diff --git a/scripts/mmteb_create_author_list.ipynb b/scripts/mmteb_create_author_list.ipynb
deleted file mode 100644
index b2acd782e6..0000000000
--- a/scripts/mmteb_create_author_list.ipynb
+++ /dev/null
@@ -1,1463 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Create points table and author list"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from __future__ import annotations\n",
-                "\n",
-                "import os\n",
-                "import sys\n",
-                "from pathlib import Path\n",
-                "\n",
-                "project_path = Path(os.getcwd()) / \"..\"\n",
-                "\n",
-                "sys.path.append(str(project_path / \"docs\" / \"mmteb\"))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from create_points_table import load_data"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Point table"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "df = load_data()\n",
-                "df = df.groupby(\"GitHub\").sum().astype(int)\n",
-                "# create a new column with the sum of the points\n",
-                "df[\"Total\"] = df.sum(axis=1)\n",
-                "df = df.sort_values(\"Total\", ascending=False)\n",
-                "# total as first column\n",
-                "df = df[[\"Total\"] + [col for col in df.columns if col != \"Total\"]]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>Total</th>\n",
-                            "      <th>Bug fixes</th>\n",
-                            "      <th>Review PR</th>\n",
-                            "      <th>New dataset</th>\n",
-                            "      <th>Dataset annotations</th>\n",
-                            "      <th>Paper writing</th>\n",
-                            "      <th>Coordination</th>\n",
-                            "      <th>New task</th>\n",
-                            "      <th>Running Models</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>GitHub</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>KennethEnevoldsen</th>\n",
-                            "      <td>597</td>\n",
-                            "      <td>87</td>\n",
-                            "      <td>326</td>\n",
-                            "      <td>68</td>\n",
-                            "      <td>35</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>81</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>isaac-chung</th>\n",
-                            "      <td>433</td>\n",
-                            "      <td>50</td>\n",
-                            "      <td>194</td>\n",
-                            "      <td>120</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>12</td>\n",
-                            "      <td>54</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>imenelydiaker</th>\n",
-                            "      <td>358</td>\n",
-                            "      <td>24</td>\n",
-                            "      <td>144</td>\n",
-                            "      <td>120</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>70</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>awinml</th>\n",
-                            "      <td>302</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>300</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>x-tabdeveloping</th>\n",
-                            "      <td>239</td>\n",
-                            "      <td>10</td>\n",
-                            "      <td>32</td>\n",
-                            "      <td>144</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>41</td>\n",
-                            "      <td>12</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>...</th>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PhilipMay</th>\n",
-                            "      <td>2</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>achibb</th>\n",
-                            "      <td>2</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>antoniolanza1996</th>\n",
-                            "      <td>2</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>cslizc</th>\n",
-                            "      <td>2</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>hanhainebula</th>\n",
-                            "      <td>2</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>98 rows \u00d7 9 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                   Total  Bug fixes  Review PR  New dataset  \\\n",
-                            "GitHub                                                        \n",
-                            "KennethEnevoldsen    597         87        326           68   \n",
-                            "isaac-chung          433         50        194          120   \n",
-                            "imenelydiaker        358         24        144          120   \n",
-                            "awinml               302          0          2          300   \n",
-                            "x-tabdeveloping      239         10         32          144   \n",
-                            "...                  ...        ...        ...          ...   \n",
-                            "PhilipMay              2          0          2            0   \n",
-                            "achibb                 2          0          0            2   \n",
-                            "antoniolanza1996       2          2          0            0   \n",
-                            "cslizc                 2          0          0            2   \n",
-                            "hanhainebula           2          0          0            2   \n",
-                            "\n",
-                            "                   Dataset annotations  Paper writing  Coordination  New task  \\\n",
-                            "GitHub                                                                          \n",
-                            "KennethEnevoldsen                   35              0            81         0   \n",
-                            "isaac-chung                          1             12            54         2   \n",
-                            "imenelydiaker                        0              0            70         0   \n",
-                            "awinml                               0              0             0         0   \n",
-                            "x-tabdeveloping                      0              0            41        12   \n",
-                            "...                                ...            ...           ...       ...   \n",
-                            "PhilipMay                            0              0             0         0   \n",
-                            "achibb                               0              0             0         0   \n",
-                            "antoniolanza1996                     0              0             0         0   \n",
-                            "cslizc                               0              0             0         0   \n",
-                            "hanhainebula                         0              0             0         0   \n",
-                            "\n",
-                            "                   Running Models  \n",
-                            "GitHub                             \n",
-                            "KennethEnevoldsen               0  \n",
-                            "isaac-chung                     0  \n",
-                            "imenelydiaker                   0  \n",
-                            "awinml                          0  \n",
-                            "x-tabdeveloping                 0  \n",
-                            "...                           ...  \n",
-                            "PhilipMay                       0  \n",
-                            "achibb                          0  \n",
-                            "antoniolanza1996                0  \n",
-                            "cslizc                          0  \n",
-                            "hanhainebula                    0  \n",
-                            "\n",
-                            "[98 rows x 9 columns]"
-                        ]
-                    },
-                    "execution_count": 4,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\begin{longtable}{lrrrrrrrrr}\n",
-                        "\\caption{Contributions by GitHub users. See \u0007utoref{tab:authors} for the mapping between authors and GitHub handles.} \\label{tab:contributions} \\\\\n",
-                        "\\toprule\n",
-                        " & Total & Bug fixes & Review PR & New dataset & Dataset annotations & Paper writing & Coordination & New task & Running Models \\\\\n",
-                        "GitHub &  &  &  &  &  &  &  &  &  \\\\\n",
-                        "\\midrule\n",
-                        "\\endfirsthead\n",
-                        "\\caption[]{Contributions by GitHub users. See \u0007utoref{tab:authors} for the mapping between authors and GitHub handles.} \\\\\n",
-                        "\\toprule\n",
-                        " & Total & Bug fixes & Review PR & New dataset & Dataset annotations & Paper writing & Coordination & New task & Running Models \\\\\n",
-                        "GitHub &  &  &  &  &  &  &  &  &  \\\\\n",
-                        "\\midrule\n",
-                        "\\endhead\n",
-                        "\\midrule\n",
-                        "\\multicolumn{10}{r}{Continued on next page} \\\\\n",
-                        "\\midrule\n",
-                        "\\endfoot\n",
-                        "\\bottomrule\n",
-                        "\\endlastfoot\n",
-                        "KennethEnevoldsen & 597 & 87 & 326 & 68 & 35 & 0 & 81 & 0 & 0 \\\\\n",
-                        "isaac-chung & 433 & 50 & 194 & 120 & 1 & 12 & 54 & 2 & 0 \\\\\n",
-                        "imenelydiaker & 358 & 24 & 144 & 120 & 0 & 0 & 70 & 0 & 0 \\\\\n",
-                        "awinml & 302 & 0 & 2 & 300 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "x-tabdeveloping & 239 & 10 & 32 & 144 & 0 & 0 & 41 & 12 & 0 \\\\\n",
-                        "davidstap & 176 & 0 & 0 & 176 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "jaygala24 & 149 & 0 & 0 & 149 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "wissam-sib & 144 & 4 & 6 & 134 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "Muennighoff & 142 & 0 & 48 & 0 & 0 & 0 & 70 & 0 & 24 \\\\\n",
-                        "orionw & 125 & 20 & 20 & 0 & 0 & 0 & 75 & 10 & 0 \\\\\n",
-                        "dokato & 112 & 12 & 6 & 94 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "gentaiscool & 110 & 0 & 0 & 110 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "jupyterjazz & 108 & 0 & 0 & 108 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "SaitejaUtpala & 102 & 0 & 0 & 102 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "vaibhavad & 93 & 8 & 4 & 6 & 0 & 0 & 75 & 0 & 0 \\\\\n",
-                        "MathieuCiancone & 88 & 0 & 0 & 88 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "schmarion & 88 & 0 & 0 & 88 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "GabrielSequeira & 88 & 0 & 0 & 88 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "digantamisra98 & 71 & 0 & 0 & 71 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "shreeya-dhakal & 62 & 0 & 8 & 54 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "Rysias & 58 & 0 & 0 & 58 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "Samoed & 51 & 22 & 2 & 18 & 0 & 0 & 0 & 0 & 9 \\\\\n",
-                        "gowitheflow-1998 & 50 & 0 & 0 & 50 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "sivareddyg & 50 & 0 & 0 & 0 & 0 & 0 & 50 & 0 & 0 \\\\\n",
-                        "asparius & 48 & 0 & 14 & 34 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "Akash190104 & 46 & 0 & 0 & 46 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "MartinBernstorff & 43 & 13 & 8 & 2 & 0 & 0 & 20 & 0 & 0 \\\\\n",
-                        "staoxiao & 40 & 0 & 0 & 40 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "akshita-sukhlecha & 40 & 4 & 0 & 36 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "rafalposwiata & 36 & 0 & 0 & 36 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "bp-high & 36 & 0 & 0 & 36 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "KranthiGV & 34 & 0 & 14 & 20 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "bjoernpl & 28 & 0 & 0 & 28 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "rasdani & 28 & 0 & 0 & 28 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "loicmagne & 28 & 28 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "jphme & 28 & 0 & 0 & 28 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "ShawonAshraf & 28 & 0 & 0 & 28 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "violenil & 26 & 0 & 0 & 26 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "mariyahendriksen & 24 & 0 & 0 & 0 & 0 & 24 & 0 & 0 & 0 \\\\\n",
-                        "dwzhu-pku & 24 & 0 & 0 & 24 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "hgissbkh & 23 & 13 & 2 & 0 & 0 & 3 & 0 & 5 & 0 \\\\\n",
-                        "jankounchained & 22 & 8 & 0 & 14 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "taeminlee & 22 & 0 & 0 & 22 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "tomaarsen & 22 & 0 & 2 & 0 & 0 & 0 & 20 & 0 & 0 \\\\\n",
-                        "kwojtasi & 22 & 0 & 0 & 22 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "mrshu & 21 & 0 & 4 & 16 & 1 & 0 & 0 & 0 & 0 \\\\\n",
-                        "crystina-z & 21 & 0 & 0 & 21 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "ManuelFay & 20 & 13 & 0 & 2 & 0 & 0 & 0 & 5 & 0 \\\\\n",
-                        "AlexeyVatolin & 20 & 20 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "Andrian0s & 20 & 2 & 4 & 14 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "rbroc & 20 & 0 & 0 & 20 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "john-b-yang & 20 & 0 & 0 & 0 & 0 & 20 & 0 & 0 & 0 \\\\\n",
-                        "mmhamdy & 20 & 0 & 0 & 20 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "manandey & 18 & 0 & 0 & 18 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "thakur-nandan & 18 & 0 & 0 & 18 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "PranjalChitale & 16 & 0 & 0 & 16 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "Sakshamrzt & 16 & 0 & 4 & 12 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "sted97 & 16 & 0 & 0 & 16 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "dipam7 & 16 & 0 & 2 & 14 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "artemsnegirev & 14 & 0 & 0 & 12 & 2 & 0 & 0 & 0 & 0 \\\\\n",
-                        "taidnguyen & 14 & 0 & 0 & 14 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "jordiclive & 12 & 10 & 0 & 2 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "guenthermi & 12 & 0 & 0 & 12 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "slvnwhrl & 12 & 0 & 0 & 12 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "Art3mis07 & 12 & 0 & 0 & 12 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "xhluca & 12 & 4 & 2 & 6 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "anpalmak2003 & 12 & 0 & 0 & 9 & 3 & 0 & 0 & 0 & 0 \\\\\n",
-                        "ab1992ao & 11 & 0 & 0 & 8 & 3 & 0 & 0 & 0 & 0 \\\\\n",
-                        "MariyaTikhonova & 11 & 0 & 0 & 7 & 4 & 0 & 0 & 0 & 0 \\\\\n",
-                        "henilp105 & 11 & 2 & 0 & 0 & 9 & 0 & 0 & 0 & 0 \\\\\n",
-                        "simon-clematide & 10 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "tmp_handle & 10 & 0 & 0 & 0 & 0 & 0 & 10 & 0 & 0 \\\\\n",
-                        "sarahooker & 10 & 0 & 0 & 0 & 0 & 10 & 0 & 0 & 0 \\\\\n",
-                        "swj0419 & 10 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "xiamengzhou & 10 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "ABorghini & 10 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "xu3kev & 10 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "malteos & 10 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "ljvmiranda921 & 10 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "howard-yen & 10 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "hongjin-su & 10 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "guangyusong & 10 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "Alenush & 10 & 0 & 0 & 6 & 4 & 0 & 0 & 0 & 0 \\\\\n",
-                        "cassanof & 10 & 1 & 0 & 8 & 0 & 0 & 0 & 0 & 1 \\\\\n",
-                        "HLasse & 10 & 5 & 0 & 0 & 5 & 0 & 0 & 0 & 0 \\\\\n",
-                        "ZhengLiu101 & 10 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "Ruqyai & 10 & 0 & 8 & 2 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "izhx & 6 & 0 & 0 & 6 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "marcobellagente93 & 6 & 0 & 0 & 6 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "monikernemo & 2 & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "NouamaneTazi & 2 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "MexicanLemonade & 2 & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "bakrianoo & 2 & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "PhilipMay & 2 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "achibb & 2 & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "antoniolanza1996 & 2 & 2 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "cslizc & 2 & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "hanhainebula & 2 & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0 \\\\\n",
-                        "\\end{longtable}\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(\n",
-                "    df.to_latex(\n",
-                "        longtable=True,\n",
-                "        caption=\"Contributions by GitHub users. See \\autoref{tab:authors} for the mapping between authors and GitHub handles.\",\n",
-                "        label=\"tab:contributions\",\n",
-                "    )\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Contributor affiliations"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "points_to_authors = project_path / \"docs\" / \"mmteb\" / \"points.md\"\n",
-                "\n",
-                "# extract table from markdown file\n",
-                "with open(points_to_authors) as f:\n",
-                "    lines = f.readlines()\n",
-                "\n",
-                "table = False\n",
-                "table_lines = []\n",
-                "colnames = []\n",
-                "head_skipped = False\n",
-                "for line in lines:\n",
-                "    if not table and line.startswith(\"|\"):\n",
-                "        table = True\n",
-                "        colnames = [c.strip() for c in line.strip().split(\"|\")[1:-1]]\n",
-                "        continue\n",
-                "    if colnames and table and not head_skipped:\n",
-                "        head_skipped = True\n",
-                "        continue\n",
-                "    if table:\n",
-                "        table_lines.append([c.strip() for c in line.strip().split(\"|\")[1:-1]])\n",
-                "    if table and line.strip() == \"\":\n",
-                "        break"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# create a dataframe from the table\n",
-                "import pandas as pd\n",
-                "\n",
-                "author_df = pd.DataFrame(table_lines, columns=colnames)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>GitHub</th>\n",
-                            "      <th>First name</th>\n",
-                            "      <th>Last name</th>\n",
-                            "      <th>Email</th>\n",
-                            "      <th>User on openreview</th>\n",
-                            "      <th>Affiliations</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>KennethEnevoldsen</td>\n",
-                            "      <td>Kenneth</td>\n",
-                            "      <td>Enevoldsen</td>\n",
-                            "      <td>kennethcenevoldsen@gmail.com</td>\n",
-                            "      <td>~Kenneth_Enevoldsen1</td>\n",
-                            "      <td>Aarhus University</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>x-tabdeveloping</td>\n",
-                            "      <td>M\u00e1rton</td>\n",
-                            "      <td>Kardos</td>\n",
-                            "      <td>martonkardos@cas.au.dk</td>\n",
-                            "      <td>~M\u00e1rton_Kardos1</td>\n",
-                            "      <td>Aarhus University</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>imenelydiaker</td>\n",
-                            "      <td>Imene</td>\n",
-                            "      <td>Kerboua</td>\n",
-                            "      <td></td>\n",
-                            "      <td>~Imene_Kerboua1</td>\n",
-                            "      <td>INSA Lyon, LIRIS</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>wissam-sib</td>\n",
-                            "      <td>Wissam</td>\n",
-                            "      <td>Siblini</td>\n",
-                            "      <td>wissam.siblini92@gmail.com</td>\n",
-                            "      <td>~Wissam_Siblini1</td>\n",
-                            "      <td>Individual Contributor</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>GabrielSequeira</td>\n",
-                            "      <td>Gabriel</td>\n",
-                            "      <td>Sequeira</td>\n",
-                            "      <td></td>\n",
-                            "      <td></td>\n",
-                            "      <td>Individual Contributor</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>...</th>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>83</th>\n",
-                            "      <td>sarahooker</td>\n",
-                            "      <td>Sara</td>\n",
-                            "      <td>Hooker</td>\n",
-                            "      <td></td>\n",
-                            "      <td>~Sara_Hooker2</td>\n",
-                            "      <td>Cohere For AI</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>84</th>\n",
-                            "      <td>kwojtasi</td>\n",
-                            "      <td>Konrad</td>\n",
-                            "      <td>Wojtasik</td>\n",
-                            "      <td></td>\n",
-                            "      <td>~Konrad_Wojtasik1</td>\n",
-                            "      <td>Wroc\u0142aw University of Science and Technology</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>85</th>\n",
-                            "      <td>tmp_handle</td>\n",
-                            "      <td>Jimmy</td>\n",
-                            "      <td>Lin</td>\n",
-                            "      <td></td>\n",
-                            "      <td>~Jimmy_Lin2</td>\n",
-                            "      <td>University of Waterloo</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>86</th>\n",
-                            "      <td>hongjin-su</td>\n",
-                            "      <td>Hongjin</td>\n",
-                            "      <td>Su</td>\n",
-                            "      <td></td>\n",
-                            "      <td>~Hongjin_SU1</td>\n",
-                            "      <td>University of Hong Kong</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>87</th>\n",
-                            "      <td>howard-yen</td>\n",
-                            "      <td>Howard</td>\n",
-                            "      <td>Yen</td>\n",
-                            "      <td></td>\n",
-                            "      <td>~Howard_Yen1</td>\n",
-                            "      <td>Princeton University</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>88 rows \u00d7 6 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "               GitHub First name   Last name                         Email  \\\n",
-                            "0   KennethEnevoldsen    Kenneth  Enevoldsen  kennethcenevoldsen@gmail.com   \n",
-                            "1     x-tabdeveloping     M\u00e1rton      Kardos        martonkardos@cas.au.dk   \n",
-                            "2       imenelydiaker      Imene     Kerboua                                 \n",
-                            "3          wissam-sib     Wissam     Siblini    wissam.siblini92@gmail.com   \n",
-                            "4     GabrielSequeira    Gabriel    Sequeira                                 \n",
-                            "..                ...        ...         ...                           ...   \n",
-                            "83         sarahooker       Sara      Hooker                                 \n",
-                            "84           kwojtasi     Konrad    Wojtasik                                 \n",
-                            "85         tmp_handle      Jimmy         Lin                                 \n",
-                            "86         hongjin-su    Hongjin          Su                                 \n",
-                            "87         howard-yen     Howard         Yen                                 \n",
-                            "\n",
-                            "      User on openreview                                  Affiliations  \n",
-                            "0   ~Kenneth_Enevoldsen1                             Aarhus University  \n",
-                            "1        ~M\u00e1rton_Kardos1                             Aarhus University  \n",
-                            "2        ~Imene_Kerboua1                              INSA Lyon, LIRIS  \n",
-                            "3       ~Wissam_Siblini1                        Individual Contributor  \n",
-                            "4                                               Individual Contributor  \n",
-                            "..                   ...                                           ...  \n",
-                            "83         ~Sara_Hooker2                                 Cohere For AI  \n",
-                            "84     ~Konrad_Wojtasik1  Wroc\u0142aw University of Science and Technology  \n",
-                            "85           ~Jimmy_Lin2                        University of Waterloo  \n",
-                            "86          ~Hongjin_SU1                       University of Hong Kong  \n",
-                            "87          ~Howard_Yen1                          Princeton University  \n",
-                            "\n",
-                            "[88 rows x 6 columns]"
-                        ]
-                    },
-                    "execution_count": 8,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "author_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\begin{tabular}{llll}\n",
-                        "\\toprule\n",
-                        "GitHub & First name & Last name & Affiliations \\\\\n",
-                        "\\midrule\n",
-                        "KennethEnevoldsen & Kenneth & Enevoldsen & Aarhus University \\\\\n",
-                        "x-tabdeveloping & M\u00e1rton & Kardos & Aarhus University \\\\\n",
-                        "imenelydiaker & Imene & Kerboua & INSA Lyon, LIRIS \\\\\n",
-                        "wissam-sib & Wissam & Siblini & Individual Contributor \\\\\n",
-                        "GabrielSequeira & Gabriel & Sequeira & Individual Contributor \\\\\n",
-                        "schmarion & Marion & Schaeffer & Wikit \\\\\n",
-                        "MathieuCiancone & Mathieu & Ciancone & Wikit \\\\\n",
-                        "MartinBernstorff & Martin & Bernstorff & Aarhus University \\\\\n",
-                        "staoxiao & Shitao & Xiao & Beijing Academy of Artificial Intelligence \\\\\n",
-                        "ZhengLiu101 & Zheng & Liu & Beijing Academy of Artificial Intelligence \\\\\n",
-                        "achibb & Aaron & Chibb & Individual Contributor \\\\\n",
-                        "cassanof & Federico & Cassano & Northeastern University && Cursor AI \\\\\n",
-                        "taidnguyen & Nguyen & Tai & University of Pennsylvania \\\\\n",
-                        "xu3kev & Wen-Ding & Li & Cornell University \\\\\n",
-                        "Rysias & Jonathan & Rystr\u00f8m & University of Oxford \\\\\n",
-                        "taeminlee & Taemin & Lee & Korea University Human-Inspired AI Research \\\\\n",
-                        "izhx & Xin & Zhang & Harbin Institute of Technology \\\\\n",
-                        "orionw & Orion & Weller & Johns Hopkins University \\\\\n",
-                        "slvnwhrl & Silvan & Wehrli & Robert Koch Institute \\\\\n",
-                        "manandey & Manan & Dey & Salesforce \\\\\n",
-                        "isaac-chung & Isaac & Chung & Individual Contributor \\\\\n",
-                        "asparius & \u00d6mer & \u00c7a\u011fatan & Ko\u00e7 University,Turkey \\\\\n",
-                        "rafalposwiata & Rafa\u0142 & Po\u015bwiata & National Information Processing Institute \\\\\n",
-                        "rbroc & Roberta & Rocca & Aarhus University \\\\\n",
-                        "awinml & Ashwin & Mathur & Individual Contributor \\\\\n",
-                        "guangyusong & Guangyu & Song & Tano Labs \\\\\n",
-                        "davidstap & David & Stap & University of Amsterdam \\\\\n",
-                        "HLasse & Lasse & Hansen & Aarhus University \\\\\n",
-                        "jaygala24 & Jay & Gala & MBZUAI \\\\\n",
-                        "digantamisra98 & Diganta & Misra & Max Planck Institute for Intelligent Systems && ELLIS Institute T\u00fcbingen \\\\\n",
-                        "PranjalChitale & Pranjal & Chitale & Indian Institute of Technology \\\\\n",
-                        "Akash190104 & Akash & Kundu & Heritage Institute of Technology && Apart Research \\\\\n",
-                        "dwzhu-pku & Dawei & Zhu & Peking University \\\\\n",
-                        "ljvmiranda921 & Lester James & Miranda & Allen Institute for AI \\\\\n",
-                        "Andrian0s & Andrianos & Michail & University of Zurich \\\\\n",
-                        "simon-clematide & Simon & Clematide & University of Zurich \\\\\n",
-                        "SaitejaUtpala & Saiteja & Utpala & Microsoft Research \\\\\n",
-                        "mmhamdy & Mohammed & Hamdy & Cohere For AI Community \\\\\n",
-                        "jupyterjazz & Saba & Sturua & Jina AI \\\\\n",
-                        "Ruqyai & Ruqiya & Bin Safi & NaN \\\\\n",
-                        "KranthiGV & Kranthi Kiran & GV & New York University \\\\\n",
-                        "shreeya-dhakal & Shreeya & Dhakal & Individual Contributor \\\\\n",
-                        "dipam7 & Dipam & Vasani & Individual Contributor \\\\\n",
-                        "Art3mis07 & Gayatri & K & R. V. College of Engineering \\\\\n",
-                        "jankounchained & Jan & Kostkan & Aarhus University \\\\\n",
-                        "bp-high & Bhavish & Pahwa & Microsoft Research \\\\\n",
-                        "rasdani & Daniel & Auras & ellamind, Germany \\\\\n",
-                        "ShawonAshraf & Shawon & Ashraf & ellamind, Germany \\\\\n",
-                        "bjoernpl & Bj\u00f6rn & Pl\u00fcster & ellamind, Germany \\\\\n",
-                        "jphme & Jan Philipp & Harries & ellamind, Germany \\\\\n",
-                        "malteos & Malte & Ostendorff & Occiglot \\\\\n",
-                        "ManuelFay & Manuel & Faysse & CentraleSup\u00e9lec && Illuin Technology \\\\\n",
-                        "hgissbkh & Hippolyte & Gisserot-Boukhlef & CentraleSup\u00e9lec && Artefact Research Center \\\\\n",
-                        "sted97 & Simone & Tedeschi & Sapienza University of Rome \\\\\n",
-                        "gentaiscool & Genta Indra & Winata & Individual Contributor \\\\\n",
-                        "henilp105 & Henil & Panchal & Nirma University \\\\\n",
-                        "ABorghini & Alessia & Borghini & Sapienza University of Rome \\\\\n",
-                        "jordiclive & Jordan & Clive & Imperial College London \\\\\n",
-                        "gowitheflow-1998 & Chenghao & Xiao & Durham University \\\\\n",
-                        "mariyahendriksen & Mariya & Hendriksen & University of Amsterdam \\\\\n",
-                        "dokato & Dominik & Krzemi\u0144ski & Cohere For AI Community \\\\\n",
-                        "Samoed & Roman & Solomatin & AI Talent Hub && ITMO University \\\\\n",
-                        "Alenush & Alena & Fenogenova & SaluteDevices \\\\\n",
-                        "ab1992ao & Aleksandr & Abramov & SaluteDevices \\\\\n",
-                        "artemsnegirev & Artem & Snegirev & SaluteDevices \\\\\n",
-                        "anpalmak2003 & Anna & Maksimova & SaluteDevices \\\\\n",
-                        "MariyaTikhonova & Maria & Tikhonova & SaluteDevices && HSE University \\\\\n",
-                        "vaibhavad & Vaibhav & Adlakha & Mila, McGill University && ServiceNow Research \\\\\n",
-                        "sivareddyg & Siva & Reddy & Mila, McGill University && ServiceNow Research \\\\\n",
-                        "guenthermi & Michael & G\u00fcnther & Jina AI \\\\\n",
-                        "violenil & Isabelle & Mohr & Jina AI \\\\\n",
-                        "akshita-sukhlecha & Akshita & Sukhlecha & Individual Contributor \\\\\n",
-                        "Muennighoff & Niklas & Muennighoff & Stanford University && Contextual AI \\\\\n",
-                        "AlexeyVatolin & Aleksei & Vatolin & FRC CSC RAS \\\\\n",
-                        "xhluca & Xing Han & L\u00f9 & Mila, McGill University \\\\\n",
-                        "crystina-z & Xinyu & Zhang & University of Waterloo \\\\\n",
-                        "tomaarsen & Tom & Aarsen & Hugging Face \\\\\n",
-                        "mrshu & Marek & Suppa & Comenius University Bratislava && Cisco Systems \\\\\n",
-                        "swj0419 & Weijia & Shi & University of Washington \\\\\n",
-                        "xiamengzhou & Mengzhou & Xia & Princeton University \\\\\n",
-                        "john-b-yang & John & Yang & Stanford University \\\\\n",
-                        "thakur-nandan & Nandan & Thakur & University of Waterloo \\\\\n",
-                        "loicmagne & Loic & Magne & Individual Contributor \\\\\n",
-                        "sarahooker & Sara & Hooker & Cohere For AI \\\\\n",
-                        "kwojtasi & Konrad & Wojtasik & Wroc\u0142aw University of Science and Technology \\\\\n",
-                        "tmp_handle & Jimmy & Lin & University of Waterloo \\\\\n",
-                        "hongjin-su & Hongjin & Su & University of Hong Kong \\\\\n",
-                        "howard-yen & Howard & Yen & Princeton University \\\\\n",
-                        "\\bottomrule\n",
-                        "\\end{tabular}\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(\n",
-                "    author_df[[\"GitHub\", \"First name\", \"Last name\", \"Affiliations\"]].to_latex(\n",
-                "        index=False\n",
-                "    )\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Author list"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "izhx has less than 10 points\n",
-                        "achibb has less than 10 points\n"
-                    ]
-                }
-            ],
-            "source": [
-                "github = set(author_df[\"GitHub\"])\n",
-                "\n",
-                "not_10 = []\n",
-                "\n",
-                "df = df.reset_index()\n",
-                "# check if all github users are in the points table and has 10 total point\n",
-                "for gh in github:\n",
-                "    if gh not in set(df[\"GitHub\"]):\n",
-                "        print(f\"{gh} not in points table\")\n",
-                "\n",
-                "    if df[df[\"GitHub\"] == gh][\"Total\"].values[0] < 10:\n",
-                "        print(f\"{gh} has less than 10 points\")\n",
-                "        not_10.append(gh)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "'ShawonAshraf'"
-                        ]
-                    },
-                    "execution_count": 11,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "gh"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "['Sakshamrzt']\n"
-                    ]
-                }
-            ],
-            "source": [
-                "missing_users = [user for user in df[df[\"Total\"] >= 10][\"GitHub\"] if user not in github]\n",
-                "print(missing_users)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# sort author_df by total points\n",
-                "author_df = pd.merge(author_df, df[[\"GitHub\", \"Total\"]], on=\"GitHub\", how=\"left\")\n",
-                "author_df = author_df.sort_values(\"Total\", ascending=False)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# create a latex author list\n",
-                "# \\textbf{First Last \\textsuperscript{1}},\n",
-                "# \\textbf{First Last \\textsuperscript{1}},\n",
-                "# [if too long add \\\\]\n",
-                "# ...\n",
-                "# \\\\\n",
-                "# \\\\\n",
-                "# \\textsuperscript{1}Aarhus University, Denmark,\n",
-                "# ...\n",
-                "# [if too long add \\\\]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "author_list = []\n",
-                "affiations = {}\n",
-                "\n",
-                "aff_id = 1\n",
-                "for i, row in author_df.iterrows():\n",
-                "    author = row[\"First name\"] + \" \" + row[\"Last name\"]\n",
-                "    if row[\"GitHub\"] in not_10:\n",
-                "        continue\n",
-                "    author_str = f\"\\\\textbf{{{author}\"\n",
-                "\n",
-                "    if row[\"Affiliations\"]:\n",
-                "        affiliations = row[\"Affiliations\"].split(\"&&\")\n",
-                "\n",
-                "        aff_string = \"\"\n",
-                "        for aff in affiliations:\n",
-                "            aff = aff.strip()\n",
-                "            if \"N/A\" in aff:\n",
-                "                continue\n",
-                "            if aff not in affiations:\n",
-                "                affiations[aff] = aff_id\n",
-                "                aff_id += 1\n",
-                "            aff_string += f\"{affiations[aff]},\"\n",
-                "\n",
-                "        # remove last comma\n",
-                "        aff_string = aff_string[:-1]\n",
-                "\n",
-                "        if aff_string:\n",
-                "            author_str += f\"\\\\textsuperscript{{{aff_string}}}\"\n",
-                "        else:\n",
-                "            author_str += \"\"\n",
-                "\n",
-                "    # if row[\"Affiliations\"] not in affiations and row[\"Affiliations\"]:\n",
-                "    #     affiations[row[\"Affiliations\"]] = aff_id\n",
-                "    #     aff_id += 1\n",
-                "    #     author_str += f\"\\\\textsuperscript{{{affiations[row['Affiliations']]}}}\"\n",
-                "    author_str += \"}\"\n",
-                "    author_list.append(author_str)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Move last author to the end\n",
-                "last_author1 = \"Niklas Muennighoff\"\n",
-                "last_author_ = [a for a in author_list if last_author1 in a][0]\n",
-                "last_author2 = \"Siva\"\n",
-                "last_author__ = [a for a in author_list if last_author2 in a][0]\n",
-                "# remove from author list\n",
-                "author_list = [\n",
-                "    a for a in author_list if last_author1 not in a and last_author2 not in a\n",
-                "]\n",
-                "\n",
-                "author_list.append(last_author__)\n",
-                "author_list.append(last_author_)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# create the latex string\n",
-                "\n",
-                "latex = \"\"\n",
-                "line_length = 0\n",
-                "max_line_length = 85\n",
-                "\n",
-                "for i, author in enumerate(author_list):\n",
-                "    _line_length = len(author.split(\"\\\\textsuperscript\")[0])\n",
-                "    if line_length + _line_length > max_line_length:\n",
-                "        latex += \"\\\\\\\\\\n\"\n",
-                "        line_length = 0\n",
-                "    latex += author + \", \\n\"\n",
-                "    line_length += _line_length\n",
-                "\n",
-                "# add the affiliations\n",
-                "line_length = 0\n",
-                "latex += \"\\\\\\\\\\n\"\n",
-                "latex += \"\\\\\\\\\\n\"\n",
-                "for aff, id in affiations.items():\n",
-                "    if \"N/A\" in aff:\n",
-                "        continue\n",
-                "    _line_length = len(aff)\n",
-                "    if line_length + _line_length > max_line_length:\n",
-                "        latex += \"\\\\\\\\\\n\"\n",
-                "        line_length = 0\n",
-                "    latex += \"\\\\textsuperscript{\" + str(id) + \"}\" + aff + \", \\n\"\n",
-                "    line_length += _line_length"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\textbf{Kenneth Enevoldsen\\textsuperscript{1}}, \n",
-                        "\\textbf{Isaac Chung\\textsuperscript{2}}, \n",
-                        "\\textbf{Imene Kerboua\\textsuperscript{3}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Ashwin Mathur\\textsuperscript{2}}, \n",
-                        "\\textbf{M\u00e1rton Kardos\\textsuperscript{1}}, \n",
-                        "\\textbf{David Stap\\textsuperscript{4}}, \n",
-                        "\\textbf{Jay Gala\\textsuperscript{5}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Wissam Siblini\\textsuperscript{2}}, \n",
-                        "\\textbf{Orion Weller\\textsuperscript{8}}, \n",
-                        "\\textbf{Dominik Krzemi\u0144ski\\textsuperscript{9}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Genta Indra Winata\\textsuperscript{2}}, \n",
-                        "\\textbf{Saba Sturua\\textsuperscript{10}}, \n",
-                        "\\textbf{Saiteja Utpala\\textsuperscript{11}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Vaibhav Adlakha\\textsuperscript{12,13}}, \n",
-                        "\\textbf{Mathieu Ciancone\\textsuperscript{14}}, \n",
-                        "\\textbf{Marion Schaeffer\\textsuperscript{14}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Gabriel Sequeira\\textsuperscript{2}}, \n",
-                        "\\textbf{Diganta Misra\\textsuperscript{15,16}}, \n",
-                        "\\textbf{Shreeya Dhakal\\textsuperscript{2}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Jonathan Rystr\u00f8m\\textsuperscript{17}}, \n",
-                        "\\textbf{Roman Solomatin\\textsuperscript{18,19}}, \n",
-                        "\\textbf{Chenghao Xiao\\textsuperscript{20}}, \n",
-                        "\\\\\n",
-                        "\\textbf{\u00d6mer \u00c7a\u011fatan\\textsuperscript{21}}, \n",
-                        "\\textbf{Akash Kundu\\textsuperscript{22,23}}, \n",
-                        "\\textbf{Martin Bernstorff\\textsuperscript{1}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Akshita Sukhlecha\\textsuperscript{2}}, \n",
-                        "\\textbf{Shitao Xiao\\textsuperscript{24}}, \n",
-                        "\\textbf{Bhavish Pahwa\\textsuperscript{11}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Rafa\u0142 Po\u015bwiata\\textsuperscript{25}}, \n",
-                        "\\textbf{Kranthi Kiran GV\\textsuperscript{26}}, \n",
-                        "\\textbf{Bj\u00f6rn Pl\u00fcster\\textsuperscript{27}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Daniel Auras\\textsuperscript{27}}, \n",
-                        "\\textbf{Shawon Ashraf\\textsuperscript{27}}, \n",
-                        "\\textbf{Jan Philipp Harries\\textsuperscript{27}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Loic Magne\\textsuperscript{2}}, \n",
-                        "\\textbf{Isabelle Mohr\\textsuperscript{10}}, \n",
-                        "\\textbf{Dawei Zhu\\textsuperscript{28}}, \n",
-                        "\\textbf{Mariya Hendriksen\\textsuperscript{4}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Hippolyte Gisserot-Boukhlef\\textsuperscript{29,30}}, \n",
-                        "\\textbf{Konrad Wojtasik\\textsuperscript{31}}, \n",
-                        "\\textbf{Tom Aarsen\\textsuperscript{32}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Jan Kostkan\\textsuperscript{1}}, \n",
-                        "\\textbf{Taemin Lee\\textsuperscript{33}}, \n",
-                        "\\textbf{Marek Suppa\\textsuperscript{34,35}}, \n",
-                        "\\textbf{Xinyu Zhang\\textsuperscript{36}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Aleksei Vatolin\\textsuperscript{37}}, \n",
-                        "\\textbf{Mohammed Hamdy\\textsuperscript{9}}, \n",
-                        "\\textbf{John Yang\\textsuperscript{6}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Andrianos Michail\\textsuperscript{38}}, \n",
-                        "\\textbf{Manuel Faysse\\textsuperscript{29,39}}, \n",
-                        "\\textbf{Roberta Rocca\\textsuperscript{1}}, \n",
-                        "\\textbf{Manan Dey\\textsuperscript{40}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Nandan Thakur\\textsuperscript{36}}, \n",
-                        "\\textbf{Simone Tedeschi\\textsuperscript{41}}, \n",
-                        "\\textbf{Dipam Vasani\\textsuperscript{2}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Pranjal Chitale\\textsuperscript{42}}, \n",
-                        "\\textbf{Artem Snegirev\\textsuperscript{43}}, \n",
-                        "\\textbf{Nguyen Tai\\textsuperscript{44}}, \n",
-                        "\\textbf{Silvan Wehrli\\textsuperscript{45}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Gayatri K\\textsuperscript{46}}, \n",
-                        "\\textbf{Xing Han L\u00f9\\textsuperscript{12}}, \n",
-                        "\\textbf{Michael G\u00fcnther\\textsuperscript{10}}, \n",
-                        "\\textbf{Jordan Clive\\textsuperscript{47}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Anna Maksimova\\textsuperscript{43}}, \n",
-                        "\\textbf{Maria Tikhonova\\textsuperscript{43,48}}, \n",
-                        "\\textbf{Aleksandr Abramov\\textsuperscript{43}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Henil Panchal\\textsuperscript{49}}, \n",
-                        "\\textbf{Weijia Shi\\textsuperscript{50}}, \n",
-                        "\\textbf{Hongjin Su\\textsuperscript{51}}, \n",
-                        "\\textbf{Jimmy Lin\\textsuperscript{36}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Zheng Liu\\textsuperscript{24}}, \n",
-                        "\\textbf{Sara Hooker\\textsuperscript{52}}, \n",
-                        "\\textbf{Ruqiya Bin Safi}, \n",
-                        "\\textbf{Simon Clematide\\textsuperscript{38}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Mengzhou Xia\\textsuperscript{53}}, \n",
-                        "\\textbf{Malte Ostendorff\\textsuperscript{54}}, \n",
-                        "\\textbf{Federico Cassano\\textsuperscript{55,56}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Lester James Miranda\\textsuperscript{57}}, \n",
-                        "\\textbf{Alessia Borghini\\textsuperscript{41}}, \n",
-                        "\\textbf{Lasse Hansen\\textsuperscript{1}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Wen-Ding Li\\textsuperscript{58}}, \n",
-                        "\\textbf{Guangyu Song\\textsuperscript{59}}, \n",
-                        "\\textbf{Alena Fenogenova\\textsuperscript{43}}, \n",
-                        "\\textbf{Howard Yen\\textsuperscript{53}}, \n",
-                        "\\\\\n",
-                        "\\textbf{Siva Reddy\\textsuperscript{12,13}}, \n",
-                        "\\textbf{Niklas Muennighoff\\textsuperscript{6,7}}, \n",
-                        "\\\\\n",
-                        "\\\\\n",
-                        "\\textsuperscript{1}Aarhus University, \n",
-                        "\\textsuperscript{2}Individual Contributor, \n",
-                        "\\textsuperscript{3}INSA Lyon, LIRIS, \n",
-                        "\\textsuperscript{4}University of Amsterdam, \n",
-                        "\\textsuperscript{5}MBZUAI, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{6}Stanford University, \n",
-                        "\\textsuperscript{7}Contextual AI, \n",
-                        "\\textsuperscript{8}Johns Hopkins University, \n",
-                        "\\textsuperscript{9}Cohere For AI Community, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{10}Jina AI, \n",
-                        "\\textsuperscript{11}Microsoft Research, \n",
-                        "\\textsuperscript{12}Mila, McGill University, \n",
-                        "\\textsuperscript{13}ServiceNow Research, \n",
-                        "\\textsuperscript{14}Wikit, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{15}Max Planck Institute for Intelligent Systems, \n",
-                        "\\textsuperscript{16}ELLIS Institute T\u00fcbingen, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{17}University of Oxford, \n",
-                        "\\textsuperscript{18}AI Talent Hub, \n",
-                        "\\textsuperscript{19}ITMO University, \n",
-                        "\\textsuperscript{20}Durham University, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{21}Ko\u00e7 University,Turkey, \n",
-                        "\\textsuperscript{22}Heritage Institute of Technology, \n",
-                        "\\textsuperscript{23}Apart Research, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{24}Beijing Academy of Artificial Intelligence, \n",
-                        "\\textsuperscript{25}National Information Processing Institute, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{26}New York University, \n",
-                        "\\textsuperscript{27}ellamind, Germany, \n",
-                        "\\textsuperscript{28}Peking University, \n",
-                        "\\textsuperscript{29}CentraleSup\u00e9lec, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{30}Artefact Research Center, \n",
-                        "\\textsuperscript{31}Wroc\u0142aw University of Science and Technology, \n",
-                        "\\textsuperscript{32}Hugging Face, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{33}Korea University Human-Inspired AI Research, \n",
-                        "\\textsuperscript{34}Comenius University Bratislava, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{35}Cisco Systems, \n",
-                        "\\textsuperscript{36}University of Waterloo, \n",
-                        "\\textsuperscript{37}FRC CSC RAS, \n",
-                        "\\textsuperscript{38}University of Zurich, \n",
-                        "\\textsuperscript{39}Illuin Technology, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{40}Salesforce, \n",
-                        "\\textsuperscript{41}Sapienza University of Rome, \n",
-                        "\\textsuperscript{42}Indian Institute of Technology, \n",
-                        "\\textsuperscript{43}SaluteDevices, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{44}University of Pennsylvania, \n",
-                        "\\textsuperscript{45}Robert Koch Institute, \n",
-                        "\\textsuperscript{46}R. V. College of Engineering, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{47}Imperial College London, \n",
-                        "\\textsuperscript{48}HSE University, \n",
-                        "\\textsuperscript{49}Nirma University, \n",
-                        "\\textsuperscript{50}University of Washington, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{51}University of Hong Kong, \n",
-                        "\\textsuperscript{52}Cohere For AI, \n",
-                        "\\textsuperscript{53}Princeton University, \n",
-                        "\\textsuperscript{54}Occiglot, \n",
-                        "\\\\\n",
-                        "\\textsuperscript{55}Northeastern University, \n",
-                        "\\textsuperscript{56}Cursor AI, \n",
-                        "\\textsuperscript{57}Allen Institute for AI, \n",
-                        "\\textsuperscript{58}Cornell University, \n",
-                        "\\textsuperscript{59}Tano Labs, \n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(latex)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": []
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "@orionw \n",
-                        "@ZhengLiu101 \n",
-                        "@staoxiao \n",
-                        "@xiamengzhou \n",
-                        "@xu3kev \n",
-                        "@swj0419 \n",
-                        "@Art3mis07 \n",
-                        "@sted97 \n",
-                        "@vaibhavad \n",
-                        "@isaac-chung \n",
-                        "@taeminlee \n",
-                        "@Samoed \n",
-                        "@mrshu \n",
-                        "@Muennighoff \n",
-                        "@kwojtasi \n",
-                        "@jankounchained \n",
-                        "@imenelydiaker \n",
-                        "@AlexeyVatolin \n",
-                        "@mariyahendriksen \n",
-                        "@KennethEnevoldsen \n",
-                        "@akshita-sukhlecha \n",
-                        "@awinml \n",
-                        "@jaygala24 \n",
-                        "@digantamisra98 \n",
-                        "@gentaiscool \n",
-                        "@Rysias \n",
-                        "@MartinBernstorff \n",
-                        "@jupyterjazz \n",
-                        "@davidstap \n",
-                        "@Alenush \n",
-                        "@MathieuCiancone \n",
-                        "@xhluca \n",
-                        "@rafalposwiata \n",
-                        "@x-tabdeveloping \n",
-                        "@ab1992ao \n",
-                        "@artemsnegirev \n",
-                        "@jphme \n",
-                        "@slvnwhrl \n",
-                        "@hgissbkh \n",
-                        "@HLasse \n",
-                        "@Ruqyai \n",
-                        "@bp-high \n",
-                        "@ljvmiranda921 \n",
-                        "@violenil \n",
-                        "@malteos \n",
-                        "@rasdani \n",
-                        "@asparius \n",
-                        "@simon-clematide \n",
-                        "@dokato \n",
-                        "@mmhamdy \n",
-                        "@john-b-yang \n",
-                        "@henilp105 \n",
-                        "@dwzhu-pku \n",
-                        "@tomaarsen \n",
-                        "@sarahooker \n",
-                        "@manandey \n",
-                        "@ManuelFay \n",
-                        "@sivareddyg \n",
-                        "@thakur-nandan \n",
-                        "@Akash190104 \n",
-                        "@shreeya-dhakal \n",
-                        "@PranjalChitale \n",
-                        "@schmarion \n",
-                        "@ShawonAshraf \n",
-                        "@loicmagne \n",
-                        "@KranthiGV \n",
-                        "@gowitheflow-1998 \n",
-                        "@dipam7 \n",
-                        "@rbroc \n",
-                        "@ABorghini \n",
-                        "@jordiclive \n",
-                        "@Andrian0s \n",
-                        "@bjoernpl \n",
-                        "@taidnguyen \n",
-                        "@MariyaTikhonova \n",
-                        "@wissam-sib \n",
-                        "@cassanof \n",
-                        "@SaitejaUtpala \n",
-                        "@GabrielSequeira \n",
-                        "@crystina-z \n",
-                        "@guenthermi \n",
-                        "@anpalmak2003 \n",
-                        "@guangyusong \n"
-                    ]
-                }
-            ],
-            "source": [
-                "# authors with 10 points or more\n",
-                "\n",
-                "{g for g in github if g not in not_10}\n",
-                "\n",
-                "# to a string @author\n",
-                "\n",
-                "for g in github:\n",
-                "    if g in not_10:\n",
-                "        continue\n",
-                "    print(f\"@{g} \")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 20,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Kenneth Enevoldsen ~Kenneth_Enevoldsen1\n",
-                        "Isaac Chung ~Isaac_Kwan_Yin_Chung1\n",
-                        "Imene Kerboua ~Imene_Kerboua1\n",
-                        "Ashwin Mathur ~Ashwin_Mathur1\n",
-                        "M\u00e1rton Kardos ~M\u00e1rton_Kardos1\n",
-                        "David Stap ~David_Stap\n",
-                        "Jay Gala ~Jay_Gala1\n",
-                        "Wissam Siblini ~Wissam_Siblini1\n",
-                        "Niklas Muennighoff ~Niklas_Muennighoff1\n",
-                        "Dominik Krzemi\u0144ski ~Dominik_Krzemi\u0144ski1\n",
-                        "Genta Indra Winata ~Genta_Indra_Winata1\n",
-                        "Saba Sturua ~Saba_Sturua1\n",
-                        "Saiteja Utpala ~Saiteja_Utpala1\n",
-                        "Orion Weller ~Orion_Weller1\n",
-                        "Mathieu Ciancone ~Mathieu_Ciancone1\n",
-                        "Marion Schaeffer ~Marion_Schaeffer1\n",
-                        "Gabriel Sequeira \n",
-                        "Diganta Misra ~Diganta_Misra1\n",
-                        "Vaibhav Adlakha ~Vaibhav_Adlakha1\n",
-                        "Shreeya Dhakal \n",
-                        "Jonathan Rystr\u00f8m ~Jonathan_Rystr\u00f8m1\n",
-                        "Roman Solomatin ~Roman_Solomatin1\n",
-                        "Siva Reddy ~Siva_Reddy1\n",
-                        "Chenghao Xiao ~Chenghao_Xiao1\n",
-                        "\u00d6mer \u00c7a\u011fatan ~\u00d6mer_Veysel_\u00c7a\u011fatan1\n",
-                        "Akash Kundu ~Akash_Kundu2\n",
-                        "Martin Bernstorff ~Martin_Bernstorff1\n",
-                        "Shitao Xiao ~Shitao_Xiao1\n",
-                        "Akshita Sukhlecha ~Akshita_Sukhlecha1\n",
-                        "Bhavish Pahwa ~Bhavish_Pahwa1\n",
-                        "Rafa\u0142 Po\u015bwiata ~Rafa\u0142_Po\u015bwiata1\n",
-                        "Kranthi Kiran GV ~Kranthi_Kiran_GV1\n",
-                        "Shawon Ashraf ~Shawon_Ashraf1\n",
-                        "Daniel Auras ~Daniel_Auras1\n",
-                        "Bj\u00f6rn Pl\u00fcster ~Bj\u00f6rn_Pl\u00fcster1\n",
-                        "Jan Philipp Harries ~Jan_Philipp_Harries1\n",
-                        "Loic Magne Individual Contributor\n",
-                        "Isabelle Mohr ~Isabelle_Mohr1\n",
-                        "Dawei Zhu ~Dawei_Zhu2\n",
-                        "Hippolyte Gisserot-Boukhlef ~Hippolyte_Gisserot-Boukhlef1\n",
-                        "Tom Aarsen ~Tom_Aarsen1\n",
-                        "Jan Kostkan ~Jan_Kostkan1\n",
-                        "Konrad Wojtasik Wroc\u0142aw University of Science and Technology\n",
-                        "Taemin Lee ~Taemin_Lee1\n",
-                        "Marek Suppa ~Marek_Suppa1\n",
-                        "Xinyu Zhang ~Crystina_Zhang1\n",
-                        "Roberta Rocca ~Roberta_Rocca1\n",
-                        "Mohammed Hamdy ~Mohammed_Hamdy1\n",
-                        "Andrianos Michail ~Andrianos_Michail1\n",
-                        "John Yang ~John_Yang3\n",
-                        "Manuel Faysse ~Manuel_Faysse1\n",
-                        "Aleksei Vatolin ~Aleksei_Vatolin1\n",
-                        "Nandan Thakur ~Nandan_Thakur1\n",
-                        "Manan Dey ~Manan_Dey2\n",
-                        "Dipam Vasani ~Dipam_Vasani1\n",
-                        "Pranjal Chitale ~Pranjal_A_Chitale1\n",
-                        "Simone Tedeschi ~Simone_Tedeschi1\n",
-                        "Nguyen Tai ~Nguyen_Tai1\n",
-                        "Artem Snegirev ~Artem_Snegirev1\n",
-                        "Mariya Hendriksen ~Mariya_Hendriksen1\n",
-                        "Michael G\u00fcnther ~Michael_G\u00fcnther1\n",
-                        "Mengzhou Xia ~Mengzhou_Xia1\n",
-                        "Weijia Shi ~Weijia_Shi1\n",
-                        "Xing Han L\u00f9 ~Xing_Han_L\u00f91\n",
-                        "Jordan Clive ~Jordan_Clive1\n",
-                        "Gayatri K ~Gayatri_K1\n",
-                        "Anna Maksimova ~Anna_Maksimova1\n",
-                        "Silvan Wehrli ~Silvan_Wehrli1\n",
-                        "Maria Tikhonova ~Maria_Tikhonova1\n",
-                        "Henil Panchal ~Henil_Shalin_Panchal1\n",
-                        "Aleksandr Abramov ~Aleksandr_Abramov1\n",
-                        "Malte Ostendorff ~Malte_Ostendorff1\n",
-                        "Sara Hooker ~Sara_Hooker2\n",
-                        "Zheng Liu ~Zheng_Liu4\n",
-                        "Simon Clematide ~Simon_Clematide1\n",
-                        "Lester James Miranda ~Lester_James_Validad_Miranda1\n",
-                        "Alena Fenogenova ~Alena_Fenogenova1\n",
-                        "Lasse Hansen ~Lasse_Hansen2\n",
-                        "Guangyu Song ~Guangyu_Song1\n",
-                        "Ruqiya Bin Safi ~Ruqiya_Bin_Safi1\n",
-                        "Wen-Ding Li ~Wen-Ding_Li1\n",
-                        "Alessia Borghini ~Alessia_Borghini1\n",
-                        "Federico Cassano ~Federico_Cassano1\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# get openreview ids for author_df\n",
-                "\n",
-                "# filter out authors with less than 10 points\n",
-                "tt = author_df[author_df[\"GitHub\"].isin({g for g in github if g not in not_10})]\n",
-                "\n",
-                "t = tt[[\"First name\", \"Last name\", \"User on openreview\"]]\n",
-                "\n",
-                "for row in t.iterrows():\n",
-                "    print(row[1][\"First name\"], row[1][\"Last name\"], row[1][\"User on openreview\"])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 21,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "First name                      Federico\n",
-                            "Last name                        Cassano\n",
-                            "User on openreview    ~Federico_Cassano1\n",
-                            "Name: 11, dtype: object"
-                        ]
-                    },
-                    "execution_count": 21,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "row[1]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": ".venv",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.20"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
diff --git a/scripts/task_selection/create_main_results_table.ipynb b/scripts/task_selection/create_main_results_table.ipynb
deleted file mode 100644
index 9d0ff19538..0000000000
--- a/scripts/task_selection/create_main_results_table.ipynb
+++ /dev/null
@@ -1,2738 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Creating data for main results table"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/au561649/Github/mteb/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-                        "  from .autonotebook import tqdm as notebook_tqdm\n"
-                    ]
-                }
-            ],
-            "source": [
-                "from __future__ import annotations\n",
-                "\n",
-                "import pandas as pd\n",
-                "\n",
-                "import mteb\n",
-                "\n",
-                "mdl_names = [\n",
-                "    \"sentence-transformers/all-MiniLM-L6-v2\",\n",
-                "    \"sentence-transformers/all-MiniLM-L12-v2\",\n",
-                "    \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\",\n",
-                "    \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\",\n",
-                "    \"sentence-transformers/all-mpnet-base-v2\",\n",
-                "    \"sentence-transformers/LaBSE\",\n",
-                "    \"intfloat/multilingual-e5-large-instruct\",\n",
-                "    \"intfloat/e5-mistral-7b-instruct\",\n",
-                "    \"GritLM/GritLM-7B\",\n",
-                "    \"intfloat/multilingual-e5-small\",\n",
-                "    \"intfloat/multilingual-e5-base\",\n",
-                "    \"intfloat/multilingual-e5-large\",\n",
-                "]\n",
-                "model_metas = [mteb.get_model_meta(name) for name in mdl_names]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "def add_aggregate_columns(results):\n",
-                "    task_names = results.columns[2:]\n",
-                "\n",
-                "    # convert to 100 scale\n",
-                "    results[task_names] = results[task_names] * 100\n",
-                "\n",
-                "    borda = results[task_names].rank(ascending=True, method=\"min\").sum(axis=1)\n",
-                "    results[\"Borda Count\"] = borda\n",
-                "    results = results.sort_values(\"Borda Count\", ascending=False)\n",
-                "    # borda str: 1 ({borda count}) 2 ({borda count}) 3 ({borda count}) ...\n",
-                "    results[\"Borda str\"] = [\n",
-                "        f\"{i + 1} ({int(borda_count)})\"\n",
-                "        for i, borda_count in enumerate(results[\"Borda Count\"].to_list())\n",
-                "    ]\n",
-                "\n",
-                "    # add mean across tasks\n",
-                "    results[\"Mean\"] = results[task_names].mean(axis=1)\n",
-                "\n",
-                "    # add mean pr. task type\n",
-                "    task_types = [\n",
-                "        \"BitextMining\",\n",
-                "        \"PairClassification\",\n",
-                "        \"Classification\",\n",
-                "        \"STS\",\n",
-                "        \"Retrieval\",\n",
-                "        \"MultilabelClassification\",\n",
-                "        \"Clustering\",\n",
-                "        \"Reranking\",\n",
-                "    ]\n",
-                "\n",
-                "    tasks = [mteb.get_task(name) for name in task_names]\n",
-                "    tasktype_to_tasks = {\n",
-                "        task_type: [t for t in tasks if t.metadata.type == task_type]\n",
-                "        for task_type in task_types\n",
-                "    }\n",
-                "\n",
-                "    for task_type, tasks in tasktype_to_tasks.items():\n",
-                "        task_names = [t.metadata.name for t in tasks]\n",
-                "        results[f\"Mean {task_type}\"] = results[task_names].mean(axis=1)\n",
-                "\n",
-                "    # add mean pr. task type\n",
-                "    cols = [f\"Mean {task_type}\" for task_type in task_types]\n",
-                "    results[\"mean pr. task type\"] = results[cols].mean(axis=1)\n",
-                "    return results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "mult_tasks = mteb.get_benchmark(\"MTEB(Indic)\").tasks\n",
-                "\n",
-                "# load task results for the specified models from mteb/results repository\n",
-                "mteb_results = mteb.load_results(\n",
-                "    models=model_metas,\n",
-                "    tasks=mult_tasks,\n",
-                "    download_latest=False,\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "mteb_results = mteb_results.join_revisions()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Indic\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/au561649/Github/mteb/mteb/load_results/benchmark_results.py:120: UserWarning: Couldn't get scores for IndicGenBenchFloresBitextMining due to No splits had scores for the specified languages..\n",
-                        "  warnings.warn(\n",
-                        "/Users/au561649/Github/mteb/mteb/load_results/benchmark_results.py:120: UserWarning: Couldn't get scores for IndicLangClassification due to No splits had scores for the specified languages..\n",
-                        "  warnings.warn(\n",
-                        "/Users/au561649/Github/mteb/mteb/load_results/benchmark_results.py:120: UserWarning: Couldn't get scores for LinceMTBitextMining due to No splits had scores for the specified languages..\n",
-                        "  warnings.warn(\n"
-                    ]
-                }
-            ],
-            "source": [
-                "mult_tasks = mteb.get_benchmark(\"MTEB(Indic)\").tasks\n",
-                "\n",
-                "# load task results for the specified models from mteb/results repository\n",
-                "mteb_results = mteb.load_results(\n",
-                "    models=model_metas,\n",
-                "    tasks=mult_tasks,\n",
-                "    download_latest=False,\n",
-                ")\n",
-                "\n",
-                "mteb_results = mteb_results.join_revisions().filter_models()\n",
-                "\n",
-                "# manual check that everything is there\n",
-                "# pd.DataFrame(mteb_results.get_scores()).to_csv(\"tmp.csv\")\n",
-                "\n",
-                "results = pd.DataFrame(mteb_results.get_scores())\n",
-                "results = add_aggregate_columns(results=results)\n",
-                "\n",
-                "\n",
-                "# create latex table\n",
-                "# column order\n",
-                "cols = [\n",
-                "    \"model\",\n",
-                "    \"Borda str\",\n",
-                "    \"Mean\",\n",
-                "    \"mean pr. task type\",\n",
-                "    \"Mean BitextMining\",\n",
-                "    \"Mean PairClassification\",\n",
-                "    \"Mean Classification\",\n",
-                "    \"Mean STS\",\n",
-                "    \"Mean Retrieval\",\n",
-                "    \"Mean MultilabelClassification\",\n",
-                "    \"Mean Clustering\",\n",
-                "    \"Mean Reranking\",\n",
-                "]\n",
-                "\n",
-                "latex_df = results[cols]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th>BelebeleRetrieval</th>\n",
-                            "      <th>BengaliSentimentAnalysis</th>\n",
-                            "      <th>GujaratiNewsClassification</th>\n",
-                            "      <th>HindiDiscourseClassification</th>\n",
-                            "      <th>IN22ConvBitextMining</th>\n",
-                            "      <th>IN22GenBitextMining</th>\n",
-                            "      <th>IndicCrosslingualSTS</th>\n",
-                            "      <th>MTOPIntentClassification</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>Mean</th>\n",
-                            "      <th>Mean BitextMining</th>\n",
-                            "      <th>Mean PairClassification</th>\n",
-                            "      <th>Mean Classification</th>\n",
-                            "      <th>Mean STS</th>\n",
-                            "      <th>Mean Retrieval</th>\n",
-                            "      <th>Mean MultilabelClassification</th>\n",
-                            "      <th>Mean Clustering</th>\n",
-                            "      <th>Mean Reranking</th>\n",
-                            "      <th>mean pr. task type</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>baa7be480a7de1539afce709c8f13f833a510e0a</td>\n",
-                            "      <td>73.754577</td>\n",
-                            "      <td>83.984927</td>\n",
-                            "      <td>87.518968</td>\n",
-                            "      <td>35.234375</td>\n",
-                            "      <td>71.873549</td>\n",
-                            "      <td>88.875417</td>\n",
-                            "      <td>53.688533</td>\n",
-                            "      <td>62.952996</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>70.186085</td>\n",
-                            "      <td>80.374483</td>\n",
-                            "      <td>76.314311</td>\n",
-                            "      <td>67.009249</td>\n",
-                            "      <td>53.688533</td>\n",
-                            "      <td>84.862788</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>51.671254</td>\n",
-                            "      <td>87.462063</td>\n",
-                            "      <td>71.626097</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</td>\n",
-                            "      <td>68.199269</td>\n",
-                            "      <td>83.070706</td>\n",
-                            "      <td>76.737481</td>\n",
-                            "      <td>38.740234</td>\n",
-                            "      <td>67.784590</td>\n",
-                            "      <td>87.696664</td>\n",
-                            "      <td>43.866162</td>\n",
-                            "      <td>59.198891</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>66.354869</td>\n",
-                            "      <td>77.740627</td>\n",
-                            "      <td>75.057575</td>\n",
-                            "      <td>64.659155</td>\n",
-                            "      <td>43.866162</td>\n",
-                            "      <td>82.604635</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>25.602675</td>\n",
-                            "      <td>85.970595</td>\n",
-                            "      <td>65.071632</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>d13f1b27baf31030b7fd040960d60d909913633f</td>\n",
-                            "      <td>60.371654</td>\n",
-                            "      <td>79.643111</td>\n",
-                            "      <td>74.908953</td>\n",
-                            "      <td>39.038086</td>\n",
-                            "      <td>63.130747</td>\n",
-                            "      <td>85.290012</td>\n",
-                            "      <td>41.113890</td>\n",
-                            "      <td>54.049160</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>64.571532</td>\n",
-                            "      <td>74.210379</td>\n",
-                            "      <td>72.795771</td>\n",
-                            "      <td>63.753851</td>\n",
-                            "      <td>41.113890</td>\n",
-                            "      <td>77.842327</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>24.607904</td>\n",
-                            "      <td>83.761455</td>\n",
-                            "      <td>62.583654</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>e4ce9877abf3edfe10b0d82785e83bdcb973e22e</td>\n",
-                            "      <td>58.193538</td>\n",
-                            "      <td>83.430444</td>\n",
-                            "      <td>74.393020</td>\n",
-                            "      <td>39.335938</td>\n",
-                            "      <td>62.739174</td>\n",
-                            "      <td>84.663212</td>\n",
-                            "      <td>40.761080</td>\n",
-                            "      <td>52.118503</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>64.720156</td>\n",
-                            "      <td>73.701193</td>\n",
-                            "      <td>73.795104</td>\n",
-                            "      <td>63.782196</td>\n",
-                            "      <td>40.761080</td>\n",
-                            "      <td>76.817269</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>29.054088</td>\n",
-                            "      <td>84.369577</td>\n",
-                            "      <td>63.182930</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>13f00a0e36500c80ce12870ea513846a066004af</td>\n",
-                            "      <td>70.063654</td>\n",
-                            "      <td>72.100459</td>\n",
-                            "      <td>69.855842</td>\n",
-                            "      <td>37.089844</td>\n",
-                            "      <td>42.137574</td>\n",
-                            "      <td>74.667530</td>\n",
-                            "      <td>27.245620</td>\n",
-                            "      <td>63.660123</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>60.204173</td>\n",
-                            "      <td>58.402552</td>\n",
-                            "      <td>67.838170</td>\n",
-                            "      <td>60.043953</td>\n",
-                            "      <td>27.245620</td>\n",
-                            "      <td>79.496827</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>27.978331</td>\n",
-                            "      <td>84.695146</td>\n",
-                            "      <td>57.957228</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>07163b72af1488142a360786df853f237b1a3ca1</td>\n",
-                            "      <td>66.289423</td>\n",
-                            "      <td>72.074816</td>\n",
-                            "      <td>73.088012</td>\n",
-                            "      <td>32.006836</td>\n",
-                            "      <td>44.302781</td>\n",
-                            "      <td>73.799887</td>\n",
-                            "      <td>22.981297</td>\n",
-                            "      <td>59.232093</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>60.022827</td>\n",
-                            "      <td>59.051334</td>\n",
-                            "      <td>72.951099</td>\n",
-                            "      <td>59.563869</td>\n",
-                            "      <td>22.981297</td>\n",
-                            "      <td>77.266212</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>32.702546</td>\n",
-                            "      <td>84.420079</td>\n",
-                            "      <td>58.419491</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>e34fab64a3011d2176c99545a93d5cbddc9a91b7</td>\n",
-                            "      <td>47.515538</td>\n",
-                            "      <td>80.415418</td>\n",
-                            "      <td>76.358118</td>\n",
-                            "      <td>38.398438</td>\n",
-                            "      <td>63.459252</td>\n",
-                            "      <td>84.668840</td>\n",
-                            "      <td>52.758018</td>\n",
-                            "      <td>62.863971</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>61.855117</td>\n",
-                            "      <td>74.064046</td>\n",
-                            "      <td>64.583366</td>\n",
-                            "      <td>61.906474</td>\n",
-                            "      <td>52.758018</td>\n",
-                            "      <td>64.334769</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>21.105169</td>\n",
-                            "      <td>78.980476</td>\n",
-                            "      <td>59.676046</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>79f2382ceacceacdf38563d7c5d16b9ff8d725d6</td>\n",
-                            "      <td>36.101692</td>\n",
-                            "      <td>74.882594</td>\n",
-                            "      <td>81.934750</td>\n",
-                            "      <td>38.691406</td>\n",
-                            "      <td>33.588414</td>\n",
-                            "      <td>54.802443</td>\n",
-                            "      <td>34.096874</td>\n",
-                            "      <td>61.699834</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>58.502887</td>\n",
-                            "      <td>44.195428</td>\n",
-                            "      <td>82.036142</td>\n",
-                            "      <td>61.943266</td>\n",
-                            "      <td>34.096874</td>\n",
-                            "      <td>57.910346</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>32.061697</td>\n",
-                            "      <td>74.332275</td>\n",
-                            "      <td>55.225147</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>bf3bf13ab40c3157080a7ab344c831b9ad18b5eb</td>\n",
-                            "      <td>19.395077</td>\n",
-                            "      <td>60.613522</td>\n",
-                            "      <td>76.874052</td>\n",
-                            "      <td>37.431641</td>\n",
-                            "      <td>11.867235</td>\n",
-                            "      <td>18.709649</td>\n",
-                            "      <td>19.788971</td>\n",
-                            "      <td>59.196945</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>49.672632</td>\n",
-                            "      <td>15.288442</td>\n",
-                            "      <td>77.849520</td>\n",
-                            "      <td>57.645424</td>\n",
-                            "      <td>19.788971</td>\n",
-                            "      <td>48.779038</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>16.675403</td>\n",
-                            "      <td>59.258690</td>\n",
-                            "      <td>42.183641</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>84f2bcc00d77236f9e89c8a360a00fb1139bf47d</td>\n",
-                            "      <td>9.627615</td>\n",
-                            "      <td>54.188273</td>\n",
-                            "      <td>44.104704</td>\n",
-                            "      <td>34.316406</td>\n",
-                            "      <td>2.110360</td>\n",
-                            "      <td>5.353099</td>\n",
-                            "      <td>-2.509332</td>\n",
-                            "      <td>18.150352</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>33.630293</td>\n",
-                            "      <td>3.731730</td>\n",
-                            "      <td>52.634533</td>\n",
-                            "      <td>45.224600</td>\n",
-                            "      <td>-2.509332</td>\n",
-                            "      <td>12.853808</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>4.012625</td>\n",
-                            "      <td>42.601772</td>\n",
-                            "      <td>22.649962</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>a05860a77cef7b37e0048a7864658139bc18a854</td>\n",
-                            "      <td>9.927654</td>\n",
-                            "      <td>58.975982</td>\n",
-                            "      <td>41.638847</td>\n",
-                            "      <td>28.911133</td>\n",
-                            "      <td>1.922509</td>\n",
-                            "      <td>4.993733</td>\n",
-                            "      <td>-5.337116</td>\n",
-                            "      <td>17.615309</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>33.121629</td>\n",
-                            "      <td>3.458121</td>\n",
-                            "      <td>55.034299</td>\n",
-                            "      <td>43.891361</td>\n",
-                            "      <td>-5.337116</td>\n",
-                            "      <td>13.923827</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>3.687981</td>\n",
-                            "      <td>47.587196</td>\n",
-                            "      <td>23.177952</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>8b3219a92973c328a8e22fadcfa821b5dc75636a</td>\n",
-                            "      <td>7.661654</td>\n",
-                            "      <td>58.541631</td>\n",
-                            "      <td>42.579666</td>\n",
-                            "      <td>32.036133</td>\n",
-                            "      <td>1.415965</td>\n",
-                            "      <td>3.573556</td>\n",
-                            "      <td>-6.275768</td>\n",
-                            "      <td>18.495051</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>31.842342</td>\n",
-                            "      <td>2.494760</td>\n",
-                            "      <td>53.667487</td>\n",
-                            "      <td>44.145491</td>\n",
-                            "      <td>-6.275768</td>\n",
-                            "      <td>6.217327</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>3.103278</td>\n",
-                            "      <td>39.181786</td>\n",
-                            "      <td>20.362052</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>12 rows \u00d7 34 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model  \\\n",
-                            "4             intfloat/multilingual-e5-large-instruct   \n",
-                            "3                      intfloat/multilingual-e5-large   \n",
-                            "2                       intfloat/multilingual-e5-base   \n",
-                            "5                      intfloat/multilingual-e5-small   \n",
-                            "0                                    GritLM/GritLM-7B   \n",
-                            "1                     intfloat/e5-mistral-7b-instruct   \n",
-                            "6                         sentence-transformers/LaBSE   \n",
-                            "11  sentence-transformers/paraphrase-multilingual-...   \n",
-                            "10  sentence-transformers/paraphrase-multilingual-...   \n",
-                            "9             sentence-transformers/all-mpnet-base-v2   \n",
-                            "7             sentence-transformers/all-MiniLM-L12-v2   \n",
-                            "8              sentence-transformers/all-MiniLM-L6-v2   \n",
-                            "\n",
-                            "                                    revision  BelebeleRetrieval  \\\n",
-                            "4   baa7be480a7de1539afce709c8f13f833a510e0a          73.754577   \n",
-                            "3   4dc6d853a804b9c8886ede6dda8a073b7dc08a81          68.199269   \n",
-                            "2   d13f1b27baf31030b7fd040960d60d909913633f          60.371654   \n",
-                            "5   e4ce9877abf3edfe10b0d82785e83bdcb973e22e          58.193538   \n",
-                            "0   13f00a0e36500c80ce12870ea513846a066004af          70.063654   \n",
-                            "1   07163b72af1488142a360786df853f237b1a3ca1          66.289423   \n",
-                            "6   e34fab64a3011d2176c99545a93d5cbddc9a91b7          47.515538   \n",
-                            "11  79f2382ceacceacdf38563d7c5d16b9ff8d725d6          36.101692   \n",
-                            "10  bf3bf13ab40c3157080a7ab344c831b9ad18b5eb          19.395077   \n",
-                            "9   84f2bcc00d77236f9e89c8a360a00fb1139bf47d           9.627615   \n",
-                            "7   a05860a77cef7b37e0048a7864658139bc18a854           9.927654   \n",
-                            "8   8b3219a92973c328a8e22fadcfa821b5dc75636a           7.661654   \n",
-                            "\n",
-                            "    BengaliSentimentAnalysis  GujaratiNewsClassification  \\\n",
-                            "4                  83.984927                   87.518968   \n",
-                            "3                  83.070706                   76.737481   \n",
-                            "2                  79.643111                   74.908953   \n",
-                            "5                  83.430444                   74.393020   \n",
-                            "0                  72.100459                   69.855842   \n",
-                            "1                  72.074816                   73.088012   \n",
-                            "6                  80.415418                   76.358118   \n",
-                            "11                 74.882594                   81.934750   \n",
-                            "10                 60.613522                   76.874052   \n",
-                            "9                  54.188273                   44.104704   \n",
-                            "7                  58.975982                   41.638847   \n",
-                            "8                  58.541631                   42.579666   \n",
-                            "\n",
-                            "    HindiDiscourseClassification  IN22ConvBitextMining  IN22GenBitextMining  \\\n",
-                            "4                      35.234375             71.873549            88.875417   \n",
-                            "3                      38.740234             67.784590            87.696664   \n",
-                            "2                      39.038086             63.130747            85.290012   \n",
-                            "5                      39.335938             62.739174            84.663212   \n",
-                            "0                      37.089844             42.137574            74.667530   \n",
-                            "1                      32.006836             44.302781            73.799887   \n",
-                            "6                      38.398438             63.459252            84.668840   \n",
-                            "11                     38.691406             33.588414            54.802443   \n",
-                            "10                     37.431641             11.867235            18.709649   \n",
-                            "9                      34.316406              2.110360             5.353099   \n",
-                            "7                      28.911133              1.922509             4.993733   \n",
-                            "8                      32.036133              1.415965             3.573556   \n",
-                            "\n",
-                            "    IndicCrosslingualSTS  MTOPIntentClassification  ...       Mean  \\\n",
-                            "4              53.688533                 62.952996  ...  70.186085   \n",
-                            "3              43.866162                 59.198891  ...  66.354869   \n",
-                            "2              41.113890                 54.049160  ...  64.571532   \n",
-                            "5              40.761080                 52.118503  ...  64.720156   \n",
-                            "0              27.245620                 63.660123  ...  60.204173   \n",
-                            "1              22.981297                 59.232093  ...  60.022827   \n",
-                            "6              52.758018                 62.863971  ...  61.855117   \n",
-                            "11             34.096874                 61.699834  ...  58.502887   \n",
-                            "10             19.788971                 59.196945  ...  49.672632   \n",
-                            "9              -2.509332                 18.150352  ...  33.630293   \n",
-                            "7              -5.337116                 17.615309  ...  33.121629   \n",
-                            "8              -6.275768                 18.495051  ...  31.842342   \n",
-                            "\n",
-                            "    Mean BitextMining  Mean PairClassification  Mean Classification  \\\n",
-                            "4           80.374483                76.314311            67.009249   \n",
-                            "3           77.740627                75.057575            64.659155   \n",
-                            "2           74.210379                72.795771            63.753851   \n",
-                            "5           73.701193                73.795104            63.782196   \n",
-                            "0           58.402552                67.838170            60.043953   \n",
-                            "1           59.051334                72.951099            59.563869   \n",
-                            "6           74.064046                64.583366            61.906474   \n",
-                            "11          44.195428                82.036142            61.943266   \n",
-                            "10          15.288442                77.849520            57.645424   \n",
-                            "9            3.731730                52.634533            45.224600   \n",
-                            "7            3.458121                55.034299            43.891361   \n",
-                            "8            2.494760                53.667487            44.145491   \n",
-                            "\n",
-                            "     Mean STS  Mean Retrieval  Mean MultilabelClassification  Mean Clustering  \\\n",
-                            "4   53.688533       84.862788                            NaN        51.671254   \n",
-                            "3   43.866162       82.604635                            NaN        25.602675   \n",
-                            "2   41.113890       77.842327                            NaN        24.607904   \n",
-                            "5   40.761080       76.817269                            NaN        29.054088   \n",
-                            "0   27.245620       79.496827                            NaN        27.978331   \n",
-                            "1   22.981297       77.266212                            NaN        32.702546   \n",
-                            "6   52.758018       64.334769                            NaN        21.105169   \n",
-                            "11  34.096874       57.910346                            NaN        32.061697   \n",
-                            "10  19.788971       48.779038                            NaN        16.675403   \n",
-                            "9   -2.509332       12.853808                            NaN         4.012625   \n",
-                            "7   -5.337116       13.923827                            NaN         3.687981   \n",
-                            "8   -6.275768        6.217327                            NaN         3.103278   \n",
-                            "\n",
-                            "    Mean Reranking  mean pr. task type  \n",
-                            "4        87.462063           71.626097  \n",
-                            "3        85.970595           65.071632  \n",
-                            "2        83.761455           62.583654  \n",
-                            "5        84.369577           63.182930  \n",
-                            "0        84.695146           57.957228  \n",
-                            "1        84.420079           58.419491  \n",
-                            "6        78.980476           59.676046  \n",
-                            "11       74.332275           55.225147  \n",
-                            "10       59.258690           42.183641  \n",
-                            "9        42.601772           22.649962  \n",
-                            "7        47.587196           23.177952  \n",
-                            "8        39.181786           20.362052  \n",
-                            "\n",
-                            "[12 rows x 34 columns]"
-                        ]
-                    },
-                    "execution_count": 6,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>Borda str</th>\n",
-                            "      <th>Mean</th>\n",
-                            "      <th>mean pr. task type</th>\n",
-                            "      <th>Mean BitextMining</th>\n",
-                            "      <th>Mean PairClassification</th>\n",
-                            "      <th>Mean Classification</th>\n",
-                            "      <th>Mean STS</th>\n",
-                            "      <th>Mean Retrieval</th>\n",
-                            "      <th>Mean MultilabelClassification</th>\n",
-                            "      <th>Mean Clustering</th>\n",
-                            "      <th>Mean Reranking</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>1 (209)</td>\n",
-                            "      <td>70.186085</td>\n",
-                            "      <td>71.626097</td>\n",
-                            "      <td>80.374483</td>\n",
-                            "      <td>76.314311</td>\n",
-                            "      <td>67.009249</td>\n",
-                            "      <td>53.688533</td>\n",
-                            "      <td>84.862788</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>51.671254</td>\n",
-                            "      <td>87.462063</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>2 (188)</td>\n",
-                            "      <td>66.354869</td>\n",
-                            "      <td>65.071632</td>\n",
-                            "      <td>77.740627</td>\n",
-                            "      <td>75.057575</td>\n",
-                            "      <td>64.659155</td>\n",
-                            "      <td>43.866162</td>\n",
-                            "      <td>82.604635</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>25.602675</td>\n",
-                            "      <td>85.970595</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>3 (173)</td>\n",
-                            "      <td>64.571532</td>\n",
-                            "      <td>62.583654</td>\n",
-                            "      <td>74.210379</td>\n",
-                            "      <td>72.795771</td>\n",
-                            "      <td>63.753851</td>\n",
-                            "      <td>41.113890</td>\n",
-                            "      <td>77.842327</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>24.607904</td>\n",
-                            "      <td>83.761455</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>4 (164)</td>\n",
-                            "      <td>64.720156</td>\n",
-                            "      <td>63.182930</td>\n",
-                            "      <td>73.701193</td>\n",
-                            "      <td>73.795104</td>\n",
-                            "      <td>63.782196</td>\n",
-                            "      <td>40.761080</td>\n",
-                            "      <td>76.817269</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>29.054088</td>\n",
-                            "      <td>84.369577</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>5 (151)</td>\n",
-                            "      <td>60.204173</td>\n",
-                            "      <td>57.957228</td>\n",
-                            "      <td>58.402552</td>\n",
-                            "      <td>67.838170</td>\n",
-                            "      <td>60.043953</td>\n",
-                            "      <td>27.245620</td>\n",
-                            "      <td>79.496827</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>27.978331</td>\n",
-                            "      <td>84.695146</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>6 (144)</td>\n",
-                            "      <td>60.022827</td>\n",
-                            "      <td>58.419491</td>\n",
-                            "      <td>59.051334</td>\n",
-                            "      <td>72.951099</td>\n",
-                            "      <td>59.563869</td>\n",
-                            "      <td>22.981297</td>\n",
-                            "      <td>77.266212</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>32.702546</td>\n",
-                            "      <td>84.420079</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>7 (139)</td>\n",
-                            "      <td>61.855117</td>\n",
-                            "      <td>59.676046</td>\n",
-                            "      <td>74.064046</td>\n",
-                            "      <td>64.583366</td>\n",
-                            "      <td>61.906474</td>\n",
-                            "      <td>52.758018</td>\n",
-                            "      <td>64.334769</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>21.105169</td>\n",
-                            "      <td>78.980476</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>8 (137)</td>\n",
-                            "      <td>58.502887</td>\n",
-                            "      <td>55.225147</td>\n",
-                            "      <td>44.195428</td>\n",
-                            "      <td>82.036142</td>\n",
-                            "      <td>61.943266</td>\n",
-                            "      <td>34.096874</td>\n",
-                            "      <td>57.910346</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>32.061697</td>\n",
-                            "      <td>74.332275</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>9 (98)</td>\n",
-                            "      <td>49.672632</td>\n",
-                            "      <td>42.183641</td>\n",
-                            "      <td>15.288442</td>\n",
-                            "      <td>77.849520</td>\n",
-                            "      <td>57.645424</td>\n",
-                            "      <td>19.788971</td>\n",
-                            "      <td>48.779038</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>16.675403</td>\n",
-                            "      <td>59.258690</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>10 (68)</td>\n",
-                            "      <td>33.630293</td>\n",
-                            "      <td>22.649962</td>\n",
-                            "      <td>3.731730</td>\n",
-                            "      <td>52.634533</td>\n",
-                            "      <td>45.224600</td>\n",
-                            "      <td>-2.509332</td>\n",
-                            "      <td>12.853808</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>4.012625</td>\n",
-                            "      <td>42.601772</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>11 (49)</td>\n",
-                            "      <td>33.121629</td>\n",
-                            "      <td>23.177952</td>\n",
-                            "      <td>3.458121</td>\n",
-                            "      <td>55.034299</td>\n",
-                            "      <td>43.891361</td>\n",
-                            "      <td>-5.337116</td>\n",
-                            "      <td>13.923827</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>3.687981</td>\n",
-                            "      <td>47.587196</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>12 (40)</td>\n",
-                            "      <td>31.842342</td>\n",
-                            "      <td>20.362052</td>\n",
-                            "      <td>2.494760</td>\n",
-                            "      <td>53.667487</td>\n",
-                            "      <td>44.145491</td>\n",
-                            "      <td>-6.275768</td>\n",
-                            "      <td>6.217327</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>3.103278</td>\n",
-                            "      <td>39.181786</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model Borda str       Mean  \\\n",
-                            "4             intfloat/multilingual-e5-large-instruct   1 (209)  70.186085   \n",
-                            "3                      intfloat/multilingual-e5-large   2 (188)  66.354869   \n",
-                            "2                       intfloat/multilingual-e5-base   3 (173)  64.571532   \n",
-                            "5                      intfloat/multilingual-e5-small   4 (164)  64.720156   \n",
-                            "0                                    GritLM/GritLM-7B   5 (151)  60.204173   \n",
-                            "1                     intfloat/e5-mistral-7b-instruct   6 (144)  60.022827   \n",
-                            "6                         sentence-transformers/LaBSE   7 (139)  61.855117   \n",
-                            "11  sentence-transformers/paraphrase-multilingual-...   8 (137)  58.502887   \n",
-                            "10  sentence-transformers/paraphrase-multilingual-...    9 (98)  49.672632   \n",
-                            "9             sentence-transformers/all-mpnet-base-v2   10 (68)  33.630293   \n",
-                            "7             sentence-transformers/all-MiniLM-L12-v2   11 (49)  33.121629   \n",
-                            "8              sentence-transformers/all-MiniLM-L6-v2   12 (40)  31.842342   \n",
-                            "\n",
-                            "    mean pr. task type  Mean BitextMining  Mean PairClassification  \\\n",
-                            "4            71.626097          80.374483                76.314311   \n",
-                            "3            65.071632          77.740627                75.057575   \n",
-                            "2            62.583654          74.210379                72.795771   \n",
-                            "5            63.182930          73.701193                73.795104   \n",
-                            "0            57.957228          58.402552                67.838170   \n",
-                            "1            58.419491          59.051334                72.951099   \n",
-                            "6            59.676046          74.064046                64.583366   \n",
-                            "11           55.225147          44.195428                82.036142   \n",
-                            "10           42.183641          15.288442                77.849520   \n",
-                            "9            22.649962           3.731730                52.634533   \n",
-                            "7            23.177952           3.458121                55.034299   \n",
-                            "8            20.362052           2.494760                53.667487   \n",
-                            "\n",
-                            "    Mean Classification   Mean STS  Mean Retrieval  \\\n",
-                            "4             67.009249  53.688533       84.862788   \n",
-                            "3             64.659155  43.866162       82.604635   \n",
-                            "2             63.753851  41.113890       77.842327   \n",
-                            "5             63.782196  40.761080       76.817269   \n",
-                            "0             60.043953  27.245620       79.496827   \n",
-                            "1             59.563869  22.981297       77.266212   \n",
-                            "6             61.906474  52.758018       64.334769   \n",
-                            "11            61.943266  34.096874       57.910346   \n",
-                            "10            57.645424  19.788971       48.779038   \n",
-                            "9             45.224600  -2.509332       12.853808   \n",
-                            "7             43.891361  -5.337116       13.923827   \n",
-                            "8             44.145491  -6.275768        6.217327   \n",
-                            "\n",
-                            "    Mean MultilabelClassification  Mean Clustering  Mean Reranking  \n",
-                            "4                             NaN        51.671254       87.462063  \n",
-                            "3                             NaN        25.602675       85.970595  \n",
-                            "2                             NaN        24.607904       83.761455  \n",
-                            "5                             NaN        29.054088       84.369577  \n",
-                            "0                             NaN        27.978331       84.695146  \n",
-                            "1                             NaN        32.702546       84.420079  \n",
-                            "6                             NaN        21.105169       78.980476  \n",
-                            "11                            NaN        32.061697       74.332275  \n",
-                            "10                            NaN        16.675403       59.258690  \n",
-                            "9                             NaN         4.012625       42.601772  \n",
-                            "7                             NaN         3.687981       47.587196  \n",
-                            "8                             NaN         3.103278       39.181786  "
-                        ]
-                    },
-                    "execution_count": 7,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "latex_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\begin{tabular}{llrrrrrrrrrr}\n",
-                        "\\toprule\n",
-                        "model & Borda str & Mean & mean pr. task type & Mean BitextMining & Mean PairClassification & Mean Classification & Mean STS & Mean Retrieval & Mean MultilabelClassification & Mean Clustering & Mean Reranking \\\\\n",
-                        "\\midrule\n",
-                        "intfloat/multilingual-e5-large-instruct & 1 (209) & 70.2 & 71.6 & 80.4 & 76.3 & 67.0 & 53.7 & 84.9 & NaN & 51.7 & 87.5 \\\\\n",
-                        "intfloat/multilingual-e5-large & 2 (188) & 66.4 & 65.1 & 77.7 & 75.1 & 64.7 & 43.9 & 82.6 & NaN & 25.6 & 86.0 \\\\\n",
-                        "intfloat/multilingual-e5-base & 3 (173) & 64.6 & 62.6 & 74.2 & 72.8 & 63.8 & 41.1 & 77.8 & NaN & 24.6 & 83.8 \\\\\n",
-                        "intfloat/multilingual-e5-small & 4 (164) & 64.7 & 63.2 & 73.7 & 73.8 & 63.8 & 40.8 & 76.8 & NaN & 29.1 & 84.4 \\\\\n",
-                        "GritLM/GritLM-7B & 5 (151) & 60.2 & 58.0 & 58.4 & 67.8 & 60.0 & 27.2 & 79.5 & NaN & 28.0 & 84.7 \\\\\n",
-                        "intfloat/e5-mistral-7b-instruct & 6 (144) & 60.0 & 58.4 & 59.1 & 73.0 & 59.6 & 23.0 & 77.3 & NaN & 32.7 & 84.4 \\\\\n",
-                        "sentence-transformers/LaBSE & 7 (139) & 61.9 & 59.7 & 74.1 & 64.6 & 61.9 & 52.8 & 64.3 & NaN & 21.1 & 79.0 \\\\\n",
-                        "sentence-transformers/paraphrase-multilingual-mpnet-base-v2 & 8 (137) & 58.5 & 55.2 & 44.2 & 82.0 & 61.9 & 34.1 & 57.9 & NaN & 32.1 & 74.3 \\\\\n",
-                        "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 & 9 (98) & 49.7 & 42.2 & 15.3 & 77.8 & 57.6 & 19.8 & 48.8 & NaN & 16.7 & 59.3 \\\\\n",
-                        "sentence-transformers/all-mpnet-base-v2 & 10 (68) & 33.6 & 22.6 & 3.7 & 52.6 & 45.2 & -2.5 & 12.9 & NaN & 4.0 & 42.6 \\\\\n",
-                        "sentence-transformers/all-MiniLM-L12-v2 & 11 (49) & 33.1 & 23.2 & 3.5 & 55.0 & 43.9 & -5.3 & 13.9 & NaN & 3.7 & 47.6 \\\\\n",
-                        "sentence-transformers/all-MiniLM-L6-v2 & 12 (40) & 31.8 & 20.4 & 2.5 & 53.7 & 44.1 & -6.3 & 6.2 & NaN & 3.1 & 39.2 \\\\\n",
-                        "\\bottomrule\n",
-                        "\\end{tabular}\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(latex_df.to_latex(index=False, float_format=\"%.1f\"))"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Europe"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/au561649/Github/mteb/mteb/load_results/benchmark_results.py:120: UserWarning: Couldn't get scores for NordicLangClassification due to No splits had scores for the specified languages..\n",
-                        "  warnings.warn(\n"
-                    ]
-                }
-            ],
-            "source": [
-                "mult_tasks = mteb.get_benchmark(\"MTEB(Europe)\").tasks\n",
-                "\n",
-                "# load task results for the specified models from mteb/results repository\n",
-                "mteb_results = mteb.load_results(\n",
-                "    models=model_metas,\n",
-                "    tasks=mult_tasks,\n",
-                "    download_latest=False,\n",
-                ")\n",
-                "\n",
-                "mteb_results = mteb_results.join_revisions().filter_models()\n",
-                "\n",
-                "# manual check that everything is there\n",
-                "pd.DataFrame(mteb_results.get_scores()).to_csv(\"tmp.csv\")\n",
-                "\n",
-                "results = pd.DataFrame(mteb_results.get_scores())\n",
-                "results = add_aggregate_columns(results=results)\n",
-                "\n",
-                "\n",
-                "# create latex table\n",
-                "# column order\n",
-                "cols = [\n",
-                "    \"model\",\n",
-                "    \"Borda str\",\n",
-                "    \"Mean\",\n",
-                "    \"mean pr. task type\",\n",
-                "    \"Mean BitextMining\",\n",
-                "    \"Mean PairClassification\",\n",
-                "    \"Mean Classification\",\n",
-                "    \"Mean STS\",\n",
-                "    \"Mean Retrieval\",\n",
-                "    \"Mean MultilabelClassification\",\n",
-                "    \"Mean Clustering\",\n",
-                "    \"Mean Reranking\",\n",
-                "]\n",
-                "\n",
-                "latex_df = results[cols]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th>AlloProfClusteringS2S.v2</th>\n",
-                            "      <th>AlloprofReranking</th>\n",
-                            "      <th>AlloprofRetrieval</th>\n",
-                            "      <th>AmazonCounterfactualClassification</th>\n",
-                            "      <th>ArguAna</th>\n",
-                            "      <th>BUCC.v2</th>\n",
-                            "      <th>BelebeleRetrieval</th>\n",
-                            "      <th>BibleNLPBitextMining</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>Mean</th>\n",
-                            "      <th>Mean BitextMining</th>\n",
-                            "      <th>Mean PairClassification</th>\n",
-                            "      <th>Mean Classification</th>\n",
-                            "      <th>Mean STS</th>\n",
-                            "      <th>Mean Retrieval</th>\n",
-                            "      <th>Mean MultilabelClassification</th>\n",
-                            "      <th>Mean Clustering</th>\n",
-                            "      <th>Mean Reranking</th>\n",
-                            "      <th>mean pr. task type</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>13f00a0e36500c80ce12870ea513846a066004af</td>\n",
-                            "      <td>56.411821</td>\n",
-                            "      <td>77.926160</td>\n",
-                            "      <td>55.422</td>\n",
-                            "      <td>78.452787</td>\n",
-                            "      <td>63.171</td>\n",
-                            "      <td>99.502429</td>\n",
-                            "      <td>91.393110</td>\n",
-                            "      <td>97.337705</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>62.970819</td>\n",
-                            "      <td>90.420705</td>\n",
-                            "      <td>89.939440</td>\n",
-                            "      <td>64.736896</td>\n",
-                            "      <td>76.050247</td>\n",
-                            "      <td>57.105263</td>\n",
-                            "      <td>17.551200</td>\n",
-                            "      <td>45.281105</td>\n",
-                            "      <td>60.269644</td>\n",
-                            "      <td>62.669312</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>baa7be480a7de1539afce709c8f13f833a510e0a</td>\n",
-                            "      <td>56.465655</td>\n",
-                            "      <td>74.677730</td>\n",
-                            "      <td>52.118</td>\n",
-                            "      <td>67.619802</td>\n",
-                            "      <td>58.476</td>\n",
-                            "      <td>99.473836</td>\n",
-                            "      <td>92.240123</td>\n",
-                            "      <td>97.916667</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>62.192929</td>\n",
-                            "      <td>90.383846</td>\n",
-                            "      <td>89.985879</td>\n",
-                            "      <td>63.241319</td>\n",
-                            "      <td>77.428657</td>\n",
-                            "      <td>54.802566</td>\n",
-                            "      <td>17.265889</td>\n",
-                            "      <td>46.895583</td>\n",
-                            "      <td>58.420108</td>\n",
-                            "      <td>62.302981</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>07163b72af1488142a360786df853f237b1a3ca1</td>\n",
-                            "      <td>57.112000</td>\n",
-                            "      <td>78.317664</td>\n",
-                            "      <td>54.619</td>\n",
-                            "      <td>73.901466</td>\n",
-                            "      <td>61.653</td>\n",
-                            "      <td>99.384579</td>\n",
-                            "      <td>88.393877</td>\n",
-                            "      <td>96.731306</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>61.729119</td>\n",
-                            "      <td>89.580187</td>\n",
-                            "      <td>91.154182</td>\n",
-                            "      <td>62.946715</td>\n",
-                            "      <td>76.481287</td>\n",
-                            "      <td>53.644095</td>\n",
-                            "      <td>15.464545</td>\n",
-                            "      <td>46.473328</td>\n",
-                            "      <td>59.815300</td>\n",
-                            "      <td>61.944955</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</td>\n",
-                            "      <td>35.150764</td>\n",
-                            "      <td>69.442880</td>\n",
-                            "      <td>39.341</td>\n",
-                            "      <td>75.116732</td>\n",
-                            "      <td>54.357</td>\n",
-                            "      <td>99.022547</td>\n",
-                            "      <td>92.728438</td>\n",
-                            "      <td>94.571508</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>58.491543</td>\n",
-                            "      <td>84.456404</td>\n",
-                            "      <td>88.753477</td>\n",
-                            "      <td>60.385125</td>\n",
-                            "      <td>75.759056</td>\n",
-                            "      <td>50.814358</td>\n",
-                            "      <td>14.980040</td>\n",
-                            "      <td>38.235917</td>\n",
-                            "      <td>55.910580</td>\n",
-                            "      <td>58.661870</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>d13f1b27baf31030b7fd040960d60d909913633f</td>\n",
-                            "      <td>34.113190</td>\n",
-                            "      <td>65.897152</td>\n",
-                            "      <td>34.447</td>\n",
-                            "      <td>75.098079</td>\n",
-                            "      <td>44.206</td>\n",
-                            "      <td>98.699878</td>\n",
-                            "      <td>87.649863</td>\n",
-                            "      <td>94.283655</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>57.186738</td>\n",
-                            "      <td>84.110742</td>\n",
-                            "      <td>87.352572</td>\n",
-                            "      <td>57.854090</td>\n",
-                            "      <td>73.669498</td>\n",
-                            "      <td>50.201567</td>\n",
-                            "      <td>14.862272</td>\n",
-                            "      <td>38.160560</td>\n",
-                            "      <td>53.854468</td>\n",
-                            "      <td>57.508221</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>79f2382ceacceacdf38563d7c5d16b9ff8d725d6</td>\n",
-                            "      <td>41.806274</td>\n",
-                            "      <td>67.204322</td>\n",
-                            "      <td>30.799</td>\n",
-                            "      <td>73.983687</td>\n",
-                            "      <td>48.908</td>\n",
-                            "      <td>98.330171</td>\n",
-                            "      <td>79.738726</td>\n",
-                            "      <td>95.205078</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>54.410453</td>\n",
-                            "      <td>79.468659</td>\n",
-                            "      <td>90.725512</td>\n",
-                            "      <td>56.599349</td>\n",
-                            "      <td>74.253507</td>\n",
-                            "      <td>41.160312</td>\n",
-                            "      <td>6.897841</td>\n",
-                            "      <td>35.783209</td>\n",
-                            "      <td>52.336680</td>\n",
-                            "      <td>54.653134</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>e4ce9877abf3edfe10b0d82785e83bdcb973e22e</td>\n",
-                            "      <td>35.393261</td>\n",
-                            "      <td>64.410023</td>\n",
-                            "      <td>27.380</td>\n",
-                            "      <td>71.746625</td>\n",
-                            "      <td>39.088</td>\n",
-                            "      <td>96.352984</td>\n",
-                            "      <td>82.592438</td>\n",
-                            "      <td>85.072037</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>55.038105</td>\n",
-                            "      <td>80.948799</td>\n",
-                            "      <td>86.374052</td>\n",
-                            "      <td>56.108885</td>\n",
-                            "      <td>71.636076</td>\n",
-                            "      <td>46.072279</td>\n",
-                            "      <td>13.967448</td>\n",
-                            "      <td>36.498080</td>\n",
-                            "      <td>54.108918</td>\n",
-                            "      <td>55.714317</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>e34fab64a3011d2176c99545a93d5cbddc9a91b7</td>\n",
-                            "      <td>30.208930</td>\n",
-                            "      <td>55.374766</td>\n",
-                            "      <td>19.775</td>\n",
-                            "      <td>74.485455</td>\n",
-                            "      <td>34.178</td>\n",
-                            "      <td>99.189150</td>\n",
-                            "      <td>72.629699</td>\n",
-                            "      <td>97.470989</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>51.843718</td>\n",
-                            "      <td>88.779284</td>\n",
-                            "      <td>85.180182</td>\n",
-                            "      <td>55.100932</td>\n",
-                            "      <td>65.683818</td>\n",
-                            "      <td>34.351709</td>\n",
-                            "      <td>16.298114</td>\n",
-                            "      <td>34.253079</td>\n",
-                            "      <td>48.660051</td>\n",
-                            "      <td>53.538396</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>bf3bf13ab40c3157080a7ab344c831b9ad18b5eb</td>\n",
-                            "      <td>40.451213</td>\n",
-                            "      <td>62.424382</td>\n",
-                            "      <td>26.634</td>\n",
-                            "      <td>69.772668</td>\n",
-                            "      <td>44.878</td>\n",
-                            "      <td>97.167386</td>\n",
-                            "      <td>74.561219</td>\n",
-                            "      <td>93.669550</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>51.731912</td>\n",
-                            "      <td>76.988968</td>\n",
-                            "      <td>88.925092</td>\n",
-                            "      <td>52.678430</td>\n",
-                            "      <td>72.536393</td>\n",
-                            "      <td>37.598783</td>\n",
-                            "      <td>5.687953</td>\n",
-                            "      <td>34.443163</td>\n",
-                            "      <td>50.198119</td>\n",
-                            "      <td>52.382113</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>84f2bcc00d77236f9e89c8a360a00fb1139bf47d</td>\n",
-                            "      <td>35.215154</td>\n",
-                            "      <td>69.630056</td>\n",
-                            "      <td>34.270</td>\n",
-                            "      <td>62.193713</td>\n",
-                            "      <td>46.521</td>\n",
-                            "      <td>26.357644</td>\n",
-                            "      <td>39.288041</td>\n",
-                            "      <td>6.588554</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>44.687410</td>\n",
-                            "      <td>29.807469</td>\n",
-                            "      <td>80.519579</td>\n",
-                            "      <td>49.249220</td>\n",
-                            "      <td>63.883592</td>\n",
-                            "      <td>37.307847</td>\n",
-                            "      <td>10.872124</td>\n",
-                            "      <td>36.190054</td>\n",
-                            "      <td>49.608487</td>\n",
-                            "      <td>44.679796</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>a05860a77cef7b37e0048a7864658139bc18a854</td>\n",
-                            "      <td>31.976537</td>\n",
-                            "      <td>67.013696</td>\n",
-                            "      <td>33.196</td>\n",
-                            "      <td>63.058263</td>\n",
-                            "      <td>47.128</td>\n",
-                            "      <td>28.503819</td>\n",
-                            "      <td>38.265164</td>\n",
-                            "      <td>6.486622</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>44.389138</td>\n",
-                            "      <td>32.061171</td>\n",
-                            "      <td>81.520946</td>\n",
-                            "      <td>49.244106</td>\n",
-                            "      <td>64.192402</td>\n",
-                            "      <td>36.243232</td>\n",
-                            "      <td>7.574194</td>\n",
-                            "      <td>32.513396</td>\n",
-                            "      <td>49.196304</td>\n",
-                            "      <td>44.068219</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>8b3219a92973c328a8e22fadcfa821b5dc75636a</td>\n",
-                            "      <td>31.106654</td>\n",
-                            "      <td>62.621726</td>\n",
-                            "      <td>28.413</td>\n",
-                            "      <td>61.660638</td>\n",
-                            "      <td>50.167</td>\n",
-                            "      <td>20.293395</td>\n",
-                            "      <td>34.484863</td>\n",
-                            "      <td>4.975517</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>43.447989</td>\n",
-                            "      <td>27.244291</td>\n",
-                            "      <td>80.187412</td>\n",
-                            "      <td>47.757474</td>\n",
-                            "      <td>62.650859</td>\n",
-                            "      <td>37.346527</td>\n",
-                            "      <td>8.775719</td>\n",
-                            "      <td>33.555435</td>\n",
-                            "      <td>47.729140</td>\n",
-                            "      <td>43.155857</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>12 rows \u00d7 87 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model  \\\n",
-                            "0                                    GritLM/GritLM-7B   \n",
-                            "4             intfloat/multilingual-e5-large-instruct   \n",
-                            "1                     intfloat/e5-mistral-7b-instruct   \n",
-                            "3                      intfloat/multilingual-e5-large   \n",
-                            "2                       intfloat/multilingual-e5-base   \n",
-                            "11  sentence-transformers/paraphrase-multilingual-...   \n",
-                            "5                      intfloat/multilingual-e5-small   \n",
-                            "6                         sentence-transformers/LaBSE   \n",
-                            "10  sentence-transformers/paraphrase-multilingual-...   \n",
-                            "9             sentence-transformers/all-mpnet-base-v2   \n",
-                            "7             sentence-transformers/all-MiniLM-L12-v2   \n",
-                            "8              sentence-transformers/all-MiniLM-L6-v2   \n",
-                            "\n",
-                            "                                    revision  AlloProfClusteringS2S.v2  \\\n",
-                            "0   13f00a0e36500c80ce12870ea513846a066004af                 56.411821   \n",
-                            "4   baa7be480a7de1539afce709c8f13f833a510e0a                 56.465655   \n",
-                            "1   07163b72af1488142a360786df853f237b1a3ca1                 57.112000   \n",
-                            "3   4dc6d853a804b9c8886ede6dda8a073b7dc08a81                 35.150764   \n",
-                            "2   d13f1b27baf31030b7fd040960d60d909913633f                 34.113190   \n",
-                            "11  79f2382ceacceacdf38563d7c5d16b9ff8d725d6                 41.806274   \n",
-                            "5   e4ce9877abf3edfe10b0d82785e83bdcb973e22e                 35.393261   \n",
-                            "6   e34fab64a3011d2176c99545a93d5cbddc9a91b7                 30.208930   \n",
-                            "10  bf3bf13ab40c3157080a7ab344c831b9ad18b5eb                 40.451213   \n",
-                            "9   84f2bcc00d77236f9e89c8a360a00fb1139bf47d                 35.215154   \n",
-                            "7   a05860a77cef7b37e0048a7864658139bc18a854                 31.976537   \n",
-                            "8   8b3219a92973c328a8e22fadcfa821b5dc75636a                 31.106654   \n",
-                            "\n",
-                            "    AlloprofReranking  AlloprofRetrieval  AmazonCounterfactualClassification  \\\n",
-                            "0           77.926160             55.422                           78.452787   \n",
-                            "4           74.677730             52.118                           67.619802   \n",
-                            "1           78.317664             54.619                           73.901466   \n",
-                            "3           69.442880             39.341                           75.116732   \n",
-                            "2           65.897152             34.447                           75.098079   \n",
-                            "11          67.204322             30.799                           73.983687   \n",
-                            "5           64.410023             27.380                           71.746625   \n",
-                            "6           55.374766             19.775                           74.485455   \n",
-                            "10          62.424382             26.634                           69.772668   \n",
-                            "9           69.630056             34.270                           62.193713   \n",
-                            "7           67.013696             33.196                           63.058263   \n",
-                            "8           62.621726             28.413                           61.660638   \n",
-                            "\n",
-                            "    ArguAna    BUCC.v2  BelebeleRetrieval  BibleNLPBitextMining  ...  \\\n",
-                            "0    63.171  99.502429          91.393110             97.337705  ...   \n",
-                            "4    58.476  99.473836          92.240123             97.916667  ...   \n",
-                            "1    61.653  99.384579          88.393877             96.731306  ...   \n",
-                            "3    54.357  99.022547          92.728438             94.571508  ...   \n",
-                            "2    44.206  98.699878          87.649863             94.283655  ...   \n",
-                            "11   48.908  98.330171          79.738726             95.205078  ...   \n",
-                            "5    39.088  96.352984          82.592438             85.072037  ...   \n",
-                            "6    34.178  99.189150          72.629699             97.470989  ...   \n",
-                            "10   44.878  97.167386          74.561219             93.669550  ...   \n",
-                            "9    46.521  26.357644          39.288041              6.588554  ...   \n",
-                            "7    47.128  28.503819          38.265164              6.486622  ...   \n",
-                            "8    50.167  20.293395          34.484863              4.975517  ...   \n",
-                            "\n",
-                            "         Mean  Mean BitextMining  Mean PairClassification  \\\n",
-                            "0   62.970819          90.420705                89.939440   \n",
-                            "4   62.192929          90.383846                89.985879   \n",
-                            "1   61.729119          89.580187                91.154182   \n",
-                            "3   58.491543          84.456404                88.753477   \n",
-                            "2   57.186738          84.110742                87.352572   \n",
-                            "11  54.410453          79.468659                90.725512   \n",
-                            "5   55.038105          80.948799                86.374052   \n",
-                            "6   51.843718          88.779284                85.180182   \n",
-                            "10  51.731912          76.988968                88.925092   \n",
-                            "9   44.687410          29.807469                80.519579   \n",
-                            "7   44.389138          32.061171                81.520946   \n",
-                            "8   43.447989          27.244291                80.187412   \n",
-                            "\n",
-                            "    Mean Classification   Mean STS  Mean Retrieval  \\\n",
-                            "0             64.736896  76.050247       57.105263   \n",
-                            "4             63.241319  77.428657       54.802566   \n",
-                            "1             62.946715  76.481287       53.644095   \n",
-                            "3             60.385125  75.759056       50.814358   \n",
-                            "2             57.854090  73.669498       50.201567   \n",
-                            "11            56.599349  74.253507       41.160312   \n",
-                            "5             56.108885  71.636076       46.072279   \n",
-                            "6             55.100932  65.683818       34.351709   \n",
-                            "10            52.678430  72.536393       37.598783   \n",
-                            "9             49.249220  63.883592       37.307847   \n",
-                            "7             49.244106  64.192402       36.243232   \n",
-                            "8             47.757474  62.650859       37.346527   \n",
-                            "\n",
-                            "    Mean MultilabelClassification  Mean Clustering  Mean Reranking  \\\n",
-                            "0                       17.551200        45.281105       60.269644   \n",
-                            "4                       17.265889        46.895583       58.420108   \n",
-                            "1                       15.464545        46.473328       59.815300   \n",
-                            "3                       14.980040        38.235917       55.910580   \n",
-                            "2                       14.862272        38.160560       53.854468   \n",
-                            "11                       6.897841        35.783209       52.336680   \n",
-                            "5                       13.967448        36.498080       54.108918   \n",
-                            "6                       16.298114        34.253079       48.660051   \n",
-                            "10                       5.687953        34.443163       50.198119   \n",
-                            "9                       10.872124        36.190054       49.608487   \n",
-                            "7                        7.574194        32.513396       49.196304   \n",
-                            "8                        8.775719        33.555435       47.729140   \n",
-                            "\n",
-                            "    mean pr. task type  \n",
-                            "0            62.669312  \n",
-                            "4            62.302981  \n",
-                            "1            61.944955  \n",
-                            "3            58.661870  \n",
-                            "2            57.508221  \n",
-                            "11           54.653134  \n",
-                            "5            55.714317  \n",
-                            "6            53.538396  \n",
-                            "10           52.382113  \n",
-                            "9            44.679796  \n",
-                            "7            44.068219  \n",
-                            "8            43.155857  \n",
-                            "\n",
-                            "[12 rows x 87 columns]"
-                        ]
-                    },
-                    "execution_count": 10,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>Borda str</th>\n",
-                            "      <th>Mean</th>\n",
-                            "      <th>mean pr. task type</th>\n",
-                            "      <th>Mean BitextMining</th>\n",
-                            "      <th>Mean PairClassification</th>\n",
-                            "      <th>Mean Classification</th>\n",
-                            "      <th>Mean STS</th>\n",
-                            "      <th>Mean Retrieval</th>\n",
-                            "      <th>Mean MultilabelClassification</th>\n",
-                            "      <th>Mean Clustering</th>\n",
-                            "      <th>Mean Reranking</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>1 (757)</td>\n",
-                            "      <td>62.970819</td>\n",
-                            "      <td>62.669312</td>\n",
-                            "      <td>90.420705</td>\n",
-                            "      <td>89.939440</td>\n",
-                            "      <td>64.736896</td>\n",
-                            "      <td>76.050247</td>\n",
-                            "      <td>57.105263</td>\n",
-                            "      <td>17.551200</td>\n",
-                            "      <td>45.281105</td>\n",
-                            "      <td>60.269644</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>2 (732)</td>\n",
-                            "      <td>62.192929</td>\n",
-                            "      <td>62.302981</td>\n",
-                            "      <td>90.383846</td>\n",
-                            "      <td>89.985879</td>\n",
-                            "      <td>63.241319</td>\n",
-                            "      <td>77.428657</td>\n",
-                            "      <td>54.802566</td>\n",
-                            "      <td>17.265889</td>\n",
-                            "      <td>46.895583</td>\n",
-                            "      <td>58.420108</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>3 (725)</td>\n",
-                            "      <td>61.729119</td>\n",
-                            "      <td>61.944955</td>\n",
-                            "      <td>89.580187</td>\n",
-                            "      <td>91.154182</td>\n",
-                            "      <td>62.946715</td>\n",
-                            "      <td>76.481287</td>\n",
-                            "      <td>53.644095</td>\n",
-                            "      <td>15.464545</td>\n",
-                            "      <td>46.473328</td>\n",
-                            "      <td>59.815300</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>4 (586)</td>\n",
-                            "      <td>58.491543</td>\n",
-                            "      <td>58.661870</td>\n",
-                            "      <td>84.456404</td>\n",
-                            "      <td>88.753477</td>\n",
-                            "      <td>60.385125</td>\n",
-                            "      <td>75.759056</td>\n",
-                            "      <td>50.814358</td>\n",
-                            "      <td>14.980040</td>\n",
-                            "      <td>38.235917</td>\n",
-                            "      <td>55.910580</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>5 (499)</td>\n",
-                            "      <td>57.186738</td>\n",
-                            "      <td>57.508221</td>\n",
-                            "      <td>84.110742</td>\n",
-                            "      <td>87.352572</td>\n",
-                            "      <td>57.854090</td>\n",
-                            "      <td>73.669498</td>\n",
-                            "      <td>50.201567</td>\n",
-                            "      <td>14.862272</td>\n",
-                            "      <td>38.160560</td>\n",
-                            "      <td>53.854468</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>6 (463)</td>\n",
-                            "      <td>54.410453</td>\n",
-                            "      <td>54.653134</td>\n",
-                            "      <td>79.468659</td>\n",
-                            "      <td>90.725512</td>\n",
-                            "      <td>56.599349</td>\n",
-                            "      <td>74.253507</td>\n",
-                            "      <td>41.160312</td>\n",
-                            "      <td>6.897841</td>\n",
-                            "      <td>35.783209</td>\n",
-                            "      <td>52.336680</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>7 (399)</td>\n",
-                            "      <td>55.038105</td>\n",
-                            "      <td>55.714317</td>\n",
-                            "      <td>80.948799</td>\n",
-                            "      <td>86.374052</td>\n",
-                            "      <td>56.108885</td>\n",
-                            "      <td>71.636076</td>\n",
-                            "      <td>46.072279</td>\n",
-                            "      <td>13.967448</td>\n",
-                            "      <td>36.498080</td>\n",
-                            "      <td>54.108918</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>8 (358)</td>\n",
-                            "      <td>51.843718</td>\n",
-                            "      <td>53.538396</td>\n",
-                            "      <td>88.779284</td>\n",
-                            "      <td>85.180182</td>\n",
-                            "      <td>55.100932</td>\n",
-                            "      <td>65.683818</td>\n",
-                            "      <td>34.351709</td>\n",
-                            "      <td>16.298114</td>\n",
-                            "      <td>34.253079</td>\n",
-                            "      <td>48.660051</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>9 (328)</td>\n",
-                            "      <td>51.731912</td>\n",
-                            "      <td>52.382113</td>\n",
-                            "      <td>76.988968</td>\n",
-                            "      <td>88.925092</td>\n",
-                            "      <td>52.678430</td>\n",
-                            "      <td>72.536393</td>\n",
-                            "      <td>37.598783</td>\n",
-                            "      <td>5.687953</td>\n",
-                            "      <td>34.443163</td>\n",
-                            "      <td>50.198119</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>10 (310)</td>\n",
-                            "      <td>44.687410</td>\n",
-                            "      <td>44.679796</td>\n",
-                            "      <td>29.807469</td>\n",
-                            "      <td>80.519579</td>\n",
-                            "      <td>49.249220</td>\n",
-                            "      <td>63.883592</td>\n",
-                            "      <td>37.307847</td>\n",
-                            "      <td>10.872124</td>\n",
-                            "      <td>36.190054</td>\n",
-                            "      <td>49.608487</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>11 (292)</td>\n",
-                            "      <td>44.389138</td>\n",
-                            "      <td>44.068219</td>\n",
-                            "      <td>32.061171</td>\n",
-                            "      <td>81.520946</td>\n",
-                            "      <td>49.244106</td>\n",
-                            "      <td>64.192402</td>\n",
-                            "      <td>36.243232</td>\n",
-                            "      <td>7.574194</td>\n",
-                            "      <td>32.513396</td>\n",
-                            "      <td>49.196304</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>12 (237)</td>\n",
-                            "      <td>43.447989</td>\n",
-                            "      <td>43.155857</td>\n",
-                            "      <td>27.244291</td>\n",
-                            "      <td>80.187412</td>\n",
-                            "      <td>47.757474</td>\n",
-                            "      <td>62.650859</td>\n",
-                            "      <td>37.346527</td>\n",
-                            "      <td>8.775719</td>\n",
-                            "      <td>33.555435</td>\n",
-                            "      <td>47.729140</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model Borda str       Mean  \\\n",
-                            "0                                    GritLM/GritLM-7B   1 (757)  62.970819   \n",
-                            "4             intfloat/multilingual-e5-large-instruct   2 (732)  62.192929   \n",
-                            "1                     intfloat/e5-mistral-7b-instruct   3 (725)  61.729119   \n",
-                            "3                      intfloat/multilingual-e5-large   4 (586)  58.491543   \n",
-                            "2                       intfloat/multilingual-e5-base   5 (499)  57.186738   \n",
-                            "11  sentence-transformers/paraphrase-multilingual-...   6 (463)  54.410453   \n",
-                            "5                      intfloat/multilingual-e5-small   7 (399)  55.038105   \n",
-                            "6                         sentence-transformers/LaBSE   8 (358)  51.843718   \n",
-                            "10  sentence-transformers/paraphrase-multilingual-...   9 (328)  51.731912   \n",
-                            "9             sentence-transformers/all-mpnet-base-v2  10 (310)  44.687410   \n",
-                            "7             sentence-transformers/all-MiniLM-L12-v2  11 (292)  44.389138   \n",
-                            "8              sentence-transformers/all-MiniLM-L6-v2  12 (237)  43.447989   \n",
-                            "\n",
-                            "    mean pr. task type  Mean BitextMining  Mean PairClassification  \\\n",
-                            "0            62.669312          90.420705                89.939440   \n",
-                            "4            62.302981          90.383846                89.985879   \n",
-                            "1            61.944955          89.580187                91.154182   \n",
-                            "3            58.661870          84.456404                88.753477   \n",
-                            "2            57.508221          84.110742                87.352572   \n",
-                            "11           54.653134          79.468659                90.725512   \n",
-                            "5            55.714317          80.948799                86.374052   \n",
-                            "6            53.538396          88.779284                85.180182   \n",
-                            "10           52.382113          76.988968                88.925092   \n",
-                            "9            44.679796          29.807469                80.519579   \n",
-                            "7            44.068219          32.061171                81.520946   \n",
-                            "8            43.155857          27.244291                80.187412   \n",
-                            "\n",
-                            "    Mean Classification   Mean STS  Mean Retrieval  \\\n",
-                            "0             64.736896  76.050247       57.105263   \n",
-                            "4             63.241319  77.428657       54.802566   \n",
-                            "1             62.946715  76.481287       53.644095   \n",
-                            "3             60.385125  75.759056       50.814358   \n",
-                            "2             57.854090  73.669498       50.201567   \n",
-                            "11            56.599349  74.253507       41.160312   \n",
-                            "5             56.108885  71.636076       46.072279   \n",
-                            "6             55.100932  65.683818       34.351709   \n",
-                            "10            52.678430  72.536393       37.598783   \n",
-                            "9             49.249220  63.883592       37.307847   \n",
-                            "7             49.244106  64.192402       36.243232   \n",
-                            "8             47.757474  62.650859       37.346527   \n",
-                            "\n",
-                            "    Mean MultilabelClassification  Mean Clustering  Mean Reranking  \n",
-                            "0                       17.551200        45.281105       60.269644  \n",
-                            "4                       17.265889        46.895583       58.420108  \n",
-                            "1                       15.464545        46.473328       59.815300  \n",
-                            "3                       14.980040        38.235917       55.910580  \n",
-                            "2                       14.862272        38.160560       53.854468  \n",
-                            "11                       6.897841        35.783209       52.336680  \n",
-                            "5                       13.967448        36.498080       54.108918  \n",
-                            "6                       16.298114        34.253079       48.660051  \n",
-                            "10                       5.687953        34.443163       50.198119  \n",
-                            "9                       10.872124        36.190054       49.608487  \n",
-                            "7                        7.574194        32.513396       49.196304  \n",
-                            "8                        8.775719        33.555435       47.729140  "
-                        ]
-                    },
-                    "execution_count": 11,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "latex_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\begin{tabular}{llrrrrrrrrrr}\n",
-                        "\\toprule\n",
-                        "model & Borda str & Mean & mean pr. task type & Mean BitextMining & Mean PairClassification & Mean Classification & Mean STS & Mean Retrieval & Mean MultilabelClassification & Mean Clustering & Mean Reranking \\\\\n",
-                        "\\midrule\n",
-                        "GritLM/GritLM-7B & 1 (757) & 63.0 & 62.7 & 90.4 & 89.9 & 64.7 & 76.1 & 57.1 & 17.6 & 45.3 & 60.3 \\\\\n",
-                        "intfloat/multilingual-e5-large-instruct & 2 (732) & 62.2 & 62.3 & 90.4 & 90.0 & 63.2 & 77.4 & 54.8 & 17.3 & 46.9 & 58.4 \\\\\n",
-                        "intfloat/e5-mistral-7b-instruct & 3 (725) & 61.7 & 61.9 & 89.6 & 91.2 & 62.9 & 76.5 & 53.6 & 15.5 & 46.5 & 59.8 \\\\\n",
-                        "intfloat/multilingual-e5-large & 4 (586) & 58.5 & 58.7 & 84.5 & 88.8 & 60.4 & 75.8 & 50.8 & 15.0 & 38.2 & 55.9 \\\\\n",
-                        "intfloat/multilingual-e5-base & 5 (499) & 57.2 & 57.5 & 84.1 & 87.4 & 57.9 & 73.7 & 50.2 & 14.9 & 38.2 & 53.9 \\\\\n",
-                        "sentence-transformers/paraphrase-multilingual-mpnet-base-v2 & 6 (463) & 54.4 & 54.7 & 79.5 & 90.7 & 56.6 & 74.3 & 41.2 & 6.9 & 35.8 & 52.3 \\\\\n",
-                        "intfloat/multilingual-e5-small & 7 (399) & 55.0 & 55.7 & 80.9 & 86.4 & 56.1 & 71.6 & 46.1 & 14.0 & 36.5 & 54.1 \\\\\n",
-                        "sentence-transformers/LaBSE & 8 (358) & 51.8 & 53.5 & 88.8 & 85.2 & 55.1 & 65.7 & 34.4 & 16.3 & 34.3 & 48.7 \\\\\n",
-                        "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 & 9 (328) & 51.7 & 52.4 & 77.0 & 88.9 & 52.7 & 72.5 & 37.6 & 5.7 & 34.4 & 50.2 \\\\\n",
-                        "sentence-transformers/all-mpnet-base-v2 & 10 (310) & 44.7 & 44.7 & 29.8 & 80.5 & 49.2 & 63.9 & 37.3 & 10.9 & 36.2 & 49.6 \\\\\n",
-                        "sentence-transformers/all-MiniLM-L12-v2 & 11 (292) & 44.4 & 44.1 & 32.1 & 81.5 & 49.2 & 64.2 & 36.2 & 7.6 & 32.5 & 49.2 \\\\\n",
-                        "sentence-transformers/all-MiniLM-L6-v2 & 12 (237) & 43.4 & 43.2 & 27.2 & 80.2 & 47.8 & 62.7 & 37.3 & 8.8 & 33.6 & 47.7 \\\\\n",
-                        "\\bottomrule\n",
-                        "\\end{tabular}\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(latex_df.to_latex(index=False, float_format=\"%.1f\"))"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Multilingual"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[\"Borda Count\"] = borda\n",
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:11: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[\"Borda str\"] = [\n",
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:17: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[\"Mean\"] = results[task_names].mean(axis=1)\n",
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[f\"Mean {task_type}\"] = results[task_names].mean(axis=1)\n",
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[f\"Mean {task_type}\"] = results[task_names].mean(axis=1)\n",
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[f\"Mean {task_type}\"] = results[task_names].mean(axis=1)\n",
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[f\"Mean {task_type}\"] = results[task_names].mean(axis=1)\n",
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[f\"Mean {task_type}\"] = results[task_names].mean(axis=1)\n",
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[f\"Mean {task_type}\"] = results[task_names].mean(axis=1)\n",
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[f\"Mean {task_type}\"] = results[task_names].mean(axis=1)\n",
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[f\"Mean {task_type}\"] = results[task_names].mean(axis=1)\n",
-                        "/var/folders/bq/3m2kv2_535q0c9ld2jmmz774yj4nph/T/ipykernel_22431/597027223.py:43: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
-                        "  results[\"mean pr. task type\"] = results[cols].mean(axis=1)\n"
-                    ]
-                }
-            ],
-            "source": [
-                "mult_tasks = mteb.get_benchmark(\"MTEB(Multilingual)\").tasks\n",
-                "\n",
-                "# load task results for the specified models from mteb/results repository\n",
-                "mteb_results = mteb.load_results(\n",
-                "    models=model_metas,\n",
-                "    tasks=mult_tasks,\n",
-                "    download_latest=False,\n",
-                ")\n",
-                "\n",
-                "mteb_results = mteb_results.join_revisions().filter_models()\n",
-                "\n",
-                "# manual check that everything is there\n",
-                "pd.DataFrame(mteb_results.get_scores()).to_csv(\"tmp.csv\")\n",
-                "\n",
-                "results = pd.DataFrame(mteb_results.get_scores())\n",
-                "results = add_aggregate_columns(results=results)\n",
-                "\n",
-                "\n",
-                "# create latex table\n",
-                "# column order\n",
-                "cols = [\n",
-                "    \"model\",\n",
-                "    \"Borda str\",\n",
-                "    \"Mean\",\n",
-                "    \"mean pr. task type\",\n",
-                "    \"Mean BitextMining\",\n",
-                "    \"Mean PairClassification\",\n",
-                "    \"Mean Classification\",\n",
-                "    \"Mean STS\",\n",
-                "    \"Mean Retrieval\",\n",
-                "    \"Mean MultilabelClassification\",\n",
-                "    \"Mean Clustering\",\n",
-                "    \"Mean Reranking\",\n",
-                "]\n",
-                "\n",
-                "latex_df = results[cols]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th>AILAStatutes</th>\n",
-                            "      <th>AfriSentiClassification</th>\n",
-                            "      <th>AlloProfClusteringS2S.v2</th>\n",
-                            "      <th>AlloprofReranking</th>\n",
-                            "      <th>AmazonCounterfactualClassification</th>\n",
-                            "      <th>ArXivHierarchicalClusteringP2P</th>\n",
-                            "      <th>ArXivHierarchicalClusteringS2S</th>\n",
-                            "      <th>ArguAna</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>Mean</th>\n",
-                            "      <th>Mean BitextMining</th>\n",
-                            "      <th>Mean PairClassification</th>\n",
-                            "      <th>Mean Classification</th>\n",
-                            "      <th>Mean STS</th>\n",
-                            "      <th>Mean Retrieval</th>\n",
-                            "      <th>Mean MultilabelClassification</th>\n",
-                            "      <th>Mean Clustering</th>\n",
-                            "      <th>Mean Reranking</th>\n",
-                            "      <th>mean pr. task type</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>baa7be480a7de1539afce709c8f13f833a510e0a</td>\n",
-                            "      <td>29.659</td>\n",
-                            "      <td>45.387432</td>\n",
-                            "      <td>56.465655</td>\n",
-                            "      <td>74.677730</td>\n",
-                            "      <td>68.606356</td>\n",
-                            "      <td>62.534994</td>\n",
-                            "      <td>61.284058</td>\n",
-                            "      <td>58.476</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>63.227169</td>\n",
-                            "      <td>80.126470</td>\n",
-                            "      <td>80.863584</td>\n",
-                            "      <td>64.942144</td>\n",
-                            "      <td>76.814671</td>\n",
-                            "      <td>57.116686</td>\n",
-                            "      <td>22.913504</td>\n",
-                            "      <td>51.538016</td>\n",
-                            "      <td>62.613273</td>\n",
-                            "      <td>62.116044</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>13f00a0e36500c80ce12870ea513846a066004af</td>\n",
-                            "      <td>41.800</td>\n",
-                            "      <td>45.078589</td>\n",
-                            "      <td>56.411821</td>\n",
-                            "      <td>77.926160</td>\n",
-                            "      <td>79.296512</td>\n",
-                            "      <td>59.760046</td>\n",
-                            "      <td>62.283243</td>\n",
-                            "      <td>63.171</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>60.930855</td>\n",
-                            "      <td>70.531726</td>\n",
-                            "      <td>79.944411</td>\n",
-                            "      <td>61.830247</td>\n",
-                            "      <td>73.327960</td>\n",
-                            "      <td>58.306711</td>\n",
-                            "      <td>22.773771</td>\n",
-                            "      <td>50.482520</td>\n",
-                            "      <td>63.778754</td>\n",
-                            "      <td>60.122013</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>07163b72af1488142a360786df853f237b1a3ca1</td>\n",
-                            "      <td>34.535</td>\n",
-                            "      <td>44.476335</td>\n",
-                            "      <td>57.112000</td>\n",
-                            "      <td>78.317664</td>\n",
-                            "      <td>73.555839</td>\n",
-                            "      <td>65.283735</td>\n",
-                            "      <td>61.278123</td>\n",
-                            "      <td>61.653</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>60.279746</td>\n",
-                            "      <td>70.579905</td>\n",
-                            "      <td>81.122211</td>\n",
-                            "      <td>60.314295</td>\n",
-                            "      <td>74.021600</td>\n",
-                            "      <td>55.750101</td>\n",
-                            "      <td>22.196821</td>\n",
-                            "      <td>51.390095</td>\n",
-                            "      <td>63.819183</td>\n",
-                            "      <td>59.899276</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</td>\n",
-                            "      <td>20.842</td>\n",
-                            "      <td>45.500507</td>\n",
-                            "      <td>35.150764</td>\n",
-                            "      <td>69.442880</td>\n",
-                            "      <td>76.163514</td>\n",
-                            "      <td>55.572114</td>\n",
-                            "      <td>56.212217</td>\n",
-                            "      <td>54.357</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>58.570572</td>\n",
-                            "      <td>71.666250</td>\n",
-                            "      <td>79.028390</td>\n",
-                            "      <td>59.916975</td>\n",
-                            "      <td>73.488372</td>\n",
-                            "      <td>54.111178</td>\n",
-                            "      <td>21.302373</td>\n",
-                            "      <td>42.923755</td>\n",
-                            "      <td>62.840466</td>\n",
-                            "      <td>58.159720</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>d13f1b27baf31030b7fd040960d60d909913633f</td>\n",
-                            "      <td>20.371</td>\n",
-                            "      <td>43.802315</td>\n",
-                            "      <td>34.113190</td>\n",
-                            "      <td>65.897152</td>\n",
-                            "      <td>74.334014</td>\n",
-                            "      <td>56.683137</td>\n",
-                            "      <td>56.115056</td>\n",
-                            "      <td>44.206</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>57.012959</td>\n",
-                            "      <td>69.437945</td>\n",
-                            "      <td>77.154385</td>\n",
-                            "      <td>58.205718</td>\n",
-                            "      <td>71.444249</td>\n",
-                            "      <td>52.721823</td>\n",
-                            "      <td>20.162060</td>\n",
-                            "      <td>42.674461</td>\n",
-                            "      <td>60.176360</td>\n",
-                            "      <td>56.497125</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>79f2382ceacceacdf38563d7c5d16b9ff8d725d6</td>\n",
-                            "      <td>22.236</td>\n",
-                            "      <td>42.445471</td>\n",
-                            "      <td>41.806274</td>\n",
-                            "      <td>67.204322</td>\n",
-                            "      <td>72.766528</td>\n",
-                            "      <td>55.342767</td>\n",
-                            "      <td>55.160328</td>\n",
-                            "      <td>48.908</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>52.005259</td>\n",
-                            "      <td>52.062937</td>\n",
-                            "      <td>81.154392</td>\n",
-                            "      <td>55.064354</td>\n",
-                            "      <td>69.661040</td>\n",
-                            "      <td>39.757752</td>\n",
-                            "      <td>16.398034</td>\n",
-                            "      <td>41.080665</td>\n",
-                            "      <td>53.374677</td>\n",
-                            "      <td>51.069231</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>e4ce9877abf3edfe10b0d82785e83bdcb973e22e</td>\n",
-                            "      <td>19.011</td>\n",
-                            "      <td>42.358118</td>\n",
-                            "      <td>35.393261</td>\n",
-                            "      <td>64.410023</td>\n",
-                            "      <td>69.163655</td>\n",
-                            "      <td>54.276235</td>\n",
-                            "      <td>54.198749</td>\n",
-                            "      <td>39.088</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>55.456701</td>\n",
-                            "      <td>67.472922</td>\n",
-                            "      <td>76.329053</td>\n",
-                            "      <td>56.500928</td>\n",
-                            "      <td>70.360817</td>\n",
-                            "      <td>49.345019</td>\n",
-                            "      <td>19.096430</td>\n",
-                            "      <td>41.735446</td>\n",
-                            "      <td>60.391010</td>\n",
-                            "      <td>55.153953</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>e34fab64a3011d2176c99545a93d5cbddc9a91b7</td>\n",
-                            "      <td>16.717</td>\n",
-                            "      <td>43.170657</td>\n",
-                            "      <td>30.208930</td>\n",
-                            "      <td>55.374766</td>\n",
-                            "      <td>74.987910</td>\n",
-                            "      <td>53.442702</td>\n",
-                            "      <td>49.986064</td>\n",
-                            "      <td>34.178</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>52.100056</td>\n",
-                            "      <td>76.351008</td>\n",
-                            "      <td>75.969075</td>\n",
-                            "      <td>54.600844</td>\n",
-                            "      <td>65.349763</td>\n",
-                            "      <td>33.169113</td>\n",
-                            "      <td>20.122117</td>\n",
-                            "      <td>39.159195</td>\n",
-                            "      <td>50.197562</td>\n",
-                            "      <td>51.864835</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>bf3bf13ab40c3157080a7ab344c831b9ad18b5eb</td>\n",
-                            "      <td>20.525</td>\n",
-                            "      <td>37.672740</td>\n",
-                            "      <td>40.451213</td>\n",
-                            "      <td>62.424382</td>\n",
-                            "      <td>68.075646</td>\n",
-                            "      <td>53.617944</td>\n",
-                            "      <td>52.245736</td>\n",
-                            "      <td>44.878</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>48.781520</td>\n",
-                            "      <td>44.563390</td>\n",
-                            "      <td>78.993229</td>\n",
-                            "      <td>51.656889</td>\n",
-                            "      <td>66.581953</td>\n",
-                            "      <td>36.614971</td>\n",
-                            "      <td>14.930329</td>\n",
-                            "      <td>39.337371</td>\n",
-                            "      <td>50.972387</td>\n",
-                            "      <td>47.956315</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>84f2bcc00d77236f9e89c8a360a00fb1139bf47d</td>\n",
-                            "      <td>21.275</td>\n",
-                            "      <td>37.267741</td>\n",
-                            "      <td>35.215154</td>\n",
-                            "      <td>69.630056</td>\n",
-                            "      <td>61.846281</td>\n",
-                            "      <td>61.473392</td>\n",
-                            "      <td>56.459312</td>\n",
-                            "      <td>46.521</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>42.470049</td>\n",
-                            "      <td>21.161317</td>\n",
-                            "      <td>70.893513</td>\n",
-                            "      <td>46.985488</td>\n",
-                            "      <td>57.599660</td>\n",
-                            "      <td>32.808557</td>\n",
-                            "      <td>16.280508</td>\n",
-                            "      <td>40.765913</td>\n",
-                            "      <td>42.234410</td>\n",
-                            "      <td>41.091171</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>a05860a77cef7b37e0048a7864658139bc18a854</td>\n",
-                            "      <td>20.714</td>\n",
-                            "      <td>37.288350</td>\n",
-                            "      <td>31.976537</td>\n",
-                            "      <td>67.013696</td>\n",
-                            "      <td>62.113993</td>\n",
-                            "      <td>57.444530</td>\n",
-                            "      <td>55.061728</td>\n",
-                            "      <td>47.128</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>42.151564</td>\n",
-                            "      <td>22.908162</td>\n",
-                            "      <td>71.679313</td>\n",
-                            "      <td>46.848558</td>\n",
-                            "      <td>57.202961</td>\n",
-                            "      <td>32.504190</td>\n",
-                            "      <td>14.586407</td>\n",
-                            "      <td>36.839927</td>\n",
-                            "      <td>44.326628</td>\n",
-                            "      <td>40.862018</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>8b3219a92973c328a8e22fadcfa821b5dc75636a</td>\n",
-                            "      <td>20.516</td>\n",
-                            "      <td>39.807785</td>\n",
-                            "      <td>31.106654</td>\n",
-                            "      <td>62.621726</td>\n",
-                            "      <td>61.280109</td>\n",
-                            "      <td>59.106889</td>\n",
-                            "      <td>54.542340</td>\n",
-                            "      <td>50.167</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>41.432105</td>\n",
-                            "      <td>20.093673</td>\n",
-                            "      <td>71.234656</td>\n",
-                            "      <td>46.198911</td>\n",
-                            "      <td>56.084065</td>\n",
-                            "      <td>32.513445</td>\n",
-                            "      <td>15.054331</td>\n",
-                            "      <td>38.037554</td>\n",
-                            "      <td>40.284573</td>\n",
-                            "      <td>39.937651</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>12 rows \u00d7 146 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model  \\\n",
-                            "4             intfloat/multilingual-e5-large-instruct   \n",
-                            "0                                    GritLM/GritLM-7B   \n",
-                            "1                     intfloat/e5-mistral-7b-instruct   \n",
-                            "3                      intfloat/multilingual-e5-large   \n",
-                            "2                       intfloat/multilingual-e5-base   \n",
-                            "11  sentence-transformers/paraphrase-multilingual-...   \n",
-                            "5                      intfloat/multilingual-e5-small   \n",
-                            "6                         sentence-transformers/LaBSE   \n",
-                            "10  sentence-transformers/paraphrase-multilingual-...   \n",
-                            "9             sentence-transformers/all-mpnet-base-v2   \n",
-                            "7             sentence-transformers/all-MiniLM-L12-v2   \n",
-                            "8              sentence-transformers/all-MiniLM-L6-v2   \n",
-                            "\n",
-                            "                                    revision  AILAStatutes  \\\n",
-                            "4   baa7be480a7de1539afce709c8f13f833a510e0a        29.659   \n",
-                            "0   13f00a0e36500c80ce12870ea513846a066004af        41.800   \n",
-                            "1   07163b72af1488142a360786df853f237b1a3ca1        34.535   \n",
-                            "3   4dc6d853a804b9c8886ede6dda8a073b7dc08a81        20.842   \n",
-                            "2   d13f1b27baf31030b7fd040960d60d909913633f        20.371   \n",
-                            "11  79f2382ceacceacdf38563d7c5d16b9ff8d725d6        22.236   \n",
-                            "5   e4ce9877abf3edfe10b0d82785e83bdcb973e22e        19.011   \n",
-                            "6   e34fab64a3011d2176c99545a93d5cbddc9a91b7        16.717   \n",
-                            "10  bf3bf13ab40c3157080a7ab344c831b9ad18b5eb        20.525   \n",
-                            "9   84f2bcc00d77236f9e89c8a360a00fb1139bf47d        21.275   \n",
-                            "7   a05860a77cef7b37e0048a7864658139bc18a854        20.714   \n",
-                            "8   8b3219a92973c328a8e22fadcfa821b5dc75636a        20.516   \n",
-                            "\n",
-                            "    AfriSentiClassification  AlloProfClusteringS2S.v2  AlloprofReranking  \\\n",
-                            "4                 45.387432                 56.465655          74.677730   \n",
-                            "0                 45.078589                 56.411821          77.926160   \n",
-                            "1                 44.476335                 57.112000          78.317664   \n",
-                            "3                 45.500507                 35.150764          69.442880   \n",
-                            "2                 43.802315                 34.113190          65.897152   \n",
-                            "11                42.445471                 41.806274          67.204322   \n",
-                            "5                 42.358118                 35.393261          64.410023   \n",
-                            "6                 43.170657                 30.208930          55.374766   \n",
-                            "10                37.672740                 40.451213          62.424382   \n",
-                            "9                 37.267741                 35.215154          69.630056   \n",
-                            "7                 37.288350                 31.976537          67.013696   \n",
-                            "8                 39.807785                 31.106654          62.621726   \n",
-                            "\n",
-                            "    AmazonCounterfactualClassification  ArXivHierarchicalClusteringP2P  \\\n",
-                            "4                            68.606356                       62.534994   \n",
-                            "0                            79.296512                       59.760046   \n",
-                            "1                            73.555839                       65.283735   \n",
-                            "3                            76.163514                       55.572114   \n",
-                            "2                            74.334014                       56.683137   \n",
-                            "11                           72.766528                       55.342767   \n",
-                            "5                            69.163655                       54.276235   \n",
-                            "6                            74.987910                       53.442702   \n",
-                            "10                           68.075646                       53.617944   \n",
-                            "9                            61.846281                       61.473392   \n",
-                            "7                            62.113993                       57.444530   \n",
-                            "8                            61.280109                       59.106889   \n",
-                            "\n",
-                            "    ArXivHierarchicalClusteringS2S  ArguAna  ...       Mean  \\\n",
-                            "4                        61.284058   58.476  ...  63.227169   \n",
-                            "0                        62.283243   63.171  ...  60.930855   \n",
-                            "1                        61.278123   61.653  ...  60.279746   \n",
-                            "3                        56.212217   54.357  ...  58.570572   \n",
-                            "2                        56.115056   44.206  ...  57.012959   \n",
-                            "11                       55.160328   48.908  ...  52.005259   \n",
-                            "5                        54.198749   39.088  ...  55.456701   \n",
-                            "6                        49.986064   34.178  ...  52.100056   \n",
-                            "10                       52.245736   44.878  ...  48.781520   \n",
-                            "9                        56.459312   46.521  ...  42.470049   \n",
-                            "7                        55.061728   47.128  ...  42.151564   \n",
-                            "8                        54.542340   50.167  ...  41.432105   \n",
-                            "\n",
-                            "    Mean BitextMining  Mean PairClassification  Mean Classification  \\\n",
-                            "4           80.126470                80.863584            64.942144   \n",
-                            "0           70.531726                79.944411            61.830247   \n",
-                            "1           70.579905                81.122211            60.314295   \n",
-                            "3           71.666250                79.028390            59.916975   \n",
-                            "2           69.437945                77.154385            58.205718   \n",
-                            "11          52.062937                81.154392            55.064354   \n",
-                            "5           67.472922                76.329053            56.500928   \n",
-                            "6           76.351008                75.969075            54.600844   \n",
-                            "10          44.563390                78.993229            51.656889   \n",
-                            "9           21.161317                70.893513            46.985488   \n",
-                            "7           22.908162                71.679313            46.848558   \n",
-                            "8           20.093673                71.234656            46.198911   \n",
-                            "\n",
-                            "     Mean STS  Mean Retrieval  Mean MultilabelClassification  Mean Clustering  \\\n",
-                            "4   76.814671       57.116686                      22.913504        51.538016   \n",
-                            "0   73.327960       58.306711                      22.773771        50.482520   \n",
-                            "1   74.021600       55.750101                      22.196821        51.390095   \n",
-                            "3   73.488372       54.111178                      21.302373        42.923755   \n",
-                            "2   71.444249       52.721823                      20.162060        42.674461   \n",
-                            "11  69.661040       39.757752                      16.398034        41.080665   \n",
-                            "5   70.360817       49.345019                      19.096430        41.735446   \n",
-                            "6   65.349763       33.169113                      20.122117        39.159195   \n",
-                            "10  66.581953       36.614971                      14.930329        39.337371   \n",
-                            "9   57.599660       32.808557                      16.280508        40.765913   \n",
-                            "7   57.202961       32.504190                      14.586407        36.839927   \n",
-                            "8   56.084065       32.513445                      15.054331        38.037554   \n",
-                            "\n",
-                            "    Mean Reranking  mean pr. task type  \n",
-                            "4        62.613273           62.116044  \n",
-                            "0        63.778754           60.122013  \n",
-                            "1        63.819183           59.899276  \n",
-                            "3        62.840466           58.159720  \n",
-                            "2        60.176360           56.497125  \n",
-                            "11       53.374677           51.069231  \n",
-                            "5        60.391010           55.153953  \n",
-                            "6        50.197562           51.864835  \n",
-                            "10       50.972387           47.956315  \n",
-                            "9        42.234410           41.091171  \n",
-                            "7        44.326628           40.862018  \n",
-                            "8        40.284573           39.937651  \n",
-                            "\n",
-                            "[12 rows x 146 columns]"
-                        ]
-                    },
-                    "execution_count": 14,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>Borda str</th>\n",
-                            "      <th>Mean</th>\n",
-                            "      <th>mean pr. task type</th>\n",
-                            "      <th>Mean BitextMining</th>\n",
-                            "      <th>Mean PairClassification</th>\n",
-                            "      <th>Mean Classification</th>\n",
-                            "      <th>Mean STS</th>\n",
-                            "      <th>Mean Retrieval</th>\n",
-                            "      <th>Mean MultilabelClassification</th>\n",
-                            "      <th>Mean Clustering</th>\n",
-                            "      <th>Mean Reranking</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>1 (1375)</td>\n",
-                            "      <td>63.227169</td>\n",
-                            "      <td>62.116044</td>\n",
-                            "      <td>80.126470</td>\n",
-                            "      <td>80.863584</td>\n",
-                            "      <td>64.942144</td>\n",
-                            "      <td>76.814671</td>\n",
-                            "      <td>57.116686</td>\n",
-                            "      <td>22.913504</td>\n",
-                            "      <td>51.538016</td>\n",
-                            "      <td>62.613273</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>2 (1258)</td>\n",
-                            "      <td>60.930855</td>\n",
-                            "      <td>60.122013</td>\n",
-                            "      <td>70.531726</td>\n",
-                            "      <td>79.944411</td>\n",
-                            "      <td>61.830247</td>\n",
-                            "      <td>73.327960</td>\n",
-                            "      <td>58.306711</td>\n",
-                            "      <td>22.773771</td>\n",
-                            "      <td>50.482520</td>\n",
-                            "      <td>63.778754</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>3 (1233)</td>\n",
-                            "      <td>60.279746</td>\n",
-                            "      <td>59.899276</td>\n",
-                            "      <td>70.579905</td>\n",
-                            "      <td>81.122211</td>\n",
-                            "      <td>60.314295</td>\n",
-                            "      <td>74.021600</td>\n",
-                            "      <td>55.750101</td>\n",
-                            "      <td>22.196821</td>\n",
-                            "      <td>51.390095</td>\n",
-                            "      <td>63.819183</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>4 (1109)</td>\n",
-                            "      <td>58.570572</td>\n",
-                            "      <td>58.159720</td>\n",
-                            "      <td>71.666250</td>\n",
-                            "      <td>79.028390</td>\n",
-                            "      <td>59.916975</td>\n",
-                            "      <td>73.488372</td>\n",
-                            "      <td>54.111178</td>\n",
-                            "      <td>21.302373</td>\n",
-                            "      <td>42.923755</td>\n",
-                            "      <td>62.840466</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>5 (944)</td>\n",
-                            "      <td>57.012959</td>\n",
-                            "      <td>56.497125</td>\n",
-                            "      <td>69.437945</td>\n",
-                            "      <td>77.154385</td>\n",
-                            "      <td>58.205718</td>\n",
-                            "      <td>71.444249</td>\n",
-                            "      <td>52.721823</td>\n",
-                            "      <td>20.162060</td>\n",
-                            "      <td>42.674461</td>\n",
-                            "      <td>60.176360</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>6 (830)</td>\n",
-                            "      <td>52.005259</td>\n",
-                            "      <td>51.069231</td>\n",
-                            "      <td>52.062937</td>\n",
-                            "      <td>81.154392</td>\n",
-                            "      <td>55.064354</td>\n",
-                            "      <td>69.661040</td>\n",
-                            "      <td>39.757752</td>\n",
-                            "      <td>16.398034</td>\n",
-                            "      <td>41.080665</td>\n",
-                            "      <td>53.374677</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>7 (784)</td>\n",
-                            "      <td>55.456701</td>\n",
-                            "      <td>55.153953</td>\n",
-                            "      <td>67.472922</td>\n",
-                            "      <td>76.329053</td>\n",
-                            "      <td>56.500928</td>\n",
-                            "      <td>70.360817</td>\n",
-                            "      <td>49.345019</td>\n",
-                            "      <td>19.096430</td>\n",
-                            "      <td>41.735446</td>\n",
-                            "      <td>60.391010</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>8 (719)</td>\n",
-                            "      <td>52.100056</td>\n",
-                            "      <td>51.864835</td>\n",
-                            "      <td>76.351008</td>\n",
-                            "      <td>75.969075</td>\n",
-                            "      <td>54.600844</td>\n",
-                            "      <td>65.349763</td>\n",
-                            "      <td>33.169113</td>\n",
-                            "      <td>20.122117</td>\n",
-                            "      <td>39.159195</td>\n",
-                            "      <td>50.197562</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>9 (603)</td>\n",
-                            "      <td>48.781520</td>\n",
-                            "      <td>47.956315</td>\n",
-                            "      <td>44.563390</td>\n",
-                            "      <td>78.993229</td>\n",
-                            "      <td>51.656889</td>\n",
-                            "      <td>66.581953</td>\n",
-                            "      <td>36.614971</td>\n",
-                            "      <td>14.930329</td>\n",
-                            "      <td>39.337371</td>\n",
-                            "      <td>50.972387</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>10 (526)</td>\n",
-                            "      <td>42.470049</td>\n",
-                            "      <td>41.091171</td>\n",
-                            "      <td>21.161317</td>\n",
-                            "      <td>70.893513</td>\n",
-                            "      <td>46.985488</td>\n",
-                            "      <td>57.599660</td>\n",
-                            "      <td>32.808557</td>\n",
-                            "      <td>16.280508</td>\n",
-                            "      <td>40.765913</td>\n",
-                            "      <td>42.234410</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>11 (490)</td>\n",
-                            "      <td>42.151564</td>\n",
-                            "      <td>40.862018</td>\n",
-                            "      <td>22.908162</td>\n",
-                            "      <td>71.679313</td>\n",
-                            "      <td>46.848558</td>\n",
-                            "      <td>57.202961</td>\n",
-                            "      <td>32.504190</td>\n",
-                            "      <td>14.586407</td>\n",
-                            "      <td>36.839927</td>\n",
-                            "      <td>44.326628</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>12 (418)</td>\n",
-                            "      <td>41.432105</td>\n",
-                            "      <td>39.937651</td>\n",
-                            "      <td>20.093673</td>\n",
-                            "      <td>71.234656</td>\n",
-                            "      <td>46.198911</td>\n",
-                            "      <td>56.084065</td>\n",
-                            "      <td>32.513445</td>\n",
-                            "      <td>15.054331</td>\n",
-                            "      <td>38.037554</td>\n",
-                            "      <td>40.284573</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model Borda str       Mean  \\\n",
-                            "4             intfloat/multilingual-e5-large-instruct  1 (1375)  63.227169   \n",
-                            "0                                    GritLM/GritLM-7B  2 (1258)  60.930855   \n",
-                            "1                     intfloat/e5-mistral-7b-instruct  3 (1233)  60.279746   \n",
-                            "3                      intfloat/multilingual-e5-large  4 (1109)  58.570572   \n",
-                            "2                       intfloat/multilingual-e5-base   5 (944)  57.012959   \n",
-                            "11  sentence-transformers/paraphrase-multilingual-...   6 (830)  52.005259   \n",
-                            "5                      intfloat/multilingual-e5-small   7 (784)  55.456701   \n",
-                            "6                         sentence-transformers/LaBSE   8 (719)  52.100056   \n",
-                            "10  sentence-transformers/paraphrase-multilingual-...   9 (603)  48.781520   \n",
-                            "9             sentence-transformers/all-mpnet-base-v2  10 (526)  42.470049   \n",
-                            "7             sentence-transformers/all-MiniLM-L12-v2  11 (490)  42.151564   \n",
-                            "8              sentence-transformers/all-MiniLM-L6-v2  12 (418)  41.432105   \n",
-                            "\n",
-                            "    mean pr. task type  Mean BitextMining  Mean PairClassification  \\\n",
-                            "4            62.116044          80.126470                80.863584   \n",
-                            "0            60.122013          70.531726                79.944411   \n",
-                            "1            59.899276          70.579905                81.122211   \n",
-                            "3            58.159720          71.666250                79.028390   \n",
-                            "2            56.497125          69.437945                77.154385   \n",
-                            "11           51.069231          52.062937                81.154392   \n",
-                            "5            55.153953          67.472922                76.329053   \n",
-                            "6            51.864835          76.351008                75.969075   \n",
-                            "10           47.956315          44.563390                78.993229   \n",
-                            "9            41.091171          21.161317                70.893513   \n",
-                            "7            40.862018          22.908162                71.679313   \n",
-                            "8            39.937651          20.093673                71.234656   \n",
-                            "\n",
-                            "    Mean Classification   Mean STS  Mean Retrieval  \\\n",
-                            "4             64.942144  76.814671       57.116686   \n",
-                            "0             61.830247  73.327960       58.306711   \n",
-                            "1             60.314295  74.021600       55.750101   \n",
-                            "3             59.916975  73.488372       54.111178   \n",
-                            "2             58.205718  71.444249       52.721823   \n",
-                            "11            55.064354  69.661040       39.757752   \n",
-                            "5             56.500928  70.360817       49.345019   \n",
-                            "6             54.600844  65.349763       33.169113   \n",
-                            "10            51.656889  66.581953       36.614971   \n",
-                            "9             46.985488  57.599660       32.808557   \n",
-                            "7             46.848558  57.202961       32.504190   \n",
-                            "8             46.198911  56.084065       32.513445   \n",
-                            "\n",
-                            "    Mean MultilabelClassification  Mean Clustering  Mean Reranking  \n",
-                            "4                       22.913504        51.538016       62.613273  \n",
-                            "0                       22.773771        50.482520       63.778754  \n",
-                            "1                       22.196821        51.390095       63.819183  \n",
-                            "3                       21.302373        42.923755       62.840466  \n",
-                            "2                       20.162060        42.674461       60.176360  \n",
-                            "11                      16.398034        41.080665       53.374677  \n",
-                            "5                       19.096430        41.735446       60.391010  \n",
-                            "6                       20.122117        39.159195       50.197562  \n",
-                            "10                      14.930329        39.337371       50.972387  \n",
-                            "9                       16.280508        40.765913       42.234410  \n",
-                            "7                       14.586407        36.839927       44.326628  \n",
-                            "8                       15.054331        38.037554       40.284573  "
-                        ]
-                    },
-                    "execution_count": 15,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "latex_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\begin{tabular}{llrrrrrrrrrr}\n",
-                        "\\toprule\n",
-                        "model & Borda str & Mean & mean pr. task type & Mean BitextMining & Mean PairClassification & Mean Classification & Mean STS & Mean Retrieval & Mean MultilabelClassification & Mean Clustering & Mean Reranking \\\\\n",
-                        "\\midrule\n",
-                        "intfloat/multilingual-e5-large-instruct & 1 (1375) & 63.2 & 62.1 & 80.1 & 80.9 & 64.9 & 76.8 & 57.1 & 22.9 & 51.5 & 62.6 \\\\\n",
-                        "GritLM/GritLM-7B & 2 (1258) & 60.9 & 60.1 & 70.5 & 79.9 & 61.8 & 73.3 & 58.3 & 22.8 & 50.5 & 63.8 \\\\\n",
-                        "intfloat/e5-mistral-7b-instruct & 3 (1233) & 60.3 & 59.9 & 70.6 & 81.1 & 60.3 & 74.0 & 55.8 & 22.2 & 51.4 & 63.8 \\\\\n",
-                        "intfloat/multilingual-e5-large & 4 (1109) & 58.6 & 58.2 & 71.7 & 79.0 & 59.9 & 73.5 & 54.1 & 21.3 & 42.9 & 62.8 \\\\\n",
-                        "intfloat/multilingual-e5-base & 5 (944) & 57.0 & 56.5 & 69.4 & 77.2 & 58.2 & 71.4 & 52.7 & 20.2 & 42.7 & 60.2 \\\\\n",
-                        "sentence-transformers/paraphrase-multilingual-mpnet-base-v2 & 6 (830) & 52.0 & 51.1 & 52.1 & 81.2 & 55.1 & 69.7 & 39.8 & 16.4 & 41.1 & 53.4 \\\\\n",
-                        "intfloat/multilingual-e5-small & 7 (784) & 55.5 & 55.2 & 67.5 & 76.3 & 56.5 & 70.4 & 49.3 & 19.1 & 41.7 & 60.4 \\\\\n",
-                        "sentence-transformers/LaBSE & 8 (719) & 52.1 & 51.9 & 76.4 & 76.0 & 54.6 & 65.3 & 33.2 & 20.1 & 39.2 & 50.2 \\\\\n",
-                        "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 & 9 (603) & 48.8 & 48.0 & 44.6 & 79.0 & 51.7 & 66.6 & 36.6 & 14.9 & 39.3 & 51.0 \\\\\n",
-                        "sentence-transformers/all-mpnet-base-v2 & 10 (526) & 42.5 & 41.1 & 21.2 & 70.9 & 47.0 & 57.6 & 32.8 & 16.3 & 40.8 & 42.2 \\\\\n",
-                        "sentence-transformers/all-MiniLM-L12-v2 & 11 (490) & 42.2 & 40.9 & 22.9 & 71.7 & 46.8 & 57.2 & 32.5 & 14.6 & 36.8 & 44.3 \\\\\n",
-                        "sentence-transformers/all-MiniLM-L6-v2 & 12 (418) & 41.4 & 39.9 & 20.1 & 71.2 & 46.2 & 56.1 & 32.5 & 15.1 & 38.0 & 40.3 \\\\\n",
-                        "\\bottomrule\n",
-                        "\\end{tabular}\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(latex_df.to_latex(index=False, float_format=\"%.1f\"))"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": ".venv",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.20"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
diff --git a/scripts/task_selection/europe_results.csv b/scripts/task_selection/europe_results.csv
deleted file mode 100644
index 6abf16e11a..0000000000
--- a/scripts/task_selection/europe_results.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-,model,revision,mean,mean (Classification),mean (Retrieval),mean (PairClassification),mean (BitextMining),mean (Clustering),mean (MultilabelClassification),mean (STS),mean (Reranking),mean (InstructionRetrieval),mean (wieghted by task type),borda_count,Total Evaluation time (hours)
-2,GritLM/GritLM-7B,13f00a0e36500c80ce12870ea513846a066004af,0.607,0.643,0.571,0.894,0.708,0.435,0.176,0.755,0.589,0.035,0.534,680.0,6.408
-7,intfloat/multilingual-e5-large-instruct,baa7be480a7de1539afce709c8f13f833a510e0a,0.61,0.635,0.555,0.899,0.767,0.46,0.173,0.772,0.575,-0.004,0.537,679.0,4.463
-11,intfloat/e5-mistral-7b-instruct,07163b72af1488142a360786df853f237b1a3ca1,0.592,0.625,0.524,0.907,0.702,0.445,0.155,0.76,0.585,-0.006,0.522,643.0,5.718
-3,intfloat/multilingual-e5-large,4dc6d853a804b9c8886ede6dda8a073b7dc08a81,0.571,0.609,0.513,0.887,0.69,0.367,0.15,0.756,0.552,-0.031,0.499,527.0,5.765
-9,intfloat/multilingual-e5-base,d13f1b27baf31030b7fd040960d60d909913633f,0.557,0.583,0.506,0.876,0.683,0.367,0.149,0.734,0.53,-0.027,0.489,438.0,2.712
-4,sentence-transformers/paraphrase-multilingual-mpnet-base-v2,79f2382ceacceacdf38563d7c5d16b9ff8d725d6,0.512,0.554,0.393,0.906,0.554,0.343,0.069,0.741,0.516,-0.011,0.451,387.0,14.898
-0,intfloat/multilingual-e5-small,e4ce9877abf3edfe10b0d82785e83bdcb973e22e,0.537,0.565,0.465,0.869,0.66,0.355,0.14,0.71,0.534,-0.024,0.475,347.0,1.901
-1,sentence-transformers/LaBSE,e34fab64a3011d2176c99545a93d5cbddc9a91b7,0.498,0.54,0.338,0.85,0.723,0.335,0.163,0.657,0.488,-0.03,0.452,296.0,2.439
-5,sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,bf3bf13ab40c3157080a7ab344c831b9ad18b5eb,0.484,0.517,0.355,0.888,0.513,0.327,0.057,0.724,0.492,-0.013,0.429,252.0,1.809
-6,sentence-transformers/all-mpnet-base-v2,84f2bcc00d77236f9e89c8a360a00fb1139bf47d,0.433,0.485,0.359,0.796,0.236,0.36,0.109,0.63,0.472,-0.031,0.379,241.5,2.887
-8,sentence-transformers/all-MiniLM-L12-v2,a05860a77cef7b37e0048a7864658139bc18a854,0.431,0.487,0.345,0.809,0.256,0.323,0.076,0.635,0.47,-0.008,0.377,221.0,1.78
-10,sentence-transformers/all-MiniLM-L6-v2,8b3219a92973c328a8e22fadcfa821b5dc75636a,0.425,0.475,0.366,0.796,0.218,0.335,0.088,0.618,0.445,-0.028,0.368,172.5,1.606
diff --git a/scripts/task_selection/europe_tasks.csv b/scripts/task_selection/europe_tasks.csv
deleted file mode 100644
index 32bdf97267..0000000000
--- a/scripts/task_selection/europe_tasks.csv
+++ /dev/null
@@ -1,97 +0,0 @@
-,Name,Type,Languages,Domains,License,Description
-0,BornholmBitextMining,BitextMining,{'dan'},"['Web', 'Social', 'Fiction', 'Written']",CC-BY-4.0,"Danish Bornholmsk Parallel Corpus. Bornholmsk is a Danish dialect spoken on the island of Bornholm, Denmark. Historically it is a part of east Danish which was also spoken in Scania and Halland, Sweden."
-1,BibleNLPBitextMining,BitextMining,"{'hrv', 'lit', 'por', 'ita', 'nld', 'dan', 'ces', 'spa', 'pol', 'ron', 'fra', 'swe', 'hun', 'eng', 'deu'}","['Religious', 'Written']",CC-BY-SA-4.0,"Partial Bible translations in 829 languages, aligned by verse."
-2,BUCC.v2,BitextMining,"{'eng', 'fra', 'deu'}",['Written'],Unknown,BUCC bitext mining dataset
-3,DiaBlaBitextMining,BitextMining,"{'eng', 'fra'}","['Social', 'Written']",CC BY-NC-SA 4.0,"English-French Parallel Corpus. DiaBLa is an English-French dataset for the evaluation of Machine Translation (MT) for informal, written bilingual dialogue."
-4,FloresBitextMining,BitextMining,"{'fin', 'nob', 'ces', 'pol', 'swe', 'eng', 'lit', 'slk', 'nno', 'nld', 'dan', 'ron', 'deu', 'hrv', 'ita', 'slv', 'spa', 'hun', 'est', 'bul', 'mlt', 'por', 'gle', 'ell', 'isl', 'fra', 'eus'}","['Non-fiction', 'Encyclopaedic', 'Written']",CC BY-SA 4.0,FLORES is a benchmark dataset for machine translation between English and low-resource languages.
-5,NorwegianCourtsBitextMining,BitextMining,"{'nob', 'nno'}","['Legal', 'Written']",CC BY 4.0,"Nynorsk and Bokmål parallel corpus from Norwegian courts. Norwegian courts have two standardised written languages. Bokmål is a variant closer to Danish, while Nynorsk was created to resemble regional dialects of Norwegian."
-6,NTREXBitextMining,BitextMining,"{'fin', 'nob', 'ces', 'pol', 'swe', 'eng', 'lit', 'slk', 'nno', 'nld', 'dan', 'ron', 'deu', 'hrv', 'ita', 'slv', 'spa', 'hun', 'bul', 'mlt', 'por', 'gle', 'lav', 'ell', 'isl', 'fra', 'eus'}","['News', 'Written']",CC-BY-SA-4.0,"NTREX is a News Test References dataset for Machine Translation Evaluation, covering translation from English into 128 languages. We select language pairs according to the M2M-100 language grouping strategy, resulting in 1916 directions."
-7,BulgarianStoreReviewSentimentClassfication,Classification,{'bul'},"['Reviews', 'Written']",cc-by-4.0,Bulgarian online store review dataset for sentiment classification.
-8,CzechProductReviewSentimentClassification,Classification,{'ces'},"['Reviews', 'Written']",CC BY-NC-SA 4.0,"User reviews of products on Czech e-shop Mall.cz with 3 sentiment classes (positive, neutral, negative)"
-9,GreekLegalCodeClassification,Classification,{'ell'},"['Legal', 'Written']",cc-by-4.0,Greek Legal Code Dataset for Classification. (subset = chapter)
-10,DBpediaClassification,Classification,{'eng'},"['Encyclopaedic', 'Written']",cc-by-sa-3.0,"DBpedia14 is a dataset of English texts from Wikipedia articles, categorized into 14 non-overlapping classes based on their DBpedia ontology."
-11,FinancialPhrasebankClassification,Classification,{'eng'},"['News', 'Written']",cc-by-nc-sa-3.0,"Polar sentiment dataset of sentences from financial news, categorized by sentiment into positive, negative, or neutral."
-12,PoemSentimentClassification,Classification,{'eng'},"['Reviews', 'Written']",CC-BY-4.0,Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg.
-13,ToxicChatClassification,Classification,{'eng'},"['Constructed', 'Written']",cc-by-4.0,"This dataset contains toxicity annotations on 10K user
-            prompts collected from the Vicuna online demo. We utilize a human-AI
-            collaborative annotation framework to guarantee the quality of annotation
-            while maintaining a feasible annotation workload. The details of data
-            collection, pre-processing, and annotation can be found in our paper.
-            We believe that ToxicChat can be a valuable resource to drive further
-            advancements toward building a safe and healthy environment for user-AI
-            interactions.
-            Only human annotated samples are selected here."
-14,ToxicConversationsClassification,Classification,{'eng'},"['Social', 'Written']",CC BY 4.0,Collection of comments from the Civil Comments platform together with annotations if the comment is toxic or not.
-15,EstonianValenceClassification,Classification,{'est'},"['News', 'Written']",CC BY 4.0,Dataset containing annotated Estonian news data from the Postimees and Õhtuleht newspapers.
-16,ItaCaseholdClassification,Classification,{'ita'},"['Legal', 'Government', 'Written']",Apache 2.0,An Italian Dataset consisting of 1101 pairs of judgments and their official holdings between the years 2019 and 2022 from the archives of Italian Administrative Justice categorized with 64 subjects.
-17,AmazonCounterfactualClassification,Classification,"{'eng', 'deu'}","['Reviews', 'Written']",CC BY 4.0,A collection of Amazon customer reviews annotated for counterfactual detection pair classification.
-18,MassiveScenarioClassification,Classification,"{'fin', 'por', 'nob', 'ita', 'nld', 'dan', 'lav', 'ell', 'isl', 'slv', 'spa', 'pol', 'ron', 'fra', 'swe', 'hun', 'eng', 'deu'}",['Spoken'],Apache 2.0,MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
-19,MultiHateClassification,Classification,"{'por', 'ita', 'nld', 'spa', 'pol', 'fra', 'eng', 'deu'}","['Constructed', 'Written']",cc-by-4.0,"Hate speech detection dataset with binary
-                       (hateful vs non-hateful) labels. Includes 25+ distinct types of hate
-                       and challenging non-hate, and 11 languages.
-                     "
-20,NordicLangClassification,Classification,"{'nob', 'nno', 'dan', 'isl', 'swe'}",['Encyclopaedic'],cc-by-sa-3.0,A dataset for Nordic language identification.
-21,ScalaClassification,Classification,"{'swe', 'dan', 'nob', 'nno'}","['Fiction', 'News', 'Non-fiction', 'Blog', 'Spoken', 'Web', 'Written']",CC BY-SA 4.0,"ScaLa a linguistic acceptability dataset for the mainland Scandinavian languages automatically constructed from dependency annotations in Universal Dependencies Treebanks.
-        Published as part of 'ScandEval: A Benchmark for Scandinavian Natural Language Processing'"
-22,SwissJudgementClassification,Classification,"{'deu', 'fra', 'ita'}","['Legal', 'Written']",CC-BY-4.0,"Multilingual, diachronic dataset of Swiss Federal Supreme Court cases annotated with the respective binarized judgment outcome (approval/dismissal)"
-23,TweetSentimentClassification,Classification,"{'por', 'ita', 'spa', 'fra', 'eng', 'deu'}","['Social', 'Written']",cc-by-3.0,A multilingual Sentiment Analysis dataset consisting of tweets in 8 different languages.
-24,CBD,Classification,{'pol'},"['Written', 'Social']",bsd-3-clause,Polish Tweets annotated for cyberbullying detection.
-25,PolEmo2.0-OUT,Classification,{'pol'},"['Written', 'Social']",cc-by-sa-4.0,"A collection of Polish online reviews from four domains: medicine, hotels, products and school. The PolEmo2.0-OUT task is to predict the sentiment of out-of-domain (products and school) reviews using models train on reviews from medicine and hotels domains."
-26,CSFDSKMovieReviewSentimentClassification,Classification,{'slk'},"['Reviews', 'Written']",CC-BY-SA-4.0,The dataset contains 30k user reviews from csfd.cz in Slovak.
-27,DalajClassification,Classification,{'swe'},"['Non-fiction', 'Written']",CC-BY-4.0,A Swedish dataset for linguistic acceptability. Available as a part of Superlim.
-28,WikiCitiesClustering,Clustering,{'eng'},"['Encyclopaedic', 'Written']",cc-by-sa-4.0,"Clustering of Wikipedia articles of cities by country from https://huggingface.co/datasets/wikipedia. Test set includes 126 countries, and a total of 3531 cities."
-29,RomaniBibleClustering,Clustering,{'rom'},"['Religious', 'Written']",MIT,Clustering verses from the Bible in Kalderash Romani by book.
-30,BigPatentClustering.v2,Clustering,{'eng'},"['Legal', 'Written']",cc-by-4.0,"Clustering of documents from the Big Patent dataset. Test set only includes documentsbelonging to a single category, with a total of 9 categories."
-31,BiorxivClusteringP2P.v2,Clustering,{'eng'},"['Academic', 'Written']",https://www.biorxiv.org/content/about-biorxiv,Clustering of titles+abstract from biorxiv across 26 categories.
-32,AlloProfClusteringS2S.v2,Clustering,{'fra'},"['Encyclopaedic', 'Written']",mit,Clustering of document titles from Allo Prof dataset. Clustering of 10 sets on the document topic.
-33,HALClusteringS2S.v2,Clustering,{'fra'},"['Academic', 'Written']",Apache-2.0,Clustering of titles from HAL (https://huggingface.co/datasets/lyon-nlp/clustering-hal-s2s)
-34,SIB200ClusteringS2S,Clustering,"{'fin', 'nob', 'ces', 'pol', 'swe', 'eng', 'lit', 'slk', 'nno', 'nld', 'dan', 'ron', 'deu', 'hrv', 'ita', 'slv', 'spa', 'hun', 'est', 'bul', 'mlt', 'por', 'gle', 'ell', 'isl', 'fra', 'eus'}","['News', 'Written']",cc-by-sa-4.0,"SIB-200 is the largest publicly available topic classification
-        dataset based on Flores-200 covering 205 languages and dialects annotated. The dataset is
-        annotated in English for the topics,  science/technology, travel, politics, sports,
-        health, entertainment, and geography. The labels are then transferred to the other languages
-        in Flores-200 which are machine-translated.
-        "
-35,WikiClusteringP2P.v2,Clustering,"{'lav', 'dan', 'ces', 'eus', 'mlt'}","['Encyclopaedic', 'Written']",cc-by-sa-3.0,"Clustering of wikipedia articles inspired by BlubrbsClusteringP2P. Labels are taken from top-level categories of the respective languages (e.g., https://lv.wikipedia.org/wiki/Kategorija:Pamatkategorijas)."
-36,StackOverflowQA,Retrieval,{'eng'},"['Programming', 'Written']",MIT,The dataset is a collection of natural language queries and their corresponding response which may include some text mixed with code snippets. The task is to retrieve the most relevant response for a given query.
-37,TwitterHjerneRetrieval,Retrieval,{'dan'},"['Social', 'Written']",CC BY 4.0,Danish question asked on Twitter with the Hashtag #Twitterhjerne ('Twitter brain') and their corresponding answer.
-38,LegalQuAD,Retrieval,{'deu'},"['Legal', 'Written']",CC BY 4.0,The dataset consists of questions and legal documents in German.
-39,ArguAna,Retrieval,{'eng'},"['Medical', 'Written']",cc-by-sa-4.0,NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval
-40,HagridRetrieval,Retrieval,{'eng'},"['Encyclopaedic', 'Written']",apache-2.0,HAGRID (Human-in-the-loop Attributable Generative Retrieval for Information-seeking Dataset)is a dataset for generative information-seeking scenarios. It consists of queriesalong with a set of manually labelled relevant passages
-41,LegalBenchCorporateLobbying,Retrieval,{'eng'},"['Legal', 'Written']",CC BY 4.0,The dataset includes bill titles and bill summaries related to corporate lobbying.
-42,LEMBPasskeyRetrieval,Retrieval,{'eng'},"['Fiction', 'Written']",Not specified,passkey subset of dwzhu/LongEmbed dataset.
-43,SCIDOCS,Retrieval,{'eng'},"['Academic', 'Written', 'Non-fiction']",cc-by-sa-4.0,"SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation."
-44,SpartQA,Retrieval,{'eng'},"['Encyclopaedic', 'Written']",MIT,Measuring the ability to retrieve the groundtruth answers to reasoning task queries on SpartQA.
-45,TempReasonL1,Retrieval,{'eng'},"['Encyclopaedic', 'Written']",CC BY-SA 3.0,Measuring the ability to retrieve the groundtruth answers to reasoning task queries on TempReason l1.
-46,WinoGrande,Retrieval,{'eng'},"['Encyclopaedic', 'Written']",CC BY,Measuring the ability to retrieve the groundtruth answers to reasoning task queries on winogrande.
-47,AlloprofRetrieval,Retrieval,{'fra'},"['Encyclopaedic', 'Written']",cc-by-nc-sa-4.0,"This dataset was provided by AlloProf, an organisation in Quebec, Canada offering resources and a help forum curated by a large number of teachers to students on all subjects taught from in primary and secondary school"
-48,BelebeleRetrieval,Retrieval,"{'fin', 'nob', 'ces', 'pol', 'swe', 'eng', 'lit', 'slk', 'nld', 'dan', 'ron', 'deu', 'hrv', 'ita', 'slv', 'spa', 'hun', 'est', 'bul', 'mlt', 'por', 'ell', 'isl', 'fra', 'eus'}","['Web', 'News', 'Written']",CC-BY-SA-4.0,Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants (including 115 distinct languages and their scripts)
-49,StatcanDialogueDatasetRetrieval,Retrieval,"{'eng', 'fra'}","['Government', 'Web', 'Written']",https://huggingface.co/datasets/McGill-NLP/statcan-dialogue-dataset-retrieval/blob/main/LICENSE.md,"A Dataset for Retrieving Data Tables through Conversations with Genuine Intents, available in English and French."
-50,WikipediaRetrievalMultilingual,Retrieval,"{'fin', 'por', 'ita', 'nld', 'dan', 'ces', 'ron', 'swe', 'eng', 'deu', 'bul'}","['Encyclopaedic', 'Written']",cc-by-sa-3.0,The dataset is derived from Cohere's wikipedia-2023-11 dataset and contains synthetically generated queries.
-51,Core17InstructionRetrieval,InstructionRetrieval,{'eng'},"['News', 'Written']",MIT,Measuring retrieval instruction following ability on Core17 narratives.
-52,News21InstructionRetrieval,InstructionRetrieval,{'eng'},"['News', 'Written']",MIT,Measuring retrieval instruction following ability on News21 narratives.
-53,Robust04InstructionRetrieval,InstructionRetrieval,{'eng'},"['News', 'Written']",MIT,Measuring retrieval instruction following ability on Robust04 narratives.
-54,MalteseNewsClassification,MultilabelClassification,{'mlt'},"['Constructed', 'Written']",cc-by-nc-sa-4.0,"A multi-label topic classification dataset for Maltese News
-        Articles. The data was collected from the press_mt subset from Korpus
-        Malti v4.0. Article contents were cleaned to filter out JavaScript, CSS,
-        & repeated non-Maltese sub-headings. The labels are based on the category
-        field from this corpus.
-        "
-55,MultiEURLEXMultilabelClassification,MultilabelClassification,"{'fin', 'ces', 'pol', 'swe', 'eng', 'lit', 'slk', 'nld', 'dan', 'ron', 'deu', 'hrv', 'ita', 'slv', 'spa', 'hun', 'est', 'bul', 'mlt', 'por', 'lav', 'ell', 'fra'}","['Legal', 'Government', 'Written']",CC BY-SA 4.0,EU laws in 23 EU languages containing gold labels.
-56,CTKFactsNLI,PairClassification,{'ces'},"['News', 'Written']",CC-BY-SA-3.0,"Czech Natural Language Inference dataset of around 3K evidence-claim pairs labelled with SUPPORTS, REFUTES or NOT ENOUGH INFO veracity labels. Extracted from a round of fact-checking experiments."
-57,SprintDuplicateQuestions,PairClassification,{'eng'},"['Programming', 'Written']",Not specified,Duplicate questions from the Sprint community.
-58,OpusparcusPC,PairClassification,"{'fin', 'fra', 'swe', 'eng', 'deu'}","['Spoken', 'Spoken']",cc-by-nc-4.0,"Opusparcus is a paraphrase corpus for six European language: German, English, Finnish, French, Russian, and Swedish. The paraphrases consist of subtitles from movies and TV shows."
-59,RTE3,PairClassification,"{'deu', 'eng', 'fra', 'ita'}","['News', 'Web', 'Encyclopaedic', 'Written']",cc-by-4.0,Recognising Textual Entailment Challenge (RTE-3) aim to provide the NLP community with a benchmark to test progress in recognizing textual entailment
-60,XNLI,PairClassification,"{'ell', 'spa', 'fra', 'deu', 'eng', 'bul'}","['Non-fiction', 'Fiction', 'Government', 'Written']",Not specified,
-61,PSC,PairClassification,{'pol'},"['News', 'Written']",cc-by-3,Polish Summaries Corpus
-62,WebLINXCandidatesReranking,Reranking,{'eng'},"['Academic', 'Web', 'Written']",CC BY-NC-SA 4.0,WebLINX is a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. The reranking task focuses on finding relevant elements at every given step in the trajectory.
-63,AlloprofReranking,Reranking,{'fra'},"['Web', 'Academic', 'Written']",CC BY-NC-SA 4.0,"This dataset was provided by AlloProf, an organisation in Quebec, Canada offering resources and a help forum curated by a large number of teachers to students on all subjects taught from in primary and secondary school"
-64,WikipediaRerankingMultilingual,Reranking,"{'fin', 'por', 'ita', 'nld', 'dan', 'ces', 'ron', 'swe', 'eng', 'deu', 'bul'}","['Encyclopaedic', 'Written']",cc-by-sa-3.0,The dataset is derived from Cohere's wikipedia-2023-11 dataset and contains synthetically generated queries.
-65,SICK-R,STS,{'eng'},,,Semantic Textual Similarity SICK-R dataset as described here:
-66,STS12,STS,{'eng'},"['Encyclopaedic', 'News', 'Written']",Not specified,SemEval-2012 Task 6.
-67,STS14,STS,{'eng'},"['Blog', 'Web', 'Spoken']",Not specified,SemEval STS 2014 dataset. Currently only the English dataset
-68,STS15,STS,{'eng'},"['Blog', 'News', 'Web', 'Written', 'Spoken']",Not specified,SemEval STS 2015 dataset
-69,STSBenchmark,STS,{'eng'},,,Semantic Textual Similarity Benchmark (STSbenchmark) dataset.
-70,FinParaSTS,STS,{'fin'},"['News', 'Subtitles', 'Written']",cc-by-sa-4.0,Finnish paraphrase-based semantic similarity corpus
-71,STS17,STS,"{'ita', 'nld', 'spa', 'fra', 'eng', 'deu'}","['News', 'Web', 'Written']",Not specified,Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation
-72,SICK-R-PL,STS,{'pol'},"['Web', 'Written']",CC-BY-NC-SA-3.0,Polish version of SICK dataset for textual relatedness.
-73,STSES,STS,{'spa'},['Written'],cc-by-4.0,"Spanish test sets from SemEval-2014 (Agirre et al., 2014) and SemEval-2015 (Agirre et al., 2015)"
diff --git a/scripts/task_selection/indic_results.csv b/scripts/task_selection/indic_results.csv
deleted file mode 100644
index 49c7a77297..0000000000
--- a/scripts/task_selection/indic_results.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-,model,revision,mean,mean (BitextMining),mean (Classification),mean (Retrieval),mean (STS),mean (Reranking),mean (Clustering),mean (PairClassification),mean (wieghted by task type),borda_count,Total Evaluation time (hours)
-7,intfloat/multilingual-e5-large-instruct,baa7be480a7de1539afce709c8f13f833a510e0a,0.718,0.703,0.709,0.887,0.537,0.91,0.472,0.785,0.715,224.0,1.887
-3,intfloat/multilingual-e5-large,4dc6d853a804b9c8886ede6dda8a073b7dc08a81,0.645,0.644,0.631,0.875,0.439,0.897,0.237,0.739,0.637,190.0,1.269
-2,GritLM/GritLM-7B,13f00a0e36500c80ce12870ea513846a066004af,0.646,0.607,0.652,0.832,0.272,0.91,0.361,0.741,0.625,165.0,2.136
-9,intfloat/multilingual-e5-base,d13f1b27baf31030b7fd040960d60d909913633f,0.625,0.612,0.619,0.833,0.411,0.877,0.216,0.71,0.611,164.0,0.851
-11,intfloat/e5-mistral-7b-instruct,07163b72af1488142a360786df853f237b1a3ca1,0.637,0.616,0.636,0.808,0.23,0.903,0.387,0.779,0.623,154.0,1.644
-0,intfloat/multilingual-e5-small,e4ce9877abf3edfe10b0d82785e83bdcb973e22e,0.619,0.612,0.613,0.808,0.408,0.87,0.239,0.69,0.606,150.0,0.736
-1,sentence-transformers/LaBSE,e34fab64a3011d2176c99545a93d5cbddc9a91b7,0.607,0.636,0.6,0.716,0.528,0.809,0.188,0.652,0.59,135.0,0.809
-4,sentence-transformers/paraphrase-multilingual-mpnet-base-v2,79f2382ceacceacdf38563d7c5d16b9ff8d725d6,0.571,0.42,0.602,0.696,0.341,0.822,0.241,0.827,0.564,127.0,0.785
-5,sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,bf3bf13ab40c3157080a7ab344c831b9ad18b5eb,0.5,0.236,0.563,0.641,0.198,0.785,0.194,0.789,0.486,91.0,0.668
-6,sentence-transformers/all-mpnet-base-v2,84f2bcc00d77236f9e89c8a360a00fb1139bf47d,0.364,0.072,0.472,0.323,-0.025,0.647,0.089,0.584,0.309,52.0,0.84
-8,sentence-transformers/all-MiniLM-L12-v2,a05860a77cef7b37e0048a7864658139bc18a854,0.359,0.078,0.46,0.329,-0.053,0.692,0.076,0.584,0.31,39.0,0.689
-10,sentence-transformers/all-MiniLM-L6-v2,8b3219a92973c328a8e22fadcfa821b5dc75636a,0.351,0.063,0.463,0.294,-0.063,0.645,0.066,0.574,0.292,27.0,0.629
diff --git a/scripts/task_selection/indic_tasks.csv b/scripts/task_selection/indic_tasks.csv
deleted file mode 100644
index 3a4cd20753..0000000000
--- a/scripts/task_selection/indic_tasks.csv
+++ /dev/null
@@ -1,32 +0,0 @@
-,Name,Type,Languages,Domains,License,Description
-0,IN22ConvBitextMining,BitextMining,"{'ory', 'kas', 'asm', 'snd', 'hin', 'mar', 'tam', 'san', 'kan', 'urd', 'mni', 'npi', 'guj', 'tel', 'doi', 'pan', 'mal', 'ben', 'gom', 'mai'}","['Social', 'Spoken', 'Fiction', 'Spoken']",CC-BY-4.0,IN22-Conv is a n-way parallel conversation domain benchmark dataset for machine translation spanning English and 22 Indic languages.
-1,IN22GenBitextMining,BitextMining,"{'ory', 'kas', 'asm', 'snd', 'hin', 'mar', 'tam', 'san', 'kan', 'urd', 'mni', 'npi', 'guj', 'tel', 'doi', 'pan', 'mal', 'ben', 'gom', 'mai'}","['Web', 'Legal', 'Government', 'News', 'Religious', 'Non-fiction', 'Written']",CC-BY-4.0,IN22-Gen is a n-way parallel general-purpose multi-domain benchmark dataset for machine translation spanning English and 22 Indic languages.
-2,IndicGenBenchFloresBitextMining,BitextMining,"{'ory', 'asm', 'gbm', 'hin', 'nep', 'mar', 'tam', 'bgc', 'mup', 'mwr', 'kan', 'san', 'urd', 'awa', 'mni', 'guj', 'tel', 'pan', 'raj', 'mal', 'hne', 'ben', 'gom', 'mai', 'bho'}","['Web', 'News', 'Written']",CC-BY-SA-4.0,Flores-IN dataset is an extension of Flores dataset released as a part of the IndicGenBench by Google
-3,LinceMTBitextMining,BitextMining,{'hin'},"['Social', 'Written']",Unknown,LinceMT is a parallel corpus for machine translation pairing code-mixed Hinglish (a fusion of Hindi and English commonly used in modern India) with human-generated English translations.
-4,BengaliSentimentAnalysis,Classification,{'ben'},"['Reviews', 'Written']",CC BY 4.0,dataset contains 3307 Negative reviews and 8500 Positive reviews collected and manually annotated from Youtube Bengali drama.
-5,GujaratiNewsClassification,Classification,{'guj'},"['News', 'Written']",MIT,A Gujarati dataset for 3-class classification of Gujarati news articles
-6,HindiDiscourseClassification,Classification,{'hin'},"['Fiction', 'Social', 'Written']",MIT,A Hindi Discourse dataset in Hindi with values for coherence.
-7,SentimentAnalysisHindi,Classification,{'hin'},"['Reviews', 'Written']",CC BY-NC-SA 4.0,Hindi Sentiment Analysis Dataset
-8,MalayalamNewsClassification,Classification,{'mal'},"['News', 'Written']",MIT,A Malayalam dataset for 3-class classification of Malayalam news articles
-9,IndicLangClassification,Classification,"{'ory', 'kas', 'asm', 'snd', 'hin', 'mar', 'tam', 'san', 'kan', 'urd', 'mni', 'npi', 'guj', 'tel', 'doi', 'pan', 'mal', 'ben', 'gom', 'mai'}","['Web', 'Non-fiction', 'Written']",CC0,A language identification test set for native-script as well as Romanized text which spans 22 Indic languages.
-10,MTOPIntentClassification,Classification,{'hin'},"['Spoken', 'Spoken']",Not specified,MTOP: Multilingual Task-Oriented Semantic Parsing
-11,MultiHateClassification,Classification,{'hin'},"['Constructed', 'Written']",cc-by-4.0,"Hate speech detection dataset with binary
-                       (hateful vs non-hateful) labels. Includes 25+ distinct types of hate
-                       and challenging non-hate, and 11 languages.
-                     "
-12,TweetSentimentClassification,Classification,{'hin'},"['Social', 'Written']",cc-by-3.0,A multilingual Sentiment Analysis dataset consisting of tweets in 8 different languages.
-13,NepaliNewsClassification,Classification,{'nep'},"['News', 'Written']",CC BY-SA 4.0,A Nepali dataset for 7500 news articles
-14,PunjabiNewsClassification,Classification,{'pan'},"['News', 'Written']",MIT,A Punjabi dataset for 2-class classification of Punjabi news articles
-15,SanskritShlokasClassification,Classification,{'san'},"['Religious', 'Written']",CC BY-SA 4.0,This data set contains ~500 Shlokas
-16,UrduRomanSentimentClassification,Classification,{'urd'},"['Social', 'Written']",MIT,"The Roman Urdu dataset is a data corpus comprising of more than 20000 records tagged for sentiment (Positive, Negative, Neutral)"
-17,SIB200ClusteringS2S,Clustering,"{'ory', 'kas', 'asm', 'snd', 'hin', 'mar', 'tam', 'san', 'kan', 'urd', 'awa', 'mni', 'npi', 'guj', 'tel', 'pan', 'mal', 'hne', 'ben', 'mai', 'bho'}","['News', 'Written']",cc-by-sa-4.0,"SIB-200 is the largest publicly available topic classification
-        dataset based on Flores-200 covering 205 languages and dialects annotated. The dataset is
-        annotated in English for the topics,  science/technology, travel, politics, sports,
-        health, entertainment, and geography. The labels are then transferred to the other languages
-        in Flores-200 which are machine-translated.
-        "
-18,BelebeleRetrieval,Retrieval,"{'mal', 'ory', 'ben', 'mar', 'tam', 'npi', 'guj', 'asm', 'snd', 'kan', 'urd', 'tel', 'pan', 'hin'}","['Web', 'News', 'Written']",CC-BY-SA-4.0,Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants (including 115 distinct languages and their scripts)
-19,XQuADRetrieval,Retrieval,{'hin'},"['Web', 'Written']",CC BY-SA 4.0,XQuAD is a benchmark dataset for evaluating cross-lingual question answering performance. It is repurposed retrieving relevant context for each question.
-20,XNLI,PairClassification,{'hin'},"['Non-fiction', 'Fiction', 'Government', 'Written']",Not specified,
-21,WikipediaRerankingMultilingual,Reranking,"{'hin', 'ben'}","['Encyclopaedic', 'Written']",cc-by-sa-3.0,The dataset is derived from Cohere's wikipedia-2023-11 dataset and contains synthetically generated queries.
-22,IndicCrosslingualSTS,STS,"{'mal', 'ory', 'ben', 'mar', 'tam', 'guj', 'asm', 'kan', 'urd', 'tel', 'pan', 'hin'}","['News', 'Non-fiction', 'Web', 'Spoken', 'Government', 'Written', 'Spoken']",CC0,This is a Semantic Textual Similarity testset between English and 12 high-resource Indic languages.
diff --git a/scripts/task_selection/mteb_lite_results.csv b/scripts/task_selection/mteb_lite_results.csv
deleted file mode 100644
index e382c9e3b1..0000000000
--- a/scripts/task_selection/mteb_lite_results.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-,model,revision,mean,mean (Clustering),mean (STS),mean (Classification),mean (Reranking),mean (Retrieval),mean (PairClassification),mean (weighted by task type),borda_count,Total Evaluation time (hours),Total CO2-eq emissions (kg)
-11,intfloat/e5-mistral-7b-instruct,07163b72af1488142a360786df853f237b1a3ca1,0.67,0.514,0.836,0.752,0.498,0.548,0.884,0.672,393.0,2.502,2.971
-2,GritLM/GritLM-7B,13f00a0e36500c80ce12870ea513846a066004af,0.664,0.508,0.825,0.77,0.496,0.532,0.873,0.667,384.0,3.111,3.409
-7,intfloat/multilingual-e5-large-instruct,baa7be480a7de1539afce709c8f13f833a510e0a,0.652,0.499,0.843,0.732,0.487,0.51,0.862,0.656,357.0,2.033,1.418
-3,intfloat/multilingual-e5-large,4dc6d853a804b9c8886ede6dda8a073b7dc08a81,0.621,0.428,0.806,0.728,0.447,0.49,0.847,0.624,270.0,2.549,1.563
-6,sentence-transformers/all-mpnet-base-v2,84f2bcc00d77236f9e89c8a360a00fb1139bf47d,0.56,0.466,0.722,0.566,0.484,0.419,0.83,0.581,211.0,1.19,0.688
-9,intfloat/multilingual-e5-base,d13f1b27baf31030b7fd040960d60d909913633f,0.602,0.422,0.791,0.7,0.443,0.461,0.836,0.609,211.0,1.17,0.691
-4,sentence-transformers/paraphrase-multilingual-mpnet-base-v2,79f2382ceacceacdf38563d7c5d16b9ff8d725d6,0.573,0.435,0.798,0.686,0.452,0.341,0.817,0.588,188.0,1.017,0.563
-8,sentence-transformers/all-MiniLM-L12-v2,a05860a77cef7b37e0048a7864658139bc18a854,0.547,0.446,0.707,0.558,0.475,0.407,0.825,0.57,172.0,0.814,0.442
-10,sentence-transformers/all-MiniLM-L6-v2,8b3219a92973c328a8e22fadcfa821b5dc75636a,0.544,0.449,0.704,0.554,0.471,0.398,0.824,0.567,149.0,0.733,0.391
-0,intfloat/multilingual-e5-small,e4ce9877abf3edfe10b0d82785e83bdcb973e22e,0.584,0.408,0.776,0.677,0.432,0.437,0.827,0.593,147.0,0.833,0.459
-5,sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,bf3bf13ab40c3157080a7ab344c831b9ad18b5eb,0.551,0.417,0.775,0.644,0.454,0.328,0.8,0.57,109.0,0.879,0.469
-1,sentence-transformers/LaBSE,e34fab64a3011d2176c99545a93d5cbddc9a91b7,0.486,0.361,0.702,0.668,0.413,0.168,0.789,0.517,49.0,1.02,0.582
diff --git a/scripts/task_selection/mteb_lite_tasks.csv b/scripts/task_selection/mteb_lite_tasks.csv
deleted file mode 100644
index 25e359e383..0000000000
--- a/scripts/task_selection/mteb_lite_tasks.csv
+++ /dev/null
@@ -1,41 +0,0 @@
-,name,type,languages,domains,license
-0,AmazonCounterfactualClassification,Classification,"['deu', 'eng', 'jpn']","['Reviews', 'Written']",cc-by-4.0
-1,ArguAna,Retrieval,['eng'],"['Medical', 'Written']",cc-by-sa-4.0
-2,ArXivHierarchicalClusteringP2P,Clustering,['eng'],"['Academic', 'Written']",cc0-1.0
-3,ArXivHierarchicalClusteringS2S,Clustering,['eng'],"['Academic', 'Written']",cc0-1.0
-4,AskUbuntuDupQuestions,Reranking,['eng'],,
-5,BIOSSES,STS,['eng'],,
-6,Banking77Classification,Classification,['eng'],['Written'],mit
-7,BiorxivClusteringP2P.v2,Clustering,['eng'],"['Academic', 'Written']",https://www.biorxiv.org/content/about-biorxiv
-8,CQADupstackGamingRetrieval,Retrieval,['eng'],,
-9,CQADupstackUnixRetrieval,Retrieval,['eng'],,
-10,ClimateFEVERHardNegatives,Retrieval,['eng'],,
-11,FEVERHardNegatives,Retrieval,['eng'],,
-12,FiQA2018,Retrieval,['eng'],,
-13,HotpotQAHardNegatives,Retrieval,['eng'],"['Web', 'Written']",cc-by-sa-4.0
-14,ImdbClassification,Classification,['eng'],"['Reviews', 'Written']",not specified
-15,MTOPDomainClassification,Classification,"['deu', 'eng', 'fra', 'hin', 'spa', 'tha']","['Spoken', 'Spoken']",not specified
-16,MassiveIntentClassification,Classification,"['afr', 'amh', 'ara', 'aze', 'ben', 'cmo', 'cym', 'dan', 'deu', 'ell', 'eng', 'fas', 'fin', 'fra', 'heb', 'hin', 'hun', 'hye', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kan', 'kat', 'khm', 'kor', 'lav', 'mal', 'mon', 'msa', 'mya', 'nld', 'nob', 'pol', 'por', 'ron', 'rus', 'slv', 'spa', 'sqi', 'swa', 'swe', 'tam', 'tel', 'tgl', 'tha', 'tur', 'urd', 'vie']",['Spoken'],apache-2.0
-17,MassiveScenarioClassification,Classification,"['afr', 'amh', 'ara', 'aze', 'ben', 'cmo', 'cym', 'dan', 'deu', 'ell', 'eng', 'fas', 'fin', 'fra', 'heb', 'hin', 'hun', 'hye', 'ind', 'isl', 'ita', 'jav', 'jpn', 'kan', 'kat', 'khm', 'kor', 'lav', 'mal', 'mon', 'msa', 'mya', 'nld', 'nob', 'pol', 'por', 'ron', 'rus', 'slv', 'spa', 'sqi', 'swa', 'swe', 'tam', 'tel', 'tgl', 'tha', 'tur', 'urd', 'vie']",['Spoken'],apache-2.0
-18,MedrxivClusteringP2P.v2,Clustering,['eng'],"['Academic', 'Medical', 'Written']",https://www.medrxiv.org/content/about-medrxiv
-19,MedrxivClusteringS2S.v2,Clustering,['eng'],"['Academic', 'Medical', 'Written']",https://www.medrxiv.org/content/about-medrxiv
-20,MindSmallReranking,Reranking,['eng'],"['News', 'Written']",https://github.com/msnews/MIND/blob/master/MSR%20License_Data.pdf
-21,SCIDOCS,Retrieval,['eng'],"['Academic', 'Written', 'Non-fiction']",cc-by-sa-4.0
-22,SICK-R,STS,['eng'],,
-23,STS12,STS,['eng'],"['Encyclopaedic', 'News', 'Written']",not specified
-24,STS13,STS,['eng'],"['Web', 'News', 'Non-fiction', 'Written']",not specified
-25,STS14,STS,['eng'],"['Blog', 'Web', 'Spoken']",not specified
-26,STS15,STS,['eng'],"['Blog', 'News', 'Web', 'Written', 'Spoken']",not specified
-27,STS17,STS,"['ara', 'deu', 'eng', 'fra', 'ita', 'kor', 'nld', 'spa', 'tur']","['News', 'Web', 'Written']",not specified
-28,STS22.v2,STS,"['ara', 'cmn', 'deu', 'eng', 'fra', 'ita', 'pol', 'rus', 'spa', 'tur']","['News', 'Written']",not specified
-29,STSBenchmark,STS,['eng'],,
-30,SprintDuplicateQuestions,PairClassification,['eng'],"['Programming', 'Written']",not specified
-31,StackExchangeClustering.v2,Clustering,['eng'],"['Web', 'Written']",not specified
-32,StackExchangeClusteringP2P.v2,Clustering,['eng'],"['Web', 'Written']",not specified
-33,TRECCOVID,Retrieval,['eng'],,
-34,Touche2020,Retrieval,['eng'],,
-35,ToxicConversationsClassification,Classification,['eng'],"['Social', 'Written']",cc-by-4.0
-36,TweetSentimentExtractionClassification,Classification,['eng'],"['Social', 'Written']",not specified
-37,TwentyNewsgroupsClustering.v2,Clustering,['eng'],"['News', 'Written']",not specified
-38,TwitterSemEval2015,PairClassification,['eng'],,
-39,TwitterURLCorpus,PairClassification,['eng'],,
diff --git a/scripts/task_selection/mult_results.csv b/scripts/task_selection/mult_results.csv
deleted file mode 100644
index 98edf2b0e1..0000000000
--- a/scripts/task_selection/mult_results.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-,model,revision,mean,mean (BitextMining),mean (PairClassification),mean (Classification),mean (STS),mean (Retrieval),mean (MultilabelClassification),mean (Clustering),mean (Reranking),mean (InstructionRetrieval),mean (weighted by task type),borda_count,Total Evaluation time (hours)
-7,intfloat/multilingual-e5-large-instruct,baa7be480a7de1539afce709c8f13f833a510e0a,0.634,0.801,0.812,0.65,0.767,0.58,0.229,0.515,0.63,-0.004,0.553,1244.0,6.884
-2,GritLM/GritLM-7B,13f00a0e36500c80ce12870ea513846a066004af,0.609,0.705,0.802,0.619,0.732,0.591,0.212,0.504,0.628,0.035,0.536,1119.0,10.675
-11,intfloat/e5-mistral-7b-instruct,07163b72af1488142a360786df853f237b1a3ca1,0.602,0.706,0.814,0.603,0.739,0.554,0.222,0.514,0.634,-0.006,0.531,1100.0,9.969
-3,intfloat/multilingual-e5-large,4dc6d853a804b9c8886ede6dda8a073b7dc08a81,0.587,0.717,0.793,0.599,0.734,0.55,0.213,0.431,0.626,-0.031,0.515,980.0,9.206
-9,intfloat/multilingual-e5-base,d13f1b27baf31030b7fd040960d60d909913633f,0.571,0.694,0.776,0.582,0.712,0.536,0.202,0.428,0.599,-0.027,0.5,811.0,4.261
-4,sentence-transformers/paraphrase-multilingual-mpnet-base-v2,79f2382ceacceacdf38563d7c5d16b9ff8d725d6,0.52,0.521,0.816,0.551,0.695,0.393,0.164,0.412,0.532,-0.011,0.452,698.0,16.15
-0,intfloat/multilingual-e5-small,e4ce9877abf3edfe10b0d82785e83bdcb973e22e,0.556,0.675,0.768,0.565,0.699,0.502,0.191,0.418,0.602,-0.024,0.488,654.0,2.893
-1,sentence-transformers/LaBSE,e34fab64a3011d2176c99545a93d5cbddc9a91b7,0.521,0.763,0.761,0.546,0.652,0.329,0.201,0.394,0.504,-0.03,0.458,589.0,3.818
-5,sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,bf3bf13ab40c3157080a7ab344c831b9ad18b5eb,0.488,0.445,0.794,0.517,0.664,0.362,0.149,0.396,0.51,-0.013,0.425,475.0,2.759
-6,sentence-transformers/all-mpnet-base-v2,84f2bcc00d77236f9e89c8a360a00fb1139bf47d,0.424,0.212,0.71,0.47,0.571,0.328,0.163,0.411,0.421,-0.031,0.362,397.5,4.772
-8,sentence-transformers/all-MiniLM-L12-v2,a05860a77cef7b37e0048a7864658139bc18a854,0.421,0.229,0.719,0.468,0.566,0.324,0.146,0.368,0.443,-0.008,0.362,355.0,2.691
-10,sentence-transformers/all-MiniLM-L6-v2,8b3219a92973c328a8e22fadcfa821b5dc75636a,0.415,0.201,0.713,0.463,0.556,0.331,0.151,0.383,0.4,-0.028,0.352,289.5,2.43
diff --git a/scripts/task_selection/mult_tasks.csv b/scripts/task_selection/mult_tasks.csv
deleted file mode 100644
index d3320dba33..0000000000
--- a/scripts/task_selection/mult_tasks.csv
+++ /dev/null
@@ -1,180 +0,0 @@
-,Name,Type,Languages,Domains,License,Description
-0,BornholmBitextMining,BitextMining,{'dan'},"['Web', 'Social', 'Fiction', 'Written']",CC-BY-4.0,"Danish Bornholmsk Parallel Corpus. Bornholmsk is a Danish dialect spoken on the island of Bornholm, Denmark. Historically it is a part of east Danish which was also spoken in Scania and Halland, Sweden."
-1,BibleNLPBitextMining,BitextMining,"{'aoj', 'ncl', 'imo', 'acu', 'eko', 'urb', 'bvd', 'cab', 'bsp', 'mam', 'amf', 'ikk', 'mqj', 'esk', 'tur', 'cpb', 'azb', 'myy', 'nld', 'mcr', 'tgk', 'cbu', 'rwo', 'wmw', 'xbi', 'mdy', 'sri', 'ziw', 'zpu', 'xtm', 'mlp', 'yad', 'cuk', 'tpt', 'shj', 'bch', 'msk', 'taj', 'lat', 'awk', 'azz', 'san', 'mca', 'kpx', 'pes', 'kqw', 'kpg', 'cek', 'cop', 'cnl', 'zai', 'agt', 'kaq', 'cjo', 'dan', 'enq', 'gnw', 'luo', 'kqc', 'kgk', 'tbz', 'mig', 'snc', 'acr', 'kpr', 'mwf', 'nhe', 'beo', 'mxt', 'rmy', 'wal', 'otn', 'sps', 'tcz', 'aak', 'bgs', 'kan', 'zsr', 'bmr', 'gun', 'kpw', 'amm', 'gmv', 'wos', 'cao', 'mkn', 'mek', 'aey', 'nhu', 'mcd', 'mgh', 'kiw', 'lgl', 'ycn', 'nna', 'apz', 'ndg', 'xon', 'ptu', 'glk', 'cmn', 'mwp', 'sab', 'qvw', 'chz', 'far', 'msy', 'ots', 'agr', 'apw', 'ebk', 'agu', 'ind', 'amx', 'jid', 'mhl', 'myk', 'pio', 'tnn', 'yaa', 'kto', 'cap', 'msc', 'ron', 'mkj', 'tam', 'naf', 'tuf', 'ppo', 'zpo', 'bao', 'meu', 'ubr', 'amn', 'zos', 'mzz', 'hin', 'wim', 'bhl', 'npi', 'bpr', 'quf', 'hmo', 'mle', 'tca', 'bjz', 'tof', 'maj', 'tod', 'uvh', 'yut', 'pol', 'zav', 'piu', 'opm', 'csy', 'ken', 'tee', 'mie', 'snn', 'pad', 'cbv', 'mya', 'xsi', 'faa', 'rgu', 'gwi', 'lin', 'reg', 'vid', 'chd', 'mto', 'nca', 'agm', 'soy', 'knf', 'kwd', 'tew', 'maq', 'cco', 'blz', 'swe', 'med', 'tbf', 'taw', 'avt', 'urt', 'ben', 'iou', 'poy', 'pjt', 'are', 'kup', 'amr', 'met', 'spm', 'tbg', 'tfr', 'hus', 'txu', 'cbs', 'buk', 'ncu', 'zpz', 'poe', 'sll', 'atd', 'hla', 'row', 'mxp', 'zpm', 'awb', 'shp', 'nsn', 'mop', 'mcb', 'mal', 'mpj', 'bus', 'hat', 'bre', 'kyf', 'qup', 'kmu', 'jac', 'jic', 'kjs', 'dgr', 'pls', 'ood', 'myw', 'rop', 'hau', 'knj', 'cha', 'ino', 'mbh', 'poi', 'tue', 'epo', 'ake', 'clu', 'mbs', 'bbb', 'zga', 'llg', 'usp', 'cbi', 'ilo', 'mir', 'gvs', 'hns', 'mib', 'agd', 'tpz', 'tel', 'mar', 'aii', 'kyq', 'dgz', 'mjc', 'cle', 'acf', 'gfk', 'ksr', 'trc', 'aia', 'atb', 'lif', 'top', 'gym', 'ngu', 'toc', 'mcp', 'hui', 'agg', 'tav', 'yon', 'awx', 'pab', 'kos', 'byr', 'hix', 'ewe', 'srq', 'nab', 'msm', 'sey', 'mpp', 'ctp', 'nko', 'sja', 'ffm', 'mph', 'ory', 'srp', 'kde', 'ktm', 'wiu', 'mgw', 'mpm', 'kze', 'haw', 'qvz', 'mvn', 'noa', 'zaj', 'mih', 'dik', 'sbs', 'ura', 'gul', 'obo', 'dji', 'ssd', 'tke', 'aon', 'mpt', 'uvl', 'nif', 'jae', 'mks', 'qul', 'xed', 'ame', 'aer', 'usa', 'zlm', 'djk', 'apn', 'pma', 'tiy', 'zpv', 'bzj', 'sxb', 'not', 'srn', 'grc', 'anv', 'guj', 'mit', 'kmg', 'pon', 'seh', 'gyr', 'kqf', 'apu', 'kvn', 'pao', 'mxq', 'adz', 'ong', 'kbh', 'bqc', 'dad', 'ipi', 'gum', 'spy', 'yva', 'wrs', 'rmc', 'snp', 'ese', 'tsw', 'zas', 'zpl', 'heb', 'tuo', 'bvr', 'upv', 'emi', 'yka', 'cux', 'crx', 'als', 'tet', 'mmx', 'alp', 'dop', 'geb', 'deu', 'waj', 'xtd', 'gam', 'kyc', 'ptp', 'ces', 'hbo', 'hub', 'mbt', 'pwg', 'toj', 'dww', 'zty', 'hrv', 'nii', 'tim', 'msb', 'kdc', 'zao', 'blw', 'qvn', 'kbq', 'tos', 'chq', 'gvn', 'tpa', 'myu', 'kmk', 'nhr', 'omw', 'kud', 'kmo', 'cbr', 'kkl', 'wed', 'otq', 'sim', 'caa', 'jao', 'uig', 'ssx', 'nnq', 'ghs', 'lbk', 'bps', 'tzj', 'aaz', 'bmk', 'kyz', 'tmd', 'ntu', 'bjk', 'tvk', 'dgc', 'cof', 'ulk', 'bzd', 'quc', 'abx', 'amo', 'nhg', 'sua', 'swp', 'vie', 'tif', 'nfa', 'tgo', 'dah', 'boa', 'apr', 'sus', 'zpc', 'cac', 'nwi', 'pri', 'otm', 'qwh', 'bba', 'aka', 'cbc', 'mav', 'cak', 'gdn', 'yre', 'wol', 'aau', 'snx', 'sgb', 'gnn', 'hun', 'wnc', 'meq', 'npl', 'kyg', 'bdd', 'inb', 'nuy', 'wat', 'gvc', 'tnc', 'mqb', 'chk', 'wuv', 'amk', 'isn', 'xav', 'bss', 'qvm', 'yaq', 'wap', 'yal', 'bjp', 'kue', 'abt', 'bea', 'qxn', 'ctu', 'bki', 'yrb', 'fai', 'sbk', 'guh', 'fuh', 'maz', 'apb', 'gdr', 'kwj', 'mgc', 'zam', 'bqp', 'ngp', 'rro', 'beu', 'bsn', 'lbb', 'kdl', 'lac', 'srm', 'ded', 'hch', 'cbt', 'sny', 'mcf', 'mio', 'auc', 'cpy', 'aai', 'cpu', 'zab', 'ntp', 'agn', 'qvh', 'arb', 'eri', 'cso', 'kbc', 'amp', 'ndj', 'bmh', 'ata', 'guo', 'hot', 'mna', 'cot', 'ter', 'too', 'prf', 'wbi', 'bkx', 'yuw', 'bzh', 'cme', 'zar', 'kgf', 'mil', 'bbr', 'smk', 'big', 'jiv', 'wiv', 'wro', 'urd', 'dwr', 'kmh', 'kqa', 'kkc', 'nch', 'ukr', 'zap', 'bon', 'gux', 'ian', 'ceb', 'kpf', 'bsj', 'nou', 'aom', 'caf', 'knv', 'lww', 'aly', 'miz', 'zca', 'cya', 'bkq', 'cnt', 'kms', 'nss', 'nhw', 'cuc', 'ape', 'klv', 'plu', 'cax', 'gai', 'bnp', 'box', 'spl', 'pan', 'spp', 'mbc', 'khs', 'wrk', 'tdt', 'cpc', 'lex', 'nhi', 'tuc', 'cth', 'pib', 'amu', 'azg', 'ztq', 'yor', 'arl', 'tnk', 'ckb', 'ncj', 'leu', 'kje', 'bco', 'vmy', 'cgc', 'yle', 'soq', 'uli', 'cta', 'muy', 'arp', 'ita', 'tac', 'kek', 'nhy', 'hop', 'con', 'udu', 'nlg', 'dov', 'mic', 'bmu', 'ubu', 'swh', 'cwe', 'kew', 'auy', 'bel', 'crn', 'nbq', 'sbe', 'yuj', 'hvn', 'jni', 'zia', 'ksj', 'gup', 'klt', 'huv', 'tgl', 'dhg', 'jvn', 'mwe', 'zad', 'kiz', 'rkb', 'mox', 'mwc', 'quh', 'cav', 'uri', 'tbo', 'eng', 'wnu', 'mbb', 'ton', 'tte', 'poh', 'mmo', 'tiw', 'iws', 'tcs', 'ikw', 'rus', 'pir', 'mxb', 'zaa', 'etr', 'aby', 'rai', 'byx', 'zac', 'gaw', 'yss', 'khz', 'mti', 'lid', 'rug', 'fra', 'aso', 'emp', 'dwy', 'stp', 'tlf', 'mva', 'tpi', 'yby', 'ote', 'bef', 'sgz', 'tnp', 'nin', 'cjv', 'ixl', 'mbl', 'tbc', 'ntj', 'daa', 'zat', 'zyp', 'urw', 'lcm', 'gui', 'heg', 'hto', 'qvc', 'wmt', 'gvf', 'mlh', 'huu', 'kwf', 'suz', 'lug', 'chf', 'gub', 'nas', 'cut', 'mau', 'aoi', 'hlt', 'twi', 'mux', 'anh', 'ttc', 'kql', 'cub', 'mcq', 'ruf', 'car', 'viv', 'nvm', 'wbp', 'for', 'bjr', 'nyu', 'qxo', 'yap', 'djr', 'yml', 'asm', 'kvg', 'wsk', 'cbk', 'arn', 'dif', 'tna', 'nho', 'ons', 'alq', 'fue', 'sue', 'mkl', 'dob', 'fuf', 'qvs', 'por', 'tgp', 'bkd', 'kik', 'nak', 'okv', 'bgt', 'mee', 'mps', 'bhg', 'mco', 'roo', 'zaw', 'zpq', 'txq', 'att', 'kbm', 'pah', 'lit', 'nop', 'spa', 'ssg', 'xnn', 'tzo', 'boj', 'aui', 'cni', 'wer', 'mpx', 'bjv', 'atg', 'maa', 'kgp', 'kpj', 'cpa', 'kne', 'nya', 'qve', 'gah', 'qxh', 'nys', 'ign', 'som', 'kwi', 'jpn', 'cui', 'ksd', 'mbj', 'tha', 'tku', 'gng', 'gof', 'qub', 'xla', 'bxh'}","['Religious', 'Written']",CC-BY-SA-4.0,"Partial Bible translations in 829 languages, aligned by verse."
-2,BUCC.v2,BitextMining,"{'deu', 'cmn', 'fra', 'rus', 'eng'}",['Written'],Unknown,BUCC bitext mining dataset
-3,DiaBlaBitextMining,BitextMining,"{'fra', 'eng'}","['Social', 'Written']",CC BY-NC-SA 4.0,"English-French Parallel Corpus. DiaBLa is an English-French dataset for the evaluation of Machine Translation (MT) for informal, written bilingual dialogue."
-4,FloresBitextMining,BitextMining,"{'kin', 'ita', 'zul', 'sin', 'kbp', 'khk', 'ast', 'ell', 'shn', 'slk', 'lim', 'uig', 'hne', 'bho', 'tzm', 'scn', 'mal', 'mlt', 'fon', 'swh', 'hat', 'ajp', 'azj', 'tur', 'bel', 'uzn', 'azb', 'nld', 'tso', 'szl', 'vie', 'hau', 'tgk', 'bos', 'kaz', 'epo', 'tgl', 'pag', 'srd', 'isl', 'kmb', 'grn', 'ilo', 'ace', 'bod', 'aka', 'tel', 'mar', 'nob', 'quy', 'eng', 'ban', 'wol', 'mkd', 'ssw', 'san', 'kea', 'prs', 'pes', 'ary', 'zho', 'hun', 'rus', 'dan', 'jav', 'umb', 'xho', 'plt', 'luo', 'gaz', 'bem', 'kab', 'fra', 'smo', 'tpi', 'ewe', 'sna', 'mag', 'crh', 'hye', 'gla', 'bug', 'amh', 'kan', 'gle', 'bul', 'ory', 'srp', 'bam', 'bjn', 'kor', 'tum', 'nus', 'lug', 'ydd', 'est', 'twi', 'afr', 'kas', 'pbt', 'fij', 'fuv', 'dik', 'min', 'sat', 'glg', 'yue', 'apc', 'lua', 'ayr', 'tir', 'arz', 'kir', 'fao', 'ind', 'fur', 'ibo', 'kmr', 'pap', 'arb', 'asm', 'lmo', 'ron', 'tsn', 'vec', 'zsm', 'awa', 'tam', 'mri', 'snd', 'sot', 'slv', 'cjk', 'nno', 'dyu', 'guj', 'lvs', 'por', 'hin', 'khm', 'cat', 'kik', 'eus', 'npi', 'aeb', 'nso', 'kat', 'urd', 'tuk', 'ukr', 'taq', 'mni', 'pol', 'run', 'kam', 'cym', 'ceb', 'lit', 'mya', 'knc', 'kon', 'lij', 'spa', 'lin', 'heb', 'oci', 'ars', 'tat', 'sag', 'mos', 'acm', 'ltg', 'acq', 'als', 'pan', 'lus', 'fin', 'bak', 'deu', 'nya', 'swe', 'ces', 'som', 'jpn', 'ltz', 'ben', 'hrv', 'mai', 'kac', 'yor', 'tha', 'ckb', 'dzo', 'war', 'lao', 'sun'}","['Non-fiction', 'Encyclopaedic', 'Written']",CC BY-SA 4.0,FLORES is a benchmark dataset for machine translation between English and low-resource languages.
-5,IN22GenBitextMining,BitextMining,"{'san', 'kas', 'mni', 'sat', 'mal', 'brx', 'pan', 'asm', 'tam', 'snd', 'kan', 'mai', 'ben', 'ory', 'guj', 'doi', 'hin', 'tel', 'mar', 'gom', 'eng', 'npi', 'urd'}","['Web', 'Legal', 'Government', 'News', 'Religious', 'Non-fiction', 'Written']",CC-BY-4.0,IN22-Gen is a n-way parallel general-purpose multi-domain benchmark dataset for machine translation spanning English and 22 Indic languages.
-6,IndicGenBenchFloresBitextMining,BitextMining,"{'san', 'mup', 'mni', 'sat', 'bgc', 'hne', 'bho', 'nep', 'mal', 'pan', 'asm', 'awa', 'tam', 'boy', 'kan', 'raj', 'mwr', 'mai', 'ben', 'pus', 'ory', 'guj', 'urd', 'bod', 'hin', 'tel', 'mar', 'gom', 'eng', 'gbm'}","['Web', 'News', 'Written']",CC-BY-SA-4.0,Flores-IN dataset is an extension of Flores dataset released as a part of the IndicGenBench by Google
-7,NollySentiBitextMining,BitextMining,"{'yor', 'pcm', 'hau', 'ibo', 'eng'}","['Social', 'Reviews', 'Written']",CC BY-SA 4.0,"NollySenti is Nollywood movie reviews for five languages widely spoken in Nigeria (English, Hausa, Igbo, Nigerian-Pidgin, and Yoruba."
-8,NorwegianCourtsBitextMining,BitextMining,"{'nob', 'nno'}","['Legal', 'Written']",CC BY 4.0,"Nynorsk and Bokmål parallel corpus from Norwegian courts. Norwegian courts have two standardised written languages. Bokmål is a variant closer to Danish, while Nynorsk was created to resemble regional dialects of Norwegian."
-9,NTREXBitextMining,BitextMining,"{'kin', 'ita', 'zul', 'sin', 'swa', 'ell', 'slk', 'uig', 'mal', 'mlt', 'tur', 'bel', 'nld', 'lav', 'vie', 'hau', 'fuc', 'tgk', 'bos', 'kaz', 'isl', 'div', 'bod', 'tel', 'mar', 'nob', 'eng', 'ton', 'wol', 'mkd', 'ssw', 'tah', 'prs', 'zho', 'hun', 'rus', 'dan', 'xho', 'nep', 'bem', 'sqi', 'fra', 'smo', 'hmn', 'ewe', 'sna', 'hye', 'amh', 'kan', 'gle', 'bul', 'pus', 'srp', 'kor', 'afr', 'fij', 'glg', 'yue', 'tir', 'kir', 'fao', 'ind', 'ibo', 'kmr', 'arb', 'ron', 'fil', 'orm', 'tsn', 'tam', 'mri', 'snd', 'slv', 'nno', 'guj', 'por', 'hin', 'khm', 'cat', 'eus', 'nso', 'kat', 'urd', 'tuk', 'ukr', 'pol', 'cym', 'lit', 'mya', 'spa', 'mey', 'heb', 'tat', 'mlg', 'pan', 'fin', 'bak', 'deu', 'nya', 'shi', 'swe', 'ces', 'som', 'jpn', 'aze', 'ltz', 'ben', 'hrv', 'mon', 'yor', 'tha', 'ckb', 'fas', 'dzo', 'ven', 'uzb', 'msa', 'nde', 'lao'}","['News', 'Written']",CC-BY-SA-4.0,"NTREX is a News Test References dataset for Machine Translation Evaluation, covering translation from English into 128 languages. We select language pairs according to the M2M-100 language grouping strategy, resulting in 1916 directions."
-10,NusaTranslationBitextMining,BitextMining,"{'bew', 'bbc', 'mad', 'sun', 'ind', 'min', 'bhp', 'jav', 'mak', 'abs', 'rej', 'mui'}","['Social', 'Written']",CC BY-SA 4.0,NusaTranslation is a parallel dataset for machine translation on 11 Indonesia languages and English.
-11,NusaXBitextMining,BitextMining,"{'bjn', 'bbc', 'mad', 'nij', 'ace', 'ind', 'min', 'bug', 'jav', 'eng', 'ban', 'sun'}","['Reviews', 'Written']",CC BY-SA 4.0,NusaX is a parallel dataset for machine translation and sentiment analysis on 11 Indonesia languages and English.
-12,Tatoeba,BitextMining,"{'ita', 'max', 'ast', 'ell', 'slk', 'nov', 'uig', 'mal', 'swh', 'bre', 'mhr', 'tur', 'bel', 'kur', 'nld', 'vie', 'cha', 'kaz', 'bos', 'epo', 'tgl', 'ber', 'isl', 'tel', 'mar', 'nob', 'eng', 'lat', 'mkd', 'csb', 'pes', 'yid', 'hun', 'rus', 'dan', 'jav', 'xho', 'kab', 'sqi', 'fra', 'ido', 'hye', 'dtp', 'gla', 'arq', 'amh', 'swg', 'gle', 'bul', 'srp', 'kor', 'ina', 'est', 'afr', 'cmn', 'glg', 'yue', 'kzj', 'arz', 'fao', 'ind', 'orv', 'ron', 'cor', 'cbk', 'wuu', 'awa', 'zsm', 'tam', 'lfn', 'nds', 'slv', 'nno', 'lvs', 'por', 'hin', 'khm', 'cat', 'eus', 'pam', 'kat', 'ang', 'urd', 'ile', 'tuk', 'ara', 'ukr', 'pol', 'gsw', 'cym', 'ceb', 'lit', 'spa', 'tzl', 'heb', 'oci', 'tat', 'fin', 'deu', 'swe', 'ces', 'hsb', 'aze', 'jpn', 'ben', 'hrv', 'mon', 'pms', 'tha', 'dsb', 'fry', 'war', 'uzb'}",['Written'],CC BY 2.0,"1,000 English-aligned sentence pairs for each language based on the Tatoeba corpus"
-13,BulgarianStoreReviewSentimentClassfication,Classification,{'bul'},"['Reviews', 'Written']",cc-by-4.0,Bulgarian online store review dataset for sentiment classification.
-14,CzechProductReviewSentimentClassification,Classification,{'ces'},"['Reviews', 'Written']",CC BY-NC-SA 4.0,"User reviews of products on Czech e-shop Mall.cz with 3 sentiment classes (positive, neutral, negative)"
-15,GreekLegalCodeClassification,Classification,{'ell'},"['Legal', 'Written']",cc-by-4.0,Greek Legal Code Dataset for Classification. (subset = chapter)
-16,DBpediaClassification,Classification,{'eng'},"['Encyclopaedic', 'Written']",cc-by-sa-3.0,"DBpedia14 is a dataset of English texts from Wikipedia articles, categorized into 14 non-overlapping classes based on their DBpedia ontology."
-17,FinancialPhrasebankClassification,Classification,{'eng'},"['News', 'Written']",cc-by-nc-sa-3.0,"Polar sentiment dataset of sentences from financial news, categorized by sentiment into positive, negative, or neutral."
-18,PoemSentimentClassification,Classification,{'eng'},"['Reviews', 'Written']",CC-BY-4.0,Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg.
-19,ToxicConversationsClassification,Classification,{'eng'},"['Social', 'Written']",CC BY 4.0,Collection of comments from the Civil Comments platform together with annotations if the comment is toxic or not.
-20,TweetTopicSingleClassification,Classification,{'eng'},"['Social', 'News', 'Written']",Other,"Topic classification dataset on Twitter with 6 labels. Each instance of
-        TweetTopic comes with a timestamp which distributes from September 2019 to August 2021.
-        Tweets were preprocessed before the annotation to normalize some artifacts, converting
-        URLs into a special token {{URL}} and non-verified usernames into {{USERNAME}}. For verified
-        usernames, we replace its display name (or account name) with symbols {@}.
-        "
-21,EstonianValenceClassification,Classification,{'est'},"['News', 'Written']",CC BY 4.0,Dataset containing annotated Estonian news data from the Postimees and Õhtuleht newspapers.
-22,FilipinoShopeeReviewsClassification,Classification,{'fil'},"['Social', 'Written']",MPL-2.0,"The Shopee reviews tl 15 dataset is constructed by randomly taking 2100 training samples and 450 samples for testing and validation for each review star from 1 to 5. In total, there are 10500 training samples and 2250 each in validation and testing samples."
-23,GujaratiNewsClassification,Classification,{'guj'},"['News', 'Written']",MIT,A Gujarati dataset for 3-class classification of Gujarati news articles
-24,SentimentAnalysisHindi,Classification,{'hin'},"['Reviews', 'Written']",CC BY-NC-SA 4.0,Hindi Sentiment Analysis Dataset
-25,IndonesianIdClickbaitClassification,Classification,{'ind'},"['News', 'Written']",cc-by-4.0,The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news publishers.
-26,ItaCaseholdClassification,Classification,{'ita'},"['Legal', 'Government', 'Written']",Apache 2.0,An Italian Dataset consisting of 1101 pairs of judgments and their official holdings between the years 2019 and 2022 from the archives of Italian Administrative Justice categorized with 64 subjects.
-27,KorSarcasmClassification,Classification,{'kor'},"['Social', 'Written']",MIT,"
-        The Korean Sarcasm Dataset was created to detect sarcasm in text, which can significantly alter the original
-        meaning of a sentence. 9319 tweets were collected from Twitter and labeled for sarcasm or not_sarcasm. These
-        tweets were gathered by querying for: irony sarcastic, and
-        sarcasm.
-        The dataset was created by gathering HTML data from Twitter. Queries for hashtags that include sarcasm
-        and variants of it were used to return tweets. It was preprocessed by removing the keyword
-        hashtag, urls and mentions of the user to preserve anonymity.
-        "
-28,KurdishSentimentClassification,Classification,{'kur'},"['Web', 'Written']",CC BY 4.0,Kurdish Sentiment Dataset
-29,MacedonianTweetSentimentClassification,Classification,{'mkd'},"['Social', 'Written']",CC BY-NC-SA 3.0,An Macedonian dataset for tweet sentiment classification.
-30,AfriSentiClassification,Classification,"{'yor', 'kin', 'pcm', 'twi', 'ary', 'por', 'tso', 'arq', 'hau', 'amh', 'ibo', 'swa'}","['Social', 'Written']",Creative Commons Attribution 4.0 International License,AfriSenti is the largest sentiment analysis dataset for under-represented African languages.
-31,AmazonCounterfactualClassification,Classification,"{'deu', 'eng', 'jpn'}","['Reviews', 'Written']",CC BY 4.0,A collection of Amazon customer reviews annotated for counterfactual detection pair classification.
-32,CataloniaTweetClassification,Classification,"{'spa', 'cat'}","['Social', 'Government', 'Written']",cc-by-sa-4.0,"This dataset contains two corpora in Spanish and Catalan that consist of annotated Twitter
-        messages for automatic stance detection. The data was collected over 12 days during February and March
-        of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia.
-        Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance
-        towards the target - independence of Catalonia.
-        "
-33,CyrillicTurkicLangClassification,Classification,"{'kir', 'bak', 'sah', 'chv', 'tat', 'rus', 'kaz', 'krc', 'tyv'}","['Web', 'Written']",CC BY-NC 4.0 DEED,Cyrillic dataset of 8 Turkic languages spoken in Russia and former USSR
-34,IndicLangClassification,Classification,"{'san', 'kas', 'mni', 'sat', 'mal', 'brx', 'pan', 'asm', 'tam', 'snd', 'kan', 'mai', 'ben', 'ory', 'guj', 'doi', 'hin', 'tel', 'mar', 'gom', 'npi', 'urd'}","['Web', 'Non-fiction', 'Written']",CC0,A language identification test set for native-script as well as Romanized text which spans 22 Indic languages.
-35,MasakhaNEWSClassification,Classification,"{'yor', 'pcm', 'lin', 'run', 'fra', 'hau', 'ibo', 'amh', 'som', 'eng', 'swa', 'xho', 'tir', 'lug', 'sna', 'orm'}","['News', 'Written']",cc-by-nc-4.0,MasakhaNEWS is the largest publicly available dataset for news topic classification in 16 languages widely spoken in Africa. The train/validation/test sets are available for all the 16 languages.
-36,MassiveIntentClassification,Classification,"{'afr', 'ara', 'ita', 'pol', 'hun', 'rus', 'cym', 'dan', 'jav', 'swa', 'ell', 'mya', 'spa', 'sqi', 'mal', 'heb', 'cmo', 'fra', 'ind', 'tur', 'ron', 'fin', 'deu', 'tam', 'nld', 'hye', 'swe', 'lav', 'vie', 'amh', 'kan', 'slv', 'jpn', 'aze', 'tgl', 'ben', 'mon', 'isl', 'tha', 'kor', 'por', 'fas', 'hin', 'khm', 'tel', 'nob', 'eng', 'msa', 'kat', 'urd'}",['Spoken'],Apache 2.0,MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
-37,MultiHateClassification,Classification,"{'ara', 'deu', 'nld', 'por', 'cmn', 'ita', 'pol', 'fra', 'hin', 'eng', 'spa'}","['Constructed', 'Written']",cc-by-4.0,"Hate speech detection dataset with binary
-                       (hateful vs non-hateful) labels. Includes 25+ distinct types of hate
-                       and challenging non-hate, and 11 languages.
-                     "
-38,NordicLangClassification,Classification,"{'isl', 'swe', 'fao', 'nob', 'dan', 'nno'}",['Encyclopaedic'],cc-by-sa-3.0,A dataset for Nordic language identification.
-39,NusaParagraphEmotionClassification,Classification,"{'bew', 'mad', 'bbc', 'sun', 'min', 'bug', 'jav', 'mak', 'rej', 'mui'}","['Non-fiction', 'Fiction', 'Written']",Apache 2.0,NusaParagraphEmotionClassification is a multi-class emotion classification on 10 Indonesian languages from the NusaParagraph dataset.
-40,NusaX-senti,Classification,"{'bjn', 'bbc', 'mad', 'nij', 'ace', 'ind', 'min', 'bug', 'jav', 'eng', 'ban', 'sun'}","['Reviews', 'Web', 'Social', 'Constructed', 'Written']",CC-BY-SA 4.0,"NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak. NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English."
-41,ScalaClassification,Classification,"{'swe', 'nob', 'dan', 'nno'}","['Fiction', 'News', 'Non-fiction', 'Blog', 'Spoken', 'Web', 'Written']",CC BY-SA 4.0,"ScaLa a linguistic acceptability dataset for the mainland Scandinavian languages automatically constructed from dependency annotations in Universal Dependencies Treebanks.
-        Published as part of 'ScandEval: A Benchmark for Scandinavian Natural Language Processing'"
-42,SwissJudgementClassification,Classification,"{'ita', 'deu', 'fra'}","['Legal', 'Written']",CC-BY-4.0,"Multilingual, diachronic dataset of Swiss Federal Supreme Court cases annotated with the respective binarized judgment outcome (approval/dismissal)"
-43,NepaliNewsClassification,Classification,{'nep'},"['News', 'Written']",CC BY-SA 4.0,A Nepali dataset for 7500 news articles
-44,OdiaNewsClassification,Classification,{'ory'},"['News', 'Written']",MIT,A Odia dataset for 3-class classification of Odia news articles
-45,PunjabiNewsClassification,Classification,{'pan'},"['News', 'Written']",MIT,A Punjabi dataset for 2-class classification of Punjabi news articles
-46,PolEmo2.0-OUT,Classification,{'pol'},"['Written', 'Social']",cc-by-sa-4.0,"A collection of Polish online reviews from four domains: medicine, hotels, products and school. The PolEmo2.0-OUT task is to predict the sentiment of out-of-domain (products and school) reviews using models train on reviews from medicine and hotels domains."
-47,PAC,Classification,{'pol'},"['Legal', 'Written']",cc-by-nc-sa-4.0,Polish Paraphrase Corpus
-48,SinhalaNewsClassification,Classification,{'sin'},"['News', 'Written']",mit,"This file contains news texts (sentences) belonging to 5 different news categories (political, business, technology, sports and Entertainment). The original dataset was released by Nisansa de Silva (Sinhala Text Classification: Observations from the Perspective of a Resource Poor Language, 2015)."
-49,CSFDSKMovieReviewSentimentClassification,Classification,{'slk'},"['Reviews', 'Written']",CC-BY-SA-4.0,The dataset contains 30k user reviews from csfd.cz in Slovak.
-50,SiswatiNewsClassification,Classification,{'ssw'},"['News', 'Written']",CC-BY-SA-4.0,Siswati News Classification Dataset
-51,SlovakMovieReviewSentimentClassification,Classification,{'svk'},"['Reviews', 'Written']",CC BY-NC-SA 4.0,"User reviews of movies on the CSFD movie database, with 2 sentiment classes (positive, negative)"
-52,SwahiliNewsClassification,Classification,{'swa'},"['News', 'Written']",CC BY-NC-SA 4.0,"Dataset for Swahili News Classification, categorized with 6 domains (Local News (Kitaifa), International News (Kimataifa), Finance News (Uchumi), Health News (Afya), Sports News (Michezo), and Entertainment News (Burudani)). Building and Optimizing Swahili Language Models: Techniques, Embeddings, and Datasets"
-53,DalajClassification,Classification,{'swe'},"['Non-fiction', 'Written']",CC-BY-4.0,A Swedish dataset for linguistic acceptability. Available as a part of Superlim.
-54,TswanaNewsClassification,Classification,{'tsn'},"['News', 'Written']",CC-BY-SA-4.0,Tswana News Classification Dataset
-55,IsiZuluNewsClassification,Classification,{'zul'},"['News', 'Written']",CC-BY-SA-4.0,isiZulu News Classification Dataset
-56,WikiCitiesClustering,Clustering,{'eng'},"['Encyclopaedic', 'Written']",cc-by-sa-4.0,"Clustering of Wikipedia articles of cities by country from https://huggingface.co/datasets/wikipedia. Test set includes 126 countries, and a total of 3531 cities."
-57,MasakhaNEWSClusteringS2S,Clustering,"{'yor', 'pcm', 'lin', 'run', 'fra', 'hau', 'ibo', 'amh', 'som', 'eng', 'swa', 'xho', 'tir', 'lug', 'sna', 'orm'}",,,Clustering of news article headlines from MasakhaNEWS dataset. Clustering of 10 sets on the news article label.
-58,RomaniBibleClustering,Clustering,{'rom'},"['Religious', 'Written']",MIT,Clustering verses from the Bible in Kalderash Romani by book.
-59,ArXivHierarchicalClusteringP2P,Clustering,{'eng'},"['Academic', 'Written']",CC0,"Clustering of titles+abstract from arxiv. Clustering of 30 sets, either on the main or secondary category"
-60,ArXivHierarchicalClusteringS2S,Clustering,{'eng'},"['Academic', 'Written']",CC0,"Clustering of titles from arxiv. Clustering of 30 sets, either on the main or secondary category"
-61,BigPatentClustering.v2,Clustering,{'eng'},"['Legal', 'Written']",cc-by-4.0,"Clustering of documents from the Big Patent dataset. Test set only includes documentsbelonging to a single category, with a total of 9 categories."
-62,BiorxivClusteringP2P.v2,Clustering,{'eng'},"['Academic', 'Written']",https://www.biorxiv.org/content/about-biorxiv,Clustering of titles+abstract from biorxiv across 26 categories.
-63,MedrxivClusteringP2P.v2,Clustering,{'eng'},"['Academic', 'Medical', 'Written']",https://www.medrxiv.org/content/about-medrxiv,Clustering of titles+abstract from medrxiv across 51 categories.
-64,StackExchangeClustering.v2,Clustering,{'eng'},"['Web', 'Written']",Not specified,"Clustering of titles from 121 stackexchanges. Clustering of 25 sets, each with 10-50 classes, and each class with 100 - 1000 sentences."
-65,AlloProfClusteringS2S.v2,Clustering,{'fra'},"['Encyclopaedic', 'Written']",mit,Clustering of document titles from Allo Prof dataset. Clustering of 10 sets on the document topic.
-66,HALClusteringS2S.v2,Clustering,{'fra'},"['Academic', 'Written']",Apache-2.0,Clustering of titles from HAL (https://huggingface.co/datasets/lyon-nlp/clustering-hal-s2s)
-67,SIB200ClusteringS2S,Clustering,"{'kin', 'ita', 'zul', 'sin', 'kbp', 'khk', 'ast', 'ell', 'shn', 'slk', 'lim', 'uig', 'hne', 'bho', 'tzm', 'scn', 'mal', 'mlt', 'fon', 'swh', 'hat', 'ajp', 'azj', 'tur', 'bel', 'uzn', 'azb', 'nld', 'tso', 'szl', 'vie', 'hau', 'tgk', 'bos', 'kaz', 'epo', 'tgl', 'pag', 'srd', 'isl', 'kmb', 'grn', 'ilo', 'ace', 'bod', 'aka', 'tel', 'mar', 'nob', 'quy', 'eng', 'ban', 'wol', 'mkd', 'ssw', 'san', 'kea', 'prs', 'pes', 'ary', 'zho', 'hun', 'rus', 'dan', 'jav', 'umb', 'xho', 'plt', 'luo', 'gaz', 'bem', 'kab', 'fra', 'smo', 'tpi', 'ewe', 'sna', 'mag', 'crh', 'hye', 'gla', 'bug', 'amh', 'kan', 'gle', 'bul', 'ory', 'srp', 'bam', 'bjn', 'kor', 'tum', 'nus', 'lug', 'ydd', 'est', 'twi', 'afr', 'kas', 'pbt', 'fij', 'fuv', 'dik', 'min', 'sat', 'glg', 'yue', 'apc', 'lua', 'ayr', 'tir', 'arz', 'kir', 'fao', 'ind', 'fur', 'ibo', 'kmr', 'pap', 'arb', 'asm', 'lmo', 'ron', 'tsn', 'vec', 'zsm', 'awa', 'tam', 'mri', 'snd', 'sot', 'slv', 'cjk', 'nno', 'dyu', 'guj', 'lvs', 'por', 'hin', 'khm', 'cat', 'kik', 'eus', 'npi', 'aeb', 'nso', 'kat', 'urd', 'tuk', 'ukr', 'taq', 'mni', 'pol', 'run', 'kam', 'cym', 'ceb', 'lit', 'mya', 'knc', 'kon', 'lij', 'spa', 'lin', 'heb', 'oci', 'ars', 'tat', 'sag', 'mos', 'acm', 'ltg', 'acq', 'als', 'pan', 'lus', 'fin', 'bak', 'deu', 'nya', 'swe', 'ces', 'som', 'jpn', 'ltz', 'ben', 'hrv', 'mai', 'kac', 'yor', 'tha', 'ckb', 'dzo', 'nqo', 'war', 'lao', 'sun'}","['News', 'Written']",cc-by-sa-4.0,"SIB-200 is the largest publicly available topic classification
-        dataset based on Flores-200 covering 205 languages and dialects annotated. The dataset is
-        annotated in English for the topics,  science/technology, travel, politics, sports,
-        health, entertainment, and geography. The labels are then transferred to the other languages
-        in Flores-200 which are machine-translated.
-        "
-68,WikiClusteringP2P.v2,Clustering,"{'sqi', 'wln', 'mlt', 'ilo', 'lav', 'min', 'cat', 'ces', 'dan', 'eus', 'bos', 'glv', 'sco', 'kur'}","['Encyclopaedic', 'Written']",cc-by-sa-3.0,"Clustering of wikipedia articles inspired by BlubrbsClusteringP2P. Labels are taken from top-level categories of the respective languages (e.g., https://lv.wikipedia.org/wiki/Kategorija:Pamatkategorijas)."
-69,SNLHierarchicalClusteringP2P,Clustering,{'nob'},"['Encyclopaedic', 'Non-fiction', 'Written']",CC-BY-NC,Webscrabed articles from the Norwegian lexicon 'Det Store Norske Leksikon'. Uses articles categories as clusters.
-70,PlscClusteringP2P.v2,Clustering,{'pol'},"['Academic', 'Written']",cc0-1.0,"Clustering of Polish article titles+abstracts from Library of Science (https://bibliotekanauki.pl/), either on the scientific field or discipline."
-71,SwednClusteringP2P,Clustering,{'swe'},"['News', 'Non-fiction', 'Written']",cc-by-4.0,"The SWE-DN corpus is based on 1,963,576 news articles from the Swedish newspaper Dagens Nyheter (DN) during the years 2000--2020. The articles are filtered to resemble the CNN/DailyMail dataset both regarding textual structure. This dataset uses the category labels as clusters."
-72,CLSClusteringP2P.v2,Clustering,{'cmn'},"['Academic', 'Written']",Apache-2.0,Clustering of titles + abstract from CLS dataset. Clustering of 13 sets on the main category.
-73,StackOverflowQA,Retrieval,{'eng'},"['Programming', 'Written']",MIT,The dataset is a collection of natural language queries and their corresponding response which may include some text mixed with code snippets. The task is to retrieve the most relevant response for a given query.
-74,TwitterHjerneRetrieval,Retrieval,{'dan'},"['Social', 'Written']",CC BY 4.0,Danish question asked on Twitter with the Hashtag #Twitterhjerne ('Twitter brain') and their corresponding answer.
-75,AILAStatutes,Retrieval,{'eng'},"['Legal', 'Written']",CC BY 4.0,This dataset is structured for the task of identifying the most relevant statutes for a given situation.
-76,ArguAna,Retrieval,{'eng'},"['Medical', 'Written']",cc-by-sa-4.0,NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval
-77,HagridRetrieval,Retrieval,{'eng'},"['Encyclopaedic', 'Written']",apache-2.0,HAGRID (Human-in-the-loop Attributable Generative Retrieval for Information-seeking Dataset)is a dataset for generative information-seeking scenarios. It consists of queriesalong with a set of manually labelled relevant passages
-78,LegalBenchCorporateLobbying,Retrieval,{'eng'},"['Legal', 'Written']",CC BY 4.0,The dataset includes bill titles and bill summaries related to corporate lobbying.
-79,LEMBPasskeyRetrieval,Retrieval,{'eng'},"['Fiction', 'Written']",Not specified,passkey subset of dwzhu/LongEmbed dataset.
-80,SCIDOCS,Retrieval,{'eng'},"['Academic', 'Written', 'Non-fiction']",cc-by-sa-4.0,"SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation."
-81,SpartQA,Retrieval,{'eng'},"['Encyclopaedic', 'Written']",MIT,Measuring the ability to retrieve the groundtruth answers to reasoning task queries on SpartQA.
-82,TempReasonL1,Retrieval,{'eng'},"['Encyclopaedic', 'Written']",CC BY-SA 3.0,Measuring the ability to retrieve the groundtruth answers to reasoning task queries on TempReason l1.
-83,TRECCOVID,Retrieval,{'eng'},,,TRECCOVID is an ad-hoc search challenge based on the COVID-19 dataset containing scientific articles related to the COVID-19 pandemic.
-84,WinoGrande,Retrieval,{'eng'},"['Encyclopaedic', 'Written']",CC BY,Measuring the ability to retrieve the groundtruth answers to reasoning task queries on winogrande.
-85,BelebeleRetrieval,Retrieval,"{'kin', 'ita', 'zul', 'sin', 'khk', 'ell', 'shn', 'slk', 'mal', 'mlt', 'swh', 'hat', 'azj', 'tur', 'uzn', 'nld', 'tso', 'vie', 'hau', 'tgk', 'kaz', 'tgl', 'isl', 'grn', 'ilo', 'bod', 'tel', 'mar', 'nob', 'eng', 'wol', 'mkd', 'ssw', 'kea', 'pes', 'ary', 'zho', 'hun', 'rus', 'dan', 'jav', 'xho', 'plt', 'luo', 'gaz', 'fra', 'sna', 'hye', 'amh', 'kan', 'bul', 'ory', 'srp', 'bam', 'kor', 'lug', 'est', 'afr', 'pbt', 'fuv', 'apc', 'tir', 'arz', 'kir', 'ind', 'ibo', 'arb', 'asm', 'ron', 'tsn', 'zsm', 'tam', 'mri', 'snd', 'sot', 'slv', 'guj', 'lvs', 'por', 'hin', 'khm', 'cat', 'eus', 'npi', 'nso', 'kat', 'urd', 'ukr', 'pol', 'ceb', 'lit', 'mya', 'spa', 'lin', 'heb', 'ars', 'acm', 'pan', 'als', 'fin', 'deu', 'nya', 'swe', 'ces', 'som', 'jpn', 'ben', 'hrv', 'kac', 'yor', 'tha', 'ckb', 'war', 'lao', 'sun'}","['Web', 'News', 'Written']",CC-BY-SA-4.0,Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants (including 115 distinct languages and their scripts)
-86,MLQARetrieval,Retrieval,"{'ara', 'deu', 'vie', 'hin', 'zho', 'eng', 'spa'}","['Encyclopaedic', 'Written']",cc-by-sa-3.0,"MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
-        MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
-        German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
-        4 different languages on average."
-87,StatcanDialogueDatasetRetrieval,Retrieval,"{'fra', 'eng'}","['Government', 'Web', 'Written']",https://huggingface.co/datasets/McGill-NLP/statcan-dialogue-dataset-retrieval/blob/main/LICENSE.md,"A Dataset for Retrieving Data Tables through Conversations with Genuine Intents, available in English and French."
-88,WikipediaRetrievalMultilingual,Retrieval,"{'srp', 'deu', 'nor', 'nld', 'por', 'swe', 'fas', 'ita', 'hin', 'ben', 'ces', 'dan', 'eng', 'bul', 'ron', 'fin'}","['Encyclopaedic', 'Written']",cc-by-sa-3.0,The dataset is derived from Cohere's wikipedia-2023-11 dataset and contains synthetically generated queries.
-89,CovidRetrieval,Retrieval,{'cmn'},,,COVID-19 news articles
-90,Core17InstructionRetrieval,InstructionRetrieval,{'eng'},"['News', 'Written']",MIT,Measuring retrieval instruction following ability on Core17 narratives.
-91,News21InstructionRetrieval,InstructionRetrieval,{'eng'},"['News', 'Written']",MIT,Measuring retrieval instruction following ability on News21 narratives.
-92,Robust04InstructionRetrieval,InstructionRetrieval,{'eng'},"['News', 'Written']",MIT,Measuring retrieval instruction following ability on Robust04 narratives.
-93,KorHateSpeechMLClassification,MultilabelClassification,{'kor'},"['Social', 'Written']",cc-by-sa-4.0,"
-        The Korean Multi-label Hate Speech Dataset, K-MHaS, consists of 109,692 utterances from Korean online news comments,
-        labelled with 8 fine-grained hate speech classes (labels: Politics, Origin, Physical, Age, Gender, Religion, Race, Profanity)
-        or Not Hate Speech class. Each utterance provides from a single to four labels that can handles Korean language patterns effectively.
-        For more details, please refer to the paper about K-MHaS, published at COLING 2022.
-        This dataset is based on the Korean online news comments available on Kaggle and Github.
-        The unlabeled raw data was collected between January 2018 and June 2020.
-        The language producers are users who left the comments on the Korean online news platform between 2018 and 2020.
-        "
-94,MalteseNewsClassification,MultilabelClassification,{'mlt'},"['Constructed', 'Written']",cc-by-nc-sa-4.0,"A multi-label topic classification dataset for Maltese News
-        Articles. The data was collected from the press_mt subset from Korpus
-        Malti v4.0. Article contents were cleaned to filter out JavaScript, CSS,
-        & repeated non-Maltese sub-headings. The labels are based on the category
-        field from this corpus.
-        "
-95,MultiEURLEXMultilabelClassification,MultilabelClassification,"{'est', 'ita', 'pol', 'hun', 'dan', 'lit', 'ell', 'slk', 'spa', 'mlt', 'fra', 'ron', 'fin', 'deu', 'nld', 'swe', 'lav', 'ces', 'slv', 'hrv', 'bul', 'por', 'eng'}","['Legal', 'Government', 'Written']",CC BY-SA 4.0,EU laws in 23 EU languages containing gold labels.
-96,BrazilianToxicTweetsClassification,MultilabelClassification,{'por'},"['Constructed', 'Written']",CC BY-SA 4.0,"
-        ToLD-Br is the biggest dataset for toxic tweets in Brazilian Portuguese, crowdsourced by 42 annotators selected from
-        a pool of 129 volunteers. Annotators were selected aiming to create a plural group in terms of demographics (ethnicity,
-        sexual orientation, age, gender). Each tweet was labeled by three annotators in 6 possible categories: LGBTQ+phobia,
-        Xenophobia, Obscene, Insult, Misogyny and Racism.
-        "
-97,CEDRClassification,MultilabelClassification,{'rus'},"['Web', 'Social', 'Blog', 'Written']",apache-2.0,"Classification of sentences by emotions, labeled into 5 categories (joy, sadness, surprise, fear, and anger)."
-98,CTKFactsNLI,PairClassification,{'ces'},"['News', 'Written']",CC-BY-SA-3.0,"Czech Natural Language Inference dataset of around 3K evidence-claim pairs labelled with SUPPORTS, REFUTES or NOT ENOUGH INFO veracity labels. Extracted from a round of fact-checking experiments."
-99,SprintDuplicateQuestions,PairClassification,{'eng'},"['Programming', 'Written']",Not specified,Duplicate questions from the Sprint community.
-100,TwitterURLCorpus,PairClassification,{'eng'},,,Paraphrase-Pairs of Tweets.
-101,ArmenianParaphrasePC,PairClassification,{'hye'},"['News', 'Written']",Apache-2.0,asparius/Armenian-Paraphrase-PC
-102,indonli,PairClassification,{'ind'},"['Encyclopaedic', 'Web', 'News', 'Written']",CC-BY-SA 4.0,IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian. IndoNLI is annotated by both crowd workers and experts.
-103,OpusparcusPC,PairClassification,"{'deu', 'swe', 'fra', 'rus', 'eng', 'fin'}","['Spoken', 'Spoken']",cc-by-nc-4.0,"Opusparcus is a paraphrase corpus for six European language: German, English, Finnish, French, Russian, and Swedish. The paraphrases consist of subtitles from movies and TV shows."
-104,PawsXPairClassification,PairClassification,"{'deu', 'kor', 'cmn', 'fra', 'eng', 'jpn', 'spa'}","['Web', 'Encyclopaedic', 'Written']",Custom (commercial),{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification
-105,RTE3,PairClassification,"{'fra', 'deu', 'eng', 'ita'}","['News', 'Web', 'Encyclopaedic', 'Written']",cc-by-4.0,Recognising Textual Entailment Challenge (RTE-3) aim to provide the NLP community with a benchmark to test progress in recognizing textual entailment
-106,XNLI,PairClassification,"{'tha', 'ara', 'deu', 'vie', 'fra', 'hin', 'rus', 'zho', 'swa', 'eng', 'tur', 'ell', 'spa', 'bul'}","['Non-fiction', 'Fiction', 'Government', 'Written']",Not specified,
-107,PpcPC,PairClassification,{'pol'},"['Fiction', 'Non-fiction', 'Web', 'Written', 'Spoken', 'Social', 'News']",GPL-3.0,Polish Paraphrase Corpus
-108,TERRa,PairClassification,{'rus'},"['News', 'Web', 'Written']",mit,"Textual Entailment Recognition for Russian. This task requires to recognize, given two text fragments, whether the meaning of one text is entailed (can be inferred) from the other text."
-109,WebLINXCandidatesReranking,Reranking,{'eng'},"['Academic', 'Web', 'Written']",CC BY-NC-SA 4.0,WebLINX is a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. The reranking task focuses on finding relevant elements at every given step in the trajectory.
-110,AlloprofReranking,Reranking,{'fra'},"['Web', 'Academic', 'Written']",CC BY-NC-SA 4.0,"This dataset was provided by AlloProf, an organisation in Quebec, Canada offering resources and a help forum curated by a large number of teachers to students on all subjects taught from in primary and secondary school"
-111,VoyageMMarcoReranking,Reranking,{'jpn'},"['Academic', 'Non-fiction', 'Written']",CC BY 4.0,a hard-negative augmented version of the Japanese MMARCO dataset as used in Voyage AI Evaluation Suite
-112,WikipediaRerankingMultilingual,Reranking,"{'srp', 'deu', 'nor', 'nld', 'por', 'swe', 'fas', 'ita', 'hin', 'ben', 'ces', 'dan', 'eng', 'bul', 'ron', 'fin'}","['Encyclopaedic', 'Written']",cc-by-sa-3.0,The dataset is derived from Cohere's wikipedia-2023-11 dataset and contains synthetically generated queries.
-113,RuBQReranking,Reranking,{'rus'},"['Encyclopaedic', 'Written']",cc-by-sa-4.0,Paragraph reranking based on RuBQ 2.0. Give paragraphs that answer the question higher scores.
-114,T2Reranking,Reranking,{'cmn'},,,T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
-115,GermanSTSBenchmark,STS,{'deu'},,,Semantic Textual Similarity Benchmark (STSbenchmark) dataset translated into German. Translations were originally done by T-Systems on site services GmbH.
-116,SICK-R,STS,{'eng'},,,Semantic Textual Similarity SICK-R dataset as described here:
-117,STS12,STS,{'eng'},"['Encyclopaedic', 'News', 'Written']",Not specified,SemEval-2012 Task 6.
-118,STS13,STS,{'eng'},"['Web', 'News', 'Non-fiction', 'Written']",Not specified,SemEval STS 2013 dataset.
-119,STS14,STS,{'eng'},"['Blog', 'Web', 'Spoken']",Not specified,SemEval STS 2014 dataset. Currently only the English dataset
-120,STS15,STS,{'eng'},"['Blog', 'News', 'Web', 'Written', 'Spoken']",Not specified,SemEval STS 2015 dataset
-121,STSBenchmark,STS,{'eng'},,,Semantic Textual Similarity Benchmark (STSbenchmark) dataset.
-122,FaroeseSTS,STS,{'fao'},"['News', 'Web', 'Written']",cc-by-4.0,Semantic Text Similarity (STS) corpus for Faroese.
-123,FinParaSTS,STS,{'fin'},"['News', 'Subtitles', 'Written']",cc-by-sa-4.0,Finnish paraphrase-based semantic similarity corpus
-124,JSICK,STS,{'jpn'},"['Web', 'Written']",cc-by-4.0,"JSICK is the Japanese NLI and STS dataset by manually translating the English dataset SICK (Marelli et al., 2014) into Japanese."
-125,IndicCrosslingualSTS,STS,"{'ory', 'tam', 'mal', 'guj', 'hin', 'tel', 'mar', 'kan', 'eng', 'pan', 'ben', 'asm', 'urd'}","['News', 'Non-fiction', 'Web', 'Spoken', 'Government', 'Written', 'Spoken']",CC0,This is a Semantic Textual Similarity testset between English and 12 high-resource Indic languages.
-126,SemRel24STS,STS,"{'kin', 'afr', 'ary', 'arq', 'hau', 'hin', 'ind', 'tel', 'amh', 'mar', 'eng', 'arb'}","['Spoken', 'Written']",Not specified,"SemRel2024 is a collection of Semantic Textual Relatedness (STR) datasets for 14 languages, including African and Asian languages. The datasets are composed of sentence pairs, each assigned a relatedness score between 0 (completely) unrelated and 1 (maximally related) with a large range of expected relatedness values."
-127,STS17,STS,"{'ara', 'deu', 'kor', 'nld', 'ita', 'fra', 'eng', 'tur', 'spa'}","['News', 'Web', 'Written']",Not specified,Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation
-128,STS22.v2,STS,"{'ara', 'deu', 'cmn', 'ita', 'pol', 'fra', 'rus', 'eng', 'tur', 'spa'}","['News', 'Written']",Not specified,SemEval 2022 Task 8: Multilingual News Article Similarity. Version 2 filters updated on STS22 by removing pairs where one of entries contain empty sentences.
-129,STSES,STS,{'spa'},['Written'],cc-by-4.0,"Spanish test sets from SemEval-2014 (Agirre et al., 2014) and SemEval-2015 (Agirre et al., 2015)"
-130,STSB,STS,{'cmn'},,,A Chinese dataset for textual relatedness
diff --git a/scripts/task_selection/task_selection_eng_lite.ipynb b/scripts/task_selection/task_selection_eng_lite.ipynb
deleted file mode 100644
index f4b76d5b9e..0000000000
--- a/scripts/task_selection/task_selection_eng_lite.ipynb
+++ /dev/null
@@ -1,3007 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Task Selection for  MTEB(eng)\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/au561649/.virtualenvs/mteb/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-                        "  from .autonotebook import tqdm as notebook_tqdm\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "1.12.48\n"
-                    ]
-                }
-            ],
-            "source": [
-                "from __future__ import annotations\n",
-                "\n",
-                "import mteb\n",
-                "\n",
-                "print(mteb.__version__)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Defining the initial scope\n",
-                "Here we define the tasks for MTEB(eng)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task ArxivClusteringP2P -> ArXivHierarchicalClusteringP2P\n",
-                        "Task ArxivClusteringS2S -> ArXivHierarchicalClusteringS2S\n",
-                        "Task BiorxivClusteringP2P -> BiorxivClusteringP2P.v2\n",
-                        "Task BiorxivClusteringS2S -> BiorxivClusteringS2S.v2\n",
-                        "Task MedrxivClusteringP2P -> MedrxivClusteringP2P.v2\n",
-                        "Task MedrxivClusteringS2S -> MedrxivClusteringS2S.v2\n",
-                        "Task RedditClustering -> RedditClustering.v2\n",
-                        "Task RedditClusteringP2P -> RedditClusteringP2P.v2\n",
-                        "Task STS22 -> STS22.v2\n",
-                        "Task StackExchangeClustering -> StackExchangeClustering.v2\n",
-                        "Task StackExchangeClusteringP2P -> StackExchangeClusteringP2P.v2\n",
-                        "Task SummEval -> SummEvalSummarization.v2\n",
-                        "Task TwentyNewsgroupsClustering -> TwentyNewsgroupsClustering.v2\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import mteb\n",
-                "\n",
-                "MTEB_MAIN_EN = mteb.get_benchmark(\"MTEB(eng, classic)\")\n",
-                "\n",
-                "\n",
-                "tasks = MTEB_MAIN_EN.tasks\n",
-                "\n",
-                "\n",
-                "# get the updated version of tasks, which uses the new implementation (typically notably faster, but SummEvalSummarization.v2 also contains a notable bug fix: https://github.com/embeddings-benchmark/mteb/issues/1156\n",
-                "new_tasks = []\n",
-                "for task in tasks:\n",
-                "    if task.superseded_by is not None:\n",
-                "        print(f\"Task {task.metadata.name} -> {task.superseded_by}\")\n",
-                "        new_tasks.append(task.superseded_by)\n",
-                "    else:\n",
-                "        new_tasks.append(task.metadata.name)\n",
-                "\n",
-                "tasks = mteb.get_tasks(tasks=new_tasks)\n",
-                "\n",
-                "# Remove tasks based on discussion on quality and use as training data. See e.g.: https://github.com/embeddings-benchmark/mteb/issues/1050\n",
-                "# The intention is to make the dataset a zero-shot evaluation dataset\n",
-                "tasks = [t for t in tasks if t.metadata.name not in [\"QuoraRetrieval\", \"MSMARCO\", \"NQ\"]]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# replace hardegatives retrieval tasks:\n",
-                "\n",
-                "hard_retrieval_mapping = {\n",
-                "    \"ClimateFEVER\": \"ClimateFEVERHardNegatives\",\n",
-                "    \"FEVER\": \"FEVERHardNegatives\",\n",
-                "    \"HotpotQA\": \"HotpotQAHardNegatives\",\n",
-                "    \"DBPedia\": \"DBPediaHardNegatives\",\n",
-                "}\n",
-                "\n",
-                "tasks = [\n",
-                "    task\n",
-                "    if task.metadata.name not in hard_retrieval_mapping\n",
-                "    else mteb.get_task(hard_retrieval_mapping[task.metadata.name])\n",
-                "    for task in tasks\n",
-                "]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "AmazonCounterfactualClassification\n",
-                        "AmazonPolarityClassification\n",
-                        "AmazonReviewsClassification\n",
-                        "ArguAna\n",
-                        "ArXivHierarchicalClusteringP2P\n",
-                        "ArXivHierarchicalClusteringS2S\n",
-                        "AskUbuntuDupQuestions\n",
-                        "BIOSSES\n",
-                        "Banking77Classification\n",
-                        "BiorxivClusteringP2P.v2\n",
-                        "BiorxivClusteringS2S.v2\n",
-                        "CQADupstackAndroidRetrieval\n",
-                        "CQADupstackEnglishRetrieval\n",
-                        "CQADupstackGamingRetrieval\n",
-                        "CQADupstackGisRetrieval\n",
-                        "CQADupstackMathematicaRetrieval\n",
-                        "CQADupstackPhysicsRetrieval\n",
-                        "CQADupstackProgrammersRetrieval\n",
-                        "CQADupstackStatsRetrieval\n",
-                        "CQADupstackTexRetrieval\n",
-                        "CQADupstackUnixRetrieval\n",
-                        "CQADupstackWebmastersRetrieval\n",
-                        "CQADupstackWordpressRetrieval\n",
-                        "ClimateFEVERHardNegatives\n",
-                        "DBPediaHardNegatives\n",
-                        "EmotionClassification\n",
-                        "FEVERHardNegatives\n",
-                        "FiQA2018\n",
-                        "HotpotQAHardNegatives\n",
-                        "ImdbClassification\n",
-                        "MTOPDomainClassification\n",
-                        "MTOPIntentClassification\n",
-                        "MassiveIntentClassification\n",
-                        "MassiveScenarioClassification\n",
-                        "MedrxivClusteringP2P.v2\n",
-                        "MedrxivClusteringS2S.v2\n",
-                        "MindSmallReranking\n",
-                        "NFCorpus\n",
-                        "RedditClustering.v2\n",
-                        "RedditClusteringP2P.v2\n",
-                        "SCIDOCS\n",
-                        "SICK-R\n",
-                        "STS12\n",
-                        "STS13\n",
-                        "STS14\n",
-                        "STS15\n",
-                        "STS16\n",
-                        "STS17\n",
-                        "STS22.v2\n",
-                        "STSBenchmark\n",
-                        "SciDocsRR\n",
-                        "SciFact\n",
-                        "SprintDuplicateQuestions\n",
-                        "StackExchangeClustering.v2\n",
-                        "StackExchangeClusteringP2P.v2\n",
-                        "StackOverflowDupQuestions\n",
-                        "SummEvalSummarization.v2\n",
-                        "TRECCOVID\n",
-                        "Touche2020\n",
-                        "ToxicConversationsClassification\n",
-                        "TweetSentimentExtractionClassification\n",
-                        "TwentyNewsgroupsClustering.v2\n",
-                        "TwitterSemEval2015\n",
-                        "TwitterURLCorpus\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# list of tasks\n",
-                "for task in tasks:\n",
-                "    print(task.metadata.name)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Loading in data\n",
-                "We will start out by loading in the relevant data for the model and tasks of interests."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/au561649/.virtualenvs/mteb/lib/python3.8/site-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
-                        "  warnings.warn(\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Getting revision for sentence-transformers/all-MiniLM-L12-v2\n",
-                        "Getting revision for sentence-transformers/all-mpnet-base-v2\n"
-                    ]
-                }
-            ],
-            "source": [
-                "def get_models():\n",
-                "    model_names = [\n",
-                "        \"sentence-transformers/all-MiniLM-L6-v2\",\n",
-                "        \"sentence-transformers/all-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/all-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/LaBSE\",\n",
-                "        \"intfloat/multilingual-e5-large-instruct\",\n",
-                "        \"intfloat/e5-mistral-7b-instruct\",\n",
-                "        \"GritLM/GritLM-7B\",\n",
-                "        \"GritLM/GritLM-8x7B\",\n",
-                "        \"intfloat/multilingual-e5-small\",\n",
-                "        \"intfloat/multilingual-e5-base\",\n",
-                "        \"intfloat/multilingual-e5-large\",\n",
-                "    ]\n",
-                "    models: list[mteb.ModelMeta] = [mteb.get_model_meta(name) for name in model_names]\n",
-                "\n",
-                "    # get missing revisions - Assuming we are using the latest revision\n",
-                "    for model in models:\n",
-                "        if model.revision is None:\n",
-                "            print(f\"Getting revision for {model.name}\")\n",
-                "            encoder = model.load_model()\n",
-                "            model.revision = encoder.model_card_data.base_model_revision  # type: ignore\n",
-                "\n",
-                "    return models\n",
-                "\n",
-                "\n",
-                "models = get_models()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Already up to date.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# load results from mteb/results repository\n",
-                "mteb_results = mteb.load_results(models=models, tasks=tasks, download_latest=True)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import mteb.task_selection as task_selection\n",
-                "\n",
-                "results_df = task_selection.results_to_dataframe(mteb_results, drop_na=False)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>Average</th>\n",
-                            "      <th>AmazonCounterfactualClassification</th>\n",
-                            "      <th>AmazonPolarityClassification</th>\n",
-                            "      <th>AmazonReviewsClassification</th>\n",
-                            "      <th>ArXivHierarchicalClusteringP2P</th>\n",
-                            "      <th>ArXivHierarchicalClusteringS2S</th>\n",
-                            "      <th>ArguAna</th>\n",
-                            "      <th>AskUbuntuDupQuestions</th>\n",
-                            "      <th>BIOSSES</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>StackExchangeClusteringP2P.v2</th>\n",
-                            "      <th>StackOverflowDupQuestions</th>\n",
-                            "      <th>SummEvalSummarization.v2</th>\n",
-                            "      <th>TRECCOVID</th>\n",
-                            "      <th>Touche2020</th>\n",
-                            "      <th>ToxicConversationsClassification</th>\n",
-                            "      <th>TweetSentimentExtractionClassification</th>\n",
-                            "      <th>TwentyNewsgroupsClustering.v2</th>\n",
-                            "      <th>TwitterSemEval2015</th>\n",
-                            "      <th>TwitterURLCorpus</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Rank</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>0.626</td>\n",
-                            "      <td>0.732</td>\n",
-                            "      <td>0.963</td>\n",
-                            "      <td>0.520</td>\n",
-                            "      <td>0.653</td>\n",
-                            "      <td>0.613</td>\n",
-                            "      <td>0.617</td>\n",
-                            "      <td>0.670</td>\n",
-                            "      <td>0.855</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.481</td>\n",
-                            "      <td>0.549</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.870</td>\n",
-                            "      <td>0.263</td>\n",
-                            "      <td>0.717</td>\n",
-                            "      <td>0.649</td>\n",
-                            "      <td>0.533</td>\n",
-                            "      <td>0.816</td>\n",
-                            "      <td>0.878</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>0.626</td>\n",
-                            "      <td>0.792</td>\n",
-                            "      <td>0.966</td>\n",
-                            "      <td>0.556</td>\n",
-                            "      <td>0.598</td>\n",
-                            "      <td>0.623</td>\n",
-                            "      <td>0.632</td>\n",
-                            "      <td>0.674</td>\n",
-                            "      <td>0.863</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.438</td>\n",
-                            "      <td>0.559</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.743</td>\n",
-                            "      <td>0.278</td>\n",
-                            "      <td>0.688</td>\n",
-                            "      <td>0.663</td>\n",
-                            "      <td>0.573</td>\n",
-                            "      <td>0.811</td>\n",
-                            "      <td>0.874</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>0.611</td>\n",
-                            "      <td>0.681</td>\n",
-                            "      <td>0.962</td>\n",
-                            "      <td>0.508</td>\n",
-                            "      <td>0.625</td>\n",
-                            "      <td>0.613</td>\n",
-                            "      <td>0.585</td>\n",
-                            "      <td>0.644</td>\n",
-                            "      <td>0.875</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.461</td>\n",
-                            "      <td>0.525</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.825</td>\n",
-                            "      <td>0.274</td>\n",
-                            "      <td>0.668</td>\n",
-                            "      <td>0.592</td>\n",
-                            "      <td>0.507</td>\n",
-                            "      <td>0.798</td>\n",
-                            "      <td>0.867</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>0.569</td>\n",
-                            "      <td>0.762</td>\n",
-                            "      <td>0.933</td>\n",
-                            "      <td>0.439</td>\n",
-                            "      <td>0.556</td>\n",
-                            "      <td>0.562</td>\n",
-                            "      <td>0.544</td>\n",
-                            "      <td>0.592</td>\n",
-                            "      <td>0.825</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.385</td>\n",
-                            "      <td>0.501</td>\n",
-                            "      <td>0.314</td>\n",
-                            "      <td>0.712</td>\n",
-                            "      <td>0.231</td>\n",
-                            "      <td>0.660</td>\n",
-                            "      <td>0.628</td>\n",
-                            "      <td>0.392</td>\n",
-                            "      <td>0.753</td>\n",
-                            "      <td>0.858</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>0.556</td>\n",
-                            "      <td>0.743</td>\n",
-                            "      <td>0.918</td>\n",
-                            "      <td>0.425</td>\n",
-                            "      <td>0.567</td>\n",
-                            "      <td>0.561</td>\n",
-                            "      <td>0.442</td>\n",
-                            "      <td>0.593</td>\n",
-                            "      <td>0.851</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.389</td>\n",
-                            "      <td>0.497</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.695</td>\n",
-                            "      <td>0.215</td>\n",
-                            "      <td>0.643</td>\n",
-                            "      <td>0.628</td>\n",
-                            "      <td>0.358</td>\n",
-                            "      <td>0.722</td>\n",
-                            "      <td>0.855</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>0.536</td>\n",
-                            "      <td>0.695</td>\n",
-                            "      <td>0.886</td>\n",
-                            "      <td>0.408</td>\n",
-                            "      <td>0.543</td>\n",
-                            "      <td>0.542</td>\n",
-                            "      <td>0.391</td>\n",
-                            "      <td>0.564</td>\n",
-                            "      <td>0.825</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.376</td>\n",
-                            "      <td>0.470</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.726</td>\n",
-                            "      <td>0.212</td>\n",
-                            "      <td>0.636</td>\n",
-                            "      <td>0.628</td>\n",
-                            "      <td>0.345</td>\n",
-                            "      <td>0.708</td>\n",
-                            "      <td>0.850</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>0.531</td>\n",
-                            "      <td>0.622</td>\n",
-                            "      <td>0.671</td>\n",
-                            "      <td>0.268</td>\n",
-                            "      <td>0.615</td>\n",
-                            "      <td>0.565</td>\n",
-                            "      <td>0.465</td>\n",
-                            "      <td>0.659</td>\n",
-                            "      <td>0.804</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.403</td>\n",
-                            "      <td>0.520</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.513</td>\n",
-                            "      <td>0.199</td>\n",
-                            "      <td>0.611</td>\n",
-                            "      <td>0.550</td>\n",
-                            "      <td>0.501</td>\n",
-                            "      <td>0.739</td>\n",
-                            "      <td>0.851</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>0.517</td>\n",
-                            "      <td>0.729</td>\n",
-                            "      <td>0.764</td>\n",
-                            "      <td>0.386</td>\n",
-                            "      <td>0.553</td>\n",
-                            "      <td>0.552</td>\n",
-                            "      <td>0.489</td>\n",
-                            "      <td>0.602</td>\n",
-                            "      <td>0.763</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.382</td>\n",
-                            "      <td>0.468</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.379</td>\n",
-                            "      <td>0.174</td>\n",
-                            "      <td>0.656</td>\n",
-                            "      <td>0.590</td>\n",
-                            "      <td>0.452</td>\n",
-                            "      <td>0.688</td>\n",
-                            "      <td>0.853</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>0.516</td>\n",
-                            "      <td>0.624</td>\n",
-                            "      <td>0.630</td>\n",
-                            "      <td>0.264</td>\n",
-                            "      <td>0.574</td>\n",
-                            "      <td>0.551</td>\n",
-                            "      <td>0.471</td>\n",
-                            "      <td>0.641</td>\n",
-                            "      <td>0.836</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.389</td>\n",
-                            "      <td>0.515</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.508</td>\n",
-                            "      <td>0.172</td>\n",
-                            "      <td>0.633</td>\n",
-                            "      <td>0.542</td>\n",
-                            "      <td>0.470</td>\n",
-                            "      <td>0.700</td>\n",
-                            "      <td>0.848</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>0.512</td>\n",
-                            "      <td>0.620</td>\n",
-                            "      <td>0.643</td>\n",
-                            "      <td>0.265</td>\n",
-                            "      <td>0.591</td>\n",
-                            "      <td>0.545</td>\n",
-                            "      <td>0.502</td>\n",
-                            "      <td>0.635</td>\n",
-                            "      <td>0.816</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.403</td>\n",
-                            "      <td>0.508</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.472</td>\n",
-                            "      <td>0.169</td>\n",
-                            "      <td>0.621</td>\n",
-                            "      <td>0.540</td>\n",
-                            "      <td>0.460</td>\n",
-                            "      <td>0.679</td>\n",
-                            "      <td>0.847</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>0.497</td>\n",
-                            "      <td>0.683</td>\n",
-                            "      <td>0.692</td>\n",
-                            "      <td>0.354</td>\n",
-                            "      <td>0.536</td>\n",
-                            "      <td>0.522</td>\n",
-                            "      <td>0.449</td>\n",
-                            "      <td>0.605</td>\n",
-                            "      <td>0.742</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.375</td>\n",
-                            "      <td>0.458</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.391</td>\n",
-                            "      <td>0.161</td>\n",
-                            "      <td>0.601</td>\n",
-                            "      <td>0.561</td>\n",
-                            "      <td>0.407</td>\n",
-                            "      <td>0.651</td>\n",
-                            "      <td>0.838</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>0.426</td>\n",
-                            "      <td>0.754</td>\n",
-                            "      <td>0.689</td>\n",
-                            "      <td>0.378</td>\n",
-                            "      <td>0.534</td>\n",
-                            "      <td>0.500</td>\n",
-                            "      <td>0.342</td>\n",
-                            "      <td>0.527</td>\n",
-                            "      <td>0.787</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.353</td>\n",
-                            "      <td>0.424</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.163</td>\n",
-                            "      <td>0.049</td>\n",
-                            "      <td>0.632</td>\n",
-                            "      <td>0.588</td>\n",
-                            "      <td>0.242</td>\n",
-                            "      <td>0.628</td>\n",
-                            "      <td>0.846</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>12 rows \u00d7 66 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                  model  Average  \\\n",
-                            "Rank                                                               \n",
-                            "0                       intfloat/e5-mistral-7b-instruct    0.626   \n",
-                            "1                                      GritLM/GritLM-7B    0.626   \n",
-                            "2               intfloat/multilingual-e5-large-instruct    0.611   \n",
-                            "3                        intfloat/multilingual-e5-large    0.569   \n",
-                            "4                         intfloat/multilingual-e5-base    0.556   \n",
-                            "5                        intfloat/multilingual-e5-small    0.536   \n",
-                            "6               sentence-transformers/all-mpnet-base-v2    0.531   \n",
-                            "7     sentence-transformers/paraphrase-multilingual-...    0.517   \n",
-                            "8               sentence-transformers/all-MiniLM-L12-v2    0.516   \n",
-                            "9                sentence-transformers/all-MiniLM-L6-v2    0.512   \n",
-                            "10    sentence-transformers/paraphrase-multilingual-...    0.497   \n",
-                            "11                          sentence-transformers/LaBSE    0.426   \n",
-                            "\n",
-                            "      AmazonCounterfactualClassification  AmazonPolarityClassification  \\\n",
-                            "Rank                                                                     \n",
-                            "0                                  0.732                         0.963   \n",
-                            "1                                  0.792                         0.966   \n",
-                            "2                                  0.681                         0.962   \n",
-                            "3                                  0.762                         0.933   \n",
-                            "4                                  0.743                         0.918   \n",
-                            "5                                  0.695                         0.886   \n",
-                            "6                                  0.622                         0.671   \n",
-                            "7                                  0.729                         0.764   \n",
-                            "8                                  0.624                         0.630   \n",
-                            "9                                  0.620                         0.643   \n",
-                            "10                                 0.683                         0.692   \n",
-                            "11                                 0.754                         0.689   \n",
-                            "\n",
-                            "      AmazonReviewsClassification  ArXivHierarchicalClusteringP2P  \\\n",
-                            "Rank                                                                \n",
-                            "0                           0.520                           0.653   \n",
-                            "1                           0.556                           0.598   \n",
-                            "2                           0.508                           0.625   \n",
-                            "3                           0.439                           0.556   \n",
-                            "4                           0.425                           0.567   \n",
-                            "5                           0.408                           0.543   \n",
-                            "6                           0.268                           0.615   \n",
-                            "7                           0.386                           0.553   \n",
-                            "8                           0.264                           0.574   \n",
-                            "9                           0.265                           0.591   \n",
-                            "10                          0.354                           0.536   \n",
-                            "11                          0.378                           0.534   \n",
-                            "\n",
-                            "      ArXivHierarchicalClusteringS2S  ArguAna  AskUbuntuDupQuestions  BIOSSES  \\\n",
-                            "Rank                                                                            \n",
-                            "0                              0.613    0.617                  0.670    0.855   \n",
-                            "1                              0.623    0.632                  0.674    0.863   \n",
-                            "2                              0.613    0.585                  0.644    0.875   \n",
-                            "3                              0.562    0.544                  0.592    0.825   \n",
-                            "4                              0.561    0.442                  0.593    0.851   \n",
-                            "5                              0.542    0.391                  0.564    0.825   \n",
-                            "6                              0.565    0.465                  0.659    0.804   \n",
-                            "7                              0.552    0.489                  0.602    0.763   \n",
-                            "8                              0.551    0.471                  0.641    0.836   \n",
-                            "9                              0.545    0.502                  0.635    0.816   \n",
-                            "10                             0.522    0.449                  0.605    0.742   \n",
-                            "11                             0.500    0.342                  0.527    0.787   \n",
-                            "\n",
-                            "      ...  StackExchangeClusteringP2P.v2  StackOverflowDupQuestions  \\\n",
-                            "Rank  ...                                                             \n",
-                            "0     ...                          0.481                      0.549   \n",
-                            "1     ...                          0.438                      0.559   \n",
-                            "2     ...                          0.461                      0.525   \n",
-                            "3     ...                          0.385                      0.501   \n",
-                            "4     ...                          0.389                      0.497   \n",
-                            "5     ...                          0.376                      0.470   \n",
-                            "6     ...                          0.403                      0.520   \n",
-                            "7     ...                          0.382                      0.468   \n",
-                            "8     ...                          0.389                      0.515   \n",
-                            "9     ...                          0.403                      0.508   \n",
-                            "10    ...                          0.375                      0.458   \n",
-                            "11    ...                          0.353                      0.424   \n",
-                            "\n",
-                            "      SummEvalSummarization.v2  TRECCOVID  Touche2020  \\\n",
-                            "Rank                                                    \n",
-                            "0                          NaN      0.870       0.263   \n",
-                            "1                          NaN      0.743       0.278   \n",
-                            "2                          NaN      0.825       0.274   \n",
-                            "3                        0.314      0.712       0.231   \n",
-                            "4                          NaN      0.695       0.215   \n",
-                            "5                          NaN      0.726       0.212   \n",
-                            "6                          NaN      0.513       0.199   \n",
-                            "7                          NaN      0.379       0.174   \n",
-                            "8                          NaN      0.508       0.172   \n",
-                            "9                          NaN      0.472       0.169   \n",
-                            "10                         NaN      0.391       0.161   \n",
-                            "11                         NaN      0.163       0.049   \n",
-                            "\n",
-                            "      ToxicConversationsClassification  \\\n",
-                            "Rank                                     \n",
-                            "0                                0.717   \n",
-                            "1                                0.688   \n",
-                            "2                                0.668   \n",
-                            "3                                0.660   \n",
-                            "4                                0.643   \n",
-                            "5                                0.636   \n",
-                            "6                                0.611   \n",
-                            "7                                0.656   \n",
-                            "8                                0.633   \n",
-                            "9                                0.621   \n",
-                            "10                               0.601   \n",
-                            "11                               0.632   \n",
-                            "\n",
-                            "      TweetSentimentExtractionClassification  TwentyNewsgroupsClustering.v2  \\\n",
-                            "Rank                                                                          \n",
-                            "0                                      0.649                          0.533   \n",
-                            "1                                      0.663                          0.573   \n",
-                            "2                                      0.592                          0.507   \n",
-                            "3                                      0.628                          0.392   \n",
-                            "4                                      0.628                          0.358   \n",
-                            "5                                      0.628                          0.345   \n",
-                            "6                                      0.550                          0.501   \n",
-                            "7                                      0.590                          0.452   \n",
-                            "8                                      0.542                          0.470   \n",
-                            "9                                      0.540                          0.460   \n",
-                            "10                                     0.561                          0.407   \n",
-                            "11                                     0.588                          0.242   \n",
-                            "\n",
-                            "      TwitterSemEval2015  TwitterURLCorpus  \n",
-                            "Rank                                        \n",
-                            "0                  0.816             0.878  \n",
-                            "1                  0.811             0.874  \n",
-                            "2                  0.798             0.867  \n",
-                            "3                  0.753             0.858  \n",
-                            "4                  0.722             0.855  \n",
-                            "5                  0.708             0.850  \n",
-                            "6                  0.739             0.851  \n",
-                            "7                  0.688             0.853  \n",
-                            "8                  0.700             0.848  \n",
-                            "9                  0.679             0.847  \n",
-                            "10                 0.651             0.838  \n",
-                            "11                 0.628             0.846  \n",
-                            "\n",
-                            "[12 rows x 66 columns]"
-                        ]
-                    },
-                    "execution_count": 8,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "_results_df = results_df.copy()\n",
-                "_results_df[\"Average\"] = _results_df.mean(axis=1)\n",
-                "_results_df = _results_df.sort_values(\"Average\", ascending=False)\n",
-                "_results_df = _results_df[\n",
-                "    [\"Average\"] + [col for col in _results_df.columns if col != \"Average\"]\n",
-                "]\n",
-                "_results_df = _results_df.reset_index().drop([\"revision\"], axis=1)\n",
-                "# remove column name \"task\"\n",
-                "_results_df.columns.name = None\n",
-                "# rename index to rank\n",
-                "_results_df.index.name = \"Rank\"\n",
-                "_results_df.round(3)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Index(['model', 'Average', 'AmazonCounterfactualClassification',\n",
-                            "       'AmazonPolarityClassification', 'AmazonReviewsClassification',\n",
-                            "       'ArXivHierarchicalClusteringP2P', 'ArXivHierarchicalClusteringS2S',\n",
-                            "       'ArguAna', 'AskUbuntuDupQuestions', 'BIOSSES',\n",
-                            "       'Banking77Classification', 'BiorxivClusteringP2P.v2',\n",
-                            "       'BiorxivClusteringS2S.v2', 'CQADupstackAndroidRetrieval',\n",
-                            "       'CQADupstackEnglishRetrieval', 'CQADupstackGamingRetrieval',\n",
-                            "       'CQADupstackGisRetrieval', 'CQADupstackMathematicaRetrieval',\n",
-                            "       'CQADupstackPhysicsRetrieval', 'CQADupstackProgrammersRetrieval',\n",
-                            "       'CQADupstackStatsRetrieval', 'CQADupstackTexRetrieval',\n",
-                            "       'CQADupstackUnixRetrieval', 'CQADupstackWebmastersRetrieval',\n",
-                            "       'CQADupstackWordpressRetrieval', 'ClimateFEVERHardNegatives',\n",
-                            "       'DBPediaHardNegatives', 'EmotionClassification', 'FEVERHardNegatives',\n",
-                            "       'FiQA2018', 'HotpotQAHardNegatives', 'ImdbClassification',\n",
-                            "       'MTOPDomainClassification', 'MTOPIntentClassification',\n",
-                            "       'MassiveIntentClassification', 'MassiveScenarioClassification',\n",
-                            "       'MedrxivClusteringP2P.v2', 'MedrxivClusteringS2S.v2',\n",
-                            "       'MindSmallReranking', 'NFCorpus', 'RedditClustering.v2',\n",
-                            "       'RedditClusteringP2P.v2', 'SCIDOCS', 'SICK-R', 'STS12', 'STS13',\n",
-                            "       'STS14', 'STS15', 'STS16', 'STS17', 'STS22.v2', 'STSBenchmark',\n",
-                            "       'SciDocsRR', 'SciFact', 'SprintDuplicateQuestions',\n",
-                            "       'StackExchangeClustering.v2', 'StackExchangeClusteringP2P.v2',\n",
-                            "       'StackOverflowDupQuestions', 'SummEvalSummarization.v2', 'TRECCOVID',\n",
-                            "       'Touche2020', 'ToxicConversationsClassification',\n",
-                            "       'TweetSentimentExtractionClassification',\n",
-                            "       'TwentyNewsgroupsClustering.v2', 'TwitterSemEval2015',\n",
-                            "       'TwitterURLCorpus'],\n",
-                            "      dtype='object')"
-                        ]
-                    },
-                    "execution_count": 9,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "_results_df.columns"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Comparing Scores with MTEB"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "scores = _results_df[[\"model\", \"Average\"]].copy()\n",
-                "# add rank\n",
-                "scores[\"Rank\"] = scores[\"Average\"].rank(ascending=False)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import pandas as pd\n",
-                "\n",
-                "mteb_scores = {\n",
-                "    \"GritLM/GritLM-7B\": 66.76,\n",
-                "    \"intfloat/e5-mistral-7b-instruct\": 66.63,\n",
-                "    \"intfloat/multilingual-e5-large-instruct\": 64.41,\n",
-                "    \"intfloat/multilingual-e5-large\": 60.89,\n",
-                "    \"intfloat/multilingual-e5-base\": 59.11,\n",
-                "    \"sentence-transformers/all-mpnet-base-v2\": 57.78,\n",
-                "    \"intfloat/multilingual-e5-small\": 57.04,\n",
-                "    \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\": 54.64,\n",
-                "    \"sentence-transformers/all-MiniLM-L12-v2\": 56.53,\n",
-                "    \"sentence-transformers/all-MiniLM-L6-v2\": 56.1,\n",
-                "    \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\": 52.45,\n",
-                "    \"sentence-transformers/LaBSE\": 45.21,\n",
-                "}\n",
-                "mteb_scores_df = pd.DataFrame(mteb_scores.items(), columns=[\"model\", \"Average\"])\n",
-                "mteb_scores_df[\"Rank\"] = mteb_scores_df[\"Average\"].rank(ascending=False)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>Average_v1</th>\n",
-                            "      <th>Rank_v1</th>\n",
-                            "      <th>Average_v2</th>\n",
-                            "      <th>Rank_v2</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>66.76</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>62.576234</td>\n",
-                            "      <td>2.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>66.63</td>\n",
-                            "      <td>2.0</td>\n",
-                            "      <td>62.623036</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>64.41</td>\n",
-                            "      <td>3.0</td>\n",
-                            "      <td>61.127328</td>\n",
-                            "      <td>3.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>60.89</td>\n",
-                            "      <td>4.0</td>\n",
-                            "      <td>56.925622</td>\n",
-                            "      <td>4.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>59.11</td>\n",
-                            "      <td>5.0</td>\n",
-                            "      <td>55.578989</td>\n",
-                            "      <td>5.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>57.78</td>\n",
-                            "      <td>6.0</td>\n",
-                            "      <td>53.148536</td>\n",
-                            "      <td>7.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>57.04</td>\n",
-                            "      <td>7.0</td>\n",
-                            "      <td>53.556709</td>\n",
-                            "      <td>6.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>54.64</td>\n",
-                            "      <td>10.0</td>\n",
-                            "      <td>51.724491</td>\n",
-                            "      <td>8.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>56.53</td>\n",
-                            "      <td>8.0</td>\n",
-                            "      <td>51.633906</td>\n",
-                            "      <td>9.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>56.10</td>\n",
-                            "      <td>9.0</td>\n",
-                            "      <td>51.215533</td>\n",
-                            "      <td>10.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>52.45</td>\n",
-                            "      <td>11.0</td>\n",
-                            "      <td>49.685354</td>\n",
-                            "      <td>11.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>45.21</td>\n",
-                            "      <td>12.0</td>\n",
-                            "      <td>42.554815</td>\n",
-                            "      <td>12.0</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model  Average_v1  Rank_v1  \\\n",
-                            "0                                    GritLM/GritLM-7B       66.76      1.0   \n",
-                            "1                     intfloat/e5-mistral-7b-instruct       66.63      2.0   \n",
-                            "2             intfloat/multilingual-e5-large-instruct       64.41      3.0   \n",
-                            "3                      intfloat/multilingual-e5-large       60.89      4.0   \n",
-                            "4                       intfloat/multilingual-e5-base       59.11      5.0   \n",
-                            "5             sentence-transformers/all-mpnet-base-v2       57.78      6.0   \n",
-                            "6                      intfloat/multilingual-e5-small       57.04      7.0   \n",
-                            "7   sentence-transformers/paraphrase-multilingual-...       54.64     10.0   \n",
-                            "8             sentence-transformers/all-MiniLM-L12-v2       56.53      8.0   \n",
-                            "9              sentence-transformers/all-MiniLM-L6-v2       56.10      9.0   \n",
-                            "10  sentence-transformers/paraphrase-multilingual-...       52.45     11.0   \n",
-                            "11                        sentence-transformers/LaBSE       45.21     12.0   \n",
-                            "\n",
-                            "    Average_v2  Rank_v2  \n",
-                            "0    62.576234      2.0  \n",
-                            "1    62.623036      1.0  \n",
-                            "2    61.127328      3.0  \n",
-                            "3    56.925622      4.0  \n",
-                            "4    55.578989      5.0  \n",
-                            "5    53.148536      7.0  \n",
-                            "6    53.556709      6.0  \n",
-                            "7    51.724491      8.0  \n",
-                            "8    51.633906      9.0  \n",
-                            "9    51.215533     10.0  \n",
-                            "10   49.685354     11.0  \n",
-                            "11   42.554815     12.0  "
-                        ]
-                    },
-                    "execution_count": 12,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# merge\n",
-                "all_scores = pd.merge(\n",
-                "    mteb_scores_df, scores, on=\"model\", how=\"outer\", suffixes=(\"_v1\", \"_v2\")\n",
-                ")\n",
-                "all_scores[\"Average_v2\"] = all_scores[\"Average_v2\"] * 100\n",
-                "all_scores"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Text(0.5, 1.0, 'Pearson correlation: 0.99, p-value: 0.0000\\nSpearman correlation: 0.97, p-value: 0.0000')"
-                        ]
-                    },
-                    "execution_count": 13,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1000x1000 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# correlation plot with model names, and a regression line\n",
-                "import matplotlib.pyplot as plt\n",
-                "import seaborn as sns\n",
-                "from scipy.stats import pearsonr, spearmanr\n",
-                "\n",
-                "sns.set_theme(style=\"whitegrid\")\n",
-                "plt.figure(figsize=(10, 10))\n",
-                "sns.regplot(x=\"Average_v1\", y=\"Average_v2\", data=all_scores)\n",
-                "for i, txt in enumerate(all_scores[\"model\"]):\n",
-                "    plt.annotate(txt, (all_scores[\"Average_v1\"][i], all_scores[\"Average_v2\"][i]))\n",
-                "\n",
-                "# add correlation coefficient\n",
-                "\n",
-                "pearson_corr = pearsonr(all_scores[\"Average_v1\"], all_scores[\"Average_v2\"])\n",
-                "spearman_corr = spearmanr(all_scores[\"Average_v1\"], all_scores[\"Average_v2\"])\n",
-                "\n",
-                "plt.title(\n",
-                "    f\"Pearson correlation: {pearson_corr[0]:.2f}, p-value: {pearson_corr[1]:.4f}\\nSpearman correlation: {spearman_corr[0]:.2f}, p-value: {spearman_corr[1]:.4f}\"\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Task selection \n",
-                "Here we do task selection for construction of MTEB(eng)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>DBPediaHardNegatives</th>\n",
-                            "      <th>SummEvalSummarization.v2</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Rank</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>0.46571</td>\n",
-                            "      <td>NaN</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>0.40407</td>\n",
-                            "      <td>NaN</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>NaN</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>0.42475</td>\n",
-                            "      <td>0.314073</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>0.42578</td>\n",
-                            "      <td>NaN</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>0.40379</td>\n",
-                            "      <td>NaN</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>0.35720</td>\n",
-                            "      <td>NaN</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>0.30667</td>\n",
-                            "      <td>NaN</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>0.36958</td>\n",
-                            "      <td>NaN</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>0.35697</td>\n",
-                            "      <td>NaN</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>0.27419</td>\n",
-                            "      <td>NaN</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>0.21176</td>\n",
-                            "      <td>NaN</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "      DBPediaHardNegatives  SummEvalSummarization.v2\n",
-                            "Rank                                                \n",
-                            "0                  0.46571                       NaN\n",
-                            "1                  0.40407                       NaN\n",
-                            "2                      NaN                       NaN\n",
-                            "3                  0.42475                  0.314073\n",
-                            "4                  0.42578                       NaN\n",
-                            "5                  0.40379                       NaN\n",
-                            "6                  0.35720                       NaN\n",
-                            "7                  0.30667                       NaN\n",
-                            "8                  0.36958                       NaN\n",
-                            "9                  0.35697                       NaN\n",
-                            "10                 0.27419                       NaN\n",
-                            "11                 0.21176                       NaN"
-                        ]
-                    },
-                    "execution_count": 14,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# results_df\n",
-                "\n",
-                "# columns with nan values\n",
-                "\n",
-                "_results_df[_results_df.columns[_results_df.isna().any()].tolist()]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "tasks_not_in_index = [\n",
-                "    \"SummEvalSummarization.v2\",\n",
-                "    \"DBPediaHardNegatives\",  # remove them until we have results\n",
-                "]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "def is_candidate_valid_removal(current_tasks: list[str], task_to_remove: str) -> bool:\n",
-                "    \"\"\"Determine if target task should be removed.\n",
-                "    This checks that all task types are present in the current tasks\n",
-                "    \"\"\"\n",
-                "    # check if removing task removes a unique task type - if so, don't remove\n",
-                "    _current_tasks = current_tasks.copy()\n",
-                "    if task_to_remove in _current_tasks:\n",
-                "        _current_tasks.remove(task_to_remove)\n",
-                "    task = mteb.get_task(task_to_remove)\n",
-                "    ctasks = mteb.get_tasks(tasks=_current_tasks)\n",
-                "\n",
-                "    # don't remove a unique task type\n",
-                "    task_types = {t.metadata.type for t in ctasks}\n",
-                "    if task.metadata.type not in task_types:\n",
-                "        return False\n",
-                "\n",
-                "    return True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 62/62 [00:01<00:00, 59.78it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 61/61 [00:00<00:00, 67.21it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 60/60 [00:00<00:00, 70.91it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 59/59 [00:00<00:00, 71.41it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 58/58 [00:00<00:00, 69.97it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 57/57 [00:00<00:00, 65.43it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 56/56 [00:00<00:00, 56.90it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 55/55 [00:00<00:00, 66.02it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 54/54 [00:00<00:00, 65.71it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 53/53 [00:00<00:00, 76.50it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 52/52 [00:00<00:00, 66.05it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 51/51 [00:00<00:00, 61.66it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50/50 [00:00<00:00, 67.87it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:00<00:00, 70.84it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 48/48 [00:00<00:00, 73.11it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 47/47 [00:00<00:00, 75.61it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 46/46 [00:00<00:00, 56.74it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 45/45 [00:00<00:00, 73.59it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 44/44 [00:00<00:00, 77.61it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 43/43 [00:00<00:00, 75.50it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 42/42 [00:00<00:00, 76.87it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 41/41 [00:00<00:00, 78.52it/s]                      \n",
-                        "Task: TwitterURLCorpus: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 40/40 [00:00<00:00, 78.11it/s]                      \n"
-                    ]
-                }
-            ],
-            "source": [
-                "from sklearn.linear_model import LinearRegression\n",
-                "\n",
-                "# remove tasks one by one\n",
-                "tasks_to_select_from = [\n",
-                "    t.metadata.name for t in tasks if t.metadata.name not in tasks_not_in_index\n",
-                "]\n",
-                "\n",
-                "tasks_removed = []\n",
-                "predicability_scores = []\n",
-                "\n",
-                "while tasks_to_select_from:\n",
-                "    most_pred_tasks = task_selection.most_predictable_task(\n",
-                "        results_df[tasks_to_select_from],\n",
-                "        sklearn_estimator=LinearRegression(),\n",
-                "        metrics=[\n",
-                "            task_selection.spearman,\n",
-                "            task_selection.pearson,\n",
-                "            task_selection.mse_with_zscore,\n",
-                "        ],\n",
-                "    )\n",
-                "\n",
-                "    # reverse the list to get the least predictable task\n",
-                "    most_pred_tasks.reverse()\n",
-                "\n",
-                "    while most_pred_tasks:\n",
-                "        most_pred_task = most_pred_tasks.pop()\n",
-                "        most_pred_task_name = list(most_pred_task.keys())[0]\n",
-                "\n",
-                "        # if the task is too hard to predict, skip it (this essentially stops the loop)\n",
-                "        if (\n",
-                "            most_pred_task[most_pred_task_name][\"mse_with_zscore\"] > 0.2\n",
-                "            or most_pred_task[most_pred_task_name][\"spearman\"] < 0.95\n",
-                "        ):\n",
-                "            continue\n",
-                "\n",
-                "        if is_candidate_valid_removal(tasks_to_select_from, most_pred_task_name):\n",
-                "            tasks_to_select_from.remove(most_pred_task_name)\n",
-                "            tasks_removed.append(most_pred_task_name)\n",
-                "            predicability_scores.append(most_pred_task[most_pred_task_name])\n",
-                "            break\n",
-                "\n",
-                "    if not most_pred_tasks:  # if no task was removed, then we are done -- can be replaced with another stopping criterion\n",
-                "        break"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1000x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# plotting the predictability scores with the tasks removed\n",
-                "import matplotlib.pyplot as plt\n",
-                "\n",
-                "# make the plot wider\n",
-                "plt.figure(figsize=(10, 6))\n",
-                "\n",
-                "for metric in [\"spearman\", \"pearson\"]:\n",
-                "    plt.plot([t[metric] for t in predicability_scores], label=metric)\n",
-                "\n",
-                "plt.xlabel(\"Tasks removed\")\n",
-                "plt.ylabel(\"Correlation (predicted vs. observed performance)\")\n",
-                "plt.legend()\n",
-                "\n",
-                "# add vline for 0.95 spearman\n",
-                "plt.axhline(y=0.95, color=\"r\", linestyle=\"--\")\n",
-                "\n",
-                "# add task names to the x-axis\n",
-                "plt.xticks(range(len(tasks_removed)), tasks_removed, rotation=90)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1000x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# plotting the predictability scores with the tasks removed\n",
-                "\n",
-                "plt.figure(figsize=(10, 6))\n",
-                "\n",
-                "for metric in [\"mse_with_zscore\"]:\n",
-                "    plt.plot([t[metric] for t in predicability_scores], label=metric)\n",
-                "\n",
-                "plt.xlabel(\"Tasks removed\")\n",
-                "plt.ylabel(\"Normalized Mean Squared error (predicted vs. observed performance)\")\n",
-                "plt.legend()\n",
-                "\n",
-                "# add vline for 0.2 mse\n",
-                "plt.axhline(y=0.2, color=\"r\", linestyle=\"--\")\n",
-                "\n",
-                "# add task names to the x-axis\n",
-                "plt.xticks(range(len(tasks_removed)), tasks_removed, rotation=90)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Compare scores with scores prior to selection"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 20,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "scores_lite = results_df[tasks_to_select_from].copy()\n",
-                "scores_lite[\"Average\"] = scores_lite.mean(axis=1)\n",
-                "scores_lite[\"Rank\"] = scores_lite[\"Average\"].rank(ascending=False)\n",
-                "scores_lite = scores_lite.reset_index().drop([\"revision\"], axis=1)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 21,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>Average_v2_full</th>\n",
-                            "      <th>Rank_v2_full</th>\n",
-                            "      <th>Average_v2_lite</th>\n",
-                            "      <th>Rank_v2_lite</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>0.63</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.67</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>0.63</td>\n",
-                            "      <td>2.0</td>\n",
-                            "      <td>0.66</td>\n",
-                            "      <td>2.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>0.61</td>\n",
-                            "      <td>3.0</td>\n",
-                            "      <td>0.65</td>\n",
-                            "      <td>3.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>0.57</td>\n",
-                            "      <td>4.0</td>\n",
-                            "      <td>0.62</td>\n",
-                            "      <td>4.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>0.56</td>\n",
-                            "      <td>5.0</td>\n",
-                            "      <td>0.60</td>\n",
-                            "      <td>5.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>0.54</td>\n",
-                            "      <td>6.0</td>\n",
-                            "      <td>0.58</td>\n",
-                            "      <td>6.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>0.53</td>\n",
-                            "      <td>7.0</td>\n",
-                            "      <td>0.56</td>\n",
-                            "      <td>8.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>0.52</td>\n",
-                            "      <td>8.0</td>\n",
-                            "      <td>0.57</td>\n",
-                            "      <td>7.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>0.52</td>\n",
-                            "      <td>9.0</td>\n",
-                            "      <td>0.55</td>\n",
-                            "      <td>10.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>0.51</td>\n",
-                            "      <td>10.0</td>\n",
-                            "      <td>0.54</td>\n",
-                            "      <td>11.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>0.50</td>\n",
-                            "      <td>11.0</td>\n",
-                            "      <td>0.55</td>\n",
-                            "      <td>9.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>0.43</td>\n",
-                            "      <td>12.0</td>\n",
-                            "      <td>0.49</td>\n",
-                            "      <td>12.0</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model  Average_v2_full  \\\n",
-                            "0                     intfloat/e5-mistral-7b-instruct             0.63   \n",
-                            "1                                    GritLM/GritLM-7B             0.63   \n",
-                            "2             intfloat/multilingual-e5-large-instruct             0.61   \n",
-                            "3                      intfloat/multilingual-e5-large             0.57   \n",
-                            "4                       intfloat/multilingual-e5-base             0.56   \n",
-                            "5                      intfloat/multilingual-e5-small             0.54   \n",
-                            "6             sentence-transformers/all-mpnet-base-v2             0.53   \n",
-                            "7   sentence-transformers/paraphrase-multilingual-...             0.52   \n",
-                            "8             sentence-transformers/all-MiniLM-L12-v2             0.52   \n",
-                            "9              sentence-transformers/all-MiniLM-L6-v2             0.51   \n",
-                            "10  sentence-transformers/paraphrase-multilingual-...             0.50   \n",
-                            "11                        sentence-transformers/LaBSE             0.43   \n",
-                            "\n",
-                            "    Rank_v2_full  Average_v2_lite  Rank_v2_lite  \n",
-                            "0            1.0             0.67           1.0  \n",
-                            "1            2.0             0.66           2.0  \n",
-                            "2            3.0             0.65           3.0  \n",
-                            "3            4.0             0.62           4.0  \n",
-                            "4            5.0             0.60           5.0  \n",
-                            "5            6.0             0.58           6.0  \n",
-                            "6            7.0             0.56           8.0  \n",
-                            "7            8.0             0.57           7.0  \n",
-                            "8            9.0             0.55          10.0  \n",
-                            "9           10.0             0.54          11.0  \n",
-                            "10          11.0             0.55           9.0  \n",
-                            "11          12.0             0.49          12.0  "
-                        ]
-                    },
-                    "execution_count": 21,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# merge and compare with the original scores\n",
-                "\n",
-                "all_scores_lite = pd.merge(\n",
-                "    scores,\n",
-                "    scores_lite[[\"model\", \"Average\", \"Rank\"]],\n",
-                "    on=\"model\",\n",
-                "    how=\"outer\",\n",
-                "    suffixes=(\"_v2_full\", \"_v2_lite\"),\n",
-                ")\n",
-                "\n",
-                "all_scores_lite.round(2)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 22,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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AAAAASUVORK5CYII=",
-                        "text/plain": [
-                            "<Figure size 1000x1000 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# correlation plot with model names, and a regression line\n",
-                "\n",
-                "plt.figure(figsize=(10, 10))\n",
-                "sns.regplot(x=\"Average_v2_full\", y=\"Average_v2_lite\", data=all_scores_lite)\n",
-                "for i, txt in enumerate(all_scores_lite[\"model\"]):\n",
-                "    plt.annotate(\n",
-                "        txt,\n",
-                "        (all_scores_lite[\"Average_v2_full\"][i], all_scores_lite[\"Average_v2_lite\"][i]),\n",
-                "    )\n",
-                "\n",
-                "# add correlation coefficient\n",
-                "\n",
-                "pearson_corr = pearsonr(\n",
-                "    all_scores_lite[\"Average_v2_full\"], all_scores_lite[\"Average_v2_lite\"]\n",
-                ")\n",
-                "spearman_corr = spearmanr(\n",
-                "    all_scores_lite[\"Average_v2_full\"], all_scores_lite[\"Average_v2_lite\"]\n",
-                ")\n",
-                "\n",
-                "plt.title(\n",
-                "    f\"Pearson correlation: {pearson_corr[0]:.2f}, p-value: {pearson_corr[1]:.2f}\\nSpearman correlation: {spearman_corr[0]:.2f}, p-value: {spearman_corr[1]:.2f}\"\n",
-                ")\n",
-                "\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 23,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>Average_v2_full</th>\n",
-                            "      <th>Rank_v2_full</th>\n",
-                            "      <th>Average_v2_lite</th>\n",
-                            "      <th>Rank_v2_lite</th>\n",
-                            "      <th>Average</th>\n",
-                            "      <th>Rank</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>0.63</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.67</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>66.63</td>\n",
-                            "      <td>2.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>0.63</td>\n",
-                            "      <td>2.0</td>\n",
-                            "      <td>0.66</td>\n",
-                            "      <td>2.0</td>\n",
-                            "      <td>66.76</td>\n",
-                            "      <td>1.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>0.61</td>\n",
-                            "      <td>3.0</td>\n",
-                            "      <td>0.65</td>\n",
-                            "      <td>3.0</td>\n",
-                            "      <td>64.41</td>\n",
-                            "      <td>3.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>0.57</td>\n",
-                            "      <td>4.0</td>\n",
-                            "      <td>0.62</td>\n",
-                            "      <td>4.0</td>\n",
-                            "      <td>60.89</td>\n",
-                            "      <td>4.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>0.56</td>\n",
-                            "      <td>5.0</td>\n",
-                            "      <td>0.60</td>\n",
-                            "      <td>5.0</td>\n",
-                            "      <td>59.11</td>\n",
-                            "      <td>5.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>0.54</td>\n",
-                            "      <td>6.0</td>\n",
-                            "      <td>0.58</td>\n",
-                            "      <td>6.0</td>\n",
-                            "      <td>57.04</td>\n",
-                            "      <td>7.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>0.53</td>\n",
-                            "      <td>7.0</td>\n",
-                            "      <td>0.56</td>\n",
-                            "      <td>8.0</td>\n",
-                            "      <td>57.78</td>\n",
-                            "      <td>6.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>0.52</td>\n",
-                            "      <td>8.0</td>\n",
-                            "      <td>0.57</td>\n",
-                            "      <td>7.0</td>\n",
-                            "      <td>54.64</td>\n",
-                            "      <td>10.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>0.52</td>\n",
-                            "      <td>9.0</td>\n",
-                            "      <td>0.55</td>\n",
-                            "      <td>10.0</td>\n",
-                            "      <td>56.53</td>\n",
-                            "      <td>8.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>0.51</td>\n",
-                            "      <td>10.0</td>\n",
-                            "      <td>0.54</td>\n",
-                            "      <td>11.0</td>\n",
-                            "      <td>56.10</td>\n",
-                            "      <td>9.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>0.50</td>\n",
-                            "      <td>11.0</td>\n",
-                            "      <td>0.55</td>\n",
-                            "      <td>9.0</td>\n",
-                            "      <td>52.45</td>\n",
-                            "      <td>11.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>0.43</td>\n",
-                            "      <td>12.0</td>\n",
-                            "      <td>0.49</td>\n",
-                            "      <td>12.0</td>\n",
-                            "      <td>45.21</td>\n",
-                            "      <td>12.0</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model  Average_v2_full  \\\n",
-                            "0                     intfloat/e5-mistral-7b-instruct             0.63   \n",
-                            "1                                    GritLM/GritLM-7B             0.63   \n",
-                            "2             intfloat/multilingual-e5-large-instruct             0.61   \n",
-                            "3                      intfloat/multilingual-e5-large             0.57   \n",
-                            "4                       intfloat/multilingual-e5-base             0.56   \n",
-                            "5                      intfloat/multilingual-e5-small             0.54   \n",
-                            "6             sentence-transformers/all-mpnet-base-v2             0.53   \n",
-                            "7   sentence-transformers/paraphrase-multilingual-...             0.52   \n",
-                            "8             sentence-transformers/all-MiniLM-L12-v2             0.52   \n",
-                            "9              sentence-transformers/all-MiniLM-L6-v2             0.51   \n",
-                            "10  sentence-transformers/paraphrase-multilingual-...             0.50   \n",
-                            "11                        sentence-transformers/LaBSE             0.43   \n",
-                            "\n",
-                            "    Rank_v2_full  Average_v2_lite  Rank_v2_lite  Average  Rank  \n",
-                            "0            1.0             0.67           1.0    66.63   2.0  \n",
-                            "1            2.0             0.66           2.0    66.76   1.0  \n",
-                            "2            3.0             0.65           3.0    64.41   3.0  \n",
-                            "3            4.0             0.62           4.0    60.89   4.0  \n",
-                            "4            5.0             0.60           5.0    59.11   5.0  \n",
-                            "5            6.0             0.58           6.0    57.04   7.0  \n",
-                            "6            7.0             0.56           8.0    57.78   6.0  \n",
-                            "7            8.0             0.57           7.0    54.64  10.0  \n",
-                            "8            9.0             0.55          10.0    56.53   8.0  \n",
-                            "9           10.0             0.54          11.0    56.10   9.0  \n",
-                            "10          11.0             0.55           9.0    52.45  11.0  \n",
-                            "11          12.0             0.49          12.0    45.21  12.0  "
-                        ]
-                    },
-                    "execution_count": 23,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# compare with v1\n",
-                "\n",
-                "all_scores_v1 = pd.merge(\n",
-                "    all_scores_lite,\n",
-                "    mteb_scores_df,\n",
-                "    on=\"model\",\n",
-                "    how=\"outer\",\n",
-                "    suffixes=(\"_v2_lite\", \"_v1\"),\n",
-                ")\n",
-                "\n",
-                "all_scores_v1.round(2)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 24,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 700x700 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# correlation plot with model names, and a regression line\n",
-                "\n",
-                "plt.figure(figsize=(7, 7))\n",
-                "\n",
-                "all_scores_v1_plot = all_scores_v1.copy()\n",
-                "all_scores_v1_plot[\"Average_v2_lite\"] = all_scores_v1_plot[\"Average_v2_lite\"] * 100\n",
-                "\n",
-                "emb_size = {\n",
-                "    \"GritLM/GritLM-7B\": 4096,\n",
-                "    \"intfloat/e5-mistral-7b-instruct\": 4096,\n",
-                "    \"intfloat/multilingual-e5-large-instruct\": 1024,\n",
-                "    \"intfloat/multilingual-e5-large\": 1024,\n",
-                "    \"intfloat/multilingual-e5-base\": 768,\n",
-                "    \"sentence-transformers/all-mpnet-base-v2\": 768,\n",
-                "    \"intfloat/multilingual-e5-small\": 384,\n",
-                "    \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\": 768,\n",
-                "    \"sentence-transformers/all-MiniLM-L12-v2\": 384,\n",
-                "    \"sentence-transformers/all-MiniLM-L6-v2\": 384,\n",
-                "    \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\": 384,\n",
-                "    \"sentence-transformers/LaBSE\": 768,\n",
-                "}\n",
-                "\n",
-                "\n",
-                "all_scores_v1_plot[\"Embedding Size\"] = all_scores_v1_plot[\"model\"].map(emb_size)\n",
-                "\n",
-                "# log scale\n",
-                "# all_scores_v1_plot[\"Embedding Size\"] = all_scores_v1_plot[\"Embedding Size\"].apply(lambda x: np.log(x))\n",
-                "\n",
-                "rename = {\n",
-                "    \"GritLM/GritLM-7B\": \"GritLM-7B\",\n",
-                "    \"intfloat/e5-mistral-7b-instruct\": \"e5-mistral-7b-instruct\",\n",
-                "    \"intfloat/multilingual-e5-large-instruct\": \"multilingual-e5-large-instruct\",\n",
-                "    \"intfloat/multilingual-e5-large\": \"multilingual-e5-large\",\n",
-                "    \"intfloat/multilingual-e5-base\": \"multilingual-e5-base\",\n",
-                "    \"sentence-transformers/all-mpnet-base-v2\": \"all-mpnet-base\",\n",
-                "    \"intfloat/multilingual-e5-small\": \"multilingual-e5-small\",\n",
-                "    \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\": \"multilingual-mpnet-base\",\n",
-                "    \"sentence-transformers/all-MiniLM-L12-v2\": \"all-MiniLM-L12\",\n",
-                "    \"sentence-transformers/all-MiniLM-L6-v2\": \"all-MiniLM-L6\",\n",
-                "    \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\": \"multilingual-MiniLM-L12\",\n",
-                "    \"sentence-transformers/LaBSE\": \"LaBSE\",\n",
-                "}\n",
-                "\n",
-                "\n",
-                "all_scores_v1_plot[\"Model Name\"] = all_scores_v1_plot[\"model\"].replace(rename)\n",
-                "\n",
-                "sns.regplot(x=\"Average_v2_lite\", y=\"Average\", data=all_scores_v1_plot, scatter=False)\n",
-                "\n",
-                "sns.scatterplot(\n",
-                "    x=\"Average_v2_lite\",\n",
-                "    y=\"Average\",\n",
-                "    data=all_scores_v1_plot,\n",
-                "    hue=\"Model Name\",\n",
-                "    style=\"Model Name\",\n",
-                "    size=\"Embedding Size\",\n",
-                "    sizes=(50, 150),\n",
-                ")\n",
-                "\n",
-                "# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
-                "\n",
-                "# place it below\n",
-                "plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.1), ncol=2)\n",
-                "\n",
-                "# x and y labels\n",
-                "plt.xlabel(\"MTEB(eng, lite)\")\n",
-                "plt.ylabel(\"MTEB(eng)\")\n",
-                "\n",
-                "\n",
-                "# add correlation coefficient\n",
-                "\n",
-                "pearson_corr = pearsonr(\n",
-                "    all_scores_v1_plot[\"Average_v2_lite\"], all_scores_v1_plot[\"Average\"]\n",
-                ")\n",
-                "spearman_corr = spearmanr(\n",
-                "    all_scores_v1_plot[\"Average_v2_lite\"], all_scores_v1_plot[\"Average\"]\n",
-                ")\n",
-                "\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 25,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "PearsonRResult(statistic=0.9579163089066131, pvalue=9.685276711455158e-07)"
-                        ]
-                    },
-                    "execution_count": 25,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "pearson_corr"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 26,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "SignificanceResult(statistic=0.9020979020979022, pvalue=5.997857446537695e-05)"
-                        ]
-                    },
-                    "execution_count": 26,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "spearman_corr"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 27,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>Average_v2_full</th>\n",
-                            "      <th>Rank_v2_full</th>\n",
-                            "      <th>Average_v2_lite</th>\n",
-                            "      <th>Rank_v2_lite</th>\n",
-                            "      <th>Average</th>\n",
-                            "      <th>Rank</th>\n",
-                            "      <th>Embedding Size</th>\n",
-                            "      <th>Model Name</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>0.626230</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>66.963823</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>66.63</td>\n",
-                            "      <td>2.0</td>\n",
-                            "      <td>4096</td>\n",
-                            "      <td>e5-mistral-7b-instruct</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>0.625762</td>\n",
-                            "      <td>2.0</td>\n",
-                            "      <td>66.448032</td>\n",
-                            "      <td>2.0</td>\n",
-                            "      <td>66.76</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>4096</td>\n",
-                            "      <td>GritLM-7B</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>0.611273</td>\n",
-                            "      <td>3.0</td>\n",
-                            "      <td>65.236396</td>\n",
-                            "      <td>3.0</td>\n",
-                            "      <td>64.41</td>\n",
-                            "      <td>3.0</td>\n",
-                            "      <td>1024</td>\n",
-                            "      <td>multilingual-e5-large-instruct</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>0.569256</td>\n",
-                            "      <td>4.0</td>\n",
-                            "      <td>62.070275</td>\n",
-                            "      <td>4.0</td>\n",
-                            "      <td>60.89</td>\n",
-                            "      <td>4.0</td>\n",
-                            "      <td>1024</td>\n",
-                            "      <td>multilingual-e5-large</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>0.555790</td>\n",
-                            "      <td>5.0</td>\n",
-                            "      <td>60.235039</td>\n",
-                            "      <td>5.0</td>\n",
-                            "      <td>59.11</td>\n",
-                            "      <td>5.0</td>\n",
-                            "      <td>768</td>\n",
-                            "      <td>multilingual-e5-base</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>0.535567</td>\n",
-                            "      <td>6.0</td>\n",
-                            "      <td>58.444347</td>\n",
-                            "      <td>6.0</td>\n",
-                            "      <td>57.04</td>\n",
-                            "      <td>7.0</td>\n",
-                            "      <td>384</td>\n",
-                            "      <td>multilingual-e5-small</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>0.531485</td>\n",
-                            "      <td>7.0</td>\n",
-                            "      <td>56.019392</td>\n",
-                            "      <td>8.0</td>\n",
-                            "      <td>57.78</td>\n",
-                            "      <td>6.0</td>\n",
-                            "      <td>768</td>\n",
-                            "      <td>all-mpnet-base</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>0.517245</td>\n",
-                            "      <td>8.0</td>\n",
-                            "      <td>57.290940</td>\n",
-                            "      <td>7.0</td>\n",
-                            "      <td>54.64</td>\n",
-                            "      <td>10.0</td>\n",
-                            "      <td>768</td>\n",
-                            "      <td>multilingual-mpnet-base</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>0.516339</td>\n",
-                            "      <td>9.0</td>\n",
-                            "      <td>54.728697</td>\n",
-                            "      <td>10.0</td>\n",
-                            "      <td>56.53</td>\n",
-                            "      <td>8.0</td>\n",
-                            "      <td>384</td>\n",
-                            "      <td>all-MiniLM-L12</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>0.512155</td>\n",
-                            "      <td>10.0</td>\n",
-                            "      <td>54.381772</td>\n",
-                            "      <td>11.0</td>\n",
-                            "      <td>56.10</td>\n",
-                            "      <td>9.0</td>\n",
-                            "      <td>384</td>\n",
-                            "      <td>all-MiniLM-L6</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>0.496854</td>\n",
-                            "      <td>11.0</td>\n",
-                            "      <td>55.130507</td>\n",
-                            "      <td>9.0</td>\n",
-                            "      <td>52.45</td>\n",
-                            "      <td>11.0</td>\n",
-                            "      <td>384</td>\n",
-                            "      <td>multilingual-MiniLM-L12</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>0.425548</td>\n",
-                            "      <td>12.0</td>\n",
-                            "      <td>48.570700</td>\n",
-                            "      <td>12.0</td>\n",
-                            "      <td>45.21</td>\n",
-                            "      <td>12.0</td>\n",
-                            "      <td>768</td>\n",
-                            "      <td>LaBSE</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model  Average_v2_full  \\\n",
-                            "0                     intfloat/e5-mistral-7b-instruct         0.626230   \n",
-                            "1                                    GritLM/GritLM-7B         0.625762   \n",
-                            "2             intfloat/multilingual-e5-large-instruct         0.611273   \n",
-                            "3                      intfloat/multilingual-e5-large         0.569256   \n",
-                            "4                       intfloat/multilingual-e5-base         0.555790   \n",
-                            "5                      intfloat/multilingual-e5-small         0.535567   \n",
-                            "6             sentence-transformers/all-mpnet-base-v2         0.531485   \n",
-                            "7   sentence-transformers/paraphrase-multilingual-...         0.517245   \n",
-                            "8             sentence-transformers/all-MiniLM-L12-v2         0.516339   \n",
-                            "9              sentence-transformers/all-MiniLM-L6-v2         0.512155   \n",
-                            "10  sentence-transformers/paraphrase-multilingual-...         0.496854   \n",
-                            "11                        sentence-transformers/LaBSE         0.425548   \n",
-                            "\n",
-                            "    Rank_v2_full  Average_v2_lite  Rank_v2_lite  Average  Rank  \\\n",
-                            "0            1.0        66.963823           1.0    66.63   2.0   \n",
-                            "1            2.0        66.448032           2.0    66.76   1.0   \n",
-                            "2            3.0        65.236396           3.0    64.41   3.0   \n",
-                            "3            4.0        62.070275           4.0    60.89   4.0   \n",
-                            "4            5.0        60.235039           5.0    59.11   5.0   \n",
-                            "5            6.0        58.444347           6.0    57.04   7.0   \n",
-                            "6            7.0        56.019392           8.0    57.78   6.0   \n",
-                            "7            8.0        57.290940           7.0    54.64  10.0   \n",
-                            "8            9.0        54.728697          10.0    56.53   8.0   \n",
-                            "9           10.0        54.381772          11.0    56.10   9.0   \n",
-                            "10          11.0        55.130507           9.0    52.45  11.0   \n",
-                            "11          12.0        48.570700          12.0    45.21  12.0   \n",
-                            "\n",
-                            "    Embedding Size                      Model Name  \n",
-                            "0             4096          e5-mistral-7b-instruct  \n",
-                            "1             4096                       GritLM-7B  \n",
-                            "2             1024  multilingual-e5-large-instruct  \n",
-                            "3             1024           multilingual-e5-large  \n",
-                            "4              768            multilingual-e5-base  \n",
-                            "5              384           multilingual-e5-small  \n",
-                            "6              768                  all-mpnet-base  \n",
-                            "7              768         multilingual-mpnet-base  \n",
-                            "8              384                  all-MiniLM-L12  \n",
-                            "9              384                   all-MiniLM-L6  \n",
-                            "10             384         multilingual-MiniLM-L12  \n",
-                            "11             768                           LaBSE  "
-                        ]
-                    },
-                    "execution_count": 27,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "all_scores_v1_plot"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 28,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "tasks = mteb.get_tasks(tasks=tasks_to_select_from)\n",
-                "\n",
-                "tasks.to_dataframe().to_csv(\"mteb_lite_tasks.csv\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# MTEB(eng) Benchmarking\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 29,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# It is possible to start the notebok from here:\n",
-                "import pandas as pd\n",
-                "\n",
-                "import mteb\n",
-                "\n",
-                "_df = pd.read_csv(\"mteb_lite_tasks.csv\")\n",
-                "\n",
-                "tasks = mteb.get_tasks(tasks=_df[\"name\"].tolist())"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 30,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/au561649/.virtualenvs/mteb/lib/python3.8/site-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
-                        "  warnings.warn(\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Getting revision for sentence-transformers/all-MiniLM-L12-v2\n",
-                        "Getting revision for sentence-transformers/all-mpnet-base-v2\n"
-                    ]
-                }
-            ],
-            "source": [
-                "def get_models():\n",
-                "    model_names = [\n",
-                "        \"sentence-transformers/all-MiniLM-L6-v2\",\n",
-                "        \"sentence-transformers/all-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/all-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/LaBSE\",\n",
-                "        \"intfloat/multilingual-e5-large-instruct\",\n",
-                "        \"intfloat/e5-mistral-7b-instruct\",\n",
-                "        \"GritLM/GritLM-7B\",\n",
-                "        \"GritLM/GritLM-8x7B\",\n",
-                "        \"intfloat/multilingual-e5-small\",\n",
-                "        \"intfloat/multilingual-e5-base\",\n",
-                "        \"intfloat/multilingual-e5-large\",\n",
-                "    ]\n",
-                "    models: list[mteb.ModelMeta] = [mteb.get_model_meta(name) for name in model_names]\n",
-                "\n",
-                "    # get missing revisions - Assuming we are using the latest revision\n",
-                "    for model in models:\n",
-                "        if model.revision is None:\n",
-                "            print(f\"Getting revision for {model.name}\")\n",
-                "            encoder = model.load_model()\n",
-                "            model.revision = encoder.model_card_data.base_model_revision  # type: ignore\n",
-                "\n",
-                "    return models\n",
-                "\n",
-                "\n",
-                "models = get_models()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 31,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# load task results for the specified models from mteb/results repository\n",
-                "mteb_results = mteb.load_results(models=models, tasks=tasks, download_latest=False)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 32,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import mteb.task_aggregation as task_aggregation\n",
-                "\n",
-                "mean = task_aggregation.mean(mteb_results)\n",
-                "weighted_mean = task_aggregation.task_category_weighted_mean(mteb_results)\n",
-                "borda = task_aggregation.borda_count(mteb_results)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 40,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import pandas as pd\n",
-                "\n",
-                "data = []\n",
-                "for model_name, revisions in borda.items():\n",
-                "    for rev, avg_score in revisions.items():\n",
-                "        total_eval_time = sum(\n",
-                "            res.evaluation_time for res in mteb_results[model_name][rev]\n",
-                "        )\n",
-                "\n",
-                "        total_co2 = sum(res.kg_co2_emissions for res in mteb_results[model_name][rev])\n",
-                "\n",
-                "        data.append(\n",
-                "            {\n",
-                "                \"model\": model_name,\n",
-                "                \"revision\": rev,\n",
-                "                **mean[model_name][rev],\n",
-                "                **weighted_mean[model_name][rev],\n",
-                "                **avg_score,\n",
-                "                \"Total Evaluation time (hours)\": total_eval_time / 3600,\n",
-                "                \"Total CO2-eq emissions (kg)\": total_co2,\n",
-                "            }\n",
-                "        )\n",
-                "\n",
-                "df = pd.DataFrame(data)\n",
-                "df = df.sort_values(\"borda_count\", ascending=False)\n",
-                "# round\n",
-                "df = df.round(3)\n",
-                "\n",
-                "df.to_csv(\"mteb_lite_results.csv\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 41,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\begin{tabular}{llrrrrrrrr}\n",
-                        "\\toprule\n",
-                        " & Rank (Borda Count) & mean & mean (weighted by task type) & mean (PairClassification) & mean (Classification) & mean (STS) & mean (Retrieval) & mean (Clustering) & mean (Reranking) \\\\\n",
-                        "model &  &  &  &  &  &  &  &  &  \\\\\n",
-                        "\\midrule\n",
-                        "e5-mistral-7b-instruct & 1 (393) & 67.0 & 67.2 & 88.4 & 75.2 & 83.6 & 54.8 & 51.4 & 49.8 \\\\\n",
-                        "GritLM-7B & 2 (384) & 66.4 & 66.7 & 87.3 & 77.0 & 82.5 & 53.2 & 50.8 & 49.6 \\\\\n",
-                        "multilingual-e5-large-instruct & 3 (357) & 65.2 & 65.6 & 86.2 & 73.2 & 84.3 & 51.0 & 49.9 & 48.7 \\\\\n",
-                        "multilingual-e5-large & 4 (270) & 62.1 & 62.4 & 84.7 & 72.8 & 80.6 & 49.0 & 42.8 & 44.7 \\\\\n",
-                        "all-mpnet-base-v2 & 5 (211) & 56.0 & 58.1 & 83.0 & 56.6 & 72.2 & 41.9 & 46.6 & 48.4 \\\\\n",
-                        "multilingual-e5-base & 6 (211) & 60.2 & 60.9 & 83.6 & 70.0 & 79.1 & 46.1 & 42.2 & 44.3 \\\\\n",
-                        "paraphrase-multilingual-mpnet-base-v2 & 7 (188) & 57.3 & 58.8 & 81.7 & 68.6 & 79.8 & 34.1 & 43.5 & 45.2 \\\\\n",
-                        "all-MiniLM-L12-v2 & 8 (172) & 54.7 & 57.0 & 82.5 & 55.8 & 70.7 & 40.7 & 44.6 & 47.5 \\\\\n",
-                        "all-MiniLM-L6-v2 & 9 (149) & 54.4 & 56.7 & 82.4 & 55.4 & 70.4 & 39.8 & 44.9 & 47.1 \\\\\n",
-                        "multilingual-e5-small & 10 (147) & 58.4 & 59.3 & 82.7 & 67.7 & 77.6 & 43.7 & 40.8 & 43.2 \\\\\n",
-                        "paraphrase-multilingual-MiniLM-L12-v2 & 11 (109) & 55.1 & 57.0 & 80.0 & 64.4 & 77.5 & 32.8 & 41.7 & 45.4 \\\\\n",
-                        "LaBSE & 12 (49) & 48.6 & 51.7 & 78.9 & 66.8 & 70.2 & 16.8 & 36.1 & 41.3 \\\\\n",
-                        "\\bottomrule\n",
-                        "\\end{tabular}\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "latex_df = df.drop(columns=[\"revision\"])\n",
-                "latex_df[\"model\"] = [name.split(\"/\")[1] for name in latex_df[\"model\"]]\n",
-                "latex_df = latex_df.set_index(\"model\")\n",
-                "\n",
-                "\n",
-                "avg_cols = [\n",
-                "    \"mean\",\n",
-                "    \"mean (PairClassification)\",\n",
-                "    \"mean (Classification)\",\n",
-                "    \"mean (STS)\",\n",
-                "    \"mean (Retrieval)\",\n",
-                "    \"mean (Clustering)\",\n",
-                "    \"mean (Reranking)\",\n",
-                "    \"mean (weighted by task type)\",\n",
-                "]\n",
-                "\n",
-                "borda_col_name = \"borda_count\"\n",
-                "\n",
-                "# multiply by 100 to get percentage values and round to 2 decimal places\n",
-                "latex_df[avg_cols] = latex_df[avg_cols] * 100\n",
-                "\n",
-                "latex_df[\"Rank (Borda Count)\"] = [\n",
-                "    f\"{rank} ({borda:.0f})\"\n",
-                "    for rank, borda in zip(range(1, len(latex_df) + 1), latex_df[borda_col_name])\n",
-                "]\n",
-                "latex_df = latex_df.drop(columns=[borda_col_name])\n",
-                "\n",
-                "\n",
-                "# column order and rename\n",
-                "cols = [\n",
-                "    \"Rank (Borda Count)\",\n",
-                "    \"mean\",\n",
-                "    \"mean (weighted by task type)\",\n",
-                "    \"mean (PairClassification)\",\n",
-                "    \"mean (Classification)\",\n",
-                "    \"mean (STS)\",\n",
-                "    \"mean (Retrieval)\",\n",
-                "    \"mean (Clustering)\",\n",
-                "    \"mean (Reranking)\",\n",
-                "]\n",
-                "\n",
-                "latex_df = latex_df[cols]\n",
-                "\n",
-                "table_latex = latex_df.to_latex(index=True, float_format=\"%.1f\")\n",
-                "\n",
-                "\n",
-                "print(table_latex)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 35,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "intfloat/multilingual-e5-small: 0.83 hours\n",
-                        "sentence-transformers/LaBSE: 1.02 hours\n",
-                        "GritLM/GritLM-7B: 3.11 hours\n",
-                        "intfloat/multilingual-e5-large: 2.55 hours\n",
-                        "sentence-transformers/paraphrase-multilingual-mpnet-base-v2: 1.02 hours\n",
-                        "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2: 0.88 hours\n",
-                        "sentence-transformers/all-mpnet-base-v2: 1.19 hours\n",
-                        "intfloat/multilingual-e5-large-instruct: 2.03 hours\n",
-                        "sentence-transformers/all-MiniLM-L12-v2: 0.81 hours\n",
-                        "intfloat/multilingual-e5-base: 1.17 hours\n",
-                        "sentence-transformers/all-MiniLM-L6-v2: 0.73 hours\n",
-                        "intfloat/e5-mistral-7b-instruct: 2.50 hours\n"
-                    ]
-                }
-            ],
-            "source": [
-                "for model, revision in mteb_results.items():\n",
-                "    for rev, results in revision.items():\n",
-                "        print(\n",
-                "            f\"{model}: {sum(res.evaluation_time for res in results) / 3600:.2f} hours\"\n",
-                "        )"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 39,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "WARNING:mteb.abstasks.TaskMetadata:Citation contains whitespace. Please ensure that the citation is correctly formatted.\n",
-                        "WARNING:mteb.abstasks.TaskMetadata:Citation contains whitespace. Please ensure that the citation is correctly formatted.\n",
-                        "WARNING:mteb.abstasks.TaskMetadata:Citation contains whitespace. Please ensure that the citation is correctly formatted.\n",
-                        "WARNING:mteb.abstasks.TaskMetadata:Citation contains whitespace. Please ensure that the citation is correctly formatted.\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\begin{tabular}{llll}\n",
-                        "\\toprule\n",
-                        " &  & languages & domains \\\\\n",
-                        "type & name &  &  \\\\\n",
-                        "\\midrule\n",
-                        "Classification & AmazonCounterfactualClassification \\cite{oneill-etal-2021-wish} & ['deu', 'eng', 'jpn'] & ['Reviews', 'Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "Retrieval & ArguAna \\cite{boteva2016} & ['eng'] & ['Medical', 'Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "\\multirow[t]{2}{*}{Clustering} & ArXivHierarchicalClusteringP2P \\cite{arXiv.org e-Print archive} & ['eng'] & ['Academic', 'Written'] \\\\\n",
-                        " & ArXivHierarchicalClusteringS2S \\cite{arXiv.org e-Print archive} & ['eng'] & ['Academic', 'Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "Reranking & AskUbuntuDupQuestions \\cite{wang-2021-TSDAE} & ['eng'] & None \\\\\n",
-                        "\\cline{1-4}\n",
-                        "STS & BIOSSES \\cite{10.1093/bioinformatics/btx238} & ['eng'] & None \\\\\n",
-                        "\\cline{1-4}\n",
-                        "Classification & Banking77Classification \\cite{casanueva-etal-2020-efficient} & ['eng'] & ['Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "Clustering & BiorxivClusteringP2P.v2  & ['eng'] & ['Academic', 'Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "\\multirow[t]{6}{*}{Retrieval} & CQADupstackGamingRetrieval \\cite{hoogeveen2015} & ['eng'] & None \\\\\n",
-                        " & CQADupstackUnixRetrieval \\cite{hoogeveen2015} & ['eng'] & None \\\\\n",
-                        " & ClimateFEVERHardNegatives \\cite{diggelmann2021climatefever} & ['eng'] & None \\\\\n",
-                        " & FEVERHardNegatives \\cite{thorne-etal-2018-fever} & ['eng'] & None \\\\\n",
-                        " & FiQA2018 \\cite{\n",
-                        "thakur2021beir} & ['eng'] & None \\\\\n",
-                        " & HotpotQAHardNegatives \\cite{yang-etal-2018-hotpotqa} & ['eng'] & ['Web', 'Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "\\multirow[t]{4}{*}{Classification} & ImdbClassification \\cite{maas-etal-2011-learning} & ['eng'] & ['Reviews', 'Written'] \\\\\n",
-                        " & MTOPDomainClassification \\cite{li-etal-2021-mtop} & ['deu', 'eng', 'fra', ...] & ['Spoken', 'Spoken'] \\\\\n",
-                        " & MassiveIntentClassification \\cite{fitzgerald2022massive} & ['afr', 'amh', 'ara', ...] & ['Spoken'] \\\\\n",
-                        " & MassiveScenarioClassification \\cite{fitzgerald2022massive} & ['afr', 'amh', 'ara', ...] & ['Spoken'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "\\multirow[t]{2}{*}{Clustering} & MedrxivClusteringP2P.v2  & ['eng'] & ['Academic', 'Medical', 'Written'] \\\\\n",
-                        " & MedrxivClusteringS2S.v2  & ['eng'] & ['Academic', 'Medical', 'Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "Reranking & MindSmallReranking \\cite{wu-etal-2020-mind} & ['eng'] & ['News', 'Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "Retrieval & SCIDOCS \\cite{specter2020cohan} & ['eng'] & ['Academic', 'Written', 'Non-fiction'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "\\multirow[t]{8}{*}{STS} & SICK-R \\cite{dadas-etal-2020-evaluation} & ['eng'] & None \\\\\n",
-                        " & STS12 \\cite{10.5555/2387636.2387697} & ['eng'] & ['Encyclopaedic', 'News', 'Written'] \\\\\n",
-                        " & STS13 \\cite{Agirre2013SEM2S} & ['eng'] & ['Web', 'News', 'Non-fiction', ...] \\\\\n",
-                        " & STS14 \\cite{bandhakavi-etal-2014-generating} & ['eng'] & ['Blog', 'Web', 'Spoken'] \\\\\n",
-                        " & STS15 \\cite{bicici-2015-rtm} & ['eng'] & ['Blog', 'News', 'Web', ...] \\\\\n",
-                        " & STS17 \\cite{cer-etal-2017-semeval} & ['ara', 'deu', 'eng', ...] & ['News', 'Web', 'Written'] \\\\\n",
-                        " & STS22.v2 \\cite{chen-etal-2022-semeval} & ['ara', 'cmn', 'deu', ...] & ['News', 'Written'] \\\\\n",
-                        " & STSBenchmark \\cite{huggingface:dataset:stsb_multi_mt} & ['eng'] & None \\\\\n",
-                        "\\cline{1-4}\n",
-                        "PairClassification & SprintDuplicateQuestions \\cite{shah-etal-2018-adversarial} & ['eng'] & ['Programming', 'Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "\\multirow[t]{2}{*}{Clustering} & StackExchangeClustering.v2 \\cite{geigle:2021:arxiv} & ['eng'] & ['Web', 'Written'] \\\\\n",
-                        " & StackExchangeClusteringP2P.v2 \\cite{geigle:2021:arxiv} & ['eng'] & ['Web', 'Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "\\multirow[t]{2}{*}{Retrieval} & TRECCOVID \\cite{roberts2021searching} & ['eng'] & None \\\\\n",
-                        " & Touche2020 \\cite{potthast_2022_6862281} & ['eng'] & None \\\\\n",
-                        "\\cline{1-4}\n",
-                        "\\multirow[t]{2}{*}{Classification} & ToxicConversationsClassification \\cite{jigsaw-unintended-bias-in-toxicity-classification} & ['eng'] & ['Social', 'Written'] \\\\\n",
-                        " & TweetSentimentExtractionClassification \\cite{tweet-sentiment-extraction} & ['eng'] & ['Social', 'Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "Clustering & TwentyNewsgroupsClustering.v2 \\cite{LANG1995331} & ['eng'] & ['News', 'Written'] \\\\\n",
-                        "\\cline{1-4}\n",
-                        "\\multirow[t]{2}{*}{PairClassification} & TwitterSemEval2015 \\cite{xu-etal-2015-semeval} & ['eng'] & None \\\\\n",
-                        " & TwitterURLCorpus \\cite{lan-etal-2017-continuously} & ['eng'] & None \\\\\n",
-                        "\\cline{1-4}\n",
-                        "\\bottomrule\n",
-                        "\\end{tabular}\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(tasks.to_latex(properties=[\"name\", \"type\", \"languages\", \"domains\"]))"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Compare CO2-eq emissions"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 42,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th>mean</th>\n",
-                            "      <th>mean (Clustering)</th>\n",
-                            "      <th>mean (STS)</th>\n",
-                            "      <th>mean (Classification)</th>\n",
-                            "      <th>mean (Reranking)</th>\n",
-                            "      <th>mean (Retrieval)</th>\n",
-                            "      <th>mean (PairClassification)</th>\n",
-                            "      <th>mean (weighted by task type)</th>\n",
-                            "      <th>borda_count</th>\n",
-                            "      <th>Total Evaluation time (hours)</th>\n",
-                            "      <th>Total CO2-eq emissions (kg)</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>07163b72af1488142a360786df853f237b1a3ca1</td>\n",
-                            "      <td>0.670</td>\n",
-                            "      <td>0.514</td>\n",
-                            "      <td>0.836</td>\n",
-                            "      <td>0.752</td>\n",
-                            "      <td>0.498</td>\n",
-                            "      <td>0.548</td>\n",
-                            "      <td>0.884</td>\n",
-                            "      <td>0.672</td>\n",
-                            "      <td>393.0</td>\n",
-                            "      <td>2.502</td>\n",
-                            "      <td>2.971</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>13f00a0e36500c80ce12870ea513846a066004af</td>\n",
-                            "      <td>0.664</td>\n",
-                            "      <td>0.508</td>\n",
-                            "      <td>0.825</td>\n",
-                            "      <td>0.770</td>\n",
-                            "      <td>0.496</td>\n",
-                            "      <td>0.532</td>\n",
-                            "      <td>0.873</td>\n",
-                            "      <td>0.667</td>\n",
-                            "      <td>384.0</td>\n",
-                            "      <td>3.111</td>\n",
-                            "      <td>3.409</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>baa7be480a7de1539afce709c8f13f833a510e0a</td>\n",
-                            "      <td>0.652</td>\n",
-                            "      <td>0.499</td>\n",
-                            "      <td>0.843</td>\n",
-                            "      <td>0.732</td>\n",
-                            "      <td>0.487</td>\n",
-                            "      <td>0.510</td>\n",
-                            "      <td>0.862</td>\n",
-                            "      <td>0.656</td>\n",
-                            "      <td>357.0</td>\n",
-                            "      <td>2.033</td>\n",
-                            "      <td>1.418</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</td>\n",
-                            "      <td>0.621</td>\n",
-                            "      <td>0.428</td>\n",
-                            "      <td>0.806</td>\n",
-                            "      <td>0.728</td>\n",
-                            "      <td>0.447</td>\n",
-                            "      <td>0.490</td>\n",
-                            "      <td>0.847</td>\n",
-                            "      <td>0.624</td>\n",
-                            "      <td>270.0</td>\n",
-                            "      <td>2.549</td>\n",
-                            "      <td>1.563</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>84f2bcc00d77236f9e89c8a360a00fb1139bf47d</td>\n",
-                            "      <td>0.560</td>\n",
-                            "      <td>0.466</td>\n",
-                            "      <td>0.722</td>\n",
-                            "      <td>0.566</td>\n",
-                            "      <td>0.484</td>\n",
-                            "      <td>0.419</td>\n",
-                            "      <td>0.830</td>\n",
-                            "      <td>0.581</td>\n",
-                            "      <td>211.0</td>\n",
-                            "      <td>1.190</td>\n",
-                            "      <td>0.688</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>d13f1b27baf31030b7fd040960d60d909913633f</td>\n",
-                            "      <td>0.602</td>\n",
-                            "      <td>0.422</td>\n",
-                            "      <td>0.791</td>\n",
-                            "      <td>0.700</td>\n",
-                            "      <td>0.443</td>\n",
-                            "      <td>0.461</td>\n",
-                            "      <td>0.836</td>\n",
-                            "      <td>0.609</td>\n",
-                            "      <td>211.0</td>\n",
-                            "      <td>1.170</td>\n",
-                            "      <td>0.691</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>79f2382ceacceacdf38563d7c5d16b9ff8d725d6</td>\n",
-                            "      <td>0.573</td>\n",
-                            "      <td>0.435</td>\n",
-                            "      <td>0.798</td>\n",
-                            "      <td>0.686</td>\n",
-                            "      <td>0.452</td>\n",
-                            "      <td>0.341</td>\n",
-                            "      <td>0.817</td>\n",
-                            "      <td>0.588</td>\n",
-                            "      <td>188.0</td>\n",
-                            "      <td>1.017</td>\n",
-                            "      <td>0.563</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>a05860a77cef7b37e0048a7864658139bc18a854</td>\n",
-                            "      <td>0.547</td>\n",
-                            "      <td>0.446</td>\n",
-                            "      <td>0.707</td>\n",
-                            "      <td>0.558</td>\n",
-                            "      <td>0.475</td>\n",
-                            "      <td>0.407</td>\n",
-                            "      <td>0.825</td>\n",
-                            "      <td>0.570</td>\n",
-                            "      <td>172.0</td>\n",
-                            "      <td>0.814</td>\n",
-                            "      <td>0.442</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>8b3219a92973c328a8e22fadcfa821b5dc75636a</td>\n",
-                            "      <td>0.544</td>\n",
-                            "      <td>0.449</td>\n",
-                            "      <td>0.704</td>\n",
-                            "      <td>0.554</td>\n",
-                            "      <td>0.471</td>\n",
-                            "      <td>0.398</td>\n",
-                            "      <td>0.824</td>\n",
-                            "      <td>0.567</td>\n",
-                            "      <td>149.0</td>\n",
-                            "      <td>0.733</td>\n",
-                            "      <td>0.391</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>e4ce9877abf3edfe10b0d82785e83bdcb973e22e</td>\n",
-                            "      <td>0.584</td>\n",
-                            "      <td>0.408</td>\n",
-                            "      <td>0.776</td>\n",
-                            "      <td>0.677</td>\n",
-                            "      <td>0.432</td>\n",
-                            "      <td>0.437</td>\n",
-                            "      <td>0.827</td>\n",
-                            "      <td>0.593</td>\n",
-                            "      <td>147.0</td>\n",
-                            "      <td>0.833</td>\n",
-                            "      <td>0.459</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>bf3bf13ab40c3157080a7ab344c831b9ad18b5eb</td>\n",
-                            "      <td>0.551</td>\n",
-                            "      <td>0.417</td>\n",
-                            "      <td>0.775</td>\n",
-                            "      <td>0.644</td>\n",
-                            "      <td>0.454</td>\n",
-                            "      <td>0.328</td>\n",
-                            "      <td>0.800</td>\n",
-                            "      <td>0.570</td>\n",
-                            "      <td>109.0</td>\n",
-                            "      <td>0.879</td>\n",
-                            "      <td>0.469</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>e34fab64a3011d2176c99545a93d5cbddc9a91b7</td>\n",
-                            "      <td>0.486</td>\n",
-                            "      <td>0.361</td>\n",
-                            "      <td>0.702</td>\n",
-                            "      <td>0.668</td>\n",
-                            "      <td>0.413</td>\n",
-                            "      <td>0.168</td>\n",
-                            "      <td>0.789</td>\n",
-                            "      <td>0.517</td>\n",
-                            "      <td>49.0</td>\n",
-                            "      <td>1.020</td>\n",
-                            "      <td>0.582</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model  \\\n",
-                            "11                    intfloat/e5-mistral-7b-instruct   \n",
-                            "2                                    GritLM/GritLM-7B   \n",
-                            "7             intfloat/multilingual-e5-large-instruct   \n",
-                            "3                      intfloat/multilingual-e5-large   \n",
-                            "6             sentence-transformers/all-mpnet-base-v2   \n",
-                            "9                       intfloat/multilingual-e5-base   \n",
-                            "4   sentence-transformers/paraphrase-multilingual-...   \n",
-                            "8             sentence-transformers/all-MiniLM-L12-v2   \n",
-                            "10             sentence-transformers/all-MiniLM-L6-v2   \n",
-                            "0                      intfloat/multilingual-e5-small   \n",
-                            "5   sentence-transformers/paraphrase-multilingual-...   \n",
-                            "1                         sentence-transformers/LaBSE   \n",
-                            "\n",
-                            "                                    revision   mean  mean (Clustering)  \\\n",
-                            "11  07163b72af1488142a360786df853f237b1a3ca1  0.670              0.514   \n",
-                            "2   13f00a0e36500c80ce12870ea513846a066004af  0.664              0.508   \n",
-                            "7   baa7be480a7de1539afce709c8f13f833a510e0a  0.652              0.499   \n",
-                            "3   4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.621              0.428   \n",
-                            "6   84f2bcc00d77236f9e89c8a360a00fb1139bf47d  0.560              0.466   \n",
-                            "9   d13f1b27baf31030b7fd040960d60d909913633f  0.602              0.422   \n",
-                            "4   79f2382ceacceacdf38563d7c5d16b9ff8d725d6  0.573              0.435   \n",
-                            "8   a05860a77cef7b37e0048a7864658139bc18a854  0.547              0.446   \n",
-                            "10  8b3219a92973c328a8e22fadcfa821b5dc75636a  0.544              0.449   \n",
-                            "0   e4ce9877abf3edfe10b0d82785e83bdcb973e22e  0.584              0.408   \n",
-                            "5   bf3bf13ab40c3157080a7ab344c831b9ad18b5eb  0.551              0.417   \n",
-                            "1   e34fab64a3011d2176c99545a93d5cbddc9a91b7  0.486              0.361   \n",
-                            "\n",
-                            "    mean (STS)  mean (Classification)  mean (Reranking)  mean (Retrieval)  \\\n",
-                            "11       0.836                  0.752             0.498             0.548   \n",
-                            "2        0.825                  0.770             0.496             0.532   \n",
-                            "7        0.843                  0.732             0.487             0.510   \n",
-                            "3        0.806                  0.728             0.447             0.490   \n",
-                            "6        0.722                  0.566             0.484             0.419   \n",
-                            "9        0.791                  0.700             0.443             0.461   \n",
-                            "4        0.798                  0.686             0.452             0.341   \n",
-                            "8        0.707                  0.558             0.475             0.407   \n",
-                            "10       0.704                  0.554             0.471             0.398   \n",
-                            "0        0.776                  0.677             0.432             0.437   \n",
-                            "5        0.775                  0.644             0.454             0.328   \n",
-                            "1        0.702                  0.668             0.413             0.168   \n",
-                            "\n",
-                            "    mean (PairClassification)  mean (weighted by task type)  borda_count  \\\n",
-                            "11                      0.884                         0.672        393.0   \n",
-                            "2                       0.873                         0.667        384.0   \n",
-                            "7                       0.862                         0.656        357.0   \n",
-                            "3                       0.847                         0.624        270.0   \n",
-                            "6                       0.830                         0.581        211.0   \n",
-                            "9                       0.836                         0.609        211.0   \n",
-                            "4                       0.817                         0.588        188.0   \n",
-                            "8                       0.825                         0.570        172.0   \n",
-                            "10                      0.824                         0.567        149.0   \n",
-                            "0                       0.827                         0.593        147.0   \n",
-                            "5                       0.800                         0.570        109.0   \n",
-                            "1                       0.789                         0.517         49.0   \n",
-                            "\n",
-                            "    Total Evaluation time (hours)  Total CO2-eq emissions (kg)  \n",
-                            "11                          2.502                        2.971  \n",
-                            "2                           3.111                        3.409  \n",
-                            "7                           2.033                        1.418  \n",
-                            "3                           2.549                        1.563  \n",
-                            "6                           1.190                        0.688  \n",
-                            "9                           1.170                        0.691  \n",
-                            "4                           1.017                        0.563  \n",
-                            "8                           0.814                        0.442  \n",
-                            "10                          0.733                        0.391  \n",
-                            "0                           0.833                        0.459  \n",
-                            "5                           0.879                        0.469  \n",
-                            "1                           1.020                        0.582  "
-                        ]
-                    },
-                    "execution_count": 42,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# plot co2 consumption"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "mteb",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.8.19"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
diff --git a/scripts/task_selection/task_selection_eu.ipynb b/scripts/task_selection/task_selection_eu.ipynb
deleted file mode 100644
index f76aba6d51..0000000000
--- a/scripts/task_selection/task_selection_eu.ipynb
+++ /dev/null
@@ -1,8037 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Task Selection for MTEB(EU)\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/au561649/.virtualenvs/mteb/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-                        "  from .autonotebook import tqdm as notebook_tqdm\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "1.12.48\n"
-                    ]
-                }
-            ],
-            "source": [
-                "from __future__ import annotations\n",
-                "\n",
-                "import mteb\n",
-                "\n",
-                "print(mteb.__version__)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Loading in data\n",
-                "We will start out by loading in the relevant data for the model and tasks of interests."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Getting revision for sentence-transformers/all-MiniLM-L12-v2\n",
-                        "Getting revision for sentence-transformers/all-mpnet-base-v2\n"
-                    ]
-                }
-            ],
-            "source": [
-                "def get_models():\n",
-                "    model_names = [\n",
-                "        \"sentence-transformers/all-MiniLM-L6-v2\",\n",
-                "        \"sentence-transformers/all-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/all-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/LaBSE\",\n",
-                "        \"intfloat/multilingual-e5-large-instruct\",\n",
-                "        \"intfloat/e5-mistral-7b-instruct\",\n",
-                "        \"GritLM/GritLM-7B\",\n",
-                "        \"GritLM/GritLM-8x7B\",\n",
-                "        \"intfloat/multilingual-e5-small\",\n",
-                "        \"intfloat/multilingual-e5-base\",\n",
-                "        \"intfloat/multilingual-e5-large\",\n",
-                "    ]\n",
-                "    models: list[mteb.ModelMeta] = [mteb.get_model_meta(name) for name in model_names]\n",
-                "\n",
-                "    # get missing revisions - Assuming we are using the latest revision\n",
-                "    for model in models:\n",
-                "        if model.revision is None:\n",
-                "            print(f\"Getting revision for {model.name}\")\n",
-                "            encoder = model.load_model()\n",
-                "            model.revision = encoder.model_card_data.base_model_revision  # type: ignore\n",
-                "\n",
-                "    return models\n",
-                "\n",
-                "\n",
-                "models = get_models()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks: 420\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# load tasks\n",
-                "eu_languages = [\n",
-                "    # official EU languages (56) - we could include the whole economic area e.g. Norway - additioanlly we could include minority languages (probably a good idea?)\n",
-                "    # germanic\n",
-                "    \"dan\",\n",
-                "    \"eng\",\n",
-                "    \"deu\",\n",
-                "    \"nld\",\n",
-                "    \"swe\",\n",
-                "    # romance\n",
-                "    \"fra\",\n",
-                "    \"ita\",\n",
-                "    \"por\",\n",
-                "    \"spa\",\n",
-                "    \"ron\",\n",
-                "    # slavic\n",
-                "    \"bul\",\n",
-                "    \"hrv\",\n",
-                "    \"ces\",\n",
-                "    \"pol\",\n",
-                "    \"slk\",\n",
-                "    \"slv\",\n",
-                "    # baltic\n",
-                "    \"lav\",\n",
-                "    \"lit\",\n",
-                "    \"est\",\n",
-                "    # finno-ugric\n",
-                "    \"fin\",\n",
-                "    \"hun\",\n",
-                "    # other indo european\n",
-                "    \"ell\",\n",
-                "    # non-indo european\n",
-                "    \"mlt\",\n",
-                "    \"gle\",\n",
-                "    # Schengen Area\n",
-                "    \"nno\",\n",
-                "    \"nob\",\n",
-                "    \"isl\",\n",
-                "    \"ron\",\n",
-                "    \"eus\",  # Basque - recognized minority language\n",
-                "    \"ron\",  # Romanian - recognized minority language\n",
-                "    \"rom\",  # Romani - recognized minority language\n",
-                "]\n",
-                "\n",
-                "\n",
-                "eu_tasks = mteb.get_tasks(\n",
-                "    languages=eu_languages,\n",
-                ")  # does not need to language - you can also filter by task types, domains, etc.\n",
-                "\n",
-                "print(f\"Number of tasks: {len(eu_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks after filtering: 381\n"
-                    ]
-                }
-            ],
-            "source": [
-                "not_include = [\n",
-                "    \"DKHateClassification\",  # # due to it being a gated dataset on huggingface (requiring to sign a form)\n",
-                "    # was added after models were run\n",
-                "    \"SouthAfricanLangClassification\",\n",
-                "    \"BrightRetrieval\",\n",
-                "    \"LitSearchRetrieval\",\n",
-                "    \"MSMARCO\",\n",
-                "    \"SpanishPassageRetrievalS2P\",\n",
-                "    \"XStance\",\n",
-                "    \"MIRACLReranking\",\n",
-                "    \"SummEvalSummarization.v2\",\n",
-                "    \"SICK-BR-STS\",\n",
-                "    \"PublicHealthQA\",  # some error in initial run of the dataset\n",
-                "    # model model had an error on this - likely contains empty examples:\n",
-                "    \"YahooAnswersTopicsClassification\",\n",
-                "    \"FrenchBookReviews\",\n",
-                "    \"SlovakSumRetrieval\",\n",
-                "    \"LegalBenchPC\",\n",
-                "    \"RomanianSentimentClassification\",\n",
-                "    \"GPUSpeedTask\",  # for speed testing\n",
-                "    \"CPUSpeedTask\",  # for speed testing\n",
-                "    \"MSMARCOv2\",  # too large to be practical for a benchmark\n",
-                "    \"SIB200Classification\",  # we will be using the SIB200 dataset for Cluster Classification so as they are the same dataset we will not include this one\n",
-                "    \"SummEval\",  # due to https://github.com/embeddings-benchmark/mteb/issues/1156\n",
-                "]\n",
-                "retrieval_to_be_downsampled = [  # TODO: Removing this list when tasks are ready\n",
-                "    \"TopiOCQA\",\n",
-                "    \"MSMARCO-PL\",\n",
-                "    \"ClimateFEVER\",\n",
-                "    \"FEVER\",\n",
-                "    \"HotpotQA\",\n",
-                "    \"HotpotQA-PL\",\n",
-                "    \"DBPedia\",\n",
-                "    \"DBPedia-PL\",\n",
-                "    \"NeuCLIR2022Retrieval\",\n",
-                "    \"NeuCLIR2023Retrieval\",\n",
-                "    \"NeuCLIR2022Retrieval\",\n",
-                "    \"NeuCLIR2023Retrieval\",\n",
-                "    \"NQ\",\n",
-                "    \"NQ-PL\",\n",
-                "    \"NeuCLIR2022Retrieval\",\n",
-                "    \"NeuCLIR2023Retrieval\",\n",
-                "    \"MIRACLRetrieval\",\n",
-                "    \"RiaNewsRetrieval\",\n",
-                "    \"Quora-PL\",\n",
-                "    \"QuoraRetrieval\",\n",
-                "]\n",
-                "not_include += retrieval_to_be_downsampled\n",
-                "\n",
-                "eu_tasks = [t for t in eu_tasks if t.metadata.name not in not_include]\n",
-                "# exlude machine translated tasks\n",
-                "eu_tasks = [\n",
-                "    t\n",
-                "    for t in eu_tasks\n",
-                "    if t.metadata.sample_creation\n",
-                "    not in [\n",
-                "        \"machine-translated\",\n",
-                "        \"machine-translated and verified\",\n",
-                "        \"machine-translated and localized\",\n",
-                "    ]\n",
-                "]\n",
-                "\n",
-                "print(f\"Number of tasks after filtering: {len(eu_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# load results from mteb/results repository\n",
-                "mteb_results = mteb.load_results(models=models, tasks=eu_tasks, download_latest=False)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "{'intfloat/multilingual-e5-small': {'e4ce9877abf3edfe10b0d82785e83bdcb973e22e': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]},\n",
-                            " 'sentence-transformers/LaBSE': {'e34fab64a3011d2176c99545a93d5cbddc9a91b7': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]},\n",
-                            " 'GritLM/GritLM-7B': {'13f00a0e36500c80ce12870ea513846a066004af': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]},\n",
-                            " 'intfloat/multilingual-e5-large': {'4dc6d853a804b9c8886ede6dda8a073b7dc08a81': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]},\n",
-                            " 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2': {'79f2382ceacceacdf38563d7c5d16b9ff8d725d6': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]},\n",
-                            " 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2': {'bf3bf13ab40c3157080a7ab344c831b9ad18b5eb': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]},\n",
-                            " 'sentence-transformers/all-mpnet-base-v2': {'84f2bcc00d77236f9e89c8a360a00fb1139bf47d': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]},\n",
-                            " 'intfloat/multilingual-e5-large-instruct': {'baa7be480a7de1539afce709c8f13f833a510e0a': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]},\n",
-                            " 'sentence-transformers/all-MiniLM-L12-v2': {'a05860a77cef7b37e0048a7864658139bc18a854': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]},\n",
-                            " 'intfloat/multilingual-e5-base': {'d13f1b27baf31030b7fd040960d60d909913633f': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]},\n",
-                            " 'sentence-transformers/all-MiniLM-L6-v2': {'8b3219a92973c328a8e22fadcfa821b5dc75636a': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]},\n",
-                            " 'intfloat/e5-mistral-7b-instruct': {'07163b72af1488142a360786df853f237b1a3ca1': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PpcPC, scores=...),\n",
-                            "   MTEBResults(task_name=TwentyNewsgroupsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=FinancialPhrasebankClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRevenueProfitSharingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FaithDial, scores=...),\n",
-                            "   MTEBResults(task_name=NYSJudicialEthicsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorQuadRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS13, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CataloniaTweetClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishPassageRetrievalS2S, scores=...),\n",
-                            "   MTEBResults(task_name=GermanPoliticiansTwitterSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIRSmall, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSubjectivityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIConfidentialityOfAgreementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowQA, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonReviewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NoRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity1LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalReasoningCausalityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISurvivalOfObligationsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkSlClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackMathematicaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBusinessLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanDPR, scores=...),\n",
-                            "   MTEBResults(task_name=NarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEstatesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Context, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExpirationDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Moroco, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBQMSumRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NusaXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIPOwnershipAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithThirdPartiesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonCounterfactualClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity2LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VieMedEVBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=InternationalCitizenshipQuestionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TelemarketingSalesRuleLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpartQA, scores=...),\n",
-                            "   MTEBResults(task_name=RTE3, scores=...),\n",
-                            "   MTEBResults(task_name=CUADExclusivityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsBenefitsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackTexRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MindSmallReranking, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHealthLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GreekLegalCodeClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalteseNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Quail, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolDictionariesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackUnixRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=DefinitionClassificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBWikimQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-OUT, scores=...),\n",
-                            "   MTEBResults(task_name=STS12, scores=...),\n",
-                            "   MTEBResults(task_name=LegalQuAD, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADWarrantyDurationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DutchBookReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCapOnLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SciDocsRR, scores=...),\n",
-                            "   MTEBResults(task_name=GreekCivicsQA, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMostFavoredNationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115FirstPartyCollectionUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPostTerminationServicesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=SICKFr, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-E, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicChatClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDVerificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OpusparcusPC, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EstQA, scores=...),\n",
-                            "   MTEBResults(task_name=YelpReviewFullClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADUncappedLiabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna, scores=...),\n",
-                            "   MTEBResults(task_name=AskUbuntuDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIExplicitIdentificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentExtractionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS-PL, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA-PL, scores=...),\n",
-                            "   MTEBResults(task_name=ARCChallenge, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=EstonianValenceClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArguAna-PL, scores=...),\n",
-                            "   MTEBResults(task_name=WikiCitiesClustering, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity4LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CzechSoMeSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PAC, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT21, scores=...),\n",
-                            "   MTEBResults(task_name=Touche2020, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLILimitedUseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STSES, scores=...),\n",
-                            "   MTEBResults(task_name=SyntecReranking, scores=...),\n",
-                            "   MTEBResults(task_name=AlphaNLI, scores=...),\n",
-                            "   MTEBResults(task_name=Itacola, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackAndroidRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STS22.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ImdbClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=DBpediaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS15, scores=...),\n",
-                            "   MTEBResults(task_name=FQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=XMarket, scores=...),\n",
-                            "   MTEBResults(task_name=MLQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=HunSum2AbstractiveRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BUCC.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADInsuranceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS14, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDomesticViolenceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IWSLT2017BitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL1, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackEnglishRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NusaX-senti, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoticeOnCompelledDisclosureLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MAUDLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGamingRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CTKFactsNLI, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115PolicyChangeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADMinimumCommitmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FiQA2018, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicenseeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DoNotTrackLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HagridRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SwedishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FinParaSTS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanSTSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AmazonPolarityClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAuditRightsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIReturnOfConfidentialInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADGoverningLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SRNCorpusBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=OralArgumentQuestionPurposeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=CanadaTaxCourtOutcomesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianParliamentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WinoGrande, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADIrrevocableOrPerpetualLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataSecurityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BSARDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115InternationalAndSpecificAudiencesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AppsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackProgrammersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115ThirdPartySharingCollectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CDSC-R, scores=...),\n",
-                            "   MTEBResults(task_name=PawsXPairClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MLSUMClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCovenantNotToSueLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=RomaniBibleClustering, scores=...),\n",
-                            "   MTEBResults(task_name=SweRecClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BlurbsClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CrossLingualSemanticDiscriminationWMT19, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsConsumerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R-PL, scores=...),\n",
-                            "   MTEBResults(task_name=RomanianReviewsSentiment, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-R, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SyntheticText2SQL, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=OverrulingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfCustomersLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PatentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterURLCorpus, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPTrainingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRenewalTermLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS17, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BulgarianStoreReviewSentimentClassfication, scores=...),\n",
-                            "   MTEBResults(task_name=MedicalQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=WebLINXCandidatesReranking, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity6LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=TenKGnadClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TRECCOVID, scores=...),\n",
-                            "   MTEBResults(task_name=AllegroReviews, scores=...),\n",
-                            "   MTEBResults(task_name=SciFact, scores=...),\n",
-                            "   MTEBResults(task_name=NorwegianCourtsBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsDivorceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAuditsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonTransferableLicenseLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=RomaTalesBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonCompeteLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADCompetitiveRestrictionExceptionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NFCorpus-PL, scores=...),\n",
-                            "   MTEBResults(task_name=CBD, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADSourceCodeEscrowLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCIDOCS, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBPasskeyRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity5LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SwednRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADRofrRofoRofnLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Context, scores=...),\n",
-                            "   MTEBResults(task_name=TweetTopicSingleClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DiaBlaBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GermanQuAD-Retrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EightTagsClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2STS, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNonDisparagementLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=STS16, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115DataRetentionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBSummScreenFDRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=EmotionClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDBPAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADTerminationForConvenienceLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackMT, scores=...),\n",
-                            "   MTEBResults(task_name=BrazilianToxicTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ArxivClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PIQA, scores=...),\n",
-                            "   MTEBResults(task_name=UCCVCommonLawLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWebmastersRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILACasedocs, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLiquidatedDamagesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PlscClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLINoLicensingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SweFaqRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackGisRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchConsumerContractsQA, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCourtsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDCertificationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAffiliateLicenseLicensorLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RedditClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PoemSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsFamilyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=GermanGovServiceRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=STSBenchmark, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Pure, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Pure, scores=...),\n",
-                            "   MTEBResults(task_name=SwissJudgementClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloProfClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Banking77Classification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTortsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CorporateLobbyingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=BigPatentClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=CUADAntiAssignmentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDSKMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FunctionOfDecisionSectionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackStatsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoSolicitOfEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=RARbMath, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADNoticePeriodToTerminateRenewalLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserAccessEditAndDeletionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BIOSSES, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MedrxivClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=Assin2RTE, scores=...),\n",
-                            "   MTEBResults(task_name=ArXivHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNeedleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Diversity3LegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DalajClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalBenchCorporateLobbying, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MasakhaNEWSClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=FalseFriendsGermanEnglish, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsImmigrationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL2Fact, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ToxicConversationsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TempReasonL3Fact, scores=...),\n",
-                            "   MTEBResults(task_name=StackExchangeClustering.v2, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEmploymentLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADVolumeRestrictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BiorxivClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=TbilisiCityHallBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADJointIPOwnershipLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADLicenseGrantLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PersonalJurisdictionLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=VGHierarchicalClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterSemEval2015, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HALClusteringS2S.v2, scores=...),\n",
-                            "   MTEBResults(task_name=PROALegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLISharingWithEmployeesLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=JCrewBlockerLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CodeFeedbackST, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=PSC, scores=...),\n",
-                            "   MTEBResults(task_name=CzechProductReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=RARbCode, scores=...),\n",
-                            "   MTEBResults(task_name=CSFDCZMovieReviewSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FeedbackQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=StatcanDialogueDatasetRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsHousingLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofReranking, scores=...),\n",
-                            "   MTEBResults(task_name=LEMBNarrativeQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=SIQA, scores=...),\n",
-                            "   MTEBResults(task_name=Core17InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=OPP115UserChoiceControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HateSpeechPortugueseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LegalSummarization, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=CUADChangeOfControlLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TextualismToolPlainLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackPhysicsRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=UnfairTOSLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SpanishNewsClusteringP2P, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-E-PL, scores=...),\n",
-                            "   MTEBResults(task_name=GerDaLIR, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CUADEffectiveDateLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SICK-BR-PC, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CUADPriceRestrictionsLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HellaSwag, scores=...),\n",
-                            "   MTEBResults(task_name=CUADThirdPartyBeneficiaryLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=Robust04InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AlloprofRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=AILAStatutes, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsTrafficLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=CosQA, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkHrClassification, scores=...),\n",
-                            "   MTEBResults(task_name=StackOverflowDupQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=PolEmo2.0-IN, scores=...),\n",
-                            "   MTEBResults(task_name=SwednClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=SNLHierarchicalClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsEducationLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SCDDAccountabilityLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=News21InstructionRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=FrenkEnClassification, scores=...),\n",
-                            "   MTEBResults(task_name=AfriSentiLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=ItaCaseholdClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LearnedHandsCrimeLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SprintDuplicateQuestions, scores=...),\n",
-                            "   MTEBResults(task_name=NollySentiBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=CQADupstackWordpressRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=ContractNLIPermissibleCopyLegalBenchClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=InsurancePolicyInterpretationLegalBenchClassification, scores=...)]}}"
-                        ]
-                    },
-                    "execution_count": 6,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "mteb_results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import mteb.task_selection as task_selection\n",
-                "\n",
-                "results_df = task_selection.results_to_dataframe(\n",
-                "    mteb_results, drop_na=False, languages=eu_languages\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
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-                            "\n",
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-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
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-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>task</th>\n",
-                            "      <th>AILACasedocs</th>\n",
-                            "      <th>AILAStatutes</th>\n",
-                            "      <th>ARCChallenge</th>\n",
-                            "      <th>AfriSentiClassification</th>\n",
-                            "      <th>AfriSentiLangClassification</th>\n",
-                            "      <th>AllegroReviews</th>\n",
-                            "      <th>AlloProfClusteringP2P.v2</th>\n",
-                            "      <th>AlloProfClusteringS2S.v2</th>\n",
-                            "      <th>AlloprofReranking</th>\n",
-                            "      <th>AlloprofRetrieval</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>WikiCitiesClustering</th>\n",
-                            "      <th>WikiClusteringP2P.v2</th>\n",
-                            "      <th>WikipediaRerankingMultilingual</th>\n",
-                            "      <th>WikipediaRetrievalMultilingual</th>\n",
-                            "      <th>WinoGrande</th>\n",
-                            "      <th>XMarket</th>\n",
-                            "      <th>XNLI</th>\n",
-                            "      <th>XPQARetrieval</th>\n",
-                            "      <th>XQuADRetrieval</th>\n",
-                            "      <th>YelpReviewFullClassification</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
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-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>GritLM/GritLM-7B</th>\n",
-                            "      <th>13f00a0e36500c80ce12870ea513846a066004af</th>\n",
-                            "      <td>0.35292</td>\n",
-                            "      <td>0.41800</td>\n",
-                            "      <td>0.26677</td>\n",
-                            "      <td>0.439404</td>\n",
-                            "      <td>0.931445</td>\n",
-                            "      <td>0.567694</td>\n",
-                            "      <td>0.671576</td>\n",
-                            "      <td>0.564118</td>\n",
-                            "      <td>0.779262</td>\n",
-                            "      <td>0.55422</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.836619</td>\n",
-                            "      <td>0.276693</td>\n",
-                            "      <td>0.924117</td>\n",
-                            "      <td>0.934731</td>\n",
-                            "      <td>0.53697</td>\n",
-                            "      <td>0.259600</td>\n",
-                            "      <td>0.784399</td>\n",
-                            "      <td>0.493916</td>\n",
-                            "      <td>0.961998</td>\n",
-                            "      <td>0.650635</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/e5-mistral-7b-instruct</th>\n",
-                            "      <th>07163b72af1488142a360786df853f237b1a3ca1</th>\n",
-                            "      <td>0.36662</td>\n",
-                            "      <td>0.34535</td>\n",
-                            "      <td>0.19001</td>\n",
-                            "      <td>0.406934</td>\n",
-                            "      <td>0.921680</td>\n",
-                            "      <td>0.597813</td>\n",
-                            "      <td>0.691183</td>\n",
-                            "      <td>0.571120</td>\n",
-                            "      <td>0.783177</td>\n",
-                            "      <td>0.54619</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.890336</td>\n",
-                            "      <td>0.287826</td>\n",
-                            "      <td>0.916219</td>\n",
-                            "      <td>0.927265</td>\n",
-                            "      <td>0.39514</td>\n",
-                            "      <td>0.287633</td>\n",
-                            "      <td>0.821737</td>\n",
-                            "      <td>0.456863</td>\n",
-                            "      <td>0.951960</td>\n",
-                            "      <td>0.618311</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-base</th>\n",
-                            "      <th>d13f1b27baf31030b7fd040960d60d909913633f</th>\n",
-                            "      <td>0.26053</td>\n",
-                            "      <td>0.20371</td>\n",
-                            "      <td>0.09611</td>\n",
-                            "      <td>0.400586</td>\n",
-                            "      <td>0.671191</td>\n",
-                            "      <td>0.407753</td>\n",
-                            "      <td>0.631008</td>\n",
-                            "      <td>0.341132</td>\n",
-                            "      <td>0.658972</td>\n",
-                            "      <td>0.34447</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.798718</td>\n",
-                            "      <td>0.241045</td>\n",
-                            "      <td>0.886177</td>\n",
-                            "      <td>0.899056</td>\n",
-                            "      <td>0.56177</td>\n",
-                            "      <td>0.167343</td>\n",
-                            "      <td>0.718563</td>\n",
-                            "      <td>0.391408</td>\n",
-                            "      <td>0.963752</td>\n",
-                            "      <td>0.597217</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large</th>\n",
-                            "      <th>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</th>\n",
-                            "      <td>0.26427</td>\n",
-                            "      <td>0.20842</td>\n",
-                            "      <td>0.10828</td>\n",
-                            "      <td>0.423193</td>\n",
-                            "      <td>0.642822</td>\n",
-                            "      <td>0.410437</td>\n",
-                            "      <td>0.636065</td>\n",
-                            "      <td>0.351508</td>\n",
-                            "      <td>0.694429</td>\n",
-                            "      <td>0.39341</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.755041</td>\n",
-                            "      <td>0.249324</td>\n",
-                            "      <td>0.905086</td>\n",
-                            "      <td>0.917812</td>\n",
-                            "      <td>0.54985</td>\n",
-                            "      <td>0.171770</td>\n",
-                            "      <td>0.749804</td>\n",
-                            "      <td>0.457246</td>\n",
-                            "      <td>0.974800</td>\n",
-                            "      <td>0.643164</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large-instruct</th>\n",
-                            "      <th>baa7be480a7de1539afce709c8f13f833a510e0a</th>\n",
-                            "      <td>0.33330</td>\n",
-                            "      <td>0.29659</td>\n",
-                            "      <td>0.15027</td>\n",
-                            "      <td>0.423486</td>\n",
-                            "      <td>0.914404</td>\n",
-                            "      <td>0.524254</td>\n",
-                            "      <td>0.669222</td>\n",
-                            "      <td>0.564657</td>\n",
-                            "      <td>0.746777</td>\n",
-                            "      <td>0.52118</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.762207</td>\n",
-                            "      <td>0.287510</td>\n",
-                            "      <td>0.918727</td>\n",
-                            "      <td>0.926954</td>\n",
-                            "      <td>0.54272</td>\n",
-                            "      <td>0.256423</td>\n",
-                            "      <td>0.806215</td>\n",
-                            "      <td>0.504125</td>\n",
-                            "      <td>0.970556</td>\n",
-                            "      <td>0.652686</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>5 rows × 381 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "task                                                                              AILACasedocs  \\\n",
-                            "model                                   revision                                                 \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af       0.35292   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1       0.36662   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f       0.26053   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81       0.26427   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a       0.33330   \n",
-                            "\n",
-                            "task                                                                              AILAStatutes  \\\n",
-                            "model                                   revision                                                 \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af       0.41800   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1       0.34535   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f       0.20371   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81       0.20842   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a       0.29659   \n",
-                            "\n",
-                            "task                                                                              ARCChallenge  \\\n",
-                            "model                                   revision                                                 \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af       0.26677   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1       0.19001   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f       0.09611   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81       0.10828   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a       0.15027   \n",
-                            "\n",
-                            "task                                                                              AfriSentiClassification  \\\n",
-                            "model                                   revision                                                            \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                 0.439404   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                 0.406934   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                 0.400586   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                 0.423193   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                 0.423486   \n",
-                            "\n",
-                            "task                                                                              AfriSentiLangClassification  \\\n",
-                            "model                                   revision                                                                \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                     0.931445   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                     0.921680   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                     0.671191   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                     0.642822   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                     0.914404   \n",
-                            "\n",
-                            "task                                                                              AllegroReviews  \\\n",
-                            "model                                   revision                                                   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af        0.567694   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1        0.597813   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f        0.407753   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81        0.410437   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a        0.524254   \n",
-                            "\n",
-                            "task                                                                              AlloProfClusteringP2P.v2  \\\n",
-                            "model                                   revision                                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                  0.671576   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                  0.691183   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                  0.631008   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                  0.636065   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                  0.669222   \n",
-                            "\n",
-                            "task                                                                              AlloProfClusteringS2S.v2  \\\n",
-                            "model                                   revision                                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                  0.564118   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                  0.571120   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                  0.341132   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                  0.351508   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                  0.564657   \n",
-                            "\n",
-                            "task                                                                              AlloprofReranking  \\\n",
-                            "model                                   revision                                                      \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af           0.779262   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1           0.783177   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f           0.658972   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81           0.694429   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a           0.746777   \n",
-                            "\n",
-                            "task                                                                              AlloprofRetrieval  \\\n",
-                            "model                                   revision                                                      \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af            0.55422   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1            0.54619   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f            0.34447   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81            0.39341   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a            0.52118   \n",
-                            "\n",
-                            "task                                                                              ...  \\\n",
-                            "model                                   revision                                  ...   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  ...   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  ...   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  ...   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  ...   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  ...   \n",
-                            "\n",
-                            "task                                                                              WikiCitiesClustering  \\\n",
-                            "model                                   revision                                                         \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af              0.836619   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1              0.890336   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f              0.798718   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81              0.755041   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a              0.762207   \n",
-                            "\n",
-                            "task                                                                              WikiClusteringP2P.v2  \\\n",
-                            "model                                   revision                                                         \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af              0.276693   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1              0.287826   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f              0.241045   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81              0.249324   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a              0.287510   \n",
-                            "\n",
-                            "task                                                                              WikipediaRerankingMultilingual  \\\n",
-                            "model                                   revision                                                                   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                        0.924117   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                        0.916219   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                        0.886177   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                        0.905086   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                        0.918727   \n",
-                            "\n",
-                            "task                                                                              WikipediaRetrievalMultilingual  \\\n",
-                            "model                                   revision                                                                   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                        0.934731   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                        0.927265   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                        0.899056   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                        0.917812   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                        0.926954   \n",
-                            "\n",
-                            "task                                                                              WinoGrande  \\\n",
-                            "model                                   revision                                               \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af     0.53697   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1     0.39514   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f     0.56177   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81     0.54985   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a     0.54272   \n",
-                            "\n",
-                            "task                                                                               XMarket  \\\n",
-                            "model                                   revision                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  0.259600   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  0.287633   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  0.167343   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.171770   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  0.256423   \n",
-                            "\n",
-                            "task                                                                                  XNLI  \\\n",
-                            "model                                   revision                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  0.784399   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  0.821737   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  0.718563   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.749804   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  0.806215   \n",
-                            "\n",
-                            "task                                                                              XPQARetrieval  \\\n",
-                            "model                                   revision                                                  \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af       0.493916   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1       0.456863   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f       0.391408   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81       0.457246   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a       0.504125   \n",
-                            "\n",
-                            "task                                                                              XQuADRetrieval  \\\n",
-                            "model                                   revision                                                   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af        0.961998   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1        0.951960   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f        0.963752   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81        0.974800   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a        0.970556   \n",
-                            "\n",
-                            "task                                                                              YelpReviewFullClassification  \n",
-                            "model                                   revision                                                                \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                      0.650635  \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                      0.618311  \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                      0.597217  \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                      0.643164  \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                      0.652686  \n",
-                            "\n",
-                            "[5 rows x 381 columns]"
-                        ]
-                    },
-                    "execution_count": 8,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results_df.head()  # inspect the dataframe"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>task</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "Empty DataFrame\n",
-                            "Columns: []\n",
-                            "Index: []"
-                        ]
-                    },
-                    "execution_count": 9,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# which tasks are missing?\n",
-                "missing_tasks = results_df[results_df.isna().any(axis=1)]\n",
-                "missing_tasks = missing_tasks.loc[:, missing_tasks.isna().any()]\n",
-                "missing_tasks  # should be empty"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Task Selection\n",
-                "\n",
-                "In this section we will do the task selection to construct a benchmark."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>task</th>\n",
-                            "      <th>Diversity1LegalBenchClassification</th>\n",
-                            "      <th>Diversity2LegalBenchClassification</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>GritLM/GritLM-7B</th>\n",
-                            "      <th>13f00a0e36500c80ce12870ea513846a066004af</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/e5-mistral-7b-instruct</th>\n",
-                            "      <th>07163b72af1488142a360786df853f237b1a3ca1</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-base</th>\n",
-                            "      <th>d13f1b27baf31030b7fd040960d60d909913633f</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large</th>\n",
-                            "      <th>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large-instruct</th>\n",
-                            "      <th>baa7be480a7de1539afce709c8f13f833a510e0a</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-small</th>\n",
-                            "      <th>e4ce9877abf3edfe10b0d82785e83bdcb973e22e</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/LaBSE</th>\n",
-                            "      <th>e34fab64a3011d2176c99545a93d5cbddc9a91b7</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/all-MiniLM-L12-v2</th>\n",
-                            "      <th>a05860a77cef7b37e0048a7864658139bc18a854</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/all-MiniLM-L6-v2</th>\n",
-                            "      <th>8b3219a92973c328a8e22fadcfa821b5dc75636a</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/all-mpnet-base-v2</th>\n",
-                            "      <th>84f2bcc00d77236f9e89c8a360a00fb1139bf47d</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2</th>\n",
-                            "      <th>bf3bf13ab40c3157080a7ab344c831b9ad18b5eb</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/paraphrase-multilingual-mpnet-base-v2</th>\n",
-                            "      <th>79f2382ceacceacdf38563d7c5d16b9ff8d725d6</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "task                                                                                         Diversity1LegalBenchClassification  \\\n",
-                            "model                                              revision                                                                       \n",
-                            "GritLM/GritLM-7B                                   13f00a0e36500c80ce12870ea513846a066004af                            0.763333   \n",
-                            "intfloat/e5-mistral-7b-instruct                    07163b72af1488142a360786df853f237b1a3ca1                            0.763333   \n",
-                            "intfloat/multilingual-e5-base                      d13f1b27baf31030b7fd040960d60d909913633f                            0.763333   \n",
-                            "intfloat/multilingual-e5-large                     4dc6d853a804b9c8886ede6dda8a073b7dc08a81                            0.763333   \n",
-                            "intfloat/multilingual-e5-large-instruct            baa7be480a7de1539afce709c8f13f833a510e0a                            0.763333   \n",
-                            "intfloat/multilingual-e5-small                     e4ce9877abf3edfe10b0d82785e83bdcb973e22e                            0.763333   \n",
-                            "sentence-transformers/LaBSE                        e34fab64a3011d2176c99545a93d5cbddc9a91b7                            0.763333   \n",
-                            "sentence-transformers/all-MiniLM-L12-v2            a05860a77cef7b37e0048a7864658139bc18a854                            0.763333   \n",
-                            "sentence-transformers/all-MiniLM-L6-v2             8b3219a92973c328a8e22fadcfa821b5dc75636a                            0.763333   \n",
-                            "sentence-transformers/all-mpnet-base-v2            84f2bcc00d77236f9e89c8a360a00fb1139bf47d                            0.763333   \n",
-                            "sentence-transformers/paraphrase-multilingual-M... bf3bf13ab40c3157080a7ab344c831b9ad18b5eb                            0.763333   \n",
-                            "sentence-transformers/paraphrase-multilingual-m... 79f2382ceacceacdf38563d7c5d16b9ff8d725d6                            0.763333   \n",
-                            "\n",
-                            "task                                                                                         Diversity2LegalBenchClassification  \n",
-                            "model                                              revision                                                                      \n",
-                            "GritLM/GritLM-7B                                   13f00a0e36500c80ce12870ea513846a066004af                            0.746667  \n",
-                            "intfloat/e5-mistral-7b-instruct                    07163b72af1488142a360786df853f237b1a3ca1                            0.746667  \n",
-                            "intfloat/multilingual-e5-base                      d13f1b27baf31030b7fd040960d60d909913633f                            0.746667  \n",
-                            "intfloat/multilingual-e5-large                     4dc6d853a804b9c8886ede6dda8a073b7dc08a81                            0.746667  \n",
-                            "intfloat/multilingual-e5-large-instruct            baa7be480a7de1539afce709c8f13f833a510e0a                            0.746667  \n",
-                            "intfloat/multilingual-e5-small                     e4ce9877abf3edfe10b0d82785e83bdcb973e22e                            0.746667  \n",
-                            "sentence-transformers/LaBSE                        e34fab64a3011d2176c99545a93d5cbddc9a91b7                            0.746667  \n",
-                            "sentence-transformers/all-MiniLM-L12-v2            a05860a77cef7b37e0048a7864658139bc18a854                            0.746667  \n",
-                            "sentence-transformers/all-MiniLM-L6-v2             8b3219a92973c328a8e22fadcfa821b5dc75636a                            0.746667  \n",
-                            "sentence-transformers/all-mpnet-base-v2            84f2bcc00d77236f9e89c8a360a00fb1139bf47d                            0.746667  \n",
-                            "sentence-transformers/paraphrase-multilingual-M... bf3bf13ab40c3157080a7ab344c831b9ad18b5eb                            0.746667  \n",
-                            "sentence-transformers/paraphrase-multilingual-m... 79f2382ceacceacdf38563d7c5d16b9ff8d725d6                            0.746667  "
-                        ]
-                    },
-                    "execution_count": 10,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# tasks with exactly the same results for all models (i.e. columns where all values are the same)\n",
-                "same_results = results_df.loc[:, results_df.nunique() == 1]\n",
-                "same_results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks before removing tasks with same results: 381\n",
-                        "Number of tasks after removing tasks with same results: 379\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# remove these tasks from the tasks\n",
-                "print(f\"Number of tasks before removing tasks with same results: {len(eu_tasks)}\")\n",
-                "eu_tasks = [t for t in eu_tasks if t.metadata.name not in same_results.columns]\n",
-                "print(f\"Number of tasks after removing tasks with same results: {len(eu_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "TbilisiCityHallBitextMining(name='TbilisiCityHallBitextMining', languages=['eng', 'kat'])\n",
-                        "BUCCBitextMiningFast(name='BUCC.v2', languages=['cmn', 'deu', 'eng', '...'])\n",
-                        "LinceMTBitextMining(name='LinceMTBitextMining', languages=['eng', 'hin'])\n",
-                        "RomaTalesBitextMining(name='RomaTalesBitextMining', languages=['hun', 'rom'])\n",
-                        "CzechSubjectivityClassification(name='CzechSubjectivityClassification', languages=['ces'])\n",
-                        "DanishPoliticalCommentsClassification(name='DanishPoliticalCommentsClassification', languages=['dan'])\n",
-                        "GermanPoliticiansTwitterSentimentClassification(name='GermanPoliticiansTwitterSentimentClassification', languages=['deu'])\n",
-                        "AmazonPolarityClassification(name='AmazonPolarityClassification', languages=['eng'])\n",
-                        "ArxivClassification(name='ArxivClassification', languages=['eng'])\n",
-                        "EmotionClassification(name='EmotionClassification', languages=['eng'])\n",
-                        "FrenkEnClassification(name='FrenkEnClassification', languages=['eng'])\n",
-                        "ImdbClassification(name='ImdbClassification', languages=['eng'])\n",
-                        "PatentClassification(name='PatentClassification', languages=['eng'])\n",
-                        "TweetSentimentExtractionClassification(name='TweetSentimentExtractionClassification', languages=['eng'])\n",
-                        "FrenkHrClassification(name='FrenkHrClassification', languages=['hrv'])\n",
-                        "ItalianLinguisticAcceptabilityClassification(name='Itacola', languages=['ita'])\n",
-                        "LanguageClassification(name='LanguageClassification', languages=['ara', 'bul', 'cmn', '...'])\n",
-                        "MTOPDomainClassification(name='MTOPDomainClassification', languages=['deu', 'eng', 'fra', '...'])\n",
-                        "MTOPIntentClassification(name='MTOPIntentClassification', languages=['deu', 'eng', 'fra', '...'])\n",
-                        "MultilingualSentimentClassification(name='MultilingualSentimentClassification', languages=['bul', 'deu', 'ell', '...'])\n",
-                        "HateSpeechPortugueseClassification(name='HateSpeechPortugueseClassification', languages=['por'])\n",
-                        "FrenkSlClassification(name='FrenkSlClassification', languages=['slv'])\n",
-                        "SpanishSentimentClassification(name='SpanishSentimentClassification', languages=['spa'])\n",
-                        "SwedishSentimentClassification(name='SwedishSentimentClassification', languages=['swe'])\n",
-                        "RedditFastClusteringS2S(name='RedditClustering.v2', languages=['eng'])\n",
-                        "RedditFastClusteringP2P(name='RedditClusteringP2P.v2', languages=['eng'])\n",
-                        "StackExchangeClusteringFast(name='StackExchangeClustering.v2', languages=['eng'])\n",
-                        "StackExchangeClusteringP2PFast(name='StackExchangeClusteringP2P.v2', languages=['eng'])\n",
-                        "TwentyNewsgroupsClusteringFast(name='TwentyNewsgroupsClustering.v2', languages=['eng'])\n",
-                        "MLSUMClusteringP2PFast(name='MLSUMClusteringP2P.v2', languages=['deu', 'fra', 'spa'])\n",
-                        "MLSUMClusteringS2SFast(name='MLSUMClusteringS2S.v2', languages=['deu', 'fra', 'spa'])\n",
-                        "LEMBNarrativeQARetrieval(name='LEMBNarrativeQARetrieval', languages=['eng'])\n",
-                        "LEMBNeedleRetrieval(name='LEMBNeedleRetrieval', languages=['eng'])\n",
-                        "LEMBPasskeyRetrieval(name='LEMBPasskeyRetrieval', languages=['eng'])\n",
-                        "LEMBQMSumRetrieval(name='LEMBQMSumRetrieval', languages=['eng'])\n",
-                        "LEMBSummScreenFDRetrieval(name='LEMBSummScreenFDRetrieval', languages=['eng'])\n",
-                        "LEMBWikimQARetrieval(name='LEMBWikimQARetrieval', languages=['eng'])\n",
-                        "EstQA(name='EstQA', languages=['est'])\n",
-                        "SyntecRetrieval(name='SyntecRetrieval', languages=['fra'])\n",
-                        "SprintDuplicateQuestionsPC(name='SprintDuplicateQuestions', languages=['eng'])\n",
-                        "XNLI(name='XNLI', languages=['bul', 'deu', 'ell', '...'])\n",
-                        "Assin2RTE(name='Assin2RTE', languages=['por'])\n",
-                        "SickBrPC(name='SICK-BR-PC', languages=['por'])\n",
-                        "STS12STS(name='STS12', languages=['eng'])\n",
-                        "STS13STS(name='STS13', languages=['eng'])\n",
-                        "STS14STS(name='STS14', languages=['eng'])\n",
-                        "STS15STS(name='STS15', languages=['eng'])\n",
-                        "STS16STS(name='STS16', languages=['eng'])\n",
-                        "SemRel24STS(name='SemRel24STS', languages=['eng'])\n",
-                        "STS17Crosslingual(name='STS17', languages=['ara', 'deu', 'eng', '...'])\n",
-                        "STS22CrosslingualSTSv2(name='STS22.v2', languages=['cmn', 'deu', 'eng', '...'])\n",
-                        "Assin2STS(name='Assin2STS', languages=['por'])\n",
-                        "-\n"
-                    ]
-                }
-            ],
-            "source": [
-                "licenses_to_remove = [\"Not specified\", \"Unknown\"]  # remove tasks with unknown licenses\n",
-                "# Note: this implicitly penalizes low-resource languages, as they are more likely to have unknown licenses - though this is probably still a reasonable choice\n",
-                "unspecified_licences = [t for t in eu_tasks if t.metadata.license in licenses_to_remove]\n",
-                "[print(l) for l in unspecified_licences]\n",
-                "print(\"-\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "['TbilisiCityHallBitextMining',\n",
-                            " 'LinceMTBitextMining',\n",
-                            " 'RomaTalesBitextMining',\n",
-                            " 'CzechSubjectivityClassification',\n",
-                            " 'DanishPoliticalCommentsClassification',\n",
-                            " 'GermanPoliticiansTwitterSentimentClassification',\n",
-                            " 'ArxivClassification',\n",
-                            " 'FrenkEnClassification',\n",
-                            " 'PatentClassification',\n",
-                            " 'FrenkHrClassification',\n",
-                            " 'Itacola',\n",
-                            " 'LanguageClassification',\n",
-                            " 'MultilingualSentimentClassification',\n",
-                            " 'HateSpeechPortugueseClassification',\n",
-                            " 'FrenkSlClassification',\n",
-                            " 'SpanishSentimentClassification',\n",
-                            " 'SwedishSentimentClassification',\n",
-                            " 'EstQA',\n",
-                            " 'SyntecRetrieval',\n",
-                            " 'Assin2RTE',\n",
-                            " 'SICK-BR-PC',\n",
-                            " 'Assin2STS']"
-                        ]
-                    },
-                    "execution_count": 13,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "import mteb\n",
-                "\n",
-                "MTEB_MAIN_EN = mteb.get_benchmark(\"MTEB(eng, classic)\")\n",
-                "\n",
-                "exceptions = [\n",
-                "    \"STS22.v2\",\n",
-                "    \"SemRel24STS\",\n",
-                "    \"XNLI\",  # assume that semrel task are fair use\n",
-                "    \"LEMBNarrativeQARetrieval\",\n",
-                "    \"LEMBNeedleRetrieval\",\n",
-                "    \"LEMBPasskeyRetrieval\",\n",
-                "    \"LEMBQMSumRetrieval\",\n",
-                "    \"LEMBSummScreenFDRetrieval\",\n",
-                "    \"LEMBWikimQARetrieval\",  # assume that LongEmbed tasks are fair use\n",
-                "    \"TwentyNewsgroupsClustering.v2\",\n",
-                "    \"XNLI\",\n",
-                "    \"StackExchangeClusteringP2PFast\",\n",
-                "    \"BUCC.v2\",\n",
-                "    \"RedditClusteringP2P.v2\",\n",
-                "    \"RedditClustering.v2\",\n",
-                "    \"MLSUMClusteringP2P.v2\",\n",
-                "    \"MLSUMClusteringS2S.v2\",\n",
-                "    \"StackExchangeClusteringP2P.v2\",\n",
-                "    \"StackExchangeClustering.v2\",\n",
-                "] + MTEB_MAIN_EN.tasks  # assume mteb tasks are fair use\n",
-                "\n",
-                "remove_due_to_license = [\n",
-                "    t for t in unspecified_licences if t.metadata.name not in exceptions\n",
-                "]\n",
-                "remove_due_to_license = [t.metadata.name for t in remove_due_to_license]\n",
-                "remove_due_to_license"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks before: 379\n",
-                        "Number of tasks after: 357\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(f\"Number of tasks before: {len(eu_tasks)}\")\n",
-                "eu_tasks = [t for t in eu_tasks if t.metadata.name not in remove_due_to_license]\n",
-                "print(f\"Number of tasks after: {len(eu_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks before: 357\n",
-                        "Number of tasks after: 343\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# remove tasks not intended to cover EU languages (these are included as they cover some languages in the EU, typically English)\n",
-                "non_eu_tasks = [\n",
-                "    \"NusaXBitextMining\",\n",
-                "    \"NollySentiBitextMining\",\n",
-                "    \"IN22ConvBitextMining\",\n",
-                "    \"IndicCrosslingualSTS\",\n",
-                "    \"IndicGenBenchFloresBitextMining\",\n",
-                "    \"AfriSentiClassification\",\n",
-                "    \"AfriSentiLangClassification\",\n",
-                "    \"PhincBitextMining\",\n",
-                "    \"NusaX-senti\",\n",
-                "    \"IN22GenBitextMining\",\n",
-                "    \"MasakhaNEWSClassification\",\n",
-                "    \"MasakhaNEWSClusteringP2P\",\n",
-                "    \"MasakhaNEWSClusteringS2S\",\n",
-                "    \"BrazilianToxicTweetsClassification\",  # not EU portuguese\n",
-                "]\n",
-                "\n",
-                "print(f\"Number of tasks before: {len(eu_tasks)}\")\n",
-                "eu_tasks = [t for t in eu_tasks if t.metadata.name not in non_eu_tasks]\n",
-                "print(f\"Number of tasks after: {len(eu_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks before: 343\n",
-                        "Number of tasks after: 233\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# remove legal bench tasks (These are English tasks focusing on legal documents)\n",
-                "legal_bench_tasks = [\n",
-                "    \"CanadaTaxCourtOutcomesLegalBenchClassification\",\n",
-                "    \"ContractNLIConfidentialityOfAgreementLegalBenchClassification\",\n",
-                "    \"ContractNLIExplicitIdentificationLegalBenchClassification\",\n",
-                "    \"ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification\",\n",
-                "    \"ContractNLILimitedUseLegalBenchClassification\",\n",
-                "    \"ContractNLINoLicensingLegalBenchClassification\",\n",
-                "    \"ContractNLINoticeOnCompelledDisclosureLegalBenchClassification\",\n",
-                "    \"ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification\",\n",
-                "    \"ContractNLIPermissibleCopyLegalBenchClassification\",\n",
-                "    \"ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification\",\n",
-                "    \"ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification\",\n",
-                "    \"ContractNLIReturnOfConfidentialInformationLegalBenchClassification\",\n",
-                "    \"ContractNLISharingWithEmployeesLegalBenchClassification\",\n",
-                "    \"ContractNLISharingWithThirdPartiesLegalBenchClassification\",\n",
-                "    \"ContractNLISurvivalOfObligationsLegalBenchClassification\",\n",
-                "    \"CorporateLobbyingLegalBenchClassification\",\n",
-                "    \"CUADAffiliateLicenseLicenseeLegalBenchClassification\",\n",
-                "    \"CUADAffiliateLicenseLicensorLegalBenchClassification\",\n",
-                "    \"CUADAntiAssignmentLegalBenchClassification\",\n",
-                "    \"CUADAuditRightsLegalBenchClassification\",\n",
-                "    \"CUADCapOnLiabilityLegalBenchClassification\",\n",
-                "    \"CUADChangeOfControlLegalBenchClassification\",\n",
-                "    \"CUADCompetitiveRestrictionExceptionLegalBenchClassification\",\n",
-                "    \"CUADCovenantNotToSueLegalBenchClassification\",\n",
-                "    \"CUADEffectiveDateLegalBenchClassification\",\n",
-                "    \"CUADExclusivityLegalBenchClassification\",\n",
-                "    \"CUADExpirationDateLegalBenchClassification\",\n",
-                "    \"CUADGoverningLawLegalBenchClassification\",\n",
-                "    \"CUADInsuranceLegalBenchClassification\",\n",
-                "    \"CUADIPOwnershipAssignmentLegalBenchClassification\",\n",
-                "    \"CUADIrrevocableOrPerpetualLicenseLegalBenchClassification\",\n",
-                "    \"CUADJointIPOwnershipLegalBenchClassification\",\n",
-                "    \"CUADLicenseGrantLegalBenchClassification\",\n",
-                "    \"CUADLiquidatedDamagesLegalBenchClassification\",\n",
-                "    \"CUADMinimumCommitmentLegalBenchClassification\",\n",
-                "    \"CUADMostFavoredNationLegalBenchClassification\",\n",
-                "    \"CUADNoSolicitOfCustomersLegalBenchClassification\",\n",
-                "    \"CUADNoSolicitOfEmployeesLegalBenchClassification\",\n",
-                "    \"CUADNonCompeteLegalBenchClassification\",\n",
-                "    \"CUADNonDisparagementLegalBenchClassification\",\n",
-                "    \"CUADNonTransferableLicenseLegalBenchClassification\",\n",
-                "    \"CUADNoticePeriodToTerminateRenewalLegalBenchClassification\",\n",
-                "    \"CUADPostTerminationServicesLegalBenchClassification\",\n",
-                "    \"CUADPriceRestrictionsLegalBenchClassification\",\n",
-                "    \"CUADRenewalTermLegalBenchClassification\",\n",
-                "    \"CUADRevenueProfitSharingLegalBenchClassification\",\n",
-                "    \"CUADRofrRofoRofnLegalBenchClassification\",\n",
-                "    \"CUADSourceCodeEscrowLegalBenchClassification\",\n",
-                "    \"CUADTerminationForConvenienceLegalBenchClassification\",\n",
-                "    \"CUADThirdPartyBeneficiaryLegalBenchClassification\",\n",
-                "    \"CUADUncappedLiabilityLegalBenchClassification\",\n",
-                "    \"CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification\",\n",
-                "    \"CUADVolumeRestrictionLegalBenchClassification\",\n",
-                "    \"CUADWarrantyDurationLegalBenchClassification\",\n",
-                "    \"DefinitionClassificationLegalBenchClassification\",\n",
-                "    \"Diversity1LegalBenchClassification\",\n",
-                "    \"Diversity2LegalBenchClassification\",\n",
-                "    \"Diversity3LegalBenchClassification\",\n",
-                "    \"Diversity4LegalBenchClassification\",\n",
-                "    \"Diversity5LegalBenchClassification\",\n",
-                "    \"Diversity6LegalBenchClassification\",\n",
-                "    \"FunctionOfDecisionSectionLegalBenchClassification\",\n",
-                "    \"InsurancePolicyInterpretationLegalBenchClassification\",\n",
-                "    \"InternationalCitizenshipQuestionsLegalBenchClassification\",\n",
-                "    \"JCrewBlockerLegalBenchClassification\",\n",
-                "    \"LearnedHandsBenefitsLegalBenchClassification\",\n",
-                "    \"LearnedHandsBusinessLegalBenchClassification\",\n",
-                "    \"LearnedHandsConsumerLegalBenchClassification\",\n",
-                "    \"LearnedHandsCourtsLegalBenchClassification\",\n",
-                "    \"LearnedHandsCrimeLegalBenchClassification\",\n",
-                "    \"LearnedHandsDivorceLegalBenchClassification\",\n",
-                "    \"LearnedHandsDomesticViolenceLegalBenchClassification\",\n",
-                "    \"LearnedHandsEducationLegalBenchClassification\",\n",
-                "    \"LearnedHandsEmploymentLegalBenchClassification\",\n",
-                "    \"LearnedHandsEstatesLegalBenchClassification\",\n",
-                "    \"LearnedHandsFamilyLegalBenchClassification\",\n",
-                "    \"LearnedHandsHealthLegalBenchClassification\",\n",
-                "    \"LearnedHandsHousingLegalBenchClassification\",\n",
-                "    \"LearnedHandsImmigrationLegalBenchClassification\",\n",
-                "    \"LearnedHandsTortsLegalBenchClassification\",\n",
-                "    \"LearnedHandsTrafficLegalBenchClassification\",\n",
-                "    \"LegalReasoningCausalityLegalBenchClassification\",\n",
-                "    \"MAUDLegalBenchClassification\",\n",
-                "    \"NYSJudicialEthicsLegalBenchClassification\",\n",
-                "    \"OPP115DataRetentionLegalBenchClassification\",\n",
-                "    \"OPP115DataSecurityLegalBenchClassification\",\n",
-                "    \"OPP115DoNotTrackLegalBenchClassification\",\n",
-                "    \"OPP115FirstPartyCollectionUseLegalBenchClassification\",\n",
-                "    \"OPP115InternationalAndSpecificAudiencesLegalBenchClassification\",\n",
-                "    \"OPP115PolicyChangeLegalBenchClassification\",\n",
-                "    \"OPP115ThirdPartySharingCollectionLegalBenchClassification\",\n",
-                "    \"OPP115UserAccessEditAndDeletionLegalBenchClassification\",\n",
-                "    \"OPP115UserChoiceControlLegalBenchClassification\",\n",
-                "    \"OralArgumentQuestionPurposeLegalBenchClassification\",\n",
-                "    \"OverrulingLegalBenchClassification\",\n",
-                "    \"PersonalJurisdictionLegalBenchClassification\",\n",
-                "    \"PROALegalBenchClassification\",\n",
-                "    \"SCDBPAccountabilityLegalBenchClassification\",\n",
-                "    \"SCDBPAuditsLegalBenchClassification\",\n",
-                "    \"SCDBPCertificationLegalBenchClassification\",\n",
-                "    \"SCDBPTrainingLegalBenchClassification\",\n",
-                "    \"SCDBPVerificationLegalBenchClassification\",\n",
-                "    \"SCDDAccountabilityLegalBenchClassification\",\n",
-                "    \"SCDDAuditsLegalBenchClassification\",\n",
-                "    \"SCDDCertificationLegalBenchClassification\",\n",
-                "    \"SCDDTrainingLegalBenchClassification\",\n",
-                "    \"SCDDVerificationLegalBenchClassification\",\n",
-                "    \"TelemarketingSalesRuleLegalBenchClassification\",\n",
-                "    \"TextualismToolDictionariesLegalBenchClassification\",\n",
-                "    \"TextualismToolPlainLegalBenchClassification\",\n",
-                "    \"UCCVCommonLawLegalBenchClassification\",\n",
-                "    \"UnfairTOSLegalBenchClassification\",\n",
-                "]\n",
-                "# ^ might be worth creating a benchmark for these tasks\n",
-                "\n",
-                "print(f\"Number of tasks before: {len(eu_tasks)}\")\n",
-                "eu_tasks = [t for t in eu_tasks if t.metadata.name not in legal_bench_tasks]\n",
-                "print(f\"Number of tasks after: {len(eu_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks before: 233\n",
-                        "Number of tasks after: 230\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# remove code tasks\n",
-                "from mteb.languages import PROGRAMMING_LANGS\n",
-                "\n",
-                "prog_langs = set(PROGRAMMING_LANGS)\n",
-                "\n",
-                "code_tasks = [\n",
-                "    t.metadata.name for t in eu_tasks if set(t.metadata.languages) & prog_langs\n",
-                "]\n",
-                "\n",
-                "print(f\"Number of tasks before: {len(eu_tasks)}\")\n",
-                "eu_tasks = [t for t in eu_tasks if t.metadata.name not in code_tasks]\n",
-                "print(f\"Number of tasks after: {len(eu_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Iterative Automated Task Selection "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "[BibleNLPBitextMining(name='BibleNLPBitextMining', languages=['aai', 'aak', 'aau', '...']),\n",
-                            " FloresBitextMining(name='FloresBitextMining', languages=['ace', 'acm', 'acq', '...']),\n",
-                            " IWSLT2017BitextMining(name='IWSLT2017BitextMining', languages=['ara', 'cmn', 'deu', '...']),\n",
-                            " NTREXBitextMining(name='NTREXBitextMining', languages=['afr', 'amh', 'arb', '...']),\n",
-                            " TatoebaBitextMining(name='Tatoeba', languages=['afr', 'amh', 'ang', '...']),\n",
-                            " MassiveIntentClassification(name='MassiveIntentClassification', languages=['dan', 'deu', 'ell', '...']),\n",
-                            " MassiveScenarioClassification(name='MassiveScenarioClassification', languages=['dan', 'deu', 'ell', '...']),\n",
-                            " MultiHateClassification(name='MultiHateClassification', languages=['deu', 'eng', 'fra', '...']),\n",
-                            " TweetSentimentClassification(name='TweetSentimentClassification', languages=['deu', 'eng', 'fra', '...']),\n",
-                            " SIB200ClusteringFast(name='SIB200ClusteringS2S', languages=['bul', 'ces', 'dan', '...']),\n",
-                            " BelebeleRetrieval(name='BelebeleRetrieval', languages=['acm', 'afr', 'als', '...']),\n",
-                            " MultiLongDocRetrieval(name='MultiLongDocRetrieval', languages=['deu', 'eng', 'fra', '...']),\n",
-                            " WikipediaRetrievalMultilingual(name='WikipediaRetrievalMultilingual', languages=['bul', 'ces', 'dan', '...']),\n",
-                            " XPQARetrieval(name='XPQARetrieval', languages=['ara', 'cmn', 'deu', '...']),\n",
-                            " MultiEURLEXMultilabelClassification(name='MultiEURLEXMultilabelClassification', languages=['bul', 'ces', 'dan', '...']),\n",
-                            " XNLI(name='XNLI', languages=['bul', 'deu', 'ell', '...']),\n",
-                            " WikipediaRerankingMultilingual(name='WikipediaRerankingMultilingual', languages=['bul', 'ces', 'dan', '...']),\n",
-                            " STS17Crosslingual(name='STS17', languages=['ara', 'deu', 'eng', '...']),\n",
-                            " STS22CrosslingualSTSv2(name='STS22.v2', languages=['cmn', 'deu', 'eng', '...'])]"
-                        ]
-                    },
-                    "execution_count": 18,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# tasks with more than N eu languages\n",
-                "eu_langs = set(eu_languages)\n",
-                "tasks_with_many_languages = [\n",
-                "    t for t in eu_tasks if len(set(t.languages) & eu_langs) > 5\n",
-                "]\n",
-                "tasks_with_many_languages"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# tasks which should be kept, e.g. due to them being known high quality datasets, unique tasks, etc.\n",
-                "tasks_to_keep = [\n",
-                "    # dataset with good coverage of languages and of reasonable quality\n",
-                "    \"WikipediaRerankingMultilingual\",\n",
-                "    \"MultiEURLEXMultilabelClassification\",\n",
-                "    \"SIB200ClusteringS2S\",\n",
-                "    \"WikipediaRetrievalMultilingual\",\n",
-                "    \"BibleNLPBitextMining\",\n",
-                "    \"MultiHateClassification\",\n",
-                "    \"XNLI\",\n",
-                "    \"TweetSentimentClassification\",\n",
-                "]\n",
-                "\n",
-                "\n",
-                "eu_langs = set(eu_languages)\n",
-                "\n",
-                "\n",
-                "def is_candidate_valid_removal(current_tasks: list[str], task_to_remove: str) -> bool:\n",
-                "    \"\"\"Determine if target task should be removed.\n",
-                "    This checks that all task types are present in the current tasks or whether the task is in the tasks_to_keep list.\n",
-                "    This is all conducted within language.\n",
-                "    \"\"\"\n",
-                "    if task_to_remove in tasks_to_keep:\n",
-                "        return False\n",
-                "\n",
-                "    # check if removing task removes a unique task type - if so, don't remove\n",
-                "    _current_tasks = current_tasks.copy()\n",
-                "    if task_to_remove in _current_tasks:\n",
-                "        _current_tasks.remove(task_to_remove)\n",
-                "    task = mteb.get_task(task_to_remove)\n",
-                "    ctasks = mteb.get_tasks(tasks=_current_tasks)\n",
-                "\n",
-                "    # don't remove a unique task type\n",
-                "    task_types = {t.metadata.type for t in ctasks}\n",
-                "    if task.metadata.type not in task_types:\n",
-                "        return False\n",
-                "\n",
-                "    # check that removing the task does not remove a unique task type within the language\n",
-                "    _languages_covered_by_task_type = [\n",
-                "        t.metadata.languages for t in ctasks if t.metadata.type == task.metadata.type\n",
-                "    ]\n",
-                "    languages_covered_by_task_type = {\n",
-                "        lang for sublist in _languages_covered_by_task_type for lang in sublist\n",
-                "    }\n",
-                "    # reduce to eu languages\n",
-                "    languages_covered_by_task_type = languages_covered_by_task_type & eu_langs\n",
-                "\n",
-                "    task_langs = set(task.metadata.languages) & eu_langs\n",
-                "\n",
-                "    if not task_langs.issubset(languages_covered_by_task_type):\n",
-                "        return False\n",
-                "\n",
-                "    return True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 20,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: STSES: 100%|██████████| 230/230 [00:03<00:00, 58.11it/s]                                    \n",
-                        "Task: STSES: 100%|██████████| 229/229 [00:03<00:00, 65.13it/s]                                   \n",
-                        "Task: STSES: 100%|██████████| 228/228 [00:03<00:00, 65.85it/s]                                   \n",
-                        "Task: STSES: 100%|██████████| 227/227 [00:03<00:00, 66.77it/s]                                   \n",
-                        "Task: STSES: 100%|██████████| 226/226 [00:03<00:00, 68.12it/s]                                   \n",
-                        "Task: STSES: 100%|██████████| 225/225 [00:03<00:00, 69.17it/s]                                   \n",
-                        "Task: STSES: 100%|██████████| 224/224 [00:03<00:00, 70.26it/s]                                   \n",
-                        "Task: STSES: 100%|██████████| 223/223 [00:03<00:00, 67.70it/s]                                   \n",
-                        "Task: STSES: 100%|██████████| 222/222 [00:03<00:00, 69.59it/s]                                   \n",
-                        "Task: STSES: 100%|██████████| 221/221 [00:03<00:00, 72.04it/s]                                   \n",
-                        "Task: STSES: 100%|██████████| 220/220 [00:03<00:00, 71.93it/s]                                   \n",
-                        "Task: STSES: 100%|██████████| 219/219 [00:03<00:00, 69.72it/s]                                   \n",
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-                    ]
-                }
-            ],
-            "source": [
-                "from sklearn.linear_model import LinearRegression\n",
-                "\n",
-                "# remove tasks one by one\n",
-                "tasks_to_select_from = [t.metadata.name for t in eu_tasks]\n",
-                "\n",
-                "tasks_removed = []\n",
-                "predicability_scores = []\n",
-                "\n",
-                "while tasks_to_select_from:\n",
-                "    most_pred_tasks = task_selection.most_predictable_task(\n",
-                "        results_df[tasks_to_select_from],\n",
-                "        sklearn_estimator=LinearRegression(),\n",
-                "        metrics=[\n",
-                "            task_selection.spearman,\n",
-                "            task_selection.pearson,\n",
-                "            task_selection.mse_with_zscore,\n",
-                "        ],\n",
-                "    )\n",
-                "\n",
-                "    # reverse the list to get the least predictable task\n",
-                "    most_pred_tasks.reverse()\n",
-                "\n",
-                "    while most_pred_tasks:\n",
-                "        most_pred_task = most_pred_tasks.pop()\n",
-                "        most_pred_task_name = list(most_pred_task.keys())[0]\n",
-                "\n",
-                "        # if the task is too hard to predict, skip it (this essentially stops the loop)\n",
-                "        if (\n",
-                "            most_pred_task[most_pred_task_name][\"mse_with_zscore\"] > 0.5\n",
-                "            or most_pred_task[most_pred_task_name][\"spearman\"] < 0.8\n",
-                "        ):\n",
-                "            continue\n",
-                "\n",
-                "        if is_candidate_valid_removal(tasks_to_select_from, most_pred_task_name):\n",
-                "            tasks_to_select_from.remove(most_pred_task_name)\n",
-                "            tasks_removed.append(most_pred_task_name)\n",
-                "            predicability_scores.append(most_pred_task[most_pred_task_name])\n",
-                "            break\n",
-                "\n",
-                "    if not most_pred_tasks:  # if no task was removed, then we are done -- can be replaced with another stopping criterion\n",
-                "        break"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 21,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 3500x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# plotting the predictability scores with the tasks removed\n",
-                "import matplotlib.pyplot as plt\n",
-                "\n",
-                "# make the plot wider\n",
-                "plt.figure(figsize=(35, 6))\n",
-                "\n",
-                "for metric in [\"spearman\", \"pearson\"]:\n",
-                "    plt.plot([t[metric] for t in predicability_scores], label=metric)\n",
-                "\n",
-                "plt.xlabel(\"Tasks removed\")\n",
-                "plt.ylabel(\"Correlation (predicted vs. observed performance)\")\n",
-                "plt.legend()\n",
-                "\n",
-                "# add vline for 0.8 spearman\n",
-                "plt.axhline(y=0.8, color=\"r\", linestyle=\"--\")\n",
-                "\n",
-                "# add task names to the x-axis\n",
-                "plt.xticks(range(len(tasks_removed)), tasks_removed, rotation=90)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 22,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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9DphtofPHNN9OZguqVFqn/Qzum84Wq+GGx6dznm6bDbQa0yaZbELzrddbeZt/w5kd1oJ7yhVLjznNtzuHCN+uB1POc2V3dG5g3o/dAkyw1zRaeyHWFqr+OVdonUV75vEw30JDwzTfTma8ar6dkSQNzWm+te9zyqcGWmnuYopJH0K/lmVVm+Gb0XxL+BZ+IHwLAAAAAAA8dd+wHb69cHtf9bLBRIdOSXSoYkm/ejzZrKEBAABAta2y52vOCwYD1Q+qm7GlNlpfNdBwQvjLtHlVrFrYBGvTwYlM9c+WJR2dov0W60OxXFHSCcA2Y4HKYs/fbuh3tqdP50u033vg6GRO+VJFkXBQm3tjrtymec3GQvfWY1lWNQjYE5vbPjvgPNbGPWpdlWohei+bb8OhoCIhO4aWbaE5w7xWjS8SvjWh5HS+pHzJ3bFXKpaOO823g4la+LknZs/dEx4Ffk9ULFfmhFEnfQj9ZgplmTV4Xj1Xzce0SzdjYQzWn5YI337kIx/Rtm3b1NHRoYsuukj33Xffgsd+/etf1wUXXKCenh7F43Gdd955+vznP+/jaAEAAAAAQCNM+PYZ2/rmXG7ab/ccmfJ7SAAAAHBYllX9QDqxQHOeaSlK0nyLeZitfE9ss2oLBavnlFdb+KI1HBzPzvn7oYnsAkcCa8vs4FKyCYsMkh433yY6wmoL2fX34z5ui75e7B1NSZLO2NCpUDDgym12VptvWyf4CNtMsVJthj+p+bbT+5Zp03xrgr5eiUbs9ttcoXXeN2QWWCg2WyIarj4O3W6/ncgWVKpYCgTm/vz7fG6+PbHp1o/mW/Pc2BYKqKPNv4gizbfwU12vwr75zW/WfYOXXXZZQwP40pe+pOuvv14f//jHddFFF+nWW2/VC17wAj3yyCMaHBw86fi+vj69/e1v15Of/GRFIhF961vf0pVXXqnBwUG94AUvaOi+AQAAAACAt1IzRf3q8WlJc5tvJencLT36zsMj2n1kuhlDAwAAgOa2ES30gbQJ9dB8i/mY82K+rXz7O9uVnClpPF3QjpM/9sMacWLY9jDhW6wTswNazWjXS3kcvg0EAuqPt+tYckbj6bw29UQ9uZ/16pERO3y7Y7DTtduM03zbskwIMBwMKO4EVA3TujrmYfjWBOi9bL6VpFgkpOlcUdlC6wTAq7s0RBaeKwOBgHpjEY2l8xrP5HVKd4dr9z+SnJFk/57bQrUAqtklwo8GWunk82sy6/3zlnluTHS0KRBwZ5FBPUzz7UyxonyprPZwaIlrAMtX16uwl7zkJXP+HggEZFnWnL8b5XJjE+gtt9yiq6++WldeeaUk6eMf/7j++7//W5/+9Kf11re+9aTjf+/3fm/O39/0pjfp3/7t3/TjH/+Y8C0AAAAAAC3m/kNTqljSaX0xndo990OaczbZzbcP0nwLAADQNKa1NBQMKNo2/4eSptG0GVtqo/Wl8guHv/riEQ2PZTSxShoTD41n1dEW1GDCvcDFenBwPCNJCgakikX4FuvH7C3im/EcaRY/eLmVd39nxAnfro55fDV50FmM/tSNCddus7Pdfi2X5jVby5nK2Y+h7ujJIcRa8603OwWUypVqwNMEfb1imm9bK3xrj2Wx5ltJ6o/b4Vu3X7eOJu3f61Bi7s++12m+nXC5aXch5v9rY3eHHp+eUa5YVq5Qrv7OvJDMOTtkRL17nppPV3tYgYBkWfYYNnQRvoV36up0rlQq1a/vfOc7Ou+88/Ttb39bU1NTmpqa0v/8z//o/PPP1+23397QnRcKBf3iF7/QJZdcUhtQMKhLLrlEP/nJT5a8vmVZuvPOO/XII4/od3/3d+c9Jp/PK5lMzvkCAAAAAAD+uG94XNLJrbeStGuzHb49MJ7VtA8r7QEAAHCyWnAnvGAbkQlVNmNLbbQ+EzibL9DQ5zR6rYbtyqdzRb3wn3+oP/7oPXNKiLC0g+N22PaczT2STm7CBdaqOc23TXiONPNvd9Sb5lvJbjCXalvWwz27D09JsneGcotpvk0XvA/fTmQK+vHeMZ4z62T+7bM7dnIIcaDT29dLk9miLEsKBKTeee7fTXGnXTbXSuFb5/1OfJ5dGmbrjds/G7fDt6b5duiExV3mdzHlU/OtWTByWn9M4aD9vs/r1t3Z7zX9FAwG1OX8vpvRTI/1pa7w7WxvfvOb9c///M96wQteoEQioUQioRe84AW65ZZbdO211zZ0W2NjYyqXyxoaGppz+dDQkI4dO7bg9aanp9XZ2alIJKIXvehF+vCHP6znPe958x578803q7u7u/q1ZcuWhsYIAAAAAACW777hCUnShdtODt/2xCI6rS8mSXrw6LSv4wIAAIAtaYKTi3wYbZqKaL7FfNKLnEMmTLIamm8ffjypbKGso1M5jaYImTXChG1/Z8fAnL8Da93ErObbTKGsUrni231bluVL8+3AKlpEsZqMpmb0+PSMgoHazlBuMOHCTN7712zv+M+H9P/71L26e9+45/e1Fkw5Af3ueRpATRutV823JnTZG4soHGo4JtYQ06Ka8SEAXq+02aVhifCt+T24H761f/6DXSc03zrz64Rf4Vunwby/s716316Hb03wNeHh89RCzHvYaRaQwmMNz6qPPfaYenp6Trq8u7tbBw4ccGFIS+vq6tIDDzygn/3sZ3rve9+r66+/Xnfddde8x77tbW/T9PR09evw4cO+jBEAAAAAgPVupljW7sN2qHa+5lup1n67+8iUX8MCAADALPUEd0xTUYrWIMwjlV/4Q/Vq8+0qaEx85Fht98z9xzNNHMnqMlMs65jT6PbbO53w7XiWJkSsCyduFZ72IfBoZAplVZyHmZeNgv2dq2ceX032OP9etmOwc8k2zkZ0+Ri+3T9mP1c+djzt+X2tBSYA2DNP+LbPeZyNpb0JQprQpVkU5aWYE77NtlLzbd4eS73Nt5Muh29HU/brpMGTmm/t38eUTzvCmVBxfzxSbd2dzHh738mcPRclPGxoX4gJurN7C7zWcPj2Gc94hq6//nqNjIxULxsZGdFf//Vf68ILL2zotgYGBhQKhebclrm9U045ZcHrBYNB7dixQ+edd57+8i//Ui972ct08803z3tse3t7taHXfAEAAAAAAO/tPjylQrmiwa52be2PzXvMuc62pHsI3wIAADRFtQlqkeCO+V6S5lvMo9p8O8851Gea3FZBY+IjI7Xw0IFxwrf1OjKZlWVJ8UhI5zlbp6fyJVrGsC7Mbr6VaiEjP5gFMaFgQNG2kGf3099pGjlbfx5fTcwidPPvYm4x4UITNvTSlNOYOUYwuy7T2YWbbweqr5fynixeMb8j0+zqJRO+zbVU+Nb+2c/3WnU2r163mubbocQJzbdOAHYqW1Cl4v2iJdOA3B9vrwZ/PW++zTWx+baD5lv4o+Hw7ac//Wk98cQTOu2007Rjxw7t2LFDp512mo4ePapPfepTDd1WJBLR05/+dN15553VyyqViu68804985nPrPt2KpWK8nme0AEAAAAAaCX3DU9IsltvA4HAvMec4zTfPnhk2rdxAQAAoCblBCcTi3wYbT64TNJ8i3ksFuDuN9vproLw7aMjqeqfh8cI39br4HhWknRaf1wdbaHqlsqHJrLNHBbgi4kT2gr9fJ40z99dHeEF/83FDWYeH1sF8/hq8sDhKUnSuc6iBbfUwrfen4vmuZ3wbX2qzbexk9tnTcP0TLHiSWOsCc/3+9B8G22zz8FWar7NOGH0ziWab/tMG6zLgVTTfDvUNbf51pwLFcuf5w9zHvR1Rqq7U3gdvk3lTfOt/+HbavMtC0jhsYZ7nXfs2KE9e/bou9/9rn7zm99Ikp7ylKfokksuWdaLuuuvv15XXHGFLrjgAl144YW69dZblclkdOWVV0qSXvOa12jTpk3VZtubb75ZF1xwgc444wzl83n9z//8jz7/+c/rYx/7WMP3DQAAAAAAvHPfgVr4diFP3dStQEB6fHpGx1N5bejyvoEBAAAANaY5b7EPo7uc8G2KDy4xD/OB9nznUN8qCd9alqVHj9XCt/uPE76tlwnfbu2zdzs5rS+m0VRehyay2uVyoyPQak5uvvUzfGvf12LN9W4YqDbfErB0i2VZ2uMsQj/P5fCteS7OeNx8myuUlS9VJEljtCLXZSpn/5zmCyHGIiF1tAU1U6xoPF2ohqjdYhpPzePZS7Xm29Z535Be5LXqbH0eNX2PJJ3wbWJu+DYSDqqzPax0vqTJbHHeYLabzOvx/nikel+TGW+ft8zzYpfL53Q9EtHwnDEAXlnW2R0IBPT85z9fv/u7v6v29vYVraS6/PLLdfz4cb3zne/UsWPHdN555+n222/X0NCQJOnQoUMKBmsFvZlMRn/+53+uI0eOKBqN6slPfrL+7//9v7r88suXPQYAAAAAAOCuUrmiXxyclLR4+LazPawzNnRq32haDx6d0nOePOTXEAEAAKDah9Fdi2wFaoI9fHCJE+VLZRWc8M1855AJ37q9fa/bnpieqTZzSdLwWLqJo1ldTMPt1v5a+PbnBydpvsW6MJE5sfnWv7CZua+udm/bBE1TptthtPXs4HhW07miIuGgnnRKl6u3HW+3g4/pvLfn4sSstkyab+sznbN/Jz3zhG8DgYD64+06OpXTeCav05znVLeMpWqhS6/FnHOwVZpv86WyCmX7tWrnEosV+mLut8GWK5aOp+zHyGDi5PBzb7xN6XxJE5mCtg/EXbvf+YzPCt/2xb1p+T2RafRtavMt72HhseDSh8xVqVT0nve8R5s2bVJnZ6eGh4clSe94xzv0qU99almDuOaaa3Tw4EHl83nde++9uuiii6rfu+uuu/TZz362+vcbb7xRe/fuVS6X08TEhO655x6CtwAAAAAAtJhfPZ5UtlBWd7RNZw4u/kHCrs3dkqTdh6f9GBoAAABmSc7atnoh5sNSmm9xovSsc2K+NjET2prMFGRZlm/jatQjI3brbcJ5HByayKrkBDWwuIPjdkuwCQptcRpwDxO+xTow6QSZTJukH9uGG6k6nr/d0G+aIDP5lp7HV5PdR6YkSWdvTKgt1HBkZ1HmubhQqqjo4fPYZIbwbaOmnJBj9wIhxAEPg+6m+bbfj+bbNqd9uUXCt7NboOORJcK3HuzYMJ7Jq2JJwcD84edeJ/A75XEIVqo1mPd3Rqr36/XuFEkndG5aaP2UcBYGThO+hccafia/8cYb9dnPflbvf//7FYnUJoanPvWp+td//VdXBwcAAAAAAFan+4YnJEnP2NanYHDxHXPOdbYi3eN8+AAAAAD/pBppvvUxVITVwTTrxSMhheZ53W9CDKWKVf3wvRU9eswO3/7Ozg2KhIMqli0dnco1eVSrw0HTfNtnt7Wd5oRvab7FWmdZVjW0tM0Jn/vZrpdynpMXe/52gwmLFcuWr82+a9kDh6ck1f49zE3xWQthMh62385uyzStqlicmR96YvM/ZmcH3d025gR6zaIoL8UidvNtrtAa84V5HMQWeK06m3ndOpktqlJxZ7HBaNL+fQ50tis8T9i+x6cQbKFUqc7h/fH2avjWt+Zbj5+r5tMdI3wLfzQcvv3c5z6nT3ziE3r1q1+tUChUvfzcc8/Vb37zG1cHBwAAAAAAVqd7nfDthdt7lzz2HKf59sGj07SoAAAA+MyEdxbbhtW0gdJ8ixOZc2Kh86c9HKq28HkRJnGLab598ild2t5vh0iHxzLNHNKqUK5YOjJhh5S3OuFD04BL+BZrXaZQ28p8qzNv+Pk8ae7L6zbBjrZZ8zgNp67Yc8Te+em8LT2u33ZbKKj2sB0D8vJ8nB0UzBXLyrZI0LKVTTkBwIWab03QfczD5tsBH8K3USd8m22R5lvzOIjPs0PDiXrj9u+mXLFcW3Q4kpyRJA0lOub9fp8TEJ3KehsQNSHbUDCg7mhb9f/V6/BtPQs9vWIeaywghdcaDt8ePXpUO3bsOOnySqWiYpETFgAAAACA9a5SsfSzAyZ827/k8WedmlA4GNBYuqDHp2e8Hh4AAABmMc2liUXDt/YHl6mZIoulMEc1fLtIoMGLLXzd9qgTvj3zlC5tHyB8W69jyRkVyhWFgwGd2m2HSkzz7eNTM55ueQ4024QTkOtoC2ooYTdW+hnwSfnYJmjaMsdbeB5fLYrlih46aodvdzmL0d1mnpMzHgZiTwwK0n67tOl6m2+9CN+a5tt4u+u3faJq822xNcK35r1OVx3h29mLxtx63TriNN8Ods3/s+/xqYF2zFk80RuLKBgM1JpvM94+b5nGZ68XiszHPD/SfAuvNRy+Peuss/SjH/3opMu/+tWv6mlPe5orgwIAAAAAAKvX3tG0pnNFxSIhnb0xseTxHW0hnTnUJUl68MiUx6MDAADAbLU2ooU/EDVNRRXLbvoDjGqgYZHwlwnftmpoq1yxtHckLUl60lCXtm8gfFuvg+P2z2hzb7S6lfKGzna1h4MqVyw9McXiSqxdE9lamM3Mgcmcf+2f5r4We/52i2nkpPl25R45llK+VFGiI6xtTmOy20zDZybvT/OtJB3n3FhUpWJVA4CJBZpvB6ohd3d/ltlCqdpCO7BAANRNsRZrvjWPg3qabyX3F42NpuzXQoMLNd/G/Qnfmv8fM5/3+hD6taxag7AfC0VOZB5rfj43Y31q+JXYO9/5Tl1xxRU6evSoKpWKvv71r+uRRx7R5z73OX3rW9/yYowAAAAAAGAVuW94XJL09K29agvVt+733C3deviJpHYfmdYLn3qql8MDAADALKY5r7N94Q9EO9qCCgcDKlUspWaKi7acYn0x589i4a/+Fm++PTieUb5UUUdbUFv6YjTfNuDQeFaSdNqsAFkwGNCWvpj2jaZ1aCKr0/pjzRoe4KkJJyDXG2+rNvo1o/nWl/Ct08g55kEj53qz54jdenvulh4FgwFP7sOEDNN578KPUycE9sYI3y4qlS/JbB7RvUD4ttow7fLjzNxeeziouBOM9VI0Yp9/rRK+TeWX3qVhtt54RIcmsq4335qG9JPuz2lC9rqB1vz/mLBvr/PfbKGsmWJZHW3unxszxYqKZfvEXyh07qVu57mZ5lt4reHm2z/6oz/Sf/3Xf+l73/ue4vG43vnOd+rXv/61/uu//kvPe97zvBgjAAAAAABYRe4dnpAkPWNbX93X2bW5R5K0h+ZbAAAAX9XTfBsIBKofmJrjAWl28+3C54/bDWJue3QkJUnaOdilUDBQDd/uP074dikHJ+zw7da+uQHb05y/H3K+D6xFE05Qqi/eXm30S/kavl26edwtAx6FAtej3YenJEm7Nnd7dh9dfjTfZuee65wbi5t2fl7RtpDaw/OHHPvjJuTubpDZ7Dww0NmuQMCbwPdsJuCbK7TGe4ZGm2/dXjQ2mrSbb4cWaL41IdgJj5tvzeIJE/JOdIQVchYATGW9ee4yz4nBgHwJfp+o9v61qErF8v3+sX4saxnU7/zO7+i73/2u22MBAAAAAACrnGVZus8J3164vf7w7Tmb7A8d9hyZlmVZvvxjMAAAAGptUEs153V1hDWRKShJcxBmMeGvxdrE+lo8tPXIsbQk6cyhLkmqhm8fn8551gS2Vpjm2639hG+x/pjm275YW1O2tq5n8YxbTChwPEO76Urtdhadn+ssQvdCvN1+3kp7GL6ddIKJHW1BzRQrNN8uwTRv9sQWDsubxUrjLi9WGnd+NyZ06bWoE7LMtEjzbbrBubI35m4YdiRlwrcLNd/a93dim7TbzHOWCRcHAgH1xto0li5oIlPQKd3zh4NXIlltaG9ryr/1m4UxFUtKF0rVvwNua7j59vTTT9f4+PhJl09NTen00093ZVAAAAAAAGB1OjSR1Wgqr0goqPO29NR9vSed0qVIOKjUTEkHxvmAFgAAwA/5UlmFUkXS0s15tVa/1mixQmuop3mx1iDWmsEc03z7pFM6Jdnj7eoIy7IIjy7l4ITdDnzaCc23W5y/H+bnhzXMNN/2OnOGVAsa+WF2qMlr/S2+iGK1yBZK1eecRv7NrFGm4TPt4Wu2SScoeMYG+7mT8O3ipnL2z6s7uvDjdaDTDmdOZAqutnSa3415Pea1WMQ+/3KtEr7NL71QbDYz3024NN+NJO2f/2DX/OFWE8g2zyleGa8239ZCwF4Hf6edBSmJqPeLRObT0RZSe9iORbKAFF5qOHx74MABlcsnT5L5fF5Hjx51ZVAAAAAAAGB1utdpvT13S3dDDVFtoaDOOjUhSdrjtIAAAADAW7ODtEt9IN2MYBFan9lOdtHm22pjYmuGth6phm/t9yOBQECnO+23+49nmjauVmdZlg5Wm2/jc75H8y3Wg9ktgs1YoOJr860T1iJguTIPHU2qYkmndndocIEt6N1gnpMzPjTfmtZ4zo3FmebbxcK3pvm2XLGqx7thbJ7QpZdiTvNttlCSZbkXIl4uE76N1xm+dbP5tlSuVJuHBxdovjW/96lswdOfl3kd3jcrhO12y++JzPvGZjbOmmZ6Nx9TwInqfiX2zW9+s/rnO+64Q93d3dW/l8tl3Xnnndq2bZurgwMAAAAAAKvLfU749hnb+hq+7rmbu/XA4SntOTKtPzpvk9tDAwAAwAlMI1o8ElIouPhWoLXwLc23qDGBhsXCX7Xm29YL3+ZLZQ2P2QHbJzkBIknaPhDX7iPT1e/hZFPZYjX8d2LzLeFbrAezm29Nq19qpqhKxVJwiedUN6R8DDUNOPN4qy6iWC12H56SJO3a3L34gStUbb4teNl8a59/OwZN8y3nxmKmskuHbyPhoBIdYSVnShrP5NXrUlNtrfHUn+bbqBO+rVhSvlRpqJzBC5k6XqvO5ubr1vFMQRVLCgUD6o/PH741AdhSxVI6X/Kszdz8/8xuQO6N2/dlHs9u83ORyEK6o206nsormeM9LLxT9xn+kpe8RJK92vOKK66Y8722tjZt27ZNH/jAB1wdHAAAAAAAWF1M+PbC7Y2Hb3dt7pF0kOZbAAAAn9Q+EF36Q95aqx+tQaip50P1vhYO3+4/nlG5YinREdbQrEay7QN2mGh4LN2sobU8E6wd7GqvBm2MLX1RSXbL2HS2qO5Y8xrPAK/M13xbsaRMwbvwlGFZVnXxQ8LH5ttWnMdXkwecf+86d0uPp/fjdfNtrlBWrmjvlk3zbX1M62bPEs+HA53tdvg2XdCOQXfue9yZqwYWCH+6LTYrbJsrlJsevk3la4sN62Fet066MN+NJGckSRs62xdc6NjRFlK0LaRcsazJTNGz5w/TwDu7AdkEf934f51PMtcCzbfOcyTNt/BSsN4DK5WKKpWKTjvtNI2Ojlb/XqlUlM/n9cgjj+jFL36xl2MFAAAAAAAt7Nj0jA5NZBUMSE/f2tvw9U3zx0NHkypXmr8tGQAAwFpngrT1tBF1NWFLbbQ+057c2b70NsrjGW+3012OR46lJElPOqVLgUAtFLFtwG5upfl2YQed8O3W/thJ34tFwhpwwh2HJ2m/xdpkmgJ7YxG1h4OKhOzohR/Pk5lCWeafTbwO+kq1xszJbEGlcsXz+1urTPPteZt7PL2fWvi27MntTzpb1IeDAW1zngPGUoRvF2OCf4s130q1x5qbLdN+N9+GQ7X5MFv05hxshAmhd9Y5V/a62PQ9krQfF7MXeM17nzHTQOvdAgfz/9M3p/m2Nrd7IWka2pc4771kHnNJwrfwUN3hW2N4eFgDAwNejAUAAAAAAKxi9x2wW2/P3ti9rA9/Tt/QqXjEXum/b5SGKQAAAK8lTXCyrvCtfQwfXGK2VB1b+ZqwR6FUUabQ/BDGbI+M2OFb09xnnF5tviV8u5BD4/bP5rS++LzfP81pvzUNucBaU2sRjCgQCCgRdZ4nfWiIN8/F4WBAHW0NRz4a1huLKBCQLMu77cnXuvF0XkcmcwoEpKc6i8+9EnfCt14FwU1QrzceqS60SM6UlC+11nN8K5nOmubbxQOw/U477biLTcKmlXig05/mW0mKtdsts7lC8xft1RaK1dd82+9F821Xx6LHmfNiwqMQbL5Urs4HA7NC2H2eN9+ahvYWCN+yews81PArsWuvvVYf+tCHTrr8tttu05vf/GY3xgQAAAAAAFah+4bHJUnP2Na3rOuHggGdvcn+AGKPsxUfAAAAvJOuBieX/kDUNBbRfIvZTHvyYgHuWCSsqLPl8ES6tbYsf3RW8+1spvl2LF3gw/oFHBxfuPlWkk7rsy8/TPgWa1CxXKkuYDHbdpvnUhM28pJ5Lu7qCM9p7fZKKBio/n+aLezRmD1HpiVJpw/EPQ+ixZ2QoWn8dNtkxrQ+t6k72qZw0D4HJzwK8K0FUzn7Z7NUA6hZsDTm4usl03jqV/OtJMXazDnY/EC2eb+z2C4Ns5k22EyhrJkVNveOpuprvjVttFMehW/NYzYUDMyZf3qcxt0JjxZVNLLLilfMY26aBaTwUMPh26997Wt61rOeddLlF198sb761a+6MigAAAAAALD63DdsN99euH154VtJOnezCd9OuzImAAAALKyRD0TNMSmCiJjFBBoSS5xDJlQw1mKhrYWab7s62rShyw5KHKD9dl4HJ+oL39J8i7XINH8GArXGwoSPDfG152//2gRNG+R4iy2iWC0eODwlSTp3S4/n99XpNN9mPGodrTbfxiIKBgO1wGiKc2MhJvjXvWT41mm+den1UqViVUPRfjbfRiN2+DbbAjsemNeq8TqbbxMdYdcC5aNO8+1QYqnmWycEm/Hm+cO0H/fF7ces4XXo1yxSWSp07qVq861Lz835UlmViuXKbWHtaDh8Oz4+ru7uk2vwE4mExsbGXBkUAAAAAABYXSYzBT06kpYkPWNb77JvZ9fmHkk03wIAAPih2pzXvnT4thoqovkWDsuyqufQUm1iJpjTSs236XxJRyZzkqQnnRC+laTtA3FJ0jDh23kdcppvTcj2RFsI32INMy2CPdE2hZwgU7UhPu9H+LbWfOuXWiNnay2iWC12O//OdZ6P4du0V823TlDPBPf643aok3NjYVPZ2pyxmIFOd0PuU7miyk5Q0LRX+yEWsc/BXLH57xtqO33UN18GAoFq++1Kw7cj1fBtc5tvzf+HWURhmMUjXrVWm8DrUov0vGSaft1qvv33ew/p/Bu/q9u+v9eV28Pa0HD4dseOHbr99ttPuvzb3/62Tj/9dFcGBQAAAAAAVpefHbBbb3cOdlZbGpZjl9N8++snUiqUKq6MDQAAAPNrpPnWfHBJ8y2MmWKlGujorLP5tpW2pN7rtN4OdrVXQxazne6Eb/cfJ3x7opliWcecQMnW/vi8x5hQ7mHCt1iDTCtl36y5wzxPJnPeh82STdjKu9rI2UKLKFYLy7KqOzyd6yw691LcNN96FL41z+UmuDfgNMUfJ3y7oGS9zbdxdx9n487vpDvapki44XjYsrVK861lWdXHwVILxWbrdy18a//8B5dsvrXvb9Lj8G3fCa93a6Ffb97fmeeqlmi+dWkB6d37xjWVLSoU9O/xhNbX8Kux66+/Xtdcc42OHz+u5zznOZKkO++8Ux/4wAd06623uj0+AAAAAACwCtw3bIdvn7G9b0W3c1pfTN3RNk3ninrkWErnbD559x0AAAC4o9YEtfQHol3V8G3zG6zQGkwQOxCQ4pHFt/I1H+6Pt1D49lEnfPukU05uvZWkbTTfLsgEarvaw+qNzT9/mObbI5M5lStWtR0UWAtM8+3sIJMJwrq1tfVias23/gWaBqrzOAHLRh2ZzGkiU1BbKKAnnzr/c46bqs23Hr1mM0G9vrh9/rnd1roWmdbNngWeM41qw7RLj7Mx53dibtcvsRYJ3+ZLFRXL9kKxePvir1Vn63UpDDuashcqDXYt0XzrnBfmucVtppX6xMIM8xounS+pUKq4HtBuRkv7iRJR+77daL4tlSu6d/+4JOlZO/pXfHtYOxo+w1/3utcpn8/rve99r97znvdIkrZt26aPfexjes1rXuP6AAEAAAAAQOu7z2m+vWiF4dtAIKBdm7v1o71j2nN0ivAtAACAh5INfCDqZ6gIq0Oq2iQWViCweLCy1iDWOqGtR46lJUlnDs0fhNpO+HZBB8ft8O1p/bEFf/dDiQ5FQkEVyhU9MZ3T5t6Yn0MEPDUxX/Ot066X8qhtdDYTaEr4GL6l+Xb5Hjg8JUk669SE2sP1BwCXq9p8WyirUrEUdHnxg2nRNAHFDc65MUbz7byK5YoyTgh1qeZbt4PMJiw/EF/+LmXLEY/Y52CuyeHb2e3PZkz16HPh91AsV6qLzoaWaL41OzB43Xzbf0LzbaKjTcGAVLGkqWxhyYbeRpn3jX4+V53IPDe78R72waPTSuVLSnSEdfZGPrNAzbJi63/2Z3+mI0eOaGRkRMlkUvv37yd4CwAAAADAOpXOl/Srx5OSpGdsW1n4VpJ2OYHbPYenV3xbAAAAWJgJ75iGtMWYDy4zhbLKFcvTcWF1SDcQ/uoz2yi3UPPtIyP2e5gnLRC+Pd0J3x4Yy8iyOOdnO+g0327tXzhQGwoGtLk3Kkk65BwPrBUT8zTfJnxtvrXvw882wWojJ+Hbhu05MiVJOndLjy/3N/t1XbbofvjRBARN+HaA8O2iTONmILB0W7V5vTSdK6pQqqz4vseb1HwbdZpvM4Xm7phhdvmIR0INhdD7XGi+HUvnZVlSOBio3t5CemJmkZo382v1PDghfBsMBqqP4wkPgr9J57lqqdC5l8z7FDeab+95zG69/a3T+9nRAXOsqDN6w4YN6uzsdGssAAAAAABgFbr/4KTKFUtb+qLa2BNd8e3t2twjSdrtfDgBAAAAb9TCO0t/IDo74OPVNsZYXRoJb9eab1sntFVtvj1l/vCt3epqt1gSNpvr0LjdBrylb/E2W/P9w4RvscbM13xrnktN2MhL5j4SfoZvq4soCFg2arezuNz8e5fXOtqC1WBYxoMm5mr4Nm6f87VgNufGfKayzuvt9vCSgb2eqN1EKrnTgjru/E78Dt/GnPBts5tvTfi2s8G50sztK1k0NpK0f/aDXe1LBn9NONecK24z/x9985wHPTH7cez2a/RCqaKZoh0gb2bzrQn+uvHcfPe+MUnSs3YMrPi2sLbUNcOcf/75uvPOO9Xb26unPe1pi24dc//997s2OAAAAAAA0PruG56Q5E7rrVRrvt07mlauUK62JQAAAMBdtebSpT8uagsF1dEW1EyxouRMUd2x5n2IitaQztffvNjXYuHb8XS+GhLaOTh/0VB7OKTNvVEdnshpeCyjDV3+btncyqrNt33xRY87zQnf0nyLtWbCCUj1zmozTERN8633C1TM4od6Fs+4ZcCFbdjXo1K5ogeP2uHb87b4s015IBBQPBJScqakdL6kIZdvfzIz9/w3zbecG/MzjZs9S7SfSnYTaV+8XWPpvMbTBQ0lOlZ032PO6y7zO/KL+bfcbLPDt85cGa9jodhs5nXr5IrCtzOSpA11/A5NAHYyW5BlWYtm8pbDLJo4sflWsv9fHzuecT34m5oVdm00/Owm8551plhRvlRWe3h5nzPMFMv6+cFJSdKzdvS7Nj6sDXWd4X/0R3+k9nZ7Mn7JS17i5XgAAAAAAMAqY8K3F213J3x7SqJDG7radTyV18NPTOvpW925XQAAAMzVaHgn0dGmmWLel1Y/tL7kTP1tYn0tFtp6dMRuvd3SF100kLF9oNMJ36Z1oUvvd9aCQ+NO+LZ/8ebbWvg25/mYAD+Z5tvZbZKm2S/lw3Nk7fnbx+bbasCSdtNG7B1NK1csq7M9rNMH/NtVurM9bIdvPditwDSymoCiCXbSfDu/6Zz98zINnEsZ6IzY4VsXWqZrzbf+hm9jbfbc1PTwrdN827XM8O1Kmm9HU/bPfqiOxVvm/vKlinLFsmIRd+d2s/htvvPAhMLdXiCXnLVDxlKNz17qjIQVCEiWZQfhB7uWF769/+CkCqWKBrvadcYG/+ZyrA51PWJvuOGGef8MAAAAAADWt5liWQ8cmZIkXbjdnVXfgUBAuzZ1687fjGrPEcK3AAAAXjEBoXrbiLo6whpN5X1p9UPrS8/6UH0p/S3WfPvoSEqS9KShrkWP294f0w8l7R/L+DCq1aFcsXR40g7fmnDtQrbQfIs1aiJzcvOtWciS9CDseCLz/O1n860JGmcKZXYpasAe59/Mdm3uXnLreTeZhSWZvLvn40yxXA1UmtDeQFftOb5csZoatGtFtebb+h6v/S4uWDK3MTBP46mXYs78kCs09z2DCd82o/l21Gm+rae9OBYJKRIKqlCuaDJbdD98m54bmJ+tz3kcT2VdDt865309O6x4KRgMKNHRpulcUclcSYOLv/Rf0N2PjUmSnrVjwPVmYqx+wWYPAAAAAAAArF57jkyrUKpooLNd25ZoPWrErs091dsHAACA+8oVSxknPFFvc16Xj61+aH2NNCebD/tzRTu01WyPOOHbM5cK3w7EJUnDxwnfGk9M51QsW2oLBbSxJ7rosSace5jwLdYYE8jqj9daBBNR+7nUBI681Izm2672sCIhO17iRiPnevHAYfvftcy/c/nFhA3TLodvzdb0oWCgGqrri0UUCEgVq3UW2bQS8zNL1Nl8a+YVN5qEx5rUfGvC+a3SfFvPQrHZ+lxYNDZSDd8u/bMPBALqjdvnx0oCv/PJl8pKOT+Hgfg8zbfO/ZpFJW4xO6XUe957yTw/T6/g+fnufeOSpIvPcKd8BGtLXTNMb29v3cntiYmJFQ0IAAAAAACsHvcN2//wdNH2PldXfe/a0i1J2u00hAAAAMBds8MY9YZ3zIenKR9a/dD60nnTvLj0+dPphLYK5YrGM3ltjri3cG85Hj3mNN+eskT41tlW9sA44Vvj0LgdpN3cG1uy3XBLnx3OncgUlJop+trSCXjFsqxqIMuEpSQp0VF7jrQsy9NmvGaEbwOBgPo7I3piekbj6YI29zZ3Hl8tdh+ekiSd5/w7l19M2DDjcvNo9dyPtVXP8XAoqN5YRBOZgsbSeW3o8jfo2eqqzbf1hm9N860LIUzTfGtu0y/xdqf5ttjc8G1mheHbyWxBlYq1rNbqkaQdfB6so/lWspvUR5J5TbrcQGses+FgoBpCnc2r5lvzPJVogdd+3dE2HVauGghuVHKmWG0xf9aOARdHhrWirhnm1ltvrf55fHxcN954o17wghfomc98piTpJz/5ie644w694x3v8GSQAAAAAACgNd07bC/CvXB7n6u3u2uT/aHE/uMZPqQFAADwgGmvjYSCag/Xt3W0Cfks94NLrC0mwN1VR6AhEAioLx7RseSMJjLNDW1ZllVtvl0qfHu603x7YDzLVtqOg06LrWm1XUxXR5v64nYg6/BETmdt5H0dVr9MoaxCuSLpxOZb+/wulCvKlyrqaKvvuXU5mtUoWA3f0nxbl1yhXH2+OXdLj6/33VltvnU3/GgCer2xuWHOgU57rjdhT9SY5tvuOh+vA05L7fgKm29nios3nnop2uaEv11uXm5U2gmAdja4UMGc3xXLDk/3xhsPL5vm28E6w+g9MdNA6+5jyDwm++KReReFmP+3CZfDt6YF3s9FIgsxAeDlNtPfu39CFcveEWOpXR+wPtV1ll9xxRXVP7/0pS/Vu9/9bl1zzTXVy6699lrddttt+t73vqfrrrvO/VECAAAAAICWUypXdP/BSUnuh2/7O9u1qSeqo1M5PXh0WhefwapyAAAANy2nNc9sL0zzLSQp2WCgwYRv3WhyW4knpmeUmikpHAzo9IHORY/d2BO1G3tLFT0+ldOWOgKna91Bp/l2a399P4stfTFNZAo6NJHVWRsTXg4N8MWEE2TqaAtWt1aXpHgkpGDADmslc0XPwreVilVb/OBzqMmEjccIWNbl4SemVa5YGuxq1yl1tl+6Jd7uTfjRBPRODCMOdLbr0ZG0xlYYGF2LTODPhCuX0u/8bFcaZF6q8dRLMWduzBaa23xrwueNNt9GwkF1tYeVypc0kS0sK3x7PGU/FobqfOybtl0T1naLed3dt8D/gwkaT7p8v81aJDIfE3xfbvj27n1jkqSLz+h3bUxYW4KNXuGOO+7QC1/4wpMuf+ELX6jvfe97rgwKAAAAAAC0voefSCpTKCvREdaThhZvjFqOc50t+R48Mu36bQMAgNZmWZYePDKtmSZvVbqWLSe4U9tSm+Zb1NrE6t2lwmx5PNHk0JZpIdw+EFckvPhHpaFgoBoyHR7LeD621eDQhP1zqKf5dvZxh53GXGC1M+HD/hOaJAOBQHU+THq4SCVTKMmy7D/7vZ23mcdpN63PA4ftf8/atbln3sZJL3W22+HHtMvn4mTGNN/OPff6O00wm/DtiaZyjTXfVn+WK1ysZB6n/Z3zN556yYRvc01+L5fO2z/7eIPhW0nqM69bl/F7KJQq1dBrveHbnmoI1t35dcJpKjfz94n64vZ5Oeny4rhkzp57Ei3UfDu9zPDtPY/Z4dtn7aAcBPNrOHzb39+vb3zjGydd/o1vfEP9/aS8AQAAAABYL+4bnpAkPWNbn4IebL96zqYeSdIewrcAAKw7H73rMf3hbT/WR3+wr9lDWbNMgLbe4KR9rP3hqfkwFeubOYfqbRMzjVtub6fbqEeP2eHbM0+pbwHhtoG4JMK3Rq35Nl7X8af12dvzHiJ8izXCBJl64yc/f5p2yaSHi1RMsLctFFD7EgsI3DbghALHCVjWZffhKUnSec7icj+ZsGHa5eZb0455YovmgBPsO865cZLpavi2vvbUWsh9ZT/LMRO6PGGhgB+iLdJ8m3Gab5fTEr6S163mcdAWCpwUVF/w/kz41uXXydUQ9gLnQY9H95tqpebb2PIXxoymZvToSFqS9MzTyURifg3PMO9617t01VVX6a677tJFF10kSbr33nt1++2365Of/KTrAwQAAAAAAK3JhG8v3N7nye2fu9n+cGL3kSlPbh8AALSmY9Mzuu37duj2l05oAe5LOR8+NrINqwnqpvI036Lx9mQTYhhvcvjWNN/Wu3vH6YRvqyzL0qFq+Lax5lvCt1grJjImfHhykKmrvU1SbtlbW9dj9uIZv9ss+1tkHl8tzL9nnbulx/f7NuHbjMvhWxNENIE9oxbM5tw40ZTTZFpv8+1A3J2fpbn+QJf/4dtYxD7/ck0O36ac8z8eWUb4Nrb88O1IckaSNNjVUfc83eMERE3A3S1mvj4xMG+Y/89UvqRiuaK2kDuLOpIzje+y4hXTvju9jJ/tTx4blySddWpCvQv8DIGGHzWvfe1rdffddyuRSOjrX/+6vv71ryuRSOjHP/6xXvva13owRAAAAAAA0GoqFUs/O+Bt+PbsTXb49shkruntWAAAwD//9J1HqluUmoZFuG85H4iaRr+Uh9tpY/VINXgO9VcbxJrbiveoE749s87w7XYnfLuf8K0ms8VqkMWEapeyxTnuMOFbrBFmDuubp82w1nzr3fNko3Ovm/qdgOUY7aZLmsoWqq9jdzk7O/nJnB+ZgrvnogmS9p0Qvt3AubGgaWfHiJ56G1Cd5ttcsazsCn5/pjl3oAmBwVi1+bYky7J8v3/DhM87fW6+HTXh20T9wede00Cbdbv51jkPOuc/DxLRNpl8sJv3bRahJBrYZcUrJvi+nFb6u/eNSZKetYPWWyxsWa/ILrroIv2///f/3B4LAAAAAABYJfYdT2syW1S0LaSnbvJm+7zuaJtOH4hr/1hGe45M6feeNOjJ/QAAgNbx0NFpfe3+I9W/H53KqVSuKOxSAw9q0tXwTv0fiNqNfvK00Q+rR7rB9mQT2mrmwrpyxdJeZ+vYJ53SWPj2AOFbHRy3fwZDiXZ1tIXquo4J6R6ZzKlcsRQK+tvUCbhtseZbEzJKLSPgU69a820zwrdO8y3tpkvafWRakv0c0l1n6NJNpukznXe3eXTCaY48sQHSnBuEb+eyLEvTucaab+ORkNrDQeVLFY2nC4r1Le+xbn4X/QuELr1kwrcVS8qXKnW/ZnBbo69VZ1tR+DZl/+yHujoavj+3w7cT1ebb+YPAoWBAPdE2TWaLmsoWNdjAmBdjgq6JOs97L5kxTDf4HtayLN29z26+vXjHgOvjwtqxrH+teuyxx/R3f/d3etWrXqXR0VFJ0re//W396le/cnVwAAAAAACgNd07bLfenr+1x7XtqOaza7Md7H3Q+dACAACsXZZl6cb/fliWJf3huRsVCQdVrlh6fGqm2UPz3dfvP6I3f/GXype826p1OeEdcyzNt6hULKULjQW4+1pgu/JDE1knBBKsu7l1+wY7fHtkMuvpY3I1OOS0127ti9d9nVO7owoHAyqUK9VtmIHVrNp8G5+v+dYsUvGh+bbd/0DTgBPeGm9yg/lqsPvwlCTp3M3eLFhfStwJG6ZdDoJPOs/hvScEigdM822KYPZsuWJZxbLd/Fpv820gEKj+PFfymsmE5M3iJz/FIrX3F9lC8147pfMrD99OLuN3YF7vDDXQfGvOj8mMu4/ZsbQJ3y4cwu5dQdB4IeZ5sBWab5cbvj00kdXRqZzCwYAu3ObNzn9YGxr+dOx///d/dc455+jee+/V1772NaXT9urQ3bt364YbbnB9gAAAAAAAoPX8zAnfXrjN2y2XztncI6nWGAIAANau7z48op/un1B7OKi3/sGTq8G4Q+twq/L33/6I/vOBx3Xv/gnP7mM521ZXQ0WEb9e9TKEks4twvedQf7z5jYmPHEtJknYOdtXdwLqhs12d7WFVLOnwOpyPZjNbqJ/WX19wWbIb1Tb3RiWtz/kca49pvj2x+VOqzYfL2dq6XuY5OBFtbvNtM7eSXw32HJmSJJ27pacp92/ChhmXm29NK+eJ5/9AVy2YzblRM+U0BbeFAoo20P5ae6wtP+g+5gQp+xcJXXolFAwoErbjaNlC8943mPBtfBnh294VLBobSdq/t8FE6zTfDizSgNwbs7835eJ9N7Ol/UTd1fewjT033/OY3Xr7tNN6lnUOYf1oOHz71re+VTfeeKO++93vKhKpPTif85zn6Kc//amrgwMAAAAAAK3HsizdZ8K3271d9W0aQsyHFgAAYG0qlCq6+du/kSRd9f+x999xktz1nT/+qo7T3RN6pift7M7OzuYgabUoLJJIxkLC4CBhH8Y2FueIwx3nL+dw3JczNhgTzA8wDvhrDD4j7Ds4Y2EfBiSMkTFKq7BKu9o4s7OTc0/nXL8/qt7VvbMTKnyqu7r6/Xw89gHanemq6an+fKo+n9f7+X7tKHZGQ1r4dmKltVq9r2WKmFNtSXZaImkz2oz51s5QEdMc0PXj90oI+vRtN1pp3yuKC/NK+PbgQIfu75EkCXt6lfFobLG1xqP1UPh2RKc1mBhu4WIKxn2Q+XajQBsZ/pI2zpPVQFP9bYI0jpcqsq1232ZHlmU8P6kUkd+kFpXXm0hQCXrSfC2Kqvn22uufPg/FMl8btZBpsysUgCTpK/oBxBQsUXC3twHmWwAIB5RrMNsg860sy6aed4iYhTBs1XyrP3wbVT9TmUIZuaK494zuu7c034bpHl3c3FUtFHGA+VadL9cyxn6+xy4tAQDu3Ncr/JwYd2E4fPvSSy/h/vvvv+7v+/v7sbS0JOSkGIZhGIZhGIZhGIZxLpMrWcwlcvB7JZzYHbX1WEeHOuGRgIVknluUMgzDMIyL+dKTExhfSqO3PYBffcN+AGhZ8+15NRwIKPdAdmEmvEObp4VSBflS41rIMo2HzMntQZ/uMElMbVeeypcadv2Q+fbQYLuh7xvtVb5+fKm1w7dX1WIII+ZboDqeT7XYeM64k1U1vLM+fAjUGOJtDB+aMdeLos3vRYdq/1tO23eP0uzMrOWwlMrD55FwbKizIedA10daoHU0XyojrQYpe9Zd/7XXxqIFW6vbIPNtl0FTdUwNzC5Z+JxRcDe2hfHUTsKq6TfToPBtvlRBuaJYmNutmG9NBKAXyHzboT/43Nnm07oyxA2GRDcjVyxrAeTYFiHs7rAyd4my7pbKFe24nQ4y3ybzJVQq+szclYqMJ1Tz7V37OXzLbI3h8G00GsXs7Ox1f3/69Gns3LlTyEkxDMMwDMMwDMMwDONcnhpXFp5u2hVFm4GWaWYIB3yaleqFybitx2IYhmEYpjHEMwX88XcuAgD+6z2HtM1RLXy73FphrfNzCe3/L9oYvk3UhCf10h7wgXKWFP5hWhMtfGtgQ70z5INPDRU0yn573oT5FgBGeyMAOHyrmW9jEUPf16rFFIw7IZvkRoG2ehjiG2m+BYCedvOt2FsFWr86vKPD9nWzzaAW6el8CbKsL2y2HRQI9Egbh7971aDhEodvNch8G90grL8VNL6YNd/KsqwF5LcKXdpJKNDY8C3dq0pS1cJrhJiFjg0LSePmW0mShIdg6dz9XmnLECxZcVcFjeu1xu1GzVW1dKrhd1lWArh6OD+fxHK6gJDfi5uHozaeHeMGDIdv3/GOd+B3fud3MDc3B0mSUKlU8Nhjj+E3f/M38cADD9hxjgzDMAzDMAzDMAzDOIinr6wAAG4f7anL8W7a1QUAeGl6rS7HYxiGYRimvvzxdy5iLVvE4cEOvP3WYe3vR2KtGda61nxrn/nfjDnP45HQHlCDRVn7gkWM89HCX0H9G+qSJFmyiFklXypr4dlDg8bCt3s5fItsoazZuEd6jJlvhzl8y7iEYrmiFa9saL5VQ0Z2FqiQVbdRNsGYNo5zwHIzXpiKAwCO74o27BwofFssy8iXKkJek4J83eEAPJ7rrfe9FgOjbmQtq7wXZN7US6/aLcDs5yyRK6FYVkLX9JmtN3QNZouNKdhLqyFLpXhQX5eGWuieNVssI2sgQJwvlTVD+kCnseAzhbRFhWDps9gTCWz5HtBxVwSFfmkObPN7EPAZjiUKJ+jzos2vnIfeZ9jHLi0BUPY/nPAzMM7G8BXyh3/4hzh8+DCGh4eRSqVw9OhRvO51r8Odd96J97///XacI8MwDMMwDMMwDMMwDuLUeH3DtzeqmxUvTHH4lmEYhmHcxthiCg8+MQEAeP9bj2qtNoGa8O1yRpixqxk4P1cTvk3YF2xJ5c2Z86ilNptvWxsyWhkx3wLWLGJWGVtMo1yR0dHmw6ABExnA5lugGpztaPMhGjY2blTNt1nh58Uw9YRshJK0scmS7Hp2FqhUzbcNCt+2k92UA5abQebbhoZvA9XrI63T9LgddP13bxLm7G1n8+16NPOtwfBtzKJhmkK7HUFfw+zLIfW46XxjzLd0rxox0OWjlo6gD36v2rHBQCiVnt8CPo/h0HUPhW8zYuYQsh/3RLYOAfdElPOMCzouXfedDrDeEnQuazrn58cvK53/7tofs+2cGPdgKHwryzLm5ubwmc98BmNjY/j617+OL33pSzh37hwefPBBeL2NGbQZhmEYhmEYhmEYhqkP84kcrixnIEnALSPddTnmcdV8++JUvKWCNwzDMAzTCnzkm+dQqsh44+F+vOZA7zX/tqtbCWsl8yVhG5BOR5bla8O3SfvCCxSeNWrOo7APh29bm5TJ66engeHbC6pV+tBAh2ED2h41fLuQzF/TSreVmFhWgscjsbDh94/Mt0upPDKF1nz/GHewmq4G6bwbmD8p3JPI2Rm+JXN9Y0JNbDfdmnJFxktq8fjxBrYq93ok4eFHuv67NynAoMAoh2+rUJix03D41lrInUK79DtpBOGAcv0ZscaKxGyhGCFJkmY4N2Kipc4lA51Bw/dLVNwkykBL99vb2Y/p5xR1f05zoNHr3k4oCK1nfi6WK3hqTAnf3rmvd5uvZhjA0CgjyzL279+PM2fO4MCBAxgeHt7+mxiGYRiGYRiGYRiGcQ3PTqwCAI4Mdtatev3QYAf8XgnxTBFTq1lt45ZhGIZhmObm8ctL+PbZeXg9Ev77Ww5f9+9tfi8GO9swl8jh6kpGC+25mblETmtnDSibt7Ism2qVuhWyLGvhHaMb0hS+tTNYxDgf7foxaBOjz7FZk5sVKNh+cLDD8Pd2hfyIRQJYThdwZSmNG3Z2iT49x0Pm25GeiOHv7Qr50RXyYy1bxORKFodM/A4YxglULYIb35No4dusfSHzavi2QeZb1aBI7wVzLZcXU0gXyggHvNjf397Qc2lv8yFbLAsrGtHMtxtYnwE2326EZr41aIynsOSyyfdySS3goxBvIwir9uVGFd1QoZhZ8y2gjPULybyh+1Yy3/Z3GOuyQMcDgLig+2QqktguhE0267ig0C/NgUaL9OyEgsB6zPQvTsWRLpQRDftxdEen3afGuABD5luPx4MDBw5geXnZrvNhGIZhGIZhGIZhGMbBXFFtR4d31G+zNOjz4oi60PXCVLxux2UYhmEYxj7KFRl/8PVXAAA/c3I39vdvfG9BrcrJuOh2KBy4qzsEAMgVK9eEcUWRLZZRrigdBYya8yhYlOTwbUuTzJszL8Y08239gzlkvj1sMvg5qtpvx5ZaYzxaz8SyEr7dHTNXDEnjOYV4GaYZIfPnpuHbkBI0yhbLKJYrtpwDzb+NMt/G2Hy7JS9MxgEAN+7s2tCOXE+oQCYtKPxI9s/tw7d8bRBxNejXZdAASu/lSrqASsV4F7AlncZTOwmp5ttMsbHm2w6L4VvAmPl2PlE13xolSqZdQV1fKDS8XRGraPNto+epjdDMtzqKYx67pGQi79gbg6fB4zjTHBgK3wLARz/6UfzWb/0WXn75ZTvOh2EYhmEYhmEYhmEYBzMTzwIAhrpCdT3uTbsUsxS17mMYhmEYprn5h+emcHY2gY42H37j7oObfh2FvCZbJKxF4dvju6Ka0W5RbV0qEjJBSRIQUTfG9ULnlbQhFMw0D7SpbtSc3BOphknqzXk1fHtwwFr4dnyxRcO3mvmWw7dM67Kyjfm21gZu1zxJr9soo2CM7aZbQkXjx4ejDT0PAIgElXu8lKBrkQKB3Ztc/2y+vZ6EyfAtjTGlimyq2wQZcxtrvlWuv2yhseFb+hyYwUzHhvmkefNtt2pIXhVkoKXroHeb64COm8iVUBJQOELFo50Gr3s7oc/gmg7z7WOXlgAAd+7vtfWcGPdgOHz7wAMP4NSpUzh+/DhCoRB6enqu+cMwDMMwDMMwDMMwjHuZjSvhj6FoncO3O6MA2HzLMAzDMG4gUyjhjx4+DwB4zxsPbGniqZpvWyOsReHAQ4Md6O9QNkmpdalIaEO0PeiDJBmz+XS06W/ZybiXlMm25z0NMiam8iVMriiFhKbDt31K+PZKi5i413NV/bnNmm+He1qrmIJxJyvbmG99Xo9W1GLHPFmpyEgVzJnHRdFrIozWSrwwqRSNH98VbeyJAIgElDmaQohWoUBgT2Tja69XneM5fFslrgaWo2Fjn9eAz6PdY5n5rNF9Fv1OGoFmvm1w+LY9aH6stGa+NRG+peMJCt+u6DTfdoX8oEfCuIC5i+a/RhWJbASdy3bh22yhjNNX4wCAu/bF7D4txiUYvtI//elP23AaDMMwDMMwDMMwDMM0A9Oq+XZH1PgCohVuGlbMty9PJ1CpyNzyiWEYhmGamP/v38awkMxjd08YD9w5suXXjsRay5RI5lslfNuGy4tpLCTFBxjIWtppIrhDLbUTbL5taci8aLSVL7U/rrf59qIabO/rCG4bQNiMvar5dmyp9cK3pXIFU6vKs+BILGLqNdh8y7iB7cy3gGL6SxfKtphvU4USZLUDvdHiB1GQSXOZA5bXkSuW8cpsAgBwXF3HaiRkYk4LCt/S3B0Nb2O+TXIwm1gzab4FlPczmSthOVXAvj5j37usjlUxk/c8IqDwd6PCt2ktfFtf8y0VTlIhpRG6w8bDvluxpL7OdteBz+tBZ5sfa9kiVtOFbU2520G2Zieab7czST99ZQWFcgU7utq0rhcMsx2G78je9a532XEeDMMwDMMwDMMwDMM0AbNrSvX+zjqbb/f3taPN70EqX8LYUhr7+9vrenyGYRiGYcQwu5bF//e9ywCA9/3QYQR9W2+GDrdQWKtckXFxIQUAODTQgf5O1XybzAk/FpmgzAR3NPOtiRa4jHvQbGJGzbcNCt9eIKu0SestAIz2Ks8g44spyLJs2BrdzMyu5VCqyAh4PRg0YXIDOHzLuIMV1WLZvUn4EFAKW2bXcrbMkxToDXg9aPObD5RZIdZOZsYiSuUKfF7DzZZdyyuzCZQqMmKRQN3XzTaC5mhR5ts4mW83C9+qYcNssYxMoYRwwDnWy0ZB71lXyHgINhYJYHwpbSrovqSab2MWQ5RWCGvm28YU7NF4afRetRYz5lt6djNjviWr9GpGzPxBBSMxHQbknkhACd8KOHbSZIcMO6Eg8Hbm28cvLwMA7tzX21L3+ow1TF3p5XIZDz30EF555RUAwNGjR/FjP/Zj8Pmc88FhGIZhGIZhGIZhGEYs6XxJW6Da0VVf863P68ENQ114ZmIVL07FOXzLMAzDME3KHz18HrliBbft6cabbxjc9utH1LDWXCKHXLHcsKBJPbiynEahVEGb34PdPWHNlkT2JJFY2RCl77HD6Mc0D0kKcBts5RtrULvy83NKsP2ghfAtmbgTuRJW0oWGBlrqzcSyEpjd1ROC12QXEgrfTq5kuJsJ07RQAGurIBPNkwkBrbvXQ6/ZyEBTdzgASQJkGVjJFNDfUd/1ISfzwmQcAHB8OOqI0FZEM9+KMY+uqEHS7sjGc38k4EWb34NcsYKlZAG7Y62dHypXZO1+yYz5lsaZJRP3TBTYtWowtUJIC9822nxr3r5qpmhsXn12G+g0/t6TVXo1I+Y+eUUNYfdEtj+X7rAf4xBTIEdzlZkuK3ZB4dvt5ubHLy8BAO7aH7P9nBj3YLgM6cyZMzh48CDe9a534aGHHsJDDz2Ed73rXThw4ABefvllO86RYRiGYRiGYRiGYRgHMLumtBntaPNpxrN6ctOuKADgxam1uh+bYRiGYRjrvDgVxz88Nw0AeP9bj+oKJfREAogEvJBlaC3P3cqFOcXMeXCgAx6PpIVZFpJ2hG+VTUdqR2wE2kRNsvm2pdGuIZPm27VsEcVyRfh5bYZmvh00X8TX5vdqJsPxpbSQ82oWJlaUn5cKIsywI9oGr0dCvlTBIrerZ5oUKhzY0nyrs7W1GZxgE/R6JM18upyqbyGF03lBXa86rq5fNRq6z0vlxVyL8fTW5mdJkrSwJ4/zyr2SLCv/31z4VnkvzZhvaazq1WE8tQsy32YbFL7VujQEzRdv0li3ojMMmyuWNXFFvxnzrXq8ZK5k+T45Vywjrb73esy39LmOCwj+0vzXaeK6twt6ht3KfLuWKeKlaWUcv2t/b13Oi3EHhsO3v/iLv4hjx45hamoKzz33HJ577jlMTk7ipptuwi//8i/bcY4MwzAMwzAMwzAMwziA6bjSNqtRrfNu2tUFQAnuMEyzUSxX8OJUHKU6hkwYhmGchCzL+IN/Vrrp3X9iJ44PR3V9nyRJ2B2LAFBsiW7mnBq+PaSaOftVWxK1LhVJNbxjfEO0avRj820rkzIZAIuqxkRAnNVLD+fnq+F2K4z2KuPRWIuFb6+q5tsRdTw2g9/rwVBUCaJcdfl4zrgXzXy7hUWw00ZDPBU+NKIguhYKcnH49lqq5tuuxp6ISiRA4Vvr4cdCqaJZXKmQZiMoMLrE4Vst5BcOeBHwGY5moTdi7nNWLFcQzyjHbqSlP+RXrr9MoTHPDFr41kKxQk+7MfMtdSxp83u0ucAInSG/dp9Mv0OzUADb75XQoaPgsjtiLGi8FfScaOY9sIsurTBm8+vxibFlyDKwry+CARPhaaZ1MTzCP//88/jIRz6C7u5u7e+6u7vx4Q9/GKdPnxZ6cgzDMAzDMAzDMAzDOIfZuGKb29HVmMUnCt+emUnU1ZLFMCL4q38fx4/+6WP40pMTjT4VhmGYhvDwmXmcGl9Bm9+D37r3kKHvJdPixLK7w25VM6cSDuzroPCtHeZb8+Y8CvwkBVnUmOZEu4YMtvL1eiTNrCWira0eVtIFLKqfI1Hh2ystFr6dUMO3uy2Yb2u/n8K8DNNMyLKsjVvdkc3HPpont2ttbQYnmG+Bavh4Oc0BS2ItW9QKM25yiPk2oho/03nr4UeyYXqkrVvJ93EwW4PCk1GT9k/NfGvwc0bjlEcyf2wRkPk202DzLYXQzUBB83imgHJF3vbr59Wiyf6ONl1dXtbj9UhaSNRqkRoZk2ORoK5z6Q6rxxVwf07PiY4y34aU62Ar8+3jl5cAsPWWMY7h8O3BgwcxPz9/3d8vLCxg//79Qk6KYRiGYRiGYRiGYRjnMaOGb4caZL7dE4ugI+hDvlTBxflUQ86BYczy0nQcgBIeZxiGaTUKpQo+8k3FevvLr91r+F5id0wN37rclHh+7trwbX+HUvC0mLAzfGt8Q7QrZJ/Rj2kOSuUKskUlSGEmAEZBhpU6BXMo2D7cE0JEh/lrKyh8O95q4dsVMt8KCt+6fDxn3Em6UEZBLQTe0nyrzpNb2fXMQubbrcKP9YDMt0scsNR4aUppVb67J7ylGbaetKtznojw7SoFScMBeDybB/l62XyrQSE/swFEs58zeu97IsEtf1d2Q+FbumesN2kB5lsqGKvIW4c2CTLfDnSaNw73qMe0GoIl863e8YjMt6sWjbuAw823W/weH7ukhG/v3Beryzkx7sFw+PYjH/kI3vOe9+Dv//7vMTU1hampKfz93/89fuM3fgMf+9jHkEgktD8MwzAMwzAMwzAMw7iH6bhSvd+o8K3HI+FG1X774lS8IefAMGaZWlXC6zNr2QafCcMwTP354hNXMLGcQV9HEO9+/T7D309hrUkXh7VyxTKuqGbfQ6qZs1/dtE3mS8gKNkZV21ZbMN/mSpDl7Q1QjPtI1YR4zIRZY9RGuU7mWy3YbtF6CwCjfa0XvpVlGVfV8clq+Ha4BcZzxr1QwUCb34OQGirbCArGJnLizbcJh5hvKWC5zAFLjRfUdarjw9GGnkctFDpMCQjfkk01Gt46SMrh2yrxLAWWTYZvI+Y+Z2Qd7m1vbAg8rBpnG2a+VcfLdguFV36vRwuQ6unYMJ9Qzbed5rvG0fVi1XxLc1ZM53XQLSj0W6nIjikUqYVC8PlSBbkNAuFzazlcXkxDkoBX7+XwLWMMw6PMD//wDwMA3v72t2tqalpc+ZEf+RHtvyVJQrncmEGUYRiGYRiGYRiGYRjxzK6R+db8AqJVbtzVhccvL+Ol6TW8o2FnwTDGoYDBjBpiZxiGaRVW0wV85jsXAQC/dc8hU0E9Ct9OuLhN+aWFFCqy0u6zr0PZaO8I+hDye5EtlrGQzGEkFhF2PCttq+l7yhUZmULZskmUaT7o+gn6PAj4DHt+tBCAnhCDCM6r5tuDIsK3sWr4tlKRG2qUqxfL6QLShTIkCdjVzeZbpnVZUYNQW1lvgWrAh8x/IrFirheJVkTB5luNFybjAIDjatG4E6B7tHTB+rUYV69/snJuRtXWyuFbMqV2mTTfUnjWaLHSclp57/WGLu2CzLcZAeFvMyTz1sO3gGKOTeRK+sK3SWXNb6DD/Np5jyADrXYd6DXfUvjWYug3XSihotZnmrU+20F7wAePpFiME7ki2vzXFtE8flmx3t4w1IXoNuMcw6zH8Cjz3e9+147zYBiGYRiGYRiGYRjG4czElfDtjq7GmG8B4NiQsolxdpY77jDNQypf0hbNp+NZrXCdYRimFfjj71xEIlfCkR2d+PFbdpl6DTItXl3JuHYMJTPnwYEO7eeTJAn9nUFMLGewkMwLDd+SAc1M+Dbk98LnkVCqyEjmShy+bUGq14+5DfUeLbRVn2DOBTLfDloP3+7qDsHnkZAvVTCbyGFng7qC1BMKyg52tl0XVDAKh2+ZZmZFDTJ1R7Ye+2hutcN8a8VcL5IYmW/THLAkHGm+Ve/RyABqBQqfbxdKq5pvOZi9Ru9ZyFyQjz5n8UwRxXIFfq++gicKxW9XKGA3Wvi2WK77M5wsy0gLDN9eWc7oCt8uJJQxcaDT/HsfFRSCpdB2j87roFsz7lqbu6hIJOD1IGiiSM8uPB4JnSE/4pkiEtki+tcFpB+7tAwAuHM/W28Z4xgeZV7/+tfbcR4MwzAMwzAMwzAMwzgYWZYxs6ZU7zdyg/nojk4AwLnZJMoVGd4WME0xzc/UajVcUChVsJwuaBtSDMMwbubSQgoPPjkBAPgfbz1iet4eiobgVcNuC8k8Biy08XQqZOY8vC4c2N+hhm8TYsMtFN5pDxoPT0qShI42H1YzRSRyRQx2ue/3wWyNFXMyUA0BGDW5mUGWZaHmW5/Xg92xMMYW0xhfTLdG+Fa1jlNw1gr0GgvJPLKFMkIBa2FehqknK2ll7twuyERttpMCAo/rSVgcf0VRtZtywBJQ2pXPJ/LweiQcG+ps9OloRAJq+DZvvWP1qhbk2/resRq+5WC2Zr4NmytWiob8mqlzNV1Av85nIPpcNtp8S3O8LAP5UsVyAY8RssWyZl9ttzheUtGYLvNtQlk777cQvtXMtxbvk5cNXgdV46614yZqikScVjTb2aaEb9fWmellWdbMt3ft623EqTFNjq6Y+dWrVw296PT0tKmTYRiGYRiGYRiGYRjGmSynCyiUKpAkNDTwMtob0dovX1lON+w8GMYIkyvZa/6bLNIMwzBu56PffAXlioy7jwzgzv3mN7H8Xg+Gosr9h1ttiZr59rrwrfJzL6gtTEVhNTzZoQWLxFv9GOeTylszL8YMhBisMpfIIZkrweuRsLdPjD16b6/yOuMt8jwyoYZvyUJuha6QX7tuagvUGKYZIPNtzzZBOmqzncjaZ77tNGkeF0WvGuZi863C85NxAEqRRzjgnI4AZPwkA6gVyIbZvU0L+74ONZid5GtDC9+GzH1ePR6p2i3AwD0TdRZodNF37WchU7AeADcCdWnwSErXDisYCaUuqNf9QIf5tfOoIAMt3WfHtvnMEvTZXssWUabksgkSarC10+R1byddm8zP40tpzK7lEPB6cNuenkacGtPk6Arf3nbbbXj3u9+Np59+etOvWVtbw+c+9znccMMN+OpXvyrsBBmGYRiGYRiGYRiGaTyzcSXw0dceRKCBLaO8HgmHdyihlDMziYadB8MYYX2wgMO3DMO0As9cWcG/vLIAn0fC+95y2PLrkS2RQmBug8K36823fR3KpvmC4ABDMm8tfNsZopba4q1+jPOh8LbZNr5mgiRmoc/WaG8EQZ8Y49oohW8XWyR8u6L8nCMx6+FlSZK08dytxRSMe9FrvqW5NWFDgYrV4hlRUDv7ZTbfAgBenIoDAI7v6mrsiayDjJ/ZYtlSmA6oWji7w1sH+ejaSORKyJfqG7h0GvGMtfAtYO6zRvdXvQ0233o9kraGnCnU95khpY6VkaB1+yqFUvX8DqrmW/PhW/qMWTffKs+PMZ0h7Kh6ncpyNThuBgq2djZ4ntoIeoZd//M9dnkZAHBid5S7MjCm0LVbdvbsWUQiEbzpTW/C4OAg3vrWt+KXfumX8J//83/GO9/5TrzqVa9Cf38/vvCFL+DjH/843vOe99h93gzDMAzDOIzTV1fxhj/6Lh4+M9foU2EYhmEYxgam1bDgDge0VT26Q2nhd5bDt0yTsN58Ox0Xay9kGIZxIt8+Ow8A+NHjQ9jX12759Xb3KKEvN4a11jJFzKkbtQcG1plv1ZalCwnB4VuL5ryOoH1Wv0YztZrBT/3lk/jmS7ONPhXHYjX8VU/z7YV5JXx7aN1nywqjvcqYNr6UEvaaTuaqWvRAoVmrcPiWaVY0821kG/OtOrem8iVULAYe15PU2nk31ihIbcwzhXLdQ3VO5AUK3w5HG3oe64kEqyGytMXfE1k/e7YJ33aF/PB5lLBjPeZ5J0MBv+g2tuytiJmwTC9R6HKbQoF6EFGDjNk6m2/TeeV4HSYLxWqJ6TTfZgol7R55oNP8e6+Fb3WYdreCQtg9Os23Pq9HC8xa+ewm1Q4ZjjbfriuOeeLyEgDgLgvdepjWRlf4NhaL4ZOf/CRmZ2fxp3/6pzhw4ACWlpZw8eJFAMDP/MzP4Nlnn8UTTzyBt7zlLbaeMMMwDMMwzuTPvnsZV5Yz+KcXZhp9KgzDMAzjeAqlimXjRL2ZXVPCgzuj5iv3RXFsSDGJnJ3l8C3THJD5ljYd2HzLMEwr8NT4CgDgNQfEbGBpYS0Xtnk/r4YDd0ZD14Vh+9WWpQtJcYUbxXIFuWIFgHlzKYUuky403/7pv17CE2PL+Mt/H2v0qTiWqvnW3KZ6T3v9wrfn55SA7KFBceHbPb3KeDS+5L7xaCMm1JDsSIzDt0xrQ+bb7m2CTDRHyjKQEhxMdYr5tj3o04yWrW6/rVRkvDi5BgA4viva2JNZR9Dnhd+rBGFTFu/ZVjL6gqQej6QFRpeSrX1tUPjWkvlWtZYuGTHfql8ba7D5FgDCAWWsStc5fEsB0IiA8C2FYbfr2EDFkuGA1/QzlnI85XpZzVgrcqT77JjO8C1Qnd+sBH8TWWfMUxtBz9prNe9tpSLjCdV8e9f+WEPOi2l+DF3toVAIP/ETP4Gf+ImfsOt8GIZhGIZpQlbSBTx6fgEAsCjYxMIwDMMwbiNXLOMH/3//hr6OIL7263c1+nR0Q2HBoS4HmG+HyHy7BlmWLbcPYxi7mVxVPj+37OnB9y4scviWYRjXk86X8NK0EkI4uVfMBhaFviZcGNai8O3BgesNwf0dyob7YlLcekttYLbd5KYomYzcFr6NZwr42vPTAIDzc0lUKjI8Hr7XXE8qT+ZFc9dPT83Gfrkiw2vje3x+XinYOyjQfLtXNd9OrmZRKFW0AJobyRRK2vgzohrIrbJLDd+u7w7BME6HzLfbBZna/F4EfR7kSxUkskXTlvmNoHm30UZBSZLQGwlgZi2H5XQBw4LM2M3I2FIayXwJbX7PhvdyjSYS9CGeKSKdt3bPFs/ot2j2tgcxn8hrBtZWRTPfhsyHYGm8Wdb5XsqyrL3vve2NN9+G1CL0ehuyyXxr9lmnlphWNLb172BBvV/q7whaWqvuERCAzRbKyKiBZyMh7O5wABPLGaxaKJCjzigi5z5RbGS+PTubwGqmiEjAi5scVkDBNA/ufRpkGIZhGKZufP3FGZRUe99iiz9MMwzDMMx2TK5kMB3P4vnJuO6FUycws6bY1nZEGx++PTTQAY+kWB9EBlEYxg5kWcaUGhQ7OdoDoPp5YhiGcSvPTqyiXJGxMxrCTkH3Dru1sJYLw7dzSjjw0GDndf/Wr7YsXRB4z0Pms5DfC7/X3DYRhS7Xt+xsdv7PM1OaFThTKLOZcxNSFs2LZBCT5WqYxw7KFRkX58Wbbwc6gwj5vShXZEyuuvsaoc9AV8iPLgtts2tx83jOuBuyENIYthUdauiIDIAiKFdkpPLOMQqSkbOZ1rbs4MWpOADghqEu+EzeV9lJRDWPpiyGb8miuZ35GaheG62+XxjPWDff9rZT+Fbf/VK6UEa+pNzLOsN8q4Rvs3U231KhmBUDLUFj/mp66+ee+YSy1tffaa1rXFQ93lq2aLpz3rIaFA54PYbeg6p110L4Vn0+bHSRyEbQOVEwHgAev7wEALh9tMf0szHD8JXDMAzDMIxlHjo9rf3/hQQHCRiGYRhmK2rDohfUjeBmgEydO6PWFhBFEAp4sa9PsYmcmU00+GwYZmsS2RKS6ibX7RS+ZfMtwzAu59T4CgDg5N4eYa+5WzXfLqUKlsMDTuP8nGK+PTS4kflWufdaSRdQUDfSrUIbolaCO2QySroofFupyHjwyQkAAMmqzs3xveZGkHnRbKDB7/VoQZQVC2at7bi6kkG+VEHQ59ECnyKQJAmjvYoF9spSWtjrOpGJZSUgS/ZxEdDv4upKBrJsLlTCMI2AQqZ6Am2dIWV8FDlP1t7/OCF8S3bG7Vqxu50XJuMAgOPD0Yaex2bQtUImUDMUyxVt7tcTPqfAaCubb/OlMrJF5T23Uryihdy3sa4SNE6F/F6EA40fJ0J+Mt/WOXxr8V61llhE3++AwrcDlsO3yvUiy9eGRI1AYe1Ye8CQhbdbs+6an7s0Q7sD5qn1UPi2tjDmsUvLAIC79vc25JwYd8DhW4ZhGIZhLDG+lMbpq3FtUyJdKFtuX8MwzcTZmQR+5cFncWkh2ehTYRimSai1PjTT2DEbV823XY033wLA0SHFDHd2hgMRjLMhG1pve1ALjS8m88iX6rvxwDAMU0+eGlc2sF49GhP2mp1tfs3E4yZboizL1fDtwPXm2+6wH36vsugiKsCgBSctbIhSkINeyw3824VFXF3JoLPNhx++aQgAcHa2ee7X60lSMy9aCJPUIbRFn60DA+3wesy3/92I0T4lfDvu8vDtVTV8KzK8vDMagiQB2WIZSzpNfgzTaIrlChIGwodUpJIQOE9SkDfg8yDo8wp7XbPEDBo53crzU2sAnBu+jQStm2/J4CpJ+iyufZoVuXWvDQpNShLQYSEASvdLeufLpZrQpRNonPlWOV5EhPk2olzzuWJly5+DOpUMdAQtHc/v9WjPWmYNtFTc1qPDVF1Lj2b5tW6+tfKcYBcUCKbPZ6FU0QqH79zH4VvGPBy+ZRiGYRjGEl9TrbevPdCnPURx+2emlfhfp67iW2fm8JVnphp9KgzDNAm18+TFheYw3xbLFcwnlfDtkKDW0VY5uoPDt0xzMKWGb3d1h9Ad9qPNryzHza1xxwiGYdxJrljGC5NKCIGM36Kg8BeZGN3AXCKHRK4Er0fCvv7Idf8uSZIWYFgQtN6SFLAhqoWKTNqYnMjfPHEFAPD2W4fxqt1RAMA57rKwIXQNWQlwUxjATvPthXklfHtwoEP4a+9VzbdjLg/fTqwoP59I823A58GQWtR51UXFFIy7oQCUJFVbgm8FBadEzpNk6nOKTbBXC1i27n5QoVTBK+q61M27oo09mU0QEb6l678r5NdVzELXRiubb+mz3xXyw2OhAMis+Za+r9GQfTdTqG/BXiqv3qsKCN+2B30IeJW1vK1+D2S+7e+0/t53WwzBLpsM31bNtxbCtzRXhZwxV9VCxQMUEH5+Mo5ssYyeSACHB8U/LzCtg6HwbbFYxM///M9jfHzcrvNhGIZhGKaJkGUZX3teCd++7cRO9HeI3QximGZgQQ2jceicYRi91JoKLs43R/h2bi0HWQYCXo9mXGg0x4a6AABnZtYafCYMszWTK1kAwHBPGJIkaQH26Xi2kafFMAxjG6evxlEoVzDQGRQa1gKA3TEl7OYm8y2ZOUd7I5ta7PrU1qULCTGFGxS+sBLeqbbTdof59spSGo+eXwQAvPPVIzg8qBR6nZtj8+1GpDTzrfXwra3m23mySovfTN+jjkfjiy4P36rFDiM91xcHWGG4R7kndtN4zrib1bQS1InqDB92rgv4iEBE8YxIRBnMp+PZpg1pnptLoFCuoDvs18Y1p9EeVO4vrXSspABgj47gOVC1rjbr71UEZAvWYwreil6Dhmn6PPY5zHybKdbXfJtWzbciwreSJGn2W5oLNmIhoZpv1Wc3K1RDsObmEAph9xoMYVPod2WLn3M7aN7rdMhcVQt9Hsl8+9ilJQDAHftilkLyDGMofOv3+/HVr37VrnNhGIZhGKbJeO5qHBPLGYQDXtxzbAB9WviWLV5M60Ch21ZeSGIYxhjNaL6dVQ2dO6JtjlmIOrJD2UC/spyxZO9gGLsh8+1wt7IRt1MN387E+Z6ZYRh38tT4MgDg9tEYJEnsfcNuNdRAJkY3QOHbrcKBooudKTBrZTOagj9uCd9+6ckJAMAbDvVhT29Eu9e8upLRwk5MFfq9W2qjrIZCVmxsSX1B/XwdtMFkNdqnhm9dbr4lM+1u0cUUqsmczbdMs0C2Q70WwU4b5klt7HWI+TYmwG76yJk5vOGPvov7/uwxyLIs6tTqBnVjumFnl/D7XlFEAuLMt906r3/NfJu0b453OhTui1oM39LnLFMoI1vYPsCqmW8jTjHfquHbfH3Dt9rzjqDxsieyvYGYusb1dwgI34bVsK9JA+2KWfOtxeMCVetzp8Vr3w60whj1HB+/rIRv79rX27BzYtyBofAtANx333342te+ZsOpMAzDMAzTbDx0egoA8OZjgwgHfNoDBRtAmVaCDJZLNm4WMQzjLmo3JZZSedPto+rJjGro3NFlffFQFLH2IAZVkwC3A2aczOSq8vnZ1a0EDKjF7gybbxmGcSmnxlcAACdHe4S/NpkXr664ZwzVzJxbhAPFh2/JnGclfKu203ZBMDVTKOErz0wCAN51xx4ASktxuve9MM/22/WkBAQaKAyworONslHypbIWjLWjjezeXmU8mkvk6t5KuV6UyhVMq/eywk3mHL5lmgyyHeoP36rzZFag+TZvff4WScygkXM9X39xBr/2t8+hWJYxtZptyo6KNIaN9oq1g4uE5mor5luyYFIwbzsofLtVUNHtkPnWagAxEvAi6FNiXXreT9qnijnEfBtSw98ZHcFhkdD1LsJ8CwA9ke1DqVXzrfXgM1mmze4bmL0OqsZd8/sVTisUqYXMt8l8CclcEaevxgEAd+2PNfCsGDdg+Go/cOAAPvjBD+Kxxx7DLbfcgkjk2huJ97znPcJOjmEYhmEY51IoVfD1F2cBAPe/aicA1JhvW/eBmmk92HzLMIxR1hepXFpM4baI+HCKSGbWlA3XoaizWugdG+rEXCKHMzMJ3LrH2e8h07pQK11qQTkU5fAtwzDupVCq4LmrqwCAV+8VPzcPU1hr2T2mSTLfHtzSfEvFzmKs6ck8bYiaDwPYYfRrFP/4/AwSuRJ294Tx+oN92t8fHuzA7FoOr8wmccsI32vWIuIaqhrE7ClGHF9Ko1SR0dHm04r2RBINB9Ad9mM1U8SVpQyODnUKP0ajmYnnUKrICPg8GBBgcatlmMO3TJOxYtR8S3Y9gUUqNOc6pZV3rw4T5GY8dHoK//UrL6BSI7u9vJgS0q69nlCx7XC32AIFkVD40Er4VjPfhnWabzuowKaAckWG1yFdtOqJZr7V+Z5thiRJiEUCmFnLYTlV0Aq7N4P2qciY22jIfJst1veZISU8fKuOd5sUG6TyJe2Y/QLGMbpuVjPm5hCas2KGzbfWQr+yLGvznlPmqlronGQZ+NdzCyhVZOyMhrSiMIYxi+GR5vOf/zyi0SieffZZPPvss9f8myRJHL5lGIZhmBbh0fMLiGeK6O8I4k61HYMWvk1wCJFpDdL5ErJFpWJ3JV1ApSI7ph07wzDOhRZBe9uDWErlcXE+hdscHhylkCAZO53C0aFOfOfcgtbmj2GchiwrBh+gxnwbVRbhpzl8yzCMC3lxKo5csYJYJIB9fe3CX5/Mi1OrWVds5JcrMi4upABsbebs7xS73iLCRkTfm8qXmvp3IcsyvvjEBADgZ189cs0z/eEdnfju+UW8wl0WriFfKqNQqgCwFmiIRarBHDugYPuhgQ7bWoGP9kawejWO8aW0K8O3EytKocPunrDw9S4KOUxy+JZpElZMmm9FFqk4zSZYa76VZVn3WPuVpyfxO//wImQZ+Mlbh7GQzOG75xdxeTGt7TU1CzSG7ep21npZLRF1rk5aCd+qc3W3zuu/JxyAJAEVWZnnae+wlYir4duukPXPa6w9qIRvdQTdKRza6xDzLYVv622+FR2+jW1jhF1IKEWSkYBXyDHJMm02BEvFbbGIsc9et2r4XcsWTT3j5YoVFMtKVYVV67MdBHwehPxeZItlfPOlOQCK9dauZwWmdfAY/Ybx8fFN/4yNjdlxjgzDMAzDOJCHTk8DAH7s5iHt5pvaIC6yAZRpEWrtleWKrC2oMAzDbEalImuLX3fsU9oZXVxwfhvb2biygOg08+3RHcoG91kORDAOZTldQLZYhiRVQ7c72XzLMEwdmV3LatalevDU+AoA4PbRHls2sAY62xDwelCqyK4YR68sp1EoVdDm92gWyI3oF9xpiMI7VjaGa42nqSa23z4zsYpXZhNo83vwH27ddc2/HVHvNc/NOf9+vZ7U/r6tXEM9dQrfHtwi2G6V0V6lyGB8KWXbMRrJxLISKhuxwQZG4du5RA65Yn0DOQxjBgpcNdJ8S69lxTouEnovShUZiay+e4EHn5zAb39VCd6+89W78ZG33Yj9/cpYOrbYfGMpFdtudR/XaCJCzLfKtafXfOvzerSvbdWOgQktfGv980pB96VNrKu1LGvGU2cEnkMNDt9GBIVv6Xre7L6VntNE2bu7twn7bgeFsHsMhrDp56zI1WvYCDRPeSQliOxEOtVA/KMXFgAAd+1vrqIPxpkYDt/WIssyZFne/gsZhmEYhnEVa9kivvOKclN634md2t9TKw2q8GMYt7M+aN6qC0kMw+hnNaO0WwOqrZgvLTh/c4EMnTuizmr/d2yoC4CysV4sVxp8NgxzPbQRN9DRhqBPWXQe0sK3OV5XYxjGVuYTOfzAJx7FA184VbfxhsK3J0ftsfp7PRJ29SjjqBtalV+gcOBAx5ZWoX613ftCUsx6S1JAK9CAz4M2v7LFJDJYVG/+5vErAID7bt55XVvgI2po8/xcEpUKz9kEhbcjAa8l4zGFtpZtCt9emK+ab+1itFcJW40tpW07RiOhcXZ3THyorCcSQCTghSxzRwimOaCxSm/4kOy0ekOpenCa+bbN70WHGmxb0mHk/Pz3x/E/vvYyAODn7xrFh37sBng8ktYtYWyxucbSbKGs7QcMdzs3fNseVNYi0nnz4cdq+Fz/vWNvjRm5FYmr71k0ZN1AS0FaPe8lfU3MYebbbJ3DtxQ2FzVeUoh1s/DtvLovTh1LrEJzjdnw7YpmvjV2Hfi9Hm1cN3PsZE2RiFNtshSIzxWVvQQSpDCMFUyFb7/4xS/ixhtvRCgUQigUwk033YQHH3xQ9LkxDMMwDONQvvHSLArlCg4NdGjGOQDoa1fNt4JMLAzjdJbWXevr/5thGGY9FNrvDvs1k9bFeeeHb2fXlAXEnQ4z3+7qDqEj6EOhXMHlJjSkMO6HWlAO91Q/O4NdSoAqWywjnmnesBLDMM7nzMwacsUKXpiM12WeLJUrePYKmW/t28AiA6MbwrfnasK3W0GbuEupaiGXFVKCwjtk3mvW8O1CIodvvay0G/3ZO0au+/fR3ggCPg9S+ZJWUMNUTWJWzYu96jriarpgS4HA+Xl9ny8rVM23zRUY08vEsvJz7bbB6ChJkmaKdMN4zrgfav2tN9BGBS5JgXNkNXzrDPMtUH0/tgsFfvbRy/jQ188CAH71DfvwP374iBbM2quGb5ttXWdqVRm7Otp86Ao753eynvagcm4pC+ZbCvKtL1TaCprnW1VYsibQfFsNMm/9XpYrMlYyTgvfKs8bmUJ9O2XQ844o823PdubbhGjzrXLdrJpYN8wUSsiqXQVi7cbDwFasu2tqwQnZZZ1IbQHqgf52rdCVYaxgOHz7yU9+Er/6q7+Kt7zlLfjKV76Cr3zlK3jzm9+MX/mVX8GnPvUpO86RYRiGYRiH8dDpaQDA/a/aeU3lGm0GrWQKbJ9jWoLrzLc22VoYhnEPS0llnOjrCGpt9eYSOUcHFtL5krZgvKPLWYtRHo+khZjPziQafDYMcz2T6mZcrQWnze/VNqHY8sUwjJ1M14QFHz4zb/vxzswkkC6U0RXy47CNbd4pBEbt0JsZMnNu937FIgFIkrqhLuC5U1R4h8K79HrNxt+duopSRcatI91aR4VafF4PDg4o9+yvzPG9JkHPLu0Ww9sUKjDSrlwv6XwJkyvKGEi/QzsY7Y0AcHP4VhlnR2ww3wLV8XySw7dME2DUfNsZogIVceMbtQB3ivkWqAa7NgsFyrKMP/6Xi/jYt84BAP7LDx7Ab9976Jp9pb19ylg6Hc8iV6yvHdMK9Ly/y8HWWwCIaOZb89diXDPf6g90xlo8fBun8K2AYLYWct/mOWA1U4AsA5JUDYs2GjLfZupovpVlGSk17NsuKnwb0We+FRa+VX9/cRMBWCqGCPg8iKjvv7FjK9fsStr4fkVCQIcVu6kNxN+1v7eBZ8K4CcPh2z/5kz/BZz/7WXzsYx/Dj/7oj+JHf/RH8fGPfxx//ud/js985jN2nCPDMAzDMA5iciWDU+MrkCTgx24euubfesIBeD0SZLl1W8kwrQWbbxmGMcpiSlmI620PorPNj0F1Qc7J9tvZNWXTuqPN5yi7CnF0SAnfnuHwLeNAyJK3q/taa/TOqPLZn+HwLcMwNlJr6vz2WfvDt0+NLwMAbtvTA4+FVvTbsTumBDTcENY6r9N86/N6tHazC8mc5eMmBYUnq1a/5gvfFkoV/O1TVwEAD9y5Z9OvOzyo3Guem03W47SaAjKJWQ0zBH3G2pUb4eKC8nzV2x40ZfzSy55eJXAVzxQ1K6ZbkGVZM9Lu7onYcgwK3151QTEF4340821E35hCAdlEtijM7p3UQk0OCt+qgbSNpBSyLOMTj5zHp/7lAgDgt+49hP/nTQeva0UeiwTQFfJDlpurmIHutYe7ndUlaj00X4sw33YbCJKSrXW9wKRVEGm+pXFnuyAz7ct2hwPweU01QRdOI8K3mUIZNOzWLXyr7g32d4i57+zR7LPG5xAKafdGAteNt3qwYr6lIpFmCd/euc++jj1Ma2F4xJ2dncWdd9553d/feeedmJ2dFXJSDMMwDMM4l398XrHe3rE3hh1d1y4qeDyS9kAtYjOIYZzOdebbFl1IYhhGP7XmWwA4oFqYLi04dzN/Oq7M6TujztxMoPAtm28ZJ0LBsF3rWvUOqZ8nDt8yDGMnUzVjzPOTcc3GYxdPja0AAE6O9th6HM18u9I84YyNyBXLuKK2dNdjCqaN3AUBRZ9V8621zejaYFGz8fCZOSwm8+jrCOLNxwY3/Tr63bwyy/eaBIV3RJgXe9q3DjKY5YIabD80aJ/1FlBaKVN3kPHl5h6T1rOUKiBTKEOSgOEee54Fd6tG3asuKKZg3I0sV83zZO3eDgoelSqy1v7bKqLM9SLZzHwryzI+/M+v4M++exkA8P63HsGv/8D+DV9DkiTNfju22DxjKT3vD/c43XxrLXxbKlc0g7Ne8zMAreNPq4p61jLK/XFUpPl2m/eSPocxA4Ziuwn5leuvnuFbuta9HgltfjEhZArDxrNFlCvXh2EX1GftfkHmW7puyhXZsEF9RS1qo/tso9Dn3ExhmajnTDshM71HAk7u5fAtIwbDI83+/fvxla985bq///KXv4wDBw6YOok/+7M/w549e9DW1oaTJ0/i1KlTm37t5z73Obz2ta9Fd3c3uru7cffdd2/59QzDMAzDiEOWZTx0Wgnf3ndi54Zf09+hPFgssgGUaQHoOh9SN1padSGJYRj9UGifFqD39yubwY4236rBHdpUdhpHd6jh29mEMJsMw4hiehPzrRa+XeOCNYZh7IPGIK9qof2XV+yz35YrMk5dUcO3e+0N31L782Y3JV5aSKEiKxurfToMSf2dytcsJqytt1Qq1TasVjdFq+bb5gvfPvjEBADgp27fjYBv860yutc8N8fhW0LkpjoFGUSvp5yfV8O3A51CX3cjRnuVwNh4EwXG9HBVLXDY0dmGoM94y2I9UGCNw7eM00kXyiiUKwD0m2/DAa92DybKEO/EUFPvBqHASkXGB/7pDP7q++MAgA/+2DH84mv3bvk6+/qU9bGxReeuj61ncqW5zLfpfMnUulm8psjKiMW1r12frdWNyLIs1HyrBZm36RRA684xk6FLOyDzbbZQv04ZFL5tD/pMmV83gsKwsgzENzDCUoHkgCDzbdDn1d47oyFYGo97dM5X69HCtxnjz3gJMrQLuO7tgs7txl1RIZ9PhgFMhG9///d/H7/7u7+LN7/5zfjQhz6ED33oQ3jzm9+M3//938cHP/hBwyfw5S9/Ge9973vxgQ98AM899xyOHz+Oe++9FwsLCxt+/aOPPoqf+qmfwne/+1088cQTGB4exj333IPp6WnDx2YYhmEYxhgvTa/h8mIaQZ8HP3TDxlYQkSYWhnE6i+pD7BF1M64VF5IYhjHGkjo/aubbfsWkRW1RnQiZOYccar49ONABv1fCWraIabaIMg6iUpFr2lBubL7la5ZhGDuhMebeYwMAgEfO2Be+PTeXQDJXQnvQp4UV7YLG1ESutOHGZ7NwnsycAx26NoWr6y3WCjfShZLWhrUjaG2zsTOkhDlEhYrqxdmZBE5dWYHPI+FnTu7e8msPqebbiZUM0hbaNbsJzXxr8foBqmY20ebb83Uy3wLAHgrfNlGrdD1MqAUOZKe1AzKZT65kuJCScTQr6hpwm9+DUEBfGF2SJHQKNsRTsYuT2nnTOE6hwEpFxn9/6CV88YkJSBLwkbfdiAfu2LPt65D59nIThW+n4mqnm+7mMN9WZCBXrBj+frrf7gr54fPqjxf1dijXRivumaQLZZRUO2o0ZD0IW2u+3Wq+pNAlGamdAAVIM8Vy3eb6VK4avhWF3+vRgpqr655BZVnWuswMCDLfArUhWIPhW/W+utekAblHNbybMd8mssp776R5aj2vHu1BOODFT9463OhTYVyE4fDtj//4j+PUqVPo7e3F1772NXzta19Db28vTp06hfvvv9/wCXzyk5/EL/3SL+Hnfu7ncPToUfzFX/wFwuEwvvCFL2z49X/7t3+LX/u1X8PNN9+Mw4cP46/+6q9QqVTwne98x/CxGYZhGIYxBllv33R0YNPWRhQmWrBoYmGYZoBCdId3KJtxrbiQxDCMMdabbw8MKJvBlxwcvp2OK4uHTg3fBnwe7FdDzGdn2EjGOIfFVB6FcgVej3SdOXpnVPnvGQ7fMgxjE7liWevU8R/vHAUAPHF52TZD6VNjivX2lpFuQ5vyZggFvFoQtZltiZqZUw13bgd1GrJa7ExBWZ+ANqy0NpRoMvPtg09eAQDce8PgthvksfYg+juCkOXq76zVod93u0Dz7co2Jjej0O/q4IC+z5cV9ro8fDvSE7HtGDujIUiSElISHcBmGJGsqMEnvdZbQuQ8Wa7ISKtt251kvo1pdtMCyhUZv/n3L+B/Pz0JjwR84ieO46du37rIhdjbq5pvm2gs1cy3Pc4O34b9XlCdV8pEIdFKWrl+ewwG+ejzspRsvfGdrLcBr8fy/TZQfe9LFVkLN24EheDNhi7tgAoWZBnIl4yHv82QzosP3wKbd2xI5UvIqOMzdSsRQXdk47DvdtA9ldHPLBE1GfoFas23zpmn1nPn/l68/Hv34qe3KcJkGCMYGumLxSJ+/ud/Ht3d3fjSl76EZ599Fs8++yy+9KUv4cSJE4YPXigU8Oyzz+Luu++unpDHg7vvvhtPPPGErtfIZDIoFovo6dm4lVU+n0cikbjmD8MwDMMwximVK/i/L8wAAN72qp2bfh1tgC2muIUu425kWdY2sw8Pkvm29RaSGIYxxuJ15ltlc2E6njW1AF4PZtfIfCuucl80ZNg7O8vP/IxzmFQDYTu62q4LolGYncO3DMPYxeya8kwe8ntx255u7O2NoFCu4N8uLNpyvFPjSvj25N6N1+lFQ7ZECoc1I1Uzp87wbaeYYufaltVW27B2BJvPfLuWKWrF5e/SYeIDqt1uzs1y+Bao2sREhL+oHe6ywODlSrqgPXcdqEP4dlQN3zZTYEwPVNxgp/m2ze/FoBqAb+ZiCsb9UIEABaH0QuGjhIB5MlXzGpuJURoBGTkXEjn8xpefxz88Nw2vR8Ifv+MEfvyWXbpfZ3+/OpYuppvChJ3IFbWA5a5uZxarEx6PhEhAuRbNWPwpyBcNG7vuejtojs83xe9UJJotOOy3fL8NAEGfV7vvWt6iYMmZ5tvq/WK9ukgk1eNEgvpM5XqhMOv6UCoVR3YEfdf8vFbRzLdpYwUcJAkyex1s9nPqgZ4LnWy+BZRxkWFEYih86/f78dWvflXYwZeWllAulzEwMHDN3w8MDGBubk7Xa/zO7/wOhoaGrgnw1vKRj3wEXV1d2p/hYVZHMwzDMIwZ/v3SEpZSBcQiAbz2QN+mX9enLpiy+ZZxO4lcCYWyUql7pMZ822oLSQzDGGNJM98qi1jRcEAL4l52qP2WwoE7upy7mXBsSAlEnGHzLeMgJleVAMHwBi0oKXy7kMyjUCfzB8MwrcX0qjJ/7+wOQZIkvOmosgb/7bPzwo8lyzJOXVHDt6Mx4a+/ERQGa+awlha+1RkOpGLnhaS1YudUXtm8FRHc6VRbrzZT+Pb/PDuJXLGCw4MduG1Pt67voW43r3ChF4Dq71uETSymmW/FhW8vqNbbXd0h4cazjaDw7ZWl5giM6WViWQkTj9gYvgWqxshmHs8Z91M1fxoLMlH4KJG1br4lm2DQ50HAZ2+XASNQZ6cryxn83xdm4PdK+LOffhV+5PiQodfZ3ROB1yMhlS9ZtvzXAyq27YkEEKnDXGMVCiGaKfynIGlP2Kj5Vvn6YnlrW6sboWB2V0hcAJE+a1sVLC1p4VvnmG+9HglBdcwiO6zdaOZbwQFQCsOu/x3MJ5TnM5HW29rjmTXfxkybb/3XvI4RaL5zkqGdYeqB4Tuz++67D1/72tdsOBXjfPSjH8X//t//Gw899BDa2jY28Lzvfe/D2tqa9mdycrLOZ8kwDMMw7uCh5xQryI8cH4J/ixaSfe20GeT8BRKGsQJZVDrafFqAJl+qONZcyTBM4ylXZG3RigK3QNV+e9GB4VtZljGjmvN2Rp0bvj2qhm/PcviWcRBTagvKjSw4sUgAAZ8HslxdpGcYhhHJdFwJBND8fc8xJXz7r+cWUCyLDf1fXEhhJV1Am9+DG3d2CX3tzaA26Feb1Hy7liliTh3/D+o03/Z1qMXOFtdbEgKDk7SpKqKddj2oVGQ8+OQEAOCBO/boNpEdUbvdnJvje02gGtwRYbTqsTF8qzfYbpXhnjC8HgnZYhnzLpIRUBiWxlu7oEK1SQ7fMg6GzLc9Bs2f1XnS+npx1VzvLJtgbbgr4PXgL955C958w6Dh1wn4PBhWn50vLzpvfWw9U2qh27DDrbcEBYTN7F2sZMh8ayzI1+av2loXU+6ZH/WwllHujaMCw7d0z7S8xXtJVtyYwUIBuwkHlPB3tlif8C1d5+2Czbc03q2uu28lGdVAp9iucd3qnGM0fLtsMYRN11o8Y/wZj54LOwVe+wzTDBheXTlw4AA++MEP4rHHHsMtt9yCSOTah673vOc9ul+rt7cXXq8X8/PXVtvPz89jcHDrm7JPfOIT+OhHP4p/+Zd/wU033bTp1wWDQQSDzppcGIZhGKbZSOVLeOSsYqW//8TOLb+WKvsWOXzLuByyV/Z1BBEO+BAOeJEplLGUKjhuEZRhGGewnM6jIgOSdK0t4kB/Ox6/vIyLC85rY7ucLqBQqkCSxC8gioRaAU/Hs1jLFNFlcEOMYexAM9/2XG8LkyQJO6MhjC+lMR3Pbvg1DMMwVqg13wLAzcPd6G0PYClVwFNjK3jNgV5hx3pqXLHe3jLSXTcT2+6Y8nNNrDRnm/fzajhwZzSkO8ComW8TSscVsy1sq+EdEeFb1ejXJObbf7u4iInlDDrafLjvhH4jH91rnptNWnrv3QK1Pm8XcA31tFOQRFz4lqzSeoPtVvF7PdjdE8b4UhpjSykMdjn3uUkvqXxJs+ftttl8u5vNt0wTYNV8mxRQpKIFmhxmE4yGAxjsbMNqpoDPPXArXndw866J27Gvrx1XljO4vJjGnfvE3avaARUM7GqSZ3kqukqbMt/S9W98ra23PYhkroSlVB77VflAK2CH+ZaCn0tb3DPR/VSvg8y3ABAO+LCaKdbNfCuyS0Mt3ZGtzbfCw7cU9jUYgqWith6T5tta426lIsPj0f/sQ+ZbEUV6DNNMGF4J+/znP49oNIpnn30Wf/mXf4lPfepT2p9Pf/rThl4rEAjglltuwXe+8x3t7yqVCr7zne/gjjvu2PT7Pv7xj+NDH/oQvvWtb+HWW281+iMwDMMwDGOQb708h1yxgr29Edy0a2uLDW0GLSbzrmq1xjDroYA5tfvR2v60WBU3wzD6WUpWWz75aizy+1Uj06V555k9ZuPK4mFfe9BRbQ3X0xXyY7hHCeGcmV1r8NkwjAKZcDYy3wLAUFRZlJ+JZ+t2TgzDtA5T6thC5luvR8LdRxT7LRXXiuKpsWUAwMnRmNDX3YrdqolxcqU5x1AK3x4c0B9CoM4JhXJF29A3Q0qgOY8CQCJCRfXgwScU6+3bbx1GOKB/M35vXwR+r4RkvqTN760MBcBEBBpiLjDfAsAeNaA6vtScBQHrIat4NOwXGhzaCCqmaNbxnGkNNPOtwfAhmf8SWZHmW2eFb70eCf/8ntfg33/7BywFbwFlvgWAsaYy3zZX+NaU+Vado7tNBPl6bSiyaQbiFL4VKAeIaftPW4Vvr92zcgoh1XybKdSnYI9C5hHB4dvNzLfU+aC/Q+z7roVgDdwny7Js2YAcVa/bilyde/RCX98ZctZcxTB2Y2jnTJZlPProozh79izGx8ev+zM2Nmb4BN773vfic5/7HP7mb/4Gr7zyCn71V38V6XQaP/dzPwcAeOCBB/C+971P+/qPfexj+B//43/gC1/4Avbs2YO5uTnMzc0hlXL+TRjDMAzDNCsPnZ4CoFhvt7N70EOd1c0ghnE6FL6lDVBaSFri8C3DMJuwuMkC6AHV/HBxwXnPtdNqcGdH1Plt9I6qRrKzM9wOmHEGW5lvAWCoS/lccfiWYRg7mN6gAOBNR5Xw7bfPzgsrlpVlWTPf3j7aI+Q19UCmxJm1LPKl+hiURHJ+TrlfOTTYqft72vxeLQS3YKHbUFKgOU8z3woIFdnNxHIa3z2/AAB456tHDH2v3+vB/n4lyHluznndKuoNBXdEBMB6asK3IsYlWZY18+2hOplvAWC0V3mmG190SfhWtYqP1MHoyOZbd7CWKeI//MXjePCJK40+FVswa76lcTIhoEiF5m8ndlyLtQfRL8D4uK9PGUvHmmAs1cy3mxTbOo2IZr41ft9Mwb/usJnwrfKZabU9EzvMt1qQOb3xe5ktlJFWzbIxx5lvlfBttk7mW+1etU7m24WkIq8QMQ5udLzVjP7wbaZQRq5YAWD+Ogj6vFpgf8XAsYFaS7vz5iqGsRPD4dsDBw5gampK2An85E/+JD7xiU/gd3/3d3HzzTfj+eefx7e+9S0MDCgLgVevXsXs7Kz29Z/97GdRKBTwEz/xE9ixY4f25xOf+ISwc2IYhmEYpsrcWg6PX1YsNved2Lnt19duBi1a2AxiGKdDC0Z96gISVR4vtlgVN8Mw+lkf2icofDu5mqnbIqReZtfImuf81qlHdyh2/rOzHL5lGk+pXMGMao7ezIQzpIbap9WvYxiGEcn0OvMtANy1vxfhgBezazm8PC1mvryynMFiMo+Az4Obh6NCXlMPve0BhANeyHI1aNxMXJhTiq4ODRprv0s2pYWElfCt2oZVSPi2ecy3X3pyArIMvP5gH0Z7I4a//4ga5DzH95pC7Ytk5CqUK6ZsfOt5cWoNiVwJ4YBXMyjWg1H1WG4x306o5tvdMfvfQypUm13LolCq2H48xh4eu7yEp6+s4n8+fqXRp2ILps23WpGKiPCt+22Ce9Xw7eVmMt/WoUhBBFXzrfFrkYJ/HL7VTzyjvM/RkLgQLFlXNzPf0nsc8HmEdCcQSVgz39Y3fGuX+XZ9xwZ6NhvoFG2+VeaQ1bT+zy2dW9Dn0d53M5D91kjwN1+qBn85fMu0GobCtx6PBwcOHMDy8rLQk/hP/+k/YWJiAvl8Hk899RROnjyp/dujjz6K//k//6f231euXIEsy9f9+b3f+z2h58QwDMMwjMI/Pj8NWQZu29OteyFB2wzi8C3jYq4331LbH77uGYbZmKVNzLex9iB6IgHIsvM2GMjISYZOJ3NsiM23jHOYS+RQrsgIeD2btp2jQBybbxmGEU25ImNuTQn276yxcbX5vXjdAaUd8CNn54Qc66kxZa/g5uEo2vzmN/eMIkmSZkucaDJboizLOEfm2wH95lsA6O+k9RbzhRtVc571zWhqp50vVRxtIM4Wyvjy05MAgHfdacx6SxxRuyy8Mtfa95qyLNeYb61vqocCXoTUsWN9kMEMD59RxrY3HOpD0Fe/MWlvr8vCt+q4Wg/zbV97EG1+Dyoy3xc3M/OJnPq/7lwXXVWDdEbDhzRPGm3bvRHa/B10b6CJiiam41nkis69r5BludrppmnMt8qcmDJjvtWuf+PXXqxFuwUmNPOtuPBnbJsgM9lYeyOBbTuY1ptwQHkfMoX6dMtICSw2rEUz0a67Z51Xn80GRJtvw8bNt7X7D1aug55NftatqJ3rRL/3DON0DIVvAeCjH/0ofuu3fgsvv/yyHefDMAzDMIzDeOj0NADg/hO7dH8PhRHZfMu4mfXm274WXUhiGEY/m5lvAWC/ar+9uOCsNrYzanBnR9T5mwlH1fDtpYWUozdpmNZgckU1TnaH4PFsvNi9QzVKc8iAYRjRzCdyKFVk+DwS+juu3QC855jSce7bZ+eFHOup8RUAwMnRHiGvZwQK3042Wfh2PpFHIleC1yNhX78xqyT9Pq0UOycFBidrrVoigkV28U8vTCORK2F3TxivP9hv6jUO7yDzrbPu1+tNtlhGuSIDgDCrWs8mLXzN8Ig6tt17bNDyaxmBbMpXVzIolZvf3npVM9/aH76tLaa42mTjOVOFQrepfKkpbOhGIdmC0RbenWr4KCHgPRFpHXcqsUgAXSE/ZBm4suzcYoaVdEEzeO5smvCtct2kTVjmKfhH87URqubb1uoWGM8qP2/UhC14M2j82ex+qTpOibWviiBUZ/NtWg35ijYAx2ruWWVZuR+WZVkrQBnoEBy+VY8XzxS1420HFbOZ+bzWQteukeI4bZ4K+uDdZC2UYdyK4fDtAw88gFOnTuH48eMIhULo6em55g/DMAzDMO7hldkEzs0lEfB68NYbd+j+vqr5llvoMu5lkSpIO5SHUK3yONlaC0kMw+inWnl+/eLXAQrfzjvTfLszKnbx0A52dLUhGvajVJFxacFZ7yPTekypFpxdW2zEDdWYb/UuojMMw+hhWp2/d0Tbrtv0euPhfng9Es7NJbVwlRVOaeHbmOXXMopmvhXwc9QTst6O9kYMmzm19RYLZkHaFBWxGe31SNrrODV8K8sy/ubxCQDAO1+92/RG8OFBpdBrfDmNbJ2CA06ETGIeCZZa2dZCYZIVi8Gcy4spXFpIwe+V8AOHzYWszTLY2YY2vweliqy1Im9mJlaU0Fs9zLcAOHzrAih4BECz77uFYrmChDr2GTXfUqELWTCtkMiJK55xKpIkafbbywvODd/SOD/QGayrZd0KHSbDt6VyBWvq9dttKXzbWsKSNc18K+7zul3nxWX1PspokUA9CPvrG75NCXzeqYUCrflSBVlV/JDIlZArKoVX1KVEFGSbLpQrSOt87+g6sBq+7VGPHc/on79ornNzkQjDbIbhq/7Tn/60DafBMAzDMIwTIevtGw/3o8tAS5l+tbWGlc0ghnE6msGyXbnetcWPNF/3DMNszFbmWy1867DQ6GxcNd92Od/kIUkSjg114rFLyzgzs4YbdnY1+pSYFmZS3Yzb1b15YGFI/VylC2UksiVD99sMwzBbMb1KxTPXz9/RcAC37+nBE2PLeOTsHH7xtXtNH2dyJYPpeBY+j4RXjURNv45ZRmLNGdY6P6eYUw8NdBj+3j4Bxc5a22pBm6KdbT5Hmw6fnVjF2dkEgj4P3n7rsOnX6esIorc9iKVUHufnk7h5OCruJJuIRE2YQVRLYwoHGDFrbcTDZ+YAAHfs60VnncNpHo+EPbEIzs0lMb6Uxp5eY1ZrJ1EsVzCjPgeOxOrzcww3qcmcqVIbvp1dy+GAiTnOqZD1U5KMWyw7Q+IKVETP305lb287Tl+NY2zRWetjtUyqxbbDWzzvOw0y36YMhm/XskVQrXDURJC0r6M1uwVSYFHkOg9ZV1czRZTKFfi813oWl9R9qVjEeeZbKtiqVwEbXeeiw7fhgBcBnweFUgXLqQLCPT4sqs9lnW0+tPnFhvFDfi+CPg/ypQpW0wVdPw+Zka2GsDXzbUb//TlZ3jsFhs4ZplkwPNq8613vsuM8GIZhGIZxGOWKjH98Xgnf3ndip6Hv7VNDiIst9kDNtA6ViqxVkJL5lkyWrdZCiWEY/dBCM4X2a6GNKScZW4vlCubVBcShDcI7TuToDiV8e3Ym0ehTYVocPebbUMCLnkgAK+kCpuNZDt8yruX5yTi+9OQEfvvNh9AvuA0jszFkvt2sAOCeYwNq+HbeUvj2KdV6e+OuLoQD9Q+CUFhLhMG3npyfV8O3g8aDSVqxc9K6+VZUOLGjzQ+s5ZDIOtN8+8UnFOvtfTfvtNz698iODvz7xTzOzSZaNnxLYQaR5sWeyNZtlPXy8Jl5AMA9Rwcsn5MZRnuV8O3YUho/0JAzEMP0ahblioygz6PZtu2GzbfNj5vNt6tpJUwUDfkN29Nprk0IKFCpmm/dHb7d16+ab50cvl1R7rWH62QHF4HZ8O2qGiLtbPNdF/bUAwVBW61boB3m22g4AI8EVGTl97Je7qDtVznQfBtSnxXrZr5Vr/OI4PCtJEmIRQKYXcthNVPAcE8Y86qEaqBT/FqHJEnoDgcwl6gebztWtBC2RfOt+v1xI+HbrNjnTIZpJozPkAAuX76M97///fipn/opLCwsAAC++c1v4syZM0JPjmEYhmGYxvHE5WXMJ/LoCvnxA4f7DH0vtdZg8y3jVuLZIkoVpeSbFpB6O2ghia97hmE2hsy3FNqv5cCAYr6dWE4jV3RGG9u5tRxkGQh4PZYX7OrF0SGlHfDZWQ7fMo1lSudm3FBUWZyfiTd/e2KG2Yw/++4l/P2zU/g/z0w1+lRaBioA2Mh8CwBvUoNpz1xZsWSaPDW+DAA4ORoz/RpWICPj1ZUMZFJyNQFkvj1owgpIQbhFC8+d1fCkIPOtZvVznvl2IZHDN16aBQD87B0jll/vsBqYPqf+DluRlA3hr5hmvjV/Xc+t5fDCZByS1NjwLQCMLzk3MKaHCTUAu7snDI/BoKFZOHzb/NTuA8y6LHxLXc7MtPCmAFKuWEG+ZG2tp2q+dXeoaW+vsj42tpRu8JlszqSOYlunQcbMtOHwrfKs0G1yXZD2TLLFMjIFZxZqiaZckbViNzO24M3weqSagqXr75mWVemDVeOpHWjm22J9roF0Xhlv7ShW6A5fWzRGxSd2hG+B6mePgvDbQSHsWLu1AqpuE50pkpr51t1FIgyzEYbDt//2b/+GG2+8EU899RT+4R/+AamU8hD5wgsv4AMf+IDwE2QYhmEYpjE8dFqx3r71ph0I+oy1yhDRBpFhnAzZK7vDfgR8yi11rxrCTeZLjgnOMQzjHIrlirZI1rfB4ldfexBdIT8qMjDukA0G2jDbEW2r26arVY4NdQEAzs4kUKk0TwiHcR/VNpRbb8YNdSn/PrPG4VvGvVxULZ+tHFarN1Orypiyc5MxaFd3GEd3dKIiA995Zd70cch8e3Jvj+nXsMLOaAgeSdnMb5bOO+WKjItqp4PDJsy3tKm7kDC/3kJhgHZBm9EdAq1+ovlfpyZRqsi4ZaQbN+zssvx6R3ZwoRdtqots40vhACvm22+fnQMAnBiOaoboelMN3zrjec4sV5eV8x+J1c/ouLvGZN5MxRSMQjpfQrIm0DfrsmcbMt+aCd/WzrU0/5qlaq53d6hpX58ylo4tph07HtC99vAmXSacSNV8a2zfYlWdm7tNdg+IBLxo8yv7J61iv01kq/fEnQLDt0BVBLO8QfdFuo/qtRi6tAMK39bDfFupyLaZb4FquHlVC98qz6EkpRJNt9olS6+Blq4DM3PWRsfVG/oFqs+Dbi8SYZiNMBy+/W//7b/hD/7gD/Dtb38bgUD1A/vGN74RTz75pNCTYxiGYRimMWQLZXzrZcUM8rYTOw1/vwgTC8M4Gc1eWbOQ0RnyIaC2XrLaKpFhGPdBi6Jej7ThgrUkSTjQr9g9KJDRaMjEuaOreVqE7+2NIODzIF0oszWJaRiFUgVzaihqs5bvxJBqpZxm8y3jUnLF6nh8fq51w2r1hsaUXZuYb4Gq/fbbZ82Fb+fWcphYzsAjAbeOdJt6DasEfB7sUIsYri43x7x/ZTmNQqmCNr/HVKtiWm9JF8qGzWUAIMuycHMeGaWshopEUyxX8LdPTQAAHhBgvQWAw4NK+PbcbMKxgSC7SQo2JwPVcIAVE/fDZ5Sx7N5jg0LOyQx71cDYlaXmGI82Y2KZzLeRuh2T7pmT+ZLWqptpHhbW7QG4zXy7YsF86/VI6AiKmSdbxXy7OxaG1yMhlS9dd205hSn1+WZXTwuZb8PmrjtJkrQ9lGYplrNKPFstVPJ7TTUi3xQah5Y2eC9pz8qq8dQOwgG6/uwP36ZrDMsii8UIWtdfWWe+7e+wyXwbNnafTFbkXosG5J7wtSFjPSSyrVEkwjAbYXi0f+mll3D//fdf9/f9/f1YWloSclIMwzAMwzSWR87OIV0oY7gnhFtMbKL1qQ8ZiRwbQBl3QgsZZHkGlIUkqnpdcujCIMMwjYMWRWORwKYW2QMDSvj20rwzzIBk4hzaIrjjNHxej2aRa2UjGdNYZuJZyDLQ5vdsu9hNLeFn4u7aoGYYYnwpDRKRjy0qoUPGXmRZ1gpoNjPfAsA9x5Tw7fcuLiJrwkD01PgyAMU638gQCJkZm6Xo5oJqgD440AGvic4CkaAPEdUcZSaQki9VUCwrH0pR4clOzXzrrPDtI2fmsZDMo7c9iB+6YYeQ19zXH4HPIyGRK7kuXKaXqjlZ3Oc+ZjF8u5Yp4skxZUy6p4Hh21G1Vfp0PNvU66ET6nhaT/NtKODViguaZTxnqsyvs7HPuWx8XLFgvgWq823CYrBcM9+6vJ130OfVOshcXnRGcXotlYrclOZb8+Fb5brttmDRpDDoRoFRN0JFJF2CrbdA1bq6lfk2ZtF4agdkvs0W7X9eoICvzyMh6BMbfgauLxqjvcIBu8y3EWMG2pUUmW+tnU+UwrcmzLeijc8M0wwYHm2i0ShmZ2ev+/vTp09j507jZjyGYRiGYZzHQ6enAQD337wTkmR8M6izzac91LD9lnEjtFC0voWPtviR5uueYZhr2ciYvZ79/Upo1Gnm26Gu5gnfAsCxIcVIdmZmrcFnwrQqtBG3qzu87b30kBa+ZfMt404u1cxppYrsyA10t7GcLiBXrECSoFlhN+Lojk7sjIaQK1bw/UvGpRpPja8AAG4f7TF9riKgVuUTTWK+PVcTvjVLf6dS8LyQMB5uqrXutQfEhHdEhYpE8zdPXAEA/PTtwwgI2ngP+rzYr3areKVFC71SOfvMtxsFSfTwnXPzKFVkHBxox2hv/Wyt6+kO+zXT15XldMPOwypkEt9dx/AtUB3POXzbfFD4ltY7Ztfc9WxD5k+z4VsKIVEoyQylckVr1+528y0A7OtT5trLi84bSxeSeRTKFXg9UlN1iooElfBj0mj4llrYb9DFSy997ZvbWt1IXB0z7Ajf0ji7fv+pUpG1MOhWa8+NIqSGbzMmij6Nksqr5uE2n6n97e1YH76lOXCg017zrR4DrSzLwkLY9HOuZgq6O35oRSItME8xzHoMrzi84x3vwO/8zu9gbm4OkiShUqngsccew2/+5m/igQcesOMcGYZhGIapI4vJPP79orLxdt8Jc4U1kiRpRlCntgZiGCtsZL4FqgsbS0nzrRIZhnEn1Fpt/bhRywF1I98p4dtZ1cTZTOZbQAkTAcDZmdYMRDCNZ3JVCQwMb2GcJIaiyuI8h28Zt3Jp3Zx2wSF2dzczrRYA9HcEtwwcSpKENx1V7LePnJkzfJxTavj2ZKPDt2o4bLJJwlr0GSBTvxmsrLdQy+r2oG/TbgxGoRCQ1XbaIrm0kMSp8RV4PRJ++uSI0Nem3x0FqVsNre25wDa+scjGQRK9PHJmHgBwbwOtt4Ayro6qgbFxBwbG9CDLshZ+Henh8C2jDwoe3TzcBUAxoRu1azoZCjJ1mwwfdgqYJ1M176fI4gensrdPKaQYc2Dh3pT6vL+jqw0+r3irpl2Q+bZQqqBY1t+NhAKGVsy3WmDUZJFNs2Gr+XaTgqW1bBFlteWM2UIBO9HMt3UJ3yrHaBd4r1rLdeHbJIVvbTLfhqsh2O1IF8rIq92GYtt04tqOaFi5fssVWXeHEyrGbIV5imHWY/iO4A//8A9x+PBhDA8PI5VK4ejRo3jd616HO++8E+9///vtOEeGYRiGYerI/31hBuWKjOPDUexVF4zNQK3CFpPuajPFMMD24dvFFqniZhhGP3rMtwcGlHn3ypIz2nJPk/k22jwmDwA4qppvz7aojYxpPBQA26WjBeVONdw+n8ihZGADjGGahUvqhrnfq4T8WjWsVk9o/t6po3jmHjV8+51zC9pmrR6WUnktWO0Y822ThLXOizDfWgrfireWUvvrpAWjn2hemFQ6INy2pxuDgq10h9VCr5Y136oBMJGBhh41HJArVpApGAun5Ypl/NuFRQCND98CwF7VvDu21Jzh28VUHtliGR5J372sSIZ7mquYgqkyn1Dmo7197Vowf86End2pkG3QbJCJ5kkrhniav9v8HvibKPBpFtqXGnNgIUO12La+Y6RVIjXztpFwPLWcNxs+B2qEJS2yZ5KwM3yrvZfXBjGpgKmzzSes44NIwvU03+bE36vWUhu+lWVZmwP7O2wy30aU60hP+HZFvS7a/B6ELXY5afN7td9bXMexgarhvdOGa59hnI7hkTcQCOBzn/scxsbG8PWvfx1f+tKXcO7cOTz44IPwer12nCPDMAzDMHXkodPTAIC3mbTeEvSgweZbxo1QuHZ9iC7WYi2UGIbRz5IO8+1gZxvagz6UKjImHNCmdHatOc23hwc7IUnKBqAd4/F8Iod/fnFWd8stpvWYUq2Twz3bf3Z624PweyVUZGCe75sZF3JpXglovvZAH4Bq8LBVqFRk3Pdnj+HNn/6eIcOUFch8u1NHIOC20R50hfxYSRfw7MSq7mOQ9fbwYAeiFjbiRTDSowTdmsGUmCuWtVb0Vsy31fUW48EmCk6KDN+S+dZKO23R0PUwqgYhRXKkxcO3SRuuoUjAqwVFjFrxvndhEdliGTujIRxTi/AaCV1z400avr26TEbHUN3DO2y+bV7IfNvfEdQKHubW3BO+tWq+FTFP0vd2tEgr731q+PayA823kyv6n/edhN/r0cb1lKHwLV3/5q+9VtsziauB5aiF92wz6L1c3y2AwrhbSR8aSciv3DfWJXxrQ6FYLVr4NlNAIlvSBBpbrflbQTPfprefQ5bU64K6Sog6Nll+tyORVd77zhaZqximFtNPTsPDw3jLW96Ct7/97Thw4IDIc2IYhmEYpkFcWkjipek1+DwSfvimHZZeq08z37bGAzXTWmxmvu1rsRZKDMPop2q+3XyzRpIk7O9XNhguzDd2gyGdL2lt0nYItoXZTSTow2hM2fQ+OyM2FCHLMn75i8/g1//uOTx6flHoazPuwYgJx+ORsKNL2bSbUW2VDOMWSuWKFj56643K82WrhW/HllJ4fjKOc3PJugWxjJhv/V4P3ni4HwDwyJk53ceg8O3JBltvgWpYazGZN2zMrDeXFlKoyMpGvJXN2X61peliwoz5Vrm/E7kZTSFMK+20RWPEQm+UI2pwenwpjVzR/gCB06Dfc7vATXVJkrQ2yno394mHz8wDAO45NgBJkoSdk1kofHulScO3E2r4diRWf6Pj7hiHb5uVBXU+Guxq08K3sy4K32rmW5Nhpk4B8yQFmlqllffePmUsnY5nHTfXTq3ad49hN2SmTuf1v6da+DYiwHybbI09kzUbzbe0rrx+/4n+26yh224iQTLf2v+8QOHbSB3Mt/NqMWQ07Eeb3x5RJQVg9dhnVwRfB2TdpUD5dlTNt60xVzFMLc5zjjMMwzAM0zDIevv6g31a+xKzaG0QTWwGMYzToUrivnWfk1ZrocQwjH70mG8B4IAavr240Nhw0uyaEtzpaPM1pVnliGq9OiM4fHt6Mo4XppQ2xq/MtabtjNkeMt/q3Ywbiiob1By+ZdzG5GoWhXIFbf5qwHM6nnVUa3q7OX01rv3/sTqZu6Y0860+G9c9RwcAAN9+ZV631f3JsWUAwMm9MRNnKJausF/b2CYTmVOh8PmhgQ5LIUFtvcVEsXMiR+Edcfd3ZDZyVPhWDcZQOFskfR1B9EQCqMjAxQYXzDWClGZfFLup3mMifFsqV/Cdc2r49uig0PMxC4VvLy2mmrJTxoV5ZZzaY4M1ejvo8zoTz9XNFs+IgcJHA51tWvHu3Jqz52S9yLKsjUsUQjIKtd+mVvRmSLaY+TYWCaCzzQdZhtY1wCk0q/kWqIYRU3n91yKFz3tEhG/TrbFnEqfwrR3m2wjJX659L5cFG09FEwoowdRssWz7/VGazLc2FSvQZ2EtW9QKXwc67BNXaPZZHeFbug6sfF43PLbO+/OkDc+aDNMscPiWYRiGYRgASjvMr52eAQDcd2Kn5dcjE4uZNogM42TKFRkr6kNsb8e1D7Gt1kKJYRj9aMbsbYpbDgxQ+LaxG/nTcWX+1mPNcyLUcvas4HbADz4xof3/STYyMRuQK5a1z7vezbgh9XM2zeFbxmVcUueyvb3t6I4EMNipbEhRsKcVOD0Z1/7/5cX6mm936ZzDX3ewDwGfBxPLGV3m/XimgPPq7/C2PY033wLVwNaEw8IZ66H37ZBqTjVLv7q5a2a9pbohKm4zmox+Vtppi4bMmXaEbyVJwpEdyu/wFcH3ms2Adg0JtolRSGDZQPj21JUVxDNFdIf9uG1Pt9DzMcv+/nZ4JMUQ1ozdwJ6ZWAUAnBiO1v3Yfe1BBH0elCsyZuO8ntwsyLKM+YQavu1ow6Da1cMt5tt0oYyCGgY3G2rr0OZJ80UqNPZ2toj5VpIk7FOL0y8vOOv+zkinG6dRDd/qM9+WK7IWJI1aCJL2qXsoS004L5rBTvMt7T+lC+VrrNAki1m/X+UUwgHl2pNlIFe0t8CGzLftAXvGy6j6e5Vl4IJaXEn74XZAhR+5YgXZwtaf3WWLpvbrjq2Gb1d1BH9L5Yr23rfKXMUwtXD4lmEYhmEYAMCzV1cxHc+iI+jDm1TzjRXI7LfIIUTGZSyn86jIgEe6/iGWqrjXt/1hGIbRjNnbmm+VjfxLDbZozarBHbLWNBtHd6jh25k1Ya+5lMrjn1+c1f6b26EyG0HGyfagT/dGC4Xc2XzLuA2yuFNhCQUOz821UPj2GvNtncK3aiBAr/k2EvThNft7AQDfPju37dc/fWUVsgzs64tse19TL5qlVblmvrUavu00b75N2WAj6qgx3zrB9JkrljGvdmGyI3wLAIcHlXvNVuyEQJvqoo1WMc18q/+6fuSMYr29+8gAfF5nbHe2+b2aNbbZ5rtcsYyX1C4fjSiu8Hgk7FLnLqeP50yVRLakBan6O4M15lt3hG+phXeb36OZG41ChngR5tvOFrIJ7u1VniHq1T1CD6VyRQuW6+104yTag8o1TGbQ7Uhki6BbOwrhmYH2UBK5EvIlfcHfZmYtowaWQ+KDsO1BHwI+5Z6ntmCJZDCONd/6q+NnpmBvtwwqVrDLfOvzerQwOt3r9dtovm0P+uDzKF1TtgvB0pxFIW2rUHGcnvBtqmZcYfMt04o442mUYRiGYZiG84Jq5HnNgV60+c0tJNWimVgSHL5l3MVSstpqyeu5tlUohW9XMgWUuEUewzAq+VJZsx70bmO+3a+aPcaWUg0dRygEONSk5tujqvl2bCktbFH3y09PolCuaJYv3hBmNoIsOLu6Q7pbig9p4Vt3bFAzDEHm2/19ytx2WA0cnm+yMJJZ0vkSztcE88aW7A8OJHNFzapmxF5/j1qA+8jZ+W2/9qmxZQDAyb0xE2doDyM9TRa+HbBqvlXuJ+OZouEAQ7VttUDzbUh5rXJFRrbY+EDFlDoXdwR9lixtW0Hj2bnZ1hjParEr0NBDbZR1mm9lWcYjZ5SCgXuPDQo9F6vQZ7zZ5ruXptdQKFfQ2x7ASKwxoTIKzDutzTyzOfOqhb0r5Eeb34tBNXzrFvMttfm2EmjrDFWLVMxih7ne6ezrVwoZLjsofDu7lkO5IiPg82j3Y81E1Xyr71qk678j6IPfQpFLV8ivhQdbQVpip/lWkiT0UreAGvER/f9eQaFL0Xg9EoJqaDizjb3VKhQujwju0lBLjxpGpy4YAzaabyVJQrfOEGzVfCvmOqBnqZX09sUjiazyvof8Xi0gzjCthK6r/sUXX9T9h2EYhmGY5oQsXSOxiJDXIwPOUiqPcqXx5hOGEcWitpBx/QN1TyQASVJazqzoqAZlGKY1oIVlv1faduF1ZzSEcMCLYlnGRAMDJNNqCLBZw7f9HW3o6whClsUYp0rlCv72yQkAwK/+wD4ASlCyyIUWzDqm1M/tsAHT3hCbbxmXcpnCt2phycEmDSOZ5aXpNVRkaBvNlxdStltBp9VxJBr2G9ps/MEjA5Ak4MWpNcyubT0WPTW+AgA4OVp/K+JmUFhrYtm54du1TBFzalvugxbNt10hv7ahabStvRbeEbgZHfJ7tcJU2nRtJBTC3tUT1l0IY5QjO6rmWyfYfutFpSJXW/kKDjSQoWtFZyjnpek1zKzlEA548ZoDvULPxSrNanp/5soqAOCWkW7bPjvbQZ+tWnM842zm1bmNgkea+TbhkvCtauOmtt9m0My3OQvm23zrhW818+2Sc8L4WrFtNASPpzHjpBVo7tZrvo2r+xvdFoN8Ho+kzfOtEL6NZ5Wf0a4isNgG3ReXNeOpc0PhYdUebnexntalwc7wrfqZoOKAgU57O8d1q9fS6jYhWArf9ggK39LrxHXsdSZsKPJkmGZCV/j25ptvxokTJ7T/3eoPwzAMwzDNyVSNpUsEMTWEWJGBFZ3WCoZpBpbUzc2NWqx6PZJW9doKC0kMw+iDQhGxSHDbxXmPR9JCShfnG2f3oODNUNTexUM7Oapu3J6dsd4O+DvnFjCzlkNPJICfv2sUQZ8H5YqMWTaVMuuggjYj99Q71c/ZNIdvGRchy3LVfKvOaxRGOj+fbImwGgWHXn+wD5KktFnVa3Q0y7Q6Bhmx3gLKs82rdncDAP5lC/ttMlfEmRmlJfnJUeeYb3erhsZJB5tvz88rIbyhrjbLLaMlSUKfurG+YDB8m7IhvCNJkvZ6SQvBIlFMriifg9099hWR7e9vh9cjIZ4pYr6FOj6lC7XtZEWbb9Xwrc5x8pEzylj1+oN9Qjp4iYTMyBfmmyt8++yEUlxx60jjiivIqn7qynLDzoExBo2BFDza0amMvSvpAnIOsKFbhWx/PRbMtzReJrIWwrdaqKl1Wnnv61MkMWOLacc8O0yp9xi7DBTbOgmj4Vu6/q2Gb4GqyGQp5f77JjvNt0D1nqn2vRRtPLWDcEC5/uw232qFYjaGQOkzUSwrY5Od5lsA6A7rNN9uIQ6yclw99+cUvu206bpnGKejK3w7Pj6OsbExjI+P46tf/SpGR0fx53/+5zh9+jROnz6NP//zP8e+ffvw1a9+1e7zZRiGYRjGJmhzQlT41uf1aO2YFpIcTGHcA5lv+zZ5gG2lhSSGYfRB48FGof2NoJDSpYXGbdaSgXNHV3OabwHg6JASvj0jIHz7xSeuAAB+8rZhtPm9mtXU6e2tmfpDxjpbNAABAABJREFUJpzhbv2bcfQ5S+ZKlmxIDOMkZtdySBfK8HkkrbtKbVjNaGCwGTl9VTEIvnpvDEPq53xs0V5zF4X4jYZvAeBNRwcAAI9sEb59ZmIVFRkYiYW1ttJOgMy3k6sZx3beofDtIYvWW6Jf3eBdMBj8TNgU3qla/Zxjvt1tYzCmze/F3l5lbHtlzvq9ZrNA5mS/t9o6WBQUJNFbpPDwmTkAwL3HBoWehwgODSrPIRfmk44dk9YjyzKenVDNt3u6G3Yet4x0wyMp69TcFaI5qJpvlfuCzpAPITUQP+8C+y2Zb3ssGCwpiJS0MEfS/NpKRsHdsTC8HgmpfMkxzw6TggU29YY6YyR1hm9X1Tm5W4DBlfZMFl2+Z5IrlpErKl2yumwz315/z0Rrz81gvs3ovP7MklLHSyOdYIyyPuTcb7v5Vp+BdkWw+bZ63O3XKqkDSmcLzVMMU4uup+ORkRHtzx/+4R/iM5/5DN797nfjpptuwk033YR3v/vd+PSnP40PfehDdp8vwzAMwzA2IMuyZr410iJ3Oyhk5JTFEYYRweIW5lsA6O24vvKYYZjWhsaN3nZ9C18H+pVQxsWFxphvZVnGzJqySWYmvOMUjqnh27Oz1gIRlxaSeOzSMjwS8DMndwOohjk4fMusx4z5NhL0ae0I2abMuAWy3o7EwgioAa02vxd7VENps7XiNoosyzg9GQcAnNgdxV7N3GXv3K6Zb00EAu5Rw7dPji1vWgjw1JhiRbx9T+OsiBuxoysEv1dCsSw7ts31eTWgeVBU+FZ9Hl00WOxMwR/RJijN6ueAIpJ6hG8B4IjaZeEVi/eazUTVnOyHJIlttx0zYL4dW0zh4kIKPo+EHzjcL/Q8RLC7J4w2vwf5UgUTy85pl74VlxfTWM0UEfR5cMNQV8POoz3oww07leOfGl+p+/GfvrKCz31vrGlC005gQQvfKvOSJEnYoRbozK45c042ggjzLQWRkvmS6WsrmauOv61C0OfFsHpPe9nme2i90PO+kWJbJxExaL4lyyZ1+rMCBUbdvmdChmuPBLQH7AkhUpCZLKf5UlkbI/SuPTcCLXxrs/mWOjV02Bi+XW+D7tcp3DB/PGXspzlpI2RZ1gLZwsK3dNxtQr8Am28ZxnBp6ksvvYTR0dHr/n50dBRnz54VclIMwzAMw9SXeKaItPrAIzJkU90McvcDNdNaLG3TuoWMz8spe1vKMgzTPBg13x5QzbcX5xuzubCcLqBQqkCSqvaaZuSoGog4N5tAqVwx/ToPPjEBAPjBIwPYpW6wcPiW2QxqeW60oI2smGz4YtwChW/J5k6Q9fOCy8O3M2s5LCbz8Hkk3LCzC/v6lPdhbMneENaUBfPt3r527OuLoFiW8ej5xQ2/5tS40gac2oI7Ba9H0uZopwbdLswpn4nDwsK3yj2a0WLnattqe8K3Vqx+ojA7Fxvl8A7ld3lu1t3jWS10/bTbEGboMRC+ffiMYui+Y1/MtpbOVvB6JK2g8nyTzHfPXFGCrseHo1rRTKM4OaoUeDxV5/CtLMv4jf/9PD78jVfw1eem6nrsZmZeNbDXrh2QHX/OFeFb1XwbMT/W1AZmUybnSQr0tZL5FlDuTwH7u0fopXqP0ZyF6u1BJfyYzusLP1LgLiogfNvX3hp7JnH1s9oV8sPjEVuoRFDBEr2XdO/k80haNwonEqLwbdHe8G0jzLd61/zNQgba1S1CsKl8CYWSsv4dExTCrjXuyvLWxSP0HOjka5Bh7MTwE9SRI0fwkY98BIVC9YNdKBTwkY98BEeOHBF6cgzDMAzD1Adql9PXEUSb2hZKBBy+ZdzItubbFmmhxDCMfqrmW53h2wFlc+HyYqohxh0yb/a1Bxu+8WqFPbEIwgEv8qUKxk2GnVL5Er763DQA4IE7RrS/H65pb83oR5Zl/Nl3L+Ffz23e0ryZSeVLWFVbsRltQzmkBuWmOXzLuISLm4VvB9TCiCYJI5nl9FWldfeRHZ1Ke/o6mW/N2LdruUdt3/6I2s69lkyhhBen1gBUg1FOggpjJh1YGCPLMs6R+XZArPl2IWHsuZPMpaI3Ren1kg0238qyrBVH2R2+PTJI41nrmG+TNrY9p0LmVL6EfGnrUMYjZ5UxisYsJ0LFJufnm2O+e2ZCmbduHelu8JkAt48qBR5PqQUf9WJ8Ka3di3/h++PbhkwYBTLOU1EIUA3fsvlWIeDzoM2vrK2YNcTbVTzjdPap99BOMd/SGlCzmm/bg8r9Wkqn+TauXf/W7xtpTdTt5tu1mvCtXcTovVRDtxTC7YkEbAv8iiCsmoCzBXuL9ej6tqNYjKg1y/ZEAgj6xO2rb4Se8C2FsEN+r/ZeizpusSxvO260apEIwxCGd9H+4i/+Ag8//DB27dqFu+++G3fffTd27dqFhx9+GH/xF39hxzkyDMMwDGMzVjfoNqNP2wxq/oU2hiG2M1j2dqgtlJLuruJmGEY/S+oiqN4q+F3dYQR9SpvSqQaEO2nDcUigDb8ReDyS1g74rMl2wA+dnkYqX8Levgju2ter/b2TAz5O5sWpNfzRw+fxvn94qdGnYgv0eY2G/Ybbge6MKhvUbL5l3MJlNXxL5j+iGkZyd1jt+atxAMCJ3VEA0My3l222dk1rz/bmAgFvOjoAAHj0/OJ14bfnJuIoVWQMdbUJXzsQAc3NE8vOm5vnE3kkciV4PZJ2LVilX23vvZA0tt5C4UnRm9E07yWyjTXfrqQLyBTKkCSxnZ02gu4zLy+mkbPZ4OUU7Lp+AKAz5INPDYxsZb+dT+RwWh1j71HHLCdClutmMd8+S+HbPY0P3962pxuSpJgu6yl0eOxyNex7bi6Jxy/XN/zbrNC6/0Bndb1jh2a+bf5nGxHmW6BapGI+fNuaRkEnmW9zxbJmenbivbAeIpr5Vt/9GplvuwW0sNf2TFwevo2rBdldAmzBm0FW02X1vVzcplOjU9DMtwWbzbd1CN/Wfib6bbbe1h6PCv43gvYfRFlvAeV3RsUjq+mt5y+a3zod2JWCYeqB4fDt7bffjrGxMfzBH/wBbrrpJtx000348Ic/jLGxMdx+++12nCPDMAzDMDYzZVPFrma+dfkDNdNabGew7I20RhU3wzD6MWq+rQ1mXJivv91jdo3Ct23bfKXzOUrh2xnjIS9ZlvHFx68AAH721SPX2CMo4HOVw7eGuKK2Ap9P5HVv9jQTkyvKZ8fMPTWF3Tl8y7iFS4sbm28pjHRxvjF293pxejIOoBq+JfPt1ZWM1gpSNLliWXsGMRs6vHlXFH0dQaTyJTw5dm2771OqgfDk3hgkyXlGpZGYc+dmMqPuiYWFdRsiw+CCgWBaqVzRNrtFG4no9RptvqXf/2Bnm9DOThsx0BlENOxHuSLj0oIzjHx2Q2EGO4xWkiRpwYKtWlI/clbpoHBid/SaNvdOgyzXzRC+XUrltU4ht+xuvNk8Gg7gkPr+PX1lZZuvFsfjl5YAAB1qWOfz3x+v27GblUpF1uah2s/jYJdyH+IG8y0FnayYb4HquGm2SKVVw7fVArbGz7P0rB4OeK+xXjYTFEbUbb6l8K2AICkZ7t0uLKmH+Zb2n+h+admG0KUdhP32h2/LFVl7/XYbDayxmjGgHvej3WHleorrMN/GBI9PPTqsu0B1fmu1eYphCFP9IyORCH75l38Zn/zkJ/HJT34Sv/RLv4RIJCL63BiGYRiGqRN2mW/71YcOo20QGcapFMsVbdF1O/Ptcpqve4ZhFLYzZm/EgQFlg+HiQv03a2lDYairOU0etRwdUsK3Z0yEb58cW8HFhRTCAS9+/JZd1/zbcI/y3sQzRW1hndmeqzU2wskGWJ3thgrazNxTV8O3zb9BzTDLqby28UOhU2K4J4w2v2J3n1huvMHKDgqlCl6aXgMA3DysGAQHO9sQDnhRrsi2hUNrAwHRsLkNL49Hwt1HFJPkI2fmrvm3J8eVANTJ0cYHszZi2MGFMRfUtvOHBzuFvabWachA+LY2aGHU0L4dZDiicFCjoN8/XQ92IkmSVlBwrgkCliJI5Sh8a8+mOoUFtjLf0th077FBW85BFHRtXFl2vhmZrLcHB9rRZXL+EA3NNU+N1cc+W6nIeEI91u/96DEAwL+eW3BE4M/JrGQKKFVkSNK16x071D2BORd0wyOzpGXzbci8+bZYriBbtKd4xunQs8R0PNvwsXRytVps68RCND1EDIZvaT4WEb4lIYHb90woHBm1MXyrmW/TeciyrI1TTjff0vWXtTF8my5Ur20yPdtB7WeiHubbaHj7e2S6DmKCrwPt2NuEb5Oa+ba15imGIUyFbx988EG85jWvwdDQECYmJgAAn/rUp/CP//iPQk+OYRiGYZj6QC2Tzbam3Awzm0EM42SoitjrkTZdQKFFDrdXcTMMox+j5lsAOKCaAi81wHw7o9ppdtjcqrceHFPDt2dnE5BlY4bFB5+8AgC4/8TO66r2wwEfetXF7kkHhnycSm3g9qoD24JbRTPfmgj8UPh2ms23jAsgA+TOaAjhwLUbL16P1FQ2QDOcm0ugUKogGvZjj2pjlSQJo71KeGDMpiAPjR87oyFLgYB7jinh2395ZR4V1U6cK5bxvGrzvd2h4Vsy3044cH6hYCZd+yLo7yTjVV63RZqCsUGfBwGfqa2hTekko1+Dzbd0X7a7DuFbADiidll4ZdZ4oVczQpvqdrXxpTDJZsGCtUwRT1xWApJOD9/2dQTRHfajIsPxZuRnVLvsLSPOGd9vH40BAJ4ar4/59uxsAvFMEe1BH37s5iH84OF+AMBfP8b2262YV8O1sUgQfm91XhnsUsK3zW6+LZYrSKhzp9XwIa0pmClSSdV8j50mRycSiwTQ2eaDLFc76TSK6h5a866V0fyttxMRSUi6LYbPgaqwZCVdcHUHlEQdzLdkXi6WZSTzJSzbZDwVTSighGFrA7KiofEy4PUg6LMvfFtrGa6H+ZZ+5/HM5s9adB2INnNXj72N+VZ9TrCrSI9hnI7hFZbPfvazeO9734sf+qEfwurqKsplpTKhu7sbn/70p0WfH8MwDMMwdYDMt2RxEwVV/C0m84YDLwzjRKoBusA17cdridVUcfN1zzBMrlhGUl3UNmK+3d+vtuVuwEbtjBbecW4bV70cHOiA1yNhJV3AvAET/+xaFg+fUVraPnDHng2/hgKWHL7VT62N0IlmQqtYMd9Si/i5RM7VG1FMa3BJDZeSxX091ErarabI01fjAIATw9FrQrB71ba5Y0v2BAem1ef6nRYDAXfuiyES8GI+kceLqsH3hck4CqUK+jqCWojYaVDYci1bxNoWm5KNgMy3hwbFhW9jkSA8ElCRq5aj7Uhq1lLxwR16TceYbwUXl2/GEdVmfG6uRcK3efuuIaDa1n15k/Dtd88voFSRcaC/3bFjESFJkvaZd/p894xqvr11pLvBZ1KFCj3Ozye3DXuI4LFLSwAU467P68EvvGYUAPDVZ6frcvxmhbrdDXReu9ZBhYVLqTwKpUrdz0sU1GJbkqrmP7No5lsTnXNobg35vdeEnFsBSZKwTy1Ov7zQ2PBtdQ+tPvcYdlAN325vHq1UZG386xFgvu0JByCp965b2Tubnbj6GTfbiUQPbX4vOtTf5XKqoHVcE208FU3Yr4RhbTXfqveqdlpvAWU8DqrFjOvnQDvoVq+nVL606by6YlMIu1vrTLH1/JXIKu99Z4sViTAMYfgO7U/+5E/wuc99Dv/v//v/wuerfnBuvfVWvPTSS0JPjmEYhmEY+5FlWVs4sMt8my2WdbeyYRgns5hSjA1bBehiNZXH9MDJMEzrQqH9gNdjaPGJAkuXFlKada5ezKpt73d0Na/Ng2jze7FPbVN4ZmZN9/f9r6euolyRcXK0Z9OgzG4Ht7d2KmSGBdz5vtW2oTRKX0cQPo+EckXGQrK5DVEMQ4a//X2bhG8H3W2+PX1VCTHdPHxtiGlvHc23Vgj6vHjDIcX49+2zSnt3Mg+eHO1xbJtdxUqvPKc5aY4pV2RcVDsZiAzfej2S9vPq7TaUtNFGREY/M6EikdC9xu5Yfe5jD+9QfqevzCZboviWAmB2mRdpPWWzQPnDZ5QxyenWW+KwGs4+7+Bwdq5YxstqocWte5wTvu3rCGJvXwSyDDx9ZdX24z2mGpXv3N8LALhjXwyHBzuQLZbxd6eu2n78ZoXMt+utf91hPwI+D2QZTf1ss6oGjaIhP7ybSBj00mHBEF+1CbZmoGlvr1rAZtM9tF4mLRTbOoUIhW8LpW3XGhO5IuhLrIbPAcDn9WgG6SWdhWPNyFodzLdA1by6nMpr3RprbaxOhMy3GRvDt1QoZrclXJIk7b61vw7m2842P2ga2qwoaFkLYQsO36rBX73m206br32GcSqGw7fj4+M4ceLEdX8fDAaRTje24ohhGIZhGOMspwvIFpWHnSHBhrtwwKdV0+rdDGIYJ7OUVB4wt2od3+b3aouhiy5eSGIYRh+0oNzXETQUVhnpCSPg9SBbLNe1DX2xXMG8ujk2ZDG84xSODXUBAM7O6Nv0LpQq+LtTkwA2t94CHL41SqFUweyae8O3sixjykIbSq9H0tqzztTxM88wdqCFb/u3Cd/OuzR8OxkHAJzYHb3m7zVr16KzzbcAcM+xAQDAI6oF/qlxJZR0ctQ5Lck3YiTmvLl5YjmNfKmCNr9Hu3cQRX8nhW/1BZtSNlpLOyy00xYJ/e5Fv9ebcXCgAx5JMU0ttsC6V0qzJ9uzqd6jmbWu39zPFct49PwigOYJ3x5sAtP7i1NrKJZl9HUE6/a50cvJ0RgA4JQ6B9lFvlTWjvEaNXwrSZJmv/3i4xMolpvX3monc1r49tp1UkmSsEN9tplda97w7XJaGddFtPDutDBP2mmubwb2qgXVdnWP0As977vBfCvLQKa4dQByVe0k0R70IeATY1zu1QKjLjbfZuoVvlXG3aVUQRureh0evg0HlOvPzvAtmW/bg/YHQE/ujSEc8OKmXV22H8vjkbQQ/Mpm4Vv1/pk6SYiCQvPbGatpruq06TmBYZyO4ZlydHQUzz///HV//61vfQtHjhwRcU4MwzAMw9QRst4OdAYR9IlvxdGvGkJbYROCcT8Upu3bpoVPn7b4wdc9w7Q6NP8ZXQD1eT3aBgOFmOrB3FoOsqyYekW3qWoUR3coxqmzs/rCt998eRZLqTwGOoNa+Ggjhjl8a4iZeBa1YhW3vW+JbEkzbJjtJkGB9+l4825QMwygP3x7ZTmN3Dabvs3GSrqAiWVlfDs+HL3m3+w2304JMt8CwBsO9cPnkXBxIYWL80k8q7YkP7k3Zvm17YSCYxMrzpGEkOH5QH+HZWveevo7lGATtf3eDjvDO50hn3qMxplvawt96hWMafN7Map+tl9xcMBSFMm8al8M2hMAo4Db8gab+/9+cQnZYhlDXW24YWenLccXDc13FxxcbPL0FcVsfutIt+PM5lTwcUq1r9vF6atx5IoV9LYHcXCgeu/yozcPobc9iLlEDt94adbWc2hW5tX5h+ajWgY7mz98S+ZbIeFbdZ40Y4hP2Giubwb29VEBW6PNt9Q9snkL1dv8Hs2emd6mUyUF7boj4q673hbYM6mb+Va7Z6ox3woOXYomrJpvs0X7ivWoUKw9KH6vez2ffPtxPPv+N9WtcxwZaGluWo9dBmTtuFuYbysVWXsONNL5j2HchOHw7Xvf+178+q//Or785S9DlmWcOnUKH/7wh/G+970Pv/3bv23HOTIMwzAMYyNTarscM+1x9dDXYawNIsM4GQrR0XW9GbEWqOJmGEYfS+o4sN24sREUWrq4UL/NWtoY2xFtg0dwQKRRHB1SNufP6DTfPvjEBADgp28fgd+7+bIJBXwmXRYitQtq0UimlamV7LZtDpsJ+vl624NaKz2jDLH5lnEBqXxJm0s2C9/2tQfREwlAloGL843dRBfN85NKSHVfX+S6DVcqqlnNFLG6jTXGDNMCAwFdIT9erQZtP/ntC8gVK+iJBHBgk9+pU3Di3EzGSwrhiaTf4HoLbYi22xCcpEBQooHmWyr0afN7ti2YFclhtdDrnM5Cr2amGmiwZ1M9toX59pEzcwCAe44NOi4kuhn0uZ9P5Ldt1dsoqLjilpHuBp/J9dyuhm9fnklo5m47ePzSEgDgzn2xa66toM+Ln331CADg898fhyy759lFFAua+fb68C2Zb+fWmvfZhuyCIsK31XnSePi21c23+8h8u5hu2OcwnS9pc1Mzm28lSUJEncO3G1fpeYWslyJopfBtVOD7thG0/7SULNgWuhQNrZXZab5N5e29V61FkiTT639moM/iZveUNEaJlml0q6+3WegXANKFkiZc6LQ5eM4wTsVw+PYXf/EX8bGPfQzvf//7kclk8NM//dP47Gc/iz/+4z/GO97xDjvOkWEYhmEYG5myuWJXC98mmrfKnWEIMt/2brOR1woLSQzD6KNqvjUeADjQr2zW1jOYRKE/2ihzA2S+vbqS2Xaj68zMGp6ZWIXPI+Gnbh/e8msp4DMdz6LsohCpXZDp9paRbvg8EgrlCuZ1tsluBqigzco9NZlvOXzLNDOXVettb3tw0w1HSZJwSGvF7a6w2umrcQDAid3Xh5jCAZ82v44tiZ3bS+WK1vp5Z1RMIOBNRxX7+zdfVgJvt+1xnhVxPZr5dtk54VsyXh62NXyrbz5NaOEd8RuiFAhK5UsNuy+ie43h7nBdr9Uj6u/2lRYI35Ll364AWM8m4dtSuYJ/eWUeALbsTOE02oM+7d7wnAPNyJWKrIVvb93T0+CzuZ6haAjDPSGUa87TDh67vAwAuGv/9Xb3n3n1bgR8Hrw4pTwnMtdCz3MDndevdwyqJsBmNt+upMSFb8kEmMgaD5JrNsEWDTTtjoXhkZR7jEYJXmgPrSvkb/qW6mSv3858S5ZLkeFbCocuunjPpH7mW2XcvbKcRqFcAWBu7bmeRALKtZfJ2x++jdQhfFtvaH1lZYPwrSzL2v2ziDmrFhoDtjLf0nNmwOtB0Gc4gsgwrsDQlV8qlfDFL34Rd999Ny5evIhUKoW5uTlMTU3hF37hF+w6R4ZhGIZhbISMMGbb424HtZ1y8wM10zroNd9y+JZhGILGATPm2wMDZL6tY/hWtdIMCWhZ7RS6IwHNKHpudutNb7LevvmGQfRvYO+pZaCzDQGvB8WyrAWemM2ZXFGurdHeCHaqIQQnhaOsQj+fFQsOh28ZN3BJnbP290e2/DqyAZ53YBjJCs9PxgEAJ3ZHN/x3st9eXkwLPe58Mo9yRYbfK2mBTKtQ+JY4OXp9KMlpjMSUMfiqg8y3dI0fHBAfvu1T71UWEvqeO1M2BidrX9NOQ+VWkIV+d52NdEfIfOuy8WwjyL7YblP4ttpF6Npr+ukrq1jNFNEd9uN2B4ZEt4KKTZw4311eTGEtW0Sb34NjarcQp3H7HmXuOTW+bMvrp/IlvKDO3Xfu673u33vbg7j/5p0AgM//+7gt59DMzKvzz9bm2+Z9Vl4VaL6l4Gwyb95826qtvIM+rza3X15sTNcM2kMb7mn+tTLd5lstfCsuREp7Jm7tFijLco351ubwrXrPRPcX7UEf2vz1s7CaQTPfFu17VkjbXCjWSHoiyjUVz1w/jyTzJS2ETcFsccfdPnxbLRLxOb5gl2HswlD41ufz4Vd+5VeQyyk3yuFwGP39/bacGMMwDMMw9YGqdu1aOOhXK98XdW4GMYyTWdJpvtXa/rh0IYlhGP1YM98q4dtLC6m6tdaj0N9QV/NvKNRyVN1MPjOztunXrGWK+Nrz0wCAd925Z9vX9HokzWR11UUhUruoblaFtY0zJ4WjrDIpwHy7Uw3fTsebd4OaYahghOztm6GFb+edF0YyS6Ui43nVfHvzcHTDr9nbq8ztY4LDt9OrZK4PweMRs9k1FA3hhp3VMNbJvc4PvNH8MhPPolCqNPhsgFyxjCvLyu/aTvPtvE4LHG2Kdthgggr6vJrlKJE1HiwSwdWae416clgN315aSDniurOTlBYAsydQ0qOGBRK5Eorl6nv58BnFwP2DRwbg8zaXTcvJ8x2ZXG8ejsLv0Pf15Kgy9zw1tmLL658aX0apImN3T3jTsePnXzMKAHjk7Jz2TMMoRmpaJ90ofDuohm+b2Xy7nBZn/hRhvrXDXN8s7O2z5x5aL9rzvqAOE41EC9/mtgvfKtddt0CLZp/LhSW1HSBsN9+q7+UlNZBO+1FOJqyGb7MF+8y31KWBLLtuguai9R0igKqpPRzwaiFnYcel8G26uOn+BM1trTxPMYzhp6nbb78dp0+ftuNcGIZhGIZpANUWufYsHNADdaNaAjGMSNh8yzCMUayYb0diEfg8ElL5Ut3MqrNq6M9N5lsAODrUBQA4O7N5O+D/8+wkcsUKDg924NaR69uFbwRt0PIm7PZUW0GHtHCUm943raDNwj01m28ZN1A137Zv+XVuNN9eXkwhmS8h5PdqpsP17NPMt2KtXdNxZTzdKXj+vufoIADFHHR40JlWxFr6OoJo83tQkYFpB4yllxZSqMiK+crMveB2UPh2Ued9Ipnz7NoUpddNbhPmsAu6r6i3+Xaoqw2dbT6UKrI2BrqRYrmCbFEJS7Tb1Mo3GvKD6gdW1WCBLMv49tl5AMC9xwZtOa6dOHm+e/qKEmi9dcS5xRVU+PHCVBy5oviwzmOXFKPuXfs3t7sfGuzAaw/0oiIDf/3YFeHn0KwspQqQZaUoNbZBOM8V5lt1HBIRaqOihUTOvPnWjuKZZsGue2i92C2wqSc0h6cL24RvqYW9gPA50dtBwhJ37pmQkTTo89huoe1Vx10q/NpoHHYaFL7N2Bi+TdncpaGRdG9hoF1OK58pO0LYZL8ulCub/u6o+LJVDe0MA5gI3/7ar/0a/ut//a/40z/9UzzxxBN48cUXr/nDMAzDMEzzIMuytnBgxdK1FZr5lsO3TJOTK5a1xU4O3zIMo5dFncbsjQj4PNjTq2wwXJyvzwYDBVWGoteba5qZo6qR7OzsxuHbSkXGg09OAFCst3pbZNHGi5sMrnahtYKOudR8u2LdfEufu7VssWEtuxnGKrQhvl349qAaTl1I5rWN3WbntNq2+qZdXZuaGavWLsHhW/W5fqfg5/ofv2UXdnWH8M5Xj8AryKhrJ5IkOWqOobDdoYEOW9pv9qumwcVUXleXhGr41p5N0c6QTz1OY8239Q7fSpKk2W/PzW1e6NXspGvuTewKNHg8kmb1IuPkmZkEpuNZhPxevPZAry3HtRMqXLgwl6xbNxO9PKuab2/Zo6/wsBHs7gljoDOIYlnGadUuL5LHLi0BAO7ct/W1Rfbbrzwz2bAxzmnMq4Uf/R3BDa37ZL5dSOZQKjenFVyo+TZULVAxOhbYPX83Aw033zbIrm8HkaASgEzltw5Akl0zKjDUGVMN90tJdzz/rWdNDSDabb0Fqubbzf7biZCRNVss23ZPRPerdhWKNRIKwW60frKsmm+pi4RIQv5qh5ONrLtAtbCksw7XPsM4FcPh23e84x0YHx/He97zHtx11124+eabceLECe1/GYZhGIZpHhZTeeRLFUiS0p7SDvo7qgttDNPMUJA24PVsW8HZq1aY0kMvwzCty5JOY/ZmHFDDSxfq1KaUWkK6zXx7bEjd9J5PbtgO+HsXFzGxnEFHmw8/dvOQ7td1UsDHySRyRc0AMtztvvBtbUGblc24jja/tqE66wBjIyOOmXgWd3zkO/joN881+lRsJV8qY2JZ2RDfLnzbHvRpYfVzDrQBmoFCQSd2bx5i2qtau66uZIQGUah4RrT5dmc0hO//zhvxO28+LPR17WR3j/oeLzcmnFELtZkn86VoqNNQsSxrrYG3IlUn822iQebbq8vVQp96c0T9HbtlPNsICn+1+T3wb1JgIIKeyLUtdR8+MwcAeP3BPtstcnYw2qt0M0nmS44wchOLyTwmljOQJOBVW8xbjUaSJNw+qlhpnxpfFvraS6m89pm9c9/m5lsAeP2BPuzriyCVL+HLT08KPY9mhbrzUCHIenojQfg8EipytSi52dDMtwLCTGS+LVdkw9ZHCjW1cjvvvb2NNd9O2iywqScRMt9uU/BLazhizbfKZ2k5ra9wrNmob/j22t9Lrw3GU9GEA8q1J8tArmhPUUbK1eFbMt9e/9xHxSK9NhiQJalaHBff5JmTnhM6W3ieYhjDT8jj4+PX/RkbG9P+l2EYhmGY5oFCAjs62xDw2bNwTmGj1Uxxw7ALwzQLizUBuu2sRWy+ZRgGADKFEtLqporZRVAK39ajhW06X9IWiqlFpFvY1R1CR5sPxfLG7YC/+IRivf0Ptwxri8F6cFuI1C7IEhOLBBAJ+rSAKoVkmp3ldAHZYhmSZN0aTcE5J4UzGOt88+U5zK7l8OWnr7pyk5EYX0qjIitWrn4dRSeH1bBavQpM7Ob0VcUgePNwdNOvGeoKoc3vQbEsa5v4IpiyyXzbjIzEnDE3VyoynrisBMXI9CyagM+jGZD0FDxTeMcuaykVqTbCCrmWKWqh30YEY8h8+8omXRbcQNLm8DZB4dvldeHbe28YsPW4dhHwebCvr74FlXp4dmIFgGLmrkdIyAonR3sAAKfGV4S+Lo3Rhwc7tjUGejySZr/9n49fQbni3vs5vSyo4duBTe75PB4JA2owl4p8mwlZlrUigO6I9c9Im98Dn2oIThicJ9l8C+xT18am41nkiva1rN+MKbWTz3B385tvO3SGb1cyZH4WN0fE1Dm+WJaRyLqv2w+tqUYFvmeb0R0OoHaLSkSRgN2EaoqoMgV7fv9a+NaF42V3hAKw1wt/aL7qsSF8W3vslQ2ODQCJLBWJuO99Zxi9GE7ZjIyMbPmHYRiGYZjmYUqr2LVv0aA77IffqzwFchCRaWaWVIutngAdVXFnCmXbFhIYhnE+1Eatze8xXXG/Xw1rXKxD+HZ2Tbkv6Gjzuc6oIkkSjqqhiLPrQhFXlzP47vkFAMDP3mFsXYNCpJMcvt2S9S0ayUi3nC5oC+PNDN1TD3S0IeizZmQj6/RMvPk2qJnNeeaKEhZZzRQxueLeYDUVN+zvb9+2WA2o2kDdYIpM50taqOrE7uimX+fxSNgTU8xdYwLNXdP0bO8yc70ZqDBmosEFHn936ipeml5DOODF3UfsCw1q3YYS26+32B3eIdNRsgHm20k1FNPbHjRUSCWKI1r4tvnHs82ge7YOm01iZHJbSeUxvpTGhfkUfB4JbzzUnOFbwJnz3TNXlIKRW0aca70lKHz73NVVoWKHxy8vAQDu2t+r6+vfdmIXomE/plaz+PbZOWHn0azMq/POwCbmW6Ba1DvXhOHbdKGMgtqlQESoTZIkrR230dAhm2+V0GZnmw+yDFypc3eDtUxRu7excx+tXpD5drv7NQr4dQsM87X5vdp9aLMasbeCrKD1KGrxeqRrrMTrTbhOxOuR0OZX4mlGDeB6ofvViCvNt8p1RUHbWqgDZ49N1wEde3WDYwPVearT4QVdDGMnphV3Z8+exbe+9S380z/90zV/GIZhGIZpHigIYacVRJIkrRXiQtJ9D9RM67BooHV8JOBFULVJU/iOYZjWYzGlbDD1tm9vzN4MMt9enE/abkucVsN+oltWO4WjQ0oo4szM2jV//6WnJiDLwOsO9mFUbWWoFwqTuiVEahdX14VvO9v82sKtG4LL1XCx9c8OmXNn2HzrGmRZxtNqwAUAnp+KN+5kbEYL36qGv+04NKiMy+fnmt8U+eLUGiqyModuFUIBoBkQxxbFBAdkWdZs2Wy+rRZ4NNJ8O5/I4WPfPAcA+K17D2HQxo4C/Z3611vISNtpU/iWwhRkPqon9PveLWAuNsPBgXZIklJ0vujSta+kzeZkgoxdK+kCHlGtt6/eG0NXHSxydkHh2/MOCt8+PaHcm9y6x/nh2/397eiJBJArVvDSdFzY6z52STHf3rU/puvrQwEvfubkbgDA578/Luw8mpV51Xy71RxH/9aM5tuVVLWYOhSwVmBJmDXEs/lW2WPaq95DX16ob/i2tsBH1LXQSCI6zLeViqy1thdt0nRzx0Ay33aF6hOErQ3cbmdwdwpUJGdX+DadV17X7mKxRtCthq0TuRJK5WuLkZbTyuep1yYDMoXwVzc13yrjiV3PmQzTDBgO346NjeH48eO44YYb8Na3vhX33Xcf7rvvPtx///24//777ThHhmEYhmFsQjPf9thbsUthRWpHxTDNCC0I6QnfSpJUXUhKu28hiWEYfSyq4Xs948Zm7O2LwCMpC2t2b+TPqsGdHTYGRBrJsaEuAMDZmWrIK1cs4yvPTAIA3mXQegu4L0RqF2T6rA3EkJmw0W3BRUCbcSIsOFXzLYdv3cKV5cw1G4svTMYbdzI2U2u+1cMh1e5+YT5le4GJ3ZyeVEJMNw9Ht/3afX2q+XZJjPl2KVVAvlSBJAE7ujh8Wzu/NOq6+sA/nkEyX8Lx4SgeuGOPrcfS1luSW6+3yLJcNZfaZM6jUFCyAQVJ1fBtY4x04YBPs1qfc0FBwUZUrx+7w7fKNb2cLuBhNXx777Hmtd4CwGGHhW+zhTLOTCsFibeO9DT4bLZHkiTcvkc5z6fGV4S85uRKBldXMvB5JNw+qi98CwAP3LEHfq+Ep6+s4kUXF1TpYV5dn+jfYr2jar5tvmcbaq0tspU7zb8Jw+Hb+tk0nUy1gM3+zlC11ENgU0+oM1d6i259yVwJ5YpyHx0VXPxCXQXJ1Okm4lnlZ6rXZ7V2fNLTrdEJhPxKgN2ubpFUrOBG823tdRVfV+xINlzRYXmCLMubmW+TeTbfMozh8O1/+S//BaOjo1hYWEA4HMaZM2fwve99D7feeiseffRRG06RYRiGYRi7mFqtz8JBn9oG0Y2tZJjWgUJvvTqriHvVxecll1pvGIbZHpr39I4bGxH0ebWN/IsL9m4wUNhvyK3mW7Ud8NnZhBbG+acXZhDPFLGrO4Q3HOo39bpuCpHahWa+rQmnkgXXDaFlKmgbFnBPTebpaQ7fuoanryghERKgt0L49sCAvvDt3r4I/F4JqXyp6a/501fjAIATu6Pbfq1oaxe9d/0dQQR8phvduYZd3SFIkmJTWt5kc9BOHj4zh2+dmYPPI+Gjb7sRXo+57gd66VfXWxYSWz93ZgplqBkKLXQhmk41VGTU6CeCRodvAeDIDiVgeW7WGQFL0STUMINd1w8RU0MD5+aSeE4dW990dNDWY9rNQbXY5PJiCsV1prJG8MJUHKWKjP6OYNOEyW4fVcK3pwSFbx+/vAQAOD4cNXRND3S24YdvGgLA9luSbGxl/B9Ui4Ka0nyryhS6I+KCRJ0hMsTrD50VyxXkisq40crmW0B5dgCAsaX6mm+15/0G3mOIhEKJqfzm5lGyWyrd/cTaft1svqXuD6IDy5tRa761svZcT8KqPTprm/m2PverjcDn9WgB3Pg6A+2SGmaP2RTCJvEEGbHXQ/Naq89TTGtjeDXuiSeewAc/+EH09vbC4/HA4/HgNa95DT7ykY/gPe95jx3nyDAMwzCMTWjmW5sXWrU2iNtsBjGMk6HwrV6DZZ/6oLvkwipuhmH0sWRw3NgMMghenLd3I386rmyIuTV8u7+/HX6vhGSuhKnVLGRZxhefuAIAeOerR0yHY9wUIrULMsPWBmLo/08sN//7pplwBGzGaebbJrRDMRvztBoSuVcNDr08s+aI4I1oyhVZ2wjf39eh63v8Xo9msHKKDdAMsizjeTVUrS98K9Z8O60+1+906fxtlKDPix1qEKjec0wyV8QH/vEMAOCXXrcXR9TCHzsh4+B2HRLIAuX1SNqmt2hos9VIqEgUIudisxweVH7fr7jVfJuz15xMkLHr2YmqUXyrtvbNwK7uENqDPhTLMsbrHBrbCHpvb9vTA0myt0BAFBS+febKqmZjtML3Ly0DAO7ap996S/zCa0YBAP/84izmmjBUKop5HeHbqvm2+d6nlbQSMOoRaL41U6RC8zfgzjCZEei54XK9zberVEzsjnttzXy7RacCMj9Hw+KDfG4O38Yz9bVUx2ospzGbjKeioeeQjA3h21K5gmxReV23jpcUgqU5iqCCEZG29muOq15fK5mN9zrJ6N5p83MCwzgZw+HbcrmMjg5lAbW3txczMzMAgJGREZw/f17s2TEMwzAMYxuViqxt0g0LaJG7FX3t1AbRfQ/UTOuwZNBgSQ+6yy5cSGIYRh8izLdA1SBot/l2do3Mt829ub0ZAZ9Hs06dmUng9GQcL08nEPR58JO3Dpt+3d0cvt2SSkXG1Mr1ppiRmHuMwdMCC9oofDu3lhMSLGAazzNqwOU/3LoLnW0+5IoVXLC5mKIRTK5kUChVEPR5sNPAZ+GQ2or7XBOHb6fjWSwm8/B5JBwb6tr260d7lfDtUqqAtax1Q+h0nDrauMPGJYLdscbMzX/08HnMJXIYiYXxX37wQF2OqRU7J7cONqXUVqDtQZ9tYTuz7bRFMOkA8+1hdTx7xaXmWwqL2W6+XWfsuvdYc1tvAUCSJBxUn+mcMN89o1r5bxnpbvCZ6OfIjk50tPmQypdwdsZawF2WZTyhmm/v3N9r+Ptv2NmF20d7UKrI+Bu1mLPVyBXLmv1uoHPz9Q4Kzjez+bZHoMGyU5sn9Rep0NgbDnjh87Z2h4N9VMC2mNa6GdUDusdwi/lWT/iWrJp2tLCPacIS9+2ZrNXdfKuMvx7JnqC0HYQofFsUH75N1wR6Iy4N39LvebUmBCvLMlbUji89tplvldddb9wlyPrcWafgOcM4EcN3aTfccANeeOEFAMDJkyfx8Y9/HI899hg++MEPYu/evcJPkGEYhmEYe1hM5VEoV+D1SFoVul3QZtB2JhaGcTIUotNrsOztcO9CEsMw+jBqzN6MA/3KRr7d4dsZtW31ji532Dw24qhqoDs7s4YHn5gAAPzI8SGtgt8MFPJwQ4jUDuaTuQ3vOd1iDK5U5GobSgHBt4GOIDwSUCzLfA/hAhaTeYwvpSFJwK0jPTg+HAUAvDC51tgTs4FL6hy1t6/dkEmcwrfNbL49rbZFPzrUiTb/9kbRjja/ZisdE2Du0sy3LrFxiaARdvVnJ1bx4JPKvcUf3n+jrmtBBGQc3K7YmYI+dgYnabM1aSBUJIJyzVzcyPAtmY4vLSRdaThPqSGdTpvbya43dt1zbMDW49WLQ6oZ+XyDzciViqyZb2/d0zzhW69Hwm17FPvtU+PLll7r/HwSS6kC2vweXcb6jSD77d89dRWZQv1t342G1joCPs+Whkd6/ptP5FBpssJCO8y3VUO8cfMtt/JWiqs8kjIf1VPyMlmn7pH1IhJU7lG3ul+j69+OECkJChaT7usWSObbegUQKcjcEwmY7uZVbyIBZSzL2jB30r1qwOdBwOfOYgUKxNeGYBO5EoplZY61y4CsmW/TG89f9KzJ5lumlTE86rz//e9HpaIsHHzwgx/E+Pg4Xvva1+Ib3/gGPvOZzwg/QYZhGIZh7GFKbZcz2Nlme9V0f4ey0La4jYmFYZyMFqLTabCstlBy30ISwzD6oOBcn8Wq8/39iiXpko3hW1mWMaPaaNzctvrokLLp/b2LS/jnF2cBAO+6Y4+l1xzm8O2WTK5U26HX3nNSOGZqNdvUhteFpNiCNp/Xg0E1SDWtBuKZ5oXMcocGOtAV9uP4rigA4IXJeONOyiaoQOSAOmfp5ZBqJG9mG/Dz6u/zhBqu1sPeGnOXVWiscPP8bZSRmPL+TqzUp717oVTB+/7hRcgy8OOv2oW7TJgUzUJB7oVEfksLXD3CO1qoqM7m29m1LEoVGQGvZ8v253azqzuE9qAPxbIs5LPtNFIU4LY5AFZr2dvf3661GW92Dqnm2/Nz9W2Xvp6LCykkciWE/F4tMN4s3D6qhG9Pja9Yep3HLinh3dv29CDoM1cocfeRAezuCWMtW8RXn5u2dD7NyHxCWTsY6AxuaVPva1cKC0sVGUvp5ios1My3EYHm25Bx8y3NqR0caELQ59XWES4LKGDTgyzL2j6a3d0j64Vmvt0i/Gin+Zb2TJabbEzQg2a+rVP4lt7L9YVLTobMt+m8ePMt3at2uNR6C1QD8bUhWLLeRgJe2wpAu9Xjrqav3+uUZVmztHeG3PveM8x2GE7a3HvvvXjb294GANi/fz/OnTuHpaUlLCws4I1vfKPwE2QYhmEYxh4oCFGPil0y/tWzIplhRJLOl5BR29b06jRYxrTwLV/3DNOqiDLf7utrhyQpi2nLNo0py+kCCqUKJAkNDS3YDbUDf34yjkK5gpuHo7hx1/YtwreCNn8mV7NNZ/OpB1e1Fo3X3nPu6ArB55FQKFe0zdtmhDbidnSJK2gbUgN0Mxy+bXqevqKY5cjUdpM63rwwFW/UKdkGFYjsNxq+Vc23lxdTTWuKPH1V+T3fbMCeR2GysSXrwYEpNt9eR73t6n/5vcu4MJ9CTySA97/1SF2OSVCxc7ZY1mxPG6FtiNoY3qHwbb3Nt3Svsas71FDrlyRJOKyOaa/MNtZuagdVe7K9gZLuGsvevS6x3gI15tv5xl4bz0wowdWbh6PwN1kL+5MUvr2yYum56/FLSwCA11golPB6JPzHO/cAAP76++Mt9xw4n1DWJQa3WTvweT3aPDW31lzPfHaYbztNFKkksmy+rWUv3UPXqchlKVVArqislQ25pNAtQuHbLe4bKcxHreZF0ufiboEUvt3KCC6Su/b34o2H+/FLr2ue7uRhNXybLdoQvlWv6YiLw7c94evNt7RXENMpDTIDjQWrmcJ1BZ+5YkUz73KhCNPKCHmy6unp2bKyjWEYhmEY56FV7NahJR+ZWJZS+ZZbjGTcAS0GhfxeRAL6qkd72927kMQwzPbIcrVlfK/Fxa9QwKsZNi7aZL+djSsbYX3tQde25gKAwzs6rvnvB+4YsfyaO7ra4PVIKJQqXGi0ARR8Wt8G2uuRtCKwerYFF83kajXwIwoO37qHp1XzLbV1vlk1o16YT2652dmMXFo0F77dGQ2ho4lNkYVSBS/PKEGqE8P623dTcODygjjz7S6XBAJEMKLOOfWYX8YWU/jMv14CAPzuDx/VWnLWi1DAq9mdtroPSdXBfEvB3mSdzbdTVFxeh/Wt7SCT6Ctz7gvfpvJkX7Q30ODzejST9w/dsMPWY9UTCmZPrmS3DMrbzbNaYZD+Ocsp3LCzCyG/F/FM0fRzcalcwVOqOdeqpfzttw2jI+jD2FIaj15YsPRazQYVT/brKNwdVLuDzDZd+Fa8+ZZCSYms/nkyyebba9jbq3Q3qJf5lp73d3S2uWatjAz2xbKMfGnjAORqxr7wrdYtMOmuboHFckWb36M2vG8b0R704Qv/8Tb8xC276nI8EYQDyvWX2cK8bBZ6/9tdHL6lZ82VGgPtcto+UzVBr50vVa4LTlNBiUeC7r1ThnEjhu8SfuAHfgBvfOMbN/3DMAzDMExzQHacephv6YG6WJYRN7C4xDBOQWsd37F1O7Va+jTzrbsWkhiG0UcqX0KuqBj8rIZvgWob74s2teWm4I5bTB6b0dnm10KgsUgAb7nR+oZ+bUDgap0Me83E5MrmBV+71bbg9TIT2gEFfkS2oKyGb5trg5q5llS+hDMzawCqbZL7O9uwo6sNFRl4eXqtkacnFFmWcdmk+VaSJBxUA0nnmjCs9spsAoVSBd1hP0Zi+seBvX3K+GfVfJvIFTXLKJtvq9DvYiGZR7Yg3qpEyLKM//7QSyiUKnjdwT782M1Dth1rK/o61W5Dic3Dt3SdtNchfJsrVlAo1c9kfVUr9Gn8Z4AKvV6ZteeevZHU4xoi/vxnXoXPPXArbthprUOFk+iOBDQ5wQWbnun08LRqvr1FtfI3E36vB7eMKKHhU+PLpl7jhak1pPIlRMN+HFXD8mZpD/rwk7cNAwA+//1xS6/VbMwnleeUgY7tw7c7uprTfLuascF8q9owEwYM8TT2drL5FgCwr7++5tvqHlrjC3xEEQlUr6V0fpPwrWZ+Fh/6Jjtntlh2VUFqbaieP6+bE1LDmRkbntHoeqrHvWqjqBpoq9fbsrr/SDIgOwgHvAioHRNqjw1Ur/3OkJ+FnUxLYzh8e/PNN+P48ePan6NHj6JQKOC5557DjTfeaMc5MgzDMAxjA1VLl/0LBwGfR2sbt5BsroU2hgGqreONPMDSQtJatljXjUeGYZwBBe8jAa+Qdlf7B9TwrV3m2zUK326/edbsHFfNkz952zDa/GIq8inQy+Hb66H3ZKNwKoVkmvl9m7Shm8RO9XPI5tvm5vTVVVRkxey6o6saCDu+KwoAeGEq3pgTs4G5RA6pfAlej4Q9aqjeCAcHlLBaI8NIZjl9VTEI3jwcNbTRtK9XmdevLGdQttAdZloNBHSH/ZpFiFFavZKdk8ZpO/g/z0zhybEVtPk9+PB9NzRss5ECfVutt1TNefZdJ7Ub3fW0317dxLLfCA4PKmG+c7PNV0ywHWQTq0eg5PhwFG86OmD7cerNIbXY5PxcY+a7hUQOkytZSBJwYne0IedglZNqQdOTqr3WKI9fWgIA3LE3Bo/H+pj9rjv3wCMBj11axisu/NxvBhV7DHRuH0xtVvMttfEWGT6k8dPIHJnUzPVsvgWq5lurBWx6oULhXQ4o8BGF1yMhpK6FpTYJgq+o5ls7DK6RgBdtfiWitOwiacmaGkDsCPrg87rDkmwHYfXas6NAkq5nV5tv1X32eKb62ama2u0L30qShG51PlxNX/u5TWhFIjxPMa2N4ZHnU5/61IZ//3u/93tIpepzo8MwDMMwjHXqab4FgP6ONqxmilhM5nF4sC6HZBhhUPi2r0O/7SAa8sPrkVCuyFhJF7TFZoZhWgMttG9g3NiKA/3KRu3FeXueuynkN9Tlng2FzfhvP3QYJ4aj+OmTu4W95jCHbzeFQk8bBWLcEFq2455aM9+ucfi2mXlabetM1lvi+HAU3zozhxcm3WO+vaQWhozEwqbasR5ucBjJCqcn4wCAE7uNte/e2R1CwOdBoVTB9GoWuw1Yc2uh8C1bb69FkiSMxMJ4eTqBq8sZLeAtksVkHh/+xisAgPe+6aDQIgyj9KvmQbr/3IhEHcI7Xo+E9qAPqXwJiVxJK0i1G2eFb5VrbSGZx3IqX7f3oB5o5tsgb6yb5dBAB/794lLD5rtnJla182jWgATdV50aX4Esy4aLHh67rIRv79zfK+R8hnvCePMNg/jGS3P4wvfH8Uf/4biQ13U6ZLEd6DRivm2eZ5tiuaLNmyLNtzQHJ7JGzLeqUdDFJkcjkPl2ajWLXLEsrKB6M6ZWNy8mbmYiQR+yxbJWWLMeCvbZEeaTJAm97UFMrWaxmMqbfg5yGms19k9mc+w039L17ObwLQXiV2rCt8tp+rza+9zRHQ5gPpHHamZ9+Nb+Ik+GaQaElV28853vxBe+8AVRL8cwDMMwjI2UK7IWsqnXBlG/jjaIDONUFrXWLfofYD0eCTF1gWopxdc9w7Qa9LnvE7ThfqDfXvPtjLp5tiPq/vDOzmgIP/+aUaGbNBT2mGziEKkd5IplzKv3fhvdc7ohfGuH+VYL38abyw7FXMszVxQj2617rg1lHh9WWmg/r4Y23QCFb/f3tZv6fjIBnmvC8O3zWvg2auj7FEuwMm5ctmDumlaf63e2wPxtFJpjJmyaYz749bNYyxZxbKgTP3/XqC3H0EvVfLv5cydtRtu9KdphwupnFbr/amQAmogEfRhRP9vNOKZtRSpXn2vIzTTafPuMWhi0/t6kmTg+HEXA58FiMo8ry8bG92yhjOcm4gCAu/bFhJ3TL7xGmQP+8fmZLYsg3MS8alrv12W+Ve5Rmsl8S8EiSVJs+qLoDCnjZ8KU+ZbHXgCIRQLobPNBloEry2nbjze5Ul+BTb1oDyrrYenCJubbtHKNdttgvgWqeyxu2jOJq+HbaJjDt1tB3eEym1x7VqDnHREd6JwKBeLjmeo8sqztXdpnvgWq48HKevMtBc+btLCLYUQhLHz7xBNPoK2NbV4MwzAM0wwsJHMolmX4PBIGBBn5toPCR1ttBjGMUzFjvgWqC0mLLlpIYhhGH5r5VlD4dr8avl1K5a9r7ySCGS28w8/1ZnBDiNQOyArbHvRprdFqaXZjcKlc0QKydphvV9IFW1rxMfZTLFdw+mocAHDbnmvNtzfu7IIkKaFJtwQ0tPBtv8nwrWolnVrNbmpfciLLqTwm1ODPTbuihr9/nxpWHls0Hxyohm8bHzp0Grt7lLbEdhTGfPfcAv7vCzPwSMBH33ZTw1vLVoudNw82URi2w+bN6Gr4tj6f5XS+pNmenBC+Bar2Wze1oM+XyiiUKwCAdg6AmebwYCcA4Px8ErIs1/34z04ohUHr702aiTa/FzcPRwEAT40tG/reZyZWUChXsKOrDaNq63oRvGp3N44PR1EoV/ClJyeEva6TIbmGIfPtFnOU01hVg4fU1UwUZMQslCrIFfU95yXzZBTkUBOgWFP3qvfQlxfsD99O2VBs6wRoLt/o2UuWZc18S23mRUMhQTeFbymAKDKw70bCNppv03UqNmwktLYazxRQqSj3kitp+0zV1xxbHQ/W70uQKZ4KTBimVTG8KvS2t73tmj/3338/Xv3qV+Pnfu7n8O53v9uOc2QYhmEYRjBUsbsj2la3TaI+dTPILRvMTGuhGSwNhm9j6kISVZ8yDNM6mB03NiMS9GlWu0uL4u23s2qAcEeXu2we9YLNtxtTa6LbqCUsvW8r6UJdDXmimEvkUK7I8HslDHSIC653tvm0NnkzTdSelalyZiaBbLGMaNh/nQ22o636dy9OxRtwduIhK/uBAXPh2+5IQDN3XphvHlMkWW/397eb2mTd26cEfy5bmNen1SKHnS6zcYlAM98KtqKl8yW8/2svA1Bshzfu6hL6+mboV+egrYqdq+Y8ewMBnVpL7frM62Sgj4b9jrEtHdmhBCzdZL6tDVNHAryxbpYDA+2QJOXet95F2plCCS/PKIHwW0aa13wLACdHlfDwqfEVQ9/32CUlrHvnvt4Nn03MIkmSZr/90pMTukOVzUoqX9ICe3rCt4Pq18yu5RoSOjfDclr5fIoOMrUHfKBLT2+RCptvr6dawGZPZyiiXJG1Qje3hW9pLk9vEL5N5ksoqaE+u823btozIRMpm2+3JuS3L3ybJPOti+9Vo+pnsiJXLeq0BxETJADZDBoPVjPXPufReq5TnsUYplEYTtt0dXVd86enpwdveMMb8I1vfAMf+MAH7DhHhmEYhmEEo1Xsdtdv0aC6GdQ8Ve4MQ5g1WPa5sIUSwzD6EG2+BaqhpovzYjcYiuWK1jZyiNtWm4ICPgvJPJtKayCj7fAmobCONr+2oUnFYc0EnfPOaAgegUYkSZIwpFqoyUrNNBdPq2GQW0e6N7w2jqvGthfU8Gazc5nMt30dpl+j0a24zUB24xPq79Moe3utBwem4tVxiLmWkZg9dvVPfvsCpuNZ7IyG8P+86aDQ1zYLhde3Ct+m6mSCqrf59qpqn97toFAM2U3dZL5Nqb/P9qBPqAWy1Wjze7EnphReXJizNzS2nucn4yhXZAx2tjX9nHG7Gr59ymD49vHLSwCAu/bHhJ/TD90wiB1dbVhOF/BPz88If30nQZb19mC1YHArBjrbIEmK7XV9q2qnQuZb0eFbj0fS3rOEzuLTRJ2KZ5oJKmAbW7LXfDufqHaPHNQRNG8m6DpMbXC/RlbLkN+LNjUoKZpeF+6ZrLH5VhdhNRhrx9qpdr/q4mKFgM+jfX5pTqX/jdlsvqU5cTWzznyb5XmKYQDA8Mjz13/913acB8MwDMMwdYRaAItsj7sdfTo2gxjGqVCIzqjBslf9+iW+7hmm5RBtvgWAA/3tePT8Ii4uiA0mza3lIMtAwOuxfaHOrXSF/eho8yGZK2FqNYMDA+YDaG6CzLdbBWKGe8JYSRdwdSWDo0Od9To1IdjZgnIoGsKF+RSHb5uUp69s3db5+HAUf//sFJ6fWqvnadnCarqgtXzf12++hfPhwQ78+8Wlpgrfkvn25t1RU9+vBQcWzQcHptVxqJ7P9s2CZqVfzaJSkYUUSbwwGcdfPzYOAPjw/Tdom8eNpl/tNLSwRUvvZE140k5o01VvqMgqV1ec1w76yA7lPvDifAqlcqVuHafspF7XTytwaKAD40tpnJtL4DUHeut23GevrAIAbtnTLdT62ghuGemGzyNhOp7F1GoGu3TIJdYyRbw0rdx33bVf/Pvu93rwrjv34KPfPIe/+v4Y7r1h0LUBrPmEstZBc892BHwe9LYHsZjMY3YtZ7uZTwQrGftaeHe2+f//7J13dBzV2Yd/s127WmnVe7GKJVe5GxeMTTWhJZAAAZKQUBLyJSQhlVRII4UQSA+EEAgESIHQIWDcMMa927Kq1Xvbpu3z/TFzZyVbZcvM7Ozufc7ROSCvZkerKXfu/b3PC5vLF7r5lg/0UfNtkEoRukeEApnPKLSkJFzhiYmEb6cw3xKrpZQt7LP5boGJFL4l5tv0FDqvOhMpOt586xW/UI+YnFP10oTGlYLFqIXd7cOI0wuWZYXwrZTnLPe+U5tvyXNfWgq9T1GSm/h/6qdQKBQKhRI2ZOIglMlJscilIURKnMKybDBEF+YEMQmxDcWJ2YFCoYhH0Hwr3sRXdS63kN/UL+4CQ88YF9QosBhEtXcmGyTkI7ZhL54hn0Vp1vRjTiEcFYefW4dQ0CZN+BYAukZp14h4g2VZ7G/jAi4rpgnfLim2AACOdo7GTfvd6WjiF72LLClRBRFr8kmb9vgwRfoDrBC+XVoSWfvuCr5lbr/NLbRqDAeX149BvlVrvFsMpaAg3QCNioHHF0DvDKHUUPH6A/jWC8cQYIFrlhRiY02uCHspDjl8pyGryzdtu3NyjEltJCKLrlaZzLehFPrITUmGESadGh5/AK0SW/nkwuam4S+xiJXpnYxNVpZFds9SEkadBguL0gEAe0O03+5uGQTLcqG9PIkMlh9fWQqjTo2GPjvO//m7eOSdRsGEmEiQrnZ55tA/x4J07rW9Y/HxbDNslzB8y4eyrSEeG0HzLb3+EipzSPcIh6TPUkRgU5KZeONsEr51uM8dNxLzbYZJujEjCeEP2hJnzYSab0PDyIdvpTDf2oTwbWL/Dci9adTpgXXcB1+AnfR96d6X+1xHHGebb/nwLTXfUpKcsMO3GRkZyMzMDOmLQqFQKBSKMomF+TaUNogUihKxuX1w+wIAwm8fn4gtlCgUSmiQMIyY5tuqPG6BobFP3PAtMWuSBTFKZNDw7bmQcGrJDOHUMv5zaxuOv3BKp4TGSRKko+bb+KN5wIFhhwd6jQqL+GDI2dTkm6HTqDDq9Mb9NYPck6pyU6PaTk1eMIwUD4Hk5gE77G4fjDo15uZF9runp2iFIp1IAnpd/PXBqFPDYqQLXWejUatQxF+fxTjPHn+vFad6rLAYtfjelfOj3p6YpBk00Gu4pZ6BaeZc5ArvkHBvJIHySAhlrCE3KhUjBCxP9sRHQcFs2JKgja9c1JLwbZ984Vt/gMXB9pkLg+KN1XO43yPU8O2upiEA0lhvCelGLR775ApU5abC6vLh1+80YP3P38XD7zQkVAiXBGjzQjTfAkA+H3juEaEYRg5GJDTfkvtwqIZ4cj+loaYgpVlGqBjO2jrduEcMOkinGwWNMcSCHIcOz7nFUsSimWGU0nzLr5k4EmfNZGyc+9zoc9nMmPiCXacE4VvBfJvg41VioB12eDDEn0Opeg0MWmmNv0Hz7eTwLXlOSKPBc0qSE3b49nvf+x4A4LLLLsN9992H++67D5dddpnwb7/+9a+FLwqFQqFQKMqkc1T+tnwkfGR3++Cc4qE+2bn9yf244JdbZVugooQOsTWb9RqhLU6oZPPHvZQTgRQKRXmwLDvBfCti+JYPNvVaXaK28u0e40ILhdSaFxU0fDsZlmUFG91MY87g5xZ/IdPOYWLCkcJ8yy1Q0/Bt/LH/DBcCWVJigU4z9dSrTqPCgkLO9ErsqfEKsbFHG76tzkuFiuFaGA7EQeHa4fZRAMCiovSoWspXTDB3hUsXHzossqTEfQtxqSD3mN3NQ1E9a7cNOfDwOw0AgO98aJ6o4zsxYBhGaP9NjIQTcfv88PAFpVKHd9KE8K088z7tCjTfAkBtAXeN//vuNhziQ4/xjF0Ib9NF9WiZy4dvG/psCATkKTZp6LPB5uIKRkj4N95ZXcGFb/eEGr5tHgQArK2ULnwLcOHet768Ab/9+FJU56bC5vLh4XcaEyqE22flxmnhGISD5tv4eLYZkjB8SO6T1vHZ75MeX0AQQtDwbRC9Ri3c90kXDinokPB5P9aQAKTdfe5xSIJ1UoZvc8zcthOpSyY134YGWV9zevyij4PsgvlW2hBqrMnkA96jTq9wv8oSsfPe9O/Lh2/PNt+6aIcMCgUAwj4Ddu3ahR/+8If4whe+IHzv7rvvxu9+9zu88847+O9//yvm/lEolCSEZVnc/8pJZJl0+OJF1bHeHQol4fD5A+jh29fKab5N1WuQolVj3OvHgM2Nsiw6ECd0DDvxzqk+AMD+MyPYVKuc9pWUCa3jI7BXZvGGhCFH4rRQolAos2N1+eDxcwskYppv0wxa5KcZ0Gt1oanfjmWl4rQMJeG+wnQavo0GsiDTQcO3ALgAHZn4nmnMGc+fW4eE5ltyPtLwbfyxlw/frpzFLFdXbMGh9lEc6RjDNUuK5Ng1SSAL3tGGbw1aNcqzTGgZdOB0rw25YbQyjgWHOrgw3dIo78WVOSbsbR1GcwTBAWK+LZLxuT7eqMg2YWfjIB7Z0ohHtjSiIseExUXpWFRsweLidCwoTINRN/PcBMuy+O5/j8PlDWBtZRY+urxYpr0Pj1yzAR3D4+i3nhtisE8IwpokXowWjH4yBMwCgWChj9LCtxvn5uAfe9qxv20EH/nD+1heloHb18/BpQvyoVbFX1iejOnMejqXFy3lWSboNSq4vAG0DztRnm2S/D33t5F7liWqghElsbwsEwzDmeP7rS7kzhAE7R1zoWXAARUDrKnIknzf1CoGV9UV4opFBXj9eA8eeacRjf12PPxOIx5/rxW3rZ+DT6+bE7cBrT6+yGOmz/xs8vlnm56xODHfShhmSkvhrqOhFAVNfE2imxzDpSInFWeGnGgZcEgWqpfyeT/WkPGgfYpiqWD4VrprFClks7p8cPv80GviPyw56uTOV0ucXtvlwjhBbuPy+Wd9FgsHwXyrT+y/gWC+dXowZJfO1H42GRPedyLkuY8WiVCSnbCfst566y1s3rz5nO9v3rwZ77zzjig7RaFQkpvGfjv+9v4Z/OrtBow5478SmEJRGr1WF3wBFlo1I+uC5mQTS+JUtIrBtoYB4b+PdY3FcE8oU0HMWzkR2I1I6G7Y4ZHNaEKhUGLPwARjttgtn6r51taNIrYpJUU51HwbHdR8OxkShslL0894HpRmcZ9b54gT/ji6V3p8AfTybVOlaENJzsfuMRcdQ8QZ+89wAZeVc2YO3y4psQAAjnSOSrxH0tLE34+iDd8CENq0n+6VrxV3pBzizbdLSy1RbaciWxzzLWVqPrN+Dq5YXCB8Ri0DDvz3cDd+9OpJfOxPu7HwB2/h0l9vx1f/eQRPvn8GB9tH4PJOboH64qEu7GwchF6jwk8/skixluFc8/TzLcRCa9SpJQ/ekfCtHObbAbsbbl8AahWDAouyAvuXLsjHG186Hx9dXgytmsGBthHc9cxBbHxwKx5/rzXuuh6R/U2l4duoUasY4ZmuXqb73QG+MGh52cxjk3giPUWLefmcYZoUPk3HribOeruwKB3pMrYDV6kYXLm4EG99eQN+d1PimHD7+Weg/IjMt/ERvpXFfBtS+Ja7l5p06rgs3JCSCr5wIZICtlAhY+1iCZ73Yw25nzumNN9yx2aGhGG+NIMWGv6YJuHBeIdcz9No+HZGUibMDzo9/hleGT7CNTPRzbf8uTnq9GDIwT37ZZmk78ySYeKObZc3gPEJfzsr/7mT4hIKJVkJ+wzIysrCSy+9hK9+9auTvv/SSy8hK0v6ikEKhZL4NPYFH5aOdo3i/OqcGO4NhZJ4dE5YoJN70iYnVY+2IeeUJpZkZlt9v/DfRztp+FZpDArm2/AnnMiDsD/AYsTpQZbC2pNSKBRpGCShfRGtt4Sq3FTsbBycNGaOFmLOK1RYaCHemBi+ZVlWseEcuQi1DXR+mgFaNQOvn0XP2HjcLGx1j46DZQGDVoVsCYxI+ekGMAwX8h1yeCS5nlDEp8/qQvuwEyoGWDZLKLOOD98e7xqD1x+ANg5NdA63D918iKIqJ/rw7dw8M9443qv48K3d7UMDHzpeyv8dI6UiJ/LgADXfzk5Zlgm/v2kZAGDI7sbRrjEc6xzD0c4xHOsaRZ/VjYY+Oxr67PjPwU4AXDBubp4Zi4vSsaAoDb9+uwEAcPdF1bIYKiMlGL49N9hEFqLlaAVKAgc2t/SBMjLWKLQYFHkNnVeQhgc/VodvXFaDv3/Qhqc/aEPH8Dh+9OpJPPx2A25cVYJPrS2Pi7GPzS3fMZQM1OSl4XiXFad7bdi8MF/y9yPm25Xl4nROUQqrKzJxsseKPS3DuHJx4bSv29XMhW+lsmPOBgnhfmhhAd443otHtjSgoS9owv3Mujn4zPr4MeH28fP6eWmhP5/kx1n4VjDfShBmShMM8bMXqQTv3/FxbMhJZW7kBWyh4PUH0DPGjbVLMhNvrG3iw7f2qcK3DulNmioVg6xUHfqsbgzZPQkhAxjlw7cWGYs84hGVioFBqzonwBktXn8Abh/Xhc6c4OZbYqUedngwbCf3K+nNt6l6jTB/O+L0IEXHnbekSI+abynJTthPyvfffz9uv/12bNu2DatXrwYA7NmzB2+++SYee+wx0XeQQqEkH039E8K3nWM0fEuhiExnDCt2ifl2YIrFoGTF5fXj/eYh4f+PU/Ot4ojGfKtVq2AxajHq9GLIQcO3FEqyQMy32RKc89W5nBWwsV+88C1p/ZgIk92xpNCSAhXDGQAG7G7Ft0yXGtKicTYrrFrFoCTDiJZBB9qHnXERQAEmj6mlCFpr1SrkmvXos7rRPTpOw7dxwj7evDavIG3WRfLyLCPSDBpYXT6c7rVhYVG6HLsoKiQwmp2qE8WMVEvMtyLa3aXgaOcoAixX0BpOy+WpqOBDy2eGHAgEWKjCKJCl5tvwyErVY1NNLjbV5Arf67O6uDBu1xiOdY7iaOcYhhwenOqx4lSPFdjPva4234w7N1TEaM9DgxyLUxU7kyCsHOGdcEJF0dI+FFqhT6zJTTPgq5fW4P82VeGFg114/L0WNA848NjOVvx11xlsXpiP29fPwdJS5QYjSQCMtj0Xh5p87trfIMP9rnfMhc6RcagYKPoYi4TVczLxxK4z2Ns6vfmWZVm838TNu66riq1ASqVicMXiAly+MH9SCPeRLY346674COGyLIs+3nybF4H5tmfMpfhCVZZlMUzMtybx/xZCkUpI5lty/6bX3rMh5tuWQWnMt92j4wiwgF6jimhNQOkI5lvPVOZb7vi3SGB+nkh2KjffQQQG8YzL64eHD34q+RquFIw6DVxej6jm24kW50Q335Jzc4RfcwSATAmkAGfDMAwsRh0GbG6MOLnQvNvnh8vLHfs0fEtJdsIerd16662YN28efvOb3+CFF14AAMybNw/vvfeeEMalUCiUaGjsD046HekYjd2OUCgJCmkBXBwDOw4JoUzVBjFZ2ds6jHGvH9mpOgw5POi1utBvcyV9YEdJkBBdpKGX7FQ9Rp1eDNrcmJtnFnPXKBSKQpHSfEtalDaJFL51uH1CazSyIEaJDJ1GhYL0FHSNjqNj2Jn093Iy5iwJIRBTksmFbzuGnUCl1HsmDsFwsXRj6kJLihC+rYvSrkmRh/1niFlu9rbODMOgrsSCnY2DONI5GpfhW3IvqhTBegsANXz4tqHPBn+AVWx73UPtowCAJbPYjUOhJCMFWjUDlzeA7jDt38R8G4tn+0QhL82AvPkGXDw/DwAXuOkZcwlm3KOdYxi0e/DLjy5WpFl1IjmC+XaK8C0JTuqlD++QgG8ooaJoCbXQRykYtGrctLoUN64swfbGATy+sxXvNQ3itaM9eO1oD5aXZeC29XNw6fw8aBR2vNmpfVFUavLTAAD1vVbJ32t/GxdMrc1Pk+UaICdkvHW6z4YRh2fKQqCWQQd6rS7o1CqsKJt9fCYHE0O4b57oxSPvNOJ0n00I4X5+YxU+d0GFIgOq1nGfYBYMZ76DBHXHvX5Yx31IV7AZ0uHxw+PnfkdpzLfc707adM+EVUZzfbxBCtg6R8bh8vph0IobtgsW26Yo8lyMFlJM43CfG34ccXBjuEwZwrdAUHwSz4w6uc9MrWIS7l4rBSn8+eqcIvwdKcTibNCqFDeOFhtipR51eoTwrRzmW4C7LgzY3MJ1wjbhXkaL9CjJTkRnwOrVq/HMM8+IvS8UCoUC4FzzLYVCERcycRBKEEJsZloMSla2nu4HAFw8Lw/720bQ1G/H8a4xXFib3IEdJTHIt26J1GCZnapDU39iTCRRKJTQCJpvxZ/4quZb63WNjsPu9kU9qUva6JkNGrqYLgKlmUY+fDuO5WWx3pvY0h5G+JYY68jPxAPBgjbpxtSFlhQcah8VAnYU5UPMaytCbOtcV8yHbztGcfPq+LtoEAt7Va444duyLBP0Gq4FZfuwE3N4o5XSOMwXii8VIRSvUatQlmVCU78dLQOOkK8pPn8Avbx5Ll6M4fEAwzAotKSg0JIiSyt4MckNIXwrR3gnTQjf+iS3G4Yz1lASKhUjWJhP9Vjx+HutePlwNw60jeBA2wiKM1Lw6XVzcP2KYsWMzwX7Ig2UiAIxvZ8ZckoSGpsIKQwKdWwST2Sl6lGdm4rGfjv2nhnGZQvOvW6/3zQIAFheloEUnbJMeCoVgw8tKsDmBZNDuD9/sx42lxff2Fwb6108BzL2sBi1YR23Bq0amSYdhh0e9FjHFR2+JS28DVqVJMeMWTDEz16kYnXJZ66PN7JTdUIXkTNDDtTyRQ1iEU4xcTxi0nHHoW2KEPiwYL6V9rjL4udME8F8S6QG6SnahAxriw0x046LaL4l4dtkCD+Tc3PY4cWwgzt/smQw3058b2LIJvcys16j2OJpCkUuQo79+3w+uN2Tb359fX24//778Y1vfAPvvfee6DtHoVCSD58/gJYBh/D/vVaX0MYmWdjdPIS5330D/9zXEetdoSQonSOxM9+S8O0ADd8KbDs9AADYWJOLxbzt6lin9OYLSuiIYb4FgCF+8pZCoSQ+UppvLUadsN1mEey3XaPcWJu2rBaHkkzuc4ynEKlUdAxzgdFQWkEHw7fxEzINFrRJd+6Q87J7NLmeieMVq8srGOxCMd8CEIzGRzris/CYFE9XixS+VasYwfB+ulf6VtyRwLKsYL5dKoL5Fgi2zW0eCP2+3mt1wR9goVMnZitcSvgQ4/6A7dx7hp0P78jRCpSEinwBFuNe8RbUp4IEY0IZayiVeQVpePBjdXjvW5tw94VVyDBq0Tkyjh+9ehJrH3hXKOqINSTQQO2L4pBr1sNi1MIfYEXraDIdB9q48O3yssQL3wLAqjncmGu6c2VX0xAAYF1Vlmz7FC4khPvGl87H96+cDwD4w7Zm/Hl7c4z37FzIWl1eBF1e8nn7bc+Ysp9tSPBQCustAKSlEPPt7OFbEoxMo23sz4FhGMF+29zvmOXV4RNvdv1wIQFFh3ty+JZlWYzy50CmxCbNnARaMyGfWTo9V0MihQ9/O8QM38rY6SPWTDTfDtqkvWdN994kfEvvUxRKkJDDt3fccQfuvvtu4f9tNhtWrlyJ3//+93jrrbewadMmvP7665LsJIVCSR7ah53w+ANI0aoxl19wOcIbRZKFlw53weML4OUj3bHeFUqCMrFljtxQ8+1kzgw60DrogEbFYF1VltBq9ljXaGx3jDKJaEN0JHybCFXcFAolNILmW2kmvkjIqVGEhdoe3qhZkE6N62IQjwZXKfD5A4KtNZRADLHJtA+Jv2gmFR0jMphv+fOym5pv44KDbSMIsNwxT1rrzkZdMTf+b+i3CcGmeKJZMN+aRdtmTR5nrVJq+LZzZByDdje0agYLCtNF2SYJDkwsRp+NLv65vsBigIoaZigActP4AIPDAx/fLptgk3Ex2qhTC9ajqWxqYtKeAOFbQq7ZgHsurcHuey/CA9cuQkWOCTa3Dz9+7SRYlo317gWPIRq+FQWGYVCTx907G/qku9853D6c7AmvMCjeWF3BhWqnCt/6Ayx2t3Dh27VV2bLuVySoVAw+s34OvnU5Z7x94I16PLu3PcZ7NRkSviX3nHAgcw69Sg/f8hbBDJM0QaKJhvjZEKzj9No7JRU5XAFbSxgFbKFCioljsYYmB4J51OuHPxAcZ9jdPnj93P9nGKUN3ybSmslE8y1ldoy8Od3pEe9ZgcynmJIgfEvOTV+AFZ6HpA7LEyz8ew87ePMtvU9RKAIhh2937dqF6667Tvj/p556Cn6/H42NjThy5Ajuuece/PKXv5RkJykUSvIwsWXhEt4Ac7QzPg0wkUJ+X2LMoVDExOsPCO2lY9GaMlcw3yp7kk0utp3uB8BNgJsNWiwqJuHb5LruKZlAgBUmgCIN0WUnUAslCoUSGoO8tUEK8y0wMXwb/UItCfUVUvOtKJTQ8C0AzmbkD7DQaVTC+G8myrLi73MTzLdShm+J+XaMhm/jAdLWOZxwS26aAYXpBrAscDzOngHcPj/a+HO2SiTzLRBsxX26T5lzIof4AvF5BWmitQkXggODoQcHSIEDNddTCJlGHTQqBiwbHIsSbDJaSxmGEd7HFoLVL1JcXj/6rNwzdiKEbwkGrRofX1WKf312DfQaFY52juGDltjbb+UMcCcLNeR+J2GxyeGOUfgDLArTDQn7vLeKH3ed6B47xyR6stuKsXEvzHqN0G0sHvjcBZW4a2MlAODbLx7DKwqStBChRn6IhWYTyU+PE/OtgzuOMiWyCJJ7JGnVPRPk2ktDTVNTSQrYBsUv4iXdI0sSaIwxkYkBRceEAOSokzsuDVoVUnTiPOtMR7Y5cdZMRmn4NiyM/LE1Lqb51p08Y1WDVo0UbTBADwBZqfKEbzP5whRyrbCO8+ZbGTqsUChKJ+TwbVdXF6qrq4X/37JlC6677jqkp3MPLJ/61Kdw4sQJ8feQQqEkFRNbFi4utgAAjnSOxm6HZMbl9QvV7oN2D/ppQJEiMr1jLgRYQKeJTWtK0gZxKhNLMrL19AAAYFNtDgBgfkEaVAzQZ3Wj30rPfyUwNu4Vqr0jfYANVnHHfwslCoUSGlKbb6t4S1JTX/R2jy6+nX2iLsbKDQl/dMRRiFQKyO9fnJESkpGRLGiNOL0htd+MNS6vXzjPSzKlO3eE8K1I5ttAgMWYU/mfb7yy7wwXjlpZHl5b5zq+8Djeuv6cGXTCH2CRqtcgLwL72XSQMFK9Qs23h9tHAQBL+b+bGFQK1q7wzbc0fEshqFSMMPY8ez4xaM6TZ1GUhITGxqUz35IimFS9BhZj4i32ZqXq8bEVxQCAR3fEvvW8XQhwJ95nHSvkuN+RwqDlCWq9BbhAZ1mWEQEWONA2MunfdjUPAgBWV2RCow55OVwRfOOyGty8uhQsC3zl+cPYygscYg0x34ba5WEiQfOtsgsLifk2U6J7C2nN7fD4Z10fIfdvGmqaGjKGbpbCfCtDsW0s0WtU0Kq5uRrHhA4sxGaZKbH1FgCy+ID7oC3+10xImD4Rx6RSQILdThHDtw4Ziw2VQMZZx5pc5tuMacy3aSnJ8blTKDMR8tOGwWDA+HhwQPzBBx9g9erVk/7dbhd/cEOhUJKLRj54WpWXijo+fHu0c0wR7bXk4FSPFb4JLT5O9ShzsYkSvwjtcS2hBSHEJtOkg4oBWJYL4CYz4x6/0PpsU00uAK7imFRsU/utMiCV1+kpWug1kVV7Z/ELoEMJUMVNoVBmJxBgMcQv1khtvj3QPhL1tYUY8Qst4S+eUc6FhG97rS64vOJNIscbxGAb6kJVql6DLH6iOB6CyxMDP1KaTUiobtDuEeV4+uq/jmDZj9+OO8NqPOD2+XGYD8+unBNewEUI38ZZ4XHThM5FDCPesyUJI50ZdCjyOnqogwv2LC0NL2Q9ExXZ3H29Z8wVcutNwXyboK1wKZFB2oD3WyePD+U255n1pKW2dAUfZLxQkmkU9RqkJG5fXwGG4Qq3iawhFrAsOyF8SxfWxaJWBvPt/jauMGhFmXj3LCWymh977W2dbIne1cSFb9dWZsu+T9HCMAx+eM1CXFVXCF+AxV1PHzjn94sFwfBt+HMd+encmIWab4PXUbt75nEfNd/OjGC+HXCIuoYsV7FtLGEYRrDfTgrfOrk1O4sM4VtSNEbmUOMZYgGl5tvQEMy3Ij7vk+ulKQnMt8Dkc9Ss10S8dhkuJHw7wl8raJEIhRIk5PDtkiVL8Pe//x0AsHPnTvT19eHCCy8U/r25uRmFhYXi7yGFQkkqGgXzrRk1+Wbo1CqMjXvRNqT8BVgxONo5eRG0vkeZbRYp8UvncGwX6NQTTSzW+H+ojoYPWobg8QVQZEmZ1KZ1UTHXVeDs6wElNpCJtmgCdNmppIVScgfOKZRkQQxj9mwsK81AdW4qRp1efP3fR6NaZCBGzYL0xFxQkJtMkw4mnRosGwxGJSOk4CucNtAlcWQNFgraMlIkDfxYjFqhlVy0i9R7W4fx4qEu+AMs/neiV4zdo0zgeJcVbl8AmSYdKrJNYf0sKTw+0hFf4/+J4VsxyTXrYTFqEWCD76EU3D4/TnRx8zRLSy2ibTfDpBNMNaHab4XwLTXfUiaQaybm26nDt6kyhXeI+Yi8rxSQQp/SBA3FAEB5tgmbF+QDAB7d0RKz/Rj3+uHnZRE0ACYec/luJr1WlySdCfwBFod4W/uKMK388caqOVkAgD285ADg7tmkK8G6qvgL3wLcPPpD19dhU00OXN4AbvvbvpgX0fXx8/m5UZlvlR6+5c23JmmCRFq1SgieWWcxxNPw7cyUZhmhYrgQ84BNvLWmTv55X+pi21hj0p07XhvlA3VyWDSzzUGDpj8Q3wKuMWK+TeDjRUyM/LEXauFpKDjcXJA3NUnCtxPPUanWH2Z6XxK+Jfcxep+iUMII337/+9/HI488gsrKSlx22WW49dZbUVBQIPz7iy++iHXr1kmykxQKJXx++VY9rvn9LmGgHA/4A6ywsFKdmwqdRoX5hWkA4s8AEykkbEcGh6do+JYiMmTioCSMIITYEBPLgF3ZE21SQ9qFbazJmRTaWFTEhW9jPZlK4RjgjZI5UbSOJ4HzAbs7aUzuFEoyI4YxezZ0GhV+e9NS6DQqvFvfj7+9fyai7bAsi25+4YuGd8SBYRhhnNUeByFSqWjnC77CCd+S18ZD4WXnMAnfSjumZhhGsFJ3RxHmDgRY/OS1k8L/H+SDGBTxIOGOFWUZYQeyFxWng2G4MOXZreKVTNOANOFbhmFQkye9DTASTvXY4PFzIetwrm+hQELbobbN7Rqh5lvKueSYuXvG2deSoJFIJvMtbz6ySmi+DYZvE7MdNOHODRUAgJcOd8UssEZCOWoVIxQFUaLHbNAKz2CnJTAbn+61we72IVWvQW1+mujbVxLEfHu0cwzjfAvrg22jcHkDyE7VY26euGMVOdGqVfjDzcuxak4mbG4fPvXXvSGPFaSgXzDfhh++zY+b8K205lsgGFKa7T5J7t/EKE+ZjF6jFuZfmkQ8Lzr4cbbUxbaxJlUw3wbto+T4txilP+YyjTowDBBggy3s45VRPnybRsO3IUEKECYee9Fid3N/g2QJ3048R+UIy5/9viP8tYLcx+ixT6GEEb694IILcODAAdx999144okn8Nhjj0369yVLluArX/mK6DtIoVDC53DHKH6/tRlHOkbx5vH4Mdp0jYzD7QtAp1EJD0x1SWaAPNY1CgC4qo4ziZ/qUdZCEyX+6ZwwcRArcqj5FizL4t16Lny7qSZ30r8tJtc9Gr5VBKRqPjsq8y33sx5fYNZ2YhSKnDg9Prx4qFNYnKKIg3DdkLjqvDY/Dd+9Yh4A4IHX63GiO/z7xpDDA48vAIaJbPGMMjWlcWRwlYp2oRV06GPOsqz4CS2TMbUcLSgL+VBGNCblV4/14EjnGNQqbuHwcMdo3JtllMZ+Pny7ig9/hEOqXoNqPsB6NI7st418UKgqR/xAi9CKO4Zt1qfiUPsIAGBJiUX0hfiKHC58G4r5lmVZ4ZpQbEns4CElPIj5ts86tfnWLFM7UBIqksd8m9jnwNLSDKwqz4TXz+KJ91tjsg+COVmvSegQUiyoIfe7XvEFHPvbuLHJ0lKLMAZMVIozUlCYboAvwAr36vebBwEAayuz4v64TdGp8finVmBhURqGHB584i97YtJlJRBgBbN6Xlr486T5/JyDze0TQqVKRGrzLRBszz17+JYaBWejkn8WCbV7RCh0DsdeYCMHJj0XgJy4XiGn+VajVgkt7InIIF4RzLdG+UKQ8QwJ34q5JmFPavOtdMUi071v0HxLijxp+JZCCTl8CwDz5s3Dl770Jdxwww1QqSb/6J133oklS5aIuW8UCiUCWJbFT187Jfz/+81DM7xaWTT2c4sqlTmpwoTQYqH94miM9ko+HG6fYP69fkUxAM544vbRQAxFPILh2xiabwUTS3w/UEdD84ADnSPj0KlVWFuVNenf5hekQ8Vw4a0+q7JNAMmAGObbFJ0aJn5CYdAe31XclMTim/85hq88fwS/29oY611JKITrRhSh/VD5xHlluHheHjz+AO5+9lDY7bp6Rrn7TE6qHjpNWNMDlBkgIZD2ODC4SkUki1XxZAzuGJHHfAsErdSRmm/dPj9+8WY9AOALm6pg0qlhd/uE529K9AQCLPad4YIeK8rDD98CQB2Z+4iTrj/+AIuWQW6Bu1oCm9xcPoxUrzDzLWnfvbTEIvq2K0hwYHD24MCA3Q03XzxDLHIUCjCh09BZ5lsSqpArvCOEisalC1eRIqfiBA/GAEH77T8+aI9JYI28Z7KEGeSkRsL73X5+bLK8LEP0bSsNhmGEAqgPWrnQ8a4mLny77qx513jFbNDiyU+vQmWOCd1jLnziL3tkD6sNOTzwBVgwTFA0EA4mvUYwsCt5znvEKb35lhgCSbvu6SDhXLmKZ+KRcLtHhEKHAgQ2cpDKH1eOCeFbYqDNkClESsQFQ3G+ZjLGBxHTqf0zJFJ03L3A6RUzfMsdx6YkGa9ODHpnyWq+5d7L6fHD5fULRSJpKcnxuVMoM0FX1yiUBOPtk33YyxtXAC58Gy8trhv54Gn1hJaFdSV8+/XuMfj8gZjsl1yc7LEiwHJVw0tKLEgzaOALsEIgl0IRg2BQIHYTB8HFoOQN3247zVlvV1dkwqib/FCSolOjOpebfD+WJNZvJTNo4yZOss3RPcASc268V3FTEoejnaN45Ug3AG78SBGPoPlW+vAtwzD4xUcXIy9Nj+YBB374ysnZf2gCxJZDzJoUcSjlDa5k3JVsONw+DPELNuGEb+PJGCyYb2UYUxdGGb596v02dI6MIy9Nj89eUIE6PjR4sG1UpD2kNA3YMTbuRYpWjQWFkbV1Jn+Xw3FSeNw54oSH71wkRQidmG8bFBa+JX+fJaUW0bdNggMtIQQHuvhrUJ7ZQItnKJOYrth5orlUDtIkNt+yLCuMFxLdfAsAF9bmojLHBJvbh+f2dsj+/nKHt5MJ4X4ngen9QBtfGFQWWWFQvLFqDhey3ds6BJvLiyP8nOrayuxY7paoZKXq8ffbVqPIkoKWQQc++fhewbYoByQwm52qh1Yd2fijIJ17tukZU274dsguvfmWXE9nM99aaahpViqkMN/yczklMRTYyEEqb751TCjkJzbLDKM8IVIydxrvayZB8y0N34ZC0Hwr3rOCnRSLJcl4deI5KoepmpBm0AgCvVGnV7iPUfMthaKA8O3vf/97lJeXw2AwYPXq1di7d++0rz1x4gSuu+46lJeXg2EYPPzww/LtKIUSB3j9AfzsDc5oc8f5c2DQqjBodwuhVqXT2Hdu+LYiOxWpeg1c3kDc/B6RcpSfEFpczLUvnFfALdqd6lHWYhMlfvH4AujlJ8liOXFATID9NuVOsknNttMDAICNNblT/vvCIq7w4GgXDd/GGjHMt0Cw+nQozieSKIkBy7LCmBEAGvrswsQyJXqI4VoO8y3ATbD9+oYlYBjguX0deP1YT8g/2zNGwrfUmicmZJzVPix/K1AlQELHFqM2rMlXEp7pHBmHP6DsAtIOGdtQBsO34Y+dR50e/PZdzm7+1UtqYNRpsKyUs58d5FvyUqJn35lgW+dIgwhL+PDtkY7RuCigJkXCFdkmSVpZz83jwki9VhfGnMpoSzxod6N92AmGCYalxaSSnwtrHXTMegyQ4pmiBLdxUcInl8y3WIPPnf4AOyE8Kc+iKHkfqSytww4PHB4/GCZoiE9kVCoGd5zP2W//uqsVXpkFFXba9lwyyP2uvtcm6v2/Z2wcXaPjUKsYSQpGlMjqCi5kfKh9FLuaBuEPsCjNNCZc2/hCSwqevn01slN1ONljxW1/2ydq2+6ZIHP5eWmRz3UQY79Sw7def0AIvEpqvg3BEO/2+eHxcdd7ar6dnsocvoBtUETzLT+Xk2jXj7Mx6c4tlhpxcMdkhkxhvkQJ347y5zI134YGCd86Rbx/OdzctpKlU8PEwG2WDAIQAsMwghl72OERDO70PkWhxDh8+/zzz+Oee+7BD37wAxw8eBB1dXW47LLL0N/fP+XrnU4nKioq8LOf/Qz5+fky7y2Fonye3duOlkEHskw63H1RNVby7Q5Jix2l08S3vZzYslClYrCIhNDipP1ipJDfbzH/+wbDt9ZY7RIlwegZGwfLAnqNSmjnEguExaAkNd863D7saR0CAGyqyZnyNYuLees3Dd/GHGKwjDZERyaSBuK8hRIlMdjROIj3m4egU6tQwU9Sk6IASvTIab4lrK3Mxuc3VgIAvvWfoyGHqYlJszA98UMLclIyweAaDyE6sWkfisxEl59mgE6tgi/ARmx5lQO72ye0IpWjmwQJx0fymfz23SZYXT7U5ptx3fJiAMCyMguA5Avfjjo92PTgNlz3x/dFDy3t49sbkzmYSKjJN0OnUcHq8uHMkPILYkhxdNWE4mkxMRu0QqCuvlcZcyKH20cBAFU5qZJYXUozjdCoGDg9fqFodjqI+TYZQoeU8CCdhgbtbgT4QpaJNjO5wpPE0GeVyHzbzhfB5KcZYNCqJXkPpfHhpUXITtWjZ8wldDCRC7nNyclEZU4qNCoGNpdP1DDi/jPcOG9egTlp/m4V2SZkp+rg9gXwx23NAIB1VVkx3itpmJNtwlOfWQ2zQYP9bSP43NMHhJCmlPTxhR155siLdwv48G2vQsO3xPrJMNKG6Mh9ciZD/MR/S5bzOBKI+bZzZBwukVrYk4LikszEHmub+OPK4Z7KfCtv+HYgRuFblmXxizfr8YOXjkdcyBAIsEKQ3kLDtyGRohU/fGtzJ9d41TLhHM2S0XwLBK27o05P0HxLDe0USmzDtw899BDuuOMOfPrTn8b8+fPxpz/9CUajEX/961+nfP3KlSvxy1/+EjfeeCP0evkWMimUeMDq8uLhdzijzZcvrobZoBVa6uxqGorlroUEy7ITFm/Mk/5tcQkXQjvckdghNNJeflExCd+SyndlLDRR4h9SsVuckQKGEd9OFCo5/ATdQJKGb3c1DcLrZ1GWZcQcvrXo2Qjm286xpAztKAlSdR1tiC6bD+8OJulxT1EOgUDQevvJNWW4bhkXxtp2euoCSEr4kOuGXOZbwpcvnoslJRZYXT58+bnD8IUQLuvmF7wKaHhHVEggc2JIM5kggZhwOy2oVAyK+cUtYpZVIp0TzL5ymB1IuK5rdDyscWH7kBNP7T4DALj3Q/MEO+nSEs582zLgwIgjeYqCfvW/BrQOOnCgbQR/390m6rb38QGXaMK3WrUKCwu5AtwjHaNi7JakEPNt9VnzN2JSw7fiPi1BK+5IOMz/XZZIYL0FuGOAFC0098/cNpeabynTkZ2qB8MAvgCLYT48QcI7WjUDvUae5SCpzbftMhrolYJBq8an15UDAB7d0SLrXJFNZnNyMqHTBAtiT/eKd7/bz1v5V5RFPjaJNxiGwao53O97hF9nIetjicj8wjT87dMrkaJVY3vDAL7y/GHJu4f08cVBuWmRh2+D5ltlFlsK1k+jTpLuDgRyPbXOcJ+cWPgg5b7EO9mpOqQZNGBZ4MzQzGPoULC5vBgVim0Te5yROkP4Vq429lm8JGjQFpu5iVeP9uAP25rx5O423PbkPjg94ReO2dw+kMtvGg3fhgQJfotpbifHcWqSdGrInBi+lVm2RczYw06PEDyXokCZQok3Ip5t8Xg86OzsRHt7+6SvcH7+wIEDuPjii4M7o1Lh4osvxu7duyPdrXNwu92wWq2TviiURORP25ox7PCgIseEG1eVAghW9u5pGQpp8T2WdI+54PT4oVUzKMua/EBTV2wBkNjmW6vLi5ZB7sFw0TnmW3HbTlGSl84RZSxOTDTfJuOxva2Bs0tunJszbQh6fkEaVAwX4Oqz0rBmrPAHWAzxIbpckcy3Qw7696TElpeOdOFUjxVmgwb/t6kKG3kD966mIdEMEcmOYMyW0XwLcKGd3358Kcx6zn7z23ebZv0ZYtIsskS+eEY5F4NWjXx+QbJdwSFSqegcibxFIwmeKflzm1jQJgdkgdrtC2A4jLDsz9+qh9fP4vzqbFwwN9htIcOkE0IehzqSw357vGsMz+wJBm5//U6DMMaLlu7RYFvnpVG2da7jQ52H4yh8K5X5FpgQvhUxjBQN5HxZWpoh2XtUhNg2l5pvKdOhVauEhdh+fi6BBGDNBq1shdjEsDuT0S8ahLFGgodizuaW1WUw6tSo77VhR6N8ne7IMZQsYQa5qcnn1gDqxQzftnH3rOVl0t2zlMjqOZNNt2srE9N8S1helok/fWI5tGoGrx3rwXdePCbpXLtgvk2LfK6jQAjfKtN8S563iNVPKkhIibTrnorg/Ztee2eCYRjBftsyEH34lowxMozahDdokvu63c3NB7MsGwygyxS+zYnhmonD7cNPXjsl/P/7zUO49Yl9k8LIoUDChwatKmk6MkRLio77nBwRhJ2nw5505tvgfUqusDyB3CMHbW44+AA1DZ5TKBGEbxsbG3H++ecjJSUFZWVlmDNnDubMmYPy8nLMmTMn5O0MDg7C7/cjLy9v0vfz8vLQ29sb7m5NywMPPID09HThq6SkRLRtUyhKoWt0HI+/1woAuPfyedCquVN7QWE60gwa2Nw+HO9WdvC8kTeZzMk2CftPIAtQp3ttCRsKIa3liywpyOIfNubmmaFiuAf+fmpKpIgAmTiQKygwHcQE6PEFZpxgSkRYlsW2es4uubE2d9rXpejUmJvHLTYncuGB0hlxehBguVZj0T7AZse4iptCAQCX148H32oAANy1sRIZJh3mF6Qh16zHuNePvXzbbEp0xMp8C3Bhxx9/ZCEA4LfvNs76N+0Z5c236TS8IzbxECKVCvI7lyZo+FYoaJMp8KPXqIXrSfdoaIvUB9tH8NrRHjAM8O0PzTvn35fx4cGDbaOi7adSYVkWP3j5BAIscMWiAiwoTIPN5cOv3m4QZfv7eLPcgsI0wd4SKcSoekTh43+WZdEsQ/i2VkHhW3+AxRG+G1O0IeuZCDU4QM23lJnIEQqeuXsGCcDKGd4JhookMt8ORT7WiGfSjVrcsJJb33p0R7Ns72uPwTGUTJD7XYNIpne724dTPdxa0Iry5ArfEvMtwH2uWTIXxMaCC+bm4JEbl0LFAM/t68ADb9RLFsAl5tu8qMy33NilV+HhW6mDTKQ990yG+Fjcv+MVUsBGnlGioSOJ7Pqms8y3To8fHl7kJXUAnZBt5tdMRCqODYffbW1Cr9WFkswU/OOO1TDrNdjbOoxP/XWvEOQMBWJKTqfhw5Ax8uFbMc23yRa+nXifyjLJO94h790+HLTY03sVhRJB+PbWW2+FSqXCq6++igMHDuDgwYM4ePAgDh06hIMHD0qxj1Fx7733YmxsTPjq6OiI9S5RKKLzq7dOw+0LYPWcTFw8LxjmUqsYnFfBVffuapKvGj4SZmpZWJhuQHaqDr4Ai5M9yg4RR8oxvhVSXUm68D2DVi20pD+VoL83RV5IUCDW7XIMWjXS+IH4gF2ZE21S0dBnR/eYC3qNCmsqZrYvLOQt2CScT5EfYq/MNOqgUUfXnpOYb2MxkUShEJ7+oA1do+PITzPg02u5wkmGYbCphhs/bj3dH8vdSwgCARZD/GJNdowW+q5ZUoTrlhUjwAJffu4QRp1Th/69/gD6+FBGITXniQ5ZqOlQcIhUKoKLVeEfV/EQvpXbfAsEz1ESuJsJlmXxU97e8tFlxUJHlYkI4dv2xDffvnCwCwfaRmDUqfHdK+fhB1ctAAA8u7cdJ7qjH2fvP8N9hmK0dSZdf050W+EVuXvRkY5RbH54B1450h31tvqsbtjcPqgYoDxbumdLwXzbF/tuQE39dtjdPhgnFElKQQU/B9Q8EJr5tpjevylTQNqBk0L+WAQnpTbfCoU+Wcl3Dty2fg7UKga7moZkmy8SAmBJEmaQG3JfEct8u691GAGWk3wkW5FlTZ5ZCD+tq8qO8d7Ix4cWFeBn1y4GADy6owV/2t4iyfsEw7cJbL51yhS+JUUqM4Zvg+Z6ysxUkgK2wejNtx1JZNdP1XMBSBJaJOFzvUaFFJkMrsKaiczCkpYBO/6yk7tWfv/KBVhbmY2/374aaQaum9gnHt8z4/k5kTG+2MySIq99NJ4xarkxpVOk8K3HF4DHx82hRFsUHS+Y9Bp8bHkxrlhUENV9ORIsRhK+5a65KVr1OWI9CiUZCfssOHz4MP785z/j8ssvx5IlS1BXVzfpK1Sys7OhVqvR19c36ft9fX3Iz88Pd7emRa/XIy0tbdIXhZJIHO8awwuHugAA37li3jntw8gkw+7mIdn3LRwa+6a3pjAMg8X8ItSROGi/GAlH+fDtoiLLpO+ThdJTPbE3vVDinw6FmG+BCSYWa3IFEUmwbW1l1qwtaBYXc+HbozR8GzOE1vEi2Ctp+JYSa8bGvfjd1iYAwFcuqRbaOwHAplquHfm20wMx2bdEYsTpgT/ABYSyUmM36Xr/NQswJ9uE7jEXvvWfqVtP9o65wLKATq1ClsztqZIBIUQ6pNwQqRSwLJs85lsZTThFFm6RujuE8O1bJ/qwv20EBq0KX720ZsrXLCuzAOCer8k1KxGxurx44I16AMAXL6xGQXoKVs3JxFV1hWBZ4P5XTkYd6iTm21VzojfLlWUZkZ6ihccXENX2yrIs7nvlBOp7bfjqv45EHTomxdPlWSboNdItylZkp0KjYmBz+dAd44DG4Q4uZL24OB1qFTPLqyOnMnd28+3YuBc2foGcmm8pU5HLP7+S51kSHJDTAkWCQnaPDwEJ7jPRjDXineIMI65YVACAC9nJAQnl0ACYNBDzbXO/PeriG4fbhx++ehIAsLEmJ+p9izdUKgZXLC6AWsXgysUFsd4dWbl+ZQm+ewXX8eLXbzeIahMk9PHz+NGZb7mfHRv3wiliu3GxGLbLE74lRSozdQUk/0ZtgrNTyZtvxXiGIsXESlhDkxqTjju2yH1+hA+fZxh152QNpIIYyoccbtkKLlmWxf2vnITXz2JjTY4gNVtSYsEzt5+H9BQtDrWP4hN/2SMEa2didJz73Kj5NnTIusS41y/Ks4Jjgqk4Wcy3APDLj9Xh9zcvk+18JWTy4dsz/Jw3sblTKMlO2OHb+fPnY3AweoOmTqfD8uXLsWXLFuF7gUAAW7ZswZo1a6LePoWSDLAsi5/wRptrlhQKAdWJrK3kzIr7zgzD5RX/gVssGvu5h6LqvKlbFgohtM7EDKEd7RoFEPw9CcHwLTXfygXLsgm7AC13i9yZyDVPNrEkC9v48O3GmtxZXjnZfBtr01OyQoKyYtgrSQhvyC5vFTeFQvjz9maMOr2oyk3FdcuKJ/3buqpsaFQMWgcdaBXBEpHMDPDXjQyjNqYV36l6DX5z41Jo1QzePNGLZ/ee2wGGmGYKLAaoJAwSJSvExKbkEKkUDNjccPsCUDGRGZVLs5Qfvo2FCaeQt5bNFr71+gP4+Ztc2PSO8yuExe2zqc41I1WvgcPjFzXkqTQeeacRg3Y3KrJNuG39HOH7915eC4NWhb2tw3jtWE/E2x9zenGabxG9XATzLcMwqCuxAAAOi1h4vK1hAIfaue15fAF8/pmDIVt8pqKJn7+pnKJ4Wkx0GpXQQrYhxscp+fyWlkrbvpuYb7vHxqedwyPW2wyjFkYdXeSinEuuUOzMjfWCbavlCwSQoBDLQgiLi4XXH0DPGH8vTsLwLQDcuaECAPDasR5hrk9KbEnWxlduiiwpMOnU8PgDOBPl8/gPXzmJ1kEHCtIN+PplUxdhJTr3XbUAH9x7keT3bCVy2/o5yDHr4fEHcExkmYPXH8CQI/rwrVmvgYkPXfUq0H47Ipf5NmV28y35tzRa+DArC4vSoWKAkz1WPL+vPaptdRKBTRKMMch93SGEb7ljLkPGAn0iA/D62RnD6GLyzql+bG8YgE6twg+uWjApuLioOB3/uGM1MoxaHOkcw81/+WDajmIEEtBNN9JzNVSME6QgLl/02RUSIE/RqiUtlqVwWPhjnczd0vsUhcIR9mrgz3/+c3zjG9/Atm3bMDQ0BKvVOukrHO655x489thjePLJJ3Hq1CncddddcDgc+PSnPw0A+OQnP4l7771XeL3H48Hhw4dx+PBheDwedHV14fDhw2hqagr316BQEoKtp/uxu2UIOo0KX5vGaFOVm4pcsx5uX0CxLSVZlkUjb06pzp26fR9ZgDrSOSrTXsnHiMMjtC5dWHh2+Ja0naLhWznwB1hc8Zv3cMmvt0tSHR5L3D6/UJ2uhKrd3LTJJpZkwOryCm1pN4UQvp1fkAa1isGg3YNeq/ImI5MBKcy3NrdP0cUwlMSkd8yFv+5qBQB8c3MtNGeFQs0GLVaWc6EhUiRAiQzSJk2M60a0LCpOxzcuqwUA/PDVE2jsmxxcIiG+gmnCeZToiAeDqxR08AGQgvSUiALoJNA66vSGZBiRG5Zl0RkDEw4JMnePzRy+/ceedrQOOpCdqsNnL6ic9nVqFYMl/DO2UucJouV0rw1/e/8MAOC+qxdApwkej4WWFNx1QRUA4IHX6yN+9jvQPgyWBeZkm0S77tfxBblidf1hWRa/frsBAHDDihIUWVLQNuTEN/99NOICv6aB6TsXiU1NPleQLFYr7nBpGbDj91ub8NaJXgAQzhupyDTpkJ6iBcti2oKoLv7+Ta23lOkgoShS7BwM38oXnDRo1cJ11xZF2H8qukfHEWABg1aFHBEKZeORhUXpWFeVBX+AxePvtUr+fuRvmErti5KgUjGYy9tvT/dFfr9741gPnt/fAYYBHrp+idASONnQaVSKeB6PBQzDYFmpBYD4Y/xBuxssC2hUjGC8iwSGYVDAP9soMXw75AiaP6WEBJXIPXoqYnH/jleKM4y455K5AIDvvXQCx6MInwcFNok/1ib3dSF8Kxz/8gXpDFq1cIwPyNAx0OX144evngAA3H7+HMzhix8nsqAwHc/eeR6yTDoc77Lipsf2YNgxfQB3lA8tU/Nt6KRM6ArqFGEtnoRv6VhVHkiBisfHdWyg9ykKhSPslZCLL74YH3zwAS666CLk5uYiIyMDGRkZsFgsyMgIr5LwhhtuwIMPPojvf//7WLJkCQ4fPow333wTeXl5AID29nb09AQNFN3d3Vi6dCmWLl2Knp4ePPjgg1i6dCluv/32cH8NCiXu8fkD+OnrnNHm0+vKp630ZxhGsN/ubh6Sbf/Cod/mhs3lg1rFoDx76t+jjrf6tgw4ojK0KBFShVyeZTynMo6Yb5sHHDSsJQOHO0ZwsseKlgEHXo/CgKREiB0nRauWvHI7FMgCSb9NeZNsUrGrcRC+AIuKHJNgdZsJg1aNan5BO1Gt30qHmG/FmLRPM2ig40NIgzJMJFEoE3n4nQa4vAGsKMsQWmmdzaZariXl1tMDcu5awjFg5+5rYhizxeC29XNwfnU2XN4AvvjsoUnjSRLii8ROSpkd8nzWMzYedfvYeCLaNtAmvQbZvC2+Q4HBZeu4T7C/FctpvuXP067R6cfOVpcXD7/DhSy/fPHcWe10wsJ8W+KFb1mWxQ9ePg5/gMVlC/KwYe65bZfv3FCBIksKukbH8ecdzRG9zz6+sG5luXhmNTL3IVbh8ZZT/TjaOQajTo1vbK7B729eBq2awRvHe/HErjMRbbOxjw/f5kgfviWtuE/LWJDc1G/Hb7Y0YvPDO3Dhr7bjl2+dxojTizSDBqvKozcczwTDMILtt5kPOZ9NFx8IKKL3b8o0COZbPnxrd8fGnBdKsCgSyFijJMMoe4tVJXHnBq7I5vl9HRhzSjtXbqcBMMmpySP3u8jCt92j4/jWC8cAAJ+7oBJr+PUgSvKxjDf+ij3GJ1KPXLM+6s45pAC4R4HhWxI+JB3MpCKNv57aXN5pW67Hwlwfz3x+YxUurM0VOn1EUszLsqwwDyHn836sMPFzBnbBfMuHb2VeP8znC8fekGFN9s/bW9AxPI6CdAO+cGHVtK+rzU/Ds3eeh+xUPU72WHHTYx9Mu6Zj5Y81Cw3fhoxKxQgBXDFEWHbapUFWzr5GpNFjn0IBEEH4duvWrdi6dSvefffdSV/ke+HyhS98AW1tbXC73dizZw9Wr14t/Nu2bdvwt7/9Tfj/8vJysCx7zte2bdvCfl8KJd55fn8HmvrtyDBq8fmN0w8QAWBtZTYAYFfToBy7FjZk4aYsywi9Rj3lazJNOsEsdCzBQmgkfLuYX2SbSH6aARajFv4Ai6b+qRdeKOLxbn3Q9vfs3uja0ygN0i6nJDNFEYsTxHzbn0Tm2628TTIU6y1hMW++iqZamxI5xHybLcKEK8MwwsTtkH3mVkUUipg09tnwz/0dAIB7P1Q77T2AXJs+aBlKOPu7nCjJfAtwk5m/ur4O2ak61Pfa8MDrp4R/I+Zb0s6eIi45qXoYtCoE2OBnnQyQjh4lmZEfVyS4rMTwLTH7ZqfqkKKb+tlVCkjIbqZj6Y/bmjHi9KIyx4QbV5bMus1lZfzCfAKab1852oMPWoah16jwvSvnT/maFJ0a3/7QPADAn7Y3CzbRcNjXOgwAWCFiIHNxCTf+b+y3CwtIkcKyLB7irbefWluOrFQ9lpRY8B3+9/7p66ci+vuTUGh1nvTh27l5pBuQtObbhj4bHn6nAZf+ejsufmg7Hnq7AfW9NmhUDDbMzcHPr1uE7V/fJMtCdEU297m2DMxsvk2GQAAlMoLzLVyoiYR35F6MJsEiq8gm+2gLfRKFDdXZqM03w+nx4+k9bZK+lxAA09OFdamoyY/8fucPsLjnn4cxNu7F4uJ0fOXiuWLvHiWOCI7xRyPucjAVfXxXtty06DvnkKCdEju9yWa+5YNKARZweKYe8xPrOC18CA2VisFD19ehOCMF7cNOfPWfR6YNNk/HqNMLBz8nqoTukVKTOiF8y7KsED6Pxm4dCZ9ZPwcA8NA7Ddhyqk+y9+kYduIP27hu2t/+0DwYdTOfW3PzzHjuzvOQa9ajvteGjz/6wZSdPKn5NjKM/JzadNfAcLDH6HknWTn7Hil3kSeFolTCDt9ecMEFM35RKBTpsbt9+PXbjQCAL11UPeuAbm0VV+l8pHMs6sUbKWjs5yaVqmdpWSi2AUYpkHaSJGQ3EYZhBNPLqR75TC/JypZTwfDt/raRc9ozxzMkfKuUBbpcMzfJNtXDaiLCsiy28TbJjTXnmremY1ERd12IB/Nt54hTctuK3AyIaL4FgiZMar6lyMkv3jqNAAtcOj8Py8umDwdV5aaiyJICjy+A3S3KLNiKB8h1QynmW4C75z74sToAwJO72/DOSW4iu4c3aFLzrTQwDIMSftzVrsAQqVSIEYgp43+2TYGfG2lBKfeYutASHDu7fecWSHSNjgttp++9fB406tmn+5aWcAvzZ4acGEqgsYnD7cNPXjsJAPi/TVUz/q0+tCgfq+dkwuUNTCpOCAWX1y+M0cW0oeaaDSiypIBloy88futEH072WGHSqXHn+RXC9z+1thxXLCqAL8DiC88cFBZZQ2HU6cEgX0hWKaP5tmXAIapFnGVZ1Pda8dD/TuPih7bj0l/vwMPvNKKhzw6tmsGmmhz84qOLsf+7F+Opz6zCDStLZTNAEfNty3TmWz58S823lOkg8y39VjdYlo1Z22qzYPWTyHyb5OFbhmFw5wbu2v7ErjOSdkwj6wk0ACYdNfmRm2//vKMZH7QMw6hT45Ebl0KnCXvZl5JALCpKh0bFYNDuFtYDxICEb/PSop/rCJpvlVekKphvTdLO6eg1KqFD2nT3SfL9NHrtDRmLUYc/3rwcOrUK75zqw593tIT186TYNtesh0ErX7FtrCDm2wALuLwBDBPzrVHeIN3HV5Xi5tWlYFngS88dRlO/NOuyP37tJNy+AM6ryMSViwtC+pmq3FQ8d+d5yE8zoLHfjhsf3Y3+swoHiGXZIvPnFu+QgnaniOZbkz7xz1slcHZAPy2F3qcoFCCC8C3B6XSivr4eR48enfRFoVCk59HtzRi0u1GeZcRNq8tmfX1xhhGlmUb4Ayz2tg7JsIfh0cgbXatzzTO+ro43wBztUH4ILRyI+ZaE7M5mXkEaAOBUT+IEQZVI9+g46nttYJhg29Dn9nXEeK/EIxgUUMYCXY45ucy3J3us6Le5kaJVY9Wc0BfnF/FFB8e7xkS1FYhNx7ATFz+0HZ98Ym+sd0VUiMFSrBAdMejS8C1FLvafGcbbJ/ugYoBvbK6d8bUMw2BTLVccsLV+QI7dS0gGbeKG9sViY00ubuNNEl//9xH0jrmE8A4J9VHEhwRQkzF8G00gRsmf295WzhIq95g606SDng9S9I2dO4548K3T8PCLSBfNC63LQrpRiyq+APZQ+6ho+xprfvtuE/qsbpRmGoVQ0nQwDIPvXzUfKgZ49WgP9vIm21A41jUGjz+A7FQ9yrLEDYCRuY9oCo8DARYPv8NZbz+zfs6k4CjDMPjZdYswJ9uE7jEXvvLPwyFboUhHnsJ0g7BYKyVFlhSYdGp4/AGcGZzaBBsqLMviRPcYHnzrNC761XZsfngnfvNuE5r67dCpVbioNhe/+lgd9n/nEjzx6VW4fkUJLDJbn4BgqLllmt+3iw/SFCnk2Z6iPMg41O0LwOryTTDnyRsIIFY/m1vcIt0Oar4VuKquEAXpBgza3fjvoS5J3sMfYIOtfGkATDJqeNN7+7ATzjAMcEc6RvHQ/7j7/X1XL8CcbJMk+0eJHwxaNRYUcutJYna4CIZvRTDf8t13eseUZb5lWRbDxHxrkvaeyTCMUNBgdU19nyT3T7nv3/HOouJ03Hf1AgDAL9+qx+7m0NfEg518kmOMYZwQMLa7fRjhxSpyFR1O5AdXLcCqOZmwu324/cn9oktedjQM4K0TfVCrGNx/9cKwuoNW5KTi+c+eh8J0A5oHHLjh0Q8mXb9Gx7nrRho134YFMd+K0YFPGKvSLg2yYDZooGIm/j/93CkUIILw7cDAAK688kqYzWYsWLAAS5cunfRFoVCkpXfMhUd3ctV637q8NuRK5nW8/XZXk/LCt019obUsXMyH0I4mkPm23+ZCz5gLDAMsmDV8S823UrL1NGe9XVpiwecuqAQAvHCwc0qzVDzSIZhvlbFAl8svBiWL+ZZYb9dVZUOvCb36sjbfDI2KwZDDg26FTUhOZHvDAFzeAI50jE5raIpHxDbfZgnm29DtYhRKpLAsiwfeqAcA3LCyRAhYzcSmGi6wtfV0v6ID/0pGieZbwjc212BBYRpGnF585fnDQvt6ar6VjhIFh0ilolOE8C352Q6FfW77zwzjb+9zdtmr6wplfW+GYQTLJQnOE453jeFFPnDznQ/ND2sRaVmpBYC4C/OxpHnAjsff4+ZLfnDV/JBsRQsK03HjqlIAwH0vn4A/xBAqCequmpMR1mceCkLXH75LTiS8cbwX9b02mPUa3L7+3BCy2aDFH25eBr1GhW2nB4T2m7NBiqcrQxhXiIFKxWBuFK24AwEWB9tH8LM36rHpwW244jfv4Xdbm9Ay6IBOo8Il8/Pw8A1LsP97F+PxW1fiuuXFSI+xsahSMN86phyPUfMtZTYMWrVgyRuwuQRzntzBSSFUNC6u+ZYEY2j4FtCqVfjMOq7A7tGdLWG31w6Fia2AqflWOrJS9cIzZENfaPNqDrcPX3ruEHwBFlcsKsDHlhdLuYuUOGJpKScWEbPArs/KzXWIEb4Nmm+VNdft8Pjh4TstSG2+BYJBvenuk7Ey1ycCH19VgmuXFSHAAl989pAQHp+NDoUJbKRGpWJg4gOQDrdPMD+f3VJeDnQaFf548zIUWVJwZsiJLzx7ED6ROp94fAHc98oJAMCn1pQLtvlwKMsy4fnPrkGRJQWtgw7c8Ohu4blsjD+HY1G4Gc+k6LhrmxjmWwft0iArKhUz6TqRRsO3FAqACMK3X/7ylzE6Ooo9e/YgJSUFb775Jp588klUV1fj5ZdflmIfKRTKBH71v9NweQNYUZaByxbkh/xzayuzAQDvh1HlJwcsy6KBbyExWyhkYVE6GAboHnOh36asB/NIOc5bbytzUpE6jTVmPh++re+10iCMhGyt58K3F9bm4oK5OchPM2DE6cVbJ/pivGfiQMy3JTK3yJ0O0gZxbNwraWs8pUCOL2KVDBWDVo1q3n4RbdtZKZloCXuX/13jHa8/gBG+1VKOaOZbEr5NjtA5Jba8fbIPB9pGYNCq8OWL54b0M2sqs6DTqNA5Mo7mBArSy8mAQs23AKDXqPGbjy9FilaN3S1DsPILOWThiyI+pQoNkUqF2+dHD7+oFU0gRonmW7vbh3v+eQQBFrh2aREuDeNZXCxIUL57QviWZVn85LVTAIAPLynEouKpCzqnYxm/MJ8I4VuWZXHfyyfg9bO4sDYXF83LC/lnv3rJXKQZNDjZY8XzIXY/2X+GG/+uKAu9q0Wo1JVYAEQevvVPsN7edv6cacOk8wrS8KNrFgIAHnq7Ae83D8667aYQOxeJSW2YrbjdPj+2ne7Ht188htUPbMG1f3gff9rejDNDTug1KmxekI/ffHwpDn7vEjz2yRX48NIiRS0WlWYZoWK4687Zxaour18o5EuWUAAlMnL5cFS/1R2z8I6ZN0/ZpjH6RYoYlv1E4sZVJTDrNWgZcGCLBPMxdv740alVYRWTU8IneL8LTcBx38sncGbIicJ0A376kUWiFwNR4pdlZeKP8cU133LbUJr5dpgfYxm0KqEdupSQQpnp7pPB+7dyxqnxAsMw+MmHF6E234xBuxtf+MdBeEMIciptDU0OSHFWrM23AFeI8tgnVyBFq8bOxkFBKhEtT+xqRcuAA9mpOnz5kuqIt1OSacTznz0PJZkpaBty4oY/70bHsBNj/BpSOjXfhgUxL4dj/J8Ocr006elYVS4sE+aZ0lJo6JlCASII37777rt46KGHsGLFCqhUKpSVleGWW27BL37xCzzwwANS7COFEjEsy8LjE6cySgmc6rHi3wc7AQDfuWJeWBMqayqzhG0MKSj0M+TwYNTpBcMEW+tNR6pegyr+NUc7lBtCC4ejfJhu8QyLpFW5qVCrGIw4vUKFMUVcXF6/YIXeVJsLjVqF61dwtoDn9rbHctdEo1Mw3ypj4iAtRSOYuxPdfjvm9AqTnRtrQmsDPJHFvBX7WNeomLslGizLJmT4dtjhAcsC6rOqOKMhO5XbDjXfUqTG5w/g529yE5S3rZ8T8uKIUafBeRXcmHFr/YBk+5fIDArmW2XaDipzUnH/NQuE/zcbNHQhR0KUGCKVku5RF1gWSNGqkRXFYk1pFve5dY2Mi2Y6iZYfv3oS7cNOFFlScN+Ec0hOCi3ctXxi+Hbr6X7sbhmCTqPC1y6rCXubZGH+SMeYYj7rSHnrRB92Ng5Cp1bhB1fND+tns1L1QqHKg/87jbHxmYNi/gCL/W3c+H5lufjh20VF6VCRwuMQLU0TefVoNxr77UgzaPCZ9XNmfO31K0vw0eXFCLDA3c8envX9SPg2FKO+WMzlixFP900fvrW6vHj5SDe+8I+DWP6jd3DrE/vwjz3tGLC5YdZrcHVdIX53Exe4/dMnluPqusJpC6BjjV6jFkKFTWcVQ5HnepNOTRd3KTNCug3129xC2+o0mcO3ZBGWLIaLwZjTK1yjSzJpAB3gQlk3nccZ3B/d0Sz69mNlTk5GasIwvb92tAf/OtAJhgEeumFJzK3tFGVBuluc7LaKJr3oF8y30RcakwLgIYdHUVKOYT5AJ4f1Fphgvp0mfGvl73fU5BgZKTo1/nDzMqTqNdh3ZgS/eHP2ICex6yfTGMPEPxNNNN9mxtDgOr8wDQ9dXwcAePy9Vvxrf2jFsdPRO+bCb7Y0AgC+dfm8qIsuizOMeP7ONSjPMqJzZBw3PvoBhhw0fBsJRr7IYVxE822qnv4N5CLTRM23FMrZhB2+dTgcyM3lgiMZGRkYGOAWZRctWoSDBw+Ku3cUShQ8+f4ZrHngXTz+Xmusd0U0fvr6KbAscMXiAqF1TKhkp+qF6undLcqx3zbybZRKM40htYQkBpijnaMS7pV8COHbounDtwatGhXZXNvBUz2hVb5TwmN3yxDGvX7kpxkE0/D1K0vAMJwtum3IEeM9jA6X1y8EXJVix2EYRrCJDiioIEAKdjQOIMACc/NSI2oNurCYhG+Vef53DI+j1+qCiq8H2ds6PO2kYTxBzpkskw4qlTj2EGLCVFIRDCUx+feBTjQPOJBh1OKzF1SG9bObajhD99bTiRGklxN/gMUwP+GqRPMt4WPLi3Hl4gIAyinKSVRIiLR9KDnCtyRkXJppjMq8lWc2QKdRwRdgFdGK9J2TfXhuXwcYBnjwY3Uxm1QWzLdj3IKgzx/AT1/nFhA/va48ovO5KicVZoMG415/SEEPpTLu8eNHr54EAHz2ggqUZZnC3sYn1pShKjcVww4PHnmnccbXNvTZYHP5YNKpMa9AfAOsSa8RzLJHwux+4fMHhP2/4/yKkI7XH12zULBCffHZQzMGsWMRvq2Zxnzbb3Xh6Q/a8Mm/7sXyH72Nu589hFeP9sDu9iHXrMct55Xiqc+swoHvXYLffHwprlxcKCwuKx0yB9QyMHkugrQ2LcpIoYZDyowEw7cuwVwqd8EVeT8x5wdIO+jsVD2Muvg4n+XgM+vmQKtmsO/MiOg2e7ubhr/kgtzvGmYoNgG4e8G9LxwFAHx+Y6VQQEuhEIosKcg16+ELsML6U7T02cQz36anaGHQcvGEfgXJZoYd3L5kmOS5X5LrqnV86iIVUvyQRgN9EVORk4oHP7YYAPDYzla8ebxnxtd3JKP5Vh8035IAuiXGBR2XLyrA3RdxhtrvvHgcB9oiH9s88MYpODx+LC214NqlRaLsX6ElBc/duQYV2SZ0jY7DzUvgLPRcDQsjCX6LEL61C+Fbar6Vi4nCIvqcQKFwhB2+rampwenTpwEAdXV1+POf/4yuri786U9/QkFBgeg7SKFECsMAvVYXdjQkhrVre8MAdjYOQqtm8M3LaiPaxtrKbABcmFApNPVzk0nVIS7c1PEhtHAXoJQIywYnPxYVW2Z87Tw+EHqShm8lYStv6txUmyssYBVnGHF+NRdAei7E1qNKhdhxUvWamD84TySXr5RX0iSbFJAA26YIrLdAMJx/vGsMLMuKtl9isaeVu6csLc1ARY4JvgCL9xpnb1mrdAYEe6V4ATpiThik4VuKhIx7/Pg132r6CxdWhx0SI9eqfWeGRW8Rm+gMOdwIsNxzSCwtEbPBMAx+eu0i3Lq2HN/cHL4pkxI6ZMHG6vJhzJn451OwDXR0xV4qFYMSvmAs1tbgQbsb3+LDDbevnyN0lIkFhencZ9I1yi18/3N/J5r67cgwavH5jVURbVOlYoTCXrHDOnLyx21N6BodR5ElJeLPQqtW4ftXcsbcp3afEeYqpmL/Ga7rw7KyDGjUYU+thkRdCT/30TEa1s+9fKQbLYMOWIxa3LquPKSfSdGp8fubl8GkU2NP6zAeerthytc53D4h/Cln+LY2n5sPaR924njXGP64rRkf+cMurPrpFnz3v8exo2EAXj+LyhwT7tpYiRc/vxYf3HsRfvzhRdgwN0fouBJPVPBdn84J3/LP9pEUdVKSi1w+HNVvdQfNpTKHz4VQkYjm22ChDz0HJpKXZsA1S7hAyaPbW0TdtjVGx08yUjtNsclE/AEWX3n+MKwuH+pKLIK5n0KZCMMwWCbiGN/l9WOUf57NM0cfvmUYBgX8s03P2Pgsr5aPYQf3O2bKZb4lRSpTdN1wef3w8AVxNNQUHZsXFuB2vhvI1/91FK2DU4t2AgFWcd0j5cDEFzMN2NxCJ+HMKDoZicWXL6rGZQvy4PEH8LmnD6A3gsLsPS1DeOlwNxgG+OHVC0UTrABAfroBz9153qTnYhqUDw+jlphvo39WCIZv6fVSLiaGb+mxT6FwhD37+KUvfQk9PVxl0A9+8AO88cYbKC0txW9+8xv89Kc/FX0HKZRIIaG5/W3DcIpw444l/gCLn752CgDwqTXlgkEpXNbyC4XvNyknFNUoWFNCs8Us5kOqRzpHFRlCC4deqwuDdjfUKkawrU5HbUHobaco4cGyLN7lw7cX1k4OR358ZQkA4F/7O+GN4zasnXzFbrHC7DiC+dYWe6OZVAQCLLaf5opANkYYvq3JN0OjYjDs8AiL3UpibysXPlg1JxMX8efQllPxb8wk5lsx7ZXZZu6BdNDuEW2bFMrZ/HVXK/qsbhRnpOAWvv1oOJRnm1CeZYTXz2JXk3IKtuKBicZsqcJYYpFm0OK+qxdEfG+ihEaKTi3cR2IdIpWDTiF8G/1CVSm/jVh+bizL4t4XjmHQ7kFNnhlfvTS2YXXBfDs6DrvbJwQk776oOqr2hqQt7cEobDKxpG3IgT/t4IJG37tyHlJ0kZtONszNwcXz8uALsPjhq6emnXPYe4b7rFaWZ0b8XrNBuv4cCaPrj88fwCN8S807N1SEZbmszEnFzz/KWaH+sK0Z79b3nfMaEgTNMulkXZDNNOmEa+mVv30PP3+zHofaRwEAS0os+ObmWrxzzwXY8tWN+ObmWiwtzRB1YTUWVJLw7aB90ve7RrlrYpFCOtpQlEvuhPGHL8Bdy+QO75BQkU3E8G3HBMs+ZTJ3bqgAALx1snfaYFEkBM3JNMwgNdW5ZjAMN2c0XdH2n7Y3Y2/rMEw6NR65YQm0Cn/upMSOZWUWAOKM8Yk4Q69RIS1FnGtBPl8k0mtVzroAMd9myiQvIWElm/vc+yS5dzIMkEpN71HzzctrsbI8Aza3D3c9fWDKNveDdi58qmKAAkv0IfN4gXQGIcFjnVoFYxTP1GKhUjF46PolqM03Y8Dmxp1/3w+XN3RDqs8fwA9ePgEA+PiqUiwqnr77bKTkphnw7B3n4fzqbNy8uhTqOH8GlRsyd+MU0XwbL51uEoGMCXNCseoQRqEojbCfzG655RbceuutAIDly5ejra0N+/btQ0dHB2644Qax949CiZjyLCOKM1Lg9bPY0zIc692Jiv8c6MTpPhvSU7T4woWRWVwAYHVFJtQqBmeGnIoJcDX2cQsJoZpvawvM0KoZjDq96BhWxu8QKcR6W52bOusCITHfnqLmW9Fp6rejc2QcOo0K66omm6wumpeH7FQdBu3uuA4TBit2lbVAR8y3JKyUiBzrGsOQw4NUvQYryjMi2oZBqxZazx3vUp71e++ZYPj2wto8AMC20/3wB+K7QEKS8C0fOB9xemZs50uhRMqww4M/bWsGAHzt0hroNZFNlpJA5rbT8XvviwUkWC+mMZsS/yghRCoX7SIGYpTwuf3rQCfePtkHrZrBQzfUwaCN7QJUIb8A2D06jke3N2PQ7kZ5lhE3ry6LartBK9ZotLsYE374ykl4fAGcX52NyxbkR729714xDzq1CjsaBoQizYmwLIt9fPFZpOP7UKgjhccdowiEOK5+4VAX2oacyDTp8Kk15WG/55WLC/GpNdzx9JXnjwhFnISmAa4YuFJG6y1hFR901qoZbJibgx9/eCH2fPsi/Pf/1uGujZWymnjloCLHBGAm8y0NHlJmhjzHNg9w864MEzSbyUWwnbZ49v92EQt9Eo25eWZsqskBywJ/2Sme/TZoTqaL6lKTolOjjD+2p7LfHu4Yxa/54qv7rl6A8myTrPtHiS8mjvGjldj08eKMvDSDaGKPgnTu2aYnApulVAw5uDkducy3Zv3090nSiSpVp4n7ojIloFWr8LubliE7VYf6Xhu++9/j55wXHfyzT0F6SlIVNpDxGvn9M0xaxQh8THoNHvvkCmQYtTjaOYZv/udoyNezpz9oQ32vDRajFl+XsJA6x6zH329bjZ98ZJFk75GoGMUM39JiMdnJmFCoIlZhDoUS70Q8evB4PDh9+jR0Oh2WLVuG7OxsMfeLQokahmEE++2OxoEY703kOD0+PPi/0wCAL15YBUsULWzNBi0W89VdSrHfEvNtdV5oCyV6jVqwxIZjgFEix/jwLVlUmwnyO7cM2MOq7qPMDllQPa8iC8azFiJ0GhWuW14MAHhuX7vs+yYWHYL5VlmLE7l8m6r+BA7fbuOtt+ursqOatFlUxF27SWhfKfSOudA25ISKAZaXZWBFeQbMBg2GHJ64v0YTy4iYIboMow4qBmBZYNhJ7bcU8fn91ibY3D7ML0jD1XWFEW9nE2+x3nq6P+47DciJFKF9SvxTwhc/JUP4low5S0QYc5ZmcWGC9qHYfG4dw07cz1tS7rmkBgsKxbekhAsx3zo9fsH0+s3NtdBpolsYXFJqAcNwx+h0ljWlsuVUH7bU90OrZvCDqxaIskhYnm3CZ/i2pD969STcvsnP350j4+i1uqBRMVhaIl34tibfDL1GBavLhzNDsxsMvf4AfvsuZ7393AUVERtfvn3FPNQVp2Ns3Iv/+8chofUowBWuAohJ0PUnH1mIp29bjQPfuwRPfWYVbjmvDHlpiWukIuHbzhHnpDkgUkhPzbeU2SDzLW38fTRVL394xyyYb2n4Vi7u3FAJAPj3gU7R7ul2N/f3S6NhBlkgxfdnh2/tbh++9Nwh+AIsrlhcgI/y89UUynQsLEqHVs1g0O4WxByR0sfbafNFHHvl8+HbSFrJS8XJbk58U54tzz2GmG+tU9wnbTRIJjp5aQb85uNLoWKA/xzsxHP7Oib9O5E9KU1gIzUmPReAJN0FMqLIIEhBSaYRf7h5OTQqBi8d7safd8xeYDRod+NXfLHK1y6tmWTopCgHEr6dykQdLtR8Kz/UfEuhnEvYM/ROpxO33XYbjEYjFixYgPZ2Loz0xS9+ET/72c9E30EKJRo2VHOh8J2NygiaRsJjO1rRb3OjJDMFn1gTndEGANZWcmbP95tj30Z4xBFsoURa6oXC4gkGmHiGBNNCaXeRa9Yjw6hFgA3aginiQMK3F9VO3Xb5xpVcy+7tDQOKMUaHi1LNtyScJFb49p/7O7D2gS040a2cgOpW3hq5qTYnqu2Q68QxhZlvifV2fmEa0gxaaNUqbJjL/a7vxrEtGpAmRKdWMUKL3kEbDd9SxKVj2Im/724DAHzr8tqoFtdXz8mEQatCn9WNUz3nGneUwu+3NuGjf3z/HDNfrCDj2hxqvqVMgBhcOxRynEoJCcqWZsW3+dYfYHHPPw/D4fFjZXmG0MY51hi0amTx4wiPL4DlZRnYvDB602uaQSt0ohGjLa1cuLx+3P/KSQDAZ9bPETUQ+oULq5Bj1uPMkBN/23Vm0r/tb+PGvwuL0mftYBMNWrUKC/kCvFCK2v5zoBMdw+PITtXjE+eVR/y+eo0av7tpGdJTtDjSMYqfvn5K+DcyF1EVxvyNWFiMOqyvzk6aRZ2cVD3Meg0CbDA8CUw03yrr2Z6iPEinIR9vzjbHYCGaGJCsrnPbaUdKh4iW/UTkvIpMLC5Oh9sXwFP8s2G0EJNYKg2AyUJNPifgODt8e9/LJ9A25ERhugE//fAixVgJKcrFoFVjPl9AeLA9ujF+n5Wb6yD3FjEImm+Vsd7i8weEZ6EVZZmyvCe5T9qmuE8Gw7fJMfaVi7WV2fgqb0H9wcsnJnUZ7EjSAh8SVuzgnzOUFr4FgDWVWfjBVfMBAD9/sx5bp+hQM5FfvnkaNpcPCwrT8PFVpXLsIiUCUngZllME4RgJ36bS8K1skGuFTq2CPkopAIWSKIR9Jtx77704cuQItm3bBoMhWOV28cUX4/nnnxd15yiUaFlblQ0Vw9k5uuMwNNdvc+HPO7jWwd/cXBtx6+CJrKvkAsnvNw/G3GTWxLc+K7KkhFWNROy9SjNAhgPLskKIbnEI4VuGYTCPt9+e6rFKum/JxJjTi/38pMqF04Rv52SbcF5FJlgW+OdZ1bDxQjB8q6yJg1w+1DggQvjW6w/gwbdOo3vMhef2KuPvNGR3CwvlpIV7pBDz7bGusZhfuyeyt5Ur5FhVniV8jwTZp2rTG08EzbfiTjgRk24szXI+fwBnBh1452Qf/ry9GV//1xF8/6Xj55jdKPHFQ283wOMPYF1VFs6vjq4riUGrFsaMpIhAabh9fvx+axP2t43gzqcOiFIlHy3kfpZNzbeUCZCFm44EN9+OOb1CuEYU820Mw7eP7mjBvjMjMOnUeOj6JVArqM1n4YTA3bc/NE+04MXEtrTxwmM7WtA+7ERemh5fvLBa1G2n6jX45uZaAMBv321Cvy1o5Np3hnt+XFkunfWWUCcUHs889+HxBfDbd5sAAHdtrIw6FFySacRD19cBAP72/hm8drQHQHAOJ9TORZTIYRhGsN+28J+71x9AL2+eU1phLUV55J41Ho1FeIeE5cecXlG6ePkDrFAUT8O3U8MwjFA09PfdZ0R5RiLjOxpmkIeaPM58W98XDN++erQb/z7QCRUD/PqGJUg30jAeJTSWllgARF9g18+PP8TsOpCfzo1llGK+re+1weHxw6zXCAZqqSH3Sev4VOZb7nvUfCs+d11QiYtqc+HxBfC5pw9gzMl91mQNTYz5jHgilQ9AkjnNTIVaYm85rwwfX1UKlgXufvYQmvqnlkUc7hjF8/u59cEfXrNAUfM5lMmYBPNt9IV6Dje1hctNpom7h6WlaGhRGIXCE3b49r///S9+97vfYf369ZNOpAULFqC5uVnUnaNQoiU9RYsl/APmzsaB2O5MBPz67UY4PX4sKbHgikUFomxzWVkGdBrOZNY8MHvrQikh1pRwF27q+L/p8e4x+APKCaGFQ+fIOEadXmjVTMgP8yR8e5KGb0VjR+MA/AEWVbmpM1a0kurIf+3viMtjrpMPLShtgY60QZy4mB0pW071CQbdXU3KsJ3vaBwAy3LnbrSTkzX5ZmjVDEad3qhbhYnJ3lbO/LVqTtAIsLEmFwzDXauUYi+IBKnax2fxYd4hh/ThW6fHh+NdY3jpcBce+t9pfP6ZA7js1zsw//tvYeOD23D7U/vxwBv1+NeBTjy1uw0vH+6WfJ8o0nCiewz/PdwFAPjWZnHCWBv5IP02hYZv97WOwMkvJp/sseLr/z4S8+IEar6lTEUsQ6RyQsy+2al6UWygJZncuHVs3CsshsnByW4rHnr7NADgB1ctUJz1hnwuH1qUj+Vl4oU/hfBtnJhvO0ec+P02Lmz6nSvmSxIIunZpEepKLLC7ffjFm6eF7+/jx78ry6U3YtWVcAV4h2fp+vPP/R3oGh1HrlmPm1eLY/a5aF4ePncB1778m/85ioY+m2BgFdMyTJmeCt4w3DLIzd31jrkQYDmzDB1rUGYjVa9BijZ4P47FQnShJQVFlhR4/AE8s6c96u31Wl3w+lno1CpRA2CJxuYF+SjJTMGI04t/HYi+ON3upvZFOSHrBI19NgT4wPm9LxwDAHx+YxVWV2TN9OMUyiSWlYlTYNcnhG+lMN8qI3y7n+/utqwsQ7awHrmuTmWID5pvaZBMbFQqBg9dvwQlmSnoHBnHPf88jECAFeY0yDN3snC2GMui0AIPhmFw/9ULsKo8Eza3D3c8deCcuaJAgMX3XzoOALh2WRGWy2SxpkQGmTt0uMUz34YjeqNEx8KidJxfnY1PrSmP9a5QKIoh7PDtwMAAcnPPtbc5HA6aaqcokvOrufbXOxqVEcYKlYY+G57fx01MfvcK8Yw2Bq0aK/iH7vebY/uZNPKVadVhLtxU5qTCpFPD6fGjqd8uxa5JDrH21uanhWw0ruUn3+p7afhWLEh7kumst4TLFuTDYtSie8yFHQ3xFeR3enwYcnDt7ZUWHiChxkG7J+pQ88SFnJZBhyJs59tOc8fKppqcqLel16iFCfiJ7ZBiybDDgwa+iGKi+SvTpBPMCvFsvyXh27ONQdEimG9tHtG26fMHsKdlCM/sacMPXzmJT/51L9b97F3M//5buPK37+FLzx3Gb95twuvHenG6zwaPPwC9RoV5BWm4cnEBNszljtGXaPg2bvn5m6fBssBVdYVYFIJRPxQ28sfFwfZRWYNvoUKMvHXF6dCoGLx6tAd/3B7bYtCg+VaZlghKbCjN4sZfXSPj8PkDMd4b6WgX2kCLs1Bl1GmEeyZZBJMal9ePrzx/GF4/i0vm5+FjK4pled9w+L9NVfjkmjLcf/VCUbe7rMwCADjaNQpvHBynP371FFzeAFbPycRVi8UpVD4blYrBfXxry38f6MSRjlGMODxo5OcgVsgQviXF5Ce7rfD4pv67uLycCR7gjg+DNvrwO+Frl87FqvJM2N0+fOLxPfAHWKTqNcinoTdZqOTNt828+ZYYPwssBqiowYkyCwzDTGoPnhqD8I5axeALF1YBAP64rTlqC2s7XwBQlJFCLWYzoFGrcPt6zn77l52tUc+3EftiLI6hZKQ8ywidRgWnx4+2YSe+8txh2Fw+LCmx4EsXi2v6pyQ+y0otALhOitFcg/us3FyHuOZbblsDdrcinj/2tcnX3YKQlsJdV8l1diJW/ntpKcoMQsY76UYt/njzcug0Kmyp78cftzcL8w5K6x4pNWff35VqvgUAnUaFP9yyDEWWFLQOOvDF5w5Nmmf75/4OHO0cQ6peg29dXhvDPaWEgpG3Ljuj7JDh9vnh9XPjXdqpQT70GjX+fttqfPEiOj6lUAhhh29XrFiB1157Tfh/Egj8y1/+gjVr1oi3ZxSKSGyYy7XM3dU0GFfGyoffaUCABS5fmC/6os66Ku4zeb9pSNTthgsJzlbnhtfGRa1isJBvwX5kFgOMUjnaNQoAWBxGQIaYb0/12GJudksE/AEW2xpIOHLm8K1Bq8ZHlhYBAJ7dG72tQ066eEuq2aBBusIma7JTdWAY7m8x4ow8iHhm0IGdjYNgmGBlcqztt/4Ai+3k+Jol3B0qi/jr3lGFhG/38UaA6txUZJ1lXrpoXh4A4N1T8Rm+dfv8gnUgW2SrlBC+tYtnvv3Gf47ihkc/wHdePI6/7mrFjoYBYXE+w6jFyvIM3LiyBN+9Yh6euHUldn5jE07+cDPe+NL5+N1Ny/Dja7gAz67mQcFmQYkfdjUNYkfDALRqBl+7dK5o2y3JNKI6NxX+AIudTcorPCFG3js3VOK+qxcAAH751mmhsCYWBM23NJRECZJnNkCnVsEXYBVj9ZGCjmFiiRFvoaqMDy4T26bU/Op/p3G6z4bsVB0euHaRIgvMFxSm44fXLBTdzF+RnYr0FC1c3gBOKbzTyo6GAbx5ohdqFYMfXrNQ0r/T0tIMXLuMew6875UTwvi3KjdVlkXJ0kwjLEYtPP7AtEW4z+/rQM+YCwXpBtywskTU99eoVfjtTUuRnaoTQheVOSZFnhuJiGC+5btWkWf7Ikty2bgokTOxkDRW1tLrlhWjyJKCQbsbz+xpi2pbUow1EpWPrSiGxahF+7ATbx7vjWpbxCSWRsO3sqBRqwRRyTf/fRR7zwzDpFPjkRuXQKsOezmXkuQUWVKQa9bDF2BxtHM04u2QuULSxU4MMo066NQqsCyEbnaxgmVZwXwrR4EdIY2Yb8d956z3Wan5VnIWFqXjh/x84q/+d1oYayeb+fbssKLFqNzwLcCtrTz6yeVI0aqxo2EAP3ujHgAw6vTg529y//3li6tFvV5RpMHIm2/HPefav8PBPsEebtLRayaFQokdYT+t/fSnP8W3v/1t3HXXXfD5fHjkkUdw6aWX4oknnsBPfvITKfaRQomKumILzHoNRp1exdgCZ8Pp8WELH1gihgAxWVPJtSfa3TIU00ByI29MrMoLv2VhHW+AORLFpEEsOdrBHYvhhG+r81KhVjEYG/cm9MK9XBzpHMWwwwOzQYMVIVQ0f3wV175zS30/+uMonNbJTxoosWJXo1Yhi1+07rdGPslGAtEb5+bg6rpCALEP3x7uGMWo04s0g0awwEbLoiJuO0q5l+3lW+6umnPupCSxSe9qHoQrysrVWDBo58LgWjUjemg9K1U36T2ixe3z4y1+MW19VTZuWz8HD1y7CP/87Boc/N4lOPT9S/Gvz63Fz65bjNvPr8Cm2lyUZBonmYJKs4xYXpYBlgVeOULtt/FEIMAKk4w3ry5DWZZJ1O2T4oGt9coK33YMO9E84IBaxWB9dTZuOa8MN60uBcsCdz97SDDEyQ0131KmQqViUMwv3pDQSCISNN+KN+Yk22qX4XPb3TyEv7zXCgD42bWLRS++UToqFYOlvBnrIG99UiIeXwD3vXICAPCpNeVCZwgp+ebmWhh1ahxqH8XP+MU8uYxYDMOgrtgCYOrCYymtt4S8NAMeuXEpSN62KsziaUrkVPDm25YBO1iWFYrraPiWEioTQwexCu/oNCp8kZ/b/tP2lqjMi2Jb9hMZo06DT55XBgB4dGdLVBIH0vqcmsTkoyaPu9fu5cOA91+zUPRnfUpywDAMlpVy49aD7aMRb4eEb/PSxHtGUqkY5KVz2+uJcQe7zpFx9Fnd0KqDY285IPdmjz8A91ldLogNN1bFM8nCDStL8NHlxQiwQIAFdGoV8pIstGnSn22+Vf4xt6AwHQ9+rA4A8Jf3WvGfA5146O0GjDi9qM5NxafWlsd2BykhkcKHb51RdsdwuLmfN+nUtEMMhUKJKWGHb9evX4/Dhw/D5/Nh0aJF+N///ofc3Fzs3r0by5cvl2IfKZSo0KhVWFvFhU13NiorODAd208PwO0LoDTTiPm87VRMFhelw6zXYGzci5PdsbHaWF1e9PIP7VW54YdvSWj1aKcyQmjhEAiwQniOhOlCQa9RC20Hp7PeUEKHGDk3zM0JyRwwN8+MZaUW+AMs/nWgU+rdE41guxxlLk6QYEO/LbJAs9vnxz/3dwDggmfE7L2reSimhmhiZDx/bg40IpkpBPNt55gi7NczhW9r880oTDfA5Q1gd3NsLeuRMGgj9kq96GYvsc23+1pH4PD4kWPW46nPrML3rpyPj68qxao5mWEZ2T68hAuu//dwlyj7RZGeYYcH337xGI51jcGkU0tSsLWxJgcAsL2hHwEFdZAg5vrlpRlCQP6+qxZgZXkGbG4f7nhyP8bGz23bJyVefwAjTu49c5IstEeZHTlDpLGiXQIbXYlMn5vV5cXX/nUELAvcuLIEF8/Pk/T9lIoYC/NS89ddrWgZcCA7VYcvXyJPa7u8NINwjyUG0hVl8hmx6vi5j8Md5859PLOnHf02N4osKbh+hbjW24msq8rGty+fB51ahUvmi9PVgzI75VkmMAxnPhu0e4LmW4U+21OUR84k823sgpPXLS9GSSZnv336g8jtt1IU+iQyn1hTDp1GhSMdo9gfRWGNXbAvKj+MkyhMLC66cnEBruMt/BRKJCwrswAADrZHdh2wu31w8MGovDRxQ4kFadyYJtaiGdLdYmFRuhAGkwOTTgOSE7OeNYdlo+ZbWWAYBj+6ZiFq+etuUUZK0oX3UvWTj/kMhZtvCVcsLsDd/HP6vS8cE8aY91+9gJri44Sg+Ta68K3NzV0/zw6SUygUitxEdPeprKzEY489hr179+LkyZN4+umnsWjRIrH3jUIRjQ1zueDAjobYmhBD5c0TnMFu88J8Sdr5adQqrK7gFoveb47NZ9LUzxnJ8tMMQnuVcCAVqPW91rizKp4ZcsDm9kGvUaE6TOvvPD6MfarHJsWuJRXv8m2pL6wJffHwRt5++/y+DkWFkGaCmG9LFGi+BYBcftJuIML2Um8e78WI04vCdAM21eZiWWkGDFoVBmxuNPbHxnwIANtOc8GwTWEcX7MxNz8VOrUKY+Ne4e8aK2wuL050cwGAqcK3DMPgwnnc776lvk/WfRODoL1S/ABdjsjh26180Hvj3JyoJgevWFwIjYrB8S6rcI+mKBOPL4C/7GzBBb/ciuf2ccUH91xaI4mlcUVZJlL1GgzaPTjerZyCp+38cX8BHw4GOKvWH25ejoJ0A1oGHfjyc4dk7fAwxNus1SombiaqKfKRDOFbKcac5HOT2hh838sn0DU6jtJMI7575XxJ30vJBMO3yjTfHukYxcPvNAAAvnX5vIjmESLlM+vmTAp7TTX+lYrpuv44PT78cRtnvf3ihVXQaaRdYLxjQwVO/PAybF5YIOn7UIIYtGrBctsyYKfmW0rY5E4wFJpjuBitVavwxU1cwcSftjfDGWFrWVJcTsO3oZFj1uPapVxo87EdLRFvx+am5lu5WVbGjcmKLCn4yUcWSbI+REkeyBj/UPtIRDIHYr016zWiB5vy07l1gd6Yh2+555+V5fKN8QHO/ksKG6yuyfdGar6VjxSdGn+6ZTmWllpwC2+NTybOPq/jaU7zyxfPxaXz8+DxBxBggSsWFWAtL+ehKB+jljv2xDLfptJiBQqFEmNo6QclKdhQzS3MH2wfER5alIrHFxCMnJctkM64s6YyaIeMBU19XLAn3PApoTgjBZkmHbx+Fqd64ssCe4y33s4vTAu7Ao+Eb0/G2e+sNHrHXDjZYwXDBK1+oXDl4gKY9Rq0DzuxuyU+bJ6dCjff5pqJ+TayIOIzH7QD4ILRahUDg1YtTJTtaopNcUG/zSWc5xfMDf34mg29Ri3YL2Jt/T7QNoIAyy16FaRPfWxdyLerf/dUvyJMveEwYA+ab8WGBCRJUC9aSPiWfN6RkmnSCcVKL1H7rSJhWRbvnOzDZQ/vwI9fOwWby4d5BWl49o7zcNv6OZK8p06jwnp+0nJrvTI6SLh9frzPj1/PvofnmPV49BMroNeosPX0AH71v9Oy7RcJ1GeZdElnyaDMTqKHb/0BVhhzlmaJH76V8nN741gPXjjYBRUDPHR9XVIHS+pK0sEwXJC63xrbBfCzaR9y4rYn98HlDWBjTY4QJpILg1aN714xDwB3XMr5bLWYLzxuHrBPms96+oM2DNo9KMlMwXXLi2XZF2oQkp+KHG7OrGXQEQzfKvTZnqI8cie0TY51eOcjy4pQmmnEkMODv++OzH5LinGKFVpcrkRuP597Tnz7VB9aBiIrsg0GwJJ3jCQ3K8sz8bdPr8SLn18rdHqhUCJlYVE6tGoGg3YPOobDlzmQ8O3Egg6xKODDt7E23+7nzbcr+OC7nJBrq9U1tfk2jV57ZaE824QXP79OsvlVJXP2HEg4nfRijUrF4KEblmBxcTqyU3X4Nv/MTokPjLx1edzrj0p2ZefNt8k8n0ehUJRByLOmarU6pC8KRYmUZBpRnmWEL8Dig5bhWO/OjLzfPAib24ccsx5LS6R72FtXlQUA2Nc6DI8vINn7TEdjP2durcqNLHzLMAwWFwdbsMcTZH+JvTccSPuTehq+jQoSVqsrtiArjHCdUafBNUu51uzP7m2XZN/EhkyqKTV8S9ogRmK+beizYe+ZYahVDG5YGWyzuo4PisUqfLudt94uLk6f1OZRDBbx1z0S7o0Ve1u5e+lM1q+1ldkwaFXoHnPhdF982boHiflWgvBtVio3gTXkcEcdSm4bcqBlwAGNisG66uiruq9Zwl3fXjrcHXeB6USnvteKTzy+F7c/tR+tg1y77Z9ftwivfnE91lRmSfrem2q5gCu5d8aafa0jcHr8yDXrMZ8vSprIouJ0/OKjiwEAf9jWjFeOdMuyXwMSXjco8Q8JiUhtcI0VfVYXvH4WWjWDfBFbkZbxQd6u0XF4/eI/s/ZbXfj2i8cAAJ+7oBIrZDYdKQ2zQYuaPO55U0n22xGHB7f+bS8G7R7ML0jD725aFpMih0sX5OPJz6zCX29dIauBLsesR5ElBSwbfAZwuH3403bOYnj3hdU0FJvAVOaYAADN/UHzbbGFBg8poZE7YT4i1sFJrVqFL/Ktgf+8owUOd3j2W4fbh0G+gFXMQp9EpyrXjItqc8GywOPvtYb9815/AC4vNwaL9TGUbGysyRW6hVEo0WDQqjG/kJtPjmSM32/l5jryJDgeBfOtNXYd3kYcHqF73vIYhG9JNw/r+NThW3rtpUjN2YFFizG+ij5S9Rq8cNdavP+ti2iHkDjDqAvmysaj6HBMrpc0fEuhUGJNyLOzLMuitLQU3/ve9/DCCy9M+0WhKJXzefvtzkZlWLum460TXHvuS+fnSbqgVJNnRpZJh3GvH4c7RiV7n+kgD7TVueaIt0EMMGe3X1Q6x/jw7aKi9LB/loRMWgcdcEUxGE123q3nAkQXRWCKvHFlKQDgfyf6MOwQx1wpJcRCVqLQtny5UYRv/7GHC0BfMi9v0gTgOt7s/UHLMHwSBDVmYxsfvt0oovWWQK4bx7pGRd92OIQSvjVo1cLfYsspZYT2QkUw34ocngaC4Vuvn8XYeHQ2fnKsrSjPEKX18iXz82DUqdE+7MTB9tGot0eJniG7G9958Rg+9MhOvNc0CJ1ahbs2VmLr1zbihpWc8VtqNtZw98ojnaMYskdmKReTbXwI+IK5OdOGn65ZUoTPbqgAAHz930dwXIaCBSmvG5T4hxhcO0Zit6goJcRMW2RJEfW6lJOqh16jgj/AomdUXBsSy7L4xn+OYsTpxfyCNHz54rmibj9eWcq3pVXKOMDl9ePOv+9Hy4ADhekGPPHplTFdULlgbg6qopjDiJQlJRYAwJEO7n725O4zGHZ4UJ5lxEdktgBT5IWYb/ed4QrnVUwwqEKhzMZES2GszbcA8JGlRSjLMmLY4cFTYdpvO/i5LYtRK8qzbzJx+/ncc9G/D3SG/Txnn9AGXex28xQKRT6WlVoARBa+JeZbKcK3SjDfHmjjPpPKHFNYkhaxSEvhrq021+SilKB1nN7zKNIy8f6uVTNxGWDUqFXQaWhBarxh0ATDt05P5HkHh5v72Xg8dikUSmIR8p1o79692Lx5Mx555BHcf//96OjowIYNG3DNNddM+qJQlMr5vBFuZ2NsTIih4A+wePtkLwBg88J8Sd+LYRjBlBYLO2RjHxe+jdR8CwBLSuLPfOsPsDjeze0vMfeGQ45ZjyyTDgEWON0bXyZJpeDy+vEefx3YFEH4dmFROhYVpcPjD+CFg51i756o2N0+jDi5iRqltqYkbRD7beFNsjk9PvyH//xvPq900r/NL0yDxaiF3e3DEZmvDz5/ADv4Io+NERxfs0HCt8e7rDEzk7q8fuG6u3qG8C0AXDiP+wxI4D1eGJQwRKfXqIWWYcTcEynERLqpRpxjzajT4LIF3PjjpcNdomyTEhkeXwCP7WjBxge34Zk97QiwwOUL8/HOPRfgm5trZZ18z0szYH5BGlgWwvUtlpDjfuMsx/03Ntfigrk5cHkD+OzfD0geHKbmW8pMlGRy47Bhh2dS2/hEgYRvxS72UqkYYZvtIluDn9nTjm2nB6DTqPDwjUvoQg0PsT0dbIu9+TYQYPHVfx3BvjMjMBs0+NtnVkmy6B8P1PFzH0c6RmFzefHoDs56+6WLq6Gh1tuEpjKbM98e5QuJ8tIM9HpFCRky3wIoYzFao1bhixdWAwAe3dEMexj22/YhbhxQqtDCciVzXkUmFhWlw+0L4OkPwuviRf5GKVo1taxTKHHMMqHALpLwLTfXMbGgQyzy07nn5N4Yhm/3tXGCiZUx6oIimG9d1HxLiQ3aCcHVDKNO1i4vlORGpWKQouUCuONRhG/tbu76qYTnHQqFktyE/MS8YsUK/PGPf0RPTw/uuecevPjiiyguLsaNN96It99+W8p9pFBEYU1lFjQqBq2DDsW2+zzYPoJBuwdpBg3Oq5C2hTAQbM3+frO84VuH2ye0y6uOInxLzLfNA/a4WcRuHrDD6fHDqFMLBpNwYBgGtQWcaae+1yr27iUFe1qHMe71Iy9NjwWF57arDoUbV5UAAJ7d267o1uxdvF0tPUW5ZhASbuwP03z76pEe2Fw+lGUZBbsqQa1isKYiNsUFB9tHYXP5kGHUoo6/RonJ3DwzdGoVxsa96BiOjT3vcMcoPP4A8tL0sy58kVDowfaRuDBFE6QO0ZHtDkYRBhz3+LG7eQhAZIUE0/Fh3p726tEeSVp8U2aGZVn870QvLv31dvzk9VOwuXxYUJiG5+48D3+8ZXnMWqxuquVM3lvrYxu+7Rh2onnAAbWKwfrq7Blfq1Yx+M2NSzEn24Su0XHc9cxBSY9pKUP7lPjHbNAi08SZz2N1/5aSTonCt0AwZCNm+LZ10IGfvHYKAPDNzbWYmye/yVSpECvW0a4xeHyxHQf8/M16vHa0B1o1gz/fsjyp/051E7r+/G3XGYw6vajIMeHqOmq9TXTIvBGZdqCtVCnhkGHUQqvmAhRKCe98eEkh5mSbMOL04qndZ0L+OdI9QKldnZQMwzC4g+8K8tTuM2F1UiNhsFSFHD8UCiUylvEFdqd6bHB6Qi98ACaYb83SmW/7be6YdK8DgP1nuEDyihiFb0lxvXU8+HdhWVa4/ip1TYeSWJDQYoZRF+M9oSQbRh0XvnV6w7s3TcROzLd0vEqhUGJM2OWqBoMBt9xyC7Zs2YLjx4+jv78fmzdvxvDwsBT7R6GIhtmgFSo8lWDtmoo3j3PW24vn5clSTb6WN98eah8N+6E7GpoHOOttdqoOGabIB/PZqXoUWVLAssAxGdoJiwGxRS4sTI+4Jeu8fC4weqqHmm8jYWt90BQZaRXn1XWFSNGq0TzgwH4FWKGmgxQaFCvUegsAuXxIaSDM8O0ze7gWhTetKoVqinOJFBfIHb7dOqEduhTt4HUaFebxAfyjXaOibz8U9rZyY75Vc7JmPYcKLSmYxxsztzfEj/2WHI9ShejECN/ubhmE2xdAkSUlqkKWs1lXmYXsVB2GHR7sVOh4SWn4AywCgegLMU71WHHL43tw598P4MyQE9mpevziusV4+QvrZSnKmgkSpN/eMAC/CL9rpGxr4I7JZaUWpKfMvgCRbtTisU8uR6peg72tw/jhKycl27dgaJ9OVFOmRiqDqxIgv5MUNjqxw7c+fwBfef4wxr1+rK3MwqfXlouy3URhTrYJGUYtPL4ATvbErtjzqd1n8Gfe7vqLjy7G2qqZCy4SnYVF6VAxXEveP25vBgB8+eK5kjxvUJRFXpoeJl2wHadSO9pQlAnDMLhsQT7Ks4yojEAAIAWc/bYKAPDojpaQ7bdkfqskg4ZvI+FDC/NRZEnBkMODFw+F3uXGTs2LFEpCUJhuQF6aHv4AG3YXSSF8K0EHiuxUPdQqBv4AG3V3sEjguruNAgBWlmfI/v4AkJbCXV8nmm/dvgC8fm7ujV5/KXIghG9NNOxNkZcUEr6NxnzLj1dN1HxLoVBiTETpvs7OTvz4xz/GJZdcgvr6enz9619HWlpk9kAKRU7O5w1ZOxvkDWOFAsuyeOsEF769lG/5LDWlmUYUWVLgC7BCmEoOGvu48G2VCGGhxcVc+8VwJw1ixTH+YZ7sdyTMK+Cut7FcDI1XWJbFuyR8G4Up0mzQ4qq6AgCc/VapdI4of3GChBudHn/Iiy7HOsdwpHMMOrUKH11ePOVr1vML9AfbR2QtLiDh7tnaoUfDwiLu+hGrooNg+DY0I8BF/Lm25VT8hG/JhK9UIbpsM7fdwTBD5xMhBtJNtTmitoPSqFW4cnEhAOC/h7pF226i0tBnw9If/g9V33kddff/Dxf8ciuu/t17+MTje/CFfxzEd/97DL98qx6P7WjBP/d14M3jvfigZQineqzoGRuH0+PDoN2Nb794DFf8Zid2NQ1Bp1Hh8xsrse3rG3H9yhJFBGuWlFiQZtBgbNyLwx2xKzrZfjr8a2xVrhkP37AEDAP8/YM2ye7b1HxLmQ0SIlVqF5ZoEGx0Eow5g+Fbhyjbe2ZPOw53jMJs0OCXH6ubsogrmWEYBktJW9oYFRm+fbIP9718AgDwtUvn4iNLpx7vJxMmvUYw/zo9fszNS8UViwpivFcUOWAYBnNyTML/U/MtJVx+d9MybP3aRmFhWwlcXVeIimwTRp1ePPn+mZB+RspCn2RAo1bh0+vKAQCP7WwJuXiUzNOZaZiBQolrGIYRxESH2kfD+tk+Gxe+zU8Xf65DrWKQx8+h9IzJ3yHmaOcYvH4WOebZu7tJBTHbTuzsSYK4DAOYdPT6S5EeEzXfUmIEMd+ORxG+dfDj1VQ6XqVQKDEm5PCtx+PB888/j0svvRTV1dU4ePAgHn74YXR0dOBnP/sZNBp6QaMon/Pnci1zdzUPxqyNyXSc7LGic2QcBq0KF/D7KTUMw2BdFWdSI62r5aCxnwvfVudG3zayrsQCAEKFqtI5yoflFkURvq3lrZf1PVawbOzsc/FI84AD7cNO6NQqIZwZKTeuKgUAvH6sB2Pj3lleHRs6+SCEks23Jr1GMPn085X0s/GPvZz19vJF+chKnXriryyLKy7w+lnsOyNPcKB3zIX6XhsYBtgg4XV8EQnfxqDowOsP4AAfxFgdYvj2wnlBY6aULd/FwunxCQtMUoXoskzcdocckVkdWJYVLMubJAh6f3gp18L47ZN9wuQF5VxYlsX3XzoOq8uHAAuMjXvRNuTE0c4x7GwcxKtHe/D0B+34/dZm/OT1U/jGf47ic08fwI2PfoDLH9mJNQ+8i/nffwsrfvwO/rGnHQEWuGJxAbbccwG+sblWURNGGrVKuK6R4LfcuH1+vM+PVzfWhHeNvXh+Hr56yVwAwPdfOo79Z8QvOhOM2dPclyiU0kxuPEbNt+Ehpvl23OPH77Y2AQC+cVkNDbFNw7JSCwCuiE1ujnSM4ovPHkSABW5cWYL/21Ql+z4olbpii/Df1HqbXEw0llLzLSUSxCzWFAONWoW7L6oGwNlvJwaOpoOGb6PnxlWlMBs0aBlwCGKC2bDxJjHaxpdCiX9I+DacMT7LsuizcnMduWbxzbcAUMA/k/WOhbYuICb7+LmhleUZMbtXErOtdTw4/ypce/UaWixKkYVUPbc+F02nWgolEox8gUE0a1B2Gr6lUCgKIeTwbUFBAb75zW9izZo1OHbsGP72t79hw4YNcDgcsFqtwheFomQWFaUjPUULm8uHIwozpb51nLPeXjA3R1YbwdpKvjV7s3w24KZ+GwCgOk888+2RDmX9PafC6w/gZDd3nVw8YeEsXKpyU6FRMbC6fOiOwaREPEOspKsrMqNuQbG0xIKaPDNc3gBeOhx6yzY56eDNt0oO3wJALt+2aiAEC6jV5cVLhzkb582ry6Z93cTigl1N8lzftvFhyCUlFmRKOFFBwvvHusZkD+Af7xrDuNePDKMWVSG2rawr5j4Pm8uH/TIFoaNh0MYFYg1alWQP7Nl8OI+YMsOlecCOzpFx6DQqrKnMEnPXAAB1xekozzJi3OvH/072ir79ROH1Y734oGUYeo0Kr35xPd65ZwP+/bk1ePxTK/Crj9Xh+1fOx90XVePWteX48JJCbKrJwdJSCypyTMgy6aCZMIG+sCgN//zsGvz+pmVCa3qlQYLeJPgtN/taR+D0+JFr1mN+QfhdV/5vUxWuWFQAr5/F554+iO5Rca0qxJhNzbeU6RAzRKokxj1+YQwnSfg2i//chqL/3J7afQYDNjeKM1Jww8rSqLeXqCyLkfm2Y9iJ257cB5c3gA1zc/CjDy9UXGAslqzgW+HW5puxWaZuSRRlUJE9IXxLiwYoCcJVdYWoyDFhbNyLv+06M+NrAwFW6BxAw7eRk6rX4CZeJPDYzpaQfsYmmG9pG2oKJd5ZVmYBABxqHwl5Pnls3AuPjxMp5KZJM9eRn86tC/TEYJ2LFGavKAtNMCEFaSnc9dU6oRCFhG+JFZdCkRqyVppJzbcUmRHMt97Izbc0fEuhUJRCyFehkZERjIyM4Ec/+hF+/OMfn/PvLMuCYRj4/ZFfHCkUqVGrGKyvysZrx3qws3EAy8syYr1LAm+d6AMAbF4o7yLKWj60c6LbilGnBxYZBtfEfFuVG334dlFROhgG6Bodx6DdLQSalEhDnw1uXwBmgwZlUUwW6zVqVOWmor7XhlPdVrr4EgZb6rnz7MLa6E2RDMPgxlUluP+Vk3h2bwc+cV6Z4haHiflWqUEuQo5Zj9ZBB/pDCN++dKgLTo8f1bmpWFk+8zV8XVU2/rm/E+81yhO+ldJEOpG5eWboNCrYXD60DTlRnm2a/YdEYm8rMQJkhlx5r1Yx2FiTgxcOdmHr6X5JwqJiMsAHYrNT9ZKd09lm7l47YIvMfEvMo+dVZAnVwWLCMAyuWVKER7Y04r+Humm75ykY9/jxk9dOAgDu2liJhUXhG+1ZloXD44fT40OOhMebWFzA22ZPdFvRb3UJhRNyQQocLpibE9FnxTAMfvmxxWgesKO+14bP/v0A/vW5NTBooy96c/v8ggVfyWNRSmwh47GOBAvfdvLFXmaDBulG8RcHSzK4z83q8mHM6Y34PWwuL/60vRkA8KWLqqHThFyLnnTUlVigYoDuMRd6x1zCgriUjDo9+NQTezFo92B+QRr+cPMyaNX0bzSRjywtgsvrx6baXGrASjIqcoLPe0ovrKVQQkWtYvCli6rxpecO47GdLfjUuvJpQ0YDdjfcvgDUKgYFFnmfQRKNW9eV4/H3WrGndRhHOkaFjnLTQazE1HxLocQ/CwrToVUzGLR70DE8LhQ5zgSx3mYYtdBrpBEGFfBzS70hdsQTi0CAxX6+2HBleQzDt/y9jwRuuf/mrr1meu2lyAQJ3UoVsqdQpoOEb52e6MO30Qq3KBQKJVpCnsneunWr8PXuu++e80W+T6EonfOrOdPrjobYtMyditZBB0732aBRMbiwJk/W985NM6A6NxUsC3zQMiT5+7m8fsH2VJ1rjnp7ZoMWFXzw7GjnaNTbkxLSIn5RUXrUi2W1+dxnd6qHGsdDxeryCtZNMcK3ALcAqtOocKrHiqMKs2kDwfBtcYbyw7cAZg3fsiyLZ/a0AwBuWl06a/iKmL1P9lgx7Igs5BgqVpcX205z9xWxjq/p0KpVmMdbH491yXvckfDtqjnhTUpeVMvd27ac6hN9n8RGaB0vob0yy8Rte8gRmfmWtIjcxIchpeDDS4sAAO81DYZkpU42/ri9Gd1jLhRZUvDZDZURbYNhGKTqNcg1GxQfvAW4UGkdb97eFoNxNHnPjVEUOBh1Gjz2yRXIMGpxrGsM975wTBSD+BBvvdWqGaSnUDMJZWqIqa1zZBz+gLzmeimRug10ik6NXP6eHI01+IldZzDi9KIix4SP8Pc4ytSY9BrU5nNjzXDa0kaKy+vHHU/tR8uAA4XpBjzx6ZXUWDIFGrUKn1hTrvhnO4r4TAzfFtLia0oCceXiQlTlpsLq8uGJ985M+zpSuFRoMdDCjCgpSE/B1XWFAEKz39r5MBgNgFEo8Y9Bq8aCQm5OJ9Qxfh8fiM2TsPg6Vubbhn4bbC4fjDo15hVEv04ZKWn89dU6fq75ll57KXLx+U2V+L9Nlbimjs6VUOQlhRfLRBW+pddMCoWiEEKerbjgggtC+qJQlM75c7mgyuGOUcFQFWveOsG1dF5TmSWJLWg2iP12V5P04dvmATtYFrAYtchOFceyS6r0j3QoL/w4kaN8SG5xsSXqbZHgXX2vLeptJQs7GwbhC7CoyDGhLEscU6jFqMOHeFv1c/vaRdmmWFhdXuEaV6RwOw4JVMwW8DvYPoL6XhsMWhWuDcHEmWPWoyaPmzx7v1la++0bx3rg9gVQnZuKBYXht0MPl0VF8odv/QEWe/l2XKvnhGevPX9uNjQqBs0DDpwZdEixe6IxOMF8KxU5vPmWvFc42Fxe7OP/DlJaludkm1BXYoE/wOLVo92SvU880jHsxJ95e+J3rpiHFJ009g8lQoKvxEIrFx3DTjT127kuFnwhXaSUZBrxh5uXQ61i8OKhLvxlZ2vU+0fuX1kmPbURUqalID0FGhUDjz8gLGLGikG7G/02cfaBBGJLJAwEkmBv23BkY4hRpweP7eACJl+5eC40NLgzK6Qt7cE2acO3gQCLr/3rCPadGYFZr8ETn14l6eI+hRKPzM0zY1V5Jq6qK5Sk6wWFEivUKgZ3X1QNAHj8vZZp5+nlGGskE7efXwEAeON476wdGYQAGC2KoVASgmWlXBe5UMO3xEYrZeejgnRu3aJ3bFyy95iKfbykZVlpRkyfD9P4Am6ra2L4lphvaXE3RR6qcs34+mW1McknUJIbI9+Rbtzjm+WV00PNtxQKRSnQFQdK0lFkSUFljgkBFtgtcRgrVN48zoVvL1uQH5P3X1vFhRh2yfB5NPXbAQDVuamiWd7q+DDrkTgx3y4uDr819dmQ8C0134YOMUVeKHJY7cZVpQCAlw93w+GO/AFBbDqHuQmrDKNW8eaooPl25hDIMx9wAeerFheGPBGwjlzfJC4u+M+BLgDAtcuKZTFYLi6yAAheV+TgdC9nBEjVa8I2AqQZtEILL3IuKhU5zLck2DtoC9/IvKuJLyTINqE8W5xCgun48BLOiPPfwzR8O5Gfvn4Kbl8AayqycPnC2IzdYsUm3uy9s2EQXn9Atvcl1ttlpRZRzLJrKrPw/SvnAwAeeOMUTnZHN54iQXoprxuU+EetYoSCqGgMrtHSb3Xh0l/vwEW/2o7u0egXODv4MWcobUsjhYRvI/3cHt3RApvbh9p8M65YVCDmriUs4S7MR8rP36rHq0d7oFUz+PMnlqMmP3bWKQpFqWjVKvzzc2vw248vjfWuUCiic8WiAlQT++2uqYvipLbsJxvzC9Owviob/gCLJ3admfG1JMxAA2AUSmIgFNiFOMbvJ+ZbCec6YmW+PcCLDVaUZ8j6vmeTxl9fSbEDAFjHffy/KXtNh0KhUKIli5e0PbOnPeK8AxmvKn0dnEKhJD40fEtJSs6v5uy3OxpjH77tHXPhcMcoGAa4dH5eTPbhvIosqBigZcCBXokfchv7uPBtVa54i2okzHq0c0yU1sFS4Pb5Ud/LDRwXFUUfvq3lg2+tQw44o6gISxYCAVaw9F04T9zw7eo5majINsHh8eOVI8oJqHWO8GaQOFicyDVzk2wzmW9HHB68eqwHAHDzeWUhb3t9NTF7S3e9bx9yYu+ZYTAM8OGlhZK9z0QW8teR491jCMjUunpvKxdgXl4WmRHgIv7c2yqzMTNcBkiITkLzLQnfjnv9YV/Dt9ZzIcSNElpvCVcuLoRaxeBIxyhaFW4slov3mwbxxvFeqBjgB1fPlyVsryQWF6Ujy6SDze3DAYltiBPZzl83xDzuP7mmDJcvzEeABX7xVn1U2yL3L7G6OlASFxIamc0yJiXff+kEhh0e2Fw+/PT1U1FvT7DRSTjmLInicxu0u4VgyT2XzKV26hAh4dvjXVa4fZG3/5uJv+8+gz9v54zEP79usVAUTKFQKJTkQa1i8KWLif22dUr7rRxjjWTjjg2c/fb5fe0zdgYkYbBUGgCjUBICMsY/1WMLaT6yz8rNdZCArBQU8Nvus7pkm+MGguZbIouIFWkpwZbrpMidmm8pFEqy8Km15ajMMaFnzIWP/vH9sLvtsSwrSLHMdLxKoVBiDA3fUpKSDXO5RZ0dDQMxD2v+7yRnvV1WmiFp+5aZSE/RCkEuqVuzN/bbAHDmW7GYV5AGjYrBsMODzhF529OESn2PDV4/iwyjFsW88Soacs0GZKfqwLJAAx9opkzPkc5RDDk8MOs1ok+oMAyDG1aWAACe3dch6rajgZwLYhxvUpPLV8/PFL79z8FOeHwBLChMQ10Y9uhVc7KgUTFoH3ZKFnR58RBnvV1flS20ypKa6rxU6DUq2Fw+tMkU4NnLGwFWzYnsHLqQN2Z+0DIkVKMqkUESopPQ6mDUqWHQqvj3C91+y7KsEF7eVJsjyb5NJMesF+zRLx3ukvz9lI7PH8B9r5wAAHzivDLU5qfFeI/kR6VicMFc7tiTK0jv9vnxfjMX/t9YI95xzzAMvrm5FhoVg22nB6IaA1PzLSVUogmRisEbx3rw5oleaFQMVAzw6tGeqJ//hIIvCcec0Zhv/7itGeNeP+qK03FJjIpd45GyLCMyTTp4/AGciNIOPhXvnOzDD17m7qlfvWQurl1WLPp7UCgUCiU++NDCAszNS4XN5cPj751rv+2g5lvR2VCdjZo8MxweP57d2z7t64IBMBpmoFASgYJ0A/LS9PAHWBwNoZtaH2++lXLdMsesh4oBvH4WQ47wO4RFQvfoOLpGx6FWMVhSYpHlPadjoqmRFDxYXTRIRqFQkoO8NANeuGsd1lRkweHx47Yn9+PpD9pC/nm3LwAfX7hhouZbCoUSY2j4lpKUrJ6TBa2aQefIONqGYmcdAoC3TnDh28sWxHYhcG2lPK3ZG/u5oGh1nnjhW4NWjXkFXAAmlEmDWHC0i9uvRcUW0Sx55HeOtBVDMrGVb3N//txsaCMwds7GdcuLoVVzdkil/D06+CBEcYbyFydIWKl/mvAty7J4Zg+3GHDz6rKwzqFUvUaYRJPCfsuyLF441AkAuHZZkejbnw6tWiVcA451SX/dY1kWe1ujC99W5KRiTrYJXj+L9xoHxNw9UZHDfMswjGC/Je8XCid7rOi3uZGiVUf8dwiXDy/hbM4vHe6OecFSrHn6gzY09NmRYdTiK5fMjfXuxIyNfJB+W7085/G+1hE4PX7kmvWYXyBu4Lk824SbV5cCAH72Rn3Ex3jQfEvDt5SZiSZEGi2jTg++9xIXdrxrYyVu4TsJ3PfyCcGwEy4sy8rSCrosK7LPrWdsHH/nJ+2/emlN0tnKo4FhGCwrtQAADopsOj/SMYovPnsIARa4YUUJvnBhlajbp1AoFEp8oVIx+NJF3PPVE++1Ysw52cTaMcwVl9PwrXgwDIPbz58DAHhiVys8vqnHgrSNL4WSWHBjfM5+e7B99jF+Hz/XkSdhobFWrRLWBqTuyknYzz/fLChMi3lYS6NWwaRTAwCsvIncJoRvqfmWQqEkPulGLZ78zCp8dHkx/AEW3/3vcfzktZMh2dDJWJVhAKNWLfWuUigUyozQ8C0lKTHpNVhexj1k7ohhAGjE4cEHLVyY6bIF+THbDwBYW8m1Zt/dPChZuMbt8wth5+pcs6jbXsybMI90joq6XbE4xu9XOMbO2ajN5z5DpYQ9lcy7xBQpUZv27FS9YNJ6bgZjhJwQ862UFjKxIObbYYdnygn/3c1DaB10IFWvwdV8EDAcSAvb9yQI3x5oG0HbkBMmnVr26zi57h2T4brXMujAoN0DnUYlvG8kkHNwyyl5jJmRIJfBkoT0hsII35JCgnVV2dBr5JlMuHRBPgxaFVoHHYotcJGDIbsbD73dAIALcFmMuhjvUezYUJ0NFQOc7rOha1T6jgOk3dQFc3MkCc598aJqmHRqHO0cw2vHeiLaxqCds7NQ8y1lNmIZvv3xa6cwaHejMseEL1xYhXsumYtMkw4NfXY8tTt0q8REhhweOD1+MAxQJIP5tnvUFVZQ+HfvNsHjC2BVeSbOr86WavcSlqVhLMyHSsewE7c9uQ/jXj82zM3Bjz+ykIaiKRQKhYLLF+ajNt8Mm9uHv7zXInzf5fWjlzcvltDwrahcvaQQuWY9+qxuvHq0e8rX2Kh9kUJJOITwbdvorK/t56+/eRJ37MznO8n1jMnT1XI/392NrBHHmrQULmRLrrnUOk6hUJINnUaFX350Mb52KVeQ99jOVtz1zAGMe/wz/pydv26adBqoVHRuiUKhxJaQwrfXXnttyF8USrywgW+Zu6NB/DBWqGyp74c/wKI234yyLFPM9gMAVpZnQqdWoXvMhTMS2YDPDDrhD7Aw6zXISxM3nFBXbAHAWXSUCAksLSoSL3xLrJf1PTbRtpmI9FtdON7FBZQ3ShS+BYAbV3LmvBcPdcHlnfmBQA5I+DYezLcZRh00/IPRkOPcICKx3n54aWFEto31fPh2d/NQSNWS4fCfg5z19vJFBTDq5J0QW8hfT+Qw3xLr7dISS1Shz4vmcefg1tMDov8txIBlWcFgKaX5FgCyU7nwJgnthcLW01zB0KbaHEn2aSpS9RpcMp8Llv/3cJds76s0fvV2A6wuH+YVpOHjq0pjvTsxxWLUCYs1JBgrJdsauONeqnt4dqoed2yoAAD88q3TERlAqfmWEirB8K08i4qEHQ0D+PeBTjAM8IuPLoZeo4bFqMM3LqsBADz8dgP6beFbhkgb6Pw0g6RFITlmPfQaFfwBFt0hhv47hp14fl8HAOCrl86lAc8ICGdhPhTGxr249Ym9GLR7MK8gDb+/aakkXUkoFAqFEn9w9ttqAMATu85g1Mk9J5O5rVS9BhlGagAUE71GjU+tLQcAPLqjZUoZB7GJUfsihZI4LCuzAAAOtY/MKOEJBFihS53U4dsCfvuk2EJq9p3higtXlsvTVWw2SMjW6jrbfEvDtxQKJXlgGAZfuLAaj9y4BDq1Cm+d6MONj+6ecb6SdmmgUChKIqRZ7vT0dOErLS0NW7Zswf79+4V/P3DgALZs2YL0dPFCZRSK1Gyo5oIru5sHI26zGS1vnegFAGxeGFvrLQCk6NRYyreVfL9ZmkByYz8XEq3KSxV98XNxCXf9Od41Br/CAl3jHj8a++0AgMV8SFgMSPj2VK816VuBz8RWPhhUV2KR1Ei3viobxRkpsLp8eD1Cc56YdI5wYYjiODDfqlSMEFjqt04O3/bbXMK18qZVZRFtf0mJBSlaNYYcHtT3ihdWd3n9ePUo97e+dlmRaNsNFRLmP95llTzISsK3q+dENym5sjwTqXoNBu1uWULD4WJ3++DycmOCbLO0ZlNyzA+GaL4dcXhwiDfPSWXxno4P88bpV450wxejMVMsOd41hmd5q/n9Vy+AmlZRY1MtH6Svl7aDRMewE039dqhVDNZLaK284/wKZKfq0DbkjMhgL5cxmxL/EGPboN0Np8cny3s63D7c+8IxAMCn1pRjeVnwXn79ihLUFafD5vbh52+cDnvbxOArtYmOYZiwrcEPv9MIX4DF+dXZWF2RJeXuJSx1JelQqxj0Wl0hh56nwx9gcfezh9A84EB+mgFP3LqSBnkoFAqFMonLFnD2W7vbh7/sbAUQLPQpyTTSQhoJuHl1KYw6Nep7bed0i2JZVrAv0kADhZI4LChMh1bNYMjhmfHZatDhhj/AgmGCAgGpyE/nwrc9Y9KHb60uL+p7OVHLCqWYb/nnIus4H751eyd9n0KhUJKJa5YU4Zk7ViPDqMWRzjF85Pfv4/Q067okfGvSy9MlkkKhUGYipPDtE088IXzl5eXh+uuvR2trK1544QW88MILaGlpwY033ojsbNrGjxI/zC9IQ6ZJB4fHj0Pto7K/v9Pjww7e5CV3q/LpWFvJncPvNw1Jsv0mPoBanZsq+rarc80w6tRwePxoGbCLvv1oONnDBYJzzHpRjb+VOanQqhnYXD7BBEE5l3f5Nu0XShxWU6kY3LCiBADwjz3tMQ1Ejzm9QoW0lC2AxSSXPzdIRT3hX/s74QuwWFZqwfzCtIi2rdOosLqCC5qIWVzwzqk+2Fw+FFlScN4c+UMd1bmp0GtUsLt9ODPkkPS9SPh2VZS/p06jwoa53L1mS730xsxwIRZak04tucmYhG+HQgzf7mgcQIAFavPNKLTIe15vmJuDDKMWg3YPdjVLM0ZQKizL4r6XT4BlgavqCrEqygB6orCxhiti29U0CLdPOts7sd4uK7UgPUW6RQeTXiOYth7Z0ihM3IUKNd9SQiU9RSscyx0y2W9/+dZpdI2Oo8iSgq/zpluCSsXg/msWAuBs/gfahsPaNnkGKZGh00I44dumfjtePMR1J/jqpTWzvJoyHUadBvMKzACAg3wBUKQ88PopbG8YgEGrwmOfXCEssFMoFAqFQlCpGHz5Yq7V7BO7WjEyIRhWmhkfc1vxhsWow/X8XOZjfOCZ4PYF4PVzc5vUvkihJA4GrRoLCjmhw0xjfCLIyE7VQyNxt4oC/tmgV4bw7cG2EbAsUJZlRK7ERt9QSePnCMh6DjXfUiiUZGdleSZe+Pw6zMk2oWt0HB/94/vY2XiuBMRBzLe0WIFCoSiAsEfMf/3rX/G1r30NanWwgkCtVuOee+7BX//6V1F3jkKREpWKEVqRT3XDlprtpwfg9gVQlmVEbb5Z9vefinVVXKjq/eZBSSyKjUL4VvzfV61isJCfNDjcMSr69qPhaCdnd1xclC6qpUGnUaEyhwsyi2nzTCTcPj92NnJhywtrpTdFfmxFCbRqBvvbRvDK0djZbzt46212qk7yAKFY5PK2wIEJ4Vt/gMU/9nAGwptXR2a9JZDr/dkmj2j4zwEu1PGRpUVQxcCEqVGrhECylBbZzhEnukbHoVExQmuyaLiwNg8A8G59X9TbEhty/Mlhr8zirREk8Dsb205zY5WNMltvAUCrVuHKxZz99qVDXbK/fyx5+Ug39reNIEWrxr2X18Z6dxTD/II05Jr1GPf6hXC+FGzn7fVyHPc3ripFeZYRg3YP/rKzJeSfc3n9sPETfdR8SwmFcA2u0XCgbRhP7j4DAHjg2kUwTWEtW1JiEQrIvv/SibC6iLQPkUCMDOHbrNA/t4ffaUCABS6Zn4clJRaJ9yyxWVbK2aAOto1GvI1/7uvAX97jAj2/+tgSLCqmXbMoFAqFMjWXLcjD/II0ODx+PLazRTDfyjHWSFZuWz8HKgbY0TCAUz1W4fukIJFhAFOczC1SKJTQCGWM32flgrD5MgRUg+Zb6QtU95/hAscrypRTXJ/Gh2ytvG08GL6lYTIKhZK8zMk24YW71mJVeSZsbh8+/cS+czrWkfFqKjXfUigUBRB2+Nbn86G+vv6c79fX1yMQSL42tJT45ny+fS0x0MrJm3wb9csW5CumbVZdiQVGnRoj/8/eXYdHcbxxAP/uXdxdiIcEiQEhuLsWdwrF2wItlFLkh0uR4lRxKxQoUKy4u0OCh0BChBAj7snN74+TJiR3OU9C3s/z3NPm7uZ27tidnZ15952sfI0Ec4bFCYNvvezVn/kWAAJEk3jiYNeK4rE4+NbZQu2f7eMoDLwrOjhK/nMn/AOy8gpha6oPXyWzpirCwdwAE9p4AQDmHXki93Ly6ibOQuakhSxk6iIOWIpP/+8O9yuhCYhJyYa5oS66BTiq9PnizN53wj8gr0D1/kp8eg6uiAK7ewc6qfx5ygpwErZ7jzXY7okD6/yczNUSzN26pi04DngSkyYZyK0otLl0vDhDZoIc7UShgOGyqK/SRpRxVNt61RMG355++h7ZeZrLdFqRZOYWYOkJ4XXPhDbVtZ5xuCLjOA5tRAGxF19oph+dW1CIG6JMy621sN/r8nn4oZMwwHrjlTfFbgaRRfw+PT5PMmFCiCzaCr7NyS/EtAMhYAzoV98ZLWtIP46mda4JMwMdPH2Xhr8+GsiWRZKNzlrz7aPkd0uS/bs9e5eG46Kb4KZ0qKHxen3qJBPzSma+vRvxAbMOPwYAfNvOW+U+PSGEkE8bx3GY1F64IsX2GxEIjk4BALhQ8K3GuFgZoYuf8Py8uUj2W3Hwl4meTrnccE4I0RxxcgVZffw4UeZbda7kKI2jufB6MlYLmW/vRgjHuRu4W2p8W/ISB9mmZeeDMYa0bGEQrpkhjTERQqo2S2M97BrTEL3qVkOBgGHGocdYfuqFJIGcpL9aSrIBQgjRNoWDb0eOHInRo0dj9erVuHbtGq5du4ZVq1ZhzJgxGDlypCbqSIjGtPAWTkCGxKQiOVO+zHPqkFcgwIXnwkxenXwdtLbdsujyeZLllNW5NDsAFBQK8CZRnPlWQ8G3oqxGIaKB2YoiJEYcfKv+DD+1RMuAUvBt6S6IlrVvU9NWawPF41t7oZaDKZKz8jHv6FOtbPNj0aLMt86WlSdQzNZUeId7fJFgp9233wIQBowY6Kp252ItB1NYG+shK69QLdmxjz56h0IBQz1XC0kG6vLgJw6+1WDmW3HwbSMP9WQEsDHRRx3RzQgXRceoOrxLyVY5a7s2l44Xb0OeIP3g6BR8yMyDqYEOAt3KZ3A40NUSLlaGyMwrxNnnFS9rsSb8dikM79Ny4GJliDEtPMu7OhVOm1rCfvSll+o7jou6G56MrLxC2JnqS2420rSu/g6o42KBrLxC/HzhlVxligbtV5Qb6kjF5ixaNjlKw8G3v14Mw+uETNiY6GN2t9oy32ttoo/vO9YEAKw881Lua2PxagsuWrjhS96g5dVnQwEA3QMcUVtLbcenTBx8+/RdKnLyFbv5Jjo5C1/tuo/8QoYufg6Y3M5bE1UkhBDyienoYw/fambIyivEXVGGQgq+1awxLTwAAEeDYyQ3SWeIgxnoBkNCPjniPv6L9+nIyiso9T3itsBOC5lvHSWZb3PAmPpX5BTLKxBI5gWC3CtQ5ltDcebbAuTkC1AgGt+mzLeEEALo6/CxZmBdTBKNKf1+6TW++eshcvILkSnJfEvtJSGk/CkcfLty5UpMmzYNq1atQsuWLdGyZUusXr0aP/zwA1asWKGJOhKiMQ7mBqhpbwrGgOtqDjaV5cbrRKTnFsDOVB/1KtgymM1E2SGvq3FpdgB4+yEL+YUMRnp8VDPXTEBgXVEw1/PYdOQWVIysfBm5BXidIAw6FgfJqZN4QlkTmYo/BeLAPvEy99qgp8PDyv51wOdx+DckFqeexGpt22LizLfaCIRQFztRplFx8GNMSrYkeHpII1eVP5/H49CkujUA4Joa2reDD2IAAH0CnVX+LFWIM2o/fZemcuCpNOLg24ZqCr4FgHa1hBkzz6sh+LZQwLDw2DM0XXYBY3beU+l3EO9/2sl8qwcASMooO8Dpkuh3aultC12+wt13teA4Dj3rCLM8H3kYUy510Ka3SZnYdEWY9Wd2Nx+VbwD4FDXzsoEOj8ObxEy8TcpU++eLg3pb1bDVWlArx3GY0VmY/XbP7UhEJJb9vf4L2tfTaN3Ip0McRKrJ4Ntn79Lw+6XXAIBFPX1hYVT2/jm0kStqOZgiJSsfK868LPP9+YUCvEsR9jm1sRR00cy30iZkH0Wl4NzzOPA44DvKeqsWLlaGsDHRQ34hwxMFbvbKzC3AmB33kJSZBx9HM6waUIey5hFCCJELx3GY3L74eVwbfY2qrJ6rJRq4WyK/kGH7jQgAQLpo+XPKJEbIp6eahSEczAxQKGAIjiq9jy9enc7eVPPBt3ai7Lp5BQIkZ+VrbDtP3qUit0AASyNdVLc11th2FGUmznybky9pe3kcYKxHY5GEEAIIrw++61ADq/rXgS6fw7+PYzF40y1EiFbHMtGn9pIQUv4Unr3n8XiYNm0aYmJikJKSgpSUFMTExGDatGng86lhI5VPC29hsOnVUO0F355+KswY18HHvsJNQDX1Egan3Qn/gPxC1ZdmF3sVJwxA9bIz0dh3drEyhKWRLvIKBXgRWzGCUZ/EpIIxoJq5gUaCucTBtxFJmVLvUq6q3iRkICIpC7p8Ds1Fx7m2+DmZ46tWwiyJsw8/1WpmbaCyZr4VHh/izLf77kRCwIAmntZqyyzb3Eu4H9xQMfj22bs0PI9Ngx6fh8/Keenc6rbGMNDlISO3AOEaCH6LT8/Bm8RMcBwQ5Ka+4Nu2tYXBt9deJSqcRa2orLwCfPXnfWy9LgySvPAiHr9cDFP688QZLLWZ+TY1Ox95BbLPtxdfJgAA2oiClstLr3rVAACXQxPwQcvtmrYt/vc58goFaOFtg44+2ruBozIxNdBFkGiZvj0KLFMvr0uhwv2+dU3t7vdNqlujTU1bFAiYXAGIiaIAem0E7ZNPg7wZXJVVUCjA9IMhKBAwdPZ1QBd/+foqOnweFvb0AwD8dScSj6NlB1rGpuRAwAB9HZ5W9n9xxrv03AKkZpc+IbtKdMz2CXQu15UJPiUcx6GeKDOWrGVpixIIGL7b9wgv3qfDxkQfm74IgpEeBe4QQgiRX/vadvAXJTHgOMDJovKMb1VWY0Wrvey+9RaZuQVIF2USM6XMt4R8kgLdLABI7+PHpQnHSO3NNH+tp6/Dl9zQHJuarbHt3IsQJpgIcreqUCsXiTPcpmUXIK3IEuoVqY6EEFIR9K3vjF2jG8HcUBcPI1Pwl2hOglZqIIRUBEqlziooKMC5c+fw119/STp/7969Q0ZGhlorR4g2tKghXDL36qsEjS5pIlYoYDj77D0AoLOfg8a3p6jaDmawNNJFZl4hQqJT1Pa5YfHCYFgvO81NgnIcB39RFkh11l0V4klrf2f1Z70FhMFbtqb6YOzTyX57600S1p9/hZgU1QZaxFlTG3lYl0uWhm/aesPLzgSJGblYdPyZVrctznxbmYJvJZlv03KQXyjA3rtRAIChjVXPeivWTBR8+ygqBRm5ygerH3oQDQBoV9tOrkxymqTD58G3mrB9KStIRhl3w4UDsLUczGBupL6lW3wczeBgZoDs/ELcepOk1GfEp+dg0MZbOPssDno6PAxu6AIAWHMuVOns7drMfGtuqAsd0c0oSZm5Ut8Xn56Dx6JMc61EfZby4mVnCt9qZigQMPz7WPtZvbXlcmgCzj6LA5/HYW53HxrslmFUM+HypJuvhqu1DYpOzkJYfAb4PO3fQAMA0zrXAscB/4bEIli0JKE0/2W+peBbIp+iwbeauP7cej0cj2NSYWagg4U9fRUq29DDCr3qVgNjwNyjT2RmkxcHD7tYGWmlnTTQ5Usmft8mlQxcvvUmCVdfJUKXz0mWoiPqUd9NFHz7NkWu968+G4ozz+Kgx+dhw7D6FDBFCCFEYcLsVsLzuYe1Ma1EogXta9vDw8YYaTkF2H8vCuniADBa9pyQT1Kg6Aa7h1KCb9+nijLfmmk+8y0gXKW06HY14W6E8Ls2EN1IXlGYGQrnropmvjWltpcQQkrV2NMah8Y3LbYyhjGt1EAIqQAUDr59+/Yt/P390bNnT0yYMAEJCcKMRMuXL8fUqVPVXkFCNK2huxX0dHh4l5qD1wnqzxr4sQeRyUjMyIOZgQ4ae1prfHuKKro0+/Uw5QKiSvMqXhic721nqrbPLE0dUZBrsAaC0JQRIgqYEi8Nrwm1HIS/aUXJ9quK1Kx8jN15D6vPhqLVTxfxw9/BeJOg3I0d4uDb8soUaaDLx4p+AeBxwKGHMbjwIk4r22WMSZYxdrasPMvy2YkG8hIycnHuWRzi03NhY6KHjj7qu0nBxcoIrlZGKBAw3AlXrn0rKBTg8KN3AIRZ1SoCcTaYxwosBSwv8e/UyEN9WW8B4USa+Ni8KDpWFfHyfTp6/3oDIdGpsDLWw19jG2FpnwAMDHIBY8CkvQ8Rl6b4YG2CKPOtrRaC6Hg8DtairA5JGdKzyF4WZb0NcDavEJk1e9V1AgAcfhhTzjXRjPxCARYeewoA+KKJO7ztNdtvqew6+jqge4AjCgUMPxwILjOLs7wuifb7QFcLmBtqf8KhtqMZetcT7uvLTr6QGSApzphdEY5PUjlUszAEjwNyCwSS4G11iUjMxKozoQCA2d19JP0rRczsWhvGenw8jEzBQdENR6URB99qcxloaVmDGWNYLfreAxu4SLLkEvUILJL5tqyA8SOPYiSrECzp4y8J3CWEEEIU1baWPTYND8KvQwPLuypVAo/HYXRz4c2VW66FIyVLOE5BmW8J+TT9t7pFSql9/Ph04biqnRYy3wKAg5nwhr1YDQXfMsaKZb6tSMxEgbbpOQWSGx+o7SWEEOmq25rgn/FNJWNONTQce0IIIfJQOPh20qRJCAoKQnJyMgwN/8te0bt3b5w/f16tlSNEGwz1+Ggouti6IlreVpNOPRFmvW1f2x66fKWST2tck+qipdlfq7Y0e1Gv4sTBt5pd/rNOhct8mwJAGDSlKT6OZgCA57FpGtuGtmy+9gbpOQUw1OWjQMDw9/1otF99Gd/89RAv3sv//dJz8nEnXDiY0rYcl2mv52opGbieeeix1CVy1SklKx+ZeYUAKlfmW/HSUvmFDL9deg0AGBDkAj0d9baT4uy3114pF3x79VUiEjNyYWWsh9Y1yzcLqZifk+Yy394WHUcN1Rx8CwDtRMfm+RfxCmX+u/oqAf1+v4GYlGx42hiLLrKF9VvQ0xe1HEyRmJGHb/56iIJCxQIBE8UZLLUURGdtLMr4nCE9+EochNi6Zvm1ZUX1qFsNHAfcf5ssCfT/lOy4EYHXCZmwNtbDpPaUOVEeC3r4wspYDy/ep+NXUcCVqi69FAbll+d+/33HmtDT4eHmmyRclnGNQJlviaJ0+Tw4ifpok/c9Qli8elYQEggYph8MQW6BAM29bNC/vnI3CdmbGUjav+WnXkjtv0YlizLfarG/6SIl+Pbqq0TcifgAPR0eJrahtlvdApzNocPjEJ+eK3N1kuCoFEw7EAIA+LKlJ/opuQ8SQgghYh187FFbNO5JNK9voDOsjPUQnZyNgw+EN9yaUiYxQj5Jfk5m0OPz8CEzr8TKIvmFAiSKEgU4aCnzraOGM9++TshEclY+9HV48Kumubk6ZYgDbdOy8yXBt2aU+ZYQQmSyNtHH/i+b4Oq0NmjvY1/e1SGEEMWDb69evYrZs2dDT6/4Msvu7u6Iifk0M2CRT18L0XK2V19pNviWMYbTT4XBtx191ZfNUd2aiTLfPnibgkwVlmYXKxQwvBZlL/W212zwbYCL8ML5VXyGSsvKq0NqVj4iRAMX4syUmlD7Ewm+/ZCZh63XwgEAawbWwaHxTdGulh0EDDgW/A6d117F2J33ylz+GQCuvUpEgYDB08YYHjbGGq65bN93rAkPG2PEpeViyb/PNb696GThhLitqX6lWpZPX4cPCyPhoNLjmFRwHDC4oavat9PMS9i+KXtzgTgDXI861SrMDRTi4P6n71JRKGN5aEWlZOXhZZwwo3YDDWQEaOZlA30dHqKTsyXZ0cuy724kRm67i/TcAjT0sMKh8U3hZv3fMW6gy8dvQwNhoq+DO+EfsPpsqNz1YYxJBpa1lcFSHOSbKCXzYX6hQHJjUJsKEuxtb2aApqJ+wpFHn1bfPyE9F+vOvQIA/NCpZrlkXK2MrE30saCHcGn7Xy+G4dk71Wd1JXAAAQAASURBVPojuQWFuPFaeINEqxrlt987WRjiiyZuAITZb6W1r5T5lihjasea0Nfh4cbrJHRZdwXLT71AVp5q1y5770bhdvgHGOrysbSPPziOU/qzRjT1QHVbYyRm5GHtudLPpeIAWG1mmRVnvi168wdjDCvPvAQADGvsJlmulKiPgS4fPtWE15wPIlNKfU9cWg7G7ryH3AIB2tayw7TOtbRYQ0IIIYSog6EeH8MaC6+BxOPMlH2RkE+Tvg4fvk7CPv7DqORir4lvMtblc7A00itRVhPE13Gaynwrznpb18VC7ck+VGUmGn9My8lHeo7w5ldqewkhpGx8HkerXxFCKgyFe5gCgQCFhYUlno+OjoapKaX0JpVTS9HE/q03H5BbUHL/VpdnsWmITs6GgS6vXIMJyuJhYwx3ayPkFQoUClySJjo5C7kFAujr8OBsqdlOkJ2pARzNDcAY8EQDS7ArQrwEvKuVESw0OEhRy1HY9r54nw6BGgPvtG3D5dfIzCuEbzUzdPJ1QKCrJbaMaIAT37ZAtwBHcBxw9lkcev56HcO23MbtN9Izl14QLWPfphyz3ooZ6PKxvG8AOA7Ydy9K40H+4ixklSnrrZhdkcClVjVsNXLR1FSU2fvF+3TJ8lnySs3Ox5lncQBQoTJ5Vbc1gaEuH5l5hQhPVE/2PAC4F5EMxgBPW2ONBJUZ6vHRRBTEef55vMz3CgQMP516gekHH6NAwNC7nhN2jW5YatvqaWuCZX39AQC/XXqNCy/i5KpPWnYB8kSZcq2NtTOwLM74nJSZV+rr998mIz23AFbGeggQZXavCHrWdQIAHH70TqGsxRXditMvkJ5bAH8nc/QPcinv6lQq3QMc0cnXHgUChh8OBCNfwazTRd0NT0ZWXiHsTPXhW618s1xNaOMFUwMdvHifLjXYXJy5mjLfEkX0rOuEs9+1QttadsgvZPj90mt0WH0Fp568V6pdjU3NxtITwpu8fuhUU+U+lJ4OD/NFQfU7b74tdQWK6HIIvnWzLpn59uyzOIREp8JIj4+vW1fXWl2qmkDxsrRvk0u8lpNfiHE77yE+PRfediZYN6gu+Dzlg78JIYQQUn6GNXErFphmok83pRLyqfqvj59S7Pm4NOGYuZ2pAXha6tdLMt+mSV9pQxV3I4TXMZpIMKEqcZbbjNwCycozZpQQgBBCCCGkUlE4+LZjx45Yu3at5G+O45CRkYF58+aha9eu6qwbIVpTy8EUNib6yM4vxP1SJpPU5fQTYdbbVjVsYahXcTNSchyHeaLJ1q3XwyV3hSrrVZwwGKy6rYlWJuHEWSBDolM0vi1ZQmKE2/d31uwyNtVtTaDH5yEjt0DmMqAVWXxaDnbcjAAAfN+xRrFMXT7VzPDrkECc/a4V+gY6g8/jcPVVIgZuvIX+f9zApZfFl6wXCBguiparblsBgm8BoKGHFb5o4g4AmHHwsUazMkdLlgCufHf7FQ3wHNrITSPbsDLWg48oW/TN19IDuEtz4nEs8goEqGFvUu4BYUXxeZykPo/VeNPBHVHb38hDc4OS7UTH6MUX0oNvc/IL8e3eh/jt0msAwKR23lg9oA70daSfR7sHVJNkrPxuX7DkuJAlIUM4sGxmoKO1rNHiYD1pmW/FbVnrGrYVKoils58D9HR4CIvPwFMVs5xWFMFRKfj7vjCz9fwePhXq964MOI7Dol5+MDfUxdN3adh45Y3Sn3VJtN+3qmGrUuZOdbAw0sP41l4AgFVnQpGTX/ImPfHxS5lviaJcrY2w5YsgbBxWH04WhohJycZXf97HyO138TYpU+7PYYxh9j9PkJ5bgHquFviiqbta6tfC2xZd/BxQKGCYd+RpiaBgcQCsazlkvhUviyoQMMnNoiObuVMQvAbVc7UAADyILD5ewhjDtAMhCI5OhYWRLjZ/EQRTWiKVEEIIqbRsTPTRN/C/G85NKPsiIZ8sSfDtR338uDThOIedmfaurzSe+fatcJw7yN1SI5+vCnGWW8b++/6U+ZYQQgghpHJROPh21apVuH79Onx8fJCTk4MhQ4bA3d0dMTExWL58uSbqSIjGcRyHlt7CbIhXXym3FLk8Tj8VZt/r7OegsW2oS5uaduhX3xmMAdMOhJQabCAv8XLi3vYm6qqeTHVcLAAA18KSEJuaXW5Z+R5HC4Pg6mg4+FaXz4OXnfC3fRZbOYOgfrv0Gjn5AtRztUCbmqUHzHrZmWDVgDq4NLU1hjZyhR6fh7sRyRix7S56/HIdp568h0DA8DgmFYkZeTDR16lQdzILs5AJAyuWn3yhse1EJwsDsCtn5lvhIJujuQHa1NRcdvDmovb+ephi7f2hB8LAvD6BzuUeEPYxPydhO/M4Wn1twO1w4aBkQw0G34qzU997+wEpWSWzvyZl5GLo5ts4HhILXT6HVf3r4LsONeT6/f/XrTbqOJsjNTsfE/c8RF6B7Eyc8eUQQCfOfCtetv5jl14IM2W3riA3EoiZGeiifW1hnaRlA61MBAKG+ceegjGgdz0n1HerOOeOysTO1ADzPvMBAKw79wqv4tKV+pxLoaL9Xkp/QNtGNnOHg5kBYlKy8eett8Vey8orQGaesI8sPp4JUQTHcejo64BzU1phQpvq0OVzuPQyAR3WXMGas6UHfH/sWEgszr+Ihx6fh5/6Bqj15oFZ3WrDQJeH2+EfcCwkVvJ8ek4+krOEGXm0mflWvK3Y1GzkFQhw/HEsXrxPh6mBDsa1oKy3miSemH/2Lq3Yfvnbpdc4GvwOOjwOvw0NhJu1cXlVkRBCCCFqMqaFh+T/KQCMkE9XoJsFAOEKcVl5/yULEa8WZy8aq9cGR3PhXMb71By1z+fFp+XgbVIWOA4IdKt4wbcGunxJxnHx3A61vYQQQgghlYvCwbfOzs4IDg7GrFmz8N1336FevXpYtmwZHj58CDu7ijFBSogyWtQQBmNdCdXMkvDhiZl4GZcOHR6HtjXtNbINdZvTzQf2Zvp4k5gpySikjFfxwuALbzvtBN/WFS3NfSU0AU2WXoDvvNP47OdrmLz3IX4+/wonHsfi5ft0lQKK5REiCr71d7LQ6HYAoJajKQDguRqCb5MycnHqyXsUqLBktCLepWRjz+1IAMDUjjXLDKpzsTLCj739cXV6G4xu7gFDXT4ex6Tiqz/vo/O6K1h3/hUAoLmXTbFl0sqbsb4OlvUJAADsuvVW4ayr8vov+LbyZb6tKwqcH93cAzp8zf3bNa1uDQC4HpYk92De26RM3I1IBo8TBudVNOKM349FGbdVlZlbgCeiLLoNPazV8pmlcbY0Qi0HUwgYcPmj8++bhAz0+f0G7r9NhpmBDnaOaoS+9Z2lfFJJ+jp8/DIkEGYGOngUlYKlJ5/LfH9ihjD4V5tZ88TbSsosGXgck5KNl3Hp4HGQ3CBUkfSsKzwOjga/Q6GgfG5yUZd/HsbgYWQKjPT4mNGlVnlXp1LrXc8JbWvZIa9QgB8OhCi8b0QnZyEsPgN8Hie5UaK8GejyMaVDDQDALxfDJEsAAkBiep7oPTyY6NPECFGeoR4fP3SqhdOTW6KFtw3yCgRYd/4VOq65IjM7/IfMPMw/+hQAMLGtF7ztTdVaL2dLI0wQZX/+8d9nyBSt3hD1QdjftDLW0+q+b2uiDwNdHgRMmHl3regadWwLT5gbUbZVTXK2NISdqT4KBExynXvm6XusOP0SALCgpy+aVq8Y7TYhhBBCVFPd1gR9RGNfftU0m1SCEFJ+HM0N4WhugEIBQ3DUf6upvRdlX7XXZuZbM2Ggb1ZeIdJy1Ltq4D3Raqe1HMxgVkFX6RDX612KOPi2YtaTEEIIIYSUTqnIFh0dHQwdOhQ//fQTfvvtN4wZMwaGhpUvwx4hRTXzEk4UPX2XJjUDnSpOP30PAGhS3brSTAyaG+liaR9/AMDmq29KLD8jrzBR5lsvO/VOBkvT0MMKgxu6orqtMXR4HLLyCvE4JhWHH73DqrOhGL/7ATqtvQKfuafQasVFjNp+Fz/++wx770TibsQHfCglCEpRSRm5iBFdKPs5aX55eh9H4TZexCqXZU4s6kMWev56HV/9eR8Ljz9TR9XK9POFMOQVCtDIw0oSFCkPezMDzOnug2vT22BiGy+Y6usgNC4DF0QBCm1rV7wbQpp52WBwQ1cAwPSDIcXuKFeXKNESwC5Wle+8/HljN1yc2hqjm3uU/WYVNPSwgi6fQ0xKtmTZ4rIceiDM7tnMywb2Ztq7619e/qLMt0/fpamlDXsQmYxCAYOThSGcLDS7L4mz355//l9w0Z3wD+jz+w28TcqCi5UhDo1vhiYKtA9iLlZGWD2gLgBg2/UInHwcK/W9CeWQ+dZaFHwr3nZRl14Kf49AV0tYGFW8jJqta9rCzEAHcWm5uPVGMzcTaENyZh6WnRJmI/+mrXeFPL4rE47j8GNvP5jqC4Pet14LV6j8pZfCIPxAVwuYG1ac/nLf+s6oYW+ClKx8/HH5teT5hAzhhJSNiX6Fy4hOKidPWxPsHNUQvw4JhIOZASI/ZGHk9rsYt/MeopNL9lkWHnuKD5l5qOVgiq9aaSbz69iWnnC1MkJcWi5+vhAGQBj4Cmg36y0gbGNcRdtcf/4V3iRmwtJIF6M03Hckwt++6LK0z2PTMHnfIwDA8CZuGNrIrRxrRwghhBB1+6lfAO7Nbg+fapof1yaElJ+ifXyxuDThOKWdFsfIDPX4sBDNm4qDf9XlboRwdbcG7hUv662YmaHwptaYFMp8SwghhBBSGSkcfMvn89GmTRt8+PCh2PNxcXHg8/lqqxgh2mZnaoDaogBGRZcil8epJ8Lg206+Dmr/bE1qW8sefeo5QcCAH/4OVjhbrEDAJMG33vbayXyrw+dhaR9/nP++NZ4v6ozz37fCxmH1Mb1zLfSr74x6rhYwNdCBgAFvk7Jw4UU8Nl0Nx4xDj9H/j5sIXHQW9RaewZgd97D3TqRkmR1FhIgyRnraGmvlLlXxvvv8vfKZb98mZWLghpuSzKk7b77FjdfqPxaKikzKwt/3ogAA38uR9bY01ib6mNqpJq7NaIupHWvA0kgXNib6aF+7YmaY/l/XWnA0FwZTrDytfEbp0jDGKnXmWz6Pg4eNscYDmIz0dCQDi9fkaO8ZYzj0MBoA0E+BzKva5GlrAmdLQ2TlFWLwxlulBnMq4k64sJ/XyMNKHdWTqZ0o+PZyaAIKCgU4/DAGn2++jZSsfNRztcA/45vBS4XM6e197PFlK08AwLQDIYhIzCz1feIbb7QZfCtepl6cdbcocaZDcXByRaOvw0e3gGoAgMMPY8q5NsrJLSjEl3/eR0J6LjxsjDGquXt5V+mT4GhuiNndawMAVp55iTcJGXKXFQfftq5ZsfZ7Po/DtE7CrMhbr4VLJoISRJlvtdlukE8fx3HoFuCIc9+3wriWntDhcTjzLA7tV1/GrxfDkFcgXJ3iwos4HH70DjwOWN43QGMrPhjo8jHvMx8AwJZrb/A6IUMSCOyq5eBb4TaNAQgzrwPA162rU+ZpLREvS3vhRTzG7LiHrLxCNPOyxpzuPuVbMUIIIYSonQ6fp9WVgQgh5aOeqwUA4GGR4FvxfJiDlm9QF28vNjVbrZ97L0L43YLcNT/OrSzxHKJ4tSXKfEsIIYQQUrkoPDvDGENubi6CgoLw9OnTEq8RUpm1rCHMfnslVL0Bh+9Tc/AoKgUcB3T0qZgBgbLM/cwHtqb6eJ2QibXnXilU9l1qNrLyCqHL5+BWDpOzunweqtuaoKOvA75uXR0r+9fBP+ObIWReR9yd1R57xzXG4l5+GNnMHS1r2EoyPCZn5ePc8zjMOPQYDX88j56/XsfP51/heWyaXG3dY9FSnHWcLTT59SRqOQizCr9NykJGruLZVN8kZGDghlt4l5oDT1tjfFZHGFA17UCIZHlZTVh3/hUKBAwta9iioYpBfuaGupjY1ht3ZrXHteltYGVc8TJFAsKBE3FG6W03wnH/7YcySsjvQ2YeskUB8tUsKHujLOJs5/IEmN+NSEbUh2wY6/HR0adi3kDB53HYPrIB7Ez18TIuHQM33lQpS8BtUfCtqselPOq5WsLCSBep2fmYtO8RJu97hLxCAbr4OeCvsY3VMtkztWNNNHC3RHpuAcbvflDqjSTigGVtTi7Zirb1ITMXhYL/zi05+YW4HibMJtu6pq3W6qOoXnWF54pTT94rfHNOeWOMYeahx7gT/gGm+jrYMKw+9HXoRkJ1GRDkghbeNsgtEGD6wRAIBGX3nXILCiVtcqsaFW+/b1fbDg3cLZFbIMAa0VL3CRnabzdI1WGir4P/da2NE5NaoKGHFXLyBVhx+iU6r7uCM0/fY9Y/TwAAo5t7oI6LhUbr0q62PdrWskN+IcP8o08lKwe4WGp/pYWiAb+2pvoY1thd63WoqsQ3r90J/4CYlGy4Wxvh1yGB0OVrJvCbEEIIIYQQoln1JJlvUyTzXnFpwjFlba8OVU00N6fOzLcZuQV4+k44V1ehM99+lOmWMt8SQgghhFQuCo+QcxyHgwcP4rPPPkOTJk1w5MiRYq8RUpm19BZO9F99laDWYPIzz4RZbwNdLbW6VIu6WBjp4cdefgCAjVdeIzgqRe6yr0RZbz1tTKBTgSblOI6Drak+Gnta4/PGbpj3mS92jmqI6zPa4vnCzjgyoRm+71ADdZyFS7kHR6Vg1dlQdFl3Fc2XX8S8I09wJTQBuQWlBxuFiIJvxUvBa5q1iT7sRFnXXr5PV6hsWHwGBm28hfdpOfC2M8HecY2xpLcfnCwMEZ2cjWUnX2iiygiLz8A/omyiUzrUUNvn6vJ5MNCt2AFUrWvaoV99ZzAG/HAgRC1Ba3kFAhx6IMw+aW+mT0FkZfgv+DapWNBjaQ49EO6nXf0dYahXcX9XLztT7P+yCZwsDPEmIRMDNtwsdYnqsuQWFOKRqJ3XRvAtn8ehjSjL5b8hsQCAL1t64tchgWo7lnX5PPw8OBDWxnp4FpuGBceelXiPOPhWmxksxTcJCBiQkvVf9ts74R+QnV8IezN9+DhW3CUeG7hboZq5AdJzC3BBlKm3svj1YhgOPYgBn8fh16GBqGFvWt5V+qRwHIelffxhrMfH3Yhk7LwZUWaZu+HJyMorhK2pPnwr4NKmHMdhRhdhRt+/70fhVVx6ubQbpOqpYW+KfeMaY83AOrAx0cebhEyM23Ufsak5cLUywpQONbVSj7ndfaDH5+Hqq0QcDxFmnS2fzLf/Bfx+09arQvfNPjV+TubQ5QvH/Uz1dbD5iwawMKqYNzwSQgghhBBCyubnZAY9Pg8fMvMkN1nGpQnHOuzNtDvW4WAuznyrvuDbR5EpEDDAycIQjubav3lUXmaGxTPdfhyMSwghhBBCKjalMt/y+XysW7cOK1euxMCBA7F48WLKeks+CfXdLGGgy0N8ei5exikWwCjL6afC4NvOvhUzY6I8Ovo6oGfdahAwYOrfwVKDTj8WFicMvvWyV37JcG0z1OOjjosFvmnnjSMTm+PO/9phaR9/tK9tB30dHmJSsrHj5lsM33oH9Redw/jd93HwfjQ+ZP4XOPU4JgUAEOCsneBbAKgtCtB6Hpsmd5nQuHQM2ngL8em5qOVgir/GNYadqQFMDXSxvG8AAGDXrbe4EabebNCAMOutgAHta9ujroazdVVEc7r5wM5UGEChaEbpolKz8/HH5ddo8dMF/HjiOQDtBX1XZnWczWGir4OUrHw8eyf9mMnJL5QEhPat76yt6inN3cYY+75sDFcrI0R+yMKAP24iIjFToc8IiU5FXoEANib68LAx1lBNi2tXWxh8y+dx+LG3H2Z2rQ0eT703dTmYG2DtoLrgOOCvO5GS4H+xRFEGS1stZrDU4fNgaaQr2v5/55CLL4WBrG1q2lXom9t4PA496joBEAapx6flIDwxE09iUnH7TRIuvojHseB32Hc3EluvhePn86+w7OQLzD3yBFP2P8JXu+5j2Jbb6PPbdcw+/BhZeZrLtF7UseB3WHlGmLl0YU9ftKyAWVY/Bc6WRpjRpRYAYPmpl4hMkn0zwCXRft+6hm2F3e/ru1mik689BAz46fRLSbtBmW+JpnEch971nHH++1YY0dQdPA7gOGBZX3+tBZ+62xhjXEtPAMJVQgDApRyCb2uIVvxwsjDEwAYuWt9+VWagy0erGrbQ4/Owfkg9eNlVnmt8QgghhBBCSEn6Onz4OgnnlR5EJiMnvxCp2cLrPW0nEnIUbU+dmW/vRghXd6vIWW+B0jLf6kp5JyGEEEIIqYhUunVq3Lhx8Pb2Rv/+/XHlyhV11YmQcmOgy0cjD2tcDk3A1dBE1HJQPetWcmYebr0RXuB1qsTBtwAw/zNfXA9LxKv4DPx8PgxTO5WdZelVvDCI2bsST8zZmRlgcENXDG7oiuy8QlwPS8T5F3E49zweCem5OPH4PU48fg8eJwzKaFLdBnFpueBxgI8WM7fVcjTF5dAEuYNvn8em4fPNt5GUmQcfRzP8OaaRJAsjADT3tsHQRq7YfTsSPxwIwenvWsJEXz133L54n4ZjwcKMWerMeluZmBvp4sfe/hi78x42XnmNLn4OCi0ZHJ2chW3XI7D3TiQy84TB8Ham+hjZzAOfN3bVUK0/HTp8Hhp7WuHc83hcf50IfymB8meexSE9twBOFoZo6K75LLDq4GxphP1fNsHQzbfwWpQBd8/YRvCyky+z551w4TmrkYeV1gLguvg5YmaXbNRztdRott0W3rb4tq031p1/hf8degK/aubwFmU8La8MljYm+kjOykdiRi5qQliXSy8TAAizZFd0vepVwx+XX+Pc83ice35e6c95EJmCZ+/SsHWEZrPo3X+bjO//DgYAjGnugaGN3DS2LQIMbeSG4yGxuB3+AdMPhmDP2EZS25VLoZVjv5/WuRbOPY/H2WdxcBItiUiZb4m2mBvqYn4PXwxr4oas3EKp/RdNGd+mOg49iMY70WRoeWS+beJpjbUD66KOiwWt9FAOfv+8PtJzCopdNxJCCCGEEEIqr0BXSzyMTMGDyGQEuQnHZQ10eVrPvuomSgJx+tl7TPzgpZabPe+9FY5zB1XwcX0zg48z31LwLSGEEEJIZaJw5ls3Nzfw+f9NcLRp0wa3bt1CVFSUWitGSHlp4S1civzKqwS1fN75F/EoFDDUcjCFq7X2JyfVydJYD4t7+QEAfr/8Go+jU8ss8ypemPnWW86gr4rOUI+P9j72WNonALdntsORCc3wbVsv1HY0g4ABdyOSsf68MIupt50pjPS0N0AhXpr8xfuyszY/fZeKIZtuISkzD/5O5tgztlGpE6gzu9aGk4UhYlKysVSUVVUd1pwVZhzs5u+o1QDliqaDjz161BFmlP7hgHwZpZ/EpGLS3odoteIStlwLR2ZeIWrYm2BFvwBcnd4GX7euTndGy6lpdWF7f11GZudDD4TZUfsEOqk9E6smOZgbYO+4JqjlYIr49FwM3HBLZobfom6Lgm81GQT7MT6Pw5etqmtlm9+280ZzLxtk5xfi690PkJVXAIGAIUmUvbw8gm+B/zLvhidmIjwxE7p8Ds28rLVaF2XUcjBDK1HmWB4nXIba3kwfnrbG8HcyRyMPK7SrZYfP6lTD4IYuGN3cA9+29cLMLrWwqJcfVg+og5/6BcDcUBcPIlMwYMNNtWa4KCoyKQvjdt5DXoEA7WvbY2bX2hrZDvkPj8dhed8AGOjycPNNEvbciSz1fdHJWQiLzwCfx6G5qC9eUVW3NcGAIGG2zZiUbADazZhNCCDcD7UdeAsARno6mN3dBwCgp8ODo7l2MyEBwizAveo5aS07PylOl8+jwFtCCCGEEEI+IYGuwqywD96mIC5dOCZnb2ag9VWJOvnao46zOVKy8jF+9wPk5Mu3+qY0+YUCPIxMAQA0qOjBt4bF53NMtRz4TAghhBBCVKNw7y08PLzEc15eXnj48CHi4uLUUilCylPLGrbAv89xJ/wDcvILYaCrWjad00/fAwA6+1XurLdinf0c0T3AEcdDYvHDgWAcndgcejqlx/EzxhAWJwq+ta+8mW+l4fE41HGxQB0XC0zpWBPRyVm48CIe557H42FkMvoHaXeJ+tri4NvYNAgETGqg4OPoVHy+5TZSs/NRx8UCO0c1hLlh6cGaJvo6WNEvAEM238bu25Ho4ueoclDM4+hUnH4aBx4HfNfBW6XP+hTM7yHMKB0al4FfL74uNRMwYwyXQhOw6cob3HidJHm+mZc1xrbwRKsKvER3RSbel+9GlN7ex6fl4IooE2OfQO0ez+pga6qPv8Y2xrCtt/EkJg2DN93CrtENEeBsIbVMQaEA9yO0H3yrTXweh7WD6qLruqsIi8/ArH+eYFa32igUMADQekCJtYlwe4kZwuDfiy/iAQgHhStLIP32kQ2QWyCAvg5P6baorosFhm25jdC4DPT9/QZ2jW4IT1v19R1Ss/MxasddJGXmwbeaGdYPrgt+JQqor8zcbYzxQ6daWHT8GZaeeIHWNe0kGWPFxNmeA10tpPZJKpLv2nvj8MMYZIsmgmxNKRCNVB1d/BywsKcvrI31ocNX+H5uQgghhBBCCCEVSKCbBQDhaoXhCZkAAHtT7d9oqa/Dx2+f10f39VfxOCYV848+xbK+AUp/3vPYNGTlFcLMQKfCr8xZNMswn8fBSI9WeSGEEEIIqUzUNlNiYGAANzdatpVUft52JnAwM0BugQB3RQFIysrKK5AEbnXy/TSCbwFgQQ9fWBvr4cX7dPxyMUzq++LScpGeWwA+j4O79aefmcjZ0gjDm7hj56iGeDy/E8a08NTq9j1sjKHH5yEzrxBRyVmlvudhZDKGbL6F1Ox8BLpaYNdo6YG3Yk29bDCssbB9n34wBOk5+SrVc9XZlwCAnnWd4PWJZERWhZWxHhb2FGaU/u1iGJ6++y+jdG5BIf6+F4VOa69g5La7uPE6CXweh551q+H4N82xe0xjtK5pR4G3SvK2M4GtqT5y8gV4EJlc4vUjj95BwITBYJU1u5qlsR52j2mMQFcLpGbnY+im27j/Vvq57VlsGjJFg5I17T/d49PGRB8/D64HPo/DPw9j8MsF4bnMylgPuloOJPo48+3Fl8Lg2zY17bRaD1VwHAcDXb5KbVENe1Mc+KopPGyMEZOSjf5/3JQrw7488gsFmLD7AcLiM+BgZoAtXzTQamZ6Aoxo6o76bpbIyC3AzEOPwRgr9ro4+LZ1Jdnv7cwMMLq5h+RvWxPtT0oRUl44jsPwJu7oFuBY3lUhhBBCCCGEEKIiR3NDOJobQMCAM8+EyYTsy2GVEwBwsjDE+sH1wHHA3rtR2H9X+VV370YIx/uD3K0q/Ip2RRMwmOjr0HwPIYQQQkglI1d0gZWVFRIThUsyW1pawsrKSuqDkMqO4zi0EGVDvPpK+lLk8rj8MgG5BQK4WRuhlsOnE8RkbaIvNViwqFfx6QAAd2sjqdlxifro8nmSDMPPY9NLvH7/7QcM23IH6TkFaOBuiZ2jG8FMzqyKM7rUgouVIWJSsrHkxAul63j/7QdcepkAPo/DpHaU9Vasq78DOvs6oEDAMO1ACJIycvHbpTC0WH4RPxwIQWhcBoz1+BjT3ANXprXBukH14Oek/aWGPzUcx6FZdWsAwI2wpGKvMcZw8EE0AKBv/cqX9bYoc0Nd7BzdCA09rJCeW4BhW+7g5uukUt97J/y/rLcVfVBSVY08rTG1Y00AwPYbEQAAGxPtZ6+0NRUG3yZl5CIrrwC33wj/DdrUqhxBiOrkYmWEv79qAj8nMyRl5mHwplu48Vq1vhhjDHOPPMG1sEQY6fGxZUQQHMppAqEq4/M4LO8bAD0dHq6EJuDv+9GS13ILCiX/zq1q2JZXFRX2ZStPOFkIJ6honyKEEEIIIYQQQkhlFehqCQC4IpoTtReNV5aHFt62+F60OuDsI0/wJEa5m/PviZIrBblbqq1ummJm+F+SAFMDShhACCGEEFLZyBUNt2bNGpiaCgMH165dizVr1kh9EPIpaCGa+BdnrVXWqafCu0Q7+Tp8cncqdgtwRFd/YbDg1L9DkF8oKPGeV3EZAABvym6qNbUdzQAIl9Qp6k74BwzfcgcZuQVo7GmF7SMbwkRf/ot4Y30d/NS3DgDgrzuRSh8bq86EAgD6BTrDvZJmEtUEjuOwsJcvLIx08fRdGhouOY+fTr1EfHou7M30MaNLLdyY2Q6zu/uUWKqbqKaZl/Bmi2thxQP8nsWm4cX7dOjxeejuX608qqZWJvo62DGyIVp42yArrxAjtt0p9Ti+XST4tir4sqUn2hUJcrUth4Fla2NhwG9iRh5uhCUhr1AAFytDVLetmm2kjYk+/hrbGE08rZGRW4ARW+/i1JP3Sn/epqtv8NedKPA44OfB9eBbjW5cKC9ediaYIpo8WXT8GeLScgAAd8OTkZVXCFtTffhWMyvPKirE1EAXpya3wLkpregmM0IIIYQQQgghhFRa9VwtAAB5BcJ5Pnuz8r3JeHxrL7SvbYe8AgG++vM+UrLyFCrPGJNkvm3gXvHHuYsmyTGVM2EOIYQQQgipOOSaJfziiy+gr68v+X9ZD0I+Bc29bMBxwIv36YgXBQYoKq9AgAvPhUtHd/J1UGf1KoyFPf1gaaSL57Fp+O3i6xKvv4oXBd+KsrESzRNnWC4afHvzdRK+2HoHmXmFaOZljW0jGsJYgcBbsSbVrfFFEzcAwIyDIUjLyVeo/I3XibjxOgm6fA7ftPNSePufOjtTA8z7zAcAUChgqOVgilX96+DqtLb4qlV1mBvSoIsmiINvQ6JTkJr93z596EEMAKC9jx3MjT6N395Qj49Nw4PQrpYdcgsEGLPjHs49i5O8LhAw3I0QB99al1c1tYrH47BqQB1JULutifaDb21E20zMyMXFl8J+Q5uadp/cTTuKMDXQxbaRDdDJ1x55hQKM330fe+9EKvw5p568x9KTwmzts7v5oF1te3VXlShoTHMP1HE2R3pOAWb98xiMMVwS7fetathWuv3e1EBXqT4VIYQQQgghhBBCSEUR6FY8O6ydWfllvgXEY7Z14WZthOjkbEze9wgCAZO7/NukLCRm5EKPz4N/JVhB0LRY8C2NMxFCCCGEVDZy9eDS0tLKfpOImVnlyVZEiDRWxnrwq2aOxzGpGLTxFmxM9GGgx4ehLg+GunwY6vFhoMsX/v/Hf+sJ/xuemIn03ALYmeqjnotFeX8ljbAx0ceCnn749q+H+PnCK3T0tZdkXgWAsPh0AMJMZ0Q7fES//4v3wt/+2qtEjNl5Fzn5ArSsYYuNw+rDQJev9OdP71ILF18mIPJDFpb8+xzL+gbIVY4xhtWirLeDG7rC2dJI6Tp8ynrVdYKxng6M9XXQtLp1pQtCqoyqWRjC08YYbxIzcftNEjr6OiC/UIAjj4TBt30Dncu5huploMvH75/Xx6S9D3HyyXt89ed9rBtUD90CHPEqPgMpWfkw0uNXquyTqrIw0sPG4fWx6kwoPm/spvXt24iy7Sam5+LSS2E24jY17WQVqRIMdPn4dUggZv3zBPvuRWHGocdIzsrHV6085WobQ6JTMHnfQzAGDGvshpHN3DVfaVImHT4PP/Wrg+4/X8W55/E4GvwOl0JpvyeEEEIIIYQQQggpL77VzKDH5yGvsGJkvgUAc0Nd/D60Pvr8fh2XXiZg/YVXmNy+hlxlxQkmApzNVZoP0xYzw//CNcwo+JYQQgghpNKRqwdnYWFR5iQ3Ywwcx6GwsFAtFSOkvHXxd8DjmFS8SczEm8RMpT+no689eLxPN4DuswBHHA9+hzPP4vDDgWD8M74ZdPk8MMYQGifKfGtnWs61rDpqiYJvIz9k4cTjWHy37xFyCwRoW8sOvw0NVHmgwUhPBz/1C8Cgjbew924Uuvg7olUN2zLLXXmViHtvk6Gvw8OENpT1VhqO49DxE82UXZE19bLGm8RMXA9LREdfB1x9lYDEjDxYG+uhpRz7d2Wjp8PDz4PrYerfwTj86B2++esB8grrICOnAABQ380SuvyqtYS6bzVzbB3RoFy2bW2sBwB4lyrMtK+vw0Njz6qRebgsOnwelvX1h5WJHn6/9BrLT73Ah8xczOxSW2bf6l1KNkbvuIecfAFa1bDFvM986GaGCqSmgym+aeuN1WdDMfufJ0jPLQCfx6G5t015V40QQgghhBBCCCGkytHX4cPPyQwPIlMAVIzgWwDwqWaGH3v54/u/g7Hu/CvUcbGQ6+btexHJAIAgdytNV1EtzIpkvi36/4QQQgghpHKQK/j24sWLmq5HhcIYQ0FBAQUSV3FfNHRCI1dTZOQUIKegELn5AuTkFyK3QIDc/ELkFAiQU1CIPNHzOQUCyWu5BcLndPk8DGtQDTk5OSrVRVdXF3x+xbw7k+M4LO7th9vhH/AkJg0bLr/GxLbeSMzIQ2p2Pngc4GlrXN7VrDKsjPVgb6aPuLRcjN/9AADQwccevwypB30d9exDjT2tMaKpO7bfiMCMgyE4/V1LmQMCjDGsOvMSgDD7YEUZuCFErLmXDf68FYnrr5MAAAfvC7Pe9qzr9MkGoerweVg1oC70dfjYdy8KU/YHw9nSEADQsJIMSn4qbE2LL+PWpLo1DPUq5jm/PHAch+mda8HKSA8/nniOTVfD8SEzH8v7+kOnlOMzI7cAo7bfRUJ6Lmram+KXIfVKfR8pX1+3ro5TT97jWaxwhZVAVwuYG9LkAiGEEEIIIYQQQkh5CHS1LBJ8qy/7zVrUt74zHkYl489bkZi89xGOf9McLlayV1a891aY+baBu6U2qqgyIz0++DwOhQIGU8p8SwghhBBS6cjVg2vVqpWm61Fh5OXlITY2FllZWeVdFVIBGAEw4gDoih7FcJDnEBKkxSM8TbV6cBwHZ2dnmJiYqPZBGmJnaoAFPXwxed8jrDv/Ch18HJCUmQsAcLUyqhTLunxKajuaIS5NuIRzZ18HrB9cD3o66g08mta5Ji6+jMfbpCwsPv4MP/WrI/W9Z5/FISQ6FUZ6fHzVurpa60GIOjTxtAHHAWHxGQiNS8fZ53EAgD6BTuVcM83i8zgs7eMPfV0edt58i6gP2QCABh4UfKtNBrp8mOjrICNXmHlYnuwNVdHYlp6wNNbD9IMhOPggGqnZefhlSPGM7gWFAnyz5wFevE+HjYk+towIgilli6iQdPk8/NQvAL1+vY4CAUNr2u8JIYQQQgghhBBCyk2gmyVwLRymBjow0qtYAaBzuvvgcUwagqNS8NWf93Hw66ZS5x2TMnLxOkG4mml9t8oRfMtxHEwNdJCSlU9jmYQQQgghlZDSveesrCxERkYiLy+v2PMBAQEqV6q8CAQChIeHg8/no1q1atDT06Mlakm5Y4whISEB0dHR8Pb2rrAZcHvWrYbjIe9w7nk8fjgQjF51hUFrXnam5VyzqqdpdWtcepmAbgGOWDuwrkYydxrp6WBFvzoYuPEm9t+LRhd/x1IDxgQChtVnQwEAI5q6w8ak4twxTYiYuZEu/J3MERKditn/PEFegQA17U3hW82svKumcTwehwU9fGGgy8fGK29gqMtHXReL8q5WlWNjokfBt3LoV98ZFoa6mLDnAc49j8fwLXew6YsgScbUxf8+x8WXCTDQ5WHzF0FwtpSdBYOULz8ncyzo6Yt/HsSgf33n8q4OIYQQQgghhBBCSJXVzMsGnjbGaORZ8RIz6Ovw8fvQQHT/+RqevkvD3CNPpCaEuf82GQBQw94EFkZ62qymSswMdEXBtxUr8JkQQgghhJRN4R5cQkICRo4ciZMnT5b6emFhocqVKi95eXkQCARwcXGBkRFN1pOKw9bWFhEREcjPz6+wwbccx+HH3v64E34ZIdGpiPogzB7tbV8xs/V+ykY390TbWnaobmui0RsIGnpYYURTd2y7HoGZBx/j9HctSywZfeJJLF68T4epvg7GtfTUWF0IUVUzLxuERKfiToRwSaq+9Z2qzA04HMdhZpda8HMyh7WxHmUrLwfWJvqISMpCdVtjuFpTH1SW9j722DmqIcbsuIc7ER8waOMt7BjVACdCYrH9RgQAYM2AuhREXkkMbeSGoY3cyrsahBBCCCGEEEIIIVWauaEuzn/fqsKOiVezMMTPg+th2Jbb2H8vGoGulhjU0LXE++6Jgm+D3CteELEsZobCkA3KfEsIIYQQUvkonA5x8uTJSElJwe3bt2FoaIhTp05hx44d8Pb2xtGjRzVRR63j8dSfJZIQVVTUi92P2ZsZYO5nvgCA5Kx8AIC3HQXfahufx8HLzlQr+820TrXgbm2E92k5WHT8WbHXCgUMa0RZb0e38KhUdxmTqqdZdRvJ//M4oKcoe3dVwXEcetSphmZeNmW/maidrSgrOGW9lU8jT2vs+7IJbEz08Tw2DT1/uY6FonPQ9M610MXfsZxrSAghhBBCCCGEEEJI5VLR5yKbedng+441AQBzjzxFSHRKiffcFSXXaOBuqc2qqczBzBAA4GhuUM41IYQQQgghilI4yvTChQtYvXo1goKCwOPx4Obmhs8//xw//fQTli5dqok6EkIqkb6BTmhT01byt7edaTnWhmiaoR4fK/rXAccBB+5H48KLOMlrRx7F4HVCJiyMdDGquUc51pKQsgW5W0JPR9gtau5tC3szGuQi2jOquQc6+thjdAtqK+XlU80MB79uAhcrQ8Sm5kDAgIFBLviqFWVZJ4QQQgghhBBCCCHkU/R1q+poX9seeYUCfP3nAyRn5kley84rxJOYVABAkFvlynw77zMfrB5QBy28KTkGIYQQQkhlo3DwbWZmJuzshFm5LC0tkZCQAADw9/fHgwcP1Fs7Qkilw3EclvYJgKWRLqyM9eBFmW8/eQ3crTCqmTBgbOahx0jNykd+oQBrz70CAHzZsjrMaKkcUsEZ6PLR0lt448CQhi7lXBtS1TT0sMLG4UFwNDcs76pUKm7Wxjj4VVO0rmmLvoHOWNTLr8Jn6CCEEEIIIYQQQgghhCiHx+OwakAduFsbISYlG5P2PUKhgAEAgqNTkF/IYG+mD2fLyjXO6mJlhD6BztDh0+q8hBBCCCGVjcI9uJo1a+Lly5cAgDp16mDDhg2IiYnBH3/8AUdHWuKVKObSpUvgOA4pKSky3+fu7o61a9dqpU5EdQ7mBjjzXSucmtQChnr88q4O0YKpHWvCw8YYcWm5WHj8GQ7ej0bkhyzYmOjhi6Zu5V09QuSyol8A9o1rjM5+1J8hpLKwMzPA9pENsWpAHUn2akIIIYQQQgghhBBCyKfJ3FAXv39eHwa6PFwJTcC6c6EAgHsRHwAAQe5WdIM+IYQQQgjRGoVnqCdNmoTY2FgAwLx583Dy5Em4urpi/fr1WLJkidorSD5tTZs2RWxsLMzNzQEA27dvh4WFRflWiqiFrak+7GjZ9irDUI+Plf0DwHHAwQfR+PHEcwDA1629YKSnU861I0Q+lsZ6aORpXd7VIIQQQgghhBBCCCGEEEKIFLUdzbC0jz8AYP2FMFx4EYe7EckAgAZuluVZNUIIIYQQUsUoHBH1+eefS/6/fv36ePv2LV68eAFXV1fY2NiotXLk06enpwcHB4fyrobW5OXlQU9Pr7yrQYhG1HezwpjmHth0NRzpOQWwN9PH0Eau5V0tQgghhBBCCCGEEEIIIYQQ8gnpXc8ZDyNTsPPmW0ze+wiFAgZAmPmWEEIIIYQQbVF5bVYjIyMEBgZ+soG3jDFk5RWUy4MxJnc9W7dujW+++QaTJ0+GpaUl7O3tsWnTJmRmZmLkyJEwNTWFl5cXTp48CQBITk7G0KFDYWtrC0NDQ3h7e2Pbtm2Sz4uKisKAAQNgYWEBKysr9OzZExEREWXW48mTJ+DxeEhISAAAfPjwATweD4MGDZK8Z/HixWjevDkA4NKlS+A4DikpKbh06RJGjhyJ1NRUcBwHjuMwf/58SbmsrCyMGjUKpqamcHV1xcaNG+X6bebPny/5vKKP7du3AwAOHDgAf39/GBoawtraGu3bt0dmZqak/NatW+Hr6wt9fX04Ojpi4sSJktciIyPRs2dPmJiYwMzMDAMGDEBcXFyxbdetWxebN2+Gh4cHDAyEmWBTUlIwZswY2NrawszMDG3btkVwcLBc34eQiuz7jjXhaWsMAPimrTcMdPnlXCNCCCGEEEIIIYQQQgghhBDyqZndzQf1XC2QllOAzLxCmOjroJaDaXlXixBCCCGEVCEKZ75ljOHAgQO4ePEi4uPjIRAIir1+6NAhhSvx66+/YsWKFXj//j3q1KmDn3/+GQ0bNpT6/r///htz5sxBREQEvL29sXz5cnTt2lXh7cojO78QPnNPa+Szy/JsYSeFlmvfsWMHpk2bhjt37mDfvn34+uuv8c8//6B379743//+hzVr1mDYsGGIjIzEnDlz8OzZM5w8eRI2NjYICwtDdnY2ACA/Px+dOnVCkyZNcPXqVejo6GDx4sXo3LkzQkJCZGZu9fX1hbW1NS5fvox+/frh6tWrkr/FLl++jNatW5co27RpU6xduxZz587Fy5cvAQAmJiaS11etWoVFixbhf//7Hw4cOICvv/4arVq1Qs2aNWX+LlOnTsVXX30l+Xv37t2YO3cugoKCEBsbi8GDB+Onn35C7969kZ6ejqtXr0oCn3///XdMmTIFy5YtQ5cuXZCamorr168DAAQCgSTw9vLlyygoKMCECRMwcOBAXLp0SbK9sLAwHDx4EIcOHQKfLwxE7N+/PwwNDXHy5EmYm5tjw4YNaNeuHUJDQ2FlRXdkksrLQJePvWMb40FkCjr52pd3dQghhBBCCCGEEEIIIYQQQsgnSE+Hh9+GBqL7+mtIysxDPVcL6PBVzj1GCCGEEEKI3BTufU6ePBnDhg1DeHg4TExMYG5uXuyhqH379mHKlCmYN28eHjx4gDp16qBTp06Ij48v9f03btzA4MGDMXr0aDx8+BC9evVCr1698OTJE4W3/ampU6cOZs+eDW9vb8ycORMGBgawsbHB2LFj4e3tjblz5yIpKQkhISGIjIxEvXr1EBQUBHd3d7Rv3x6fffYZAOG/iUAgwObNm+Hv74/atWtj27ZtiIyMLBZUWhqO49CyZUvJ+8TZbHNzc/HixQvk5+fjxo0baNWqVYmyenp6MDc3B8dxcHBwgIODQ7Hg265du2L8+PHw8vLC9OnTYWNjg4sXL5b5u5iYmEg+LyIiArNnz8a2bdvg5+eH2NhYFBQUoE+fPnB3d4e/vz/Gjx8v2e7ixYvx/fffY9KkSahRowYaNGiAyZMnAwDOnz+Px48fY8+ePahfvz4aNWqEnTt34vLly7h7965k+3l5edi5cyfq1auHgIAAXLt2DXfu3MHff/+NoKAgeHt7Y+XKlbCwsMCBAwfK/D6EVHR2Zgbo7OcAjuPKuyqEEEIIIYQQQgghhBBCCCHkE+Vobog/htVHHWdzjGruUd7VIYQQQgghVYzCmW937dqFQ4cOqS3T7OrVqzF27FiMHDkSAPDHH3/g33//xdatWzFjxowS71+3bh06d+6MH374AQCwaNEinD17Fr/88gv++OMPtdSpKENdPp4t7KT2z5V324oICAiQ/D+fz4e1tTX8/f0lz9nbC7NQxsfH4+uvv0bfvn3x4MEDdOzYEb169ULTpk0BAMHBwQgLC4OpafFlOXJycvD69esy69GqVSts3LgRgDDL7ZIlSxAaGopLly7hw4cPyM/PR7NmzRT6bh9/P3GArrQg7dJERkaiV69emDp1KgYMGABAGLDcrl07+Pv7o1OnTujYsSP69esHS0tLxMfH4927d2jXrl2pn/f8+XO4uLjAxcVF8pyPjw8sLCzw/PlzNGjQAADg5uYGW1tbyXuCg4ORkZEBa2vrYp+XnZ0t1+9LCCGEEEIIIYQQQgghhBBCCCEEaOBuhSMTm5d3NQghhBBCSBWkcPCtubk5PD091bLxvLw83L9/HzNnzpQ8x+Px0L59e9y8ebPUMjdv3sSUKVOKPdepUyccPny41Pfn5uYiNzdX8ndaWppCdeQ4DkZ6Cv9M5UJXV7fY3xzHFXtOnIVSIBCgS5cuePv2LU6cOIGzZ8+iXbt2mDBhAlauXImMjAzUr18fu3fvLrGNokGk0rRu3RqTJ0/Gq1ev8OzZMzRv3hwvXrzApUuXkJycjKCgIBgZGanl+wkEArnKZmZmokePHmjSpAkWLlwoeZ7P5+Ps2bO4ceMGzpw5g59//hmzZs3C7du3YWNjo3AdS2NsbFzs74yMDDg6OpaaRdjCwkIt2ySEEEIIIYQQQgghhBBCCCGEEEIIIYQQQohm8BQtMH/+fCxYsADZ2dkqbzwxMRGFhYWSjKxi9vb2eP/+fall3r9/r9D7ly5dCnNzc8mjaJbSqs7W1hZffPEF/vzzT6xdu1aSrTYwMBCvXr2CnZ0dvLy8ij3Mzc3L/Fx/f39YWlpi8eLFqFu3LkxMTNC6dWtcvnwZly5dQuvWraWW1dPTQ2Fhobq+IgCAMYbPP/8cAoEAu3btkgQhi3Ech2bNmmHBggV4+PAh9PT08M8//8DU1BTu7u44f/58qZ9bu3ZtREVFISoqSvLcs2fPkJKSAh8fH6n1CQwMxPv376Gjo1Pi91VXwC8hhBBCCCGEEEIIIYQQQgghhBBCCCGEEEI0Q+Hg2wEDBiA5ORl2dnbw9/dHYGBgsUdFM3PmTKSmpkoeRQMlq7K5c+fiyJEjCAsLw9OnT3H8+HHUrl0bADB06FDY2NigZ8+euHr1KsLDw3Hp0iV8++23iI6OLvOzOY5Dy5YtsXv3bkmgbUBAAHJzc3H+/Hm0atVKall3d3dkZGTg/PnzSExMRFZWlsrfdf78+Th37hw2bNiAjIwMvH//Hu/fv0d2djZu376NJUuW4N69e4iMjMShQ4eQkJAg+S3mz5+PVatWYf369Xj16hUePHiAn3/+GQDQvn17+Pv7Y+jQoXjw4AHu3LmD4cOHo1WrVggKCpJan/bt26NJkybo1asXzpw5g4iICNy4cQOzZs3CvXv3VP6+hBBCCCGEEEIIIYQQQgghhBBCCCGEEEII0RwdRQt88cUXuH//Pj7//HPY29uXyCKqCBsbG/D5fMTFxRV7Pi4uDg4ODqWWcXBwUOj9+vr60NfXV7qOnyo9PT3MnDkTERERMDQ0RIsWLbB3714AgJGREa5cuYLp06ejT58+SE9Ph5OTE9q1awczMzO5Pr9Vq1Y4fPiwJPiWx+OhZcuW+Pfff9GsWTOp5Zo2bYqvvvoKAwcORFJSEubNm4f58+er9F0vX76MjIwMNG3atNjz27ZtQ6NGjXDlyhWsXbsWaWlpcHNzw6pVq9ClSxcAwv09JycHa9aswdSpU2FjY4N+/foBEAYZHzlyBN988w1atmwJHo+Hzp07S4JzpeE4DidOnMCsWbMwcuRIJCQkwMHBAS1btiyR1ZkQQgghhBBCCCGEEEIIIYQQQgghhBBCCCEVC8cYY4oUMDY2xunTp9G8eXO1VKBRo0Zo2LChJGBRIBDA1dUVEydOxIwZM0q8f+DAgcjKysKxY8ckzzVt2hQBAQH4448/ytxeWloazM3NkZqaWiKQNCcnB+Hh4fDw8ICBgYGK34wQ9aF9kxBCCCGEEEIIIYQQQgghhBBCCCGEEEII0SxZMaZFKZz51sXFRe7sp/KYMmUKvvjiCwQFBaFhw4ZYu3YtMjMzMXLkSADA8OHD4eTkhKVLlwIAJk2ahFatWmHVqlXo1q0b9u7di3v37mHjxo1qqxMhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIaXhKVpg1apVmDZtGiIiItRSgYEDB2LlypWYO3cu6tati0ePHuHUqVOwt7cHAERGRiI2Nlby/qZNm2LPnj3YuHEj6tSpgwMHDuDw4cPw8/NTS31I2UxMTKQ+rl69qvX6LFmyRGp9unTpovX6EEIIIYQQQgghhBBCCCGEEEIIIYQQQggh5NPFMcaYIgUsLS2RlZWFgoICGBkZQVdXt9jrHz58UGsF1U1WSuCcnByEh4fDw8MDBgYG5VTDii8sLEzqa05OTjA0NNRibYT7nLT9ztDQEE5OTlqtjybQvkkIIYQQQgghhBBCCCGEEEIIIYQQQgghhGiWrBjTonQU/eC1a9eqUi/yCfDy8irvKhRjZWUFKyur8q4GIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCqgCFgm/z8/Nx+fJlzJkzBx4eHpqqU7lTMBkwIRpH+yQhhBBCCCGEEEIIIYQQQgghhBBCCCGEEFIx8BR5s66uLg4ePKipupQ7XV1dAEBWVlY514SQ4vLy8gAAfD6/nGtCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYRUbQplvgWAXr164fDhw/juu+80UZ9yxefzYWFhgfj4eACAkZEROI4r51qRqk4gECAhIQFGRkbQ0VH4kCWEEEIIIYQQQgghhBBCCCGEEEIIIYQQQogaKRzJ5+3tjYULF+L69euoX78+jI2Ni73+7bffqq1y5cHBwQEAJAG4hFQEPB4Prq6uFAxOCCGEEEIIIYQQQgghhBBCCCGEEEIIIYSUM44xxhQp4OHhIf3DOA5v3rxRuVKalJaWBnNzc6SmpsLMzEzq+woLC5Gfn6/FmhEinZ6eHng8XnlXgxBCCCGEEEIIIYQQQgghhBBCCCGEEEII+WTJG2OqcObb8PBwlSpWWfD5fPD5/PKuBiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgipQFRKpckYg4KJcwkhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIqbSUCr7duXMn/P39YWhoCENDQwQEBGDXrl3qrhshhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIRWKjqIFVq9ejTlz5mDixIlo1qwZAODatWv46quvkJiYiO+++07tlSSEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghpCLgGGNMkQIeHh5YsGABhg8fXuz5HTt2YP78+QgPD1drBdUtNTUVFhYWiIqKgpmZWXlXhxBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYRUAGlpaXBxcUFKSgrMzc2lvk/hzLexsbFo2rRpieebNm2K2NhYRT9O69LT0wEALi4u5VwTQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEFLRpKenqzf41svLC/v378f//ve/Ys/v27cP3t7eitdQy6pVq4aoqCiYmpqC47jyrg7RMHEUujKZjlUpS+WpfHmWr8x1p/JVu3xlrjuVr9rlK3PdqXzVLl+Z607lq3b5ylx3Kl+1y1fmulP5ql2+Mtedylft8pW57lS+apevzHWn8lW7fGWuO5Wv2uUrc92pfNUuX5nrTuWrdvnKXHcqX7XLq7ptUrkwxpCeno5q1arJfJ/CwbcLFizAwIEDceXKFTRr1gwAcP36dZw/fx779+9XrrZaxOPx4OzsXN7VIFpmZmamdMOnSlkqT+XLs3xlrjuVr9rlK3PdqXzVLl+Z607lq3b5ylx3Kl+1y1fmulP5ql2+Mtedylft8pW57lS+apevzHWn8lW7fGWuO5Wv2uUrc92pfNUuX5nrTuWrdvnKXHcqX7XLV+a6U/mqXV7VbZPKQ1bGWzGeoh/at29f3L59GzY2Njh8+DAOHz4MGxsb3LlzB71791aqooQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEVAYKZ74FgPr16+PPP/9Ud10IIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCKnQFM58S0hloq+vj3nz5kFfX1+rZak8lS/P8pW57lS+apevzHWn8lW7fGWuO5Wv2uUrc92pfNUuX5nrTuWrdvnKXHcqX7XLV+a6U/mqXb4y153KV+3ylbnuVL5ql6/MdafyVbt8Za47la/a5Stz3al81S5fmetO5at2eVW3TT5NHGOMyfNGHo8HjuNkfxjHoaCgQC0VI4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGkotGR943//POP1Ndu3ryJ9evXQyAQqKVShBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYRURHJnvi3Ny5cvMWPGDBw7dgxDhw7FwoUL4ebmps76EUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBSYfCUKfTu3TuMHTsW/v7+KCgowKNHj7Bjxw4KvCWEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghnzSFgm9TU1Mxffp0eHl54enTpzh//jyOHTsGPz8/TdWPEEIIIYSQEiIjI1HaAg6MMURGRpZDjQghRLOo3SOEEEIIIYQQQioOVa/T6TqfEEIIIYSQyk/u4NuffvoJnp6eOH78OP766y/cuHEDLVq00GTdCCGEkAqvsLAQV65cQUpKSnlXpUrauXMncnNzSzyfl5eHnTt3lkONiLZ4eHggISGhxPMfPnyAh4eHxrabnZ2Na9eu4dmzZyVey8nJKXO/u3LlCgoKCjRVPfKJU3X/06T4+HgsWbKk3LZfFZRXu/exvLw8vHz5UuG2rKCgAMHBwTh9+jROnz6N4OBg5Ofny10+JSUFZ86cwZ9//omdO3cWexBCqh4675CqLCYmpryrQAipgCiIj5QHxhju3buHAwcO4ODBg3jw4EGp++GnStXr9Ipyna8qZccJKqro6GiMGzeuvKtBpFi4cCGysrI08tkpKSnYs2ePRj6bkKooLS1N7kdVk5aWhsOHD+P58+flXRW1KCwsLO8qEFKuOCbnVRCPx4OhoSHat28PPp8v9X2HDh1SW+UIkUdISIjc7w0ICJD7vTk5Odi3bx8yMzPRoUMHeHt7K1O9KisnJwcGBgblXQ21SUlJwZ9//omJEyeWd1XkUlBQgJycHJiYmChclvZ9xRkYGOD58+eVakCsInn16hUuXryI+Ph4CASCYq/NnTtXZlk+n4/Y2FjY2dkVez4pKQl2dnYVsrOvyEWkmZmZBmuiupSUFNy5c6fUf7vhw4drdNs8Hg9xcXGwtbUt9vzbt2/h4+ODzMxMtW8zNDQUHTt2RGRkJDiOQ/PmzbF37144OjoCAOLi4lCtWjWZ+520fbYquHPnDurXry+5ljh+/DhWrFiBsLAwODo64ttvv1VovynP/Q8ABAIBwsLCSt1+y5Yt1b49dex/ZVGlvxMcHIzAwECNt7vZ2dlgjMHIyAiA8Jj/559/4OPjg44dO2p028pSV7tfHu1eUVlZWfjmm2+wY8cOAMJ90tPTE9988w2cnJwwY8aMUssJBALMnTsXv/76K1JTU4u9Zm5ujokTJ2LBggXg8aTfG3zs2DEMHToUGRkZMDMzA8dxktc4jsOHDx/KrP/Ro0dLfZ7jOBgYGMDLy6tc+nIPHjzA3Llzcfz4ca1vuyzr16+X+73ffvut1NcePHgAXV1d+Pv7AwCOHDmCbdu2wcfHB/Pnz4eenp7KdS2NuuqvrOjoaBgYGMDGxgYAcPXqVfzxxx+IjIyEm5sbJkyYgCZNmqh9u8Cn1d+URlvnHW2rCv92hw4dwvz582WO50VFRYHjODg7OwMQ9iP37NkDHx8flQMhytq+QCDAihUrcPToUeTl5aFdu3aYN28eDA0NVdquOrx//x4//vgjtmzZInfAQV5eXqn9VVdXV01UUWLXrl34448/EB4ejps3b8LNzQ1r166Fh4cHevbsqbHt3rp1C8eOHZP823Xu3Flj26oMVBkfVFRiYiIyMzPh5uYmee7p06dYuXIlMjMz0atXLwwZMkTj9ShNWloadu/ejS1btuDevXtq/WxNzU0oQ9UxsujoaBw9ehSRkZHIy8sr9trq1aullqPjTnVxcXGYOnUqzp8/j/j4+BLBq5rs78TGxuL8+fOwsrJC+/bti/XNMzMzsWrVKqnjsxcvXsTo0aPx9u1bSZ05joOHhwe2bt2qkbGRsqSlpeHChQuoWbMmateurfHtqXqdXt7X+apSdpxg/Pjx+OmnnyTnp7/++gs9evSAsbExAOH41JAhQ3DixAntfJGPVPRrjdDQUKSkpKBhw4aS586fP4/FixdLzrn/+9//ZH5GUlISQkJCUKdOHVhZWSExMRFbtmxBbm4u+vfvr7HjJzMzE1OnTi3W1/75559LHAOyaHJ8vbz/7VNSUmBhYSHzPYWFhdi+fbvknPFxP//ChQsarGHp9Xn8+DHc3NxgaWkpV5mKOkanSfKOUWlifEod42PKHrs8Hq/YOG5pGGPgOE6u407amAnHcdDX19fYGKNYdHQ0LCwsSlxf5efn4+bNmzL7PgMGDEDLli0xceJEZGdno06dOoiIiABjDHv37kXfvn3L3P6TJ0+krkp/+PBh9OrVS6Hvow6hoaHYvHkzdu3ahdjYWI1tJzMzE8uWLZPa9r1580Zj2yZEHjryvnH48OFlNoyElIe6deuC4zipd9OKX5N10p4yZQry8/Px888/AxAOTDdp0gRPnz6FkZERpk2bhrNnz5Y5MVZejb66JmdUDWYQCAT48ccf8ccffyAuLk5yoT1nzhy4u7tj9OjRctdTWaoE8ZXm/Pnz2LJlC/755x8YGRlpLPg2Ozsb9+/fh5WVFXx8fIq9lpOTg/3795caSHTs2DEkJSVhxIgRkud+/PFHLFq0CAUFBWjbti327dsn9YJH1X1f2gVSaXr06KH28hWFn58f3rx5o9LFoLJBXMruO4Bqv7+6/u02bdqEr7/+GjY2NnBwcCgRTFPWcStu3z8WHR0Nc3NzueuoKkWCAC0sLNR6sSnNixcv0KNHD4SGhhZ73tLSUu4+naxgprICoaTtd6qes6ZMmSLZxpw5cyTnLEA40HP79m3UrVu31M9T9btPnz4dfn5+uHfvHlJSUjB58mQ0a9YMly5dknvyuqJl/zh//rzUfsvWrVvVuq0mTZpIBkaPHTuGXr164fPPP8fAgQPx8OFDjB49Gqampujdu3eZn6Xs/qcut27dwpAhQ4pNMBXdflnH7sKFCzF16tRi+y8gbNNXrFhRatunjv1PGk33d+rVqyf3sffgwQOZr/fs2RN9+vTBV199hZSUFDRq1Ai6urpITEzE6tWr8fXXX8ssr44BakXP2aq2+6q0e4D62v2ZM2ciODgYly5dKjah3r59e8yfP1/qpNqMGTOwfft2LFu2DJ06dYK9vT0A4QTzmTNnMGfOHOTl5WH58uVSt/39999j1KhRWLJkSYnjRl69evUq9bqx6PVi8+bNcfjwYZmTBa9fv8batWslWQl8fHwwadIkVK9eXWqZ06dP4+zZs9DT08OYMWPg6emJFy9eYMaMGTh27Bg6deok13fQdtD/mjVr5Hofx3EyJwe+/PJLzJgxA/7+/njz5g0GDRqE3r174++//0ZWVhbWrl1bajlV911V6q+Odqtv376YM2cOunfvjiNHjqBPnz7o3r07mjVrhtDQULRq1QqHDh1C9+7dSy2fn5+PWbNm4dChQ7CyssJXX32FUaNGSV6XddOFOvqb6gpermgTg+oSEhKCoKCgEsFJpSnPawWBQFDqzQ0CgQDR0dFy9SGUueFpw4YNknZv0qRJaNSoES5cuIDvv/8eoaGhZfbVhgwZgnHjxmHYsGF4//49OnToAF9fX+zevRvv378v8zpRle3/+OOPmD9/Ptq3bw9DQ0OsW7cO8fHxau8bS5OcnIzx48dL6j9jxgxMnDgR8+fPx8qVKxEQEIBt27aV+TmvXr3CqFGjcOPGjWLPq+Nasyy///475s6di8mTJ+PHH3+UbMvCwgJr166VGnzbp08fubdRWgKQAwcOYODAgTA0NISuri5Wr16N5cuXY+rUqWV+nri/JQ9ZQYiqUKXdVXV8UB1jPN988w2qVauGVatWARBmKG/RogWqVauG6tWrY8SIESgsLMSwYcNKlFXn9UJRFy9exNatW3Ho0CGYm5uXea0ZHByMY8eOwcrKCgMGDJDcQAMIxzImT55coi1QdW5C1f2+KGljZBkZGWUmyTh//jx69Ogh6af6+flJggECAwOlllPluCtKlQBQdVEmmENdbceIESMQGRmJOXPmwNHRUaG5WFWO37t376Jjx44QCATIz8+Hk5MTDh8+DF9fXwDCfWfBggWl/vZhYWHo3r07GjVqhDVr1qBWrVpgjOHZs2dYv349unbtipCQEHh6espdP2V8HMgSFBSkcCCLMlS9Tle1fFHx8fF48uQJ6tevD3Nzc8TFxWHHjh0QCATo1q2b5CZIRUVFRWHevHky+0DKjhNs2LAB8+fPlwQvffnll2jUqJFkf8nNzcXp06eVqnd5UOZaPTg4GKtWrcK1a9cQGxsLHo8HT09P9OrVCz/88IPMudzp06fD399fEnwbHh6Ozz77DC1atEBAQACWLl0KIyMjTJ48udTyd+7cQceOHZGWlgYLCwucPXsW/fv3h46ODgQCAZYtW4Zr166V2v6r2meYM2cOdu3ahaFDh8LQ0BB79uzBuHHj8M8//8j9ueU9vq6uG46WL18Od3d3DBw4EICwPTt48CAcHBxw4sQJ1KlTp9RykyZNwvbt29GtWzf4+fkpHL9TWFiIp0+fwtvbu8QNhllZWQgLC4Ofn5/Um+QnT54Mf39/jB49GoWFhWjVqhVu3LgBIyMjHD9+HK1bty6zDuoao1OWOq6TFZ1XkWeMqqzxtaIUuU5Xx/iessfuxYsX5dq2vMoaM3F2dsaIESMwb948yb+xOq41YmNj0bNnT9y/fx8cx2HIkCH47bffJOexDx8+oE2bNjKvs69cuYJZs2YBAP755x8wxpCSkoIdO3Zg8eLFcvVZOnXqhGvXrpWIRzh48CCGDx9e5k07qgQPF5WVlYV9+/Zh69atuHnzJoKCgmT2i1+9eoW5c+diw4YNJc5vqamp+Prrr7F48WKZ/cYxY8bg8uXLGDZsmML95Y8pe5OyOtoO8umSO/MtIRXV27dv5X5v0Y5wUX5+fliyZInkQmDbtm34/vvv8fDhQ7i6umLUqFGIj4/Hv//+K/PzBw8eLLPRnzRpUpl1lHZiKnq3V8+ePWFlZSV5TV13DXXs2LFYMEOtWrUUCmZYuHAhduzYgYULF2Ls2LF48uQJPD09sW/fPqxduxY3b94stZy6AgLKCuKTd3A2KioK27Ztw7Zt2xAZGYlBgwZh2LBhaNeuHXR1dUstc+LECcmk6KhRo1CrVi3Ja8nJyejbt6/USUVVMtm1adMG/fr1w4QJEwAAN27cQIsWLbBw4ULUrl0bs2bNQpcuXaQO7qm678vKUFaUtH1PlfKqdpbVObB/6tQpzJw5E4sWLUL9+vUld2iLlZWRSNkgLlWzIKry+6v6by/m5uaG8ePHY/r06XJ9npj43y84OBi+vr7Q0fnvfqLCwkKEh4ejc+fO2L9/f5mfpeqNE4pm47t8+bI8XxEA0KpVK7nf+zFpd2mLMxHI44svvpD6Wo0aNdC1a1eFA6FUPWe1adMGgPB3bNKkSbHJDz09Pbi7u2Pq1KmlZu1W9bvb29vj3LlzkkFrxhjGjx+PEydO4OLFizA2Ni4z86i0jBaKUCUYp6gFCxZg4cKFCAoKKrXfUtrAiSoTgzweD+/fv4ednR1atGiB5s2bY+nSpZLXlyxZgmPHjkntLxSl7P4npmpWmbp166JGjRpYsGBBqb9dWcH/ymQkUsf+V5Qy/R1ZZGWGWLBggdyfM2/ePJmv29jY4PLly/D19cXmzZvx888/4+HDhzh48CDmzp1b5jJNEydOlAxQl/ZvV9ZgpDLnbFXbfVXaPUB97b6bmxv27duHxo0bw9TUFMHBwfD09ERYWBgCAwOlTlY7ODhgx44dUgNMT58+jeHDhyMuLk7qto2NjfH48WOVJm7Pnz+PWbNm4ccff5RMUN25cwdz5szB7NmzYW5uLpnw27Jli9S69ujRA3Xr1kWzZs0AANevX5cEanTo0KFEmS1btmDs2LGwsrJCcnIyrK2tsXr1anzzzTcYOHAgJk2aJFdGGWX2PXVdZ6nK3NwcDx48QPXq1bF8+XJcuHABp0+fxvXr1zFo0CBERUWVWk5d+64y1NFumZiY4PHjx/Dw8EDjxo3Ru3fvYn3eX375BVu3bpV6rTF//nz88ccfmDp1KlJSUvDLL79g4MCB2LBhAwDhuczR0bFE3xVQT39T3hsLOY6T2V9Wtd2VpqyMRG3atClz/+c4DufPn9fI9sXK61ohLS0NY8aMwbFjx2BmZoYvv/wS8+bNk6xAIG9/UZnM48uWLcPcuXMREBCAFy9egDGGWbNm4eeff8akSZPw5ZdfljmBamlpiVu3bqFmzZpYv3499u3bh+vXr+PMmTP46quvZO5zqm7f29sbU6dOxZdffgkAOHfuHLp164bs7Gy5r4NVGSP68ssvcerUKfTv3x+nT5/Gs2fP0KlTJ/B4PMyePRuNGzeWqw7NmjWDjo4OZsyYUeqxJ20yX0yVwHkfHx8sWbIEvXr1KtZnePLkCVq3bo3ExMRSy40cOVKu7wag1ADk+vXro0GDBvj111/B5/OxdOlSrFixQq5znLi/VRaO46R+d1WD8FRpd1UdH1THGI+Hhwe2b98uaZtWrlyJP/74Ay9evICOjg5WrlyJAwcO4NatWyXKqvN6ISYmBtu3b8e2bduQkpKC5ORk7NmzBwMGDJB5Xjhz5gw+++wzeHt7Iz09HZmZmfj7778l+4a0dlPVuQlV93vgv31v3bp1GDt2bKlBfHw+H9evX5f62Q0bNkSXLl2wYMECyXFrZ2eHoUOHonPnzlLnBVQ57sTKCgCV55ylTOD0x8oaryotmEMdbQcAmJqa4urVq3IHWxalyvHboUMHuLi4YPPmzcjMzMT06dOxf/9+nD17FvXq1ZP520+cOBHPnz8vtS/FGEP79u3h4+MjSfohiyrnTQcHB5w+fRp16tTBnj17MG/ePAQHB2PHjh3YuHEjHj58WOb2AcXPe6pep6taXuzSpUvo3r07srKyYG9vj1OnTqF79+4wNDQEj8dDREQEjh49qtRKQfL0d5UdJyg6RgigWFlAsdWdpM3zFJ1THTFihNzHK6BY9lNlrtVPnz6N3r17o2vXrjA0NMShQ4cwatQoGBsb4+DBg2CM4dq1a3BwcCh1my4uLti/f78kac7ixYtx4MABPHr0CIBwHOLnn3+W/P2xDh06wN3dHatXr8aGDRuwbt06dO7cGZs2bQIAjBo1CsnJyaWODavaZ/Dw8MBPP/2E/v37AwDu37+Pxo0bIzs7u9gcjyzqGF+XRp5/+8GDB5e44ahWrVqSG45OnjyJLVu2lHrDUVEeHh7YvXs3mjZtirNnz2LAgAHYt28f9u/fj8jISJw5c6bUcjY2Nti5cye6du2q1Hfcvn07fvnlF0n/oKiCggI0btwYkydPxueff15qeWdnZxw+fBhBQUE4fPgwJkyYgIsXL2LXrl24cOGCzP6GmLJjdKrOi6jrOlmZeRV1UscKYYpSx7GrDjt37sSsWbMwYsSIYvvOjh07MHv2bCQkJGDlypX44YcfJBnA1XGt8cUXX+Dly5f45ZdfkJKSghkzZoDjOJw5cwaWlpYyx+jEDA0NERoaChcXFwwfPhzVqlXDsmXLEBkZCR8fH2RkZMhVvz///BPXr1+XnCP27duHUaNGYfv27ZJ/n4+VFTws775/69YtbN68GX///TdcXV3x/PlzXLx4ES1atJBZbty4cbCwsMBPP/1U6uvTp09HWloafv/9d6mfYWFhgX///VcyJq8MZW9SVlfbQT5xjBDCTE1N2atXryR/Dxo0iI0dO1by98OHD5mjo2OZn2Nubs6uXbumUl1at27NzMzMmLGxMQsMDGSBgYHMxMSEmZubs0aNGjELCwtmaWnJnj59Kilz6dIluR+yWFtbsydPnjDGGNu0aRMLCAhghYWFbP/+/axWrVpl1r169ers3LlzjDHGTExM2OvXrxljjD1//pxZWFhILbd9+3a5H7K4urqyZcuWlVnP0uTl5bH9+/ezjh07MkNDQ9a7d2/2999/Mx0dnWK/dWl2797N+Hw+69atG2vevDkzMDBgf/75p+T19+/fMx6PJ7V8r169WLdu3VhCQgJ79eoV69atG/Pw8GBv374ts7ytrS178OCB5O/vvvuOderUSfL3v//+y7y8vKRuW137fnmYP3++3A9NlC+K4zjJg8fjSR7iv8tSp04d1r9/f/bs2TOWnJzMUlJSij2kUWXfqShMTU0lbYUixP82HMexqVOnFvv3WrJkCduzZw/Lzc2V67MGDRrEHB0d2bRp09iaNWvY2rVriz3K4u3tzSZNmsQyMzMV/h6a9OjRI43++xsZGSn1b6euc9aIESNYamqqstVXiqmpKXv27FmJ5ydMmMCcnZ3ZlStXyvzNOY5jXbt2Zb1795b5kGXevHnM3t6erVixgs2aNYuZm5uzcePGSV5///494ziuzO/j4ODAdu7cWeb7ihoxYoTcj9K+e1xcHGOMMTs7O3bv3r1ir7948UJmf6EoZfc/sc6dOzMfHx/222+/sX/++YcdPny42EOe7Rc9fyqK4zgWHx9f4vnz588zGxubUsuoY/9Tpb9TFk23OWKGhoaS81z//v0l5+nIyEhmaGhYZnlra2v277//Kr19Zc/Z6lAe7V5RhoaGkuOuaF//0aNHzMzMTGo5IyMjFhISIvX14OBgZmxsLHPbvXv3Zvv27VOi1v/x9fVl169fL/H8tWvXmI+PD2OMsbNnzzIXFxepn1G3bl02ffr0Es9Pnz6d1atXr9Qy/v7+7KeffmKMMXbgwAHGcRxr0qQJi4qKUqj+yux76rrOUpWpqSkLDQ1ljDHWvn17Sf/q7du3zMDAQKPbLk/m5uYsODiYMSY874n/XywsLIwZGRlJLe/l5cWOHTsm+fvVq1fMy8uLjRgxggkEgkrR12dM9XZXmrLOO5MnT5b6GD16NDM0NFTp95P3vFde1wrffvstq1GjBvv777/Zpk2bmJubG+vWrZvkGkne/qIy9a9Ro4akXbly5QrjOI5169aNZWRkyP0ZxsbGLDw8nDHG2GeffSYZ65Gn3VB1+3p6eiwyMrLYc/r6+nK326qOEbm4uLDz588zxhgLDw9nHMexmTNnyrXtooyMjNjz588VLic2YcIEZmxszAYMGMAmTZpU4jiSxcDAgEVERDDGivcZQkNDNdruGxsbF+uj5+bmMh0dHck1iKa1bt1arkebNm3Uvm1VxwfVoei/O2OMdenShf3www+Sv1++fMmsrKw0tv0DBw6wLl26MGNjY9avXz92+PBhyT4gz7VOkyZN2P/+9z/GGGMCgYAtX76cmZiYsJMnTzLGKvYYm3jf4jiONW3atNj+1rFjRzZu3DhJX0waExMTFhYWxhhjzMLCQjJH8OjRI+bm5ia1nDqOu/bt27ORI0eywsJClpaWxr7++mtmbW0t2afL+u1Pnz7N9PT0mK+vL3N1dWXW1tbswoULktfl/bfbsWMHc3Z2ZrNnz2ZHjx5lR48eZbNnz2YuLi5sw4YNbPHixczCwoL9+OOPcn83edWuXbvYMawtlpaW7OXLl8WeW7p0KbO0tGR37tyR+dv5+vqyo0ePSv3so0ePMl9f3zLroOp508DAQHLeHjZsmOR67e3bt2VeZxal7HlP1et0Vcs3b96cTZgwgaWnp7MVK1YwJycnNmHCBMnrU6dOZU2bNi217JEjR2Q+1qxZU+axo+w4QdExwo/LMqZYmztjxgxmbm7OmjdvzqZMmcKmTJnCWrRowczNzdmkSZNYhw4dGI/Hk2u8T0yRMS5lrtXr1q3Lfv/9d8nfZ86ckczB5uXlsXbt2pU6tipWdL9njLG2bduy2bNnS/4OCwtj5ubmUstbWlpKxhjz8vIYj8djt2/flrx+//595uTkJPuLK0lHR4fFxMQUe67oWJ88OI6TzJXLeihDnn97d3f3YnMXK1asYNWrV2f5+fmSvxs1alTmtor+O3777beS8f2XL1/KHCN3dHQs0XYronnz5uyvv/6S+vq+fftYixYtpL5e9Npo7NixbNKkSYwxxt68ecNMTU3lqoOyY3Sqzouo6zpZmXmVskRFRRWbn5elPMYZ1HHsimVmZrLnz5+z4ODgYg95tG3bttQx4n379rG2bdsyxhjbuXMnq1mzpsL1kqVatWrF2smcnBz22Wefsbp167KkpCS5zlve3t5s3759LCMjg9na2kqu+x89esSsra3lrsvEiROZr68vS0pKYrt372aGhobswIEDMssMHz6cNWrUiN29e5edPXuW1a9fnwUFBbEPHz4wxsre91euXMl8fHyYk5MTmzp1Knv06BFjjMl9rVWjRg12584dqa/fu3eP1ahRQ+ZnuLu7lzo/poimTZuyli1bshMnTrCHDx+yR48eFXtIo662g3zaKPiWfJKePn3KTp48WeJiURpzc/NiA1Du7u5sy5Ytkr/Dw8PlGhxWR6O/Zs0a1qdPn2IX3CkpKaxfv35s7dq1LDMzk/Xs2ZN17NhRpe2URtVgBmmD60+fPlVooENZygbxMSYcpG7RogXbsGGDpKPBmHydhrp167J169ZJ/t63bx8zNjZmmzdvZoyVPVBgZ2dXLCBBIBCwr776irm6urLXr1/LLG9gYFCsY9ugQQPJ5D5jjEVERMic0FXXvl/VqRJAyJjyQVyq7DsVxahRo4oNNClq+/btLCcnR6U6qHrjhKpBgIypdrEpjaKBcNnZ2Sw1NbXYQxZ1BEKpIiUlhSUlJZV4PikpSeFBa3m/e4MGDaQOqkyYMIFZWFjIFXw7cOBAhQNXi1JXMI6VlZVkck0bOI5jFy9eZMHBwczNza3ExfaLFy+YiYmJXJ+l6v5nYmLCHj58qHT5Nm3aSCZhFSEeGObxeCUGic3MzBiPx2Pjx48vtaw69j9V+jvfffedzMfnn3+ulXOOv78/W7duHYuMjGRmZmbsxo0bjDHhAI29vX2Z5VUdoFY18FpMmXa/tIBtMVnBrdIo2u63aNGCrV+/njEmPIbevHnDGBMO+BUN7vhY165dWceOHVlCQkKJ1xISEljnzp1Zt27dZG578+bNzNXVlc2bN48dOHBA7uu8ogwMDNjjx49LPB8SEiLp70ZERMi87tHX1y81cOHly5dMX1+/1DJGRkaSADKBQMB0dXWV6neoa99TRVRUFPv111/Z9OnTS7QBsrRp04YNHz6c7dy5k+nq6kq+x6VLl2QGc0ij6L6rav2V1aNHDzZjxgzGGGOdOnUqds3ImPCGV29vb6nlDQ0NJfuOWHR0NKtRowYbOnQoi4mJUajd1UR/Ux7KtruaOO/k5+eztWvXMltbW+bl5SVzwrEs8va1Fb1W+PjfR9ZDFldXV3bx4kXJ3wkJCaxhw4asY8eOLCcnR+7+ojLXOh8HA+jp6ZW46aosDRs2ZNOnT2dXrlxhBgYGkkmQmzdvlhkIoOr2eTxeiXNu0fNeWVQdI+Lz+ezdu3eSvw0NDZW6SSooKIhdvXpV4XJiqgTO165dWxLgUnR8cP369VJvVlGHjwN5Pt7+p0zV8UF1sLOzKzZhaW1tXWwSODQ0VKPjw3w+n/3vf/9jaWlpxZ6Xd0LYzMysxPXx7t27mbGxMTt27JhCY2yKzk2oiypBfPb29pJ5jdq1a0vq++jRI5n/buo47lQJAGVMfYHT5RXMwZgwgLhjx44l+n6aZmlpWWqfYsWKFczCwoIdOnRI6m9namoqs75v3ryRa4xH1fOmugJZlD3vqTo+qep1ftG2Kz8/n+no6BQb7woNDZUahClOHFI0scjHj7KOHWXHCdQZfDtmzBi2cOHCEs8vWrSIjRkzhjHG2Ny5c1n9+vUlr5WVFKFNmzZyb1+Za3UDA4Nix494rEDcB7xy5QqztbWVWr5oEFhhYSEzMzNjx48fl7z+7NkzmcHPRW90Y6zk76/JG2VL62ubmprK3ddmTLj/rFu3TqmbjNetWyfzMW3atDL/7dV1w5Gjo6MkALVGjRps//79jDHhGLmsINaVK1ey8ePHM4FAUOY2SmNra1tm+y0tOQRjwmvN06dPs4KCAubi4iLZ9548eSJ3Yg1lx+hUnRdR13WyJuZVFJnPU3VOUpnxMXUcu/Hx8axbt27FklgVfcjDwMCg1LHZ0NBQyf7y5s0bueJaFGFsbFxiu/n5+axXr14sICCAhYSElPkdfv31V6ajo8MsLCxYnTp1WGFhIWNMeJ3cunVrheozZMgQ5u3tzYyMjOS6uUTV4GHxtVZBQUGx5+W91vq43fxYWWPyjDG2a9cu1q9fP5WCzpW9SVldbQf5tGkvBzghWvDmzRv07t0bjx8/BsdxkiU+xOn2paX6rl27No4dO4YpU6bg6dOniIyMLLYEydu3b2Fvb1/m9hctWoS5c+dix44dSi2BDAArVqzA2bNniy1Tb25ujvnz56Njx46YNGkS5s6dW+YyMVlZWYiMjEReXl6x5wMCAqSW8fLywuHDh9G7d2+cPn0a3333HQDhkhlF6yONj48Prl69WmIJrQMHDqBevXpllv9YTk5OifrLqkf//v0lSxAqqqCgABzHgeO4EstslOXVq1f47LPPJH8PGDAAtra26NGjB/Lz89G7d2+Z5T9ekoHjOPz++++YOHEiWrVqhT179kgt6+TkhOfPn8PV1RUZGRkIDg4utmxnUlKSzH1RXfu+WGZmJi5fvlzqvvftt99qvLy2DR8+HL/++qtkSb3g4GD4+PgovFx3o0aNEBYWBi8vL4XKqbLvlEaV31/Zsl5eXpgzZw5u3boFf3//Er9dWdv18fHBo0eP0KhRo2LPi5fMCQoKklkeEC5pamVlVeb7pOnUqRPu3bun1FLYCQkJGDlyJE6ePFnq65peoqLoknJJSUkKbb9bt2744Ycf8OzZs1L/7Xr06CF3PZQ5Zw0aNAifffYZxo8fX+z5/fv34+jRozhx4oTMbSrz3Xv37o2//vqr1CWjfvnlFwgEAvzxxx8ytwsA69evlyyrpoyYmBj4+flJ/vby8sKlS5fQtm1bDBs2TOrSLR8bM2YM9uzZgzlz5ihdF0W1a9dO0j+7fv06GjRoIHnt4cOHcHV1letzVN3/XFxcSiwFp4hvvvkG33//Pd6/f1/q9qXtu2vXrgVjDKNGjcKCBQtgbm4ueU28rKB4ybiPqWP/U6W/I89SjS1btizzPYWFhVizZo1kCbWPj/uylsWaO3cuhgwZgu+++w7t2rWT/F5nzpyRq7/5/fffY926dfjll1/KXI68NMqes8VUaff9/f2xZcsWdOvWrdjzK1euxJw5c5CdnV3m9lVp95csWYIuXbrg2bNnKCgowLp16/Ds2TPcuHFD5jLpf/zxB7p27QpHR0f4+/tL+pZxcXF4/PgxfHx8cPz4cZn1Hjt2LABg4cKFJV6TtSRUUfXr18cPP/yAnTt3SpYmTEhIwLRp0yRt0atXr+Di4iL1M2xtbfHo0aMSS38+evRIaruenZ0t6Y9zHAd9fX04OjqWWd+PqbrvFaXodRYgXBKwR48e8PT0xIsXL+Dn54eIiAgwxhAYGCiz7Nq1azF06FAcPnwYs2bNknyHAwcOoGnTpnLVWZV9V9X6K9tuLVu2DC1atMC7d+/QvHlzzJo1C3fv3kXt2rXx8uVL7Nu3T2a77eDggNevX8Pd3V3ynJOTEy5evIg2bdpgxIgRMustpq7+ZnR0NI4ePVrqbyBtGXNA+XZXXecdsd27d2Pu3LnIzs7G/PnzMW7cOJlLI0pbIlcsPT1dru0qeq1Qt27dYuNZ0pTV9iUkJBQbm7GxscG5c+fQqVMndO3aFZs3b9ZI/QEgNzcXBgYGkr/19PQUvt5avnw5evfujRUrVuCLL75AnTp1AABHjx6VLC2pqe0zxjBixAjo6+tLnsvJycFXX30FY2NjyXOHDh0qtbyqY0SMsWL7Jp/Ph6Ghodz1F1u+fDmmTZuGJUuWlNpfLavd19PTU/qcM2XKFEyYMAE5OTlgjOHOnTv466+/sHTpUpn7XmBgIM6fPw9LS0upS0iLPXjwoNTnN2/eLFk6ExD2f7dv315sCXp5xpfu3bsntd2X9m+vboq0u6qOD35MmTGexo0bY/369di0aRMOHTqE9PR0tG3bVvK6eInVsih73h09ejR+/fVXXLp0CcOGDcPAgQNhaWlZ5vbE9PX1kZKSUuy5IUOGgMfjYeDAgZKlpWVRdm6iqAMHDkj97tL2e7Ft27YBAMLCwvD69Wu0bNkShoaGkmVUZWncuDGuXbuG2rVro2vXrvj+++/x+PFjHDp0CI0bN5ZZVh3HXU5OTrG/Z8yYAR0dHXTs2BFbt26VWfbp06fYtWsXAOFvPW3aNDg7O6Nfv37Yu3dvsXEHWW7cuFFq36xevXq4efMmAKB58+aIjIyU+hnKth0DBw5EVlYWqlevDiMjoxJttiLLRyty/Pr5+eHGjRslxjCmTp0KgUCAwYMHS91ORkaGzHbFyMgIWVlZZdZX1fPm5MmTMXToUJiYmMDNzQ2tW7cGAFy5cgX+/v5lbl9M2fOequOTql7n6+npSY6fvLw8CASCYsdTdna21LkKR0dH/Pbbb+jZs2eprz969Aj169eXuX1lxwkA4fiOeB/Ky8vDjz/+KBknk2ffEdu/fz/u379f4vlBgwahfv362LRpEwYPHlzs3Fl0PK405ubmGD58uFzbV+Za3cnJCS9fvpRc671+/RoCgQDW1tYAAGdnZ5nLj7du3RqLFi3Cb7/9hr///hsCgUCy7wPAs2fPil1HfszFxQVv3ryRvGfv3r3FxiliY2OLteGyKNpnYIyhXbt2xfq7WVlZ+Oyzz6Cnpyd5rqxz3qBBg5QaXy/aP5KmrPFpMzMzpKSkSK637ty5g9GjR0te5zgOubm5ZW6nT58+GDJkCLy9vZGUlIQuXboAEF4Ly9qfrl27hosXL+LkyZPw9fUtcYyX1VfNzMyUeb2bnp4u8xgcOXIkBgwYAEdHR3Ach/bt2wMQzsnVqlVL5rbFlB2jU3VeRF3XyeUxr1KUKnOSyo6PqePYnTx5MlJSUnD79m20bt0a//zzD+Li4rB48WK5+tqAsP3asmULli1bVuz5LVu2SPaXpKQkqdcByl5reHp6IiQkpNiYsI6ODv7++2/0798f3bt3L7Pu48ePR8OGDREVFYUOHTqAx+NJPnvx4sVSyx09erTEc3369MHVq1cxePBgcBwneY+0ebHU1NRiv4m+vj4OHTqE/v37o02bNvjzzz9l1n3RokXYtm0bdu3ahcGDB2PYsGHFjsWymJub4/Xr1yViiMTCwsLKHKNYtWoVXr9+DXt7e7i7u5do+8o6bwDCmILExES56y2mrraDfNoo+JZ8UiZNmgQPDw+cP38eHh4euHPnDpKSkvD9999j5cqVUstNmzYNgwYNwr///ounT5+ia9eu8PDwkLx+4sSJMgf3AfU0+qmpqYiPj4ePj0+x5xMSEiSdYQsLixKdgaLvU3ZiTdVghrlz5+KLL75ATEwMBAIBDh06hJcvX2Lnzp1lTqiLqTKpqkoQ37t373Dw4EFs2bIFkyZNQpcuXfD555/LNTloZmaGuLi4YvtMmzZtcPz4cXTv3h3R0dEyy9eqVQv37t1D7dq1iz3/yy+/AJAdQNS/f39MnjwZ//vf/3DixAk4ODgUGxC9d+8eatasKbW8uvZ9QHhB2LVrV2RlZSEzMxNWVlZITEyEkZER7OzsyhxkVaW8qoE8ypbfvXs3Vq5cCVNTUwBAixYt8OjRI4UveJQN4lJl3/mYKr+/KmU3btwIExMTXL58ucSAHMdxZe43EyZMwLRp00oE38bExGD58uW4fft2md9d1RsnVAkCVOVi09LSUmYbVVBQUGbdp02bhosXL+L333/HsGHD8OuvvyImJgYbNmwocfH6MXUEQqlyzrp9+3apgR6tW7fGrFmzyty2Mt995syZmDlzptTP/O233/Dbb7/J3K4ywX4fU1cwTk5ODjZu3Ihz584hICCgxL4rK5BGTJGJwfDw8GJ/F52cA4QD7dOnT5er7qruf2vXrsWMGTOwYcMGmYPR0vTt2xcAMGrUqGLbFU9qStv+F198AQDw8PBA06ZNFbpZQx37nyr9nYsXL8pdV1kWLFiAzZs34/vvv8fs2bMxa9YsRERE4PDhw5g7d26Z5fv164fmzZsjNjZWEogDCAO7y5qUA1QfoFb2nC2mSrs/ZcoU9O3bFyNHjsTq1avx4cMHDB8+HI8fP5b7hhtV2v3mzZsjODgYS5cuhb+/P86cOYPAwEDcvHlT5qSmi4sLgoODcfr0ady6dQvv378HADRs2BBLlixBx44dJYON0ggEArm+nyxbtmxBz5494ezsLBmMjYqKgqenJ44cOQJAOIE8e/ZsqZ8xduxYjBs3Dm/evJEEjV6/fh3Lly/HlClTpJYrGpBQWjACUHZAgqr7nqrBqzNnzsTUqVOxYMECmJqa4uDBg7Czs8PQoUPRuXNnmWUDAgLw+PHjEs+vWLFC7hsBVNl3Va2/su1W7dq1cfv2bcyePRs//fQTMjMzsXv3bujo6KBBgwbYu3cvevXqJbV827ZtsWfPHrRr167Y89WqVcOFCxeKTa7Koo7JDVWCl5Vtd9V13jl16hRmzJiB8PBwTJ06FVOmTCkWQCmNhYWFzPOjPIFMgOLXCh/3l5Tl6uqK58+fF7u+NzU1xZkzZ9CxY0e5zpmA8tc6c+bMKRZMsXjx4hJBDrL6mq1bt0ZiYiLS0tKKTRCNGzdOrms2VbYv7q8V9fnnn5e5TTFVx4g+ntTMzs4uMaEJlD3GKJ4E/7gNKau/KqbKDUtjxoyBoaEhZs+ejaysLAwZMgTVqlXDunXrMGjQIKnlevbsKQl6ltU+SuPq6opNmzYVe87BwUESlAfIN86wd+9eDB8+HJ06dZIcM6GhoYiLi5P72AFUC+BVtN1VdXywKGXHeBYtWoR27drhzz//REFBAf73v/8VO3737t0ruXldFmXPuxs2bMDatWuxf/9+bN26FZMnT0anTp3AGJOrL1m3bl1cvHixRKDZoEGDwBgrtW34mLJzE2Lr16/HrFmzMGLECBw5cgQjR47E69evcffuXUyYMKHM8h8+fED//v1x8eJFcByHV69ewdPTE6NHj4alpaXM8/7q1aslgV4LFixARkYG9u3bB29vb5nttTqOO1UCQAH1BE4DqgdzqNJ2rF27Vq46lkXR43f48OG4fPlyqclMpk2bBsaYzJvFnj17Jrm++5i8gQ2qnjeVDWT5mLLnPVXHJ1W9zm/WrBlmzJiBGTNmYOfOnQgMDMTixYuxb98+cByHRYsWSU1OUb9+fdy/f19q8K08N4QpO07QsmVLvHz5UvJ306ZN8ebNmxLvkYeBgQFu3LhRIljxxo0bkhuyBAJBsZuzxDcrKCskJETy/8pcqw8fPhxjxozBrFmzoK+vj9WrV6NHjx6S/t6jR4+KHRMfW7x4MTp06AA3Nzfw+XysX7++2DXOrl27it0A87FBgwYhPj5e8vfHwd/y3PAGKNdnmDdvXonnpO2D0qgyvq6O6y113XC0Zs0auLu7IyoqCj/99JNk3Cg2NrZEQH9RFhYWCvVJP+bt7V3qeVfs2rVrJW46L2r+/Pnw8/NDVFQU+vfvL+m/8/l8zJgxQ646KDtGp+q8iLquk9Uxr6IKVeYklR0fU8exe+HCBRw5cgRBQUHg8Xhwc3NDhw4dYGZmhqVLl5Zoi0qzcuVK9O/fHydPnpQEat+7dw8vXrzAgQMHAAB3797FwIEDSy2v7LVGly5dsHHjRsm8kJg4ALdv375l9lkAICgoqMR5uazvLevaeOvWrZIb1WRd56saPCyem7p8+TK2bt2KRo0awcvLC4wxJCcnyywLCM/pP//8s9Rz0/r169GiRQuZn6HMGMHHlL1JWV1tB/nEaSvFLiHaYG1tLVkmx8zMjL148YIxxtj58+dZ3bp1ZZY9d+4cmzx5Mlu2bFmJdOXz588vlkpcmvnz58t8yGPIkCHMw8ODHTp0iEVFRbGoqCh26NAh5unpyT7//HPGGGN//fVXsSVSPi7frFkzdvfuXWZsbMzOnDnDdu3axWrWrFls2RFpYmNj2YMHDySp7hlj7Pbt23KnYL9y5Qpr3749s7W1ZYaGhqxZs2bs9OnTcpVljLHx48ez2rVrswMHDjBDQ0O2detWtmjRIubs7Mz+/PNPmWXd3d2lPjw8POSuQ1hYGJs1axZzdnZmHMexIUOGsDNnzpRIpS/Ws2dPNnfu3FJfu3jxIjM2NpaZan7JkiWsS5cuUl//+uuvGcdxpb6WlZXFhg0bxiwsLFitWrXYlStXir3eunVrtmzZMqmfzZh69n3GGGvVqhUbO3YsKywslCxTExkZyVq2bMkOHjyo0fJz5sxhjo6ObOXKlczAwIAtWrSIjR49mllbW5dY3lWd5ctaHkle0pZ0KmtpJ1X2nY+p8vur+m+vCmNj41J/87KWNqtbty6rV6+e5GFqaspMTEyYn59fseflWRJTlaW5HBwcJEt9mJqaSpbZO3LkCGvWrJnMsmUtqyRreSUxFxcXyTFuamoqWSJr586dMvctdVHlnGVkZFTq8mshISFyLSmjynfftWsXy8jIKHMbpSltSUZFjR49mo0aNarU16Kjo5mXl5dcS5y0bt1a6qNNmzZlll+3bh0zMTFhEydOZHp6euzLL79k7du3Z+bm5pIlHysqCwsLpqenx3g8HjMxMWGWlpbFHmWJiIiQ+ZBHYWEhe/nyJbt69Sq7fPlysYcsqux/RSna32GMsdTUVHbmzBl2/PhxmUsjyuLp6Sk5vk1MTCRLdK1bt44NHjxYqc9UxIgRI2Q+yqLsOVtMlXafMcYePHjAfH19mZeXF7OysmJdunRhsbGxZZYTU7bty8vLYyNHjlRoKbGKqLCwkJ08eVKynOCpU6eKXfeURSAQsNWrVzMnJyfJv7+TkxNbu3at1OX+3NzcZF6nyHutouq+p8p1FmPFj1cLCwv25MkTxphwWTw3N7cyyycnJ7NNmzaxGTNmSJZlvX//PouOji6zLGOq91lUqb862i3x8ofv3r1jeXl5cpWJiIhgp06dkvp6TExMmX09xlRvdxgTLl8uvuYV9/fT09NZjx492G+//SazrCrtrirnndu3b7PWrVszAwMDNnnyZJaQkKBQ+UuXLsn1KIsq1wqq+Oabb1i/fv1KfS0tLY01atRIru0rU/9WrVrJ7GfK09fcsmWL0uccdWxfFaqOEZU1tijvGKOq+26vXr2Yubk58/DwYN27dy+xHLO8MjMzVb7+0TZ/f3/2yy+/MMb+a/MEAgEbO3as1H/bj/31119MV1eXde/enenp6bHu3buzGjVqMHNzc7n6nIq2u+oYHxRTZYwnISGBHT58mN26davEa8ePH5fruFbX9UJoaCibOXMmq1atGjMzM2ODBw+WWf9Dhw6xyZMnS3199+7dZS4Hq8rcBGOM1axZk+3Zs4cxVnyMcc6cOWzChAlllh82bBjr1KkTi4qKKlb+1KlTzMfHp8zy5WXTpk2SeY/SLFu2jLm7u0t9vUOHDmzFihWlvrZnzx6mq6sr1znvyJEjTE9PjwUEBLDRo0ez0aNHszp16jB9fX3JEte//fZ/9t46Lort/x9/7QILLF2CggioICKCqAiKhYWKhWJhd11b7EDs1ms3YgfXuHZjiwEoBnbrtVDBBJ6/P/jtfHbZmNmZQe/7fn0+HvNQZvY158zMOa9zXr1Ya0lmMXiHUPxMHa2yPCJEThG6booFvuueUP0kIEzOT09PR8mSJSGRSODt7Y2nT5+icePGMDQ0hKGhIRwcHHD58mWNtImJidi/f7/We2dmZurcM/xb9ASxsbEwNTVF//79ER8fj/j4ePTv3x9yuRyTJk0CAMyZMwe1a9dWoXvw4AGWL1+OhQsXMvIhV+ga/1zmwI8fPxAdHY0iRYrAzs4Obdu2VZFXLly4wKof/PHjB5KTk/Hs2TO1a8nJyXjz5o1ez6SMrKwsfP36lfV3v8ouJFS/npubi/T0dFy/fh0/fvzQmz4lJQX29vaMbnnMmDEq19u1a4eePXvy7l9BY/r06Sp7FmUkJyfDzs4O06dPL/B+8NHRCbWLiCUnF4SsmZyczHm9E6JnEKrfEwILCws8ePAAAODq6orTp08DyLPncl0zFb8fPnw4s0aPGDGCuS8b+MoaP378wIcPH3ReZ7MLde7cWedRkIiOjkbdunU1Xvvx4wcaN26s137r48ePWLp0KQIDA2FgYIDg4GDMnj1b6++vXLkCY2NjNG/eHBcuXEBGRgYyMjJw/vx5REREwNjYWOt+RUwozxPlg23uiMU7fuO/jd/Ot7/xn4K1tTUj6Hl4eODYsWMA8pwL9Fm0fyU+ffqEbt26MZt2qVQKmUyG7t27M44WV69exdWrVzXSi2FY+5X41Y5gysjJycHevXvRvHlzyGQy2NnZafzdiRMnMGXKFK33OXbsGCfF+v86rKysGKWylZUVbty4AQA4f/48vLy8CpReqGKeL71YzrdiOHEJhZD3L/TbK5Cbm6vVcUUbbG1tcfbsWbXzZ86cgbW1tVY6rgZNroETfCGWsMkXZmZmePToEQDA2dmZWT/u378PMzOzAm9fyJpVo0YN9OvXT+18nz59EBISwtq2kGe3t7eHmZkZ2rRpg7179+p0VsyPEydO8FLqKUMsZxyhEGoYzI8fP34w36SgIcRpXQycO3cO7u7uGhX1bEK6kPGnCVz3O1evXkXhwoWZflpaWuoch9ogl8uZ7+zk5MQoVe7duwdLS0tW+szMTIwZMwbBwcEoXrw43N3dVY6ChtA1Wyjf//jxI1q1asUY0vQdr0J4n6WlpSCjmjYFek5ODqe5f+LECYSHh6N48eIoXrw4GjVqpOZY8jPx8eNHfPz48ae1J3TsCZWzHB0dmT2et7c3du3aBSBPOc82dhSGqRIlSsDQ0JBZL0aPHo327duztg0I37MI6b9QviUUX758EUQvxn7zVxhnhK47EokEcrkcAwcOZIx5mo7/BaSlpWH//v3YtWuXyqEL79690+lE8PHjR04OmL8KCqNp0aJF0a5dO6xYsYLhW/92/Fd0REIDlv6XIZfLGb5pa2vLOFXduHEDTk5OnO4h1AnvVxrFxdLx8IXY625OTg52796NJk2aQCaTidrX/BBqmzA1NWX2dQ4ODkhOTgaQ51xna2vLSu/o6MjQKMvo9+7d46zjSUpKwrp167Bu3TpcunSJE82vhhiO0woIceYQyjuys7Oxfft2xMbGIjY2FgkJCXrL+3znb2xsrN6yHpt8wlVOEbpuRkREaAwumD59ulYnCU3gu+4J1U8CwuV8AGqOlkeOHMGePXsEOWBygVA9gTJOnz7NyeFTE9avX4+goCAmqD4oKAgbNmxgrn/+/FlFrjp27BjkcjkjaxgZGSE+Pp5ze1zHf0HZdtzd3Qv823LBr94z8MH9+/dRpkwZxv5etGhRXLx4Ue/7iBFwBOSt8cuWLUNsbCxiYmJUDjb8888/OHXqFE6dOqVXsOr3799Ro0YNGBoaIiwsDAMHDsTAgQMRFhYGQ0NDVK9enTVo+MiRIxg5ciS6du36Ux0IhdpFfqWcnD+oI/9Rs2bNn+K8J0Q/JhQVKlRgvl+jRo3Qvn17PH36FNHR0fDw8CjQthUQKmucOnWKd9tNmzZVORo2bIhixYrBysqKU4Dr9+/fERoaivT0dL3bFsN5WBtSU1MxYMAAODg46Pzdnj174ODgoOb46uDgwKrjUsalS5eYYJsrV67o1Ve+Qcr/6zq23/g5+O18+xv/KYSEhOCvv/4CALRp0wZhYWE4ffo0OnToAB8fH1Z6bQbh3NxcvZxBhDB9BT59+oSUlBSkpKTg06dPnOmEGNYUEVnajp8BsRzB+Djx6cI///yjM2JHDIiVyY4PxBj79vb2zIavZMmSzAb65s2bkMvlBUovdLPMl14ikeD48ePMXDUzM8PevXuZvxVHQUOMsSPk/Qv99nFxcShTpgyMjY1hbGwMX19frFu3jlO/W7dujerVqyMjI4M59/79e1SvXh2RkZGc7vErIVTY3Lx5M9q2bYsWLVpgyZIlerfv6+vLCAS1atXCkCFDAOQ5njs7O7PSC3WEErJmnT59GiYmJqhatSrjKF21alWYmJhw6oOQZ//x4wf27NmDtm3bwszMDA4ODujTpw/OnDnD2u7Zs2eZjCkKxMXFwc3NDQ4ODujevTtnRbNQZxxlKLLt6wOhhsH80Ce6G/j1jnh3795Fv379UKtWLdSqVQt//PEHYyBng5+fHyIjI3Hjxg28f/+eifRVHLogZPyxQdd+p27duqhcuTLOnj2LK1euoFmzZihRooTebXh6ejKK6SpVqmDq1KkA8vgZm4IGyOP7hQsXRnR0NObOnYt58+apHFzBV0EtFEL4/unTp+Hm5oaAgADcuHEDK1asgIWFBVq2bIl3795xal8I7+vQoQPmzJnDqR1lfPjwAZGRkTAxMUGhQoUwduxYFSPyy5cvWed+fHw8DA0N0bJlS8ZhrmXLljAyMlIxqLFBqHHg/v37GhWc6enpOo3yQrOqiAGhclaTJk2wfPlyAMCQIUNQokQJTJo0CQEBAahVq5ZO2lq1amHYsGEAVB1Bzpw5w9mBSOieRUj/hfItbXj8+DGnsWdhYYGOHTvi0KFDemVqVkAM44YYxhl9+a7QdUdo1umcnBxMmzYNlStXRoUKFTB8+HB8/vyZc/ti4N69eyhbtqxaZi2FgYILHj9+XMC95IekpCTW3zx9+hTr169Hjx494OXlBalUCmdnZ0RFRendnr7OHMnJyYiNjcWiRYvUsiZ/+PChwI3KYuH9+/eYNWsWk8Fxzpw5rHtNMZC/0oziCAgIQOXKldGhQwfGKVEbsrOzMXPmTFSsWBGOjo6cK1V8/vwZp06dQlpamtq1L1++IC4ujrX/zs7OjNOcr68vE3B49uxZzs6fQp3wfqVRXIiOR4z3X1DrLgBOWfK6du3KuQpYfgi1Tbi7uzO2hPLly2Pp0qUAgIMHD3Kq0GJubs58O+U9V1JSEquM/uTJE4SEhEAikTDzTCKRoEqVKqy6AjG+O8DPAfTfBCG8486dOyhZsiTkcjnDM+VyOby8vDjrGQD+87ds2bKQSqUIDg7WuPb9m2Fvb68182yhQoUKvH2h+kkx5PxfCb56Ak2wsLDgldiED6pUqYImTZrg+fPnePfuHfr06YPChQv/lLbFgBiV1XSBq6zKl+fs3bsXXbt2xbBhw5j9jgLv3r1jtUezZY/Upedp3rw5SpUqhY0bNyIhIQGVK1dGQEAA67MWBJYvXw4DAwM4OjrCz88P/v7+zKGrImNmZiY6d+4MAwMDRkY0NDREly5d1KqbasP3798xffp0+Pn5QS6Xw9TUFH5+fpg+fTq+ffumk3bChAmQSqUIDAxEkyZN1BwKuUKIjk6oXURMOZmrXYUtyONnBTkK0Y8Jnbvx8fFYs2YNgDxfGnt7e0ilUpiYmGDz5s2cn+H9+/c4ePAg4uPjERcXp3KwQaisYWRkBDc3N4wcOVLj3ldf5OTkoEePHpyzTSvzXT4Q4jzMBi6Vvj5//oyEhATMmDED06dPx19//cWZb7169Qo1a9ZUk1dCQ0N/mn3n36pj+41/B3473/7GfwoHDhxgSlncuXMHXl5ekEgksLe3x9GjR7XSiWEQBv4dTF+IYU0R3aY4+vbtiypVqsDKygr9+/fXSGNtba2mANd2cIFQo6oQJz4gb9HftWsXZs6cifnz52P//v2cI8xr1aqFNWvW6Iwc0gW+meyEbHbFGvtAXokvhfNDt27dEBgYiPXr16NevXoIDAwsUHqhm2W+9GKV1wKEOXGJkQVRyPsXQjt79mzI5XJER0czGZyGDRsGuVzOSXH39OlTeHh4wMrKiinrYm1tDS8vL86bYG2R4u/fv+ecRZGvE6AQYXPx4sWQSCTw9PSEn58fpFIphg4dyqm/CsyZM4fJ+HX48GGYmJjA2NgYUqmU1YlNDEcooc4gV69eRdu2bVG6dGmUL18enTt35ix4Cnl2ZWRlZWH9+vVo0KABZDIZa7/DwsJUMnKkpqbC0NAQ3bp1w+zZs+Hk5ITx48dzaluoM05OTg5iYmJgaWnJOHFYWVlh4sSJnO4n1DCYH/o434ox/oRklTlw4ABkMhkCAwMxaNAgDBo0CIGBgTA2NsahQ4dY6eVyuSjZ2/Qdfwrw2e/Y2dmplP55//49JBKJ3vue4cOHY/LkyQDy1llDQ0OUKFECMpkMw4cPZ6W3srJiHPX5QAwFtZA1WwjfV7wjZUXW3bt3ERQUxGmfDAjjfbGxsbC2tkbz5s0xZcoUztkj+/fvD09PT2zbtg0rVqxAsWLF0LBhQ0aZ//LlS0gkEp1tlypVSuO+YPbs2ShVqhTbYwMQxzhQrVo1jRk04uPjUb16dY00YmVVAYSNPaFy1r1795igsszMTPTs2RO+vr6IiIhgzYxgaWnJ9FPZEeThw4cwNjbm1H+h67aQ/gvlW9rAdd1LSEhAixYtYGpqCicnJwwYMICT46ICYhg3hBhn+PJdsdYdvpg4cSKkUinq1q2LJk2awMTEhLfDJV9ZITw8HE2aNMHr169hbm6OGzdu4NSpUwgMDOQccCSVSlGtWjUsX76ctwMH3/5/+vRJzWH56tWrCA8P1yvgKisrCwcOHEDHjh1haGgIAwMDvZ9BH2eOgwcPQiaTwcfHB66urrCzs1NxFOWqJ+GrI9LmuJr/YIPC2c7Z2ZnJpuTi4qI2t9jAJ2BpxIgRsLKyQkhICAYPHozBgwejatWqsLKywoABA1CnTh1IpVLs3LlT6z3Gjh2LwoULY9asWTAxMUFsbCy6du0KOzs7rfuO27dvo1ixYowuplq1anj+/Dlzneu3a9OmDROQNnHiRDg4OKBbt24oVqwYp4xEgHAHXj58V6gxXAG+Oh6x3r/QdXfr1q1o1qwZfHx8UK5cObRq1UqvzOmNGzeGsbExXFxcMHToUCbIlAv42iYU6Nq1K1OBaeHChTA1NUXt2rVhbW2ttcSyMurXr8+UvjY3N8f9+/eRk5ODyMhING/eXCdtvXr1UKlSJSaDIQDcunULwcHBqFevnlY6sb47INwBVIjjtAJCnDmE8I769esjLCwMb9++Zc69efMGYWFhaNCgAef+C9HRXr9+HSNHjoS7uzuMjIzQoEEDbNiwQet+LX/yCW0HV/BdN01MTFTGrQI3b96EiYmJXvcC+K17QvSTYsj5uvDu3Tud41dowBFfPYEm8K0qqMC3b9/w5MkTPHr0SOXQBCsrKxWnqaysLBgYGPDKJrt27VqmqiIADBs2DFZWVggODuadRfDGjRs67RIF7XzLVVblw3M2bNgAAwMDNGzYECEhITAxMcH69euZ61zWDYlEAjc3NzRr1kxNt8Om53F0dFRxPnv+/DmkUqleyW3q16+vEtA2depUvH//nvn7zZs38Pb2Zr2Pq6urxszdbOjRowc8PDywb98+fPjwAR8+fMDevXtRvHhx9OrVS+/76QsnJye97O6aIFRHJ9QuIlROFmpXEQN85XS++jEx5m5+ZGVl4fLly3rt+Xbv3g0LCwtIJBJYWVnB2tqaObjYpITKGq9fv8aff/6JypUrQyKRwM/PDzNmzNA7sY0ybt26xbnCysCBAwXpIoU4D//48QMzZsxAuXLlYGZmBhsbG1SqVAlLly4VlBAvNzcX+/btY5VXWrZsiQoVKqjIumlpaahQoQJat27NuT0hQcpi6Nh+47+L3863v/Gfx9u3b1kZvhgGYUA8pp+UlIRhw4ahVatWamUH2CBW1JAyxo8fzxhn80O5RPPs2bNhY2OD1q1bM8J169atYWNjwzn6VYhRVagT365du+Dg4KDmROni4oKTJ08yv9MWgd+/f384OTnB1NQULVq0wM6dOzlF+SjAJ5Od0M2uWGMfyBu3CoPUq1evUK9ePVhYWCAgIICToloIvdDNMl96sUoLCXXiEiMLopD3L4TWzc1NowJw7dq1cHNz49T3zMxMLFu2DH369MGQIUMQFxen19zTpqx6+fIljIyMWOnFysYH6Cdsli5dmjHKKPrBJdOwLjx8+BA7duzgpBwXwxGqINYsvtDn2fNDIXD7+PiwKhicnJxUHGZGjRqFKlWqMH9v3bqVk3IOEO6MM2LECDg4OGDx4sWMUWTRokVwcHDAqFGjWOn1NQyyORCUKlWKs4JG6PgTmlXG399f4/owfPhwTs4QNWvWxP79+1l/xwX6jD+A/35HE69UGHSF4Ny5c5g9ezZ2797N6fdubm5qzgT6QKiCWuianR/68H1tpYtycnIwceJEvdsG9ON9fLNHurq6qhjBX79+jcDAQNStWxdfv37lpJyVyWQaHdbv3LnD2XlTDOOAhYWF1n5YWVlppBErq4rQsSdW0AkfODg4MMEaygbVQ4cOwcXFhdc9hazbQsGVbylkUm3H3Llz9TJMfPz4EatXr0adOnVgYGCAkiVLcipFmR98jBtCnJf58t2CWne4okSJEkxgEZA3b2Qymd6GNCGygp2dHfPeLS0tGceSo0ePwt/fn1P7V65cwdChQ+Hi4gJjY2M0adIE27Zt45wFlk//Hz9+jKCgIEilUhgZGWHQoEHIyspC+/btIZPJ0KpVK43lWZVx8OBBjBw5EsHBwTAxMUG5cuUwcOBA7Ny5k5eBQx9njuDgYGYvnJubi+nTp8Pc3JzZu3E1KvLVESmy5rEdbAgJCUGnTp1UMq7/+PEDHTt2RNWqVVnphQQsdevWTePeJDY2Ft26dQMAjBs3DuXLl9d6Dw8PD8aRxdzcnNmjz58/H23atNFIoyjd+fr1a9y5cwcNGzaEu7s743jD9du9ffsWz549A5C3z5o6dSoaNWqEwYMHcx5/Qh149eW7YhrD+ep4xHr/+cF13c3JyUHLli0hkUjg5eWFJk2aoEmTJvD09IRUKmXWnDdv3iAhIUHnvd69e4dly5ahevXqkEqlKF26NCZPnqyz0oE2cLFNKD+D8pzdtGkT/vjjDyxYsIA1Cx0AXLt2DYUKFUJYWBhkMhlatGgBb29vODo6ssq5JiYmGiv4Xbp0SWdlIrG/u74OoMoQ4jgNCHfmEMI75HK5xuyt+ma7FqqfV+D06dPo06cPHBwcYGFhofE3upJSKCen4Aq+62bFihU17onHjx+vl9wlRqAuHxSEnK8MXU6UYgQc8dUTaAJf59v09HSEhISolbHWNQa1yRp82vf09GQCLM6ePQtTU1MsW7YMjRo14hy0kx9szq8SiQTr1q1jlTm1QSxZlQ/P8ff3V3HM3rJlC8zMzLBy5UoA3MZdnz59YGNjw9xLOXCBDRKJBC9fvlQ5Z2ZmppecKZVKVcZP/kA/rmsf32zPdnZ2GoNNjh07Bnt7e073EFJR0dbWVq+s7JogVEcn1C4iVE4WalcRCjFtklwhxtwVI+tqyZIlMWDAANHWZn1tE8q4f/8+Jk2aBB8fHxgYGPCuIr13717Oc7dfv36wtLRE+fLl0aNHD0ZHrDjYwNd5+PPnz6hSpQoTpD5gwAAMGDAAdevWhVQqRcOGDZGTk4O7d+8yNl823L9/H2PGjGHmYcOGDXX+3tLSUmNCiwsXLmjVy+eH0CBlobzjN/7b+O18+xv/KfAtvS6GQRgQh+lv2rQJRkZGCA8Ph0wmQ3h4ODw9PWFlZcWr3AAfw1p+3Llzh5OCKSIiAn/++afa+T///BNNmjTh1ba+DgF8nfjOnDkDIyMjNG/eHGfPnsX79+/x/v17nDlzBhERETAxMcHNmzcRHR2t08CZk5ODgwcPomPHjrC0tISNjQ26d++uVYmiDVwz2Qnd7Io19v9tELJZ1odeoVDVhU2bNrH+RqgTlzL4ZkH8VTA2NtboxJKens7ZmYYvFMokTcqqhIQE9O3bF56enqz3EeIEKETYNDExUTH85OTkQCaTqWQYYYOQEhliOELlB98168uXL4wzh+Jgg9DyIIq5Vr9+fchkMhQvXhxjxozBzZs3ddIZGxurtF2lShVMmjSJ+fvBgwcwNzfXqy98nXEKFy6sURG7c+dOFClShJVeX8OgsbExOnbsqNWBoGfPnpzXHKHjT2hWGWNjY41ZTG7fvs2p/YSEBJQuXRpr1qzBpUuX9M4Mw3f8CdnvSCQSHD9+XKWfZmZm2Lt3r159F1oWLD4+Hi1atOCtYBOqoBa6ZouhZLxz5w4OHDjAZBPUJ7L8V5RGMjU1VTNifPz4EcHBwQgNDcX9+/dZ537x4sVVnOAUWLJkCecy9GIYBywtLbU6JWjj3WJkVQHE3S8C+juvCskk1rVrVzRt2hTfv39nnCcfPXqEcuXKYcCAAZzuIXTsCuk/X74ltkOCMtLS0uDv78+JviBLynEBX74rdN3Jn3VL26ENMplMbdwZGxvrnc1EiKxgbW3N8E8PDw/GsH337l2djlCakJubi2PHjqFbt26wsbGBlZUVp0y+fPrfqlUr+Pv7488//0TNmjUhlUpRoUIF9O3bl/P7k0gkKFSoEKZPn66SRYov9HGmUM7WrcCGDRtgZmaGPXv26KUnEUtHxAeKfV1+pKWlcRo/QgKWLC0tte7VFVlfb968qVPukcvljPOek5MTYwi7d++e1syxhQoVUnFey83NRa9eveDq6op79+79VB2XGA68+kAMY7hQiPX++a67c+bMga2tLfbs2aN2bdeuXbC1tcXMmTPh4+PDuawrkFdKeMaMGShVqhRr5m2+tgkxkZGRgUmTJiEyMhL169fH6NGjOemJSpYsiQsXLqidv3DhAooXL66VriDnHRcH0PwQ4jgttjOHPrCxsdGYROH06dO8KgsJxdWrVzFkyBA4OztrzR4rVlIKZfBZN3fv3g1DQ0N06NCBSRLTvn17GBoa4q+//uLcthiZJPnoJxXgK+fnby//cerUKa1zUKyAI7GwYcMGXjy0cuXKqFatGvbt24erV68iOTlZ5dAETfYAuVyO5cuXc3JeVYapqSmzZ4mOjkb79u0B5AUTaJN38jtL5T/atWvH6nzLdrDRF5SsygZNjq7Hjh2Dubk5lixZwnncff36FRs3bkTt2rUhl8sRGRmJAwcOsM4dqVSKu3fvqswTCwsLpKSkcJ67+Z2388saXJ+hS5cueju+AnljTlNiguvXr3NKzpK/oqJEItGromJ0dLTg4AAxdHSA8CBlvnIyH7tK586dWQ8ulQ4AYXoGvvoxMeaukKyrCsjlckFZ0oXaJvIjOzsbe/bs4aSjy8/rBw4ciFatWsHc3Bx9+/bl1J6iAqymQ1/nX32ch8eNGwdXV1eNerjk5GS4urqif//+cHZ2xoIFC7Te5+vXr1i/fj1q1qwJIyMjSKVSzJkzh9OeydzcHFevXlU7f+XKFc6ygtAgZQX48o7f+G/jt/Ptb/ynwLf0uhgGYUAcpu/r64uFCxcy97t37x5yc3PRvXt3jBs3jpW+IAxr69atQ+HChVl/Z2ZmplW5zjVCW4hRVYgTX/369dGjRw+t13v06AF7e3vY2dlxjhL/8uULtm7dypSD1xdcMtkJ3eyKNfaBvAwmQrIQCaEXulnmS+/j46PTEKhwpmeDUCeu/NA3CyIg7P0LofXx8WGyDue/Z5kyZTjfJy0tDfv37+cc3Q1ARZGUX7kkk8ng6emp0WiTH0KcAIUImxKJRK38mb4R+kJKZIjhCCVkzcrKykLfvn3h4OCgltmAy7gX8uytWrViskz37dsXZ8+e5Uzr6urKZBf99u0bTE1NceTIEeZ6amqqIMOKPs44xsbGuH37ttr5W7du8SrNx4by5ctj8eLFWq9fvXqVM88SOv6EZpVxcXHB1q1b1c5v2bIFRYsWZaXXptTmotwWMv6E7Hd0Kea59h3Iy+rQoUMH3mXB/P39YWFhAXNzc5QpU0bvEsxCFdRC12whfP/NmzcIDQ1l3rWC33fu3FlrlYr8+BWlkby8vLB37161858+fUJwcDCnvfLixYshk8nQq1cvrFu3DuvWrUPPnj1hbGyskRdoghjGgfDwcERGRqrImdnZ2WjevDnCwsI00oiRVQUQPvaEOq8KySSWkZHBZEY3MDBA0aJFYWRkhGrVqnE2rgodu0L6z5dvFSlSRGdJdX3WPSBPZtmyZQuaNGkCY2NjuLq6cqryIYZxQ4jzMl++K3Td0ZWFi0s2LqlUqnGvre/cFSIrhISEME4jbdq0QVhYGE6fPo0OHTrAx8dHr34o4/Lly5z3i3z6X7hwYZw7dw5AXgYsiUSCuXPn6tXHuXPnolmzZrCzs0ORIkXQpk0bLFu2TOPelQv0ceZwcHDApUuX1M5v2rQJcrkcS5Ys4aXjEaojUiAlJYWTnqFQoUI4ePCg2vkDBw6gUKFCrPRCApYKFSqkMTg+Li6OaTstLU3nfTw9PZkMyVWqVMHUqVMB5GXpcnBw0EhjYWGhkd/07dsXLi4uSExM5PTu+ZY+FxP68l2xHFmEQKz3z3fd9fX1xapVq7ReX7lyJaRSKcLCwjhlkQWA79+/46+//kLz5s1hYmLCGqTK1zahQPHixTF+/HjevE45Y2V+KOwN2rBz504EBgaqZI1LSkpCUFCQTgdGsb67JnBxANUFfRynAeHOHEJ4R/v27eHj44Pz588jNzcXubm5OHfuHMqUKYOOHTtyvo8QHa3CAaN06dIwMDBAaGgoVq5cqbUM77Vr13i1wxX6rJt///03KleuDLlcDjs7O9SsWVPvQBe+655Q/aRQOV9Bp+3QtWcWM+BIX2RnZyMlJYVxNlZGVlYWUlJSOK8BcrmcNRg9P4Q6rypDudKLv78/k83z7t27WnWMUqkUAQEBWh2oKlSowOo8q6mSH1eIJavy4TnKsoIyTpw4AXNzc4wePVrvcffw4UNMmDABHh4ecHV1xadPn7T+VtOcUT7H5duL5Xw7ZcoU2Nvbo2PHjpg1axbnQNHQ0FBERkaq2DU/f/6MyMhI1KpVi7VdoRUV+/fvD2tra1SrVg39+vXTO/MmII6OLj/0sYtogj5yMh+7StOmTbUejRo1gqmp6U9JTMJXPybG3OWbdVUZzZo1w5YtWzj/Pj+E2iYUOH36NHr37s0EibVr14610mF+Xh8aGopWrVph2bJlKs6gPxNcnYc9PT2xfft2rde3bt0KiUSi1YH80qVL6N27N6ytrVGhQgXMnz8fL1++hKGhIWddZePGjVGtWjWVxGhPnz5F9erV0bRpU073EBqkrAn68I7f+G/jt/Ptb/ynwLf0uhgGYUAcpi+Xy5lIbFtbW8Yx5MaNG3BycmKlF2JYU6RXVxxNmzZFpUqVYGBgwKmknaurK2bNmqV2ftasWXB1deXUByFGVSFOfDY2NhqdcBRISUmBRCLR6FytCS9evMDcuXNRvnx5SCQSVKpUiROdvpnshG52xRr7AFC2bFlIpVIEBwdj0aJFemeuFEIvdLPMl75GjRoICgrSmBFhy5YtMDQ0xIwZM1jvI9SJC+CfBVEBIe9fCO327dthYGCAevXqYeLEiZg4cSLq1asHQ0ND1jKAQF7Wm7Jly6oZ57kqOIE8w7yQ7OBCnACFCJsSiQQ9e/ZUUWzIZDJ06dKFs7JDSIkMMRyhhKxZffr0gbe3N7Zv3w5TU1OsXr0asbGxcHFxUSmvqQ1Cnr1t27a8DGkA0KtXLwQHByMxMRGDBw+GnZ2diuFv/fr1qFChgl735OuMExgYiD/++EPtfL9+/TitW/oaBvv3768zw+Hdu3dRo0YNTvcSOv6EZpWJiYmBtbU1pk2bhsTERCQmJmLq1KmwtrbmpDgUkhlGyPgTst8RK6uN0LJgQkswC1VQC12zhfD99u3bo169enjy5ImKYv/AgQMoXbo0Kz0gjPexZWfQhj/++AMtWrTQeO3jx4+oVKkSpzU7ISEBVapUga2tLWxtbVGlShWdxqL8EMM4kJaWBjs7OxQvXhydOnVCp06dULx4cTg4OGg1PouRVQUQPvbEcLwWWoL59OnTWLRoEaZPn47Dhw/r1bYYZb349p8v32rUqBHGjh2r9XpycjIkEgnrfQ4cOIAOHTrA0tIStra26NGjBxPIwwViGDeEOC/z5bsFkU1NH0gkEjRo0EBFR2JoaIi6deuqnGODEFnhwIED2LFjB4A8I5qXlxckEgns7e2Z0rZc8eTJE0yfPh1+fn4wMDBASEgIpyxLfPovlUpVgg7MzMxw69YtvfqrjNTUVPz5559o1qwZjIyM4OzsrPP3Qp056tSpg5kzZ2q8tnHjRiY7iz7gqyPSBK68448//oCLiws2b96Mx48f4/Hjx9i0aRNcXFw4ZR0XErAUGxsLU1NT9O/fH/Hx8YiPj0f//v0hl8uZqh9z5sxB7dq1td5j+PDhjI5v8+bNMDQ0RIkSJSCTybTKOhUrVtRavrZv376wtrbm9O34lj5XhlAHXn35rlD9YLly5Zj9gb+/v1qAG5dgN7HeP99118TEhMk8qAkPHz6EVCrl5HirKYvRkSNHWLPp8bVNKDBnzhzG4apChQqYN28eXrx4wYkWyMuYril4YN68eRoTg1hbW8PGxoY5ZDIZpFIpZDKZyv91ychifXcF9HUA1QZ9HacB4c4cQnjH+/fv0bhxYyYhgOL9N23aVK9n56ujVchk/v7+mDlzJp4+fcpKI5FIEBgYiOXLl+Pjx4+c+8gFYq6bXMF33ROqnxQq51taWmL69Ok4ceKExmPFihVa56AYAUd89QRr1qxB+fLlNeq2fvz4gfLlyyM+Pp7l6fNQoUKFX1rto23btggICEDXrl0hl8vx5s0bAHlZ17UFzHl6eup8PjbnV6lUKsj5VixZlQ/PadKkidZEU8ePH4eZmZnee+3Hjx8jJiYG7u7ucHZ21ul8q22u5D90IX+gZv4gTa7Ot3wDRa9du4YiRYrAzs4OoaGhCA0NhZ2dHZydnXH9+nXWdoVWVBQj86YYOjqAv11EAb5yslC7ijJ27tyJ0qVLw9ramgk4ZIPQxCR89GNiz119sq4qY+XKlXB1dcX48eOxfft2vRIyAcJtEyNGjICbmxtkMhkaNmyIjRs3/pKqCUKhr/Nw/oqe+fH48WOd39/AwAADBw5U0w3p43z7+PFj+Pv7w8jICB4eHvDw8ICRkRHKlSvHWccpNEhZAb684zf+2/jtfPsb/1noU3pdLIOwGEzf2dmZcYrw9fXFxo0bAQBnz57VWlZNGUIMawoDsuLo0qULhg8frnER0oQ1a9bAwMAA4eHhiI2NRWxsLMLDw2FoaIg1a9ZwuocQo6oQJz4TExOdhruHDx+yRth/+PABq1evRu3atWFoaAhPT0/ExMRwLp3BJ5Od0M2uWGNfgevXr2PkyJFwd3eHkZERGjRogA0bNnDeePKlF7pZ5kv/6dMnlC9fHnXq1FFRpm7duhUymQzTpk3j1L5QJy4hWRCVIeT7CaG9dOkSoqKiEBAQgICAAERFRWks6awJ4eHhaNKkCV6/fg1zc3PcuHEDp06dQmBgIBITEzndQyjEcEIF9Bc2q1evrlPRoY+yg2+JDKGOUELWrKJFizIZKSwsLJhI33Xr1qF+/fqc+/Czy4O8fv0aVatWhUQigYWFhdr6FBoaypR8Y4NQZ5wTJ07AzMwM3t7e6NKlC7p06QJvb2+Ym5tzmj9CDYNCIWT8Cc0qk5ubizlz5sDZ2Zlx+nd2dsa8efM4lwb8FRBjvyMWhJYF4wuhCmqha7Yy9OX7jo6OjOOFslHu3r17nKtMKMCH9+XPytCwYUMUK1YMVlZWOh3Q3r17p/Pdfvz48aeU3xarLNezZ88wcuRINGjQAM2bN0dMTAzevn2r9fdiZFUBhI89MZxXlaFPJrG4uDiN7Xz79k1jZkRdEGvd1jcTGqA/30pMTNSpuM7MzOQ09k1NTREZGcnL+Ss/+Bo3AP7Oy0L5rph48uQJ52DL/PoRbQcbxJIVFHj79q1ee42lS5eiWrVqMDAwgI+PD6ZMmaKX0zKf/uc3RltYWPDKwpebm4vLly9j9uzZCA8PZ7Jn+/v766QT6syRkJCAgQMHar2+YcMGTgFjQnVE2pCcnMxp3fj27Rv69+/POHBJpVIYGxtj4MCBnHi/0ICl9evXIygoiHHqCwoKwoYNG1TupU8VorNnz2L27NnYvXu31t9MmTJFpyzYu3dvTo4kAL/S58oQw4FXH74rVD84YcIERn/DN9hNzPcP6L/u2tjYaCyDqkBqaiqsra1Z2y1SpAhMTEzQtGlTQXslfWwT+XH79m2MGzcOJUuWhKGhIerUqcNpz7RixQo4ODioBOLPmjULlpaWGmX8tWvXcj60QczvzscBND/4Ok4Dwp05AOG8Iz09Hbt378bu3bs1ZrTjAj462lGjRukdFJ+YmIjOnTvDwsICZmZm6NChgyBdrNB189KlS0zAB1e9sjL4rntC9ZNC5fwaNWpg+vTpWq/rcqIUI+CIr54gJCQEmzZt0np9y5YtnMs/Hz16FMHBwTh+/DjevHmjEuT6M7LYv3//Hn379kXjxo1V5L9x48YxQUf50bZtW537TTbnV6GZb8WSVQH9ec6JEycwZcoUrfc7duwYJznr69ev2LhxI2rXrg0TExO0aNECe/fuFZTJkivyB2rmD9Js0KBBgWdAzMrKwvLlyzF48GAMHjwYK1as0Bh8qK3/QisqCoVQHZ1Qu4hQOVmoXQXIc34MCQmBXC5HdHS0XoHyYuoZuOrHxJq7yuCadVUZYmQsB/jbJipXrswrAZkyXr16xeh2+fDypKQkDBs2DK1atVJLrMcGvs7D2gJ2FLh48aLOSgF169aFhYUF2rZti/379zN7c32cb4E8PdGhQ4ewYMECLFiwQO/kDkKDlIXyjt/4b+O38+1v/KfBtfS6mAZhoUy/TZs2mD17NgBg4sSJcHBwQLdu3VCsWDFOi6YyhBjW+OL8+fNo27Ytkw2hbdu2TJk4fcDXqMrXic/X1xerV6/Wen3VqlXw9fXVeQ8TExMULlwYAwcO1MvxUwE+meyEbnYL0hni9OnT6NOnDxM19TPohTry8KH/559/UKpUKbRo0QK5ubnYtm0bjIyMNGZh1gahTlxCsiBqg5DvJ/Tb6wM7OzvGuGJpaclEzR09epTVKKuMzMxM7N27F0uWLOFc3kcZQp1QFeAjbIqNX1UiQ981y8zMjMlq4+zsjAsXLjD30dcJTQGhz56UlMRZyZORkaFxzr59+5ZzCUwxnHGePXuGUaNGISIiAhERERg9erRKBn8u4GsY/JUQK6sMkLd28Mnysm7dOlSuXBmFCxdmBPS5c+fy4h0At/Enxn5HGWXKlBFcyh7gVxZMiGFNiIJabMdrffi+ubk50tPTmf8rlOJJSUmwtbXVu20FhPC+nJwc9OjRQ6ex7/91iJFVBRBv7InhvKpvJjFtWXnevHkjaL/Bd+zyyYSWH0LLGeoDsTOJibHf1Nd5WQjfVYbQdcfCwuKnGhQVEEtW4AMXFxcMGzZMr2zF+aFv/yUSiUo2RYlEAisrK5XsimyVBsLDw2FjYwMDAwMEBARg8ODB2LVrF96/f8/aXzGdOYRAqI5IG7g63yqQlZWF1NRUpKam6pWR59/kOP+roU/pc2UIdcJTBhvfLQhj+L8JXNbdBg0aoFevXlqv9+zZk5Mj3PLlyznxGi7gapvQhXPnzum1Zk+fPh3Ozs548OABpk2bBktLS5w+fZpX2z8bfBxAlSHUcVosZw4F+PIOMfEzdLSZmZlYvXo1qlWrBolEgpIlS2LatGl6B2fzXTdfvXqFmjVrQiKRqOw9QkND1ZzLdIHvuidUPylUzl++fLlO/fXLly+1Bk6IFXCUH1z0BA4ODjqD+O7fv6/TiUcZ+Svh6RvoqsDPlBVevHghyFmnU6dOosuJYuBn2YV69+4NGxsblC1bFvPmzRPkBAfk7SG4Zn0FxAvUVIYiOcTPgBgVFRV48uSJXlV1xIJQu4gYcjJfu0paWhqTuKxLly68358YegYx9GN8oG/W1YLGz9LxffjwAe3atYOBgQGzdhkaGiIqKoqzTWrTpk0wMjJCeHg4ZDIZwsPD4enpCSsrK058h6/zcMuWLREREaH1ekREBCIjI3Xe4/Hjx5gwYQLc3Nzg6OiI/v37w9DQUGPlgYKC0CBlMXjHb/x38dv59jf+cxBaev1X4+3bt8zmLCcnB1OnTkWjRo0wePBgXuVB+RjWkpKSmEgpXVEsPws/wxFszpw5sLW1xd69e9Wu/f3337Czs2OcorXh0KFDPyWq8n8FV69exZAhQ+Ds7Mwri55QeqGbZX3oHz9+DFdXV9SqVQsymQyxsbG82gT4O3GJDSHvn41WOeI8fyS6vpHp1tbWTBYlDw8PHDt2DEBe+XpTU1NO/b1y5QqcnJxgaWkJAwMDODg4QCKRwMzMTGd5HzEhlrB5+vRpXllZ/i0lMvRZs3x9fRmjZa1atTBkyBAAwPz581lL0SpDzGcvVaoUZ56jGKuasHDhQk73+DfwivzgYhi8cOEC5s2bhxEjRmDEiBGYN28eY5z42RAjqwwfLF68GPb29pg0aRJMTU0Z5f6aNWt4GTYAbuNPjP2OMoRkReBbFkwsw5oYELJm8+H79evXx5gxYwD8X0m7nJwcREZGonnz5nq1Lybvu3XrFpycnHT+5vXr15g+fTqaNm2KoKAgBAUFoWnTppgxY4bW72ZjY8MoAvOXxNXHgUtsvH//HrNmzULXrl3RtWtXzJkzR2+nfaEQa7+or5zFN5OYpqwqQJ4Dmb7fT8jYFZIJDRBezlCBhw8fIi0t7afLjmLtN3+VcQYQno1HKP3GjRuRmZnJm15ffPnyBTNmzED9+vVRvnx5zqXflfErMvILzaIIAEOHDsWePXt48VcxnDk2b96Mtm3bokWLFrzXR746IjYZ+dSpUz/NiUuo4/y3b9/w5MkTPHr0SOXggri4OJ2HNjx48ADLly/HwoULce3aNc591QaxSp8LccL72Xw3NzcXSUlJ2LZtG7Zv347Lly8L4iV81z19190zZ87AyMgIkZGRuHDhAj58+ICMjAycO3cOLVq0gJGR0U9xQhXLNnHhwgUMGDAATk5OkMvlaNWqFWfa6Oho2NnZwdraGufOneNMd/fuXYwePRqtW7dmAqf27dvH6nAv9rzjCzEdp4WCC+8YNGgQs6/IX26bb/nt/BCqX9+5c6dewdV37tzBqFGjULRoURgZGaFRo0acafmumy1btkSFChVUHDfS0tJQoUIFtG7dWq978Vn3hOonxZTz9cXJkycFV9bQBjY9gVwu15mtPCUlBXK5nFNbQgNdFdBHVkhJSWHGa0pKis5DE06ePIkfP35w7lt+fP/+Xc0GoHC0HjZsGE6dOsXrvkJlVS48JycnB9OmTUPlypVRoUIFDB8+XO/ATIlEgmLFiqFp06ZqWR/1yQCpwM/O+qqMuLg4lClTBsbGxjA2Noavry/WrVun9rtdu3Yx8zV/ZnZ9M7ULraiYk5ODmJgYWFpaMs5rVlZWmDhx4k/TcwjVif0KOfnx48fo1KkTDA0N0bRp05/qcJgffPRjYsxdvllXCwJi6fgUeP78Oaus27JlS5QsWRIHDhxg5PsDBw7Ay8uL817f19eXsR8qeFdubi66d++utRKKGEhLS4O5uTkqVaqELVu2ICUlBcnJydi0aRMCAwNhbm6uV5Du4cOH0aZNG5iYmKBkyZIYOXIkLl++rPa7+fPnM1UJ8ifO4pNISwG+Qcr/5qqXv/Hr8dv59jf+U+Bbev3s2bPYs2ePyrm4uDi4ubnBwcEB3bt31+rMVFBMXyj4GNaePHmCkJAQNWeGKlWqcI68ys7Oxvbt2xEbG4vY2FgkJCTwysbJ1agqlhNfTk4OWrRoAYlEglKlSqFZs2Zo2rQpvLy8IJVK0axZs1/mWKsrk92PHz8ECdlCxr4mKDJXli5dGgYGBggNDcXKlSs5G8yE0gvdLOtLr6xE2bJlC4yNjdGyZUtOCpafAX2ycALC3r8+tMrZzzRFpesTmR4SEoK//voLQF7m8LCwMJw+fRodOnSAj48Pp+euXr06unfvjpycHEZYefz4MapVq4YdO3ZwugdfiC1s6huhr2+JjIJyhOKzZs2ZM4dZWw8fPgwTExMYGxtDKpVi3rx5rG0WRHmQZ8+ecb6HtbW1xgCXefPmFWhmAKHKYW3gYhh89eoVs88oVqwYAgMDERgYiGLFikEikSAkJERnmZ1f7YhXrlw5JhDK399fzQFGH2cYb29vhncpK3ivXbsGOzs7Xv3jMv7E3u/wUU4LLQvGx7AmpoJaKITw/WvXrqFQoUIICwuDTCZDixYt4O3tDUdHR86lOAuC9+3du1enE9PFixdhY2MDZ2dndOzYEdHR0YiOjkbHjh3h4uICW1tbjZmN1q5dy+xD16xZw8uBq1mzZowcoMsgw9Uoo8g+5OzszNC5uLjAzs5Oo4JQGRkZGdi2bRtmzpyJWbNmYceOHT+lBGZ+8HVe5ZNJTMErpVIpfH19Vfhk2bJlYWFhwZoVQQGhY1dIJjS+fGvVqlVqAQ3du3dn9rve3t6CsrjWqlWLU6CYWPtNfYwzBcV3f7Xz7c/OnNu2bVvY29ujV69eGD9+POfS7/81KJd/5gKhzhyLFy+GRCKBp6cn4yg5dOhQvfogBNpkZC6ystjrHl+kp6cjJCREUAY6a2trlcPMzAwSiQTGxsZa9/vHjh2DXC5nsggZGRkhPj5e7/4LLX2eH3wdePXhu0L1g8pturu7M99L8c2KFy/OuvaJte4JkRcSEhJgb2+vNvbs7Oywfft2TvfQhkWLFrFWx+Jrm1Agf1WZunXrIi4uDp8+fdJKo80OUbRoUURFRXG2TZw4cQKmpqaoXbs2ZDIZs95NnTpVpwOgWPNOF/R1AP1V0Jd31KhRg3EWZnOC0gd89Ltfv37VGGCk0BXog8zMTCxbtgy2trY/JVjE0tISFy9eVDt/4cIFWFlZFXj7QvWTYsj5fKGtQokYYNMT+Pn56ZRDFy1aBD8/vwLomXboIytIJBI1+0b+bNm69j1C332nTp3Qo0cP5u+PHz+iaNGicHBwQNmyZWFoaKgx8F4BMWVVfXnOxIkTIZVKUbduXTRp0gQmJiZ6V+Pp2LGjqJlnxXK+1dd5efbs2ZDL5YiOjmbk42HDhkEul2POnDkqv80/5sTM1K4vRowYAQcHByxevJixIyxatAgODg4YNWqUVrp/i6zCF0LtKqampmrf+1fopvnqx8SYu3yzrorpiyPUNqENXBKjyOVyjXJZYmIi54ATuVzOBBvb2toiNTUVAHDjxg3W5Bi6wMV5+Ny5cyhdurSKzkIikcDb2xtnzpzRSZudna3Refvdu3dYsGCB1uQQbm5uePPmDfN/bcfPSqT1G7+hCxIAoN/4jf8IoqKiKCoqiurVq0cGBgac6erXr081atSg4cOHExHRtWvXKCAggDp16kTe3t40c+ZM6tmzJ02YMEGN1t3dnS5dukR2dnbk7u6utQ2JREL3799n7UuHDh2oZs2aVK1aNSpevDjnZ1Bg5MiRtHnzZnr+/DnVqVOHoqKiqEmTJiSXy1lpw8LCKCMjg+Li4sjLy4uIiG7fvk2dO3cmS0tLOnDggE76u3fvUsOGDenp06cq9EWLFqW9e/dyep5ly5bRxo0b6cyZM1SqVCmKioqitm3bUrFixTT+3sDAgF68eEGFChUiqVRKEolE7TcASCKRUE5ODmv7W7ZsoU2bNlF6ejoREZUsWZLatGlDrVu3ZqXVhlGjRtHLly9p9erVvOi9vb0pPT1dY/87d+5MMpmMli1bRkREnz59Ih8fH/r69SsVLlyYbty4Qbt27aIGDRpovLeQsZ8fQUFBlJSURGXLlqWoqChq06YNOTs7c35OIfQHDx6kjRs30s6dO8nQ0JBatGhBUVFRVK1atQKlV4w5xRhTLKn5/6/p2wUEBNDRo0fJxsaGypUrp3HsKnDlyhVOz5EfusZOfgh5//rSnjx5kqpUqUKGhoZ08uRJnfeuXr26zusHDx6krKwsioiIoLt371J4eDilp6eTnZ0dbdmyhUJDQ1n7b21tTRcuXCAvLy+ytramc+fOkbe3N124cIE6duxIt27dUqOxtbWl9PR0sre3JxsbG53f7927d1qvValShaKioqhly5Zkb2/P2lc2WFhYUEpKCnl4eHD6fdGiRalNmzYUFRVFfn5+rL+Pi4uj1q1bk7GxMa1du1bnc3fs2JH1fnzWrPv375O7u7ta248ePaLLly9TiRIlqGzZsqxt6/vsYmPlypU0atQoSkxMpFKlShER0ezZs2nixIn0999/U9WqVXnfu3bt2nT//n2N+w6pVEovX75UWTc1iQNc1s309HTasGEDbdq0iR48eEChoaEUFRVFERERZG5urvb7Fi1a0PPnz2nNmjXMPkGB27dvU5cuXahIkSK0bds2je0JHX+DBw+m2NhYMjMzo8GDB+t8tjlz5qidi4mJoWHDhpFcLqcJEybobH/8+PE6729qakq3bt2iYsWKqczbO3fuUNmyZenLly866YVCrP1OgwYNaNWqVVS4cGHONHK5nMLDwykqKooaNGhARkZGerVpZWVFR44coYoVK6qcv3jxItWtW5cyMjLUaPKPe234GWu2UL7/4cMHWrhwIaWkpFBmZiYFBARQ3759OX8DIbwv/7wBQC9evKC9e/dSx44daeHChRrpgoKCyM/Pj5YuXar27gBQr169KDU1lc6dO6dXf7igc+fOtGDBArKwsKDOnTvr/O2aNWtY71e1alUqUaIErVixggwNDYmIKDs7m7p160b379+nxMREjXTr16+nfv360cePH1XOW1lZ0dKlS6lVq1Ya6cQce/rKWfmxYsUKioyMJGtra06/J8rjm4p/hwwZorI2yGQycnNzo+bNm5NMJmO9l9B1m0//FeDLt4KCgqhnz57M2Dtw4AA1atSI1q5dS97e3tSvXz8qXbo0rVy5Uus9zp8/T3v27KHv379TrVq1KCwsjLm2aNEievPmDeuaI8Z+09nZmd69e0dhYWEUFRVFjRo1ImNjY62/F8p3tYHPuqOMqVOnUu/evXmNAyLue22xZAUrKyvat28fValShVd/dUGXrChW/588eUISiYRcXFyIKG+t3rhxI5UuXZp69Oihs3+5ubk0efJkWrp0Kb169YrS09PJw8ODxo4dS25ubtS1a1ettP7+/tSrVy/q1auXxuuLFy+m5cuXU3JyssbrPj4+1LJlS2ZurV+/nnr27ElZWVk6+8wVbDoiNhlZAU2ysvK616lTJ53fTtO6t3v3bqpfvz4ZGRnR7t27dbbfuHFjrdcU8v6IESOocOHCav3gK3/duXOHevfuTcOGDaN69eqpXQ8JCSF7e3tasmQJmZiY0JgxY+ivv/6i58+f69WOqakp2djYUKtWrSgqKooqVKigd18/fvxIO3bsoI0bN9KJEyfIw8OD0Vtz0Y/qy3eF6geJ8vS6fn5+VKlSJRowYACVKlWKANCNGzdowYIFdOnSJUpNTdXKA8VY94iEywufP3+mgwcP0p07d4goT9apV68eJ904ALp79y59//6dvLy8mP0eEVGtWrXowYMHOvX7fG0TCkilUqpYsSK1bduWWrduTY6Ojqw0uuwRymCzTQQHB1NkZCQNHjxYZb27ePEiRURE0NOnTzXSiTXvdKFUqVJ0584dvfYMyli8eDG9efOGxo0bp3ZtwYIF1KNHDzIxMaEFCxbovE///v11XheDdwiFvjra169fU4cOHejIkSOUm5tLFStWpPXr11OJEiX0bjsxMZFWr15NO3bsIKlUSi1btqSuXbtSUFCQkEdiXTctLCzo1KlT5O/vr3L+6tWrVL16dTUZTBlC1j2x9JNEwuX8N2/e0OrVq+ncuXP08uVLIiJycnKiypUrU6dOncjBwUEjnfKenS/46glmzJhBM2bMoGPHjqm9p5SUFKpVqxZFR0dTdHS0RvrU1FQqU6YMSaVSSk1N1dlHrt+hd+/eFBsby0lmevToEbm6upJEIqFHjx7p/K0mmVvou/f09KSFCxdS3bp1iShPNpwyZQrduHGDrKysaPjw4XTx4kU6fvy4Rnqx1mw+NqWSJUvS0KFDqWfPnkREdOTIEWrYsCF9+fJFp+xYkChTpgzt37+fihYtyun3q1evpoyMDJXx36NHD1q1ahUREXl5edHBgwdZ7+fu7k4xMTHUoUMHlfNxcXE0YcIEevDggZ5P8nNQpEgRWrp0qRpf3LVrF/Xp04eePXumkU5sHZ0m6LKLcIEuOVmoXYXL+NalIxFLTuerH/uVc1dMXxyhsoY2JCUl0efPn3XatF1dXWnv3r3k6+urcj41NZUaNGigdb+tDBcXF9q/fz/5+vpS2bJlaeTIkdSmTRs6d+4chYWF0YcPH3j1Xx9/guTkZBW7Urly5VhpYmNjacKECVS7dm0yNTWlgwcPUps2bVT2d1euXKGAgABe/deFiIgIWrt2LVlaWlJERITO3yYkJPBqQ5/39xv/YfwSl9/f+I2fAH0yYzg5OalkWxo1ahSqVKnC/L1161Z4e3uL2j9t6Nq1K0qWLAmJRAIXFxdERUVhxYoVSE9P50TPN2oIAExMTHDlyhW185cuXeJUvr1+/foICwvD27dvmXNv3rxBWFgYGjRowKkPLi4uGDZsGJKTkzn9/sSJE0x5FrHKy/DBhw8fcOjQIfz9999qJVU7dOigs0QHG3RlsitZsiQOHjzI/L1w4UIUKVKEiSqNjo7WGR0v5tgfNWoU0tLSOP1WbHpTU1NERkZi586dvEol8aV/+PAhp0MTJkyYwGSc0pTFSIyMRvpk4RTy/oXQPnr0SGPWltzcXJ1RdqtWrdIakfn27Vu9Sj/Y29szPFZR8gMAbt68qTXaUIxsfAUBfaO0f3WJDD5rVv7MAC1btsTLly/1blvIs2vLsP7x40d8+/aN832mT58OZ2dnPHjwANOmTYOlpaUoZTAXLlyolXc8fPiQeXY+vEsZEokEgYGBmDdvHqdvYG5urnGfocClS5dgbm7Oeh++KKisMnzg7e2NnTt3AlCdtwsWLNCaOVesbFa/GkLLgpmbm+Pq1atq569cuVJgWaN/xprNBdrWTMU1LhDC+/LPk9DQULRq1QrLli3TWS7RxMREZ5ndmzdvspZA1ZYV5s2bNz+t9Dag/VnS0tK0ykuXL1+GoaEhOnbsiOTkZHz9+hVfvnzB5cuX0b59exgZGWmVfcQce/rKWdpw584dHDhwgCktx2VMrV27Vu/Mlfkh1p6FT//58i3lDBQA0KtXL5XMccePH4ebm5tW+m3btkEqlcLMzAzW1taQSqWYOXMmr74Ixb+pjPOvBNe9tliygre3t6BKKps3b0bbtm3RokULtcxif/31l9b2xep/SEgIUzL1xYsXsLCwQHBwMOzt7VmzR8bExMDDwwPr16+Hqakp8943b96MoKAgnbTTp0+HnZ2dxneXnJwMOzs7TJ8+XSu9iYkJk0kGyKscIJPJ8Pz5c53tcoVQHVFBQqyMWnK5XOfaLwRJSUnw8vLSeM3KykpFN5GVlQUDAwMmUw5X8C19rgwTExMULlwYAwcO1Jjhnw368l2h+kEA6Nu3L0JDQzVey83NRWhoKPr166eVXui6p4BQeYEv7t+/jzJlyjBZnFxdXXl9OwX47H246v8LAmZmZrh//z4A1fXuwYMHMDY21kon1rwrSISGhmrNhiVmNi0hvKNz584ax35mZqZeWeX01dF27twZTk5OmDJlCubMmQMvLy+9dCLPnj3D5MmTGXtWlSpVsHr1ao1ZdHVBiG2lcePGqFatGp49e8ace/r0KapXr46mTZvqbFfIuieWflKonM+30gyQ98z537e+4Ksn+P79O2rUqAFDQ0OEhYVh4MCBGDhwIMLCwmBoaIjq1avrtNOwZZ7lumeJi4vTaGP49u1bgWbcFvru5XI5w7OBvCyif/zxB/N3WloaHBwctNKLtWbzsQvJZDK1rLrGxsacq68q49ixY1qvKcqyFwQqVaqE1atXM3/v378fhoaGWL9+PS5fvozg4GB07dqV9T7Gxsa4c+eO2vn09HSda69Y4zYiIgLTpk1TOz99+nS0aNFCK52xsTFu376tdv7WrVus+r2Chi67iAJ85WQx7Sp8ILZNUl/9mFhzd926dahcuTIKFy7MvKu5c+cytpKCxq+SNQBg2bJlqF27Nl68eMGce/HiBerWrYulS5dyukebNm2YzOUTJ06Eg4MDunXrhmLFiunMGp2Tk6Mx86wCFy9e5OVLk52djatXrzIVI7WhRIkSKs94+PBhyGQyvfbOMTExGit5ff78WaeOqVOnTsx3Z8uczhe6eMdv/L+D3863v/GfQk5ODiZOnIgiRYrAwMCAURKNGTMGK1eu1EpnbGyssmGoUqUKJk2axPz94MEDTs4gfJm+Jjx9+hQbN25Ez549mVT1zs7Oet1DX5QsWRIXLlxQO3/hwgUUL16clV4ul6sIbAokJyfDzMyMUx+EGFX5OvEB2h2p8h+acPXqVRQuXJgR8C0tLRnnvYKGUCFbrLGvjG/fvuHWrVs6lSti0wvdLP/Kzfa/DUK+Hx9avs40+ekKFy6sYiDVB3Xq1MGGDRsAAN26dUNgYCDWr1+PevXqITAwkNc99YGYwuaGDRtUFNzZ2dmsNImJiYiKikJQUBCePn3K9InNke9XOUIpK1cBYWWh+D47WylYV1dXjBs3jpPgGB0dDTs7O1hbW+PcuXO8noMvTp48qXG+/vjxg1OpHX0Ng3Z2djoF+OPHj8POzo7TvX61I567u7tGY+L79+85GeVWrFgBZ2dnbN68GWZmZti0aRMmTZrE/F8ThJaUU8bly5dV9mw7d+5EkyZNMHLkSK0O5Ldv31bbJx45cgQ1atRAxYoVMXnyZE5tA8Ddu3cxevRotG7dmvmO+/btw/Xr11lphRjWgF9nWFEGX74v1rjny/v4ws3NTee7jYuLQ7FixXTeIz/vV+DZs2ecFfsbN27Ueo1rOfFChQqpOLYocODAARQqVEgjTadOnXQaLZo3b653mTY+EOq8+ubNG4SGhjJroGLt7dy5MwYPHixGF1khZOwK7T8fvmVqaqpidClbtqxKCbxHjx7pHL8BAQHo2bMns5+bMmWK1lLrbBBrv8nHeZkv3+3du7dKme2NGzeq7HPfv3+P+vXr62w7LS0NvXv3hr+/P5ycnODk5AR/f3/07t2bV/DgqVOnOJdkFAP79u1DWFgYL+Pd4sWLIZFI4OnpCT8/P0ilUs68TixYW1vj1q1bAPJKQ1auXBkAcPDgQdb9UvHixXHkyBEAqvv9mzdvwtraWietGM4c+Z0hxCpFyxdfvnzhpJ9SRs2aNTU6b3748KHAnX8rVKhQYPuKq1evag240rRf4Pvtfvz4gcOHD2Pp0qWMzujZs2cqfEkXxHDgBbjzXaH6QQDw8fHB7t27tV7fvXs3fHx8tF4Xuu4pg8+6y1aClq0UbfPmzVGqVCls3LgRCQkJqFy5MgICAjj1VwG+tgllvH//HitWrMCIESOYBBeXL19m9j7a8P37d3h4eODGjRt69VkBZ2dnplys8rxJSEiAh4eHVjox591/AXx5hzZZ7/Xr1zAwMNC7H1x1tC4uLip2jPT0dBgYGHDa7yjWVScnJ0RHRzNrvr4Qalt5/Pgx/P39YWRkBA8PD3h4eMDIyAjlypXj5czHFWLpJ4XK+ZUqVUKPHj202sV69OihNXBJIpGgQYMGv6z8+/fv3zF9+nT4+flBLpfD1NQUfn5+mD59OmtyA7Gc4Pi+f10l47mUkBf67m1tbVXkmcKFC2P9+vXM3/fu3dOZUEnMNRvQzy4klUo17rWV9zFcYW1tjUuXLqmdnzdvHucA/aysLNy8eRMpKSkqhy6I5bzs4+OjUZ8aGxuLMmXKaKUTSz9ob2+v0aafmpqqVccFAIGBgSr7TAX69euHSpUqcW7/V0AsOVmoXeVXgq9+TIy5u3jxYtjb22PSpEkqQbZr1qz5KQlRFBBimxACf39/mJubw8jICMWLF0fx4sVhZGQEc3NzlCtXTuXQhrdv3zJ2kZycHEydOhWNGjXC4MGDdTrATpw4EVKpFHXr1kWTJk1gYmLCSx89YMAARq7Jzs5GlSpVIJFIYGZmhuPHj2ulE8N5+1fbBH/jN9jw2/n2N/5T4JsZw9XVldkMffv2DaampoySH8jbaHIxconJ9LOysnDw4EGMGDECQUFBkMlk8Pf350TL17C2c+dOBAYGqkTCJiUlISgoCH/99RdruzY2NoySThmnT5/Wy0j4KxzB2BypdEXJ1q1bF5UrV8bZs2dx5coVNGvWDCVKlOD4tP8HPpkUhQrZYo19IM/JvEuXLjAwMFBRMPfr1w9Tp04tcHqhm2U+9Onp6WjdurVGw1dGRgbatGnDSekm1IlLjCycQt6/EFptUd4PHz7UmnVWQSeWA2ZSUhITJf3q1SvUq1cPFhYWCAgI4JQdTgjvKShh8/bt2xg2bBicnJx0/m779u0wNTVFt27dYGxszLT/559/sjoziOEIBei/Zon17YU8e1xcHFxcXDBmzBjs3r0bu3fvxpgxY1C0aFEsW7YMkyZNgrW1tZryTJvxr2jRooiKiuJkENSFhw8fIi0tjbORV4x9iz6GwT59+qBYsWJISEhQ4ZsfPnxAQkIC3NzcdGZSUobQ8Sc0q4y29l++fAkjIyNWegBYv349SpQowWTjcHZ21mmQFSOblQIVKlTA9u3bAeTtFYyNjdGmTRuUKFECAwYM0EjTtGlTjB07lvn7/v37MDU1Rd26ddG/f3+Ym5tj7ty5rG2fOHECpqamqF27NmQyGTP3pk6dqqKo1gahhjWh417omi2E7/NdM5XBh/exRcazYeHChTA2Nkb//v2xa9cunD9/HufPn8euXbvQv39/mJqaYtGiRRppFTxRKpVi8uTJKnxyzpw5aNq0KWcZycrKCvv27VM7P3DgQNb1UoE//vgDLi4u2Lx5Mx4/fozHjx9j06ZNcHFx0Tp3SpYsicOHD2u95+HDh1GyZEnWtoWOPUCY82r79u1Rr149PHnyRGXtPXDgAEqXLq2Tlk3W4gIh67bQ/vPlW6VKlcKOHTsA/J/jhLJh8MKFC3B0dNRKb2ZmppIF59u3bzA0NNTIw3RBjP2mEOdlsYLtLCwsVPZ8L1++1Em/b98+yGQyBAUFYfz48Vi8eDEWL16M8ePHo3LlyjA2Nubk2PH582eVIOuHDx9i7ty5Gh3xuTyHAlzWnX/++Qc1atSAVCqFubk5bGxsVA5dKF26tErGn/j4eM5rhVj9NzMzYwIkGzVqxGRV4mLMNzExYWQD5TmblpbGKbhbiDOHRCJBz549MWjQIOaQyWTo0qWLyrmCRmZmJvr27QsHBwdevFPbfvXVq1cwNDRkpRcSsHT06FEEBwfj+PHjePPmjd6Ow4C6U8vOnTuxZMkS+Pj4ICwsTCONRCLBunXrVOjkcjmWL1/O6gSjjIcPH6JUqVKQy+UqOo7+/fujZ8+enPoPCHPg1ZfvCtUPAnl8VldQ8/3793UG6Atd9xTgu+7qylrKJXupo6Ojyp7o+fPnkEqlemXwFJK1GwBSUlJgb2+PEiVKwNDQkKEfPXo02rdvz0pfpEgR3s63Q4YMQUhICJOp/M6dOzh9+jQ8PDx0ZpATa94B0KrLyMnJ4VzpQwiuXbum9RoX2wgf3vHhwwdkZGRAIpHg7t27Krzy3bt3iIuLQ+HChTk/g746WqlUqpJ9Dchz5ueS4KBRo0bYuXMnp8B/XRDDtpKbm4tDhw5hwYIFWLBggU75Sxv0XffE0k8KlfOFVJqRSCRo1aqVzgxw2rLACdUT/Fug7f0nJyfr3G/rypTMJfOukHcP5GX0HjFiBIA8OV8qlapUaTh06JDOhEpirdl87EKaHI8NDQ1Rt25dvZ2+V6xYAQcHB5U5MGvWLFhaWiIxMVEn7T///IOGDRvy0lOI5by8fft2GBgYoF69epg4cSImTpyIevXqwdDQEAkJCVrp+I7b/DAxMdEYOMFWoerEiRMwMzODt7c3unTpgi5dusDb2xvm5uas712Bly9fol27dihcuDAMDAx46YmUwdUu8m+QkxVIS0vD/v37OTnsi9k+X/2YGHPX29ub2VMpt33t2jXOSVmys7OxcuVKtGnTBrVq1ULNmjVVDjYItU1YW1ur6WZsbGxga2uLIkWKoFq1aiqZsZWhq6JZQVfXEyPzLJAXsKfwI/rrr79QuHBh3L59G2PGjGECrjVBDOdtbbzv6NGjsLe353QPsYOUT5w4gb1797Jm/v2N/zfw2/n2N/5T4JsZo1evXggODkZiYiIGDx4MOzs7FWX8+vXrUaFCBdb2xWD6I0eORHBwMExMTFCuXDkMHDgQO3fu5My0hRjWrK2tIZPJIJVKIZPJVP7PxcjTvn17+Pj44Pz588jNzUVubi7OnTuHMmXKoGPHjpz6L9QRjK+i4sSJE5wOTbCzs8Ply5eZv9+/fw+JRMLZoKDcf30zKQoVssUa+0CeIrF8+fI4deoUzMzMmG+3c+dOTk4RQuiFbpb50nfv3h3Dhg3Tej06Ohq9evVibV+oE5cYWTiFvH8+tAqDpVQqVTNs9u/fH5UqVdK5URfT+VYohDgBiiFsKpCVlYXVq1cjJCQEBgYGqFSpEmbMmKGTxt/fn1EgK7d/5coVrQo2MR2h+KxZ+YU0vpHxfJ5dgdDQUGzZskXt/JYtW5gynevWrVMrh8rFGMilnOGqVauY0jIKdO/enZnz3t7ealGkmqBt3bx9+zan7AD6Gga/fv2KXr16MfsLExMTmJiYMHuN3r17s2ZXEWv88c0qo1CCaTIwJiQkoG/fvvD09GRtXxlZWVmcHKnEyGalgKWlJe7evQsAmDZtGurWrQsgL2DKxcVFI42LiwvOnj3L/B0bGws/Pz/m75UrV6r8rQ1BQUHM+FWeexcuXOBc5UGIYU2oglroms2H7wtdM5XBh/eJERm/efNmVKpUCYaGhowRytDQEJUqVdLITxVQ8EWJRIKiRYuq8EpPT0/UrVsX58+f59SHv//+G1ZWVipOFf369UORIkU4l8b+9u0b+vfvz/AxqVQKY2NjDBw4UCv/MjMz0+ks8OjRI06KfqFjT6jzqqOjIxOUpDx27t27x+oI99dff2Hnzp3MsW3bNowaNYo16EAZQtZtof3ny7emTp0KJycnTJw4ETVq1FDLFjh37lzUqlVLK71YmeTE2G8Kdb7ma9DWtd9nc74tW7asStBIfowfPx6+vr46+w7kVclQlKJ8//49HB0d4eLiAhMTEyxevJiVXoisUKtWLZQsWRLTpk3TWFZSF0xMTFQcZ3JyciCTyVT0BVwgpP+BgYEYPnw4EhMTYWJiwszBc+fOsa75AQEBiI+PB6D67WNiYhASEqLXM+iL6tWrq5VQzn/oMsqcPXsWe/bsUTkXFxcHNzc3ODg4oHv37pwyCvbp0wfe3t4M/169ejViY2Ph4uKi4lSZH4psXRKJBMePH1fJ4HXlyhVMmTKFNeM8IDzAXZOegkv55/z3UL6Xo6Mj2rRpo3UcC3GCUUaTJk3Qrl07fPv2TWX8HT9+nLNDmFAHXn35rlD9IKB9vivAxneFrnsKiCEv8IFEIlErGW9mZqaXvkFI1m4g7zsq9IzK9GfOnOE0bydPnoyOHTvyrmbVrVs3Zr9uZGQEqVSKdu3a6XSuFGPeffjwAZGRkTAxMUGhQoUwduxYlTbZxp5YVVqKFCmi8Xtv376d036dD+9g0+kaGBioVKljg746Wk2OEBYWFnqN+8+fP2PXrl2YOXMmZs6ciV27dunlhCmWbUUo9F33hOonxZLzhVSaYeP7uiBWBj1lfPnyBWvXrsWiRYv0rraVnp6OZcuWITY2FjExMSqHJvj7+6NcuXKQSqXw9fVVyTZYtmxZWFhYIDIyUtDz6IKQdw/8nz3Lw8MDpqam6NKli8r13r17o0OHDlrpxVqz+diF2ByO9S39PX36dDg7O+PBgweYNm0aLC0tcfr0aVa6tm3bokqVKkhKSoKZmRkOHTqE+Ph4eHl54e+//9ZJK5bzMgBcunQJUVFRCAgIQEBAAKKionDlyhWNvxV73FasWFHjHBk/fjxr9v9nz55h1KhRiIiIQEREBEaPHq1SpYwNYWFhKF26NBYvXqymM9KVSEyoXURMOZmvXeXevXsoW7YsswfILztxbZ+vnM5XPybG3NUWZJuens45oU/fvn1hZmaGli1bYsCAAUylG8XBBqGyxpw5c2BnZ4d27doxtol27drB3t4ekydPZvSuy5cvV6HLzs7GyZMnNTp+8sGrV69w7do1zlm7xcg8m5+me/fuTDKK+/fv6xz7Qpy3FQ7PUqlUzfnZ0tISUqkUffr04dR/vkHK06ZNw5gxY5i/c3NzUa9ePWb+Ojo6Fnjm5N/498OQfuM3/kN49uwZlShRQu18bm4u/fjxQytdbGwsRUREUPXq1cnc3Jzi4uJIJpMx11evXk1169bVSm9jY0MSiYQkEgl5enqSRCJhruXk5FBmZib16tWL0zNMmzaNHBwcaPz48RQREUGenp6c6BT4888/acWKFdS0aVOaNm0ac75ChQo0dOhQnbTz5s3Tq638WLBgAXXs2JGCg4PJyMiIiIiys7OpcePGNH/+fE73mDRpEi1dupQ6dOhAmzdvZs5XqVKFJk2apJFm8ODBREQkkUho7NixJJfLmWs5OTl04cIF8vf319lu1apVaebMmbR79276/v071apVi8aPH0+mpqasfX737h25uLgwf1tbW5OZmRm9ffuWLC0tWekVWLt2LY0ePZo6depEgYGBRER08eJFiouLozFjxtDr169p1qxZZGxsTKNGjSIionHjxlH9+vVp69at9OLFC+rUqRMVLlyYuedff/1FVapU0dqm0LGvjJ07d9KWLVsoKChIZQ74+PjQvXv3CpR+xIgRNGnSJBo8eDBZWFgw50NDQ2nhwoWsbfOlP3nyJK1fv17r9ZYtW1Lbtm21Xt+9ezfz/4MHD5KVlRXzd05ODh09epTc3d1Z+89n7OSHkPfPh/bq1atERASArl27pjLuZDIZ+fn56eRZCp6r7e+fgQULFjBtr1y5kszNzZlrOTk5lJiYSKVKldJ5jwcPHlC5cuXUzhsbG1NWVhanfpw/f55WrlxJ27ZtI1dXV7p58yYdP36cqlatykp7+/Ztqlatmtp5KysrysjI0Egzd+5cIsr7dkuXLiUDAwPmmkwmIzc3N1q6dCmnvvNZswBQp06dyNjYmIiIvn79Sr169SIzMzOV3yUkJOhsm8+zK3D27FmNz1iuXDk6d+4cERGFhITQ48ePVa4/ePBA5325Yvny5dSzZ0/m7wMHDtCaNWto3bp15O3tTf369aOYmBhauXKlRvqIiAgiyhu7yu+SKG/spqamUuXKlVn7MWjQIOrcuTPNmDFDhXc2aNBAI+8zNjamJUuW0PTp0+ny5cv08uVLIiJycnKi8uXLc1ozhY6/jx8/EvICEOnTp09kYmKi8uz79u2jQoUKaaVv2rQpEeW9u44dO6pcMzIyIjc3N5o9ezbrcyhDLper7F20wcTEhL58+cL8ff78eZo5c6bK9czMTE5tAqDc3FwiIjpy5AiFh4cTEVHRokXpzZs3GmnevHmjst85fvw4NWrUiPm7Ro0aNGTIENa2r127Rhs3blQ7X6hQIa1t54dEIqE6depQnTp1OP2eKG9+KtaKWrVqkaHh/4nCOTk59ODBAwoLC9NKL9aazYfvC10zlcGH961bt44WL17M8J0jR45Qw4YNaeXKlSSVSjm126pVK2rVqhX9+PGD+c729vaMzKANCr5Zs2ZNSkhIIBsbG07taULDhg1p8eLF1LhxYzp8+DCtWrWKdu3aRcePH+ckc+Xk5ND58+dpwoQJNHXqVGaPU7x4cZ1z+PPnzyq8Jj+MjY3p69evWq+LNfb4yFnKyMrK0vic7969U1lHNEHBO5XRokUL8vHxoS1btlDXrl1Z2xeybhMJ6z9fvhUdHU2fP3+mhIQEcnJyom3btqlcP3PmDLVp00Zn2/n3mNnZ2bR27Vqyt7dnzvXv31/nPcTYbx46dIgOHjyosg4QEZUsWZIePXqkkUYo3xWK9PR0ioqK0nq9TZs2NH36dNb7XLlyhdl/bN++nRwdHenq1au0Y8cOGjduHPXu3VsjnRiywtmzZ+ncuXPk5+fH2s/8+Pbtm8r+WCqVkkwmU9lL6IIY/Z8+fTo1a9aMZs6cSR07dmSeY/fu3Yzcqg3jxo2jjh070rNnzyg3N5cSEhLo9u3btG7dOvr77785PYMyvn79Slu2bKGsrCyqU6cOlSxZUutvT5w4off9lTFx4kSqUaMGs7+6du0ade3alTp16kTe3t40c+ZMKlKkCE2YMEHnffbs2UPr1q2jGjVqUOfOnalq1apUokQJKlasGG3YsEHr+Pb392fmXmhoqNp1U1NT+vPPP1mfA4BGGfvp06cqa5EmHD9+nPX+bFDsVV+/fk0ymYy1TWUaoTh16hSdPXtWZc9FROTm5kbPnj3jdI8BAwZQhQoVKCUlhezs7JjzzZo1o+7du7PS68t3heoHFbhx4wYjp+UH215djHWPiP+6e+7cOXr79i0z94jy9rHjx4+nrKwsatq0Kf35559a132JREKZmZkqumCpVEqfPn2ijx8/Mud0ya18bRMKXLp0iZYvX6523tnZWet3UUZSUhIdPXqUDh06RL6+vnrpSWQyGa1YsYLGjh1L169fp8zMTCpXrpxOfkkkzrwbO3YspaSkUHx8PGVkZNCkSZPoypUrlJCQwMxDAFrphw8fTr6+vsza8uDBA2rUqBFVrVqVypYtS1OnTiW5XE4DBw7U2Y9u3bpR7dq16cyZM+Tk5ERERFu2bKEuXbrQ2rVrWZ+DD+84fvw4AaDQ0FDasWMH2draMtdkMhkVK1aMihQpwtq2AvrqaAGo2bMU315Z1nv37p3G9nbv3k3dunVTm5v29va0atUqFZ2BNvCxrSj2KVzAtldWQN91T6h+Uiw5f+jQodSjRw+6fPky1apVixwdHYmI6NWrV3T06FFasWIFzZo1SyOtEF26UD3B4MGD6cePH8ye5Pv37xQUFEQ3btwguVxO0dHRdPjwYQoODma914oVK6h3795kb29PTk5OajaDcePGqdEoZNTk5GSqV6+eyl5XoV9s3rw5a9t8IdSOUb16dbp8+TIdOnSInJycKDIyUuW6v7+/zv22WGs2H7vQmjVrWO+rD6Kjo+nt27dUoUIFysnJoYMHD1JQUBAr3bFjx2jXrl1UoUIFkkqlVKxYMapTpw5ZWlrS1KlTqWHDhlppO3bsSH379qW0tDQ6duwYlSpVisqXL89cP3v2LJUpU4ZT/8uXL6/TxqgMscft2LFjKSIigu7du8fIDUePHqVNmzapjYn8KFKkCE2ePJlzW/lx+vRpOnXqFKvtPj+E2kWEysli2FUGDBhA7u7ujD7v4sWL9PbtWxoyZIhWfq2AGHI6X/2YGHPX3d2dkpOTqVixYirnDxw4QN7e3pzusXnzZtq6dSs1aNCAVx+E2iZOnz5NkyZNUvP7WbZsGR06dIh27NhBZcuWpQULFqjIfQYGBlS3bl26efMmWVtb8+o7EdHly5epY8eOdPPmTbX9sUQioZycHI102dnZavppIyMjTjKKMhwdHenGjRtUuHBhOnDgAC1ZsoSI8vTfyra6/MhvSyMiateuHac2582bRwCoS5cuFBMTo7IvU/A+tv1Camoq8//8Mm9OTg4dOHCAnJ2dtdJv2bKFhg8fzvy9fft2SkxMpFOnTpG3tzd16NCBYmJiaOvWrZye6Tf+m/jtfPsb/ymULl2aTp06pbZob9++XaOxSQF7e3tKTEykDx8+kLm5udrisG3bNpUNTH6IwfQVuHr1Kp08eZJOnDhBs2fPJplMRtWrV6caNWpQjRo1WA3DfA1r2dnZJJFIqF69eoyAri+sra1p165ddPfuXbp58yYREXl7e2tUOmoDH6OqGIqKyZMn04QJE6h27dpkampK8+fPp3/++YdWr17Nqd/5F2oAdPPmTfr06RNzrmzZsjrvERcXR7Nnz6aWLVsy5xo1akS+vr60bNkyOnr0KLm6utLkyZMZB0qhQrbQsa+M169fa3RYysrK4qRMEEIvdLPMl/7x48c6nbTs7e3pyZMnWq+L5cTFZ+zkh5D3z4dWYYzr3LkzzZ8/Xy9HdSJ15bAmxTCRduUwEWk0RGrCsWPHNJ4XwwlViLA5e/ZsWr16NX348IHatGlDiYmJ5OfnR0ZGRirGPV1wcnKiu3fvkpubm8r506dPk4eHh0YaMR2h+KxZ+ecKVwEtP/g8uwJFixalVatWqTgMExGtWrWKihYtSkREb9++FfRudOHOnTtUoUIF5u9du3ZRkyZNGAP8lClTqHPnzlrpFfsUAGRhYaFiXJTJZBQUFMTJIMzXMGhpaUk1a9Zkvb8mCB1/1tbWKgFT+SGRSCgmJkYrvcKw6O7uTklJSSrOT/rg7du3NG7cODp+/Dj9888/agZLTbzL39+f4uPjaerUqXTq1Cl69eqVCh+7d+8eZ6NchQoVaNKkSVS7dm06efIkoyR58OCB1n2gra0tvXjxgooWLUq5ubl06dIlJgCKKM9QossgqoC1tTW9ePFCzVnw6tWrWhUcCxYsoB49epCJiQmrkU2bYU2oglqsNZsP3xe6ZiqDD+97/PixikKzdu3aJJFI6Pnz52oOIWwwMjJScQThCjGceIiI2rZtSxkZGVSlShVycHCgkydPcpZVlJWk7u7u5Ovry7nd/E6zymBzHBVr7Al1Xq1atSqtW7eOYmNjmf7k5ubSjBkzePP0oKAg6tGjB6ffClm3iYT1nw/fUvRt3LhxNHHiRI3X2YxZrq6utGLFCpVzTk5OFB8fz/wtkUhYHQrEMG7wMc78aoO2m5sb7d27l7y8vDRe37t3r9o70YTPnz8zQUaHDh2iiIgIkkqlFBQUpNXxmEgcWaFUqVKcjYCakD84+fv37zR58mQVfjRnzpwC63+NGjXozZs39PHjR5U9W48ePVgDj5o0aUJ79uyhiRMnkpmZGY0bN44CAgJoz549rME3Yjpz8EFycjLDa4jyjIOVKlVi5nPRokVp/PjxrM637969Y/ibpaUlsz8MCQnR6vRNlLefA0AeHh508eJFcnBwYK7JZDIqVKiQTqOYGI7z1atX13mdDRkZGTR69GjasmULvX//noiIHBwcqHPnzmrjuiCQm5ur0Wj59OlTlaBDXRDqwKsv3xWqH1SgVq1aGvf0EolEq2OaAlKplCZOnMh73VOA77obExNDNWvW5O34rtAz5T+n0Fkonl+bQZuIv21CAWNjYxVHXwXS09NV5rI2WFtbC15bXV1dGb3Gzwpy37lzJ8XFxVGNGjWIKG8P0bBhQ2rUqBETiKarL5cuXaLo6Gjm7w0bNpCnpycdPHiQiPJ08n/++Ser821MTAy9e/eOateuTYmJiXTgwAHq1q0bxcfHc3qvfHiHgl8+ePCAihYtyjm4URv01dEKcaQ5e/YstWjRgho3bkxDhgxh9pU3btyg2bNnU4sWLejkyZOcnOD0ta0o9ikKvH79mj5//sw4s2RkZJBcLqdChQqx7pX5rntC9ZNiyfl9+/Yle3t7mjt3Li1evJgZgwYGBlS+fHlau3atis1AGVx0ONogVE9w6NAhmjJlCvP3hg0b6PHjx3Tnzh1ydXWlLl260KRJk2jv3r2s95o0aRJNnjxZxSmGDePHjyeivHW5devWrAGZuqBNTyWRSMjExIRKlChB1apVU9l/CXn3Cnh7e2uV59jkbKGyqgJCbYJ8oOl9Ozs7k1wup2rVqtHFixfp4sWLRKTb+T4rK4vpu42NDb1+/Zo8PT3J19eXrly5orMPQpyXP378yMx3TWu+MvLzBeVx26pVK52B3lzQqFEj2rlzJ02ZMoW2b99OpqamVLZsWTpy5Ijafj41NZXKlClDUqlUxYlNE9hs4UR5MhGfeSDULkIkTE4Ww65y7tw5OnbsGNnb25NUKiWpVEohISE0depU6t+/P+PzoAliyOkFod/jisGDB1Pfvn3p69evBIAuXrxImzZtoqlTp2p1mM4PmUyml99JfvCVNRQ4ePCgxkDuWrVqMYlJGjRoQCNGjFD7TZkyZej+/fuckihoQ5cuXcjT05NWrVpFjo6OnHlt/qAhIs2BQ2xJjTp37kwtW7akwoULk0Qiodq1axMR0YULF3Q6fgvZcyr2XO7u7lS5cmXWZB6aIDRI+cGDByq8bd++fdSiRQsmwHXMmDFqMvhv/L+H3863v/GfgtDMGNoMosoRx5ogBtNXwM/Pj/z8/BihICUlhebOnUt9+/bVqsBRBl/DmqGhIfXq1YtxmtUXHz9+JHNzc5JKpVSiRAlm45Obm6siTLCBj1FVDEWF0EhdTcrp8PBwFeU027fjm0lRiJCtAN+xr4wKFSrQ3r176Y8//iCi/1OKrly5kpNRSwi90M0yX3orKyu6d++eVqPt3bt3dY5HsZy4+I4dZQh5/0Jo+W64xYiyPHHiBBUrVowaNmzIi2+L4YQqRNgcPnw4DR8+nCZOnKjTeKoL3bt3pwEDBtDq1asZBem5c+do6NChNHbsWJ20YjhC8VmzxIqOF/Lss2bNosjISNq/fz9VrFiRiPKMPbdu3aLt27cTUV7GmVatWmm9R/PmzSkwMFBNMTxjxgxKSkrSqeT88uWLCm85e/asStZADw8Pnc6vinfo5uZGQ4cOVcvKwRV8DINfvnyhy5cvk62tLZUuXVrl2tevX2nr1q3UoUMH1rb5jj+xssoIzWLcvn17unv3LnXt2pWzkkSsbFZEeYq6du3a0c6dO2n06NHMvm379u1ao/Nr1KhBsbGxtHjxYtq2bRvl5uYyxlGiPGNZ/v2bJrRu3ZqGDx9O27ZtY5R7Z86coaFDh2r99nPnzqWoqCgyMTFRM7IpQ5cTmlAFtVhrthC+Lwb/48P7xIiMT0lJoT179pCtrS21bNlS5f19/PiRBg4cyBr09vTpU9q9ezc9fvyYvn//rnJNm1Jc2UFcGQ4ODhQQEECLFy9mvYcy+CpJNUX3K0MXDxBr7Al1Xp0xYwbVqlWLLl26RN+/f6fo6GhKS0ujd+/e0ZkzZ/Tuz5cvX2jBggWc9upEwtZtof3nw7eI8vaIL1680BmspwsPHz7kRZcfYhg3+BhnxDAMjhs3jjGK5TeIff78WSftxIkTqW3btnTixAmqXbu2WhawAwcOaAzAzI8SJUrQzp07qVmzZnTw4EEaNGgQERH9888/OmU9MWSFadOm0ZAhQ2jy5Mnk6+urJrPoar9atWp0+/ZtlXOVK1em+/fvM3/r4j1i9P/BgweUnZ2tljXxx48frN+PKG/cHT58WO92xXLm0LaGKDtSNGnSRE1v8v79e5VgppMnT1L9+vWZvytWrKgzSFcBDw8PevDgAbm6ulKpUqVo69atFBgYSHv27NGZKUchW/HNRimW4/ypU6do2bJldP/+fdq2bRs5OztTfHw8ubu7U0hIiFa6d+/eUXBwMD179oyioqJUnLn+/PNPOnz4MJ0+fZpSU1Pp/PnzGvd+ylnjlaH87XSt5XXr1qV58+YxgYaKjKjjx4/nnGFJqAMvH74rVD8oVqUWoeC77qakpKhk89fX8V0MHYdQ20Tjxo1p4sSJTMYkiURCjx8/puHDh3Oad0LlhXXr1tHMmTPpzp07RETk6elJw4YNo/bt27PSCpl3r1+/VtEL2dvb05EjR6hevXrUoEED1v2KWFVaiPIqNEVFRVFQUBA9e/aMNm3aRE2aNOFEK4R3KJ7/8+fPGuUdLo5MRPrraNnkFF2YNGkSde7cmZYtW6ZyvnLlylS5cmXq2bMnTZw4kfbt28d6L31tK8r8auPGjbR48WJatWoVE3R1+/Zt6t69u0p2RG3gu+6JpZ8U4z58K80cP35cL/uPMoTqCR4/fqyiFzx06BC1aNGCmQsDBgzgvOa+f/+et8NL6dKlKTk5mSpVqqRy/sKFC2RgYKDi5KcNc+fOZRzAFXvm9+/fk1wuJ3Nzc/rnn3/Iw8ODjh8/zgQ3CHn3uuDh4UEHDx5kzVouVFZVQIhdqFmzZhrlEeV1o23btmrBlNr0ggYGBnTmzBlGvmcLVPXy8qLbt2+Tm5sb+fn50bJlyxjHRbagcSEBRzY2Nsy7VySJyA82m7IQ3p0fDRs21JnlVwF/f396+fIlFSpUiHFi0xawxWYLJ8pLajZixAjmvXOFULuIUDlZDLtKTk4OIw/Y29vT8+fPycvLi4oVK6bWt/wQQ04Xqt/jO3eJ8qoMmJqa0pgxY+jz58/Utm1bKlKkCM2fP59at27Nqf9Dhgyh+fPn08KFC3k5+fOVNRSwtbWlPXv2MLohBRR6b6I8535NMt+kSZNo6NChFBsbS+XLl1cbP1z8W+7fv087duzQ2wFZSOZZZUyYMIHKlClDT548ocjISMaZ18DAQKPDsZhQDgr4+vWr2n6ZTUcnJEg5OztbxXH53LlzKoF9RYoU4VzV8Tf+w8Bv/MZ/DImJiahduzYcHBxgamqKKlWq4ODBg5xomzZtimbNmqkdERERaNu2LcaNG4dbt25xuteXL1/w4cMHlYMLcnNzcfnyZcyePRuNGjWCjY0NDAwMUK5cOQwcOJCVfsWKFXB2dsbmzZthZmaGTZs2YdKkScz/daF69er466+/OPVTGQkJCShZsiSysrLUrmVmZsLT0xO7d+/mdK8pU6agdOnSOH/+PCwsLHDq1CmsX78eDg4OWLBggd594wqZTIbHjx+rnDM2NsaTJ09YaR8+fMjpYEPJkiUxfPhwtfPDhw+Hp6cnACApKQlFihRR+82uXbs0Hrt378ahQ4dw//59nW2LMfZPnToFc3Nz9OrVCyYmJhgwYADq1KkDMzMzXLp0ifX5hdAPGTIEISEhePHiBSwsLHDnzh2cPn0aHh4emDBhAmvbfOkjIyPRtGlTrdcbN26MFi1asLYvFELGjgJC3r/Qb5+UlIRhw4ahVatWamOwIDFjxgx4e3ujUKFCGDRoEK5du1ag7WnD+vXrUaJECUgkEkgkEjg7O2PlypWsdFOmTEHJkiVRtGhRREdHM/03NDREWloap7Zzc3OZNULRvomJCcaMGcOJ/smTJ1i0aBGGDx+OQYMGqRxcIGTNEgqhz37//n0MHz6cGasjRozAgwcPOLdvb2+P1NRUtfOpqakoVKiQTtpSpUphx44dAIDXr1/DwMBAZa5duHABjo6OnPvCF127dkXTpk3x/ft3mJub4/79+3j06BHKlSuHAQMGqP3+9u3bKFasGCQSCaRSKapVq4Znz54x11++fAmpVMq5fSHj7+HDh8jJyeHcliZkZmZi7969WLJkCebPn69ysMHc3BzJycl6t3njxg3MmzcPmzdvVuv/smXLcPXqVb3vqYwvX77gx48fGq89ePAAxYsXh0QigaGhIRYvXqxyvUmTJpz2qt++fUO3bt1gaGgIiUQCIyMjSKVStGvXDtnZ2YL6/78Cvnw/MzMTY8aMQXBwMIoXLw53d3eVgwv48D6JRIIGDRqorM+GhoaoW7cupzX74MGDkMlk8PHxgaurK+zs7HDs2DHmOpe5f+TIEcjlcpQpUwaGhobw9/eHtbU1rKysULNmTa10NWrU4HTouocy9u/fD39/f+zZswfPnz/nJe/9KoghZ2VkZGDSpEmIjIxE/fr1MXr0aDx//pyVztraGjY2NsxhbW0NAwMDWFhYYNeuXZzaFrpuC+k/X74lkUjw6tUrzv0rSPDlOwpcu3YNhQoVQlhYGGQyGVq0aAFvb284Ojri7t27BdLn6tWrc5q/unDmzBm0atUKrq6ukMlkkMlkcHV1RatWrXD27FlO/di2bRvzzevUqcOcnzJlCsLCwgQ9IxsU30sqlaocinP/dlSrVg1r165VOx8fH4/q1asXWLsKmV6B1q1bo3v37szfV69eReHChVnvU6NGDVhaWsLMzAwBAQEICAiAubk5rKysUKlSJYa35Ze9XF1dcfLkSQB5/MPU1BRHjhxhrqempsLGxoa1/Tlz5jD7ysOHD8PExATGxsaQSqWYN28eK70CaWlp2L9/v5q+iA1r167Fly9fOLejjO3bt8PU1BTdunWDsbEx7t27BwD4888/Ub9+fZ20AwYMQJkyZfDy5Uu1ay9evICvry9atGgBS0tLjeMLADNHFHNIeS4pyyLv3r3TSP/kyROULl0a3t7eMDQ0RFBQEOzs7ODl5cWZr7ds2ZIZdwpZ6dOnTwgNDUWnTp1Y6fnyXaH6Qb7Iv9brOtjAd901NjZW0e1WqVIFkyZNYv5+8OABzM3NhT0oBwixTWRkZKB27drMXqlo0aIwMjJCtWrVkJmZWaD9nj17NuRyOaKjo5lxM2zYMMjlcsyZM4eVXsi88/Lywt69e9XOf/r0CcHBwfDz89O57hUpUgQXLlwAAOTk5MDS0hJ///03c/3GjRuwtLTUSKtpvmzfvh1FixZF165d9eKbQnjHP//8g4YNG6qt+YqDK4TqaPWBjY2NRr2WAikpKbC2tma9j1DbioeHB65cuaJ2/tKlS3Bzc+P2MBC27gmBGHI+X8TFxXE6NEGonsDKygrp6enM325ubli1ahXz94MHD2BiYsLpObp06YIlS5ZwfGpVVKxYEdu2bVM7v2PHDgQGBnK6x8aNG1GjRg2V9fnOnTsIDQ3F5s2b8eTJE1SpUgXNmzdnrgt59wDU9JCKw8DAACNHjmTVT4olqwrhOR07doSVlRWKFSuGiIgIREREwM3NDdbW1mjZsiW8vLxgbGyM06dPC+6nJsTHx2PNmjUA8viFvb09pFIpTExMsHnzZr3v9+XLF6xduxaLFi1SkUXy48SJE4zu9cSJEzoPbcjOzsbMmTNRsWJFODo66r3Xyo+kpCSsW7cO69at0/rdHj58iNzcXOb/QmzhQN7eUSaTQSqVwtzcnPMz/FvsIkIQEhLC+GK0adMGYWFhOH36NDp06AAfH5+f0ge++jFAvLmblZXFiw81bdoUVlZWcHd3R3h4uN42ZaG2ieXLl8PAwACNGjVCbGwsYmNj0bhxYxgaGjJ6tlmzZqFly5ZqtPn3yHx0PE2aNMH27ds5/fa/hqysLPTt2xcODg6C9st84Ofnx6wZjx49gkQiUdEFnTlzBs7OzgXah9/490MCiFDb4Dd+4z+CTp060c6dO8na2prKly9PRERXrlyhjIwMqlu3LqWkpNDDhw/p6NGjGjOLff78maKjo2nr1q309u1btetcor1sbGwoMzOT/Pz8qHr16lSjRg2qWrWqzqwW+bFhwwaaMGEC3bt3j4jyoi1iYmJUor80YevWrTRy5EgaNGiQxogbbRHWdevWpZYtW1K3bt00Xl+9ejVt2bKFKfWkCwBoypQpNHXqVCYTirGxMRMJxIZLly7R1q1bNUaI60qVb2BgQC9fvlSJdLGwsKDU1FRB6f/1we7duykyMpJKlSqlMZNieHg4LVmyhO7cuaOWmUsqlWqMNFSODg8JCaGdO3dqjIQTOvYVuHfvHk2bNo1SUlIoMzOTAgICaPjw4ZzL8vKl//79O/Xt25fWrl1LOTk5ZGhoSDk5OdS2bVtau3Yta1ZQvvRXr16l4OBgCg8Pp+joaCaS79atWzRjxgzau3cvnT17lgICAlifPSsri06ePKlx7LKVxxIydpQh5Pvxpd28eTN16NCB6tWrR4cOHaK6detSeno6vXr1ipo1ayZaFgFdOHfuHK1evZq2bt1KXl5e1KVLF2rbtq1eWbT5ZOPLj8+fP1NmZqbeEecnT56k1atX0/bt26lEiRKUlpZGJ0+e5JwBkyhvDty9e5cyMzOpdOnSKlketOHo0aPUuHFj8vDwoFu3blGZMmXo4cOHBIACAgLo2LFjnNrms2atXLmSTp06RTVq1KDOnTvTli1baMKECfTt2zdq3749xcTEcHtw4vfsYsDU1JSSk5PVIoBv3bpF5cqV01lieNq0aTR//nzq06cPHTt2jF6/fk3Xr19nrs+bN4/+/vtvOnLkCGs/tm/frnXdZCux9eHDB2rRogVdunSJPn36REWKFKGXL19ScHAw7du3T20f0axZM/rx4wetXbuWMjIyaODAgXTjxg06ceIEubq60qtXr6hIkSKc9ktijT++WWWuXr1KDRo0oM+fP1NWVhbZ2trSmzdvmLKGytHymlCxYkX6888/OZVfLAh4eHhQUlIS2dnZqZzPyMiggIAArf3Pzs6mtLQ0cnBwUMsQnJKSQi4uLmr31IbHjx/T9evXKTMzk8qVK8ealUOBiRMn0tChQ9XK8H758oVmzpxJ48aN00mfk5NDc+fO1TruFSWddUHImq0Mffl+mzZt6OTJk9S+fXumtJMyBgwYwLltfXgfW7k2BbSt2ZUrV6aaNWvS5MmTCQDNnDmTYmNjadu2bRQWFsZp7gcGBlL9+vUpJiaGLCwsKCUlhQoVKkRRUVEUFhams/y2mFCuiKH8/sGx0oVQCBl7QuUsIVi7dq3K+5JKpeTg4ECVKlXSO0vHr1q3ifTnW1KplF69esWpTLQ2iJUxXgG++02ivHV/4cKFKvv9vn37smYFEoPv/mq8fPmSXrx4QX5+fgwfuHjxIllaWuosq6cAX1nh5MmTOu+bvxRoQYFv/y0tLenKlStqGVnu3r1LFSpUoIyMDJXztra2lJ6eTvb29mRjY6Mzi42ucWNtbU1JSUnMHHV3d6exY8dSly5diCgvq7S3t7fO/TZR3p761KlTtGbNGkY2/PDhA3Xr1o1CQkKoe/fu1LZtW/ry5YuKvqt3796UkpJC06dPZ0qpP3/+nGQyGRHlyT/z5s2jpKQkne3nx6NHj+jy5ctUokQJThkQ79+/T82aNaNr166p6IsU77Ug16xy5crRoEGDqEOHDsy67eHhQVevXqX69evrzEbl5uZGy5Yto3r16mm8fuDAAWrQoAGNHz+eybCdH0ePHqXRo0fT5MmTKTAwkIjy5uzYsWNpzJgxZGVlRT179qRKlSrRqlWrNN4jOzubNm/eTKmpqQzPi4qKUiktqwtPnz6levXqEQCmPO6dO3fI3t6eEhMTOfFhPnxXqH5QgYyMDLp48SL9888/almUNa07cXFxrM+jANdsbfquu8WKFaP4+HiqVq0aff/+naytrWnPnj1Uq1YtIiK6du0aVa9e/X9i3VFkd1Z8d0U5Vy7gK+O7u7tTTEyM2veNi4ujCRMmsGZGFjLv+vfvTy9evNCYKfDTp09Up04dSkpK0sq3oqKi6OPHj0yVlvHjx9PLly8ZncSOHTto4sSJlJKSokbLpeIdEfdMfnx5R1RUFD169IjmzZtHNWrUoL/++otevXpFkyZNotmzZ3PKSqiAvjraxYsXU0JCAtna2lLPnj2ZOUOUl1U4MDBQo47A1NSUbt26pbUi3KNHj6hUqVKs661QyOVyOnnyJKMTV+DixYtUo0YNTtn2+UKMCi9iyPl8+yGVSsnc3JwMDQ21ln+XSCQa+aZQPUFwcDBFRkbS4MGDKS0tjcqHX/aWAAEAAElEQVSWLUt3795lbHEnT56kjh07cqoGMnXqVJozZw41bNhQY7UIXbKyubk5paamqlWDUZSX/vTpE2v7xYsXpx07dpC/v7/K+atXr1Lz5s3p/v37dPbsWWrevDm9ePGCiIS9ewW9s7MzGRqqFjh+9OgRFSlShIyMjEgikWjV74khqyrA1y40YsQI+vjxIy1cuJDhxbm5uTRgwACysLCgyZMnU69evSgtLY1Onz4tuJ9s+Pz5M926dYtcXV1ZKw4NHjyYfvz4wZQo//79O1WqVInS0tJILpdTdnY2HT58mDX77+PHj6lo0aJq8x4APXnyhFxdXTXSjRs3jlauXElDhgyhMWPG0OjRo+nhw4e0c+dOGjduHGfd5NOnT6lNmzZ05swZxgchIyODKleuTJs3b1bJKq+MxMREqly5str4y87OprNnz1K1atVY22bbO2rbL4ppFxEKvnuugwcPUlZWFkVERNDdu3cpPDyc0tPTyc7OjrZs2UKhoaGc2hfDJskHv3rusq0/XG3KfG0TRERnzpyhhQsXMpmKvby86I8//tBaUVABMXQ8b968oY4dO1JgYCCVKVNGbc1r3Lgx6z2EoH///lSiRAk1PrNw4UK6e/cuzZs3r8Da7tu3Lx0/fpxiY2Opffv2tGjRInr27BktW7aMpk2bRlFRUZzvdePGDY1zR9v7W7FiBQ0aNIhatWpF58+fJ2tra5VM0ZMmTaILFy7Qnj17+D3cb/w38NPdfX/jN/7FGD58OHr37q2SSSwnJwf9+vXDyJEjkZubix49eqBKlSoa6fv06QNvb28mw8Pq1asRGxsLFxcXrF+/nlMf/v77b9GyJukbNZQ/Mj1/dLo2FC5cWGck3507dzhlFVHGt2/fkJaWhgsXLuDTp0+caDZt2gQjIyOEh4dDJpMhPDwcnp6esLKyYs0qITRSF4DWDHo5OTl49OgRp2fgm0nxyJEjqFSpEo4cOYKPHz/i48ePOHLkCIKDg7F3716cPn0aPj4+6NKli0Z6oWP/34JHjx5h79692LJli0rkdEHS79mzR2OUlYODA+dMXleuXIGTkxMsLS1hYGAABwcHSCQSmJmZcY5wF5qF81fB19cXCxcuBJCXDebevXvIzc1F9+7dMW7cOI00NjY2eP36NQD27Cr6ICsrC2vXrkXFihVhZmbGmRfzzcYnNj5+/IilS5ciMDAQBgYGCA4OxuzZswusvYoVKzLfSPHtPn36hMaNG6tlxOQCrmvW3LlzYWZmhoiICBQuXBiTJk2CnZ0dJk2ahJiYGFhaWmLZsmV6t68v3r9/j4MHDyI+Pp5zVgJlVKxYETExMWrnx48fj4CAAJ20OTk5GDt2LPz9/REWFoYbN26oXG/RogWnbHbz58+Hubk5+vXrB5lMhp49e6J27dqwsrLCqFGjOD0HkJflYNGiRZg+fToOHz6s9XeFChVSyYqSm5uLXr16wdXVFffu3dMr863Q8Sc0q0z16tXRvXt35OTkMO0/fvwY1apVY6LvdeHixYsIDQ3FiRMn8ObNG72yZz558kTj3uj79+9MljU2aMtw8fLlSxgZGWml+/Dhg8b9TnZ29k/L+imVSjX2/c2bN5y+3dixY1G4cGHMmjULJiYmiI2NRdeuXWFnZ8cpa7EYazZfWFlZFVjGj4KEpaWlWoa2DRs2wMzMDHv27OE0983NzZl7WFtb4/r16wCA5ORkFCtWrED6rQl8s5IokJ6ejmXLliE2NhYxMTEqBxvEGnv6yFkpKSnMnE9JSdF56MKjR4+YDCmarhUUxOo/X2iSMTUd2qApY7xyJhJ9M8b/Kgjlu8o4ffo0vn79WkA9LRj8Slnh+fPniI+Px969e/Ht2zeVa5mZmZx4j5D+W1paas1Epyn75Nq1a5nvu2bNGqxdu1broQtBQUGMHHT9+nVIpVKVbJ8nTpzgtHYUKVJEY0WR69evM1VlLl++DDs7O5Xrr1+/RtWqVSGRSGBhYYGEhASV66GhoXrttfkiPDwcTZo0wevXr2Fubo4bN27g1KlTCAwMRGJiIiu9kIxapqamjD5CsVcGgHv37sHY2FgnrUwm01mF6smTJzAwMNB5Dx8fH5w5c0bt/OnTp1G6dGkAedmEixYtqvM+QvHjxw/Ex8dj2LBh6N27N1asWIHPnz8XaJtC9YMAsHv3blhYWEAikcDKygrW1tbMwSeb2s9Cr169EBwcjMTERAwePBh2dnYqvG/9+vWoUKGCVvrv379j2LBhKF68OCpWrKiSgRH431h3hcj4xsbGGvX76enprPMWEDbv3r17x+zvNeHjx48699oPHjxgMvwLqdLyK+Hk5MRk77WwsMDt27cB5GXmLUh9/Pz58yGXy9G3b1+0a9cOMpkMU6ZMYa7rGve+vr5YvXq11nuvWrUKvr6+nPvC17YSHh6OcuXK4fLly8y5S5cuISAgAI0aNeLcvr7rnhgVXgDhcr6QfpQuXRp2dnYYMGBAgclE2pCQkACZTIbQ0FA4OjoiPDxc5Xp0dDQiIyM53cvNzU3rwSYr29raaqyIcebMGU6Zm4G8fU9SUpLa+YsXL8LU1BRAHp8yMzNjrgl99z179oS/v7+aTphrRTyhsqoYsLe3Z3idMm7fvs3sr1NTU2FlZaX1HhEREZg2bZra+enTpxdoRUofHx8V29/q1athY2PDZIft1KkTGjRowHofvjpODw8PJsO7sr5s/vz5aNOmDefnqFevHipVqqRS9fTWrVsIDg5GvXr1RO+3GBDDLiKGnCyWXUWBt2/fatWbaYK+crqY+jF95265cuWYygf+/v4oV66c1uM32LF7925YWVlp9enRheTkZMTGxmLRokWMjV2BDx8+oHPnzqztFylSRGOG7MuXLxd45teiRYvi+PHjAFQrHq1bt461wo4C9+7dQ9myZdWqdnCxC65atQpNmzZFr1698OLFC5VrvXv3VtP9/Mb/e/jtfPsb//MQ0wlL6GZfDKafHw8fPkRaWprg0shc2+JTKsLExAQ3b97Uev3GjRucS8QIAR8nPgU6derE6dCEDx8+IDIyEiYmJihUqBDGjh2rUprgZyhnhRoW+I59ZUeb/I5DXByJhNL/W/D582ckJCRgxowZmD59Ov766y9kZWVxphfqxMUXQt6/WN9OLpczRjlbW1vGMe/GjRtwcnLSSCOWUTY/Tp06hc6dO8Pc3ByVKlXibBTT1wnwZwibqampGDBgABwcHNSuNWvWjPkuQhRsv8oRqlSpUtiwYQOAPEck5XIuALBy5UqUL19eI61Yzy6GMXL37t0wNDREhw4dmPHavn17GBoaMmWHChpeXl7YuHEjAFWj+NixY9G3b1/R27OwsFBTiAFA37594eLigsTERM7rpdDx17ZtW1SpUgVJSUkwMzPDoUOHEB8fDy8vL5XylNpgZWXFKCatrKyY5zp//jy8vLxY6dPT01GhQgW9ykg/f/4cFStWhFQqhYGBAdq3b6/iOMdlv6EolymRSLBu3TqVEpoJCQno27cvPD09NdImJCSgZMmSGte3zMxMeHp6Yvfu3RppBw0axJRJHTRokM6DDRKJBP/884/a+aNHj8Le3p6VXqiCms+aLRbfd3Nz0ziH2CAW7+MLBwcHjUq5TZs2QS6XY8mSJaxj19HRkXl2b29vxsiRnJysYsDShV9ZzhP4v7Jkjo6O8PPzg7+/P3NwWfN/xX5R2VFfWylhLspdvoYZoWNXSP/F4FsSiQStWrXiJWMCeaX0GjZsiNevX+POnTto2LAh3N3dGecDXXxfDL4jlnFGLMMgkLeXUOxXhGLkyJGcDAs1atRAzZo1tR5s4BMw9Pr1azUdzPXr19GpUydERkYye2FduHjxIqytrWFpaQlTU1OUKFFCxbGJq55CSMBTeHg4IiMjVfQj2dnZaN68OcLCwljb5guxnDnMzMwYHZ8yjh8/zjgP37t3DxYWFhrpMzIyNJatfPv2rZqRNz9ycnKwatUqNGzYED4+PihTpgwaNWqEuLg4zkZZOzs7Zn5aWloye9ejR4/C39+flV6I47y7uzsTlKcsZ8TFxcHb21snbZEiRXDq1Cmt1xMTE1kD/E1MTHDt2jW186mpqYx+8uHDh4xDDJC3T/7+/Tvzf11HQUEMviuG43HJkiUxYMAAvfRa2vDlyxdOOiIx1l2hju/jx4+Ho6MjZs6cidGjR8PKygo9evRgrr98+RISiUSNTqhtYv78+Uype21lxNnKhysgRMb38fHB5MmT1c7HxsaiTJkyrG3zmXdi4sePH0hOTsazZ8/UriUnJ+PNmzcF0q5YvMPCwoLRj7q6ujLOmPfv32d9Z0J0tKVLl1bZV5w5cwYODg4YO3YsAN37hTlz5sDW1hZ79+5Vu/b333/Dzs6OU1IAobaVf/75B/Xr14dEIoFMJmPKmNevX1+vxDT6rnvBwcEMT8nNzcX06dNhbm6O/fv3c+q3AnzlfLH6cf78efTo0QNWVlYoX748Fi9e/NNsMUeOHMHAgQMxbdo0tTVnwoQJGvdhYqN169aoXr06MjIymHPv379H9erVOTv/NmjQAAEBASpBZ1euXEH58uXRsGFDAHl64Py8VOi7T0hIQNGiRfHnn38y5/RxvuUrq4plF7K2ttbIG3ft2sU4Pqenp+t0gra3t1dJ9KBAamoqChUqpLP97OxsrFy5Em3atEGtWrX0kvOU7f9A3jjq3r078/fVq1c5JaTSpuN8+PAh5HK5Vjq5XM7oBZycnJjgg3v37sHS0pK1XQVMTEy0BkvqWnu09fv27dtaZSPg32EPFktO/tl2lfzQV04XS78H6D93J0yYwPD4CRMm6DwKCmLaJoA8ef327ds4deoUTp48qXKw4f3795g1axa6du2Krl27Ys6cOSprEBuKFSuGvn374uXLl5xpAPGChrQF7N25c4dTwJ4QmJmZMbzP2dmZCVy7f/8+Z/uA0CDl3/gNXTBkz437G7/x78bcuXPJwsKCiEhwKvPs7Gy6desWeXp6qpy/desWU1bIxMREa+m7d+/eMeVJLC0tmZIgISEhrKVQV69eTRkZGTR48GDmXI8ePZgyTF5eXnTw4EEqWrSoGm1AQAAdPXqUbGxsqFy5cjpL8+kqdaCtRBAb3Nzc6NKlS1rLLV66dEnnvSMiImjt2rVkaWlJEREROttKSEjQeu3evXtMCSaZTEZZWVkkkUho0KBBFBoaqrMMuZDS9mPHjqWUlBSKj4+njIwMmjRpEl25coUSEhKYsoLQUjomP/Qt66bAvXv3mDKIyrC0tGRKy5QsWZLevHmjkZ7v2LexsaEXL15QoUKFyNraWuPYg44yvELoBw8eTLGxsWRmZqYybzRBU4kNofTKMDU1pWbNmun8jS4kJyfTsmXLSCqVkoGBAX379o08PDxoxowZ1LFjR9Z5QcRv7Ah5/0K/vfJ9FOWbnJ2d6fr16+Tr60sZGRlay4Ipl5zp1KmT1ntzwfPnz2nt2rW0du1a+vjxI7Vr144uXLigVtZXF27evEmbNm0iIiJDQ0P68uULmZub08SJE6lJkyZq/L9JkyZkbGxMRERNmzYV1H9t8PX1pXnz5tHMmTPVrllZWTHfy8rKincbZmZmTEmOwoUL071798jHx4eISCuvIRK+Zj169IhCQkKIKK+cqYGBAQUFBTHXq1evTkOHDtV4P7GefciQIdSlSxeaMmUKyeVyXvdo1KgR7dy5k6ZMmULbt28nU1NTKlu2LB05cuSnlRB+/PgxUwrH1NSUmYvt27enoKAgWrhwoRrNggULqEePHmRiYkILFizQef/8pWdKlSpFly5dIm9vb5Xzinb0KYnDd/wpcOzYMdq1axdVqFCBpFIpFStWjOrUqUOWlpY0depU1pKORkZGTFmlQoUK0ePHj8nb25usrKzoyZMnrO1HRUWRkZERbdy4kRwdHXXOAwVGjBhBUqmULly4QBkZGTRixAiqWbMmHTp0iCkZy7bfUPAciUSiVr7LyMiI3NzcaPbs2RpplyxZQtHR0RrHvJmZGQ0fPpwWLlxIjRo1Urt+9epV+vHjBxHlzWttz6vrPSjKT0skEvL09FT5bU5ODmVmZlKvXr200ivw8uVLpvSdubk5ffjwgYiIwsPDaezYsaz0fNZssfh+bGwsjRs3juLi4vTiPWLwPiHlNP39/en48eNUvnx5lfOtW7cmAJxKDwcFBdHp06fJ29ubGjRoQEOGDKFr165RQkKCyhqgC926ddNZzpMrMjIyaNWqVXTz5k0iIvLx8aEuXbqwvttJkybR5MmTafjw4bza5TP2hMpZDx48YMpQspUa1gVtvCkzM5NMTEy00gkdu0L6LwbfIspbN7mUFteEs2fP0pEjR8je3p7s7e1pz5491KdPH6patSodP36cKaWsCWLwHX9/f3r58iUVKlSI/P39NZYxJ2IvwyyU7yqDq1zNBc+ePeO0ZucvH/vjxw9KTk6m69evc+Jf+soKRER//PEHFSlShFmT//nnH6patSoVKVKEihcvTp06daKcnBxq37691nZHjRpFzZo1o5UrV1JWVhYNHz6cqlevTocPH6Zy5cqx9ltI/xWYPn06VatWjby8vKhq1apERHTq1Cn6+PEjHTt2TGe7tWvXpnbt2lFERIRGXYcuNGvWjPbt20d///031a1bl/744w+V63K5nPr06cN6nyZNmlCXLl1o9uzZTCnrpKQkGjp0KDOvLl68qKZHUUAb37K1tdXZLgBq3Lgx7du3j/z8/MjX15cA0M2bN6lTp06UkJBAO3fuZO1/Tk4Ooy+1t7en58+fk5eXFxUrVowpj6kLGzZsoBUrVlDDhg1pwoQJ1KZNGypevDiVLVuWzp8/r7Ocbffu3WnAgAG0evVqkkgk9Pz5czp37hwNHTqUdd7Xq1ePRo8eTYcPH2Z0agp8+/aNxo4dS2FhYTrvUb58eRo2bBitW7eOWQdev35N0dHRzLe8c+eOio61adOmDM/TxTd18bzdu3dT/fr1ycjIiHbv3q2zj5pkHzH4rlD9IFEef+zfvz9vWVfBc7Zu3Upv375Vu66p72Ksu/b29pSYmEgfPnwgc3NzMjAwULm+bds2Mjc310q/YcMGWrlyJYWHhxNRnr6pfv361LlzZ2afq6l9obaJuXPnUlRUFJmYmNDcuXO1/k4ikbCWkeYj4ysQExNDrVq1osTERKpSpQoR5ZXUPXr0KG3dupX1OfjMO2V8+fKFLl++TLa2tmo6ua9fv9LWrVt16sYNDQ3Jz89P4zVt5zXh6NGjdPToUY36VU3yjhi8gyjP9nP79m1yc3MjPz8/WrZsGbm5udHSpUupcOHCOvssREf74MEDlRLJlStXpmPHjlHt2rXpx48fNHDgQK3tDhgwgM6ePUvh4eHk5eVF3t7ezHp1584datq0qU56BYTaVhwcHGjfvn2Unp5Ot27dIqI8/ZO29Vkb9F330tLSKD4+nojyvm90dDS5uLhQixYtaPPmzcy4ZwNfOV+sflSqVIkqVapE8+bNo23bttGaNWuYvc7q1asZmUITVq5cSadOnaIaNWpQ586dacuWLTRhwgT69u0btW/fXqc9joioVq1aVKtWLY3Xxo8fz/Lk4mDWrFlUrVo1KlasGLNHTk5OJkdHR+a9smHVqlXUvn17Kl++PFP+Ozs7m2rVqsXYd83NzdX0bULePVHenjcwMJA6dOhAe/fu1dvGyVdWFcsu1L59e+ratSuNGjVKZa89ZcoUht+fPHmS0fdqQmZmptp+kShPx/nx40edzzFgwABau3YtNWzYkMqUKaOXjkgqlarwpfPnz6vsca2tren9+/da6RX2SIlEQmPHjlWZ+zk5OXThwgU1OVQZLi4u9OLFC3J1daXixYvToUOHKCAggJKSkljHjTKKFi3K7L+UkZOTQ0WKFFE7r9ArSSQS6tSpk0pbOTk5lJqaqrKm5IdYY0cIxJKThey5srKyaNq0aVr3G4o9uy7oK6eLpd8j0n/uKvh5Tk4O1axZk8qWLUvW1tZ6tSnUpiiWjo8ob763bduWHj16pLY/YRu7ly5donr16pGpqSkFBgYSUZ7/weTJk5l5zIa3b9/SoEGDyNHRkfW3ypgwYQINHTqUJk+eTABo5syZ1LhxY9q2bRurfK2MEiVK0IEDB6hfv34q5/fv38/4SBUUPDw86MGDB+Tq6kqlSpWirVu3UmBgIO3Zs4fzmDp37hwdO3aM7O3tSSqVklQqpZCQEJo6dSr179+frl69WqDP8Bv/bfx2vv2N/3kojB3Z2dkkkUioXr16ei84Cgjd7Ath+suXL6eePXsyfx84cIDWrFlD69atI29vb+rXrx/FxMTQypUr1WjFdOS6d+8ezZs3jzEmly5dmgYMGEDFixfXShMREUGjR4+mOnXqqL37ly9f0pgxY6hdu3Za6cVyhuLjxCcGdu7cSXFxcVSjRg0iyvsGDRs2pEaNGjHKdi4btj179lBUVBRlZmaSpaWlCo1EItGpYBSq4OQ79o8dO8YYjo4fP876jPkhhF7oZlmMzTab45kCbMpxoU5cfMeOkPcv9NsrUK1aNTp8+DD5+vpSZGQkDRgwgI4dO0aHDx/WqnxTxr59+8jAwIDq1auncv7QoUOUk5ND9evX10rboEEDOn78ONWtW5dmzpxJDRs2JEND/bdG+joBiiFsKpCdnU1z586lTZs2UXp6OslkMvL09KTOnTtTjx49GIWfMhSKOAAUExNDDg4OZGpqqnfbfB2hhK5ZcrmcsrKymL8dHBzUDGjZ2dkaacV6dqHGSAUaNmzI6uSpDYsXL6aEhASytbWlnj17qsyXN2/eUGBgIKuixsnJid69e0fFihUjV1dXOn/+PPn5+dGDBw+0GjaEGAabNWtGmzZt0ugosnDhQsrNzaWlS5fq7LMCQh3xsrKyGAWzjY0NvX79mjw9PcnX11dnoJIC5cqVo6SkJCpZsiRVr16dxo0bR2/evKH4+HgqU6YMK/3169fp6tWr5OXlxf6w/z+OHDlCf/31F1WoUIGI8gyhkZGRFBoaSkePHiUi9v2GQpnn7u5OSUlJKg6UXPq8ePFirderVatGY8aM0XhNeZ04ceIE5zaVMW/ePAJAXbp0oZiYGJU9o0wmIzc3NwoODma9j1AFNZ81Wyy+P3v2bLp37x45OjqSm5ubGo/XNnaF8r5Dhw5Ro0aNqGTJkvTp0ycaN24cbdu2jWrWrElEeYbyuLg4rc63vXv3psTERI3X2rRpQwBoxYoVOvswZ84cyszMJKI8x4DMzEzasmULlSxZkjVISoH9+/fT3r17GWcCPhCiJH3//j1FRkbybpvP2BMqZykCKH/8+EExMTE0duxYcnd350yvbFAaN26c3gYloWNXSP/F4Ft8HbwV+PLli8reVCKR0JIlS6hfv35UvXp12rhxo1ZaMfiOWMYZvnz3+fPnGg1+yti8eTO1bt2aV7/i4uI4/U7bfmfChAkMX9IFPgFD58+fp7Vr1zJ/r1u3jmxtbSk5OZkMDQ1p1qxZtGjRIp3Ot5cvX6ZFixaRVColCwsLWrx4Mbm6ulKtWrXo4MGD5Orqytp3vv1XoHTp0pSamkoLFy6klJQUMjU1pQ4dOlC/fv1YHVB9fHxo5MiR1KdPH2rYsCG1a9eOGjRooFG+0QQxnDmWLVtGgwYNotatWzPyhaGhIXXs2JEZF6VKldKopxMStLJ27VpKTEyko0ePMmutAseOHaOmTZvSunXrdOqIiIjKlClDKSkp5O7uTpUqVaIZM2aQTCaj5cuXczKKCXGcHzFiBOXm5lKtWrXo8+fPVK1aNTI2NqahQ4eqOUPnx8SJE6lChQpUsmRJ6tu3L5UqVYpx5lq8eDF9+/aN1q1bp/Meq1atoiZNmpCLiwujB3vy5Al5eHjQrl27iCjPWUN576ps9M5vAOcKoU54YvBdofpBojwH6EuXLvE2nkZHR9Px48dpyZIl1L59e1q0aBE9e/aMli1bRtOmTdNII8a6qwBfx/dnz56pyHIlSpSgEydOUGhoKLVv355mzJihkU6obUL5Wwt1huAj4yvQvHlzunDhAs2dO5dx8Pf29qaLFy9yckbhM+8USE9Pp7p169Ljx49JIpFQSEgIbd68mXE6/fDhA3Xu3Fkn33vx4gUdPXqUbG1tqXbt2irOWFlZWTR79mwaN26czmeIiYlheBDXYD0xeAdRnhPYixcviChvnQoLC6MNGzaQTCZT2RNoghAdrb29PT158oTc3NyYc2XKlKFjx45RaGgoPX/+XCutVCqlbdu20ZYtW2jTpk0qjq8TJkzgvEcTy7bi5uZGAKh48eK89Lv6rnvGxsaUkZGhcq5t27YklUqpVatWWgOb84OvnC92PxT7NDc3Nxo/fjxt3ryZFi5cqHW/Pm/ePBozZgwTNPP8+XOaO3cuDRo0iHJycmj27Nnk7OxMPXr00NnunTt3aNeuXfTw4UOSSCTk7u5OTZs2ZV2DxEqs4uzsTKmpqbRhwwZmv9q5c2dq06YN532nk5MTHT58mG7dukXp6elElOdQr6zzy7+nU4a+7z5//48cOULTpk2jcuXKcQ5WFCKrimUXmjt3Ljk6OtKMGTPo1atXRETk6OhIgwYNYoKW69atq9MpzNfXl7Zs2aLG3zdv3syaXGXz5s20detWatCggd599/b2pj179tDgwYMpLe3/Y++9o6JIvv/v9ww5g4iISEYREBVzRFDEnHMWc0LM6JoFc8SIWcEcUDFnMGAO4GIAUUBd06qIiEq6zx88078ZJnRP97DuZ7++zumjdM/tru6uvlU3VFUSMjIyZN5xenq6yr6AJLmLiPDw4UOZNktXVxdVq1ZVOrkIUORjv3DhAurUqYOgoCD06dMHW7ZsQUZGBsaNG8f5PpYsWYKgoCCsXbuW8TffuXMHwcHBWLp0qdzvJX0sIoKJiYmMf0ZXVxd169bFkCFDlF5PU3VHSFxEU3aykD6XJiYHUNdOF+rfk4bvt6ulpYWAgAA8fvxYbf+U0JiiJm2N4cOHo2bNmjhx4oTa72/cuHFo164dNm3axPRV8vPzMXjwYIwdO1ap/1yaTp064dKlSyrzdhShqUFD48ePx+jRo/Hhwwc0adIEQNHgtWXLlgmeJJGNwMBAJCQkoHHjxpgyZQratm2LNWvWIC8vj3N8gO8g5by8PEybNo3RPcOHD8fAgQOZ4+/evUO5cuVKbODAb/5HKOGZdX/zm38UAwMDuaX51CE/P5/CwsKobNmyzBT7ZcuWpXnz5jFL3aSnp9PLly8Vyi9fvpxZgubcuXOkr69Penp6JBaLaeXKlSqvLb3cOhHR8OHDqXPnzszfly5dIkdHR9byx8XF0efPn7ncrhynT58mXV1dql27NjO9fu3atUlPT4/Onj2rVC4rK4s8PT3JxMSERowYQStXrqSVK1fS8OHDycTEhDw8PCgrK4v1+oWFhZSens55qffi9OzZk1nKaO7cuWRlZUWDBw8mBweHEltGl6io3j1//lxmX1ZWFtWrV4+aNGlCz58/5zRVv5Bl3Z48eUJubm6kq6tLLi4u5OLiQrq6ulSpUiV6+vQpEREdPnyYIiMjFcoLrft5eXk0Z84cpcfZECr/q3B0dGTduCxj3KxZM2aJr8GDB1Pt2rVp586d1Lx5c6pduzarvNAlAYU8f6Hv7uPHj8yScAUFBbRgwQJq27YtjR8/nlkqVxVeXl4Klzc7deoUValSRaWsSCSicuXKCVoCnIioffv2tHHjRiIimjBhArm6ulJYWBhVr16dmjZtqlJWT09PTn9wJScnhxo0aEBisZgCAgIoODiYgoODKSAggMRiMbVu3ZoKCgro2bNntG3bNjn5goIC0tHRoeTkZF7XT01NZZa7zM7OpmHDhpGXlxd16tSJU1vMt81q0KAB7d27V+nxY8eOsS6JKPTeO3bsSPv27eMlqwnCw8PJ0NCQRo0aRX369CFdXV2aP38+c5zrEjGDBg1ilvNZs2YNGRgYkL+/P5mbm9PAgQNLrPyaQGj9q1mzJp0+fZqIiNq2bUt9+/alV69e0eTJk8nZ2ZlV/vbt28yyPO/evaPmzZuTiYkJVa9ene7fv88q36hRI2YpXq4YGRnJ1dm8vDzq0KEDValShRITEzm9d77o6+vT48ePlR5/9OgRs5yoMnJzc0lLS0vhcqRciY2Npby8PN7yISEhzHKqe/fuJW1tbXJ1dSVdXV0KCQlhlRfaZgvR+0KX5uKr+zS1nOavRuhynkREDRs2pAEDBsjUwby8POrfvz81atRIpezAgQNp/fr1vK8tpO4JtbOIipYsV7fu+vr6kq+vL4lEIqpfvz7zt6+vLwUEBNDQoUM51Ueh7TYRv/ITCdNb0sv68aFWrVpK7bdRo0aRubk55yXh+OodoqJnEBgYyPscfPWup6enXD9x165dzFKBe/bsIR0dHV5l0gQpKSlKlxCXho+toK+vL9OfadmyJU2aNIn5++nTp1SqVCmV17WwsFC4NP2SJUvI3NycoqOjOdUfIbaOUAoKCujMmTPUv39/MjU1JQsLCxoyZAjFxsZykk9OTqYlS5bQqFGjaPTo0bRs2TJmOVB1+Pr1KyUkJFBCQgJ9/fqV9fdCl3Ns1qwZLViwQOnxefPmUUBAAGs5Tp8+TYcOHSKiovrq5uZGIpGISpcuTRcuXGCVr1ixIt24cYOIimwwSZn27t1LVlZWrPJERD9//qSkpCS6efMmp2cn4fnz59SiRQuZ5VDFYjE1b95c4RKXiigoKKBTp05ReHg4hYeH0+nTp6mgoIBVLjc3l5o0aSKozRGKEL3L1z949OhRZtu8eTPZ29vTrFmz6ODBgzLHFC0xWxw7OztmqXDpZZkjIyOpZcuWrPcu1F7gi5OTE50/f15u/+vXr6lixYrUrFkzVr0pJDaRm5tLzs7Ogvqrv9rG5/vddejQgVq3bk0fPnyglJQUat26NTk5OTHLyrLpTU0tIV22bFmlfS82NK07vn37Rnfv3qUPHz5wluHjo+3ZsyeNHTtW4bE///yTrKysStzWExpb+fbtGw0cOJC0tLRIS0uLaetHjx6tsj0tjrrtXrNmzWjJkiUKz7V7927S0dHh9OyE2vmaKMerV69o3rx55OrqSjY2NjRp0iSV/h8iokqVKjH26b1790hbW5s2b97MHN+8eTPVqFFD5Tnmz59PWlpaJBaLqWzZsmRtbU1isZh0dHSU3pMEX19fxlaQtjOLb35+firP86vh8+yVcefOHVq5ciWnmIpQW5VIszG9L1++0JcvX9SWi4mJIW1tberXrx9t376dtm/fTn379iVtbW06fPiwSlkbGxumb6Qu0dHRpKurS02aNCFra2tq06aNzPHJkydT165dWc8zYMAAXvddnOvXr9OyZcsoJiZGLTlzc3PS1dUlsVhMurq6Mv+3sLCQ2aSZPXs2Y5f/0wiNi2jKThbS5zIzM6OrV6+yXkMVQux0vv4xRaj77daoUUNhf/ufQhO2hqGhIWebtDjK4itJSUlkYGDA6RxhYWFUunRp6t+/Py1dupTpd0s2ZVhZWdGdO3fk9u/Zs4cMDQ1p/fr1nPt869atI1tbW8ZWd3Jyoh07dnCS1SRpaWl06NAhhd+0Mho2bMi0Dz179qQWLVrQ1atXqV+/fuTp6alUbtasWWRtbU1LliyhadOmkZmZGQ0dOpQ5/vbtWxKJRLzv5Tf/DX4n3/7mP0Xjxo1ZO9Rc4dvZl0YdpV/cOVelShWZRjI9PZ01oYFIWGCtWrVqCoNfISEhrAlomZmZNGLECCpVqhTT2FpYWNCIESM4GXtEwoOqQpP4+OLm5qYw+e/r169Ur149qlq1KqcOi6GhIa9AkAS+Ds7i8K37xsbG9OLFC7XlhMoL7Sz/Sse+BKFJXELrDpGw9yf03QtBX19f4bVfvHhBhoaGKmXZnJtcnZxCkgCFGJszZ84ke3t7hW3MgwcPyN7ensaMGUO2tra0atUqhefw8PCg69ev87q+JuDTZl29elXld7F27VpavXo163mE3LvQYCRRUfLxkiVLqFatWmRtba3SoaWo7BIHNxHRtWvXyMrKimbMmEFE3ANLBQUFMglke/bsoaCgIFq1ahX9/PlTpawmAoO/kqioKCYp/c6dO1S6dGkSi8Wkr6+vMrlbU+zfv588PDxo27ZtdOfOHSahQ7IpwsvLiw4ePCi3X5KAa29vz9lBEhQUpNAZs3r1agoODlYoU6lSJYqKilJ6zsjISHJzc2O9tpOTEz148IBTORVx9+5dmQFrR44cofbt29PUqVNZ660i1HVQC22zf7WTkY/uMzU1pWfPnsns27VrFxkZGdGxY8fUSr79/Pkzbdq0iaZMmUIfP34koqJ3+urVK5Vyt27dYgKh0ty4cYNu377N6dpRUVHUpUsX3oOViIQ5SefPn8/LOSpBSN3TRPJqv379aPny5bxkNRFQEtpnEVJ+vnpr3rx5dOzYMZl9O3bsIEdHR7KysqIhQ4bQjx8/lMrPnz9fZZLSiBEjODl2NaF3NBmc4ap3fX19qW7dugq/2X379pG2tjYtXrxY5TlycnLoypUrlJSUJHfs+/fvgoIDkZGRZGNjw/o7PrZCmTJlZOqcpaWlTB8gOTmZjIyMVF63UaNGShP+Fy1axAwUL4nyS/P582daunQpDRo0iAYNGkTLly+nzMxMVrnifP/+nfbv38/ZvyIkmUMoQgetWFtbq9Tr9+7dI2tra15l+/jxIxUWFnL6rdABS5rg06dPdPPmTbp58ybTb/gnKF26tKA2UxNJeEL0Lh//oMSXy7Zx+f6MjIyYpElbW1u6efMmERUlVbPpLiLh9gJfBg0apDRZ4tWrV+Tq6sp6/0JjE+XKlRNkYwux8cViscJErL///rvEky/LlCkjY+MVFhbS8OHDyd7enlJTU1n1pr+/PwUGBlJBQQFlZWXRiBEjyNLSku7du0dE3H0kpUqVkrN71EGo7tAE6vpoExISaOvWrUqPP3z4UKlv9PXr1zRhwgSF/fzMzEyaOHEivX37lrUMQmMrY8aMoRo1atCVK1fIyMiI8ZEfOXKEqlWrxnp9Ceq2e9HR0UoTl4mKbGZfX1/O1+eLkHLs27ePWrRoQQYGBtShQwc6evQoMxELGwYGBoyuJyry8UonvaekpJC5ublS+YsXL5JYLKZZs2bJxO4+fvxIM2bMIC0tLYqLi+NUFk2QlJREp06d4u3j3bx5M/Xs2ZOaNm1Kfn5+MpsihDx7TSDUVpXwK+NCEo4fP07169cnQ0NDsrS0JD8/P06D9ZYuXUojR47k3Dcuzvnz52ns2LG0cOFCOZt19uzZzEAkdfjy5QsdPnyYdwK2ukgSlrls0uTk5Mjcc1paGq1YsYLOnDnDes309HROmzKExkU0ZScL6XNpYnIAIXa6EP+YUE6dOkXVqlWjY8eO0V9//cXkI2giJ4crQm0NPz8/xrZXlzJlyij8Tk6fPk1lypThdA6+k4FpatCQNO/fv1drkO2/Ab6DlF1dXWXazZSUFHJ1daUBAwZQYWHh/8zkJL8pWX4n3/7mP8W+ffvI2dmZVq9eTfHx8ZwSGf4tVKpUiVH2Hz58IC0tLZkRKDdv3uTkXBcSWNPT01PoIHr69Cnp6elxOkdhYSG9f/+e3r17x8to+dWJYHwICgqiLl26KDyWlZVFderU4dTg/uqZFIXSrl07OSPsn5IX2lnmKx8fH68RJ4VQNFF3hDx/IbInTpxgZp+U5syZM3Ty5ElWeWtra4Ud4nPnznGejedXIsTYrFixosJEPAn79+8nkUikcqRtTEwMNWzYkFfyuSYSoUoiCS0vL48ZiKEKIfcuNBhJRDRjxgyysbGhpUuXkr6+PoWGhtKgQYPI0tKSNQnLwMBAzrH58OFDsra2pilTpvxjhp46gcGOHTty3rigifonjbqzyjx//lxhnyk5OZmT01lZ3VFVhyZPnqx0lrO8vDxq164d5/derlw5hSOd7969S7a2tgpl/vjjD7K3t1cYPHvz5g3Z29sziSaq2Lx5M7Vq1Yp3AkXNmjUZ3Zeamkp6enrUs2dPcnV1VZo4/G9CE07GO3fuUFRUFEVFRTEBZa7w0X2aGhmfkJBAVlZW5OrqStra2kxQdNq0adS3b1+VsrVq1aIDBw7I7T906BCnGYeJigYampiYkLGxMVWuXFntme6JhDlJha6UIBShdlZoaCiZm5tT586daf78+WonDwtFSLtNJKz8fPVW8+bNaeHChczfiYmJpK2tTYMHD6Zly5ZR2bJladasWXxuRy00oXd+RXDm69evVKNGDWrWrBnl5uYy+/fv30+6uroyz1YRT58+JQcHB6Zt9fHxob/++os5zrW/VLyf0qFDB6pTpw5paWlxGqjHh3bt2tHAgQOpoKCADhw4QLq6ujJJCcePH6dKlSqpPMemTZuod+/eSo8vXLiQdXUlody+fZtKlSpFtra2zPMrX748WVpa0t27dzmf582bN7RixQqqUaMGiUQiqlOnjsrfayqZIzs7m6ZPn0716tUjFxcXcnJyktmUIXTQio6OjkxdLc7r169JV1eXtfyZmZkK9dbHjx95BTbZEuc13d8Xyvnz52nq1Kk0aNAgCgwMlNnYGDt2rOAEY6FJeL8yKC4ULy8vJumladOmNGHCBCIqmqlMma0hjVB7gS9paWkK/VMSXr9+zer7EhqbmDdvHvXv31/QSh98UTYL4uvXrzlNCELE/7szMTFR6FsYNWoUlS9fni5fvsw6i13x2QsXLFhAFhYWdOvWLc5t/uTJk2nu3Lmsv1OGEN3RqVMnhX2bRYsWKY07KEKof14dJkyYQEOGDFF6fNiwYTR58mTW8wiNrdjb2zN2jrGxMWNnpqSkkImJCev1lcF3JkkJXP2TEoTY+WwoS+oUiUTk4OBAf/zxh5x9xGYrWVpayny35cuXl0n4SklJIWNjY6Vl6tatm8yMccUZMmQI9ejRg+3WBJOamkpVqlSR8ctJ/s/VxzZq1CgyMjKibt26UXBwMI0dO1ZmU4SQZy/hr7/+oqioKDpx4oRcsl92djbNmTNHqaymbFUhOuft27fUp08fsrGxYQbNSW8lTYcOHcjMzIycnJyoTZs2v6S/2rVrV2YSkZycHKpQoQLp6OiQtra2yrjP/PnzacuWLXL7t2zZwmonExUlji5cuJDq169PNWvWpJCQELVWSmrWrBmTwPr582cqU6YMlS9fnvT19WndunUqZaXfcfHvjc03TiQ8LvJvsJM1MTmAEIT694R8u8VjIVzfOxHJ2ePKNjaE2hrR0dFqT6oiISgoiMqXL0979+6ljIwMysjIoD179lD58uVLPK7xbxk0JAQ+E8pwgcsgZUW659WrV1SxYkXq3bs3vX79+nfy7W9IRESE3/zmP4JYLJbbJxKJQEQQiUQoKChQKf/u3TtMnDgRFy5cwPv371H882CTHzNmDFxdXTFmzBiZ/WvWrMGzZ8+wcuVKpbILFy5EeHg4Ro4ciYsXL+LDhw/4888/meMrV67E8ePHcf78eZVlOH36NKZOnYrQ0FDUqFEDRkZGMsdNTU2VytrZ2WH58uXo2rWrzP79+/dj4sSJyMjIUHltCe/fv8fTp08BAG5ubihTpgwnOQA4duwYFi9ejPXr16Ny5cqc5QDg5MmT0NLSQvPmzWX2nz17FgUFBWjZsqVa5+PK58+f8ddff8HT01Ph8a9fv+LevXto3LixyvNs2bIFc+fORWBgILy8vKCjoyNzvF27dirlL1y4wNTdwsJCmWNbt25VKSu07gNAREQE5syZg969eyuse2zlFyK/ZcsWREdHIyoqCqVKlWItq6bkW7RoAT8/P4SEhAAAHj58iOrVq2PAgAFwd3fHkiVLMGzYMMyePVvleV68eIH8/HxUqFBBZn9KSgp0dHTg6OjIWn4hdQcQ9vyFyFapUgULFy5Eq1atZPafPn0aISEhSEhIUFnuYcOG4fr16zh8+DBcXFwAAM+ePUPnzp1Rq1YtbN68WaW8NHx11+3bt1FYWIg6derI7L958ya0tLRQs2ZNpbLS7ZZIJGL+z6Xd0tfXR0pKCuzs7BQef/nyJRwdHVWew8LCAjk5OcjPz4euri4MDAxkjn/69EmpbO3atTF58mR06dJFZn90dDQWLVqEmzdvKpWVIKTNUkZCQgKqV6/OqreE3LsmcHFxwapVq9C6dWuYmJjgwYMHzL4bN25g9+7dSmXt7e2xa9cuNGrUSGb/o0eP0KRJEzRv3hw7d+5kfQbbtm2DsbGxXLt/4MAB5OTkoH///irl58+fj+TkZGzevBna2toqfxsYGKjyePFysaGJ+ieExo0bY+DAgXLPaOfOndi8eTNiY2NVyqenp6s87uDgILcvPz8fOTk5Sr+L/Px8vH79WqFscfT19fHnn3/C1dVVZv+zZ89QuXJl/PjxQ07m69evqFevHjIyMtCnTx+4ubkBAJ48eYJdu3bBzs4ON27cgImJicpre3t749mzZ8jLy4ODg4Pcd3/v3j2V8mZmZrh37x5cXFywaNEiXLx4EWfOnMG1a9fQo0cPvHz5UqX8ggULYG1tjYEDB8rs37p1Kz58+MC06coQ2mYL0fvv379Hjx49EBsbC3NzcwBAZmYm/Pz8sHfvXlhZWam8NsBP9wUEBCAgIAATJ06UO7Znzx70798fBQUFrDrH398f1atXx+LFi2FiYoKEhAQ4OzsjPj4evXr1QlpamlJZY2NjJCYmwtnZWWb/ixcvUKVKFXz9+lXltQFgzpw5Ko/PmjWL9RxjxozB4cOHsXTpUtSvXx8AcO3aNUyaNAmdO3dWae8JRWjdE2JnAYCTk5PSYyKRCM+fP1cpf+fOHezfvx8ZGRnIzc2VORYdHc16faHttpDy89VbNjY2OHbsGNMXnDZtGuLi4nD16lUARe3trFmz8OjRI6XXTktLw7lz55Cbm4vGjRvzendC9I6EsLAwLFu2DE2bNlXYZyvuA5FGiN798OEDfHx8ULlyZezfvx+HDh1Cr169MHv2bPzxxx8qy9yxY0fk5eVh+/btyMzMxNixY/Ho0SPExsbC3t4e7969Q7ly5Vjvv3gfRiwWw8rKCk2aNEFAQIBKWYCfrZCYmIimTZsiKysL+fn5+OOPPxAaGsoc79u3L4yMjBAREcF6faEIsXUaNWoEV1dXbNq0iekr5ufnY/DgwXj+/DkuX76sVDYrKwuHDh3C7t27ERsbC2dnZ/Tu3Ru9e/dm7D5ldO/eHebm5tiwYYPC40OHDsXXr1+xZ88elefp2bMn4uLi0LdvX9jY2Mh8PwAQHBysUK5MmTI4deoUatSoIbN/7969GDRoEJYtW4ZRo0YprXtaWlp4+/at0nada91t2bIl2rZti5EjR8rsj4iIQExMDE6ePKlSXl003d8Xwpw5czB37lzUrFlT4bs7fPiwSvmgoCBERkaiQoUKCnXe8uXLWcswbtw46OnpYeHCherfAITpXSH+QQCIjIxE9+7doaenJ7M/NzcXe/fuRb9+/VTKr1ixAlpaWhgzZgzOnz+Ptm3bgoiQl5eH5cuXK/12JAi1F4Ty8+dP5Ofny12XC0JjEx07dsSFCxdgbGwMLy8vuTIo6zNx9dnb29vL7Vu1ahWAojobGhoKY2Nj5lhBQQEuX76MtLQ03L9/X+W5hXx3tWvXRlBQEPr27St3bPTo0di1axeysrKUPr9SpUohNjYWVapUkdm/dOlSzJs3D1u3bkWXLl1Yn39wcDAiIyNRpUoVVKlSRc6/yvbtC9EdVlZWuHjxIry8vGT2P3z4EP7+/nj37p3Ka0tQ10f77ds3TJw4ETExMcjNzUXTpk2xevVqTrZl5cqVERERgYYNGyo8Hh8fjyFDhiApKUnleYTGVgwNDfHnn3/C2dlZxs5MSEiAj48Pvnz5wnovJQFX/6Qm7HxlJCcnY8uWLYiMjMSbN2/kjjs6Osp9q8VRZis1bNgQQUFB6N69u0K548ePY+rUqXj48KHC405OToiKilJaf65cuYJ+/frhxYsXSstW3L5Qhqq2r23bttDS0sLmzZvh5OSEW7du4ePHj5gwYQKWLl0q53tVROnSpREZGSkX31CFkGcPFPXRAwICUFhYiLy8PNja2uLIkSPMd8TWX9SErQoIiwu1bNkSGRkZGD16tMJ2o3379iqvLRS2viuX/urt27exZ88eJCcnAwAqVqyIXr16qbSRpClbtizOnDmDqlWrYvfu3Zg1axYSEhKwY8cObNy4UWnb6+joiN27dzN+KQk3b95Ejx49VH43ABAaGorZs2fD398fBgYGOHPmDHr27MmpnwgU1fm4uDh4enpi8+bNWL16Ne7fv49Dhw5h5syZePz4sVJZbW1tlC9fHgMGDEDbtm2VxhSqVq2qcL+m4iJCcXZ2RuPGjRERESHTZ/77779Ru3ZtuW/X29tbpo4/e/YMRARHR0e5/gaXvq4QO12of0/ItxsXF6fy3KryKMRiMRwcHNCrVy+VsduStjWE9Pdzc3MxadIkREREID8/HwCgo6ODESNGYOHChXL217+F6tWr48KFC7CwsJCry8UpSVvN1tYWMTExcr6We/fuoV27dnj16hXrOb58+YKCggK5XJBPnz5BW1tbaezN2dkZmzZtQtOmTWX2//XXX/Dz84ODgwMuXLhQ4rrnN/9ufiff/uY/BZ9EBmmEdvaFKP3CwkLMnj0bx44dQ9myZbF8+XK4u7szx7t27YoWLVpg0KBBKssgJLA2d+5crFixAlOmTJEJJi9atAjjx4/HjBkzVF47KysLo0aNwt69e5nraGlpoXv37li7di3MzMxUygPCgqpCk/iEQER49uwZcnNz4ebmxpqEpAhFHTYJbO9OaGBBE4aukPILlRfaWf6VAXVAeBKX0Gcv9BxCZA0MDPD48WO5hJG0tDR4enri27dvyguNoo5yixYtcOfOHZQvXx5AUdKpj48PoqOjGaelKr5+/YqRI0fy1l1CkgCFGJvKgroSbt++jVatWuHDhw9Kz7F9+3aVhpKq5EtNJEJpIhmkOFyd20LuXRMYGRnh8ePHsLe3h42NDU6cOIHq1avj+fPn8Pb2Vhkc6NWrF6ytrbFixQq5Y0lJSfDz88PHjx9Zn0HFihWxYcMG+Pn5yeyPi4vD0KFDmWR0ZfANDGoCofWvc+fOqF27tlzCz+LFi3H79m0cOHBApbypqSnu3bunMHm1Zs2ayMzM5H4zAsnPz8ePHz9kgqRsVK5cGcOHD8fo0aNl9q9evRrr169X2m59+fIFU6dOxb59+/D582cAgLm5OXr06IF58+bBwsKC9dqzZ89W+e2xJUCampri7t27qFChApo1a4Y2bdogODgYGRkZcHNzw/fv31XKC3VQC22zhej97t274/nz54iMjGTshEePHqF///5wdXVlTSIC+Om+w4cP4/Llywp1DgDs3r0bmzZtwqVLl1ReWzpxWjoomp6eDjc3N4VJ3xIsLS1x/Phx1KtXT2Z/fHw8WrduzdTHkqa4k5SIoKur+484SYXWvV856ESSqNO8eXOcPXsWAQEBSE5Oxrt379CxY0dOQa1f2W7z1VvFB0o1bNgQLVu2xLRp0wAU9Xe9vLyUtlmXLl1CmzZtGL2mra2NrVu3ok+fPmqVX4jekSAkOCNU7758+RINGzZEhQoVcOXKFcyYMQPTp09nLbO1tTXOnz/PJLEQEUaOHImTJ0/i0qVLMDIy4pTAKBS+tsLff/+Na9euoWzZsnIBtRMnTsDDw0PlewGAGzdu4NixY0wyTYsWLf6x8gNFtt79+/dRqVIlmf2PHj1CzZo1kZOTo1LWwsIC3bt3R+/evTkHsAHNJHMARX2cEydOoEGDBpyvDQgftCIWi9GyZUulbcrPnz9x+vRp1rpbqlQpXLt2Tca3CBQNnGrQoAE+fvyoUl7ogKVfiY2NDRYvXqwwkY8Lxe2j4rD1eQDhCbx89a5Q/yBQ5A958+aNXFD748ePKFOmjNp6Mz09HXfv3oWrq6tccqQihNoLfPnw4QP69euH8+fPo7CwELVq1cLOnTvlbD5VCI1N8E0E0tLSYv4vCfdx9bFI6lp6ejrKly8vcy5dXV04Ojpi7ty5cm1RcYR8dwsWLMCVK1eUDgoYOXIkIiIi5JLJJfj4+KBXr14YPny43LHFixdj5syZyMvLY627qr59kUiEixcv8pYHVOsOAwMDPHjwgBnkKuHJkyfw9vZmtXMlqOujHT9+PDZu3IjevXvDwMAAu3fvRoMGDTjpCmm/liIyMjLg7u7O6tsFhMVWfHx80LVrVwQFBcHExASJiYlwcnJCUFAQUlJScPr0aU7n0XS7l5CQAG9vb6X1VoIm7HxpcnJysG/fPmzduhXXr19HzZo10blzZ0yaNEmt87Bx7do1GBkZoVq1agqPr1u3DoWFhXJ+JwmGhoZITk5m/PnFefXqFSpUqKCy7ksSsby9veUmk5FGVX0uXbo0Ll68iCpVqsDMzAy3bt2Cm5sbLl68iAkTJrAOPACAcuXKITY2FhUrVmT9raZo1qwZ7OzssHnzZnz79g0hISHYv38/zp07B29vb9bkW6G2qgQhcSETExNcuXJFaR3iQkFBAVasWKF0oG9J+jkmT56MpUuXwtjYmPFPp6amIicnBxMnTsSiRYtYz2FgYIDk5GTY2dmhX79+KFeuHBYuXIiMjAx4eHggOztboZy+vj4eP34s1198/vw5PDw8VPrXAKBChQqYOHEihg0bBgA4f/48Wrduje/fv6t8pxIMDQ3x5MkT2Nvbo1u3bvD09MSsWbPw8uVLuLm5qbTz3r59ix07dmDbtm3IzMxEnz59MGjQIDmbRRmaiItowk4Wi8VwdXWFubk5YmJiULZsWQDKE9/ZJgSQhktf91dOTCLk283IyICdnZ1cX5+I8PLlS6V9CqAo3r5161bExsaiZcuWGDhwIFq1asWpzkoj1NYQ2t8Hitrq1NRUAEWT9BgaGrLKSCgoKMD27duVDrZU1V/dt2+fzIArRX1nRcyZMweTJk2CoaHhL7PVAH4TyhSH7yDlwYMHg4iwZcsWuWOvX7+Gr68vnj9//jv59v86/9gcu7/5zT9AXFycwmWZ8vLyOC0pZ2xsTPfv3+d9fT09PUpJSZHbn5KSQnp6erzPqw6xsbEqN1UUFhbS8uXLydbWlpn239bWllauXMk63TpR0VIxFSpUoNOnTzNLWJ4+fZrc3Nyoe/funMq/bds22r59u9JNFfr6+gqXen7x4gUZGhpyuj4fnj9/TpUrV2aWR7C3t+e15LUQypYtS5GRkbzlhdb9X82sWbNo9uzZSreSktfT06OMjAzm7wYNGlBYWBjz94sXL1QuryTBxMREqe4wMzNjlf9fxtrami5cuCC3/9y5c2RlZcXpHIWFhXTmzBlavHgxrV69mi5fvqxWGYTqLiMjI2ZJM2meP3/O+v7T09MV6tfCwkJKT09nLXenTp2UHu/UqRN17dpV5TmEUKpUKYqPj5fbf+3aNTI3N+d0DiFtljIePHhQIst7hIeH0/fv35n/810STELFihXpxo0bRFSkOxYsWEBERHv37mWt+wkJCbR161alxx8+fMhJ9+np6SltN7ksKzlgwACVW0kitP6VLl2aEhMT5fYnJiayLh1PVLScsKJlAO/cuaP0uz969CizbPbRo0dVboqIiYmhbdu2yewLCwsjPT090tLSombNmsksrayKLVu2kIGBAc2cOZP53mbMmEGGhoa0ceNGVvnCwkJ6//49vXv3jlMfUZP4+flRv379KDIyknR0dJj2MzY2lhwcHFjl9fT06Pnz53L7U1NTOfXXhbbZQvS+qakp3bp1S27/zZs3f2l/getymlZWVsx3I70c6NmzZ6l8+fIqZXv06EGNGzemzMxMZt/nz5+pcePGJdrWKePbt2+UmJhIiYmJai0T9/LlS1q7di2FhITQuHHjZDY2hNY9IXYWEdGcOXMU3mtOTo7K5SyJipafXrNmDRH9v3dfWFhIQ4YMoZkzZ7JeWxMIKT9f7O3tGT/Ez58/ycDAgM6fP88cT0xMJAsLC6XyDRo0oPbt29Nff/1Fnz59opEjR5KNjY3a5RCidzQBX70rvWzfvn37SE9Pj7p168Z5ST+hS1gX5/bt2xQZGUmRkZF0584dznJCbAVVvHr1SuXxAwcOkFgsJiMjIzI3NyexWExLlixR+zpCyl+mTBk6c+aM3P7Tp0+z9rfOnj1LBQUF6hX2/8fAwIBevnyp9PjLly859XUdHR0V1iE2hC7nyNbH5trXNjQ0VNrfNTAwYJV3cHCga9euye2/ceNGiS/FKpRSpUrRs2fPfmkZfH19VW4lhVD/IFHRcrDv37+X2//gwQOV7ZYEaT/Z/xKBgYFUtmxZmj9/Pi1fvpzc3NzUfldCYxN80dLSIgcHB5o1axbduXOHHjx4oHBTha+vL2d7UhG/8rvbtGkT9enTR+nxf2IJaaHUqlVLYZ901qxZVL169RK7rqOjI+3fv5/5+86dO6Stra2wHhfH0tJSZb2Oi4sjS0tL1vMIja1cuXKFjI2Nafjw4aSvr0/BwcHUrFkzMjIyUqvPpul2j6t/UlN2/vXr12nQoEFkampKlStXJi0tLVYfuaI+uqZg8xOIRCJ69+6d0uNsS8cTEY0cOZIsLCyoWrVqFB4ezmsJcXNzc+Y5ODs708WLF4mI6NmzZ5z6S0RES5cupZEjR6rlHxP67C0sLOjp06cy+xYsWEAWFhZ069Yt1ucn1FbVBO7u7gp9q+owY8YMsrGxoaVLl5K+vj6FhobSoEGDyNLSkrOPXpovX77QunXrqEaNGip/t337dtLX16fVq1czvl4iotzcXAoPDyd9fX3asWMH6/UqVKhA+/bto+zsbLKysmLiZA8ePFCpP11dXSkqKkpuf2RkJDk5ObFeV1dXV66/pqenp9KGksbLy4vCw8MpIyODTE1NGR/9nTt3yNramtM5iIr098CBA8nExITq1KlDGzduZLUBhcZFNGUni8ViSk1NpY4dO1K5cuUYPc5Fd2kCIXa6UP+YkG9XLBYr1P1///035+f26tUrCgsLI1dXVypXrhyFhIRQcnIyr/L8U+Tn51NCQgLl5OTIHcvJyaGEhATO/o9Ro0aRkZERdevWjYKDg2ns2LEymzLWrVtHIpGIKlasSFWrViWxWEwTJ07kfU+/Ak9PT1q9erXc/lWrVpG7uzunc1hYWCj08zx+/JhKlSqlVC4tLY1Onz6t9Pjr1685+dd/89/md/Ltb/5TCG20hXb2NaH0haKpwFpWVhZlZWWpdW1DQ0O6cuWK3P7Lly+XaPKrBE0k8fGhc+fOVKlSJdq9ezdFR0dT/fr1S9QppgihDk5NGLo7duygHz9+yO3/+fMnJ0NTqPyvQFNOCj5JXJpGyPMXIjt06FDy8vKSqb8pKSlUpUoVGjRokFK5+Ph4OnbsmMy+7du3k4ODA1lZWdGQIUMUlkkRQnWXkCRAIe1WUlISGRsbU506dWjfvn2UkJBADx48oD179lDt2rXJ2NiY/vzzzxK7viYSoUoiGYSrc1vde3d0dKS///6b+b+yjYuDi4goJCSE5s2bR0RFCbfa2trk6upKurq6FBISwukcQrGzs1OY6HnkyBGytbUt0WsfOHCAunbtSnXq1CFvb2+ZjQtC65++vj49efJEbv/jx485JWO0adOGunbtSvn5+cy+/Px86ty5M7Vo0UKhjHRgQTLISdGmrP76+voyyWtERTpGLBZTWFgYHTp0iCpVqsQpgU/CunXrZAZcOTk5cW5vExIS6MCBA3TgwAGFSR2qcHJyYr4laT5//szp+0lISKDKlSuTqampjDN19OjR1LNnT1Z5oQ5qoW22EL2rbLDUvXv3yMTEhPXaQq+vDK56d9CgQdShQwfKzc0lY2Njev78OaWnp5O3tzcFBwerlH316hU5OzuTmZkZk7Ribm5Obm5unBM88vPzacmSJVSrVi2ytrYmCwsLmU0VgYGBnDZVnD9/ngwNDaly5cqkra1N1apVI3NzczIzMyM/Pz/W8v/q/qKQumNoaMgM9ihVqhSjNx49ekRly5Yt8esLleert4YPH0716tWjy5cv0/jx48nS0pJ+/vzJHN+5cyfVrFlTqbyZmRklJSUxf3/79o20tLQUlkUVmvjuhQRn+OpdSZso/a+i/yujVq1aShPQRo0axQTa2Hj58iU1bNiQRCIRoy9EIhE1aNCAU3BSEwPWpHnz5g2NHj2aNRmgevXqNGzYMKavMn/+fF4BdCHlDwoKovLly9PevXspIyODMjIyaM+ePVS+fHlWvU9UlLRx7tw5ioiIYPxTr1+/pq9fv6qU00QyBxFRVFQUdenSRa1BFv8mfH19afTo0XL7R44cSQ0bNmSVVzdxvlq1anL9emVbSTN58mSaO3cub/nAwECFPtHs7GzW9l5T8NW7QvyDkncoFovJy8tL5p1VqVKFTExMONlaYrGYfHx8aOPGjbySOYXaC3wpX768TEA1OTmZtLS0OPuWiIS3uX5+fvT582e5/V++fFHZX3zz5g0tXLiQ3NzcyNramiZMmMBr8IAQhH53ikhLS6OkpCTegzH+aYTojpiYGNLW1qZ+/foxg+P69u1L2tradPjwYc5lUNdHq62tLZcgaWBgwMkn16pVKxo8eLDS44MGDaKWLVuynkcTsZVnz57R4MGDqVatWuTu7k69e/dW21chdKBucbjayULt/KVLl5KHhwfZ2trSxIkTmUR7bW1tGVtCESKRiBwdHSkwMJAiIyM5J95xge3+RSIRzZs3T+nEBmFhYZye348fP2j37t3k7+9PhoaG1LVrVzp9+jTnRNiGDRsy31jPnj2pRYsWdPXqVerXrx95enpyOkeHDh3IzMyMnJycqE2bNtSxY0eZTdn9C3n2FhYWCgciLlmyhMzNzSk6Olrl8xNqq0oQEhc6c+YMBQQEKJwYgivOzs50/PhxIir6liR9oPDwcE4+QgkXL16kPn36kKGhIdnY2NDIkSNV/r5WrVq0fPlypceXLVtGtWrVYr3u2rVrSVtbm8zNzalKlSpMe7dq1SqVA4AWLVpElpaWtHXrVkpLS6O0tDTasmULWVpa0vz581mvKxaL5QZaSfx0XDhw4ADp6OiQWCwmf39/Zv/8+fOV+sVV8fbtW/Lz8yOxWMwriV4dNGUnS9ucU6ZMIQMDA4qKilIr+ZbvAF+iXxeTJBL27Sob5JeWlsYrjyQ2NpZ8fX1JLBZztjv42BpCJ1XZtm0b1ahRQyaWJCEvL49q1Kih0G+mCEtLSzpx4gSn30rj4eEhE0uJiori9cwHDRpEly5dUltOEwidUIZI+CDl3/xGFb+Tb3/zn0JZo/306VNOhqrQzr4mlL6QoDARv05TTk4OHT16VKFz6MuXL3T06FFOjkY7OzuFDVZCQgLnJB4hnT6+SXxCsba2lknc++uvv0gsFlN2djarrKZmUhTq4NSEofu/GBAXKq8pJwWfJC5Nz8Ip5PkLkc3MzKS6deuStrY2k7yora2tNOAgoUWLFrRw4ULm78TERNLR0aHBgwfTsmXLqGzZsjRr1iyV15YgVHcJSQIUamxev36dPDw8mOQDSTKCu7u7wtkaFF1f0bt7/fo1awKiJhKh+NSd4rOdFd/27dvHSecIufeSID4+npYtW0YxMTGsv/3w4QOlpaXJ7Pvzzz9pwIAB1LVrV9q1axena06ePJkcHBzo4sWLlJ+fT/n5+XThwgVycHCgCRMmsMrzDQyGh4eTsbExjR49mnR1dWnYsGHk7+9PZmZm9Mcff3Aqu9D6J3RWmaSkJLK0tCQXFxdm9jEXFxeysrKihw8fcroHdZGeNZSIaNy4cdS8eXPm7xMnTpCrq6va533//j1rEouEmzdvMjPSSCc+eXl5KZypRRHKvr23b9+Sjo6OWmWX5vv37zKzTShDqIOaT5stjRC9365dO/Lx8ZEJjr569YoaN25MHTp0YL225Pqa1n0PHjwgkUjE+rvMzEzy9/cnc3Nz0tLSIjs7O9LR0SEfHx9O/ebs7GzasGEDjRw5kiZMmEA7duzg9M4lCJkRRRIY69ixI3Xo0EHppopatWoxs7xKZn/9+vUrtWvXjtatW8dafqF1T2hfXVndvXDhApUuXVqlrK2tLdPX8vLyot27dxNRUdtnamrKem3J9YXUXSHl56u3Pnz4QI0aNSKRSEQmJiYUHR0tc7xJkyYq2z1F15WeNZormghuCKk/fPWu5LdsmzLmz5+vMuFjxIgRnHRX8+bNqU6dOjKDdp48eUL16tWTaYeVwcdW+PTpE/Xo0YMsLS3JxsaGwsPDqaCggGbMmEEGBgZUp04d2rt3r8rrGhkZycyW/fPnT9LW1laZlKqp8ktfc8yYMaSrq8vYKnp6ejR27FhWH1NaWhpVqlSJDA0NSUtLi6n3Y8aMoWHDhqmU1VQyR7Vq1cjExISMjY2pcuXKghJIv3//Ttu3b6e1a9dympUnNzeXtLS0BPUrr169Svr6+tSoUSNmVZ9GjRqRvr4+p9Vi1E2cV7WakLqrEwllzJgxZG5uTj4+PjR69Gi1Z5tXpvM+fPhAWlpanMogNIGXr94V4h+UvB+RSEQTJ06UeWfz58+n3bt3y/i8lHHv3j2aOHEilS9fnvT09Kh9+/Z04MABzkmsJWUvsCEWi+nNmzcy+6QHEHFBaGxC2b2/e/eOtLW1OZWBzyxyEoSs0iDku9uyZQstW7ZMZt+QIUOYtsPd3Z3XjMqxsbF04sQJtZLAb9++TZMmTaLu3btzSqCTRqjuOH78ONWvX58MDQ3J0tKS/Pz81F4VSl3doSgBy8TEhFMC1sWLF0lLS4smTJhAb9++Zfa/ffuWxo8fT1paWgonSimOkNiKJlG33dOUf1Kona+lpUV//PGHXEIPl+TbS5cu0axZs6hx48akr69PYrGYXF1daejQobRnzx6Z96oubMm3Dg4OKic3kGzqkJaWRrNnzyZnZ2eyt7fn5Os6ffo0HTp0iIiKYolubm4kEomodOnSnOovEb+VwYQ++0aNGtH69esVHlu0aBHp6empfP5CbVUJQuxEc3Nzxk4wNjZWOxZOVNROSwYLlC1blu7evUtERUnzbL4GyeyZLi4uZGlpSWKxmPbu3cspcdvQ0FClXZ6amsrZ1r59+zZFR0fL1Nfjx4/T1atXlcoUFhbS5MmTmbojFovJ0NCQ86o+IpGIWrVqJdPGaWtrU0BAAOd2782bN3Tv3j2ZPsbNmzfp8ePHnMpAVJQoKpmxu1atWrR+/XrWPovQuIim7OTidT8qKor09fUpMDCQte4LHeBLVDIxSS7+MSJ+366kPygWi2nYsGEyfcQxY8ZQnTp1qH79+qzXlvD9+3eKiooiPz8/MjAwoO7du5eorSF0UpWGDRvSnj17lJZp37591KhRI07lt7GxkZv5nAvFV48uKCggXV1d+uuvv9Q6T7t27UhPT4/Kly8vM+jnn0LIhDJE/AcpP336lG7evCmz7/z58+Tr60u1atViJlr6zf9ttPGb3/wH6NSpEwBAJBJhwIAB0NPTY44VFBQgMTER9evXZz1P9+7dkZOTAxcXFxgaGkJHR0fm+KdPn1TKDxw4ED9//sS8efMQGhoKAHB0dMT69evRr18/TvcyZ84cbN68GRMmTMD06dMxbdo0pKWl4ciRI5g5cyarPBFBJBLJ7c/Ozoa+vr5CmY0bNyImJgbt2rWTO2ZqaopVq1bh5cuXGDVqlMprT58+HePHj0dUVBTKli0LAHj79i0mTZqEGTNmsJZdUn5F/Pz5E7q6uiplFy9ejBYtWqBSpUooX748AODVq1do1KgRli5dyun6fHj//j0qVKjA/G1jYwMDAwO8f/8eTk5OKmVXrFiB3r17Q19fHytWrFD6O5FIhDFjxig9/uPHD2zcuBHnz59HlSpV5Oru8uXLVZZDaN0HlNe9V69ewczMrETl09LSUFBQILf/58+fePXqFeu1+cqHhoaiU6dOaNy4MYyNjbFjxw6Zerp161YEBASwXn/RokXw8fGBm5sbGjVqBAC4cuUKsrKycPHiRYUymqo7EoQ8fyGyZmZmiI+Px7lz55CQkAADAwNUqVIFPj4+KuUePHjA6FkA2Lt3L2rXro1NmzYBAOzs7DBr1izMnj1b5XkA4bpr6dKl8PHxgYODA7y9vZnyWVtbIyoqSqHM+PHjARS9nxkzZsDQ0JA5VlBQgJs3b6JatWqs165bty6SkpJw//59pKSkAAAqVKjAlEMZq1atYq6/efNmGBsby1z/8uXLqFSpkspz2NraIjExEbt27WLeXWBgIHr27CmnQ5TBp82qVq0aRCKRwvZCsl/ROSUIvfe8vDxUqlQJx48fh7u7u9LfqUu9evVQr149Tr8NCgpCuXLlsGzZMgBF7VCjRo1Qrlw5uLi4YMCAASgoKEDfvn1Vnic0NBRpaWlo2rQptLWLzILCwkL069cP8+fPZy1HbGwscnNz5fb/+PEDV65cUSq3bt06bNy4ET179sT27dsxefJkODs7Y+bMmZzaG0B4/ZsxYwY6deqE1NRUNGnSBABw4cIF7NmzBwcOHGCV9/DwQGJiItasWcNcv1+/fhg9ejRKlSrFKh8ZGYnu3bvL9FkBIDc3F3v37lXYb/z69SssLS2Zv69evYquXbsyf3t6euKvv/5ivXZxrKysOP3u0aNHaNq0Kdzd3bFz506m/j969AgrVqxA06ZNcePGDXh4eCiUj4mJYf5/5swZmfahoKAAFy5cYO03ScjMzMTBgweRmpqKSZMmoVSpUnj06BGsra1ha2urUnbSpEn4+PEjRo4cydRffX19hISEYOrUqazX5tNmA5rR+2vWrEG7du3g6OgIOzs7AMDLly9RuXJl7Ny5U6WsJvS+KlTpXQlmZmY4d+4crl69isTERGRnZ6N69erw9/fndA0jIyMMHTqUdxl37dqFTZs2oXXr1pg9ezZ69uwJFxcXVKlSBTdu3FDZXxoxYgT27NmDFy9eIDAwEH369OH0rUvz+PFj7NmzBwCgra2N79+/w9jYGHPnzkX79u0xYsQIlfJ8654EvnaWhYUFRCIRRCIRKlasKPOuCwoKkJ2djeHDh6u8to+PD86dOwcvLy907doVwcHBuHjxIs6dO4emTZuqlBVad4WUX6jeKl26NC5fvowvX77A2NgYWlpaMscPHDggcz+KKH7dwsJCXLhwAX/++SezT5EdD2iuvwko77MlJCSwfgt89a6DgwOnsilj6tSpKs+/bt06rFu3jvU8cXFxiI+Ph5ubG7PPzc0Nq1evZr5FVfCxFaZMmYL4+HgMGDAAZ86cwbhx43D69GmIxWJcvHgRdevWZb1uTk4OTE1Nmb91dXWhr6+P7OxslClThlVeSPmlrxkeHo4FCxYgNTUVABifAxvBwcGoWbMmEhISZPo/HTt2xJAhQ1TK2tvbM3ahqt+w0aFDB9bfKGL8+PHIy8vD6tWrART17+rVq4ekpCQYGhpi8uTJOHv2rEo/pY6ODuzt7RX6KbjSoEEDXL9+HUuWLMH+/fsZW3vLli0yPixlDBkyBGPHjkVeXp5Mf3ny5MmYMGGC3O9nzZrFu6yaJjExkdFv0voSUN1nycrKAhVNVoKvX7/K2KQFBQU4efIk5+9nx44dWLhwIUxMTGT2f//+HZGRkdi6datKeb56V4h/UPIOHR0d0b17d6U2ORve3t7w9vbG4sWLERsbi927d2Po0KEoLCxEp06dlN67Ju0FvhRvq7W0tJT2oaQRGptITExk/v/o0SO8fftWRv706dOsdo6Ehg0bomHDhpg/fz569uyJ4cOHo3Pnzqzt9YULF9CuXTs4OzvjyZMnqFy5MtLS0kBEqF69Out1+X53QFFsYtiwYczfp0+fxrZt2xAZGQl3d3eMHj2aiZkoYtGiRcjOzmb8hESEli1b4uzZswCAMmXK4MKFC/D09FRZDokt3rx5c5w9exYBAQFITk7Gu3fv0LFjR6VymtIdrVu3RuvWrVl/pwp1fbREJOMTAor6EG3btpWxEe7duycn6+fnh7Vr1yI4OBgrVqyAqakpRCIRvnz5Ah0dHaxevZppP1QhJLYiKZuOjg68vLwAAEePHsW2bdvg4eGB2bNns8aUJKjb7gn1T0oQYucDRb69bdu2ISoqCj179kTfvn1RuXJlVjkA8PX1ha+vL4Ci9iM+Ph6xsbGIjY3Fjh07GB9oUlISp/OpQ1pamsbPKRaLmWfP1ofaunUrevfujebNmzP7XF1d8eTJE3z69ImxI7mwbds2tcsq9Nn369cPcXFxCm3ZyZMng4gQERGh9PqasFUBYXGhlStXsp6fjfLly+PNmzewt7eHi4sLzp49i+rVq+P27dtyPlcJhw4dwpYtW3D58mW0bNkSy5YtQ8uWLWFkZAQvLy9O711LS0uhT1xCXl6e3DNVRs2aNVGlShW8ePECLi4u0NbWZm0LRCIRFi1ahBkzZuDx48cwMDBAhQoVlN5zcfr37y+3r0+fPpxkJZQtWxbZ2dk4d+4cfHx8YGBggFq1arE+vzdv3iAyMhLbtm3D58+f0bt3b1y7do2z3hIaF9GUnVxc9/fp0wcuLi4q+wsSBg8ejLy8PDx+/JjxMzx9+hSBgYEYPHgwTp8+zXoOPna6Jvx7AL9v9/79+wCKntvDhw9l2mZdXV1UrVoVEydOZD3PzZs3sWXLFuzfvx/Ozs4YOHAgDh06BAsLC1ZZIbZGYWGhwv9z5enTpyr9OLVq1cLjx485nWvChAkIDw/HmjVrOLdTQJH/18jIiPlbLBZDV1cX379/53wOoKif9fnzZxw4cAC7d+/G8uXLUalSJfTu3Ru9evWCo6OjWudTlxEjRmDEiBH48OEDDAwMOLVV0oSFhcHf3x8JCQmMT/rChQu4ffs2YzsoIiQkBF5eXqhduzYA4MWLF2jbti0aNWqEKlWqYMGCBTA0NMTYsWN539tv/vcRERfvwW9+8y8nMDAQQJFjs1u3bjAwMGCO6erqwtHREUOGDEHp0qVVnmfHjh0qjyvqkCqDr9J3cXHBqlWr0Lp1a5iYmODBgwfMvhs3bmD37t0K5SSBtfDwcAwZMkRhYE1LSwvXrl2Tk61duzZmzJiBtm3bKjz38ePHMXfuXNy6dUvumLe3t0zjnpKSgp8/fzKBjIyMDOjp6aFChQoKnTQSJEHVcePGITQ0VGFQNS0tjemgKYOI1E7iE4qWlhaSk5NlklfKly+Pq1evynQypDv0msbPz0/pMZFIxBqQF1L3JXUgISEBnp6eMs66goICvHjxAi1atMD+/fs1Li/pLHfo0AE7duxQ2Fk+d+4cnj59qvDaQuUlKHNSfPr0CcbGxpycfH/99ZdMEleVKlU4J3EJQcjzF/ruhaCvr4+UlBTGKdmwYUO0bNkS06ZNA1DkwPPy8sLXr18VymtKd0n49u2bTBJglSpVVCYBSr7ZuLg41KtXT87YdHR0xMSJEzkFRfkgMSLT09NRvnx5mboruf7cuXNRp06dErm+kDYrPT2d0zWUJWxo4t5tbW1x/vx5wcm3T58+xerVqxnD2t3dHUFBQTLJHcruYfv27WjcuDGAImdLREQEnjx5Am1tbSxduhQHDx7EjRs3OJUjOTmZqbteXl6syS6SwGC1atVw8eJFGT0lCQxu2LBBqSPd0NAQjx8/hoODA8qUKYNz586hatWqSElJQd26dfHx40dO5RbKiRMnMH/+fDx48ID5bmfNmsU815JES0sLb968kXPqffz4EWXKlFEYJHB1dcXatWvRvHlzZGdnw9LSEhcvXkSDBg0AFAWcmjdvjg8fPnAqw8GDB7F//35kZGTIOYwV6b1u3bohPz8fhw4dknPsEBE6deoEHR0dpTpfLBYDgMLglI6ODhwdHbFs2TK0adNGZbkTExPRtGlTmJubIy0tDU+fPoWzszOmT5+OjIwMREZGst47UJTkz8dBDfBrszWl94kI58+fx5MnTwAU6Q0uyaslqfcTEhJQvXp1QQlCioiJiUHLli2ho6Mj4yBVhLLkQ2mMjIzw+PFj2Nvbw8bGBidOnED16tXx/PlzeHt748uXLyrlf/78iejoaGzduhXx8fFo3bo1Bg0ahICAAE7OzrJly+LSpUtwd3eHh4cHFi5ciHbt2iEhIQENGjRAdnY26zn41D2hdtaOHTtARBg4cCBWrlwp01+W1B22wSOfPn3Cjx8/UK5cORQWFmLx4sWIj49HhQoVMH36dJVOcqF1V0j5NaW3+CK5vipEIpHSb08TekcSnPny5QuTUCFBOjizdu1a1rKqq3dTUlIwc+ZMbNiwQc6W/vLlC0aMGIGwsDA4OzuzXluCZGClZLAuFypWrIidO3cyDnYJt27dQq9evfDs2TPWc6hrK9jb22P79u1o0qQJ0tLS4OzsjClTpnAaHCVBLBYjLCxM5psPCQnBpEmTZHxjXAZqqlt+aYgIHz9+hEgkkkmiZcPS0pJJejYxMUFCQgKcnZ2RlpYGDw8P5OTkcD7XP03lypUxf/58pm3atm0bJkyYgPv378Pe3h4DBw7E+/fvceLECZXn2bJlC6KjoxEVFVXifgFFEBGmTJmCVatWySXOc5kc4H8RSdKOMkQiEebMmcP4HRQhScKzsLBASkqKjK+woKAAx44dw5QpU5QOmhOqd4X6B0uKe/fuYdCgQUhMTFTabv0b2l0zMzOZZ56ZmQlTU1OZNlnRgFGhsQnpuqcoXGdgYIDVq1dj4MCBrPcRHx+PrVu34sCBA3Bzc8PAgQMxdOhQ1n5F7dq10bJlS8yZM4fRu2XKlEHv3r3RokUL1oFiQrC0tERsbCyTPCkJqB88eBBA0cDfwMBAvHjxQqF89erVERISgu7duwMoShrr378/zp07B3d3d/Tr1w+Ghoas/skqVapg2LBhGDVqFPMMnJycMGzYMNjY2GDOnDkK5TShO4TC10er7J6Ko2qAxevXr7F//348e/YMRISKFSuiS5cunPtbQmMrtWrVwpQpU9C5c2c8f/4cHh4e6NSpE27fvo3WrVtzThJSt90T6p8sfm0+dr40cXFx2Lp1Kw4ePAhXV1ckJSUhLi6O8RlxJTc3F9euXcOpU6ewYcMGZGdn87L12fwEFy9exOjRo3Hjxg2Fff369esjIiKCdbCbtJ1+9epVtGnTBoGBgWjRooVKvVfcL1euXDnEx8erlTT0/v17lcl6+fn5uHfvnpwdoQxNPft/gl8ZF5JmypQpMDU1xR9//IF9+/ahT58+cHR0REZGBsaNG4eFCxfKyWhrayMkJARTpkyRGSSlo6ODhIQEpZMKSOPr64tGjRrJTA4jzfTp03H16lXExsaqPE9OTg6CgoKY+GxycjKcnZ0RFBQEW1tbTJkyhbUsv4KPHz+iW7duuHTpEkQiEVJSUphESAsLCyYxVhE6OjqwtbVF//790a5dO6U2ZZUqVRTuFxoX0aSdrIh3797hyZMnKuMLBgYGiI+Pl5tA5+7du2jUqBFnW1ddO10T/j2hBAYGIjw8nFe+hKenJ96/f49evXph4MCBqFq1qlrymrA18vLy0KJFC0RERKgVuzUyMsL169eV1uvExETUq1cP3759U3hcMtBPgiQm5+npKfe+o6OjFZ5DLBZj6NChMrHYtWvXok+fPjJ1gW0yt+K8evUKe/bswdatW5GSkoL8/Hy15H8FDx48wJIlS2TiglOnTlX5Tu3s7LB//37mGwkLC8PBgwfx4MEDAEX+m9WrVzN//+b/Jr+Tb3/zn2LOnDmYOHGizMiN/zX4BoWFBNYsLCyQkJCgdOaPjIwMVK1aFZ8/f5Y7xtVBA6h20vzqRDAhKHKwSY/4lPxflaFcUjMp/hNI6sCcOXMwYcIEGaNF8u46d+6sNAFViLzQzvKvduxrAqF1R8jz5yu7atUqDB06FPr6+kxCiDKUGbkODg6IioqCj48PcnNzYW5ujmPHjjEj1R4+fIjGjRsrnUVTU7pLKHyNzfHjxyM0NBRGRkZMIqsyVBlLfn5+iI6O5jQyFNBcItSvTj6WlEGde5dm/vz5SE5OxubNm2Wci+pw6NAh9OjRAzVr1mQMths3buD27dvYu3cvOnfurFTWwMAAT548YRz4rVq1QuXKlbF48WIARY66evXqlVgSq9DAoLOzMw4dOgRvb2/UrFkTQ4YMwbBhw3D27Fn06NFD6Xer6UQ8dUlMTETlypUhFotlZiZShDJHigSxWIx3797JzTqbkJAAPz8/hc9g6tSpOHLkCP744w+cPHkS8fHxeP78OdNn2rhxIyIjI3H16lXWe1m1ahWmTZuGAQMGYOPGjQgMDERqaipu376NUaNGYd68eXIyVlZWOHXqFGrWrKnwnLdv30arVq1Yk3+dnJxw+/Zt1kFxyvD390f16tWxePFimWSc+Ph49OrVq0RmT9EkQpyMmoCP7mOr70+ePEHPnj05BYUuXLiACxcu4P3793IzBRSfBU0sFuPt27coU6aMyqAZWz9bgpubGyIjI1GnTh00bNgQbdq0wZQpU7Bv3z4EBQXh/fv3rOeQkJ6eju3btyMyMhL5+flISkpiHXTZoUMHtG7dGkOGDMHEiRNx9OhRDBgwgHkf58+f53x9ddCUnSUJ3PJt94QipN0GhJVfqN761QjRO78yODN06FCYm5sz/ZvihISEICsrC+vXr1d5nsLCQoSFhWHZsmVMkruJiQkmTJiAadOmsSYjHT16FPPnz8fatWuZNvDOnTsICgpCSEgI79lRVaGtrY2XL1/CxsYGQNHApTt37nAKBEtwdHRkHRggEonw/PlzQWVVxtu3bzF58mTExMQwAyJNTU3RsWNHLFiwANbW1irlLSwscO3aNXh4eMi091evXkXnzp3x7t07pbKaSubgi6mpKe7duwdXV1cAQM+ePWFiYoKNGzcCKAr0tGrVinXFAm9vbzx79gx5eXlwcHCQ83UqGiyVlZXF3HNWVhZrObnAZ8BSQUEBVqxYoXSgF9fVLv5p4uLiQERo0qQJDh06JJP0rKurCwcHB5QrV07lOYQm4f0qvVuqVCkkJyejdOnSrLP9cX1/r169wu7du7F79278+eefqFevHnr37s06o9avanfZJiaQoGqCAr6xifT0dBARnJ2dcevWLRk7UVdXF2XKlFE5g56iWeQGDhzIeRY5ADKTgFhYWODq1avw9PREQkIC2rdvX6J2lvQAXQCoWrUqBg0axPgEMzIy4ObmpnRmLgsLC8THxzN+0cDAQBQUFDADM2/cuIGuXbvi5cuXKsthZGSEpKQkODo6yiQEP378GE2aNMGbN28UyvHVHZr87oT6538lQmMrZmZmuHfvHlxcXLBo0SJcvHgRZ86cwbVr19CjRw/W914cIQN1/w18/foVu3fvxtatW3H37l3Url0bXbp0Ueo/zs3NxY0bN3Dp0iXExsbi5s2bsLOzg4+PD3x8fNC4cWOFcUOhfoJ27drBz88P48aNU3h81apVuHTpEg4fPqz0GiNHjsTevXthZ2eHgQMHonfv3pzbDml/AwCZ/iZXiifwenl54eTJk8xkIe/evUO5cuWUPgO+z/7fAF+dUxJ9VWmuX7+O69evo0KFCkonmxo2bBj27dsHT09P9O3bF927d4eFhYVaybfHjx9Hhw4dMH78eEyYMIGxbd6+fYtly5Zh5cqVOHz4MGtcMTg4GNeuXcPKlSvRokULJCYmwtnZGUePHsXs2bNlBkl36tQJ27dvh6mpqVwiXnGUJd9pin79+uH9+/fYvHkz3N3dmW/nzJkzGD9+vMrZsqXtb2XxBVU6X2hc5FfbyYBmBvgKgY9/TNPf7rNnz5CamsrMmsxltnixWAwjIyNoa2sL6jMJtTWsrKyYCQW4Uq1aNQwfPlypHSRZKVJZ4qZkoB8XlM3G7uvry6nuqzNYMy8vDydOnMDOnTtx4sQJlCpVCq9fv+Ysz4Xq1avjwoULsLCwkJtUqzhcJtLii4GBAZKTk5k2vmnTpqhfvz4zCCM1NRU1atRAZmZmiZXhN/9+fk3U5De/KSFmzZqF/Px8nD9/HqmpqejVqxdMTEzw119/wdTUVGFAVGiHQdNKn88yGQBw6dIlAPwCa/n5+fjw4YNSQ+7Dhw9KR6poKilNMnJd3aCqJpL4hCJ59kLQ0dHBjx8/NFAa7miqsyx0WToh8pKkDb6dZaHyQtBUEpfQuiPk+fOVXbFiBXr37g19fX2sWLFC6e9EIpHS77ZVq1aYMmUKFi1ahCNHjsDQ0FAmeJqYmAgXFxfWsvNFU0mAEkNIXWPz/v37ePLkCby9vVXOCM5mTEn0V25urszSRsro0KED45hUlWjAlgglpM2SJjMzE7du3VKYxNWvXz+VsureuzS3b9/GhQsXcPbsWXh5eckF1rg4uCZPnoypU6di7ty5MvtnzZqFyZMnq0y+NTU1RWZmJuNkunXrFgYNGsQcF4lE+Pnzp0JZTSRuv3jxQlBgsEmTJoiJiYG3tzcCAwMxbtw4HDx4EHfu3FHpPNRU/eNLtWrVmOuzLS+o7PqSvqJIJJJb2lF6ZghFzJw5E69fv8aYMWNQtmxZ7Ny5U+Y579mzR6ljuTgSh07Pnj2xfft2TJ48Gc7Ozpg5c6ZSB9XXr19VJsqULVtW6Wzj0kjPVvTjxw+1+w23b9/Ghg0b5Pbb2trKLM8qjVAHtSYTr/nofU0mEfHRfZpaTnPOnDmYO3cuatasCRsbG1YZoUt6Fadjx464cOEC6tSpg6CgIPTp0wdbtmxhZkRRB3WWs5SwfPlyJvFvzpw5yM7Oxr59+1ChQgWl+lYTdY+vnVWcxo0bIzU1Fdu2bUNqairCw8NRpkwZnDp1Cvb29iqX8eUz23dxhLTbQssvVG/9avj2N4H/l2Dk5OSkVnBGE4HBuLg4lUvtduvWDb169WIty7Rp07BlyxYsXLiQmfnr6tWrmD17Nn78+KFwwIk0AwYMQE5ODurUqcPcf35+PrS1tTFw4ECZwUaSNlSorUBEMs9aS0tLZhZFLghJkhJa/qysLNSvXx/Z2dkIDAxEpUqVQER49OgR9uzZg6tXr+LevXsqBy0EBARg5cqVTMKqSCRCdnY2Zs2ahVatWqks08qVKzFkyBCFNoaZmRmGDRuG5cuXK2w3NZEIJRaLZdrMGzduYMaMGczf5ubmCge3F4dPYreFhQWjb83NzRWWn8sAcWmMjY1Rq1YttcohWR5+woQJmD59OqZNm4a0tDQcOXKkxGbN1YTekcxQ9eLFC9jb26u1jKeES5cuCUrg5at3hbJixQpm9jehy0Bv2LABu3fvxrVr15glSI8ePcpp9kfg17W76qx4pww+sQng/82MybfPa29vLzeLXGFhoVz/UZWtYmRkxCTK29jYIDU1lekf/f333wplNJUI5ODggLt378LBwQF///03kpKSZGbrfPv2rcrly/Pz82XiJtevX5dZ8rVcuXJK70EaCwsLxqa1tbXFn3/+CS8vL2RmZqqchY6v7tDkdyfUP8+Hu3fvMoMKFdmpHTp0wMqVK1lnphMaWyEi5ts5f/48k+xmZ2fH6b0Xh0+7x8c/WVKDhUxMTDBs2DAMGzYMDx8+ZPrBinyATZo0wc2bN+Hk5ITGjRtj2LBh2L17NzMATBVC/QQJCQlYtGiR0uMBAQFYunSpyjJERETA3t4ezs7OiIuLQ1xcnMLflVQiYvF7T0tLQ15ensrfSBDy7Nni0NKUVCISX51TEn1VaerVq8c6SGnDhg1YuXIl9u/fj61bt2Ls2LFo3ry5jC5ho02bNlixYgUmTpyIZcuWMW3Uly9fmNlXuUzoc+TIEezbtw9169aVeRaenp5ITU2V+a307PzFV0f4pzl79izOnDkjN8N5hQoVWGcFVzaLPVeExEUAYXaypvJBlixZgqCgILkBvsHBwSr1nqZiknz8Y5r6dj99+oSuXbvKzZo8aNAg1lmTlSWVqotQW0PiT1Y0s7YyevXqhenTp6N+/fpy/fGEhATMnDkTkydPViqviXtnm4lbHS5duoTdu3fj0KFDKCwsRKdOnXD8+HE0adJEY9eQ0L59e6afz3fwuybyYUqVKoU3b97Azs4OhYWFuHPnjkzfKjc3V2mb/5v/O/xOvv3Nf4r09HS0aNECGRkZ+PnzJ5o1awYTExMsWrQIP3/+REREhJyM0A6DJpS+NEKDwnwCa56enjh//jxq1Kih8PjZs2dVBiPZePPmDebNm4c1a9aw/lbdoKomkviEoqnlqUeNGoVFixZxnklRqINT04Zu//79kZmZiZ07dyI1NRWTJk1CqVKlcO/ePVhbW8PW1rbE5IV2ln+FY18TSVwS1K07ihDy/NWVffDgAeOQ4Gtsh4aGolOnTmjcuDGMjY2xY8cOmZHUW7duRUBAAK9zS1CluzSVBMjX2Lx06RKTyCLRm927d8eqVatYZ5GS5vv37xg9ejTnpY00nQglJBnk2LFj6N27N7Kzs+UcTiKRiDX5Vt17l8bc3FxlciwX3rx5o7CMffr0wZIlS1TK1q1bF6tWrcKmTZsQHR2Nr1+/yhi20iMwi3P//n3GEXzv3j2lz1nV8xcaGNy4cSMjO2rUKGZZ4Xbt2mHYsGFK5YTWP6HJFC9evGASjfnqLom+ePDgAZo3b650ZghFGBgYMDP3KEKdgFVGRgbq16/PnFcSYOzbty/q1q2rUO85ODjg1q1bSuvWzZs3OQXUCwsLMW/ePERERODdu3fMtzdjxgw4OjrKOEwVoaenp9BJUnyZSmmEOqg12Wbz0ftCkoiKw0f3CXWMS4iIiMD27dvRt29fTr+X/mYHDhyI8PBwmSUB1UXaKdq9e3c4ODgwMxVwSVxXtJzlmjVrWJezlCA9g46RkZFC27Q4mqx7QpNX4+Li0LJlSzRo0ACXL1/GvHnzUKZMGSQkJGDLli3MssCKUOZ8/PnzJ+fZt4S020LLL1RvCUVZUEUkEkFfXx+urq7MDMeKEBLckKBucEYTgcGMjAyVy7iWLl2a00xmO3bswObNm2WCT1WqVIGtrS1GjhzJmnzLJxlGqK1ARDKDdL5//462bdvKfS8lFUwXWv7w8HBoaWkhKSlJrm2ePn06GjRogFWrVuGPP/5Qeu5ly5ahefPm8PDwwI8fP9CrVy+kpKSgdOnS2LNnj8ryC0nm0EQilLu7O44dO8bM+pSRkcGs/AEU+S652Gx8BoxKlp8E+CUzaSqRbteuXdi0aRNat26N2bNno2fPnnBxcUGVKlVw48aNEvHPSesdVUl6XHBwcMCVK1ewYcMGPH/+HAcOHICtrS2ioqLg5OSEhg0bKpXVRAKv5Dxc9a4m3pt04qnQJNSwsDD07NkTq1atUns5WODXt7tC4BObKE5UVBQiIiLw4sULXL9+HQ4ODlixYgWcnZ3Rvn17hTIFBQXIyMhAaGgowsLCAHCfRW7u3LmYMGEC6tati6tXr8Ld3R2tWrXChAkT8PDhQ0RHR6Nu3boKr6up765///4YNWoUkpKScPHiRVSqVEkmThEfH69yFl8XFxdcvnwZzs7OyMjIQHJyMnx8fJjjr169gqWlJWs5fHx8cO7cOXh5eaFr164IDg7GxYsXce7cOWalLVWoqzsSEhLQpUsX6OnpwcnJCfXr1xeccM/Xv6ssiUi6vzlgwACZ9mzZsmVo0qSJUju1WbNmWLJkicrBVIDw2ErNmjURFhYGf39/xMXFMasivHjxgrW91YT+5Ouf1KSdrwwvLy+sXLlSqZ/xypUrsLGxQZMmTeDr64vGjRtz+lYA4X6Cd+/eKV0aHShaCYJtZaV+/frxbmclA+OV/a0plJ1TyLMviZU3+KKuzhHaV1XE06dPsXr1ajx+/BhAUV88KCgIbm5uSmUMDAzQv39/9O/fHykpKdi2bRvu3LmDBg0aoHXr1ujSpQurTggKCkLHjh1x4MABpKSkACia0bRz585KfafF+fDhg0Kb99u3b3J1p2PHjkzccvv27ZzOX1J8+/ZNZul6CZ8+fWKdLVziO87IyICdnZ3CbyQjI0OpvJC4iFA0lQ/CZ4Cv5JqaiEny8Y9p6tsdO3YsdHR0kJGRIbOSa/fu3TF+/HiV/ilNDJQDhNsa+fn52Lp1K5NXU3xiHkUTLIwbNw6nTp1CjRo14O/vj0qVKgEomiX+/PnzaNCgAeeJIV68eIH8/Hy5mXdTUlKYFX1LEltbW3z69AktWrTAxo0b0bZt2xJdJcDCwoLxuwcGBqJ8+fKc/PDFzyE0H8bX1xehoaFYt24dDhw4gMLCQvj6+jLHHz16VOLP/jf/A9BvfvMfon379tSnTx/6+fMnGRsbU2pqKhERXbp0iVxdXRXKxMbGUl5eHvN/VZsiwsPD6fv370RElJ6eTgUFBRq9p/j4eFq2bBnFxMRw+v3Hjx+pSZMmJBKJSCwWM88gMDCQxo8fr1Bmw4YNZGRkRMeOHZM7FhMTQ0ZGRrRhwwaV1/3zzz9p9erVtGHDBvr8+TMREX348IGCg4NJX1+fPDw8OJU/JyeHBg4cSFpaWqSlpcWUf/To0bRgwQK532dmZnI67z/Bly9fFG5ZWVn08+dPVvkOHTqQiYkJ2djYUEBAAHXs2FFmK86AAQMoKyuL+b+qTRFC635xEhISyMrKilxdXUlbW5t5d9OmTaO+ffuWqHxBQQHNnTuXypUrJ1Nvpk+fTps3b2a9tlB5PqSlpVFhYSHzf1UbG+rWHUUIef7qyorFYnr37h0REfn5+TE6gw+ZmZmUn58vt//jx4+cvjtN6S6+9O3bl5o3b04vX76UabdOnz7Nem2RSMQ8RyIiExMTRp4rY8aMoRo1atCVK1fIyMiIkT9y5AhVq1ZN7vcWFhb04cMHIipqVyQ6iC982iwJFSpUoODgYPr27Ruva6t775qmZcuWtHXrVrn9W7dupYCAAJWyCQkJVLp0adLV1SWxWEzTp0+XOd6nTx8aNmyYUllN9lUiIyOpfv36ZGNjw+ir5cuX05EjRzR2DQlC69/27dvpx48fzP9VbYrw9vamT58+ERHRnDlzeNc9yfUl/cdfgZOTE927d4+IiGrUqEERERFERHTmzBmysLBQKDNz5kyyt7enhw8fyh1LTEwkBwcHmjFjBuu158yZQ87OzrRz504yMDBgvr29e/dS3bp1WeUHDRpEHTp0oNzcXDI2Nqbnz59Teno6eXt7U3BwsEKZo0ePUm5uLuu5laHJNpuP3re3t6dHjx4pPefjx4/Jzs6O0738St1XqlQpevbsGeffS5dPLBbT+/fveV87NzeXAgMD6fnz57zkR4wYQRYWFlSlShVauXIlo4v48vXrVzmbQRGarHvq2lnFqVu3Li1btoyISKbu3rx5k2xtbRXKhIeHU3h4OInFYpo3bx7zd3h4OC1fvpw6dOjAud4Jrbt8yi9BqN4SiqSfJBKJZDbJPrFYTD4+PkwbVRwh/U0JsbGxZGBgQP7+/qSrq8ucY8GCBdS5c2e53wvVu0RE1tbWdOHCBaXHz58/T9bW1qzn0dPTo6dPn8rtf/LkCenr66uUzcvLox07dtDbt2/ZC6xBZs+ezWnjgvR3J72tWrWKNm7cSBcvXlRoTwmhTp06Cvu5ErZs2cLp28nLy6OdO3fSpEmTaMSIEbRp0ybKyclhldPT06OUlBSlx1NSUkr03UdHR5Ouri41adKErK2tqU2bNjLHJ0+eTF27duV0rs+fP9OmTZtoypQp9PHjRyIiunv3Lr169Urh7zt27Mi0KTt27GD6vlyR9jP1799fbT+TBENDQ0pPTyciorJly9Ldu3eJiCg1NZVMTU3VKpO6FBYWUnp6Oqe6ooyDBw+SgYEBDR48mPT09Bidt3r1amrZsiXn81y+fJl69+5N9erVY95ZZGQkXblyhVVWHb0r1D+oiu/fv3Pqs0gj6bvw5Ve3u+bm5mRhYSG3lSpVisqVK0c+Pj5KdRyf2IQ069ato9KlS1NYWJjMvW/bto18fX2VyrH1E9PS0ujFixcKZSU+utTUVEpISCAiouzsbBo2bBh5eXlRp06dWPuaQr+7goICmjFjBlWrVo1atGghZ/t06dJFpX9248aNZGRkRAMHDiQPDw+qX7++zPHQ0FA5XayIjx8/0uvXr5kyLViwgNq2bUvjx49X2s+SRl3doa2tzbQz0r5SIfD1706ZMoXMzMyoYcOGNH78eBo/fjw1atSIzMzMKDg4mJo1a0ZisVjG1+Ps7MzUGUUkJiaSk5MT57Lzja0kJCRQ5cqVydTUVKZ/NHr0aOrZs6fKa2qi3ePrn9SknZ+Xl0eLFy8mb29vMjIyIgsLC6pTpw5FRESo1MnZ2dl06tQpCgkJodq1a5Ouri5VrlyZRo0aRQcOHBBkg7Ph7OxMhw8fVnr80KFDatUfdRGJRDL6XiQSkZmZmZzuZzuH9HcrrfeJiN6+fUtisVih7K989pqEr87Jy8ujOXPm0MuXLwVd/+DBg6StrU1169alcePG0bhx46hevXqkra1NBw8eVOtcBQUFFBMTQ+3btyddXV1B5eJKo0aNaNWqVUREjI+TqEh/NW/eXOa30n4xTbUZfGnZsiUTj5CUu6CggLp27arQP6AIZffw999/K/1uiITFRaQRYifn5+dTXFwc77gmW1yCLUYhFCH+MaHfrrW1NT148EDu2qmpqWRkZMTpHE5OTvT333/L7f/8+TOndkOoreHr66t08/PzUyqXm5tLixYtoqpVq5KhoSEZGBhQ1apVadGiRZxi2RJ8fHwU1o2oqChq3Lgxq7xEVxbfxo8fT3/88Qdt3bqV8T0oYuPGjYJi+uqipaXF6Aq+uk8T+TAvXrwgV1dXEolEpK2tTevWrZM53r59exo7dqzaZfvNf4vfybe/+U9RqlQpevLkCRHJNtovXrwgAwMDlbJ8OwyaUPqahG9grXfv3iQSicjd3Z06dOhAHTp0oEqVKpFYLKYePXqovObRo0dJR0eHCQK6uLjQxYsXqXTp0tS8eXM6deoU5/KrG1TVZBKfUCSBT2Wbvb09zZw5U2nSE18HuVAHp6YM3SZNmtCkSZOISPb7u3btGjk4OJSovNDO8q9w7GsyiUsTwRUhz19dWVNTU8a5KBKJfpkzSYju0lQSqhBjk83BxwV7e3u6fv26nHxKSgqZmJjI/V6TiVBEwpJBDA0N1b5fadS99+Lk5eXRuXPnKCIignn/r1+/pq9fvyqVOXr0KLOtX7+erKysaNSoURQVFUVRUVE0atQoKlOmDK1fv571+h8+fKAjR47QjRs35I4dP35caYKZdLupzFHBFb6BQRcXF5o1a5bCRBhVCK1/48aNo+zsbCIiiouLYwxurujr6zNtpab6fD9//qSXL19Senq6zKaKatWqkbe3t9xWvXp1ql+/PvXr148uXryo8hyDBg1iglJr1qxhAvvm5uY0cOBAhTLfv3+n+vXrk5aWFrVo0YLGjRtHY8eOpebNm5OWlhbVq1ePU0Kxi4sLnT9/nohkv73Hjx+Tubk5q3xmZiZTVi0tLbKzsyMdHR3y8fFh3m9xhDqoNdlm89H7mkgikiBU933+/JnOnDlDUVFRtGPHDpmNjcmTJ9PcuXM5lZOIyN/fn7y8vGjAgAEkEomoR48eFBgYqHDjgqmpKe/kW5FIRA4ODtShQwe5QU5cBzw9f/6cWrVqRYaGhjI2gsSGUIQm657Q5FUjIyPm+RW3tfX09BTKODo6kqOjI4lEIrKzs2P+dnR0pIoVK1JAQIDCdkwRQusun/JLEKq3hHL+/HmqU6cOnT9/nrKysigrK4vOnz9P9erVoxMnTtDVq1fJ09NTqf7WRHBD3eCMJgKDXbt2pQ4dOig93q5dO+rSpQvreWrXrk1BQUFy+0ePHk116tRhlTcwMOCU4C6NpgesCcHR0ZGMjIxIJBJRqVKlqFSpUiQSicjIyIisra0ZOygjI4OREVp+CwsLxjeniMePH7MmMyjjr7/+olGjRqn8jaaSOfi8ewnnz5+nsWPH0sKFC+V09+zZs+nSpUus5+CTzKCjo0N//fUXEfH79jSROE9EVLFiRUa/N2jQgBnksXfvXrKyshJ8flUUFBSQjo4OJScn8z5HtWrVmL6NtM67d+8ep6R/IuEJvHyC4ppIPCYqSggaNWoUWVlZKfRvckGSeFy3bl21E49/dbu7fPlysrS0pD59+tCqVato1apV1KdPHypdujTNmzePeacbN26UkxUSmyAicnd3Z/SXtPzDhw/J0tJSqdz06dNV2rfp6enk7++v8Fhx3xIfhH53fOzz4mzZsoU6dOhAw4cPpzdv3sgcGzFiBEVHR6uU18SAG3V1h6urK/3xxx8UGxtLIpGIjhw5QnFxcQo3rvD17w4ePFihrRYaGkqDBw8moqIBuTVq1GCO6enpqbSvnj9/ztlOJRIeWynO9+/fWds0TbR7fP2TmrLzc3JyqEGDBiQWiykgIICCg4MpODiYAgICSCwWU+vWramgoICePXtG27ZtU3murKwsOnnyJE2aNIlq1apFurq65OnpyVoGPn6C0aNHU+XKlRX6kXJycqhy5coK+/CKUNZfzc7OVuov0ETim1gspmfPntGXL18oMzOTTExMKCEhgUkcT05O5txu8n32ROoP1tIkQmJKxsbGSgeGcMXZ2VnhRAAzZ84kZ2dn3udlaxelffyqNjauXLlCxsbGNHz4cNLX12cGOxgZGdGdO3dkfmttbc1M0vUrY2pERf2SMmXKUIsWLUhXV5e6dOlC7u7uZG1tzXnQvbJ7SEtLI0NDQ5WyfOMi0vCxk6VhawNLAk35GYT4xyQyfL9dY2Njpr8ofe3bt29TqVKlOJ1DWd/17du3pKOjwyr/q20NReTn5zMDwNgwMTFR2H9ISUkhMzMzVnlfX18yNTUlIyMjql69OlWvXp2MjY3JzMyM6tSpwwxMSUpKUvc2SgQ7Oztat24dpaWlkUgkort378rF0thiakIHKUvIy8ujBw8eKHxXDx48EBRr/c1/g9/Jt7/5T2Fubs40BtIN5pUrV6hMmTKs8nw6DJpQ+lw76lw660ICa/v27aP27duTh4cHubu7U/v27Wnfvn2s16xVqxaNHTuWvn79SitWrCCRSESVK1emW7duscoWR92g6r8liY+oqMEuX748TZ8+nWJiYigmJoamT59OdnZ2tGHDBgoLCyNzc3OaN2+eRq+ricCCJgxdU1NTxrCSfndpaWmcOuxC5IV2ln9FZ7skkriEIOT5qyvbqVMnsra2Jl9fXxKJRNSgQQPy8/NTuJUkQnSXppJQhRibxa8rPTqaK9IJk9LXf/DggcIZiTSdCCWkzerYsSOnNkoZ6t67NGlpaVSpUiUyNDSUmT1wzJgxKkdWF5+xTtnG1THLh1KlSjGOKaHtJt/A4PLly6lmzZokFoupZs2atHLlSrngmCKE1j+hs8rUrVuX/P39afbs2SQSiWjSpEk0Z84chRsbycnJ1LBhQ7lgEpf3z2dGmuIUFBTIBDf37NlDQUFBtGrVKpUjrX/+/EkLFy6kqlWrkoGBATNCe8GCBZydFvr6+kwii3S9SUpK4pwERlTUv1+7di0tWrSIzp07p/K3Qh3Ummyz+eh9Tc4II0T3xcTEkImJCTMjjLm5ObMpS6KSHkEfHBxM5ubm5OPjQ6NHj5YbYV+ct2/fUkhICHXp0oXEYjG1bNmSGSRYfONCv379aPny5Zx+Wxy2WZC4DHiqX78+1atXj/bu3UuXLl3iNLJek3VPaPKqra0tXbt2TU4+OjqaNajl6+vLabYwVQipu0TCyq8pvcUXT09PpuzSXL16lRmsdO7cOaUzY2kiuKFucEYTgcF79+6Rnp4ede7cmW7evEmZmZmUmZlJN27coE6dOpGenh4zm6YqYmNjycjIiNzd3WngwIE0cOBAcnd3J2NjY7p8+TKrfOPGjVXqYEVoesCaEHbv3k2+vr4yQdCUlBRq0qQJ7d27l16+fEkNGjSQmaFIaPm1tLRUJi+9efOGtLS0lB5XtjrJ2LFjOa1OoqlkDj7vXpM0bdpU7WQGLy8v6t+/P23fvp1EIhGtXr1aLglGVTKMpmbUCgkJYXxfe/fuJW1tbXJ1dSVdXV0KCQnhdU518PDwYNo8PhgYGDA+suJ2Khf/FpHwBF4+QXFN+AeJiEaOHEnu7u5MAvHWrVspNDSUypcvTzt37mSVF5p4/Kvb3U6dOikcEBsREUGdOnUiIqJVq1ZR5cqV5X4jNDah7N6Tk5NVJuHZ2dlRtWrVFK5SEhERQSYmJtSiRQuFsprypwv57v4NflEiYYMuJPLq6I7Dhw8zCT6KVjng4yPi6981NTVVmsgh6Ws/fvyYjI2NmWPly5dXOXHByZMnqXz58pzL/itiK5po9/j6JzVl50tWKVI0C/GDBw/I3t6exowZQ7a2tswMm8ooKCigGzdu0IIFCyggIIAZOKoKPn4CoiJ7v1y5cmRnZ0eLFi2iI0eO0JEjR2jhwoVkZ2dH5cqV45wMr+zdffjwQWWfUyjFE8aV/c0FPs+eSPhqlEIRElNq166d4Jk9DQwMFOqu5ORk1kEvyvrIO3bsoMjISJWymvTvP3v2jAYPHky1atUid3d36t27NyUmJsr9btasWayDFNQZKCWUzMxMCgsLo65du1LLli1p2rRpzCBAVUh8gGKxmIYNGybjFxwzZgzVqVNHbgb7koCPnSxNjRo1mJgyF5TN7q5oU4am/AxC/GNEwr5dIbMmS/JkRCIRRUZGyuTOREdH06hRo6hixYqsZdCUrZGSkkKnT59mBj4KWf3jwYMHnL9dU1NTZkVDae7cuSPTT1PGihUrqFOnTjJ1LTMzk7p06UIrV66kb9++Ufv27WVW55ROXlU1KQXXlXjVYcOGDcxM18o2Np0rdJDyb37DFW385jf/IQICArBy5Ups3LgRACASiZCdnY1Zs2ahVatWrPJNmjRBXFwcHB0dOV9z+vTpCAoKwujRoyESiVCrVi253xARRCIRCgoKFJ6jQ4cOnK6l6hwSvn37BkNDQ7n9nz59gp6enkrZbt26oVu3bpzKIs3Tp0+xe/duGBsbIygoCBMnTsSKFSsUPgs2Pnz4gDJlysjt//btG0Qikdx+f39/+Pn5wd3dHQDQsWNH6OrqKjz3xYsX1S6POuzYsQPLli2TeYZt27aFl5cXNmzYgAsXLsDe3h7z5s3DH3/8ofAc+fn5iI2NRWpqKnr16gUTExP89ddfMDU1hbGxsUIZsViMChUq4OPHj6hQoQKvsvOp+8XR09NDVlaW3P7k5GRYWVmVqPzr16/h6uoqt7+wsBB5eXms1xYqz4dq1aohMDAQDRs2BBFh6dKlSt/xzJkzWc/Hp+5II+T5qyu7c+dO7NixA6mpqYiLi4Onp6dCvVXSCNFd9erVQ4cOHVCjRg0QEcaMGQMDAwOFv926davS8zRq1AiRkZEIDQ0FUKTnCwsLsXjxYvj5+aksAxFhwIABjG7/8eMHhg8fDiMjI5nfRUdHKz1HzZo1ceLECQQFBTHXB4DNmzejXr16cr/fuXMnVqxYgdTUVIhEInz58gU/fvxQWU5VCGmzWrdujUmTJuHRo0fw8vKCjo6OzPF27dqplFf33qUJDg5GzZo1kZCQAEtLS2Z/x44dMWTIEKVyhYWFKs+rLqtWrVK4XyQSQV9fH66urvDx8YGWlhZzrHPnzmjcuDFsbGwgEolQs2ZNmePSPH/+XOX1X7x4AW9vb7n9enp6+Pbtm1K5cePGYdy4cUhOTsauXbuwdu1aTJw4EX5+fujTpw/69eunUE5o/XN0dMSqVasQEBAAIsL169dhYWGh8Lc+Pj5y+7Zv345Zs2bh+PHjEIlEOHXqFLS15c0pkUjEqrcHDBgAbW1tHD9+nHkXXPn7778xYcIEzJgxQ2Z/WFgY0tPTcfbsWcyaNQuhoaFo3769nHx+fj7mz5+PgQMHonz58gCAHj16oEePHqzX1tXVRUhICEJCQjiXtzgeHh64cuUKHBwcZPYfPHhQYX1SRsOGDdGwYUNOvx0+fDjat28PkUgEkUiEsmXLKv2tor62JttsPnq/VatWmDFjBlq0aAF9fX2ZY9+/f8esWbPQpk0bldeVIET3TZgwAQMHDsT8+fM5t9v379+X+btatWoAgD///FNmv6JvwNraGgsXLgQAODk5ISoqSkbnqkuFChUwd+5cXLt2DTVq1JBrL8eMGaNUdvv27byvKyEhIQF3796Fm5sbZxlN1j117azi9OjRAyEhIThw4ABTb69du4aJEycq1dsSLl26JPN3fn4+fvz4wamPKkFI3RVafk3pLb6kpqbC1NRUbr+pqSnTVleoUAF///23Qnkh/U0J5ubmePPmDZycnGT2379/H7a2tnK/F6p3AcDb2xsHDx7EwIEDcfjwYZljlpaW2L9/P6pXr85a9saNGyM5ORlr167FkydPAACdOnXCyJEjUa5cOVb5kSNHYsKECXj16pVC3VGlShU5GaG2gre3N6fv8t69e6y/mT59Og4dOgQXFxdmn6urK5YuXYrOnTvj+fPnWLx4MTp37qyx8hMRxGKx0jKJRCIQkcJjMTEx6NKlC/Lz8wEAixcvxqZNm9CtWzfUqFEDhw8fRosWLVjvOTo6GhUrVsTo0aMZvfvkyROsXbsWBQUFmDZtmspzAPzevTSJiYkK90v66vb29iptntu3b2PDhg1y+21tbfH27VuFMhERERg/fjxOnDgBkUiE6dOnK6xLIpFIoe6zsrLCjRs30LZtW8aXyQdJ+w0A3bt3h729Pa5fv44KFSqgbdu2vM6p7vUnTZqE9evXo3LlymrLly1bFs+ePZPzkV29ehXOzs6czvH06VOFNoWZmRkyMzNZ5dXVu4Bm/IMAcOzYMURGRsLX1xeBgYFo1KgRXF1d4eDggF27dqF3794q5cPCwhAREYF+/fph7969zP4GDRogLCyM9fq/ut09c+YMFi1aJLe/adOmmDBhAoCi/vmUKVPkfiM0NuHk5IQHDx7I3fvp06cZ37ci/vzzT4wePRo1a9bErFmzEBISglevXmHgwIG4ffs2li5diqFDhyqVr1ixIuv3/unTJ5XHhXx3ytoEdVHkmwSK3oOenp7SmIGE2rVrK3z+XFFXd3To0AEdOnRAdnY2TE1N8fTpU4X9dXXg69/V19dHfHy8nH88Pj6esUELCwtl7FF/f3/MmzdPYbtMRJg3bx78/f05l51vbKWgoAArVqzA/v37kZGRgdzcXJnzqqq7mmj3+PonNWXn7927F8uXL1fYL6latSqWLl2K7t27IzAwkLGlJBQWFuLOnTuIjY3FpUuXcO3aNXz79g22trbw8/PD2rVrWe0FPn4CoMjej4+Px4gRIzB16lRGD4hEIjRv3hxr166FtbW1ynNkZWWBiiYZw9evX2WeY0FBAU6ePCn4m1JFcTtXHTTx7AFg/PjxGDBgABYvXgwTExNmf6tWrdCrVy/e5eOKkJhSy5YtMWXKFDx8+FBhX5vNtw8Avr6+uHLlipzuunr1Kho1aqRSNjg4WObvvLw85OTkQFdXF4aGhujbt69SWU36+F1cXLBp0ybW382ePRs9evTAs2fP0K5dO2zbtg3m5uYaK4e6mJmZcbKpiiPxExIRHj58KNM26+rqomrVqpg4cSLrefjERaThYydLExYWhokTJyI0NFRh/S3uwzE3N2dtY9hySTQVkxTiHwOEfbuLFy9G06ZNcefOHeTm5mLy5MlISkrCp0+fcO3aNZXXleTSiEQi9O/fX+aYjo4OHB0dsWzZMtbyC7U1Pn78iG7duuHSpUsQiURISUmBs7MzBg0aBAsLC05lEIKPjw8WLFiAPXv2MPW7oKAACxYs4BQrWbJkCc6dOydTR83MzDB79mwEBAQgODgYM2fOREBAgMxxSf01MzPT8B2pZujQoejZsyfS09NRpUoVnD9/Xu34QKVKlTB16lT4+fmBiLB//36FflYArN/A+PHjFe6X1j3t27dHqVKl1Crjb/4j/JOZvr/5TUnz8uVLZtZWbW1tqlu3LllaWpKbmxunUQzr16+nsmXL0oQJE2j37t2cZ53Nysqihw8fkkgkogsXLtCDBw8Ubv8EfEYNzZgxQ2YZPHVnJdLE0ucSGjVqxIzAlZ7FcfTo0dS8eXO53+fk5ND69etp4sSJJBKJaOjQoTR27FiFW0mjr6+vcHYJ6VGWz58/Vzriku9MikRFI4wbNmyocIYDLvCt+9IMGjSIOnToQLm5ucy7S09PJ29vbwoODi5R+erVq1NUVBQRyda/OXPmUMOGDVmvLVSeD0+ePKHu3bszsz9WrlyZqlWrJrd5e3uznktI3ZEg5PkLkfX19WVmM/qnEaK7NDUbn5AlerjMwMc2C586SxsVx9HRUfAyGkJGugodWS7k3oUuJakp+C6PdOrUKVq9ejWJRCIKDQ2llStXKtzYcHd3Z2ZXlX4Oq1at4qS7pLl+/TpVq1aN8whfPvVPk7PKCF2a09DQkB4/fsxLls+MNMUxMjLiPeN9YWEh3b59mw4cOEAHDx6ke/fuqTWy+8iRI2RmZkYLFy4kQ0NDWrJkCQ0ePJh0dXXp7NmzrPLKZhtmm3X48ePHdOzYMRKJRLR9+3ZmVpXimyI02Wbz0fuanBFGiO7ju5zmvwVHR0elG9eZg4Xg6+vLOktzcTRZ99S1s4rz8+dPGjx4MGlra5NIJCIdHR0Si8XUp08fpcsEx8TEyC1tGhYWRnp6eqSlpUXNmjXjbHsKqbt8yy9BqN4SSoMGDahFixYys5q8f/+eWrRoQY0aNSKioplvlc3woYklISdMmEANGzakN2/eMEvcXb16lZydnWn27NkKZYToXWlycnIoOjqaFi9eTIsWLaLDhw/L+C9KGmV9BVV9BqG2wuzZszltXDAwMKDbt2/L7b916xbTb33x4oXMDC9Cyy8SiZjZzhRt5ubmSp+dplZWSktLo5YtW8r0+ST3wnW1ED7vvri8qtlZ9PT0qF+/fgpn6CUisrKyYma0ke5rnz17ltNMgnz6q/+2GbX4Ym5uzsyOo6+vL1cH2Zg/fz55eHjQjRs3yMTEhK5cuUI7d+4kKysr1lkDJTg5OTHtvvT727FjB7m7u7PK89G7RML9g0RFtoJkBTdbW1u6efMmERX5NLnMBiV05uBf3e7a2dkpXC1h+fLlzCzzCQkJCmcwFhqb2LRpE9na2tLevXvJyMiI9uzZQ2FhYcz/2Thy5AhZW1tT1apVydTUlPz9/VlnchWJRBQeHi5o6XUiYd+dpmbfZdNf9vb2NHPmTCooKFAov2/fPnJ2dqbVq1dTfHw8JSQkyGxsCNEdsbGxrH1SLvD10YaGhpKBgQGNGTOGoqKiKCoqisaMGUOGhoYUFhZGREXfgL+/PyPz7NkzMjMzo9q1a9O+ffuYGNjevXupVq1aZGZmptB3oQy+sZUZM2aQjY0NLV26lPT19Sk0NJQGDRpElpaWFB4ervKammj3+PonNWXn6+npKV0WnYgoIyNDaTlMTExILBZTuXLlqHfv3rR582bO9oEETfgJPn36RLdu3aKbN2+qFZtke3daWlpM/ZVGeul2VX1WLn0Gvmji2RMJX41SKELiQny/Hem45fr168nKyopGjRrF6K5Ro0ZRmTJlFM5iz0ZycjI1bdqUTp8+zen30r7pjIwMmjFjBk2aNInTCitEpHQVXbbVdGfPnv2P2sTFiYuLU7lxYcCAASpneWWDb1xEAh87WZri9ZVtBs7iq18p21avXq30njUVkxTiHyt+73ziKnxnTZbg6OjI6HA+CLU1+vbtS82bN6eXL1/K6N3Tp0+zrtKjDHVmvk1KSiJLS0tycXFhYsAuLi5kZWXFyQY0MjKiS5cuye2/dOkSE09KTU3ltEraP8327ds5r74ozbVr16hOnTpUunRpEovFcjP1c5mxX4Kvry+ZmpqSkZERVa9enapXr07GxsZkZmZGderUYc4jWQ3lN/+3+J18+5v/HHl5ebRz506aNGkSjRgxgjZt2sRM+c6G0A4DX6WvSfgE1opPsW5iYqKWwSwqNsW/oaEhbdy4kVcCp5Cg6q9M4iMiqlChgsLl80JCQphA6O3bt6lcuXIK5du3b099+vShnz9/ynTYLl26RK6uriqvLTSwILTuExV1mP39/cnc3Jy0tLTIzs6OdHR0qFGjRpSdnV2i8kI7y7/asS80iUtI3ZEg5PkLffe/Ck3pLqFJqEKNTaFwXdqoJNBEMogQ+N4736Ukw8PDOW9cELo80oABAygrK4vTtRQhNDBIRHTz5k0KDg6msmXLkqGhIXXv3p13ebjy9etXEolElJyczCxhXXwraWrWrElXrlzhJVumTBmFSwXv2LGDqX9JSUlUunRppefguzzUxYsXycnJSS6RxcXFhbNzlYjo8uXL5O/vT1ZWVmRgYEANGjSgM2fOcJItnnTo6elJhoaGZGpqyikBUaiDWmibTcRP72siiUgCX93HdzlNCZmZmfTx40e5/R8/flTocA8PD2cSkjShM381z549I39/f9q+fTvduXNH7WC+0LonNHlVQkZGBp04cYL27dvHuqy1r68vrVmzhvn72rVrJBaLKSwsjA4dOkSVKlWicePGcb62Jvos6pRfGiF6SyhPnjwhNzc30tXVJRcXF3JxcSFdXV2qVKkSPX36lIiKBpioWhpTaH9TSHDmVwcGiYi+f/9ON2/epGPHjqnd109LS1O5saGJAWtCaNWqFVWvXl1mWcJ79+5RjRo1qHXr1kRUlKynaPl0In7lZ0vgUpXIJT3IKD8/n7S0tNQeuCAN32QOIuHv/siRI+Tm5kabN2+mxMRESkxMpM2bN5O7uzvt3buXdu7cSeXLl6cJEyYolBc6wDktLY3X0peaSpxPTk6mDRs2UGhoKOfBUppCaBJhYWEhY9dI+l36+vrMwFEuCE3g5at3hfoHiYi8vLwoNjaWiIiaNm3K1NHw8HCytbVllReaeEz0a9vdjRs3kpaWFrVt25ZCQ0MpNDSU2rVrR9ra2rR582YiIlq6dCl169ZNobyQ2AQR0c6dO8nV1ZWpe7a2tsx12Xj79i35+/uTSCQiY2Nj5j2qQhP2DZGw704kElGrVq0EL2O7Y8cOKl++PE2fPp1iYmIoJiaGpk+fTnZ2drRhwwYKCwsjc3NzmjdvntJyCBl0oa7ukLaB+C5BXRwhPtqdO3dS3bp1GX1Rt25d2rVrF3M8JydHbsDI7du3ydPTUyb5SCQSkaenp9oDZ/jGVpydnen48eNEVKRzJD6y8PBw6tmzJ+t1NdXu8UETdr6VlZVKe+7WrVtK/UMRERGMPcEXoX4CIcTGxtKlS5dIJBJRdHS0TAJbfHw8vX79WqGcdBx327ZtgvoMEgoKCujp06d05coVTomImnj2RMIHawnlV8SFVMUy+cQ1i3P79m1yc3NT+ZvExERycHAgsVhMbm5udP/+fbK2tiZjY2MyNTUlLS0tOnz4MKd7+V8c7KbseatTblWDbrj4eYTGRYTayWxJtOqQlZVFGzZsoFq1apXoxCTF4esf40tubi41adJE8LVU2bhcfU9CbA1ra2tmwr3iAw25DFRUhDrJt0REr1+/pqlTp1KrVq2oc+fONGfOHIU+d0X06tWLnJycKDo6ml6+fEkvX76k6OhocnZ2pj59+hAR0Z49e6hGjRoycjk5OXT06FGF8cQvX77Q0aNHf3mOFBeE2j4rVqygTp06yfTPMzMzqUuXLrRy5Ur69u0btW/fngICAjRR3N/8jyEi0tCaLr/5zb+YN2/eYN68eVizZs2vLopClC2PoAhVy6FK+PLlC9asWYOEhARkZ2ejevXqGDVqFGxsbBT+XiwW4+3bt8wSLCYmJkhISOC8lJqq5QQliFQslVCc1NRULFy4UKb8ISEh8PLy4iT/q4iJiUHXrl1RqVIlZtn6O3fu4MmTJzh48CDatGmD9evXIyUlBcuXL5eTt7S0RHx8PNzc3GTeQVpaGjw8PJCTk6P02jt27FBZtuJLMJQkV69eRWJiIvPu1FleSoj8lStXMHfuXJl6U3xphJKU/5UIqTvFEfL+uMqOHz8eoaGhMDIyUrpEgwRF34qm0LTu0iQ/fvzAmjVrOC2xU1IcPHgQXbp0kdm3atUqDB06FPr6+qxtF5f2ClC/zfonUHTv0nTv3h1mZmbYuHEjTExMkJiYCCsrK7Rv3x729vbYtm2bQrniS4Z++PABOTk5zBJRmZmZMDQ0RJkyZZhlpFXh4uKCQ4cOMcu4S7h//z6zPFJ8fDw6d+6MN2/esJ6PD7t27cLs2bORmpoKAChXrhzmzJmDQYMGKZVJTk7Grl27sGfPHrx48QJNmjRB79690alTJ5VLkGuy/sXFxaFBgwbQ1tZW+TtpYmJi0LJlS+jo6CAmJkblb9mWRrt48SKmT5+O+fPnK1yWUNmyN0DRslbz58/HkCFDmP7G7du3sXnzZvzxxx+YNm0aVqxYgZMnT+LcuXMKzxEREYE5c+agd+/enJeHevbsGapWrYo6deogODgYlSpVAhHh0aNHWLVqFe7cuYPExETO/UdF3LlzBzVr1lRbLisrCwMGDEDHjh1VLgv3b4ar3v/8+TOePXsGIkKFChVgYWGhsTKw6b4tW7Zg7ty5CAwMVGs5TQktW7ZE27ZtMXLkSJn9ERERiImJwcmTJ2X2Ozk54c6dO7C0tJTTn9KIRCJOOlNCbm4uXrx4ARcXF7V0gFBu3LiBXr16IS0tjdkn+v+XXv+n+hslYWdFR0dj9uzZCpdXL1OmDM6cOcMs2zZ+/Hg8evQIp0+fBgCcPHkSwcHBSElJ4X19gL3uqkJV+bnAV2+pS2FhIc6ePYvk5GQAgJubG5o1a8apP6sMPv3Nly9f4uHDh8jOzoa3t7egZc3Z4OonYWvzT58+jX79+uHvv/+WO/ar+vpCSUxMRM2aNeWWVVbE27dv0bdvX1y4cIHR2/n5+WjatCmioqJgbW2NS5cuIS8v719h+wr1T/2bqF27NkJDQ9G8eXOZ/WfOnMGMGTNw69YtHDlyBBMmTGD60tJ8+fIFXbp0wZ07d/D161eUK1cOb9++Rd26dXHq1Cm5/htQVDcqV64MsVjMqtcULU8tzZw5czBp0iS1lpCWsGnTJowYMQKlS5dG2bJlZZZYFYlEuHfvntrn/BXk5ubi2bNnyM7OhoeHB4yNjfH9+3ely7tKQ0SYP38+FixYwPhk9PT0mOVpuaKu3hXiH3z+/DkcHR0RHh4OLS0tjBkzBufPn2eWY8/Ly8Py5cvllmkuzoIFC7Bz505s3boVzZo1w8mTJ5Geno5x48ZhxowZcsueq8M/1e5eu3YNa9aswdOnTwEUtbtBQUGoX78+r/PxiU3k5OQgOzub0YevX7+Gra2t0t/v2bMHo0ePRrVq1bBu3Tps2bIF4eHhGDlyJBYsWCC3rL0ELS0tvHnzpkSXZmdDLBajW7durN+WMl+LhKZNm2LYsGHo1q2bzP79+/djw4YNuHDhAqKiojBv3jw8efJETj49PV3l+YsvT6wMrrpD+tmLxWKFy1HztRWE+ufV5cGDB0hJSQERoWLFinJ+Ki7wja0YGRnh8ePHsLe3h42NDU6cOIHq1avj+fPn8Pb2xpcvXzhdX0i7JxQhdn737t2Rn5+PQ4cOKTzeuXNnaGlpYf/+/ZoqrgxC/QSaID09HXZ2doJsIyFIbP309HQUT7koaXtj8ODB+PjxI/bv349SpUohMTERWlpa6NChA3x8fLBy5coSu7Y0QnXOjx8/lLZT/zQPHjyAj48PsrKylP6mZcuW0NbWxpQpUxAVFYXjx4+jefPm2LRpEwAgKCgId+/exY0bN1ReKyEhQebvvLw83L9/H8uXL8e8efPQqVMn5lj16tVx4cIFWFhYwNvbW2GbIaGk+9rF9aqk3DNmzMC8efPQtGlT1nOULVsWW7ZsQevWrWX2L126FDNmzMD3799VyguNi/wb7OTLly9jy5YtOHToEMqVK4dOnTqhc+fOTBv4T8PHP6but2tlZYX4+HhBvqSmTZsiMjJSrl988+ZN9O3bl/Gb8YGLrWFiYoJ79+6hQoUKMv6KO3fuoHnz5vj48aOcDNszffLkCXr27PmP+Keys7Mxbtw4REZGIj8/HwCgra2N/v37Y8WKFTAyMsKDBw8AQOb7Cg8PR0xMDC5cuKDwvP7+/ujQoQNGjx6t0fKWKlUKycnJKF26NCwsLFTqvk+fPrGeLz09Hfb29irPowpbW1ucO3cOHh4eMvuTkpIQEBCA169f4969ewgICFDoh/zNf5vfybe/+c+QlJSES5cuQVdXF926dYO5uTn+/vtvzJs3DxEREXB2dkZSUhLn83HtMGhC6asKJkujbmBZGlWBtf+V4IaioOq/JYlPwosXL7BhwwaZoOiwYcPg6OjIKmthYYFr167Bw8ND5h1cvXoVnTt3xrt370q49EVo2tC9d+8eZs6ciePHj/8SeaGO+ZJy7Gsyiask646Q569M1s/PD4cPH4a5uTn8/PyUyotEIly8eFHt6/4TaCIJ8MOHD7h58yZ0dXXRtGlTaGlpIS8vD+vWrcOCBQuQn59fop3z/Px8PHnyBLq6uqhYsSKz/+jRo5g5cyaePHmCnz9/ysiUVCJUcbgmg8TFxWHp0qV4/PgxAMDDwwOTJk1Co0aNVMrxuXdpXr16hebNm4OIkJKSgpo1ayIlJQWlS5fG5cuXOQWtdu/ezQTE3NzcAABPnz7FkCFDMGzYMPTu3Zv1HIaGhrh8+bKcjrp9+zYaN26MnJwcpKWloXLlysjOzgYAdOrUCdu3b4epqamMA08R0dHRrGWQoE5gUCwWo1atWujVqxd69OgBa2trTtcQWv+ysrKYpFZVDlRAcfKrdH9JlUOfi2NdIl+838g1sLZr1y6FweBevXoBAL5//w6RSKS0PedT/tGjR+Px48cKHSxEBH9/f3h4eGD16tUqy56dnQ0tLS2ZwOODBw8wY8YMnDx5kreT6eHDh2jbtq1MYqMEoQ5qTbXZ/4t6Xxqh9b5UqVK4du0a3N3dZfY/efIEDRo0UOic1CQ5OTkICgpiklKSk5Ph7OyMoKAg2NraYsqUKSV6fQ8PD7i7u2Py5MmwtraWq4eKgvma7C+qgi15dcOGDTh37hx0dXURHByMOnXq4OLFi5gwYQKSk5PRr18/rF+/Xk7OwMAAT58+hb29PYCiRLSuXbti0qRJAIocnh4eHvj27ZvK8gmtu3zLL6Gk9NY/QUnrHWXBGU0EBrn4Sbj0OStUqICAgADMnDmTc5+jOKmpqVi5cqVMnzM4OBguLi4Kf18SA9akSUhIgLe3NwoLCznLPHnyRMZPIel/KqKky68KsViMHTt2wMzMDADQs2dPrFy5Uu7d/ROJHAAQFRWFiIgIvHjxAtevX4eDgwNWrlwJJycntG/fXqWsgYEB7t+/j0qVKsnsf/LkCby9vfH9+3dOA2avXbsmM2hCVTJD8f6qZJCHhH9q0IeDgwNGjhyJkJCQErsGG6mpqdi2bRtSU1MRHh6OMmXK4NSpU7C3t4enp6fa5/v58yfWrl2LxYsX4+3bt5zlhCTwKkLooBFVFE/C7N69O1atWoUfP37g7t27cHV1ZU3aBoQnHv+vtruajk1IePv2LebNm4ctW7Yo1RWdO3fGmTNnsGDBApnk5vj4eAQGBgIAtm/fjnr16snJFo8LCIHvd6epMhgYGCAxMVEuoSMlJQVVq1ZFTk4OXrx4AU9PT7UmKhCCKt0hPSg4Li5O5XkaN24sqBxc/bu5ubl4//69XB9D0p8vafjEVtzc3BAZGYk6deqgYcOGaNOmDaZMmYJ9+/YhKCgI79+/L/Fy8/VPaoJHjx6hTp068PT0xPjx45mB0o8fP8aKFSvw6NEj3LhxQ+U3+O3bNyxcuBAXLlxQ+P5V9beF+gk0wezZszFz5ky5snz58gXDhw/Hnj17lMqePHkSWlpacoOlzp49i4KCArRs2ZL1+tWqVUPFihUxZ84c2NjYyNk+kn6tIoQ8e4DfYK1/Ai46p6CgAPPnz0dERATevXvH+GhmzJgBR0dHlRM7aILiPhYiwps3b7BmzRrY2dnh1KlTSmVLly6NixcvokqVKsjOzoapqSlu376NGjVqACjq79etWxeZmZm8ynbixAksWbIEsbGxzD7pAQJz5sxRKT9r1ixe1xVKXFwcxo8fj7t377L+dvHixZg5cyYCAwOxfPlyfPr0Cf369cPDhw+xYcMGdOzYUaU8n7iIItSxkxWRk5ODjIwMuYGxyvrMb9++xfbt27FlyxZkZWWhW7duiIiIQEJCglwyX3E0YacL9Y8Bwr7dcePGQU9PDwsXLlR5DVW0bt0aN27cwLp169C9e3cUFhZi7ty5mD9/PkaOHMk66ECordGqVSvUqFEDoaGhzMQ8Dg4O6NGjBwoLC3Hw4EE5GUW2uQQ+NnpmZiZu3bqlsN3o168fp3NkZ2czbYyzs7PKSXGAIp/ujBkz0LZtW4XHjx8/jrlz5+LWrVucrs+VHTt2oEePHtDT08P27dtV+heVDTTV5CBlY2NjHD9+HL6+vjL7Y2Nj0bZtW3z9+hXPnz9HtWrVWGOQv/kP8g/Osvub35QYR48eJR0dHWZ5AxcXF7p48SKVLl2amjdvTqdOneJ0nvz8fJo7dy6VK1eOtLS0mKnip0+frnR5p5JYooQv79+/p2PHjtGZM2coPz+fiIqm8V+5ciVZW1uTpaWlQjmxWEzPnj2jL1++UGZmJpmYmFBCQgLv5Y2KU1BQQMeOHWP9XV5eHj18+FBuuZUjR45QlSpVSFdXV07G19eXPn/+zPxf2ebn58e7/P8U3bp1oyFDhhARMUsKfv36lZo0aUIDBgxglX/27BlNmzaNevTowUyZf/LkSfrzzz9ZZfnUfWlOnz5NEyZMoKlTpzKyjx8/pvbt2zNLJZWk/NevX+WWcLt//z61adOG01INQuX5IL20gdDlcYTWHSHPX+i7+zejSndJL+vi6OiodHNyclIof+XKFTIzM2Pece3atSkpKYkqVKhA7u7utH79erWWJVSXhw8fMksjicVi6tixI719+5Z8fHyoVKlSFBISQi9fviyx6xPxb7MkREVFkba2NnXr1o1Zdrxbt26ko6MjsyxecTR170KXknR2dpZZ1kjCnTt3yNHRkdM5+CyPNGDAAGZpmAEDBqjc+PDmzRsaPXo0GRgYKP3NP7GMkSLEYrGM3lW0nJeQZcnUQZNLU/1TeHp6UkxMjNLjMTEx5OnpqfR4RkYG1a1bl8RiMeno6NC4cePo27dv1LdvX9LV1aXu3bvTjRs3eJfvypUrZG5urvCY9JLns2fPVrkpQhNttlC9/+DBAwoNDaW1a9fShw8fZI59+fKFAgMDVT6ff4PeNzQ0VLh0XGJiokqdoYj8/Hy6f/++WkuIjxkzhmrUqEFXrlwhIyMjpt9y5MgRqlatmlrX54OhoSGzlDpXNNVf5GNnSViwYAHp6OhQjRo1yMjIiAwNDWnevHlUtmxZWrBggcp34OLiQqdPnyaiov62rq4uXb16lTl+9+5dpUugShBad4WUv6T1ljqcP3+epk6dSoMGDaLAwECZTRma6m9GRERQ586dqWfPnsz9XrhwgapVq0aGhoY0fPhwORmheleTmJiYyCxFqS6nT58mXV1dql27No0bN47GjRtHtWvXJj09PTp79qxCGaG2AhvqLkmoLkLKb2FhwbRT5ubmckveS2+KKMklZNVl3bp1VLp0aQoLCyMDAwOm3di2bRv5+vqyylerVo369+9PP3/+ZPbl5uZS//79mXbn6tWrcn3/nJwcGTt0ypQpTN0bN24cTZo0SW7ZbwlpaWnMMpxpaWkqN0V4e3szerFatWrk7e2tdFOFiYkJ87x+BbGxsWRgYED+/v6kq6vLlGXBggVKl54lIvrx4wdNmTKFatSoQfXq1WOWC966dSvZ2NhQ+fLlaeHChbzL9ePHD1q2bBlZW1ur/B0fvSsNX/9g8SU4pZdR5cPPnz8pKSmJbt68SV+/fmX9/b+p3VV3+XChsYlPnz5Rjx49yNLSkmxsbCg8PJwKCgpoxowZZGBgQHXq1KG9e/cqla9fv75SOzsnJ4fGjBlDOjo63B8AD/h+d0SytroQKlSoQCEhIXL7Q0JCqGLFikRUtJx4uXLllJ4jMjKS6tevTzY2NoyuXLFiBR05ckSpTEnrDq4I8dEmJydTw4YN1fKRuLu7yyxxPGLECBlb9d27d2rbeXwICQmhefPmERHR3r17SVtbm1xdXUlXV1dhfZBGE+0eX/8kEdGmTZuoX79+tHXrVqb8lSpVIicnJ5o5cybnZ3D9+nXy8PCQ8XWJRCJyd3ena9euscr36NGDbGxsaPLkybRixQpauXKlzPZvp3z58lSvXj2ZNuvSpUtkZ2dHtWrVUinr5eVFJ06ckNt/6tQpqlKlCqfr87H1JWjq2V+5coXWrl1LixYtonPnzvEqi7oIjQvNmTOHnJ2daefOnTJ97b1791LdunWVykm+My6bKhTZGNbW1tSzZ0/666+/WGVV9Znevn0ryGZJSUkhQ0ND3vK/isePH5ORkRHn39+7d488PT3J1dWVSpUqRS1btqQ3b95wkuUTF9Ek79+/p9atWyuMLyh7923atCFTU1Pq2bMnHT9+nImJaWtrU1JSEus1hfoZhPjHpOH77RIRjR49mkxNTalGjRo0dOhQGTt33LhxnK5PRLRmzRoyNDSknj17Ur169ahcuXJ05swZlTKasjUePnxIZcqUoRYtWpCuri516dKF3N3dydraWqnvic02V2WjFycmJoZMTExIJBKRmZkZmZubM5syP4smMDc3p/T0dKXH09PTlcZmfjXF/euSfpK0/ufqa+rVqxc5OTlRdHQ0vXz5kl6+fEnR0dHk7OxMffr0ISKiPXv2UI0aNUr0nn7z7+R38u1v/hPUqlWLxo4dS1+/fqUVK1aQSCSiypUr061bt9Q6j5AOg6b5+fMnPXnyhPLy8jj9XkhgrXgCirK/1SUlJYWmTp1KNjY2pK2trfK3/4aEAE3w+fNnOnPmDEVFRdGOHTtkNjZevnxJHh4e5O7uTtra2lS3bl2ytLQkNzc3VgekEAcnkbC6v3nzZhKJRGRpaUlisZisrKwoKiqKzM3NadiwYfTo0aMSkxfaWf43OfaFIKTuCHn+Qt+9Ir58+UKHDx+mx48fqy2rKdTRXXxp3Lgx9ezZkx4+fEgTJ04kkUhEFStWpAMHDpTI9YrTqlUratq0KR07dox69epFIpGIKlWqREuWLOGd9KtOIpQmkkEqVapEy5cvl9u/bNkyqlSpklI5Tdx7XFycwvY5Ly9PaTCuOAYGBgr7KTdv3uQcnHjz5g35+/uTSCQiXV1d0tXVJbFY/P+xd9ZhUazv/793gWVpRFAQRUApFTtAbOxuxQ7swBbjmNgF6rEDbLBbMFBUPFiAqAiKIBy7sEAUeP/+4Nr5srC7MzuziJ/z83Vdcykz88w8M/vME3eiRYsWePXqFQDg0qVLrIIHdeGrGIyKimKESor4/v07goODedWJS/u7fPky87sVhfGrzBnoV5GVlYW0tDQ8e/ZMbuNCUFAQ4zxW8JrK5ixGRkZITk5Wes2nT5/C0NBQ6fFevXqhevXqWLduHZo2bQqxWIzatWtjzJgxas3xCgrS/f39MX36dJQpUwZeXl6cr/OrEdLvh4aGQiKRoHLlyrCxsUHJkiVx6dIl5jgXoX5R9Pvq0qRJE4wdO7bQ/tGjR6NBgwYqy/r4+DAOYdnZ2ahfvz5EIhEMDAwQHh7O6f42Nja4ceMGAHnFyOPHj2FkZKTGk/Cjffv2OHToUJHfpyBC11mOjo6MI2lERAREIhHatWuHr1+/st7b19cXzs7O2LVrF3r37g0bGxu5cWDz5s3w8PBQeQ2hbVdI/TXVbwll3rx5zHypU6dO6Ny5s9ymDE3MNzWlnOFDZGRkIUe4oKAg2NrawsLCAsOGDVM4lhVk8ODBnBxKlVG9enWlhjxsBohFhTrGt9nZ2di2bRu8vLzg6emJpk2bym2a5ndyUBeKi4sLY0CVf9yIi4tjdRQEgOvXr6NkyZKwsLCAp6cnPD09UapUKZQsWZIZj3bt2oXly5fLldu4cSPat2/P/G1oaIh69eoxzuWWlpYK10GaQFOG80OGDMHGjRuLpI5ccHNzw6pVqwDI/3ZRUVGwtrZWWm7atGkwMTFBt27dGHnAsGHD4Orqiv3796tcy8gQaoQntN8VIh/UtPGtuvwu4+6NGzdgZ2dXSCmrSj4uVDcxfPhw2NjYYPLkyahSpQpjtNSuXTumv1BFTk4O6zlcZRV84fvdAYXbHl+OHz8OiUSCqlWrYujQoRg6dCiqVasGXV1dZk6xYcMGpcYdfJ0uNNF3nD17FlevXmX+Xr9+PapVqwYvLy9O8y2hMtr69eujUaNGOHPmDKKjoxETEyO3KaLg71bQ8eLVq1cQiUSsdc+PEN2KjBs3bmDVqlUqnYdlaGLc4yufXLNmDQwMDNC1a1dYWVnBz88PJUuWhJ+fH+bPnw9jY2Ns3ryZwxP/H3fv3kVwcDCCg4MVOvwrw8TERM5J8n+NDx8+oEePHjAyMsKWLVswZcoU6OjoYObMmaz6ValUqlDWlZyczNn4sWnTppwDQBWkqN79nTt3GAPEokATeqEKFSrgwoULAOTHjfj4eJUGXAUNDQ0MDCASiRgHP5msiK+jIxdEIhHevHnD/C0LiCODq/FtwQBY6enpiI+PR69evVCtWjWl5VJTU+XmRlFRUfDx8VG7z+BLbGys3BYTE4OzZ8+icePGrDKe/Hz+/Bm9evWCtrY2tLW11VojCtWLCF0n9+nTBx4eHrh16xYMDAwQFhaG3bt3w8nJCadOnVJYRktLCxMnTizkMMXV+FYoQuRj+eH77QKaDaLm6+sLkUgEHR0dTo4mmlxrpKenw8/PDz169ECbNm0wa9YsVqN9TeHg4AAfHx9m/qIuX79+xezZs+Hu7o4KFSrAzs5OblOGoaEhbt++rfT47du3VeqGNMGdO3fkAnscO3YMnTp1wowZM+Scngsi1Ek5P1++fIG3tzfT54jFYkgkEgwbNoz5lqKjoxEdHS3sYf/wP8kf49s//CcwNjZmPAuzs7OhpaXFy7tPyIQB4N/p5+fbt28YMmQItLS05CKQjh07FkuWLFFaTohijc0ARR1DlIyMDAQFBaFhw4YQi8Vo3LgxNm7cyEx2lVEUBgG/2ohPE95GfCMpChFwAsLavqurK6MsOnToEEQiEdzd3TlPVoWUFzpZ/l0E+4pQ14iLb9sR8v6F/vYA0KNHD6xbtw5AXv/h4OAAHR0daGtr/1IDFb59lyK4GAGamZkxC+qMjAyIxWKVUTQ0jYWFBTP5T09Ph0gkwq5du9S6hhBDKE0Yg0gkEoVRBR4/fgxdXV2l5TTx7Mqisrx7946zIUT79u1Ro0YN3Llzh9l3+/Zt1KxZEx06dFCrPvHx8Th+/DiOHz+OR48ecS6XkZEht0hPSUnBmjVrWI11+SoGC743RYoZru9PE4Z4Qli6dKmcgXH37t0hEolQpkwZpYqpgkRERKBv375wd3fHv//+CyDPACO/wk0RfCLSFIRPG2ZTiLL9flZWVkz7eP36NUQiEdasWcOpvvkpKGS3t7dHvXr1MGPGDCaqsyqKQkDNZcwW0u+7u7tj5syZAIDc3FwsW7YMhoaGjHKHy7ejib4PyFs3tG/fHhUqVECFChXQoUMHREREcCp77do1SKVSNGzYkFFgNmzYEFKplPUa1tbWuHXrFgDg6NGjKFOmDBISEjB79mzUr1+f0/3zK9Dzz3djYmJgbGzM6RpC2Lx5M8qVK4e5c+fi0KFDTL8t2/jApe0JXWdJpVKkpqYyf0skEpXC1vxkZGSgf//+MDU1hbOzc6HfuUmTJqxRwIS2XSH111S/JRRLS0te36sm5puaUM7w7XdbtWol1z7u3bsHbW1teHt7Y9WqVbC0tMTcuXNZ7//t2ze0bdsWAwcOxMqVK9WKhgQAurq6CqMJJiQkqJxzKoPLWqGgErbgdvXqVc5j/pgxY2BgYICePXvCx8cHEyZMkNuKov7/FaRSKaN8yT9uJCYmQiqVcrrG58+fsXHjRiaSz6ZNm1jnKw0aNJAzGCpoALl7925OAQICAwPlFL9Tp06FiYkJ3N3dOUfVUYf839XixYthbm7O+7sTioGBAWMAkf/9JScnq/xu7ezsmDE5Li4OIpEIgwcPZhR1XBBqhCe03xUiHxSLxSoNSVTRpUsXJmtaly5dVG7K+F3G3WrVqqFHjx54+PAhPn78iPT0dLlNEUJ1E+XKlcPFixcB5LVTkUiEGTNmCH+YXwjf7w4AFi1apBGHGyDPKXT69OlMe/P19VXpRJofvk4Xmug7qlSpwkTfvHfvHiQSCWbMmAE3NzdOmYmEymj19fXV1qFoOvJjcUVyEwpf+aSzszMTGffu3bvQ1taWcxjbtm3bL4uaZmtryyuIhgwhcgJNMmPGDMYQS6brYqN06dJM/5uf8+fPw8LCgtM1jhw5gkqVKmHnzp24fft2IcNEVQh598WZkVATeiFlc+0HDx5wjp66d+9eeHh4yMnDHz16hIYNG2LPnj2c66IuIpEIbdu2ZcYabW1ttGzZkvm7bdu2nPo/RZnZRCIRbGxsEBkZqbRcgwYNGBnBy5cvYWRkBHd3d5ibm2P+/Pkae062ehd0UnJ3d+c8lsgygNSsWRMPHz7E1q1bYWRkhJ49e6q11uSrFxG6Tra0tERUVBSAPB2HLNPU8ePHlRog37hxA97e3jAyMkLdunWxbt06vH37ViPGt1zW6ULkYwWvI/TbFcKHDx/QtWtXmJiYYMuWLejbty8MDAzw999/qyynqbXGs2fPlM7xuAZGKcjhw4fh6urK6Vx9fX1BDpJ8I67Xq1dPpQx38eLFqFevHu96caF27dqM3UBSUhJ0dXXh5eWFihUrwsfHp0jvXZAvX74w4zyXLC9/+P+DP8a3f/hPoCnPfKETBk10+nzToRa3IdfNmzcxfPhwGBsbo0aNGli5ciW0tLQ4Txg1YRBQ3EZ8Qr2NhERSFCLgBIS1fX19fUaImZubCx0dHbW8dYWUFzpZ/l0E+0KNuIS0HSHvX+hvD+QJuGTPuHfvXlSsWBHfvn3Dhg0bfkkKaKF9F8DPCFDRuCUkHa66KLq/shSFyhBiCKWJMatChQrYtGlTof0bN25ExYoVlZbTxLMX9G6XkZCQwDl64ps3b9CmTZtC3tlt2rTRSMQXLrRo0YKJSPXx40eUKlUKZcuWhVQqxYYNG5SW46sY5KKY4RoVRaghntCoMra2toxHdVhYGExNTREaGoqhQ4eiRYsWrOUPHToEPT09eHt7Q1dXl3kP69atYxWO84lIUxBlbTgmJkZlGujw8PBCSgTZdvHiRZWCZbFYLOfQYGBgoJZQVFMIFVDzHbOF9PvGxsaFzt27dy8MDAxw8uRJTkpNTfR9QtJpyoiOjkafPn1QqVIl1KpVC4MHD+ZUD11dXUaJM2zYMGZt9fTpU879bsOGDbF27VoA8sYkY8eORatWrThdQwhCU6jzbXtC11lsEV2KGqFtV0j9f5d+y8zMjNc8URPzTU0oZ/j2u5aWlsxYDwAzZ86UU2KFhITAxcWF9f7btm2DtrY2DA0NUb58eU7pGPNTtmxZhISEFNofHByMcuXKsZbnu1ZQlr5SXYebkiVLKkylyxUhDk+nT5/GuXPnCu0PDQ3FmTNnWO/95MkTjB07lokaO27cuF+6ZnJxcWHWKPnnrWvXri3SqMeWlpZyhmLm5uZyfyckJHByGnF0dGTm7JGRkdDT08PmzZvRoUMHlQaQMtQ1nFeV+lTd704o1tbWzFw9/28nSwepDB0dHcYpDsjrA/MHWeCCUCM8of2uEPkgmyGJKuPZQYMGMYblgwYNUrkp43cZd/mkDxeqm9DS0pKLlqWnp/dLIqBpEr7fHaA5hxuh8HW60ETfYWBgwPT1c+fOZSJV37lzB6VLl2YtL1RGW7t2bVZH4IJo2vhWXd3K7du30aRJE8bwPz/p6elo0qQJZxkJwN9hjK98Uk9PT85IR1dXF/fv32f+fvz4MWtQlIkTJzLOGQVTd6uTynv37t3o3r07L72WJuQEmmDt2rXQ19dHnz594OTkhEqVKnH6/YcPHw5XV1e5Oebjx4+ZCNpcULbG5zJn5/vuiyIjoTpoQi9Us2ZN7N69G4B8/zF//nzW7Egy7O3tFUZ5vn37NmxtbVWWVfatTJo0CTNnzsSOHTvw/v17hWXZ5jpscx4ZBQNgRUREID4+njVis6mpKTNHCggIYGThoaGhv2SuXTBSZGpqKjIzM9W6hkQiwfTp0/Hjxw9m35MnT+Dm5sYpoJRQhK6T82eIs7GxYdr/06dPWbMafv36Fdu3b4eHhwd0dHQgFovh7+/PKaiEDL5yBk3I9zTx7QqhTJky8PDwkKv7gQMHYGZmhrZt2yotp6m1Bt/APJs2bUK3bt3g5eXFZNy9ePEiqlevDn19fYwcOZLT/bt06cI7eyTAP+L65s2bGV1EQU6cOAEDA4Mij76dX0eydOlStGzZEkCeMX/ZsmU5XeNXOyn/4f8vtOkPf/iPEBoaSiYmJkRElJubSxcvXqT79+/LndOxY0eV16hUqRJdvXqVypcvL7f/0KFDVKNGDdY6JCYmUvXq1YmI6ODBg9S4cWPat28fXb9+nXr37k3+/v6s1zh27BgFBweTm5sbiUQiZn/lypUpKSlJabmPHz+Subk5ERHp6emRvr4+ValShfV+RERisVjuXooQiUSUnZ2t8FjVqlXp8+fP1KdPH4qMjKTKlSsTEZGvry+n+xMRvXv3jsqUKUNERCYmJmRgYEBubm6cyxMRRURE0KxZs4iI6OjRowSA0tPTKSgoiPz8/Khbt25qXU9dnj9/TuPHjyd9fX1e5Zs2bUovX76kUqVKye3/9OkTNW3alHJycpSWNTU1pZcvX5KdnZ3c/ujoaLK2tma9t5C2n5mZyTyzSCQiXV1dsrKyYr2nJsq/fv2aeeZSpUqRvr4+tWnThvO9hZbXFJs2baK9e/cSEdH58+fpwoULdO7cOQoJCaGpU6dSWFiYyvJC2o6Q9y/0t5fV0czMjIiIzp07R926dSN9fX1q164dTZ06Va1rqYsm+i6ivO+kX79+RER08uRJSklJoUePHtHu3btp1qxZdP36dYXlHj58SK9evSIiIgCUkJBA3759K1THokAkEtGXL19IKpUSABKJRJSZmUmfP3+WO8/Y2FjpNd69e0eWlpZERHTmzBnq0aMHOTo60pAhQyggIEDl/YWMWTImT55M48ePp5iYGKpfvz4REV2/fp0CAwNV3l/Is3ft2pW5xqBBg0hXV5c5lpOTQ/fu3WPqwoaFhQWdOXOGEhMT6dGjR0RE5OzsTI6OjpzKy+4ZGBhIFy9epDdv3lBubq7c8UuXLqksf/fuXVqzZg0R5bVjS0tLio6OpsOHD9OcOXNo1KhRCsu9ePGCXFxciIjI1taWpFIp8w0IhW0+IkNI+yMimjp1Ki1btoyIiOLi4mjSpEk0efJkCg8Pp0mTJtHOnTtVln/16hWVK1eOiIhOnTpFPXv2pJYtW5KtrS3Vq1eP9f5+fn60adMmGjBgAB04cIDZ7+HhQX5+firLxsTE0J07d8jZ2Zn1PgWpUaMGiUQiEolE5OnpSdra/7cczMnJoeTkZGrdurXS8p6engSg0H6RSMR8T6oQi8Vy/5dIJGo/AxFReno6PXnyhCQSCdnZ2ZGRkRHnsvfv36e6desSEVFISAi5urrS9evXKSwsjEaOHElz5sxRWV7ImM2339fV1aX09HS5fX369CGxWEy9evWiVatWsT63Jvr9RYsW0fLly2nixInMvvHjx9Pq1atp4cKF1KdPH9Z6VK9enXl/6lC6dGl6+PAhWVlZ0blz52jjxo1ERJSRkUFaWlqcrrF48WJq06YNPXz4kLKzsykgIIAePnxIkZGRdOXKFbXrpC4F+2h14dv2NLHO+uuvv5g5348fP8jPz49Ze8tYvXq1Wtf8/Pkz7d27l7Zv3063b99Wep4m2q6Q+muq3xKCt7c37du3j/766y+1ywqdb2ZlZZFUKmX+lkgkzNydK3z73Y8fP1Lp0qWZv69cuSK3VqtTpw6lpaWx3n/WrFk0f/588vX1lfs9uTJs2DAaPnw4PX36VG7OuWzZMpo0aRJreT5rhfDwcLXrqQyJREIVK1bkXZ7vWocob121dOnSQvtzc3PJ19dX5do7NDSUOnbsSNWrVycPDw8iynvvlStXppMnT1KLFi14PxNXJk2aRGPGjKHv378TALp58ybt37+flixZQtu2beN0jcePH1N4eLjCubqytp+enk5ZWVnM32/fvpU7npubK3dcGWlpacxvf+zYMerevTsNHz6cPDw8qEmTJqzl+/TpQ8OHD6f+/fvTq1evqHnz5lSlShXau3cvvXr1qlD9k5OTWa/5q+jduzdNnz6dDh48SCKRiHJzc+n69es0ZcoUGjBggNJyOTk5cv28trY2GRoaqnXvf//9l2rVqkVERFWqVCFdXV2aOHEi53WO0H5XiHxw4MCBcn+rs8aTrZ8A0Pz588nCwoL09PQ4l5fxO4y79erVoydPnqjddwrRTQCQW5tpaWnxen/FCd/vjogoNjZWbh184MABqlevHm3dupWIiMqVK0dz586lefPmsdYjPT2dbt68qbDfZauHnZ0dxcTEFJKPnzt3jpGBKEITfYdEIqGMjAwiIrpw4QJTVzMzs0LzXkUIldEuW7aMpk2bRosXLyZXV1fS0dGRO65ori2TLRTcxxd1dSurVq2iZs2aKaybiYkJNW/enFasWEF79uzhdD11xz0ZfOWT+vr6cvNyCwuLQu1GmT5ORnR0ND169Ihq1KhB0dHRSs9j+11WrVpFSUlJVLp0abK1tS30+9+9e1dpWU3ICYTSunVrun37NgUFBVH37t0pMzOTJk2aRG5ubjR//nyaNm2a0rLLly+n1q1bk7OzM5UtW5aI8uZQjRo1opUrV3K6v5A5EN93HxAQQMuWLaOpU6fS4cOHqUePHrRhwwaKi4tjnqMo0YReaM6cOTRw4EB6/vw55ebm0pEjRyghIYF27dpFp06d4nSNly9fKvxOcnJy6PXr1yrLRkdH0927dyknJ4ecnJyIKE/Hr6WlRc7OzrRhwwaaPHkyXbt2jSpVqiRXlk1uzJXGjRvzKvfz509GL3HhwgVmfuHs7EwvX77USN1UUXCc5ENYWFih569QoQJdv36dFi1axFpeqF5E6DrZycmJEhISyNbWlqpVq0abN28mW1tb2rRpE+u3YGBgQEOGDKEhQ4ZQQkICbd++nZYuXUq+vr7UokULOnHiBOv9+a7TNSHfU/fblenUuHDkyBHWc0aOHEmzZs2SWzf06tWLPDw8aPDgwSrLamKtoUwH8vXrV7l1XH6WLl1Kc+bMoapVq9KjR4/o+PHjNGvWLFq3bh35+PjQiBEjqESJEpzuL9OdP3z4UOGcjc0WqkSJEmrL9YiIhg8fThEREdSxY0dydnZm+s1Hjx5RYmIi9ezZk4YPH672ddUBAPOtX7hwgdq3b09EeWuFd+/ecbrG4sWLGb3CjRs3aP369eTv70+nTp2iiRMnsrbBb9++0dKlS5X2PU+fPlX3sf7wH+KP8e0f/jMUFBCOGDFC7m+RSKTSAI1I+GRfE53+27dvCxnQEeV15myLZL6KtaNHjyq95o0bN2jt2rUqlcUJCQnUq1cvatq0aaFFCFc0oVQtTiM+IqJWrVrR7du3yd7enld5ZRO29+/fk4GBgcqyQgScRMLb/rZt2xjhUHZ2NgUGBjKGdTLGjx9fJOWFTpZ/B8G+UCMuIW2HSNj7F/rblytXjm7cuEFmZmZ07tw5xgjt48ePShcqmkITfRcRfyPAgkZssjEjvxEb27jFFwByRp4A5AztudxfqCGUUGOQUaNGkaWlJa1atYpCQkKIiMjFxYWCg4OpU6dOSssJeXaZIAIAGRkZySnDJBIJubm50bBhw1Q9diFsbW0JAFWoUEFO2cYFHx8fCgwMpHbt2lGVKlXUVnJkZGQwRothYWHUtWtXEovF5ObmRs+ePVNa7ndQDAptf8nJycx3f/jwYerQoQMtXryY7t69S23btmUtX6JECUpLS6Ny5crRuXPnGEUhAE7fbUJCAjVq1KjQfhMTk0JGlgWpVKkS53llQTp37kxEeQa8rVq1klPsSCQSsrW1VeqsJNSgQvbtydrp169fqUaNGoUMoT58+KD0GikpKTRmzBgKDQ1l+k9tbW3q2rUr+fv7M0ZaWVlZcsbx+REqoBYyZvPt96tXr07h4eGMIYeM3r17E4BC6yBFaKLff/r0KXXo0KHQ/o4dO9LMmTNZ65CamqryuI2NjdJjgwcPpp49e5KVlRWJRCJq3rw5ERFFRUVxNkRv0KABxcTE0NKlS8nV1ZXCwsKoZs2adOPGDXJ1deV0Db78/PmT9PT0KCYmRm1nExl8257QdVajRo0oISGB+bt+/fqCBInh4eG0Y8cOOnLkCJmYmFCXLl1Uni+07XKpv7LxUxP9lib4/v07bdmyhS5cuEBVq1YtJFhXpRjRxHxTqHKGb79bunRpSk5OpnLlytGPHz/o7t27NH/+fOb4ly9fCr0LRfz48YN69erFy/CWKO/5jYyMaNWqVTRjxgwiIipTpgzNmzdP5TpHBp+1Al8lrCImT55MAQEBtH79el4GMUIcnh4/fqxwneXs7ExPnjxRWdbX15cmTpxYyHjX19eXpk+f/kuMb729vUlPT49mz55NGRkZ1KdPHypTpgwFBARQ7969Wctv3bqVRo0aRebm5mRpaSn3/kUikVIjnrJly9L9+/cZZVZB7t27x8mowtDQkN6/f082NjYUFhbGGItLpVLKzMxkLc/XcP7z589kaGhY6JvLzc2lr1+/qpTraYrFixfTmDFjqFy5cpSTk0OVKlWinJwc6tOnD82ePVtpOQByTpbfv3+nkSNHFpKrqFLIacIIT0i/K0Q+qAlDEgBUsWJFevDgATk4OKhd9ncYd8eNG0eTJ0+mV69eKVRoK5NTCNFNAJBzjszMzKQOHToUklGqMoArbvh+d0Sac7g5efIk9e3bl+lrCva7bN8AX6cLTfQdDRo0oEmTJpGHhwfdvHmTgoODiSjPEIyrIZ0QGa1sfeXp6Vno2ZS1XbZ2y2Y4WhB1dStRUVEqAyh07NiRtm/fzvn+fMc9vvJJZ2dnunfvHmPYXbCNP3r0iGxtbVXWOTw8nLS0tOjly5eM81avXr1o7dq1ct8UGzJZER+Eygk0gSwggszpVE9PjzZu3Ejt27cnb29vlca3JiYmFBkZSefPn6fY2FjS09OjatWqUcOGDTnfX4ghIt93n5SURD169CCiPMM2bW1tWrFixS8xvJUhVC/UqVMnOnnyJC1YsIAMDAxozpw5VLNmTbUc7Tw9PWnEiBG0bds2qlmzJhER3blzh0aNGsX0a6rub2ZmRjt37mTmp58+fSJvb29q0KABDRs2jPr06UMTJ06k0NBQTvVRl4MHD9L+/fspMTGRJBIJOTo60uDBg6lVq1Yqy1WuXJk2bdpE7dq1o/Pnz9PChQuJKC9gRsmSJYukrjKys7NpzZo1Cus9fPhwzmtO2Zr3yZMnlJSURI0aNSI9PT0SiUScHI+F6kWErpN9fHwYecbcuXOpdevWtHfvXpJIJBQYGMj5Ok5OTrR8+XJasmQJnTx5knbs2MGpHJ91uhD5WH7U/Xbzr2MA0NGjR8nExIRq165NRHnfbHp6Omcj3fzt4/v374weuWzZsnT+/Hml5YSuNWTraVkbze8slJOTQ1FRUUyAvoLs3LmTtm7dSgMHDqSrV69S48aNKTIykp48ecJJh58fmf5xwYIFhY5xkfEtXLiQ5syZQ0FBQWoHk9uzZw917NiR9u3bR4mJiQSAnJycaP78+dSzZ0+1rsWH2rVrk5+fHzVv3pyuXLnC6ASTk5M5z3uEOil7e3vTlStXqH///oye4g9/kPHH+PYP/wmERhGSIXSyr4lOv3bt2nT69GkaN24cEf3fRGfbtm3k7u6usixfxZoiIUBCQgL5+voyQitFg7iMp0+fUmBgII0aNYoyMzPJy8uL+vbtq9aAowmDgOI04iPi722kiUiKQgScRMLavo2NDROJgIjI0tKSdu/eLXeOSCRSutAWUl7oZPl3EezzNeLSRNsR8v6F/vZERBMmTKC+ffuSoaEhlS9fnpncRkREFLkhjCb6LiJ+RoDFHRVIE9G0hBpCacIYpEuXLqxGOwUR8uwyZaStrS1NmTJF7YVxfjIyMmjcuHEUFBRERHkKFXt7exo3bhxZW1tzisB84MABCgkJ4WQsqoiKFSvSsWPHqEuXLhQaGspEqXjz5o1KpbgQxWBBo+tHjx7R169fiYjUMigV2v6ERpXp2rUr9enThxwcHOj9+/eMYjA6OpqT17ylpSU9efKkkCLl2rVrrIomPhFpZMydO5eI8tpwr1691JofCY1qIFSZn5aWRm5ubqSjo0MLFy5kFFQPHz6kjRs3kpubG0VHR1NERATFx8fT9OnTFV5HqICa75gtpN8fNWoURUREKDzm5eVFAOTGY0Voot8vV64cXbx4sVAbv3DhAmMUqgpbW1uVY6yq9zdv3jyqUqUKpaWlUY8ePZg5j5aWlloR6ytUqKDwXR06dIi6d+/O+TrqoqOjQzY2NoKcavi2PaHrrMuXL/Ous4znz59TYGAg7dy5k9LT0+njx4+0b98+6tmzJ+u8S2jbFVJ/TUWzEcq9e/cYAX7BCHqq3p8m5puaUM7w7Xfbtm1Lvr6+tGzZMjp27Bjp6+vLKcHv3btHFSpUYH2GgQMHUnBwMG/lv0gkookTJ9LEiRPpy5cvRERqRVzXRORuojyl0o8fP+T2cTFivHbtGoWHh9PZs2epcuXKheYNbFE9hNTfxMSEnj59Wmi+w0XBFB8fzxiw5GfIkCGcskppir59+1Lfvn0pIyODvn79qtBZXhl+fn60aNEipXMSZbRt25bmzJlD7dq1KzRXy8zMpPnz51O7du1Yr9OiRQvy9vamGjVqUGJiIrNmePDgAasxDxE/w/mjR4/S9OnTKSYmppAiLzMzk+rUqUMrV65UaKSjSSQSCW3dupXmzJlDcXFxjKyHzRhUSORXGUKN8IQ6vQiVDwpFLBYzayR1jW9/l3FX5ow4ZMgQZh+bnEKobkK2TpOhymDvd4Xvd0ekOYebyZMn05AhQ2jx4sW8MtPxdbrQRN+xfv16Gj16NB06dIg2btzIRKo+e/asyuw0MoTKaPnMubm0W3UyEaqrW3n+/LnKOZmhoaFaESCFOOrykU8uW7ZM5XwoNTW1kBG/IgpmJzp79myh4AZsFPwt1UGonEATKDO2ateuHcXFxSk8duPGDXr//j21b9+eRCIRtWzZkl6+fElz586ljIwM6ty5M61bt06pY7ciHj58SKmpqYXm7KqiEPJ995qIPCsETeiFiIgaNmyo0liOjR07dtDAgQOpdu3aTJ+RnZ1NrVq1Ys1UsWLFCjp//rzcmsrExITmzZtHLVu2JB8fH5ozZw61bNmSd/2UkZubS15eXnTw4EFydHRkZNnR0dF08OBBGj58OG3cuJHev39PERERhfqXZcuWUZcuXWjFihU0cOBAqlatGhERnThxgnEiKAoyMzOpRYsWdOPGDWrevDkTWCI+Pp5Gjx5NJ0+epBMnTlBycjJdvXqVBg0apPRa79+/p549e1J4eDiJRCJ6/Pgx2dvb09ChQ8nMzIw18rRQvYjQdXL+sb5WrVr07NkzevToEdnY2BQyQueClpYWde7cmbNBPp91uibkezLU+Xbzz/GnT59OPXv2pE2bNjH1zMnJodGjR3N20szNzaVFixbRpk2b6PXr14xe7a+//iJbW1saOnQoaz34IIswD4Di4uLkdGESiYSqVatGU6ZMUVg2NTWVmjVrRkR5705HR4fmz5/PS78odN0hJNo9EVHPnj2VGtpmZmYWaZAef39/6tu3Lx07doxmzZrFzD0OHTrEOSOpUCfls2fP0unTp5kMTX/4Q37+GN/+4Q8FEDLZ10SnzzcdqqYMuV68eEFz586loKAgatWqFafoTNbW1jRr1iyaNWsWXbp0iXbs2EEeHh6Mt6O3tzdrGm1NGAQUpxEfEX9vI01EUhQi4JTBt+2npKSoXUZT5YVOln8XwT5fIy5NtB0h71/ob09ENHr0aKpbty6lpaVRixYtGMNne3t71tTrQtFE30XEzwhQE6l5hKCJaFpCDKE0MWalpaWRSCRiPPpv3rxJ+/bto0qVKqlMb6KJZ582bZqccPvZs2d09OhRqlSpEmeB3IwZMyg2NpYuX74sp0hp3rw5zZs3j5MxmdD0SHPmzGE8+D09PRknn7CwMDnDrIIIUQxyMbrmglBDPKFRZdasWUO2traUlpZGy5cvZyI9vHz5kkaPHs1aftiwYeTj40M7duwgkUhEL168oBs3btCUKVNYvfv5RKQpiEw5+OPHD4XpcRRFIL137x7rdYm4R4JSl3nz5pGTkxOFhobKGaJ07tyZJk6cSK1bt6YOHTrQ7du3GQcsRQgVUPMds4X0+2yKvJ49e7J6Zmui7+ObTlNGwTSYP3/+pOjoaFq9ejWnlHIFjWPT09M5t6vs7Gx69OgRE5FDxvHjx2nOnDn06NGjIjW+JSKaNWsWzZw5k3bv3s0rvRfftqfJ9PWKePr0KY0cOZLCwsIKHTt8+DBt376dIiIiqE2bNrRq1Spq06YNGRgYkKurK6c+X5MRQNVFaL+lKfj+hpqYb2pCOcO33124cCF17dqVGjduTIaGhhQUFCSn3NixYweneVdOTg4tX76cQkND1YocnJmZSefPn6emTZsyhh2yfz9//kyXL1+mVq1asSrkhTgMffv2jaZPn04hISH0/v17hc/GhqmpqdrGIPkRUv9OnTrRhAkT6OjRo4yh9JMnT2jy5MmsqRAtLCwoJiamkEwjJiZGLQNYITRr1oyOHDlCpqampK+vzxg4fP78mTp37syayvTjx49MRDJ1mDlzJoWEhJCTkxONHTuWGbcSEhJo/fr1lJ2dzcmY/O+//6bZs2dTWloaHT58mDF2v3PnDnl5ebGW52M4v3HjRpo2bZpCozcDAwOaPn06rV+/vsiNbxcsWEBTpkyhcuXKyRn+ZGZm0ooVK5RGL9SEjEioEZ7QflcT8kGhLF26lKZOnUobN25UK+I/l3G3qLID5ac4nKWFGL79LvD97og053Dz/PlzGj9+PC/DWxnKnC6eP3/OGMQWRBN9h42NjcLsc2vWrOFUXqiMls+cW9PtVl3dioWFBSUkJJCdnZ3C6z169EgtAyi+DmN85ZNsRhvDhw+nN2/ecK6/jILGuOpw584dio+PJ6K896FKNihDqJxAU1y9epU2b95MSUlJdOjQIbK2tqbdu3eTnZ0dNWjQoND5CxYsoCZNmjByybi4OBo2bBgNHDiQXFxcaMWKFUy2CzaePn1KXbp0obi4OEa+SfR/Topcxi4+715o5FkhaEIvZG9vT7du3Sr0faWnp1PNmjU5OR9ZWFjQmTNnKDExkR49ekREeQbzXHQ6nz59ojdv3hTK1PH27VsmMIOpqWkhY2pNEBAQQBcuXKATJ04wbVDGiRMnaPDgwVShQgUKDAxUGLW9SZMm9O7dO/r8+bNcqvrhw4cLGgPZWLp0KaWlpVF0dHQh+W9sbCx17NiRJk6cSIcPH2Z1QJw4cSLp6OhQamoqE2CBKC9696RJk1iNb4XqRYSukwuir6/PRF/+FWgiQxhfhHy7O3bsoGvXrskZCGtpadGkSZOofv36tGLFCtb7+/n5UVBQEC1fvlxO/12lShXy9/dXanwrVMYnk80NHjyYAgIC1MrokpWVJafPkEgkvGTDBckf+ZcrQqLdE+WNK2vXri20/9u3b9S+ffsilUNXrVpVoVPNihUrODu3C3VSLlGihEZ+uz/8R8Ef/vAfIjAwEKdOnWL+njp1KkxMTODu7o6UlBTW8nZ2dnj37l2h/R8/foSdnR3vemVmZuLHjx+cz3/y5Am8vb1Rp04duLi4oG/fvrh37x7v+8uIi4tTeiw9PR3Tpk2Dnp4e3N3dERERIehe6enp+Pvvv1GrVi2IRCJUqFBB0PUA4P3796zn3Lp1C0eOHMGXL1+YfadOncK1a9cE37+omTdvHr5+/cqr7Pz58/Ht27dC+zMyMjB//nzW8ppu+5mZmWqX+f+ZHz9+YMWKFRg/fjzu3r3L7F+9ejW2bt3KWl5I22Hj48ePWLdu3S8rm52djejoaHz48IHXPYXCt+86ePAgVq9ejbS0NGZfYGAgjh07prLc2bNncfXqVebv9evXo1q1avDy8vpl7+DJkyeYNWsWevfujdevXwMAzpw5g/v376t9rY8fP2q4dspp0KABdu3aBQB4+fIljIyM4O7uDnNzc079Xnp6Og4ePIgVK1Zg5cqVOHz4MD59+sTp3i1atMDGjRsB5D1zqVKlULZsWUilUmzYsIHTNWxsbHDjxg0AgKGhIZKSkgAAjx8/hpGREadrrFy5EqNHj0Zubi6n8xXx8uVL3L17Fzk5Ocy+qKgoxMfH876mMlJSUjhtfFGn/T179gzt2rVD1apVsW3bNmb/hAkTMG7cON514Epubi78/PxgYGAAkUgEkUgEqVSK2bNns5a9fPmyyo0LiYmJaNCgAcRisdwmEokgFosVlpEdk9VX0aasrCKysrKQlpaGZ8+eyW3KKFOmjFx/WZArV65AJBJh+/btrPfOzs4u1McmJyczfaAqhI7ZQF5bDQ0Nxe7duxEUFCS38SEmJkblu//06RPnjY0jR47Aw8MDZmZmMDMzg4eHB+tYx8apU6fQuHFjlecsXboUBw4cYP7u0aMHxGIxrK2tERsbq7JsXFwcypcvz7TzLl264NWrV2jUqBHMzMwwffp0ufG7qKhevToMDQ2hq6sLR0dH1KhRQ25jQ0jb+/nzJ4KCgvDq1SvBz1EQVe1PS0sLM2fOxOfPn+X2a2tr48GDB7zv2bZtW7x48UKtMhMnTuS8FSQ1NVWujURFRcHHxwebN2/m/Qy/krlz58qN8zLS09PRu3fvX1IHIf1ueno6srOzC+1///49srKyWMs3adJE6da0aVOl5fz9/dGsWTOlxz09PbF+/XrW+wP81wqjR4+Gi4sLDh06BD09PezYsQMLFy5E2bJlsWfPHk731gR865+eng43Nzdoa2vD1tYWtra20NLSQtOmTVnnbfPnz4epqSmWLl2KiIgIREREYMmSJTA1NcWCBQs08VisiEQihW309evX0NbWZi0/ZMgQZs2gLk+fPkWrVq3k5l5isRitWrVi1g1FTXh4OExNTSEWizF48GBm/4wZM9ClSxeFZaysrPD48WOl13z8+DGsrKw0XteCiMVihb/du3fv1Jqvyvj06ROOHj1aJGskdXn48CEmT56s9LhQ+aAmMDU1hUQigVgshlQqRYkSJeQ2PiQkJGDatGmwtLTUcG2LhgcPHuDs2bM4fvy43MaGqjZ27tw5TVZR4wj57t6+fYuGDRtCJBLByMgIR44ckTverFkzzJw5k7UOXbp0QXBwsHoVZ+Hly5cYO3Ys9PT01C6rTt9x584dOf3PsWPH0KlTJ8yYMYPTfIcPsbGxzBwxNjZW5caHzMxMrFixQpNVlmPQoEFo0KCBwmO5ubnw8PDAoEGDOF+Pz7gHCJdPKoNtnS9DLBbjzZs3zN+GhoZ4+vSpWvd6/fo1mjZtCpFIxPTVIpEIzZo1k7u2MopCTqAOsrmyt7c3dHV1mbnSunXr0KZNG4VlLC0tcevWLebvmTNnwsPDg/k7JCQELi4unO7fvn17dOrUCW/fvoWhoSEePnyIq1evom7duqx6Vr7vvnz58sz8WtkmRKfNB3V1gsrm2q9evYJEIlHrWllZWXj06BF+/vzJuUyfPn1gZ2eHI0eOIC0tDWlpaThy5Ajs7e3Rr18/AMD+/ftRq1YtterCBVdXV5Xyy23btkEsFqN169YKx4CMjAy5+V5KSgrWrFlT5HMFR0dHHDp0SOnxkJAQiEQiDBkyhPVapUuXRkxMDAB53UhSUhIMDAxYy2tCLyKE3NxchISEYNSoUejWrRu6dOkit/0K+K7TlcnCJk2ahJkzZ2LHjh0q7TGEfLumpqYK63fs2DGYmpqqLCujQoUKuHDhAgD5thMfH8/pGpqW8XGZ74lEIowYMYJ51xKJBEOGDGGVRyoiOzsbCxYsQJkyZaClpcU8/+zZs+X0XEWFvb095syZI7fv69evaNCggdJ5mabQxG/38eNHjBkzBh07dsTZs2eZ/XPmzIGfnx9r+d27d6N79+4K19x/+MMf49s//KdwdHTExYsXAQCRkZHQ19fH5s2b0aFDB06THaGTfSGdvqYU4gX5/PkzNm/ejDp16ihdrC9btgxmZmaoVKmSxhfFmZmZWLlypdqLpfyEhoaiR48ekEqlapUrbiM+dVG2YAoNDWUtK1SxoImFrpAJX05ODrZv34527dqhcuXKqFKlCjp06ICgoCBOi6fc3FzcunULBw8exKFDh3Dnzp1iW3QVB0LajjIuXLgALy8vSKVSmJmZFVlZHx8fpn1kZ2fDw8MDIpEIBgYGCA8P51t9wQjtu7gaAVapUgWnT58GANy7dw+6urqYMWMG3Nzc1BIQ8+Xy5cvQ09ND8+bNIZFImO92yZIl6Natm8qyQgyhZOzYsQMhISGF9oeEhCAwMFBlWVNTUzx69AgAEBAQgPr16wPIGzPYhIu7d++GiYlJIcNBU1NTuWdSRsmSJRnj5K1bt6Jq1arIyclBSEgInJ2dWcsDgJ6eHvO+8wsJYmJiYGxszOkanTt3homJCezs7NC+fXvBQh6ZoODhw4dql81/jQ0bNhSJYDI/mmh/QhDq8CUjKysLDx48QFRUlJzjUFFTv359NGrUCGfOnEF0dDRiYmLkNkVoyng6ISFBbcNfAJBIJCoNJNPS0qCjo8N6/+ISUMs4ceIEjIyMIBKJYGJiAlNTU2bja4wQExMDkUik9Ljs3XLZioPHjx9DX19f5Tm2tra4fv06ACAsLAympqYIDQ3F0KFD0aJFC5Vl27ZtC09PT5w8eRJ9+vSBSCSCs7MzVqxYgYyMDI09Bxvz5s1TuRU1enp6ghwclKFKKTx8+HCYmJigfv362LhxI7MuE2p8m3/c5EqTJk1gYmICfX19xuDZwMAAxsbGrMaYRaVQV0WXLl2Y9X/B8V3d8b5s2bJwd3eXe2fh4eEoV64c6tSpw1r+69ev+Ouvv1C5cmUYGBjA0NAQrq6uSg28ClLc/S5f6tSpgxMnTig9fvLkSU7vTxFc1wrlypVj1kRGRkaMUeOuXbuUGhL8CtRxeMrNzUVoaCiWL1+OdevWcXb0zs3NxerVq2Ftbc3M1a2treHv71/ka32ZoZFIJEJ4eLic8dHdu3exePFilC9fnvU6ixcvhrm5OQYOHIiVK1ciICBAbuPC+/fvERUVhaioKE4O6fnRhKOnuobzUqlUpdLx4cOHasv2+CASiRQarFy8eBHm5uas5Xv06ME4EmdkZMDBwQE6OjrQ1tZWaXBQVHz9+hXbtm2Du7s7RCIRKleurPRcTRse82Hnzp0IDAxUunHl27dv2LFjBxo0aAAtLS3Uq1cPy5cvL5I6Hz9+nAmcUdBgVh0D2qSkJFStWrWQ4yLXubaenl4hx47v379jzJgx0NXV5f+AvwCh3x0g3OFm27ZtsLGxwdy5c3Ho0CHOv92HDx/Qu3dvlCxZElZWVggICEBOTg7++usv6OnpoV69epxkRUL6jtq1azPnJCUlQSqVwsvLCxUrVoSPj4/KsgXHF1VbfvLrA1Q526pqu2/evMHJkycRGhrK/HY/fvyAv78/SpcujZIlS6qsuxCePHkCExMT1K1bF8HBwYw848CBA6hTpw5MTExUOoQogo/DmBD5pCq4Gt+KRCK0bduWWRtoa2ujZcuWaq0Zevbsidq1a8vJAx88eIDatWv/Mmc9IVSvXp1xZs6/Vrx79y5Kly6tsIyuri5SU1OZvz08POSMbpKTk2FoaMjp/iVLlmRkkcbGxkx7uHjxIqpXr66y7P/6u+ejE5T1ySKRCLt27ZLrp48cOYIxY8bA0dGR0/2/ffuGIUOGQEtLS+7+Y8eOxZIlS1SW/fLlC7y9vRmHIbFYDIlEgmHDhjGBbqKjoxEdHc3xbXBHKpWqDECQkpICsVisdOwrGBikdOnSagcG4UPB76YgqampnOebhoaGSExMZP4v++1u3brFSR+pab2IuowfPx66urpo3bo1Bg4ciEGDBsltxQHXdXqTJk1gbGwMAwMD1KxZEzVr1oShoSFMTExQr149RkZdUG6niW934sSJKFmyJFatWoWrV6/i6tWrWLlyJczNzTkbn0qlUka+mb/tPHjwgJPhtlAZH5/5XuPGjVU6hrM5h+dn/vz5sLe3x549e+R0jAcOHICbmxunawjhyZMnsLKywpo1awDk2SG5u7ujYcOGRRYkTEbB387Y2LjI5bMFqV69OoyMjGBoaIgqVaqoHVzjD/9t/hjf/uE/hZ6eHjNhnTZtGvr37w8AuH//vkohj6Ym+0I6fTaluLrRxK5cuYIBAwbAwMAADg4OmD59Om7evKn03vr6+ujYsSMvpd7379/h6+uLWrVqwd3dHUePHgWQZ1RVpkwZlCtXDkuXLuVcdyBvcTFnzhyUL18exsbG6NWrl0IDrfwUhxFfQEAA49GpjnBLEUIiKfIVcGpyoct3wpebm4t27dpBJBKhevXq6N27N3r16sUIqzt16qTyvpcuXYKdnV0hoXaFChVw5coVTnXPzs7GihUrUKdOHZQuXVojUTnUQagRlyaicAJ5i+P58+fD1tYWYrEYffr0wdmzZzlF7uZb1tramvEyP3r0KMqUKYOEhATMnj2bEVYWFZrqu4QYARoYGCA5ORlAXlQymcHrnTt3lAoHNYmbmxtWrVoFQH6hGhUVBWtra5VlhRhCyXBwcMClS5cK7b98+TJr35P/3XXo0IH5rZ49e6ZSoXvnzh1oa2tj4MCBiImJwffv35GZmYk7d+6gf//+0NHRUWp8KCP/fKNHjx6M0VRqairnaCgNGzbE2rVrAchHpRg7dixatWrF6RoFhTrqCnk0qVS+dOkS+vXrB319fVhZWWH06NEqz8/MzERUVBROnjypdjQgQHj7ExpVRqjDV0FSUlLw4MEDhZEJAc1HpNHX1y+2yF18DH+BvKgeqpxKzp49y8kQRqiAWuiY7eDgAB8fH416R7Mp5fJHRg4MDISlpSV8fX2Zb87X1xdWVlasxhBCvcsLOhWmp6cjPj4evXr1QrVq1VSWlUqljIB//PjxGD58OIA8Y262qAYWFhaMsiQ9PZ2Z9/6vIbTtNW7cmJnraBK29peRkYHAwEA0atQIurq66NixI7S0tFRmZWGDj/HtqlWr0KFDBzll+ocPH9CpUyesXLlSZdmiUqirYtCgQUzEYKHj/YcPH9CjRw8YGRlhy5YtmDJlCnR0dDBz5kzWyEBZWVmoVasWdHV10blzZ/j6+mL69Ono2LEjJBIJ3NzcWOf7xaUY5AKbIYUqheizZ884RVURulaQ1cHa2hpRUVEA8qKiqlIq1ahRg2nr1atXL6QMUEcxwKf+kZGROHnypNy+wMBAlC9fHhYWFhg2bBi+f/+u9J4Fo3V//vy5UATtoiS/fE6REZK+vj6nSPvFHYlMqKMnH8N5Z2dn7N69W+nxXbt2wcnJSY2nUA+ZolgsFjP/l23GxsYQi8Ws6xRAPhLX3r17UbFiRXz79g0bNmxgNaKRkZmZieXLl6NNmzaoVasWL6XctWvXMHjwYBgYGEAsFmPy5Mmsc3hNGEAWNzdu3MDQoUNhbGyMKlWqQEtLS3CGNjYKGiHyzfQhJPohAAQHB8PMzAxt2rTBq1evEB0dDRcXFzg5OSmV6xc3mvruNAHf32748OGwsbHB5MmTUaVKFYjFYrRp0wbt2rVjMhZxQUjfYWxsjCdPngDIG3tbtmwJIK8fKFu2rMqyBccYWYad/FE0DQwMCo09KSkpjEMLHyfbq1evMo7tYrEYdevWxYMHD+Dg4AAXFxds3LiR1dlRqG7l1q1bqFy5cqGxu3Llymp/M3wdxvjKJ9nganzLtlbgsmYwNjZW+L6ioqJgYmKisuzvkCVET0+P+Q0KRtBU5rhgY2PD6I6ysrKgp6fHRFIE8uZOXPVCpqamjFzX3t6ekXU/efKEVU4s5N3/DvDRCebvlwv21RKJBI6OjoXWEsoYP348atWqhatXr8LAwIC5/7Fjx1T2u9nZ2bhy5Qo+fPiAL1++MDLVXxUYoUSJEirXgvfu3VO51tREYBA+WFhY4Pbt20qP37x5k/N8s02bNkwGOJluJCcnBz169GANCgPwk5Nocp1cokQJZq1VHAiRM6xZswZdu3aVC/iWnp6O7t27w9/fH9++fUOnTp2YuYgMTXy7OTk5WLZsGcqUKcOULVOmDJYtW6bQAUsRNWvWZNac+fv8+fPnc4q8KlTGp4m1ohD4RP4tUaIE3r59CwCF5ut87CFiY2NhZmaGgIAAuLm5oXHjxkVueAtoRj4r1Em5uINr/OH35o/x7R/+U1hYWDApOKtXr84oVZ88eaJSMaGpyb6QTj+/Ujw8PBx6enrYu3evWqmEX758iSVLlqBixYooVaoUxo4dyymqkCKvLHUW6NOmTYOJiQm6desGKysraGtrY9iwYXB1dcX+/fs5T5iysrKwf/9+eHp6QiqVon379tDS0pIzjlFFcRjx2dra4t27d8z/hShW+CyYhAo4NbnQ5ZvqYceOHTAyMlJogHfx4kUYGRkpTcMsi5TWtGlTHDt2DI8ePUJ8fDwOHz6Mxo0byy24VfHXX3/BysoKK1euhFQqxcKFCzF06FCULFmSc0QaIQg14hKy2P7x4wdCQkLQsmVL6OnpoUuXLjh48CCnvkNIWRm6urqMgG7YsGFMNIenT5/CyMiI0zX4oqm+S4gRYH4PUg8PD0YwmZyczCulnboYGBgwwsH8321ycjJrVBUhhlAydHV1GeFofpKTk1kF1HXr1sX06dMREREBqVTKLHhv3Lih0nB40KBB6N69u9Lj3bp1k0svpwhXV1cEBAQgNTUVxsbGiIyMBADcvn2bs9H01atXYWhoiJEjR0IqlcLHxwctWrSAgYGBSiGWJhEqKPj333/h5+eHChUqoGTJkhCLxThw4ABrNLKzZ8/CwsKClzJThtD2JySqDMDf4Wv79u2MwbuMYcOGMUoiFxcXhREENBGRpuDz5xcycCExMRG9e/dWmIkhPT0dXl5enMZcvoa/Pj4+cHV1VWhM8Pr1a1StWpXTbydUQC10zNbX19d4umiuSjkgL2Xrvn37Cu3fu3cvGjdurLKs0MgAipwNRSIRbGxsmH5UGVZWVsxY6+joyDjlPXr0iHW+UDDDQ/7oGr+ajx8/YuvWrfD19WUiGN65cwf//vsva1mhbS84OBj29vZYt24dIiMjNZJKFlCv/SUmJmLGjBkoU6YMjI2N4eXlhcOHD6t9z8qVK6uMtqKIMmXKMN9+fuLi4lhToBeVQp0Lubm5ePbsmUaiNM+YMQMikQg6OjpyimVVyKKWyeQc+YmPj0fp0qUZZyJlFJdiUE9PT27MaNu2LV68eMH8/erVK5Vt19DQUOWc7Pbt25yiYQlZK7i6ujJyIE9PTybVfEBAgMr57rx58xjjEaGKAT71b926tZwT471796CjowNvb2+sWrUKlpaWmDt3rsr7FlW0bi6kpKQgOTkZIpEIt27dkjM+evHiBed1YnEj1NGTj+H8zJkzYWNjwxhO5+fly5ewsbHhlDqeL4GBgdi5cydEIhECAgLkoq3u27ePdb4hI/9ao3///pg+fTqAvH6fSzQlIC+Vsbm5OUaOHIm5c+dy/vZev36NZcuWwcnJCZaWlpg4cSJu3brFKmf5nQwg+UbfXblyJSpVqgRra2tMmTKFWasKjZj/KxES/VBGWloamjdvjpIlS0IqlWLkyJG/dVpTTX13xUm5cuWYebas/58xY4ba1xHSdxgZGTFrlObNm8Pf358pq858c+/evfDw8JCbuz169AgNGzbEnj17FJb58eMHBg8ezMgHudK4cWN4eXkhLi4OU6ZMgUgkgqOjIw4ePMj5GprSrURHRyMkJATBwcG8o1TydRjjK59kQ511llAMDQ0Vvre7d++yrreLI0tIQezs7HD+/HkA8vLtoKAguLi4KCwzcuRIuLu7IyIiApMmTULJkiXlnPH37NmD2rVrc7p/gwYNGEdXLy8vtG7dGteuXcOAAQNURquX1Zfvu5dx8+ZNLFu2DJMnT+aVwlwIQtK/29raMsZgfLGxsWGcJPLf//Hjx6zvT1dXV+1+T1O0bdsWI0eOVHp8xIgRKjOdaCIwCB969uyJrl27Kj3etWtX9OjRg9O14uLiUKpUKbRu3RoSiQTdu3eHi4sLSpcuzTijaBpNr5OLK7CF7P585QxlypRROLe+f/8+ypQpAyBvzagsej3fb7egky3fjM/Hjh2DiYkJli5dCn19faxYsYKJYh0WFsZaXqiMTxNrRSHwifwbGBjIOECrylCiTpaSyMhIGBgYoFmzZr8ss5wm5LPFnY32D/9t/hjf/uE/RZ8+fVCzZk0MHToU+vr6zML9+PHjrIscQPhkX5NKOXWj+bRv355RYJ46dYpRBvwKAaWdnR0TqS4uLg4ikQiDBw9WKxXg2LFjUbJkSbi5uWH9+vXMb/e/YsSnCfgsmDQl4NTEQpdvqocWLVqoTAGzaNGiQh52MsaMGYNmzZopPJabm4tmzZph7NixrHW3t7dnIokZGhoyi7uAgAB4eXmxlhcKXyMuReXVXWxbWFigYcOG2Lx5s5xXF5dvT0hZGTY2NkxqsnLlyjG/w/379zkbcPJFE30XIMwIsEOHDmjVqhUWLFgAHR0dxvgmNDQUDg4O6j6S2lhbWzOL9Pzf7ZEjR2Bvb6+yrBBDKBnlypVTGOn02LFjrALq8PBwmJqaQiwWyxnLzpgxQ6URkoODAyOQVcT58+dZ3/3Bgweho6MDsVgsJ8xYvHgxWrdurbJsfp48eQJvb2/UqVMHLi4u6Nu3L2eHE03AV1Bw6NAhtGnTBgYGBujevTuOHTuGrKwszt9+xYoVMXr0aIVKea4IbX9CosoA/B2+6tWrhx07djB/nz17Ftra2tizZw/u3LkDd3d3DB06tFA5oRFpCnLx4kW4u7sjPDwc7969KxSRVBHDhg3D1KlTlV5z2rRpKgXHMvgY/gJ5kRsdHBxgZGSEUaNGISAgAP7+/hgxYgSMjIzg4ODAKR2zUAG10DG7S5cuCA4OZj0vP2zRjoODgzkr5fT09BQaniYkJLA+v1Dv8oJOhREREYiPj2eNvAnkzfnKly/PGCLIopHs37+fNSKFWCzGkydPmGi7RkZGiI2N5dTuNUlsbCwsLCxQsWJFaGtrM2PurFmzmHakvmSYowABAABJREFUCqFtT5nBPpvhPls0ECcnJ7WVwjk5OThx4gQ6deoEiUSiVlm+GBoaKsyGcunSJVYDyqJSqHMhJycHOjo6gg3G165dC319ffTp0wdOTk6oVKkSa6R/AGjUqFGh9NcFr9uoUSOV1yguxaAiw/v8MpZXr15BJBIpLV+vXj2VWTAWL16MevXqsdZDyFph9erVjDPo+fPnIZVKoaurC7FYzBjlqEIW0Ylr+klN1d/S0pJxjAbyDDI9PDyYv0NCQpQaQsgoqmjd/z8h1NGTj+H858+fUblyZWa+5u/vD39/f4wcORJGRkaoVKnSL4lifPnyZU7zC2U4ODggODgYX79+hYWFBWOUFxMTwzmFurGxMa5du6b2vaVSKfr164dz587JZcVgW2v9TgaQBftfGc+fP1cpH9fS0sLMmTMLGbj/SuPbHz9+oFmzZrzHXSHRD2WkpaWhUaNGMDU1hY6ODubPn680Q8rvhNDvrjjR0tKSc9DR09Pj1eaE9B1NmzbFgAEDsGvXLujo6ODx48cA8t4rlwwvMuzt7RlZRX5u374NW1tbpeWMjY3VNkIzMzNj3lNGRgbEYjGOHTum1jWKik+fPmHDhg2oVasW5zJ8Hcb4yic1uc4XSseOHdGoUSM8f/6c2ffvv/+icePG6Ny5s8qyxZElpCCLFy9GpUqV8M8//8DIyAhXr17Fnj17YGFhodRR8O3bt2jYsCFEIhGMjIxw5MgRuePNmjXj7DB07tw5xqn08ePHcHJygkgkgrm5OdMPKEPIuwfydGcikQjOzs6F0ppzTWEuBKHp3wui7rolf7Td/PePiYmBsbGxyrK1atXi7JSqaa5fvw4dHR306NEDUVFRjLzqxo0b6N69O3R0dFTOIzURGIQPDx48gKGhIerVq4fg4GDExsYiJiYG+/fvR926dWFoaKjQ6VkZ6enp8PPzQ48ePdCmTRvMmjVLbjwuKjSxTg4MDETv3r1/mdFhQYTIGZRlCw4PD2fkY0lJSWrZVXB9l5pyso2IiEDz5s1hYWEBPT09eHh4qMyYlx+hMj4+8z1lGacHDRqExYsXKww2ogwhkX8LGkBzRZls2MzMDM7OzmpneOGLJuSzxZ2N9g//bf4Y3/7hP8XHjx8xZswYdOzYEWfPnmX2z5kzB35+fryvyRVNKuXUNb7V0tLCxIkTCwkHf4WAMr/BGJA36VPXeEgmYC0ohP9fMeL78eMH7O3t8fDhQ97XELJgKgoBp7oLD74TvtKlS6v0SL97967S569cuTJOnDihtOyJEyc4Gd7r6+szCmFLS0vcuXMHQN4Cg22Rrgn4GnHJENJ2SpQogUaNGmHLli1yhidcvj0hZWXMnTsXJiYmcHZ2ho2NDeN9t337dqWpiTSFJvouQJgR4LNnz9CuXTtUrVoV27ZtY/ZPmDAB48aNU7su6jJ58mQ0aNCAiUzw+PFjXLt2Dfb29qwevkIMoWRMmzYN5cuXx6VLl5CdnY3s7GxcvHgR5cuXZyJ7qSI7O7tQKpDk5GSVKYTzp+9VxLNnz6Cvr89675cvX+Lu3btySrCoqKgi93jWZHokvoohoWO2kZGRYA92oe1PaFQZvg5fZmZmcv3MyJEj5VJphYeHq1SI8Y1IU5D8hncFo5AqU+44OjqqTNt4+/ZtODo6st6bj+GvjA8fPmDkyJFM6kxZKs0RI0YwvwEbQgXUfMbs48ePM9u2bdtgY2ODuXPn4tChQ3LHFDkjAOzRjtWJeuzo6KjQiHrq1Kmsv19xRv/88eMHVqxYgfHjx8spk1evXo2tW7eqLFuwrSv7u6jx9PRk3n3+ufL169c5KdSFzhf5Gu6zRQMRklbr58+fKsfk/CQmJmLFihUYM2YMxo4di9WrV6u1Xu7fvz9sbW1x+PBhpKWlIS0tDYcOHYKdnR0GDBigsixfhbqmqFSpklopjwvSqlUrlCxZkolClpGRwUTeX7Zsmcqy5ubmKpVncXFxrMbfxaUY5GJ8q+rb37x5MwwMDBRmgjlx4gQMDAw4pdPVhMOajJSUFBw+fFitaNVCIzrxqb+urq5cdGoPDw85mVxycjKr0XtRRetWh8DAQEauBOSNlSYmJnB3d1fab06cOJFJt1gw8tivjkQm1NGTr+F8eno6Ro0aBTMzM7n52qhRozilcdQEd+7ckZtzHzt2DJ06dcKMGTPkotop4++//4a2tjZMTU1RrVo1Zs23du1aNGnShFMdXFxceLVVJycn2NraYubMmXJrS65rreI0gJSlhheLxVi0aJFcuvjVq1ejc+fOKqO/Ll68GA4ODihXrhymTZuGuLg4AL8+8q25uTlv41sh0Q+BvPWsqakpOnTogDdv3iAsLAzW1taoX7++xrNnaBqh3x1fAgICkJmZyfxf1aYMsVgsZ/QgS3+tLkL6jtjYWFSpUgXGxsZyc+uxY8eqFRhCT09PaQp7VX33gAEDsHr1as73ARTPtfjKejShWwHyHOv69esHfX19WFlZqRXxW4jDGB/5pCbX+UJJTU1F9erVoaOjA3t7e9jb20NHRwc1atRggt0oozjlBDJyc3Ph5+cHAwMD5h1KpVImpb0q0tPTFWY1eP/+vaC+6/3795wCfAh59wBQqlQp7Ny5k3c9hSLECGzp0qU4cOAA83f37t2ZFPRcHEUBoGHDhoyBdf6+e+zYsWjVqpXKsmfPnkX16tVx8uRJvHjx4pc7aB85cgTm5uaFZLMlS5ZksrUpQ1OBQfhw48YNVKpUSU62JhKJ4OLiwqwbufDs2TOl34gyOZEm9SJC18kZGRlo1aoVDA0NUaVKFbXvLxQhcoY+ffrAzs4OR44cYeRjsmA8/fr1A5A3J1XmwCLk2/0dnGyFyvj4zPeUZZzu3Lkz7OzsUKJECWbtw4bQyL98DKC5yob5yoe5ogn5LB8n5RIlSjAB7ApmmSm4/eH/b/4Y3/7hD/kQOtnXpFJOXePbGzduwNvbG0ZGRqhbty7WrVuHt2/f/hIBpSYEVPv27UPz5s1hYGCAnj174uTJk8jOzv6fMeID8lI1CBEQCVkwCRVwamKhy3fCp6Ojo9Kb8fnz50ojYhkZGSlMWS/j6dOnnFKBOjo64p9//gGQN9mSReI9cOAALCwsWMsLRWjUbiFtJzMzE3v27EHTpk2hp6eHrl274siRI9DR0WH99oSULVj/1atXywmUAgMDizxagqaE65owQi0usrKy4O3tDW1tbSYNsVgsRr9+/VjTqQoxhMp//549ezL31tHRgZaWFgYPHlxkyhllUXhksBliaJInT55g1qxZ8PLyYup05swZlUYumkyPxFcxNHz4cJiYmKB+/frYuHEjI/TiOmYPHjxYzticD0Lbn9CoMnwdvgoKN6pWrSqnBOSinOATkaYgBSOQFtwUkT+ahSJSUlI4RXPiY/hbkNzcXLx+/RqvX79WO1q5UAE1nzFbkTJNmYJNEWxGk+pEPT59+jSkUimqVKmCoUOHYujQoXB1dYVUKmVSLilDE46GT548wdixY+Hp6QlPT0+MGzeuyNLJyWBr76ravSbJH3E7/1ovJSUFurq6rOWFzhd/R7imUl28eDG0tbUhFothaWmJ0qVLQywWQ0dHBytWrOB0r2/fvmHUqFFM1FCxWAyJRIJRo0YxhnKq4KNQ1xQnTpxAgwYNOAviC9K8eXO5SEoyTp06BUtLS5VltbW18fLlS6XHX7x4AR0dHZXXKC7FoFDjWwDo27cvo0js3LkzOnfuDGdnZ4jFYvTu3ZtTPYp7rSA0ohOf+tvY2ODKlSsA8ub7enp6cnW4d+8eq1LidzBEcXR0ZJzTIiMjoaenh82bN6NDhw5KZXxNmjRhHJkLRh/71ZHIhDp6CjWcz83NxZs3b3jN14RSu3ZtxmghKSkJurq68PLyQsWKFZksWWzcvn0bR44cYdo8kNdvcjUqOHPmDFq3bs0rstO1a9cwePBgGBoaombNmli9ejW0tbU5yRyLywAS+L+U8SKRCOXKlZNLGe/o6IiWLVsysjdVXL58GQMGDIC+vj6qVq0KLS0tXlGE+TJhwgQmK4y6CIl+COQFByiY3v7Dhw/o0aPHb5/VTRPfHR9sbW2ZeXH+NldwUxWBUyQSwdXVlTGY0dLSQuXKlXkZ0ijrO/i24czMTPz48YPz+e3bt0eNGjWYwBKyOtWsWRMdOnRQWm7hwoUwNTVFt27dsHjxYk6GyyKRCOHh4YxjjIGBAU6fPs3bYYavbuXff/+Fn58fKlSogJIlS0IsFuPAgQNqjz2/2mFMk+t8TZCbm4uwsDCsXbsWa9euVZk1LD/FmSWkIFlZWXjw4AGioqLkvsHfHb7vHsgLJCM0S4oQhBiB2draMvOqsLAwmJqaIjQ0FEOHDpVbN6ri6tWrMDQ0ZJxLfXx80KJFCxgYGOD27dsqyxZcZ/xqB20gT05x5MgRLFu2DMuWLcORI0cYuT8bxRUYREZ0dDSCg4MRHBysMOI6G2KxWKE85d27d0rfvyb1IkLXyT169IC5uTlGjhyJuXPn/lIDRECYnOHLly/Md5pfPjZs2DBGPhYdHa00aJaQb1cTTrZ2dnYKg3B8/PiRNeJ6bm4unj17hs+fPwuS8d26dUtj872cnBwMGTIE7du351xGSOTf38EAWghC5bN8nJQDAwMZm6P8GWYUbX/4/5s/xrd/+E+xY8cOxrsnPyEhIZw6PE1M9jWllONrBPb161ds374dHh4ejILL39+/SNO6iUQitG3blgmTr62tjZYtWxYKn8+Fp0+fYs6cObCxsWG8/mQRerhQXEZ8QF6Kl4EDBwqKMMF3wSRUwKmJtg/wm/AVNIAsiCqlqKaM6KZPn45FixYByDO41dbWRsWKFSGRSHgL3NVBE1G7NbHYlhkCli1bFiKRCH369EFYWBirEabQssWFpvouIUaAxakYy09qaipOnz6N4ODgYhHYJSQkICQkBCdPnlRLsHzw4EH06NED9erV46wUEYlE2LVrV6Fok7ItKCiItd+QKcyVbVy4fPky9PT00Lx5c0gkEsYYZMmSJXKRUJWhifRIAH/FUEZGBgIDA9GoUSPo6uqiY8eO0NLS4mQY9O3bN7Rt2xYDBw7EypUrOUej0SSaiiqjLs7Ozowy9u3bt9DS0pITBkdFRbEqdfhEpNEEpUuXVqkwvnDhAieFlKYMIF+/fo2IiAhERESoPc8WMmYWRaaNX01qairjHNilSxfMnDlTLkKhMoQ6Gp47dw4SiQR169Zlov7VrVsXurq6nDzzgbwUd2fPnuUUMfh3I3/k2vxGgGFhYShbtixreU20vV27dqF+/fqwsrJixts1a9YUW3rYmJgYiEQiledcunQJYrEYc+fOlVtnv3//Hn/99Re0tLQYIz8ufP36lRHqczG6/R0wNTVllCJSqVSjURVkkRuUIWStlp/iUAwWrLuRkZGcjIVr3YODg9GpUydUqlQJLi4u6NSpE4KDgznXg+9aIScnB9u3b0e7du1QuXJlVKlSBR06dEBQUJBaxiRCIzrxqf/IkSPh7u6OiIgITJo0CSVLlpRb2+zZswe1a9dWed/fwRAlfwS8adOmoX///gDysiuxRXz+L8DHcD4jIwPHjx9XKIP89OkTjh8/ziitipL8Di9Lly5Fy5YtAeQZtXIZc+fPn6/Q8CEjIwPz58/nVIc3b96gSZMmEIvFMDQ05NV3f/nyBVu2bIG7uztEIhGaNGmCLVu2qOyXi8sAMj9NmjTRSJTjz58/Y9OmTahbty60tLTg7u6OVatWaaCGqhk7diyMjY1Rq1YtDB8+XHDUaq7RDwEwqdsVIct68Lsi9LsrTjQRyUtTkVuF8ubNG7Rp0wYikQgSiYSZQ7Zp00blupmP4bKmI7eqq1s5dOgQ2rRpAwMDA3Tv3h3Hjh1DVlYW72A0QhzG+Mgn/ysUd5YQIM/JX9Hc4+vXr3J1KioyMzOxfPlytGnTBrVq1fqlbWDZsmW/bHxXBl8jMKlUysiixo8fj+HDhwPI0xOok8n0yZMn8Pb2Rp06deDi4oK+fftyym6oSja5bt06zvcvCtLS0jBs2LBirQNXcnNzeTnaiUQihXPalJQU1qyEmtCLCF0n6+vr4+rVq7zvLxRNBMb58uULIx9Tx2FByLeriTmDMruAV69eKQ3kJSMnJwc6OjrF6rSgiJiYGFhZWf2Se2nCAPrjx4/YunUrfH198f79ewB5uu78mWZ/V4Q4Kf/8+RNBQUF49epVUVfzD/+jiACA/vCH/wiOjo60efNmatq0qdz+K1eu0PDhwykhIUFleT09PUpMTKRy5cqRj48Pff/+nTZv3kyJiYlUr149+vjxY5HVvWvXrnJ/nzx5kpo1a0YGBgZy+48cOcL5mgkJCbR9+3bavXs3paenU4sWLejEiRMaqW9+Bg8ezOm8nTt3cr4mAAoLC6Pt27fTiRMnyNzcnLp27Upr167lW80ip0uXLnTx4kUyNDQkV1dXQb+dupiYmNDdu3epQoUKtGzZMrp06RKFhobS9evXqXfv3pSWlqayfHG2fbFYTG3atCFdXV2Fx7OysujcuXOUk5OjsOylS5fIzMxMYdl3795RixYtFJZVxT///EORkZHk4OBAHTp0UKvsf4Hc3Fw6d+4c7dixg06ePElGRkb07t27Iiv77ds3unLlCqWmptKPHz/kjo0fP573c7BRFH2XutSpU4d8fX2pW7du9PTpU6pcuTJ16dKFbt26Re3atSN/f/8iu7emePjwocLfrmPHjmpdRzYlFYlEnM5fu3YtzZo1iwYNGkRbtmyhwYMHU1JSEt26dYvGjBlDixYtUlhOLBZzun5ubq7SYxMnTpT7++fPnxQTE0P379+ngQMHUkBAAOv13d3dqUePHjRp0iQyMjKi2NhYsre3p5s3b1LXrl3p33//Zb2GVCql+Ph4srOzY3+gIuTx48e0c+dOCgoKoq9fv1K7du2oe/fuheY2MrZv304jR44kqVRKJUuWlPvNRSIRPX36lPO9NdX+ZHz//p20tLRIR0eH0/kZGRkK71+1alWF5y9dupQCAgJo9OjRdOnSJXr79i3dv3+fOe7v70+nTp2iCxcuKL2nn58frVq1ijw9PalWrVqF5htc+8309HTavn07xcfHExFR5cqVaciQIWRiYqLw/J49e9LPnz/p6NGjCo936tSJJBIJHTx4kNP9+fL582caM2YMHThwgBnftbS0qFevXvT3338rrf/vTHp6OpmamnI+9+bNm/TmzZtC/dSAAQOKoHby5OTk0OfPn6lEiRLMvpSUFNLX16dSpUqpLFujRg1q1aoVLV26VG6/r68vhYWF0d27d5WWffr0KXXp0oXi4uJIJBIVGjO4zPXOnDlDWlpa1KpVK7n9oaGhlJubS23atGG9hhC8vb3p/fv3FBISQmZmZnTv3j3S0tKizp07U6NGjYp8zN+4cSPNmTOHJkyYQIsWLaL79++Tvb09BQYGUlBQEIWHhyss17RpU9axWSQS0cWLF9WuU2xsLNWsWVPl79erVy8yNTWlzZs3Kzw+fPhw+vLlC+3fv1/t+6uiZs2adPHiRSpRogTVqFFD5TtQ1XY1QVBQkMrjAwcOLLJ7i8ViqlKlCmlrays8np2dTQ8ePFB7vfUrEIvFZGJiwvx26enpZGxszMwFAdDnz59/y7oDoA4dOtCZM2eoWrVq5OzsTAAoPj6e4uLiqGPHjnTs2DFO18o/983fjgGQSCQqkud/9+4dde3ala5du0aGhoYUFBREXbp0YY57enqSm5ub0vn670KpUqUoNDSUatSoQTVq1KBJkyZR//79KSkpiapVq0Zfv35VWvbnz5+kp6dHMTExVKVKlV9Ya8V8//690HzV2NiYtdyrV6/o5cuXVK1aNaYt3bx5k4yNjcnZ2bnQ+QEBAXTixAmlY0Lz5s2pS5cuNGbMGB5PwR1jY2O6c+cOOTg4UIsWLah9+/bk4+NDqamp5OTkRJmZmSrLa2lp0cuXLwvNbd6/f0+lSpXi9N00b96cUlNTaejQoVS6dOlC44i6fXd8fDwj4/3w4QP9/PlT4XlC5YOa5MePH5ScnEwVKlRQOo5wJS4ujrZv30779u2jN2/eaKiGiimoV8iPSCSiS5cuKT3+6dMnysnJKSSn/PDhA2lra3P67rKzs+ny5cuUlJREffr0ISMjI3rx4gUZGxuToaEh9wf5xQj97oTy8+dPcnZ2plOnTpGLi0uR3ksZ1tbWdOHCBV73z8nJoTVr1lBISIhCGcOHDx/Uul5iYiI9evSIiIicnZ3J0dFR7Tqx8ezZM07nlS9fntN56upWtLW1afr06eTr60tGRkbMfh0dHYqNjaVKlSpxum9+1B33iPjLJ/NTHOv8tWvX0vDhw0kqlbLq3NjkTELkBJpA2bj97t07srS0pOzs7CK9f9++fSksLIy6d++ucMyfO3eu3N+afPe5ubnUrl07SkxMpEqVKhWSZxalTlIoZcqUoUOHDlH9+vXJycmJ/Pz8qEePHpSQkEB16tShz58//9L6yOQK27Ztozt37hTrOpGLrOT27dtKx4xf8btv376d1qxZQ48fPyYiIgcHB5owYQJ5e3urLDdp0iQiyls3DBs2jPT19ZljOTk5FBUVRVpaWnT9+nWV1xGqFxG6TnZ2dqaQkBClOoD/MkK+Xba5g6o5g8y+pXPnzhQUFCSnA8jJyaGLFy/S+fPnWW2BKleuTNu3byc3NzeV5yljyJAhKo/v2LFD7Ws+efKEateuTenp6WqXffr0KWVmZpKLiwsn3aeic2Sydi5t/969e9S8eXMyMTGhlJQUSkhIIHt7e5o9ezalpqbSrl271H4GdTh06JDSvq+o5bNERPr6+hQfH895fvuH/78QJvX4wx9+M1JTUxVOtMqXL0+pqams5UuUKEFpaWlUrlw5OnfuHPn5+RFR3mSL60Sbb6df0FCgX79+nO6nCicnJ1q+fDktWbKETp48yWvA50JRGKaJRCJq1aoVtWrVij58+EC7du3idJ/iMuIjIjI1NaVu3brxLs+m2FYl3AXACGYuXLhA7du3JyKicuXKcTKa1ETb9/b2pn79+lGTJk04nS9jwIABrAp9VUImT09PUuRHkn+yyMb9+/flFGJubm7MxPvYsWPUuXNn1mtoAnWNuGQIaTuKEIvF1LZtW2rbti29ffuWdu/eXWRlo6OjqW3btpSRkUHfvn0jMzMzevfuHSOcK8rvVtN9Fx8jwMTERKpevToRER08eJAaNWpE+/btYxRjRW2I061bN6pbty5Nnz5dbv/y5cvp1q1bKo3oNGEIRUS0a9cuWrFiBSOocXR0pKlTp1L//v1VltuwYQNt2bKFvLy8KDAwkKZNm0b29vY0Z84clUoJVUa1XFmzZo3C/fPmzVOphM9PXFwc7du3r9D+UqVKcTZ2r1KlCj19+lSQ8e2///5LJ06cUNh2V69ezekaDg4OtHjxYvLz86PTp0/T9u3bycvLi7KyshSeP2vWLJo/fz75+vpyNoYuiKbaX0GkUimn896+fUuDBg2ic+fOKTyu7P7Tpk2jjIwMOnLkCFlaWhb6xq5fv05eXl4q7719+3YyNTWlO3fu0J07d+SOiUQiTv3m7du3qVWrVqSnp0d169Ylorzfe9GiRRQWFkY1a9YsVGbGjBnk7u5O3bt3p2nTppGTkxMRET169IiWL19OoaGhFBkZqfB+9+7dY62TDLYxb9iwYRQdHU2nTp0id3d3IiK6ceMG+fj40IgRI+jAgQOs99CEgJrvmL1s2TKytbWlXr16ERFRjx496PDhw2RlZcUYWSnj5MmT1LdvX/r69SsZGxsXMlxXNl+6d+8eValShcRiMetvwVZ/LS0tOYUaEZGtra3KMjLi4+MpJCSk0P4hQ4awjnc+Pj5kZ2dHFy9eJDs7O7p58ya9f/+eJk+eTCtXruR0f19f30KGv0R5811fX98iN75dtWoVde/enUqVKkWZmZnUuHFjevXqldoGaHzb3rp162jr1q3UuXNnufdQu3ZtmjJlitJysnmKIr58+UL79u1T2t9rgps3b6qcT/bv35+zQlqdb79Tp06Mc+CvWgsog49xrZmZGSUmJpK5uTmVKFFC5VpB1bypoKJYEVzWwMWhGBQ6158zZw75+voyysCPHz8W6v/UQZ21QmBgIEVERNDFixcLGYFdunSJOnfuTLt27eLU9pUZ1quLOvU3NzeniIgI+vTpExkaGpKWlpbc8YMHD7IakClzHBeJRCSVSqlixYpF7oDWokUL8vb2pho1alBiYiK1bduWiIgePHjAOvbp6OiQjY1NsSrtv337RtOnT6eQkBB6//59oeNc6mZpaUmWlpZy+2RzR0Xs3buX/vrrL6XHJ0yYQAsWLChy49vatWuTn58fNW/enK5cuUIbN24kIqLk5GQqXbo0a3llsqTY2Filzt8FiYyMpBs3bqic26mDi4sLrVy5kpYuXaoysIJQ+aAmyMzMpLFjxzLOI4mJiWRvb0/jxo0ja2tr8vX1Vfuarq6u5O/vTytWrNB0dQshpN/s3bs3dejQgUaPHi23PyQkhE6cOEFnzpxRWf7Zs2fUunVrSk1NpaysLGrRogUZGRnRsmXLKCsrizZt2sS7bkWN0O9OKDo6OvT9+/civ48qxowZQ8uWLaNt27apbXA+f/582rZtG02ePJlmz55Ns2bNopSUFDp27BjNmTNH7bo4OjryMrhVx2g+KCiIpkyZImc4JQR1dStDhw6lv//+my5fvkz9+/enXr16CZqrEak/7hHxl0/K4LvOF8qaNWuob9++JJVKlco5ZXVgkzMJkRMI4fPnz4S8DL/05csXOZleTk4OnTlz5pcY/546dYrOnDlDHh4enM7X5LsfP348hYeHU9OmTQsFOPjd6dq1K/Xp04ccHBzo/fv3jEwmOjqaKlasyPk6SUlJtHPnTnr69Cn5+/tTqVKl6OzZs2RjY0OVK1dmLR8REUHbt2+nw4cPU5kyZahr1670999/836uX8GBAwdowIAB1KpVKwoLC6OWLVtSYmIivX79Ws7psaiYM2cOrV69msaNGycnm504cSKlpqbSggULlJaNjo4morw5a1xcHEkkEuaYRCKhatWqqZRRyRCqFxG6Tl61ahVNmzaNNm3a9Ev6O2Xw0Ul++/aNli5dShcvXlTo9MEWGEXItyvEYFEmmxOJRIXkZDo6OmRra0urVq1ivc7SpUtp6tSptHHjRl6OsgWDlf38+ZPu379P6enp1KxZM7WvR0R0/vx51nnbz58/yc/Pj+7evUtubm7k6+tL/fr1Y2TtTk5OdObMGdb2mJyczKuOMiZNmkSDBg2i5cuXyzk/tW3blvr06SPo2mzkd3g6fvx4IYcndeHjpFy3bl2Kjo7+Y3z7B8X8uiC7f/hD0VOuXDmFaUePHTsGa2tr1vJjxoxB+fLl0bx5c5QsWZIJs79//35O6UECAgJgaGiIsWPHQiKRYMSIEWjevDlMTEwwc+ZM9R+IJx8/fsStW7cQGxurMN3K/wJv377llNohP3fv3oWlpSWMjY2hpaUFCwsLiEQiGBgYKE2P9DsxYcIEuW3MmDHw8PCAiYkJxo8fr7Js06ZNMWDAAOzatQs6Ojp4/PgxgLzUKeXLl2e9t9C2DwAdO3aErq4uypYtiylTpiA6OppTOSGwpaLkmpKyTJkycilIZRw6dIg1xYkmePPmDdq2bQuxWKxwY0NI25EhSw+5YsUKBAQE4OzZs8jOzi7yso0bN8awYcOQk5PDpGBOTU1Fo0aNmNTsvztJSUmoWrVqoXRrXH4/IyMjJsVJ8+bN4e/vDyAv9YVUKi3yupubmytMw3Tv3j2UKlVKZdn27dujU6dOePv2LQwNDfHw4UNcvXoVdevWRUREBKf7r1q1Cvr6+pg2bRqTOnzq1KnQ19fH6tWrVZbV09Njvm8LCwvExMQAABITE2FmZsbp/orIycnByZMneZV9/Pgx5xSi1tbWuH79OgD59ONHjhyBvb09p2sITY904cIF6Ovro0qVKtDW1kb16tVhamoKExMTNG3alFMdlKEqnWGJEiWYdJR8Edr+srOzsWLFCtSpUwelS5dWOw1snz594OHhgVu3bsHAwABhYWHYvXs3nJyccOrUKUHP9ito0KABBg0aJJfO8efPnxg4cCAaNmyotNzJkydhYWEhN0aJRCJYWFgonIPLKJhCStXGhrK0XhEREZzG7P3790NHRwft27eHRCJB+/bt4ejoCBMTEwwaNIi1vNAx29bWlvn2w8LCYGpqitDQUAwdOlQutaUiHBwc4OPjozANsiryp+NiSw3KhpB0mmXLlkVISEih/cHBwShXrpzKsiVLlmRSXxkbGzMpeS9evIjq1auz3hvIS4uWnJxcaH9ycvIvme/JuHbtGv7++28sW7YM58+f51xOaNuTSqXMuJl/3ElMTFR7zvHz50/4+/vDwsICFStWxP79+xWeVzB1WcEtODiYte56enpIS0tTejwtLY1T/YV++8XNkydPMGvWLPTu3Zv5ns+cOYP79+8rPD8wMJBJ7R4YGKhyK2r+V9+9WCyWm88YGRkx34068FkrtGjRAkuWLFF6zUWLFjHpvIsaIWsdISgbr/LPJxo1aqSR1PbK+PjxI8aMGYOOHTvi7NmzzP45c+bAz8+Ptfy2bdvQtm1bJg3jr2b06NFwcXHBoUOHoKenhx07dmDhwoUoW7Ys9uzZw+kat27dwtSpU9GrVy906dJFblOEqakpnj17pvR6z549UyuNMF9iY2NRpUoVGBsby6WKHzt2LLy8vJSWMzU1RYkSJSAWi5n/yzZjY2OIxWKMHj2aUx1q1KiBGzdu8H6Gf//9FwEBARgzZgwmTpyITZs2cWrvQuWDmmD8+PGoVasWrl69CgMDA6bvPHbsGOu87efPn1i+fDlq1KgBAwMDlChRAvXq1cOmTZt4pTQWwuPHj3Hu3DlkZGQAAKf7lyhRAg8fPiy0Pz4+npOcolOnTujXrx+ysrLk5mvh4eGoWLGimk/wa+H73WmSRYsWYeDAgXLrXDaqV69eaF2jbGOjc+fOMDIygpWVFVq2bMmp35Rhb2/PyBIMDQ0ZmUlAQIDa7y8tLQ1///03pk+fjokTJ8ptyvj27RuGDBkCLS0taGlpMW1v7NixSuckBedKxUFGRgYCAwPRqFEj6OrqomPHjtDS0kJcXByv66k77gHC5ZN81/m/E0LkBEJgkzFpaWlxmrMJxcXFhXOqbk1jaGj4y+WQBedIqjZV/PjxAytWrMD48eNx9+5dZv/q1auxdetWTnW5fPky9PT00Lx5c0gkEqbvWrJkCbp166a03MuXL7FkyRJUrFgRpUqVwtixY6GtrY0HDx5wum9RExMTo3Kt5erqivXr1wP4P/lObm4uhg0bhjlz5hR5/czNzbFv375C+/ft24eSJUtyusagQYPUtgHIj1C9iFBMTU0hkUggFothaGiotm5BKELW6b1794aVlRWmTZuGNWvWwN/fX25jQ+i3++TJE4wdOxaenp7w9PTEuHHj1NIV2dra4u3bt5zPL0j+304qlWrkt8vJycHw4cOxbNkyhcdlus+C265du+Dj4wMDAwOlslUZkyZNgoWFBby9vWFvb4+OHTvCyckJBw4cQEhICFxdXdGnTx9e9VcHY2Nj5vfKv15JSUmBrq5ukd7bycmJ6Xvy3/uvv/7CmDFjOF3j69evGDNmTCEdF1cZV3BwMOzt7bFu3TpERkYWknX/4f9v/hjf/uE/xbRp01C+fHlcunQJ2dnZyM7OxsWLF1G+fHlMnjyZtbzQCYMmOn0hJCcno23bttDS0mIGCYlEgt69e+PVq1fMeTIl3O/Gx48fMXr0aJQsWZKpf+nSpeHr68tJ+PA7GPH9/PkT58+fx6ZNmxjD5+fPnzPGrHyYO3cua/sVKuDUxEIXAD58+IDNmzejcePGEIvFqFSpEhYtWqTQ0EGGbGFYnMyZMwf29vZ4+fIls+/AgQPQ19dXaCSiaYrKiItL2wHyJv4yY/X8W9myZXHlyhXmPEUGykLKAoCJiQljQGNiYsIoKf755x84OTmp9bzFhRAjwOJWjEmlUub95yc+Pp7VkEUThlC2trYICgoqtD8wMBC2trYqy9rZ2TH9Va1atbBp0yYAQGhoKK9F8uPHjzFjxgxYWVlBW1tb7fIAsGvXLlhZWXE6d/LkyWjQoAFevnwJIyMjPH78GNeuXYO9vb1cP66KgoYI+Y0huSwU69SpwwjkZOPmly9f0LFjR2zYsIG1fEhICLp06YLKlSujRo0a6NWrF86dO8dabsKECVi0aBH7A6pAaPv766+/YGVlhZUrV0IqlWLhwoUYOnQoSpYsiYCAANbylpaWiIqKApBnjJOQkAAgr0/08PBgLZ+amipnTBYVFQUfHx9s3ryZtayMrKwsPHr0SC3FogypVIr4+PhC+x88eAA9PT2VZTMyMnDkyBEsX74cy5Ytw7FjxxiFtDLyO8QcPXoUFSpUwKZNmxihxKZNm+Dg4ICjR4+y1r1cuXIKnQZiY2M5OdsJFVALHbOlUilSU1MB5BkmDB8+HACQkJDAaoyir6/Py/ArJSWFmWsJcVji62g4f/58fPv2DfPnz4epqSmWLl2KiIgIREREYMmSJTA1NcWCBQtU3tvU1JSZS9jb2+PSpUsA8gS2bG1WRunSpXHx4sVC+8+fPw8LCwtO1+BDRkaGnFOHr6+vnAJ86tSpyMzMZL2O0Lbn4uKCY8eOAZBfq65du1YtpeiePXtgb28PKysr/P333yr7IDZjby7jVX7jcUW8evWK05inCeXU58+f5ZQ5QtZ46sBXoVjUfPr0CRs2bECtWrVUnlfcikG+FGx7+b8bdeCzVihdurRKZ9a7d++idOnSnOsQERGBvn37wt3dHf/++y+AvHmrImcWTdRfE1y4cAH16tXDhQsX8PnzZ3z+/BkXLlyAu7s7Tp8+jWvXrqFy5coYMmRIkdVBKNWrV4ehoSF0dXXh6Oj4S41RgLw5U3h4OAAw6w0g77dv06YNa3k+hvOGhoa4ffu20mvevn0bhoaG6j+MhsjMzMSPHz+UHg8MDMTOnTshEokQEBAg56iwb98+REZGcr5XaGgo6tevj/DwcLx7904tg4C///4burq6EIlEMDExgYmJCUQiEfT19Rm5c25urpz8TsbvYABpY2PDGB7n7zsfP34MIyMjpeUyMjLg4eEBsViMli1bwsfHBz4+PmjZsiXEYjHatWuHnJwcPHnyBDt37iyy+r979w7NmjVj5imy+g8ePBiTJk1SWVZfX1+pgzOXOauZmRmzts3/7pKTkznPeX832L47TcLH+HXevHmcNzYGDRqkclOFvr4+47xgaWmJO3fuAMiTmxsbG3N+B3ydrPkYzbPN0/kgRLeSmJiIGTNmoEyZMjA2NoaXl5daOiG+DmNC5ZN81/maRLZmL0hGRgbmz5+vsmxxBiS6fPkywsPDIRKJcOTIEVy+fJnZIiMj8fz58yK9v4wzZ86gdevWnILAFETIuwfyxlxF8r2ihM2581c6erq5uWHVqlUA5MfNqKgopTLC9u3bM33EqVOnmCAy/0vGt/r6+ozO1czMjJl7PHz4EJaWlkVePxMTEyagTH4SEhJgYmJS5PcHhOtFAGHr5OJu+0LW6SYmJrh27VqR11ER586dg0QiQd26dRm5aN26daGrq4uwsLBfUoei+u0ePXqk9PtTJBcViUQwNjZGnTp1WA1vgbz+/vTp0wDyvjWRSIQzZ84wxy9fvsxJNwLktfP69evDysqKGbvWrFnDyI1VYWFhwcx78ve7YWFhKFu2LKf780UTAZmEOikLkXH/4b/PH+PbP/ynyMrKQs+ePSESiaCjowMdHR1oaWlh8ODByMrKKvL7F1UUPi6kpqaidOnSKFu2LBYvXoyjR4/i6NGjWLRoEcqWLQtbW1t8/PgRx48fx9KlS4u0Lnx4//49HB0dYWBggOHDh2PNmjVYs2YNhg0bBgMDA9SqVQuZmZmIiopSahRT3EZ8KSkpcHZ2hr6+vpyH+Pjx4zFixAje11UnkmJBfqWAsyBpaWlYvnw5nJ2doaWlpfS8gl7yPXv2lDMWF8Lhw4fh6urK6dyxY8eicuXKeP/+Pfbu3Qs9PT0cOnRII/VgQ6gRlzK4tJ3r169DR0cH3bp1Q2RkJD5+/IiPHz/i+vXr6Nq1K2OgNW3atEICHyFlZZibmzMLdQcHB8ZwLz4+/pdGoROCECPA4laM1alTR+FvM3fuXNSsWVNlWU0YQunq6jJK4PwkJiayekkOHTqUeWfr169njFJMTU05K+EzMjIQFBSEhg0bQiwWo3Hjxti4cSNrH1RQgdO5c2fUq1cPWlpanA1ns7Ky4O3tDW1tbWbeIhaL0a9fP86Ro/MLlRVtbOSPpmJqaspEz4uJiVFp/J2Tk8PMt5ycnNCpUyd06tQJjo6OEIvFGDlyJIA8peWRI0cKlR83bhxMTEzQqFEjjB07lnMklvwIbX9Co8oYGRkxQk4bGxtGYPX06VNO92/QoAF27doFIC/agrGxMdzd3WFubs4qXOcTkaYgpUqVQmhoaKH9586dUxr1OjIyslBUaJmhvIWFBYYNG8bJwatOnTqMoCg/p0+fZu13AGDz5s1o3ry5nMPMy5cv0bJlS0bJpQqhAmqhY7aVlRUT+dbR0ZFx8nn06JFKYwQgr+8JDg5mvUdRwdfRUDbXy83NxerVq2Ftbc0Ip6ytreHv78/qiNWgQQPGONvLywutW7fGtWvXMGDAAFSuXJlT/YcPHw5XV1e5aAqPHz9G1apVMXToUE7X4MPGjRvRvn175m9DQ0PUq1cPTZo0QZMmTWBpacka7R0Q3va2bt0Ka2trHDhwgImo4Ofnxym6ApAXVaRatWowNjbGggUL8PXrV9YymshSIRKJsGjRIgQEBCjc/Pz8OEfNVvfbj46OljNQMzQ0LBRJ6ebNm6z3FgofhWJBcnJykJCQgKtXr+LKlStym7pcunQJ/fr1g76+PqysrFijQBaHYlAT0Zg0ZXzLZ62go6ODFy9eKL3m8+fPIZFION1fplDw9vaGrq4u8wzr1q3jZICpCYc7PlSuXJkZL/Nz7do1VKpUCUCe8wRb5HQhFPxW1P12hBpxCcXAwIAx5LK2tmbGkKdPn8LAwIC1PB/D+Xr16qmUOS5evBj16tVT91F+OZcvXxYsS1NkDMBFKXfq1CloaWlh8uTJcv3AixcvMHHiROjo6ODq1avw8vLiZJQj41fKB/X09Ji+Jn/fGRMTo9KIcM6cObCxsVEYMSgmJgY2NjYYP348rK2tsXbt2qKpPID+/fujVatWSEtLk6v/uXPnmP5HGU2aNMHYsWML7R89ejQaNGjAem9TU1PG8Cf/va9evcqanegPwoxfixtHR0f8888/AAAPDw9mbX/gwAG1HAX5OlnzMZoXiUR48+YN57qxoSndSk5ODk6cOIFOnTpxni8B/B3GhMoni3udDyiPYvzu3TvWtVZxByQC5B2Oi4M3b96gSZMmvCJgCnn3ALBjxw707NnzfzJyclBQkMqNCwYGBox8uKDTijLdgpaWFiZOnFjIePRXGt8W1C0U3Jo2bary97e2tmbW1a6ursw3GBkZqZbDBl9k8vyCTJ48WaV8oEuXLowTGts7YEOoXkToOrm4EbJOt7W1VZipgStCvt3q1atj+vTphfZPnz5dpYNqQEAAE7hAmXxQthUXp0+fhrm5eZFdX1tbmzESB/ICfOTvx168eKHSFkPGhg0bYG5uDj8/P7l1286dO9GkSRPW8kOHDkXnzp3x48cPGBoa4unTp3j27Blq1KgBHx8f9R9MDTQRkEmok7LQTMx/+G+jTX/4w38IiURCwcHBtHDhQoqNjSU9PT1ydXWl8uXLcyq/a9culccHDBig8rilpSV9+PCBypcvTzY2NvTPP/9QtWrVKDk5mQBwfg4+zJs3j5ycnCg0NJSkUimzv3PnzjRx4kRq3bo1dejQgW7fvk0HDhwo0rrwYcGCBSSRSCgpKYlKly5d6FjLli2pf//+FBYWRmvXrlV4DR0dHRKLxUREVKpUKUpNTSUXFxcyMTGhtLS0In8GHx8fql27NsXGxlLJkiWZ/V26dKFhw4bxvu6NGzfkflN14FpOaNsvyM+fP+n27dsUFRVFKSkphX7T/BT8Ns6cOUNLlizhfK/NmzfT+fPnSSKRkI+PD9WrV48uXbpEkydPpsTERM51X7duHfXt25fc3Nzo+fPntH//furUqRPnegjh27dvVKpUKSIiKlGiBL19+5YcHR3J1dWV7t69y/u6XNqOn58fDR48mDZv3iy3v379+lS/fn0aMWIENWzYkADQxYsXNVZWRo0aNejWrVvk4OBAjRs3pjlz5tC7d+9o9+7dVKVKFR5P/evJyckhIyMjIiIyNzenFy9ekJOTE5UvX54SEhJUlq1atSrFxcUV2r9ixQrS0tIqkvrm56+//qKuXbtSUlISNWvWjIiILl68SPv376eDBw+qLFulShWKjY0lOzs7qlevHi1fvpwkEglt2bKF7O3tOd2/YsWKFBISQjNnzpTbHxwcTA4ODirLbtmyhXJzc4mIaMyYMVSyZEmKjIykjh070ogRI1SWvXXrFm3bto0OHDhAFSpUoL59+1JkZCRt2LCBKlWqxFpvExMTub/FYjE5OTkx4xUXJBIJbd26lebMmUNxcXH09etXqlGjButz56dx48acz1WEgYEB/fjxg4iIrKysKCkpiSpXrkxERO/evVNaLiAggC5cuEAnTpyg9u3byx07ceIEDR48mCpUqECBgYEK++C4uDiqUaMGERHdv39f7phIJOJUd6Ht79WrV+Tq6kpERIaGhvTp0yciImrfvj399ddfrOWdnJwoISGBbG1tqVq1arR582aytbWlTZs2kZWVFWv5+/fvU926dYmIKCQkhKpUqULXr1+nsLAwGjlyJM2ZM0dp2RkzZlBsbCxdvnyZWrduzexv3rw5zZs3j3x9fVnv36tXLxo6dCitXLmS6tevT0RE169fp6lTp5KXl5fCMgsWLKAmTZowv3lcXBwNGzaMBg4cSC4uLrRixQoqU6YMzZs3T+W94+LiyM7OrtB+Ozs7evjwIWvdN27cSE+ePCEbGxuysbEhIqLU1FTS1dWlt2/fyo1JisbQEiVK0JcvX4iIyNramu7fv0+urq6Unp5OGRkZrPcXOmZ37dqV+vTpQw4ODvT+/Xtq06YNERFFR0dTxYoVVZZt164dTZ06lR4+fEiurq6ko6Mjd7xjx46s9yciSkpKIn9/f4qPjyciokqVKpGPjw9VqFBBZbnU1FSmvejp6THvsX///uTm5kbr169XWE421xOJRDRx4kSaOHEiU1Y2frIxe/Zs+vbtGxHltcX27dtTw4YNqWTJkhQcHMzpGsuXL6fWrVuTs7MzlS1bloiI/v33X2rYsCGtXLmS0zX4sHfvXpo2bZrcvn379jF91Z49e+jvv/+miRMnqryO0Lbn7e1Nenp6NHv2bMrIyKA+ffpQmTJlKCAggHr37q203M2bN2n69On0zz//0MiRI+nChQtkbm7Oej8i4rwOV4WNjQ1t3bqV9Rw2+Hz769atowYNGsjt2717N1lbWxMA2rFjB61du5Z2797N8Wn4ERcXR/v27Su0v1SpUirHaxn//PMP9enTh549e1Zo7SUSiSgnJ4f1Gs+fP6fAwEDauXMnpaen08ePH2nfvn3Us2dP1rFbaL/LB39/f8HXEIlE9OXLF5JKpQSARCIRff36lT5//ix3nrGxscrr8Fkr5OTkkLa2cpGtlpYWZWdnc3oOPz8/2rRpEw0YMEBOFuTh4UF+fn6s5YWsdYSQlJSk8N0aGxvT06dPiYjIwcGB0zfAlyZNmhTal7+9s307c+fO1XSV1MLe3p6Sk5PJxsaGnJ2dKSQkhOrWrUsnT54kU1NT1vJJSUnUrl07Ispbu3z79o0Zy5s1a0bz588vVGbIkCE0adIkqly5cqF1wsmTJ2nRokW0evVqjTyfKsRiscq+ie23y7/O+v79O7NmksH23RMRhYeHs56jiBUrVpCvr2+h79PKyopWr15N+vr61KJFC7K0tFRLdsZXrsiH2rVr0+nTp2ncuHFE9H/fzbZt28jd3V1puQMHDtDq1aupatWqhY5Vq1aNVq5cSb169aLBgwcz1y4KwsLCKDQ0lJkvynBwcKBnz56pLOvn50fNmzen2NhY8vT0JKI8GcutW7coLCyM9d4tW7Ykf39/2rJlCxERM/bMnTuX2rZty/OJfg1CvztNsHPnziK/BxvZ2dl0+fJlSkpKoj59+pCRkRG9ePGCjI2NydDQUGm5Ll260MWLF6levXo0btw46tevH23fvp1SU1NZ1wn5iY+Pp/379xMRkba2NmVmZpKhoSEtWLCAOnXqRKNGjVJY7u3bt8xaIz+yvl8Zjo6OrHPBDx8+cKq7pnQrYrGYOnToQG3atKEXL15wLsdn3CMSJp8k0tw6XwiyuW5BYmNjyczMTGVZvnICody7d4+qVKlCYrGYPn36pFC+LkPRuKJJvLy86Pnz57R48WIqXbo0Z9kmkbB3T0S0du1aRqdqa2tbqP0I0S0po+B6SBWq5kw+Pj5yf//8+ZMyMjJIIpGQvr4+J72eqakpvXz5spCcMTo6mqytrRWWuXbtGm3fvp1q1apFLi4u1L9/f5UykaKgoG5B0XFVz9+oUSM6f/48ubq6Uo8ePcjHx4cuXbpE58+fZ+YfRc327dspLCyM3NzciIgoKiqKUlNTacCAATRp0iTmvPxzfxMTE6a9s70DNoTqRYSuk2W8efOG3rx5w4wDMoq63xGyTl+4cCHNmTOHgoKCSF9fX+17C/l24+PjKSQkpND+IUOGqJTjrFmzhvr27UtSqZTWrFmj9DyRSETjx49nfYakpCTauXMnJSUlUUBAAJUqVYrOnj1LNjY2jH5MGfnbN1FeP/7y5Us6ffo0DRw4UGm5nz9/UuvWrWnTpk1q6QBl5OTkyPXx2tracjpksVjMyRZp3bp1tHXrVurcuTMtXbqU2V+7dm2aMmUKa/lVq1ZR9+7dqVSpUpSZmUmNGzemV69ekbu7Oy1atEjNp1KPZs2a0YkTJ6hGjRo0ePBgmjhxIh06dIhu375NXbt25XSNDx8+MDJ5Y2NjZp7aoEEDpfPk/GhC1v2H/y5/jG//8J/E0dGRHB0d1S4ndLKviU6fL+fOnaPg4GCFwlQ9PT1auHAhNWnShLZt2/bLDArV4dixY7R582aFRpqWlpa0fPlyatu2Lc2dO1fp5KW4jfiuXr1KkZGRJJFI5Pbb2trS8+fPWcsXbCOyCdvt27dZDYGECjg1sdAlylMu7Nu3jw4fPky5ubnUtWtXOnXqFGPUp2mWLl1Kc+bMoapVq9KjR4/o+PHjNGvWLFq3bh35+PjQiBEjqESJEgrLnjhxotC+rl270tWrV8nLy4tEIhFzTlELuYQacQlpO//88w8tW7ZM6fExY8bQ1q1b6e7du1StWjWNlZWxePFiRii3aNEiGjBgAI0aNYocHBxox44dKuv+uyDUCDA9PZ0OHTpESUlJNHXqVDIzM6OHDx9S6dKllQqJNEWHDh3o2LFjtHjxYjp06BDp6elR1apV6cKFC6wCDE0YQs2fP5969epFERER5OHhQUR5BoAXL15UuAjPj1gsZhwuiIh69+7NSVBWtWpV+vz5M/Xp04ciIyOZxTQXg0UZQhQ6jRo1ohMnTjAK7+joaGrRogXp6enxut7Vq1dp8+bN9PTpUzp48CBZW1vT7t27yc7OrpDBUEHc3Nzo2rVr5OLiQm3btqXJkydTXFwcHTlyhBGcKWLnzp20YsWKQgp1orz+cvny5TR8+HBq2bIlTZgwodA5fBXR+RHa/sqWLUsvX74kGxsbqlChAoWFhVHNmjXp1q1bpKury1rex8eHXr58SUR5hhWtW7emvXv3kkQiocDAQNbyP3/+ZO5z4cIFZpxxdnZmrquMY8eOUXBwMLm5ucmN/ZUrV6akpCTWexMRrVy5kkQiEQ0YMIAx3tHR0aFRo0bJCV3yExMTQwsXLmT+PnDgANWtW5cxiitXrhzNnTuX1fjWxcWFlixZQtu2bWPmTD9+/KAlS5aQi4sLa907d+7M4QmVI1RALXTMXrNmDdna2lJaWhotX76cUcC+fPmSRo8erbKsTOG4YMGCQse4GtCFhoZSx44dqXr16nL9buXKlenkyZPUokULpWWFOBoWnKdyNbqV0apVK+b/FStWpEePHtGHDx+oRIkSnBVbJiYmFBkZSefPn2ccNatWrUqNGjVSqy7q8uTJE8bYnyjP+CX/+FW3bl0aM2YM63WEtj0ior59+1Lfvn0pIyODvn79qlDBXhA3NzfS09OjkSNHkp2dnUIjUCJSKNy+d+8ep3qpUkqkpKRwugYbfL79yMhIGjt2rNw+Nzc3Zn6np6dHPXv21Ej9VMFHoZifkSNHMoZQVlZWaimDDx8+TNu3b6eIiAhq06YNrVq1itq0aUMGBgbk6urK6VrFoRhUpfDgCgA5uRIAxnlI9jeXvpfPWgEADRo0SOmcJCsri/NzJCQkKOznTExMKD09nbW8Jhzu+FCrVi2aOnUq7dq1iywsLIgozzBo2rRpVKdOHSIievz4MZUrV67I6vDx40e5v3/+/EnR0dH0119/cVYqKVrr3b1795es9QYPHkyxsbHUuHFj8vX1pQ4dOtD69evp58+fnAxg+RjODx8+nCIiIqhjx47k7OxMTk5ORET06NEjSkxMpJ49e9Lw4cM195BKOHr0qNzfst8uKChIqfFUfjIyMmjatGkUEhJC79+/L3Scy5yLr0HA3bt3Czk456d///60ePFiunLlikLnk9/BAHLx4sXUpk0bevjwIWVnZ1NAQAA9fPiQIiMj6cqVK0rLPXv2jHFQVIRs/bN9+/aiqDbDt2/fFBoifPjwgXWt6OHhQTdu3KAVK1ZQSEgIM9/cvn27SiW7lpYWvXz5klatWkWtWrWiSpUq0ffv36lPnz70+PFjMjc3Zwwqf1eEfneaQl3jV3XWE2xGpM+ePaPWrVtTamoqZWVlUYsWLcjIyIiWLVtGWVlZtGnTJqVl86/De/XqRTY2NnTjxg1ycHCgDh06cKofEX8na75G8/PnzxdsQCVDqG6lIA8ePKCaNWty7vf4OozxlU/K0MQ6ny+y9i8SiQoZUufk5NDXr19p5MiRKq9RXAGJqlevTq9evaJSpUpR9erVSSQSKbxfUb9Dorx1440bN5TqPxShiXdPJFxGxgdTU1PO/aaqd19wrk2UN78fNWoUTZ06ldP1e/fuTdOnT6eDBw+SSCSi3Nxcun79Ok2ZMkWpTtPNzY3c3NzI39+fgoODaceOHTRp0iTKzc2l8+fPU7ly5dSWWamLUGeR9evX0/fv34mIaNasWaSjo0ORkZHUrVs3mj17tiaqqJL79+9TzZo1iYgYWbS5uTmZm5vLBdoo2E7yP7cmHGaE6EWErpPv3LlDAwcOpPj4eN5OzkIQsk5ftWqVIKN9Id+uhYUFxcTEFJoXx8TEqJRRJicnK/w/H65cuUJt2rQhDw8PioiIoEWLFlGpUqUoNjaWtm/fTocOHVJZPjo6Wu5vsVhMFhYWtGrVKhoyZIjScjo6OpzlpMoIDQ1l5l25ubl08eJF5pvj0m6J8t5fftmWDF1dXUbfpgoTExM6f/48Xbt2je7du0dfv36lmjVrUvPmzbk/CE+EOjwRCXdSJsoLzLBp0yZKTk6mGzduUPny5cnf35/s7Ox+SxusP/xCfmWY3T/8oSiYOHEik/KyYNpiPmmMC5KYmAhPT08mFboqcnJy8PPnT+bv/fv3Y9y4cVi7di2ysrJ43Z8rEokEaWlpSo+npaVBR0enSOsgBC71ZwuXf+vWLSbt9OvXr9GqVSsYGRmhZs2aiImJ0Wh9FSE0NVjBVFhDhgzB9OnTFaaFLsixY8fktoMHD2LmzJmwtrbGtm3beD2POm0fAMqUKQOpVIrOnTvj4MGDnFJPA3mpdfKnqJKlKeCCo6MjAgMDAQAREREQiURo164dpzS4shSAbBuX9D5C2b17N3bu3AkAuH37NszNzSEWiyGVSnHgwAHW8kLajlQqVZkKISUlBVKpVONl/0ucO3cOhw8fBpCXjs3JyQkikQjm5ua4ePGiyrKxsbEwNzdHxYoVoa2tzfQbs2bNQv/+/Yu87prm/fv3aqf5un37Nvr27YuaNWuiZs2a6Nu3L5M6hI2IiAj07dsXbm5uTMqVXbt24erVq0rLSCQS9O/fH2FhYXJ1/VWppQqmMDYyMuKVwhgQnh4pKSmJSU/09etXjBgxAq6urujatavKb1sqlTIpbBWRkpICsVjMOvd5/Pgxzp07h4yMDAAQnCJOnfY3ffp0LFq0CEBeGkdtbW1UrFgREolEYeojNr59+4Y7d+7g7du3nM6vW7cupk+fjoiICEilUmaecuPGDdYU4nzTuCqr971793Dv3j3WFHW6urpITU1l/vbw8ICfnx/zd3JyMgwNDVnvGRUVhVKlSsHCwgKenp7w9PSEhYUFSpUqxaRDLkrev3+P58+fA8ibuy9ZsgQdOnTApEmT8OHDB9byQsfs4oZvei+AfzpNkUjEKQX8ryQzM/OXpaWUSqVM+jdFxMfHK02HmB+hbW/hwoWc59j5KV++PGxtbVVudnZ2CsvK5tJFOdf++PEj1q1bx3oen29fT09Pbo26evVqJkUiADx79ozTbyeUyZMno0GDBnj58iWTEu3atWuwt7fnlLpeX1+fSaOmLlpaWpg5cyY+f/4st1+deZPQflcTZGdn49ChQ1i4cCEWLlyII0eOIDs7W2UZtjSWXNNZ8lkrsKXNVid9tp2dHc6fPw9Aft4QFBQEFxeXIqm/Jnj06BGcnJwgkUhQoUIFVKhQARKJBM7OzkhISAAAHD16FLt27SqyOijj8uXLqFmzJut5sbGxsLCw+G3WeikpKTh8+DAz/2fDy8sLq1atAgAsWLAAFhYW8Pb2Rvny5VlTwQYHB6NTp06oVKkSXFxc0KlTp2JPqQ0Ae/fuRceOHVnPGz16NFxcXJj11o4dO7Bw4UKULVsWe/bs4XSvK1euqNyUoa+vr3J9mJSUBH19faXHi0I+yIcnT57A29sbderUgYuLC/r27cukR1aGhYUFbt++rfT4zZs3izSVq4w2bdpg9uzZAP5PRpmTk4MePXqgW7duRXLP/HKCnz9/Yvfu3Zg6dSpGjRqFrVu3Mmvm/0W4fneaICUlBc7OztDX14eWlhbzLY0fPx4jRoxQWCYwMJDzxkanTp3Qr18/ZGVlyY254eHhqFixouYelKUOW7ZsAZA3h6xYsSL8/PxQs2ZNeHp6Ki139epVGBoaYuTIkZBKpfDx8UGLFi1gYGCg9LssKN8SilDdSkFiYmIgEok4ny9k3OMjn/wdCAwMxM6dOyESiRAQECDX3vft24fIyEjWa/CVEwglJSWFWdMXdwroGjVq4MaNG2qV0cS7Ly7yr4UCAwNhaWkJX19fHD9+HMePH4evry+srKw49ZuKuHXrFpycnDidm5WVBW9vb2hra0MkEkFHRwdisRj9+vVjXe/l59GjR5g6dSosLS0hlUrRoUMHXnXnyuDBg1m3ovx+fkeysrLw5csXzucL1YsIXSdXrVoVXbp0wT///IPk5ORf3u8IWafPmzdP5cYXLt/u/PnzYWpqiqVLlyIiIgIRERFYsmQJTE1NsWDBAt73Vgc3NzdmvM//20dFRbHqZYQyYcIEXrongJtNAxcZq4uLC44dOwZA/vnXrl3LqhsoyK+Ur2uK1atXIyAgAABw/vx5SKVS6OrqQiwWw9/fn7X8hg0bYG5uDj8/Pzk93c6dO9GkSZMirfsffn/+GN/+4X+eJk2a4OPHj8z/lW1NmzblfQ91JvvFRfny5VUa2p09exbly5f/dRVSkzJlyqgURkRERMDKyuoX1kh9evbsiWHDhgH4P+Hsly9f0KxZM86KMU0jVMCpTtvfsmUL8y2qg0gkQtu2bdGlSxd06dIF2traaNmyJfO3bFOEVCqVMwSSSCQqBfX/K6hrxCUEV1dX7NixQ+nx7du3w9XVVeNl/+twNQL09PTE1KlTAcgvdK5fv/5b99m/A3wFLP/++y/8/PxQoUIFlClTBpMnT8bdu3eho6Oj0oiEi+EYFwOygsqJ/L+7ulSvXh1BQUGFrnP37l2ULl2a1zW5UKJECZVK+3v37sHU1FTp8Xfv3qFZs2aMQEBW78GDB2PSpEkary8XIiMjsWrVKpw4ceKX3C88PBympqYQi8UYPHgws3/GjBmsSp2GDRti7dq1AOQdVsaOHYtWrVqpLJudnY3Y2FiFytuMjAzExsYiJydHYVkbGxvGUCArKwt6enq4cOECc/zevXucDSi/fv2KzZs3Mw5yW7Zs4eQ48zvCZcw+fvw4fvz4wfxf1VbU6OrqIjExsdD+hIQEViNCvo6GipRJXJTZBeeCqjYu5OTkYMGCBShTpoycMn727NlFaoxSsWJFHDp0SOnx4OBgVKhQQe3rqjtfrFq1KsRiMdzd3fH333//knkmmxI0JSUFcXFxvK594cIFeHl5QSqVwszMTOl5DRs2lFujHD9+nLMBS4kSJXDt2jWlx69du/ZLDMeFKhSbNm2Ks2fP8rr38OHDYWJigvr162Pjxo2MseyvclrSBI8fP4aDgwP09fVRo0YN1KhRA/r6+nBycsKTJ0+KpU58HNb4snjxYlSqVAn//PMPjIyMcPXqVezZswcWFhbMfEJdflX9c3JycPbsWQQEBCAgIADnzp1TOk/5lcTHx8PAwID1vP/1tV5RGM5nZmZixYoVmqymWiQlJXH67cqVK4fw8HAAYJwegDxDKi7KfECxclQsFjObMurUqYPVq1crPb5q1SrUqVOHUx3y8ysNIFVx8OBBpcd69uyJrl27Kj3etWtX9OjRoyiqJUdcXBxKlSqF1q1bQyKRoHv37nBxcUHp0qUVjhv5HXM+ffqkclOGpo0Yfye4fneaoLiNX83MzBinu/z3T05Ohp6eXqHz2daGfNaJfJ2sAfWN5sVisUbbraZ1KzExMWo5+vEd94QagP0OXL58mZFZqEtxBiT6XQgNDUX9+vURHh6Od+/ece77AWHvXsbHjx+xdetW+Pr64v379wCAO3fuMIbgRUmzZs2wb9++Qvv37t2Lxo0b87pmdHQ0jIyM1CqTmpqK06dPIzg4WKHMiyvZ2dk4evRokRvfikQi2NraokuXLujcubPSLT9scwyube53YMeOHRg7dizj1Obr6wuJRAKxWIzmzZvj3bt3rNcQqhcRuk42NDTk7eRcVPxKOYMiuHy7ubm5WL16NaytrZk1krW1Nfz9/TnVPTExEYcOHWL0IadOnULDhg1Ru3Zt+Pn5cbqGgYEBU77gfK2oHezHjh0LY2Nj1KpVC8OHD9dIEEF12bp1K6ytrXHgwAEYGBhg//798PPzY/7Pxq+Wr8fGxnLe+KCuk7KLiwuOHj0KQL79xMXFoWTJkrzq8If/DiKgCPM+/OEP/xFiYmKoUaNG9Pnz50LH1AkRryqdplAmTJhAly5doosXLzIp+WS8efOGWrRoQU2bNiV/f/8iq4MQhgwZQklJSXT+/PlCqYWysrKoVatWZG9v/1unof/333+pVatWBIAeP35MtWvXZlKDRUREcErrqmmePn1KVatWpa9fv/Iqr6rta4rBgwdzOk9RGhKxWEyvX79m2ryRkRHdu3evUErWPyhnzZo15OfnR7t376a2bdvKHTt9+jQNHDiQZs6cSZMmTdJY2Ro1anBOTcSW4uR/HRMTE7p79y5VqFCBjIyMKDY2luzt7enZs2fk5OTEpA/SJGZmZpSYmEjm5uas6fUKptTr2rUr5/scOXKE9ZxPnz7R+fPnKSUlhUQiEdnb25OnpycZGxuzlq1RowZNnDiRBgwYIPfuoqOjqU2bNvTq1SvWa1y6dIl27NhBR44coe/fv9OUKVPI29tbLsWwjKCgINbryVCV6lgsFjNp0YhIru7qoq+vTw8fPiRbW1u56zx9+pRJU1kUtGvXjmxsbGjjxo0Kj48cOZJSU1PpzJkzCo8PGDCA3rx5Q9u2bSMXFxem3qGhoTRp0iR68OCBwnKabn/qoqgfVAaXVL45OTn0+fNnKlGiBLMvJSWF9PX1Vc4Zrl27Rm3atKF+/fpRYGAgjRgxQi6Na61atZSWDQwMpPXr11NUVBRpaWnJHcvOziY3NzeaMGEC9evXr1DZUaNGUWxsLC1btoyOHTtGQUFB9OLFC2betnfvXvL396dbt26xPrsQ+KTSVWcew6X/UZf8333+dJQF4ZKW7MqVK7Ry5UqKj48nIqJKlSrR1KlTqWHDhpzqUq5cOVq9ejX16NFDbn9ISAhNmTKFUlNTOV1HHQr2e1zhOkck4paubsGCBRQUFEQLFiygYcOG0f3798ne3p6Cg4PJ39+fbty4oVb9uOLj40MXLlygO3fukFQqlTuWmZlJtWvXpubNm1NAQECR3D8/Dx48oL1799KBAwfo33//pRYtWlDfvn2pc+fOCtMrExHduHGD3r9/T+3bt2f27dq1i+bOnUvfvn2jzp0707p161jTMOfny5cvtH//ftq+fTvdvn2bczq+tLQ02rlzJ+3cuZNSU1Opd+/e1L9/f/L09CyUJk9GwfZnbGxMMTExnMZdT09PqlmzJq1YsULh8cmTJ1NMTAxdvHiRU/2FkpaWRnFxcfT161eqUaOGyvTV+Tl69CjNnj2bpk6dSq6uroXeFZucIjMzk0JCQmjHjh0UFRVFrVq1otOnT1NMTAxVqVJFYZni7nfz07ZtWwJAe/fuJTMzMyIiev/+PfXr14/EYjGdPn1aYTm28YYor9/Ozs7WeJ2J8tJ16+npqXzPXABAixcvpiVLljApk3V1dWnKlCm0cOFCTVW3SPn+/Tvp6upyXkNqioLyPgD08uVLWrp0KWVnZ9O1a9dUli+Otd7atWs5nzt+/HiN35+I6O3btxQVFUUSiYQ8PT1JS0uLfv78SRs2bKAlS5ZQdna2ytTnRUVmZibNmDGDzp79f+ydd1QT2/f2nwDSi4pdQUAUAQVsWLCgWMDeuwJiF3tBvfarXsu19wJSVLBhb1hRrKiAFan2jqICipT9/sGb+SWkTTITwPv1s1bWgpmcmZNkZs7Z+zx779N4+vSp3PcaGhri8ePHMDc3R7Vq1RAeHg5nZ2ekpqaibt26rHxsX79+Ffs/JycHMTExmDt3LpYsWQI3Nzep7YKCgjBmzBj8+++/GDlyJLS0tAAUzNW3bduG6dOnY/PmzfDy8mL3wf8/XP2DbMnNzUV8fDy0tbXF7OqjR49i3rx5iI+PR3Z2ttS2jx8/RuPGjWFvb48pU6agdu3aICI8efIEa9aswePHj3Hz5k3Y29ur9TMABb/fxo0bERcXx5RSHTduHCpXrizxXk1NTbx9+5aZ60t7VhGR3Lm+hoYGFi9eDENDQ7n9Utd9qy6Uue/4QFh61sbGRuy5++zZM9jZ2THjIBt+/vyJX79+iW1TNGcpU6YMrl27Bjs7O7HzR0VFoVevXnj//r3Y++XZhqIURflqVVDVzpMF32srcXFxqF+/vtq/Oz78k1ztfD5R5dovbtLS0mBqagqgwGbasWMHfvz4ga5duxbJdyi8lws//xU9+wujynd///59tG3bFiYmJnj27BmePn0KKysrzJkzBy9evEBwcLASn0R59PX1ERcXJ2GbJiQkwMnJSe5z99ixY2L/C+faGzduhJmZGU6fPi2zbcuWLXHs2DGmRPixY8fQrl076Onpqf5hipBx48YhNDQU1atXh7e3NwYPHszYqrJgY58qe82pSuvWreX25eLFizL3LVmyBEuWLIGLiwvu3buHvn374siRI5g0aRI0NDSwfv16dO7cWea6gxCu6yJc7eTu3btjyJAh6NWrl8L3/tfgcu+K8v37dwAF62NsOHz4MPr27cvcC9u3b8eoUaPg6uoKTU1NnD17FosXL4afn5/c41SrVg379+9Hs2bNxK6dw4cPY9q0aUhOTpZow9eaduvWrWXuEwgEcu8dPtmzZw8WLFjAfNYqVapg4cKF8PHxUdi2qP3rwt9bkaSxqObLenp6iI+PR/Xq1cWun8TERDg4OODHjx9q78MfSi5axd2BP/xBnXz79g0XL15E7dq1Ubt2bYXvlzdhcHFxkdrGycmpRDz058+fj1OnTqFGjRoYPHiwmINy7969qFSpEubNm6e283Nl0aJFaNiwIWrWrIlx48aJ9X/z5s3Izs6WaiiWJBFftWrVEBcXh3379jHOWR8fHwwaNEim0adI+CZKYRGcIn78+IH169ejatWqCt+ryrUPFAihAgMDYWxsrFAUJUsIxUYsIY+5c+cyYoFfv35h8eLFMDExEXsPGxFUZmYmIiMj8eLFCwknhzqc21xFXHxdOxMnTsT169fRuXNn2NjYwNbWlrn3EhMT0a1bN0yaNInXtt27d2fV75IMXyJAHR0dqeKEhIQEiUAKvlizZg1j0K5Zs0apRezC9xYXdu/eDV9fX4nPb2Jigq1bt6Jfv35y2z99+hQtW7aU2sf09HRWfWjTpg3atGmDr1+/Ys+ePQgICMC///6LOnXqSCy2yxPUKsvZs2eZ7zI/Px8XLlzAw4cPxd7TtWtXhcepVKkSkpKSYGFhIbY9KipKpqiIj2fHX3/9BVdXV6SlpWHatGliY/aqVatw9OhRXLp0SeZxIyIicPbsWVSrVk1se82aNfH8+XOZ7bhef4XHOnlI+/5jYmJYtWXz/QYEBKB169YSwSKFf0tpNG/eHLGxsVi2bBnq1q2LiIgI1K9fHzdu3EDdunXltvX398e0adMkhLcAoKWlhRkzZmDjxo1Sxbd///03evbsiVatWsHQ0BBBQUFiAVMBAQFo3769wv4DQEhICLZt24aUlBTcuHED1atXx5o1a2BlZYVu3brJbXv48GGx/4VChqCgICxcuFBqm9KlS3NyUHMds/Pz86X+rSy7d++Gt7c3evbsycxNrl27Bjc3NwQGBmLgwIEKjzFixAiMHDkSKSkpaNasGXOM5cuXs/qcV69exbZt25CcnIyDBw+iatWqCAkJgaWlJZo3by61japiKa5zxMIEBwdj+/btcHNzw+jRo5ntjo6OiI+P5/VcosyePRv79++HjY0NfH19GSHK06dPsXHjRuTm5mL27NlS2/It+re3t8fSpUuxdOlSXLt2DXv37sWkSZMwevRomWLJhQsXonXr1oz49sGDB/Dx8YGXlxdsbW2xcuVKVKlSBQsWLFB4/itXrsDf3x+HDh1ClSpV0LNnT2zcuFFum5ycHBw5cgQ7d+7E1atX4e7ujpUrV2LAgAH466+/YGdnp/C8oigTfz527Fj0798fFhYWGDNmDLOompeXh82bN2PDhg3Yu3evUudXhUWLFmHatGkwMzODmZkZs/3Hjx9YuXKlQjtfuBg0bNgwZpvQh8HGT6GnpwdPT094enoiMTERu3btwp07d+Di4oJOnTqhd+/eEnNjrs9dPomMjMTNmzfFFjNNTU2xbNkyubZu4fFGlBs3bmD9+vUyn+l82AqlSpWCubk55+9HIBDgr7/+wvTp05GUlISMjAzY2dnJFXgVd8ATUDBeLlmyBFu3bsX79++RkJAAKysrzJ07FxYWFqwWhbgiy9/XpEkTVoHhxWXrsUEgEEj1c3AVzkdFRaFz58749u0bBAIBGjZsiF27dqF79+7Q0tLCggULeLWrZFHY5iEifP/+Hfr6+ti9e7fC9lZWVkhNTYW5uTlq166N/fv3w9nZGcePH2dEHoqQZru0a9cO2tramDJlCu7evSu1naenJx48eABfX1/MmjULNWrUABEhJSUFGRkZmDBhgtLCW2X8g1x4+PAhOnfujJcvXwIAunXrhi1btqBv3754+PAhRowYITPgASgQm507dw4+Pj7o378/8xsSEWrXro2zZ88WifD2xYsXMDMzw19//SV1n7m5udi2ixcvMmOMPDtYEVu3bpVqpwmRdd+WFLjed3yQn58vddx89eoVK2FHZmYm/Pz8sH//fqSlpUnsVzQmt2/fHmvXrsX27dsBFPxmGRkZmD9/vkTSAmF/SwqiInJR0tLSUKFCBamfne/+K7u2oigpDhvBNx8BY1z9k3zY+VzJysrCjBkzVL72VfET8MGDBw/QpUsXvHz5EjVr1kRYWBjc3d2RmZkJDQ0NrFmzBgcPHlT7WgSXZz/X737KlCnw8vLCihUrxJ5zHTt2LJJrx8zMDDt27MCKFSvEtu/cuVPMdpVG4d9FIBCgfPnyaNOmDVatWiW3bVRUlNga3uDBg1kH2ZYENm3ahNWrVyM8PBwBAQGYNWsWOnXqBB8fH7Rv316qLc3lOuMbJycnsf9zcnIQGxuLhw8fKpzrBwYGwt/fHwMGDMCdO3fQuHFj7N+/n/Fb1KlTR8xnJwtV1kVEUcVOFmXnzp3w9PTEw4cPUadOHYkgZzZrO8rCxU7nkpCnMFzuXVHYim6FLFmyBDNmzMDixYsRGBiI0aNH459//mHWoLdv3441a9YoFN/2798ffn5+OHDgAAQCAfLz83Ht2jVMmzYNQ4cOldqGr3GEy32ckJCA9PR0ODs7M9suXLiAxYsXM8kRZPmXCzNo0CAMGjQIWVlZyMjIUCrAqaj966mpqZyPwWeQsqWlJWJjY1G9enWx7WfOnIGtra1K/fvDf4c/mW//8J+ib9++aNmyJXx9ffHjxw84Ojri2bNnICKEhYUpjEAqHG1ceMIgLbpdnkikMIUfxHzz5csXzJ49G/v27WMM+9KlS6Nv375YsmQJE/1ZUklNTcXYsWMRERHBLG4IBAK0a9cOGzduhLW1tUQbWSILacyfP5+3vkrjypUraNasGZOVQkhubi6uX78u1QnDVyZFRQ5ORRN9Va59oCAj2fr162FkZAQvLy+5E3a+BRQA4OrqykrQoWhCGxMTg44dOyIrKwuZmZkoW7YsPn36xGQfTElJ4avLDPIi3ESRFe3G17UjZN++fQgNDUVCQgKAAhHcgAED0L9/f7W2/V3hKxvf8OHDkZaWhv3796Ns2bK4f/8+NDU10b17d7Rs2bLEZivnyr1799C4cWMMGjQIkydPZsSbjx8/xtq1axEWFobo6Gg4OjrKPIaVlRW2b9+Otm3bikUYBgcHY9myZXj8+LFKfYuNjUVAQIBUg+zNmzdYvXo15s2bJ+F0//r1KxYvXoxp06ahYsWKMo/PJrMJWyHKP//8g927dyMgIADt2rXDqVOn8Pz5c0yePBlz587F+PHjJdrw9ew4fPgwRo4cKeGMKVOmDLZt2yZ3zmVkZIR79+6hZs2aYr/dnTt30KFDB6nOZj4oSVllatasiZSUFFStWhWtWrVCq1at4OrqKnWuwycVKlTA7du3ZYp8U1NT4ezsjI8fP8o8xtevX2FoaCixMPz582cYGhpKVDAozJYtWzBv3jxMmjQJixcvxqNHj2BlZYXAwEAEBQWp7ITau3cv9u3bh6NHj0rsi4yMZH2cVq1aSWzjOmbzha2tLUaOHInJkyeLbV+9ejV27NjBZMmRBxFh7dq1WLVqFd68eQOgILp9+vTpmDBhgtx51aFDhzBkyBAMGjQIISEhePz4MaysrLBx40acOnVKZrZrLhmRfv78iYiICLRu3VrCKfvt2zdcvnwZHTp0YJV1VVZk+uPHj+Hs7KzWTHCpqakYM2YMzp07J2HnbN68WebCgDqvvdjYWOzevRthYWFIS0uTGZlfuXJlHD9+HA0bNgRQEIARGRnJZHw8cOAA5s+fL3PcfffuHbO48u3bN/Tt2xdbt25FXFwcK+FshQoVULt2bQwePBh9+vRhsoWXKlWK1TG4Zpz38/PDypUrYWRkxLQRCqCmTJkiMysun6gihhBFkc9CFT9Ffn4+Tp48CX9/f5w+fVoiiyHX5y6flC1bFidOnGACDoRcu3YNXbp0USrI9enTp5g5cyaOHz+OQYMGYdGiRVK/P75sBX9/f4SHhyMkJERhJiRZDBs2DOvWrZN4hmZmZmL8+PFSRaR8Zx5XheLKVi5K4XtHQ0MD5cuXl8hiLovf0dbjmlHL1dUVVapUwezZsxEUFIRVq1ahZs2aWLJkCXr37q2ubksQGBgo9jmEv13jxo3Fqk7IYs2aNdDU1MSECRNw/vx5dOnSBUSEnJwcrF69GhMnTlS5b/Hx8WjYsKHCecfNmzcRGhqKxMREAP/nZ2nSpIncdlz9g1zo1KkTsrOzMWnSJISGhiI0NBQ2Njbw8fHBuHHjlMpGFxsbK+Zjqlevnrq6LQGXcVco3JWW/fDly5cSwl0hfGcQLQ643nd80K9fP5iYmGD79u1MdbTy5cujW7duMDc3VzhmjRs3DpcuXcLff/+NIUOGYNOmTXj9+jW2bduGZcuWYdCgQXLbl4SqeKpUihG2k3YNvnnzBjVq1CiSLF7Krq3Iy4TGNtCMj0ySXP2TfNj5XOFy7avqJ+ADDw8PaGlpYebMmQgJCcGJEyfQoUMH7NixAwAwfvx43L17Fzdv3lRbH3JycuDu7o6tW7eyrkwiCtfnTnFUWhDl1KlT6NWrF6ytrdG4cWMAwO3bt5GYmIhDhw5JDTzgAz4ry5UEnj9/jsDAQAQHByM3NxePHj1iLQItSSxYsAAZGRn4999/Zb5HR0cHSUlJjDhbR0cH9+/fh42NDQDg9evXsLS0lEiQVBhV1kVEUcVOFuX48eMYMmSI1CAOda0tcLHTg4KC0L9/f+jo6ChcH1JHsKSibMlAwfcmr7KUkZERYmNjUaNGDeTn50NbW1usUhDbSge/fv3CuHHjEBgYiLy8PGhpaSEvLw8DBw5EYGCg3GA4vkhKSkJycjJatmwJPT09Zq4hjx49eqBu3bpYtGgRgAJfs729PVq0aIHatWsjICAAf//9t8xkWkLatGmD8PBwiaDSb9++oXv37gr9y8XpX1cVttWSBQKBQj3Izp07sWDBAqxatQo+Pj7YuXMnkpOT8c8//2Dnzp3/aV3EH1hAf/jDf4iKFStSbGwsERHt2bOHrK2tKTMzkzZv3kxOTk7F3LuiIz8/n96/f0/v37+n/Pz84u6O0nz+/Jlu3bpFt27dorS0tOLuDms0NDTo/fv3Ets/ffpEGhoaaj33rl27KDAwkHkFBwfT6dOn6fPnz2o9L59kZGTQnDlzqGnTplSjRg2ytLQUe6mTVq1a0YgRIygvL48MDQ0pOTmZXrx4QS1btqRDhw6p9dz/dbKysqRu//z5M61fv56+fv0qsS89PV3mvv8a6enp1LZtWypdujRpamqSmZkZlSpVilq2bEkZGRlqP7+bmxvt2rVLqe/6x48fdPToUfr27ZvEvq9fv9LRo0fp58+fco/h5eVFvXv3lrm/V69e5O3tLfcYS5cuJTs7O7p58yYZGRnR1atXaffu3VS+fHlav349uw+jJFOnTqURI0bI3D9q1CiaMWOGWs4tjfz8fFq8eDEZGBiQQCAggUBAurq6NGfOnCI5f2ZmJoWHh9Py5ctp+fLlFB4eTpmZmQrbeXh4MH00NDSklJQUysvLoz59+lCvXr3ktuXj+uNCbm4uxcXFSX22ZWVlUVxcHOXl5bE61qtXr2j37t00cuRIsrGxIQ0NDapatSoNGjRIbjsu8w19fX2Ki4uTuT8uLo709fVZ9V9VbG1t6fDhw0REzJhLRPTgwQMyNTVV+bjJyclkYGDARxd5Z926daxf8tDW1qbExESJ7YmJiaSjo6OwHzk5ORQUFETv3r0jIqJv375JvZdk4eTkREFBQUQk/tvdu3ePKlasyPo4yrB27Vpq06aNzP1ubm60YcMGVseqX78+hYSEEJF4/xcuXEjNmzfn3lkWpKWlFaudk5KSQosXLyY7OzvS1NSkNm3a0M6dOyk9PV1mGx0dHXrx4gXzv4uLCy1evJj5PzU1lQwNDaW27dy5MxkbG9OAAQPoxIkTlJubS0REWlpa9OjRI1Z9LlOmDLVs2ZK2b98uNl9hewyBQEDBwcF09OhROnr0KOnr69P27duZ/4Uvedy4cYMmTJhAHh4e5OHhQRMmTKAbN26w6j8fCAQC+vDhg8T2CxcuULly5YqsH7KQNiaVJIYMGUL29vZ08+ZNys/Pp/z8fLpx4wbVqVOHPD09WR3j9evXNHz4cCpVqhR17tyZHjx4oN5O/3+cnJzI0NCQdHR0qFatWlSvXj2xFxtkzRs+fvxImpqafHeZN2rUqEHnz58nIvFn9pMnT6h06dLF2TXWFKet9/XrV6lz0ry8PLm23+XLl1m/pFG2bFnm2ZyVlUUaGhp05MgRfj5UMfLs2TM6dOiQ3Hl0YeLi4sResbGxdPr0aWrVqhW5uLiora/F6R8sX748xcTEEFHB9S8cg5Xh69evFBERQSdOnJA69hUFssbdZ8+eKbSVVLXVZLX7g3K8fPmS7OzsyNbWlrS0tKhJkyZkampKNjY2rL5fMzMzunTpEhERGRkZMbZXcHAweXh4sOpDTk4O7d69m6ZPn05jxoyhHTt2yPSNEhXM52xtbWX6R+3s7CgyMpLVuYmIjhw5IvY6cOAAzZ49m6pWrUo7d+6UeL/QDtXQ0KAlS5aI2aarV6+m7t27F9m6mrL3z7Nnz1i95MF13CPi7p/kaufzAZdrvzj8BEJMTU2Zsfn79+8kEAjozp07zP4nT56QiYmJWvtARFSuXDlKSEhQqS3X50758uXp3r17RCT+/UdERFC1atVU6pOyvHjxgmbNmkU9evSgHj160OzZs8V8COpAIBCIPS9EP/vvyIsXL2jhwoVkaWlJVatWpe/fv0t9X0JCAvXv31/mmDFgwIBi/R4SExOpTJkyct+j6Ld79+4dq/V0rusiXO3k6tWr07hx4xgf6x/kM2nSJJkvHx8f0tPTU/i783XtCHn+/DmdPHmS9u3bx+oZzsea9qdPn6hNmzYkEAhIQ0OD6b+3tzdNmTJFbttq1arR9evXmf///vtvcnR0ZP7fuXOn2P+yKPw9Cnn//j1paWkpbF8c/vU7d+6Qq6urzO/e1dWV0YcVBbt37yZra2vm2SNrnv2H/z20FMtz//CH34evX78y2UDOnDmDXr16QV9fH506dcL06dPVdt67d+9i2rRpOHr0qNRMeN27d8fatWvlZvDjk7y8PNy/fx/JyckYOHAgjIyM8ObNGxgbG/8W0XJlypRB2bJlkZyczGRFIDlRP1++fMHu3bvh6ekp9fsPDg6Wuo9vZPUxLS0NBgYGMtvxkUlR2ZJz6oBrtNTw4cMRGRmJIUOGoHLlyiqXKBblyZMn8Pf3lxtpCRRk1Ni2bRs0NDSgqamJ7OxsWFlZYcWKFfD09FSqnIcy5OXl4dGjR6hZs6ZEBpAfP34gMTERderUkZmtkeu1w0dpLaCgDIO0LKGZmZno3Lmz1CyGGzduxP3796VGoJqYmODq1av49u2b1DJ/JQU+svGZmJjg3LlzuHbtGlNSrX79+mjbtq26uw+goPz0rFmzMHbsWHTq1AmDBw9Gx44dJcrkiLJt2zYcO3ZMasYcY2NjrF+/Hi9evICvr6/MY1y7dg2bN2+WuX/06NEYO3as3L7PnDkT+fn5cHNzQ1ZWFlq2bAkdHR1MmzZNYWQzUBDtKO85Iy3C8cyZM9i6davMNkOHDsWIESOwfPlyhefnA1XLI/Ex7gCAvr4+evToIbH91atXWLRoEVNqsTArVqyAm5sb7ty5g1+/fmHGjBl49OgRPn/+jGvXrsk9Jx/XHxdCQkKwceNG3Lp1S2JfqVKlMGzYMEyaNAmDBw9WeKyqVati0KBB6NGjB65evYrQ0FDs2bMHYWFhcstykozCJdnZ2QqzztasWRPXr1+Hg4OD1P1RUVEqZepQhtTUVKmZq3R0dJCZmanSMdmU0k1MTMS8efOwbds2qdf9mDFjsHjxYplZMriM2VxLQAsxMzPDhQsXJDIknz9/XmE5PwDQ0tLC6NGjmcw5ypb34lpOUxX27NmDuXPnytw/adIkLFq0iNU9P2/ePHh6euL169fIz89HeHg4nj59iuDgYJw4cYLPbsukbNmyYuXB2MB1viikSZMmiI6OhoODA7y9vTFgwABW5acrVqyI1NRUmJmZ4devX7h3755Y9ZHv37/LnDecPn0aEyZMwJgxY1R+trx58waHDh2Cv78/Jk6cCA8PDwwePFgpW6Fw1o5Ro0aJ/a8oK0mDBg1kZhr89OkTypUrx7ovyiDMXigQCFCrVi2xz5yXl4eMjAy55RhllVUzMTFBrVq10LRpU9Z9CQ4OlrlPIBBgyJAhEtu5Pnf5Yv369fDy8hLLpJabm4uuXbti3bp1ctt+/foVS5cuxYYNG+Dk5IQLFy6gRYsWrM7Lh63ApbTht2/fQERM1kvRbK15eXk4deqU3Ax8fGYeV4XXr19LrQiQn5+PnJwctZwT4LccYXHZeocPH4afnx9iY2Ohr68vtu/Hjx9o1KgR/v33X3Tp0kWiLddM1F++fGGeiXp6etDX12cyEakbReXHRZE1FxYSHByMfv36Mdd39erVUb16dfz69QvBwcEyy5GK4uTkJDUjY5MmTRRm0hLy8+dP3L9/Hx8+fJAo7y4rg21x+gc/ffqEKlWqACi4/g0MDBRm6hUlNjYWHTt2xLt37wAUzFX379+PDh06qKW/hZkyZQqAgnFt7ty5YvdPXl4ebt26JVFmuTCyfMMZGRlys2bLsvFKOnzed3xQrVo1xMXFYd++fcxz18fHB4MGDWKVefnz58/MvMTY2JjJjt+8eXOMGTNGYXth5lZhKV8hubm5uHLlilRbau3atRgxYoRUv6uJiQlGjRqFNWvWSG0rjW7dukls6927N+zt7bFv3z74+PiI7RPaqkSErVu3imV709bWhoWFhVw/GJ8ou7bCR5VJPiowcPVPcrXz+YDLtV8cfgIhnz9/RqVKlQAAhoaGMDAwEMu0XaZMGXz//l2tfQCAwYMHw9/fH8uWLVO6LdfnTteuXbFo0SLs378fQMEY9uLFC/j5+SmsBMsXZmZmWLp0Kav3CsdaNqxevVru/rNnz8LExARAgY1w4cIFPHz4UOw96sz4z5Xs7GyEh4cjICAAUVFR6Ny5MzZu3Ah3d3eZ/p2VK1fCzMxM5phhZmaGlStXYsuWLeruvlRu3LjBqkrI48ePmfkeESE+Pp7Jlvnp0ydW51J1XYSrnSwkLS0NkydPVrh+wjd82ukfPnyQamdIm7NxvXel+cZzc3OxadMmLFmyBFWrVsXff/8t97hC/5is/5XF3NxcZlUKafCxpj158mSUKlUKL168gK2tLbO9X79+mDJlClatWiWz7adPn1CtWjXm/0uXLonZ9K6urpg6darM9qLzdtF7ECi49s+cOcPKT1wc/vVVq1ahTZs2Mp997dq1w8qVK+WuqQEF94ihoaHEMzY/Px8ZGRmsdUTCuX5WVhYyMjJ+6+olf+CXP+LbP/ynMDMzw40bN1C2bFmcOXMGYWFhAAqcv7ImfHxM9vl66PPB8+fP4e7ujhcvXiA7Oxvt2rWDkZERli9fjuzs7CJzlqhKWloa+vbti0uXLkEgECAxMRFWVlbw8fFBmTJlpE48ilvEJxRmCgQCeHl5iU1qhULowiUuRVm9ejW+ffsm8/r5/v07Vq9eLSHm4urg5NPQBYDLly9LLQXy8+dPXL16VWH706dP4+TJk3BxcWHdL2lkZmYiLCwM/v7+uHnzJuzs7BSKb0uVKsVMtipUqMBMfE1MTPDy5UtO/ZEHVxGXqteOkNKlS3MurQUAJ0+eRJkyZcSEGJmZmXB3d5fZ5tChQ3INiVGjRmHatGklWnzLVQSYk5MDPT09xMbGwsXFhfO1rwrr1q3DmjVrcP78eezduxdDhw6FpqYmevfujUGDBkl1QvMhhHrz5g1q1aolc3+tWrXw+vVruX2X52D58eOHwoWVwuVXcnJyEBMTgzNnzsgM2ElNTZVrkFerVg3Pnj2Te15RQkJCsHXrVqSmpuLGjRuoXr061qxZAysrK6kLJoURLY8kWnZbUXkkrs8ORaSlpcHf31+m+LZOnTpISEjAxo0bYWRkhIyMDPTs2RPjxo1D5cqV5R6b6/V38eJF+Pr64ubNm1KFQM2aNcOWLVtkLmz5+/tj2rRpUssPaWlpYcaMGdi4caNC8W1ERAQuX76My5cvIyYmBra2tmjVqhUOHjwo89xCMYhAIMDOnTvFnIl5eXm4cuUKateuLfe8AwcOxJw5c9CsWTOJuUFcXBzmzZuHGTNmyD0GVywtLREbGyuxUHbmzBkxp5MsFJXSlQVXBzWXMTs1NVXh52LD1KlTMWHCBMTGxjJzy2vXriEwMFChgEyIs7MzYmJiVFqorFSpEpKSkmBhYSG2PSoqSm3iucTERLkBjA4ODkw5ZkV069YNx48fx6JFi2BgYIB58+ahfv36OH78ONq1a8dXl3mHL9G/m5sbAgICxMYLNnTs2BEzZ87E8uXLceTIEejr64uJD+/fv48aNWpIbRsVFQV/f380aNAAtra2GDJkiNLlt3R1dRmnZnJyMnbt2oUJEyYgNzcXS5YsgZeXF9q0aSOzLFzhRQRV6N+/Pw4ePCgxb37//j3c3NwkFvj4Yu3atSAiDBs2DAsXLmQWFoH/E0PIE9DKEv6np6czY96xY8eYAGZ5FC6xnpOTg6ysLGhra0NfX1+q+La4Fwbz8/OxcuVKHDt2DL9+/UL37t3h6ekJgUAAW1tbqcJOUVasWIHly5ejUqVKCA0NZTU3E4WPgKH58+crdU5RhLaeULxdGIFAIGa/Faa4A57s7Oxw9epVifHq4MGDai0/z1fADPB/As7Ctt6vX78QFhbGSsCpClu2bMGMGTMkhLcAYGBgAD8/P2zcuFGq+BbgLpwvvJj+9OlTiQArdYjwZIldC8OmDKy3tzfc3d0lFtG+f/8Ob29vVr9d4fmfhoYGypcvz0qQABTMjYcMGYK0tDSJfYU/Q0kRQAoEAkbEIPQl/fjxQyLwW9aipp+fHywtLXHo0CHo6uri77//hq+vL+u5HldiYmIAFFy3Dx48EAts1NbWhqOjI6ZNmya1LVfh7vz582FoaIicnByZQU3qDPhRFT7vOz5QRfwqipWVFeP3qV27Nvbv3w9nZ2ccP35cItmENFq3bo23b99KPDu+fv2K1q1bS/0O4uLi5Ppe2rdvr9CvzYYmTZpg5MiREtuFz6rWrVsjPDxcTLhYVKi6tsL22afoucd13OPqn+TDzucKl2u/OPwEohS20fhI6KIsubm5CAgIwPnz59GgQQMJsbi8tTWuz51Vq1ahd+/eqFChAn78+IFWrVrh7du3aNq0KZYsWcL1o7Hi6tWr2LZtG1JSUnDgwAFUrVoVISEhsLS0RPPmzcXeKxxrFcHmd+QaZFucjB07FmFhYTAzM8OwYcMQGhrKaoyPjIyU6/vs27cvBg4cyGdXpVI4WRER4e3bt7hz545cv70QNzc3sblD586dAYCZU7D5/VVdF+FqJwvp2bMnLl26JNMfpi74sNPv3r0LT09PPHnyRGIOJ+u+4fPeBQrWeObNm4cfP35gwYIFGDlyJBMsLQsiEgtMz8jIQL169Zi1fbbBbESEgwcP4tKlS1LFx+Hh4VLb8bGmHRERgbNnz4qJaIGChCnPnz+X2++yZcvi7du3MDMzQ35+Pu7cuSOm8/j165fc70A4bxcIBGjTpo3Efj09PWzYsEFuH4Di8a/funULM2fOlLm/S5cu2Llzp9xjcAlSFkU0EZ2+vj5zLLaJ6P7wH6dI8uv+4Q9FxKZNm0hLS4tKly5Njo6OTIm19evXk6urq9Q2rq6urF6tW7eWeV4rKyu5pcfu379PlpaW3D4cS7p160aDBw+m7OxssXTvly5dImtr6yLpAxeGDBlCHTp0oJcvX4r1/8yZM2RnZye1jaOjI1OOUBrnz59Xa3kkLy8v8vLyIoFAQP369WP+9/LyopEjR9LSpUvp48ePMtvb29vT1atXZe6/du2a1M8uLEsgTGsv6yWrzAIf1z7R/5XSEwgEdOnSJbGyevfu3aOlS5dS9erV5R6DiMjCwoIeP36s8H2yiIqKIm9vbzIwMCANDQ2aOnUqPXnyhFXbdu3a0Z49e4iIaPjw4eTs7Ey7d++mDh06kLOzs8p9UkTz5s0pNDRU5v59+/ZRixYtZO5X9doRwkdpLSKipKQkqly5Mq1Zs4aICspYN23alFq0aCGznKahoSE9f/5c5jGfP39ORkZGcs9b3DRq1IiOHTsmc//x48epUaNGco9haWlZpOUwFPHjxw/av38/OTo6ynx2lC5dWuFvp6gUrKzSJkKULREj5OfPn7Rq1SpOZc02btxIXl5eUveZmprKLfcXGRlJpqamrM6zefNmKleuHC1evJj09PSY8W7Xrl0y5yyFUbU8EtdnhyJiY2NV+v3YwPX669KlC61evVrm/nXr1lH37t1l7i9fvjylpqbK3J+SksKqBLhAIKAKFSrQ8uXL6cuXLwrfT1QwTlpYWJBAICAzMzPmfwsLC6pVqxa1b9+ebt68KfcYv379IldXV9LS0iJ3d3emvJO7uztpaWlRq1at6NevX6z6oyo7duygqlWrUlhYGBkYGFBoaChTJkzemChEtIyuMqV0a9WqRbdv35a5/86dO1SrVi2Z+7mO2XwRHh5OLi4uVLZsWSpbtiy5uLgoVc553759ZGVlRRs2bKDr169LlESWB9dymqpgaGgoVjayMHfu3CFDQ0O1nLukwPe1l52dTfHx8ZSTk8Pq/R8/fqQWLVqQQCAgIyMjCg8PF9vfpk0bmj17ttxjZGRkkL+/P7m4uFCpUqVIQ0OD1q5dS9++fWPdb1Hy8vLo1KlT1KtXL9LW1mY99qpKw4YNadiwYWLb3rx5Q7Vr16ZevXqp9dxEBXN2tr8XW5KTk6lp06Y0ZswYlY+RkJBAbm5udObMGan7uT53ubJo0SLS0NCg9u3bU7du3UhXV5e8vb1ZtxcIBKSvr09du3ZlSqhKe8mCD1uBiOjLly+0Y8cOmjlzJqWlpRER0d27d+nVq1dy212+fJkuXbpEAoGAwsPDxWy769ev0+vXr+W256v/qnLkyBEyMTGhZcuWkb6+Pq1cuZKGDx9O2traFBERobbz8omq5ee5UrlyZanlq4UkJiZS5cqVZe4fMWIETZ8+Xeb+GTNm0OjRo6XuY+OnUtdnZ1t+XFEJcuHn+PDhg8T22NhYhaV0+cLa2prGjh3LqpQtV/8gXwjPIXzJ+l8WpqamdPfuXeb/L1++kEAgUFi+lW+8vLyUPqfQhyoQCKhZs2ZiftX27dvTyJEjWZWz7dmzJ+Xn50tsf/fuHdnb2yvVp6KAz/uOD7g+d1evXk3r1q0jIqJz586Rrq4u6ejoMHNXRch6djx9+lSmj1NHR0fhM1tXV1fhueWRlZVFEydOVGrelZubSzExMQrtbD5QdW2FrzGHy7gnC2X9k1ztfK5wufaLw08gRCAQUMeOHZl5uZaWFrVv3575v2PHjmof+4jkr7MpWlvj+twRcvXqVdq0aRMtX75c7lop3xw8eJD09PRo+PDhpKOjw/i3N2zYQB4eHkXWj98NgUBA1atXp+7duytla+rq6sodU589e0Z6enrq7DoRkdhz2svLi4YNG0Z+fn509uxZhW35mjeoui7C1U4WsnjxYipXrhx5enrSv//+S+vWrRN7qQs+7HQHBwfq0aMH3bx5k1JTU4t0znb69GlydHQkY2NjWrRokcz1Y2kUXhOQ9VLEhAkTSEdHh9zd3cnT01PiepYFH2vahoaGjE0gqoGJjo6msmXLym07cOBA6ty5M7148YJWrVpFhoaGYt/fwYMHycHBQWb7Z8+eUWpqKgkEAoqOjhb7zd+8eUO5ublyz09ElJOTQwsXLqSXL18qfC+f6OjoUEpKisz9KSkpCufL7dq1ox07dsjc7+/vT+3bt1fYF1lr2+/fvyctLS2F7f/w3+aP+PYP/zmio6MpPDycvn//zmw7ceIERUVFqe2cfDz0+aJs2bIUHx9PROIDd2pqapFMurlSsWJFRogm2v/k5GQyMDCQ2qakiPgWLFig1ERRiL6+vsL+6+vrS2wvKQ5OUWe6NCeXvr4++fv7KzxOSEgI9e7dmzIzM1mf+/3797R8+XKysbGhSpUq0eTJkyk6Opq0tLTo0aNHrI8THR1NFy9eZI7ZoUMHMjIyovr161NMTAzr4ygLVxGXqteOOoiLi6OyZcvSunXrqEmTJtSqVSu594OJiQnduHFD5v4bN26QiYmJGnrKH3yIUHfu3EkdO3ZkFtKLk7dv39KaNWuoQYMGJBAIqHHjxlLfx4cQSiAQUHBwMB09elTqKygoSKZz9OfPnzRz5kxq0KABNW3alA4fPkxERAEBAVS5cmWqVq0aLVu2jN2HlkJycrLMMaNjx440fPhwmW19fHxYOxZtbW2ZvouOdw8ePFAoIvr69Sulp6eTQCCgpKQk+vr1K/P6/PkzBQUFyV1QV/ezQ5b49uPHjxJj0sOHD8nLy4v69OnDBEHIg+v1Z25uLjfQ48mTJ2RmZiZzv76+vlyBYlxcHKvvbs2aNdSjRw8yNTWlKlWq0IABA2jbtm309OlThW1dXV05LYD9+vWLli9fTo6OjqSvr096enrk6OhIy5cvp+zsbJWPqwy7d+8ma2trZq5QtWpV2rlzp1rPydVBzZfwevLkyVJfU6ZModmzZ1NAQIBaxwRZC5JsFibz8/MZobSwra6uLs2ZM0dt/W3cuLHcZ/rSpUtljleFsbS0pE+fPkls//LlS5EFSqoCX9deVlYWDRs2jDQ1NUlTU5MZd3x9femff/5R2D49PV2qIzYtLU2pZ0d8fDxNnz6dKlWqRLq6utSlSxfWbaXx4cMHWrVqFav3BgcHU7Nmzahy5crM82D16tUKF7Y/fPhAtWvXpsmTJxMR0evXr6lWrVrUp08fJuBXndy9e5fu37/P/H/kyBHq1q0bzZo1i9NzOzIykmrUqMGpb9HR0WRjYyN1X3EvDFpbW9PWrVuZ/8+dO0fa2tqsfzNpizDSXrLgw1aIi4uj8uXLk7W1NWlpaTH37V9//UVDhgxh9TmePXsmVcilCD76z5UrV65Q27ZtqXz58qSnp0cuLi6sFnT54OvXr1Kvlby8PNaivOIScOrq6soNRH78+LFcHyUX4Twb/9SDBw/Yf5gixsnJierVq0caGhpUt25dqlevHvNycHAgIyMj6tOnj9xjXL9+nY4fPy62LSgoiCwsLKh8+fI0YsQI+vnzp8K+GBkZUVJSEqt+lxT/INfgbmkLmYaGhnJ97kXB169f6fDhw6wC/FUR7ooiLeDn7du3RRbw87ujivhVHs+ePaNDhw4pDFIUiqQ0NDTEhIA9evSgrl27koWFBXXo0EFqWysrK8Y3JI1Dhw4pZauULl2aypQpw7xKly5NmpqaZGRkREePHpXZbuLEiYxNnpubS82aNSOBQEAGBgZ06dIl1ufngrJrK3yNOaqOe+r2TxYnbK99ouLxEwhhM1eXN18vibD97qXNNwIDA6l69epKzTe44uTkREFBQUQk7t++d++eTPF5bm4uxcXFUVZWlsS+rKwsiouLKxI7uzhR1dasWLEiXbhwQeZxz58/zykpye8A13URIarayUJEk2IUfqnTx8iHnW5oaCg38EcWXO7dW7dukaurK+nq6tKkSZPkJiyTd34+KFOmDJ08eVLpdnysaXt4eDDjo9DOycvLoz59+iic66ekpFCNGjVIIBCQlpYWbd68WWx/t27daNKkSew+DAcMDAzk+qnVQbVq1ej06dMy9586dYqqVasm9xhcg5T5SkT3h/82f8S3f/hPIRTPKQvXyT4fD32+KF26NCM6FDV2rl69ShUqVCiSPnBBlaifkiLiy8rKEhOOPnv2jNasWaNwcYjPTIrKwoehy0e0FFGBoW5kZESGhoZUp04dsUWOevXqSW2jq6tLgwcPpjNnzoj1U1nxbXHBVcSljmsnMzOTnjx5olQmPCHXr18nAwMDatOmjdRrShRXV1fy8/OTuX/GjBmss38WF3yIUJ2cnMjQ0JB0dHSoVq1arK57Pvn69SsFBARQ27ZtSUtLi2rVqkULFy6Uu8jHhxBKUUYeeSKwGTNmkImJCfXq1YsqV65MWlpaNGLECKpbty6FhoZyNsKXL18u00i6ePEiaWpq0tSpU8UyEL17946mTJlCmpqach1googKUkTHu4SEBIUBO4UzCBV+aWpq0uLFi2W2V/e4I0t8279/f5oyZQrz//v376lMmTJkb29PXbt2pVKlSlFwcLDcY3O9/rhmlXF0dKQtW7bI3L9p0yZydHSUuV8a9+/fpw0bNlCPHj2oVKlSVLVqVaXaK5uRJjc3lyIjI4skg40iMjMz5WbBlkZCQgKtXLmSxo0bR76+vrR69Wrm/pEHVwc1X8JrV1dXMjY2JgMDA6pfvz7Vr1+fDA0NycTEhBo3bswslkqbx7x48UIssvzWrVs0ceJE2rZtm8LzCuFDkJGdnU2PHj2iW7duMQGPisZ9Vdm2bRsZGBhILCoRER07dowMDAxYf35Zkenv3r0jbW1tzn1VF3xdexMmTKAGDRrQ1atXycDAgLlvjhw5otYqIbLIzc2lw4cPcxbfsoVrxvkXL16Qubk5TZ48mWrWrEn9+vXjzfGviIYNG9LBgweJqCBISEdHhwYMGEDW1tY0ceJElY+bmpoqM8CVLTExMTLFLMW9MKitrU0vXrwQ26ajo1NkGTr4sBXc3NyYTGyi88Vr166xduqfPn1arOLBxo0bydHRkQYMGCB3LvC/nHk8PDycatasKTUwOCMjg2rVqiU32xAfAk4u1K5dm0JCQmTuDw4OlimaJ1KPcP7bt2+0bds2cnZ2VmsGujt37pCrq6tU8WN6ejq5urrKrTyzYMECWrBgAQkEApo2bRrz/4IFC2jp0qW0d+9ehUEP7u7uYvbK/fv3SUtLi4YPH06rVq2iSpUq0fz58xV+Fm9vb7UHp5U0pC1kGhgY0MmTJ5X2T3GhT58+tGHDBiIqmOPWrFmTSpUqRVpaWsx4rC6KO+BHFbjed3zARfzKB1yq4vn6+lKdOnXox48fEvuysrKoTp06NH78eNZ92bVrl0qVYqpUqULR0dFERHT48GGqUqUKPX36lObMmUPNmjVjfX4uqLq2UhhlxxxVxz2+/JN82PklgaL0E5RUEhMT6cyZM8zn5iLsU4S0+UapUqWUnm9wRU9PjxFhFU6mpKOjI7XNrl27qEGDBlLvkZycHGrQoIHcuWxhVA2y/R3p06eP3IptXbt2pd69e6u9H/n5+RQdHU0HDhyggwcP0r1795S+3lW1U7mui3A9f3HDh53erVs3lea0XO5dYaKuSZMmSWQJZpsxuFKlSuTn58eqmoQ8LCwsWFfNFYWPNe0HDx5QhQoVyN3dnbS1tal3795ka2tLFStWZBV4mZOTQ7GxsVIzNMfGxkpNOlGYwMBAOnHiBPP/9OnTycTEhJo2bcpqbaBr166sMgzziZeXFzVv3lzqvvz8fHJxcVEYbMM1SJmvRHR/+G/zR3z7h/8U2traZGVlRX///bfEIos8uE72+Xjo80Xfvn1pxIgRRPR/UTPfv3+nNm3a/BZRnqpE/ZQUEV+7du0YQc6XL1+oQoUKVK1aNdLV1ZWIQBKFSyZFrg5Ovg1dLoguakh7ScPGxoYsLCxo9uzZYpMmZcW3rVu3llr2++vXrwpLA3GBq4iLzyycHz58oE6dOsk0WgsjXFAs/CpbtizVrl1boYD04MGDpKWlRRs2bBC7/nJzc2n9+vVUqlQpOnDgAKu+Fxd8iFBVue75RFdXlypXrkyTJk1inOyK4FMIpQqWlpZMto4HDx6QQCAgb29vpR08ha9hJycnqlSpEmlqasrt/9atW5nyX0KRnIaGBuno6Mh91hfG1taWcQKKOifXr1+vUHjNtTwS12eHvHJYPXr0oNatW0t9blhYWIhlOlq5ciXVqFGDKaW9cuVKhfcM1+uPa1aZ5cuXk6mpqdRF39jYWDI1NaXly5fL/QxC8vPz6e7du7Rq1Srq3Lkzk5FGkQiOj4w0iqo2qBN/f3+Vz7106VLS1NQkDQ0NqlSpElWsWJE0NDSoVKlStHLlSrltuTqo+RJer1mzhnr27Ck2d0tPT6fevXvT2rVrKTMzk7p16ya1zFHz5s0Zgfrbt2/JyMiImjZtSuXKlaOFCxcqPLc6ULacpioMGjSIBAIB2draUvfu3al79+5Uu3Zt0tDQoP79+ytsL8yqLi3renh4OI0bN06pEqxFDV/Xnrm5OROwKDruJCYmFkmVEK4oWmBRBJeM80KePn1KFSpUoEGDBql1IbUwxsbGjBN+2bJlzPMhKiqKU5DvsWPHyM7OjtV7C1cpOHLkCG3ZsoXs7e3J3d1dapviXhjU0NCQyH5XlBkU+bAVRH970ev22bNnMhezC1OnTh0mq8v9+/dJW1ubZs2aRU2aNJHrI+Iz8/jvBtdyhHwIOLkwe/ZsMjc3FwsWFPL27VsyNzen2bNny2zPp3A+MjKShg4dSgYGBlSzZk3y8/OTm12QKwMGDKBFixbJ3L9kyRIaNGiQwuMEBgZKFcKxoVKlSmK29ezZs8nFxYX5f//+/WRra6vwOJmZmdSxY0fWpWxLggBSlKSkJPrrr7+of//+TPDTqVOn6OHDhzLbyCshz7ZSAx+IVmXbs2cPWVtbU2ZmJm3evJlVwFJ0dDRNnz6d+vXrp7B8tDSKM+BHFfi677jARfxamNu3b9Py5ctp6tSpEtVKFKFKVbx3795RlSpVyMzMjJYvX05HjhyhI0eO0LJly8jMzIyqVKki9XnON6IBSiNGjGACvFJSUorMVlB1bUWIqmOOquMeX/7JkmLnnz9/nmbNmkU+Pj7k7e0t9lKWovATlCQ+ffpEbdq0YcYp4Zzd29tbLAmBLFT57vmab3DF0tKSzp07R0Ti9kpQUJDM8zdv3pxCQ0NlHnPfvn3UokULVufnGmT7u3Hv3j3S0dGhXr160a1btyg9PZ3S09Pp5s2b1LNnT9LR0aG7d++qtQ8XL14kS0tLsTmbhoYG1ahRQ26yj8IUtlN1dHRY2alc10VknZ+tnVzc8GGnf/z4kTp27EgLFiyggwcPSvh7ZMHl3q1evbrcbMFsMgYvWrSIatSoQRoaGtS8eXPatWuXUpV0hQQGBlL//v2VDhDha007PT2dFi9eTH369CEPDw/666+/6M2bNwrbyaropiy1atVi5j3Xr18nPT092rZtG3Xp0oWVvbJlyxaqVKkSTZ06lfbu3cv6+uFCUlISmZiYkLOzM+3bt49iY2MpNjaWwsLCqFGjRmRiYqIwmzPXIGW+EtH94b/NH/HtH/5TfPz4kVavXk2Ojo6kpaVF7du3p3379il0anOd7PPx0OeLly9fkp2dHdna2pKWlhY1adKETE1NycbGRunMYsWBKlE/JUXEZ2pqyjiRd+zYQQ4ODpSXl0f79++n2rVry2zHJZMiVwcnn4bu0qVLpUb1+Pv7q7XEUlRUFHl7e5OhoSHVr1+fVq9eTVpaWnLLihdGVia09+/fk5aWFp/dFYOriIvPLJwDBw4kFxcXio6OJgMDA4qIiKCQkBCysbERi4ITokg0ykZAOnv2bBIIBGRsbExOTk7k5ORExsbGpKGhIVdQX1IobhEqV/Lz82n79u0qGahchVBcKFWqFL169Yr5X1dXV6wUM1sKX6eLFi2iLVu2sIp6ffXqFa1evZrGjh1LY8aMoTVr1iidRW3Hjh1UtWpVCgsLIwMDAwoNDWXKtMl7Louiankkrs8OVcu6Fc4m4uHhwWRzIyoQNcnKci8Kl+uPa1aZX79+kaurK2lpaZG7uztNmjSJJk2aRO7u7qSlpUWtWrWiX79+KfwMnTt3pjJlypCmpibVr1+fpkyZQkePHpUaCFIYPjLSNGjQgM6fP8/qvXxjbW1NGhoaZGZmRoMHD6YdO3awmidfvHiRNDQ0aP78+WIZCNLS0mju3Lmkqakp18nL1UHNl/C6SpUqUgOEHj58SFWqVCGighLz0sSApUuXpvj4eCIiWrduHfN7nz17Vq6DsrATTN5LGiWhnOa+ffuoW7dujJ3TrVs32rdvH6u2hUUboi9tbW2qVauW1LG8pMDXtSe6ECW6KBYbG0vGxsb8dloNCIUIwteBAwdo9uzZVLVqVVaZAZXNOF+4ZK/wpaOjQ8bGxmLb1I2RkRGT2aNt27a0du1aIiooJygvK4No+UXR14sXL+jw4cNkZWXFekFfmgiqYsWKNGDAAJmLBMW9MCgQCCSy3wn9RMoKoVSBD1uhfPnydO/ePSISv24jIiJYC69FSwLOnz+fCWq+e/euXEFEcdg6su47aS91wrUcoZDAwMAiKfdbmG/fvpG9vT0ZGRnRmDFjaO3atbR27VoaPXo0GRkZkZ2dHX379k1me67C+bdv39I///xD1tbWVKFCBfL19S2y6kRWVlZyM6Pev39f6TKwP378oMDAQNq0aROrLEs6OjpiCSFcXFzEsm+lpqayyhq9c+dO0tLSIkNDQ4nFammfoSQIIIVcvnyZ9PT0qG3btqStrc08u/755x+55VTZlJAvihKnurq6zG84ZMgQxj/1/PlzhRnjQ0NDqVSpUtS5c2fS1tamzp07U61atcjExEQpIUdxBfyogjruO1VRRfwqypIlS0ggEFDt2rWpVatW5OrqyrzYJGdQNXPrs2fPyMPDQ0LI5OHhoXTQ0O3bt2ny5MnUqVMn6tGjB82cOZOVj9zc3JzOnj1Lubm5ZGZmxviCHz58qLB8NV+osrbCx5ij6rjHl39SVTufTxYsWEAaGhrk7OxM3bp1Y3xtwpc0SoKfoE6dOrRo0SKlEjGpgyFDhlCHDh3o5cuXYnP2M2fOKAx2VOW7J+JvvsGVpUuXkp2dHd28eZOMjIzo6tWrtHv3bipfvjytX79eapvy5cvLHc9TUlKoXLlyrM7PR5Dt78bx48epfPnyEgHJ5cuXV5vwTUhiYiLp6+tT69at6ciRIxQfH09PnjyhQ4cOUatWrcSqLClCVTtViKrrInydv7BQnmvQAlv4sNOPHTtGJiYmSlWjJOL33uXCpUuXmGAbY2NjGj58ON28eZN1+6ysLOrQoYNSVXiFcF3Tfv78uczr9vnz53LbytIxKIuenh5zrhkzZtCQIUOIqGDOx+b3U6WaKR9ER0eTvb29RAZae3t7VsFWXIOU//AHNvwR3/7hP8vdu3fJ19eXTE1NydTUlMaPHy8zwp+PCQPXhz6f5OTkUEhICE2fPp3GjBlDO3bs+K1KvKgS9VMSRHyiE5Y+ffowosMXL14oLMmnaiZFrg5OPifL1atXp2vXrklsv3nzJllYWLA6xpcvX2jHjh00c+ZMSktLI6KCe1nUkSWL79+/0/bt26lp06YkEAjI1dWVtm/fLpHtSBRhyTpppe3u3btHS5cuZV3OUxX4EHHxlYWzUqVKdOvWLSIqWOB/+vQpERUIdkSjpvnm1q1bNGHCBOrYsSN5eHjQxIkTmX78DvAlQo2Ojqbg4GAKDg6WWzaGT/Ly8qhUqVIql2nhIoQSIkv8dezYMYqIiJC6yFA4i1lRZjDjm927d5O1tTVjnLIVEAnhUh6Jr2eHMlSoUEFsLmZqaipW4ighIYF1+WtVrz8+ssr8+vWLli9fTo6OjqSvr096enrk6OhIy5cvZ53FbNq0aXT8+HFKT09n9X5R+MhIc/r0aXJycqLjx4/TmzdvJIRZ6ubVq1e0e/duGjlyJNnY2JCGhgZVrVpVriCgb9++NHLkSJn7R4wYofC5y8VBzZfwWlaG4kuXLjELI8nJyVJ/S1HHcJcuXZiFLEUCPFmZw0T/l5c9lK9ymsWNhYUF64xXJQm+rr0WLVowi1+iY6evr69ay/Cqmz179lDXrl0Vvk/ZjPOi5XoVvdRN69ataejQoRQcHEylSpViRIGXL1+Wa6vIyxasqalJo0aNUmv2TaLiXRhUNViIT7jaCj4+PtS9e3f69esXc98+f/6c6tWrx4z/iihTpgwjQHFxcWEW4lJTUxX6KYo64K6k3HdcyxEKKc4y0unp6TRmzBgqW7YsM96XKVOGxowZo9BO4CKc79y5MxkbG9OAAQPoxIkTzByhqMS3iqo7pKSkyP3tJk+eTL6+vsz/2dnZ5OjoSKVKlSITExMyMDCg69evy+2Dubk5ExCWnZ1Nenp6YkFv9+/fZyUgr1ixIi1ZsoTy8vIUvpeoZAkgmzRpQqtWrSIi8TH31q1bVLVqVZnt5syZw1RFkcbz58+pbdu2/HZWCjVr1qR9+/ZRRkYGlS9fnglMFQY9yaNu3bq0ceNGIvq/z56fn08jRoygefPmSW1TkgJ+VIHrfccnqopfhVSoUIF27dql8vm5Zm79/Pkz3b59m27duqVSyevp06eTQCAgIyMjcnR0JEdHRzI0NCRNTU3Gdvzx4wddvHhRou38+fPJxMSEateuTebm5kzwiL+/PzVp0kTpvqiCsmsrfI05qo57fPknVbXz+aRSpUpM9l22lAQ/gUAgIFNTU9LU1KQOHTrQwYMH5Y4j6kI0Y7rouJecnKzQz6nKd0/E33yDK/n5+UwyCeGcU1dXl6luKg19fX25c5a4uDjS19dndX5lg2z/K2RlZVF4eDitWLGCli9fTocPH1YpwYqyjBs3jtq0aSN1X35+PrVp00ZsLi0PLnYqEbd1ET7OX1go36lTJ6pevTqZmJioNciXiLudXr16dRo3bpzSmfX5vHf54Pv377Rjxw5ycXEhgUBAdnZ2jA0ijz59+lC5cuVo9OjRNH/+fKWrkXJZ09bQ0JAqoP306ZNC4Spf4lvRIG8nJydmDEpKSmK9NlecxMTE0P79+2nfvn0UExPDuh3XIGUhgYGBYgnLpk+fTiYmJtS0aVOxxEN/+N/kj/j2D/9pXr9+TfPnzycdHR0yMDAgTU1Nat68uUSJKz4nDKo+9PlC1bJo/wWKW8RXt25dWrduHb148YKMjY0Zh/ydO3dYRcqpkkmRq4OTz2tfVl+Sk5NZlcSMi4uj8uXLk7W1NWlpaTGG8l9//cVEXrHl8ePHNGXKFKpQoYLczLWFxfKFX/r6+lKz+fIJHyIuPrJwGhkZMY4+c3NzioqKIqKCa0iRsXn79m2pkYU3b94UK3/0X4WLCPXly5fUvHlzZjG0TJkyJBAIyMXFRenfUBXs7OyYEtTFgaxMhKIlJVu2bCnmMCmcxUxaBjM2Do5Xr17RunXraNy4cTR58mTaunUr68UNvg2szMxMlQxnruWRVH12KIrs9vb2pmHDhkm069q1Kw0bNozy8vLowIEDpK2tLfadnzhxQm6meL7gM6sMHyg7d+MjI40s4WVRlXIVkpmZSWfOnCFPT0/S0tIiTU1Nme+1sLAQc6oW5sqVK6yCfbg4qPkYswcOHEiWlpYUHh5OL1++pJcvX1J4eDhZWVnR4MGDiaggY1aDBg0k2jo7O5Ofnx9duXKFdHV1mcWdGzduyBUyiHLu3DmqX78+nTlzhhFbnzlzhho2bEgRERFS2/BVTlNZZGXtlPb6r8PHtXf16lUyNDSk0aNHk66uLk2cOJHatWtHBgYGRRb4ow7YLGgS8ZNxvriIi4ujOnXqkLGxsdhCgK+vLw0YMEBmO9Hyi6Kve/fu0ffv31Xqy8ePH5W+57Kysujw4cNFvjBYUti/f7/KtkJ6ejq1bduWSpcuTZqammRmZkalSpWiFi1asM7s16VLF+rQoQMtWrRILEPb2bNnqWbNmgrb8xFw97vBtRyhkJJQRjo/P58+fPhA79+/V2rcVlU4r6mpSZMnT5YI8Cwq8W21atXo9OnTMvefOnVKbtZoe3t7sc8XEBBAZcqUYTJreXl5UceOHeX2YfTo0dS0aVO6cuUKTZkyhUxNTcXG6t27d1PDhg0VfpYyZcrIrAAmjZIkgDQwMGD6IiqESU1NlesfNDMzIycnJ3rw4IHEvq1bt5KRkRG5u7urp9MibNq0ibS0tKh06dLk6OjICKDXr1+vsIS1vr4+41srW7YskwXz8ePHVKlSJaltSkrggapwve/4hKv4tVKlSioHqBOpXhWPDwIDA0lXV5c2bNggFpj369cvWrduHenp6dG+ffvI1dWV/v77b6nHOHDgAK1evVrMLxQYGMgEsKkbZddW+BxzVBn3+PJP8mHnc6Vs2bJKjTlExecnEEUgENDr16/p8OHD1KVLF9LS0qLy5cvT1KlTlaqKyBVDQ0PmOhQd96KjoxVW+FLluyfib77BF9nZ2fTo0SO6deuWQlvT0dGReVZLY9OmTeTo6MjqvMoG2f4XKUpNgL29PR07dkzm/mPHjpG9vT2rY3G1U7mui3A9vzTy8vJo5MiRrKpTcYWLnW5oaKjSc4frvZuVlUVXr16VOkb/+PGDgoKClO6TkBMnTlDZsmVZrWvo6+vLXWNQJwKBQGrCsGfPninUYggEAgoODlapqp0oAwcOpPr165OPjw/p6+vTp0+fiKggUZK8+zcrK0ss4/LMmTNp8uTJzGv69OklXqPEJUhZSK1atZjgzOvXr5Oenh5t27aNunTponbh/R9KPn/Et3/4z/Hr1y86cOAAeXh4kJaWFjVp0oR27NhBGRkZlJqaSoMGDSJbW1uxNnxO9kXJzc2lmJgYlSKVVcXIyIiGDh1KERERrLMjlCRq1KhB8+fPV8nR9fz5c5mfWVG6fj44cOAAlSpVijQ0NKhdu3bM9qVLl7JyDkdGRkqNzM3JyZFZRpmrg5PPa9/a2lrqAlVwcDCr7Bpubm5M+XFRQ/natWsqZ5/NycmhQ4cOydwvLFknEAgoOjparJTdmzdvfptMbnzQsGFDOnPmDBEVGJ5DhgyhV69e0YwZM8jKykpu20aNGtGBAwckth86dIicnZ3ltq1evTotXLiw2MtDFRcdOnSgxo0bM+XFiIji4+OpadOmRZKF7tixY9S8eXOpi1uKuHv3rlg5tSNHjlC3bt1o1qxZrIVA58+fp8aNG9P58+fp27dv9O3bNzp//jw1bdqUTp48SVFRUWRvby8m5OQji9mmTZtIR0eHBAIBmZiYMGV29PX1ae/evURUsFAtjAAtTEkxsLiWR1Jl3CEqMPQtLCyoR48eElHe8kqjxcXFUbly5UhbW5s0NDQksiAMHjyYRo0apbDfRNyihIVwySpjaWnJOCZE+fLlC6sxLy8vjxYtWkRVqlQhTU1NZsybM2eOwuzHfGSkkSXIEr7UydmzZ2nWrFnUtGlT0tXVpXr16tGkSZPoyJEjcn8HPT09ueLwly9f/hZZLb5//07Dhw9n7gMNDQ3S1tamESNGMEKqmJgYqUF8ly5dotKlS5OGhoZYCbNZs2axfvbY29tLdTBeuXJF5oIwX+U0lUVe1s7CL7acP3+eZs2aRT4+PkVWEq4kkZSURMOHD6dGjRqRra0tDRo0qEh+S3WRlZVFEydOpFq1arF6v6oZ50+ePMnMk0U5e/YsnTp1Sul+88WPHz9YZT2W9x422aC/fPlCY8eOJVNTU+aeq1ixIs2cOZOViDYoKIgZq0TJzs7mtLjyO5CTk0NBQUH09u1bTseJioqiTZs20fLly+ncuXNKtX3+/Dl16tSJHBwcxK73SZMm0fjx4zn1i29KStAFX+UIi7uMtKwMkNKeZ7LaKyucv3HjBg0fPpyMjIzI2dmZNmzYQB8/fiwy8a2Xlxc1b95c6r78/HxycXGRaysaGRkx2cWJiPr3708jRoxg/o+JiaHKlSvL7cPHjx+pRYsWTPbJ8PBwsf1t2rRhdf1MmjSJlixZovB9QkqSALJq1apMZSxR/54w4EwWX79+pSFDhpCOjg4tXbqU8vLy6Pnz5+Tm5kbGxsZFkjVaSHR0NIWHh4sJiE6cOMEEqsuiatWqzNyqbt26jH/h+vXrZGxsrL4OFyNc7zs+4Sp+Xb58OevM8tJQNnNrYZGmvJciGjVqRKtXr5a5f9WqVaShoUH169cv0rUqZVB2bYXvMUfZQF2+qizwYedzZcaMGbRo0SKl2hSXn0CUwlkA37x5Q0uXLqWaNWuShoYGNW3aVO2JVYiIPDw8GB+nMANyXl4e9enTh/HVykKV756Iv/kGXyQmJtKZM2eYCqzyRNjLly8nU1NTqUmBhFnm2YoXf+cgWy7k5uaq7FfmgmgCH2mkpKQwVb0UwdVO5bouoi47OT4+XmbAVUlh6NChtGPHDqXbcbl3nz59StWrVxdLuCNa8fjdu3dKJwTJzMykXbt2UcuWLUlDQ4Nq1qxJ//zzj8J2NjY2cpOSseHKlSs0aNAgatq0KTMWBgcHyxT1CgWqGhoaNGrUKDHR6oQJE6hx48aMv0AW0hKISUtqpIgvX77QuHHjqGvXrmL247x582jx4sUy223ZsoU6d+7M/G9oaEiNGzcmV1dXcnV1pUqVKrHKPMyF3Nxc2rlzJw0YMIDc3NyodevWYi+2qBqkTCQ+358xYwaTPO7hw4esK0n/4b/LH/HtH/5T+Pr6kqmpKZUtW5YmTpwoVUz09u1bEggEYtv4muxPnDiRmaTl5uYyqe5llZdVB+Hh4dS7d2/S09OjSpUq0cSJE3+rzJOrV6+mhg0bkkAgoIYNG9LatWtZL1TxIcThytu3b+nevXtiIuBbt27JLVkoRJX+c3Vw8mnoCo8VEBDACFj9/f3J1NSUli5dqrC9sbExE20n6px/9uwZq8y5oly+fJlOnjxZYp2JheEq4uIjC2dISAhTWu3OnTtUrlw50tDQIF1dXQoLC5Pb1sDAgPm9RGFjbK9Zs4YcHR1JU1OT2rZtS6GhoVIX50s6eXl59PTpU7p69SpFRkaKveShq6srVeB5584dVuVtuFK6dGlGAKarqytR5lAeDRs2pIMHDxLR/2W4HjBgAFlbW7NerLC3t2cW5USJiooiOzs7IirI0mhmZqbcB5PDiRMnSFNTk6ZOnSpm4L9584YmT55MpUqVoqtXr9KAAQNkZqRS1cBycnKievXqsXqxgWt5JFXHzbFjx1KZMmXIycmJ1q1bR2lpaaz6S1TgHD5y5IjUbNknTpxgnXlWVpmd169fsxJgpqenS+13WloaK0GHrPO/e/eOtLW1FbZfuHAhWVlZ0e7du0lPT495hoaFhbES0BZ3RhouCAQCqlChAi1fvpy+fPmiVDt5GaLZOunGjx9P69atk9i+YcMGVs8urmO2kO/fv1NcXBzFxcUplYEyNzdXYn6TmprKOnu2rq6uVBspLi5O5r3DVzlNZREVhAcGBlKlSpVo5syZTCT/zJkzqXLlyqyzgC1YsIA0NDTI2dmZunXrpjBgoKQSHR1NwcHBFBwczCpj7YULF4ql9CbfFC7JLMwEamRkxCqzgyjKZpyvW7cuk1FFlNOnT5ODg4NS5y4OevbsKdWZ/O7dO4VZadLS0qhWrVpkYGBAI0eOpDVr1tCaNWtoxIgRZGBgQA0aNKAfP37QrVu3pD5biUqGnV6c6OnpKV0ZoSRlFOEj4I4tbIIuiiJLP1/lCIu7jDTXDJBchPMZGRnk7+9PLi4ujJBq7dq1rL43LiQlJZGJiQk5OzvTvn37KDY2lmJjYyksLIwaNWpEJiYmYuLawpiYmIglA7CwsBAT7aSmprL+7dLT06UGdKelpbG6d8aPH08mJibUsmVL8vX1FXsGTJ48WeL9JUkAOXXqVGrevDmT8TkxMZGioqLIysqKVSnXI0eOUMWKFcnR0ZGMjY2pbdu2v00JzwEDBjCLzosWLaLy5cvT8OHDqXr16qxEdCU14EceXO87PlFW/FqYvLw8cnd3JysrK+rcubPSAlhlM7eyFW+yuXf19fWl+maFJCcnk0AgkGmDL1y4UO6rqFBlbaW4xhw+4Wrnc2XChAlUunRp1mMOUfH5CQr3QdZ3dOnSJRo8eHCRlNB+8OABVahQgdzd3UlbW5t69+5Ntra2VLFiRYXZJVX57kXhOt/gyqdPn6hNmzbMHF34HPL29qYpU6ZIbfPr1y9ydXUlLS0tcnd3p0mTJtGkSZPI3d2dtLS0qFWrVqyCTIWoGmT7O8PVr6wqfPlm+YDruoi6OHnyZJEI8Lj4WRYvXkzlypUjT09P+vfff2ndunViL1lwuXe7d+9OnTp1oo8fP1JiYiJ16tSJLC0tmXmbMtfOtWvXyMfHh4yNjUlfX5+GDh2qcB1WlBMnTlCHDh3kCsnlcfDgQdLT06Phw4eTjo4Oc/9t2LCBPDw8pLYRClQFAgE1a9aM+d/V1ZXat29PI0eOVJiUTtH9p26aN28ulvlaVMdBVKAxUOfzh4ho3LhxZGBgQH379qWJEycy16DwVRSUL1+eWdN3cnJiqh0lJSUVyZzjDyUbARER/vCH/whubm4YPnw4evbsCR0dHanvyc3NxbVr19CqVStmW05ODtq3b4+oqCi0bdsWtWvXBgDEx8fj/PnzcHFxwblz51CqVCm5569WrRqOHDmChg0b4siRIxg7diwuX76MkJAQXLx4EdeuXePvwyrg+/fvOHjwIEJDQ3Hx4kVYWVlh8ODBmDdvXpH1gQsJCQnYs2cPQkNDkZqaitatW2Pw4MEYOnSozDYaGhp49+4dKlSoILb9+fPnsLOzQ2Zmprq7zQkNDQ28f/8e5cuXF9uekJCAhg0b4tu3bxJtkpOT0aBBA9jY2GDq1KmwsbEBUHDtrlq1CgkJCbhz5w6sra2lnpOvax8AiAgzZ87E+vXr8evXLwCArq4u/Pz8WF13FSpUwNmzZ1GvXj0YGRkhLi4OVlZWOHfuHIYNG4aXL19KtFm+fDkyMjLw999/M33w8PBAREQEc8wLFy7A3t5e4fkTExNx6dIlfPjwAfn5+WL71H3fyLp2379/D3Nzc2RnZ8ttb2Njgy1btqBNmza4ceMG3NzcsHbtWpw4cQJaWloIDw9Xuk9ZWVmIj4+Hubk5ypUrJ/e9pqamOHHiBJo2bSq2/fr16+jUqRO+fPmi8Hz37t1DYGAgQkNDkZeXh4EDB2LYsGGoX7++0n0vam7evImBAwfi+fPnKDytEggEyMvLk9m2Vq1a2L17N5ydncW23759GwMHDkRSUpJa+iwkKChI7n5PT0+Z+0xMTHDv3j3UqFEDy5cvx8WLF3H27Flcu3YN/fv3l3rPFkZPTw/R0dGoU6eO2PYHDx7A2dkZP378wPPnz2Fra4usrCyZxxGey8zMTOE5XV1d0bx5cyxevFjq/jlz5mDVqlWoVKkSLl++jOrVq0u8R/R5Va9ePUyZMgVDhgxBcnIyHB0dkZGRIfXYCxcuZP7++fMnNm/eDDs7O+beuXnzJh49eoSxY8fin3/+UfhZunbtil+/fsHFxQV///03UlNTUbVqVURERMDX1xcJCQly26sy7gjJzs5GeHg4AgICmHvdx8cH7du3h0AgkNkuJycH7u7u2Lp1K2rWrKnwMxZm/fr1AIDJkyfj77//hqGhIbMvLy8PV65cwbNnzxATEyP3OB4eHujSpQvGjh0rtn3r1q04duwYTp06JbXdsWPHAADdu3dHUFAQTExMxM5/4cIFnDt3Dk+fPpV7fmtra2zbtg1ubm5iY158fDyaNm3K6rnJlatXr2Lbtm1ISUnBgQMHULVqVYSEhMDS0hLNmzdX23nXrl2LK1eu4MqVK9DR0UGrVq3g6uoKV1dX1KpVS2Y7DQ0NLF68WOw3F+X79++YN2+e3GcuAFStWhXHjh1DgwYNxLbfu3cPXbt2xatXr+S25zpmFzctW7aErq4uQkJCULFiRQAFfR86dCh+/vyJyMhIiTYaGhrw8PBg7Kvjx4+jTZs2MDAwEHufKvMNtgjtvAEDBoht37t3L7Zv347Lly8rPEblypWxYsUKDBkyRE29VC+vXr3CgAEDcO3aNZQuXRoAkJ6ejmbNmiEsLAzVqlWT2k5TUxNv375lrtkmTZrg0KFDqFq1alF1nRcCAwPFxhcNDQ2UL18ejRs3RpkyZRS2DwgIQOvWrWFpaan0ufX09PDkyRNYWFiIbX/27Bns7e3VbmdqaGjIHVsVPfcaNWoEBwcH+Pv7M9vevn2LNm3awN7eHgcPHpTZdtKkSbhw4QLOnz/PPDOEvHv3Du3bt4eNjQ0iIiKwfv16qXNHWfONuLg4tG7dGp8/f5bb/98dV1dXTJo0Cd27d2fdZuvWrTh58iSOHz8OADAyMoK9vT309PQAFNjrM2bMwOTJkxUe68WLF3L3m5uby93fqFEjzJw5E7169UJKSgrs7OzQs2dPREdHo1OnTli7di27D8UCaWOQNB48eABfX1/eziuNr1+/YtasWdi3bx8zLytdujT69++PJUuWsHruNG7cGK1bt0anTp3Qvn173Lx5E46Ojrh58yZ69+6tcM7BlXLlyiEyMhL29vbYuXMnNmzYgJiYGBw6dAjz5s3DkydP5LYvPH4ISUtLQ4UKFRQ+e4Q8ffoU/v7+CAkJQXp6Otq1a8fMqdXBnTt34OXlhcePHzPPTiKCnZ0ddu3ahUaNGsls27RpU/Tp0wdTpkzBo0eP4ODggKSkJGbsiIyMhKenJ549e6a2/gtp3bq1zH0CgQAXL14U28bVP8gnv379wrhx4xAYGIi8vDxoaWkxfp7AwEBoamrKbf/+/XsMHjwYFy5cgIGBAU6cOCHmx1c3w4YNk7s/ICBA5r7Pnz/j58+fqFKlCvLz87FixQpcv34dNWvWxJw5cxQ+OxwcHLBs2TJ07NhRbPuZM2fg5+eHuLg49h+kCOFy3/GJg4MDhg8fjh49eqBOnTo4c+YMmjZtirt376JTp0549+6d3Pa+vr7YuXMnWrdujYoVK0rMv3bt2iW3/cGDBzFw4EDk5eXBzc2N8U//888/uHLlCk6fPs3tA8rB2NgYt2/fZnz7hXn69CkaNWok089Tr149sf9zcnKQmpoKLS0t1KhRA/fu3eO9z+qA65hz4cIFXLhwQer6gLx7X4gy/smShLJjDlAy/ASy/DOifPv2DcbGxmrrg5CvX79i48aNiIuLQ0ZGBurXr49x48ahcuXKctup8t2XJIYOHYoPHz5g586dsLW1ZfybZ8+eZeZT0sjJycGaNWuwd+9eJCYmgohQq1YtDBw4EJMmTYK2trbSfcnKykJGRobc6+G/QnH5lTU0NHDx4kWULVtW6v5Pnz6hXbt2rOwErnYq13URruefMmWK2P9EhLdv3+LkyZPw9PTExo0b5bbniqzn35s3b1CjRg38+PFDZlt5fjGBQICUlBSZ+1W9dytWrIjz58+jbt26AAq+r7Fjx+LUqVO4dOkSDAwMUKVKFbnXzooVK7Br1y5m7crHxwcDBgyAkZGRzDbSKFOmDLKyspCbmwt9fX0J/YMiH1W9evUwefJkDB06VOz+i4mJgYeHh9z5pre3N9atW6fSuCTLPleWK1euyN3fsmVLqdsrV66MGzduML7R8uXLIzo6mvk/ISEBjRo1wtevXzn1Tx7lypVDcHCwhK3EBiMjI/Tt2xc+Pj5o1qyZyn0YNGgQ4uPjUa9ePYSGhuLFixcwNTXFsWPHMHv2bDx8+FDlY//h9+eP+PYPf/j/8DHZ19XVRVJSEqpVq4aRI0dCX18fa9euRWpqKhwdHeWKWNTJ48ePMWjQINy/f5+1c7wkcfPmTYwZM0Zm/4WT3HXr1mHEiBHQ19dn9uXl5eHWrVvQ1NRUu/i5devWchdFZRnKPXv2BAAcPXoU7u7uYsLxvLw83L9/HzY2Njhz5ozU9lwdnHwbuhkZGXjy5An09PRQs2ZNmUL4wgwfPhxpaWnYv38/ypYti/v370NTUxPdu3dHy5YtpS7q1a9fH35+fujXrx8A4MCBA/D09MS5c+dga2uLoUOHQl9fH/v375d77h07dmDMmDEoV64cKlWqJPY7CgQCtTkY+RJx6evrM0JZPz8/vH37FsHBwXj06BFcXV3x8eNHtfRfyIABA/D27VscPXqU+Qzp6eno3r07KlSooPD7FyUnJwebN2+Gn58fcnJyULduXUyYMAHe3t5y76/ixMnJCbVq1cLChQtRuXJliX6K/q6FOXr0KJYuXYpNmzahYcOGAAru6fHjx8PPz0+pRfqixtjYGHfv3kXNmjXRrl07dO7cGRMnTsSLFy9gY2Mj18AX0rx5cxgZGSE4OJgRZHz8+BFDhw5FZmYmrly5gvPnz2PcuHES90Fubi4WLlyI9evXM2JXQ0NDjB8/HvPnz5cZNGBsbIzo6GhmMbIwT58+ha2tLZ49eybTycKHgTV8+HBUrlyZCR4QMn/+fLx8+ZKVU//FixcYO3YsXr58iQkTJsDHxwdAgTA1Ly+PEaoWhuu4U5jnz58jMDAQwcHByM3NxaNHj2QKJIECw1y4AKksQufQ8+fPUa1aNbGFW21tbVhYWGDRokVo3Lix3OOULVsW165dg62trdj2+Ph4uLi4IC0tTWo7DQ0NAAVjQ2EzqlSpUrCwsMCqVavQuXNnuefX09NDfHw8qlevLuakefz4MZydnWUKuAFg0aJFco/NJmDk0KFDGDJkCAYNGoSQkBA8fvwYVlZW2LhxI06dOiVTfMw3Dx48QGRkJC5evIgTJ06gQoUKMoUoFhYWrMaB1NRUuft1dXXx8OFDCeFBUlIS6tSpg58/f0ptx9eYnZmZiWXLlslc0JPn4AQKFnT379+PFy9eMMFOQtjMV5KSktCjRw8kJCQwC4IvX75EzZo1ceTIEamCDG9vb4XHBRQvRnNBX18fcXFxEs+NhIQEODk5yQ3QEGJqaorbt2+jRo0a6uqmWnF3d0d6ejqCgoKYMezp06fw9vaGsbGxzGd2YYe86DPnf4maNWsiJSUFVatWRatWrRjhPxsRUqVKlbB37160adNGbPv58+cxcOBAfPjwQV3dBlAwXouSk5ODmJgYBAUFYeHChcz4L4uPHz+iZcuW8PDwwOrVq/HmzRu0bt0ajo6OCAsLY8Y2aVhYWGDbtm3o0KGD1P1nzpxBx44dMX/+fMyfP19sX7169SAQCBAXFwd7e3toaWkx+/Ly8pCamgp3d3elbIXfkf3792PWrFmYPHkyGjRoICFIcHBwkGjTokULzJgxA126dAEged/u3r0bmzZtwo0bNxSen6t4m4+AOz74/v07QkNDsXPnTty9e7fIfFtEhE+fPoGIUL58eaVs0suXL6NHjx749u0bPD09mfn97NmzER8fr1YxCiDuJ+jbty/s7e0ZW8PGxkbh2Mm3cD4vLw/Hjx9HQECAWsW3QmJiYpCUlMT4uJycnBS2OXz4MPr374/mzZvj0aNHaNSoESOCBwA/Pz+kpqbKfW4pEm4KYWPvKUtJEUAKefHiBR4+fIiMjAzUq1ePlf0XGhoKX19fODk5YfPmzfD398e6deuYAFVdXV2197tHjx5i/+fk5ODhw4dIT09HmzZt1HrvFnfAD1dUue/4hKv41cjICGFhYejUqZPKfXj37h3evn0LR0dHZo51+/ZtGBsbyxTG8oGrqytatGgh4WMSMmfOHERFRbEKWhTy7ds3eHl5oUePHkUSwKjq2oo0VBlzFi5ciEWLFqFhw4ZS/buHDx+W2k5V/6QoXO384qAk+Am8vb2xfv16pYVXf+CPSpUq4ezZs3B0dBSzWVJSUuDg4CDXv/kH1eHiV+aC0LaUJi0SbleUjKbwsWSh6Biqrovwdf7CwnlhgHibNm0wbNgwMf8Hn/CVmKSoMTY2xq1btyTWYnx9fXH06FHs3bsXrq6ucr/38uXLY/DgwfDx8ZFI5qMMXBISAQV29uPHj2FhYSHx3LOzs5O5tgAU2Irr1q2TGLcyMzMxfvx4uTYim4ATNkjzAYreC7J+Az09PcTGxspcV42Pj4eTk5Pcz8+VKlWq4PLly3ITuMhCQ0MDdnZ2ePz4MWxsbDB8+HAMHTpUwt+hiPT0dMyZMwcvX77EmDFj4O7uDqBgXVdbWxt//fWX0n37w38H9Tz5//CHYkTV7JWlSpXCjBkzMGPGDJXPXbFiRTx+/BiVK1fGmTNnsGXLFgAFEXeKovr55ufPnzh27Bj27t2LM2fOoGLFipg+fXqR9oErt2/fxt69e7Fv3z58+/YNffr0kfo+4SSSiPDgwQMxsai2tjYcHR0xbdo0tfe3sEMxJycHsbGxePjwocLskUBB/42MjJhsNkBB/5s0aYIRI0bIbN+wYUM8fPhQZQcnH9e+KIaGhio59FetWoXevXujQoUK+PHjB1q1aoV3796hadOmWLJkidQ2qampYouVp06dQu/eveHi4gKgwLEo67oRZfHixViyZAn8/PyU7jcXhMJKgUAgcY2IirgUYWhoiLS0NJibmyMiIoIRpOvq6rISQAJAr1694OzsLPEdrFixAtHR0Thw4IDMtv/++y9atmyJ6tWrM5kSYmNjUbFiRYSEhLA6f05ODg4fPoxdu3bh3LlzaNKkCXx8fPDq1SvMnj0b58+fx969e1kdq6hJTEzEwYMHVcog4+XlhaysLDRu3JgxyHNzc6GlpYVhw4aJLdypKytYcnIydu3aheTkZKxbtw4VKlTA6dOnYW5uLjdrdMOGDbF48WK0bdsWkZGRzJiXmpoqkRlNFv7+/ujWrRuqVasmJgKzsrJihCYZGRmYM2eORNvx48cjPDwcK1asYDLH3rhxAwsWLEBaWhrTn8Lk5eXJdXyXKlUKenp6cqObN23axBhYhw4dgqmpKQDg7t27ElkZZXHgwAHcuXNHYvvgwYPRsGFDVoux5ubmOHHihMT2NWvWyG3HddwpjKjjjY1zbfDgwfD398eyZctYn0OIUFjZunVrhIeHs8o6Jo3s7Gzk5uZKbM/JyZH73BTOLS0tLREdHa0wM7gs7OzscPXqVYnMygcPHpTIOFOYwgs+hTPSsBHfLl68GFu3bsXQoUMRFhbGbHdxcZGZFZpPiAgxMTG4fPkyLl26hKioKOTn58t1dvCVYcza2hpnzpyRyJh3+vRpuWJEvsbs4cOHIzIyEkOGDJG6oCeP9evX46+//oKXlxeOHj0Kb29vJCcnIzo6GuPGjWN1DGtra9y/fx/nzp1DfHw8AMDW1hZt27aV2Rd1LpaxxczMDDt27MCKFSvEtu/cuZN1VqHhw4dj7969mDt3rjq6qHYiIyNx/fp1MSenjY0NNmzYgBYtWhRjz4qG6OhohIaGIiEhAdra2rCxscHQoUMlHPeySExMxOvXr3H58mVcuXIF//77L0aNGoXKlSvD1dUVu3fvltm2W7dumDRpEg4fPsyIt5OSkjB16lR07dqVl88nj27dukls6927N+zt7bFv3z6F4tvy5csjIiKCyWp+4sQJ1K9fH3v27JErvAUKMuTKmw/WqVMHGhoaEsJb4P+em7GxsejQoYPYopAwYKZXr15yz/9foH///gCACRMmMNsULUwmJSUxGWGAArtO9LdydnZm/dwvvOgmFG+vXr1app0tChEx85/z588zAUZmZmb49OkTqz5w4cqVK/D398ehQ4dQpUoV9OzZE5s2bVL7eQHgx48fjOgWKAj+Onz4MGxtbWUK0kVxdXXFp0+f8O3bN7E5qzBYX91YW1vjyJEj6NGjB86ePctkSv7w4YPcTDtC4bxAIICbm5tM4byyCIOriyrAVFilRBl69OiBU6dO4cSJE2jfvj3Gjx8vtl9fX1+ickZhAgMDGd9IUec84eof5Btzc3OFWcNE6dWrF86ePYt//vmH+e5XrFiB7t27w9vbG6dOnUJgYKBE1SW+kSawy8/Px5gxY1gHcX348EHq2oS0gAtRTExMkJKSIiG+TUpKkgjeKImoct/xSe/evdG8eXNG/CrEzc1NQlQtjbJly3IO1KtUqRIqVaoktq1wtSshwuctGxQJMKdNm4bu3bsjOzsbU6dOZfxy7969w6pVq7B27VqlhePGxsZYuHAhunTpUiTiW1XXVqShypizdetWBAYGKv1ZVfVPCuHDzi8OSoKfoCT0QTSz7qlTp8R8jZqampzE/L8DmZmZUue1nz9/Zp2UR1n4fHb+rnDxK3NBUcIDZeBqp6q6LsLX+S9dusTqPHwj/HxEhK1bt0pNTLJ169Yi6cuvX7+kznelzf9r166NO3fuSPjwhBmC2fjW3rx5wyqgRRHKzikKU6lSJSQlJUnM16OiohQmOggKCsKyZcskxLc/fvxAcHCw3DVBT09PsXU8VSmcGVt47c+dO1futV+tWjU8fPhQpvj2/v37Mquy8cXUqVOxbt06bNy4UaVkXRcvXsTbt2+xc+dOLF26FLNnz0bnzp0xfPhwuLu7szpm6dKlpWa2Fq16+of/Xf6Ib//wn0JR9kq2peOVmTCI4u3tjb59+zIL6W3btgUA3Lp1S62RzaKcPXsWe/fuxZEjR6ClpYXevXsjIiJCZpr4kkZCQgL27NmD0NBQpKamok2bNli+fDl69uwpM4uecJLLJV0/H8ia1C9YsEBupKHQSWBhYYFp06ap7EwtbgcnUJBlQ1aUtiIHn4mJCc6dO4eoqCjcv3+fKc8jvI+kkZubK2bE37hxA5MmTWL+r1KlCqsFwS9fvrAS6fINXyKudu3aYfjw4ahXrx4SEhKYkguPHj2SMABkceXKFSxYsEBiu4eHh0IxUdWqVXH//n3s2bMHcXFx0NPTg7e3NwYMGKDQGLp37x527dqF0NBQaGhoYOjQoVizZo3YM7NHjx5FnqFFGRo3boykpCSVxLd8lmlVhcjISHh4eMDFxQVXrlzBkiVLUKFCBcTFxcHf319uGeI1a9Zg8ODBOHLkCP766y/m8x88eJB12Q4bGxs8fvwYERERTCkgGxsbtGvXjhEYyHKU7927F2FhYfDw8GC2OTg4wMzMDAMGDJDp3La3t8fRo0dlluk9cuSIXJEJwI+Bpaenh2vXrklk/7l27RrrbD6qlkfiY9zJzs5GeHg4AgICEBUVhc6dO2Pjxo1wd3dXKOTJzc1FQEAAzp8/LzUD3OrVqxWen6uDy9nZGdu3b8eGDRvEtm/duhUNGjRQ2F7U2fjz50+lMzDNmzcPnp6eeP36NfLz8xEeHo6nT58iODhYquNQFGmR66IZadjw9OlTqXNDExMTpKenszqGqnTp0gXXrl3Dt2/f4OjoCFdXV4wYMQItW7ZE6dKlFbZX5fsWZcqUKfD19cXHjx+ZLJYXLlxgFiRlwdeYffr0aZw8eZIJFFKGzZs3Y/v27RgwYAACAwMxY8YMWFlZYd68eUoFaAgEArRv3x7t27dXug9Cirqc5po1a9CrVy+cPn2ayWx9+/ZtJCYm4tChQ6yO8fPnT2zfvh3nz5+Hg4ODxByFzbOnODEzM0NOTo7E9ry8PFSpUkVmO6GAStb/vwMzZszAv//+C0NDQ8aRfe7cOaxcuZIJoPv58ydu3Lght2xn1apVMWjQIPTo0QNXr15FaGgo9uzZg7CwMLni2xUrVsDd3R21a9dmHMmvXr1CixYt8O+///L7YZWgSZMmGDlyJKv3mpmZ4dy5c2jRogXatWuHkJAQVtdBuXLl8OzZM5kO9NTUVJlZN4SCXAsLC/Tr169IshWWRFRZoExPT0d2djbzf+FKJvn5+WL75SEqPhLSsGFDVKlSBStXrmQqIsiCj4A7ZXn37h0CAwPh7++Pb9++oW/fvsjOzsaRI0dgZ2enlnNKo1u3bujZsydGjx6N9PR0ODs7Q1tbG58+fcLq1asxZswYhcfQ1NSUCBZja6NzZd68eRg4cCAmT54MNzc3RhAUEREh13f0uwrnC5d+lYe8Md/NzQ0tW7aU6suYP3++Qh/TmDFjGJ+mt7c3Bg8eLLM0b2F69uyJwMBAGBsbK7w35fnYits/KOu3EAgE0NXVhbW1Nbp16ybxvbx79w4xMTESNnKzZs0QGxuLmTNnolWrVhL+xqJAQ0MDU6ZMgaurq9zkBXfv3oWnpyeePHkiIb5mkwmuuAN+lIWv+45PlBG/FmbBggWYP38+du3apXKQhDK+cT6DETp37ow1a9Zg2rRpWLVqFRN0/fXrV2hqamLlypVMRn1l+Pr1q1rLB4ui6toKX/z69UulMsSq+ieF8GXnKwtfY44oRe0nKG5OnDiBuXPnMn66fv36iWUoFwgE2LdvH3r37i3WTh3ffXHRokULBAcHM1m3BQIB8vPzsWLFCqm2eZkyZVj7I2Rd/6LPzp8/f2Lz5s2ws7Nj5ro3b97Eo0ePFAZM/c5w8StzobDYlwtc7VRV10X4On9hIiMjkZmZiaZNm6qcLIQNfCUmefXqFY4dOyZ1vqJozpaQkAAfHx9cv35dbLu8AOMePXogNDRUaoDLxo0bkZ+fr1A03K1bN4SGhjJznGXLlmH06NHMekJaWhpatGiBx48fyz2OEFWD5UaMGIGJEyciICAAAoEAb968wY0bNzBt2jSZCR++ffsGIgIR4fv372L+sby8PJw6dUphRtvt27dL9PX9+/fYunUrMjMz0bVrVyboXh7SKrW2a9cO2tramDJlCu7evSu1XceOHTFv3jx06tRJwr/348cPLFy4UO0BJ1FRUbh06RJOnz4Ne3t7CZudzZjp6OiIDRs24N9//0V4eDj8/f3RuXNnVKlSBd7e3gqrTl65ckXu/t9Fj/UH9SCgog7B/sMf1Ej16tUxduxYlbNXqjJhKMzBgwfx8uVL9OnTh1mgCgoKQunSpaVmrOEbfX19dO7cGYMGDULHjh15iQIqSjQ0NNCoUSMMHDgQ/fv3V9tiTlGSlJQEZ2dn3p0lXB2cfBi6ooSFhWHo0KHo0KEDIiIi0L59eyQkJOD9+/fo0aOHUpHIP3/+hI6OjsL+OTk5YdKkSfDy8sKLFy9gYWGBhw8fMgty169fR9++fWWWsBbi4+ODRo0aYfTo0az7WJLgo8yBrJIR8fHxqFevHusMusqiqamJdu3awcfHB927d5f6zMrMzISvr2+JiGaXxuHDhzFnzhxMnz4ddevWlfgMioy14qRp06bo06cPpkyZIlYi5fbt2+jZs6fCe0caP3/+hJaWltpK6wipUKECIiMjJaJlnzx5gpYtW0qIFIQEBQVhzJgx+PfffzFy5EixjMPbtm3D9OnTsXnzZnh5eck9/5cvX+Dv748nT54AKMgcOWzYMNYLq8uWLcPChQsxYsQIZiHo1q1bCAgIwNy5czFz5kyFx+BaHklVxo4di7CwMJiZmWHYsGEYNGiQUkJEecIogUDAqpRgXl4eAgMDceHCBalOEkXHuHbtGtq2bYtGjRrBzc0NQIEAMzo6GhEREQqzSObn52PJkiXYunUr3r9/j4SEBFhZWWHu3LmwsLBQmIUQAK5evYpFixYhLi6OCTiZN2+eyoLIBw8eoEuXLqwyxFpZWWH79u1o27at2L0fHByMZcuWsXZSqcL06dPRqlUrtGjRQqqzRxG6urpwdnZmSsY3a9ZM6ajvLVu2YMmSJXjz5g2AAhHMggULMHToUKX7oyyWlpY4deoU62ydoujr6+PJkyeoXr06KlSogHPnzsHR0RGJiYlo0qQJ0tLSWB3nwoULMu8deRH2fJTT5MKrV6+wZcsWsefu6NGjWS/s8fHsKU6OHj2KpUuXYtOmTWjYsCGAgsX98ePHw8/PT+bivYaGBurUqcOMd/fv30ft2rXFqoUAJTcjTFBQEEaPHo2VK1di1KhRzHWWk5ODLVu2YObMmQgMDMSWLVvg5uYmNVs+UCA2u3z5Mi5fvoyYmBjY2toyz5GWLVsqXLAgIpw7d44JNHNwcChWh+qPHz8wa9YsnD59Gk+fPpXYL8vWy8rKgo6Ojlh2FHm23rBhw5CcnIxz585JXDPZ2dno0KEDrKysWGXsVzXI+X+RmjVrYtmyZTIFjvv378fs2bORlJSk8jmSkpLg6OiosIT5/fv3MXDgQLx8+RJTpkxhRNXjx49HWloa79VJunTpgitXrqBTp04YNGgQ3N3doampiVKlSiEuLq5IxbflypVDZGQk7O3tsXPnTmzYsAExMTE4dOgQ5s2bx4xHsrC0tJQ7V09JSeG7yxJwKX8eFBT0Wwnn5Y3zorAZ83v16oWDBw9K/H7v37+Hm5sbHj58KLe9aKDi9evX0alTJ/j4+KB9+/ZyrwnR8tmKynmL+kdKmgCydevWuHfvHvLy8hgfU0JCAjQ1NVG7dm08ffoUAoEAUVFRYvd0fn6+wkDOK1euFNv4e+rUKXh6esr0NQAFi7k1atSAn58fKlasKPF7KxKtfP36Fe7u7rhz545EwE94eDirYMWihM/7jg9at24t9x5T1Id69eohOTkZRAQLCwsJ+0bRfJlP37iqvHr1CgcPHmQC3GvWrInevXsrtJkKl+cmIrx9+xYhISFo1apVsVYjU9faSmH8/PxgaGiodKUUVf2TQviy85VF1TGnMMXtJ5BHXFwc6tevrzb/aNeuXdG9e3emcp2ofw0oCOK8fPkyTp06JdaOr+++JPDw4UO4ubmhfv36uHjxIrp27YpHjx7h8+fPuHbtmkQ2cUXl3kVhk51y+PDhqFy5MiP+FTJ//ny8fPmSlZ36u8K3X1kZ1Ck+Y2unqmtdRNH5ly9fjoyMDOaaIyJ4eHggIiICQMGYcOHCBYXJXYqTCxcuoGvXrrCyskJ8fDzq1KmDZ8+egYiYe1keLi4u0NLSwsyZM6VWdZMmbOYDDQ0NvHv3jhGpGhsbIzY2lnnmvn//HlWqVFH423MNliMiLF26FP/88w+ysrIAADo6Opg2bZrEs0i07/KuV4FAgIULF8pdy/f29oa2tja2bdsGAPj+/Tvs7e3x8+dPVK5cGY8fP8bRo0eZ5FzKEh8fj4YNG8oMeHr//j2cnJygra0NX19f1KpVC0BBkpeNGzciNzcXMTExatX1cBkzNTU18fbtW6ki52fPnsHf3x9BQUEKhf3S7FXR31Zdc44//B78Ed/+4T9F4YFWWbhOGLhm4uKD79+/S6Sr/51ITEyUyHDAlszMTCxbtkymmKAoFjakERISAj8/P0bgIUr9+vVx4cIFlClTRmG5lMIOPq4OTr4NXQcHB4waNQrjxo1jHA2WlpZMOVdFGSFVETLt2LEDkydPRr9+/XDz5k2ULl0a165dY/YvXrwYt27dwvHjx+We+59//sHq1avRqVMnqeJJ0RKh6kJVIQxfODs7o3PnzhIZwhcsWIDjx4/LjHYT5fHjx1IjJeVl53j+/DmvEbPFgazJtqzAjW/fvrE+trozeRsaGuLBgwewtLQUcxA+e/YMtWvXxs+fP2W2tbKyQnR0NExNTcW2p6eno379+qyfuape+4sWLUJ8fDx27drFZMDOzs6Gj48PatasKbX8sZBp06Zh9erVMDIyQo0aNUBESElJQUZGBiZMmKCwPNGVK1fQpUsXmJiYMAKou3fvIj09HcePH2ftYNq/fz/WrVsnJiSbOHEi+vbty6p9XFyc2P+FyyMpitB+//49pk2bxnz/hc0CWYaihoYGzM3NFY5b6szO4Ovri8DAQHTq1EnqnI1NianY2FisWLFCTEg1a9YsVvOQRYsWISgoCIsWLcKIESPw8OFDWFlZYd++fVi7di1u3Lih8mdTlaioKHTp0kWidJA0/vnnH+zevRsBAQFo164dTp06hefPn2Py5MmYO3euRIldPrhx4wbS0tKYctUAEBwcjPnz5yMzMxPdu3fHhg0bFJali4qKwpUrV3D58mVcv34dubm5aNiwISOia9euHes+ffz4EXp6ejKrK8iCy5i9e/duHD16FEFBQUpnU7KyssKhQ4dQr149NGzYECNGjMCoUaMQERGB/v37s1qMXLhwIRYtWoSGDRtKvXekldkVMmbMGISHh2PRokUS5TS7d++uMKPPH5SnsIAyMzMTubm5YoEjWlpaMDAwkPn7s83KLm/cLE6cnZ0xYMAAmRnrV69ejenTp8PJyQnnz5+XKaLV0NBA+fLlMXXqVIwcObLEiVfkUfg6EGbK0NfXx+7du6XOtfmy9V69eoWGDRtCR0cH48aNQ+3atUFEePLkCTZv3ozs7GxER0fLFdAmJiZi2LBhnIKcfzeOHTsGDw8PlCpVCseOHZP7Xmm/38SJE3H+/HncvXtXakaRhg0bom3btli3bp3CvhS2PYRimgULFiA+Ph6xsbGKP5AUfv78yYhi+URLSwsTJkzAmDFjxOZkxSG+1dfXR3x8PMzNzdG3b1/Y29szC/k2NjbMQpssCv8+wrn6mTNnMH36dFbBdly4ePEimjVrxtlP+b8onG/UqBEcHBzg7+/PbHv37h1at24Ne3t7uRViCvP8+XMEBgYiODgYubm5ePTokdJzT0WUNAHk2rVrcfXqVezatYvxaXz9+hXDhw9H8+bNMWLECAwcOBA/fvzA2bNn1d4fZSksZhY+N0+ePAlPT0+pVXCEGBkZISYmRqXKSKLnK0kBP78TheeLOTk5iI2NxcOHD+Hp6alw3FQ0b1Y0X+bqG+dKTk4ORo0ahblz58LS0lKptoXfL5w7t2nTBrNmzSrW9SZ5ayt8MnHiRAQHB8PBwUGpSilc/JMAP3Z+cVKS/QRxcXGoV6+exByGLywtLXHmzBkm0KSw+PbBgwdwc3PDhw8f1HL+ksLXr1+xceNGMRHouHHjULlyZbWf28TEBHfu3JHw5SYmJqJhw4ZFlrn7fw0+xGdc7VSu6yKqnr9+/frw8/NDv379AAAHDhyAp6cnzp07B1tbWwwdOhT6+vrYv3+/3PNzhUtiEmdnZ3h4eGDhwoXMc6tChQpM8KuiCi8GBga4e/euUhWfe/fujeHDh6NDhw4qV+MqLL4t/MxlK77lGiwn5NevX0hKSkJGRgbs7Ozk2niRkZEgIrRp0waHDh0SS+Cjra2N6tWry61qBgC1atXCxo0bGYH9pk2bsHTpUjx+/BgmJibw8/PD7du3FVaMvH//vtj/wmt/2bJlyM3NRVRUlMy2qampGDNmDM6dO8esJQoEArRr1w6bN29WWZ9VFBS+fqQh9FPKo/C4Inz2zJ07F0uWLGES/fzhf5M/4ts//Kfgmr1SlQmDKHxk4lKFb9++Mc5MRaIudQu5ipMBAwYgMjISQ4YMkSommDhxolrPX3gyL5yw3LlzB3PnzpXqaFm4cCGmT58OfX19zg6+4sbAwACPHj2ChYUFTE1NcfnyZdStWxdPnjxBmzZt8PbtW7ntVRUyBQQE4Pjx46hUqRLmz58vVlps7NixaNu2rUJDS55DUiAQqF24zUUIA0hOloUIS/qZm5srFDMdP34cPXv2xMCBA8XKcIeGhuLAgQNyS6GlpKSgR48eePDgASM6FZ4fkG9s8yXgLE6eP38ud39hY01RlKMo6hYjVKtWDfv370ezZs3EjNXDhw9j2rRpSE5OltlWlrHy/v17mJmZsSoFyeXa79GjBy5cuAAdHR0mOCYuLg6/fv2SMHCkiUBv3ryJ0NBQJCYmAijIBjJgwAA0adJEYb/r1q2Lpk2bYsuWLUzmuLy8PIwdOxbXr1/HgwcPFB5DnZw8eRIrV67E5cuX5b7Pw8MDL168gK+vr9TvX1bGfi8vL1bXsKLsDElJSUhOTkbLli2hp6fHyrgVUq5cOQQHB6scycsVa2trbNu2DW5ubmL3Tnx8PJo2bcpKAKsqfGSkUSVCmyseHh5wdXVlKlQ8ePAA9evXh5eXF2xtbZmslgsWLGB9zNzcXERHR2Pbtm3Ys2cP8vPzWT03c3NzcfnyZSQnJ2PgwIEwMjLCmzdvYGxsrFAMwXXM5pJNafjw4TAzM8P8+fOxadMmTJ8+HS4uLrhz5w569uwpJhCRReXKlbFixQqpZb4UYWJiIlFOEyjIAjZgwIAiWdTIysqSGujDJsv8rl270L9//yKxz/iC72C53xEDAwM8ePBApgM3JSUF1tbW+Pz5s1xB7dq1a3HlyhVcuXIFOjo6jM3u6urKZGuQR2ZmJiIjI6Vef+oO1AsMDBR71gjFEI0bN1ZrOUMhqampGDt2LCIiIiSc6xs3blQoMCqurCjFieg8WV4WR1niYz4zikizPYgIZmZmCAsLY0QSspg3bx5at26NZs2aKbQp+eDmzZvw9/fHvn37YGtriyFDhqB///6oXLlykYtvHRwcMHz4cPTo0QN16tTBmTNn0LRpU9y9exedOnXCu3fvVDrupk2bcOfOHbVnMjM0NERubi4aNWoEV1dXtGrVCi4uLqzHwf9F4byQjx8/omXLlvDw8MDq1avx5s0btG7dGo6OjggLC1OYnVWUly9fYteuXQgMDMSvX78QHx/PSnwbGhqKAQMGSN03ffp0rFy5knUfipqqVavi3LlzEvfro0eP0L59e7x+/Rr37t1D+/bt8enTp2LqpWwKi5lFRYjDhg2TW+Wne/fuGDJkiMzM5X8oHhYsWICMjAz8+++/aj0PF984Xxn8TExMEBsbq7T4tiSgytoKn6haKYWrf5IPO784KU4/gaI1n69fv+Ly5ctqm7Po6uoiPj4eFhYWAAoq0zg6OjJ+ntTUVNSuXRvZ2dlqOf9/iZ8/f0rY2WzWsitVqoRly5ZJVLALDAyEn58f3r9/z2c3//D/4UN8xtVOlQXbdRFVz1+mTBlcv36dyXbu7e2NvLw8BAcHAyiwZ/v06YOXL1+q1H+2cElMYmRkhNjYWNSoUQNlypRBVFQU7O3tERcXh27duimsqteoUSOsWbMGzZs3Z91fNzc3XL58GVWqVIG3tze8vLyUFmryJb7lI1hOVZ4/fw5zc3OVBMgGBgZ4+PAhM8fr2bMnqlWrxqwVPX78GK6urgoDPoTXfmGJYJMmTRAQEMBKI/X582emEpO1tTXraqB88fHjR6YKmI2NDcqXL6+wjageRx1ERkZiypQprBKZ/eG/i3rrAf/hD0WAqADB2toac+fOxc2bN1XKXmlnZ8fJ6Xf+/HkmE9eaNWs4ZeJShjJlyjCp0kuXLi110C7JzvGyZcsiISEB5cqVk1keU4i8SN/Tp0/j5MmTcHFxUUc3FVK4dLKGhgZsbGywaNEimaU+RJ1GJUVcq6qhW6ZMGXz//h1AgaP94cOHqFu3LtLT0xVmhAEKst9t374dbm5uYgJ6R0dHxMfHy2w3bNgwdOvWjRFvvnz5Ejt27MCPHz/Qv39/VtkhUlNTFb5HnWzduhWBgYEqCWEAwMnJSe59U6pUKfTr1w/btm2TmfWmS5cuOHLkCJYuXYqDBw8y2TXOnz+PVq1ayT3/xIkTYWlpiQsXLsDS0hK3b99GWloapk6dqtCx/ezZM6nPpezsbLx+/Vpu25KCspl7RSMPnz17hpkzZ8LLy0ssO0BQUBD++ecfXvspjf79+8PPzw8HDhyAQCBAfn4+rl27hmnTpsksvy6avevs2bNiz768vDzmOmADl2u/dOnSEgtabEqPCx32TZo0kSq0ffHiBXx8fHDu3DmZx0hKSsLBgwfFSjZrampiypQpjKOFLXfv3mUy39rb26NevXpKtZeGjY0NoqOjFb4vKioKV69ehZOTk1LHDwwMVK1j/5+0tDT07dsXly5dgkAgQGJiIqysrODj44MyZcpg1apVCo+hra3N2UGSnJyMXbt2ISUlBWvXrkWFChVw+vRpmJubKywN9fr1a6nnz8/PR05OjtQ2fM13CjvPhIvBnp6emDVrltx+CxEIBPjrr78wffp01hHaXImNjRUT9oaFhaFx48bYsWMHADCLTWzEtwkJCUz5+MuXLyM7OxudO3eGq6urwrbPnz+Hu7s7Xrx4gezsbLRr1w5GRkZYvnw5srOzsXXrVrntuY7Z8oJZFLF9+3Ymk8G4ceNgamqK69evo2vXrhg1ahSrY/z69QvNmjVT6fw6OjrMopIolpaWEuXo+ebjx4/w9vbG6dOnpe5nY+PMnDkTEydORJ8+feDj46Py91CU/FcFtcqgqakpN6AnJycHhoaGCjPZTpo0CZMmTQJQIP6PjIzEmTNn4OvriwoVKuDVq1cy28bExKBjx47IyspCZmYmypYti0+fPkFfXx8VKlRQu/i28EKispw6dQqampro0KGD2PaIiAjk5eVJLJSLkpKSAktLS5w+fRpfvnxhgpaUca7HxsZyCnL+HRHNOqNKpq2KFSvi+vXrGDNmDGbOnCk1owjbUn6Fs54I5w3W1tZyBWRCbty4gdWrVzMiTqF/SxkRpzII5+hr167Fvn37EBAQgClTpiA/Px/nzp2DmZlZkWXfmzdvHgYOHIjJkyfDzc2NsdciIiI4zdk9PDwwa9YstYtvv3z5gtu3byMyMhKRkZFYu3Ytfv36hYYNG6J169ZYvHix3PZeXl7Q0tLCiRMnpC7olnTu3LmD/fv3Sw2aUFSho3z58oiIiGAWlE+cOIH69etjz549rIS32dnZCA8PR0BAAKKiotC5c2ds3LgR7u7urIW7Y8aMQenSpSWe0ZMnT0ZYWFiJFt9+/foVHz58kBDffvz4kUkaUbp0aVYBu8WBomxR8ti5cyc8PT3x8OFD1KlTR2JtQl5lKCHFGfDDFS73nToZPHgwnJ2dlRLf/vz5E/v27UNmZibatWvHqkIOF9944SBOoZApKChIqYy53bt3x5EjR2RWjSjJqLK2wieq3vuq+ieF8GHncyUtLQ3z5s3DpUuXpGZQlOejKk4/wfHjx9GuXTuZ82J1r4WWLVsWSUlJzOcXVkcTkpiYqNBm4vLdFyeKynILkVcpITMzE35+fti/fz/S0tIk9rP5/SZNmoQxY8bg3r17cHZ2BgDcunULAQEBmDt3Lqs+/i4o8iWLou7rpvDzGgDatWsHbW1t1uIzrnaqLNiui6h6/tzcXLGg1Bs3bjC+JgCoUqVKkQSXhYWFYf/+/SolJjEwMGDmaZUrV0ZycjKzFsKm78uXL8eMGTOwdOlSqVocaXqCCxcu4Pnz59i1axeCg4OxZMkStGrVCsOHD0evXr1YBfoKBAKJe0AVG9XNzQ1xcXEqry316NFD6nmFybCsra0xcOBAJiv6/fv3UadOHWhoaODr169yE/fISy6hq6uLHz9+MP/fvHlTzCbU1dVFRkaGwv4X1kMIr31lKuaULVuWeeYWJZmZmRg/fjyCg4OZ8VJTUxNDhw7Fhg0b5Apr1a3BqVixIiMI/sP/Ln8y3/7ht4etwIdN9sqLFy9izpw5Sk0YZKFqJi5ViIyMZLLJREZGyn2vIhFdcRAUFIT+/ftDR0dHIqtPYeQtQFtaWuLUqVNMxNn/GlwcnHwYugMHDkTDhg0xZcoU/P3339iwYQO6deuGc+fOoX79+gr7oKenh/j4eFSvXl0sYu3x48dwdnaWOml88OABunTpgpcvX6JmzZoICwuDu7s7MjMzoaGhgczMTBw8eJCT0KUoMDU1xe3bt1GjRg2V2h89ehR+fn6YPn06M+G9ffs2Vq1ahfnz5yM3NxczZ85Ev3791JLloVy5crh48SIcHBxgYmKC27dvw8bGBhcvXsTUqVMRExMj0UYo4OzevTuCgoKkCjjPnTv320xWk5OTsXbtWkZEaWdnh4kTJyr8Td3c3DB8+HCJjDZ79+7F9u3bFUbocuXXr18YN24cAgMDkZeXBy0tLeTl5WHgwIEIDAwUE5cKES4WSouOLFWqFCwsLLBq1Sqx0vKy4Hrtq4K5uTlMTU0REhKCOnXqiO3btm0bk2FClsALKMjgNn36dIlny5EjR7Bs2TLcvHlTYT8+fPiA/v374/Lly4xgKD09Ha1bt0ZYWBiraE2u5Zns7OywZ88eXgS/yjB06FB8+PABO3fuhK2tLfO8P3v2LKZMmYJHjx4pPMaqVauQkpKCjRs3quRkiYyMhIeHB1xcXHDlyhU8efIEVlZWWLZsGe7cuaOwlGyDBg0wefJkDB48WGzMWrRoEc6dO4erV69KtOFrvvO7oquri8TERGYRqnnz5vDw8MBff/0FoCAYoW7dusxipSyqVq2KHz9+MBkrW7VqBQcHB9bXQffu3WFkZAR/f3+Ympoyv93ly5cxYsQIRlgmi+J4bvGJn58fDA0NVVqE4FpOkwuDBg3C8+fPsXbtWri6uuLw4cN4//49Fi9ejFWrVqFTp04Kj5Gbm4vjx48jMDAQp0+fhpWVFby9veHp6SlWOaEkoaiqiSiKbNX3799j2rRpTEm6wmN4SQzSBABXV1e0aNFCZlbuOXPmICoqitWciYgQExODy5cv49KlS4iKisL3799Rt25dqfNV0T7UqlULW7duhYmJCeLi4lCqVCkMHjwYEydOVJhxSRVkVbaQhqLMzw4ODli2bJnEosyZM2fg5+cnUa5RFE1NTSbQFwD69euH9evXsxZ+AqplRfnD/1HcGUWAgufnrVu3cOXKFURGRuL69evIzs5Go0aN5JYk5IunT5/C398fISEhSE9PR7t27cQCAtXJu3fv8PbtWzg6OjJ20O3bt2FsbKyyoHzFihXYvHmzwoxCfPPo0SOsXLmStY+Sa3Ww4iQsLAxDhw5Fhw4dEBERgfbt2yMhIQHv379Hjx49WAufExIS0KJFC7Rr1w4hISGs5ptjx45FWFgYzMzMMGzYMAwaNAjlypVT+jOcPHkSgwYNwokTJ5jn5/jx4xEeHo4LFy7I/V2KWwA5aNAg3LhxA6tWrUKjRo0AANHR0Zg2bRqaNWuGkJAQhIWF4d9//8WdO3fU3p+i5Pjx4xgyZIjUORybpBiKAn5KcnUovu47dRASEgI/Pz+8efNG6v4pU6YgJycHGzZsAFDgK3N2dsbjx4+hr6+P3NxcREREKAze4+obl8bevXuxb98+HD16lNX7hfaRm5sbGjRoAAMDA7H9ogJuZeawxSme/oP66dixI5KSkuDj4yO1BLc8H1Vx+gkcHBwwceJE+Pj4SN0fGxuLBg0aqM3W7d+/P7KysmTOSzt37gwDAwPs27dP5jG4fPfFiaxs3aJVzQQCAXJzc2UeY9y4cbh06RL+/vtvDBkyBJs2bcLr16+xbds2LFu2DIMGDWLVl/3792PdunXMuoytrS0mTpyIvn37qvDJSi6/Q2Wk+Ph4NGzYkJUAkCtc10VUxcnJCZMmTYKXlxdevHgBCwsLPHz4kAk6u379Ovr27Ss3wJsPqlSpgsuXL7Oq5FSY7t27o1OnThgxYgSmTZuGo0ePwsvLC+Hh4ShTpgzOnz8vt73o+qAoyiSBu3jxIgICAnD48GHo6OhgwIABGDZsGBo0aCD3vB4eHsxYc/z4cbRp04aZ62RnZ+PMmTMKz//p0yd4enrC2dlZpWA5Ly8vHDlyBKVLl2b6e+/ePaSnp6N9+/aIi4vDs2fPcOHCBbi4uEhURpK2rgoothXc3Nzg7OyMf/75B1evXoWrqytevXqFypUrAwDOnTuHMWPGMP6j/yKjRo3C+fPnsXHjRiYRX1RUFCZMmIB27dphy5YtMtvevXtX7vXFlsL+WuGzZ9myZcjNzS0SH9kfSi5/xLd/+IMIfEwYpGXiatmyJVxdXTFx4kS19FuUFy9ewMzMTOpnePnypdxIw9+d3bt34+jRowgKClJb2ni+4StakauDkw9D9/Pnz/j58yeqVKmC/Px8rFixAtevX0fNmjUxZ84chSVRVREyeXh4MCVMQ0JCcOLECXTo0IHJojd+/HjcvXtXqhBO6Ag1MDDAlClT5PZt9erVCj8/F7gIYQDA2dkZf//9t0Q2q7Nnz2Lu3Lm4ffs2jhw5gqlTpyI5OZmPLotRpkwZ3Lt3D5aWlqhRowZ27tyJ1q1bIzk5GXXr1pWa3YFPAWdxc/bsWXTt2hVOTk7MhP/atWuIi4tjIvBloa+vj7i4OIkMGgkJCXBycmKVNZoPXr58iQcPHiAjIwP16tVjldHD0tIS0dHRKi0kCuF67avCt2/f4Ovri/3792P+/Pnw8/PDq1evMGzYMERHR2PlypUYOXKk3GPs27cPM2bMwPjx45nsuTdv3sSmTZuwbNkysSAQWYKYfv36ISUlBcHBwcz7Hz9+DE9PT1hbWyM0NFThZ+FanikiIgKrVq3Ctm3bpGapUBeVKlXC2bNn4ejoKPa8T0lJgYODAysHXY8ePXDp0iWULVsW9vb2Ek4SRQtDTZs2RZ8+fTBlyhSxPty+fRs9e/ZU6CA7evQok2l20aJFWLhwIZ4+fYrg4GCcOHFCbdUO+OLnz5/YsGGDzMwa9+7d4/2c1atXR0hICFq2bIlfv36hdOnSOH78OFOK7MGDB2jVqpXC7AxOTk6Ij49H/fr1GQFu8+bNWc/9hFlkbGxsxH77Z8+ewc7OTuFzV9Xn1u3bt9GgQQOpQQ1AgYPw6NGjChcHrl69im3btiE5ORkHDx5E1apVERISAktLS1bCtokTJyI4OBgODg5wcHCQuHfkzXm4ltPkQuXKlXH06FE4OzvD2NgYd+7cQa1atXDs2DGsWLFCacfW+/fvsXv3bgQFBSE+Ph7u7u7w8fFBly5dlColrW4UlZ8VRZGt6uHhgRcvXsDX11dqBsNu3bqp3E91cuLECXTv3h1TpkzB1KlTGdHnu3fvsGrVKqxduxbh4eHo0qWL3ON06dIF165dw7dv3+Do6MiI91u2bKkwa27p0qVx69Yt2NjYoHTp0rhx4wZsbW1x69YteHp6yq3SoSryHPKisPFT6Onp4cmTJxJj/bNnz2Bvb4/MzEy5/ZBX1o8NfAY5/64UdRZDZYSpbLIwCklISMClS5dw/vx5HDlyBCYmJkVaMj4vLw/Hjx9HQEBAkYhvL168iGbNmimVAUaUevXqiT1riQjv3r3Dx48fsXnzZoU2B1dE/ZORkZHIzs5GixYtmPmTcCyXxe8snHdwcMCoUaMwbtw45rllaWmJUaNGoXLlylKzSMryz2VlZUFHR0dsDidvvqqhoQFzc3OJ378wbOZKe/fuha+vL86dOwd/f38cPXoUly5dkrvIXhIEkBkZGZg8eTKCg4MZ0Y2WlhY8PT2xZs0aGBgYMIIIZauwqAtFv5co8mwlCwsLdO7cGXPnzlUqUEVIcQT88IUq9x3fFP5+hIvhd+7cYaogSaNOnTpYunQpMybu2rWLSSZgbm6OYcOG4cOHDzh58qTc83P1jUtDGT8JID9RTeHkNN7e3qz7UZziaXXSs2dPBAYGwtjYWOH9pU4BMlc7nytGRkaIiopSODeQRnH6Cby9vaGvr49NmzZJ3f/kyRN07NhRbVUPY2Ji0LRpU3Tp0gUzZsxgxuenT59i+fLlOHnyJK5fv4769evLPAaX7744kRXASUQICwvD+vXrYWhoKLf8ubm5OYKDg+Hq6gpjY2Pcu3cP1tbWCAkJQWhoKE6dOqWu7v+BI3yJz54+fYoNGzaICad9fX1ZBf+psi7Ch528Y8cOTJ48Gf369cPNmzdRunRpXLt2jdm/ePFi3Lp1C8ePH2d9LlXgkpgkJSUFGRkZcHBwQGZmJqZOncrMV1avXq2w0iefSeC+f/+OvXv3Yvbs2fj69atcwT7beYuiOQvXYLmZM2fi27dv2LhxI+NDzs/Px8SJE2FkZIQlS5Zg9OjRePToEaKiovD8+XOYm5tDIBDg+fPnco8t77sXJpOpXLky3r59iwEDBsDf35/ZP3bsWGRmZkoV6otWEVdESa60Ua5cORw8eFCi+uGlS5fQt29ffPz4UWZbDQ0NWFlZYdiwYfDy8kKVKlVU6oMsf22TJk0QEBDwWwYv/4E/VM/b/oc/lDC+ffuGW7duMZHJbDLGFYZLWSlAMhOXn5+fUpm4+MDS0lIsM42Qz58/w9LSssRmNBJSOLOOkLS0NFSoUEFu/1etWoXk5GRUrFgRFhYWEot66hCScBXPrl27lvk7LS0NixcvRocOHcTKzwsFlPJYunQp1qxZwzg4161bJ+bgVMTx48cZQ9fb2xstWrSAtbU1qlevjj179igU3+bm5jLCV6Bg8jFz5kyF5xVl3rx58PT0xOvXr5Gfn4/w8HAxIZM0oqOjmYyrjo6O2L59O8aOHctMeEWFcYWJiYlhSoPLy3RVFPz8+RPbt2/H+fPnlRbCAAWCJWmT8urVqzMlLJycnPD27Vux/XyVQK9Tpw7jVG/cuDFWrFgBbW1tbN++XebCvFDoxYeAs7iZOXMmJk+ejGXLlkls9/PzkyvCMzMzw44dO7BixQqx7Tt37lSqRBlXzMzMYGZmhtzcXPz8+ZNVG1HH5c+fP1ValOZy7atalsvY2BjBwcHo1asXRo0ahX379iE1NRXOzs64f/++QucCACZT8YwZM6TuExpf8oz1M2fO4Pz582JCXTs7O2zatIl1OT2u5Zn69euHrKws1KhRA/r6+hLfv7pKVGVmZkoVSn7+/JlViSGgQAjVo0cPlfvw4MGD/8femcf1tH3///V+l+ZRSqRURKVSrjlDJfOU+SqEcHGFjBlulDJdEhkypQypzFyzqEyZlSkVTeaxCEW1fn/063x7956nio/n43EetM9Ze+9z3mfYe62110JUVBRXuYGBgUhOJP3798exY8cQEBAAdXV1+Pn5oUWLFkId7stxdXXFiBEjMHDgQJGcjmQdkcbLywtnzpzB4MGD0bp16yoZq/bq1Qu+vr5YsWIFDh8+DDU1NXTs2JHZn5KSIlI02bt37yIvL4+Jvjd//nw8fPgQ9vb2cHZ2RlBQkEB5fpHenj17JlIaa0nfW+3ateMY42ppaeHu3bvMdzIvLw/Dhw8X6Hx74MABjBw5Eh4eHrhz5w6KiooAlKX2Xbp0qUiGiZSUFMbJ4f79+0KPr4i06TSl4cuXL8y109XVxdu3b9GkSRPY2tpKNMavW7cuOnTogLS0NKSlpeHevXvw9PSErq4uduzYwaXEqy4qvuezsrLg6+uL0aNHc8wVIiMjsWzZMqF1Xbp0CRcvXqwxTi6i0qdPH6xZswazZs3C6tWrmWwJ+fn5UFBQwL///ivU8RYALC0t8ddff6Fjx448UyQKolatWsz8wsDAADk5ObCysoK2tjZyc3PFPykRkKWBWFtbG0+fPuVyvs3IyOCKhiYPXF1dAYDL+C7OIuefGWFRDOVh2BA184so1788G0dl582FCxcKjbosaxQUFODm5lZlmW369euH4uJitGrVinHYd3R0hKqqqkjylftZPlZ3cnKqEoOMpaUl9PX1MW3aNPj6+sLW1lasMZ8k6URrCk+ePGGi4ispKeHLly9gsVjw8fGBi4sLTyfAivo5aRg1apTMxtbu7u7Iy8uDo6Mj9PX1kZCQIDQ9qrT6QVmgoaGBrVu3Ys2aNYyjn7m5OTQ0NJhjatp4RFbvlffv38PHx0cix1ugbK6zefNmsNlsKCgooKioCObm5li5ciU8PT1rtPOtJM+drKk8xmOz2WjatCkCAgIE6llycnKYiHUAmLlyuX5o2rRpIqV1rhidXhLdeGW+ffuGdevWwcjISGQZccaQNcGhtrrTqGtrazPtiztHKEdS/WQ5spjnS4ulpSVHKmtxqE49QVhYmMCxrJWVldwcb4GyhRsxMTEYN24cly5OV1cX0dHRAh1vAemufXXCy1n43Llz8PX1RVpaGubMmYOZM2cKrOPDhw+MTkxLS4t5Vjp06IBJkyaJ1Z9bt24xDpzNmjWr8ixvVQ2/LEksFgvKyspQUlKSa/v29vYCnc9E4cCBA/jzzz/RsmVLRseVlJQEW1tbREdHc71XKiOJXUQW8+Tx48dDQUEBx44dQ6dOnbgW9rx48QJjx44VqR1puHTpEi5cuICTJ0+KHZikos1WXV0dYWFhYrUtqwzLmZmZiIiIQEREBPLz8xndET9kNW7x9vbGiBEjJF4st337dly+fJkjeAObzYa3tzfat2+PpUuXYsqUKYzdo6K9URTbIz86d+6MW7du4cyZMzA0NMSQIUM49tvb2zOZcSuzZs0akdpgsVg12vn269evPH8zAwMDkQJZubi4YO3atVi0aBG6d++OcePGoW/fvnyDpfCi8rii/N0j6aLt3/xa/Ha+/c0vwd27d9GrVy+8evUKQNlqwdjYWK4okMKQdsCgr6+P1NRUvHr1Cq9evcLr16/x7du3Ko3CWjGtR0UKCgp+ihc/v8g+RUVFQicMVWWAqYi0yvmK6T8GDRqEgIAATJkyhSmbOnUq1q9fj3PnzsHHx4dvPdIqOKWd6CoqKmLixInMBFcSJHFk+vDhA5MmWENDA+rq6hxRBHR1dfmmr644OZPW8V5aBDnCiKKAtLS0xPLly7FlyxbmOfnx4weWL1/OGPWeP3/ONShds2YN42gkzb28cOFCJmJWQEAA+vTpg44dO0JPT09gWiVAto4F1cWjR48QGxvLVT527Fih13XNmjUYNGgQTp48iTZt2gAoi46Ynp6OAwcOyKO7AMoc7t+/f4/Ro0czZUFBQViyZAmKi4vh4uKCmJgYgVE5SktLERQUhLCwMLx+/RppaWkwNzfHP//8A1NTU74pvyoizb0/cuRIgWm5hNG2bVvY2toiLi4O6urqWLhwociTX1nct6WlpVxKEaDMwaeyop4f0o5bZGVgFpeOHTti586dTApxFovFRIVxdnYWqQ5plS06Ojp4+fIlV1SYO3fuiGzY6tixI86ePStR+82aNcO8efMwefJk9O7dGyNGjECvXr143hOA5EYgfvz33384ceIEE627KliyZAkGDhyIzp07Q0NDA5GRkRxju/DwcJEdz3V0dNCvXz84Ojqiffv2OHLkCPbu3Ytr164Jdb7t1q0bQkJCsGXLFgBl919BQQEWLVokkkFV0vdW5TEurzGvsAiXgYGBCAsLw6hRoxAdHc2UOzo6IjAwUFjXAUg35qlO42zTpk3x+PFjmJqaonnz5kzE7rCwMLEcSV6/fo1du3Zhx44dePr0Kdzc3PDff//B1dUVX758QUBAADw9PYVGIqgqKr7nAwICEBwczCwAAcocw2xtbbFlyxahaQWNjY2F3mM1FW9vbwwYMAD79u1Deno6AMDCwgKDBw8Wati9evUq3r9/j3///Zcp27lzJxYtWoQvX77Azc0NoaGhAhd/ODg44MaNG7CwsEDnzp3h5+eHd+/eYdeuXbCxsZHNSVZCGoV8Zfr374/p06fj0KFDzCKHjIwMzJw5U2jUUxaLxfVuE3fMVd1zrerGx8cHffv2ZaIYJiUlcUQxlAeijiVFYeLEidDX18fMmTMxefJkDue5X52PHz/i+vXrSEhIQEJCAkJCQvD9+3e0bNkSzs7OQr+98kyzLApTp05FYmIiAgIC8N9//4mdMeBndpyvqAsyMjLC/fv3YWtri7y8PL5GOVml542IiJBYll9WJn19fbRo0QIbN25kyvgt+KoJDpDlaGhoVLmTvqTI6nkdOHAgLly4INKiQl5Ux4IfWSHJcydrJJ2vsNlsjnFyUlISRyAMHR0dfPz4UeT63rx5w9MJU9DzUNkJlYjw+fNnqKmpYffu3SK3XZHycxI0dissLMSZM2fg7OzMtRj106dPiI+PR/fu3UVeKC0u1aWXKqfiPSPp/SOtflIW83xp2bhxI3x9feHn58czBbegBTfVqSeQ130pDv3790fXrl1x+vRpjrlqt27dRFroKM21ryncvn0bc+fOxcWLFzFu3DicOHGCK7gSL8zNzZGZmQkTExNYWloiNjYWrVu3xrFjx4RmpynnzZs3+PPPPxEfH8/I5OXlwdnZGdHR0RIF6foZ0NHREfiuadCgAUaPHo1FixbJJbuTLJzP5syZw2SUq8iiRYswZ84coc63kthFZDVPHjt2LF8H24rjdXkibWASoCzqbMXxD5vN5jvfrxztWBCCxjuFhYXYv38/wsPDkZiYCGNjY3h5eWHMmDFVtnBD2sVyxcXFSE1N5cpGkpqaysyRVVRUeD6jJiYmzMJeJycnsecMVlZWHMF8KiIos86vYIcHyoKbLFq0CDt37mTeN9++fYO/v7/QLKBA2Zhr48aNOHLkCMLDwzF48GDUqVMHnp6e8PLyEphhphxZ6mt/8+vBop/V+vKb31Sge/fuKCgowKpVq6CiooIlS5bg3r17zGRHELIaMJRTMRJXQkKCWJG4pKFcQbt27VqMHz+eQ5FeUlKCa9euQUFBgSP9QU2iPOS9j48PlixZwjHAKykpQWJiIrKysqo9Qqk80dDQwN27d7miWGRkZMDe3l5geqkGDRrg5MmTsLW1hZ2dHebNm4fhw4fj6tWr6NGjB/Lz8wW2bWdnh9DQUHTu3Bmurq6wt7fHqlWrsG7dOqxcuVJo+m2gLDWaj4+PRClri4uLsXTpUowdOxYNGjQQWY7NZuP169fMJFpTUxMpKSmMM9Xr169Rv359vkYhUVYgslgsjtQNNZErV66gX79+YLPZzHvq3r17KCkpwX///Ye2bdti165dePXqFWbPnl0lffrw4QPf6AXr1q3DhAkToKKiIjTdRU1eZVeOsbExgoODuVYaxsbGYtasWcjJyREo/+zZM2zatIkjvc7EiRPlOtl0dnbG4MGD8ffffwMou4c6duyIgIAAWFlZYcGCBejZs6fAyLMBAQGIjIxEQEAAxo8fj/v378Pc3BwxMTEICQnB1atX5dZ/QLq0XHv37sWUKVNgb2+PjRs3Yvv27Vi7di0mT56MZcuWVclilf79+yMvLw979+5lUpw8f/4cHh4e0NXVxaFDh3jKySuNcFVy//59dOnSBS1atMD58+fRr18/PHjwAB8+fMDly5dFVjoUFxcjPj4eT548gbu7OzQ1NfHixQtoaWkJdQyZNWsWrl27hn379qFJkya4ffs2Xr9+jVGjRmHUqFEiGV/z8vKwf/9+PH36FLNmzULt2rVx+/Zt1K1bVyQH3tLSUpw7dw5RUVE4dOgQFBQUMHjwYHh4eMhsBTk/rK2tER0dXS3G8Pz8fGhoaHCtKP7w4QM0NDSELrY6ePAgk0L54cOHqF27Njp06MAoroS9E549e4bu3buDiJCeno6WLVsiPT0dderUQWJiokgGAkkQlrpd2JgFANTU1PDw4UOYmppyyD99+hTW1tYiRy6vDBHh1KlT2L59O/bv3y9RHfJm9+7dKC4uxujRo3Hr1i306NEDHz58gJKSEiIiIjBs2DChdfTt2xenT59GkyZNMG7cOIwaNYojOhVQZrgxNDSUqeOarFBTU0NycjIsLCw4ytPS0mBvby/UqeHMmTNYvXo147j8v0LPnj2ZrDRA2Ri5RYsWGD16NKysrPDvv//ir7/+wuLFi/nWcfPmTXz+/BnOzs548+YNRo0axaTk2759u9yi9926dQuzZs3CkSNHuIyu+fn5cHNzQ0hIiND3Xn5+Pnr06IGbN28yc61nz56hY8eOOHjwoEDDJpvNRs+ePRnD9rFjx+Di4sJlSJZnGt6fHR0dHVy7dg1NmzaFjo4Orl69CisrK1y7dg2enp5ITU2t7i4K5PDhw0hMTER8fDwePXoEBwcHsZ04fxUePHiAf//9F3v27OEbSZ9fFCpeVJUzRV5eHi5evMjoKR88eAAHBweh+kFZphOtatzd3dGyZUvMmDEDS5YsQWhoKPr374+zZ8+iRYsWQt9ZJ06cgIKCAldQhzNnzqCkpAQ9e/YUqR95eXnIyMiAkpISzMzMhGZZEHUhIovFwvnz53nuk1Y/KCtu3ryJ2NhY5OTk4ESnCfcAAQAASURBVPv37xz7auo34+PHj9i9ezc8PT15fnd37tzJc19FgoKCEBISgt69e/OMGC1Mx9WtWzeMHj0a7u7uGD9+PFJSUjB16lTs2rULHz9+xLVr1yQ/QTkj7XNXnbRr1w5DhgzBjBkz8ODBA9jZ2SEjI4PRLyckJMDT0xNZWVkC67l16xY8PT3x6NEjrkVvwhYtVE4RXO7I1KZNG4EL43mxc+dO/Pvvv4xtrEmTJpg9ezZGjhzJdezatWtx9OhRxMXF8azL1dUVbm5uHMFCfsOJNPpJQH7zfHFIT0+Hu7s7V1aZn2HBDQC8evUK165dY4IzGRoaok2bNkzQluogLy9PJAfSn/naP3nyBPPnz8eBAwcwdOhQBAYG8s2CyIs1a9ZAQUEBU6dOxblz59C3b18QEX78+IHg4GCRFioOGzYMT58+xc6dOxmHtIcPH8LT0xONGzfG3r17JT6/mszOnTuxYMECjB49mol0ef36dURGRmLhwoV4+/YtVq1ahdmzZ2P+/PlyaX/YsGFcDvDfv39HdHQ0Ro0aJbQONTU1pKSkcNnD09PT0bx5c546ruq2i9TEuZ443L17F/Pnz2ciqmtqanJcZxaLhatXr6JVq1Zcsmw2myPboyB4vbeuX7+O8PBwxMTEoLCwEAMGDMDYsWPRpUsXkRatyDIroKenJzp27Ihx48aJXGdFpk6dir1792L+/PnMtbpx4waWLl0Kd3d3rF27Ftu2bUNERAQuXbrEIbt7925Gv5KRkQEjIyN07tyZccatrPPlR3p6Ot+I+35+fnzlPn36BA0NDS6n/NLSUhQUFNTI+7Yi9+/fR/fu3VFUVMSMu5KTk6GiooLTp0+jWbNmfGUr22aAMntseHg4IiIikJWVBUdHRyQmJnLJCvNjqMjP4NPwGzlCv/nNL4Cenh7dunWL+fvjx4/EYrEoPz9fqCyLxSI2m838K2gTh3fv3tH+/ftp5MiRpKioKLa8uDg5OZGTkxOxWCxq374987eTkxN169aNJkyYQGlpaXLtgzSYmpqSqakpsVgsMjY2Zv42NTWlJk2aULdu3SgpKUloPR8/fqStW7eSr68vvX//noiIbt26Rc+ePZNb358/f04zZ87keb/l5eXRrFmz6NWrV0LrMTExoVWrVnGVr1q1ikxMTATKDh8+nFavXk1ERAEBAaSvr0/jxo2jhg0b0oABA4S2HRwcTGvXriUiorNnz5KKigopKysTm82mkJAQofJERDExMWRubk6hoaF05coVSk5O5tiEoa6uTpmZmSK1VQ6LxaJevXrRgAEDaMCAAaSoqEjdunVj/u7Vq5fAZ4/FYpGpqSkNGDCA3Nzc+G5VRXp6Op06dYq+fv1KRESlpaUiy3769Ik2bdpEPj4+5OPjQ2FhYfTp0yeBMvn5+SJvopKTk0M5OTkCjzE1NaV3794REVHDhg05nveKm5mZmcjtVif+/v6ko6NDy5cvp8TEREpMTKRly5aRjo4OBQQE8JX7/v07ubi4VMu7WV9fn27fvs387ePjQ927d2f+Pn78ODVu3FhgHY0aNaJz584REZGGhgY9efKEiIgePXpEOjo6YvcpNzeXcnNzRT6+ZcuWdPXqVbHbGThwIKmrq9O6des4yi9fvkxNmjShJk2a0JUrVwTWERkZKXAThZycHLK3t6datWqRubk5mZubU61atcjBwUHgdWCxWCJtoo47MjIyaMGCBfTnn3/S69eviYjoxIkTdP/+fZHkJSUvL48CAwNpyJAh1LNnT1qwYAG9ePFCZPmsrCyytLQkNTU1UlBQYO6/qVOn0l9//SVUvqioiMaNG0eKiorEYrGoVq1axGazacSIEfTjxw+h8snJyaSvr0+NGzcmRUVFpv0FCxbQyJEjRT6Pcr59+0axsbHUvHlzvr/dt2/f6MiRIzzf7fn5+XTkyBEqLCwUqb0TJ05Qjx49KCsrS+y+Vjf6+vo0aNAgCg0NpZSUFInq+PHjB+3evZtmz55NkyZNoq1btzLfXlER95vNYrGYZ4yI871JRPTq1Suhz62ZmRmdPXuWSz4yMpKsrKzE6j8R0dOnT2nhwoXUoEEDUlZWpt69ews8/t27dzR58mSysrIiPT090tXV5diqki9fvtCtW7fo7du3IsuMHTtW6Pu9tLS0xj4XTZo0odmzZ3OVz549m5o0aSJUXkdHh5SUlIjNZpOGhka1/n6SkpaWRps3b6YlS5aQv78/x8YPQ0NDunHjBvP3/PnzydHRkfk7NjZWouenKhg+fLjAsWRQUBB5eHiIVFdpaSmdPn2aVq5cSaGhoZSQkCCS3OjRo0XahJGYmEgeHh7Url07Zm6+c+dOunjxokj9+JmpU6cOM963sLCgU6dOEVHZmFlNTU1u7cbFxZGVlRVfPYW1tbXI90FFuWPHjtGoUaOoVq1apKysLKvu1kgeP35MmzdvpuHDh1P9+vVJT0+P3NzcKCQkhO7evctTRhTdojhjdVnw7t07OnDgAE2ZMoVsbGyIzWaTnp5elbVfHbx//56eP39OREQlJSW0bNky6tu3L82YMYM+fPggVN7W1paOHz/OVX7y5Emys7MTKp+ZmUm9evUiBQUF5ndXUlKiP//8k0M/KOrYXRyk1Q/Kgr1791KtWrWoT58+pKSkRH369KEmTZqQtra2SN+M6iIgIIAGDx7Md/+QIUMoMDBQYB389Fui6rhu3LhB58+fJyKi169fU/fu3UlTU5NatGhBd+7cEet8qhppnztJ0dHR4RrX8tv4cfDgQVJSUiIXFxeqW7cu9enTh2P/nDlzaMiQIUL7YmdnRwMGDKCkpCTKzMykrKwsjo0X27dvl+m7YPXq1aSmpkZz5syhI0eO0JEjR2j27NmkpqZGwcHBXMe3atWKjh49yre+Y8eOUatWrWTWP17IyrYiC/bt20dDhgyhNm3akIODA8fGD0n1k+XIep4vCa1ataJ27dpRdHQ0XbhwgeLj4zk2QVSnnqCgoIA8PDxIQUGBFBUVycDAgAwMDEhRUZEUFBRoxIgR9OXLF7n2gYho+fLlFB0dzfw9ZMgQYrFYVL9+fb5jxnKkufbVyaRJk0hJSYm6d+8us+9TVlYWHThwQCRbYjlaWlp0/fp1rvJr166Rtra2TPpVE3FxcaGYmBiu8piYGHJxcSGisvl206ZN5dI+m83m0HGW8+7dO5HnOT179qTw8HCu8vDwcOrWrRtPGVnYRaSZJ9e0ud6PHz/o7NmzHLbg58+f0+fPn3keP3bsWAoKCmL+1tDQoD179lB8fDxduHCBRo4cSSNGjOApW3E8c+jQIWrUqBGFhYUx9v+wsDCysLCgQ4cO8ZRnsVhkb29PoaGhEo0LK+qfPD09SUtLi4yNjRlfABMTE9LS0hJprhEYGEh16tQhT09PWrVqFa1du5ZjE0ZxcTEFBgaSoaEhc88ZGhpSUFAQFRcXExFRdna2UFvnixcvaO/eveTh4SGWH9GWLVtIQUGB6tatS82bNyd7e3tmEzReOXjwIFlYWPD8LhYUFFCTJk0EjglrCl++fKEtW7bQjBkzaMaMGSLbdfi9t8o5d+4cubu789wnaI73M/o0/EZ+/Ha+/c0vQWVjNlHZoOHp06dCZaUdMFTkwIED5O3tTba2tqSgoED6+vo0YMAAWrt2rdBJlqwYPXq0WI5yNQ0nJyeJFXKydoQRlZkzZ9L48eP57v/rr79ozpw5QuvZsWMHKSgoUJ8+fWjJkiW0ZMkS6tOnDykqKtKOHTsEyspawSnJRJffJEfUyUa/fv0oIiJCrH5KawyePHky6erqkr29Pa1du5Zx2K5q3r17Ry4uLsy1Kr93x4wZQzNmzJBbu6JMFkVZfPDjxw9auHAhaWlpMcdraWnRggUL6Pv373Lrf02htLSUgoODycjIiLn3jYyMKCQkRKgzVkVjfFWioqJC2dnZzN+tWrWilStXMn9nZWUJdQZQUVFhjAcVlcMPHjwgdXV1kfpRUlJC/v7+HPeOtrY2BQQEUElJiUDZ69evk4uLC8XHx9O7d+9Edhhv374932v+9etXmjp1KtWqVUtg2zo6Ohyburo6sVgsUlZWFkuxXFpaSmfOnKF169bRunXrGGV7VREfH0+qqqrk6upKSkpKzG+4bNkyGjRokNzazc7O5vtsVLwvBdG/f38aMWIEFRUVcdx/Fy5cEOo4XpGcnBw6fvw4xcTEiPUsdunShXGCq9j+5cuXqWHDhiLXQ0T08uVLWrNmDf3xxx/EYrGoTZs2PI8LCQlhFKj8+hQaGipSm2/evCEnJ6ef2glP1rx48YL+/vtvocdJ+s1msVh04cIFZo6hrq5Ox48fZ/6Oi4sT+r1dunQpWVtbU1JSEmlqatLFixdp9+7dpK+vz7WggB+FhYW0e/ducnZ2ZpzOg4ODRZo/9OzZkywsLGj58uW0Y8cOioiI4NhqKleuXKFjx45xlEVGRpKpqSnp6+vT+PHj5eL8ImuOHz9OKioqZGNjQ15eXuTl5UW2trakoqLC00GoMpV/r5/l9ytHUuWysrIyx8IwR0dHDseZzMxM0tDQENi2s7Mzffz4kas8Pz+fnJ2dxT8ZETE3Nxc4H0tJSfkpFLv79+8nVVVVGjduHCkrKzPvzdDQUOrZs2c1907+dO3alfbs2UNEROPGjaPWrVvT7t27qXv37tS6dWu5tdu3b1+eTjblrF27VuSFpuXOm+X6rnLnzapcqFodsFgsMjAwoKCgIEpOThZpcWxlhwlBm7ypqJ+sU6cODRw4kNauXSvyuRD97zrOq6io8FwcnpmZKXSenJOTQ3Xr1qUGDRrQ0qVL6dChQ3To0CEKCgqiBg0akKmpKX38+JGOHDlCy5cvl3nfq8sBsiK2tra0fv16Ivq/uVJpaSmNHz+e/Pz8qqQPktC8eXNmgTEvzp07R/b29lXYo9+IgrAxrqjj3XPnztH06dNp+fLlXE4JixcvpgsXLgjti4aGBqWnp4vV/8qOAPXq1RM7OEVFTE1NeS4Kj4iIIFNTU65yHR0dgXqY7OxsiRbYi4OsbCvSsnbtWtLQ0KApU6aQkpIS/fXXX+Tq6kra2to0f/58vnKS6ifLkcU8X1pUVVUpNTVVItnq1BN4eXkxi9vKHZ6IyhyjTp8+TU2aNKFx48bJtQ9EZc/d5cuXiYjozJkzpKOjQ6dPnyYvLy/q2rWrQFlprn11wmKxSFVVlctJXVSndVmhoaHB0/n39u3bpKmpKff2qwsVFRWeeuy0tDRSVVUlorIF9+X/lzUsFovevHnDVX737l2R9cqbNm0ifX19+vvvv2nXrl20a9cu+vvvv8nAwIA2bdrELCA5cuSITPsuzTy5Js31JAlMYmlpyRGQp3JgiKSkJKGBwIjKbIm89JDHjx+nFi1a8JTp27evzBZDzJkzh8aNG8f13p8wYQLNmjVLqLwsnSfFDV5FVOY8evr0aZo3bx61bduWlJWVyd7enqZPny6SvImJiUTzyK5du9LWrVv57t++fTtfx/dfAV6+ZL/5jaz57Xz7m1+CysZsXgZtUZwIJRkwVEQWkbh+IzmydIQRh2bNmgk0PFy+fJmsra1FqispKYnc3d2Zyam7u7tIEX9rApVX84uyur8imzZtIkNDQ5o5cyZFRUVxTK5kPcGqSGFhIUVFRZGrqyupqanRkCFD6NSpU2JFnZWWkSNHUvfu3Sk3N5fj3j116pTI9w5RmdPjyZMnRb52FSeDERERZGhoSL6+voycr68v1atXT6iibOLEiWRgYMC1cMHQ0JAmTpzIV+779+9kbm5ODx8+FPkcazqfPn0SGnG4ItOnT6e5c+fKsUe8adSoERN16/Pnz6SkpESXLl1i9t+6dYvq1KkjsI4WLVrQrl27iIjznevv708dOnQQqR++vr6kr69PGzduZO6dDRs2kL6+vkDFNlGZMqlly5Ziry4W5tRLRGJHASvvT5cuXZjryg9RVlcnJiaK3b4ktG3blomKVPE3vHbtGhkZGcmtXVmsjq9duzajnK7Y98zMTKkUiwcOHCBbW1uhx2lpaVFGRgZX+1lZWSJFgcvPz6fw8HBydXUlRUVFatKkCfn7+zN18kKWEWm6dOnyUzpRllNcXEz79+9nFisdOHCAQ+HGj/v371NoaCht3ryZcaR7+/YtTZ8+nVRUVET65kr6za64KEnSxUqlpaUUGBjIOPyzWCxSUVGhhQsXCu33zZs3adKkSaSjo0MtW7aktWvX0qtXr0hRUZEePHggVJ6o7F6vqgWFlSkuLqZt27bR8OHDqUuXLuTs7MyxCaJHjx4cSsmUlBRSVFSkcePG0erVq8nQ0JAWLVok5zOQDbm5uTRv3jwmssP8+fOFZhz4VZBUuWxiYsJ814uKikhVVZXDsSYlJUWocYifgvb169ekqKgodp9ERVlZWeCC4qdPn5KKiopIdRUUFNDx48dp06ZNYkf1kBZ7e3vGCaTie/P27dtUt25dubdf3QiKYijPd6qJiYnAedajR4/I2NhYaD02NjYczpvr1q0Ta5Huz8y0adPIwcGBlJWVqV27djRv3jw6ffp0lURQkwWDBw+m0NBQunfvnkTyP7Pj/PHjx3nOy06fPk0nTpwQKl+3bl2Ki4vjKj979izp6+sLlB07dix16tSJvn37xrXv69ev1KlTJ+rQoQOpqKjQ4cOHOfYPGDCAmSeWf+v5bTUZNTU1xnmwdu3ajI784cOHZGhoWI09E4yGhoZQJ0RRHXmKioooNTVVpKwqFamuBT+yQNrn7legf//+tH//frFkhGVIERdlZWWeDsBpaWk8dRUaGhp08+ZNvvXdvHlT6EI1aZGlbUUamjZtSlFRUUTE+Tv8888/AhfqSqqfLEeaeb6s6Nixo8QBAapTT6Cjo8M4vfLi0qVLcnceJypzhCyfl0+dOpUmTJhARGVZFIS1L821r04WL14s0iYIb29vnvPR0NBQmjZtmkj96NevH3Xq1IlZeERE9OzZM+rcufMvvVDQwsKCp01p7ty5TGakGzduUP369WXabvniZzabTba2thyO1nZ2dqSpqSlSpHgi2Wf3ExVZzZOrG0kCk6iqqnJEY60cjCE7O1sku4aKigrPa/jw4UO+OiphUUfFoU6dOjwXLaSmplLt2rVl0oa8aNeuHamoqJCDgwP5+PjQ4cOHxV4gqampKdFYsV69egIXiaWnp1O9evXErlfeHDlyhAnyVdn3QRw/kvj4eLHnZpXJz8/naV8uKSn5qQMj/kZ2KOI3v/lF6NKlC4iIo6xPnz5gsVggIrBYLJSUlAis4969ezAzM+MqNzMzw8OHD4X24c2bN+J1Wk7cvHkTsbGxyMnJwffv3zn2HTx4sJp6JTrPnj3D0aNHefY/ODiYr9yNGzewefNmrnIjIyO8evVK5v0sJzMzEyYmJnz3N2jQAFlZWSLV1aZNG+zZs0fsPpw4cQIKCgro3r07R/mZM2dQUlKCnj17CpSfOnUqGjdujKlTp3KUr1+/HhkZGQgJCRHah+zsbLRv3x6KipyfluLiYly5cgUNGzYUKD958mQAvH9jUZ5fSVFWVsbw4cMxfPhwZGdnIyIiApMnT0ZxcTEePHgADQ0NubRbkTNnzuD06dNo0KABR7mFhQWys7OFyj99+hQDBgzAvXv3mHceUHbdAPC9dp07d2b+HxAQgODgYAwfPpwp69evH2xtbbFlyxZ4enrybT8qKgrR0dEc95mdnR2MjY0xfPhwbNq0iadcrVq1UFhYKPT8fiY0NTXFOr64uBjh4eE4d+4c/vjjD6irq3PsF/TOk4YhQ4Zg+vTpmD9/Pk6cOAFDQ0O0bduW2X/z5k00bdpUYB1+fn7w9PTE8+fPUVpaioMHD+Lx48fYuXMn/vvvP5H6ERkZiW3btqFfv35MmZ2dHYyMjDB58mQEBQXxlfXw8ECtWrUQFRWFunXrMve7MNhsttBjOnXqJFJdFbGwsMDy5csxYsQIpKam8j0uJCQE48ePh5aWFtc+bW1t/PXXXwgODkbHjh351nH+/HlMmTIFSUlJXPXk5+ejffv22LRpk9DzuHfvHqKiorjKDQwM8O7dO4Gy0lA+LqtMQUEBVFRURKqjtLSU57vt2bNnQp/DzZs34+zZs1BSUsK0adPQpk0bnD9/HjNnzkRaWhpGjRoltH1lZWV8+vSJqzwtLQ36+vpC5evWrQtdXV0MGzYMy5YtQ8uWLYXKpKeno3nz5nz329nZIT09XWg9AHDlyhVcvXpVYH01lYyMDPTq1QvPnz9n3lPLli2DsbExjh8/jkaNGvGUO3r0KAYPHozi4mIAwMqVK7F161YMHToUf/zxBw4dOoQePXoIbV/Sb3ZmZqaop8gXFouFBQsWYPbs2cjIyEBBQQGsra2hoaGBb9++QVVVla9smzZt4O3tjaSkJKHvd35YWlri27dvknZfKqZNm4aIiAj07t0bNjY2Ir/zAeDu3btYsmQJ83d0dDTatGmDrVu3AgCMjY2xaNEiLF68WNbdljkNGjTA0qVLpa6nsLCQa57F67tUk/j48SOGDBkitlyvXr3g6+uLFStW4PDhw1BTU+P4xqakpPB9b6SkpDD/f/jwIcecsqSkBKdOnYKRkZHYfRIVfX19PH78mKeOAgBSU1NRp04dofXcuXMHvXr1wtevX/HlyxfUrl0b7969g5qaGgwMDLjmgLLm8ePHPMck2trayMvLk2vb1Q0RQVtbG6qqqiguLoaBgQFOnTpVJW2/fv0atWrV4rtfUVERb9++FVrPxIkT0blzZ9jY2Miyez8F5XqQvLw8XLx4EQkJCViwYAEePHgABwcHXL58madceno6/Pz8sHnzZp5j9UmTJiEwMBDm5uZy7f++ffukkg8MDERYWBhGjRqF6OhoptzR0RGBgYHSdk+u+Pr6Yvny5VzlpaWl8PX1Faoj69+/P6ZPn45Dhw4x34iMjAzMnDmTY+7Ki1OnTiEmJobnvEZVVRVLliyBk5MTtm3bhv79+3Ps19bWZsY42traAtvhh7T6QVmgq6uLz58/AyjTyd6/fx+2trbIy8vD169f5d6+pCgoKODFixd8dbwvXrwQqk/4+vUrvL29ERkZCaBsfmhubg5vb28YGRnB19dXoHx8fDzXGA0oG7tdvHhRxDOpHqR97qThxYsXCA4Ohp+fH8/3bmBgIGbNmoW6detyyVYc7wnDzs5O4P5t27bB09MT9+/fh42NDdd3WNj7QxY0btwYsbGxmD9/Pkd5TEwMLCwsuI5v1qwZo5PkxZkzZ9CsWTO59LUcWdpWpCEnJwft27cHUPa+Ln+PjRw5Em3btsX69et5ykmqnyxHmnm+rPD29sa0adMwe/Zs2Nract27gu796tQTlJaWQklJie9+JSUllJaWyr0furq6yM3NhbGxMU6dOsWMk4hIqE1LmmtfnSxatEjqOg4cOICjR49ylbdv3x7Lly8XySa5fv169OvXD6ampjA2NgYA5ObmwsbGBrt375a6jzWVVatWYciQITh58iRatWoFoMyek5qaiv379wMos5cPGzZMpu26ubkBKNOzde/encN2qqSkBFNTUwwaNEikuiR9NqW1i8hqnlzO169fefoyyPvZvXjxIq5cucL1DjQ1NcXz5895yqioqCA7O5vRafv4+HDsz83NhZqamtC2rayssGzZMmzbto1p//v371i2bBmsrKx4ylT235GG4uJipKamcum3U1NTq+Sd//r1a8yaNQtxcXF48+YN17kJeu+npqZCXV0dlpaWsLS0hJWVFXR1dcVqf8iQIThz5gwmTpwoltzHjx8Zuwgvfvz4gY8fP4pVZ1Xg5uaGV69ewcDAgHkH8UKYH0lFfwhJOHToEObOnYu7d+9yPSffvn1Dq1atsGrVKvTt21eqdn7zc/Pb+fY3vwSyMGYDkg0YKvPkyRPs2LEDT548wdq1a2FgYICTJ0/CxMRE7ooKoMyYPGrUKHTv3h1nzpxBt27dkJaWhtevX2PAgAFyb19a4uLi0K9fP5ibmyM1NRU2NjbIysoCEaFFixYCZaV1hJEUVVVVZGVl8VUSZWVl8VWS8OovPwQZxKVVcMpiouvs7IyXL1/CwMCAozw/Px/Ozs5CFQ1VMSgWBpvNZpxX5eXsy4svX77wnNR8+PABysrKQuWnTZsGMzMzxMXFwczMDNevX8f79+8xc+ZMrFq1SqQ+XL16FWFhYVzlLVu2xLhx4wTKKisrw9TUlKvczMxMoAIMAP7++2+sWLEC27Zt43Lcrsm0aNECcXFx0NXVhYODg0DF6u3bt/nuu3//PvNuS0tL49gnrrJWHPz8/PD8+XNMnToVhoaG2L17NxQUFJj9e/fuFTpJ6N+/P44dO4aAgACoq6vDz88PLVq0wLFjx9C1a1eR+vHhwwdYWlpylVtaWuLDhw8CZe/fv487d+5I7EQmDxQVFfHixQuBxyQnJ2PFihV893fr1k3ocyuKA++aNWuEOt/q6Ojg5cuXXE49d+7ckYsz0YwZMwCU3dv//PMPx3uvpKQE165dg729vUh1devWDSEhIdiyZQtTZ0FBARYtWoRevXrxlVu+fDn8/PxgZ2eH1NRUHDlyBAsWLEBoaCimTZuGv/76SySFR79+/RAQEIDY2Fim/ZycHMydO1ckJePRo0fRpUsXkZzByykuLsbbt2/5jjfevn0rUIFSkeo0jkjL1KlT0ahRIyQlJaF27doAgPfv32PEiBGYOnUqjh8/zlMuMDAQf//9N5YsWYJt27ZhxowZmDp1Kk6cOMEoqkVB0m92+SKkHz9+8FXyiur0rqSkBGtrawBAUVERgoODsXLlSoGLzbp06YLt27fjzZs3GDlyJLp37y72d2bjxo3w9fWFn58fT2OyPJ03o6OjERsbK/D55sfHjx85DO0JCQkcY+NWrVohNzdXJv2UN4WFhUhJScGbN2+4xs7CjPlfvnzB3LlzERsbi/fv33Ptr8qxryRIqlxesmQJBg4ciM6dO0NDQwORkZEc49Pw8HB069aNp6y9vT1YLBZYLBZcXFy49quqqiI0NFS8ExEDV1dXBAUF8VwYQEQICgqCq6ur0Hp8fHzQt29fhIWFQVtbG0lJSahVqxZGjBiBadOmyaPrHBgaGiIjI4NrvnDp0iW5Ox9WJ5mZmejXrx+zkLtBgwY4cOCASAtuZEG5w1vjxo157k9JSUG9evWE1vP3338DKNONZWZmolGjRj/VvE0WlJSU4MePHygqKkJhYSGKiorw+PFjvsf/+++/MDY25jtWNzY2xr///st3kaos2bVrF8LCwpCZmYmrV6+iYcOGCAkJgZmZGZfjZ2V+Zsf59PR0ZqxUEUtLS2RkZAiVX7lyJXr06AFLS0vGOP3s2TN07NhR6Fzt3bt3PPUj5Zibm0NRURFjx47l2rdjxw6e/xeH6nSALKdTp044e/YsbG1tMWTIEEybNg3nz5/H2bNn0aVLF7m3LykODg44fPgwx8Lkihw6dAgODg4C65g3bx6Sk5MRHx/P8f12dXXF4sWL+TrfVveCH1kg7XMnDcHBwfj06RPf9+7nz58RHBzMUxdTPt6rHMiAF8LGy1evXsXly5dx8uRJrn38HALKx5r8/hYXf39/DBs2DImJiXB0dAQAXL58GXFxcYz+oiJjx47FjBkz0KxZM/Tp04dj37FjxxAUFCS3wADlSGNbkSWGhob48OEDGjZsCBMTEyQlJaF58+bIzMwU6DQkK/2kJPN8WVHuoFfx2yRqUKPq1BP06dMHEyZMwPbt27nez3fu3MGkSZOqxAlm4MCBcHd3h4WFBd6/f898a+/cucN3LF6ONNe+JvL9+3d8//5dpIA279+/57nYSEtLS2T9mLGxMW7fvo1z584xwTCsrKxEmif/zPTr1w+pqanYvHkzY1Pq2bMnDh8+zIxDJ02aJPN2y52uTU1NMWzYMJGDaFTmx48f6NGjB8LCwnguDBGEtHYRWc2T3759izFjxvD85gPy17FJEpikfKxbPj6ozMGDB4WOdQEgLCwMffv2RYMGDRgn45SUFLBYLBw7doyvnKzsnWPGjIGXlxeePHmC1q1bAwCuXbuG5cuXY8yYMSLVIWkQOAAYPXo0cnJy8M8//6BevXpindf79+9x7949xMfH4/Tp01iwYAGUlJTQuXNnODs7Y/z48ULraNy4Mf755x8kJSXxXLTBb4G9qakpbt68ydMeC5Q58AsLolYdVNR/y8KPhJcvDFB2f6qoqKBx48Y8gyBs2rQJc+bM4WkXUldXx9y5c7F+/frfzrf/61RDtN3f/EZulIcd58Xbt2+Fyl+7do0MDAxIX1+funTpQl26dCF9fX0yMDCga9euCZWPj48nVVVVcnV1JSUlJSbs+7Jly2jQoEGin4gU2Nra0vr164no/9LjlJaW0vjx48nPz69K+iANrVq1YvpZ3v/Pnz9Tv379aOPGjQJlvby8yM3Njb5//04aGhr09OlTys7OJgcHB5HTlEhCr169aNy4cQL7xS8lX3naDFE2QaioqDAp3SqSmZlJampqQs+BX0qq9PR0kdJMlJ/LmzdvuMofP34sclq26qCwsJCioqLI1dWVVFRUaPDgwXT8+HGRUtPLip49ezKppMrv3ZKSEhoyZIhI7w49PT0m9aeWlhaTciMuLo7s7e1F6kOTJk1o9uzZXOWzZ89mUtXww9/fn4YPH06FhYVMWWFhIXl4eAhNL+Tm5kaamppUr1496tat20+TSnHx4sVMutFFixZJnF7pf53WrVuTt7c3V/mUKVOoTZs2AmWrMy1X5XQmhw8fpk2bNlGzZs2oR48eAmX5vW/LSU9PF5pCWlbpkWbOnEkdOnSgly9fkqamJqWnp9OlS5fI3NxcLveuk5MTOTk5EYvFovbt2zN/Ozk5Ubdu3WjChAmUlpYmUl25ublkbW1NVlZWpKioSG3btiU9PT1q2rSpwBRGTZo0oYiICCIiSkxMJBaLRb1796aCggKxziUvL49cXV1JR0eHFBQUyNjYmGrVqkWdOnUSua4fP37Q2bNnKSwsjD59+kRERM+fP6fPnz/zPL5NmzYCU64vXbpU6HNTzunTp6l9+/Z04cIFevfuHeXn53NsNRk1NTUmdW1F7t69S+rq6nzltLS0mGevuLiYFBQUJHqHSPvNHjhwIJWWlnKVv3r1ipo1a8ZTprCwkHx9femPP/6gdu3a0aFDh4iIKDw8nOrVq0cNGjQQeG+Uk5OTQ/7+/mRqakp169alqVOnkqKiosD3SUWkTacpDfXq1aPHjx9LJGtiYkIJCQlEVJYCWFVVlc6dO8fsT0lJIV1dXZn0U56cPHmS9PX1JU7DN3nyZLKysmLSiIeHh9OSJUuoQYMGtHv37io4A+lYunQp1alThzw9PWnVqlW0du1ajk0YeXl5VFxczFX+/v17Kioq4imTlZVFmZmZxGKx6MaNG5SVlcVsL1684FmfLMnIyCBtbW1q3bo1xcTE0N27d+nu3bsUHR1NrVq1Im1tbYFjinK0tbWZ+YG2tjbzzCclJVHTpk3leg5EZb+dtbU1JSUlkaamJl28eJF2795N+vr6tG7dOrm3X10MGjSILC0tKSoqig4ePEjt27enFi1aVFn7U6ZMIRsbG/r27RvXvq9fv5KNjQ3PcTivY8eOHUsKCgqkoKDA6LmmTJlCy5Ytk3m/axLe3t5ka2tLCgoKVKdOHRo4cCCtXbuWkpOTeX7Ly2nSpAldv36d7/6bN28KnWPLgo0bN1KdOnUoMDCQVFVVmd9ux44d5OTkJFTezMyMGStVTGUaGRlJVlZW8uu4DKhbty7FxcVxlZ89e5b09fVFqqO0tJROnz5NK1eupNDQUGYsIYyGDRvS6dOn+e4/efIkNWzYkO/+Tp06kb+/PyUmJgrUc/NDWv2gLHj//j2T/rmkpISWLVtGffv2pRkzZoidVrUq2b9/PykqKlJoaCjHN764uJjWrVtHtWrVon379gmsw8TEhK5evUpEnM9Nenq6QN1oRf0wr7Gempoabd++XQZnKT9k8dxJSrNmzejixYt891++fJmsra157qs4vjt06BA1atSIwsLCKDk5mZKTkyksLIwsLCyYOZggGjZsSH///Te9evVK5L6zWCzS0dEhXV1d0tXVJRaLRdra2szf5Zs43Lx5k9zd3alFixbUokUL8vDwoNu3b/M93sPDg1gsFllZWZGbmxu5ubmRpaUlsdls+vPPP8VqWxKksa3IEi8vL0YXtn79esbOp6OjQ2PHjuUrJ6l+UlbzfFlQ8TngtQmiOvUEHz58oB49ehCLxaLatWuTpaUlWVpaUu3atYnNZlPPnj3p48ePcu0DUZld+t9//6WpU6dyPGvBwcG0detWgbLSXPvqJjw8nKZMmcLoE3x9fUlJSYnYbDa5urrSu3fvBMo3a9aMQkNDucrXrVsndKwZFxdHVlZWPHWYeXl5ZG1tTYmJiWKczW8koaioiHJzcyk7O5tjE4U6deqIbAeoiLR2EVnNk93d3cnR0ZFu3LhB6urqdObMGdq1axc1bdqU/vvvP9FORgqGDh1K48ePJ6L/009//vyZXFxcaPTo0Txlyse669ev57B/izPWLaegoIA2b95MPj4+5OPjQ1u2bBFoE6k83uG3iUJJSQmtWLGC6tevz4yV69evTytWrBBJT3fu3DlSU1MjGxsbUlRUJHt7e9LR0SFtbW1ydnYWKq+hoUF37twRqa+CKC0tpRs3bpCnpycpKiqK/M00NTXlu5mZmfGVmz9/PpmYmPAcp758+ZJMTExo/vz5Ep9PVRAZGcnhh1BOUVERRUZGilRH+fiEl269/N9OnTpxzVvr1asn1K5br1498U7oN78cv51vf/NLIYkxuzLiDhgq0rZtW1q9ejURcSrYrl27RkZGRiKehXSoqakxStbatWszzgkPHz4kQ0PDKumDNGhoaFBGRgYREeno6ND9+/eJqMyhQpBymkg2jjCScP78eVJQUKCZM2dyDFpevXpFM2bMIAUFBZ7KR6Iyh+3yLSIiggwNDcnX15dx6PL19aV69eoxjkL8kFbBKc1Et9xJks1mU69evTgcJ/v160empqbUvXt3oX3w9vbmaTgPDQ2Vm/P0pEmTSFdXl+zs7CgkJEQkJ315cO/ePTIwMKAePXqQkpISDR48mKysrKhu3brM8yAIHR0devr0KRERmZub0/nz54mozGCvqqoqUh+OHz9OKioqZGNjQ15eXuTl5UW2trakoqJCx48f5zq+spOspqYm1alTh1m4UKdOHdLS0hLqQDt69GiB2/8Subm5lJubW6VtLlmyhLl3JOHjx4+0detWmjdvHr1//56IiG7dukXPnj0TST4+Pp7U1dXJysqKxo4dS2PHjiUrKyvS0NAQqiCLjY0la2tr2rFjB928eZMxjJRv8oTXxKxu3bo0fPhwevHihUBZc3NzgUabAwcOCJwkE8nGgZeobFI6btw4UlRUJBaLRbVq1SI2m00jRoyQq0PR6NGjZeLg+ePHD9q1axfNnj2bJk2aRFu3bqWvX78KlFFRUaGcnBzmbyUlJbp586bEfbh48SJt2LCBVqxYIZaxJSsriywtLUlNTY3DkWXq1Kn0119/8ZTZvHkzqaur07Fjx7j2HT16lNTV1Wnz5s0itV/x3q1q44i06Orq0uXLl7nKL126JFBJx2KxOByzK47VxUHab3bLli25DHcvXrwgS0tLvs67c+bMIW1tbRo0aBDVq1ePFBUVafz48WRra0t79+6V6Hk9c+YMDR8+nFRUVMjCwoLmzZtHt27dEijTqlUrateuHUVHR9OFCxc4xrHx8fFi90EcVq1aRZMnTxbo7MSPiRMnUrt27SgxMZFmzJhBenp6HM6Wu3fvppYtW8qyu3KhcePGNHnyZLGM+RUxNjamCxcuEBExCy6IiHbu3FklxmxpkVS5/LNz48YNatasGZdTTrNmzQQ691WkolHLwsKCTp06RURlRqmqcMQqLS2lwMBAUldXZ74/KioqzEKGX5W6detyOAK9ePGC2Gy2XHUTFXn16hXVr1+fjI2NacWKFXT48GE6fPgwLV++nIyNjal+/foivU+mTp1Kf/zxB128eJHU1dWZb+fhw4dFXuj5szJ48GAKDQ2le/fuiSWnoqIi0FkiKytL5Hm6NFhZWTHzjorjnnv37pGenp5Q+Z/ZcX7ChAlka2vLMTZLT08nOzs78vLykmvb06ZNI1tbW56L01+/fk12dnYCdVyenp5kamrKOFx26dKFAgMD6cqVKyKN+arTAfJXYP78+cRisUhLS4vs7e3J3t6etLS0iM1m09y5c4XKV3R0r/jc3b17l7S0tPjKVfeCH1lQnc+dmpqaQGef7OxskcY8rVq14qkDPX78uEgLaCraNUQlIiJCpE3exMTEUP/+/ZlFzv3796eYmBi5t0sknW1FlpSUlNCPHz+Yv/fu3Uve3t60bt06vov1iCTXT8pjnl8dVKeeoJyHDx9SeHg4LV26lJYuXUrh4eH06NGjKmn7f5XyxV2urq5Uu3ZtmjhxIhkaGtLy5ctp5cqV1KBBA5o4caLAOrZv306qqqrk5+fH3C///PMPqamp0ZYtWwTK9u3bl4KDg/nuX7t2Lbm5uUl0bj8LHz9+pNOnT9OuXbsoMjKSY5M3aWlp1KFDB6n0ytOnTxdpbFUZae0isponGxoaMkHbNDU1mYABR44cIUdHRzHPSnwkDUwyZ84cvmPdWbNmya2/LBaL1q5dK/PxjiSBRKQJAkdUNs8WtKiJF/7+/vTlyxe6desWrV69mvr27Uu6urqkqKhIDg4O5OPjQ4cPHxarTnH59OkTNWvWjDQ1NWnSpEkUEhJCISEhNHHiRNLU1CRra2smQExNhc1m87y/3717J/K759y5c9SmTRs6d+4cffr0iT59+kTnzp2jdu3a0fHjx+nSpUvUrFkzLvuNioqKwLHFw4cPRbLJ/ubX5rfz7W9+KSQxZssSdXV1xompooItMzNT5Oih0mJkZMQ43Nra2lJUVBQREV25ckWgkq+mULduXWbVmpWVFR05coSIhEczq8ilS5ckcoSRhrCwMFJWViY2m82s3mKz2aSsrCzSYI2IyMXFhfm9KrJnzx7q3LmzQFlpFZzSTHTLnSRZLBYNGzaMw3FywoQJtHTpUpGcWuvXr8/TAerWrVtyc15nsVjUsGFDcnNz43Imreroq3l5eRQYGEhDhgyhnj170oIFC4Q68SUkJND379+pQ4cOjFFt+PDh1KNHD7p06RKNGjVK5IUHRGUR8ebNm8ec9/z58zmc1CoizGn2f8mB1szMjOdK7o8fPwp1BikpKSF/f39mgstms0lbW5sCAgKqJPqynZ0dsdlsateuHW3YsEEsB/Tk5GTS19enxo0bk6KiIvPNW7BgAY0cOVLkep4/f07z58+ngQMH0sCBA2nBggVMlBxB8Iv8V9OdB2WxuloWDrwVyc7OpuPHj1NMTIxEK87FZcyYMTwn8gUFBTRmzBi5tl05Snv5ynBx4fX7iUP//v1pxIgRVFRUxDFmvHDhAjVu3JivnKwi0lQ2hlSHcURSRo4cSc2aNaOkpCQqLS2l0tJSunr1KtnY2JCnpydfORaLRTt37mQWOJWPcSpHshYFSb7Z5bx584YsLS3Jx8eHiMregU2aNKEhQ4bwfe+bmZkxfbt37x6xWCwaM2aMRI6olfnw4QOtW7eO7O3thb47VVVVmeiZVY2bmxtpa2uTmZkZ9enTR6yx2tu3b6ljx47EYrFIU1OTDh48yLHfxcWlxq/sJypT5otrzK+Iuro645RgZGTEGAmePn0q8jzrf5WIiAiOyCWzZ88mbW1tateundyiEVU2Hty5c4diY2MpJiZG7AgbXbt2pT179hAR0bhx46h169a0e/du6t69O7Vu3VpWXRZKUVERPXjwgK5du8Y3yvuvBIvF4jLaVdQZVQVZWVnUs2dPjqge5VHARO2HpFEc/5fh5/xYzrlz56hu3bpy70dFJ+CKv11aWppIRqGf2XE+Ly+P2rZtS4qKisxCDUVFRXJ2dhY5Al5BQQEdP36cNm3aJFa09Q8fPpCFhQVj1Fy7di2FhITQX3/9RZqammRhYcEsXBVEZmYmbd++nUaNGkUmJibMOKZHjx60cuVKvnLV6QBZzq1btzgyVRw+fJj69+9P8+bNE+jAVlO4du0aTZ06lXr16kU9e/akadOmiZQRj6gsAma5c3rFueaUKVNECkzwMyOL505S9PT0BEanTkhIEGnRgYqKCs9oeqIa00eNGiU0yqW8ECW7noKCQrX0TRRkYVupLiTVT8pzni8KR44cYSKsV9aLiKMnqU49QXUi7JoJun6yuvbVSePGjRlb5o0bN4jNZtP+/fuZ/SdOnCATExOh9WzcuJGMjIyYZ8fMzEwk51FZZYX7WTl69Chpamoy0dJ1dHSYrSoyO7Vv3546depEJ06coDt37jBZeso3UZgyZQppaWnRH3/8QRMmTGACopVv/JCFXUQW82RNTU0mEJqJiQldunSJiMp0bFWx0JJIssAkRERXr16lqVOnUs+ePalnz540depUZs4vKjt37iRHR0eqV68eM+cMDg7m60BaOSiGtIibUbAi0gSBIyrLKNitWzee2Ub4Ue40qqCgQC1btqSZM2fS0aNHKS8vT+Q6ZEFeXh5NmjSJateuzdz7urq6NGnSpBqdoaQcfhmY7969K/K7r1mzZnwDu5Rnyjh79izXN8TS0pJ27drFt96dO3dWSXax39RsWERE+M1vfhHevn2LTp06oWfPnggODsaLFy/g7OyM5s2bIzo6Gmw2W2gdu3btwubNm/H06VNcvXoVDRs2xJo1a2Bubo7+/fsLlG3QoAFiY2PRvn17aGpqIjk5Gebm5jh06BBmzZqFJ0+eyOpU+eLu7o6WLVtixowZWLJkCUJDQ9G/f3+cPXsWLVq0wMGDB+XeB2lwc3ND7969MX78eMyaNQtHjhzB6NGjcfDgQejq6uLcuXN8ZXfu3Ilhw4ZBWVmZo/z79++Ijo7GqFGj5Nr358+fIzY2FhkZGSAiNGnSBIMHD0aDBg1EkldTU0NycjIsLCw4ytPS0mBvb4+vX7/ylc3Pz0ePHj1w8+ZNpr1nz56hY8eOOHjwIHR0dIS2v2nTJgQFBeHFixcAAFNTUyxevFjk6+bv749Zs2ZBXV1dpOMro6Kigvv376Nx48Yc5RkZGbCxsUFhYaFE9Qpi9OjRYLFYQo/bsWOHzNuWBQoKCnj58iXu3LmDL1++YODAgcjIyECfPn2QlpYGPT09xMTEwMXFpbq7+kvDZrPx6tUrGBgYcJS/fv0axsbG+P79O1/ZefPmYfv27fD394ejoyMA4NKlS1i8eDHGjx+PoKAgufYdAB48eIA9e/YgOjoaz549Q9euXeHh4QE3NzeoqanxlXN1dUWLFi2wcuVKjm/elStX4O7ujqysLIn79OzZMwQEBGDLli18j8nOzhZYR8OGDSVuXxzKh9KivEuAsvuiRYsWUFBQwJQpU9C0aVMAQGpqKjZs2ICSkhLcvn0bdevW5VuHt7c34uPjcePGDaioqHDs+/btG1q3bg1nZ2esW7dOwrOSL+XvrsrPzLt372BoaIji4mKR6nnx4gUuXbqEN2/eoLS0lGPf1KlTecqw2WxMmDCBubc3bNiAESNGQFtbm+O44OBggW2rqKigdevW6Ny5M5ydndGuXTuoqqqK1G8A0NPTw5UrV9C0aVOO5ycrKwvW1tYCv/mxsbGIiopCeno6M95wd3fH0KFDRW7/ZyYvLw+enp44duwYatWqBQAoLi5Gv379EBERwfVbliPKPIDFYqGkpESm/eVFbm4uOnTogEGDBuG///5DixYtsGfPHigoKPA8XklJCZmZmTAyMgIAqKqq4vr167C1tRW77R8/fjDXrTLXr19H69at+cp26tQJfn5+cHV1FbtdaRkzZozA/aKM1fLz86GhocF1nT98+AANDQ0oKSlJ1Ud5M3bsWDg6OsLLy0sieTs7O4SGhqJz585wdXWFvb09Vq1ahXXr1mHlypV49uyZjHv869C0aVNs2rQJLi4uuHr1Krp06YKQkBD8999/UFRUlMscu+K30sXFReQ5HS9u3ryJz58/w9nZGW/evMGoUaNw5coVWFhYYPv27bC3t5dp339ThoKCAtLS0qCvr8+UNWjQAJcuXYKpqSlTpqWlJfe+fPz4kdFTWFhYQFdXV2RZNTU13L9/H+bm5hxjluTkZHTq1An5+fly7Hn1s2vXLoSFhSEzM5PREYaEhMDMzIyvjnDo0KH48eMHDh06xHN///79oaSkhH379smz67C2tsayZcvQv39/jt8uNDQUO3bswO3bt0Wq5/v378jIyEBBQQGsra2hoaEh137LCiLC2bNnkZycDFVVVdjZ2aFTp04iyd65cwe9evXC169f8eXLF9SuXRvv3r2DmpoaDAwM8PTpU4HyHz9+xPz58xETE4O8vDwAgI6ODoYOHYqgoCDo6emJfT5Pnz5FeHg4QkNDUVBQwHfMKgv9oLS0atUKvr6+GDRoEJ4+fQpra2sMHDgQN27cQO/evRESEiL3PlQXly5dQs+ePTFixAhERETgr7/+wsOHD3HlyhUkJCTgjz/+ECgfGRmJOnXqoHfv3gCAOXPmYMuWLbC2tsbevXurTM8hKdI8d9LQu3dv1K9fH1u3buW5f9y4cXjx4gVOnDghsJ4WLVrAxsYG27ZtY+YG379/x7hx43D//n2h782goCCEhISgd+/esLW15Zp38dNTAICfnx+jW6is5xGFI0eO8N139epVrFu3DqWlpRz69U+fPolcf1WMV6S1rciCwsJCpKSk8NQz9evXj6eMpPpJWc7zJaGiPluQvkSYnqQ69QTlPHv2DDo6OlxjlB8/fuDq1atyeQ9VvmYsFgsV3S0q6okrXz9ZXfvqRFlZGRkZGTA2Nmb+TklJYfTcz58/h5mZmUC7SEXevn0LVVVVkceZ/GyJ5WRkZMDW1hbfvn0Tqb6fjSZNmqBXr15YunSpQPuNvFBXV8etW7dgaWkpcR3Ozs5897FYLJw/f57nPlnaRaSZJ7dq1QqBgYHo3r07+vXrBx0dHSxbtgzr1q3D/v37q8QfRFIE6YbfvXuHOnXqCJTftGkT/Pz8MH36dAQGBuLBgwcwNzdHREQEIiMjceHCBS4ZfvYgScjOzkaPHj2Qk5ODoqIipKWlwdzcHNOmTUNRURHCwsIEyhsaGuLChQuwsrKCtbU1li9fjn79+iE5ORmOjo4oKCgQKK+rq4uvX7+iuLgYampqXNfyw4cPXDLl730VFRWZjKmePXuGo0ePIicnh+s9K8yuBZSN2d+9ewcigr6+vsi2zerCwcEBLBYLycnJaNasGRQVFZl9JSUlyMzMRI8ePRAbGyu0LlVVVdy4cQM2NjYc5ffu3UPr1q3x7ds3ZGdnw8rKisNGt2DBAuzevRvXr1/nst2+evUKbdq0wYgRI6rEpv+bmstv59vf/HKIa8yuiCQDhorMmjUL165dw759+9CkSRPcvn0br1+/xqhRozBq1CgsWrRIVqfJlw8fPqCwsBD169dHaWkpVq5cyRjWFi5cKNbgsTp4+vQpCgoKYGdnhy9fvmDmzJlM/4ODgwUqGfkN3t6/fw8DAwO5T1QTExPRvn17jo8+UOYQcuXKFaGT/KZNm6J///5YuXIlR/mcOXNw5MgRPH78WKC8rBSc4k50y/n27RuIiJnsZWdn49ChQ7C2tka3bt2EytvY2GDixImYMmUKR3loaCg2bdqEhw8fitWfnw1JlHv8nD6BsneBrq6uWIPmixcvMosP9u3bByMjI+zatQtmZmbo0KGDeCckBvv370dsbCzPiYKoBsHq4OjRowDKFg1ERkZyOHyVlJQgLi4OZ8+eFfjs1q9fH2FhYVy/8ZEjRzB58mQ8f/5cPp3nw+XLlxEVFYV9+/ahsLBQoCJeW1sbt2/fRqNGjTiMudnZ2WjatKlUDvPJyclo0aJFjVUwAmULPv7991+kp6cDKFN6zZ49GyNHjhQqm52djUmTJuH06dMczrvdu3fHhg0bYGZmJlBeFg68QNl3Y//+/bhw4QLPd4+snYk+ffoEIoKuri7S09M5nFFKSkpw7Ngx+Pr6MotABFFuyFRSUoKenh7Hu47FYvE1iDs5OQl9LwpS8JVz6dIlJCYmIj4+HleuXEFxcTFatmyJzp07w8nJCV27dhUor6uri8uXL8Pa2prj+bl06RIGDRqE169fC5SXlsTERIH7q8JAKi3p6elITU0FAFhZWfFVuMsDSb7ZlUlLS0PHjh3RtWtX7Nq1S+B9qaCggFevXjHPjKamJlJSUoS+K3gxaNAg7N+/n6u9N2/ewMXFBffv3+cru2/fPixevBizZ8/maUy2s7MTuz+/EZ2vX79iyJAh0NfXF9uYDwBr1qyBgoICpk6dinPnzqFv374gIvz48QPBwcGYNm2aPLsvE6RVLkuKmpoaUlNTYWJigrlz5+Lly5fYuXMnHjx4ACcnJ7x9+1bmbWprayMpKQlWVlZgs9l4/fo1x3fzZ6KwsBChoaF8xxs1ebwvDWw2m+tdS0RMWfn/q3q8++nTJ5w/fx5NmzaFlZWV0OM7deqEIUOGwNvbm+P74+3tjfT0dJw6daoKel09VNQRBgUFMU7IwnSEd+7cQbt27dCnTx/MmTOHY6y+cuVKHD9+HFeuXEGLFi3k2v9t27Zh8eLFWL16Nby8vLBt2zY8efIEy5Ytw7Zt2/Dnn38KlB87dizWrl0LTU1NjvIvX77A29sb4eHh8ux+teLk5IQmTZogLCwM2traSE5ORq1atTBixAhMmzYNAwcOFKkeImK+EZIYNbOzsxEfH89sb968Qdu2bdG5c2f4+fkJbLc6HCDLqagrWLFiBc6fP4/Tp0/j8uXL+PPPP5Gbm1tlfZEUafRjT548wfLly5GcnIyCggK0aNECc+fOFcmhrjoW/PwKXLhwAV27dsX06dMxe/ZsRhfy+vVrrFy5EmvXrsWZM2eEBie4fv06M0Yun9ukpKSAxWLh2LFjAhcqAhA4PxOkpwCArl274urVqyguLkarVq0Y3YKjo6NYC30r8vjxY/j6+uLYsWPw8PBAQEAAh22F11iFHzVZPycrTp06hVGjRuHdu3dc++QxZpPlPL86qU49wcuXL9G/f3/cunULLBYL7u7u2LhxI2PXev36NerXry/3+/fcuXOYO3culi5dinbt2gEoc3pfuHAhli5dKlRH+DNS2S5VUbcJyP/aN2rUCKtXr4abmxvP/QcPHsSsWbOELpj6WVFXV8e9e/eY613VtGrVCmvWrJGrzZAfsrKL5Ofno6SkBLVr1+Yo//DhAxQVFYU6SO7evRvFxcUYPXo0bt26hR49euDDhw9QUlJCREQEhg0bJt2JioAkgUkA/rrh169fo0uXLgJ1w0DZIs+lS5fCzc2N49m/f/8+nJyceH5HBdmyxaW83e3bt0NPT49pPz4+HuPHj2fsdYLkJQ0CB5QtlhOEp6cnV5ks9XpxcXHo168fzM3NkZqaChsbG2RlZYGI0KJFC6F2rcDAQHh4ePxU4w1/f3/m35kzZ3L4rygpKcHU1BSDBg0SKbBGhw4doKmpiZ07dzK/x9u3bzFq1Ch8+fIFiYmJOHfuHP7++28O2/7nz5/Rrl075OTkYMSIERzvnj179sDY2BhJSUlc+pPf/I9RRRF2f/ObKuXx48dkYGBAHh4eYqVpsbKyYtIVVEzJdu/ePZFSExUVFdG4ceNIUVGRWCwW1apVi9hsNo0YMYKKi4slOhdx+PHjB0VGRnKlNvxZKC4upoSEBIlTUcki3Lw0lKcNqMy7d+9ESoF+/PhxUlFRIRsbG/Ly8iIvLy+ytbUlFRUVOn78uDy6LFO6du1KmzZtIiKijx8/koGBATVo0IBUVFRESg+1fft2UlVVJT8/Pybt9T///MOkhf6VOXnyJOnr6/NNU8UPfve8JOzfv59UVVVp3LhxpKyszLz/QkNDqWfPnlzH29vbk4ODg0ibINauXUsaGho0ZcoUUlJSor/++otcXV1JW1u7xqeArpxKrOKmpKRETZo0oWPHjgmsQ1lZmR4/fsxVnpqaKlJKO1lz584dmjlzJhkZGQltX19fn27fvk1EnN/MM2fOUIMGDaTqx927d0V6b+7cuZPat2/Pkd5mzZo1fNPbyIrVq1eTmpoazZkzh0kDNnv2bFJTU6Pg4GCR6/nw4QNdv36drl27JnZaF1mkR5o6dSopKytTjx49yNPTk0aPHs2xyRphaRAVFBQoMDBQpLoaNGhAgYGBVFJSIvN+isuPHz/oypUr5OnpSYqKiiLdu0OHDqXx48cT0f+lI/38+TO5uLjwvPb5+fkib6LA73tTvv2GP5J8s8vTvlXelJWVSUtLi6OMFywWi3r16kUDBgygAQMGkKKiInXr1o35u3wThZYtW9LYsWM5yl68eEGWlpY0aNAggbKSptP8jWzYtm0bKSoqkoaGBjVs2JBJ52tqaio0pR4vsrKy6MCBA5ScnCyH3sqec+fOkZqaGtnY2JCioiLZ29uTjo4OaWtrk7Ozs1zbrjjmsbe3p507dxIRUUZGBqmrq8ulzYEDB1LdunXJycmJWCwWOTo6krOzM89NGPzSPefn58v92hERubu7U506dWjixIm0aNEiWrx4Mcf2q1I+pxW2yZshQ4ZQaGgoERF9/fqVLCwsqFatWqSoqMiRGpYfFy9eJA0NDZo4cSKpqKjQtGnTqGvXrqSurk43b96Ud/erFWl0hMeOHSN9fX2u8a6+vn6VphDevXs3NW7cmPluGxkZ0bZt20SS5afjevv2bY1MXb527Vr69u0b839BmzC0tbWZFNra2tpMWuOkpCSJU0kWFRWJlAY1MjKSxowZQ2ZmZqSpqUndu3enpUuX0uXLl5kU1TUdTU1NSktLIyIiV1dXCgkJISKi7OzsatFziIu4+jFZoqqqStnZ2URENGfOHBo5ciQREd2/f5/q1Kkj17YlQZbPnbSEhYWRsrIysdlsZv7FZrNJWVlZJL10OQUFBbR582Ym7fWWLVuooKBAjj3/P378+EGXLl2ipUuXUvfu3UlTU5OUlJTI0dFRrHqeP39O48aNo1q1alGfPn3o3r17PI+rOB6JiIggQ0ND8vX1ZfRcvr6+VK9ePYqIiJDF6fElISFBpE3eNG7cmCZPniyRbU0S/aQs5/nS8P37d3JxcWHe2+JSnXqCUaNGUZs2bejGjRt09uxZ+uOPP6hly5aMjvXVq1fEYrHk2geishTWFy9e5CpPTEwkS0tLvnLSXvvqhMVi0YULFyg5OZmSk5NJXV2djh8/zvwdFxfH8/d3cHBgfh9h9iVBTJkyhWxsbJhvUEW+fv1KNjY25O3tLZuTrYEMGDCAYmJiqq39uLg4ateuHV24cIHevXsnkW66Irm5uZSbmyvy8bKwi/To0YM2bNjAVb5p0yaJxntfvnyhW7du0du3b8WWlYQdO3aQkpKSRDpCaXTDREQqKirMt67iPDktLa1Kxvq1a9dm5moV28/MzCRVVVWh8k+ePGF0oQUFBfTXX3+Rra0tDRw4kDkvWcNisfjaB4TZBSrTqlUr8vPzI6L/O//Pnz9Tv379RBrz2tnZEZvNpnbt2tGGDRuq7J6VBREREVRYWChVHampqdS0aVNSUlKiRo0aUaNGjUhJSYksLS0ZW/2hQ4cY3W9F8vLyaNKkSVS7dm3m3aOrq0uTJk0S2777m1+T35Fvf/PTwy+y49evX6GsrMwR8ZZXqPeKqKqqIjU1FQ0bNuRYrZOeng47OzuRU1Tk5OTg/v37KCgogIODAywsLMQ7KSlQU1PDo0ePanwaKn6oqKjg0aNHYq24kWW4eWngt3IpLS0NLVu2FCmVU25uLjZt2sQRyW3ixIlM+paKrFu3DhMmTICKiorQFBq8Vrm1aNECcXFx0NXVZa4hP0SJRlSnTh0kJCSgWbNm2LZtG0JDQ3Hnzh0cOHAAfn5+ePTokdA6Nm3ahKCgICbqoampKRYvXoxRo0YJlf2ZsbCwQLdu3eDn5yd0RWZF2Gw2evbsCWVlZYHHiRIZw8HBAT4+Phg1ahTH++/OnTvo2bMnXr16xXF8+UozoCya1caNG2Ftbc2s7k5KSsKDBw8wefJkLFu2jG+7lpaWWLRoEYYPH87Rrp+fHz58+ID169cL7Xt1Y2Zmhhs3bghNx8KLNm3aoE2bNlzPsLe3N27cuIGkpCRZdZMvmZmZiIqKQlRUFB4/fozOnTvD3d0dgwcP5pu+HShL2/f+/XvExsaidu3aSElJgYKCAtzc3NCpUyepUkmKEvlW0khUssDMzAz+/v5c76bIyEgsXrwYmZmZcmu7MtKkR6pduzZ2796NXr16ybGH/0dCQgKICC4uLjhw4ADHynIlJSU0bNgQ9evXF6kuPT09XL9+HY0aNZK6X1Qh+rA4pKWlcUSiKioqQqdOneDk5CQ0guSzZ8/QvXt3EBHS09PRsmVLpKeno06dOkhMTORaBS7riDSVU0T/+PEDd+7cwT///IOgoCB06dJFpLaqihkzZoh8rCjRL3mlkF6zZg3Mzc35ppAuR5JvtrAV8RXhtTp+zJgxIsnu2LFD6DFv375Fp06d0LNnTwQHB+PFixdwdnZG8+bNsXfvXoHZQiRNpykNT548QVBQEBNdz8TEhCP9l4KCAi5dusSsNv+VMTQ0xNSpU+Hr6yswNSUvSktLERERgYMHDyIrKwssFgtmZmYYPHgwRo4cWePTiwFA69at0bNnT/j7+zNjRgMDA3h4eKBHjx6YNGmS3Nr28PBAamoqHBwcsHfvXuTk5EBPTw9Hjx7F/PnzhUYFkYRv374hMjIST548werVqzF+/Hi+6STXrFkjsC5+EUbevHkDIyMj/PjxQ2b95oW2tjZOnDgBR0dHubbzG94YGhri9OnTaN68OaKiorBo0SIkJycjMjISW7ZswZ07d4TWIU0Ux58ZaXWE3759w6lTpzjSZ3fr1q1aUsN+/foVBQUFIkUakmW2iqrEzMwMN2/ehJ6enlTRJ4GyKLXlWbiaNGmC0NBQdO/eHampqfjjjz/w5csXgfI7duzA7du30bZtW3h4eGDevHkIDg5GcXExXFxcEB0dDT09PZ6ybDYbJiYm8PX1hZeXF9+UsBWRVj8oa1xcXGBsbAxXV1d4eXnh4cOHaNy4MRISEuDp6YmsrCy590EaxNWPVaa0tBQZGRk8o5AJi0BsYGCA06dPw8HBAQ4ODpgxYwZGjhyJJ0+eoHnz5kLT4FY1snzuZMHz588RGxvL8d4dPHgwGjRoIPe2KyOpngEo0zVcuHAB586dw+HDh6Gtrc0zilxl8vPzsXTpUoSGhsLe3h4rVqxAx44dRWqzS5cuGDduHIYPH85RHhUVhS1btiA+Pl7s8xCVivoOfubqqsgWoKWlhTt37oitZ5JUPynLeb60VPzuiUt16AnKMTIywqFDh5io1EVFRRgyZAhyc3MRFxeHHz9+VEnkW34prFNSUtCmTRuBY0Zprn11Uv7c8npmy8t5Pbf+/v6YPXs21NTUOOxLvBCUSVZW0U9/VrZv346AgACMGTOGZ8RpUbNySUq5TopfphdRnrnS0lIEBgZi9erVzPhGU1MTM2fOxIIFC0TSe0lrF7l8+TJXNpjU1FQ4Ojri/fv3ItdVHRgbG2PixImYN2+e2DpCQbrh6OhoofVZW1tj2bJl6N+/P8dYOTQ0lJkHyRNpMgqWlJTg8uXLsLOzg46OjtR9KSws5MrMxStqMpvNRkhIiEB7K8DbLlAZTU1N3L17F40aNYKuri4uXbqEZs2aITk5Gf379xdprvXgwQPs2bMH0dHRePbsGbp27QoPDw+4ublVi75CVG7cuIHS0lK0adOGo/zatWtQUFBAy5YtRaqntLQUZ86cQVpaGoCy7CNdu3YV+VkiIrx79w5EJFGGm9/8uvx2vv3NT4+0xuyKVPeAQRY4OTnBx8dHqONATaVly5ZYsWKFWE4fsgw3Lwnl6eaOHDmCHj16cDhClpSUICUlBU2bNpV5OkZpFZyymuiWUzEd69ChQ9GsWTMsWrQIubm5aNq0Kb5+/Sr8pP4/b9++haqqKsdv+SsjqXKPzWZj6NChQtOPiaKkU1NTw8OHD2Fqasrx/nv69Cmsra1RWFjIV3bcuHGoV68elixZwlFe/vsLSkdZccGAgYEBzp49i+bNmyM9PR1t27at8ZNcaUlISEDv3r1hYmLCkZYqNzcXJ06cEFlJLilt27bFjRs3YGdnBw8PDwwfPhxGRkYiyebn52Pw4MG4efMmPn/+jPr16+PVq1do164dTpw4AXV1dYn7JYrzrSTpbWSFiooK7t+/z5XmPj09Hba2tgKfF1kibXokMzMznDx5EpaWlvLsJhfZ2dkwMTGRalI6Z84c1K5dG76+vhLXsXPnTvz7779MKqImTZpg9uzZGDlypFBZIyMjfPv2DU5OTnByckLnzp1hZ2cn1jkVFxcjJiaGw5HFw8OD5zs9ISGB+X9WVhZ8fX0xevRojvdGZGQkli1bJpKShh8JCQmYMWMGbt26JXEd8sDZ2Vmk41gsltDUStI67kv6za5J5ObmokOHDhg0aBD+++8/tGjRAnv27BHoeFtdTJ8+HaqqqsxCHk1NTfj5+THOQzExMTAxMUFYWFh1drNKqF27Nm7cuCH2vUdE6Nu3L06cOIHmzZvD0tISRIRHjx7h3r176NevHw4fPiyfTssQWSiXJSUvLw8LFy5Ebm4uJk2ahB49egAoG+sqKSlhwYIFcmsbKHsHHjp0SGzDQEpKCgDA3t4e58+f5xgvlJSU4NSpU9i8ebPcnaCsra0RHR0t15SzNZHt27fDy8uL7/7Pnz/Dx8cH27Ztk2s/VFVVkZaWBmNjY4waNQr169fH8uXLkZOTA2tr6xrnyFWTkFZHmJuby3MhdU1H2KIvFosFf39/ub/7qpNu3bph9OjRcHd3x/jx45GSkoKpU6di165d+PjxI65du8ZXNigoCEFBQXB0dMTt27cxdOhQHD58GNOnTwebzca6devQp08fbNq0iad8WFgY4uPjkZCQgMLCQnTo0IGZb/zxxx88f5ua5gCZkpICDw8P5OTkYMaMGYxO0dvbG+/fv0dUVJTc+yAN0ujHkpKS4O7ujuzsbC6HJFGcUapjwc9vOElPT8eFCxd4Ok/7+fkJld++fTvWrFnD6BksLCwwffp0jBs3TqBcuYNrQkICioqK0LFjR0bfIIquYeXKlVixYgUMDQ2xdOlSsW1DampqSE5O5nIATEtLg729vVh6fXHR09ODpqYmRo8ejZEjR/INbiDMYUVaxo4dC0dHR4HjN15Up35SVvj4+EBZWRnLly+v7q6IhYaGBu7cucNx3xYXF2PIkCF4+vQpdu/eDXt7e7k733bq1AkqKirYtWsX4+z5+vVrjBo1CoWFhRw6vcr8rNdemNN1OfJ0vs7OzsakSZNw+vRpjgUP3bt3x4YNG36qlOriIshBrCoWKwi6pwGgc+fOQuuYN28etm/fDn9/f2ah7qVLl7B48WKMHz8eQUFBYvXp06dPOH/+PJo2bcrlUMsLdXV1JCUlcS0ovXfvHtq0aSP0u8cvUASLxYKKigoaN26M/v37c9ltZIW0gUmk0Q1v27YNixcvxurVq+Hl5YVt27bhyZMnWLZsGbZt24Y///xToj6JyrBhw6CtrY0tW7ZAU1MTKSkp0NfXR//+/WFiYiLUHi5JELiKfPnyBXPnzkVsbCxP+zWv54/fonhJMDQ0xIULF2BlZQVra2ssX74c/fr1Q3JyMhwdHcXW8Vy+fBlRUVHYt28fCgsLRQokV120bt0ac+bMweDBgznKDx48iBUrVgicp8uKwMBAeHh4/NLfmN9IjqLwQ37zm5qNNA4GlZkxYwb+/vtvFBYWgohw/fp17N27lxkw8JNZsmQJ1NXVhUblEiUSl7RMnjwZM2bMQG5uLv744w8u56eabvAKDAzErFmzsGTJEp795+VIVK7ENTU1xbBhw6CiolIlfS2nXPFDRNDU1ORwmlFSUkLbtm0xfvx4keq6ePEiNm/ejKdPn2Lfvn0wMjLCrl27YGZmhg4dOnAcWzGyoiRRFis61IriXCuMxo0b4/DhwxgwYABOnz4NHx8fAGURlYQ5gFVGX18fCQkJ+Pr1K9q2bSvWisWfkcGDByM+Pl6iidK6detkNmDPyMiAqakpR/mlS5dgbm4uUHbfvn24efMmV/mIESPQsmVLgc63hoaG+PDhAxo2bAgTExMkJSWhefPmyMzM5BvtoCaSkJCAVatWMRGera2tMXv2bKHOs507d8bjx4+xceNGJuL1wIEDMXnyZJEjgEpDly5dEB4eDmtra7FltbW1cfbsWVy6dAkpKSmM86Crq6tQ2fJFC/zIy8sTWkdmZiYcHBy4ypWVlYVGIpKWxo0bIzY2FvPnz+coj4mJqdJIBX/++Sf69u2LyZMnc5THxsbi6NGjOHHihED5xYsXw9/fH+Hh4UKd+KUlJSUFNjY2YLPZyM/Px7179/geK8pYZdmyZejTpw9OnTrFc4W/sDFXcHAw/vnnH0yZMoVDwTdx4kS8e/eO+YbxQ19fH6mpqXj16hVevXqF169f49u3b2KtDFZUVISHhwc8PDyYspcvX2L27NlcUb8rKi0DAgIQHBzMEZGmX79+sLW1xZYtW6QaG9etWxePHz+WWF5erF27Fs2aNZOJc2hoaCi2bt0KNzc3DuNGy5YtMWvWLKHy0nyzAeDEiRNQUFBA9+7dOcrPnDmDkpIS9OzZU6R6cnNzAUAipx5jY2OcPXsWHTt2RNeuXbFr1y6RHcd5RQ0OCQmBmZmZXBb/xcXFYfv27RxlgwYNYsYmpqamQg3Zvwqenp6IiYnh+vYIIyIiAomJiYiLi+NyZD9//jzc3Nywc+fOGp9pQl1dnYkmUa9ePTx58gTNmjUDALkbtHV0dHhmYxC2gFFWVFwUIE4UNXt7e7BYLLBYLLi4uHDtV1VVRWhoqOw6yofVq1dj7ty5CAsL+2kz9EjCjBkzcOjQIWzbtg2GhoYc+06fPo3x48dXyTzX2NgYV69eRe3atXHq1ClER0cDKIsSJEh3IkrUfRaLheLiYpn2tyYhiY6wIqampujQoQNGjBiBwYMHV5lew9nZWaTfLi4ujue+CxcuyCxbxc/K0qVL8fnzZwBlzrSjRo3CpEmTYGFhwTUuqUxERAS2b9+O4cOH4+bNm2jTpg1iY2MxaNAgAICNjQ0mTpzIV37ixInM/ocPHyIhIQHx8fFYuXIlioqK4OjoCGdnZ45xq7T6QVljZ2fHc77377//1sjFXpWRRj82ceJEtGzZEsePH0e9evXEXnC6YcMGZsHPgQMHmAjJt27d4opI+pv/IzExUaTjhEUe3rp1KyZNmoQ6derA0NCQ4/djsVhCnW/9/PwQHBwMb29vjoWyPj4+yMnJQUBAAF/ZiRMnQl9fHzNnzsTkyZPFDkjh6+sLVVVVNG7cGJGRkXwD1vDLjmZsbIytW7di5cqVHOXbtm2T+0KSly9f4tChQwgPD8fKlSvRq1cveHl5oUePHlUaSWz9+vUYMmQILl68yFPPxC9yuCz1k9LM86WhuLgY4eHhOHfuHE+bnDAdW1XrCcoxNzdHSkoKhy5WUVER+/btw5AhQ9CnTx+5tV2R8PBwDBgwACYmJsxvl5ubCwsLC6ELXaW99tWFLOd1379/57ngwcTERGgfTpw4IVX005+VyteqqhHFuVYYkZGR2LZtG0eUXjs7OxgZGWHy5MlCnW+HDh2KTp06YcqUKfj27RtatmyJrKwsEBGio6OZsTc/WrdujS1btnDpRMLCwvDHH38I7f+dO3dw+/ZtlJSUMJGX09LSoKCgAEtLS2zcuBEzZ87EpUuXJLK9CcPLywv79u2TODCJNLrhcePGQVVVFQsXLsTXr1/h7u6O+vXrY+3atXJ3vAXKdEzdu3dnFsW5u7szGQX37t0rVN7GxgZPnz6V2Hlyzpw5uHDhAjZt2oSRI0diw4YNeP78OTZv3sx3IYUsxzNt27bFpUuXYGVlhV69emHmzJm4d+8eDh48iLZt24pdn7q6OlRVVaGkpMTMgWsqDx8+RIsWLbjKHRwc8PDhQ5HriYuLQ1xcHM9vjyB/BqDMH2LRokVo06YNRowYgaFDh0qUFfc3vya/I9/+5pdCFsbsPXv2YPHixXjy5AkAoH79+vD39+e74rViFBpBUblEicQlC3iteBOU5qOmUbH/FQcj4vRf0smatPj7+2PWrFkSR3s8cOAARo4cCQ8PD+zatQsPHz6Eubk51q9fjxMnTgh1opIF0ly7/fv3w93dHSUlJXBxccHZs2cBlDlIJSYm4uTJkzzlVqxYgYKCAiZqKhGhZ8+eOHPmDICylGtxcXGMcf1X5OvXrxgyZAj09fXFUu4pKCjg5cuXMnG+XbZsGXbv3o3w8HB07doVJ06cQHZ2Nnx8fPDPP//A29ubr6yhoSGWL1+O0aNHc5RHRERg7ty5AtN8jBs3DsbGxli0aBE2bNiA2bNnw9HRETdv3sTAgQOFGrZqArt378aYMWMwcOBAxonv8uXLOHToECIiIuDu7s4l4+npiS5dusDJyUnu7yZRETclXmFhocSLHWSRWq06o9UfOHAAw4YNg6urK8dvHhcXh9jYWAwYMEBubVdE2vRI3759w4ABA3D58mWYmppyvXtkeQ0rru4VlppMlG99YGAg/Pz80LRpU9StW5fLKCZszGVmZgZ/f38uZ7PIyEgsXrxYJKN1Xl4eEhMTkZCQgISEBDx8+BD29vZwdnYWqCB88OABLly4ACUlJQwdOhQ6Ojp49+4dgoKCEBYWBnNzczx48ICvvCwi0pRHQyyHiPDy5UssX74cxcXFuHTpktA6qhIFBQW8evUK+vr6MDc3x40bN/im6hWGtCmkJf1ml2NnZ4fly5ejV69eHOWnTp3C3LlzkZyczFe2uLgY/v7+WLduHbOKXUNDA97e3li0aBHftMS6uro83+1fv36FsrIyhxPEhw8f+LYvbdRgSdDU1MSjR4+YVLE+Pj5YuHAh8/tnZ2fD0tJS6O/2KzB16lTs3LkTzZs3h52dnciLDrp16wYXFxe+CvmlS5ciISEBp0+flnmfZYmbmxt69+6N8ePHY9asWThy5AhGjx6NgwcPQldXF+fOnZNr+x8/fsT27duZhVZWVlYYO3as3KKYVEaSaO3lUffMzc1x/fp1jtTxSkpKMDAwqBInqLdv32Lo0KFITEyEmpoa170r6L3zM5OVlYUxY8YgJSUF69evx/Dhw/H582dMnz4du3btwqxZs+Dv7y9SSnlp2LhxI6ZNmwYNDQ00bNgQt2/fBpvNRmhoKA4ePMj33X3kyBG+dV69ehXr1q1DaWlplWV8qC7E1RFW5M6dO9izZw9iYmLw9u1b9OjRAyNGjEDfvn05MibJGkGLyD5//oyoqCgUFRUJHXNnZ2fD2NhY7DSmNYGSkhJERETwNajJUz+rrKyMjIwMxvlGWVmZyYgFAM+fP4eZmRlXelJhvHjxAhs3bkRoaCgKCgpqtH43NzcXLBaLGb9dv34dUVFRsLa2xoQJE6q5d8KRRj+mrq6O5ORkriw5/wtU53NXccEIP3OnKLqGhg0bYvLkyZg7d65E/dDX18e6deu4HKX37t0Lb29vgQvGDh8+jMTERMTHx+PRo0dwcHBgIt926NBB6ELf0aNHi6TP46dnO3HiBAYNGoTGjRszqXyvX7+O9PR0HDhwgGv+Ki9ycnKY+WVRURE8PT3h7+8PRUX5x5Havn07Jk6cCBUVFejp6XHpmfhFDpdWPynpPF+WSGPXrA49QTlz587F3bt3ec5li4uLmYiOVfHNJCKcPXuWCa5hZWUFV1dXoc9lTbApS0JOTo5Ixwmye6SlpcHLywtXrlzhKP9ZbNn/ywhb9CJssQtQFn00JSUFTZo04Sh//Pgx7O3ther5DA0Ncfr0aTRv3hxRUVFYtGgRkpOTERkZiS1btuDOnTsC5S9fvgxXV1e0atWKycQbFxeHGzdu4MyZM0KD6oSEhODixYvYsWMHE3wqPz8f48aNQ4cOHTB+/Hi4u7vj27dvctG3lZSUoE+fPvj27ZtIgUlkpRvmJV9QUCATG7U4FBcXIzo6miMoEL+MgpU5deoU5s2bJ1YQuIqYmJhg586dcHJygpaWFm7fvo3GjRtj165d2Lt3L09fDllGvn369CkKCgpgZ2eHL1++YObMmbhy5QosLCwQHBws0uKIzMxMREVFISoqCo8fP0bnzp3h7u6OwYMHyz3TgDTo6enhv//+Yxa5lXPlyhX07t0bHz9+FFqHv78/AgIC0LJlS56LJQ8dOiS0jgcPHmDPnj2Ijo7Gs2fP0LVrV3h4eMDNzU2s4Dy/+fX47Xz7m18KaYzZlamuAYO0CEv3UdMjzUiTriI9PR1jx479aSdrDg4O8PHxwahRozgUNXfu3EHPnj3x6tUrvrLSKjhlNdF99eoVXr58iebNmzMGmuvXr0NLS4tvWvMWLVpg7ty5GDZsGICyVUOenp44e/YsrKysMGrUKKipqSE2NlakPvyMSKrck+WAnYiwdOlSLFu2jHHcUlZWZiJRC2L58uXw9/fH+PHj0bp1awDAtWvXEB4ejn/++Ufg6svS0lKUlpYyStTo6GhmovDXX39BSUlJ6nOTN1ZWVpgwYQKXkTM4OBhbt25lnDQq4uTkhGvXruH79+8wNTWFs7MzXFxc4OLiwhUZS95I4swBlClIWrdujc6dO8PZ2Rnt2rWTe/RUoCzq56xZsxAVFVWt6W1u376N4OBgDiecmTNn8ox2IS+kTY80dOhQXLhwAYMHD+ZyYAVkExW9nOzsbJiYmIDFYslkrKKrq4s1a9ZwOf2LioqKCu7fv89lFE1PT4etra1YjiTv379HfHw8jhw5gr1796K0tJTvd/Po0aMYPHgwEyXO3NwcW7duxdChQ/HHH39g+vTpTDpzfjRt2hT9+/fnikgzZ84cHDlyRKTItfwcoNu2bYvw8HC+3+zqQk9PDydOnECbNm3AZrPx+vVrDicycZDWMCbpN7scVVVVPHr0iCuSVlZWFpo1ayYwMs6kSZNw8OBBBAQEcERSWrx4Mdzc3PimL+YX9YgXgiInV0c6zfIo6+Xji8pcv34drq6uNTollqwQZJQDwNeoaWhoiFOnTsHe3p7nflHmGjUBWSiXJSUxMRF9+/aFtrY2WrZsCaAsAl1eXh6OHTsmkmFJGvhFa9+wYQMCAwOFRmuvblxdXZGTkwMvLy+e4w1ZZjOqiYSEhGDhwoVwcnLCvXv3oKGhgYiICLRq1arK+nDz5k3k5uaia9euTCS948ePQ0dHh7mnROHx48fw9fXFsWPH4OHhgYCAgBqvY5IV0ugIiQjx8fGIiorCgQMHUFpaioEDBwqNqCJLiouLsWHDBgQFBUFbWxtLliwRab6Ul5eH69ev89Qz1eSI6VOmTEFERAR69+7N06C2Zs0agfIuLi44ePAgdHR0OMo/ffoENzc3gTq2ynqaimMmoCwNdf369YXq2d68eYMLFy4gPj4e8fHxSEtLQ61atdC2bVs4OzvznatVpwNkOR07dsSECRMwcuRIvHr1Ck2bNkWzZs2Qnp4Ob29vodFDqxtp9GMuLi6YM2eO0DmdIKp7wY+kSPvcSYOenh40NTUxevRojBw5km/0KWHOBFpaWrh7967QCMf80NHRwY0bN3gulG3durVIWZ6AMuedixcvYt++fdi7dy/YbHaVLHZ59uwZNm3axHHvTZw4scqjsAJlTiFeXl5ISEjA27dvq+T+NzQ0xNSpU+Hr6yvSwhNZ6SclnefXFKpDT1BOSUkJvnz5wtdRqri4GM+fP/+fGa9WJRUd9XgF9BDFrujo6AhFRUX4+vry/G40b95cxr3+uVm3bh0mTJgAFRUVrFu3TuCxwgIDSAu/IGDliGJPbtOmDdq0acN1Lt7e3rhx4waSkpIEyquqqiItLQ3GxsYYNWoU6tevj+XLlyMnJwfW1tbMYgZB3L17F//++y/u3r0LVVVV2NnZYd68eSJlNjQyMsLZs2e5oto+ePAA3bp1w/Pnz3H79m1069ZNLu9BcQOTyEo3DJRFBnV2dpY4cmx1I20QOA0NDTx8+BAmJiZo0KABDh48iNatWyMzMxO2trYi3XvVSdu2bXHjxg3Y2dnBw8MDw4cPh5GRUXV3SySGDx+Oly9f4siRI8y4Pi8vD25ubjAwMBDJj6RevXpYuXKlUPu3qFy+fBlRUVHYt28fCgsL/ydsFL8RAP3mN78QKioqlJmZyVWemZlJampqQuW3b99OT58+lbj9Xbt20ZcvXySWl5bv37+Tubk5PXz4sNr6IA3fv38nFxcXSktLk0i+ffv21KlTJzpx4gTduXOH7t69y7HJAwcHB/rw4QMREdnb25ODgwPfTRiqqqrM/auhoUFPnjwhIqInT56QsrKyQNm///6b1NXVaejQoTRt2jSaPn06xyYMWV+7nJwcysnJEelYHR0djnt29OjRNHLkSObvq1evUoMGDcTuw89E3bp1KSgoiEpKSsSSi4+Pp+/fv1NkZCQVFhZy7S8qKqLIyEix6iwqKqIHDx7QtWvX6PPnzyLLxcTEUPv27UlXV5d0dXWpffv2FBMTI1bbPytKSkqUnp7OVZ6eni7w2S0sLKTz58+Tn58fderUiZSVlYnNZlPTpk1p4sSJFBsbK89uExHR6tWrSU1NjebMmUNHjhyhI0eO0OzZs0lNTY2Cg4MFyl68eJGCgoKoa9eupK6uTsrKyuTo6Ejz58+nM2fOyK3PbDabXr9+TUREu3fvpsaNGxOLxSIWi0VGRka0bds2ubVNVPatGjNmjFTjBVnh5OREU6ZM4SqfPHkydejQQai8mpoaXbx4UR5dkzt169aVeLxARNSsWTMKCgriKl+yZAnZ2NjwlRszZgx9+vSJDhw4QN7e3mRra0sKCgqkr69PAwYMoLVr1wr8brZq1YqmT59Onz9/pjVr1hCLxSIbGxu6fv26yH0/fvw4qaiokI2NDXl5eZGXlxfZ2tqSiooKHT9+XKQ6srKyOLacnBz69u2byH2oasaPH0/KyspkampKbDabTExMyMzMjOcmjK1bt5KRkRFFR0eTuro67d27lwIDA5n/C0PSb3ZF+bi4OK7ys2fPkr6+vkBZLS0tOnHiBFf58ePHSUtLS6L+iIOKigplZWUREedYNS0tjVRUVOTSZrt27Xg+q+UEBARQu3bt5NL2r0KtWrXoxYsXfPc/f/6clJSUqrBHPx82NjY0fvx4Ki4uZsqKi4tpwoQJAr8ZssLU1JTnmD4iIoJMTU2FykdERNB///3H/D179mzS1tamdu3aMc+0PFFVVZXbfPxn4OvXrzRgwABisVikoaFBKSkp1daX0tJSKi0tFVvu+fPnNG7cOKpVqxb16dOH7t27J4fe/W9w69Ytsre3JzabXWVt7t69m8zNzalevXq0YcMG+vHjh0hyR48eJU1NTWKxWKStrU06OjrMpqurK+deS4eenp7I42JesFgsZs5ZkdevX5OioqJQ2QsXLlBycjIlJyeTuro6HT9+nPk7Li5O4O8/adIksrKyIjabTUpKStShQwdauHAhxcXFiTRel1Y/KAt0dHQoNTWViIjWrl1L7du3JyKi06dPizRerymIqh8r/22Tk5Pp4MGDZG1tTTt27KCbN29y7EtOThbaZkJCAmlpaZGxsTENGDCABgwYQCYmJqSlpUUJCQmyPD2ZI+1zJw1FRUUUHR1N3bp1I1VVVRo0aBCdOHFC7G/e2LFjadOmTRL3Y8qUKeTj48NVPnPmTJo8ebJQ+Xfv3nHoGthsNunp6ZGbm5vEffqZKCwspD179lCXLl1ITU2NhgwZQidPnqyy9nV1dSkjI0Pk42Wln6zueT4RUV5eHr1//56r/P3795Sfny9Qtjr0BOWYmZnRu3fv5NqGqJw7d47mzZtHXl5eNGbMGI5NENJc++pEQUGBGjZsSIsWLaKbN29y2RNFsSuqqanRo0ePqqjHPz+mpqbM/W5qasp3q4qxVl5eHsf29u1bOnPmDLVp04bOnTsnUh3x8fGkrq5OVlZWNHbsWBo7dixZWVmRhoYGJSYmCpW3sLCgmJgYKigoIH19fUbXevfuXdLT05Pq/ERBXV2dLly4wFV+4cIF0tDQIKIy276mpqZc2tfR0aEdO3bIpW5hNG7cmNhsNhkbG9OIESNo69atPO2jsiYhIUGkTRjx8fECN2HY2toyx3Xp0oVmzpxJRGXzHiMjI+lOUkw+f/5M+fn5HJsw5s+fTw8ePKiC3smeZ8+ekbm5OWlra5OTkxM5OTmRjo4ONW3aVGSflNq1a4s13hPGnTt3aObMmWRkZCT3cc9vaj6/I9/+5pfC0NAQUVFRcHFx4Sg/d+4c3N3d8ebNG4HyFhYWePr0KYyMjNC5c2d07twZTk5OIqeK0tfXx7dv39CvXz+MGDEC3bt3r5I0jhUxMjLCuXPnuFJQ/yzo6+szEYzERV1dHbdu3arSaG3+/v6YPXs21NTU4O/vL/BYYREEzc3NsWXLFri6unKsEt65cyeWL1+Ohw8f8pWtU6cOdu7cKXEKKFlcO0lTJFWOAmJpaYnp06dj4sSJAMpS2DRt2vSXTidcu3Zt3LhxA40aNZJIXkFBAS9fvuSKwvP+/XsYGBgIXKk3duxYkdqQZVSeyunOBWFnZyezduVF48aNMXv2bPz1118c5WFhYVi9ejUTUVYYhYWFuHLlCk6ePIktW7ZUSSpJMzMz+Pv7c0UtioyMxOLFi5GZmSlSPcXFxbhx4wY2b96MPXv2CIz8OXDgQEREREBLSwsDBw4UWO/Bgwe5ynhFfK7qaPXa2tq4e/duta/ulTY9kqWlJWJjY6v8OTMxMYGTkxMzzpLk3bds2TK8fPlS6Ep/fhw4cADDhg2Dq6srE/Ht8uXLiIuLQ2xsLAYMGMBTrvx9a2Njg06dOjHnUTn6MD+0tbVx69YtNG7cGCUlJVBWVsapU6fg6uoqVv/lEZEmLy+PK7pXTeLUqVPIyMjA1KlTERAQAE1NTZ7HTZs2TWhd0qSQlvab/ddff+Hq1as4dOgQU0dGRgYGDRqEVq1aYdu2bXxlDQwMkJCQwDXOf/ToETp16oS3b98Kbf/EiRNQUFBA9+7dOcrPnDmDkpIS9OzZk6+stFGDJWHr1q2YPn06YmNj0bt3b459x44dw59//omQkBCMHz9e5m3XFIR9K4GySA0HDhzguU9BQQGvXr3iGy1a1Ah8NYmCggKuSH7C0sJJg6qqKu7evcukDC9H1HSI0iJttPamTZti06ZNcHFxwdWrV9GlSxeEhITgv//+g6KiIs/xlixp0aIFNm7ciLZt28q1nZrI5cuXMWbMGCgqKiIkJATbtm3DiRMnEBQUJNL3SlZs374da9asYeYlFhYWmD59OsaNGydQLj8/H0uXLkVoaCjs7e2xYsUKoePLXwFnZ2ehKYJZLBbi4uJEqu/Zs2dMSsf79++jXbt28PDwYHQe8uLUqVPw9fVFZmYmZs2ahRkzZnCl1BREkyZN0KtXLyxduvSnS51Yv359xMfHc6WxFUa5rsLe3h7nz5/niLRYUlKCU6dOYfPmzcjKyuJbB78MEwCYckERldq1awdnZ2c4OzvD0dFR7GsvrX5QFmhoaOD+/fswNTVFv3794OjoiLlz5/6y+j1Bvzkg2u9ejq2tLdq1a4dNmzYxNoWSkhJMnjwZV65cwb1792Tef1kh6XMna3Jycph090VFRfD09IS/vz+TcUsQy5YtQ3BwMHr37s0zhTOvSIIzZsxg/l9cXIyIiAiYmJgw455r164hJycHo0aNQmhoKN+2bW1t8ejRI+jq6nLoGqpaX/P161fk5OTg+/fvHOXy7Mf169exY8cOREdHw9TUFGPGjMGIESOqPNqzj48P9PX1MX/+fJGOl5V+UhbzfGnp2bMn+vbti8mTJ3OUh4WF4ejRozxTaJdTHXqCcmSZFVAapElhLc21r05evXqFyMhI7NixA3l5eRgxYgS8vLzEsku3atUKa9asQYcOHeTY099UJQkJCZgxYwZu3bol0vEvXrzAhg0bkJqaCqBMvz158mTUr19fqOzGjRsxbdo0aGhooGHDhrh9+zbYbDZCQ0Nx8OBBvtmhyrl9+zZq1arF6PSPHDmCHTt2wNraGosXLxaakdPDwwNXr17F6tWrmaw2N27cwKxZs9C+fXvs2rUL0dHRWLVqFW7evCnK5RALQ0NDXLx4USJfCml0w+U8f/4c8fHxSExMREJCAtLT01GvXj04OTlh9+7dYvdJFMrH3AAEjrvlrd9cs2YNFBQUMHXqVJw7dw59+/YFEeHHjx8IDg6Wu64nMzMTU6ZMQXx8PIc+UNT5xs/Oly9fsGfPHiQnJzMRq4cPH87XB6Uyc+fOhYaGBv755x+J+5CZmcnoeB4/fozOnTvD3d0dgwcPFppp4ze/Nr+db3/zSyGNMbscaQYMxcXFOHXqFPbu3YsjR45ATU0NQ4YMgYeHB9q3by+TcxTG0qVLkZaWhm3btomkVKpp+Pj4QFlZGcuXLxdb9mefrC1btgy7d+9GeHg4unbtihMnTiA7OxvTp0+Hn58fvL29+cpKq+CUxbWTNEWSvb09pk+fjtGjRyMnJwempqa4f/8+k67jypUrGDp0KJ49eyZx32o64ir3KsMvBXdycjKcnZ3x4cMHgbINGzaEg4MD3wkLIFhJBJQ5be3fvx9Pnz7FrFmzULt2bdy+fRt169blSlkhzDBRzs8yUdi0aROmT5+OsWPHMu/6y5cvIyIiAmvXruVyyq3M9+/fcfXqVcTHx+PChQu4du0a6tevj86dO8s9Fam0zhxpaWlMGsz4+HgUFRUxRgJ+k8wxY8Zg3bp1TEpAQYbtHTt2cJVJm3JeFnh6esLe3r5GpHmWJj3S8ePHERoairCwMJiamsq/s/+f3bt3IzExEfHx8cjIyOBa9CRK3wcMGIDz589DT08PzZo145pci+JIdOvWLQQHB3Mo+GbOnAkHBwe+MtIq9oWloa0qVqxYAVNTUwwbNgwAMHToUOzfvx/16tXDiRMnanRqt4rvEGmRxDAm7Tc7Pz8fPXr0wM2bN9GgQQMAZU45HTt25JneuCIBAQFITU3Fjh07oKysDAAoKiqCl5cXLCwshC70AsqMpsuXL+dyyDh16hTmzp2L5ORknu3KIp2mpAwfPhwxMTGwtLRknB8fP36Mx48fY9CgQSKllPqZGTNmjEjH8fpmAmXvnZ49ezL3TGWKiopw6tSpGj/mqk7lsqOjI2bPng03NzeO8sOHD2P58uVC0yFKi42NDdzd3bneO4GBgYiJiRHqiKOmpobU1FSYmJhg7ty5ePnyJXbu3IkHDx7AyclJ7gb9M2fOwN/fH0FBQTwdWeTpOF2dzJw5E+vXr8eUKVMQFBQEFRUVAEBMTAymTJmCZs2aYceOHXJfzOXn54fg4GB4e3tzzNPXr18PHx8fBAQE8JRbuXIlVqxYAUNDQyxduhT9+/eXaz9rEoLG+J8/f0ZUVBSKioqEvnc2b96MqKgoXL58GZaWlvDw8IC7u7vcUx9fv34dc+fORVJSEiZOnIgFCxbwTcEuCHV1ddy7d6/Kx6myYPXq1Xj69CnWr18v1JG6IsIMuqqqqggNDRW4iDk7O1uktsS9D75//47v379DQ0ND4HE1wQGyTZs2cHZ2Ru/evdGtWzckJSWhefPmSEpKwuDBg2u8fm/AgAE87xsWiwUVFRU0btwY7u7uzLhU1N8cEP67V/eCH2mQ9LmTF5mZmfDy8kJCQgLevn0rkiOnoG8yi8XC06dPucqdnZ1F6g+vFNAV2bBhAzp37gwbGxuR6pM1b9++xZgxY3Dy5Eme++U51maz2TAxMYGnpyf++OMPvsf169dPbn0Aypyrd+7ciebNm8POzo5rzBocHMzxt6z0k7KY50tL7dq1cfnyZS7HydTUVDg6OuL9+/c8+12degKg5jjfSpPCWpJrX9O4dOkSduzYgX379sHa2hpeXl7w8vLiSO1eTsV03Ddv3sTChQuxdOnS/6l5ojwoLi5GYWGh0HGivElNTUXLli2Z4Ezy5ubNm8jNzUXXrl2Zcz9+/Dh0dHSYYBv8aNWqFXx9fTFo0CA8ffoU1tbWGDhwIG7cuIHevXsjJCREoHxBQQF8fHywc+dOFBcXAwAUFRXh6emJNWvWQF1dHXfv3gVQZgOXNdIEJpFEN8yPr1+/4uLFi9i7dy/27NkDImKuh6zR09NjbIojR47kO8cV5vzILzhU+VjfxMSErw6VF9nZ2UywlapYNOXo6AgiwrRp01C3bl2ucXfnzp25ZGbMmIElS5ZAXV2dY+EYLyqPd341pk2bhp07d8LOzk6k8V5l2rZtixs3bsDOzg4eHh4YPnw4lw/Eb/53+e18+5tfCmmM2ZWRdsDw9etXHDp0CFFRUTh37hwaNGjARNeSJwMGDEBcXBw0NDRga2vLFdVC3lFtpMXb2xs7d+6EhYUF/vjjD67+C/ronT9//qeerBERli5dimXLluHr168AAGVlZcyePRvz5s2DqqoqX1lJFJyynuhqa2sjOjqaa1XciRMnMHz4cOTn5/OU27p1K3x8fDBs2DAkJSVBR0cHly9fZvYHBgbi2rVrOHbsmEjn9TMirnKvHAcHB7BYLCQnJ6NZs2YcDvclJSXIzMxEjx49BDql/P3339i7dy8aNmwocWSBlJQUuLq6QltbG1lZWXj8+DHMzc2xcOFC5OTkYOfOnRzHy9IwUVM4dOgQVq9ezRGFcvbs2XyN1OWOh+XOtiYmJozzYadOnZhvmLyRxpnDyMgI3759g5OTE0dEDnkbWdhsNrS1tYW2I8jpXFoCAwOxevVqdOnShee3ilc0lJqIrq4uvn79iuLiYqipqXG9e+R5Dct5+fIlEhIS8N9//yEmJkZg1OSKCHNG4+eEJi1sNhvp6elCjSv8vptsNhuRkZGMEmj48OEICQlB3bp1OY4TxagkTUQaMzMz7NmzB+3bt8fZs2cxdOhQxMTEIDY2Fjk5OThz5ozQOqqDHz9+MAZpSY2S4eHhcHZ2ltjZSdJvdkWICGfPnuVYod2pUyehcuXjfGVlZcZBOjk5Gd+/f2eiX5fDb8yvqqqKR48ecTncZ2VloVmzZvjy5QuXTMUI+9JEDZaG6OhoREdHIy0tDUBZ1Mbhw4fL1ZD3qyCt825NQRLlsqyIiYnBnDlz4O3tzUQxS0pKwoYNG7B8+XIOQ6k8FO2SRmsvx8DAAKdPn4aDgwMcHBwwY8YMjBw5Ek+ePEHz5s3lbhgrN7pW/s1+9agcjRs3xo4dO3hGin39+jUmTJiA8+fP4/Pnz3Lth76+PtatW4fhw4dzlO/duxfe3t549+4dTzk2mw1VVVW4uroKzOhU03VMsqK4uBgbNmxAUFAQtLW1sWTJEqHfIGNjYwwfPhweHh5VurCp/LebMGGCwPGOsDnLwIED8eeff2Lo0KGy7qJcqBwpvjxyrTgL9bKzs0FEMDc3x/Xr1znG/EpKSjAwMKiSDGflkQLbtm0LDw8PzJs3D8HBwSguLoaLiwuio6Ohp6fHU7YmOEDGx8djwIAB+PTpEzw9PZlFxfPnz0dqamqNf2+MHj0ahw8fho6ODuMIePv2beTl5aFbt25ITk5GVlYW4uLiuBw7EhMT0b59e66AGMXFxbhy5YrQMX91L/gRF1k8d7KkqKgIBw4cQHh4OK5evYrevXtj7Nix6NGjh9zblhXfv39HZmYmGjVqVKWBVTw8PJCdnY2QkBA4OTnh0KFDeP36NaP/qpyFRJbwctCrTFWMGQU5UvNynpaVflIW83xpUVdXR1JSEldWp3v37qFNmzaMraoiNUFPUFnHxg95O27r6enh+vXrEmVIkuTa11Rev36N4cOHC1z0UHGhE/B/c8KK/OrzRGk4duwY3r9/j9GjRzNlQUFBWLJkCTNOjImJga6urlz7Udl5kYjw8uVLLF++HMXFxbh06RJf2fT0dPj5+WHz5s1c+vP8/HxMmjQJgYGBYi0ALHd1Emfsq62tjdu3b6NRo0ZYsWIFzp8/j9OnT+Py5cv4888/kZubK1I9BQUFzOIcc3PzKnOAliYwiSS64YqcOXOGCcZz584dWFlZMUFVOnXqJLf77/v37zh06BDCw8Nx8eJF9OrVC15eXujRo4fECy55UatWLQwbNgybN29mFlHXJDQ0NHDr1i2uxXqCcHZ2xqFDh6CjoyP2eKcm8vDhQ552MVG+99Ke/4IFC+Dh4cEEkPvNbyry2/n2N78ckhqzAdkPGN69e4fo6GiEhYXh0aNHVTJZqC5nFFkhzUevOox6urq6Ig/qRHVi+v79OzIyMlBQUABra2ts3rwZ//77L169esVxnLQKTllPdKVJkRQeHo5jx47B0NAQixYtgqGhIbNv8uTJ6Nq1q1Cj8s+MpPe9v78/8+/MmTM5JnZKSkowNTXFoEGDhKZIKSoqwsGDBxEeHo4rV66gd+/e8PLyQrdu3US6v11dXdGiRQusXLmSI4rjlStX4O7uLjAl4/8q5VEd5s6di4EDB3I53lUV0jhz2NvbIzU1FS1atGAccDt06CBWWkwXFxeei2M+ffoENzc3nvc+m81GSEiIUMWqp6enyP0QF0miocgDadMjRUZGCtwvz2v49etXXLp0iXFCLx93OTk5Yc2aNXJrV5hyBSj7DfktuBImL+y7KQujkiwi0qiqqiItLQ3GxsaYNm0aCgsLsXnzZqSlpaFNmzb4+PGj0DqqC3Nzcxw6dEhiJxYLCws8ffqUK+Jy5Qjg/KhOBZWoTpQA/zG/oaEhoqKi4OLiwlF+7tw5uLu7482bN1wyskqnKQmfP38WGuU4ISFBro6Xv6kZSKJclhXC3t3ipJOWlNu3byM4OJhjoZewaO3leHh4IDU1FQ4ODti7dy9ycnKgp6eHo0ePYv78+bh//75c+lxOQkKCwP2/6vP79etXoePiXbt2SRQhSxx0dHRw48YNrswCaWlpaN26NfLy8njKCctQUU5N1zHJgj179sDPzw/fvn3DwoULMWHCBJEconjpWKoCU1NTkca7wuYs27dvR0BAAMaMGcNzkba8HVnERRbjJFmQk5Mj0nEmJiY8y4OCghAUFARHR0fcvn0bQ4cOxeHDhzF9+nSw2WysW7cOffr04cguVdMcIIGyOcmnT584dOlZWVlQU1Or9giFwvD19cWnT5+wfv16ZgxQWlqKadOmQVNTE0FBQZg4cSIePHjA5VxS0RmtIu/fv4eBgYHQcUJ1L/gRl5ry3F2/fh07duxAdHQ0TE1NJQ4uICsyMjLw5MkTdOrUCaqqqiJ9D759+4YpU6Ywepq0tDSYm5vD29sbRkZG8PX1lWuf69WrhyNHjqB169bQ0tLCzZs30aRJExw9ehQrV64U6Ej1v4qs9JM14TlydnaGjY0NQkNDOcr//vtvpKSk4OLFi1wy1aknqNgHYVSFE6c0KawlufY1jStXriA8PBz79u1D06ZNMXbsWEyYMIHn7yNsbliRX3WeKA3Ozs4YPHgw/v77bwBl175jx44ICAiAlZUVFixYgJ49e8o9ciW/zJZt27ZFeHg4LC0t+cpOmDABOjo6WLlyJc/9c+fOxadPn/hmUq3I9u3bsWbNGqSnpwMo0/lOnz4d48aNEyqrpaWFW7duwcLCAl27dkWfPn0wbdo05OTkoGnTpiJnG5Dkmy8LpPEFkUQ3XBE2mw19fX3MnDmT+T2rmpycHERERCAyMhJFRUXw9PSEv7+/SPP0I0eOYO7cuZg9ezZat24NoGwsuXr1aixatAjFxcXw9fXFsGHDsGrVKp51xMXFIS4uDm/evEFpaSnHPnlnM3V2dsaCBQvg6uoq13ZqIk+fPsWAAQNw7949jndQ+TP3e9HGb6qb3863v/lNBWQxYCiPeLtnzx7ExcVxRLoQNOD8jfRUh1GvouPS+/fvERgY+P/YO/Owmrb/j7/PaU6TKRSlCEVRhotLo+9NhsxDCinzcClS9yIql4RERUUaTCHhGjNVVGZNhjQoMo+hQtP6/dHT/nU6Y52zO9Xt9TzneWrvs85ae5+z1/BZn8/7A0tLS5Z0jrGxsVi3bh3X1IW/fv3Chg0bcPnyZUrpdvz48QgLC8PatWshISGBJUuWwNXVlaWcsIYZUS90G0OKpP8qERERmDZtmkii8J4/f47w8HAqXcqjR4/4RmvWjBKt6Xz7/Plz9OzZkyU1MCdyc3Ph5+dHORPo6elh+fLl9YoWFzdFRUVsiy1OCphubm5UoEfPnj0p5y8TE5N6pQQVhvv372PHjh31cuYoLCzE9evXkZCQgISEBDx+/Bj9+vWDmZkZ/vnnH77luaUHe//+PdTV1VFWViZwmf8iwqZHEhdDhw5lcbatVnymOyofqDKucOPmzZvYtWsXKisrufZbTCYTJ06c4LuJR6eBWBSKNGpqaoiOjsbQoUPRs2dPbNy4EVOmTMHTp08xcOBAFnX8xkZoaChiYmJw4MCBem+mvnr1CvHx8VT/lZ2djU6dOsHU1BQHDx4UcYvZKS4uRkJCAscIbbqVsxcsWICbN2/i5MmT1Dibk5ODSZMmYeDAgdi3bx9bGVGl06wPpqamiI2N5ZruKyEhAWPGjKFdObIF8SNO47I4szaUlZVhwYIFWLduXb0VuwsLC7F27VoUFBRg0aJFlPrb+vXrIS0tjTVr1oiyyS3woaKiAhkZGdDU1GyQuc+yZcsgJSXFtgG7atUq/PjxA4GBgbS3oaly8eJFuLm5IS8vD6tWrYKzszNbxovapKeno0+fPmAymVzTWVbTGBzneMHLoaW5q5FFRESgXbt21Lx69erVCAkJgZ6eHpU5iBs1lXE5qXDxC9bQ0dGBp6cnbGxscO/ePfz22284duwYJk2aBAC4cOECFi5cyDI2NQbHreZE+/btkZSUhB49erAcz8rKwtChQ/Hx40dkZGRg+PDhbAEM3ObNWVlZGDBgAN91VmMI+GmKVAe4z549m1Ir5gSnoAFRpuH99OkTpk6diri4ODAYDGRnZ0NbWxsODg5o3bo1tm/fzrXs8uXLkZSUBD8/P4wcORLp6enQ1tbG6dOnsWHDBqSkpPCsW1iUlJSQnp6Orl27QlNTE4cPH8bvv/+OvLw89O7du0mpb4qCly9fAgDPzGTNyT6ZlJSEESNGYODAgZTa7tWrV3H37l1cunSJYyYHcdoJarahMXwHwqSwrs+9bwy8efMGkZGRCAsLw5cvX2BrawsHB4d6Z6lqQTBqZrUBqsawx48f4+LFiwCqspAuX76cckali9o2kmrfCkH2J3v27ImDBw9i4MCBHM/fv38fM2bMwNOnT3l+jru7O3x9fbFs2TKW/fiAgAA4OTnB09OTZ3lzc3N06dIFI0aMgKOjIx4/fozu3bsjISEBs2fP5isoJMyYL27qYxuuiZ+fH65fv47r169DRkaG2ts0NTVlmz/TTV5eHhwdHXkqbtdm0KBB8PLygqWlJcvxal+OO3fu4NSpU1i5ciXHjNYeHh7w9PTEgAED0KlTJzZn65MnTwp3UXzIzc3FwoULYWdnhz59+rCNOfzsDB8+fOA6dmdkZLApsTcmxo4dCwkJCezbtw9aWlq4c+cOPn36hJUrV2Lbtm20jZmiXC+00Lxpcb5todkhzGa2sBOG6dOn4+zZs5CXl8fUqVNha2tLTfpaaP5MmjQJZmZmWLp0KcvxgIAAXLlyBadOneJYztXVFcHBwRgxYgSSk5MpRbtbt27h77//xpQpUxokrV19qK2uceXKFa4pkgRV13j//j3HaLHGvjHVGCgtLeV477gpqnCioKAAYWFhCA8PR2lpKTIzM/k639Zc9Nd0vr18+TIcHBx4pmiJjY2FtbU1+vXrx6K8mpaWhjNnzuB///ufwG0XF3l5eVi6dCni4+NZHPYE2RApKirCjRs3WFTXe/ToARMTEyqSuSnw6dMnxMfH4/Tp0zhy5AgqKyt5Xnf1RnS/fv0oVZ5qKioqcPHiRQQHB3M0cnBTkvkvUp/0SN++faMcwvlt/HFyHBcFbdq0AZPJxB9//FGneZaRkRGuXr2K1q1bw9DQkGcU+YMHDwRuz9OnT+Hm5oYzZ87A1tYWnp6eXDfUG4NhXxSKNEuXLsXZs2eho6ODlJQU5OfnQ0FBAVFRUfDx8anT/WtoDA0NkZOTg7KyMmhqarI5wdSl7SUlJbhx4waOHDmCQ4cOgRDCVfVYVKSkpGDUqFEoKSlBcXEx2rRpg48fP1IqYHQrZ3/9+hUjR47EvXv3qI3Ely9fYvjw4RyVyAHRpdOsD/r6+pTacW1nhOvXr2PUqFGYM2cOm0pMC80PYY3LTRllZWWkpqbW2/lWHDQnB0RhWbFiBfT19eHo6IiKigqYmJggOTkZ8vLyOHv2LExNTUVeZ83NgPLycoSHh0NDQ4NSUbx9+zZevHiBWbNmtfSfHLhz5w5cXV1x69YtLFy4EGvWrBE4QLLmXJGTIlSL41zDkJeXh/LycjbF5+zsbEhJSbGlWK1Nz549sWfPHpibm+PmzZuwsLCAn58fzp49C0lJSZ72LUlJSXTu3Bn29vYYO3YsV/UlblkcZGRkkJOTgy5dulD/p6enU8rvr169gpaWFpvNu7ERHR2NY8eOcbTPN+a1BlCV5SwiIoLNUfPff//F7Nmz8eXLF2RnZ2PQoEFUxpBq++jp06cxcuRIlsCxiooK6jusdo7hhjgDfoRF2OdOGIRRvxQ0DS8AxMXF8Tw/a9YsvH//Hvv27YOuri5lH42NjYWzszMePXrEtaympiaOHj2KwYMHs9hWc3JyYGRkRHuA7MCBAylREWtra6ioqGDz5s3YtWsXoqOjOTqfiIpdu3ZxPK6srIwePXo02P5aZWUlFdRcVFQEAFBUVMTKlSuxZs0att9Zc7NPpqamYuvWrUhNTaUyiv71119sz3Q14rQTVMPvO6ioqMC7d++gpqZGWxsA4TMk1fXeNwakpKSgrq6O2bNnw9ramm19Xg2vtV5YWBgUFBQwZcoUluPHjx9HSUkJrRnZmipycnJ4+vQptd83aNAgTJkyBS4uLgCq5hF6enooLi4WZzN5Iicnh8zMTK7zmOfPn0NXV5dv0Ef79u2xa9cu2NjYsBw/cuQIli1bho8fP/Isn56eDltbW7x48QLOzs6UeNSyZcvw6dMnHD58mGd5YcZ8cVMf2zA3MjIykJCQgGvXruHs2bNQVVWlAljo4tevXzhx4gT279+PmzdvYvTo0XBwcKACzfkhJyeHlJQUNsG86oxRP378QH5+PvT09Dj+Djt16gQfHx/asxhx49atW2wZZ+tiZ+jYsSNCQ0PZBFy2bduGdevWCaz6LA7atWuHa9euwcDAAMrKyrhz5w569uyJa9euYeXKlVyD1SZOnIjw8HAoKSmx+bXUhtNaX9D1At1ZEVto/PDX3m6hhSYEv81sfs63K1aswIoVKwD8/4Th4sWLWLp0qUATBgkJCRw7dgyWlpZidZZsygZOALh37x7X9nMa9Pht5lVD96ZebGwstmzZwnZ85MiRPFNDHT9+HJGRkbC2tsbDhw9hYGCA8vJypKWlCZyeQlgDZ30XurXTKlUrcVRTvVkgCPfv38fs2bPx5MkTFnWQ5roxJexkrybZ2dlwcHBAcnIyy3FB792vX78QExOD/fv3IzExEWPGjEFAQABGjhwpkAHb2toanp6eOHbsGICq7+3FixdwdXVl+03Uxs3NDU5OTvD29mY77urq2iScb+3s7EAIwf79+9GhQ4c6pZVRUFCAlZUVrKysAFQZJH19feHv74+goKAG+90/evSIpS4JCQn07t2b43sdHBywc+dOXL58mXIafvz4Mdq0aYNhw4Zh+/btfFU/+/XrBwaDAQaDwZbeBqhaAHNzBGgMcWPcohsZDAZkZWXRvXt3jBs3jvYUh4QQytn+ypUrGDNmDICqvpebgal169aUYVpFRYXj75XufvfTp0/IyMhAfHw8YmNjsWbNGkhLS1NO5/PmzeNYbty4cdQm5rhx44RO4fT69WusX78eERERsLS0RGpqaoMqNGRnZyMuLo5j0IS7uzvXcsXFxdTGQuvWrfHhwwf06NED+vr6As/zduzYga5du6KgoAA+Pj5UkMWbN2+wePHiel5RwzB+/Hihyl+6dIkl4EFXVxcmJiaIjo6GsbExxzKiHLOdnJwwduxYBAUFQVlZGbdu3YKUlBTs7OywfPlynmU/ffoEd3d3rr8bQTa1lJWVkZycjMuXLyMtLY3a2OF27dV4eHjwTadJB7GxsRg+fDjs7e0RGRlJHb9x4wbGjBmD2bNntziO/Uf48OEDcnNzWdT9GmqtUPO3x4lZs2bRVjdQ1e+dOnWKayYVQfjy5QtCQ0NZMh04ODjQNlfp168f5YBYPe/jNIdrjuu82kRHR8POzg4AcObMGeTl5SEzMxMHDhzAmjVrkJSUJPI6a284VKsAVjvOtGvXDu3atWvUG4LiZPDgwZCTk8PChQuhpaXFdeOVk40xLy+PUpHJy8ujtZ2CIM50mOLE3t4eDg4ObDay27dvY9++fYiPj+dZvqCgAN27dwcAnDp1CpMnT8b8+fPx+++/83WYf/nyJSIiIhAWFoagoCDY2dnB0dERurq6ArW9rKyMxXFTWlqaxaFFUlKSZ78pTgfIanbt2oU1a9bA3t4ep0+fxpw5c5Cbm4u7d+9SaZIbMzNnzoSjoyP+/vtvSpXt7t272LRpEzXmJyQksNhMqufJhBAoKipCTk6OOictLY3BgwdzXefWpLE51NYFYZ87Yajdv9WFmg61/Jxr+XHp0iXExsayqaXq6Ojwdaz+8OEDRwfC4uLiBklfvXz5crx58wZAVXaEkSNH4tChQ5CWlkZ4eDitde/YsYPj8cLCQnz9+hVDhw7Fv//+S7uNbc2aNQgNDYW3tzclEJGYmIgNGzbg58+fbBm+RGWfFMU6XxT069cPhw4dqlMZcdkJquH3HTx8+BBGRka0rzeE7Tvqc+/FTUVFBV68eAEvLy9s3LgRAPv3wW+tt3nzZgQHB7MdV1VVxfz581ucbzmgrq6OJ0+eQENDA0VFRUhLS2PpQz99+gR5efkGacvVq1fZsimuWLGCb7YiZWVl5Obmcp3z5OTkCCQIUlZWhgEDBrAd79+/v0DCCgYGBsjIyGA7vnXrVoH8O4QZ80VFfX1B6msbrgkhBCkpKYiPj0dcXBwSExNRWVlJqxr6nTt3EBYWhqioKHTt2hVz5szBsWPH6jw/6NWrF7y9vRESEgJpaWkAVb8nb29vyiH31atX6NChA8fypaWlGDp0qHAXIwQODg4wNDTEkSNH6rwfDVTtbU6aNAlz5syBr68vPn/+jFmzZiEjI4Ov07m4qaiogKKiIoAqu9br16/Rs2dPaGpq8lTLrhksVJ95iyjXCy00b1qUb1toVlQrp1VvZqelpbFsZvPbLAc4Txi+f/8OfX39OqX3+fnzp0hSwNeVmgbOkJAQNgOnIGnAxUlUVBRmzZoFS0tLXLp0CX/88QeysrLw7t07TJgwgWN6NE5qIrVpiE09TU1N/Pnnn1i5ciXL8e3bt2PXrl1cJ9zS0tLIy8uDuro6gCqnszt37tRJ2t/ExAQODg5sC9KDBw8KZODs0aMHgoOD2SJ2EhISMH/+fL4pPkRB37590a1bN7i6unKcMDZlAzQn5syZg127dkFRURH29vY8J8j80gL+/vvvkJSUhJubG8c0F9wUVQBg8eLFiIqKQpcuXeDg4ABbW1uBVX2q+fr1KyZPnox79+7h+/fvUFNTw9u3bzFkyBCcP3+eZ2pOWVlZZGRksBnns7KyYGBgwDX1e2NCQUEB9+/fp5Ro6kJlZSXu3r1LOYIlJSWhqKgIGhoaMDMzoy0l5I0bN+Ds7Iy7d+8CqFJyKCkpYXF8j42N5WgsqVYV6NOnD4yNjWFqagoTE5M69VnPnz8HIQTa2tq4c+cOy6JcWloaqqqqjVbxG6iKdHzw4AEqKiqo7z0rKwsSEhLo1asXnj59CgaDgcTEROjp6dHWjvqkR0pISKD6jISEBJ6fz8+JWhQQQnD//n0EBATg0KFDfFWTRcHXr1+xadMm+Pv7o1+/ftiyZYvAKWm0tLRw7949tG3bVqg27N27F4sWLUK7du3QsWNHln6bwWDwNJCJU5GmOVCdCm3lypWYP3++QNH8ohyzVVRUcPv2bfTs2RMqKiq4efMmdHV1cfv2bcyePRuZmZlcy44aNQo5OTlwdHTkOFeia2NC3IrPubm5GD58OKZMmYKdO3ciMTERVlZWsLW1RVBQkFja1ELDo6enB11dXaxevbrB1wqtW7dm+b+srAwlJSWQlpaGvLw87Rvi1QpcFhYW6N+/P9vcml+Q8fXr1zF27FgoKytTm1P3799HYWEhzpw5U6cNFkF5/vw5NDQ0wGAw+G48Nbd1Xm1kZWWRk5ODzp07Y/78+ZCXl4efnx/y8vLQt29f2pXsWqg7Xbt25buBxWAwhFKrrw4coJP6psPkpkBYG359jzhRUlLCgwcPKAfaanJycjBgwAAUFhbyLF8zu4+hoSGcnZ0xc+ZM5Obmom/fvpQiIj8SExMRFhaG48ePQ09PD46OjnB0dOQZ5MxkMlmywwwdOhTHjh2jNvY/fvyI//3vf1zXTMLaB0VBr169sH79etjY2LAoeLq7u+Pz588ICAigvQ3CUFFRAW9vbwQEBODdu3cAgA4dOmDZsmVwdXWFhIQEXrx4ASaTyeZw4eHhgVWrVvG0g/FC3AE/wiDscydOHBwc+L6HwWAgNDSU53sUFRXx4MED6OjosPz27927B0tLS3z69IlrWWNjY0yZMgXLli2DoqIi0tPToaWlhWXLliE7O5uvarKoKSkpQWZmJjQ0NOpsJxYlz549g52dHfr164fdu3fTWpeamhqCgoLYVK9Pnz6NxYsX49WrV7TUK651fk0ePHgAKSkpyrZ7+vRphIWFQU9PDxs2bKCck2oibjsBwGqr4URaWlqDON9Wk5OTg9zcXBgbG0NOTk6g+V597n1jQFDnQl5rPVlZWWRmZrIFBuXn50NXV7dRqy+Ki7/++gunTp3C33//jfPnzyM5ORnPnj2j9lFCQkIQGRkpUFY0Ydi9ezeWL1+OyZMnU+rkt27dQnR0NHbs2MEz2Grq1KkoKyvjuhYZN24cpKWlcfz4cZ5tWLZsGaSkpNhSvK9atQo/fvxAYGBgHa+qbggz5osCcfqCjB07FklJSfj27Rv69u1L7Q8aGxvXSTW3rjCZTGhoaGD27NlUgDEnao/jtUlOToa1tTWYTCYl2paRkYGKigqcPXsWgwcPxoEDB/D27VtKVbomrq6uUFBQwLp164S7oHrSqlUrpKWlsc2560JKSgpmzpyJX79+4fPnz/jtt9+wf/9+dOzYUYQtFT3Dhw/HypUrMX78eMyYMQNfvnzB2rVrERISgvv37+Phw4e0t+HDhw9cncwzMjLqtE/eQjOEtNBCM0JZWZlkZmZSfz9+/JgQQsitW7dIz549+ZYfM2YMad26NZGQkCBGRkbE2dmZnD59mnz58kWg+isqKoinpydRU1MjEhISJDc3lxBCyNq1a8m+ffvqd1F1pGfPnuTw4cOEEEIUFBSoNqxbt44sWbKkQdogDPr6+iQgIIAQ8v/tr6ysJPPmzSPu7u4cy+Tn5wv0opuwsDAiISFBxowZQ7y8vIiXlxcZM2YMkZSUJGFhYVzLMZlM8v79e+p/BQUF8uzZszrVraioSLKzs9mOZ2dnE2VlZb7lZWRkSF5eHtvxvLw8IisrW6e21OTr169k9+7dpH///nzfq6CgwPEaWuCPvLw8efLkSb3KMhgMoqmpScaPH08mTJjA9SUIN27cIIGBgWTLli3k8uXLApXp3LkzOXbsGNvxo0ePki5dutTpWsSFqampwNdbzZYtW4iVlRVRUlIiDAaDdO7cmdjZ2ZHQ0NA6P//1Yfr06WTnzp3U/woKCiQhIYHk5+eTvLw84uTkRCZOnMixLIPBIO/evaO9jY2ZHTt2kIkTJ5KvX79SxwoLC8nkyZOJn58fKS4uJuPGjSN//PEHre1IS0sjffr0IUpKSmTDhg3U8aVLlxIbGxu+5Z8/f04qKyvZjldWVpLnz5+LtK2EEOLh4UGKi4vJ/fv3yfbt28nYsWNJ69atiaSkJDE0NCROTk7k1KlTAn2WlpYW+fjxI9vxL1++EC0tLa7ltmzZQtq0aUP09PQErosONDQ0iLe3d73KHjhwgJpX3Lt3j7Rr144wmUwiKytLoqKiBP6crKwsEhwcTLy8vIiHhwfLq7Hz5csXsnfvXuLm5kY+ffpECCHk/v375OXLl3zL7tixg0yYMIG0bduWqKmpERsbGxIcHEyePn1Kd7MJIYS0a9eOZGVlEUII0dHRIRcvXiSEEPLkyRMiLy/Ps6yCggJJTU0Vug1FRUXk3LlzZM+ePWTnzp0sL04wmUyx9/tpaWmkdevWZPbs2URJSYnMmzdPrO1poeGRl5dvVGuFrKwsYmFhQT3DdNK1a1euL15jXjV9+vQh8+bNI+Xl5dSx8vJyMn/+fNKnTx86m04IISQhIYGUlZWxHS8rKyMJCQm01y9uNDQ0SGxsLCkvLyddunQhZ8+eJYQQ8vDhQ6KiokJr3aWlpURCQoJkZGTQWk8LnJk9ezYpKipiO56Xl0eGDRtGe/0dO3YkkZGRdS7Hq8+pS98jTpSUlMiDBw/Yjt+7d48oKCjwLT9jxgxiZGREHB0diby8PLXuOH36NOndu3ed2/P27VtiZmZGmEwmNXflBoPBIEwmkzAYDLZX9XEmk8m1vLD2QVEgJydH2WHbt29PzV+zsrJImzZtGqQNouLr168s6366UVFRYXm1atWKMBgMIiMjQ1q3bt1g7agPwj53wlB7TVP9Cg8PJ8nJyXzLMxgM0rVrVzJhwgQyfvx4ri9+WFlZkbVr1xJC/t++X1FRQaZMmUImTZrEs+yNGzeIgoICWbhwIZGVlSXLly8n//vf/0irVq3IvXv3BLsRzZSEhATSrVs32uuRkZHhaBfIzMwUan+EH6Ja5wvDgAEDSHR0NCGEkNzcXCIjI0NsbGxI9+7dyfLlyzmWaQx2An6kpqbyHDNFxcePH4m5uTk1Rlfvyc6ZM4c4OzvzLFufe99c6NKlCzl9+jTb8VOnThF1dXUxtKjxU1JSQmbOnElUVFRIr169yPXr11nOm5qa1tveXBfU1dWJv78/2/GAgACipqbGs+yDBw+IjIwMmTRpErl9+zYpLCwkhYWF5NatW2TixIlERkaG3L9/n2NZJycn6rVs2TKiqKhIevfuTRwdHYmjoyO1T7J06VKO5VVUVEjr1q0FevGD15jPbV9NlAjrC1JX23BNVq1aRc6cOUMKCwuFu4g6wml9xGm9JAjfvn0je/bsoX5PQUFB5Nu3b1zfX/O3t3z5cqKiokKMjY3J0qVLWc45OTmJ6nK5MmbMGGrcqC/fvn0j06ZNI5KSkkRSUpKEh4eLqHX0cvHiRXLixAlCSNX6tmfPnoTBYJB27dqRq1evCvQZ1c8NJ1atWsW3fIcOHSi7Xk22bt1K63yxhaaBpLidf1toQZRISUlR6gGqqqp48eIFdHV1oaysjIKCAr7le/XqhQULFmD48OH1kh3fuHEjIiIi4OPjw5JKqk+fPvDz84Ojo2OdP7OuvHjxgpK7l5OTw/fv3wFUpcwaPHhwo1cXyM3NxejRowFUqR9Wp1ZycnKCubk5PDw82Mo0FqUce3t76OrqYteuXVTKYV1dXSQmJuK3337jWo4QAnt7eyqt3M+fP7Fw4UI2lQReaYwZDAb1Xdfk69evAkX2qqqqIj09nS3KNC0trV7qfnFxcdi/fz9iYmKgrKyMCRMm8C1jYWEhdLRWU8Xc3BwxMTFsUYHfvn3D+PHjce3aNZ7l9fT0uKaY58esWbNEprgzbNgwDBgwADIyMgJ/5rx58zB//nw8e/aM6ruSkpKwZcsWODs7i6RddLNv3z4sXLgQr169Qp8+fVhSQgKgoidr4ufnB1NTU2zbtg1mZmYN/ru/d+8e1qxZw3Ksc+fOVH86c+ZMqi/mxPfv3/mqu3NLD/Tvv/8K3E5+UariYuvWrbh8+TLLNSorK2PDhg34448/sHz5cri7u+OPP/6gtR3CpkfS0tLCmzdv2FQqPn/+DC0tLZErQ3h4eGDhwoUYNGgQDA0NYWJignnz5sHY2LjO8678/HyO7fv16xdevnzJtZybmxvk5OTQvXt3REREICIiguP7eI251dQ3tRNQlf57ypQpfOvgRHXqaqAqldbz58/rrEjDT3nX3d29Xm1rCNLT0zFixAgoKysjPz8f8+bNQ5s2bRATE4MXL17wVYtasWIFVqxYAaAqGjkhIQEXL17E0qVLoaqqyvP3Awg/ZhsaGuLu3bvQ0dGBiYkJ3N3d8fHjRxw4cAB9+vThWbZXr15CK3+kpKRg1KhRKCkpQXFxMdq0aYOPHz9CXl4eqqqqHFXsiBiT5VQrQnbt2hWHDh3ChAkTMH78eGzdupVFLVKQlHQtNG3Mzc0b1VpBR0cH3t7esLOz46lYLQqETV2fk5OD6OholrmBhIQEnJ2d+faZosDMzIzjfOPr168wMzNrMCUqcTFnzhxMnTqVUh6tzixx+/ZtKqUhXUhJSUFDQ6PZ3+PGSlpaGgwMDHDw4EFKESoiIgJ//vknzM3Naa+/vukwhe1zGgPGxsbYvHkzjhw5QvV9FRUV2Lx5M4YNG8a3fGBgINauXYuCggKcOHGCsovdv38fNjY2ArcjOTkZ+/fvx/Hjx9GzZ08EBgbyVYMS9v4Lax8UBR07dsTnz5+hqakJDQ0N3Lp1C3379kVeXp5Y55X1oT5zTGHXibXJzs7GokWLOCpvNSaEfe6EoWbK7ZoUFhbi69evGDp0KP7991+uaYkXLVqEI0eOIC8vD3PmzIGdnV2dUxgDgI+PDywsLHDv3j2UlpZi9erVePToET5//oykpCSeZYcNG4bU1FR4e3tDX18fly5dgpGREW7evNkgCloVFRUIDw/H1atX8f79e1RWVrKc57fOpRMNDQ28ffuW9nr69u2LgIAANgX4gIAAnlnlhEUU63xhycrKQr9+/QAAx48fh4mJCQ4fPoykpCRMnz4dfn5+bGWaWn9OJ05OTpCSkqL2o6uZNm0anJ2dsX37dq5l63PvGwM+Pj5YtmwZ5OTkAFTt51TvCwFVeweurq48FattbGzw559/QlFRkcrGkpCQgOXLl2P69On0X0QTRE5OjucavqHSoRcWFmLkyJFsx//44w+4urryLGtoaIjo6Gg4ODiwqd+2bdsWx44dg5GREceytbMTV6ufVmeBa9euHdq1a4dHjx5xLC/K50mYMV8UCOMLUh/bMADcvHkTnz59wtatW6ljkZGRWL9+PYqLizF+/Hj4+/tT/YCoqT03EQZFRUUsXLhQ4PfX/u1V99u1lVbpzm4DVCkPOzk5USqrtfej+e2pJiUlUXPd9PR0JCUlYdmyZTh//jyCgoLYsn81JiwtLam/u3fvjszMTHz+/BmtW7cW+N4vWrQIKioqsLKyYjnu5OSEqKgolt83J5ydnTFp0iTMmTMHvr6++Pz5M2bNmoWMjAwcPny47hfVQvNCvL6/LbQgWv73v/+RQ4cOEUIImTt3Lhk0aBA5ePAgsbS0JIMGDeJaLjk5mZw5c4blWEREBOnatStp3749mTdvHvn58yff+rt160auXLlCCGGNNHry5AntiibVaGlpUVHm/fv3J0FBQYQQQmJjYxt9hDwhVRFz6enphJAqFdzqCJTk5GSipKTEt/z169eJra0tGTx4MKV+FhkZSW7cuEFfo4XE3t5eoBcvxowZQ6ZMmcKmZjRp0iQycuRIvm1YvXo10dTUJNeuXSPl5eWkvLycXL16lWhqapKVK1cKdB0vX74kGzduJN26dSNt27YlTCaTREVFcVRV5MSHDx/IqFGjyIYNG0h0dDQ5ffo0y6s5w01J9N27d0RSUpJjmWr1ja9fv5KrV6+SIUOGkLi4OPLx40eWcw2h0CGM6ndlZSXx9fUl6urqVHSiuro68fPzE/i3I25u3rxJtLS06qxIQ0iVGhU3Pnz4IOqmUsjKypIXL15Q/584cYIUFxdT/+fn5xNpaWmOZauvi9uL33ULEqFalyhVcdCqVSsSFxfHdjwuLo5SdMnNzSWKiooN3LK6wWAwWJTXq8nPz+erwFnf+t69eydUv1Q9JjAYDBIZGckyTsTExJAlS5aQHj16cC0/e/ZsocdcQqqUdRQUFMjSpUuJtLQ0WbBgARkxYgRRVlYmf//9N9/yDg4OZM+ePXW6dlEijPKuuLGwsCAuLi6EENb5dlJSEtHU1BToMyorKykF5jFjxhAVFRUiISFB+vXrx7dsfcbsmty9e5dcu3aNKmNpaUkUFRWJkZERSUlJ4Vn2zp07xNzcnMTHx9d7vDcxMSHz5s0jFRUV1P178eIFMTY2piLHGxO1x5yaY4SgY20LzYPg4GDSpUsXsn79+kazVkhJSaF9rL958yb5+++/yapVq8iFCxfq9RlDhw4lJ0+eZDt+8uRJ8ttvvwnZQv5wm288ffq00c+VRMXx48eJr68vKSgooI6Fh4c3iAr/vn37yKhRo/iqbbbAmStXrpC//vqLODo6kjlz5rC8+FFaWkpWrVpFpKWlyV9//UWmTJlCFBQUSEhISAO0vMrO4+npWa+ylZWVJCsrizx8+JCjcnVj5+HDh6Rt27akW7du1Py+W7dupH379rQrQb9+/Zp4e3uTnj17ElVVVeLk5NSg6tPC2gdFgaOjI5WZJSAggMjJyZERI0YQFRUV4uDg0CBtEIa3b98SOzs70qlTJyIhIcFm8+CFsOtEbty9e1egrH7iRJzPHS9yc3PJkCFDyKJFi3i+7+fPn+Tw4cNkxIgRRF5enkyZMoVcvHixzrbJL1++EC8vLzJlyhRiZWVF1qxZQ16/fi3MJTQIS5YsIa1atSJTp04ly5cvJytWrGB5iZN///2X6Onp0V5PfHw8adWqFdHV1SUODg7EwcGB6OrqEgUFBTZ1SVEiinW+sCgqKlIZekaMGEH8/PwIIVUZsxqziltaWhrP19GjRxvEXtChQwdKvbimnSo3N5e0atWKZ9mmeu9rKx8rKipS101I1VjK797/+vWLTJ06lTAYDCIlJUWkpKSIhIQEmTNnDvn16xdtbW8OPHv2jPrd1CQrK4tjhlNRY2NjQ3x8fNiOb926lUybNk2gzygpKSExMTHEx8eHbNmyhZw8eZJlf4ouysvLibe3Nxk6dCgZMGAAcXV1JSUlJfX6rMLCQrJx40aWMf/58+cNkq1LGF+Q+tqGR44cybKfkJ6eTiQlJcncuXPJ9u3bSceOHcn69euFv7gGICcnhyxdupRYWFgQCwsL8ueff5KcnBxxN0sghN1TlZaWJq6urix70zk5OWTw4MGNWnVcVJmdzp49S5SVlVn8hpYuXUrU1NQEzjD84MED0rt3b9K9e3fSpk0bYmVlRd68eSNUu1poHrQo37bQrNi0aRMV3fPPP/9g1qxZWLRoEXR0dBAaGsq1nKenJ0xNTTFmzBgAVSpYjo6OlJLp1q1boaamhg0bNvCs/9WrVxyVeCorK1FWVlb/C6sD5ubm+Pfff2FoaIg5c+bAyckJ0dHRuHfvHiZOnNggbRAGY2NjXL58Gfr6+pgyZQqWL1+Oa9eu4fLly7CwsOBZ9sSJE5g5cyZsbW2RkpKCX79+AahSd9i0aRPOnz8v8vbWVNziBze1hLCwMKHb4e3tDRMTE/Ts2RPDhw8HANy4cQPfvn0TKDLdy8sL+fn5sLCwgKRk1dBQWVmJWbNmYdOmTTzLnjhxAqGhobh+/TqsrKywfft2WFlZoVWrVtDX1xc42ujmzZtISkrChQsX2M4xGIxmqdaTnp5O/f348WOWKP6KigpcvHgR6urqHMuqqKiw3FtCCNszQghpkHsnjOp3aWkp5s+fDycnJ6r/VlRUpLW9osbBwQGGhoY4cuQIOnToUKfoxunTpyM6OpqtzLt372BhYcEWOSkqFBUVkZubiy5dugAA2/iQl5fHU+ElOjq6XioggGgjVMXFuHHj4ODggO3bt2PgwIEAgLt372LVqlUYP348AODOnTvo0aOHyOuuSxTn58+fOR6vVpVmMBhYt24d5OXlqXMVFRW4ffs2Fb0rahgMhlAKldX3l8FgYPbs2SznpKSk0LVrV56KEuHh4fWuuya7d+9GSEgIbGxsEB4ejtWrV0NbWxvu7u5c73tNunfvjnXr1uHWrVscI5S5RZgDolGkEUZ5V9zcvXsXwcHBbMfV1dUFUsMZO3YskpKS8O3bN/Tt2xempqaUAjMvJTJhxuyaDBgwgPpbVVUVFy9e5FumGhUVFXz79o1NLa8u431qaiqCg4PBZDIhISGBX79+QVtbGz4+Ppg9e3ajWy80lGpHC42fakUKT09PtnN0z3drq/YTQvDmzRsEBATg999/p63e6OhoTJs2DXJycpCSkoKvry+2bNmCVatW1elz/vzzTyxfvhw5OTkYPHgwAODWrVsIDAyEt7c3S//GKWNDfanuTxgMBkumF6Cq30xPT6+XKmdT4+fPn5g8eTLb8drzGLoICAhATk4O1NTUoKmpyZZhh58K438ZDw8PeHp6YsCAAZRycV2QkpLC1q1bIS8vDy8vL0hKSiIhIYFSwaWDmtljKisrERISgitXrsDAwIBtvunr68vxM/Ly8mBtbY3Hjx8DqJpjnThxglr3NAV69+6N9PR0BAQEIC0tDXJycpg1axaWLl0q8Dr2y5cvCA0NxZMnTwBUZbZycHDgW15DQwPq6uqYPXs2rK2tISUlhcrKSpa+FuDe37548UKg9mloaHA8Lqx9UBSEhIRQa5QlS5agbdu2SE5OhrW1NRYsWNAgbRAGe3t7vHjxAuvWravzsy/sOpEbkpKSeP36db3LNwSieO7oQFtbG97e3nBwcOD5PhkZGdjY2MDGxgbPnz9HeHg4Fi9ejPLycjx69AgKCgpcy86ePRsWFhYwNTWFhoYG1q5dK1Dbvn37RtlH+O0x0J3pIyoqCseOHcOoUaNorYcT3K7969evuH//PlauXNkg8yYTExM8ffoUu3fvpjJbTJw4EYsXL4aamhpt9YpinS8sAwYMwMaNGzFixAgkJCRgz549AKrmBB06dKC9/vrSr18/MBgMjiq81ccbQoWwuLiYxbZazefPn/kqQDbVe1/7nnP6DviVf/v2LcLDw7Fx40akpqZCTk4O+vr6jSbTaWPG3t4eDg4O0NHRYTl++/Zt7Nu3D/Hx8SKvs6YquJ6eHv755x/Ex8dTa5tbt24hKSkJK1euFOjz5OTkBMqYyomysjLIyckhNTWVbxax2mzatAkbNmzAiBEjICcnh507d+L9+/fYv39/nduhrKzMll0yLS0NoaGhCAkJqfPn1QVhfEHqaxtOTU2Fl5cX9X9UVBR+++037N27FwDQpUsXrF+/nq8vjSg4cOAAgoKCkJeXh5s3b0JTUxM7duyAtrY2xo0bx7NsbGwsrK2t0a9fP8qml5SUhODgYJw5cwb/+9//eJavzihSe377+fNnSEpK0j5nE2Z/lRCCkJAQTJs2jcU+0K1bNyQlJeGff/4RRRNpQVSZnUaPHo3du3fD2toaly9fRmhoKE6fPo24uDiB93O7d++OPn364MSJEwCqlO47duwoVLtaaB4wSF1nRC200Azp1KkTzpw5Q22Gr1mzBgkJCUhMTARQle5j/fr1lOGZG/3794eTkxPs7OygqKiItLQ0aGtrw9PTE5cvX8aNGzdov5bKykpUVlZSDpRRUVFITk6Gjo4OFixYAGlpadrbIAyfP3/Gz58/oaamhsrKSvj4+FDtX7t2LU+5e0NDQzg5OWHWrFks9z8lJQVWVla0pCdiMpkCL+DpNpS8fv2axcBpYGAgkIGTEIKCggK0b98eL1++rPNCV1JSEq6urnBzc2NxmpSSkkJaWhr09PQEan/Xrl0xZswYrFu3rlEbFkRJzd8Pp+FYTk4O/v7+HA3ECQkJAtdjYmJS/0YKQPfu3REcHAwLCwuWZy8zMxNDhgzhmDbvw4cPmDVrFq5cuYLKykoMHDgQhw4dQrdu3WhtKx20atWq3mmQBw4cCAMDA5YAkbdv38LMzAy9e/dGdHS0KJtKMXbsWLRv356rUcHe3h4fP37E2bNn2c4xmUy8ffuWLXXwf4mioiI4OTkhMjIS5eXlAKr6wtmzZ2PHjh1o1aoVUlNTAUDkTqwRERECv5fb5oSZmRkAUJv/NecG0tLS6Nq1K1atWsVmwBMWJpMJZWVlvuOmIJuSWlpauHv3Ltq1ayeq5tUJeXl5PHnyBJqamlBVVcXly5fRt29fZGdnY/Dgwfj06RPP8lpaWlzPMRgMPHv2jOv5pUuXIjw8HKNHj+a4Gcwt5WZNHB0dMXDgwDqlV2osqKqqIjY2FoaGhixjzuXLl+Hg4ICCggKe5V1cXGBiYoLhw4dDWVlZ4HqFGbNrYm5ujpiYGDZH32/fvmH8+PE8nSIGDRoESUlJLF++nGOwhyDjffv27am5dY8ePeDv7w9LS0tkZmaif//+KC4u5vsZLbTwX4PJZLL8z2Aw0L59e5ibm2P79u3o1KkTLfX2798fAwcORGBgICQkJLB582Zs3bq1zs47tdtfm5ob06Jcs86ZMwdA1dxl6tSpVEpS4P/nG/PmzRPbWN5QyMrKYtCgQTAxMYGpqSmGDh3Kci/oxsPDg+f59evXN1BLmh6dOnWCj48PZs6cWa/yZWVlcHNzQ2BgIFauXInExERkZWUhNDSUNuem6nk+PxgMBtc5x+TJk/Ho0SO4u7tDVlYW27Ztw8+fP3H//n1RNpU2ysrKMHLkSAQFBdV7PXP9+nWMHTsWysrKlK34/v37KCwsxJkzZ6jUyJyo2edymzvy6m8lJCSov6vL1Q685tdf19c+KApu3bqFM2fOoLS0FBYWFhxTEjd2FBUVcePGjXqt5YVdJ/IK+OnSpQtHwYLGgCieOzrJz89Hnz59UFRUJND7CwoKEBYWhvDwcJSWliIzM5On862pqSlu376N0tJSdO3aFWZmZjA3N4e5uTnPjXgJCQm8efMGqqqqXPcYGsoBU01NDfHx8bQEkfOD1/4Kg8HA3LlzsWvXrka/r1VfRLHOF5b09HTY2trixYsXcHZ2puaHy5Ytw6dPnxptGuXnz58L9D66nTlHjRqF/v37w8vLC4qKikhPT4empiamT5+OyspKnvb9pnrva+8P1LTPAVXCImpqalz7rsrKSsjKyuLRo0eNctxo7CgpKeHBgwds+1E5OTkYMGAACgsLRV4nL1t2TfjZtSMjIwX6nFmzZvE8r62tjZMnT6Jv374CfV41Ojo6WLVqFRUQduXKFYwePRo/fvzgazsRhLS0NBgZGdE+bgvjC1Jf27CsrCyys7MpQZ9hw4bBysqKckDOz8+Hvr4+JbJEF3v27IG7uztWrFiBf/75Bw8fPoS2tjbCw8MRERHBV8jB0NAQlpaW8Pb2Zjnu5uaGS5cu8Q1QtrKywtixY7F48WKW40FBQfj3339pEYKrRhjHc6Dp972hoaGIiYnBgQMHhF7b7t69G87Ozmjfvj3i4uIE3t9PSkqCnZ0d2rRpg4MHDyIpKQnOzs6wsrJCUFAQTz+mFpo/Lcq3LTQr6ruZ/eXLFxZnv4SEBFhZWVH/Dxw4kO9GPgC4u7tj9uzZePXqFSorKxETE4OnT58iMjKSowMTHTCZTJYJ4vTp0zF9+vQGqVsU1BwsmUwm3NzcBC779OlTjgZwZWVlWhYbAKsaV35+Ptzc3GBvb09F+928eRMRERHYvHkzLfUDrAZOfiq1nCCEoHv37tRkq64TLkdHRwQGBiI+Ph4zZ87EtGnT6jW5+PTpE5ycnP4zjrdAVQQzIQTa2tq4c+cO2rdvT52TlpaGqqoqy8ZLTRrC8CYo9VH9dnV1RWpqKjw9PSErK4vg4GDMnTu3SSrcmZub19v59vz58zA2NoazszN8fX3x+vVrmJmZoW/fvoiKiqKhtVU4OztjxIgRaNu2LVxcXChD2fv377FlyxYcPHgQly5doq3+ajip19XE3d2d9jbUBwUFBezduxc7duygjEna2tosmzF0KcfOnj0bFRUV2LZtG/79919qU3P9+vUCO3JUP2dz5szBzp07aY/GrYmHh0edHB65kZeXJ4LW1J+OHTvi8+fP0NTUhIaGBm7duoW+fftS/To/hGm/KBRphFHeFTfW1tbw9PTEsWPHAFQZdV+8eAFXV1dMmjSJa7mbN2/i06dP2Lp1K3UsMjIS69evR3FxMcaPHw9/f3+uqiTCjNk1iY+PR2lpKdvxnz9/8g3Ue/jwIVJSUtCzZ0++9XDD0NAQd+/ehY6ODkxMTODu7o6PHz/iwIED9TLaNSSVlZXIycnhqPjMywmmhaaPsMZlYRGXav/Tp09x9OhRqm9ZuXIl3N3d8f79+zoFQYlrzKzO8FId1FNbcfW/wpUrV3D9+nXEx8djx44dKC8vx4ABAyhnXH6qKsLS4lxbf0pLS4VSZx4wYABKSkoQHx+PwYMHgxACHx8fTJw4EQ4ODti9e7cIW1uFKNbTiYmJiI6OxrBhwwAAgwcPRufOnVFcXNwknmMpKSk2ldm6smTJEkybNg179uyh+uCKigosXrwYS5YsQUZGBteywva5DAYDnTt3hr29PcaOHUttqAuCsPZBYRGVYru46dKlS50V/KoRdp1YnemlmtoBP40VUTx3dJKRkcHX+e7Xr1+IiYnB/v37kZiYiDFjxiAgIAAjR47k64wTHx+PX79+ITk5GfHx8YiPj8fBgwdRVlYGHR0dyhm3dvaZa9euUXsh165daxCFTm6sXLkSO3fuREBAQIO3g9vYpaSkBB0dHZ6Oz6IkOzsbp0+fRn5+PhgMBrS1tTF+/HiBHc7qiyjW+cJiYGDAcWzbunWrQHYOcdFYFFJ9fHxgYWGBe/fuobS0FKtXr8ajR4/w+fNnJCUl8SzbVO+9sDCZTOjo6ODTp09N0gFM3DAYDI4OjtWKnHQgKrvC8uXLuZ5jMBgoLi5GeXk5X+fbNWvW4O+//66zE96LFy9YbOojRowAg8HA69ev0blzZ4E/R9wI4wtSX9twhw4dkJeXhy5duqC0tBQPHjxgCfb9/v072x4DHfj7+2Pv3r0YP348iwPtgAEDBFp3PHnyhNpXqImDgwP8/Pz4lr99+zbHLDKmpqZsSsiiRlj116be99Y3s1PNDEE1ad++PYyMjFhsM9wyBFVjbm4OJycneHl5QUpKCrq6ujAzM4OdnR309fXx8uXLOl5VC82JFufbFpoV9d3MFtWEYdy4cThz5gw8PT3RqlUruLu7w8jISCCZemERNi2ZuBFEQZbBYFAKg5zo2LEjcnJy0LVrV5bjiYmJVMSlqKnpAOnp6QlfX1/Y2NhQx6ytraGvr4+QkBDa0iMJa+AUdrIVHBwMPz8/HDt2DPv378eKFStgaWkJQkidNqonTpyIuLi4Jql8Wl+qDUTCbuhz+/4ZDAZkZWWhoaHBN8WRMOjp6eHGjRtsBq/o6GgYGhpyLHP58mWEh4fD0tISADBmzBjo6uri169ftLaVDsaOHQsnJydkZGRwdGKztrbmWrZ9+/a4dOkStbF59uxZGBkZ4dChQyKJtOWGmZkZ/P394eTkBF9fXygpKYHBYODr16+QlJSEn58fW7qzajQ1NUVm/Dt58iTL/2VlZcjLy4OkpCS6devWaJ1vq1FQUICBgQG+ffuGK1euoFevXujVqxft9YoqPVK1U0xDMn36dJGpJl+9ehVXr17l6IhXn1RRdUGY1E7CIi0tXS9n/5qEhIRAQUEBCQkJbErqDAajUTvfbt++HZMnT4aqqip+/PgBExMTvH37FkOGDOGZGsnT0xOmpqYYM2YMgKoNWEdHR9jb20NXVxdbt26Fmpoa19RYwo7ZNcfqx48fs2RkqKiowMWLF6Gurs7zMwYMGICCggKhNuU2bdpEGej/+ecfzJo1C4sWLYKOjg6LCntj49atW5gxYwaeP39eJ/W4FpoHokot1tQoKSlhCZCRlpaGrKwsioqK6jSWintT+r/u/Dls2DAMGzYMf//9N8rLy3H37l0EBwfDx8cH3t7e/7nfdVNi7ty5OHz4MNatW1ev8gMGDMCuXbuoDSEGgwFXV1f88ccf9VbTFYZv377h2rVrfNcs79+/Z7ENderUCXJycnj//j3tDkiiws7ODqGhoWxqRoKSk5OD6OholnWvhIQEnJ2d+ap1Cdvnvnz5EhEREQgLC0NQUBDs7Ozg6OgIXV1dvmXF7QC5efNmzJs3j0WxfdOmTU3O+dbPzw9ubm4IDg5mszHzQ9h1orgCfkSBsM+dMHz79o3j8a9fv+L+/ftYuXIlT7v84sWLERUVhS5dusDBwQFHjhypszK/jIwMzMzMKAXynz9/Ijk5GRcuXEBISAhCQkLYnG9r7iuYmprWqT5Rk5iYiLi4OFy4cAG9e/dms23GxMTQVndjEJjYvHkz3N3dUVlZCVVVVRBC8OHDB7i6utLej4linU8XsrKy4m4CTz5+/Iji4mKWsffRo0fYtm0bFWQ9Y8YM2tvRp08fZGVlISAgAIqKiigqKsLEiROxZMmSemdJaez3HgD27dtHOceXl5cjPDyc6jsFUb709vaGi4sL9uzZ0+iDwRsbxsbG2Lx5M44cOcISKLZ582Zqn6mxwilLJgC8efMGHh4e2L9/v0D+FPV1wisvL2d7vqSkpLiKCDUm0tPT0adPHzCZTL5zfgMDA67n6msbHjVqFNzc3LBlyxacOnUK8vLyGD58OEv7GmJ/Py8vj+O+s4yMjEAZ3dq3b4/U1FQ2f4jU1FSBbG2/fv3i6KtSVlaGHz9+8C0vLPV1PK+mKfe948aNq1eQWEpKCsfj3bt3x7dv36jz/D6bEIKQkBBMmzaNZa7crVs3JCUl8dybauG/AYPUN4y3hRYaEdWTjH79+rFEDAP/v5kdHByM/Px8juUXLVqEtLQ0asIQERGB169fU7L8hw4dgp+fH+7evVvvNt67d49KVUYH/FITAfydV8XJ6dOnuZ67efMmdu3ahcrKSvz8+ZPr+zZv3oyDBw9Sk/Pz58/j+fPncHJywrp167Bs2TI6mk4hLy+PtLQ0tglbVlYW+vXrh5KSEtrqdnJygoyMTL0NnGfOnIGPj49IJlvZ2dkICwtDREQEioqKMHr0aEyePJmvkfmff/6Bn58fRo8e3eRU+ERBdnY24uLiODqR8XNA5Oe8LiUlhWnTpiE4OJgWw83p06cxe/Zs/PXXX/D09ISHhweL6jenxbKEhARevXrFkn6tVatWePToUZ03N8QNLydZQR2CsrKyMHz4cPzvf//DgQMHGkxloqCgANHR0cjOzgZQlXZn8uTJVOoYXqxfvx4ODg4id+j49u0b7O3tMWHCBLFsSgvC1KlTYWxsjKVLl+LHjx/o27cv8vPzQQhBVFQUT/VNUSCq9EjFxcXw9vbm6sDKK0VUfaiZUlFYPDw84OnpiQEDBqBTp05sz0xtx25RI0xqp2pevnyJf//9Fy9evGALHuMVYbt9+3Y8e/ZMLIo0jYnExESkp6ejqKgIRkZGGDFiBM/3d+rUCWfOnKHm42vWrEFCQgISExMBAMePH8f69evx+PFjvnXXZ8yuOVZzWoLLycnB398fDg4OXOs9fvw4NmzYABcXF45zJV6G1aZOv3790KNHD3h4eHB85kWhqN1C40aUqcXqQnFxMbZs2YKYmBhKBUtLSwuTJ0/GqlWrIC8vT1vdTCYTGzduZFH7cnV1hYuLC4tDCL91Ej9HMX6KMsLy7t07rFq1ippv1O4D/wvOp1lZWZQSXrU6nrGxMUxNTXkq/4gCfmvF/8L9rws1FVEqKysREREBAwMDGBgYsI27vOZr/FKwN0TQaX3XLBISEsjKymJR+e/cuTMSExNZ1uoNmT2jrixbtgyRkZHQ0dFB//792Tbj+anZ/P7773BxcWFTIT116hS8vb1x69YtrmV9fHywbNkyKitJUlISBgwYQH3f379/h6urq0DKx4mJiQgLC8Px48ehp6cHR0dHODo68lzzCWsfFAYFBQWkpqZSgYKlpaVo1aoVXr16JbIAzIagdevWKCkpQXl5OeTl5dme/c+fP3MtK4p1YlNF2OdOGHiNdQwGA3PnzsWuXbu43n8mkwkNDQ0YGhryHDMFcUAtLS3FzZs3ER8fj7i4ONy+fRtqamowMTHhGSSso6MDW1tb2NraikWJbM6cOTzP0xm8LW4Hyri4OIwYMQLr1q3D8uXLqYx+nz9/hp+fHzZt2oRr167Rlm1FXOv8Nm3aICsrC+3atUPr1q15/vZ59XvixMbGBmpqapQy+Pv379GrVy+oqamhW7duuHDhAkJDQxudfbk53PuuXbsKJKbEy7Zcc7yVlpZmy+jWWK+9MfD48WMYGxtDRUWFcn68ceMGFWxHl0Mdtz1eZWVl9OjRA3PnzmVZQwjC9+/fsWXLFuzcuRO9e/fG5s2bqUAWXtQUUOMEtyBgJpMJKysrlrXYmTNnYG5uzjJ34Tbm89vnLiwsREJCAi3rbCaTibdv30JVVZWa+3CyMdMlUvDx40dMnDgRiYmJUFBQQEREBCZMmECdt7CwwODBg2l3QNTT08PmzZsxbtw4KCoqIi0tDdra2vD390dYWBhXx+tqPD09sWPHDri5uVGZbpKSkrBlyxY4OzvzDb41MzNDnz594O/vz3J8yZIlSE9P55vZTlgMDQ2Rk5ODsrKyOjmeV9PS99afyspKyMrKUpmkW2ihNi3Oty00C4TdzBbVhKGoqAgSEhIsA1VqairWrVuH8+fP07qpkZaWxvF4tVF9165dUFBQwPv372lrg6h5+vQp3NzccObMGdja2sLT05OnkxchBJs2bcLmzZspR1cZGRmsWrUKXl5etLe3Z8+eGDduHHx8fFiOr169GqdPn8bTp09pq1tYAycdk63KykqcO3cOoaGhuHDhAn79+sXz/bwUVPgt1Js6e/fuxaJFi9CuXTt07NiRxXDBYDD4TpZPnz5NbcQPGjQIAHDnzh1s374d69evR3l5Odzc3DBt2jRs27ZNZO1+9uwZtLS0wGAwcOPGDXh6eiItLY1yhHJ3d8cff/zBsayEhATevn3LshhXUlJCWlpak1HTqS/cjGolJSWQkZFhUddpzAudfv364eHDhzAxMYGjoyMmTZoksg3kjIwMjB07lmvQjLjp2LEjYmNj0bdvXxw+fBjr169HWloaIiIiEBISwjWSUlTIyMggJyeHxUlaVlYWOTk5dUqPZGNjg4SEBMycOZOjM5uonUFqGoiEpVOnTvDx8Wl0BnRBuXr1KqytraGtrY3MzEz06dOHcoYwMjLCtWvXuJadMGEC4uLi0KZNG5Eo0lTPnZuKI29BQYFAAQK1kZWVRXZ2NlV22LBhsLKyotJB5efnQ19fn686R33H7GrFVm1tbdy5c4dl/JOWloaqqipfVXFOjhbVhlZBDavm5uaIiYmBiooKy/Fv375h/PjxPH974qRVq1ZIS0sTWvW5haaLsMbl+lCdcv7hw4ewsrJCr169QAjBkydPcPHiRRgZGeH69eu0pdUTxYYmAMqBoJqysjKUlJRAWloa8vLytM83rays8OLFCyxdupTjfGPcuHG01i9u1NXV8ePHD5iamsLU1BQmJiYwMDBosHG3dqBzWVkZUlJSEBERAQ8PDzg6OjZIO5oKgmz0AlXPHr8xs3379pTTnTio75qFkxNbzcD+usw7xAWv71GQ7+7o0aNYvXo1li1bhsGDBwOoUuEPDAyEt7c3iwptbaeo2gGHSkpKSE1NpTJyvXv3DmpqanW6f+/evaPWbh8+fOAZhCJuB8ja672aG+JNhYiICJ7n6cpsJs6AH1Eg7HMnDLWzuVSjpKQEHR0dlkAmTtjb2ws0LnNzQL1+/TqLs62GhgZMTExgYmICY2Njgew0O3bswOHDh/HgwQMYGRnBzs4O06ZNYxEtaK6I24Fy2rRpUFFRQXBwMMfz8+fPx/fv33HkyBFa6hfFOr8+REREYPr06ZCRkRFbvycsWlpaCA8Pp9STt23bhqCgIGRmZkJSUhLbtm1DdHQ0z6CZ+iKMAmVzuPf8ePnyJTw9PRESEsL1Pc312huK169fIyAgAGlpaZCTk4OBgQGWLl1Ka7Ayt0CNwsJCpKWlobCwENevXxfI+besrAz+/v7YtGkT2rZti3/++QeTJ08WdZPZ4BdsUg23MV/Y8sLw/PlzaGhogMFg4Pnz5zzfy8uXQljb8NevX6GgoMBmx/78+TMUFBRoDzbbt28fNmzYgO3bt8PR0RH79u1Dbm4uNm/ejH379mH69Ok8yxNC4Ofnh+3bt+P169cAADU1Nbi4uODPP//kOydMSkrCiBEjMHDgQFhYWACo2uu5e/cuLl26xKIGTAf1dTyvpin3vdra2rh79y7atm3LcrywsBBGRkYN4kfSu3dvhIaGUnaCFlqoSYvzbQvNAlFsZgP1nzAUFBRg6tSpuHPnDiQkJLB06VJs3LgRCxcuxNGjRzFhwgQ4OTnht99+E+5C68iVK1fg5uaGrKwsODs7Y+XKlVBUVGzQNtSH169fY/369YiIiIClpSU2b95cp0i90tJS5OTkoKioCHp6enwNbKLi/PnzmDRpErp3705913fu3EF2djZOnDiBUaNG0Va3sAZOuidb79+/b1IqFw2NpqYmFi9eDFdX13qVHzRoELy8vGBpaclyPDY2FuvWrcOdO3dw6tQprFy5Erm5uaJoMgD2TaVp06Zh165d6NChA9+yTCYTysrKLAuZwsJCKCkpsRgdG7Pz6ahRo3DkyBFKbc/b2xsLFy6kFq2fPn3C8OHD2VQU+T1vNWmIhY4wqsspKSkICwvDkSNHUF5ejunTp8PBwQEDBw4Uqk2JiYkYO3Ys1zRE4kZOTg5ZWVno0qULZs2aBTU1NXh7e+PFixfQ09NDUVERrfVzcl5XVFREenp6nZzXVVRUcO7cOfz+++90NJNW2rZtizt37jRIKqNqRJXaCajqt62srODh4UFtSKuqqsLW1hYjR47EokWLuJYVlSJNZGQktm7dSilf9+jRAy4uLo3eoVlCQgLDhg2DnZ0dJk+ezOZUxg1NTU0cOHAAxsbGKC0thYqKCs6cOUMZyTIyMmBiYsJ33BF2zBYGYQyr1XBzgn///j3U1dUbbZo1c3NzrF69GiNHjhR3U1oQE8Ial+vDzp07sXnzZiQkJLClgc3MzISpqSnWrFlDe4YVOsjOzsaiRYvg4uLCtoYQNYqKirhx4wb69etHaz2NlX79+iEzMxNGRkaUA+6wYcPE7kR1+PBhHD16lGcWohaEQ5wKpED91yzcnNhq0xjShNMFv2wivJyias+1ajuf1sX5Njk5Gfv378fx48fRs2dPODg4YP78+TzbJ04HSFEptjdFXrx4IdD7NDQ0OB4Xd8BPC8JRrZzr6uqKiRMnCmQX5UZWVhYOHTqEI0eOIC8vD2ZmZrCzs6M9U4E4EacDZXX9Bw4c4Jqq/caNG5g1axby8vJoqV8U6/z/KnJycsjMzKTu0ahRo9CnTx9KHCcrKwtDhgzBp0+fRF63uBUoGztpaWkwMjL6T177f5XKykrMmzcP79+/x5kzZ7i+jxCCyMhIuLu7o7y8HOvXr4ejo6NAPhwtiIamahuuyaFDh7BhwwZqv1tNTU2g4OLy8nIcPnwYlpaW6NChAyXCUVffmdTUVGzduhWpqamU8/tff/3VooZKM9x+u+/evUOXLl3YsktyQthsoKLMJN1C86PF+baFFkTA9OnT8fTpUzg6OiImJgYJCQkwMjLCb7/9Bjc3tzqp0ImCBw8ewNXVFTdu3MDcuXPh7u7eJBwfv379ik2bNsHf3x/9+vXDli1b6hQhdPDgQUycOFGsm1gFBQXYs2cPMjMzAQC6urpYuHBhvRTamhovX76EiooKm7NzWVkZbt68SVtqpuZAbSWUuiInJ4eUlBT06tWL5XhmZiYMDQ3x48cP5OfnQ09Pj1KFFgW1J7p1uQ5BHVAbc5QdHYo2DY2wqsvVlJWV4cyZMwgLC0NsbCx69eoFR0dH2Nvb80wFvmvXLpb/CSF48+YNDhw4ABMTExw+fLh+F0YzPXr0wMaNGzF69GhoaWkhKioK5ubmSEtLg4WFBT5+/Ehr/cKmR6pGS0sL58+fZ1Fuaiq4urpCQUGBbxogUSJKw7qioiJSU1PRrVs3tG7dGomJiejduzfS0tIwbtw42lWffX19sW7dOixdupRyvk5MTERgYCA2btwIJycnWusXhpSUFBw+fBhRUVH48OEDRo4cCTs7O4wdO5an+vaiRYuQlpaGLVu24NSpU4iIiMDr16+p4LpDhw7Bz88Pd+/e5Vm/sGN2REQE2rVrh9GjRwOoypAQEhICPT09HDlyhLaNtWqH8X79+uHatWssShgVFRW4ePEigoODG63i+MmTJ7F27doGT8XZwn8bExMTTJ06FUuWLOF43t/fH9HR0QI7qjU27t27Bzs7O2rtShd6eno4dOgQDA0Naa2nMVOtAJSQkICEhAQ8fvwY/fr1g5mZGe0pGbnx7NkzGBgY0B401pyoTuPaq1cvtrU3J8SpQAqIf83SlOHnCFWT2nM3YZ1v37x5g8jISISFheHLly+wtbWFg4NDk9jYE5Vie2Pi58+fbJu4SkpKbO+r6TDCKbMIPwXL5hzw0xB8/PgRxcXFLM/jo0ePsG3bNhQXF2P8+PGYMWMGbfW7ubkhPj4eKSkp6NmzJ0xMTCi1+5qO53Xl1q1bWLRoEdLT02mzLebm5uKff/7B/v37AVQ5iNecG0hISCAxMZHtdylKxOlACQDy8vLIysriuof38uVL6Ojo4MePH7TU3xj4+vUrLl++TKlua2trw8LCgmN/15jo0KEDLl26hL59+wIA2rVrh+DgYEyaNAlAVcChoaEhLfNdUSlQNtV7z4+6Ot8KOt7+lxGlKARdpKWlwcrKilIT5YS+vj6ePXuGZcuWYcWKFVz38/l9/5yyddSkMe/J1Zd///1X4PdaW1uzHWvqtmFOlJSUoKioqE4+MPLy8njy5AktNviaGWPopLCwENHR0cjNzYWLiwvatGmDBw8eoEOHDlBXV2d7/7dv3wT+7MbY91b/9sePH4+IiAiWPeeKigpcvXoVly9fFigDtbDZQOnIJN1C86HF+baFZoW4NrPV1NQQExODwYMH4/379+jYsSN8fX2xYsUKWurjRm5uLv7++2+cOHECU6dOxcaNG5tMWi8fHx9s2bIFHTt2xKZNm+qVerJ9+/b48eMHrK2tYWdnB0tLy5ZouXpQ14XumzdvMG7cONy/fx8MBgMzZszA7t27KSdcQR0QCSGIjo7mqsBZ1zTaTQlHR0cMHDgQCxcurFd5Q0ND9O3bFyEhIZQTUVlZGebNm4e0tDSkpKQgKSkJdnZ2Io3S57ep1NwRhaLN+fPnISEhwaY4dunSJVRUVMDKyoq+C4DoFBxLS0tx8uRJ7N+/H9euXcPQoUPx+vVrvHv3Dnv37sW0adM4lqut0spkMtG+fXuYm5vjr7/+arRq7bt378by5cuhoKAATU1NPHjwAEwmE/7+/oiJiUFcXByt9YsqvdHBgwdx+vRpREREiF19ra4sX74ckZGRMDAwgIGBAZsjHh3ODKIyrANVaYDj4uKgq6sLPT09eHt7w9raGmlpafj9999pd4TR0tKCh4cHm3pOREQENmzYQJuiiyghhCA+Ph6HDx/GiRMnUFlZiYkTJ1KbhrX5+PEjJk6ciMTERCgoKCAiIgITJkygzltYWGDw4MF8naCEHbN79uyJPXv2wNzcHDdv3oSFhQX8/Pxw9uxZSEpK8p3vHDhwAEFBQcjLy8PNmzehqakJPz8/aGlp8Zw/1zRKczIByMnJwd/fHw4ODvW6LroRVyrOFhoXdTUuC0v79u0RHx+P3r17czz/8OFDmJmZ4cOHDyKvuzYJCQnYtm0bnjx5AqDKodXFxUWodHapqakwNjaukxG+Ply6dAnbt29HcHAwunbtSmtdjZ1Pnz4hPj4ep0+fxpEjR1BZWSmW/uvHjx/466+/cOHCBYE2J/6rTJ06FcbGxli6dCl+/PiBvn37Ij8/H4QQREVFUY4d3BCnAikg/Jrl1atXOHHiBLKysgBUzWEmTpxIS39LB/fu3cOxY8fw4sULNhsXnfYlYe0EUlJSUFdXx+zZs2Ftbc1V6bQl8Ig+iouL4erqimPHjnF09uP03UlKSqJz586wt7fH2LFjISkpyfGzqx3EatNcAn7E9dzZ2NhATU0N27dvB1Cl3NarVy+oqamhW7duuHDhAkJDQ2nP8lJUVIQbN24gPj6ecsbt0aMHTExMYGZmJnA67Tt37lAK9d++fcPYsWMRFRVFS5tXrFgBOTk5bN68GUBVn1VTyOXo0aPQ0NBAUFAQLfUD4nWgBLirqFXTEOIK9V3ni4KDBw9i6dKlbGsCZWVlBAUFcbXpNgbGjRuHdu3aYe/evYiJiYGtrS3evn1LZUg6d+4cVq1aRa2h6KCsrAwLFizAunXr6pSNDGja954fgjjf1me8/S/TFNSWc3JyMGDAABQWFnJ9T037HicnRUHtfLUzuJSVlSElJQURERECKaA2Rfhl56iG2/1r6rZhUWFqaooVK1Zg/Pjx9Spvb2+PwMBAtuDa/Px8zJw5Ezdu3BBBK7mTnp6OESNGQFlZGfn5+Xj69Cm0tbWxdu1avHjxApGRkWxl+Dmr16Qx9r3Vv31O/Z6UlBS6du2K7du3Y8yYMXw/S9hsoHRnkm6hacPZCtBCC02UTZs2Yc+ePQCAmzdvIiAggNrMdnJyos3I8+7dO2phpaqqCnl5edodpmqzePFihIaGwszMDPfu3WtyKR3d3NwgJyeH7t27IyIiguvgxes7fPPmDS5evIgjR45g6tSpkJeXx5QpU2Bra4uhQ4fS1XQWbty4geDgYDx79gzHjx+Huro6Dhw4AC0tLa6pi0SFMAZOYRa6bm5uYDKZuH37NgoLC+Hm5gYzMzNcunSJMnQIEuexYsUKBAcHw8zMDB06dGiQ6LDGQvfu3bFu3TrcunWLo5obv5R8gYGBsLa2RufOnakNmIyMDFRUVODs2bMAqpSNFi9eLNJ2MxgMtu+pvt9baWkpR6drbin5mgtubm4c05BWVlbCzc2N9rHky5cvmDJlSr3L379/H2FhYThy5AhkZGQwa9YsBAYGonv37gCqNoj+/PNPrsbCpuDgx4nFixdj0KBBKCgowP/+9z9q8aetrY2NGzfSXj8/p1pB2b59O3Jzc9GhQwd07dqVre8RVPlYHKSnp1NznYcPH7Kco2v8qOlQK2xA1+DBg5GYmAhdXV2MGjUKK1euREZGBhXMxQlRKtK8efOG49xo6NChePPmTT2vqmFhMBgwMzODmZkZFi1aBEdHR0RERHB1vm3Xrh2uX7+Or1+/QkFBgS1A6/jx42zq/ZwQdswuKCig+shTp05h8uTJmD9/Pn7//XeYmpryLLtnzx64u7tjxYoV+Oeff6j5mYqKCvz8/HhuyuXl5YEQAm1tbdy5cwft27enzklLS0NVVbVRB6011fGiBdFR27g8b948tGnTBjExMVyNy8JSWFiItm3bcj3ftm1bfP36VeT11ubgwYOYM2cOJk6cSPUxSUlJsLCwQHh4OF8lt9rqKNWZBgICAuptbK4L06ZNQ0lJCbp16wZ5eXm2frO5q0LExMRQTjiPHz9GmzZtMGzYMGzfvp1Kr0wnrVu3ZlNf/P79O+Tl5XHw4EHa62/KXL9+HWvWrAFQpcBOCEFhYSEiIiKwceNGvs63dAfk8UOYNcvu3bvh7OyM0tJSKhj727dvcHFxga+vr8htC6ImKioKs2bNgqWlJS5duoQ//vgDWVlZePfuHUvwFTf4jSn80r/v27ePmleWl5cjPDycUr+sTm3KjYqKCrx48QJeXl7U91TbpsbPIUFcDpDNhdWrVyMuLg579uzBzJkzERgYiFevXiE4OJij/QaoUsaMiIhAWFgYgoKCYGdnB0dHR4GzzDx+/JjnWsDMzAyenp71uZwGQ9jnThhu3bqF8PBw6v/IyEi0adMGqampkJSUxLZt2xAYGEi7862CggKsrKwoW97nz5/h6+sLf39/BAUF8Xxus7KycOjQIRw5cgR5eXkwNzfHli1bMHHiRIHWqfXl6tWrCA0NZTk2adIkKmCga9eumDt3Lm31A1X2kV27dlEOlN+/f4e5uTl1Pisri/asgjX77drw67eFRZh1vrA8ePAAc+bMga2tLZycnNCrVy8QQvD48WP4+flh5syZ6NWrF9fAAXHj5eUFCwsLHDx4EOXl5fj777+p/Sigql+ie74tJSWFEydO1DkzV1O/96KgPuPtf5m8vDzKltdYbWSXL19Gjx49eL5HVGskTn3j5MmT0bt3bxw9erRZOt/W3jutK03ZNmxkZISrV6+idevWMDQ05Ln/w29Pa/HixVi5ciVevnzJMUMNvyDHtLQ0GBgY4ODBgxgyZAiAKofMP//8k2X+QhfOzs6wt7eHj48Pi3jRqFGjuNoHaz53+fn5cHNzg729PdX+mzdvIiIiggrGamxU//a1tLRw9+5doTJLtG7dmkX1ua60ONe2wIsW5dsWmhXy8vLIzMyEhoYGXF1dqVRdjx49gqmpKW2KNBISEnj79i01UVFSUkJaWlqdIx2FgclkQlZWlm/qu8bqSGNvby+Qs4ygDkclJSU4efIkDh8+jCtXrqBz587Izc0Vtpk8OXHiBGbOnAlbW1scOHAAjx8/hra2NgICAnD+/HmcP3+etrr5GTj53bclS5YgLi4OXl5eHBe6tra2XMuqq6vj5MmTGDRoEADg169fmDJlCgoKCnD16lWUlZUJFB3epk0bHDx4EKNGjar7DWji8OorBE3J9/37dxw6dIhFlWbGjBm0KocymUxYWVlRab45pb0HeG/uZGVlwdHREcnJySzHm4KSXe2+X1FREenp6dT3KYgygpycHJ48ecKmApafn4/evXujuLiYtvYDwik46uvrIzMzE3/88QfmzZuHsWPHsi3OP378CFVVVTbDgKDRs9yc6MTNs2fPmoXCs4eHB8/z69evb6CWNE2ys7O5qrW7u7vzLPvs2TMUFRXBwMAAxcXFWLlyJZKTk6GjowNfX1+Ozr2iVKTp06cPZsyYgb///pvl+MaNG3H06FFkZGTw/Qxx8/LlSxw+fBiHDx/Gw4cPMWTIENja2tZbkVZQhB2zVVVVERsbC0NDQxgaGsLZ2RkzZ85Ebm4u+vbty1PNR09PD5s2bcL48eNZVNQePnwIU1PTlvTRLTRrRowYASMjI8q4XP37T05OxowZM2hJi1d7rlebhlDBAgBdXV3Mnz8fTk5OLMd9fX2xd+9evkpOtdVRGAwGlWlg+/bt6NSpk8jbXJP/uiqEqqoqjI2NqfTT+vr6DVp/eHg4i62lOtPEb7/9xuKc0AI7cnJylMPPrFmzoKamBm9vb7x48QJ6enp1UuB7+fIlAHBNad2YOHfuHMaNG4cVK1Zg5cqVVB/x5s0bbN26Ff7+/jh9+nSjtt0YGBhgwYIFWLJkCTVmaGlpYcGCBejUqRPfdVDtZ6OsrAwlJSWQlpaGvLw8z6CBrl278rVv8poz8suwUQ23YEBh7YOi4urVq7h69SrHtVJjXedXo6GhgcjISJiamkJJSQkPHjxA9+7dceDAARw5coSvfTcxMRFhYWE4fvw49PT04OjoCEdHR55qZVJSUigoKEDHjh05nn/z5g00NTXZnKkbE8I+d8IgJyeHzMxM6rkYNWoU+vTpAx8fHwBVtschQ4ZwFJwQJZWVlbh79y4VdJOUlISioiJoaGjAzMyM5/PHZDIxcOBAzJgxA9OnT0eHDh1obWs1ioqKePLkCTU+OTk5Ye3atVQA2vPnz9GrVy/8+PGDtjakp6fDwsIC3759oxwovby8qPMzZ85Eq1ataFPfFaTfBuhzdhPnOn/OnDkoKirC8ePHOZ6fPHkylJSUGnW//fHjRyQlJaFjx4747bffWM6dO3cOenp6tO/Tzp49G/369WNbr/Giqd/7iRMn8jxfWFiIhIQEnmtlYcfbFhqe2oG91Xz9+hX379/Hvn37sG/fPkyfPr2BW/b/PHv2DAYGBrRnlRMnZWVlGDlyJIKCgqCjoyPu5jQIHh4elHO1sHtawmZXKysrw99//41du3Zh5cqVyMnJwYULF+Dr64t58+bxvxghUVZWxoMHD9CtWzeWecPz58/Rs2dP/Pz5k2d5CwsLzJ07FzY2NizHDx8+jJCQEMTHx9PYeuGo/o44UVJSIlB2z/pkA61LxjBemaRbaP60KN+20KxQUFDAp0+foKGhgUuXLsHZ2RkAICsrS6uBgBCCHj16UB1+UVERDA0N2QZwOhVdmrqDTM3odFEgLy8PS0tLfPnyBc+fP6c1tUw1GzduRFBQEGbNmsWSCur333+nXQlx06ZN2LFjB2Xg3LlzJ4uBkx9nzpyhFrpz5szB8OHD0b17d2hqauLQoUM8nW+/fv3KsjEhIyODmJgYTJkyBWZmZgKr6SgrKzcLZ7b6IArjnaKiIu0OR7WpvVFuZ2dX58+YM2cOJCUlcfbsWXTq1KlJKR4TQmBvb085H//8+RMLFy6knI9//frF9zOUlZXx7NkzNufbnJwcNidmOhBGwXHq1KlwcHDgmXa0Xbt2HCNyw8PDoampCUNDQ4GUsRsb3bt3R+fOnWFiYkI5UlQrWTYlmvrcAah6VnJzc2FsbAw5OTmeC3BRsnfvXixatAjt2rVDx44dWepkMBh8nW9rjneCbiKJUpHGw8MD06ZNw/Xr1ynVw6SkJFy9ehXHjh0T6DPERXBwMA4fPoykpCT06tULtra2OH36tNBqxIIi7Jj9v//9D3PnzoWhoSGysrIox5VHjx7xTceel5cHQ0NDtuMyMjICB2tERESgXbt2GD16NIAqpZGQkBDo6enhyJEjDXYf64M4U3G2IH7u3r2L4OBgtuPq6up4+/YtLXUSQmBhYcE1dXR5eTkt9dbm2bNnGDt2LNtxa2trtiAKTgirjiIszd25lh/v378Xa/329vZirb8p06VLF9y8eRNt2rTBxYsXKTvPly9fICsry7d8ZWUlNm7ciO3bt1Obv4qKili5ciXWrFkjcNrQuuDs7AwvLy+0atWKsolyw9fXl+PxrVu3ws3Njc2O1alTJ/j6+kJeXh4+Pj6N2vk2NzeXmutIS0ujuLgYDAYDTk5OMDc357th++XLF7Zj2dnZWLRoEVxcXHiW5RcM8vLlS54KpsLOxYS1D4oCDw8PeHp6YsCAAU3OzgNU2e+r11hKSkqUPX/YsGFYtGgR3/LDhg3DsGHDsGnTJtjY2GDhwoWYNGkST4WlyspKnkpjTCazUQenA8I/d8KgpKSEwsJC6vm5c+cOi+Idg8EQyEZXX3x8fChn2+/fv0NdXR2mpqbw8/ODmZmZQI5/T58+FYsDDZPJxOvXrynn2x07drCcf/fuHZutUNQYGBjgyZMnXB0op0+fjt69e9NWPx1BfHVBFOv8+pKUlITdu3dzPb9w4cJGrXafl5fH0x5Q3SfRjY6ODjw9PZGUlMRRRZGTfb2p33tlZWW+5/llChB2vP2v8/TpU/j7+1N737q6uli2bJlAGdHqy/jx4zkeV1RURM+ePUXiePvgwQO4u7tTGT3rwo8fP7Br1y6e+1XNASkpKaSnp9e7fFO0Da9fv54KVHJ0dISNjU29haeEte1LSUlh69atkJeXh5eXFyQlJZGQkECpyNKNjIwMR2fQrKwsruIBNbl58ybHvagBAwbQnu1AWEaMGIHIyEi2Z/z27duYOXMmJU7Gi/pkA1VRURF4TdvY12wt0EuL820LzQphNrOFoaFUA3jRHBxoREG14u2hQ4dw9epVdOnSBTY2NoiOjqa97qdPn8LY2JjtuLKyMgoLC2mtW1gDpzALXW1tbaSnp7MYCCUlJXH8+HFMmTIFY8aMEegaNmzYAA8PD+zfvx9ycnIClWlulJaWIi8vD926deO6yV/Nv//+CysrK0hJSXGNOK3G2tpalM2kEEXfl5qaivv37/NV7W6MCOJ8zM/IVK0odPLkSXTr1g1AlTPhypUrafveahISEgIFBQUkJCQgISGB5RyDweDqfFtWVobw8HBMnjy5XsaMRYsWUan05syZAzs7O6FSfTQ0BQUFiI+PR0JCAnx8fDBv3jyoqanBxMQEZmZmjX6RWpPCwkJER0cjNzcXLi4uaNOmDR48eIAOHTo0akPVp0+fMHXqVMTFxYHBYCA7Oxva2tpwdHRE69atsX37dlrr37hxI/755x+4urrSWk9N8vPzoaamRv0/d+5cFmN3165dKVU1fkyaNAm3b9/Gjh07cOrUKQBVRto7d+5w3PRpTGzcuBE2NjbYtWuXWNPv1WXMrklgYCDWrl2LgoICnDhxglIUun//PlvEeW20tLSQmprKZgS9ePGiwCltN23ahD179gCoMrYFBATAz88PZ8+ehZOTU6NNRSzOVJwtNA6ENS7XB0HW2PzSzouCLl264OrVq2yBPleuXKE9Ba+oyM3NRVhYGHJzc7Fz506oqqriwoUL0NDQoNWZorHx8+dPNtVCOlQx6rIRxy+l4n+ZFStWwNbWFgoKCtDU1KRSwl+/fl0gBeM1a9YgNDQU3t7eVLBTYmIiNmzYgJ8/f+Kff/4ReZtTUlJQVlZG/c0Nfmk6OQU7VDNz5kzs2rWr/o1sAFq3bk2lCVdXV8fDhw+hr6+PwsJClJSU1OszdXR04O3tDTs7O2RmZta7bZ8+fUJoaChCQkI4nvfx8cGyZcsou1hSUhIGDBhABf1+//4drq6uXB12xOkAWU1QUBDCw8Mxc+ZM2uuiA21tbeTl5UFDQwO9evXCsWPHMGjQIJw5cwYqKip8yycnJ2P//v04fvw4evbsicDAQL7lGkvAjzDQ8dwJyuDBg7Fr1y7s3bsXMTEx+P79O0va32oVc7rw8/ODqakptm3bBjMzs3oFZ+vo6IjFPtO7d29cuXKFymhXm9jYWPTp04eWumvSrl07rmu6vn37wtPTk2u/KQoIIcjJyUFpaSl69uxZpzW+sIhinV9fXr9+zTNFfI8ePfDq1Sta2yAM3bp1g6amJszMzKiXOLIMhIaGQkVFBffv38f9+/dZznGzrzf1ey+KfSFhx9v/MidOnMD06dMxYMAAyuHv1q1b6NOnD6KiomizU4gqsDc2NhaXL1+GtLQ05s6dC21tbWRmZsLNzQ1nzpyBpaUl389o3bo1y5qGEILv379DXl5eYEGopoydnR211qwrTdU2nJCQgLCwMKxatQrOzs6YPHkyHB0dMXz4cIE/49u3b8jKykJpaSkGDRpUL3tiWVkZ3NzcEBgYiL/++guJiYmYOHEiQkNDGyRA1draGp6enpSACoPBwIsXL+Dq6irQs9+lSxfs3buXyhBRzb59+xq9jVFWVhYGBgbYvXs3pk2bhsrKSnh6emLTpk0CB6xwCyLgRVxcHPV3fn4+3NzcYG9vT/W/N2/eREREBJWxsoX/MKSFFpoRX758IUuWLCHW1tbkwoUL1HF3d3eyceNGMbZMfPz69Yt8//5d3M1oEKZNm0ZatWpF2rdvT5YsWUKSk5MbtH4tLS1y+fJlQgghCgoKJDc3lxBCSEREBNHV1aW1bnV1dZKenk4IIURfX58cPnyYEEJIcnIyUVJS4lteX1+fxMfHE0IIsbCwICtXriSEELJz506irq7Os+zq1avJH3/8wfFcWVkZsba2Jkwmk28bSkpKiKWlJVFQUCB9+vQhhoaGLK/mTHFxMXFwcCASEhJEQkKC+u0sXbqUbN68mWMZBoNB3r17R/3N7SXIvRcnAwYMIDdu3BB3M8RGYWEhGTx4MJGUlCRdu3YlXbt2JZKSksTMzIx8+fJF3M3jiZqaGnn8+HG9y//8+ZMcPnyYjBgxgsjLy5MpU6aQixcvksrKShG2smHIysois2fPJpKSko3+matJWloaad++PenevTuRlJSk+p41a9aQmTNnirl1vJk5cyaxtLQkBQUFLGPuxYsXiZ6eHu31KyoqUnUKSuvWrcmHDx8IIYSoqKiQ1q1bc31xQklJidy+fZvr59++fZsoKirWqU1NEXH3EfUZs4XFw8ODFBcXk7179xJ1dXUSFRVFWrVqRY4cOUI2btxI/S0IcnJy5Pnz54SQqjlc9bP+8OFD0q5dO1raLwp0dXXJyZMnCSGs8+yMjAzStm1bMbashYbC0dGRjB8/npSWlhIFBQXy7Nkz8vz5c2JoaEiWL18u7ubRyu7du4m0tDRZuHAhiYyMJJGRkWTBggVERkaGBAUF8SxbVFRE1q1bR3r37k1atWpFFBQUiL6+PtWvNATx8fFETk6OjBgxgkhLS1PP7+bNm8mkSZMapA3ipKioiCxZsoS0b9+eMJlMthcdVK8Dea0Tm8JasTFw9+5dEhMTw2JXO3v2LElMTORbtlOnTuT06dNsx0+dOkXU1NRE2k5RIi8vz3Oem5ubS+Tl5RuwRXXHxsaGbN++nRBCiKenJ2nfvj2ZO3cu0dTUJBMmTKj356akpAg9305NTeX57DGZTMrWQwj7uuPt27c8ywtrHxQFbdq0ITk5OQ1SFx34+vqSnTt3EkIIuXz5MpGVlSUyMjKEyWQSPz8/jmVev35NvL29Sc+ePYmqqipxcnIiGRkZAte5YcMGgV6NGbqeO0FIS0sj7dq1I9LS0oTJZJK1a9eynLezsyMLFiygtQ2EEFJaWsr1XLUtgBvV19DQ9pmQkBAiLy9Pzp49y3bu33//JfLy8iQkJIS2+gWBX78pLM+ePSN9+vSh5mZdunQhd+7coa2+akS5zq8vNfcXOMFvzBE3cXFxZP369cTExITIysoSJpNJunfvTubPn0+OHDlC3r59S2v9FRUV9S7b1O+9KKjPeNtCFdra2mTdunVsx93d3Ym2tjbt9RcUFHA9d/PmTZ5l9+3bRxgMBmnbti1hMpmkffv25MCBA0RFRYUsWLBA4P2msLAwEh4eTr0iIyPJhQsXyOfPn+t0LU2VpUuXEiUlJdK/f38yf/584uTkxPLiRVO1DVdTVFRE9u/fT4yNjQmDwSA6OjrE29ubvHnzhme5lJQU0qlTJ8pWoqSkRC5evFjn+g0MDEj37t2p33plZSXx9vYmMjIyZNGiRfW6prpQWFhIRowYQVRUVIiEhATp0qULkZKSIsOHDydFRUV8y587d47IysqSPn36EEdHR+Lo6Ej09fWJrKwsOXfuHO3tF5aAgAAiLy9PbGxsyJAhQ4iamhqJjY1tsPrNzc2pNXZNDh06RExMTBqsHS00ThiENMFcvy200MgpLS3F+/fv2aLANDQ0aK03LCwMDx48wODBg2Fra4u//voLvr6+KC8vh7m5OaKioih1reaIra0tbG1tYWlpyTNVF11s3rwZBw8exP79+/G///0P58+fx/Pnz7FixQq4u7tj2bJltNU9Y8YMDBgwgEox6O/vj3HjxuHy5cswMjLiG6m2Y8cOSEhI4M8//8SVK1cwduxYEEJQVlYGX19fLF++nGvZ8vJylJSUcFXsKS8vx6tXr/imqqhWMJw8eTI6dOjApsTSnNWdly9fjqSkJPj5+WHkyJFIT0+HtrY2Tp8+jQ0bNvBUrGnqXLt2DWvXrsWmTZugr6/PluKBDiWoxgYhBJcvX0ZaWhrk5ORgYGDAUUW7sbFp0yZkZWVh3759QitCPH/+HOHh4YiMjER5eTkePXoEBQUFEbVU9JSUlCAxMRHx8fGIj49HSkoKevXqBVNTU5iamjYZBcYRI0bAyMgIPj4+UFRURFpaGrS1tZGcnIwZM2aIPfUeLzp27IjY2Fj07duXpe3Pnj2DgYEBldqXLhwdHTFw4EAsXLhQ4DIRERGYPn06ZGRkEBERwfO9nFJ0Dx06FGPGjOGaYtzLywsXLlxAcnIy1899/fo1fH194e7uzta/fv36FRs3bsSqVavQoUMHAa5IPNy9exdHjhyh0gj16NGDmgc1BKIYs798+YLQ0FCW1HAODg5cFcAlJCTw5s0bqKqq4tChQ9iwYQNyc3MBAGpqavDw8GBJq8oLVVVVxMbGwtDQEIaGhnB2dsbMmTORm5uLvn370v7s1Bc5OTlkZmZCU1OT5ZnPzs6GgYEBfvz4Ie4mtkAzX79+xeTJk3Hv3j18//4dampqePv2LQYPHowLFy6wpfakm2/fvuHQoUMIDQ3FvXv3aK/v5MmT2L59O0u/4eLiwnPOUVpaiqFDh+Lhw4ewsrJCr169QAjBkydPcPHiRRgZGeH69eu0pxIeMmQIpkyZAmdnZ5bn986dO5g4caLAqu1NlSVLliAuLg5eXl6YOXMmAgMD8erVKwQHB8Pb2xu2trYir/P58+cCv7cxppRsLsjKyiI9PZ1N1ezp06fo169fox27Bg0aBBsbGzg5OXE87+vri6ioKNy5c6eBWyY4nz9/xs+fP6GmpobKykr4+PggOTkZOjo6WLt2LVq3bs2zfO3sQoQQvHnzBgEBAejSpQsuXLhQ77alpaXByMiIazpKJpOJt2/fQlVVFQBY+k2gKgW8mpoa1/LC2gdFgaurKxQUFLBu3Tra62oInj9/jvv376N79+5c1cKlpKSgrq6O2bNnw9ramuvY2pzVxoV97oTl48ePSEpKQseOHfHbb7+xnDt37hx69+5Na2ZEoCojQnR0NJtN+927d7CwsMDDhw+5lrWwsED//v3FYp+xsbHB0aNH0atXLypd+dOnT/H06VNMmjSJUlYTF/z6TWGZPHkyHj16BHd3d8jKymLbtm34+fMnm4KpqBHlOr++MJlMREREsGRVqklhYSHmzJnTJFIo//z5E8nJyZSt9s6dOygrK0OvXr3w6NEjWuqs+R0CgIuLC/766y+Bsrs1p3svKgQZb1uoQl5eHunp6WxK69nZ2ejbty/tivN6enpITExk+60nJSVh9OjRPDPBGhgYYObMmXBxccGJEycwZcoUDB48GMeOHROLcnVTxczMjOs5BoOBa9eucT3fVG3DnMjJyUFYWBgOHDiAt2/fYuTIkVwzxVpaWqKoqAjbtm2DrKwsvLy8kJGRgezs7DrV6ejoiF27drHZIVNSUjBz5kye8z1RkpSUhLS0NBQVFcHIyAgjRowQuGxBQQH27NlDZXTR1dXFwoULG73ybTV//fUXtmzZAklJScTHx2Po0KENVre8vDzS0tJYskEDVZk2+vXrR3v/20IjR5yevy20QAefP38mW7duJQ4ODsTBwYFs3bqVfPr0qUHqfvr0KRk2bBibkklDKIps3LiRUpNp06YNWbhwIenYsSPx9vYmPj4+pHPnzmThwoW0tuG/TmVlJRWVXK0kIysrS9atW0dKSkporfvTp0/k1atXhJCqiNvNmzeTsWPHEmdn53pF+uXn55MTJ06QtLQ0UTeVK/Ly8v9ZBVQNDQ0qSq6mmlt2djZPVRUrKytSWFhI/b9582YWtdSPHz/SrrosLDVVlxq632yhioKCAhIYGEhcXV3rFCE7fvx4oqioSDp16kT++OMPMmHCBJZXXXjx4gXx8PAgWlpaRF1dvdErtktJSVFqNqdPn26yEdVKSkqUIlHNvic/P5/IyMiIs2l8UVBQIFlZWdTf1W2/e/cuadOmDe31b9q0ibRr147Mnj2bbNu2jezcuZPlRQeiUKRZuXIlmTdvHtfzCxYsIKtXrxa6rXTh4uJCGAwGUVRUJH379iV9+/YlCgoKhMlkNli76ztmV5OQkECUlJRIly5dqP5SQ0ODKCkpkYSEBI5lOCmiFBcX81RJ4caMGTOIkZERcXR0JPLy8uTjx4+EEEJOnz5NevfuXefPayh0dXXJqVOnCCGs933Xrl3NPkNCC6wkJiaSwMBAsmXLFirrSENy7do1YmdnR+Tl5UmnTp3I4sWLG7wNguLn50c6dOhAMjMz2c49efKEdOjQgezatYv2drRq1Yo8e/aMEML6/Obl5TX6+YYo6NKlC4mLiyOEVClYZmdnE0IIiYyMJFZWVrTXX93PE1I15163bh1ZtWoVuX79Ou11N0WcnJwotZjaa6O6rJUIIWTQoEFk2bJlbMeXLl1KfvvtN5G3vTZFRUVk7dq1ZMiQIaRbt25ES0uL5cWN8PBwIicnRwIDA0lZWRl1vKysjAQEBBA5OTkSFhZGe/vrS1lZGYmIiBBK7Y6TSnSHDh2IjY0Nef36tVDt46fgWHveV7PfJIS/Ep6o7YOCUvPZWL58OVFRUSHGxsZk6dKldX52miK1fy+c1Mfra+P6+vUr2b17N+nfv7+IWy06RPHc0UlBQQHPdbCoGDBgAHFwcGA59ubNG9KrVy++av/its8cOXKEjBs3jujq6hJdXV1ibW1Nu+qqoNCtfNuhQweWPYnXr18TJpMpkHqcMIhynS9MG5pbpoRfv36Ra9euERcXF6KkpERr+2t/h3XJktUc731dKC0tJebm5pRtt4W6YWVlRfbv3892fP/+/VyzlIqSOXPmkP79+5Nv375Rx6rtnb6+vjzLysvLk7y8PEJI1Z66lJSUQFlFCKlSiRf01QJ3mqptmBtFRUUkODiYtGnThme/2bZtW3L//n3q/y9fvhAGg0G+fv0qsrb8/PlTZJ9Vm5KSEnLmzBnqfzc3N5Y1louLC/nx4wdt9TcGPn/+TCZOnEiUlZVJSEgIsbW1Ja1atSKBgYECf0Z5eTnZunUrGThwIOnQoYNAGSlr0qNHD+Li4sJ23MXFhfTo0aNO19NC80M4mbIWWmhkXL9+HWPHjoWysjKlfuXv7w8vLy+cOXOGdiXBOXPmQFJSEmfPnkWnTp3YopzpJDw8HKGhobCxscG9e/fw22+/4dixY5g0aRIAoE+fPnVSZ2sq7Nq1C/Pnz4esrCx27drF871//vknrW1hMBhYs2YNXFxckJOTg6KiIujp6SE4OBhaWlp4+/YtLfWWl5fj7NmzsLS0BFAVNevm5iZw+bKyMowcORJBQUFUpI6mpmadFXAIIYiOjkZcXBxH5Wd+6hpdunT5T6iccuLDhw9UhHRNiouLefYjsbGx+PXrF/X/pk2bMHXqVKioqACo+m08ffpU5O0VJXFxceJugtgpLi5GQkICXrx4gdLSUpZzdPdbV69ehbW1NbS1tZGZmYk+ffogPz8fhBAYGRnxLKuiokKNMfXh169fiImJwf79+5GYmIgxY8YgICAAI0eOBJPJrPfnNgSjRo1CYmIioqKi8PbtW7x9+xampqZsqlaNHRkZGXz79o3teFZWFtq3by+GFgnO8OHDERkZCS8vLwBVY3C1ug6vyG9RERISAgUFBSQkJCAhIYHlHIPvxCpZAAEAAElEQVTBEOjZraysRE5ODscxk9Ocdd68ebh27RrGjh3LVZFm3rx5POu8ePEigoKCuJ6fNWsW5s2bhy1btvBtf0MTEREBf39/7Nq1CwsWLKDUpMrKyrBnzx64urqid+/emDVrFq3tqO+YXc2SJUswbdo07Nmzh8rUUFFRgcWLF2PJkiXIyMjgWK72Z8vLy0NeXr7O7Q8MDMTatWtRUFCAEydOUFkx7t+/Dxsbmzp/Ht14enpi1apVcHZ2xpIlS/Dz508QQnDnzh0cOXIEmzdvxr59+8TdzBZo5MePH7h69SrGjBkDADh79iw1/z1//jwuXboET09PyMrK0taGV69eITw8HGFhYSgsLMSXL19w+PBhTJ06tUHX/HUlJiYG69ato8aLmvTq1Qtr1qxBdHQ0rRlagKo545s3b6ClpcVyPCUlBerq6rTW3Rj4/PkzpVippKSEz58/AwCGDRuGRYsW0VZvRkYGxo4di4KCAujo6CAqKgojR45EcXExmEwmduzYgejoaIwfP562NjRFUlJSUFZWRv3NDUGefR8fH4wePRpXrlzBkCFDAAA3b95EQUEBzp8/L5oG82Du3LlISEjAzJkz62SjnD17NjIyMrB06VL89ddf6NatGwghePbsGYqKivDnn3/C3t6e3sYLgaSkJBYuXEgphdeH2nPzujBx4kSe53kpgQmLsPZBYaj9vPTr1w8A2JSfGvO4WZOrV6/i6tWrHNdq+/fvZ3t/Xl6eyNsQFxeH/fv3IyYmBsrKypgwYYLI6xAVonju6OTTp08IDQ1FSEgIrfWcP38exsbGcHZ2hq+vL16/fg0zMzP07dsXUVFRPMuKyz7z/ft3KCoqYvr06Zg+fTrH9yQkJMDExIS2Noib9+/fs6iXderUCXJycnj//j3b/FXUiGqdX1+EGe8aC6Wlpbh16xbi4uIQHx+P27dvo0uXLjA2NkZAQECD/nZJHRINN4d7LwxSUlJIT08XdzOaLNbW1nB1dcX9+/cxePBgAMCtW7dw/PhxeHh4sCh/Wltbi7z+ffv2YfLkyRg7dixiY2ORnJwMa2trbNy4kWcWVaDKxlPdzzEYDMjIyKBTp04C1duvXz8wGAy+zxqDwfhPqUbXlaZmG+bG9evXsX//fpw4cQJMJhNTp07lqRj/+fNnFnVlFRUVtGrVCp8+fRLIN+HYsWMYP348pKWlAQAvX76EmpoatZdZUlKCgIAArF69Wsgr40xERATOnTtH2UcDAgLQu3dvyMnJAQAyMzPRqVMnrhlsanLjxg0EBwfj2bNnOH78ONTV1XHgwAFoaWlh2LBhtLRfFPTp0wdaWlpISUmBlpYW5s2bh6NHj2Lx4sU4d+4czp07x/czPDw8sG/fPqxcuRJr167FmjVrkJ+fj1OnTsHd3Z1v+R07dmDSpEm4cOEClW3jzp07yM7OxokTJ4S+xhaaNgxSl9lgCy00cvT19TFkyBCOm9nJyclcN7NFRatWrXD//n306tWL1no4ISMjg5ycHEoSXkZGBunp6dRG26tXr6ClpcXm2NXU0dLSwr1799C2bVuexhAGg4Fnz57R0oZfv35hw4YNuHz5MmRkZODi4oLx48cjLCwMa9euhYSEBJYsWQJXV1da6geqDDNPnjypd8rI9u3bU6nA6svy5csRHBwMMzMzdOjQgc14FBYWxrP8uXPn4O/vj6CgINrTgDU2jI2NMWXKFCxbtgyKiopIT0+HlpYWli1bhuzsbFy8eJFjOWHTEbYgflJSUjBq1CiUlJSguLgYbdq0wcePHyEvLw9VVVXa+q1qBg0aBCsrK3h4eFC/H1VVVdja2mLkyJG0OQQsXrwYUVFR6NKlCxwcHGBra4t27drRUhedpKenU86XN27cgKSkJExNTXHo0CFxN00g5s6di0+fPuHYsWNo06YN0tPTISEhgfHjx8PY2Bh+fn7ibiJXHj58CAsLCxgZGeHatWuwtrbGo0eP8PnzZyQlJaFbt27ibiJPbt26hRkzZuD58+dsBkN+BsKoqChERUUhKysLAKCjowMbGxuuG2U1adWqFZ48eQINDQ2O51+8eAFdXV0UFxfX4WoahsaSArm+Y3Y1cnJySE1NZXOG45WCmslkQllZma+zQrVDV3OiMaTibEG8BAUF4dy5czhz5gyAqvlubePy6tWrBTIu15UTJ04gNDQU169fh5WVFezs7GBlZYVWrVohLS0Nenp6Iq+zmtatWwvsoMTt2W/fvj3i4+PRu3dvjucfPnwIMzMzfPjwod7tFIRVq1bh9u3bOH78OHr06IEHDx7g3bt3mDVrFmbNmoX169fTWr+4MTAwgL+/P0xMTDBixAj069cP27Ztw65du+Dj44OXL1/SUq+VlRUkJSXh5uaGAwcOUA55e/fuBQAsW7YM9+/fx61bt2ipv4UqXr9+jcDAQJZ0josXL4aamhrtdauoqODcuXP4/fff61X+1q1bOHLkCJWGs0ePHpg+fTq1wd+YMTU1hZOTE8aNG9fgdc+ZM0eg93GzkTGZTGzcuBEKCgoAAFdXV7i4uFDr5e/fv8Pd3Z3rekFY+2ALVRuynp6eGDBgAEfH9ZMnT3ItW1ZWRgUJ1ubjx4987R5NNeAHEO9zx4+0tDQYGRk1iH20oKAAw4YNw6RJk3D27FkYGRnh0KFD1F4VN8RlnzE1NUVsbCxkZGQ4nk9ISMCYMWPw/ft3WuoHBAtaSEhIoO37k5CQYHNy7ty5MxITE1n2KEQtGvJfXueLCnNzc9y+fRtaWlowMTHB8OHDYWJiIrAjn7Dw25tpgTdOTk6QkZGBt7e3uJvS5BBUuIROJ9TS0lKMHj0aJSUlSE9Px+bNm7F06VK+5fjNdavhJGzx/Plzgdv3X5gL37t3D8eOHeMo6sNPDKup8vr1a4SHhyM8PBw5OTkYOnQoHB0dMXXqVLRq1YpnWSaTiWvXrqFNmzbUsaFDh+LYsWMsTrkGBgYcy9e0UQNV84LU1NQG248fPnw4Vq9ejbFjxwJgH3MOHjyIwMBA3Lx5k+fnnDhxAjNnzoStrS0OHDiAx48fQ1tbGwEBATh//nyDBArXFy8vL6xZs4atD3z58iXmzJmDy5cv8/2Mbt26YdeuXRg9ejQUFRWRmppKHbt16xYOHz7M9zMKCgqwZ88eFjvPwoULKR+tFv67tDjfttCsqM9mtigZOHAgduzYIZaokBYnPPHh6uqK4OBgjBgxAsnJyfjw4QPmzJmDW7du4e+//8aUKVP4GtiERVgDpygWum3atMHBgwcxatSoepVv3bo1SkpKUF5eDnl5eTZjdXM2NCUmJlIb+uHh4ViwYAEeP36M5ORkJCQkoH///hzLNZfnvrCwEKGhoZRCRu/eveHg4ABlZWUxt4x+qtVSg4KCoKysjLS0NEhJScHOzg7Lly/na4AWlpqLi9atWyMxMRG9e/dGWloaxo0bh/z8fFrqZTKZ0NDQgKGhIU8jc2M3EhBCkJKSgri4OMTFxSE2NhaEEJSXl4u7aQLx9etXTJ48Gffu3cP379+hpqaGt2/fYsiQITh//jxfg4W4+fr1KwICApCWloaioiIYGRlhyZIlDWZkr6Z6OVWXzdB+/fqhR48e8PDw4Lihy6n/q1ak4QU/RZp27dohJiaGazaI69evY+LEifj48aMAV9GwtGrVChkZGVw3MZ49ewZ9fX3aHYfrO2ZX8/vvv1OBWjU5deoUvL29OTpBMZlM+Pn58R0XZ8+eLdA1fPnyhWXc1dXVhYODA4vxsbFQe64DVCkJFBUVcVQgbqH5ISrjcn2QlJSEq6sr3NzcWPpfKSkp2p1vIyIiBH4vt2dfSkoKBQUF6NixI8fzb968gaamJu0BuqWlpViyZAnCw8NRUVEBSUlJVFRUYMaMGQgPD6d9rSxuduzYAQkJCfz555+4cuUKxo4dC0IIysrK4Ovry1cZqL60a9cO165dg4GBAYqKiqCkpIS7d+9S41RmZiYGDx5MqwpnC+JFS0sL58+fh66ubp3KVavON6Tynqg5duwY/vrrLzg5OaF///5s6xpuG6pAVTaDLVu2ICYmBvn5+WAwGNDS0sLkyZMb5L507dqV77qCl8BAY3SA/PbtG65du4ZevXqJRbCirnTq1Ak+Pj6YOXNmnctOmjQJ0dHRbN/hu3fvYGFhwaYEXI04A35EhTDPHd00pPMtUKVWO3z4cPzvf//DgQMHBLIVcLPPDB48GBcuXKDNPqOvrw9tbW2cPHmSzZHh+vXrGDVqFObMmQN/f39a6geED1oQFiaTyfYdEUKoY9V/i/r3I+p1vjBs3rwZHTp0gIODA8vx/fv348OHD7SKygiDlJQUOnXqhPHjx8PU1BQmJiaUgmNDwGQyMX/+fGpuEBgYCDs7O7bv1NfXl+tnNNV7LwqWLVuGyMhI6OjocBw3eN23FhoeTkrF379/h42NDUaPHs0i4sJrzBd2rlvNp0+fqOe9oKAAe/fuxY8fP2BtbY3hw4fzLNsciIqKwqxZs2BpaYlLly7hjz/+QFZWFt69e4cJEybwHTObkm24GisrK1y5cgXt2rXDrFmz4ODgwDHTEzeqx3tOrnHVx3mN9+Lej+/UqRNu3rxJBQa1b98ed+/epf7PysrCwIED8fXrV56fY2hoCCcnJ8yaNYvlGlJSUmBlZUVbFufGQk1xmk6dOuHcuXMwMjLCs2fPYGhoyPf+tdACLyTF3YAWWhAlRkZGePLkCdtg++TJE/Tt25f2+rds2YLVq1dj06ZN0NfXZ3MeFHV0bG0eP35MDYqEEGRmZqKoqAgAGqUTBd1UVFQgIyMDmpqaaN26NW31HD9+HJGRkbC2tsbDhw9hYGCA8vJypKWlNZgqweLFi+Hs7IyCgoJ6GTjLy8uxf/9+XLlypd4LXWVlZaGiehuzwiLdDBs2DKmpqfD29oa+vj4uXboEIyMj3Lx5E/r6+lzLMRgMtt9YY1fCqM29e/dgaWkJOTk5DBo0CEDV7+2ff/6h7kNzJjU1FcHBwWAymZCQkMCvX7+gra0NHx8fzJ49m3bn21atWlEOF506dUJubi6ljsZv3ODmOMtgMCArK4vu3bvD3t4eZmZmbO+ZNWtWk/ut1sTX1xfx8fFITEzE9+/f0bdvXxgbG2P+/PlNyrijrKyMy5cvIykpicWBdcSIEeJumkAoKytjzZo1Yqs/MjISW7duZVEDc3FxEWiTNjs7G9HR0ejevbvA9VWn8hJGkea3337DgQMHuDrfRkZGUn1xY0NCQoKng1hZWVmDOHDVd8yu5s8//8Ty5cuRk5PDkhouMDAQ3t7eLMbsmvO36dOni8TZ9Pr16xg7diyUlZUxYMAAAIC/vz+8vLxw5swZrr8NcSLuVJwtiJecnByWZ0tWVpbFMWDQoEFYsmQJLXU7OjoiMDAQ8fHxmDlzJqZNm0brurImothkr6ys5NkvMpnMBnFCkZaWxt69e+Hu7o6MjAwUFRXB0NBQqKwrTYHKykps3boV//77L0pLS/H69WusX78emZmZuH//Prp3706rI9Lnz58px2sFBQW0atWK5ffbunVrWlXsmgPFxcXw9vbmmnqe04Zweno6+vTpAyaTyTeVLt2OaF5eXnB3d0dERESdxk0PDw8sXLiwSY+11RkhaipmCbKhWlpaChMTEzx8+BBWVlaUs/yTJ0/wzz//4MKFC7h+/TpXZVNRwC8A9uXLl/D09OR6Xlj7oCiYOnUqjI2NsXTpUvz48QMDBgxAfn4+CCGIiorCpEmTaG+DMJSWlmLo0KH1KvvixQvMnTsXoaGh1LE3b97A3NycqxI9AEybNg2urq44evQo34DLxkp9n7umDrdsBSUlJThz5gyLIyAvYYlq+0xiYiLS09MbzD4TGxuL4cOHw97eHpGRkdTxGzduYMyYMZg9ezatjrcAfU61ghIXFye2ukW1zheW4OBgjkpvvXv3xvTp0xutA2hhYSFu3LiB+Ph4bNmyBTY2NujRowdMTEwoZ9yaisaixtjYGE+fPqX+Hzp0KNv8kJ/9u6nee1Hw8OFDau+nOrtXNU1536C50q9fPzbHxer/g4ODERISItCYL6zYS0ZGBsaOHYuCggLo6OggKioKI0eORHFxMZhMJnbs2IHo6Gg20YPmxqZNm7Bjxw4sWbIEioqK2LlzJ7S0tLBgwQK+wiRN0TYMVAVcREdHY8yYMfXaA8jLy6OhVQ1HYWEhfv36Rf1fO4tVZWUly3luPH36lON3rKys3GiDs318fLBs2TIqC1pSUhIGDBhA7ZN9//4drq6u2L17N9/P6ty5M968eQMNDQ1069aN2tu5e/cu13232ty4cQPBwcF49uwZjh8/DnV1dRw4cABaWlpiEWhsofHQonzbQrPi6NGjWL16NZYtW8ZxM7um2gMdxsbqTUBukbJ0GpmEjdhpDqxYsQL6+vpwdHRERUUFjI2NcfPmTcjLy+Ps2bMwNTWlpV5paWnk5eVBXV0dQJUC8507dwRywBAVnNKM1OV75+QcV/Nzrl27xrcNERERuHjxIvbv309NgFqgFyaTCSsrK2pCeObMGZibm1ObK79+/cLFixcb9XM/fPhwdO/eHXv37oWkZFVMUHl5OebOnYtnz57h+vXrYm4hvbRv3x7JycnQ0dFBjx494O/vD0tLS2RmZqJ///60KziOHz8eo0ePxrx587Bq1SqcPn0a9vb2iImJQevWrXHlyhWuZf/66y/s2bMH+vr6lLPe3bt3kZ6eDnt7ezx+/BhXr15FTExMo1LdEQUDBw6kDLnDhw//T6g0NxY+fvyI4uJiltRRjx49wrZt21BcXIzx48djxowZtLfD19cX69atw9KlS6lUvomJiQgMDMTGjRv5pj83NzfH6tWrMXLkSIHrFIUiTVxcHP73v/9hxYoVcHFxQYcOHQBURWb7+Phg586duHTpEszNzQVuV0NR/bx5eXlxPL927VokJiYiPj6+YRtWR/ilhuM0f6ud1koY9PX1MWTIEOzZs4cyVFZUVGDx4sVITk5GRkaG0HWIkpZUnC1wy25TTWZmJvr164efP3/SUv+PHz9w7Ngx7N+/H7dv34alpSXOnTuH1NRU9OnTh5Y6gSqVQEHhFuTLZDLRp08fao5dm/Lycjx69KjB1wrl5eX4+fMnlWayueLl5YUNGzZgxIgRkJOTQ2xsLGxsbLB///4GqZ/JZOLdu3eUw4GioiLS09OhpaUFoOlkSREnNjY2SEhIwMyZMzlmKuCkWlxTDYefnY7ue29oaIjc3FwQQtC1a1c2h9EHDx5wLMdJdb6pwS8lLbc0tDt37sTmzZuRkJDANu5kZmbC1NQUa9aswbJly0TW1rrCT8FTWPugKOjYsSNiY2PRt29fHD58GOvXr0daWhoiIiIQEhKClJQU2tsgDK6urlBQUMC6devqXPbDhw8wNjaGlZUVfH198fr1a5iZmaFv376IioriuhZYsGABjh49it69e7ME/DSE2r6oqO9zJwr4Ba4XFhYiISGBlt+/KLIV8OLBgwdwd3fH2bNn61xWUHJzczF8+HBMmTIFO3fupLK92NraIigoiLZ6q6mt+MkJBoPB4tTeHBDlOl9YZGVl8eTJE2qeWM2zZ8+gp6dH21pL1Hz//h2JiYmIi4tDfHw80tLSoKOjw1V1vDHQXO59Cw1PQkICtm3bRimX6unpwcXFhTZhEH7jfE14jfk3b97Ep0+fMGbMGOpYZGQk1q9fT9n2/f39uTrBWVlZQVJSEm5ubjhw4ADOnj0LS0tL7N27F0CVovL9+/c5ZhZrTrRq1QqPHj1C165d0bZtW8THx0NfXx9PnjyBubk53rx5w7VsU7MNi5qysjKuwZQfP35Eu3btOJ4Tt/Ktjo4OvL29uQYyHjt2DH///TdycnJ4fo62tjZCQkIwYsQIlmuIjIyEt7c3Hj9+TEfzhaL2nElJSQmpqan1uvdubm5QUlLC33//jaNHj8LOzg5du3bFixcv4OTkxDdD9IkTJzBz5kzY2triwIEDePz4MbS1tREQEIDz58/j/Pnzwl9wC02WFuXbFpoVNjY2AIDVq1dzPEe3sVGcUbJNPWJHFERHR8POzg5AlRNifn4+MjMzceDAAaxZswZJSUm01FtRUQFpaWnqf0lJyQbfSBT2+xfFb3fq1Kk4cuQIVFVV67SxU82LFy94ntfQ0BC6jY2N8vJyVFRUsCwk3717h6CgIBQXF8Pa2ppnlFRto231778ms2bNEl2DaeDevXssjrdA1TO0evVqKuqyOWNoaIi7d+9CR0cHJiYmcHd3x8ePH3HgwAFaHTqq8fX1pRTSPTw8UFRUhKNHj0JHR4ev4vXHjx+xcuVKtg2pjRs34vnz57h06RLWr18PLy+vZud8e/fuXXE3QSiENXKJk2XLlkFNTQ3bt28HALx//x7Dhw+HmpoaunXrBnt7e1RUVNQrRWhd8Pf3x549e1j6WGtra/Tu3RsbNmzg63y7bNkyrFy5Em/fvuWYLYFTkJgoFGnMzMwQGBiI5cuXY8eOHVBSUgKDwcDXr18hJSUFf3//Rul4CwCrVq3C+PHj8evXL6xcuZJyHH779i22b98OPz8/nDx5krb6hR2zq6nPnE2U8bI5OTmIjo5mUQiQkJCAs7Mzy++qMeHh4dES5PAfpnPnznj48CFX59v09HR07tyZtvrl5OQwe/ZszJ49G9nZ2QgLC8O9e/fw+++/Y/To0Zg8eTItmQpUVFT4Op3zs22sX7+ebz10qg+eOXMGnz59gr29PXXsn3/+gZeXF8rLy2Fubo6jR482mJpwQxMZGYndu3djwYIFAIArV65g9OjR2LdvH99ADFFhb29PjVs/f/7EwoULWQI1W+DNhQsXcO7cOSrQShDy8vIoh2dx2+mEUXtq6mpj9XXyi4mJwbp16ziOOb169cKaNWsQHR0tVudbfoj7dwcAX79+pVLWXrx4EZMmTYK8vDxGjx4NFxcXMbeOM87OztTflZWVCAkJwZUrV2BgYMC2VuNlK2nfvj0uXbpErQvOnj0LIyMjHDp0iGffHxwcDD8/PyrgZ8WKFbC0tAQhhE11u7FCp3MtP/itFZSVlWmzj4oiW0FsbCwuX74MaWlpzJ07F9ra2sjMzISbmxvOnDkDS0tLEbSUO926dcPFixdhamqKr1+/4uTJk7CxsWkQx1sACA8Ph6amJgwNDUW69q0rr169wokTJygFzp49e2LixImU4IqoaUy6WF26dEFSUhKbA2hSUhLU1NTE1Kq606pVK7Rp0wZt2rRB69atISkpSTkmNgSlpaXIy8tDt27duAZA1qa53HthyMnJQW5uLoyNjSEnJ0etc1vgzsGDBzFnzhxMnDiRUpxPSkqChYUFwsPDaRGmENU47+HhATMzM2pfIiMjA46OjrC3t4euri62bt0KNTU1bNiwgWP5u3fv4tq1azAwMEDfvn0REhKCxYsXU/OsmuJszZmamWzU1dXx8OFD6Ovro7CwECUlJTzLNkXbsCiZPn06oqOj2fqZd+/ewcLCgmfARmxsLDXvrKysxNWrV6n3060aO2rUKLi7u2P06NGQlZVlOffjxw94eHhg9OjRfD9n3rx5WL58Ofbv3w8Gg4HXr1/j5s2bWLVqVb2CDxuC2nMmYeZQNZ1rp02bBk1NTUoga+zYsXzLb9y4EUFBQZg1axaioqKo47///js2btxY73a10EwgLbTQjMjPzxf41ULzQ0ZGhhQUFBBCCJk3bx5Zvnw5IYSQZ8+eEUVFRdrqZTAYZNSoUWTChAlkwoQJRFJSkvzxxx/U/9WvpkB2dja5ePEiKSkpIYQQUllZKXDZKVOmkHbt/o+9M4+H8nv//2uGyJ6i3VoKleLTvtKGSml5a9HKu0X7Tqto3xcpyq5Cor1U72wp2pEiZGujUiqkhfP7w898m2YGMffMmLmfj4dH5pw593m5m3vu+1zXda5LjcybN484OzuTTZs2sf3UBIPBIEwmk+ePODJz5kwyZ84c1usvX74QDQ0Noq6uToyMjIi0tDS5fPmyEBVST/Pmzcm1a9c42iMiIkjz5s2FoEiw3L9/n0RGRhJCCCkoKCDm5uZESUmJmJiYkMePHwtXXA0oKyuTjIwMjvaMjAyirKxMCCEkNTWVKCoqClqaQIiNjSW2trakd+/e5NWrV4QQQgICAsitW7eErKxmLCwsyI4dO1ivk5OTibS0NPn333/J3r17ScuWLYmzs7PwBFaDtrY2iY6OZr3evXs3adeuHfn58yfrda9evSjXISsry/Xzn56eTmRlZWscz2AwOH6YTCbrX15kZmaSVq1akcWLFxNCCLl16xZRVFQkc+fO/Sv9r169Ivv27SPz588nDg4OZP/+/axnKFHm0KFDREZGhjCZTKKqqkpUVVUJk8kkMjIy5MCBA5TOLS737L59+5KzZ89ytJ89e1Yg187fwmAwSEFBgbBl0AiRxYsXE0NDQ/Lt2zeOvtLSUmJoaMj6ThQU5eXl5MKFC2TMmDFERkaGkjmio6Nr9ePm5kbJ/PzA1NSUHD58mPX69u3bhMlkki1btpCwsDCir69Pli1bJkSF1CIjI0Py8vLY2n63WVDNzJkza/VDwxttbW3y7NmzOo//8OED6/e8vDyyYcMGsnLlShIbG8sPeZTBYDBIkyZNWM9avH5EnYCAANK3b1/SqlUrlh14//795Ny5czzHqKmpkZSUFJ79T548IWpqanzX+jckJiaKvI1MT0+PhISEkOLiYqKurk5u3rxJCKnU3qxZMyGr446pqWmtfszMzGp1vOfPn5PmzZsTW1vbv7KtVpGenk7WrFlDWrduTZSVlcnkyZNJWFjYXx9H0NTluhMnLl++TCIiIjjar127Rq5cucJ1jJeXF2EwGKRZs2aEyWQSdXV1EhgYSJo0aULmzp1br/tQbfj8+TPr58qVK0RWVpZMnDiRFBUVsfVRyfz584mqqirp1q0bOXjwICksLKR0Pm64u7sTWVlZwmAwiIqKClFRUSEMBoPIysoSd3d3gesRNDt37iTNmjUjPj4+LP+pt7c3adasGdm2bZuw5fGkvLyc3L17l+zcuZNYWFgQJSUlwmQyiYaGBpk+fTrx9fUViC+4pKSE2NnZESkpKSIlJUVevHhBCCFk4cKFZPv27dWObajnnh98+PCBDB48mGULrTpvs2bNIsuXLxeyOtFGX1+f7Nu3j6N97969RF9fn/L5/fz8yKVLl1ivV61aRVRUVEifPn1qvOZatmxJ7t+/z3q9du1a0q9fP9br06dPEwMDA57j/7QTKioqsj47hBCSn58v8s/K/GDy5Mlk7969hBBCXF1dibq6Ovn333+JlpZWjfEIDc02zG+6d+9O7Ozs2NrevHlD9PX1yfjx43mO4+bP4ebfoYr8/HzSsmVLoqmpSXbt2kXOnTtHzp07R3bu3Ek0NDRIq1atSH5+fo3HqaioIFu2bCEKCgos3Y0bNybr16+nTHt94ed1z8tOExMTU6vxcnJyJDs7m0PHixcvauUXpBFv6OBbGho+8+nTJ7Jnzx5ib29P7O3tyb59+0hRURHl8+bm5tbqR5zR1NQk165dI79+/SIaGhqsh/+UlBTSpEkTyuYVFadWfQyc/FjoysvL1yvoLDExke3n/v375NixY0RfX79BGJjrgp6eHlvg6eHDh0nr1q1Z3xmrV68mpqamwpInEBYtWkTatm1LgoODSV5eHsnLyyNBQUGkbdu2rAB6GsHw9etXNsN6Tcb15s2bE39/f452f39/VuD006dPhe6cpIIzZ84QOTk58u+//xJZWVnWd6abmxuxtLQUsrqaqa+RS5g0btyYzYhnaWlJVq1axXr9/Plz0rRpU8p1dOrUiWzdupWjffPmzaRz5841jq/PJrGkpCSiqqpKZsyYQZSVlcns2bPr/Hc0RF6+fEn27dtHHBwcWIHDfwY3UQG/7tn+/v7V/lBNcHAw0dTUJLt37ya3bt0it27dIrt37yba2tokODiYJCUlsX5EASaTSQffSjj8Mi5ThTA+n1++fCGenp6kR48edTbsf/78mRw5coT873//47O6/0NdXZ08evSI9XrZsmXE3Nyc9fry5cukffv2lM0vbJhMJnn37h1bm6KiIsnKyhKSIpq/JTAwkEyYMIGUlJT81bjk5GSipaVFmEwm6dixI3n8+DFp0aIFUVRUJMrKykRKSoqrs1NUYDAY5ODBg8TPz6/aH1HmyJEjRE1NjWzZsoXIycmx1mu+vr7VPq9JS0uTt2/f8ux/8+YNadSoEd/1/g21Cb4VdgCku7s7kZaWJk2aNCFdu3Yl5eXlhJDKjXTiaOPiFawuKytLlJWV6xW0LogNP/yirtcdP5g1a1aNP38GWVBBly5duG7IvHr1KjEyMuI5ZteuXYSQSlsTg8Egffr0EdhmnT+TYfwePFKbDcL8oqysjJw6dYoMHTqUyMvLk3/++YdERETUKXj9b7l06RKRkpIiK1asIG/evGG1v3nzhixbtqzBbLStDxUVFWT16tWkcePGrP97eXl54uLiImxp1aKkpEQYDAZp3bo1sbW1JV5eXiQzM1PgOhYvXkz+97//kVu3bhEFBQXW99+5c+dIt27dqh3bUM89P5g2bRoxNzcnL1++ZAtgioiIIIaGhkJWJ9rIyMjwTIoiiOCvDh06sDZX3blzh8jJyRFPT09iZWVVY+CnrKwsmx23X79+ZMuWLazX2dnZ1SZzYTAYbOvsP9fYkhJ8W1hYSF6/fk0IqXxe3L59O7GysiLLly8nHz9+rHZsQ7MN85t3796xbQZ//fo16dChA/nnn39Y6xZRJSsri5ibm3M8s5mbm7MFo9aG79+/k6dPn5K7d++Sr1+/UqSYP/Aj+JZfdhodHR1y48YNDh3+/v4i61OlERwMQkSotgUNTT2pKR0+1eXXHzx4AHNzc8jJyaFnz54AKksgfPv2DdevX4eJiQllc/9eHqDqsv49ZT6poSSlOLBp0yYcOHAArVq1QmlpKdLT0yErKwsfHx8cP34c8fHxwpZIGUePHsXGjRuxdOlSbN26FSkpKdDV1YWfnx/8/f0RFRVV7fjp06fj3bt38PLygoGBAZKSkqCrq4tr165h+fLlePr0aY0a9PX1cfr0aa6lsuvD5cuXsXv3bkRHR/P1uKKAgoICUlJSWGWFxo0bh7Zt2+LQoUMAgGfPnsHU1BTv3r0TpkxK+fHjB1atWgUPDw/8+vULANCoUSM4ODhgx44dIln2np8MHjwY4eHhaNKkCVv7ly9fYG1tjcjISErnz87OxsKFCxEdHY2ysjJWe23uGVu2bMG2bdswe/Zs9OjRA0DlPc/Lywtr167FunXrsH//fly5cgU3btyg9O8QNMbGxli2bBmmT58OJSUl1nfm48ePYWlpifz8fGFLrJbGjRsjIyMDGhoaAID+/fvD0tIS69atAwDk5OSgS5curNJFokSLFi1w/fp1dO3aFQCgpqYGT09PVtnsjIwMGBsbo7i4mFIdYWFhmDhxIoYOHcoqRXz79m3cvHkTp0+fxtixY/k+55cvX1i/3759G2PHjoW1tTU8PT3ZnvmUlZVrdbyMjAxERUXh3bt3HKVMN27cyB/RFPDlyxeef2NmZibat29Pybz8umf/WV7958+fKC0thYyMDOTl5fHx40dK9FdRU6lzBoMhUusGJpOJ/Px8NG/eXNhSaIRIdnY2HBwccOPGDba17rBhw3DkyBHo6upSOn9oaCiCgoKQnp4OGRkZdOjQAbNmzaK8BPCfxMbGwtvbG2FhYWjdujXGjRuH8ePHs57DakNUVBR8fHwQHh4OFRUVjB07Fu7u7pTolZOTw/Pnz6GpqQkA6NmzJ/755x9WyfHc3FwYGhqipKSEkvmFDZPJhKWlJdt65uLFixg8eDAUFBRYbeHh4cKQR1MLjI2N8eLFCxBCoK2tzVF6/tGjR1zHWVpaQlpaGk5OTggMDMSlS5dgbm6O48ePA6gshfrw4UMkJCRQqr+8vBz79+/H6dOnkZeXhx8/frD183rmEId7r6GhIbZt2wZra2u29VpKSgpMTU3x4cMHruOkpKSQn58PdXV1rv0FBQVo3bo1pc9I48aNq7a/qKgIMTExPDXU1z7ILx48eICXL19i2LBhUFRUBFBp32vSpAlr/SSqfP78GeXl5WjatClb+8ePHyEtLc2xFvH396/1sWfMmFFnXe/evRPp67Ku1x0/YDKZ0NLSgrGxcbVlaM+ePUuZBqDy2Sc1NRXa2tps7Tk5OejUqRPXZx4FBQU8ffoU2traIIRAVlYWUVFRArtOYmJiavW+QYMGUazk/8jNzYWfnx8CAgLw69cvPH36lPU9QgWmpqbo378/z1LB69evR1xcnFj6Jv6kuLgYqampkJOTg56ensjb5T09PWFmZoYOHToIVYeWlhZCQkLQu3dvtu+/zMxMmJiYsNnzeNHQzj0/aNmyJa5du4auXbuynbesrCwYGRlRbtttyLRv3x6rVq3C3Llz2do9PDywd+9eZGRkUDq/vLw80tLSoKmpCUdHR7x9+xYBAQF4+vQpTE1N8f79e55jtbS0EBgYiIEDB+LHjx9o0qQJLl68iCFDhgAAnjx5gkGDBlW7Vvl9nf3nGvv79++IiIgQCZsmVeTk5ODGjRv48eMHBg0ahM6dO//V+IZmG6aCly9fon///hg/fjwuXboEExMTnDx5ki3WhhfC8k38zsePH5GZmQmg8vvgz3ULN+zs7Gp1bB8fn3ppowImk4ktW7awngcdHR2xatUqqKmpAQC+fv2KjRs3Vvt55ZedZvv27Thx4gR8fHwwbNgwXLlyBbm5uVi2bBk2bNiARYsW8emvpmmISAtbAA0NP1myZAnb6z+d2VQH3y5btgyjR4/G8ePHIS1deXn9+vUL//77L5YuXYrY2FjK5mYwGGjbti1mzpwJKysr1vySxKZNm9C5c2e8fPkS//zzD+vhW0pKCk5OTkJWRy1ubm44fvw4rK2tsWPHDlZ79+7dsXLlyhrHX79+HdeuXUPbtm3Z2vX09JCbm1srDXv37sXq1avh4eHBYWSsDx07dsT9+/f5djxRonHjxvj27RvrdUJCAnbv3s3WL+5GBhkZGRw8eBDbt2/HixcvAADt2rWDvLy8kJUJhujoaA4nKACUlZXh1q1blM8/depUEELg4+ODFi1asAXw1cT69euho6ODw4cPIzAwEEDl9Xr8+HFMmTIFADBv3jw4ODhQol2YPH/+HAMHDuRoV1FRQVFRkeAF/SUtWrRAdnY2NDQ08OPHDzx69AguLi6s/q9fv3IEF4gKvXv3xqFDh3D8+HGEh4fj69evGDx4MKs/PT2dFVRMJePHj8fdu3exf/9+nDt3DgBgYGCAe/fuwdjYuNbHefbsGddgiNGjR3O8t0mTJhwbq06fPo3Q0FDW69oaxY4fPw4HBweoqamhZcuWbMdlMBgiHXw7cuRI/PfffxyOiOfPn2PIkCF49eoVJfPy65796dMnjraMjAw4ODiwAtKoJDs7m/I5+MmfgeE0komOjg4iIiLqZFyuDxUVFZg8eTJCQ0PRoUMH6OvrAwAeP36M0NBQzJkzB0ePHkVhYSFiY2Mp2XiRn58PPz8/eHt748uXL7CxscH3799x7tw5GBoa1uoYr1+/hp+fH3x9fVFUVIRPnz7h1KlTsLGx+atnv7+lTZs2SE1NhaamJoqLi5GUlIT9+/ez+gsLC8X6mZ9bgNXUqVOFoISmrlhbW9dp3P379xEZGQkjIyN07doVx44dw/z581lOzkWLFqF37958VModFxcXeHl5YcWKFVi/fj3WrVuHnJwcnDt3rtpnPSq/FwRFdnY212dyWVnZagP+CSEYMmQIT5tq1YZhKlFRUamxvzr7dn3tg/yie/fu6N69O1vbyJEjBTZ/fZg0aRKsrKwwf/58tvbTp0/jwoULuHLlClt7fQJq/6S6DT+iHHgL1P264wcODg4ICgpCdnY2Zs2ahalTp1L+nMgNFRUVZGVlcdjFMzMz2Tbe/M63b99Yz0MMBgOysrJo1aoV1VJZCDKotrYwmUxW4I8ggn4ePXoET09Pnv3Tpk1jbboVdxQVFf9qY5+wiYiIwLVr12p8H9Wb3d6/f8/1O7qkpKTWz1UN7dzzg5KSEq7rwY8fP0pE8HFdsLOzw8GDB7FixQosXrwYiYmJ6Nu3L4DKRA1+fn44ePAg5ToUFRVRWFgITU1NXL9+HcuXLwfAaTvlxogRI+Dk5ISdO3fi3LlzkJeXx4ABA1j9ycnJaNeuHc/xfz53cVtjUx0LIkyioqIwatQo1nmWlpaGj4/PX9kaGpptmAo0NDRw48YNDBgwAMOGDUNgYGCtv69HjhyJGzduoHHjxmztVPsmfqdp06asJIC1xc/Pr1ab1UQRTU1NVpAsULl5o8on/ft7qoNfdhonJydUVFRgyJAhKC0txcCBAyErK4uVK1fSgbc0dPAtjXghbGf2gwcP2AJvgcoHn9WrV3MYHPnNq1ev4O/vD19fX3h4eGDq1Kmwt7eHgYEBpfOKGhMmTGB7XVRUxFcjqKhSXwMnPxa6U6dORWlpKStw8s/ArZoyuf25A5gQgrdv32LTpk3Q09OrlYaGRrdu3RAYGIjt27fj1q1bKCgoYAsie/HiBVq3bi1EhdQTEBCAHj16wMDAAF26dGG1l5WV4fTp02K7UE5OTmb9/uzZM7ZMqeXl5YiIiECbNm0o15GUlISHDx+iY8eOdRpva2sLW1tbnv1ycnJ1lSbStGzZEpmZmRwOlbi4OMqz7/GD+hq5hMnmzZsxZMgQnDhxAr9+/cLatWvZMokGBwcLzHn0v//9DydOnKjT2KysLIwdOxZPnjxhOZWA/wt24OZg4meWqi1btmDr1q1wdHTk2zEFhaKiIsaOHYsLFy6wnrlTU1MxePBg2NjYUDYvlfdsPT097NixA1OnTkVaWhq/JHNFS0uL0uPT0FBJXYzL9eHgwYP477//cOHCBYwaNYqt78KFC5g1axbatWsHPz8/Sp5ZraysEBsbi5EjR+LAgQOwsLCAlJQUPDw8ajU+LCwM3t7eiI2NhaWlJfbu3QtLS0soKCigS5culAfY/fPPP1i6dCnWrl2LK1euoGXLlmyG7AcPHtT5GbQh4OvrK2wJNPXE2dm5TuM+fvyIli1bAqh8blFQUGB7XlVVVRVIhYmTJ0/i+PHjGDlyJDZt2oTJkyejXbt2MDIyQkJCAhYvXsx1XNWGroaMjo4OEhMTOZ57IiIiqrWT1ub/vKriBlXU97tDWAGQy5cvx+bNm6GgoMAKwODFvn37KNPBD+7evctVo6mpKataDC+uXLkCKSkpjuz4169fR3l5OSwtLbmOE4UNP/WlrtcdP3B3d8e+ffsQHh4OHx8frFmzBiNHjoS9vT2GDx8usO+0MWPGYOnSpTh79izLppKZmYkVK1Zw3WBbhZeXFyuT169fv+Dn58fK5FUFr+9sflJRUYHMzEyu1XG4bUDnJ9+/f2f9/8XFxWHUqFE4fPgwLCwsaszQV1/Ky8ur3YDeqFEjscz8N27cOPj5+UFZWbnGrOuiWqnhz0pywqJ79+64fPkyK+im6jvHy8sLffr04Xi/OJx7fjBgwAAEBARg8+bNACrPW0VFBXbt2gUzMzMhqxNN/P39sWPHDjg4OKBly5bYu3cvTp8+DaAyKURISAjGjBlDuY5hw4bh33//hbGxMdLT0zFixAgAwNOnT2u0O27evBnjxo3DoEGDoKioCH9/f8jIyLD6fXx8MHz4cJ7jJX2dvWHDBgwbNgxHjx5F48aNsX79eqxevfqvgm8l0TasqqrK9XmwtLQUFy9eRLNmzVhtNcUyKCoqYty4cQL3TdQXUdmsVhdycnLqfQx+2WkYDAbWrVuHVatWITMzE8XFxTA0NKS0SgNNw4EOvqURewTpzFZWVkZeXh7LQFbFy5cvoaSkROncLVu2hKOjIxwdHREXFwdfX1/06tULhoaGsLe3h729PeWGCmGzc+dOaGtrY+LEiQAAGxsbhIWFoVWrVrhy5QqMjIyErJA66mvg5MdC98CBA3+t+3f+zOgHVDp9NDQ0EBwcXK9jiyobN26EpaUlTp8+jbdv32LmzJls2Q3Onj0r8uX46svMmTOhoKAAPz8/NifW58+fMWvWLLENvu3WrRsYDAYYDAZb8FYVcnJycHNzo1xHjx498PLly3oFPvz48YOrYb6mnYYNmdmzZ2PJkiXw8fEBg8HAmzdvEB8fj5UrV2LDhg3Cllcj9TVyCRMjIyOkpqbi9u3baNmyJXr16sXWP2nSpFpnAvxbalMmrgpepYeqWLJkCXR0dHDz5k3o6Ojg3r17KCwsxIoVK7Bnzx6uY/gZVPzp0yf8888/fDueIAkPD8fQoUNha2uL4OBgPH36FEOGDIGtrS2ljnyq79nS0tJ48+YNP6RWS0BAQLX94nrfpaGpC76+vti9ezdH4C1QmaF8165dmDNnDoYPH46lS5fyff6rV69i8eLFcHBwqNNmxIkTJ8LR0REhISGU2yO4sXHjRrx+/RqLFy9Gy5YtceLECbYSfkFBQbCyshK4LhoaQfCnbUMYwaz5+fmsDa6Kior4/PkzAGDUqFHVrllmzJhRqw1aoliOsorly5djwYIFKCsrAyEE9+7dQ1BQELZv3w4vLy+e4+oacC1KCCsA8vHjx/j58yfrd140hMDu79+/c81y/PPnzxqzuTk5ObFlHK6ioqICTk5OPINvhb3hhx/U9brjF7Kyspg8eTImT56M3Nxc+Pn5Yf78+fj16xeePn0qEIf4rl27YGFhAX19fVZ1uVevXmHAgAE81/m1yeTFYDAoD75NSEjAlClTkJuby5ENjeqy0/Pnz0dwcDA0NDRgZ2eHoKAgjuBjKunUqRPOnz+PZcuWce0/d+4cOnXqJDA9gkJFRYX1naysrNwgvp//RFSC8LZt2wZLS0s8e/YMv379wsGDB/Hs2TPcuXMHMTExHO8Xh3PPD3bt2oUhQ4bgwYMH+PHjB1avXo2nT5/i48ePuH37trDliSS/fz+PHTtWaJtx3N3dsX79erx8+RJhYWGswMWHDx9i8uTJ1Y5VU1NDbGwsPn/+DEVFRTYbAVBZBYAOYuNNSkoK7ty5w7JJ7969G56enigsLGQLIK0OSbQN1zd+4XeE5ZuoL6KyWa0+BAQEYOLEiRxJ4378+IHg4OAaP7v1sdPY2dnV6n2ibCehoR4GaWh5pWlo6kBiYiIGDhz4V0ETdWHx4sU4e/Ys9uzZw1bqYdWqVRg/fjxfb+61oaCgAJMnT0ZMTAzev3/fYHaw1BUdHR2cPHkSffv2xY0bN2BjY4OQkBCcPn0aeXl5uH79urAlUoaXlxc2bdqEvXv3wt7eHl5eXnjx4gXLwDlp0qRqx6ekpGDIkCEwMTFBZGQkRo8ezbbQFUQGxD8NEUwmE+rq6mjfvj3Pkn/iQGpqKq5fv46WLVvin3/+YQuSP3bsGHr27Ilu3boJTyDFMJlM7Nmzh7VDc9OmTQAqv79at24tlpkFALCM2bq6urh37x7U1dVZfTIyMmjevDmH4YEKXrx4gXnz5mHq1Kno3LkzR6aH6jYtZGRkwM7ODnfu3GFrr8qUJK7/d0Dl37ht2zZs374dpaWlAMAqLVK1iaEhwMvI9fHjRygqKrIF5IoyZWVlHGV+qKCq9GF11Pbzr6amxipzo6Kignv37qFjx46IjIzEihUrqnVYA/XPSGNvb48ePXpg3rx5Nb5XFCkqKoKpqSn09PQQGxuL6dOnY/fu3ZTPy4979oULF9heV2X6P3z4MDQ0NHD16lUqpLP4fUc3UBlEUFpaChkZGcjLy9e4u5+GRpKQk5PD8+fPeW4oys3Nha6uLr59+0bJPTMhIQHe3t4ICQmBgYEBpk2bhkmTJqFVq1ZISkqqcbPJ3LlzERISgk6dOmHatGmYOHEiVFVV0ahRo1qNp6GRdMrLy7F//36WTenHjx9s/bzumUwmE5aWliyH0MWLFzF48GBWyfHv378jIiKC8vVSx44dERAQgF69eqF///4YNWoUnJycEBISgkWLFuHdu3c89demHOXZs2epks4XTp48iU2bNuHFixcAgNatW8PFxQX29vZ1Ot6XL19w8uRJeHt748GDB/yUylfqax+kAczMzNC5c2eODdELFixAcnIybt26xXOsnJwcUlNTOark5OTkoFOnTjyzDxsZGWHp0qU8nbre3t6sDT/nz58X2bU6v6+7uvLy5Uv4+vrCz88PP378QFpamsACeQghuHHjBpKSkiAnJwcjIyPKs8byg27duqFDhw5wcXFBq1atOGwfKioqlM3NZDKhqakJY2Pjam0uVGUA9ff3h4ODA/bs2YM5c+aw/BC/fv2Cp6cnVq1ahSNHjmDmzJmUzE8jHrx48QI7duxAUlISiouLYWJiAkdHR7ZKfzScfP78GYcPH2Y7bwsWLGDb7E7zfzCZTGRkZLD5krhRU1IIfvP161cEBQXBy8sLDx8+FGu/kLBhMpnIz89H8+bNWW1KSkpISkqqdUVG2jZcf4Tlm+AnVZvVAgICBLpZrT5ISUnh7du3bJ9/ACgsLETz5s2r/e6pr51GXOwkNNRCB9/SiBXCdmb/+PEDq1atgoeHB2uHfKNGjeDg4IAdO3Zw7MSgijt37sDHxwehoaHo2LEj7OzsMGfOHLHPfCsnJ4f09HRoaGhgyZIlKCsrg6enJ9LT09GrVy98+vRJ2BIppb4GzvoudPPy8qrtF+csmDR1p2qxWFWCvV+/fggMDMSXL1/EOvhWVKjKbPF72Q4Gg1GrAMJ+/fpBWloaTk5OXA3zXbt2pUq2yPDjx48GXVrk8+fPKC8v59ic8/HjR0hLSwvcUPc3VFRUYOvWrfDw8EBBQQHS09Ohq6uLDRs2QFtbmxLnHrdsFbyoKUutqqoqHj16BB0dHbRr1w5eXl4wMzPDixcv0KVLF1ZQNzfqmpHm0KFDrN9LSkqwb98+jBw5El26dOEIvBdEScu/gdsGurdv32LYsGEYNWoUW3YpUf7cAuB4HmcwGFBXV8fgwYOxd+9eoTgYMjIy4ODggFWrVnGUyKWhkWSaNm2K6OhonpuRnjx5goEDB1K+ziwpKUFISAh8fHxw7949lJeXY9++fbCzs6sxo+23b99w+vRp+Pj44O7duzA3N8fly5eRmJiIzp07U6q7Ch8fH5iZmUFHR0cg89HQ8IuNGzfCy8sLK1aswPr167Fu3Trk5OTg3Llz2LhxI8/npVmzZtXq+FRna3NycoKysjLWrl2LkJAQTJ06Fdra2sjLy8OyZcu4ZucEKgMMg4KCoKWl1eDKUXKjtLQUxcXFHA662hIVFQUfHx+Eh4dDRUUFY8eOhbu7O59V8hdRCYBsqNy+fRtDhw5Fjx49MGTIEADAzZs3cf/+fVy/fh0DBgzgObZly5Y4deoUR4Wj//77D1OmTOEZ9C7sDT/8pr7XXV34/v07K5NXXFwcRo0ahVmzZsHCwkLsfSL8QEFBAUlJSWjfvr3A5545c2atMo9Red9cuXIl9u3bByUlJbRr1w6EEGRlZaG4uBiLFy/G/v37KZtbFBg8eDDCw8PRpEkTtvYvX77A2toakZGRwhEmAdDnnuZvqCkxhKCTosTGxsLb2xthYWFo3bo1xo0bh/Hjx6NHjx4CmV8SYTKZ8Pf3Z9sUM3nyZBw4cAAtWrRgtY0ePfqvjitJtuErV65ASkqK4++8fv06ysvLuVaqECffRBXC3KxWV5hMJgoKCjg2ICQlJcHMzKzawPH62mnEzU5CQw108C2NWCEqzuzS0lKWgbNdu3aQl5enfM63b98iICAAvr6++PTpE2xtbWFnZycwh5oo0Lp1a5w5cwZ9+/ZFx44dsWXLFvzzzz94/vw5evToQXnmY1FBGAZOoOaFH68FX3p6OoqKitCzZ09W282bN7FlyxaUlJTA2toaa9eu5bteUUJTUxOmpqYYNGgQTE1NBZJpWFT4fadaXl4eRo8eDQaDAQ8PD/Tt21fsg2/9/f2hpqaGkSNHAgBWr16NY8eOwdDQkPUgTyWGhoYwMDDA6tWr0aJFC45ruLr5FRQU8PDhQ+jr61OqkYY6LC0tYWVlhfnz57O1e3h44MKFC7hy5YqQlNWMq6sr/P394erqitmzZyMlJQW6uroICQnBgQMHEB8fL2yJ1TJgwACsWLEC1tbWmDJlCj59+oT169fj2LFjePjwIVJSUniOrWtGmtoGPTEYDGRlZdX+jxEAvJ4xqpaytd00wA/E9Z794MEDTJ06FWlpacKWQkMjMowcORKampo4evQo1/558+YhLy9PoPfL58+fw9vbG4GBgSgqKsKwYcM4NiHzIiMjA76+vvD390dxcTFGjhyJCRMmYNy4cZRq1tPTQ1ZWFtq0aYNBgwaxvj+FEdxBQ/M3tGvXDocOHcLIkSOhpKSExMREVltCQgJOnTolbIl/RXx8POLj46GnpwcrK6tq3/t7ENudO3caXDnK+vL69Wv4+fnB19cXRUVF+PTpE06dOgUbG5sG9fcLyz5YUlKCHTt24ObNm1wrdYjaWoMbiYmJ2L17NxITE1nZS9esWQM9Pb1qx82dOxfx8fE4e/Ysa52QmZnJCkTx8vLiOk5UNvw0VObPn4/g4GBoaGjAzs4Otra2UFNTE4qWkpISxMTEcM2YXtMm15s3b2L//v1ITU0FABgYGGDp0qUYOnQoZXqrGDx4MFavXg0LCwvK5xJVEhISEBQUhIyMDABAhw4dMGnSJPTu3VvIyqiHWyZFAHj37h3atGmDnz9/CklZw+DRo0do1KgRK8vt+fPn4evrC0NDQ2zatKnaTROSdu6Tk5PRuXNnMJlMJCcnV/ve6irySSpMJhNhYWE1BnzVlBSiPuTn58PPzw/e3t748uULbGxs4OHhQVfXERC12VBUV/u4pNiGjYyMsGPHDowYMYKtPSIiAo6OjkhKSuIYI0q+ifrQUDerVVVHSEpKQqdOndiqJZeXlyM7OxsWFhY4ffo0pTok3U5CUzN08C0NDR8JCAhAjx49YGBgwNZeVlaG06dPY/r06ZTN3ahRI7Rp0wYzZszA6NGjOTKYVSHOC5aFCxfi0qVL0NPTw+PHj5GTkwNFRUUEBwdj165dePTokbAlihT8Xuj++UD68+dPPH78GPv27cPWrVt5OnXHjh2LLl26wNXVFQCQnZ2NTp06YcCAAdDX14ePjw82b96MpUuX1u4Pa4CcOHECsbGxiI6ORmZmJodjuibjfkPmTwNTaWkpbG1tcfPmTZSUlIj0QoUfdOzYEUePHsXgwYMRHx+PIUOG4MCBA7h06RKkpaUpK6lWRX0yW/To0QP79+9H//79KVAmmvAqAfk7DAYD3t7eAlBTf5o2bYrbt29zPLekpaWhX79+KCwsFJKymmnfvj08PT0xZMgQttJKaWlp6NOnj8CckqWlpVydajXdN69du4aSkhKMGzcOmZmZGDVqFNLT09GsWTOEhIRwZEr6HWFmpBEW/Mw6XF/E9Z6dmJiIgQMHSsxmNRqa2nDnzh2YmprC2toaK1euhL6+PgghSE1Nxd69e3H+/HlERUWhX79+AtdWXl6OixcvwsfHp9bBt1VUVFTg8uXL8Pb2xtWrV/H9+3eKVP4fr1+/RnR0NGJjYxETE4OMjAy0atUKpqamOHHiBOXz09DUBQUFBaSmpkJTUxOtWrXC5cuXYWJigqysLBgbG+Pz58/CligQGko5ShMTE9y8eROqqqo1li7nZR8MCwuDt7c3YmNjYWlpialTp8LS0pL1/E0HFNSOyZMnIyYmBtOmTeO6WXDJkiVCUlZ/qpzqvPj8+TMsLCzw4MEDtG3bFgDw6tUrDBgwgGtmwypEccNPbeDHdccPmEwmNDU1a9RAtY3t8ePHGDFiBEpLS1FSUoKmTZviw4cPkJeXR/PmzasNPD9y5AiWLFmCCRMmoE+fPgAqg0HPnDmD/fv3Y8GCBZRqP3v2LNavX49Vq1ZxrY4jzj4lV1dXrFy5UiAJdESNKp9Qt27dEBkZyRbQV15ejoiICHh6erJVTKPhpEePHnBycsL48eORlZUFQ0NDjBs3Dvfv38fIkSNx4MABjjGSeu5/9wVVBbNxC1MR9QA2YcErWFtQWFlZITY2FiNHjoStrS0sLCwgJSWFRo0a0c/KYoCk2Ibl5OSQmpoKbW1ttvacnBx06tQJJSUlHGNEyTdRV0Rps9rf4uLiwvp3xYoVbPYIGRkZaGtrY/z48QKtENJQ7CQ0goUOvqWh4SNMJhMKCgrw8/PD+PHjWe0FBQWUl0//fUdKlZHpb0oRiwM/f/7EwYMH8fLlS8ycORPGxsYAgP3790NJSQn//vuvkBXyl/oaOAW10L18+TJ2796N6Ohorv0aGho4ffo0y7C4ZcsWnDlzBomJiQAAb29vuLm5sV6LO2/fvkVMTAwuXbqEkJAQVFRUiPV16+LiglWrVnEYOJ2dnREbG4uoqCghKRMM8vLySEtLg6amJhwdHVlZzJ8+fQpTU1O8f/+e0vmtrKwwc+ZMtntWbYmMjMT69euxbds2rob5hlJe5W8YO3Ysz77y8nL8999/+P79e4O5ZhUUFJCQkMDKzFDFkydP0KtXL5SWlgpJWc3IyckhLS0NWlpabMG3z549Q8+ePVFcXEzp/O/fv8esWbNw9epVrv11+Qx8/PgRqqqqNe6SpTPSiA5/e88uKSnBzp07ER4ejpycHDAYDOjo6GDChAkCc/b9GaRHCMHbt29x+PBhaGho8PxM09BIKmfPnsWcOXM4SpepqqrC09OzTs9QosS7d+8E6rgrLS3FrVu3EBQUhJMnT4IQgl+/fglsfhqav6Fjx44ICAhAr1690L9/f4waNQpOTk4ICQnBokWLeJaPFyWeP38ONzc3tiyKixYtQseOHWt9jIZSjvJ320KVc44Xzs7OXNulpaXh6OgIJycnKCkpsdpFPaBAVAIgq2jSpAkuX74slM0p/GDmzJlwd3eHgoICW3tOTg6mTZuGW7duVTueEIIbN24gKSmJlTV34MCB1Y4R5Q0/1cGP644fzJw5s1bZpniVkeUXpqam6NChAzw8PKCiooKkpCQ0atQIU6dOxZIlS6qtNtC2bVs4OTlh4cKFbO3u7u7Ytm0bXr9+Tal2btnOGkoWt/rye1U2SeP3TH7c/EJycnJwc3OrVTICSUZFRQWPHj1Cu3btsHPnTkRGRuLatWu4ffs2Jk2ahJcvX3KMkdRzn5ubC01NTTAYDOTm5lb7XqorAjZEhB18Ky0tjcWLF8PBwYEt+YCoPyvTsCPptuGWLVvi1KlTHMlP/vvvP0yZMqVBrPPrgqhsVqsP/v7+mDhxIho3bixsKQ3GTkIjWKRrfgsNTcNAFJzZQKXRadq0aXjy5Ak2bdokkDmBymyhkk6jRo2wcuVKjvZly5YJQQ31jBkzBrKysgAAa2vrvx6fnZ0NdXV11u9U0bFjR9y/f59n/4cPH1jZIAAgKiqKrQSiqakpVqxYQZk+UaG0tBRxcXGIjo5GVFQUHj9+jM6dO8PU1FTY0iiFl/G9JqO9uKCoqIjCwkJoamri+vXrWL58OQCgcePG+PbtG+XzW1lZYdmyZXjy5AnXANrRo0fzHFtV9m7IkCFs7eJsmD979izX9vPnz2Pt2rWQlZXFxo0bBayq7vTs2RPHjh2Dm5sbW7uHhwf+97//CUlV7TA0NMStW7c4DLFnzpxhbb6hkqVLl6KoqAh3796Fqakpzp49i4KCAmzZsgV79+6t0zFrKhlWxaJFi7BixQrk5+fXOSPN+PHj0bNnTzg6OrK179q1C/fv30doaGjthQsYX19fKCoq4p9//mFrDw0NRWlpKWbMmEG5hrrcs3/8+IFBgwYhJSUFlpaWsLKyYjnUt27diqtXryI2NpZn9Qp+8eczI4PBgLq6OgYPHlznzy4NjTgzduxYmJub49q1a6wStHp6ejA3N28w2bFCQ0MRFBSE9PR0yMjIoEOHDpg1axbMzc0F4rS7fv06oqOjER0djcePH8PAwACDBg3CmTNnagxGoqERJmPHjsXNmzfRq1cvLFq0CFOnToW3tzfy8vIahJ0pLCwMkyZNQvfu3dmyKHbu3BnBwcHVbh7gVo7y8OHDIl2O8nfbQl2D/Ozt7eHu7o7o6GhMmzYNEydOhKqqKr8kUkZ97YP8RlVVtdZrG1EkKSkJRkZGOHHiBOva8ff3x+LFi6utUFIFg8HA8OHDMXz48FrP2bdvX4SEhGDOnDkICwtj61NVVUVQUJDIBd4C/Lnu+IGfn5/Q5v6dxMREeHp6gslkQkpKCt+/f4euri527dqFGTNmVBt8W1RUxHWD7fDhwznW7VQgyb4lSc5PlZ2dDUIIdHV1ce/ePZavCKjMJNe8eXNISUkJUWHDgBCCiooKAJXBW6NGjQJQmXTmw4cPXMdI6rn/3Y5LB9f+PVpaWkL9XMTFxcHb2xv/+9//YGBggGnTpmHSpElC0yPpZGRkICoqCu/evWN9B1VRnY9K0m3DY8aMwdKlS3H27Fm0a9cOAJCZmYkVK1ZU6w+tQhR8E3Vh+vTptdqsJspUndsfP35w/dxrampSOn9DtJPQCBY68y2NWPDjxw/07duX5cz+fXd4REQETExMBOLMrtp1lpWVhbFjx6Jfv34IDAzEly9fKM98S/N/PHv2jGsJ6No8NNHUnT9LUVTtltu0aRPS0tJ4Zq5t06YNzp49i549e6KiogKqqqo4deoURo4cCQBITU1F7969xbqkY9++fVmOaFNTUwwaNAgDBw5sEE4efsHtumUwGGyB2OKIra0t0tLSYGxsjKCgIOTl5aFZs2a4cOEC1q5di5SUFErnr25BUFMAbXWlVp48ecKRrUMcuX37NpycnPDo0SMsXLgQTk5ODeq6vX37NoYOHYoePXqwgqhv3ryJ+/fv4/r16xgwYICQFfLm/PnzmDFjBtasWQNXV1e4uLjg+fPnCAgIwKVLlzBs2DBK52/VqhXOnz+Pnj17QllZGQ8ePECHDh1w4cIF7Nq1C3FxcdWOLysrg5ubG08DWXUZqfiRkUZdXR2RkZFcsx4PHToUBQUFNR5DWHTo0AGenp4wMzNja4+JicGcOXPw/PlzSuev6z374MGD2L59O2JiYjiyzaWlpcHU1BTr1q3DokWLqJRPQ0PzF0RGRmLhwoVISEjgyOj/+fNn9O3bFx4eHiJ7v6yoqMDkyZMRGhqKDh06QF9fH0Dl+iozMxNz5szB0aNHUVhYiNjY2Goz/NcHJpMJdXV1rFixAnPmzOFZcpuGRtSJj49HfHw89PT0GsQ6tV27drC1tYWrqytbu7OzM06cOIEXL15wHdeQy1Hyg2/fvuH06dPw8fHB3bt3YW5ujsuXLyMxMRGdO3cWtrwGwYkTJ3D+/Hn4+/s3mI0qv/Pz50+sXbsWhw4dwooVK5CZmYmrV69i3759mD17do3jS0pKEBMTw9U2vXjx4mrHlpaWNugNP5KOuro67ty5Az09PXTo0AFubm4wNzdHWloa/ve//3EtY1zFlClTYGxsjFWrVrG179mzBw8ePEBwcDDV8iUWJpOJgoICtuBHGpq/YfDgwdDQ0MDQoUNhb2+PZ8+eoX379oiJicGMGTOQk5MjbIkixZ9ZN3lB+3N5M3jwYISHh3Osrb98+QJra2tERkZSOn9JSQlCQkLg4+ODe/fuoby8HPv27YOdnR1b9Qga6jh+/DgcHBygpqaGli1bsgVVMhgMgVS7aKh8/vwZFhYWePDgASsx2KtXrzBgwACu19WfCNs3IclkZGTAzs4Od+7cYWsXREIoSbeT0NQOOviWRiwQFWf27yVq8vLyMHr0aDAYDHh4eKBv376Ufunv2rULixYtgpycHIDKgJru3buzMh98/foVjo6OOHLkCGUahE1V0POTJ09YQSgAWA+ddPAzJ/xc6P5eKqcKQgg0NDQQHBzMyhbxJ7a2tvjy5QuOHDmC0NBQODs7Iz8/n1XeLSwsDK6urkhKSqqV1oZI06ZNwWQyMXz4cJiamrLKlEkCkn7dFhUVYf369Xj58iUcHBxYWS6cnZ0hIyODdevWCVlh7fn69SuCgoLg5eWFhw8fivX/3bNnz+Do6IiIiAhMnz4dLi4ubBm8GxKJiYnYvXs3EhMTWSUx16xZw1Y6SlS5desW6/5QXFwMExMTbNy48a8yDNUVZWVlJCcnQ1tbG1paWjh16hT69euH7OxsdOrUCaWlpdWOt7W1xfXr1zFhwgS0aNGC4/5ZXdYgfpRlk5OTQ2JiItfnZmNjY4Fk3q4rjRs3RlpaGrS1tdnac3JyYGBgQLn2ut6zBw0aBBsbGyxYsIBrv5ubG86cOVPtxgYaGhrBMnr0aJiZmfHMcHno0CFERUXxzIwvbPbv348tW7bA39+flX2pigsXLmDWrFlYs2YN/Pz8MH36dKxevZoSHQcOHEBsbCxiY2MhKyuLQYMGSdyah4ZGGMjLyyM5ORnt27dna8/IyEDXrl15Pq825HKUqqqqtcrm8/Hjx1odLyMjA76+vvD390dxcTFGjhyJCRMmVJu9kgYwNjbGixcvQAiBtrY2RzKMhhIM4OzsjM2bN0NaWhoxMTE87Zq/8/jxY4wYMQKlpaUoKSlB06ZN8eHDB8jLy6N58+bIysriOq4hb/jh93XXkBk+fDhmzpyJKVOmYPbs2UhOTsbixYsRGBiIT58+4e7duzzHbtmyBXv27EG/fv3YspXfvn0bK1asYPtc1BTEXVcCAwPh4eGB7OxsxMfHQ0tLCwcOHICOjg7GjBlDyZyiAJPJhIqKSo2fY3H/DNc1gyINkJycDFtbW+Tl5WH58uUsm96iRYtQWFiIU6dOVTte0s79n0kFfvcJ/d4mzr6F+lKVCOzPSjbv3r1DmzZt8PPnT4Fpef78Oby9vREYGIiioiIMGzas1n5nmrqjpaWF+fPnCyQ7vjhCCMGNGzeQlJTE8onVtjKTsH0Tkky/fv0gLS0NJycntGrViuPZrWvXrpTN3ZDtJDSCgw6+pRELRMWZ/ecDb2lpKWxtbXHz5k2UlJRQulj4PfAXqAwKSUxMhK6uLgCgoKBA7LPvWllZQUpKCl5eXtDR0cG9e/dQWFiIFStWYM+ePSJpoKwv9TVw8nOh++f1VZXhqH379pCWluY5LicnB8OGDcOLFy8gJSWFQ4cOwcHBgdVvbW0NHR0d7N+/v0YNDRVCCJ48eYLo6GjExMQgNjYWMjIyGDRoEMzMzGqVWaOhIonXrajw8+dPVgBefTL4xMbGwtvbG2FhYWjdujXGjRuH8ePHo0ePHnxUKxq8fPkSGzduxIkTJzBq1Chs27YNBgYGwpZFIwR69OiBLVu2wNzcHKNHj0aTJk2wfft2HDp0CGfOnOGZSawKFRUVXLlyRWilQ3v27IlRo0ZxGPE3bdqEixcv4uHDh0LRVRs0NTVx+PBhjo1B58+fx4IFC/Dq1StK56/rPVtdXR3R0dHo1KkT1/6UlBSYmZnh/fv3lGkvKSnBzp07ER4ejpycHDAYDOjo6GDChAlYuXIlnVGLhuYPtLS0EBERwfNen5aWhuHDhyMvL0/AymqHkZERli5dCjs7O6793t7emDNnDoYPH47z589DRkaGck1PnjxBTEwMIiMjcenSJTRv3pzy720amvrw/PlzuLm5ITU1FQBgYGCARYsWcWxgEkVGjBiBf/75B7NmzWJr9/X1RXBwMK5du8Z13MyZM2tlZ/L19eWLTn7i7+/P+p0QAgcHB7i6unIEJvxtKdCKigpcvnwZ3t7euHr1Kr5//84XvfxElAIgXVxcqu2vbqOhKPDz5084OTnB3d0dK1asQFxcHNLT0+Ht7Y0RI0ZUO7ZqY4mHhwdUVFSQlJSERo0aYerUqViyZAnPwO2GvOGHquuuIfLgwQN8/foVZmZmePfuHaZPn87KhOvt7Y1u3brxHKujo1OrORgMBs8g7vpw9OhRbNy4EUuXLsXWrVuRkpICXV1d+Pn5wd/fH1FRUXyfU1RgMpk4cOAAVFRUqn2fOH+G6QyK1FBWVgYpKalqK7LS5x5QUlJCUlISy5dNw5vk5GQAQLdu3RAZGYmmTZuy+srLyxEREQFPT0+hZFsuLy/HxYsX4ePjQwffCoA/Y0BqC20brj/C9k1IMgoKCnj48CGrspcgach2EhrBQQff0ogFouDMBiqNi6tWreJ4OHF2dkZsbCylRoo/A3//XLBIQvCtmpoaIiMjYWRkBBUVFdy7dw8dO3ZEZGQkVqxYgcePHwtbIt/ht4FTWAvdX79+4enTp1BXV0fr1q3Z+pKSktC2bVs0a9ZMoJqEBSEEDx8+xOHDh3Hy5ElUVFTQ162Y8+nTJ3h7e7M5dO3s7NiMJ1Shq6uLs2fP/vWOwPz8fPj5+cHb2xtfvnyBjY0NPDw8kJSUBENDQ4rUCh95eXkwGAwsXLiw2qDJhlIW68qVK5CSkoK5uTlb+7Vr11BRUQFLS0shKRN9Tpw4gV+/fmHmzJl4+PAhLCws8PHjR8jIyMDPzw8TJ06sdryhoSGCg4NhZGRUp/nrm5Hm4sWLGDduHKZMmYLBgwcDAG7evImgoCCEhobC2tq6TroEgaOjI0JCQuDr68vakR4TEwM7OztMmDABe/bsEZiWv7lnN2rUCC9fvkTLli259r99+xZaWlocpWn5xY8fP9C3b1+kpKTA0tIS+vr6IIQgNTUVERERMDExQWxsbLVOIRoaSaNx48ZISUnhyBpZRWZmJrp06SKyWS3k5OTw/PlzaGpqcu3Pzc2Frq4uvn37RnngLSEEjx8/RnR0NKKiohAXF4evX7+iS5cuEvG8T9MwCQsLw6RJk9C9e3e2LIT3799HcHAwxo8fL2SF1ePh4YGNGzfCxsYGvXv3BlCpPzQ0FC4uLmy2l4ayfvlbqLBxvXv3jsPmJgrQAZD8oyozdGBgIHr37g1CCHbt2gVnZ2fY2dlVW1WuSZMmuHv3Ljp27IgmTZogPj4eBgYGuHv3LmbMmIG0tDSu4xr6hp/foYOoGiaGhobYtm0brK2t2f4PU1JSYGpqig8fPghbImXwyiApSdAZFIUHfe7p+8bf8Hv1UW7hPXJycnBzc+O5AZdGfLC3t0ePHj0wb968Wo+hbcP/R0lJCWJiYpCXl8dhi6+pwoAo+SYkjR49emD//v3o37+/sKXQ0HCH0NCIAdLS0uTt27c8+9+8eUMaNWokQEWCh8FgkIKCAtZrRUVF8uLFC9br/Px8wmQyhSFNYDRp0oRkZWURQgjR1dUlkZGRhBBCMjMziZycnDClCYw//98FMf758+fk7t27bG3//fcfMTU1JT169CBbt26tk5bv37+Tr1+/1mlsQ+Phw4dk7969xMrKiqiqqhJpaWlibGxMli1bRs6dOydseZQi6ddtTEwMUVZWJhoaGmTs2LFk7NixRFNTkygrK5OYmBjK5/fy8iIjRowghYWFtR4zatQooqysTCZPnkwuXbpEfv36RQipvBc/ffqUKqkiAYPBqPGnId1ru3TpQi5fvszRfvXqVWJkZCQERTWjo6NTqx9BU1JSQh4+fEjev39fq/dfuXKFWFhYkJycnL+e68iRI0RNTY1s2bKFyMnJse7bvr6+xNTUtNbHuXTpEunbty+Rl5cnzZo1I2ZmZiQ6Ovqv9Qia79+/ExsbG8JgMEijRo1Io0aNiJSUFJk1axb5/v075fPX9Z7NZDLJu3fvePZT/ax+4MAB0qJFC5KWlsbRl5qaSlq0aEEOHTpE2fw0NA0RXV1dcvbsWZ79YWFhQrnn1BZVVVWSlJTEsz85OZk0adKEch2jRo0iqqqqREpKipiYmJDly5eT8+fPk0+fPlE+Nw1NfdDV1SUbNmzgaN+4cSPR1dUVgqK/ozZrl4a2fvlb6mojO336NBk7dizp1KkTMTY2JhMnTiQREREUKKSO+toHJRk7OztSXFzM0f7o0SPSqVOnaseqqamR9PR0Qgghenp6rM9NamoqkZeX5zlOVlaWZGRk8OzPyMggjRs3ro18oSPJnz0zMzOuzzefP38mZmZmghf0FzRu3Jhln/j9/zA9Pb3BfPbqCpPJZPOrSSJKSkoSe93WlSZNmhBVVdVa/VQHfe4l+77xt+Tk5JDs7GzCYDDI/fv3SU5ODuvnzZs3LD8Njfizbds2oqamRmbMmEH27NlDDh48yPbDDdo2XMmjR49Iy5YtibKyMpGSkiLq6uqEwWAQBQWFWtn4hO2bkGRu3rxJ+vTpQ6KiosiHDx/I58+f2X5oaIQN7zrgNDQNiIqKCkhJSfHsZzKZAs0c+ezZM47dMgwGA1ZWVgLTIIl07twZSUlJ0NHRQa9evbBr1y7IyMjg2LFj9K5JCnF0dESXLl3Qs2dPAEB2djasrKwwYMAAGBkZYfv27ZCXl8fSpUt5HsPX1xePHj1C7969YWtrizVr1mDfvn349esXBg8ejODgYLHOfNuzZ08YGxtj0KBBmD17NgYOHFhjqStxQdKv2wULFmDixIk4evQo6z5WXl6O+fPnY8GCBXjy5Aml8x8+fBiZmZlo3bo1tLS0oKCgwNbPrbTV1atXsXjxYjg4OEBPT49SfaJGRUWFsCXwlYyMDK6ZivX19ZGZmSkERTWTk5MDLS0tTJkyRahZSVxdXdlKMcnLy8PExATfvn2Dq6srNm7cWO347t27o6ysDLq6upCXl+fYUV5dOVg3NzccP34c1tbW2LFjB9sxV65cWeu/YeTIkRg5cmSt3y8qyMjIICQkBJs3b0ZSUhLk5OTQpUsXaGlpCWT+ut6zCSEYMmQIpKW5L8F//frFb6lshIeHY8OGDVzLZOvr62PdunU4c+YMFi1aRKkOGpqGxIgRI7BhwwZYWFigcePGbH3fvn2Ds7MzRo0aJSR1NdOnTx8cPXoUR48e5drv7u7OyuZJJfr6+pg7dy4GDBggMWscGvHg7du3mD59Okf71KlTsXv3biEo+jvEbe0iCCoqKjB58mSEhoaiQ4cOrHKWjx8/RmhoKObMmYOjR4+isLAQsbGxGDt2rJAViybl5eXYv38/Tp8+zTWbVHVrHVHA29uba7uxsTEePnxY7VhjY2Pcv38fenp6GDRoEDZu3IgPHz4gMDAQnTt35jmuTZs21WbbT05ORqtWrWr/R9AIhejoaK6VTMrKynDr1i2O9uXLl9f62Pv27auXtprQ0dFBYmIix7q6uozM4gIhpFZlhMWZf/75B9evX/+rDIqSzoEDB/hyHPrc0/wNVd/R9HM+zbFjx6CoqIiYmBjExMSw9TEYDK7ZW2nbcCXLli2DlZUVPDw8oKKigoSEBDRq1AhTp07FkiVLahwvbN+EJDN06FAAwJAhQ9jaq57lxLmKME3DgA6+pRELhO3MriIrKwtjx47FkydPwGAwWGUfqhbvVH/pe3l5QVFREUDl3+zn5wc1NTUAwNevXymdWxRYv349SkpKAAAuLi6sANBmzZohODhYyOrElwcPHmD16tWs1ydPnkSHDh1w7do1AICRkRHc3Nx4Bt9u3boVW7duRb9+/XDq1CnExcXh3LlzcHV1BZPJxKFDh7B+/XqeTmNx4OPHj1BWVha2DKHw+3Xr6uqKUaNGsa7bkJAQIaujnszMTJw5c4ZtA4mUlBSWL1+OgIAAyuevS2n5uLg4eHt743//+x8MDAwwbdo0TJo0if/iRJiSkhKOQOWGiIqKCrKysqCtrc3WnpmZKbJ/X0hICHx8fLBv3z5YWlrCzs4OI0aMAJPJFKgOFxcXzJs3jxV8W0VpaSlcXFxqDL6dPHkyXr9+jW3btqFFixZ/5ejJzs6GsbExR7usrCzr+7S2/PjxA+/eveMw2vIqUS5KdOjQAR06dBD4vHW9Zzs7O9f4HirLVz979gympqY8+83MzODq6krZ/DQ0DZH169cjPDwcHTp0wMKFC1kOirS0NLi7u6O8vBzr1q0TskrerFu3DqampigsLMTKlSvZSgru3bsX58+fR1RUFOU6fg9SLCsr4whkpqERVUxNTXHr1i2OYLi4uDgMGDBASKpqJj4+HoWFhWybAwICAuDs7IySkhJYW1vDzc0NsrKyQlQpmhw8eBD//fcfLly4wLG54sKFC5g1axbatWsHPz8/roHZNJW4uLjAy8sLK1aswPr167Fu3Trk5OTg3LlzNa6ThMnp06dhbW0NGRkZAMCrV6/QunVr1lqztLQUhw8fZrOB/sm2bdtYNvitW7di+vTprI3LvIJ6gYa/4UfSSU5OZv3+7Nkz5Ofns16Xl5cjIiICbdq04Rj3+PHjWh2fysDQqo3Fy5cvx4IFC1BWVgZCCO7du4egoCBs374dXl5elM0vCsyYMQOOjo41vs/Hx0cAaoRD+/btsWHDBiQkJKBLly4cG8RrKsEticyYMYMvx6HPfeV3nKQHwNeFwMBAeHh4IDs7G/Hx8dDS0sL+/fuhq6uLMWPGCFseDcVkZ2f/9RjaNlxJYmIiPD09wWQyISUlhe/fv0NXVxe7du3CjBkzMG7cuFodR1i+CUlGEDZMGpr6wCBV0YE0NA0YFxeXWr2vNk7v+mBlZQUpKSl4eXlBR0cH9+7dQ2FhIVasWIE9e/ZQapzX1taucYHCYDCQlZVFmQZR5OPHj1BVVZWYxZuSkhKSkpLqnDFUWVmZlYW0tsjJySE9PR0aGhoAKncc9e3bF5s3bwYAvHjxAv/73/9QVFTEdbyenh5cXV0xefJkPHjwAL169cLp06dZAShXr17FvHnzkJubW6e/qaFQVFSEM2fO4MWLF1i1ahWaNm2KR48eoUWLFlwNtOKMJF23/fr1w6pVqziCYM+dO4cdO3YgISFBOMJqQUlJCSsQ8t69eygvL8e+fftgZ2cHJSUlYcujFEVFRdjY2MDOzg79+/cXtpw6M3fuXMTHx+Ps2bNo164dgMrA23HjxqFnz54i7WB5/fo1/Pz84Ofnh9LSUkybNg329vYCy8bMZDJRUFAAdXV1tvbIyEhMnDgR79+/r3a8vLw84uPj0bVr17+e29DQENu3b8eYMWPY7vtubm6sTPI1kZGRATs7O9y5c4etvaHsEn716hUuXLjANZMW1RmBgIZ5z27UqBFevnyJli1bcu1/+/YttLS0uGZqoqGRZHJzc+Hg4IBr166xba41NzeHu7v7X62bhMHZs2cxZ84cjiyDqqqq8PT0pDTov4qKigps3boVHh4eKCgoQHp6OnR1dbFhwwZoa2vD3t6ecg00NHXBw8MDGzduhI2NDXr37g0ASEhIQGhoKFxcXNC6dWvWe0ePHi0smRxYWlrC1NSUFUj05MkTmJiYYObMmTAwMMDu3bsxd+5cbNq0SbhCKeDPLJLu7u6YOnUqR9ZtXs+LRkZGWLp0Kezs7Lj2e3t7Y86cORg+fDjOnz/PCtIUReprH6wP7dq1w6FDhzBy5EgoKSkhMTGR1ZaQkIBTp04JXFNtkJKSwtu3b1kVVpSVlZGYmMg6hwUFBWjdujUla6WCggKYmJhASkqK54afqvWGqFHf604cYDKZLBsmN3ernJwc3NzceH63CJPfP/cnT57Epk2b8OLFCwBA69at4eLiIvbPakwmE1paWjA2Nub6/1fF2bNnBahKsFS3ppFEn2J9KCsr47CrVLeBWxLP/Z9+n6KiIigrK3MkVhD1TPnC5OjRo9i4cSOWLl2KrVu3IiUlBbq6uvDz84O/vz8doEbDFdo2XIm6ujru3LkDPT09dOjQAW5ubjA3N0daWhr+97//1Sq5ibB9EzQ0NKIJHXxLQ8NH1NTUEBkZCSMjI6ioqODevXvo2LEjIiMjsWLFilrvZqaCV69ewdXVFceOHROaBqqoreFKHHcn19fAyY+Fbps2bXD27Fn07NkTFRUVUFVVxalTp1ilrFNTU9G7d298/vyZ63hZWVlkZmaygndlZWWRnJzMMjS/fv0aOjo6Yv3An5ycjCFDhqBJkybIycnB8+fPoauri/Xr1yMvL08gGVBphENISAhWr16NRYsWsTl03d3dsWPHDrbSbkZGRpRo4EcQ2fPnz+Ht7Y3AwEAUFRVh2LBhuHDhAiV6RYFz587Bz88PV65cgba2Nuzs7DB9+nQ2B3xD4PPnz7CwsMCDBw/Qtm1bAJXPCwMHDkRYWBiaNGkiXIG1JCYmBps2bUJsbCw+fPgAVVVVyuaqum9+/vwZysrKbPfQ8vJyFBcXY968eXB3d6/2OCYmJjhy5Ajruq8NVRlpTp06hU2bNmHv3r2wt7eHl5cXXrx4wcpIU5tM1P369YO0tDScnJzQqlUrjs0OdQkKFhQ3b97E6NGjoauri7S0NHTu3Bk5OTkghMDExASRkZGUzk/FPfvLly84efIkvL298eDBAwpUVzpV8/PzOQLGq6AymICGRhz49OkTMjMzQQiBnp4epfcaflNaWopr164hIyMDQOXmR3Nzc47s7VTh6uoKf39/uLq6Yvbs2SzHYEhICA4cOID4+HiB6KCh+VtqW1lB1DYutWrVChcvXkT37t0BVGbBjomJQVxcHAAgNDQUzs7OePbsmTBlUoKZmVmt3scrIEFOTg7Pnz/nWQUiNzcXurq6+Pbtm8gF3opSAKSCggJSU1OhqamJVq1a4fLlyzAxMUFWVhaMjY152geFDZPJRH5+Piv49s8A5to8Lw8ePBjh4eEca+kvX77A2tq62rVKQ93wU5vrjsFgUL5OEya5ubkghEBXVxf37t1jW3PJyMigefPmbBWvqiMzMxMvXrzAwIEDIScnx9ogSxV/fu6BymfH4uJitjZxZsGCBQgKCoKWlhZmzZqFqVOnomnTpsKWRdOAKCkpgaOjI06fPo3CwkKOflF6ThQF/P39a/U+fmUXFkcMDQ2xbds2WFtbsz2vpKSkwNTUFB8+fBC2RBoB8LcBoLRtuJLhw4dj5syZmDJlCmbPno3k5GQsXrwYgYGB+PTpE+7evVvteGH7JiSdW7duwdPTE1lZWQgNDUWbNm0QGBgIHR2dBp0oiUY8oINvacQeQTizq1BVVcWjR4+go6ODdu3awcvLC2ZmZnjx4gW6dOmC0tJSSuevjqSkJJiYmIjlQ5Mk706ur2OBHwtdW1tbfPnyBUeOHGE5cfLz81kly8PCwuDq6oqkpCSu4/lh3G7oDB06FCYmJti1axfb33/nzh1MmTIFOTk5wpZIGWVlZXBzc0NUVBTX0ue1yeDYkKnJoctgMCjNRJmcnIyhQ4dCRUWFL0Fk5eXluHjxInx8fMQ6+LaK9+/fIzAwEH5+fkhNTYW5uTns7OwwevRoSEtLC1terSCE4MaNG0hKSoKcnByMjIygq6vbIDbslJWV4cyZM/Dx8UFCQgJGjx4Nf39/Skvo+vv7gxACOzs7HDhwgM2ZLSMjA21tbfTp06fG41y/fh0uLi7YunUr17Jy3LJi8DMjjYKCAh4+fAh9ff1avV+U6NmzJywtLeHi4sK6ZzZv3hy2trawsLCAg4MDpfPz854dFRUFHx8fhIeHQ0VFBWPHjq0xcLuuMJlMdO7cmed3069fv/D06VOxft6ioZE0IiMjsXDhQiQkJHDcVz5//oy+ffvCw8OD0go9QGUpVU9PTwwZMoTtezMtLQ19+vTBp0+fKJ2fhkbSaNy4MTIyMlgbnPv37w9LS0usW7cOAJCTk4MuXbrg69evwpQpkjRt2hTR0dE8N74+efIEAwcOFMnvLVEKgOzYsSMCAgLQq1cv9O/fH6NGjYKTkxNCQkKwaNEivHv3jnINdYEf9klugYwA8O7dO7Rp0wY/f/6sUUdD3vBDU3cKCwthY2ODqKgoMBgMZGRkQFdXF3Z2dlBVVcXevXspmZdXVR9J4/v37wgPD4ePjw/u3LmDkSNHwt7eHsOHD5eIymw09WPBggWIiorC5s2bMW3aNLi7u+P169fw9PTEjh07YGtrK2yJNGKGnJwc0tLSoKWlxfa8kpGRASMjI3z79k3YEmkopi4BoLRtuJIHDx7g69evMDMzw7t37zB9+nRWJlxvb29069at2vHC9k1IMmFhYZg2bRpsbW0RGBiIZ8+eQVdXF4cPH8aVK1dw5coVYUukkXAaRlQADU0d4ObMpprOnTsjKSkJOjo66NWrF3bt2gUZGRkcO3ZMKGW+JAUHBwcEBQUhOztb4nYn17d8CD92j27duhXDhg2DlpYWpKSkcOjQIVbgLQAEBgZi8ODB1R7j2bNnyM/PB1AZCJaWlobi4mIAkIhdmvfv34enpydHe5s2bVjnRVyxt7fH9evXMWHCBPTs2VPiDJrZ2dlCnX/58uWYOXMmK4isihEjRmDKlCl/fTwpKSlYW1vD2tqajypFF3V1dSxfvhzLly+Hm5sbVq1ahStXrkBNTQ3z5s2Dk5OTwLLK1RUGg4Hhw4dj+PDhrLakpCR4e3uLbPDt3bt34e3tjdOnT7OcUWFhYQJxSlbdN3V0dNC3b1+OoNnaYmFhAQAYMmQIW3t1wfa/bzCytbWFra1tnTPSGBoaNtj7a2pqKoKCggAA0tLS+PbtGxQVFeHq6ooxY8ZQbuCq7z379evX8PPzg6+vL4qKivDp0yecOnUKNjY2lN4DnZ2da3yPIMrP09DQCI4DBw5g9uzZXDd0qKioYO7cudi3bx/lwbevX79G+/btOdorKipqFYREQyNo4uPjUVhYiFGjRrHaAgIC4OzsjJKSElhbW8PNzY3SDV/1oUWLFsjOzoaGhgZ+/PiBR48ewcXFhdX/9evXOj/DNnSysrIwb948XL9+nWt/nz59cPToURw9epRrv7u7e6022gkDUSovPHbsWNy8eRO9evXCokWLMHXqVHh7eyMvLw/Lli0TtjxKSE5OZv3+u40TqNykHBERUevKQqqqqujRowffNVJNXFycxGeb8vf3h5qaGqsa3OrVq3Hs2DEYGhqyMqvyYtmyZWjUqBHy8vLYqmBNnDgRy5cvpyz4FgA6dOhQ41pU3Mu/y8rKYvLkyZg8eTJyc3Ph5+eH+fPnswKRFBUVhS2RcugS2nXn4sWLCAgIgKmpKWbNmoUBAwagffv20NLSwsmTJ2sMvpX0c//161c2myeTyZSIa64+6OjoIDExkeO+EhERwXYPoRFf1qxZg5UrV7ICQMPCwtgCQLlB24YrqaoQAwDNmzdHRETEX40Xtm9CktmyZQs8PDwwffp0BAcHs9r79euHLVu2CFEZDU0ldPAtjVghLGd2FevXr0dJSQmAytKKo0aNwoABA9CsWTOEhIRQPr+k4u7ujn379rF2J69Zs4benfz/qcmx8Cd1Wehqa2sjNTUVT58+hbq6OkfZdRcXF1Y5c14MGTKEbd4qJ9fvWT/FGVlZWXz58oWjPT09XewzD1y6dAlXrlxBv379hC1FKFRneBcEkhz4zQ8KCgrg7+8PPz8/5ObmYsKECbC3t8erV6+wc+dOJCQk1Pr7l6Z2dOrUCe/evcOUKVMQExODrl27CkXHoEGDWL+XlZVxGMe5BTr9Tl2d43/eD+Xl5esU4L1z506sXr0a27Ztq3XmXVFBQUGBdb5btWqFFy9eoFOnTgAEs2GnrvfssLAweHt7IzY2FpaWlti7dy8sLS2hoKCALl26UP6sUxsDKw0NjXiRlJSEnTt38uwfPnw49uzZQ7kOQ0ND3Lp1i+O598yZMzA2NqZ8fhqav8XV1RWmpqYsu8STJ09gb2+PmTNnwsDAALt370br1q2xadMm4QrlwYgRI+Dk5ISdO3fi3LlzkJeXZwuyT05ORrt27YSoUHh8/foVN2/e5Nm/bt06mJqaorCwECtXroS+vj4IIUhNTcXevXtx/vx5kQpy5YYoBEDu2LGD9fvEiROhqamJ+Ph46OnpwcrKSojKaubatWus6iYVFRW4efMmUlJSAABFRUU8x3Xr1g0MBgMMBoNrAgI5OTm4ublRollUGDx4MNq0aYPJkyfD1taWtUaTJLZt28YK3o+Pj8fhw4dx4MABXLp0CcuWLUN4eDjPsdevX8e1a9c4bOh6enrIzc2lVLeLiwtbVR9Jh8lksnwS4p79r4qaMijSVM/Hjx9ZyZeUlZVZwer9+/evMQhLEs99YmIi1q5dy8pQ2Lp1a7aqsQwGA/Hx8Q1yI4qgWL58ORYsWICysjIQQnDv3j0EBQVh+/bt8PLyErY8GgFQlwBQ2jZcyeDBgxEeHo4mTZqwtX/58gXW1tY1VgoRtm9Cknn+/DkGDhzI0a6iolLtWo2GRlDQwbc0YoGwndlVmJubs35v37490tLS8PHjR6iqqop98KCwoXcnc6cmxwK/FrrS0tI8A6BqCowSduZPUWD06NFwdXXF6dOnAVSe97y8PDg6Oor9TsM2bdqwZVyVNAICAqrtnz59OqXzS3Lgd30IDw+Hr68vrl27BkNDQ8yfPx9Tp05lW7D37duX3mlOAampqVBQUEBAQAACAwN5vo/qrCylpaVYvXo1Tp8+jcLCQo7+mhw0vwfv/g38ykgzdOhQAH+XeVdU6N27N+Li4mBgYIARI0ZgxYoVePLkCcLDw9G7d2/K56/rPXvixIlwdHRESEiIyN33vnz5gpMnT8Lb2xsPHjwQthwaGho+UVBQUG12S2lpabx//55yHRs3bsSMGTPw+vVrVFRUIDw8HM+fP0dAQAAuXbpE+fw0NH9LYmIiNm/ezHodHByMXr164fjx4wAADQ0NODs7i2zw7ebNmzFu3DgMGjQIioqK8Pf3h4yMDKvfx8eHreoFzf/Rt29fhISEYM6cOQgLC2PrU1VVRVBQkMhvHBbFAMg+ffqIbMbgP/mzQtjcuXPZXvNai2VnZ4MQAl1dXdy7d4/NniIjI4PmzZtDSkqK/4JFiDdv3iA4OBhBQUHYsWMHjIyMYGtri8mTJ9eYlEFcePnyJSvb/7lz5zBhwgTMmTMH/fr1g6mpabVjS0pKuG6s/fjxI+WZ1idNmvTX1XTEje/fv7MSu8TFxWHUqFE4fPgwLCwswGQyhS2PcuqSQZHm/9DV1UV2djY0NTWhr6+P06dPo2fPnrh48SJHcNefSOK5d3Nz49goFBgYiDZt2oAQAh8fHxw6dKhau6+k8++//0JOTg7r169HaWkppkyZgtatW+PgwYOYNGmSsOXRCAB+B4BKkm04OjqaI5EKUJlg5datWzWOF7ZvQpJp2bIlMjMzoa2tzdYeFxdHVyCnEQno4FsasUCUndlNmzYVyDzjxo2rtl+SdnxI4u7kusLPhW55eTn8/Pxw8+ZNvHv3DhUVFWz9vHaLCTvzpyiwd+9eTJgwAc2bN8e3b98waNAg5Ofno0+fPti6dauw5VHK3r174ejoCA8PD4n8LCxZsoTt9c+fP1FaWgoZGRnIy8tTHnwryYHf9WHWrFmYNGkSbt++zXNzQuvWrbFu3ToBKxN/fH19hS0BALBq1SpERUXh6NGjmDZtGtzd3fH69Wt4enqyZXqqjqKiInh7eyM1NRVAZVZfOzu7ajPO8Csjjahn7KqOffv2obi4GEDl+SguLkZISAj09PQEUo6vrvdse3t7uLu7Izo6GtOmTcPEiROhqqpKud7qiIqKgo+PD8LDw6GiooKxY8cKVQ8NDQ1/adOmDVJSUlhBIH+SnJyMVq1aUa5jzJgxuHjxIlxdXaGgoICNGzfCxMQEFy9exLBhwyifn4bmb/n06RNatGjBeh0TEwNLS0vW6x49euDly5fCkFYr1NTUEBsbi8+fP0NRUZEj4C80NFRiN6jXhrFjx8Lc3BzXrl1DRkYGgMrMk+bm5nWqOCFoRCUA8vnz53Bzc2OtdQwMDLBo0SJ07NhRYBr+lj/tmH9DlT2rPsdo6KipqWHhwoVYuHAhsrOzcerUKfj7+2PNmjUYOHBgjVnExAFFRUUUFhZCU1MT169fx/LlywEAjRs3xrdv37iOefPmDVq3bo0BAwYgICCAtfmDwWCgoqICu3btgpmZGWWa6YQxwPz58xEcHAwNDQ3Y2dkhKCgIampqwpYlUOgS2nUjKysL2tramDVrFpKSkjBo0CA4OTnBysoKhw8fxs+fP2u0U0niub9z5w4WLlzI1ta7d29W4JScnBxsbGyEIa1BYWtrC1tbW5SWlqK4uFjiN1FIGvwKAJUk23BycjLr92fPnrFV/iwvL0dERATatGlT43GE7ZuQZGbPno0lS5bAx8cHDAYDb968QXx8PFauXIkNGzYIWx4NDRjk9zrfNDQNlLlz5yIkJASdOnVic2Y3atQISUlJMDQ0FIiOsrIyuLm5ISoqimvw4aNHjyibe9asWbV6n6gErfAbbruTZ82aJTG7k3mRlJQEExMTnkHIBgYGOHXqFKvcp5KSEpKSklgL3bt378LGxqZW5a0WLlwIPz8/jBw5Eq1ateIw3u3fv5/ruLy8vFr9LZqamrV6X0MmLi4OycnJKC4uhomJCSszoTjz/v172NjYIDY2FvLy8hzZuajOXimKZGRkwMHBAatWrWLLqE4Fnz9/xoQJE/DgwQN8/foVrVu3Rn5+Pnr37o2rV69CQUGB0vkbKqWlpQ3C8cqL2mzYiYmJoTewVIOmpiYCAgJgamoKZWVlPHr0CO3bt0dgYCCCgoJYGeV58eDBA5ibm0NOTg49e/YEANy/fx/fvn3D9evXuZaWYzKZyM/Pp42pIkJd7tnfvn3D6dOn4ePjg7t378Lc3ByXL19GYmIiOnfuLADVwOvXr+Hn5wdfX18UFRXh06dPOHXqFGxsbGjHKw2NmLFo0SJER0fj/v37aNy4MVvft2/f0LNnT5iZmeHQoUNCUkhDI5poaWkhMDAQAwcOxI8fP9CkSRNcvHiRVTHgyZMnGDRokESuVRs6NdnIIiMjsXDhQiQkJEBZWZmt7/Pnz+jbty88PDwwYMAAQcitN1UBkEFBQUhLSxNYAGRYWBgmTZqE7t27szLeJiQk4P79+wgODhb5jb5fvnzh+P+vIjMzk+emFgDw9/eHmpoaRo4cCQBYvXo1jh07BkNDQwQFBUnUpvPy8nJcvXoVGzZsQHJyskTYF2xtbZGWlgZjY2MEBQUhLy8PzZo1w4ULF7B27VqkpKRwjFFVVYW7uzu6du2KwYMHw8TEBJGRkRg9ejSePn2Kjx8/4vbt22jXrh0lmmk7Q+U50NTUhLGxcbVr4vDwcAGqEiwtW7ZEVFQUDAwMYGhoiB07dmD06NFISkpCv379WEFGNOxISUnh7du3rOtn4sSJOHToEMrKyvDw4UO0b98eRkZG1R5DEs+9vLw80tPTWZuC9u/fD3t7e9a9Ny8vDx06dEBZWZkwZYo8v379QnR0NF68eIEpU6ZASUkJb968gbKyMr3RTgLIyspCcXExjIyMUFJSghUrVuDOnTusANDqnjkl1TZclbgNqKz+9ydycnJwc3ODnZ2doKXR1BJCCLZt24bt27ezqjjLyspi5cqVbNWLaGiEBR18SyM2iIIz29bWFtevX8eECRPQokULjocUZ2dngeiQNP7cnWxraytxu5N5UZNjgZ8LXTU1NQQEBGDEiBF/pfH3LCxVt6Tfr52GUAKbpu4MHToUeXl5sLe35/q9+WfJP0nhwYMHmDp1KtLS0gQy3+3bt5GUlCRRgd/14U/jahWFhYVo3ry5yH9fidOGnYcPH7KyKRkaGnINWqUCRUVFPHv2DJqammjbti3Cw8PRs2dPZGdno0uXLjUaxwcMGID27dvj+PHjkJauLEby69cv/Pvvv8jKykJsbCzHGF6fu7py69YteHp6IisrC6GhoWjTpg0CAwOho6PDkRVflNDV1cX9+/fRrFkztvaioiKYmJggKytLSMr+joyMDPj6+sLf3x/FxcUYOXIkJkyYUGNwfF0JCwuDt7c3YmNjYWlpialTp8LS0hIKCgoC3axIQ0MjOAoKCmBiYgIpKSksXLiQlW0wLS0N7u7uKC8vx6NHj9gyfNLQ0AAODg5ISkrCzp07ce7cOfj7++PNmzeQkZEBAJw8eRIHDhzA/fv3hayU5k9qCpwqLS1FRkYGz/Xa6NGjYWZmhmXLlnHtP3ToEKKionD27Fm+6BUEwgiAbNeuHWxtbeHq6srW7uzsjBMnTuDFixeUa6gPAwYMwI0bNzg2rjx//hxDhgzBq1eveI7t2LEjjh49isGDByM+Ph5DhgzBgQMHcOnSJUhLS4t18F4Vt2/fxsmTJ3HmzBmUlZVhzJgxYl0+/XeKioqwfv16vHz5Eg4ODqy/2dnZGTIyMlyrIx05cgSOjo6wsLCAh4cHPDw82OxzCxYsEEilAklm5syZtQo2agg2srpibW2NkSNHYvbs2Vi5ciXOnz+PmTNnIjw8HKqqqvjvv/+ELVEk+TN4/c/ENrVBEs9906ZNcfHiRfTr149r/+3bt2FlZUVvdKuG3NxcWFhYIC8vD9+/f0d6ejp0dXWxZMkSfP/+HR4eHsKWSCOCSLptODc3F4QQ6Orq4t69e1BXV2f1ycjIoHnz5hxVY7ghLr6JhsyPHz+QmZmJ4uJiGBoa0hsOaEQGOviWRiwRtDO7ChUVFVy5coXnooGGGiR5d3J9HQv8XOi2bt0a0dHR6NChQ+3E/3+kpaXRtm1bzJw5E1ZWVqwgpD/p2rXrXx1X1Dl06BDmzJmDxo0b15htavHixQJSJXjk5eURHx8vdv+/9SUxMREDBw7Ely9fKDn+t2/fcPPmTYwaNQoAsGbNGnz//p3VLy0tDVdXVw5nE00lvDKDvHnzBu3ateNZTpCGf7x79w6TJk1CdHQ0mjRpAqDSwGFmZobg4GA24wkVGBkZwc3NDYMGDcLQoUPRrVs37NmzB4cOHcKuXbuqdcgClTupHz9+DH19fbb2Z8+eoXv37qydu7/Dz4w0YWFhmDZtGmxtbREYGIhnz55BV1cXhw8fxpUrV2rM3CtMeJ2HgoICaGpqsn2X8Qsq79kVFRW4fPkyvL29cfXqVUr0A5Xf646OjnBycoKSkhKrXdCVQmhoaARLbm4uHBwccO3aNbaNjubm5nB3d4eOjg4l8zZt2hTp6elQU1ODqqpqtWtW2qlKI2p8+PAB48aNQ1xcHBQVFeHv789WfnPIkCHo3bs3tm7dKkSVNNxwcXGp1ft4JUfQ0tJCREQEDAwMuPanpaVh+PDhta7gJEyEGQApLy+P5ORkjgyxGRkZ6Nq1K9e1jihhaWkJBoOBCxcusGyUqampGDx4MGxsbHDw4EGeY+Xl5ZGWlgZNTU04Ojri7du3CAgIwNOnT2Fqaor3798L6s8QOGvWrEFwcDDevHmDYcOGwdbWFmPGjGnQVYMERXZ2Nuzt7fHs2TMcO3YMo0ePFrYkGgmjPhkUJRl+BN9K4rkfMmQITExMsHv3bq79K1asQGJiIm7evClgZQ0Ha2trKCkpwdvbG82aNWN97qKjozF79mxkZGQIWyKNCELbhvmDMHwTNDQ0DQPuEU40NA0cPT09bNu2DVu2bGE5sydPnkz5Da9NmzZsDyw0gmH69OliXQqhOqytres13tjYGOfOneMZfBseHg5jY+NaHWvFihU4ePAgDh8+/Ff/H69evYK/vz98fX3h4eGBqVOnwt7enqezQ1zYv38/bG1t0bhxY+zfv5/n+xgMhlgH3+rr60t0oOKFCxfYXhNC8PbtWxw+fJjSjRz+/v64fPkyK/j28OHD6NSpE+Tk5ABUOhVbtWrFM+OPpFIVdMdgMODl5cW2o7K8vByxsbEcwZQ01LBo0SJ8/foVT58+Zd0vnj17hhkzZmDx4sUICgqidP5Zs2YhKSkJgwYNgpOTE6ysrHD48GH8+PGj2u/0KpSVlZGXl8fxeXn58iXPZ8mKigq+aAeALVu2wMPDA9OnT0dwcDCrvV+/ftiyZQvf5uEnv39fXrt2DSoqKqzX5eXluHnzJrS1tSmZm8p7NpPJhJWVFaysrPDu3bv6SuWJvb093N3dER0djWnTpmHixIlQVVWlbD4aGhrRQEtLC1euXMGnT5+QmZkJQgj09PQov/7379/Pup/t379fYtfrNA0TNTU1xMbG4vPnz1BUVOTIfhMaGkpnVhFR6ltxrKCgAI0aNeLZLy0tLfLBk38GQB48eFDgAZCmpqa4desWR/BtXFwcBgwYIDAddSU8PBxDhw6Fra0tgoOD8fTpUwwZMgS2trbYt29ftWMVFRVRWFgITU1NXL9+HcuXLwcANG7cWOxtX7GxsVi1ahVsbGwkuiLdp0+f4O3tzarQY2BgADs7OzRt2pTnGB0dHURGRuLw4cMYP348DAwMOJJTPHr0iFLdNJLN78GiCgoKdNbMWsJgMDjWOX+77pHEcz9//nxMmjQJ2tracHBwAJPJBFBp2zty5Ajc3Nxw6tQpIasUbW7duoU7d+6wKnNUoa2tjdevXwtJFQ3V1HeTM20brsTf3x9qamoYOXIkAGD16tU4duwYDA0NERQUxHPTgzB9EzSVlJWVwc3NDVFRUXj37h2Hv4x+XqYRNnTmWxqJ4d27d3wr08uLq1ev4tChQ/Dw8BDLHYk04kdYWBgmTZqEAwcOcF3orlixAqdOncKECRNqPNbYsWMRFRWFpk2bolOnThwOi9pkHo6Li4Ovry9CQ0NhaGgIe3t72Nvbs3TRiB/Xr1+Hi4sLtm7dii5dunB8bpSVlYWkTDD8+dlmMBhQV1fH4MGDsXfvXspKyw0YMACrV6+GlZUVAM6d+SdOnIC7uzvi4+Mpmb+hUpUdLjc3F23btmVzxMvIyEBbWxuurq7o1auXsCRKDCoqKvjvv//Qo0cPtvZ79+5h+PDhKCoqEqie3NxcPHz4EHp6eujSpUuN71+8eDHOnj2LPXv2oG/fvgAqs1OtXLkS48ePrzabEj+Ql5fHs2fPoK2tzXb9Z2VlwdDQEGVlZZTOXxeqvi8ZDAb+XMI2atQI2tra2Lt3L2tTgagSGhqKoKAgpKenQ0ZGBh06dMCsWbNgbm5O+dzfvn3D6dOn4ePjg7t378Lc3ByXL19GYmIiOnfuTPn8NDQ0NDQ0NDSiTLt27bB3716eG93Dw8OxcuVKkS4l2q9fP9ja2go1ANLDwwMbN26EjY0NevfuDQBISEhAaGgoXFxc0Lp1a9Z7RTXDZ1FREUxNTaGnp4fY2FhMnz6dZ4a+37G1tUVaWhqMjY0RFBSEvLw8NGvWDBcuXMDatWuRkpIiAPU0wiI2NhZWVlZQUVFB9+7dAQAPHz5EUVERLl68iIEDB/Icm5ubi1mzZiElJQVz587lCL6t7+YCGpraUlxczBHMIu72+brCZDJhaWkJWVlZAMDFixcxePBgKCgosL2vthU5JencOzo6Yvfu3VBSUmL5I6qyAC9fvrxW91xJRlVVFbdv34ahoSGbXTcuLg7jx49HQUGBsCXSUIC/vz8mTZoEWVlZ+Pv7V/veGTNmcG2nbcNAx44dcfToUQwePBjx8fEYMmQIDhw4gEuXLkFaWprnd7a4+CYaMra2trh+/TomTJiAFi1acASg08/LNMKGDr6lESuE6cwGgPfv38PGxgaxsbGQl5fnCCKjyynSiCL8WujOmjWr2n5fX99aayooKMDkyZMRExOD9+/fV5sdQByIi4tD//79hS1DKPy+YPkdQggYDAbKy8uFIUvsadWqFeLj41k7MdXV1XH//n3W6/T0dPTo0QOfP38WnkgRxszMDOHh4RK5M1hUUFJSwq1bt9CtWze29sePH2PQoEH48uULJfNGRkZi4cKFSEhI4DCAf/78GX379oWHh0eNGZ1+/PiBVatWwcPDA79+/QIhBDIyMpg/fz62bt3KykJNFbq6ujh27BiGDh3KZqQNCAjAjh078OzZM0rnrw86Ojq4f/++0IIJ6nrPrqiowOTJkxEaGooOHTqwsh6npqYiMzMTc+bMwdGjR1FYWIjY2Fi28tZUkJGRAV9fX/j7+6O4uBgjR47EhAkTMG7cOErnpaGhkTyGDh2KqVOnYty4cWLrPKahoRENzMzMasw4x2AweJYxXrRoEaKjo3H//n00btyYre/bt2/o2bMnzMzMWBVRaLhT2w30omTz4bZ+fPv2LYYNG4ZRo0Zhx44drPbq7mVFRUVYv349Xr58CQcHB1hYWACodATLyMhg3bp1/BcvRC5cuABLS0s0atSIo7LTn4hqoDU/6dKlC/r06YOjR4+yNmuXl5dj/vz5uHPnDp48ecJ13PHjx7FixQoMHToUnp6eUFdXF6RsGhpkZ2dj4cKFiI6OZtuMTdvnq6cmf1gV1fnFJPHcp6SkoHPnzrh79y5OnTqFjIwMAJUVbSdPnszauEPDm4kTJ0JFRQXHjh2DkpISkpOToa6ujjFjxkBTU/OvfLE0kouk2obl5eWRlpYGTU1NODo64u3btwgICMDTp09hampaY6UTYfsmJBkVFRVcuXKF0qqxNDT1gQ6+pRELRMWZPXToUOTl5cHe3p7rjgteO41oaOpKfR0LorbQvXPnDnx8fBAaGoqOHTvCzs4Oc+bMEfvMtzIyMmjTpg0mT54MW1tbdOrUSdiSBEZMTAzPvidPnmDhwoUCVCM5yMnJITExER07duTan5aWhm7duolk9ksaGgAYM2YMioqKEBQUxMqc9Pr1a9ja2kJVVRVnz56lZN7Ro0fDzMwMy5Yt49p/6NAhREVF1Xr+0tJSvHjxAkBlpq2jR49i9+7dyM/P55tmbmzfvh0nTpyAj48Phg0bhitXriA3NxfLli3Dhg0bsGjRIkrnrwvx8fEoLCxk2z0eEBAAZ2dnlJSUwNraGm5ubqyMI1RR13v2/v37sWXLFvj7+3PsgL9w4QJmzZqFNWvWwM/PD9OnT8fq1aupkM9BRUUFLl++DG9vb1y9ehXfv38XyLw0NDSSw5IlS3D69Gl8/vwZI0eOxNSpUzFixIhqS7vT0NDQ1AVez+gA8PXrV5w6dQrfv3/nGchSUFAAExMTSElJYeHChaz1clpaGtzd3VFeXo5Hjx6hRYsWlOivK3QAZP1hMplc7atVrrOqDFfiGghVV5hMJvLz89G8efNqbbeSct542dqeP3+Obt264du3bxxjLCwscO/ePRw4cADTp08XlFQaGjb69esHQgiWLFnC1a85aNAgISkTfyTx3DOZTPTo0QP//vsvJk2aBCUlJWFLanC8evUK5ubmIIQgIyMD3bt3R0ZGBtTU1BAbG0t5FWAa4cMr8QiDwYCsrCxkZGRqfSxJsw03b94c165dg7GxMYyNjbF8+XJMmzYNL168QNeuXVFcXMx1nKj4JiQZQ0NDBAcHw8jISNhSaGi4Qgff0ogFouLMlpeXR3x8PLp27UrJ8Wlo/qS+jgVRWOhW7Srz9fXFp0+fYGtrCzs7O4kpcQEAHz58QHBwMIKCghAfHw8jIyPY2tpi8uTJaNu2rbDlCZSvX78iKCgIXl5eePjwoVgb50tKSrBz506Eh4cjJycHDAYDOjo6mDBhAlauXAl5eXnK5tbT08OOHTswfvx4rv2nT5/G2rVrkZmZSZmGhkx5eTn8/Pxw8+ZNvHv3jqMkWGRkpJCUSQ4vX77E6NGj8fTpU2hoaLDaOnfujAsXLlD23amlpYWIiAgYGBhw7U9LS8Pw4cORl5fHtf/79+/YtGkTbty4AVlZWaxatQrW1tbw9fXF+vXrISUlhQULFsDR0ZES/VUQQrBt2zZs374dpaWlAABZWVmsXLkSmzdvpnTuumJhYQEzMzPWuXny5AlMTEwwc+ZMGBgYYPfu3Zg7dy42bdpEqY663rONjIywdOlS2NnZce339vbGnDlzMHz4cJw/f/6vjKT84t27d7SBnoaGhhIqKirw33//4dSpUzh79iykpKQwYcIE2NraiqVDmYaGRnT49esX3N3dsXXrVqioqGDz5s2YNGkSz/fn5ubCwcEB165dYwu8NDc3h7u7O3R0dAQlvdaISgBkQ3ZIV7cx/E9qum99+vQJ3t7eSE1NBQAYGBjAzs5O7Kt60VQG0VWt8X/n3Llz2LFjBxISEjjGDBs2DL6+vhJn/6URLRQVFfHw4UOeSRpoqEMSz/2tW7fg6+uLM2fOoKKiAhMmTIC9vX2NVcRo2Pn16xeCg4ORnJyM4uJimJiYwNbWlvJqajSiAa+NY1W0bdsWM2fOhLOz818lt5IE27CtrS3S0tJgbGyMoKAg5OXloVmzZrhw4QLWrl2LlJQUruNExTchyVy9ehWHDh2Ch4cHtLS0hC2HhoYDOviWRiwQFWe2iYkJjhw5QpfFoBEqf+NY4OdCV0dHp9qH/aysLK7tjRo1Qps2bTBjxgyMHj2aZwYkSdnJlJ2djVOnTiEoKAhpaWkYOHCgRATyxcbGwtvbG2FhYWjdujXGjRuH8ePHo0ePHsKWRgk/fvxA3759kZKSAktLS+jr64MQgtTUVERERMDExASxsbGUZQRbsmQJ/vvvPzx8+JBrOc3u3btj6NChOHjwICXzN3QWLlwIPz8/jBw5Eq1ateL47tu/f7+QlEkWhBD8999/SEtLA1Dp1Bw6dCilczZu3BgpKSlo37491/7MzEx06dKFa0YbAHB0dISnpyeGDh2KO3fu4P3795g1axYSEhKwdu1a/PPPP6zylILgx48fyMzMRHFxMQwNDaGoqCiwuf+WVq1a4eLFi+jevTsAYN26dYiJiUFcXBwAIDQ0FM7Oznj27JnANP3NPVtOTg7Pnz+HpqYm1/7c3Fzo6uri27dvlAbehoaGIigoCOnp6ZCRkUGHDh0wa9YsmJubUzYnDQ0Nze+UlZXh4sWL2Lp1K548eSLWm+1oaGiEy8mTJ7Fx40Z8+/YN69evx5w5cyAtLV2rsZ8+fUJmZiYIIdDT04OqqirFahs+lpaWMDU1lWiHdGxsLKysrKCiosJatzx8+BBFRUW4ePEiBg4cKGSFwuHjx48SEXwcEhKC1atXY9GiRSz/UEJCAtzd3bFjxw62TbySYuemaRiYmZlh3bp1lNvUaDiR5HNfUlKC06dPw8/PD7du3UL79u1hb2+PGTNmoGXLlsKWR0Mj0gQEBGDdunWYOXMmevbsCQC4d+8e/P39sX79erx//x579uzBqlWrsHbtWraxkm4bLioqwvr16/Hy5Us4ODjAwsICAODs7AwZGRmsW7eO6zhR9E1IGu/fv4eNjQ1iY2MhLy/P4T//+PGjkJTR0FRCB9/SiAWi4sy+fv06XFxcsHXrVnTp0oXjS19ZWZmyuWlogLo7Fvix0P0zSO/nz594/PgxIiIisGrVKjg5OXEd9/uuu6oAtj9vTZJSnqyK8vJyXL16FRs2bEBycrLY/u35+fnw8/ODt7c3vnz5AhsbG3h4eCApKQmGhobClkcpBw8exPbt2xETE8Oxsz0tLQ2mpqZYt24dZaXfCwoK0K1bN8jIyGDhwoXo0KEDgMpSeIcPH8avX7/w+PFjkSunKSqoqakhICAAI0aMELYUGgHTrl077N27lyOTTRXh4eFYuXIlzw0nurq6OHDgAEaPHo2UlBQYGRlh5syZ8Pb2rnYDC01l4HNGRgYr03H//v1haWnJMojl5OSgS5cu+Pr1q0B11fae3bRpU0RHR/N0sj558gQDBw7Ep0+fKNFZUVGByZMnIzQ0FB06dIC+vj4AIDU1FZmZmZgzZw6OHj2KwsJCxMbGYuzYsZTooKGhkWzy8/MRHByMEydO4NGjR+jZsyfXLHA0NDQ09SEiIgJOTk7Izs7GypUrsXz5cigoKAhbltChOgBSXBzSvr6+UFRUxD///MPWHhoaitLSUsyYMYPn2C5duqBPnz44evQoa1NleXk55s+fjzt37uDJkyeUahcGpqam8PPzg7a2Ntf+8PBwLFiwAG/fvhWsMCFQU2Y5BoMBQojE2blpRJ8XL15g3rx5mDp1Kjp37szh16SDxamDPveVZGZmwtfXF4GBgcjPz4eFhQUuXLggbFkii6amJkxNTTFo0CCYmZlBV1dX2JJoBMyQIUMwd+5c2NjYsLWfPn0anp6euHnzJgIDA7F161ZW4hLaNlw/RNU3IUkMHToUeXl5sLe3R4sWLTj8adWt02hoBAKhoREDVFVVSVJSEs/+5ORk0qRJE8p1MBgMwmAwCJPJZPupaqOhoYqrV6+Srl27EmVlZeLq6kqKi4vrfKyMjAyydu1aoqGhQRo1akSsrKzqpe3w4cNk5syZPPtzcnJq9SMJxMXFEQcHB6Kurk6UlJTI1KlTydWrV4UtixJGjRpFlJWVyeTJk8mlS5fIr1+/CCGESEtLk6dPnwpZHfUMHDiQHD58mGf/oUOHyMCBAynVkJWVRczNzVn3qap7lbm5OXnx4gWlczd0WrVqRZ4/fy5sGRLJnTt3yMWLF9na/P39iba2NlFXVyezZ88mZWVllM2/cOFC0rlzZ/Lt2zeOvtLSUtK5c2eyaNEinuMbNWpEXr16xXrduHFjkpycTIlWXkRGRpI9e/aQuLg4QgghHh4eRENDg6ipqZF///2XlJaWClRPbdHU1CQxMTGEEEK+f/9O5OTkyH///cfqT05OJqqqqgLT87f37BEjRpB58+bx7J87dy6xtLSkQiohhJB9+/aRpk2bclw/hBBy/vx50rRpU7J7927SqVMnsnPnTsp00NDQSB6fP38mPj4+ZOjQoURaWpp06NCBuLi4kMzMTGFLo6GhETPu3r1LTE1NSePGjcnSpUvJ+/fvhS1JYAwaNIhkZ2fz7A8LCyMtW7akVIOsrCzJy8tjve7Xrx/ZsmUL63V2djZRVFSkVAM/0NPTI5GRkRzt0dHRpEOHDtWObdy4MUlLS+NoT0tLI40bN+abRlFi1KhRRElJiXh4eLC1FxYWkokTJ5LGjRuTbdu2CUmdYKmtjVtS7Nw0DYf4+Hiio6PDsg9X2Yhpvyb10Of+/yguLiaenp6kadOmEve3/y2BgYFk9uzZRE9PjzAYDNK2bVtia2tLjh07RtLT04Utj0YANG7cmOv/dXp6OpGTkyOEVPr/qn4nhLYN/87Hjx/J7t27iZ2dHbGzsyO7d+8mhYWF1Y4RNd+EJCInJ0cSExOFLYOGhie1q7NEQyPiVO0oP3r0KNd+d3d39OnTh3IdUVFRPPvEcWc7jfC5d+8eHB0dkZCQgHnz5uG///6DmppavY7Zvn17rF27FlpaWlizZg0uX75cr+NZWlpizZo18PX15dqvpaVVr+OLA2vWrEFwcDDevHmDYcOG4eDBgxgzZgzk5eWFLY0yrl69isWLF8PBwQF6enrCliNwnj17BlNTU579ZmZmcHV1pVSDjo4OIiIi8PHjR2RmZgKovP4loRRgfVmxYgUOHjyIw4cP09lKBYyrqytMTU0xatQoAJXPV/b29mylTFu3bk1ZKdP169cjPDwcHTp0wMKFC1mZq9PS0uDu7o7y8nKepYmAyqxHv1dhkJaWhqKiIiVauXH8+HE4ODhAR0cH69atg7OzM7Zu3Ypp06aByWTixIkTaNasGXbs2CEwTbVlxIgRcHJyws6dO3Hu3DnIy8tjwIABrP7k5GS0a9eOch11vWevW7cOpqamKCwsxMqVK6Gvrw9CCFJTU7F3716cP3++2rVEffH19cXu3btZ187vjB49Grt27cKcOXMwfPhwLF26lDIdNDQ0kkeLFi2gqqqKiRMnYvv27ayMiDQ0NDT8pnfv3pCTk8O8efOgo6ODU6dOcX3f4sWLBayMepSUlGBkZITdu3dj7ty5rPaPHz9i/vz5OH/+PDZu3EiphhYtWiA7OxsaGhr48eMHHj16BBcXF1b/169fOTL6iSJ5eXnQ0dHhaNfS0kJeXl61Y01MTJCamspR4Sg1NRVdu3blq05R4eLFi/Dx8cHy5ctx9uxZeHl54f79+3BwcEDbtm1x//59dO7cWdgyBQJt46ZpqNjZ2cHY2BhBQUFcM8nRUAd97oHY2Fj4+PggLCwMTCYTNjY2sLe3F7YskWbq1KmYOnUqAODt27eIiYnBpUuXMH/+fFRUVNDZ1SUADQ0NeHt7c9jwvb29WZlZCwsLoaqqyuqjbcOVxMbGwsrKCioqKiz7lJubGzZv3oyLFy9i4MCBXMeJim9CktHX18e3b9+ELYOGhicMQv6o7U1D0wC5c+cOTE1NYW1tXa0zu1+/fgLV9fXrVwQFBcHLywsPHz6kH3hp+A6TyYScnBzmzJnD1TBcRW0dC7wWur17966zxl27duHIkSPIycnh2b9o0SLIyckBAG7fvo3u3btDVlYWQOV15OjoiCNHjtRZg6jTr18/2NrawsbGpt7B0w2FhIQEeHt7IyQkBAYGBpg2bRomTZqEVq1aISkpCYaGhsKWSCmNGjXCy5cv0bJlS679b9++hZaWFn78+CFgZTS1YezYsYiKikLTpk3RqVMnDgdieHi4kJSJP6JQyjQ3NxcODg64du0aqpZSDAYD5ubmcHd3r/Z+zGQyYWlpybrHXbx4EYMHD+Yog0vVZ6hz586YO3cuFi1ahIiICFhZWcHLy4tVkic0NBRr1qxhBeSLEh8+fMC4ceMQFxcHRUVF+Pv7s5W/GjJkCHr37o2tW7dSqqM+9+yzZ89izpw5+PjxI1u7qqoqPD09MX78eH5KZUNOTg7Pnz+HpqYm1/7c3Fzo6uri27dvbAHiNDQ0NPXlxo0bGDJkSI2lmGloaGjqi7a2do1BKwwGA1lZWQJSJFiqAiB79+7NEQDp5+dHeQCkg4MDkpKSWA5pf39/vHnzhvVsefLkSRw4cAD379+nVEd90dTUxOHDhzF69Gi29vPnz2PBggV49eoVz7EhISFYvXo1Fi1axLKlJiQkwN3dHTt27ICBgQHrveJWSjwvLw/Tp0/HvXv3UFFRgXXr1mHt2rWQkpIStjSBERAQUG3/9OnTBaSEhubvUFBQQFJSEtq3by9sKRKHpJ77N2/ewM/PD35+fsjMzETfvn1hb28PGxsbDhspDXdKS0sRFxeH6OhoREVF4fHjxzAwMICpqSn2798vbHk0FHPhwgX8888/0NfXR48ePQAADx48QFpaGs6cOYNRo0bh6NGjyMjIwL59+wDQtuEqunTpwkrqV/WcWl5ejvnz5+POnTs8k+mJim9Ckrl+/TpcXFywdetWdOnShcMvq6ysLCRlNDSV0MG3NGKDMJ3ZfxIbGwtvb2+EhYWhdevWGDduHMaPH896AKKh4Rf8cCzwa6FrbGzMpoUQgvz8fLx//x5HjhzBnDlzuI6TkpLC27dv0bx5cwCVD0eJiYnQ1dUFABQUFKB169Z08LqYUlJSgpCQEPj4+ODevXsoLy/Hvn37YGdnByUlJWHLowwpKSnk5+dDXV2daz/9uRdtZs2aVW0/r0zfNPWncePGyMjIYO3g7t+/PywtLVnZZnNyctClSxd8/fqVci2fPn1CZmYmCCHQ09Nj20nOi5o+O1VQ9RmSl5dHamoqKyOPjIwMkpKSWE7gvLw86Onp4fv375TMzw8+f/4MRUVFDifux48foaioKPLGwdLSUly7dg0ZGRkAAD09PZibm1Oe7b5p06aIjo7m6eR/8uQJBg4ciE+fPlGqg4aGRjL59esXoqOj8eLFC0yZMgVKSkp48+YNlJWVBZoBnoaGhkbcEWYApLg4pB0dHRESEgJfX19W5quYmBjY2dlhwoQJ2LNnD8+xNW00YTAYIISAwWCInb3n+vXrsLe3B5PJRH5+PjZu3Ig1a9ZI1OabP20CP3/+RGlpKWRkZCAvL8/ht6KhERWsrKwwc+ZMgfpQaSqRxHNvaWnJquA5ffp02NnZcWSMp6mevn37sgXbDho0CAMHDqyVbZpGfMjOzoanpyfS09MBAB07dsTcuXOhra3N9f20bbgSOTk5JCYmcnzvPH/+HN26dasxs2pD9000ZKrWFX/GxYjr+oqm4SEtbAE0NPxi7NixMDc3F4ozGwDy8/Ph5+cHb29vfPnyBTY2Nvj+/TvOnTsn9hkcaYQHr2yytYWfC90xY8awPfAwmUyoq6vD1NQU+vr6PMf9uQdEUveEBAYGwsPDA9nZ2YiPj4eWlhYOHDgAHR0djBkzRtjyKENBQQF2dnaws7PD8+fPWaVSnJycMGzYMFy4cEHYEimBEIIhQ4ZAWpr7o9ivX78ErIjmb6CDa4WHKJUyVVVV/euNVcL+7JSVlbEyzQOArKwsKwtv1WtR//5RUVHh2t60aVOBaajLPTsyMhILFy5EQkICWxACUGm069SpEzw8PNjKVfGTqh39R48e5drv7u6OPn36UDI3DQ2NZJObmwsLCwvk5eXh+/fvGDZsGJSUlLBz5058//4dHh4ewpZIQ0MjJsTHx6OwsJCtlGpAQACcnZ1RUlICa2truLm5sT3/ihtpaWl48eIF1NXVkZ+fDyaTKbAS1mpqaoiNjeXpkA4NDW0QGy42b96MnJwcNptNRUUFpk+fjm3btlU7Njs7WxASRYqSkhIsW7YM/v7+WLt2LdatW4fr169jzpw5OHfuHAICAtgy/ooz3IJVMjIy4ODggFWrVglBEQ1N7bCyssKyZcvw5MkTrpnk/swETsM/JPHcN2rUiJWZU5Kyo/OTtLQ0KCgoQF9fH/r6+jAwMKADbyUQHR0d7Nixo9bvp23DlZiYmCA1NZUjFiI1NRVdu3atcbwo+CYklaioKGFLoKGpFjrzLY1Y8Lsz+8+U4p8/f0bfvn0pdWZbWVkhNjYWI0eOhK2tLSwsLCAlJYVGjRpJRPl0GuFRX8fC6NGjYW9vX6+F7pcvX2r1Pl7p/qsyIlRlvlVSUkJSUpJEZb49evQoNm7ciKVLl2Lr1q1ISUmBrq4u/Pz84O/vL3EPlOXl5bh48SJ8fHzENvj292DB6nB2dqZYCU1doTO4CQdxKWUqLKSkpJCeng51dXUQQqChoYG4uDjWjviCggLo6+uL9T23vtT1nj169GiYmZlh2bJlXPsPHTqEqKgonD17lhLdd+7cgampKaytrbFy5Uro6+uDEILU1FTs3bsX58+fR1RUFPr160fJ/DQ0NJKLtbU1lJSU4O3tjWbNmrHWetHR0Zg9ezZr8zQNDQ1NfbGwsICZmRkcHR0BVGZvMjExwcyZM2FgYIDdu3dj7ty52LRpk3CFUkB1AZCtWrWSqABIfpGeno6kpCTIycmhS5curOohNOzo6OhASUkJfn5+MDExYbUXFRVh4cKFCAsLw6ZNm1jXpSTy4MEDTJ06FWlpacKWQkPDleoyVNOZ5KiFPvc0dYEQgidPniA6OhoxMTGIjY2FjIwMBg0aBDMzM8yePVvYEmkEwK1bt+Dp6YmsrCyEhoaiTZs2CAwMhI6ODvr378/xfto2XElISAhWr16NRYsWoXfv3gCAhIQEuLu7Y8eOHWxrJl5ZgmloaGi4QQff0ogFwnZmS0tLY/HixXBwcICenh6rnQ6+paEaUXAs1JRFo6Z0/3TwLWBoaIht27axHNNVf39KSgpMTU3x4cMHYUukoaH5jT8zuKWnp0NXVxdLliyhM7hRjLiUMhUWf96zq+7Rf74W53tufanrPVtLSwsRERE8gx7S0tIwfPhw5OXlUab97NmzmDNnDke5U1VVVXh6ekpUmUMaGhrB0axZM9y5cwcdO3Zk+97MycmBoaEhSktLhS2RhoZGTGjVqhUuXryI7t27AwDWrVuHmJgYxMXFAajMvOrs7Ixnz54JUyYl0AGQokFAQEC1/dOnTxeQEsHh5OQEV1dXniV2z549CwcHB+Tn5wtYmeiQmJiIgQMH1jqBBQ0NDQ0NTW0hhODhw4c4fPgwTp48iYqKCtquKwGEhYVh2rRpsLW1RWBgIJ49ewZdXV0cPnwYV65cwZUrV7iOo23D1W96ACo3PtA+EtEhOTkZnTt3BpPJRHJycrXvpYOlaYQN91rHNDQNjKoMaLwYPnw49uzZQ9n8cXFx8Pb2xv/+9z8YGBhg2rRpmDRpEmXz0dBUkZSUhC1btrBeBwcHo1evXjh+/DgAQENDA87OzpQG3/6e4Y0QghEjRsDLywtt2rSp9TG8vLxYmSJ//foFPz8/qKmpAagsIS7uZGdnw9jYmKNdVlYWJSUlQlBEI0y+fPmCkydPwtvbGw8ePBC2HBouLFmyBN27d0dSUhKaNWvGah87diy9s5xixKWUqbCQtEzqVFDXe3ZBQQFH+cDfkZaWxvv37/mikRdjx46Fubk5rl27xso0qaenB3Nzc8jLy1M6Nw0NjeTCy/n36tUrKCkpCUERDQ2NuPLp0ye0aNGC9TomJgaWlpas1z169MDLly+FIY1yJk6cyDUAskmTJjhx4gTGjx8PBwcHOvi2lrx69QoXLlxAXl4efvz4wda3b98+nuOWLFnC9vrnz58oLS2FjIwM5OXlxTL4dseOHSgvL0dsbCyMjIzQpEkTtv6xY8di4MCBwhEnYP6s3kUIwdu3b3H48GGxzyJHIz6UlZWhcePGwpYhkdDnnqYmXF1dsXLlSqSlpSE6OhrR0dGIi4vD169f0aVLFyxatAiDBg0StkwaAbBlyxZ4eHhg+vTpCA4OZrX369ePLWbgT2jbcKVtn6bh0K1bN1YCt27durGCo/+EDpamEQXo4FsasUDYzuzevXujd+/eOHDgAEJCQuDj44Ply5ejoqICN27cgIaGBu1UoqEEUXAs/LmYk5KSQu/evVmZa2tCU1OTFSwMAC1btkRgYCDbe8S9tJuOjg4SExM5/s7qMuTRiB9RUVHw8fFBeHg4VFRU2LJ50ogWt27dwp07dzgcm9ra2nj9+rWQVEkWKioqXNubNm0qYCUNC9oAW3/qes9u06YNUlJS0L59e679ycnJaNWqFV+1/k5kZCQWLlyIhIQEjvvL58+f0alTJ3h4eGDAgAGUaaChoZFMhg8fjgMHDuDYsWMAKg3ixcXFcHZ2xogRI4SsjoaGRpxo0aIFsrOzoaGhgR8/fuDRo0dwcXFh9X/9+rVa+3FDhg6A5B83b97E6NGjoauri7S0NHTu3Bk5OTkghLBlFebGp0+fONoyMjLg4OCAVatWUSVZ6EhJSWH48OFITU3l+OwBYNu0LM5YW1uzvWYwGFBXV8fgwYOxd+9e4YiioakF5eXl2LZtGzw8PFBQUMCq8LVhwwZoa2vD3t5e2BLFFvrc0/wNLi4umDdvHnr27AljY2MMGjQIs2fPxsCBA3naymnEk+fPn3N9tldRUUFRURHXMbRtuBJxjzcQN7Kzs6Gurs76nYZGlKGDb2nEAmE7s6tQUFCAnZ0d7Ozs8Pz5c3h7e2PHjh1wcnLCsGHDOHY/09DUF3FwLOTk5FTb/+rVK7i6ugpGjJBYvnw5FixYgLKyMhBCcO/ePQQFBWH79u3w8vIStjwaCnn9+jX8/Pzg6+uLoqIifPr0CadOnYKNjQ1bKXga0YLO4CZ8SkpKsGPHDty8eRPv3r1DRUUFW39WVpaQlIkuf1PiUllZmUIlDZu63rNHjBiBDRs2wMLCgiOTybdv3+Ds7IxRo0ZRpvvAgQOYPXs21/9bFRUVzJ07F/v27RN7AysNDY3g2bt3L8zNzWFoaIiysjJMmTIFGRkZUFNTQ1BQkLDl0dDQiBEjRoyAk5MTdu7ciXPnzkFeXp7t2SY5ORnt2rUTokJqoQMg+cOaNWuwcuVKuLi4QElJCWFhYWjevDlsbW1hYWHx18fT09PDjh07MHXqVKSlpVGgWDTo3LkzsrKyoKOjI2wpQuNPuwQNTUNh69at8Pf3x65du9gqenXu3BkHDhygA0AphD73NH9DVbbHjx8/0rZbCadly5bIzMyEtrY2W3tcXBzPxFi0bbiSgICAavvFsVJFQ+b3YOnc3Fz07dsX0tLsIY6/fv3CnTt36MBqGqHDINzyMtPQNDAWLVqE6Oho3L///9q79/ie6///4/f3TgwzI6eJNWxGiIU0ZZu0OeSYFCuFKDkkh1JyDKUQah9U26icmUPRVOyQQyjMaWMjc2gOOTZz2vb+/eHr/Ws2TLHXe+/drpeLS3u/Hq/3e3fP8H7v9Xo8n8+tud7MbtSokQIDAzV9+vR8z5aZmanvvvtO4eHhNN/inuvTp4/i4+MtNxbmzJmjP//807Ia49y5czV16lRt3bo13zK5uLgoPj4+zyvf3kl8fLx8fX1tfruAuXPnavTo0Tpw4IAkyd3dXWPGjOECi41aunSpwsLCFBcXp5YtW+rFF19Uy5YtVbx4ccXHx6tWrVpGR8RtPP/883J1ddUXX3whFxcX7dy5U2XLllW7du1UpUoVRUREGB3R5nXp0kWxsbF66aWXVLFixRzN6jdv9wnJzs7ujk39ZrOZLXry4N+8Z584cUK+vr6yt7dXv379VKNGDUlSYmKiQkNDlZmZqW3btmXb0eBe8vDwuO3qvImJiQoKCtLhw4fvy/cHULhlZGRo4cKFio+PV1pamnx9fRUSEiJnZ2ejowGwIX/99Zc6duyo9evXq0SJEpozZ062VZ2eeuopNW7cWOPHjzcw5f3VoEEDTZw4UU899ZTRUQosFxcX7dixQ9WqVZObm5vWr1+vhx9+WPHx8WrXrt0dFxHIzY4dO9S0adO7mhBZ0ERFRendd9/VBx98oEcffVTFixfPVqdJCLBe1atX16xZs/TUU09lu7eTmJioxx9/PNdVvXFvMPa4G3Z2djpx4oRlFUgUXh9++KG+/fZbhYeH6+mnn9bq1auVkpKit956SyNGjFD//v1zPIdrw9e5ublle3zt2jWlp6fLyclJxYoV05kzZwxKhjuxt7dXamqqypUrl+346dOnVa5cOe5pwXCsfAub8P777ysyMlLe3t63vJk9fPhwQ7LZ29urffv2ObYdAu6FDz74QB07dpS/v7/lxsI/t0EPDw9XUFBQvudixc67FxISopCQEKWnpystLc3y4fHYsWOqVKmSwelwrz3//PN65513tHDhQlZKLYBYwc14P/zwg1atWqUmTZoYHaXAiI6ONjqCzfg379nly5fXxo0b1adPH7377ruW1SpMJpOCg4MVGhp63xpvpevNv7fbDcHBwUGnTp26b98fQOHm4OBg+bfzhtTUVA0dOlSff/65gckA2JIHHnhAcXFxOn/+vEqUKCF7e/ts9cWLF6tEiRIGpcsf48aN05AhQ2iA/A+KFy+uq1evSpIqVqyoAwcO6OGHH5Z0vcH7dm5eeMNsNis1NVWff/65zf/s2qpVK0lS27Zts10XLiwTPC9evKiJEycqMjJShw4dkslkkqenpzp16qQhQ4aoWLFiRkcEbunYsWO57iqalZWla9euGZCo8GDscbe8vb3veP+V5kHbN2zYMGVlZempp55Senq6mjZtqiJFimjIkCG5Nt5KXBu+IbdJDUlJSerTp4+GDh1qQCLk1Y2fK252+vTpHD/3Akag+RY2weib2YBRrOHGQseOHbM9vnz5sl5//fUcH3QiIyPvaw5bUaxYMRUrVkzHjx/X+PHjFRYWpvT0dKNj4R7r2bOnQkNDFRMTo5deeknPP/98jhmXsF4PPvig4uPjtWDBAu3cuVNpaWnq2bMnK7jlIzc3N5UuXdroGAWKv7+/0RFszt2+Z3t4eGj16tU6e/askpOTZTab5eXllS///leqVEm7d+/O9aaOdH0b5ooVK973HAAKlz179ig6OlpOTk7q3LmzSpUqpb/++kvjx4/XzJkz79luKQDwT66urrkeLww/PxT2Bsh7oXHjxlq/fr1q1qypVq1aafDgwdq1a5ciIyPVuHHj2z735sU3TCaTypYtq2bNmmny5Mn3MbXxbjfZc9euXfmYJP9dvXpV/v7+2r17t1q2bKk2bdrIbDYrISFB48eP1w8//KC4uLjbNrwARqpVq5Z++eWXHNs1L1myRPXr1zcoVeHA2ONujRkz5pafdVF4mEwmDR8+XEOHDlVycrLS0tJUq1at2/YDcG341ry8vPTRRx/pxRdfVGJiotFxcJMbfSgmk0mvvPKKihQpYqllZmZq586d8vPzMyoeYEHzLWyGkTezAaMZeWPh5u/94osv3vfvaSvOnj2rN954Qz/99JOcnJw0bNgw9evXT6NHj9akSZNUt25dtq+3UbNmzdLUqVO1aNEihYeHa+DAgQoODpbZbFZWVpbR8ZAHDg4O/HtnoA8++EAjR47UnDlzWEHmP0hPT9fhw4ctKzvdULduXYMSWa97+Z7t5uamhg0b3ufE2bVq1UojRoxQixYtVLRo0Wy1S5cuadSoUXrmmWfyNRMA27Zy5Up16tRJGRkZkqSPP/5YX375pTp37qxHH31Uy5YtU4sWLQxOCQC2pTA3QN4rU6ZMUVpamqTrDS5paWlauHChvLy8NGXKlNs+tzBfz7l5sufff/+t+fPn66uvvtLvv/+ufv36GZTs/psxY4aOHj2q+Ph4y46MNyQmJiogIEAzZ8685Up0gNFGjhypl19+WceOHVNWVpYiIyO1b98+ff311/r++++NjmfTGHvcrRdeeCHHlusofNatWyc/Pz8VLVpUtWrVytNzuDZ8ew4ODvrzzz+NjoFc3OhDMZvNcnFxybYAkpOTkxo3bqxevXoZFQ+wMJlvLBEKAIABbl4592bnzp1TbGysTa7O8dprrykqKkrPPfec1qxZo7179yo4OFh2dnZ6//3377iiBmxHUlKSIiIiNGfOHKWlpal169bq1KnTHf9+wBhVqlRRQECA/P39FRgYyKpt+aR+/frZVm+6MdnqoYceyrGCzLZt2/I7XoFy6tQpde/eXT/88EOudVt8z/2vCvp79okTJ+Tr6yt7e3v169fPclM4MTFRoaGhyszM1LZt29gtBMA906hRIzVp0kQffPCBvvrqKw0aNEgPP/ywwsPD830CAgAUVjc3QPI5H/dbXFycwsLCtHTpUrm7u6tjx4569tlnbfq939/fX507d1bfvn1zrX/22WdasmSJYmNj8zkZkHe//PKLxo4dq/j4eKWlpcnX11cjR45UUFCQ0dFsHmOPvLK3t1dqairNt1CJEiWUkZGhhg0bWu5TNWnS5La7MnJt+LqVK1dme2w2m5WamqrPP/9clStXvuX9EhhvzJgxGjJkSI6dlwFrQfMtAMBQ3bt3z9N5trgCbJUqVTR79mw1a9ZMhw4dUtWqVTVs2DBNmDDB6GgwSFZWllatWqWwsDD98MMPunLlitGRkItvv/1WcXFxiomJUXJysipVqiR/f3/5+/srICBAXl5eRke0SWPGjMnzuaNGjbqPSQq+kJAQpaSkaOrUqQoICNCyZct04sQJjRs3TpMnT1br1q2Njmh1bOE9OyUlRX369NGaNWt04zKAyWRScHCwQkND5enpaXBCALbE1dVVv//+u6pXr67MzEwVKVJEUVFRat68udHRAMDmFcYGyHulatWq2rp1q8qUKZPt+Llz5+Tr66uDBw/m+ryLFy9q4sSJioyM1KFDh2QymeTp6alOnTppyJAhNr1jy/HjxzV79myFhYXpwoUL6ty5s2bOnKn4+Pg8r8ZWkJUtW1YxMTF6+OGHc63v3r1bgYGBOnXqVD4nAwDYEjs7Ox0/fpzmW+jatWvasmWLYmNjFRsbq40bN+rq1atq0KCBAgMDNW7cuFyfx7Xh63+P/slkMqls2bJq1qyZJk+erIoVKxqUDEBBR/MtAAAGcXBw0JEjRywf5osVK6bffvutUFyYxp2dPHmSCykFQGpqqmJjY/X9999r4cKFysrKYjUhWL2KFStqxYoVatSokUqWLKnffvtN3t7eWrlypT7++GOtX7/e6IhWx5bes8+ePWtZOdrLy0tubm5GRwJgg26+Meji4qL4+Hh2DACA+6SwN0DeK7dqbDlx4oSqVKmS6yTpq1evys/PT7t371bLli3l4+Mjs9mshIQERUVFydfXV3FxcTl2bLEFbdq0UVxcnFq3bq2QkBC1aNFC9vb2cnR0LDR/9hwdHXXkyBFVqFAh13pqaqo8PDx09erVfE4G3L3Lly9r4cKFSk9PV/PmzVlgIB8x9gD+rT179uiTTz7R3Llz83R/imvDKIhOnDihIUOGaO3atTp58qRubnPkviyM5mB0AAAACiuz2SwHh///Vmxvb3/bbUFgexYvXqz58+dr//79cnJykre3t7p3767g4GAab61cenq61q9fr5iYGEVHR2v79u2qXbu2AgICjI5WKGzdulVZWVl67LHHsh3fvHmz7O3t1aBBA4OSFQwXL160/Bvj5uamU6dOydvbW3Xq1NG2bdsMTmedbOk9283NjVXPAOSLNWvWyNXVVdL1HR7Wrl2r3bt3Zzunbdu2RkQDAJvyzwbIqVOnWhogZ86caXS0AuOfW9D+8/1Lun4jd+3atXrooYdyfe6MGTN09OhRxcfHW7bwvSExMVEBAQGaOXOm+vfvf1+yG+mHH37QgAED1KdPn0LbKJaVlSV7e/tb1u3s7GgGgFUaNGiQrl27ps8++0zS9YkEjRs31t69e1WsWDENHTpUP/30kx5//HGDk9oexh7Af7F//37FxMQoJiZGsbGxunLlip588klNmjQpT/enuDaMguiVV17R4cOHNWLECFWsWFEmk8noSEA2rHwLAIBB7OzsVLt2bUszz86dO+Xj4yMnJ6ds59EIZXuysrLUpUsXLV68WN7e3vLx8ZEkJSQkKDk5Wb1799aMGTN0+vRpxcXFqUOHDgYnxj/5+flp+/btqlmzpgICAuTv76+mTZsyQzgfNWrUSG+//bY6deqU7XhkZKQmTpyozZs3G5SsYGjYsKHGjRun4OBgtW3bVqVKldKHH36o6dOna8mSJTpw4IDREa0O79kAcHdu3sovNyaTiWYUALgHHBwccm2ALEyrj/5XN963TCZTjlWUHB0d9dBDD2ny5Ml65plncjzX399fnTt3Vt++fXN97c8++0xLlixRbGzsvQ9usF9//VVhYWFauHChatasqZdeekkvvPCCKlasWGj+7N38s+LNMjIytGfPHj7zwOrUrl1bEyZMsEyGi4iI0ODBg7V9+3ZVqVJFPXr00MmTJ7Vq1SqDk9oexh7Af2FnZ6eyZcvqzTff1DPPPKM6derQiJgHFy9e1MSJExUZGalDhw7JZDLJ09NTnTp10pAhQ1SsWDGjI+I2XFxc9Msvv6hevXpGRwFyxcq3AAAYZNSoUdket2vXzqAkyG/Tpk3Tzz//rJUrV+a4cbNy5Up1795d1apV0+zZs9WtWzeDUuJWEhMTVbx4cfn4+MjHx0c1a9ak8Taf7d27V76+vjmO169fX3v37jUgUcHy5ptvKjU1VdL196IWLVpo7ty5cnJy0uzZs40NZ6V4zwaAu5OVlWV0BAAoNNavX6+wsDA9+uij2RogkXc33rc8PT21detWPfDAA3l+7t69e2+7ylhgYKDGjh37XyNapcaNG6tx48aaOnWqFi5cqPDwcA0aNEhZWVn66aefVLlyZbm4uBgd8766+WfF3Dz77LP5kAS4O4cPH87WIP/jjz+qU6dO8vDwkHT92lGrVq2MimfTGHsA/8WAAQMUFxensWPH6vvvv1dAQIACAgL0xBNP0EB6C1evXpW/v792796tli1bqk2bNjKbzUpISND48eP1ww8/KC4uTo6OjkZHxS1Urlw5xyRJwJqw8i0AAEA+q1u3rgYOHKgePXrkWg8LC1Pv3r0VFBSkFStW5FhZEcYym83atWuXZVufuLg4OTk5yd/fX4GBgerVq5fREW1emTJl9P333+fYfm3jxo1q3bq1zp49a1Cygik9PV2JiYmqUqXKXd1kBgAAAGA9Ll68aGmA3LJlizIzMzVlyhT16NHD5hsg/6tNmzbp9OnT2SZIf/311xo1apQuXryo9u3b67PPPlORIkVyPNfR0VFHjhxRhQoVcn3t1NRUeXh46OrVq/ctvzXZt2+fwsLC9M033+jcuXN6+umntXLlSqNjAbhJqVKltHXrVsuK6Z6enhoxYoTlevWhQ4dUs2ZNXbp0yciYNomxB3AvnDt3Tr/88otiY2MVGxurPXv2qH79+tqwYYPR0azOtGnT9OGHHyo2NlY1atTIVktMTFRAQICGDx+u/v37G5QQd/Ljjz9q8uTJmjVrlh566CGj4wA53HkPOAAAcN9lZGTo559/1qxZs/T3339Lkv7880+lpaUZnAz3Q1JSkpo3b37L+o0ajbfWyWQyqW7duhowYICWLFmiH374QU8//bQWL16s119/3eh4hUJQUJDeffddnT9/3nLs3Llzeu+99/T0008bmKxgKlasmHx9fWm8zYP58+ffsjZ06NB8TAIABcc333yjJk2ayN3dXSkpKZKkTz/9VCtWrDA4GQDYluLFi6tHjx5av369du3apcGDB+ujjz5SuXLlLFtbI3djxozRnj17LI937dqlnj17qnnz5ho2bJi+++47ffjhh7k+NysrS/b29rd8bTs7O2VmZt7zzNaqRo0a+vjjj3X06NHb/vxUGFy4cEEzZsxQgwYNjI4C5FCzZk199913kqQ9e/bo8OHDCgwMtNRTUlJUvnx5o+LZNMYewL2QmZmpa9eu6cqVK7p8+bKuXLmiffv2GR3LKkVGRmrEiBE5Gm8lycfHR8OHD9eSJUsMSIa8ev755xUTE6Nq1arJxcVFpUuXzvYLMBor3wIAYLCUlBS1aNFChw8f1pUrV7R//35VrVpVb775pq5cuaKZM2caHRH3WOnSpRUTE6O6devmWt+1a5eaNm3K6p1WZuzYsRoyZIgSExMVExOjmJgYrV+/Xn///bfq1KmjgIAA+fv7sx19Pjh27JiaNm2q06dPq379+pKkHTt2qHz58patLXFrgwYNyvW4yWRS0aJFVb16dbVr146LFrkoVaqU5s+fr5YtW2Y7/tZbb2nBggVKTU01KBkAWKcZM2Zo5MiRGjhwoMaPH6/du3eratWqmj17tubMmaPo6GijIwKATcvMzNR3332n8PBwVh+9jYoVK+q7776zNEkOHz5csbGxWr9+vSRp8eLFGjVqlPbu3ZvjuXZ2dqpdu7YcHBxyfe2MjAzt2bOnUDXgFnbR0dEKDw9XZGSkXF1d1aFDB4WGhhodC8hm2bJleuGFF/TEE09oz549atiwoaUhVJLeeecd/fHHH1q0aJGBKW0TYw/gv+jfv79iY2O1d+9eubm5qWnTpvL391dAQIDq1Kkjk8lkdESrU7ZsWcXExOjhhx/Otb57924FBgbq1KlT+ZwMeTVnzpzb1l9++eV8SgLkjuZbAAAM1r59e7m4uCgsLExlypRRfHy8qlatqpiYGPXq1UtJSUlGR8Q91rp1a1WpUkUzZszItf7666/r8OHDWr16dT4nw+3Y29srNTVV7u7uql+/vvz9/eXv76+mTZvK1dXV6HiFzsWLFzV37lzFx8fL2dlZdevWVZcuXeTo6Gh0NKsXGBiobdu2KTMz0zLbe//+/bK3t5ePj4/27dsnk8mk9evXq1atWgantS6rVq1SSEiIvv/+ez3xxBOSrl/wjIyM1Nq1a+Xj42NwQgCwLrVq1dKECRMsP/Pc+Fln9+7dCggI0F9//WV0RAAAVLRoUSUlJVkmcj7xxBNq2bKlhg8fLun6FuB16tSx7Fb1T2PGjMnT9xg1atS9Cwyrc+zYMc2ePVsRERE6d+6czp49q3nz5qlz5840wcBqrV27Vt9//70qVKig/v37q1ixYpbamDFjLM1cuPcYewD/1nPPPWf5N6J27dpGxykQHB0ddeTIEVWoUCHXempqqjw8PHT16tV8TgbAVtB8CwCAwcqUKaONGzeqRo0a2W5IHzp0SLVq1VJ6errREXGPbdy4UQEBAWrfvr2GDBkiHx8fmc1mJSQkaPLkyVqxYoWio6PVpEkTo6PiH+zs7HT8+HEVLVpUJUuWNDoO8K9NnTpVv/zyiyIiIix/ls+fP69XX31VTzzxhHr16qWuXbvq0qVLWrNmjcFprc+8efPUr18//fTTTwoLC7P8m+3t7W10NACwOs7OzkpMTJSHh0e2n3WSkpJUt25dXbp0yeiIAADIw8ND33zzjZo2baqrV6+qVKlS+u677/TUU09Jur5Dkb+/v86cOWNwUlibpUuXKiwsTHFxcWrZsqVefPFFtWzZUsWLF1d8fDwTWgEAwD1z7do1vfbaaxoxYoQ8PT2NjlNg2Nvb6/jx4ypbtmyu9RMnTsjd3Z2dKqzcgQMHFBERoQMHDmjatGkqV66cfvjhB1WpUuWWqxoD+SX3fXAAAEC+ycrKyvUD/dGjR+Xi4mJAItxvfn5+WrhwoXr37q2lS5dmq7m5uWn+/Pk03lopk8lE462V+OabbzRr1iwdPHhQmzZtkoeHhz799FNVrVpV7dq1MzqeVfvkk0/0008/Zfuz7OrqqtGjRysoKEhvvvmmRo4cqaCgIANTWq+uXbvq3LlzatKkicqWLavY2FhVr17d6FgAYJU8PT21Y8cOeXh4ZDseFRWlmjVrGpQKAIDsWrVqpWHDhmnixIlavny5ihUrpieffNJS37lzp6pVq3bXr3vhwgXNnTtXYWFh+u233+5lZFiJ559/Xu+8844WLlzIdVwUGDt37szzuXXr1r2PSQofxh7Af+Ho6KilS5dqxIgRRkcpUMxms5566ik5OOTeHpeRkZHPiXC3YmNj1bJlSzVp0kRxcXEaP368ypUrp/j4eIWFhWnJkiVGR0QhR/MtAAAGCwoK0tSpU/XFF19Iut7cl5aWplGjRqlVq1YGp8P90qFDBwUHB2vNmjVKSkqSJHl5eSk4ODjbNlOwLt7e3nfcLpCVcO6/GTNmaOTIkRo4cKDGjRtnmcDg5uamqVOn0nx7B+fPn9fJkydzrMBz6tQpXbhwQZJUqlQptln6P4MGDcr1eNmyZeXr66v//e9/lmNTpkzJr1gAUCAMGjRIffv21eXLl2U2m7VlyxbNnz9fH374ob766iuj4wEAIEn64IMP1LFjR/n7+6tEiRKaM2eOnJycLPXw8PC7mpwYHR2t8PBwRUZGytXVVR06dLgfsWEFevbsqdDQUMXExOill17S888/Lzc3N6NjAbdVr149mUwm3Wpz3Bs1k8nEKoD3GGMP4L9q3769li9frrfeesvoKAXGqFGj7njOs88+mw9J8G8NGzZM48aN06BBg7JNeGvWrJk+//xzA5MB15nMt/p0BwAA8sXRo0cVHBwss9mspKQkNWjQQElJSXrggQcUFxencuXKGR0R99i6devUr18//frrrzlWUT1//rz8/Pw0c+bMbKuswHh2dnaaOnWqXF1db3veyy+/nE+JCq9atWppwoQJat++fbYtrHfv3q2AgAD99ddfRke0aiEhIdq0aZMmT56shg0bSpK2bt2qIUOGyM/PT998840WLFigSZMmsTqTpMDAwDydZzKZtG7duvucBgAKnrlz52r06NE6cOCAJMnd3V1jxoxRz549DU4GAEB258+fV4kSJWRvb5/t+JkzZ1SiRIlsDbk3O3bsmGbPnq2IiAidO3dOZ8+e1bx589S5c+c7TuJFwXbp0iUtWrRI4eHh2rx5s4KDg7Vq1Srt2LFDtWvXNjoekENKSkqez715Bwv8N4w9gP9q3Lhxmjx5sp566ik9+uijKl68eLb6gAEDDEoG3D8lSpTQrl275Onpme2e4KFDh+Tj46PLly8bHRGFHM23AABYgYyMDC1YsEA7d+5UWlqafH19FRISImdnZ6Oj4T5o27atAgMDbzkzdfr06YqOjtayZcvyORlux87OTsePH6ch3go4OzsrMTFRHh4e2X7QTkpKUt26dXXp0iWjI1q1tLQ0vfXWW/r6668tWyo5ODjo5Zdf1qeffqrixYtrx44dkq6vyAEAwL2Qnp6utLQ0PksBAGzK0qVLFRYWpri4OLVs2VIvvviiWrZsqeLFiys+Pj7HjiOwbUlJSYqIiNCcOXOUlpam1q1bq1OnTurYsaPR0QAAgA3w9PS8Zc1kMungwYP5mKbgu3DhgubOnauwsDAWIrFiDz74oBYtWiQ/P79s9wSXLVumIUOGWCb8A0ah+RYAACCfeXh4KCoqSjVr1sy1npiYqKCgIB0+fDifk+F27O3tlZqaSsOIFahVq5Y+/PBDtWvXLtsP2p999pkiIiK0bds2oyMWCGlpaZaLcVWrVlWJEiUMTlSwHD16VNL1Cz8AAAAACicHBwe98847GjZsWLYtUB0dHWm+LcSysrK0atUqhYWF6YcfftCVK1eMjgTc1t69e3X48GFdvXo12/G2bdsalKjwYOwBIP9FR0crPDxckZGRcnV1VYcOHRQaGmp0LNzCkCFDtHnzZi1evFje3t7atm2bTpw4oW7duqlbt24aNWqU0RFRyDkYHQAAgMJo5cqVatmypRwdHbVy5crbnstFFttz4sQJOTo63rLu4OCgU6dO5WMi5AVz1qzHoEGD1LdvX12+fFlms1lbtmzR/Pnz9eGHH+qrr74yOl6Bcfz4caWmpqpp06ZydnaW2WxmO9Q7yMrKsmztlZaWJklycXHR4MGDNXz4cNnZ2RmcEACM5+vrq7Vr18rNzU3169e/7XsLE2YAAAVdz549FRoaqpiYGL300kt6/vnn5ebmZnQsGMzOzk5t2rRRmzZtdPLkSaPjALd08OBBdejQQbt27ZLJZLJc/7zxGT4zM9PIeDaNsQfwX9387wZu79ixY5o9e7YiIiJ07tw5nT17VvPmzVPnzp0ZQys3YcIE9e3bV5UrV1ZmZqZq1aqlzMxMde3aVe+//77R8QCabwEAMEL79u0t29e3b9/+lueZTCYustigSpUqaffu3apevXqu9Z07d6pixYr5nAp3kpWVZXQE/J9XX31Vzs7Oev/995Wenq6uXbvK3d1d06ZN0wsvvGB0PKt3+vRpde7cWdHR0TKZTEpKSlLVqlXVs2dPubm5afLkyUZHtFrDhw9XWFiYPvroIzVp0kSStH79eo0ePVqXL1/W+PHjDU4IAMZr166dihQpYvmaGxgAAFs2a9YsTZ06VYsWLVJ4eLgGDhyo4OBgmc1mriMUIosXL9b8+fO1f/9+OTk5ydvbW927d1dwcDA7KMGqvfnmm/L09NTatWvl6empLVu26PTp0xo8eLAmTZpkdDybxtgD+Le+/vprffLJJ0pKSpIkeXt7a+jQoXrppZcMTmadli5dqrCwMMXFxally5aaPHmyWrZsqeLFi6tOnTpctyoAnJyc9OWXX2rkyJHatWuX0tLSVL9+fXl5eRkdDZAkmcws4QUAAJCv+vfvr5iYGG3dulVFixbNVrt06ZIaNWqkwMBATZ8+3aCEQMGRnp6utLQ0bmbdhW7duunkyZP66quvVLNmTcXHx6tq1apas2aNBg0apD179hgd0Wq5u7tr5syZOValX7Fihd544w0dO3bMoGQAAAAArEFSUpIiIiI0Z84cpaWlqXXr1urUqZM6duxodDTcB1lZWerSpYtlC1wfHx9JUkJCgpKTk9W7d2/NmDFDp0+fVlxcnDp06GBwYiC7Bx54QOvWrVPdunXl6uqqLVu2qEaNGlq3bp0GDx6s7du3Gx3RZjH2AP6NKVOmaMSIEerXr1+2xSFCQ0M1btw4vfXWWwYntD4ODg565513NGzYMLm4uFiOOzo6Kj4+XrVq1TIwHf6NjIwMXb58WSVKlDA6CiBJYk9MAAAMduTIEaMjIJ+9//77OnPmjLy9vfXxxx9rxYoVWrFihSZOnKgaNWrozJkzGj58uNExAas1duxYrVu3TpJUrFgxS+PtxYsXNXbsWCOjFQg//vijJk6cqAcffDDbcS8vL6WkpBiUqmA4c+aM5WbqP/n4+OjMmTMGJAIA6/bqq68qJibG6BgAAOQbLy8vTZgwQUeOHNG3336r9PR0denSxehYuE+mTZumn3/+WStXrlRiYqKWL1+u5cuXa9++fVq2bJkWLVqkSZMmyd/f37I6HWBNMjMzLY1IDzzwgP78809JkoeHh/bt22dkNJvH2AP4Nz777DPNmDFDEydOVNu2bdW2bVt9/PHH+t///seCPrfQs2dPhYaGqkWLFpo5c6bOnj1rdCTk0XfffafZs2dnOzZ+/HiVKFFCpUqVUlBQEP8/YRVovgUAwGAPPfSQ/P399eWXX/IBsZAoX768Nm7cqNq1a+vdd99Vhw4d1KFDB7333nuqXbu21q9fr/LlyxsdE7Bao0ePVsuWLTVlypRsx9PS0jRmzBiDUhUcFy9eVLFixXIcP3PmjGWbcOTukUce0eeff57j+Oeff65HHnnEgEQAYN1OnTqlFi1aqHLlyho6dKji4+ONjgQAQL6ws7NTmzZttHz5cibe27CIiAh98skneuaZZ3LUbjTDvPPOO6pcubIGDhyY/wGBO6hdu7blM/pjjz2mjz/+WBs2bNDYsWNVtWpVg9PZNsYewL+RmpoqPz+/HMf9/PyUmppqQCLrN2vWLKWmpqp3796aP3++KlasqHbt2slsNisrK8voeLiNKVOm6OLFi5bHGzdu1MiRIzVixAgtWrRIR44c0QcffGBgQuA6k9lsNhsdAgCAwmz79u2aN2+eFixYYLk5/eKLL6pNmzY0QRUCZ8+eVXJyssxms7y8vOTm5mZ0JMDq2dnZaf78+erbt6/atGmjWbNmycnJSSdOnJC7u7syMzONjmjVWrVqpUcffVQffPCBXFxctHPnTnl4eOiFF15QZmamli5danREqxUbG6vWrVurSpUqevzxxyVJmzZt0pEjR7R69Wo9+eSTBicEAOtz9uxZLV68WPPmzdMvv/wiHx8fhYSEqGvXrnrooYeMjgcAwD2xePFizZ8/X/v375eTk5O8vb3VvXt3BQcHGx0N95Gzs7P27dunKlWq5FpPSUlR1apVdenSJTk5OeVzOuDO1qxZo4sXL6pjx45KTk7WM888o/3796tMmTJauHChmjVrZnREm8XYA/g3ateura5du+q9997LdnzcuHFauHChdu3aZVCygiMpKUkRERGaM2eO0tLS1Lp1a3Xq1EkdO3Y0OhpuUq5cOa1Zs0b169eXJA0aNEh79+5VVFSUJGn16tV688032WEChqP5FgAAK2E2mxUTE6N58+Zp6dKlysrKUseOHRUeHm50NACwKnZ2djp+/Lj+/vtvtWnTRqVKldLy5ctlNptpvs2D3bt366mnnpKvr6/WrVuntm3bas+ePTpz5ow2bNigatWqGR3Rqv35558KDQ1VYmKiJKlmzZp644035O7ubnAyALB+R48e1fz58xUeHq6kpCRlZGQYHQkAgP8kKytLXbp00eLFi+Xt7S0fHx9JUkJCgpKTk9W7d2/NmDFDp0+fVlxcnDp06GBwYtxLpUuXVkxMjOrWrZtrfdeuXWratCm7naFAOXPmjNzc3GQymYyOUugw9gDuZOnSpXr++efVvHlzNWnSRJK0YcMGrV27VosWLeKz5l3IysrSqlWrFBYWph9++EFXrlwxOhJucvNEt0aNGum5557T0KFDJV2f6FarVq1sq+MCRrAzOgAAALjOZDIpMDBQX375pX7++Wd5enpqzpw5RscCAKtz4wJ0tWrV9Ouvv6pkyZJ69NFH9dtvvxmcrGCoXbu29u/fryeeeELt2rWzrLKxZcsWTZw40eh4Vs/d3V3jx4/X0qVLtXTpUo0bN47GWwDIg2vXrum3337T5s2bdejQIZUvX97oSAAA/GfTpk3Tzz//rJUrVyoxMVHLly/X8uXLtW/fPi1btkyLFi3SpEmT5O/vz4pMNujxxx/XjBkzblkPDQ217JoCFBSlS5em+dMgjD2AO3n22We1efNmlSlTxvK584EHHtCWLVtovL1LdnZ2atOmjZYvX64jR44YHQe5qFSpkhISEiRJaWlpio+Pl5+fn6V++vRpFStWzKh4gAUr3wIAYCWOHj2qefPmad68edq9e7cef/xxhYSE6PXXXzc6GgBYlRsr35YrV07S9RnKAwcO1IwZM5SVlcXKt/9SfHy8fH19Gb87OHfunMLCwiwXfR5++GH16NFDrq6uBicDAOsUHR2dY3ePkJAQNWvWjBvLAIACr27duho4cKB69OiRaz0sLEy9e/dWUFCQVqxYIScnp3xOiPtp48aNCggIUPv27TVkyBD5+PjIbDYrISFBkydP1ooVKxQdHW1ZmQ6wNoGBgbf9TL5u3bp8TFO4MPYAkL8WL16s+fPna//+/XJycpK3t7e6d++u4OBgo6PhFt59910tX75c7733nlavXq2NGzfq4MGDsre3lyR98cUX+vrrr7V+/XqDk6KwczA6AAAAhd2sWbM0b948bdiwQT4+PgoJCdGKFSvk4eFhdDQAsEoRERHZGh3t7Ow0ffp0+fr6KjY21sBksHW//fabgoOD5ezsrEaNGkmSpkyZovHjx+vHH3+Ur6+vwQkBwLpUqlRJZ86cUYsWLfTFF1+oTZs2KlKkiNGxAAC4Z5KSktS8efNb1m/UaLy1TX5+flq4cKF69+6tpUuXZqu5ublp/vz5NN7CqtWrVy/b42vXrmnHjh3avXu3Xn75ZWNCFRKMPYC7YWdnd8cJzCaTSRkZGfmUqODIyspSly5dtHjxYnl7e8vHx0eStH37di1evFi9e/fWjBkzdPr0acXFxbGCsBUZOXKkjh07pgEDBqhChQr69ttvLY23kjR//ny1adPGwITAdax8CwCAwSpXrqwuXbooJCREjzzyiNFxAMBqrVu3Tv369dOvv/6qkiVLZqudP39efn5+mjFjhpo2bWpQwoKNlW/v7Mknn1T16tX15ZdfysHh+lzWjIwMvfrqqzp48KDi4uIMTggA1uXLL7/Uc889p1KlShkdBQCA+6J06dKKiYlR3bp1c63v2rVLTZs21dmzZ/M5GfJTenq61qxZo6SkJEmSl5eXgoOD2QYXBdbo0aOVlpamSZMmGR2l0GHsAeRmxYoVt6xt2rRJ06dPV1ZWli5fvpyPqQqGTz/9VOPGjdOcOXP0zDPPZKutXLlS3bt317vvvqvZs2erW7duevvttw1KCqCgovkWAACDmc1mtlsFgDxo27atAgMD9dZbb+Vanz59uqKjo7Vs2bJ8TmYbaL69M2dnZ23fvt0yO/6GvXv3qkGDBkpPTzcoGQBYv6NHj0qSHnzwQYOTAABw77Ru3VpVqlTRjBkzcq2//vrrOnz4sFavXp3PyZAf8jJJeObMmXryyScNSgj8O8nJyWrUqJHOnDljdJRCh7EHkFf79u3TsGHD9N133ykkJERjx45lV9Vc1K1bVwMHDlSPHj1yrYeFhal3794KCgpitwor1axZM0VGRuaY3H/hwgW1b99e69atMyYY8H8cjA4AAEBhZzKZdO7cOYWFhSkhIUGSVKtWLfXs2TPbtuoAUNjFx8dr4sSJt6wHBQWxKsRtdOzY8bb1c+fO5U+QAqxkyZI6fPhwjubbI0eOyMXFxaBUAGC9srKyNG7cOE2ePFlpaWmSJBcXFw0ePFjDhw+XnZ2dwQkBAPhvhg8froCAAJ0+fVpDhgyRj4+PzGazEhISNHnyZK1YsULR0dFGx8R9MnXqVPXq1StH460kubq66rXXXtOUKVNovkWBs2nTJhUtWtToGIUSYw/gTv7880+NGjVKc+bMUXBwsHbs2KHatWsbHctqJSUlqXnz5res36jReGu9YmJidPXq1RzHL1++rF9++cWAREB2NN8CAGCw3377TcHBwXJ2dlajRo0kXd8CY8KECfrxxx/l6+trcEIAsA4nTpyQo6PjLesODg46depUPiYqWO40ocPV1VXdunXLpzQF0/PPP6+ePXtq0qRJ8vPzkyRt2LBBQ4cOVZcuXQxOBwDWZ/jw4QoLC9NHH32kJk2aSJLWr1+v0aNH6/Llyxo/frzBCQEA+G/8/Py0cOFC9e7dW0uXLs1Wc3Nz0/z58y3vgbA9TBJGQXfzRG2z2azU1FT99ttvGjFihEGpCgfGHsDdOn/+vCZMmKDPPvtM9erV09q1a5ngkwfOzs46d+6cqlSpkmv9woULKlmyJI23Vmjnzp2Wr/fu3avjx49bHmdmZioqKkqVKlUyIhqQjclsNpuNDgEAQGH25JNPqnr16vryyy/l4HB9XkxGRoZeffVVHTx4UHFxcQYnBADrUK1aNU2ePFnt27fPtR4ZGakhQ4bo4MGD+RsMhcbVq1c1dOhQzZw5UxkZGZIkR0dH9enTRx999JGKFClicEIAsC7u7u6aOXOm2rZtm+34ihUr9MYbb+jYsWMGJQMA4N5KT0/XmjVrlJSUJEny8vJScHCwihUrZnAy3E9FixbV7t27Vb169VzrycnJqlOnji5dupTPyYC86d69e7bHdnZ2Klu2rJo1a6agoCCDUhUOjD2Au/Hxxx9r4sSJqlChgiZMmKB27doZHanAaN26tapUqaIZM2bkWn/99dd1+PBhrV69Op+T4U7s7OxkMpkkXZ+kcjNnZ2d99tln6tGjR35HA7Kh+RYAAIM5Oztr+/btObaw3rt3rxo0aKD09HSDkgGAdenfv79iYmK0devWHNuvXbp0SY0aNVJgYKCmT59uUEIUFunp6Tpw4ICk603h3FAHgNwVLVpUO3fulLe3d7bj+/btU7169WhEAQAUeOvWrVO/fv3066+/qmTJktlq58+fl5+fn2bOnMmqZDaKScIAACA/2NnZydnZWc2bN5e9vf0tz4uMjMzHVAXDxo0bFRAQoPbt22vIkCHy8fGR2WxWQkKCJk+erBUrVig6OprdKqxQSkqKzGazqlatqi1btqhs2bKWmpOTk8qVK3fbvw9AfqH5FgAAg5UvX17ffPNNjtnMa9asUbdu3XTixAmDkgGAdTlx4oR8fX1lb2+vfv36qUaNGpKkxMREhYaGKjMzU9u2bVP58uUNTgoAACTpscce02OPPZZjYkz//v21detW/frrrwYlAwDg3mjbtq0CAwP11ltv5VqfPn26oqOjtWzZsnxOhvzAJGEAAJAfXnnlFcsKoLcTERGRD2kKnmXLlql37946c+ZMtuNubm6aNWuWnn32WYOSAbAFNN8CAGCwAQMGaNmyZZo0aZL8/PwkSRs2bNDQoUP17LPPaurUqcYGBAArkpKSoj59+mjNmjWWbWZMJpOCg4MVGhoqT09PgxPCVkVHR2vbtm1q3LixmjRpolmzZmn8+PG6dOmS2rdvr+nTp8vZ2dnomABgVWJjYy3b+z3++OOSpE2bNunIkSNavXo1qwACAAo8Dw8PRUVFqWbNmrnWExMTFRQUpMOHD+dzMuQHJgmjoHNzc8u1mctkMqlo0aKqXr26XnnlFXXv3t2AdLaNsQeA/JWenq41a9YoKSlJkuTl5aXg4GB2tSsgvvnmG82cOVN//PGHNm3aJA8PD3366aeqWrWq2rVrZ3Q8FHIORgcAAKCwmzRpkkwmk7p166aMjAyZzWY5OTmpT58++uijj4yOBwBWxcPDQ6tXr9bZs2eVnJwss9ksLy8vubm5GR0NNuzLL79Unz595OnpqeHDh2vUqFEaP368XnrpJdnZ2enbb79VmTJleN8GgJv4+/tr//79Cg0NVWJioiSpY8eOeuONN+Tu7m5wOgAA/rsTJ07I0dHxlnUHBwedOnUqHxMhP5UvX14bN25Unz599O677+Y6SZjGW1izkSNHavz48WrZsqUaNWokSdqyZYuioqLUt29f/fHHH+rTp48yMjLUq1cvg9PaFsYeAPLHunXr1K9fP/3666/q0KFDttr58+f18MMPa+bMmUwQt2IzZszQyJEjNXDgQI0fP16ZmZmSrk9kmTp1Ks23MBwr3wIAYCXS09N14MABSVK1atWYaQcAgJWoXbu2XnvtNfXv319RUVFq06aNvvrqK7388suSpMWLF+vdd99VcnKywUkBAAAA5Kdq1app8uTJat++fa71yMhIDRkyRAcPHszfYMh3TBJGQfTss8/q6aef1uuvv57t+KxZs/Tjjz9q6dKl+uyzz/TFF19o165dBqW0TYw9AOSPtm3bKjAwUG+99Vau9enTpys6OlrLli3L52TIq1q1amnChAlq3769XFxcFB8fr6pVq2r37t0KCAjQX3/9ZXREFHI03wIAYJAePXrk6bzw8PD7nAQAANxOsWLFlJCQIA8PD0mSk5OT4uPjLVvLHj58WF5eXrpy5YqRMQHAauR1a+0qVarc5yQAANxf/fv3V0xMjLZu3aqiRYtmq126dEmNGjVSYGCgpk+fblBCALi1EiVKaMeOHapevXq248nJyapXr57S0tJ04MAB1a1bVxcvXjQopW1i7AEgf3h4eCgqKspyLf9miYmJCgoKyvO1LOQ/Z2dnJSYmysPDI1vzbVJSkurWratLly4ZHRGFnIPRAQAAKKxmz54tDw8P1a9fX8yFAQDAel2+fFnOzs6Wx0WKFFGRIkWyPc7IyDAiGgBYJU9PT8vX/9x++Z/HTCaTZZs4AAAKqvfff1+RkZHy9vZWv379VKNGDUnXb+KHhoYqMzNTw4cPNzglAOSudOnS+u6773KsBvjdd9+pdOnSkqSLFy/KxcXFiHg2jbEHgPxx4sQJOTo63rLu4OCgU6dO5WMi3C1PT0/t2LHDsjjKDbdrqgbyE823AAAYpE+fPpo/f77++OMPde/eXS+++KLlogoAALAeJpNJf//9t4oWLWppGEtLS9OFCxckyfJfAMB1JpNJDz74oF555RW1adNGDg5cggQA2Kby5ctr48aN6tOnj959991sk06Cg4MVGhqq8uXLG5wSAHI3YsQI9enTR9HR0WrUqJEkaevWrVq9erVmzpwpSfrpp5/k7+9vZEybxNgDQP6oVKmSdu/enWOl8Rt27typihUr5nMq3I1Bgwapb9++unz5ssxms7Zs2aL58+frww8/1FdffWV0PEAmM0vtAQBgmCtXrigyMlLh4eHauHGjWrdurZ49eyooKCjbylAAAMA4dnZ2ua7YePNjVnAEgOuOHz+uOXPmKCIiQufOndOLL76onj17shoFAMCmnT17VsnJyTKbzfLy8pKbm5vRkQDgjjZs2KDPP/9c+/btkyTVqFFD/fv3l5+fn8HJbB9jDwD3X//+/RUTE6OtW7eqaNGi2WqXLl1So0aNFBgYqOnTpxuUEHkxd+5cjR49WgcOHJAkubu7a8yYMerZs6fByQCabwEAsBopKSmaPXu2vv76a2VkZGjPnj0qUaKE0bEAACj0YmNj83Qeq5EAQE7r169XRESEFi9erFq1aqlnz57q2bOn7OzsjI4GAAAAAAAAG3bixAn5+vrK3t5e/fr1U40aNSRJiYmJCg0NVWZmprZt28ZuFQVEenq60tLSVK5cOaOjABY03wIAYCWOHDmiiIgIzZ49W1evXlViYiLNtwAAAABswokTJ9SlSxfFxsbq1KlTKl26tNGRAAAAgEIvKytLycnJOnnypLKysrLVmjZtalCqwoGxB4D8kZKSoj59+mjNmjW60SJnMpkUHBys0NBQeXp6GpwQd5KRkaGYmBgdOHBAXbt2lYuLi/7880+VLFmSfgoYjuZbAAAMdOXKFUVGRio8PFzr16/XM888o+7du6tFixasBAUAgBXixggA3J2NGzcqPDxcixcvVo0aNdSjRw/17t2bn3cAAAAAg/3666/q2rWrUlJSdHPLgMlkUmZmpkHJbB9jDwD57+zZs0pOTpbZbJaXl5fc3NyMjoQ8SElJUYsWLXT48GFduXJF+/fvV9WqVfXmm2/qypUrmjlzptERUcg5GB0AAIDC6o033tCCBQtUuXJl9ejRQ/Pnz9cDDzxgdCwAAHAL3BgBgLxJTU3V119/rYiICJ09e1YhISHasGGDateubXQ0AAAAAP/n9ddfV4MGDbRq1SpVrFhRJpPJ6EiFBmMPAPnPzc1NDRs2NDoG7tKbb76pBg0aKD4+XmXKlLEc79Chg3r16mVgMuA6Vr4FAMAgdnZ2qlKliurXr3/bCyuRkZH5mAoAANxKvXr15O3trTFjxuR6Y8TV1dWgZABgXRwdHVWpUiW9/PLLatu2rRwdHXM9r27duvmcDAAAAMANxYsXV3x8vKpXr250lEKHsQcAIG/KlCmjjRs3qkaNGnJxcVF8fLyqVq2qQ4cOqVatWkpPTzc6Igo5Vr4FAMAg3bp1YzYzAAAFSFJSkpYsWcKNEQC4g8zMTB0+fFgffPCBxo0bJ0msGA4AAABYmccee0zJyclc5zAAYw8AQN5kZWXleg3x6NGjcnFxMSARkB3NtwAAGGT27NlGRwAAAHeBGyMAkDd//PGH0REAAAAA3EH//v01ePBgHT9+XHXq1MmxYwU7Vdw/jD0AAHkTFBSkqVOn6osvvpB0fUJ/WlqaRo0apVatWhmcDpBM5puXnQAAAAAAAJKknTt3Wr4+cOCA3n//fQ0dOpQbIwAAAAAAoECzs7PLccxkMslsNrNTxX3G2AMAkDdHjx5VcHCwzGazkpKS1KBBAyUlJemBBx5QXFycypUrZ3REFHI03wIAAAAAcAt2dnaWmx+54cYIAOT01FNPqW/fvurYsWOu9b/++kuNGjXSwYMH8zkZAAAAgBtSUlJuW/fw8MinJIUPYw8AQN5lZGRowYIF2rlzp9LS0uTr66uQkBA5OzsbHQ2g+RYAAAAAgFu5082Qf+LGCABcZ2dnJzs7Ow0fPlxjxozJUT9x4oTc3d2ZtAAAAAAAAAAAKLAcjA4AAAAAAIC1oqEWAP6dGTNmaMiQIdq5c6e+/fZbFS9e3OhIAAAAQKG3cuVKtWzZUo6Ojlq5cuVtz23btm0+pSocGHsAAO5elSpVFBAQIH9/fwUGBqpq1apGRwKyYeVbAAAAAADy4MMPP1T58uXVo0ePbMfDw8N16tQpvfPOOwYlAwDrYmdnp+PHj+v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-                        "text/plain": [
-                            "<Figure size 3500x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# plotting the predictability scores with the tasks removed\n",
-                "\n",
-                "plt.figure(figsize=(35, 6))\n",
-                "\n",
-                "for metric in [\"mse_with_zscore\"]:\n",
-                "    plt.plot([t[metric] for t in predicability_scores], label=metric)\n",
-                "\n",
-                "plt.xlabel(\"Tasks removed\")\n",
-                "plt.ylabel(\"Normalized Mean Squared error (predicted vs. observed performance)\")\n",
-                "plt.legend()\n",
-                "\n",
-                "# add vline for 0.5 mse\n",
-                "plt.axhline(y=0.5, color=\"r\", linestyle=\"--\")\n",
-                "\n",
-                "# add task names to the x-axis\n",
-                "plt.xticks(range(len(tasks_removed)), tasks_removed, rotation=90)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Constructing the Benchmark"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 23,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "['BornholmBitextMining',\n",
-                            " 'BibleNLPBitextMining',\n",
-                            " 'BUCC.v2',\n",
-                            " 'DiaBlaBitextMining',\n",
-                            " 'FloresBitextMining',\n",
-                            " 'NorwegianCourtsBitextMining',\n",
-                            " 'NTREXBitextMining',\n",
-                            " 'BulgarianStoreReviewSentimentClassfication',\n",
-                            " 'CzechProductReviewSentimentClassification',\n",
-                            " 'GreekLegalCodeClassification',\n",
-                            " 'DBpediaClassification',\n",
-                            " 'FinancialPhrasebankClassification',\n",
-                            " 'PoemSentimentClassification',\n",
-                            " 'ToxicChatClassification',\n",
-                            " 'ToxicConversationsClassification',\n",
-                            " 'EstonianValenceClassification',\n",
-                            " 'ItaCaseholdClassification',\n",
-                            " 'AmazonCounterfactualClassification',\n",
-                            " 'MassiveScenarioClassification',\n",
-                            " 'MultiHateClassification',\n",
-                            " 'NordicLangClassification',\n",
-                            " 'ScalaClassification',\n",
-                            " 'SwissJudgementClassification',\n",
-                            " 'TweetSentimentClassification',\n",
-                            " 'CBD',\n",
-                            " 'PolEmo2.0-OUT',\n",
-                            " 'CSFDSKMovieReviewSentimentClassification',\n",
-                            " 'DalajClassification',\n",
-                            " 'WikiCitiesClustering',\n",
-                            " 'RomaniBibleClustering',\n",
-                            " 'BigPatentClustering.v2',\n",
-                            " 'BiorxivClusteringP2P.v2',\n",
-                            " 'AlloProfClusteringS2S.v2',\n",
-                            " 'HALClusteringS2S.v2',\n",
-                            " 'SIB200ClusteringS2S',\n",
-                            " 'WikiClusteringP2P.v2',\n",
-                            " 'StackOverflowQA',\n",
-                            " 'TwitterHjerneRetrieval',\n",
-                            " 'LegalQuAD',\n",
-                            " 'ArguAna',\n",
-                            " 'HagridRetrieval',\n",
-                            " 'LegalBenchCorporateLobbying',\n",
-                            " 'LEMBPasskeyRetrieval',\n",
-                            " 'SCIDOCS',\n",
-                            " 'SpartQA',\n",
-                            " 'TempReasonL1',\n",
-                            " 'WinoGrande',\n",
-                            " 'AlloprofRetrieval',\n",
-                            " 'BelebeleRetrieval',\n",
-                            " 'StatcanDialogueDatasetRetrieval',\n",
-                            " 'WikipediaRetrievalMultilingual',\n",
-                            " 'Core17InstructionRetrieval',\n",
-                            " 'News21InstructionRetrieval',\n",
-                            " 'Robust04InstructionRetrieval',\n",
-                            " 'MalteseNewsClassification',\n",
-                            " 'MultiEURLEXMultilabelClassification',\n",
-                            " 'CTKFactsNLI',\n",
-                            " 'SprintDuplicateQuestions',\n",
-                            " 'OpusparcusPC',\n",
-                            " 'RTE3',\n",
-                            " 'XNLI',\n",
-                            " 'PSC',\n",
-                            " 'WebLINXCandidatesReranking',\n",
-                            " 'AlloprofReranking',\n",
-                            " 'WikipediaRerankingMultilingual',\n",
-                            " 'SICK-R',\n",
-                            " 'STS12',\n",
-                            " 'STS14',\n",
-                            " 'STS15',\n",
-                            " 'STSBenchmark',\n",
-                            " 'FinParaSTS',\n",
-                            " 'STS17',\n",
-                            " 'SICK-R-PL',\n",
-                            " 'STSES']"
-                        ]
-                    },
-                    "execution_count": 23,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# we now have the tasks:\n",
-                "tasks_to_select_from"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 24,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "tasks = mteb.get_tasks(tasks=tasks_to_select_from, languages=eu_languages)\n",
-                "\n",
-                "# we can now create a benchmark\n",
-                "benchmark = mteb.Benchmark(\n",
-                "    name=\"MTEB(Europe)\",\n",
-                "    tasks=tasks,\n",
-                "    description=\"Benchmark for evaluating document embedding models for European languages\",\n",
-                "    citation=\"\",\n",
-                "    reference=\"\",\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 25,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th>Languages</th>\n",
-                            "      <th>Domains</th>\n",
-                            "      <th>License</th>\n",
-                            "      <th>Description</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Type</th>\n",
-                            "      <th>Name</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"7\" valign=\"top\">BitextMining</th>\n",
-                            "      <th>BornholmBitextMining</th>\n",
-                            "      <td>{dan}</td>\n",
-                            "      <td>[Web, Social, Fiction, Written]</td>\n",
-                            "      <td>CC-BY-4.0</td>\n",
-                            "      <td>Danish Bornholmsk Parallel Corpus. Bornholmsk ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>BibleNLPBitextMining</th>\n",
-                            "      <td>{hrv, lit, por, ita, nld, dan, ces, spa, pol, ...</td>\n",
-                            "      <td>[Religious, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>Partial Bible translations in 829 languages, a...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>BUCC.v2</th>\n",
-                            "      <td>{eng, fra, deu}</td>\n",
-                            "      <td>[Written]</td>\n",
-                            "      <td>Unknown</td>\n",
-                            "      <td>BUCC bitext mining dataset</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>DiaBlaBitextMining</th>\n",
-                            "      <td>{eng, fra}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>English-French Parallel Corpus. DiaBLa is an E...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>FloresBitextMining</th>\n",
-                            "      <td>{fin, nob, ces, pol, swe, eng, lit, slk, nno, ...</td>\n",
-                            "      <td>[Non-fiction, Encyclopaedic, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>FLORES is a benchmark dataset for machine tran...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NorwegianCourtsBitextMining</th>\n",
-                            "      <td>{nob, nno}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>Nynorsk and Bokmål parallel corpus from Norweg...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NTREXBitextMining</th>\n",
-                            "      <td>{fin, nob, ces, pol, swe, eng, lit, slk, nno, ...</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>NTREX is a News Test References dataset for Ma...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"21\" valign=\"top\">Classification</th>\n",
-                            "      <th>BulgarianStoreReviewSentimentClassfication</th>\n",
-                            "      <td>{bul}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Bulgarian online store review dataset for sent...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>CzechProductReviewSentimentClassification</th>\n",
-                            "      <td>{ces}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>User reviews of products on Czech e-shop Mall....</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>GreekLegalCodeClassification</th>\n",
-                            "      <td>{ell}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Greek Legal Code Dataset for Classification. (...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>DBpediaClassification</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>DBpedia14 is a dataset of English texts from W...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>FinancialPhrasebankClassification</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>cc-by-nc-sa-3.0</td>\n",
-                            "      <td>Polar sentiment dataset of sentences from fina...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PoemSentimentClassification</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC-BY-4.0</td>\n",
-                            "      <td>Poem Sentiment is a sentiment dataset of poem ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ToxicChatClassification</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Constructed, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>This dataset contains toxicity annotations on ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ToxicConversationsClassification</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>Collection of comments from the Civil Comments...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>EstonianValenceClassification</th>\n",
-                            "      <td>{est}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>Dataset containing annotated Estonian news dat...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ItaCaseholdClassification</th>\n",
-                            "      <td>{ita}</td>\n",
-                            "      <td>[Legal, Government, Written]</td>\n",
-                            "      <td>Apache 2.0</td>\n",
-                            "      <td>An Italian Dataset consisting of 1101 pairs of...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>AmazonCounterfactualClassification</th>\n",
-                            "      <td>{eng, deu}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>A collection of Amazon customer reviews annota...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MassiveScenarioClassification</th>\n",
-                            "      <td>{fin, por, nob, ita, nld, dan, lav, ell, isl, ...</td>\n",
-                            "      <td>[Spoken]</td>\n",
-                            "      <td>Apache 2.0</td>\n",
-                            "      <td>MASSIVE: A 1M-Example Multilingual Natural Lan...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MultiHateClassification</th>\n",
-                            "      <td>{por, ita, nld, spa, pol, fra, eng, deu}</td>\n",
-                            "      <td>[Constructed, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Hate speech detection dataset with binary\\n   ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NordicLangClassification</th>\n",
-                            "      <td>{nob, nno, dan, isl, swe}</td>\n",
-                            "      <td>[Encyclopaedic]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>A dataset for Nordic language identification.</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ScalaClassification</th>\n",
-                            "      <td>{swe, dan, nob, nno}</td>\n",
-                            "      <td>[Fiction, News, Non-fiction, Blog, Spoken, Web...</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>ScaLa a linguistic acceptability dataset for t...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SwissJudgementClassification</th>\n",
-                            "      <td>{deu, fra, ita}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>CC-BY-4.0</td>\n",
-                            "      <td>Multilingual, diachronic dataset of Swiss Fede...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>TweetSentimentClassification</th>\n",
-                            "      <td>{por, ita, spa, fra, eng, deu}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>cc-by-3.0</td>\n",
-                            "      <td>A multilingual Sentiment Analysis dataset cons...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>CBD</th>\n",
-                            "      <td>{pol}</td>\n",
-                            "      <td>[Written, Social]</td>\n",
-                            "      <td>bsd-3-clause</td>\n",
-                            "      <td>Polish Tweets annotated for cyberbullying dete...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PolEmo2.0-OUT</th>\n",
-                            "      <td>{pol}</td>\n",
-                            "      <td>[Written, Social]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>A collection of Polish online reviews from fou...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>CSFDSKMovieReviewSentimentClassification</th>\n",
-                            "      <td>{slk}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>The dataset contains 30k user reviews from csf...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>DalajClassification</th>\n",
-                            "      <td>{swe}</td>\n",
-                            "      <td>[Non-fiction, Written]</td>\n",
-                            "      <td>CC-BY-4.0</td>\n",
-                            "      <td>A Swedish dataset for linguistic acceptability...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"8\" valign=\"top\">Clustering</th>\n",
-                            "      <th>WikiCitiesClustering</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>Clustering of Wikipedia articles of cities by ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RomaniBibleClustering</th>\n",
-                            "      <td>{rom}</td>\n",
-                            "      <td>[Religious, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>Clustering verses from the Bible in Kalderash ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>BigPatentClustering.v2</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Clustering of documents from the Big Patent da...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>BiorxivClusteringP2P.v2</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Academic, Written]</td>\n",
-                            "      <td>https://www.biorxiv.org/content/about-biorxiv</td>\n",
-                            "      <td>Clustering of titles+abstract from biorxiv acr...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>AlloProfClusteringS2S.v2</th>\n",
-                            "      <td>{fra}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>mit</td>\n",
-                            "      <td>Clustering of document titles from Allo Prof d...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>HALClusteringS2S.v2</th>\n",
-                            "      <td>{fra}</td>\n",
-                            "      <td>[Academic, Written]</td>\n",
-                            "      <td>Apache-2.0</td>\n",
-                            "      <td>Clustering of titles from HAL (https://hugging...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SIB200ClusteringS2S</th>\n",
-                            "      <td>{fin, nob, ces, pol, swe, eng, lit, slk, nno, ...</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>SIB-200 is the largest publicly available topi...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WikiClusteringP2P.v2</th>\n",
-                            "      <td>{lav, dan, ces, eus, mlt}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>Clustering of wikipedia articles inspired by B...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"15\" valign=\"top\">Retrieval</th>\n",
-                            "      <th>StackOverflowQA</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Programming, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>The dataset is a collection of natural languag...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>TwitterHjerneRetrieval</th>\n",
-                            "      <td>{dan}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>Danish question asked on Twitter with the Hash...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LegalQuAD</th>\n",
-                            "      <td>{deu}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>The dataset consists of questions and legal do...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ArguAna</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Medical, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>NFCorpus: A Full-Text Learning to Rank Dataset...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>HagridRetrieval</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>apache-2.0</td>\n",
-                            "      <td>HAGRID (Human-in-the-loop Attributable Generat...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LegalBenchCorporateLobbying</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>The dataset includes bill titles and bill summ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LEMBPasskeyRetrieval</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Fiction, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>passkey subset of dwzhu/LongEmbed dataset.</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SCIDOCS</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Academic, Written, Non-fiction]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>SciDocs, a new evaluation benchmark consisting...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SpartQA</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>Measuring the ability to retrieve the groundtr...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>TempReasonL1</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>CC BY-SA 3.0</td>\n",
-                            "      <td>Measuring the ability to retrieve the groundtr...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WinoGrande</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>CC BY</td>\n",
-                            "      <td>Measuring the ability to retrieve the groundtr...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>AlloprofRetrieval</th>\n",
-                            "      <td>{fra}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-nc-sa-4.0</td>\n",
-                            "      <td>This dataset was provided by AlloProf, an orga...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>BelebeleRetrieval</th>\n",
-                            "      <td>{fin, nob, ces, pol, swe, eng, lit, slk, nld, ...</td>\n",
-                            "      <td>[Web, News, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>Belebele is a multiple-choice machine reading ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>StatcanDialogueDatasetRetrieval</th>\n",
-                            "      <td>{eng, fra}</td>\n",
-                            "      <td>[Government, Web, Written]</td>\n",
-                            "      <td>https://huggingface.co/datasets/McGill-NLP/sta...</td>\n",
-                            "      <td>A Dataset for Retrieving Data Tables through C...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WikipediaRetrievalMultilingual</th>\n",
-                            "      <td>{fin, por, ita, nld, dan, ces, ron, swe, eng, ...</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>The dataset is derived from Cohere's wikipedia...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"3\" valign=\"top\">InstructionRetrieval</th>\n",
-                            "      <th>Core17InstructionRetrieval</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>Measuring retrieval instruction following abil...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>News21InstructionRetrieval</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>Measuring retrieval instruction following abil...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Robust04InstructionRetrieval</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>Measuring retrieval instruction following abil...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"2\" valign=\"top\">MultilabelClassification</th>\n",
-                            "      <th>MalteseNewsClassification</th>\n",
-                            "      <td>{mlt}</td>\n",
-                            "      <td>[Constructed, Written]</td>\n",
-                            "      <td>cc-by-nc-sa-4.0</td>\n",
-                            "      <td>A multi-label topic classification dataset for...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MultiEURLEXMultilabelClassification</th>\n",
-                            "      <td>{fin, ces, pol, swe, eng, lit, slk, nld, dan, ...</td>\n",
-                            "      <td>[Legal, Government, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>EU laws in 23 EU languages containing gold lab...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"6\" valign=\"top\">PairClassification</th>\n",
-                            "      <th>CTKFactsNLI</th>\n",
-                            "      <td>{ces}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC-BY-SA-3.0</td>\n",
-                            "      <td>Czech Natural Language Inference dataset of ar...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SprintDuplicateQuestions</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Programming, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>Duplicate questions from the Sprint community.</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>OpusparcusPC</th>\n",
-                            "      <td>{fin, fra, swe, eng, deu}</td>\n",
-                            "      <td>[Spoken, Spoken]</td>\n",
-                            "      <td>cc-by-nc-4.0</td>\n",
-                            "      <td>Opusparcus is a paraphrase corpus for six Euro...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RTE3</th>\n",
-                            "      <td>{deu, eng, fra, ita}</td>\n",
-                            "      <td>[News, Web, Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Recognising Textual Entailment Challenge (RTE-...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>XNLI</th>\n",
-                            "      <td>{ell, spa, fra, deu, eng, bul}</td>\n",
-                            "      <td>[Non-fiction, Fiction, Government, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td></td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PSC</th>\n",
-                            "      <td>{pol}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>cc-by-3</td>\n",
-                            "      <td>Polish Summaries Corpus</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"3\" valign=\"top\">Reranking</th>\n",
-                            "      <th>WebLINXCandidatesReranking</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Academic, Web, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>WebLINX is a large-scale benchmark of 100K int...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>AlloprofReranking</th>\n",
-                            "      <td>{fra}</td>\n",
-                            "      <td>[Web, Academic, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>This dataset was provided by AlloProf, an orga...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WikipediaRerankingMultilingual</th>\n",
-                            "      <td>{fin, por, ita, nld, dan, ces, ron, swe, eng, ...</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>The dataset is derived from Cohere's wikipedia...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"9\" valign=\"top\">STS</th>\n",
-                            "      <th>SICK-R</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Semantic Textual Similarity SICK-R dataset as ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STS12</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, News, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>SemEval-2012 Task 6.</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STS14</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Blog, Web, Spoken]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>SemEval STS 2014 dataset. Currently only the E...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STS15</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Blog, News, Web, Written, Spoken]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>SemEval STS 2015 dataset</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STSBenchmark</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Semantic Textual Similarity Benchmark (STSbenc...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>FinParaSTS</th>\n",
-                            "      <td>{fin}</td>\n",
-                            "      <td>[News, Subtitles, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>Finnish paraphrase-based semantic similarity c...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STS17</th>\n",
-                            "      <td>{ita, nld, spa, fra, eng, deu}</td>\n",
-                            "      <td>[News, Web, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>Semeval-2017 task 1: Semantic textual similari...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SICK-R-PL</th>\n",
-                            "      <td>{pol}</td>\n",
-                            "      <td>[Web, Written]</td>\n",
-                            "      <td>CC-BY-NC-SA-3.0</td>\n",
-                            "      <td>Polish version of SICK dataset for textual rel...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STSES</th>\n",
-                            "      <td>{spa}</td>\n",
-                            "      <td>[Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Spanish test sets from SemEval-2014 (Agirre et...</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                                                                             Languages  \\\n",
-                            "Type                     Name                                                                                            \n",
-                            "BitextMining             BornholmBitextMining                                                                    {dan}   \n",
-                            "                         BibleNLPBitextMining                        {hrv, lit, por, ita, nld, dan, ces, spa, pol, ...   \n",
-                            "                         BUCC.v2                                                                       {eng, fra, deu}   \n",
-                            "                         DiaBlaBitextMining                                                                 {eng, fra}   \n",
-                            "                         FloresBitextMining                          {fin, nob, ces, pol, swe, eng, lit, slk, nno, ...   \n",
-                            "                         NorwegianCourtsBitextMining                                                        {nob, nno}   \n",
-                            "                         NTREXBitextMining                           {fin, nob, ces, pol, swe, eng, lit, slk, nno, ...   \n",
-                            "Classification           BulgarianStoreReviewSentimentClassfication                                              {bul}   \n",
-                            "                         CzechProductReviewSentimentClassification                                               {ces}   \n",
-                            "                         GreekLegalCodeClassification                                                            {ell}   \n",
-                            "                         DBpediaClassification                                                                   {eng}   \n",
-                            "                         FinancialPhrasebankClassification                                                       {eng}   \n",
-                            "                         PoemSentimentClassification                                                             {eng}   \n",
-                            "                         ToxicChatClassification                                                                 {eng}   \n",
-                            "                         ToxicConversationsClassification                                                        {eng}   \n",
-                            "                         EstonianValenceClassification                                                           {est}   \n",
-                            "                         ItaCaseholdClassification                                                               {ita}   \n",
-                            "                         AmazonCounterfactualClassification                                                 {eng, deu}   \n",
-                            "                         MassiveScenarioClassification               {fin, por, nob, ita, nld, dan, lav, ell, isl, ...   \n",
-                            "                         MultiHateClassification                              {por, ita, nld, spa, pol, fra, eng, deu}   \n",
-                            "                         NordicLangClassification                                            {nob, nno, dan, isl, swe}   \n",
-                            "                         ScalaClassification                                                      {swe, dan, nob, nno}   \n",
-                            "                         SwissJudgementClassification                                                  {deu, fra, ita}   \n",
-                            "                         TweetSentimentClassification                                   {por, ita, spa, fra, eng, deu}   \n",
-                            "                         CBD                                                                                     {pol}   \n",
-                            "                         PolEmo2.0-OUT                                                                           {pol}   \n",
-                            "                         CSFDSKMovieReviewSentimentClassification                                                {slk}   \n",
-                            "                         DalajClassification                                                                     {swe}   \n",
-                            "Clustering               WikiCitiesClustering                                                                    {eng}   \n",
-                            "                         RomaniBibleClustering                                                                   {rom}   \n",
-                            "                         BigPatentClustering.v2                                                                  {eng}   \n",
-                            "                         BiorxivClusteringP2P.v2                                                                 {eng}   \n",
-                            "                         AlloProfClusteringS2S.v2                                                                {fra}   \n",
-                            "                         HALClusteringS2S.v2                                                                     {fra}   \n",
-                            "                         SIB200ClusteringS2S                         {fin, nob, ces, pol, swe, eng, lit, slk, nno, ...   \n",
-                            "                         WikiClusteringP2P.v2                                                {lav, dan, ces, eus, mlt}   \n",
-                            "Retrieval                StackOverflowQA                                                                         {eng}   \n",
-                            "                         TwitterHjerneRetrieval                                                                  {dan}   \n",
-                            "                         LegalQuAD                                                                               {deu}   \n",
-                            "                         ArguAna                                                                                 {eng}   \n",
-                            "                         HagridRetrieval                                                                         {eng}   \n",
-                            "                         LegalBenchCorporateLobbying                                                             {eng}   \n",
-                            "                         LEMBPasskeyRetrieval                                                                    {eng}   \n",
-                            "                         SCIDOCS                                                                                 {eng}   \n",
-                            "                         SpartQA                                                                                 {eng}   \n",
-                            "                         TempReasonL1                                                                            {eng}   \n",
-                            "                         WinoGrande                                                                              {eng}   \n",
-                            "                         AlloprofRetrieval                                                                       {fra}   \n",
-                            "                         BelebeleRetrieval                           {fin, nob, ces, pol, swe, eng, lit, slk, nld, ...   \n",
-                            "                         StatcanDialogueDatasetRetrieval                                                    {eng, fra}   \n",
-                            "                         WikipediaRetrievalMultilingual              {fin, por, ita, nld, dan, ces, ron, swe, eng, ...   \n",
-                            "InstructionRetrieval     Core17InstructionRetrieval                                                              {eng}   \n",
-                            "                         News21InstructionRetrieval                                                              {eng}   \n",
-                            "                         Robust04InstructionRetrieval                                                            {eng}   \n",
-                            "MultilabelClassification MalteseNewsClassification                                                               {mlt}   \n",
-                            "                         MultiEURLEXMultilabelClassification         {fin, ces, pol, swe, eng, lit, slk, nld, dan, ...   \n",
-                            "PairClassification       CTKFactsNLI                                                                             {ces}   \n",
-                            "                         SprintDuplicateQuestions                                                                {eng}   \n",
-                            "                         OpusparcusPC                                                        {fin, fra, swe, eng, deu}   \n",
-                            "                         RTE3                                                                     {deu, eng, fra, ita}   \n",
-                            "                         XNLI                                                           {ell, spa, fra, deu, eng, bul}   \n",
-                            "                         PSC                                                                                     {pol}   \n",
-                            "Reranking                WebLINXCandidatesReranking                                                              {eng}   \n",
-                            "                         AlloprofReranking                                                                       {fra}   \n",
-                            "                         WikipediaRerankingMultilingual              {fin, por, ita, nld, dan, ces, ron, swe, eng, ...   \n",
-                            "STS                      SICK-R                                                                                  {eng}   \n",
-                            "                         STS12                                                                                   {eng}   \n",
-                            "                         STS14                                                                                   {eng}   \n",
-                            "                         STS15                                                                                   {eng}   \n",
-                            "                         STSBenchmark                                                                            {eng}   \n",
-                            "                         FinParaSTS                                                                              {fin}   \n",
-                            "                         STS17                                                          {ita, nld, spa, fra, eng, deu}   \n",
-                            "                         SICK-R-PL                                                                               {pol}   \n",
-                            "                         STSES                                                                                   {spa}   \n",
-                            "\n",
-                            "                                                                                                               Domains  \\\n",
-                            "Type                     Name                                                                                            \n",
-                            "BitextMining             BornholmBitextMining                                          [Web, Social, Fiction, Written]   \n",
-                            "                         BibleNLPBitextMining                                                     [Religious, Written]   \n",
-                            "                         BUCC.v2                                                                             [Written]   \n",
-                            "                         DiaBlaBitextMining                                                          [Social, Written]   \n",
-                            "                         FloresBitextMining                                      [Non-fiction, Encyclopaedic, Written]   \n",
-                            "                         NorwegianCourtsBitextMining                                                  [Legal, Written]   \n",
-                            "                         NTREXBitextMining                                                             [News, Written]   \n",
-                            "Classification           BulgarianStoreReviewSentimentClassfication                                 [Reviews, Written]   \n",
-                            "                         CzechProductReviewSentimentClassification                                  [Reviews, Written]   \n",
-                            "                         GreekLegalCodeClassification                                                 [Legal, Written]   \n",
-                            "                         DBpediaClassification                                                [Encyclopaedic, Written]   \n",
-                            "                         FinancialPhrasebankClassification                                             [News, Written]   \n",
-                            "                         PoemSentimentClassification                                                [Reviews, Written]   \n",
-                            "                         ToxicChatClassification                                                [Constructed, Written]   \n",
-                            "                         ToxicConversationsClassification                                            [Social, Written]   \n",
-                            "                         EstonianValenceClassification                                                 [News, Written]   \n",
-                            "                         ItaCaseholdClassification                                        [Legal, Government, Written]   \n",
-                            "                         AmazonCounterfactualClassification                                         [Reviews, Written]   \n",
-                            "                         MassiveScenarioClassification                                                        [Spoken]   \n",
-                            "                         MultiHateClassification                                                [Constructed, Written]   \n",
-                            "                         NordicLangClassification                                                      [Encyclopaedic]   \n",
-                            "                         ScalaClassification                         [Fiction, News, Non-fiction, Blog, Spoken, Web...   \n",
-                            "                         SwissJudgementClassification                                                 [Legal, Written]   \n",
-                            "                         TweetSentimentClassification                                                [Social, Written]   \n",
-                            "                         CBD                                                                         [Written, Social]   \n",
-                            "                         PolEmo2.0-OUT                                                               [Written, Social]   \n",
-                            "                         CSFDSKMovieReviewSentimentClassification                                   [Reviews, Written]   \n",
-                            "                         DalajClassification                                                    [Non-fiction, Written]   \n",
-                            "Clustering               WikiCitiesClustering                                                 [Encyclopaedic, Written]   \n",
-                            "                         RomaniBibleClustering                                                    [Religious, Written]   \n",
-                            "                         BigPatentClustering.v2                                                       [Legal, Written]   \n",
-                            "                         BiorxivClusteringP2P.v2                                                   [Academic, Written]   \n",
-                            "                         AlloProfClusteringS2S.v2                                             [Encyclopaedic, Written]   \n",
-                            "                         HALClusteringS2S.v2                                                       [Academic, Written]   \n",
-                            "                         SIB200ClusteringS2S                                                           [News, Written]   \n",
-                            "                         WikiClusteringP2P.v2                                                 [Encyclopaedic, Written]   \n",
-                            "Retrieval                StackOverflowQA                                                        [Programming, Written]   \n",
-                            "                         TwitterHjerneRetrieval                                                      [Social, Written]   \n",
-                            "                         LegalQuAD                                                                    [Legal, Written]   \n",
-                            "                         ArguAna                                                                    [Medical, Written]   \n",
-                            "                         HagridRetrieval                                                      [Encyclopaedic, Written]   \n",
-                            "                         LegalBenchCorporateLobbying                                                  [Legal, Written]   \n",
-                            "                         LEMBPasskeyRetrieval                                                       [Fiction, Written]   \n",
-                            "                         SCIDOCS                                                      [Academic, Written, Non-fiction]   \n",
-                            "                         SpartQA                                                              [Encyclopaedic, Written]   \n",
-                            "                         TempReasonL1                                                         [Encyclopaedic, Written]   \n",
-                            "                         WinoGrande                                                           [Encyclopaedic, Written]   \n",
-                            "                         AlloprofRetrieval                                                    [Encyclopaedic, Written]   \n",
-                            "                         BelebeleRetrieval                                                        [Web, News, Written]   \n",
-                            "                         StatcanDialogueDatasetRetrieval                                    [Government, Web, Written]   \n",
-                            "                         WikipediaRetrievalMultilingual                                       [Encyclopaedic, Written]   \n",
-                            "InstructionRetrieval     Core17InstructionRetrieval                                                    [News, Written]   \n",
-                            "                         News21InstructionRetrieval                                                    [News, Written]   \n",
-                            "                         Robust04InstructionRetrieval                                                  [News, Written]   \n",
-                            "MultilabelClassification MalteseNewsClassification                                              [Constructed, Written]   \n",
-                            "                         MultiEURLEXMultilabelClassification                              [Legal, Government, Written]   \n",
-                            "PairClassification       CTKFactsNLI                                                                   [News, Written]   \n",
-                            "                         SprintDuplicateQuestions                                               [Programming, Written]   \n",
-                            "                         OpusparcusPC                                                                 [Spoken, Spoken]   \n",
-                            "                         RTE3                                                      [News, Web, Encyclopaedic, Written]   \n",
-                            "                         XNLI                                              [Non-fiction, Fiction, Government, Written]   \n",
-                            "                         PSC                                                                           [News, Written]   \n",
-                            "Reranking                WebLINXCandidatesReranking                                           [Academic, Web, Written]   \n",
-                            "                         AlloprofReranking                                                    [Web, Academic, Written]   \n",
-                            "                         WikipediaRerankingMultilingual                                       [Encyclopaedic, Written]   \n",
-                            "STS                      SICK-R                                                                                   None   \n",
-                            "                         STS12                                                          [Encyclopaedic, News, Written]   \n",
-                            "                         STS14                                                                     [Blog, Web, Spoken]   \n",
-                            "                         STS15                                                      [Blog, News, Web, Written, Spoken]   \n",
-                            "                         STSBenchmark                                                                             None   \n",
-                            "                         FinParaSTS                                                         [News, Subtitles, Written]   \n",
-                            "                         STS17                                                                    [News, Web, Written]   \n",
-                            "                         SICK-R-PL                                                                      [Web, Written]   \n",
-                            "                         STSES                                                                               [Written]   \n",
-                            "\n",
-                            "                                                                                                               License  \\\n",
-                            "Type                     Name                                                                                            \n",
-                            "BitextMining             BornholmBitextMining                                                                CC-BY-4.0   \n",
-                            "                         BibleNLPBitextMining                                                             CC-BY-SA-4.0   \n",
-                            "                         BUCC.v2                                                                               Unknown   \n",
-                            "                         DiaBlaBitextMining                                                            CC BY-NC-SA 4.0   \n",
-                            "                         FloresBitextMining                                                               CC BY-SA 4.0   \n",
-                            "                         NorwegianCourtsBitextMining                                                         CC BY 4.0   \n",
-                            "                         NTREXBitextMining                                                                CC-BY-SA-4.0   \n",
-                            "Classification           BulgarianStoreReviewSentimentClassfication                                          cc-by-4.0   \n",
-                            "                         CzechProductReviewSentimentClassification                                     CC BY-NC-SA 4.0   \n",
-                            "                         GreekLegalCodeClassification                                                        cc-by-4.0   \n",
-                            "                         DBpediaClassification                                                            cc-by-sa-3.0   \n",
-                            "                         FinancialPhrasebankClassification                                             cc-by-nc-sa-3.0   \n",
-                            "                         PoemSentimentClassification                                                         CC-BY-4.0   \n",
-                            "                         ToxicChatClassification                                                             cc-by-4.0   \n",
-                            "                         ToxicConversationsClassification                                                    CC BY 4.0   \n",
-                            "                         EstonianValenceClassification                                                       CC BY 4.0   \n",
-                            "                         ItaCaseholdClassification                                                          Apache 2.0   \n",
-                            "                         AmazonCounterfactualClassification                                                  CC BY 4.0   \n",
-                            "                         MassiveScenarioClassification                                                      Apache 2.0   \n",
-                            "                         MultiHateClassification                                                             cc-by-4.0   \n",
-                            "                         NordicLangClassification                                                         cc-by-sa-3.0   \n",
-                            "                         ScalaClassification                                                              CC BY-SA 4.0   \n",
-                            "                         SwissJudgementClassification                                                        CC-BY-4.0   \n",
-                            "                         TweetSentimentClassification                                                        cc-by-3.0   \n",
-                            "                         CBD                                                                              bsd-3-clause   \n",
-                            "                         PolEmo2.0-OUT                                                                    cc-by-sa-4.0   \n",
-                            "                         CSFDSKMovieReviewSentimentClassification                                         CC-BY-SA-4.0   \n",
-                            "                         DalajClassification                                                                 CC-BY-4.0   \n",
-                            "Clustering               WikiCitiesClustering                                                             cc-by-sa-4.0   \n",
-                            "                         RomaniBibleClustering                                                                     MIT   \n",
-                            "                         BigPatentClustering.v2                                                              cc-by-4.0   \n",
-                            "                         BiorxivClusteringP2P.v2                         https://www.biorxiv.org/content/about-biorxiv   \n",
-                            "                         AlloProfClusteringS2S.v2                                                                  mit   \n",
-                            "                         HALClusteringS2S.v2                                                                Apache-2.0   \n",
-                            "                         SIB200ClusteringS2S                                                              cc-by-sa-4.0   \n",
-                            "                         WikiClusteringP2P.v2                                                             cc-by-sa-3.0   \n",
-                            "Retrieval                StackOverflowQA                                                                           MIT   \n",
-                            "                         TwitterHjerneRetrieval                                                              CC BY 4.0   \n",
-                            "                         LegalQuAD                                                                           CC BY 4.0   \n",
-                            "                         ArguAna                                                                          cc-by-sa-4.0   \n",
-                            "                         HagridRetrieval                                                                    apache-2.0   \n",
-                            "                         LegalBenchCorporateLobbying                                                         CC BY 4.0   \n",
-                            "                         LEMBPasskeyRetrieval                                                            Not specified   \n",
-                            "                         SCIDOCS                                                                          cc-by-sa-4.0   \n",
-                            "                         SpartQA                                                                                   MIT   \n",
-                            "                         TempReasonL1                                                                     CC BY-SA 3.0   \n",
-                            "                         WinoGrande                                                                              CC BY   \n",
-                            "                         AlloprofRetrieval                                                             cc-by-nc-sa-4.0   \n",
-                            "                         BelebeleRetrieval                                                                CC-BY-SA-4.0   \n",
-                            "                         StatcanDialogueDatasetRetrieval             https://huggingface.co/datasets/McGill-NLP/sta...   \n",
-                            "                         WikipediaRetrievalMultilingual                                                   cc-by-sa-3.0   \n",
-                            "InstructionRetrieval     Core17InstructionRetrieval                                                                MIT   \n",
-                            "                         News21InstructionRetrieval                                                                MIT   \n",
-                            "                         Robust04InstructionRetrieval                                                              MIT   \n",
-                            "MultilabelClassification MalteseNewsClassification                                                     cc-by-nc-sa-4.0   \n",
-                            "                         MultiEURLEXMultilabelClassification                                              CC BY-SA 4.0   \n",
-                            "PairClassification       CTKFactsNLI                                                                      CC-BY-SA-3.0   \n",
-                            "                         SprintDuplicateQuestions                                                        Not specified   \n",
-                            "                         OpusparcusPC                                                                     cc-by-nc-4.0   \n",
-                            "                         RTE3                                                                                cc-by-4.0   \n",
-                            "                         XNLI                                                                            Not specified   \n",
-                            "                         PSC                                                                                   cc-by-3   \n",
-                            "Reranking                WebLINXCandidatesReranking                                                    CC BY-NC-SA 4.0   \n",
-                            "                         AlloprofReranking                                                             CC BY-NC-SA 4.0   \n",
-                            "                         WikipediaRerankingMultilingual                                                   cc-by-sa-3.0   \n",
-                            "STS                      SICK-R                                                                                   None   \n",
-                            "                         STS12                                                                           Not specified   \n",
-                            "                         STS14                                                                           Not specified   \n",
-                            "                         STS15                                                                           Not specified   \n",
-                            "                         STSBenchmark                                                                             None   \n",
-                            "                         FinParaSTS                                                                       cc-by-sa-4.0   \n",
-                            "                         STS17                                                                           Not specified   \n",
-                            "                         SICK-R-PL                                                                     CC-BY-NC-SA-3.0   \n",
-                            "                         STSES                                                                               cc-by-4.0   \n",
-                            "\n",
-                            "                                                                                                           Description  \n",
-                            "Type                     Name                                                                                           \n",
-                            "BitextMining             BornholmBitextMining                        Danish Bornholmsk Parallel Corpus. Bornholmsk ...  \n",
-                            "                         BibleNLPBitextMining                        Partial Bible translations in 829 languages, a...  \n",
-                            "                         BUCC.v2                                                            BUCC bitext mining dataset  \n",
-                            "                         DiaBlaBitextMining                          English-French Parallel Corpus. DiaBLa is an E...  \n",
-                            "                         FloresBitextMining                          FLORES is a benchmark dataset for machine tran...  \n",
-                            "                         NorwegianCourtsBitextMining                 Nynorsk and Bokmål parallel corpus from Norweg...  \n",
-                            "                         NTREXBitextMining                           NTREX is a News Test References dataset for Ma...  \n",
-                            "Classification           BulgarianStoreReviewSentimentClassfication  Bulgarian online store review dataset for sent...  \n",
-                            "                         CzechProductReviewSentimentClassification   User reviews of products on Czech e-shop Mall....  \n",
-                            "                         GreekLegalCodeClassification                Greek Legal Code Dataset for Classification. (...  \n",
-                            "                         DBpediaClassification                       DBpedia14 is a dataset of English texts from W...  \n",
-                            "                         FinancialPhrasebankClassification           Polar sentiment dataset of sentences from fina...  \n",
-                            "                         PoemSentimentClassification                 Poem Sentiment is a sentiment dataset of poem ...  \n",
-                            "                         ToxicChatClassification                     This dataset contains toxicity annotations on ...  \n",
-                            "                         ToxicConversationsClassification            Collection of comments from the Civil Comments...  \n",
-                            "                         EstonianValenceClassification               Dataset containing annotated Estonian news dat...  \n",
-                            "                         ItaCaseholdClassification                   An Italian Dataset consisting of 1101 pairs of...  \n",
-                            "                         AmazonCounterfactualClassification          A collection of Amazon customer reviews annota...  \n",
-                            "                         MassiveScenarioClassification               MASSIVE: A 1M-Example Multilingual Natural Lan...  \n",
-                            "                         MultiHateClassification                     Hate speech detection dataset with binary\\n   ...  \n",
-                            "                         NordicLangClassification                        A dataset for Nordic language identification.  \n",
-                            "                         ScalaClassification                         ScaLa a linguistic acceptability dataset for t...  \n",
-                            "                         SwissJudgementClassification                Multilingual, diachronic dataset of Swiss Fede...  \n",
-                            "                         TweetSentimentClassification                A multilingual Sentiment Analysis dataset cons...  \n",
-                            "                         CBD                                         Polish Tweets annotated for cyberbullying dete...  \n",
-                            "                         PolEmo2.0-OUT                               A collection of Polish online reviews from fou...  \n",
-                            "                         CSFDSKMovieReviewSentimentClassification    The dataset contains 30k user reviews from csf...  \n",
-                            "                         DalajClassification                         A Swedish dataset for linguistic acceptability...  \n",
-                            "Clustering               WikiCitiesClustering                        Clustering of Wikipedia articles of cities by ...  \n",
-                            "                         RomaniBibleClustering                       Clustering verses from the Bible in Kalderash ...  \n",
-                            "                         BigPatentClustering.v2                      Clustering of documents from the Big Patent da...  \n",
-                            "                         BiorxivClusteringP2P.v2                     Clustering of titles+abstract from biorxiv acr...  \n",
-                            "                         AlloProfClusteringS2S.v2                    Clustering of document titles from Allo Prof d...  \n",
-                            "                         HALClusteringS2S.v2                         Clustering of titles from HAL (https://hugging...  \n",
-                            "                         SIB200ClusteringS2S                         SIB-200 is the largest publicly available topi...  \n",
-                            "                         WikiClusteringP2P.v2                        Clustering of wikipedia articles inspired by B...  \n",
-                            "Retrieval                StackOverflowQA                             The dataset is a collection of natural languag...  \n",
-                            "                         TwitterHjerneRetrieval                      Danish question asked on Twitter with the Hash...  \n",
-                            "                         LegalQuAD                                   The dataset consists of questions and legal do...  \n",
-                            "                         ArguAna                                     NFCorpus: A Full-Text Learning to Rank Dataset...  \n",
-                            "                         HagridRetrieval                             HAGRID (Human-in-the-loop Attributable Generat...  \n",
-                            "                         LegalBenchCorporateLobbying                 The dataset includes bill titles and bill summ...  \n",
-                            "                         LEMBPasskeyRetrieval                               passkey subset of dwzhu/LongEmbed dataset.  \n",
-                            "                         SCIDOCS                                     SciDocs, a new evaluation benchmark consisting...  \n",
-                            "                         SpartQA                                     Measuring the ability to retrieve the groundtr...  \n",
-                            "                         TempReasonL1                                Measuring the ability to retrieve the groundtr...  \n",
-                            "                         WinoGrande                                  Measuring the ability to retrieve the groundtr...  \n",
-                            "                         AlloprofRetrieval                           This dataset was provided by AlloProf, an orga...  \n",
-                            "                         BelebeleRetrieval                           Belebele is a multiple-choice machine reading ...  \n",
-                            "                         StatcanDialogueDatasetRetrieval             A Dataset for Retrieving Data Tables through C...  \n",
-                            "                         WikipediaRetrievalMultilingual              The dataset is derived from Cohere's wikipedia...  \n",
-                            "InstructionRetrieval     Core17InstructionRetrieval                  Measuring retrieval instruction following abil...  \n",
-                            "                         News21InstructionRetrieval                  Measuring retrieval instruction following abil...  \n",
-                            "                         Robust04InstructionRetrieval                Measuring retrieval instruction following abil...  \n",
-                            "MultilabelClassification MalteseNewsClassification                   A multi-label topic classification dataset for...  \n",
-                            "                         MultiEURLEXMultilabelClassification         EU laws in 23 EU languages containing gold lab...  \n",
-                            "PairClassification       CTKFactsNLI                                 Czech Natural Language Inference dataset of ar...  \n",
-                            "                         SprintDuplicateQuestions                       Duplicate questions from the Sprint community.  \n",
-                            "                         OpusparcusPC                                Opusparcus is a paraphrase corpus for six Euro...  \n",
-                            "                         RTE3                                        Recognising Textual Entailment Challenge (RTE-...  \n",
-                            "                         XNLI                                                                                           \n",
-                            "                         PSC                                                                   Polish Summaries Corpus  \n",
-                            "Reranking                WebLINXCandidatesReranking                  WebLINX is a large-scale benchmark of 100K int...  \n",
-                            "                         AlloprofReranking                           This dataset was provided by AlloProf, an orga...  \n",
-                            "                         WikipediaRerankingMultilingual              The dataset is derived from Cohere's wikipedia...  \n",
-                            "STS                      SICK-R                                      Semantic Textual Similarity SICK-R dataset as ...  \n",
-                            "                         STS12                                                                    SemEval-2012 Task 6.  \n",
-                            "                         STS14                                       SemEval STS 2014 dataset. Currently only the E...  \n",
-                            "                         STS15                                                                SemEval STS 2015 dataset  \n",
-                            "                         STSBenchmark                                Semantic Textual Similarity Benchmark (STSbenc...  \n",
-                            "                         FinParaSTS                                  Finnish paraphrase-based semantic similarity c...  \n",
-                            "                         STS17                                       Semeval-2017 task 1: Semantic textual similari...  \n",
-                            "                         SICK-R-PL                                   Polish version of SICK dataset for textual rel...  \n",
-                            "                         STSES                                       Spanish test sets from SemEval-2014 (Agirre et...  "
-                        ]
-                    },
-                    "execution_count": 25,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# create a dataframe with tasks\n",
-                "import pandas as pd\n",
-                "\n",
-                "data = []\n",
-                "\n",
-                "eu_langs = set(eu_languages)\n",
-                "\n",
-                "for t in tasks:\n",
-                "    data.append(\n",
-                "        {\n",
-                "            \"Name\": t.metadata.name,\n",
-                "            \"Type\": t.metadata.type,\n",
-                "            \"Languages\": set(t.metadata.languages) & eu_langs,\n",
-                "            \"Domains\": t.metadata.domains,\n",
-                "            \"License\": t.metadata.license,\n",
-                "            \"Description\": t.metadata.description,\n",
-                "        }\n",
-                "    )\n",
-                "\n",
-                "tasks_df = pd.DataFrame(data)\n",
-                "# tasks_df\n",
-                "\n",
-                "# print all rows\n",
-                "pd.set_option(\"display.max_rows\", 100)\n",
-                "_tasks_df = tasks_df.set_index([\"Type\", \"Name\"], inplace=False)\n",
-                "_tasks_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 26,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "(74, 4)"
-                        ]
-                    },
-                    "execution_count": 26,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "_tasks_df.shape"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 27,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "tasks_df.to_csv(\"europe_tasks.csv\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Reviewed\n",
-                "\n",
-                "To ensure that these tasks are appropriate we ask speakers of the language to suggest improve upon the selection of the tasks:\n",
-                "\n",
-                "| Name                                 | Evaluated languages            |\n",
-                "|--------------------------------------|--------------------------------|\n",
-                "| Kenneth Enevoldsen (@KennethEnevoldsen) | Danish (dan), English (eng), Swedish (swe), Norwegian (nno, nob) |\n",
-                "\n",
-                "Kenneth: \n",
-                "  - Norwegian: \n",
-                "    - looks reasonable both datasets are good\n",
-                "  - Danish: \n",
-                "    - ~~`DanishPoliticalCommentsClassification` has a questionable license and quality (have added filtering based on missing license added)~~ \n",
-                "    - Danish looks reasonable\n",
-                "  - Swedish: \n",
-                "    - Datasets are reasonable\n",
-                "  - English: Might be worth excluding \n",
-                "    - ~~`NusaXBitextMining`, `NollySentiBitextMining`, `IN22ConvBitextMining`, and `IndicGenBenchFloresBitextMining`. Given their targets~~\n",
-                "    - ~~Might be worth removing legal benchmark tasks (CUAD, MUAD etc.)? Not really what the benchmark seeks to test (some legal would be reasonable, but 100+ seems excessive)~~\n",
-                "    - reasonable\n",
-                "  - Other: \n",
-                "    - ~~It might be worth excluding:`AfriSentiClassification` given its intended target.~~\n",
-                "    - ~~a few more non-EU tasks (notably Masakhane tasks)~~\n",
-                "    - ~~some code-retrieval tasks in the benchmark~~\n",
-                "    - reasonable"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Benchmark Performance"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# It is possible to start the notebok from here:\n",
-                "import pandas as pd\n",
-                "\n",
-                "import mteb\n",
-                "\n",
-                "_df = pd.read_csv(\"europe_tasks.csv\")\n",
-                "task_names = _df[\"Name\"].tolist()\n",
-                "\n",
-                "eu_languages = [\n",
-                "    # official EU languages (56) - we could include the whole economic area e.g. Norway - additioanlly we could include minority languages (probably a good idea?)\n",
-                "    # germanic\n",
-                "    \"dan\",\n",
-                "    \"eng\",\n",
-                "    \"deu\",\n",
-                "    \"nld\",\n",
-                "    \"swe\",\n",
-                "    # romance\n",
-                "    \"fra\",\n",
-                "    \"ita\",\n",
-                "    \"por\",\n",
-                "    \"spa\",\n",
-                "    \"ron\",\n",
-                "    # slavic\n",
-                "    \"bul\",\n",
-                "    \"hrv\",\n",
-                "    \"ces\",\n",
-                "    \"pol\",\n",
-                "    \"slk\",\n",
-                "    \"slv\",\n",
-                "    # baltic\n",
-                "    \"lav\",\n",
-                "    \"lit\",\n",
-                "    \"est\",\n",
-                "    # finno-ugric\n",
-                "    \"fin\",\n",
-                "    \"hun\",\n",
-                "    # other indo european\n",
-                "    \"ell\",\n",
-                "    # non-indo european\n",
-                "    \"mlt\",\n",
-                "    \"gle\",\n",
-                "    # Schengen Area\n",
-                "    \"nno\",\n",
-                "    \"nob\",\n",
-                "    \"isl\",\n",
-                "    \"ron\",\n",
-                "    \"eus\",  # Basque - recognized minority language\n",
-                "    \"ron\",  # Romanian - recognized minority language\n",
-                "    \"rom\",  # Romani - recognized minority language\n",
-                "]\n",
-                "\n",
-                "eu_tasks = mteb.get_tasks(tasks=task_names, languages=eu_languages)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Getting revision for sentence-transformers/all-MiniLM-L12-v2\n",
-                        "Getting revision for sentence-transformers/all-mpnet-base-v2\n"
-                    ]
-                }
-            ],
-            "source": [
-                "def get_models():\n",
-                "    model_names = [\n",
-                "        \"sentence-transformers/all-MiniLM-L6-v2\",\n",
-                "        \"sentence-transformers/all-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/all-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/LaBSE\",\n",
-                "        \"intfloat/multilingual-e5-large-instruct\",\n",
-                "        \"intfloat/e5-mistral-7b-instruct\",\n",
-                "        \"GritLM/GritLM-7B\",\n",
-                "        \"GritLM/GritLM-8x7B\",\n",
-                "        \"intfloat/multilingual-e5-small\",\n",
-                "        \"intfloat/multilingual-e5-base\",\n",
-                "        \"intfloat/multilingual-e5-large\",\n",
-                "    ]\n",
-                "    models: list[mteb.ModelMeta] = [mteb.get_model_meta(name) for name in model_names]\n",
-                "\n",
-                "    # get missing revisions - Assuming we are using the latest revision\n",
-                "    for model in models:\n",
-                "        if model.revision is None:\n",
-                "            print(f\"Getting revision for {model.name}\")\n",
-                "            encoder = model.load_model()\n",
-                "            model.revision = encoder.model_card_data.base_model_revision  # type: ignore\n",
-                "\n",
-                "    return models\n",
-                "\n",
-                "\n",
-                "models = get_models()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# load task results for the specified models from mteb/results repository\n",
-                "mteb_results = mteb.load_results(models=models, tasks=eu_tasks, download_latest=False)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 20,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import mteb.task_aggregation as task_aggregation\n",
-                "\n",
-                "mean = task_aggregation.mean(mteb_results)\n",
-                "weighted_mean = task_aggregation.task_category_weighted_mean(mteb_results)\n",
-                "borda = task_aggregation.borda_count(mteb_results)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 21,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import pandas as pd\n",
-                "\n",
-                "data = []\n",
-                "for model_name, revisions in borda.items():\n",
-                "    for rev, avg_score in revisions.items():\n",
-                "        total_eval_time = sum(\n",
-                "            res.evaluation_time for res in mteb_results[model_name][rev]\n",
-                "        )\n",
-                "\n",
-                "        data.append(\n",
-                "            {\n",
-                "                \"model\": model_name,\n",
-                "                \"revision\": rev,\n",
-                "                **mean[model_name][rev],\n",
-                "                **weighted_mean[model_name][rev],\n",
-                "                **avg_score,\n",
-                "                \"Total Evaluation time (hours)\": total_eval_time / 3600,\n",
-                "            }\n",
-                "        )\n",
-                "\n",
-                "df = pd.DataFrame(data)\n",
-                "df = df.sort_values(\"borda_count\", ascending=False)\n",
-                "# round\n",
-                "df = df.round(3)\n",
-                "\n",
-                "df.to_csv(\"europe_results.csv\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 22,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th>mean</th>\n",
-                            "      <th>mean (Classification)</th>\n",
-                            "      <th>mean (Retrieval)</th>\n",
-                            "      <th>mean (PairClassification)</th>\n",
-                            "      <th>mean (BitextMining)</th>\n",
-                            "      <th>mean (Clustering)</th>\n",
-                            "      <th>mean (MultilabelClassification)</th>\n",
-                            "      <th>mean (STS)</th>\n",
-                            "      <th>mean (Reranking)</th>\n",
-                            "      <th>mean (InstructionRetrieval)</th>\n",
-                            "      <th>mean (wieghted by task type)</th>\n",
-                            "      <th>borda_count</th>\n",
-                            "      <th>Total Evaluation time (hours)</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>13f00a0e36500c80ce12870ea513846a066004af</td>\n",
-                            "      <td>0.607</td>\n",
-                            "      <td>0.643</td>\n",
-                            "      <td>0.571</td>\n",
-                            "      <td>0.894</td>\n",
-                            "      <td>0.708</td>\n",
-                            "      <td>0.435</td>\n",
-                            "      <td>0.176</td>\n",
-                            "      <td>0.755</td>\n",
-                            "      <td>0.589</td>\n",
-                            "      <td>0.035</td>\n",
-                            "      <td>0.534</td>\n",
-                            "      <td>680.0</td>\n",
-                            "      <td>6.408</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>baa7be480a7de1539afce709c8f13f833a510e0a</td>\n",
-                            "      <td>0.610</td>\n",
-                            "      <td>0.635</td>\n",
-                            "      <td>0.555</td>\n",
-                            "      <td>0.899</td>\n",
-                            "      <td>0.767</td>\n",
-                            "      <td>0.460</td>\n",
-                            "      <td>0.173</td>\n",
-                            "      <td>0.772</td>\n",
-                            "      <td>0.575</td>\n",
-                            "      <td>-0.004</td>\n",
-                            "      <td>0.537</td>\n",
-                            "      <td>679.0</td>\n",
-                            "      <td>4.463</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>07163b72af1488142a360786df853f237b1a3ca1</td>\n",
-                            "      <td>0.592</td>\n",
-                            "      <td>0.625</td>\n",
-                            "      <td>0.524</td>\n",
-                            "      <td>0.907</td>\n",
-                            "      <td>0.702</td>\n",
-                            "      <td>0.445</td>\n",
-                            "      <td>0.155</td>\n",
-                            "      <td>0.760</td>\n",
-                            "      <td>0.585</td>\n",
-                            "      <td>-0.006</td>\n",
-                            "      <td>0.522</td>\n",
-                            "      <td>643.0</td>\n",
-                            "      <td>5.718</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</td>\n",
-                            "      <td>0.571</td>\n",
-                            "      <td>0.609</td>\n",
-                            "      <td>0.513</td>\n",
-                            "      <td>0.887</td>\n",
-                            "      <td>0.690</td>\n",
-                            "      <td>0.367</td>\n",
-                            "      <td>0.150</td>\n",
-                            "      <td>0.756</td>\n",
-                            "      <td>0.552</td>\n",
-                            "      <td>-0.031</td>\n",
-                            "      <td>0.499</td>\n",
-                            "      <td>527.0</td>\n",
-                            "      <td>5.765</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>d13f1b27baf31030b7fd040960d60d909913633f</td>\n",
-                            "      <td>0.557</td>\n",
-                            "      <td>0.583</td>\n",
-                            "      <td>0.506</td>\n",
-                            "      <td>0.876</td>\n",
-                            "      <td>0.683</td>\n",
-                            "      <td>0.367</td>\n",
-                            "      <td>0.149</td>\n",
-                            "      <td>0.734</td>\n",
-                            "      <td>0.530</td>\n",
-                            "      <td>-0.027</td>\n",
-                            "      <td>0.489</td>\n",
-                            "      <td>438.0</td>\n",
-                            "      <td>2.712</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>79f2382ceacceacdf38563d7c5d16b9ff8d725d6</td>\n",
-                            "      <td>0.512</td>\n",
-                            "      <td>0.554</td>\n",
-                            "      <td>0.393</td>\n",
-                            "      <td>0.906</td>\n",
-                            "      <td>0.554</td>\n",
-                            "      <td>0.343</td>\n",
-                            "      <td>0.069</td>\n",
-                            "      <td>0.741</td>\n",
-                            "      <td>0.516</td>\n",
-                            "      <td>-0.011</td>\n",
-                            "      <td>0.451</td>\n",
-                            "      <td>387.0</td>\n",
-                            "      <td>14.898</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>e4ce9877abf3edfe10b0d82785e83bdcb973e22e</td>\n",
-                            "      <td>0.537</td>\n",
-                            "      <td>0.565</td>\n",
-                            "      <td>0.465</td>\n",
-                            "      <td>0.869</td>\n",
-                            "      <td>0.660</td>\n",
-                            "      <td>0.355</td>\n",
-                            "      <td>0.140</td>\n",
-                            "      <td>0.710</td>\n",
-                            "      <td>0.534</td>\n",
-                            "      <td>-0.024</td>\n",
-                            "      <td>0.475</td>\n",
-                            "      <td>347.0</td>\n",
-                            "      <td>1.901</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>e34fab64a3011d2176c99545a93d5cbddc9a91b7</td>\n",
-                            "      <td>0.498</td>\n",
-                            "      <td>0.540</td>\n",
-                            "      <td>0.338</td>\n",
-                            "      <td>0.850</td>\n",
-                            "      <td>0.723</td>\n",
-                            "      <td>0.335</td>\n",
-                            "      <td>0.163</td>\n",
-                            "      <td>0.657</td>\n",
-                            "      <td>0.488</td>\n",
-                            "      <td>-0.030</td>\n",
-                            "      <td>0.452</td>\n",
-                            "      <td>296.0</td>\n",
-                            "      <td>2.439</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>bf3bf13ab40c3157080a7ab344c831b9ad18b5eb</td>\n",
-                            "      <td>0.484</td>\n",
-                            "      <td>0.517</td>\n",
-                            "      <td>0.355</td>\n",
-                            "      <td>0.888</td>\n",
-                            "      <td>0.513</td>\n",
-                            "      <td>0.327</td>\n",
-                            "      <td>0.057</td>\n",
-                            "      <td>0.724</td>\n",
-                            "      <td>0.492</td>\n",
-                            "      <td>-0.013</td>\n",
-                            "      <td>0.429</td>\n",
-                            "      <td>252.0</td>\n",
-                            "      <td>1.809</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>84f2bcc00d77236f9e89c8a360a00fb1139bf47d</td>\n",
-                            "      <td>0.433</td>\n",
-                            "      <td>0.485</td>\n",
-                            "      <td>0.359</td>\n",
-                            "      <td>0.796</td>\n",
-                            "      <td>0.236</td>\n",
-                            "      <td>0.360</td>\n",
-                            "      <td>0.109</td>\n",
-                            "      <td>0.630</td>\n",
-                            "      <td>0.472</td>\n",
-                            "      <td>-0.031</td>\n",
-                            "      <td>0.379</td>\n",
-                            "      <td>241.5</td>\n",
-                            "      <td>2.887</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>a05860a77cef7b37e0048a7864658139bc18a854</td>\n",
-                            "      <td>0.431</td>\n",
-                            "      <td>0.487</td>\n",
-                            "      <td>0.345</td>\n",
-                            "      <td>0.809</td>\n",
-                            "      <td>0.256</td>\n",
-                            "      <td>0.323</td>\n",
-                            "      <td>0.076</td>\n",
-                            "      <td>0.635</td>\n",
-                            "      <td>0.470</td>\n",
-                            "      <td>-0.008</td>\n",
-                            "      <td>0.377</td>\n",
-                            "      <td>221.0</td>\n",
-                            "      <td>1.780</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>8b3219a92973c328a8e22fadcfa821b5dc75636a</td>\n",
-                            "      <td>0.425</td>\n",
-                            "      <td>0.475</td>\n",
-                            "      <td>0.366</td>\n",
-                            "      <td>0.796</td>\n",
-                            "      <td>0.218</td>\n",
-                            "      <td>0.335</td>\n",
-                            "      <td>0.088</td>\n",
-                            "      <td>0.618</td>\n",
-                            "      <td>0.445</td>\n",
-                            "      <td>-0.028</td>\n",
-                            "      <td>0.368</td>\n",
-                            "      <td>172.5</td>\n",
-                            "      <td>1.606</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model  \\\n",
-                            "2                                    GritLM/GritLM-7B   \n",
-                            "7             intfloat/multilingual-e5-large-instruct   \n",
-                            "11                    intfloat/e5-mistral-7b-instruct   \n",
-                            "3                      intfloat/multilingual-e5-large   \n",
-                            "9                       intfloat/multilingual-e5-base   \n",
-                            "4   sentence-transformers/paraphrase-multilingual-...   \n",
-                            "0                      intfloat/multilingual-e5-small   \n",
-                            "1                         sentence-transformers/LaBSE   \n",
-                            "5   sentence-transformers/paraphrase-multilingual-...   \n",
-                            "6             sentence-transformers/all-mpnet-base-v2   \n",
-                            "8             sentence-transformers/all-MiniLM-L12-v2   \n",
-                            "10             sentence-transformers/all-MiniLM-L6-v2   \n",
-                            "\n",
-                            "                                    revision   mean  mean (Classification)  \\\n",
-                            "2   13f00a0e36500c80ce12870ea513846a066004af  0.607                  0.643   \n",
-                            "7   baa7be480a7de1539afce709c8f13f833a510e0a  0.610                  0.635   \n",
-                            "11  07163b72af1488142a360786df853f237b1a3ca1  0.592                  0.625   \n",
-                            "3   4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.571                  0.609   \n",
-                            "9   d13f1b27baf31030b7fd040960d60d909913633f  0.557                  0.583   \n",
-                            "4   79f2382ceacceacdf38563d7c5d16b9ff8d725d6  0.512                  0.554   \n",
-                            "0   e4ce9877abf3edfe10b0d82785e83bdcb973e22e  0.537                  0.565   \n",
-                            "1   e34fab64a3011d2176c99545a93d5cbddc9a91b7  0.498                  0.540   \n",
-                            "5   bf3bf13ab40c3157080a7ab344c831b9ad18b5eb  0.484                  0.517   \n",
-                            "6   84f2bcc00d77236f9e89c8a360a00fb1139bf47d  0.433                  0.485   \n",
-                            "8   a05860a77cef7b37e0048a7864658139bc18a854  0.431                  0.487   \n",
-                            "10  8b3219a92973c328a8e22fadcfa821b5dc75636a  0.425                  0.475   \n",
-                            "\n",
-                            "    mean (Retrieval)  mean (PairClassification)  mean (BitextMining)  \\\n",
-                            "2              0.571                      0.894                0.708   \n",
-                            "7              0.555                      0.899                0.767   \n",
-                            "11             0.524                      0.907                0.702   \n",
-                            "3              0.513                      0.887                0.690   \n",
-                            "9              0.506                      0.876                0.683   \n",
-                            "4              0.393                      0.906                0.554   \n",
-                            "0              0.465                      0.869                0.660   \n",
-                            "1              0.338                      0.850                0.723   \n",
-                            "5              0.355                      0.888                0.513   \n",
-                            "6              0.359                      0.796                0.236   \n",
-                            "8              0.345                      0.809                0.256   \n",
-                            "10             0.366                      0.796                0.218   \n",
-                            "\n",
-                            "    mean (Clustering)  mean (MultilabelClassification)  mean (STS)  \\\n",
-                            "2               0.435                            0.176       0.755   \n",
-                            "7               0.460                            0.173       0.772   \n",
-                            "11              0.445                            0.155       0.760   \n",
-                            "3               0.367                            0.150       0.756   \n",
-                            "9               0.367                            0.149       0.734   \n",
-                            "4               0.343                            0.069       0.741   \n",
-                            "0               0.355                            0.140       0.710   \n",
-                            "1               0.335                            0.163       0.657   \n",
-                            "5               0.327                            0.057       0.724   \n",
-                            "6               0.360                            0.109       0.630   \n",
-                            "8               0.323                            0.076       0.635   \n",
-                            "10              0.335                            0.088       0.618   \n",
-                            "\n",
-                            "    mean (Reranking)  mean (InstructionRetrieval)  \\\n",
-                            "2              0.589                        0.035   \n",
-                            "7              0.575                       -0.004   \n",
-                            "11             0.585                       -0.006   \n",
-                            "3              0.552                       -0.031   \n",
-                            "9              0.530                       -0.027   \n",
-                            "4              0.516                       -0.011   \n",
-                            "0              0.534                       -0.024   \n",
-                            "1              0.488                       -0.030   \n",
-                            "5              0.492                       -0.013   \n",
-                            "6              0.472                       -0.031   \n",
-                            "8              0.470                       -0.008   \n",
-                            "10             0.445                       -0.028   \n",
-                            "\n",
-                            "    mean (wieghted by task type)  borda_count  Total Evaluation time (hours)  \n",
-                            "2                          0.534        680.0                          6.408  \n",
-                            "7                          0.537        679.0                          4.463  \n",
-                            "11                         0.522        643.0                          5.718  \n",
-                            "3                          0.499        527.0                          5.765  \n",
-                            "9                          0.489        438.0                          2.712  \n",
-                            "4                          0.451        387.0                         14.898  \n",
-                            "0                          0.475        347.0                          1.901  \n",
-                            "1                          0.452        296.0                          2.439  \n",
-                            "5                          0.429        252.0                          1.809  \n",
-                            "6                          0.379        241.5                          2.887  \n",
-                            "8                          0.377        221.0                          1.780  \n",
-                            "10                         0.368        172.5                          1.606  "
-                        ]
-                    },
-                    "execution_count": 22,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 23,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\begin{tabular}{llrrrrrrrrrr}\n",
-                        "\\toprule\n",
-                        " & Rank (Borda Count) & mean & mean (wieghted by task type) & mean (BitextMining) & mean (PairClassification) & mean (Classification) & mean (STS) & mean (Retrieval) & mean (MultilabelClassification) & mean (Clustering) & mean (Reranking) \\\\\n",
-                        "model &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
-                        "\\midrule\n",
-                        "GritLM-7B & 1 (680) & 60.70 & 53.40 & 70.80 & 89.40 & 64.30 & 75.50 & 57.10 & 17.60 & 43.50 & 58.90 \\\\\n",
-                        "multilingual-e5-large-instruct & 2 (679) & 61.00 & 53.70 & 76.70 & 89.90 & 63.50 & 77.20 & 55.50 & 17.30 & 46.00 & 57.50 \\\\\n",
-                        "e5-mistral-7b-instruct & 3 (643) & 59.20 & 52.20 & 70.20 & 90.70 & 62.50 & 76.00 & 52.40 & 15.50 & 44.50 & 58.50 \\\\\n",
-                        "multilingual-e5-large & 4 (527) & 57.10 & 49.90 & 69.00 & 88.70 & 60.90 & 75.60 & 51.30 & 15.00 & 36.70 & 55.20 \\\\\n",
-                        "multilingual-e5-base & 5 (438) & 55.70 & 48.90 & 68.30 & 87.60 & 58.30 & 73.40 & 50.60 & 14.90 & 36.70 & 53.00 \\\\\n",
-                        "paraphrase-multilingual-mpnet-base-v2 & 6 (387) & 51.20 & 45.10 & 55.40 & 90.60 & 55.40 & 74.10 & 39.30 & 6.90 & 34.30 & 51.60 \\\\\n",
-                        "multilingual-e5-small & 7 (347) & 53.70 & 47.50 & 66.00 & 86.90 & 56.50 & 71.00 & 46.50 & 14.00 & 35.50 & 53.40 \\\\\n",
-                        "LaBSE & 8 (296) & 49.80 & 45.20 & 72.30 & 85.00 & 54.00 & 65.70 & 33.80 & 16.30 & 33.50 & 48.80 \\\\\n",
-                        "paraphrase-multilingual-MiniLM-L12-v2 & 9 (252) & 48.40 & 42.90 & 51.30 & 88.80 & 51.70 & 72.40 & 35.50 & 5.70 & 32.70 & 49.20 \\\\\n",
-                        "all-mpnet-base-v2 & 10 (242) & 43.30 & 37.90 & 23.60 & 79.60 & 48.50 & 63.00 & 35.90 & 10.90 & 36.00 & 47.20 \\\\\n",
-                        "all-MiniLM-L12-v2 & 11 (221) & 43.10 & 37.70 & 25.60 & 80.90 & 48.70 & 63.50 & 34.50 & 7.60 & 32.30 & 47.00 \\\\\n",
-                        "all-MiniLM-L6-v2 & 12 (172) & 42.50 & 36.80 & 21.80 & 79.60 & 47.50 & 61.80 & 36.60 & 8.80 & 33.50 & 44.50 \\\\\n",
-                        "\\bottomrule\n",
-                        "\\end{tabular}\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "latex_df = df.drop(columns=[\"revision\"])\n",
-                "latex_df[\"model\"] = [name.split(\"/\")[1] for name in latex_df[\"model\"]]\n",
-                "latex_df = latex_df.set_index(\"model\")\n",
-                "\n",
-                "avg_cols = [\n",
-                "    \"mean\",\n",
-                "    \"mean (BitextMining)\",\n",
-                "    \"mean (PairClassification)\",\n",
-                "    \"mean (Classification)\",\n",
-                "    \"mean (STS)\",\n",
-                "    \"mean (Retrieval)\",\n",
-                "    \"mean (MultilabelClassification)\",\n",
-                "    \"mean (Clustering)\",\n",
-                "    \"mean (Reranking)\",\n",
-                "    \"mean (InstructionRetrieval)\",\n",
-                "    \"mean (wieghted by task type)\",\n",
-                "]\n",
-                "\n",
-                "borda_col_name = \"borda_count\"\n",
-                "\n",
-                "# multiply by 100 to get percentage values and round to 2 decimal places\n",
-                "latex_df[avg_cols] = latex_df[avg_cols] * 100\n",
-                "\n",
-                "latex_df[\"Rank (Borda Count)\"] = [\n",
-                "    f\"{rank} ({borda:.0f})\"\n",
-                "    for rank, borda in zip(range(1, len(latex_df) + 1), latex_df[borda_col_name])\n",
-                "]\n",
-                "latex_df = latex_df.drop(columns=[borda_col_name])\n",
-                "\n",
-                "# column order and rename\n",
-                "cols = [\n",
-                "    \"Rank (Borda Count)\",\n",
-                "    \"mean\",\n",
-                "    \"mean (wieghted by task type)\",\n",
-                "    \"mean (BitextMining)\",\n",
-                "    \"mean (PairClassification)\",\n",
-                "    \"mean (Classification)\",\n",
-                "    \"mean (STS)\",\n",
-                "    \"mean (Retrieval)\",\n",
-                "    \"mean (MultilabelClassification)\",\n",
-                "    \"mean (Clustering)\",\n",
-                "    \"mean (Reranking)\",\n",
-                "]\n",
-                "\n",
-                "latex_df = latex_df[cols]\n",
-                "\n",
-                "table_latex = latex_df.to_latex(index=True, float_format=\"%.2f\")\n",
-                "\n",
-                "\n",
-                "print(table_latex)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Counter({'BitextMining': 7,\n",
-                            "         'Classification': 21,\n",
-                            "         'Clustering': 8,\n",
-                            "         'Retrieval': 15,\n",
-                            "         'InstructionRetrieval': 3,\n",
-                            "         'MultilabelClassification': 2,\n",
-                            "         'PairClassification': 6,\n",
-                            "         'Reranking': 3,\n",
-                            "         'STS': 9})"
-                        ]
-                    },
-                    "execution_count": 15,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "from collections import Counter\n",
-                "\n",
-                "Counter([task.metadata.type for task in eu_tasks])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "74"
-                        ]
-                    },
-                    "execution_count": 16,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "len(eu_tasks)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "mteb",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.8.19"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
diff --git a/scripts/task_selection/task_selection_example.ipynb b/scripts/task_selection/task_selection_example.ipynb
deleted file mode 100644
index c21ea5e616..0000000000
--- a/scripts/task_selection/task_selection_example.ipynb
+++ /dev/null
@@ -1,1858 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Task Selection: An Example\n",
-                "\n",
-                "This is an example of how to perform task selection using MTEB when creating a benchmark. The goal here is to subsample a potentially large number of tasks down to the one with the most information. We do this as a feature selection approach, where we remove a task if its performance if predictable by the performance of other tasks. See the paper for more information.\n",
-                "\n",
-                "For this example we will be using Danish (dan) as it has relatively few tasks, but there is not reason to limit to only Danish tasks."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/au561649/.virtualenvs/mteb/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-                        "  from .autonotebook import tqdm as notebook_tqdm\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "1.12.48\n"
-                    ]
-                }
-            ],
-            "source": [
-                "from __future__ import annotations\n",
-                "\n",
-                "import mteb\n",
-                "\n",
-                "print(mteb.__version__)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Loading in data\n",
-                "We will start out by loading in the relevant data for the model and tasks of interests."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Getting revision for sentence-transformers/all-MiniLM-L12-v2\n",
-                        "Getting revision for sentence-transformers/all-mpnet-base-v2\n"
-                    ]
-                }
-            ],
-            "source": [
-                "def get_models():\n",
-                "    model_names = [\n",
-                "        \"sentence-transformers/all-MiniLM-L6-v2\",\n",
-                "        \"sentence-transformers/all-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/all-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/LaBSE\",\n",
-                "        \"intfloat/multilingual-e5-large-instruct\",\n",
-                "        \"intfloat/e5-mistral-7b-instruct\",\n",
-                "        \"GritLM/GritLM-7B\",\n",
-                "        \"GritLM/GritLM-8x7B\",\n",
-                "        \"intfloat/multilingual-e5-small\",\n",
-                "        \"intfloat/multilingual-e5-base\",\n",
-                "        \"intfloat/multilingual-e5-large\",\n",
-                "    ]\n",
-                "    models: list[mteb.ModelMeta] = [mteb.get_model_meta(name) for name in model_names]\n",
-                "\n",
-                "    # get missing revisions - Assuming we are using the latest revision\n",
-                "    for model in models:\n",
-                "        if model.revision is None:\n",
-                "            print(f\"Getting revision for {model.name}\")\n",
-                "            encoder = model.load_model()\n",
-                "            model.revision = encoder.model_card_data.base_model_revision  # type: ignore\n",
-                "\n",
-                "    return models\n",
-                "\n",
-                "\n",
-                "models = get_models()\n",
-                "\n",
-                "danish_tasks = mteb.get_tasks(\n",
-                "    languages=[\"dan\"]\n",
-                ")  # does not need to language - you can also filter by task types, domains, etc."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "BornholmBitextMining\n",
-                        "BibleNLPBitextMining\n",
-                        "FloresBitextMining\n",
-                        "NTREXBitextMining\n",
-                        "Tatoeba\n",
-                        "AngryTweetsClassification\n",
-                        "DanishPoliticalCommentsClassification\n",
-                        "DKHateClassification\n",
-                        "LccSentimentClassification\n",
-                        "MassiveIntentClassification\n",
-                        "MassiveScenarioClassification\n",
-                        "NordicLangClassification\n",
-                        "ScalaClassification\n",
-                        "SIB200Classification\n",
-                        "SIB200ClusteringS2S\n",
-                        "WikiClusteringP2P.v2\n",
-                        "DanFeverRetrieval\n",
-                        "TV2Nordretrieval\n",
-                        "TwitterHjerneRetrieval\n",
-                        "BelebeleRetrieval\n",
-                        "WikipediaRetrievalMultilingual\n",
-                        "MultiEURLEXMultilabelClassification\n",
-                        "WikipediaRerankingMultilingual\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# just to see what tasks we are working with\n",
-                "for task in danish_tasks:\n",
-                "    print(task.metadata.name)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# load results from mteb/results repository\n",
-                "mteb_results = mteb.load_results(\n",
-                "    models=models, tasks=danish_tasks, download_latest=False\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "{'intfloat/multilingual-e5-small': {'e4ce9877abf3edfe10b0d82785e83bdcb973e22e': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]},\n",
-                            " 'sentence-transformers/LaBSE': {'e34fab64a3011d2176c99545a93d5cbddc9a91b7': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]},\n",
-                            " 'GritLM/GritLM-7B': {'13f00a0e36500c80ce12870ea513846a066004af': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]},\n",
-                            " 'intfloat/multilingual-e5-large': {'4dc6d853a804b9c8886ede6dda8a073b7dc08a81': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]},\n",
-                            " 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2': {'79f2382ceacceacdf38563d7c5d16b9ff8d725d6': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]},\n",
-                            " 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2': {'bf3bf13ab40c3157080a7ab344c831b9ad18b5eb': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]},\n",
-                            " 'sentence-transformers/all-mpnet-base-v2': {'84f2bcc00d77236f9e89c8a360a00fb1139bf47d': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]},\n",
-                            " 'intfloat/multilingual-e5-large-instruct': {'baa7be480a7de1539afce709c8f13f833a510e0a': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]},\n",
-                            " 'sentence-transformers/all-MiniLM-L12-v2': {'a05860a77cef7b37e0048a7864658139bc18a854': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]},\n",
-                            " 'intfloat/multilingual-e5-base': {'d13f1b27baf31030b7fd040960d60d909913633f': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]},\n",
-                            " 'sentence-transformers/all-MiniLM-L6-v2': {'8b3219a92973c328a8e22fadcfa821b5dc75636a': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]},\n",
-                            " 'intfloat/e5-mistral-7b-instruct': {'07163b72af1488142a360786df853f237b1a3ca1': [MTEBResults(task_name=LccSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=WikiClusteringP2P.v2, scores=...),\n",
-                            "   MTEBResults(task_name=AngryTweetsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TwitterHjerneRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BornholmBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanishPoliticalCommentsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=NordicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=ScalaClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200Classification, scores=...),\n",
-                            "   MTEBResults(task_name=MultiEURLEXMultilabelClassification, scores=...),\n",
-                            "   MTEBResults(task_name=TV2Nordretrieval, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=DanFeverRetrieval, scores=...)]}}"
-                        ]
-                    },
-                    "execution_count": 5,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "mteb_results"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Computing the most predictable task\n",
-                "\n",
-                "We will start out by constructing a dataframe from the results. Where we will compute the most predictictable task given a model and a set of tasks."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import mteb.task_selection as task_selection\n",
-                "\n",
-                "results_df = task_selection.results_to_dataframe(\n",
-                "    mteb_results, drop_na=False, languages=[\"dan\"]\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>task</th>\n",
-                            "      <th>AngryTweetsClassification</th>\n",
-                            "      <th>BelebeleRetrieval</th>\n",
-                            "      <th>BibleNLPBitextMining</th>\n",
-                            "      <th>BornholmBitextMining</th>\n",
-                            "      <th>DanFeverRetrieval</th>\n",
-                            "      <th>DanishPoliticalCommentsClassification</th>\n",
-                            "      <th>FloresBitextMining</th>\n",
-                            "      <th>LccSentimentClassification</th>\n",
-                            "      <th>MassiveIntentClassification</th>\n",
-                            "      <th>MassiveScenarioClassification</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>NordicLangClassification</th>\n",
-                            "      <th>SIB200Classification</th>\n",
-                            "      <th>SIB200ClusteringS2S</th>\n",
-                            "      <th>ScalaClassification</th>\n",
-                            "      <th>TV2Nordretrieval</th>\n",
-                            "      <th>Tatoeba</th>\n",
-                            "      <th>TwitterHjerneRetrieval</th>\n",
-                            "      <th>WikiClusteringP2P.v2</th>\n",
-                            "      <th>WikipediaRerankingMultilingual</th>\n",
-                            "      <th>WikipediaRetrievalMultilingual</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-base</th>\n",
-                            "      <th>d13f1b27baf31030b7fd040960d60d909913633f</th>\n",
-                            "      <td>0.562846</td>\n",
-                            "      <td>0.907317</td>\n",
-                            "      <td>0.949219</td>\n",
-                            "      <td>0.332181</td>\n",
-                            "      <td>0.40416</td>\n",
-                            "      <td>0.364127</td>\n",
-                            "      <td>0.822655</td>\n",
-                            "      <td>0.601333</td>\n",
-                            "      <td>0.606893</td>\n",
-                            "      <td>0.679691</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.758533</td>\n",
-                            "      <td>0.756863</td>\n",
-                            "      <td>0.411994</td>\n",
-                            "      <td>0.509961</td>\n",
-                            "      <td>0.92682</td>\n",
-                            "      <td>0.912300</td>\n",
-                            "      <td>0.42163</td>\n",
-                            "      <td>0.203832</td>\n",
-                            "      <td>0.897135</td>\n",
-                            "      <td>0.90878</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large</th>\n",
-                            "      <th>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</th>\n",
-                            "      <td>0.576886</td>\n",
-                            "      <td>0.946187</td>\n",
-                            "      <td>0.976562</td>\n",
-                            "      <td>0.296079</td>\n",
-                            "      <td>0.40868</td>\n",
-                            "      <td>0.394338</td>\n",
-                            "      <td>0.826064</td>\n",
-                            "      <td>0.615333</td>\n",
-                            "      <td>0.636954</td>\n",
-                            "      <td>0.711836</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.801533</td>\n",
-                            "      <td>0.782353</td>\n",
-                            "      <td>0.465025</td>\n",
-                            "      <td>0.516162</td>\n",
-                            "      <td>0.95369</td>\n",
-                            "      <td>0.950800</td>\n",
-                            "      <td>0.35219</td>\n",
-                            "      <td>0.206153</td>\n",
-                            "      <td>0.912693</td>\n",
-                            "      <td>0.92426</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large-instruct</th>\n",
-                            "      <th>baa7be480a7de1539afce709c8f13f833a510e0a</th>\n",
-                            "      <td>0.645081</td>\n",
-                            "      <td>0.923867</td>\n",
-                            "      <td>0.992188</td>\n",
-                            "      <td>0.552233</td>\n",
-                            "      <td>0.40513</td>\n",
-                            "      <td>0.448807</td>\n",
-                            "      <td>0.929799</td>\n",
-                            "      <td>0.708000</td>\n",
-                            "      <td>0.718527</td>\n",
-                            "      <td>0.774748</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.824433</td>\n",
-                            "      <td>0.828431</td>\n",
-                            "      <td>0.586632</td>\n",
-                            "      <td>0.507129</td>\n",
-                            "      <td>0.93690</td>\n",
-                            "      <td>0.955333</td>\n",
-                            "      <td>0.77233</td>\n",
-                            "      <td>0.242545</td>\n",
-                            "      <td>0.900087</td>\n",
-                            "      <td>0.90626</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-small</th>\n",
-                            "      <th>e4ce9877abf3edfe10b0d82785e83bdcb973e22e</th>\n",
-                            "      <td>0.562655</td>\n",
-                            "      <td>0.854937</td>\n",
-                            "      <td>0.839258</td>\n",
-                            "      <td>0.371457</td>\n",
-                            "      <td>0.39601</td>\n",
-                            "      <td>0.348182</td>\n",
-                            "      <td>0.762986</td>\n",
-                            "      <td>0.586000</td>\n",
-                            "      <td>0.561197</td>\n",
-                            "      <td>0.640282</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.721500</td>\n",
-                            "      <td>0.747059</td>\n",
-                            "      <td>0.386207</td>\n",
-                            "      <td>0.508008</td>\n",
-                            "      <td>0.90379</td>\n",
-                            "      <td>0.863838</td>\n",
-                            "      <td>0.29358</td>\n",
-                            "      <td>0.195741</td>\n",
-                            "      <td>0.883712</td>\n",
-                            "      <td>0.89263</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/LaBSE</th>\n",
-                            "      <th>e34fab64a3011d2176c99545a93d5cbddc9a91b7</th>\n",
-                            "      <td>0.511079</td>\n",
-                            "      <td>0.739557</td>\n",
-                            "      <td>0.989583</td>\n",
-                            "      <td>0.456256</td>\n",
-                            "      <td>0.34537</td>\n",
-                            "      <td>0.383403</td>\n",
-                            "      <td>0.838430</td>\n",
-                            "      <td>0.500667</td>\n",
-                            "      <td>0.582313</td>\n",
-                            "      <td>0.652589</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.353867</td>\n",
-                            "      <td>0.599510</td>\n",
-                            "      <td>0.284970</td>\n",
-                            "      <td>0.506104</td>\n",
-                            "      <td>0.76295</td>\n",
-                            "      <td>0.957067</td>\n",
-                            "      <td>0.14380</td>\n",
-                            "      <td>0.177777</td>\n",
-                            "      <td>0.825927</td>\n",
-                            "      <td>0.69096</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>5 rows \u00d7 22 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "task                                                                              AngryTweetsClassification  \\\n",
-                            "model                                   revision                                                              \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                   0.562846   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                   0.576886   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                   0.645081   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e                   0.562655   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7                   0.511079   \n",
-                            "\n",
-                            "task                                                                              BelebeleRetrieval  \\\n",
-                            "model                                   revision                                                      \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f           0.907317   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81           0.946187   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a           0.923867   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e           0.854937   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7           0.739557   \n",
-                            "\n",
-                            "task                                                                              BibleNLPBitextMining  \\\n",
-                            "model                                   revision                                                         \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f              0.949219   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81              0.976562   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a              0.992188   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e              0.839258   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7              0.989583   \n",
-                            "\n",
-                            "task                                                                              BornholmBitextMining  \\\n",
-                            "model                                   revision                                                         \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f              0.332181   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81              0.296079   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a              0.552233   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e              0.371457   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7              0.456256   \n",
-                            "\n",
-                            "task                                                                              DanFeverRetrieval  \\\n",
-                            "model                                   revision                                                      \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f            0.40416   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81            0.40868   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a            0.40513   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e            0.39601   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7            0.34537   \n",
-                            "\n",
-                            "task                                                                              DanishPoliticalCommentsClassification  \\\n",
-                            "model                                   revision                                                                          \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                               0.364127   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                               0.394338   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                               0.448807   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e                               0.348182   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7                               0.383403   \n",
-                            "\n",
-                            "task                                                                              FloresBitextMining  \\\n",
-                            "model                                   revision                                                       \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f            0.822655   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81            0.826064   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a            0.929799   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e            0.762986   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7            0.838430   \n",
-                            "\n",
-                            "task                                                                              LccSentimentClassification  \\\n",
-                            "model                                   revision                                                               \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                    0.601333   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                    0.615333   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                    0.708000   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e                    0.586000   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7                    0.500667   \n",
-                            "\n",
-                            "task                                                                              MassiveIntentClassification  \\\n",
-                            "model                                   revision                                                                \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                     0.606893   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                     0.636954   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                     0.718527   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e                     0.561197   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7                     0.582313   \n",
-                            "\n",
-                            "task                                                                              MassiveScenarioClassification  \\\n",
-                            "model                                   revision                                                                  \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                       0.679691   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                       0.711836   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                       0.774748   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e                       0.640282   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7                       0.652589   \n",
-                            "\n",
-                            "task                                                                              ...  \\\n",
-                            "model                                   revision                                  ...   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  ...   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  ...   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  ...   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e  ...   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7  ...   \n",
-                            "\n",
-                            "task                                                                              NordicLangClassification  \\\n",
-                            "model                                   revision                                                             \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                  0.758533   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                  0.801533   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                  0.824433   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e                  0.721500   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7                  0.353867   \n",
-                            "\n",
-                            "task                                                                              SIB200Classification  \\\n",
-                            "model                                   revision                                                         \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f              0.756863   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81              0.782353   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a              0.828431   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e              0.747059   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7              0.599510   \n",
-                            "\n",
-                            "task                                                                              SIB200ClusteringS2S  \\\n",
-                            "model                                   revision                                                        \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f             0.411994   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81             0.465025   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a             0.586632   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e             0.386207   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7             0.284970   \n",
-                            "\n",
-                            "task                                                                              ScalaClassification  \\\n",
-                            "model                                   revision                                                        \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f             0.509961   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81             0.516162   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a             0.507129   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e             0.508008   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7             0.506104   \n",
-                            "\n",
-                            "task                                                                              TV2Nordretrieval  \\\n",
-                            "model                                   revision                                                     \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f           0.92682   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81           0.95369   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a           0.93690   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e           0.90379   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7           0.76295   \n",
-                            "\n",
-                            "task                                                                               Tatoeba  \\\n",
-                            "model                                   revision                                             \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  0.912300   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.950800   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  0.955333   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e  0.863838   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7  0.957067   \n",
-                            "\n",
-                            "task                                                                              TwitterHjerneRetrieval  \\\n",
-                            "model                                   revision                                                           \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                 0.42163   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                 0.35219   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                 0.77233   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e                 0.29358   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7                 0.14380   \n",
-                            "\n",
-                            "task                                                                              WikiClusteringP2P.v2  \\\n",
-                            "model                                   revision                                                         \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f              0.203832   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81              0.206153   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a              0.242545   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e              0.195741   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7              0.177777   \n",
-                            "\n",
-                            "task                                                                              WikipediaRerankingMultilingual  \\\n",
-                            "model                                   revision                                                                   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                        0.897135   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                        0.912693   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                        0.900087   \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e                        0.883712   \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7                        0.825927   \n",
-                            "\n",
-                            "task                                                                              WikipediaRetrievalMultilingual  \n",
-                            "model                                   revision                                                                  \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                         0.90878  \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                         0.92426  \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                         0.90626  \n",
-                            "intfloat/multilingual-e5-small          e4ce9877abf3edfe10b0d82785e83bdcb973e22e                         0.89263  \n",
-                            "sentence-transformers/LaBSE             e34fab64a3011d2176c99545a93d5cbddc9a91b7                         0.69096  \n",
-                            "\n",
-                            "[5 rows x 22 columns]"
-                        ]
-                    },
-                    "execution_count": 13,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results_df.head()  # inspect the dataframe"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: WikipediaRetrievalMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22/22 [00:00<00:00, 67.96it/s]      \n"
-                    ]
-                }
-            ],
-            "source": [
-                "from sklearn.linear_model import LinearRegression\n",
-                "\n",
-                "task_predictablity = task_selection.most_predictable_task(\n",
-                "    results_df,\n",
-                "    sklearn_estimator=LinearRegression(),  # model to predict performance on a held out task given the performance on the other tasks\n",
-                "    metrics=[\n",
-                "        task_selection.spearman,\n",
-                "        task_selection.pearson,\n",
-                "        task_selection.mse_with_zscore,\n",
-                "    ],\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "[{'BelebeleRetrieval': {'spearman': 0.9878787878787878,\n",
-                            "   'pearson': 0.9970507409976126,\n",
-                            "   'mse_with_zscore': 0.005898518004774879}},\n",
-                            " {'NTREXBitextMining': {'spearman': 0.9515151515151514,\n",
-                            "   'pearson': 0.9867856182994142,\n",
-                            "   'mse_with_zscore': 0.026428763401171528}},\n",
-                            " {'TV2Nordretrieval': {'spearman': 0.9515151515151514,\n",
-                            "   'pearson': 0.9779518119166111,\n",
-                            "   'mse_with_zscore': 0.04409637616677795}},\n",
-                            " {'MassiveScenarioClassification': {'spearman': 0.9151515151515152,\n",
-                            "   'pearson': 0.9595171124139267,\n",
-                            "   'mse_with_zscore': 0.08096577517214652}},\n",
-                            " {'WikipediaRerankingMultilingual': {'spearman': 0.9151515151515152,\n",
-                            "   'pearson': 0.9478418113623516,\n",
-                            "   'mse_with_zscore': 0.10431637727529648}},\n",
-                            " {'WikipediaRetrievalMultilingual': {'spearman': 0.9030303030303028,\n",
-                            "   'pearson': 0.9295224767277619,\n",
-                            "   'mse_with_zscore': 0.14095504654447644}},\n",
-                            " {'BibleNLPBitextMining': {'spearman': 0.8787878787878788,\n",
-                            "   'pearson': 0.9964031787166682,\n",
-                            "   'mse_with_zscore': 0.00719364256666383}},\n",
-                            " {'Tatoeba': {'spearman': 0.8787878787878788,\n",
-                            "   'pearson': 0.9854416845271496,\n",
-                            "   'mse_with_zscore': 0.029116630945700793}},\n",
-                            " {'SIB200Classification': {'spearman': 0.8666666666666665,\n",
-                            "   'pearson': 0.9331750993371765,\n",
-                            "   'mse_with_zscore': 0.1336498013256467}},\n",
-                            " {'SIB200ClusteringS2S': {'spearman': 0.8666666666666665,\n",
-                            "   'pearson': 0.9223883525531887,\n",
-                            "   'mse_with_zscore': 0.15522329489362224}},\n",
-                            " {'AngryTweetsClassification': {'spearman': 0.8424242424242423,\n",
-                            "   'pearson': 0.9073414635456171,\n",
-                            "   'mse_with_zscore': 0.185317072908766}},\n",
-                            " {'DanFeverRetrieval': {'spearman': 0.8424242424242423,\n",
-                            "   'pearson': 0.9259938436309683,\n",
-                            "   'mse_with_zscore': 0.14801231273806378}},\n",
-                            " {'LccSentimentClassification': {'spearman': 0.8424242424242423,\n",
-                            "   'pearson': 0.9132684082289415,\n",
-                            "   'mse_with_zscore': 0.17346318354211748}},\n",
-                            " {'MassiveIntentClassification': {'spearman': 0.8303030303030302,\n",
-                            "   'pearson': 0.8485440619293967,\n",
-                            "   'mse_with_zscore': 0.30291187614120596}},\n",
-                            " {'MultiEURLEXMultilabelClassification': {'spearman': 0.8060606060606059,\n",
-                            "   'pearson': 0.8779012689047206,\n",
-                            "   'mse_with_zscore': 0.244197462190559}},\n",
-                            " {'DanishPoliticalCommentsClassification': {'spearman': 0.7818181818181817,\n",
-                            "   'pearson': 0.8426768615531222,\n",
-                            "   'mse_with_zscore': 0.31464627689375585}},\n",
-                            " {'FloresBitextMining': {'spearman': 0.7818181818181817,\n",
-                            "   'pearson': 0.9180176990639936,\n",
-                            "   'mse_with_zscore': 0.16396460187201242}},\n",
-                            " {'WikiClusteringP2P.v2': {'spearman': 0.7575757575757575,\n",
-                            "   'pearson': 0.824686598274594,\n",
-                            "   'mse_with_zscore': 0.3506268034508118}},\n",
-                            " {'ScalaClassification': {'spearman': 0.5636363636363636,\n",
-                            "   'pearson': 0.5013461087446984,\n",
-                            "   'mse_with_zscore': 0.9973077825106031}},\n",
-                            " {'TwitterHjerneRetrieval': {'spearman': 0.05454545454545454,\n",
-                            "   'pearson': -0.17697172918390386,\n",
-                            "   'mse_with_zscore': 2.3539434583678074}},\n",
-                            " {'BornholmBitextMining': {'spearman': -0.09090909090909088,\n",
-                            "   'pearson': -0.1256947260720855,\n",
-                            "   'mse_with_zscore': 2.251389452144171}},\n",
-                            " {'NordicLangClassification': {'spearman': -0.1515151515151515,\n",
-                            "   'pearson': -0.02762353090535816,\n",
-                            "   'mse_with_zscore': 2.0552470618107166}}]"
-                        ]
-                    },
-                    "execution_count": 15,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "task_predictablity  # task ordered by predictability of fist metric"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Task Selection\n",
-                "\n",
-                "In this section we will do the task selection to construct a benchmark."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Manual Curation\n",
-                "Naturally you can always select your datasets manually and there might be plenty reasons to do so:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# if you wish you can do some manual filtering here, which we will do in this example:\n",
-                "tasks_to_remove = [\n",
-                "    \"DKHateClassification\",  # due to it being a gated dataset on huggingface (requiring to sign a form)\n",
-                "    \"MultiEURLEXMultilabelClassification\",  # due to it being very large\n",
-                "]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "BornholmBitextMining - CC-BY-4.0\n",
-                        "BibleNLPBitextMining - CC-BY-SA-4.0\n",
-                        "FloresBitextMining - CC BY-SA 4.0\n",
-                        "NTREXBitextMining - CC-BY-SA-4.0\n",
-                        "Tatoeba - CC BY 2.0\n",
-                        "AngryTweetsClassification - CC-BY-4.0\n",
-                        "DanishPoliticalCommentsClassification - Not specified\n",
-                        "DKHateClassification - CC-BY-4.0\n",
-                        "LccSentimentClassification - CC-BY-4.0\n",
-                        "MassiveIntentClassification - Apache 2.0\n",
-                        "MassiveScenarioClassification - Apache 2.0\n",
-                        "NordicLangClassification - cc-by-sa-3.0\n",
-                        "ScalaClassification - CC BY-SA 4.0\n",
-                        "SIB200Classification - cc-by-sa-4.0\n",
-                        "SIB200ClusteringS2S - cc-by-sa-4.0\n",
-                        "WikiClusteringP2P.v2 - cc-by-sa-3.0\n",
-                        "DanFeverRetrieval - CC BY-SA 4.0\n",
-                        "TV2Nordretrieval - CC0\n",
-                        "TwitterHjerneRetrieval - CC BY 4.0\n",
-                        "BelebeleRetrieval - CC-BY-SA-4.0\n",
-                        "WikipediaRetrievalMultilingual - cc-by-sa-3.0\n",
-                        "MultiEURLEXMultilabelClassification - CC BY-SA 4.0\n",
-                        "WikipediaRerankingMultilingual - cc-by-sa-3.0\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# we also want somewhat permissible licenses\n",
-                "\n",
-                "for t in danish_tasks:\n",
-                "    print(t.metadata.name, \"-\", t.metadata.license)\n",
-                "\n",
-                "# based on this we will probably also remove:\n",
-                "tasks_to_remove += [\"DanishPoliticalCommentsClassification\"]  # ambiguous license"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[]\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# we also want to removed machine translated datasets\n",
-                "machine_translated_datasets = [\n",
-                "    t.metadata.name\n",
-                "    for t in danish_tasks\n",
-                "    if t.metadata.sample_creation\n",
-                "    in [\n",
-                "        \"machine-translated\",\n",
-                "        \"machine-translated and verified\",\n",
-                "        \"machine-translated and localized\",\n",
-                "    ]\n",
-                "]\n",
-                "\n",
-                "print(machine_translated_datasets)  # there is none"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "tasks_to_select_from = [\n",
-                "    task.metadata.name\n",
-                "    for task in danish_tasks\n",
-                "    if task.metadata.name not in tasks_to_remove\n",
-                "]"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Iterative Automated Task Selection \n",
-                "\n",
-                "Here we do the iterative task selection for Danish. We have designed the code to be flexible enough to allow benchmark developers to adjust the assumptions they make."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 20,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# tasks which should be kept, e.g. due to them being known high quality datasets, unique tasks, etc.\n",
-                "tasks_to_keep = [\n",
-                "    \"AngryTweetsClassification\",\n",
-                "    \"DanFeverRetrieval\",\n",
-                "    \"BornholmBitextMining\",\n",
-                "]\n",
-                "\n",
-                "\n",
-                "def is_candidate_valid_removal(current_tasks: list[str], task_to_remove: str) -> bool:\n",
-                "    \"\"\"Determine if target task should be removed. This simply checks that all task types are present in the current tasks or whether the task is in the tasks_to_keep list.\"\"\"\n",
-                "    if task_to_remove in tasks_to_keep:\n",
-                "        return False\n",
-                "\n",
-                "    # check if removing task removes a unique task type - if so, don't remove\n",
-                "    _current_tasks = current_tasks.copy()\n",
-                "    if task_to_remove in _current_tasks:\n",
-                "        _current_tasks.remove(task_to_remove)\n",
-                "    task = mteb.get_task(task_to_remove)\n",
-                "    ctasks = mteb.get_tasks(tasks=_current_tasks)\n",
-                "\n",
-                "    # don't remove a unique task type\n",
-                "    task_types = {t.metadata.type for t in ctasks}\n",
-                "    if task.metadata.type not in task_types:\n",
-                "        return False\n",
-                "    return True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 21,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:00<00:00, 75.74it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 19/19 [00:00<00:00, 83.23it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 18/18 [00:00<00:00, 84.55it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 17/17 [00:00<00:00, 84.32it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 16/16 [00:00<00:00, 77.37it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 15/15 [00:00<00:00, 85.79it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14/14 [00:00<00:00, 86.38it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:00<00:00, 89.55it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12/12 [00:00<00:00, 81.43it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 11/11 [00:00<00:00, 80.33it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 10/10 [00:00<00:00, 86.61it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 9/9 [00:00<00:00, 88.41it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 8/8 [00:00<00:00, 90.58it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7/7 [00:00<00:00, 89.17it/s]\n",
-                        "Task: WikipediaRerankingMultilingual: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:00<00:00, 92.14it/s]\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# remove tasks one by one\n",
-                "tasks_removed = []\n",
-                "predicability_scores = []\n",
-                "\n",
-                "while tasks_to_select_from:\n",
-                "    most_pred_tasks = task_selection.most_predictable_task(\n",
-                "        results_df[tasks_to_select_from],\n",
-                "        sklearn_estimator=LinearRegression(),\n",
-                "        metrics=[\n",
-                "            task_selection.spearman,\n",
-                "            task_selection.pearson,\n",
-                "            task_selection.mse_with_zscore,\n",
-                "        ],\n",
-                "    )\n",
-                "    # reverse the list to get the least predictable task\n",
-                "    most_pred_tasks.reverse()\n",
-                "\n",
-                "    while most_pred_tasks:\n",
-                "        most_pred_task = most_pred_tasks.pop()\n",
-                "        most_pred_task_name = list(most_pred_task.keys())[0]\n",
-                "        if is_candidate_valid_removal(tasks_to_select_from, most_pred_task_name):\n",
-                "            tasks_to_select_from.remove(most_pred_task_name)\n",
-                "            tasks_removed.append(most_pred_task_name)\n",
-                "            predicability_scores.append(most_pred_task[most_pred_task_name])\n",
-                "            break\n",
-                "\n",
-                "    if not most_pred_tasks:  # if no task was removed, then we are done -- can be replaced with another stopping criterion\n",
-                "        break"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 22,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 640x480 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# plotting the predictability scores with the tasks removed\n",
-                "import matplotlib.pyplot as plt\n",
-                "\n",
-                "for metric in [\"spearman\", \"pearson\"]:\n",
-                "    plt.plot([t[metric] for t in predicability_scores], label=metric)\n",
-                "\n",
-                "plt.xlabel(\"Tasks removed\")\n",
-                "plt.ylabel(\"Correlation (predicted vs. observed performance)\")\n",
-                "plt.legend()\n",
-                "\n",
-                "# add task names to the x-axis\n",
-                "plt.xticks(range(len(tasks_removed)), tasks_removed, rotation=90)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 23,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 640x480 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# plotting the predictability scores with the tasks removed\n",
-                "import matplotlib.pyplot as plt\n",
-                "\n",
-                "for metric in [\"mse_with_zscore\"]:\n",
-                "    plt.plot([t[metric] for t in predicability_scores], label=metric)\n",
-                "\n",
-                "plt.xlabel(\"Tasks removed\")\n",
-                "plt.ylabel(\"Normalized Mean Squared error (predicted vs. observed performance)\")\n",
-                "plt.legend()\n",
-                "\n",
-                "# add task names to the x-axis\n",
-                "plt.xticks(range(len(tasks_removed)), tasks_removed, rotation=90)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Constructing the Benchmark"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 24,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "['BornholmBitextMining',\n",
-                            " 'AngryTweetsClassification',\n",
-                            " 'SIB200ClusteringS2S',\n",
-                            " 'DanFeverRetrieval',\n",
-                            " 'WikipediaRerankingMultilingual']"
-                        ]
-                    },
-                    "execution_count": 24,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# we now have the tasks:\n",
-                "tasks_to_select_from"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 25,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# but some of the task above seem to be quite hard to predict, so we might want to include some of them back based on a threshold\n",
-                "benchmark_tasks = (\n",
-                "    tasks_to_select_from + tasks_removed[-1:]\n",
-                ")  # chosen somewhat arbitrarily based on the plot above [-4:]\u00a0or [-5:] might be more reasonable\n",
-                "# but this makes running the benchmark faster, which is useful for this example"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 26,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "tasks = mteb.get_tasks(tasks=benchmark_tasks, languages=[\"dan\"])\n",
-                "\n",
-                "# we can now create a benchmark\n",
-                "benchmark = mteb.Benchmark(\n",
-                "    name=\"mteb(dan)\",  # we recommend that the name is prefixed with \"mteb\" and that the language indicated using the ISO 639-3 code (3 letter code)\n",
-                "    tasks=tasks,\n",
-                "    description=\"Benchmark for evaluating Danish document embedding models\",\n",
-                "    citation=\"\",\n",
-                "    reference=\"\",\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Congratulations you just created your own benchmark. Once completed you can use the benchmark however you like. If you do believe that others would find the benchmark useable, we encourage that you submit a PR with the benchmark to mteb so that other can reproduce your results."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Running the benchmark\n",
-                "\n",
-                "You can naturally run the benchmark simply in `mteb` using:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 27,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #262626; text-decoration-color: #262626\">\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 </span><span style=\"font-weight: bold\">Selected tasks </span><span style=\"color: #262626; text-decoration-color: #262626\"> \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[38;5;235m\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 \u001b[0m\u001b[1mSelected tasks \u001b[0m\u001b[38;5;235m \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Clustering</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[1mClustering\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">    - SIB200ClusteringS2S, <span style=\"color: #626262; text-decoration-color: #626262; font-style: italic\">s2s</span>, <span style=\"color: #800000; text-decoration-color: #800000; font-style: italic\">multilingual </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold; font-style: italic\">1</span><span style=\"color: #800000; text-decoration-color: #800000; font-style: italic\"> / </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold; font-style: italic\">197</span><span style=\"color: #800000; text-decoration-color: #800000; font-style: italic\"> Subsets</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "    - SIB200ClusteringS2S, \u001b[3;38;5;241ms2s\u001b[0m, \u001b[3;31mmultilingual \u001b[0m\u001b[1;3;31m1\u001b[0m\u001b[3;31m \u001b[0m\u001b[3;31m/\u001b[0m\u001b[3;31m \u001b[0m\u001b[1;3;31m197\u001b[0m\u001b[3;31m Subsets\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-                            "\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\n",
-                            "\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Classification</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[1mClassification\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">    - AngryTweetsClassification, <span style=\"color: #626262; text-decoration-color: #626262; font-style: italic\">s2s</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "    - AngryTweetsClassification, \u001b[3;38;5;241ms2s\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">    - NordicLangClassification, <span style=\"color: #626262; text-decoration-color: #626262; font-style: italic\">s2s</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "    - NordicLangClassification, \u001b[3;38;5;241ms2s\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-                            "\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\n",
-                            "\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">BitextMining</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[1mBitextMining\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">    - BornholmBitextMining, <span style=\"color: #626262; text-decoration-color: #626262; font-style: italic\">s2s</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "    - BornholmBitextMining, \u001b[3;38;5;241ms2s\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-                            "\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\n",
-                            "\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Reranking</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[1mReranking\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">    - WikipediaRerankingMultilingual, <span style=\"color: #626262; text-decoration-color: #626262; font-style: italic\">s2p</span>, <span style=\"color: #800000; text-decoration-color: #800000; font-style: italic\">multilingual </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold; font-style: italic\">1</span><span style=\"color: #800000; text-decoration-color: #800000; font-style: italic\"> / </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold; font-style: italic\">16</span><span style=\"color: #800000; text-decoration-color: #800000; font-style: italic\"> Subsets</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "    - WikipediaRerankingMultilingual, \u001b[3;38;5;241ms2p\u001b[0m, \u001b[3;31mmultilingual \u001b[0m\u001b[1;3;31m1\u001b[0m\u001b[3;31m \u001b[0m\u001b[3;31m/\u001b[0m\u001b[3;31m \u001b[0m\u001b[1;3;31m16\u001b[0m\u001b[3;31m Subsets\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-                            "\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\n",
-                            "\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Retrieval</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\u001b[1mRetrieval\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">    - DanFeverRetrieval, <span style=\"color: #626262; text-decoration-color: #626262; font-style: italic\">p2p</span>\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "    - DanFeverRetrieval, \u001b[3;38;5;241mp2p\u001b[0m\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-                            "\n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "\n",
-                            "\n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# we can now run the benchmark\n",
-                "evaluator = mteb.MTEB(tasks=benchmark)\n",
-                "\n",
-                "model = mteb.get_model(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
-                "results = evaluator.run(model)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Aggregating Scores across Benchmark\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 29,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Already up to date.\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "WARNING:mteb.load_results.load_results:Validation failed for SIB200ClusteringS2S in intfloat/e5-mistral-7b-instruct 07163b72af1488142a360786df853f237b1a3ca1: Missing subsets {'kac_Latn', 'bug_Latn', 'lit_Latn', 'som_Latn', 'jpn_Jpan', 'srd_Latn', 'mal_Mlym', 'hrv_Latn', 'luo_Latn', 'dik_Latn', 'ita_Latn', 'ell_Grek', 'zho_Hant', 'ary_Arab', 'est_Latn', 'hun_Latn', 'sin_Sinh', 'hye_Armn', 'ltg_Latn', 'fij_Latn', 'hin_Deva', 'nqo_Nkoo', 'tpi_Latn', 'yor_Latn', 'ben_Beng', 'szl_Latn', 'amh_Ethi', 'bul_Cyrl', 'ron_Latn', 'lmo_Latn', 'sag_Latn', 'ilo_Latn', 'mar_Deva', 'epo_Latn', 'kat_Geor', 'snd_Arab', 'san_Deva', 'hau_Latn', 'tgl_Latn', 'khk_Cyrl', 'mni_Beng', 'fra_Latn', 'kaz_Cyrl', 'gaz_Latn', 'scn_Latn', 'min_Latn', 'ace_Latn', 'ajp_Arab', 'urd_Arab', 'tam_Taml', 'run_Latn', 'ban_Latn', 'fuv_Latn', 'bel_Cyrl', 'srp_Cyrl', 'hne_Deva', 'dyu_Latn', 'kir_Cyrl', 'bem_Latn', 'bod_Tibt', 'tha_Thai', 'pap_Latn', 'tso_Latn', 'ydd_Hebr', 'tgk_Cyrl', 'umb_Latn', 'lug_Latn', 'lao_Laoo', 'nob_Latn', 'lim_Latn', 'nno_Latn', 'ast_Latn', 'tzm_Tfng', 'cym_Latn', 'ind_Latn', 'ibo_Latn', 'jav_Latn', 'lvs_Latn', 'npi_Deva', 'pes_Arab', 'fin_Latn', 'lus_Latn', 'sun_Latn', 'tuk_Latn', 'ars_Arab', 'fur_Latn', 'lua_Latn', 'bak_Cyrl', 'kin_Latn', 'spa_Latn', 'fao_Latn', 'kor_Hang', 'twi_Latn', 'war_Latn', 'arb_Latn', 'azb_Arab', 'kas_Deva', 'xho_Latn', 'aeb_Arab', 'guj_Gujr', 'apc_Arab', 'grn_Latn', 'aka_Latn', 'mya_Mymr', 'kab_Latn', 'nso_Latn', 'yue_Hant', 'bam_Latn', 'bho_Deva', 'slv_Latn', 'slk_Latn', 'kbp_Latn', 'kam_Latn', 'taq_Tfng', 'knc_Latn', 'dan_Latn', 'ewe_Latn', 'uig_Arab', 'eus_Latn', 'ory_Orya', 'vie_Latn', 'sot_Latn', 'lij_Latn', 'tur_Latn', 'cat_Latn', 'kmb_Latn', 'kmr_Latn', 'awa_Deva', 'nus_Latn', 'ceb_Latn', 'sat_Olck', 'smo_Latn', 'heb_Hebr', 'crh_Latn', 'bjn_Latn', 'acq_Arab', 'mai_Deva', 'ltz_Latn', 'bos_Latn', 'glg_Latn', 'lin_Latn', 'plt_Latn', 'por_Latn', 'nya_Latn', 'asm_Beng', 'swe_Latn', 'ayr_Latn', 'gle_Latn', 'oci_Latn', 'pol_Latn', 'arz_Arab', 'tel_Telu', 'azj_Latn', 'ssw_Latn', 'tum_Latn', 'zsm_Latn', 'vec_Latn', 'mri_Latn', 'quy_Latn', 'als_Latn', 'shn_Mymr', 'wol_Latn', 'kan_Knda', 'isl_Latn', 'khm_Khmr', 'nld_Latn', 'pan_Guru', 'cjk_Latn', 'fon_Latn', 'tir_Ethi', 'ces_Latn', 'kik_Latn', 'kea_Latn', 'kon_Latn', 'deu_Latn', 'eng_Latn', 'sna_Latn', 'tat_Cyrl', 'pbt_Arab', 'prs_Arab', 'ckb_Arab', 'uzn_Latn', 'gla_Latn', 'acm_Arab', 'dzo_Tibt', 'mos_Latn', 'zul_Latn', 'hat_Latn', 'afr_Latn', 'mag_Deva', 'tsn_Latn', 'ukr_Cyrl', 'swh_Latn', 'mkd_Cyrl', 'pag_Latn', 'mlt_Latn'} for split test\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# load task results for the specified models from mteb/results repository\n",
-                "mteb_results = mteb.load_results(models=models, tasks=benchmark)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 64,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "WARNING:mteb.task_aggregation:Model intfloat/e5-mistral-7b-instruct revision 07163b72af1488142a360786df853f237b1a3ca1 has missing scores for some tasks\n",
-                        "WARNING:mteb.task_aggregation:Model GritLM/GritLM-7B revision 13f00a0e36500c80ce12870ea513846a066004af has missing scores for some tasks\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "WARNING:mteb.task_aggregation:Model intfloat/e5-mistral-7b-instruct revision 07163b72af1488142a360786df853f237b1a3ca1 has missing scores for some tasks of type Classification\n",
-                        "WARNING:mteb.task_aggregation:Model intfloat/e5-mistral-7b-instruct revision 07163b72af1488142a360786df853f237b1a3ca1 has missing scores for some tasks of type Reranking\n",
-                        "WARNING:mteb.task_aggregation:Model intfloat/e5-mistral-7b-instruct revision 07163b72af1488142a360786df853f237b1a3ca1 has missing scores for some tasks of type BitextMining\n",
-                        "WARNING:mteb.task_aggregation:Model intfloat/e5-mistral-7b-instruct revision 07163b72af1488142a360786df853f237b1a3ca1 has missing scores for some tasks of type Clustering\n",
-                        "WARNING:mteb.task_aggregation:Model intfloat/e5-mistral-7b-instruct revision 07163b72af1488142a360786df853f237b1a3ca1 has missing scores for some tasks of type Retrieval\n",
-                        "WARNING:mteb.task_aggregation:Model GritLM/GritLM-7B revision 13f00a0e36500c80ce12870ea513846a066004af has missing scores for some tasks of type Classification\n",
-                        "WARNING:mteb.task_aggregation:Model GritLM/GritLM-7B revision 13f00a0e36500c80ce12870ea513846a066004af has missing scores for some tasks of type Reranking\n",
-                        "WARNING:mteb.task_aggregation:Model GritLM/GritLM-7B revision 13f00a0e36500c80ce12870ea513846a066004af has missing scores for some tasks of type BitextMining\n",
-                        "WARNING:mteb.task_aggregation:Model GritLM/GritLM-7B revision 13f00a0e36500c80ce12870ea513846a066004af has missing scores for some tasks of type Clustering\n",
-                        "WARNING:mteb.task_aggregation:Model GritLM/GritLM-7B revision 13f00a0e36500c80ce12870ea513846a066004af has missing scores for some tasks of type Retrieval\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import mteb.task_aggregation as task_aggregation\n",
-                "\n",
-                "mean = task_aggregation.mean(mteb_results)\n",
-                "weighted_mean = task_aggregation.task_category_weighted_mean(mteb_results)\n",
-                "borda = task_aggregation.borda_count(mteb_results)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 65,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
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-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th>mean</th>\n",
-                            "      <th>weighted_mean</th>\n",
-                            "      <th>borda_count</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>intfloat/multilingual-e5-large-instruct</td>\n",
-                            "      <td>baa7be480a7de1539afce709c8f13f833a510e0a</td>\n",
-                            "      <td>0.631669</td>\n",
-                            "      <td>0.611051</td>\n",
-                            "      <td>64.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>intfloat/multilingual-e5-large</td>\n",
-                            "      <td>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</td>\n",
-                            "      <td>0.536125</td>\n",
-                            "      <td>0.505508</td>\n",
-                            "      <td>55.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>intfloat/multilingual-e5-small</td>\n",
-                            "      <td>e4ce9877abf3edfe10b0d82785e83bdcb973e22e</td>\n",
-                            "      <td>0.526768</td>\n",
-                            "      <td>0.503706</td>\n",
-                            "      <td>50.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>intfloat/multilingual-e5-base</td>\n",
-                            "      <td>d13f1b27baf31030b7fd040960d60d909913633f</td>\n",
-                            "      <td>0.525225</td>\n",
-                            "      <td>0.498132</td>\n",
-                            "      <td>50.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>79f2382ceacceacdf38563d7c5d16b9ff8d725d6</td>\n",
-                            "      <td>0.430674</td>\n",
-                            "      <td>0.420396</td>\n",
-                            "      <td>36.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>sentence-transformers/LaBSE</td>\n",
-                            "      <td>e34fab64a3011d2176c99545a93d5cbddc9a91b7</td>\n",
-                            "      <td>0.443979</td>\n",
-                            "      <td>0.446280</td>\n",
-                            "      <td>34.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>sentence-transformers/paraphrase-multilingual-...</td>\n",
-                            "      <td>bf3bf13ab40c3157080a7ab344c831b9ad18b5eb</td>\n",
-                            "      <td>0.413172</td>\n",
-                            "      <td>0.402389</td>\n",
-                            "      <td>29.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L12-v2</td>\n",
-                            "      <td>a05860a77cef7b37e0048a7864658139bc18a854</td>\n",
-                            "      <td>0.399563</td>\n",
-                            "      <td>0.382440</td>\n",
-                            "      <td>28.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>sentence-transformers/all-mpnet-base-v2</td>\n",
-                            "      <td>84f2bcc00d77236f9e89c8a360a00fb1139bf47d</td>\n",
-                            "      <td>0.372704</td>\n",
-                            "      <td>0.352972</td>\n",
-                            "      <td>22.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
-                            "      <td>8b3219a92973c328a8e22fadcfa821b5dc75636a</td>\n",
-                            "      <td>0.377550</td>\n",
-                            "      <td>0.355880</td>\n",
-                            "      <td>22.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>intfloat/e5-mistral-7b-instruct</td>\n",
-                            "      <td>07163b72af1488142a360786df853f237b1a3ca1</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>GritLM/GritLM-7B</td>\n",
-                            "      <td>13f00a0e36500c80ce12870ea513846a066004af</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>NaN</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                model  \\\n",
-                            "5             intfloat/multilingual-e5-large-instruct   \n",
-                            "9                      intfloat/multilingual-e5-large   \n",
-                            "6                      intfloat/multilingual-e5-small   \n",
-                            "10                      intfloat/multilingual-e5-base   \n",
-                            "11  sentence-transformers/paraphrase-multilingual-...   \n",
-                            "0                         sentence-transformers/LaBSE   \n",
-                            "8   sentence-transformers/paraphrase-multilingual-...   \n",
-                            "4             sentence-transformers/all-MiniLM-L12-v2   \n",
-                            "2             sentence-transformers/all-mpnet-base-v2   \n",
-                            "7              sentence-transformers/all-MiniLM-L6-v2   \n",
-                            "1                     intfloat/e5-mistral-7b-instruct   \n",
-                            "3                                    GritLM/GritLM-7B   \n",
-                            "\n",
-                            "                                    revision      mean  weighted_mean  \\\n",
-                            "5   baa7be480a7de1539afce709c8f13f833a510e0a  0.631669       0.611051   \n",
-                            "9   4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.536125       0.505508   \n",
-                            "6   e4ce9877abf3edfe10b0d82785e83bdcb973e22e  0.526768       0.503706   \n",
-                            "10  d13f1b27baf31030b7fd040960d60d909913633f  0.525225       0.498132   \n",
-                            "11  79f2382ceacceacdf38563d7c5d16b9ff8d725d6  0.430674       0.420396   \n",
-                            "0   e34fab64a3011d2176c99545a93d5cbddc9a91b7  0.443979       0.446280   \n",
-                            "8   bf3bf13ab40c3157080a7ab344c831b9ad18b5eb  0.413172       0.402389   \n",
-                            "4   a05860a77cef7b37e0048a7864658139bc18a854  0.399563       0.382440   \n",
-                            "2   84f2bcc00d77236f9e89c8a360a00fb1139bf47d  0.372704       0.352972   \n",
-                            "7   8b3219a92973c328a8e22fadcfa821b5dc75636a  0.377550       0.355880   \n",
-                            "1   07163b72af1488142a360786df853f237b1a3ca1       NaN            NaN   \n",
-                            "3   13f00a0e36500c80ce12870ea513846a066004af       NaN            NaN   \n",
-                            "\n",
-                            "    borda_count  \n",
-                            "5          64.0  \n",
-                            "9          55.0  \n",
-                            "6          50.0  \n",
-                            "10         50.0  \n",
-                            "11         36.0  \n",
-                            "0          34.0  \n",
-                            "8          29.0  \n",
-                            "4          28.0  \n",
-                            "2          22.0  \n",
-                            "7          22.0  \n",
-                            "1           0.0  \n",
-                            "3           0.0  "
-                        ]
-                    },
-                    "execution_count": 65,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "import pandas as pd\n",
-                "\n",
-                "data = []\n",
-                "for model_name, revisions in borda.items():\n",
-                "    for rev, avg_score in revisions.items():\n",
-                "        data.append(\n",
-                "            {\n",
-                "                \"model\": model_name,\n",
-                "                \"revision\": rev,\n",
-                "                \"mean\": mean[model_name][rev],\n",
-                "                \"weighted_mean\": weighted_mean[model_name][rev],\n",
-                "                \"borda_count\": avg_score,\n",
-                "            }\n",
-                "        )\n",
-                "\n",
-                "df = pd.DataFrame(data)\n",
-                "df.sort_values(\"borda_count\", ascending=False)"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "mteb",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.8.19"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
diff --git a/scripts/task_selection/task_selection_indic.ipynb b/scripts/task_selection/task_selection_indic.ipynb
deleted file mode 100644
index 3abaedaf93..0000000000
--- a/scripts/task_selection/task_selection_indic.ipynb
+++ /dev/null
@@ -1,2811 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Task Selection for MTEB(Indic)\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/au561649/.virtualenvs/mteb/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-                        "  from .autonotebook import tqdm as notebook_tqdm\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "1.12.48\n"
-                    ]
-                }
-            ],
-            "source": [
-                "from __future__ import annotations\n",
-                "\n",
-                "import mteb\n",
-                "\n",
-                "print(mteb.__version__)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Loading in data\n",
-                "We will start out by loading in the relevant data for the model and tasks of interests."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Getting revision for sentence-transformers/all-MiniLM-L12-v2\n",
-                        "Getting revision for sentence-transformers/all-mpnet-base-v2\n"
-                    ]
-                }
-            ],
-            "source": [
-                "def get_models():\n",
-                "    model_names = [\n",
-                "        \"sentence-transformers/all-MiniLM-L6-v2\",\n",
-                "        \"sentence-transformers/all-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/all-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/LaBSE\",\n",
-                "        \"intfloat/multilingual-e5-large-instruct\",\n",
-                "        \"intfloat/e5-mistral-7b-instruct\",\n",
-                "        \"GritLM/GritLM-7B\",\n",
-                "        \"GritLM/GritLM-8x7B\",\n",
-                "        \"intfloat/multilingual-e5-small\",\n",
-                "        \"intfloat/multilingual-e5-base\",\n",
-                "        \"intfloat/multilingual-e5-large\",\n",
-                "    ]\n",
-                "    models: list[mteb.ModelMeta] = [mteb.get_model_meta(name) for name in model_names]\n",
-                "\n",
-                "    # get missing revisions - Assuming we are using the latest revision\n",
-                "    for model in models:\n",
-                "        if model.revision is None:\n",
-                "            print(f\"Getting revision for {model.name}\")\n",
-                "            encoder = model.load_model()\n",
-                "            model.revision = encoder.model_card_data.base_model_revision  # type: ignore\n",
-                "\n",
-                "    return models\n",
-                "\n",
-                "\n",
-                "models = get_models()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks: 55\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# load tasks\n",
-                "indic_languages = [\n",
-                "    \"asm\",\n",
-                "    \"awa\",\n",
-                "    \"ben\",\n",
-                "    \"bgc\",\n",
-                "    \"bho\",\n",
-                "    \"doi\",\n",
-                "    \"gbm\",\n",
-                "    \"gom\",\n",
-                "    \"guj\",\n",
-                "    \"hin\",\n",
-                "    \"hne\",\n",
-                "    \"kan\",\n",
-                "    \"kas\",\n",
-                "    \"mai\",\n",
-                "    \"mal\",\n",
-                "    \"mar\",\n",
-                "    \"mni\",\n",
-                "    \"mup\",\n",
-                "    \"mwr\",\n",
-                "    \"nep\",\n",
-                "    \"npi\",\n",
-                "    \"ori\",\n",
-                "    \"ory\",\n",
-                "    \"pan\",\n",
-                "    \"raj\",\n",
-                "    \"san\",\n",
-                "    \"snd\",\n",
-                "    \"tam\",\n",
-                "    \"tel\",\n",
-                "    \"urd\",\n",
-                "]\n",
-                "\n",
-                "\n",
-                "indic_tasks = mteb.get_tasks(\n",
-                "    languages=indic_languages,\n",
-                ")  # does not need to language - you can also filter by task types, domains, etc.\n",
-                "\n",
-                "print(f\"Number of tasks: {len(indic_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks after filtering: 46\n"
-                    ]
-                }
-            ],
-            "source": [
-                "not_include = [\n",
-                "    \"MSMARCO\",  # too large\n",
-                "    # was added after models were run\n",
-                "    \"XStance\",\n",
-                "    \"MIRACLReranking\",\n",
-                "    \"HinDialectClassification\",\n",
-                "    \"IndicNLPNewsClassification\",\n",
-                "    \"SIB200Classification\",  # we will be using the SIB200 dataset for Cluster Classification so as they are the same dataset we will not include this one\n",
-                "    # to be downsampled\n",
-                "    \"MIRACLRetrieval\",\n",
-                "]\n",
-                "\n",
-                "indic_tasks = [t for t in indic_tasks if t.metadata.name not in not_include]\n",
-                "# exlude machine translated tasks\n",
-                "indic_tasks = [\n",
-                "    t\n",
-                "    for t in indic_tasks\n",
-                "    if t.metadata.sample_creation\n",
-                "    not in [\n",
-                "        \"machine-translated\",\n",
-                "        \"machine-translated and verified\",\n",
-                "        \"machine-translated and localized\",\n",
-                "    ]\n",
-                "]\n",
-                "\n",
-                "print(f\"Number of tasks after filtering: {len(indic_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Load data"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# load results from mteb/results repository\n",
-                "mteb_results = mteb.load_results(\n",
-                "    models=models, tasks=indic_tasks, download_latest=False\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "{'intfloat/multilingual-e5-small': {'e4ce9877abf3edfe10b0d82785e83bdcb973e22e': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]},\n",
-                            " 'sentence-transformers/LaBSE': {'e34fab64a3011d2176c99545a93d5cbddc9a91b7': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]},\n",
-                            " 'GritLM/GritLM-7B': {'13f00a0e36500c80ce12870ea513846a066004af': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]},\n",
-                            " 'intfloat/multilingual-e5-large': {'4dc6d853a804b9c8886ede6dda8a073b7dc08a81': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]},\n",
-                            " 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2': {'79f2382ceacceacdf38563d7c5d16b9ff8d725d6': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]},\n",
-                            " 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2': {'bf3bf13ab40c3157080a7ab344c831b9ad18b5eb': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]},\n",
-                            " 'sentence-transformers/all-mpnet-base-v2': {'84f2bcc00d77236f9e89c8a360a00fb1139bf47d': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]},\n",
-                            " 'intfloat/multilingual-e5-large-instruct': {'baa7be480a7de1539afce709c8f13f833a510e0a': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]},\n",
-                            " 'sentence-transformers/all-MiniLM-L12-v2': {'a05860a77cef7b37e0048a7864658139bc18a854': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]},\n",
-                            " 'intfloat/multilingual-e5-base': {'d13f1b27baf31030b7fd040960d60d909913633f': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]},\n",
-                            " 'sentence-transformers/all-MiniLM-L6-v2': {'8b3219a92973c328a8e22fadcfa821b5dc75636a': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]},\n",
-                            " 'intfloat/e5-mistral-7b-instruct': {'07163b72af1488142a360786df853f237b1a3ca1': [MTEBResults(task_name=IndicGenBenchFloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=GujaratiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPDomainClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicLangClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliHateSpeechClassification, scores=...),\n",
-                            "   MTEBResults(task_name=FloresBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultiHateClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IN22GenBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MultilingualSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XQuADRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TamilNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NepaliNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=UrduRomanSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SemRel24STS, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliDocumentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=IndicCrosslingualSTS, scores=...),\n",
-                            "   MTEBResults(task_name=IN22ConvBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MassiveScenarioClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRerankingMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=BengaliSentimentAnalysis, scores=...),\n",
-                            "   MTEBResults(task_name=TweetSentimentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SanskritShlokasClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MalayalamNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=HindiDiscourseClassification, scores=...),\n",
-                            "   MTEBResults(task_name=SIB200ClusteringS2S, scores=...),\n",
-                            "   MTEBResults(task_name=MintakaRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=OdiaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=MarathiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=KannadaNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LinceMTBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=Tatoeba, scores=...),\n",
-                            "   MTEBResults(task_name=MultiLongDocRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=PhincBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=XPQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BelebeleRetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=BibleNLPBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=MLQARetrieval, scores=...),\n",
-                            "   MTEBResults(task_name=TeluguAndhraJyotiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=LanguageClassification, scores=...),\n",
-                            "   MTEBResults(task_name=WikipediaRetrievalMultilingual, scores=...),\n",
-                            "   MTEBResults(task_name=MTOPIntentClassification, scores=...),\n",
-                            "   MTEBResults(task_name=NTREXBitextMining, scores=...),\n",
-                            "   MTEBResults(task_name=PunjabiNewsClassification, scores=...),\n",
-                            "   MTEBResults(task_name=XNLI, scores=...),\n",
-                            "   MTEBResults(task_name=SentimentAnalysisHindi, scores=...)]}}"
-                        ]
-                    },
-                    "execution_count": 6,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "mteb_results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import mteb.task_selection as task_selection\n",
-                "\n",
-                "results_df = task_selection.results_to_dataframe(\n",
-                "    mteb_results, drop_na=False, languages=indic_languages\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>task</th>\n",
-                            "      <th>BelebeleRetrieval</th>\n",
-                            "      <th>BengaliDocumentClassification</th>\n",
-                            "      <th>BengaliHateSpeechClassification</th>\n",
-                            "      <th>BengaliSentimentAnalysis</th>\n",
-                            "      <th>BibleNLPBitextMining</th>\n",
-                            "      <th>FloresBitextMining</th>\n",
-                            "      <th>GujaratiNewsClassification</th>\n",
-                            "      <th>HindiDiscourseClassification</th>\n",
-                            "      <th>IN22ConvBitextMining</th>\n",
-                            "      <th>IN22GenBitextMining</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>TamilNewsClassification</th>\n",
-                            "      <th>Tatoeba</th>\n",
-                            "      <th>TeluguAndhraJyotiNewsClassification</th>\n",
-                            "      <th>TweetSentimentClassification</th>\n",
-                            "      <th>UrduRomanSentimentClassification</th>\n",
-                            "      <th>WikipediaRerankingMultilingual</th>\n",
-                            "      <th>WikipediaRetrievalMultilingual</th>\n",
-                            "      <th>XNLI</th>\n",
-                            "      <th>XPQARetrieval</th>\n",
-                            "      <th>XQuADRetrieval</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>GritLM/GritLM-7B</th>\n",
-                            "      <th>13f00a0e36500c80ce12870ea513846a066004af</th>\n",
-                            "      <td>0.651366</td>\n",
-                            "      <td>0.419482</td>\n",
-                            "      <td>0.548942</td>\n",
-                            "      <td>0.721005</td>\n",
-                            "      <td>0.726886</td>\n",
-                            "      <td>0.638675</td>\n",
-                            "      <td>0.698558</td>\n",
-                            "      <td>0.370898</td>\n",
-                            "      <td>0.380802</td>\n",
-                            "      <td>0.676261</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.311205</td>\n",
-                            "      <td>0.690237</td>\n",
-                            "      <td>0.616112</td>\n",
-                            "      <td>0.375781</td>\n",
-                            "      <td>0.496861</td>\n",
-                            "      <td>0.846951</td>\n",
-                            "      <td>0.832380</td>\n",
-                            "      <td>0.665936</td>\n",
-                            "      <td>0.397125</td>\n",
-                            "      <td>0.88930</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/e5-mistral-7b-instruct</th>\n",
-                            "      <th>07163b72af1488142a360786df853f237b1a3ca1</th>\n",
-                            "      <td>0.614661</td>\n",
-                            "      <td>0.435547</td>\n",
-                            "      <td>0.497658</td>\n",
-                            "      <td>0.720748</td>\n",
-                            "      <td>0.759505</td>\n",
-                            "      <td>0.637100</td>\n",
-                            "      <td>0.730880</td>\n",
-                            "      <td>0.320068</td>\n",
-                            "      <td>0.403273</td>\n",
-                            "      <td>0.668236</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.313787</td>\n",
-                            "      <td>0.711112</td>\n",
-                            "      <td>0.654907</td>\n",
-                            "      <td>0.379297</td>\n",
-                            "      <td>0.494694</td>\n",
-                            "      <td>0.844201</td>\n",
-                            "      <td>0.825875</td>\n",
-                            "      <td>0.716875</td>\n",
-                            "      <td>0.377082</td>\n",
-                            "      <td>0.88243</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-base</th>\n",
-                            "      <th>d13f1b27baf31030b7fd040960d60d909913633f</th>\n",
-                            "      <td>0.650504</td>\n",
-                            "      <td>0.518994</td>\n",
-                            "      <td>0.485035</td>\n",
-                            "      <td>0.796431</td>\n",
-                            "      <td>0.800805</td>\n",
-                            "      <td>0.825293</td>\n",
-                            "      <td>0.749090</td>\n",
-                            "      <td>0.390381</td>\n",
-                            "      <td>0.547230</td>\n",
-                            "      <td>0.748925</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.391938</td>\n",
-                            "      <td>0.857164</td>\n",
-                            "      <td>0.786698</td>\n",
-                            "      <td>0.380469</td>\n",
-                            "      <td>0.403741</td>\n",
-                            "      <td>0.837615</td>\n",
-                            "      <td>0.829165</td>\n",
-                            "      <td>0.721940</td>\n",
-                            "      <td>0.394047</td>\n",
-                            "      <td>0.95313</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large</th>\n",
-                            "      <th>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</th>\n",
-                            "      <td>0.739715</td>\n",
-                            "      <td>0.540234</td>\n",
-                            "      <td>0.487615</td>\n",
-                            "      <td>0.830707</td>\n",
-                            "      <td>0.804803</td>\n",
-                            "      <td>0.855500</td>\n",
-                            "      <td>0.767375</td>\n",
-                            "      <td>0.387402</td>\n",
-                            "      <td>0.588946</td>\n",
-                            "      <td>0.775150</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.424952</td>\n",
-                            "      <td>0.881030</td>\n",
-                            "      <td>0.792410</td>\n",
-                            "      <td>0.364063</td>\n",
-                            "      <td>0.416054</td>\n",
-                            "      <td>0.859706</td>\n",
-                            "      <td>0.860075</td>\n",
-                            "      <td>0.740601</td>\n",
-                            "      <td>0.437062</td>\n",
-                            "      <td>0.97010</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large-instruct</th>\n",
-                            "      <th>baa7be480a7de1539afce709c8f13f833a510e0a</th>\n",
-                            "      <td>0.762216</td>\n",
-                            "      <td>0.496533</td>\n",
-                            "      <td>0.584896</td>\n",
-                            "      <td>0.839849</td>\n",
-                            "      <td>0.844991</td>\n",
-                            "      <td>0.905643</td>\n",
-                            "      <td>0.875190</td>\n",
-                            "      <td>0.352344</td>\n",
-                            "      <td>0.632050</td>\n",
-                            "      <td>0.795942</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.485856</td>\n",
-                            "      <td>0.935997</td>\n",
-                            "      <td>0.796409</td>\n",
-                            "      <td>0.373828</td>\n",
-                            "      <td>0.438792</td>\n",
-                            "      <td>0.874621</td>\n",
-                            "      <td>0.865210</td>\n",
-                            "      <td>0.756606</td>\n",
-                            "      <td>0.440010</td>\n",
-                            "      <td>0.95971</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>5 rows \u00d7 46 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "task                                                                              BelebeleRetrieval  \\\n",
-                            "model                                   revision                                                      \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af           0.651366   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1           0.614661   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f           0.650504   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81           0.739715   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a           0.762216   \n",
-                            "\n",
-                            "task                                                                              BengaliDocumentClassification  \\\n",
-                            "model                                   revision                                                                  \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                       0.419482   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                       0.435547   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                       0.518994   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                       0.540234   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                       0.496533   \n",
-                            "\n",
-                            "task                                                                              BengaliHateSpeechClassification  \\\n",
-                            "model                                   revision                                                                    \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                         0.548942   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                         0.497658   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                         0.485035   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                         0.487615   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                         0.584896   \n",
-                            "\n",
-                            "task                                                                              BengaliSentimentAnalysis  \\\n",
-                            "model                                   revision                                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                  0.721005   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                  0.720748   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                  0.796431   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                  0.830707   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                  0.839849   \n",
-                            "\n",
-                            "task                                                                              BibleNLPBitextMining  \\\n",
-                            "model                                   revision                                                         \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af              0.726886   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1              0.759505   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f              0.800805   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81              0.804803   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a              0.844991   \n",
-                            "\n",
-                            "task                                                                              FloresBitextMining  \\\n",
-                            "model                                   revision                                                       \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af            0.638675   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1            0.637100   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f            0.825293   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81            0.855500   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a            0.905643   \n",
-                            "\n",
-                            "task                                                                              GujaratiNewsClassification  \\\n",
-                            "model                                   revision                                                               \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                    0.698558   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                    0.730880   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                    0.749090   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                    0.767375   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                    0.875190   \n",
-                            "\n",
-                            "task                                                                              HindiDiscourseClassification  \\\n",
-                            "model                                   revision                                                                 \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                      0.370898   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                      0.320068   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                      0.390381   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                      0.387402   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                      0.352344   \n",
-                            "\n",
-                            "task                                                                              IN22ConvBitextMining  \\\n",
-                            "model                                   revision                                                         \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af              0.380802   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1              0.403273   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f              0.547230   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81              0.588946   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a              0.632050   \n",
-                            "\n",
-                            "task                                                                              IN22GenBitextMining  \\\n",
-                            "model                                   revision                                                        \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af             0.676261   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1             0.668236   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f             0.748925   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81             0.775150   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a             0.795942   \n",
-                            "\n",
-                            "task                                                                              ...  \\\n",
-                            "model                                   revision                                  ...   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  ...   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  ...   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  ...   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  ...   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  ...   \n",
-                            "\n",
-                            "task                                                                              TamilNewsClassification  \\\n",
-                            "model                                   revision                                                            \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                 0.311205   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                 0.313787   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                 0.391938   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                 0.424952   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                 0.485856   \n",
-                            "\n",
-                            "task                                                                               Tatoeba  \\\n",
-                            "model                                   revision                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  0.690237   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  0.711112   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  0.857164   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.881030   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  0.935997   \n",
-                            "\n",
-                            "task                                                                              TeluguAndhraJyotiNewsClassification  \\\n",
-                            "model                                   revision                                                                        \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                             0.616112   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                             0.654907   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                             0.786698   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                             0.792410   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                             0.796409   \n",
-                            "\n",
-                            "task                                                                              TweetSentimentClassification  \\\n",
-                            "model                                   revision                                                                 \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                      0.375781   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                      0.379297   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                      0.380469   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                      0.364063   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                      0.373828   \n",
-                            "\n",
-                            "task                                                                              UrduRomanSentimentClassification  \\\n",
-                            "model                                   revision                                                                     \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                          0.496861   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                          0.494694   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                          0.403741   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                          0.416054   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                          0.438792   \n",
-                            "\n",
-                            "task                                                                              WikipediaRerankingMultilingual  \\\n",
-                            "model                                   revision                                                                   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                        0.846951   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                        0.844201   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                        0.837615   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                        0.859706   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                        0.874621   \n",
-                            "\n",
-                            "task                                                                              WikipediaRetrievalMultilingual  \\\n",
-                            "model                                   revision                                                                   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                        0.832380   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                        0.825875   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                        0.829165   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                        0.860075   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                        0.865210   \n",
-                            "\n",
-                            "task                                                                                  XNLI  \\\n",
-                            "model                                   revision                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  0.665936   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  0.716875   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  0.721940   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.740601   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  0.756606   \n",
-                            "\n",
-                            "task                                                                              XPQARetrieval  \\\n",
-                            "model                                   revision                                                  \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af       0.397125   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1       0.377082   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f       0.394047   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81       0.437062   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a       0.440010   \n",
-                            "\n",
-                            "task                                                                              XQuADRetrieval  \n",
-                            "model                                   revision                                                  \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af         0.88930  \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1         0.88243  \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f         0.95313  \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81         0.97010  \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a         0.95971  \n",
-                            "\n",
-                            "[5 rows x 46 columns]"
-                        ]
-                    },
-                    "execution_count": 8,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results_df.head()  # inspect the dataframe"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>task</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "Empty DataFrame\n",
-                            "Columns: []\n",
-                            "Index: []"
-                        ]
-                    },
-                    "execution_count": 9,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# which tasks are missing?\n",
-                "missing_tasks = results_df[results_df.isna().any(axis=1)]\n",
-                "missing_tasks = missing_tasks.loc[:, missing_tasks.isna().any()]\n",
-                "missing_tasks  # should be empty"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Task Selection\n",
-                "\n",
-                "In this section we will do the task selection to construct a benchmark."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>task</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>GritLM/GritLM-7B</th>\n",
-                            "      <th>13f00a0e36500c80ce12870ea513846a066004af</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/e5-mistral-7b-instruct</th>\n",
-                            "      <th>07163b72af1488142a360786df853f237b1a3ca1</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-base</th>\n",
-                            "      <th>d13f1b27baf31030b7fd040960d60d909913633f</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large</th>\n",
-                            "      <th>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large-instruct</th>\n",
-                            "      <th>baa7be480a7de1539afce709c8f13f833a510e0a</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-small</th>\n",
-                            "      <th>e4ce9877abf3edfe10b0d82785e83bdcb973e22e</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/LaBSE</th>\n",
-                            "      <th>e34fab64a3011d2176c99545a93d5cbddc9a91b7</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/all-MiniLM-L12-v2</th>\n",
-                            "      <th>a05860a77cef7b37e0048a7864658139bc18a854</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/all-MiniLM-L6-v2</th>\n",
-                            "      <th>8b3219a92973c328a8e22fadcfa821b5dc75636a</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/all-mpnet-base-v2</th>\n",
-                            "      <th>84f2bcc00d77236f9e89c8a360a00fb1139bf47d</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2</th>\n",
-                            "      <th>bf3bf13ab40c3157080a7ab344c831b9ad18b5eb</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/paraphrase-multilingual-mpnet-base-v2</th>\n",
-                            "      <th>79f2382ceacceacdf38563d7c5d16b9ff8d725d6</th>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "Empty DataFrame\n",
-                            "Columns: []\n",
-                            "Index: [(GritLM/GritLM-7B, 13f00a0e36500c80ce12870ea513846a066004af), (intfloat/e5-mistral-7b-instruct, 07163b72af1488142a360786df853f237b1a3ca1), (intfloat/multilingual-e5-base, d13f1b27baf31030b7fd040960d60d909913633f), (intfloat/multilingual-e5-large, 4dc6d853a804b9c8886ede6dda8a073b7dc08a81), (intfloat/multilingual-e5-large-instruct, baa7be480a7de1539afce709c8f13f833a510e0a), (intfloat/multilingual-e5-small, e4ce9877abf3edfe10b0d82785e83bdcb973e22e), (sentence-transformers/LaBSE, e34fab64a3011d2176c99545a93d5cbddc9a91b7), (sentence-transformers/all-MiniLM-L12-v2, a05860a77cef7b37e0048a7864658139bc18a854), (sentence-transformers/all-MiniLM-L6-v2, 8b3219a92973c328a8e22fadcfa821b5dc75636a), (sentence-transformers/all-mpnet-base-v2, 84f2bcc00d77236f9e89c8a360a00fb1139bf47d), (sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2, bf3bf13ab40c3157080a7ab344c831b9ad18b5eb), (sentence-transformers/paraphrase-multilingual-mpnet-base-v2, 79f2382ceacceacdf38563d7c5d16b9ff8d725d6)]"
-                        ]
-                    },
-                    "execution_count": 10,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# tasks with exactly the same results for all models (i.e. columns where all values are the same)\n",
-                "same_results = results_df.loc[:, results_df.nunique() == 1]\n",
-                "same_results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks before removing tasks with same results: 46\n",
-                        "Number of tasks after removing tasks with same results: 46\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# remove these tasks from the tasks\n",
-                "print(f\"Number of tasks before removing tasks with same results: {len(indic_tasks)}\")\n",
-                "indic_tasks = [t for t in indic_tasks if t.metadata.name not in same_results.columns]\n",
-                "print(f\"Number of tasks after removing tasks with same results: {len(indic_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "LinceMTBitextMining(name='LinceMTBitextMining', languages=['eng', 'hin'])\n",
-                        "LanguageClassification(name='LanguageClassification', languages=['ara', 'bul', 'cmn', '...'])\n",
-                        "MTOPDomainClassification(name='MTOPDomainClassification', languages=['hin'])\n",
-                        "MTOPIntentClassification(name='MTOPIntentClassification', languages=['hin'])\n",
-                        "MultilingualSentimentClassification(name='MultilingualSentimentClassification', languages=['urd'])\n",
-                        "XNLI(name='XNLI', languages=['hin'])\n",
-                        "SemRel24STS(name='SemRel24STS', languages=['hin', 'mar', 'tel'])\n",
-                        "-\n"
-                    ]
-                }
-            ],
-            "source": [
-                "licenses_to_remove = [\"Not specified\", \"Unknown\"]  # remove tasks with unknown licenses\n",
-                "# Note: this implicitly penalizes low-resource languages, as they are more likely to have unknown licenses - though this is probably still a reasonable choice\n",
-                "unspecified_licences = [\n",
-                "    t for t in indic_tasks if t.metadata.license in licenses_to_remove\n",
-                "]\n",
-                "[print(l) for l in unspecified_licences]\n",
-                "print(\"-\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "['LanguageClassification', 'MultilingualSentimentClassification']"
-                        ]
-                    },
-                    "execution_count": 13,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "exceptions = [\n",
-                "    \"LinceMTBitextMining\",\n",
-                "    \"SemRel24STS\",\n",
-                "    \"XNLI\",  # assume that semrel task are fair use\n",
-                "    \"MTOPDomainClassification\",\n",
-                "    \"MTOPIntentClassification\",\n",
-                "]\n",
-                "remove_due_to_license = [\n",
-                "    t for t in unspecified_licences if t.metadata.name not in exceptions\n",
-                "]\n",
-                "remove_due_to_license = [t.metadata.name for t in remove_due_to_license]\n",
-                "remove_due_to_license"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks before: 46\n",
-                        "Number of tasks after: 44\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(f\"Number of tasks before: {len(indic_tasks)}\")\n",
-                "indic_tasks = [t for t in indic_tasks if t.metadata.name not in remove_due_to_license]\n",
-                "print(f\"Number of tasks after: {len(indic_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# remove legal bench tasks\n",
-                "# and code tasks\n",
-                "# Note: none of these tasks are included so we can just skip it."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Iterative Automated Task Selection "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# tasks which should be kept, e.g. due to them being known high quality datasets, unique tasks, etc.\n",
-                "tasks_to_keep = [\n",
-                "    # dataset with good coverage of languages and of reasonable quality\n",
-                "    # \"HinDialectClassification\",\n",
-                "    # \"IndicNLPNewsClassification\",\n",
-                "    \"IN22ConvBitextMining\",\n",
-                "    \"IN22GenBitextMining\",\n",
-                "    \"IndicGenBenchFloresBitextMining\",\n",
-                "    \"IndicLangClassification\",\n",
-                "    \"IndicCrosslingualSTS\",\n",
-                "    \"LinceMTBitextMining\",\n",
-                "]\n",
-                "\n",
-                "\n",
-                "_langs = set(indic_languages)\n",
-                "\n",
-                "\n",
-                "def is_candidate_valid_removal(current_tasks: list[str], task_to_remove: str) -> bool:\n",
-                "    \"\"\"Determine if target task should be removed.\n",
-                "    This checks that all task types are present in the current tasks or whether the task is in the tasks_to_keep list.\n",
-                "    This is all conducted within language.\n",
-                "    \"\"\"\n",
-                "    if task_to_remove in tasks_to_keep:\n",
-                "        return False\n",
-                "\n",
-                "    # check if removing task removes a unique task type - if so, don't remove\n",
-                "    _current_tasks = current_tasks.copy()\n",
-                "    if task_to_remove in _current_tasks:\n",
-                "        _current_tasks.remove(task_to_remove)\n",
-                "    task = mteb.get_task(task_to_remove)\n",
-                "    ctasks = mteb.get_tasks(tasks=_current_tasks)\n",
-                "\n",
-                "    # don't remove a unique task type\n",
-                "    task_types = {t.metadata.type for t in ctasks}\n",
-                "    if task.metadata.type not in task_types:\n",
-                "        return False\n",
-                "\n",
-                "    # check that removing the task does not remove a unique task type within the language\n",
-                "    _languages_covered_by_task_type = [\n",
-                "        t.metadata.languages for t in ctasks if t.metadata.type == task.metadata.type\n",
-                "    ]\n",
-                "    languages_covered_by_task_type = {\n",
-                "        lang for sublist in _languages_covered_by_task_type for lang in sublist\n",
-                "    }\n",
-                "    # reduce to eu languages\n",
-                "    languages_covered_by_task_type = languages_covered_by_task_type & _langs\n",
-                "\n",
-                "    task_langs = set(task.metadata.languages) & _langs\n",
-                "\n",
-                "    if not task_langs.issubset(languages_covered_by_task_type):\n",
-                "        return False\n",
-                "\n",
-                "    return True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 44/44 [00:00<00:00, 67.54it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 43/43 [00:00<00:00, 78.31it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 42/42 [00:00<00:00, 68.35it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 41/41 [00:00<00:00, 72.58it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 40/40 [00:00<00:00, 69.07it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 39/39 [00:00<00:00, 71.52it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 38/38 [00:00<00:00, 71.72it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task LinceMTBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 37/37 [00:00<00:00, 75.46it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task LinceMTBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 36/36 [00:00<00:00, 75.47it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task LinceMTBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 35/35 [00:00<00:00, 68.08it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task LinceMTBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 34/34 [00:00<00:00, 71.83it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: SemRel24STS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 33/33 [00:00<00:00, 72.40it/s]                        \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task LinceMTBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: IndicCrosslingualSTS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:00<00:00, 70.50it/s]               \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task LinceMTBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task IN22ConvBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: IndicCrosslingualSTS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 31/31 [00:00<00:00, 72.52it/s]               \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task LinceMTBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task IN22ConvBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: IndicCrosslingualSTS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 30/30 [00:00<00:00, 72.86it/s]               \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task LinceMTBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: IndicCrosslingualSTS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:00<00:00, 73.24it/s]               \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task LinceMTBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task IN22ConvBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: IndicCrosslingualSTS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 28/28 [00:00<00:00, 72.68it/s]               \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task IN22ConvBitextMining is not a valid candidate for removal\n",
-                        "Task WikipediaRerankingMultilingual is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: IndicCrosslingualSTS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 27/27 [00:00<00:00, 71.63it/s]               \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task WikipediaRerankingMultilingual is not a valid candidate for removal\n",
-                        "Task IN22ConvBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: IndicCrosslingualSTS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 26/26 [00:00<00:00, 73.27it/s]            \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task WikipediaRerankingMultilingual is not a valid candidate for removal\n",
-                        "Task IN22ConvBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: IndicCrosslingualSTS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:00<00:00, 72.95it/s]            \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task WikipediaRerankingMultilingual is not a valid candidate for removal\n",
-                        "Task IN22ConvBitextMining is not a valid candidate for removal\n",
-                        "Task LinceMTBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: IndicCrosslingualSTS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 24/24 [00:00<00:00, 72.80it/s]            \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task WikipediaRerankingMultilingual is not a valid candidate for removal\n",
-                        "Task IN22ConvBitextMining is not a valid candidate for removal\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: IndicCrosslingualSTS: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 23/23 [00:00<00:00, 72.96it/s]            \n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Task IN22GenBitextMining is not a valid candidate for removal\n",
-                        "Task IndicGenBenchFloresBitextMining is not a valid candidate for removal\n",
-                        "Task WikipediaRerankingMultilingual is not a valid candidate for removal\n",
-                        "Task IN22ConvBitextMining is not a valid candidate for removal\n",
-                        "Task LinceMTBitextMining is not a valid candidate for removal\n",
-                        "Task IndicCrosslingualSTS is not a valid candidate for removal\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n",
-                        "Threshold reached\n"
-                    ]
-                }
-            ],
-            "source": [
-                "from sklearn.linear_model import LinearRegression\n",
-                "\n",
-                "# remove tasks one by one\n",
-                "tasks_to_select_from = [t.metadata.name for t in indic_tasks]\n",
-                "\n",
-                "tasks_removed = []\n",
-                "predicability_scores = []\n",
-                "\n",
-                "while tasks_to_select_from:\n",
-                "    most_pred_tasks = task_selection.most_predictable_task(\n",
-                "        results_df[tasks_to_select_from],\n",
-                "        sklearn_estimator=LinearRegression(),\n",
-                "        metrics=[\n",
-                "            task_selection.spearman,\n",
-                "            task_selection.pearson,\n",
-                "            task_selection.mse_with_zscore,\n",
-                "        ],\n",
-                "    )\n",
-                "\n",
-                "    # reverse the list to get the least predictable task\n",
-                "    most_pred_tasks.reverse()\n",
-                "\n",
-                "    while most_pred_tasks:\n",
-                "        most_pred_task = most_pred_tasks.pop()\n",
-                "        most_pred_task_name = list(most_pred_task.keys())[0]\n",
-                "\n",
-                "        # if the task is too hard to predict, skip it (this essentially stops the loop)\n",
-                "        if (\n",
-                "            most_pred_task[most_pred_task_name][\"mse_with_zscore\"] > 0.5\n",
-                "            or most_pred_task[most_pred_task_name][\"spearman\"] < 0.8\n",
-                "        ):\n",
-                "            print(\"Threshold reached\")\n",
-                "            continue\n",
-                "\n",
-                "        cand_removal = is_candidate_valid_removal(\n",
-                "            tasks_to_select_from, most_pred_task_name\n",
-                "        )\n",
-                "\n",
-                "        if cand_removal:\n",
-                "            tasks_to_select_from.remove(most_pred_task_name)\n",
-                "            tasks_removed.append(most_pred_task_name)\n",
-                "            predicability_scores.append(most_pred_task[most_pred_task_name])\n",
-                "            break\n",
-                "        else:\n",
-                "            print(f\"Task {most_pred_task_name} is not a valid candidate for removal\")\n",
-                "\n",
-                "    if not most_pred_tasks:  # if no task was removed, then we are done -- can be replaced with another stopping criterion\n",
-                "        break"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 3500x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# plotting the predictability scores with the tasks removed\n",
-                "import matplotlib.pyplot as plt\n",
-                "\n",
-                "# make the plot wider\n",
-                "plt.figure(figsize=(35, 6))\n",
-                "\n",
-                "for metric in [\"spearman\", \"pearson\"]:\n",
-                "    plt.plot([t[metric] for t in predicability_scores], label=metric)\n",
-                "\n",
-                "plt.xlabel(\"Tasks removed\")\n",
-                "plt.ylabel(\"Correlation (predicted vs. observed performance)\")\n",
-                "plt.legend()\n",
-                "\n",
-                "# add vline for 0.8 spearman\n",
-                "plt.axhline(y=0.8, color=\"r\", linestyle=\"--\")\n",
-                "\n",
-                "# add task names to the x-axis\n",
-                "plt.xticks(range(len(tasks_removed)), tasks_removed, rotation=90)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 3500x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# plotting the predictability scores with the tasks removed\n",
-                "\n",
-                "plt.figure(figsize=(35, 6))\n",
-                "\n",
-                "for metric in [\"mse_with_zscore\"]:\n",
-                "    plt.plot([t[metric] for t in predicability_scores], label=metric)\n",
-                "\n",
-                "plt.xlabel(\"Tasks removed\")\n",
-                "plt.ylabel(\"Normalized Mean Squared error (predicted vs. observed performance)\")\n",
-                "plt.legend()\n",
-                "\n",
-                "# add vline for 0.5 mse\n",
-                "plt.axhline(y=0.5, color=\"r\", linestyle=\"--\")\n",
-                "\n",
-                "# add task names to the x-axis\n",
-                "plt.xticks(range(len(tasks_removed)), tasks_removed, rotation=90)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Constructing the Benchmark"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 28,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "['IN22ConvBitextMining',\n",
-                            " 'IN22GenBitextMining',\n",
-                            " 'IndicGenBenchFloresBitextMining',\n",
-                            " 'LinceMTBitextMining',\n",
-                            " 'BengaliSentimentAnalysis',\n",
-                            " 'GujaratiNewsClassification',\n",
-                            " 'HindiDiscourseClassification',\n",
-                            " 'SentimentAnalysisHindi',\n",
-                            " 'MalayalamNewsClassification',\n",
-                            " 'IndicLangClassification',\n",
-                            " 'MTOPIntentClassification',\n",
-                            " 'MultiHateClassification',\n",
-                            " 'TweetSentimentClassification',\n",
-                            " 'NepaliNewsClassification',\n",
-                            " 'PunjabiNewsClassification',\n",
-                            " 'SanskritShlokasClassification',\n",
-                            " 'UrduRomanSentimentClassification',\n",
-                            " 'SIB200ClusteringS2S',\n",
-                            " 'BelebeleRetrieval',\n",
-                            " 'XQuADRetrieval',\n",
-                            " 'XNLI',\n",
-                            " 'WikipediaRerankingMultilingual',\n",
-                            " 'IndicCrosslingualSTS']"
-                        ]
-                    },
-                    "execution_count": 28,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# we now have the tasks:\n",
-                "tasks_to_select_from"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 41,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "tasks = mteb.get_tasks(tasks=tasks_to_select_from, languages=list(indic_languages))\n",
-                "\n",
-                "# we can now create a benchmark\n",
-                "benchmark = mteb.Benchmark(\n",
-                "    name=\"MTEB(Indic)\",\n",
-                "    tasks=tasks,\n",
-                "    description=\"Benchmark for evaluating document embedding models for Indic languages\",\n",
-                "    citation=\"\",\n",
-                "    reference=\"\",\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 42,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th>Languages</th>\n",
-                            "      <th>Domains</th>\n",
-                            "      <th>License</th>\n",
-                            "      <th>Description</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Type</th>\n",
-                            "      <th>Name</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"4\" valign=\"top\">BitextMining</th>\n",
-                            "      <th>IN22ConvBitextMining</th>\n",
-                            "      <td>{ory, kas, asm, snd, hin, mar, tam, san, kan, ...</td>\n",
-                            "      <td>[Social, Spoken, Fiction, Spoken]</td>\n",
-                            "      <td>CC-BY-4.0</td>\n",
-                            "      <td>IN22-Conv is a n-way parallel conversation dom...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>IN22GenBitextMining</th>\n",
-                            "      <td>{ory, kas, asm, snd, hin, mar, tam, san, kan, ...</td>\n",
-                            "      <td>[Web, Legal, Government, News, Religious, Non-...</td>\n",
-                            "      <td>CC-BY-4.0</td>\n",
-                            "      <td>IN22-Gen is a n-way parallel general-purpose m...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>IndicGenBenchFloresBitextMining</th>\n",
-                            "      <td>{ory, asm, gbm, hin, nep, mar, tam, bgc, mup, ...</td>\n",
-                            "      <td>[Web, News, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>Flores-IN dataset is an extension of Flores da...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LinceMTBitextMining</th>\n",
-                            "      <td>{hin}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>Unknown</td>\n",
-                            "      <td>LinceMT is a parallel corpus for machine trans...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"13\" valign=\"top\">Classification</th>\n",
-                            "      <th>BengaliSentimentAnalysis</th>\n",
-                            "      <td>{ben}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>dataset contains 3307 Negative reviews and 850...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>GujaratiNewsClassification</th>\n",
-                            "      <td>{guj}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>A Gujarati dataset for 3-class classification ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>HindiDiscourseClassification</th>\n",
-                            "      <td>{hin}</td>\n",
-                            "      <td>[Fiction, Social, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>A Hindi Discourse dataset in Hindi with values...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SentimentAnalysisHindi</th>\n",
-                            "      <td>{hin}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>Hindi Sentiment Analysis Dataset</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MalayalamNewsClassification</th>\n",
-                            "      <td>{mal}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>A Malayalam dataset for 3-class classification...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>IndicLangClassification</th>\n",
-                            "      <td>{ory, kas, asm, snd, hin, mar, tam, san, kan, ...</td>\n",
-                            "      <td>[Web, Non-fiction, Written]</td>\n",
-                            "      <td>CC0</td>\n",
-                            "      <td>A language identification test set for native-...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MTOPIntentClassification</th>\n",
-                            "      <td>{hin}</td>\n",
-                            "      <td>[Spoken, Spoken]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>MTOP: Multilingual Task-Oriented Semantic Parsing</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MultiHateClassification</th>\n",
-                            "      <td>{hin}</td>\n",
-                            "      <td>[Constructed, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Hate speech detection dataset with binary\\n   ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>TweetSentimentClassification</th>\n",
-                            "      <td>{hin}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>cc-by-3.0</td>\n",
-                            "      <td>A multilingual Sentiment Analysis dataset cons...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NepaliNewsClassification</th>\n",
-                            "      <td>{nep}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>A Nepali dataset for 7500 news articles</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PunjabiNewsClassification</th>\n",
-                            "      <td>{pan}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>A Punjabi dataset for 2-class classification o...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SanskritShlokasClassification</th>\n",
-                            "      <td>{san}</td>\n",
-                            "      <td>[Religious, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>This data set contains ~500 Shlokas</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>UrduRomanSentimentClassification</th>\n",
-                            "      <td>{urd}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>The Roman Urdu dataset is a data corpus compri...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Clustering</th>\n",
-                            "      <th>SIB200ClusteringS2S</th>\n",
-                            "      <td>{ory, kas, asm, snd, hin, mar, tam, san, kan, ...</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>SIB-200 is the largest publicly available topi...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"2\" valign=\"top\">Retrieval</th>\n",
-                            "      <th>BelebeleRetrieval</th>\n",
-                            "      <td>{mal, ory, ben, mar, tam, npi, guj, asm, snd, ...</td>\n",
-                            "      <td>[Web, News, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>Belebele is a multiple-choice machine reading ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>XQuADRetrieval</th>\n",
-                            "      <td>{hin}</td>\n",
-                            "      <td>[Web, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>XQuAD is a benchmark dataset for evaluating cr...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PairClassification</th>\n",
-                            "      <th>XNLI</th>\n",
-                            "      <td>{hin}</td>\n",
-                            "      <td>[Non-fiction, Fiction, Government, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td></td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Reranking</th>\n",
-                            "      <th>WikipediaRerankingMultilingual</th>\n",
-                            "      <td>{hin, ben}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>The dataset is derived from Cohere's wikipedia...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STS</th>\n",
-                            "      <th>IndicCrosslingualSTS</th>\n",
-                            "      <td>{mal, ory, ben, mar, tam, guj, asm, kan, urd, ...</td>\n",
-                            "      <td>[News, Non-fiction, Web, Spoken, Government, W...</td>\n",
-                            "      <td>CC0</td>\n",
-                            "      <td>This is a Semantic Textual Similarity testset ...</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                                                             Languages  \\\n",
-                            "Type               Name                                                                                  \n",
-                            "BitextMining       IN22ConvBitextMining              {ory, kas, asm, snd, hin, mar, tam, san, kan, ...   \n",
-                            "                   IN22GenBitextMining               {ory, kas, asm, snd, hin, mar, tam, san, kan, ...   \n",
-                            "                   IndicGenBenchFloresBitextMining   {ory, asm, gbm, hin, nep, mar, tam, bgc, mup, ...   \n",
-                            "                   LinceMTBitextMining                                                           {hin}   \n",
-                            "Classification     BengaliSentimentAnalysis                                                      {ben}   \n",
-                            "                   GujaratiNewsClassification                                                    {guj}   \n",
-                            "                   HindiDiscourseClassification                                                  {hin}   \n",
-                            "                   SentimentAnalysisHindi                                                        {hin}   \n",
-                            "                   MalayalamNewsClassification                                                   {mal}   \n",
-                            "                   IndicLangClassification           {ory, kas, asm, snd, hin, mar, tam, san, kan, ...   \n",
-                            "                   MTOPIntentClassification                                                      {hin}   \n",
-                            "                   MultiHateClassification                                                       {hin}   \n",
-                            "                   TweetSentimentClassification                                                  {hin}   \n",
-                            "                   NepaliNewsClassification                                                      {nep}   \n",
-                            "                   PunjabiNewsClassification                                                     {pan}   \n",
-                            "                   SanskritShlokasClassification                                                 {san}   \n",
-                            "                   UrduRomanSentimentClassification                                              {urd}   \n",
-                            "Clustering         SIB200ClusteringS2S               {ory, kas, asm, snd, hin, mar, tam, san, kan, ...   \n",
-                            "Retrieval          BelebeleRetrieval                 {mal, ory, ben, mar, tam, npi, guj, asm, snd, ...   \n",
-                            "                   XQuADRetrieval                                                                {hin}   \n",
-                            "PairClassification XNLI                                                                          {hin}   \n",
-                            "Reranking          WikipediaRerankingMultilingual                                           {hin, ben}   \n",
-                            "STS                IndicCrosslingualSTS              {mal, ory, ben, mar, tam, guj, asm, kan, urd, ...   \n",
-                            "\n",
-                            "                                                                                               Domains  \\\n",
-                            "Type               Name                                                                                  \n",
-                            "BitextMining       IN22ConvBitextMining                              [Social, Spoken, Fiction, Spoken]   \n",
-                            "                   IN22GenBitextMining               [Web, Legal, Government, News, Religious, Non-...   \n",
-                            "                   IndicGenBenchFloresBitextMining                                [Web, News, Written]   \n",
-                            "                   LinceMTBitextMining                                               [Social, Written]   \n",
-                            "Classification     BengaliSentimentAnalysis                                         [Reviews, Written]   \n",
-                            "                   GujaratiNewsClassification                                          [News, Written]   \n",
-                            "                   HindiDiscourseClassification                             [Fiction, Social, Written]   \n",
-                            "                   SentimentAnalysisHindi                                           [Reviews, Written]   \n",
-                            "                   MalayalamNewsClassification                                         [News, Written]   \n",
-                            "                   IndicLangClassification                                 [Web, Non-fiction, Written]   \n",
-                            "                   MTOPIntentClassification                                           [Spoken, Spoken]   \n",
-                            "                   MultiHateClassification                                      [Constructed, Written]   \n",
-                            "                   TweetSentimentClassification                                      [Social, Written]   \n",
-                            "                   NepaliNewsClassification                                            [News, Written]   \n",
-                            "                   PunjabiNewsClassification                                           [News, Written]   \n",
-                            "                   SanskritShlokasClassification                                  [Religious, Written]   \n",
-                            "                   UrduRomanSentimentClassification                                  [Social, Written]   \n",
-                            "Clustering         SIB200ClusteringS2S                                                 [News, Written]   \n",
-                            "Retrieval          BelebeleRetrieval                                              [Web, News, Written]   \n",
-                            "                   XQuADRetrieval                                                       [Web, Written]   \n",
-                            "PairClassification XNLI                                    [Non-fiction, Fiction, Government, Written]   \n",
-                            "Reranking          WikipediaRerankingMultilingual                             [Encyclopaedic, Written]   \n",
-                            "STS                IndicCrosslingualSTS              [News, Non-fiction, Web, Spoken, Government, W...   \n",
-                            "\n",
-                            "                                                             License  \\\n",
-                            "Type               Name                                                \n",
-                            "BitextMining       IN22ConvBitextMining                    CC-BY-4.0   \n",
-                            "                   IN22GenBitextMining                     CC-BY-4.0   \n",
-                            "                   IndicGenBenchFloresBitextMining      CC-BY-SA-4.0   \n",
-                            "                   LinceMTBitextMining                       Unknown   \n",
-                            "Classification     BengaliSentimentAnalysis                CC BY 4.0   \n",
-                            "                   GujaratiNewsClassification                    MIT   \n",
-                            "                   HindiDiscourseClassification                  MIT   \n",
-                            "                   SentimentAnalysisHindi            CC BY-NC-SA 4.0   \n",
-                            "                   MalayalamNewsClassification                   MIT   \n",
-                            "                   IndicLangClassification                       CC0   \n",
-                            "                   MTOPIntentClassification            Not specified   \n",
-                            "                   MultiHateClassification                 cc-by-4.0   \n",
-                            "                   TweetSentimentClassification            cc-by-3.0   \n",
-                            "                   NepaliNewsClassification             CC BY-SA 4.0   \n",
-                            "                   PunjabiNewsClassification                     MIT   \n",
-                            "                   SanskritShlokasClassification        CC BY-SA 4.0   \n",
-                            "                   UrduRomanSentimentClassification              MIT   \n",
-                            "Clustering         SIB200ClusteringS2S                  cc-by-sa-4.0   \n",
-                            "Retrieval          BelebeleRetrieval                    CC-BY-SA-4.0   \n",
-                            "                   XQuADRetrieval                       CC BY-SA 4.0   \n",
-                            "PairClassification XNLI                                Not specified   \n",
-                            "Reranking          WikipediaRerankingMultilingual       cc-by-sa-3.0   \n",
-                            "STS                IndicCrosslingualSTS                          CC0   \n",
-                            "\n",
-                            "                                                                                           Description  \n",
-                            "Type               Name                                                                                 \n",
-                            "BitextMining       IN22ConvBitextMining              IN22-Conv is a n-way parallel conversation dom...  \n",
-                            "                   IN22GenBitextMining               IN22-Gen is a n-way parallel general-purpose m...  \n",
-                            "                   IndicGenBenchFloresBitextMining   Flores-IN dataset is an extension of Flores da...  \n",
-                            "                   LinceMTBitextMining               LinceMT is a parallel corpus for machine trans...  \n",
-                            "Classification     BengaliSentimentAnalysis          dataset contains 3307 Negative reviews and 850...  \n",
-                            "                   GujaratiNewsClassification        A Gujarati dataset for 3-class classification ...  \n",
-                            "                   HindiDiscourseClassification      A Hindi Discourse dataset in Hindi with values...  \n",
-                            "                   SentimentAnalysisHindi                             Hindi Sentiment Analysis Dataset  \n",
-                            "                   MalayalamNewsClassification       A Malayalam dataset for 3-class classification...  \n",
-                            "                   IndicLangClassification           A language identification test set for native-...  \n",
-                            "                   MTOPIntentClassification          MTOP: Multilingual Task-Oriented Semantic Parsing  \n",
-                            "                   MultiHateClassification           Hate speech detection dataset with binary\\n   ...  \n",
-                            "                   TweetSentimentClassification      A multilingual Sentiment Analysis dataset cons...  \n",
-                            "                   NepaliNewsClassification                   A Nepali dataset for 7500 news articles   \n",
-                            "                   PunjabiNewsClassification         A Punjabi dataset for 2-class classification o...  \n",
-                            "                   SanskritShlokasClassification                 This data set contains ~500 Shlokas    \n",
-                            "                   UrduRomanSentimentClassification  The Roman Urdu dataset is a data corpus compri...  \n",
-                            "Clustering         SIB200ClusteringS2S               SIB-200 is the largest publicly available topi...  \n",
-                            "Retrieval          BelebeleRetrieval                 Belebele is a multiple-choice machine reading ...  \n",
-                            "                   XQuADRetrieval                    XQuAD is a benchmark dataset for evaluating cr...  \n",
-                            "PairClassification XNLI                                                                                 \n",
-                            "Reranking          WikipediaRerankingMultilingual    The dataset is derived from Cohere's wikipedia...  \n",
-                            "STS                IndicCrosslingualSTS              This is a Semantic Textual Similarity testset ...  "
-                        ]
-                    },
-                    "execution_count": 42,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# create a dataframe with tasks\n",
-                "import pandas as pd\n",
-                "\n",
-                "data = []\n",
-                "\n",
-                "_langs = set(indic_languages)\n",
-                "\n",
-                "for t in tasks:\n",
-                "    data.append(\n",
-                "        {\n",
-                "            \"Name\": t.metadata.name,\n",
-                "            \"Type\": t.metadata.type,\n",
-                "            \"Languages\": set(t.metadata.languages) & _langs,\n",
-                "            \"Domains\": t.metadata.domains,\n",
-                "            \"License\": t.metadata.license,\n",
-                "            \"Description\": t.metadata.description,\n",
-                "        }\n",
-                "    )\n",
-                "\n",
-                "tasks_df = pd.DataFrame(data)\n",
-                "# tasks_df\n",
-                "\n",
-                "# print all rows\n",
-                "pd.set_option(\"display.max_rows\", 100)\n",
-                "_tasks_df = tasks_df.set_index([\"Type\", \"Name\"], inplace=False)\n",
-                "_tasks_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 43,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "(23, 4)"
-                        ]
-                    },
-                    "execution_count": 43,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "_tasks_df.shape"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 44,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "tasks_df.to_csv(\"indic_tasks.csv\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Benchmark Performance"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# It is possible to start the notebok from here:\n",
-                "import pandas as pd\n",
-                "\n",
-                "import mteb\n",
-                "\n",
-                "_df = pd.read_csv(\"indic_tasks.csv\")\n",
-                "task_names = _df[\"Name\"].tolist()\n",
-                "\n",
-                "indic_languages = [\n",
-                "    \"asm\",\n",
-                "    \"awa\",\n",
-                "    \"ben\",\n",
-                "    \"bgc\",\n",
-                "    \"bho\",\n",
-                "    \"doi\",\n",
-                "    \"gbm\",\n",
-                "    \"gom\",\n",
-                "    \"guj\",\n",
-                "    \"hin\",\n",
-                "    \"hne\",\n",
-                "    \"kan\",\n",
-                "    \"kas\",\n",
-                "    \"mai\",\n",
-                "    \"mal\",\n",
-                "    \"mar\",\n",
-                "    \"mni\",\n",
-                "    \"mup\",\n",
-                "    \"mwr\",\n",
-                "    \"nep\",\n",
-                "    \"npi\",\n",
-                "    \"ori\",\n",
-                "    \"ory\",\n",
-                "    \"pan\",\n",
-                "    \"raj\",\n",
-                "    \"san\",\n",
-                "    \"snd\",\n",
-                "    \"tam\",\n",
-                "    \"tel\",\n",
-                "    \"urd\",\n",
-                "]\n",
-                "\n",
-                "\n",
-                "indic_tasks = mteb.get_tasks(tasks=task_names, languages=indic_languages)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Getting revision for sentence-transformers/all-MiniLM-L12-v2\n",
-                        "Getting revision for sentence-transformers/all-mpnet-base-v2\n"
-                    ]
-                }
-            ],
-            "source": [
-                "def get_models():\n",
-                "    model_names = [\n",
-                "        \"sentence-transformers/all-MiniLM-L6-v2\",\n",
-                "        \"sentence-transformers/all-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/all-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/LaBSE\",\n",
-                "        \"intfloat/multilingual-e5-large-instruct\",\n",
-                "        \"intfloat/e5-mistral-7b-instruct\",\n",
-                "        \"GritLM/GritLM-7B\",\n",
-                "        \"GritLM/GritLM-8x7B\",\n",
-                "        \"intfloat/multilingual-e5-small\",\n",
-                "        \"intfloat/multilingual-e5-base\",\n",
-                "        \"intfloat/multilingual-e5-large\",\n",
-                "    ]\n",
-                "    models: list[mteb.ModelMeta] = [mteb.get_model_meta(name) for name in model_names]\n",
-                "\n",
-                "    # get missing revisions - Assuming we are using the latest revision\n",
-                "    for model in models:\n",
-                "        if model.revision is None:\n",
-                "            print(f\"Getting revision for {model.name}\")\n",
-                "            encoder = model.load_model()\n",
-                "            model.revision = encoder.model_card_data.base_model_revision  # type: ignore\n",
-                "\n",
-                "    return models\n",
-                "\n",
-                "\n",
-                "models = get_models()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# load task results for the specified models from mteb/results repository\n",
-                "mteb_results = mteb.load_results(\n",
-                "    models=models, tasks=indic_tasks, download_latest=False\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import mteb.task_aggregation as task_aggregation\n",
-                "\n",
-                "mean = task_aggregation.mean(mteb_results)\n",
-                "weighted_mean = task_aggregation.task_category_weighted_mean(mteb_results)\n",
-                "borda = task_aggregation.borda_count(mteb_results)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import pandas as pd\n",
-                "\n",
-                "data = []\n",
-                "for model_name, revisions in borda.items():\n",
-                "    for rev, avg_score in revisions.items():\n",
-                "        total_eval_time = sum(\n",
-                "            res.evaluation_time for res in mteb_results[model_name][rev]\n",
-                "        )\n",
-                "\n",
-                "        data.append(\n",
-                "            {\n",
-                "                \"model\": model_name,\n",
-                "                \"revision\": rev,\n",
-                "                **mean[model_name][rev],\n",
-                "                **weighted_mean[model_name][rev],\n",
-                "                **avg_score,\n",
-                "                \"Total Evaluation time (hours)\": total_eval_time / 3600,\n",
-                "            }\n",
-                "        )\n",
-                "\n",
-                "df = pd.DataFrame(data)\n",
-                "df = df.sort_values(\"borda_count\", ascending=False)\n",
-                "# round\n",
-                "df = df.round(3)\n",
-                "\n",
-                "df.to_csv(\"indic_results.csv\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\begin{tabular}{llrrrrrrrlrr}\n",
-                        "\\toprule\n",
-                        " & Rank (Borda Count) & mean & mean (wieghted by task type) & mean (BitextMining) & mean (PairClassification) & mean (Classification) & mean (STS) & mean (Retrieval) & mean (MultilabelClassification) & mean (Clustering) & mean (Reranking) \\\\\n",
-                        "model &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
-                        "\\midrule\n",
-                        "multilingual-e5-large-instruct & 1 (224) & 71.80 & 71.50 & 70.30 & 78.50 & 70.90 & 53.70 & 88.70 & NaN & 47.20 & 91.00 \\\\\n",
-                        "multilingual-e5-large & 2 (190) & 64.50 & 63.70 & 64.40 & 73.90 & 63.10 & 43.90 & 87.50 & NaN & 23.70 & 89.70 \\\\\n",
-                        "GritLM-7B & 3 (165) & 64.60 & 62.50 & 60.70 & 74.10 & 65.20 & 27.20 & 83.20 & NaN & 36.10 & 91.00 \\\\\n",
-                        "multilingual-e5-base & 4 (164) & 62.50 & 61.10 & 61.20 & 71.00 & 61.90 & 41.10 & 83.30 & NaN & 21.60 & 87.70 \\\\\n",
-                        "e5-mistral-7b-instruct & 5 (154) & 63.70 & 62.30 & 61.60 & 77.90 & 63.60 & 23.00 & 80.80 & NaN & 38.70 & 90.30 \\\\\n",
-                        "multilingual-e5-small & 6 (150) & 61.90 & 60.60 & 61.20 & 69.00 & 61.30 & 40.80 & 80.80 & NaN & 23.90 & 87.00 \\\\\n",
-                        "LaBSE & 7 (135) & 60.70 & 59.00 & 63.60 & 65.20 & 60.00 & 52.80 & 71.60 & NaN & 18.80 & 80.90 \\\\\n",
-                        "paraphrase-multilingual-mpnet-base-v2 & 8 (127) & 57.10 & 56.40 & 42.00 & 82.70 & 60.20 & 34.10 & 69.60 & NaN & 24.10 & 82.20 \\\\\n",
-                        "paraphrase-multilingual-MiniLM-L12-v2 & 9 (91) & 50.00 & 48.60 & 23.60 & 78.90 & 56.30 & 19.80 & 64.10 & NaN & 19.40 & 78.50 \\\\\n",
-                        "all-mpnet-base-v2 & 10 (52) & 36.40 & 30.90 & 7.20 & 58.40 & 47.20 & -2.50 & 32.30 & NaN & 8.90 & 64.70 \\\\\n",
-                        "all-MiniLM-L12-v2 & 11 (39) & 35.90 & 31.00 & 7.80 & 58.40 & 46.00 & -5.30 & 32.90 & NaN & 7.60 & 69.20 \\\\\n",
-                        "all-MiniLM-L6-v2 & 12 (27) & 35.10 & 29.20 & 6.30 & 57.40 & 46.30 & -6.30 & 29.40 & NaN & 6.60 & 64.50 \\\\\n",
-                        "\\bottomrule\n",
-                        "\\end{tabular}\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "latex_df = df.drop(columns=[\"revision\"])\n",
-                "latex_df[\"model\"] = [name.split(\"/\")[1] for name in latex_df[\"model\"]]\n",
-                "latex_df = latex_df.set_index(\"model\")\n",
-                "\n",
-                "latex_df[\"mean (MultilabelClassification)\"] = None\n",
-                "\n",
-                "avg_cols = [\n",
-                "    \"mean\",\n",
-                "    \"mean (BitextMining)\",\n",
-                "    \"mean (PairClassification)\",\n",
-                "    \"mean (Classification)\",\n",
-                "    \"mean (STS)\",\n",
-                "    \"mean (Retrieval)\",\n",
-                "    \"mean (MultilabelClassification)\",\n",
-                "    \"mean (Clustering)\",\n",
-                "    \"mean (Reranking)\",\n",
-                "    \"mean (wieghted by task type)\",\n",
-                "]\n",
-                "\n",
-                "borda_col_name = \"borda_count\"\n",
-                "\n",
-                "# multiply by 100 to get percentage values and round to 2 decimal places\n",
-                "latex_df[avg_cols] = latex_df[avg_cols] * 100\n",
-                "\n",
-                "latex_df[\"Rank (Borda Count)\"] = [\n",
-                "    f\"{rank} ({borda:.0f})\"\n",
-                "    for rank, borda in zip(range(1, len(latex_df) + 1), latex_df[borda_col_name])\n",
-                "]\n",
-                "latex_df = latex_df.drop(columns=[borda_col_name])\n",
-                "\n",
-                "\n",
-                "# column order and rename\n",
-                "cols = [\n",
-                "    \"Rank (Borda Count)\",\n",
-                "    \"mean\",\n",
-                "    \"mean (wieghted by task type)\",\n",
-                "    \"mean (BitextMining)\",\n",
-                "    \"mean (PairClassification)\",\n",
-                "    \"mean (Classification)\",\n",
-                "    \"mean (STS)\",\n",
-                "    \"mean (Retrieval)\",\n",
-                "    \"mean (MultilabelClassification)\",\n",
-                "    \"mean (Clustering)\",\n",
-                "    \"mean (Reranking)\",\n",
-                "]\n",
-                "\n",
-                "latex_df = latex_df[cols]\n",
-                "\n",
-                "table_latex = latex_df.to_latex(index=True, float_format=\"%.2f\")\n",
-                "\n",
-                "\n",
-                "print(table_latex)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Counter({'BitextMining': 4,\n",
-                            "         'Classification': 13,\n",
-                            "         'Clustering': 1,\n",
-                            "         'Retrieval': 2,\n",
-                            "         'PairClassification': 1,\n",
-                            "         'Reranking': 1,\n",
-                            "         'STS': 1})"
-                        ]
-                    },
-                    "execution_count": 16,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "from collections import Counter\n",
-                "\n",
-                "Counter([task.metadata.type for task in indic_tasks])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "23"
-                        ]
-                    },
-                    "execution_count": 17,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "len(indic_tasks)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "mteb",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.8.19"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
diff --git a/scripts/task_selection/task_selection_mult.ipynb b/scripts/task_selection/task_selection_mult.ipynb
deleted file mode 100644
index fff022d9ef..0000000000
--- a/scripts/task_selection/task_selection_mult.ipynb
+++ /dev/null
@@ -1,4202 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Task Selection for MTEB(Multilingual)\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/au561649/.virtualenvs/mteb/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-                        "  from .autonotebook import tqdm as notebook_tqdm\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "1.12.48\n"
-                    ]
-                }
-            ],
-            "source": [
-                "from __future__ import annotations\n",
-                "\n",
-                "import mteb\n",
-                "\n",
-                "print(mteb.__version__)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Loading in data\n",
-                "We will start out by loading in the relevant data for the model and tasks of interests."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Getting revision for sentence-transformers/all-MiniLM-L12-v2\n",
-                        "Getting revision for sentence-transformers/all-mpnet-base-v2\n"
-                    ]
-                }
-            ],
-            "source": [
-                "def get_models():\n",
-                "    model_names = [\n",
-                "        \"sentence-transformers/all-MiniLM-L6-v2\",\n",
-                "        \"sentence-transformers/all-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/all-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/LaBSE\",\n",
-                "        \"intfloat/multilingual-e5-large-instruct\",\n",
-                "        \"intfloat/e5-mistral-7b-instruct\",\n",
-                "        \"GritLM/GritLM-7B\",\n",
-                "        \"GritLM/GritLM-8x7B\",\n",
-                "        \"intfloat/multilingual-e5-small\",\n",
-                "        \"intfloat/multilingual-e5-base\",\n",
-                "        \"intfloat/multilingual-e5-large\",\n",
-                "    ]\n",
-                "    models: list[mteb.ModelMeta] = [mteb.get_model_meta(name) for name in model_names]\n",
-                "\n",
-                "    # get missing revisions - Assuming we are using the latest revision\n",
-                "    for model in models:\n",
-                "        if model.revision is None:\n",
-                "            print(f\"Getting revision for {model.name}\")\n",
-                "            encoder = model.load_model()\n",
-                "            model.revision = encoder.model_card_data.base_model_revision  # type: ignore\n",
-                "\n",
-                "    return models\n",
-                "\n",
-                "\n",
-                "models = get_models()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks: 564\n"
-                    ]
-                }
-            ],
-            "source": [
-                "mult_tasks = mteb.get_tasks()\n",
-                "print(f\"Number of tasks: {len(mult_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks after filtering: 510\n"
-                    ]
-                }
-            ],
-            "source": [
-                "not_include = [\n",
-                "    \"DKHateClassification\",  # # due to it being a gated dataset on huggingface (requiring to sign a form)\n",
-                "    # was added after models were run\n",
-                "    \"SouthAfricanLangClassification\",\n",
-                "    \"BrightRetrieval\",\n",
-                "    \"LitSearchRetrieval\",\n",
-                "    \"HinDialectClassification\",\n",
-                "    \"MSMARCO\",\n",
-                "    \"SpanishPassageRetrievalS2P\",\n",
-                "    \"SummEvalSummarization.v2\",\n",
-                "    \"IndicNLPNewsClassification\",\n",
-                "    \"XStance\",\n",
-                "    \"MIRACLReranking\",\n",
-                "    \"KorFin\",\n",
-                "    \"Ocnli\",\n",
-                "    \"Cmnli\",\n",
-                "    \"QBQTC\",\n",
-                "    \"SICK-BR-STS\",\n",
-                "    \"PublicHealthQA\",  # some error in initial run of the dataset\n",
-                "    # model model had an error on this - likely contains empty examples:\n",
-                "    \"YahooAnswersTopicsClassification\",\n",
-                "    \"FrenchBookReviews\",\n",
-                "    \"SlovakSumRetrieval\",\n",
-                "    \"LegalBenchPC\",\n",
-                "    \"RomanianSentimentClassification\",\n",
-                "    \"GPUSpeedTask\",  # for speed testing\n",
-                "    \"CPUSpeedTask\",  # for speed testing\n",
-                "    \"MSMARCOv2\",  # too large to be practical for a benchmark\n",
-                "    \"SIB200Classification\",  # we will be using the SIB200 dataset for Cluster Classification so as they are the same dataset we will not include this one\n",
-                "    \"SummEval\",  # due to https://github.com/embeddings-benchmark/mteb/issues/1156\n",
-                "]\n",
-                "retrieval_to_be_downsampled = [  # TODO: Removing this list when tasks are ready\n",
-                "    \"TopiOCQA\",\n",
-                "    \"MSMARCO-PL\",\n",
-                "    \"ClimateFEVER\",\n",
-                "    \"FEVER\",\n",
-                "    \"HotpotQA\",\n",
-                "    \"HotpotQA-PL\",\n",
-                "    \"DBPedia\",\n",
-                "    \"DBPedia-PL\",\n",
-                "    \"NeuCLIR2022Retrieval\",\n",
-                "    \"NeuCLIR2023Retrieval\",\n",
-                "    \"NeuCLIR2022Retrieval\",\n",
-                "    \"NeuCLIR2023Retrieval\",\n",
-                "    \"NQ\",\n",
-                "    \"NQ-PL\",\n",
-                "    \"NeuCLIR2022Retrieval\",\n",
-                "    \"NeuCLIR2023Retrieval\",\n",
-                "    \"MIRACLRetrieval\",\n",
-                "    \"RiaNewsRetrieval\",\n",
-                "    \"Quora-PL\",\n",
-                "    \"QuoraRetrieval\",\n",
-                "]\n",
-                "not_include += retrieval_to_be_downsampled\n",
-                "\n",
-                "mult_tasks = [t for t in mult_tasks if t.metadata.name not in not_include]\n",
-                "# exlude machine translated tasks\n",
-                "mult_tasks = [\n",
-                "    t\n",
-                "    for t in mult_tasks\n",
-                "    if t.metadata.sample_creation\n",
-                "    not in [\n",
-                "        \"machine-translated\",\n",
-                "        \"machine-translated and verified\",\n",
-                "        \"machine-translated and localized\",\n",
-                "    ]\n",
-                "]\n",
-                "\n",
-                "print(f\"Number of tasks after filtering: {len(mult_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# load results from mteb/results repository\n",
-                "mteb_results = mteb.load_results(models=models, tasks=mult_tasks, download_latest=False)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import mteb.task_selection as task_selection\n",
-                "\n",
-                "results_df = task_selection.results_to_dataframe(mteb_results, drop_na=False)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>task</th>\n",
-                            "      <th>AFQMC</th>\n",
-                            "      <th>AILACasedocs</th>\n",
-                            "      <th>AILAStatutes</th>\n",
-                            "      <th>AJGT</th>\n",
-                            "      <th>ARCChallenge</th>\n",
-                            "      <th>ATEC</th>\n",
-                            "      <th>AfriSentiClassification</th>\n",
-                            "      <th>AfriSentiLangClassification</th>\n",
-                            "      <th>AllegroReviews</th>\n",
-                            "      <th>AlloProfClusteringP2P.v2</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>WikipediaRetrievalMultilingual</th>\n",
-                            "      <th>WinoGrande</th>\n",
-                            "      <th>WisesightSentimentClassification</th>\n",
-                            "      <th>XMarket</th>\n",
-                            "      <th>XNLI</th>\n",
-                            "      <th>XPQARetrieval</th>\n",
-                            "      <th>XQuADRetrieval</th>\n",
-                            "      <th>YelpReviewFullClassification</th>\n",
-                            "      <th>YueOpenriceReviewClassification</th>\n",
-                            "      <th>indonli</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>GritLM/GritLM-7B</th>\n",
-                            "      <th>13f00a0e36500c80ce12870ea513846a066004af</th>\n",
-                            "      <td>0.355864</td>\n",
-                            "      <td>0.35292</td>\n",
-                            "      <td>0.41800</td>\n",
-                            "      <td>0.809611</td>\n",
-                            "      <td>0.26677</td>\n",
-                            "      <td>0.408941</td>\n",
-                            "      <td>0.450786</td>\n",
-                            "      <td>0.931445</td>\n",
-                            "      <td>0.567694</td>\n",
-                            "      <td>0.671576</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.917722</td>\n",
-                            "      <td>0.53697</td>\n",
-                            "      <td>0.341335</td>\n",
-                            "      <td>0.259600</td>\n",
-                            "      <td>0.741047</td>\n",
-                            "      <td>0.506027</td>\n",
-                            "      <td>0.947917</td>\n",
-                            "      <td>0.650635</td>\n",
-                            "      <td>0.374902</td>\n",
-                            "      <td>0.555207</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/e5-mistral-7b-instruct</th>\n",
-                            "      <th>07163b72af1488142a360786df853f237b1a3ca1</th>\n",
-                            "      <td>0.389854</td>\n",
-                            "      <td>0.36662</td>\n",
-                            "      <td>0.34535</td>\n",
-                            "      <td>0.823778</td>\n",
-                            "      <td>0.19001</td>\n",
-                            "      <td>0.428429</td>\n",
-                            "      <td>0.444763</td>\n",
-                            "      <td>0.921680</td>\n",
-                            "      <td>0.597813</td>\n",
-                            "      <td>0.691183</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.909265</td>\n",
-                            "      <td>0.39514</td>\n",
-                            "      <td>0.356845</td>\n",
-                            "      <td>0.287633</td>\n",
-                            "      <td>0.779189</td>\n",
-                            "      <td>0.474599</td>\n",
-                            "      <td>0.933592</td>\n",
-                            "      <td>0.618311</td>\n",
-                            "      <td>0.330566</td>\n",
-                            "      <td>0.579942</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-base</th>\n",
-                            "      <th>d13f1b27baf31030b7fd040960d60d909913633f</th>\n",
-                            "      <td>0.296610</td>\n",
-                            "      <td>0.26053</td>\n",
-                            "      <td>0.20371</td>\n",
-                            "      <td>0.777889</td>\n",
-                            "      <td>0.09611</td>\n",
-                            "      <td>0.370099</td>\n",
-                            "      <td>0.438023</td>\n",
-                            "      <td>0.671191</td>\n",
-                            "      <td>0.407753</td>\n",
-                            "      <td>0.631008</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.887509</td>\n",
-                            "      <td>0.56177</td>\n",
-                            "      <td>0.363027</td>\n",
-                            "      <td>0.167343</td>\n",
-                            "      <td>0.709983</td>\n",
-                            "      <td>0.415317</td>\n",
-                            "      <td>0.958011</td>\n",
-                            "      <td>0.597217</td>\n",
-                            "      <td>0.315869</td>\n",
-                            "      <td>0.509662</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large</th>\n",
-                            "      <th>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</th>\n",
-                            "      <td>0.330127</td>\n",
-                            "      <td>0.26427</td>\n",
-                            "      <td>0.20842</td>\n",
-                            "      <td>0.802889</td>\n",
-                            "      <td>0.10828</td>\n",
-                            "      <td>0.398049</td>\n",
-                            "      <td>0.455005</td>\n",
-                            "      <td>0.642822</td>\n",
-                            "      <td>0.410437</td>\n",
-                            "      <td>0.636065</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.908209</td>\n",
-                            "      <td>0.54985</td>\n",
-                            "      <td>0.360570</td>\n",
-                            "      <td>0.171770</td>\n",
-                            "      <td>0.739017</td>\n",
-                            "      <td>0.472794</td>\n",
-                            "      <td>0.970637</td>\n",
-                            "      <td>0.643164</td>\n",
-                            "      <td>0.347949</td>\n",
-                            "      <td>0.517360</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large-instruct</th>\n",
-                            "      <th>baa7be480a7de1539afce709c8f13f833a510e0a</th>\n",
-                            "      <td>0.375337</td>\n",
-                            "      <td>0.33330</td>\n",
-                            "      <td>0.29659</td>\n",
-                            "      <td>0.854500</td>\n",
-                            "      <td>0.15027</td>\n",
-                            "      <td>0.432675</td>\n",
-                            "      <td>0.453874</td>\n",
-                            "      <td>0.914404</td>\n",
-                            "      <td>0.524254</td>\n",
-                            "      <td>0.669222</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0.915935</td>\n",
-                            "      <td>0.54272</td>\n",
-                            "      <td>0.368923</td>\n",
-                            "      <td>0.256423</td>\n",
-                            "      <td>0.784905</td>\n",
-                            "      <td>0.518825</td>\n",
-                            "      <td>0.965380</td>\n",
-                            "      <td>0.652686</td>\n",
-                            "      <td>0.347656</td>\n",
-                            "      <td>0.561701</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>5 rows × 507 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "task                                                                                 AFQMC  \\\n",
-                            "model                                   revision                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  0.355864   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  0.389854   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  0.296610   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.330127   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  0.375337   \n",
-                            "\n",
-                            "task                                                                              AILACasedocs  \\\n",
-                            "model                                   revision                                                 \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af       0.35292   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1       0.36662   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f       0.26053   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81       0.26427   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a       0.33330   \n",
-                            "\n",
-                            "task                                                                              AILAStatutes  \\\n",
-                            "model                                   revision                                                 \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af       0.41800   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1       0.34535   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f       0.20371   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81       0.20842   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a       0.29659   \n",
-                            "\n",
-                            "task                                                                                  AJGT  \\\n",
-                            "model                                   revision                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  0.809611   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  0.823778   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  0.777889   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.802889   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  0.854500   \n",
-                            "\n",
-                            "task                                                                              ARCChallenge  \\\n",
-                            "model                                   revision                                                 \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af       0.26677   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1       0.19001   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f       0.09611   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81       0.10828   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a       0.15027   \n",
-                            "\n",
-                            "task                                                                                  ATEC  \\\n",
-                            "model                                   revision                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  0.408941   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  0.428429   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  0.370099   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.398049   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  0.432675   \n",
-                            "\n",
-                            "task                                                                              AfriSentiClassification  \\\n",
-                            "model                                   revision                                                            \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                 0.450786   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                 0.444763   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                 0.438023   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                 0.455005   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                 0.453874   \n",
-                            "\n",
-                            "task                                                                              AfriSentiLangClassification  \\\n",
-                            "model                                   revision                                                                \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                     0.931445   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                     0.921680   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                     0.671191   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                     0.642822   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                     0.914404   \n",
-                            "\n",
-                            "task                                                                              AllegroReviews  \\\n",
-                            "model                                   revision                                                   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af        0.567694   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1        0.597813   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f        0.407753   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81        0.410437   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a        0.524254   \n",
-                            "\n",
-                            "task                                                                              AlloProfClusteringP2P.v2  \\\n",
-                            "model                                   revision                                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                  0.671576   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                  0.691183   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                  0.631008   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                  0.636065   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                  0.669222   \n",
-                            "\n",
-                            "task                                                                              ...  \\\n",
-                            "model                                   revision                                  ...   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  ...   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  ...   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  ...   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  ...   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  ...   \n",
-                            "\n",
-                            "task                                                                              WikipediaRetrievalMultilingual  \\\n",
-                            "model                                   revision                                                                   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                        0.917722   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                        0.909265   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                        0.887509   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                        0.908209   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                        0.915935   \n",
-                            "\n",
-                            "task                                                                              WinoGrande  \\\n",
-                            "model                                   revision                                               \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af     0.53697   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1     0.39514   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f     0.56177   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81     0.54985   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a     0.54272   \n",
-                            "\n",
-                            "task                                                                              WisesightSentimentClassification  \\\n",
-                            "model                                   revision                                                                     \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                          0.341335   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                          0.356845   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                          0.363027   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                          0.360570   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                          0.368923   \n",
-                            "\n",
-                            "task                                                                               XMarket  \\\n",
-                            "model                                   revision                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  0.259600   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  0.287633   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  0.167343   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.171770   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  0.256423   \n",
-                            "\n",
-                            "task                                                                                  XNLI  \\\n",
-                            "model                                   revision                                             \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  0.741047   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  0.779189   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  0.709983   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.739017   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  0.784905   \n",
-                            "\n",
-                            "task                                                                              XPQARetrieval  \\\n",
-                            "model                                   revision                                                  \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af       0.506027   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1       0.474599   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f       0.415317   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81       0.472794   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a       0.518825   \n",
-                            "\n",
-                            "task                                                                              XQuADRetrieval  \\\n",
-                            "model                                   revision                                                   \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af        0.947917   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1        0.933592   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f        0.958011   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81        0.970637   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a        0.965380   \n",
-                            "\n",
-                            "task                                                                              YelpReviewFullClassification  \\\n",
-                            "model                                   revision                                                                 \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                      0.650635   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                      0.618311   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                      0.597217   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                      0.643164   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                      0.652686   \n",
-                            "\n",
-                            "task                                                                              YueOpenriceReviewClassification  \\\n",
-                            "model                                   revision                                                                    \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af                         0.374902   \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1                         0.330566   \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f                         0.315869   \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81                         0.347949   \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a                         0.347656   \n",
-                            "\n",
-                            "task                                                                               indonli  \n",
-                            "model                                   revision                                            \n",
-                            "GritLM/GritLM-7B                        13f00a0e36500c80ce12870ea513846a066004af  0.555207  \n",
-                            "intfloat/e5-mistral-7b-instruct         07163b72af1488142a360786df853f237b1a3ca1  0.579942  \n",
-                            "intfloat/multilingual-e5-base           d13f1b27baf31030b7fd040960d60d909913633f  0.509662  \n",
-                            "intfloat/multilingual-e5-large          4dc6d853a804b9c8886ede6dda8a073b7dc08a81  0.517360  \n",
-                            "intfloat/multilingual-e5-large-instruct baa7be480a7de1539afce709c8f13f833a510e0a  0.561701  \n",
-                            "\n",
-                            "[5 rows x 507 columns]"
-                        ]
-                    },
-                    "execution_count": 7,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results_df.head()  # inspect the dataframe"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>task</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "Empty DataFrame\n",
-                            "Columns: []\n",
-                            "Index: []"
-                        ]
-                    },
-                    "execution_count": 8,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# which tasks are missing?\n",
-                "missing_tasks = results_df[results_df.isna().any(axis=1)]\n",
-                "missing_tasks = missing_tasks.loc[:, missing_tasks.isna().any()]\n",
-                "missing_tasks  # should be empty"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Task Selection\n",
-                "\n",
-                "In this section we will do the task selection to construct a benchmark."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>task</th>\n",
-                            "      <th>Diversity1LegalBenchClassification</th>\n",
-                            "      <th>Diversity2LegalBenchClassification</th>\n",
-                            "      <th>IFlyTek</th>\n",
-                            "      <th>TNews</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model</th>\n",
-                            "      <th>revision</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>GritLM/GritLM-7B</th>\n",
-                            "      <th>13f00a0e36500c80ce12870ea513846a066004af</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/e5-mistral-7b-instruct</th>\n",
-                            "      <th>07163b72af1488142a360786df853f237b1a3ca1</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-base</th>\n",
-                            "      <th>d13f1b27baf31030b7fd040960d60d909913633f</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large</th>\n",
-                            "      <th>4dc6d853a804b9c8886ede6dda8a073b7dc08a81</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-large-instruct</th>\n",
-                            "      <th>baa7be480a7de1539afce709c8f13f833a510e0a</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>intfloat/multilingual-e5-small</th>\n",
-                            "      <th>e4ce9877abf3edfe10b0d82785e83bdcb973e22e</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/LaBSE</th>\n",
-                            "      <th>e34fab64a3011d2176c99545a93d5cbddc9a91b7</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/all-MiniLM-L12-v2</th>\n",
-                            "      <th>a05860a77cef7b37e0048a7864658139bc18a854</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/all-MiniLM-L6-v2</th>\n",
-                            "      <th>8b3219a92973c328a8e22fadcfa821b5dc75636a</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/all-mpnet-base-v2</th>\n",
-                            "      <th>84f2bcc00d77236f9e89c8a360a00fb1139bf47d</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2</th>\n",
-                            "      <th>bf3bf13ab40c3157080a7ab344c831b9ad18b5eb</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>sentence-transformers/paraphrase-multilingual-mpnet-base-v2</th>\n",
-                            "      <th>79f2382ceacceacdf38563d7c5d16b9ff8d725d6</th>\n",
-                            "      <td>0.763333</td>\n",
-                            "      <td>0.746667</td>\n",
-                            "      <td>0.0</td>\n",
-                            "      <td>0.0</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "task                                                                                         Diversity1LegalBenchClassification  \\\n",
-                            "model                                              revision                                                                       \n",
-                            "GritLM/GritLM-7B                                   13f00a0e36500c80ce12870ea513846a066004af                            0.763333   \n",
-                            "intfloat/e5-mistral-7b-instruct                    07163b72af1488142a360786df853f237b1a3ca1                            0.763333   \n",
-                            "intfloat/multilingual-e5-base                      d13f1b27baf31030b7fd040960d60d909913633f                            0.763333   \n",
-                            "intfloat/multilingual-e5-large                     4dc6d853a804b9c8886ede6dda8a073b7dc08a81                            0.763333   \n",
-                            "intfloat/multilingual-e5-large-instruct            baa7be480a7de1539afce709c8f13f833a510e0a                            0.763333   \n",
-                            "intfloat/multilingual-e5-small                     e4ce9877abf3edfe10b0d82785e83bdcb973e22e                            0.763333   \n",
-                            "sentence-transformers/LaBSE                        e34fab64a3011d2176c99545a93d5cbddc9a91b7                            0.763333   \n",
-                            "sentence-transformers/all-MiniLM-L12-v2            a05860a77cef7b37e0048a7864658139bc18a854                            0.763333   \n",
-                            "sentence-transformers/all-MiniLM-L6-v2             8b3219a92973c328a8e22fadcfa821b5dc75636a                            0.763333   \n",
-                            "sentence-transformers/all-mpnet-base-v2            84f2bcc00d77236f9e89c8a360a00fb1139bf47d                            0.763333   \n",
-                            "sentence-transformers/paraphrase-multilingual-M... bf3bf13ab40c3157080a7ab344c831b9ad18b5eb                            0.763333   \n",
-                            "sentence-transformers/paraphrase-multilingual-m... 79f2382ceacceacdf38563d7c5d16b9ff8d725d6                            0.763333   \n",
-                            "\n",
-                            "task                                                                                         Diversity2LegalBenchClassification  \\\n",
-                            "model                                              revision                                                                       \n",
-                            "GritLM/GritLM-7B                                   13f00a0e36500c80ce12870ea513846a066004af                            0.746667   \n",
-                            "intfloat/e5-mistral-7b-instruct                    07163b72af1488142a360786df853f237b1a3ca1                            0.746667   \n",
-                            "intfloat/multilingual-e5-base                      d13f1b27baf31030b7fd040960d60d909913633f                            0.746667   \n",
-                            "intfloat/multilingual-e5-large                     4dc6d853a804b9c8886ede6dda8a073b7dc08a81                            0.746667   \n",
-                            "intfloat/multilingual-e5-large-instruct            baa7be480a7de1539afce709c8f13f833a510e0a                            0.746667   \n",
-                            "intfloat/multilingual-e5-small                     e4ce9877abf3edfe10b0d82785e83bdcb973e22e                            0.746667   \n",
-                            "sentence-transformers/LaBSE                        e34fab64a3011d2176c99545a93d5cbddc9a91b7                            0.746667   \n",
-                            "sentence-transformers/all-MiniLM-L12-v2            a05860a77cef7b37e0048a7864658139bc18a854                            0.746667   \n",
-                            "sentence-transformers/all-MiniLM-L6-v2             8b3219a92973c328a8e22fadcfa821b5dc75636a                            0.746667   \n",
-                            "sentence-transformers/all-mpnet-base-v2            84f2bcc00d77236f9e89c8a360a00fb1139bf47d                            0.746667   \n",
-                            "sentence-transformers/paraphrase-multilingual-M... bf3bf13ab40c3157080a7ab344c831b9ad18b5eb                            0.746667   \n",
-                            "sentence-transformers/paraphrase-multilingual-m... 79f2382ceacceacdf38563d7c5d16b9ff8d725d6                            0.746667   \n",
-                            "\n",
-                            "task                                                                                         IFlyTek  \\\n",
-                            "model                                              revision                                            \n",
-                            "GritLM/GritLM-7B                                   13f00a0e36500c80ce12870ea513846a066004af      0.0   \n",
-                            "intfloat/e5-mistral-7b-instruct                    07163b72af1488142a360786df853f237b1a3ca1      0.0   \n",
-                            "intfloat/multilingual-e5-base                      d13f1b27baf31030b7fd040960d60d909913633f      0.0   \n",
-                            "intfloat/multilingual-e5-large                     4dc6d853a804b9c8886ede6dda8a073b7dc08a81      0.0   \n",
-                            "intfloat/multilingual-e5-large-instruct            baa7be480a7de1539afce709c8f13f833a510e0a      0.0   \n",
-                            "intfloat/multilingual-e5-small                     e4ce9877abf3edfe10b0d82785e83bdcb973e22e      0.0   \n",
-                            "sentence-transformers/LaBSE                        e34fab64a3011d2176c99545a93d5cbddc9a91b7      0.0   \n",
-                            "sentence-transformers/all-MiniLM-L12-v2            a05860a77cef7b37e0048a7864658139bc18a854      0.0   \n",
-                            "sentence-transformers/all-MiniLM-L6-v2             8b3219a92973c328a8e22fadcfa821b5dc75636a      0.0   \n",
-                            "sentence-transformers/all-mpnet-base-v2            84f2bcc00d77236f9e89c8a360a00fb1139bf47d      0.0   \n",
-                            "sentence-transformers/paraphrase-multilingual-M... bf3bf13ab40c3157080a7ab344c831b9ad18b5eb      0.0   \n",
-                            "sentence-transformers/paraphrase-multilingual-m... 79f2382ceacceacdf38563d7c5d16b9ff8d725d6      0.0   \n",
-                            "\n",
-                            "task                                                                                         TNews  \n",
-                            "model                                              revision                                         \n",
-                            "GritLM/GritLM-7B                                   13f00a0e36500c80ce12870ea513846a066004af    0.0  \n",
-                            "intfloat/e5-mistral-7b-instruct                    07163b72af1488142a360786df853f237b1a3ca1    0.0  \n",
-                            "intfloat/multilingual-e5-base                      d13f1b27baf31030b7fd040960d60d909913633f    0.0  \n",
-                            "intfloat/multilingual-e5-large                     4dc6d853a804b9c8886ede6dda8a073b7dc08a81    0.0  \n",
-                            "intfloat/multilingual-e5-large-instruct            baa7be480a7de1539afce709c8f13f833a510e0a    0.0  \n",
-                            "intfloat/multilingual-e5-small                     e4ce9877abf3edfe10b0d82785e83bdcb973e22e    0.0  \n",
-                            "sentence-transformers/LaBSE                        e34fab64a3011d2176c99545a93d5cbddc9a91b7    0.0  \n",
-                            "sentence-transformers/all-MiniLM-L12-v2            a05860a77cef7b37e0048a7864658139bc18a854    0.0  \n",
-                            "sentence-transformers/all-MiniLM-L6-v2             8b3219a92973c328a8e22fadcfa821b5dc75636a    0.0  \n",
-                            "sentence-transformers/all-mpnet-base-v2            84f2bcc00d77236f9e89c8a360a00fb1139bf47d    0.0  \n",
-                            "sentence-transformers/paraphrase-multilingual-M... bf3bf13ab40c3157080a7ab344c831b9ad18b5eb    0.0  \n",
-                            "sentence-transformers/paraphrase-multilingual-m... 79f2382ceacceacdf38563d7c5d16b9ff8d725d6    0.0  "
-                        ]
-                    },
-                    "execution_count": 9,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# tasks with exactly the same results for all models (i.e. columns where all values are the same)\n",
-                "same_results = results_df.loc[:, results_df.nunique() == 1]\n",
-                "same_results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Index(['Diversity1LegalBenchClassification',\n",
-                            "       'Diversity2LegalBenchClassification', 'IFlyTek', 'TNews'],\n",
-                            "      dtype='object', name='task')"
-                        ]
-                    },
-                    "execution_count": 10,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "same_results.columns"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks before removing tasks with same results: 510\n",
-                        "Number of tasks after removing tasks with same results: 506\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# remove these tasks from the tasks\n",
-                "print(f\"Number of tasks before removing tasks with same results: {len(mult_tasks)}\")\n",
-                "mult_tasks = [t for t in mult_tasks if t.metadata.name not in same_results.columns]\n",
-                "print(f\"Number of tasks after removing tasks with same results: {len(mult_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "TbilisiCityHallBitextMining(name='TbilisiCityHallBitextMining', languages=['eng', 'kat'])\n",
-                        "BUCCBitextMiningFast(name='BUCC.v2', languages=['cmn', 'deu', 'eng', '...'])\n",
-                        "LinceMTBitextMining(name='LinceMTBitextMining', languages=['eng', 'hin'])\n",
-                        "RomaTalesBitextMining(name='RomaTalesBitextMining', languages=['hun', 'rom'])\n",
-                        "HotelReviewSentimentClassification(name='HotelReviewSentimentClassification', languages=['ara'])\n",
-                        "OnlineStoreReviewSentimentClassification(name='OnlineStoreReviewSentimentClassification', languages=['ara'])\n",
-                        "TweetEmotionClassification(name='TweetEmotionClassification', languages=['ara'])\n",
-                        "CzechSubjectivityClassification(name='CzechSubjectivityClassification', languages=['ces'])\n",
-                        "DanishPoliticalCommentsClassification(name='DanishPoliticalCommentsClassification', languages=['dan'])\n",
-                        "GermanPoliticiansTwitterSentimentClassification(name='GermanPoliticiansTwitterSentimentClassification', languages=['deu'])\n",
-                        "AmazonPolarityClassification(name='AmazonPolarityClassification', languages=['eng'])\n",
-                        "ArxivClassification(name='ArxivClassification', languages=['eng'])\n",
-                        "EmotionClassification(name='EmotionClassification', languages=['eng'])\n",
-                        "FrenkEnClassification(name='FrenkEnClassification', languages=['eng'])\n",
-                        "ImdbClassification(name='ImdbClassification', languages=['eng'])\n",
-                        "PatentClassification(name='PatentClassification', languages=['eng'])\n",
-                        "TweetSentimentExtractionClassification(name='TweetSentimentExtractionClassification', languages=['eng'])\n",
-                        "PersianFoodSentimentClassification(name='PersianFoodSentimentClassification', languages=['fas'])\n",
-                        "FilipinoHateSpeechClassification(name='FilipinoHateSpeechClassification', languages=['fil'])\n",
-                        "FrenkHrClassification(name='FrenkHrClassification', languages=['hrv'])\n",
-                        "IndonesianMongabayConservationClassification(name='IndonesianMongabayConservationClassification', languages=['ind'])\n",
-                        "ItalianLinguisticAcceptabilityClassification(name='Itacola', languages=['ita'])\n",
-                        "LanguageClassification(name='LanguageClassification', languages=['ara', 'bul', 'cmn', '...'])\n",
-                        "MTOPDomainClassification(name='MTOPDomainClassification', languages=['deu', 'eng', 'fra', '...'])\n",
-                        "MTOPIntentClassification(name='MTOPIntentClassification', languages=['deu', 'eng', 'fra', '...'])\n",
-                        "MultilingualSentimentClassification(name='MultilingualSentimentClassification', languages=['ara', 'bam', 'bul', '...'])\n",
-                        "TurkicClassification(name='TurkicClassification', languages=['bak', 'kaz', 'kir'])\n",
-                        "HateSpeechPortugueseClassification(name='HateSpeechPortugueseClassification', languages=['por'])\n",
-                        "KinopoiskClassification(name='KinopoiskClassification', languages=['rus'])\n",
-                        "RuSciBenchGRNTIClassification(name='RuSciBenchGRNTIClassification', languages=['rus'])\n",
-                        "RuSciBenchOECDClassification(name='RuSciBenchOECDClassification', languages=['rus'])\n",
-                        "FrenkSlClassification(name='FrenkSlClassification', languages=['slv'])\n",
-                        "SpanishSentimentClassification(name='SpanishSentimentClassification', languages=['spa'])\n",
-                        "SwedishSentimentClassification(name='SwedishSentimentClassification', languages=['swe'])\n",
-                        "TurkishMovieSentimentClassification(name='TurkishMovieSentimentClassification', languages=['tur'])\n",
-                        "TurkishProductSentimentClassification(name='TurkishProductSentimentClassification', languages=['tur'])\n",
-                        "YueOpenriceReviewClassification(name='YueOpenriceReviewClassification', languages=['yue'])\n",
-                        "RedditFastClusteringS2S(name='RedditClustering.v2', languages=['eng'])\n",
-                        "RedditFastClusteringP2P(name='RedditClusteringP2P.v2', languages=['eng'])\n",
-                        "StackExchangeClusteringFast(name='StackExchangeClustering.v2', languages=['eng'])\n",
-                        "StackExchangeClusteringP2PFast(name='StackExchangeClusteringP2P.v2', languages=['eng'])\n",
-                        "TwentyNewsgroupsClusteringFast(name='TwentyNewsgroupsClustering.v2', languages=['eng'])\n",
-                        "MLSUMClusteringP2PFast(name='MLSUMClusteringP2P.v2', languages=['deu', 'fra', 'rus', '...'])\n",
-                        "MLSUMClusteringS2SFast(name='MLSUMClusteringS2S.v2', languages=['deu', 'fra', 'rus', '...'])\n",
-                        "RuSciBenchGRNTIClusteringP2P(name='RuSciBenchGRNTIClusteringP2P', languages=['rus'])\n",
-                        "RuSciBenchOECDClusteringP2P(name='RuSciBenchOECDClusteringP2P', languages=['rus'])\n",
-                        "ThuNewsClusteringFastS2S(name='ThuNewsClusteringS2S.v2', languages=['cmn'])\n",
-                        "ThuNewsClusteringFastP2P(name='ThuNewsClusteringP2P.v2', languages=['cmn'])\n",
-                        "SadeemQuestionRetrieval(name='SadeemQuestionRetrieval', languages=['ara'])\n",
-                        "CodeEditSearchRetrieval(name='CodeEditSearchRetrieval', languages=['c', 'c++', 'go', '...'])\n",
-                        "LEMBNarrativeQARetrieval(name='LEMBNarrativeQARetrieval', languages=['eng'])\n",
-                        "LEMBNeedleRetrieval(name='LEMBNeedleRetrieval', languages=['eng'])\n",
-                        "LEMBPasskeyRetrieval(name='LEMBPasskeyRetrieval', languages=['eng'])\n",
-                        "LEMBQMSumRetrieval(name='LEMBQMSumRetrieval', languages=['eng'])\n",
-                        "LEMBSummScreenFDRetrieval(name='LEMBSummScreenFDRetrieval', languages=['eng'])\n",
-                        "LEMBWikimQARetrieval(name='LEMBWikimQARetrieval', languages=['eng'])\n",
-                        "EstQA(name='EstQA', languages=['est'])\n",
-                        "SyntecRetrieval(name='SyntecRetrieval', languages=['fra'])\n",
-                        "GeorgianFAQRetrieval(name='GeorgianFAQRetrieval', languages=['kat'])\n",
-                        "ArEntail(name='ArEntail', languages=['ara'])\n",
-                        "SprintDuplicateQuestionsPC(name='SprintDuplicateQuestions', languages=['eng'])\n",
-                        "FarsTail(name='FarsTail', languages=['fas'])\n",
-                        "XNLI(name='XNLI', languages=['ara', 'bul', 'deu', '...'])\n",
-                        "Assin2RTE(name='Assin2RTE', languages=['por'])\n",
-                        "SickBrPC(name='SICK-BR-PC', languages=['por'])\n",
-                        "CMedQAv1(name='CMedQAv1-reranking', languages=['cmn'])\n",
-                        "STS12STS(name='STS12', languages=['eng'])\n",
-                        "STS13STS(name='STS13', languages=['eng'])\n",
-                        "STS14STS(name='STS14', languages=['eng'])\n",
-                        "STS15STS(name='STS15', languages=['eng'])\n",
-                        "STS16STS(name='STS16', languages=['eng'])\n",
-                        "SemRel24STS(name='SemRel24STS', languages=['afr', 'amh', 'arb', '...'])\n",
-                        "STS17Crosslingual(name='STS17', languages=['ara', 'deu', 'eng', '...'])\n",
-                        "STS22CrosslingualSTSv2(name='STS22.v2', languages=['ara', 'cmn', 'deu', '...'])\n",
-                        "Assin2STS(name='Assin2STS', languages=['por'])\n",
-                        "-\n"
-                    ]
-                }
-            ],
-            "source": [
-                "licenses_to_remove = [\"Not specified\", \"Unknown\"]  # remove tasks with unknown licenses\n",
-                "# Note: this implicitly penalizes low-resource languages, as they are more likely to have unknown licenses - though this is probably still a reasonable choice\n",
-                "unspecified_licences = [\n",
-                "    t for t in mult_tasks if t.metadata.license in licenses_to_remove\n",
-                "]\n",
-                "[print(l) for l in unspecified_licences]\n",
-                "print(\"-\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "['TbilisiCityHallBitextMining',\n",
-                            " 'LinceMTBitextMining',\n",
-                            " 'RomaTalesBitextMining',\n",
-                            " 'HotelReviewSentimentClassification',\n",
-                            " 'OnlineStoreReviewSentimentClassification',\n",
-                            " 'TweetEmotionClassification',\n",
-                            " 'CzechSubjectivityClassification',\n",
-                            " 'DanishPoliticalCommentsClassification',\n",
-                            " 'GermanPoliticiansTwitterSentimentClassification',\n",
-                            " 'ArxivClassification',\n",
-                            " 'FrenkEnClassification',\n",
-                            " 'PatentClassification',\n",
-                            " 'PersianFoodSentimentClassification',\n",
-                            " 'FilipinoHateSpeechClassification',\n",
-                            " 'FrenkHrClassification',\n",
-                            " 'IndonesianMongabayConservationClassification',\n",
-                            " 'Itacola',\n",
-                            " 'LanguageClassification',\n",
-                            " 'MultilingualSentimentClassification',\n",
-                            " 'TurkicClassification',\n",
-                            " 'HateSpeechPortugueseClassification',\n",
-                            " 'KinopoiskClassification',\n",
-                            " 'RuSciBenchGRNTIClassification',\n",
-                            " 'RuSciBenchOECDClassification',\n",
-                            " 'FrenkSlClassification',\n",
-                            " 'SpanishSentimentClassification',\n",
-                            " 'SwedishSentimentClassification',\n",
-                            " 'TurkishMovieSentimentClassification',\n",
-                            " 'TurkishProductSentimentClassification',\n",
-                            " 'YueOpenriceReviewClassification',\n",
-                            " 'RuSciBenchGRNTIClusteringP2P',\n",
-                            " 'RuSciBenchOECDClusteringP2P',\n",
-                            " 'ThuNewsClusteringS2S.v2',\n",
-                            " 'ThuNewsClusteringP2P.v2',\n",
-                            " 'SadeemQuestionRetrieval',\n",
-                            " 'CodeEditSearchRetrieval',\n",
-                            " 'EstQA',\n",
-                            " 'SyntecRetrieval',\n",
-                            " 'GeorgianFAQRetrieval',\n",
-                            " 'ArEntail',\n",
-                            " 'FarsTail',\n",
-                            " 'Assin2RTE',\n",
-                            " 'SICK-BR-PC',\n",
-                            " 'CMedQAv1-reranking',\n",
-                            " 'Assin2STS']"
-                        ]
-                    },
-                    "execution_count": 13,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "import mteb\n",
-                "\n",
-                "MTEB_MAIN_EN = mteb.get_benchmark(\"MTEB(eng, classic)\")\n",
-                "\n",
-                "\n",
-                "exceptions = [\n",
-                "    \"STS16\",\n",
-                "    \"STS17\",\n",
-                "    \"STS22.v2\",\n",
-                "    \"SemRel24STS\",\n",
-                "    \"XNLI\",  # assume that semrel task are fair use\n",
-                "    \"LEMBNarrativeQARetrieval\",\n",
-                "    \"LEMBNeedleRetrieval\",\n",
-                "    \"LEMBPasskeyRetrieval\",\n",
-                "    \"LEMBQMSumRetrieval\",\n",
-                "    \"LEMBSummScreenFDRetrieval\",\n",
-                "    \"LEMBWikimQARetrieval\",  # assume that LongEmbed tasks are fair use\n",
-                "    \"TwentyNewsgroupsClustering.v2\",\n",
-                "    \"XNLI\",\n",
-                "    \"StackExchangeClusteringP2PFast\",\n",
-                "    \"BUCC.v2\",\n",
-                "    \"RedditClusteringP2P.v2\",\n",
-                "    \"RedditClustering.v2\",\n",
-                "    \"MLSUMClusteringP2P.v2\",\n",
-                "    \"MLSUMClusteringS2S.v2\",\n",
-                "    \"StackExchangeClusteringP2P.v2\",\n",
-                "    \"StackExchangeClustering.v2\",\n",
-                "] + MTEB_MAIN_EN.tasks  # assume mteb tasks are fair use\n",
-                "\n",
-                "remove_due_to_license = [\n",
-                "    t for t in unspecified_licences if t.metadata.name not in exceptions\n",
-                "]\n",
-                "remove_due_to_license = [t.metadata.name for t in remove_due_to_license]\n",
-                "remove_due_to_license"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks before: 506\n",
-                        "Number of tasks after: 461\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(f\"Number of tasks before: {len(mult_tasks)}\")\n",
-                "mult_tasks = [t for t in mult_tasks if t.metadata.name not in remove_due_to_license]\n",
-                "print(f\"Number of tasks after: {len(mult_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks before: 461\n",
-                        "Number of tasks after: 351\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# remove legal bench tasks (These are English tasks focusing on legal documents)\n",
-                "legal_bench_tasks = [\n",
-                "    \"CanadaTaxCourtOutcomesLegalBenchClassification\",\n",
-                "    \"ContractNLIConfidentialityOfAgreementLegalBenchClassification\",\n",
-                "    \"ContractNLIExplicitIdentificationLegalBenchClassification\",\n",
-                "    \"ContractNLIInclusionOfVerballyConveyedInformationLegalBenchClassification\",\n",
-                "    \"ContractNLILimitedUseLegalBenchClassification\",\n",
-                "    \"ContractNLINoLicensingLegalBenchClassification\",\n",
-                "    \"ContractNLINoticeOnCompelledDisclosureLegalBenchClassification\",\n",
-                "    \"ContractNLIPermissibleAcquirementOfSimilarInformationLegalBenchClassification\",\n",
-                "    \"ContractNLIPermissibleCopyLegalBenchClassification\",\n",
-                "    \"ContractNLIPermissibleDevelopmentOfSimilarInformationLegalBenchClassification\",\n",
-                "    \"ContractNLIPermissiblePostAgreementPossessionLegalBenchClassification\",\n",
-                "    \"ContractNLIReturnOfConfidentialInformationLegalBenchClassification\",\n",
-                "    \"ContractNLISharingWithEmployeesLegalBenchClassification\",\n",
-                "    \"ContractNLISharingWithThirdPartiesLegalBenchClassification\",\n",
-                "    \"ContractNLISurvivalOfObligationsLegalBenchClassification\",\n",
-                "    \"CorporateLobbyingLegalBenchClassification\",\n",
-                "    \"CUADAffiliateLicenseLicenseeLegalBenchClassification\",\n",
-                "    \"CUADAffiliateLicenseLicensorLegalBenchClassification\",\n",
-                "    \"CUADAntiAssignmentLegalBenchClassification\",\n",
-                "    \"CUADAuditRightsLegalBenchClassification\",\n",
-                "    \"CUADCapOnLiabilityLegalBenchClassification\",\n",
-                "    \"CUADChangeOfControlLegalBenchClassification\",\n",
-                "    \"CUADCompetitiveRestrictionExceptionLegalBenchClassification\",\n",
-                "    \"CUADCovenantNotToSueLegalBenchClassification\",\n",
-                "    \"CUADEffectiveDateLegalBenchClassification\",\n",
-                "    \"CUADExclusivityLegalBenchClassification\",\n",
-                "    \"CUADExpirationDateLegalBenchClassification\",\n",
-                "    \"CUADGoverningLawLegalBenchClassification\",\n",
-                "    \"CUADInsuranceLegalBenchClassification\",\n",
-                "    \"CUADIPOwnershipAssignmentLegalBenchClassification\",\n",
-                "    \"CUADIrrevocableOrPerpetualLicenseLegalBenchClassification\",\n",
-                "    \"CUADJointIPOwnershipLegalBenchClassification\",\n",
-                "    \"CUADLicenseGrantLegalBenchClassification\",\n",
-                "    \"CUADLiquidatedDamagesLegalBenchClassification\",\n",
-                "    \"CUADMinimumCommitmentLegalBenchClassification\",\n",
-                "    \"CUADMostFavoredNationLegalBenchClassification\",\n",
-                "    \"CUADNoSolicitOfCustomersLegalBenchClassification\",\n",
-                "    \"CUADNoSolicitOfEmployeesLegalBenchClassification\",\n",
-                "    \"CUADNonCompeteLegalBenchClassification\",\n",
-                "    \"CUADNonDisparagementLegalBenchClassification\",\n",
-                "    \"CUADNonTransferableLicenseLegalBenchClassification\",\n",
-                "    \"CUADNoticePeriodToTerminateRenewalLegalBenchClassification\",\n",
-                "    \"CUADPostTerminationServicesLegalBenchClassification\",\n",
-                "    \"CUADPriceRestrictionsLegalBenchClassification\",\n",
-                "    \"CUADRenewalTermLegalBenchClassification\",\n",
-                "    \"CUADRevenueProfitSharingLegalBenchClassification\",\n",
-                "    \"CUADRofrRofoRofnLegalBenchClassification\",\n",
-                "    \"CUADSourceCodeEscrowLegalBenchClassification\",\n",
-                "    \"CUADTerminationForConvenienceLegalBenchClassification\",\n",
-                "    \"CUADThirdPartyBeneficiaryLegalBenchClassification\",\n",
-                "    \"CUADUncappedLiabilityLegalBenchClassification\",\n",
-                "    \"CUADUnlimitedAllYouCanEatLicenseLegalBenchClassification\",\n",
-                "    \"CUADVolumeRestrictionLegalBenchClassification\",\n",
-                "    \"CUADWarrantyDurationLegalBenchClassification\",\n",
-                "    \"DefinitionClassificationLegalBenchClassification\",\n",
-                "    \"Diversity1LegalBenchClassification\",\n",
-                "    \"Diversity2LegalBenchClassification\",\n",
-                "    \"Diversity3LegalBenchClassification\",\n",
-                "    \"Diversity4LegalBenchClassification\",\n",
-                "    \"Diversity5LegalBenchClassification\",\n",
-                "    \"Diversity6LegalBenchClassification\",\n",
-                "    \"FunctionOfDecisionSectionLegalBenchClassification\",\n",
-                "    \"InsurancePolicyInterpretationLegalBenchClassification\",\n",
-                "    \"InternationalCitizenshipQuestionsLegalBenchClassification\",\n",
-                "    \"JCrewBlockerLegalBenchClassification\",\n",
-                "    \"LearnedHandsBenefitsLegalBenchClassification\",\n",
-                "    \"LearnedHandsBusinessLegalBenchClassification\",\n",
-                "    \"LearnedHandsConsumerLegalBenchClassification\",\n",
-                "    \"LearnedHandsCourtsLegalBenchClassification\",\n",
-                "    \"LearnedHandsCrimeLegalBenchClassification\",\n",
-                "    \"LearnedHandsDivorceLegalBenchClassification\",\n",
-                "    \"LearnedHandsDomesticViolenceLegalBenchClassification\",\n",
-                "    \"LearnedHandsEducationLegalBenchClassification\",\n",
-                "    \"LearnedHandsEmploymentLegalBenchClassification\",\n",
-                "    \"LearnedHandsEstatesLegalBenchClassification\",\n",
-                "    \"LearnedHandsFamilyLegalBenchClassification\",\n",
-                "    \"LearnedHandsHealthLegalBenchClassification\",\n",
-                "    \"LearnedHandsHousingLegalBenchClassification\",\n",
-                "    \"LearnedHandsImmigrationLegalBenchClassification\",\n",
-                "    \"LearnedHandsTortsLegalBenchClassification\",\n",
-                "    \"LearnedHandsTrafficLegalBenchClassification\",\n",
-                "    \"LegalReasoningCausalityLegalBenchClassification\",\n",
-                "    \"MAUDLegalBenchClassification\",\n",
-                "    \"NYSJudicialEthicsLegalBenchClassification\",\n",
-                "    \"OPP115DataRetentionLegalBenchClassification\",\n",
-                "    \"OPP115DataSecurityLegalBenchClassification\",\n",
-                "    \"OPP115DoNotTrackLegalBenchClassification\",\n",
-                "    \"OPP115FirstPartyCollectionUseLegalBenchClassification\",\n",
-                "    \"OPP115InternationalAndSpecificAudiencesLegalBenchClassification\",\n",
-                "    \"OPP115PolicyChangeLegalBenchClassification\",\n",
-                "    \"OPP115ThirdPartySharingCollectionLegalBenchClassification\",\n",
-                "    \"OPP115UserAccessEditAndDeletionLegalBenchClassification\",\n",
-                "    \"OPP115UserChoiceControlLegalBenchClassification\",\n",
-                "    \"OralArgumentQuestionPurposeLegalBenchClassification\",\n",
-                "    \"OverrulingLegalBenchClassification\",\n",
-                "    \"PersonalJurisdictionLegalBenchClassification\",\n",
-                "    \"PROALegalBenchClassification\",\n",
-                "    \"SCDBPAccountabilityLegalBenchClassification\",\n",
-                "    \"SCDBPAuditsLegalBenchClassification\",\n",
-                "    \"SCDBPCertificationLegalBenchClassification\",\n",
-                "    \"SCDBPTrainingLegalBenchClassification\",\n",
-                "    \"SCDBPVerificationLegalBenchClassification\",\n",
-                "    \"SCDDAccountabilityLegalBenchClassification\",\n",
-                "    \"SCDDAuditsLegalBenchClassification\",\n",
-                "    \"SCDDCertificationLegalBenchClassification\",\n",
-                "    \"SCDDTrainingLegalBenchClassification\",\n",
-                "    \"SCDDVerificationLegalBenchClassification\",\n",
-                "    \"TelemarketingSalesRuleLegalBenchClassification\",\n",
-                "    \"TextualismToolDictionariesLegalBenchClassification\",\n",
-                "    \"TextualismToolPlainLegalBenchClassification\",\n",
-                "    \"UCCVCommonLawLegalBenchClassification\",\n",
-                "    \"UnfairTOSLegalBenchClassification\",\n",
-                "]\n",
-                "# ^ might be worth creating a benchmark for these tasks\n",
-                "\n",
-                "print(f\"Number of tasks before: {len(mult_tasks)}\")\n",
-                "mult_tasks = [t for t in mult_tasks if t.metadata.name not in legal_bench_tasks]\n",
-                "print(f\"Number of tasks after: {len(mult_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of tasks before: 351\n",
-                        "Number of tasks after: 343\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# remove code tasks\n",
-                "from mteb.languages import PROGRAMMING_LANGS\n",
-                "\n",
-                "prog_langs = set(PROGRAMMING_LANGS)\n",
-                "\n",
-                "code_tasks = [\n",
-                "    t.metadata.name for t in mult_tasks if set(t.metadata.languages) & prog_langs\n",
-                "]\n",
-                "\n",
-                "print(f\"Number of tasks before: {len(mult_tasks)}\")\n",
-                "mult_tasks = [t for t in mult_tasks if t.metadata.name not in code_tasks]\n",
-                "print(f\"Number of tasks after: {len(mult_tasks)}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Iterative Automated Task Selection "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "[BibleNLPBitextMining(name='BibleNLPBitextMining', languages=['aai', 'aak', 'aau', '...']),\n",
-                            " FloresBitextMining(name='FloresBitextMining', languages=['ace', 'acm', 'acq', '...']),\n",
-                            " IN22ConvBitextMining(name='IN22ConvBitextMining', languages=['asm', 'ben', 'brx', '...']),\n",
-                            " IN22GenBitextMining(name='IN22GenBitextMining', languages=['asm', 'ben', 'brx', '...']),\n",
-                            " IndicGenBenchFloresBitextMining(name='IndicGenBenchFloresBitextMining', languages=['asm', 'awa', 'ben', '...']),\n",
-                            " IWSLT2017BitextMining(name='IWSLT2017BitextMining', languages=['ara', 'cmn', 'deu', '...']),\n",
-                            " NTREXBitextMining(name='NTREXBitextMining', languages=['afr', 'amh', 'arb', '...']),\n",
-                            " NusaTranslationBitextMining(name='NusaTranslationBitextMining', languages=['abs', 'bbc', 'bew', '...']),\n",
-                            " NusaXBitextMining(name='NusaXBitextMining', languages=['ace', 'ban', 'bbc', '...']),\n",
-                            " TatoebaBitextMining(name='Tatoeba', languages=['afr', 'amh', 'ang', '...']),\n",
-                            " AfriSentiClassification(name='AfriSentiClassification', languages=['amh', 'arq', 'ary', '...']),\n",
-                            " AfriSentiLangClassification(name='AfriSentiLangClassification', languages=['amh', 'arq', 'ary', '...']),\n",
-                            " AmazonReviewsClassification(name='AmazonReviewsClassification', languages=['cmn', 'deu', 'eng', '...']),\n",
-                            " CyrillicTurkicLangClassification(name='CyrillicTurkicLangClassification', languages=['bak', 'chv', 'kaz', '...']),\n",
-                            " IndicLangClassification(name='IndicLangClassification', languages=['asm', 'ben', 'brx', '...']),\n",
-                            " MasakhaNEWSClassification(name='MasakhaNEWSClassification', languages=['amh', 'eng', 'fra', '...']),\n",
-                            " MassiveIntentClassification(name='MassiveIntentClassification', languages=['afr', 'amh', 'ara', '...']),\n",
-                            " MassiveScenarioClassification(name='MassiveScenarioClassification', languages=['afr', 'amh', 'ara', '...']),\n",
-                            " MTOPDomainClassification(name='MTOPDomainClassification', languages=['deu', 'eng', 'fra', '...']),\n",
-                            " MTOPIntentClassification(name='MTOPIntentClassification', languages=['deu', 'eng', 'fra', '...']),\n",
-                            " MultiHateClassification(name='MultiHateClassification', languages=['ara', 'cmn', 'deu', '...']),\n",
-                            " NordicLangClassification(name='NordicLangClassification', languages=['dan', 'fao', 'isl', '...']),\n",
-                            " NusaParagraphEmotionClassification(name='NusaParagraphEmotionClassification', languages=['bbc', 'bew', 'bug', '...']),\n",
-                            " NusaParagraphTopicClassification(name='NusaParagraphTopicClassification', languages=['bbc', 'bew', 'bug', '...']),\n",
-                            " NusaXSentiClassification(name='NusaX-senti', languages=['ace', 'ban', 'bbc', '...']),\n",
-                            " TweetSentimentClassification(name='TweetSentimentClassification', languages=['ara', 'deu', 'eng', '...']),\n",
-                            " MasakhaNEWSClusteringP2P(name='MasakhaNEWSClusteringP2P', languages=['amh', 'eng', 'fra', '...']),\n",
-                            " MasakhaNEWSClusteringS2S(name='MasakhaNEWSClusteringS2S', languages=['amh', 'eng', 'fra', '...']),\n",
-                            " SIB200ClusteringFast(name='SIB200ClusteringS2S', languages=['ace', 'acm', 'acq', '...']),\n",
-                            " WikiClusteringFastP2P(name='WikiClusteringP2P.v2', languages=['bos', 'cat', 'ces', '...']),\n",
-                            " BelebeleRetrieval(name='BelebeleRetrieval', languages=['acm', 'afr', 'als', '...']),\n",
-                            " MintakaRetrieval(name='MintakaRetrieval', languages=['ara', 'deu', 'fra', '...']),\n",
-                            " MLQARetrieval(name='MLQARetrieval', languages=['ara', 'deu', 'eng', '...']),\n",
-                            " MultiLongDocRetrieval(name='MultiLongDocRetrieval', languages=['ara', 'cmn', 'deu', '...']),\n",
-                            " WikipediaRetrievalMultilingual(name='WikipediaRetrievalMultilingual', languages=['ben', 'bul', 'ces', '...']),\n",
-                            " XPQARetrieval(name='XPQARetrieval', languages=['ara', 'cmn', 'deu', '...']),\n",
-                            " XQuADRetrieval(name='XQuADRetrieval', languages=['arb', 'deu', 'ell', '...']),\n",
-                            " MultiEURLEXMultilabelClassification(name='MultiEURLEXMultilabelClassification', languages=['bul', 'ces', 'dan', '...']),\n",
-                            " OpusparcusPC(name='OpusparcusPC', languages=['deu', 'eng', 'fin', '...']),\n",
-                            " PawsXPairClassification(name='PawsXPairClassification', languages=['cmn', 'deu', 'eng', '...']),\n",
-                            " XNLI(name='XNLI', languages=['ara', 'bul', 'deu', '...']),\n",
-                            " WikipediaRerankingMultilingual(name='WikipediaRerankingMultilingual', languages=['ben', 'bul', 'ces', '...']),\n",
-                            " IndicCrosslingualSTS(name='IndicCrosslingualSTS', languages=['asm', 'ben', 'eng', '...']),\n",
-                            " SemRel24STS(name='SemRel24STS', languages=['afr', 'amh', 'arb', '...']),\n",
-                            " STS17Crosslingual(name='STS17', languages=['ara', 'deu', 'eng', '...']),\n",
-                            " STS22CrosslingualSTSv2(name='STS22.v2', languages=['ara', 'cmn', 'deu', '...'])]"
-                        ]
-                    },
-                    "execution_count": 17,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# tasks with more than N eu languages\n",
-                "\n",
-                "tasks_with_many_languages = [\n",
-                "    t for t in mult_tasks if len(set(t.metadata.languages)) > 5\n",
-                "]\n",
-                "tasks_with_many_languages"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# tasks which should be kept, e.g. due to them being known high quality datasets, unique tasks, etc.\n",
-                "tasks_to_keep = [\n",
-                "    # dataset with good coverage of languages and of reasonable quality\n",
-                "    \"WikipediaRerankingMultilingual\",\n",
-                "    \"MultiEURLEXMultilabelClassification\",\n",
-                "    \"BibleNLPBitextMining\",\n",
-                "    \"SIB200ClusteringS2S\",\n",
-                "    \"WikipediaRetrievalMultilingual\",\n",
-                "    \"MasakhaNEWSClassification\",\n",
-                "]\n",
-                "\n",
-                "\n",
-                "def is_candidate_valid_removal(current_tasks: list[str], task_to_remove: str) -> bool:\n",
-                "    \"\"\"Determine if target task should be removed.\n",
-                "    This checks that all task types are present in the current tasks or whether the task is in the tasks_to_keep list.\n",
-                "    This is all conducted within language.\n",
-                "    \"\"\"\n",
-                "    if task_to_remove in tasks_to_keep:\n",
-                "        return False\n",
-                "\n",
-                "    # check if removing task removes a unique task type - if so, don't remove\n",
-                "    _current_tasks = current_tasks.copy()\n",
-                "    if task_to_remove in _current_tasks:\n",
-                "        _current_tasks.remove(task_to_remove)\n",
-                "    task = mteb.get_task(task_to_remove)\n",
-                "    ctasks = mteb.get_tasks(tasks=_current_tasks)\n",
-                "\n",
-                "    # don't remove a unique task type\n",
-                "    task_types = {t.metadata.type for t in ctasks}\n",
-                "    if task.metadata.type not in task_types:\n",
-                "        return False\n",
-                "\n",
-                "    # check that removing the task does not remove a unique task type within the language\n",
-                "    _languages_covered_by_task_type = [\n",
-                "        t.metadata.languages for t in ctasks if t.metadata.type == task.metadata.type\n",
-                "    ]\n",
-                "    languages_covered_by_task_type = {\n",
-                "        lang for sublist in _languages_covered_by_task_type for lang in sublist\n",
-                "    }\n",
-                "\n",
-                "    if not set(task.metadata.languages).issubset(languages_covered_by_task_type):\n",
-                "        return False\n",
-                "\n",
-                "    return True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Task: AFQMC: 100%|██████████| 343/343 [00:06<00:00, 56.82it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 342/342 [00:05<00:00, 64.28it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 341/341 [00:06<00:00, 56.49it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 340/340 [00:05<00:00, 66.81it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 339/339 [00:05<00:00, 64.88it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 338/338 [00:05<00:00, 66.39it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 337/337 [00:04<00:00, 68.22it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 336/336 [00:05<00:00, 67.12it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 335/335 [00:05<00:00, 66.83it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 334/334 [00:05<00:00, 57.06it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 333/333 [00:04<00:00, 68.36it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 332/332 [00:04<00:00, 68.10it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 331/331 [00:04<00:00, 66.25it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 330/330 [00:04<00:00, 68.03it/s]                                    \n",
-                        "Task: AFQMC: 100%|██████████| 329/329 [00:04<00:00, 68.10it/s]                                    \n",
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-                        "Task: STSB: 100%|██████████| 144/144 [00:02<00:00, 69.16it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 143/143 [00:01<00:00, 72.87it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 142/142 [00:01<00:00, 72.11it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 141/141 [00:02<00:00, 69.79it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 140/140 [00:01<00:00, 70.98it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 139/139 [00:01<00:00, 69.84it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 138/138 [00:02<00:00, 68.07it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 137/137 [00:02<00:00, 67.21it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 136/136 [00:01<00:00, 69.94it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 135/135 [00:01<00:00, 72.47it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 134/134 [00:02<00:00, 66.28it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 133/133 [00:01<00:00, 72.88it/s]                                     \n",
-                        "Task: STSB: 100%|██████████| 132/132 [00:01<00:00, 71.74it/s]                                    \n",
-                        "Task: STSB: 100%|██████████| 131/131 [00:02<00:00, 65.15it/s]                                    \n"
-                    ]
-                }
-            ],
-            "source": [
-                "from sklearn.linear_model import LinearRegression\n",
-                "\n",
-                "# remove tasks one by one\n",
-                "tasks_to_select_from = [t.metadata.name for t in mult_tasks]\n",
-                "\n",
-                "tasks_removed = []\n",
-                "predicability_scores = []\n",
-                "\n",
-                "while tasks_to_select_from:\n",
-                "    most_pred_tasks = task_selection.most_predictable_task(\n",
-                "        results_df[tasks_to_select_from],\n",
-                "        sklearn_estimator=LinearRegression(),\n",
-                "        metrics=[\n",
-                "            task_selection.spearman,\n",
-                "            task_selection.pearson,\n",
-                "            task_selection.mse_with_zscore,\n",
-                "        ],\n",
-                "    )\n",
-                "\n",
-                "    # reverse the list to get the least predictable task\n",
-                "    most_pred_tasks.reverse()\n",
-                "\n",
-                "    while most_pred_tasks:\n",
-                "        most_pred_task = most_pred_tasks.pop()\n",
-                "        most_pred_task_name = list(most_pred_task.keys())[0]\n",
-                "\n",
-                "        # if the task is too hard to predict, skip it (this essentially stops the loop)\n",
-                "        if (\n",
-                "            most_pred_task[most_pred_task_name][\"mse_with_zscore\"] > 0.5\n",
-                "            or most_pred_task[most_pred_task_name][\"spearman\"] < 0.8\n",
-                "        ):\n",
-                "            continue\n",
-                "\n",
-                "        if is_candidate_valid_removal(tasks_to_select_from, most_pred_task_name):\n",
-                "            tasks_to_select_from.remove(most_pred_task_name)\n",
-                "            tasks_removed.append(most_pred_task_name)\n",
-                "            predicability_scores.append(most_pred_task[most_pred_task_name])\n",
-                "            break\n",
-                "\n",
-                "    if not most_pred_tasks:  # if no task was removed, then we are done -- can be replaced with another stopping criterion\n",
-                "        break"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 20,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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162HJkiXw6NEjOHv2LBQqVAjmzp0LhQsXhqCgIFnHfPfuHaSkpEChQoWEbXFxcTBr1ixISUmB4ODgfySJmkcGsBYY1KtXD86ePQtpaWlQoUIFqFmzJtSqVQuqVq2q155RkZ6eDjdv3oRChQqBg4NDFn5dJtnlu89qbFBubJhVD0xJSYFp06bpvEZSMZPsQMqP/E/zb4trp6SkwLBhw0T2/YIFC7T8fYQ2WZ3b/02+1cePH8OhQ4fg58+fULNmTfD29ta7PyJCQkIC/Pz5E0qUKCFZ7KlUKv9We+Pdu3fw+PFjUCgU4ObmBo6OjpL7/VPySB1eHSan4ivq/JvuR+Kfge4BIruhlk3EL8OQIUMgNTUVFixYAACZRpKfnx/ExcWBpaUljBgxAg4dOqQVvDEUuCpQoAB06dIFwsLCQKlUQkZGBkyZMgWWLFkCr1+/Fpy948aNAzc3N+jWrZvWMQwFNnmCLL6+vqBQKCTbrQOA8J5KudPlHJGiadOmzPuqI0ex40nOkuLIkSOwcuVK2LFjB1haWnJV1Ur9zn/S+RkRESFKFFHHzs4OPn/+DBERETB9+nSt93VVkH379g1mzpwp2zmcE50fs3ovGnqOmjdvznz8NWvWMO9ra2sLDg4OzElLrEF/VZAiISHhXxkgMnQPTJw4UXaym6HECfXjDho0CNasWQONGzcGb29vpuugOl+aPHv2DOzs7LLten79+hWioqJg1apVcPbsWShfvjwMGTIky0lu06dPBzc3N2jTpg0AALRu3Rq2b98O+fLlg7/++gt8fHwAAMDb2xsePnxo0MG9Z88eeP/+PXTp0kXYNmXKFAgPD4e0tDSoXbs2REVFCQ5WOc+qoQCxpiwKCgqC5s2bQ+/evSEpKQkqVaoEJiYm8O7dO4iIiIA+ffoI+44ePRquX78OsbGx0LBhQ2F73bp1YcKECToTdLO7e15aWhqkp6eLkqNev34NS5YsgZSUFGjatClUq1YNAABGjhwJ3t7ecOnSJUhKSoLBgwdD1apVITY2Vud8tXTpUjh06BCYmprCoEGDoFKlSnD06FEYOnQoxMfHa425S5cukJiYCOPGjZMMJOri3xDsk5N4bwg/Pz/BYbNnzx4IDg6GDh06QJs2beDq1avQrVs3sLGxgWbNmkl+Xl+C+dWrV7mvZ1ZqJB88eABz586FO3fuAACAl5cXDBo0CIoUKcKdcMUjj4KDg7nGqR4sN0RERAQAyA8mZvc94+DgINwvus6p+nlU/VaFQgHjxo0T6V3p6elw/vx58PX11fl92dmVi9fmyQkdRn0srERERHDpatHR0QCQeX7j4uKgWLFiWoHDr1+/QkJCAnh7e2dbBzUViAgHDhyAlStXwrZt24Ttcu7FjIwMyfFlZGTAs2fPwNXVFZYtWwa7d++W1IFtbW1h/vz58PTpUzhy5Ajzb4iOjs5SkiaPvjZ58mSYMmUKjBw5Uuf3zJkzh2ncCoUiS8kEQ4cOhdDQUJg6dSpzlw13d3e4ePGiViAjKSkJypYtCw8fPoQpU6bAhAkToG7dumBhYQHz5s2DN2/e6H2G27dvDz179oSOHTvCq1evoF69elCyZEnYuHEjvHr1Sks/0vVMqSc2dOvWjemaAsibT+UQExMj6DDdu3cHd3d3uHv3LowaNQr27NkDDRo0AAB5yaUtWrSAcePGQWBgIOzatQuaN28OgYGBULVqVYiPj4eaNWtCdHQ0BAYGAgDA2bNn4f3798L/AJmra4SFhQmJFgsWLNBKdr948SJs2rQJ4uPjwdTUFEqUKAEdO3bUsoNU6NMzr127xvw7VfMjK3KeabnzQHBwsKTvSd3nVK1aNdi5cyc4ODjoDLqr7l9XV1et854THWBZCx7k6mr379+H0NBQOHPmjGi75nn/+PEj9O3bV3g2Ro0aBf3794cJEybArFmzoHTp0rB69Wrm771x4waUL18efv78CQBscheA34f48uVLCAoKgsuXL4NCoYD27dvDn3/+KSSCfvjwAfz9/bPVN3Hs2DFYtWoVREdHg52dnaCvjxo1CtasWQPTpk2DBg0aCEXrr1+/hoMHD8K4cePg58+fIl+ZIdvx2LFj2TLmnLapDB1/6dKlMGHCBOG69OrVCypVqgTu7u4AAPDjxw+IiYkRfW7JkiWwZs0a6Nixo97vzm4/8o8fP+DBgweS7+mTpcuWLYNevXoJ/x84cABWr14N69atA09PTyFBe8WKFczjlYLlvFy8eBHq168PGRkZkJqaCi4uLrBz504oWbIkAAB8+fIFJk6cKMitrCQhnTt3Dtq3bw9PnjyRlL//5sTx7IbXt8aia+rTM1me69atW0ONGjWgf//+8O3bNyhfvjw8fvwYEBE2b94MLVq0YBqvOqmpqTBmzBiIjo6GXLlyQe/evSE0NFR4X7Mg+O/QqeUkrvLqDTwsXrwYxo8fD4MHD4YpU6YI58Le3h7mzp0rStDlKWYfMGAA5M+fH2bPng0AmV0hq1evDvnz54ciRYpAly5dID09XVI+5NSKHTwywFCBgTqHDh2CtLQ0OH/+PJw4cQKOHz8O8+fPhx8/fkCFChXg1KlTov0HDx4MpUqVgm7dukF6ejrUrFkTzpw5A5aWlrB3716oVauW1new2L0qdPnuv3z5wtQQSTM2KJWgy/JM88aGWfVAFd27d4fjx49Dx44duXy36uhKjub1I1+/fh327NkDuXLlgtatWwsFkACZOvLgwYNh1apV2aoHqI+dVyfVBU+RnCGbbf369RASEgIWFhYQGRkJPXv2NFiwxBo3ze5iiqdPn0JYWJiW/+HNmzdw69YtKFeuHNjZ2cHr169h7dq1kJGRAY0bN4ZSpUppHeufWjkgq75VfciJxx07dgwCAwPh27dvAABgbGwMq1atgg4dOkh+7tGjR9C0aVO4ffs2AGTKh+3bt0P58uVF+8mxN+TcL3FxcdCnTx84ffq06P2aNWvC4sWLoUSJEqLtcuQRIsLly5eFBODChQtLnuvDhw9D3bp1dR4nIyMDpk6dKhTaGUKlw2QlvmKoAEMVU5R7P+bUajYsfmc5cvr69eswe/ZsOHXqFLx8+RKUSiW4u7tDcHAwDB8+nKnA21CxjtyGQqxynVXPSE5OBmtra61909PTISUlRfitWZFJcvxfxK8DddAlfhm8vb1h6tSpwmSzevVqGDp0KFy9ehVcXV0hNDQU3rx5A/v27RN9bt26dTBmzBjo0qULVKxYEQAyO9usXbsWxo4dC2/fvoVZs2bB8OHD4ffff4dJkybB2rVrYdKkSdCjRw+4desWuLu7Q1RUFMydOxfOnj0rOj5LBx+eiiqeKqZChQoxB6tVho+c4E27du30KnaDBg3S+mz9+vVFyVkeHh46k7NUPH36FFavXg2rV6+GxMREaNu2LXTs2BHq1KnDPNFJGXi8zk9DCcl2dnZcxoC3tzcsWbJESOzS5MyZM9CjRw+Ii4vTeo+12vivv/4SHH2hoaHg4eEh7Pvx40do0aKFEMCV2/nRUED5jz/+YDonchIEPnz4AF27dmU6PgDA2rVruQKbqu4rLHTu3JlpP1WlKU/gvGbNmlzH1jyPup5v9aB/ly5doGrVqtna/VOT4sWLQ6NGjZgSJ3Lnzg3r1q2DRo0aGTyuyji8fv06lCxZUlTBmp6eDo8ePYKGDRtC48aNmccqdT3PnTsHK1asgK1bt4KrqyvcuXMHjh07BtWrVweATAcpK2FhYVrbChcuDBs3boQqVarAoUOHoHXr1hAVFQVbtmyBxMREOHjwIABkBqhGjx4N4eHhUK5cOaFbjgqVoeHv7w8tW7aEfv36AUCmTKlevTpMmjQJPD09YcyYMRAQECAkCPDOGwCZ803fvn0NBohV5M6dG44fPw4lS5aEFStWwIIFC+Dq1auwfft2GD9+vJCYqDp2VFQUVK5cGWxsbOD69evg7u4OCQkJULZsWUljm0Vm8DpuunbtCqamprB06VIAAPj8+TOULFkSvn//Ds7OznD79m3YtWsXNGrUCPLmzQuHDx8WnGCICH379oW//voLjh07BlZWVqJnaNq0aTB+/HgoXbo03L17FxARxowZAwsWLIBBgwZBr169tIxeGxsbOHnyJJfjinW+8/f3N3huFAqFKFGMxwDv37+/kHgvpTdIBZwMzb2PHz+GV69eQZ48eaB69epQrVo10bwzdepU2LNnj5aeBmA4wfz58+dc1xNAd+WtIWJiYqBp06bg6+sLVatWBQCA06dPC450fSs7aFKzZs0syyN9+Pv7M+2nUCiEuU5u93Wee4ZF5wkLC4OqVauCsbExHD9+XO9YatasKfzW48ePg5+fn+g6mJqagpubGwwbNkyycy1PtzKWgMmFCxe4bJ6s6DCGnIKsrgbVPcCjq6mSltasWQMLFy6E8+fPCx2yVaSlpUHlypVh8ODB0KFDh2xJRn706BGsWrUK1qxZA2/fvoW6deuKOn/y3IvJycnQvXt32LNnD9ja2kKvXr0gLCxM+B3q+lTFihVh3Lhx0KRJE8lx7d27FyZNmiQkgrCwevVqg/e3Opp6Jo++ZmtrC9euXRMSg7IDudfTysoKbt68yTUWpVIpzCHqvH79GlxdXeHHjx9QrFgxGDZsmJAsdPjwYWjcuDF8+/ZNp+7k4OAA586dgxIlSsD8+fMhKioKTp8+DQcPHoTevXtr2fb+/v5w5coVSE9PF4Ip8fHxYGRkBB4eHnDv3j1IS0uDhQsXgpubm8HfxWo7aMKTKLJy5Uro0aMH5MqVCz5+/AiOjo4QEREBAwYMgDZt2sCgQYOEjnJyOslYW1vDzZs3oXDhwlC5cmVo1qyZSN9cuHAhrFq1SgiyBQQEQK1atYR9bt68CWXLloUuXbqAp6cnzJw5E3r16gUTJkwQjjFixAiYNWsWWFtbC/fNgwcP4Nu3b0IC5Pfv3+Hs2bPg7+9vUM9UFdMZQn1+BGCfv1hRXX+588CRI0dgzJgxMGXKFJGvbNy4cTB27Fiws7MTkgJXrlxp8PqamJhAmzZtYOnSpWBubs7cAVYFSwJ7UFAQrF+/Hnbv3q2zoKFu3brQrFkzGDBggCxdTaU/jBo1SnIeUF3/Xr16wYEDB6BVq1YQExMDt2/fhgYNGoBSqYSxY8dC5cqVub5X08ZnlbtPnjxh/o5ChQpB586d4d69e7Bw4UJISkqCUaNGgUKhgIMHD4KDgwO8fv0anJ2dBZ1gyJAhEB4eDlZWVgYLd9QT0p8/fw5r1qyB1atXQ1JSEnz8+BEiIyOhdevWwjnNly8frF27Vkjy1yQmJgY6deoEr1+/Fv0GXtuRFxabKis6CcvxNect9d8KIO2zcXR0hAsXLkCRIkX0jkeOP0AfuvxThmSpQqGA2NhYwQbr06cPvH37Viicio2Nha5du8KjR4+0vpOnoIrlvNSrVw8KFiwIK1asgJSUFBg5ciRs2bIFDh06BGXKlNE63yp5KCcJydfXF4oXLw4TJ06UlDF2dnai/w3Z4aVLl+bye2T3qiqa15/n+DxdUXltWU1YfSX58uWDmJgY8PHxgcjISAgLC4Pr16/D2rVrYdmyZXD16lWm71M/LxMmTIAlS5bAsGHDICkpCRYuXCjMlwCgJXflwFtY2a5dO63EVQ8PDyFxdf/+/bBy5UpR4iqv3qDrXtD0Ufv7+4OXlxdMnToVgoODRfLu1q1bUKtWLXj37p3w+YCAAEhMTIT+/ftLPkPq91XhwoVhzZo1gs40a9YsWLJkCdy9exeMjY1h1qxZsG3bNjh37pzoGKwrdvAmXwPwyQBnZ2eYMWOGwcILTeLj4+HYsWNw+PBh2LlzJ9jZ2YnOIUBm4tnOnTuhfPnysHPnTujXrx8cO3YM1q9fD0ePHhUlhPHYvap7cd68edCjRw/JZBgjIyOthDMA/bFBExMT0b6szzRvbJhVD1Rhb28P+/btE/x7LLAkR/P6kQ8ePAhNmjSBYsWKwefPnyElJQW2bt0q+LrUr1FW9AB9Y1+xYgXzyifqxQEqeJOjDT2ndnZ2MGPGDGjVqhUAAFy+fBkqV64M3759k+yKCsAXN83q6pOaSOlTsbGxEBgYCF+/foW8efPCgQMHIDAwECwsLECpVMLjx49h9+7dooZY2bGigi7djtWHKMe3aujYPL4Plc5QrVo1yJ07NyxevBjMzc1h7NixsGPHDnjx4oXk51q2bAlxcXEwfvx4MDc3h1mzZsH379/h8uXLzN+tC9775dWrV+Dt7Q1OTk7Qu3dv8PDwAESE27dvw/Lly+H9+/dw69YtkY+LVx4dO3YMunXrJpKjqiTdVatWiYoSTU1NoWfPnjBjxgwt/+GtW7egc+fO8OrVK3j+/DnTd6uQG19RfdaQfyJfvnyQmJjIfT+y+jJY9S91O5nF78wrp2NiYqBZs2bQqFEjsLCwgOjoaAgNDQUrKyvYvn07ICKcOnUK8uXLJ/o8b7EOjw4GwC7XefSMHTt2wMiRI+HatWta92JKSgqULVsWZs2aBU2aNMlSvIfX/0X8WlCCLvHLYGtrC1euXIGiRYsCQGbSqI2NDSxbtgwAMiviGjVqpKVc1alTB3r16gWtW7cWbd+yZQssXboUjhw5AuvXr4cpU6bA3bt3oWjRorB06VKoU6eOyCFw9+5d8PPzg48fP4qOwxLYzErgNLuRE7yRY2iyJmelpqbCzp07YcWKFXDy5Elo2LAhtG/fHtq1awfXr1/X2dGGB17np6GE5KSkJObvDgsLAysrK7hz547OLnyJiYng6ekp2UZfl4J69OhRaNOmDbx9+xYiIyOhU6dO0LBhQ/j06RNcunQJVqxYASEhIQCg7Qxq1qwZpKamwpo1a4ROgbdv3xY6BepKzmQJKCsUCjh16hT3deNJEGDh3/DM5eQyb7qOPWfOHJgyZQoEBASInE4HDhyA3377DR49egTr168HT09PcHFx4boHnj17Brt374bExEShq48Kzc5QPIkT+fPnh9jYWChevLjBfVWJaBMnToShQ4eKluNUKdQtWrTgSnBTZ/bs2bBq1Sr49OkTtGvXDjp06AA+Pj5gYmKSbfIIAMDCwgLi4+OhYMGCMGjQIPj+/TssXboU4uPjoVKlSsI8o26IqcshTQMmT548EBMTA2XKlAGATMPw9u3bcODAAQDITAYYNGgQ3L9/X/aYeRNzLC0t4e7du+Dq6gqtW7eGkiVLQlhYGDx9+hRKlCghWurV0tJSKIZRn3evX78ONWrU0FruFIBNZvAmLhYvXhwWLlwoOLUWLVoEU6dOhdu3b4OdnR2MHDkSLly4AMeOHQNbW1s4f/681hKH/fv3h127dkFkZCTUqlVLuEYlSpSA33//HTp37gwnT56EmjVrQqNGjSAqKkor8VqFl5cXbNy4UbiuLLDOd7/99pvOY3z+/BkiIyPhx48fIhnAY4DzJN6rMDT3/vbbb0KQOm/evPDXX39BuXLlhPfv3bsHlStX1tLTAAwnmPNeT4DM5zMgIMBgAZGqS6iKMmXKQIMGDWDatGmi7aNGjYKDBw8yBUH/F2G9Z3h1Hl66du0K8+bN41o2nDVoxhowsbKykmXzyEFOMn12U716dejXrx+0bdtW8v0tW7bAwoUL4cSJE7KT0H78+AHbtm2DlStXwqlTpyA9PR1mzZoF3bp107rWPPJr0KBBcODAAZgyZQokJSXB5MmTwdvbG6Kjo8HU1FQUbHdwcIDr16/rtQd8fHwkZVhOwaOvdevWDSpUqAC9e/fOtu+Xez2bN28Obdu21bLvpVB1nQgODoa1a9eK7L709HQ4cuQIHDp0CO7duwdmZmaQkJAgWkrb3NwcEhISoECBApLHt7a2hlu3boGbmxs0bdoUqlatCiNHjoTExEQoUaKE0KlFxdy5c+HkyZOwevVq4d779OkTdO/eHapVqwY9evSA9u3bw7dv37Q6I7Lw9etXSV1ds3sfT6JI6dKloWPHjjB8+HDYvn07tGrVCipXrgxbtmzROi9ybDB7e3s4ceIElC5dGvLmzQuHDh0SjffBgwdQunRpwVZ2dnaGPXv2CF1sxowZA8ePHxe6gm3duhXCwsKErjdr166F3r17C4m7quB+amoqLF68WOjguXjxYqhTpw6MHTs2221TgJyfv+Tg7e0Ny5YtgypVqoi2nz59Gnr27AlxcXFw+PBhCA0NhcTERNi1axeMHDkShg8fLrI1Z8+eDWFhYZCWlgajRo2CNm3awKxZs7gL/Fj9DS4uLjB16lSDBQ+XLl2SpatZWVnB5cuXRQnUUri6usKaNWugdu3a8PjxY3B3d4dRo0bB1KlTmX6vJpo2fk7IXQAAFxcX2LFjh3ANf/z4Aa1atRK6uKemporuRX9/f9ixYwfY29sbLN46duwYbN++HVauXAknTpyAgIAA6NChAwQEBICVlZWWTW1lZQXnzp2T7PwFkNm1pkqVKvDlyxdhmxzbkVU2qmCxqbJSIMVyfDkJuiNHjgRra2sYN24c89iyA13+KUOy1NLSEu7cuSMk6fj4+EC3bt2ErmG65lIAPh2W5bzkypULzp07J/JLTZs2DWbMmAExMTHg6uoqOt+8ifHqqJ4Fla5vCEN2OE9X97CwsGwv8NS8/jlZQGqIZ8+ewaRJkwS7SR1WX4m6v65Tp06QP39+mDZtGiQmJoKXl5dIHulD/bwUK1YM5syZI3T/T0hIgICAAKhWrRqsWrUK3rx5k2UdgLe4Vk7iKq/eMHr0aFi8eDGUKlVKmHMuXrwIN27cgC5dusDt27fhyJEjEB0dDW3btoW7d+9CoUKFRPLu/v37ULp0aZEc4Clmt7CwEI4LANCoUSPw9vaGGTNmAECmnuHn5wfv378XfY5VF5STfM0jA1gLLwAyu5LHxsbC8ePH4cePH1C9enWoVasW1KpVSzKRX93O6dmzJ1haWsLcuXPh0aNH4OPjIyp44bF7eZNh5MYGWZ9p3tgwqx6oonDhwvDXX39p+RP1wZIczetHrlKlCvj7+8OUKVMAEWHmzJkQHh4OW7duhYYNG2abrcGT2M0Lb3K0oefUxMQEnjx5Avnz5xe2qccrpJAbN2XBUEfMhw8fwtChQ0XHrl69Ovj4+MC0adNgyZIlMHfuXAgODoaFCxcCAMDw4cPhzJkzovMuN7FfHV26Hav+Jce3mhP+SXt7ezhz5owgR75+/Qq2trbw+vVrrdWVADKLdLZt2yY02nr58iUUKFAAkpOTdcZvVPDaG4YYOXIkHD58GE6fPq2V/Pft2zeoVq0a1K9fX9S8hEceJSQkgI+PD1SqVAkGDRokSgCeP38+XLp0CW7cuCHYHufPn4cuXbpAWloarFmzBqpWrSp0zQ0PD4cWLVrAokWLuDvoK5VKpoZoUk0YWAsw3N3dhXgeK6y+DFb9S73rspy4mSHKlCkDvXr1EnwHhw4dgoEDB8KdO3cgNTUVAgICoGDBglor/PDKdN6GQqxynUfPqF+/PrRu3Rq6d+8u+Z2rVq2CqKgokU9Vjkzi9X8RvxhIEL8IdnZ2GB8fL/zv5uaGK1euFP5/9OgRmpuba33O3Nxc9DkV8fHxaGFhgYiIDx8+FP42NzfHx48fIyKitbU1PnjwABER4+Li0MrKSus4lpaWwj45RVxcHO7fvx937dolev1duLm54e3bt7k+Y2FhgU+ePEFExFatWuGECRMQETExMVE414iITk5OWL16dVy6dCl++PBB2G5sbIxxcXHZMPrMa3T//n3m/e3s7PDUqVPZ8t2IiI6Ojnj8+HGd7x8/fhwdHR1F2+zt7dHBwQGVSqXwt+pla2uLSqUS+/bti4iIvr6+OG/ePOGzUVFRaGVlhStWrEBExFevXqFSqRTez5MnD964cUP4PyMjA3v37o2urq744MEDrf1VzJkzB5s3b46fPn0StiUlJWHLli1x7ty5mJKSgkFBQVi/fn3OM/T3PEdy+PbtG3769En0YuXatWuS5xERMSUlBe/cuYPXr18XvVT89ttvel8dOnSQPHbz5s1x8eLFWtuXLFmCzZs3R0TE+fPno5GREdc9cPjwYbS0tERvb280NjZGX19ftLe3Rzs7O/T399f6vmbNmmFUVBTTeZo1axb27dsXMzIymPZHRFyzZg1+//6deX8Vhq6nkZER/v7775iWlibanp3yCBHR2dkZT58+jYiIxYsXxy1btiAi4t27d9HGxkbYLzY2Vu9Lhbm5uSBvERErVKiAM2bMEP5//PgxWlpaZmnMoaGhkveWLkqVKoXz5s3DxMREtLW1xTNnziAi4qVLlzBv3ryifatXr47z589HxMx59+HDh4iI2L9/f2zQoIHk8XNCZlhaWgrfjZh5Hw8YMED4Py4uDp2cnBAx8xyvW7dO8jj9+vVDe3t70TNkbm6OiYmJwv+mpqZ46dIlveOJiYnB+vXr46NHj7h+A898p05qairOnTsXnZycsGjRorhp0ybR+9bW1nj16lWmYzk7O+O9e/e4vt/Q3KtQKPDYsWN4/fp1LFSoEF64cEH0/t27d9Ha2lryszY2NnrvF97rqRpPmzZtsEuXLnpfmpiZmUnqpffu3UMzMzPJMRiaM+SQlpaGM2fOxAoVKmDevHlFeoaDg0OWji0H1nuGV+dRh+U8vnnzRud3q8+b6uTKlQsTEhIMjt3Pzw9///13RMycd6dPn47W1ta4f/9+0djl2jyasOgwjo6OuG/fPoPHykmcnJz0yrmHDx9i7ty5ZR370qVL2KdPH7S3t8fy5cvjvHnz8NWrV3rndR755erqiseOHRP+f/v2LVasWBHr16+P379/F92P1tbWeuX+pUuXdMowXlhlBo++NnXqVMydOzd27twZZ82ahfPmzRO9pHj69CkuWrQIR44cqaXLZoUVK1agq6srhoWF4bZt2/TayAqFAhUKBSqVSuFv1cvU1BSLFy+Oe/bsQUREpVKpJQPU9RIpKlasiCNHjsQTJ06gubk5Xrt2DRERz549iy4uLlr758+fX/Leu3XrFubPnx8RES9fvqxlGxq6pm/evMHGjRujUqmUfGlStGhR4XcjIt6/fx+LFi2KXbp0wYyMDNG9a2lpKTyjGRkZaGJikq22ctOmTXHUqFGIiNigQQOt+2n58uVYrFgx4X8zMzORTlW1alWcPHmy8P+jR49Ez1KFChUwIiJC5/fPnj0blUolli1bVvBH5ISeyTp/aV5jfS9DGJoHzM3N8ebNm1qfu3HjhjDPPH78WPDfVKhQAQ8cOKC1/4EDB7BChQqIiLhjxw50d3dHRMP6lyas/gZjY2OR7aPJkydP0N7eXrauVr58eTx58qTB8RoZGeGLFy+E/y0sLLJkM2r6D+TIXRX6fIhWVlZaumhqaioGBwdj6dKl8caNGzp1KRZUNnVycrJou9Tc26hRI6xfvz6+fftW6zhv377Fhg0bYuPGjUXbeWxHXtmoIis2FQssx1coFPj69Wvhf3X/NKK0zjtw4EC0t7fHGjVqYP/+/bN17tWHLt+XIVnq4eGB27dvR8TM621kZCTSlc6fP6/lO1DBo8OynBcHBwdJuTpz5ky0t7fH6OjoLD0X6vj7+wv6Pws8dnhO0KxZM70vf3//bDs3WUWfH5b1uS5WrBhGRUXhly9f0MnJCY8cOSIcW10/05TJmq8RI0YIY7GwsNCyd549e4bFixfHkJAQfP78ud5zmBM6tXr8CxExICAAhw8fLvx/7949zJUrl9ZnePSG7t2746RJk7T2Dw8Px+7duyMi4vjx47FcuXLo6emJO3fuRESxvJs/fz6WKVNG9HlPT0+8cuUK0+/MkyePoJ8jZsqObdu2Cf/Hx8dnKd7Ho1Or4JEBI0aMkDyHUigUCsyTJw9Onz4dP3/+bHB/V1dXjImJwbS0NCxYsCDu3bsXETPtEnt7e619We1eFV26dGGKo8iNDbI+07yxYVY9UMX69euxZcuWmJKSwvwZMzMzfPr0KSIi9ujRAwcNGiSMQxUX4PUj29raavmlNm7ciFZWVrhnzx69vjIeWMauDk9c29LSEu/cucM8FkPPqZR9b2Njo9e+lxs3ZUGXX0L9pXls9euampqKxsbGIp0gPj4e7ezsRJ9h8VHKjT2y6l9yfKs54Z/U1KcRtXVqzf1fvXol2mZlZaX3npFrbxiiTJkyev11mzZt0pofeeRRv379sHbt2pLvZWRkYO3atbF///6i7d++fcNBgwahiYkJ9uvXD8uVK4d58uQR9HkpDOkwCoUC582bh2vWrNH7kqJ27dqS5ygqKkr4bevWrcOiRYvi+/fvtfZ7//69znmK15fBg5y4mSHMzc1FuqbKd6fyV5w4cUKIa6rDK9N5dDBEdrnOo2c4Ozvrnf/v37+Pzs7Oom1JSUnc9wCv/4v4tZDuw08Q/4N4enrCnj17YMiQIRAXFweJiYmi6pQnT55A3rx5tT5XsGBBWLlypVanspUrVwrdcd6/fy9U9nh5ecHJkye1Kty3bdsm2cWuQYMGcOnSJe7lPlkqqh4+fAjNmjWDmzdvipbOUlWZSFXqpaSkwPHjxyWPrepCIMX379+19ldVk4SHh8P48eNh7dq1zB1kihYtCjt37oRmzZpBTEyM0K3vzZs3oiqVtLQ0YWkzzWVt9cHzOytVqgQJCQnM3QkcHBwgV65czGMxRKVKlWD9+vWiJSHUWbdunVCBo2Lu3LmAiBAaGgoTJ04UdXtSVRurloy5f/++qHtM69atwcnJCZo2bQqpqanQrFkz0bE1l3FRKBSwePFi6N+/P9SsWRMiIyMlxzlz5kw4dOiQ6PrZ2dnBhAkToH79+jBo0CAYP3680H2S5xqxPEdly5aFI0eOgIODg8El06Q6ELJWMaovZ6dZQQ8g/dyx8vbtW+jatSvs379f8n3VsVmWTJO6n2JiYmD69Ola2+vUqQNDhw4FgMxuAQMHDuS6B0aPHg3Dhg2DiRMngo2NDWzfvh3y5MkDISEh0LBhQ63va9y4MQwfPhxu374NpUqV0lqGSrVsNwDAqVOn4NixY7B//34oWbKk1r6aHY0AMuX0tWvXoFKlSqLtqmWyVB21APiuZ3h4OKxevRrWr18P7dq1g44dO4K3t7fWZzQ/P2fOHNiyZYvk/SVV3dm8eXNo3749FCtWDN6/fw8BAQEAkHndixYtCp06dYJFixYJnSxUHQM0z40KFxcXoUv3ly9f4Pr166Kq4vfv3+uV3SzPatGiRWHcuHFChyPNsWg+0+PHj4f27dvDb7/9BnXq1BHk1cGDB7Xm0qlTp0JAQADcvn0b0tLSYN68eXD79m04c+aMzk5scudefZibm4u6cpw7dw5mzpwpel/VLaVZs2awadMmyWr0hQsXQkZGBixZskTY9uPHD1G1s6mpqcF5pk2bNvD161coUqQIWFpaap1zqXuLd75TsXHjRhg/fjx8+/YNJkyYAD179tRa7qtgwYLMy90PHToU5s2bBwsXLmRe3pJl7q1Tp44whtOnT0OFChWE965evaqzA0KrVq2E5cal4L2eKubPn6+1XLohnJyc4Nq1a1pL51y7dk3rWKxzhvr/rPJo4sSJsGLFChg6dCiMHTsWxowZA48fP4adO3fC+PHjJb/v0qVLOo8tJasB2Luvs94zvDoPAN95LFWqFKxcuRIaN24s2mfWrFkwbtw4yQ5e3bt3h8jISIPdyuLi4mD9+vUAkDnvjhgxAgoUKAAtW7aEzZs3C/ezXJsHgF+HMTU15ZIXvPfAtm3bdO6v0tVSUlL0Lkf9+fNnUdd1KXTZMZUqVYIBAwbAuXPnhG6MhuCRX2/fvhXZjLlz54bDhw9DgwYNoFGjRrBixQrhvZIlS8Lhw4dFnb/VOXjwIJQsWVJrO8s5VB8Pj8zg0deWLVsG1tbWcPz4ca25WaFQaOkBR44cgaZNmwor0Xh7e8Pjx48BEaFs2bKS41Ohzy4FAOjRowcAAEyaNEnrs5pLX6q6VRUuXBguXrwIuXPn1vm9iAhdunQRddv8/v079O7dW9QtRf1enz59OjRr1gxmzpwJnTt3Fro/7N69W8u+A8jslvvmzRutTlBv374VngN7e3vh97Ne08GDB0NSUhKcP38eatWqBTt27IDXr1/D5MmThaWL1Xn+/LlIxy1atCjExsZC7dq1oWPHjkJXMYBM21GlRyoUCjAzMxOW+GTBkA02bdo0qF69Orx48QKqVasGY8aMgYsXL4Knpyfcu3cPoqKiRHNw3rx54dGjR1CwYEH4+fMnXLlyRdSt7/Pnz6J7OS4uTu8S2sHBwcLS4fb29gDAr2eyyEbW+cvX11fvsukqdC09zzMPlCtXDoYPHw7r1q0TVux5+/YtjBgxQpiT7t+/L/jNbt68KbkcbaFCheDmzZvC+F++fAkAhvUvTVj9Dbt27YK3b9/q1Pvevn0LaWlpACBPV5s+fTqMGDECpk6dKikbVeNDRJG+bGRkBBYWFjqPq2+uA8i8d9XhlbsAbD5Ed3d3uHHjhkgXNTY2hq1bt0KrVq2EDo9ShIaGwrx588DGxka0PSUlBQYMGACrVq2Cbt26waJFiyA2NhY6duwIbdq00dlJacmSJdCoUSNwdnaGUqVKCfrN69ev4ebNm+Dl5QV79+4VfYbHduSVjSrk2lQAhucwnuOPHz9ekL8/f/6EKVOmCL5BKd3oxo0bQjejW7duid7Tp9MY8gc4ODjo/bzqedPEkCzt3Lkz9OvXD+Li4uDo0aPg4eEh0pPOnDmj0x/Do8OynBdvb284c+aMln9w2LBhkJGRAe3atTP4Pbdv35Y8h+r6FADAgAEDYOjQofDq1StJGaM5Bh47PCfQXHVO6v1OnTpl+Xvk+NZ4YH3uBg8eDCEhIWBtbQ2FChUSlvY9ceKEqNs3Syc/1TyVL18+ePDgAbi5uQnvubi4wLFjx8Df3x+6dOmi8xhZ0an1YWtrC0lJScK8fuHCBejWrZvwvkKhgB8/fog+w6s3bNmyRXJZ8LZt20K5cuVg+fLl0K5dO4iIiIA5c+ZAv3794Pv374CIcOHCBdi0aRP88ccfIpsKIDNmMmrUKFi6dKnonEpRuXJlmD9/Pixfvhyio6Ph8+fPULt2beF9VbdkTVh1QR6dWgWPDPj+/TssW7YMDh8+DKVLl9baV92nEh0dDSdOnIDNmzdDWFgYlClTRuigW61aNS2/cNeuXaF169ZCR7u6desCQKZvXbN7LI/dq0LVpS8hIQEePHgANWrUAAsLC2FVOBVyY4OszzRvbJhVD1Qxe/ZsePDgAeTNmxfc3Ny09peKUeXNmxdu374Nzs7OcODAAVi8eDEAZM7tqnPA60c2MzPTWvWzffv2oFQqoU2bNnr1Hp4YHsvYAeTFtb28vODdu3c6x6mJoecUEaFOnToiff3r16/QpEkTUWdn9WskN24KYNj/6ezsDH/++adO2/TatWta/iJTU1P4/v07AGTqghkZGcL/qvFq3nMsPkq5sUdW/UuOb5VHt+PRGWJiYkS6TEZGBhw5ckSkE6p0NYVCAV++fBHZdEqlEj5//iyy5dTlgFx7w9D98vDhQ73zfPny5eHhw4eibTzyKDY2VtR9Vx2FQgGDBw+G0aNHi7abm5vDnDlz4M2bN/Dnn3+ClZUVXLp0Sae/lVWHadu2LbfNDpBpK0jFa8qUKQNnz54FAIBq1arBw4cPYfPmzdC3b1/Rflu2bIHdu3fDX3/9pXUMXl+GLh4+fAi9e/eGgwcPCtvkxM0MyWkXFxe4d++eoBc9ePAAMjIyhE7RBQoUkFwFglWmq+DRwQDY5TqPnvHx40ed9idAZmd+zdXp2rZtC02aNOG6B3j9X8Qvxt+fE0wQ/wzR0dFoamqKtWvXxrx582JgYKDo/REjRmCrVq20Prdr1y40NTXF0qVLY7du3bBbt27o4+ODZmZmQoXrn3/+KVTs7Ny5E+3s7HDatGloaWmJM2fOxO7du6OpqSkePHhQ6/g8HXwQ+SqqAgMDMSgoCN++fYvW1tZ4+/ZtPHnyJFasWBFPnDihdewrV65gvnz50NbWFo2MjNDJyQkVCgVaWVlh4cKFtfb/8uUL9uvXD52cnPSOxdfXF21sbNDa2hq9vb2xTJkyopcUW7duRRMTE1QqlVivXj1h+9SpU7Fhw4bC/9++fcMNGzagv78/WlhYYPPmzTE6OhpNTEx0Vsny/s7o6Gj08vLC1atX46VLlwx2nuGpNGPpQHf06FE0MjLCoUOHiirwXr16hUOGDEEjIyOhIl+T2NhY/Pnzp94xODs749mzZyU/a21tjWPGjBFdTzmdAhEzqwXVq5hUHDt2TOhS9ODBA7SxseG+RizP0YQJE4RrMmHCBL0vdXirGPv27Yuenp64bds2tLCwwFWrVmF4eDgWKFAAN2zYIOyn2dlY82VjY6N1/Pbt22PVqlXx4sWLaGVlhQcPHsT169djiRIlhEr1rFCwYEHJDlERERFYsGBBRES8fv06Ghsbc90D1tbWQuWtvb093rp1CxEzO0gUKlRI6xg8lcC8HY0QM+/hrVu3am3fvn07VqxYUbSN9XqqExsbi506dUJLS0ssXbo0GhkZ6ewUNm7cOHR2dsZZs2ahubk5hoeHY7du3dDR0VFnZ6OfP3/izJkzceDAgaKKw4iICFy+fDkqlUpRda+his1Ro0ahh4cHrlu3Dtu2bYuurq6iLsBLly7FqlWrSn6W9Vl1c3PT+ZJ6phERX758iVeuXMH09HRh2/nz5yWrJh88eIDdu3fHChUqoKenJ4aEhOisqEbkn3tZZHXt2rWFDm4nTpxApVIp6op18OBBLFKkiM4x6UOhUGCvXr2ECmFTU1MMDQ3V2/1ETuUw73y3f/9+9PHxQVtbW5w0aRJ++fJF52/g6egbHByMdnZ2WLhwYQwMDNTqsiOFobn38ePHote7d+9E769duxbXrl0r+dmsdCDTheZzysrEiRPR3t4ep02bhidOnMATJ07gH3/8gfb29lqdUXjnDB555O7uLhxDXcbPmzcP27Vrp3XsTZs2oYmJCQYGBqKpqSkGBgZi8eLF0c7OTqes5um+znrP8Oo8vOdx+vTpaGZmhr1798avX7/is2fPsHbt2ujk5ITR0dGSv5O1W5mTk5Nkx5NNmzahpaUlLl68GJVKpWybB5F/zuPpYs97D8ybNw+tra2xf//+aGpqir169cK6deuinZ2d0EkYEdHHx0dvh/ZFixahj4+P1nYWO6Z+/fpoY2OD7du3x/379wu/U183Hh75VaJECckOH58/f0Y/Pz/08fERxrJ06VKhe40mu3fvRisrK1y6dKmsc6iCV2bw6Gu8VKhQAcePH4+I/9eh5PPnz9i0aVP8888/tfZntUtzEkM6qS7dNC0tTdTtCTGzi6vUHNG+fXssXLgwRkdH49OnT/Hp06cYHR2N7u7u2KFDB0TMfNbKlSsn7M9yTfPly4fnz59HxEy9UdWNY9euXZI6YOHChfHw4cNa258/f47FixfHevXqCeddoVDglClThDnT3Nwcx40bZ3Au5bHBEhISsG3btmhjYyPcgyYmJlilShXcsWOHaN/evXujn58fnjhxAocMGYKOjo7448cP4f0NGzZg+fLlhf9tbGz0duzQXMECkU/PZJWNrPOXpr6j7yUFzzxw9+5dLFGiBJqammKRIkWwSJEiaGpqih4eHsI9tGPHDsFu9PX1xc6dO4vO98+fP7Fz587o6+uLiIinTp1CNzc3ROTXv1j9DUqlEqdNmyb5+1XfW6lSJdm6mrocVH9pykaFQoGlSpUS/GJGRkZYsmRJnf4yqWPqO74cWHyII0aM0LnqUWpqKjZt2lTnOHSdU1X3UxVfv37FNWvWYI0aNdDMzAybNm2KRkZGkp0X09PT8a+//sLx48djz549sWfPnjh+/Hjcv3+/yI5Uh9V25JWNKnhtKt45jOX4NWvWxFq1ahl8ZRUWf4Ahu1SXfWpIlqanp+O4cePQ19cXGzZsqLVyW8uWLYUu45rIWYlJH8uXLxfmYSmmTZsmyDZNHjx4gKVLl9bqjKfr+uvSu3TJAB47nHeVlJxeVYXn+HJ8a5ro66DL81xfvHgRo6OjRV1I9+7dK3sFgW7dumFoaKjke8+ePcOiRYvqHDevTq3+G4YPH45t2rSRtGeaNm2KoaGhmJ6ejlu3bkVTU1ORPrt371708PAQHZNXb8iTJ4+kj2bt2rWYJ08eRMzsrqlaLWXDhg1YtGhR4blwcXGRlAH29vZoamqKSqUSra2t9d5X169fx9y5cwv7jx07VvR+hw4dsFevXlrfwaoL8ujUKnhkgD75L7WinYqkpCTcs2cPdurUCU1MTHSu1rR161aMiIgQuuchZsp8VTdjFTx2r4r3799j7dq1hd+l8mt37doVhwwZIuzHExtUf2ZZn2ne2DCrHqiCJ0alIiwsDO3s7NDDwwNdXV2FVQJXrlyJlStXFsbB40euV68ezpw5U/L7IiMjhTitJrwxPJaxI/LHtRERjxw5gn5+fnjs2DF89+6dwRWhDD2nhq6N1DWSGzdl8X82adIEx40bJ3lsxMw5TKFQiLYFBQVhYGAgnjp1Cnv27Inly5fHxo0b45cvXzAlJQVbtmwpirUj5uyKCqz6lxzfKo9ux6oz6PN5Sfm+9D37uuSAHHuD5X4xZMu+evVKZH8h8skjGxsbg6uZaa7ylZCQgNWqVcO8efPi0qVLsXLlypgvXz6tOUMFiw4j12ZHzFz1YOTIkVrbR44cicWLF0fETH1IoVBIrhB9584drdUCVGRXLElKN+WNm7HI6YkTJ2KBAgVw8eLFuGrVKvT29hYdSzVnasIq01Xw6GCI7HKdR8/w8PDA9evXS55vxMyuySVKlBBtc3Bw4L4HeP1fxK8FJegSvxSHDx/GwYMH47Rp07QSOCZMmCDpzEfMVCZGjhwpTHCjRo3Sq3ycOHEC69ati05OTmhhYYFVq1bFmJgYyX15A5s8gVNHR0fBoLS1tcW7d+8iYuakppoA1KlZsyb26NED09PTBYUnMTERa9SoIbnMAGvwRo6hiciXnIWYqeCNGTMGCxQogAqFAtu3b48HDx7UWm6e93eyOD58fX1FQRTWhGRWY2DJkiVoZmaGSqVSSOxUKpVoZmam16GGmBmwuHfvHp48eRKPHz8ueiFmGmoqRVeTY8eOoZWVlehenDp1KgYEBOj8vj59+mgZg4h8AeXsuEbZlSDAm6xQsGBBQZbY2NgIyyWsW7dOdN7kBCl4DLZPnz7hwYMHce/evXqXhVFn2bJlaGRkhE2aNMHw8HAMDw/Hpk2borGxseDUnDVrFpYqVYrrHsibN6+gwHp6egrOyGvXrkkuBZbTWFlZSSasShmOrNdTiuTkZFyyZAlWrFgRjYyM0M/PD2fPni3ahzfJjQXN5Xf0Lb2DmBkA7dixI9rb26OHh4eWo6tWrVo6g9i8z2p28/PnT+zatavepYKk4JUZLLI6NjYWLSws0N3dHS0sLLQCKH369MFOnTqJtq1fv15vUqsKlgCrPuc6K6yO/vPnz2OtWrXQ3NwcBw8eLLmsrCY8BricxHs5xUCssCaYs15PROllsljIyMjAiIgIdHFxEQWe5s6dq+WE5HXy8cgjS0tLYXnofPny4eXLlxExM8Bsa2urdexSpUrhwoULhWM/ePAAMzIysEePHjp1EJ5gIus9w6vzqH4fz3m8cuUKlixZEosWLYq5cuXCgIAAfPnypeR3IrIHzXgCJnJtHt45j8cpyHsPlChRAiMjI0X7I2bK4379+gn7TZ8+XWTzqKNaSnb69Ola77HaMYmJiThx4kR0c3PDvHnz4sCBA9HY2FjSMYfIJ78GDBiALVu2lDxOcnKykCCmIiQkBBUKBXp6emJwcDAGBwejh4cHKpVKbNu2rexzqEJuIhIvGRkZBoMmvMVdcgqqeDl8+DCOHj0au3Xrhl27dhW95LJy5UouHebz589C8a8qyGNqaoo9evQQ5p+rV68Ky1ayXlP14Iqrq6uQQKK+XKs6PIkihQoV0juP6irWklOYqFoK+MWLFzqLVN++fYvVq1dHhUKBNjY2WgG+2rVrixLYa9asqZWMoc6YMWOwZs2aom08eiarbJQzf8mBdx5IT0/H/fv3C4GmAwcO6EyKPH36NDo6OqKTkxPWqVMH69Spg3ny5EFHR0ch+XjdunU4Y8YMROQv8GP1N7i6ujIVPMjV1WJjY/W+VPAG/Q0dV/P46rDIXUQ2H2JqaqreJadTU1O1EsA/ffqESUlJqFAoMCEhQRRU+/DhA65du1ZrGUkV8fHxOHr0aMyfPz/a2tpiu3btZNuZvLYjr2xUwZtAyTuH8R5fDqpnyBA56Q8wJEuPHz+Oqampso4tpyAUkf288MCbhMRbfMFjh/MmubLu/+jRI1y2bBkuXLhQ0OlYyMkCUin0JejmxHOXkZGB8fHxeOvWLb338uPHjyWXyFXx/PlznUXYvDo1IlvxkNzEVR69ITw8HC0sLHDgwIG4fv16XL9+PQ4cOBAtLS1x8uTJiJjZqKBu3bqiz6WkpOidv3mLBd6+fYs7d+7Ec+fOab23d+9eyTmFVReUk3wtpwCLlXfv3uH27dtxwIABWKpUKVQqlejo6IjBwcFZOi6v3YuI2LFjR2zQoAE+ffpUZMseOHBAMkkI0XBsULMQgvWZ5okNy9HT5GAoOZrXjxwdHY2DBw/W+X0bN26ULOyRowewJHbzxrUR+ZOjcyKeKDduyuL/PHHiBO7fv1/nsb98+aJ1j8XHx2OxYsUEP9KzZ8+EWJ+xsTE6OTkJ/lwVrPeMnNgjj/7F61vlOXZOxOMQ5dlrcuwN1sRVTbtL/RUfH58lu8GQrfzq1SvR8RcsWIBWVlbYvHlz4X5JT0/HadOmobm5OXbo0AE/fvwoOgaLDiPXZkdkL8AwNjaWLOa8ceOGzmskp1mRFFK6KW/cjEVOp6am4ogRIzB//vzo6OiI7du3F8X7zp8/L+STaMJarKPazqODscp1Hj3j999/R1dXV1EzPBUvX75EV1dXraYWlpaW3PcAr/+L+LWgBF2C+I/BEzi1t7cXnATu7u549OhRRMw0VqUmDTs7O8HYsbOzEwLP586d06oYQcxa4lpOkp6ejvv27cMWLVqgqakpOjo6it7n/Z0sjg/W4IpmQjKPMfDs2TOMiIjAvn37Yp8+fXDOnDkGHcNnz57FwoULSzofVApJbGwsTp06Vecxjh49qjMhigeegDLvNcpJeJMVrKyshKQlFxcX4bMPHz7McjIqq8F29epVdHZ2Fq61ra2tXmeuOqdOncK2bdsKSW1t27bF06dPZ2ncQUFBuGzZMkREHDp0KBYtWhQnT56MZcuWxTp16mTp2CrevHmDJ0+exJMnTxp0CuTKlQvPnDmjtf306dNob28v2pZd1/PGjRs4aNAgdHJyEm3nTXJTER8fj0uXLsXw8HCcOHGi6MWboJsV5DyrLAHiL1++4NixY9HPzw+LFCmChQsXFr3UsbW15U7Q5YVVVt++fRvnzp2Lmzdv1goyLF26VEiYUZE7d260srLCdu3a4b59+7QKSrJKWloabtu2TUi4j46O1vsdrI5+hUKBlpaWOHjwYK0KYF3VwHI6+vIgtxhIRWpqqvAsyoXnesbGxsoOKKtITk7G5ORkne/zOvl45FHx4sWFAFXVqlXxjz/+QETEzZs3a8k51bFVY8mVK5fg1Lh9+zbmy5dPcvxygomGkKPz8J7H5ORkbNOmjeDwzo77G1F+wIQH3jmPxynIew9YWFgIcsfJyQmvXbuGiJnzn3p1+s+fP7FWrVpobGyMDRs2xMGDB+PgwYOxYcOGaGxsjDVr1pRM1JNjxxw8eBDbtWuH5ubmWKxYMRw9erRWMIOHDx8+6E1SSE5O1nLeR0VFYVBQEHp5eaGnpycGBQVhVFSU5OdZz6EKuYlIrKxduxa9vb3RzMwMzczMsFSpUjq7y/AWd/Fez9jYWAwMDBS6dzVp0kRnJx7EzDlGqVRixYoVMSgoSEiQVr3kogq6FyxYEDt06IDLly8Xxq6Pz58/C92d1LuzacJ6TcuXLy/YCk2aNMGOHTvis2fPcMSIEeju7q513KwkirCS0wnjSUlJkvP0+/fvRR0u9uzZg0ZGRjh8+HCRA//ly5c4bNgwNDY2xt27d8seB6tszIrNHhcXh/v37ze4YgRiztqyiJlybfHixUIHpiVLlujVZXjg8TewFDxo6mrJycmioKa+Z+/fAo/cReTzIZ48eZJ5HFJBNfWXkZGRkGyli/T0dNy9ezcGBQWhqamp1nu6PqOp3/PYjryyUQVv8hTvHCY3OevUqVNCVyMp0tPTceLEiWhraytcGzs7O5w0aZLOc8zqD9i8eTO2b98eW7ZsqXflAx6y0jGLR4flOS/h4eHcvgk5SUg88NjhvAkrLPsfPXoULS0tBf+kiYmJ3q5VcsftQEKuAAEAAElEQVTDYstqJutovvz9/XUmq7A+d82bN5cscJ8+fbooceDhw4fo7e0t3FMFCxbECxcu6D0f37590/u+FHIaJrAWD8lJXOVlw4YNWLlyZSGhvHLlyrhx40bh/a9fv8o6L/8W/g6dWoWhAgNvb280MjLC3LlzY/PmzXH+/PmSRbDqsBYwyrF78+bNK9iv6n7tBw8eGNRJdcUGs7rSRE5y6dIlIRFdfbU8Kf5N93xOxfB449qIf19ydE6QE/5PdTRXkTt8+DDu2bNHazsrcmOPPPoXr2+V1z8pJx6XE8ixN1gTV+WuvsIijxQKBR47dkyr+7jqdeTIEdHxHRwcdBbP37p1C8uVK4f58+cXbf87mj6xFGDUqlUL+/fvr/XZvn37YrVq1bJlHLrQVzzGyr8p14IXVrnOo2ckJydjyZIl0cbGBvv06YNz587FuXPnYu/evdHGxga9vLy0fFRy74Gc9H8R/20oQZf45dDlVMzIyNCZmPHx40eMiYnB9evXC0sg61sKOSfhCZxWq1ZNWNKxXbt22LBhQzx16hR26tQJS5YsqXXs3LlzY3x8PCJmtvdXKYZ37txBS0tLrf15gzc8hqaqIk/Xi5U3b95odazk/Z05SU4bAz4+PtiqVSu8ffs2fvz4EZOSkkSvrMDTKVAdloByTl8jnmXSeJMVSpUqJSh7derUwaFDhyJipgPZxcVFtC9vkILVYKtfvz5WqVIFz5w5g1euXMFmzZph0aJFGc8OO6z3wIMHDwSn3pcvX7BXr15YqlQpbN68uU6nF2vixJcvX7Br165oZGQkOAWMjY0xNDRU51L3bdu2xZo1a4qegY8fP2LNmjW1lv3muZ4saCYJ8Sa5If5fp+O8efOij48P+vr6Cq8yZcpoGchWVla4b98+g0tryoHnWeUJELdt2xadnZ1xxIgROGfOHMFQUr3U6dSpE0ZERGTL79EFi6zu2rUrt4GVmpqKe/bswfbt26OVlRU6OTlh3759ZSXFX7x4UfT//fv3sVixYmhpaSkk3FtaWmKJEiUEJ45c5HbD44Un8T6rsDo89CWY81zPM2fOaHVuW7t2Lbq5uaGTkxP26NFDMoD+8OFD4ZlTJz4+XsuBxOvk45FHI0eOxClTpgjvGxsbY9GiRdHU1FRyeSgXFxch6ahUqVJCV88zZ87o1HfkOOJy4p7hOY+qpYnKli2Lt2/fxuXLl6ONjQ22bt1aawl7KbKzK5ccmye75zx1eO+BwoULC/ZCuXLlcMmSJYiYuUyvpq728+dPnD59Ovr4+KClpSVaWFigj48PTp8+XZRop05WktA+fPiA8+fPR19fX51yg+deTExM1Pu+XHjOIaK8wACrvjZ79my0tLTEESNGCMmBw4cPR0tLS8k5nLe4i+d6rl+/Ho2NjbF169ZCQUnr1q3RxMREFPBXJ1++fHqT2lRcu3YNw8PDcdGiRVrd5T99+iTZbffZs2e4YcMG7NmzJ5YoUQKVSiW6uLhgSEiIwe8zBOs1Xb9+Pa5evRoRM2323Llzo1KpRHNzc9y8ebPO47MGh1k7xKmTXQnjiYmJovMuJ6Fr/vz5QtKnyl5UKpVoYmKCc+bM4TqWJnLmR1Z4l01XjYFnHsiJztJSsHaARWRPYDdU8HD16lVRcqS1tbVWUqmuZKqPHz/irFmzhE48ERERWfa/qDoMValSBcuXL48jR47Er1+/6tyfV+4i8vkQTUxM0M3NDUePHq21hLQmsbGxeOzYMVQoFBgdHS0Kqp05cwafP3/OdS5Uz/CnT5+wVatWaG5ujnny5MFx48aJEu81Ozch8tmOcmUjLzmdGK/CxsZGb+HuqFGj0MnJCf/880/hGVq0aBE6OTlpdRFSweIP+PPPP1GhUGDx4sWF5UWHDRuW5d+TlY5ZPPCcl9KlS6NSqUQ/Pz9JXUAKOUlICQkJ2L9/f6Eb04ABA7Js4yPy+6hZ9q9atSoGBQXhixcv8MOHD9i3b1+dHbOzMh4WW9ZQ8o6+FXtYyZ07t84OW3ny5BH+b9GiBXp4eGBkZCRGR0djlSpVsGzZsnqPbWNjg507d8aDBw/qtPU0kdMwQU5xLSs5pTdormyoepUtWxarVKmCnTp1Ep4tRL5i9q9fv+LJkycl57pv375lS3yQN+GSVQbwFBgsXLgQb968yTwGOQWMPHavtbW1ML+oJ+hevHhR53LSUkjFBnnhjQ3z6IGvX79Gf39/VCgUgp2hUCiwdu3aOv0IZmZmWL16dRw7diwePnxYrz6ojqFCHcTMrs66VlySgkUPmDdvnnCP62vyoN7ogTeunVPs27cPu3XrhsOHD9daRenDhw8649S8cdN/2+qTKnT5KHM69phV36oheONxT58+lbQrf/78qbOrKCty7A2W+0VON18eeaSvI7lUAvCLFy/0noe0tDScNGmSaBuLDqOpS+h6ZYVTp06hubk5Vq9eXWgEU716dTQ3N9db6K+Cx5ehib54FavfObtzLeTIdHV4GwrxwKpnJCUlYZ8+fTBXrlzCfevg4IB9+vSRlDFZvQcIQhNK0CV+GeQ4bhEzl7izsbFBhUKBdnZ2aG9vL7xUgU3V3ywvKXg6+PAETg8cOCC0qL9//z6WKFECFQoF5s6dG48cOaJ17Hr16glBye7du2PFihVxw4YN2KBBA6xYsaLW/qzBGzmGpqr7lerVr18/rFq1KtrZ2eHAgQO19v/69Svu2rULZ86cifPmzcP9+/frnNR5fycin/OzcOHCktWHHz9+1EpYYjEG+vTpIzIAIiMjRQbex48fdXb6srS0ZOrChJh5DVevXq13uUJ1crLzo5xrxPMc8SyTxpusEBERIRzj0KFDaG5ujmZmZqhUKkWJhXKCFKwGm6Ojo6ij28ePH1GhUDBd2/T0dLx37x6ePHkSjx8/LnppklP3AE/iRM+ePdHd3R3/+usvoZvRvn37sEiRIti7d2/J4z979gzd3d3Rzs5OWKLH3t4eS5QooaXEs15PFampqThjxgwsU6YMWllZoYODA1aqVAmXLFkiaYjxJrkhZiYpSHXkUMFrICPKdzixPqu8AWI7OzshEcMQ4eHhaG9vjy1atMCpU6cyGYKIfDKDRVZnpYMPYuYyfBs2bMBGjRqhqamppHz5/PmzluP16tWrGBgYqHVNAwICsGHDhvj+/Xth27t377Bhw4bYqFEjnePIqWAfIrsBLifxXgVPMZA6hhJ0eTuQGbqeDRs2FD3HN27cQGNjY+zevTvOnj0b8+XLh2FhYVrHrVGjhmTXgPXr12stsc3r5JMjj1ScPXsWZ8+erbOLYLt27YSgyKRJk9DJyQm7d++OhQoV0rmMLE8wkfee4dF5eM6j6lypF2MkJCRg5cqVdSa48nYrMxQwkWvzIPLPeSpYnIK890C3bt2E7tcLFy5ECwsLrFu3Ltrb24uWAZUre7MrGVmzg64c+aVUKrFGjRq4bNmybAk2qGA9hyp4ZQaPvubm5iYZwFyzZg26ublpbect7uK5nh4eHpK6x+zZs9HDw0NrO2JmcoKhuTAmJgZNTU2xZMmS6Orqio6OjqJEAH3PHmLmvHHgwAHs3LkzGhsbo5GRkdY+PCsMIMpPLktJScHLly8bTCxiSRSR0yEOUX7nSk0053e5CV1Pnz7FiIgI7NOnD/bp0wcjIiL0Ov9Z9Uxe2cgzf/Eum47INw/ISczQtwqIFLz6V3bSrVs3QS9CzEwO2bhxo5Bs2rFjR+zQoYPW51SJIy4uLkInngIFCmjZ6LqSiTRfKiZNmoRKpRLr16+PQUFBaG5urjfYyCt3Efl8iG/fvsUFCxZglSpVUKFQoI+PD86YMUNvodHjx4+Zk8q2bNmCzZo1w5IlS2KZMmWwTZs2Wp25Bg4ciMWLF8etW7fi8uXLsVChQti4cWOhOOfVq1daywjLtR0R2WUjIp9NJUcnkWOzGVpZx9nZWbK79s6dO7U6Wqlg8Qd4eXmJVjRZv349czBYnyxVKBRZLshj0WF5z8utW7dw9OjRWLhwYTQxMcFGjRrhxo0bdeqBvElIBw4cQFNTU6xYsaLQjalixYpoZmaGBw8elPwOVjucN2GFZX87OztRYmNKSgoaGRkxde3LyQJSObA8d+bm5kKXMnXu3LmD5ubmwv958+YVdSJ/8eIFKpVKvQld0dHR2LJlS7SwsMB8+fLhoEGDtAq1NZHTMIGleCggIECUcPjHH3+IlqZ+9+4denp6io4rd0WKHz9+4NOnT/HJkyeilzqjRo1COzs7rFatGg4ZMgSHDBmC1atXRzs7Oxw0aBDWq1cPlUol7ty5k6uY/d69e1ioUCHBj1qjRg1RkpE+/Z7H58iTfM0jA+QUXvz48QPv3r1rsKiOtYBRHR67NyAgAMeOHYuImfPXw4cPMT09HVu1aoUtWrTQ2p8nNoiYqQ+pOnQjIg4fPhzt7OzQz89P9GywxIbVYdUDVbRu3RrLly8v8sXHxcVh+fLlhVUdNDl58iROmTIF69Wrh1ZWVmhmZoZVq1bF33//Xec8gGi4UAcRsWnTpmhmZoYFChTAYcOGCV2MdcGiB7i5uQkyn7XRA29cWwVvkZy+53Tjxo1oZGSEjRs3xmrVqqG5ubmoA6i+5583Zsbq/5RTEKyPDx8+aNkLLD7KrMQeEQ3rX3J8q6zHRmTXGV68eIEVKlQQijM7duwoitNr3gM/f/7E4cOHY5EiRbBChQq4cuVK0fca8gkhstkbObVaKY88+ju6kbPoMAqFAt3c3LBZs2ZaegWLjsFagHH16lVs3749enl5Ybly5bBr166SDVTUYfFlGPIJqIr41eH1O8vJtdDk9u3bgoyWI9NVyGkoxCPXef3rGRkZ+ObNG3z9+rXBJGo59wCv/4v4daAEXeKXQY7jFjGzomTQoEF6E0LUl4aaPXs2Ojg4YNu2bQUHb9u2bdHBwUEyEMjbwSerHRzev3+vc6K5ePGiEER8/fo1NmjQAG1sbLBs2bKSxhhr8EaOoamLsLAwwVmtYteuXejk5KSViFagQAFRUqGqIwHv7+R1fuoK+r169QpNTExE21iMAc3EA01jWp9i7+/vj/v375d8T5OBAwdivnz50MLCAlu2bIk7d+6UXBJYhZzOjxcvXsThw4djmzZttJYS09yP5xrxPkc8y6Rl9Zl7/Pgxbt++XatjaVaCFCp0GWxS96DKmaWPs2fPYuHChSWTO6XuMdZ7gLf6midxwtHRUfLYR48exdy5c+v8ji9fvuDSpUuxb9++OHToUFy7dq3e+12FruuJmOkMrFq1qhA4HTRoEA4aNAjr16+PSqUSGzdujOnp6ZiQkCDcU5oYSnJDNOxQ4zWQs+JwYn1WeQPEbm5uWonCupDTxZVXZrDI6uzo4KMKdJcsWVJ0zhMTE7Fy5cpCt7bffvsNU1JSsGPHjmhqaopt2rTRWlbQ0tJSsnOLvup7OcE+VngMcDmJ94aKgQwlP6iWNZZCTgcyRN3XEzEzmKEeTPv9999FS3Zv2bJFK6CFKF72VvP82tnZ6RwLIl9SAaJ+ecTb4eX9+/dCd7T09HT8448/sEmTJjhkyBCdDhOeYCLvPcOr86ij7zzqWjYvPT1dqxOACt6gmaGAiVybRwp9cx4in1OQ9x5IT08XBQU3bdqEAwYMwPnz54u64sqVvTxJaBcuXMDffvsNGzdujM2aNcORI0fqnKPkyK8rV67gsGHDsECBAmhmZoZBQUG4detWUXcbfUvTqb/UYT2HujAkM3j0NTMzM0nZFR8fj2ZmZgbHYgie62lqaqpTjuoay4gRI3Q+wyr8/PyEZzYjIwOnT5+O1tbWgi0mpU/FxMTg6NGj0c/PD83NzbFMmTI4ePBg3Llzp+RzwbPCgBS6rinPUvXqsCSKyOkQh8hug6nmZV2vOXPmZEuCrnoi0ZMnT3DcuHE4bNgwyUQLHj2TVzbyzF/ZsWy6vnmANzHD0CogmvDqX7wJ7Ibw8PAQFXtpJjeeO3cOXV1dtT5XrVo17NKli0j+pqamYufOnbF69erCNlXnFUMvFUWLFhU6oSNmyjpTU1OdiTzZJXf1+RBVPHz4ECdPnowlS5ZEIyMjvateGQqCpqenY+vWrVGhUGCJEiUwKCgIg4KCsHjx4qhUKoW59N27d5g7d26RL+Dt27dYsWJFrF+/Pn7//l1S7vLYjnJlI69NxVsgJddmM5Sga2Zmhvfu3dPafvfuXVFioTos/gBzc3PRSh/p6eloampqsJOWIVmqUCiwUaNGWr5Ffb5GFTw6rJzzouLUqVPYt29fdHJyQhsbG8l9eJOQfH19JRNOR44cKSlLeexw3iRXuX4SQ/ei3PGow+Jb44H1uatQoYJk0D0sLEyk/ygUCnz16pVoHysrK4N+W8TM5XJXrVqF9erVQyMjIyxWrFi2BvpZiofkxCp49Yb4+HisVq0a07Lc3bt3l9TVw8PDsXv37oiIOH78eCxXrhxXMXtwcDA2btwY3759i/fv38fGjRtj4cKFhQRhXf5SXp8jT/I1jwzgKTD4+vUrhoaGopGRERoZGQnXs3///kJyvDosBYyasNi9Km7evIl58uTBhg0boqmpKbZs2RI9PT0xb968Wt/LGxtEzCwAUMnYM2fOoIWFBS5duhSbNGkimjtYYsPqsOqBKmxtbSWLF8+fP2/Qx6c69pkzZ4QiT33Jf6yy98OHD7h06VKsWbMmKpVK9PLywilTpmit2oXIH8PLCoZ0Ut7kaEPPqa+vr6h4KyoqCq2srHDFihWIqD9ewhs3ZfF/ZrUgWAqpZhUsPkq5sUdW/UuObzUrzT506QydOnXCSpUq4cWLF/HQoUNYrlw5LF++vGCna/pXw8LCMG/evDhz5kwcM2YM2tnZYc+ePYX3pfyxcuwNOcU3iJl+qiNHjuDevXslfQ1ZlUeaqHdll7uSoCH69u2LDg4OwvOqPrcbgrcAgwdWXwavTwCR3++cHXKadeVJQ/A2FOKV6zx6hiaxsbG4b9++bGucwev/In4tKEGX+GVwdXXldtwiZia4sBguKpo3b44LFizQ2r5gwQIMCgrS2i6ng486+gKnvEtpZBVdwZvsVOzu378vUo5Onz6NJiYm2KJFCzxz5gx+/PgRP378iKdPn8bmzZujubk53rlzB0eMGCHbWcXq+FApWgqFAtetWycKDEZHR2O/fv2wePHier9LyhjQNHg0jWl9xld0dDR6eXnh6tWr8dKlSwaXuE9PT8eYmBjs3Lkz2traooODA/bo0UOnUaSCpfPjpk2b0MTEBAMDA9HU1BQDAwOxePHiaGdnl+UlxHifI95l29QxlKzAuoyCnCAFq8GmUCjw2LFjomttZWWF+/bt03v9fXx8sFWrVnj79m38+PEjJiUliV760HcP8FZf8yROWFhYSCbJ3Lp1S9YSHZrwLL81fvx4dHV1lTy3165dQ1dXVxw4cCC6uLjg/PnzEZE/yQ0RMTQ0FBcvXqzzfZYlQjdt2iT8nRWHEyu8AeL169djy5YtmR2gvGR17tUlqxMSEgTDWNdLE9WzExAQgKamplikSBEcO3Ys3rlzR9inTZs26OvriwsWLEB/f39UKpVYvnx57Nevn84uVQ4ODpKOv1OnTul0MrDOd4aWr5HqQMVjgMtJvDdUDGRmZoadO3fW6ejo1auXzvucJ8Gc5XoiZj4T6vKlatWqOHnyZOH/R48eobW1tdZ32traSnYGvnTpktb+vE4+HnlkY2ODnTp14lpeMyeRc8/I1XlYuH//Ph44cEDoeK0viCCnW5m+gIlcmweRb85DlJeMmt1k1/LGuuyY4cOHo0KhQBsbG/Tx8UEfHx+0trZGIyMjoQv2t2/fBGen3MIhxMz75OjRo9i9e3d0cHBAOzs7oQOKqitEWFgY7ty5U+crK/DKDB59rWTJkqJOlCrCw8PR29tbaztvcZcm+pIKixQpIkpyU7F48WKdyzIOHDgQ7e3tsUaNGti/f38hMUP1QsyUz5rB4o0bN6KVlRXu2bNH8tlTKBSYJ08enD59uqjrmC54VhhAZL+mPEvVS6EvUUROhzgp9BUm6lo5QqrQUKFQ4JQpU5h1mBs3bmChQoVQqVRiiRIl8OrVq5g3b160trZGW1tbNDIyEjofqsiqnmkI1vlLzrLpPPMAb2KGoVVANOEt8GNNYGcteLCwsBDp2RERESJd/smTJ5J2jMoHpUlcXJzO886Cqamp1vUxMzPTaQvwyl3ErPkQ09LScM+ePejr66tTz2AJgkZERGCuXLm0AriImT63XLly4cyZM7FkyZJoYmKilQiQnJyMfn5+WLt2bXz48GGW7Fi5spE3gVITQwVSco+/ceNGvde3YsWKOGDAAK3t/fv3x0qVKhkcty6kOt2yJAkZkqUKhQLbtGmDXbp00fuSgkeHzcp5uXr1Kg4dOhRdXFwMJvOqoy8JyczMTLJb07179yRlktyVdRD5k1x1+Uk0feSWlpa4bNky0Ta5x1fBYsuyLIEstcoEIvtzt3v3bjQ2NsZOnToJzVw6duyIxsbGIn1BqVRq+Y9sbGzw+vXrev1HmsTFxemVu3J0apbiITmxCl69oUqVKlijRg3866+/8OrVq3jt2jXRSx1bW1uddonKz3/nzh20trbmKmbPkyePaN+MjAzs3bs3urq64oMHD3Ta1nJ1QZbkax4ZwFNgMHDgQCxXrhyePHkSrayshOu5c+dOycIulgJGXeize9VJSkrCyZMnY6tWrTAgIADHjBmjFTeRGxu0sLAQYkMjRozAjh07ImJmPEHdbueNDfPqgdbW1nj16lWt7VeuXNFZ2IGYeb2XLl2K7dq1Q2dnZ8yVKxcGBwfrLdpkTdBV5+nTpzhjxgz08PCQXOElq6SlpeHVq1e1EqLk6KS8ydGGnlOpoomjR4+itbU1Ll68mDlewhI3ZUFOQbChGMXJkye1PsPio5Qbe+T1IfL4VnmOzer/zp8/P54/f174//v379ikSRP09fXF9+/fa53zokWLimyY+/fvY9GiRbFLly6YkZEheY2y6ovRxcePH7FTp07o7e2N3bt3x0+fPmHVqlUFH0nevHm1rpFceaROcnIyLl26VOg8rELOSoKsOsz3798xMjIS69ati5aWltiqVSs8cOCAwSJT3gIMxMx7x1CsD1HeajasZMXvrAtNH6fmq0OHDkzyTpdMV8HbUIhXrqvQp2dMmzZN6NCv2rdBgwaiZ+PWrVs6j816D/D6v4hfC0rQJX4ZLCwsZDlumzVrhlFRUczfY2VlpdMhIDXB8Hbw4Qmc8i6lER4ezlSprYI1eJMdip2KdevWobOzs/B/QECAqApNk549e2Lu3LnR0dFRcN7w/k5Wx4fmEvLqL1NTUyxevLhWkIHFGMhKgi7PEveafPv2Dbds2YI+Pj5Mype+ToGImctiLVy4UPQbMjIysEePHjh+/HjRvrzXiPc54lkmjTdZgXUZBTlBClaDTV+gWt/1t7S0lDyPrOi7B3iqr3kSJ2rXro2tWrUSPUtfv37FVq1aGVzSJS4uDvfv36/VaUsdnmUxihcvjtu2bdP5/pYtW1ChUIgc/nKS3KZOnYq5c+fGzp0746xZs7QSCkqWLKk3yUOVLK8iKw4n1meVN0Ds6+uLNjY2aG1tjd7e3jqXepULr8xgldX6gvxSz12bNm2ESvp+/frhmTNnJI/t7OyMZ8+eRcTMSleFQoFz5szRO56OHTtiyZIl8dy5c5iRkYEZGRl49uxZ9Pb2xs6dO0t+hnW+09d5SlcHKh4DXE7ivaFioHLlyuGff/4p+VnEzOCprvucNcGc9XoiZhrpqk4eP378QAsLCzx8+LDw/o0bNyQTqQMDA7FVq1YinS4tLQ1btGiBDRs2FO3L6+TjkUe8y2vyLMmtgieYmNViDX06T0ZGBm7ZsgX79OmDLVq00NuV6927d1i7dm3heVfN6V27dtVaAUJFVrpyIWoHTOTaPIj8S0HxOAV574EiRYpgWFiY5LlRhzfZTgWLHbNmzRo0NzfHBQsWiDpU/vz5E+fNm4cWFhYYFRWFtWrVwvDwcETMvsKhy5cvi4LtFy9exN69e6O9vT2WKVMGFyxYYPAasZ5DFbwyg0df27ZtGxoZGWGDBg1w0qRJOGnSJGzQoAEaGxtjdHS01jF4i7t4kgr//PNPNDU1xd69e+O6detw3bp12KtXLzQzM5P8PYiItWrV0vlSdYp0cnLCS5cuaX1206ZNaGlpiYsXL9Z69ubMmYPNmjVDR0dHzJ8/P7Zr1w6XLl2q85rxrDCAyH5N5SxVrwvNRBG5HeJYbbD8+fPrTU7XnN8VCgUWLFiQWYdp2LAhBgYG4qlTp7BXr17o4uKCXbt2xfT0dExPT8e+fftqJWjx6Jly5kd19M1fvMumI/LNA7yJGSzL6qrDW+DHmsDOWvDg4OCg93i6it7y5MmDMTExWtsPHDiAefLkMTg+FdevXxfZbEqlUtJ/oOtZ4pW7iPw+RMTM89CnTx+hQ2iHDh10ruLEEgQtVaqU1nKw6qxYsQKVSiU2bNgQixcvjvv27dPa5/Pnz+jn58fsy9KFXNnIm0DJWyDFcvy0tDS8fv26kNCgTkpKCl6/fl1L34+NjUUrKyv09PTE0NBQDA0NRU9PT7S2tta5LDuLP0ChUGCvXr1EQV5TU1MMDQ3VKnZRx5AszUqRFo8Oy3teVB2lvby80MjICGvXro0rVqzQWfTOm4RUoEAB3LJli9b2qKgoLFiwoNZ2Hjuct4Cc1U9i6KXrOc3uAlJ9Sx83adIELSws9PoEWJ/rvXv3YpUqVdDS0hIdHR3R399fq4hGyn+kvs3QeYmKisKgoCA0MzNDV1dXnR2FeXVqVuTEKnj1BktLS8lERyny5MkjmQizdu1aYe6Ni4vD3LlzcxWz29jYSOre/fr1wwIFCuCJEyckr5OcFTs00ZV8zSMDeAoMXF1dBb+j+vW8f/++ZPyOpYCRBU27V4V6Z1BNVPElRHmxQcRM201V/O7r6yt0d05ISBDJRt7YMK8e2LRpU6xRo4ao2cazZ8+wZs2aOpdlz58/Pzo4OGCzZs1w3rx5eO3aNYOJaIiGC3U0+fnzJ+7YsQNbtGiB5ubmkgXkvDG8QYMGCQ1B0tLSBB3LyspKNC/L0Ul5k6MNPafqvnh1YmNj0draGseMGcOsZxqKm7L4P+UWBPPGKlh8lHJjj6z6lxzfKo9ux+r/trKy0pr7U1NTMTg4GEuXLo03btwQ/U4LCwutWOezZ8+wePHiGBISgs+fP9c6L3LsDZb7pVu3blisWDGcPHkyVqpUCf38/LBy5cp47tw5vHDhAtaqVQsDAwNFn5Ejj1QcP34cO3XqhFZWVlisWDEcOXKkKE4jZyVBOTrM48ePccKECeju7o6urq74+fNnnfuyFmCkpKRgv3790MnJyeBKZipychWxnGhYpVQqsWzZsjr9nuXLl5f8rawyXQVvQ6HsKH7W1DPKlCkjWpVry5YtaGFhgadOncL3799j48aNsVWrVqJjyLkHeP1fxK8FJegSvwwlSpSQ5bhdsWIFurq6YlhYGG7btk1vIhdipjE7a9Ysre2zZs2SXP6Ot4MPT+CUdymN0qVLo1KpRD8/P1y0aJHBJZBZgzdyFDvN5Ifg4GCsVKkSGhkZiVr6Ozg4SDoaVVy/fh0VCoUoQZj3d/I6P93c3JiXj2YxBrKSoMu6xL0mL1++xDlz5mC5cuVQoVDo7ArB2ikQMVPhVRkouXLlEq7b7du3MV++fKJ9ea8R73PEs0wab7IC6zIKcoIUrAaboeuu6/r7+/vrDKbpguceUGGo+ponceLmzZuYP39+dHR0xNq1a2Pt2rXR0dERXVxcdFa6PXjwAEuXLq3lTJBSqHmWxdDsiKlJYmKi1vF5k9wQDS/LWatWLaxcubJk4DMqKgqNjY1xxowZwrasOJxYn1XeADHPsi6GOqBIwSszWGV1dHQ0xsbG6n2p0759eyZHo1KpFCW3WFlZCcsU6+Ljx4/YtGlToUjE1NQUlUolBgcH6wwO8s53PPAY4HIS7w0VAw0cOBAHDRqkc3wJCQlYq1YtyfdYE8xZryciYu/evdHPzw9PnDiBQ4YMQUdHR9GS8xs2bMDy5ctrfS4uLg4dHR2xSJEiQleoIkWKoJOTk2jpKER+J58cecS6vCbPktwqeBxxWSnWMKTzDBw4EM3MzLBhw4bYuXNnvV25OnbsiA0aNMCnT5+K9LUDBw6gl5eX5PdnpSuXVMBErs2DyL8UFI9TkPceiIiIEByA5cuXx7lz5+LLly+19uNNtlPBYsdUqFBBsqOKitmzZwtOTNUxsnIvPn36FKdPn44+Pj5oZGSE1apV0+qY/+3bN1y/fj3Wrl0bLS0tsU2bNjqXs2Y9hyp4ZQZvouulS5cwJCQEy5Yti2XLlsWQkBDJjuAqeIq7eJPLo6OjsWrVqpgrVy7MlSsXVq1aNcsdiOvVq4czZ86UfC8yMhJNTEz0BvBu3LiBCxYswGbNmqGJiQm6uLho7cO7woCcYA/PUvUq9CWKyO0Qx2qDNWnSBMeNG6fz/WvXromWkeRN6HJ0dBQ6y3z+/BkVCoUoEfvOnTtaqwLx6Jly5kcVhuYv3mXTEfnmAd7EDEOrgGjCW+DHmsDOWvBQu3ZtHDZsmM7jDBkyBGvXrq21fcCAAVigQAHcvHkzJiYmYmJiIm7atAkLFCigVxfVROrebdSokchPZmxsjPXr19dZOMQrd3l8iKNGjUI3Nzc0NTXFxo0bY2RkpEHZxBIENTc3FzraSfH48WNUKpX448cPHDBgALZs2VJyv+TkZKxUqZKW3JVjOyLyyUZem4p3DmM5/urVq7FcuXKSdklqaiqWK1cO169fr/Xe8+fP8ffff8fmzZtj8+bNccyYMXpX6GHxB9SsWVNvoYt6sYs6hmSpUqmUnaDLG9hmPS+qe87X1xdnzpyJz549MzgW3iSkiRMnor29PU6bNg1PnDiBJ06cwD/++APt7e0lkx95kxF5CshzelWVnCwgVWfnzp3o5eWF9vb2QgMHTbLbV2LIbyTlPzpw4AB26tQJbW1tMVeuXNizZ0+h6FcfPDo1IlvxkGbRiGbBiFSsgldvKF++PHPBVnh4OFpYWODAgQNx/fr1uH79ehw4cCBaWloKKxVFRERg3bp1uYrZK1SoICRuatKvXz+0t7eX1O/lrNiByJZ8zSMDeAoMLCwshDla3Zdx7do1ydUGeWW6Oix2r729vWTx49y5c0UJw3Jig4iZ/ruyZctit27d0NLSEt+9e4eImd361QvZeGPDvHpgYmIi+vr6oomJCbq7u6O7uzuamJhgmTJldNpsPj4+aGZmhn5+fjh69GiMiYnR0sHkFOqokOo6ePjwYckkYN4YnouLiyCbd+zYgfnz58d79+7h2LFjsUqVKsJ+vHFtRP7kaEPPaVBQkFZTIRXHjh1DKysrvfY9T8yMxf8ppyDY1tYWp0+frnOOWb58udZnWHyUcmOPrPqXHN8qj27HqjOUKlVKshmPKknX1dVVdP4KFy4sar6h4vnz51i8eHGsV6+e3nuG1d5guV/y588v6BHPnj0Tuh6rOH/+PObNm1f0GV559PLlS/zjjz+waNGimCdPHuzfvz8aGxtL+m/kriTIq8MkJibixIkTsXDhwuji4qI3QZe1AKNv377o6emJ27ZtQwsLC1y1ahWGh4djgQIFcMOGDZKfYfVlqOZMfS9NnwOL37lMmTKCXenr66vV/EizEVLx4sUlbUMVuhrbsMp0FbwNheQWP+vTM+zt7UWyokuXLkIXfcTMFTsKFCggOp6ce4DX/0X8WlCCLvHLIMdxi6i/0ltq/9WrV6ORkREGBgZieHg4hoeHY2BgIBobG+Pq1au19ucNbMrt4MC6lMatW7dw9OjRWLhwYTQxMcFGjRrhxo0bJR3trMEbOYamZvJDaGgojhw5UmsyNjc315to+vjxY8nuYzy/k9f5yQOLMaCZyKmZxKlvWW4ePn36hKtWrcK6deuisbExFi9eHCdOnKhz+SmeToGImcqaymFSqlQpjIyMRETEM2fOSDp6eK6RnE5Y6pw5c0bnMmlynzlDyzXJDVKokBM8N0R0dDR6eXnh6tWr8dKlS6IlaaSWpeG9BxDZqq9VY2FNnEhJScFly5bhkCFDcMiQIbh8+XJJB5SKwMBADAoKwrdv36K1tTXevn0bT548iRUrVtTZGYZl+S1djhIVFy5c0LnMCGuSGwufP3/GcuXKYb169UQB/i1btqCpqanW0hpZdTixPqu8AWJWNDufNG7cGAsVKoR2dnZaQWoVvDKDVVZnxzLrUmgGP2xsbJg7FMTHx+Pu3btx9+7dBjtkZ2W+e/r0qd6gGY8BLifxPitV3oaQ04HMEG/fvsXq1aujQqFAGxsbrePUrl1bWLpMk+fPn+Po0aOxUaNG2KJFC5w4caJoyVIpeOYMufLI0PKarEtyq8PqiOO9Z3h0HgcHB8mEVyny5s0rOEbVncgPHjyQXEkDUV63Mn0BE7k2jzqsS07yJqPKuQfu3buH48ePx2LFiqGxsTHWq1dP1B1JruxlsWMMJRI9ePAAFQqFqGu9HPm1ZMkSrFGjBhoZGWHJkiVx6tSpeu0bFQ8fPkR/f39UKpV6ZYChc6jr2CwyIycSXaUwVNzFm1wuF31LLEZHR+PgwYN1fnbjxo2ShSAZGRl4+fJlnD17NgYGBqK9vT0aGRlJLiWblRUGeOYBlqXqEdkSReR2iGO1wU6cOKG3wPDLly8iGcOb0CWnWJZXz+SRjbw2uyb6lk1Xh2Ue4LVjDa0Cogmv/sWbwG6o4GHbtm1obGyMCxcuFOm4aWlpOH/+fDQxMcGtW7dqHffHjx84cOBAoThOqVSimZkZDh48mEsmXbt2TXRvafrIdL2yC0M+xCpVqjAlYqjDEgR1cHCQ9D2ouHHjBtrb2yNipp6ob/nJ5ORkredIju2oglU28tpUvHMYy/GrVauGmzZt0jnGqKgovcuC8sDju+PBkCzNiv2dlYIqffz+++/cyxPzJiFlZGRgREQEuri4CPEJFxcXnDt3rqR857HDeZNcs5IUy0JOFpAiZiYpV6tWDS0tLXHEiBF6E+R5n+tLly4JiaLZ4fdCzEw+atWqFVcxjyYsy9WzFA9pFo1oFow0atRIS07y6g1HjhxBPz8/PHbsGL57987gcsIbNmzAypUro4ODAzo4OGDlypVx48aNwvtfv37Fb9++cRWzT506FQMCAnSezz59+oiKaVTw6oI8yde8MoC1wKB69eo4f/58RBQnXPfv3x8bNGig8xzwwGP3Ll++HJ2cnETJjLNmzUJbW1uRn0RubPDjx4/Yr18/bNq0qciWGD9+vChpjDc2LEcPzMjIwIMHD+L8+fNx/vz5eOjQIZ2/R338u3btwiFDhmC5cuXQwsIC/fz8BD+i3EKd/Pnzo7m5OQYHBzPb0zx6gJmZmWDP9ejRQ0hafvjwoc6VVlnj2rzJ0Yae09jYWJw6darO33306FGdurecmJkh/6ecguBatWrh9OnTdX6nZkEgojwfJSus+pcc36oc3c6QzjBixAisX7++5GdTU1OxadOmonPerVs30cqZ6jx79kwoMNMHq71h6H4xMjLCFy9eCPtbWFiIfAYvX76UPD6rPAoMDERbW1ts164d7t27V5A1uhJ05a4kqI4uHeb79+8YGRmJdevWRXNzc2zZsiXu27fPYHEXawFGwYIFheRmGxsbIb62bt06nToCqy9j8ODBOl/dunWTXN2Bxe88YcIEQQazNEJq3769Xn+mlKxA5JfpvA2FeOU6i56h6dMrUaKEKJH2yZMnWnqDnHuA1/9F/FpQgi7xyyDHcSuXc+fOYfv27YVAWfv27fHcuXM695cb2ORN0DO0lIYmp06dwr59+wpL1emCJXgjx9BkoVSpUrhq1Sqd769cuRJLlSql9xiGfiev4wMxMxC4b98+XLx4MdPEq88YYEnk1NX1DzFTSahSpQo6OzsLysicOXO07jFzc3N0dnbGwYMHMzlUeToFImYuqzl79mxERJw0aRI6OTlh9+7dsVChQgYDISz34t+RICA3KVbXck1ZhdVgQ0T09vY2uHQi77I0PPcAT/V1TqPeCcvW1lboRHrkyBHJZAhNdF3P1q1bY/PmzXV+rnnz5lrLY0hhKMlNHVWARZM3b96gh4cHtmzZEjMyMnDr1q1oYmIiWbmZFYeTJqzzBitygxrp6enYs2dPvU4oOTJDn6zOzgTdixcvipzxCoUC7e3thUCDQqFAOzs74X/VK6vIme9UGFq2hdcA5028l1MMxENWEsw1r6c6SUlJkjL0/fv3oo662QHPnKHCkDziWV5T83O6luTWhaFgIs89w6PzuLm5MS+taW1tLSx/pu7wuXjxIubKlUvn53i6lRkKmGS3zaNPh5GTjKpCzj1w9uxZrbFkpXsaon47xsbGRu+1v3v3ruRcxyu/ChQogMOHD2decvbp06cYHh6ORYoUQWdnZxw5ciSmpqYyfVbqHOpCjsxQRz1wrhlQNxRgV4e1uAuRPbmcF31LLA4ZMkT2cQMDA9HBwQGNjIywbNmyOGTIENy1a5co6VsdnhUGpDB0TXmWqkdkSxSR0yFOk+wsTOTV1xQKBXeHOET5tqkh2chrs2cH2WXL8nY6R+TTv7KawC5V8DBixAhUKBRoa2uLvr6+6Ovri7a2tqhUKvV210XMnAtu3LiBN27ckJWoqJmgy0J2yV0VvD5EQ7AEQRs1aoS9e/fWeYxevXrpTZaSgyHbkVc2yrWpWOcwluM7OTnp7CyFmHnP586dW9RBT7NA21DBtq5zxeIPOHXqFFPSjz5ZGhsby6z/aGJIh83u88IKaxKSiuTkZExOTta7j5yVdXgLNnn3513yNbsLSOPi4oRmKqGhoUz+Atbn+vXr1+jv748KhULkv6ldu7ZIn5CiUaNGomQaTQxda0Pw6NSGiof+jqIRzdXODBV38cJTzC4HHl1QbvI1iwxg5eTJk2htbY29e/dGc3NzHDRoENarVw+trKz0NqNAzLRPWZ4jXrt3+vTp6OLigo8ePcJp06ahra0tnjp1SrRPdsQGc4Ks6oGsvHv3Drdt24YdO3ZEY2Nj4dmQW6izbNkynbYoC4b0AFdXV4yJicG0tDQsWLAg7t27FxEzk3xVBVhSsOikcpKjcyqeyBs31UTK/ymnIHjZsmV6E8FevXol6UfgXVEBkS32yOpDlONbzYp/ElFaZ0hNTdVrN6WmpoqS/x4/fowHDhzQuf/z589xzZo1ku/x2hvqSN0vWVmVlwUjIyP87bffhOukQleCrtyVBFXo0mH69OmDDg4OWLp0aZw7dy5X8ShrAYaVlZWwwouLiwueP38eETPtKV0J44jyY0mpqak4d+5cdHJywqJFi0rKcl6/syFevnzJ1ChCE7kynVUH45XrLHqGj4+P0EzxyZMnqFAoRPfs6dOntVY0k3MPyPF/Eb8OlKBLEP9xDAXZ5Cw/r+Lq1as4dOhQdHFxkaw0lSK7ExEvXrwoVDBKOQMiIiIwV65ckp3N9u7di46OjkJSqC54fieL4+PKlSuYL18+tLW1RSMjI3RyckKFQoFWVlZMEy9Pcp4h/vzzT8ydOzdOnjxZtFTR6tWrtQy2nFqSTMX79+8FQy49PR3/+OMPbNKkCQ4ZMsTgMn5y7kV9rF27Vu9LH6zJCizLNWnCGqSQY7BpGmFS8C5Lw4qc6mt97Nq1S3BYagb1DC01hZi5jIUqsO7u7o5Hjx5FxMwl7i0sLCQ/w3I94+Li0NraGitVqoRRUVF4/fp1vHbtGm7atAkrVqyI1tbWOh0CvElua9euRW9vbzQzM0MzMzMsVaqU1rJriYmJ6OrqinXq1EFTU1MMDw/XebzsQv1ZNTMzE7bzBoizEtRQcffuXcyXL1+2/TZNNGW1m5ubsCRaVvHw8BDJlzVr1jC9fvvtN/zy5QsiotZygYaWD9SE19HPImMQczYIwlIMdP78eZw7dy6OGjUKR40ahXPnzhWM6pxC83qqo5I/UixcuFBy+8ePH3HWrFnYrVs37NatG0ZEROgMriLyzxks8kju8pqIhpfkloInmMgCj86zZs0abNu2LZOjKyAgAMeOHYuI/5fElZ6ejq1atcIWLVpkacwqshowYYFHh5HjFOS9B86fP4+DBg3CfPnyCV0OVUgl2xnq6K0LTTumZs2awvWUYsyYMVizZk3u79GEpVjpx48fuHnzZqxXrx6am5tjs2bNcM+ePcwBH33nUJOsBAbUUU+elgquGwqwZ7W4S/16Ojg4CI569YIXqZcUrEssbt68Gdu3b48tW7ZkWsps2LBhuGfPHr0yPDswdE3lLFWPmPVEER54E8YfP36McXFxWrJAvZMIC3I6xMmFRTbyzF/fvn3DGTNmYEBAAJYrV44rYVWOLftPIyeBnaXg4ezZszhw4EAMCAjAgIAAHDhwIJ49ezbL4zVkH508edLgvRUZGSno/ohZl7uIWfMhIiK+ePFCCF5pwhIEPX36NJqYmGCrVq3w/Pnz+OnTJ0xKSsKzZ89iy5Yt0cTERJSc8/btW5w+fToGBwdj5cqVsXLlyhgcHIwzZsxgthsRpW1HubJRHbnJU6y+VV3Ht7S01Js8ev36dbS0tBTpUeoF2ixdAqVg9d3xJmhKcebMGdyzZ49o29q1a9HNzQ2dnJywR48een1P+nTY7D4vO3fuNOhrVJHdifEq5NrhvD5qlv1ZfQdyjq/Plk1MTMQuXbqgsbExBgcHSy6FzYK+57p169ZYvnx50bHj4uKwfPny2LZtW73Hzcp50UdWdWo5hZXZgZzirh8/fuDTp0/xyZMnold2o0vPlEt26tRZKTBISEjA7t27Y4UKFdDT0xNDQkKE1RA1SU9Px4kTJwqFS0qlEu3s7HDSpEk6z4ucJh0jRoxAR0dHtLe3l9S/eGKD/1TxhSbz5s0TunxqNvZhafSzfft2HDBgAJYqVUqIPTZr1gznzZsnJCWxFupkN4b0gLCwMLSzs0MPDw90dXUV5umVK1di5cqVRfvK1UmzKzk6PT0dp02bhlWqVMHy5cvjyJEjs5QAx4ou/+fx48dld0//O2Cdw1h8iHJ9q7z+SRb/96dPn/DgwYO4d+9eZtvi+/fvIhtNH1m1N3TdLwqFAqdMmSLIEnNzcxw3bpzw/+TJk1GpVMqWR2fPnsXu3bujjY0NVqxYERcsWIBv377VmaArdyVBQzqMQqHAQoUKYXBwsOCjkXplhVKlSgl6R506dXDo0KHC+dJM5MwqGzZsQHd3d3R2dsZFixbJLkZUJyMjAy9evIhbt27Fbdu24eXLl7Xm4+PHj8v6Lh6ZnhVY5TqLnrFs2TK0srISuoP7+fmJ3letiq7O33kPEL8GCkREIIhfgLNnz8L79+8hMDBQ2LZu3ToICwuDlJQUCA4OhgULFoCZmRnMnz8fevbsCebm5jB//ny9xx04cKDWtvT0dNi5cyfcuXMHAABKliwJTZs2BSMjo2z7PadPn4aNGzfCtm3b4Pv37xAUFAQhISHQsGFDYZ+2bdvC3r17wdLSElq3bg0hISHg5+en97iPHj2CyMhIiIyMhHv37kHNmjWhffv20LJlS7Czs5P8zLNnz4TP3Lp1C/z8/MDZ2RnWrVsn+xw+e/YM2rVrB6dPnwZ7e3sAAEhKSoIqVarA5s2boUCBAgAAkJGRAW3atIHt27dDiRIlwNPTExAR7ty5A/fv34egoCDYtm0bKJXKLP9OVmrVqgXFixeHJUuWgJ2dHVy/fh1MTEygQ4cOMGjQIGjevLnWZ75//w67d++GyMhIOHDgAOTNmxfatWsH06ZNA3d3d7h48SI4Ojpyj8XLywumTp0KwcHBYGNjA9evXwd3d3e4desW1KpVC969e5el3yrFpUuX4OvXr1CjRo0sHScnr5GDg4Po/9TUVPj69SuYmpqCpaUlfPjwQeszLM8cAMDSpUshMjISTp8+DR4eHhASEgLt27eHQoUKGRyXra0tXLt2Ddzd3SXfHz16NGzevBlevHgB9erVg5CQEAgKCgJLS0uDx1a//jmN5j2wfPlyaNWqlfAsS5ErVy6Ij4+H3Llzg4ODAygUCp37JiUlwatXryBPnjxaz7Y6CoUC0tPTtbZXr14dhg4dCsHBwdC+fXv4+PEjjB07FpYtWwaXL1+GW7duCfvyXs9z585Bt27d4M6dO8JvQETw8PCAFStWQJUqVUT7x8TEQGRkJOzcuROMjY2hZcuWEBISovf5iYiIgHHjxkH//v2hatWqAABw6tQpWLRoEUyePBnq1Kkj7Hv37l3o1KkTBAUFwZgxY0THKV26NAAApKWlQXp6OpiZmQnvvX79GpYsWQIpKSnQtGlTqFatms7x6HpWe/bsCS9fvhSuk9Q1RUTJ69SmTRt4+PAhrFu3Djw9PQEA4Pbt29C5c2coWrQobNq0Sed4VPz111/QuXNnePv2rcF9WdEnq3Vx/PhxSElJAT8/Py3Zo4sXL15Aamoqk9xQx9/fH3bs2AH29vbg7++vd99jx45xHdsQWZUxu3fvhoCAADAxMYHdu3fr3bdp06bcx3/z5g20aNECTp8+Da6urpA3b14AyLzXExMToWrVqrB9+3bIkycPAAAkJyeDra2t8Lc+VPvpQt/1dHBwgMOHD0O5cuVE2+fNmwfjxo3T+u5Lly5BgwYNwMLCAipWrAgAABcvXoRv377BwYMHoWzZssK+vHMGjzyytLSEwMBACAkJgUaNGoGJiYnec5CcnAzbt2+HyMhIiI2NBXd3dwgJCYGQkBAoUqSIzs8dO3YMIiMjYfv27ZCRkQHNmzeHkJAQqF27NuzZsydH7xkV3759g2bNmsHp06fBzc1N67deuXJF+PvWrVtQp04dKFu2LBw9ehSaNm0KcXFx8OHDBzh9+rTwW2/cuAHe3t6gVCrhxo0ber9fJatZ4bF5NMmKDmMI3nsgPj4eNm7cCJs2bYJHjx5B7dq1ISQkBJo3bw7W1tbCfhMnToThw4eL7mtD+pQ6UnZMSEgI9O7dG/bu3QvBwcEwZMgQGDp0qCA3Xr16BbNnz4a5c+dCdHQ0IGKO34uOjo5gY2MDnTt3ho4dOwqyShN1ecR6DlWwyAwefW3Hjh1QtWpVMDY2huPHj+v9fTVr1hT97+LiAh8+fICGDRtCSEgINGnSRPKe1UTX9bSwsIC2bduCmZkZrFmzRu+4O3furLUtX758EBMTAz4+PqL57uHDh1C6dGn48uULLF68GPr16wfFihUDCwsLuHnzJgwZMgRmzpxpcNwAmTqGubk5076ssM4DVatWhZCQEGjdujXkzp07W8eg4tOnT3Do0CF4/PgxKBQKKFy4MNStW9fgHGrIBlu1ahUkJSXBkCFDhM/07NkTVq5cCQAAJUqUgJiYGChYsCAAALx79w5SUlJEci0uLg5mzZolyMj27dsL73Xt2pXp961evZrtRGggd35kISQkBA4ePAgtW7aEvHnzat33YWFhWp8xNA80b94c1qxZA7a2tpJ+DXWio6O5x5yd+pcufv78CTt27ICVK1fCyZMnISAgAEJDQyEgIEC2z07OedFlH6nQZSepoznfHT9+XLbcBZDnQ9TE09MT4uPj9Y7bEDt27ICePXtq+WUcHBxg6dKl0KJFCwDI1IEbNGgAlpaWULduXZF+f+TIEfj69SvExMRA+fLlDX6nlO34d8hGdfTpJLz4+vpC7969dX72zz//hGXLlsGuXbvA1dUVFAoFPHnyRO8xdemDcnx32eGfCggIgFq1asHIkSMBAODmzZtQtmxZ6NKlC3h6esLMmTOhV69eMGHCBO5jP3nyRNZ5+fHjB6SlpYGVlZVoHw8PD7h//77O5+Lr16+wY8cO2LhxIxw5cgQKFiwI7dq1g5CQEPDw8ICyZcvCkSNHwMHBAcqUKaNXdqjbJnLh9Xvw7s97/VmOz2LLWlpagkKhEPnSpMiK7WhnZweHDx+GChUqiLZfuHAB6tevD0lJSTo/K/e5qFu3Ljx8+BAePnyo9Z5cnVrFq1evYPPmzbBhwwa4cuUKVKxYEc6dO6dz/ydPnkBKSgp4eHiAUqnMcb1Bxf379yE0NBTOnDkj2q6aSwcNGgTh4eFgZWUl0hmliIiIEP7m1TNzCh4ZcO3aNZHPXKFQgFQKgCEdwxCjR4+GlStXwsSJE0W+6QkTJkCPHj1gypQp3MfUFUOcNWsW1KhRQ/CBAfxfPJEnNqhUKvWeG/X/v379yhzX5L3PCxcuDJcuXQJHR0coXLiwzn0VCoXkc50nTx6oUaMG1KpVC2rWrAmlSpXS2sfKygrOnj2r059048YN8PPzg5SUFL3jVfHnn3/Cu3fvYPz48Vrv8eoB27Ztg6dPn0KrVq2EGO/atWvB3t4egoKCACB7dNKsEh4eDhMmTIC6deuChYUFxMTEQLt27WDVqlV6P5eRkQFr1qyB6Ohoke3bsmVL6Nixo87nV5//U6FQgJGRkRBnyW6yw0eZnbFHVt+qXFj939euXYNGjRrB69evARHBxsYGtmzZAg0aNJA87tu3b6FTp05w+PBhyMjIgAoVKsCGDRugaNGiOsci194wdL+4ubnpnSvUyYo8SklJgaioKFi1ahVcuHAB0tPTISIiAkJDQ8HGxkZr/0+fPoG1tbWW3f3hwwewtrYGU1NTYRuLDtOlSxem3ynXXwMAMGfOHDAyMoKBAwfC4cOHoUmTJoCIkJqaChERETBo0CAAyJov48CBAzBq1Ch49OgRDBs2DIYMGSKyJ+TGzY4dOwbdunWDJ0+eCPObSiatWrVKuOezIl8MyfQhQ4bI0sFyktWrV8Pu3bvB2dkZwsLCBD8CAEDfvn2hXr160KxZM2Eb6z1AEKxQgi7xy8DjuMuKkZSQkACNGzeGZ8+eQYkSJQAA4N69e1CwYEHYt28fFClShCuwqemQ5km2UAV2GjRowBRoqFy5Mly8eBFKly4NISEh0K5dO3BxcdG5v77gTVYNzYYNG0JSUhKsXbtWdB67du0Ktra2cODAAdH+UVFRsGnTJoiPjwcAgGLFikG7du2gbdu2sn5nVpyf9vb2cP78eShRogTY29vD2bNnwdPTE86fPw+dO3eGu3fvCvuyGAPqzgNeLCws4O7du1CoUCGRkXT//n0oXbo0fPv2zeAxfv/9d3j16pVB41OFroBMp06dwN/fH2rUqGHQiGK5Rll5jqS4f/8+9OnTB4YPHy4ysngTnNQd6T4+Pga/Vx1DhmxWAkSNGjWClStXgrOzs2h7TiTF6boHEhIS4MGDB1CjRg2wsLAQHKUAmUp7VhIneIiJiYGUlBRo3rw5JCQkQGBgIMTHx4OjoyNERUVB7dq1hX3lXs9r166J5FGZMmUk9+NNcgMAKFy4MEycOBE6deok2r527VqYMGECPHnyRHAqqjsXNf9WXZ+uXbuCqakpLF26FAAAPn/+DCVLloTv37+Ds7Mz3L59G3bt2gWNGjXSGou+ZzUrAWKeoIamYYeI8PLlS9i3bx907twZFi5cCABZkxkssnr69Onw5csXCA8PF8YREBAABw8eBIBMB+qRI0egZMmSes+FPp4+fQoKhUIwdi9cuACRkZHg5eUFPXv2lHXM7Ar2/fHHH9CnTx9RIj6PAT537lzuxHuegqoTJ07AixcvYPXq1YJeoeLevXsQGhoK+fPnh61btwKA2DHBm2DOw4oVK+D333+HEydOgIeHBwAAzJ49GyZNmgR79+6F6tWri/avXr06FC1aFJYvXw7GxsYAkJlk3717d3j48CGcOHFC2Jd3zuCRR58/f5Z0tOnCwsICHBwcoE2bNhASEsKUKGHIEacZXNEFzzWS0nlat24Nx44dY05w+vTpEyxcuBCuX78OX758gbJly0K/fv1E86+hwJCcsasCJmfPnpWdrMAy58nVG3jvAaVSCRUqVID27dtD27ZtRc4yQ7AEBliTkRcsWADDhg2DtLQ0Ibj06dMnMDIyghkzZsDgwYNz5F4EEOtT6sdllUe855BFZsjV1xITE6FgwYJa+yMiPH36FFxdXUXbWYq71MnJ5HKAzHvqypUrUKxYMdH9pSqaeP/+PZQsWRJat24tyIQNGzZAr1699AY9MzIyYMqUKbBkyRJ4/fo1xMfHg7u7O4wbNw7c3NygW7duWdJf/u7kMhWaiSIbNmyA/v37awUp7OzsYMmSJdCmTRutY7DaYJUrV4ZevXoJibQHDhyAJk2awJo1a8DT0xP69+8PXl5esGLFCgAAaNeuHeTPnx9mz54NAJkFPB4eHpA/f34oUqQI7N+/H1auXAkdO3bk+s1yr5Oc+VEKqfnLzs4O/vrrL71JSJoYmge6du0K8+fPBxsbG4PJy6tXr+YOxvwd+hdrwcPjx48NHsvY2Bjy5csHQ4cOFc6LoSChKjhoyD5SIZVIq0LffMcrdwH4fYhSXLx4Eb5+/ap33ACGixJUybX3798HgEybWpWMq6Jy5crg4+MDS5YskfydvXv3hhs3bsDZs2eF7ay2Iy9ZsalY5jDe48+YMQNmzJgBR48e1UqkuH79OtSpUwdGjBgBI0aMELafOHECqlSpItgYKtLS0uDMmTOSRXu8fmQVuu5dHllqZmYGe/bsEeTmmDFj4Pjx43Dq1CkAANi6dSuEhYXB7du3AUC+DstyXuQkZKhgSUJSL0ibMGGC3vMSFhYmOxDOW0Aup+AcAKBPnz4QHh5uUDfJ7gJSfXq6CvX5Rc5zbWNjAydPngRfX1/R+1evXoWaNWvqTdjw9vaG/fv3SyZ7njt3Dvbs2QM/f/6EOnXqiJpFLFq0CN69eydZeMOrUwOwFQ+xJq6OHz+eS2+QmySm8j2OGjUKnJ2dta7V4MGDZRWz8+iZ2R2nAPg/nbpz587MMqBLly7MBQYODg6yk4ny588PS5Ys0YoV7Nq1C/r27QvPnz/Xezx1VHavlG4ihVQ8kSU2yFp8Ua1aNbh27RpzXFNdP2bVA3Ma1kKda9euCdsQERISEuDnz59QokQJ0bxXp04dePTokdZ559UDHj58yJTAyaqT8iZH8zynjo6OMGzYMOjVqxcAABw+fBgaN24M37590zmfICI0adIE/vrrL/Dx8QEPDw8hYfzmzZvQtGlT2Llzp9bnWBIR5caI3717B6tWrYKzZ8/Cq1evACCzALlKlSrQpUsXcHJyyhYfZXbHHll8q3KPzer/btCgAXz58gVmzZoF5ubmEB4eDjdv3hTsE01CQ0Nh//79MHDgQDA3N4elS5eCs7NztjdJyWrxTU5x7949WLlyJaxfvx6SkpKgXr16wnVZvXo11KlTh1nOA8jTYQzBE0sKDAyEwoULa8mJJ0+ewOXLl6Fo0aIiXUSOL+PChQswcuRIOHfuHPTu3RvGjBkjqR/L8TsnJCSAj48PVKpUCQYNGiTIo9u3b8P8+fPh0qVLcOPGDXB3d5ctX1hkOm9DoZwo7lL3r2dkZMDMmTNh165dkJqaCnXq1IGwsDCwsLDQ+tzDhw+57oF/YzIy8e+EEnSJXwZnZ2cux51cGjVqBIgIGzduhFy5cgEAwPv376FDhw6gVCph3759WUpEkxtkY+nIM2bMGAgJCQEvLy+mY2YlEdEQFhYWcObMGa2ktsuXL0P16tXh69evso/N8jt5nZ/qODk5wZkzZ6BYsWJQvHhxWLBgATRo0ADu3r0L5cqVEwVpWR2IchN0vby84I8//oCgoCCRA3zBggWwevVqwYGYnJwM58+fh58/f0LFihXByclJOEbnzp3h6dOncPToUabv1NUpsHv37nDixAlISEgAFxcXqFmzplDpW6xYMdG+LNcoJxI6L126BB06dBAlUfM+c+pJp7z8nV1uVeREUonmPfD+/XshwUmhUMD9+/fB3d0dQkNDwcHBQQiQy2HdunXQpk0bLSP058+fsHnzZlES66pVqyAkJETSYP3w4YOkYyYr11NFeno63Lx5EwoVKqTVQZU3yQ0AwNzcHG7duqUV7Ll//z6UKlUK7t27x3Qc1fUpXrw4LFy4EOrXrw8Amc79qVOnwu3bt8HOzg5GjhwJFy5ckHQmsM4bvAFinqCGpmGnVCrByckJateuDaGhoYJDMSsyg0VWly1bFkaOHCkkmWzduhU6d+4Mhw4dAk9PT+jUqRNYWlrCli1bhM/ocoArFAowMzMTVQ0DZCZn9uzZEzp27AivXr2C4sWLg7e3N9y/fx8GDBgg6mQQGhoK8+bN07q/UlJSYMCAAUICR1bmO0PkdEdfnmKgt2/fwokTJ3Qmy1++fBlq1aoFnz9/BgC+BPOqVatmqQv1jBkzYP78+XDq1CmIioqCqVOn6kyosbCwgKtXrwrJvCpu374N5cuXz5J+xCuPHjx4AKtXr4YHDx7AvHnzIE+ePLB//35wdXXVSkQ/dOgQ1KlThykoqiInHHEA/DqPlZUVxMTE6L2GKnTJOtV7KlkntyuXPlQBk2/fvsm2eVjmPLl6A+89cP/+fS39kBUWfYrHjnn69Cls27ZNlCTUsmXLbOmUFBUVBbt37xaC7eoBtJ07d8KnT5+gc+fOshK5snIOsxtd3Rjev38PefLk0alj6ivuUofnesoZS6NGjaBcuXIQHh4ONjY2cOPGDShUqBC0bdsWMjIyYNu2bWBhYQF37twBNzc3AMhMvrWwsIDHjx9rBalUTJo0CdauXQuTJk2CHj16wK1bt8Dd3R2ioqJg7ty5cPbsWZH+snbtWr2/LStFbOvXr4clS5bAo0eP4OzZs1CoUCGYO3cuFC5cWOhkxIp6osiVK1egUqVKEBISAr/99psoKDB37lzYvHkzXLx4Ueu6sdpgjo6OEBsbK3SO6tOnD7x9+xa2bdsGAACxsbHQtWtXePToEQBk6g5r1qwRnpVZs2bBkiVL4O7du2BsbAyzZs2Cbdu26e0OJ4VcPVPO/KjrmJrzl5eXF2zevJmrE3t22D7q8OqCvAV+cpJhWAseNIsddaFQKMDHxwfWrVsH3t7eevfNbvTNd3Llroqc6Oqdnp4OU6dO1VuUwIMuvVjF3bt3oUyZMqLCdFbbUQWrbMyKTcUyh/EePzU1FerXrw+nTp2CunXrCufo7t27cPjwYahatSocOnRIZNfKuWd4/cgqIiMjISgoSKvTLI8s7dWrF9y/f1/Qx6pVqwYBAQHCykGPHz+GUqVKCfadXB2W5bxkJSEjOxLjNZFrh/MWkPPsz+O7k3N8Ob41Q8h5roOCgiApKQk2bdoE+fPnBwCA58+fQ0hICDg4OMCOHTu4x7Ft2zZo06YNWFhYgImJCSQnJ8P06dNh2LBhzMdg1akB2IqHeAukWJGbJGZlZQWXL1/WOR/IhUfPzIk4hb7kaxYMFRj4+/vLLowyNzeHGzduQPHixUXb7927B76+vkxNYVSo272/GpMmTYJhw4ZpFSF++/YNZs6cKdmxFsCwP463UOfRo0fQtGlTwU9UoEAB2L59u8HiQV49QKlUQs2aNaFbt27QsmVLJl1Tn07KmxzN85z27NkTEhISRH4fc3NzSEhIEBpnSH3HoEGDYNeuXVrz79GjRyE4OBgWLlyoNeex+D+VSiW8fv1a5MM0BOtqE05OTtnuo1QfN6/+xepblavbseoMuXPnFq1Wl5SUBLly5YKkpCTJ1VwKFiwIK1asEJo/3b9/Hzw9PSElJUVvAi2vL4blfjl69Cj0798fzp07pzXWT58+QZUqVWDJkiWixiBy5ZEm6enpsGfPHli1apWQoGthYQE/f/6EQoUKgb+/v/BiKexj0WGOHTumU+ddtGgR9OvXDwD4YklPnjwR2QBt2rSB+fPn62x+IKdZkVKpBAsLC+jZs6fe8UitQm2I/v37w507d+DIkSNa7yEi1K1bF7y8vGDBggWy5Itq/Lwy3RA5UfSirmfwdEfXtAMN3QOsNphCoWDOtyH+R0GC+EUwMzPDxMRE4f+qVavi5MmThf8fPXqE1tbWWp+7efOmzmPu2LFDa5ulpSXeuHFDa/u1a9fQysqKc9RZIz09HSdNmoT58+dHIyMjfPDgASIijh07FlesWKHzcz9+/MC7d+9iamqq3uNnZGQwjWPixImYkpKitf3r1684ceJEyc8UK1YMz58/r7X9/PnzWKRIEeH/T58+Mb2kYP2dvNSrVw83btyIiIjdu3fHihUr4oYNG7BBgwZYsWJF0b7JyckGj6dQKHDdunW4a9cuvS8pli9fji4uLrh582a0srLCTZs24eTJk4W/ERGvXr2Kzs7OqFQqUaFQoK2tLR44cCCLZ0E3z549w8jISOzVqxd6eHigUqlEFxcXyX1z6hrp4urVq2hjY5Pl45w4cQJDQkKwcuXK+OzZM0REXLduHZ48eVLv5zZu3Ihfvvw/9r46LIru/fuzCyydUqKUIogICiI2IiZ2PrYittjdiInd9YiKjYWBHYgdYIANIootBiKiInC/f3Dt/HZ2ZtmZRb/v732/fq5rrmvnzNkzcc65z90nhznPz8/n1Nm6dSvVrl2bSpcuTc+ePSMioqVLl9LBgweZOo8fP+bMnzNnzlBAQABVr16d5syZo/G7aYKePXtS06ZN6cWLF2RkZMTQohMnTlClSpU49aVSKb17945T/uHDB5JKpb+tbunSpSk9PV3t84vtzxEjRjA0Nj8/n+rUqUMSiYQMDQ3p3LlznPpPnjyhKVOmUJcuXZjnO3bsGN27d4+3fQ8PD94+nDVrFlWuXFnt+yjDwMCAnj59ypy3a9eOhg0bxpzfv3+frKysim1D3VwV009ERK1btyZ/f3969eoVU/by5UuqX78+tW3bVu07/W4IodVmZmb04MED5jw4OJh69uzJnF+9epXKli3L+o9EIiGpVKrycHBwoOnTp1NBQQFzj0ePHhER0fLly6l27dpERHTy5ElydnZmta3qm2dmZpKWlpbAN1eN+/fv0+DBg6lq1apka2tLtra2VLVqVRo8eDDdv3+/RG1v2bKFfvz4wSn/+fMnbdmyRaM2S5UqRfHx8Sqvnzt3jkqVKsV77fnz57w8T2FhIT1//pyCg4NpwIABTHl2djbZ29uTlZUVeXl5kba2Nh09erTY5xs/fjyVKlWKzMzM6OrVqyrrWVtb08mTJznlJ06cIGtra065kDVDEULpUXx8POnr61OjRo1IJpMxdH3evHnUoUMH3rZ//fpFp0+fpnXr1jFz6tWrV/T161eV70tElJqaSidOnKDc3Fwi4uc/hY4ZTXgeNzc3SkpKKraOHGJpHRHR+fPneWnnr1+/6Pz584LuqwhNZR45NOVhhEDsGPj8+TNt2LCBJk6cSB8/fiQiops3bzLPpQpz586lz58/F1tHqBxDRCzeTB3E0K81a9aQRCIhV1dXqlKlCkmlUho7dqzgewmB2G8ohmaIGe8SiYTev3/Pqfvs2TMyMDDgbSMwMJBZJ+U0pk+fPjR69GhOfTH9KZFIeJ/71atXpKenx/ufu3fvkrW1NTVr1oxkMhl17NiR3N3dycbGhp48eaLyHRX5Xj6UL1+ezpw5w6n78OFDMjMzE/xOxUFIn65Zs4YsLS1p9uzZpK+vzzzH5s2bKSAgoET3Dw4Opo4dO6q83qFDB+rTp4/G7evr6zPvRUTk5eVFy5cvZ86fP3/O6lc9PT1W/aCgIBo3bhxz/vjxY7KwsND4eTSBpuujOhw7doyaNWvGel8hELoO7Ny5U2Ubv4OWqeO/iIiioqIYmhsVFVXsIUd8fLyg49mzZ2qP5ORkunr1KrVv357q1q1LREQNGjTgXYO+fPlCDRo0KPadv3//LkiXJcfFixd51xwi8XSXSHMd4vfv3ykqKopWr15NKSkpKuuFh4dTuXLlaPv27SxaEx0dTTVr1iSiIhlHyEFE5OTkVKxssGXLFnJ0dFR5XR3+JG1UhJg1TAzy8vJo/vz5VKVKFTIwMCB9fX2qUqUKzZ8/n37+/Mmpr2rMPH78WK2erCS6u/3795Onp6fo/zk4ODC88s+fP0lfX59ZU4mIkpOTydzcXHS7yhDyXcqWLcuSKVJSUkhLS0vl/FSF79+/q63j7OxMHz584JR//vyZoxMQCyF6D03rayIniX0esbq1P4GMjAyqWrUq6ejoULly5ahcuXKko6ND3t7e9OLFC5X/+/btGz18+JCSkpJYBxGRj48PDRw4kNERz507V/DYFstTExGdOnWK0UGpgoWFBcv+NWjQIJYe4Ny5c+Tk5CToGRXx7NkzhiaqW4MV4evrK1he7dOnD+/YysnJ4fCkYvnM/wTE0AB18y4+Pp6h2+fOnSuWL1KGn58fS3csx9ChQ6lGjRqavh4RFa1f5cqVY+lY1eHmzZusMXnw4EFq06YNTZo0iVnz1NnW1NnZhEAsH6gJbRSij8vLy6OAgADS1tamZs2a0ciRI2nkyJHUrFkz0tbWpvr161NeXh7TZocOHahixYq0c+dOiomJodq1a5OPj4/g9xbKB9y+fZuGDx9OVlZWZGpqSgMGDOC1AWvKk/5OSKVSXvle0YaijMaNG9O8efNUXp8zZw41adJE5fXi9J8SiYSaN29O7dq1K/ZQRI0aNWjAgAEqZaoBAwYwPLgcQnSUgwcPZsmqO3fuZOnNPn/+TEFBQSrfUx00mRdiIYRn4NMdFTcGpFIpvXnzhlVmYGBQrP2xJPJGceOlVatWtGTJEpX/Xb58OcfG9ie/+48fPyguLo7CwsLI39+f9PT0SCqVkqurKw0cOJCio6Pp7du3nPsK5WHMzMwoMTGRc99ly5ZpbO9X7n91+j1FCNFlEBE5OjqSk5NTsYfy2i5U7+zh4UGHDx9W+YyHDx8mDw8P5l3F0hci4TRdDjE82J+Ci4sLrVu3jjk/ffo0yWQyXt63JGPgL/6iOPx10P2L/xpoqrizs7PjZbj27dvHq9Q2Nzeny5cvc8ovXbrE274mTI9Qw6kQ5bcicnNzKSQkhLS0tFiCz9ChQ1UKFkKMN5q848GDB8nPz48SEhKYsoSEBKpZsybLMVqdc5P8ekneU6zyMyEhgeLi4oiI6N27d9S0aVMyNjYmHx8funPnDqe+OmFAIpGoPYpjkLdv304uLi5M3TJlyrAE2SZNmlDt2rXpypUrdOvWLWrXrh25uLiobE8OVc7Q2dnZvMp+Ob59+0YnT56kiRMnUs2aNUkmk1HVqlVZdcT2kdgxpqx0OXjwIK1du5Y8PDyoWbNmnPpinBX27dtH+vr61K9fP9LV1WWefeXKlYIF08ePH9O4cePI1taWVS5UYGvbti1NmzaNOX/69Cnp6+tTkyZNaPjw4WRkZERLly4V9CzFQegYsLGxYca+IhOblpbGG7ggxnFClbHkzp07HJqrCUOtSX+WKVOGoV0HDhyg0qVL0+PHj2nq1KmMQ6Ucmji57du3j7S0tKhp06Y0c+ZMmjlzJjVt2pS0tbUpJiaGUlJSqEuXLrwG3aysLOratSvrvS0sLFgOlaVLl6bt27cz52lpaaSvr8/7LELnqlgDsRCjRkFBAUVERFDt2rXJ19eXJkyYwCgk1EGTdUkdrVYeT25ubrR27VrmnE9xv2XLFipbtixNnTqVDh8+TIcPH6apU6eSvb09rV+/nmbPnk1mZmaMQ7ahoSGj1GnVqhVFRERw2v7y5QtlZWWRRCKhJ0+esObmp0+faMuWLVS6dGnedxS63h07doxkMhnVrFmTwsLCaM2aNbRmzRoKCwuj2rVrk66uLsfpUYwArkn/qAsGGjJkCDk6OlJMTAxrbnz58oViYmLIycmJhg4dytu2uuepUKECy2l21apVZGdnR1lZWURU5HyrSKNVORrY29tT9+7dOc4Hihg2bBiVLVuWoqOjKSMjgzIyMmjXrl1UtmxZGjFiBKuuWCWfGHpUs2ZNWrx4MRGxx/7169d5g26ePXtGFStWJAMDAxatGD58OA0cOJBTX/59hSrihI4ZTXieI0eOUNOmTQUFdGjiDPO7FaAlcVYQu+aJcUYVOwaSkpLI0tKSXFxcSFtbm6k/ZcoUVvBDSSDUCc3Q0JD69OkjyOgrpj8rVapEM2bMYM63bdumcpzI8fLlS1q+fDmFhobSqFGjaN26dfTp0yfeumK/oViaIYRfGzVqFI0aNYqkUikNHDiQOR81ahQNHz6catSoweGNiMQHdxGp7085XZVKpTRnzhwWrV2yZAm1bduWI5MoIisri2bPnk2dOnWioKAgmjJlCr1+/Zr1PZTfUSaTUUhICKtMEYrOoorvef/+fZXBvfn5+bR3716GB9y3b59KQ6jQPnV3d2dkbMXnuHv3rsrgFT48e/aM7t+/z1JsV6hQgU6fPq3yP6dPn6YKFSrwXhMig1WsWJH2799PRP8ThKRomLl+/TrZ2Ngw59bW1iyZvFSpUrRv3z7mPCUlpcSB1WLogCbro1C8f/+eAgICSCqVkpGREZmbm7MOPohZB0xNTenYsWOcNkaOHMmRY4mI0ZHwYdWqVZwyMd/x169ftGXLFo5R708gOzub1q9fT35+fsxzpKamMvRbFW189+4daWtrc8pzcnIoNDSUrKyseHVaysjNzWXxvM+ePaOlS5cyvKimdJdImA5x1KhRLJ7558+fjNxmampKhoaGdOXKFd72hQQlqDNQKhopV61aRbq6ujR8+HA6dOgQXbt2ja5du0aHDh2i4cOHk76+Pq1evZqINJMdNaWNmjhQigmQ+t0OmnKDq1Qq5RhmW7duTU5OTtS0aVPe/wrVB6xbt446dOhAXbt2pWvXrhER0dmzZ6lq1apkYGBAgwYN4rStjgYMGjSIatWqRRcuXKDRo0dTqVKlWHqo7du3k6+vL+9zC+FhxXwXTRwy5BDrhKSKxrx9+5Z0dHQ45WIN4WKdXIXWF6O706R9TXRr9+/fp+PHjwtyzBMz7woLC+nUqVO0YsUKWrFiRbF80Pv376lFixYq7RpERbJIamoq85+fP3+StrY27zhQhiY8NZH64CFNHFffvn1LPXr0oNKlS5OWlpba9U4Mzp49S7Vq1aJz587Rhw8fig12ERPMLpbPVHcPoTI+H08thxgaUJLAC3WIj48nQ0NDcnd3p5CQEAoJCSF3d3cyMjKiCxculKhtoiKbrBgHXV9fX4anT0tLI11dXeratSu5uLgwujIhNjZlO1t+fj5FRkZS165dqWHDhtSgQQPWoQyxfKCqPjp79ixZWlryvqtQfZyYQB0bGxsWz/H69WuSSqVqg5U1sScTFdGY/fv3U6tWrUhHR4c8PDxo8eLFzLcQa9cm+v3O0XwOa9ra2tSkSROVDms2NjZ0+/Ztle9969YtXpohRP8pkUioc+fOFBwcXOyhCD09PXr48KHK53n48CGHVguhX8p1jI2NWTaRt2/f8tI6oTpETXSrYvSTQnkGiURC586dYwXOGBoa0tGjRznBNPLvovzcxsbGxTp1ayJvCBkvDg4OxdLQhw8fkr29PatME3oUEBDAoYvF0Ug5vn//TnFxcTRt2jSqV68e6enpcdZgMTzMhg0byMrKijXeFy1aRCYmJirXJHXJ+UrinPknnZ2Ftm1sbFysLPL06VMmgYcm9EUR6mi6umdXlVCoJMHPREVz/ejRoyy9uUwmYyU2ISpKdsIXSPfXQfcv/hT+Ouj+xX8NNFXcTZ8+ncqVK8dStEVHR5OBgQHt2bOHU79nz57k4eFB165do8LCQiosLKSrV69S5cqVqXfv3pz6YjP4iDGcis3IM3z4cKpWrRpdvHiRDA0NmfoHDx7kNVYKNd5owtiZmZmRTCYjqVRKMpmM9VvRqGRkZCQo+0lJ3lOs8lMMhAgDqu4vFt++feNtp1SpUnTz5k3m/PPnzySRSNRmaxGb+XHSpElUq1Yt0tPTI29vbxo5ciQdPHiQ16ngd/WRqnnEp3ixsbGhrl27sgztROKdFapWrcoIfYrzTpUALse3b99o06ZNVLduXdLS0qIaNWrQggULWHWECmxly5ZlGcVmzZpFVapUYc4jIyNZ54o4c+YMTZo0ifr27Ut9+vRhHcoQOgaMjIyYTDqKz52QkMDKUCXGcaJq1ark7e1NUqmUPD09ydvbmzm8vLzI2NiYOnXqxHlesQy1Jv2pyND379+fUQQ+ffqUowAV6+QmR2JiInXv3p18fHzIx8eHunfvTrdu3WLuqZgJTBnjx49nGcACAwNp4sSJRFRkGJRKpax5cOrUKVbmckWom6slMRCrM2rMnDmTpFIpNWnShNq0aUN6enqCIy3F0gwhtLpKlSq0efNmIioyRkgkEpbj8+XLlzl9GhgYSLt37+bcb/fu3RQYGEhERYZZNzc3IirKUDFhwgS6cOEC6enpMQ4mV69eZdpWNy+1tLRY2TSFfBfl9c7Ly4sVBKCMsLAwThYkMQK4JsY7dcqJHz9+0KBBgxh+Qk9Pj4nWlslkNHjwYI0zkInNQi3E8YAvQpqoSLE4fPhw5j2kUinp6urSyJEjOc8vVsknhh4ZGhoy76xYNz09nXR1dTltt2nThnr06EE/f/5k1T937pxKB1kxijihY0YTnkeRJ1Xl4FQSWifUaCY0O35JnBXErnliFI5ix0BgYCCzlinWv3z5Misbnru7O5MZlqgok0dmZiZz/u7dO94gEzFOaAcOHKA2bdqQjo4OVahQgebNm8fK8K4IMfRLT0+PpSwtKCggmUzG4UXlWL16Nenq6pJEIiFTU1MyNTUliURCBgYGTCbLwsJChh8Q+g3lEEozxPBrAQEBFBAQQBKJhGrXrs2cBwQEUJMmTWjAgAG82RbFBncJ6U85bZVIJGRvb8+it66urtSkSRPGaUgT1K9fn/V+fIeyAtnHx4e2bdvGec/w8HAmG6ci7t27R+XKlSMDAwOG7zU0NCQnJyde44LQPlXlKJySksLLF23cuJFZL+To378/sy65u7szCm9DQ0NWhhBlPH/+nNfIJlQGmzdvHtna2tLMmTMpICCAyQAix9KlS6lhw4bMeevWrSkkJIQKCgpo7969JJPJWPLokSNHqGLFiiqfVwjE8JlCaeOVK1coNjaW9d8tW7aQk5MTWVlZUf/+/Tl8QMOGDalChQoUERFBmzdvVplRVhFi1oEjR46Qqakpy5g/dOhQsrOz4zUEi81qI9Ywq+wspA5iAh6IirJJ9erViwwNDalChQo0YcIEunHjBhEVOW/s2bOHkpKSeA25t27dorlz5/LS3iFDhpC7uztDxzZt2kSzZs2ismXLsoIm5WjcuDETBPj582eysbGhsmXLkp6eHq1Zs0ZjukskTIfo4eHBcmDbtGkTmZubM1kPg4ODqXnz5rztaxKUoA7R0dFUo0YN0tbWZvQ72traVKNGDZacpYnsKJY2yiFWhyg2QEpTHaWqTMdygyufYXbAgAE0d+5cFm+lCCG6u3nz5pGOjg5Vq1aNDA0NycDAgObMmUO2trY0b948lfNOHS3NzMykevXqkUQiIWNjY8aJTo7AwECaPHkyb9tCeFgx30UThww5hDohyZ1H+XZZi4mJodDQUHJ1dRX8rnxyuFgnVyH1NdHdafI8YmTZtLQ08vLyYnQnirphVU4Tf8o20K1bN6pTpw4lJCSQoaEhnTp1irZt20Zubm505MgRlfcW6iAglqcmEhY8pInjarNmzahSpUq0Zs0aOnDgAB08eJB18CElJYXWr19Ps2bNovDwcNahCOU+5Evcokkwu1g+U/F5hPCCYnhqMTRAk8ALFxcXCgsLKzYTPt/7TJ48mdq3b0/t27enKVOmqJSTif6HHigfPj4+VLt2berVqxcT0DVnzhzq3bu34MzsJiYmzM4mERERTJbSS5cucXYzE4PQ0FAyNDSkf/75h0aMGMFkopUfcsh5PqF8oJmZGZmbm5NUKmV+yw8TExOSSqU0ZMgQ3mcSq48TAolEwgl0U7yPKoi14Snjx48ftGTJEkbPoaurSz179iQnJyfRO81o4hxd3DxV56jG57Cmo6OjUp8jb1smk3HKheg/NbERa7LbhBAdpTobmyoHXXX8V0l0q2L0k0J5BmU+QdmerOzQL5FIOPNZrr9TFSiribwhZLzo6uqygnuUkZqayrRfEnqkTBNDQ0OpTp06ZGpqSsOHD1d5/58/f1J8fDxNnz6d/P39SVdXl2MHEcvDzJ8/n8qUKUPp6ekUERFBJiYmdOnSJZXPoC45nzJ/ry6DtiI0cTKX48WLF8XuZCBU76yOZijO09/lg6KKpj9+/FijhEJC6XpERARNnTqVOS8sLKSmTZsy89XGxoYJ7uOT21T1bUnGQE5ODk2dOpVq1apF5cuXJ2dnZ9bxF//d0MZf/MV/CWbNmoX27dujfv36MDIywpYtWyCTyZjrmzZtQpMmTTj/Cw8Px6dPn9CoUSNcuHABJ06cQL9+/bBt2zZ06NCBU3/FihXo3bs3atWqBR0dHQBAfn4+WrdujeXLl7PqAYBEIkFkZCSMjIyYawUFBbhw4QIqVqzIaX/lypXYsGED2rZti4iICKbc19cXY8eOZdV99eoVXFxcOG0UFhbi169fnPKDBw9i9+7dqFmzJiQSCVPu4eGBtLQ0Tv3Zs2dj3bp16NWrF6Kjo5nyOnXqYPbs2TA3N4dEIoFEIoGrqyurzYKCAuTk5GDQoEGcdgFg2bJlvOV873Lt2jUcPnwYeXl5aNiwIcLCwqCvr6/yP0Lf8/Dhw8zvkydPwtTUlPX8Z8+ehbOzs6DnVIWJEydi9uzZGD16NIyNjZnywMBArFq1CgBYz6gK9+7dQ+XKlYutY2BgAAMDA075p0+fULZsWebczMwMhoaG+PjxI0xMTFS2FxUVhSlTpiA4OBh+fn4AgBs3bmDLli2YOnUqMjMzsWjRIujq6mLy5MmIiIiAlZUVwsLC0L59e7i6uqpsW2gfaTqPCgsLAQCZmZmQyWSsvlWGmDkHAI8fP4a/vz+n3NTUFFlZWZzya9euITIyEnv37oWDgwMePnyIc+fOoV69epy66enp8Pb25pTr6uri27dvzPmHDx9YfXru3Dm0atWKOQ8ICMCYMWM47YSHh2PmzJnw9fVF6dKl1Y49oWPAzs4OW7duxaxZswAU9VdhYSEWLFiABg0aMO0tXboUAEBEWLduHbS0tJhrMpkMTk5OWLduHQCgbdu2AIA7d+6gadOmrL6X11Wm0XJ6pOqcD2L7EwBsbGzw4MEDlC5dGidOnMDatWsBALm5uax3AoC7d+9i586dnDasra3x4cMHlc9VrVo1bN++nffa+fPnVV4DgH/++QfdunVjzqdPn46goCDs2bMHb968QXBwMEqXLs1cP3DgAOrUqcPblrq5amZmBqCoT+/evctad2UyGapUqcI7j4Ci/mncuDEaN27Me33r1q1Ys2YNBg4cCAA4c+YMWrRogcjISEilUt7/aEozhNDq0NBQDB06FBcvXsS1a9dQq1YtVKpUiakbFxfHmb9XrlxhxrQivL29cfXqVQBA3bp1kZGRAQCYP38+2rVrh4ULF6J3796oUqUKgKL1Sj4Hz507ByJCYGAg9u/fDwsLC6ZdmUwGR0dH2NnZse4ndr1LSUlB9+7dOc8tR9euXTF//nwAQHZ2NqgoKBBfv36Fnp4eq+1jx47B2tqaeW/5vGzYsCG0tbVZddPT09GsWTPeexIR73xOSkqChYUFdHV1sXbtWsyfPx83b97E27dvAQC2traoVq0a73o3evRoAEXjZdq0aaw1tKCgANevX0fVqlXx6NEjfP/+nbl27do1LFy4kDnX09NDTk4Oc56enq7y2xWHgoICXLt2DTNmzMC8efOY9bB8+fK867vQNUMOMfTIzMwMb9684fBBt2/fRpkyZThtXLx4EVeuXGHRAABwcnLCq1eveN4WOHXqFE6ePMlazwCgQoUKeP78OQDxY0YTnkcIT3r79m0A4mhd+/btARSNr+DgYOjq6rKePTk5GbVr12bKJkyYAE9PT2aup6eno1WrVqhXrx68vLwwb948GBgYaCzzAOLXPFXz7uXLlxzeSuwYSExMxL///sspL1OmDDN/AeDRo0fIz89nzrdv346xY8fC0tKSecYfP35w2lEnxyiibdu2aNu2LTIzM7Ft2zZERUVh2rRpaNq0KUJCQtC6dWtUr15dNP36+fMnDA0NmXOpVAqZTMaiJ3IcPXoUw4cPx8iRIzFmzBhmnX7z5g2zJtjb22PNmjWoWLEivL29BX9DOYTSDDH82rlz5wAAffr0wfLly4uVLRTx7ds3lXKL4lyRQ0h/ymlvgwYNEBMTA3Nzc0HPIsePHz+QnJyM9+/fM7KEHK1bt0Z8fLyo9oAiHqx379549eoVCgsLERMTg8ePH2Pr1q04cuQIp36/fv3g4eGBxMRE5vk/f/6M4OBgDBgwAFeuXGHVF9qnzs7OuHPnDhwdHVn1Tpw4AXd3d87///33X4b/ktfbvHkztm7dCnd3dwwdOhTh4eGIjIxEbm4ua+3nexa+OSpUBhs/fjxyc3MRExMDW1tb7N27l9XO5cuX0bVrV+Z81qxZaNiwIbZv3478/HxMnjyZNRaio6NRv359lc9bHDThM4XSxpkzZyIgIAAtW7YEULRm9+3bF8HBwXB3d8fChQthZ2eHGTNmMP+5cuUKrl69yvCLQiBmHWjRogXWrFmD1q1b4/Tp09i4cSMOHTqEc+fO8cr6CxcuRFBQEOs7LF68GDNnzsTRo0eZekL5L2X4+fnh9u3bnHHMhzVr1mD06NHIy8tj6FJ2djZGjx6NyMhIdO3aFUSEM2fO4ObNm9i4cSOys7Pxzz//4OfPnzh48CCLz9fS0kLnzp2ZdSAwMJBzT319faxcuZJTHhsbi61btyIgIAB9+vRBvXr14OLiAkdHR+zYsYPDc9+6dYuhw/v27YONjQ1u376N/fv3Y/r06Xj48CEA8XQXEKZDzMjIYL37qVOn0LFjR+a7jxgxAs2bN+dtv1KlSrh48SKnj/bt28fQqqtXr+Ljx4/MWAeK5L6wsDB8+/YNbdu2xcqVK5m1oHPnzujcuTN+/frF8KuWlpaMPlaxDbGyo1jaqKkOUShPIqb90aNH49evX8yYy8vLQ82aNfHgwQMYGBhg/PjxOH36NGrVqoXNmzcDKKI7Y8eOZfEm6iBEd7d582Zs2LABvXv3xsWLF1G/fn1cuXIFT5484b2XUFpqaWmJCxcu4MuXLzAyMuLoWvbu3cuS3RUhhIcV812IiKP3zsnJgbe3N2t8ffr0ifPfrVu34t9//0XDhg1ZOvIqVarg0aNHzLlcByaRSNC7d29WGzo6OnBycsLixYuZMjFyuBxC9B5i62uiu9PkecTIsiNGjICzszMzZ27cuIGPHz9izJgxWLRoEauukHlnZmbGjFt1GD58OOs8Li4Ohw4dgq+vL6RSKRwdHdG4cWOYmJhg3rx5aNGiBQBw5kJ+fj6ioqIYmYevbUA8Tw0UfR9fX18kJSWhVKlSTHm7du3Qv39/AEDv3r0RGhqK+/fvIy4uDhUrVkS1atWYuleuXOHYKS5duoSLFy/yruF82LBhAwYPHgxLS0vY2tpy9LnTp09nzuUyR3EwMzNj2amUIZFIEB4ezioTy2eK5QXF8NRiaIB8nBIRjI2NWXYymUyGmjVrMn0px5AhQ7Bz507MmjULPj4+6NGjBzp37gxbW1sVXxSws7PDnDlzVF5XRrNmzbB27VqWXiMhIQHJyckIDg7GgwcP0KhRI8TExCAhIQFnz57FqVOn4OnpyaHBMTExrHMiYmS0M2fOMHyEvb19sfp1dYiOjsaePXtU8jZyVK1aVRQfuGzZMhARQkJCEB4ezqItctpYq1Yt3nuJ1cfJ8ePHD+zevRvfvn1D48aNUaFCBeaaRCJBTk4Oa6xIpVJ8/foV2dnZTJkyXynWnixHYmIiNm3ahOjoaBgaGmLs2LHo27cvXr58ifDwcDx//lywXTs5OZn5/eDBA5auo6CgACdOnGB9F6HzVM4HiEFBQQFLF6QMLS0tlv5KDiH6TyE2YmWMHTsWAwYMwM2bN9GwYUPY2NgAAN69e4ezZ89iw4YNzLonVkepCdTxX5roVoW2rQihPINY/b0mY0asvAEIGy9lypTBvXv3eOcRUDRv5HrFktAjuVyqjBkzZrBsIXl5ebh27Rri4+MRFxeH69evw9HREf7+/ujfvz+2b98Oe3t7VhtieZjx48fj48eP8PX1RUFBAU6ePImaNWvyPh9QpF9r1KgRLl++zKx1u3fvRkhICKKiotC5c2fWXPjx4wcGDRpU7HqkqS5DEZUqVcKdO3dQrlw5VrkmdjNlmqgIxbGuCX1RhDqa7ubmBqlUKpgHE0vXd+/ejQkTJjDn+/btw4ULF3Dx4kW4u7ujV69eCA8Px549e0BEHBrH17cxMTGcukLGgBz9+vXD+fPn0bNnT0G+Fn/x34W/Drp/8V8DIYo7RWZcEStXrkT37t1Rs2ZNvHr1Crt27UKbNm1465qZmeHQoUN48uQJoxh3d3fnMEJiDJuKEONsIUT5rYjMzEyOcg4oYoT4Fg91xpv169drxNjl5+dDIpGgadOmjNCgCrNmzcKMGTPQqFEj6OvrY/ny5Xj//j02bdqk8j9C31Os8pNP+OZDXFwc81uIMEBEvO18/foVu3btQmRkJG7evImCggJOnY8fP2L69Ok4d+4crzFZrhxWZnKICA8fPsTXr1+ZMi8vL9Z/t2zZgsWLF+Off/5hylq1agVPT0+sX78eZ8+ehYODA+bMmYPJkyfj9u3bOH/+POLj47F48WLIZDLUr18fAQEBCAgIYDFmQvtIk3mUlZWFKVOmYPfu3fj8+TMAwMrKCn369OEwzYB4BydbW1s8efIETk5OrPJLly6xmOrFixdj06ZN+PLlC7p27YoLFy6gSpUq0NHRYSlAFSFUYLOwsMCbN29gb2+PwsJCJCYmMsIBUCQQ8Y2rdevWISoqCj179uS9vzKEjoHp06fj33//RWJiIvLy8jB+/Hjcv38fnz59wuXLl5n/inGcCAsLA1BkLOncuXOxhn85lI0mfAYTgG00EdqfiujTpw/++ecfhvFu1KgRAOD69esco7xQpVp2djbLeFwcMjIyeOePHJaWlnjx4gVzXr9+fdy8eROnTp2Cra0tOnXqxKpftWpVRnGqDHVzVYxjzooVKzBgwADo6empNW4MHz4cGRkZLAVpo0aNIJFI8Pr1a46CQg5N114htLp///7Q0tJCbGws/P39mTEqx+vXrxESEsIqs7e3x8aNG1mOJwCwceNGRinx8eNHZi4EBATgw4cPyM7OZs2PAQMGMLRL7lSSnp4Oe3t7lQZnRYhd75ycnHD06FG4ubnxtnf06FGGTokxgmhivBMbDGRiYsIKDCgOQhWDgwYNwrZt2zBv3jxcvHgR7969Y/EEaWlpHKdoTaClpYUmTZrg4cOHcHZ2hqenZ7H1xSr5xCj5u3TpggkTJmDv3r1M0MXly5cxduxY9OrVi9N2YWEhL5/y8uVLlYZzIYo4TcaMWJ5HeU7wQRMnRLFGs8TERIwfP54537FjB1xdXXHy5EnmmVeuXImRI0dqLPMIXfM0UQqKHQO6urq8611KSgqsrKx4nx/g55s1kWP4YGVlhdGjR2P06NFYuXIlxo0bh2PHjsHS0hLu7u6oV6+eaOcDZd4zLy8Pc+bMYclNS5YswcKFCxknBUWULl0aS5YsgYGBARo3bgxbW1vMmzcPgPhvKJRmaOLoKtZQUa9ePUHBXXKI6U8hBnxlnDhxAr169eI17kokEt6xLQRt2rRBbGwsZs6cCUNDQ0yfPh0+Pj6IjY3lDVC6c+cOyzkXKFoH58yZg+rVq3PqC+3T0aNHIzQ0FD9+/AAR4caNG9i1axfmzZuHyMhITrupqanw9fVlzg8dOoQ2bdowjoRz585Fnz59mOvKDi2KUDXfhMpgly5dwvTp0zFz5kzedpQdKby8vPDw4UPGCFOjRg3W9S5durCcD8VAEz5TKG28c+cOMx+AImeBGjVqYMOGDQCKeMqwsDCWg27FihV5Hf6Lg1jZp1u3bsjKykKdOnVgZWWF8+fPqzQC9uvXjwl+v3TpEnbv3o25c+fi2LFjrGBATQ2zQ4YMwZgxY/Dy5UtUq1aNYzSRr+1CAx46d+6Mjx8/on379li2bBmaNWsGLS0tXlkBKBqzRIRy5crhxo0bLDork8lgbW3NWZOBIr5G/m1NTEwYWbRu3boYPHgwp35ubi4zNk6dOoX27dtDKpWiZs2ajEEW0MxALESHKJVKWWvttWvXMG3aNObczMyM0bMoQ0hQQnh4OBo0aCDKGR0oklkUg0yVoYnsKJY2ipWp5BC6holp/9SpU5g7dy5zfceOHcjIyEBqaiocHBwQEhKC2bNns5zjleVXIRCiu8vIyGBko3r16kFHRwfh4eEqHV6F0lK5I0lx0NbWhq2tLRo3boxWrVppxMMK+S6azDc5hCbXkOtznZ2dkZCQwHLM5IMmzohiA8iF1FfU3XXp0kWlUygf/lQA6dWrVxEXFwdLS0tIpVJIpVLUrVsX8+bNw/Dhw5l1CBA27yQSCctBJTMzE7m5uUzAelZWFgwMDGBtbc1xov327Rszh8zNzZGZmQlXV1d4enri1q1bAAAHBwdmvZfD1tYW27ZtY84lEgmvg65YnhoQFjwk1nEVKOJVVNk4+DB79mzMmTOH5XChCkKCqzQJZpdKpZg5c6ZgPlMsLyiGpxZDAzQJvBg1ahRGjRqFlJQU7NixA6tXr8bYsWPRoEED9OjRA7169UJycjIqV64MqVTKcp7hg7IdCShyBhozZgyLbwCK+vr58+c4deoUwsLCMGvWLHh6eqp03ueDr68vZs+ejUaNGuH8+fNMsoz09HRe+6IqvbNEIoGenh5cXFzg7+8PmUymkrdVhFg+UE5PnJ2dUbt2bU5gUXEQoo8TE6gD/I/NRBFExPB/cudHZblFrD15yZIl2Lx5Mx4/fozmzZtj69ataN68OaO7dnZ2RlRUFOzt7QXbtcU6R2tqGxACPscvRfz8+ZO3XIj+Uwz9lCM0NBSWlpZYunQp1qxZw/SflpYWqlWrhqioKMaup4ljv1AI5b/27NkDQJxuVRPeTijPICTwUxFC9MfKECtvAMLGS/PmzTFt2jQ0a9aMYzf9/v07wsLCGHmrJPRIFXr06AE/Pz/GAdzU1BTW1tZo1aoVQkNDER0dXWwACKCeh+Gj42XKlIGBgQH8/f1x48YN3LhxAwB/EJO65HzHjh3jvJM6lMTJXA5Vc10TG0jDhg1V6skVndo1oS+AcJru6OiIs2fPCubBxNL19PR0Ft9x7NgxdOzYkdEzTZ06lbF5881TVX2rXFfIGJDj+PHjOHr0qMrEV3/x342/Drp/8V8HVYYhxQVBMTpajvbt2+PixYvo2rUrJBIJU6d169ZMnezsbBgZGUEqlcLFxYUR3goLC1nOVYDmGXzEOFuIzcjj6+uLo0ePYtiwYQD+x5gdGRnJ60irznijKWOnra2NQYMGMQ7OxUGTLBhC31Os8jM+Ph6Ojo5o0aKF4HcVIgz07t2bJRBduHABGzduxP79+2FnZ4f27dtj9erVvO337NkTT548Qd++fWFjY6MySoePUWvZsiWLUVMWwMVmfqxSpQqqVKnCMMNJSUlYunQpQkNDOYZJoX0kdh59+vQJtWrVwqtXr9C9e3dmzjx48AArV67E6dOncenSJSQnJ+PatWsYPny4aAen/v37Y8SIEdi0aRNj8Ll69SrGjh3LUkBNmDABEyZMwMyZM3kNdXwQKrAFBARg1qxZWLNmDfbu3YvCwkIEBAQw1x88eMCZt0CRskZMJKzQMfDu3Tu8efMGq1atgrGxMXJyctC+fXuEhobyGtHEOE6IEXw1MZoI7U9FzJgxA5UrV8aLFy/QqVMnRjDW0tLCxIkTWXWFOrmZm5vjzZs3sLa2ZgwtypDPVSsrK6SlpalUIjx58oSj4HB3d1cZlTtgwACV30foXBXy7ZcuXYru3btDT09PZfSt/B7Dhw9Hfn4+R8Ggo6PDmyFeDk3XXqGKm169ekFbW5s3wGTNmjWcdhctWoROnTrh+PHjjGNNYmIiHj16hH379gEoyibRuXNn5vnz8/NZWQ4A4NevX8jNzWWVyfs/NzcXGRkZyMvLY11XFFrFrnczZ85Et27dEB8fj0aNGnEi8E+cOMEY0sQYQTRxvBcT5f39+3fcvHkTFhYWHOebHz9+YM+ePax5J9TpsiRZqDt06AA/Pz+O0WnBggVISEjgGHwqV66Mp0+fCsrgL1bJJ8bpdu7cuQgNDYW9vT0KCgpQqVIlFBQUoFu3bpg6dSqn7SZNmmDZsmVMNk95do6wsDCVmUiEGBM1GTNCeJ7Pnz8LDopQHBdi1hmxRjOx2fGFyDzKELrmaaIUFDsGWrdujZkzZzIKeolEgoyMDEyYMEGUoU4VNAnAeffuHbZs2YKoqCg8f/4cHTt2ZDICzJ8/Hzdu3MDmzZsFj0V/f388fvyYVVa7dm08ffqUOZevq7du3cL69etVttWzZ0/MnTsX58+fh4ODAwDx31AszRDr6JqYmIg9e/bwrknK2QYWLFiAhg0bqg3ukkNsf758+RKHDx/mfZYlS5Zw6g8bNgydOnXC9OnT1QaQKgbFKULR2NumTRtmLtarVw+nT58utk05XF1d8e7dO3h4eLDK379/z2s4Ftqn/fr1g76+PqZOnYrc3Fx069YNdnZ2WL58Obp06cJp9/v37yzad+XKFfTt25c5L1euHCsQQh2/zsfXCpXBGjRowPDIQmFpackKuH758iXs7OwglUqZTHWaQBM+Uyht/Pz5M2vsnT9/HkFBQcx59erVWQF4ABAREYExY8Zgzpw58PT05Ogo+PgadeuAqvFtZWUFHx8fFr/LN5eEZLXRNPO2fKwqGt/49BlCAx5+/PiBvn37YsKECRzemw/ysaocFK0O5cqVQ3p6OhwcHFCxYkXs2bMHfn5+iI2NZZy7FOHi4oKDBw+iXbt2OHnyJEaNGgWgiA4ofysxdBcQpkN0d3dHbGwsRo8ejfv37yMjI4Pl5PX8+XOVdFJIUEJSUhKrb9Q5oyclJSE2NhYWFhb4559/WHJMdnY2Ro4ciU2bNmkkO4qljWJlKjmErmFi2tc00/G+fftUjhm5s6AihOgDfv78yfr2MpmsWH5UKC0tbicsOQoLC5GamorIyEiMHTtW42yu6r6LJg4ZcohNriE0m5smzohiszKKqS/PBqYcGHP9+nVoaWmxnBQ1aV+MLFtQUMAEOlhaWuL169dwc3ODo6MjhzcXO6937tyJNWvWYOPGjUxA8+PHj9G/f39WplQ53Nzc8PjxYzg5OaFKlSpYv3494xwm1yk8e/as2HsWB7E8tfyd1QUPiXVcBYp0NxMnTmTeUR0+f/7MSSBQHC5evIj169fj6dOn2Lt3L8qUKYNt27bB2dkZdevW1SiYXSzE8oJieWrFewiBJoEXrq6uCA8PR3h4OK5du4bBgwejT58+6NWrF6pWrYq3b9/C2tqacZ5R5fjDN4b27NmDmzdvcsq7dOmCatWqYcOGDejatSuWLFmCxMREUc+9dOlS9OjRAwcPHsSUKVMY2Wjfvn28to6lS5cyzvSKO5MYGBjAyMgI79+/R7ly5dC3b18sX74cq1atKjb7naZ8oKJz+Y8fPzhrDB8fLEQfJzZQR5MgVkC8PXnt2rUICQnh6E0VIQ9mGDp0qCC7tljnaLHztF27drx9ryjfd+vWDW5uboL4Ab6kBkL0n+fOnSuWf1IFobtNiNVRTp8+nXESVQ40V7ZRiOW/xOhWNeHtxCadUBWQIB8DDg4OogKQFCFW3gCEjZepU6ciJiYGrq6uGDp0KMOTPHr0CKtXr0ZBQQGmTJnCalcTeqQKV69eZfH/VapUwe3bt3HhwgUmOCogIEBloipAPQ8jT4qkDC0tLVy+fJnhc1QFMQHFJ+fTxJasqS5DCMTaQMTwC5rSF6E0fdOmTQgICBDMg4ml6/n5+aw5ePXqVYwcOZI5t7OzY+ifJrYbTWBubq7RN/2L/xLQX/zFfxnatm1L7dq14xzt27enbt260fTp00kikQg6pFIp025MTAxVqFCBvn37xrlnTk4Oubq60uHDh0v8/Bs2bKAyZcpQdHQ0GRoa0q5du2j27NnMb2VcuHCBGjVqRFZWVqSvr0916tShkydP8rZ98eJFMjIyokGDBpGenh6NGDGCGjduTIaGhpSYmMipP3fuXKpUqRJdu3aNjI2N6eLFi7R9+3aysrKiFStW8N7j+/fv9OXLF9bBh/r169OBAwfUfg+ZTEYZGRmsMl1dXXrx4oXK/4h9T6FYsGABubu7k7W1NY0aNYru3r2r9j9jxoyhunXr0ps3b8jY2JhSU1Pp0qVLVK5cOZoxYwZT782bNzRv3jxycXEha2trGjp0KGlra9P9+/eLbd/IyIju3LlTbJ1nz54JOpRRoUIFmjBhAqd8woQJ5OrqSkRECQkJZGdnR0REhYWFdPPmTVq8eDG1atWKzM3NSUtLi7y9vWnkyJGsNv5UH40YMYIqV65Mb9++5Vx78+YNeXp6UseOHcnExISioqKISPycKywsZK7LaYWenh5NnTqVVW/u3LlUoUIFsre3p/HjxzPjRV2/bt++nVxcXJi2y5QpQ5GRkaw66enpVL58eZJIJKStrU1r1qxhXW/Tpg3nmxMRjR8/nmbOnKny3soQOwbE4MWLF7R69WqaMGECjRo1inUoIj8/nxYuXEjVq1cnGxsbMjc3Zx0lhdD+1BQ/f/6kfv36kba2NkkkEtLR0SGpVEo9evSg/Px8pl58fDz9+vWL+V3c0alTJ2rbtq3Ke7Zu3Zo6duzIKT906BDvcfjwYTp16hQ9ffqU8x8xczUhIYHGjRtHnTt35qzBmkAikVDz5s1Z7Whra1OTJk1K3LYyhNJqIiJ9fX1emqkKT58+pQkTJjDPO3HiREpPT+et6+/vz9AmRWzbto3q16/PKnv//j21aNGCpFIp71FSXL58mTp37kwODg4kk8lIJpORg4MDde7cma5cucKp/+zZMyooKCjxfVUhPj6e8vLyVF5//PgxOTo6Mvybv78/vXr1irn+9u3bEn2XBw8e0LJlyyg6OprznuvXr6fbt2/z/s/S0pKSk5M55cnJyWRtbc0pP378OFWtWpViY2Pp9evXankqIWuGHELpkSKeP39OR48epd27d1NKSgpvHaIiml6pUiVyd3cnbW1tqlmzJpUqVYrc3Nzo3bt3vP+5e/cuWVtbU7NmzUgmk1HHjh3J3d2dbGxs6MmTJyrvVRyE8jxSqZR5LvmYUT6UZQGiIp5/6tSpVKtWLSpfvjw5OzuzjpLAzs6Orl+/TkREBQUFZGJiQkeOHGGuP3jwgExMTJhzITLPo0ePWPcQu+ZFRUXR9+/fBT2/2DGQlZVFjRo1IjMzM9LS0iJ7e3vS0dEhf39/ysnJYepJpVJ6//49c25kZMRar1TNbTFyzP79+6lly5ako6NDVapUoZUrV9Lnz59ZdZ48eUI6OjqCvoUmMDAwoLS0NJXX09LSyMDAgFUm9BsqQgzNIBLOr+3atYt0dHSoZcuWJJPJqGXLluTq6kqmpqYUHBzM23ZWVhbNnj2bOnXqREFBQTRlyhR6/fo1b10x/XnmzBkyMDCgypUrk7a2NlWtWpXMzMzI1NSUGjRowNu+sbGxYLoTEBBAJiYmZGhoSD4+PuTj40NGRkZkampKNWrUIDMzMzI3N1crx/Hh6NGj5OHhQXv37qUXL17QixcvaO/eveTp6UlHjx7lXQ/E9um3b99U0mU5KlasSPv37ycioszMTNLS0mLxfdevXycbGxvR76cIoTKYRCJR+7zqYGxsXOz8+pMQShsdHBzo/PnzRFS0Xuvr69OZM2eY68nJyRzZR1FvpW79kkPdOhAQECDokM+l5cuX8x729vbUvXt3VllJIVSfYWxszFn/FPHo0SOSSCR04MAB6tevHxkbG5Ofnx+tXLmSMjMzBelhiIju379Px48f58hWyliyZAnz/qdPnyY9PT3S1dUlqVRKy5Yt49Tfu3cvw6M1btyYKZ87dy41a9aMOdeE7hKp1yHGxMSQTCajwMBAsrGxoZYtW7L+P378eOrUqZPa76MKurq6LB1fnTp1aPbs2cx5eno6GRkZERHRyZMnSSaTkYeHBzk4OFCpUqUoLi6OqavIA5RUdhRCGzWFJrpVdTA1NWXx5k5OTrRx40bmPD09nfT09Fj/Wb58ORkZGdHQoUNJJpPRwIEDqVGjRmRqakqTJ0/mvY8QfYBEIqGBAwcy/IFMJqOQkJBi+YbfjdjYWLK3t2fOxfCwmnwXMTh48CCZmppSREQEGRgY0MKFC6lfv34kk8no1KlTvP/Jycmho0eP0tq1azn0VRli5HAxeg+x9atXr0579+7ltLF//37y8/Mr8fOIkWXr1q3L2By6du1KzZo1o0uXLlGvXr3Iw8ND0LdShXLlytGtW7c45YmJieTk5MQp37ZtG23evJmpY2lpSVKplPT09Cg6OrpEzyKHGJ6aiOiff/6h/v37E9H/yFZfv36lwMDAYteP79+/U1RUFK1evZpSU1M5183MzEgmk5FUKiUjIyO1+tuQkBBau3atoHfct28f6evrU79+/UhXV5fh61auXElBQUG8//n27Rs9fPiQkpKSWIfi8yo/o6pDU2jKU4uhAXv37qVOnTpRjRo1yNvbm3WowvXr12nEiBFka2tLBgYG1LlzZyIqoieFhYXMbzF2JCIia2tr2rJlC6d8y5YtjP7r/v37ZGlpqfLZxOL79++MPl0RO3fupICAAJaMl5qaSoGBgRQdHU0vXrygOnXqkJ2dHZmampKzszO1bNlSsD5bKB/47ds3Cg0NJSsrK9G62+L0cXKaKUeXLl2YeU1EdPv2bSpdunSx7QvBn7LhEYmza/9J9O7dm0xNTcnR0ZHat29P7du3JycnJzIzM6N//vmH3NzcSFdXly5duqTxPYToP7ds2SLo+E+gfv36gmRCZQjlvzTRrYrh7cTqv1XpheWHrq4u9erVi7m/qvXDwsKC7OzsyN/fnzZt2sS5j1B5Q6i+/NmzZxQUFMToAOTvERQUxGvn04QeKdPEtm3bUo0aNUhLS4vDq339+pWOHz9O48ePJz8/P9LR0SEPDw8KDQ2lPXv2qNQNi+Fh1IHP9rlv3z6yt7envn37Fkuv/1OYO3cuR+f8p/Gfpi/qeDCxqFKlCsNPP3/+nCQSCUtnc/nyZSpTpkxJH1sUtm3bRh07duT1GfuLv5AQaZi3+i/+4v9RBAcH4+DBgzAzM0O1atUAFEW7Z2VloUmTJkhKSsKzZ89w9uxZUanHmzRpgn/++Qf9+vXjvb5p0ybs3r2b2YZWEWIz+OzYsQMzZsxAWloagKLoj/DwcFaEraZIS0tDREQEkpKSkJOTAx8fH0yYMIF3G2Uiwty5czFv3jwmKk5XVxdjx45lbb+Ym5uL8ePHY8+ePfj48SOnHVURtZMmTcKoUaOK3Z5QS0sLb9++ZUXQGBsbIzk5udjscmLeEyjaNuL8+fO8faQcfXX16lVs2rQJe/bsgZubG0JCQtCtWzfeaKm8vDyEhoYiKioKBQUF0NbWZiJeo6KioKWlhVatWuHChQto0aIFunfvzmytqKOjg6SkpGK34KxevTpWrlzJyUrzO3D48GF06tQJFStW5M382LJlS6xduxapqalYsmQJzM3NkZOTgypVqqB+/foICAhAvXr1eLPCAOL7SMg8cnJywvr169G0aVPeNk6cOIHmzZsjLCyMFWWuyZzLy8vDkydPkJOTg0qVKqncTvr8+fPYtGkT9u3bBxcXF9y/fx/nz59XS39yc3ORk5OjMltUfn4+7t+/DysrK05WjKSkJJQtW5YToThixAhs3boVXl5e8PLy4kTSKtOj4sbAwoULERoaivXr1+PKlSsYN26cyndR3vbq7NmzaN26NcqVK4dHjx6hcuXKePbsGYgIPj4+iIuLY+pOnz4dkZGRGDNmDKZOnYopU6bg2bNnOHjwIKZPn64yOlIshPYnUEQTXFxcOPdetWoVnjx5gmXLlnH+k5GRgXv37iEnJwfe3t7FZmnKyMiAvb09J3qbiPDixQt8/PgRtWrVQsuWLTF+/HhWhOyCBQtw9OhRXLlyBT4+Pqz/S6VS3uwHipmn6tati4MHD7Kiy4XM1ejoaPTq1QtNmzbFqVOn0KRJE6SkpODdu3do164dJxpx5syZGDt2LGe7nu/fv2PhwoWYPn06a+vk4qAq0lHM2iuEVssREBCAkSNHMhHcvxMmJia4desWJ0vekydP4Ovry9oCtXv37nj+/DmWLVuGgIAAHDhwAO/evcPs2bOxePFilRnixKx3mkBIRt+CggIsXbpUZZYi+da/qsAX5d27d2/8+vULUVFRyMrKwsiRI/HgwQPEx8fDwcEB7969g52dncptysVmIBMKfX193Llzh5mncjx69Aje3t6crakVI4wVaQCpyHYvh7o1QxFi6JEY5OfnIzo6GsnJyQyt6N69O2uXAGV8+fIFq1atYtEXvuzrJR0zypCvw9ra2jh//nyxdRUzDHTt2hXnz59Hz549Ubp0aQ6dHjFiBG8bQrKVde/eHdnZ2Ux2/LCwMLx9+5bhkffv34+ZM2ciKSkJQMlkHjFrnhhoMgbkOxvI6ytnSJBKpahcuTKzjV1ycjIqVqzIbCMm54eU54ZQOQYoytDWpUsX9OvXj+F3lPH9+3csWLAAU6dO/a1jUQ4/Pz907dqVyZSojCVLliA6OprZvk0R6r4hH4TQDDH8mpeXFwYOHIjQ0FAYGxsjKSkJzs7OGDhwIEqXLs3ZYlksxPSnn58fgoKCEB4ezjyLtbU1I2fxbSkfEhKCOnXqCJK3ly1bhosXL2Lz5s2M/Pflyxf069cPdevWRXh4OGrUqIHCwkLcuHGj2ExMyuOFbw2Q822K53zrgZh1QB0iIiKwfPlyDBkyBHFxccjMzMS9e/eY68uWLcORI0dw5syZEt1HiAwmlUrx7t07lj5ALOTjQFX2bE0ghs8UQhsHDx6MpKQkzJ8/HwcPHsSWLVvw+vVrhtbt2LEDy5YtQ0JCAvMfMeuXMn7XOiAk4z9QNH4Vs4fL8Sf4L0NDQ9y9e1dlfz99+hSenp749u0bgCLeePfu3di0aRNu3LiBgoICLFmyBCEhIUwmQeX/t2vXDnfv3mXJVvI5qopXk+P58+e4efMmXFxceLeHBoC3b9/izZs3qFKlCkMXbty4ARMTE1SsWBHAn6W7Z8+exZEjR2Bra4thw4axZLbw8HBG31Mcfvz4gd27dyM3NxeNGjVi+E1HR0ds27YN/v7+yMvLg5mZGWJjY9GwYUMARVve169fH58+fULt2rXRoEEDzJkzB0SEhQsXYtasWdi7dy+aNWvG4u9LKjuKhRiZSswaJrT9WrVqoVOnTkymYy8vLzx58oSZk+fPn0fv3r1ZmTkrVqyIsLAwdO3alUUXp0+fjk+fPmHVqlW8z6JOHxAQEFDsWgcUzQ9FvkEOsfpyVcjKykJISIhGdEPod1mzZg1iYmJgYWGBgQMHMmMWKNoJw8/Pj5fOAUWZP+V8vPwbTp8+HU2aNOHUvX37Npo3b47c3Fx8+/YNFhYW+PDhAwwMDGBtba3yHkLkcDF6D7H1jYyMkJyczKG98i1pv379ynlmsc8DCJNlT548iW/fvqF9+/Z48uQJWrZsiZSUFJQqVQq7d+/m3UoXEDavDQwMcP78eY7McOPGDQQEBHCyCiojNzcXjx49goODAytbr9jdgEqCly9fomnTpiAipKamwtfXF6mpqbC0tMSFCxdgbW2N0aNH49evX8z2wnl5eahRowbu378PAwMD5Ofn4/Tp06wMmlu2bCn2vsqZJ+fNm4clS5agRYsWvDsBKNJSb29vjBo1Cr169WLN09u3byMoKIiVhTYzMxN9+vTB8ePHeZ9Dvk6re97inh0QRr804anF0IAVK1ZgypQpCA4Oxr///os+ffogLS0NCQkJCA0NxZw5c5i6KSkp2LFjB3bt2oX09HQEBgaie/fuaN++PS8feOHCBdSuXZu1nTxQxNdeuXIF/v7+nP/Mnj0bc+fORf/+/Zk5kpCQgMjISEyePBlTpkzB0qVLcezYMZw+fVpUVvdy5cohISGBY+vIysqCj48PhzaWL18e+/fvR9WqVVnlt2/fRocOHfD06VNcuXIFjRo1YnY144My3yCWDwwNDcW5c+cwa9Ys9OzZE6tXr8arV6+wfv16REREoHv37irvXRzMzMyQkJDA0EBnZ2dMmzYNISEhAIoyc7u7u3N0jppAHR+gKvsoH1TxvmLw4MED3jGjuBOuHELm6cSJE5GdnY1Vq1YxfHdhYSFGjBgBY2NjzJkzB4MGDcL9+/dx6dIltc+3b98+dOzYkVOuTv8plUphZGQEbW1tldvRSyQSjg5B6G4Tys8oZkeF3w1NdatiIVT/fejQIUyYMAHjxo2Dn58fgKJ1ffHixQgLC0N+fj4mTpyIzp07Y9GiRVi6dCnmzJmDoKAgVv0TJ05g1KhRSE9Px7Zt27By5Ur0799fo2cXqi8HirKDP3nyBESEChUqqMwarQk9UpaxpFIprKysEBgYyMvHKuLr16+4ePEiTp8+jc2bNyMnJwf5+fkivoJ4CM2eL9epRUZG4uLFiwgICECfPn2we/duzJgxAz9//kTPnj2Llan/lC1JExtIVlYWbty4gffv33Myvffq1UsUfSkJTRfKgylDHV3fsGEDRo0ahc6dO+PatWswMzNj7RQxe/ZsXL9+HbGxsYKfXQ5NaChQxJumpaWBiODk5MThY/8TtPQv/hfjP+kN/Bd/8b8BEyZMoMGDB7OixwsKCmjo0KE0adIkKiwspAEDBlCdOnVEtVu6dGne6GA5UlNTeaMSNcngI8efzOAgBj9//qT79+/T9evX6evXr5zrQ4YMIXd3dyaaedOmTTRr1iwqW7Ysbd++nbdNVRmLlbO9/CcyKN66dYtsbW3JxMSEtLS0yMrKiiQSCRkaGhabDe3bt28UFRVF1atXJ0NDQ5XZgomKj3jV0tKiUaNGccqFZG65ceMGBQYGUnx8PH348KHYTHuqMioUFBTQ8+fPea+Jyfx45MiRYr9BSSB0HslksmKzK7948YK0tLRUXv+Tcy47O5vWrVtHfn5+pKWlRbVq1aLFixdr3N6XL194+zQ/P19lPwjJgqQMVWNAMZuV4vwtLhO5HNWrV6fp06cTUVG2hrS0NPr69Su1bt2akw24XLlyTAZBIyMjJkp0+fLl1LVrV6aeubk5ZWZmEpH6LAglhZ2dHW+U+M2bN39LpJ5iZkdFfPjwgfmesbGxvNGuVlZWKiNAz5w5QzVq1KAzZ85QdnY2ZWdn05kzZ6hWrVp09OhRunTpEnl4eFBISIjoZ/b09KRVq1YR0f/0aWFhIfXv35/pa7HvWBJouvYKyRa6e/duKleuHK1cuZKuXLmiNhL08+fPdPLkSdq2bZvaqFQTExOV2VjkmaTksLW1ZbJtGhsb0+PHj4moKFJYFY+j6XonBGIy+k6bNo1Kly5NixYtIj09PZo1axb17duXSpUqpTLDmroob2tra1am2sLCQho0aBA5ODhQWlpasRl0hWYge/HiBS8flJeXx2S9U0b16tUpPDycUx4WFkY+Pj6ccnUZtP8kRo0axWTdVM549Z/MgMUHsWNGE55HCExNTUVnzRCalSs9PZ3JgikkO/6fknkU8aez2AvBjBkzBB2qoE6OISJR0e5ix+Lr169p27ZtdPToUfr58yfrWk5ODkMfoqKiSF9fn1avXs3KAPTr1y9atWoV6evrM9kC/lMQw68ZGBgwMoKFhQVDjx88eEC2trZERJSUlMSMV+W1U2hWBSH9qcgvmpmZ0b1794iI6M6dO+To6Mj7n2/fvlHz5s2pd+/etGjRomIzVdnZ2fHKaPfu3SM7OzuKioqiq1evUqlSpWjz5s0UFRWl8lCGujVAzHrg7e1Nnz59IiKiqlWrcjJqFZddq6CggKZNm0ZVq1alZs2a0YMHD1jXO3bsyJulNyUlhdavX0+zZs2i8PBw1lEcipPB+PQBfEdxkI/f34WS6HhUITMzk+rVq0cSiYSMjY0pJiaGdT0wMPC3ZHL83wRNMsBu3bqVateuTaVLl2ayty1dupQOHjzI1KlevTotWbJE5X0XL15M1atX57326NEjGjduHNna2pKenh61atWKU6dly5bUpk0byszMJCMjI3rw4AFdvHiR/Pz86MKFC2I+QYkghO7+pzBq1CgaOnQoc/7z50+qUqUK6ejokKmpKRkaGjI7cAwaNIhq1apFFy5coNGjR1OpUqVY6+P27dvJ19eXiIpkI+Xs5jt27CBDQ0OKjY3VaIeMktBGOTSVqYSsYULb1yTTseJOMFZWVsxuXCkpKWRhYVHMV/sz+BO0VA4xPKyQ77J8+XIyMDCg0NBQ6tGjB8lkMpo7dy7TRkl3a1FE/fr1qX///lRQUMCsXxkZGeTv789k4lSEJjvrCN0lRUx9CwsL3p12Ll++TGZmZiVuv6T4+PEjkxmUD0LndcuWLcnb25tu3rzJlCUmJpKPjw/vmiEEfLsBKWaPUx5fv4On/vXrF23bto3GjRtHgwcPpg0bNlBubi5z3cPDg6Vb3LRpE5mbmzMZVoODg6l58+Yava8cTk5OKg9lWqqvr8+seYp8XVpaGunq6rLqduvWjerUqUMJCQlkaGhIp06dom3btpGbmxtrh5qSQCj90oSnFkMD3NzcaOfOnZzvMm3aNAoNDWXVlUgk5OfnR8uWLePdeVAZmuptt2/fTjVr1mTobc2aNWnHjh3M9dzcXPr+/bvo7OWqdtV4+/Yt7043+vr6lJCQwCm/ceMG6evrE1GRDsbQ0FDlu/BBLB9ob29P586dIyJ21tutW7dysj8ry0+qDiKimjVrMnale/fukVQqZWXMjI+PZ8m+eXl5NG7cOCpfvjxVr16dlXWfqGTrWHF2IVU2X02QlpZGXl5enPupWu+EzlNLS0tGp66Ix48fU6lSpYioaDcTU1NTIiqin3fv3uX85+DBg+Tl5UUymUyj96tUqRKVKlWKRowYITjTpJjdJuTQdOeAS5cu0Y8fP4p9HqH8lya61T+pn6xevTqdOHGCU37ixAlGbjxw4ACVK1eOiIjat2/PmwF+3bp11L59e/L29qaIiAiqXLmyxvLGn4AYelQSFBQU0LVr1ygiIoKaNm1KRkZGJJFIyMnJSWMepn379hQREcG51/z583l3EhWCpUuXkqGhIbVv355Kly5Ns2fPplKlStHs2bMpPDycTExMaP369bz/FarLcHd3p48fPzLngwcPZmzYRETv3r1j1iQ5xOqdDx8+TMbGxiSRSMjU1JTMzMyYQz43xNCXktB0sTyYGLq+ceNGatu2LQ0aNIjevHnDujZ48GCODk0INKGhcmhqp/iL/w78ddD9i/86iGWoiYqY9UmTJlHfvn2pT58+rEMOPT09evjwocr7PnjwgLN9GJE4w6YQiHVCU3TUU3beVLdtslBowtgJ3SYnODhY0FGS9xSr/JTj4sWL1KdPHzIyMqIaNWqwFFlicPXqVY23VkxJSSFfX99it7X88uULderUifT09Mja2pqmTZvG2srjdyqSFfHs2TO6f/8+w3CXpI+EziM7Ozu6ePGiyme6cOGCRtv7tGvXjnmukhiH5UhOTqYRI0aQlZWVRgaimJgYqlChAq9DSU5ODrm6utLhw4dFv6cYlGTbKzGOEwYGBowzla2tLaMET0tLY231HRUVxSgJ1DlDlLQ/dXV1eQM2UlNTSVdXt8RObhKJhLWdt+I3V9zeOjc3l2JiYmjBggU0f/58OnDgQLFORh4eHnT58mVO+aVLl6hSpUpEVLTtqqKTsdC5KtZArOodz549+1u2Ovvda68ixAimQgRkRbRs2ZI6derEotH5+fnUoUMH1pa2REXrrfybOzg4MIqtp0+fcgR7OTRd75QxadIkFo9EJE4AF+p4rwh1wUDGxsYcYwcRUWhoKJUtW5YuXLigcq1T52D++vVrql69OkmlUtLS0qKePXuyjOrFraOHDx8mbW1t6tWrF0ODevbsSdra2sy2m2Igds0QQ48CAgKYLZaK285Mrsg+dOgQ5eXlMb+LO+TQVBEndMyUhOcRsv2Sk5MT7zgrDmKMZr9+/aI7d+7Qq1evOO3cuXOHPnz4wJwLlXm0tbU1XvPUKQXFjoHly5czW8Kp2hb9d22FLgaq1rjs7GyOU60Y+nXjxg0yMzMjExMT0tfXJxcXF4bnIeKOxzFjxpBEIiETExPy9vamqlWrkomJCUmlUsY5W+w3LIkjkhh+rUyZMsza7+npyYz5K1euMPxaSYK7xMDGxoaZp+7u7sz4u3PnjkoDbGRkJGlra5ORkRE5OjoW6yBgaGjIyL6KOHfuHBNMk5aWRsbGxiV6D1UQ2qe2trYMX/ifUBz/+++/pKWlRTY2NlSlShWqWrUqc5TE8CSRSKhz585q9QHF4XdvIaiOz9RkfZQjKyuLd9vNjx8/MvQoMzOTI2Pdu3ePgoODqVOnTiwnCCLNZVlNth4VA7EBfmvWrCFLS0uaPXs26evrM+vp5s2bWVus/o6Ah/z8fDpw4ACvs1WpUqUY/sDExIQePXpEREVyTNWqVVl1CwoKaOPGjdSiRQvy8PCgypUrU6tWrWjLli0qHcXkvJaqQw4hdJdI80BWVcFOhYWFnGAnMY5cYpzRrayseANjd+3aRQYGBrR27VrRa8aMGTNKTBt/l0xV0vbPnDlDI0eOpIiICI4eYMaMGZy1ytnZmQkGrVatGq1bt46IigyTiv3/O/TIQpw4/qTMLsawLeS7VKpUiUVbL1++TFZWVjRt2jQi+r16VVNTU4aumJqaMjzNtWvXyM3NjVP/P+GMKARdunSh+vXrU1ZWFlP2+fNnql+/PsdZXCj+kwGkQufd+/fvKSgoiCQSCclkMpLJZMx20nzOg/n5+RQZGUldu3alhg0b8tL0tm3bUosWLSgzM5NSU1OpRYsW5OzszNBa5fH1n+CpFe07REX9279/f+b89u3bVLp06f+I3YmoaJ6ePn2aiNgy9ZYtW8jd3Z1VV5NgdkV8//79t9kpNIEYGiAm8EKs87sqve3jx49/i5wjVE8i55klEglt3bqVxUfHxMRQaGgoubq6ctpv3rw5+fj4sJIg3Lp1i6pVq0YtWrQgoiJ9XeXKlUU9txg+kKhIdpTP5TJlyjBj8+nTpxzZVFF+Uj68vb3JwMCAmddiA3XCwsLIxsaGFi5cSFOmTCFTU1MaMGAAc/3t27ckkUiISDwfoM4uJD9MTU1LlFxFrHO00HlqZmbGK5cdOnSICTBJSUkhMzMzunv3Ljk6OjI22Hbt2tHbt2/J39+fLCwsaMKECUzyIE30n9euXaMBAwaQqakpVatWjdasWVMs/axVqxbDNxcWFtL8+fPJyMiIjh8/zvSr8logRkepCGNjY7VBr0L5L010q+raLgnPoMr34+HDh4zPR3p6OmNvMTQ0VGkbNDQ0pBkzZtDdu3fJwMBAsLzxO4Jv1EEMPZIjNzeXDh06RAsXLqTly5fTiRMnePUV169fp/nz51NQUBBjC7O3t6eePXvSpk2bGBuWpjyMpaUlKzGLHMnJyWRtba3R96hYsSLD39+6dYu0tbVZQTORkZFUrVo13v8K1WUoB5cozyNF2iuHWLtZhQoVaMSIEWqTTwilL0JpOp/dXywP9n87+FkTGvoXfyEE2upz7P7FX/z/hfz8fDx69Aiurq6s8kePHjHp0/X09JhtE8LDwzFz5kz4+vrybqcgh5OTExITE5nt5JSRmJgIR0dHTvnDhw+xa9cuAIC2tja+f/8OIyMjzJw5E23atMHgwYPh4+ODs2fPwtzcHN7e3sVuDbZ06VJmmz2+bdSVYW5ujjdv3sDa2hpmZma8bZPCNpnt27dHVFQUTExM0L59+2Lblqfp//TpE7OFlYmJCZNiv27durxbiALg/VZ8ELr9nJaWlqj3VMSdO3ewfv16SKVSaGlp4efPnyhXrhwWLFiA3r17s77D69evERUVhaioKGRnZ6NHjx64fv06a/up0aNHY9asWTA0NMTo0aOLfe4lS5agZs2aqFmzJpYtW8ZsrTh69GgUFhbi9OnTsLe3591aESjaDllHRwc7d+6EjY0N73tPmzYNSUlJ2LZtG7KysjB79mzcunULMTExzJaZpGJbA3VbIwDApk2bkJWVxXrXAQMGYOPGjQAANzc3nDx5Ek5OThr3kZB5BABNmzbFlClTcPr0aebd5Pj58yemTZuGZs2aiZpzt27dgqmpKVPH1NRUZV2h8PT0xLJly7Bw4ULMnTsXurq6AIC2bdsK+v/atWsxfvx41laTchgaGmLChAlYtWoVWrVqVeJnVTcGfv36hfDwcEybNk3wFqeGhobMdhWlS5dGWloaPDw8ABRtDaiIsmXL4s2bN3BwcED58uVx6tQp+Pj4ICEhgfluAHu7seDg4GLvHx8fX6L+dHFxwYkTJzB06FBW+fHjx5kt1n79+gWgaPyoGl/K5fI5JJFIMG3aNFb/FhQU4Pr166ytufT19dGuXTvBz52WlsZsxawIExMTZiuwChUq4NWrV3j//r2ouWpubs5sWVimTBncu3cPnp6eyMrKYm3xZ25uDolEAolEAldXV1bbBQUFyMnJwaBBg5gyTbcYEUIzxNJqOdLT04utq4gxY8YgJCQEc+fO5Z2vypg/fz78/f3h5uaGevXqASjaDjM7O5uzJaibmxseP34MJycnVKlSBevXr4eTkxPWrVvHu90RIG69Kw6vXr3CixcvWGVxcXE4dOgQfH19IZVK4ejoiMaNG8PExATz5s1DixYtmLpv375ltkIzMjLCly9fAAAtW7bEtGnTeO8ZGxuLrVu3MlsO1atXDy4uLnB0dMSOHTtQsWJFJCYmwt3dnfU/+ZakfFudyZGWlsY8n0wmw7dv3yCRSDBq1CgEBgbi2bNnkEqluH79OrKysjBx4kQ0aNAAp06dYraNUrWOtmrVCgcPHsTcuXOxb98+6Ovrw8vLC2fOnFG59XRWVhY2btyIhw8fAgA8PDwQEhICU1NTtGnTRtSaIYYenTt3jjmPj49X23bbtm3x9u1bWFtbF/ssirSiatWqzH+qVq3K2hJQ1X8A4WNGE55HzPZLs2bNwvTp07FlyxZBcxoo2lKtdu3aAIrotpxW9uzZEzVr1mRtJ6ytrY0qVarwtqNcLlTmkUqlGq95O3bswIYNG9CiRQvMmDEDXbt2Rfny5eHl5YVr165h5MiRosbA0qVL0b17d+jp6WHp0qXF1lfeHloINJFjAKhc6+QoW7YsgoODERYWJop+TZ48Ge3atUNkZCS+ffuGCRMmoH79+jh9+jS8vb0591m0aBE6duyIXbt2ITU1FQDg7++Prl27ombNmgAg+huKpRmKEMOv+fv74/Tp0/D09ESnTp0wYsQIxMXF4fTp08wW0Onp6bCysmJ+q4Om/VmzZk1cunQJ7u7uaN68OcaMGYO7d+8iJiaG+Y7KmDJlCsLDwzFx4kS1W+K1adMGISEhWLx4MWvr1rFjxzLf+MaNG3B1dUWjRo3Qo0cPtG/fnpcH48Pnz59Za0ClSpXQp08fWFhYMPcX2qcGBgYoKChAgwYN4OXlBTMzM0HPoAlmz56NOXPmYMKECSrriJXB5FixYgWsra01frZJkyZp/F8+qOMzMzIyRK+Pcqii0/L+B4Bhw4bBzs4OixcvBgC8f/8e9erVg52dHcqXL4/g4GAUFBSgZ8+eTJuarAP9+vUrdutRZRQUFCAqKgpnz57llR2VeVl1/JfydpIrV67Ehg0b0LZtW0RERDDlvr6+GDt2LHPeu3dv3L17F0OHDsWkSZNQvnx5EBGePn2KnJwcDB8+XK28qKWlhbZt2/L2X0FBAaOfsbS0xOvXr+Hm5gZHR0c8fvyYqUdEaN26NY4dO4YqVarA09MTRISHDx8iODgYMTExOHjwIKd95a2Yf/36hTt37uDevXssmVcI3QXE6xCzs7PRr18/xMbGwsTEBAMHDkRYWBiz1fz79+/h7OzMGrsZGRksndipU6fQsWNHRu83YsQING/enPlmFy5cwJcvX2BkZMTZwn7v3r3MVttVq1bFuXPnUK1aNVadLl26gIg4W44L2aI0LCwMAEpEG4XIVJquYULbB4CGDRuy+loR8vdURGBgIA4fPgxvb2/06dMHo0aNwr59+5CYmMh6RrF6ZD4EBQXhzp07jK6YD0L1fJpAHQ+ryGcK+S7p6ekMPw8AtWvXRlxcHBo1aoRfv35h5MiRrPtbWFggJSUFlpaWjA5EFZS3qdXR0WF4EWtra2RkZMDd3R2mpqYcGRxQL4efPXtWlN5DUz3JokWL4O/vD0dHR4bPvXPnDmxsbLBt2zamnpj2NdWtffv2DRERESrXI7n+SxFC552VlRWOHTuGlJQUPHr0CABQsWJFjkwmx4gRIxAVFYUWLVqgcuXKvO9w5coVnDlzBpaWlrC0tERsbCyGDBmCevXq4dy5czA0NGTVF8tTA8Dhw4cRFBQEHR0dHD58uNi6rVu3hlQqZcnN165dY8k6ZmZm+Pz582+hF0LQv39/jBgxAps2bYJEIsHr169x9epVjB07liODffv2jeEbzc3NkZmZCVdXV3h6eqrcblguq+3ZswcfP37kXNfUTqEJxNAAW1tbfPr0CY6OjnBwcMC1a9dQpUoVpKenc/QeFSpUQFZWFvbt24e0tDSMGzcOFhYWuHXrFmxsbFCmTBkAYMa6RCJBcHAwS/deUFCA5ORkFj3kQ15eHu/cc3BwYH4L1ZPIeTGJRMJZ93V0dODk5MTwxIrYuHEjevbsiWrVqjHbTufn56Nhw4bIyMjA58+fYWRkhO/fv8PHx0fluyiPGaF8oBzlypVDeno6HBwcULFiRezZswd+fn6IjY3l8B+3b9/mfYY7d+5g4sSJuHfvHvr37w8AaNeuHY4dO4YjR46gSZMmGDZsGOs/BgYGGDJkCHO+Y8cOREZGomXLlgCKbCdBQUHo06cPo1eXz1+x81qonXf58uWieFJlXL16FXFxcbC0tIRUKoVUKkXdunUxb948DB8+nPP9hM7Tnj17om/fvpg8eTJLvp87dy5j/zx//jw8PDwwYcIEuLi4YNWqVdi1axd27dqFhw8fom/fvjhx4gT09fWZ+2ui/6xRowZq1KiBZcuWYe/evdi8eTOjZ9i0aRNrPgLA/fv3mTVWIpFg/PjxKFu2LDp27Ijo6GjmfRQhRkepCFV6b0UI5b800a2qa/vNmzca8QxA0ToeERGBf//9l9Ed//r1CxEREYw/yKtXr2BjYwOgiM+LjY3FqFGjWO3IbVhhYWFITk6GkZGRYHlDU325GIihR0AR79CvXz+O/q9MmTLYsWMH/P39ARTxIjVr1oStrS0aNGiAJUuWoEGDBihfvjynTU14GADIycnh2PuBonUgOztb5f/Onj2rkh98/vw56tatCwDw9vaGlpYWS2dYv359lp5BEWJ1GXKo6lNFiLWbvXr1CsOHD1c7l4TSF6E0nQ9ieTCxdP13QxMa+hd/IQR/HXT/4r8OYhhqAFi3bh2ioqIY44UqtG/fHlOmTEHjxo0ZRkyOt2/fYurUqejRowfnf0IMm2KMbHJBND8/HxKJBE2bNuU8jyLi4uIYY46i44UqaGK8EcvYyZGWloZly5axjI8jRozgZdzUQex7KkKo4qN58+Y4d+4cmjRpgoULF6JFixbQ1uaSWU0ViIaGhggJCUFISAgeP36MjRs3IiIiAhMnTkTjxo15FWj37t3D7du34ebmpvL9Dh48iC1btiAgIABA0Rhr0aIFWrVqxbTJ94yxsbHo3r07cnJyYGJiwqojkUiY+fTvv/9i4MCBzLUTJ05g8+bN2Lp1K9zd3TF06FCEh4eXqI+EOgjIne0rVKiA0NBQVKxYkTGCrVmzBj9//sTWrVuxefNmUc4KckdxIkJ4eDisrKxYArcq5OfnY+nSpdi1axdSUlIgk8ng6uqKPn36YMCAAdDR0dHIQHTv3j2sWbNG5XV/f39MnToVgObODYCwMaCjo4P9+/erdKrjgxjHiXbt2uHs2bOoUaMGhg0bhh49emDjxo3IyMjgCMFyHDt2DFpaWmjatCmr/NSpUygoKNC4P+UYPXo0hg4diszMTAQGBgIoEvYWL16MZcuWMcoyQJiTmxxygYOIcPfuXZbQKZPJUKVKFYwdOxYrVqwQ1J6yc1O1atUwbtw4bN26lRGEMzMzMX78eGa9TE1NRdmyZUXPVaEG4mXLloGIEBISgvDwcNY6I5PJ4OTkhFq1agEo6q9WrVqhQoUK+Pr1K6ZPn469e/eiQYMGAIDv379jy5YtvA66QmiGprRajJAqVECWo1KlSkhOTsaqVauQlJQEfX199OrVC0OHDmU5ZgBFhp43b94AKDLANmvWDDt27IBMJkNUVBRv+2KNfaqwZcsWTpkYAVyo470i1AUDTZkyBbt27eLl51atWoXCwkKsW7eOt211DuZnzpzBgQMH4OvrCwC4fPkyOnXqhMDAQJw9exYA/zoqR4sWLVgOysUhMTERTZs2hb6+Pvz8/AAUGT7nzJmDU6dOiV4zxDrdAkVKSH19fdy5cweVK1dWWU9RsaWs5FIFTRVxQseMJjzPyJEjkZWVhevXryMgIAAHDhzAu3fvMHv2bI6hZ/HixUhLS4ONjQ2cnJwYQ48cfMomMUazN2/e4OzZs7CwsECjRo1Y68C3b9+wePFiTJ8+HYBwmad69eowNjbWaM1TpxQUOwYU+1xo/6tyajA1NYWrqyvGjh2Lxo0bM2WaOKFFRUVhypQpCA4OZubdjRs3sGXLFkydOhWZmZlYtGgRdHV1RdGvmzdvYvXq1ZBKpTA2NsaaNWvg4OCAhg0b4uTJkywDpRzywD1VEPsNS+KIJIZfW7VqFX78+AGgyNlVR0cHV65cQYcOHRieVL5+Cg3u0rQ/lyxZgpycHABFwbg5OTnYvXs3KlSowHIkUUReXh46d+6s1jkXANavX49Ro0ahS5cuyM/PB1Bk8OvduzfjNF2xYkVERkZi8+bNmDRpEoYMGYIWLVqgR48eaN68OYd2yHHhwgW0atUKpqamzJqzYsUKzJw5E7GxsfD39xfdp1paWmjSpAkePnwoygltzZo1iImJgYWFBQYOHMji5z58+AA/Pz+Wg8vnz5/RqVOnYtvUxGFcnVOoMipVqoRLly4xfNOQIUMwc+ZMJsjr/fv3cHJyYgWQiYU6PlOT9REQHph27do1Fq+3detWWFhY4M6dO9DW1saiRYuwevVqhifSVPY5fvw4jh49ijp16gh6fiFOSIoQGuAnR3p6Om9wg66uLr59+8YqExLwoCkqV66MpKQkODs7o0aNGliwYAFkMhn+/fdfljNiVFQULly4gLNnzzLyixxxcXFo27Yttm7dyqzZcqgKvpgxYwZD2wBhdBcQr0PUJNhJqCOXIoQ4ow8ePBgXLlzgrde1a1cQETZs2ACgSM6cOnUqE7T9+vVrLF26FKNGjUJBQQEWL16MMmXKYMCAAQA0p42AMJmqJEHeYmS21NRUHDp0CM+ePYNEIoGzszPatm3L6xj777//MjQpNDQUpUqVwpUrV9C6dWuWTq8kujs5hDhxiAkEEgsxhm0h38XS0hIvXryAk5MT87/KlSsjLi4OgYGBeP36NatNsY7xivD29kZCQgIqVKiA+vXrY/r06fjw4QO2bdvGK5upk8N1dHRE6T001ZOUKVMGycnJ2LFjB6PL6NOnD7p27crie/5kAKkcYgNMAPG6EicnJxARypcvz2sbkCM6Ohp79uxhghT48P37d1YbEokEa9euxdChQ1G/fn3s3LmTVV8sTw2ID651d3dHbGwsRo8ejfv37yMjI4O1lj1//hw2NjbYsmWLKHqhqQP4xIkTUVhYiIYNGyI3Nxf+/v7Q1dXF2LFjOY6JmgSzjx8/HufOncPatWvRs2dPrF69Gq9evcL69etZQUFyiKFfYnlqMTRAaOAFACQnJ6Nhw4YwMzPDs2fP0L9/f1hYWCAmJgYZGRnYunUrgP9Zs4gIxsbGLJ5RJpOhZs2aLL23IlJTUxESEoIrV66wyvmctIXqSeT02dnZGQkJCSz+uDjY2tri9OnTePToEVJSUgAUjQ03NzeEh4dDV1cXDRo0UGsTVoZQPlCOPn36ICkpCfXr18fEiRPRqlUrrFq1Cr9+/VIpm8qRnp6OadOmYffu3Wjfvj3u37+PChUqMNfFBOq8evWKNX5cXFwQHx+PwMBA9OzZEwsWLGCu/Q4+4MGDB8jIyGDmCVC0Runq6grmSZUh1jla6DxdunQpbGxssGDBArx79w4AYGNjg1GjRjHBp02aNGGS/pw6dQpVq1ZFvXr1sGvXLkyePJl3HGmq/wTA2AOcnJwQFhaG6OhorFq1iqNz0tXVRVZWFqusW7dukEql6Ny5M6/zupC59/r1a9jZ2RX7jNHR0ejSpQurTCj/pYluVV3b8nNAHM8AAKtXr0br1q1RtmxZeHl5AQDu3r2LgoICHDlyBEBRcI/c6X3atGkYPHgwzp07x+gQExIScOzYMcb2cPr0aQQEBAiWN0oyXoRCDD26cuUKOnbsiNatW2PMmDFMQpQHDx5g8eLFaNq0KW7fvo3NmzfD0NAQDx8+VOmjQEQ4ceIENm7ciH379gEQx8MARQmvdu/ezejE5YiOjmYFiipCXXI+AwMDlh7BysqKCRSVQ67zU4ZYXYYYiLWbNW3aFImJicUGRipCKH1RBB9NB7gJccTyYELp+q9fvzBlyhSGnxo0aBBCQkKY6+/evYOdnZ1o53WxNLQkQZh/8V+GP5id9y/+4n8l8vPzafbs2WRra8ukw7e1taU5c+YwqfefP3/ObDVhYWHBpIgvDtnZ2eTh4UHGxsY0ePBgWrZsGS1btowGDRpExsbGVKlSJcrOzub8r02bNvTvv/8SUdG2pS4uLjR79mzy8fGhhg0bcp79/PnzgrdfVNzGRh1+/fpF4eHhzHurg3zbutzcXLV1lyxZwmxRcfr0adLT0yNdXV2SSqW0bNky3v+cOHGCZDIZ+fn5MVtL+Pn5ka6uLp06dUrQM/JB7HsSETVu3JjZyqBfv37k5+dH27dvp6ZNm5Kfnx9TTyKRkJ2dnejtYUuC4rZWJCKqV68es8WTKujr69PTp09ZZdnZ2VSrVi0KDAykp0+f8qbpF7o1guJ29kREgwYNog4dOjDn586dIycnJ+Zckz4SM4+ePn1KzZo1Y22PIZVKqWnTppytR8TOuYKCAtLR0RG0LVRubi7VqVOHpFIpNWnShEaMGEEjRoygJk2akFQqpRYtWlBBQQE9efKE2WJTV1eX01d8ULXtihwPHjxgtl8JDg5maJPYrWGFjoFevXrRkiVL1D63HGlpacx2LDk5OTRw4EDy9PSk9u3bq6VpV69epcWLF9Phw4dV1vH09KSjR49yyo8fP05eXl7MuZj+VMaaNWuoTJkyzBhzdnamLVu2sOrk5eWRlpYW3b17V1TbwcHBxW5fpLjtsqqDb9vZR48ekZubG8lkMipfvjyVL1+eZDIZVaxYkdlu5MCBA7R161YiEjdXP378yGzJXlBQQPPmzaNWrVrR6NGjmW2YFREfH8/acpYPJdliRAzN0ARbt26l2rVrU+nSpZkxu3TpUjp48CCrXrt27Wj37t0lvp8QfPv2jW7evMlsF8YHoeudJvD19aUTJ04QEVGrVq2oZ8+e9PLlSxo/fjyVK1eOVXfChAk0Z84cIiKKjo4mbW1tcnFxIZlMRhMmTOBt39PTk+Lj44mIqGHDhjRmzBgiKtrep0yZMiV69q5du9LixYuJiGjmzJlkZWVF/fr1I0dHR2rXrh0ZGhpy6MSvX7+obdu25OXlRcnJyb9tu5u6detScHAwZ0vm3r17U7169Vh1ha4ZROLpkbOzM7M9opC2AwMDRdHSvLw86tOnj+DnFzpmNOF5xGy/pMlWyH379mWuybfWbtSoEZmZmVFISAhT78aNG2RmZkYmJiakr69PLi4udO/ePea6Ms0TK/Nosua5urrStWvXiIioTp06NG/ePCIq6gMrKyumntgxkJeXR+XKlRO0pV1UVBTvsWzZMurZsyfJZDIOTyBGjiEq2k6bj1bv3r2bAgMDiaiI7ru5uYmiX+bm5rzbzy1cuJDMzMwoJiaG6dOePXuyZMk7d+5QXl6eymcW8w3lEEMziErGr6mDiYmJ4GcR25+aYOTIkUy/CsXXr1+ZLQa/fv2qsl5BQQGdPHmSevfuTSYmJmRubk79+/dn1jRFVK5cmfr378/aMjA/P58GDBjAu/2q0D6tVq0anTlzRuCbFa2tBgYGFBoaSj169CCZTEZz585lrvPxYCEhIbR27VpB7YuRwZS3BBRbX8gWgmIhlM8UQxtPnjxJMpmMPDw8yMHBgUqVKkVxcXGs55Z/cz09PdYcDAoKonHjxjHnjx8/5mxrTCR+HRC79WipUqV4ZTBVUMd/KcPd3Z3htRW3Y12xYsVv18MUhxMnTjDbnaemppKbmxtJJBKytLSks2fPMvUaN27MrJt8mDNnDjVp0kTwfVNTU1Vu+SsUQnSIDg4OdO7cOeY8MzOT/Pz8qEmTJvTjxw/e+V+zZk2mL+/du0dSqZRFm+Lj48nR0bFEz64OmmxRKpY2yiFGptJkDRPa/ty5c0lLS4ukUinZ2tqSjY0NSaVS0tHRoYULF4p+L2UI1QfI9QCKUJyjRES7du3i1PmTMrtQHlYounbtSiNHjuS9du/ePbKysuKVB3/9+kVbtmyht2/fCr5XQkICQ//fvXtHTZs2JWNjY/Lx8aHbt29z6ouRw/9fhVhZ1tTUlC5duiTqHkLn3bdv3ygkJIS0tLRIS0uLGedDhw7lpfmlS5dm5EtVqF69OqOHU0ZoaCiZmZmp1DeI4anFICYmhmQyGQUGBpKNjQ21bNmSdX38+PHUqVMn0e0GBAQw/F9AQIDKo0GDBrz///nzJ92/f5+uX7+ukv/etm0bo2dPTEwkS0tLkkqlpKenR9HR0bz/sbe3Z9Y+Y2Njxn6wdetWCgoK4tQXSr804anF0ICCggKW7mjXrl00bNgwWrFiBf38+ZNVNzAwkOEXFWn05cuXedfoGTNmMNvFC0Xt2rXJ39+fjh07Rrdv36Y7d+6wDkUI1ZP8b4NQPlAVnj17Rvv37y92m/rMzEwaOnQoMwdv3Lihsm5KSgotXLiQQkNDaejQobR48WLW+iuHs7MzL8/z6tUrcnV1pcaNG3PGoiY2vLS0NPLy8uJsXS+VSlnti7Fry1G3bl06cOAAERWty82aNaNLly5Rr169yMPDg1NfEz7jy5cvxdpklGVNIyMjtbKVWP3ny5cvac6cOeTi4kKlS5emcePGqbQDNm7cWCXPt3PnTtLR0eH0q5C55+HhwZHVd+zYwdCEXbt2kY6ODueeQvkvTXSrYvSTmtjjsrOzae3atYyvwrp163j9PeS4dOkSdenShfEJ6NKlC12+fJlTT6y8IXa8lATF0aOgoCAaMGCAyv8OGDCALC0tqVSpUirtB0+fPqWpU6dS2bJlSVdXl1q0aMG6LoaHOXz4MGlra1OvXr0YvXDPnj1JW1uboQvKsLW1VclfERWNI1V8ARFRbGwsrx6OSLguQyqV0vv375lzIyMj1jvz8QFC9M6HDh1ijsjISHJwcKCwsDDat28f69qhQ4dYbYuhL0TCabocYnkwoXQ9LCyMbGxsaOHChTRlyhQyNTVljU9N9XxiaWhUVBT9+PGD+V3c8Rf/3fjroPsX/9VQx1ATFSkUZs6cKai9rKwsGjx4MFlYWDALkbm5OQ0ePJjXAYlIvGFTjOG0fv36KpkPPhgZGVF6erqguiVxXBMiaFatWpXXCWfChAklNq6IeU8i4YoPdYKDsgChqTAgBnv27KFKlSrR5s2bKTExkTESyw8iIjc3N15D2devX6lWrVpUpUoVXmbKwMCAV6BXhrJA7eXlxThsExU5h8idReUQ20eaOAh8+vSJrl+/TtevX6ePHz+qbFuss0KlSpXo6tWrautNnz6dHBwceOfBnTt3yMHBgYYPH05lypShFStWEJFwga1ixYq0bds2ldflTiQlhdAxMGvWLDIzM6MOHTrQ3Llzafny5azjPw09PT3e8ZWenk4GBgasMqH9qQrv378v1ilDjJObKnz58oUOHDhQrLAkFAUFBXT8+HGmb06cOEEFBQUq64udq0Jx8+ZNlmP/wYMHqU2bNjRp0iRGiWxiYsIJoNmxYwcZGhpSbGxssQ66YmiGWFq9Zs0asrS0pNmzZ5O+vj4zRzZv3kwBAQGsumIEZDk+f/5MixYtor59+1Lfvn1pyZIllJWVJejZ1EGMoj83N5cuXrxI9+/f57Tz/ft3jkO6JkYQOYQ43msSDCQU6hzMPT09ad++fZz/yZ10HRwcVI7F/Px8WrhwIVWvXp1sbGzI3NycdShDVQDG/fv3SV9fn1UmVsknhh5FRkZS8+bNi10/FWFpaSmaZyyJMVHVmNGE5zE2NmbonIODA2PMffr0KeebawKhRrNGjRpRnz59qKCggLKzs2nw4MFUqlQpunXrFhEVH5QgROYhEr/miXFGFTsG7OzsRDl+qcLixYupVq1arDKxcoyenh5v3ZSUFGYMqBoPxdGvevXqqXRYnD9/PkPDiIqUtsU5FfJB7DfU1BFJCI4ePco4hyji5MmTdOzYMU65mOAusf1548YNxnCjiGvXrlFCQgLvf4YNG0ampqbk7+9PQ4cOZYwy8uN34fv377Rnzx6V8peenh49evSIU/7o0SOOPEUkvE+PHz9OVatWpdjYWHr9+jVDM1TRjkqVKjFOKkRFhnsrKyuaNm0aEfHTo7lz55KlpSX17t2bFi1apFYeECqDzZkzh2JjY1llW7ZsIScnJ7KysqL+/fszCnIifqOpsoNuSYNqxPCZQmmjmMA0a2tr1npeqlQpFp+SkpJChoaGvPcRsw5s27aNOnbsqDZYUw4hTkiKEBvgt2HDBipTpgxFR0eToaEh7dq1i2bPns38liMlJYW6dOnCO7azsrKoa9eugmRcMfj48SMVFhayymxsbHid6eS4desW2djYCL7H1q1bqXTp0sy5WLpLJEyHqEmw059y5FLE58+facOGDTRx4kSGP7158ya9fPmSee7nz58z9XV1dVmBTqmpqWRmZsZqUyxtlEOs85RY3aqQ9uPi4kgqlVJYWBhrvnz8+JGmTZtGWlpadP78eVa7mzZtoj179nDut2fPHpVGRCH6AE2dOP5kIJAYHlbId0lKSqJNmzapvN/du3dVOpRo4oQkBkLlcLF6D0112vfv36fjx4+r1X38yQBSsQEmRMLn9fDhw6latWp08eJFMjQ0ZNaTgwcPUtWqVTntLlq0iIYMGcJZIxQxd+5cXidQOQYPHqzS6UBswgQxwUNnzpyhkSNHUkREBIcXmDFjBuPQ+vz5c0HH/y0ICWY3NDRknrFMmTJMAO/Tp095eSqh9EsTnvpPQVHHqsgfP3v2jHR1dTn1c3NzWf3+7NkzWrp0KZ08eVLlPQwMDATrrsU4FxMVyWt8MsXKlStpxIgRnPL8/HyKjIykrl27UsOGDalBgwas43eCjw/UBDk5OTRjxgwyMTEhHx+fYr81kbhAnb59+6p0fH758iW5uLjwjkWxdoGWLVtSmzZtKDMzk4yMjOjBgwd08eJF8vPzowsXLjD1xNq1icQ7R/8JPkMqldKTJ0/oy5cvlJWVRcbGxpSUlKSWhxSi/9y9ezc1a9aM9PX1qW3btnTo0CFW4C4fYmJiVAYQERXxY8q2CiFzLyAggGrWrMkrB+7evZu0tbVpwYIFnGuaJOQQCjFt/w573O+CJvLGnwq+EQNzc3OW7U4ZSUlJJJFIOLLPjx8/aPv27dSgQQPGuXHJkiW87yqWhzly5AjVrl2bDAwMqFSpUtSgQQPewHc51CXnu3TpUrHy+urVq2nlypW814TqMiQSCXl6ejKO3FpaWuTh4cGce3p6quUD+PTOcv8kdYe8bU3oC5Fwmq4K6ngwoXTdxcWFpRdMTU0lFxcXCg4OpsLCQo35KU1o6F/8hRD8ddD9i79Qg+HDh5OZmZkoY1xhYSG9f/+e3r1791uEL0WIMZzu3r2bypUrRytXrqQrV67wOmcqonXr1qIiN0rquFYcdHV1eZVBjx8/5lUKiIHY9/yT+NPCgCqmS5H5GjZsGHXs2JH3/9nZ2VSjRg1e5kVo5seKFSsyTFRmZiZpaWlRYmIic/369escw9P/pj4S66xw+PBhqlu3rlolsqurK69Dlxx79uwhiUTCUo4IFdgmT55MDg4OvFk43rx5Qw4ODoyBtyQQOgbEZnIV4zgxd+5c2rhxI6fuxo0bKSIigvd5bGxseBUzp0+f5mRLEdqfmkKskxsRUadOnRjhLzc3lypUqEA6Ojqkra1N+/btoytXrohyVCgJhM5VsQZiX19fZn6kpaWRrq4ude3alVxcXBjFqpWVFYuWyLFr1y4yMDCgtWvX/jZFthha7e7uzigRFZXad+/epVKlSrHqChGQFZGQkEAWFhZUpkwZateuHbVr147Kli1LpUqVops3b7Lqtm/fnncOzJ8/XyXNF4rHjx+To6Mj85z+/v70+vVr5roQoVeIEaQkUAwGkn8rIYcmGD9+vMoMZ79+/aLWrVur/B7Tpk2j0qVL06JFi0hPT49mzZpFffv2pVKlSvEaF6ytrXmV8CdOnCBra2tWmVglnxh6VLVqVTIyMiJdXV1ydXVVu1vAyJEjRStbxSrihEATnkeTrFOJiYm0bds22rZtG+NAW1KYm5tzHJvmzZtH5ubmdOPGjd9ivCvpmlecM6rYMTBnzhzq3bu32mzq6vD48WNeZ3cxckyFChVUBg66uroSURF9trOzE/VsGzZsoO7du6u8HhERwewyoc6pkA9iv6FYmiGGXxO6e4EcYoO7xPRn9erVae/evZzy/fv3q8wWLyaDV05ODk2dOpVq1apF5cuXJ2dnZ9ahCm/evKGlS5dStWrVSCKRUI0aNTh1ateuzWuoPHDgAG99oX2qzH/ID1X8iL6+PscQe/fuXbKxsaGJEyfy0iOx8oBQGaxp06Ysfic5OZm0tbWpX79+tHjxYrK1taWwsDDWu/5pB10xEEobxQSmtW7dmkJCQqigoID27t1LMpmMZQQ6cuQIVaxYkfc+YtaBqlWrkrGxMRkZGVHlypXV8gJCnJBKiu3bt5OLiwsznsuUKcPKkkpE1L9/f1ZGYWWMHz+eBg0apPEzZGVl8fJSHz9+ZM07HR0dFv+sjFevXpFMJuOUK/Oubdu2pRo1apCWlhbLAVAs3SUSpkPUNMBbqCOXJkhKSiIrKytycXEhbW1tZk5PmTKFevbsSURFjuqKDnlly5ZlOV+kpqaSkZERq12xtFFT/And6j///FNsVqv+/ftTly5dWGUVKlRgZeaWIz4+nuF3lCFEH6CpE8d/EsXxsJp8FzEQ64T09OlTlYFjQpykipPDxeqoxdQXm2FLbPtiZFmxASZi4ODgwMxnRR4jNTWVjI2NOfXbtm1Lpqam5OzsTC1btvwt+glFaJIwQZPg2uKgTD9/F03903oeRfypHZs04anF0AAxgRdWVlaM7kJx7J46dYrKli3LaaNx48ZMsOnnz5/J2tqaypYtS3p6erRmzRre9/X19aWLFy/yXisp7OzseHXEN2/e5O2j0NBQMjQ0pH/++YdGjBhBI0eOZB3KMpyqQxlC+UA5xDgW29jYkIGBAU2YMIHu3LnD4dMU+TWxgTrPnj3j1dvL8erVK971XqwNr1SpUswzmpiYMMGnZ8+eZQUxiLVrq8LvcI5++/Yt9ejRg0qXLs04PCseilBF39TROyH6T4lEQo6OjjR58mQOLS9JIhwhTnjK+Pr1K1WrVo0aN27M2tlpz549JJPJVNrklKEuIUdJdKvFta2JPS4lJYXWr19Ps2bNovDwcNbBh4KCAnr8+DFdvHiRzp8/zzoUoYm88Sf05UTi6JHyrj3KePbsGSuIPDExkQYPHkxmZmbk6+tLy5cvp7dv35K2tjZv8heiP5/0SUxyPj78+vWLd6cQMRCb9O1PQVP6IpSm/07w0XU+furly5fk6upK3bt3p1evXv0RPd/vGAN/8d8JCRER/uIv/ovw7t07jB07FmfPnsX79++hPAUKCgpY5w0aNFDZlkQiQVxcHO+19+/f4/HjxwAANzc3WFtb89ZLSEhAYWEhatSowSq/fv06tLS04Ovryyo/ceIEJk2ahFmzZqFatWowNDRkXTcxMWF+S6VS3mcmIkgkEs67rlu3DuHh4ejevTtv261bt2adx8bGYsGCBVi7di0qV67M+34AMHz4cLi4uGD48OGs8lWrVuHJkydYtmwZ5z/29vZYsmQJOnXqxCrfs2cPxo4di4yMDJX3Uwex75meno78/HxUqFCBVZ6amgodHR04OTnx3kfIGNi4cSNiYmKwbds2WFhYaPxOqvD8+fNirzs6OuLz5894/fo1PDw8eOt8/foVt27dQv369VnlGzduxMyZM9GnTx94enpCR0eHdV3+HSMiIrB8+XIMGTIEcXFxyMzMxL1795h6y5Ytw5EjR3DmzBmmTGwfiZ1HYiBmzgGAubk5cnNzkZ+fD5lMBn19fdb1T58+AQD09PSQmpoKe3t73vu+ePECTk5OrHmqOKclEgnzW3lOf/36FbVq1UJGRgZ69OgBNzc3AMCjR4+wY8cO2Nvb49q1azA2NgYABAYGCvoWyvRO6BgQCz8/P4wfPx4dO3ZklcfExGD+/Pm4fv06U+bk5ISdO3eidu3arLrXr19Hly5dkJ6ezml/4MCBuHr1Kg4cOIDy5csDAJ48eYIOHTqgevXqiIyMZOoK7U8fHx+cPXsW5ubm8Pb2ZvWPMm7dusX89vb2xpMnT/Dr1y84OjpyxpdiXTlsbW1x8uRJVKlSBTt37kRYWBiSkpKwZcsW/Pvvv7CxsUGDBg0wYcIEAMDdu3fh4+OD4OBguLu7Y+HChRg4cCBmzJjBafvs2bPM+lhYWMi6tmnTJk59oXPVy8sLERERaN68Oev6iRMnMGHCBCQlJbHKTU1NcevWLZQvXx7z589HXFwcTp48icuXL6NLly548eIFmjRpgiZNmmDs2LGc59q1axd69+6NgoICzloHiKcZYmi1vr4+Hj16BEdHRxgbGyMpKQnlypVDamoqvLy88P3792L/Xxzq1asHFxcXbNiwAdra2gCA/Px89OvXD0+fPsWFCxeYulZWVoiLi4Onpyerjbt376JRo0Z49+4dp32h6127du3w69cvREVFISsrCyNHjsSDBw8QHx8PBwcHvHv3DnZ2drzfXgjmzZsHGxsbhISEsMo3bdqEzMxMZmwLRZ8+fQTX3bx5M6fs2LFj0NLSQtOmTVnlp06dQkFBARo3bozc3FzOeiBHfn4+Xr16BUdHR8618uXLY8WKFWjRogWMjY1x584dpuzatWvYuXMnq/7w4cNx4MABLFq0iKF7ly9fxrhx49ChQwcWTyV0zZBDDD2aMWNGsXQuLCyMdT5s2DBs3boVFSpU4KUVS5Ys4bQxe/ZsLF68GA0bNuT9jyJfKXTMiOF50tPT4ezsjO3btyM/Px/BwcG4efMmmjVrhk+fPkEmkyEqKgqdO3dm/v/+/Xt06dIF8fHxMDMzAwBkZWWhQYMGiI6OhpWVFeeemzdvhpGREYfn3bt3L3Jzc9G7d28AgIWFBeLj4+Hl5cWqt2jRIsyZMwebNm1Cx44dmX4VK/MAwtc8TSB2DLRr1w5nz56FkZERPD09OfVjYmIE3ffu3bto3Lgx3r59yyoXKscAwOHDh9GpUydUrFgR1atXBwAkJibi0aNH2LdvH1q2bIm1a9ciNTUVVlZWv5V+ySGVSvH27VtGnlBcX1RB7DcUSzPE8Gv6+vp4+PAhR2569uwZPDw88O3bN1a5s7OzyveSSCR4+vQpq0xMfxoZGSE5OZnz7dLT0+Hl5YWvX78W+3916Nq1K86fP4+ePXuidOnSHFo5YsQI5nd2djb279+PnTt3Ij4+HuXKlUP37t3RvXt3hkdVxO7duzF+/HgMGzYMNWvWBABcu3YNq1evRkREBNzd3Zm6Xl5egvv0/Pnzxb6Tshzo4OCAHTt2oF69eqzyBw8eIDAwEE2bNsX27ds15gMA4TJY6dKlERsby/BuU6ZMwfnz53Hp0iUARbQ0LCwMDx48AABoaWnh7du3DD02NjZGcnIyM+ZKysMA4vhMobTR2toax48fR7Vq1VjXo6Oj0bdvXyxevBihoaEoKChAcnIyGjZsiOzsbOTn52Py5MmYNWsW85+ePXvC0NAQ69at4zy7mHUgPDy82O+gzAu0a9cO586dg4WFBTw8PDiyozJNUsd/BQUFqbx3bm4ucnJyeHUwbm5u2L59O0PPlXHz5k1069aN0eWIRVBQEFq1aoUhQ4awytetW4fDhw/j2LFjALhjURmqxqIyXyuVSmFlZYXAwEA0adKEKRdLd+VtKUNZhzh8+HC8efMGe/fu5dT9+vUrGjdujISEhBLNIbFo1KgRfHx8sGDBAtb6eOXKFXTr1g3Pnj1D3bp1MWzYMBbfpogjR45g0qRJuHv3LlMmljbKIVaHKGYNE9q+s7Mztm3bhrp16/K2cfHiRfTq1YulL9HT08OjR494x4y7uzuvHCtEH5CTk4OAgABYWFjg6NGjzNzfu3cvevTogZkzZ/LyR39SzycGQr7Lt2/fMHbsWBw+fBh5eXlo2LAhVq5cqXJ+K2LPnj2YNGkSRo0axfsNlXn/+vXrIyQkhJER5Ni+fTsiIyMRHx+v0XsC4nXUYuq3atUKWlpaiIyMhLOzM27cuIGPHz9izJgxWLRoEYenENu+OllWTsfkePLkCYgITk5OnPWITw8ndF4bGBjg3r17KFeuHIseJSUlwd/fH1++fGH9X52uQq6fePbsGU6fPo28vDzUr19fEK0AxPPUADBq1Cjo6uoiIiJCbfsJCQnYtWsXUlJSAACurq7o1q0ba35qa2ujbNmyCA4ORqtWrRhdljKqVKkCABw5ShXEmLUV9TwdOnSAn58fh+4sWLAACQkJvOvb0qVLoaWlheHDh+PMmTNo1aoViAi/fv3CkiVLWPw9IJx+acJTi6EBrq6uWL9+Pce+ef78eQwYMIDF6/Tr1w8fP37Enj17YGFhgeTkZGhpaaFt27bw9/fn2O8sLS1x/vx5eHh4IDIyEitXrsTt27exf/9+TJ8+HQ8fPuR8x7i4OEydOhVz587ltSOYmJgItvk5ODiwzvX09HDv3j24uLiwyp88eYLKlSvjx48fnOffunUrR08th1QqhaOjI7p166bSrguA0/dC+UA5ypQpg8OHD3N4/Fu3bqF169Z4+fIl65nkkPNnyudyfq1z584wMzPD+vXreZ97wIAB+Pr1K3bt2sUq//nzJ/Lz8zk0VBXE2vDMzc1x69YtODs7o3z58oiMjESDBg2QlpYGT09P5Obmct5V1Tsq48uXLygoKOCsF58+fYK2tjZHZyt0ngYFBSEjIwNDhw7lle/btGnD/FbHO8qhzEMK0X86OTkVq4cFVNN1PqSkpGDjxo3YunUr3rx5w5SXK1cO9evXx7p166Crq8uUf/jwAX5+fkz7mZmZ8Pf3R+XKlbFnzx7s378f3bp1w4wZMzB58mRBz6AKmuhWxUCsPW7Dhg0YPHgwLC0tYWtry+oHiUTCqX/t2jV069YNz58/56xVyuNXE3lDjL5cDMTQIy8vL4waNUolH7Np0yYsW7YMycnJAIp4gWHDhmHQoEGMrRoAdHR0kJSUhEqVKnHa0ISHEYMRI0Zg69at8PLygpeXF2dN4rNVKCIpKQk+Pj689Kgkugx1EGs327p1Kzp37syazwCQl5eH6Oho9OrVS2P6IpSmyyGWBxNK18uVK4cNGzagYcOGrHqvX79GgwYN4OjoiLNnz/52HQXfGNDS0hL03/+kvuQv/vfhr4PuX/zXQQxDrQmys7MRGhqK6OhohsBqaWmhc+fOWL16NUxNTVn1xRg2AXGGUyHOmaraVgaf4CPUeCOGsZNj5syZWLp0KSZOnMhyQJk/fz5Gjx6NadOmFftuxUHse4pVfn79+hVDhgwRNAY0cc77EyAiPHnyBHl5eXBzc1OpLFOE0O9YWFiIGTNmIDY2Fra2tliyZAnLeNypUyc0a9YMffv2Fd22HGLnkRiIdVaIiooqlpmVjyNVRlY5EhIS0Lx5c2RmZjJlYgS2L1++YNKkSdi9ezc+f/4MADAzM0OXLl0wZ84cmJubs97R0dERLVq04Agiili6dCnrXGg/zZw5E2PHjoWBgQGrzvfv37Fw4UJMnz6dVS7GcUJPTw8PHz7kCGxPnz5FpUqVOAo4oOjbNGvWDImJiShbtiyAIodof39/xMTEMII/ILw/w8PDMW7cOBgYGIhyXBPr5AYUGVtTUlJgb2+PXr16wc7ODhEREcjIyEClSpVgbGwsylFBjvDwcMycORO+vr686+OBAwc4zyJ0DIg1EJuYmODmzZuoUKECGjdujJYtW2LEiBHIyMiAm5sbvn//jgMHDuDChQuccSnHzp07sWHDBpw7d45zTSzNEEOrK1WqhHnz5qFNmzYsg8zKlSuxefPmEtF1fX193L59GxUrVmSVP3jwAL6+viyhV19fH3fu3GEpPYAiR31vb29eA6vQ9c7GxgZnzpxhnH+JCEOGDMGxY8dw7tw5GBoachwKxAjgmjjeaxIMJBRiHczlyM/Px48fP2BkZKSybUNDQzx8+BAODg4oXbo0jh49Ch8fHzx9+hTe3t4cA15eXh7GjRuHdevWIT8/H0QEmUyGwYMHIyIigqVsEavk04QeCUVxAW8AeOepGEWcmDEjlOeRr40NGjRgjrJlyyI3NxePHj2Cg4MDLC0tWf/p3Lkznj59iq1btzK8zoMHD9C7d2+4uLhwjB+AcKOZv78/unXrhkGDBnHaWLBgAaZPn45fv34x804TmUfomieHGKWg2DEg1FitDiNHjsSjR49w4sQJVrlYZ+T09HSsX7+eMT67ublh4MCBnHVNLP26du0aYmNjGWeOZs2a8b6HVCpFXFwco5SsXbs29uzZw/Axcig6cYj9hmJphhh+zdbWFjt37uQEhZ05cwbdunXD+/fvi723Oojpz1KlSuHIkSOoVasWq86VK1fQokULhm/WFGZmZjh69Cjq1Kmjtq6+vj7Mzc3RuXNndO/eXa2zUXF8F8A1Wgrt04yMDNjb23PmPxHhxYsXHAN4t27dYGNjw8uD3b9/Hw0aNMDHjx9LpHAWKoMpBz3WrVsXQUFBmDJlCoAiXtPT05MZj1KpFJUrV2Zof3JyMipWrAiZTAagaO2+f/9+iZ5dDJ8plDaKDUz78OEDLl++DFtbW47R+ejRo6hUqRLvOit2HRADsTRJLP8VGBjIkeWAIh1d27ZtmWBTxWA6Pjx//hzu7u4cY5JQWFhY4PLlyyydB1DEg9epUwcfP34EUDQWg4KCOIYyOX7+/IkTJ05oPBY1obt/MsBbEbm5ucjIyEBeXh6rXNkRUSgUgzsV5a/nz5/Dzc0NP378wOXLl2FoaIiqVavytrFmzRoUFhZi6NChTJlY2iiHWB2iWJ5ESPsGBgZISUnh8AlyvHz5EhUqVGDJhA4ODli1ahXHmebQoUMIDQ3l1d0K1Qdo4sTxJ/V8YnhYId9l9OjR+Pfff9G9e3fo6+tj586dqFOnDq8ORRlinZBMTExw69YtXic0X19fZGVlscrFyOFiddRi6ltaWiIuLg5eXl4wNTXFjRs34Obmhri4OIwZMwa3b9/mfIffGUAqBnxyr9B57e/vj06dOmHYsGGsIKBhw4YhNTWVI5MIwblz59CyZUtmvmpra2PTpk3o0aOH+JcTAKHBQ+PHj8eiRYtgZGTEyANpaWnIzc3F2LFjMX/+fADA27dvsWXLFmzevBlZWVno0aMH+vbty1kn5ZDL4d7e3sU64QqZX3zQJJhdGc+fP8fNmzfh4uLCu3YJpV+a8NRiaICYwIsvX76gY8eOSExMxNevX2FnZ4e3b9+iZs2aOH78OGccGBgYMHqRf/75Bx4eHggLC8OLFy/g5ubGy0fJ6R3fuiqnd4qOLfL+F2KPqVy5MgYNGsRaxwFg5cqVWLt2LUcHbmdnh/j4eLi6unKeEyjSnW/atAnx8fEICgpCSEgImjdvrlYeE8oHyiHGsVgdnyaHo6Oj6ECdzMxM9OrVC2fOnEFhYSGqV6+O7du3c55LGWJtePXq1cOYMWPQtm1bdOvWDZ8/f8bUqVPx77//4ubNm0xSH7F2bUC8c7TQeWpsbIyLFy+q5CF/B/60I6Icubm52L17NzZt2oSrV6/C19cXHTp0wLhx45g6UqkULi4uMDMzw+HDh2FrawuAP3jwxYsXqFu3LipUqICLFy9i2rRpmDp1qsr7C+W/NNGtiuHtxOq/HR0dMWTIEMEB91WrVoWrqyvCw8N59bGK/gGayBt/aryIoUdLly7F7NmzsW3bNo68fvToUfTu3RuTJ0/G6NGjAQBNmzbF1atX0apVK/Ts2RNNmzaFRCIp1kFXDAoKCrB06VLs2bOHV87kSzihaXI+OYpz0NXUliQEYvXOWlpaePPmDSfY5OPHj7C2ti6R7ksoTZdDLA8mlK7369cPRISNGzdynvHVq1cICAjA06dP/4iDrre3NyvRlZyP7d27N7y9vVX+t6S+aH/x/zbUe2D9xV/8f4ZLly79UYa6f//+uH37Nsvod/XqVYwYMQIDBw5EdHQ0q/6DBw/g4+PDacfb25sjOAL8Dgyq8Pz5c9SuXZvjeJCfn48rV65wBBnlbInqsHTpUkHKr48fP3Ick4EihcKHDx94/zNt2jQYGxtj8eLFmDRpEoAiwXnGjBkaR4DJIfY9b9++zWtgrVmzJkfoB4qYAaFjoE2bNr9NgSjH4cOHERQUBB0dHRw+fLjYuq1bt0Z6ejpat27NjLeyZcti//79ag3EQr+jVCrFzJkzMXPmTN7rfJHpYvtI7DwSAzFzDgCCg4MF1WvQoAHmzp2L/fv3816PiIjgCAnOzs7FCmyKMDU1xZo1a7B69Wp8+PABRAQrKyve8TZ//nxs3rwZe/fuRffu3RESEiIoG4PQfgoPD8egQYM4Drq5ubkIDw/nOOjq6uri3bt3HIePN2/ecOiZvb09Ll++zBFML1++DDs7O97nMTU1xZUrV3D69GkkJSVBX18fVapU4c3YIbQ/lZ1uhUJMXTns7e1x9epVWFhY4MSJEwxN+fz5M/T09PD582fY2Ngw9c+fP8+KyqxevTpnvABFgk1UVBR69uwp+FmEjgFTU1M8ffqUoxR+8uQJb1S+r68vZs+ejUaNGuH8+fNYu3YtgCKnH/m7tWvXDu3atVN5z3/++QcBAQG818TSDDG0evTo0QgNDcWPHz9ARLhx4wZ27dqFefPmITIyEitWrMCAAQOgp6eHFStWFNuW8nonzyah7KD74sULJiO2HJ6enti9ezdnfkVHR6tUeghd775//86aixKJBGvXrsXQoUNRv359TtZXALhw4QLveA8KCsLixYtZZW/fvkXp0qU5da2srFhR/YrYv38/75pXu3ZtRERElMhBNzU1lfebVaxYEU+ePEFsbCw+fvzIohdz5szBrFmzkJ+fj8DAQOzevZsVGCFH2bJl8ebNGzg4OKB8+fI4deoUfHx8kJCQwOuwIZPJsHz5csybNw9paWkAirLwKtNXQNyaAYijR+XKlUNCQgJKlSrFKs/KymIcjBUhdi0FwOuIrQpCx4wYnicuLg7x8fGIj4/Hrl27kJeXh3LlyiEwMBANGjRAmTJlOP85ceIEzpw5wzLGVKpUCatXr2ZltVNERkYGr3LV0dGRlT2mV69eOH/+PK+D7vjx40FErGyImsg8Qtc8OdavX8873z08PNClSxeW8lrsGBDqgCtX9irjy5cvuHXrFlJSUljZxeUQKsfI4ezsLCiLlBj6tW/fPnTu3Bn6+vrQ0dHBkiVLMH/+fF4HPABo2LAhy0jdsmVL1nVlA5jQbyiHWJohhl9r06YNRo4cydm9YMyYMbw7LogN7hLTn02aNMGkSZNw6NAhRkbNysrC5MmT0bhxY6Ze+/btERUVBRMTE7Rv377YNhUzf5qbmwveGeXw4cNo2LChWkOvHGLoIiC8T52dnXkV9p8+fYKzszNHiT1x4kTcvHmT954eHh6Ii4vjlXFevnyJw4cP8xpMlLOTCKUZNjY2SE9Ph729PfLy8nDr1i1WZtevX7+yAhCVjW18SvEOHToIurcqiOEzhb7n4MGDeWkZUJS1mYiwYcMGpszS0lKlwr9FixZ49eoV7zWx64AYiKVJ6vgvZcTHx3PGFQD8+PEDFy9eZM5NTU2Rlpam0kH3yZMnKndFEAJ55jFl/Pr1i+UII8TZuVevXiqvJSYmMtnpKlWqxAn8FUt3AWE6RHNzc16eVg5jY2OVzrmZmZkIDg5W6aCmqcFMV1cX2dnZnPKUlBQmw5a6oIkBAwZwnJbF0kY5xOoQxfIkQtr/8eMHE3jABx0dHc586dq1K4YPHw5jY2P4+/sDKNIljBgxAl26dOFtR6g+wMrKCqdOnULdunXRuHFjXLx4EdOnTy82w9qf1POJ4WGFfJcDBw5g8+bNzI4YPXv2RM2aNZGfn682AYLYtV0ikfBm+5dnmFKGGDlcrI5aTP2CggJGZ2FpaYnXr1/Dzc0Njo6OKjOWi2lfE92aGAid13PnzkVQUBAePHiA/Px8LF++HA8ePMCVK1cEZ1bMzs7Gjh07sHHjRiQmJmLatGlo3Pj/sPedUVEs3drPDDmDqGAgGhAUBTNGTJhzzoI5cjBhVgwYjjljBPQYjjlnBQMGEAGPioCKOSsiYgL294M1/U7P9Mx0Nfje+93js1YvmOrq6u7qql07VzOsW7cOxsbGmDZtGiZOnCjKQZeVpwaAf/75h5t7iuBEVURERGDVqlVYuXIlhg4dyvFaP3/+xLp16xAcHIyKFSuiX79+sLe3R3BwMIKDg3H58mVs3boVtWrVgoeHBwYOHIiBAwfyeOHhw4dj586dePToEfz9/dGnT59C3XUwKytLkD4aGBgIriVAvs5NeQc8JycnjXwEIJ5+SeGpWWhA8eLFkZSUpKaLTUxMVNPlWFlZ4cyZM7h8+TKSkpKQlZWFqlWromnTpoLPV7ZsWRw8eBAdO3bEqVOnEBQUBCA/86UmPkoM3yuTyURlXFbF2LFjMWrUKLx9+5YLTjp37hyWLFkiqA8cN24cVqxYgdWrVwvSma5du6Jr1654/vw5wsPDERQUhKFDh6Jv374YOHCgWjZtBcTygQqULVsWJ0+eVOMPTpw4oSZnOzk5IScnB6GhoQgICNAYhAPkO1Nq2vUTyOdxlHcZCg4ORkJCAmbPng1jY2OEhYVh8ODBOr8Zqw1v2rRpXJKO2bNno02bNqhfvz5sbW2xe/durh6rXRvId04Tynjp6+vLBW8qQ+w8dXBwYMrYrYpv376p8Vyqc0QML6DY5UsKrl27hk2bNmHPnj1wdHTEvXv3cOHCBUFbmEwmw8mTJzF+/HhUq1YNBw8eVNt5RJEVFQD+/PNP9OvXDx06dEC7du1451QDGMTyX1J0qyy8HSvP8PHjR7Vdz7QhNTUVe/fu1engDkiTN1h5R7FgoUeBgYGIiYlBmzZt4ObmBnd3dxAR7t27h9TUVE4WVeDUqVN4+vQptmzZguHDh+Pr16/cziaaeD0WHiYkJASbNm3CuHHjMG3aNEydOhXp6ek4ePCgIK8DSLNViIVYXYaNjY3g+1tZWaF8+fIYP348T08JsNvNVHeRUODZs2ecTlQqfRFL0xVg5cHE0vXp06cjOTlZ8BlLlSqF6OhonDlzRvR7sUC1b2/cuIHNmzdjxYoVcHFxQUBAAHr37q1Vh/Ib/0LQb/zGvwzu7u4UHx/PdE1sbCxNmDCBunfvTh07duQdqjA1NaVLly6plV+8eJFMTU3VyosUKUIxMTFq5VeuXCFra2u18sePH1NeXp5aeV5eHj1+/JhXJpfL6fXr12p13717R3K5XK08IiKCvn37plb+/ft3ioiIUCsXi4oVK9KqVavUyleuXEnu7u46r8/MzKTMzEzJ91cF63taWloKjpm4uDgyNzdXK2cdA4UNmUzGfXeZTKbxUIyBzp07U4UKFWjHjh20f/9+qlOnDlWtWvWXP6c2sH4j1nnEApY5RyR+3t25c4fMzc2pVq1atHv3bkpMTKSEhATauXMn1axZk8zNzemff/6R1LYyEhMTac+ePbRnzx5KSkrS+q4xMTE0aNAgsrS0pBo1atC6devo06dPWq8RA5lMRm/evFErP3fuHBUtWlStvEePHtSwYUPKyMjgyj5+/EgNGzakrl278uouXLiQbG1tacuWLZSenk7p6em0efNmsrW1pdDQULX3O3LkCK8sPDycnJycqFixYjR48GC1cSelzwcOHEgXLlwQPKcKFxcXevfunVr5x48fycXFRfCaNWvWkL6+PllbW1PlypUpNzeXiPJpqq+vLzk6OlJ0dDQR5c8ZExMTOnv2LHd9UlIS2djYqLVbpEgRSktLE/XcCoidq0OGDCFPT09e+6mpqVS5cmUaOHCg2vWJiYlUqVIlsrS0pFmzZnHlo0aNop49e4p6toSEBI3f6FfSDCKi7du3U9myZTl6W6pUKdq0aRMRETk7O3Pf3NnZWeMh9P1Hjx5NpUuXpl27dtGTJ0/oyZMntHPnTipdujQFBgby6h4+fJj09fWpX79+FB4eTuHh4dS3b1/S19enAwcOCD632PWuRo0aFBkZKdjGyJEjydraWq3vjY2NKTk5Wa3+vXv3yNjYmFdWtmxZ2rZtm1rdyMhIjfPCyMiIUlNT1cpTU1PJyMiIV7Znzx7q2rUr1apVi7y9vXmHEOzs7OjcuXNq5WfOnKFixYqRr68vrV69miu/cuUKyeVymjt3Lu3bt48qVKhAQUFBgm0HBwfTvHnziIho165dpK+vT2XLliVDQ0MKDg7m6vn7+4s6lMFKv1jokTKvoYxXr16RgYGBWrm/v78gL5eVlaX23AqEhITQly9f1Mqzs7MpJCSEVyZ2zEjleb5+/Urnzp2j6dOnU/369cnIyIjkcjl5eHjw6pmbm9OtW7fUro+PjycLCwvBth0cHOjQoUNq5QcPHqRSpUrpfDZNkCLzsI4ZIyMjevjwoVr5gwcP1OYd6xho1KgRffz4Ua3806dP1KhRI+63r6+v4NGuXTuaMGGC4PNJwcWLF6l3797k4+NDz549I6L88aXK87PQr6pVq9LQoUMpJyeHiIhCQ0MF12ci4ngcbcft27d514jtQwVYvz8Lv5aRkUG1a9cmfX19bp3T19fX+IxS+C+xePbsGbm6upKVlRU3XqytrcnNzY2ePHnC1RswYAA3Zvv3708DBgzQeChj27Zt1KVLF0H6JYSfP3/SmTNnaP369dz9nj9/Tp8/fy7QexKJ70dNvHp6enqhya9nz54lU1NTqlSpEunr65OXlxdZW1uTlZWV4HgUK4MNGzaMfHx86OLFizR27FiytbWl79+/c+e3b99O1atXL5R3EAsWPlPK+lgQvHz5kkaNGkUmJiaC51nmXk5ODv35559Uo0YNsrOzIxsbG95RUOjivxRITEykxMREkslkdOHCBe53YmIixcfHU2hoKDk5OXH1u3btSh06dNB433bt2lGXLl0kP7evry+NGjVKrXzEiBFUr149ye0q8PTpU6pXrx7JZDKur2UyGdWtW5eePn3K1WOlu0Tivr+QTB0REUHOzs4aZWoFevXqRXXr1qXY2FgyMzOj06dP07Zt28jNzY2OHj0qsUfy5e8OHTrQjx8/yNzcnB4+fEiPHz8mb29vNRlJE4RkR6m0kVWHyAox7ctkMpo3bx6tWLFC8Jg7d67a+37//p26detGMpmMDAwMyMDAgPT09Mjf359HV5UhRh+gPCd3795NRkZG1K1bN155YmKiWhu/UmZn4WHF9Iu+vj49f/6cd52JiYmgzlAV0dHR9PPnT7Xynz9/cnodZbRp04a6du3K8Y9E+fS4c+fO1KJFC7X6LHL4r0S9evU4PUTPnj2pRYsWdPnyZerXrx9VrFixwO1L0a3FxsZSZGQkRUZGUlxcnNb2WeZ1WloaDRo0iGrUqEHu7u7Uu3dvnfpYIqLz589Tnz59yNTUlEqUKEEjRowgIiIrKyu6c+cOV+/Lly+kp6cn+L6q+FU8dY0aNWjp0qUazy9ZsoRq1Kih8fyrV6+oUaNGJJfL6f3792rnv337Rjt27KCmTZuSqakpde3alU6ePMnjDb28vNT0OZoO1WdX1ScQEc2cOVOjbkAul1ODBg1ow4YN9OHDB43vpcCvpF8sNGDixInk5ORE58+fp5ycHMrJyaFz586Rk5MTjRs3TtT9bt68Sa1bt1Yr37NnDxkYGJBcLqemTZty5aGhoYK0SCxevnxJCxYsIDc3N7Kzs6Nx48bR3bt3RV27du1aKlWqFKeLdXFx0WjT7NChA1lZWZGLiwu1adNGp82XiCgqKop8fX1JLpdrHAesfODmzZvJxMSEZsyYQVFRURQVFUXTp08nU1NT2rBhg+A9zMzM6NGjRxp6IR+adHYKvHr1ikcDSpcuTSdPnuR+p6SkkJ6enkaeToHCsCe/f/9eTe6TQrtMTU0FaW1SUpKg7CN2np46dYr8/Px09rkysrKyaOTIkVSsWDGSy+VqhyrE6D9lMhk5OzuTv78/RUZG8vh+TVi8eDF5eHhQqVKlaPz48ZSQkEBE+XyL8rqiDOWxM2nSJDIxMaFt27bxxozCtqz8V+h/VYjlv6ToVll4O1aeISAggNatWyd4XyE0atSITpw4IaquFHmDRV/OAin0aNeuXdS+fXtyd3cnd3d3ateuHe3cuVPnvc6cOUM9e/YkY2NjKleuHE2ePJlu3rzJq8NCB1xdXTl50tzcnLNBrlixQrQtkRXabI9idRkKm53qsXz5curbty8ZGhrS4cOHeW2I1Tsr+CS5XE6enp48vqhy5cpkYWHB6W6l0BdNEKLpCrDyYKx0/b8NbWPg69evtG3bNmrcuDGZmppS9+7d6fTp0//lJ/yN/6347aD7G/86sDLUO3fuJAMDA2rTpg0ZGhpSmzZtqHz58mRlZaVmjCPKN7ILLRiJiYmCRnYWwyYRG1Oiibm7f/++ICPLKviIrc/C2GVnZ9OhQ4cEjVSfPn2iQ4cO6RQMdYH1PVmVnyxjQIoCsbBhZ2fHcy548eIFyeVyysrKUqu7YsUK+vr1K/e/tkMVLAa8X+kgwArWZ9GkAHn+/Lma8vvq1avk4eHBCa0KIdbd3Z2uXLki2LZYge369etUqVIlNcHY09OTbty4ofWdv3z5QuHh4VSjRg0yMzPjOemyjAFra2uysbEhuVzO/a84LC0tSS6XcwpnZYh1nCDKN9JPnDiRjI2NuT40NTUVZPRbtGhBCxYs4H4nJSWRgYEBDRo0iJYsWUL29vY0c+ZMtT4X+z0VaNeuHRkZGVHp0qV5yg8hsDq5KRAbG0v79+/nOW8cPXqULl++LNlRYeLEiTR79myN9xSC2PkhxUAshK9fv9KPHz9E1U1ISCCZTCZ4jpVmSKXVX7580aoQZcX3799pzJgxZGhoyI13IyMj+uOPPwTXxqNHj1KdOnXI1NSUbG1tqVGjRhQVFaWxfbHrXWhoKLVs2VJjO8OHD1frexYBnMXxXgGxwUArVqwgc3NzGjVqFBkaGtLQoUOpadOmZGVlRVOmTBFsW5eDebFixXjGuqCgIGrevDn3+9ixY1S2bFnBtlURExNDS5YsUVO+KBQlHTt2pA4dOmg8VK9hUfKJoUeHDh2iQ4cOkUwmo8jISO73oUOHaP/+/TRy5EgqX768WhuaaMXbt29JT09PsC9Y1l+xY4aF5xHC9+/f6fz58zRhwgRuHVNGu3btqEGDBjwD/bNnz6hhw4YanYEKajSLioqiY8eOqRmIpBgRWNc8FmdU1jGg6Vlev35N+vr6Wt9DGU+fPqXBgwerlbOMr71795KJiQkNGjSIjIyM6MGDB0REtGrVKjVayEK/zMzMeIEF379/J319faZ1IzMzk8LCwqhmzZqCsiBLH7LSDBZ+jSifZzt16hQtWrSIVq1aJeh0outZNAV3sfLrWVlZFBYWRiNGjKBx48ZRRESEaP5CF7y8vMjCwoLMzc2pUqVKWh0E0tPTqUKFCmRqakp6enrc2BozZgwNHTpUsP20tDQaNWoUNWnShJo0aUKjR4/WGGCl65sGBQVRUFAQyeVyGjp0KPc7KCiIxowZQ7Vq1aI6deqoXf/27VtKT0/nlf3zzz80YMAA6tq1K/31119q19SoUYNmzJhBRPkGkwcPHtDnz5+pXbt2tHbtWrX6Yr/p27dvqX79+iSTycjCwoL279/Pq9+4cWON6/uvAgufKWV9VODr168UHh5Oa9asoZSUFK78w4cP1KNHD7K1taUSJUrQihUrKDc3l6ZPn04mJiZUq1Yt2rVrl2CbLOvA9OnTqUSJErR48WIyNjamOXPm0MCBA8nW1lZQH0DEFiQlNsBPVZZWPUxNTWnz5s1c/fj4eDIyMqLOnTvT9evXKSMjgzIyMujatWvUqVMnMjIyUjMOsuDy5ctkbGxM9evXp1mzZtGsWbOofv36ZGxsTBcvXuTV/fHjB+np6akFWGhD8+bNqVatWjyHu+TkZPLx8eHxn0RsdJdInA5RSKbW19fXKlMrYG9vT9evXyciIgsLC7p//z4R5fOXdevW1f3yGpCRkUFNmzYla2tr0tPTIwcHBzIwMKAGDRqI5vGUjWtSaaMCrDrEX6GjdHJy0hoMqjiEcP/+ffr777/pyJEjarReyrNLdeL4lXo+KQGh2vpFLperzR0LCwtRgWKs3//OnTtka2tLZcqU4QKFypQpQ8WKFROkJSxyOKveg6X+yZMnad++fUSUT8vd3NxIJpNR0aJFBR0YWNtn0a2JDXRQBuu8Fotnz57R3LlzqUyZMmRra0tyuZx27drFc2wQejcFL6ULrDw1kbjgIVNTU633f/DggaD8cOXKFRo4cCAvMYQi6YAmpKen06xZs8jV1ZUcHR05PahijRVzKENKMHt8fDyNHz+eSpcuTUZGRtS+fXvas2ePRhuVWPolhadmoQFiAy9OnjxJ48aNo8mTJ3Pf9d69e9S+fXuSy+Ua9X8vX76k+Ph43je8fv063bt3j/udmJjInVcNzNAVqHHp0iUKCAggCwsLqlWrFm3YsEHneCEievPmjc5gR23Bl6o2X4WTTaNGjcjExIS6d++u8duz8IEKsDgWE+XrncLDw7W+H2ugjlwup5cvX/LaMDU11alP+lVBAKx2bSJ252ix89Ta2prTw5ubm4sKShwxYgS5u7tzuqQtW7bQnDlzqHTp0rR9+3a1+mL68cKFCzRz5kxq2LAhZwcrC1mDngABAABJREFUW7YsDRkyhHbu3EmvXr1Su15PT4+mTJnCW7uItDvoqj7Ltm3byNjYmPz9/blnERNALsRDiuW/pOhWWXg7VntcaGgoFS1alPr370+LFy/WaQffv38/eXh40NatWykuLk6Q1hVE3viVAe2s9EgscnJyaMGCBVSnTh2qXr06BQcHU3Z2Nn348IFWrlxJXl5eov1bhHgYU1NTLjDO3t6ek+cfPHhAlpaWGp9LW3I+XWvW7t27NfY3a7IiTViyZAn5+PjwysTqnRVrkEwmo/Hjx/P4otDQUNqxYwfHC0ihL1LAyoOJpev379/n9AwKnD17lnx9falGjRpcchxWFGQMKOPhw4daA9N+49+H3w66v/GvAytD7enpyWVFUyg/8vLyaPDgwZyBSRlhYWHUtGlTnkDz8uVL8vPzo/Xr16vVZzVsijGcKhgIuVxOrVq14jEV7dq1I2dnZzXFvba2ExISBPuGxXgjlrFbvnw5NW7cWK1cgSZNmvCy1EkB63uyKj9ZxoBU5zyxEBPFKpPJ1BgsMzMzQUVyQTI/shjwWL8R6zxigVhnBYVAJpfL1RQgS5cupQ4dOpCXl5fgPeLj42n37t20e/duwYwMrAKbIjtvjRo1aMeOHXTr1i26desW/fXXX1S9enWysLDQKIQT5SvA/P39uQy/2dnZ3DmWMRAeHk5bt24lmUxGK1as4EUB7tixQzBKWQFWx4nPnz/TjRs36Pbt2xqVZPb29hQbG8v9njJlCs8Q+Pfff3OOfAX5nkT5hvGwsDBq2LAhl2Vx3rx5nGJLqpObMr5//07Jyclq2VakOiqMGTOGrK2tqUGDBjRq1CjeONOU/ZNlrrIaiD9+/EgbN26kSZMmcYLLzZs3ucyFuqAtglHK2iuWVmdnZ/OimNPT02nZsmV06tQpXr0fP36Qq6ur6EwQyvjy5QslJSVRUlKS6Ax9YsC63rGARQBncbxXQGwwkJubG+3YsYOI+Eat6dOn08iRIwXb1uVgbmxszMuOVKNGDVq0aBH3uzAyEI4YMYJsbGzIy8uLVqxYoVWYZ10zWOiRkEFdcRgaGlL58uV5WdU+ffpEGRkZJJPJKC0tjT59+sQdHz58oIiICCpRooTge7Ao4sSOGRaehyifzkZHR9OsWbPI19eXTExMqHz58jRo0CCKjIxUy4r15MkT8vLyIgMDA3J1dSVXV1cyMDAgb29vjcZesUazBQsW0LRp03jv3Lx5c67/7ezseJn3WWQeqWueGKUg6xhgzYSoC5rWAhY5xsvLi+OblelGfHw82dnZ8eqy0K+CGNujo6OpX79+ZGZmRuXKlaPg4GAuAIu1DwtiGChsR1epwV1SAqpYwJKNmMVBoH379tSnTx/6/v0779tfuHBBMLDj5MmTZGhoSDVr1uS+Uc2aNcnIyIiXiUHsN1XwQDKZjPfb19eX/Pz8aMiQITzHTwV69OhBY8eO5X6/fv2abGxsqGLFitSuXTsyMDBQy7SvnMXE2tqao1cJCQmCc5rVYTwjI0PN8EiUn71DmZaqjivF4ezsTH5+foWS0UIMn8lKG4OCgniGie/fv3PrjZWVFZmZmXEBnkOGDCFHR0caN24cF7DZsmVLat26NV29elXwmaWsA6xZaliDpMQG+KWnp9OjR49IJpNRbGwszyj84sULwXFx5MgRwYxWxYoVE8xqz4pbt25Rr169yMPDg6pVq0b+/v6Cc4ko3wFNW0CnKoyNjTVmcZSaRYZFh8giU6vCwsKCk4cdHR3p8uXLRJRvsCqMDDiXLl2iNWvW0MKFC+nMmTNM1yrzC1JpowKsMhXrGvYrZTZWiNEHSHXi+JV6PikBodogk8nUMlPp6elRxYoVde7WIsUJ6fnz5zR58mRq1aoVde7cmUJCQjTKhyxyOKuOWkz9zZs3a9TRacuwJbZ9Kbo1lkAHBcTOu5s3b/KSdhw8eJDat29PkydP5vEje/fupZYtW5KZmRl16dKFDh48yAXrqeprhd5NoedQLlOGVJ6aSFzwkIWFBc8JUxXJycnc+H3x4gWXEbV48eIUFBTERKuePHlCISEh5OLiQqVKlSqUXSZYg9kVyMvLo/Pnz9OgQYPIxsaGrKysBHc8EEu/pPDURGw0gEh7gMGmTZtIJpNxDuLFihWjbdu2kbW1NQ0dOlSnzjI1NZVOnjzJ2Q5U57TyPNakS9IUqKGArozLvwrXrl2jwYMHk5WVFXl7e9OqVatEZVBm4QOVIcaxmIho3bp1ZG9vT+PGjaMdO3bw6ICCFrAG6kgNNGG14WVlZdG0adPIx8eHypQpQy4uLrxDql2biN05Wuw81ZThUnEIwcHBgdtd0cLCggvMjoyMFHR4Zw2mELvLV2hoKJUrV44cHBxo4sSJHO0Vm0FXgZiYGLKzsyuw86dY/kuKblVM21Ltcax2cE00TpnWFUTekBJ8wwpt9EhZd6HrUGD27Nkkl8vJz8+P2rdvzzl9K0PhVCuFhylfvjxdu3aNiIjq1q1L8+fPJ6L8DL/KGWuVoSs5n641S9vaVVjJiu7fvy9oY2Wxm4WHh3MJt8RALH0h0k3ThcDCg4ml6x06dKDp06dzvxX6BT8/PxozZgyZm5vTsmXLRPeBAgUZA0T5QYFz5syhMmXKUIkSJSg4OFhw55Tf+PdBRkSE3/iNfxEiIiK0nu/fvz/vt5mZGe7cuQNnZ2fY2toiKioKnp6euHfvHho3boyXL1/C29sbMpmMuyY1NRXfv3+Ho6MjAODJkycwMjJCuXLlEB8fr3bPL1++4K+//kJiYiJMTExQuXJl9OzZEwYGBlydsWPHAgBWrFiBwYMHw9TUlDuXm5uL69evQ09PD1euXIG/vz/3rt26dYOJiQlX19DQEM7Ozhg8eDCKFi0KANzzJyYmomLFitDX1+e1/ejRI7Ro0QJ///03AGDlypUAgKCgIMyZMwfm5ua8+hcvXkR6ejpu3bql9q5v376FiYkJ7xpl1KxZE9OnT0fbtm0Fzx89ehSzZ8/GjRs3BM9rA+t7KuPFixdYvXo17xuNGjUKRYoU4bWtgK4xcPjwYQBAhw4dEBERASsrK96znDt3DmfOnMH9+/eZ31MZenp6ePnyJYoXL84rf//+PYoXL47c3Fzo6ekhJSUFxYoV486XLl0aly9fhrOzM1dmaWlZoGcpU6YMVq5cidatW8PCwgIJCQlc2bVr17Bjx44CfSMx84gFLHMOAFxcXAAAjx8/RunSpaGnp8fVV8y72bNno1atWszP0qhRIwBAdHQ0fHx8YGhoqNb2+PHjUa5cOQBAt27dkJOTg3379vHGJQAQETp16gQDAwNeP7548QLh4eEIDw9HZmYm+vTpg4CAAHh4eDA/ryqio6NRt25d3vf8n4CxsTFSU1Ph4OAAAKhXrx5atmyJqVOnAgDS09Ph6emJz58/F+r3fPbsGXbu3IktW7YgNTUVOTk5kMvlAACZTAZVVszAwADOzs5YsmQJ2rRpo9ZednY2Ro8eza1nKSkpcHV1xejRo1GqVClMmjQJAPDp0yeYm5vznh0APnz4AHNzc944Av4zzoQgk8lw/vx57ndB5qoYJCUloUmTJrC2tkZ6ejru378PV1dXTJs2DU+ePEFkZKTONhITE1G1alXk5uYKnhdDM6TQaj8/P3Tq1AnDhg1DRkYG3NzcYGhoiHfv3mHp0qUYPnw4V7dUqVI4e/Ys3N3dRfcNEeH9+/eQyWSwtbUVfZ1Y6FrvNOHZs2cA8tcPTTh27BhCQ0ORkJDAtT1z5kw0bNhQsH5WVhbu3bsHExMTlCtXDkZGRlqfYd26dZg3bx5evHgBAHB2dsasWbPQr18/ro6pqSnu3bsHJycnFC9eHGfOnEGVKlWQmpqK2rVr4/3794JtExHOnDnD65cGDRoAAMqWLYs1a9agefPmyMrKgq2tLc6fP4+6desCAOLj49G8eXO8fftWsO379+9j1apVuHfvHgDA3d0do0ePhpubG6/e9+/fsX//fmzZsgUxMTFo3bo1Bg4cCD8/Px6tZ10zpNAjFxcXxMbGcrykJsjlcrV1SBkymQwhISEcHQYAGxsbyGQyfPr0CZaWlrzrc3NzkZWVhWHDhmHNmjVq7ekaMyw8T+PGjXH9+nW4uLigYcOGqF+/Pho2bIgSJUpofWciwtmzZ5GcnAwg/3s2bdpU6zVAPi1XjC9PT084OTnxzletWhXBwcHo3r07AGDPnj3o378/zpw5A3d3d/Tr1w+mpqYc3WWReaSueUSESZMmYeXKlfjx4weA/LU2ODgYM2bMAMA+BpTrC6kqTExMsGrVKgQEBGh9PwVU1wIpcoypqSnu3r0LZ2dnWFhYIDExEa6urnj48CE8PDzw7ds3tfuKoV9yuRxz587lPUNwcDAmTJjAm1tjxowBALx69Qrh4eHYvHkzMjMz0a1bN6xfvx6JiYk8fo21D1lphlisXLkSQ4YMgbGxMdfvmqB4x4iICBARAgICsHz5ct66q3gWHx8f3j0A3d8zJCQELVu2hIGBAbe2a0K7du3UyuRyOV69eqUmU7158walSpXCz58/tbapCba2toiJiYGbmxtvbKWnp8PDwwPZ2dm8+t7e3mjevDkWLFjAK580aRJOnz7N6RpYv6m/vz9WrFghWt5zcXFBeHg4t34vXrwY69evR3JyMvT19bF48WLs3bsX165d466xt7fHhQsX4O7uDg8PDyxYsADt2rVDYmIi6tati6ysLADsMhgrNNHGjIwM3Lx5E7t378bevXs16iPEQhefyUobK1WqhNDQUG58bt26FePGjcOtW7fg6OiIgIAAvHnzBseOHYOjoyPCw8PRuHFjpKenw9XVFZMmTUJoaKjG+0lZB8zMzHDv3j04OjqiRIkSOHbsGKpWrYqHDx/C29sbnz594t2jQoUKmDlzJnr27Mkb7zNmzMCHDx+wevVqtefSxn8VFF+/fsXJkyeRlpYGIkL58uXh5+eHDx8+YPbs2diwYUOh3EcXNm/ejP3792Pbtm06+W0AKF++PLZv346aNWvyym/cuIFWrVrh2bNnTHQXAJMOkUWmVkWNGjUwd+5cNG/eHO3atYO1tTXmz5+PlStXYu/evXjw4IHO9/9VEJIdWWmjMsTIVAXRrepq//z58xg1ahSuXbum9vyfPn1CnTp1sH79ehw4cABz5syBmZkZR381YenSpdz/v1ofoEBh6/kU0MXDjh07lqlfQkJCRN135syZ3P+dOnUCABw6dAgtWrTg8Yq5ublISkqCm5sbTp48yfp6atAlh7PqPVjqq+qlS5YsiZiYGJ7spQqW9qXIsiYmJoiJiYG3tzev/s2bN1G/fn01/ksBMfO6Ro0amDRpEjp37szJCp06dUJsbCxat26N5cuXAwD09fURHByMSZMmwcLCgvfMQry9LshkMh79YuWpASAzMxNEBBsbG6SmpvJk5tzcXBw5cgSTJk3Cixcv4Ovri/r162POnDmCzzNt2jRcvnwZUVFRMDAwQKlSpdC/f3+0a9dO4/ytXLky97+y7uPy5cto06YN/P390aJFC1H98d9AfHw8Bg4ciKSkJEG9oxj6JYWnLmxUrlwZffv2xYQJE7Bv3z507doVtWvXxt9//61Vv/f+/Xt069YNFy5cgEwmQ2pqKlxdXREQEAAbGxssWbIEQD5v6ejoCJlMhsePH2t9FlUdSExMDLZs2YI9e/bAzc0NAQEBGDJkiMYxsHfvXvz999948uQJR9sVUMhJb968UZPplJGTk4P4+Hj4+/vjzZs36NWrFwICAlClShWtz/7fhLY5oEoLWNq0srLiyScZGRmwtLTk3e/Dhw8ApPMBPXv2RHR0NPr27YsSJUqoyUMJCQkAxNu1VZGQkIA///yTt95NnjxZoy7jV/EZ5ubmuHv3LhwdHVG6dGns378fNWvWxKNHj+Dp6cnJvgXRfwLAjx8/cOXKFZw4cQJhYWHIysoS/P7R0dHYsmUL9u7di7Jly+LOnTucvU4sXr9+jeTkZDRs2BCpqamYMWMGwsLCBPnM4cOHY+7cuXB1deWdE6NDVK7LolsVq58EpNnjWMBC61jkjYKOl8KCLl0GkP89lOlRuXLlMH78eAwdOhQAcPbsWbRu3Rpfv35Vo2lSeJhJkybB0tISU6ZMwe7du9GnTx84OzvjyZMnCAoKUtOhAfnr39ChQzFy5EhOP+Hi4oKhQ4eiRIkSovXOqmuXch8UVJdx+/ZtNGvWDK9evVI7x2o3Y4UY+qKLpgcGBhb4OcTQdQcHB/z999/cuJg7dy727t3LrSmbN2/GqlWruN9ioWsuK6A8Bn78+IEDBw5g8+bNuHTpElq2bImAgAC0bNlSzU7/G/9e/HbQ/Y3f0IHSpUvjxIkT8PT05Ah/z549cfXqVbRo0QKfPn0SrYgD+Mo4FkgxnIaEhGD8+PEwMzPT2rbi+UNCQjBu3DieUljRdufOnbl7/kpHRBsbGyQmJnKOrap48uQJqlSpgo8fPzK3zfqeUtoWg5kzZ/7XhAG5XI7Xr1/zlGpAvvGhUaNG+PDhgyBDrWCglf9XZr5+/vyJChUq4OjRo6Idy8QY8H7lN2KFVGeFRo0aYf/+/bCxsdHYNquyXwGxAluxYsVw4sQJVK9eXfB8bGwsWrVqxTmLtWrVChcuXICfnx8CAgLQunVrnc60rGPgwYMH2Lp1Kx48eIAVK1agePHiOHHiBBwdHVGxYkUcPnxYtOOEwpHY0tKSM2powv79+7n/nZycsG3bNjRo0AA/fvyAtbU1jhw5giZNmgDIF3gaNmzIKZsAcd9TG37+/Iljx45h+/btOHbsGIoUKYLnz59z58U6uSkjMDAQV65cwfLly9GiRQskJSXB1dUVhw4dwqxZswQNeIUNMXP15cuXGDFiBLOBGACaNm2KqlWrYtGiRTwDfkxMDHr16oX09HQkJSVpbTM5ORk9e/aUpJhUQAqtLlq0KKKjo1GxYkVs2rQJq1atwq1bt7Bv3z7MmDGDc8IEgNDQUKSkpGDTpk0659yrV68wceJEHD58mDN4W1paomPHjpg/fz7s7OxQpEgRpKSkoGjRopzSRhOUx7lU5OXlYe7cuViyZAmnXLSwsMC4ceMwderU/zHDibZgIFdXV+zbtw/e3t6oXr06Bg8ejKFDh+L06dPo0aOHpH6ZPHkyDh48iClTpuD48eOIiYnBw4cPOf5ow4YNiIyMxOXLl9Wu3bdvH3r06IHq1atzSoRr164hNjYWu3btQufOnQXv+fjxY4SHhyMyMhI5OTm4c+eO2vtKcbhipUcA8O3bNxgbGwuei46OBhGhcePG2LdvH894aWhoCCcnJ5QsWZJ3jRRFnFiw8DwGBgYoUaIEOnToAF9fXzRs2PCXOMWLhY2NDWJiYrg119/fH7m5uVzAwrVr19C1a1c8ffpU8j2krnnalIKsY+Dx48cgIri6uuLGjRs8HtbQ0BDFixdnUmipOtxIkWNcXV2xYcMGNG3alLcmRUZGYsGCBbh79y5Db/0Hzs7OOpXaMpkMDx8+RNu2bXHx4kW0bt0avXv3RosWLaCnpydoxJfah2JoBgu/FhgYiLi4ONja2nL9ru0dlSE2uEvs9/Tx8eEcbFkMmgpew8vLC+fPn+eN39zcXJw8eRJhYWFIT0/X+pyaYGNjgytXrsDDw4M3ti5fvozOnTvj9evXvPrGxsa4ffu2mvyRkpKCypUrqzmLs64DaWlpePDgARo0aAATExMefVSGiYkJkpOTOSV0q1atUKlSJSxatIh7Hh8fH17QS4cOHdC6dWsMHjwY48ePx6FDhzBgwACO5pw9exbAr3MYF4ulS5di7969iImJ+SXtK8BKGy0tLREfH4+yZcsCAOfkqnAiTUhIQKtWrfDixQvo6+vj6dOnXECJqakp4uLiRAVesqwDbm5uiIyMRK1atVCvXj20adMGkyZNwu7duzF69Gi8efOGV19qkJRYREREoGjRomjdujUAYOLEidiwYQM8PDywc+dOjYYzVegK8hNCZmYmN88yMzO11lWdj97e3khLS8PPnz/h5OSkpr9TDfI/dOgQQkNDsWbNGk7ej4uLw+jRo/Hw4UPcvXtXEt0FxOkQpcjUCmzfvh05OTkYMGAAbt68iRYtWuDDhw8wNDREeHg4F4QkBefOncO5c+fw5s0b5OXl8c5t2bKlQLKjWNrIil+pW23Xrh0aNWqEoKAgwfMrV67EhQsXkJGRgQMHDsDa2hq+vr4a30s1YJdFdyfVieO/AU08bKNGjST1CwtYHOOTkpJQqVIlyOVynWNZ2clRLFj1Hiz1VYOclPmdwnoegE2W1Rbo0KtXL6SlpelsQxOsrKwQHx+PMmXKYOHChTh//jxOnTqFK1euoEePHpysNnToUOzevRsVK1ZE37590b17d9jY2Ajy9gUBS8IEluCho0ePokOHDhg7dizGjRsHOzs7APl6qyVLlmD58uU4cOAANwaU2wDUgwiVefARI0Zg165dcHBwQEBAAHr37q3zu+bm5mLZsmUanTMLQ/8F5Aem79ixAzt27MA///wDHx8f9O7dG8OGDZPUnlie+sKFC6JpQHh4OJPNQTk5ERHByMgIFy5c0Om4169fP7x58wabNm2Cu7s7N69PnTqFsWPH4s6dO7z6P3/+xNChQzF9+nStPMrLly8RGRmJrVu34uPHj+jduzcCAgJQqVIlrc+zcuVKTJ06FQMGDMCGDRvg7++PBw8eIDY2FiNHjsS8efMAqCez8fT0xPHjx7nAo9evX6NkyZIgIpiZmUFfX1+nbpWVD6xatSrOnTsHGxsbtaQ/qhBK9iQGYgN16tevD0B3gLcCikBvqTY8a2trHDt2TOf4EmvX/lUoCG8P5K/Fq1atQsOGDdG0aVN4eXlh8eLFWLlyJRYtWsQluWDVf/748QPXrl3DhQsXEBUVhevXr8PBwQENGjRAgwYN0LBhQ412dQD4/PkzduzYgS1btuDmzZuoWbMmunTpopNWqGLIkCGwtrbmaJUqgoODkZmZiXXr1gme/5WOhWLalqr//pUQI2/8Cn25FHoUHR0tun1FAIqRkRHS0tI4Wgvk67fS0tI0BoMUJOnT1atXcfXqVZQrV05j8LWY5Hz/0/jjjz+QnJzMHLAnxUYohb6Ipem/GiYmJkhJSeHGV5MmTVCnTh0ukOzBgweoVq0aMjIyfvmz2NrawsLCAv3790ffvn01BgUVNCHeb/z/jf/ZVHa/8Rv/JRSEoW7QoAHOnDkDT09PdO3aFYGBgTh//jzOnDnDKaBZnW5ZDJuKDCkXLlwAwGZkmzlzJnJycnD27Fk8ePAAvXr1goWFBV68eAFLS0tOcFI8v7OzM7p3767RyUKBR48eAdBuvJEqaObk5ODt27caBYm3b98iJydH+4trAMt7sio/WceAwmDwq4QBRZ/LZDI0adJEYxQr8J+xxQIDAwPBbGHaULp0abx8+RKOjo4oU6YMTp8+japVqyI2NpYTlFi+kZR5xAIpc075uh8/fuDRo0coU6aMmhBx69YtJCcnw9vbW6szpeq82bp1KwDdAtvnz5855agQ7O3teRltTp48iRIlSuDJkycICQnR6HCuPFdZxkB0dDRatmyJunXr4uLFi5g3bx6KFy+OxMREbN68GXv37kWHDh04pX2HDh00tiWTydCvXz/ufVWjRbWhVatWmDRpEhYuXIiDBw/C1NSUU0QB+fO+TJkyvGvEfE8hXLhwATt27MC+ffuQl5eHTp064ejRo2jcuDGvnoKeAtqd3JRx8OBB7N69G7Vr1+a9e8WKFf9rWYfEzFUXFxf0798fxsbGWLZsmca2ZDKZmoNubGwswsLC1OqWKlWKixj18vISNNgo2lSdF1JohhRanZ2dzWVAOX36NDp16gS5XI7atWurRV3Gxsbi3LlzOH36NDw9PdUUjwoH88zMTNSpUwdZWVnw9/dHhQoVQES4e/cudu7cicuXLyM+Ph7Lli3j7q3IzKILBTH2TZ06FZs3b8aCBQs4Qfzy5cuYNWsWvn37xinAxaBTp06SHO+FoBqUoozGjRvj8OHD8Pb2hr+/P4KCgrB3717ExcXx7suS+XHGjBl4/vw5xowZA3t7e2zfvp1nYN+5c6dGZdDEiRMxefJkzJ49m1c+c+ZMTJw4UaODrsJ4RkQaHUjErhkKsNCjvLw8zJs3D+vXr8fr16+5TN7Tp0+Hs7MzBg4cCOA/irhHjx5xWVN0QaHwd3Fx0aqIkzJmWHiejIwMXLp0CVFRUVi4cCF69uyJ8uXLo2HDhpzDrmKssRo/pATq5OTk8BTLV69exR9//MH9LlmyJC9LsxQjgtQ1z9zcHDVq1BA8xzoGFMZJVeeawoIYOUYVgwcPRmBgILZs2QKZTIYXL17g6tWrGD9+PKZPny6ZfrE4dZ44cQJjxozB8OHDdTomSu1DMTSDhV/78OEDZzBQpi9i0LBhQ53BXcrt6vqeyn3B0i8KXkMmk6nxcMB/shFLDZDx8/PD8uXLOSdLmUyGrKwszJw5E61atVK7tlixYkhISFAbAwkJCYJKX7HrwIcPH9C1a1e1DFgDBw7kZcBSwNLSEhkZGdxYu3HjBkf3Fe/x/ft33jVLly7lgnlCQkKQlZWF3bt3o1y5cryARKkyWGGhTZs2mDt3LvN1rHwmK22Uy+U8nvfatWuYPn0699va2poLYiYiHu3W09PjOX5pA8s60LFjR5w7dw61atXC6NGj0adPH2zevJnLUqMKe3t7fPjwAU5OTnB0dMS1a9dQpUoVPHr0iHs3KZm3FQgNDeWMwFevXsXq1auxfPlyHD16FEFBQTr5x4LAxsaGc/awtrYW/J5Cgc8AtNJSIQwYMADZ2dmoVasW921ycnKgr68PU1NTHn1gdUgSo0OUIlMr0KdPH+7/atWq4fHjx0hOToajo2OBdGIhISGYPXs2qlevLpi5B2CXHQE22ihFpmLhSVjbT0xMxMKFCzXW8fPzw+LFi3H06FGOzkZFRWltVxksurs///wTDg4OgvTcysoKDg4O+PPPP7Fu3bpfrudThSYeVnn9YekXFijWaEXgiTYnJC8vL47/0jWWpQQos+o9frVOW0r7LLLsn3/+idGjR6sFOgQGBmLx4sVcPSnzmoi45z979iznROzg4IB3795x9cPCwrB8+XL8/fff2LJlC/744w80b96cd31hQCxPDeTzAGKDh9q0aYNly5Zh/PjxWLJkCcfzf/r0icv8qnh3Vjlg/fr1cHR0hKurK6KjozU6Aimv6yEhIdi0aRPGjRuHadOmYerUqUhPT8fBgwcxY8aMAgezh4WFYceOHbhy5QoqVKiA3r1749ChQ7zgHyn0SyxPzUIDGjRowO3uER8frzXAAMjfUUCxY4VMJoORkZHOXYOAfH3nqVOn1ByrypUrJ5hxzsDAAPv27ePxr0JwdHRUy7icl5enNgdV9ZNr167Fhg0b0LNnT4SHh2PixIm8nRoUUO239PR0td1QiIij0WLAyge2b9+e0+2w8oFisXz5cgwePFjj2jt06FAsXbqU4+FUd5XVBVZ7sgI2NjaidowQa9dmtfuLnacdO3aUzNsD+bJsYmIiGjZsiEmTJqFt27ZYvXo1fv78yZN9xeo/Aajt8jV06FDs2LFD1HxVwMLCAkOHDsXQoUNx+/ZtTqe/fft2Jlt+dHQ0tm/frrFOt27d0KtXL43nNfFfrLpVlraVIYZnYNXbSuVhWeQNlvEiFlLokaadELUhJydHrZ8NDAy07kbFwsOowsfHR6ezso2NDWcjL1WqFP755x94enoiIyNDbTeFjIwM3LhxQzAYVLFzoxRdhqax9enTJ8THxyMlJQUXL15k1juz2gil0hcxNJ2VB5Piz1WkSBG8fPkSDg4OyMvLQ1xcHK9vf/z4Icg3sUDMGACAjx8/4uPHj5gzZ46gblHb2vEb/x78zqD7G/8KKEdGaooG1kQUP3z4gG/fvqFkyZLIy8vDokWLEBMTg3LlymHatGmiM0y9fPkS8+bNw+rVq3nR41K3JBHjbPH48WO0aNECT548wffv3znnicDAQHz//h3r169XazcjI4PbWm7ChAkoUqQI4uPjYWdnh1KlSgk+i5DxJiQkBBMmTICpqanO7LLKzq21a9dGx44dERwcLFh3/vz5OHToUIG39tH1nqrfqDCUn8pjQAhinfPE4L+RiZYl8yPAvs0E6zfShMJidsQ6OH39+hWjRo3iIo8V82706NEoVaoUJk2aBEA9Yrt79+5YuXKlVsdaTQKb6vZRbm5uCA0N1ejYtXfvXkydOpXbnk7KdnyA+DHg4+ODrl27YuzYsbxsGTdu3ECnTp24qGGxUBZ4WfDu3Tt06tQJly9fhrm5OSIiItCxY0fufJMmTVC7dm2eU6HY76mMUqVK4cOHD2jRogV69+6Ntm3baowEFuvkpgxTU1P8888/cHV15fVnYmIiGjRooLadrDYUhlOkprlqYmIiOsO2KooXL45Tp07B29ub945nzpxBQEAAnj59yrzFSGHRDF20unLlyhg0aBA6duyISpUq4eTJk/Dx8cHNmzfRunVr3pY0iqw5mqBQCM+ZMweRkZGIiYlRcz598+YN6tatC39/f7x7945THl28eBF16tTRSZ8Lst6VLFkS69evVzOOHjp0CCNGjMDXr19FC+Dt27fHypUrYWFhgQEDBmitq+gXKcFAeXl5yMvL4/pl165dHF83dOhQ3m4BUjM/ssDU1BRJSUlcRjwFUlNTUaVKFZ5CiHWbR7FrhgIs9Gj27NmIiIjA7NmzMXjwYI4m7d69G8uXL8fVq1fVnufSpUsICwvDw4cPsWfPHpQqVQrbtm2Di4sL6tWrJ9g/2hRxiuwTLGOmIPj8+TMuX77MRZInJiaiXLly+Oeff0RnKTtw4AAAaVm5vLy88Mcff2DAgAF48uQJnJ2d8c8//3DZlWJiYlC3bl28fv1akswDiFvzCrJusI6Bbdu2Yf369Xj06BGuXr0KJycnLFu2DK6urmjfvj0A6HyGjIwMREdHa6TrYpzQiAihoaGYP38+NyeNjIwwfvx4zJkzB/7+/r98LF67dg2bN2/G7t274e7ujr59+6JHjx4oUaKE1ixbYvpQAVaaoQvKvG7jxo2xf/9+WFtbi7pWNbjr3r17cHV1xYIFCxAXF4e9e/cKXqfpeyorhAMCArBixQredsKaIDYbcUREBHr06AEjIyOd2YeUjZ/Pnj3jnDFSU1NRvXp1pKamomjRorh48aKa0+3s2bOxbNkyTJo0CXXq1AEAXLlyBQsXLsTYsWPVDN5ivylrBqz27dujaNGi2LhxI/bv34/evXvj1atXnG7k2LFjGD9+PG/XAKn4VZkrNUHbFoLaUBA+UwxtVJan7ty5g8qVKyMtLY3jUaKjo9G/f3+kp6dDLpejUqVK3BxISkpChQoV1GR/oYxcUmQfBa5du8bxVEKBSYMGDYKDgwNmzpyJNWvWYMKECahbty4XJLV58+YC8V+mpqacs2dwcDCXge3OnTvw9fXlBbFog5QMusrZfXRlFJJi0FSGNhqj7Ni+cOFCxMTEiKa7gDgdohSZ+lejRIkSWLRoEfr27auxjpTtKVloY2HoELXxJKztGxsb459//lGTMRRIS0uDp6cnfvz4wa3Vrq6uiI2NZd4xQpfuzs3NDdu3b9foLHHz5k306tUL9+/f/6V6PhYe9tChQ5L6RZNMKpPJYGxsjLJly2LAgAFctngFcnJyEBUVpdEJiXWL+MLYWYdVR62pvp6eHl69esXxUJaWltwWwiworABSIN+ZIDs7mwtuAP4T6KDsKP3x40c12UrXvG7cuDEcHBzQtGlTDBw4EHfv3kXZsmV567QQUlNTsXXrVkRERCArKwutW7dGly5deGNVk8OP8vhS7VcpPLXyeNOFZ8+eYc+ePUhNTQWQn524c+fOvAx5Cjx58gQODg6C7T558oRL1qJLnlJAWa4qU6YMVq5cidatW8PCwgIJCQlc2bVr19C8eXNJvLoCDg4O6NmzJ3r37o0qVaoIXieFfonlqU+ePCmaBnz69IlzLBcDuVyOuXPncraj4OBgTJgwQc0xXjUwysLCAvHx8ShXrhxPbxsXF4fmzZsL7ozQv39/eHl5adSbKJ5HATEZlxUQu1ODrqzeigy6v5IPVHbievLkCUqXLs28A9mXL18QHR0tmDF6zJgxcHJywsmTJzXq5ZOTk+Hn54cnT54w3VcILPbk7du349ChQ4iIiOAcw4Ug1q7NavcXO0+BfL6ssHj7x48f4+bNmyhbtqzGTPe6HBF/1S5fP3/+RGhoKJMtXzX7t9D7uru7Izs7m4n/ysnJYdKtStVPiuEZWPW2UnlYVl2MAlIdV1VRGPQIyE9YI0SPFONdLpejZcuWPBvpkSNH0LhxYx7vpfydWHmY+/fvY9WqVZweyt3dHaNHj4abm5vgM/fq1QvVq1fnnLFXrVqF9u3b48yZM6hatSr3LEeOHEHv3r2RlZWllixKkZwAkGZLUpUJFLC0tISbmxuGDx8OFxeXX653lkpfxNB0Vn2pFH+u3r17IzMzE2vXrsWePXswc+ZMvHr1ihtb+/btw+zZs5GYmKjznYQgdgwA4jNMF1Qv9Bv/n4N+4zf+BYiKiqKfP39y/2s7lPHz50+KiIigV69eibrPP//8Q6tWraKwsDD6+PEjERG9ffuWAgMDydjYmDw8PAr8Lu/fv6fGjRuTTCYjuVxODx48ICIif39/Gjt2LK9u+/btqU+fPvT9+3cyNzfn6l64cIHKli2r1nZiYiIVK1aMypYtS/r6+lz9qVOnUt++fdXqZ2dnU0BAAOnp6ZGenh5Xf9SoUdSmTRv6+vUrERE9fvyYcnNzRb1fWFgYmZmZ0ZEjR9TOHT58mMzMzCgsLExUW5og5j3T09MpLy+P+1/boQzWMZCbm0uzZ8+mkiVL8vpw2rRptGnTpgK9JxFReHg49x104dOnT4JHZmYmff/+Xa1+hw4dyMLCgkqUKEF+fn7UsWNH3qELMTExtGTJEjp8+LDaOdax+CvBMueIiMaMGUPVqlWjS5cukZmZGVf/4MGD5OXlxdWTyWT0+vVr7reFhQVXVxP69u1LzZs3p6dPn/Lm9MmTJ3lja8aMGeTo6Ei3b99WayMpKYmcnJxo+vTpDL0gDLFjwMzMjB4+fEhExHvuR48ekZGRERER2djY0Nu3b4kov28zMzM13lcul9ObN2+4/5X7UQwyMjIoJydHrfz9+/dqY13s91TGhg0buPmvCyEhIeTq6krbt28nExMTrv1du3ZR7dq1Ba+pX78+rVy5kojy+1PRt6NGjaLmzZuLuq8CAwYM4Pp6wIABWg8haJurymO8UaNGovuEiGjgwIHUoUMH+vHjB/eOjx8/Jm9vbwoMDGR6x8IAC63es2cPGRgYkFwup2bNmnHloaGh1KJFC0n3r1WrFm3ZskXj+c2bN1Pt2rVJX1+f41nEzg2p6x0RkZGREd2/f1+tPDk5mYyNjSk8PJy+fftGRPnrkbbj0KFD9OPHD1H9ocCsWbPoy5cv3P/aDlZkZGQwXyMFLVu2FPy2W7ZsIT8/P+738OHDycbGhipXrkzLly/n6KU2iF0zFGChR2XKlKGzZ88SEZ+u37t3j6ytrdXa3rt3L5mYmNCgQYPIyMiIq79q1Spq2bKl4PNHRUWRiYkJNW3alAwNDblr5s+fT507d5Y0ZhRg5XmI8unAtWvXaP78+eTn50empqYkl8uJiMjR0ZHu3r2r8X737t0jBwcH7ndiYqJo3liBDRs2kJmZGQUEBJCHhwfVqVOHd37OnDnk4+MjSeZRQMyap7xu9O/fX/S6wToG1q5dS0WLFqW5c+fyxuPWrVvJ19eXq6dr7RowYAB17txZrX1tcsz8+fMF++f79+90584dun79On3+/JkrL8hYJCJasWKF4LFy5UrasGEDnT9/nuNbsrKyaPPmzVS3bl1urVm+fLkg3yS2DxUQQzNY+DVLS0tuXshkMo53E4PatWvTkiVLiIhPY65fv06lSpVSq6/reyqPZ2U+srDBqj9QXLN9+3aaMGECDR8+nDZu3EjZ2dmCdfPy8mjp0qVUqlQpkslkJJPJqFSpUrR8+XJuLVeG2HXAzs6OEhISiIjf3w8ePCAzMzO1dhMTE6lo0aJkaGhIcrmcpk2bxjvfp08fGjp0qMZ3/vz5sxr9VQWrDFZYCAwMZOanCwKxtHH//v1kaGhIjRs3Jjs7O2rTpg2vnYkTJ1LXrl2JSDdPpI03Eiv7/Pjxg/z9/Tk5RAxyc3O5NYqIaOfOnTR69GhauXIlt/YWhP8qVqwYxcfHExGRl5cXRUZGEhFRWlqa4DjWhISEBG59F4uOHTty4zgiIoLjf8Xi48ePtHHjRpo0aRK9f/+eiIhu3rxJz54949XTRWMKQneJ2HSIYmXqoKAgysrK4v7XdkyfPp127dol2K42FClShNLS0piuEQMW2lgQmUoMT8LavqurKx04cEDju+3bt49cXFyoSJEidO3aNSKSNmbE6O6MjY0F31uB9PR0MjExYbqvFLDwsFL7ZdKkSWRlZUX16tWjsWPH0tixY6l+/fpkZWVFgYGB1KxZM5LL5XTw4EHumvT0dKpQoQKZmpryvv+YMWO4tdTb25s+fPhARPkym0IG1gQWOVwZrDpqMfVlMhlZW1uTjY0N2djYkEwmIysrK+634hACy/OwyLK6+kRxLF68mHleJyYmUqVKlcjS0pK31o4aNYp69uyp7bNx73z48GFq3749GRoa8s4peCIFD6g4FGVyuZwaNGjAjRUidp5agYsXL1Lv3r3Jx8eHW4siIyPp0qVLOt9BEzTpqN69e8e87qrC1NSUHj9+TERE9vb2dPPmTSLKp9eWlpa8tSg6OprHj4iBEJ9dGBDLU7PQAOV+dnFxoXfv3ml9BicnJ3J2dtZ6uLi4qF3XsmVL7nkVetvc3Fzq2rWroPxNlK+zsLa2ps6dO1NoaKia/Euke66lp6fTo0eP1Np2cXHh+MBq1arR+vXriYjo1KlTPBqjao9RnhdERK9eveKNR019+PHjR65fWPlAPT097hmk2DXi4+PJ3t6eLC0tSU9Pj4oVK0YymYzMzMy4ZzIyMqLU1FSNbaSmppKxsbFauTK9Vj6KFClCJUuWpAYNGvB0mKw2PC8vL7KwsCBzc3OqVKkSeXt78w4FxPKkUu3+YvHz508KCQmhp0+fSrpeLHTpP4nydUEnTpyg4OBgqlmzJhkaGlKlSpVo5MiRtGfPHo28ys+fP2nRokXk7e1NZmZmZGNjQ7Vq1aL169er0bacnByKjo7WacOxs7Ojc+fOaTx/9uxZsrOzIyI2/otVtypVPynFHverwKqLIRI3XsSioPTozZs31Lp1a5LL5YKHAmJ0t6rfiYWH2bt3L+nr61Pt2rU52dLHx4f09fVp7969gs/+/v17ev78ORHl81/z58+ntm3b0tixY3m8VLly5SgwMFAn//0rbElPnz6lwYMHF1jvrMDXr18FdXFS6YsYms7Kg0mh648ePaKyZcuSTCYjfX19Wrt2La/N9u3b0x9//CGt00j8GPiN3xCL3w66v/GvghSG2sTERKsiUYFDhw6RgYEBpxwpU6YMnT9/nooWLUrNmzenEydOcHVZDJuqYHG2KFKkCCUnJxORulOckPKzcePGNGHCBLX6V65cIScnJ7X62ow3ACQzdr179yaZTEbu7u7UoUMH6tChA1WoUIHkcjn16NFDdDuaIOY9WZWfRGxjQIH/ljDw/ft3evr0KT1+/Jh3KEOhzNN0ODo60owZMziHEinOfGIh5hsVZB6xgNXBydHRka5evar27KmpqWRhYcHV06UQEoJYge3r169Up04d0tPToxYtWlBQUBD98ccf1Lx5c9LT0yMfHx9Bx21NTpSfPn2iRo0aqZWLHQOlSpWiK1euqD33/v37ydXVlYiIyXHCzs6Oc+yWYkRigdjvKRWsTm5ERJcuXSJzc3MaNmwYGRsbc4YeMzMziouLk/QceXl59PjxY40OIZqgba7KZDLJBuKMjAxq2rQpWVtbk56eHjk4OJCBgQE1aNCAE+iU8fHjRzp16hRt27aNIiIieIcCBaEZrLT65cuXFB8fz3PAu379Ot27d0+t7s+fP+nMmTO0fv167pmeP3/Oc/6ysbHh1nMh3Lt3j2xsbKhs2bI0ZcoUioqKIplMRgcPHqTo6GjBQwEp650CNWvWpNGjR6uVjxo1imrVqsUkgEtxvF+xYgVzMFCZMmVo5syZgo7Fqs/D6mDu5eWlpojw9vamqlWrUp06dahfv350/vx5OnToEHesW7eOihUrRiNHjqRt27bRtm3baOTIkVS8eHFat24d17ZMJiMnJyfq0KGDWjCEpuAYViUfCz1SNrQr171z545g215eXtx8VK4fHx/PKW1VoUsRV5BgDTE8z/Tp0+nq1au0cOFCatGiBVlYWJBcLicHBwfq168fbd26lesDVuMHq9FMgc2bN1OHDh1o2LBh9PLlS9654cOH0/79+4lIuhFBzJonVSnIOgbc3d05xxLl+rdv3yZbW1uu3tKlS7XeNzMzU82ZmYgtAEfTmpGVlUX+/v4FDhxydnYmMzMzkslkVKRIESpSpAhnYLOzs+PkiidPnvCuS05OpgkTJpC9vT0ZGxtT27ZteefF9qECYmgGC7/WqVMnsrOzI19fX5LJZFS3bl1q1KiR4KEKMcFdytD1PZs2bUqenp40YMAAkslk1KNHD/L39xc8NCElJYXCwsJozpw5FBISwjuUIVZ/oA0vXrygkSNHaq2TmZmpk5cRuw6Ym5tTSkqKWr3Y2FgqUqSIYNtv376lgwcPcg5Myjh69Kia4+bDhw+pVatWXHCD4lDQY1WwymBiockxMCAggLy8vMjU1FQSPy2Vz2ShjWfPnqU//viDFixYoMavzZo1iy5cuMD83KpgkX0sLS1FO+iKXZek8F8K9OrVi6pWrUoDBw4kU1NTbm09dOgQVaxYkaunjY/q2LEjNWrUiNlRyMDAgF68eKH2DmLA6tigjcYUhO4SsesQxcDX15f7jr6+vloPHx8fsrS0pH79+jHdY+LEiTR79mzR9cXIjkRstLEgMpUYnoS1/VGjRlGlSpUE9T7Z2dlUqVIlGj16NA0ePJiMjIzI2dmZ44FdXFwEDyGI0d2xOHH8Sj0fCw8rtV8GDRokOBbnzJlDgwYNIqL8gPpq1apx58Q4IRkbG3P0UwyNkeqMyKr3EFNfrDNsQZ9Him5NFwoyr1Xx9etXZhlK9TufPXuWatWqRWfPnuX4wLNnz5KPjw8dO3aMLl++TBUrVqSAgADuGlaemkhc8JCyTkPboQxNesH09HQyNTVVK9clgymjfPnyHE9at25dLrhh165dVKxYMUnB7KpQOC3Xrl1b0GlZKv0Sw1Oz0ICCBl6Ixe3bt6l48eLUokULMjQ0pC5dupC7uzvZ2dlpDJoR4wQ8bdo0rTTr8ePH1LRpU7XygQMHck7xq1ev5pzXrK2teXNCLpdTWloaffr0iTIyMsjCwoISExM5R6WUlBQeH6hqv1Hg1atXZGBgQETsfKCDgwOtXbuW0tPTSSaT0c2bN9VsdkK2OwUaNmxIgwcPptzcXG5eP3nyhBo0aED79u0jIvGBOqpYunQp2draUp8+fWjlypW0cuVK6tOnDxUtWpTmzZvH0YUNGzYQEbs9WWwAoVielNU5Wso8NTc3F3QKF8K5c+fI3d1dMAA1IyODPDw86OLFi2rnpARTZGZm0vHjx2nChAlUo0YNMjQ05Mk8RPk8X926dUkul5Ofnx8FBgZSYGAg+fn5kVwup9atW1Nubi6lpaXR1q1biShfv6lLxuvatSt16NBB4/l27dpRly5diIiN/2LVrUrVT7LwDD9+/CA9PT3BREhC+PHjBzVu3JiTIXRBii5GavCNEApKj3r16kV169al2NhYMjMzo9OnT9O2bdvIzc2Njh49yvQsqmDhYVxdXQUTUs2YMYOzPyuDJbje1NRUp92eqGC6DE1QBA8XRO+clZVFI0eOpGLFiml1olaGGPpCJI6ms/JgUoOff/78SQkJCZzTtTISEhJE216EIHYMKONXBqb9xv//0L0n+W/8xv8h6Ovr488//0S/fv1EX1OzZk0kJCRo3K5Bgblz52LkyJGYM2cONm3ahLFjx2LMmDE4fvy42jZeP378QGZmJooWLYqIiAgsXLhQ1BabAHD69GmcOnUKpUuX5pWXK1dObYubvLw8we1Ynj17Jni/uLg4bNiwQa28VKlSgls8Hjx4ELt370bt2rV5Kd0rVqwImUyGffv2oVWrViAiPHv2DN++fRN8J8UWRgps374d7dq1w44dO5CSkgIigpubG0JCQtCtWzfBNlgg5j3v3buHL1++wMbGBiEhIRg2bJjWbVcAtjGgQGRkJDZs2IAmTZpg2LBhXHmVKlWQnJxcgLfMR2pqKgICAhATE8MrJ4HtjcPDwzF16lQMGDAANWvWBADcuHEDERERmDZtGt6+fYvFixfDyMgIU6ZMEbVVgqbtt4SgvEW6mG9UkHnEApY5BwBv375V244WyN96SHXrA9XtGXRt4fXlyxfBcfjhwwfe9iDGxsa4cOECli1bhp07d3LbKpQvXx5z585FUFAQr74CUVFRatuQAPlbyV26dEmtXOx2GT169EBwcDD27NkDmUyGvLw8XLlyBePHj+fosY+PDzp06IBq1aqBiDBmzBiYmJgItjds2DC0b9+e60N7e3uN92bd9lAVYr+n1C11nj9/LrjtZF5eHn7+/Cl4fb169ZCQkIAFCxbA09MTp0+fRtWqVXH16lV4enqKfTUeiAhly5bFnTt3UK5cOdHXaZurMpkMjRo14rbT6tixo9rWugootnBXwMrKCmfOnMHly5eRlJSErKwsVK1aFU2bNlW7VtcWI4oxVhCawUqr7e3t1calgq4qQ3XLrmbNmsHCwgILFy7kbdmVmZmpdXtaa2trZGZmYsuWLRg2bBjmz58PmUzG225WGcr0X8p6p8CiRYvQunVrnD17Fj4+PgCAq1ev4unTpzh+/DgaN26M4OBgmJmZoVGjRtz2NEIoVqwYrl27hrZt24revnrs2LHo0aMHjI2N4eLiorV9BUaOHIkdO3Zgzpw5qFq1Kvr06YPu3burfS9zc3O8f/8exYsXR1RUlMb5qIwWLVpg3bp18PT05L53bGwskpKSMGDAANy9exdNmzYV3BZz7dq1WLt2rdqzKsZbv379mLf0FrtmKMBCjzw8PHDp0iU1/njv3r3w9vZWa+P+/fto0KCBWrmVlRUyMjIEn//27dvYsWOHWnnx4sXx7t07SWNGATE8z7Rp0zB37lyUKFECjRo1wrJly+Dr64syZcqotVeqVCmt2wgnJSWhRIkS3G9ra2s8evQIxYsXR3p6OvLy8kQ9d0BAAAICAgTPKY8fKTIPIG7N69ixI7dNrfK2U7rAOgYePXokOJaMjIzw5csX7veUKVNga2sr+K5fvnxBy5YtBbfX1CbHPHjwgFc3IiICCxYsUFszvn79isjIyAKNRQAIDQ3Fhg0bsGnTJm58paWlYejQoRgyZAjq1q2LHj16ICgoiLeFm5ubGxYtWoT58+fjyJEj2LJlC69dsX2ogBiawcKvbd++HREREXjw4AGio6NRsWJF0euLtbU1Xr58qbYt3K1bt9S2yQR0f8+TJ09i2bJlePDgAWQyGT59+qRRNhXCxo0bMXz4cBQtWhT29vZqvMaMGTO43zVr1sStW7d06g/u3LmDCxcuwNDQEN26dYO1tTXevXvHbbWo2GZVE8TwMGLXgfr16yMyMhJz5szh3ikvLw+LFi3SuOVe0aJF0b59e8FzrVu3Vivr06cPiAhbtmyBnZ2dznnCKoOJxa1btwTLLS0t0axZM+zfv595221AOp/JQhubNGmCJk2aCLYzc+ZM5mcWgljZBwA6dOiAgwcPat2mWAF9fX0sWrRI57okhf9SYM2aNZg2bRqePn2Kffv2cVsy3rx5Ez179uTqWVlZaW3HysqKef2sUKECJk+ejEaNGoGI8Pfff8PS0lKwrmrbY8eOxYABA7Bo0SLemGnVqhV69eqldr02GlMQuguw6RA7duwoOI+Vt1rv1asXLly4wJ1T/l8T4uLiNI5zZYwdO5b33Bs2bMDZs2dRuXJlGBgY8OouXbqU+1+s7Aiw0caCyFRieBLW9qdNm4b9+/ejfPnyGDVqFLe1a3JyMtasWYPc3FxMnToVdnZ26NSpE9LS0jBmzBgMHjyYSa8mRnfXoEEDrFq1Co0bNxZsY+XKlahfvz6AX6vnY+FhN2zYIKlf/v77b9y8eVOtvEePHqhWrRo2btyInj178sbkpUuXEBMTo6YrcXZ2xvPnzwEAXl5e8Pf3R7169UBEWLx4MbcdvSpmzJiBVatWiZbDlcGq9xBTv3///jrvWxjPo0uWzczMFH1fBf0uyLxWhbGxsWB5ZGSkxmtkMhn69u3L/Q4MDMSGDRtQp04drqxJkyYwNjbGkCFDcOfOHSxfvpwnL7Ly1EC+fWP9+vXo168fdu3axZXXrVsXc+fOBZDPA+iCQu+koNcymQzTp0/n9WFubi6uX78OLy8vtet1yWDKsk/Hjh1x7tw51KpVC6NHj0afPn2wefNmPHnyBEFBQdi3bx9WrlwJPz8/EBGuXr0KGxsbwecW4sv27duHvn37onfv3rh16xa+f/8OAPj06RNCQ0Nx/PhxyfRLDE/NQgM6d+6Mhg0bokSJEpDJZKhevTr09PQE6yq22JaCSpUqISUlBatXr4aFhQWysrLQqVMnjBw5kqf7UMajR490thsREYGjR49i27ZtqFSpEu9cWFgYJkyYgLp166pdt2HDBk6vMnLkSNja2iImJgbt2rXD0KFDuXpEhPLly/N+K8vMCnle2aZ16tQpHv+Ym5uLc+fOcfOKlQ+cNm0aRo8ejVGjRkEmkwnaDYVsdwokJCQgLCwMcrkcenp6+P79O1xdXbFo0SL0798fnTp1QqtWrTB9+nS0aNFCjf58/foVM2fORJs2bdTavnz5MubOncujuUB+358+fRr79u1D5cqVsXLlSgwePJjZnqxNZlF+V7E86dGjR/HlyxdYWlrC398fLVq00LreSZmnjRs3RnR0NJydnbXWA4Dly5dj8ODBgt/fysoKQ4cOxdKlSzm+RwFd+k8hmJmZoUiRIihSpAhsbGygr6+Pe/fu8eosWLAAT58+xa1bt1C5cmXeucTERLRr146jkcHBwQDy5/bDhw+1ysSTJ0+Gj48PunTpgokTJ/L4zEWLFuHUqVOcPZqF/2LVrUrVT7Lovw0MDODo6CjaxmhgYICkpCRRdQFpuhgp40UTCkqPzp8/j0OHDqF69eqQy+VwcnJCs2bNYGlpifnz5wvqhsSChYd5+fKloAzfp08f/Pnnn2rl+vr6GDZsmNqcEULz5s0RFxenU0dXEF2GLhRE7zxx4kRcuHAB69atQ9++fbFmzRo8f/4cYWFhWLBggeA1YugLII6mOzs7M/FgrHRdAX19fVSpUkXwnKZysRA7BpQhZP8DgO/fv2u0kf/Gvwe/HXR/418HFoYaAEaMGIGxY8fi6dOnqFatGszMzHjnFYzt/fv3sWPHDpibm2P06NEYP348li1bJsjQsBg2VQ2tLM4Wfn5+WL58OSckyWQyZGVlYebMmWjVqpVaG0ZGRoIKq5SUFBQrVkytXJvxxsjICH/88Ydkxq5bt26F4owrBDHvyar8BNjGgAJSnPNYMGDAAOjr6+Po0aOcYkYTIiIisGTJEl6/t23bFp6enggLC8O5c+fg6OiIefPmYcqUKQCAnJwcREVF4cGDB+jVqxcsLCzw4sULWFpawtzcXJTCDoDaOBDzjQoyj1jA6uBUvXp1HDt2DKNHjwbwH6fbTZs2cQ5sQP74HzBgANfGt2/fMGzYMDUao+zMySKwGRoaIjg4mBOutUFZaLx79y5PgZKbm4uTJ09qVNzqGgNAvuPJyJEj4eDggNzcXHh4eCA3Nxe9evXC1KlTAeQbE8U6TmzZsgU9evRAWloa2rVrh61bt2p1XiwIxH5PKysr7pwuo68yWJ3cFChTpgw2btwo+j66IJfLUa5cObx//57JQVfbXC1RogSmTZsm2UAM5Dsj16tXT2udcePGISAgAKGhoVrbLwjNYKXVcXFx+Pvvv/HkyRM1p3flOR0YGIjq1asjMTGRcyIA8hVMgwcP5n4TEeRyucZ3k8lkICJ06NABHTp04AzO9+/f1ynASlnvFGjYsCFSUlKwZs0azkDWqVMnjBgxAiVLlmQSwKU43pcsWZI5GCgoKAhBQUFISUnBX3/9hTVr1mD8+PFo1KgR+vTpwylzmjZtyuxg/u7dO4wbNw7Tp0/nnZ87dy4eP36M06dPY+bMmTh27Bji4uI0vp8QwsPDmeoD7Eo+Fno0Y8YM9O/fH8+fP0deXh7279+P+/fvIzIyEkePHlVr297eHmlpaWr89+XLlzUqN3Qp4vr27Ss5WEMMz5OWlobo6GhRRitW40dBjGaajMsymQxGRkbcOGWVeQBxa55UpSDrGHBxcREMkjx58iQ3LwFg27Zt6Nu3L6ytrXnBXllZWWjRogXevHmDqKgotfbFOKFlZmaC8ncdwufPn3nfNjc3F8ePH0fx4sUxZMiQAgUOTZs2Dfv27eM5f5ctWxaLFy9G586d8fDhQyxatAidO3cWbFNPT4+j/8oQ24cKiKEZLPyaiYkJZ9iLi4vDwoULRfNrYoK7lKHre9rZ2XGKZxcXF2zbto237urC3LlzMW/ePFF89YgRIzBu3Dg8e/ZMo/7g8OHD6NKlC3JycgDkB7xs3LgR3bp1Q7Vq1XDgwAG0aNFCre3Xr19j/PjxOHfuHN68eaOm8FUdX2LXgUWLFqFJkyaIi4vDjx8/MHHiRNy5cwcfPnzAlStXNL7rypUrBcuVHfQaNGgAPT09JCYm4ubNm5zxThdYZTCx0OUg+OzZMwwZMkTQ0KwNUvlMVtqoydin6HNHR0c1Jz9NiI+PVysTK/sA+c7Ss2fPxpUrVwTH+pgxY3i/mzRponNdksJ/KWBtbY3Vq1er1QsJCeH9FhtkyoL169dj7NixOHbsGGQyGaZNm6bReVWVhsXGxiIsLEytribHBl00RirdBdh0iFZWVjh48CCsra1RrVo1APljKiMjA35+fti9ezcWLlyIc+fOCTrSaELlypW1OqwpoOpsr3Du+ueff3jlqt9BrOwIsNHGgshUYngS1vbt7OwQExOD4cOHY/Lkydx6IZPJ0Lx5c6xZswZ2dnYAwK03N2/eRGBgIJNDrBjdHYsTx6/U87HysFL6xdjYGDExMWq6g5iYGI6PzMvL4/GUYpyQwsPDMXPmTBw9ehQymQwnTpyAvr66WU8RNMRqCFeAVe/xq3XahRlAam1trfObq9oppMzr3NxcLFu2TKM+6MOHD7zfgYGBvN8/f/5EdnY2DA0NYWpqynPQffDggaDTl6WlJSczlitXjuecw8pTA+KCh8QGmAL/oddEhNu3b/PWdUNDQ1SpUgXjx4/nysTKYMpQdjDp3r07HB0dcfXqVZQrVw5t27ZF7dq1mYPZlSHGabkg9EsXTz1s2DAcP35cFA2Ij49nDjDIy8tDeHg49u/fj/T0dMhkMri4uKBLly7o27evxrljZWXF6fVZ8OPHDzx69AhlypRRe49//vkHo0aNQvXq1TFz5kwEBwfj2bNnCAgIQGxsLBYvXowhQ4bwrsnJyUFoaCgCAgK44L4ePXqgR48eavcWEywEgJOVZDKZWqCBgYEBnJ2dsWTJEgDsfOCQIUPQs2dPPH78GJUrV8bZs2eZZFMDAwNOR1y8eHE8efIE7u7usLKywtOnTwGID9RRxalTp7Bw4UK18iZNmmDcuHEA8vVfkyZNAsBuTxZCSkoKNm/ejMjISLx8+RKAeJ6U1Tlayjxt2bIlJk2ahNu3bwvy3sp6qMTERMH+U8DPzw+LFy9WKxfjiJiXl4e4uDhERUXhwoULuHLlCr58+YJSpUqhUaNGWLNmjZqud9euXVi6dKmacy6Q77C2ePFidO/eHf7+/pz8N3fuXIwfPx5z5swRfF9LS0t4e3tj7969CAgIwIEDB3jnbW1t8ffff6Nq1aoA2PgvVt2qVP0kqz1u6tSpmDJlCrZt24YiRYrobF8RJKLJ+VEZUnQxUoJvNKGg9OjLly/cmmxjY4O3b9+ifPny8PT0FNQ3sICFh/H19cWlS5fU+MbLly+rOcQrIDY5X+vWrTFhwgTcvXsXnp6easGgChpQEF2GLhQkYdWRI0cQGRkJX19f+Pv7o379+ihbtiycnJzw119/oXfv3pLoiyao0vQ///yTiQcraPCzprYVOsr27duLmsfKEDsGgP/wdDKZDJs2beLJDrm5ubh48SIqVKjAdP/f+L+H3w66v/GvAwtDDYAT5JSNCwpHGGWh/fPnz9wioaenBxMTE40OByyGTVWwOFssWbIEzZs3h4eHB759+4ZevXohNTUVRYsWxc6dO9XabteuHWbPno2///6ba/vJkycIDg4WNAhrM940bNgQe/bsYWbsZsyYgUmTJnGK8o8fP2pUIkqFmPdkVX4CbGNAAanOeWKRkJCAmzdvilrwY2JiuGyNyvD29sbVq1cB5DvLPXnyBIC4zI8sCjtliPlGBZlHLGB1cAoNDUXLli1x9+5d5OTkYMWKFbh79y5iYmK4TLaAehaJPn366HwWVoGNiHDz5k1Ouebq6govLy81YVVRJpPJBLOamJiYYNWqVWrlYrN/GhoaYuPGjZgxYwZu376NrKwseHt78xxBWR0nKlSogAoVKmDmzJno2rWr5CwWuiD2eyobelmMvqxObgC4OagJqlnJxWLBggWYMGEC1q1bp5alQBO0zdUuXbpINhDPnj1b63llo+bz588xZswYnWOgIDSDhVbv2rUL/fr1Q/PmzXH69Gn4+fkhJSUFr1+/VhNAxWTLAf6T4UGToknVQcfc3BwXLlyAi4uL4NqlDCnrnTJKliyJefPmCbbNKoCzOt4XJMq7fPnyCAkJQUhICK5du4bhw4fD39+fE+ylZCCTkjHpV4J1zWChR+3bt8eRI0cwe/ZsmJmZYcaMGahatSqOHDmCZs2aqbU9ePBgBAYGYsuWLZDJZHjx4gWuXr2K8ePHqzk0K6BLETdz5kzJwRpieJ4ZM2agYsWKotpjNX5IzcoFQKdxuXTp0hgwYACaN2/OJPMA4tY8qUpB1jEwduxYjBw5Et++fQMR4caNG9i5cyfmz5+PTZs2cfW6dOmCjIwM9OzZE8eOHYOvry+XOff169eIjo5GyZIl1doX44Sm6GuZTMbLsqOATCZDSEgIpk6dWqDAoZcvX3KOmsrIycnhnLNKliyJz58/i24TEN+HCoihGVIdXcUaQhUQE9ylDBanQjGZm1Tx8eNHdO3aVVRdMfoDKTuvAPnBl0+ePMH06dN1Bl8C4tcBKRmwAGDZsmV4+/YtsrOzOZn948ePMDU1hbm5Od68eQNXV1dcuHABNWrUwNOnT0U76ErJJFMYeP/+PTZv3szsoCuVz2SljUKynDIMDAxQoUIFtGnTRicPKASxsg8AbN68GdbW1rh586Ya/yOTydQcdMXo4gqSAfbixYtazws5GxUW6tSpg2vXrgHID3xMSUkRleUFYHdsEKujZKW7AJsO0d7eHr169cLq1as5J5G8vDzOkXHXrl0YNmwYunXrhpSUFJiZmWk0mCmwdOlSGBoaaswkqAwp7weIlx0BNtpYEJlKzBompX0nJyccP34cHz9+RFpaGogI5cqV06hjleK8LkZ3x+LE8Sv1fFJ5WJZ+GT16NIYNG4abN29y63lsbCw2bdrEJTo4deoUL1uoGCckNzc3zilQLpfj3LlzWmkMqxyuAKuOWlf9IkWKICUlBUWLFoWNjY3W9UvVcZX1eXTJsmIzVd2+fZv7X8q8CwkJwaZNmzBu3DhMmzYNU6dORXp6Og4ePCioT/n48aNaWWpqKoYPH44JEybwyqtVq4YJEyZwO3gA+Q7+EydO5MZbamoqHBwcuGtYeWqALXjo/fv3nCzw9OlTbNy4Ed++fUPbtm05hxgFvfb398eKFSs0OlkoIFYG0wYfHx+eDCAlmF0ZYpyWC0K/WHhqJycnnTSAJcCAiNCuXTscP34cVapUgaenJ4gI9+7dw4ABA7B//34cPHhQ7TopfFd2djZGjx6NiIgIAPm8jqurK0aPHo1SpUph0qRJsLS0RGRkJDp37oyhQ4di9+7dePToEWrWrImkpCRBRyqxOzUA+YkGxEBh03JxcUFsbCyKFi2qsa4UPtDCwgKVKlXC1q1bUbduXaYARG9vb8TGxqJcuXJo2LAhZsyYgXfv3vGyDrME6iijSJEiOHLkiNoOGUeOHOEcmr58+cKNKVZ7sgLZ2dnYvXs3tmzZgqtXr6J69eo8PlEsT8rqHC1lno4YMQIABHW5quvo69ev1Ry3lKGvr4+3b9+qlYtxRLS2tsaXL19gb2+vc5cvBR4/fiy4o58CisDOzZs3c2UK3qNdu3a8vlSVNdq0aYPHjx/j5MmTHJ9Zvnx5+Pn58fhsFv7rxYsXTLpVqbwdqz1u9erVSEtLQ8mSJeHk5KQmx6o6oubk5GDLli04e/asoNyrPJak6GKkBN9oQ0HokZubG+7fvw9nZ2dUqVIFYWFhcHZ2xvr167XqksRAFw+jnOm8Xbt2CA4Oxs2bN1G7dm0AwLVr17Bnzx6NPIPY5HyKJDpC9krlOVHQ3Wy0YdasWZL1zh8+fOD4N0tLS47nrlevHoYPHw5AGn1RhjaazsqDFST4+datW4iPj0dubi5HP1JSUqCnp4cKFSpg7dq1GDduHC5fvgwPDw9R7waIHwNAPk8H5NPM9evX8xKyGBoacvPjN/7loN/4jX8ZZDKZxkMul6vVT09P13ootxsZGUmHDh2iQ4cOkampKW3YsIH7rThU4ezsTO/evRP9/Ldv36bixYtTixYtyNDQkLp06ULu7u5kZ2dHaWlpavV//vxJ27dvpwkTJtDw4cNp48aNlJ2dLdh2RkYGNW3alKytrUlPT48cHBzIwMCA6tevT1lZWWr1L126RObm5jRs2DAyNjamwMBAatasGZmZmVFcXBxXLzw8nL59+ybq/eRyOb1+/Zr7bWFhQQ8ePBB1rViwvqdMJuM9kyZIGQMHDx4kKysrWrBgAZmamtKff/5JgwYNIkNDQzp9+nSB37V69ep06dIlUXXLlStHwcHBauXBwcFUvnx5IiKKjY2lkiVLEhFR+/btqU+fPvT9+3cyNzfnvtOFCxeobNmyBXpu1m/EOo9YwDrniIjS0tJo0KBBVKNGDXJ3d6fevXtTUlJSoTxPRkYGzZ07l7p27UotW7akqVOn0osXL9TqnT9/nlxcXEgul/NoXJkyZSg6OppXNz09nR49ekQymYxiY2N5NO7FixeUk5Mj+CwFHQP79u0jT09PCb3w34XY75mdnU2HDh2izMxMtXOfPn2iQ4cOCdLCixcvUtOmTalYsWJkYmJCdevWpVOnTml8HsW31HRIhbW1NRkaGpJcLidjY2OysbHhHUJgnati4eXlxTsqVqxIpqamZGlpSd7e3ry6HTt2pN27dzO1z0ozWGi1p6cnrV69moiImxd5eXk0ePBgmjFjBq+utbU13blzh1eXKH99LV68OFcvPDxc1PHp0yfumk+fPmk9hCB2vVPG169f6fr163TkyBGN693nz59JJpNRSkoKZWRkCB7KmDVrFn358kXU/TMzM+n27dskk8no3LlzlJCQIHgI4fr16xQYGEj29vZkampK3bt3F6zn6+tLHz9+1PksxYsXp4iICLXyiIgI7nveuXOHzMzMaMWKFaKOgkLsmqEAKz0SQmxsrFpZXl4ezZ07l8zMzLg1ydjYmKZNm6axne/fv9OgQYNIX1+fZDIZGRgYkFwupz59+tDPnz95dVnGDBE7zyMG6enp1LJlS7V1t2XLlvTw4UON1w0YMEBw3dCEiIgIKl26NE2bNo0OHz5Mhw8fpmnTppGDgwOFhYXR3LlzydramlnmUUDMmnfv3j06cuQIyWQyCg8Pp4MHDwoeypAyBrZv305ly5bl6pcqVYo2bdokWHfhwoVkaWlJFy5coPr165Orqys9ffpUY9ti5JioqCi6cOECyWQy2r9/P0VFRXFHTEwMPX/+XK1d1rFIRNSqVSuqWrUqxcfHc2Xx8fFUrVo1at26NRERHT58mCpVqsTULhFbHxKx0wxtCAoK4niBoKAgrYcmPHnyhI4dO0a7d++mlJQUjfV0fc8VK1bQ169fiYgk0d2AgABat26dqPcWoz+wtLSk1NRUIiLKyckhPT09OnPmjM62zc3N6datW6KeQ4GCfNOvX7/Sn3/+qfH8jh07yNfXlycTpaamUuPGjWnXrl309OlTqlu3LnXu3JnS0tKoadOmFB4eTnFxcZSYmMg7VCFFBisMJCQkFIifJmLjM1lp48GDB8nNzY02bdpESUlJlJSURJs2bSJ3d3fatWsXbd++nUqXLk3jxo2T/Py/SpZlXZfE8l/a2i8MGYkV6enplJeXJ7r+wIEDqUOHDvTjxw8yNzenhw8f0uPHj8nb25sCAwMF29d0DBw4sMB0V6wOsWjRonT//n218vv375OtrS0RESUlJZGenh73HX19fTUejRo1Et1nqsjIyKD379+rlb9//15N7pEiO6pCF21klanE6laltq8NHTt25PqoY8eOWg8hsOgDsrOzaf/+/bRo0SJauHAhHThwQCvP9Cv0fGJ52IL0y/bt26l27dqcHqV27dr0119/8fpBwZMQET19+pQ8PDzI3d2d9PX1qXbt2mRra0tubm4F/s6scjirjlpXfWV7wNatW7XqMoTA+jxSZdnMzEwKCwujGjVqaFwvxM47V1dXOnr0KBHl820KnmXFihXUs2dPndcrEBsbS25ubryy5ORkcnNzI0NDQypTpgyVKVOGDA0NqUKFChw9PnDgAEVGRqq1J5anJiIKDQ0lDw8PunbtGllYWNClS5do+/btVKxYMVq5ciUR5dN3Jycnksvl5ObmRrdu3SI7OzsyNzcnS0tL0tPTowMHDvDaffPmjcZ7KvMZUmQwIqKUlBQKCwujOXPmUEhICO9QRlRUlJo+QRdcXFw4Xl1ZdxcREUHu7u5q9VnpFwtPXdjYsmULWVhY0Pnz59XOnTt3jiwsLAT1XFL4rjFjxlC1atXo0qVLZGZmxvXjwYMHycvLi1f31atX1LRpU5LJZGRubk5RUVFa36Ndu3YaaYkm5Obm0v379+nSpUsUHR3NOxTQxtMJrWGsfKAUxMbGct/r9evX1Lx5c7KwsKCqVasK6j8/fPhAN27coOvXr9OHDx+0tr1hwwbS09Ojtm3b0pw5c2jOnDnUrl070tfX5/QIixcvpm7duhERu13g6tWrNHDgQLK0tKRKlSqRnp4eXbx4UfBZWOzaROz80a/gM1xdXdVonzL27dtHLi4uauVi9J/r168X5L21oVixYoK8pAI3btygokWL8sqUaa7QIQUsOkRW3aoU/SQRG88wa9YsrYcqxMo8P378oMaNG+tcl1XBoi//1di2bRtt3bqViIji4uKoaNGinG1x165dhXIPTTyMNv2CGB24prq69OZiIFaXoUvOaNSokdqzsOqdPT09ubnbpEkTTle0YsUKKlWqFBFJoy9EbDSdiJ0HY6Xry5Yto06dOvF0ABkZGdSlSxdavnw5ffnyhdq3b09+fn6i25QKX19fnWvub/x7ISNSSbn1G7/xG5KgbetpBVQjKaTi06dPWL16NRITE5GVlYWqVavqzG6jjJcvX2LevHmC2/8B+RHRSUlJXNtNmzbV2NaDBw+wYMEC3rMEBwfD09NT0rvJ5XK8evWKi6CxsLBAYmKizky0UsDynmIgdQxcunQJs2fP5vXhjBkz4OfnV6DnAfK3Spg2bRpCQ0MFU+8rR60fPnwYXbt2RYUKFbjI+7i4OCQnJ2Pv3r1o06YN1q1bh9TUVCxduhS2traIiYmBm5sb7zulp6fDw8MD2dnZGrdoEoJqhh2g8L+RVBR0zimwd+9edOnSpdCf79u3b1i9ejW3JVhaWhqqVKmCWrVqITAwEBUqVAAR4e7du1i5ciXi4uKQlJTENK9IYJsYMWMgLCwMZ86cgaGhIQIDA1GrVi2cP38e48aNQ0pKCvr164d169Zh5cqVGDJkCIyNjXWOm/DwcJw7dw42Njbw9vbWmoWjoFupaIPq91yxYgUOHz6Mc+fOCdZv2rQpOnTogFGjRolqPy4uDtWrV1crT0xM5P3++fMnbt26haVLl2LevHno1KkTw1v8B4oMBpqgmvVZGUJzdezYsZgzZ47obEm6kJmZiQEDBqBjx468bf42b96M2bNnw9/fX+cWIwWBWFptZmaGO3fuwNnZGba2toiKioKnpyfu3buHxo0bc9t1Aflb71lZWWHDhg2wsLBAUlISihUrhvbt28PR0ZE5i5Genh5evnyJ4sWLQy6XC84N0pBRVgpOnjyJfv368bZQVED1HtHR0ahbt66kbG5iEBERgR49euiM8k5JScFff/2FnTt34tGjR2jcuDF69+6NTp06adyuUizmzp2L0NBQDB48WDBj0tSpU7Fs2TJMmTKFF9WvyJSiiHzOyMiAqakpihcvzm1VWZhQXTPEQJUeZWVlcbsFKJCQkIDp06fj+PHjGsfXjx8/kJaWhqysLHh4eMDc3Bxfv37VuKUckJ+JR1P2dalg5XlYIDZLmVQ0adIEQ4cORbdu3Xjlf//9N8LCwnDu3Dls27YN8+bNQ3JycqHdV4iHCQkJwYQJE5gyAkgZA9nZ2cjKyuLkg+fPnwtu2zZp0iT8+eefcHZ2RlRUFC97lBDEyjGPHz+Gg4ODKF5fCl69eoW+ffvi3Llz3BqWk5ODJk2aYNu2bbCzs8OFCxfw8+dPyfKB2D4UgjLNYOHXDhw4gAMHDsDa2lprxlOZTCZ6e7f9+/dj1qxZSEpKUjun7Xu6uLggLi4Otra2atsAqj6LEN2dP38+li5ditatWwvyGkJyjDZIlXk9PDzw119/FXinFeVv+vbtW1y/fh2GhoZo0qQJ9PT08PPnT6xduxbz589HTk6O4DoPAGXKlMG+fft4mQCB/KwVnTt3xsOHDxETE4POnTvjwIED6NWrF9LT07l6MoGsn8ooLBmMBYmJiahatWqh8EksEEsba9asiTlz5qB58+a88lOnTmH69Om4ceMGDh48iHHjxuHBgweC90pKSkL16tXVtt3WBU2yrLZtiv+b+PTpE++3QkaaPn065s2bhyZNmvyyeyclJaFSpUqQy+WC9EkZqlvMfvr0CV26dEFcXBw+f/6MkiVL4tWrV6hduzZOnDihlslHGxo1alTodBcQ1iHa2NggIiJCTdY6fPgw+vfvj48fPyI1NRU1a9YUzBBZmGjZsiXatm3LZThTYP369Th8+DCOHz/OlYmVHQtCG6WgsHWrYuHv74+VK1fCwsIC/v7+Wutqk03/t+juxEIXD1tY/SIWOTk52L17N+/79+7dm1sDDh8+jJYtW8LAwICXLUwIqnOSVQ5n1VH/Sp12YbWvSbd28eJFbN68Gfv27UPJkiXRqVMndO7cWetuBrpgZmaGe/fuwdHRESVKlMCxY8dQtWpVPHz4EN7e3mprlSYkJCSgQYMGahnW8/LycPr0aaSkpADIz1zXrFkzZjlFG09NRAgNDcX8+fORnZ0NID/bu2LLcyCf7urr62PSpEnYtm0bjh49iubNm2Pjxo0A8rNJ37x5k8sqCuRn5t28eTNat27Nu9/ixYsxffp0fP36lVfOIoNt3LgRw4cPR9GiRWFvb8/ThclkMkRFRXE2EKGs9coQyvA7f/58bN++HVu2bEGzZs1w/PhxPH78GEFBQZg+fTqXAV0qdPHUy5cvh7W1Nbp3746wsDCtbYWHhyM8PByWlpY69cP79++Hn58fGjdujEmTJgnWCQ0NRXR0NE6dOsUrl8J3OTk5Yffu3ahduzZP/klLS0PVqlW5b7Nz506MGjUKXl5eWLt2LTZv3owVK1ZgxIgRmD9/PoyNjdXaXr9+PUJCQtC7d29ROwhdu3YNvXr1wuPHj9V2JVOWTZo0aYLIyEg12fn69evo27cvUlJSmPnAgmYX/9W4cuUKVq9ejfv37wPIpzOjR49GnTp1NF6jiw9YsmQJtmzZgk+fPqFnz57o06cPqlSpAgMDAyQmJorOZKjNrv348WM4Ojrq3GWmIPj27Zvg+FNg9OjRiIqKQmxsrFq9r1+/ombNmmjUqJFGXUph6z+7d++OnJwc7Nu3T/B8586doaenx2VAFguxNl9VPQmLDpFVtypFPykETTzDr0KxYsUQExMj6VsXxngpbHqUnZ2N5ORkODo6as08XhBo42FY8PjxY63nhTK2FzZ0yRlA/s7Ne/fuZW774cOHcHZ2xooVK6Cnp4cxY8bg7NmzaNu2LYgIP3/+xNKlSxEYGMjcNgtNz8zMlMyDsdL1UqVK4cyZM2pryp07d+Dn54fnz58jPj4efn5+hSrLC0HbevHy5ctfqtf8jf/9+O2g+xv/auhiqBXYtm0b1q9fj0ePHuHq1atwcnLC8uXL4eLiImrrNVWwGDbFGvtUnS3u3LmDCxcuwNDQEN26dYO1tTXevXuHefPmYf369XB1dcWdO3dEtR0fH48ZM2Zo3G5dCObm5khPT2dm7P6bDrqqUH7Pgig/CwuFIQwolFiq/a/JEPro0SOEhYXxFH1Dhw5V29YKyDfIXLlyBR4eHrzvdPnyZXTu3BmvX7/WagBXhiZjuCqUv9GvmEcsEHJwysnJQXJyMgwNDXnbcB06dAgzZsxAcnIyvn//Lul+LAaiUaNG4d69e4KOokSEpk2bwsPDA6tWreKdGzBgANasWaOmxEpPT0ffvn1x6dIlXrmuMRAUFIQZM2agcuXKSE5OBhFh6tSpWLVqFQIDAzF06FBOuGZxnOjfvz8ncOva1mzmzJlaz2sD6/esWbMmpk+fjrZt2wq2d/ToUcyePRs3btzgyqQ6uQnh2LFj+PPPPxEVFcXwlr8G8fHxaNasGR48eFCoBuLbt2+jbdu2POcObcp6ZTr3q2iGKq0uXbo0Tpw4AU9PT1SuXBmTJ09Gz549cfXqVbRo0YKnxH727BmaN28OIkJqaiqqV6/Obdl18eJFpi33AL7xTXUbYlUotnUryHpXrlw5+Pn5YcaMGYJbo7EI4L6+vv8Vx3u5XI4aNWqgV69e6NGjh+BzA5DsYP7XX38JKrJ79eoFIF8hK5PJOP5zx44dnMFBsfXO/fv3MXjwYAwdOhS9e/eW9J5SnArE0KOnT5+iW7duuHHjBvT09DBq1CjMnTsXw4YNw+7du9GxY0cEBQWhVq1aOp/x+/fvWLNmDRYtWoRXr16JfjeFIk5fX79AY4aF5ylsdOrUiclopgwTExMkJSWpKV5TU1NRpUoVZGdn49GjR6hYsSJnUBUj8/xKHkYTWMfAq1evMG/ePGzevJl7N9X+U2zNqWpAU+1HbdDkhJadnY0nT56oObUNGDCgUOhXcnIybzwqaEJhQqgPAfE0o6COrmIgNrhLLAojQI71XXXpD+RyOSIiImBlZQUA6NmzJ5YvX662Jqmuu6dPn8aSJUu4bQO1Qcw3PXjwINq0aYPMzEzIZDJUr14dW7duRYcOHaCvr48xY8agf//+Gh3oTU1NcfHiRTWZNTY2Fg0bNkR2djbS09NRqVIlODo6wt3dHRMnToSdnZ3aPBFr/JASZMICqQ66hclnaqONJiYmuHXrFipUqMArT05Ohre3N75+/coLlhRCYmIivL29ua17FWBdB8RsU6wJmtalwg7wA/L547Fjx+LmzZui6kuBsh5LESSnrG7X5YwO5DtDKDug6QqUX758Oe7duwcg33k/MDBQ9BaUQmDVIY4ZMwY7d+7ElClTeIFpoaGh6NWrF1asWIFNmzYhPDwcly9flvxcYlCkSBFcuXIF7u7uvPLk5GTUrVsX79+/58rEyI6XL19mpo2/SoeoWMP+N+goWaCquxODMWPG/I/r+QoLP378wJs3b9TorKOjo+g2lJ2QVGmMJijGcEEM4ZrAqqNWrX/8+HHo6empBZicPn0aubm5aNmypei2hdoXq1t79eoVwsPDsXnzZmRmZqJbt25Yv369oIOYlHnn5uaGyMhI1KpVC/Xq1UObNm0wadIk7N69G6NHj8abN2/U7qEMIsLLly+xevVqODg44MSJE0z9ooyC8tTagoeKFi2K8+fPo3LlytyWxbGxsahWrRqAfPpbu3ZtZGRkcO0tWrQIM2bMgL+/P5YuXYoPHz6gX79+uH37NsLCwtCxY0fe/WfNmoUZM2aojflPnz5h2LBhvG3unZycMGLECAQHBwu+S0GD2cU4LReEfuniqb99+4YbN27A19dXI38H/Ge7Z5YAA3t7e5w8eVLNOViBW7duoWXLlqJ1Ntr4LlNTU/zzzz9wdXXl2RESExPRoEEDfPr0CZ07d8apU6cwf/58nuNzTEwM9z7h4eHw8fHhtS1WN6yAl5cXypcvj5CQEJQoUUJtTCjktNatW+PatWtYu3Ytunfvjry8PMyePRuhoaEYMWIEli9fzswHKicYCA8P16o/0JYs438zVO3J+vr6CA4OxuzZs3nbfQs5c7HwpKzO0VLmaW5uLkJDQ7F+/Xq8fv2ak3mmT58OZ2dnDBw4kLvm9evXqFq1KqcvVeh0kpOTsWbNGuTm5iI+Pl6jLloVQo6IX758wYIFC3Du3DlBXkNZN3H37l3UqlULFStWxNixY7kkPvfu3cOyZctw9+5dXLt2DRUrVlS7tyb9V+XKlUXZfAuiE/rVkGKPy8jIwN69e/HgwQNMmDABRYoU4b6lpuD3tLQ0PHjwAA0aNICJiYlgAqSgoCAYGRlhwYIFBX4vKY6r/1vpUWHrBQuK6OhoLF68mCd/T5gwAfXr1+fqSNFlLFu2DEFBQRrrff78GS1atMDXr1+Z9c7KvA+Q77C/cuVKfPv2DTdv3kTZsmXVgofF0hcWms7KgxUk+Nnc3BxHjx6Fr68vrzwqKgpt27bF58+f8fDhQ3h5eemUkVQhZgwow8PDAzt27FDjr/bt24dhw4bh7du3TPf/jf9j+C9l6v2N3/hfg5ycHJo9ezaVLFmS9PT0uK1Upk2bJrjd59q1a6lo0aI0d+5cMjEx4epv3bqVfH19Rd83NzeXjhw5QkT87TOcnZ01HqrbXbx584aOHDlCp06d4rac//HjBy1fvpzs7Oy4beQOHTpEBgYGXFr+MmXK0Pnz56lo0aLUvHlzOnHihNrznTx5ksaNG0eTJ0/m3vHevXvUvn17bgsJZfz8+ZNu376tlvb+4MGDVLlyZdLT05O0jZVcLqe0tDT69OkTZWRkkIWFBSUmJoramlsMxL6ncup8qVsEq0J5DCjw+fNnta1Zbt26RW3atCmUbRh/xZYkCnTr1o0GDx5MRMRtxfj582dq3LgxDRgwQHK7Yr+R1HnEArFzjih/G1bF9l5yuZw6duxIr169ogYNGlCRIkUoODhY61bL2nDp0iWysrLixlzNmjXpzp07VK5cOXJ3d6d169bxxlHFihXp8OHDGts7fPgwVaxYUa3cy8uLXF1dKSYmhisLDw8nS0tL6tChg1p9XWOgfPny3By/ePEiyWQyat26teAWR//bIOV7Wltb0+PHjzW2+fjxY7K2tiai/K1ZateuTXK5nAwMDCgoKIi+fPlCffv2JUNDQ+revTtdu3aN6ZlTU1PJ1NSU/WWVkJaWRlOnTqUePXpwNPD48eP0zz//qNVlXTcKA5cuXeL6kBUFoRkstLpnz560ZMkSIiKaPXs2FStWjAYNGkROTk6CW2Dq2rLLxsaG3r59S0T5Y0yxVabQIQUFWe8sLCy0bjMtl8t5bStvd6c4FOXK2/OI3TpKSt+I3TZKeSuiX7UdL1H+9mfK29orEBcXR87OzpLaZF0zWOhR9+7dycvLi1atWsVts1S9enUaOXKk4Br37ds3mjRpElWrVo18fHy4rd62bNlCJUqUoNKlS9OCBQvUrlu/fj117tyZevbsyd373Llz5OXlRaampjRs2DBJY+ZXIiEhgebMmUNr1qzhxqUCnz59In9/f+73gAEDKDMzk/tf26GKcuXKUXBwsFp5cHAwlS9fnojytzwsUaKEaJmHZc3z9vbmtmjy8vIib29vjQcR+xj48OED9ejRg2xtbalEiRK0YsUKys3NpenTp5OJiQnVqlWLt02arv4T6kddcoyhoSGv/M2bN9S6dWtBGiaVfv1KsPYhK80oDHz69IkOHDhA9+7d45XPnz+fDAwMqFq1amRmZkampqY0b948sre3p/nz5wtuD8b6PVWRk5NDt27dKrStx8ToD1i24VNd3wwNDUkul5O5ubnGtU7sN23YsCH17NmTbt++TePHjyeZTEbly5enPXv2iHrXVq1aUdWqVXnrWHx8PFWrVo1at25NRPlyR6VKlcjU1JRSU1NFtcsig7FCyhaCYsDKZ0pdH728vKh///70/ft3ruzHjx/Uv39/bmvgy5cva+UhEhIS1N5RiuzDsk0xkThd3K/gv+7du0dmZmai60uB8nbG6enpWg8FsrOzefqhSZMmUVBQEHdMmDCBvn79qnavkydPkqGhIdWsWZOrW7NmTTIyMhLc8l0BTXSXSJoOMScnh+bOnUv29vbcdfb29jRv3jxu3j5+/Fht3MTGxtKECROoe/fuavNPKkxNTXnboyuQlJREJiYmzO1JoY1SZSqxa9iv0FGqIjs7m7ddanp6Oi1btkzjdr8sujtdh4I2/ko9HysPK6VfUlJSqF69ehplXlX8888/tGrVKgoLC+No39u3b+mPP/4gY2Nj8vDwYH5PIjY5XBmsOmqW+p6ennTs2DG1Nk6cOEGVK1cWfA8x7bPIsm3atCFLS0vq2bMnHT16lKNV+vr6dOfOHbX7S5l3wcHBNG/ePCIi2rVrF+nr61PZsmXJ0NBQUIYTasvOzo569uxJL168UKt/9uxZmjx5Mg0cOJD8/f15hzKk8NTa8O3bN1qyZAnZ2dmp9Q1Rvk5YQQeIiF69eiU4ZuLj46lixYpUtmxZKlKkCLVs2ZJevnwpeM/SpUuTj48Pr90LFy6Qg4MD1ahRg1fXwsKCV08VylsqF8RW8v37d7pz5w5dv36dPn/+zDtXEPrFwlMXNgwMDATHmgLPnz/XKU8pQxvfVb9+fVq5ciUR/ceOQEQ0atQoat68ORER1alTR6PeLjs7m8aMGUMGBgain0cTWGST1atXk6mpKfXs2ZN8fHyoZMmSvDVACh9YELx7945GjBhB7u7uZGtrWyi6YWXk5ubS/fv36dKlSxQdHc07lMFiFwgNDaVy5cqRg4MDTZw4kW7fvk1E6vSXlSdVpdOK9U2ZpirTaSnzNCQkhFxdXWn79u08+X7Xrl1Uu3Zttf5LT0+nli1b8p5F0R+KMa8MMfpPZfTo0YNKlChBEydOpGXLltHy5ct5hyquXr1KHh4ePH5AJpORu7s7XblyRa2+Lv0XK1j5r40bN1K/fv1oy5YtRJTfzxUqVCAXFxeaMWNGgdqWao9LTEykYsWKUdmyZUlfX58bA1OnTqW+ffuq1X/37h01btyY63NFfX9/fxo7diyv7qhRo8jS0pKqVatGQ4YM4cmDQUFBam2zjpdfDdXnVRxjx46lKVOm0JYtW+j9+/dMbYrlYVasWCH60ITIyEiqU6cOlShRgqPRy5Yto4MHD3J1tm3bRvr6+tStWzeuvW7dupGBgQH99ddfXD0pugxjY2OKiIgQfLasrCyqW7cuubm5SdI76+LVhCCWvoil6UTsPBgrXVdGr169yMXFhfbv309Pnz6lp0+f0v79+8nV1ZX69OlDREQ7d+6katWqae0HVYgdA8oYPnw4GRkZcXq9rKws6t+/P5mYmNDSpUuZ7v8b//fw20H3N/51YGWo3d3dOWOJ8gJ2+/ZtUYah1NRUmjx5MpUoUYL09fUlPzeL4bRGjRr0xx9/0OfPn2nZsmUkk8moUqVKdOPGDcG2N23aRDKZjGxtbUkul1OxYsVo27ZtZG1tTUOHDqW7d+/y6v9KR0RVxaGm31LA+p6FBaEx8Cuc8woDHz9+pFOnTtG2bdsoIiKCd6ji6dOn5OHhQe7u7qSvr0+1a9cmW1tbcnNz4zF+qvj+/TslJydzTJky/qe+kRBYnRVatWpFTZo0oSNHjlCvXr1IJpNRhQoV6M8//yywUwOrgcjCwoIePXqksb2HDx+Subm5WvmPHz9o/PjxZGhoSJMnT6auXbuSubk5bdiwQbAdXWPA2NiYnjx5wtU3NDSkuLg4tpcn7Y4TT5484dGb69evU2BgIIWFhTHfRxlSvqe5ubnW94uLi+P6ndXJTRmqAQsZGRl079496t69O1WpUkXyO0dFRZGJiQk1bdqUDA0NufVu/vz51LlzZ17dgs5VbQZiInUBe/ny5RQcHEwlS5aknj17Sn5HVkih1e/fv6fnz58TUb5Sc/78+dS2bVsaO3as2hiOjo4WpIU/f/7kFJ/h4eGSAl5OnDhBly5d4n6vXr2aqlSpQj179iw0JyR/f3/B4CYFCssIogksfXP9+nXOACeEb9++0e7duyU9hyq+f/9OT58+pcePH/MOIZiYmAjyZ9evX5fkUEDEvmaw0KMSJUrQ1atXiYjo9evXJJPJaNmyZRqfZeLEiWRlZUWdO3fm+KDBgweTp6cn7dy5U/CbFLYxURNYeB5dOHXqFBkaGlLFihXJ0dGRbG1t6fz589x5TQZKKTh06BAZGhpS5cqVaeDAgTRw4ECqUqUKGRkZcY4+a9euJR8fH9EyD8uax6oUZB0DQ4YMIUdHRxo3bhxVqlSJM2S0bt2aG3sFgRQ5plevXlS3bl2KjY0lMzMzOn36NG3bto3c3Nzo6NGjBX6mnJwc2rRpE/Xs2ZOaNGlCjRo14h2sYO3DgjppKr+HJn6ta9eutGrVKiLKN6yWK1eODAwMSF9fn/bu3cvVYw3ukvI9AwMDubUrJyeH6tSpQzKZjMzMzOjChQta31GbHKNAQfUHqtC2tmniA8R+0yJFinDK8+zsbJLL5TxDhC68fPmSmjZtSjKZjAwNDTnn4WbNmtGrV6+IiOj8+fN06tQpatOmDe9ba8KvdhgX49Cvyvf+CkhZH4mIrly5Qra2tlSsWDFq0qQJNWnShIoXL062trbc/I6MjKRFixZpvLeQg64U2cfR0ZG7p/JYT01NJQsLC7X6rLo4ViQmJvKOhIQEOnHiBDVs2JDq1q1b4PYLG+vWraM2bdpwv83NzalWrVqc4c7e3l7QcOPl5aUxUEfZsVAs3SVi1yGqQmwg/c6dO8nAwIDatGlDhoaG1KZNGypfvjxZWVkVKMjb19eXRo0apVY+YsQIqlevHnN7BaWNYvErdatS0KxZM1q3bh0R5fPJxYsXp9KlS5OxsTGtXbuWV/d/k+5OLKQGVLH0S506dahBgwZ0/PhxunXrFiUkJPAOZUhxjNcEhTOAAqxyOKveQ4qexNjYWFBP+ejRI7VA818VQKqnp0dBQUFqzn+aHHQLA1evXqUlS5ZoTaIgFrNmzeJ4o/bt21OHDh14hzKkJExgCR6SyWT05s0b7lplZ0sizfJvZmYmde/enfT19UlfX19Nj6WMDx8+UNeuXcnCwoI2bNhA48ePJwMDA5oyZYoaLx4QEMDN0/8fwcJTC0GVBiggJsBALpfzvqUqNH1LKXzXpUuXyNzcnIYNG0bGxsYUGBhIzZo1IzMzM06fnpubq/FZFFB1FCUiioiI4PSDyvj+/bugjqdRo0ZMdHbSpEkkk8nIwMBA0KlRKm7evMkLMjp48CC1b9+eJk+ezAvIU0bLli2pXLlytGDBAkFdaEFw9epVcnFxUXOIUrXJSuUDoqKiqF+/fmRqasolebp8+TJ3npUn/W84R5cpU4bOnj1LRHyZ5969e1qTiHz48IFu3LhB169f16jHlKL/tLKy4vWZWMTHx9Pu3btp9+7dgokiFBCj/4qJiVFLRBUREUHOzs5UrFgxGjx4MDcfWfivZcuWkZmZGXXq1IlKlChBc+fOJVtbW5o7dy6FhISQpaUlz+bHyttJtcc1adKEJkyYQET8MXDlyhVycnJSq9+3b19q3rw5PX36lFf/5MmTasFXLEGpv1pfLoUe+fr6kqWlJZmZmVHVqlWpatWqZG5uTlZWVlSrVi0u4JyFzxLLw6g61puZmZFMJuOCFRR6Pk2BfWKT81WoUEFQLl+yZAlVqFBB9HsJYc+ePWRsbEyHDh3ilX/+/Jnq1q1L5cqV4+yMrJDioMtKX3TRdCkoCF3//PkzDRo0iOOj5HI5GRoa0uDBg7nxc+vWLbp16xbTM0kdA0ePHiV7e3uqV68elSlThqpUqcI5M//Gvxu/HXR/418HVoba2NiYI/TK9VNSUsjY2FjwHtnZ2RQREUH169cnuVxODRs2pHXr1nECtTZoMmyyGE4tLS25CNCcnBzS09OjM2fOaLynp6cnZ8DZu3cvyWQy8vHx0ciUshpvWBg7XYrDgjjysL6nNmhSfCigawwUxDmPFRcvXqTevXuTj48PPXv2jIjyjXbKjltE+ZHYFhYWJJPJyMrKiqytrblDU/StrsyPyvjy5QsFBASQnp4eL2POqFGjaP78+URUeN+oMDJhsTorFCtWjGPsMjIySCaTUWRkpOT7K4PVQKTKfKtCl6PQjBkzOKWTcjZdIWgbA7oUtprA4jhRr149rp9fvnxJFhYW5OPjQ0WLFqWQkBCd99IEKd+zVq1agpmuFAgNDaVatWoREbuTmzKEMqDIZDJydHTU+b20oXbt2lzmV+X17vr161SqVCleXda5ymIgJlIXsF1dXalWrVo0efJkLuukMqKioqhNmzZUpkwZKlOmDLVt25YuXrwo+t010QxWWv3z50+KiIgQtd4T8TPbKOPdu3cFduarVKkSl6EmKSmJc7yvXbs2kwFc23r35csXatWqFfXv358WL14sOjpZF36F471qX6tmWGFxoNTkYM6aMYkoP4uPt7c33bx5kyuLi4ujqlWrUtu2bVlekQPrmsFCj+RyOW98m5mZUXJyssa2XVxcOCXT7du3SSaTkb+/P6doEYIUYyLrmJHC82iDj48PTZkyhYiI8vLyaOHChWRubs4ZfLSNL9ZsZUT5gTbBwcFcxrlJkyapGbxZZJ5fycOwjgEHBwc6d+4cEeUb7GUyGU2ePLlQnoVImhOavb09Xb9+nYjyaYciy92hQ4fUjI9S6NfIkSPJzMyMunXrRoGBgfTHH3/wDlaw9qFURyQWfs3Ozo5zTPnrr7+obNmy9OXLF1q7di0v0yZrcJeU71mqVCmKjY0lIqIDBw5QyZIl6f79+zRt2jSqU6eO4DVi5Bjld2DVHxQ2xH5TIYW9tsz4mnDv3j06dOgQHTp0SOOaEBYWRg4ODjRz5kzau3cvV19xKFBYDuOaoCtTRWZmpsZxIBVCfKaU9VH5GdetW8dlp1m/fj2PP1YN5lM9Ll26pLYmSVkHlI1YymM9ISGBLC0t1epLNW4rv5e2AD+h7CoKGUXTNb8C4eHhvOCNCRMmkJWVFfn4+PCMSPXq1eM5bKkazLZt2ybouGxkZCSYWe7+/ftkZGTE/RZLd4nYdYhS4enpSatXryai/7xvXl4eDR48WC0jFgsuX75MxsbGVL9+fc4IX79+fTI2NhaUCXXJjoVFG5UhJFMVZpC3Lh2lGNja2nI752zcuJEqV65Mubm59Pfff6sZHln0ASxOHNpQ2BnvxYKlX0xNTUXTG6mO8QsWLODthNClSxeSyWRUsmRJNSdgsWDVe0jRadvZ2XH8qTLOnDlDxYoVk9w+iyx79epVGjRoEFlYWFDNmjVp1apV9PbtW8kOuoUx71hgb28vWkaTkjCBJXhIJpNRq1atOHlUX1+f/Pz8uN+tWrVS4zUUGf6rVq1Kd+/epY0bN5KFhQV169ZN67yePHkyp6NW8BFE/MD+0NBQKlq0qCjdlNhg9o4dO3LBJ7p2YNAFsfRLDE/NQgPEBBiofkvVQ+hbKq6TwnelpaXRoEGDqEaNGuTu7k69e/cWzILPClb96v79+8nDw4O2bt1KcXFxag7HCnz48IE6depEVlZWtGHDBurduzeZmZnRmjVrBJ9DLB+oQPXq1Tm9+IMHD8jIyIh69uxJZcuWpcDAQMF7mJubS6b3ulClShXq2rUr3b17lz5+/EgZGRm8Q4GC2vAyMzNp/fr1VLNmTdLT0yMfHx9asmTJf40nFYKmeapJvr9z547OXTpSU1Pp5MmTHF+nKutJ0X86Ozv/0kAoMfqv5s2b82xgSUlJpK+vT4MGDaIlS5aQvb09zZw5k/neFSpU4LJRxsfHk76+Pi8xyKZNm5gzXypDqj3O0tKSkwWUx0B6ejpP/lJAWQ5Trv/gwYMC7ezyq3crlUKPli1bRp06deIFa2ZkZFCXLl1o+fLl9OXLF2rfvj35+fmJfg4pPMxff/1FdevW5a2dycnJVL9+fdq+fbvgNWKD6w0NDQUzrqempgp+fyFo02Vs3LiRTE1NOT1qVlYW1atXj8qWLSvonCtW76wagCPGNi+Vvmii6ar4byQUIsp31FWs56o7HkiB1DGQm5tLI0aM4PjYkydPFvhZfuP/Bn476P7Gvw6sDLW7uztnzFKuv3LlSrVtr27cuEFDhgwhS0tL8vb2psWLF5Oenp5WJY9YwyaL4ZQ1MsbU1JQz6ufl5ZGBgYHWKBdW440Uxu5XgPU9FWBRfIgdAwVxzmPB3r17ycTEhAYNGkRGRkbcOFi1apXa9vPlypWjwMBAnpOINojJ/KgMMVtgSv1GBcmEpQkFdYo1NzcXvZW6LrAaiGQyGV24cEFNuaQ4zp07J6ic+vHjB40dO5aMjIxoypQp1KBBA7K3txfcho5I9xiQyWQ0dOhQznhsaGhIAQEBOrdqYXGcsLa25gSvFStWcOdPnToladtDBaR8z7CwMDIzM1MzPhHlO4OZmZlxghKrk5syVAMWLl68SPfu3dOazU0MzMzMOCFNed149OiRmqDBOldZDMSskLLFiFiaIYVWm5iYiI7MV3ViV+D+/fuCmceOHTsmKMidOnWKjh8/ziszMzPjvtHMmTO5bHA3b97ktiVUBauxb9OmTaSvr0/m5ubk5OSkdds+FgFciuO9rmAgXbzRq1evSCaTCbYt1sGcJWOSAm/evKGWLVuqZUpp2bKl1kALbWBdM1jokapyx8LCQqtyx8DAgAsOIsrnw3UZX6Qo4ljHDCvPowvKyloF/vrrL25N0Oagy5KViwUsMo9UHkaMUpB1DOjp6fG22DQxMSnUjFZSnNCUdyZwdHTk1ruHDx+qZbqWQr9sbW018lpSwNqHUh2RWPg15Xndt29fLvvj48ePeeORNbhLyvc0MjLixu3gwYM5efThw4eCay+RODlGARb9AVG+kXrUqFFcNtTRo0er9f/Pnz/VnJdevXpFs2bNogkTJqgFXor9pqoyg5mZGR07dkyjgbogUDXea8rG9KszV4rdQrAgEMNnSlkfxUIomE9X4JCUdUDMNsXKYNXFsQb4qWZTefLkCX39+lXrO/wKlC9fnnNCi4mJIRMTEwoLC6O2bdvynHjs7e15wTVFixbl/b5//76go3Pp0qXp77//VivfvXs3OTg4cL/F0l0iadl1Xr16RX369KESJUqQnp6eqO1vlWXIIkWKcGP+7t27ZG9vr/V+unDr1i3q1asXeXh4ULVq1cjf319wDIuRHQtKG8XKVFIDpH6FgyZRPr+g2PWja9euXLaxJ0+eqPE7LPoAqU4cv0LPpwBLQBVLv1SvXl1tTdYEqU5Izs7OXPbG06dPk7W1NZ06dYoGDhxIzZo1U6svRg5n1XtI0ZMMGTKEPD09efxIamoqtyuI1Pal6NaysrJo8+bNVLduXTIwMCC5XE7Lly8XDAZXQNe8i4uLI19fX8Fs4hkZGeTr6ys4P1m2hi5SpIjoYAEpCRNYgofE7EigGhhuaGhIwcHB9OPHD64sLS2NateurZYUQIGVK1eSqakp9erVi9zc3MjDw4PrR23b02vTTYkNZh8wYAA3Jljek+jX0i8WGiAmwKB///7M35Lofw/fpYAm/WpCQoJgELYmmUSVTy5ZsiTVrVuXN3927dpFRYoUoVatWqm1K5YPVEBZl7Tg/7H33VFRZNvXp5ucUUCSIKCAICgYMEdUzGJAxZyzIjqKOWLEAOacxzwYZxTMmLOIEQMKYw6gEgzA/v5gdf26uqq7qwqYN+977rVqaRe3bt+qvnXuCfucO28eQ2I7f/48SpcuzXuvVatWLZLdhfhgbGzMS0LiayclhseHO3fuICwsDDY2NpJ0UgXEkqOFvqeVK1fGtm3bOOOZMWOG2p0aPnz4gEaNGjHzSXFNnz59MHr0aKadFP/ntm3b0LFjR63+zPDwcIa4qW6t4YvNCfF/2dnZMb4gAJg4cSIreX3Pnj3w8vLijEmb/qWscwEFvhuFDAMK9AZ1iZ1CdDup8TgbGxum6rDyHIiPj+d9T5VtaeX2165dQ8mSJbV+nzoU1W6l6iBFHjk4OPD6G+/evQsHBwcABTEbMbtJSdFh3NzceCtDX79+HS4uLrzXCE2uL1u2LFavXs25ftWqVShXrhxv32J9GfPnz4e5uTlOnz6NunXrws3NTW3Cg1C/s7ZkKr4kI6HyRROUZboqxBYUEivXiwtS5sCTJ08QEBAAZ2dnxMfHY9KkSdDX18fYsWNZuvAv/G/iF0H3F/7nIFahXrduHRwdHbFr1y6YmJhg586diIyMZP6vgK+vL8qUKYMJEyawlEZtWdhCA5tiAqcKp64i49bY2Bhr165VW6VGrOEjNngjRrHTFkySy+XQ0dFR+12aINXAE+r4EDMHCkPOEwM/Pz8mAKl8vzdv3uQQtIyNjQUbvID4zGQhW2BK/Y2kVMLSBikEpydPnuDz58/IyMiAmZkZEhMTORWLpI5FTIBIXRa7OmeTAhUrVkS5cuWY3yk/Px/z5s2DgYEBhgwZwnvPmuZA/fr1NW7TwrdVCyCOOKFMQmzdujUT+Hnx4kWhqpRJ/T27desGmUwGLy8vZpu58uXLQy6Xo0uXLqz+xZDc/gk4Ojoysk753YuNjYWbmxurrdh3VUyAWIH09HRcu3YNiYmJGgMlUrYYESozpMjq+vXrayWSKAxguVzOqVTRpk0buLi48BIbfH19eUlcR48eRcWKFVnnlLcPql27NuOQSklJ4QQSFRAb7LO1tcXs2bMFbUEnxgCXQrzXlgwkhKCrjkwglGAupmKSKh49esToZ4rqBFIhds0QI49kMhlTZVaxXZSFhQXzWXGo61uIU02KI07snBGr82iDjY0Nr1N0586dMDY2xqpVq9TOLzFVuRRIT09HXFwctm3bhi1btrAOBcTYPFLXPCFOQbFzQMqcEQMpJLSqVasyyRGtW7dGjx498Pfff2PcuHGc9VGK/LK3ty/0e68Msc9QKhFJjL7m7u6O3bt3IzMzEzY2NkzQ8vbt2yxHvdjkLim/p7OzM+Li4pCbmwsnJyfG2Xv37l21wR4hdowCQv0HQMEWh/r6+ggICGDuLSAgAAYGBoiPj2fa9e7dGwMHDmQ+f/nyBU5OTrCxsUHFihWhq6vL0g+E/qZSbQYFcnNzsX79eoSGhiIwMBANGzZkHVJQHJUrlVGcWwgqIETPLIysS05Oxpo1azBr1izMmDGDdQDSdiWSsg4I2aZYGWJ9ccWZ4FecUA4sjxs3Dj169ABQIGOsra2ZdoaGhhrtigcPHvBWY5kxYwYsLS0xb948JCQkICEhAXPnzoWlpSVmzpzJtBMqdwHxPkQAaNasGby9vbFy5Urs378fBw4cYB18cHR0ZEi5vr6+2LFjB4ACAgsfGbk4IMR2LKxsFGpTSU2QEmuzCYWvry9iYmKQmpoKc3NzZnee69evc/yHYvwBUkkcxeHnU0BMQpWY53Ly5EnUrFkTp0+fxocPHzTKUKn+T2XfysiRIxn94NGjR7x6jBA7XKzfQ4qfJCMjAzVq1ICuri5DnNTR0UHDhg05lWiLM4FUFQ8fPsTYsWNhZ2cHQ0NDtbvYaHvvQkNDWTJYFZGRkejWrRvnvJitoceNG6fxO5QhpWBCcSYPAVC7I2JeXh7vfQUFBcHKyorZRSE7O5vROebPny95HGKT2fPz8/HixQtRlc3Fyi8xOrUYGSAmweCfQHHsIubn5wd/f3/I5XL4+vrC39+fOSpWrAgzMzOEhIRwrhO6bfbMmTN5fZ5paWlo3Lgx57xQPVABMzMzZu1v3LgxoqOjAWiOa1y9ehWNGjXCmTNntK41YtGwYUNmJyhNkLqGbdmyhbdq/vfv37Fu3TpJOqkCYsnRQt/TAwcOwMLCAvPmzYOxsTGioqKYLdSVbXZl9OjRA0FBQUhLS2M9m2PHjsHb25tpJ8X/6efnBzMzM5iamsLHx4c155UTghs0aIDr168jLy9PVGxOiP/LwMCARRStXbs2IiMjmc8pKSkwNTXljF2b/mVlZcWq3lm6dGnW+/j48WPefoX0DUjXGfr164fg4GD8+PGD+Y1evHgBf39/3gJkzZs3x+TJkwH832+al5eHkJAQdOjQQWtVdnXkSSnzRQykyCN1iSenT59mfqunT5+qTYbngxQdxsjIiHcXiitXrqhd74Qm169cuRL6+voYPHgwtm7diq1bt2LQoEEwMDDgJW0C0nwZERERkMvlcHNzY71fqhDqd5aSTCVUviigTaarQqwOJlauZ2ZmYvLkyahZsybKli0LV1dX1iEVUuaAqakpOnfuzLJzLly4gLJly/6r/Vm/8M9Al37hF/7HMHXqVOrVqxe9fPmS8vPzKTY2lh49ekRbt26lI0eOcNr379+fjIyMaPLkyZSdnU1du3YlBwcHiomJoS5dujDtHj16RJ07d6aGDRuSt7e34PF8+PCB7OzsiIjor7/+opCQEPLw8KC+fftSTEwMq+39+/fpzZs3REQEgB49ekRZWVmsNhUrViQiol69erHODxo0iPVZJpNRXl4e83n9+vVkampKRES5ubm0efNmsra2Zl0zcuRI5tqvX7+SoaEhASCZTEY5OTn05csXVntzc3NmrPn5+UREdOLECWrVqhURETk5OdGHDx9Y1+zfv5//QRHRpUuXaOnSpUxfUiDmPhV48+YNOTk5ERHRkSNHqFOnTtS0aVNycXGh6tWrM+3EzgG5XM76v76+vuT7UodHjx5RvXr1OOctLCwoIyODdS4oKIiuX79Obm5ugvpW/Paq+PjxI5mYmHDOv3//nkqVKsU5n5WVxepHym8k5j0SAzHvHADy8PBgzgMgf39/1mfV904MAgMDCQDzWfEeyWQyTt8pKSmSvqNq1aq0dOlS5veTyWQUERFBTZs2pR49enDaa5sDZ86ckTQOW1tbun//Ptnb29OxY8do1apVRESUnZ1NOjo6rLYVKlSg1atXU8uWLen48eM0a9YsIiJ69eoVWVlZSfp+Ium/5/bt26lNmza0Y8cOSk5OJgDk6elJM2bMoE6dOnH6Vzy/zMxM8vf3Z8kFIqJPnz5xxrZ3717auXMnJScnk76+Pnl4eFCfPn0oKChI8v0SEXXp0oUiIiJo7969JJPJKD8/ny5cuEC//fYb9ezZk9NezLvq5OREly5dopIlS9KxY8do165dRESUnp5OhoaGrGueP39Ow4YNo7i4OGbO6+rqUvv27Sk6OppsbW2JiOj79+9kYGBAz549o9atW3PG16ZNG5o4cSLvvYqRGWJl9dChQ2n06NGUlpZGVapU4cjDihUrkoWFBREVzAMzMzMyMjJi/q6vr081atSgAQMGcPp+/Pgx7/pSvnx5evLkCetcnTp1aPTo0VS7dm26evUq7d69m4iIkpOTqXTp0rxjF7reKfDjxw/q3LkzZ97yISUlhRn7H3/8Qa1bt6Y5c+bQzZs3qUWLFqy2P3/+JAMDAyIq0BvatGnD3Ofr1695+09OTiY/Pz8iKnhH6tevTzt27KALFy6w9DUp+Pz5M5UsWZKIiI4dO0YdOnQgY2NjatmyJY0dO5Zp5+3tzdFrhMLFxYUAUNmyZUlXt/Ammpg1Q4w82rRpk6hxAKDevXszv+e3b99o8ODBnPciNjaW9XnKlClkbGxMRAXzLDIyknlvFFi8eDHzf7FzRqzOow1+fn50+vRpqlKlCut8ly5dCABHL1dGdnY2mZmZERFRfHw8tW/fnuRyOdWoUYNevHjBaX/48GHq1q0bZWZmkrm5OWsdlslkjLwWY/NIXfPu3r1LAQEBRES0Z88e8vX1pQsXLlB8fDwNHjyYpk6dKnoOAKDAwEDmPcjJyaHWrVtzZO/NmzfVPlNNEGvHEBGFhYUx82jatGnUrFkz+v3330lfX582b97Muk6K/BozZgzFxMTQ8uXLefUqsZDyDMXIDAXE6GujRo2ibt26kampKZUpU4YaNGhAREQJCQnk6+vLtKtXrx49evSI+VyrVi169uyZ2nuV8nv26dOHOnXqRPb29iSTyahx48ZERHTlyhUqX7487/cItWOIhPsPiIjGjx9P4eHhNG/ePM75iIgIatKkCRERXbhwgZYvX878fevWrZSXl0ePHz8mCwsLioiIoKioKNZ6KuQ3lWozKBAWFkabN2+mli1bko+Pj9r5+/PnTzIyMqLbt2+Tj4+P1n7F2GBi0bFjR8rIyKDQ0FD6888/qUGDBpSVlUXNmzent2/f0tmzZ8nBwUFS3woI0TOlro/r1q2jIUOGkLW1NdnZ2XHWgKlTp1L9+vVFj1nKOlCnTh26ffs2zZs3j3x9fSk+Pp4qV65Mly5dYr3XCoj1xQnRv5YuXSr4HlV9CMUFU1NT+vjxIzk7O1N8fDyNHj2aiIgMDQ0pJyeHaVe6dGm6e/cueXp68vZz584dXp19ypQpZGZmRosWLaIJEyYQEZGDgwNNnz6ddY9C5a4CYn2I58+fp3PnzjH6txDUq1ePjh8/Tr6+vhQSEkJhYWF06tQpOn78OAUGBgruRxWpqaka/+7s7Mz8X4jtWFjZKNSmkrKGielfLKZOnUpdu3al8PBwCgwMpJo1axJRgX6qLA8UEOoPSE9PZ2x4IqKzZ89S8+bNmc/VqlWjtLQ0Tv/F5ecjEqbDKiDmuSh0CtX5rE6XiouLY2yc/Px8OnnyJN29e5fVRqFLKlCiRAlKS0sjJycnOnbsGEVGRjLfwedvFGqHi/V7iG1vYWFBFy9epOPHj1NiYiIZGRlRpUqVqG7duoXqvzC+NSIiT09PWrBgAc2dO5cOHz5MGzdu5G2n7b0rWbIkjR8/Xu39t2nThjZs2MA537ZtWypZsiRt2rSJedc/f/5M/fv3pzp16tCAAQOY+Ve+fHlau3YtnThxgipWrEh6enqsvpTtYyE6tarelpeXx3rOurq6zDteFFDoJ0+ePKGnT59SvXr1yMjIiGQyGU2ZMoXTPi8vj+7cucPoZUZGRrRq1Spq1aoV9e/fn8aNG0dERF++fCFTU1POb56fn8/YzMrQ19en7OxsIiqw1xT2c8mSJTnyl6hgjpUrV47u3btH7u7ugu5VrPwSqlMTiZMB5cqVowMHDlC7du0oLi6OwsPDiYjo3bt3zHNp37691vvR1dUlOzs7atKkCbVu3Zpyc3NpyZIlvL7pgQMHqh2/sm2ijO/fv0uOiwUHBxMR0e3btykoKIg1Z/X19cnFxYU6dOjAua5MmTKC+leem9++fWN82KVLl6bjx49z2gvVAxWoWrUqRUZGUuPGjens2bOMTZ2SksJaO5VhaWlJX758oUaNGrHOFzb2REQ0YsQIGjNmDL1584Z8fX05ckbZBpMSw+vTpw81a9aMY1t//fqV0T3F6qQKpKWlUbly5YiI6MCBA9SxY0caOHAg1a5dm9GDlSH0PW3bti0dPnyYZs6cSSYmJjR16lSqXLkyHT58mLHXVREfH09xcXEcXd7d3Z3j5xPr/1TMeW04ffo06ejo0OvXr+n06dNERNS5c2daunSp2rlFJMz/ZWtrSykpKeTk5EQ/fvygmzdv0owZM5g+vn79ypk7RNr1r/Lly9OdO3fIy8uLiIijHz58+JBcXFx4xy3UPylFZ1i0aBF17NiRSpUqRTk5OVS/fn168+YN1axZk2bPns0Zy4IFCygwMJCuX79OP378oHHjxtG9e/fo06dPdOHCBUZuExW8t/v37ycLCwuqWrUqERHduHGDMjIyeOWz2PkiBlLkUdu2balv3760aNEiqlatGhERXbt2jX777Tdmrl69epXla9AGKTpMYGAgDRo0iNavX0+VK1cmooLnOGTIEEY/V8Xo0aNp2LBh9O3bNwJAV69epZ07d9LcuXNp/fr1TLshQ4aQnZ0dLVq0iPbs2UNERF5eXrR7925q27Ytb99CY0mqv7Genh5ZW1tTWFgY67yyX0io31lsDIdIuHxRQJtM79+/P+u8WB1MrFzv378/nT17lnr06MH4fIsCUubAypUrOZyKWrVq0a1bt2jUqFFFMq5f+O/FL4LuL/zPQYxCnZubSzt27KCgoCDq1q0bZWdnU2ZmJm9w7tmzZ7R582YaMmQI5eTkUGhoKHXr1k3rAiAmsCk0cCqWwOrs7Ezr1q1jPtvZ2dG2bdtYbWQyGWNQiQ3eiFHs+BazR48e0fjx4xlCwsyZM0Xdn9T7VECo40PMHCisA1Eo7Ozs6MmTJxzD5fz58xxSikI5vH//Pq8BrlD0FEqjTCZjBRWJ/s95VqtWLc5YqlatSn/++SeNGDGCuZ6owJhXOLil/kZi3iMxEENWUBi6xQGxASKhTiZV8DmLiYj8/f3pxo0bzGepc0AoxBAn5s+fT+3ataOoqCjq1asXVapUiYiIDh06xBjlUlCY37NTp04sMq4ycnJyyMjISJKBlJ+fT6GhobR3717y8PBgnsWtW7do7969NHDgQFq1ahV9/PiREhISqF27dqL6nzNnDg0bNoycnJwoLy+PvL29KS8vj7p27UqTJ09mtRX7rgoNEKelpVGNGjVIT0+PZs2axThk7t+/T6tWraIaNWrQrVu3KCEhgR48eEARERHk5OREJ0+eZIw1BU6cOMEEUFQhVGZIkdUK8o2ynFKVGYrf38XFhX777TfepAY+WFhY0LNnzzgy/cmTJ5w+li9fTkOHDqV9+/bRqlWryNHRkYiIjh49Ss2aNePtX2ywr1evXrR79261RGhliDHApRDvhSQDqRJ+Hj58SJmZmUREGom1Qgnm8+fPp3HjxtGcOXN411HVwBBRwZwbMWIEbdmyhYgKiMZubm40YsQIcnR01BjkUwexa4YYeaSJaCqkfffu3bVeI8URJ3bOCNV5hGLIkCGUkJDA+7fQ0FACwJKZyhASNFPGmDFjqG/fvjRnzhzGKcsHMTaP1DVPiFNQ7ByYNm0a5z6KElJIaMpjrlKlCr148YIePnxIzs7OnACUFPl1/vx5On36NB09epQqVKjAmY+qBD1tEPsMpRKRxOhrQ4cOpYCAAEpLS6MmTZow66ibmxsrOCE2uUvK7zl9+nTy8fGhtLQ0CgkJYeawjo6OWpkrxI5RRrdu3bT6D4iIHjx4wDh3ldG3b1+Kjo5mPr98+ZJFCDh58iR16NCBCcT06tWLJcuF/qZSbQYFdu3aRXv27OEk2qhCT0+PnJ2dBQeLpRDGxaB///706dMnatu2LR08eJCmTp1Kr169KhJyLpEwPVPK+khEFBkZSbNnz6aIiAhRY/r27Rv9+PGDdU55jZG6DpQtW5Z3fdu3bx917NiRdU5scFuI/rVkyRJB4+TzIRQXmjRpQv379yd/f39KTk5m3o979+6xdPgWLVrQ1KlTqWXLlpyExZycHJoxYwa1bNmS079MJqPw8HAKDw+nr1+/EhExiT7KECp3iUhSEryTk5Naoo06LF++nL59+0ZERJMmTSI9PT26ePEidejQgWNvioGLi4tG36uyvBBiOxZWNgq1qaQmSIm12YSiY8eOVKdOHXr9+jXjVyEqkMmqvgUx/gCpJI7i8vMRiUuoEvNcxMpSKSSk9u3bU9euXcnd3Z0+fvzIkJ1v3brFmddEwuxwsX4PMe0vXbpEHz9+pFatWpFMJqOmTZvS69evadq0aZSdnU3BwcG0bNkyll+xOBNI1UFHR4eCg4PVEhO0vXcvX77klcUKmJqa8ibrRUVF0fHjx1lrsoWFBU2fPp2aNm1KYWFhNHXqVGratCn9+PGDSYpQJXKrykApBROkJg8JxcePH6lTp050+vRpkslk9PjxY3Jzc6N+/fpRyZIlaeHChaz2fORHogJbPikpiYgKCr1ERETQ7du3ObZxTk4OVatWjRYuXMhKzBCbzC6Xy5n3TShBV6z8EqpTE4mTAUISDFSJXXzIz8+nx48f0/r16yksLIzOnTtHly5dosaNGzOFYR48eEBDhw6lw4cP06FDhyglJYXOnTtHvXv3ZhKqZDIZi9RJVLBGJyQkqE2U1AaF7evi4kKdO3fm6FTacP/+fUpNTeXoyYp1IT8/n2bPnk2rV6+mt2/fMj67KVOmkIuLC/Xr1491nVA9UIHo6Gjq1q0bHThwgCZNmsT8hvv27VMbW+nWrRvp6enRjh07yNbWtsgISETEkJn79u3LnOOzwaTG8NQVe/n777/JwsKiULFQseRoMe9p3bp11cokPmRlZfH66z59+sRa78QmKBNx/T2aoKqrHz16lJP4qgoh/q8WLVrQ+PHjaf78+XTgwAEyNjZmJdzcuXOHypYty+lbm/61bds2jTGS1NRUjq4ktG8iaYRFogI5efz4cbpw4QIlJiZSZmYmVa5cWS3x08fHh5KTk2n58uVkZmZGmZmZ1L59exo2bBjZ29uzxhEREUGdOnWi1atXM/MuLy+Phg4dyvEJS/GXi4EUebRmzRoKDw+nLl26UG5uLhEVJHX06tWLsdXLly/PIrxqgxQdZuPGjdSrVy+qWrUqY1fk5uZSUFCQ2u8Wk1zfrl07UbFWobEk1TU4NDRUa9/FVbCKSJx8IdIu01UhVgcTK9ePHj1Kf/75J9WuXVvUfQiB2DmgIOf++PGDUlJSmKI8ZmZmankYv/C/g18E3V/4n4RQhVpXV5cGDx5MDx48ICIiY2NjtYFwR0dHmjRpEk2aNIlOnTpFGzdupNq1azPZg/379+fNEhIa2CxsBQdNeP78uaj2Yh2OUhQ7ogKFYtq0abRlyxYKCgoSXHFHHcTepwJCHR9i5kBRORC1YcCAARQWFkYbN24kmUxGr169okuXLtFvv/3GyU5XVGvkI0ArG+BSKz/OmTOHmjdvTvfv36fc3FyKiYmh+/fv08WLF+ns2bNEJP03klIJSxvEvnNSKhUJhdgA0Z07dwS146s8tW3bNlq9ejWlpKTQpUuXqEyZMhQdHU2urq4MwUPIHEhNTWUUZm1QzeoUQ5xo0KABffjwgb58+UIlSpRgzg8cOFAjcUkbCvN7jhw5kreaU1ZWFrVq1YpOnz4tmuRGRBQTE0MnTpygQ4cOMUQFBQ4dOkR9+vShsmXL0ubNm3kr3mqDvr4+rVu3jqZOnUpJSUlMEITPAS32XRUaIJ4+fTp5enpSXFwcy1gNDg6m8PBwatasGbVu3ZquX7/OGLZjxoyhkSNH0u3bt5k15cKFC7R582a1lXWEygwpslqM7Bg3bhzLWfbixQvav38/eXt7U9OmTTnt27ZtS6NGjaL9+/czjq4nT57QmDFjOIRCZ2dn3mpkmogMYoN9eXl5tGDBAoqLi9NawUWMAS6FeK8tGSg1NVUQ4YcPQgnmYismERFNmDCBEhMT6cyZMyzidOPGjWn69OmSCLpi1wwh8khqsF/KOyTFESd2zgjVeYRCm5OmU6dOvJndROKrlb18+ZJGjhwpaI0TavNIXfOEOAXFzgGxzkCxEGvH/Pz5k8qXL09HjhxhkkaMjY2ZigyqkCK/LC0tRSfVaILYZyiViCSW6Fq1alWmIkheXh4lJSVRrVq1WPqbNjx79owGDx5M8fHxRCSdVKhKHMzIyNAoC4XYMQo0atSIYmNjydLSkuU/+PLlCwUHB9OpU6eYtjY2NnT79m2OrnX79m0WqVfV+Xz58mWKiopi/V2RcEIk/jd9/PgxHTx4kJ4/f04ymYxcXV0pODhYa5VxfX19Xv2AD5MmTaKJEyfStm3bmEoifChOv4cyxo0bR58+faLAwEBycXGhM2fOqN1hQCyE6JlS/QHp6ekUEhIiqG1WVhZFRETQnj176OPHj5y/K691YteB3NxcevjwIVMxTQEF4fnhw4ec94xI/bp0/fp1Rj4oIET/+qfmixisWLGCJk+eTGlpafTHH38w6+GNGzdYAbeJEyfSnj17yNPTk4YPH848x0ePHtHy5cspNzeXlQSXk5NDx48fp4YNGzIkMMW/X758oTNnzlBQUBAr6F8UclcdoqOjafz48bRmzRq1laxUofzuy+VySXouH27dusX6/PPnT7p16xYtXryYU9VKjO147NgxMjU1pTp16hBRwW+7bt068vb2phUrVqh9jkJtKqlrmFibTQzs7OyYanJfvnyhU6dOkaenJ8evJsYfIJXEURx+PgXEBraFPhcxslTq7nBLliwhFxcXSktLowULFjBEt9evX9PQoUM57YXY4WLXJDHtZ86cSQ0aNGBs76SkJBowYAD16tWLvLy8KCoqiqkCLqV/Kb41KdD23r1//54ePXpErq6uvNc/fPiQk9hHVFBh7d27d5ydkt6/f88QqC0tLenHjx/FWhiCSHrykFCEh4eTnp4epaamMnYVUUFFx9GjR3MIukRE586dozVr1tDTp09p37595OjoSNu2bSNXV1eqU6cOrVq1isaNG8drG5uYmFBERAQtX76cRdCVksw+b948Gjt2LK1atUpQbEqs/BKjU4uRAUISDMS8b0eOHKFu3bqRpaUl3bp1ixNXSExMpDZt2lB4eDj98ccfTEKZwg8JgEVCU9y7i4sLrV69WvA4+KCYvz9+/KB3795xZKxyNX2iAruyXbt2lJSUxPgEif6P3KbQkyMjI2nLli20YMECVrzLx8eHoqOjOQRdoXqgAhUrVmQI58qIiopSm4xy9+5dunXrltpdGAoDobq12LiAv78/yWQykslkrN1+iAqedUpKCuno6FB6ejqVKFGCZs6cSb/99puo2I5YcrTQ99TNzY2uXbvG0Q8yMjKocuXKvITaunXr0tatWxn9QrFT4YIFC1i+Qam7TxIVzCkFb6FChQq8/kNViE2uI+L3f82aNYvat29P9evXJ1NTU9qyZQurCvbGjRt54xra9C9tpLqBAwfSu3fveP8mRLeTqjNs3bqVOnfuTLVr12aN8cePH7Rr1y5OHC41NZWcnJxo0qRJnL5SU1NZ8mjjxo10/vx51vuuo6NDo0ePplq1arH8PoWZL0IgRR6ZmprSunXraMmSJcy74ObmxkrEELPjilTY2NjQX3/9RcnJyfTw4UMiKiAGa6vcqy65/uXLl4x+kJaWRjKZjNGbr169Sjt27CBvb28aOHAgb79CY0lS/ELFVbBKGdrkixCZzqdTidXBxMr1EiVKaPQ3SoWUOZCTk0PDhw/nLcpTunRp0Un3v/D/GfALv/A/BldXV3z48IFzPj09Ha6urpzz9evXx/79+yV9V0ZGBlasWIEqVapAJpOhbNmyvO327t2LxYsXIy0tjTm3efNmHDhwQNT3JSUlsT5v3rwZR44cYT6PHTsWFhYWqFmzJp4/fy643/T0dCxbtkzUWD5+/Ki1TU5ODn78+ME5n5GRgXHjxsHIyAg1a9ZEQkKCqO8Wg5ycHK1tfvz4gaioKIwcORI3b95kzi9evBjr1q3TeK3QOVBcyM/PR2RkJExMTCCTySCTyWBoaIjJkycXuu/p06cjMzNT1DVPnjxB//79Ua1aNXh5eaFbt264c+dOoccCFN17VFhkZGRg7969iIqKwsKFC/HHH3/g8+fPRdL30aNHce7cOebz8uXLUalSJYSGhuLTp0/MeZlMBrlczvzmfIdcLuf0v3LlSlhbWyMyMhJGRkZ4+vQpAGDTpk1o0KABp72mOdCgQQPWYW5uDmNjY/j7+8Pf3x8mJiYwNzdHw4YNBd17eno67/ns7GxkZWUxn58/f44lS5bg2LFjgvoVgidPnmDSpEno0qUL3r59CwD466+/cPfuXd72bm5umDp1KutcZmYm6tSpgzp16rDOp6amsubslStXEBYWhjVr1nD69fX1xYYNG9SOc/369ZDL5WjWrBm+f/8u+P4UmDFjButZKpCdnY0ZM2aI6kvbupGbm4tbt26x5i0AODg4sOa4Ks6ePQuZTMZ5DrGxsahduzZKliyJkiVLonbt2lrf/X+DzGjSpAlWrVoFoOCZlSpVCqVLl4ahoSFWrlzJaZ+RkYEaNWpAV1cXLi4ucHFxgY6ODho2bMh5R27cuMGSrwcOHEDbtm0xYcIEtfND7Hqn+p4rH6rv9osXL9CyZUtUrFgR69evZ86PGjUKI0aM4PSdm5vLmR8pKSnMO6iKxMRE+Pj4wNzcHNOnT2fODx8+HKGhoXj+/LmgQx2uXbuG2NhYfP36lTl35MgRnD9/nvl85swZjQcfnJ2dcenSJQCAqakpI3cfP34MMzMzteMRivT0dMTFxWHbtm3YsmUL6xCCR48eYdy4cbCzs2POrVixAoGBgQgJCcGJEydY7d+/f8+rTysjLS2N9d4VFcTOmX8St2/f5l13FXj9+jVu3ryJvLw85tyVK1fw4MEDTtt27dph9+7dWr9TqM3z+fNnwYcqTp8+DUtLS8jlcvTp04c5P2HCBLRr107j+D5//oz9+/fz3mNx4+fPn9iyZQvevHkjqL2DgwPu378vuP9/y1zU9Gz59KTk5GRERUVh2LBhGD58OBYtWsTIJKFQp6+FhYUxsj83Nxe1a9eGTCaDiYkJTp8+Lbh/be+SEMybNw+7du1iPoeEhEAul8PR0RGJiYlqrxNqx8hkMt7f+u3bt9DV1WWdmzFjBiwtLTFv3jwkJCQgISEBc+fOhaWlJWbOnMm0a9SoEcaPHw8ASEhIgFwux6tXr5i/x8fH89qZ06ZNY8kVBTIyMtClSxcAwJw5c6Crqwu5XA47OzvY2tpCLpdDT08PUVFRap8HACxcuBBDhw5Ffn6+xnYA4OfnB1NTUxgYGMDDw4OxCRTHP4V27dqxDgMDAwQEBHDOFxZS9UxtsrFv376M7qgNQ4cOhZeXF/bt2wcjIyNs3LgRs2bNQunSpbF9+3at17do0YI1zxRISkpCmTJlIJfLIZfL0a5dO7x58wb16tVDyZIlERERwbvOf/36FdnZ2axzt27dQqtWrdS+10L0L6DgufHN9by8vCKzxYsDz549Q1BQEMt2l8vlCAoK4sjf6OhoNGrUSG1fgYGBWL58OfO5MHL33r17OHr0KA4ePMg6lGFpaQl9fX3I5XKYmpqiRIkSrEMZivvSdOjo6Ah5ZKJw5MgR1K9fn3NeqO3o4+ODP//8EwBw584dGBgYYMKECahRowZ69+6t9nsL40MUguLqPyQkhLHhs7Oz4e7uDj09Pejq6mLfvn2S+33//j3q1q0LmUwGMzMzxMbGsv7eqFEjTJw4kffa4rLZxeiw2p5LYmIiI38SExM1Hv8JiLXDixp2dna4du0a83nixImoXbs283nPnj3w8vIqsu/7/v070tLS8OLFC9ZRWGh773r37s3x9ymQn5+P2rVr88qNrl27wtXVFbGxsYydHBsbCzc3N3Tv3h0AsHPnTlSpUkXSuMPDwwUfxQ1bW1vcvn0bANv38fTpU5iYmHDaK/SX/v37w8DAgGm/bNkyNG/eHABgb2+Px48fq/3Ox48fw97evtBjV17zDA0NNa55CoiRX2J06sJAoWeKsW2VkZ6eDlNTU41rwp49eyCTydC3b1/O3xo0aMCxk4sKycnJqFOnDke/UBcDadWqFdq2bYv379/D1NQU9+/fx7lz5xAQEMCKRZYtW5bxeynP2wcPHsDS0rLQ4xYTF1Cgbt26OH78eKG/u6ihKc46ffp0TJ8+HTKZDL/99hvzefr06ZgzZw527NgBQ0ND5lnI5XLR/pP09HQMGzYMbdq0wdGjR5nzU6dORWRkJO81Qt5Tdfb9mzdvoK+vz9tvUlISSpUqhWbNmkFfXx8dO3aEl5cXbG1t8eTJE8H3dP/+fYwZM4Z17u3bt2jYsCFkMhkjg2QyGRo1aoR3796x2srlctY5U1NTPHv2TON35ufnY8+ePRgyZAg6dOig0U7OyMhAbm4up4+PHz/yxh4K40MENPuDxPQt9r1TNx8/fPjAOx4x7S0tLXnXhQMHDgiWMeribGIhRR4p8PjxYxw7doyx94tiPVOnr4wePRoTJ07Exo0bOTyU79+/4+HDh/j586ek73z9+jWGDx8OIyMj5lydOnWwdetW5u9mZmaoWbMmrK2tNcZMhfoypKC4/M5C5YsQmS4lPq0KsXJ927Zt6NixI2+MuzCQMgdGjhyJKlWq4Ny5czAxMWH0hwMHDsDPz69Ix/cL/334RdD9hf85iFWod+/eDTc3NyxbtgwXL16U5FzLycnBwoUL1SrsqlAX2OTDly9fsGbNGlSrVo2j3Hl4eODkyZMAgIsXL8LY2Bhr1qxB69atBSm8J06cQGhoKAwNDVGyZElB44mLi0NISAgMDQ2Zc2IUu/nz56NkyZLw9vYuNpJUbm4uZs6cCQcHB+jo6DAL4+TJk1nOyqKE2DlQ1Pj+/Tvu3buHK1eusBTCwkAdMTIuLo7VTizpIy8vDxs2bEDLli1RoUIF+Pj4oHXr1tiyZYsoxV7Me6QJGzduxJ49ezjn9+zZg82bN7PObdu2DRYWFhwyrKWlJYsIIBVCA0RSiWheXl5MQoKy0ykpKQlWVlac9kLnwKJFi9C6dWuW0fDp0ye0bdsWCxcu5PQrhjihSnC0tbXVSHAUizNnzsDIyAiNGzeGvr4+80zmzp2LDh068F7z5MkT2NvbY8mSJQAK5HTNmjVRt25dDqFZjHJvaGioMbDw/PlzyOVyycaPWGcDH9StG0IDxPr6+hqJe2lpadDT0xN4R+IgRGZcv34d27Ztw7Zt23Djxg217ZKTk7FmzRrMmjULM2bMYB3KsLKyYoje69atQ8WKFZGXl4c9e/agfPnyvH3n5+cjLi4OCxYswLJly9QmsVStWpVxmj99+hSGhoYIDQ1FuXLlEBYWpvVe/5MoSuK9umSgwqCoHF8AWMkQynL39u3bMDc3L1Tfhw4dgpmZGWQyGSwsLGBpackc6oJIAJCVlYWNGzeiTp060NHRQfXq1bFgwQIAQExMDIyNjTFs2DB0794d+vr6mDNnDnPtmzdveOVFXl4eZsyYAXNzcyZQYmFhgZkzZ/ISajIzMzFlyhRUqFABJiYmMDU1ha+vr8ZEguJO1igMbt++DZlMJqittqDZ+vXr4ezsjGnTpmHfvn1qyTNCbR4hpBnFwQehTkEppI+9e/ciJCQE1atXL3Iyn5GRkeCExdmzZ6NXr16CHLz/prloZGTEImsBwLdv3zBs2DAYGBiwzkshaYrR1xwdHRlyxv79++Hg4IBHjx5h8uTJqFWrluB7Ug7ISCWXu7i44MKFCwAKiK2WlpaIi4tDv3790KRJE8FjUYXCPyCTyXD69GmWz+DmzZuYM2cOypQpw7omPz8fixcvhqOjI2M3ODo6Ijo6mmX3KPRRNzc3GBkZcQLeQ4YMQc+ePTljKl26NGrWrMki+p0+fRpOTk6oVq0aTp06BblcjmnTprHe448fP2LKlCnQ0dHB2bNn1d5zcHAwLCws4OrqilatWmkM3ik77PkOVZw9e1bjIRW9e/cWdBQ11OmZYmXjnDlzYG1tjV69emHhwoWIiYlhHcpwcnJi9GwzMzOGwLJ161aG2KIJynqJMlq0aIHAwEAcPnwYXbt2hUwmQ/ny5REVFcUh4AIF/qAaNWowMiU8PBxZWVno0aMH9PX10blzZ1y+fFnreNTpX7GxsXB3d+fVDzIzM+Hh4YFDhw5p7b+oIDSpVhkfP37ElStXcOXKFbXJ7tWqVdN4H4cPH0a1atWYz1Lk7tOnT1GxYkVOwi+fHrB582aNhzIOHDig9oiIiICRkRFnXSoKPH78GMbGxpKvNzExQUpKCoCChAeF/X/jxg3Y2toWxRBZePv2LZKSkv5jpE5l8tzvv/+OcuXKISsrCytXrmQFEVXljqZDGWJJHHwoKj8fIFyH1fZclHVuTcnymnwqT548wfDhwxEYGIjAwECMGDFCLYGnqIpx8CE/Px/Xrl3D3r17sW/fPty4cUOQH1aTn8TAwACpqanM59q1a7MC6ikpKTA1NeXtNzs7GwcPHkRUVBRiYmJw7Ngx3jkEFCSWiiHnFTWePHkCCwsLBAQEYPfu3bh9+zZu376NXbt2oVq1arCwsOAlkn79+hX9+/dnyJ9yuRz6+voYMGAAMjMz0a5dO5w7dw63bt3i6FlCkosaNGgACwsL3oIJmhKsiwOmpqZITk5m/q/QMa5du8Ybd/Lz82MSi5Xb37x5k5HBhoaGGpMS79+/z4pRAdKS2Tdt2iR4zVMHTfJLjE4tRgYUR+KF6jutitTUVMHvXFH61mrVqoV69erhr7/+wq1bt5h3UHGowsrKilljzc3N8fDhQwDAyZMnWWueoaEh81yV5+G9e/d4ieVi9UDVuIC5ublW0s+ePXvg7e2NTZs24fr164XWGw4ePMj4TVX9S5qStQDxcdbNmzerJfLWqFEDjRs3ZkhfY8eO5fjU+XzrRQnl91RxzzKZDFu3bmU9h9jYWAwbNgweHh5q+8rIyEBkZCRCQkLQvHlzTJo0CS9evMCAAQM0jiEzMxPr169HzZo1IZPJUKFCBdbfO3XqhKpVq7J8hvfu3UPVqlWZJFwFZDIZWrRowcgSXV1dNG3aVKOMGTlyJAwMDNCsWTP06tWryO3kwhALtSVsC+1bLNlOJpNxyM+K8fD519W1f/78Occ2CQ8Ph5WVFRYtWoRz587h3LlzWLhwIaytrdUmz6jG2WrVqiUpAV4VUuTRhw8f0KhRI0bfUsiAPn36YPTo0ZLHAoAp+mRiYoLKlSujcuXKMDU1hYWFBapXr87EN+7du4esrCz07dsXOjo6LFk0fPhwzJ07l9Xvp0+f0KVLF1hZWcHe3h4xMTHIy8vDlClTYGRkhOrVq7N8nZaWlswaERMTw9jScXFxWouUKFCU611x+p3FyBdAs0zngxQdTAz8/PxgZmYGU1NT+Pj4FFksQcocKO6iPL/w341fBN1f+J+BVIVanVNN1cHz7ds3jB8/HlWqVEHNmjUZktvGjRvh4OAAJycnzJs3j9O/1Ao+Z8+eRc+ePWFiYgJ3d3dERETg6tWrrDZGRkYMmWvcuHHo0aMHAODu3buwtrbm7Tc1NRUzZsyAi4sL5HI5unbtiqNHj2oktzx//hxTp05FmTJlYG5ujs6dO7MIjWIUO5lMBmNjY7Rp00a000koZsyYATc3N2zfvp1FjNm1axdq1KjBaS/U8SFlDuTm5iIqKgrVqlWDra2toAzswuD58+e4d+8eQ4aJiYlhFCgxjnVAeOVHbcQP5XcpPz8fLVu2hEwmg5+fH7p06YLOnTszQaK2bdvy3pfU90gI3N3dcerUKc75M2fOsGTGjRs3oKuri169euH27dv49u0bcnJycOPGDfTo0QN6enq8DiExKO4AkTqnU3JyMsehCQifAw4ODrzVZpOSkngrGYghTkghOIpBjRo1sGjRIgDsZ3LlyhU4OjqqvS4xMRElS5ZETEwMatSogfr16/NWGxaj3JcoUULjfL5z506hsvfVOQ9Onjypds0AhK0bQgPEZcqU4RC8lXH06FEOuUVKdq9YmSEmO33t2rXQ0dGBra0tKlWqBD8/P+ZQNQSV1+mQkBCGoJKamsrK1L148SIOHz7Munbz5s0oU6YMbGxsMGDAAHz79o31d3NzcyaoN2/ePDRt2hQAcP78eZQuXZr3uRRlsE/V+SXGAJdCvBczD3JycnDlyhUcPnxYq7MZ0EwwX79+faEqJtWtWxdLly4FwK5kMHz4cAQFBfFeIxTu7u4ICwsTnDV86dIl9OvXD+bm5vDx8YGOjg6HAO7t7Y3ff/+d+XzhwgXY2NhgypQpANQTdMePHw8bGxusXLmSeR4rVqyAjY0Np2LW9+/fUaVKFRgYGCA4OBjjx49HREQE2rRpA319fdSoUYOjlwqZM4XReQoLTU5ksUEzbdXxxdo8ylWeN2/eDDs7O4wfP565Zvz48bC3t+cNPIpxCgolfSgQExMDU1NTDB8+HPr6+hg0aBAaN24MCwsLtVXWxEDMLinBwcEwMzODvb291mCGUPnl7+/POGYV64O6Qyp2796NkiVLonnz5njz5g1u3boFLy8veHp6suxGqSRNMfqagYEBI6MHDBjAJIo8e/ZMlGNS+V0SQi7nI2UYGhoyQeWRI0di4MCBAAqIHap61M+fPznr65s3bzB9+nSMHTuWFXxVHg/f+2lsbMzaAUC1kvOXL1/w5csXtfd+//59REdHY9euXZzEhjVr1uDWrVucaz59+oSQkBCYmZlh7dq1+O2336Cnp4eJEyfi58+f6NSpE3P/fBgwYACvE16B4iS5qpNxmhIG/i0Qo2eKlY2KHRT4DlXbwcTEhNEzHR0dceXKFQAF7x0foUAV6gi6NjY2zHzLyMhg1ht16Ny5M/z8/LBs2TI0bNgQcrkcVatWxbBhwzQm5glN8GvSpInGqqEbNmxg9OB/AlKrrmqDpaWlxmTNFy9esGSYFLkrtJpcUeDhw4cIDg6Gjo4OevbsWShioWpSRkZGBh48eIDOnTujUqVKrLZibAZFwBcoIBUq2qSkpLBsNVWItamuX7+OChUq8K4ffPKuuAiayutjjx49EBERAaBgbinLDFXZo9ixS9lONjExERywVofi9POJ0WG1PZfnz58zJFYpu7UcO3YM+vr6CAgIYCqDBQQEwMDAAPHx8Zz2YotxCLXDT506BVdXVw45v2zZsmoTY4T4SZydnZnrv3//DiMjI9YuLHfu3OH1fx88eBA2Njacd6J06dKs8SjsZ7HkPLEQ8t5du3YNFSpU4OiEFSpU4MRsgIK17ezZs/j06RO+fv3K2MnKhTV69+7N6IdS9C6xBROAAn/JggUL0Lx5c1SpUqXIbJPmzZszO/opfB95eXkICQnhLYBgZGTE+MBVK+4qkjrKly+Pbdu2qf3OrVu3wtPTk3Xun0hmFyu/xPy2YmSAWD1TCGxsbHD9+nW1f7969apaH3JxkcoAwNjYWNTuPJaWloz8cHNzY2I/T548Ya3xlStXZuaY8jycMWMGb9VssXqgFNKP0Hi1UKgmmojZjVFsnFUBvmrnp06dQufOnVG1alXI5XL4+PiwfOrqfOsKiCVHa3tPVZ+t8qGvrw8PDw+Or14bNPkGz58/jz59+sDExARyuRxjxozhndPm5ua8a8qVK1dgYWHBOiclObVEiRLMHC5qFJZYqOn5ielb6HunmG9yuRy+vr6stbBixYowMzNDSEgI016hx8nlcgwaNIhV9XXkyJGoXr06J1EyLy8P8+fPh4ODAzO/HBwcMH/+fLVJSUWVAC/1uSijR48eCAoKQlpaGktGHjt2DN7e3pLHAgBLlixB+/btWUn3GRkZ6NixI6Kjo5GVlYW2bduiadOmoqqVDhw4EM7OzhgzZgx8fHwgl8vRvHlztGzZkiFUKkM5Jt+6dWuG4/HixQvemDlQdLuI8aE4C1aJkS/KELqDhVgdTKxcF1sYQCikzIHiLMrzC//9+EXQ/YX/GUhVqIU618aNGwcLCwt06NAB9vb20NXVxYABA+Dr64udO3eqVabEBDZfv36NuXPnoly5cihVqhSGDx8OXV1dxmGsChsbG2bbJT8/PyZo8uTJE5Zz9cePH9izZw+aNm0KIyMjtGvXDnv37tXY9/fv37Fz504EBgbC0NAQrVq1go6ODu82n2IUO76svKIO9ondmkao40PKHJgyZQrs7e2xcOFCGBoaYtasWejXrx+srKwKRRDZsGEDQypUYMCAAYyD0MvLC6mpqXBxcWG2PxYT7AOEEyOViR+nT5+GkZERfv/9d96tvzdu3AgzMzNeQuzJkydhZmbGuy13cVXCAgoCWwrlSxkpKSks5at3797o2LGj2n46dOjA2l5FCoQGiJKTk9GlSxfe7TwzMjIQGhrKG2z18vJiKlcrvxtLly7ldX4InQOmpqa8hsepU6d4K2WIIU4IJThKhYmJCeOwU34mKSkpWqv8XLx4ESYmJmjUqBFvNSlF/0KV+xYtWmDw4MFqv2/QoEGCqmGpQpFtKpfLmf8rDkW1y6FDh7KuEbtuCA0Qh4WFwdfXl5co/PbtW1SsWJFjrEnZYkSszBCTPers7MybkMMHX19fxMTEIDU1Febm5rh48SKAgiCtMum+WbNmrD7v3LkDPT099O/fH4sWLYKdnR2mTZvG6tvMzIypTtK4cWNER0cD0Gw4Cl3vjIyMWL+R6hbIfCRNMQa4FOK90GSgo0eP8gb6NDmyNTm+iKhQFZPOnTsHU1NTDB48GIaGhggLC0OTJk1gYmKiMeghBMbGxoK2p1+4cCG8vb3h6OiI3377jQng8L3PykEyBZKSkmBra4vx48erJeja29vzEqAPHDgABwcH1rno6GjY2toyuqMyHjx4AFtbW4bUrICQOVMYnaew0ORELuqgWWGCCI0aNcKOHTs453///XfeLaLFOAWFkj4U8PT0ZMaivP5OmTIFw4YNE/g01EPMLili7BKh8mv69OlM8KC4HIhAQeX5xo0bw8rKCoaGhhg8eDCHtC+VpClGX3N2dkZcXBxyc3Ph5OTEEBzu3r0rKrlI+V1StSU0Hcqwt7dndAAPDw8msfThw4cc0lrv3r1Zz+bLly9wcnKCjY0NKlasCF1dXSZ49fz5c6SkpEAmk+HatWssn8GrV694bUExlZwLgwkTJkAmk0FPT49FinFxcWE5u1WRkJAAFxeXIhtHeno61q1bh/HjxzNVQm/cuIG///6b0zYjI4N1vH//HvHx8ahevTrrHv6NEKNnipWNYuDr68vM/8DAQGZ71JiYGI2JhgpUqFCBt0KachAfYFfE44O9vT0T6Hr79i1kMhmz04gmCA08/lPbWwtFcSXVmpqaatQNr1+/zrKtpchdbdXklP0LYiqXK+Ply5fo378/9PT00KpVKyQlJQl/CGrAl7Ahk8ng7OzM2FcKiLEdW7dujaCgIMycORN6enqMrIqLi4O7u7va8YglUFasWBHt2rXD5cuXkZKSopXUWdjd0tTB3d0du3fvRmZmJmxsbJjvuH37Nu+OSkCBjli7dm2W3v7w4UPUrVsX27dvlzwWoHj9fGJ0WKHP5cePH+jTp4/WraNV4efnx8h+ZURERPD64cQW4xBihyuqTTds2BAHDhzAw4cP8eDBA/zxxx+oX78+i+igDCF+ksGDB6NmzZpISEjA6NGjYWVlxSIGb9++HVWrVmX1e+HCBejp6aFDhw64ePEi0tPTkZ6ejgsXLqB9+/ZM1dRx48Yx761Ycp5YiHnvbt26hT179mD37t28CVTKMDAwEDRn8vPz8eLFC7W+RXUQWzABALp27Qpra2sMHjwY06ZNKzLbROyW766urjh+/DgAtj24ZcsWeHl5AQAmTpwIZ2dnJuFNGa9fv4azszMnuVNKMrvYHceKU36JkQHFoWd26tQJ7du3V/v39u3bswhrynBwcCgWUhlQIOs02TWqqFOnDpOwGxoaimbNmuH8+fPo2bMnq2LpgQMHYGFhgXnz5sHY2BhRUVFM1Wu+JAqxeqAU0o+UZJDigtg4a3JysqBq56r2hhCIJUcLfU9dXFzw/v17UWNRB1Xf4Nu3bzF//nx4enrCzs4O4eHhuHbtmsa4vKmpKe/acvPmzSKpyuji4lJs62lhiYWafKti+hb63inWPZlMht9++421Fs6ZMwc7duxg6TSKqvQymQy1atViVapv2rQpBg4cyLKbVZO2tdlRChRVArwqpMgjZZ+2ajJNYf0ZDg4OvO/B3bt3mVjCjRs3YGVlJapaqZOTE6PTKXx4EyZMUDuOgIAAREREICEhAYaGhsz9Xrp0Sa1fpbhI1EDxFqwSK1+EynQFxOpgxZX8LBZS5kBxFuX5hf9+/CLo/sL/HIpSoVaGq6srQzxISkqCTCZDnz59tG4FJTSw2apVK5ibmyM0NBRHjhxhAnyalPWuXbuicuXK6NevH4yNjRlSwsGDB1mGpo2NDerWrYs1a9awsk7U9T18+HBYWVmhRo0aWL58OdOvuvZSFLvihNitaYQ6PqTMATc3NyZQYmpqyignMTExCA0NlXyP1atXx8aNG5nPR48eha6uLrZv344bN26gZs2a6Nevn+T+AenESHWVeIACQ0p1ywllzJ49m7f6jRiCgFg4OTmpJRUpK1/u7u6M45APx48f1xjEEQKhAaIBAwZg7NixavsZN24cL9Fz3bp1cHR0xK5du2BiYoKdO3ciMjKS+b8qhM6BHj16wMXFBX/88QfS0tKQlpaGffv2wdXVlXc7XjHECaEER6lwdHRkxqI8d2NjY+Hm5sa0U1f9rmTJkihfvrzaahNilHtFkCIkJARXrlxhqgNdunQJHTt2hJ6eHs6fPy/6Hjdv3oxNmzZBJpMhJiaGtU3bjh07OEFNQPy6ITRA/OnTJ7i7u8PMzAxDhgxBTEwMoqOjMWjQIJiZmcHd3Z2z7auU7F6xMkNM9qiZmZkgUiRQsH27np4e5HI5y/k3Z84cNGvWjPlsZ2fHGPVAQRCidu3azOc9e/YwAQoFGjZsiJ49e2Lr1q3Q09NjiAtnzpzhVCFWQOh6x0fMUL7nN2/eQCaTsfoWY4BLWV+EzoNy5cph6NChvAEcddDk+DIxMSlUxSSgIHGqf//+qFatGry8vNCtWzfehCexaNeuHXbv3q21nY6ODiZOnMghj/G9z05OTrwV1O7duwdbW1v07NmT1wFjYGCAR48ecc4/fPiQowvWq1cPy5cvVzvepUuXol69eqxzxZ2soQ3aqifv3r1brRO5uMhZUmweIyMjXpLVo0ePeJ+jGKegWNKHMnHRxsaGWR+Tk5N5t0AVi6KuOqM8bjFzUVE5qyi3bFZGWloa6tWrB0tLS+jp6WHGjBmc6qtSSZpi9LVp06bBwsIC5cuXh7OzM1OVdsOGDazKOtqqCXt6eha6euqwYcNQpkwZhrisqFK2c+dOjp7m7u7Oquy/fPlyODg4ICMjA0DBWtmgQQPJYxFTyVmBrVu3olatWrC3t2fekSVLljBJdqpYunQpjI2N0bVrV3h6esLb25t5n4yMjDRWME1LSysyez0xMRE2NjYoV64cdHV1Gb1h0qRJjL4hBGfOnEHlypWLZEzFBTF6phRCnFAsXryYSfo9fvw4DA0NYWBgALlcziRuSYFcLseTJ08YW8TMzAyJiYlqCZpyuZyld5mYmPAm4ahCaOBRyvbWxQmpVVe1oXr16hqTAOfMmYPq1aszn4XKXWVoqyanTFBSV8Vc3TqakZGBcePGwcjIiCHrFRVUkzISEhLw4MED/Pz5k/cehdqOL168QMuWLVGxYkXWNs2jRo3CiBEj1I5HLIHS1NRUI8m8sP0LxYoVK6CrqwtLS0tUqlSJ0ReWLl2qdq1zc3NjCkMo4/r164VO8ChOP58YHVbMczE3NxdN0DUwMFCrf/MlhQstxqE8Jm12+LBhw9CoUSPe8eXn56NRo0YYPnw4b9/a/CTv379H3bp1IZPJYGZmhtjYWFbbRo0acQiUzZs315g8NnDgQFhbW8PKyorRacSS88SiMO/d58+fsXLlSlSpUoXztypVqghKPMrLy4Oenp7GhBg+iC2YABT8rlJ8i0LAt+W7crK3MubMmQNvb29cvnwZZmZmOHfuHLZv3w4bGxuG8PDlyxdUqFCB8SFGR0cjOjoagwcPhpmZGby9vTk7VEhJZldHFnz58iXvNcUpv8TIgOLQM+/duwdTU1NUr14du3fvRmJiIm7fvo2dO3ciICAApqamvKRwoPhIZUBBMlHNmjVx+vRpfPjwQWvi0LFjx/DHH38AKCBweXp6QiaTwdramnlOCiQkJKBx48awsbGBkZERateurXb3N7F6oBTST3Hhx48faNSokSg5IzbOWpzVzsWSowvznkr136gSTA0NDdG9e3ccO3aM5afRFPNv06YN6tWrh5cvXzLn/v77b9SvXx/BwcGSxqWMzZs3o0uXLqITQoRAm/5VGN+qGN1O7Hu3efNmzs5KmtC7d29BRFtAWtJ2USXAq0KKPFJO1lWWAdeuXSu071ZdtdnTp08zOszTp09hZmYmqlqpjo4OS/cwMjJS+74pvs/S0hJyuZxVgGvChAlqkyOLc70rzhiIWPkiVqaL1cGKe0dhoZAyB4qzKM8v/PfjF0H3F34BwhVqTaQbZbIcUKBcCyFXCA1s6ujoIDw8nGMgaVLW09PTMWzYMLRp0wZHjx5lzk+dOhWRkZHM5xIlSqBevXpYu3YtS3FU17eCyKHq4FDX/t9kaALit6YR6viQMgeMjY0ZZcrOzg43btwAUKBYFqbMfcmSJVnfPXjwYNZ2UadPn2Y5y3/8+AE3NzdW1QNtkEqM1ETQtbW11Vhd4ObNm7x9iyEIiMW4ceNQpkwZnDp1Crm5ucjNzcXJkydRpkwZpgoRwN5ClA8vXryAsbFxocYiNEDk4eHB6yRX4Pr166ztrZWxfft2lCtXjiGrODo6sr5LGULnQFZWFoYMGcIEhOVyOfT19TFkyBBkZmZy+hVDnBBKcJSKMWPGoE6dOkx1ncePH+P8+fNwc3NjVY7QVv1OXbUJscp9bGwsrK2tOYFQKysr3q3QxeDMmTO8QUw+iF03xASIP336hMGDBzPbIyq2Sxw0aBCTEKIMKUkgYmWGmOzRvn37MhnbQvD69WvcvHmT5Yi7cuUKi2hgYGDAqmBWu3Zt1jqekpLCCa4kJibCx8cH5ubmrHk3fPhwtQkgQtc7IQRdVYeZGANcyvoidB6YmZnxVmfRBCGOL6kVk4oaBw8eZI7169fD2dkZ06ZNw759+1h/U048mTNnDtzd3eHk5IRx48Yxlcz43ufQ0FCMGjWK97vv3r0LGxsbXmdpQEAAL5Fh+PDhLDIJAFhbW6sN6AAFSVCqQVAxc0aKzqMN2qonayJ9CgmaxcTEICcnh/m/pkMTtNk8Hh4evAk+Y8eO5dUbxDgFxZI+XF1dGXlUpUoVrF69GkABgYZvG1yxEEOob9iwIe+z+/z5Mxo2bMg6J0V+Ca2cJRY7d+6EpaUlWrdujXfv3iE+Ph6Ojo6oVasWS2ZLJWmK0deAAp1t8eLFrO/avHkzi1gqRZdSRlZWFh48eKCxIvKPHz8QFRWFkSNHskhFixcvxrp161htjY2NWb9Nu3btWLLs3r17sLGxYV0jZutxMZWcAWDlypWwtrZGZGQkKwCxadMm3vcoKCgIVlZW2Lt3L4CCbR8VzuH58+drrUzEt577+/szyVnaCNXKCAwMZOSLst5w4cIFtYlDfHjw4EGhK7EUN8TomUJkY3h4OGMzKW+TyXdowvPnz/HHH39o3Bo+OTkZUVFRGDZsGIYPH47FixdzbHdVYqa6zwrI5XLWzgtmZmaCZJ7QwKOU7a2LE1KrrmrDmjVrYGJiwlsF/9ChQzAxMWFIIAoIkbvK0FZNTtleFFO5fP78+ShZsiS8vb3Vfvc/hX+igIBYAmXbtm1F2fJi+xeD69evIzY2llnTAeDIkSNqyXpGRkZqCZqFDRAXp59PbGBb6HPp2bMnFi9eLGospUuXZu5NGbt374aTkxPnvNBiHAoIscMrVKiAQ4cOqR3joUOHePsW4yfJyMjg3U3g48ePrOpzQIG/SZM/XbH1uPJ3iyXniYWU9+7UqVPo3r07jI2NYW9vz9mZCigoqOHn54fDhw/j1atXGsft7e3Nu/WyJogtmAAU7K6mSVeQihcvXqgtYsLnT8/Pz2eKRihsa0NDQ0yePJnVLiMjA0OGDEHJkiVZPsQhQ4bwbn8sJpldYWPL5XLMnj2bZXcvXrwYwcHBvLveCJFfUnVqMTJASuKFEFy6dAne3t4sHVAmk8HLy4u5bz4UF6kMYO8kJCRxiA8fP37UWmxJG8TqgVJIP0CBzr5mzRrMmjULM2bMYB2FgbW1tSiCrtg4q5hq50+ePMHw4cMRGBiIwMBAjBgxQqM/Vyw5WqieMW/ePOzatYv53LFjR8hkMjg4OIgmFasSdD09PeHi4oKJEyeynoummH9qair8/Pygp6cHNzc3uLm5QU9PD/7+/hr9OkKRnZ2NoKAgmJqawsfHR6M8Egtt+ldhfKtidDux711qairr2V65cgVhYWEc20sdPn/+jP379/POfSlJ21ISMYVAijxq3rw5sy4rqoTm5eUhJCREY7V1IejatStcXV0RGxvL6DCKokndu3cHUOB/rFKliqhqpar+CeX26pCbm8vRKVJSUtT604pzvSvOglVi5YvYHSzEFhQSItdLlCjBFChR3R1W9SgMxM4BoPiK8vzCfz9+EXR/4X8OhVGoNRELpSzqgPDA5qVLl9C/f3+YmZkhICAAy5Ytw/v37zUq60KRk5OD7du3o2HDhjAyMkL79u0RGxsLPT093r537NiBxo0bw8TEBJ06dcLhw4eRm5urdixSDc3igtitaYQ6PqTMAQ8PD1y+fBlAgYKhqB67a9cuTrBXDFQz7ypWrMgibfAFHxwcHESRVaQSIzW9R3p6emoz54GCzHR9fX3OebEEATH4/v07OnXqxGwLq6enBx0dHfTp04flRJYS4C4uKGcv8+H58+dagyVZWVlatxISOwcyMzMZwgEfMVcBMcQJQBjBUSq+f/+O/v37Q1dXl5kDcrkc3bt35w0uSIFY5T4rKwuxsbGYP38+5s+fj9jYWM521VJw48YNloFw4MABtG3bFhMmTOAETMSuG4D4AHF+fj7evn2Lt2/fanSQSkkCESsztGWPKjvp58yZA2tra/Tq1QsLFy4URZ5TB2dnZ5w9exZAwZw0MjJiVVm5c+eOYCMzJycHP3784P2b0PVOCkFXjAEuZX0ROg/69OmjNulAHYQ6vqRUTAIKjPVJkyYhNDSUea5//fWXRpKqOvA5MdU5NlVx5swZ9OzZE8bGxqhYsSJ0dHQ4QefExERWhX5VJCUl8ZLnzpw5AxMTE3h5eaFv377o27cvvLy8YGpqyqmgpquri9evX6v9jlevXkFPT491TuycEavzaIM2sqemKspCgmYuLi7M++ji4qL2UK78JsXm+fPPP2FoaAgfHx/069cP/fr1g6+vLwwNDZktpZQh1ikohvTRr18/Zi4tX74cRkZGaNy4MSwtLdG3b1/e8RcX1Ol4b9++ha6uLuucFPkltHKWWBgbG3O28vv06RNCQkJYASepOqxYfa048e7dO7Rs2ZK3mmNh9O+SJUuy9Bp7e3vWlt1Pnz7l6NSqWyAbGRmp3QJZbODJy8uLCd4or71JSUm8VbAaN27M0l0UOHLkCOzs7CCTyThkA+UjMjKSM47p06czeqcYIrVyBT/lsT9//py3QqAqWfn27ds4evQo6tevz9pF4N8IsXqmOtmoCBg3aNCASRKoX78+a6tM5UM1YUAs5syZA11dXcjlctjZ2cHW1hZyuRx6enqIiopi2mkjZqoSNGUyGStQIpPJYGFhoTVYIlT/krK9dXFCatVVIejWrRtDgAkODkZwcDDKly8PuVzObCdfGAitJvfz50/MmDFDMAlAJpPB2NgYbdq0Qbt27dQehYFQEocY21GVKKfpUIZYAuX79+/RokULTJ8+XWNSndT+ixOtWrWCv78/U2wAKJBplStXRuvWrQvVd3H6+YorsD1r1ixYWlqiQ4cOmDNnjiBfwIwZM2BpaYl58+YhISEBCQkJmDt3LiwtLTFz5kxOe6HFOBQQYoebmZkxxHU+PHv2jLfaanFV8RPi01T1aRcFOU8ThL53f//9NyIjI1G2bFlYWVlBLpdj165dan1aqjqgpnEfOnQIderUYRJqhUBswQSgwBfRrFkz0RX9tEG5CrsyPnz4oPE3+v79O+7du4crV66wdCVV5Ofn4927d1p9iGKS2RV2tkwmg5OTE8v29vDwQNOmTZm4jjKEyC+pOrVYGSA28UIMbt26hd27d2P37t281dRVUVykMkBc4pAYuLq68haLSE9P5921TYoeKDYusHbtWujo6MDW1haVKlWCn58fcxR2fRw1ahSzq5MQiI2zCq12fuzYMejr6yMgIIBJRAwICICBgQFvv4B4crRQPcPFxYWxy+Lj42FpaYm4uDj069eP5fMBoFHPbdeuHRo2bMiRd+fPn0efPn1gamqKypUrY/HixdDV1dXos8zPz0d8fDyWLl2KpUuXatzVUyxCQkJgbW2NwYMHY9q0aYKTpYVAm/5VGN+qUN0uPz8fL168wJcvXwS/d3Xq1GEScxRFfGrWrAlra2teUnxISAiWLVsGoIDw7O7uDj09Pejq6nKS8sQmbSsgNs4mFGLlUVJSEkqVKoVmzZpBX18fHTt2hJeXF2xtbUUXSFHF169fGXmirMMMGDCA0WFu3bqFW7duiapWKpPJ4Ovry5DOdXR0UKFChSIloxfnelfcBavEyBexO1iILSgkRK4rV7hW3hmW7/iFX/i3QAYA9Au/8D8EV1dX+v3336lWrVp0/Phx6tSpE+3evZv27NlDqampFB8fr/ZaMzMzSkxMJDc3N87f5HI5NW/enAwMDIiI6PDhw9SoUSMyMTFhtYuNjWV9/vnzJ8XExFBaWhr17t2b/P39iYhoyZIlZGZmRv3792e1z8rKot27d9PGjRvp6tWrlJeXR4sXL6a+ffuSmZkZq+2mTZvI1NSUQkJCWOf37t1L2dnZ1KtXL859PH36lDZt2kRbtmyhly9fUmhoKPXu3ZsaNWpEOjo6rLYpKSm0efNm2rx5M2VnZ9OnT59o9+7d1LFjR06/eXl59OXLFypRogRz7vnz52RsbEylSpXitC9unDt3jmbOnEmJiYmUmZlJlStXpqlTp1LTpk05bTMyMmjy5MmUlpZGQ4YMoWbNmhER0bRp00hfX58mTZpERNLmwPjx48nc3JwmTpxIu3fvpu7du5OLiwulpqZSeHg4zZs3T9L9eXl50ezZs6l9+/b04cMHsrOzoytXrlCVKlWIiOjq1avUpk0bevPmDXPNnDlzKDk5mdavX0+6urqCvufNmzf0+vVrqlSpEsnlcqZvc3NzKl++PO81ZmZmdOfOHXJ1deX8TUdHh968eUM2Nja81759+5YcHBwoLy+PdV7seyQFycnJlJiYSEZGRuTr60tlypRh/V0ul9OWLVvIwsKC9/qMjAzq06cPZ+xicPPmTdLT0yNfX18iIjp48CBt2rSJvL29afr06aSvr09ERHZ2drRjxw5q1KgRbz8nT56kbt26sX5/IqJGjRpRbGwsWVpass5/+fKFgoOD6dSpU5y+pMyB/0akpaVRUlISZWZmkr+/P7m7u2tsn5GRQfv27aOnT5/S2LFjqWTJknTz5k2ytbUlR0fHYhnj33//TTNnzqS1a9dKur5atWo0fvx46tChAz179oy8vb2pffv2dO3aNWrZsiVFR0fzXidm3RCLd+/e0aNHj4iIyNPTk3e9OHPmDLVr146+fPlCvXr1oo0bNxIR0cSJE+nhw4ecdZdIvMxIS0ujNm3a0L1798jJyYk55+PjQ4cOHaK6desKuh+ZTEbPnj1jPjds2JBkMpna9op3bsiQIZSYmEjz58+nAwcO0JYtW+jVq1fMO//7779TdHQ0Xbt2TdA41EHoeqcqq83NzSkxMZGR63yy+s6dO9StWzdKTU2l0aNH07Rp04iIaMSIEfTx40fasWMHayxiZYvQeZCdnU0hISFkY2NDvr6+pKenx+pn5MiRvM9m3759lJaWRiEhIVS6dGkiItqyZQtZWlpS27ZtiYioV69e5OfnR+Hh4YKeNxHR2bNnqXnz5lS7dm1KSEigBw8ekJubG82bN4+uX79O+/btE9xXUeHr16+0Y8cO2rhxI924cYMCAgKoY8eONHr0aJo5cyb99ttvZGxsLLrfV69e0YoVK+jhw4dEVKCrDB06lBwcHFjtpOoCYuaMFJ2nOHHjxg1KTU2lJk2akKmpKRER/fnnn2RpaUm1a9eW1KdUmyctLY1WrVrF+p0GDx7MyD5l7Nu3j7p27Up5eXkUGBjI9Dl37lxKSEigo0ePElGBzC1fvjwdOXKEvLy8BI0/Pz+f8vPzmd9n165ddPHiRXJ3d6dBgwYx8q8w2LZtG61evZpSUlLo0qVLVKZMGYqOjiZXV1dq27Yt3blzh4iI/Pz86NSpU1SyZEnm2ry8PDp27BitWbOGnj9/zupXrPw6duwYTZgwgWbNmkVVqlTh2A7m5uaS7u/Ro0fk6emp9t579OhBRAU6bGRkJDP3VPH161eaOnVqoXRYogI79uzZs5Samko/fvxg/U2d7BWKbt260YsXLyg6OpoaNGhA+/fvp7dv31JkZCQtWrSIWrZsybnm/v37vGNp06YN8//AwEAKCAiguXPn0rlz56hBgwb0999/k729PRERHT9+nIYMGUJPnjxhrjE2NqaHDx+Ss7MzRURE0OvXr2nr1q107949atCgAb1//55p++LFC433pWpvGBkZ0cOHD6lMmTIs38Tjx4+pYsWKlJOTI/iZffjwgapWrapRD1EgJSWFcy4vL48uXLhAFStW5NgOfChVqhTFxcWRv78/a+zHjx+nvn37UlpaGqu9XC4nmUxGqi7LGjVq0MaNG//VdoYYPVPdupqTk0NRUVE0depUSWPIz8+nzZs3U2xsLD1//pxkMhm5urpSx44dqUePHpzf/fTp09S4cWOaMmUKhYWFMX6bT58+UXR0NM2ZM4dOnTpF9erVEz2WLVu2CGrH558Son99/fqVatasSampqdS9e3dG7j18+JB+//13cnJyosuXL3P8Zf+t2LNnD+3YsYMeP35MAMjDw4O6du1KnTp14rQtCrn76dMnKlGiBGfOmJmZUVJSErm4uGjto3fv3oJkzaZNmwSNSRVxcXHUpk0b8vPzY/SmCxcuUGJiIh0+fJiaNGnCtBVjOyrkkCYAIJlMxlojhdpUChw+fJh69OhBX7584fSv2reU/sXg77//pkOHDvHOmcWLF3Pav3//nnr16kXHjh1j7Krc3FwKCgqizZs3F8rfW5x+PqE6rAJCnwufn1MBVV+AAgAoOjqaFi1aRK9evSIiIgcHBxo7diyNHDlS0LujCULscLlcTm/evFH7e6mzv7T5SRRyWywqVqxI4eHh1KdPH96/b9y4kaKjoxldnajArtaE+vXrSxqLAtreu/Lly9OGDRsoISGBmjdvTt27d6fmzZuTiYkJJSYmkre3N2+/msadlJREw4cPZz6XKFGCsrOzKTc3l/T19cnIyIjV/tOnT2r7ysrKoqdPnxIRUdmyZTn2hjLev39PnTp1ooSEBDI2Nub4TDR9jybI5XJ6+/Ytx85/8eIFeXt7U1ZWFut83759KSYmhrN+Z2Vl0YgRI2jjxo2Uk5NDx48fp4YNG3Laffnyhc6cOUNBQUFMrEYTvn37Rjo6Opz7JSrw3cXGxrLiWpogRn6J1an/jVDo60LklRDd7p/At2/faNmyZXT69Gl69+4d5efns/5+8+ZNIiK18vHt27fk7OxM379//8fGrECZMmVo6NChFBERUeR9jxgxgrZu3Uru7u68vgk+XUBMnPXUqVM0efJkmjNnDq9PVuH78Pf3p6CgIE5sdPz48RQfH8/8PspITU2loUOHUlpaGo0cOZL69etHRETh4eGUl5dHS5cuZbUX+p4aGRlRcnIyOTk5UVhYGH379o3WrFlDycnJVL16dUpPT2f6VLduqYJP583MzKSdO3fSpk2b6PLly1S/fn3q2rUrBQcHq/WPFgdMTEwoLi6O6tSpU+R9i9W/iqPv/Px8MjQ0pHv37mmN7ylQokQJunz5Mnl6etLSpUtp9+7ddOHCBYqPj6fBgwdz9Ds7OzuKi4ujSpUq0Y4dO2jatGmUmJhIW7ZsobVr19KtW7eYtgqfoTIUfhA+O+DfiM+fP9Py5ctZMmDgwIEUGRkpOU6pjMzMTOYZu7m5qfVdPn36lObNm8caR0REBBNLV2DGjBmCvlehMxMVzC+FP13VFuCTR4primu9+7fE5IXKdG1Qp4OJkeu5ubm0Y8cOCgoKIltb20LeGRdi54COjg69fv2aoz98/PiRSpUq9V/xbv9C8eEXQfcX/ucgRqFWxZAhQ2jWrFlkbW3N+VthlG+pePToEW3YsIG2bdtGGRkZ1KRJEzp06BDzdw8PD1qzZg01bNiQdd3Zs2dp4MCBDOmJD/n5+XTs2DHauHEjHT58mMzMzOjDhw+8bQFQfHw8bdiwgQ4dOkTW1tbUvn17jtHz/zuKYg5cvnyZIR+0bt1a8ljmzZtHMTExNHToUDp16hS9f/+e7t69y/w9Ojqajhw5QidOnGDOtWvXjk6ePEmmpqbk6+urlVwuFO3bt2d91kRcViU5q+L79+907Nix/5jyosnhxGdM8UHV4SMGqgTKChUqULt27TgEyk6dOtHPnz9p//79vP20bduW9PX1ae/evZx74HM6vXv3jhwdHennz5+Sx379+nW1Cqy6uSWEOCG17+LEnTt3qHHjxmRhYUHPnz+nR48ekZubG02ePJlSU1Pp7t27dPLkSSpRogT5+/trdGCqM/D4kJiYSJUrV5b8flhYWNDNmzepbNmyNH/+fDp16hTFxcXRhQsXqEuXLhzihCq0rRtiAsRfvnyhYcOG0a5du5j70dHRoc6dO9OKFSs4RPh/IgkEAJ04cYJFWmvcuHGh+lQlcv78+ZNu375Nd+/epV69elFMTAwRFZBo2rdvT+fPnydTU1PasmULtWvXjrkuMDCQatSoQbNnz2bO5eXl0ZIlS9S+G1KDKkQFssLCwoKZuxkZGWRubs7IQQD05csXQXNRUxBELITMgw0bNtDgwYPJ0NCQrKysWO+fuqCpUCiIYIGBgbyObD4iRM2aNSkkJIRGjx7NIixdvXqV2rdvT3///bfk8fAhIyNDVNAnKSmJNmzYQDt27KB3796pdS4UJeRyOfn4+Kglzubm5tK9e/cKpQsUl85DVPCMr169yhvk6dmzp+R+icQRXQtj84iBUKego6MjnThxQjBBt7ixatUqmjp1Ko0aNYpmz55Nd+/eJTc3N9q8eTNt2bKFTp8+zSLm8LlNjIyMaNmyZdS3b99CjUVZh1SWSUXhkM/NzaUzZ87Q06dPqWvXrmRmZkavXr0ic3Nzxqnt4uIimaRJJExfu3XrFrVo0YKys7MpKyuLSpYsSR8+fGDks0L2aktcISp4RidPnmSds7e3p4MHD1JAQACZm5vT9evXycPDgw4dOkQLFiyg8+fPM22fPXtG7dq1o6SkJBYBVPG9ys9bkURhb29Pr1+/ptDQUNqwYQPz96FDh1JWVhaLgKhMRPX396fRo0dTjx496OnTp1SpUiXKzMzUeH+a4O3tTXPnzqW2bduy1oxly5bRpk2bROmNRQFDQ0N68OCBRkKSAv3796ePHz/Snj17qGTJknTnzh3S0dGh4OBgqlevHicRTJW8LJfLycbGhgwNDYvyFv7jEOO0//nzJxkZGdHt27fJx8dHbZ8AqHXr1vTXX39RpUqVqHz58gSAHjx4QElJSdSmTRs6cOAA65rOnTuTpaUlrVmzhrfPgQMH0tevX2nnzp3Mub/++ot0dHQoKCiI1TYuLo7y8/OpefPmQh9DofH582eaMGEC7d69m1nfLC0tqUuXLjR79mzBhJqixrdv3ziyUWrShVgIlbvK+Pz5M+Xl5bESUogK7AZdXV3W2Nu2bUvt27fnJVb/0xBL4hBqO27ZsoXGjx9PvXv3ppo1axIR0aVLl2jLli00d+5cFjm5MOQ/FxcXatWqFU2ZMqVYgolCcfLkSWrTpg25ubnRw4cPycfHh54/f04AqHLlyrwJ2wokJyczdnL58uXJw8Pjnxq2ZAjVYQvzXLRBNZD89etXIiJBCQXZ2dm8+lfFihUFfbeyHS6XyznJaMr48OEDNWnShFcnLQ4/yZIlSygyMpK2bdtGLVq0YP3tzz//pF69etHEiRNp9OjRhfqeooSuri5FRETQ+PHjWb+fnp6eRoKuKhRr7fr16+nGjRusZ64t4aWo5HHjxo0pNTWV+vXrR7a2thzdXOz3KH6nmJgYGjBgACspKS8vj65cuUI6Ojp04cIF1nXqdCRFIZLc3FyKiYmhQ4cOcWwD5Xtp164dDRs2TNSY1eHHjx+UkpJCZcuWLdJEXzE6tQJCZYDYxAsx2LBhAy1ZsoQeP35MRETu7u40atSoIimUIgUZGRm0YcMGevDgARERVahQgfr27ctbTKVbt24UHx9PHTt25J3nCrJmcHAwpyBLXl4enTx5ko4fP64xvipUDxRL+jE3N6fbt2/zFpEqLFTjyMqQyWSFWvOI/s/3ofq8VX0fhoaGlJSUxCFQJicnU8WKFenbt2+FGocYODg40L59+6hWrVrk6elJkZGRFBISQo8ePaJq1arxJlgVFg8ePGDi/p8+faJFixbRwIEDydDQUGvMvbDJz+XLl6c9e/YI1ifEQqj+JcW3KrTvChUq0IYNG6hGjRqCxmxqakp3794lFxcXatOmDdWuXZsiIiIoNTWVPD09OYnSyv7Ynj17koODA82bN49SU1PJ29ub5RMSm7StwMmTJ2nJkiWMvPPy8qJRo0YVWgeTQkTlQ2HjlP8mLF26lCZNmkS9e/emtWvXUp8+fejp06d07do1GjZsGCse99+IpUuXSpYvQmX6PwVjY2N68OCB2vdGKqTMAXVci1evXlHZsmVFFVj4hf//8J8vF/QLv/APo0SJEpSWlkZOTk507NgxioyMJKKCBUPbYrFq1Sq1fyss8VYoEU0Znp6etGDBApo7dy4dPnyYqfygQGpqKq9hX6ZMGUpNTdU4HrlcTi1atKAWLVrQ+/fvadu2bWrbymQyCgoKoqCgIPr06RNt3bqV8zyKSrErCvTv35+6d+9ODRo0EHWdNseHlDlw9+5dVnCtRo0ajGFw4MABCg4OFt0nEdG4ceMoOzubYmNjyc7OjkPEvHDhAoWGhrLOWVpaUocOHQR/h9DKj6pOkO7du6u9pmfPnlqD8prILVLeIyHYunUrRUVFMQ4nDw8PGjt2LFN5jKhwxFuhSE5OJj8/PyIqqIRdr1492rFjB0OgVAS2J0yYQDVr1qSOHTvSuHHjWFWEFixYQHFxcXTx4kWmX+WqE/fv32dV1lVUiOOr+ip0DuzatYt69uxJQUFBFB8fT02bNqXk5GR6+/Yti2SogBjihNi+xaJDhw4UEBDAyUpfsGABXbt2jfNuERU4n3v37k0LFixgOeZbtGhBXbt2pT59+jAkdKnveHEAADOPT5w4Qa1atSIiIicnJ7UJGsrQtG5oCxCrGncDBgygW7du0ZEjR1jB0LCwMBo0aBDt2rWL1V5HR4cTeBdSzUmMzJDJZNSkSRNWBSZVfPnyha5cuUI/fvyggIAArRnuS5Ys4T0/ffp0lrPG2tqaEhIS6PPnz2RqasqpTLx3715O9vCMGTNo/fr1NGbMGJo8eTJNmjSJnj9/TgcOHNBaja041jt1UEe0kUK8FzIPJk2aRDNmzKDx48cLTqwgEkYw37BhA1laWtKNGzfoxo0brDYymYzXSZqUlMSpHkxUQPAS8t5pwvz588nFxYU6d+5MREQhISH0xx9/kL29PUPa0QZfX1+Kjo6mqKgoIuInKQrFuXPnaM2aNfTs2TPau3cvOTo60rZt28jV1ZVVmUE5Q10d+PQVMXNGrM4jFIcPH6Zu3bpRZmYmmZubcwjg6nQYoUEzPT09wYEIoTbPnTt3yMfHh+RyOUsf4AOfg97Ozo7s7OxY5wICAjjthg0bRvPnzxdctVjbWLSNSxuWLVtG69ato+DgYBahp2rVqvTbb78RUQEhFQBDmleW6fr6+lSqVCneSvFi5dfp06dFj18IXrx4Qc2aNaPU1FT6/v07NWnShMzMzGj+/Pn0/ft3Wr16NRERpwKwUIjR18LDw6l169a0evVqsrCwoMuXL5Oenh51796dwsLCmHYKPZcPiurefFWKsrKyGKdniRIl6P379+Th4UG+vr4cWzMsLIxcXV3p5MmT5OrqSlevXqWPHz/SmDFjaOHChay29evXpxs3blB8fDzZ2dlxdqbx8/PjzPcmTZpQ//79yd/fn5KTkxliyb179zhrknJyrTJkMhkZGhpSuXLlWPb86NGjadiwYfTt2zcCQFevXqWdO3fS3Llzaf369UREVLJkSUpOTiZra2veqpfK0Jask5GRQdu3b2dVb1OGj48PPXv2TBCZYNGiRdSxY0cqVaoU5eTkUP369enNmzecBCOiArvq5MmTgqu//lshRM9UBC5UkZiYyCFK6enpkbOzs1a/1ebNmykhIYFOnjzJCbKfOnWKgoODaevWraw16erVqxp9Pj169OCsYePHj+fd8QcAjR8/Xi1B98aNG0wg0dvbmypXrqz2e4Um+FlYWNDKlStpxYoV9OHDBwJANjY2/5G5kpWVRREREbRnzx76+PEj5+9Sg1RTp06l8ePHM8Sm9PR0jcRjoXJXGV26dKHWrVvT0KFDWef37NlDhw4dor/++os517x5cxo/fjwlJSXxJqYV1gcjBg8ePKA9e/Zwzvft25d3FxihtuPWrVtp8eLFLN9ZmzZtyNfXl9auXUtnzpzROC6h5KmPHz9SeHi4aHJuYQmaqpgwYQL99ttvNGPGDDIzM6M//viDSpUqRd26dWOqhaqDh4dHsZFyi8vPJ1SHlfJchJL4dHV1afDgwYxMFELMff/+PfXu3ZuOHTvG+3ehMkbVDg8MDOS19ZSruClDOWlEm59ELMLCwujixYvUqlUr8vT0JC8vLybR5PHjx9S2bVsaNWpUsdsLfFD33vXr149WrFhBZ86coR49elDnzp1FJYckJCTQhg0b6I8//iAHBwdq3749rVixgtVGKgFXrG1y8eJFunTpkiB/gRAoKgUCoKSkJNZOKPr6+lSpUiXG/iIq8KsBIAD09etX1lzNy8ujv/76i9H7f//9d5oyZYra7x41ahTNnDmTRdCVksyek5NDw4cPZ0jSycnJ5ObmRiNGjCBHR0caP3487/cLlV9idGoxMkBbgkFhMHXqVFq8eDGNGDGC5bcNDw+n1NRUmjlzJucavnOqfUrF9evXKSgoiIyMjBhZvnjxYpo9ezbFx8dz7vfIkSP0119/qd2xSJl0pPru6enpkYuLCy1atIhznVg9UJn0c/DgQQ7phw8hISFM5c6iRnH5JsT2b2NjQ7dv3+YQdG/fvi2oYICYJDlt72n79u2pa9eu5O7uTh8/fmRsnFu3blG5cuUE3Y9YeHl50cKFC2nevHl06NAhGjNmDHXr1o0MDQ3VxhKI1PuexWDRokU0btw4Wr16taDYilgI0b+k+laF6nbz5s2jsWPH0qpVqzQmvipQoUIFWr16NbVs2ZKOHz9Os2bNIqICop2VlRWnvZOTE126dIlKlixJx44dY+JY6enpHP1LCpFw5cqVFBYWRh07dmTsusuXL1OLFi1oyZIlkpNSpMij4kRWVhbNmzePTp48yUvUVk04Vezy+ezZM4qOjqZSpUrR0aNHydnZmSpUqCB5HCtXrqS1a9dSaGgobd68mcaNG0dubm40depUrbsXFNcuYkVZsGrJkiWS5YvYNaMwBYWEyPWAgAC6detWkRN0xcwBBclZJpPR+vXrWTHbvLw8SkhI+FfvCPYL/xDwC7/wP4Zhw4ahTJkyaNy4MaysrPD161cAwM6dO+Hv789qm52djXPnzuHevXucfnJycrBly5ZCj+fp06eoWLEiZDIZ5HI5ZDIZ83+5XM57TXp6Oq5du4bExER8+fJFbd9OTk44ePAg5/yBAwfg6OjIOZ+dnY2DBw8iKioKMTExOHr0KHJzc7Xew/v37/H582e1f4+JiYGpqSmGDx8OfX19DBo0CI0bN4aFhQUmTpyotf+iRps2bWBgYIDSpUvjt99+w61btzS2f/fuHVq0aMH8JqpHYeDg4IBnz55xzu/btw/GxsaF6ru4MWrUKNYxbNgw1K5dGxYWFhg5cuQ/OhYp75FQLFq0CMbGxhg3bhwOHjyIgwcPYuzYsTA2NsbixYsF95OXl4fDhw8XaixmZmZITk4GADRu3BjR0dEAgBcvXsDQ0JDV9vDhw7CxsWHNVZlMBhsbG45cUH5WimenfBgbG2PDhg2c8QidA76+vli+fDkAwNTUFE+fPkV+fj4GDBiAqVOncvpt1aoV2rZti/fv38PU1BT379/HuXPnEBAQgISEBFZbsX2LhbW1Ne7cucM5f+fOHZQqVYr3GnNzczx58oQ1JgB4/vw5DAwMCj0mdbh9+3ah5nvDhg3Rs2dPbN26FXp6enj8+DEA4MyZMyhTpgzvNULXjfr162PAgAHIy8tjnklqairq1auHP/74g9Pe2NgY586d45xPSEjglY179+5FSEgIqlevDn9/f9bBB7EyY8SIEYiJieGcX7ZsGcLCwgAAt27dgr29PdOXubk5jh07xvv92vD48WOUKFFC0rUKuLm54ciRIwAK5qFiTsbExCA0NJT3muJc73JzcxEVFYVq1arB1tYWJUqUYB3K2LlzJ/T09NCqVSvo6+ujVatW8PDwgIWFBXr37q32O4TMgxIlSjDPQihu3rwJOzs7mJubQ0dHBzY2NpDJZDAxMYGrq6u4B6ECR0dHXLhwAQBbXsTGxsLNza1Qfbu4uDB9x8fHw9LSEnFxcejXrx+aNGnCavvz508sWLAA/v7+MDExQYkSJVC9enWsXr0a+fn5TDuZTIZ3796JHsu+fftgZGSE/v37w8DAgLnPZcuWoXnz5oW4ywJInTNFDXd3d4SFhSErK0vwNSdOnICxsTF8fHygq6sLPz8/WFpawsLCAg0bNuS0nz17Nnr16oWfP39q7FeozSOTyfD27Vvm/+p0AXUy4Nq1axg7diw6d+6Mdu3asQ5lBAcHw8zMDPb29mjatKnGtspj0XRoGpc2GBoa4vnz5wDY715ycjJHpxKDf8tcBIC2bduie/fu+P79O+seT58+jXLlygnuJz09HcuWLeOcF6OvWVhY4OHDh8z/79+/DwC4fPkyPD09NX7/z58/ER0dDRsbG5QrVw47d+7ktKlatSqz5rZu3Ro9evTA33//jXHjxnFkqZWVFRITEwEU6GyKcZ08eRJ+fn5CHolGpKenY9iwYWjTpg2OHj3KnJ86dSoiIyNZbdW9c8rzu169evj06RNzzfbt21GuXDmmraOjI9avX8/8ffPmzfj27Rvzf02HOpw4cQKhoaEwNDREyZIl1bY7evQo/Pz8cPjwYbx69QqfP39mHXw4f/48VqxYgfnz5+P48eOcv+fn56Nly5aQyWTw8/NDly5d0LlzZ0Zva9u2rdrx/FsgRM+0tLREiRIlIJfLmf8rDnNzc8jlcgwdOpTT9/r169GiRQt8/PhR7fc3adIEc+fOVfv32bNno2nTpqxzRkZGSEtLU3tNWloaRzYaGhoiJSWF0zYlJYVXX3/79i0aNmwImUzG3KtMJkOjRo14dYvi1L+KE0OHDoWXlxej+2zcuBGzZs1C6dKlsX37dsn9yuVyZr0GCnwDCrnOBylyt0SJEkw7ZTx48IAjC/h0BW06Q3GhdOnS2LNnD+f87t274eTkxDkv1HY0MjJi/C/KePToEYyMjNSOR6xN1bNnT6xbt07IrUrqXyiUbUZLS0vcvXsXQIGfQZ0/ACiQDytWrEBERATCw8NZR2FQnH4+QLgOK+a5ZGVloW/fvtDR0YGOjg7zjg4fPlytXK5fvz72798veNxdu3ZF7dq1ce3aNZiYmCA+Ph7btm2Dp6cnY/8rQ4gd/vz5c0GHKlxdXXH79m3BYxeLXbt2oW3btvDy8oKXlxfatGnD0gNVdSZNR2Eh5L3Lzs7G5s2bUa9ePRgYGKBNmzbQ0dFBUlISb5+vX7/G3LlzUa5cOZQqVQrDhw+Hrq4ubxxKgSdPnmDSpEno0qULsyb89ddfzLxUhRTbxN/fH5cuXRLzeAShd+/eGuNHCmj7PXV0dBid2tLSEi9evFDb14sXL2Bpack6N2XKFNjb22PhwoUwNDTErFmz0K9fP1hZWfH6/QBg5MiRqFKlCs6dOwcTExPm3T5w4ACv/SBWfonRqcXIgGrVqjG+cYU9+PXrV7Rp0wYrV65U+9yEwNraGjt27OCc37FjB6ysrHiv8fPzYx0VKlSAsbExzM3N1fpvhaJOnTro3bs3y0/y8+dP9OrVC3Xr1uW09/LyYmxCTXBxccH79+8Fj0OsHujp6ck8R2WbfcqUKRg2bBjvd8yZMwfW1tbo1asXFi5ciJiYGNZRFHj8+DGOHTuG7OxsAGD5BVVtF02HVMyYMQOWlpaYN28eEhISkJCQgLlz58LS0hIzZ87kvSYzMxPDhg3jxMPUvXdC39MfP34gKioKI0eOxM2bN5nzixcvFqW/acLff/+NmJgYDBs2DOHh4Vi9ejXLB/BPwtLSEvr6+pDL5TA1NS2y31QBIfqXFN+q0L5V79HQ0FDrPZ4+fRqWlpaQy+Xo06cPc37ChAm8/swVK1ZAV1cXlpaWqFSpEvLy8gAAS5cuRYMGDTjtnzx5guHDhyMwMBCBgYEYMWKExtiFo6Mjr49u+fLlcHBwUP+AtECKPFKHwsYpAaBLly6wt7fHuHHjsGTJEkRHR7MOZZw5cwZGRkZo3Lgx9PX1mbHPnTsXHTp0YNr5+flx7D91hwJGRkaMHmxjY8PovsnJyWr9ZcXpy/g3+Z3FQqwOJlau7969G25ubli2bBkuXryIxMRE1iEVYuaAi4sLXFxcIJPJ4OTkxHx2cXGBh4cHmjZtisuXL0seyy/8/4FfBN1f+J+DUIX60aNHKFOmDCsw9vLlS+bvb968KRIHj5jAZkpKClq0aAEdHR1mAdLX10eXLl3w5s0bpp0iIDdu3DiUKVMGp06dQm5uLnJzc3Hy5EmUKVMGY8aMYfV98OBBRklQPkqXLo2zZ88y7RRk0vT0dAwdOhRWVlbMWGxtbTF+/HiO4lyUil1R4dOnT1izZg3q168PuVwOb29vzJ49mzfAJNb5KQZTp06Fm5sbXr9+zZzbtWsXjI2NeQMMYpGamsoKtF25cgVhYWFYs2YNb/ufP3/i+PHjWL16NUP+fvnyJUPqEIJp06Zx5pdQKMiVYiHmPRILFxcXXjL+5s2b4eLiovX6x48fY8KECSRy9KUAAQAASURBVLC3t4eurm6hxiKWQJmdnY3Y2FgsWLAA8+fPx4EDBxjHijKeP3+OlJQUyGQyXLt2jeWAf/XqlSCivjJU54CxsTHzbpUsWZIhvN6/fx92dnac68UQJ8T2LRaGhobM9yvjwYMHagk8NjY2zPqiLPPi4+NRunRp3mu+fPnCcr6KeecUKKzhm5iYCB8fH5ibm2P69OnM+eHDh/MSOsWsG2IDxE5OTrzE6MTERE6CiZQkELEyw8HBAdevX+ecv3HjBjOepk2bolatWrh48SJu3ryJdu3aiSJAKWPr1q2wt7eXdK0CxsbGTKDCzs4ON27cAFAgZ83NzXmvKc71TowBLoV4L3QejBo1CrNnzxY1drEE8+/fv+Phw4daCZQAMGbMGNSpUwevX7+GmZkZHj9+jPPnz8PNzY31HkqBoaEhUlNTARQElAYOHAigQMdVDlJlZ2ejdu3akMvlaNq0KcLCwhAWFoamTZtCLpejZcuWyMvLw5MnTyCTyQQ541Xh5+fHrKXKcvHmzZuwtbUVfE+fP3/GypUrUaVKFdZ5KXOmKHQeVRgbG2sky/BBbNBMKNFVqM3z/PlzRvcSG5QX4xTs3bu3xkMV+/fvR9myZbF69WrGkbZ69Wq4u7tj//79GsclBF5eXjhw4AAA9pxcunQp44h99OgRrly5wrruxIkTaNCgAapVq8YrS6QmDiUkJKBbt26oWbMm/v77bwAFawFfsopQlCxZkll7le8xJSVFI7FIAW0kTTH6mrW1NUNycnd3Z8i0Dx480JiUuH37dri5ucHe3h4rVqxQK1e3bduGTZs2AQCuX78Oa2trJtiya9cuVltLS0vGpnVzc8OpU6cAFARFlJ+LIjFPyCEVJ06cQPXq1XHixAl8+fIFX758wYkTJ1CzZk38+eefOH/+PCpUqIC+fftyrs3KymKR9QqL1NRUzJgxAy4uLpDL5ejatSuOHj2KHz9+qL1GlZDHR57Pzs5mJSmOHz+eRd4aO3YscnJymL9v3LgRZmZmzO+ijJMnT8LMzKxIEqWLE0L0zM2bN2PTpk2QyWSIiYlhEad37NiBixcv8vbt5+cHU1NTGBgYwMPDgzeIZGtrqzEJmW/tVU7W4AOf/8vW1hYnT57ktD1+/DhsbGw45zt16oSqVauyCKD37t1D1apV0aVLF057sfoXID5przjg5OSE06dPAwCj2wEFMr0wSUmqv5GyXOeDFLlrbGysNjlVyLrxT2PGjBnIysoSReIQYzt6eHhg7NixnO8dO3YsPDw81I5LrE0VGRkpimhTXDabra0t8356eXkx69vt27dhYmLCe43YRDMxKE4/nxgdVsxzEUviA8QHku3s7Bj91MzMDI8ePQJQoLfUrl2b014KGVEohCSNFCeU7QFttkNhIfa9S05OxoQJE+Dg4ABzc3OEhoay1q5WrVox548cOcL4XTURdIWST5QhxTaJi4tDrVq1cPr0aXz48EFQAlZR4syZMzh9+jRkMhliY2Nx5swZ5rh48SIrPmdqasrrq1Pg+vXrMDU1ZZ2Tkszu7OzMkJaV19/Hjx/DzMyM016s/BKiUysgRgZITbwQAgsLC7VJLBYWFoL7+fz5M9q1a4etW7cWajyGhoZ48OAB5/y9e/d4dZi//voLzZo1k+xPUAexeqAU4pcy2Uf1KCzx68OHD2jUqBEz9xRzvU+fPhg9ejQA7Umg2hJChfg+8vPzsXjxYjg6OrISU6Ojo9XGDsWSo4tTzxCDFStWwMDAADKZDBYWFrCwsGAK5ihi6vn5+YxfT6H/qiI7OxszZswo9HikJvkKgVD9S4pvVYxuJ+Uec3NzOaTplJQUtTb0tWvXEBsby/IxHzlyBOfPn2e1O3bsGPT19REQEMD4SAICAmBgYID4+Hjevk1MTBjZoozk5GS1OrsQSJFH6lAUBF0LCwvO81KHGjVqYNGiRQDY6/SVK1dYscTp06cLPhRwdXVl3r8qVapg9erVAAr0JXWkdSm+DKEozoJVUuSLGH+2WB1MrFxXlzxc2CRiKXOgQYMG/7FEi1/49+MXQfcXfkENgoOD0bJlS7x//x6PHz9Gy5Yt4erqypBdioqgKzSwmZqaCltbW5QuXRpz5szB/v37sX//fsyePRulS5eGi4sL0tPTcfDgQcybNw9AAUGkU6dOkMlk0NPTg56eHnR0dNCnTx98//6d6fvChQvQ09NDhw4dcPHiRaSnpyM9PR0XLlxA+/btGQN33LhxmDFjBj5+/AgPDw+YmJhg4MCBWLJkCZYsWYIBAwbAxMQEVapUQU5ODq5cuYKYmJgiVeyKA2lpaViwYAHKly8PHR0dzt/FOj/FYvjw4ahQoQI+fvyI33//HUZGRti3b1+h+wUKsocVDo7Xr1/D3NwcNWvWhLW1NUehev78OcqXLw9jY2NWpYeRI0di0KBBgr+zMJUfVavCdOrUiUU+V4firIRlYGCg1uBRVw01OzsbW7ZsQd26dSGXy1G/fn2sWrVK0L1oglAC5cWLFznVehWEYhsbGwwYMIAh8hcHVOeAo6MjE+jz9fVlnAsXL17kJQoKJU5I6VssqlWrxmt8TJs2DZUrV+a9pl+/fggODsaPHz9gamqKZ8+e4cWLF/D392dVW1V2zJmamnIqQly9epXVryoJS/Vo2LBhsVQrysnJ4ZAzxK4bYgPEa9asQePGjVnJC69fv0bTpk0ZA0gBKUkgYmWGOjnw+PFjRg5YWVkxJFigIJFFJpNpDGCo/obBwcGoXr06dHR0Ck3O9PDwYLIxa9euzVTs2bVrFy9pAhC23kmtlCDGAJdCvBc6D0aMGAELCwvUq1cPw4cPF1TlSSjBXErFpO/fv6N///7Q1dVl9DW5XI7u3buLTo5Qhb29PVNB18PDg0n8efjwISuINHXqVDg7O/MGgG/fvg1nZ2eMHDmScYyrEomEODONjIyY31T593n69KmgyuKnTp1C9+7dYWxsDHt7e05VQbFzpqh0HlW0a9cOu3fvFnWN2KCZWKJrcaI4nYLVqlXDn3/+yTn/559/ql1/xWDdunVwdHTErl27YGJigp07dyIyMpL5P1BgD06ZMoW55tmzZzAyMkLTpk0xcuRImJqaYsmSJax+pciv4qowbWlpyQT4ld+7c+fOqd0FQAxJU4y+1qRJE/z+++8AgP79+yMgIADbt29HUFAQAgICOH0fPXoUlSpVgrm5OWbOnInMzExR956VlYUbN27wVjuqU6cOQ9QIDQ1Fs2bNcP78efTs2RMVKlRg2mmqDqmpUuTZs2c1HsqoUKECI6eVcf78eXh7ewMoIDvyVYAUiry8PDx69Ajnzp3jjOXHjx/Ys2cPmjZtCiMjI7Rr1w579+7VWr1NAWXCBN8BAKtWrUKrVq2Ya0xNTVG9enU0aNAADRo0gJ2dHWtXEinVX/9tEKNnnjlzRiMJWhVCgkh6enp49eqV2j5evnwJfX191jmZTIbZs2dziIGKIzIykjPXBw4cCF9fX1Z1n8ePH6NixYro168f53vNzc059g1QEDTjI3KITfD7t+zcZGJiwvgNHR0dGd362bNnhQqYiiXoipW7QEEQafjw4ZzzQ4cORZ06ddR+lzLJ/p+Ewn8khsQhxnb8888/YWhoCB8fH/Tr1w/9+vWDr68vDA0NeXUUBcT6EMUSbYrLR9m2bVusXbsWQEESYbly5RAZGYnKlSsjMDCQ95rirM5YnH4+MTqsmOcilsQHiA8km5mZMbqms7MzQ1xQ6KmqkEJGVMUff/wBX19fznkhSSNFhfz8fJw8eRJHjhzhDXYXt+0g9b3Ly8vDoUOH0LZtW9baq6Ojg/DwcA7BUZMOJpR8ogwptgkfUVQquaFdu3aMT0ybT1MVyomk6lC9enUmDsaHOXPmoHr16qxzUpLZjYyMmOet/Oxv377Ne41Y+SVEp1ZAjAyQknghFAp/mirGjBnDuwuEJty5c6fQhOFSpUohLi6Oc/7YsWO8tu+7d+/QoEED3iqhRkZGjG6jTjdWl0QjVg+UQvopTvTo0QNBQUFIS0tjzfVjx44x9mlhIMT38fPnT2zZsoWJoykSWbVBLDla6Hu6ZcsWjUdhcOTIEejo6GDMmDEs++3Vq1cIDw+Hnp4ezp07h9DQUCY+pRo/VeDDhw//+C4WYiFU/5LiWy3uXTaLC35+foiIiOCcj4iIUKtLhYaGYsGCBZzzUVFR6Ny5s+SxiJFH/0Sc0sXFhXd3Fz6YmJgw/knVAgWF3dG0X79+jK9l+fLlTLKUpaUlbzI9ULhdxLShOAtWiZUvYv3ZYnUwsXJdbOERoZAyB37hFzRBl37hF/7HsHXrVo1/79mzJxERXbx4kU6cOEHW1tZkbW1Nhw8fpqFDh1LdunXp9OnTZGJiUiTjycvLIzMzMyIisra2plevXpGnpyeVKVOGHj16xLSbPn06eXp6UlxcHBkaGjLng4ODKTw8nJo1a0atW7em69ev065du4iISF9fn3bv3k2zZs2ixMREMjIyIl9fXypTpgxrDJGRkdSnTx9as2YN63ytWrWoVq1aNGjQIKpbty4BoJMnT9LMmTNJX1+fnj59Sra2tqxrZs6cSU2bNqUePXpQfHw8LV26lOzs7OjTp09UpkwZcnZ2psuXL1OlSpUoJSWFABTJc5SKnz9/0vXr1+nKlSv0/Plzzv0QEWVlZVGpUqWIiKhEiRL0/v178vDwIF9fX7p582ahx7Bs2TLq1q0b1ahRg16+fEk7d+6ktm3bFrpfIqK7d+9SQEAAERHt2bOHfHx86MKFCxQfH0+DBw+mqVOnMm3DwsKoatWqlJiYSFZWVsz5du3a0YABAwR/56VLl1hzVAxU58Nff/1Fc+fO1Xqd0PdICsqVK0d79uyhiRMnss7v3r2b3N3dWeeuXbtG69evp127dlHZsmWpW7dudPHiRVq5ciV5e3sXahxERBUrVqSkpCTO+aioKNLR0WE+z5w5kxo0aECtWrUiIqKkpCQaMGAA9erVi7y8vCgqKoocHBxo+vTpdOjQIWrevDnp6enRoUOHNH5/mzZtBI1TdQ7Uq1ePjh8/Tr6+vhQSEkJhYWF06tQpOn78OAUGBnKu9/HxocTERHJ1daXq1avTggULSF9fn9auXUtubm6stmL7FospU6ZQ+/bt6enTp9SoUSMiIjp58iTt3LmT9u7dy3vNokWLqGPHjlSqVCnKycmh+vXr05s3b6hmzZo0e/ZsIip47+vUqcO6btu2beTo6EgAaOPGjbR06VLatm0b83cLCwuNY7WwsGDWsKIE3/ssdt04f/48Xbt2jdzd3al+/fo0depU+vDhA23bto18fHw4/a9atYqePHlCzs7O5OzsTEREqampZGBgQO/fv2d9b2pqKtWqVYuIiIyMjOjr169ERNSjRw+qUaMGLV++nNO/WJlRrlw5OnbsGA0fPpx1/ujRo8yc/PTpE5UuXZr5m6WlJZmYmNDHjx/J3Nyc58lyf1O5XE6enp7MWloYtGvXjk6ePEnVq1enESNGUPfu3WnDhg2UmppK4eHhvNcIWe+io6MljefNmzfk6+tLRESmpqb0+fNnIiJq1aoVTZkyhdW2RIkSzO/o6OhId+/eJV9fX8rIyKDs7Gze/oXOg6SkJPL39yeigjVSGTKZjLdvPT09ksvlRERUqlQpSk1NJS8vL7KwsKC0tDSm3YQJEygxMZHOnDlDzZo1Y843btyYpk+fTuPHj+f0ra+vT+vWraOpU6dSUlISZWZmkr+/P2d9kYL27dtT165dyd3dnT5+/EjNmzcnIqJbt25RuXLlmHa7du2ixYsXU8WKFTl9VKpUiRYuXEidO3emPn360KZNm6hLly7MPBEKOzs7evLkCbm4uLDOnz9/niPXFXj58iVt3ryZNm3aRBkZGZSenk47duygTp06cX4rsXOmqHQeVbRs2ZLGjh1L9+/fJ19fX9LT02P9nW8dNTExoR8/fhARkb29PT19+pQqVKhAREQfPnzgtN+0aZOgsQi1eVTx9OlTio6OpgcPHhARkbe3N4WFhVHZsmV527Zs2ZKICuZyVlYWyWQyCg8Pp0aNGtGMGTNY7XNzc+nMmTP09OlT6tq1K5mZmdGrV6/I3NycTE1NWW2TkpLI1dWV852urq50//59jfcmBP379ycjIyOaPHkyZWdnU9euXcnBwYFiYmKoS5cuRER0/fp1GjduHHPN77//Th4eHhQXF0dEBXrZsmXLaNSoUUwbKfIrMjKSVq9eTT179mTsOCKi2rVrU2RkpOR7bNq0KUVHR9PatWuJqEDGZWZm0rRp06hFixZMu58/f9KBAwdo/fr1dO7cOWrWrBlFRUVRaGgoTZo0Sa0OK0ZfmzNnDvNcZs+eTT179qQhQ4aQu7s7bdy4kWl39epVioiIoMuXL9PgwYMZe1wsjI2NqXLlyrx/mzx5MmVlZRFRgc7cqlUrqlu3LllZWdHu3buZdvn5+aK/l4ioQYMGnHPKMisvL4/5/9OnT3l1BHNzc3r27BkREbm7uzOy4O3bt/Tbb7/RyZMn6d27dxzbSblvIqLLly9T165d6cWLF5y2MpmMrKysqHz58tS9e3fatWsXlShRgoiIQkNDBd1r/fr1tbb5/fffWe8REdGOHTuYObJ9+3ZasWIFo5vcuXOHFixYoLa/5s2b09KlSwWN7z8FMXqm8jP89u0bsx4ooDo/pk2bJuj7dXXVu3t1dHQoNzeXdc7Z2ZnWrVunsV+FTq7AggULqFmzZlS+fHlGB/7777+pbt26tHDhQs71+fn5nHWRqEDP4nvfhOpfCqxcuZLWrl1LoaGhtHnzZho3bhy5ubnR1KlT6dOnTxrvrSjh5uZGKSkp5OzsTOXLl6c9e/ZQQEAAHT58mCwtLSX3K5PJ6OvXr2RoaEgAGJn+5csXVjvFnBEqd5URGRlJjRs3psTERMaWPnnyJF27do3i4+NZbfPy8mjOnDm0evVqevv2LSUnJ5ObmxtNmTKFXFxcqF+/fpLvVSgUck2he4SHhzP3rHgHVSHGdmzRogU9fvyYVq1axehFrVu3psGDB5OTk5PacYn1IaakpIi67+LyUS5evJgyMzOJiGjGjBmUmZnJ+L0WL17Me82DBw9o586dRESkq6tLOTk5ZGpqSjNnzqS2bdvSkCFDJI+nOP18YnRYMc/l/fv3vHaSon8+iP39PT096dGjR+Ti4kKVKlWiNWvWkIuLC61evZrs7e057YXa4WvWrKHjx4+Tvr4+hYWFUfXq1enUqVM0ZswYSk5O5rUd2rZtq/a+CoOMjAwKCwujmzdvUo0aNWjRokXUokULunjxIhEVrAnx8fEs+7W4bQep751cLqfWrVtT8+bN6dWrV8z58+fP04YNG6hKlSrk5eVFPXr0YGwQdUhKSqIdO3ZwzpcqVYrXblSMVaxtcvr0aY3jEAMLCwtmjmjzaRIV6IE+Pj4kl8vp8+fPvD5wBSpWrEh9+/al0aNHU4UKFRgfuAKHDx+m2bNnc97T0qVL0+vXr8nZ2ZnKli1L8fHxVLlyZbp27RoZGBjwflfVqlXpzz//pBEjRhDR/+n269evp5o1a3Lai5VfQnRqBcTIgBo1atD58+fJy8uLWrRoQWPGjKGkpCSKjY2lGjVqCP5OddiwYQPFx8czfV25coVSU1OpZ8+eNHr0aKadujVEgc+fPzOySSo6d+5M/fr1o4ULFzLr/IULF2js2LG8tk1oaCi9fPmS5syZQ7a2tixZ9ttvv1FWVhYZGhrSkiVL1H6nTCajkSNHss6J1QMbNWpEhw4dIn9/f+rTpw+Fh4fTvn376Pr169S+fXuJT0M64uPjKS4ujuXfJiqwSV+8eEFExNE9NUHVlhHi+9DV1aXBgwczupc6nU4Vnz59YmxMc3NzRv+vU6cOrz4i9D0NCwtjXffz50/Kzs4mfX19MjY2LlQsJioqisaPH8/x+9jb29PixYvJ2NiYmjRpQnZ2dkyMVGEHqCIxMZFKliwpeSyqePfuHb17945jp/H5joVCqP4lxbcq1j/59OlT2rRpEz19+pRiYmKoVKlSdPToUXJ2dmZ8sgq4urpq1HcUvhsF+vbtq/E5KNtiDx48oD179nDa9O3blxWDUfaBeHt70+zZs+nMmTPMGnT58mW6cOECjRkzRuN3a4IYefRPxClnzZpFU6dOpS1btpCxsbHGtpaWlvT69WuOLnjr1i1ydHQs1DjWrl3LvAfDhg0jKysrunjxIrVp04YGDRrEe41YX4YYSNHthEKsfBHrzxarg4mV66rcp6KC0DkwevRomjVrFpmYmLD0ID5o041+4f9z/IeIwb/wC/8xWFpasg4TExPIZDIYGBiwsoDMzMx4s3OGDRuG0qVLIyEhoUgy0oRW8HFwcNC4xenZs2chk8mwYcMG0WMoUaIE7zZ2CiQmJkImkzHbJJYpU4apfsiHo0ePQiaTMRkl/8bsklOnTqF///4oUaIELCws0KdPH5w4cYI3M7tq1arM/bZu3Ro9evTA33//jXHjxsHNzU30d/Nti7pv3z44OTmhX79+RbJdqgImJiZMNlXr1q2ZrPIXL17A0NCQ1VbsNrjFUflRbFUYBYS+R1Kwb98+6OjoICgoCDNnzsTMmTMRFBQEXV1dxMbGMu18fX1RpkwZTJgwgal+B2iufiAF6enpWLduHcaPH89sI3fjxg1m+wigIPvs2rVrzOeJEyeyKjrs2bMHXl5eANjPXEx1MED4HPj48SOzBVleXh7mzp2L1q1bY/To0byVL44dO8Zs9fH48WN4enpCJpPB2tqas42q2L6l4MiRI6hVqxaMjY1hZWWFhg0bcioY8OHcuXNYsWIF5s+fj+PHj7P+Vr58edaW46pz/fLly3B2di6S8QsFX4UM5UMZYteNa9euMZX13r59i6CgIJiZmaFy5cpMVXVliNluRkq1AbEyY8OGDTAyMsLUqVOZChZTpkyBsbExU01HJpPh9OnTrO0oTUxM8Oeff2rcovKfwsWLF7Fo0SIcOnRIbZuiXu+UIaaib2hoKFMZZubMmbCxsUH//v1RpkwZ3soqQPFWnRBagUxMxaS6desiPT2d+Xzw4EFkZ2cXapyq+PHjB6KiojBy5EiWvFm8eDHWrVvHfDYwMEBqaqraflJTUxkZoC6TWhvmzJkDb29vXL58GWZmZjh37hy2b98OGxsbLF26lNV23759aN68OUxMTNCxY0ccOHAA379/17ieip0zYnUeoRC7jgLSqpX9/PkTx48fx+rVq5lKIi9fvmRtnSbU5lGG2K3VxFSxF1u12N/fHz169GDt+vH9+3f06NGjyCtyZWVl8c5rQ0ND1rvRqFEjTJ48mfn85MkTTtVHKfKrsBWmVaF4T9PS0uDt7Q0vLy/o6uqiRo0asLKygqenJ+t+bWxsULduXaxZs4alO2nTYcXoa0Kh2Mpx1KhRoioVtW/fnrd61vz589GxY0et3/vx40etFbqEIiMjg3W8f/8e8fHxqF69Ok6cOMFqW7t2bTRr1gzv3r1jzr179w7NmjVD3bp1ARRU0FVspd6sWTN4e3tj5cqV2L9/Pw4cOMA6VFGpUiWEhITg/v37SE9P54ytRIkSqFevHtauXcuq+C/GftG2nZ2dnR0zvwHA2tqa9fnRo0cseSGl+uu/DWL0zKysLAwbNgw2NjZa9W8FtNmDMpkMLVq0UFvNpkWLFkVWXSk/Px9xcXFYsGABli1bxqkSrYw2bdqgXr16rK2p//77b9SvXx/BwcGc9mIrwP5bdm5avHgxI6eOHz8OQ0NDGBgYQC6XIzo6WnK/6ra9LkxlQz7cunULXbt2hbe3N6pUqYI+ffrwbmE9Y8YMuLm5Yfv27azKgrt27UKNGjUKPQ4hkMlkLPkpBP9EpbrC2FT5+fla16PitNnEojirMxann6+4dmKqW7cuY9sodlMCCipNBgUF8V5z9uxZ/Pz5k3P+58+fvDJ127Zt2LRpEwDg+vXrsLa2hlwuh6GhIXbt2sVpL8QOnzt3LvT09FClShWYmJjA2NgYs2fPhp2dHebOnfuPb8/ar18/uLu7IzIyEtWrV0fNmjVRo0YNXL58GVevXkWDBg1Y1fmB4rcdCvveqdvqOTMzExs2bEDt2rWZ3XSio6N5q0U6OjoyOy8o2wyxsbFqxyDFNvlPQtVHrVjfNNnW3bp1g0wmg5eXF4KDgxEcHIzy5ctDLpejS5cunO+IiIjA7NmzARS8B7q6uihXrhz09fV5qxgCBf5dU1NTDB48GIaGhggLC0OTJk1gYmKC69evc9pLkV9Ct4gWIwOePn3K+CEzMzMxaNAg+Pr6on379oWqJAeA2Q1D29GwYUPmGlWbLjo6GhEREXBwcBBc0Vsdvn//jpEjR0JfX5/RjQwMDDBq1CjeXQSNjIx4/dGFhVg9MC8vj7UG7Ny5EyNGjMDSpUtZ8kwVaWlpWLFiBSIiIgTtDCYUpqamjN6nLGeuXbvG6NPa4geabBmhvo/69esz75BQ+Pr6MvGawMBAjBkzBkDBvOOrMl4YPSM5ORmBgYEaY+RCYGZmxvgm+fDw4UPIZDK8ePGC2dFOLpdzdrczNzeHXC4XXb2aD9evX0eFChV45W9hbQ2h+pcU36oY3e7MmTMMR0FfX5+Zh3PnzkWHDh04fUdHR7OOqKgodO3aFSVLluTd/UexFimOli1bokyZMrCwsOCsvaVLl2Z2vFPG7t27WTspadpxQ9vuG0IhVR4VF/z8/GBmZgZTU1P4+Pho3KlhzJgxqFOnDl6/fs1UWj1//jzc3NxYcWqpO0OKhZTdbISiOHQ7qfJFrD9brA4mVq4DBbpTrVq1YG9vz+g6S5Ys4fWZFjUaNGjAxPuE6ka/8L+JXwTdX/gF8CvU1apVw9atW3nbDxs2DJaWlkXi/BYa2NTX10daWpraftLS0qCnp4fw8HBmC1BV40ydsWZoaKjRKH/+/DmLzClkLDo6Osznf5ti5+DgAENDQwQHB2Pv3r28RroyxDo/tUHqdqlSEBAQgIiICCQkJMDQ0JBxPFy6dImjwIjdBld1S+W+ffsiIiKCdzshoZDL5awAi7JDWxOKgyCgjOvXr6Nbt26oXLkyKleujG7durHITkDBe9GjRw/Ex8ezAipFSdBNTEyEtbU1ypUrB11dXeY3mjRpEnr06MG0UyVb1a5dG5GRkcznlJQUmJqaFno8xTEH1KEoiRP/JHJycnjHbWRkxJKjixcvZhEjXrx4wTFi+vTpo/UoTNKDKslj7969mDhxIhwdHbF+/XpWW7HrRnFCShKIFJmxcuVK1raprq6urK2stAUPikquFyekrHe5ubnYt28fZs2ahVmzZiE2Nha5ubmcdmIMcCnEe7Hz4PHjxzh27BhDitUkX4QSzMVse6iakGJmZiYoIaU4YGNjwxtUUuDq1auwtrYGwB23UOTn5yMyMpIhicpkMhgaGrLIjgro6Ohg4sSJnKCkpvVU7JwRq/MUJ8QGzcQSXZWhLYggdms1MU7Btm3bonv37vj+/TvrmZ8+fRrlypXj9H3lyhWUKlUKNjY2CAwMRGBgIGxsbFCqVClmm8jCYNasWVp1TAcHB+a78vLyYG5uzmwRDBRsH6b6bkuRX66urkwij/Kz2bJlC5NQJQbK7+nPnz+xbds2jB07FkOGDMG6des4yQBFQdJUoLD6WpkyZSQFHKytrXkTh+7cuVNk73RmZib+/PNPrFq1SithmA9nzpzhbLH88OFDeHp6Ql9fH2XLlkXZsmWhr6+P8uXLM1sn79+/n/FLmJqaMslPQmBsbMxsAceHnJwcbN++HQ0bNoSRkRHat2+P2NhY6OnpCfrthWxnZ2hoqDHo+ODBA5bOq2oPquLNmzf/en1KjJ45dOhQeHl5Mc9y48aNmDVrFkqXLo3t27dz+k5MTISNjY1Ge1DVRlN3iEF6ejqWLVum9u/qbB5lpKamws/PD3p6enBzc4Obmxv09PTg7+/P618Sm+D3b9siWIHnz5/jjz/+KHSinrbtr/m2wS5OlC1blkk6UF67Hjx4AEtLy39kDDKZTFCQVRlSbMesrCw8ePCAlXSp6feUYlNt2bIFPj4+MDAwgIGBAXx9fdX6pIvaR1kYSEk0E4ri9PMVF2lRLIkPKPxW1VlZWfh/7L11XBTr+z5+79LdiihKSImEiomBDSaiqNjd3XUMDgYGdiuCjYF6bNSjYh8DsBEDu4+oYBHX7w9+O5+d3dllZpb1nPf3eL1e89KdnZ19dnjmmTuu+7qvXr2Kt2/fcr7Pxw93d3dHXFwcgEKSoEQiQfPmzZn8gio4Ozvj3bt3Svs/fPigEUHEwcGBWc+ePXvGFEPLcOnSJZQsWZL1GW37Dpred6mpqZBIJGqPuXv3LsaOHQt7e3sYGhqiZcuWrPf5kk/kIcY3OX36tNqtuPD9+3dWgSlQ+MyU2RNC2hQnJCSgdevWTHFi69atebdH51PMDhQWZ/bp0wdVq1aFl5cXOnfurFK4QOj6JbRFtDyKWgP+TVD061xcXFC9enVMnDiRk5QuBjk5Obh+/TquX7+OnJwclcdVqlSJKbDXJorLDpTH8ePHYWxsjIoVK0JXVxf+/v6wtLSEhYWFxqSfkJAQJk4ny8vl5+cjPDycIS7K251xcXGwt7fHhAkTGMGhCRMmoFSpUsxzRR58Yx8JCQlwcXHB0qVLcf78eV42mFBytKZ2xuXLl4ulVb26WPCDBw9gbGwMAIiLi8OGDRsgkUiwePFixMXFMdvWrVtx/vx5jcYig6+vL9q0aYOLFy/i0aNHxdYiHtBu0YiQc9eoUYM5Vn4eXrp0SSXpjwvLli3j7Vfn5+ejX79+iI6OZu2fMWMGLC0tMWfOHCQnJyM5ORmzZ8+GpaUlIiMjeY/l/0UUJdojj+/fv6NPnz7Q1dWFRCJhip66dOnCylPJ3zdFbfKQFdHUqFFDbRGNDEJjGUKgDcEqseuLpvHsomwwoev6ihUrYGtri6ioKFaebsOGDQgKCir6QqiB0DnwC7+gDr8Iur/wC/8/FA3qWbNmqXWABw4cWGRgRSy4EpvlypVTS3o7fPgwypUrJ6pCw8fHB7GxsSrPvX79evj4+DCvi1LzTU5ORqlSpYr6mf8Y1qxZw1KtE4r/pcDHyZMnGTJ5z549mf0TJ05Uckzat2+Pvn37Avg/B/zz589o0KCB4ASeWCiq/ejq6qJJkyZKqj988LMJnc+ePUNUVBRcXV3h4OCA0aNH49q1a7wT3HzQsGFDjB07FgDb4D137hzKlSvHHFe2bFkmaPr9+3cYGRmxFLuuX7/+U5OUjx8/VrsJxcePH3lv/wTy8/MRGRkJBwcHFnlqypQpDNHVysoKZ8+eVXmOs2fPKv2NJBIJnJycGLViVVtxY8uWLWjVqhVrn9DnhjZRXEUgqtaM3NxcxMfH49WrVwAKle0UkwhA0ckD2abNSl0uhXZVGx8U9bzLyMiAm5sbjI2NmeplY2NjeHh44P79+2rPzTcJwhd858G7d+/QoEEDhjAtuz979uyJUaNGaTQGIYpJYhXji8K+ffvw48cP5v985kD79u0RFham8pxhYWEIDw/XeGxA4TPp1q1buHTpEud9BAD9+vWDhYUFatWqhZUrVzLBpeIsePk32DxiIZToqgh1SQQDAwNOlbz09HTOynchQUExqsXZ2dlYvXo1U1y4Zs2aIokCfOHr6wupVIqaNWti+fLlnOtcp06d0KJFCzx58gQLFiyAqakp6/t37doFX19fjcciRGGaD4QS6TUlaaqCv7+/ksKFqk1TqCKB3rlzB4aGhiqVRLk2Lly7dg329vYwNzeHjo4O7OzsIJFIYGJiwpuAcufOHU41wfz8fBw+fJgh+x45cgT5+fmc5/Dy8lIqElSH+vXr4/Dhw7yOvX//PiZPnowyZcpAIpGgU6dOSEpK4iy6kcHf358pVpK/r69du8YQZ8qXL49du3apPEdCQgJcXV2Z1z9T/fVnQpWd6ejoyJCOZGQboDDQzxWP4usPFheOHz+OiIgIGBoaKqnQ8vF5FFFQUICkpCQsWbIES5YsUeoyogn+jZ2bfjbErLvyPrMQ31q+YFN+Lt66dUtj5VS+4EogFpVcFeI7vnnzBs2bN+etCqcKRflUCxYsgLGxMcaNG8fY6WPHjoWxsTFiYmI0Pr86aOqbalOdkQvFFecryobV5LoIIfEBqpWg09PTlbqwFAe4/HDFrhH6+vpqCzhlUGVzvnr1Cnp6eqLHqKOjw1LTNzIyYsUXXr58qVKNVlu+gyKE3neqFHS5kJeXhz179igRdPmSTwDNOvaoKjoXuvbJIzY2FkOGDGGKjyZMmMAonTZq1IiT6F0c+Pr1K+bNm6eVc8uwc+dOXsepW7/42NS/wI28vDykpaVxzu8vX74gLS2N07c6evQoatWqhZMnT+Ldu3ecNs+9e/ewa9cuJrZ34MAB1KlTBwEBAYiKihL9PFIkm6rbuFC1alVMnToVwP/Nl8+fP6NVq1ZYsWKFqDHJcOPGDZQoUQLBwcHQ19dHu3bt4OXlhZIlS3LGeRs0aMAolcpjy5YtqFevntJ+vrGP4hC/EEOOFmJnpKSkaPycrlq1qlpbb8GCBahatSpr36lTp5i4rzZgamqqtshXE2izE6aQc5uYmDD3tWJ8UkgXqwcPHgiaA3fv3oW9vT1rX0FBAWJiYliiMKVLl8aiRYsErzG3b99mFEb5QtP16N+GJ0+e4ODBg0hISOCMb4uBJkU0/2sQur4Udzy7KBS1rnt5eTHK6PL39o0bN2BjYyP6e/9Lc+AXfg4kAEC/8Au/QKmpqVS3bl369OnTPz0UTowYMYL+/PNPOnHiBNnZ2bHee/PmDTVu3Jjq169PixYtEnzuhQsXUlRUFG3atImaNWvGeu/gwYPUvXt3mjRpEo0aNYqIiHr16kUPHjygY8eOkb6+Puv479+/U9OmTcnS0pIiIyN5fb+vr6/gMf8Cf+Tn59OnT5/IysqK2ZeZmUnGxsZUokQJZt+zZ8+oadOmBIAyMjIoICCAMjIyyNbWlpKTk1nHags9e/bkddyGDRu0PJL/w8ePH+nYsWOUmZlJEomEXFxcqGHDhmRubq7yM3/++SfFxsZSYmIiffv2jcaMGUN9+vQhd3d3jcZiYWFB165dI1dXVzIzM6O0tDRycXGhx48fk4eHB3379o2IiAYOHEhpaWkUHR1Ne/fupfj4eHrx4gVzv27ZsoUWLVpEly9fZs79+fNnunfvHnl4eJCpqSldu3aNFi1aRF+/fqXQ0FDq3Lmz6HFLpVKSSCQq38/Pz6ewsDDe59u7d6/a8xERASCJREL5+fm8zyuDtbU13bt3j2xtbcnKykrtd/39999K+yIjIyk+Pp4iIyOpb9++dPPmTXJxcaGEhARatGgRXbhwgRo2bEiVK1emefPmcZ539OjRlJqaSidOnGD2DR48mLZt20blypWjnj17UpcuXcja2lrw7xOKhw8fkq+vL2VnZzP7+Dw3DAwMqGTJkry+49q1a6zXfObMz4SxsTHduXOHypUrp/G54uPjeR/bvXt3QeeWSqW8jhN7byiiWbNmBIC2bNnCzMX3799Tly5dSCqV0sGDB3mfS4j9pW79LQrdunWjN2/e0Lp168jLy4tZR48ePUqjRo2iW7duiT732bNnKSQkhLp06UJxcXHUv39/un37Np0/f55Onz5NVapUYY6VSqX06tUr5tkqv6ZrAvnzqpsP8nPg9u3bVL16dfL29qZRo0aRp6cnAaA7d+7QwoUL6fbt23Tx4kXy9vbWaGxC8PXrV9qxYwfFxsbSpUuXqGnTpnTw4EFKTU2lihUrEpFmc0abNs/p06dp/vz5dOfOHSIiqlChAo0dO5bq1Kkj+pzysLGxofPnz5OHhwdr3mRmZlKFChXoy5cvaj+vzudxdHSkmJgYCg8PZ+3fsWMHjRkzhp48eSJ63FZWVnTu3DmqUKECa9xnz56ltm3b0uvXr0WfWyxu3bpFW7Zsoe3bt9OzZ8+ocePG1LlzZwoNDSVjY2PKzMykxo0b04MHD0hHR4eWLFlCAwcOZD4fGhpKzs7ONGPGDN7fybV+AaBZs2bR7Nmzmb+fgYEBjRkzhn7//XfBv0sqlVJUVBSZmpqqPW7YsGFK+x48eEAbNmyg+Ph4ev78OUVERFCPHj2oQYMGpKOjI8he8/Pz433stGnTiIjowoUL9P79e2rRogXz3saNG2natGmUk5NDoaGhtHTpUjIwMGB9vlq1atSiRQuaOnUqa//06dNp//79gvxNLj8jKCiI3N3dadWqVWRhYUFpaWmkp6dHXbp0oeHDh7Ouy/Xr11mfBUAvX76kOXPmUF5eHp09e5bze799+0YGBgZq7Z+kpCRasGABrV69mpycnIr8LXv27KEpU6bQ2LFjycfHh/T09Fjvc12XgoICOnr0KK1fv572799PZmZm9O7dO87zGxsb0+3bt8nJyYl1Xz98+JAqVKhA3759o+HDh9Px48fp6tWrZGhoyPr8169fKSAggBo1akSLFy8mon+nP6hNmJqa0u3bt6ls2bJUpkwZSkxMpGrVqtGjR4/Ix8eHZX8T8fcHc3NzycjIiPXc5IunT5/Shg0baMOGDfTkyRPq2LEjde3alRo2bMiaQ3x8np+JgoICKigoIF1dXSIi2r59O50/f57c3Nyof//+SrGr4sSSJUt4H8u19vJBUf4RUeF6I1tPi4LsOB0dHXr58iVjO3J9B5dvXaVKFRo5ciR16dKFNRcjIyPp2LFjdObMGQG/ThwU7eniRufOnenx48e0aNEiCgoKoj179tDr168pKiqKFixYQM2bNy+W75HZEt26dWPtj4+Pp+nTp9OjR4+K5Xu4oE3f9H8ZP+O6yGyHffv2UXBwMMu2yc/Pp+vXr5OHhwcdOXKEicfzQUxMjOCxSKVSev36NZNvMDMzo+vXr5OzszPn8X/88QcRFdrC8fHxZGFhwRr7iRMn6NixY5Seni54LLLxqPOVX79+TQ4ODj89LqQJ0tLSqHLlysUy5qdPn9KNGzcoOzubKlWqRG5ubkrHKF5Dc3NzSk1N5RVv+PjxI+t1bm4upaSk0G+//UYzZ86khg0bChrvzJkzaebMmRQYGEjXrl2j9u3b0969e2nEiBEklUppyZIl1KJFC1q5ciXrc+/fvycbGxvmN69du5a+fv1KrVq1YvnWb9++pUuXLpG+vj41bNiQdHR0KDc3l1asWEGzZ8+mvLw8io2N5T3eVq1asV7n5eXR3bt3SV9fnxXX37dvH02dOpXu3r1L379/F3RNFFGUTT1o0CDe59qwYUOR9oIMXDFtvqhfv77a7/nzzz9Fn1sI4uLiaNmyZXTp0iXS0dFhvZeXl0c1atSgESNGUJcuXVjvyeJlir9BZvPs2rWL2rdvz9hGa9asof79+1NQUBDp6OjQ0aNHKSoqisaPHy/YDpSdsyhqhqrYrZmZGaWmppKrqytZWVnR2bNnydvbm9LS0qh169aUmZnJezxc+PjxIy1btozS0tIoOzubKleuTIMHD6ZSpUopHWtsbExpaWlK69C9e/fI399fKT7FN/bx+PFjtWMsjvg8X8ieeTLI/Ptly5aRo6MjHT58WPS54+PjaeDAgTR//nzq168f48fk5eXR6tWraezYsbRixQrq0aMH5+e/fftGP378YO3TJGZOVPhs79q1K7Vt21aj82gKbcZWy5QpQzt27KBatWqx1tw9e/bQmDFj6MGDB7zOM3fuXFqxYgXve+7QoUPUvXt3evv2Lef7nz9/JqLCe5wvcnJyaPv27bR+/Xq6ePEiVahQgW7evMn785quR/806tatS3/88QdZWloSUeH92rhxYzIyMhJ0HnX3UqVKlWjkyJHUrVs31nxJSUmhkJAQevXqVbH8FnX4WXkzefBZX/is6YprqDoo2mBCYWRkRHfv3qVy5cqx/lYZGRnk6+tLX79+FXVeoXPg5MmTdO3aNapRowYFBgbS6tWraebMmQzXYsmSJYLn6C/8vwXdf3oAv/ALPxvqDOrAwECl4zdv3kxt2rQhExOTYhuDkMRmYmIiERUGzw8dOkSurq7UpUsXFnli69atZG9vr5SQVMSnT5/ozz//JE9PT/L09GT2Dx8+nM6fP08tWrQgDw8P8vLyYs6dkZFBrVu3phEjRjDHR0ZGUkBAALm5udHgwYNZY1mxYgV9//6d3r59S/v37//XGHZhYWEUFxdH5ubmRV7/xMRErQc/5ZGTk0OnT5+mJ0+eKBk8YpM3MsTGxlL9+vWVgqpcCd0yZcpQWloaJSQkMA547969qXPnzoyxUBRZUR5igjxCEq1i7iOh2Lx5Mw0ZMkTJALawsKBVq1ZRhw4dOD/XoEEDatCgAX38+JG2bNlCsbGxNH/+fKpYsaJS0l4IDAwMOI3xe/fusYj7v//+O4WFhVG9evXI1NSU4uPjWQnJ2NhYatKkCfM6OTmZWrRoQdnZ2WRlZUXbtm2jdu3aUenSpUlHR4cSExPpy5cv1LdvX1FzICUlhbVfFtyNiYmhmTNnEhGxAvpF4eTJk7yPFYOFCxcyjvDChQt5/14ZNm7cSGvWrKGGDRvSgAEDmP1+fn509+5dIiIaNGgQdezYkZycnGjgwIFMYDA/P59WrFhBS5cupa1bt7LOu3z5coqJiaHExESKjY2liRMnUvPmzal3797UpEkTwePkg69fv9KSJUuodOnSrP18nhu+vr6ix7Rnzx7Wa9mciY+P5yRCnTlzhlavXk0PHjygXbt2UenSpWnTpk3k7OxMtWvXJiLN1oxq1apRSkqKoADgly9fONd1bSY2CwoKBH9Gk+fd6dOn6eLFiyyiuI2NDc2ZM4cCAwMFOeChoaGCifdC1lMZCSkpKYmOHj1KZcqUYb3v5ubGCgBXqlSJ9/yVEcxr165NqampNGfOHPLx8aGkpCSqXLkyXbhwgXx8fJQ+d/ToUWbtKygooBMnTigF0YQGJuTnAN/5UKFCBTp27Bj17t2bOnbsyPxuAOTp6UlHjx4VRc7V5J4zMjKi7t27U/fu3SkjI4M2bNhAV65cocDAQGrevDm1a9eO2rVrJ7pYg4/NIwabN2+mnj17UlhYGGPDnTt3jho2bEhxcXHUqVMnItLMniooKOC0nZ89e8YK4gr1eYiI+vbtS/369aOHDx9SrVq1mPFHR0cza4XYoGCTJk1o0aJFtGbNGiIq9AGys7Np2rRpTKHHzwzaERF5e3vTrFmzaNasWXTu3DnaunUrjRgxggYMGECfPn0iJycnunPnDt26dYvs7OzIwcGB9fkZM2ZQmTJlyNLSUqPCIYlEQpMnT6axY8fS/fv3KTs7mypUqFAkwVYdVq1apZSgVPxOLj/D1dWVoqKiKDIykiFptmjRgiFpCrHX+JLE5DFjxgyqX78+Q9C9ceMG9e7dm3r06EFeXl40b948cnBwoOnTp7M+99tvv1FYWBg9ePCAGjRoQEREJ06coG3bttHOnTspNDRU8FjkkZqaSqtXryapVEo6Ojr0/ft3cnFxoblz51L37t1Z652/vz9ngqNGjRpK5ICCggKaOXMmrVq1il6/fk337t0jFxcX+u2338jJyYl69+7NOr5Dhw705csXcnV1JWNjYyXCreJ6IUuq9erVi9knG5uq+SiVSikkJIRCQkLo7du3tGnTJpXXxd7enu7fv6/kW549e5YhgEyaNIl27NhBHh4eNGTIEIbYkJ6eTsuWLaO8vDyaNGkS89n/VeKt2Geei4sLPXr0iMqWLUuenp60Y8cOqlatGu3fv59JMsmDrz+op6dHZcuW5R1ryc3Npb1799K6devozJkzFBwcTPPmzaOIiAiaPHkyVahQQekzfHweeQwbNozKly+vtPYsW7aM7t+/T4sWLRJlf8kglUpZxUkdO3akjh078jqXpli4cCGv41StvXyg6B/J48KFC7RkyRIqKCgQvPb++eefjC0vxM+eOnUqde/enZ4/f04FBQWUmJhI6enptHHjRjpw4ICgMYiFWF+Tj+9IVHht9u3bRwEBASSVSqlcuXLUuHFjMjc3p9mzZ7MIupr4VC9fvmTsLnnUqlWLXr58qfH51eHfRrrVZpxPiA0r9rrIE97l8f79eypRogRrTZbZVADIzMyM5YPo6+tTjRo1qG/fvkSkHFNTBdk9Icam/u2338jY2JiIiH78+EFRUVFKdp9sbsnsKolEonSt9PT0yMnJiRYsWMB7DFxYt24dYwvn5eVRXFwc2draEtH/EVgUsWnTJlq9ejU9fPiQLly4QOXKlaOFCxeSi4sLtW7dWvAYhNx3qshTMoglK8sjMjKSxowZQ46OjuTo6Mjs//r1K82bN09tXkiIRhOXvd+4cWPS19enUaNG0dWrVwWNOy4ujtavX08RERF05coVql69Ou3YsYOxUytWrMiyI27cuEEtW7akp0+fkpubG23fvp2Cg4MpJyeHpFIpLVy4kHbt2kWhoaF09uxZatGiBX369IkkEgkFBATQhg0bKDQ0lHR1dWn69OnUvXt33nk9Rfv45s2b1KJFC3r69CkREbVu3ZpWrlxJ7du3p5s3b1Lfvn2ZwnRN1q+ibGoha4AY8R4x8Pf3Z73Ozc2l1NRUunnzJmtd0Hb+Zv369TRmzBhO31dXV5fGjRtHy5YtUyLoFmXzjB49msaNG0dRUVEUFxdHAwYMoNmzZzM50jVr1tDChQtp/Pjxgu1ATQtvTExMmFhzqVKl6MGDB0zMTlVhJV88efKEHB0dafLkyZzvlS1blrXP0dGR1q5dS3PnzmXtX7duHWudkoFv7INv/F0oOVrMfFSMJUgkErKzs6MGDRpo/Kzr3r073bhxg4YMGUITJ04kV1dXAkAPHz6k7OxsGjZsmNLz5cuXLzRu3DjasWMHvX//Xumcmuba161bR927d6ebN29SxYoVlWIOQmNxYmKIfGOrYuOTHTt2pPHjx9POnTtJIpFQQUEBnTt3jsaMGaNUMEeknCMAQK9evaK3b9/SihUrlI5XtB9k8ViZqI0qCCHmnjt3jtavX087duygr1+/0siRIyk2NpbF++ADbRYCioFQ4aSzZ8+ycm9dunThXZCUk5ND48ePL/JeSk9Pp7p16yq9b2FhQVlZWcxrTWIZRUHTuDNfCF1f+KzpfOOxsrFrUvzs7OxMqampSs+QI0eOkJeXF+/zKoLvHCAiWrt2LQ0cOJCcnZ1p8uTJNG3aNJo5cyZ17dqVpFIpbd68mcmf/sJ/F78Iur/wn4NQg3rkyJE0YMAAatWqFXXp0oWaNm2qNtnJB0ISmzJYWVnRpUuXaNKkSbR9+3Zm0be0tKROnTrRzJkzlZQU27dvT3Xr1qUhQ4Yw6jSZmZkEgLZv384EQqRSKe3cuZMSEhJo27ZtTDLFw8ODpk+frpTUKFOmDF24cIEGDRpEEydOZII8EomEGjduTMuWLVMy3P9pWFhYMAaMubl5kcaM0OCnWKSkpFCzZs3oy5cvlJOTQ9bW1vTu3TtG3VZTgu7s2bOpb9++VLp0aapXrx7Vq1ePgoKCqHz58krHJicnU61atahz584stdS8vDxKTk6munXr/rQgDx+IuY+E4Nq1a9SzZ0/q3LkzjRw5kiGi3759mxYtWkRdu3YlT09PtepgFhYWNGjQIBo0aBClpqYKqtjnQqtWrSgyMpJ27NhBRIXz78mTJzR+/HhWVatMAfDjx49kamqqtGbt3LmTZSRPmTKFwsPDKTIykmJjY6lDhw40ZMgQmjVrFhERRUVF0fLly6lv376i5gDXNQoICCAHBweaN28ehYWF/auS8fKOclFBdi48f/6c8x4rKCig3NxcIiokTIwaNYqGDh1KkyZNYpxGWQBm1KhR1K5dO6VzGBgYUEREBEVERNDjx48pLi6OBg0aRHl5eXTr1i2NyDyKTi8A+vz5MxkbG9PmzZtZxwp9bggFV/KkXbt25O3tTQkJCSzCyu7du6lr167UuXNnSklJYZQrPn78SLNmzaJDhw4RkWZrxqBBg2j06NH07NkzqlKlilJwX16B7u3bt9SzZ0+VFfT5+fn04sULiomJoalTpypVnn78+JGioqJozJgxvBWINYEmzzsDAwPO5Fh2djbp6+sLcsDFEO9VkaC4zi8LIOTk5DCJR3n8/fffLNUisWQuV1dXWrt2La9jFYNy/fv3Z73+mdXpNWrUoFu3blFqairdu3ePiApJy5UqVRJ9Tvl7DgDt2bOHLCwsKCAggIiIrl69SllZWUUGyN3c3GjWrFkUFRVFBw8eZJJ7mhRr8LF5xGDmzJk0d+5cGjlyJLNv2LBhFBMTQ7///jsTRNbEnuJDdCUSl0T47bffyMzMjBYsWEATJ04kImLIkDKbVGxQcMGCBdS0aVNGVbNTp06MavG2bdtUjlnx/pZ9d3HfGyYmJmRkZET6+vqsdU1XV5dlx5w7d44CAgLIwMCA2a9p4VCvXr1o8eLFZGZmxiLB5eTk0NChQ0XZj1euXNFIUVAVSVOovfbhwwfavHkzde/enfN5t3HjRtZ7aWlpFBUVxRyzfft2ql69OrOuOjo60rRp05QIui1btqS9e/fSrFmzaNeuXWRkZES+vr50/PhxqlevHhEVqj8kJSVR/fr1lRIfnz59olOnTlHTpk2V1HmJCskmMuJfiRIl6MmTJ+Tl5UUWFhZMwl4GxQSHVColOzs7JfVYokIbOz4+nubOncuQcIgKiQqLFi1SIugKXTs0TbbY2dmpJaf07duXhg8fTrGxsSSRSOjFixd04cIFGjNmDP32229ERFSyZEk6f/48DRw4kCZMmKAUO1ixYsVPsXe0DbF2Zs+ePSktLY3q1atHEyZMoJYtW9KyZcsoNzeXk2TH1x8kIpo8eTJNmjSJNm3aVGTXjdKlS5Onpyd16dKFtm/fznTfiYiIUPkZPj6PPHbv3s1JGqtVqxbNmTOHFi1aJMr+4lOwpaurS/b29lrrPvIzEptc/lF6ejpNmDCB9u/fT507d6bIyEjB665sjSQqTGg5OjpyqskprnWtW7em/fv3U2RkJJmYmNDUqVOpcuXKtH//fmrcuHFx/Wy1ENMUkK/vSFT4HJY9S62srOjt27fk7u5OPj4+SklVTXyq8uXL044dO1jFCkRECQkJjCKdNmOUmvimxd35RptxPqE2rJjrompOfv/+XUnFW2ZTOTk50ZgxY9QSCIXamkIT4XXr1mURSGvVqkUPHz5U+TlZIaizszNdvnyZIc4WF8qWLcvyqe3t7ZUKhhRJYitXrqSpU6fSiBEjKCoqipl7VlZWtGjRIlEEXSH3nbrYhHxxlCaYMWMGDRgwQCme8eXLF5oxY0aRwi2aomTJkqKIxk+ePGGKHwICAkhXV5el7u/r68sUIxARjRs3jnx8fGjLli20adMmatGiBTVv3pyZE0OHDqU5c+ZQaGgoTZkyhZo1a0aTJk2i+Ph4WrBgAbVp04ZmzZrFiqmKKWYnIho/fjyVL1+eli1bRtu2baNt27bRnTt3qHfv3nTkyBEWsV6T9asom3ro0KGiz60tqCKlTp8+ndUBQtv5m/T0dKpRo4bK96tWrcoocMqQm5tLkZGRtGrVKk4Fatl5ExISmEKEvn37UqNGjZj3mzRpwpB1hdqBmqq/1qhRg86ePUteXl7UrFkzGj16NN24cYMSExPVXgs+cHZ2Vllo4uzsrPRcX7hwIbVt25YOHz5M1atXJyKiv/76izIyMmj37t0qv0dfX5+zAFAe6enptHTpUubv5+XlRUOHDiUPDw/W9/OBjBwtZj6KXT/4Yv78+dSuXTvatm0bZWRkEFGhKmhERATn33Ps2LF08uRJWrlyJXXt2pWWL19Oz58/p9WrVxcL4evChQt07tw5zryGmDi1mBgi39iq2PjkrFmzaPDgweTo6Ej5+flUoUIFys/Pp06dOtGUKVOUzqFoV8liPEFBQZyEWEX7QXb8ggULmOLpolTIiQqvt3x3zTdv3lBcXBzFxsbSx48fKSIigk6dOkU1a9akXr16CSbnEv1cNWo+kBdOEhM7F+Ifjhs3jte9xKcwnUh8LokPtC1YJYPY9UXdmi50DdWk+HnUqFE0ePBg+vbtGwGgv/76i7Zt20azZ8+mdevWCRqHPPjOASKixYsX08KFC2no0KF05MgRatmyJVP4QFTYoW3ixIm/CLr/deAXfuEX1CI3Nxf79+9Hp06dYGJiAjs7OwwaNAjnzp37x8ZUUFCA169f4/Xr1ygoKFB5XMmSJZGamgoA2LJlC8qXL4+cnBysWLEC/v7+xTKWv//+G5cuXcKlS5fw/v37Yjnnfwn16tVD3759kZ+fD1NTUzx48ABPnjxB3bp1sXv37mL5jmfPnmHz5s3o168fPDw8IJVKUbp0aXTu3Jl1nFQqxevXr5U+/+7dO0il0mIZC19kZ2djypQpqFmzJlxdXeHs7MzafgZ69OiBdu3aqXy/bdu26Nmz508ZiwxZWVlo1KgRLC0toaOjA0dHR+jp6aFu3brIzs4WfV4LCwvcuXMHAPD9+3dIpVJm7QCAjIwMmJqaajx+RWRkZMDY2Jh5/fXrV+zbtw+fPn1SOvbjx4/Yt28fvn37BgC4d+8eOnbsiI8fPyodm5WVhYiICDx48EDjMTZs2BAbNmzg/B5VqFy5MjZt2gQAzH0NADNmzEDt2rVZx164cAHDhg1DSEgIQkJCMGzYMFy4cIHX9zx58gQzZsyAs7MzSpcujc+fP/MeIxc2bNiAuLg4Ztu4cSMOHz6Mv//+W9T5vnz5gr///htLlixR+XdS9Z4qPHjwACYmJqx9/v7+iI+PB8C+3teuXUPJkiVFjV0REolEaZNKpcy/8ujUqRMCAwNx+fJlmJiYICkpCZs2bYKHhwcOHDgAABg9ejT69u2r8vv69++PcePGiRrriRMn4OXlpfKaV6hQAadPnxZ1bkV07doV3t7euHjxIgoKClBQUIALFy6gYsWK6N69e7F8hzpkZmby3mQICQnBlClTABTOl4cPHyI/Px/h4eFo27atRuP5NzxHFy9ezHuTx8ePH5GUlIQDBw7gzZs3xT6ucePGoU+fPsjLy2P25eXloV+/fhgzZozg83FdZyHQ1t9KX18fGRkZSvszMjJgYGAg+rzyePr0KSpUqAAvLy/o6uqiRo0asLGxgYeHh0bXJTc3F/Hx8Xj16hUA4NOnT5zP41OnTvHeuL5j8+bNGDt2LAYOHIi1a9fiy5cvnOM5duwYKleujCNHjuDjx4/4+PEjjhw5goCAACQlJYn+nfJ4+PAhoqKiUKFCBejo6KBBgwZYt24dsrKyVH7GzMysWOwLeaiaj2/fvoWOjk6xna+4IMRei4yMVGtPh4eHIyoqinltYGCAJ0+eMK8DAwNZ7z969Ei0Tbpo0SI0aNBA5fsNGzbE0qVLOd9r3LgxtmzZAgDo06cPqlWrhs2bN6Np06aoVq2aqPEAgKurK44fPw6AbcPcuXMHlpaWos8rFDJ7RtWmCgUFBYiKioKJiQljHxkaGjLPWUW8f//+V+ygCGRmZmL37t1IS0vjfF+IP+jv7w9TU1MYGBjA3d0dlSpVYm3ysLKyQt26dbFmzRqWDamrq4tbt25xjkWIzwMU3t/aeEbK2+XqNqlUikqVKuHGjRuiv6sofPz4Efn5+Ur78/PzBfk8ReH58+fo06cP9PT00KJFC9ZvErruyuPfYMtqG0J8x4CAABw5cgQA0LJlS3Tt2hXPnj3DuHHj4OLiUmxj2rVrF3R0dNC0aVNERkYiMjISTZs2ha6uLhITE4vte1RBE9907969rG3nzp2YNGkSSpcujXXr1mlryKIg1IYVcl1k/pVUKsXMmTNZPldMTAxCQ0M1jsPn5eUhLS2N03b+8uUL0tLSONef/wq8vLywZ88eAOx7+8aNG7CxsdH69wuNS4iBRCLh9NVPnDgBW1tbpWM3btyIffv2Yd++fTA2NsaaNWuY17KNC2lpaawtNTUVhw8fRr169RAYGChq3PLPFvm/DwC8evWK9YyxsbFh7KDPnz9DIpHgypUrzPt37tyBhYUFAMDa2pqxU758+QKpVIq9e/cKHqMq2NnZISUlBUChDSa7rsUNPja1kDXg+fPnGD16tMq44JgxYxi/v7iRkZEBKysrrZybC8bGxirtZqBwPsvnHmSwtbXFvXv3VH5O6LwFhNmBV65cQVBQkMq/UVBQECs3I48HDx4wvzk7Oxv9+/eHj48PwsLCtLbOZGZmcl5HoDA/MXHiRLRp0wZt2rTBpEmTWD69PL5+/Yq5c+ciJCQEVapUUemf7Nq1i4l3jRw5EiNHjkTNmjWhq6uLXbt2afQb/9fh6OiIkydPAiiMT8n8q40bNyIkJETj85crVw6DBw8utjVCTAyRb2xVk/gkADx+/BgHDx5EQkKC2vVAGxgxYoTKrXfv3jAyMlJaYwwNDdGlSxccOXKEtdao89n5QJP16J9GUWu1OvC9l2bNmoUKFSrg4sWLMDMzw5kzZ7B582bY2dlhyZIlxfdj/gUQur7wXdN/JjZv3ozy5csz9lRx+KVC5oCRkRHrWaynp4fbt28zrx8/fgx9fX2NxvML//v4RdD9hV8QgJycHGzevBnNmjWDvr6+RgFZIYlNReTm5uLYsWNYtWoV8/nnz58rkbMMDQ0ZZ6hr164YP348gMIHgIzgJEt489lUISMjA0eOHGECBDLS8L/VsKtfvz4+fPigtP/jx4+oX78+8/pnBD8tLCxw9+5d5v+yB/XFixfh4eGh0bkVkZOTgyNHjqB79+7Q1dVVSvircsDT09NhZmbGvP4ZQZ6OHTuiVKlSGDduHBYuXIhFixaxNhk0uY+KgpubG44dO6by/WPHjsHNzU1pv5OTkxKhuLjJxWfPnsXy5csRHR2tdox8ITToJHQOKK4lWVlZuHPnDjp06AA/Pz/mOCHEib59+2Ls2LEqjx03bhwGDBig/ofzwLBhw2Bvbw8jIyO0a9cOe/fuxY8fP9R+Zu/evbCwsMCcOXNgbGyMefPmoU+fPtDX11ciFak719u3b5X2ffv2DVu3bkWjRo1gaGiIdu3a4eDBg/9YImbo0KGc+7OzsxEUFKRRglgRX758wfDhw+Hu7s7ab2RkhEePHgFgz90HDx4oJfvFrhlCkiz29va4dOkSgEJHNj09HQCwb98+Jpnh7e2NM2fOqPyt586dQ4UKFdRdDpVo2bIlYmJiVL6/ePFihIaGsvaJfd59+PABrVu3hlQqhb6+PvT19SGVShEaGqqW4KYKP4N4f+PGDZQoUQLBwcHQ19dHu3bt4OXlhZIlS+L+/fusY4USzBXXUhmeP38OQ0NDjcbNF05OTrw2+edRSkoKSpUqxQQuzM3NGTJCccHW1paxd+Rx9+5dWFtbc34mPj5e5SZLiomdM3xtHqFwdXXFqlWrlPavXLkS5cuXZ+3TxJ4SQnQVAsUgUnHi9OnTyM3NVdqfm5vLWTSgap1MTk6Gp6enxuOpXr06pFIp/P39MW/ePDx79ozX51QFe8XMRZlNJJFIcP/+fZat9PfffyM+Ph6lSpUS/NtUrUV8PseHpCnEXvPz82MIqFw4fvw4i7BStmxZZj58//4dRkZGrM9fv35ddMK3atWq+OOPP1S+v3//flStWpXzvcuXL+PPP/8EUFgg0LRpU5iZmaFy5cpITU0VXRxhaGjI3HPyc+vWrVssf10Gvv66qu+Oi4vD+fPnlX6fpgSr79+/49atW7h06ZLGBWP/yxBjZ8bHx3Pant+/f2dIhFzg4w9Onz5d7aY49s2bN6N+/fowMjJCWFgYEhMToaenpzLZJ8TnAQrXdS4S/JIlS+Dl5cW8Fmp/8SFEPXz4EBcuXEBYWBgnebg4kJiYCDc3N+Tk5Ci9l52dDXd3d7VrEB9kZWVh3LhxMDIyQs2aNZGcnKx0jNB1Vx5CSBnOzs549+6d0rEfPnz4aUXVYiDEd9y0aRM2bNgAoDC+aWtrC6lUCkNDQ2zfvl3p3JrEEK9cuYLOnTujcuXKqFy5Mjp37oxr164V2/nVQRu+6ZYtW9CqVSvBYwG0G+cTAiHXReZjSSQSODo6svwud3d3NGnSBBcvXuQ8z6tXr9ClSxeUKlUKOjo6Ku2vDRs2oEqVKqyCRxlyc3NRpUoVpmiiuHH79m2MHj2a873jx49j4sSJ6N27N3r27MnafiZU2VT37t3TyBf/NxCjLS0tYWVlBalUyvxftpmbm0MqlWLQoEGszxRVtMJVbC7/Wa7Cl5o1azLiDkIgkUhw8uRJhvBrYmKCgwcPMq9PnDjBGouQGDXXsYpxHRnEFLNznV8diUvT9UudTS1kDdCmKEBR2Lhxo5IPq8113c/PDytXrlT5/vLly1m5BxlGjBjB5Eq5IJVKWTaRmZkZHj58yLxWzJUItQMjIiIQGRmp8vtnzpypJK6jTchIsFKpFP3792dejxw5EsOGDUP16tVRq1Ytjb+nU6dOsLW1xYABAzBt2jSV/omLiwt+++03pc9PnTqVMycvhBzNZz4OGzaMdQ3UbcWFr1+/4tKlS9i/f7/aYgoTExM8fvwYAFC6dGkmB/Hw4UMlURMxULeO/iwIia1qC8XBmxCK3NxcLFq0CHZ2dihfvjy2bdvGet/DwwNOTk6YNGkS63msKUH337YeKeL169e4ceOGUgERoFlBEt97SUhhenGLFcnjZ+TNhK4vfNd0MTaYpsXPOTk5xSZeIWQOiCnw+YX/Hn4RdH/hPwG+xjQfg/rt27dYunQpvL29NVpExSr4ZGZmwtPTE8bGxtDR0WEW9mHDhqF///6sY93c3JCQkIDs7GzY2dnhxIkTAIDU1FSmeryoRKwqhUCgUEWjQYMGzPuysfTs2ROjRo361xp2qpLWr1+/hq6uLvP6ZwQ/5at13dzcGELMnTt3VFalCsHRo0cxceJE1KxZE4aGhqhUqRJGjBiBvXv3MqqYsupWqVSKZs2aMa/btGmDVq1awcnJCU2bNmXO+TOCPBYWFjh79myRx2mihFUU5I1RLjx+/Jjzb6RIJp43bx46deoEa2trzJ49W9RYgEIip46OjlYUf4QGnYTOAa51RiKRoGzZsiySgBDihLu7O/766y+Vx165ckWJyCkW+fn5OHr0KLp37w5zc3NYWVmhb9++KqtvgUICUaNGjWBnZwcjIyMEBgbi6NGjSseFhYVxKqG/evUK3t7erH0DBw6ElZUVfH19sWjRIk4Cr1AoOrbqNi64uLhg6tSprH3Z2dmoXbs2ateuLTpBrJh0kKmEmZmZKTnVzs7ODDFB3uGJj49nJfsB7a4ZMpiZmTFJ37JlyzJr2cOHD2FkZASgUOFBzPrCB2XLlmVVZSrizp07cHR0ZO0T+rzLz8/HnDlzUKtWLQQEBCAsLAz79u3DH3/8oVThLsQBF0O8F1MMlJWVhaioKISHhyMkJASTJ0/GixcvlD7Pl2CuqWLSxo0bUatWLZQqVYpJKsbExBSr8os6NGnSBLVq1cL58+dx7do1tGnTptgDnpaWlpy/Z+/evSpVIi0tLVmbLAhiYGDAEPSEzhmhNo9QrFixAvr6+hgwYAA2btyIjRs3on///jAwMFAKLou1p9QRXcPDwzXyeerVq8coT6mDmKCgUFU+Q0NDTpsnLS2tWAjvkyZNEhXAVkXQFbN+FeWH6ejo8C5ikcf06dORk5MjuAiIL0lTiL1mampa5PNOnhQ/YMAAhnQ2atQo2NjY4Pv378z7mzdvRkBAAIBCxU/Z71C0GxQ32TFFjUWsaq2Y4giAnwKp/L2jas4o+uuqvt/S0hISiQSBgYG8FGyLIlj17NmTM7GZnZ3904k5/waIsTOFro1iCb1CcP/+fUyePBllypSBRCJBp06dkJSUxGkn8vV5AGD9+vUwMjLC1KlTGSWj3377jUmiyVCcBX6KUOzgUpxo3Lgx1q5dq/L99evXo0mTJqLPHx0dDWtra1SoUEGtjSh03QXEkTJUxdVevXr1r1aE4eM7njhxgtPWysnJwdWrV1X64dqOIWrr/NrwTbk63/CFNn12ITasmOsSFBQkuANRcHAwKlSogBUrVmDPnj1K9pgMtWvXViJqyCMhIQF16tRhXmvaWSc7Oxvr1q1DzZo1IZFIlOJTQKHNKZVKUa1aNbRu3RqhoaGsTVPMmDFD7SYPLy8v5nrJ39tLlizRSMGL732nSVytKMTFxWHDhg2QSCRMwZVs27p1K2fxlSZQLHJ58uQJvn79Kvp86pTuuexYxWIRWecjGRQJuurIv/LXXUwxu1QqZQops7KyYGZmhrS0NJXkLLHrFx+bWsgaoE1RABnkYylt2rRBaGgoqlevDh0dHaVCMG2u69HR0SzVZXnIcqDR0dFK7w0ZMgTm5uaoUqUK+vXrpxQvkUgkLB9TIpHAwsKCFaeWn7dC7UAXFxe1a8L169dVFjx169at2DqjyRAUFISgoCBIJBLUqlWLeR0UFIQmTZqgX79+KsnpycnJ6Ny5M2rWrMkUPm/cuJFzDpqbm/PK+RkZGXEqqN67d4+JrcsglBzNZz66ubmxroGqTV7sSRMcPnwYtra2vIopfHx8mJxUw4YNmQKaxYsXo3Tp0hqPpVu3bmrnshgIjSEKia2KiU8WFBRgx44dGDhwINq2bau0ngHCeRP+/v5K6qGqNi5s3rwZLi4uKFWqFJYvX87piwCFhbo9e/aEqakpKleujJiYGOjq6qrNBRUFTdYjbeLKlSsMD0fVfaFJQZLQe4mriEaxgEubsYyfIVgl9JrwXdOF2mBii5/5ivOJBZ85UJTteO/evV8E3V/4RdD9hf8G+BjT6gxqmXJuSEgI9PX14erqiilTpoiqGpZBrIJP69at0aVLF3z//p0VcDp58qQSmWL58uXQ1dWFpaUl/Pz8mGqTJUuWICgoCIBmbSC6du2Kpk2b4unTp6yxHDlyBBUqVPjXGXayoIxi8CYtLQ3Xrl3DrFmzUK5cOeZ4ocFPMdBWu1QZJBIJSpQogejoaE7DBAB69OiBHj16QCKRoEOHDszrHj16oF+/fpg1axYrAfEzgjxOTk68nApNlLCKQlHqY0IrnZYtW4YePXqIGosMzs7OWlGdlkgk8PHxYZxEHR0deHt7M699fHxYv1XoHFBcS5KTk3Hnzh0lR1MIcUJeHYMLmZmZSgGb4sDXr1+xY8cO+Pn5cf79c3NzMWPGDDx9+pTX+QICAtCrVy/WvhcvXsDT0xNt27Zl7ZdIJChXrhxCQ0OVAgeKQQS+ENISlgv3799HqVKlsHDhQgCFLdFr1qyJOnXqIDs7W1SCGAAr4RAXF4eNGzfi8OHDnMkuIS1GNF0zbt26hcOHD6utwuXTCtXGxkZtMPX06dOi2zCqah8sQ0ZGhhK5TejzLjIyElKpFE2aNEHr1q1haGiokogjxAEXQ7zXZjEQX4K5JopJK1asgK2tLaKiomBkZMTYUhs2bGBsNW3DxsYGV69eZV5/+PABEomkWFUARo4cCRsbGyxYsABnzpzBmTNnMH/+fNja2gpSnLh37x4aNmzI3GNC54xQm0cMEhMTERgYCGtra1hbWyMwMJCTSCPWnlJH5iIijXyehIQEuLi4YOnSpTh//rzKhLJYMqoQ1eI6deqgcePGLBXhV69eoUmTJqhbt67K7xaK79+/4+7duyqD34rYsmWLUht5QNz6derUKZw8eRISiQSJiYksW+n8+fN4/vy5sB+jACFFQOqgSNIUYq9ZWFjgwoULKo+9cOEC06IWKCQO16lTBxKJBGZmZkqtvRs0aIBJkyYBKLQVZERFRbtBcQMKk/vy7XEVceXKFZiamqp8Xxvgo0B66tQpZn6KaduoiAcPHqBmzZoYOHAgr2PVEaxUrUdv375V6tbyX4AYO1PV2piamsqpFi2U0PvhwwesXbsWEyZMYEjZV69e5aUanp+fj0OHDqFt27bQ19cvlhbhK1asQOnSpRkfw9nZWYlYrIkCLFD4m9esWYMpU6Zg7dq1rK4OeXl5WuviVKpUqSJtcDGq6DJIJBIYGxujVatWan1BoesuIIyUIfN/FNWK9u3bh8TERAwePLjYCmW1AT6+o+J9Vr16dV73jNgY4sGDBzm7Vxw5cgSHDh3S+PxFobh9U1Wdb/hCm3E+ITZscVyXvLw8pKSkqCXtmpqaIiUlRf3AAdjZ2TGFwFx4+PAhbG1tmddiyIjA/xE/TExMIJVKMXr0aJX5D3t7e6aziTbg7+/P2ry9vWFsbAxzc3MlgsvatWtRunRpbN++HSYmJti2bRujdKXuvikKfO87PrE1TZPx8jbh/xL4KN3Lx3clEgmrmFZXVxdNmjRhXjdr1oxFzOF73cUUsysStFS9lkHs+sXHphayBmhTFEAG+VhKjx490KtXL4wfP56zUEub6/qPHz8QFBQEXV1dBAcHMy3ig4ODoauri3r16nEWrhYVLynKv5T3MwHhdqCBgQGLeK6Ihw8fqixMbt26NfT09FC+fHnMnDmTdzcgPujRo4egWOCuXbtgZGSEPn36wMDAgIlpLl26lLMdupeXF69ihZCQEMTGxirtj42NVSp4E0qO1uZ8FIvy5ctj0KBBvLqSxsTEMJ15jh07BkNDQxgYGEAqlbI6j4pFVFQUbG1t0b17d8yfP19tRyC+EBND5BtbFXPuYcOGwcDAAMHBwejevbvSegYI500U1b1GVSebw4cPw8/PD+bm5oiMjOSMN3Lh8+fPWLNmDVNIFRQUhDVr1nDGFoqCJuuRNuHr64s2bdrg4sWLePTokdqOlmKgyb307ds3LFiwACVLlmTt1zSWoQ4/Q7BK6DXhu6YLtcHEFj/zFecrDqiaA0Jtx1/4b+IXQfcXfqEIdOjQASYmJrCzs8PgwYOLrSpZrIKPtbU10yJYnhT76NEjTjLa5cuXkZiYyGqLc+DAAV5VLUWhZMmSTHJDsTWciYnJv86wk38QcgVsjI2NsX79euZ4ocFPMVDXLpVPgLYoLFy4EG3atIGNjQ0cHBwQERGB1atXMy3X5TF9+nReDsDPCPJs2rQJ7dq146yQkoc2lbC4Ek7yW3x8vCBD6sGDBxq1zQaAdevWoVmzZrwUr4RAqPOorTkghDhRsmRJRhWcC8ePH1cyjjXFy5cvsXDhQlSpUgUSiQTVq1fnPM7ExETt2iGPN2/ewNPTkyGoPX/+HO7u7ggPD1dq4cEVMODahIBvoFyd05uWlgZra2ssXrwYNWrUQL169Zi1REyCWCiEtBgRu2Y8ePAAvr6+SsF/xbaTAL9WqM2aNUOfPn1UjqN3796cgUw+cHFxUauAuXv3bqXiGKHPu/Lly7Mq1o8dOwZ9fX3OtjNCHHAxxHu+xUBv375VOvfNmzfRo0cPhIeHM8Uy8hBKMBejmOTl5cX8veRtqRs3bmhMhFGlnDpq1ChMmjQJsbGxeP/+PWfwQlGlRlPk5+cjOjoaDg4OzP3j4OCA6OhoTjUidbh8+TI8PDwAiC/W4GvzaBNin6VCia5CwFfVSEhQUKxqcUZGBipWrMgUR7q6ukJfXx/e3t5qE1988eXLF/Tq1Qs6OjqsriRDhgwR1fFAk8KhzMxMTiKtpuAqAnr58iVnEZA6KJI0hdhrQUFBaluHjhs3jrMYISsri3NteP/+PUtRVwiqV6+OOXPmqHx/1qxZKm07mfqtqk0GMS3YhCiQFhdOnz4NV1dXtceoI1jJlCAkEgmjDCHb/v77b8THx2tERPxfhRA7U6awI5VKWYWSlSpVgq+vL8zMzBAeHq50DiGE3rS0NKY9pq6uLrPOTZ48GV27dhX02968eYMFCxaw9jk7O+Pdu3dKx3748EHJ1szNzUV8fDyTdH7z5o1S62YZhNpfbdq0wc6dOwEU2na2traws7ND9erVUbJkSdjb22ukKMQXhoaGaov4b9++rVH8i68vKHbdBfiRMhTtA/lNX18f7u7u2L9/v+jfqW3w8R0VbWNV6vmKEBtD9PHxwcGDB5X2Hz58GL6+vhqfvyho4psK6XzDF9qM8wmxYcVcl+HDhzNdB/Ly8lCrVi1IJBKYmJjg5MmTnOfx8vLCtWvXihy7sbGxWr83LS2N5TsI8cNfv36N6OhoeHh4wN7eHiNHjsTly5eLbJlsbW3909tgf/z4EW3atOEkBm/evBnly5dn7m3FLhBiwPe+4xNT07Qj2tWrV3H9+nXm9d69e9G6dWtMnDhRrX3Mt2PP+fPnldbv+Ph4ODk5wc7ODn379uVU8S9u8HnWyWKfQq67mGJ2oaI2QtcvITa1kDVAm6IAYqDNdR0oJOlGR0fDz88PxsbGMDIygp+fH6Kjo0X7jkLjVELtwDJlyuDw4cMqjz906BDKlCmj8n2Zbe7r68uQk3fu3Km2i44YfPz4EXv27FH52/z9/ZliO3l76dq1a5x5mUOHDiE4OLhIct3KlSuZfPymTZuwadMmDB48GCVKlMDKlStZOTqh5Gi+8zEvLw9paWlK6ohAoa+clpbG6fuLgZmZmejnaWZmJnbv3i1apV0RQjoC8YU2iYVizm1lZcVpe/9MXLp0CUFBQTA0NMSIESM0Eou4ffs2Ro8ejRIlSogiIWq6HmkLpqamxRL/5QvFe+nbt2+YMGECqlSpgpo1azL5m9jYWJQqVQplypRRijGKFSvig39CsKqo9YXvmi7UBhO6rgsV5+MLMXNArCDiL/y38Iug+wv/GYg1qDt16oSDBw9yOmWaBFbEKvhYWloywTF5p+fMmTMoUaIE61gZ8VMocnJycOfOnSLbMJmamjIqGvJjuXz5Mqytrf91hl1mZiYePXoEiUSCy5cvs4I1L168UPobCw1+/ttx/fp1LF26FG3atIGenp5SS4IvX76wCLGZmZlYuHChUoL4ZwR5/P39YWZmBlNTU1SsWFFlGxBtKmFp0h6DC9HR0aKMQHn4+/vD1NQUBgYGcHd359UeRRsQOgd27NiBNm3aMKq8HTp04FSIEUKcCA8PV9syr1WrVmrbifDFx48fERsbi0aNGkFXVxfu7u6YMWOG2qBJq1atWFX0ReHJkycoW7YsRo4cCTc3N3To0EFwIPCfxvnz52FiYoIGDRqwnrNiE8T37t3DvHnzMHjwYAwZMgQxMTFFJkX5tBgRu2a0aNECrVu3xtu3b2Fqaorbt2/jzJkzqFatGpKTk9WOi6sV6p9//gkdHR2MHj1aSR1y1KhR0NHRUUtAV4chQ4agYsWKnC0Iv3z5gooVK2Lo0KGs/UKfd/r6+njy5AnrGAMDA07laCEOuBjiPd9ioI4dO2LUqFHM/tevX8PKygre3t5o1aoV9PT0lJJ8mhLM+SgmyQdX5G2pe/fuaVzEFBQUBHNzc5iYmKBy5cqoXLkyTE1NYWFhgerVqzMJda7gBVdryOKCYhtIoUhJSWGCWWKLNfjaPELx5MkT1n1w6dIlDB8+HKtXr1Y6VuizlC/RVZMkAt9CDSFBQU1UiwsKCnD06FFGqSMpKanYiKzDhg1DlSpVcObMGZiYmDD33t69eznVDE6dOoUWLVowZOGWLVuy1n9NCocOHz7MUlNetmwZ/Pz8EBERIZj0Lw8hRUCqwEXSFGKv7dq1C7q6uli6dCnLtsnLy8OSJUugp6fHEOuEQrG1rLoNAFavXg0TExNO4tgff/wBExMTznsVKGyDKb/NmzcPnTp1grW1NUPoFtuCrSjwbZksZJ1+9OgRi3QtlGBVVLtHHR0d0W37/pchxM6UFUBKJBKMGTOGVRQ5a9YsbN26lUUoEEPobdiwIaMmJG9jnDt3TmO/FFCtTvLq1Svo6+sr7TcyMuKldCPU/rKysmJIAyEhIejUqRNz7X78+IHevXtzqqsUNzw9PbFp0yaV72/cuJEpMNImtLnuysPJyUnjrgP/JNT5jmIJumJjiIaGhpwEwEePHrGO11aMUhPfdMOGDbw73/CFNuN8QmxYMdfFwcEBly9fBgDs2bMHDg4OSE9Px5QpU1CrVi3O7zx69CiaNGlSZJG3n58fVq5cqfL95cuXw8/Pj3ktxA83NDREly5dcOTIEZaNWBRBd9y4cWq72WgL169fV/scy8nJUdsRTQg0ve8+ffqE1atXo1q1ahqrZQUEBGDXrl0ACgvoDAwMEBERgfLly2P48OGcnxHSsSc4OJhl41+/fh26urro06cPFixYAHt7e0ybNk30+LXl83CB67qLKWYXCqHrlxCbWsgaoE1RABkKCgpw+fJl7Ny5E7t27cK1a9dU+ura7mSSl5eH06dPi5pHGRkZOHLkCGMDyH6Dvb09xo8fz+Q+i4JQO7BHjx6oXbs257EFBQUIDAzkLcJx9epVDBkyBIaGhrC1tcWIESN4j1sR4eHhWLp0KYBCu8jNzQ16enrQ1dVl1h95GBkZMc8vRQEnAwMDpePfvHmDoKAgSKVSmJqasnxQ+WJDPnk52f0jlBzNdz5u2LABVapU4czR5ObmokqVKmr/5kLQs2dPjQtK/s0QSiwUElsVQ1p0cnLi1R353r176NixI2cMOysrCxERESwf4e+//8aSJUtUHi//nkwwbMSIEUoqxWIVi3Nzc7F7925BnwGKdz0qTrRu3Zpz3VEFvgVJfDFu3DhYWFigbdu2KFWqFHR1ddG3b1/4+Phg27ZtnGuDNsWK/gnBqqLAd00XaoMJXdeFivPxhZg58Au/wAe/CLq/8J9BcRnUMge/atWqGgVWxCr4tG/fHn379gXwf+pmnz9/RoMGDZSMJH19fbi4uOD3339XItJw4c2bN2jevLnKoIAiQkJCGJUJ2Vjy8/MRHh6Otm3b/msNO74QGvwUg/r16+PDhw9K+z9+/Kiy/bBQFBQU4OrVq1iwYAFatGjBJFwVyQeNGzdmfu+HDx9QokQJlClTBoaGhlixYgVz3M8I8vBVctVECUtbkCVPZZu/vz/s7e2ho6OjMunPF0Lao4jB77//zksxke8cyM/PR/v27SGRSODh4YHWrVujdevWcHd3h1QqZdrLvHv3DomJiYKIE9euXYOBgQHatm2LS5cuISsrC1lZWbh48SLCwsJgYGDAatkuFoaGhihVqhRGjBjBJFqKwsqVK2Fvb4/Ro0dj69atSurLXEhPT0eJEiXQuXNnrajoqcOVK1cQFBSkMngQFBTEagWrOMdlm7W1NTw9PVn7xCSIZ82aBR0dHUilUtjb26NkyZKQSqXQ09PDvHnzeP0mVS1GxK4ZNjY2TFLG3NycUbI/ceIEay398eMHXFxceKl0rVq1imkRIyPGSKVSGBgYsNZcoXj16hUcHBzg6OiI6Oho7N27F3v37sWcOXPg6OgIBwcHpZZZQp93UqlUSb1NleKqEAdcDPGebzGQk5MTqzp13rx5cHV1ZVpEzps3T+lvL5RgLlYxSRYokg9mL1myROPCi4ULFyIsLIx1b2dlZaFdu3ZYtGgRcnJy0Lp1axCRygDGP9l6R3Ht3Lt3L1auXAlvb28EBwcDEF+swdfmEYratWszRO+XL1/CzMwMNWvWhK2tLWbMmME6Vqg9xZfo+jOSCGKCgpqoFn/9+rXYn41ly5Zlgqby915GRoaSmsGmTZugq6uL9u3bM0Hy9u3bQ09Pj1Hf1qRwqGLFiox6x/Xr16Gvr4+JEyeiRo0aGvtKQoqA+JI0hRJdJ02aBIlEAnNzc6ZFsbm5OaRSqdo1tigUldDm8mM7d+4MiUQCLy8vhIaGIjQ0FJ6enpBKpejYsaPgMSxbtoz5G4ltwcb3dxZn4eAff/yBChUqMK+FEqxOnTqFkydPQiKRIDExkaUCcf78eTx//lzw7/x/AWLszLi4OM6iKkUIJfQChTarrKhQfp3LzMzkTJgDRd9XwP89n7k6ziQmJmLw4MGcCkj16tVTaxPKINT+MjIyYn5nqVKllJQo09PTNe7YwQeTJk1C2bJlOVvTvnz5EmXLlsWkSZO0Pg7ZWMSuu5cvX8bYsWPRoUMHVjFQmzZtivxertjW/wIUfUdFX8fMzIxXnERsDFGVXXXs2DHY2dlpfH4+0JZvKgbajPMJtWGFXhf5otW+ffsypMmHDx+qVMuytLSEvr5+kYnt6OhoVlxCHqmpqbCxsUF0dDSzT4gf7uHhAScnJ0yaNImVDC+KoDts2DBYWlqibt26GDJkiFLnFm3hzJkzSmqb69evL9YOMDKIve9Onz6Nbt26wcTEBG5ubhg/frxahT8+kH+uz5kzh7Erz549q1IARUjHHnt7e1bcc9KkSQgMDGRe79ixA15eXqLHr+jzGBgYFJvPI4O66y6mmF0e9+/fx+TJk9GxY0eGAH7o0CHcvHmTOUbo+iXEphayBmhTFEB2fmdnZ6UOY66urpxFyD8jf1NU8b4i3r17hwYNGjBjl90bPXv2xKhRoxAZGQlXV1dIpVLUrl0bGzZsUNvpUagdeP/+fVhYWKBatWpISEhAamoqUlNTsX37dlStWhUWFha8lCNfvHiBOXPmwMPDAyYmJujWrRsaNmwIXV1dxMTE8L4eMsh3TN2yZQvKly+PnJwcrFixgrOQ2dnZGceOHQPAXmPi4+M514uGDRvCzc0Nc+bMUfJBhYidyEMoOZrvfKxduza2bdum8riEhATUqVNH1JgVkZOTg2bNmqF79+6YP39+kSTN48ePY+LEiejduzd69uzJ2v6NEGp/CYmtiolPxsXFoWPHjpzCBvLo27cvU/DKhXHjxjH5TQCIjIxUKxgUHh7OFF2UK1dOrVpxUYrFfJ5JfFFc61Fx4+3bt2jWrBmmT5+OXbt2qc2xCilIksdff/2F6OhojB49WsmOdXZ2Zr7nxo0bkEgk6Nmzp9r4tCbdbIrCzxKsErK+8F3ThdpgQtd1oeJ8fCFmDshw8OBBTpGyI0eO4NChQ6LG8wv/7+AXQfcX/jPQ1KAu7sCKWAWfp0+fokKFCvDy8oKuri5q1KgBGxsbeHh4KFWHv337FjExMfDz84Ouri6aNGmChIQEla1dOnXqhMDAQFy+fBkmJiZISkrCpk2b4OHhgQMHDigdf+PGDZQoUQLBwcHQ19dHu3bt4OXlhZIlS+L+/fv/WsNu1qxZnNUy69evZzlnQoOfYqBKdeb169ei2lEookWLFrCysoKOjg4qV66MUaNGYd++fZyJExsbG8aAX7t2LXx9fZGfn48dO3bA09OTOU7bQR4h0EQJS1tQJM1GRkZi5cqVvKoy/2n4+vpCKpWiZs2aWL58uUpFHL5zICYmBtbW1px/n3379sHa2hrz5s2Dt7c3cy8JIU7s378fdnZ2SoljOzs70e0U5VFQUIA1a9aoDcBxoSjyhCIBRrYZGBjA3NycMxmjTURERKhVPZk5cyY6d+7MvC6KKK5IGheSIP7zzz8hlUoxbdo0Fink/fv3+O2336Cjo8MEe8W0GBG7ZlhaWjKBXhcXF0ah/v79+0pV2A4ODrzb6D579gwxMTEYNGgQBg4ciIULF3Kq0ApFZmYmQkJClILlISEhnAFroc87iUSipOApszMUk/hCHHAxxHu+xUCKFfUhISGsIFt6ejqsra1ZnxdKMBejmLR27VqULl0a27dvh4mJCbZt28a03VVnt/KBg4MDZ0L15s2bcHBwAFCotGFpaVmkcmpRik5cqFSpEnMfqyL2q1OA51o/S5YsiYiICLx48QKA+GINvjaPUFhaWjIE/sWLFzN/96NHjyoFVsXaU0URXYX6PIoBTnWbDGKCgkJVi/Pz8xEZGQkHBwfo6OgwwdUpU6YUi7KIfMBWPpGUmpoKc3Nz1rGenp6cya0FCxYw80WTwiETExPmHps2bRratm0LoPD+LA71A75FQEJImkKJrpcuXcKwYcPQrFkzhISEYPjw4bh06ZJGv0s+gR0XFwd7e3tMmDCBma8TJkxAqVKllBJ9CQkJaN26NeNTt27dGgkJCaLG8ODBA4Zww7cFmyo7kGsD+LXuVVS5VqUk/OTJE+zZswcuLi5KiS0xyMzM/OmFZf9maOqbfv36FXFxcVi+fLlK1au4uDje7aXt7OwYsqr8OpeUlKSSyCMr7JJtO3fuxKRJk1htwhWLeOQ3fX19uLu7c16DhIQEuLi4YOnSpTh//rxKBWih9lf16tWxZs0aAIW2hyIhLSkpCfb29ryumSb49OkTvL29YWZmhoEDBzKK2wMGDICZmRkqVKiAT58+aX0cMohZd7dt2wY9PT20aNEC+vr6aNGiBdzd3WFhYaFEnpozZw62b9/OvG7Xrh0kEgkcHBxYxZ3/FgjxHSUSCUupWkdHh+kGpM5+FRtD7NevH3x8fFhdejIyMuDr64vevXtrfH6+EOOb/vXXXxg5ciSaN2+ONm3aYMKECbx9YVXQZpxPjA0r5LqULVsWR48eRV5eHhwdHZlY+s2bN1W2b1dMZKtKbP/48QNBQUFMK/MRI0ZgxIgRCA4Ohq6uLurVq8dqbS40EX727Fn07NkTpqamqFy5MmJiYqCrq6v27xkUFKRyKw7RCUWC0qJFizB+/Hg4ODgo2Zvly5eHVCqFo6MjunTpgrVr1xZLzkHIfffy5UvMnj0b5cuXR4kSJTBkyJAiSc5CYGZmxtgHjRo1wqJFiwAUtktW1X1HSMceAwMDlsBLYGAgqyPCo0ePNFI55evzVKxYEZGRkbzEZgD+111MMbsMp06dgpGRERo1agR9fX3mOs6ePZv5HYD49YuPTS10DdBW4UVGRgaMjY1Rv3597N27F3fv3sWdO3ewe/du1KtXj9WdRoafkb+pUqUKjh8/zvv4rl27omnTpnj69Cnr3jhy5AirkPHkyZNMTtjc3Bx9+vTBxYsXlc4nxg68fPkyvL29lZT/vL291eadf/z4gV27dqF58+bQ09NDlSpVsHLlSlZxfmJiosrnjjoYGhoy917Xrl2ZuP3jx49ZHVhkmDVrFipUqICLFy/CzMwMZ86cwebNm2FnZ4clS5YoHW9kZFTsdqJQcjTf+WhnZ6c2Fvrw4UPY2toWy29Yt24ddHV1YWpqqkTcVIwlTp8+HVKpFNWqVUPr1q2ZeIxs0xSKhLziIAALtb+ExFbFxiebNm1aZPdWd3d3tffilStXWIWpfn5+ateh48ePcxLdhYLvM0kIxK5H2sQff/wBCwsLXgXqQgqSZJg5cyYkEgk8PT1Rr149JTtWT08Pz549Y443NDTE9evX1Y5Zm91sfoZgldD1he+aLtQG+7cUP4uZAzL4+PgwhWnyOHz4MHx9fYttjL/wv4lfBN1f+M9AjEGt7cCKWAWf3NxcbNq0CWPHjsXAgQOxdu3aIqu9ZK1ObGxsYGNjg6FDhyo9OO3t7ZmAvZmZGdLT0wEUJu7lK6blkZWVhaioKISHhyMkJASTJ09mSBPAv9OwK1euHM6dO6e0/+LFi3BycmJeCw18CIEsCcXVVvratWuYNWtWsbSdHDNmDPbv34+srKwijzUyMsLjx48BFDo2MnLdkydPlAhoP0Nd48OHD1i7di0mTJiA9+/fAyicx/IGEVD8SliKUEVS+eOPP5CUlKQVhYaicPnyZWzcuBEbN25U25JHDG7evImJEyfC2dkZenp6aNasGbZs2aJEUuUzB3x8fNS2jli3bh2kUimCg4NZhQNCiBNfvnzBnj17MHfuXERHR2PPnj2CCbWqkJ+fDz09PdFtoVShqASMppXjQuHi4qK2Zd/169c1bvPGN0Hcvn179OvXT+V5+vbty9zXYluMiFkzateuzTj5ERERCA4OxtmzZ9GtWzd4e3uzjp05cya6d+/OKLP+k/j777/x119/4dKlS2pbvQl93skrd6rbAOEOuFDiPd9ioBIlSrDsHhsbG1abpHv37nEGm4UQzMUoJgHA5s2bUb58eSbIJE+C0QSqlHtPnjzJJNYePHgAfX19tfP18ePHaNSokeDvnz59OkOC1aYCvJhiDSE2jxDIJx1btmzJED1UJU3F2FNFEV2F+jyqVJPlXyuqkIoJCgpVLZ4xYwZcXFywefNmFpl2+/btqFGjhsrfxxd16tRhEkbyCuBDhgxB06ZNWcfq6+tzJvgzMjJYKpRiC4esrKwY/zIwMJBJTj569EjwfPyZRUDFSXTVFA0aNMDWrVuV9m/ZsgX16tXT2vdGR0czPhvfFmw/ww5Up4Kqo6OD/v37s2xvsQSrn9mq+H8FfO3MkSNHYsiQIczr79+/w8/PD3p6erCwsICJiQnOnz+vdH4h7T579+6N0NBQ/Pjxg1nnHj9+jEqVKqlsha0KW7ZsQatWrVj7nJycVBZ0ckGIUr8Q++vAgQOwtrbGhg0bsGHDBjg5OWHdunU4d+4cYmNj4ejoqFb5qDiRlZWFgQMHwtramvmNVlZWGDhw4P/EPeHj44Nly5YB+L/EZkFBAfr27YupU6eyjnVycmLiaklJSbC0tMTRo0fRu3dvNG7c+KePvSgI8R2FFKPKQ2wMMSsrCzVq1ICuri5DxtDV1UX9+vVZ80abMUqgUJiCyyfIzc3lVEQcO3YsJBIJzMzM4OfnBz8/P5iamkJHR4exgb9+/coUtwqBtuJ82k5sT5s2DRYWFvD09ETZsmWZgor169cXi/3648cPREdHw8/PD8bGxjAyMoKfnx+io6OVxDjEkhE/f/6MNWvWoGbNmpBIJAgKCsKaNWuUOuj8DCgqybm4uKB69eqYOHEiZ8HDs2fPsHnzZvTr1w8eHh6QSqUoXbo0q+BcKPjedy1atIC5uTkiIiJw4MABZk0pzjxS/fr10a1bN2zcuBF6enqMf3Lq1CmVOQQhHXvKli3L3Ovfv3+HkZERi2h0/fp1jXwIvj6PRCKBjY0NdHR00LRpU+zatUtlvELodRdazC5DjRo1sGDBAgDs63jp0iWULl2adayY9YuvTS1kDQC0IwowePBgNGjQgPO9goICNGjQgGXjyqDt/M3hw4fh7++P/fv348WLF0qFioqQV4qV/5s+ePCAMzb4+fNnrF27FoGBgZBIJKhQoQIzJ2QQawempKRgx44dSEhIQEpKSpG/1cbGBpaWlhg0aJDK4z98+MDKc/KFm5sbEhISkJ2dDTs7O6ZoXFaQoIiCggJGZED2mw0NDZmuq4qoVKmS2vbv8jh16hRatGgBV1dXuLq6omXLlkhOTlY6Tgw5ms98NDY2VpsvSUtLg7GxMa/fUhRKliyJmTNnIj8/v8hj7e3tGXVZbUCRkNe8eXOUK1cOFhYWvDpqcEGo/SUktirGtgsPD4etrS0GDBiAadOmqbTxFcU+FJGZmcl6fpmamjKxZi48fvxYbX6AL4Q8k4RC6HqkTZQrVw6DBw9WWTwjDyEFSTKUKFECGzZsUHlOxa4qqrpHKkJbXcQA7QtWCV1fhKzpQmwwscXPcXFxLLHBsWPHwsLCAjVr1lR7L6uC2DkAFM5JrvzMo0ePiu3Z8Qv/u/hF0P2F/wyEGtQ/I7ACCE9s8ml/qArPnz/HtGnTYGBgABMTE+jo6KB27doMecLMzIx5YJQtWxZnz54FUJjI14SsAPy7DDtVLW8ePHig1OpRaOCDLxQJy4qbsbGxWmKjGBQ1d3x8fLB48WI8efIE5ubmTELwypUrnOpd2lJ+BArvRzs7O5QvXx66urqMQT158mR07dpV6XhtEgRUqQPJJxPr1q3LBFqePXuGxYsXY/DgwRg5ciRWrVpVbMm4p0+fonbt2kyAx8rKChKJBIGBgcV27eVx9uxZDBo0CHZ2dpzOY1FzwNDQUK1TmpmZCalUqtG9FB8fz6ki9f37d8THx4s+rwwVKlTg7WR8+fKFVX09YcIEVluUsWPHarSGawtFtQF7+PChSkf2r7/+4lQOuHjxIqs1Hl84OTmxgtKKSE5OZgKMmrQYEbpmHDlyBLt37wZQSMjy8PCARCKBra2tksJlaGgozMzMUKpUKSVFWcVAVnE7jZpAW887QHgSRCjxnk8xUKtWrdCrVy/k5+dj586d0NfXZ63NBw4cUKmcypdgLkYxSR45OTmcqvpi0alTJzg7OyMxMRFPnz7F06dPkZiYCBcXF3Tp0gXA/yml+fv748aNG0rnWLVqFczMzBAcHCxqDBKJBNWqVcPq1atFq8a9ffuWM6Eijy9fviAxMZH3nBFq8/BFtWrVMH78eCQnJ8PQ0JBJ+Fy4cEFlkFSoPVUU0VWTJMKxY8dQuXJlHDlyhElkHTlyBAEBAUhKSmIdKzQoKFS12NXVlUkIywdX79y5I0oJRhFnzpyBqakpBgwYAENDQwwfPhyNGzeGiYmJUvGTq6srVq1apXSOlStXwtXVlbVP6FwEChMOTZs2RWRkJKs6/+jRo3BzcxP0uzQhf2pDBU8GbRM5jYyMOAuq0tPTYWRkpFJRlmvjgqIKuL+/P+zt7aGjo8OQC4S2YBOLe/fuYfXq1fj9998xY8YM1iaDvLqw/Hbt2jV8/vyZdT5NCFaKrYr19fWLvVXx/yL42Jne3t6stTI2NhZWVlaMglqPHj3QrFkzpXMLafeZlZWFRo0awdLSEjo6OnB0dISenh7q1KmjVomdC6rICkLAVwFaBiEKsLt27UKZMmWU/HZDQ0OMGDFCdEtDsSgoKMCbN2/w+vXrf0Rl+tOnT7hy5Qpzv1+9ehVdu3ZFu3btsHnzZpWfMzY2ZmKC1tbWjDrM7du3lVSI5RXWhg0bxhRZpqenF8tzurihie8oBGJ9qoKCAiQlJWHu3LlYunQpJyFWk/PzgVQq5fRF3r17p0Sij4uLg6GhIZYuXcoiBf/48QOLFy+GkZEREhISEBQUhN9//13UeLQV5xNqwwr12Xfu3ImYmBiWPR8XF8eQJGXQxC7hC7FkRBlu376NUaNGoUSJEsXS5a048PXrV8yfP1+tz5aTk4MjR46ge/fu0NXVhY6Ojkbfyee+09HRwciRI5Xs0eLMI6WlpaFixYowNzdnEYiGDBmCiIgIzs8I6dgzYMAA1KxZE8nJyRg1ahRsbGxY68rmzZsREBAgevx8fR6JRILnz59jz549aNmyJXR1dWFnZ4fRo0cr+SZirzvfYnYZTExMmHtG3jd99OiRUi4JEL5+acumVreGiFWY9vb2xh9//KHy/T/++ENJyEAGbedvuIqMuQrBgMK/o2zeyP9NL1++rNRdSxGy4jCu8wLatwM3btyotfzC8uXLoaurC0tLS/j5+TGE0SVLlqhty/79+3fcunULly5dUvI35XH06FHUqlULJ0+exLt371Q+8zZt2gRdXV20b9+eUVFv37499PT0sGXLFqXziiFHFzUf/fz8mNibqmvl5+en8n0hsLKyYnVSUAdra2vexxYX8vPz0a9fP406NQixv4TGVoXadsbGxmrzTzKULFlSbafY48ePs+wRCwsLtXnECxcuwMLCgnn95csXnDlzhvN59fXrV5W5TaHPpP9VmJqa8p7rQgqSZLC3t1crzKTYQZKre6Qq0ro2uojJoE3BKqHrC981XR58bTAx67q7uztzz54/fx5GRkZYvXo1WrZsKarAQJM5oGr9OHbsGOzs7ASP5Rf+38Ivgu4v/Gcg1KD+GYEVMTAzM0O3bt2QlJTEq6Lux48f2LlzJ0JCQqCrq4saNWpg7dq1yM7OxqNHj9C5c2d4eXkBAAICAnDkyBEAhUGTrl274tmzZxg3bhxcXFyUzu3q6opp06YJVpfMy8tDSkrKP6YgUr58ec7k6caNGzVWieQLWbtoiUSCy5cvsxJTL168KLYEkpD2wDt37oSenh6kUilL8WTWrFmiiTli0bBhQ0blRt6gPnfuXLEoCwvB8ePHUb16dRw/fhyfPn3Cp0+fcPz4cdSsWRMHDx7E2bNn4e3tjV69emH58uUwMDCARCKBhYUF04LD2NiYUfUqKChgWo0KRdOmTVG9enWmxQsA3L17FzVr1lRSeysOpKSkYPTo0ShdujQnQbMohRUrK6silVm5kndXr15ltYrYu3cvWrdujYkTJyolnYQkkcTgjz/+QO3atTmJa4pYuXIlWrRowbw2NTVF9erVmdYo9vb2StX1Bw8eZNZdeRw9ehSHDh3SePx8UKZMGRw+fFjl+4cOHVLZBrdq1aqcbVl2796NatWqMa/LlSuHGTNmFNmizsjISC0x7enTp8xc1KTFSHHg/fv3nIFWPqqyMhS306joHKrb/gnwdcDFEu/VFQOlpaXB1tYW+vr6kEqlSmoOXbp0Qf/+/YX9IAVoWzFJKD5//ow+ffowv1kqlUJfXx99+/ZliDkpKSk4c+YMunbtCgMDA8yaNQv5+fl4/PgxGjZsCHNzc41aDSYnJ6Nnz54wMzODiYkJunfvzql4oYgPHz5g0KBBsLGxYcZesmRJTJgwoViCTtqyeU6ePAlLS0tIpVJW67eJEyeqvO+EqpUVRXTVJIng7e3NGaROTk7mJNEKIaMKVS1WpX5w69YtjUliMty/fx99+vRB1apV4eXlhc6dO3M+R1asWAF9fX0MGDCA6WDQv39/6OvrK7VtFJMoe/z4MZo3bw5fX1+WfT5ixAhW62FtQgxJU4gNpm0ip7u7O6dC5tixY+Hu7q5WUVZx44KiokpkZCRWrlzJUszl24JNE7LwmjVroKOjg5IlS8LPz49R45ARiBWhTknx7du3GhOs+LYq/gVlmJmZsYgRHTt2RN++fZnXKSkpKFWqlNLnhLT7lOHs2bNYvnw5oqOjcezYMcFj/fLlC4YPH85q3ynD8ePHMXHiRPTu3btY2p9qgry8PPz111/Yvn07tm7dipMnT4ouDtIEqpTuuXw+beD06dMwMzODRCKBtbU1jh49CjMzM3h6esLb2xtSqRRr1qzh/Gzp0qWZ56CPjw8Twzh//jzMzc1Zx5YqVYpR0HV3d8eOHTsAFMYnikMZqrghxnf8/ffftdoxKSQkhNXtavbs2fjw4QPz+t27d0y89mdAIpFwqqSmp6cr/U2rVq2KmJgYledasGABpFIpKleu/K9UjxaS2C5un12GomwTVaQyIUrqMgglIyoiNzeXKVgG+McdxOLbt2+YMGECqlSpglq1ajHdjGJjYxlVYJl9KsPRo0cxceJE1KxZE4aGhqhUqRJGjBiBvXv3/pQ5eOHCBfTp0wdmZmaoVq0ali5dirdv3/6UPNLXr1/V2nx8O/a8ffsWderUYXyCxMRE1vsNGjTQqJ0wX59HIpGwfIwXL15g1qxZcHNzg1QqRc2aNRlRk5913UuXLs088+R9U1kBtKYQalPLug5OnDhRbdfB2rVrc8bW7t69K1plUV7ghwsPHz5kOjb9TKgqUJRtiggJCWHigjJFvPz8fISHh3O2iM/JycGGDRtQt25dSKVSuLm5Yfbs2cz7RRXb5+bmcpKz8vLysG7dOkRERKBhw4aoX78+a5OHoq2tatMUly9fRmJiIotoe+DAAUbMiQsZGRk4cuQI0+VVFTGZi0TN9czz9PTktDMWLFigUlxB9r3FRY6Ojo6GjY0NZ25LpiisCWFVHiNGjMDMmTN5HTtu3DhERkYWy/cKwd27d5UK9oSCbwxRTGxVSHzSw8NDbc5ShvDwcISGhqp8v1WrVmjXrh3zOigoSK1C6rhx4xiie3p6OsqVK8cSgJLvTPzq1SuV8SltPZMKCgqwY8cODBw4EG3btv3Hc0ndunXD2rVreR0rpCBJhujoaLWdhYR0kPyZ0KZgldD1he+argofP37Enj171IpDCFnX5XMP48aNY8Tebt68qdRBnQ80mQP9+vWDj48Pi/CckZEBX19f9O7dW/BYfuH/Lfwi6P7CfwZCDeqf5eALIaIBhUZWu3btYGRkBHt7ewwfPlylSuGQIUNgY2MDa2trDB8+nJNg9vLlS0gkEgCFlYkySf8rV67A1tYWUqkUhoaG2L59u9JnY2JiEBAQAIlEgoCAACxatAgvX75UOm748OFM4CUvL49pBaOq9bK2IZsLsbGxDCl2/fr1sLGxwaxZs5SOd3Z2xrt375T2f/jw4acResVCaHvgly9f4tq1ayzy96VLlzjbtWpT+dHc3JwxXOSdjMzMTJVVgPfv38fkyZMRERHBBEUOHTrEkFjEwtvbm3F45HH27FlUqFABQGHVk62tLXR0dDB69GiWM/XixQuMHDkSenp6OHPmDCIiIpTUjfjC0NCQk9x75coVjVWuZXj48CGioqJQoUIF6OjooEGDBli3bh0rcSRDUcSMZs2aYcCAASq/q3///ggJCVHaHxAQwLSelylbR0REoHz58kpOk6okUmpqarG0b7a0tGTIbYaGhkoto+VRu3ZtloqA/NwFCtdYxfvOx8eHIazI4/Dhw/D19dV4/HzQo0cP1K5dm/O9goICBAYGqnQ0TExMWL9RBsWA7MKFC+Hn5wcdHR00atQI27Zt43QkFQPxipAPTmjSYkTos1db+KecRlV/T20+77KyspjEgTzev3+vRELSlHhfUFDA6bC/ffsWe/fu5VR9PnDggMr5Exsby5AO5LFjxw4lFUo+ikmKSozqtuLA58+fkZaWhrS0NLVKFnv37mVIX+bm5mjUqFGxKTlnZ2cjNjYWdevWhUQigZubG+bMmcNpN75//x7u7u4wMTFBv379sHDhQixcuBB9+/aFiYkJqlSpgq9fv+LSpUtYvHgxAGDo0KHM/+WxdOlSlcE2ITaPEOTl5SklgR89eqRybRM634siumqSRDA0NOT0F9LS0lQqqfOFUNXiypUrMwV18s/TGTNmqHxm8cGJEydUtkhVhHxCKDExEYGBgbC2toa1tTUCAwOxceNGhhwng4GBAerUqYMpU6bgxIkTTILqnwDfIiCxJE1Vz+znz58rzRdtEzkPHjwIQ0NDVKxYEb1790bv3r3h4+MDQ0NDHDx4kJWcjYuLg729PSZMmIB9+/Zh3759mDBhAkqVKsWpLMwXfFuw8SELqwpmly1bVomQog5hYWGcz8NXr17B29tbY4IV31bF/0Xk5+cjPT0dZ86cwenTp1kbUKiuI1/o7OTkxOqg8+jRI851l0+7T026elhaWrJ8HZn6rpmZmZL60PTp0yGVSlGtWjW0bt1aqR2qKty6dQuHDx9m7j/ZxoXk5GR07twZNWvWZEgnGzdu5KV49E+hKKV7baNOnTro1asXnj17hsjISFhaWmLixInM+7///rvKQp2IiAimoDQyMhJ2dnbo06cPypUrp5SUHTx4MMqVK4dGjRrBxsaGsTG3bdtWbDZscUKM7+jr68uQwZYvX463b9/y+i6+PpWiDWhmZsbyrdUl5oFCAo2scEhR/V8IZEl3WQxHPhHfqlUrODk5KRWEGxsbc8YBZHjw4AEkEgmLcCwU2vTZhSS2hfjsisr2qpTugaLJZKpIZUKU1IFCQhdXsUJ2dnaRRK5Tp07h4MGDSjaAtgkL48aNg4WFBdq2bYtSpUpBV1cXffv2hY+PD7Zt28YpaiGRSFCiRAlER0drNO9Uge99nZ2djfXr1yMwMJApCF20aNE/UjCiCL4de7Kysjiv8fv3739KvEyVfwwUkra6dOmiVLAp5LqHhoZyEsrDwsLQqVMnTJ06lSWMAQCjR49G7dq1mXsuIyMDZ8+ehYuLC0vNuKjfoMrHF2JTy4rP+XQdDA4ORkhICMv/laniDxs2TGkcfCAkbquIf0ssFihU0y9RogSCg4Ohr6+Pdu3awcvLCyVLlmSRac6dO4fevXvD3NwcxsbG6NatG2chteLfvGLFiiyRClXXZfDgwTAxMUH79u0xfPhwjBgxgrXJQyKRwMnJCW3atFGyu/nY4NrAu3fv0KBBA8Z/lc3Hnj17YtSoUUrH833m6evrc6o8Z2RkFJtCaFHz8cePHwgKCoKuri6Cg4OZv0lwcDB0dXVRr149tcURQjB06FBYWFigbt26GDJkCMt3GzlyJOvYYcOGwdLSktexxYmDBw+KylUUBVXPbKGxVXkUVTh/4MABNG3aVG2xAQBcu3YNBgYGaNu2LS5duoSsrCxkZWXh4sWLCAsLg4GBAa5evcocv2vXLujq6mLp0qWs52heXh6WLFkCPT09RugmNDQUzZs3x9u3b5GRkYHmzZvD2dmZsTnVraVCn0l8MWzYMBgYGCA4OBjdu3f/x4moUVFRsLW1Rffu3TF//nxGTVu2KYJvQZIM+fn5CA4OhouLC1q0aFGshGRtdhHTpmCV0PVFqB8THh6OpUuXAiiMW7m5uUFPTw+6uroMH0AT2NnZMfwJf39/xme6f/9+sYl98EVWVhZq1KgBXV1dODk5wcnJCbq6uqhfv75WfJVf+N/CL4LuL/xnINag1nZgRQgRTR6fPn1CbGwsGjduDB0dHbi5uSkF4ho0aICtW7dyBh1lyM3N5XxQAoWBm6tXrxYZhE5PT8fUqVPh5uYGXV1dNG7cmBXQLF26NEMi3rNnD0qVKoX09HRMmTJFKan9M1BQUIBx48bB0NCQSYIaGxurJE2qCjy8evUK+vr6Go+HT5tSsdBme2BtqUgAbENKftxJSUmcSp6nTp2CkZERGjVqBH19feb42bNnc1Y9C4Eqssr169eZJGhmZiakUikmT56s8jyTJ0+GoaEhnJycRBOe3NzcOKutL126pNRiWQyqV68OqVQKf39/zJs3T6nyXhFFKaycO3cOenp6CA8Px6VLl/Dx40dkZWXhwoULaNeuHfT09Dirr+UJ2nPmzEGTJk0AFJKiZX9/GcFNKpXCx8eHRWjz9fWFmZkZwsPDNb0kgtpD29vbsxx7W1tb1uv09HQl5SFDQ0POYMCjR49UtiAvbty/fx8WFhaoVq0aEhISkJqaitTUVGzfvh1Vq1aFhYWFytZn1tbWDMlKHufOnVOpjjx06FDY2trCysoKgwcPZgUyJBIJZs6cqeRwy7aoqCjG0dSkxQjfZ6+21Qn+TU4joN3nXXBwMJYvX660f+XKlUpEfbHE+/j4eFSsWBEGBgYwMDCAj48Pc02BQluwQYMGgpX/3dzcOFt7nzp1ilNNrijIqzBOmDAB5ubmqFGjBhPsqFmzJszNzTFhwgTB59YEr169QqNGjSCRSGBqaqrSPtQUGRkZmDRpEtNqu2XLlqz3hw8fjooVK6pUofTx8UG7du1gbm7OrMMODg6cxISrV6+KVob5WRCiVgYUTXTVJIlQp04dNG7cmHXtX716hSZNmqBu3bqcn+GrnihUtXjv3r2wsLDAnDlzYGxsjHnz5jGK0ElJSZxj4QPFQGb16tVV2juGhoYq1Qc+f/6MWrVqwcPDg7X/zJkzmDlzJho3bgwTExMYGBggMDAQkyZNUjnux48fq93Egm8RkFCSpuyZLJVKlZ7ZMTExCA0Nhb+/P+scP4PI+eTJE0ZRpU2bNpg0aRKncr/MR1bEli1bUK9ePc5zq1K4/fTpEyuZzKcFG19CjixoLQ9F8lZRCAgIQK9evVj7Xrx4AU9PT7Rt21ZjghXfVsX/NVy4cAHOzs6sluLyyiIAUKNGDYYIefPmTUilUhZZ8NSpU5ydY/i0++TT1UPVPb9hwwaWr7Nx40YcPnyYM4lkb2/PsrOKwoMHD+Dr68tcB0W1FUXs2rULRkZG6NOnDwwMDJi5unTpUs4iT1X4+++/NVaSEYKilO61DQsLC6bY6Pv375BKpcw8AQrtMFXKdu/fv8fz588BFCYsZ8+ejZYtW2LUqFFKc+DHjx+YN28ehg0bxioijomJ4a109DMh1ne8efMmJk6cCGdnZ+jp6aFZs2bYsmWL2o4OfH0qxeMUC3xVJeafPn2K2rVrM88YKysrSCQSBAYGqu1GowqypLtEIkGHDh1Yifh+/fph1qxZSnFhMzMztUVtxaGkLDZezgdCEttCfHZ5ZXt/f394e3vD2NgY5ubmxUZcF6qkruq3vn37Fjo6OgAK427yHWYKCgrQtGlTZp0uWbKkxuIHQuDs7MwUbty4cQMSiQQ9e/ZUq5q1cOFCtGnTBjY2NnBwcEBERARWr16N9PT0YhmTmFjJ3bt3MXbsWNjb28PQ0FDJ7xUzBqFdINavX69VJXAh4OvzFEUABaCybTJQ9HXv3r07LCwsUK5cOYSFhSEsLAxOTk6wtLRE+/bt4eHhAQMDA1a8+vv37+jTpw90dXUhkUgYv7ZLly4qCeN8CxkBYTa1kK6DX758Qa1atdC+fXsUFBQwpFRNSHwSiQQnT55kisAVtxMnTqicj9pc1wHhhV1ZWVmIiopCeHg4QkJCMHnyZEZ0JTo6Gp6enkwx2urVq9Xmg/k802UiSfKwsbHh9Nm5MGjQIFhZWcHf3x+LFy/mFEHQFELj3127dkXTpk3x9OlT1m8+cuQII2wjBq6urli1apXS/pUrV6J8+fKcn3n16hW6dOmCUqVKQUdHp8g1ks98/PHjB6Kjo+Hn5wdjY2MYGRnBz88P0dHRxUoql/lpXJuikrKQY8VAkZA3YsQIdOjQAaamphg8eLBG554zZw5LCCw8PBwSiQQODg4sf0UM8vLyeHeSlRfkMTU1VSvIs3//ftjZ2SnNJzs7O84i00mTJkEikcDc3JyxB83NzSGVSlnquiVKlGARxAsKCjBgwACULVsWDx48UEvQFfpM4gsrKyve69HPgIzUyLWpE5LhW5A0ePBgwYTkJ0+eFNkhFNBuFzFtClZpe30pWbIkc69v2bIF5cuXR05ODlasWMGKI5uamqJXr16cwmnq0KlTJ1SuXBm9e/eGsbExU1y3b98+eHt7azx+gP8cAArv66NHj2Lu3LlYunQpZ4HPL/w38Yug+wv/KWhqUBd3YAXgR0QrCrdu3YK/v3+xtHPXFBcuXFAai4GBARMk7tu3L+PgPHz48B9tfff582f89ddfuHHjBieJWabkIpFIsHHjRpa6S2JiIgYPHiyKmCMPoW1KhUJIe2CZkaVqU0RxKz/Ko3fv3ggNDcWPHz8YZZPHjx+jUqVKnAEb+QSn/O+8dOmSxsScwMBABAcHs4zeN2/eIDg4GHXq1AFQqKArlUqVKuzlcffuXUgkEo2IFnv37kW1atVYqtmXL19GjRo1mJZvmmDSpEm8FMKFKKwkJiYyatzym42NjcqqODMzM4ZA16hRIyxatAgAWxlKRm6TSCQYM2YMi/A2a9YsbN269adX3xsaGqqdA3fu3FGq8i5ZsiRDdJfHsWPHYGdnV+xjVIXLly/D29ubFfCXSCTw9vbGX3/9pfJzHTt2RL169VgKyx8+fEC9evXUEqR//PiBRYsWwcDAAFKpFH5+fli/fj3KlSun1vmWbYBmqi18n71i1Ql27tyJ8PBwVK9eXa0i6s9wGvngZzzvrKysONvV3LlzB9bW1gA0I94vWLAAxsbGGDduHDP2sWPHwtjYmEVCsbW1FUzQNTAwUEmkl0+uCFFMkqF3796sZKgMU6dO1bg1XXZ2NqZMmYKaNWvC1dUVzs7OrE0eW7duhbW1NRo0aMDYu/r6+hgxYkSRygNix7Z69WpYW1sr2a/lypVT2wb68OHDkEgkLFUAAwMDQeoaQm0eIeB7/4tRK5Odvyiiq1ifJyMjAxUrVoS+vj5cXV3h6uoKfX19eHt7c15foeqJQlWLk5OT0ahRI9jZ2cHIyAiBgYE4evSoyvHzQVGJM3ns3LkThoaGSkH37Oxs1K5dG+7u7gyRiQu5ubk4f/48unfvDl1dXZW+mphkOx/wLQISStKUPYslEgkcHR1Zz2d3d3c0adJESan830TkNDIy4nwOpKenqyQLF/U3Klu2LKZOncrMbbGtNT99+oTVq1ejatWqnH/7Xr16McqcfPDmzRt4enoySfjnz5/D3d0d4eHhyM/P15hgxbdV8X8Nfn5+CA8Px+3bt/HhwwdGaUe2AYU+kr6+Pho0aICSJUuyCLVAoX/NZfPwafcppquHGFhbW7MUxopCixYt0Lp1a7x9+xampqa4ffs2zpw5g2rVqiE5OVnpeH9/f4ZYK/8brl27Jkh5OzU19afGyopSutc2xJI+/19HcSh+nj17FoMGDYKdnR3n2ijUpxL7t2ratCmqV6/Oij3cvXsXNWvW5LQd+WL69OnIzs7mdWy9evU4fRgZJk+erLLohS+KI16uCkIS25r67B8/fkSbNm3UFjTIOoJ17NixyI5gfJTUZd+blZUFiUSC+/fvswqMZIULpUqVAgBUqlSJRZjZsWMHjIyMcPbsWbx//x7NmzfnVQAva1OraUcSeVsRKLRp5UksReH69etYunQp2rRpAz09PY3iwsURK8nLy8OePXs0ziPt3buXte3cuROTJk1SqxBXvnx5SKVSODo6okuXLli7dq3KAnxtF6jz9Xl69OhRLMI4qq77+PHjMXDgQJZPmp+fjyFDhmDixIkoKChAv379EBgYqHTOx48f4+DBg0hISOD0J8QUMsrOy9emFtp18MOHD/Dz80O7du1QokQJjBkzRt1lKxKKhVaKhWjq2lprc10vrsIuGWxtbTFixAhO0RYuiH2my4SM+OLbt2/YunUrGjVqBGNjY4SHh+PIkSOCfE51UIzpNG/eHOXKlYOFhQVnIZM82Ur+Nz948IDJPaalpTH3mypit2yTYcWKFdDX18eAAQOYbgH9+/eHgYEBJ3EXKBSGqFChAlasWIE9e/YorZmK0OZ8/F+GIimvQYMG6NChA1avXs27G5UqODk5MYS7pKQkWFpa4ujRo+jduzcrvgnwj63KIKSTrBBBHqCw2CExMRFz585FdHQ09uzZo7ZY79KlSxg2bBiaNWuGkJAQDB8+XEl0yczMjDNPMnjwYJQpUwbJyclF+mxFPZOEwsnJSWMb7n8JpqamrA7BqpCbm4spU6YwRGupVApzc3NMnjxZpQCGNrqI/SzBqqIgZk2XwdDQkCG3du3alSGtP378mMVXkeWlJRIJPD09MX/+fE7fTREfPnzA4MGD0apVKxw+fJjZP3XqVERFRYn+zWLmwC/8gjr8Iuj+wi+IQHEFVgB+RDQufP36FQkJCWjdujUMDAxQtmxZVgWWDELUWcPCwjhbZkZHR6Ndu3Zqf8elS5cwfPhw2Nvbw9jYGB06dGDeK1u2LI4ePYq8vDw4OjoyRs/Nmzc1VnHVJhSDC/Kbvr4+3N3dWa0jxUBom1KhENIeWLGFzuDBgxEYGAgLCwvOtkfaVH7MyspCo0aNmJaaMpW9unXrciYMTExMGEUA+d/56NEjjdve3L17Fx4eHkpkFU9PTyaAsmfPHpZyLxcePHigsSKqfHWnvr4+6//qKj2LG0IVVnJycrBnzx5ER0cjOjoaiYmJap3Y+vXro1u3bti4cSP09PSY4DGXklRcXJxWyGPy4JssKV++vNpWHAkJCUpKx/369YOPjw8ruZ2RkQFfX1/07t27GH8FP1y7dg07duxAQkICUlJSijz+2bNncHFxgYWFBRO4sbS0hIeHB2cl4Y8fP5CQkIDg4GDo6OggMDAQsbGxiIyMRMmSJREREaGFX6UMvs9eMeoEixcvhqmpKYYMGQJ9fX30798fjRo1goWFBSZNmsQ6tridRpmjzmeTx8943hkbG3Mm1a5fv86QFTQh3js5OXGqo8XFxTGkbqDwWcdlL6mDo6MjZ1X83r17Wck+MYpJ5ubmnEG0e/fuKSluC0XHjh1RqlQpjBs3DgsXLmTarcs2GcLCwmBiYoIlS5awPn/u3Dm4u7vD3d2dUylbDE6fPo3u3bvD1NQU5ubm6NOnDy5cuMA6Rl9fX63619OnTxm1Jxm8vb051SaXLFkCLy8vpf1CbR6+EHL/i1Erk0Eo0VUIZNXdskRiUlKSymSPUPXEfwOEEHSBQvVDY2NjnDx5EsD/kXPLly+vUnk3PT0dq1evRkREBEqVKgVra2uEhoay7jt5yJTrZdvly5exZs0aeHp6Yvfu3aJ/K98iILEkzaCgIN6t2X4GkZOvYpK7uzujOiWPsWPHqiRYxMfHo0yZMpgyZQr++OMP/PHHH5gyZQocHR2xevVqREVFwdLSEjNnzsSXL19Ydm5mZiYWLlyotvDg9OnT6NatG0xMTODm5obx48dzFkjNmjULtra26NatG68Wf0AhKbBs2bIYOXIk3Nzc0KFDB0ZV5WcQrP6LMDY2Vkl+kcfx48cxYsQIzJkzR8k3mj59OrPuKKKodp9iunrI8Ndff2HkyJFo3rw52rRpgwkTJnAmDoFCEnFkZGQRv/L/YGNjwyRpzM3NGXLhiRMnOMkqRkZGzLgVE/7yPr4qhWvZdubMmZ9KSC1K6V7bkEqlrOSVmZkZSz2RD0H39evXuHHjhtoEW3x8vNrt/0WkpKRg9OjRKF26NGe8VqhPpfi3khWny6Dqb2VoaMhSLZbhypUrP4UEDhSqieno6GDs2LGszgsvX77EmDFjoKuryyoUEAOx8XJ1EJPYLg6f/fr165yq6IDwjmB8lNSBosmQOjo6zPgtLS1Za32PHj0YEQbZubnIStpqU1vUvaEKBQUFuHr1KhYsWIAWLVowMWWuZwxf/IxYiabYsmULWrVqpfL9Z8+eYfPmzejXrx88PDwglUpRunRpdO7cmXWc2AJ1vtCWzyMUtra2nITI9PR02NjYACi8Zy0sLASfW0who1AU1XWQyxa6e/cuHB0dMXDgQNZ+McjMzOS1cUEb67oMQgq75H/7wYMHWcR7Wd5SKOFGLEF3/vz5GDRokCiCbWZmJqZPnw4XFxeULVsWnz9/FnwOPsjPz0e/fv0QHR2t9J6pqSnzN5X/zZcvX2bEGOSvTVEEb3kkJiYiMDAQ1tbWsLa2RmBgICfRVn4sfHIZMmhzPv4CN+TJecOGDUO/fv0AFK6/8jwBIbFVGbTZSVYbuceqVauqjKcOHjyYKcr9mYiLi0PHjh3x5cuXn/q9YiE2DyZD2bJlecXRBwwYgBIlSmDVqlWMX7xq1SrY29tjwIABnJ/RRhexf4tgldg1HSjsUpmQkIDs7GzY2dkxMevU1FTGBpP/jtTUVAwZMgTW1tbQ19dHWFgYDh06VGxFKXwhZg4A/LsO/sJ/D7r0C7/wH8WPHz/ozZs3VFBQwNpftmxZpWOzsrLo/v37pK+vT87OzmRmZkahoaEUGhqq8TgCAgIoKiqKGjVqRKdPn6aVK1cSEdGjR4+oZMmSSscfPXqUtm7dSnv37iVdXV1q164dJSUlUd26dZWOXbt2LQ0cOJBsbW3J3t6eJBIJ855EIqGpU6eyjk9OTqbp06crnSckJIQWLFigtP/evXu0ZcsW2rZtGz169IgaNGhA0dHRFBYWRqampsxxPXv2pPbt21OpUqVIIpFQo0aNiIjo0qVL5Onpye9CFTOuXLlCO3bsoCdPntCPHz9Y7yUmJhIRMXPD2dmZLl++TLa2tsU+jg8fPlB4eHixn1eGqVOnUvfu3en58+dUUFBAiYmJlJ6eThs3bqQDBw6wjl24cCHnOaZPn07Z2dlK+xs3bkx9+vShSpUq0b1796hZs2ZERHTr1i1ycnLSaNwWFhZ07NgxOnv2LF2/fp2ys7OpcuXKzNxRhKWlJb18+ZKcnZ1Z+1NSUqh06dIajcXDw4Nu375NSUlJdO/ePWZf48aNSSqVEhFRaGgo+fn50b59+2jkyJGc59m7dy95e3trNJZFixZp9PmikJ+fT3FxcXTixAnO9fHPP/8kIqINGzYQEZGTkxONGTOGTExM1J53165d1KFDB6U188ePH7R9+3bq1q0ba//ChQupS5cutHfvXpo8eTKVL1+eOU+tWrVYx3bv3p05F981XQhOnz5NISEhFBgYSMnJyTRz5kwqUaIEpaWl0fr162nXrl3Msc2aNaOpU6dS8+bNydDQkHWer1+/0owZM6h58+as/XPnzqXg4GDy9PSkMmXKEBHRs2fPqE6dOjR//nyNxi4GlSpVokqVKvE+vnTp0nT9+nXasmULpaWlkZGREfXs2ZMiIiJIT0+POe7atWu0YcMG2rZtG0mlUurWrRstXLiQ9Qxo06YNVa1alYiIvn37pnQN+eDp06dEROTo6Kj2OL7P3uXLl1NMTAwlJiZSbGwsTZw4kZo3b069e/emJk2asJ6rMqxYsYLWrFlDERERFBcXR+PGjSMXFxeaOnUq/f3336xjLS0tadmyZUrnmDFjhuDfTkSi7ZKf8byrVq0arVmzhpYuXcrav2rVKqpSpQoREU2bNo2ICteWDh06CJoDL1++VFofiIhq1apFL1++ZF7n5eVRbGwsHT9+nKpUqaK0fsXExCidIyIigoYNG0ZmZmaMvXX69GkaPnw4dezYkTkuJSVF6bOfPn2iHj16UJs2bTjHbWRkROfOnSM3NzfW/nPnzom6B+Rx+PBhOnjwIAUGBqo97tWrV5SSkqI0hlq1alFqaipNmDCB6tWrp2Qv8cWLFy8oLi6O4uLi6P79+1SrVi1asmQJtW/fnvP5YWtrS5mZmcyaqIhHjx5RiRIlWPtGjRpFQ4YMobdv31KDBg2IiOjEiRO0YMECzmenUJuHL4Tc/0KfpfKwt7cne3t71r5q1aqJHrc8JBIJNWnShJo0aVLksT9+/OC871SBj/3NhezsbKXnu7m5Oe/vlYdEIlHyibjWchn69OlDf//9N7Vu3Zr27dtHU6dOpRcvXtDp06c5bczSpUvT169fKSgoiIKCgmj8+PHk6+ur9jv8/PyU9gUEBJCDgwPNmzePwsLCBP7KQrRu3ZpGjBhBe/bsIVdXVyIiun//Po0ePZpatWrFHFe5cmXasmUL/f7775zn2bRpE1WuXFlp/8mTJ3mPpWzZskq+B5Hqe1Eodu/eTV27dqXOnTvTtWvX6Pv370RE9PHjR5o1axYdOnSI9Z1t27alw4cPU/Xq1YmI6K+//qKMjAzavXs35/nj4+NpwYIF1L59e2Zfy5YtycfHh1avXk0nTpygsmXL0syZM+nUqVMUFhZGAwYMoKysLKpWrRrp6+vTu3fvKCYmhgYOHEhEhWtvXFwcrV+/nj59+kTt27en79+/0969e6lChQqc41izZg2ZmppScnIyJScns96TSCQ0bNgwpc84OjrSsWPHqE6dOtS4cWPatGkTMx/HjBlDoaGh9P37dxo9ejRjA7169YpZP9Xdm0+ePFH5HpHmdvj/KqpXr073799nfBhVaNiwIdWtW5dlM8swbdo0evfuHefndHR0yMrKirVP3v/Oyspi7gEiordv37KOLSgoYL0vw7hx42j+/PlkampKLi4uRER07NgxmjdvHs2cOZPGjx9P3759owsXLlD9+vXp27dvtGbNGjp+/Dj5+voq/Q5Fmyo/P5/MzMyIqPBZ/+LFC/Lw8KBy5cpRenq60njs7e3p/v37SrGFs2fPMuMjKrSn1a2xANS+X9yYOnUqderUiUaOHEkNGzakmjVrEhFRUlKSID9LLABQw4YNSVe3MOT/5csXatmyJenr6xNRoS2sClevXqXu3bvTnTt3CADrPYlEQvn5+czr4cOHs97Pzc2lL1++kL6+PhkbGyv5+P9G8PEdHz16RFu3bqWtW7dSeno61atXj2bMmEHt2rVTOlaoTwWAevToQQYGBkRU6AMPGDCAsQm57lPZeHNzc5X25+fnk4ODg9rvLAq7du1Saatdu3aN+X+LFi1o4cKFNGbMGFqwYAFZWFgQUeFzV0dHh+bNm0ctW7bUaCxC4+V8IPOXU1NTqWnTpqzYtb6+Pjk5OVHbtm1ZnykOn/3jx4/08eNHzvcmTJhAUVFRNGrUKGaNJCJq0KAB5/dGR0dTmzZtaN68edS9e3fGjvzjjz9Y/sDJkycJADVo0IB2795N1tbWrN9arlw5Zr7k5eUx85CI6MKFCzRixAjmtYODA+czKTk5mSZPnkxERHv27CEAlJWVRfHx8RQVFaV0LfmiqHtDBnkbpWXLlnTu3Dn69OkT+fn5UVBQEPXt25fq1q1LlpaWosZB9HNiJZqiRo0a1K9fP5Xvly5dmjp37kxt2rShM2fO0LZt22jLli20fft22rx5M3PcwIEDmRxPz549qUuXLqx5oym05fMIRV5eHt29e5fc3d1Z++/evcs85wwNDVm2w6hRozjPJZFIyNDQkMqXL0+tW7emR48eERFR/fr1KTExUcleUwUhNnWrVq0oMjKSduzYwYzhyZMnNH78eGrbtq1KuwgArVq1ilavXs3YRvLPdb4oV66c4M/IoI11XYb09HTO/KiFhQVlZWUxrw8cOEC//fYbE7/r0KED5eTkMO9LJBJKSEig2NhY2rZtG/N8mzNnDg0YMIBZT96/f0916tSh27dvM5/7/PkzGRoaMtc3OzubPn36RETE/KuIs2fP0smTJ+nw4cPk7e2tZE+r88WkUilJJBICIOpvyRdSqZRGjRpFQUFBNG7cONZ7derUoY0bNzKxBIlEQgUFBTR37lyqX78+ERX+fe3s7Jj/80WbNm1UxlK54OjoqGS/qoO6+fjjxw/e659irI8vwsLCKC4ujszNzXmtf3yPVTdnxOD06dOUk5NDNWvW5L2mqYKVlRU9ffqUHB0d6ciRIxQVFUVEpDSHhcRWZXj+/Dmn/11QUMBpNxMRvXnzhjOf6Ovry3ptaWlJ1apVo3r16lH9+vWpZs2aZGRkpHS+d+/eUU5ODmudvHXrFs2fP59ycnIoNDSUOnXqRESF83vbtm3UtWtXpfMsW7aMCgoKaNWqVcw+Vc8hLnDlNvigffv2tG3bNipRogQ5OTkprUfyvsDPwrNnz+iPP/7g9E3k82Dfvn2jFStWUIUKFRj/++LFi3Tr1i0aNGgQ57mnT59O06ZNow0bNpCxsbHKMWzdupW2b99OISEhzD5fX19ydHSkiIgIZu2QR+3atWnUqFEUGBhIf/31FyUkJBBRIadGVb6jKGiSN1MHIWtRYmKi6DWdiGjEiBHUuXNnMjU1pXLlylFQUBARFfoUPj4+Ssf7+fnR0qVLaf78+ZSYmEjr16+nFi1akIODA/Xs2ZMiIyNZxyvGSRXBZSfwgZg5MGPGDIqMjKSAgACGG/ULvyCDBEIsll/4hf8HcO/ePerduzedP3+etZ/LMc3MzKTBgwfT0aNHGeNeV1eXwsLCaNGiRYzj+P37d1YgSwiuX79OnTp1oqdPn9KoUaOYh+zQoUPp/fv3tHXrVtbxxsbG1KJFC+rcuTM1a9aMM5kjQ7ly5WjQoEE0fvx4XmMxMjKi1NRU8vDwYO2/e/cuVapUib5+/craL5VKqWrVqtSpUyfq2LGjWkd6165d9PTpUwoPD2cMkPj4eLK0tKTWrVvzGl9xQUYIbNq0KSUlJVGTJk3o3r179Pr1a2rTpg1DmPgZ6N27N1WtWpUGDBigte84c+YMRUZGUlpaGkN0nTp1Ki/iBVFhIr9atWpKzk9WVhZNmTKFnj59SgMHDqTg4GAiKjQU9fX1mSCtpvj27RsZGBioNWDGjBlDly5dop07d5K7uztdu3aNXr9+Td26daNu3box95U2ER8fTwMHDqT58+dTv379mIRYXl4erV69msaOHUsrVqygHj16aH0sYjFkyBCKi4uj5s2bcxqNYkkUOjo69PLlSyVC1fv376lEiRK8g0jfvn0jXV1d5toSEWVkZFCvXr14reliULNmTQoPD2eSJWlpaeTi4kJ//fUXhYWF0bNnz5hjX79+Tf7+/qSvr09Dhgxhgr3p6em0bNkyysvLo5SUFKW1EgAdO3aMIbj6+vqKdhaE4mc490SFc6Bx48bUu3dvCg0N5Xx25eTk0JAhQ2jDhg1kaGjIBD6CgoKoVq1anIEPosJ7bMaMGbRkyRKGWGdqakpDhw6ladOmcX6X0GevDI8fP6a4uDjauHEj5eXl0a1bt1hJPaLC5/SdO3eoXLlyVKJECTp27Bj5+flRRkYG1ahRg96/f886/sOHD7R+/Xq6c+cOERF5eXlRr169ijUR8m/AuXPnqFGjRlS1alVq2LAhERUSKC9fvkxJSUlUp04dpc8IId5XrFiROnXqRJMmTWLtj4qKooSEBLpx4wYRERMg5oJEImEKERTH0bVrV9q5cyez/hQUFFC3bt1o1apVDOFBFW7cuEEtW7akzMxMpffmzJlDM2bMoL59+zIJ1UuXLlFsbCz99ttvNGHCBLXnVgdnZ2c6dOgQeXl5qT2uoKCAKThRheTkZFHrUkhICB0/fpxsbW2pW7du1KtXLyU7UxG9evWiBw8e0LFjx5Su7ffv36lp06bk4uJCsbGxrPdWrlxJM2fOpBcvXhBRYcBq+vTpgggiqmwevhB6/4tB/fr1VdpEZ86cYSX21UHVbzxx4oTKQh3Faz5+/HgyNTWl3377rcjvE2p/P3r0iIYMGUKnTp2ib9++Mfs1fb5LpVKqWLEicy9fv36dPD09leaaYuB5woQJNG/ePHJycqJTp06pJPP4+/vT3bt3qXLlygxJt3bt2mqDvapw//598vPzYyUMheDjx48UHBxMV65cUSoCSkxMZJKLBw4coNDQUBo1apRakqYi0YZvYRcRUbdu3ah+/fpUr149FrGuuFCpUiUaOXIkdevWjWWvpaSkUEhICL169Yp1/LNnz2jlypWsZ++AAQNU/l2NjIzo+vXrSoUMGRkZ5OfnR1++fKFHjx6Rt7c3GRsb0+nTp8nb25vWrVtHS5cupZSUFNq9ezdNnTqV7ty5Qy1btqTk5GRq3rw5de7cmYKDg0lHR4f09PQoLS1NJUFXBhlJhoskYmVlxblGfPnyhQwMDEhHR4fZ9/fff9PSpUtpzJgxlJeXp0Swmjt3LougowhZUlgVtJko/jdjz549NGXKFBo7diz5+Pgo2aLyCb+2bdvSrl27lK7j69evqWHDhnTz5k3WfmdnZ7XX/OHDh+Tm5kZz5sxRSYzasWMHTZo0ie7fv8/si4+PpwEDBtC8efOof//+zJhzc3Np5cqVNGHCBIqLi6OVK1dSw4YNacqUKYJtqjp16tDo0aOZxOSHDx9oypQptGbNGrp69arSb509ezZt3ryZYmNjqXHjxnTo0CF6/PgxjRw5kn777TcaOnQoERUSLyZPnswQ7hWRkZFB/fv3/6nz8dWrV/Ty5Uvy8/NjbKy//vqLzM3NtV6kzpc0yBUj8fPzI1dXVxo/fjyVLFlSaa4VRcrJyMiggQMH0tixY6lp06b8B/0TIcR3rFGjBl2+fJl8fX2pc+fOFBERoXEBuDx69uzJ6zhFG2nfvn00a9YsWr58OQUEBBBRYRHU0KFDafz48aKLNpcsWUKTJ0+mHj160Jo1a6hnz5704MEDunz5Mg0ePJhmzpyp9Jlnz57Rrl27mGJ2Nzc3ateuXZEFs3wg1mfng/j4eN6J7bp16zI2TK1atdR+ZsmSJazXAOjly5e0adMmqlevHueYTU1N6caNG4wgiMyGyczMJE9PT5YdLEN+fj59+vSJRZTJzMwkY2Njpbjb48ePydHRUa2/5+/vTyNGjKAePXrQkydPyMnJiW7evMnYI+fPn6f27duz4l9EhfbRvXv3yNHRkbp160YODg40Z84cevLkCVWoUEF04aOYe2Ps2LFUr149qlOnDmPP/Bfw9etXmjhxIh0+fJiz2CUpKYlOnTpFp06dopSUFPLy8mLibHXr1lUiW33//p0pUD9//nyRBerFAaE+T1paGlWuXFn0c33YsGG0bds2mjRpEiMScPnyZZo1axZ16tSJFi9eTOvWraO4uDg6e/YsERX64NeuXaP8/HwmnnHv3j3S0dEhT09PSk9PJ4lEQmfPni3SjueCEJv648eP1K5dO7py5Qp9/vyZHBwc6NWrV1SzZk06dOgQXblyhff31qtXT/BYZRBDgtHmuu7i4kJr1qyhRo0asdbSjRs30pw5cxgibatWrSg0NJR69epFRMQ6lqhQSOPUqVN05MgRevXqFbOmmpubU2pqKnPc69evycHBgfnbKP4NFQvEVMUxilrvFG0A+Xv07Nmz1KJFC+rZsycFBwcXGdfTBIcOHaLu3bsrFf7dvHmTGjZsSJUrV6Y///yTWrVqRbdu3aK///6bzp07xxQKy5CcnEy1atVi5XaICu2z8+fPa5QPSUpKogULFtDq1at5iQepm4+XL19mCmuLgkw8Rih69uxJS5YsITMzM17PPb7His1tR0dHU3Z2NkO2BkAhISGUlJREREQlSpSgEydOaCRANGTIEDpw4AC5ublRSkoKZWZmkqmpKW3fvp3mzp3LxOHExFarVKlCI0eOpC5durDu68jISDp27BidOXOGOVZIUSJRIZE+OTmZTp06RefPn6e8vDwKCAhgnqeNGzcmokKBDwcHB0bs7M2bN+Tp6UkODg7k6upKhw8fpvXr13OScouCOt9bEUKK6eXRvn17OnnyJLVr147TF/wZOXZ5nDhxglq1akUuLi509+5dqlixImVmZhIAZs2RoU+fPlSqVCkl4YFp06bR06dPleLZRIVxxAcPHhAAtYTkEiVK0OnTp5XyK3fu3KG6desqrYtEhYU3gwYNoqdPn9KwYcOod+/eREQ0cuRIys/PV/IZxKC4BKuErEWK64uYNf3q1av05MkTaty4MZNXPXjwIFlaWjIiM6r4BESFPs/69espPj5eqcCJ6zkoP4/F2o5i5kCpUqVo7ty5ou73X/h/H78Iur/wn0NgYCDp6urShAkTOAlosmrep0+fUtWqVUlPT48GDRrELLy3b9+mlStXkq6uLqWkpFBycjLduXOHNwmWL759+8Yk6eTx+fNn3ol3RcexKFSrVo1atGihpKw7ffp02r9/P129epW1PyMjQylJyQWxSojagq+vL/Xv358GDx7MGOvOzs7Uv39/KlWqFGdCQwhZQQhmz55NMTEx1Lx5c87kHZcK0s/Gpk2baPz48Qzh5WegoKCAZs6cSatWraLXr1/TvXv3yMXFhX777TdycnJiDFoZfvz4QYMHD6a4uDjKz88nXV1dys/Pp06dOlFcXBwrES0GfP/+Y8aMoZiYGDIzMyNXV1cCQA8fPqTs7GwaNmyYKIKrqgprLohVk5PB1taWNm7cyKghc6Fy5cp04sQJsrKyokqVKqkNIMqcGKlUSq9fv2Yq62RIS0uj+vXrK5GEXFxc6PLly2RjY8Pan5WVRZUrV6aHDx8y+/iu6WIhNFny6NEjGjhwIB07doxx8CUSCTVu3JhWrFihFVKKJuDr3KsiLspw+/ZtzkpWmULf48ePBaks8A18EBWqfCQmJlJkZCRTIXvhwgWaPn06hYaGclYwqoKqZ68MT58+pQ0bNlBcXBz9+PGD7t69q0TQdXFxod27d1OlSpUoICCA+vbtS/3796ekpCTq2LEja74nJydTy5YtycLCgkmuXr16lbKysmj//v0/jagtD20974gKlYrmzp3LIqNPnDiRk/AklHi/e/du6tChAzVq1Ihx5s+dO0cnTpygHTt2CFJdUIV79+4xY/fx8eE9p8+ePUstW7akDx8+cL6/Y8cOWrx4MYsoNnz4cJZSoxhs3ryZ9u3bR/Hx8aLIgcWBVq1aUe/evalFixa8n8XPnj2jgIAAMjAwoMGDB5OnpycBoDt37tCKFSvo+/fvdPnyZZUBp7dv35KRkZHSvckHmto8Qu5/GV6/fk1jxoxh7jtF91xxvisq9efm5lJqairdvHmTqlWrRp07d+Y1Vq4kQlHV3Xv27GG9Hj58OG3cuJF8fX2LVE8Uan8HBgYSABo+fDhnYFhsMlEIcUlROeDQoUPk5+enRM5RVCjJysqi5ORkOn36NJ0+fZpu375N/v7+VL9+fU5yi6KtJyNzTJ8+ne7evUupqam8xswFvkVAYkiaQgq7+vTpQ8nJyXT//n0qXbo08zyvV68eL3+yKBgbG9Pt27fJycmJZa89fPiQKlSowEluEQJ3d3cKCwujOXPmsPZPmDCB9uzZQ+np6XTlyhVq3bo1ffjwge7evUtly5al9u3bk7e3N5MU8PDwoC9fvpCuri4NGzaMBg4cyPr96gi6WVlZNHnyZEpISGCeJ1ZWVtSxY0eaOXMm83eLj4/n/btk68CzZ89o586dlJGRQUT8CVZpaWms17m5uZSSkkIxMTE0c+bMn6aE9m+DqqQAlx1TtWpV8vX1pfXr1zP7Xr16RfXr1ydvb29Wtw4iosWLF7Ney675kSNHaOzYsTRhwgQaPnw4HT9+nK5evcrZ1SMgIIAaNWrEOle1atUoIiJCZTeYmJgYGjt2LPn7+9Px48dFqScdPXqUcnJyKCwsjO7fv08tWrSge/fukY2NDSUkJDAK+DIAoFmzZtHs2bPpy5cvRERkYGBAY8aMYSXe6tevTyEhIUqKXjKkpaVRpUqVlOxabeHPP/8sksT3b4WZmRmlpKQUqf6sDleuXKEuXbrQ3bt3i3FkxQchvuPkyZOpc+fOoshWxe1TKRZf5OTkUF5eHqswXFdXl0xMTEQXmnl6etK0adMoIiKC9SyVqZUpqrnm5uZS//796bffflPqZqVNFOWzCwGfxHZUVBQlJyez4hIyGyYwMJDlZyleB6n0/2PvrKOiWts2fg3doIQYpAqCgIjYRShh5zFQUMHuQLHFjmNiBwgW2IrYSoiKioGJICGoB1QwDqgocH9/sGZ/s5lggtFz3uNvrVnKnl0z8+wn7rhuBRgaGsLNzQ2zZ88WaM+vV68eDh8+jDZt2rC+9xMnTmDGjBnIyMhg7Z+VlYXS0lKBa2hlZWWBQUkfP37E7du3BX5WX19f7Nq1C1OnTsWAAQOQlJQEPT09XL9+nfUd3Lp1C9HR0axjrayssHTpUnTt2hUWFhaIjIyEm5sbUlJS4O7uLlQJvjq5efMmCgoK0K1bN2ZbREQEFi5cyKjVhYSESC2swos8bSXiULkfICL8/fff0NDQwP79+1kVMrhw2+D06dMxatQoidSExUlQl4TqWvPIOq6XlZVh5cqV2Lx5M/Lz8wEAtWrVYpIcFBUVkZOTAwUFBSbJccOGDbh27RrCwsIY2/unT58QEBCAdu3aYeTIkRg8eDC+fv2KCxcuSJTIyP1MvIgzp75+/TpLjEVY1UF5UZ1BMNXRr4ub2GVhYYHz588zgdaVA3QfPXrE9F+8AbqV96scoBsfHy/WfcoSFD1u3DhERkbCxMQEI0aMgI+PT7WrelcW8uA+pzExMfDz8xOo7P7p0yds3ryZ1R7Hjx+P2rVr8+0rSsTFwMBA7DUGd64jbH6koaHB157EnR9V5zzj34STkxNmzZqFAQMGAACOHDkCPz8/XLp0CTY2NvD19YWGhgaj3i0NP378wMaNG5Gbm4thw4Yx1UXWr18PbW1tBAQEAJDOtnrq1Cn4+flh9uzZWLx4MYKDg1mVZHl9SbIkJZaWluLOnTvYsWMHDhw4gPLycqYfsLCwwN69e5nn/M8//8T27duRmpoKJSUl/Pnnnzh69CiSkpLQr18/BAQEwNPT8x+jrKmpqYkLFy6gXbt2v/pWAFTYKLy9vREcHMz0wUZGRkySO28Qva6uLpKTkwXOj52dnQVWsqjKPswNSF68eDFSU1MRFhbGzCdLSkrg7++Phg0b/vTAZXkLVklCdQlzVUZBQYE1BgtCUKWkyr8zdz41f/58LFu2jBENkhRp2oC+vj5u377Nl6jym98AvwN0f/MfRFNTE3fv3q1StcLf3x8vXrzAhQsXBDo1vLy8UF5ejuTkZERGRkqtArtgwQK4urqiTZs2Qo1Fnz9/Zhb/VQXr8QboSarOGh0djT59+mDw4MGs8sCHDh3CkSNHpFZhkEQJ8WegqamJJ0+ewNzcHPr6+oiLi4O9vT2ePXsGNzc3VilsQPJgBUkQZcTmcDisQER5U9nYw12AJycnY/78+XyTjIcPHwo8D7e8k6mpqdQG0MWLFyM8PByLFy/GyJEj8fjxY1haWiIqKgobNmzAzZs3BR6Xk5ODx48fo6ioCE2bNq0Wh7+kv39SUhIOHTrEcnAPGjQIrVq1kur6VWXR8yLr5LtOnTqIi4vjK/PFS3BwMAIDA6GhoVHlIubkyZPgcDhISUlB48aNWZl0ZWVlyMrKgpeXF9/CXtgEPD8/HyYmJqwgUHH7dGmR1FnCpbCwkFGlatCggUg11OLiYsTHxwsMcP0nBOmLIjMzE71798ajR4+YwAPg/42y3DYpSdB1ZUQZPoCKBXjlEiNARSDVoEGDBC7AxRl7uUiqThAQEAATExMsXLgQW7ZsQWBgINq2bYvk5GT06dOHFYBhb2+P1q1bY9u2bUzwYllZGcaNG4cbN24wqq/SII2qnTzHO0mQNvD+7t27WL9+PSvQdfr06QLLCb948QIZGRno0KED1NXVq6X8sTSKSfJE3EzwfyJZWVkYN24cLl68yJfssHnzZoGBI6WlpYiLi0NGRgYGDx4MbW1tvHnzBjo6OnyOREnnPOIiyfPPxdvbGzk5OZgwYYLA9i7uGmPRokUoKirCn3/+KdW9A5Jnd0uinijp/FtLSwt3796tUnFZnkirbMeloKAAcXFxOHXqFA4dOsQ3fnER1F8TEUxMTBAZGckEEMkbSYM0xUnsqszr169ZwctpaWmoXbs2nyKbpIirmMTLly9fBM69KpczBCrKRvfv3x+NGjVilLaSk5ORmpqKo0ePolu3bti2bRvS09Nx+fJlBAQEoHfv3rCzs8P58+fRunVr3L17F127dkVeXh6SkpKwZ88eREVFwcbGBkOHDsXAgQNRu3ZtgQG6hYWFaN26NV6/fg0fHx9W8vDBgwdhYmKCGzduyFxysrqIiYnBmjVrEBcX96tv5Zfw8uVLke/zOvzevXuHDh06wNvbG+vWrcObN2/g6uqKJk2aIDIyUmwlrC1btiA5ORlhYWFSVfXQ1NTEo0ePhCYTZmZmokGDBigsLGSCe8LCwjBw4ECZbDuFhYVCVZ+5fP/+HS9evEBRURFsbW35xvRdu3bh69evQtdN+fn52L59+09znGlpaaG0tBTNmzdnBfH9ChvY+/fvkZ2dDQ6Hw4y/oujVqxeGDh0qdVl6oCIhr0OHDhIlGv9MpFk7AuBb54pCHmsqaZIvJEUatTJdXV08ePBALgG6kqzZJUUaxzbXLhEfH4+4uDhcvXoVCgoKMicBSVoRrGPHjhgxYgTf77x//37s3r2bb+yNjo6Gj48PioqKoKOjw2qPHA6HCXIJDQ1FdHQ0jI2NsXDhQhgbGzP7jRs3Dp06deJbQ23duhWTJ09mytTeu3cPCgoKCAkJwfHjx6VWcJMEb29vuLi4MMIpjx49gpOTE4YNGwYbGxtGGX7RokUyXeefYCvZu3cv67rc4NuWLVsKnQNu2LABCQkJSEhIgKqqKuObcXFxEWn7BcRLUJcEcdc8VSV4ffr0CXFxcdUSgMIdq6oSvKhbty4uXbrEN0d/8uQJPDw88Pr1a9y7dw8eHh54//59tVWok3VOHRYWBi0tLfTv35+1/ciRI/jy5YvU4wUgXRCMPPt1cRO71NTUkJqayiQzJCcno0mTJoy9LCsrC40aNcKPHz8kCtCVlXfv3jEq2NbW1nwiJ0DFM2RqalqlYErlBGJJqGzj4U00GTFiBJ9KoqQIE3FJS0tDkyZNsH37drHOw227ssyPpGmP375947MfyCqYAwCHDh3CoEGDBL4XGBiINWvWyHyNqqhRowZu3LjB2BqGDx+OsrIyREREAKjwefbv3x+5ublyvxdpbKuA+JVkpUlKTEtLYxTp4+LiUFJSgg4dOsDFxQWTJ08GUFFZIDU1lVnvd+nSBXZ2dli9ejVzjtatW6OgoADu7u6Ii4tDnTp1MHz4cAwbNkxsYZ8RI0Zg48aNfIlfxcXFmDhxotRJQ40aNcLhw4cF2sR+Bdra2njw4AHq16+PGjVqIDExEY0bN0ZKSgp69uzJqlRobGyMlStX8lWv3bt3L2bNmsUk5EhD7969ceXKFaiqqjI+qZSUFHz//p1vrOP2v/KsIiZvwSpJENWnOzs7C7QJvHr1CqdPnxZoj+UKffDGIFQH8fHxmDZtGp8IobhI0wYkqTr4m/8evwN0f/Ofo3nz5li/fn2VWUB169ZFVFSU0P0SEhLg4uKC3bt3MyVRpKFz5864efMmY8DnGkp4Dfi8WSjCAm64RkTeBX5xcbHE6qwxMTFYvnw5Hjx4wKgsLVy4kMn6qlmzJtLS0piMRlGLQa6RTxIlxJ9BvXr1cO7cOdjb2zPqfYMGDcLNmzfh5eXFZ2D4t0nRS/MbAfxBCLwL8MqLGO77os6trKyMAQMGYMeOHRKrxzRo0AA7duyAu7s7ywCSmpqK1q1bC1UhlAfi/v7cgB5hhoqcnBz4+/vj0qVLEl2fNwM7OzsbQUFBGDZsGEvtJTw8HCtWrJDJsAYAa9euRWZmJjZv3lwtmZvcAN7g4GBMnz6dZchVUVGBubk5+vbty5SVPn36NIAKx2B4eDirLF1ZWRmuXLmCS5cuscq2idunS4ukzhJJuX//Prp06YIvX76guLgYNWvWxPv375nShD8zSF8aunfvDkVFRezevRsWFha4ffs2CgoKMH36dPz5559o3749ANFB16ampigpKeE7tziGD0C6EiPijL2AdOoE5eXlKC8vZ/qCyMhI3LhxAw0bNsTo0aNZZdTV1dXx4MEDvgC058+fw9HREV+/fhV5LVGcOnWK9TfXWB4eHo7g4GA+JXJA/uNdRkYGwsLCkJmZiQ0bNsDIyAjnzp2Dqakpq0SWPAPvCwoKmJJNHA4H6enpsLS0xIgRI1CjRg2mDNW0adOwZMkSaGpq8ilIVIZrPJBGMYnL3bt3mcDixo0bCwwqlhRxM8H/aWRmZjIlvD98+MAEC4pKdnj58iW8vLyQk5ODkpISRnl/8uTJKCkp4TPySzrnERdJnn8u2trauHbtGhwdHaW+LlARdN6iRQuBShLiOhHkmd0t6fzb1dUVc+fO/elKQLJy/PhxZtx6+vQpatasiXbt2jFBWoIMpZXVdrjtsUGDBjI7wOSZBCROYldlvnz5gsTERMTGxiIuLg737t2Dra0t7t+/L9O9iKuYBFQ4P4cPH45z584JPJcwJ2tWVhZ27NjBlPG2trbG6NGj+ZTqjh49isGDB6OsrAzu7u5MScgVK1YgISGBdd3i4mJERUUhNDQUt2/fRllZGdatW4cRI0awxowpU6bgypUruHz5MiuoEqhQW/Xw8IC7uzufs//s2bNQVFTkKzV/8eJFlJWV8QWoVVURQVwkLVX8Xyc3Nxft2rVD3759cebMGTg5OeHAgQMSVYHJzMyEo6Mj4wCRtKqHjo4Obt++LXTu9fz5czRv3pzlYKlVqxa+fv2K/v37w9/fH23atJH0o4tEXo5HefPjxw/cvn2bSUS4ceMGvn//DmdnZ7i6umLp0qVyv4cnT55g7NixLAVMoCKwb9u2bUKTX96/fw8/Pz+0aNECdnZ2fDZE3r6Au3bnwk122rx5M0xMTIT2sb8aSdeOERERWLNmDTMftbKyQmBgoMj10r/NhshFGrUyPz8/ODo6ClXflgVx1+zSII1jm2ufiI2NRXx8PGOfkDUwU9KKYDo6Orh37x5fUMmLFy/g7OyMjx8/srZbWVmhS5cuWL58eZVO7oKCAiaQPzc3l0mA6N69u9DqPsnJycjNzRVZplae1K5dG9HR0UxForlz5yI+Ph6JiYkAKgIRFy5cKDBZS9Lr/Bufa14ePXqE+Ph4XL16FWfOnIGRkRFfkpykCeqSIO6aR1lZGZ07d+ab83IpLCzEmTNnfqpCnJaWFs6cOQMXFxfW9ri4OHTv3h1///03ay4mTSKjIATNqSdNmoQGDRrwreU2b96MFy9eYMOGDcw2Kysr7Nixgy/oMj4+HqNGjWLZ16sLUUEw8uzXuVSV2FWnTh1EREQItTNcvHgRfn5+ePv2LfLy8pigI21tbTx8+JCx+1VXgC53XhsREcEoLSsqKsLX1xchISGsfnvYsGFi+WuEJRBXJ5XLiguDq0jPDbw/deoUvLy8WAGxZWVlePjwIaytrXH+/Pnqv1khiNsei4uLMWvWLBw+fFhgslJ19EV6eno4dOgQ3/p86tSpiIyMZCW0FxQUYMGCBYiNjRWo0C1tJYXKQeiNGjXClClTGPGvnJwcWFtby+SrCA8Ph4GBAbp27QoAmDlzJnbu3AlbW1scOnSICWyVxrYqCZImJdatWxdfv35lElw6duwIBwcHvuexVq1auHjxIjOPNDAwwI4dO5jrpKeno2nTpigqKgJQYcsOCwtDREQEXr58iY4dOyIgIAB9+/YVGTQuTLX0/fv3MDY2RmlpqdjfBS8xMTEICQnB9u3bBVZk+NkYGxsjNjYWNjY2sLW1xcqVK9GjRw+kpKSgbdu2zPcIACtXrkRwcDBGjhyJFi1aAABu3bqF0NBQzJ8/H0FBQSKv9e3bN0RFRaG4uBidO3dmCX+JK94A/H//K88qYvL2m4nTv0jbp1+5cgU9evRgYj3s7OyQnZ0NIoKTk5PICq6ykJqaCmdnZ1abkQRp2oAkVQd/89/jd4Dub/5zXL16FfPmzcPy5csFBq1yndWqqqrIyMhgStlU5tWrV7C0tORzXklDaWkpbt26xagJ3bhxAyUlJWjevDkSExMRHx/PGA+rKpdSOUNIGNKqs4aHh2PgwIFQVVXly9yujKBgwaqUEH8GgwcPhrOzMxN8ExISgp49e+LSpUtwcnLiyzL9t0nRy/obicupU6cwa9YsBAYGMpPe27dvY+3atVi4cCFKS0sRFBSEAQMGSKzmxpttyLs4fPr0KVq0aME3kZK0dJQkiPv7m5qaQl9fH/v27YOdnR3rvR07djCZnrI4qtzd3REQEMCXUXvw4EHs3LlTZpWq3r17IzY2FjVr1kTjxo35+kdpM7B526QouMZeXiVWLtxSfWvXrmWVrhO3T5cWSZ0lksJVrdi+fTt0dXWRkpICZWVlDBkyBJMnT/7ppYGTk5Nx+PBhgcEZgn5/AwMDXL16FQ4ODtDV1cXt27dhbW2Nq1evYvr06UyAoCRB14D4hg9A+jIzVY29gOTqBElJSYiOjmayJ728vIQeA1Q4BgMDA/kU6k+ePImVK1ciKSlJ5PHScPDgQURFRfEF8ALyHe/i4+Ph7e2Ntm3bIiEhAc+ePYOlpSVWrlyJ5ORkVglncQPvpakw4Ovri7dv32L37t2wsbFhxpcLFy5g2rRpePLkCYCK4MATJ05AT09PIpVQSXn79i0GDhyIuLg4RpHu48ePcHV1RWRkpEDVjP91KhsaBwwYgE2bNgl10AEVfYy2tjb27NkDfX195neNi4vDyJEjmaCKfyK2trY4cOCAzEHZ+/btw6xZs/DmzRsA0jkR5JndLen8OyMjA2PGjMGQIUMEBgnJquiQn5+PGTNmMHPHyvMOadcnRkZGTDJJx44dYW9vL9N9yoI0SUD79u3Djh07kJmZiZs3b8LMzAzr16+HpaUln5qzJIldc+bMQVxcHO7fvw8bGxvGON2hQ4dqUX0VVzEJAHx8fPDy5Uts2LABLi4uOHHiBPLz87F06VKsXbuWcRbJQl5eHv766y80adKEmd/evn0bOjo6IgMg9+zZg3379uHjx4/o3LkzEwBnbm6OHTt28AXacjl//jzGjBnDUhABKp6TlStX8gUHnD9/HrNmzWLK6YpbEaEy1VWq+H+RjIwMbNiwgUm+sbW1xeTJk4XOsdLS0tC+fXt07twZ+/btkzhZcvXq1di6dStfGxC3qoeLiwvat2/P97xwmTdvHhITE1nrzdLSUkRHR2Pv3r04d+4cLC0tMXz4cPj5+bGUF8VNaK8ccCsvx+PP5smTJ1izZs1Ps3/l5eXBzs4OhoaGGDNmDBo1agQiwtOnT7Fr1y4UFBTg8ePHAstFRkdHY+jQoQLn1ZVVRSsHanE4HCbZae3atQLLGv8TkGTtuG7dOsyfPx8TJkxgAg0TExOxZcsWLF26VGhQqjzWVJIoEktrA5FGrYw7drq7u6NZs2bQ1NRkvS9rMpA4a3ZpkMSxPXjwYFZALncOw7VPSGKzEWVTE7cimK6uLuLi4vjWDnfv3oWLiwv+/vtv1vaqFNKBisDN7t27Izc3Fw0bNkRkZCS8vLxQXFwMBQUFFBcX4+jRoyIr60miMl2dqKmpIT09nan40K5dO3h7e2Pu3LkAKoQO7O3t+b4XSflVvgFh1esEIWx9RES4f/8+E2CemJiIv//+G/b29qwkOWkS1OWBg4MDJk+eLDCpHKhQam/WrJnU46k060AfHx/cvHkTa9euZapp3LlzBzNmzECbNm2wb98+REZG4s8//0RycrLEiYySzKnr1q2L06dPo1mzZqxj7t27hx49erCCriurxXLJzs6GjY2NTIF2wqgqCEZe/bq4DBw4EF++fOFLNOLSrVs3aGpq4siRI/D29mbmCtHR0XBzc2PGuZKSEpw/f55pL+L6Byq3r9GjR+Py5cvYvHkza64xadIkdO7cGdu2bZPqc8qbqkSkgIrxgDtf5wY3hYeH448//mAFwHJFXAYOHFhltQcuguY6kianAuK1x/HjxyM2NhZLlizB0KFDsWXLFrx+/Ro7duzAypUr4ePjI9Y9iyImJgY+Pj44c+YMYwufOHEijh8/jitXrrDmK126dMGLFy/g7++PWrVq8f0O0vp7HR0dMWXKFAwbNgw5OTkwNzfH48ePGeXwGzdu4I8//pCp+pG1tTW2bdsGNzc33Lx5E506dcL69etx5swZKCkpyaT+nJubCw6Hw8Rz3L59GwcPHoStrS1GjRrF2leSpESg4rtJTU2Fk5MT46tq164dX+JTz549YWBggF27duH48ePw8fFBXl4eY/OKiYnBjBkzGBsBL1evXkVoaChOnDgBVVVVDBo0CCNGjGD19Z8/fwYRoUaNGkhPT2f5DMrKyhAdHY2goCDGLiwpNWrUwJcvX1BaWgoNDQ2+70Xa4G9p6dWrF7p27YqRI0dixowZOHXqFIYNG4bjx4+jRo0auHz5Mmv/w4cPY+PGjazKipMnT8Yff/zB2m/atGn48eMHQkJCAFT4f1u0aIGnT59CQ0MDpaWluHjxYrUkIMujipg8BavE7V/E6dNHjhzJN49s0aIFvL29ERwczMR9GBkZwcfHB15eXhg7diyAinVN5XmOOFSeN3PnUytXrkRpaelPmWdwkac/8Tf/fn4H6P7mPwdvABgvlctYmZubY+fOnUJVtIQ5wWQhLS0NsbGxuHz5Mk6ePAldXV28f/+etU9OTg5MTEwE3n9ubi6TlfhPQ1wlxJ9BYWEhvn37hjp16qC8vByrV69mMvDmzZvH5ySu7mAFaVT5/om0aNECS5Ys4Vv0XrhwAfPnz8ft27dx8uRJTJ8+HRkZGRKdu1mzZpg6dSqGDBnCCtBdvHgxLl26hGvXrrH2r67SUYIQ9/f//PkzJkyYgMOHD2PhwoWYNWsWXr16hREjRuDOnTtYs2YN32JQUjQ0NJCSksJnqE9LS4OjoyMTkCAtVWWChYWFVanKzAt30SbJAhmoUKG8c+eOWIZgcft0WcnNzcWjR4+qdJZIip6eHm7dugVra2vo6enh5s2bsLGxwa1bt+Dn54fU1NRquY44REZGwtfXF56enrh48SI8PDyQlpaG/Px89O7dW2AGfo0aNXDv3j1YWFigfv362L17N1xdXZGRkQF7e3um3KMkQdeA+IYPQLoSI7yIGnslUSc4evQoBgwYAHV1dSgrK+Pz589YtWoVZsyYIfS4qKgozJw5ExMnTkSrVq0AVAT5btmyBStXrmQpO1VXeaHMzEw4ODgINJbLMzivdevW6N+/P6ZNm8bq12/fvo0+ffqwDBPiBt5LUmGA2xcYGxvjwoULaNKkCes+RH0v8mTAgAHIzMxEREQEq2S5n58fGjRogEOHDkl8ztu3b6NZs2ZCHQQlJSU4deoUn4Hqn0Jlxe3KKg6C0NfXx40bN2Btbc3aPzs7G7a2tjKPj5Jw7do17NixAxkZGTh69Cjq1q2Lffv2wcLCQqDx7OLFi1i7di127NghlkJB5SAArrEpOTmZUfMHpHMiiJPd3adPH+zduxc6OjpVBiTw9rmSzr+TkpIwePBg1lqLO5ZUx/ju7e2NnJwcTJgwQeDcsXIwanUjzCEoCEnVS7lImgS0bds2LFiwAFOmTMHSpUvx5MkTWFpaYu/evQgPD+crUyxJYhdXIWvq1Kno06ePRKq7klCVYhJQoYB26tQptGjRAjo6OkhOToaVlRVOnz6N1atXCzXYfvz4Ebdv3xaYDOjr68v8/+rVq2jTpo3EFUS4cB0roaGhTDsRJ3m4QYMGfGW21dXV8ezZM4FO+caNGzNqXOJWRKiMuKWK/2tcuHABPXr0gKOjI+Nov379OlJSUhAdHY0//vhD4Jzly5cvUFVVZY3flZ1glZPGiAh5eXl49+4dtm7dKvV688yZM+jVqxemTZuG6dOnM0kxeXl5WLt2LTZs2IDjx4+je/fuAo/Pz8/H/v37ER4ejtTUVHh5ecHf3x/du3eHkpISzMzM0LRpU771AC9cFUpJHY9Xr17FhAkTkJSUxOes//TpE9q0aYPt27cLbcfVDa/tixvU1759e2ZNI++Sk7NmzcLly5dx/fp1vn7o69evaNeuHTw8PLBixQq+Y83NzdGtWzfMnz9fZGLUvxlJ1o73799HcHAwq48HKhyRixYtQlZWlsBryGNNVVUFK16knSNJo1ZWuYIIL9IKQwhCHHu5JEji2FZQUICBgQFGjBgBNzc3PruENIpKstC9e3eoq6vj0KFDzHhRVlaGAQMGoLi4mE8UoE+fPhg4cKDItZ+3tzejKLxv3z6cOXMGnp6e2LVrF4CKIKG7d+8KTCDes2cP1q9fzyRENmzYEFOmTEFAQIDMn1UczMzMsG/fPnTo0AHfv3+Hnp4eoqOjmef50aNH6Nixo8xBJb+qTC332a/KhStsfdS9e3dcv34dnz9/RpMmTZgkwg4dOjAJwrzXkiRBXRqeP3+OkJAQVvDMhAkTWMFnw4cPh4aGBrZs2SLwHM+ePUOXLl2E9sFVIc06sKioCFOnTkVERAQTcKikpAQ/Pz+sX78empqaTBCto6OjxBXqJJlTq6mp4fHjxwJVtO3s7FjrAVNTU2zevJlvPXnq1CmMHz9epgAhWYNgqrtf//btG0JCQoSq/t27dw9AxdjeunVrdO/eHTNnzmTWpc+fP8eqVasQExODGzduMIFbVcHt1xUUFGBmZgY/Pz+RydeV25eBgQGOHj3Kp84cGxuLP/74Q2BVuOqmqmeeF+73yE30rAwRITIyEps2bYKWlhbevn3Lej84OBgzZszgS+gBxJvriLIHiZucKghR7dHU1BQRERFwcXFhqdjv27cPhw4dwtmzZ0Xes7gcPHgQEyZMwKVLl7Bnzx6cOnUKsbGxfLYTbW1tJCYmVvuaYteuXZg6dSoGDBiApKQk6OnpsapxLF26FLdu3UJ0dLTU19DQ0EBqaipMTU0xa9Ys/PXXX4iIiMCTJ0/g4uLCau+S2lbbt2+PUaNGYejQocjLy4OVlRXs7OyQnp6OiRMnYsGCBcy+kiQlcvn48SMr0PLp06dwdHSEq6srli1bBqCiX3R3d8fnz59RWlqKOXPmsBJghw4dCk1NTb4qb7z8/fffOHjwIObMmYNPnz6xklKrekY4HA6Cg4OZJCVJCQ8PF/m+rNVbJSUzMxNFRUVwcHBAcXExpk+fzqxN1q1bxyguS4qdnR2WL1/OjI1hYWGYPn067t+/D1NTU4wYMQJv375FTEyMzJ9BHlXE5ClYJWn/IqpPF3b+Bw8eoH79+qhRowYSExPRuHFjpKSkoGfPnowPQEFBgal4OWzYMNSpU0es8wubN7dq1QqhoaFyUR3+zW+kQbZ6ib/5zb+Qyo5FYfTq1YvJpq2sXvb27VvMmjVLZOa4uHBVLysb7ufNmycwGMfCwkKgikhhYSEsLCyYyePnz59x69YtJvtHmAJbzZo1kZaWBgMDgyoD7yobs4QpmhQUFMDIyIi5l8pKiLNmzRKqhChvSktLGUMjUDFgi1PeYOfOnbh8+XK1SNHfv38fP378YP4vTzp16oQhQ4agT58+Aidm0gRbcnn06JHASbCZmRkePXoEoMIoxVuCRVwWLFgAPz8/vH79GuXl5Th+/DieP3+OiIgInDlzhm//yMhIHD58WObSUYIQ9/fX0dFBREQE+vbti9GjRyMqKgpZWVlo0aIFHj58KPWCgRcTExPs2rULq1evZm3fvXs3oxYhC+I4C3hLZRUUFGDp0qXw9PRkjIU3b95kgrS5DB48mLVA7tSpE+zs7HDgwAHk5eWxFsgAWEbWb9++iQxwELdPlxUTExOYmJigtLSUL/hBFpSVlZkgYyMjI+Tk5MDGxga6urrIzc2ttuuIw/Lly7F+/XqMHz8e2tra2LhxIywsLDB69GihCkh2dnZISUmBhYUFWrZsidWrV0NFRQU7d+6EpaUlHj9+DECyoGugQhGD1/AxZ84cgYYPoCLIuXJJoqqeB3HH3r1794p1v0BF6eqRI0diy5YtUFRUxIoVK7B8+XKRAbpcNeyZM2cKfK86g9GACqf8pk2bULduXYHvV/d4x8ujR49w8OBBvu1GRkZ8RnhuqbnKTvLK38XVq1cZJThx+4Li4mKBgd6FhYVVqnwDYJ5LbhuTVTHp/PnzuHz5MisY29bWFlu2bBGaJFYVrVu3Zs3PdHR08ODBAybA9ePHjxg0aNA/NkBXGoQp0r169YopjS3LnEdcjh07hqFDh8LHxwf3799HSUkJgIoAoeXLlws03A8YMABfvnxB/fr1xVIo4FUiByrmstbW1li8eDGrzURHRzNOhOHDh6N9+/Zo0KABzMzMcODAAYEBug8fPoSjoyMAMP13ZXR1dZnvsfK9iIJXtVGc+feIESPQtGlTHDp0SGDWvqwkJibi2rVrzOetTjIyMhAWFoaMjAxs3LgRRkZGOHfuHExNTdG4cWMAEHsdKUv//+DBA+zYsQMKCgpQVFRESUkJLC0tsXr1avj5+fH1XyEhIdi1axd69eqFlStXMtudnZ0FjmV6enro3bu3WPdy//59xMfHIy4uDmvXroWKigqjQMcNJJYGaZQ5i4uLmf6xRo0aePfuHaysrGBvb884HCsTHR0NHx8fFBUVQUdHh9UeORwOK3irR48eTJlMbhCEJGVbFRUV0atXL1YbMTAwQHZ2ttAA3aysLIHKqLq6usjMzOQL0H3x4gXLgH7z5k1cvXoVBgYGUFBQgIKCAtq1a4cVK1Zg0qRJQteslcdeYaWK/2sEBQVh6tSprOeIu33WrFms9ZSkVO47uN+5i4uLTM6Gbt26Yf369ZgxYwbWrl3L9O+fPn2CoqIi1qxZIzQ4F6go59muXTukpaUhLS0Njx49gp+fH2rUqIEePXogISEBWVlZGD58OIYMGSJUyReo6Fs4HA44HI7AvoHreOSyYcMGjBw5UqC9Q1dXF6NHj8a6det+WoBuo0aNYGhoiMmTJyMoKAj29vY/1fZ16dIlBAUFCVxDq6urIzAwEKtXrxYYoFtQUICpU6f+zwbnApKtHf/66y+Byklt2rQRaeeSx5qKt7/Nzs5GUFAQhg0bxrLDhIeHC/xdxYXb/3MZOHAgBg4cKPIYaQPkxEFSe7kkrFq1CjNnzhTLsV1QUIBr164hLi4Os2fPxrNnz+Do6MjMYaQJuq1KrIGXyu1l1apV6NChA6ytrZl+7dq1a/j8+bNAJaauXbsiMDAQT58+FfhZe/TogTt37jCVkZo0aYKdO3di3LhxTHvgTSjmZcGCBVi3bh0mTpzIaotTp05FTk4OFi9eLPbnlJYuXbogKCgIq1atwsmTJ6GhocHq7x8+fFgtqrfytJWIQtZnrFGjRhg9ejTat29f5drN19dXruPVsWPHMHDgQDg7OzPtJSkpCfb29oiMjGT65u3bt4tc/9jY2Mj0vUizDtTS0sKuXbuwfv16JvHA0tKSlQzIez5uMM65c+fEqlAnyZy6QYMGOH/+PCZMmMDazq1mwMugQYMwadIkaGtro0OHDgAqqltNnjy5yv69KhwdHUUGwQhCnv26v78/Ll68iH79+qFFixZC23LTpk0RFRWFgIAAvt+hRo0aiIyMhJOTk8R9++3bt7Fnzx7Gls5Voa6qWsyXL18EzruMjIx+WqK5ND5uQcFbly9fRlBQENLS0jBz5kxMnz6dbx9hVe4A8e26XL9jZdLT0xm1V14aNWrEVBThRdz2WFhYyDxbOjo6jI2uXbt2jNpjdTB48GB8/PgRbdu2haGhIeLj4/kC8bmfRx7q1yNHjoSioiKio6PRoUMHvt/qzZs3YttfhKGlpYWCggKYmpri4sWLzHxITU2N9Zmksa0+fvyYqfJ6+PBh2Nvb4/r167h48SLGjBnD8j9OnDgRQ4YMkSgpUU9PDz169EDbtm3Rpk0bnDp1CocOHcKtW7cYP5WDgwOePXuG69evw9jYGC1btmSdY+DAgQLbKJesrCzs3bsXe/fuxadPnxj/CJfY2FgQEdzc3HDs2DHWmlpFRQVmZmZiBzIK4mcH4FYF75hWVWAzl7t37zJJQI0bNxaYMJGTk8P6HbhjB9d/P3nyZFacQUFBARYsWCA0AUSQD0FQFbGgoKBqqSImrt9MGiTtX0T16YLQ1NRkqrbWrl0bGRkZjJ28sn/Qzc0NGzduxMKFC+Hp6YmAgABGXEAYleeH3PmUtAIKXKRpA7/5jSh+K+j+5jdC+PDhA1q2bIm8vDwMGTKEKQv37NkzHDx4EMbGxkhKShLpWBAH7gAxffp0jBs3TqDKT+X98/Pz+QJuX758CVtbWxQXF+PBgwfo0qUL8vLyAFRkpRw+fFhgSUze0vOSZkhVVjjj8ubNG9SvX58ZyCVRQvwZaGho4NmzZ2IHTP6bpegnT56Mw4cP49OnT+jatSuGDBmCLl26MIahqn5zXir//k2bNmUMt1wljR8/fmDkyJFISUnB/fv3cf36dQwZMkQqw9m1a9ewePFipKSkoKioCE5OTliwYIHAgCVJS0dJgqS/f35+PoYMGYIrV65AU1MTZ86cQceOHavlXs6ePYu+ffuiQYMGzALv9u3bSE9Px7Fjx6otQPndu3d4/vw5gIrSM8IC/Pv27QtXV1c+o+DmzZuZjGOgwsCVlJQEa2trbNq0CVFRUawFcmVVlfLycixbtgzbt29Hfn4+0tLSYGlpifnz58Pc3FxombPqJDo6GgUFBRg2bBizbdmyZViyZAlKS0vh5uaGqKgomRdUHh4eGDZsGAYPHoyRI0fi4cOHmDRpEvbt24cPHz7g1q1bMn4S8dHU1MSTJ09gbm4OfX19xMXFwd7eHs+ePYObm5tAJ+SFCxdQXFyMPn364MWLF+jWrRvS0tKgr6+PqKgouLm5yXxfBQUFiIuLYwwf1VEaVtyxVxzDE4fDwZ49e6ClpYUHDx4wxrPv379DU1MTr1+/FlhCFqgYu8VF0iD/ysGIRIS///4bGhoa2L9/v0BFRnmOd/Xq1cPhw4fRpk0blsLpiRMnMGPGDJbSenx8vMhzVe5TS0tLsXz5cowYMUJo8BKXLl26oFmzZliyZAm0tbWZBIqBAweivLwcR48e5TumtLQUwcHB2LRpE6Owq6WlhYkTJyI3N5evxK8wBBn4tbW1BTqH7t+/j44dO0pUzpZLVQq0+fn5qF27Np8x4Z+CoqIi8vLymLGH+zuJUugaMGAAdHV1sXPnTmZ/Q0ND9OzZE6ampggLC5NpziMuTZs2xdSpU+Hr68v63u/fvw9vb29mbs6LvBQKtLS08PTpU5iamqJevXo4fvw4WrRogaysLNjb2/90tWgub9++FWjMquyQ09TUREpKikCHRHVga2uLAwcOiFS3kYb4+Hh4e3ujbdu2SEhIwLNnz2BpaYmVK1ciOTlZYB8jLwwNDRl1CSsrK4SEhMDT0xOpqalo1qwZo57KRV1dHampqTAzM2O13/T0dDg4OFSrEyglJQXr16+XueQ7V6lIXGVOoEI1j5tg1qNHD+jp6WHFihXYtGkTjh49KrDyh5WVFbp06YLly5dXuYb98eMHbt++zSir3LhxA9+/f4ezszNcXV2xdOlSiT/niBEjkJGRgUuXLvEpGJaUlMDT0xOWlpZ8zvDRo0fj5s2bOHHiBBOc8uLFC/Tt2xfNmzfH7t27AVRdEeFnqpD/L6CmpoZHjx4JrHri4OBQrcl+1c2rV69w5MgRlhJiv379hAYw5ufnY9++fQgLC0NmZiZ69eoFf39/dOrUCcXFxVi8eDEiIyORlpaG48ePIzQ0FDdu3EDXrl3h7+8PDw8PvgCK+Ph4iRyPZmZmOH/+PCvZiZfU1FR4eHggJydH1q9HLKZMmYKEhAQ8ffr0l9jA9PT0kJycLHT8fPHiBZydnfHx40e+9/z8/NC+fXuhypeyBBX+G7Gzs8PgwYMxZ84c1valS5ciKipKaICIvG2I7u7uCAgIYJI9uRw8eJAJNpEWcdXiBcEdh6srwE9Se7mk5wakq8T04sULLF26lDWH+fbtGy5evAhXV1cmOZDL58+fERcXB09PTyYhVFQbqYyggKU3b95g8+bNSElJgbq6OhwcHDBhwgSBPgpRa1XuZxVn/VinTh2+78XQ0BCbNm3ia4uHDh3CxIkTZVLDFJf379+jT58+SExMhJaWFsLDw1kJZO7u7mjVqhUryVsa/m2+gZs3b6KgoIBVrSoiIgILFy5EcXExevXqhZCQELGSlKuL+vXrw8fHhy9we+HChdi/f7/E1fekRV7rQF7EqVAnLaGhoZgwYQICAwMZm+uVK1eYigcjR45k9v3+/TuGDh2KI0eOMIG+5eXl8PX1xfbt2wUqo4tLZXumOEEw8uzXdXV1cfbsWaZ6RVV8+fIFFy5cYM15PTw8xFYBFMa3b99w9OhRhIWFISkpCd27d4e/vz86d+4scH93d3fo6+sjIiKC+e6+fv0KPz8/FBYW8pVw/ydy7949zJo1C9euXUNAQAAWLFgg1A6en5/PiGK9ffuWbw0vbPz9+++/cejQIezevRt3794VuJ+xsTEOHjzI54u4fPkyBg8ezKfmK257dHBwQEhICDp27IhOnTrB0dERf/75JzZt2oTVq1dLrUQtbF595MgRODk5sZJLeOfVd+7cQVBQEBYsWAA7O7tqVc+UNz4+PkhNTWWS8XNycqCvr4/Tp09jzpw5jFCANLZVLS0tPH78GObm5kwg7axZs5CTkwNra2uWPYtXxVMcjh8/zlRIefr0KWrWrIl27doxCdncgPWsrCyRtmtBcPuM0NBQJCQkwMTEBMOHD8fw4cOFrsFfvnwJU1PTak+quXfvHpSVlWFvbw+gQm09LCwMtra2WLRokUxjhiz8/fffrL5CQUGB73l9+/YtBg4ciLi4OKZCwMePH+Hq6orIyEiWf1tPTw937txh7DUWFhaYP38+4wfMzs6GjY0N02a6dOmCFy9ewN/fX6CAhCC7vTyriEnqN5MESfsXSfv0Xr16oWvXrhg5ciRmzJiBU6dOYdiwYTh+/Dhq1KjBjHnctUnNmjVx6tQphIaG4sKFCzAwMICfnx/8/f3lVplNEOK2AWmrDv7mv8fvAN3f/CeoXHJFFLzO4Q8fPmDOnDmIiopijNd6enr4448/sGzZMujr68t8bydPnkRCQgLi4uLw7NkzNG3aVKABnzth37hxI0aOHMky7JeVleHWrVtQVFTE9evX4enpiaKiIvz5559QU1PDkiVL8OjRI2bBKSubNm0CAEydOhVLlixhTYbKysqQkJCA7OxsltKOOCUgfhYuLi6YOnWq3EvXikKSoC9ZKS8vx+XLl3Hw4EGcOHECioqK6NevH3x8fGSarN24cQM9evSAgoIC89w8evQIZWVlOHPmDFq1aoV9+/YhLy8PgYGBYp9XkkArLpKWjpIXhw4dwoQJE+Do6IitW7cymdPjxo3DihUrZM7UAiqcptu2bWOVAxszZky1KOgWFxdj4sSJiIiIYJwxioqK8PX1RUhICJ9DsXJAIpcXL17A0dGRFcgm7gIZABYvXozw8HAsXrwYI0eOxOPHj2FpaYmoqChs2LABN2/eZO3PLXmTmZmJI0eOVFnyRhxcXV3Rr18/jB8/HkBFe2/fvj0WL14MGxsbzJ07F97e3jI7HpOTk/H333/D1dUVb9++ha+vLxNMs2fPHrko+wmjXr16OHfuHOzt7eHg4IDZs2dj0KBBuHnzJry8vPDp0yexzlNYWIgaNWogJCQEo0aNgpqaGjNuCGPSpEmsv8U1fEiLuGOvJIE/gpJWKju2fiaVg/64hoGWLVvKHFguDTNmzMCtW7dw5MgRWFlZ4d69e8jPz4evry98fX0lzritjLa2Nh49esSnEliZx48fw93dHU5OTrh69Sp69OiBJ0+eoLCwENevXxdonBs7diyOHz+OxYsXs5SBFi1ahF69emHbtm1S33fPnj3x8eNHHDp0iAk2ef36NaO0wRtUJi7SOlj/KSgoKMDb25txFkZHR8PNzY3PUcJryHj16hU8PT1BREhPT4ezszPS09NhYGCAhIQEoc6B6kZDQwNPnz6Fubk563vPzMyEra3tTw3Kqi4nAhHh/Pnz2LNnj0zBpXfv3oWfnx+ePXvG158KCoTo3r07hg0bxqdyV11cvHgRa9euxY4dO6rsNyShdevW6N+/P6ZNm8ZqA7dv30afPn1kKiMqKZImAdna2mLFihXo2bMn695DQkIQFhYmUF22tLQUcXFxyMjIwODBg6GtrY03b95AR0eHtUYkIty/f58Z2xMTE/H582c4ODigY8eOWL9+vVSfcfz48Th06BDMzMzEUuYEgP3796O0tBTDhg3D3bt34eXlhcLCQqioqGDv3r0YMGAA3zGampp49OiRVOP5kydPsGbNGpmCkV+9egVnZ2eoqqpi/PjxrOThrVu3oqSkBMnJyXzrgU+fPsHLywvJycnMuurVq1do3749jh8/zjgu2rdvj+nTp6NXr14YPHgwPnz4gHnz5mHnzp24e/cuS1H79OnTYt+3oGSg/wImJiZYt24d+vfvz9p++PBhzJgxgxUoevbsWSgqKvIlU1+8eBFlZWXw9vaWKFnnZzpku3fvjgsXLsDKygoBAQHw9fXle/7evn0LY2NjVqDfy5cvsXfvXqZM9JMnTwQ6xF++fAkTE5MqE6GElXrm8uLFC9jb28tFaUoUHz9+xLVr1xgb2JMnT9C0aVNWqVh5IKzSFZf8/HzUrVuXVS6Vy7Jly7BhwwZ07dpVoNKmuPPSf2KwmjQcO3YMAwYMQKdOnZiAn+vXr+PKlSs4fPiw2Cry1Y2GhgZSUlIEJgE4OjpKnVRRlVq8MGWgiIgIrFmzhrE5W1lZITAwEEOHDpXqPriIu2aXBkkc2wUFBUwVAK59Qk9PDx06dEDHjh0xefJkbNy4EadPn8aVK1cEnq9Tp07o1asXX3L7P4XKYiCVEySFrR8rBzhwSUtLQ4sWLQQmAsiLT58+QUtLi09Vq7CwEFpaWr8sqKQ6uHv3LhPEUHmc//TpE3r16oUNGzaw7GTe3t5MFUOgwl7v5OSEYcOGwcbGBmvWrMHo0aOxaNEigdf8+PEjXrx4ARUVFVhYWPAFnkuDhoYGHj58yDdep6eno0mTJnx9V15eHm7dusUEYnHVCI2NjWW6D2nXgcnJyTh8+DBycnIY9Tcu0gRZyDKn3rZtG5YtW4Y3b94AAMzNzbFo0SKhiRRpaWlMQL+9vX21VPmLiIjAgAED+IK8v3//jsjISIH3Is9+3dbWFpGRkTIp8X78+BF6enoyV8rikpWVBX9/f8THx+Pdu3cC16mPHz+Gp6cnSkpKmGc4JSUFampquHDhAqMsKG8+fPiA/fv3w8/PT2A/ExERwfdeRkYG5syZg2PHjuGPP/7A0qVLq1wve3t7IycnBxMmTEDt2rX5/HiV/bUJCQnYs2cPjh07hjp16qBPnz5MsmllxE1O5SJue1y/fj0UFRUxadIkXL58Gd27dwcR4cePH1i3bh0mT55cxbcrGHGTdSrPq9PT0zF48GA++4ws6pk/a7358eNHzJs3D7m5uRg7diy8vLwAVCRqqKioYO7cuQCks622bNkSrq6u6Nq1Kzw8PJCUlIQmTZogKSkJ/fr1Y9niqkpKrIyRkRE6dOjA+KW4AayV4fqRXF1dmZcw//bt27cRGhqKqKgofPv2Db1798aIESPg7u4u0L/98OFD2NnZQUFBocp4F2n7webNmyMoKAh9+/Zlvus+ffrgzp076Nq1q0yVgCThwYMHmDNnDqOUrK2tzZojcDgc3Lx5k9UPDBgwAJmZmYiIiGASd58+fQo/Pz80aNAAhw4dYvbltds+efIEDg4OePHiBTPvjY+Ph5+fH7Kzs5nrJyYmSuSPTElJYdYP165dq7YqYvJG0v5F0j49MzMTRUVFcHBwQHFxMaZPn874wtetW8fMTwT5WV+/fo3Q0FDs3bsX2dnZjDBGVX5nXir7oMVF3DYwfPhwbNq0Cdra2nJN1vrN/wD0m9/8B+BwOKSgoMD8K+oliPLycsrPz6f8/HwqLy+X231+/PiRoqOjydfXl5SVlUlVVZV5z8XFhVxcXIjD4VCbNm2Yv11cXMjDw4NGjRpFaWlpRESkr69Pd+/eZY798OEDcTgc+vTpE981P336JPaLi7m5OZmbmxOHwyETExPmb3Nzc7KysiIPDw9KSkoS+Bnfv39PR48epaFDh5KSkpLQ71yeREVFkaWlJYWEhNCNGzcoJSWF9RJGeno6nT9/nr58+UJEJFNb4HA4ZG5uTr1796ZevXoJfVU3X79+pcOHD1OTJk2Y7/7169c0ffp0ge3j48ePNGPGDMrLyxN4vs+fP9O2bdto6tSpNHXqVNq+fTt9/vxZ5vvU1NSkrKwssffv1asX6erqkoWFBXXr1o169+7NelUXubm5lJubK/C9Pn36kKamJm3atIm1/fr162RlZUVWVlZ048YNqa/9/ft3cnNzY55zeTBq1CiytLSks2fPMs99TEwM1a9fn8aMGcO3v6mpKf3555982//8808yNTVl/m7RogXNmjWLEhISSE1NjR48eEBERDdv3qS6devyHV+/fn26fPkyERFpaWlRRkYGERE9e/aM9PT0WPsePXqU1NXVKSAggFRVVZl9Q0JCyNvbW8pvgsjQ0JDu3bvH/D116lTy9PRk/o6JiaEGDRpIff5/IoMGDaK1a9cSEdHixYvJ0NCQAgICyMzMTKznKCcnh3Jycpi/zc3N6f3790REZGZmxhoreF8WFhZ85zI0NKS+fftSSEgIPXz4UOR1379/T+PGjSMbGxvS19enGjVqsF5VIWrsHTduHNWoUYMcHR1p48aNVFBQIPQ8HA6Hli1bRhs3bmReampqNH/+fNY2XsLDw0W+pGHPnj307ds3qY7lUp3jHZeSkhIKCAggJSUl4nA4pKysTAoKCjRkyBD68eMH3/4JCQnk4+NDrVu3plevXhERUUREBF27dk3g+Xv06EF79+4V614+fvxIS5cupf79+5O3tzfNnTuX3rx5I3R/HR0dOnv2LN/2mJgY0tHRoa9fv9KpU6cEjn+fPn2iU6dOCf1NcnJyyNHRkZSVlcnS0pIsLS1JWVmZmjZtKnS8qQoOh0P5+fnM37z9KBFRXl7eL5l/icuwYcPEelXmx48ftH//fgoMDKSxY8fSrl27mDbMRZY5jzhYWFjQpUuXiIj9vYeHh5ONjY3Q4168eEFz586lgQMHMr/d2bNn6fHjx0REpKenx9e3CXtxWbduHdPnXLp0idTU1EhVVZUUFBRow4YNVX6WzMxMmjdvHtWrV49UVVWpa9euAvc7cuQI9e/fn1q2bElNmzaNnYqIAAEAAElEQVRlvXhxcHCg3r17U1JSEmVlZVF2djbrVZkdO3aQiYkJLVy4kI4ePUqnTp1ivWRFT0+PVFRUSEFBgbS0tCQeO4ShqalJmZmZRMRuA1lZWazxhYjoypUrZGNjI7Q92traUnx8vNT3cufOHbp69SoREeXn55Onpydpa2uTk5MT3b9/n2//Xbt2Ud26dSkyMpI0NTXp0KFDtHTpUub/lcnOzqZGjRqRhoYGKSoqMp910qRJNHr0aNa+enp6pKSkRM2aNaNp06bR6dOn6cOHD1J/Nl6+fftGBw8epE6dOpGGhgb179+fzp8/L/bYVVxcTHfv3qV3794J3ad3794UFRUl1vmeP39OO3bsoEGDBlGdOnVIX1+fevXqRRs2bGDmwNKQmZlJXl5ejF2Da9vw9PSk9PR0oceVl5fThQsXaPXq1RQSEiKwTZ0/f56OHTtGRERpaWlkbW1NHA6HDAwMmDk5F+61q3r9k8cZeRMcHEx6enq0cuVKSkhIoISEBFqxYgXp6enR4sWLWfva29tTTEwM3znOnTtHDg4ORERi2bGq8ztPS0ujHTt20JIlSyg4OJj14mXEiBFVrm/Ly8v5+vicnBwKDg4mCwsLqlu3Lv39998iz1FcXEzPnj0TarextLSkEydOCD3+2LFjAtcb8ub9+/d07NgxmjBhAtnZ2ZGCggLp6+vL/boKCgr04sULoXa9tLQ0oW1F2HpN2Jrt34ika8fk5GTy8fEhJycncnJyIh8fH5adQBTyWFMREVlZWVFgYCDf9sDAQLKyspL6vA0bNqTJkydTcXGx2MesXbuWNDQ0aObMmcwcLTAwkDQ0NGjdunVS30tlRK3Z5Y2CggIZGRlR3759adOmTQLtE82bN6fTp08LPUd0dDQ1b95c4HvDhw8XuI4sKiqi4cOHExFRSkoKlZWVMf8X9RLF169fBW7ncDjUpUsXxoaqpKREHh4ezN9dunQR2G9MmDCBpk6dyrd9+vTpNG7cOJH38m9FXs+1KAYNGsQ3f+Bl2bJl5OPjw9pmbGxMd+7cYf6eM2cOtW3blvn78OHDAtenWVlZ1KVLF1JUVGTmGCoqKjRw4EDWOlkam5O3tzeFhobybQ8NDSUPDw/m76KiIvLx8SFFRUVSUlIiIyMjMjIyIiUlJVJUVKQhQ4ZI1E9VRpp14KFDh0hZWZm6detGKioq1K1bN7KysiJdXV2BtgmiCvvEpUuXWL6S169fM/MeaebUP378oPDwcOa3ePv2bZXzKHmhoKDAsjtxef/+vVhz0uru18+ePUteXl4CbQuCWLlyJUVGRjJ/9+/fnzgcDtWpU4d69OjB2J38/PxIR0eHTExMmD7R1NSUdHR0hP72ubm5tGTJEqpfvz7Vrl2bZs2aJdDuyaW4uJh27txJ06ZNo2nTpgm0ZcmbxYsXU79+/YS+379/f1q6dCnz99ixY0lFRYU8PT0F2haEoaWlVeX+f/31F61YsYIaNGhARkZGNGHCBFJSUqInT56IPO7jx4/UqlUrUlJSYuavSkpK5OrqWqXtQZL2mJ2dTceOHatyzJUXzZs3p9atW1NkZCTFxsZSXFwc6yUNP3u9WRXS2FZjY2NJT0+PFBQUmPkTEdHs2bP5/FpLly4lAwMD8vPzoz///JPlt6nsu5GE2NhYWrhwIXXs2JHU1NRIQUGBGjRoQKNGjaJDhw6xxlEOh0OOjo4UEhJChYWFVZ6b19bPG+9SnXYYHR0devHiBRFV9JHcsTkxMZHq1asn9XklZcSIEbRs2TLmby0tLTpw4ADFxcVRbGwsDR06lIYMGcJ377dv3+Y7161bt0hXV5e17fjx46SiokJubm5Uq1Yt6tatG+v9mTNnUv/+/Zm/nZ2d6ebNmzJ9pgcPHpCfn1+1xcRI6jcTF0n7F3H6dF6+f/8u9D1em6ywOQaXy5cv0+DBg4lItA2juuwZ1dEGfvMbXn4H6P7mPwGv8/fEiRNUv3592r59O2O82r59OzVs2FCkUV+ecA33EydOJHt7e8ZwLyhAc9iwYQKduLxUDswgqhgouc7iyvtWNfkWFrzs4uIi1uSR97MpKiqSoaEh9e7dmzZu3CiTk1JahE1chU1g379/T25ubsz73AXB8OHDadq0aVLdgyRBX9XFX3/9RevXr6dmzZoRh8Ohli1bElGF4XTkyJFCjxs9ejTNnDlT7vfHiySBVkRVB/PIQllZGQUHB5OOjg7zLOjq6tLixYsZAzkRUZs2bYQGz3758oUmTZpEysrKMt2LgYGBXAN09fX1KTY2lm/71atXycDAgG97WFgYKSoqUrdu3WjJkiW0ZMkS6tatGykpKVFYWBiznyQLZCIiNTU1xqDGuwh/8uQJaWpqsvZ1dHRkAhl597137x7VqlVLsi+g0j28fPmS+bt58+a0evVq5u/s7GzS0NCQ+vxchBmJPn36RK6urjKfXxIKCgro9evXRFTR7lesWEHdu3enadOmCe3rf/z4QfPmzWM9Hzo6OjR37lyRC67qxNvbmxo2bEgrV66ksLAw2rt3L+slDHHHXnEDf0QFIQtbCOrp6bFempqaxOFwSFVVVeoAscoL2Nq1a4ud8CCP8a4yOTk5FBMTQ1FRUUL7M2kC77dt20bGxsY0ffp0OnjwoNBgvpcvXwp1ovE+87wYGhrS06dP+bY/ffqUDAwMaMOGDeTm5ib0M7u7u1NISIjQ98vLy+nixYu0adMm2rRpE2OElBYOh0OxsbHMPFdTU5NiYmKYv69cufKfCZx68+YNjR8/nvlb3nOe5cuXk62tLSUlJZG2tjZdu3aN9u/fT4aGhnwJPFzi4uJIXV2dOnXqRCoqKkx7X7FiBfXt25eIiK9fE/UShjhOhG/fvtH+/fvJ1dWVCaJft26d0LXHxo0bSUtLiyZMmEAqKio0evRo6tSpE+nq6tKcOXNY+2ppaYkMIqyMvAP/pP0eq6Ju3bp0/fp1ImLPS44fP06Wlpasfbt37y4yeGXjxo1ySdgTxf79+6lBgwbMd123bl3avXu3wH179uxJQ4YMoZKSEtZnjY2N5UtiOnPmTJVr2OogOzubFi1aRJaWlmRqalptDuvdu3eTqampWAHjHA6HjIyMaNmyZZSSklLtgRuFhYV069YtunXrllzXkAUFBT8l6OR/kfLyclq3bh3VrVuX9Sxt2LCB7ztVU1MTOE/Lyspi1hqVnSKiXrKyc+dOUlRUpFq1alGTJk3I0dGReXETL27cuEHR0dGs48LDw8nc3JwMDQ1p5MiRfIE7vPNpNTU16tevH8XExLDW05V5+/Ytde3atUr7FDcAVlDg2ZcvX8jOzo4mTpwoy9ciEby2LwMDA+rTpw9t3LhRLv2BIKqy8ck6jpaWllJKSorAwJEvX76wAgn/iUi7dpQEea+pYmJiSE1Njezs7Mjf35/8/f3J3t6e1NTUBAb8i4uGhgYrqU8czM3NBSaW7t27l8zNzaW+Fy6S2MslRVzHNjdhThR6enpC15JEFevMysnmXIQ5oN+9e0eKiopEJFtQRmlpKS1evJjq1KnDSqiaN28eM8eTJEGSK9AwdepUmjhxImlra1Pjxo2ZtmhnZ0c6Ojo0YcKEKr+3fxM/w1YiDEtLS5FruIcPH/LZmlRVVVkJ9G3btmUF12VlZZGWlhbrmJycHKpVqxbVq1ePli9fTidOnKATJ07QsmXLqF69emRubk4fPnygU6dO0cqVKyX+HNu2bSNDQ0MaP3487du3j/bt20fjx48nIyMj2rZtGzOv7tSpEzVs2JDOnz9PpaWlzPGlpaV04cIFsrKyooCAAImvz0WadaC9vT1t3ryZiP5/jVdeXk4jR46kBQsW8O0vSSKjpKirq4sdhEpUESy6ZcsWmjVrFuv5FRRcLwkcDofevn3Lt/3Bgwci7Zny6tffvn1LLi4uYgdem5ubM+v2ixcvkp6eHl24cIH8/f2pc+fOzH4zZ86kgIAAvrY4atQomjFjBrOtpKSEIiMjqXPnzqSmpka9e/em6Oho1nH/ZJo0acKXmMnL5cuXydHRkfmbw+GQuro6X5K2qKRtIiIbGxuRiU7dunUjHR0dGjRoEJ05c4b5/sQJ0CUSLzmVF3nOM+SFuro6paamVus5f+Z6U5z5l6S21fLycnr58iV9/vyZz4eVlZXFN8+SJoivKmGFynz9+pWuXLlC8+fPp/bt2zOCCba2tkRUYQ+UJNkkOzubWUtWFjyoSgBBXLS1tRlfTadOnRhxh5cvX5KamprU55WURo0asfqJysIjSUlJLHEo7j6CAkXv3btH2trafNsvX75MU6ZMoZUrV/L9DosWLWL5yG/fvk1ubm4UFxdH79+/Fypsx0t5eTndvXuX1q5dS927d6caNWqQoqIiNW3alKZMmSLO1yAUeQlWEUnev1TVp1emT58+Am0ieXl51LhxY+ZvQTFOvxJp2sBvfiOK3wG6v/nP0bx5c4EGy5iYGHJyciIiYpwP4rxkxc7OjmW437Rpk8zZd5UDMwQFZ3CvwTu53rt3LxkbG1NQUBBjFAkKCqLatWvLZKiWRAnxZyDpBHbo0KHk6elJubm5rMng+fPnmQm1NMiq9iQOnz59otDQUOrUqRMpKSmRlZUVBQcHM5lwRESNGzcWmVl1/fp1kZ/zyZMndO7cuWpVNhM30OpnEBQURIaGhrR161bm2dmyZQsZGhqygk/EcULJooJGRDRlyhSaNWuWTOcQhbq6usAgtMePHwsNRk1KSqLBgwczfeLgwYMFKmiXlpaKtUAmInJycqJ9+/YREXsBFhwcTO3ateO7Z65Tm3ffjIwMmbLv69evT+fPnycior///ptUVFQoMTGRef/u3bsCg5YlRdhiIz8/n5SUlGQ+v7wZM2YMGRkZ8SW9GBsbM6rL379/J0tLS4FtSxSlpaV09OhRJvj72LFjAg2bWlpaEid7SDv2yivwh5e0tDRyd3dn2p+kVKWeKgp5jXeiOHbsGNnb27O2SRN4L24wnzQqH8HBwTRo0CBWsMm3b9/Ix8eHFi1aJJVikjjKmQkJCULPKYqqnLb/a8qGjx8/ppCQENqxYweT8PDu3TuaMmUKqampsdqurHOeqigvL2cUR7nfuZqaGs2bN0/oMa1atWLUy3nb+61btwSqzMuD5ORkGjt2LOnp6ZGzszNt3LiR8vLyqnSCWFtb08GDB/nuff78+azAaKKKYM6jR4/K70P8Q5g+fTq1a9eO/vrrL9LW1qb09HRKTEwkS0tLWrRoEWtfU1NTkWPjs2fPyMTEROp7kSUJqLi4uEpjaM2aNRnDbWW1YHV1dYHHyFt1TBxlztLSUtq9ezcNGjSI3N3dydXVlfUShCQB45MnT6amTZuSqqoqtW7dmmbPnk0XLlyQSelLFoqKiigmJoa2bdvGpwxTueqIoFf//v1p4sSJIse53wjn8+fPIivM1KpVi65cucK3/dKlS2RoaCjPWxOIqalplUE3Xl5erH0ePnxISkpKFBAQQGvXriVjY2NauHAh8/7YsWOpRo0a5ODgQBs2bBCpVs3L4MGDqW3btnTnzh3S1NSkixcv0r59+8ja2prOnDnD7JeXl0d16tQhExMTWrVqFZ08eZJOnjxJK1euJBMTE6pTp45M6viS0q9fPwoJCaFHjx79tGvyUh3O9ZKSEkpNTRWo9hYWFkbNmjUTuC778eMHNWvWjFnL/xORZu34+PFjli21qoDNn7Gmys3NZRKee/fuTXPmzGEF40mDJGrxXFRVVQUmYKWlpcmshigPezkXSR3bVSlhamlpUXJystDrJScn8wVDfvr0iT5+/EgcDodP9bqwsJDCw8Opdu3aRCRbUEZwcDBZWlrS/v37SV1dnfmskZGR1KpVK0m/OlZFP1Gvn51wLm9+ha2Ei6qqqkDRFS6ZmZl8gTOmpqaMDbqkpITU1dVZwXcPHz7kC1ocMWIEdejQQWjCS4cOHahdu3akpqZGJ0+elPhziKsYC4AJnBREYmKi0IB3eaGhocHYnmvWrMn4t54+fUrGxsZ8+0uSyCgpHTt2FFtk6PLly6ShoUF2dnakpKREjo6OpKenR7q6ulI/o1y/qYKCAtnb27P8pA4ODqStrc1SHuRFnv26u7u7RAk4ampqzLg5adIkGjVqFBFVVEPhbV8GBgYCg5VSU1OpZs2azN81a9YkMzMzWrBgAaWnp4usUHrq1ClG1KKyz+tX+cC0tLSqTDThDXJbtGiRWK/KXLhwgTw8PIQKSSgqKtLUqVP5BB3EDdDl8vXr1yptDeK2x4kTJwpUVQ0JCaHJkyeLfU+V6d27N9MmqlqT89K+fXuZhR1+FeLOvyS1rZaVlZGysrLchI3EEVYQRklJCV29epUCAwMZgRuiqhVCBSHNMZLg6upKvr6+FBERQcrKyswcPy4ujszMzOR23cqoq6uzqgpWFo54+fIl3zqjR48e1KFDB0Z8iIjo1atX1LFjR5kD7tPS0sjZ2VmixFd5VhGTl2AVkeT9S1V9emWcnZ1pxIgRrG1v3ryhRo0asZ6luLg4kcrzgvj06ZPAGI2ysjKZg2ilaQN5eXk0ZMgQql27Nqsyhahq7r/57/A7QPc3/znU1NSEqqBxjRm8i4igoCDS0dGhVq1aMdmlrVu3Jh0dHQoKCpL5fjZv3iyx4f7OnTsUGBhIAwYMEDhZlzYww83NjXGy83LgwAHq2LGjwHuRVxauPImPjxc4uP/48UNgAGWtWrUYA37lAMDKap7SIq+gLzU1NapduzZNmTKFVdqKFw0NjSoX4IKCMzMyMsjBwYGvvVXHBOOfVC61du3aAg0iJ0+epDp16vzUe5kwYQLp6OhQs2bNaNSoUdX+zLm5uVH//v1ZxtgvX75Q//79yd3dXebzi8vJkydJV1eXVq5cSRoaGrRmzRoKCAggFRUVunjxImtfacuJV0VQUBA1atSIIiIiaODAgWRqaspyQu7YsYNVHk5SuM49QQkV9+7do+XLl//UhS9RRaKKoKDQCxcu0NmzZwUeo6OjI/C9mJgY0tHRYf6uU6eORAG66enp1LBhQ9LQ0GAMvRoaGmRtbc1KMCCSrsSINGMvkeQleaXlzp07ZG1tLdWxsgToymu82759O/Xt25cGDRrEBPBfuXKFHB0dSUNDgwnm5iKvwHsi4SofolSxe/XqRdra2mRgYEDu7u7k7u5OBgYGpKOjQ7179yZlZWXy9PQUqAhOJFgxSZ7KmVU5bWXNqP8ncerUKVJWVmbmCPXr12dU3z09PencuXOs/aWd80hKSUkJPXnyhG7dusX0E8JKFGpqajIO18pBjrzt/fXr1zR9+nShQd0zZsxgBSBJ4kRQVFSkKVOm8DmeqnKC8Kr3GBoaMv1HWloay1lFVBE03aVLF1q0aJFIBVJ5Bq8L4+vXr9WW/V5SUkIBAQGkpKREHA6HUSIeMmQIXzCTsKAWLunp6TKpVMg7CUhPT49pH7xt99q1a2RkZMTaV56qY5Iqc44fP540NTXpjz/+oMmTJ9OUKVNYr+riw4cPdPr0aZo+fTo5OzuTuro6tWnTptrOLw737t0jY2Nj0tHRYSrZcDgc0tTUJAsLC7HU8nx9fcnLy4vU1dVp/vz5RCTecyprYuJ/hVGjRpG9vT1rfpuenk4ODg7k7+/PbEtLS6OBAwcK/c4HDRoksfKlILS1tas8j6RlszkcDpmZmVGvXr3Edjxzr3Pr1i3mvp4/f05EFWN/5XVYdnY2eXt789klvL29RQY1/YZNcXExjRgxghQVFVmKfxMmTKAVK1YQEVG7du3o0KFDQs8RFRVF7du3/yn3Kw3irB0TEhLI2dmZ+VtLS4uvbYlyXP4MG2J1wTsXk0Qtnkvjxo1ZJWi5LFmyhOzs7GS6N2nX7OIgiWNbHCXMli1bikxuWL58OVPFjEtVateKioosxVNpqV+/PhOYyftZnz179tODHP/N/Mrnul69enzrWl7Onj3LV3p6zJgx1Lp1a0pISKBp06aRvr4+lZSUMO/v37+f1c8RVdjtRCWzxsfHE4fDoT179kj5ScRDR0dHqA+DqELFjNfeKAvirgPr1q3LBOXa29sz/rMbN24IvBdxExmlmVNHRUWRpaUlhYSE0I0bN1j25MrBhc2bN2cUfrn38ffff1OPHj1o69atYn1HleH6TDkcDs2YMYPlR12+fDkdPHiQ1dZ4kWe/rq6uLlECTu3atZlAcCsrKzp8+DARVQTe8gai6unpCQxIP3nyJKsPrey7EhXEU1kV/Z/gA9PV1RU5P7p58yZfmXhp0NPTIxUVFaFKxzdv3qSAgADS1tamFi1aUEhICL17906sAN2ysrIqFeN5Ebc91qlTR2ASzt27d2VKqh82bBiT9CNJddDDhw+Tra0thYWFUXJyssg+QBaKi4vp2bNn1Xp+SQMLJbGt2trayq0EvSTCCiUlJRQfH0+LFi0iFxcXUldXZ5TfIyIiGJu0NAqh8lYVTUlJYSoh8AbYT5gwgQYNGiS361amRo0aLKGkyiQmJvIlGeXk5JCjoyMpKyuTpaUlWVpakrKyMjVt2pQV7Fu5PYt6cWnevDm1bt2aIiMjKTY2VqzEV3lWEZOn30zS/qWqPr0yb9++pUaNGjGxDK9fvyYrKyvq37+/TFV4jh8/Tg0bNhQojFBUVERWVlYyiQ5I0wa8vLzI1taWtm7dSidOnGASybmv3/y3UcJvfvMfw8bGBitWrMDu3buhoqICAPj+/TtWrFgBGxsbAMDChQuZ/QMCAjBp0iQsWbKEdZ6FCxciNzdX5vsZP348cw9ZWVmoX78+lJSEP5qRkZHw9fWFp6cnLl68CA8PD6SlpSE/Px+9e/cGAGRlZUl1Lzdv3sT27dv5tjs7OyMgIIBv+5UrV9CjRw9YWloiNTUVdnZ2yM7OBhHBycmJtW9GRgbCwsKQkZGBjRs3wsjICOfOnYOpqSkaN24s1f1Ki6urK/766y8YGRmxtn/69Amurq4oKytjbS8uLoaGhgbfeQoLC6Gqqlot96SgoAAOhwMi4ru+LJw+fRru7u5QUFAQuo+6ujqys7Nhamoq8P3s7Gyoq6vzbZ88eTIsLCxw5coVWFhY4Pbt2ygoKMD06dPx559/ynTf5eXlVe7j5OSEK1euoEaNGmjatCk4HI7Qfe/duyf1vRQWFqJRo0Z82xs1aoTCwkKpzysNjx8/Zp6ttLQ01nuiPr+4bNy4EZ6enqhXrx6aNGkCAEhJSYGamhouXLgAAPj8+bNY52rfvj3i4uKk+n169uyJ6OhoLF68GJqamliwYAGcnJwQHR2Nzp07s/YdOXIkJk+ejNDQUHA4HLx58wY3b97EjBkzMH/+fEk+PosFCxbg9evXmDRpEoyNjbF//34oKioy7x86dAjdu3eX+vyOjo7gcDjgcDhwc3Pje19dXR0hISFSn18agoKCsHLlSr7t5eXlCAoKgre3N997qqqqMDc359tuYWHBjLFAxVi3atUq7N69W+QYx2XSpEmoX78+kpKSULNmTQBAQUEBhgwZgkmTJiEmJobZd+vWrQgKCsKCBQtgZ2cHZWVl1rl0dHT4zi/J2FtSUoLjx48jNDQUiYmJ6NatGzZv3gwvLy9W3zpt2jSBx+vq6sLKygp9+vQRe8xQUlLCmzdvxNq3Mtx2JexvUchjvFu5ciUWLFgABwcHpKam4tSpU5g7dy5CQkIwefJkjB49GjVq1GAdY2xsjBcvXvC1rcTERFhaWkp1H9zfh8PhYP78+azPWVZWhlu3bsHR0VHgsXp6eujbty9rm4mJCfN/IoKysjJ0dXUFHv/u3TuUlpaytqWkpGDVqlVC79fDw0PqsdTMzAwA8OPHD77ngcv79++lOvc/jaVLl2L8+PFYsmQJdu/ejWnTpmHSpEk4e/Ysmjdvzre/tHMeSVFRUYGtrS2Aij5k3bp1WL16NfLy8vj21dPTw19//QULCwvW9vv376Nu3brM3+vWrcPnz58F9mm6urr4+++/sW7dOqZdHTt2DKdPn+bbt02bNli5ciU2bNjAbHN3d8eePXvw9u1bDB06FJ6enmL1G8bGxigsLISZmRlMTU2RlJSEJk2aICsrC0TE2vfmzZu4fv06zp07x3ceDofDzH83bNiAkSNHCv2co0ePxrp169C+ffsq708UxcXFmDVrFg4fPoyCggK+96Wdj6uoqGDXrl2YP38+Hj9+jKKiIjRt2hQNGzbk27du3bp4/PgxGjRoIPBcDx8+RO3atSW+h4cPHzL/f/r0KavdlZWV4fz580zbkmU+7eHhgQ0bNmDnzp0AKn7HoqIiLFy4EF26dGHtO3XqVCgrKyMnJ4dZcwPAgAEDMG3aNKxdu1bizwkA48aNQ2RkJExMTDBixAgcOnQIBgYGIo+JjIzE4cOH+e6xuikrK8OPHz9QUlKCb9++oaSkBM+fP5frNSszdepUdO/eHdu3b4euri6SkpKgrKyMIUOGYPLkyejTp4/Y5zpz5gzGjRuHxYsXi/Wcrl+/Hh06dKjOj/OPRtpnafXq1fDy8kKjRo1Qr149AMCrV6/Qvn171jxgzZo1MDExEfqdm5iYYM2aNdi2bZtMn6N///64ePEixowZI3SfDx8+oFatWszf8fHxrHVC8+bNWbYyX19fqdaqxcXFjM2mRo0aePfuHaysrGBvb8/XH5mZmeHs2bP48OEDXrx4ASJCw4YN+eaYP4t9+/Zh+/btyMrKws2bN2FmZoYNGzbAwsICPXv2/CX3JA6zZ89GSkoK4uLi4OXlxWzv1KkTFi1ahKCgIDx//hytWrUSeo7mzZvj2bNnP+N2pUKctePWrVsxdOhQ1vbY2FiYmZmBiLBp0yZs27YNnTp1EniNn2FD/PbtGx4+fIi3b9/y2c969Ogh9nl69erFt23x4sV823jnarwEBwdjwIABSEhIQNu2bQEA169fx5UrV3D48GGx70MQktrLJeH58+cCxyhdXV18/PiRtW3y5MlwdnZGSkoK9PX1me29e/fGyJEjAQAjRozAtGnT0LhxY3Tr1o11fHR0NJYtW4Z169axtsfGxoKI4ObmhmPHjjF2D6BiPmlmZoY6deoIvf+QkBDmWbOxscHEiRNhbW3Nt+/r168FzjXLy8vx48cPgeeXBSLC+fPnsWfPHhw9erTaz/+r+BnPtTA6deqEZcuWsfplLkSEZcuW8fVHS5YsQZ8+fdCxY0doaWkhPDycZaMLDQ2Fh4cH65j3798LtO9xsbS0hJKSEkaMGCHxZ/jx4we8vLywfft2gesiXrp164ZRo0Zhz549aNq0Keu9+/fvY+zYsTLZY6VZB3bo0AGXLl2Cvb09+vfvj8mTJ+Pq1au4dOkS3N3d+fYvLy8XeJ5Xr15BW1ub+VuaOfXAgQMBVNhNuXB9SpX76mfPnuHQoUMAKuyMX79+hZaWFhYvXoyePXti7NixAr8jUXD9pubm5hgwYADU1NTEPlae/XqjRo3w9etXsffv06cPBg8ejIYNG6KgoICZy96/f5/VZw4fPhz+/v7IyMhAixYtAAC3bt3CypUrMXz4cGa/2NhYsa/NO26L4wP7GTRt2hQnT54UOsc7ceIE3/MoiO/fv+P79+/Q0tIS+D6vHUoQrVq1QqtWrbBhwwZERUUhNDQU06ZNQ3l5OS5dugQTExPWM8TL0qVLER4ejtWrVzPjMwDY2dlhw4YN8Pf3Z+0vbnssKCgQaO/V0dGRybYaFhYm8P9VMWDAAABg9cXC+gBpePfuHYYPHy7QdgdIbysDJJt/AZLZVleuXInAwEBs27YNdnZ2Vd7Lq1evcPr0aeTk5OD79++s9yrP2R49eoSDBw/yncPIyIjVBtzc3HDr1i1YWFigY8eOGD16NA4ePCjUrlcd/tzqxMHBAY8ePeLbvmbNGpZfVN5w+yPu2qIyx48f5+uPTExMcO/ePVy+fBmpqakAKubHledHXJ8s12Yt6jfgtvXHjx/j/v37AufZwujatSsA4MWLF8jIyECHDh2grq7OPKeyIA+/GRdJ+5eq+vTKGBoa4uLFi2jXrh2ACjujk5MTDhw4IDCGRZBfg3tPampqaNCgASwsLLBt2zbMnDlT4FxdU1MTs2bNwubNm6WeP0rTBhITE3Ht2jWh/sbf/Mf5FVHBv/nNr+TWrVtkZGREhoaGjAqaoaEhGRkZMeocvOjo6AgsjZCWllYt2bpfvnypUqGCF3t7e9q8eTMR/X92THl5OY0cOZLJiOXCLZUiCEFlBa2srCgwMJBve2BgIFlZWfFtFzcLV5YSEPJAmHre8+fPWRmyXLy9vZnyGVpaWpSZmUllZWXUv39/me5fUrUnaamqFFuXLl0oICBA6PH+/v4CS7zp6+szWVM6OjpMZjhXFVHeLFq0iMmIkrScjiS0aNGCJk6cyLd9woQJfAoY/wsUFxfTzp07adq0aTRt2jTatWsXKzO1KpUP3szwn/H7SFNO/J9AdnY2ZWVlEYfDoTt37rBULd+8eSOwZKi8UVNTE1iOJCsrS6iiZHBwMA0aNIi+ffvGbPv27Rv5+PiwfluuAmnt2rXJw8OjSsUsDQ0NRp2ClwcPHvCpk0hTYkTcsVeSkrzCSjs6OjqSlpYW1a9fn0+5s7Iq0cmTJ2nbtm3UuHFj8vLyEnotUXA4HNLT02MyVTkcDunq6laZwUokn/HOysqKKSeXkJBAHA6HunbtSkVFRUKPWb58Odna2lJSUhJpa2vTtWvXaP/+/WRoaEibNm0SepyoMt7c34PD4VCbNm1Yv5GHhweNGjVK6lJY0igmyVs5k4ioT58+Aku65eXlUePGjWU69z8FHR0d5nssLS0lRUVFkYpm0s55quLbt28UFBREzZo1o9atWzMlJ0NDQ6l27dpUr149oW1k+vTp1K5dO/rrr79IW1ub0tPTKTExkSwtLVn9aOPGjUUqGl2/fp1VXlVYG0tPTxeYUc9VCDc3N6datWrRpEmTSElJSaT6ub+/P3OPmzdvZub7enp6fCWrzMzMaPz48VWWGTc1NRV5zWfPnpGJiYnIc4jDuHHjyMbGhimxFxoaSkuWLKF69erR/v37ZT6/OEyYMIHs7OyElpG1s7MTOAetCt65miAlHg0NDUb5atGiRUypbknna7m5uWRra0s2NjakpKRErVq1In19fbK2tuZT9ZCX6pg0ypy1a9dmlDhFsXHjRua3qTymVH7xMnHiRLK3t2eVy9y4cSOlpKRUWWazutHV1WXWaLq6usyzlZSUJLFS/4cPH5jv8mc9p/8meNemCxculOhZKi8vpwsXLtDq1aspJCREoPqwlZUV3b59W+j1k5OTBdpsJGX58uVkYGBAfn5+9Oeffwps69KUzZYGZ2dnprpH9+7daejQofTq1SuaOXMmWVpaVnn8p0+f6MSJExJV8agOtm7dSgYGBrR06VJWOfmwsDBycXGR+/XFWasLWyOZmpoyylO8fXV6ejpjK9PQ0BCpnpWSklIt1QjkhThrxwYNGrAU1SpXJLl37x7Vrl1b6DXkZUPkcu7cOUYR/VdXnCKq6H8GDx5MTk5O5OTkRD4+PnTv3j2ZzyupvVwSJKnEJK4Spo+PD3E4HLKxsaFevXpRr169qFGjRqSgoEADBw4Uei/Z2dkSzQ+OHj3KzLt4K/0pKSnR0aNH+fZ3cnKiffv28d1/cHAwtWvXTuzrVkVmZibNmzeP6tWrR6qqqtS1a9dqO/c/AXk/16J48eIF6erqUosWLSgqKooePHhADx48oMjISGrevDnp6uoKtS18/PhRoH2xoKCAT+XUzMyMLly4IPQ+zp07J1OlLwMDA7FsLoWFheTl5UUcDodq1qxJjRo1okaNGlHNmjUZdXxZykRLsw4sKChgymaXlZXRihUrqHv37jRt2jQqLCzk2/+PP/6gkSNHEtH/t5e///6b3NzcWGqY0sypJamUVKtWLeb8NjY2jBq6INuqtJSUlFBubi69fPmS9RKEPPv1CxcuUJs2bSg2Npbev39fpTLy9+/fac2aNTRp0iTWmLVu3TratWsX83dZWRmtWrWK6tSpw4y1derUoVWrVlWL7T48PJxlU+dSUlLCKI3+DLhjS0hICOtzlZaW0qZNm0hZWZmOHDnCOiY0NJQmTJjAPDdBQUGMkmKnTp3o/fv31XJvqampFBgYSMbGxqSmpkbdu3cXuJ+kivHitsfGjRtTSEgI3/GbNm2SqXojEVGHDh0oODiYEhISRPryeZF3tbTBgwdT27Zt6c6dO6SpqUkXL16kffv2kbW1NZ05c0amc1c1/5LFtsqr5KmmpibSD3L58mXS0NAgOzs7UlJSIkdHR9LT0yNdXV1ydXXlO3fdunUZxW3e+z5+/DhrXaqkpEQmJiY0ceJEOnbsmMhnoLL/Rtir8jHLli2TyD71b4TbH23evJkVKyGoP5K0Chvvs3LixAmqX78+bd++nVGI3b59OzVs2JBpe0RE7du3F+lrEIQ8q4hJ6zcTh59VjfH58+dkZGREPj4+ItdBwqqF867fO3ToQMbGxlX62USt36tCmjZgY2NTLWvi3/xv8jtA9zf/SYqKimjHjh2MIWvnzp1CA0Vq1apFYWFhfNvDwsL4SndKw6RJk6hZs2Z07do10tTUZAbqkydPCgxy1NDQYAKoatasyQQwPX36lIyNjVn7ShqYERMTQ2pqamRnZ0f+/v7k7+9P9vb2pKamRjExMXz7a2lpMaUY9fT0GOfugwcPWAYbSUpAyBOug1ZBQYG6dOnCctr26NGDzM3NydPTk++4R48ekZGREXl5eZGKigr169ePbGxsqFatWnyl1sVFkqAvWRCnFNvVq1dJUVGRpk+fzgqayMvLo2nTppGioiJduXKF79x6enpMuUhLS0u6evUqEVUYDnmN1NIgSVlmeRMXF0eamppkY2NDI0aMoBEjRpCNjQ1paWlVe4llScjNzWWV5/hZ8JZu2Lt3LxkbG1NQUBATYBgUFES1a9dmAvKk5cOHD7Rr1y6aPXs2FRQUEFFF6aBXr14J3F9QyZvqYMmSJf+Zsqi1atUS+KxfunSJDA0Nmb8rB71oa2uTgYEBk/RiYGBAOjo6rIAYSUo1EVWUsuEaPngRVMJGmhIj4o690pbkrcynT5+oW7dufOWABC0ua9WqRYMGDaI3b95UeV5B7N27V6yXIOQx3qmpqVFOTg7zt4qKisDSYLxIE3hfVRlvLsOGDZO6xNDbt2/p2rVrdO3aNVaiz44dO0hTU5Oio6P5jjl9+jRpamrSjh07WNstLS1Zxp7KHDt2jHXf0uDs7MwXJPnmzRtq1KjRL0mQkgeVy3tVDqCojLRznqqYOXMm6erqUt++fal27dqkpKREI0eOJHt7ezp06JBIx01JSQkFBASQkpIScTgcUlZWJgUFBRoyZAjrOA0NDaGOLiKily9fsgJiZHEiXLx4kQYNGkRqamrUsGFDmj17Nt29e5dvv7KyMvrx4wfz96FDh2jixIm0adMmPocv77pBFD8jeJ2IyMTEhGJjY4mImMBoIqKIiAiJg7SnTp3KrCW560thL17y8vKoTp06ZGJiQqtWrWLKXK1cuZJMTEyoTp06VQY0C0LSJCAOh0MtWrSgHTt2MMl84vLjxw/at28fBQYG0tixY/kSu7hoaWkxDnne5/TOnTtUs2ZNiT8jFz8/vyrnGJXnGX/++SeNGzeuymAYc3NzxqFibm4u9FW5r+7Xrx+FhITIrXyrJPAGQjRs2JAJdnz27JlMAXQ/6zn9zf+jpqYm0iGSnZ0t8xqcSLy2LmnZ7OHDh1f5qjxfISLat28fY4tLTk4mAwMDxuEaGRnJt3///v2Zce/Lly/UsGFDUlZWFhq0Ji9sbGyYOR5vf/fo0SPS19eX+/U5HA6Zm5vTwoUL+cooVlVSkTegmPfeHzx4wAgUNGnShLZt2yb0+lu2bKEmTZpU74eqRsRZO1Zevxw7doxVLjM7O5tUVFSEXkMeaypeGjRoQOPGjZNqjvBvQlJ7uSRI4tjW09NjymrzPhfXrl3j8wtERUVRz549mQSmnj17UlRUFN/1U1JSmIADccvrcrG0tKT58+fzbV+wYIHA5IWTJ0+Srq4urVy5kjQ0NGjNmjUUEBBAKioqdPHiRTG/McF8+/aN9u/fT66urswaZt26dXIr6fsrkfdzXRV37tyhxo0b8yXiNW7cWGQCjyRMnjyZ7O3tBYqa5Ofnk4ODg0w2+SlTptCsWbPE3v/p06cUGhpKy5cvp+XLl1NoaCg9e/ZM6utzqc51oDDETWSU95y6Z8+etHPnTiKqSAxu0KABLV26lJycnMjd3V3q8xJVJLy0a9dOIrEEefbrvHZVce9HUoQF+/K+V9WrMgoKCgJL1r9///6nJ93MmTOHOBwO6ejokKOjIzk6OpKOjg4pKCjwPbvcRLROnTpRzZo1acyYMWRsbEwrV66k1atXU7169WjMmDECr/PixQuaO3cuDRw4kPnsZ8+eZXzLwigtLaUTJ04IDdDlXS/xjtdPnjwRGJAubnvcs2cPqaur04IFC5j54vz580lDQ4N5vqTFz8+PzM3NmURqd3d3Wrp0Kd24ceOXiLcQERkbGzNCZtra2kxy86lTp6ht27Yynbuq+ZcstlVJ/CDiCo9xEVdYoaioiM6dO0ezZs2iFi1akIqKCtnZ2dH48ePpyJEjrPGVw+HQxo0bJbpvDodDJiYmEtmnJKG0tJTWrFlDzZs3p1q1aokl9iIvZs6cKbQ/mjFjBrNf9+7dad26dULPs3HjRurVq5fA95o3by4w7iUmJoacnJyYvw8fPky2trYUFhZGycnJVc7ViYiGDh1Knp6elJuby+qPzp8/zxLXkIZ/mmBVVX26sEB0VVVV0tHREdnGLl++TC1btqTLly/T58+f6fPnz3T58mVq3bo1xcTEUGJiIjVu3JgUFRVFzhGfPn0q03xKmjZw4cIF8vDwECiI9Zvf/A7Q/c1vqmDFihWkpqZGEydOpH379tG+fftowoQJpKGhIXNmJ5F4ChW81K1blwnKtbe3p4MHDxIR0Y0bN/gUfaUJzMjJyaHZs2czQUdz5sxhGaZ5ETcLV1NTkwlwq6w2IEjBS15wHbQcDocGDBjActqOGjWKli9fLjRQ9uPHj7R06VLq378/eXt709y5c6UOniKqvqCvqujZsycNGTKESkpKWN99bGwsNWjQgNlv+/btpKqqSgoKCsyESUFBgVRVVfkWJfHx8fT9+3dq164d43gaNGgQeXl5UWJiIvn6+sqszFenTh2BAVx37979qUHdXF6/fk1z5syhPn36UJ8+fWju3LlM5vzPpKysjIKDg5nFiIKCAunq6tLixYtlUl5OTk4mFxcXoZmGLi4ujOoZL25ubkwfxMuBAweoY8eOUt9PSkoKGRoaUoMGDUhJSYlpt3PnzqWhQ4dKfV5pcHBwIAUFBWrdujVt2bKl2oPp9+7dy8o+DgwMJF1dXWrdunW1ZiWKw6hRo8je3p7lXEhPTycHBwfy9/dntokTCCMs8FZchg4dSo0bN6akpCQqLy+n8vJyunnzJtnZ2ZGfnx9rX3V1dUbRRlzEHXulCfwRxq1bt8jU1FSi+/wVyGO84zVAcdVDxEGSwPuOHTvSyJEjqaysjPlNc3JyqEOHDnTs2DFmv+HDhwsMQCsqKqLhw4cLPDf3PUVFRcbwoaSkRCNGjGCc9ZIqJslLOZOXt2/fUqNGjZigwNevX5OVlRX179+/2tX6fxUcDociIiKYJBGugbyyOjUvksx5xMXCwoK5zqNHj4jD4dDw4cMlUsN6+fIlxcTEUFRUlEBlIX19fYGqilzi4+NZgT/V4UQoLCykTZs2kaOjo8zOIV9fX5YSjTB+RvA6UcX6hBvwXLduXcYBkZmZKbGakIuLC6PiJExN3cXFRaASR3Z2Nnl7e7MUAbjKUD8rQSghIYGGDx9O2trapKmpSX5+ftWehPYrVccq06tXL9LV1SULCwvq1q1bta+//kl07tyZDhw4QEREAQEB1KJFC9q/fz95enpSixYtpD7vz3pO/61YWFgIVMv58OGDwO9FVAUALsIS6bhcvnyZatWqVT0foArevXtH7du3Jw6HQ9ra2nT8+HHW+25ubjRnzhzmb27AaO/evZk5kqBXVRQXF9Pdu3eFrsd4lboPHDhADRo0oOLiYtq6detPqfDDRVhwQFpa2k8JXL9z5w6NGTOG9PT0qGnTphQSEiJQ3U8Q7du3Z4ITeefsEyZMYJLZV61axaqmxMuDBw9IX1+fVq1aVU2fpvoRZ+1oaGjIBG8JIjY2lgwMDESeo7rXVLxoa2tXa0CgOMpTleeg4lRWUlRUlOm+JLWXS4Ikjm1xlTAlgTfJUJgylLCgMnV1dYEBfWlpaUITNRISEqhTp05kaGhI6urq1LZtW5FKqVWRnJxMY8eOJT09PXJ2dqaNGzdSXl4eKSkpMcHM/4vI87kWRuXn8v79+3T48GGKioqi+/fvV+u1CgsLqWHDhqStrU1jx46ljRs30oYNG2j06NGkra1NDRs2ZAQUpGHChAmko6NDzZo1o1GjRolMZJQn0qwD7969y6rydfLkSerZsyfNnj2bLzGViziJjNLMqcPDw0W+eMnIyGDG66KiIho9ejTZ29tTnz59ZLY5t2nThjp06EBnz56l+/fvM8rO3Jcg5NmvV064EZSAU9lOJOrFS1XVKYmqHheF9enCKn0+ePDgpwfEEVXYridNmkRdunQhb29vmjx5ssCqsw0aNGB8Qnfu3CEFBQVWQtzZs2cF2sDlWWlVUsV4Sdrj1q1bqW7dusz4bGFhUa0Kx1lZWbRnzx7y9fUlU1NTZp3l5eVFq1evplOnTjEKu5K0XWnQ1tZmAspMTU0pMTGRiCr6SFkTQquaf1WHbVUcxBUe4yKusEJlPn/+TGfPnqXAwEBq3rw5qaioMH7zyoIT4iDNMZIwf/58ql27Nv3555+kpqZGS5YsIX9/f9LX1/8lyrw3b96kSZMmkbe3N3l7e9OkSZOYZ5aLLNWd1NTUBB5bOZhT2BxdVAKIvKqI8VJdglWy9C/i9OniigkJEhRq3LixUBEnbqDzpUuXSElJien/BRERESFxFTFexG0DlYORuareWlpavzTg/Tf/PH4H6P7mP0lERAS1bduWateuzSxG161bJ1RFIioqitq0acN0nG3atBGYAS8N4ihU8DJo0CBGjXbx4sVkaGhIAQEBZGZmxudQlHdghrhZuOKWgPhZLFq0SGRp7Z9BdQZ9iULcUmxERK9evaJ169bRuHHjaOzYsbR+/XqBCq3czN7z588zQU/p6elkbW1NHA6HDAwMpFKf40WcsszilACR52QnNzeXMdL/LIKCgsjQ0JC2bt3KZGdt2bKFDA0NWU5QSRk0aBAtXrxY6PvLli0jHx8fvu3q6uoCg4ieP39O6urqYv9GlXF3d6fAwEAiYrfb69ev8y2Sv379SqtXryZvb29q1qwZNW3alPWqDh4/fkyzZ88mCwsLUlZWpi5dutCBAwdYKjrSYmVlxTwvN27cIHV1ddqxYwd17979pweJfPz4kVq1akVKSkpM1q2SkhK5urrKVD5OGj58+EA9evQgDodDKioqzGKmV69e9PHjR9a+0pQYkXTsrQ4yMjJIS0tL6PvcQOTqYv78+XT16lWBAaA/Ew6HQ6NHj2acLioqKjRixIhqd8aIW8ZbmDrFu3fvhDqRR40aRZaWlnT27FlG9SImJobq16/PUoQQVzGJSH7KmZXJyckhU1NTmjp1KjVs2JAGDBjwy1QY5IEgA4k4jm1x5zzioqyszFJ4V1NTYznwqoMuXbpQQECA0Pf9/f35FH8kdSKIKqknyClDVDEO37p1i6Kjo0Ua7pYuXVpl2XSinxO8TlSR6Mh11Lm7u9P06dOJqEJd4VckgxUWFtLt27fp1q1bYgdSVYWkSUBFRUUUGhpKHTp0IA6HQw0bNqSVK1fSX3/9JfQar1+/pqioKAoJCRFZVu9Xq47xIu/1F1GFvaFNmzYse8P69euF2hvkxZ07d5gqJ/n5+eTp6Una2trk5OQkU1DHz3pO/60Ic5zl5eWRsrIya5u4FQD69+8vMoi1R48e1K9fv+r7EGIgbtnscePGUY0aNcjR0ZE2btwoU3CPKHhVT4cOHcoofb18+bLanGDiYGNjwzzrvOuMTZs2Vdv6VBy+fv1K+/btIzc3N9LQ0KABAwZUqZZ57do10tLSojFjxpCamhpNnjyZOnfuTJqamkwC9ffv38nFxYWUlJTIy8uLpkyZQlOmTCEvLy9SUlKijh07il2i91cgztqxW7duQhP3iCrseV27dq3uWxOb4cOH0+7du6vtfNIoT4lSZ541axapq6vLLMjwM9bs4ji2q1LCFFc1kTfQMjs7m1n7S1pK1tvbm0JDQ/m2h4aGkoeHR7V8L1WhqKhIU6ZM4Qt2/18P0P0V8NovfoZtrrCwkMaMGUM1atRg1pA1atSg0aNHy1yuXtJExtzcXIHP5ffv30UmrlaFNOtAZ2dnJvgwIyODVFVVadCgQdSgQQOZVIWlmVPr6emxXtxAN1VV1Z8a8KGhoSGxovGvsMXyIiy4RpD6LhdxqlMSVR0gXLnKm6OjIzVt2pQUFBTI3t6e5ctwcHAgbW1t6t+/v9y/E2lRUVHhq5bGOya8evWKb91DJN9Kq5IqxkvTHt++fVutlRuFkZGRQXPnzmVEeion9khi/5QUZ2dnpvJO9+7daejQofTq1SuaOXNmtfnyhc2/ZLGtvnz5UuSLF3GFxwRdQ5SwQmXKysooKSmJVqxYQR4eHqShocH8RsL8E6KQ5hhJsLS0ZGyIvEHMGzdu5KsK+TMQtabkJu3KokTftGlTGjp0KMt2UVJSQkOHDmWt2yWdqxPJr4qYPJClf5F39Ww1NTWB1ckePnzI/K7Z2dmkpKREpqamAn1pf/31F5mamsoUPyFuG5AlGPk3/y04RET4zW/+Q2zbtg0LFizAlClTsHTpUjx58gSWlpbYu3cvwsPDERsb+1Pvp0OHDujfvz8mTpwIbW1tPHz4EBYWFpg4cSLS09Nx/vx51v6FhYX49u0b6tSpg/LycqxevRo3btxAw4YNMW/ePNSoUYO1f25uLtq1a4e+ffvizJkzcHJywoEDB6CoqCjwfq5du4YdO3YgMzMTR44cQd26dbFv3z5YWFigXbt2rH0zMzNRVFQEBwcHFBcXY/r06cy9rFu3DmZmZgCAGTNm4NatWzhy5AisrKxw79495Ofnw9fXF76+vli4cGE1fqNV8/XrVxARNDQ0AAAvX77EiRMnYGtrCw8PD4HHfPv2DQ8fPsTbt29RXl7Oeq9Hjx5yv2dpqVGjBq5fvw5bW1toa2sjJSUFlpaWSExMRN++fZGfn8/sm5CQgDZt2kBJSYl1jtLSUty4cQMdOnQAACgoKCAvLw9GRkZ81yssLESNGjXA4XBkum87OzuMGTMGEyZMYG0PCQnBtm3b8PTpU4SHh4t9Pj8/P5nuRxApKSlwcnJCWVlZtZ9bGHXq1MH27dv52typU6cwbtw4vH79Wqrz1q9fHydOnICDg4PA9x89eoSePXsiMzOTtd3a2ho9e/bE6tWrWdtnzpyJU6dOYc6cOcw2IsLYsWOxePFivrZT+ffR1dXFvXv3UL9+fVa7ffnyJaytrfHt2zdmXx8fH1y8eBH9+vVDrVq1+Npedfcv169fx8GDB3HkyBF8+/YNnz9/lul8GhoaSE1NhampKWbNmoW//voLERERePLkCVxcXPDu3btqunPxICJcunQJKSkpUFdXh4ODA/Psy8rRo0dx+PBh5OTk4Pv376z37t27J/CY9PR0pKamAgBsbGzQoEEDvn2OHDmCRYsWITAwEPb29lBWVma9L6hdSzr2VgcHDx7E6tWr8eDBA9b2iIgIrFmzBunp6QAAKysrBAYGYujQoTJdr3Pnzrh58yZKS0vRvHlzdOzYES4uLmjbti3U1dWFHlfd452Li0uVYwKHw8HVq1dZ9xASEoLY2FiB9yGovRgaGjJzECsrK4SEhMDT0xOpqalo1qwZ/vrrLxARatSogfT0dBgaGjLHlpWVITo6GkFBQXjz5g3fuQ0MDHD06FG4uLiwtsfGxuKPP/6Q+jl9+fIlxo4diwsXLoC7JONwOPD09MSWLVtgYWEh1Xkrk5aWhvbt26Nz587Yt2+fzGP0b/hRVFREXl4e0654+5WqICIcPXpUaHs/fvw4gIr21rlzZ0yZMgWBgYGoVasWACA/Px+rV6/Gxo0bcfHiRbi5ufFd4927d1BXV4eWlpbIe+nbty+OHj3K10bevn0LNzc3PH78mLX9/Pnz8PX1xfv37/nOxeFwWHMkUd8Fh8Nh5hj5+flwcnKCoqIiJkyYAGtrawBAamoqtmzZgrKyMty7d4/5/NKyfv16KCoqYtKkSbh8+TK6d+8OIsKPHz+wbt06TJ48Warz7t+/H3369GHWGdLw+fNnXL16FdbW1rCxsZH6PNbW1ti2bRvc3Nxw8+ZNuLu7Y8OGDThz5gyUlJSYtiWIFy9eICwsDPv27UNeXh68vLxw+vRp1j579+7F6NGjoaKiAn19fVa74f1NuXz69AmbN29GSkoKioqK4OTkhPHjx6N27dpSf0Z5Mm3aNLH3XbduHfN/XnvDsmXL8Pjx419qb5AHP+s5/bfBfUZ69eqF8PBw6OrqMu+VlZXhypUruHTpEp4/f85sd3FxgZWVFbZv3w5dXV2kpKRAWVkZQ4YMweTJk9GnTx8AwP3799G6dWt069YNM2fOZH3nq1evRkxMDG7cuAEnJyeZP8erV69w+vRpgXN23rYuCSUlJTh+/DhCQ0Nx48YNdO3aFf7+/vDw8OAbc8R99irfi5WVFZYuXYquXbvCwsICkZGRcHNzQ0pKCtzd3QWOVfJg9+7dWLRoEdauXQt/f3/s3r0bGRkZWLFiBXbv3o2BAwf+lPvgJSsrC/7+/oiPj8e7d+9Qs2ZNoftmZGRg5cqVrL561qxZsLe3Z/b58eMH1q9fj4MHDyI9PR1EBCsrKwwePBhTpkyBiorKz/hYUiHO2jE2NhadOnXCtGnTEBgYyNgv3r59i1WrVomcc3GRpw3xy5cv6N+/PwwNDQV+hkmTJkl0PjMzM5w/f17onCM1NRUeHh7IyckReZ7nz58jKCgI0dHR8PHxweLFixm7sDTIc80+YsQIbNy4Edra2qztxcXFmDhxIkJDQ1nbS0tLERkZiYcPHzLPhY+PD9TV1aGgoCD2+op3bqyoqIi//vpLoG1VFNu3b8eCBQvwxx9/oFWrVgCApKQkHDlyBMHBwahTpw6zb+W2VlRUxNcedXR0JLo+AHh6euLmzZvo3r07hg4dCk9PT3A4HCgrKyMlJQW2trYSn/PfwK/wDejq6iIpKQk2NjZQUFBAfn4+y5YhL4iIsXMYGhr+VBvCX3/9hZ49e+Lu3bvgcDgYPHgwtm7dyqxl8/PzUadOHant8dKsA3nt1KtWrcLVq1dx4cIFXL9+HQMHDkRubi7fMW/evEFiYqLA9sLtp6trTp2eno6xY8ciMDAQnp6efO8nJyfj2bNnAABbW1s0a9ZMvC9LBM2bN8f69ev5/IWikGe/npCQUOW1ebl8+TJmzZqF5cuXo3Xr1gCAmzdvYt68eVi+fDk6d+4MoGJer62tjT179kBfX5/xUcTFxWHkyJGMLVdSgoODmX+nT5/OstWoqKjA3Nwcffv2/SVzKnH8w5X9g7z+G0D4c6qlpYVHjx7BwsKCdUx2djYaNWrE8vlIe++LFy9mzWMXLFgg0Of7K3wDonj58iXi4uKY19u3b9GqVSt07NgRCxYs+Gn3sX//fpSWlmLYsGG4e/cuvLy8UFhYCBUVFezduxcDBgyQ27Vlsa1WNR/jbYu9evVC165dMXLkSMyYMQOnTp3CsGHDcPz4cdSoUQOXL1+W6v7Ly8uRnJyMuLg4xMbG4vr16yguLkbdunXh6urKvMzMzET62EV9RkmPkQRNTU08e/YMpqamqF27NmJiYuDk5ITMzEw0bdoUnz59kst1hSHMRp2fnw93d3c8fvwY9evXx9q1a9GrVy+B5zh+/DhmzJjBZ58EgNu3bzNzAK7/8OHDh+BwOIiOjkaLFi2kvvcuXbqgWbNmWLJkCdOOzczMMHDgQJSXl+Po0aNSn1sav5m8kKZPLysrw8mTJ5l5SePGjdGjRw+BMUvt2rWDtrY2IiIimH7h3bt38PX1RXFxMRISEnD58mWMHTsWqqqqyMnJwZAhQ1jzqQMHDsDExARJSUl8az95UFZWhj///BOnT5/G9+/f4e7ujoULF4r0x/7mP8gvCQv+zW9+ITY2NkzpGN6MjkePHrHKwvLy4cMH2rVrF82ePZtR+7h79y4rm0taxFGo4PLjxw8KDw+XWFHt+fPnZGRkRD4+PiLV+Y4ePUrq6uoUEBBAqqqqzHcTEhLCp8hVWlpK8fHxYmVuS1sCQl507tyZtm3bRkQVv62RkRHVq1eP1NTUBJY2PnfuHKNkI4+sRHkiSSk2YRl479+/Z31OYaV3qpPqKMssbx48ePDTf39VVVV6/vw53/bU1FSZymWqqqqKLKOcmZkp8PwxMTGkpqZGdnZ25O/vT/7+/mRvb09qamoUExPDtz9vnysKQ0NDunfvHt8xFy9epHr16rH21dHRYUrt/Azu379P06dPp7p161ZLiVLez+ro6EgRERFERPTixYufqvQkCdwsf3FeXDZu3EhaWlo0YcIEUlFRodGjR1OnTp1IV1dXpuxFIunKzEgy9ooLV9W68ishIYHWr19PhoaGtHnzZtYxa9euJQ0NDZo5cyajOBkYGEgaGhoiVYzE5cePH5SYmEjLly9nVPNUVFSobdu2Avf/p4x3gwcPJgMDAxozZgwtXLiQFi1axHoJoqoy3lWVm1NUVKSlS5cKPLe6urrAkkePHz8mdXV1iRWTKlOdypnClMtVVVVJR0fnP19KJz4+XqyXpHA4HOrSpQv17t2bevfuTUpKSuTh4cH8zX0JYtKkSaSqqkpeXl4CKyzwsn37dlJVVSUFBQXmt1ZQUCBVVVWBc1hJcXZ2phEjRrC2vXnzhho1aiSw3GCDBg1o3Lhx1aL2zEt2djZ5e3uzFG0UFBTI29tb5HxF1mseO3ZMYMluSTAwMCBNTU0aNGgQxcTEiLXW6d+/P4WEhBBRhVJSw4YNSVlZmZSUlFhlIiVFXV2dUQqZOXMmDR06lIgq+q6qSnMTVSjq7tixg2rWrClwDKhXrx4tXbq0Wiqz/BMRpfIlSvFLGnuDvBCmtPbp0yeBSmWS8Cue0386wtS4uBUhrKysKDo6mnWMuBUAiIiio6PJ0NCQbw5jaGhYLeVMiYguX75MGhoaZGdnR0pKSuTo6Eh6enqkq6src5vhkp2dTYsWLSJLS0syNTXlU6Gq/IwpKSlRy5Ytq1Ta27JlCykpKZGenh41adKE6Zs2bdpELi4u1XLv4rJ//35q0KAB8/vXrVu3WhVPxSU3N5eWLFlC9evXp9q1a9OsWbPox48fP/0+/kmIu3bcsmULU8WFd86loqLCjNnCkPeaavfu3aSkpERaWlpkZmbGVL8xNzfnK4UuDrIoTxFVqOkHBASQsrIydevWTaDKkTTIY83ORZqqKsLgVUbcu3cvGRsbU1BQELO+DwoKotq1a/MpJUlbplicCiK8bS0zM5O6dOnCqLZVVW5dXHJycig4OJjMzc2pVq1aNGnSJFJSUhJZZvjfzK+ylfTp04dq1apFLi4uxOFwqG3btuTq6irwJS9KSkqqXTEyNzdXaAUbX19fatmyJd25c4cuXbpEzZo1I2dnZ8ZWkpeXRxwOp9ruRZx1oLa2NqOE16lTJ9qwYQMRVagpCuofw8LCSEVFRax+urrm1Hfu3OGbO+bm5lK7du0YJWSuMnLbtm1lqiBERHTlyhVq3bo1xcbG0vv378Wyf8mzXxf2bFZWxOXSuHFjunbtGt/2hIQEatSoEfO3JNUpRXH37l2B6vt79+6lb9++iX0eeSOuf5jD4VBsbCxj+9bU1KSYmBjm7ytXrgj83v9JlVZFtUdra2umz6nKDyIL4eHhNHz4cLKwsCBtbW3y9PSk5cuX0/Xr1wWqh37//p3c3NzEUm+tLoqLi+nu3buMYqksFBUV0bx586h169ZUv359srCwYL1ksa0+ePCA9bpz5w7t3LmTGjVqxFSB5ZKRkcH0+UVFRTR69Giyt7enPn36MGqYU6dOZarwVq4CKKwqoLa2NikoKFCdOnXIx8eHdu/eXa0VoxYtWlQtFT2FYWVlRUlJSURE1LZtW1qxYgUREUVGRpKhoaHcrisMcWzUslZ34to9ub/lzp07BVZflrRKljyriEnjNxMHafoXSfv09PR0atiwIWloaDB9qIaGBllbWwv8XlJTU8na2ppUVFSofv36VL9+fVJRUaFGjRoxcRInTpygiIgI+vjxI40dO5Zq1qzJqgIxduzYaqlUJ24bWLx4MSkoKJCHhwf17NmT1NTURFbn+c1/k98Kur/5z6Guro7U1FSYmZmxMjrS09Ph4OCAr1+/svZ/+PAhOnXqBF1dXWRnZ+P58+ewtLTEvHnzkJOTg4iICJnvSRyFCi4aGhp49uyZUBUCYeqlX758gaqqKisLpbCwkLVP06ZNMXXqVPj6+rK+m/v378Pb2xt5eXms/dXU1PDs2TOxFd5ycnLw+PFjFBUVoWnTpmjYsKFYx1U3BgYGiI+PR+PGjbF7926EhITg/v37OHbsGBYsWMBk7nBp2LAhPDw8sGDBgn+dCtCrV6/g6ekJIkJ6ejqcnZ2Rnp4OAwMDJCQksLLthGXip6WlwdnZmVEKVVBQgLe3N1RVVUVeW5Qqlzhs27YNy5YtY9QMzc3NsWjRIvj6+gKARMql0ihCVMWvUNBt2bIlWrZsiU2bNrG2T5w4EXfu3EFSUpJU5zUxMcGuXbvg5eUl8P1z585h1KhRAhUBcnNzsW3bNpbK6ZgxY2BiYsK3b+VsamEEBASgoKAAhw8fRs2aNfHw4UMoKiqiV69e6NChAzZs2MDsa2tri8jISKHqv9VBVlYWDh48iIMHD+L58+fo2LEjBg8ejH79+rGUsaTBx8cHqampaNq0KQ4dOoScnBzo6+vj9OnTmDNnDp9aYXWzadMmjBo1CmpqanztqjJchQdulj9QkbG5detW2NraMooDSUlJePLkCcaNG4cVK1YAABo1aoSFCxdi0KBBrHawYMECFBYWYvPmzVIr1b18+VLkvsLGS0nGXnHgZmoLmlobGBhg2rRpmDlzJhQUFJjtFhYWCA4OZvo1LuHh4Vi0aBGysrKkupfKpKWlITY2FpcvX8bJkyehq6srUEXsZ413xKMWKwhdXV2cPXsWbdu2FfucycnJ+Pvvv+Hq6oq3b9/C19eXUdQNDQ3Fx48fQURwc3PDsWPHWMphKioqMDMzY6kN8eLu7g59fX1ERERATU0NQIUav5+fH44cOcL6TUXxM8aLX60u/yvZt28ftm/fjqysLNy8eRNmZmZYv349LC0t0bNnTwBsRQVhy+DKyq/iMHz4cLH2CwsL49tWs2ZN7N+/H126dBHrHK9fv8bhw4fx4sULRrGuX79+qFevHpycnHDlyhXUqFEDTZs2FakeISij/t27d+jQoQO8vb2xbt06vHnzBq6urmjSpAkOHTrEl82uo6OD+/fvo379+mLdO5eq+gAuHz58YD5nw4YN+SqFSEt5eTn27t2L48ePIzs7GxwOBxYWFujXrx+GDh0qk0JUaWkpzp8/j0OHDuHUqVPQ0NBA//794ePjgzZt2gg8xtjYGBcuXECTJk1w8OBBLFy4ECkpKQgPD8fOnTtx//59qe7FyMgIFy5cQNOmTdG0aVNMmzYNQ4cORUZGBpo0aYKioiKBxyUkJCA0NBTHjh2DgoIC/vjjD/j7+zMqbVz09fVx+/Ztkb9/VWp7XExNTcX/YFKSkZGBZcuWMap4pqamrO9AUVERiYmJjNKCtEhqb5AnwhRW3r59i7p16+LHjx8yX0Nez+m/GQsLC9y5cwcGBgZV7ltVBYDi4mLW/l+/fsX58+dZY4CHh4dMqt28tGjRAt7e3ggODmbar5GREXx8fODl5YWxY8fKfI3c3FyEhYVh7969+P79O1JTU0UqvIu7hgQq5oO5ubno3Lkzc86YmBjo6elJNK+sLr58+YKioiK5qRwJ4vv37zhx4gT27NmDa9euwdvbGyNGjIC3t7fQSlq8lJeX48WLFwLVeIRVVvn27RuioqJQXFyMzp07/zJbnzhIsnbMzc3F0aNHGXW8hg0bol+/fgJtHbzIe01lbGyMSZMmISgoSOx1iCikVZ769OkTli9fjpCQEDg6OmLVqlVo3769zPfDS3Wv2T9//ix2VZWq1Bi58D4X7u7uCAgIwKBBg1j7HDx4EDt37kRcXByzTd4qaFzatm0LIsLkyZMFVp3q2LGjzNe4dOkSwsLCcOLECZiYmKBfv37o169ftai6/1P4Vb6Br1+/Ijw8HBkZGVi7di1GjhwpdMxfv369zNcLCwvDvXv30KpVK/j4+GD27NlYt24dSktL4ebmhsjISOjr60t17vLycixduhRr165l5uDa2tqYPn065s6dy/RndevWxYkTJxjlupKSEvTv3x+5ubm4cuUKfvz4IZOCrjBevXqFxYsXY+fOnXzvubm5wcTEBJ06dYK/vz+ePn2KBg0aID4+Hn5+fsjOzmbtb2JigjFjxmD27Nli99OyzqkfPHiADh06sPwlXl5e+PjxI8LDw5k1zvPnzzF8+HDo6OjIpBDK/VyV+xQiEmlTqe5+nUtlZccfP37g/v37mD9/PpYtWwZ3d3fW++rq6rhz5w7s7OxY2x8+fIiWLVsyazZJqlNeuHABly5dgoqKCgICAmBpaYnU1FRGXd7T0xNnz55lXe/OnTsoLy9Hy5YtWdtv3boFRUVFODs7y/S9SIq4/mFRNnDudkHtQN6VVj9+/IijR48iMzMTM2bMQM2aNRkV6rp16/LtL6w9Hj9+HIGBgdDQ0GD5QQQhyz0rKCjA1NQUQUFB8Pf356uKIAje9eO/jUGDBiE+Ph5Dhw5F7dq1+fqPylUHhSHItiqMmJgYrFmzhpmDlZWV4fr163BwcICenp7Q41xdXXHixAno6enB1dVV6H68VQF37NgBV1dXWFlZiX1/4vLw4UOx95XWTxoUFAQdHR3MmTMHUVFRGDJkCMzNzZGTk4OpU6di5cqVUp1XWkTZqCMjI5l4BnlXd5K2Spa8qohJ4zcTF0n7F0n79C5duoCIcODAAcY3V1BQgCFDhkBBQQExMTF81ygvL8fFixeRlpYGoKJaXOfOnYXOr4gI79+/BxFVWxUISdpAw4YNMWPGDIwePRpAhWJ/165d8fXr12pZu//mf4PfAbq/+c9ha2uLFStWoGfPnqxFRkhICGOE4KVTp05wcnLC6tWrWfvfuHEDgwcP5luAyxsXFxdMnTqVCTSojCyBGRoaGnj69CnMzc1ZnzUzMxO2trZ8cvTOzs5YtWoV3wL3nw5vSfk//vgDjRs3xsKFC5Gbmwtra2t8+fKFtb+0wQf/FEpLSxEVFcWaCHJLsQFgymaeOnUKXl5erMDbsrIyPHz4ENbW1ozRhuuor0qSX5KFkiiElWWWtoxcdfErAnTj4+PRtWtXmJqassov5ebm4uzZs1I7Q4YPH44XL17g2rVrfO8REdq3b4+GDRvK/JuK61z99OkT+vXrxwTc1alTB3l5eWjdujXOnj0LTU1NZt9z585h06ZN2L59u0zlE4XRqlUr3LlzBw4ODvDx8cGgQYMEGnSk5ePHj5g3bx5yc3MxduxYJkh64cKFUFFRwdy5c6vtWoKwsLBAcnIy9PX1xS4/zktAQABq166NJUuWsLZz+1RuEAxvcomRkREuXbqEJk2aID09Ha1atUJBQYFIY0fle+EaPv5JCHP26ujoMIb1r1+/svpONTU1PH78GA0aNGAdk56eDnt7e5lKe3EdgPHx8SgpKUH79u3h4uICFxcXODg4COw/5T3eRUREYM2aNYyT28rKCoGBgRg6dChrP3kG3r98+RKmpqZijR/csqP5+fnw8vJCSUkJmjRpAqBiDFBTU8PSpUuZZyc7OxtBQUEYNmwYq48ODw/HihUr/ucCYv9JiGso0dfXh7a2NoYNG4ahQ4cKDZ6SNflCEiwsLHDu3Dk0atRI5nMFBwfL7ETIzc1Fu3bt0LdvX5w5cwZOTk44cOCAwKCeESNGoG3btvD39xfr/vbs2YP169ezAl2mTJmCgIAAsY6vDogI3bt3x9mzZ9GkSRM0atQIRIRnz57h0aNH6NGjB06ePFkt1/ry5QtOnDiBgwcP4vLly6hXrx4yMjL49lNXV0daWhpMTEzg6+uLOnXqYOXKlcjJyYGtra3QQNqqkCQJ6M2bN9i7dy/27t2LFy9eoE2bNvD398cff/zBmnfxMnPmTNSsWRNBQUFC70HYfJ3rsAMqxvXS0lKpPqMkTJkyBerq6kzykLa2NhYsWMAExkRFRcHU1BTbt2+X6TqS2hvkAdd54+joiKtXr7KSUsrKynD+/Hns2LFDZlvGp0+fUFZWxjo/UJEIrKSkJJckyf81PDw8MGzYMAwePBgjR47Ew4cPMWnSJOzbtw8fPnzArVu3WPvn5uZWGRwoC9ra2njw4AHq16+PGjVqIDExEY0bN0ZKSgp69uwpdZspKSnB8ePHERoaisTERHTr1g3Dhw+Hl5dXlU4KcdeQmZmZYgXx/q/Dnev4+flh6NChQoP/BD2fSUlJGDx4MF6+fMkXcMENspg2bRp+/PiBkJAQABUBwS1atMDTp0+hoaGB0tJSXLp0iZkL/xeR95qqZs2auHPnTrWdf+LEiYiLi8OdO3eYZEQuX79+RYsWLeDq6spK6F29ejVWrVoFY2NjLF++XKiN+p9GVXZEDoeD4OBgVrCgsP24//LOYTQ0NJCSksLn1E5LS4OjoyPL3qygoIClS5eKTFAA/j9RWlq0tLRw9+5dmROQxOHDhw/Yv38/QkND8fDhw59qL5U3/wTfAG+wkDxYtmwZli1bhrZt2+LevXv4448/cPLkSUyZMgUKCgrYtGkTunXrhm3btkl1/tmzZ2PPnj0IDg5mgkoSExOxaNEijBw5EsuWLQNQ0Wbv37/Peo5KS0vRv39/ZGZmYv/+/XB0dKz29iXKzv/w4UP4+PggJycH06ZNY9bSEydORMH/sXfm8VC1//9/zdjJVmm33qmbIkp72dq0kPaFItrLXUK0KZLkbtGiaLFUSpvSrpIlpE2hRZZId3d7qagI1+8PvznfOWaGWYx0fzwfj/N4cM11rnOdmXOu5b1++IAjR47Q6vPjyFgXX758wfXr19G1a1fo6enRPjt79iztf0IIXr16hV27dkFdXR2XLl2iPpOTk0NaWhqMjY1p59y7dw+DBw/m0IEJQlJSUp2fN4Txf0OQlJSEZcuW4d69e7RyU1NTyMrK4tChQ5ThFsuo6MePH9T9TZkyBcrKyti7dy+VplxNTQ1jx46FhoYGpSs5cOAA5syZg5YtW+LTp09o1aoVtm7dChcXF0yZMgVLlizh+C2BGue45cuXY+LEibTymJgYbNq0iWMvIG741Q/X5/DEoraupqKiAosWLUJERASqqqogKSmJqqoqTJ8+HREREXw5k/GiMQJtNTQhISGU7P7Hjx8YNGgQzM3NYWZmhl69enFds7i6ukJGRkZsxpK8AqgwGAzIysqic+fOGDt2LIcMgB9UVFRw4cKFRnWczM/PR48ePWiOr4IGHmsoCCF49+6dUM5ZdRnFA3UbxgtLeno6ZaxpbW3dIG0KCj8y6ufPn2PBggWIi4ujBYQYMWIEgoOD6/yd8/LykJCQwNU51dvbG0CNjM/f3x+2tra0cfHhw4cwNzfnGgRHnIhTbybo+CLomK6goID09HQOp5zMzEwMHDhQaPk3Cz8/P9jZ2TX4uy3IMyAjI4P8/Hya3E5WVhb5+fno1KlTg/armd8XyV/dgWaaaWyWLVuGRYsW4cePHyCE4Pbt2zh69Cg2btyI/fv3c9S/c+cOQkNDOco7duzIEVGWX758+UIJw+uLAlpbaL5w4UIsW7YML168QK9evTgUpqIYf7Rr1w75+fnQ0tKilaekpHBVdPj5+cHd3R3r16/n6MvKlSuxadMmKCgo1BsVkT0SYmPQuXNnnDlzBuPGjUNcXBxcXV0B1EQS4qakmDhxIhITE39bA11JSUnY2dnBzs6OKnv16hU8PDywa9cuygiFEAJFRUWa8Zi0tDT69euHOXPm0NrcsWNHo0WBUVNTQ1JSEr59+4Z+/fpRhm7sXkn1GUUJA8twmRclJSVCtSsKZmZmePr0KXbv3k1FrB0/fjwWLlzIM/IjP6xevRq9evVC37594ebmRvM03LJlC3JzcxEREcH13Bs3biA0NBTPnj3DiRMn0LFjRxw6dAja2toYNGiQUP1RVlbG1atXkZKSgqysLMqwfOjQoRx1TUxM8OPHD+jo6EBeXp7D07h2pHBBGTJkCMLCwqCvry9SO7xQUVHBrl27OMrrM6xqKNgjtAoTrfXEiRO4e/cuR7m9vT1MTEwoA9127drh48eP0NTUhIaGBtLT09GjRw8UFhZSG+ft27ejW7duQgnjuEXODAoKgra2Nk1ZyI9hv7CGQnUZiJeXlyM4OBiBgYG0tUPnzp1x/PhxrFy5klb/2LFjInvCz58/H2pqanBzc8PChQvrVfwB4p3vtm7dijVr1mDx4sU0Zcz8+fPx/v17ai4GgC1btsDT07PBDO+zsrLQvXt3MJlMfP78GdnZ2Tzrsgs3WM+mgYEB8vLyEBUVRY2906ZNozm7AICvry+2bt1Ki5hkY2MDAwMD7N27t9ENdC9evAgJCQmMGDGCVn7lyhVUVVVh5MiRjdofcbJz507s27cPtra2NCGSiYkJ3N3dqf9fvXqF06dPIywsDIGBgRg1ahScnZ1hZWXVIB7V7LCiztdnSLVu3Tr4+PggLCysTucnfqJ4WVhYUBGVhI3ioa6ujqtXr2Lw4MEYNmwYDh06xPO72bVrFyZNmoQbN27AwMCAYw5mNyjw9vamFFTsazVXV1cUFxfD19dXqP4KSkREBJKTkxEfH8/hGHL9+nXY2tri4MGDHJHNhUFeXh4jRozAp0+f8Pz5c44sHSzU1dVx8+ZNtGzZEpcvX0Z0dDSAGiOH2oYyghAcHEw5AZ06dYqKdnXv3j3aODVy5Ehcu3YNrVu3xsyZM+Hk5MSXEcfGjRsxZswYXL58mevvv3XrVp7RfwkhiI6Oxo4dO/ianxqC+Ph4HDhwgFY2YcIEap+rpaVFMxYfP348IiIioKSkVO++gD1ziKDyBnFgZGQEBoMBBoMBS0tLjs/l5OQo4zpRmDp1KqytrbFw4UJa+fHjx3H27FmOCFH/SyQlJWHz5s3Ue6+vrw8PDw8Oh0p/f398/foVQI1hzMyZM7FgwQLo6upyPK9AzXM6aNAg2NvbY+LEiQ0esVhBQQEVFRUAgPbt26OgoADdunUDAKEVTwsXLkR0dDTU1dXh5OSEo0eP8hVdWFA6d+6MTp06wczMjFJs13aCEycWFhZ87TPi4+PF2o9Pnz7h06dPWL9+Pfz8/Dg+r0txO3/+fJiYmODChQtco1oBNetIf39/6v+oqCgUFxcjLy8PGhoacHJygp+fH9foN00FfveOLB4/fozi4mLq3WBhY2PDtX1xyxAdHBxw7Ngxjj2ksKxevRoxMTHo0qULz8hTtR2Hvby8ICcnh86dOyMyMpJnsAhRM2s1NAkJCXxnVfn06RPXNr59+4bt27djx44dHLJyVnaqwMBAWvn+/fu57glCQkLqlH8wGAyuBrr8zjEA0Lt3byoghbhRVVWFi4sLXFxcGsUhqTFpCroBdjk4vxlJBCEiIgIHDhzAtGnTcPfuXfTt2xfHjx/HhAkTAADdu3fH/PnzhW4/MjIS+/fvp42dhoaG6NixIxYuXEgZ6Oro6CArK4smE5OUlMSJEycwadIkjBkzRug+CIuhoSFXOdLff//N9R12dnbGiRMn6nRkZGfy5MkwNTXF4sWL8f37d5iYmKCoqIjaM7F+AwAc0c4ZDAbU1NRgaWmJLVu20D5TV1fnmjGjqqpKJD0C0HQMcOujbdu2ePr0KUd5WFgYxo0bBw0NDWp8fvHiBXR1dWkOu1u2bMGIESMo49Tp06dT2SmPHj1K1du+fTs2bdoEDw8PnDp1CpMmTcLu3buRnZ1dp0HO48ePuUYbNzY2xuPHj0W4c+HgVz8srKxWWloa+/btg7e3N7Kzsxs00+qyZcvg6OhIBdpiMWrUKEyfPl3k9isqKrga8YmSDWj+/PnUuPr48WMkJSUhMTERgYGBKC8vx8CBA2FhYUGTa1ZWViIsLAzXrl3jah8gqq79/v37yMjIQFVVFbV2yM3NhYSEBP7880/s3r0bbm5uSElJEVhfpqqqKrBhL7+y1dp2FiznhXXr1nE8X927d8ezZ8/4NuI7fPgwxo8fX2/WGnl5eTx//pzK0DB69Gjs37+fipj69u1boSPAN1SWRUHo168fRyatxoYfGbWmpiYuXrwocCT6ffv2YcGCBWjdujXatWtHa5fBYFAGuoWFhRyOLkCNIWbtjEeNkUWsofVm7Ag6vgg6psvIyFDyL3ZKS0shLS3N9Zz4+HjEx8dzHX9ZumcWJ06cwNq1a9G3b1/Y29tj8uTJDSJ7EuQZqKys5JDlS0lJNUgGs2b+OzRH0G3mf5KoqCisW7eOimDUoUMH+Pj4cI3+xJ4alN0z4urVq3BycuKa8r0+WBHZ2rRpU29UodqLNW4e/Ly8owQ1zNi4cSPl6T5s2DBcvHgRz58/h6urK9asWQMXFxeefWG/B0IICCH49OmTQCkgGouTJ09i+vTpqKqqgqWlJa5evQqg5v6Tk5NpnsZAjfB10qRJUFNTq9f4oCnx6NEjJCQkQFpaGpMnT4aKigrev3+PDRs2ICQkBDo6Onj06BFV38fHB+7u7jyjZLFgf34bmk2bNqG0tJSKxkkIwciRI3HlyhUANe9jfHw8pSRkIUgaOX4RJV11Q+Pg4IAhQ4bA3NxcbCmA7969C0dHRzx+/JiW/ltfXx/h4eHo3bs3xzmnTp3CjBkzYGdnh0OHDuHx48fQ0dHBrl27cPHiRY5ogMHBwbC3t+eITFh7Yf/jxw++DVKGDh2K4uJiODs7c03X15AGceIQgAM1ip8DBw5QyhU9PT04OTkJ5Ync2LRr1w4BAQFwdHSklUdERMDT05NK9TV79myoq6tj7dq1CA4OhoeHBwYOHIi7d+9i/PjxOHDgACQkJPD69WuoqalBR0cHd+7c4St1niApRmJjY3m2c/PmTezYsQPV1dVCRa4tLy/HunXrqHRmy5cvh62tLcLDw7Fq1Soq1Y6npyd1zqlTpzBlyhQMHTqUMlpNTU1FfHw8jh8/jnHjxgncDxZnzpxBcnIyEhMT8eTJExgbG1MRdAcNGsRVqCTO+U5bWxs+Pj4cRm+RkZFYt24dTdD07t07TJ48GcnJyXwb3r958wbu7u7Uhp19i1NdXY03b97Q1l280rCxr6METTsqSMSkxsDQ0BABAQEYNWoUrfzy5cvw9PREZmZmo/ZHnAiTUr64uJgaJ8rLy+Hg4AAfHx9ISgrvw1pZWQkfHx/s2LGD8vpu0aIFXFxcsHbtWq7p6r5//45x48YhNTUVWlpaHHVYym32PUNdERNq7x3qUyKoqqpynde+ffsGGRkZmsKx9rt34MABzJ8/H7KysmjVqhWHMJM98rqamhp27NjBsVY7evQoXFxcGi3iwPDhw2FpaclTWerv74+kpCTExcUJfQ1W5NyoqCjEx8dDXV2dMurnFil59+7dWLJkCVq0aAFNTU1kZGSAyWRi586diImJ4ZkuraGwsbGBs7MzxowZI5CTjJ+fH7y9vdG1a1eONVhde7xr167By8sLubm5WLZsGdzc3GgKNHGhqKiIJ0+eUApSV1dXrF69mlprPH/+HH/++Sc1XsyaNQs7duygom7Xtf6rvR8QRN4gDljRL3V0dHD79m1aCm9paWm0adNGpOhELFq2bInU1FSOaFA5OTkYOHAgPnz4IPI1fkcOHz6MWbNmYfz48bT13enTpxERESGSgvj+/fuIiorCsWPH8O7dO1hZWcHe3h7W1ta0TDjCYmtri9GjR2POnDlwd3dHbGwsHB0dERMTA1VVVVy7dk3gNlmpW42Njet8j3gZ8vEbQffly5dUBKqkpCTk5eWhQ4cOMDMzg4WFhdijtbM7m9Xm69evOHLkCMrLy8UeUbK+iHYsuBnWKCgoIDMzs07DZiUlJWRkZFB1pk2bBkVFRSol+IMHDzBq1Cj8+++/QvRe/Aiyd3z27BnGjRuH7Oxs2v6B9Rzz+i3FLUP866+/cPDgQfTo0QOGhoZcHWQERdDIU/XNiyyEkZeJ06mWxfPnz6Guri5QmtHq6mqEhYXBx8cHTCYT69atg4ODA62NixcvYsKECejcuTOVrvz27dvIy8vDqVOnaPsyQfeaLASdYwoKCjB//nzY29uje/fuHM+LoNG3GiPFclOkqegG+M1KJAy1I37JyMhQWfWAmnlWW1ubw1mBX2RlZZGVlcWR+vvp06cwMjKi1uCenp548OAB1/1YZWUlFUWvMSPovnjxAgwGg9pH3L59G0eOHIG+vj7mzp3LUb+qqgpjxozB9+/feToystOuXTvExcWhR48eOHLkCNauXYvMzExERkZi7969PJ0e6yM2Nhb+/v4IDg6GiYkJgBr5v4uLCzw9PTmMfQWhPgdiU1NT6u/GGNdrj00s47yAgABUVlYiJSWF4xxCCK5evUo54uvp6WHo0KEcfa2srER0dDQtiEhth30FBQU8evQIWlpaIIRARkYGCQkJ9UYKbdWqFc6fP8+ReSAtLQ2jR4/m6SwiLvjVDzeUEVplZSV+/PjRIE67ysrKyMjIwB9//EHbPzx//hxdu3alZP2CPo+5ublwdnZGWloarU5DRytl599//8Xu3buxc+dOlJaW0q4hbl17UFAQbty4gfDwcCqY1efPnzF79mwMGjQIc+bMwfTp0/H9+3eB5WaHDx9GbGwsIiMj6zR2FUa2yu13JYRAXV0d0dHRtHfs8uXLWLFiBdfAYwBn0DQ1NTV8//4dNjY2sLe3x4gRI7jKU2qv7WrvY9+8eYP27dtzyGebChs3bkTbtm3h5OREKw8LC8O7d+9oOi1xIYqMWlA0NTWxcOHCeu9LkCxZjZFFTBi9Gb+IOr7UN6bPnDkTGRkZOHDgAPr06QMAuHXrFubMmYNevXpxBAnz8fGBr68vTExMuDoQnz59muMajx49QlRUFKKjo/HPP/9g2LBhsLOzg62tbb1G9rwQ9BkYOXIkTT537tw5WFpa0saapubI2kzj0myg28z/NN++fUNpaWmdwrDZs2fjw4cPOH78OFq2bImsrCxISEjA1tYWpqamCAoKEvi6SUlJGDhwICQlJZGYmFjnhqC20Ly+9CHsHjOCGmYQQuDv74+NGzdSRiQyMjJUlFxu91EXTdmT9vXr13j16hV69OhBCVNv374NJSUlDsW5IMYHTYWzZ89i4sSJ1EJPR0cH+/btw+TJk9GrVy8sXboUVlZWQrUtrBCZH3r27AlPT09MmTIFQI3Hk4ODA65evQo9PT3MnDkT8vLyOH78OO28pmYU1dCYm5vj1q1bqKiogJaWFiwsLGBpaQlLS0u0a9euQa/14MED5OXlgRCCLl26wMjIiGddY2NjuLq6YubMmbSF6f379zFy5EiuqZtqw21hLysriz59+lDK1P79+/OMKigvL4+bN29SaefFgTgF4MnJybC2toaysjIlLL137x5KSkpw7tw5mkBT3FRVVSEiIoKnRyK3DVhAQAB8fHwwZ84c2qYqLCwMa9asoQygqqurUV1dTRm+RUdHUyly5s2bB2lpabRq1QoXL15E3759wWQy8ebNG5pBCS9ETTPz9OlTeHl54dy5c7Czs4Ovr69Q3qeenp4IDQ3F0KFDkZaWhnfv3mHWrFlIT0/HypUrMWnSJK7Cm4yMDGzdupVmoO3m5sbVK1NYPn/+jBs3buDEiRM4evQomEwmVyNkcc53srKyePjwIYfCPy8vDwYGBrT+CGN4P3LkSBQXF2Px4sUcG/a3b99i9uzZYDAYAq2jBE072rVrV4wdO5YjYtLy5csRGxvLNWqHOJGTk8OTJ084Il8UFRWhW7duHB6+vzOipJQvLCyEs7MzkpKS8O7dO5GcIxYsWICYmBj4+vrSosSuW7cOtra2XNOBTp48GQkJCZg4cSLX550VCZeVstrR0REzZszg6QHOcoLhV4nAK+IZN2q/e+3atcNff/0FLy+veg0cVFRUcOfOHa5rtT59+jRaZoJ27drh8uXLPNc3rDWMsJlSpk6divPnz0NeXh6TJ0+GnZ0dX2m+7969ixcvXmDYsGHUmHPhwgWoqKiIlP5PnE5Aqqqq2LZtG4eTDi8yMjLg6emJGzduYPbs2fD29m60jBzA/2VoYK1XanP79m0MHTq03gw3gsCPvOF3hld6uuzsbPTt2/e334MJi56eHubOncthsLl161bs27ePFk3b0tISMTExHOmqv3z5AltbW54KEEIIEhMTceTIEZw6dQrV1dUYP348RwQRQXn27BlKS0thaGiIsrIyuLm5UWv2rVu3CrVGFtSQr7aBxYABA3D8+HGO6GP1GX7l5eVhw4YNiIqKQnV19S9JtV5ZWYng4GBs2LABysrKWL9+PaZOndro/eAXS0tLLF++vE5ZUe35XFtbG2vWrKEUuUVFRdDT0+PqHNUUEGTvaG1tDQkJCezfvx/a2tq4ffs2Pnz4ADc3N2zevJlrtFJA/DLEuhSnAERy7BE08pQ4EKdTLTslJSW4ffs2V9lHbafSmJgYrFy5Eu/evcOKFSvg4uLC0ynin3/+wZ49e2hrr/nz53NEfhM2+IEgcwxQk554+vTpKCoqospESYNcX4pl9mv8inFXXDQF3QCvrETBwcHw8/Or01GEH/gxLBI28h8A9O3bF3379sWOHTto5S4uLrhz5w7S09MB1Mgmy8rKuGY5BGrm1pcvXzZ41Li6DHQHDx6MuXPnYsaMGXj9+jW6du2Kbt26IS8vDy4uLlSUPRaCOjLKyckhNzcX6urqmDlzJjp06ICAgAAUFxdDX1+fZ8pnboEkahs3lZWVobKykpLHsv5WUFAQyYiHVxAhFuzfY2OM67zGpn79+iEsLIyro2xDUt/7w4tp06bh1atXiI2NpWQ5JSUlsLW1RZs2bTh0YOKGX/0wu3yb23NYe445d+4cPnz4QJMdbNiwAevXr0dlZSUsLS1x7NgxkdYd/AbaEvR5ZOnxvby8uBqINYRO6u3bt0hISEBiYiISExORm5sLKSkp9OvXDxYWFkJnyBKGjh074urVqxzRcR89eoThw4fj5cuXyMjIwPDhwwV2tDc2NkZBQQEIIXUGKBBGtlrbVoHJZEJNTQ2dO3fmCMRQV+AxbuuXyspKXL58GUePHkVsbCzk5eUxadIk2NnZYcCAAbR2xTmP1kbQLB/1oaWlhSNHjtDuCajR9U2dOrVRoviKIqMWFCUlJTx48IDnOO3r6wt3d3ccOXIE69atw5YtW+Ds7Iz9+/ejoKCAypLFvr/nFQyldhaxt2/fCt3vxgxYxQthx/SSkhI4ODjg3Llz1PtfWVkJGxsbREREcAT2at++PQIDA4XWw6empuLIkSM4ceIEfvz4IbC8V5hnoCkFfmumCUOaaeZ/jAMHDpBnz57xXb+kpIQMHTqUqKioEAkJCaKurk6kpKSIqakpKS0tFWNPOamoqCA6Ojrk8ePHfNWXlZUlhYWFHOWFhYVEXl6e53nl5eXk0aNH5NatW+Tr1688+2JpaUlyc3Pr7cehQ4dIWVkZX31ubIqLi0lxcXGdddq2bUs2bNhAqqqqGqlXotO7d2+ydOlS8vXrV7Jt2zbCYDBI9+7dye3bt2n1jI2NycePHwkhhBgZGRFjY2OeB4vExERSUVFBIiMjyY8fPziuXV5eTiIjI4Xqt4qKCu35dnR0JDNmzKD+v3nzJunUqRPHeV26dCEeHh4c5R4eHqRLly5C9aWp8ePHD3L9+nXi7e1NTE1NiYyMDGEymaRr165k/vz55Pjx443eJzk5OWqMadGiBSkoKCCEEFJQUEBkZGSEbvfGjRtkw4YNZNiwYURBQYHIyMiQgQMHkpUrV5IrV67Q6hobG5ObN28Kfa362LJlC5GXlyfLly8nsbGxJDY2lnh4eBB5eXmydetWkdvv3r07mTNnDqmsrKTKKisrydy5c0n37t1Fbl8QFi1aRBQUFMjkyZPJkiVLyNKlS2kHL44dO0YGDBhAVFVViaqqKhkwYAA5duyYwNefM2cOkZGRIVpaWoTJZBINDQ2ira3N9WBHVlaWFBUVEULoz2Fubi6RlZXleb2XL1+S2bNnEykpKTJmzBiSnZ0tcJ/Z0dbWJrGxsYQQQrKzswmDwSCzZs0i1dXVXOtXVFSQWbNmCbQmEZT379+TU6dOERcXF2JgYECYTCZp1aoVsbW15VpfnPNdt27dyIYNGzjK169fz/Gsy8nJkQcPHgjUfosWLcj9+/dF6SIHDAaDqKurEy0tLZ4H+/N44cIFIisrS7p3706cnZ2Js7MzMTAwILKysuTChQsN2jd+aNu2LYmPj+cov3r1KlFTU2v0/oiTffv2kY4dO5Lo6GiioKBAjh49Svz8/Ki/a/Pjxw8SFRVFhgwZQuTl5cmkSZPIpUuXRO6HkpISuXjxIkf5hQsXiJKSEtdz5OXlyY0bN+ptu7y8nERHR5Phw4cTOTk5MmHCBHLx4kWeY8yAAQOIqakpuXjxIrl//z558OAB7WgIVFVVSX5+Pl91Fy9eTFxdXTnK3dzcyMKFCxukP/wgJSVF/v33X56fv3z5kkhLSwvd/vTp08mFCxdo87ogVFdX8/xNBSUpKYkoKSkRdXV1Mm7cODJu3DiioaFBlJSUSFJSksjtt23blq+9YH5+Ppk8eTKRkJAg06ZNo+bpxqZ///5c5yEWvr6+pH///lw/s7CwIJ8+feIo//z5M7GwsGioLjY4ERER5Pz589T/Hh4eRFlZmfTv359aO4mCubk5Wbx4MUf5woULyaBBg0Ru/3dFWlqa5OXlcZTn5eVx7JEYDAZ58+YNR903b94QSUlJvq537949YmRkRJhMpnAdbmIwGAzCZDIJg8HgOFjl3O61rKyMxMXFkRUrVpD+/fsTWVlZYmRkRJYuXUrOnDnT6Pdx+PBhoqOjQ9q3b0+Cg4PJz58/G+W6nz9/5utgkZmZSR0xMTFEX1+fhIeHk7t379I+y8zMJIQQ0q9fP7JlyxZCCCEPHz4kTCaTtp9JTEwkmpqajXKvwiDI3rFVq1bUfSspKZGcnBxCCCHx8fHEyMiI5zV+RxkiITXy7w8fPnCUf/jwgfbM/ApycnKIra0tkZCQIDNnzhR5Djt79ixRVFQkDAaDKCsrExUVFepQVVWl6iUmJpK+ffsSeXl5smLFClJSUiLqrVDwGv/rQ5A5hhBC9PT0yPjx40l6ejopLCwkRUVFtENQap9f1/Ffoim811paWlzl7REREURLS0vk9hkMBklISKDGfAUFBXLhwgXq//j4eJHWGomJiURBQYHo6ekRJycn4uTkRPT09EiLFi1IcnIyVU9bW5u8f/9e5PupDWs/xOuwsLDgeX8qKirUHLB9+3YyYMAAQgghcXFxHDJKVv3w8HC++6arq0uOHTtGSktLiZqaGiXHefDgAWnVqhVH/cjISNK9e3ciIyNDZGRkiIGBATl48CAhpOZ54PcQhZKSEtrx7t07cuXKFdK3b19y7dq1es9v6HG99vhTXFxMvn//Xuc5165dIytWrCDOzs5k1qxZtCMpKYmvgwWDwSAbNmwg27dvJ9u3byeysrJkzZo11P+sozb//PMP0dHRIcrKysTc3JyYm5sTFRUV0rVr13p1luKkPv2whIQE0dTUJGvXriV3797lkDfVljuZm5uTXbt2Uf+npqYSJpNJ/Pz8yKlTp8iff/7JVV4kCM7OzsTW1pZUVFSQFi1akGfPnpHnz58TY2NjsmTJkjrPret5lJeXJ0+ePBGpb7xYsGAB0dPTI0wmk0hLS5NBgwaR1atXk/j4eJ7Pr7jXawoKCiQhIYGjPCEhgbRo0YIQUqP7U1RUFLjtdevW1XmwEEa2KgiJiYl1HnVRVlZGDh8+TEaNGkWkpaWJjo4O9VnttR37XoMQQl6/ft0ge/aCggJiaGjIsW9mMpkitS8jI8NVTyWqrrep4uTkRPbs2cPzcyaTSf2ehw8fJp07d6a+644dO5L9+/fzdZ2rV6+SXr16EUVFRbJ27Vry5csXkfotjN6MX/gdX0Qd03Nzc8nZs2fJ2bNnue5rWLRs2ZJvvQM37t+/T9zc3EjHjh3r1BPzoqGegWaaqU2zgW4z/3N07tyZMJlMoq6uTuzt7cm+ffvqnABY3LhxgwQHB5NNmzaRq1evNmh/1q5dy5dykxBCOnTowLeBLr+GGbU3oLyO2rRu3Zqvfrdu3ZooKCiQadOmiaSwbih+/vxJVq9eTZSUlKhFq5KSElm1ahWpqKjgqC+I8UFTQUlJiXquKysriYSEBNfndt26dZTxNL8bJBbsixN23r9/L/RGoPampWvXrrRF8vPnz7kupBraKGrcuHHUgrM+Ad6v4vv37yQ+Pp64u7tTz7Io/Pvvv+TQoUPkwoULpLy8nPZZaWkp8fHx4ThHW1ubeq7Yf7vIyEiip6dHCBHcsaA2P3/+JGlpacTBwYFISkpy3GdcXBwZMGAASUhIIO/fv+epeBQWcQvAZWVlKSEvOzk5OUJtGkShVatWDWpEWFuhW9fB4tKlS2Tnzp2EwWCQ9evXk6CgIK4HO3p6epTSnf053LFjB825gEVJSQlZvnw5kZOTI/3796cpAkRBSkqK/PPPP9T/srKyJCsrq85zlJSUxGag2717dyIhIUFat25Nxo8fT3bs2EH7rrkhzvnu5MmTREJCgowYMYL4+voSX19fMmLECCIpKUliYmJodYUxvNfT0yMZGRn11lNXVyczZswg+/fvr/dehVGavnjxgqxcuZKaI1auXPnLhOpz584lBgYGtPvMy8sjhoaGxNnZ+Zf0SZzwIyi5desWmT9/PlFRUSFGRkZk+/btXAVPwqKmpsZ1vnv8+DFp3bo113O6du1a77tZm+fPnxMfHx+io6NDOnbsSFauXMlh+COMEuHChQvk8uXLHOVxcXFcheNLly6t0+DR1dWVOlxcXIiioiLp1q0btVbr3r07UVJS4mrgJy6YTCZ5+/Ytz88bSmBOCKlXIcjO/v37Sbdu3Yi0tDSRlpYm3bp1I/v27RPp+uJ2AvL39ycuLi511lmwYAGRlpYmI0aMaHAnCkHZu3cvkZeXpxmssjh79iyRl5cne/fu5XouP0aU5ubmxMLCos7D0tKyYW+qHrp06ULJA9LS0oicnBwJDQ0l1tbWDbKPSUlJIbKysmTw4MHUnnHw4MFEVla2wdZXvyN//PEHCQkJ4Sjfs2cP6dy5MyHk/9bJtY1hMjMzSUZGBvH396/TyPHFixdk06ZNpEePHkRCQoIMGjSoTuWSMHz9+rXB91b8IKzhl5SUFGnTpg1xdXUlsbGxlCNyY3Pp0iXSo0cPoqSkRHx9fRvdsZ9dOcvtqG3gXJdBNDej6JiYGCItLU0sLS1J27ZtyZgxY2jXX758OZk0aVKj3rMgCLJ3VFFRofZqOjo65Pr164SQGscTOTk5ntcQ156qPtnUuHHjyPjx44Vu38rKigQHB3OU79mzh4wcOVKUrgtNQzvVstDV1SVLliypM5jEyJEjiZSUFJk3bx559eqVQO2XlZWRJ0+e8JR7EEKXxwoCP3MMO/Ly8nzpPZqpm6agG5CRkeH6W+bm5jaI4YywDjKC8PLlS7Jy5Uoyfvx4Mn78eLJq1Sry8uVLjn4IY7xeH46Ojnwd3FBQUKACVFhbW5OAgABCCG89Bb+OjCyCg4OJpKQkUVFRIT169KAMwXfs2EHMzc1pdcUdSEJUEhMTSc+ePXl+Lq5xnRvcHCxZrFu3jjCZTNKnTx8yduxYYmtrSzvY11N1rZFYaGpq1uncX9vBn53S0lISGhpKFi5cSNzc3EhkZCRXHWVT4tWrVyQgIIB07dqVtG3blri5udWp+1FTU6PJbF1dXcmIESOo/y9cuMB1DhMEYQJt8fM8mpiY8OVULwz9+vUjK1asIFeuXOF7TSDu9dr06dOJtrY2iYmJIS9evCAvXrwgMTExREdHh9jb2xNCCDl69Cjp1auXyNfihTCyVUJq1uiLFy8mQ4YMIUOGDCEuLi5imbvfvXtHdu7cSbp160YbB2rLGxUVFWl6n4aSN44ZM4aMHTuWvHv3jrRo0YI8fvyY3Lhxg/Tp00ckOUznzp3JoUOHOMoPHjzIc/wSJ4LKqAXF39+ftG7dmjg4OJDNmzdzOFRwW4+UlZXxvUa5d+8eGTp0KJGRkSGLFi1qsLWNOANW8Tu+NMaYTkiNbMHX11egc549e0b8/PyIvr4+kZCQIJaWlmT//v1COVqK+gw00wwvGITUk4+mmWb+g7x8+RKJiYlITk5GUlIS8vLy0L59e5ibm+Pw4cON2pdt27bhyJEjyMjIQM+ePWFvb48pU6bwTFvv7++P3Nxc7N+/nyM1Q23mzZuHmzdv4vTp0/jjjz8AAPn5+ZgwYQJ69+6N/fv3A6hJvaCpqQljY+M6U1SdPn2a9r+rqytkZGQQEBBQZz/4TQHRWAiaIsPV1RVqampYuXJlo/dVWIRNqyPoNbiloM/MzISFhYVQaZKMjIywdOlSODo6ori4GFpaWnj48CGVUiUtLQ2TJ0/GP//8w3Euv2nk+GHWrFnYsWMHlUq6rpScjZ2KoKKiAjdv3kRiYiISEhJw69YtdOjQAWZmZkKnNL1z5w6GDx+O6upq/Pz5Ex07dsSZM2fQrVs3ALzTr2zcuBGHDx9GWFgYhg0bhosXL+L58+dYunQpvL294eLiAqAmNc61a9egp6fHd59yc3OptD6JiYkoLy+HqakpzM3NsWTJEqoeKyVN7d+ICJmurzaysrJ4+PAhOnfuTCvPy8uDgYGByGkVBw4cCA8PD9ja2tLKz5w5g4CAACrFW2PQoUMHJCYmokuXLgKdV1JSgpMnT+LZs2dwd3dHy5YtkZGRARMTE6FTH7K/g7wQJsVIYGAgNm3ahHbt2sHf3x9jx44V6F7rQkJCAq9fv6bGREVFRWRlZUFbW5vnOQ4ODjAyMhI5FSE3goODYWZmhu7du/N9jrjnu3v37mHr1q3IyckBUDNOu7m5wdjYmFbvypUr8PHxwYYNG2BgYMCR8opbqsMrV65gy5YtCA0NhZaWFs8+HD58GMnJyUhMTER+fj46duwIMzMzmJmZwdzcnErXCwifdrSp8PnzZ1hZWeHu3btUWuh//vkHgwcP5ppO+79CXSnlmUwmNDQ04ODggF69evFsQ9h0YL6+vsjJyUF4eDiV9ra8vBzOzs7Q1dXlmpbuwoUL2LlzJ0JCQup8drlRWFgIZ2dnJCUl4d27d2jZsiX1We/evbFt2zYMGjSI7/YMDQ0REBCAUaNG0covX74MT09PjlRdf/31Fw4ePIgePXrA0NCQ4129f/8+X9flluZTXDCZTIwcOZJnWuLy8nJcvnxZ6PVDdXU1NmzYgJCQELx58wa5ubnQ0dHBmjVroKWlBWdnZ45zvL29sXXrVri4uND2Jrt27YKrqyt8fX2F6oucnBwePHiArl270sqfPn0KIyMjkdOPjxs3DtevX0erVq3QrVs3jt8/JiYGTCYTsrKy9aYUZaUyFDfTpk3DsWPH8Oeff1Lfy9OnT/H06VNMmDCBI4VoVlYWgJo9yvXr12nvWFVVFS5fvozQ0FAUFRXVOZd//foVR44cQXl5eaOme5aXl0dOTg40NDTg6emJV69e4eDBg3j06BHMzc3x7t07ka/x4MED/P3333jw4AHk5ORgaGiIFStW0ObT/zX27NmDpUuXwsnJiZJ3pKamIiIiAtu3b8e8efOoNLwAuK6V5eTksHPnTjg5OdHKQ0NDceTIEaSmpuLPP/+EnZ0dpk+f3mBpngsLC7F48WIkJibS9jkNtbfil8rKSvj7+8PJyYlaw9SHra0tUlJSIC0tDXNzc+oQdG8jLLdv34anpyfS09Mxf/58rFq1Cq1bt26Ua7NTO70rL8zMzAAAz58/57tt1nMWHx+P8+fPo127dnBxcYG8vDxVx8fHB+bm5lT7TQVh9o6DBw+Gm5sbbG1tMX36dHz69AmrV6/G3r17ce/ePTx8+JDrtcS1pxJ3usyWLVsiNTWVQ26Tk5ODgQMH4sOHD0K1KwyfP3+Gv78/du7cCSMjI2zatAmDBw9usPYVFBSQnZ1dp4yUyWRSaeDrkgmyyz7fvXuHWbNm4dKlS1zrssZQ1vqCHwwNDQEATk5O2L59Ow4fPlzvHMOOtbU1HB0dMWHCBL6vKSgNnWK5KdIUdAPdu3fH9OnTOfrg5+eHY8eOITs7W6T2+Z0PGmrNwYvaOo2mQN++fWFhYYHRo0dj+PDhSE9PR48ePZCeno6JEydy6Ck2btyIV69eYceOHXxf4+7du3jx4gWGDRuGFi1aAKiRFaioqGDgwIFUPW1tbfj4+GDmzJm08yMjI7Fu3Tqe6bW5wU22Jio5OTkwMTFBaWkprVzc4/qmTZugpaWFKVOmAAAmT56MkydPon379rh48SJ69OhBq19f2uxWrVpReqEZM2bwXNOx0nEXFhbWKf/9XRg3bhzXOY/BYEBWVhadO3fG9OnTaTKGlJQUhIeH48SJE9DX14ezszOcnZ0pnQ1Qs7d5+vQpNDQ0AAB9+vTBpEmT4OHhAaBm/NHX10dZWZnI95CSkoKsrCyUlpaiZ8+eGDp0KEed+p5H9jTod+/exerVq+Hv78+3jFpUKioqUFFRQY0F7Ih7vVZaWgpXV1ccPHgQlZWVAABJSUk4ODhg27ZtUFBQwIMHDwDUyEkEhaVHKigogIeHB6VHatu2LTp27AhAONlqXFwcbGxsYGRkRI2ZqampyMzMxLlz5zBs2DCqLq91GOs519DQ4JAZfvv2DadPn0ZUVBTi4+Ohrq6OadOmwc7OjpJ3MZlMKCsrU+9QSUkJlJSUqHeBEIIvX76IvKdu3bo1rl+/DkNDQygrK+P27dvo2rUrrl+/Djc3N75lsbUJDAxEYGAg/v77b1haWgKo2fctX74cbm5uWLFihUj9FhRBZdSCUteYzWAwUFRUxNX+oT4KCgqwcuVKnDp1CpMnT4afn1+D2mUIozfjF37HF0HG9GXLlvF9/a1bt9L+X7JkCQ4ePAhDQ0Oueofa9fv164c7d+7A0NAQdnZ2mDZtGjWuCAMvG5hmmhGVZgPdZv6n+fbtG27cuIGjR48iKioKhBBq0Xnw4EG+2qi9GRaW3NxcREVF4ejRoygsLISFhQXs7e052h83bhzi4+PRokULGBgYQEFBgfZ5TEwM9Te/hhmLFi3C0aNHoampiVmzZsHe3p6mfOSFi4sLDh48CF1dXfTq1YujL7UnR+D/FrJHjhzBtWvX0KlTJxQUFPD1HTUUysrKiI6OxsiRI2nlFy9exLRp0/D582daeX3GB9zu81fDZDIRGRlJCQmmTZuGoKAgtG3bllZPGGGpsbExGAwGMjMz0a1bN5qheFVVFQoLC2FlZcWh4OaHffv2wdXVFVOmTEF6ejpUVFSQmppKfe7n54dbt27h3LlzArf9O8MyJmMZ5GpoaFAGZaampnwrLXkxbNgwqKurY//+/SgrK4OnpyeOHz+Oq1evwtjYmKeBLiEE/v7+2LhxI759+wYAkJGRgYeHB1asWAE5OTkAgjkWADUGvd+/f6eUqmZmZjA0NOQqIKpPASmqYlDcAvBjx45h+fLlcHFxQb9+/QAA6enpCA4ORkBAAG0zxFLMiIstW7bg2bNn2LVrV50KKHaysrIwdOhQKCsro6ioCE+fPoWOjg5Wr16Nx48fY9u2bXy1wy7g//nzJ2VUVJdxKbvxZFRUFNatW0fNJx06dICPjw+HIRSTyYScnByGDh0KCQkJnm2zz6X8Utvo69y5c7C0tKxznvbz88OWLVswZMgQrvPoX3/9JXA/alNRUYHCwkL88ccf9b5/TWW+E8bwXlVVFd++fUNlZSXk5eU5+s7NaeTVq1dISkrC+fPncezYMVRXV9PaFlYx9O3bN64KSnG/w9wghODq1avIzMykDKdMTU0bvR/iJiwsDBYWFvUqRNgVBLwQxQCJtU6XkZGhFECZmZmoqKjAkCFDaHVZY4Ggz255eTlOnTqFsLAw3Lx5E6NHj4aTkxOsrKxEViLIycnhyZMnHIbCRUVF6NatG4eyxMLCgud30ZhGt4IgbuMWX19fREZGwtfXF3PmzMHDhw+ho6ODY8eOISgoCDdv3uQ4R01NDTt27MC0adNo5UePHoWLiwvev38vVF/E7QRU33cZHh4OHx8fvtripmARF9HR0YiOjkZubi4AQFdXF9OmTaMZZbEQ1oiSRWVlJYKDg7FhwwYoKytj/fr1XK8jLtq0aYO4uDgYGxvD2NgYy5Ytw4wZM1BQUIAePXpwKM6baThOnz6NLVu20JxHPTw8KOew58+fgxACHR0d3L59mybwl5aWRps2bbiuVdkVgbUNDRqCgQMHghCCJUuWoG3bthxrscY0umzRogUePnwosPNKVlYWkpKSkJSUhBs3bkBSUhLm5uaIiooST0f/P6x9xty5c+tcjzTE+l5cJCcnY8CAARx7hsrKSqSlpfG1hiwpKcHFixcxffp0cXVTKITZO8bFxaGsrAzjx49HXl4erK2tkZubi1atWiE6OppjbceiqeypBEVBQQHp6ekwMDCglWdnZ6Nv376UzEfciNOplsX48eMxdepUTJ48mWedyMhIvtpycHCg/razs8Pz588RFBQEc3NznD59Gm/evKH2/aNHjwbwf+sLXmo51mfs+xL2Z7i+OYadvXv3ws/PD05OTlz3BKIY0T579gzjxo1DdnY27X5Yc0djOiWJm6bwXp86dQpTpkzB0KFDacZH8fHxOH78OMaNGyf2PghDXl4evL29ERoayrEH/fz5MxYsWEAzXqmt0+BFYxqAJyYmYty4cfjy5QscHByoABkrV65ETk4OhwyRH0dGXtR+j2pTXyCJiooKvmW6oryjtQ3cCCF49eoVAgICUFlZiZSUFOqzxhjXtbW1ERUVhQEDBuDq1auYPHkyjh07huPHj6O4uBhXrlyh1W/VqhVu375NBTWqTUVFBU6fPo2wsDDcuHEDo0aNgrOzM6ysrLh+v6wASBYWFrC0tISFhYVARkFNxdHB0dERZ86cgYqKCuXUnpGRgZKSEgwfPhyZmZkoKipCfHw8zXAcqAnwMm3aNK4O5J07d0ZwcDBGjBiB0tJStGrVCtevX6fayMjIwIgRIxrEgbQ++Hke2eUAwP/Jo9lpKAfG8PBwZGRkoF+/frCzs8OKFSuwdetWVFZWwtLSEtHR0WjVqhVVv7HWa6WlpXj27BkAQEdHh6uxsKDUpUcqLi6mbDKEka0aGxtjxIgRHMHEvLy8cOXKFZpTeO3ftzZSUlKYMmUKQkNDISsri6lTp+L8+fOQl5fH5MmTYWdnRznXsyPM2lEYVFVVkZGRAW1tbfzxxx/Yv38/LCwsUFBQAAMDA6GfAUIIvLy8sGPHDmoskpWVhaenJ7y9vUXqszAIKqNuaGobXPOCXW6/cOFCHDhwABYWFggICBDKiJ2ffgHiCVjF7/giyJhel+6AHW56BEH1DqtWrYKdnR0V8E1UhHkGmmmGH5oNdJv5n+PKlStURMb79+9DT0+PippmamoKVVVVADUDb4sWLSApKVmnwEwcA296ejoWLFiArKwsrlEF66K2Mplfw4zy8nLExMQgLCwMaWlpGD16NJydnTF8+HCek4+wSvn3798jOjoaISEhePLkSaML7Nq0aYOkpCQOL6AnT57A1NSUYyP4Oxof8GuAoqSkxLfQhvWssxTtPj4+cHNzo23OpKWloaWlhQkTJkBaWlqIntcY2Zw7dw7t2rXD2rVradGkFy5ciGHDhvEUOorDKMrS0pJrpMEvX77A1ta2UX5/VsQ/T09PjB8/nsPQWlRatmyJ9PR0WnShgIAABAYGIi4uDhoaGlwNdFlUVFQgPz8fpaWl0NfXR2hoKP7++2+8fv0agGCOBUCN921OTg569uxJGekOGjSIFpmnsRC3ALy+d5WbYqYhGT9+PO1/VnQ4fgXIQ4cORc+ePREYGEiL1J2Wlobp06ejqKhIqH7p6Ojg9OnTdRoecDOerCtyJoB6I2KzEMYwSxijr/o8dVmCMGH4/v07Fi9eTAmHWFEcXVxc0LFjR3h5eXGcI475rj6hF6ttloMUIJzhfX1CMHbh17dv35CSkkI5PrDWg+bm5jSjch8fH3h4ePA99vAbMamZhkdXVxfPnj3jiIhcW2klbvgdB4D/Gwv4fXZv376N8PBwREdHQ0tLi6tTnahKhHbt2uHIkSNUpAQW165dw/Tp0/H27Vu+748X+fn5KCgogKmpKeTk5Lj28Xemc+fOCA0NxZAhQ2jzYk5ODvr3749Pnz5xnKOiooI7d+5wRBzNzc1Fnz59UFJSIlRfmpITUFPg69evdUbmB2rmH/Y5RlgjSgCIioqCt7c3vn//jtWrV2Pu3Ll8Oao1JHZ2dsjJyYGxsTGOHj2K4uJitGrVCmfPnsXKlSt5Rn/kl4yMDEhJSVHC+9jYWISHh0NfXx/r1q0Tej/YDG/EPWa2aNEC9+7d44i8/SsYO3Ysxo8fL7ACkxCC+/fvIyEhAQkJCYiLi6M544sLLS0tvta8oqzvBaWgoADh4eEoKCjA9u3b0aZNG1y6dAkaGhpUthx2eGWP+PDhA9q0acPXWjYzMxM9e/ZscuteYfaO3Pj48SNUVVXr/K1/RxkiUNPv7t27Y+fOnbTyRYsWISsrCzdu3GiUfojTqZbFgQMH4Ovri1mzZjWo0Wr79u0RGxuLPn36QElJCXfv3kWXLl1w9uxZBAYGUgZrwkSuFtZ5tC6Zk6hyJmtra0hISGD//v3Q1tbG7du38eHDB7i5uWHz5s0NGh3zV9NU3uuMjAxs3bqVZpzNLSuRMBQXF/NVjxUtjV/mzp0LFRUVBAYGcv3c09MTX758oTIaisOpdvz48YiIiICSkhKHHLQ2vMaWqqoqfPnyhdIdAjVGQvLy8hzvpaD6O6BmXNq2bRvy8vIA1Mg4li5ditmzZ9Pq1RdIYteuXbT+eXl5wdHRkZapJTIyEhs3bhTJSIyXo0G/fv0QFhZGy6DSGOO6nJwccnNzoa6ujiVLluDHjx8IDQ1Fbm4u+vbty7EP9/T0RIsWLbBmzZp62y4uLkZERAQiIyNRXl4OBwcH+Pj40PZ27FkAb926hYqKCujo6FDGuhYWFlz1OU3N0cHLywtfvnzBrl27qHexuroaS5YsgaKiIjZs2ID58+fj0aNH1JyWlpaGsLAwnDhxAl27doWTkxPmzp1Le5dXrFiBM2fOYOXKlbh48SLS0tLw7Nkz6nnYu3cvDh48SDPsFob4+Hhs27aNNkYuXbqUFkWXn+fx/fv3WL9+PV/XFMWBccOGDdiwYQMGDhyIjIwMTJ48GWfOnMHSpUvBZDKxY8cOjBkzhpbxtbHWa+KQ3fGrRxJGtiorK4vs7GyucjVDQ0NadpjY2Fh4enrCw8MDffr0AVAjc92yZQvWrl2LyspKeHl5YcqUKdi8eTPs7OxgZ2eHESNG1DmGNRbCZvmoi6qqKqSmplLr4idPnkBOTg66uro8M5CJm8aQUdcFk8lEUFBQvQ5D7HNpY2QRE2fAKn7Hl8Ya0381wjwDzTTDD80Gus38z8FkMqGmpgY3NzdKQMCNbt264c2bN7C3t4eTk1OjKC1v376NI0eO4NixY/jy5Qusra0RHR0t9uvW5vnz54iIiKDSWDx69EhkDzl+UkA0FsKkyPivwm4U8uHDB/j5+WHEiBE0oU1cXBzWrFnDkbY1MjISU6ZMgaysbKP2mRviNIriJQR/+/YtOnbsiJ8/fwrdNr94eXlRTgVdu3alDI/MzMwaJG1my5YtkZiYyDHObd68GRs2bEBYWBgmTpxIfY/l5eVYt24drl69SkXMtbW1RXh4OFavXg0JCQksWrQInp6eAIQTTJaUlCA5OZmKfvT48WMYGRnBwsICU6ZMQffu3cFkMutNDdgQY/e9e/c4hDsNJQAXRjHTkAgj8GBHWVkZGRkZ+OOPP2iClefPn6Nr16404UdBQQGCgoKo71FfXx9LlizhGq3gwIEDiImJwaFDh3hGdG9OMVI3S5YsQWpqKoKCgmBlZYWsrCzo6OggNjYW69atEzrdkaDExsby/OzmzZvYsWMHqqurac+KOBkwYADNIJcViZxdycKNkpIS3L59G2/fvkV1dTXtM1a2A34jJjUmZWVlSEpK4uq80pQjuAnDy5cvkZiYSM0deXl5aN++PczNzXH48OFf3T2RYTnrODg4UJFMavPw4UOOKCa84CawmzdvHm7evInTp09TY3N+fj4mTJiA3r17Y//+/TzbY6X15BXV/8OHD5g8eTISEhLAYDCQl5cHHR0dODk5QVVVFVu2bOGr300dOTk55OTkQFNTkzYvPn78GH369OEasdTFxQVSUlIckbfc3d3x/ft3BAcHC9WXX+0E1NQwNzdHXFwcT+VCUlISxowZg69fv4p0ncuXL8PLywuFhYVwd3fHsmXLOJzTGouSkhKsXr0aL168wIIFC2BlZQWgJmKxtLQ0Vq1aJVL7vXv3hpeXFyZMmIBnz55BX18f48ePx507dzB69GgEBQU1wF383pSWlnKsG9ijx0VGRqJ169bUGmH58uXYu3cv9PX1qSxHWVlZjbb3sbCwwKpVq7imgm1sQkJC4OPjAzs7O66ZJmob0G3duhWJiYlISUnB169f0aNHD5iamsLc3ByDBw+ud633XyMpKQkjR47EwIEDkZycjCdPnkBHRwcBAQG4e/cuTp48yXEOr/1Vbm4uTExMaJH6edGUDXT53TvWZ7wF1KT6bdeuHYYNGwZra+uG6OIvJzU1FUOHDkXv3r2p6GTx8fG4c+cOrly50mjGluJ0qmUhLqNVJSUlZGVlQUtLC5qamjhy5AgGDhyIwsJCdOvWTaSodkwmE3l5efU+w+JItc0LcaVYboaTnz9/Yt68eVizZk29WWOEhd3oiFsUV2H3DV27dsXhw4fRu3dvrp/fu3cP06dPx9OnTwEIb4xeF7NmzcKOHTugqKhY7xgjytgiLN7e3ti6dStcXFxoOpldu3bB1dUVvr6+VF1BAkkMGTIEs2fP5sjUcuTIEezduxeJiYlC97m2PJulc+WmJ2qMcb1Dhw44efIkBgwYgK5du8LPzw+TJk3C06dP0bt3b441jKBpswGgsLAQzs7OXCPEsvPjxw+kpaVRBru3b9/Gz58/8eeff+LRo0e0uk3N0UFNTQ2pqam0IC5AzVpwwIABeP/+PbKzszFw4ECsWrUK4eHh+PTpE+zs7ODk5MQzE973798xb948KijP3r17afdmYWEBKysrSpckDLt378aSJUswceJE6j1KT0/HyZMnsW3bNixatAhA4zyP/KKrqwtfX19MmzYNd+/eRd++fXH8+HFMmDABAHDp0iXMnz+f9r6Je70mTtmdIHokQVFXV8fWrVsxadIkWvnx48fh7u5Oc0Lp06cP1q9fjxEjRtDqsvTgt2/fxpkzZ+Dm5saR/ffHjx+/XB/OnuUjPz8fY8aMobJ8HDt2jMOglV9kZWXx5MkTsa0zBEUUGTUvli1bhvXr10NBQQHLli2rs25QUJDA65GmmEVMEPgdXxpjTOcXQX5TQTNeiGNN2kwzQLOBbjP/gwQFBSE5ORnJycmQkZGhDN3Mzc05Nh63bt1CWFgYjh07hs6dO8PZ2Rl2dnYNKuzKzc1FVFQUjh49isLCQlhaWsLOzg7jx49vkLQRwhhmvHjxAuHh4YiIiEBFRQVycnJE6gu/KSDESW0B+7Vr13imyBDFW/d3ZsKECbCwsMDixYtp5bt27cK1a9dw5swZrudVVFRwNVgS1KOeG2/fvuXadm3FoziMoljKTyMjIyqqKIuqqipcvnwZoaGhQkcIFYbS0lLcuHGDFgW8S5cuMDMzg4WFBSZOnChUu6amppg+fTrmz5/P8VlgYCC8vb3x8+dPSgjr6emJ0NBQDB06FGlpaZSBdHp6OlauXIlJkyY1mDfphw8fkJiYiNjYWBw9ehTV1dUghFAL47pSA/6vGJz8SthTJ7MLVq5evQonJye8ePECQI3gwMbGBkZGRjQBcmZmJs6dO4dhw4bR2jU2NkZ+fj5+/vwJTU1NDoV8RkZGc4qRetDU1MSxY8fQr18/2m+Tn5+Pnj178qVgFxdPnz6Fl5cXzp07Bzs7O/j6+uLz588iG5/wEyWsZcuWYDKZGD58OM/1X21Y/SwtLeWIPM+eTYHfiEmNxf379zFq1Ch8+/YNZWVlaNmyJd6/f09FeGnMCG6Nybdv33Djxg0cPXoUUVFRtKh5O3bs4HqOsrIyunTp0mjr0y9fvlD7ifreRVY9cUQSqs3nz59hZWWFu3fvUoa2//zzDwYPHsw1m0B1dTW11mIZnioqKsLNzQ2rVq2i9XnmzJl4+/Yt9u/fDz09PWpMiouLw7JlyzgUVb8rvXr1gqurK+zt7Wljr6+vL65evUpFG2AXGlZWViIiIgIaGhpUpNtbt26huLgYM2fO5IhcwC/icALq2bMn4uPjoaqqCmNj4zrnYEEirIgSPYJfDAwMqAj9td+n5ORkjBo1CrNmzaK+77Nnz/Ldto2NDW7fvg1PT0+kp6dj/vz5WLVqVYM40jVl2BVsmzZtwvXr1xEXF4fU1FRMnTqVWgf+r1FYWIjFixcjMTGRpmTkZtjStWtX7NmzB5aWlrh58yaGDBmCoKAgnD9/HpKSkoiJiaEpBbjtfRrS2L6goADz58+Hvb09unfvzmGs0JjRtgU1oOvduzcl3xs8eHC9UU7+6/Tv3x+TJk3CsmXLaPPR7du3MX78eMqxBvg/eVlsbCysrKxojgxVVVXIyspC165dcfny5Xqv25QNdPndO/LjxFpdXY23b98iKSkJ7u7uNOOp35kHDx7g77//xoMHD6gsbCtWrOCIRvZfp2XLlsjNzUXr1q3rjZjMLm/o3bs3FfjAxsYGKioq2LhxI3bs2IGTJ09yGHmwU1968/oy0/wKpytxpVhuhjvKysp48OCB2AxnJCUl0alTJzg6OsLa2ppn5oe6Ml1xg92BkRvPnz+Hnp4e9bzwiubOoqqqCm/evEGHDh0E6oeonDx5EsePH+f6noq6l1FTU8OOHTs4DGmPHj0KFxcXvH//nlbObyAJeXl5ZGZmco0oaWRk9J96RxcvXozz589DV1cX9+/fR1FREVq0aIHo6GgEBgZy/Eb8RsUuLy/HqVOnEBYWhps3b2L06NFwcnKiHB/roqKiAqmpqbh06RJCQ0NRWlrKMUY3NUcHVVVVREZGcjjCnT17Fg4ODvj06RPy8vLQpUsXaGpqwsHBATY2Nhx7BhaNuXfo1KkTvLy8OHSbwcHB8Pf3x8uXL4VqNzw8HC1atOAw/Dxx4gS+ffsmUuREGRkZ5OfnQ11dnfqfte4GagIRaGtrc4w54lyviVN2x68eSRh8fX2xbds2eHl5YcCAAQBqdE+bNm3CsmXLaNGy5eTkcP/+fY7AYazsQ9+/f0dRURH09fXx7ds3VFdXY8OGDQgJCcGbN2+oLIVr1qyBlpYWnJ2dAdRkSuQno0pd60Fh4SfLR32YmJhg06ZNlGHmr0ZQGTU/WFhY4PTp01BRUalzHgBq5IR1rUcak8Z02hbn+FJWVoaAgADEx8dztft49uyZwFkP+P1Nhcl4Ud+atJlmhKVx8+s100wTYOnSpVi6dCkAIDs7G0lJSbh8+TIWL16MNm3a0ITUffv2Rd++fREUFIQTJ04gPDwc7u7usLW1RVhYWIOE9v/zzz/Ru3dvLFq0CFOnTuUrdT2/AoH6DDPYDXTLy8sRExODsLAwpKSkYMyYMdi1axesrKzqVIzcvXuXZ19Yhq4SEhI4fvz4L00BUVs5w/JCZMHaBLFoiNRHTYW8vDwkJCRwXfB4e3tTf8fFxWHTpk0c51tZWXFNhZ6XlwcnJyekpaXRyhtCKHzv3j04ODjgyZMnNK99Xm1fv34dsbGxMDExAZPJhKamJoYNGwYlJSVs3LhRKANdIyMjMBgMMBgMrl6HcnJyQhtMCEuLFi0wcuRIjBw5EkDNxmvr1q3YuXMnQkJChP7OZ86ciaSkJK4GusuXLwchBCEhIVTZiRMncPDgQdjY2ODhw4cwNDREZWUlMjMzRdoEOjk5Yfv27bh69SplhPz48WO0bNkSgwYNwpYtW2BmZgYVFRUqakhhYaHQ1xOER48e0b5fCQkJrmlBBeXgwYN1fs6KzNkYFBYWorKykmOzlZeXBykpKWhpaXGcY2NjA19fXxw/fhxAzXtaXFwMT09P2jjr5eUFV1dXBAQE0M738vKCp6cnh4Gura0tX3328fH5rZXvvLw6GQwGZGVl0blzZ4wdO5ZnVIa6ePfuHdfNY1lZGe09bcz57t9//8XatWsRGRmJESNG4MGDB1R0BXbjE9b4K4jhfe0oYRs2bECbNm2QmZmJAwcOUFHCPnz4gOzsbCQmJiIuLg6rVq2CtLQ05egwZ84cjrbd3Nzg5OQEf39/yMvL87y/srIy6jtXVVXFu3fv0KVLFxgYGDSK8VltXF1dYW1tjZCQECgrKyM9PR1SUlKwt7fHkiVLGr0/4uTKlSs05xU9PT2YmZnh5MmTMDU1pept27aN6/klJSX4/PkzBgwYgLNnzwr1zgE1z5e3tzfPdRdLga+qqkoJeFRUVLjOnbXXPLXbqg9hlAjKyspIS0vD1atXkZmZSQni2L9DdlatWoUDBw4gICCAcr5ISUnBunXr8OPHD2zYsIGqe+XKFcTFxXFE2NXV1RXIkLSp4+3tDQcHB7x8+RLV1dWIiYnB06dPcfDgQZw/f56qV1vRxoqKzBLUt27dGq1btxZJ+SGOyPtjx46l9sBjx44VOcVhYxIXF4fBgwfD0dGRtv66ceMGxowZAwcHB9rant+1COs97devH+Tk5DB//nxoa2vjyJEjXOs3dvTyT58+4cCBAzQFvpOTk9DjHDuEEGpsunbtGsaMGQOgZm9d25Dgfwl7e3sQQhAWFoa2bdvW+Z68ePECnTt3BgCcOXMGEydOxNy5czFw4ECYm5sDqFmjN9be5927dygoKKAZKP6qaNuCznt37twRU08EJz4+nqfSKSwsrFH6kJ2dzXUcatOmDcf7ydpPEUKgqKgIOTk56jNpaWn069eP6xr5d4PfvaMgEdPOnz+PhQsX4uHDh/8JGaKRkRGioqJ+dTd+Odu2bYOioiL1N7/rnSVLluDVq1cAaiJjWVlZISoqCtLS0oiIiOB6jiDpzU+ePCnw/F2f8Ti7XFhQunfvjszMTGhra6Nv374IDAyEtLQ09u7dCx0dHaHbbSo0Nd2Ara0tzpw5w5HhrqH4559/EBkZifDwcISEhMDe3h7Ozs7Q09MTqV1lZWUUFBTw3Jvk5+fTguHUF0/q4cOHIjmCWFpacjXs+fLlC2xtbbkab+zYsQOrVq2Co6MjYmNjMWvWLBQUFODOnTtUVM7aCGLQ+/PnT5iYmHC00atXL8rZuHY5P1mC1NXVsW/fPgQGBtLK9+/fz6ELE4b4+HgOQ+GlS5dyzcTw8+dPyMnJ0eSADcm2bdugpaWFFy9eIDAwkAo29OrVKyxcuJCjfkJCQp3t3b59G+Hh4YiOjoaWlhZmzZqF48eP1zkGV1RUID09HQkJCUhMTMStW7egrq4OU1NT7Nq1i2sWo6qqKmq+ad26Nf7991907doVmpqaVFTpxmTGjBlwdnbGypUrqajXd+7cgb+/P6WjYKVXLy4uxvr16+Hn5weA893ltncICwuDhYWFWBwNSkpKuBpODx8+XKQojhs3bkRoaChHeZs2bTB37lyRDHR//vxJszGQlpamGTtLSkpyHevEuV4Tp+yOXz0Sv7JVdtasWQNFRUVs2bIFK1asAFATWXvdunUcMpg///wTAQEB2Lt3L6SlpQHU/BYBAQGU0e7Lly8pWw0/Pz9ERkYiMDCQtifq3r07goKCKANdlt0JN4qKihAaGory8nK+vitBaQj5jp+fH9zd3bF+/XquGWwaM0sDILiMmh/Yx/765gF+AmU0FkZGRiLpzQS9lrjGl9mzZyMpKQkzZsxA+/btue6x2J1q+dm7C/KbCkpzjNNmxEVzBN1m/ichhOD+/ftITExEQkIClQLPwMCgTq/E5ORkrF27FsnJyXj//n2DpMjLy8sTyPOEXSCwd+9eDoEAuyKcFRWOZZiRmZlJM8xgCZcWLlyI6OhoqKurw8nJCXZ2dnxF+4mOjsbMmTMxYsQIXLlyBcOHD0dubi7evHmDcePGcRVqN4UUEPzQ1FMf8cu+ffuwYMECtG7dGu3ateOI+scuENLU1MRff/0FNzc3WhtbtmzBjh07ODZgAwcOhKSkJLy8vLgupgT1qK997h9//AFPT0+uSs3aQj1xpJF7/vw5CCFUlBn2NHLS0tJo06ZNoxucV1dX486dO5QRUmpqKkpLS6GhoQELC4tGexalpaVRWFiIjh07AqgxVr59+zYMDAy41q/Pe5QVxZHlkda9e3cqHamZmRnPdoGacXnAgAEckR0qKyuRlpYm9Ibtxo0bWLZsGaXoVVRUxLdv32jKkri4OJHTv9aeR37+/Ilv375BWloa8vLyjRr91czMDE5OThyCpcOHD2P//v1cU599/vwZEydOxN27d/H161d06NABr1+/Rv/+/XHx4kVqIy8rK4vs7GyuURsMDQ2FSmH0X0gxYmFhgYyMDFRVVVHe8bm5uZCQkMCff/6Jp0+fgsFgICUlBfr6+gK1bWpqikmTJsHFxQWKiorIysqCtrY2XFxckJeXR0XAaoz57vPnz/D398fOnTthZGSETZs2caTcev78OTQ0NMBgMOoV+HFT7AgSJYwFIQT37t3Drl27EBUVherqaq5CDAUFBWRnZ9erZBQlYpI4UFFRwa1bt9C1a1eoqKjg5s2b0NPTw61bt+Dg4ICcnJxG7Y84YaVTdHNzw9y5c4Xyon/27Bns7e1hZGSE3bt3C9WPUaNGIT8/H87OzlzXL6zxNSkpiVpHsZQbvOCmxOGHLl26IDQ0lMN7PCkpCXPnzm0QZU+HDh0QEhLCEV0lNjYWCxcupEUoUVRUREZGBnR1dWnv6N27dzFixAh8+PBB5P40FW7cuAFfX19kZmaitLQUPXv2hLe3N4YPH96o/WhKTkBNhYKCAgwePBiTJk3C9u3bkZKSgpEjR8LOzo7mjCYMWlpafEVLaczo5cnJybC2toaysjKl9L937x5KSkpw7tw5kRQbQI2Bg7q6OoYOHQpnZ2c8fvwYnTt3RlJSEhwcHBo100hTokWLFrh37x61tqsL9ihCxsbGWLZsGWbMmIGCggL06NGDik7OD4JEreaFvr4+9PT0sHz5cr724U2NGzduIDQ0FAUFBTh58iQ6duyIQ4cOQVtbG4MGDWqUPvj4+MDX1xcmJiZc5SSnT59ulH506tQJx48fx4ABA2jz7unTp+Hu7s51Xerj4wN3d3cOZSw7vLIRsHj58iU2b97cJCPoimPvWFJSAicnJygrK/+WMkRhsjo0JvwEhRCE+p5fFg3lTPPt2zfk5ORAQ0ODp5yd3/Tmwj7DtSNq/vz5E4WFhZCUlMQff/whkiOpuFIsNxWamm6AlblkyJAhXA1nGtIJLCUlBeHh4Thx4gT09fXh7OwMZ2dnoYxVJk+ejJ8/f/Kc/8aOHQtpaWmcOHECAP1754aokdp5vUtv375Fx44d8fPnT45z/vzzT6xduxbTpk2jzane3t74+PEjdu3aRasviP4OAFxcXCAlJcWRetnd3R3fv39HcHAwKisrUVVVRTPme/PmDUJCQlBWVgYbGxuOtc7FixcxYcIEdO7cGX379gVQY3ial5eHU6dOYdSoUYJ/gf+f3bt3Y8mSJZg4cSKViSg9PR0nT57Etm3buBous9YhouiMGpr8/HwUFBTA1NQUcnJy1HqayWRCQ0MDDg4OlDMtN1iyEEtLS9y6dQva2towMzPD4MGDYWZmhvbt29d5/cGDB8PNzQ22traYPn06Pn36hNWrV2Pv3r24d+8eHj582KD3Wx9VVVUICAjArl278ObNGwBA27Zt4eLiAk9PT0hISKC4uBj//vtvvfcGcO4ddHV18ezZM3Ts2BFmZmZU9guWw6IoTJ8+HcbGxvDw8KCVb968GXfv3kV0dDSt/MePH9i5cydPI1DW/CgrK4ucnByOwCVFRUXQ09PD9+/fhe4zk8mkZe4cMGAAjh8/ThnHvn//HsOGDaONdxkZGZCSkqL0ZbGxsQgPD4e+vj7WrVtHGZwKizhld/zqkfiVrfLi69ev1L1wIy0tDTY2NmAymVS00ezsbFRVVeH8+fPo168fDh06hNevX8PDwwOdO3dGaGgohgwZQvtOcnJy0L9/f3z69IlnXz5+/Ij169djz5496Nu3LzZt2kRlzhIWfqKQCgP7HM/+nf8KZ11x4uTkVG8dBoOBAwcOCNSuIBnHBF1/i6o34xd+xhdR7lNFRQUXLlyggnw0NO/evaPZcbCTnZ1dp51BM800Js0Gus38z2FtbY3U1FR8+fIFPXr0oAzATE1NuSrzX758SXkPl5WVwd7eHk5OThzpD0ShpKSEMtzw8PBAy5YtkZGRgbZt21IGcCwEEQjwa5jB2nDWN5nWFn4aGhpi3rx5WLRoEdUXbW1tzJs3D+3bt4ePjw8A8J0C4lfy5csXREVF4cCBA7h79+6v7k6DoampiYULF/LlJRoREYHZs2dj5MiRlNDm1q1buHz5Mvbt2wdHR0dafQUFBdy7d69B3wUWioqKuH//Pt+b86ZmFNXQBAYGUga5X79+RceOHWFubg4LCwuxeR3XhYSEBF6/fk0tdtmN/7ixfft22v8/f/7E/fv3cfnyZXh4eFARmoVROPBKM/Hhwwe0adNG6I3jtGnT0L9/f0rAraioiAsXLkBTUxOEEMpo/dSpU0K1Xxd5eXlYsGABPDw8MGLEiAZvnxdKSkrIyMjgeO/y8/NhYmKCkpISnuempKQgKyuLMkSqbbisrq6OrVu3ckRyPH78ONzd3VFcXMzRZn1z438hxUhQUBBu3LiB8PBwSuH5+fNnzJ49G4MGDcKcOXMwffp0fP/+HXFxcQK1zTI6sre3R0REBObNm4fHjx8jLS0NSUlJdQqYG5LAwEBs2rQJ7dq1g7+/P8aOHVvvOcIY3rdo0QLZ2dnQ1tamrY+Kiorw559/YuXKlXB3d0dOTg7l5MDuoMVaD3Lr3/jx4zF16lRMnjy5zn4fPnwYlZWVcHR0xL1792BlZYWPHz9SEZOmTJlS7703JGpqakhLS4Ouri66dOmCnTt3YsSIEcjJyUGvXr1QVlbWqP0RJ0FBQUhOTkZycjJkZGQoIT/LWY1fkpOT4eTkhPz8fKH6oaioiJSUFIEUTsXFxVBXV+dYgxNC8OLFC2hoaADgbVCgrKyMLl26UEoxFsIqEcrKypCUlMTVEKK20ldWVhZZWVkc3/HTp09hZGREu8aoUaPQq1cvrF+/nlo3aGpqYurUqaiurqaiXP/OVFZWwt/fH05OThzRRnghzkhC4nYC0tHRwZ07d9CqVStaeUlJCXr27NmohqiCkJWVBXNzc9jY2OD06dOYMmUK9u7d+6u7JRYMDAzQv39/7Nmzh3IsrKqqwsKFC5GWlobs7GyR2s/KyoKdnR2Ki4uxbNkyrF27FkCNkcGHDx94RhH+r2NhYYFVq1bx5chnZ2dHpdI8evQoiouL0apVK5w9exYrV67kUMo7OjoiODiYwyinqKgIM2bMwI0bN0Tqu4KCAjIzMxtESd4QJCUlYfPmzVRkNn19fXh4eHA4egHAqVOnMGPGDNjZ2eHQoUN4/PgxdHR0sGvXLly8eBEXL15slD63b98egYGBmDFjRqNcjxfu7u64desWTpw4gS5duiAjIwNv3rzBzJkzMXPmTOp9FRR+ZQ+NlemGX/4Le0dxwP69MJlMvrI6NBbCBIWoD36e39rONOKSObHgN715QxqZf/nyBY6Ojhg3blyDj1UNkWK5Ge7U9fyKywnszZs3mDZtGpKSkvDu3TuhIvTdv38f/fv3x5gxY7B8+XLKgSknJweBgYG4cOEC0tLS0LNnT77aE9ZAl5US2sjIiGYQB9Ssjy9fvozQ0FCuDmby8vJ48uQJNDU10aZNG1y9ehU9evRAXl4e+vXrx2Gwxo/+jj2jVmVlJSIiIqChoUEZbt26dQvFxcWYOXMmdu7ciVmzZkFaWpqK5Pn161d069YNP378QPv27fH48WPExsZyGN2+ePECe/bsofSAenp6mD9/vsgRdDt16gQvLy8sXryYVh4cHAx/f3+asy6LAwcOICYmBocOHWqQaI+14TeLJFAzhk+ePBkJCQlgMBjIy8uDjo4OnJycoKqqyjP7Ejvsc6OUlBTat28PW1tbSr5Ye5/Mjabs6MBy3GloB52XL18iMTERycnJSEpKQl5eHtq3bw9zc3O+IkPzws/PD5s3b8bAgQNpRuOpqalwc3Oj3cdff/0FOzs7XLlyBRMnTuRqBMpaK2toaGDXrl1cHdMXLVrENSAEv7DWXrwiYXJbg/Xu3RteXl6YMGECnj17Bn19fYwfPx537tzB6NGjERQUJHR/gLpld1VVVQ2iC0tNTaU5tNfeNwsjWxU0M+TXr18RFRWF3NxcAEDXrl0xffp0rka9cnJyyMnJgaamJm1Mf/z4Mfr06cPVqfb79+/YunUrNm/eDE1NTfj7+4vkFMEOa17mFYVU2Kx54gogIQqCyKj5hZWB19jYuM4IqYI61vr4+MDDwwPy8vKUbQwvhN2LA+ILWAXwN76Icp/a2tq4ePEi39kZjh49imnTpnH9zMPDA3///TetrF27djhw4ABHRuXNmzdjzZo1IjlUNNNMg0KaaeZ/DHd3d3Lu3DlSUlJSZ71jx44RKysrIicnR2xtbUlsbCyprKxs8P5kZmaS1q1bk86dOxNJSUlSUFBACCFk1apVZMaMGRz15eTkSFFRESGEEDU1NfLgwQNCCCG5ubmkZcuWtLqtW7cmubm5hBBCdHV1yeXLlwkhhDx58oTIy8tT9RwcHIijo2O9R23k5eVJYWEhIYSQli1bkqysLEIIIY8fPybt2rWj6vn4+BAdHR1y+PBhIicnR91jdHQ06devn+BfWgNy/fp1Ym9vT+Tl5Un79u3JwoULOepYWFiQT58+cZR//vyZWFhYNEIvhUdRUZH6vvkhPT2dTJ8+nRgbGxNjY2Myffp0kp6ezrWuiYkJuXHjRkN1lcbYsWPJyZMn+a5/6NAhEh4eTggh5O7du6R169aEyWQSWVlZEh0dLfD1Y2Nj+T4ag/bt25Np06aRvXv3kry8PLFc48KFC8TZ2Zl4eHiQJ0+e0D77+PEj7VlnMBhk1KhRZNy4cWTcuHFEUlKSDB8+nPqfddTHrl27aGMLg8Eg+fn55PPnz3Ue7DAYDPL27VuOtp8+fUoUFRUF/RooOnfuTLKzs6n/W7RoQXuXMjIySPv27YVuvz7u3LlDunbtKrb2uaGkpEQyMjI4yu/evUtatGhR7/nfv38n1dXVXD/z8fEhKioqJCAggCQnJ5Pk5GSyceNGoqKiQnx9fTnqZ2ZmEjU1tTrnRgaDQd68eSPILTY5OnToQB49esRR/vDhQ9KhQwdCCCH37t0jrVq1Eqr9/Px8Mnv2bNK7d2+ip6dH7OzsqLmaG+KY7xgMBpGXlyc2NjYcYwSv8YLJZHL9bd+/f0+YTCbX63Ts2JGkpqYSQujva0xMDNHR0aHalJCQICYmJsTNzY2cPXuW53qQfZzfv38/0dDQIGvXriUnT57kex4oKysj9+7dI+/evePru2pohg0bRqKiogghhMyePZv06dOHHD58mIwYMYL06dPnl/SpMcjKyiI7d+4k48aNI1JSUqRjx458n1tYWEgUFBSEvraJiQm5efOmQOfw+7xraWlxPVRUVAiDwSADBw4kHz58oOqrq6tzfT7PnDnD8zvJyMgg7dq1I0pKSkRCQoKoqakRBoNBFBQUiLa2Nkf9Pn36EBcXF47yxYsXk759+9LKsrOzSZs2bYiVlRWRlpYmEydOJHp6eqRt27YkPz+f9xf0m6GgoEDtj/hFW1ub2tOJm9zcXDJkyBBqXygKvObh169fEykpKaKqqkqNfyoqKkRVVZXn0RiwryUvXrxIZGRkyJQpU0hJSQnPdSYLHx+fOo+miqysLMnJyeEoz8nJIbKysmK77vfv30lFRYXY2m/q5Ofnk6FDh5KIiAhy9+5dkpmZSTvY+fTpE1m0aBGxsbEhly5dosq9vb2Jn58fR9tGRkZER0eHpKWlUWURERFESUmJ2Nraitz3MWPGCLQPFyeHDh0ikpKSZPLkyWT79u1k+/btZPLkyURKSopa37BjZGREIiMjCSH0tWBGRgZp27Zto/W7ZcuWTWJeKy8vJ7NnzyaSkpKEwWAQKSkpwmQyib29fZ2yzRMnTpBJkyaRvn37UnIh1vE705h7x99JhpiYmEh+/vxJCCEkISGBJCYm8jwaGwMDA7Jr1y5CyP+909XV1WTOnDnE29u70frB69l5+fIlx1xaWVlJ9u/fT6ZNm0aGDBlCLCwsaAc3VFRUyLNnzwghhOjo6JDr168TQmrmEjk5OaqelpYWef/+fUPdFsnKyiKampoN1t5/nd/pvW4IUlNTibOzM1FSUiK9e/cme/bsIVVVVUK3d+7cOaKmpkaYTCbtUFNTE1i2/uDBA55yobpgMBjUdRkMBschLy9PDhw4wPVcbW1tSl7aq1cvEhISQgghJC4ujutehh/9nbm5OV8H6/nS1dUlcXFx1DV27dpFOnToQMm0li9fTszNzQX+XoRFQUGBq44iNzeXp0zFyMiItGjRgsjIyJAuXbo06Dpj7969REJCgrRt25b06NGDGBkZUQe3tmfMmEFGjBhBXrx4QVs3Xr58mejr6wt8/dLSUnLp0iXi6elJ+vTpQ6SlpUn37t3JokWLyIkTJ7jqLnjx4cMHnvL1psKmTZvIt2/fqP9TUlLIjx8/qP+/fPlCFixYUGcbZWVl5PLly8TBwYFISkoSCQkJkfrES2ZW+2DJtpSUlEhKSkq97S5fvpxoamqS69evk8rKSlJZWUni4+OJpqYmcXNzE6nPRUVFfB3sKCkpUXuNgIAAMnz4cEJIzW/QqVMnkfpDiHhld5GRkbTnhEV5eTm1jyNEONmqqakpiYiI4Cg/dOgQMTMzE7iv7PTs2ZMcOnSIEELfZ/r4+JBBgwbR6lZWVpI9e/aQdu3aES0tLXLw4MEGf5+VlZX5enZ/dwSVUfPLwoULiaqqKjEyMiLbt2+nydJ/B4TRm/GLuMeXQ4cOkYkTJ5KysjK+6isrK5OLFy9ylC9dupRmg8Ri06ZNREZGhsyfP598+/aN/PPPP8TS0pKoqamRmJgYkfvfTDMNhWT9JrzNNPPf4ObNm/jw4QPNo+LgwYNYu3YtysrKYGtri507d1JpYqZOnQoNDQ24urqibdu2KCoqQnBwMEe7oqYPcnV1xaxZsxAYGEjzzho1ahSmT5/OUb9du3b4+PEjNDU1oaGhgfT0dPTo0QOFhYUc3j7Gxsa4c+cOdHV1YWZmBm9vb7x//x6HDh2iRWiKiIgQqu+qqqpUuoiOHTvi4cOHMDAwQElJCb59+0bVO3jwIPbu3YshQ4Zg/vz5VHmPHj1+SXrlly9fIiIiAuHh4SgpKcGnT59w5MgRTJ48mauHf2JiIod3FlCTBkXUCDXiZtKkSbhy5Qrte6+Lvn37Iioqiufn7CnvNm3ahOXLl8Pf3x8GBgaQkpKi1RXFu3b//v1wcHDAw4cP0b17d462a3us2tvbU3/36tULz58/rzeNXF3Y2tryVa+xooj8+++/AGoin9X+Lli8f/9eqHsFgCNHjmDmzJmwsrLC06dPsXPnTuzfvx92dnYAgIqKCpoHZe00MuzfvyCMHDkSK1asoEU+qSvSIWHzGh4/fjyAmt/A0dGRluKrqqoKWVlZGDBggFD9AoB//vkHysrK1P+RkZFo164d9X/Lli3Fmo5bUlKS+t0bC1NTU2zcuBFHjx6lRVnbuHEjz5Sw/EZIX7NmDRQVFbFlyxasWLECQE1q9HXr1nGdR5ctWwZHR8c658ba0RB+Rz5//oy3b99CX1+fVv7u3TtqvFVRUeE6B/HDH3/8gX379vFdXxzz3cyZMwWOnkN4pGj+8OEDz5S7U6dOhaenJ06cOAEGg4Hq6mqkpqbC3d0dM2fOpDx6P378yNf8xG0e8PX15Sirax6Ql5fnOwqMOPD396fWaRs2bMDMmTOxYMEC6OrqCpym6XeAEIL79+8jMTERCQkJSElJQXV1Nc/URtzIzs4WKRXU7t274eXlBW9vb67rF27PHq/nvbS0FLKystT/dUWie/bsGezt7bF69Wrs3r0bQE1Ehb/++guKioqU93xSUhKWLFmCqVOncm3H1dUV1tbWCAkJgbKyMtLT0yElJQV7e3uuERgCAwMxevRoXLt2jYpQcvPmTbx48YIjUmH37t3x9OlT7Nq1C4qKiigtLcX48eOxaNEivlIj/i4MGTIESUlJHNE56mLVqlVYuXKl2CIJsaOrq4uAgADY29sLvQ87e/Ys9XdcXBxtvVRVVYX4+Hhoa2tj5cqV1BwuahSXhkBFRYUjTd/x48epdLqkjgiBtSNn1E4PXTsiU3x8PM90g2FhYQ11S/XSs2dPPHnyhIpUxuLJkydiTS3LPnb9L/Lu3TsUFBRg1qxZVBmvCEgqKiocaZEB8IxEcvv2baxcuRLm5uZwc3NDfn4+Ll26hK1bt2LOnDki993a2hqurq5U+r/69uHiZMOGDQgMDISrqytV9tdff2Hr1q1Yv349h7zs6dOnXKPFKCsr15kJpKGZPXs2jhw5gjVr1jTaNbkhLS2Nffv2wdvbG9nZ2SgtLYWxsTFHVCl22FNyx8bGcqTkBoDr169j8eLFSE9P51jXfP78GQMGDEBISAjXKMe/ksbcO/5OMkT2SFjm5ua/riNcKCgooKIfSUtLo6ysDAwGA66urrC0tKw3YhMvCCHIz89HRUUFunbtyhF9igUrewWDwcD+/fvRokUL6rOqqiokJydzZBRbsmQJIiIiMHr0aHTv3p2vfXD37t2pjHR9+/ZFYGAgpKWlsXfvXujo6FD12PcClZWVSExMREFBARXt7d9//4WSkhKtn3Xx+fNnfP78ma+6vBBXiuWmyK9+r9PT03Hu3DlUVFRgyJAhsLKyavBrvHr1CgcPHkR4eDg+ffoEOzs7pKamNkiWjzFjxuD58+e4fPky8vPzQQhBly5dMHz4cMjLy9PqsiLd8uLp06dC9YGlO9PR0cHt27dpcgJpaWm0adOGkoXWxtLSEmfPnoWxsTFmzZoFV1dXnDx5Enfv3qXk0+zwo79LSEgQqP8vX76kzeHx8fGYMGECtRdzcHDgGln8xo0bCA0NxbNnz3DixAl07NgRhw4dgra2Nk9ZLz+wspF4eHjQymNjYzFmzBiu5/CrbxEGPz8/bNiwga8skgBw5coVxMXFcWS+0dXVrTd9ODcUFBRgZWVFvZtfv35FSkoKEhISEBgYCDs7O+jq6tKyY/DKpiNumUBdvHnzBu7u7tS4XlvfzNrHrFixAo6OjpCTkwNQo+N58OABNW99+/YNoaGhlHyKxZUrV6isZvfv34eenh7MzMxw8uRJkaI+AoJnb+jYsSPXaKm1Wb9+PYqKijBkyBBqzVBdXY2ZM2fC399fqL6yEEb+SAih5ttr165R75u6ujrev38vUn+AmnVJbm4uh+xu7ty58PPzEyn70KxZs2BlZcWRDeDr16+YNWsWZs6cCUA42er9+/cxcOBAjvJ+/fpxRPoGataZQUFBVKaWbt264a+//sIff/zBUdfb2xsODg54+fIlqqurERMTg6dPn+LgwYM4f/48Ve/48eNYvXo1SkpKsGrVKixYsADS0tJ8fDOCoaqqKtZx4tu3b1wj1hoaGortmtwQVEbNL8HBwdi6dStiYmIQFhaGFStWYPTo0XB2dsbw4cMbNAtERUUF1zUyK1ueMAijNxOk7YYeX2pn7s7Pz0fbtm2hpaXF8W5nZGTQ/o+KisK0adNw/vx5as3i4uKCmJgYruuo5cuXY9iwYZgxYwYMDQ3x8eNH9O3bF1lZWTTdfjPN/GqaDXSb+Z/B19cX5ubm1ISSnZ0NZ2dnODo6Qk9PD3///TdlLATUTJAMBqPOtIwMBkNkA927d+9yXdR27NgRr1+/5igXRCAgbsMMU1NTXL16FQYGBpg0aRKWLFmC69ev4+rVqxgyZAhV7+XLl1xTJFZXV+Pnz58i94NfTp06hQMHDiA5ORkjR47Eli1bMHLkSCgoKMDAwIBjUcMuEHr8+DHt92ClPurYsWOj9V8YOnfujDVr1iA9PZ2rgs3R0ZHvtpSUlLgqttl/a1aZqIarN2/eRGpqKi5dusTxWV1tV1RUoLCwEH/88YdIRlFN1fBv6tSpOHnyJMez+ubNGwwZMoQjBSq//P3339i6dSs1nh0/fhxOTk748eMHZWDJjjCpBLlx8uRJjs0ktzJusASQhBAoKipSAiGgRrDar18/kZTUioqKKCgooFJ+1R5fCwsLGyTFE7uRC1BzP69evcKuXbu4ChXESUBAAMzMzNC1a1dKoXrjxg18+fIF169f53qOn58fIiMjERgYSPu+u3fvjqCgIOr5qaiowNy5c+Hq6krNS3UJwe7cuUOlbWOH19z4uzJ27Fg4OTlhy5Yt6N27N4Cae3d3d6cE17dv367TcJ2dL1++UM8lu0MFN9ifX3HOd4I4AYlieO/v749FixZBXV0dVVVV0NfXR1VVFaZPn47Vq1fDx8cHDAaD7/dWmHmgqqoKERERPBWUvN4jcWFiYkL93aZNG1y+fLlRr9+YWFtbIzU1FV++fEGPHj1gbm6OOXPmwNTUFCoqKlQ9Xu/F58+fce/ePbi5uXE4oQiCiooKvnz5wpGGkNvaiJXSksFgYM2aNTTFZFVVFW7dugUjIyO+rqujo4OAgAA4OTlRZcIoER48eIDQ0FAwmUxISEigvLwcOjo6CAwMhIODA8dcaGZmhqdPn2L37t2Usef48eOxcOFCdOjQAUCNonDIkCEwNzeHhoYGVq9ezdc9/a6MHDkSXl5eyM7ORq9evTiEo9yM23bt2oX8/Hx06NABmpqaHOfUFlCKiqhOQKz5icFgcLwvrNSBW7ZsoSlmRXmvGgpBleDssNJLs8OeHpodHx8f+Pr6wsTEhGu6wcbkr7/+wpIlS5Cfn0+ly01PT0dwcDACAgJo8z+/yhZB0lZ//PhR8E7/B3BycoKxsTGOHj3KNV1qbT59+oQDBw5QykE9PT04OTlx3RNJSUnh77//hry8PNavXw9JSUkkJSVRThKiwnLsFdQpSRw8e/YM1tbWHOU2NjZYuXIlR3m7du2Qn5/P4SCRkpJCM3ITB+xpqqurq7F3715cu3YNhoaGHDKYrVu3irUvtVFXV6fWx9nZ2fj06RNUVVW51t29ezf27t2LadOmISIiAsuXL6el5AZqHC7mzJnDdU2trKyMefPmYevWrU3OQLcx+N1liOvWrYO3tzeYTCat/PPnz5g/fz6OHj3aqP3hNyiEIBQWFsLGxgaPHz+m2j116hS1F2eHld6cEIKQkBCa4Z60tDS0tLQQEhJCOyc6OhrHjx8XKIXx6tWrUVZWBqBm7B0zZgwGDx5MpTevzfPnz2FlZYXi4mKUl5dj2LBhUFRUxKZNm1BeXs7RJ5ahMQuWzOnQoUMYOXIk3/3kxuzZs+tMsfxfoCm81ydPnsSUKVMgJycHKSkpbN26FZs2bYK7u3uDXkdDQwMdO3aEg4MDbGxsICUlherqag6DWWENdOTk5DjWzdwwMjLiK+W7oLAM4YSR9ezdu5c6b9GiRWjVqhXS0tJgY2ODefPmcdQXRH/Hy0izNrKysrTUzOnp6bRARLKyshxp1k+dOoUZM2bAzs4OGRkZKC8vB1Azrvv7+3M41dYH+3iir6+PDRs2IDExkVqHpqenIzU1FW5ublzPFyWld318+vQJkyZN4rt+WVkZh3E4ULN/YZdFAsChQ4cQEhKCwsJC3Lx5E5qamti2bRt0dHQwduxYru0rKCigZcuWaNmyJVRVVSEpKUmt9VlISUlBQ0OjUdfX9eHo6Iji4mKsWbOmznG99vvJ7X3lhpWVFdTU1ODm5oaLFy/S5HUNTWVlJX78+MHTcWXLli3w9PRESEgIT0NZQghev36NiIgI+Pn54cGDB5CTk4OBgYFIzv0siouL+arHbsxnYmICPz8/DB06FElJSdizZw+AmjVO27ZtRe4TULOmX7VqFa0sMzMTBw4cEMlAl9f4XTtQjiCyVRYMBoNaN7Lz+fNnjvpxcXGwsbGBkZERpX9LTU1FaGgozp07h2HDhtHqjx07FufOnYOvry8UFBTg7e2Nnj17ctSdOnUq5OTkMG3aNDx//hxeXl5cvwdR94Pr16+Ht7c3IiMjuY5jwvLu3TvMmjWLq04eQKOPVYLKqAVBRkYG06ZNo36riIgILFy4EJWVlXj06BHfDm+8yM3NhbOzM9LS0mjlothOiDtgFcDf+CKoTFAU55zRo0dj9+7dsLGxwdWrV3HgwAHExsYiISGBp760c+fO6N69O06dOgUAmDJlSrNxbjNNDgbhd+XUTDO/Oe3bt8e5c+coY4VVq1YhKSkJKSkpAIATJ05g7dq1lJCusWjTpg3i4uJgbGwMRUVFZGZmQkdHB1evXoWTkxNevHhBq19dXY3q6mpK0R4dHY20tDTo6upi3rx5YvHI4sXHjx/x48cPdOjQAdXV1QgMDKT6snr1akro36tXL7i6usLe3p52j76+vrh69WqjRZCQlJSEp6cnvLy8aIZhUlJSyMzM5IhgyGQyqYUGt6FSTk4OO3fupBlDNDW0tbV5fsZgMFBUVMT3YqqqqooWRbU+2CNxCIqWlhbGjBmDNWvW8LWx/PbtG1xcXBAZGQkAVBRPFxcXdOzYkedm6Hejd+/eMDQ0pBnYv379GhYWFujWrRtOnjwpVLstWrRAdnY27XlJSEiAjY0N/v77b4wbNw4dOnQQeOPg6+sLNzc3DBo0iMOw+/Xr13j37h12796NuXPnAqh5516/fs3hRVsXPj4+cHd3F9k7sDbW1tZQU1PjGeXM0dER79+/p3nJCkNt5ReDwYCamhosLS2xZcuWRo8q+O+//2LXrl3IzMyEnJwcDA0NsXjxYp5G0507d0ZoaCiGDBlCG99zcnLQv39/5ObmYubMmbh27Rqqq6vRu3dvREVFcfVGZkfQufF3pbS0FK6urjh48CAqKysB1MxVDg4O2LZtGxQUFPDgwQMA4MtQT0JCAq9evUKbNm1ocxg73AQBTWW+Y0Wai4yMxOTJkzkM77W0tDBnzpw6o4W/ePGCa5QwJpMJZWXleuc8UYyJFi9eTEVM4ibIZil5GwtLS0vExMRwCLy/fPkCW1vbRjcYFiceHh4wMzPD4MGDaULd2vB6L4Ca8Xf27NnYsWOH0OvpPn36QFJSEkuWLOFqlMW+NrKwsAAAyrCK/Zqs593d3b3OSHfsFBUVoXv37igtLQUhBC9evICamhr++ecfvpUIampq1Fq+S5cu2LlzJ0aMGIGcnBz06tWLMh4QBHNzc9y6dQsVFRXQ0tKChYUFLC0tYWlp+Z8UkNWe19nhJYStLwKcsIrMupyA1NXVeQrd+UVbWxt37twRKIPD27dvuTowNHYkjoYiOzsb1tbWKCoqosrat2+PwMBAzJgx49d17P9T1/MI8I7qWhes/RY/NAXD7F+BgoICMjMzuTop1yY5ORnW1tZQVlamZFX37t1DSUkJzp07xxFJ6ufPn/Dy8kJwcDDc3NyQkpKC3NxcHDhwQCCDsN+Bzp07w8PDg8PwJSQkBJs3b0Z+fj6tfOPGjTh8+DDCwsIwbNgwXLx4Ec+fP4erqyvWrFkDFxcXsfWVNafXB4PBaLT119KlS2FgYABnZ2dUVVXBzMwMaWlpkJeXx/nz57lGS5WXl8eTJ0+gqamJNm3a4OrVq+jRowfy8vLQr18/fPjwAZqamrh8+TL09PS4XjcnJwfDhw/n2+Dgv0RT2VMJC8uY+/Dhw5RRe2JiImbOnIl27drh9u3bjdqf6dOnw8TEBMuWLcP69euxc+dOjB07FlevXkXPnj0RExMjcJsTJ07Eo0eP4O3tDVlZWWzevBk/fvzAvXv3eJ5jYWGBmJgYnobt7HTo0AGJiYl8O9jy4uPHjzyV37a2tlBUVMSBAwfQqlUrSlaSmJiIOXPmIC8vj1a/tlyYyWRSMqcVK1bwFT2QFyoqKrhw4UKjO5c3Jk3hve7Vqxd69+6N4OBgSEhIYOPGjfj7778b3BGKfd3I656FMSo5ePAgX/VYkRP5jV4qinFcXl4eEhISuO5LamfGECZ6saD6Ox0dHZw+fbrODBdDhgxBnz59sHHjRty4cQPm5ub4559/KLnx1atXsWDBAtr6yNjYGK6urpg5cyZNtnr//n2MHDlS4AAIdemZ2GEwGHVG0L537x4taqWxsbFA/eCGs7MzevfuzXcWyVGjRqFXr15Yv349FBUVkZWVBU1NTUydOhXV1dWUjmXPnj3w9vbG0qVLsWHDBjx8+BA6OjqIiIhAZGQk5QRaXV2Nu3fvUhmdUlNTUVZWho4dO8LCwoI6aj+3Bw4cQExMTKNk0+EHRUVF3Lhxo14ZdG0dDvvzBdQEk+GmSwoKCkJycjKSk5MhIyMDMzMzmJubw9zcXOi589y5c/jw4QMtENGGDRuwfv16VFZWwtLSEseOHeOYx9+9e4fJkycjOTkZ8vLyHE51Hz9+RHV1NWRlZfHo0SO+ZXOCwO78wxpva+vQao+7WVlZsLOzQ3FxMZYtW0bJi1xcXPDhw4c6A46JQmZmJnr27CmUYSErgmZmZia6detGy15QVVWFwsJCWFlZ4fjx4wAEk62ysLa2hpycHEdmyClTpqCsrIwm/zI2NsaIESMQEBBAa8PLywtXrlyhOclXVlbC398fTk5OHBG3a2Nubl6v3qEh9oPGxsYoKCgAIYSvKKT8Ymdnh+fPnyMoKAjm5uY4ffo03rx5Az8/P2zZsoXKbNFYiENGzY0XL14gPDwcERERqKioQE5OjsgGugMHDoSkpCS8vLy46oiEyWjVEHqz+uBnfPkVMsHdu3dj2bJlUFNTQ0JCAk9ZW2pqKuzt7dGyZUscPnwYqampWLZsGUaOHImQkBC+9nPNNNMYNBvoNvM/g6ysLPLy8qhoiIMGDcLIkSMpT7CioiIYGBhQXlaNlbZt9uzZ+PDhA44fP46WLVsiKysLEhISsLW1hampqUjpQJuKYUZsbCwcHBywYsUK+Pr6wsfHh5YCorZHmriYN28ejh07hm7dumHGjBmYMmUKVFVVeRroPn/+XOjUR78L7Aa3RUVF8PLygqOjIy1NcWRkJDZu3NioClZFRUU8ePCgXiM+FkuWLEFqaiqCgoJgZWWFrKws6OjoIDY2FuvWreMa9YpfuEUPYqe24E6cvHv3Dqamphg5ciS2bt2Kf//9FxYWFujRoweio6PrVcLzokOHDoiJiaEia7FISkrCmDFjsGTJEmzcuFHgDTjLWHD37t20TQhLIWBubk5LCSiMga64SEhIwNChQ7Fs2TJ4eHhQfXr79i02bdqE7du348qVKxyevL8rP3/+hJWVFUJCQgQSOMnJySEnJweampo0Ydzjx4/Rp08fTJ48GZcuXcJff/0FWVlZhIaGon379vVGsRPn3NgUKS0tpYTXOjo6QgsBkpKSKAFAYmJinUIhdmFWU5vvxGF4z2QyERQUVKfxJsBbcBAfH49t27bRotstXboUQ4cOpeq0bt0aBw8ebDIGMrzG1Ldv36Jjx46NmsVAXNy8eRMfPnygReo8ePAg1q5di7KyMtja2mLnzp2UVzkvRyMlJSXo6uqKLICTl5fH/fv3OdLJ18WsWbOwfft2kaOynzt3Dl5eXnj06JHQSoThw4fD0dER06dPx5w5c5CVlYW//voLhw4dwqdPn3Dr1i1a/by8PMTGxlIOXzo6OrC1teVQ3JWXlyMtLY1KZXjr1i38/PkTurq6lMGuINFumuGPpuQEdO/ePTg4OODJkycNougXlerqauTn53NVyguSWjMlJQXW1tb49OkTVdaqVSvcvn2b732MOBEkPasgRg5VVVXYvHkzzp49SxkrrF27lqYg+F/G2toajo6OmDBhQr11DQwM0L9/f+zZs4emSFy4cCHS0tKQnZ1Nq9+jRw98+/YNhw4dQr9+/UAIQWBgINauXQsnJyeONLKCwG8Et8Ziz549WLp0KZycnKhIMKmpqQgPD8fmzZs5DG4JIfD398fGjRup6JoyMjJwd3fH+vXrG73/v5pOnTrhzJkzMDExwZkzZ7Bw4UIkJibi0KFDuH79OlJTUznO0dHRwalTp2BsbAwTExPMmTMH8+bNw5UrVzB16lR8/PgRsrKyePjwIU+lWH5+PgwMDGhR/v5XaGp7KkH59OkT5s2bh8uXL2PLli3Izc3F9u3b4eHhAR8fH5oxRWPAb1AIQWjXrh1OnjxJpUh99eoVOnXqhC9fvvC19+RmPMPOli1b8OzZM+zatUts0WRZkTu7du1Kk8EUFRVBX19f6OjCwqCtrY2LFy/yNNj/L9AU3usWLVrgwYMH1LhbUVEBBQUFvHz5skHlp+IyjK3rXWUwGCgrK0NlZWWj7Qf27duHBQsWoHXr1mjXrh3tXWUwGDTDptrRi798+SKW6MX8GGkmJSVh5MiRaN++PV69eoVp06bRAngsXLgQZWVlNMMZeXl5PH78GFpaWrTx4tmzZ9DX18ePHz8a9D7q4+3bt5g6dSoSExMpfWVJSQksLCwQHR1Ne78EZePGjdi6dStGjx7NNYtk7SyoDx8+xJAhQ9CzZ09cv34dNjY2ePToET5+/IjU1FRqL6evrw9/f3/KOYL1HT58+BDm5uZUym8lJSWUlZWhXbt2lDGuubl5vXtCY2Nj5Ofn4+fPn42STac+9PX1ERUVVa/RtLAGuuxkZ2cjKSkJ169fx/nz59GmTRv8888/AvfZwsICEydOxKJFiwAAaWlpGDx4MHx9faGnp4dVq1ZRujR2hg4diuLiYjg7O3M1AmXJh7t164YDBw5w6M0aAklJSXTq1AmOjo6wtrbmudbix5jvx48fkJCQ4Hj2GwpRDHRZTuk+Pj5wc3OjyV5ZhoUTJkygnBeEka0+fvyYyqLGLTMk+/5WVlYW2dnZHPLS3NxcGBoacoyNLVq0wMOHDzkytfwqxOXk3759e8TGxqJPnz5QUlLC3bt30aVLF5w9exaBgYFUsLvGQlAZtSCUl5cjJiYGYWFhSElJwZgxYzBr1ixYWVkJrWNnR0FBAffu3aPpvhsKcQWsqgtxjC8/fvzAsWPHUFZWhmHDhlHvI3t2InZOnDiBnj170ubV2uO6jIwMXF1dsX79eqqvBQUFsLe3x4sXL4SaY5ppRiyQZpr5H0FDQ4MkJSURQggpLy8ncnJy5Nq1a9TnWVlZRFVVlfrf2tqabN26lWd727dvJ7a2tiL3q6SkhAwdOpSoqKgQCQkJoq6uTqSkpMjgwYNJaWkpVe/58+d8HewwGAzy5s0bjmu+efOGSEpKCt1nBoNBmExmnYeEhATtnOTkZDJ06FCipqZG5OTkyMCBA0lcXJzQfRCWb9++kYiICGJqakpkZGSIjY0NkZCQINnZ2Y3el6aGpaUlOXLkCEd5VFQUMTMz4yjPzMzkemRlZZHc3Fzy48cPofsyc+ZMsm/fPr7ra2hokJs3bxJCCGnRogUpKCgghBCSl5dHFBUVhe4HIYQYGRnRjm7duhF5eXmipKREjI2NRWpbGIqLi4mGhgZxdXUlurq6ZMqUKaSyslKkNseOHUu8vb25fpaQkEAUFBQIk8kUuF1eYxAvtLS0yPv37wW6xuvXr4m9vT1p3749kZCQ4BiLRCE4OJhIS0sTJpNJVFRUiKqqKmEymURaWprs3LlTpLabIq1btya5ubkCndOzZ09y6NAhQgj93fPx8SGDBg0inTp1IpcvX6bq5+bmEgkJiXrHB15zo6mpKW1u/K/x+fNncvr0afLkyZNf3ZXfjvHjx5OAgACO8k2bNpGJEycKPB6xExwcTCQlJcnUqVPJ9u3byfbt28m0adOIlJQU2bVrF1Wvffv25OnTp0LfQ0PBmo8ZDAZJSEigzdEZGRnE39+faGpq/upuNghWVla03z0rK4tISkqS2bNnky1btpB27dqRtWvXNlp/Bg8eTK5evSqWtj9//sz1KC4uJqdPnyY6OjrEx8eHqq+vr0+tjfjlzp075Pr164SQmv3CiBEjiKKiIunZsye5f/8+ra6/vz+RlJQkTCaTtGvXjrRt25YwmUwiJSVF/v777zqv8/37dxIfH0/c3d2JkpKSyPN1M7+Oa9eukRUrVhBnZ2cya9Ys2sGOoaEhGTduHElPTyeFhYWkqKiIdjQmN2/eJNra2oTJZBIGg0E7eD2LrLGfdQQFBRFPT0/SoUMHMm3aNFrd5cuXE19f38a4lV+Gr68vYTKZZPjw4WTs2LFEVlaW4zf/XyY0NJSoq6uTtWvXkpMnT5LY2FjawY6srCzJycnhaCMnJ4fIyspylDs5OXFdC2dkZJBu3bqJ3HdtbW3y4MEDkdsRBXY5XExMDBk4cCBp2bIladmyJRk4cCA5ePAgGTBgAM/zy8vLyaNHj8itW7fI169fG6PLdfKr1vcyMjLkxYsXhBBC5syZQ5YsWUIIIeTZs2c8ZSTOzs5k3bp1hBBCdu3aReTk5Kg9mZOTEyGEEB0dHXL69Gme1z116hTR1tZuuBtpptFZsWIFYTAYREpKiiY3/y/AYDDI69evaWUKCgrk2bNndZ4XGRlJunfvTmRkZIiMjAwxMDAgBw8e5Khna2tLlJWViba2NhkzZgwZN24c7eBGaWkpWb16Nenfvz/5448/iLa2Nu2ojYqKCnn06BEhhC6DuXHjBmnTpg1H/ZKSEvLhwweO8g8fPpDPnz/Xed/1cejQITJx4kRSVlYmUjvN1A03WQb7b/+78u+//5J58+YRKSkpMmLECKr83bt3HPuDhw8fEkdHRzJp0iQSFRUl0nU1NDS4yo640bNnTzJv3jxK7u7v70/TH7KTmZlJqqqqqL/rOmpjZGREWrRoQWRkZEiXLl2IsbEx7WDx+PFjEhQURKKjo6lrsQgNDeXYs2tra1PyCfZnJjIykujp6fH1HTQkkydPJiYmJuTx48dU2aNHj4iJiQmZOnWqSG1raWnxPHitS0pKSoifnx+ZNGkSGTlyJFm1ahX5999/aXVkZWWp55H9O8zNzaWt1UNCQoSSB65du5asW7eO59HYxMXFkeHDh5PCwsI66zEYDLJhwwZqfywrK0vWrFlD/e/n58dzb11dXU3u3btHtmzZQsaMGUPJ/o2MjITqs5qaGsnIyKD+d3V1pY0pFy5cIJ07d+Y4T05Ojq99z9mzZ8mgQYPEoj9+9eoVCQgIIF27diVt27Ylbm5utPejKfHgwQORZXcRERHk+/fv9dYTVrb68uVLsmLFCjJq1CgyYcIE4uPjw3UN1KlTJ3L8+HGO8mPHjhF1dXWOchsbGxIRESFwf8TBz58/iY+PD7XPa0gUFRWpd19DQ4OkpKQQQmr2j3Jycg1+vfoQREYtCAsWLCCqqqrE0NCQBAUFkXfv3jVQj/8PExMTcuPGjQZvtynz/ft3Dp0FC1dXV7J48WLq//LyctKjRw8iJSVFlJWViYKCAklNTSWEEGJubs7XYWFhQbt+dXU1zzGmqqrqPy+nbeb3ojmCbjP/MyxYsACZmZnYtGkTzpw5g8jISPz777+UV1ZUVBSCgoJw584dAGj0tG0pKSnIyspCaWkpevbsSYvIBvBOyUv+f5oLoMbLt7KyEllZWQBq0mFfv36d5nlbVVWFy5cvIzQ0lJaKUxBiY2N5fnbz5k3s2LED1dXVfHnh3r17l0rl2Njk5eUhPDwckZGRKC0txejRozFx4kSMHz+ea11+Ux81Nf755x+cPXsWxcXFqKiooH3G7mEkLy+PzMxMrp6DRkZGHFEY6koTDQBSUlKYMmUKQkNDISsrK1CfN2zYgKCgIL69nuXl5akUQ+weu5mZmTA1NcXnz58Fun59fPnyBY6Ojhg3btwvSV+bm5uLwYMHY9iwYTh06JDIkTmSkpKQlpaGFStWcP08ISEBBw8eRHh4uEDtMplMvHnzRmAPeFYEKH6iMowcORLFxcVYvHgx13QhY8eOFejatXnx4gVOnjxJpQnU1dXFxIkTqWjsolBWVoZNmzYhJiaGij6ora2NiRMnwt3dHfLy8iJfQxBcXV0hIyPDkd6nLuqLkG5lZYWXL1/S0pgrKCjg0aNHfHkd1zc3/u5MnjwZpqamWLx4Mb5//44ePXqgqKgIhBBER0fzFXmNF7q6urCzs4OdnZ1AETSbwnz35s0buLu7Iz4+Hm/fvuWItsjNW19NTQ3Xr1+HgYEBrTw7OxtDhw7F+/fv8erVK6EizHTq1AleXl5YvHgxrTw4OBj+/v54+fIlgMaJmMQPTSENZ2PRvn17nDt3jlpLrlq1CklJSZRX/4kTJ7B27Vo8fvwYAPD+/XuUlZXR5pdHjx5h8+bNVMTd6dOnC92fEydOYN26dfDw8OC6fjE0NOQ4p6ysDAEBAdTzXvu9Y0XXrmvdxWAwMHv2bOzYsYPa25w7dw6BgYHYs2dPg0dCZEWZX7NmDZYsWUJFRfr48SOCgoLg7++P69evc0QiraiowM2bN6m0j7du3UKHDh1gZmaGsLCwBu3jryQpKQmbN2+mIm7r6+vDw8ODZ+aV+tbUjR1dll98fHzg6+sLExMTrmuw06dPU38rKiri/v37PCMuNiZGRkbo0qULfHx8uPabW6T1+tJDs0coqa6uRmRkJAwNDWFoaMgxDtSO8CBO6kspzEolLCi6urpwd3fHvHnzAADXrl3D6NGj8f379waJNvK7U9d3UDti9MCBA+Hh4QFbW1tavTNnziAgIADp6elUWX0ZL8rLy6mI8cLSFNLsysnJITQ0lOvzWVpaihEjRuDDhw/IyckBAL7XM401z4hzfS8Impqa2LdvH4YMGQJtbW3s2bMHo0ePxqNHjzBo0CBa5G8W/KTkdnFxQWJiIu7cucMh5/n+/Tv69OkDCwsL7Nixo1Hus6nSFPZUwrBz5054eXnB1tYW9+7dg4SEBI4cOSJUGlZh+PLlC991hck+ISEhgdzcXJqMqlOnTkhJSaHJJ9jb3rp1K9asWYPFixdj4MCBAGrkFMHBwfDz84OrqytVl5V6lhfcZGrTpk1DUlISZsyYwXVdsmTJEtr/U6ZMgbKyMvbu3UulZVdTU8PYsWOhoaHBcY2RI0fC2toaCxcupJWHhITg7NmzuHjxYp19rgtxpVhuqvyq95rJZMLPz48WcdDT0xMeHh60NMa1ZeWCEhgYCBcXFyojQmpqKkxMTKi1xdevX+Hp6SlStH5WO6zMZN26dcPGjRthYWFBfT5t2jR06NABW7ZsAVATdfXPP/9Ehw4d8Mcff+DSpUs4cOCA0PJ4JSUlPHjwgIr0WReCRC9mjyjK2t9xk8dwyx4irmiIGzduxOHDhxEWFoZhw4bh4sWLeP78OVxdXbFmzRqObAT8wE1vBtTsobp06YLZs2fz1AMoKyvj2rVr6N27N6389u3bGD58OEpKSgTuj7jR19fHxo0bMXbsWJrOaefOnQgPD//PjXOqqqr49u0bKisrIS8vzzGuf/z4EQCgpaVVr9yTwWBQ8iwW1tbWSE1NxZcvX9CjRw+Ym5vDzMyMinwqDHJycnj69Ck0NDQAAH369MGkSZPg4eEBoCY6uL6+PsrKymjn9ezZE7t37643Mi77dyItLc2RNYb1nYhKSkoKwsPDceLECejr68PZ2RnOzs5gMplo2bIlcnNz0bp1a6iqqtb53QvbH17vNouSkhIkJSU1inxKGNmqIPj6+mLbtm3w8vKiZWrZtGkTli1bhjVr1tDqh4SEwMfHB3Z2dujVqxdH5FIbGxuOa/CrkxcGRUVFZGdnN3hE3969e8PPzw8jRoyAjY0NVFRUsHHjRuzYsQMnT55EQUFBg17vV8FkMqGhoQFjY+M636WYmBiB2mXfx9y9exerV6+Gv78/12dYlCx6wujN6kKU8aWsrAyenp44fvw4Pnz4wFGf1Zfu3bvD39+felfCw8Ph5uaG+/fvQ0NDA05OTnj79i0uXLggUN/ZETabYDPN/AqaDXSb+Z/h/fv3GD9+PFJSUtCiRQtERkZi3Lhx1OdDhgxBv379sGHDBgD45WnbMjIy4O3tjfPnzwOoSSHBDZaQf8eOHWjRogXevn37Swwznj59Ci8vL5w7dw52dnbw9fWljB9KS0shISFB27w8ePAAa9aswcWLF3+50rm6uhoXLlzAgQMHcOnSJZSXl9M+FyT1UVMjPj4eNjY20NHRQU5ODrp3704ph1gpfFh07doVY8eORWBgIK2N5cuXIzY2Fk+fPqWVx8bGUgLBPn36AKgRqGzZsgVr165FZWUlvLy8MGXKFGzevFmgftdWgrPDbXNvamqKSZMmwcXFhRJQa2trw8XFBXl5ebh8+bJA1+eH7OxsWFtbC23ozi+8FsXfvn2DjIwMLZVaQwkEGgomkwllZeV6BTa1+21kZISHDx/CzMwMzs7OmDBhAk9ls6KiIm7cuAEjI6OG6najUFFRgQEDBuDhw4cYOXIk/vzzTxBC8OTJE1y+fBk9e/ZEcnKy2NISccPFxQUHDx6Erq4uV2EDu/Dg2bNn0NbWBoPBwI0bN+Dr64vMzEzKkNbb2xvDhw+HhIQEXr9+TRPOKikpITMzs873/MWLFw1iBN3UadeuHeLi4tCjRw8cOXIEa9euRWZmJiIjI7F3717cv39f6La3bduGI0eOICMjAz179oS9vT2mTJlCM5auTVOZ74QxvGelZK6d/ionJwfGxsYoLy+npV8ThNpKGRZ5eXkwNjZGaWkpAGDcuHFISEhAy5Yt0a1bN473V1DBjrA0hTScjYWsrCzy8vKo8WLQoEEYOXIkVq1aBQAoKiqCgYEBvn79CkD8yj5uRlksxRw3RRyrT/wo5ZOSkrheU0lJCbq6ujSFLSCcEsHS0hIxMTEcipEvX77A1taWWjdOmTIFKioqCA0N5dqnuXPn4uvXrzh69CiSk5NpBrkaGhowMzOjlDCdOnXi2sbvyuHDhzFr1iyMHz+eMuJITU3F6dOnERERwdUAvLbz48+fP3H//n1ERkbCx8cHzs7OAvejMZyA2rdvj8DAQL7eF1tbW8yYMaPRDNPqQkFBAZmZmQ1qLMxuVFAXDAaDtv8SN7VTCv/8+RPfvn2DtLQ05OXlhd47yMjIID8/n7ZWk5WVRX5+/n/unRY3x44dw/Lly+Hi4kIph9PT0xEcHIyAgACaw7ihoSHU1NQog0lx0BTS7J48eRIzZszAsWPHaMrOsrIyWFlZ4e3bt0hISECHDh0A1My9mpqaMDY25ir/YsHuNCBOxLm+F4R169YhKCgI7du3x7dv35CbmwsZGRmEhYVh3759uHnzplDtvnnzBj179oSEhAQWL15Mrb1zcnIQHByMqqoqZGRkoG3btg15O78VTWVPJShWVla4e/cuQkJCMHHiRHz//h3Lli1DREQEfHx8sHz5crH3oT6nJXaEkSVza5898AW3Nbu2tjZ8fHw4nAYiIyOxbt06FBYWCtwPdlRUVHDhwgVq3Vgf//zzD0aMGAFCCPLy8mBiYoK8vDy0bt0aycnJHPvdli1bIjU1lSMASU5ODgYOHMhVmc4v4jIqbIr8yvdaWEM4QZGQkKA5Ndc2ZOUnZX1d/Pz5Ezt37oS/vz9atWqFDRs2YOLEiRz1tLW1ERERATMzMwDA5s2bERISgpycHEhKSmLz5s04efIkzYlJEJydndG7d2/Mnz+/3rrsRrcs2I002Xn+/Dk0NDTAYDDw/PnzOtvlJyAFLyIjI9G6dWuMHj0aQI3eZu/evdDX18fRo0dpbZP/x95Zh0Wxvv//vUs3qAiitAUIxrGVVgGDYxwDRSXsRkSwUBT7qBgcC5RQwUJs0KOCIrYCNiEodqKCgcD9+4Pfzpdld2GD8nx4XddcMDPPPPPs7swTdxJhxYoVWLlyJRN4RU5ODnPmzMGyZcvEur8gR4S8vDykpqYiLy8PFy9e5OskLEiGf+fOHVhbW4vkpFERnPlg+fcmLS0Nbdq0AZvNZgIcCYJjABgSEoIlS5Zg3bp18PT0REhICLKysrBy5UqEhIRgxIgRErXVyMgIN27cQMOGDbmO5+XloUOHDhK/16ISHh5e4fmxY8cKVc/z58+xdOlS7Nixg+u4j48PrK2tYWlpydcxVhyaN2+O4OBgODg4ID8/Hw0bNsT58+eZcfX27dtwcHDAu3fvuK47c+YMAgICsHz58goN6KrqOxGWN2/eMHLCd+/eoUGDBggPD8eIESMgJydXbe2pzMmIg6gBfMpSXFyMDRs24MCBA3wNVznyCXFkq3FxcVBWVkbPnj0BlAbV2LlzJ0xNTREcHMwlGyEiBAUFYd26dXj58iUAQEdHBz4+PpgxYwZP3yGKAy4gmk5eHP78808MHjy4yp+9PXv2oKioCG5ubrh16xYcHR3x8eNHyMrKIiwsDMOHD6/S+1WGsDJqUXFzcxNqvSFOsKqy9ZZdY5Q/JolNTFUHrJKkf5k6dSouXLiAZcuWYfTo0QgODsaLFy+wfft2rFq1CqNGjQJQ2p/evn2bkcO6uLhARUWFGSNSUlLQt29f5n0UFzMzM4SGhlbqeFFPPbVNvYFuPf9zfP78GcrKyjyGCR8/foSysjITdcrY2Bjr1q3jiWTCISYmBnPmzJFokRQfH4+zZ89CVlYW48aNYyZsHENXBweHCj3Z//33X/j5+SE9PR2zZ8+Gt7c3VFRUatQw4+XLl1i8eDHCw8Ph4OCAlStXMgvw3NxcDBs2DNevX2eE94GBgZg0aRL279+PQYMGwcvLC126dKmStlQFb9++5RFm6uvrY8qUKfD19a2lVolP586d4eTkhICAAEaA1LhxY4waNQqOjo6YPHkyU/bUqVMYMmQImjdvzvwm169fR0ZGBg4fPoy+ffvy1L1s2TI4ODhwHY+Pj8eiRYtw/fp1xMbGwtvbu9q965KSkuDk5ARXV1eEhYVh4sSJePDgAZKTk5GYmIg//vijWu45YMAAvpFnqpLKJsVlqepFmaSw2WwEBQVVKnDh1+47d+5g9+7diIqKQlFREUaMGAEPDw8eL3tTU1Ps3bsX7du3r9K2l6U6omRs3LgRK1euRGJiIl+DQhsbGyxYsECsaAbiUpFxSXmDkvKC++HDh2PTpk08ilh+Rtp5eXlQVVXlEm6UNxCRkpJCz5494erqir/++ovHwOS/goKCAtLT06Grq4sxY8ZAR0cHq1atwrNnz2BqasoYfkpCeno69u7di6ioKGRnZ8PW1haurq58o5LVlfFOHMP7zp07o3///jzv5JIlS3D8+HHcunVL7PaMHDkS7du3Z6IvcPj7779x8+ZNREdHAxAvYlI9kqGvr4/IyEhYWVmhsLAQ6urqOH78OOzt7QGUOtNYW1szfUx1K/vEUcSJqpQXFnGE9vwUkEDp/Lhp06b49esXgNLvMTIykhF8l+fSpUsYM2YMsrOzmcgEvr6+GDx48H/eYMfExAQTJkzgiqYGlDq57Ny5k4mqKwz79u3D/v37K8xewo+acgJq2LAhrl+/DmNj40rLvn//HmPHjkXnzp3Rpk0bnnvzizhSXdjZ2WHu3LlwdHSstGxdi8xZFWRkZGDy5Mnw8fHhWccJCz8HrLJOkv+r9O3bF1FRUczaZ9WqVZg0aRKjUPrw4QMsLS2ZqO5Axco+gFcRKU7GC1GoK8ZWISEhmDlzJk6ePAkbGxvGOPf169dISEhA06ZNmbJTp05lDFLc3d3h6upaa9F/gZqZ3wvLoUOHkJubi6FDhzLG8+Hh4VBXV+dS3AmbGYwTmezp06eYPHky4uPjuYxgHBwcEBwc/D/dDwB1Z00lKr1790Z4eDhj/M7h5MmTGDduHF69elXtbSjrkJaTkwM/Pz+4ubmhW7duAEqztoWHh2PlypViyb8EObyVh7NWAAQH8MjIyIC5ublQ2eMqwtDQEKdOnRKYwY8fRUVFiI6O5so2NGrUKB6HPKDUMenq1at8M8106dKFJ1uaKG1YsWIFPDw8/iecc37X91oUyq8FyxuiimugS0SIiIiAv78/ioqKsHjxYnh6egrUTSkoKODRo0fM2rlv375o06YNE1AkPT0d3bp1E9u4fOXKlVi/fr1QWftqKnqxKLRq1Qpbt26FnZ0drly5gl69emHDhg04ceIEpKWl+TqGFxYWIjMzE/n5+TA1NeVx7q0qSkpKMH78eLx9+xbHjx/nOf/nn38iLy8PUVFRzFjz4sULjBo1ChoaGhI7U0VERGDt2rVMFryWLVvCx8eHcSgVN8rx3r17sWTJEka/paOjI7YjbXkEyWDevHkDXV1dHgPG34XU1FR06NChwv7ix48fImfd5Me8efMQGxuL+fPn49SpU0hOTsaTJ0+YPmbHjh2IiIhgMm1x4KzDqsOAThySk5Oxa9cuHDx4EK1atYKHhwcmTJjwn8pQ4+/vj5CQEHh7e2PhwoVYsGABcnJyEBsbC39/f6YvFUe2am5ujtWrV6Nv3764e/cuOnbsCG9vb1y4cAGtW7dmZPJFRUXYt28fHBwcoKWlxQR0UFFRqbLPKYpOXhzEiegrDt++fcOjR4+gp6fHNebVFMLKqOsKwq4zAO61hqjUpYBVenp6iIiIgI2NDZcRbmRkJKKiohj7JnV1ddy4cYNxNDc0NMSiRYsYmWtOTg5MTEx4AiIKm3WQQ3VmE6ynnqqk3kC3nnoEUN1p20JDQzF+/Hg0aNAAnz59QsOGDbF+/XpMnz4dw4cPx8yZMwUK527fvg1fX19cunQJ48aNg7+/v1gR4STh8+fPWLFiBTZv3ox27dph9erVPGlbR4wYgcePH8PT0xMxMTFITExEhw4d0KVLF/j5+dWaAO/58+dQV1fnEUb8+vULV65c4UnHK0rqo7qGiooKUlJSYGxsDA0NDSQlJcHMzAypqan4888/eaK/5ubmYuvWrUy6SBMTE0yaNIlvJEsFBQXcuXMHrVu35jrOiVb4/ft35OTkwNTUVGyBryhkZWVh1apVXFE8fX19eYTQolL+HScivHr1CpGRkbC2tsa+ffskqr8uIEyUEhaLhaKiIpHrFTdiJYdfv37h+PHj2L17N+Lj49G6dWt4enrCzc0NampqOHPmDNatW4ft27dXeVoXoPqiZFhbW2PYsGGYOnUq3/ObN2/GoUOHRFrY1STlf1tB/aSwBubllWt37tzBvn37EB0djXfv3sHR0RGurq4YMGCAxKl76xItW7ZEYGAg+vXrB0NDQ0RHR8POzg6pqamwt7fH+/fvq/R+V69exeTJk5GWlsZX0FhXxjtxDO+PHz+OwYMHY+TIkbCzswNQ6rEeFRWFgwcPCnS4EobAwED8/fff6NGjB6Mcvnr1Ki5fvgxvb2+utEQ1qZipDFEiq/yuTJ48GampqVi9ejViY2MRHh6Oly9fMg53e/fuRVBQEG7cuAGg+pV94iCsUv79+/coKCjg+t3u37+Pv//+GwUFBRg4cCDf6KzCwIke065dO5w/f57LsKm4uBhxcXHYvn07M29UVFREenq6wLn88+fP0aJFC3z//h1+fn5ISEjAnTt30KpVK1hbWzNpDGtDwFvdyMnJ4f79+zxGHJmZmWjTpo1IRhxPnjyBhYWFyMZcNeUE5OvrC2VlZZ70f/w4fvw4Ro8ezTcqUk0rv44cOYKFCxcKlS6xKiJzfvnyBefPn0fr1q151k21xc2bN+Hq6sqs+USFzWbDycmJa052/Phx2NnZcSmHaipyfF1BnMhzlSkfy6Kvry9SxovfnTVr1mD58uU4evQo/P398eLFCyQmJvIde37+/ImYmBjs2rULycnJ6NevHzw9PdGnTx+ho3FWFTU9vxeGygwgyhpJ8Ys6J8hQ4dOnT8jMzAQRoUWLFv9Zp0pRqStrqqrk/fv3NT5vs7e3x7hx4+Di4sJ1fN++fdixYwcSEhJqpB1t2rTByJEjMX/+fK7jgYGB2L9/P+7evYusrCwsX76ccdjR09Pjmr9JSUkhKSmJZ14GlEYrO3r0KMLDw4XKcCCqQZOtrS3atGmDzZs3cx2fOnUq0tLScOnSJaHrKk91pViui/wX3+vyVJeBrrm5OZ48eYLp06dj1qxZAp9zjlxFS0sLZ86cQdu2bQEAjRo1wvbt25lMHOUzGYmKKFn7RIlefOzYMaHbUN54qjK5fNnvXFFRkTGY8vX1xatXrxAREYH79+/DxsaGJ0ooULoWzcrKgpWVFRQUFPhG9asqUlNT4eTkxDcKXm5uLpydnXH//n1G15Sbm4s2bdrg2LFjEukK169fj0WLFmHatGmM83NSUhKCg4MRGBgILy8viaMcf/v2Dfn5+VWih+U8LwMHDkR4eDhXcJPi4mKcO3cOZ8+e5cloWZP8+PGDx0BY2LTsggx0S0pKsHz5cmzbtg1v3rxBeno6jIyMsGjRIhgYGIhl9Pz9+3dMnDgRx48fh7a2Nnbs2MGlp7a1tYWjoyOPg0VlOhd+BnSSfCf84Ly/u3fvxqdPnzBq1Ch4eHhUaFj2+fNnnD17lsmUZGRkBHt7e4naUVMYGxtj06ZN6NevH5feetOmTbh69apEelZlZWXcu3cPBgYGWLJkCe7du4dDhw7h9u3b6Nu3L16/fs2UVVRUxMOHD6tNLi6qTl5URI3oKy5FRUX48eNHtTl1CEJUGXVd5NmzZ9DV1eXrAJCbm8s4vopDdQasErV/UVZWxoMHD6Cnp4dmzZohJiYGnTt3RnZ2NszNzZm5Wrdu3TB06FDMnj0b9+/fh4WFBTIzM5k5WWJiIsaOHcvzmwqbdZCDONkE66mnVqB66qmHL69fvyYdHR3S1dWl1atXU2xsLMXGxtKqVatIV1eXdHR06PXr12LXb25uTmvWrCEiokOHDhGLxaJu3bpRbm6uwGsy6dyE8QABAABJREFUMzNp2LBhJCUlRS4uLpSVlVXhPcLCwujEiRPMvo+PD6mpqVG3bt0oJydH7LavXr2aGjRoQKamphQbGyuwXJMmTejKlStERPTmzRtisVi0YcMGse8rKS9fvqROnToRm80mKSkpGj16NH39+pU5//r1a2Kz2TzXeXh40NatW2uyqVWGlpYWPXjwgIiITExM6OjRo0RElJKSQkpKShLV3a5dOxo7diz9/PmTOVZYWEhjx46ldu3aERFRUlISGRgYiFx3SUkJHThwgCZPnkxDhgyhQYMGcW0cPn/+LNQmCQYGBlybkZERdenShebNm0dfvnyRqG5ROXnyJMXFxfEcj4+Pp1OnToldL6d/47f5+vqSgoICycnJiVwvm82mN2/eiN0uIqKfP39SdHQ09enTh6SlpcnKyoqaN29OKioqFB0dTerq6iQrK0tsNpuUlZVJQ0ODa5MUPT09WrVqlcT1lKdRo0Z07949gefv3r1LjRo1qvL7VhUsFovrt1VWVq50TBKHkpISOn/+PI0bN440NDRITU2N3N3dq/w+tUVwcDBJS0uTuro6tW3bloqLi4mIaNOmTWRjY1Nl97l27RrNnDmTtLW1SVFRkYYPH863XF0Z7+Lj46lPnz6UnZ0t0nUnTpyg7t27k6KiIjVs2JBsbW0pISFB4vaUHwcEbYaGhhLfqypp2bIlnTt3joiIkpOTSUFBgbZv304DBgzgGkt/Z969e0eWlpbEYrFIRUWFYmJiuM7b2dnR/Pnzmf3GjRtTSkoKs9+wYUM6dOgQs5+eni7x/CgiIoK6d+9OTZo0YebbGzZsEDhnjoyMpL/++osKCgoqrHfEiBE0e/ZsZv/NmzekoaFBZmZm5OzsTDIyMhQREcH32u/fv1c4N2KxWMRms4nNZhOLxeLZFBUVKTQ0lKt8ReM7vzn1169f6dSpUzR37lzq3LkzycjIkJmZGU2ZMoUOHjxY4Wf/nTA2NqZt27bxHN+6dSs1b95c6Hq+fftGM2fOpJYtW4rcBisrK9qyZYvA85s2bSIrKyuR6y3PjBkzSF1dnaysrGjatGnk5eXFtZVFX1+fpk6dKtH6uarg94xznv3yz+2UKVNIQ0OD2rVrRxs3bqQPHz5UWv/QoUNp8+bNRFT6O7Zo0YJkZGRIWlqaq7+pTe7cuUMqKipiX+/m5ibU9r9GZfNjQfIGUbCxsRG42draSlQ3h0+fPtHOnTvJz8+PeeZv3bpFz58/r5L6RcHX15fYbDYZGRnRs2fPhLomJyeHlixZQkZGRqSnp8cl86kJamp+XxlFRUW0dOlS0tHRISkpKeZZXLhwIYWEhHCVlZKSIn19fVq8eDHdvHmTUlJS+G71CEddWVOJQ2ZmJk2bNo3s7e3J3t6epk+fXi3rfGFQUFCg9PR0nuOPHz8mBQUFiep+/vw5bdy4kaZOnUpTp06lTZs2CezjDh06RFJSUuTg4EBLly6lpUuXkoODA0lLSzNrj5kzZ5Kfnx9zjbKyMq1Zs4bCwsIoLCyMnJycaOLEiXzrb9euHamoqJCysjK1adOG2rdvz7WVR0VFhcaMGUNnzpxh+peKSEpKInl5ebK0tKQlS5bQkiVLyNLSkuTl5enixYvCfF0CcXZ2prCwMInq+F2oK+91QkIC9e/fn4yNjcnY2JgGDBgg8e/IobrmMeXn3OW38nNwZ2dn8vDwoOLiYjp48CDJysrSx48fmfMnTpyg1q1bi/kpqw9+awxB647ylJfFHzx4kObPn09NmzblGbM1NTXp9u3bRFTaf3BkAJmZmTyyjPfv35OdnR1zX87v6e7uziVbqEoyMjJITU1N4PmSkhI6c+YMbdq0iTZt2kRnz56tkvsaGBhQeHg4z/GwsDAe3VRhYSG5u7vTkydPquTe4lB+HVp2k5WVpZYtW9Lx48drvF35+fk0depU0tTU5Pu+CktKSgrf8gEBAWRkZER79uwhBQUF5pmMjo6mrl27VtnnqEqq6jvhh7S0NOnr65O/vz/dvHmTUlNT+W4cIiMjSU1NjeeZUVdXp+joaEk/arWjqKhIT58+JSIibW1tunXrFhERZWVlkaqqKldZUWWrGhoadP/+fSIi6tGjB23fvp2IiLKzs3nmjdbW1nTkyBGR2i7K+FudOvnq4NixY7R7926uY4GBgSQnJ0dSUlLUu3dvrnG4uhFVRl0XEaQXf//+vcT9hrh6s8oQp38xNzdndG/29vbk7e1NREQbN26kpk2bMuViYmJIVlaW7OzsSEtLi/r3789Vz9y5c2no0KE89aupqVFSUpLQn4Gz9hK01VNPXUG6tg2E66mnrqKlpYXk5GRMnjwZ8+bN45u2TZIUrVlZWRg6dCgAYPDgwZCWlsbatWsFeopOmTIFoaGhsLW1xc2bN4UKX79ixQps3boVQGkasC1btiAoKAgnTpyAl5eX2BFt/Pz8oKCggObNmyM8PFxghMQ3b94wHjCNGzeGoqIinJycxLpnVeDn5wc2m41r164hLy8Pfn5+sLW1xZkzZ5hIH8QnMlLz5s2xaNEiJiVYRamP6hpdu3ZFUlISTExM0LdvX3h7e+Pu3buIiYlB165decpfunQJ27dvx5MnT3Dw4EE0bdoUkZGRMDQ05EljHBwcDGdnZzRr1oyJNHX37l0UFxfjxIkTAEojf02ZMkXkds+aNQvbt2+Hra0ttLS0BHp1q6urV+jxTVWQkiY7O1vsa6saPz8/vulMS0pK4OfnJ/b7VTa9JYfHjx/Dz88Px48fx6hRo7B06VKR6+X3PgnLrVu3sHv3bkRFRUFOTg5jxoxBcHAwE5Fu8+bNmDFjBoKCgsS+hzB8+vSJ6aurkry8PDRs2FDg+YYNG+Lz589Vft/KuHnzJg4cOIBnz57xeISXHTNYLBbPuydM9IXCwkK+6UgEeY2yWCzY2trC1tYWkydPhqenJ8LDw3+rVNIVMWXKFHTu3Bm5ubno3bs34wFtZGSEwMBAiepOT0/H3r17ERUVhezsbNjZ2WH16tUYPHiwQM/nujLeDR8+HN++fYOxsTEUFRV52iHI27Vfv35MtNiqpLJxQJKISdVJbm4u02fGxsbir7/+woQJE9CjRw/Y2NjUaFuqi0aNGuHixYv4/PkzlJWVedJkHjx4kOt579q1KzZt2oSdO3ciJiYGX79+ZSIuA2BSUovL1q1b4e/vj1mzZmH58uXM/ENdXR1BQUF8x9t169YhKysLWlpaMDAw4HneOZHar169irCwMOZ4REQEGjRogJSUFEhLS+Pvv/9GcHAwk76xoKAAvr6+OHDgAN+IwGXnRtnZ2SAiGBkZ4fr161xp62VlZdG4cWOe7zYkJERgX8JJEVcWZWVlODk5MXOVjx8/Yv369di8eTO2bdtW4+kDqwtvb2/MmDEDKSkp6N69OwDg8uXLCAsLw8aNG/leo6GhwROp8OvXr1BUVMSePXtEbsODBw8qfMdtbW3FmteVJy0tjVmT3rt3j+tc+TnBhw8f4OXlJdH6uaoQZW4fHByM9evXM5E5582bV2lkzosXL2LBggUASiPrEhHy8vIQHh6OwMBAJvpXTVA+khf9/0wgW7ZsYSJLiQMnPWQ9khMREVHh+TFjxnDtX7hwoTqbg7S0NPTq1QtqamrIyclhsj7FxMTg2bNnlba3Khg8eDDXvoyMDBo1asQTHUWQPKtsyuLaGFuqc34vCsuXL0d4eDjWrFmD8ePHM8fbtGmDoKAgrghlz58/R3h4OHbv3o1t27bB1dUVnp6elUb3r4c/dWVNJSrx8fFwdnZGu3btmDHi8uXLMDU1xfHjx9G7d+8abY+uri527tzJZLrgEBISItF8/Z9//sHs2bNRWFjIRIP68uULfHx8sH79eh4Z5pAhQ3Dt2jVs2LABsbGxAEqzjV2/fp2JXHXu3DmEhobyXMeJPGpgYIBx48bxbY+omV7Cw8Oxb98+/Pnnn1BTU8Pw4cPh6uqKjh078i3fo0cPXLlyBWvWrMGBAwegoKAACwsLhIaGMmlmxcXJyQl+fn64e/dutaZYrgvUhfd6z549cHd3x+DBg5n7Xb58Gfb29ggLCxM7m0pZyq7xioqKEBYWxkTP5rfGEwZR5y7Lli2Dvb099uzZg6KiIsyfP58rQnt0dLREqaE5FBYWIjs7G8bGxpCWllxNXl7OKQr85AR//fUXzMzMsH//fq4xu3fv3hg3bhzat2+P9PR09O3bF0Bpdp3y0ay9vLwgIyODZ8+ecY3pw4cPx+zZs7Fu3Tqx2yyIs2fPomXLlgLPs1gs9O7du8rHlFevXjHr77J0794dr1694jomIyODw4cPC8wE06FDB5w7dw4aGhpo3759hfJucTPrcZ4XQ0ND3Lhxo85kF5o7dy4uXLiArVu3YvTo0QgODsaLFy+wfft2vjopUYmIiMCOHTtgb2+PSZMmMcfbtm0rdnaXshQVFSEhIQFZWVkYOXIkVFRU8PLlS6iqqgqUX3379o2vHoSj76zO76S4uBjPnj3DsmXLmHVCeX0aR7d5+/ZtuLu7Y9SoUfDy8kLr1q1BRHjw4AGCgoIwevRotG7dmok+Xhdp1qwZXr16BT09PRgbG+PMmTPo0KEDbty4wZWdRxzZas+ePTF79mz06NED169fx/79+wGAb/avKVOmwNvbG8+fP+c7fymbVQkQffwVVSdf26xfvx5//fUXs5+cnAx/f38sXboUJiYmWLBgAZYtW1Zj2XrEkVHXNUhApPr8/HyRMmHwQ1y9WUWI27+4u7sjNTUV1tbW8PPzw4ABA7Blyxb8+vWL63kZNGgQTp06hRMnTqBPnz48Gd0UFRX52pFoaGhwRVCujPJZWuupp67CIkmsZ+qp53+E6kjbVlnaIH7l5eXlK02LWXZRKE7KG2Fwc3MTyhArIiICr1+/ZiZQqqqqSE1NrTCVUHXStGlTHDlyBJ07dwZQmgZx6NChyM3Nxblz5/Dr1y++qZpESX1U13jy5Any8/NhYWGBgoICeHt7Izk5GS1atMD69eu50ngcPnwYo0ePxqhRoxAZGYkHDx7AyMgIW7ZswalTp3Dq1Cme+r9+/Yq9e/ciPT0dANCqVStm8SsJDRo0wJ49exghkyDKpqIhIvTt2xchISFo2rQpVzlxBHceHh5ClatJQ0EFBQU8fPiQR+CWk5MDMzMzFBQUSHyPly9fYvHixQgPD4eDgwNWrlxZYVqd6sDc3ByPHj1Cnz59MH78eAwYMIBn0fX+/Xs0btxYIgGoMHh6eqJTp05cQqOqQEpKiqt/LI+4qeMkITo6GmPGjIGDgwPOnDmDPn36ID09HW/evMGgQYO4jDHKpzbml9YY+D/FeXp6Ojw9PZGcnMx1vjIj+ufPn2Pfvn3Yt28f7t27h27dumHUqFFV/nvUFk+ePKm2FIlsNhudOnXCyJEjMWLECKGMourKeCfI8YdDZYvtHz9+YP/+/SgoKEDv3r0lVjxWxqxZs6CgoICVK1cCKJ3T+fv7M3O8/fv3Q09PD9u2bavWdpSncePGiI+PR/v27dG+fXvMnj0bo0ePRlZWFtq2bSt2SsjfmbS0NNjb2+PLly+Msm/ZsmXM+dGjR0NJSUns38rU1BQrVqzAwIEDueb29+7dg42NDd+01gEBARXWuXjxYgClc4BHjx4xc7e+ffuiTZs2jNFCeno6unXrxhjjTp06FRcuXMCyZcv4KhFGjRol1mcEhEv1CXAbQpaUlODGjRtISEhAQkICLl++jPz8fOjp6cHW1vY/ZfB35MgRrFu3Dg8fPgRQasTh4+PDV4kAAGFhYVzfJ5vNhqamJrp06SLWulNGRga5ubnQ1tbme/7Vq1fQ19fnUUBVJ2PHjoWlpaVA45TfhadPnyIsLAwREREoKirC/fv3eRR9CgoKjLH/mDFjoKOjg1WrVuHZs2cwNTWt0b63fOpDFosFTU1N2NnZYd26dWjSpEmNteV/gfLzexUVFaSlpTHzK37z+/Lv+K9fv/Dt2zfIyspCUVGxQuXK8+fPAUCidMDl6dWrFzp06IA1a9ZwjWPJyckYOXJkjaSRdHd3F6pc2XHj58+fjCF9UlIS+vfvD3d3dzg6OlaYAvS/TPPmzbF9+3bY29tz/ZaPHj1Ct27d8OnTJ77XJSUlYffu3Th48CBMTU3h6ekJT0/P/9nvURzqyppKVNq3bw8HBwceYxM/Pz+cOXNGbCMkcTl16hSGDBmC5s2bo0uXLgCA69evIyMjA4cPH65UXsiPkydP4s8//8SsWbPg7e3NjIOvXr3C2rVrsXnzZhw9elTkulVUVPDw4UOmP/by8sLChQsZp+ynT5+idevW+P79O9d1RUVFWLFiBTw8PETuy79+/YpDhw4hKioK58+fh5GREVxdXeHv7y9SPZJQUymW6wJ14b02MTHBhAkT4OXlxXV8/fr12LlzJ7P2EBdh1ng19Vnfv3+Py5cvQ1tbm3n/OZw8eRKmpqZi65e+ffuG6dOnM3Kn9PR0GBkZYfr06WjatCn8/Pz4Xnfu3DmcO3eOb9CBsrqBX79+wdHREdu2bZNYFvXkyRNYWFhwrR/y8vKwcOFC5ObmYvLkyXB0dARQKjeQlZVlHAUBQFtbG/Hx8Wjbti3XXIBfvcJS3gGQw+fPn3Hr1i2EhIQgJCQEI0aM4FsuMTERf//9N/O8mpqawsfHB5aWliK3pSxt2rTByJEjMX/+fK7jgYGB2L9/P+7evct1fOzYsWjXrh3P+wSUymg4BtLCymvERZARF1D6rCoqKkpUv6jo6ekhIiICNjY2UFVVxe3bt9G8eXNERkYiKiqK0Q+Wd6grT15eHhITE3nGgbJyrbLP5IMHD9C5c2eJ1spPnz6Fo6Mjnj17hp8/fzLv9syZM/Hz508eOd+7d+/g7u6O06dP862P03ZhvxNx2ywM+vr6cHd3R35+Pg4ePMi3zF9//QVVVdU6HdTEz88PqqqqmD9/Pvbv3w9XV1cYGBjg2bNn8PLyYuag4shWnz17hilTpiA3NxczZsxgHBu8vLxQXFyMTZs2MWX5zV84Dp785i+ijr+i6OTFoTKHe1HngmX1BwAwe/ZsPHjwAHFxcQBK5+QzZ85ERkaGeA3+H2L27NkAgI0bN2L8+PFcfXhxcTGuXbsGKSkpXL58Wex7SKo340dV9S9Pnz7FrVu30Lx5cx5Dd6D0PW3WrBnPO0hEyM3N5QnitGfPHhw9ehTh4eECx8MvX75U2KaycJwz66mntqmPoFtPPUKgoaGBTp06VXm9FXklc+B4ZImz2FNWVsaHDx+gp6eHM2fOMJMDeXl5HqGgKJSN3lUR4eHhaNmyJbPIzM/PR/v27XkGX3E8esTh8+fPXAowOTk5xMTEYOjQobC1tRUYoaouRVAVlbKGX5UZnAQGBmLbtm0YM2YMoqOjmeM9evQQGOlFRUWlWgzl1NTUhDJaK294KyUlha5du1aJwVtYWBj09fXRvn17iSLBViVqamp48uQJj4FuZmYmj2GkqHz+/BkrVqzA5s2b0a5dO5w7d05i4Zi4DBs2DB4eHjyG1mVp1KgRIxDNysrC7t27kZWVhY0bN6Jx48Y4ffo09PT0YGZmJlFbqitKBhHB3t5eYJSGoqIiseqVhBUrVmDDhg2YOnUqVFRUsHHjRhgaGmLixIk8BhzlF3qurq4V1u3u7g5paWmcOHECTZo0qVTov337duzbtw+XL19G69atMWrUKBw9elRiAUZdo3nz5mjWrBmsra1hY2MDa2trJuKppDx+/FhkZUBdGe9EESTMnj0bv379wubNmwGURkHp2rUrHjx4AEVFRcydOxdnz55Ft27dJGrT8+fPcezYMb5RFSSJmFSdiBJZ5X8FCwsLPHz4UKCyb8SIERKNG9nZ2YxAsyxycnICnWiEneOrqqoiLy+P6QevX7/OFUmHxWLh58+fzP7x48cZJYK7uzssLS3RvHlz6OvrY+/evXwNdMPDw9GoUSMmEvXcuXOxY8cOmJqaIioqirm3KAZaa9asYQxyv379iqZNm8LGxgZBQUGwtbWtNce96mTQoEEYNGiQ0OXd3Nyq9P4lJSUVRpNgs9lVajCRmZmJrKwsWFlZQUFBga+SsWXLlpg3bx6SkpLqRDTByMhIbNu2DdnZ2bhy5Qr09fURFBQEQ0NDgYbUgHCROXV1dXHlyhU0aNAAcXFxzLrq06dPEkfKEJXqdmSrhxsigpubG+PA9uPHD0yaNIlZp5XtoznwM5LMyMjA5MmT4ePjw3OupKQEgYGBWLduHaPAVlFRgbe3NxYsWCCxEeWNGzewfft2nuNNmzbF69evJapbWER12JgyZQqio6Ohq6sLDw8PREVF1XgUstmzZ2PZsmVQUlJi5G6CqKnIQy9evOC7rigpKcGvX78EXtezZ0/07NkTK1asgIuLCyZNmoQhQ4aIFLnmf526sqYSlYcPH+LAgQM8xz08PKo9cxE/+vbti4yMDGzdupUxfBgwYAAmTZokdgTdtWvXws/Pj0fG2aRJE6xfvx6KiopYs2YNl4GutbU1PD09MXToUCgoKPCtl81m4+XLl4yR7YYNG7jOv3nzhmfuA4DJplc+WrowqKiowN3dHe7u7njw4AFGjRqFgIAAvkYZxcXFiI2NZb5HMzMzODs7Sxx97H9pnlEX3usnT55gwIABPMednZ15jBLFobI13vPnz6skC0d5bt++DX9/fyYbX3Z2doVzckmzJs2bNw+pqalISEhgjFuBUielJUuW8DXQDQgIwNKlS9GxY8dKZZoyMjJIS0uTqI0A8P37d2zatIlHNq6uro4tW7bwbWN5CgoK+Bq0fPz4kStapSgIivqtoqKCVq1aVWicyy8KZVJSUpVEgQ4ICMDw4cNx8eJFrijw586d4zu2tWjRAkuXLsXly5d5ImhqaGjA3NwcnTp1gqenJ1xcXCQORiOIXr16ISIigud3vnbtGkaPHs0ExakpPn78yMgyVVVVGb1tz549MXnyZKacmppahfWoqanxHdtMTU1x6dIlHvn+oUOH+MrSRGHmzJno2LEjUlNTubIWDho0iCubBIdZs2YhLy8P165dg42NDY4cOYI3b94way0Own4n4iCKnuPy5cv4559/BJ6fNGmSWJlMa5KyTmDDhw+Hnp4erly5ghYtWnCNb+LIVvX09JhxpCzl52Sc+kVB1PFXFJ28OBw5coRr/9evX8jOzoa0tDSMjY1FNtD9+vUr1zuTlJTElVHUzMwML1++lKzRYiCsjLoucefOHQClsqG7d+9CVlaWOScrK4u2bdtizpw5Et2jOqLEVlX/oq+vX+HvYmhoiFevXjFBbTh8/PgRhoaGPHJWYbIOVpZluSz/JefBen5v6g1066mnltDT08POnTuZfW1tbURGRnKVYbFYEhno1rZhRl2LhGVkZIS0tDQugyVpaWkcPHgQQ4cORf/+/Su8vqpTH9U1Hj9+DCsrK57jampqyMvLA1DqJe3k5AQZGRmBHtMcJElltmTJEgQEBGDXrl0CBeDVzeTJk5nU8O7u7nB1da11pRQn0seRI0dgbGwMoNQowtvbW6Lve82aNVi9ejW0tbURFRVVoWFCdfPr1y+EhYXhr7/+qtBAl0NiYiKcnJzQo0cPXLx4EcuXL0fjxo2RmpqK0NBQHDp0SKL27NixA8rKykhMTOSK2Axw99GiIkyfXpMpkIFSQ2fOgldWVhYFBQVgsVjw8vKCnZ0dl7BX1P49JSUFt27dqjQKPIfAwEC4uLhg06ZNdTo1k6Tk5uYiISEBiYmJTPpZHR0dWFtbw9bWViKjzhYtWiAvLw+HDh1CVlYWfHx80KBBA9y+fRtaWloVvl91YbwT1vD+zJkzWLFiBbO/d+9ePHv2DBkZGdDT04OHhwcCAwNx8uRJsdty7tw5ODs7M1HH2rRpg5ycHBAROnTogJycHOjo6DDlx40bxyWsNjAwYKLc1STBwcFMZJXDhw8zwrZbt27BxcWlxttTV2jUqJHAca5t27ZYunQpduzYIVbdhoaGSElJ4RFGxcXFVZgmWph3tWvXrti0aRN27tyJmJgYfP36FXZ2dkwdnIidHMRRIqxYsQJbt24FAFy5cgVbtmxBUFAQTpw4AS8vL6504kSEzMxMFBYWolWrVgL7iqCgINjY2ODvv/+Gra1tlTkh1GU4v+eTJ08wZ84cvr+nKEpbflEHKqKmnIA+fPiAYcOG4cKFC2CxWMjIyICRkRE8PT2hoaHBpdTiOKZW9XxKHERNl8gvMueWLVsERuacNWsWRo0aBWVlZejr68PGxgYAcPHiRZibm1f756un9hDGgU0YI6wWLVpg1apVcHV15Un1umDBAoSGhmLVqlWM8UFSUhKWLFmCHz9+YPny5RJ8glKlJ78IJOnp6QIzf9Q227Ztg56eHoyMjPj2MRzKjmFVzZ07dxijV45Sjh/CKo6qAnENIJKTk7Fr1y4cPHgQrVq1QnBwMNTV1au5tf9N6sKaShQ0NTWRkpLC4+SZkpLCo0StKZo1ayZxv1aW27dv83VC4DB69GiuCGtAaWThOXPmYPr06Rg2bBg8PT150hObmZnh33//ZTK2lSc+Pl5gZio7OzskJiaKLKf/8eMHjh07hn379iEuLg5aWlp8HTsyMzPRr18/PH/+HK1atQIArFy5Erq6ujh58iQjW6xHOGrzvdbV1cW5c+d41lP//vuv2EbrovDhwweEhoaKtVaOj4/H2bNnISsri3HjxjGyFT8/Pxw/fhwODg5MWWNjY+jr68PW1pbZqjJbQGxsLPbv34+uXbtyjctmZmbIysrie822bdsQFhaG0aNHC3UPV1dXZr4mDBoaGlxtISJ8/foVioqKPAFlrKysYGtrC2tra3Tv3r1CB0BLS0tEREQwWYNYLBZKSkqwZs0a2NraCtW28khimL98+XKsWbOGKwrljBkzsH79eixbtkwiA90hQ4bg2rVr2LBhA2JjYwGURr28fv0633lPaGgo1NXVcevWLdy6dYvrHIvFQmJiInbv3o05c+Zg9uzZ+Ouvv+Dp6VnlwUzk5eVhYWGBf/75B8OHD0dJSQmWLl2KFStW1IqxpZGREbKzs6Gnp4fWrVvjwIED6Ny5M44fP841HxRX7+vv74+xY8fixYsXKCkpQUxMDB4/foyIiAi+xpWicOnSJSQnJ3MZxAGlMtkXL17wlD9//jyOHj2Kjh07gs1mQ19fH71794aqqipWrlzJ6EiE/U7EYc2aNZg+fTqj/7x8+TI6duzIGNB//foVvr6++Oeff/Dy5Uu0bNlSYF0tW7bk+znrMt26deMbUENY2eqXL1+YiJiVRdEsWy49PR2FhYXo3LmzUGtcccffwsJCvlHXy0cJFRV+680vX77Azc1NpGABHJo2bYqHDx9CT08P+fn5SE1N5TJs/vDhQ41H8wZEk1HXFS5cuACgNFjRxo0bqy1ia1UHrBK3fym/buLAYrEgLy+P5s2bw8rKinEKFBQILT8/n+98RpBTUFk43zlQ6mzm5+cHNzc3pm+5cuUKwsPDmcyX9dRTJ6B66qnnt+bnz5/09etXvuc+ffpEU6dOJWdnZzp9+jRz3N/fnwIDA2uqiXWGuXPnUp8+ffie+/XrFzk7OxObzeY5V1BQQB4eHiQlJUVSUlKUlZVFRETTpk2jlStXVmubxUFDQ4PevXtHRETq6uqkoaEhcCuLoaEhnT17loiIlJWVmc8ZHh5OJiYmRETEYrHozZs3zP+CNn7foyh8+/aNHBwcSFlZmdq0aUPt27fn2gRRtt1VwY8fP2jfvn3Uq1cvUlRUpKFDh1JcXByVlJRU2T1EIS8vj7p27UrS0tJkYGBABgYGJC0tTba2tvTp0yex62WxWKSoqEjOzs40aNAggVtNoaOjQw8ePBCqbNeuXWndunVExP37X7t2jZo2bVptbfwv0rRpU0pLSyMiInNzc9q3bx8RESUnJ5OqqqpEdXfs2JEuXbokdPnaesdqm/T0dBo7dixJS0tL3I+mpqZSo0aNqHnz5iQtLc28GwsWLKDRo0fzvaaujHcJCQmkoKBAvXr1IllZWaYdK1eupCFDhnCVVVFRoYyMDGZ/xIgRNH78eGb/zp071KRJE4na06lTJ/L39yei/+tnvn79Ss7OzvTPP/+QqqoqXbt2TeD1165dIxUVFYnaUE/NkJKSIta7FxAQQAUFBbRz505q2rQpRUdHk5KSEkVFRVFgYCDzPz9SU1NJU1Oz0neV807LysoSm82mhQsXctXj6upKEydOZPbNzc0pISGBiIjs7e3J29ubiIg2btwocHxUUFCgp0+fElHpvJlz/3v37lGjRo2Yck+ePKE2bdoQm80mNptNurq6dP369Qq/o8LCQoHnOPPW/wLC/p6c+XJF82lx59RLliwRapOU0aNHk4ODA+Xm5nLNweLi4sjU1FTi+qsLExMTOnLkCBFxzx3v3r1LDRs25Co7efJk0tDQIAsLCwoKChL6Wb1x4wbFxMRwrdNPnDhBSUlJVfMhhCA/P58WLVpEZmZmpKSkRMrKymRubs70V/XUbe7cucN37tCkSRM6evQoz/HY2FjS0dGR+L6enp40cOBAKiwsJGVlZXry5Ak9ffqU2rdvTzNnzpS4/upg7Nix5ObmVun2v0ZsbCypqanRqlWrSFFRkdauXUvjxo0jWVlZOnPmDFfZly9f0qpVq6hVq1bUuHFj8vLyort379ZSy39/6sqaSlg440JAQACpq6vTqlWr6OLFi3Tx4kVauXIlqaur09KlS2utfQUFBfTw4UNKTU3l2sRBUVGxQplhVlYWKSoq8hz/9esXHT58mJydnUlGRoZMTExo7dq19Pr1ayIi2rFjBykqKtKJEyd4rj127BgpKirSjh07+N5z69atpK2tTd7e3rRv3z46evQo11aeuLg4GjNmDKmqqlKDBg1owoQJlJiYKPAzOTk5kaOjI3348IE59v79e3J0dKS+ffsKvE4YAgICKtz+S9SF9/qff/4hWVlZmjRpEkVERFBERARNnDiR5OTkaNu2bdV+f3HXyiEhIcRisahhw4bEZrNJU1OTIiMjSV1dnSZOnMgj/71w4QItXryYrK2tSV5enthsNjVv3pwmTJhAUVFRzHsnLgoKCszvV3YtkJKSIlD22aBBA8rMzBT6HtOmTSNVVVX6448/aMKECeTl5cW1lWf37t0UFhbGbBEREXT69Gn6+PEjT9lly5ZR7969SUlJieTk5KhHjx60YMECOnPmDM8c/+7du9S4cWNydHQkWVlZ+uuvv8jExIS0tLRE+jz8yM3NFXjuypUrfI/Lyspyye84ZGRkkJycnETtEZbi4mKRyufn59OuXbvIysqKWCwWtWjRglatWkWvXr2qsjZt2bKFFBUVycXFhbp160Y6OjoUHx9fZfWLwvr162njxo1ERHT27FmSl5cnOTk5YrPZFBQUVCX3uHjxIvXq1Ys0NTVJQUGBevToUSWfV11dne7fv09E3O/2pUuXqHHjxjzlVVRUKDs7m4iI9PT0mLX6kydPSEFBgSlXnd8Jm81mdK2cNpWdp7x+/Zrpd8vqZflRtmxd4ujRo4w8sPwcp/wmqmy17PfHkZ+V38rK1Tg6As5xVVVViouLq/QziDr+Pn78mHr27FlhW6qDtLQ00tfXF/k6Pz8/at26NUVERNCIESNIT0+PioqKmPPbt2+nHj16VGFLhUNYGfX/GqLozYRF3P7FwMCAlJSUiMViUYMGDahBgwbEYrFISUmJtLS0iMVikbGxMY0bN468vLyIzWbTxIkTueZEM2bMoC5dulD37t3FantZ7OzsGJ12Wfbu3UvW1tYS119PPVVFvYFuPfX8RuzatYumTZtGe/bsIaLSiRNHSd+rVy96//59LbewYn7+/Em5ubn09OlTrq2m+PXrF33+/LnC8zk5OTzHZ8yYQX/88QddunSJlJSUmAlPbGwstWvXrtraKy5hYWH048cP5v+KtrKsWLGCTE1N6erVq6SiokKXLl2iPXv2UKNGjWjTpk01+hmGDh1KjRo1okmTJtHixYuFNijgKBGrg5ycHFqyZAkZGRmRnp6eQMP46qakpITi4+NpzZo1tHnz5goF8cJS15Say5cvp7Fjx9KvX78qLaukpMT85mWFL9nZ2TUm3KtqPn/+TP/88w/98ccfNXpfFxcXxth56dKlpKmpSePGjSN9fX2JDbTPnTtH3bp1owsXLtD79+/p8+fPXFt5rl+/Tl5eXtSvXz/q168feXl50Y0bNyRqQ12koKCA4uPjad68edStWzeSl5endu3a0axZsyg2Nlaiuu3s7MjHx4eIuN+Ny5cvCxTW1JXxThTDezU1NUpPT2f2DQwMKDQ0lNnPzs4meXl5idqjrKzMKC/U1dXp3r17RFSqwNHX16du3brR8uXLBV6/dOlS6tatm0RtEJePHz/S2rVrycPDgzw8PGjt2rVcytl6uBFX6VhWKLxnzx5q3rw5Y2DZtGlTCgkJEXitvb290O/qu3fvKDY2lq5evcpTz4kTJxjlApF4SgRNTU26ffs2ERG1a9eOIiIiiIgoMzOTlJSUmHJDhgyh1q1b0759+ygmJoa6d+9OHTp0qOgrosGDB/N1vnj9+jWZmZlVeO3vhLC/Z05OjtBbXUVLS4tSUlKIiPuzZmVlcT0vdQ15eXnmey3b7vT0dJ7xgsVikb6+Pg0cOLBOOLEJy8+fP+mPP/4gOTk5GjhwIPn5+ZGvry85OzuTrKwsde3atUKj+XpqjvJKydjYWNq6dSuZmZmRo6MjT3k5OTl6/Pgxz/FHjx5JPN8hKnUI7dWrF6mrq5OUlBTp6uqSjIwMWVpaUn5+vsT111OzCGsAIS0tTfr6+uTv7083b97kMYSUxCDyf5G6sqYSFs48tqSkhNavX09NmzblmscGBQXVigPt27dvqV+/fnyNLcQ1bujUqROtX79e4Pl169ZRp06dKqzjzZs3tGzZMpKXlycZGRn6888/6dy5czRixAhisVhkYmJCAwcOpIEDB5KJiQmx2WwaOnSowPpEddRSUFCgoUOHUmxsrFBjuaKiIuOIXZaUlBSJ52vt2rXj2szMzEhRUZFUVVUrDK7wO1JX3uuYmBjq0aMHYwTRo0cPiWVHwiLuWtnc3JzWrFlDRESHDh0iFotF3bp1q9DAk8P379/p3LlztGjRIrK0tGTWs5I4A1paWjJ6jrJ6hGnTppGDgwPfa+bOnSuSo4KNjY3AzdbWVuy2l+XXr1+UnJxMK1euJAcHB5KRkeErB8/Ly6Nly5bR0KFDycnJiRYsWEAvX76U+P4mJiZ8ZUtJSUmkpqbG9xpjY2O+xmxbt26l5s2bi9WOFy9ekLe3N1/Zcl5eHs2ZM4fLqLu8QeScOXOElpFlZGTQ/PnzmfnxgAEDxGozP/z8/IjFYpGMjAxdvny5yuqVlJycHDp8+HCVzAN//fpFAQEBQr374jBs2DAmYAPn3f769SvZ2dnx1Wt17NiRMc4cMGAAjR49mp4/f05z584lIyMjgfepyu+kvFFc+eBD5Q10IyIiBBq3hoeH10kDXVECPokqW01ISGD0hwkJCRVuRER9+vSh7t27U3JyMt2+fZsGDRokdN8jyvjbvXt3srKyolOnTtGdO3coJSWFa6suLl26ROrq6iJf9+3bNxo9ejSpq6tT69at6eLFi1znbWxsaNWqVVXVTKERVkZdV7lx4wb5+PjQ8OHDq1SGWB0Bq8TtX/bt20c2NjZcTj8ZGRlkZ2dH0dHRlJubSz169CBNTU2ysbEhFotF3bt355oX9enThyZMmMCl3xMXBQUFvvU8fvyYy/Ginnpqm3oD3XrqqWWE9TYPDAxkvGIaNGhAkyZNIm1tbVq1ahWtWbOGmjVrRpMmTeKpvy4YZtSWx1hVoaenx3j+lp3wZGRk/Kci4pWUlDCeiJyFj7y8PC1atIi+ffvGlHNycqK8vDxmf+XKlVzRW9+/f89E3BUXRUVFoaJtlp/YSktLU58+fapFaf7s2TMKCAggQ0NDatq0aa0Z6P4vMHDgQFJRUaEmTZpU+ns2bdqUEVyVfT9jYmIqFKaIQm5uLgUHB5Ovr2+lUQ8k4fz58+Tq6kqKiorUpEkTmjJlSpXWXxkfPnygFy9eEFFpRIGVK1fSgAEDaPbs2XyjNohCeWFLReOAj48PsVgsUlFRobZt21Lbtm1JWVmZ2Gw2zZ07V6J21DVkZGSYKFVHjx6V+Hsui6qqKrM4Lvtu5OTkCDReryvjnSiG92WFEvfu3SM2m83lqJGQkCCW93hZtLS0mKguJiYmTCQjjlJTkohJ1UliYiKpqqqSrq4u03/q6emRqqpqlTh3/BcRV+nIz9O8oKCgQu9zDuK8q/zIzc3lih5dHmGUCCNHjqQOHTqQp6cnKSoqMs5/R48e5TKi1dLS4pqnvXz5kthsdoXGWx07diQPDw+uY69evaLWrVuL7eFfFxHn9yzrZPns2TNatGgRzZkzh0coXhVUpROQsrIyI/gs+1lv3LhBDRo04ClfU/OpyjAxMWEUKWXbvWnTJh5jEmGd2Ly8vJjnv/xnq43PGhQURFpaWvTo0SOecw8fPiQtLa0ad8Cshz/8FJNaWlrk4uLC13iic+fONH36dJ7j06ZNoy5dulRZu5KSkig4OJhWr17NZNmpRzjy8/Np4cKF1K1bNzI2NiZDQ0OurS7CTzle1VmS/peoK2sqYeE3j/3y5Qt9+fKlllpUysiRI6lHjx5048YNUlJSojNnzlBkZCS1atWK77pLGMLCwkhBQYGCg4O5HMJ//fpFW7ZsIQUFBdq9e7fA669du0aTJk0idXV10tPTI39/f/L09CQFBQXy9vamqKgo+vPPP8nExIRMTEzI2dlZYBYNcRH1d9HQ0OBr7JWUlMSTWa0q+Pz5Mw0aNIgxoviv8Lu919WBuGtlRUVFxpG0pKSEZGRkRM4q8fPnTzp//jz5+PiQqqqqRGPSpUuXSFlZmSZNmkTy8vI0c+ZMJiLtzZs3mXJl5+8zZ84kdXV1srKyomnTplXJ/F6QM4ywDjKPHz+m7du304gRI6hJkybUoEEDGjhwIBERhYaGMsFbqgt3d3f6448/uPokjgxKkCNEdUSB9vb2rlAOMnHiRC5Zcvkxr3zE0srIz8+n7du3U4MGDapkbvTx40caPHgwqamp0Y4dO2jUqFGkpKREwcHBEtddF1FSUuJyLK9KcnNzydTUlExMTEhaWpq6du1KDRs2pFatWvGVzUVGRjJj/s2bN6lRo0bEZrNJXl6eoqOjiag0E5SdnV2VGI7xQ1QD3erIwFSXkES2KgwNGzakW7duMfufPn0iFotVYVAvcVBUVKSHDx9WaZ1l2bhxI9cWFBREvr6+pKOjQy4uLtV235pGWBl1XSQqKopkZGSof//+JCsrS/3796eWLVuSmpqaxIGwqiNglbj9i5GREd25c4fn+O3btxnZx+XLl0lbW5uIiNzc3ER634qKimjt2rXUqVMn0tLSqjBDNBFRy5YtmaAZZfHx8aGWLVsKfd966qlupFFPPfXUKkeOHOHa//XrF7KzsyEtLQ1jY2P4+/sDAMLCwhAaGgoXFxfcvHkTXbp0wYEDBzBkyBAAQJs2bTBp0iSuui5evIgBAwZATU0NHTt2BABs3rwZy5Ytw/Hjx2FlZVUDnxBwd3eHtLQ0Tpw4gSZNmoDFYtXIfQVBRDh06BAuXLiAt2/foqSkhOt8TEwM1/67d+/QuHFjnnoKCgpq/bMIQ0lJCTIzM/l+1rLPAIvFwoIFC+Dj44PMzEzk5+fD1NQU27dvh6GhIV6/fg0AiI+Px8+fP5nrVqxYgWHDhkFdXR0AUFRUhMePH0vUZl1dXaiqqlZaTk1NjWvf1dVVovuW5+fPn4iJicGuXbuQlJSE/v37Y8uWLXB0dASbza7SewlLQUEBEhMT8ezZMxQWFnKdmzFjhlh1Dh48uNIyLBYLhw8fFqt+UVFXV2f6tsoYMWIEfH19cfDgQbBYLJSUlODy5cuYM2cOxowZI3Fbzp07B2dnZxgZGeHRo0do06YNcnJyQETo0KGDxPW/ePECYWFh2L17N/Ly8vDp0yfs27cPw4YNq9H+paioCCdOnICDgwMAgM1mw8/Pr8rqv3DhglDlwsPDsXnzZmzatAkTJ06EjIwMgNKxcevWrfD19YWZmVmV/LZ1gb59+yIpKQnR0dF4/fo1Xr9+DRsbG7Rs2VLiuuXk5PDlyxee4+np6dDU1OR7TV0Z79TV1fHq1SsYGhpyHb9z5w6aNm3KdWzu3LkYMWIETp48ifv376Nv375c1506dQqdO3eWqD1du3ZFUlISTExM0LdvX3h7e+Pu3buIiYlB165dMX78eJw/fx4DBgxA69at0apVKwDA48eP8fjxYwwZMgTjx4+XqA3iMHXqVAwfPhxbt26FlJQUAKC4uBhTpkzB1KlTcffu3Rpv03+Z8u+IoqIiFBUVK71OnHeVHx8+fEBoaCh27NiBX79+wdHREdu2bUOLFi0AAPr6+tDX16+wjuDgYCxcuBC5ubk4fPgwGjZsCAC4desWXFxcmHJv375l6gWAJk2aQEFBAW/fvuV5bzmcOnUKVlZWmD17NtavX4+XL1/C1tYWbdu2RXR0tNCfs64jyu959+5dDBgwALm5uWjRogWio6Ph6OiIgoICsNlsbNiwAYcOHcLAgQMlbteFCxewa9cuxMTEQE1NDYMGDZK4TktLS0RERGDZsmUAwMzD1qxZA1tbW66y1T2fEoalS5dizpw5mD17NqZOnYofP36AiHD9+nVERUVh5cqVCAkJ4bomLCxMqLptbW3x69cvAKVjlSBqaiyNiYnBokWLmPGoLK1bt8aCBQtw6NAhTJ8+vUbaU49gyq/PK2PNmjXo168f/v33X3Tr1g0AcOXKFeTm5uLUqVNit+P79+84d+4c+vfvDwA4ceIEs+Y/deoUzpw5g6VLl0JeXl7se/yvMG7cOCQmJmL06NF1Qv5VWFjIVxakp6fH/J+dnV3TzfpPU1fWVKJQvl0qKiq11JL/4/z58zh69Cg6duwINpsNfX199O7dG6qqqli5ciX69esncp1jx47F3bt3MW3aNMybNw/GxsYgIjx58gT5+fmYMWMG3NzcuK55+/YtIiMjsXv3bmRkZGDAgAGIioqCg4MD870NGzYMgwcPRn5+PkaMGMH33omJibC2tha5zQDw5csXRkZKRHznmhzKy1L79++PCRMmIDQ0lFkXX7t2DZMmTYKzs7NY7akIVVVVBAQEYMCAARg9enSV119b/I7vtahUJhvOy8sTq97v378z62IWiwU5OTk0adKkwmsKCwtx9epVXLhwAQkJCbh27Rp0dXVhZWWFLVu2iP0uAUDPnj2RkpKCVatWwdzcHGfOnEGHDh1w5coVmJubM+XKz+nbtWsHALh37x7XcXF//3bt2oHFYoGIKizHYrFQXFzM7I8cORKJiYn4+fMnrKysYG1tDT8/P1hYWDBtGT9+PPr37888szo6OkhOToaBgYFYbeVHSEgI/vrrLwwYMADx8fFITk6Gs7MzAgMDMXPmTL7XTJ48Gdra2li3bh0OHDgAADAxMcH+/fvx559/itWOuLg4bNu2TeD5MWPGYPz48Vi9ejXf85V9/xwuXryIXbt24fDhw2Cz2Rg2bBg8PT3FanNZ2rRpA0NDQ9y5cweGhoYYP3489u/fjylTpuDkyZM4efKkxPcQlXPnzuHcuXN854+7du2SqG57e3skJiZW6bPIoVmzZkhNTcX+/fuRmpqK/Px8eHp6YtSoUVBQUOApX1aP+Mcff+Dp06d49OgR9PT00KhRIwCAjIwM0tLSqryt4iDqurGuwU9OyQ9xZasZGRk4evQocnJywGKxYGRkhD///BNGRkZMmY8fP6JZs2bMvrq6OpSUlPDhw4dKddF5eXk4dOgQnjx5gjlz5qBBgwa4ffs2tLS0eHQVpqameP/+faVtFpcNGzZw7bPZbGhqamLs2LGYN2+e2PXa2dkhJiaG0fNz+PLlCwYOHIjz58+LXbc4CCujrousWLECGzZswNSpU6GiooKNGzfC0NAQEydOrHQOVBmi6M2ERdz+5dWrVygqKuI5XlRUxNiT6Ojo4OvXrwCA3bt3i1R/QEAAQkJC4O3tjYULF2LBggXIyclBbGwsYztVlg0bNmDIkCE4ffo0unTpAgC4fv06MjIyasy2oZ56hIFFws5A66mnnhrjy5cvcHNzw6BBgxhhlpycHDIzM6Grq8vsp6WlMUq3Fy9ewNDQkMtgz9zcHN26deNrmJGcnFxjhhlKSkq4desWWrduXSP3q4yZM2di+/btsLW1hZaWFs+kv/wkwcrKCkOHDsX06dOhoqKCtLQ0GBoaYvr06cjIyEBcXFxNNl8krl69ipEjR+Lp06c8AgeOgOfnz59YsmQJzp49Czk5Ofj4+GDgwIHYvXs3Fi5cCCkpKUydOhW+vr4ASif8r1+/ZoQ8KioqSE1NZRY7b968gY6ODpfwSFROnjyJzZs3Y9u2bdWyYBeGKVOmIDo6Grq6uvDw8MCoUaOYxXltcefOHfTt2xffvn1DQUEBGjRogPfv30NRURGNGzfGkydPxKrX3d1dqHKiTqBrgsLCQkydOhVhYWEoLi6GtLQ0iouLMXLkSISFhTF9n7h07twZTk5OCAgIYJ71xo0bY9SoUXB0dMTkyZPFqvfw4cMIDQ3FxYsX4eTkBFdXVzg5OUFJSQmpqakwNTWVqN3ioKioiIcPH1ZqwFWddO7cGS4uLvDy8uJ7fv369YiOjsb169druGXVS1paGhITE5GYmIhLly5BWloaNjY22Lt3r9h1jhs3Dh8+fMCBAwfQoEEDpKWlQUpKCgMHDoSVlRWCgoJ4rqkr492cOXNw7do1HDx4EC1btsTt27fx5s0bjBkzBmPGjMHixYu5yp87dw4nTpyAtrY2pk+fziW4CwgIgLW1NWxsbMRuD0dha2FhgYKCAnh7eyM5ORktWrTA+vXrmXcmOjoa0dHRSE9PBwC0aNECLi4uApW01Y2CggJSUlJ4DLQeP36Mdu3a4fv377XSrtpEGKVjYmKiyHMYNpsNNTW1SpVzHz9+5DkmzrvKj9TUVHTo0IFpu6amJvOcVjVSUlI8BqfNmjVDUlIS17ytvIA7NzcXPXv2xJAhQ3DixAl06NABe/fulXisrkuI8ns6OTlBWloafn5+iIyMZBxldu7cCQCYPn06bt26hatXr4rVlup2Arp37x7s7e3RoUMHnD9/Hs7Ozrh//z4+fvyIy5cvw9jYmClbXfMpUZCSksKrV6/QuHFj7N27F0uWLEFWVhaAUoFxQEBAlShZ6wKamppISEiAmZkZ3/P37t2Dra0t3r17V8Mtq6cqePnyJYKDg/Ho0SMApYYNU6ZMgY6Ojth1btu2DSdPnsTx48cBlK7xzczMGGX2o0ePMHfuXIFz9Hr+D3V1dZw8eRI9evSo1XZkZGTAw8MDycnJXMeJiMfYh8OvX78YB8nyvH//vtblIb8LdWVNJSySzGOrE1VVVaSlpcHAwAD6+vrYt28fevTogezsbJiZmeHbt29i13316lVERUUhIyMDANCyZUuMGDECXbt25SkrKysLY2NjeHh4wM3Nja8DnaWlJdhsNhITE/neLzExEf3792eU02VZunRphW319/fnmsOw2Wy+v5WgdzsvLw9jx47F8ePHmfe7qKgIzs7OCAsL4wl8UBUkJSVhwIAB+PTpU5XXXVvU1nutoaEh9Lxd0ne0umTDbDYbgYGBUFZWBgD4+vrCx8eHZ0zhBJ2ws7PDtWvXYGhoCGtra1haWsLa2lpig5aa5ubNmzhw4ADfABsxMTF4+vSp0HWVldOy2Ww0atQIHh4esLOzQ8+ePXmM1yrT3VQVhYWF6NevH759+4a0tDSsXLkS06ZNq9J7VIaSkhIePnzI5XhUlmfPnsHExAQFBQUARPtuXr58ibCwMISFhSEzMxPdu3eHp6cnhg0bBiUlpSpp/7Jly7BgwQKeQDDPnz+Hu7s7zp49WyX3EZaAgAAsXboUHTt25OtoVj7YlKhs27YNAQEBGDVqFP744w+e71ESx5GLFy+ie/fukJbmjktXVFSE5ORksYNVeXl5QU5ODqtWrRK7bYKorH/8+vUr/P39JdKz1iUqk1OKOydduXIl/P39UVJSgsaNG4OI8O7dO0hJSWHFihWYM2cOU//58+fRoEED5tru3bvjwIEDXIa7FhYWXPWnpaWhV69eUFNTQ05ODh4/fgwjIyMsXLgQz549Q0REBJfz1M2bN7Fw4UKsWLEC5ubmPOsrYQJT1Qbl+0cOb9++RdOmTRmn9HoqR0lJCffv34eBgQEaNmyIhIQEmJub4+HDh7Czs8OrV6/ErltUvVl10q9fP7x+/RohISFo3749gFIbhvHjx0NbWxsnTpxAt27d8PnzZzx48KBSvUz54HnGxsbYtGkT+vXrBxUVFaSkpDDHrl69in379vHUkZubi61bt3LJyyZNmsTYVtVTT12g3kC3nnrqKJyISjk5OQDEM4qsK4YZnTp1woYNG9CzZ88auV9lNGjQAHv27EHfvn2FKp+UlMQY0IWFhWHixIl48OABkpOTkZiYiD/++KOaWyw+7dq1Q8uWLREQEMB3Ua2mpgZfX19s374dvXr1QnJyMt69ewd3d3dcvXoV8+fPx9ChQ7kMJ2rCQFdDQwPfvn1DUVERFBUVeRYxNaEYYLPZ0NPTQ/v27StcFJafNFYnnMia27Ztg5qaGlJTUyEjIwNXV1fMnDlTqEi4/1Vyc3Nx9+5d5Ofno3379lVmkFR24q+hoYGkpCSYmZkhNTUVf/75J9NHi4q0tDR8fX3h5+fHFZ1GRkam1gx0bWxs4OXlJXbUgsrIy8tDaGgoHj58CAAwMzODh4cHl0JISUkJd+/eFSg0fvLkCczNzRnB6n8FIsKdO3dw4cIFXLhwAfHx8SAivh6owvL582f89ddfuHnzJr5+/QodHR28fv0aXbt2xenTp/kKk+vKeFfdhveiUFxcjMuXL8PCwoLHg5zD169fK40yJUnEJHHp0aMH43RTltjYWKxatUpso7/fmepUOgYFBVWq4B47dizPMUHvardu3XDq1CmhFT/lDXTFVSJ8+vSJq682MTGBh4cHlwCbn2EAxyig7P/85oLp6emwtLRE7969ERkZ+Z+JOMVBlL63UaNGOH/+PCwsLJCfnw9VVVXcuHGD6WsfPXqErl27ihytqiadgD5//owtW7YwEWo6dOiAqVOn8ijQq2s+JQr8lA3fvn1Dfn4+34hovzMyMjLIzc2FtrY23/OvXr2Cvr4+j7FAPTVLQUEBVq9ejZiYGCbSj6GhIf766y/MmTNHqEhBVYWlpSXmzp2LAQMGAOBd4+/ZswfBwcG4cuVKjbXpd8XQ0BCnTp2CiYlJrbajR48ejBMIP1lQ27Ztea4ZMmQIDh06xFP2zZs3sLe354kaWA9/6sqaSlgkmcdWJ506dUJgYCAcHBzg7OwMdXV1rFy5Eps2bcKhQ4cYJxtR4ETTF6V/vXTpEiwtLSssY25uDiMjIxw5coTHyOrixYvo27cv3N3dsXnzZp5rOcpsDuUz692+fRuJiYnMOy3ICJiDoDVnRkYGHj58CBaLBRMTEzRv3rzCeoRh06ZNXPtEhFevXiEyMhLW1tZ8lea/K7X1XoeHhwtdtqbfUWExMDCodM3HYrGYoBMyMjJo0qQJBg4cCBsbG1hbWzNR8yShqKgIxcXFkJOTY469efMG27ZtQ0FBAZydnYXWXX358gXnz59H69at+QakiY6OxpgxY+Dg4IAzZ86gT58+SE9Px5s3bzBo0CAeecOHDx+Yz5ibm4udO3fi+/fvcHZ25ul/Pn36hEuXLiEhIQGJiYl4+PAh2rVrBxsbG9jY2KBPnz7VZqDLL5Lo169f4eLign79+nE5XZY3cKsuGjVqhJiYGIHGlxcvXsTgwYOZaJZsNhsTJkxgxoHg4GC4urryjIEPHz7Ev//+i0aNGmHMmDHw8PDgm53kv0aTJk2wZs2aaouAXlFGSkEyJGEp68xSlg8fPqBx48YoLi7G7Nmzha5v/fr1AEodpyMiItCiRQu+RsWccuIgav8IlBqjamlpwcPDg6vcrl278O7dOybIUl2kMjmlOHPSCxcuoFevXli0aBFmzpwJDQ0NAKX646CgIKxYsQLnz5+HlZUVI8vkZxbFOc7vOezVqxc6dOiANWvWcPWnycnJGDlyJHJycnjkpGVlpOWPVaXB9fPnzwGAy8BYVDh9e7t27XgMmIuLixEXF4ft27fXiNyuPMLIqOsizZo1w+nTp2Fubg4LCwvMmzcPLi4uuHLlChwdHfH582ex665OvZmo/cvr168xevRonDt3jssR0N7eHpGRkdDS0oKjoyMmTZqEgQMHVqqXKT8/KuuE06RJE5w8eRIdOnTAkydP0L59e4m+x3rqqU3qDXTrqaeOUt7bvLx3V3nPrvfv36N3795ck7u6Yphx/vz5OuUxZmhoiNOnT4sU0TcrKwurVq3iUj77+vpypT6qi3AU8RUJXo2MjBAUFARnZ2fcu3cPFhYWcHNzQ2hoKN8FopSUFF6/fs1EjSgbOQCoGgPdygSQNSF0dHNzE8popCajyqqrq+PatWto1aoV1NXVceXKFZiYmODatWsYO3Ys4xX2uyPIKJrFYkFeXh7NmzeHm5sbT/pkoHQB8OPHD8bzWFK0tbVx4cIFmJiYwNTUFKtWrYKzszNSU1PRo0cP5Ofni1XvxIkTsX//fpiZmWH06NEYPnw4NDQ0atVA98CBA5g3bx68vLz4CpwkEa7evHkTDg4OUFBQYNIq3rhxA9+/f2fSyQGl48H169cF9s+PHz9Gp06dKkzp+Duxfv16JCQkICkpCV+/fkXbtm1hZWUFGxsbWFpaMkIlSUhKSkJaWhozdvXq1avC8nVpvBPV8P7SpUvYvn07srKycOjQITRt2hSRkZEwNDSUyElIXl4eDx8+5EkdxMHGxgbx8fFcip6yVBQxqTrZv38/5s6di+nTpzPRoK5evYrg4GCsWrWKy3CkppQn/1UERRkQhcuXL3O9d5W9q+Upb6ArjhLh4sWLGDBgANTU1NCxY0cApanD8vLycPz4cUbpVZlhAIeBAwfyHc+/ffsGOTk5LqFhTUdlq26E+T2ry+mtLjoBVdd8ShTYbDbevHnDN/JdVVJQUIBVq1YJTAsqbsYLUSi/XitPVazX6pGMwsJCdO/eHffu3YOTkxNat24NIsLDhw8RFxeHDh064OLFi0xa1TZt2oDNZleaYlXc8bxJkya4cuUKEwVdU1MTN27cYPbT09PRqVOnegWIEOzZswdHjx5FeHh4jRpZl0ecTFadOnWChYUFQkNDmWOvXr2CnZ0dzMzMcOjQoepo6n+SurSmqoyqmMdWB3v27EFRURHc3Nxw69YtODo64uPHj5CVlUVYWBiGDx8ucp2CjHck5eXLl7C0tESPHj0QERHBHL906RL69euH0aNHIzg4WOj6+GXW4/Ds2TPo6uryNfrIzc0VGEWyOii/PuakWLazs8O8efMqdWL93fid3uvfmYKCAsYA9cKFC0hJSUHLli2ZzEjW1tZizefd3d0hKyuL7du3Ayg1LDUzM8OPHz/QpEkTPHjwAEePHuUb0GXYsGGwsrLCtGnT8P37d7Rt2xY5OTkgIkRHR2PIkCFc5S0sLDBx4kQmtXVqaipXauuAgAAA/xegJzc3Fy1atEB0dDQcHR1RUFAANpuNgoICHDp0iEe3V5bMzEwEBgZi7969KCkpQXFxMc9aQFVVlWmDJPAzbiu7z8/ArbqjQPfr1w86OjpMFpryjBs3Di9fvsSpU6cAlMrvhDGIVFZWhqenJ/r3718tQQLWrFmD6dOnM9kqLl++jI4dOzJyxa9fv8LX1xf//PNPld+7Iho2bIjr169zZcP5XRC03k9PT0fHjh3x5csXvrokfrBYLJw/fx4AKrymbLnq4Pnz51i6dCl27NjBHDMwMMC+ffvQvXt3rrLXrl3DiBEjkJ2dXW3tkZTK5JRBQUEiz0mHDx8OdXV1pm8vz4QJE/D161dERUUJHbm8fHZJNTU13L59G8bGxlxyu6dPn6JVq1b48eOH0HJSQLAzlbCUlJQgMDAQ69atY2RpKioq8Pb25huRuzLKGhfzMxlTUFDA5s2beYw2qxthZdR1kZEjR6Jjx46YPXs2li1bhs2bN+PPP//E2bNn0aFDhyoJ+lUdAavE7V8ePXrEZJRs1aoVX4cWzlpFU1OTGfsqo1WrVoiIiECXLl3Qs2dP9O/fH35+fti/fz+mT5+Ot2/f8lzD0Q8+efIEBw8erDL9YD31VCX1Brr11FPLVORtbmVlhaioKAD8F8AcBHl31RXDDM6EsCY8xoQhPDwccXFx2LVrl9ATgd8VOzs7zJ07F46OjgLLyMrKIjs7G02bNgVQOuG+fv26QAEjm82Gk5MTIzA4fvw47OzsmAXVz58/ERcXV6/wrQbKpoFp2bIlNm/eDAcHBzx69Ah//PHHfyaq6Lx587B161aYm5tzGXOmpaXBzc0NDx48wL///otp06Zx9aHLly/HsmXLUFRUBDs7O+zfv19iA8eBAweiX79+GD9+PObMmYOjR4/Czc0NMTEx0NDQwL///it23d+/f8eBAwewa9cuXLt2DQ4ODjh58iRSUlLQpk0bidotDvwW7xV5D4uCpaUlmjdvjp07dzKppoqKijBu3Dg8efIEFy9eBADGMHXZsmV861m4cCGSkpKQkJAgdlvqEp06dWKUDJaWltWSXrI8t2/fhr+/P06cOFHt96oqhDG8P3z4MEaPHo1Ro0YhMjISDx48gJGREbZs2YJTp04xwnhx6NixI1avXg17e3u+5yWJmFSdVCaQq6r3+3dCGGEii8XiMkwRhupS9pelsij5eXl5SExMZH5LcZQI5ubm6NatG7Zu3coooYqLizFlyhQkJyfj7t27IrX5vxDxSVi+f/+Oc+fOoX///gBK5zI/f/5kzktLS2Pp0qWQl5dnjpVXIFWV01t1OwG9f/8eBQUFXEqL+/fv4++//0ZBQQEGDhyIkSNHAiiNVOft7Y1Ro0ZV23xKWGoqhbeLiwsSExMxevRovlErZ86cKVH9wsBms9GmTRue9J4cioqKcP/+/f+Zvr8usnHjRqxcuRKJiYk8yotHjx7BxsYGCxYswPTp07mM5yqTC4n7mwrKvlS2Te3atcOPHz/Eqv9/ifbt2yMrKwtEBAMDAx4H9du3b9dIO8TJZPXu3TtYWVnByckJ69evx8uXL2Fra4u2bdsiOjpaZGVvPb8HNTGPrQq+ffuGR48eQU9Pj0n9LCqiGCNXlk0LKJ3faWtro3fv3ujVqxfs7e0xdOhQbNy4kYm4OmrUKGzbtk3ktpbPrMdBmAiBHOpSpPZ6xEMU5/S6mjL7ypUr+PDhA7NOAoCIiAgsXryYWTts3rxZoMPz169fkZSUhAsXLiAhIQGpqalo0aKFyFHdW7ZsiS1btqBPnz4ASiOnrlixAg8ePGAyDF6/fh0XLlzguVZbWxvx8fFo27Yt9u3bh8WLFyM1NRXh4eHYsWMH7ty5w1Ve2NTWTk5OTLT7yMhInDhxAg4ODoyx6fTp03Hr1i2uIDsfPnxAYmIiEhISkJCQgAcPHkBdXR1WVlawtrbGzJkzedY9eXl5UFVV5RnHRV33CGvcBvyfgVtZmcCHDx+YyOjdunUDUPp8xMfHY9GiRfDy8hKpPUBp9MzevXtj1qxZ8PHxgZaWFoDStfSaNWuwceNGnDlzBnZ2diLXXZ2U78tVVVWRkpJSpRkqxcHX1xfKyspYtGhRjd5XEjiysqNHj8LR0ZGrLykuLkZaWhpatWqFuLi42mqi2JR3xAcEB5F48uQJTE1N6/R6rTI5ZWJioshzUkNDQ0RGRgpc81y6dAljxozhMiz89esXzxqNw/v373nmmY0bN0Z8fDzat2/PZaB79uxZeHh4IDc3l6u8qPWLyrx58xAaGoqAgAD06NEDQGmAliVLlmD8+PFYvny5SPU9ffoURAQjIyNcv36dy8hdVlYWjRs3rtFshhyqWkZdk3z8+BE/fvyAjo4OSkpKsGbNGkavv3DhwioJysOhKgNWVWf/UlJSAnl5edy/f19oY2I/Pz+oqqpi/vz52L9/P1xdXWFgYIBnz57By8uLJxp3deoH66mnKuEvra+nnnpqjA0bNnDtc7zNx44di3nz5jHHxfF8c3FxAQDMnTuX77maMszgJ9ioTYYNG4aoqCg0bty4QoVJVaY+qi2mT58Ob29vvH79mm/0YgsLCxQXF0NWVpY5Ji0tXeFkrrwBhaurK0+ZMWPGSNTuZ8+eVXi+JiNC1CXat2+PGzduoEWLFrC2toa/vz/ev3+PyMjIWjHorC7ev38Pb29vHmFQYGAgnj59ijNnzsDAwACxsbGMgW5ycjL8/f2xdOlSmJiYYMGCBVi2bJlEaYaA0giDHE/UgIAA5OfnY//+/WjRooXEdSsoKGDs2LEYO3YsMjIysHv3bty8eRM9evRAv3798Ndff1VqkFWVVKeH9c2bN7mMc4HSvmbu3LmMBywAzJkzBwMHDsTPnz/h7e3NCFZfv36NdevWISgoCEeOHKm2dtY0N27cqJZ64+PjcfbsWcjKymLcuHEwMjLCo0eP4Ofnh+PHj8PBwYGrfF0Z744fP44PHz7Azc2NOSas4X1gYCC2bduGMWPGIDo6mjneo0cPBAYGStSuwMBAzJkzB8uWLePr4R8fHw9LS0u4ubnxREzq378/xo4dW+PGuUD1vtO/K2FhYdDX10f79u35GjeJizh1iaqorMyAX01NjWv+Jc78OzMzE4cOHeISvEpJSWH27NlczzaHFy9e4PDhw1xe8oMHD2acvn53o1tRCA8Px8mTJ5nfc8uWLTAzM2OcAR89eoQmTZrwKB3d3NyY3/jHjx+YNGkSl9ObOGzfvh1BQUGME9CsWbPg4OAAIuKJ5ioO06dPh46ODtatWwcAePv2LSwtLaGjowNjY2O4ubmhuLgYo0ePRkBAACZNmlSt8ylRCAgIqHZnmNOnT+PkyZOMoqQ2WLx4caVlykf6qqdmiYmJwaJFi/gaxLZu3RoLFizAoUOHMH36dGRnZzNKsuoa25s1a4Z79+4JNNBNS0uTKG3m/xIVRbmrSVavXo25c+eKlMlKU1MTZ86cYeb9J06cQIcOHbB3795641whqCtrKlH5XWK3KCoqMpl3JEHYSI7CvMslJSV4+/YtAgMDmQjoNjY2+Pz5M44cOQIXFxexjHMB4PPnz3yjlvNLmQwA+fn5XI5ghYWFsLa2ZiK1DxgwgInUvnz5cpw+fZqJ1F4VVEWK5bpIbb/X6urqlT6zdd3pNiAgALa2tsw66e7du/D09ISbmxtMTEywdu1a6OjoYMmSJXyvV1JSQoMGDdCgQQNoaGhAWlqaSXUtCi9evOAyCDl37hyGDBnCrA3Gjh0rMEve58+fmayWcXFxGDJkCBQVFdGvXz/4+PjwlNfQ0GAyKDVt2hT37t2Dubk58vLy8O3bN6bcjRs3cP78eVhYWKBt27bYsWMHpkyZwoy5ZYPucGjcuDEaNWoES0tLjB8/HjY2NjxBVqor21/5qJLCUFYmMGTIECxduhTTpk1jjs2YMQNbtmzBv//+K5aBrq2tLYKDgzFz5kxs2LABqqqqYLFY+Pz5M2RkZLB58+YKjXMLCwuRnZ0NY2Njgc6N1UH5sbc2x+LZs2cz/5eUlGDHjh34999/YWFhwTNGVMXa/dy5c9iwYQNXyvpZs2aJnEWKA+cdJiKoqKhwBWSSlZVF165dMX78eInbnZmZiaysLFhZWUFBQUHgeFzd6Orq4vLlyzwGdJcvX4aOjk6Nt0cUKpNTirPeePPmDZP1hR+GhoZ4/fo117ERI0bg0KFDPL/fmzdvYG9vz+MA4uzsjKVLl+LAgQMASueSz549g6+vL1+5iqj1i0p4eDhCQkLg7OzMHLOwsEDTpk0xZcoUkQ10OX17VcgLqxJRZdR1haKiIsbhBih9rv38/CSuVxK9mbCI2r8UFxcjLCxMYAaxssFB2Gw2WrRogQ8fPghtoFvWAHf48OHQ19dnDJ0HDBjAU7469YP11FOlUD311FPn+P79O/3999+kpaUlUT05OTlCb/9LDB06lBo1akSTJk2ixYsX05IlS7g2Dm5ubjRhwgRm/8uXL6Srq0uamppkYWFB0tLSdPLkydr4CELDYrF4NjabzfzllOnbty8NGjSIBg0aRNLS0tSnTx9mn7PVdLvZbLbA7X+VGzdu0Pnz54mI6M2bN+Tg4EAqKirUoUMHunPnTu02rgpRVVWljIwMnuMZGRmkqqpKREQNGjQgBQUF5pyXlxc5ODgw+ydPnqTmzZtXf2OrmOLiYjp27Bj9+eefJCsrW9vNqTIaN25M8fHxPMfj4uKocePGXMc2bdpEsrKyxGazSUNDgzQ0NIjNZpOsrCwFBQXVVJNrjIsXL9KoUaOoa9eu9Pz5cyIiioiIoEuXLolVX0hICLFYLGrYsCGx2WzS1NSkyMhIUldXp4kTJ9KDBw94rqkr452NjQ1t2bKF2b98+TKx2WwKDAykw4cPU+vWrcnLy4vvtQoKCpSdnU1ERMrKypSVlUVERFlZWSQnJydRu8qPoZyt7FiamZlJTZo0oRkzZhAR0aVLl0hZWZkmTpwo0b3rqVqmTJlCGhoa1K5dO9q4cSN9+PCh1tri6OhIq1atYvbT0tJIWlqaxo0bR+vWrSNtbW1avHixxPfJyMiguLg4+vbtGxERlZSUCCzbvXt3OnLkCM/xI0eOUJcuXbiOBQcHk5ycHLFYLFJTUyM1NTVisVgkJydHwcHBPHWcPHmS4uLieI7Hx8fTqVOnRPxUdY+ePXvSsWPHmP2y/RARUWRkJHXt2pXrGjc3N6E2SUlPT6d58+aRjo4OqaqqkouLCx0+fFjs+gwMDCghIYHZX7t2LRkbG9OvX7+Yfc7zwmKx6M2bN5J9gCqiptpiYGDAd6ytp56yNGrUiO7duyfw/N27d6lRo0Y8x9+/f8/8/+zZM1q0aBHNmTOHLl68KFF7ZsyYQaampvT9+3eec9++fSNTU1NmjlPP7wG/uWv5+asgHj9+TI0bN6ZRo0ZVOG+oh5u6sqb6r1BUVEQhISHk4uJC9vb2ZGtry7WJA4vFInV1dUbOIGgTlVOnTpGmpiZ9/vyZTp06RXJycjR8+HDKy8ujz58/Mxs/Nm7cyLUFBQWRr68v6ejokIuLC1POy8uLvLy8iM1m08SJE5l9Ly8vmjFjBnXp0oW6d+/OlA8KCiItLS169OgRzz0fPnxIWlpatGnTJpE/a1mKi4spICCAVFVVmT5GTU2Nli5dSsXFxRLVXVeo7fc6ISFBqG3z5s3V1gZJ0dbWphs3bjD78+fPpx49ejD7Bw4cIBMTE2a/uLiYrl27RqtXryZHR0dSUVEhNptNurq6NGbMGNq9e7dYuqwGDRrQ/fv3mf0mTZrQnj17mP2srCwuOXNZWrRoQfv376f8/HzS1NSkc+fOERFRSkoKNWzYkKe8i4sLrVu3joiIli5dSpqamjRu3DjS19fn0rGUX5+UX0O+fv2aZ8yuaP5Yk4SFhdGJEyeYfR8fH1JTU6Nu3boJ/H2UlJQEyvuVlJQkas/z589p/fr1NGXKFJo8eTJt2LCBcnNzBZYvKCggDw8PkpKSIikpKeZ7nzZtGq1cuVKitgiDOL99dWFjYyPUJu7YW5bg4GCSlpamESNGMOOei4sLycjIcMmDxWHJkiWUn58v0jU3btwgHx8fGj58uEBd6Pv378nOzo6ZQ3N+J3d3d5o9e7ZEba6MlJQUnudg9erV1LBhQ9q1axej2w8NDaWGDRvSihUrqrU9VcmzZ8/o2bNnEtdTmZyH37vUsWNH8vDw4Dr28uVLat26NQ0ZMoSnjry8POrVqxepq6uTlJQU6erqkoyMDFlaWvJ95kStX1Tk5OTo8ePHPMcfPXpE8vLyEtUdERFB3bt3pyZNmjB9+fr16yk2NlaiesVBFBl1XUNBQaHK7W4k0ZsJi6j9y9SpU0lJSYmGDRtGM2fOpFmzZnFt5Tl27Bj17NmT7t69K1R7BMm/EhMT+ZavTv1gPfVUJfUGuvXUU0v8+PGD/Pz86I8//uCaaOzatYt0dHRIV1eXS2n/9OlToba6yqdPn+jvv/8mT09P8vT0pPXr11NeXl6ttEVRUVEow6cWLVpwGZRt2bKFdHR0mHbPnTuXbGxsqq2dVYEwhtk1ZRwgCikpKVzbjRs3aMeOHdS6dWuJDArq+T1o3LgxhYeH8xwPDw9njDnl5OS4FCedOnWiNWvWMPs5OTmkqKhYpe36+vUrl3JFkIKlqqgNgxZ+i/ANGzZIvAifPn06NWvWjKKjoxkBTFRUFDVr1oxmzpzJUz43N5fWr19PkydPZgSrVSG0qWscOnSIFBQUaNy4cSQnJ8csGjdv3kxOTk5i1Wlubs68C4cOHSIWi0XdunWrUDBdV8Y7TU1Nun37NrMviuG9oaEhnT17loi4F+Dh4eFcyh5xqEwZxiE1NZU0NDRo7NixpKqqSuPHj5fovpISHh5e4fa/yo8fP2jfvn3Uq1cvUlRUpKFDh1JcXFyNG6CIqqh0d3evdCsrABZHiRAdHU16enq0du1aunTpEl26dInWrl1LBgYGFB0dTampqZSamkqbN28mKSkp8vb2ppcvXzLXv3z5kry8vPgqqs3Nzfkqr0+fPk0WFhYifnt1D21tbUYISFRqfFd2//Hjx4yTUW1RVU5A8vLyXIJmJycn8vHxYfYfP35MDRo0IKJSZcnbt2/Fb3QVwmaza2RuFRkZSX/99RcVFBRU+73E4fPnz/TPP//QH3/8UdtN+Z9GWlqaXr16JfD8y5cvSUZGhtlPS0sjfX19YrPZ1KpVK7pz5w5paWmRsrIyqaqqkpSUFF/llbC8fv2atLW1SU9Pj9asWUOxsbEUGxtLq1evJl1dXWrSpAm9fv1a7PrrqXmEnb8KMlaUk5MjVVVViYwW/9eoK2uq/wqiKnyFgcVi0caNGyksLKzCTZx6yxrE89sXZGhlYGDAtRkZGVGXLl1o3rx59OXLF6Ycx0CKxWJR9+7duYym+vTpQxMmTKD09HSmvJWVVYXGTps2bSIrKyuRP2tZ/Pz8SFNTk/755x9mnRAcHEyampo0f/58iequK9Tl9/rLly+0fft26tSpU50OZiEnJ8clz+vRowcFBgYy+9nZ2aSsrMzsq6ioEIvFIh0dHRo1ahSFhIRQZmamxO2ws7MjPz8/Iip1lGez2Vxr2TNnzpCxsTHfazlGherq6tS2bVvGAH3Tpk18f/8PHz7QixcviKh0DbZy5UoaMGAAzZ49mz5+/MiUK79WUlZWpidPnjD7gow0f/36RWfPnqVt27Yx/cSLFy/o69evXOUMDQ25jFs4fPr0iQwNDfl+VmFp2bIlY6icnJxMCgoKtH37dhowYIDAQC96enr0999/8xz/+++/SU9PT6L2iMqMGTPojz/+oEuXLpGSkhIjL4mNjaV27dpV+/3rkoFuTdK0aVO+DgWcvrUmiYqKIhkZGerfvz/JyspS//79qWXLlqSmpsalCx09ejQ5ODhQbm4u1+8UFxdHpqam1dpGfga6JSUlNHfuXJKXl2fmGYqKihQQEFCtbakKfv36RQsXLuRy7FFVVaUFCxZQYWGhWHWyWCxavnw5j8MTZwsMDOT5Dt++fctlzPjixQtq2bIlDR06tEIHo6SkJAoODqbVq1czOgh+iFu/sHTu3JmmT5/Oc3zatGkSGa7+888/1KhRIwoMDCQFBQXmWd+9e3etzHWElVGnpqbWeNsqw9rausqNmiXRmwmLqP1Lw4YNRXJUU1dXZwIzycvLC3SSFFf+VZ36wXrqqUpYRL9JHqN66vmP4evri+3bt6NXr15ITk7Gu3fv4O7ujqtXr2L+/PkYOnQoT+h+DpzXtmyKBOKTzqiyMP9l0+BWJzdv3oSDgwMUFBTQuXNnAKUpfL5//44zZ85USYoyUWjdujUOHDgACwuLCsspKSnh3r17TDj/wYMHo1mzZti0aRMA4MGDB7CxscHbt2+rvc31lHLy5EmsXbsWCQkJtd2UWsHOzg4xMTFQV1fnOv7lyxcMHDiQK2XE70xgYCBWrFiB8ePHo1OnTgBK+4yQkBDMnz8fCxYsQKNGjaCnp4fbt28jPz8fDRs2xPnz55l0wrdv34aDgwPevXsnUVuys7Mxbdo0JCQk4MePH8xxfn2uOBw8eBBRUVFIT0+HrKwsWrZsCXd3dyYFSk2ydetW+Pv7Y9asWVi+fDnu3bsHIyMjhIWFITw8XKx06RwKCwvh4+ODbdu2oaioCAAgIyODyZMnY9WqVVzpAoHSZ5pf2lWgNL1N8+bNxW5LXaJ9+/bw8vLCmDFjoKKigtTUVBgZGeHOnTtwcnLiScEkDEpKSrh//z4MDAxARJCTk8OFCxcqTLVdV8Y7BQUFPH78GHp6egCAzp07Y+jQoUzKwKdPn8LU1BQFBQU8165cuRJ79uzBrl270Lt3b5w6dQpPnz6Fl5cXFi1ahOnTp1dbu798+cL8f/nyZQwaNAgDBw7E9u3bueZqgp7p6qJ8SqNfv37h27dvkJWVhaKiIj5+/Fij7amLPH36FGFhYYiIiEBRURHu378PZWXlGrm3vLw8MjIyoKurCwDo2bMnnJycsGDBAgBATk4OzM3NmdSYbDYb+vr6aN++fYXpD48cOQKgdJ7/9u1bhISEwMTEhOlf4uPjMXv2bNy/f5/n2srSybFYLFCpky8WLFggMD3UwoULkZSUxDVfU1BQwMOHD3lSz+Xk5MDMzIzve/07oaCggJSUFIHp4R89eoR27dpxzSVqk7dv36Jx48ZiXaulpYUzZ86gbdu2AIBGjRph+/btTGq/jIwMtG/fHvn5+WCz2VBTU6s09WNN9EdsNhuvX78W+3MLS/v27ZGVlQUigoGBAU9a0Nu3b1fr/QVx4cIF7Nq1CzExMVBTU8OgQYMQHBxcK22pp1S+8/r1a2hqavI9/+bNG+jo6DBrDScnJ0hLS8PPzw+RkZFMysSdO3cCKE2BfOvWLVy9elXsNmVnZ2Py5Mk4e/Ysl8ypd+/e+Oeff2BkZCR23f9LFBcXY8OGDThw4ACePXuGwsJCrvN1bf4VHh4udNmyaarr4aWurKn+KzRq1AgRERHo27dvldUp7lzg0KFDAt/p27dvIzExUah6rK2tRbovP9zd3bFx48ZK15aamppISEiAmZkZ3/P37t2Dra2tRDIzHR0dbNu2jSvFMgAcPXoUU6ZMwYsXL8Suu65QF9/rixcvIjQ0FIcPH4aOjg4GDx6MIUOGMPLTuoa+vj4iIyNhZWWFwsJCqKur4/jx47C3twcA3L17F9bW1sz4uH37dtja2qJly5ZV2o7ExEQ4OTmhSZMmePXqFVxcXBAaGsqcnzJlCgoKCgSOizdv3kRubi569+7NyAxOnjwJdXV1LnlbTk4Ozp49i8LCQlhbW6NNmzYC28Rms+Hk5MTIRI8fPw47OzsoKSkBAH7+/Im4uDgu2fPTp0/h6OiIZ8+e4efPn0hPT4eRkRFmzpyJnz9/Ytu2bVz18+vz3rx5A11dXZ7+TBQUFRXx6NEj6OnpwdfXF69evUJERATu378PGxsbvn1LWFgYxo0bBycnJ3Tp0gUAcO3aNcTFxWHnzp1cabvFISMjAxcuXOCbZtvf359rX19fH/v370fXrl255LGZmZno0KEDl5yvOmCz2QgMDGSeJV9fX/j4+KBRo0YAgK9fv8Lf319ivYOofP78GcXFxWjQoAHX8Y8fP0JaWlpiuaaysjJSUlJ45PplZQiSUNl4XRYLCwtMnDgRU6dOZZ4BQ0NDTJw4EU2aNEFAQAAAQFtbG/Hx8Wjbti3Xs/LkyRNYWFhI1ObBgwdXeD4vLw+JiYl8n4P8/Hw8fPgQCgoKaNGiBY9upS4yefJkxMTEYOnSpejWrRsA4MqVK1iyZAkGDhyIrVu3ilyngYFBpfImoHStW5bc3Fz07NkTQ4YMwYkTJ9ChQwfs3buXyw7j+/fvOHfuHPr37w8AmDdvHn7+/Mmcl5aWxtKlSyEvL89zP2HqF5fExET069cPenp6XN9jbm4uTp06BUtLS7HqNTU1xYoVKzBw4ECuZ/3evXuwsbHB+/fvJW67KAgro64KHW1Vc+DAAcybNw9eXl74448/mHGdQ2W2KfyQRG8mKsL2Lzo6OkhISBB6zhYWFlbh+8qRN4gr/6pN/WA99YiCdG03oJ56/lc5ePAgIiIi4OzsjHv37sHCwgJFRUVITU3lO0CxWCw0a9YMbm5uGDBgAKSlK399Z86cybVf3jCjpgx0vby84OzsjJ07dzLtLioqwrhx4zBr1ixcvHixRtrBYd26dZg7dy62bdvGYyRQFnl5eXz//p3Zv3r1KtauXct1XtJFY03x4MEDvgvT8kLUuk6rVq1w48aN2m5GrZGQkMBXePbjxw9cunSpFlpUPSxcuBCGhobYsmULIiMjAZT+9jt37sTIkSMBlColjh8/jsjISJw6dQra2tro2rUrU8fNmzcFGsmIgqurK4gIu3btgpaWllALfmEoKSmBi4sLDh48iJYtW6J169YAgDt37uDgwYOYMGECtm7dig8fPuDixYsYNGhQldy3IjZv3oydO3di4MCBWLVqFXO8Y8eOmDNnjkR1y8rKYuPGjVi5ciWysrIAAMbGxlBUVORbvl+/fvj33395Fn+PHz+Gvb09nj9/LlF76gqPHz+GlZUVz3E1NTXk5eWJVef379+Z75XFYkFOTg5NmjSp8Jq6Mt41bdoUDx8+hJ6eHvLz85GamooNGzYw5z98+CDwmfHz80NJSQns7e3x7ds3WFlZQU5ODnPmzJF48Z2Wlsb3OIvFgry8PFq1asXjNHXgwAEcPHiQ2a8NYdGnT594jmVkZGDy5MmM8OZ/HTabzQj0avr30dLSQnZ2NqMYu337NqMAAEoVMmUN+yZPnoyoqChkZ2fD3d0drq6uPEqTspw5cwbx8fFo1qwZ1/EWLVrg6dOnfK8pL7AWRJs2bTB69GiB50ePHs0orTmoqanhyZMnPHPvzMxMHkHl70izZs1w7949gXOPtLQ0nt+iuqnICUgSI9WuXbti06ZN2LlzJ2JiYvD161fY2dkx59PT0xnDcwAICAiAmpqaRJ+lKiivpK0uBg4cWCP3EYYXL14gLCwMu3fvRl5eHj59+oR9+/Zh2LBhVTanrUc8iAj29vYC5TochzYON27cwPnz52FhYYG2bdtix44dmDJlCqO0mj59OtdaSBwMDQ0RFxeHjx8/IjMzEwDQvHnzCseaengJCAhASEgIvL29sXDhQixYsAA5OTmIjY3lMQ6pCb59+8ZXFsRRCtYb3VYddWVN9V9BVla2yh1zxRn7Nm3ahAULFsDNzQ1Hjx6Fu7s7srKycOPGDUydOhVA1RjeAmDkHBXNGXfv3i1UXXl5eWjYsKHA8w0bNsTnz59Fa2A5Pn78yMiyytK6des654wgLnXlvX79+jXCwsIQGhqKL1++YNiwYfj58ydiY2Nhampa7feXhL59+8LPzw+rV69GbGwsFBUVuYyH0tLSYGxszOzHxcUhPj6+0npjYmJEaoe1tTVu3bqFM2fOQFtbG0OHDuU6365dOyawDD86duyIjh07ch3r168f1/6FCxfQv39/5pmRlpbGrl274OrqyrfO8mMwv3Ll9XczZ85Ex44dkZqayvWODxo0COPHjwcAHDt2jDkeHx/PtRYrLi7GuXPnGKNzcVFWVsaHDx+gp6eHM2fOYPbs2QB435myuLm5wcTEBJs2bWJ+PxMTEyQlJTEGu+Kyc+dOTJ48GY0aNYK2tjZXf89isXjmYO/eveO7Ji4oKKiRdZKenh5jaASUGoFydCBly9Q0I0aMwIABAzBlyhSu4wcOHMCxY8dw6tQpiep3dnbGkSNHeGSSR48eZYwgxUWY8bosWVlZzDssKyvL/PZeXl6ws7Nj5HMFBQV8ZdEfP36U2Ci2MjmJmpqaQB2+srJynXXMEMS+ffsQHR0NJycn5piFhQV0dXXh4uIiloFuTk6OWG3R1dXF2bNnYWlpid69eyMyMpLn3Q8PD8fJkyeZZ3PLli0wMzODgoICgFJH/CZNmsDLy0us+sXF2toa6enpCA4OxqNHjwCUGntPmTIFOjo6YtebnZ2N9u3b8xyXk5OrlaAKwsqo6yIjRowAAMyYMYM5JqlBsSR6M1ERtn/x9vbGxo0bsWXLFqGe74occcrOHcSVf1WnfrCeeqqSegPdeuqpJZ4/f44//vgDQKmSW05ODl5eXgIHsefPnyM8PBy7d+/Gtm3b4OrqCk9PT5iYmAi8R10xzLh58yaXcS5QKqCYO3cuj2CjJnB1dcW3b98Y47DyUY04QsR27dohMjISK1euxKVLl/DmzRsu5XNWVpZEE96a4MmTJxg0aBDu3r3LTACB/xNK1zXPMg7lvZSJCK9evcKSJUvQokWLWmpV7VHWOOzBgwdcUTWLi4sRFxeHpk2b1kbTqo1Ro0Zh1KhRAs8vXboUb968wYwZM6CtrY09e/ZweaBGRUVhwIABErcjNTUVt27dqhJj37Js3LgR//77L44dO8YjgDp27Bjc3d1hbGyMsLCwGnOmqM5FeEREBDp16gQTExOYm5szx3/8+IEDBw7wfEZlZWUMGjQIx44dY8aOhw8fws7ODsOGDZOoLXUJbW1tZGZm8hisJSUlSRSlLCQkhInCUFRUhLCwMCYKA4eyAoK6Mt4NHToUs2bNwvz580U2vGexWFiwYAF8fHyQmZmJ/Px8mJqaVkk01Hbt2lUoZJCWloatrS3mzJkDWVlZie9XnbRo0QKrVq2Cq6srI8T7X+Pnz5+IiYnBrl27kJSUhP79+2PLli1wdHSs1Du/KhFVURkcHIz169czbZ83bx769esHT09P9OnTh+cZFUeJoK+vL1TbS0pKeObPZZGRkeGZY/7555+YNWsWjhw5wnyuzMxMeHt7/3YOY/zo27cv/P390a9fP57oGd+/f0dAQACPAre6qG4noGXLlsHe3h579uxBUVER5s+fzxWxOzo6mstQZcSIEdUetbYusXjx4tpuAg4fPozQ0FBcvHgRTk5OWLduHZycnKCkpARzc/N649w6gDDPCScqNVDad2trawMonScrKSlxvXcaGhpMxHVJadCgQYUGKvVUzN69e7Fz507069cPS5YsgYuLC4yNjWFhYYGrV69yzcGrE06WrtOnT/M9z08WdOrUKUhJSfFkczlz5gyKi4u5lOn18FJX1lT/FURV+AoDRykuCv/88w927NgBFxcXhIWFYe7cuTAyMoK/vz9fI9SSkhJkZmbyjeDIzzm3pKQEgYGBWLduHWPoqaKiAm9vbyxYsABsNhuDBw9GWFgYVFVVK422xzF6KykpqTBKG5vNllgm3LZtW2zZsoXHMW/Lli1MpoXfnbrwXg8YMAAXL15Ev379EBQUBEdHR0hJSXFFSq3LLFu2DIMHD4a1tTWUlZURHh7OJTfZtWsX+vTpw+yXzxpXlZiYmAjUpU2YMIFrf/bs2Vi2bBmUlJQY41NBrF+/HgCwaNEi9O7dG1u3boW8vDwWLlyIuXPnCjTQFdbgviyXLl1CcnIyj+zJwMCAiVrNcRhksVg8RsAyMjIwMDDAunXrRL53WXr37o1x48ahffv2SE9PZ6Kd379/v0K5QpcuXbB3716J7s2PwMBALF++HL6+vkKV79ixI06ePMkY7HDGhpCQECYiZXUirlFhdXPt2jXmeS6LjY0Nk+1JVMqOEaampli+fDkSEhKY7/nq1au4fPkyvL29xWv0/0fU8brs+qlp06a4d+8ezM3NkZeXh2/fvjHlLC0tERERgWXLlgEofVZKSkqwZs0a2NraStRmYfsAceYBdRE5OTm+AbMMDQ2rXZ6uoaHBdw747ds3HD9+nMvhgfO87N27F3PnzuUqv2/fPkZns2fPHgQHB8PLy0us+iVBR0cHy5cvl7ieshgaGiIlJYWnD4+Li6vQDqW6EFZGXRepDuNiSfRmFSFJ/5KUlIQLFy7g9OnTMDMz49EVlC8/Y8YMnnUDUKrD6N+/P5PBVVz5V3XqB+uppyqpN9Ctp55aori4mGvSKS0tXeEgoa2tDV9fX/j6+iIpKQm7d+9Gly5dYGpqCk9PT3h6egplVFAbhhmqqqp49uwZj1d9bm4uVFRUaqQNZQkKChKqnL+/P5ycnHDgwAG8evUKbm5uXFEIjxw5UmHK8LrAzJkzYWhoyHhGX79+HR8+fIC3tzf+/vvv2m6eQNTV1XkWNEQEXV1dREdH11Krag+OcRiLxeISBHNQUFDA5s2ba6Fl1UthYSFfpYaenh4UFBQQEREh8FrOZF5SOnXqhNzc3Co30N29ezfWrl3L1zvc2dkZa9aswYQJE9CnTx/MmjWrSu8tiOpchLu5uUFJSQlhYWFcxgafP3+Gu7s7j4FuTEwMevXqhVGjRiE6Ohr379+Hvb09Ro0axVdQ+Lsyfvx4zJw5E7t27QKLxcLLly9x5coVzJkzB4sWLRKrTmGiMLBYLC7jgLoy3vn7++PFixdiGd7v2bMHgwcPhqKiYpVHkDly5AiTbo5jsHL9+nWsW7cOixcvRlFREfz8/BAfH1+nx1YO0tLSePnyZW03o1aYMmUKoqOjoaurCw8PD0RFRfEYr9cUoioqgVJhtouLC1xcXPD06VOEhYVhypQpKCoqwv3797nWEuIoESoaV4H/i95jZmaGo0eP8o0SAQCxsbE86XTXrFkDR0dHtG7dmokK9vz5c1haWv4W701lzJ8/HwcOHECrVq0wbdo0Jr3X48ePsWXLFsaQtSaobicgCwsLPHz4EJcvX4a2tjZPtKMRI0Yw/XC9IWjtMHz4cPj6+mL//v21st6up3LEMeQu/z7Vv191k9evXzMOicrKykyEyv79+4s9vxeHWbNmIS8vD9euXYONjQ2OHDmCN2/eMIaA/PDz8+PKpMKhpKQEfn5+9Qa6lVBX1lT/FURV+ArD2LFjhTLe2rVrF/P/s2fP0L17dwClsjeOMnj06NHo2rUrtmzZwpS9evUqRo4ciadPnzIBEjgIipa1YMEChIaGYtWqVczzkZSUhCVLluDHjx9Yvnw51NTUmD5f2KwEokZqF4c1a9Yw2Y/4pVj+L1AX3uvTp09jxowZmDx58m8ZtKJRo0a4ePEiPn/+DGVlZR7D8YMHD3KtY8UxWq2MY8eOwcnJCTIyMlzRZfnBcR69c+cOfv36xfwviLLzsXv37iE5OZl5RtauXYvt27fjw4cPFUa0FoWSkhK+fcnz58+ZeT9Hjm5oaIgbN25Ui8wjODgYCxcuRG5uLg4fPsx8vlu3bsHFxUXgdVlZWdi9ezeePHmCoKAgNG7cGKdPn4aenh6PDEEUPn36xBMVuSJWrFgBJycnPHjwAEVFRdi4cSMePHiA5ORkJCYmit0OUYmIiMDw4cN5nKgLCwsRHR1dY0E7OPz8+ZPv+PDr1y+BkZEro2yER6DUsOrBgwd48OABc0xdXR27du3CwoULxboHINp4DZQ6zZw9exbm5uYYOnQoZs6cifPnz+Ps2bOwt7dnyq1Zswb29va4efMmCgsLMXfuXNy/fx8fP37E5cuXxW6vKJSdB6iqqv6268Bp06Zh2bJl2L17N/PM//z5E8uXL8e0adNErm/Tpk2YMGEC5OXl+Rr9lUVYm4CyZGZmcgWakZeX57LB6Ny5MxOdWZz6RUVQhr+ySEtLQ1tbW6wsOLNnz8bUqVPx48cPEBGuX7+OqKgorFy5EiEhIeI0WSKElVHXRarDuFgSvVlFSNK/qKurixT04eTJk9DQ0ODKIFhQUABHR0eesqLIvzw8PIS6f9k1Xj311CYsKi8tqKeeemoENpsNJycnZiJ6/Phx2NnZ8aR4rUjg+ObNG7i4uCAxMRHv3r0TetKVkpICKysrniil1cWMGTNw5MgR/P3338wi6fLly/Dx8cGQIUNqZPIqLg8fPuRKfVR2Ar5jxw507twZ7dq1q70GVkKjRo2YVABqamq4fv06WrVqhfPnz8Pb27tCAVNtUl4QwmazoampiebNmwsULv+X4Qj3jYyMcP36dWhqajLnZGVl0bhx4wojY/xuZGRkwMPDA8nJyVzH+aUAsbOzQ0xMDE+EhS9fvmDgwIE4f/68RG3JysrCpEmT4OrqijZt2vAohTipQUVFQUEBjx8/Fpiu6unTpzAyMsL3799rLCJnSEgIlixZgnXr1sHT0xMhISHIyspiFuGc1CziwGaz8ffffzPRI5YsWQKgdBzT0dHhK1zOy8uDjY0NWrRogYsXL2LMmDFcqQT/CxARVqxYgZUrVzLe+Zy0Kxyjupridx/vNDU18f37dzg7O8PV1RUODg5V1i927twZy5Yt44kmFh8fj0WLFuH69euIjY2Ft7c3MjIyRIqYVJ2UVzxxotFv2bIFurq6AiOq/Zdhs9nQ09ND+/btKxTs1GTECUGKyo8fP0JZWVngGJCbm4vdu3cjLCwMhYWFePToEZdi8969e7C3t0eHDh1w/vx5ODs7cykRykbn5VDWGx0oVcJ8+/YNsrKyUFRUZKI8hIeHY/Lkyfj7778xYcIEZm5WVFSE7du3w8fHB//88w9P6ioiwtmzZ5GamgoFBQVYWFjU+HtRnWRnZ2Py5Mk4e/YsV9aK3r17459//pEoMrooWFhYYNasWQIFlKGhoYwT0NGjR6tknvHjxw+eyMFA6Tv3+vXr/6kIusXFxdiwYQMOHDjAN618TaR8njhxIvbv3w8zMzOMHj0aw4cPh4aGBmRkZJCamlrnUyH/r/Plyxfs3bsXoaGhuHnzJoDK5Uc/f/5EXFxcnc2Q879Eq1atEBERgS5duqBnz57o378//Pz8sH//fkyfPh1v376tkXY0adIER48eRefOnaGqqoqbN2+iZcuWOHbsGNasWYOkpCSeaxQUFPDw4UOeyFY5OTkwMzOrldSmvxu/+5qqLuHu7l7heXGM+NhsNvT19dG+fXseA9qyHDlyhPnfyMgIhw8fRvv27dGxY0eMHz8eEydOxJkzZzBixAiucb1du3Zo2bIlAgIC0KRJE571Bj/jWh0dHWzbto0no8TRo0cxZcoUJiKmqJRVfFeEpJH/X758yZVi2cTEROIUy3WN2n6vr169itDQUOzfvx8mJiYYPXo0RowYgSZNmtTP64Sk7JqkogA34qad5ncfDioqKkhNTa2yteDw4cOhpqaGHTt2QEVFBWlpadDU1MSff/4JPT29ajFwFoavX78iKioKISEhuHXrFt/vMTExEU5OTujRowcuXryIhw8fwsjICKtWrcLNmzdx6NAhse/v6emJTp06YdKkSUJfk5WVhVWrViE1NRX5+fno0KEDfH19uQzyqhspKSm8evWKZ7384cMHNG7cuMbn9ra2tmjTpg1PIJipU6ciLS0Nly5dqtH2iIIo4zVQui7/8eMHdHR0GGf25ORktGjRAgsXLuSSj33+/BlbtmzhelamTp3K5bBRT+UMGjQI586dg5ycHBNpPzU1FYWFhVxG0YBwcllDQ0PcvHkTDRs2hKGhocByLBYLT548Ebm9CgoKSElJERi459GjR2jXrh1+/Pghct3iwGazubLkCoLFYqFt27aIiIhAmzZtRLrH3r17sWTJEmRlZQEonacGBATA09NT7HaLi7Ay6rrMgwcP+MoF/wuZ5MQhKysLlpaWmDt3LmbNmoWvX7/CwcEB0tLSOH36NCPfElX+Jc4ar556apN6A9166qklKhM0cuC3qE5OTsauXbtw8OBBtGrVCh4eHpgwYQKPgKGuGGYUFhbCx8cH27ZtYzwwZWRkMHnyZKxatUpgmt3q4tmzZxWeF2Qw9zuioaGB27dvw9DQEMbGxggJCYGtrS2ysrJgbm7Ola6lnnrqCj169IC0tDT8/Pz4KjXKpsoTZPjx9u1bNG3alIl2IC6cCChlU09xFsKSCG4bNGiAhIQEgQa+d+/ehZWVFT59+iRW/eJSXYtwzu/05MkTDBo0CD169EBkZCS+fPnCGOjycxp59eoVevfujf79+3NFc1JVVZWoPXWNwsLC+rQrZdi1axdsbW0rFK6Vp6ioCHFxcYiKisLRo0ehqKiIoUOHYtSoUYxzkLgoKCjgzp07PJkAHj16hPbt2+P79+/IyclB69atoaOjI1LEpOqk/LyQxWJBU1MTdnZ2WLdu3f+kINnNzU0oT/CaVGp9/vwZxcXFPI52Hz9+hLS0NFd/9/PnT8TExGDXrl1ISkpC//794e7uDkdHR76KxqpQImRkZGDy5Mnw8fHhMlKfM2cO1q9fDxUVFRgbG4OI8OTJE+Tn52PGjBk8UVL+l/j48SMyMzMBAM2bNxcrcoUk1JQTUElJCZYvX45t27bhzZs3SE9Ph5GRERYtWgQDA4NaEeDXBfz9/RESEgJvb28sXLgQCxYsQE5ODmJjY+Hv719j6e2/f/+OAwcOYNeuXbh27RocHBxw8uRJpKSkiKykqadmuHDhAnbt2oWYmBioqalh0KBBCA4OBiCZ/KiemsXPzw+qqqqYP38+9u/fD1dXVxgYGODZs2fw8vLiG6G2OlBVVUVaWhoMDAygr6+Pffv2oUePHsjOzoaZmRlfWZC2tjb27dvHk7Xn33//xciRI2vMuLieeqqLqVOnIioqCvr6+nB3d4erq2ul87Rx48ZBV1cXixcvRnBwMHx8fNCjRw/cvHkTgwcPRmhoKFNWSUkJqampaN68udBtkpeXR1paGpN9gcPjx4/Rrl07noiF379/BxFBUVERQOm87siRIzA1NeXJvlHPf4uCggLs378fu3btwvXr11FcXIz169fDw8OjPmNCHYHNZiM8PJzLGN/FxQVBQUHQ0tJijklimPP8+XM4ODiAiJCRkYGOHTsiIyODiVRcXj5+7tw5nDt3jq8TeVVEkrt48SJCQ0Nx+PBh6OjoYPDgwRgyZAg6derEU7Zbt24YOnQoZs+ezWW4fP36dQwePBjPnz8X6d5lI2YWFBRg/fr16NevH8zNzXkCbNTUGkxU2Gw23rx5wxWMBSg1WrS1ta1xA7TLly+jV69e6NSpE2Mwee7cOdy4cQNnzpyBpaVlldynsLAQ2dnZMDY2rrJgPKKM178z1R2spjoRdk0LVO+69tSpU5CSkuIJxHHmzBkUFxczWUM42YjLZoMsy4EDBzB//nxG/sePHz9+8BhniqvXevr0aaVlSkpK8ObNG6xduxZv374V26j+27dvyM/Pr3PO9oJk1HUNjg707t27XEbVHJ2EJDqi6uwDxKm7qKgICQkJyMrKwsiRI6GiooKXL19CVVWVr44zLS0Ntra2WLx4MaKioiAnJ4eTJ09yBS8UVf4lzhqvnnpqk3oD3Xrq+U149eoVIiIisHv3bnz69AmjRo2Ch4dHhcq1umaY8e3bN8boy9jYmBEm1jQcTzNBlJ8c6enpwcbGBtbW1rCxseEbcayuYmlpCW9vbwwcOBAjR47Ep0+fsHDhQuzYsQO3bt3CvXv3aruJXKT/P/bOO67m9/3jr3MalIaUTYukEGXz0RANyf5kZNbHCAlFdsreIxHaoayMbNKQsqKBUokyKkRGEdX9+6Nf72+nc8rpdFa8n49HD8573O/r1Dn3uO7rel3p6SgsLKRKiAMVToD169ejqKgIo0aNElp5YHEkMDAQKioqsLKyAgAsXboUhw4dgq6uLjUB/RNo0qQJEhIS2ILhqlJZ0qVHjx64efMmy4S7rKwMV65cwcGDB1kCa3lBV1cXOjo6WLp0KVq2bMnWd/D6O7eysoKqqioOHDjA8fycOXOQk5MjsrKA/F6EV1UEyMnJwYgRI8BgMODt7Y0BAwagrKysxr656gK2voHRfxsRERHYtWsXUlNTAVSo2SxcuBBDhgzheL24jHdaWlrIyspC27ZtYWRkRNnD7UZncXExzpw5g2PHjuHGjRto164dNf/gBX19fXTv3h2HDh2igtl+/fqFmTNnIikpCY8ePcLt27dhYmKCUaNG1UkxiYbG0tIS1tbWmDt3Lstxb29vnD9/nhoH5s6di9DQULRv3x52dnawtbUVSKlKTjx48ACTJ0+mlLEquXPnDkJCQpCRkQEA6NSpEyZMmIB+/fpxbKeoqAjR0dEc1QPEdcOsISKsJCAPDw8EBgbCw8MDM2fOxOPHj6GpqYnjx49j9+7diI+Pr1f7DZUOHTpg7969sLKygry8PBITE6ljd+7cwbFjx4RuU0ZGBvz9/REYGIhv377BysoK48aNw5gxY4RuCw0rb968QUBAAPz9/VFYWIhPnz7h2LFjsLGxabClS2lYiY+PR3x8PLS0tHgqOckrvXv3xvr162Fubo4RI0agadOm2LRpE/bu3YtTp05xnBvPnj0b8fHxOHPmDLUOyMzMpAJtRFHatKGSkZGByMhIjgFRa9asEZFVNABrwltcXBysrKxgb28PMzMzjv1ueXk5ysvLqQCi0NBQSmFv9uzZLMlOgwcPxtKlSzmWaa2Jvn37om/fvmylmR0dHXH//n3cuXOH5biZmRnGjBmDOXPmoLCwENra2pCWlsaHDx+wc+dOODg41OXXwROCLrEsjoiLr6SSZ8+ewdfXF8HBwSgsLMTQoUPZhFpo2Pn16xcsLCzg7e0NLS0tru8rKirC5s2bawx0rVRmrE2dtxJ++DRLS0sRGhqK5ORkKhHX1tYWMjIyLNe5u7vDw8MDvXr14uij4lVJLi8vDwEBAfD19cWXL19gY2MDb2/v3yo6y8nJISUlBRoaGiwBupUJ73VVoeQ2qZ+TeubDhw8hJSVFqeWeO3cO/v7+0NXVxdq1awVeza6yqlNSUhK6dOnCEqRaVlaGFy9ewMLCAidOnBCoHZxITEzEtm3bkJiYSFU+Wr58eZ2+MzVRXFwMR0dHBAYGAgCV5Ovo6Ii2bdti2bJlPLddl/EaAIyMjGBvb49///2X7buTnJyMrl27gslk/nbM47W6Iq8IWqzmb0BPTw+bN2/GsGHDWI5fuXIFrq6uSEpKAgA4OTnhxo0bSEhIYKsY9f37d/Tq1QtDhgzBnj17WM4VFRXB1dUVJ06cQEFBAdvzhbGvlZmZie7du9e5Akpdgy1FQU0+anHC2toaEhIS8PHxgYaGBu7du4eCggI4Oztj+/bt9Up2EGQfUNe2s7OzYWFhgZycHJSUlFB9upOTE0pKSuDt7c3xOfHx8Rg6dCj69u2LCxcusPXBvFDXNR4NjSihA3RpaBoIUlJSaNu2LaZNm4YRI0awZYFWIuwFATcEBQWhd+/e0NHRYTn+48cPnDhxAlOnThWqPZUT7Ep+/fqFR48eYefOndiwYQPbRuWRI0cQExODqKgoZGZmsgUM8WNxKiiuXr2KoqIijBkzBpmZmRg+fDjS09OhrKyM48ePsymjiJrRo0ejW7du8PDwAABK3WXQoEHo3Lkz/Pz8sG7dOixcuFC0hooIbW1tHDhwAIMHD0Z8fDxMTU2xe/duXLhwAZKSkkItyS1IevfujV27duGff/6p8ZqqwZycpjIyMjLw9PSssbwzt/CigMINcXFxMDY2xqhRo+Di4oLOnTuDEILU1FTs2LED586dQ2RkJAYOHMjX54qK6ou74uJi2NraIiIiAkVFRSgrK0N0dDTX7RkZGQnKVKHAzeeSwWDUK7t///79cHJywrhx49C/f38AFcF0p06dwq5duzBv3jy2e8RpvHvz5g2ioqIQExOD6OhoZGRkoHXr1jA2NsaRI0d+e/+HDx8QGhoKb29vpKam1sv5FRcXhxEjRoDJZFLzrJSUFJSVleHChQvo168fgoODYW9vj6dPn/K9v6D5s2nWrBlu377NNk9OS0vDwIEDKWcuk8mEqqoqtZHDic+fP+P69et830RITEyEoaEhpXTu4eEBFxeXOiXbPXr0CMOGDUNxcTGKiorQrFkzfPjwAbKysmjRogVP5eZoOCOsJKCOHTvi4MGDMDU1ZdlgTUtLQ//+/YVeBUBcaNKkCVJTU6GqqorWrVvj4sWLMDAwQFZWFvT19fH582eR2VZeXo6LFy/C19cXly9fRklJichs+ds5ffo0fH19ERMTA0tLS0yePBmWlpbU2oMuV01TX44cOYLS0lJMnz4dCQkJsLCwwMePHyEtLY2AgACMHz+e7Z7Pnz/DwsICDx48QLt27QBUKPUNGjSIo5IODWcOHz4MBwcHqKiooFWrVizzNgaDgYcPH4rQuoZDfn4+XFxcqIC46n4ffgQ3ZGdnIyAgAEFBQSgtLcWTJ09Ygg9KS0uxceNG2NnZUd+J2jhz5gxWrVqFJUuWcFRw5DT/jo6OpuZulev2+Ph4vHr1CpcuXWLbwFdRUUF0dDS6dOkCHx8feHp64tGjRzh9+jTWrFmD1NRUGBgYICIiAkpKSrWuHQDw9HkURollcUOcfCVVKSsrQ3h4OPz8/OgAXS5p3rw5FbTHLRMnTkR0dDSmTJnCMdDVycmJ32byhdatW2Pr1q2YMmUK39q0trZGTEwMrKysYGtrCwsLC0hISEBKSuq3c9h27drhxIkTGDBgAMv68cyZM3BxcalXYn1d6d27N5YtW4axY8ciKysLurq6GDNmDO7fvw8rKyvs3r1boM93d3en/nV2dmYZe6SlpaGuro6xY8cKPFC4LlSKZtQHJycn3L59G7t374aFhQWSk5OhqamJc+fOYe3atXj06BGfrP09CxcuxLFjx1BSUgIbGxvY29tTyeZV9zFqG/OEKSIiLLEaYfD+/Xs8e/YMQMV+Z3UFaW5ZvHgx19fu3LmT+r+MjAxSU1Ohrq7Ocs3Lly/RpUsXKqg1Pz8fPXr0gLS0NObPn09VO3j27Bn27duH0tJSPHr0iEUhHahQ84yMjMS6deswZcoUeHl54c2bNzh48CA2b94MW1tbnt5vUVERXFxccP78efz8+ROmpqbw9PTk+PsrKyvD48ePWSqR/g5egy2FTXUftTiioqKCmzdvQk9PD4qKirh37x60tbVx8+ZNODs789TXCbIP4LXtUaNGQV5eHr6+vlBWVqbG9aioKMycORMZGRk1rkWys7PRokULluBcfq2Tf7fGo6ERNXSALg1NA6FqBm5NQWniqirIZDLRpEkTBAQEsJSDyM/Pp0qbiwMXL17Etm3bEBUVVeM1ubm5iI6OxoULF3D8+HGUl5eLjf3c8vHjRygpKYll1lD79u1x4sQJyim9fv16nDp1ComJiQAAX19feHp6Uq//NmRlZZGWlgZVVVW4urpSytpPnjyBsbEx3r9/L2oT+cLNmzexatUqbNy4keOmhoKCAlVCvrIUVtWFqLS0NFq0aAEJCYl622JtbY3p06fXWMqmPpw5cwazZs1iK1elpKSEgwcPCuSZ1RH0xk0l7u7uWLJkCVswl5ubG2JiYhAZGclz2w2R0aNH13iurKwMN27cQElJSb3Gl3bt2mHZsmWYP38+y3EvLy9s3LgRb968qfV+cRnviouLcevWLYSEhODo0aMghKC0tLTGa8+cOYOjR48iIiIC7du3x8SJE2Fra1urIjc3fP36FUePHkV6ejqACgdiZSZ5JbwoJgmKoqIibNmyBWFhYXj58iUYDAY0NDQwbty4OgdW0giWJk2a4M6dO5RySyUpKSno27cvVYJ6+vTpv527BQQEID8/n+dNhOqbuoQQ5ObmYt++fWjfvj0uX74MgFUVnVuMjY3RqVMneHt7Q1FREUlJSZCSksLkyZPh5OREK3nyEWElAcnIyCAtLQ1qamosG6xPnz5Fnz598O3bNz69o4aFtrY2goKC0LdvX/zzzz8YPnw4li1bhuPHj8PR0VFsSsS/e/dO7MoV/k1ISkrC1dUVy5YtY5lLcBPcQCP+PHv2DJ6enixVLBwdHaGtrS0ym4qLiylfQm0K/IQQXL9+HUlJSZRqmqGhoRAtbfioqalh7ty5cHV1FbUpDRpLS0vk5ORg/vz5HAPiRo4cWe9nvHr1Cv7+/ggICMDPnz+RlpbGtnkrJyeHx48fswVxcIKTeiY3lYDevn0LLy8vSglMR0cHc+fORZs2bdiureoXtLGxQZcuXeDm5oZXr15BW1sbxcXFLP6XyiCwmnBzc/vt+6qOMEssiyPi4iuh4Y1FixahUaNG2Lx5M9f3NG3aFBcvXhSpiEJMTAxX11Uds5WVlXHv3j2+Kj5LSkpiwYIFcHBwYAly5mYO6+Ligrt37+LkyZPo1KkTHj58iPz8fEydOhVTp07lqT/iFUVFRTx8+BAdOnTAli1bcPPmTVy9ehW3b9/GhAkT8OrVK6HYERgYiPHjx7Opc4qK6dOnw8vLi6XUOFARuDhlypR69+Vqamo4fvw4+vXrx+JDyMzMhIGBAU8Bd9yougOck2RKS0tx/vx5BAYG4vLly+jYsSPs7OxgbGyMnj17gsFg/HbME1ZFS2GJ1QiSoqIiODo6IigoiFIil5CQwNSpU+Hp6VlnX7WJiQnL64cPH6K0tJRac6Wnp0NCQgI9e/bEzZs3qetatWqFY8eOsYlX3bhxA5MmTWLx2bx48QIODg64fv06S5XHoUOHYv/+/dDU1GSzS1VVFUFBQTA2NoaCggIePnyIjh07Ijg4GCEhITwnyy9evBiHDh2Cra0tGjdujJCQEAwcOJBnNfTqcBNsKUy49VGLI0pKSnj48CE0NDTQoUMH+Pj4wMTEBM+fP0e3bt0oX39dEGQfwGvbysrKiIuLg7a2Npsyvq6uLrUu4RZ+zQO4WePR0IgSyd9fQkNDIw68ePGiTteLW2CGu7s7pkyZgpSUFKxdu1aoz+YWbW1t3L9/n+O54uJixMbGIioqCpGRkXj06BG6du0KY2Nj4RrJB8S5vNiHDx9YVCkiIyNZykAaGxvD2dlZFKaJBXJycigoKICqqiquXbtGZYk2btwY379/F7F1/GPIkCEAAFNTU5bjVTc1Kp0f1cuK8Rtra2ssWrQIKSkpHIOFR4wYwXPbo0ePhrm5Oa5evUotcLW0tGBubi60PnrkyJFo1KgRgIpFuKCoaXFV0wLN398fcnJy+Pfff1mOnzx5EsXFxZg2bRrfbRQmNTlOzp07hxUrVqBRo0b1Ln1aWFjIMVDUzMys1s1icRjvrl27hqioKERFReHRo0fQ0dGBkZERTp06VWOAwIQJE3DhwgXIysrCxsYGq1evppI9+IG8vDzmzJlT6zWOjo5wdnZGXl4e14pJguDnz58wMjLC48ePYWlpCWtrayo4b8OGDbh8+TJiYmJqrMZAI1z69OmDQ4cOwdPTk+W4t7c3evbsSb0OCAj4bVtr166lElbqunYA2McBBoOB5s2bY/DgwdixYwd1nJcc38TERBw8eBBMJhMSEhIoKSmBpqYmtm7dimnTptEBunxkwIABOH78OGbNmoXTp0+znFNSUqKc+PVFV1cXt27dYtuQOnXqFPT19evdfkNl9OjRiIiIQN++feHo6IjJkyfD19cXOTk5WLRokVBtOXnyJEJCQpCeng5paWl06tQJM2bMgLm5OR2cK2Ls7e3h5eWFqKgoTJkyBePHj4eSkpKozaLhA6dPn8aECRPQq1cvlioWXbt2RWhoqFCSMAEgNjaWpSKNrKwsDAwMfnsfg8GAmZkZzMzMBGneH82nT5/Y1rE0dSc2Nha3bt1Cjx49+Npu1fKnsbGxGD58OPbt2wcLCwuOAbampqaIjo7mKkCXl/k3ALRp0wYbNmzg6tqOHTvi7NmzGD16NK5evUrNLd69ewcFBQUArP4XQQS8cRuMpKGhgS1bttRJvU2cEQdfCU39KS0thZ+fH27cuIGePXuyBSJWVVmsRElJqc77KRkZGYiMjMS7d+/YfNe8+Ptq+5xVBtUwGAyWhPb//vsPx44dw+rVq+v8vJqIjY2Fr68vevbsCR0dHUyZMgUTJkzg6t6NGzdi3rx5aN++PcrKyqCrq4uysjJMmjQJq1atqpddY8eORZ8+fdj8nVu3bsX9+/dx8uRJluOEEOrvcuPGDQwfPhxAhXjMhw8f6mVLXaj0b//8+ZPjZ0VVVVVotgAVlUf19PRw5MgRah4bGBiIBQsW8KUS5/v37zmuQ4uKingWE+rRowdLcjqnILOakmQkJSUxZswYjBkzBu/evcOhQ4ewevVqlJWVYdiwYXx73/zgxYsXQhGrESSLFy9GdHQ0wsPDKZ9UbGwsFixYAGdn5xqrQNVEVcGXnTt3Ql5eHoGBgdS6+tOnT5gxYwZbNYKRI0di4cKFOHPmDJXAkJmZCWdnZ7a9Pg0NDVy5cgUfP35EZmYmgIq5WG1jwsePH6nAXQUFBUqc559//oGDg0Od3mNVzpw5A39/f2qdMXXqVPTr1w+lpaWQlKx/uNetW7cQFxfHptytrq7+W6EXQcCtj1oc6dq1K5KSkqChoYG+ffti69atkJaWxqFDhzgGdXODIPsAXtuuKUnt9evXVCK6sJJv6rrGo6ERJbSCLg3NH8jPnz8xYMAAKjCjqmrSlStXYGBgINTAjMqSIFlZWRg9ejQGDhyI4OBgfPnyRSQKutUzMSszr9auXYu0tDQ2ddYBAwZQAULGxsYwMjKCoaFhg9hA+/HjBzw9PWt0CIlbab22bdvizJkz6NOnD8rLy6GkpIRjx47BysoKAJCamop+/fqJtDSsKLG1tUVaWhr09fUREhKCnJwcKCsr4/z581ixYgUeP34sahP5QnR0dI3nUlJS2BRBg4OD4e3tjRcvXiA+Ph5qamrYtWsXNDU1662qUtvkvT6q5Tdv3sT8+fNx584dahOlks+fP2PAgAHw9vZmcyA0dJ4+fYqcnBz8/PmTOsZgMFgC8QGgU6dOOHjwIFsmdHR0NGbNmkWVQfpTuH37NpYtW4aHDx9i/vz5WLZsWb3HmEmTJkFfXx9LlixhOb59+3Y8ePAAoaGhbPeIy3jHZDLRvHlzODs7Y9asWVyV07W1tYWtrS3Mzc0F5pDk9PkF/heoz6tiEr/Zs2cPNm3ahOjoaDaltrS0NBgbG2PlypVwdHQUij00tXP79m0MGTIEvXv3phJTIiIicP/+fVy7dk0sxwEmk4n8/Pw6laGrWkq0U6dO8PT0hLm5OdLS0tCzZ0+qhBwN/yguLhZoEtC5c+cwbdo0LF++HB4eHnB3d8ezZ88QFBSECxcuYOjQoXx5TkMnPj4e8fHx0NLSYpvvCIry8nJMnDiRUqeqVJFPTU1FZmYmZs2ahQMHDqCgoAAxMTG1KvvTCI7v37/jxIkT8PPzw927d2Fubo6LFy8iMTGxwZcC/5vp0KEDbG1t4eHhwXLczc0NR44cEVr5ZmlpabRt2xYTJ07E5MmTuVZlLioqQnR0NMc574IFCwRh6h+Hvb09evfu/dvkPpra0dXVxdGjR/ma9DN37lyEhoaiffv2sLOzg62tba2K0kBF0py7uztsbW05BvPxmrTNjeKfpKQkWrVqxRIEcurUKUyaNAllZWUwNTXFtWvXAACbNm1CTEyMUNTEBF1iWRwRF18JTf2p7mesCoPBYFFZrOTIkSM4d+4cAgMDuVpLHT58GA4ODlBRUUGrVq1YAg8ZDAZP+zE17YUUFxdjz5492Lt3LzQ1NVn2BpycnBAUFAQ9PT3o6emx7QVyCkbmlqKiIhw/fhx+fn64d+8eysrKsHPnTtjZ2bFUh+BETk4OHj9+jG/fvkFfX59FiZdXmjdvjps3b3KsTDRkyBDk5+ezHB88eDDat2+PIUOGwN7eHk+fPkXHjh0RHR2NadOm8VQinBcyMjJgZ2eHuLg4luPC9iNW8uvXL6xYsQJ79+6Fs7MzMjMzcfnyZezcuRMzZ86sd/uGhob4999/4ejoCHl5eSQnJ0NDQwOOjo7IyMjAlStX6txmVYVbQgi6du2KS5cusSWT1JZccu/ePfj7+yM0NBQKCgqYPn064uLiEBMTA0tLy1oVKesj3vK3oaKiglOnTrElHERGRsLGxqZe1UHbtm2La9euoUuXLizHHz9+DDMzM7x9+5Y69vnzZ1hYWODBgweUWNTr168xaNAghIWFcbUPURt6enrw9PSEkZERhgwZgh49emD79u3Yu3cvtm7ditevX/PUrpSUFLKzs1kqLFStrFBflJSUcPv2bejq6rKoocbGxmLs2LFs/ShNzVy9ehVFRUUYM2YMMjIyYG1tjfT0dCgrKyM0NJRNmKqhMn78eCgqKuLQoUNUn968eXOMHDkSqqqq8Pf3Z7n+1atXYDAY1Pfu3r17OHbsGHR1dTFr1iye7eBljUdDI0roAF0amgbC1q1b4ejoCBkZGQAVm/m9evWilA+/fv0KV1dX7N+/X+wCM6qWwc3JycGIESPAYDDg7e2NAQMGCH2hWVWuvxJCCNq3b4/Q0FA2xb1mzZqByWTCzMwMxsbGVInehoCtrS2uXbuGcePGoWXLlmzvW5ilg7jB1tYWX758wf79+3Hy5Em4ubkhLy+PcoCfPn0aHh4eSEpKErGloqGwsBCrVq3Cq1ev4ODgQKljurm5QVpaGitXrhSxhYLh69evCAkJgY+PDxISElj6jAMHDmDNmjVYuHAhNmzYgMePH0NTUxMBAQEIDAxkyaQVJ0aMGAETE5MaldT27t2LyMhIvpWoETWVCRopKSkcs9qrjwONGzdGWloam0rNy5cvoaOj88coRj99+hSurq64cuUKpk6dCnd3dxYV8fqwfv16bN++HQMHDmRR77p9+zacnZ1ZAsMrN9vFZbzbvXs3YmJiEBMTg0aNGsHIyEik9nD7+RWXsmdGRkawsbHBvHnzOJ739PTEqVOnak2GoBEuiYmJ2LZtGxITE6ly0suXL+dpo6p6CbCaqM8mApPJhKKi4m8VTipVIoAK9e7p06dj0qRJmDlzJpKTk7FgwQIEBwfj06dPuHv3Ls/20LAizCSgW7duUXPzb9++wcDAAGvWrKGVF0XMrl27sH79egQGBlJqUJWcP38eM2bMwPLlyxEQEICpU6di6dKlIrKUppKMjAz4+/sjMDAQ3759g5WVFcaNG0erizdAZGVlkZycjI4dO7Icz8jIQPfu3XkqZ8kLHz58QGhoKEJCQhAfHw89PT3Y2tpi4sSJNa43Hj16hGHDhqG4uBhFRUVo1qwZPnz4AFlZWbRo0QJZWVlCsb2hs2nTJuzcuRNWVlYcq2rQgc7cce3aNezYsQMHDx7kSr2WG5hMJlRVVaGvr1/rPDYsLIzlnprgFDzFKYF89+7d0NDQYEkgr/RN/25rjsFgoHv37ggKCqKSN/Ly8pCbm4vu3btT9t27dw8KCgro3LkzlJSUuFYirDpf5xZBl1gWR8TFV0LDG8nJyejatSvPCmr6+vp4/vw5CCFQV1dn69erB9yqqalh7ty5tVavqi/l5eXw8/ODu7s7mEwm1q5di2nTprG8R16CkXnh2bNn8PX1RXBwMAoLCzF06FCOfonq6v78REZGBomJiRz3QvX19dn8yMnJybC1tUVOTg4WL15M7ZE5OjqioKAAx44dE4id1Rk4cCAkJSWxbNkytG7dmq3vFlVyg5ubG9atWwdJSUlER0fzrUJZbGwsLC0tMXnyZAQEBGD27Nl4+vQp4uLiEB0dzVJFileqBhbWxrt37xAcHAx/f38qgO6///6Dubk5GAwG9V2qDJbmNGaLIoga4L9Ct7CQlZVFQkICdHR0WI4/efIEffr0qVfivry8PMLDwzkG/44YMQJfv35lOU4IwfXr15GUlET5YWuq2ldXdu3aBQkJCSxYsAA3btygKtv9+vULO3fuhJOTE0/tSkhIIC8vjyUhSkFBgVJqrS91DbakqRsfP36s0xy9NgTZB9Sl7devX8Pc3ByEEGRkZKBXr17IyMiAsrIybt26xaaYPmjQIMyaNQtTpkxBXl4eOnXqhK5duyIjIwOOjo48287LGo+GRpTQAbo0NA2EqkGuQMXEKzExkVpo5OfnU2q04haYUamgW2l7cXExbG1tERERgaKiIqEvYqq/70qlvo4dO3IsBUEIQUpKCqKiohAdHY2YmBhIS0vDyMgIJiYmfMkeFRSKioq4dOkSX8rYCoOXL19i6NCheP78OSQkJLB3716Wsh+jRo2ChoYGdu3aJUIraYRFTEwMfH19cfr0abRp0wZjxozB2LFj0bt3b+oaXV1dbNy4EaNGjWJxwDx+/BjGxsb1Kkv169cvysHHbxUrNTU1XLlyhc0hUUlaWhrMzMyQk5PD1+dygtuFIS8bN5VYW1tDQkICPj4+0NDQwL1791BQUABnZ2ds376dLUhIVVUV+/btYwsgO3fuHObNm8dzprG48OrVK6xZswZHjhzB8OHDsXHjxho/C7zCrWOGwWBQm+3iON6lpKQgOjoaN2/exIULF9CiRQvq7793717MmjULjRs3xt69e2ttpz4b4XX9/Iqa5s2bIyoqik01oJLHjx/DxMSkXsoENOJL9U1HbjcRioqKsGXLFoSFheHly5dgMBjQ0NDAuHHj4OLiwqIUxGQysXv3bigqKtZqS2W5RgB48OABvn79ChMTE7x79w5Tp06lFHV9fX35Xr74b+ZvSwISR549ewZPT0+kpqYCAHR0dODo6Mi2YSwo9PT0sHDhwhqVfnx9fTFr1iyYmZnh3LlzbCUMaURHeXk5Ll68CF9fX1y+fBklJSWiNommjgwbNgz//vsvZsyYwXK8UpXr6tWrQrfpxYsXOHbsGEJCQpCWlgZDQ0OOQTmVAWfe3t5QVFREUlISpKSkMHnyZDg5OdEB41xS2zqs6tqLpnaUlJRQXFyM0tJSyMrKsgXE8eKfmD59Ole+D14DEOqSQP675E6gYkzIz8/Htm3b8O7dO9y6dYsrOwIDA7m2uep8nVs0NDSwdetWqsRyQkIC+vXrh+/fv/OlxLI4Io6+Ehruqbqvpqmpifv370NZWZnr+93d3Ws9X10Apfq+Hb8JCwvDihUr8P79eyxfvhyOjo6UiI8oKSsrQ3h4OPz8/DgG6PKq7s8Nffr0wfDhw9mCe9auXYvw8HAkJCRw1c6PHz8gISEhtMqjTZo0QUJCAlXxRNT8+vULy5Ytg5eXF5ydnREbG4v09HT4+vpi2LBhfHnG8+fPsXnzZpYkX1dXVzb1Y17hNkBXWloaHTp0gJ2dHaZPn85RBf7Lly8YOXIkIiMjuW5X0AhCoVtYmJqaQllZGUFBQWjcuDGAiqoy06ZNw8ePH3Hjxg2e2546dSpu3bqFHTt2oE+fPgCAu3fvYsmSJRg0aFCd5kb8Jjs7GwkJCejYsSP09PR4bofJZKJr164sc63k5GR07tyZxafD62egpmBLFRUVxMTEsAVbCpK6+KjFidrUtqvi5+fH8zME2Qfw0nZpaSlCQ0ORnJxM9em2traU2GBVlJSUcOfOHWhra2Pv3r04fvw4bt++jWvXrmHOnDk8r5MFvcajoeE3dIAuDU0DoXqQa/UFQdUAXXELzHB3d8eSJUvYJk1ubm6IiYkRW4VLThBCkJCQgH379uHo0aMoLy8XSZYkt+jq6iI0NLReE39hU1paiidPnqB58+Ys5ToAICkpCe3atauTE+1P49OnT/D19WXZ8Lezs2MpedeQycvLQ0BAAHx9ffHlyxfY2NjA29sbSUlJHB13MjIySEtLg5qaGku/mJGRAT09vXorrWpqauLMmTN8z1hv3LgxHj9+zKasVElmZia6desmFKXYqg4KQggcHBzg4eHBtujmZeOmEhUVFdy8eRN6enpQVFTEvXv3oK2tjZs3b8LZ2RmPHj1iud7V1RXHjx+Hv78/lb0cHR0NOzs7jBs3Dtu3b+fZFnFAVlYWDAYD8+fPrzWBQpRlskQ93hFC8OjRI0RFRSEyMhKxsbH4+vUrunXrRn1eNDQ08ODBAygrKwt0I7wun19uFZMEiZSUFF69eoVWrVpxPJ+bmws1NTW2ssU0ouHSpUuQkJCAubk5y/GrV6+ivLwclpaW9Wqfm02Enz9/YsCAAXj8+DEsLS3RuXNnEEKQmpqKK1euwMDAADExMdQmVfV1CY14IU5JQH8jp0+fxoQJE9CrVy8WBfv79+8jNDQUY8eOFbgNMjIyePbsWY1lDrOzs6GpqYnv37/TwblizLt37+h+tgHi7e2NNWvWwMbGBv369QNQ0QecPHkS7u7uLD4OYc71y8rKcPnyZaxevRrJyckc5/VNmzbF3bt3oa2tjaZNmyI+Ph46Ojq4e/cupk2bhrS0NKHZS0Pzu0CK+vgnBIWgEsgzMzPRvXt3FBUVoaioCJs3b0ZERARHZSthBIALusSyuCNqXwlN3VFWVsalS5fQt29fMJlM5OfncwzG4xf29vbo3bs35syZw9d2o6Oj4erqipSUFDg5OcHV1fW3SbNARR/y/PlzGBoaQkZGhlIEFTa8qPtzS3h4OMaMGYNJkyZh8ODBAICIiAiEhITg5MmTGDVqFB/eAf/p3bs3du3aJTBl4bpSWe0hODgY/fr1AyEEW7duhZubG+zs7LB//36e2v3y5QtX11WvAMQL3AbS3rp1q06CC+ISoCsMhW5BkZKSAgsLC5SUlFB7bUlJSWjcuDGuXr1aYzwDNxQXF8PFxQV+fn749esXAEBSUhL29vbYtm0bVaG1kqKiIkRHRyMnJ4fNPy6IaheFhYVo2rRpvdr4XbJIJfWpmluXYEtBUVcftTjBZDKhpqYGfX39Wqtk1EcwQZB9QF3bLigooGI1Xr16hcOHD+P79+8YMWIEx/5VTk4Ojx8/hrq6OkaMGIGBAwfC1dUVOTk50NbW/mOqptLQ/BZCQ0PTIGAwGCQ/P596LScnR54/f069zsvLI0wmkxBCiKSkJMnNza2xrbdv3xIpKSnBGSumPHv2jNy9e5fl2I0bN4ixsTHp3bs32bBhA8f7EhISyI4dO4i1tTVRUlIikpKSRF9fnyxatIicPXtWGKbzzKVLl4iFhQV5+fKlqE2pFyUlJeTr16+iNkPkREdHEwUFBdK+fXsyevRoMnr0aKKqqkoUFBRIdHS0qM2rN8OHDycKCgpk4sSJ5MKFC6S0tJQQUtGnPXnyhOM9Ojo61Pewar+4d+9eoq+vX2+bfHx8yLBhw0hBQUG926qKpqYmOXPmTI3nT58+TTQ0NPj6TG6pPr7wg6ZNm5KsrCxCSMV7v3nzJiGEkMzMTCIjI8N2fUlJCbGxsSEMBoNISUkRKSkpIiEhQWbMmEFKSkr4apsoYDAYv/2pHNOFibiMd8OHDydKSkpEQkKCGBgYkMWLF5Nz586RT58+Cc2GqnD7+d2/fz9RUVEh69evJzIyMtT3yN/fnxgbGwvNXiaTSd69e1fj+apzRhrR061bN3Lx4kW245cvXyZ6enr1bp+bPn337t2kZcuWJC0tje1camoqadmyJdm7dy91jMlksqxLuMHExITjd/jz58/ExMSkTm3R1E6jRo1IRkZGjeczMjJI48aNeW5fQ0ODq5+/FU1NTbJ69Wq242vWrCGamppCsUFJSYkkJSXVeD45OZk0bdpUKLbQ1M6JEyfI6NGjSZcuXYi+vj4ZP348uXLliqjNoqkH3MzzhTnXj42NJQ4ODqR58+ZEXl6eTJ48mVy+fJnjtSoqKiQ9PZ0QQoiWlhb1WUxNTSWysrJCsZeGRhy5ceMGWb58ObG3tyczZsxg+alK48aNKf9r1Tl4eno629zr27dvZM6cOaRNmzZERUWFjB8/vsY1XGlpKUlMTCSEEDJhwgTSunVrsnTpUrJr1y6ye/dulp/a+P79O/n8+TPLDy9wWm/Ky8tTa+Y/EXHxldDwxsyZM0mjRo2Iuro6YTKZRFVVVaBrmI0bNxIVFRUybdo0sn37drJnzx6WH16wtLQkUlJSZPbs2bXu+1Xlw4cPZPDgwdS8o7JPmjFjBlm8eDFPdvCLrKwssn79etKlSxciISHBF5/AhQsXyIABA4isrCxRVlYmJiYmJCoqijrftGlToqSkxNWPsIiIiCD9+/cnkZGR5MOHD3zpo+uDnZ0d+fbtG9vxhw8fki5duvDcbuVnsKYffs6N5eTkBDIeCWK/hBfk5eXFwg5eKSoqIocOHSKLFy8mixcvJocPHybFxcV8a//bt28kKSmJJCUlcfwsE1LxeW7VqhVRUFAgEhISpHnz5oTBYJAmTZrwZRzYvHkzCQ0NpV7/+++/hMlkkjZt2lDzOZqaqauPWpyYO3cuUVJSIj169CB79uzh+54yIYLtA7htOzk5maipqREmk0m0tbXJo0ePSMuWLYmcnBz1veK0992nTx/i6upKYmJiSOPGjanvQ3x8PGnbti2/3w4NjdhCK+jS0DQQ6qKgKyEhgby8vBozgateK0yePn3KlpHGYDBgbW0tlOePHj0a3bp1g4eHB4CKMn9dunTBoEGD0LlzZ/j5+WHdunVYuHAhy32SkpLQ19eHkZERjIyMYGhoyFV2sjjw/v172NjYICYmhm8l4QSNv78/Hj58iH79+sHW1hbLly/Hzp07UVpaisGDByM0NPSvVdDt1q0b+vfvjwMHDkBCQgJAhRLO3LlzERcXh5SUFBFbWD8kJSWxYMECODg4QEtLizouJSVVo4Kuj48P1q5dix07dsDe3h4+Pj54/vw5Nm3aBB8fH0yYMKFeNunr6yMzMxO/fv2CmpoaW7YtryVDHB0dERUVhfv371MlfSr5/v07+vTpAxMTE+zdu5dn23lFEBnhgwYNgrOzM0aNGoVJkybh06dPWLVqFQ4dOoSEhAQ8fvyY433p6elISkqCjIwMunXrBjU1Nb7Z9CeyePFirq/duXMn2zFxGe+WLFkCIyMjDBo0iOfnl5WVISUlBWpqalBSUqqXPdx+fgWlmFRXOJW8qkqlUj2t8iMeyMjIIDU1Ferq6izHX758iS5duqCoqKhe7XPTpxsZGcHGxgbz5s3jeN7T0xOnTp1CdHQ0APCkOlST6u67d+/Qtm1bSuGCpv506NABO3bsqFElKCwsDC4uLjwrrFUqQkyaNKlWdU8nJyee2m/oyMrKIjk5ma1KQkZGBqVIJGisrKygqqqKAwcOcDw/Z84c5OTk4NKlSwK3hYYz5eXlmDhxIk6ePIlOnTpRZW1TU1ORmZmJWbNm4cCBAygoKEBMTAxGjx4tYotpGhrLly9HaGgo3r59i6FDh8LW1hYjR46stRyomZkZpk+fjkmTJmHmzJlITk7GggULEBwcjE+fPuHu3btCfAcNm9evX+P8+fMcVbk4rcNoaufHjx9sv0d+qOxxg7u7Ozw8PNCrVy+0bt2aTXWyqgKWrq4uNm3ahJEjR7LMwT09PSlfZyWLFy/GoUOHYGtri8aNGyMkJAQDBw78raJW06ZNcfHixVor8VSlqKgIrq6uOHHiBAoKCtjO87ImFHSJZXFEXHwlNLxz5coVZGZmYsGCBfDw8IC8vDzbNV+/fsWqVavYjpeVlWHXrl04ceIEx369+v6KICo8MZlMSEpKokmTJrWq31a1ZerUqXj37h18fHygo6ND9UlXr17F4sWL8eTJkzrbwU+4UffnJ3UpcS8slXYmkwkAbH9T8v8qx+LktyspKUGjRo14urfSlwRUvLdhw4bBx8cHbdu2ZbnOyMiozm3r6+uz/P44jUcA5zHp1KlTNX6vq18vLgq6glLoFjS/fv1C586dceHChRqrPQkLY2NjdOrUCd7e3lBUVERSUhKkpKQwefJkODk5YcyYMfVqX0NDA0ePHsWAAQNw/fp12NjY4Pjx49Rn7dq1a3x6J6wkJyejV69ePFfMU1VVhbGxMYyMjGBiYiKyz3pdfdTiRklJCcLCwuDn54e4uDhYWVnB3t4eZmZmfFGvF2QfwG3blpaWkJSUxLJlyxAcHIwLFy7A3Nwchw8fBlCx952QkIA7d+6w3BcVFYXRo0fjy5cvmDZtGvz8/AAAK1asQFpaGsLCwvj+nmhoxBHOu7Y0NDRiiY+PD+Tk5ABUBFcEBARARUUFQIUDoRJCCExNTWsNzBAmWVlZGD16NFJSUsBgMChp/8rJiLAWmg8ePMDSpUup10ePHkWnTp1w9epVAICenh48PT3ZAnQ/fvwoNMcvv5k4cSLevHmDjRs3omXLliIpX1QXNmzYgA0bNmDgwIE4duwYYmNjcfbsWXh4eIDJZGLv3r1YtWpVjZvNfzqZmZk4deoUFZwLABISEli8eDGCgoJEaBl/iI2Nha+vL3r27AkdHR1MmTLltwG2//33H2RkZLBq1SoUFxdj0qRJaNOmDfbs2VPv4FwAAiuBtWrVKoSFhaFTp06YP38+tLW1AVSUnfby8kJZWRlWrlwpkGeLglWrVlFBZh4eHhg+fDgGDRoEZWVlHD9+vMb7OnXqhE6dOgnLTKFTVFTEFvRdHx49esTVdTWNBeIy3m3bto36/48fP9iC2DmxcOFCdOvWDfb29igrK4OhoSHi4+MhKyuLCxcuwNjYmGd7uP38vnjxAvr6+mz3N2rUqN5BlnWBm1JWwiixTsMdioqKyMrKYgvQzczM5Gv/UBtPnz6t9TtiYmJCJbgBFZtV3JS78vPzQ3JyMstz8vLyqNdlZWW4cuUK26YMTf0YNmwYVq9eDQsLC45JQG5ubhg+fDjP7R8/fhx+fn7YuXMnLC0tYWdnh2HDhlGbi387xsbGuHXrFluAbmxsbJ1KaNaHlStXwtjYGAUFBXBxcWEpCbhjxw6cO3cOkZGRQrGFhjN79uzBjRs3cP78ebbv4/nz5zFjxgx06NABAQEBmDp1qoispKkL8fHxKCgoYPl7BgUFwc3NDUVFRRg1ahQ8PT15DmyoKzExMViyZAlsbGwov+Hv2LhxI+Vb3LBhA6ZOnUolz/r6+grS3D+KiIgIjBgxApqamkhLS0PXrl3x8uVLEEJgYGAgavMaDIIILOUFb29vBAQEYMqUKTVe4+HhARcXFyxevBjz5s3Djx8/QAjBvXv3EBISQiWQV+XMmTPw9/fHv//+C6AikK5fv34oLS2t0Z8PAEpKSmjWrBnX9i9duhSRkZE4cOAApkyZAi8vL7x58wYHDx7E5s2buW6nKpzWmyNHjuSprYaCuPhKaHjHwsICAJCQkAAnJycqQPfr168ICQmBj48PEhISOAbouru7w8fHB87Ozli1ahVWrlyJly9f4uzZs1izZg3b9S9evOC7/f7+/nW+59q1a7h69SratWvHclxLSwvZ2dn8Mq3O3L59G0ePHsWpU6fw48cPjBw5Eps2beJL2z9//sS7d+9QXl7OclxVVVVoQbd1QVzWZCdOnMCoUaOooNbXr1+jTZs21Bq/uLgY+/btY9lbrQvVA28lJCTQr18/vgQAVt+74XY82rt3L1auXInp06fj3LlzmDFjBp4/f4779+9zDAxkMBhisbfasWNHrF69Gnfu3EG3bt3YBJkWLFggIstqR0pKCj9+/BBY+0VFRdi8eTMiIiI49gFVEyMSExNx8OBBMJlMSEhIoKSkBJqamti6dSumTZtW7wDdvLw8tG/fHgBw4cIF2NjYwMzMDOrq6ujbt2+92q4NQki9Yj82btyImJgYbNmyBTNnzkTbtm2pxCRjY2MWQSVBUlcftbjRqFEjTJw4ERMnTkR2djYCAgIwd+5cSjClMsanLlQVcRJkH8Bt2/fv38fNmzehp6eH7t2749ChQ5g7dy41Zjg6OqJfv35s7VcK2Hz58oVFUGfWrFm1JhLT0Pxp0Aq6NDQNBHV19d8uACozcN3d3blqk5sADn5gbW0NCQkJ+Pj4QENDA/fu3UNBQQGcnZ2xfft2oW1UysjIID09nZocm5qaYsCAAVi3bh0A4Pnz5+jZsycKCwvZ7i0sLMSpU6fw/PlzLFmyBM2aNcPDhw/RsmVLsQ4qkJWVRXx8PLp37y5qU7hCS0sLHh4emDhxIh48eIC+ffvixIkTVCDR5cuXMWfOHJE6kUTJwIEDsWTJEjbHw9mzZ7F582a2jLSGSlFRERX8ce/ePZSVlWHnzp2ws7PjqHBQSXFxMb59+1armps4kZ2dDQcHB1y9epUlccHc3BxeXl61Ki4IEmFlhH/8+BFKSko1jm1/g+qQnJwcbGxsYGdnh3/++UfU5gAQj/GuvLwcGzZsgLe3N/Lz85Geng5NTU2sXr0a6urqsLe3Z7unXbt2OHv2LHr16oWzZ89i3rx5iIyMRHBwMG7evInbt2/z1UZOn9+6KCbR0FQye/ZsxMfH48yZM+jQoQOAiuDcMWPGoE+fPmyb+XVFQUEBSUlJtY4pUlJSePXqFVq1asXxfG5uLtTU1Ki+uFJBVV9fH7W5E86cOQMmk0l9TzhdKyMjA09PT9jZ2dXlbdHUQn5+PgwMDCAhIVFjElBlv14f3rx5g4CAAAQEBKC4uBhTpkyBvb290Jz24oq3tzfWrFkDGxsbyiF9584dnDx5Eu7u7mjTpg117YgRIwRmx5kzZzBr1iw2RS8lJSUcPHiQTtQQMXp6eli4cGGNfZ+vry9mzZoFMzMznDt3jk0Bikb8sLS0hLGxMZXAkpKSAgMDA0yfPh06OjrYtm0bZs+ejbVr14rWUBqB06dPH1haWsLd3Z1aE7Ro0QK2trawsLCAg4ODqE1sEFSu59atW8cxsNTW1lYodigrK+PevXvUPJ0TEhISyM3NRYsWLXD06FGsXbsWz58/BwC0adMG7u7ubGtYKSkpZGdns8wLZGVlkZaWBlVV1RqfdeTIEZw7dw6BgYFcbWSrqqoiKCgIxsbGUFBQwMOHD9GxY0cEBwcjJCSEVtOvA+LgK6HhHzExMfD19cXp06fRpk0bjBkzBmPHjkXv3r3Zru3QoQP27t0LKysryMvLIzExkTp2584dHDt2TATv4PfIy8vj4cOH0NLSYvFRPXjwAObm5hyTHwQJL+r+3JKRkQE7OzvExcWxHOdGiVaUKu3iQtVxDKh4/4mJiRyrt/IDcVCj7dy5M9zc3DBx4kQWe9asWYOPHz/i6NGjLD7fwsJCKCgosCUmC7tCqSAUuoXFxo0bkZ6eDh8fn1qTkXhh4sSJiI6OxpQpUzhWPKha4al58+aIi4uDlpYWOnXqBE9PT5ibmyMtLQ09e/ast8hGmzZtcOrUKQwYMADa2tpYv349/v33Xzx79gy9e/fGly9f6tV+TSQlJcHAwIAv39Pc3FxER0fjwoULOH78OMrLy4WWHFdXH7U48+rVK/j7+yMgIAA/f/5EWloaTwG63O4T17cP4LZ/qUvFbxoaGg4QGhqaP4JXr16RmTNnitoMjigrK5OkpCRCCCEKCgokLS2NEEJIREQE6dGjh9DsaNOmDbl79y4hhJCysjKioKBALly4QJ1/+vQpUVBQYLsvKSmJqKiokI4dOxJJSUny/PlzQgghK1euJFOmTBGO8Tyir69P4uPjRW0G10hLS5OcnByW15WfF0IIef36NZGSkhKFaWJBaGgoUVVVJdu2bSO3bt0it27dItu2bSPq6uokNDSUJCUlUT9/CmlpaWTJkiWkVatWpHHjxsTa2prtml+/fpHr168Tb29v8uXLF0IIIW/evCFfv37liw2fPn0ihw8fJsuWLSMFBQWEEEISEhLI69ev+dL+x48fyb1798jdu3fJx48f+dJmXVi0aBHLj7S0NLGzs2M7Lkxu3LhBZGVlSdeuXYmkpCTp0aMHadq0KVFUVCQmJiZCtUWQnDlzhowcOZJISUkRLS0tsmnTJvLmzRu+PiMjI4NcuXKFFBcXE0IIKS8vr/FacRnv3N3diaamJjly5AiRkZGh7AgNDSX9+vXjeE+jRo3Iq1evCCGEzJw5kzg5ORFCCMnKyiLy8vI82/Lz508iISFBUlJSarW3qKiIHD58mLRt25aEhoaSJk2akJCQELJ+/Xrq/+LA58+fyf79+0nPnj1FbQrN/1NYWEj69etHJCUlibq6OlFXVyeSkpJk8ODB5NOnT3Vur2nTpkRJSYn6YTAYRFFRkeWYkpISyz1MJpO8e/euxjbz8vIIk8mkXs+dO5coKSmRHj16kD179lBjIydevnxJXrx4QRgMBrl//z55+fIl9fP27VtSWlpa5/dI83tevnxJLC0tCZPJJAwGgzAYDMJkMomlpSXJysri+/OioqKIsbExYTKZIpnLiBOVv+/f/VT9TgmKoqIiEhYWRrZs2UK2bNlCwsLCSFFRkcCfS/N7GjduTLKzs2s8//LlS8JkMklJSYkQraKpD61atSL379+nXq9YsYIMHDiQen3ixAmio6MjdLuePHlCLl++TM6dO8fywwkTExOOc4/Pnz//UWswQSMnJ0cyMzMJIRXzssePHxNCCElMTCRqamoitKxh0b59exIZGUkIIUReXp5kZGQQQggJCgoilpaWQrNj6dKlxMPDo9ZrGAwGyc/PZzlWVFTEdqwqnObf8vLyv52n9ejRg8jLyxM5OTnStWtXoq+vz/JTnSZNmlDjTdu2bSm/eFZWFmnSpEmtz+KVpKSkP853Ky6+Epr6kZubSzZt2kQ6duxIWrRoQebPn08kJSXJkydPar1PVlaW+h61atWKJCQkEEIIef78Oce9JEIq9uq8vLyIq6uryPyrlpaWZNWqVYSQirEpKyuLlJWVkX///ZeMHTtWaHZUMmDAAOLl5UXev38vkLYNDQ3JpUuXyKNHj0hiYiLLT3W+fftG5s2bR5o3b06YTCbbjzCJiYkhtra2pH///tReQ1BQELl165bQbKg+jsnJyVH9HCHsPqH6Ur19USAjI0NevnxJCCGkefPm1OckPT2dNGvWjAQEBHD1Q8M9o0aNIvLy8qR169bEzMyMjB49muWnPigqKpLY2Fiurh06dCg5evQoIYSQ//77j/Tp04ccOXKEmJubkz59+tTLDkIImTdvHlFTUyNDhgwhysrK1B5lSEgIx7kav0hMTKz397SoqIhcvXqVLF++nPTr1480atSI9OjRgyxcuJBPVv6euvqoxY0fP36QY8eOkSFDhpDGjRuTcePGkYsXL5KysjJRm8Y3GAwGy9+oco5RSU1/o7y8PDJ58mTSunVrIiEhIdKxl4ZGlPA3RYWGhkZkFBQUwNfXF4cOHar1ui9fvuDo0aPw9fXFgwcPhGJbWVkZpXqpoqKCt2/fQltbG2pqanj27JlQbAAq5PPXrVuH/fv34+TJkygvL2cplfD06VO28sIAsHjxYsyYMQNbt25lUe8cNmwYJk2aJATLeWfz5s1wdnbGhg0bOJYkELds4F+/frGUfJSWlmaxWVJS8q/Oupo4cSIAcCwnNHHiRDAYDK4ywxsS2tra2Lp1KzZt2oTw8HD4+fmxnM/OzoaFhQVycnJQUlKCoUOHQl5eHlu2bEFJSQm8vb3r9fzk5GQMGTIEioqKePnyJWbOnIlmzZohLCwMOTk5CAoKqlf7QIWSGSeFBmHx6NEjltcDBgzge7b1jx8/4OnpicjISI5lhqoriy5fvhwuLi6U6tDp06dZVIf+FEaNGoVRo0bh/fv3CA4ORkBAAFavXg1zc3PY2dlhxIgRPGeUFxQUwMbGBpGRkWAwGMjIyICmpibs7e2hpKSEHTt2sN0jLuNdUFAQDh06BFNTU8yZM4c63r17d6SlpXG8p2XLlnj69Clat26NK1eu4MCBAwAqlLUlJCR4tkVKSgqqqqq19qnu7u6YM2cO/vvvP8jIyGDVqlUoLi7GpEmT0KZNG+zZswcTJkzg2QZ+EBkZCT8/P4SFhUFRURGjR48WqT00/0NRURFxcXG4fv06kpKSICMjAz09PWhqamLp0qW/ndtXZ/fu3XW2gRACU1PTGvub6iXSvLy8sHPnToSFhcHPzw/Lly+HlZUV7O3tYWZmxqJUoaamBgBs/T6NYFFTU8OlS5fw6dMnZGZmghACLS0tlhJi/ODHjx84deoU/Pz8cPfuXfz7779/fVkycfis37x5E/Pnz8edO3fY+vvPnz+jS5cu8Pb2FlolGxp2ZGRkUFhYWKNK4pcvX6CgoEAr5zYgPn36xKJMHh0dDUtLS+p179698erVK6HZk5WVhdGjRyMlJYXyEwCgxmhOc9uoqCiOSkQ/fvzArVu3BGvwH0STJk2o32Pr1q3x/PlzdOnSBQDw4cMHUZrWoPj48SOlwqSgoEAp1P3zzz8CVyFevHgx9f/y8nIcOnQIN27cgJ6eHptftbK6T3WlNllZ2VrnRJzm38XFxbC2tmbp+6v7SqpX1PodmpqaePHiBVRVVdG5c2ecOHECffr0QXh4OJo2bVqntriF1LPEsjgiLr4SGt6xtrZGTEwMhg0bht27d8PCwgISEhJc+Y3btWuH3NxcqKqqokOHDrh27RoMDAxw//59ln2MSiIiIjBixAhoamoiLS0NXbt2xcuXL0EIgYGBQb3eR1lZGXbt2oUTJ05wrDhWVc1z69atMDU1xYMHD/Dz508sXboUT548wcePH/leZYobBPnMxMREJCQkoHPnzlxdv3TpUkRGRuLAgQMcVdqFxenTpzFlyhTY2tri4cOHKCkpAVCxZtu4ceMfrXL+u2qx3GBgYICIiAgoKSlBX1+/1jarj6etWrXCx48foaamBlVVVdy5cwfdu3fHixcvQAjBtGnT6m0fDStNmzYVWCUfJSUlNGvWjKtrN27ciK9fvwIANmzYgKlTp8LBwQFaWlrw9fWtty27du2Curo6Xr16ha1bt1KKqbm5uZg7dy7P7f5OebfyPfHKgAED8OjRI+jo6MDY2BjLli2DoaEh3/2Iv6OuPmpxYu7cuQgNDUX79u1hZ2eHkJAQqKioiNosgTB9+nRqDvTjxw/MmTMHTZo0AQBqLON0T05ODlavXs1R6ZqG5m+BDtCloflLEGVgRteuXanSun379sXWrVshLS2NQ4cOCbWMyYYNGzB06FCoqalBQkICe/fupSYMABAcHIzBgwez3Xf//n0cPHiQ7Xjbtm2Rl5cnUJvrS2Ugm6mpKctxcQ7ifPr0KfV7JYQgLS0N3759A0BvZrx48ULUJogMCQkJKpixKk5OTujVqxeSkpKgrKxMHR89ejRmzpxZ7+cuXrwY06dP/6Od8JGRkQJ/hr29Pa5du4Zx48ahT58+v118paamIiQkBEBFYP73798hJycHDw8PjBw58o8rC9q8eXMsXrwYixcvhqenJ5YsWYJLly5BRUUFc+bMwbJly+oc8LRo0SJISUkhJycHOjo61PHx48dj8eLFHAN0xWW8e/PmDTp27Mh2vLy8HL9+/eJ4z4wZM2BjY0Mt7ocMGQIAuHv3LtcO+ppYuXIlVqxYgeDgYI7OvsqABwCwtbWFra0tiouL8e3bN6rUjyioLD/v7++PwsJCfPr0CceOHYONjQ3tABEzGAwGzMzMYGZmRh1LSkriKvmuOrxsIri5uf32mupO9EaNGmHixImYOHEisrOzERAQgLlz56K0tBRPnjxhK9kVGBgIFRUVWFlZAQAVfKyrq4uQkBAqkJeGvwgqCeju3bvw9fXFiRMnoKmpCTs7O5w+fVrojntxIj4+HgUFBRg+fDh1LCgoCG5ubigqKsKoUaPg6enJcROf3+zevRszZ87kmIypqKiI2bNnY+fOnXSArgjp378/Dhw4QCUUVcfLywv9+/cXslU09aFly5Z48eIF2rdvj58/f+Lhw4dwd3enzn/9+pUtsE+QODk5QUNDAxEREdDQ0MC9e/dQUFAAZ2dnbN++neXa5ORk6v9V/TFARSDQlStX6PLtdaBfv36IjY2Fjo4Ohg0bBmdnZ6SkpCAsLAz9+vUTtXkNhtoCSxUVFQX67OpJzD169AAAPH78mOV41TVVp06dfrvGqho4x2n+PXLkyN/axs28vSozZsxAUlISjIyMsGzZMlhbW2Pfvn349esXFVwsCP609aa4+EpoeOfy5ctYsGABFYRVF0aPHo2IiAj07dsXjo6OmDx5Mnx9fZGTk4NFixaxXS9I0QF3d3f4+PjA2dkZq1atwsqVK/Hy5UucPXsWa9asYbm2a9euSE9Px759+yAvL49v375hzJgxmDdvHlq3bl0vO7jl/PnzsLS0hJSUFM6fP1/rtSNGjOD5Obq6unXaNwoPD0dQUBCMjY0xY8YMDBo0CB07doSamhqOHj0KW1tbnm2pC+vXr4e3tzemTp2K0NBQ6vjAgQOxfv16odggDMaMGcPyunowVyVhYWF1anfkyJHU+rquCSyDBw/G+fPnoa+vjxkzZmDRokU4deoUHjx4wGYvUDGXr+r/ZTKZPJWq5wevX7/G+fPnOQbpC3Jsry/+/v4Ca3vdunVYs2YNAgMDf7uH0qtXL+r/LVq0wJUrV/hqi5SUFFxcXNiOcxov6kLTpk1rnV9V7vnzSlpaGpo0aYLOnTujc+fO0NHREYmPjxcftbjg7e0NVVVVaGpqIjo6GtHR0Ryvq2tfV5WqiYRVYTAYaNy4MTp27IiRI0dyHbBeHW76l+p7D5MnT2ZrZ+rUqWzHYmNjcevWLWptRUPzt0IH6NLQ/MGIS2DGqlWrUFRUBADw8PDA8OHDMWjQICgrK+P48eNCs0NdXR2pqal48uQJmjdvjjZt2rCcd3d3R7t27djua9SoEcfstPT0dDRv3lxg9vIDYQTe8RtTU1OWxW7lJndVddi/FTp4hZ1bt24hLi6OTV1KXV0db968qXf7tBO+Qn1pzpw5uHbtGs9tXLhwAZcuXcLAgQO5uv5vUx3Kz89HYGAgAgICkJ2djXHjxsHe3h6vX7/Gli1bcOfOnTr//q9du4arV6+yjWtaWlrIzs7meI+4jHe6urq4desWW5936tQp6Ovrc7xn7dq16Nq1K169eoV///2XctBKSEhg2bJl9bJn3759yMzMRJs2baCmpsbmQAbqrpgkSE6fPg1fX1/ExMTA0tISO3bsgKWlJZo0aYJu3br91ePo3wg3mwh13eivDpPJpOZpNSV/bdy4kQpEi4+Px759+7B7925cuHABixYtqpdzkka4dOnSBe/evcOkSZMQHR2N7t27i9okscDDwwPGxsbU2iUlJQX29vaYPn06dHR0sG3bNrRp0wZr164VuC1JSUnYsmVLjefNzMzYAvRohMvKlSthbGyMgoICuLi4oHPnziCEIDU1FTt27MC5c+ca5Fr+b2bYsGFYtmwZtmzZgrNnz0JWVpYlCD45ORkdOnQQmj3x8fG4efMmVFRUwGQywWQy8c8//2DTpk1YsGABSwBijx49wGAwwGAwOCaty8jIwNPTU2i2N3R27txJJZm7u7vj27dvOH78OLS0tMQ6aELcqCmw9OfPnyzB74KAl/7X3d29ToHD9Zl/FxYW4tSpU3j+/DmWLFmCZs2a4eHDh2jZsiVbMH3VYJAhQ4YgLS0NCQkJ6NixI/T09Hi24W9DXHwlNLwTGxsLX19f9OzZEzo6OpgyZQrXlY6qKqqOHz8eqqqqiI+Ph5aWFqytrdmuF6TowNGjR3H48GFYWVlh7dq1mDhxIjp06AA9PT3cuXMHCxYsYLleUVERK1eu5Pl59WXUqFHIy8tDixYtag2grK+QzJYtW7B06VJs3LiRqyqSolRpr8qzZ89gaGjIdlxRURGFhYVCswMArl69So1j5eXliIiIoBJT6mtL9fGRUzAXL1QdS+s6rh46dIiqfjNv3jwoKysjLi4OI0aMwOzZs5GYmIgVK1ZQKsZt2rRBcXExdT+DwUB8fLzQqyIKUqFbWLx7946qrKutrc0XcYsdO3bg+fPnaNmyJdTV1dn6gKoKyoMHD0ZYWBhbJYEvX75g1KhRuHnzZr3tASoSHzkFOfKajHDz5k2B+vQLCgqQkpKCqKgoXL16FStXroS0tDSMjIxgYmLCFzEkbqivj1qUTJ06VeD7Lo8ePcLDhw9RVlYGbW1tABXzUQkJCXTu3Bn79++Hs7MzYmNjoaurW6e2ue1feA22b9++PcseBQ3N3wodoEtD8wciboEZ5ubm1P87duyItLQ0fPz4EUpKSkK3RVJSssZN5JqOjxgxAh4eHjhx4gSAisVXTk4OXF1dxTZTqxIjIyNRm1An/maFWG4ICgqq9TynrLQ/nfLyco4OvNevX7Mo3vIK7YSvCO6KiIioVxtt27at09/jb1EdCgsLg7+/P65evQpdXV3MnTsXkydPZnEQDRgwgEUBl1uKioo4Boh+/PixRvU+cRnv1qxZg2nTpuHNmzcoLy9HWFgYnj17hqCgIFy4cKHG+8aNG8fyurCwkC8lyX6nwpCYmFhnxSRBMn78eLi6uuL48eN86QdpGhb83kT48uULjh49Cl9fXzx48IA6XlJSgrCwMPj5+SE2NhbDhw/Hvn37YGFhASaTydbOq1evKGXss2fPYty4cZg1axYGDhwIY2PjerxjGmGTmpqKJk2aICgoCMHBwTVeJ6w+T1xITEzEunXrqNehoaHo27cvDh8+DKDCEe3m5iaUAN38/PxalTolJSXx/v17gdtBUzMDBgzA8ePHMWvWLJw+fZrlnJKSEkJCQrhObKMRD9atW4cxY8bAyMgIcnJyCAwMZEki9fPzY1HKFzRlZWXUPFBFRQVv376FtrY21NTUqA3xSipL+WpqauLevXss61xpaWm0aNECEhISQrO9oVO1UliTJk24Kp9O8z927dqFRYsW1RpY6uDggNWrV4vQSnYmTJjA1+opycnJ6NWrF1tQR3JyMoYMGQJFRUW8fPkSM2fORLNmzRAWFoacnJzf+g3V1NTqnfwv6BLL4oi4+EpoeKdfv37o168fdu/ejePHj8PPzw+LFy9GeXk5rl+/jvbt23PtP+nfv3+tlQ4EKTqQl5eHbt26AQDk5OTw+fNnABUCJ6tXr2ZRxf8dwgjSrwyArP7/qrx69QoeHh71ek5lFS1uq0jWptJePWhPkLRq1QqZmZlQV1dnOR4bGyvUyqMAuyLi7NmzWV7XZx9XkMqpvFKZwFbJhAkTWIL2PT098c8//7DcExwcjLZt24IQAj8/P+zdu7dWn4ggEKRCt6D58uUL5s2bh9DQUOo7KSEhgfHjx8PLy6teFRLqoqAcFRXFNr8CKpSdb926xbMNlWRlZWH06NFISUmhxAyA/32HeE1GELTvlMFgQE9PD3p6enB0dERCQgL27duHo0eP4vjx40IL0K2NmnzU4kJAQIDAn1Gpjuvv708ln3z+/Bn//fcf/vnnH8ycOROTJk3CokWLcPXq1Tq1Lej+Zffu3Vi2bBkOHjzINu7R0PxN0AG6NDQNBE5lNapSNYuxIQRm8Cqvzw/KysoQEBCAiIgIvHv3js05UD1DbseOHRg3bhxatGiB79+/w8jICHl5eejfvz82bNggTNN5orCwEL6+vkhNTQVQoXplZ2cn8JJwvEArxNaOk5MTy+tfv36huLgY0tLSkJWV/SsDdM3MzLB7926qBDiDwcC3b9/g5uaGYcOG1bt92gnPH3bs2AFXV1d4e3tz9T3/W1SHZsyYgQkTJuD27ds1Bsu1adOmTmoXb9++RZs2bTBo0CAEBQVRwUIMBgPl5eXYunUrTExMON4rLuPdyJEjER4eDg8PDzRp0gRr1qyBgYEBwsPDMXToUI73bNmyBerq6hg/fjwAwMbGBqdPn0br1q1x6dKlem08/C5z293dvc6KSYLE3t4eXl5eiIqKwpQpUzB+/Pi/uuz83wa/NhEiIyPh5+eHsLAwKCoqYvTo0dS5uXPnIjQ0FO3bt4ednR1CQkKgoqJSa3tycnIoKCiAqqoqrl27RpXkaty4Mb5//87ju6URBeK4uSYOfPr0CS1btqReR0dHw9LSknrdu3dvvHr1Sii2tG3bFo8fP6aC4quTnJwstLK2NDUzevRomJub4+rVq8jIyABQUenA3NxcZCr8NLyjoqKCmJgYfP78GXJycmwBrSdPnhRqGdyuXbsiKSkJGhoa6Nu3L7Zu3QppaWkcOnSILeCjcn1WU+AMDe98+/aN7fdaXcWPhpUVK1ZAWVmZzb+lpqYGZWVlWFhYoKCgQKA2/M4HX5WwsDCBiE8QQlBaWsp2fPHixZg+fTq2bt3K4vMfNmwYJk2axHb93r17ObZftQyuoaFhnYLwBV1iWRwRF18JTf1p0qQJ7OzsYGdnh2fPnsHX1xebN2/GsmXLMHToUJw/f57jfc+ePYOnpye1v6KjowNHR0dKva4qghQdaNeuHXJzc6GqqooOHTrg2rVrMDAwwP3799GoUSNKFb/697B6gBjAe5AYv/n48SP8/PyoxEZe4Fb5PCsrC+rq6jWqtP/69UuofueZM2fCyckJfn5+YDAYePv2LeLj4+Hi4iLURJSGOgesiwDUx48f6xTAHhcXh/nz57Mc69evHzWPlpGRgY2NDffG8glBKnQLmpkzZ+LRo0e4cOECleQQHx8PJycnzJ49G6GhoTy3zY3qatW//9OnT1mqY5aVleHKlStslQh4wcnJCRoaGoiIiICGhgbu3buHgoICODs716uSUWX1stpgMBgc54+14eHhARcXF6SlpSEqKgpRUVGIjY3F169f0a1bNzg6OopcCKw2H/XfxrZt23D9+nWWNaWioiLWrl0LMzMzODk5Yc2aNTwlBwu6fxk/fjyKi4vRoUMHyMrKsgkL/G1CDzR/L3SALg1NA+F3QR+KioqU81LcAjN+/PgBT09PREZGcgyIrVpeQhg4OTkhICAAVlZW6Nq1628ntYqKirh+/TpiY2ORnJyMb9++wcDAgMrMFWcePHgAc3NzyMjIoE+fPgAqAt82bNhAOXDEiZycHK6uU1VVFbAl4smnT5/YjmVkZMDBwQFLliwRgUWiZ8eOHTA3N4euri5+/PiBSZMmISMjAyoqKtRior7tc3LC9+vXj3bC14FevXrhx48f0NTU5Grx9beoDuXm5v42AENGRqZOpX26dOkCLy8vbNu2DYMHD8aDBw/w8+dPLF26FE+ePMHHjx9x+/ZtjveK03g3aNAgXL9+nevrvb29cfToUQDA9evXcf36dVy+fBknTpyAi4sLrl27JihTAfBfMak+HDx4ELt378aJEyfg5+eHhQsXwtzcHISQBut0/xOpS/JdXajPJsKbN28QEBAAf39/FBYW4tOnTzh27BhsbGxY5sve3t5QVVWFpqYmoqOjER0dzbG9sLAw6v9Dhw7Ff//9B319faSnp1NJNE+ePKGz5hsY/FAm/xNp2bIlXrx4gfbt2+Pnz594+PAhSwnur1+/1qpqy0+GDRuG1atXw8LCAo0bN2Y59/37d7i5uWH48OFCsYWGMzdv3sT8+fNx584dts2lz58/o0uXLvD29sagQYNEZCENr9TkuxN2kvqqVatQVFQEoCKZzNraGoMGDYKysnKNm9+BgYFQUVGBlZUVAGDp0qU4dOgQdHV1ERISQidUc8mLFy8wf/58REVF4cePH9TxmlT8aFgJDg7GlClT0LRpU5YSwN++fYOlpSXevXuHqKgogdpQ9XtMCMGZM2egqKiIXr16AQASEhJQWFhIzecFVaqVk7/6/v37OHjwINvxtm3bsgSaVLJr1y68f/8excXF1N7Ap0+fICsrCzk5Obx79w6ampqIjIxE+/btubJL0CWWxRFx8pXQ8A9tbW1s3boVmzZtQnh4OPz8/Dhed/r0aUyYMAG9evWigsru3LmDrl27IjQ0lE3AQZCiA6NHj0ZERAT69u0LR0dHTJ48Gb6+vsjJycGiRYtYgmcePXoEFxcXLFmyhCUYbseOHdi6dWu97BA3uA0e09LSQm5uLqXSPn78eOzdu5dFpV0YysKVLFu2DOXl5TA1NUVxcTEMDQ3RqFEjuLi4wNHRUWh2VPLly5caE4kyMzNrTAAVFbt3767T9VUD2GuDwWCgUaNGLFUlPDw8WBLTW7dujfz8/Do9nx8IUqFb0Fy4cAFXr15lERUwNzfH4cOHhaL+W/n3ZzAYGDx4MNt5GRkZeHp61vs58fHxuHnzJlRUVCil5n/++QebNm3CggUL8OjRI57aPXPmTK3P3Lt3L09+f3d3d8yZMwd9+vSBvr4+jIyMMHPmTBgaGopUjIRbH/XfxufPn/Hu3Tvo6uqyHH///j1V5aJp06YcVaJ/h6D7l7r22TQ0fyoMIigPAg0NjUj5/v07FZhx9+5dmJub4+LFi0hMTETXrl2FaoutrS2uXbuGcePGoWXLlmyTp7oEHvEDFRUVBAUF8UVdU9wZNGgQOnbsiMOHD0NSsiIno7S0FP/99x+ysrIQExMjYgtZqarYwCmzm97Q4MyDBw8wefJkpKWlidoUkVBaWorQ0FAWJ7mtrS1kZGT49ozbt28jKSnpr3TCJyUlwcDAoF7fuyFDhiAnJwf29vYcx4HqwT6ampq4f/8+lJWVWY4XFhbCwMAAWVlZPNsiTkhISCA3N5ctsLOgoAAtWrTg6Xe+f/9+uLq6wsLCAt7e3vD29mb57M6bN++PVM2TkZFBeno62rdvDycnJ/z48QMHDx5Eeno6+vbtyzHBgVvKysqwa9cunDhxAjk5OWwOjs+fP3P8O4oLGRkZ8Pf3R2BgIL59+wYrKyuMGzeuTspQNPxnxowZXF1XV7VSWVlZpKeno127dgAqNuXt7e2pTZacnBx06tSJJWDk9OnT8PX1RUxMDCwtLTF58mRYWlqiSZMmSEpKYnP6TZ8+nStnaFXbCwsLsWrVKrx69QoODg6U893NzQ3S0tJ1UgqnES8SEhIoJSldXV2xSwAUFg4ODkhKSsKWLVtw9uxZBAYG4u3bt1SJ+6NHj2L37t24f/++wG3Jz8+HgYEBJCQkMH/+fErZKy0tDV5eXigrK8PDhw9ZFH9phMuIESNgYmLCUsK9Knv37kVkZGStm3A0NHXl48ePtSqNaWtr48CBAxg8eDDi4+NhamqK3bt348KFC5CUlGRJvKGpmYEDB4IQAicnJ45rX1ErUDUEfHx84OTkhIsXL8LY2BhFRUWwsLBAXl4eoqKi+KJuxi2urq74+PEjvL29KZ9lWVkZ5s6dCwUFBWzbtk0gz63JD9OiRQtcvXoV+vr6kJeXR1JSEjQ1NXH9+nXY2dmxqfWHhITg0KFD8PHxQYcOHQBUBFnNnj0bs2bNwsCBAzFhwgS0atUKp06dEsh7oaFp6HTo0AG2trbw8PBgOe7m5oYjR47g+fPnIrKsIigrPj4eWlpasLa2ZjnXp08frF27lm0P7NKlS1i9ejUSEhKEaWqN8MPvDAC3bt3CwYMHkZWVhZMnT6Jt27YIDg6GhoYGFRDIZDKRl5dH+e+q9qOi5OfPn8jMzMS3b9+gq6sr1KoLVakUS6ie5Pns2TOYmpri9evXIrGLX2RnZ3N9rb6+PsLDwzFw4ECO52/fvg1ra2uhKz6OGjUKVlZWmDlzJlxcXHDu3DlMnz4dYWFhUFJSwo0bN4RqT11QVVXFxYsX0a1bN5bjycnJGDZsWJ0/X82aNUN6ejpUVFR+q6b88eNHZGdngxACTU1N3Lt3jyUAW1paGi1atKhTRYGaUFJSwsOHD6GhoYEOHTrAx8cHJiYmeP78Obp164bi4uJ6P6OSZ8+eYdmyZQgPD6fGqbomVVb2i40bNxaLSh919VH/bdja2lLJNpUVOe/fvw8XFxcMGDAAwcHBCA0Nxfbt2/HgwYM6td2Q+xcamoYEHaBLQ/MXIOrADEVFRVy6dKnGxYywadOmDaKiotCpU6car9m7dy9mzZqFxo0b11gOrJIFCxbw20S+ISMjg0ePHqFz584sx58+fYpevXrxdTHADyQlJdGuXTtMnz4d1tbWVFBxdbp37y5ky8SbxMREGBoaUhlyNPXn+/fviIiIoNTFli9fjpKSEuq8pKQkPDw82BxWDRF9ff1aHRjFxcXIyMiol6NUVlYW8fHxXH93qztMK8nPz4eqqirL36IhU9P7fPv2LTp06MBz2fcXL17A3t4eT58+xaFDh1iUh6ojLuNdXZ1q1WnTpg1OnTqFAQMGQFtbG+vXr8e///6LZ8+eoXfv3vXqH9esWQMfHx84Oztj1apVWLlyJV6+fImzZ89izZo1WLhwIce/o7hRXl6OixcvwtfXF5cvX/5jvkc0rDRr1qzOmwiSkpJwdXXFsmXLWErlSklJ0c5Pmhp59+4dJkyYgKioKDRt2hRARSC2iYkJQkNDWTY7/gY+fPiAMWPGIDY2FnJycggMDGRRRjU1NRVqBYbs7Gw4ODjg6tWrLImP5ubm8PLygoaGhlDsoOGMmpoarly5Ah0dHY7n09LSYGZmxnWFGRqaSrjx80lKSqJVq1YYOnQoSzCPrKws0tLSoKqqCldXV+Tm5iIoKAhPnjyBsbEx3r9/L0jT/xjk5OSQkJDAsew5Dfds3boVGzZswLlz57BmzRq8efMG0dHRVBKasGjevDliY2PZ/p7Pnj3DgAEDUFBQwFO7v1ufJicnw8jIiM0P899//6GgoAAnTpxAs2bNkJycDAkJCYwaNQqGhoZsylQdOnTA6dOn0aNHD5bjjx49wtixY5GVlYW4uDiMHTsWubm5XNkuqBLL4oa4+EpoRI+srCySk5PZ1EMzMjLQvXt3sdtfqURGRgYPHz5km2+mpqbCwMCAZ58jv+FHgO7p06cxZcoU2NraIjg4GE+fPoWmpib27duHS5cu4dKlSwDEN0BXXLC0tASDwcD58+epfbnU1FQMHjwYNjY22LNnj4gt5I4fP36wiSvUNfDQ1NQUBgYGNSbiODs7IzExERERETzbyQtZWVn49u0b9PT0UFRUBGdnZ8TFxVEK3eJc8eLQoUM4efIkgoOD0apVKwBAXl4epk2bhjFjxmD27Nl1ai8wMBATJkxAo0aNEBAQUOvcRJiVoAYNGgRnZ2eMGjUKkyZNwqdPn7Bq1SocOnQICQkJePz4cb2f8fbtW7i5uSEwMBDm5ubYtGkTz8JsTCYT+fn5YuPDo33UtfPt2zcsWrQIQUFB1FxbUlIS06ZNw65du9CkSRMkJiYCANv8/3cIs3/hRz9NQ9NQ4Rz5REND80ehpaWFjRs3Yv369VRgxsSJE4UWmNG2bVuWiZSocXZ2xp49e7Bv374aJ+27du2Cra0tGjdujF27dtXYFoPBEGsnnIKCAnJyctgCdF+9eiVWf5NKXr9+jcDAQPj7+8Pb2xuTJ0+Gvb19jRuXfxvnz59neU0IQW5uLvbt2yc2AfDCRlVVFcbGxjAyMoKJiQnfHGqBgYG4ePEiFaC7b98+dOnShVLlTUtLQ+vWrWtUvWpIjBo1SuDP6Ny5M1eO36qf8atXr7KU0SkrK0NERMQfUQq9cnOHwWDAx8eHRRmhrKwMMTExbP12XdDQ0MDNmzexb98+jB07Fjo6OmwJDw8fPgQgPuPdrl27qHFp165ddS5VNGbMGEyaNAlaWlooKCiApaUlgIqNx/qWYDt69CgOHz4MKysrrF27FhMnTkSHDh2gp6eHO3fu8FQ+ShQwmUxYW1vD2toa7969E7U5NAJCX18fZ8+erXFeEBYWBn19fZZj9vb28PLyQlRUFKZMmYLx48dTJXD5yadPn+Dr60uprero6MDOzk7oZb9p+IOjoyO+fv2KJ0+eUHP1p0+fYtq0aViwYAFCQkJEbKFwUVFRQUxMDD5//gw5OTk25ZWTJ08KVQlJTU0Nly5dwqdPn5CZmQlCCLS0tATy3aapO/n5+ZCSkqrxvKSkJB0MScMT3JQhLS8vR0ZGBnx8fODi4kIpAsrJyaGgoACqqqq4du0aFi9eDABo3Lix2ATxNAR69+6NV69e0QG69WTp0qX4+PEjTE1Noa6ujqioKKEH5wIVFZvS0tLY/p5paWn1Wgc2bdq01jVvZQWx6uzYsQPjxo1DixYt8P37dxgZGSE3Nxf9+/fnmASUm5vLMVC2tLQUeXl5ACqSXb9+/cq17YIqsSxuiIuvhEb0GBsb49atW2y+pdjYWAwaNAhA/ZPOa6P6fkBtVE3Q19HRwaZNm+Dj40NV9fj58yc2bdok1L2W3yUPFRYW1vsZ69evh7e3N6ZOnYrQ0FDq+MCBA7F+/XrqdWV5+6qIslT6jx8/4OnpicjISLx7946t76z03QqLsLAwDBkyBLa2tggNDcWTJ09gamoKW1tb7Ny5U6i21JWioiK4urrixIkTHJNnagoAf/r0KcdqaXPnzsWECROgrq4OBwcHMJlMqp39+/fD09MTx44d4/8b+Q1V976aNGkCb29vodvAKwcOHEBmZiZUVVWhqqoKoKLKV6NGjfD+/XscPHiQupabz/60adPw5csXlJSU1EmMLDAwECoqKrCysgJQMec8dOgQdHV1ERISUu8gxFWrVqGoqAgA4O7uDmtrawwaNAjKysos/RMvfP78GRs3boSnpyd69OiBiIgIahyqD506dfptXygstWhh+agbKnJycjh8+DB27dpFVRnV1NRk8TfWNTC3EkH3L7z20zQ0fxq0gi4NzV/Ku3fvhKb0dvnyZezduxfe3t5ikcE3evRoREZGolmzZujSpQvbxtifVLZvwYIFOHPmDLZv344BAwYAqFBOc3FxwdixY8U66zU2Nhb+/v44efIkdHV1YW9vD3t7e2ox/DdS/b0zGAw0b94cgwcPxo4dO/7IsvW/48iRI4iJiUFUVBQyMzPRtm1bGBkZwcjICMbGxtDS0uKp3UGDBmHp0qWUolD1jPojR47Ay8sL8fHxfHsvfzLXrl2Du7s7NmzYgG7durH1u5XZkZWfcQaDgepTVCkpKairq2PHjh1U4HRDpVK1Ljs7G+3atWMJ4pGWloa6ujo8PDzQt29fnp+RnZ2NGTNm4PHjx5g9ezZbgK6bmxvPbYsjv379wp49e/Dq1StMnz6dCkCsDPz977//eG67SZMmSE1NhaqqKlq3bo2LFy/CwMAAWVlZ0NfXx+fPn/n1NvjKyZMnERISgvT0dEhLS6NTp06YMWMGzM3NRW0ajQA5ffo0JkyYgN27d3PcRHB2dsaxY8cwbtw4lvu+f/+OEydOwM/PD3fv3oW5uTkuXryIxMREnlUYqhITEwNra2soKiqiV69eAICEhAQUFhYiPDwchoaG9X4GjXBRVFTEjRs3qJJqldy7dw9mZmZ82WiloflT6dChA3bs2FFjolxYWBhcXFyoDRcaGkFw4cIFzJ07l1JqtrW1RVpaGvT19RESEoKcnBwoKyvj/PnzWLFiBV/Unv4Gnj9/jjlz5mDy5Mno2rUr29pXT09PRJY1DKoHWFy6dAndu3dH27ZtWY4Ly3e7ePFiBAUFYcWKFejTpw8A4O7du9i8eTOmTJnCc8BSVFQUV0FhRkZGHI/HxsYiOTkZ3759Q8+ePWFqasrxOisrK+Tl5cHHx4daIz969AgzZ85Eq1atcOHCBYSHh2PFihVISUnh6b0A/CmxTEMjrnh7e2PNmjWwsbFBv379AAB37tzByZMn4e7ujjZt2uDmzZsYNGgQxo4di8DAwFrbq6uSI7d7IQwGgyW45d69e7C2tgYhhBp7kpOTwWAwEB4eTvVpgmbGjBlcXefv78/zM2RlZfH06VOoq6uz+PCzsrKgq6uLHz9+AKj4XVpaWqJRo0YAgPDwcAwePBhNmjRhaU9YY4ytrS2uXbuGcePGoWXLlmzjgih8t4WFhdSeSkxMDKZOnVqjiqw4MW/ePERGRmLdunWYMmUKvLy88ObNGxw8eBCbN2+Gra0ty/VZWVkYPXo0UlJSWPYhKv8GZWVlcHV1xbZt2yAvL0/tCVUqTC5evFjkv5dv376xBXWLswKlu7s719dy+9nnRtUfYA3809bWxoEDBzB48GDEx8fD1NQUu3fvxoULFyApKSmQ7//Hjx9/m7zxO7Zu3YotW7agVatW2LhxI0aOHMkX25hMJnbv3v3bRE9hqhAL2kdN83sE0b/UtZ+moflToQN0aWj+YMQlMOP9+/ewsbFBTEwMZGVl2ZzTwsq8quR3ToHqzoDY2Fj8888/gjRJYPz8+RNLliyBt7c3SktLQQiBtLQ05s6diw0bNlBqoOJMfn4+Jk6ciOjoaLx//55WWaOpkdzcXERHR+PChQs4fvw4ysvLec66a926NeLj4ym11ubNm+P+/fvU6/T0dPTu3VtsA/PEjaqBt1WpVIWp/nfS0NDA/fv3oaKiIjQbRYGJiQnCwsL4ngV8+PBhODs7Y8iQITh48CDXJYrEZbwbMmQIJk+ejDFjxoiFY1FbWxtBQUHo27cv/vnnHwwfPhzLli3D8ePH4ejoKHZqtOXl5Zg4cSJOnjyJTp06UWrMqampyMzMxKxZs3DgwAEUFBQgJiaGpQQ7zZ9BfTcRMjIy4O/vj8DAQHz79g1WVlYYN25cnRQpqtOtWzf0798fBw4coJISysrKMHfuXMTFxdUrIIBGNMjLy+PWrVscSyYbGRn9tnQzDc3fjKOjI6KionD//n00btyY5dz379/Rp08fmJiY/LakNg1NfSgsLISdnR21CV1YWIhVq1bh1atXcHBwgIWFBYCKDXJpaWmsXLlSlOY2GO7cuYNJkybh5cuX1LHKwA9Oa18aVoQRyFUXysvLsX37duzZswe5ubkAKvxFTk5OcHZ2ZlPMFxTx8fEoKChgSVYODAyEm5sbiouLMWrUKHh6elJBZ5Xk5eVhypQpiIiIoPzxpaWlMDU1RXBwMFq2bInIyEj8+vULZmZmdbaLnyWWxRlx8ZXQiAZeA2TFgaKiIhw9ehRpaWkAKlR1J02axBaQ2tDR1NTEoUOHMGTIEJYA3aCgIGzevBlPnz4FIH5jjKKiIi5duiTSyoic1u25ubkYOnQohg8fjs2bN1PHxcFHWxOqqqoICgqCsbExFBQU8PDhQ3Ts2BHBwcEICQnBpUuXWK63traGhIQEfHx8oKGhgXv37qGgoADOzs7Yvn07lJSU0LVrV9y9exfHjh1DRkYGgIpqtRMnTqSC9YXNixcvMH/+fERFRVGB50DNeyx/OtHR0dT/CSEYNmwYfHx82BK7qiY8ycrKIi0tDaqqqnB1dUVubi6CgoLw5MkTGBsb81zJxs7Ojqvr/Pz8eGqfyWRCRkYGQ4YMqXX+WdcAYyaTiby8PKEJutUVQfioGxpjxoxBQEAAFBQUfvu+6xNgLuj+pa79NA3NnwodoEtD8wciboEZQ4YMQU5ODuzt7Tlmggoz84oXpKWl0bZtW0ycOBG2trbo0qWLqE2qM8XFxXj+/DmACrWeAwcOYNu2bVQ5M3EkLi4Ofn5+OHnyJLS1tWFnZ4dZs2b91Qq6NJwpLi5GbGwsoqKiEBkZiUePHkFHRwfGxsa1lqGrDRkZGSQmJtZYljItLQ09evRgWag0VExMTH6bvctgMBAREcHzM6o6S6qTkpKC+fPnA+C86RQUFAQ3NzcUFRXVuOlE8z8sLCxw79497N69G1OnTq3TveIy3jk5OeHEiRP4/PkzrKysMHnyZAwbNqzWUtCV1FSarGqZv7qybNkyKCgoYMWKFTh+/DgmT54MdXV15OTkYNGiRSzOanFg165dWL9+PQIDA9nUps+fP48ZM2Zg+fLlCAgIwNSpU7F06VIRWUojCB4/fsy3TYTy8nJcvHgRvr6+uHz5MkpKSni2q6Zx9dmzZ+jRowddOrsBMnLkSBQWFiIkJARt2rQBALx58wa2trZQUlKqtfwxDc3fTn5+PgwMDCAhIYH58+dTfWNaWhq8vLxQVlaGhw8fomXLliK2lIaGpq7o6upCR0cHS5cu5egDpVVFGy6VQUz8CFDiRvGNwWCgtLQUAGBpaQljY2O4uroCqPCj9OzZE9OmTYOOjg62bduG2bNnY+3atRzbSktLQ3p6OoCKBNSafF3cUr3E8pYtW/hSYllcERdfCU3DoKZERQaDgUaNGkFaWlrIFv35bNq0CUeOHIGfnx+GDh2KS5cuITs7G4sWLcLq1avh6OgoahM5oquri9DQUJGq69c0HlVVlG0IwZ9ycnJ4+vQpVFVV0a5dO4SFhaFPnz548eIFunXrhm/fvrFcr6Kigps3b0JPTw+Kioq4d+8etLW1cfPmTTg7OyMpKQm9e/fGf//9hwkTJkBeXl5E74yVgQMHghACJycnjvPMmpT3xYkHDx4gNTUVQMV3oGfPnnxru3oVTE60aNECV69ehb6+PvT19bF48WJMmTIFz58/R/fu3dk+K9zCZDKhpqYGfX19tsqQVeHVVzZ9+nSuFHjrmmAgISGB3NxcsQ3QrYSfPuqGxowZM7B3717Iy8vXWYCuLgi6f6lrP01D86ci+ftLaGhoGhp79uzBjRs3cP78+RoDMzp06EAFZgiauLg4xMfHo3v37gJ/liB4+/YtQkNDERISgs2bN0NPTw+2traYOHEi2rVrJ2rzOFJSUoK1a9fi+vXraNSoEZYsWYJRo0bB398fFhYWkJCQwKJFi0RtJhuV2Yr+/v749OkTbG1tcfv27T9SfaGuFBUVYcuWLQgLC8PLly/BYDCgoaGBcePGwcXFBbKysqI2USQMGDCAJSB32bJlMDQ0rLciabt27fD48eMaNy2Sk5PF9vtfV6or31Xl69evOHbsWL0XvNUXb1+/fkVISAh8fHyQkJBABei6u7vDxMSEGrtSUlJgb2+P6dOnU5tObdq0qXHTqaFRVlaGgIAARERE4N27d2xlY27evMlTm7x+PsVlvNuzZw927dqFGzdu4NixY5g6dSokJCQwbtw42NracnQGcFOajFeqBuCOHz8eampqiIuLg5aWFqytrXluV1D4+/tj27ZtbHNAoCJQeevWrZg1axbMzMywcOFC4RtII1D09PSoTYT169fXaxOByWTC2toa1tbW9VaKNjAwQGpqKtu4mpqa2mDXCH87+/btw4gRI6Curo727dsDAF69eoWuXbviyJEjIraOhka8admyJeLi4uDg4IDly5ezzFvMzc3h5eVFB+fSiIRPnz7B19eX2jTX0dGBnZ0dXcmoDmRnZ+P8+fPo2LGjqE2h4TP8VA6sLTgjPj4ee/fuZfEPJCYmYt26ddTr0NBQ9OnTB4cPHwYAtG/fHm5ubjX6Sjp37kwJeNSXqiWWQ0JC+FZiWZwRF18JjXDhVUSgadOmtQZRtWvXDtOnT4ebmxtPQiRFRUWIjo7mmJy+YMECFltrQxj7gsJi2bJlKC8vh6mpKYqLi2FoaIhGjRrBxcVFbINzAWDHjh1wdXWFt7e3yBJ4IiMjRfJcfqOpqYkXL15AVVUVnTt3xokTJ9CnTx+Eh4ejadOmbNeXlZVR/jIVFRW8ffsW2traUFNTw7NnzxAdHQ1/f384Oztj0aJFGDduHOzt7UWejJKUlISEhIR6J9qIgtevX2PixIm4ffs29TcpLCzEgAEDEBoaKrTxdOjQofjvv/+gr6+P9PR0DBs2DADw5MkTqnomLzg4OCAkJAQvXrzAjBkzMHnyZL6uoQICAvjWVlUaio4jP33UDY2qQbeCVHgXdP9S136ahuZPhVbQpaH5A9HT08PChQtrLKng6+tLBWacO3dO4Fm7BgYG2L9/v8jKflRHQ0OjVidJVlZWjedevHiBY8eOISQkBGlpaTA0NOQpgErQuLq64uDBgxgyZAji4uLw/v17zJgxA3fu3MGKFSvw77//Cq0MW12QkpJC27ZtMW3aNIwYMaJGtURRZhWLgp8/f2LAgAF4/PgxLC0t0blzZxBCkJqaiitXrsDAwAAxMTFcqUv+aTRr1gxMJhNmZmYwNjaGsbExOnXqVO92nZyccOPGDSQkJHAsO9urVy8MGTIEe/bsqfezxJHS0lJ4eXlhw4YNUFRUxLp16zBhwoR6txsTEwNfX1+cPn0abdq0wZgxYzB27Fj07t0bQEWpyPDwcPTq1QsAsHLlSkRHRyM2NhYAcPLkSbi5uVGlyRo68+fPR0BAAKysrNC6dWu2sYlXBWh+IE7j3Y8fPxAeHo4NGzYgJSWFY7Dt70qT8epAPX78OM6fP4+fP3/C1NQUc+bMqe/bETgyMjJ49uwZVFVVOZ7Pzs6GpqYmvn//Tiu3/IHcunUL/v7+OHXqFMrLy+u0iXDy5EmEhIQgPT0d0tLS6NSpE2bMmAFzc/N623X8+HEsXboUjo6O1Jrgzp078PLywubNm6Gjo0Nd+7fN8xoyhBDcuHGDpWTqkCFDRGwVDU3D4tOnT8jMzAQhBFpaWvVONKSh4ZWYmBhYW1tDUVGRWo8lJCSgsLAQ4eHhMDQ0FLGFDQNra2tMnz4dY8eOFbUpNHwgPz8fLi4uVFJt9a00fqoJPnv2DMuWLUN4eDhsbW3h4eFBBWw1btwYGRkZVFLUP//8A0tLS6xcuRIA8PLlS3Tr1g1fv35ls4/fScGCKrHcUBAnXwmNYOGkXG1gYMAiIsBJuTooKAgrV67E9OnT0adPHwDAvXv3EBgYiFWrVuH9+/fYvn07lixZghUrVtTJpkePHmHYsGEoLi5GUVERmjVrhg8fPkBWVhYtWrRg2deqPqf89esXiouLIS0tDVlZWXz8+JGH34p48/PnT2RmZuLbt2/Q1dWFnJycqE2qlffv38PGxgYxMTGQlZVl29v5E/9GgmLXrl2QkJDAggULcOPGDVhbW4MQgl+/fmHnzp1wcnJiuX7QoEFwdnbGqFGjMGnSJHz69AmrVq3CoUOHkJCQgMePHwOoCIg/ceIEAgICcOvWLXTs2BH29vaYNm0aWrVqJfT3aWJigpUrVzZIv4uFhQUKCwsRGBhIBQA+e/YMM2bMgIKCAq5cuVLvZ3CjoFtYWIhVq1bh1atXcHBwgIWFBQDAzc0N0tLS1NyKF0pKShAWFgY/Pz/ExcXBysoK9vb2MDMz40r9luZ/CNJH3ZD5/v07CCGUYFd2djbOnDkDXV1dmJmZ1attQfcvde2naWj+VOgAXRqaPxBxC8y4du0a3N3dsWHDBnTr1o1toclPBQJuqB5Q9+vXLzx69AhXrlzBkiVLsGzZslrvLysrw+XLl7F69WokJyeLZWkXTU1N7N69GyNGjMDjx4+hp6eH6dOnw9fXV6wXAlWzxivtrD5MiXs5HUGwZ88ebNq0CdHR0WzZa2lpaTA2NsbKlSvFOiNcUBBCkJKSgqioKERHRyMmJgbS0tIwMjKCiYkJZs6cyVO7+fn56NGjB6SlpTF//nwq6PfZs2fYt28fSktL8ejRoz9S2ero0aNYs2YNvn//jlWrVmHWrFmQlOS96EJeXh4CAgLg6+uLL1++wMbGBt7e3khKSoKuri7LtbxuOjVUVFRUEBQURGVqixviMN7l5eUhNDQUR44cwcOHD9GnTx/cuXOH7brflSZ79OhRnZ994MABzJs3D1paWpCRkUFKSgoWL16Mbdu28eOtCYxmzZohKiqqxiDHlJQUGBoa4tOnT0K2jEaY1GUToby8HBMnTsTJkyfRqVMnSl0rNTUVmZmZmDVrFg4cOICCggLExMRg9OjRdbbnd8pADaVsIg0NDQ0NzZ9Kt27d0L9/fxw4cIAKuisrK8PcuXMRFxeHlJQUEVvYMDh06BDWr18POzs7jj7QESNGiMgyGl6wtLRETk4O5s+fzzGplh/qsW/fvoWbmxsCAwNhbm6OTZs2sVUSU1NTQ3BwMAwNDfHz5080bdoU4eHhMDU1BVCxxjMyMmIL5hJEUrCgSiw3JMTBV0IjeHgVETA1NcXs2bNhY2PDcvzEiRM4ePAgIiIiEBwcjA0bNlCJjtxSKY1OC+0AAI5RSURBVEzh7e0NRUVFJCUlQUpKCpMnT4aTkxPGjBlT6/0ZGRlwcHDAkiVL/vogJ3FgyJAhyMnJgb29Pcdy4tOmTROqPf7+/pCTk8O///7LcvzkyZMoLi4Wuj31ITs7GwkJCejYsSNH/+jVq1dRVFSEMWPGICMjA9bW1khPT4eysjJCQ0Op8bUqmZmZ8Pf3R3BwMPLy8mBhYYHz588L4+1QPH/+HHPmzMHkyZPRtWtXtnmmOCe8y8jIIC4uDvr6+izHExISMGjQIBQXF9f7GfLy8khOToaGhka926ov2dnZCAgIQFBQEEpLS/HkyROxTxoQB4Tho27ImJmZYcyYMZgzZw4KCwuhra0NaWlpfPjwATt37oSDgwPPbQu7f/ldP01D88dCaGho/jiUlJRIUlJSjeeTk5NJ06ZNhWYPg8EgDAaDMJlMlp/KY+LCvn37yPTp02s8HxsbSxwcHEjz5s2JvLw8mTx5Mrl8+bIQLeQeKSkp8vr1a+p148aNSXJysggt4o6XL19y9fO3YWhoSPbt21fj+b179xJDQ0MhWiSelJeXk/v375Np06YRSUnJevcvWVlZxNzcnOqvKvssc3Nz8vz5cz5ZLT5cvnyZdO/enSgoKBAPDw/y7du3erc5fPhwoqCgQCZOnEguXLhASktLCSGESEpKkidPnrBdr6qqSqKjowkhhJSUlBAZGRly48YN6nxycjJRUlKqt13iQuvWrcmzZ89EbQYboh7vPn/+TPz8/MiQIUOIpKQk6dSpE3F3dyeZmZk13tO0aVOSlZVFCCFEU1OT3Lx5kxBCSGZmJpGRkeHJDl1dXbJ27VrqdXBwMJGVleWpLWEybNgwMmfOnBrPz549m1haWgrRIhpRk5GRQVasWEHat29PpKSkiLW1Ncv5nTt3kmbNmpHw8HC2e8+dO0eaNWtGtm3bRrp06UK2bNnCkw3czvH+xnleQyMuLo7tsxIYGEjU1dVJ8+bNycyZM8mPHz9EZB0NDQ0NDa80btyYpKWlsR1PS0sjjRs3FoFFDZNK3wGnH3HygdJwh5ycHHn06JFA2i4sLCRLly4lMjIypH///iQmJqbGa+fMmUNds3jxYqKsrExKSkqo80eOHCG9evViu09ZWZlcvHhRIPb/jYjaV0IjXBo1akRycnKo1wMHDiTr16+nXr948YLIycmx3de4cWOSnp7Odjw9PZ3yT2VlZfHkq1JUVKTGakVFRfL06VNCCCF37twh2traXLVx//59rq9tCNy8eZNs376dxMbGEkII8fb2Ju3btycqKirkv//+I8XFxSK2sGZkZGRIYmKiqM2g0NLSovypVYmKiiKdOnUSgUXCpaCggJSXl9d6zbdv38jBgwdJs2bNRDKvi4+PJxoaGmzzy4Ywz9TS0iJ3795lO3737l3SoUMHntocPXo0y4+kpCQxMzNjO16djx8/km3bthE7OztiZ2dHtm3bRgoKCniyoSZycnKIu7s70dDQIG3btiVfv37la/t/KsLwUTdklJWVyePHjwkhhBw+fJjo6emRsrIycuLECdK5c+d6tS2o/oX2I9PQsMK7HBoNDY3YUql4ceDAAY7nvby80L9/f6HZExkZWeM5cVLgsLS0xPLly9ky/JcvX47Q0FC8ffsWQ4cOxZ49ezBy5EiqhIA4UlZWxqKOLCkp2SCy8ypLt9Gw8vTpUxgbG9d43sTEBB4eHsIzSAzw8PCAi4sL0tLSEBUVhaioKMTGxuLr16/o1q0bHB0dYWRkVK9naGho4MqVK/j48SMyMzMBAB07dkSzZs348RbEhnv37sHV1RV37tzBnDlzcOPGDaioqPCl7cuXL2PBggVwcHCAlpbWb68fNmwYli1bhi1btuDs2bOQlZVlKcuenJyMDh068MU2ccDZ2Rl79uzBvn37xELdXFzGu5YtW0JJSQnjx4/Hpk2bKLWS2ujatSuSkpKgoaGBvn37YuvWrZCWlsahQ4dqLWtVG1lZWSzqEJMmTYK9vT1yc3PRunVrntoUBitXroSxsTEKCgrg4uKCzp07gxCC1NRU7NixA+fOnat1bkbz59GxY0esWLECampqWL58OS5evMhy3t/fH9u2bcPw4cPZ7h0xYgS2bt2KWbNmwczMDAsXLuTJBnqO9+fg4eEBY2Nj6vOSkpICe3t7llKvbdq0YSv1SkNDQ0Mj3hgYGCA1NZWtak9qaiq6d+8uIqsaHuXl5aI2gYaPtG/fnq2yFz/YunUrtmzZglatWiEkJOS3Srzr1q3DmDFjYGRkBDk5OQQGBrL4ff38/DiWtZWWlkbHjh35bv/fhrj4SmiES8uWLfHixQu0b98eP3/+xMOHD+Hu7k6d//r1K5u6HFDRb/j6+mLz5s0sx319famKYQUFBVBSUqqzTVJSUlR1mhYtWiAnJwc6OjpQVFTEq1evuGpDUlISb9++rfOzxZHDhw/DwcEBGhoaWLlyJdzc3LBhwwZMmTIFTCYTR44cgbKyMtvfQlzo3Lkzvn//LmozKHJycjgqj6qpqSEnJ0cEFnHP3r17OR5nMBho3LgxOnbsCENDQ66rLfr5+bG8jomJgZ+fH06fPg0mkwkbGxvY29vX2+66YmdnB319fYSEhHBUXRZntm3bBkdHR3h5eVG+/gcPHsDJyQnbt2/nqU1FRUWW15MnT/7tPTExMbC2toaioiJlh6enJ9atW4fw8HAYGhryZAsAlJSUICwsDH5+foiNjcXw4cOxb98+WFhY/LayGE0FwvBRN2SKi4shLy8PoKJ69ZgxY8BkMtGvXz9kZ2fXq21B9S+1+ZF1dXWxdetW2o9M81fBIILwMNDQ0IiUuLg4GBsbY9SoUbUGZgwcOFAk9n39+hUhISHw8fFBQkKC2JSB2rp1K/bv34+XL1+yHB84cCBsbW1hY2PDt6A1QcNkMmFpaYlGjRoBAMLDwzF48GA0adKE5bqwsDBRmFcjW7duhaOjI2RkZAAAt2/fRq9evaj38fXrV7i6umL//v2iNFPoSElJ4dWrV2wlqSvJzc2Fmpoafv78KWTLRIeEhARyc3PRpk0b6Ovrw8jICEZGRjA0NGRbmNPUDpPJhIyMDGbNmlVr+Z8FCxbUue07d+7A19cXx48fh46ODqZMmYIJEyagdevWSEpKgq6uLsv1Hz58wJgxYxAbG0ttOlUtU2Nqaop+/fphw4YNdbZFHBk9ejQiIyPRrFkzdOnShc2xL+w+WlzGu+vXr8PU1LROjiteSpP9DiaTifz8fDRv3pw6Ji8vj6SkJJ6DfoXFmTNnMGvWLLYSp0pKSjh48CDGjh0rIstohE1Nmwj9+vWjrpGRkcGzZ8+gqqrKsY3s7Gxoamri+/fvLIEAdSEoKKjW81OnTuWpXRrhw2upVxoaGhoa8eb48eNYunQpHB0dqXnCnTt34OXlhc2bN0NHR4e6li5BSfO3cO3aNezYsQMHDx6Euro639qt9MMMGTIEEhISNV5X3Sfw+fNnyMnJsd3z8eNHyMnJsc3Vd+zYgaysLLFJCm6oiIuvhEa4ODg4ICkpiRIRCAwMxNu3b6nv2dGjR7F7927cv3+f5b7z58/j33//RefOndG7d28AFUFoaWlpOHXqFIYPH44DBw4gIyMDO3furJNNZmZmmD59OiZNmoSZM2ciOTkZCxYsQHBwMD59+oS7d++y2FEVQghyc3Oxb98+tG/fHpcvX+bl1yJWdO3aFbNnz4ajoyOuXLkCa2tr+Pj4UMn2J0+exPLlyynhDXHj2rVrcHd3x4YNG9CtWzc2v7CCgoJQ7VFVVcW+ffswYsQIluPnzp3DvHnz8Pr1a6HaUxc0NDTw/v17FBcXU8Hvnz59gqysLOTk5PDu3TtoamoiKysLampq0NfXrzUB58yZM3j79i0CAgIQEBCAzMxMDBgwAPb29rCxsWHbZxUWTZo0QVJSUoNMvlFSUkJxcTFKS0shKVmhH1j5/+q/z+r+bH7SrVs3SuSscj5VVlaGuXPnIi4ujmdRsblz5yI0NBTt27eHnZ0dbG1t6TkDDwjDR92Q0dPTw3///YfRo0eja9euuHLlCvr374+EhARYWVkhLy+P57YF1b/QfmQaGlboAF0amj8UcQzMiImJga+vL06fPo02bdpgzJgxGDt2LOWoEBb6+vosTklCCPLy8vD+/Xvs378fs2bNEqo9gmDGjBlcXVddLVjUVAZdtmjRAkCFEyIxMZEKhMrPz0ebNm3EJqhbWEhISCAvL48lSKwqf+PvhclkIi8vD40bNxa6s+pPQ11d/bcbNQwGA1lZWTw/o6ioCMePH4efnx/u3buHsrIy7Ny5E3Z2dlTGZ1XquunUUPldXy1ufbQwKS0tRVRUFJ4/f45JkyZBXl4eb9++hYKCAteK8B8/foSSkhLPG5FMJhOzZs1iUcXx8vLC5MmTWRIB6rqhIiyKi4tx9epVZGRkAAC0tLRgbm5Oq/z8BdR1E6FZs2aIioqqMdgmJSUFhoaG+PTpE882VVcH+vXrF4qLiyEtLQ1ZWVmBOt9p+Evjxo2RkZFBqT/9888/sLS0xMqVKwEAL1++RLdu3fD161dRmklDQ0NDU0d+lxzHYDBACAGDwfirfA+8EB0dje3btyM1NRUAoKuriyVLlrBUh6FpGFQNJpGVlWULnuJ1Djt9+nSu1qn19QmIW1IwDU1Doj4iAi9evMDBgweRnp4OANDW1sbs2bPrHej/4MEDfP36FSYmJnj37h2mTp2KuLg4aGlpwc/Pj0Xxvvq4zmAw0Lx5cwwePBg7duwQ68pQ3CIrK4vU1FSqYo+0tDSSkpKopKKcnBxoaWmhpKRElGbWSOXfqPp4IKr5lqurK44fPw5/f39KRTQ6Ohp2dnYYN24czyqnwiAkJASHDh2Cj48PVX0vMzMTs2fPxqxZszBw4EBMmDABubm5+PTpE9TU1DBjxgxMnjyZY7VES0tLqsrg1KlTYWdnx1ZlQhRYW1tj+vTpDVJ4ITAwkOtrq1a04zcyMjJITExk+3s+e/YMPXr04FnVmslkQlVVlS3+oDr03Kt2hOGjbsicOnUKkyZNQllZGQYPHozr168DADZt2oSYmJh6Jd8Iqn+h/cg0NKzQAbo0NH8w4hCYkZeXh4CAAPj6+uLLly+wsbGBt7c3R+VEYbF27VqWCTKTyUTz5s1hbGyMzp07c7wnODgY3t7eePHiBeLj46Gmpobdu3dDQ0Pjt2XIaLinMuiyMkC3ulLh3xiIClT8Xrp27UplllantLQUT548+at+L5yULWkaBs+ePYOvry+Cg4NRWFiIoUOHsqk60IgOcRjvsrOzYWFhgZycHJSUlCA9PR2amppwcnJCSUkJvL29qWvt7Oy4arN6aTJuMDY25ip4/ebNm3VuW5DcvHkT8+fPx507d9gSGD5//owBAwbA29ubDhD4Q+FlE8HKygqqqqo4cOAAx/Nz5sxBTk4OLl26xFdbMzIy4ODggCVLlsDc3JyvbdMIDjU1NQQHB8PQ0BA/f/5E06ZNER4eTimVp6SkwMjIiA66pqGhoWlg1KUkZmUgDA07R44cwYwZMzBmzBiqctnt27dx5swZBAQEYNKkSSK2kKYu/C6YRJABJPyATgrmH+LgK6ERDX+LiEBDpKHvJUVHR9d63sjISEiWVPDz509MmTIFJ0+epPahysvLMXXqVHh7e4v1Z71Dhw44ffo0evTowXL80aNHGDt2LLKyshAXF4exY8fi5cuXCAsLg5+fH+Li4mBlZQV7e3uYmZlRfuARI0bA3t4ew4cPr1XpXtgcOnQI69evh52dHUfV5erqxzTsDBw4EEuWLMGoUaNYjp89exabN2/GnTt3eGpXWMlXfzqi8lE3JPLy8pCbm4vu3btTiR737t2DgoJCjTEu3CCo/oX2I9PQsEIH6NLQ/IGIS2CGtbU1YmJiYGVlBVtbW1hYWEBCQgJSUlIiCdD98uULV9dV/50dOHAAa9aswcKFC7FhwwY8fvwYmpqaCAgIQGBgICIjIwVh7l9JQ3eqCAp3d3eurnNzcxOwJeIDk8mEoqLibxe99KT+98THx6OgoADDhw+njgUFBcHNzQ1FRUUYNWoUPD090ahRI74+t6ysDOHh4fDz8/urA3T5oRTLL8RlvBs1ahTk5eXh6+sLZWVlahyIiorCzJkzqcQjoKIv4LY02d/CiBEjYGJigkWLFnE8v3fvXkRGRv5Vv5O/CV42EeLi4mBsbIxRo0bBxcUFnTt3BiEEqamp2LFjB86dO4fIyEgqyISfPHjwAJMnT0ZaWhrf26YRDLyWeqWhoaGhofkb0NHRwaxZs9jm4jt37sThw4cpVV2ahs/Hjx85qu7R/HmIi6+EpuFw69YtHDx4EFlZWTh58iTatm2L4OBgaGho4J9//hGKDR4eHnBxcWET6/n+/Tu2bduGNWvWCMUOQSIhIYH09HQ0b94chBC0b98esbGxlFJxfn4+Onfu/NftJdWX9PR0JCUlQUZGBt26dWsQiVmysrKIiYmhSqhXcv/+fRgZGaG4uBgvX75E165d8e3bN+p8dnY2AgICEBQURIngCNsfXxdqq3jRkKpc/PjxAz9//mQ5JqwqmcePH8fSpUvh6OiIfv36AQDu3LkDLy8vbN68mVLgBlCjiiuN4BClj7ohkZmZiefPn8PQ0BAyMjKU8np9EFT/QvuRaWhYoQN0aWj+QMQlMENSUhILFiyAg4MDtLS0qOOiCtBlMpm1TlBqKh2jq6uLjRs3UgFDlYFCjx8/hrGxMT58+CBo0/8a6ABdGm5hMpnYvXs3S5l5Toi7mok4YGFhARMTE7i6ugKoyFg0MDDA9OnToaOjg23btmH27NlYu3ataA39A6mLUqwwEJfxTllZGXFxcdDW1max4+XLl9DV1UVxcTF17bx58xASEvLb0mR/E2pqarhy5QqLQ7EqaWlpMDMzQ05OjpAtoxFnzpw5g1mzZrEltigpKeHgwYMCK5+XmJgIQ0NDrhPpaERPfUq90tDQ0NCIL0FBQbWenzp1qpAsadg0atQIT548QceOHVmOZ2ZmomvXrvjx44eILKPhF9euXYOPjw/Cw8N5LoMsTMQpKbihIi6+EpqGwenTpzFlyhTY2toiODgYT58+haamJvbt24dLly7VS/UvPz8fLi4uiIiIwLt379gS1avumUhISCA3N5faZ6mkoKAALVq0+CP2V6rv91UPUKppv0+UJCcno2vXrmAymUhOTq71Wjo4kHusrKyQl5cHHx8f6OvrA6hQz505cyZatWqFCxcuIDw8HCtWrEBKSgp136tXr+Dv74+AgAD8/PkTaWlp9NgoIIqKiuDq6ooTJ06goKCA7bywvqe1BSECFYGI4th3/E2IykfdECgoKICNjQ0iIyPBYDCQkZEBTU1N2NnZQUlJCTt27BC1iWzQfmQaGlY418qmoaFp0FRmotSEmZkZtm/fLnA7YmNj4evri549e0JHRwdTpkzBhAkTBP7cmqiazU4IwbBhw+Dj44O2bdvWet+LFy+oRV1VGjVqhKKiIr7b+bfj4+NDLYJLS0sREBAAFRUVAMDXr19FaZpY8uXLFxw9ehS+vr548OCBqM0RKhMmTGBzMtLUnaSkJKxfv556HRoair59++Lw4cMAgPbt28PNzY0O0BUATk5O6NWrF5KSkqCsrEwdHz16NGbOnCl0e8RlvCsvL+foAHv9+jXk5eVZjnl5eWHnzp1UabLly5dzLE1WX16/fo3z588jJyeHLcN/586dfHkGv8jPz2crQVQVSUlJvH//XogW0TQERo8eDXNzc1y9epVSqdbS0oK5uTmb4g4vVFdKJ4QgNzcX+/bt++tVDxoaKioqiImJqbHU68mTJ+kNLRoaGpoGiJOTE8vrX7/+r707j6s5+/8A/rq3RavEFLKkKBU1aWyTocJI1hhjJHsY2cYSmrEkY50RWRprG2PJkq91YoYWsosSoixlK1uhDdX9/eHh/lyVte7n3no9Hw+P6fM5n+7nxXBu93ze55xXyM3Nhbq6OrS0tFig+5Hq1auHw4cPFyvQ/e+//1CvXj2BUtGXSk1NRVBQEEJDQ5GZmQkXF5cPFrUrgncnBX///ffQ1dXFokWLBJkUrKwUZayElMPcuXOxevVqDBo0CFu3bpWeb9Omjcz46+cYMmQI0tLSMHPmTNSuXfujFqN5V3x8fIWZ2K6Mq1fb2tpKF6ixtbWVFgO+S6jiQGUa/3xbYGAgBg4ciG+++UY6JlpQUIAOHTogMDAQAKCjowM/Pz+8ePFCOo587NgxdOvWDStXrkTnzp0/WLypSPLz86GhoSF0jI82depUREZGYtWqVRg4cCACAgJw9+5drFmzBgsXLpRbjps3b8rtXvR5ynuMWplNnDgRampqSEtLk1mc5aeffsKkSZPKrEC3LPsXjiMTyWKBLlEFpCiFGa1bt0br1q3h7++PsLAwBAUFYdKkSSgqKsK///6LevXqFSu0KU8ODg4yxyoqKmjdurV0ddbSmJiY4MKFC8W2cnnf6nT0eerXry8tCgSAWrVqYePGjTLXKMOWOvIQGRmJoKAghIeHQ09PT2bGWWVQVkV3BGRmZqJmzZrS4+joaLi4uEiPW7Rogdu3bwsRrcI7evQojh8/Lt3O5Y0GDRrg7t27cs+jKO93nTp1gr+/P9auXQvg9b/37Oxs+Pj4oEuXLsWur1KlCtzc3ODm5ibdmmz06NFltjXZ4cOH0aNHD5iamiIpKQlNmzbFrVu3IJFIYGdn90WvXR7q1KmDxMTEYkUBbyQkJKB27dpyTkWK7MiRIxg7dixOnjxZ7OeJp0+fokmTJli9ejXatm372fdwdXWVORaJRDAwMED79u0VcnY/fVhpuxhUlIe9RESVTWZmZrFzycnJ8PT0xJQpUwRIpJwmT56M8ePH48KFC7C3twcAxMbGIiQkBMuWLRM4HX2Kly9fIjw8HOvXr0dsbCw6duyIO3fu4Pz587C2thY63kdRtEnBykpRxkpIOVy9ehXt2rUrdl5PTw9ZWVlf9NrHjh3D0aNHYWtrW+o1+vr6EIlEEIlEMDc3lxlDLywsRHZ2NkaNGvVFORTFu8/7lMHNmzdhYGAg/VqRKNv459tq1aqFf//9F0lJSbh27RoAoHHjxmjcuLH0GicnJ4wePRr9+vVDvXr1MGzYMGzZskW6OJAyKCwsxPz587F69WpkZGRId+ObOXMmGjRoAA8PD6Ejlmrv3r3YsGEDHB0dMXToULRt2xaNGjWCsbExNm3aBHd3d7nk4PNlxSaPMWpldujQIRw8eBB169aVOW9mZobU1NQveu3y7l84jkz0Ggt0iSogRSvM0NbWxrBhwzBs2DBcvXoVgYGBWLhwIby9vfH9998XW1FL0UyaNAljxoxBfn4+JBIJTp8+jS1btmDBggVYv3690PEqlFu3br23/c6dO5gzZ458wiigu3fvIiQkBMHBwcjKykJmZiY2b96Mvn37VrqC1ZJmltPnqVmzJm7evIl69erh5cuXiIuLg6+vr7T9+fPn7530QZ/vU1aKlQdFeb/z8/ODs7MzrKyskJ+fj/79+yM5ORlfffUVtmzZ8t7vfbO9nUQiKbOVJn799Vd4eXnB19cXurq62LlzJwwNDeHu7o7OnTuXyT3KUpcuXTBz5kx07ty52EznvLw8+Pj4oFu3bgKlI0Xk7++PESNGoGrVqsXa9PT08PPPP2PJkiVfNPhZVFT0JRFJAeXk5GDhwoXS7VXf/X9848YNgZIREVFZMTMzw8KFCzFgwAAkJSUJHUcpeHp6olatWvDz88O2bdsAAJaWlggLC0PPnj0FTkcfa9y4cdiyZQvMzMwwYMAAhIWFoUaNGlBTUyu26pMiU7RJwcpKUcZKSDnUqlULKSkpaNCggcz5Y8eOfXChmA+pV6/eB8fE/f39IZFIMGzYMPj6+soUxKirq6NBgwb49ttvvyiHInj27NlHX1vSWIdQ3i4OTE1Nhb29PVRVZUs1CgoKcPz4cbkXEirb+GdJLCwsYGFhUWr76tWrUb9+fZiamiI6OhrR0dElXhceHl5eEb/IvHnzEBoaij/++ENmok3Tpk3h7++v0AW6T548kfaBVatWxZMnTwAA3333HTw9PeWW40M7IHDXEGHJY4xameXk5JS4ivCTJ09QpUqVL3ptZe5fiJSJSMIKF6IKZ9y4cYiKisKZM2dKLMxo2bIlnJycsHz5coESvp6Js3fvXgQFBQlWoKurq4v4+PiPGhjZtGkTZs+ejevXrwMAjIyM4Ovryx9I5Cw+Ph52dnaCbO8jpJ07dyIwMBAxMTFwcXHBgAED4OLiAm1tbcTHx8PKykroiKTEPD09ER8fj0WLFuF///sfQkNDce/ePekDnE2bNsHf3x9nzpwROGnF89NPP0FPTw9r166Frq4uEhISYGBggJ49e6J+/foIDg6WeyZFeb8rKChAWFgY4uPjkZ2dDTs7O7i7u0NTU7PYtSVtTTZ06NAy25pMV1cXFy5cQMOGDaGvr49jx46hSZMmiI+PR8+ePT84uUTeMjIyYGdnBxUVFYwdO1a6WkRSUhICAgJQWFiIuLg4mZWzqXIzNjZ+7+pPSUlJ6NSpE9LS0uScjBSZm5sboqOjMXDgwBK3V313q3QiIlJOFy5cQLt27T6pEIZKdvbsWTRv3lzoGPQRVFVVMW3aNHh7e8tMnlVTU1OqcTh9fX3ExsbCyspKZhz82LFj+OGHH5CRkSF0RKWhKGMlpPgWLFiAv//+G0FBQfj+++9x4MABpKamYuLEiZg5cybGjRv32a996NAh+Pn5Yc2aNcUKgN8VHR2NNm3aFCv+rCjeTNB/H4lEApFIpLDPklRUVHD//n0YGhrKnH/8+DEMDQ3lnlvZxj/fVlhYiJCQkFInEB85cgQAMGTIkI9a6EaIMfmP0ahRI6xZswYdOnSQeV9PSkrCt99+W+KOGIrCxsYGK1asgIODAzp27AhbW1ssXrwYy5cvxx9//IE7d+7IJYe+vr7M8atXr5Cbmwt1dXVoaWlJC4dJGByjfr8uXbrgm2++we+//y59nmhsbIx+/fqhqKgIO3bs+OzXVub+hUiZVMyfzIkquRkzZiA8PBzm5ualFmZMnz5d0IwqKipwdXUttt2tvH3sqqPu7u5wd3dHbm4usrOzpR+a7969izp16pRnRCL89NNPmDZtGsLCwgRZVZMqtt9//x29e/eGg4MDdHR0EBoaKrO6SlBQEDp16iRgworrS1aKLS+K8n6nqqoqzfLG/fv3MWXKFKxcuVJ6bvTo0di6dWu5bk2mra2Nly9fAgBq166N69evo0mTJgCAR48elem9ykLNmjVx/PhxeHp64tdff5WuriISieDs7IyAgAAW55KMjIyM966UrqqqiocPH3726+fk5GDRokUIDw/HrVu3IBKJYGJigj59+sDLy6vEmf+k+P755x/s378fbdq0EToKERGVgXcnz0skEty/fx8rV65kX/8JsrOzoaKiIjOx8MKFC5g5cyYOHDigsEVCJGvjxo0ICgpC7dq10bVrVwwcOBAuLi5Cx/pknTp1gr+/P9auXQvg9WfC7Oxs+Pj4oEuXLgKnUy6KMlZCis/b2xtFRUXo0KEDcnNz0a5dO1SpUgVeXl6fVZyrr68v8wwrJycHDRs2hJaWVrHP8U+ePEFBQQEKCwvh4OAgPZ+RkYHVq1cjJycHPXr0wHfffff5v0EFERkZKXSEL/amgPhdjx8/hra2ttzzKNv459t++eUXhISEoGvXrmjatGmpz31DQkLkG6yM3b17t8Sdc4uKivDq1SsBEn28oUOHIj4+Hg4ODvD29kb37t2xcuVKvHr1CkuWLJFbjpKKDJOTk+Hp6YkpU6bILQeVrLzHqJXdH3/8gQ4dOuDs2bN4+fIlpk6dikuXLuHJkyeIjY39otdW5v6FSJmwQJeoAmJhRsl69+4tc5yfn49Ro0YV+7D7vu1LtLS0oKWlhfT0dMybNw+BgYHIzc0tl7xEb3h4eCAgIABRUVEYOHAgfvrpp2IzPYk+11dffYWYmBg8ffoUOjo6xbZr3L59O3R0dARKV7HVrVsX8fHx2Lp1KxISEpCdnQ0PD49SV4qVJ6He7y5duoTIyEioq6ujb9++qFatGh49eoR58+Zh9erVxVa9l8fWZK1bt8axY8dgaWmJLl26YPLkybh48SLCw8PRunXrz37d8mRsbIwDBw4gMzMTKSkpkEgkMDMz43sHlahOnTpITEwscRAOABISElC7du3Peu2XL1/CwcEBiYmJcHFxQffu3SGRSHDlyhXMmzcP//zzD2JiYt47+EqKSV9fH9WrVxc6BhERlZF3J9CLRCIYGBigffv28PPzEyaUErl9+zb69u2L06dPS3eymDt3LkaNGoWwsDD06tULx48fFzomfSQ3Nze4ubnh5s2bCAkJwZgxY5Cbm4uioiJcvnxZaVbQLW1ScI0aNQSbFKzs+GyAPkQkEmH69OmYMmUKUlJSkJ2dDSsrq88eW/X39/+k60eMGAF1dXWsWbMGAPD8+XO0aNEC+fn5qF27NpYuXYrdu3crfZH+2wXIyubNc0qRSIQhQ4bIbEteWFiIhIQE2Nvbyz2XMo5/vrF161Zs27ZN6f9ef4iVlRWOHj0KY2NjmfM7duxAs2bNBEr1cSZOnCj9umPHjkhKSsK5c+fQqFEj2NjYCJgMMDMzw8KFCzFgwAAkJSUJmqWyK88x6oqgadOmuHbtGlauXAldXV1kZ2ejd+/eGDNmzBf/uShz/0KkTFigS1RBsTCjOD09PZnjAQMGvPf6zMxMjB49Gv/++y/U1dXh7e2NsWPHYvbs2Vi8eDFsbGwUdqsTqljWrFkDf39/bNu2DUFBQZgwYQKcnZ0hkUiKbddD9Lne7SPfYPFN+VJVVf3g+1F5U5T3uz179qBPnz4oKCgA8HpG8Lp169C3b19888032LVrFzp37izzPYMGDfro1fA/15IlS5CdnQ0A8PX1RXZ2NsLCwmBmZibXGf6fQ19fHy1atBA6Bim4Ll26YObMmejcuTM0NDRk2vLy8uDj44Nu3bp91muvWrUKd+7cQXx8vHRXjzeSkpLg6OiI1atXf9E2nySM33//HbNmzUJoaChXQSYiqgA4tvBlpkyZgvz8fCxbtgzh4eFYtmwZjh49ilatWuH69euoW7eu0BHpM5iYmMDX1xezZ8/GoUOHEBgYiAEDBmDChAno3bs3li9fLnTE91LkScHKQFHGSki5HDlyBPb29tDQ0CiTYv7Bgwd/0vWxsbEyO09t2LABhYWFSE5Ohp6eHqZNm4Y///yzQhYy5ubmIi0tTboK7BtCF/+9680YvEQiga6urkx/rK6ujtatW2PEiBFyz6XM45/q6uqlFvRVJLNmzcLgwYNx9+5dFBUVITw8HFevXsWGDRuwb98+oeN9EmNj42KFgEJSVVXFvXv3hI5R6ZXnGHVFoaenVy67ZFek/oVIkYkkb5bWJCIiGT///DMiIiLw448/4uDBg7h8+TKcnZ0hFosxY8YMhZ81qozeXeX4XVlZWYiOjq70WwImJycjODgYoaGhyM7ORteuXdGnT58P/vkRkWKpX78+HB0d4eDgACcnp2Krw8qLorzftWzZEm3atMHvv/+O9evXY9KkSWjSpAmCgoJYZEpUjjIyMmBnZydd7e1NIW1SUhICAgJQWFiIuLi4z9qBw8HBAX379sWYMWNKbF+xYgV27NhR6urXpFiaNWsmMynizUTQBg0aFFsFOS4uTt7xiIiIBGNkZCRdYe7BgweoVasWlixZggkTJggdjcrYkydPsGHDBgQHByM+Pl7oOO/1+PFj1KhRA8DrVZ7XrVuHvLw89OjRA23bthU4neJTlLESUi46OjooKChAixYtpGN+bdq0KbOi+KKiIqSkpODBgwfFJte0a9cO2traSExMhImJCYDXz1vq1q0rnVBw+fJlODo64sGDB2WSRxE8fPgQQ4cOxT///FNiu6I+S/L19YWXl1exHT7p0/n5+eHGjRtYuXJluS/kILSjR49izpw5iI+PR3Z2Nuzs7DBr1ix06tRJ6GgfdPjwYRw+fLjE/isoKEguGfbs2SNzLJFIcP/+faxcuRL16tUrtR8h+SjPMeqKICIiAjo6Ovjuu+8AAAEBAVi3bh2srKwQEBDwxYv0KXP/QqQsWKBLRFSK+vXrIyQkBO3bt8etW7dgamoKb29vzJ8/X+hoFdbQoUM/6jquTvBaUVER9u/fj8DAQPzzzz948eKF0JGI6BP8/fffiImJQVRUFFJSUlCnTh04ODjAwcEBjo6OMDMzk0sORXm/09PTk25tVVhYiCpVqiAiIgIdO3aUa4533b59GyKRSLrq1enTp7F582ZYWVlh5MiRgmYjKiupqanw9PTEwYMH8WaIQCQSwdnZGQEBAdKHe5/KwMAAUVFRaNKkSYntiYmJcHJywsOHDz87O8mPr6/vR1/r4+NTjkmIiKgs5eTkYNGiRQgPD8etW7cgEolgYmKCPn36wMvLiyulfwQVFRXcu3dP+rBYR0cH586dK7aDAJE8XLx4Ed27d8ft27dhZmaGrVu3onPnzsjJyYFYLEZOTg527NgBV1dXoaMqNEUZKyHl8urVK5w+fRrR0dGIjo7G8ePH8fLlSzRv3hxOTk6YO3fuZ7/2yZMn0b9/f6SmpuLdR/sikQiFhYWoUaMGjh49Kl2918jICH/++Sfc3d0BADdu3EDTpk2Rm5v7+b9JBePu7o7U1FT4+/vD0dERu3btQkZGBubOnQs/Pz907dpV6IhKwdTUFGfOnJFO7HgjKysLdnZ2uHHjhkDJPqxXr16IjIxE9erV0aRJk2ITiMPDwwVKRm/4+vpizpw5aN68OWrXrl2skHrXrl1yySEWi2WORSIRDAwM0L59e/j5+aF27dpyyUGlK68x6orA2toaixYtQpcuXXDx4kU0b94ckydPRmRkJCwsLFg7QaQEWKBLRFQKVVVV3L59W/oDuZaWFs6ePVsmWxMRlbUHDx7A0NBQ6BhE9Jnu37+P6Oho7Nu3D2FhYSgqKpLbCg+K8n4nFouRnp4u7ct0dXURHx8v2MrCb7Rt2xYjR47EwIEDkZ6eDnNzczRt2hTJyckYN24cZs2aJWg+orKUmZkpXRXVzMzsi2feq6mp4fbt26hVq1aJ7ffv34exsXGxLSiJiIhIPl6+fAl7e3skJibCxcUFFhYWkEgkuHLlCiIiImBnZ4eYmJhihQ4kS0VFBenp6TAwMAAAVK1aFfHx8ZX6AXJFMGzYsFLbRCIRAgMD5Zjm47m4uEBVVRXe3t7YuHEj9u3bB2dnZ6xbtw4AMG7cOJw7dw4nT54UOKliU5SxElJuly5dwp9//olNmzZ98Vifra0tzM3N4evrW2KBm56eHjp06ICWLVtiwYIFOHr0KBwdHXHnzh3p3+N///0Xnp6eSElJ+aLflyKpXbs2du/ejZYtW6Jq1ao4e/YszM3NsWfPHvzxxx84duyY0BFLlJGRAS8vL+mKou+Wa8h75d93x2XfyMjIQP369RV6cZgPLfxTEYvW8vPzERYWhtzcXHTs2FFuC318rtq1a+OPP/7AwIEDhY5CSqKsx6grAh0dHSQmJqJBgwaYPXs2EhMTsWPHDsTFxaFLly5IT08vk/soW/9CpExUhQ5ARKSoJBIJVFX/v5tUUVEps62IiD7H9u3bsWXLFly7dg3q6uowNzfH0KFD4ezszOJcIiWVm5uLY8eOISoqCpGRkTh//jyaNm0KR0dHuWVQpPe7gwcPQk9PD8DrVcIPHz6MxMREmWt69Ogh10yJiYlo2bIlAGDbtm2wtrZGbGwsDh06hFGjRrFAlyoUfX19tGjRosxer6ioCCoqKqW2i8Vihd1ukt7vzJkzKCoqQqtWrWTOnzp1CioqKmjevLlAyYiI6FOsWrUKd+7cQXx8fLHVXpOSkuDo6IjVq1dj3LhxAiVUDhKJBObm5tJiqezsbDRr1qzYKl1PnjwRIh59pszMTJnjV69eITExEVlZWWjfvr1AqT7szJkzOHLkCGxsbPD1119j7dq1GD16tPTv47hx49C6dWuBUyo+RRorIeVx7do1REVFISoqCtHR0Xjx4gXatm2LxYsXf/FYX3JyMnbs2IFGjRqVes2sWbPg4uKCbdu24f79+xgyZIjMipC7du1CmzZtviiHosnJyZE+G9HX18fDhw9hbm4Oa2trxMXFCZyudEOGDEFaWhpmzpxZYsG1vOzZs0f69dvjssDrIuHDhw+jQYMGAiT7eBWxAPdtkyZNwqtXr7BixQoAryfYtW7dGpcvX4aWlhamTJmCf//9F99++63ASUv3ZlIg0ccq6zHqikBdXV26Av5///2HQYMGAQCqV6+OZ8+efdZrVoT+hUiZsECXiKgUEokEHTp0kA7E5eXloXv37lBXV5e5TpE/5FPFUFRUBDc3N2zfvh3m5uawsLAAAJw/fx7bt2/HyJEjsWrVKjx+/BgxMTHo1auXwImJ6GPY29vj/PnzsLS0hKOjI7y9vdGuXTu5zwZWpPe7wYMHyxz//PPPMsdvtuyTp1evXqFKlSoAXg98vCkQtrCwwP379+WahUjZvNu/vKugoEDOiaisjBkzBlOnTi1WoHv37l0sWrQIp06dEigZERF9ivDwcMycObNYcS7w+ufd6dOnY8eOHSzQ/YCKXhhSWZW03XJRURE8PT3RsGFDARJ9nCdPnkh3sNDR0YG2trbMOIO+vj6eP38uVDyloUhjJaQ8LCwsYGBggF9++QXe3t6wtrYus8LLVq1aISUl5b0Fug4ODjh37hwOHTqEWrVq4ccff5Rpt7W1lU5CrygaN26Mq1evokGDBvj666+xZs0aNGjQAKtXr1bo7eqPHTuGo0ePwtbWVtAcrq6uAF6Pub47LqumpoYGDRrAz89PgGSfpqCgAFFRUbh+/Tr69+8PXV1d3Lt3D1WrVoWOjo7Q8b7IoUOHMH/+fOnxpk2bkJaWhuTkZNSvXx/Dhg3D3LlzsX//fgFTvt/w4cOxefNmzJw5U7AMOTk5WLRoEcLDw3Hr1i2IRCKYmJigT58+8PLygpaWlmDZiD7Gd999h0mTJqFNmzY4ffo0wsLCALyeHFS3bt3Pes2K0L8QKRMW6BIRlcLHx0fmuGfPngIlocpu2bJl+O+//7Bnzx5069ZNpm3Pnj0YOnQoGjZsiJCQEOmMOSJSfElJSdDW1oaFhQUsLCxgaWkpyFY9ivJ+V1RUJMh9P6RJkyZYvXo1unbtin///Re///47AODevXuoUaOGwOmIFNu7/UtJfvjhBzkkobJ2+fJl2NnZFTvfrFkzXL58WYBERET0OS5fvvzeFf2cnJwwZ84c+QVSUu8WtFDFJRaLMWnSJDg6OmLq1KlCxynVuwWBQq3MqMwUZayElMv48eMRExODOXPmYN++fXB0dISjoyO+++67Ly7+GjduHCZPnoz09HRYW1tDTU1Npt3GxgYAYGlpCUtLyxJfY+TIkV+UQRH98ssv0gn0Pj4+6Ny5MzZt2gR1dXWEhIQIG+496tWrB4lEInQM6XisiYkJzpw5g6+++krgRJ8uNTUVnTt3RlpaGl68eIHvv/8eurq6WLRoEV68eIHVq1cLHfGLpKWlwcrKSnp86NAh9OnTB8bGxgBe/xvo0qWLUPE+Sn5+PtauXYv//vsPNjY2xfqvJUuWlOv9X758CQcHByQmJsLFxQXdu3eHRCLBlStXMG/ePPzzzz+IiYkplotIkaxcuRKjR4/Gjh07sGrVKtSpUwcA8M8//6Bz586f9ZoVoX8hUiYiiSL89EdERESlsrGxwYQJEzBs2LAS2wMDAzFy5Eh06tQJu3fvLraSAxEpJolEgosXL0q3vYuJiYG6ujocHBzg5OSEESNGCB2RAERFRaFXr1549uwZBg8ejKCgIADAb7/9hqSkJISHhwuckIhI/mrUqIF9+/YV2+Ls+PHj6Nq1a7EtoYmISDGpqanh9u3b0tU233X//n0YGxvj5cuXck5GpLgOHDiAwYMH4+HDh0JHKZFYLIaLi4t0J5i9e/eiffv20NbWBgC8ePECERERct+dhqgyycrKwtGjRxEdHY3o6GhcunQJzZo1Q2xs7Ge/plgsLnZOJBJBIpGUuOPUxo0bsXr1aty8eRMnTpyAsbEx/P39YWJiUqELznNzc5GUlIT69esrdLHpoUOH4OfnJ13xVygnTpzA48ePZRaG2bBhA3x8fJCTkwNXV1esWLFC+p6iiFxdXaGrq4vAwEDUqFED8fHxMDU1RVRUFEaMGIHk5GShI36RatWq4cyZMzAzMwPwuph65syZ0ueFt27dgqWlJfLy8oSM+V5OTk7vbY+MjCzX+y9btgwLFixAdHR0sZ1DkpKS4OjoiOnTp3PXEKp0KkL/QqRMWKBLRPQRKvL2KKT4NDU1cfXqVdSvX7/E9tTUVJiamiIvL4/FuURKSiKR4Ny5c1i5ciU2bdqEoqIiuT8s27JlC9zc3EpsmzJlCv7880+5ZSnpIcLSpUthamoqyEOEwsJCPHv2TGaF41u3bkFLSwuGhoZyz0NUETx79gybNm1CYGAgzp49K3Qc+kRubm64f/8+du/eDT09PQCvH0K7urrC0NAQ27ZtEzghERF9DBUVFaSnp8PAwKDE9oyMDBgZGbGQ7yOJxeL3rlTKP0flMmnSJJljiUSC+/fvY//+/Rg8eDBWrlwpULL3Gzp06EddFxwcXM5JKob27dsjPDwc1apVkzn/7NkzuLq64siRI8IEI4X2+PFjREdHIzIyElFRUbh8+TL09fXx6NGjz37N1NTU97a/We0OAFatWoVZs2ZhwoQJmDdvHhITE2FqaoqQkBCEhoaWezEcfZi+vj5yc3NRUFAALS2tYit3PnnyRC45OnfuDCcnJ0ybNg0AcPHiRdjZ2WHIkCGwtLTEn3/+iZ9//hmzZ8+WS57PUaNGDRw/fhyNGzeGrq6utED31q1bsLKyQm5urtARv8i3336LH3/8EZMmTcKlS5dgY2ODlJQUmJiYAACio6MxePBg3Lp1S9igCszBwQF9+/bFmDFjSmxfsWIFduzYgejoaDknI3q/Z8+eoWrVqtKv3+fNdZ+C/QuRfKkKHYCISNFV9O1RSPFpamoiKyur1ALdNz+gsziXSDnMmTMHXl5eSEpKQlRUFKKionDs2DE8f/4c1tbWGDduHBwcHOSey9PTE9WqVYOLi4vM+YkTJ2Lr1q1yK9B99yHCm4fY+vr68Pf3F6RAV0VFRaY4F4Cgq1sQKbPIyEgEBQUhPDwcenp66NWrl9CR6DMsXrwY7dq1g7GxMZo1awYAuHDhAmrWrImNGzcKnI6IiD6WRCJBhw4doKpa8mOCgoICOSdSbrt27ZI5fvXqFc6fP4/Q0FD4+voKlIo+1/nz52WOxWIxDAwM4OfnV+ouV4qAhbdlKyoqqsRVxPPz83H06FEBEpEiGzduHKKjo6UFue3atcOIESPg6OgIa2vrL3rttwtwP2TFihVYt24dXF1dsXDhQun55s2bw8vL64tyKJp3J1O8IRKJoKGhgUaNGqFnz56oXr26nJO9n7+/v9ARAADx8fGYO3eu9Hjr1q1o1aoV1q1bBwCoV68efHx8FLpAt7SFLu7cuQNdXV0BEpWtqVOnol+/fti/fz8uXbqELl26SIvngNcr+7ds2VLAhKXr3bv3B68RiUTYuXNnuea4fPkyHB0dS213cnLCnDlzyjUD0efQ19fH/fv3YWhoiGrVqpU4GbS0lfQ/hjL3L0TKiAW6REQf8Msvv6B58+aIj49HjRo1pOd79erF7cdJLr799lusWrUKq1atKrE9ICCg2PbCRKS4fH19MWrUKLRs2RLNmjWDg4MDRowYgXbt2klXIRTCpk2b4Obmhn379uG7774D8PrBQnh4uFxX1lC0hwgmJibvXQXrxo0bckxDpJzu3r2LkJAQBAcHIysrC5mZmdi8eTP69u373n9fpLjq1KmDhIQEbNq0CfHx8dDU1MTQoUPh5uZWbOUfIiJSXD4+Ph+85ocffpBDkoqhpMmEffr0QZMmTRAWFgYPDw8BUtHn4gqTlVtCQoL068uXLyM9PV16XFhYiIiICNSpU0eIaKTA0tPTMXLkSDg6OqJp06Zf/Hp79uyBi4sL1NTUsGfPnvde26NHD+nXN2/elE6kfFuVKlWQk5PzxbkUyfnz5xEXF4fCwkLp1vXXrl2DiooKLCws8Ndff2Hy5Mk4duwYrKysBE77/wYPHix0BABAZmYmatasKT2Ojo6WWbyhRYsWuH37thDRPlqnTp3g7++PtWvXAnhd8JmdnQ0fHx906dJF4HRfrlevXjhw4AD27duHTp06Ydy4cTLtWlpaGD16tEDp3k/IZx1vy8rKknm+/64aNWrg6dOnckxE9HGOHDmCp0+fwtDQsFw+myhz/0KkjEQSiUQidAgiIkVW0bdHIcV3/PhxODo6wtXVFV5eXrCwsIBEIsGVK1fg5+eH3bt3IzIyEm3atBE6KhF9BLFYjPT0dGhoaHzWtjPlafPmzRg7diz+/fdfBAYGSvsXc3NzuWXQ1NREUlISjI2NZd53k5OTYWNjg7y8PLllAYBly5bJHL9ZBSsiIgJTpkyBt7e3XPMQKZOdO3ciMDAQMTExcHFxwYABA+Di4gJtbW3Ex8cr1MMxIiIiovJy48YN2NjYIDs7W+goRPSRxGKxdDJhSY9RNTU1sWLFCoVeTZnk69WrV/j5558xc+ZMmdXnvsSbMURDQ0OIxeJSr3t35TwrKyssWLAAPXv2lBlbW7FiBYKDgxEXF1cm+RSBv78/jh49iuDgYOk469OnTzF8+HB89913GDFiBPr374+8vDwcPHhQ4LSyrl+/juDgYFy/fh3Lli2DoaEh/vnnH9SvXx9NmjSRSwZjY2Ns3LgR7dq1w8uXL1GtWjXs3bsXHTp0AABcvHgRDg4OePLkiVzyfI47d+7A2dkZEokEycnJaN68OZKTk1GjRg0cPXoUhoaGQkckgamoqCA9PR0GBgYltmdkZMDIyOizViAlKm9isRjGxsZwcnKS/qpbt67QsYjoM3AFXSKiD6jo26OQ4rO3t0dYWBhGjhxZbKsXfX19bNmyhcW5REpGJBIpXHEuAPTv3x9ZWVlo06YNDAwMEB0djUaNGsk1g4mJCS5cuFBs676IiAhYWlrKNQvweiX9kgQEBODs2bNyTkOkXH766SdMmzYNYWFh/Lm5Atq4cSPWrFmDGzdu4MSJEzA2NsbSpUthampa4gqCRESkXJ49e4ZNmzYhMDCQP/d+gby8PCxfvpwrbSqhZs2albjbw9vbpg8ZMgROTk4CpKPydvPmTUgkEpiamuL06dMyRT3q6uowNDSEioqKgAlJ0aipqWHnzp2YOXNmmb1mUVFRiV9/yKRJkzBmzBjk5+dDIpHg9OnT2LJlCxYsWID169eXWT5F8Oeff+Lff/+VGWfV09PD7Nmz0alTJ/zyyy+YNWsWOnXqJGDK4t6sVNumTRvExMRg3rx5MDQ0RHx8PAIDA7Fjxw655OjSpQu8vb2xaNEi/O9//4OWlhbatm0rbU9ISEDDhg3lkuVz1a1bF/Hx8di6dSsSEhKQnZ0NDw8PuLu7Q1NTU+h4X+Tt1dw/xMbGphyTKDeJRIIOHTpAVbXk0qiCggI5JyL6eEeOHEFUVBSioqKwZcsWvHz5Eqampmjfvr20YPftldA/FvsXIvljgS4R0QdU9O1RSDn06tULzs7OOHjwIJKTkwEAZmZmcHZ2hpaWlsDpiOhTmZubf3Bbd3msTDBp0qQSzxsYGMDOzg5//fWX9NySJUvKPc+bTMrwEMHFxQW//vorgoODhY5CpLA8PDwQEBCAqKgoDBw4ED/99BP09fWFjkVlYNWqVZg1axYmTJiAuXPnSic06uvrw9/fnwW6RERKLDIyEkFBQQgPD4eenh569eoldCSloa+vL/M5TyKR4Pnz59DS0sLff/8tYDL6HJ07d8aqVatgbW2Nli1bAgDOnDmDhIQEDBkyBJcvX0bHjh0RHh7On30qoDeThj+lKJLI1dUV//vf/zBx4sQye80TJ07g8ePH6Natm/Tchg0b4OPjg5ycHLi6umLFihWoUqWKtH348OHQ1NTEjBkzkJubi/79+8PIyAjLli1Dv379yiybInj69CkePHhQbIeehw8f4tmzZwCAatWq4eXLl0LEK5W3tzfmzp2LSZMmyUxobt++PVauXCm3HL///jt69+4NBwcH6OjoIDQ0FOrq6tL2oKAghStuftfjx49Ro0YNDBgwALdv38a6detw9epVnD17VqbYWBnZ2tpCJBKVuJI7AGnbu6tokywfH58PXvPDDz/IIQnRp3N0dISjoyMAID8/H8ePH5cW7IaGhuLVq1ewsLDApUuXPul12b8QyZ9IUtq/OCIiAlD69ihfffUVYmJiuD0KlbsjR45g7NixOHnyZLEVN58+fQp7e3usXr1a6QcbiCoLsVgMf39/6Onpvfe6wYMHl3uWj13pRyQS4ciRI+Wc5v9t2rQJs2fPxvXr1wEARkZG8PX1hYeHh9wyfMgff/yBv/76C7du3RI6CpFCy8vLw7Zt2xAUFIRTp07B2dkZ+/fvx4ULF9C0aVOh49FnsrKywvz58+Hq6iqzZWpiYiIcHR3x6NEjoSMSEdEnuHv3LkJCQhAcHIysrCxkZmZi8+bN6Nu37wcnFtL/CwkJkfnzEovFMDAwQKtWrThJSQmNGDEC9evXL7Ya5ty5c5Gamop169bBx8cH+/fv5yrTFVxycjIiIyPx4MGDYgW7s2bNEigVKaK5c+fCz88PHTp0wDfffANtbW2Z9vHjx3/ya7q4uMDR0RHTpk0DAFy8eBF2dnYYMmQILC0t8eeff+Lnn3/G7NmzS/z+3NxcZGdnV9jnWO7u7jhx4gT8/PzQokULAK8nU3h5ecHe3h4bN27E1q1bsXjxYoXqq3V0dHDx4kWYmJjIfKa+desWLCwskJ+fL9c8T58+hY6OTrGVwZ88eQIdHR2Zol1FcfHiRXTv3h23b9+GmZkZtm7dis6dOyMnJwdisRg5OTnYsWMHXF1dhY762VJTUz/62nd3oyOiiuvly5eIjY3FP//8gzVr1iA7O/uTi2jZvxDJHwt0iYg+QkFBgcz2KHZ2dhViexRSDj169ICTk1OpM++XL1+OyMhI7Nq1S87JiOhziMVipKenV9iB8bKkCA8R3t3WVCKRID09HQ8fPsRff/2FkSNHCpaNSNkkJycjODgYoaGhyM7ORteuXdGnTx/07t1b6Gj0iTQ1NZGUlARjY2OZh4nJycmwsbFBXl6e0BGJiOgj7Ny5E4GBgYiJiYGLiwsGDBgAFxcXaGtrIz4+vthqdESViZ6eHs6dO4dGjRrJnE9JScE333yDp0+fIikpCS1atMDz588FSknlbd26dfD09MRXX32FWrVqyYwPiEQixMXFCZiOFI2JiUmpbSKRCDdu3Pjk16xduzb27t2L5s2bAwCmT5+O6OhoHDt2DACwfft2+Pj44PLly58XWsllZ2dj4sSJ2LBhg3SbelVVVQwePBhLly6FtrY2Lly4AOD1aoGKom7duti2bRvs7e1lPlPv2rULXl5e0kULqHQuLi5QVVWFt7c3Nm7ciH379sHZ2Rnr1q0DAIwbNw7nzp3DyZMnBU5KiuzZs2fYtGkTAgMDFaqIn+htL1++xMmTJxEZGYmoqCicOnUK9erVQ7t27dCuXTs4ODigfv36Qsckog9QFToAEZEyUFVVxYABA4SOQZVUfHw8Fi1aVGp7p06dsHjxYjkmIqIvoQwrUN25cwfA68FiIWlpaUFLS0vQDD179ixxFSxHR0dYWFgImIxI+ZiZmWH+/PmYO3cu9u/fj8DAQLi5ueHFixdCR6NPZGJiggsXLhRbQSEiIgKWlpYCpSIiok/1008/Ydq0aQgLC5PZXpk+XkJCwkdfa2NjU45JqKxpaGjg+PHjxQp0jx8/Dg0NDQBAUVGR9GuqmObOnYt58+ZJVy8lep+bN2+W+WtmZmaiZs2a0uPo6Gi4uLhIj1u0aIHbt2/Dzs4Ohw8fhr6+frHJ5u+qSIXlOjo6WLduHZYuXSotgDY1NYWOjo70GkUqzH2jX79+mDZtGrZv3w6RSISioiLExsbCy8sLgwYNEjqeUjhz5gyOHDkCGxsbfP3111i7di1Gjx4NsVgM4HWBbuvWrQVOWfYuX76MtLQ0vHz5UuZ8jx49BEqknCIjIxEUFITw8HDo6emhV69eQkciKlH79u1x6tQpmJiYwMHBAT///DM2b96M2rVrl/m92L8QlS8W6BIRlWDPnj1wcXGBmpoa9uzZ895r+UMJlbeMjAyoqamV2q6qqoqHDx/KMRERfQlF3cCiqKhIuhVfdnY2AEBXVxeTJ0/G9OnTpYOb5UGRHyKUtkUgEX0+sViM7t27o3v37njw4IHQcegzTJo0CWPGjEF+fj4kEglOnz6NLVu2YMGCBVi/fr3Q8YiI6CN5eHggICAAUVFRGDhwIH766Sfo6+sLHUup2NraQiQSST/nve+zzKduO0rCGjduHEaNGoVz587JbJu+fv16/PbbbwCAgwcPKmThF5WdzMxM/Pjjj0LHICX0Me8LH6NmzZq4efMm6tWrh5cvXyIuLg6+vr7S9ufPn0NNTQ09e/bEvXv3oK+vD1dX1y+6pzJKT0/H/fv30a5dO2hqakIikSj0Ignz58/HmDFjUK9ePRQWFsLKygqFhYXo378/ZsyYIXQ8pfDkyRPUqlULwOtCbW1tbZmfY/X19SvUCvc3btxAr169cPHixRJ/9uTPmR929+5dhISEIDg4GFlZWcjMzMTmzZvRt29fhe4vqHI7evQoateujfbt28PR0REODg6oUaNGmd6D/QuRfLBAl4ioBK6urtLtx983mCESifhDCZW7OnXqIDExsdiKHW8kJCSUy0w5IiofRUVFQkco0fTp0xEYGIiFCxeiTZs2AIBjx45h9uzZyM/Px7x588rt3j179kSVKlWkXyvCgJhYLP5gDpFIJN0+j4hKt337dmzZsgXXrl2Duro6zM3NMXToUDg7O8PQ0FDoePQZhg8fDk1NTcyYMQO5ubno378/jIyMsGzZMvTr10/oeERE9JHWrFkDf39/bNu2DUFBQZgwYQKcnZ0hkUgU9nOLonl7tcTz58/Dy8sLU6ZMwbfffgsAOHHiBPz8/PDHH38IFZE+04wZM2BiYoKVK1di48aNAIDGjRtj3bp16N+/PwBg1KhR8PT0FDImlbMff/wRhw4dwqhRo4SOQkpiw4YN+PPPP5GcnAwAMDc3x5QpUzBw4MDPer0uXbrA29sbixYtwv/+9z9oaWmhbdu20vaEhAQ0bNgQPj4+EIvFaNGiBTw8PODm5lYpVsd//Pgx+vbti8jISIhEIiQnJ8PU1BQeHh7Q19eHn5+f0BFLpK6ujnXr1mHWrFm4ePEisrOz0axZM5iZmQkdTam8O3arCGPK5eWXX36BiYkJDh8+DBMTE5w+fRqPHz/G5MmTucPmB+zcuROBgYGIiYmBi4sL/Pz84OLiAm1tbVhbW1fovzek/LKysnD06FFERUVh0aJFcHNzg7m5ORwcHKQFuwYGBl90D/YvRPIhkijqEl5EREQE4PWKHVFRUThz5kyxbfPy8vLQsmVLODk5Yfny5QIlJKKKwMjICKtXry62Mvzu3bsxevRo3L17V6Bkwti9e3epbSdOnMDy5ctRVFSE/Px8OaYiUi5FRUVwc3PD9u3bYW5uDgsLCwDAlStXkJKSgpEjR2LVqlV4/PgxYmJiuJ2cksrNzUV2djaLrYmIKoDk5GQEBwcjNDQU2dnZ6Nq1K/r06YPevXsLHU0ptGzZErNnz0aXLl1kzh84cAAzZ87EuXPnBEpGRJ9rwYIFWLJkCbp27Qpra+tiu5yNHz9eoGSkiJYsWYKZM2di7NixMpPfAwICMHfuXEycOPGTX/PRo0fo3bs3jh07Bh0dHYSGhsp8du7QoQNat26NefPm4ejRowgODsaOHTtQVFSEPn36wMPDQ6agt6IZNGgQHjx4gPXr18PS0hLx8fEwNTXFwYMHMWnSJFy6dEnoiB+loKAA+fn50NHRETqK0hCLxXBxcZEu+rB37160b98e2traAIAXL14gIiKiwiyy9NVXX+HIkSOwsbGBnp4eTp8+jcaNG+PIkSOYPHkyzp8/L3REhaWqqopp06bB29tbZuKCmpoa4uPjYWVlJWA6ok/z/PlzHDt2DJGRkYiKikJ8fDzMzMyQmJj42a/J/oVIPligS0T0Abdv30a9evWEjkGVWEZGBuzs7KCiooKxY8eicePGAICkpCQEBASgsLAQcXFxqFmzpsBJiUiZaWhoICEhAebm5jLnr169CltbW+Tl5cklx/DhwzFgwAA4OjrK5X6f4urVq/D29sbevXvh7u6OOXPmwNjYWOhYRApr6dKlmDt3LkJDQ9GtWzeZtj179mDo0KH49ddfERISgkGDBmHq1KkCJaVPNWfOHHz33Xdo3769zPmcnBz4+flh1qxZAiUjIqKyUFRUhP379yMwMBD//PMPXrx4IXQkpaCpqYm4uDhYWlrKnL9y5Qrs7Ozk9pmKiMqOiYlJqW0ikQg3btyQYxpSdCYmJvD19cWgQYNkzoeGhmL27Nkyq65/qqdPn0JHRwcqKioy5588eQIdHR2oq6tLz+Xk5GDbtm0ICQnB0aNH0ahRI3h4eGDw4MGoVavWZ2dQRLVq1cLBgwfx9ddfQ1dXV1qge+PGDdjY2CA7O1voiDL27t2Lx48fY8iQIdJz8+bNw++//46CggK0b98eYWFh0NfXFy6kkhg6dOhHXRccHFzOSeRDX18fcXFxMDExQcOGDbF+/Xo4OTnh+vXrsLa2Rm5urtARFdbPP/+MsLAwNGnSBAMHDsRPP/0EfX19FuiSUioqKsKZM2cQGRmJyMhIHDt2DPn5+V80GYH9C5F8sECXiOgDVFRU8N1332HAgAHo06cPPxiTIFJTU+Hp6YmDBw/izVu3SCSCs7MzAgIC3jtYTET0MVq1aoVWrVoVW4173LhxOHPmDE6ePCmXHD179sTBgwdhYGCAfv36YcCAAfj666/lcu/S3Lt3Dz4+PggNDYWzszMWLFiApk2bCpqJSBnY2NhgwoQJGDZsWIntgYGBGDlyJDp16oTdu3fLPFAkxSYWi6GmpoYFCxZg0qRJ0vMZGRkwMjKqMCvUEBER8ODBA66S/pHs7OzQtGlTrF+/XvpzzcuXLzF8+HAkJiYiLi5O4IT0KcRi8Xu3PObPO0T0Lg0NDSQmJqJRo0Yy55OTk2FtbS3ILkwpKSkIDg7Gxo0bkZ6ejs6dO2PPnj1yz1FedHV1ERcXBzMzM5kC3bNnz8LZ2RmPHz8WOqIMJycn9OnTB2PGjAEAHD9+HG3btsWcOXNgaWmJ6dOnw8XFBUuWLBE4KSmatm3bYvLkyXB1dUX//v2RmZmJGTNmYO3atTh37twXrZ5ZGeTl5WHbtm0ICgrCqVOn4OzsjP379+PChQsc5yeFVlRUhLNnzyIqKgqRkZGIjY1FTk4O6tSpAycnJ+mvL1lIhv0LkXywQJeI6APOnz+PzZs3Y+vWrXj48CE6d+6MAQMGoHv37tKtU4jkJTMzEykpKZBIJDAzM2PBOBGVmejoaHTt2hX169fHt99+CwA4ceIEbt++jQMHDsh1O7zMzExs374dmzdvxtGjR2FhYQF3d3f0798fDRo0kFuOp0+fYv78+VixYgVsbW2xaNGiCr0tIFFZ09TUxNWrV1G/fv0S21NTU2Fqaoq8vDwW5yoZsViMLVu2YMyYMejevTvWrFkDdXV1FugSESmp7du3Y8uWLbh27RrU1dVhbm6OoUOHwtnZWehoSuX06dPo3r07JBIJbGxsAAAJCQkQiUTYu3cvWrZsKXBC+hS7d++WOX716hXOnz+P0NBQ+Pr6wsPDQ6BkRKSomjZtiv79++O3336TOT937lyEhYXh4sWLguTKycnBpk2b8OuvvyIrK6tCfV7r0qULvvnmG/z+++/Q1dVFQkICjI2N0a9fPxQWFmLnzp1CR5RhaGiIgwcPolmzZgCASZMm4fLly4iIiAAAHDhwAL/88guSk5OFjEkK6ODBg8jJyUHv3r2RkpKCbt264dq1a6hRowbCwsKK7XBEpUtOTkZwcDBCQ0ORnZ2Nrl27ok+fPujdu7fQ0YiKqVq1KnJyclCrVi1pMa6joyMaNmxYZvdg/0IkHyzQJSL6SBKJBFFRUdi8eTN27tyJoqIi9O7dG0FBQUJHIyIiKhP37t1DQEAAkpKSAACWlpYYPXo0jIyMBMt0584dbNmyBUFBQUhOTkZBQYFc7vvHH39g0aJFqFWrFubPn4+ePXvK5b5EFUn16tURFRUlLVB518WLF9GuXTtkZmbKORl9KbFYjPT0dDx//hzdu3dHtWrV8L///Q8SiYQFukRESqSoqAhubm7Yvn07zM3NYWFhAQC4cuUKUlJSMHLkSKxatQqPHz9GTEwMevXqJXBixfemCOrtz1T9+/eHtra2wMmorGzevBlhYWHFCnip4rpz5w727NmDtLQ0vHz5UqaNq1zS23bu3ImffvoJHTt2RJs2bQAAsbGxOHz4MLZt2yb399GYmBgEBQVh586dEIvF6Nu3Lzw8PNC6dWu55ihPiYmJ6NChA+zs7HDkyBH06NEDly5dwpMnTxAbG1umBUxl4d2JzC1btsSPP/6IKVOmAHg9kdnKygo5OTlCxiQl8eTJE+jr6793xX8qXVFREfbv34/AwED8888/ePHihdCRiIpZs2YNnJycYG5uLtf7sn8hKnss0CUi+gxxcXHw8PBAQkICHz4TERGVk1evXmH//v34+++/sX//flSvXh13796Vy73FYjE0NTXRsWNHqKiolHpdeHi4XPIQKaM3q3KvWrWqxPZRo0YhLS0NBw4ckHMy+lIqKiq4f/8+DA0N8ezZM/Tt2xeXLl3C6tWr0aNHD35GIiJSEkuXLsXcuXMRGhqKbt26ybTt2bMHQ4cOxa+//oqQkBAMGjQIU6dOFSip4nv16hUsLCywb98+WFpaCh2HytGNGzdgY2OD7OxsoaOQHBw+fBg9evSAqakpkpKS0LRpU9y6dQsSiURaEEj0tnPnzmHJkiUyEzUmT54sXTG1vN27dw8hISEICQlBSkoK7O3t4eHhgb59+1bYySJPnz7FypUrER8fj+zsbNjZ2WHkyJGYO3cu1q5dK3Q8GY0aNUJAQACcnZ2RnZ2NGjVq4MiRI9KC7ri4ODg7O+Phw4cCJyWqXB48eABDQ0OhYxARUQWmKnQAIiJlcefOHWzevBmbN29GYmIivv32WwQEBAgdi4iIqMxkZWUhMDAQV65cAQA0adIEw4YNg56enlxzREZGFluxft++fXLdSmfQoEGcHUz0haZPnw5HR0c8fvwYXl5esLCwgEQiwZUrV+Dn54fdu3cjMjJS6Jj0Gd6e6121alUcOHAAEyZMgKurq3ChiIjokwUHB+PPP/8sVpwLAD169MAff/yBkSNHolOnTpgwYYL8AyoRNTU15OfnCx2DylleXh6WL1+OOnXqCB2F5OTXX3+Fl5cXfH19oauri507d8LQ0BDu7u7o3Lmz0PFIAX3zzTfYtGmTIPd2cXHBf//9h6+++gqDBg3CsGHD0LhxY0GyyJOenh6mT58ucy4+Ph6BgYEKV6D7448/YsKECfjtt99w4MAB1KpVS2ZF47Nnz1aK/2f06ZycnN47Vs0JIx+2fft2bNmyBdeuXYO6ujrMzc0xdOhQODs7sziXKjX2L0TywQJdIqIPWLNmDTZv3ozY2FhYWFjA3d0du3fvhrGxsdDRiIiIyszZs2fh7OwMTU1NtGzZEsDrrRrnzZuHQ4cOwc7OTi456tSpgydPnqBz585Yu3YtunfvjipVqsjl3m8LCQmR+z2JKhp7e3uEhYVh5MiR2Llzp0ybvr4+tmzZIl0lhpRLcHCwzOQNsViM5cuXw87ODtHR0QImIyKiT5GcnIyOHTuW2v6mbffu3VBXV5dXLKU1ZswYLFq0COvXr4eqKh+9KLt3t3SVSCR4/vw5tLS08PfffwuYjOTpypUr2LJlCwBAVVUVeXl50NHRwZw5c9CzZ094enoKnJAUgVgs/uAkb5FIhIKCgnLNoaamhh07dqBbt27v3Q2KhDNr1izcvXsX48ePR61atfD333/L/L/asmULunfvLmBCUlS2trYyx69evcKFCxeQmJiIwYMHCxNKSRQVFcHNzQ3bt2+Hubk5LCwsAADnz5/H9u3bMXLkSKxatQqPHz9GTEwMevXqJXBiIvli/0IkHxwlIiL6gLlz58LNzQ3Lly/H119/LXQcIiKicjFx4kT06NED69atkz5MLigowPDhwzFhwgTExMTIJcfs2bPx448/olq1anK5HxGVr169esHZ2RkHDx5EcnIyAMDMzAzOzs7Q0tISOB19qiNHjmDs2LE4efJksckTT58+xZ9//olVq1YJlI6IiD6VpqYmsrKyUL9+/RLbnz17hqpVq7I49yOdOXMGhw8fxqFDh2BtbV1sK/Hw8HCBktHnWLp0qUzBnVgshoGBAVq1agV9fX0Bk5E8aWtr4+XLlwCA2rVr4/r162jSpAkA4NGjR0JGIwWya9euUttOnDiB5cuXo6ioqNxz7Nmzp9zvQV9GU1MTGzZsKLWduwxRaZYuXVri+dmzZyM7O1vOaZTLsmXL8N9//2HPnj3Fdg7Zs2cPhg4dioYNGyIkJASDBg0SKCWRcNi/EMmHSPL2voRERFSMRCLhFtdERFThaWpq4vz589IZ5G9cvnwZzZs3R25urtwz3blzBwBQt25dud+biL7c28WcVatWlWl7+vQp7O3tsXr1arRt21aghPSpevToAScnJ0ycOLHE9uXLlyMyMvK9D6iJiEhxdO3aFfXr1y91csWoUaOQlpaGAwcOyDmZcho6dOh724ODg+WUhIjKiqurK7p27YoRI0bAy8sLu3fvxpAhQxAeHg59fX38999/QkckBXX16lV4e3tj7969cHd3x5w5c7groxzFx8fDzs4OhYWFQkcpUfv27REeHl5sgYJnz57B1dWV24nTR0tJSUHLli3x5MkToaMoLBsbG0yYMAHDhg0rsT0wMBAjR45Ep06duHMI0VvYvxCVLa6gS0T0ASKRCFlZWQgMDMSVK1cAAFZWVvDw8JDZ1pWIiEiZVa1aFWlpacUKdG/fvg1dXV255SgqKsLcuXPh5+cnnZ2rq6uLyZMnY/r06RCLxXLLQkRfxt/fHyNGjChWnAsAenp6+Pnnn7FkyRIW6CqR+Ph4LFq0qNT2Tp06YfHixXJMREREX2L69OlwdHTE48eP4eXlBQsLC0gkEly5cgV+fn7YvXs3V3L7BCzAVX4JCQkffa2NjU05JiFFsWTJEunYhK+vL7KzsxEWFgYzMzMsWbJE4HSkiO7duwcfHx+EhobC2dkZFy5cQNOmTYWOVeH07t37ve1ZWVnyCfKZoqKipKtzvy0/Px9Hjx4VIBEpqxMnTkBDQ0PoGAotOTkZHTt2LLX9TRuLc4lksX8hKlss0CUi+oCzZ8/C2dkZmpqaaNmyJYDXS/3Pnz8fhw4dgp2dncAJiYiIvtxPP/0EDw8PLF68GPb29gCA2NhYTJkyBW5ubnLLMX36dAQGBmLhwoVo06YNAODYsWOYPXs28vPzMW/ePLllIaIvw2LOiicjIwNqamqltquqquLhw4dyTERERF/C3t4eYWFhGDlyJHbu3CnTpq+vjy1btkh/JqfS6evrl7j7lp6eHszNzeHl5YXvv/9egGT0qWxtbSESifChjSdFIpHCrspIZaewsBB37tyRFmNra2tj9erVAqciRfX06VPMnz8fK1asgK2tLQ4fPszJqOXoQ4vn6OnpKeRW9W9PBLl8+TLS09Olx4WFhYiIiECdOnWEiEYK7t2idIlEgvv37+Ps2bOYOXOmQKmUg6amJrKyslC/fv0S2589e4aqVauyOJcqLfYvRPIhknxopIGIqJJr27YtGjVqhHXr1kFV9fW8hoKCAgwfPhw3btxATEyMwAmJiIi+3MuXLzFlyhSsXr0aBQUFAAA1NTV4enpi4cKFqFKlilxyGBkZYfXq1ejRo4fM+d27d2P06NG4e/euXHIQ0ZfT0NBAYmIiGjVqVGJ7SkoKrK2tkZeXJ+dk9LkaNmwIPz8/uLq6ltgeHh4OLy8v3LhxQ77BiIjoi+Tm5uLgwYNITk4GAJiZmcHZ2RlaWloCJ1MOoaGhJZ7PysrCuXPnEBYWhh07dqB79+5yTkafKjU19aOv5Vb1lYOGhgauXLkCExMToaOQAvvjjz+waNEi1KpVC/Pnz0fPnj2FjkQKSiwWSyf1lFSioampiRUrVmDYsGHyjkYKbujQoTLHYrEYBgYGaN++PTp16iRQKuXQtWtX1K9fH6tWrSqxfdSoUUhLS8OBAwfknIxIMbB/IZIPFugSEX2ApqYmzp8/X2zL78uXL6N58+bIzc0VKBkREVHZy83NxfXr1wG8LsSS90N5DQ0NJCQkwNzcXOb81atXYWtry0I+IiXCYs6KZ9y4cYiKisKZM2eKbXGWl5eHli1bwsnJCcuXLxcoIRERfYojR45g7NixOHnyJKpWrSrT9vTpU9jb22P16tVcAfALLVmyBDt27MDx48eFjkJEn6h58+ZYtGgROnToIHQUUmBisRiampro2LEjVFRUSr0uPDxcjqlIEaWmpkIikcDU1BSnT5+GgYGBtE1dXR2Ghobv/TtERJ/u+PHjcHR0hKurK7y8vGBhYQGJRIIrV67Az88Pu3fvRmRkJHcOISKicsUCXSKiD6hZsyY2btxYbIbQwYMHMWjQIGRkZAiUjIiIqOJp1aoVWrVqVay4a9y4cThz5gxOnjwpUDIi+lQs5qx4MjIyYGdnBxUVFYwdOxaNGzcGACQlJSEgIACFhYWIi4tDzZo1BU5KREQfo0ePHnBycsLEiRNLbF++fDkiIyOxa9cuOSerWK5du4bWrVvjyZMnQkehT7Rx40asXr0aN2/exIkTJ2BsbAx/f3+YmJhwhcxKIiIiAr/++it+//13fPPNN9DW1pZpf3dyA1VOQ4YMka6K+j7BwcFySENERO/atWsXRo4cWezncX19faxZswY//PCDQMmIiKiyYIEuEdEHjB8/Hrt27cLixYthb28PAIiNjcWUKVPwww8/wN/fX9iAREREXygyMhJxcXFo3bo12rRpgzVr1mDevHnIy8uDq6srli9fDk1NTblkiY6Olm479e233wIATpw4gdu3b+PAgQNcvYtIibCYs2JKTU2Fp6cnDh48KN2SUyQSwdnZGQEBAdz+l4hIiRgbGyMiIgKWlpYlticlJaFTp05IS0uTc7KK5eLFi/j++++Rnp4udBT6BKtWrcKsWbMwYcIEzJs3D4mJiTA1NUVISAhCQ0MRGRkpdESSA7FYLP367QJMiUQCkUiEwsJCIWIRUQVQ0iSQpUuXwtTUlJNAqBh9ff0SJwKIRCJoaGigUaNGGDJkSLGt6un/5ebm4uDBg0hOTgYAmJmZwdnZWe47CBIpGvYvRPKhKnQAIiJFt3jxYohEIgwaNAgFBQWQSCRQV1eHp6cnFi5cKHQ8IiKiL7Ju3Tp4enrCxMQE06dPh4+PD+bNm4eBAwdCLBbj77//Ro0aNeT2nufg4IBr164hICAASUlJAIDevXtj9OjRMDIykksGIiobNWvWxPHjx+Hp6Ylff/21xGJOFucqH2NjYxw4cACZmZlISUmBRCKBmZkZ9PX1hY5GRESfKCMjA2pqaqW2q6qq4uHDh3JMVDEFBgbC1tZW6Bj0iVasWIF169bB1dVV5vNw8+bN4eXlJWAykicWYhNReXh3EsibYn99fX34+/uzQJeKmTVrFubNmwcXFxe0bNkSAHD69GlERERgzJgxuHnzJjw9PVFQUIARI0YInFaxHDlyBGPHjsXJkyfRq1cvmbanT5+iSZMmWL16NRcGoUqL/QuRfHAFXSKij5Sbm4vr168DABo2bMgZdUREVCE0bdoUP//8M8aNG4eIiAh0794d69evx+DBgwEA27dvx6+//oqUlBSBkxKRMmMxJxERkeJp2LAh/Pz84OrqWmJ7eHg4vLy8cOPGDfkGUzKTJk0q8fzTp08RFxeHa9euISYmBt98842ck9GX0NTURFJSEoyNjaGrq4v4+HiYmpoiOTkZNjY2yMvLEzoiEREpKSsrK8yfPx+urq4y7zGJiYlwdHTEo0ePhI5ICuaHH37A999/j1GjRsmcX7NmDQ4dOoSdO3dixYoVWLt2LS5evChQSsXUo0cPODk5YeLEiSW2L1++HJGRkdi1a5eckxEpBvYvRPLBAl0iolIMGzbso64LCgoq5yRERETlR0tLC1euXIGxsTEAQF1dHfHx8dJtbtPS0mBmZoYXL16Ua46P3Ta3fv365ZqDiIiIiKiyGDduHKKionDmzBloaGjItOXl5aFly5ZwcnLC8uXLBUqoHJycnEo8X7VqVTRu3Fi6YwkpFysrKyxYsAA9e/aUKZ5asWIFgoODERcXJ3REkoOYmJj3trdr105OSYioIuEkEPpUOjo6uHDhAho1aiRzPiUlBba2tsjOzsb169dhY2ODnJwcgVIqJmNjY0REREifd7wrKSkJnTp1+ujnE0QVDfsXIvlQFToAEZGiCgkJgbGxMZo1awbOZSAioooqPz8fmpqa0uMqVaqgSpUqMscFBQXlnuPtB9Zv3ndFIpHMOZFIJN3yjYiIiIiIvsyMGTMQHh4Oc3NzjB07Fo0bNwbw+iF1QEAACgsLMX36dIFTKr7IyEihI1AZmjNnDry8vDBp0iSMGTMG+fn5kEgkOH36NLZs2YIFCxZg/fr1QsckOXF0dCx27u2xCo5RENHnMDExwYULF6QLJrzxviJCqtyqV6+OvXv3FlsFdu/evahevToAICcnB7q6ukLEU2gZGRlQU1MrtV1VVRUPHz6UYyIixcL+hUg+WKBLRFQKT09PbNmyBTdv3sTQoUMxYMAA6Q8hREREFYVIJMLz58+hoaEhLYLNzs7Gs2fPAED6X3nkqFu3LoYMGYLu3btDVZUfVYiIiIiIylPNmjVx/PhxeHp64tdff5WZKOfs7IyAgADUrFlT4JRE8uXr64tRo0Zh+PDh0NTUxIwZM5Cbm4v+/fvDyMgIy5YtQ79+/YSOSXKSmZkpc/zq1SucP38eM2fOxLx58wRKRUTKjpNA6FPNnDkTnp6eiIyMRMuWLQEAZ86cwYEDB7B69WoAwL///gsHBwchYyqkOnXqIDExsdjqoG8kJCSgdu3ack5FpDjYvxDJh0jCZSGJiEr14sULhIeHIygoCMePH0fXrl3h4eGBTp06ycyUJyIiUlZisbjElWrfPS7vVWHS09MRGhqK4OBgZGVlYcCAAfDw8OCqEUREREREcpCZmYmUlBRIJBKYmZlBX19f6EhEghCLxUhPT4ehoaH0XG5uLrKzs2XOUeUWHR2NSZMm4dy5c0JHISIltWnTJsyePRvXr18HABgZGcHX1xceHh4CJyNFFRsbi5UrV+Lq1asAgMaNG2PcuHGwt7cXOJliGzduHKKionDmzBloaGjItOXl5aFly5ZwcnLC8uXLBUpIJDz2L0TljwW6REQfKTU1FSEhIdiwYQMKCgpw6dIl6OjoCB2LiIjoi0RHR3/UdfKcHXvs2DEEBwdj+/btsLKygoeHBzw8PCAWi+WWgYiIiIiIiCofsViMjIwMGBgYCB2FFFhSUhKaN2+O7OxsoaMQkZLjJBCi8pWRkQE7OzuoqKhg7NixaNy4MYDX7+UBAQEoLCxEXFwcdw4hIqJyxQJdIqKPdPv2bQQHByMkJAQvX75EUlISC3SJiIjKUUZGBtzc3BAdHY2HDx+ievXqQkciIiIiIiKiCkwsFkNPT++Du6c9efJETolISAkJCTLHEokE9+/fx8KFC1FQUIBjx44JlIyIlF1BQQGioqJw/fp19O/fH7q6urh37x6qVq3KZ49UoqKiIqSkpODBgwcoKiqSaWvXrp1AqZRDamoqPD09cfDgQbwpjxKJRHB2dkZAQABMTEwETkgkLPYvROVPVegARESK7MWLFwgPD0dQUBCOHTuGbt26YeXKlejcuTNX8SMiogpHUT6EHz9+HEFBQdi+fTsaN26MgIAAVKtWTW73JyIiIiIiosrL19cXenp6QscgBWBrawuRSIR31zpq3bo1goKCBEpFRMouNTUVnTt3RlpaGl68eIHvv/8eurq6WLRoEV68eIHVq1cLHZEUzMmTJ9G/f3+kpqYWe08SiUQoLCwUKJlyMDY2xoEDB5CZmYmUlBRIJBKYmZlBX19f6GhEgmP/QiQfXEGXiKgUo0ePxtatW1GvXj0MGzYM7u7u+Oqrr4SORUREVC6E/hB+//59bNiwAcHBwcjMzIS7uzuGDRuGpk2blut9iYiIiIiIiN4Qi8VIT0/nVuME4HUR3dvEYjEMDAygoaEhUCIiqghcXV2hq6uLwMBA1KhRA/Hx8TA1NUVUVBRGjBiB5ORkoSOSgrG1tYW5uTl8fX1Ru3btYiv9c2IREX0u9i9E8sECXSKiUojFYtSvXx/NmjV775Zm4eHhckxFRERUPoT+EK6mpoY6depg8ODB6NGjB9TU1Eq8zsbGplxzEBERERERUeWloqKC+/fvs0CXiIjKTY0aNXD8+HE0btwYurq60gLdW7duwcrKCrm5uUJHJAWjra2N+Ph4NGrUSOgoRFTBsH8hkg9VoQMQESmqQYMGvbcwl4iIqCJJTk7Gjh07BPsQXlhYiLS0NPz++++YO3cuAHA7HSIiIiIiIpIrrmlD78rJyUF0dDTS0tLw8uVLmbbx48cLlIqIlFlRUVGJY5x37tyBrq6uAIlI0bVq1QopKSksoCOiMsf+hUg+WKBLRFSKkJAQoSMQERHJjdAfwm/evCnIfYmIiIiIiIjeKCoqEjoCKZDz58+jS5cuyM3NRU5ODqpXr45Hjx5BS0sLhoaGLNAlos/SqVMn+Pv7Y+3atQBeL0qQnZ0NHx8fdOnSReB0pIjGjRuHyZMnIz09HdbW1sV2n+Ouc0T0udi/EMmHSMLpwERERERElVJCQoL06+vXr2PGjBmYMmUKP4QTERERERERUaXn6OgIc3NzrF69Gnp6eoiPj4eamhoGDBiAX375Bb179xY6IhEpoTt37sDZ2RkSiQTJyclo3rw5kpOT8dVXXyEmJgaGhoZCRyQFIxaLi50TiUSQSCTcdY6Ivgj7FyL5YIEuEREREVElJRaLpR+0SyLPD+EdOnTAmDFjSn249ejRI7Rs2RI3btwo1xxERERERERERABQrVo1nDp1Co0bN0a1atVw4sQJWFpa4tSpUxg8eDCSkpKEjkhESqqgoABbt25FQkICsrOzYWdnB3d3d2hqagodjRRQamrqe9uNjY3llISIKhr2L0TyoSp0ACIiIiIiEsbNmzeFjiAVGRmJ6OhoTJ8+Hb6+vsXaCwsLPzhQQERERERERERUVtTU1KSrihkaGiItLQ2WlpbQ09PD7du3BU5HRMpMVVUVAwYMEDoGKQkWyBFReWH/QiQfLNAlIiIiIqqkFO2D96pVq+Dl5YWEhAT8/fff0NbWFjoSEREREREREVVSzZo1w5kzZ2BmZgYHBwfMmjULjx49wsaNG9G0aVOh4xGRkqpfvz4cHR3h4OAAJycnmJqaCh2JFNCePXvg4uICNTU17Nmz573X9ujRQ06piKgiYP9CJH8iSWn72RIRERERUaWxYMEC1KxZE8OGDZM5HxQUhIcPH2LatGnlen+xWIz09HQ8fvwYPXv2RJUqVbB7927pAHVGRgaMjIxQWFhYrjmIiIiIiIiIiADg7NmzeP78OZycnPDgwQMMGjQIx48fh5mZGQIDA2Frayt0RCJSQn///TdiYmIQFRWFlJQU1KlTBw4ODnBwcICjoyPMzMyEjkgK4M14uaGhoXQ195KIRCKOmRPRJ2H/QiR/LNAlIiIiIiI0aNAAmzdvhr29vcz5U6dOoV+/frh582a53v/tAYGnT5/Czc0Np06dQlhYGDp27MgCXSIiIiIiIiIiIqpQ7t+/j+joaOzbtw9hYWEoKiri+CcRERFRBVN6KTwREREREVUa6enpqF27drHzBgYGuH//vlyz6OnpYf/+/RgxYgS6dOmCpUuXyvX+RERERERERETt27dHVlZWsfPPnj1D+/bt5R+IiCqM3NxcHDp0CCtWrMCyZcuwY8cONG3aFOPHjxc6GimRO3fuYOTIkULHIKIKiP0LUdligS4REREREaFevXqIjY0tdj42NhZGRkblfn+RSFTseOHChdiwYQNmzpyJ4cOHl3sGIiIiIiIiIqI3oqKi8PLly2Ln8/PzcfToUQESEVFFYG9vjxo1asDb2xv5+fnw9vbG/fv3cf78eS5UQJ/k8ePHCAwMFDoGEVVA7F+Iypaq0AGIiIiIiEh4I0aMwIQJE/Dq1SvpKjCHDx/G1KlTMXny5HK/v0QiKfF8v379YGFhAVdX13LPQERERERERESUkJAg/fry5ctIT0+XHhcWFiIiIgJ16tQRIhoRVQBJSUnQ1taGhYUFLCwsYGlpCX19faFjEREREVE5YYEuERERERFhypQpePz4MUaPHi1dHUZDQwPTpk2Dt7d3ud8/MjIS1atXL7HN1tYW586dw/79+8s9BxERERERERFVbra2thCJRBCJRNJJzG/T1NTEihUrBEhGRBXB48ePcfHiRURFReHgwYOYPn061NXV4eDgACcnJ4wYMULoiERERERUhkSS0paqIiIiIiKiSic7OxtXrlyBpqYmzMzMUKVKFbnct7CwEJcuXYKZmRk0NTVl2nJzc5GSkoKmTZtCLBbLJQ8RERERERERVU6pqamQSCQwNTXF6dOnYWBgIG1TV1eHoaEhVFRUBExIRBWFRCLBuXPnsHLlSmzatAlFRUUoLCwUOhYpifj4eNjZ2fHvDBGVOfYvRGWLK+gSERERERGGDRuGZcuWQVdXFy1atJCez8nJwbhx4xAUFFSu99+4cSNWrlyJU6dOFWtTV1fHsGHDMGHCBAwYMKBccxARERERERFR5WZsbAwAKCoqEjgJEVUkc+bMgZeXF5KSkhAVFYWoqCgcO3YMz58/h7W1NcaNGwcHBwehY5IC6d2793vbs7Ky5BOEiCoc9i9E8sUVdImIiIiICCoqKrh//z4MDQ1lzj969Ai1atVCQUFBud6/bdu2GDNmDPr161di+7Zt27By5UrExMSUaw4iIiIiIiIiIgAIDQ3FV199ha5duwIApk6dirVr18LKygpbtmyRFvISEX2MN+OvRkZGaNasGRwcHODg4IB27dpBT09P6HikgIYOHfpR1wUHB5dzEiKqaNi/EMkXC3SJiIiIiCqxZ8+eQSKRQF9fH8nJyTLbNhYWFmLv3r3w9vbGvXv3yjWHoaEhTp8+jQYNGpTYfvPmTbRs2RIPHz4s1xxERERERERERADQuHFjrFq1Cu3bt8eJEyfQoUMH+Pv7Y9++fVBVVUV4eLjQEYlIiYjFYqSnp0NDQwNVq1YVOg4RERERyYmq0AGIiIiIiEg41apVg0gkgkgkgrm5ebF2kUgEX1/fcs+Rk5ODZ8+eldr+/Plz5ObmlnsOIiIiIiIiIiIAuH37Nho1agQA+N///oc+ffpg5MiRaNOmDRwdHYUNR0RKSSQSsTiXvtizZ89w5MgRWFhYwMLCQug4RFSBsH8hKh8s0CUiIiIiqsQiIyMhkUjQvn177Ny5E9WrV5e2qaurw9jYGEZGRuWew8zMDMePH4eNjU2J7ceOHYOZmVm55yAiIiIiIiIiAgAdHR08fvwY9evXx6FDhzBp0iQAgIaGBvLy8gROR0TKyNzcHCKR6L3XPHnyRE5pSFn07dsX7dq1w9ixY5GXl4fmzZvj1q1bkEgk2Lp1K3744QehIxKRkmL/QiQfLNAlIiIiIqrEHBwcAAA3b95E/fr1PzhAXF769++PGTNmwN7evliRbnx8PGbNmoWpU6cKko2IiIiIiIiIKp/vv/8ew4cPR7NmzXDt2jV06dIFAHDp0iU0aNBA2HBEpJR8fX2hp6cndAxSMjExMZg+fToAYNeuXZBIJMjKykJoaCjmzp3LAjoi+mzsX4jkQySRSCRChyAiIiIiImHFxMS8t71du3blev9Xr16hU6dOOHbsGDp27CjdOicpKQn//fcf2rRpg3///RdqamrlmoOIiIiIiIiICACysrIwY8YM3L59G56enujcuTMAwMfHB+rq6tJiBiKijyEWi5Geng5DQ0Oho5CS0dTUxLVr11CvXj0MGjQIRkZGWLhwIdLS0mBlZYXs7GyhIxKRkmL/QiQfXEGXiIiIiIjg6OhY7Nzbq+kWFhaW6/3V1NRw6NAhLF26FJs3b0ZMTAwkEgnMzc0xb948TJgwgcW5RERERERERCQ31apVw8qVK4ud9/X1FSANESk7oXYuI+VXr149nDhxAtWrV0dERAS2bt0KAMjMzISGhobA6YhImbF/IZIPFugSEREREREyMzNljl+9eoXz589j5syZmDdvnlwyqKmpYerUqZg6dapc7kdERERERERE9D5Hjx7FmjVrcOPGDWzfvh116tTBxo0bYWJigu+++07oeESkRLixMX2uCRMmwN3dHTo6OjA2NpYuthETEwNra2thwxGRUmP/QiQfIgl/EiQiIiIiolJER0dj0qRJOHfunFzuJ5FIcO7cOdy6dQsikQimpqawtbXlChNEREREREREJFc7d+7EwIED4e7ujo0bN+Ly5cswNTXFypUrceDAARw4cEDoiEREVEmcPXsWt2/fxvfffw8dHR0AwP79+1GtWjW0adNG4HREpMzYvxCVPxboEhERERFRqZKSktC8eXNkZ2eX+70iIyPh4eGB1NRU6YoSIpEIJiYmCAoKQrt27co9AxERERERERERADRr1gwTJ07EoEGDoKuri/j4eJiamuL8+fNwcXFBenq60BGJiKgSKiwsxMWLF2FsbAx9fX2h4xBRBcL+hah8iIUOQEREREREwktISJD5FR8fj4iICIwaNQq2trblfv+UlBR069YNDRo0QHh4OK5cuYLLly9j+/btqFu3Lrp06YIbN26Uew4iIiIiIiIiIgC4evVqiZOF9fT0kJWVJf9ARERUKU2YMAGBgYEAXhfPOTg4wM7ODvXq1UNUVJSw4YhIqbF/IZIPVaEDEBERERGR8GxtbSESifDuBhutW7dGUFBQud/f398frVu3xuHDh2XOW1hYoFevXujYsSOWLl2KFStWlHsWIiIiIiIiIqJatWohJSUFDRo0kDl/7NgxmJqaChOKiIgqnR07dmDAgAEAgL179+LmzZtISkrCxo0bMX36dMTGxgqckIiUFfsXIvngCrpERERERISbN2/ixo0buHnzJm7evInU1FTk5ubi+PHjsLCwKPf7R0VFYcKECSW2iUQiTJgwAZGRkeWeg4iIiIiIiIgIAEaMGIFffvkFp06dgkgkwr1797Bp0yZMnjwZnp6eQscjIqJK4tGjR6hVqxYA4MCBA/jxxx9hbm6OYcOG4eLFiwKnIyJlxv6FSD64gi4RERERUSVXVFSEw4cPIzw8HLdu3YJIJIKJiQn69OmDgQMHQiQSlXuGtLQ0WFtbl9retGlTpKamlnsOIiIiIiIiIiIA8Pb2RlFRETp06IDc3Fy0a9cOVapUwZQpUzB8+HCh4xERUSVRs2ZNXL58GbVr10ZERARWrVoFAMjNzYWKiorA6YhImbF/IZIPrqBLRERERFSJSSQS9OjRA8OHD8fdu3dhbW2NJk2aIDU1FUOGDEGvXr3kkiM7OxtaWlqltmtpaSE3N1cuWYiIiIiIiIiIRCIRpk+fjidPniAxMREnT57Ew4cPoaenBxMTE6HjERFRJTF06FD07dsXTZs2hUgkQseOHQEAp06dksvud0RUcbF/IZIPrqBLRERERFSJhYSEICYmBocPH4aTk5NM25EjR+Dq6ooNGzZg0KBB5Z7l8uXLSE9PL7Ht0aNH5X5/IiIiIiIiIqIXL15g9uzZ+Pfff6Ur5rq6uiI4OBi9evWCiooKJk6cKHRMIiKqJGbPno2mTZvi9u3b+PHHH1GlShUAgIqKCry9vQVOR0TKjP0LkXyIJBKJROgQREREREQkjE6dOqF9+/alftCeP38+oqOjcfDgwXLNIRaLIRKJUNLHkzfnRSIRCgsLyzUHEREREREREVVu06ZNw5o1a9CxY0ccP34cDx8+xNChQ3Hy5En89ttv+PHHH7nlLxEREREREX0UrqBLRERERFSJJSQk4I8//ii13cXFBcuXLy/3HDdv3iz3exARERERERERfcj27duxYcMG9OjRA4mJibCxsUFBQQHi4+MhEomEjkdERJVQdHQ0Fi9ejCtXrgAArKysMGXKFLRt21bgZESk7Ni/EJU/rqBLRERERFSJqaurIzU1FbVr1y6x/d69ezAxMcGLFy/knIyIiIiIiIiISP7U1dVx8+ZN1KlTBwCgqamJ06dPw9raWuBkRERUGf39998YOnQoevfujTZt2gAAYmNjsWvXLoSEhKB///4CJyQiZcX+hUg+WKBLRERERFSJqaioID09HQYGBiW2Z2RkwMjICIWFheWaY9CgQQgICICuri4AID4+HlZWVlBTUyvX+xIRERERERERve3dsRJdXV0kJCTAxMRE4GRERFQZWVpaYuTIkZg4caLM+SVLlmDdunXSVS+JiD4V+xci+WCBLhERERFRJSYWi+Hi4oIqVaqU2P7ixQtERESUe4GuiooK7t+/D0NDQwBA1apVceHCBZiampbrfYmIiIiIiIiI3vbuWMnevXvRvn17aGtry1wXHh4uRDwiIqpkqlSpgkuXLqFRo0Yy51NSUtC0aVPk5+cLlIyIlB37FyL5UBU6ABERERERCWfw4MEfvGbQoEHlnuPdeYOcR0hEREREREREQnh3rGTAgAECJSEiIgLq1auHw4cPFyug+++//1CvXj2BUhFRRcD+hUg+WKBLRERERFSJBQcHCx2BiIiIiIiIiEhhcKyEiIgUyeTJkzF+/HhcuHAB9vb2AIDY2FiEhIRg2bJlAqcjImXG/oVIPligS0RERERECuHy5ctIT08H8HoF3aSkJGRnZ8tcY2NjI0Q0IiIiIiIiIiIiIiK58/T0RK1ateDn54dt27YBACwtLREWFoaePXsKnI6IlBn7FyL5EEm4dywREREREQlMLBZDJBKhpI8nb86LRCIUFhYKkI6IiIiIiIiIiIiISL4KCgowf/58DBs2DHXr1hU6DhFVIOxfiOSHBbpERERERCS41NTUj7rO2Ni4nJMQERERERERERERESkGHR0dJCYmokGDBkJHIaIKhv0LkXyoCh2AiIiIiIiIhbdERERERERERERERLI6dOiA6OhoFtARUZlj/0IkHyzQJSIiIiIiwT169Ag5OTkyhbqXLl3C4sWLkZOTA1dXV/Tv31/AhERERERERERERERE8uXi4gJvb29cvHgR33zzDbS1tWXae/ToIVAyIlJ27F+I5EMkkUgkQocgIiIiIqLKzc3NDUZGRvDz8wMAPHjwABYWFjAyMkLDhg3xzz//IDAwEAMHDhQ4KRERERERERERERGRfIjF4lLbRCIRCgsL5ZiGiCoS9i9E8lH6vzQiIiIiIiI5OXnypMxM3A0bNqB69eq4cOECdu/ejfnz5yMgIEDAhERERERERERERERE8lVUVFTqLxbPEdGXYP9CJB8s0CUiIiIiIsGlp6ejQYMG0uMjR46gd+/eUFVVBfB6G53k5GSB0hERERERERERERERyc+RI0dgZWWFZ8+eFWt7+vQpmjRpgqNHjwqQjIiUHfsXIvligS4REREREQmuatWqyMrKkh6fPn0arVq1kh6LRCK8ePFCgGRERERERERERERERPLl7++PESNGoGrVqsXa9PT08PPPP2PJkiUCJCMiZcf+hUi+WKBLRERERESCa926NZYvX46ioiLs2LEDz58/R/v27aXt165dQ7169QRMSEREREREREREREQkH/Hx8ejcuXOp7Z06dcK5c+fkmIiIKgr2L0TypSp0ACIiIiIiojlz5qBjx474+++/UVBQgN9++w36+vrS9q1bt8LBwUHAhERERERERERERERE8pGRkQE1NbVS21VVVfHw4UM5JiKiioL9C5F8sUCXiIiIiIgE9/XXX+PKlSuIjY1FrVq10KpVK5l2Nzc3WFpaCpSOiIiIiIiIiIiIiEh+6tSpg8TERDRq1KjE9oSEBNSuXVvOqYioImD/QiRfYqEDEBERERERHTlyBO3atYOTk1Ox4tynT59iypQpuHPnjkDpiIiIiIiIiIiIiIjkp0uXLpg5cyby8/OLteXl5cHHxwfdunUTIBkRKTv2L0TyJZJIJBKhQxARERERUeXWo0cPODk5YeLEiSW2L1++HJGRkdi1a5eckxERERERERERERERyVdGRgbs7OygoqKCsWPHonHjxgCApKQkBAQEoLCwEHFxcahZs6bASYlI2bB/IZIvFugSEREREZHgjI2NERERAUtLyxLbk5KS0KlTJ6Slpck5GRERERERERERERGR/KWmpsLT0xMHDx7Em9IekUgEZ2dnBAQEwMTEROCERKSs2L8QyQ8LdImIiIiISHAaGhpITExEo0aNSmxPSUmBtbU18vLy5JyMiIiIiIiIiIiIiEg4mZmZSElJgUQigZmZGfT19YWOREQVBPsXovKnKnQAIiIiIiKiOnXqvLdANyEhAbVr15ZzKiIiIiIiIiIiIiIiYenr66NFixZCxyCiCoj9C1H5EwsdgIiIiIiIqEuXLpg5cyby8/OLteXl5cHHxwfdunUTIBkREREREREREREREREREdGnE0kkEonQIYiIiIiIqHLLyMiAnZ0dVFRUMHbsWDRu3BgAkJSUhICAABQWFiIuLg41a9YUOCkREREREREREREREREREdGHsUCXiIiIiIgUQmpqKjw9PXHw4EG8+ZgiEong7OyMgIAAmJiYCJyQiIiIiIiIiIiIiIiIiIjo47BAl4iIiIiIFEpmZiZSUlIgkUhgZmYGfX19oSMRERERERERERERERERERF9EhboEhERERERERERERERERERERERERERlSGx0AGIiIiIiIiIiIiIiIiIiIiIiIiIiIgqEhboEhERERERERERERERERERERERERERlSEW6BIREREREREREREREREREREREREREZUhFugSERERERERERERKRlHR0dMmDBB6BhKJSQkBNWqVRM6BhEREREREREREVUSLNAlIiIiIiIiIiIiKgcikei9v2bPni10RCIiIiIiIiIiIiIqJ6pCByAiIiIiIiIiIiKqiO7fvy/9OiwsDLNmzcLVq1el53R0dISI9VFevXoFNTU1oWMQERERERERERERKS2uoEtERERERERERERUDmrVqiX9paenB5FIJD3OycmBu7s7atasCR0dHbRo0QL//fefzPf/9ddfMDMzg4aGBmrWrIk+ffqUeq/9+/dDT08PmzZtAgBERUWhZcuW0NbWRrVq1dCmTRukpqaW+L23bt2CSCRCWFgYHBwcoKGhIX2d9evXw9LSEhoaGrCwsMBff/1V7Pu2bduGtm3bQlNTEy1atMC1a9dw5swZNG/eHDo6OnBxccHDhw+l31dUVIQ5c+agbt26qFKlCmxtbRERESFtt7e3x7Rp02QyPnz4EGpqaoiJiQEAvHjxAl5eXqhTpw60tbXRqlUrREVFyXxPSEgI6tevDy0tLfTq1QuPHz8u9c+PiIiIiIiIiIiIqKyxQJeIiIiIiIiIiIhIzrKzs9GlSxccPnwY58+fR+fOndG9e3ekpaUBAM6ePYvx48djzpw5uHr1KiIiItCuXbsSX2vz5s1wc3PDpk2b4O7ujoKCAri6usLBwQEJCQk4ceIERo4cCZFI9N5M3t7e+OWXX3DlyhU4Oztj06ZNmDVrFubNm4crV65g/vz5mDlzJkJDQ2W+z8fHBzNmzEBcXBxUVVXRv39/TJ06FcuWLcPRo0eRkpKCWbNmSa9ftmwZ/Pz8sHjxYiQkJMDZ2Rk9evRAcnIyAMDd3R1bt26FRCKRfk9YWBiMjIzQtm1bAMDYsWNx4sQJbN26FQkJCfjxxx/RuXNn6WucOnUKHh4eGDt2LC5cuAAnJyfMnTv3E/8vEREREREREREREX0+keTtUU4iIiIiIiIiIiIiKnMhISGYMGECsrKySr2madOmGDVqFMaOHYvw8HAMHToUd+7cga6ubrFrHR0dYWtrCzMzM0yfPh27d++Gg4MDAODJkyeoUaMGoqKipOfe59atWzAxMYG/vz9++eUX6flGjRrh999/h5ubm/Tc3LlzceDAARw/flz6fevXr4eHhwcAYOvWrXBzc8Phw4fRvn17AMDChQsREhKCpKQkAECdOnUwZswY/Pbbb9LXbdmyJVq0aIGAgAA8fPgQRkZGOHLkiLQg197eHu3atcPChQuRlpYGU1NTpKWlwcjISPoaHTt2RMuWLTF//nz0798fT58+xf79+6Xt/fr1Q0RExHv/HxARERERERERERGVFVWhAxARERERERERERFVNtnZ2Zg9ezb279+P+/fvo6CgAHl5edIVdL///nsYGxvD1NQUnTt3RufOndGrVy9oaWlJX2PHjh148OABYmNj0aJFC+n56tWrY8iQIXB2dsb333+Pjh07om/fvqhdu/Z7MzVv3lz6dU5ODq5fvw4PDw+MGDFCer6goAB6enoy32djYyP9umbNmgAAa2trmXMPHjwAADx79gz37t1DmzZtZF6jTZs2iI+PBwAYGBigU6dO2LRpE9q2bYubN2/ixIkTWLNmDQDg4sWLKCwshLm5ucxrvHjxAjVq1AAAXLlyBb169ZJp//bbbxEREfHePwMiIiIiIiIiIiKisiIWOgARERERERERERFRZePl5YVdu3Zh/vz5OHr0KC5cuABra2u8fPkSAKCrq4u4uDhs2bIFtWvXxqxZs/D111/LrP7arFkzGBgYICgoCO9ulBYcHIwTJ07A3t4eYWFhMDc3x8mTJ9+bSVtbW/p1dnY2AGDdunW4cOGC9FdiYmKx11FTU5N+LRKJSjxXVFT0CX86gLu7O3bs2IFXr15h8+bNsLa2lhb9ZmdnQ0VFBefOnZPJduXKFSxbtuyT7kNERERERERERERUXligS0RERERERERERCRnsbGxGDJkCHr16gVra2vUqlULt27dkrlGVVUVHTt2xB9//IGEhATcunULR44ckbY3bNgQkZGR2L17N8aNG1fsHs2aNcOvv/6K48ePo2nTpti8efNH56tZsyaMjIxw48YNNGrUSOaXiYnJZ/++q1atCiMjI8TGxsqcj42NhZWVlfS4Z8+eyM/PR0REBDZv3gx3d3eZ31dhYSEePHhQLFutWrUAAJaWljh16pTMPT5UoExERERERERERERUllSFDkBERERERERERERU2ZiZmSE8PBzdu3eHSCTCzJkzZVaZ3bdvH27cuIF27dpBX18fBw4cQFFRERo3bizzOubm5oiMjISjoyNUVVXh7++PmzdvYu3atejRoweMjIxw9epVJCcnY9CgQZ+U0dfXF+PHj4eenh46d+6MFy9e4OzZs8jMzMSkSZM++/c+ZcoU+Pj4oGHDhrC1tUVwcDAuXLiATZs2Sa/R1taGq6srZs6ciStXrsDNzU3m9+zu7o5BgwbBz88PzZo1w8OHD3H48GHY2Niga9euGD9+PNq0aYPFixejZ8+eOHjwICIiIj47MxEREREREREREdGnYoEuERERERERERERkZwtWbIEw4YNg729Pb766itMmzYNz549k7ZXq1YN4eHhmD17NvLz82FmZoYtW7agSZMmxV6rcePGOHLkCBwdHaGiooKpU6ciKSkJoaGhePz4MWrXro0xY8bg559//qSMw4cPh5aWFv78809MmTIF2trasLa2xoQJE77o9z5+/Hg8ffoUkydPxoMHD2BlZYU9e/bAzMxM5jp3d3d06dIF7dq1Q/369WXagoODMXfuXEyePBl3797FV199hdatW6Nbt24AgNatW2PdunXw8fHBrFmz0LFjR8yYMQO///77F2UnIiIiIiIiIiIi+lgiiUQiEToEERERERERERERERERERERERERERFRRSEWOgAREREREREREREREREREREREREREVFFwgJdIiIiIiIiIiIiIiIiIiIiIiIiIiKiMsQCXSIiIiIiIiIiIiIiIiIiIiIiIiIiojLEAl0iIiIiIiIiIiIiIiIiIiIiIiIiIqIyxAJdIiIiIiIiIiIiIiIiIiIiIiIiIiKiMsQCXSIiIiIiIiIiIiIiIiIiIiIiIiIiojLEAl0iIiIiIiIiIiIiIiIiIiIiIiIiIqIyxAJdIiIiIiIiIiIiIiIiIiIiIiIiIiKiMsQCXSIiIiIiIiIiIiIiIiIiIiIiIiIiojLEAl0iIiIiIiIiIiIiIiIiIiIiIiIiIqIyxAJdIiIiIiIiIiIiIiIiIiIiIiIiIiKiMsQCXSIiIiIiIiIiIiIiIiIiIiIiIiIiojL0f3zEfYU8AReKAAAAAElFTkSuQmCC",
-                        "text/plain": [
-                            "<Figure size 3500x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# plotting the predictability scores with the tasks removed\n",
-                "import matplotlib.pyplot as plt\n",
-                "\n",
-                "# make the plot wider\n",
-                "plt.figure(figsize=(35, 6))\n",
-                "\n",
-                "for metric in [\"spearman\", \"pearson\"]:\n",
-                "    plt.plot([t[metric] for t in predicability_scores], label=metric)\n",
-                "\n",
-                "plt.xlabel(\"Tasks removed\")\n",
-                "plt.ylabel(\"Correlation (predicted vs. observed performance)\")\n",
-                "plt.legend()\n",
-                "\n",
-                "# add vline for 0.8 spearman\n",
-                "plt.axhline(y=0.8, color=\"r\", linestyle=\"--\")\n",
-                "\n",
-                "# add task names to the x-axis\n",
-                "plt.xticks(range(len(tasks_removed)), tasks_removed, rotation=90)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 21,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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-                        "text/plain": [
-                            "<Figure size 3500x600 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# plotting the predictability scores with the tasks removed\n",
-                "\n",
-                "plt.figure(figsize=(35, 6))\n",
-                "\n",
-                "for metric in [\"mse_with_zscore\"]:\n",
-                "    plt.plot([t[metric] for t in predicability_scores], label=metric)\n",
-                "\n",
-                "plt.xlabel(\"Tasks removed\")\n",
-                "plt.ylabel(\"Normalized Mean Squared error (predicted vs. observed performance)\")\n",
-                "plt.legend()\n",
-                "\n",
-                "# add vline for 0.5 mse\n",
-                "plt.axhline(y=0.5, color=\"r\", linestyle=\"--\")\n",
-                "\n",
-                "# add task names to the x-axis\n",
-                "plt.xticks(range(len(tasks_removed)), tasks_removed, rotation=90)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Constructing the Benchmark"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 22,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "['BornholmBitextMining',\n",
-                            " 'BibleNLPBitextMining',\n",
-                            " 'BUCC.v2',\n",
-                            " 'DiaBlaBitextMining',\n",
-                            " 'FloresBitextMining',\n",
-                            " 'IN22GenBitextMining',\n",
-                            " 'IndicGenBenchFloresBitextMining',\n",
-                            " 'NollySentiBitextMining',\n",
-                            " 'NorwegianCourtsBitextMining',\n",
-                            " 'NTREXBitextMining',\n",
-                            " 'NusaTranslationBitextMining',\n",
-                            " 'NusaXBitextMining',\n",
-                            " 'Tatoeba',\n",
-                            " 'BulgarianStoreReviewSentimentClassfication',\n",
-                            " 'CzechProductReviewSentimentClassification',\n",
-                            " 'GreekLegalCodeClassification',\n",
-                            " 'DBpediaClassification',\n",
-                            " 'FinancialPhrasebankClassification',\n",
-                            " 'PoemSentimentClassification',\n",
-                            " 'ToxicConversationsClassification',\n",
-                            " 'TweetTopicSingleClassification',\n",
-                            " 'EstonianValenceClassification',\n",
-                            " 'FilipinoShopeeReviewsClassification',\n",
-                            " 'GujaratiNewsClassification',\n",
-                            " 'SentimentAnalysisHindi',\n",
-                            " 'IndonesianIdClickbaitClassification',\n",
-                            " 'ItaCaseholdClassification',\n",
-                            " 'KorSarcasmClassification',\n",
-                            " 'KurdishSentimentClassification',\n",
-                            " 'MacedonianTweetSentimentClassification',\n",
-                            " 'AfriSentiClassification',\n",
-                            " 'AmazonCounterfactualClassification',\n",
-                            " 'CataloniaTweetClassification',\n",
-                            " 'CyrillicTurkicLangClassification',\n",
-                            " 'IndicLangClassification',\n",
-                            " 'MasakhaNEWSClassification',\n",
-                            " 'MassiveIntentClassification',\n",
-                            " 'MultiHateClassification',\n",
-                            " 'NordicLangClassification',\n",
-                            " 'NusaParagraphEmotionClassification',\n",
-                            " 'NusaX-senti',\n",
-                            " 'ScalaClassification',\n",
-                            " 'SwissJudgementClassification',\n",
-                            " 'NepaliNewsClassification',\n",
-                            " 'OdiaNewsClassification',\n",
-                            " 'PunjabiNewsClassification',\n",
-                            " 'PolEmo2.0-OUT',\n",
-                            " 'PAC',\n",
-                            " 'SinhalaNewsClassification',\n",
-                            " 'CSFDSKMovieReviewSentimentClassification',\n",
-                            " 'SiswatiNewsClassification',\n",
-                            " 'SlovakMovieReviewSentimentClassification',\n",
-                            " 'SwahiliNewsClassification',\n",
-                            " 'DalajClassification',\n",
-                            " 'TswanaNewsClassification',\n",
-                            " 'IsiZuluNewsClassification',\n",
-                            " 'WikiCitiesClustering',\n",
-                            " 'MasakhaNEWSClusteringS2S',\n",
-                            " 'RomaniBibleClustering',\n",
-                            " 'ArXivHierarchicalClusteringP2P',\n",
-                            " 'ArXivHierarchicalClusteringS2S',\n",
-                            " 'BigPatentClustering.v2',\n",
-                            " 'BiorxivClusteringP2P.v2',\n",
-                            " 'MedrxivClusteringP2P.v2',\n",
-                            " 'StackExchangeClustering.v2',\n",
-                            " 'AlloProfClusteringS2S.v2',\n",
-                            " 'HALClusteringS2S.v2',\n",
-                            " 'SIB200ClusteringS2S',\n",
-                            " 'WikiClusteringP2P.v2',\n",
-                            " 'SNLHierarchicalClusteringP2P',\n",
-                            " 'PlscClusteringP2P.v2',\n",
-                            " 'SwednClusteringP2P',\n",
-                            " 'CLSClusteringP2P.v2',\n",
-                            " 'StackOverflowQA',\n",
-                            " 'TwitterHjerneRetrieval',\n",
-                            " 'AILAStatutes',\n",
-                            " 'ArguAna',\n",
-                            " 'HagridRetrieval',\n",
-                            " 'LegalBenchCorporateLobbying',\n",
-                            " 'LEMBPasskeyRetrieval',\n",
-                            " 'SCIDOCS',\n",
-                            " 'SpartQA',\n",
-                            " 'TempReasonL1',\n",
-                            " 'TRECCOVID',\n",
-                            " 'WinoGrande',\n",
-                            " 'BelebeleRetrieval',\n",
-                            " 'MLQARetrieval',\n",
-                            " 'StatcanDialogueDatasetRetrieval',\n",
-                            " 'WikipediaRetrievalMultilingual',\n",
-                            " 'CovidRetrieval',\n",
-                            " 'Core17InstructionRetrieval',\n",
-                            " 'News21InstructionRetrieval',\n",
-                            " 'Robust04InstructionRetrieval',\n",
-                            " 'KorHateSpeechMLClassification',\n",
-                            " 'MalteseNewsClassification',\n",
-                            " 'MultiEURLEXMultilabelClassification',\n",
-                            " 'BrazilianToxicTweetsClassification',\n",
-                            " 'CEDRClassification',\n",
-                            " 'CTKFactsNLI',\n",
-                            " 'SprintDuplicateQuestions',\n",
-                            " 'TwitterURLCorpus',\n",
-                            " 'ArmenianParaphrasePC',\n",
-                            " 'indonli',\n",
-                            " 'OpusparcusPC',\n",
-                            " 'PawsXPairClassification',\n",
-                            " 'RTE3',\n",
-                            " 'XNLI',\n",
-                            " 'PpcPC',\n",
-                            " 'TERRa',\n",
-                            " 'WebLINXCandidatesReranking',\n",
-                            " 'AlloprofReranking',\n",
-                            " 'VoyageMMarcoReranking',\n",
-                            " 'WikipediaRerankingMultilingual',\n",
-                            " 'RuBQReranking',\n",
-                            " 'T2Reranking',\n",
-                            " 'GermanSTSBenchmark',\n",
-                            " 'SICK-R',\n",
-                            " 'STS12',\n",
-                            " 'STS13',\n",
-                            " 'STS14',\n",
-                            " 'STS15',\n",
-                            " 'STSBenchmark',\n",
-                            " 'FaroeseSTS',\n",
-                            " 'FinParaSTS',\n",
-                            " 'JSICK',\n",
-                            " 'IndicCrosslingualSTS',\n",
-                            " 'SemRel24STS',\n",
-                            " 'STS17',\n",
-                            " 'STS22.v2',\n",
-                            " 'STSES',\n",
-                            " 'STSB']"
-                        ]
-                    },
-                    "execution_count": 22,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# we now have the tasks:\n",
-                "tasks_to_select_from"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 23,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "tasks = mteb.get_tasks(tasks=tasks_to_select_from)\n",
-                "\n",
-                "# we can now create a benchmark\n",
-                "benchmark = mteb.Benchmark(\n",
-                "    name=\"MTEB(Multilingual)\",\n",
-                "    tasks=tasks,\n",
-                "    description=\"Benchmark for evaluating document embedding models for European languages\",\n",
-                "    citation=\"\",\n",
-                "    reference=\"\",\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 24,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th>Languages</th>\n",
-                            "      <th>Domains</th>\n",
-                            "      <th>License</th>\n",
-                            "      <th>Description</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Type</th>\n",
-                            "      <th>Name</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"13\" valign=\"top\">BitextMining</th>\n",
-                            "      <th>BornholmBitextMining</th>\n",
-                            "      <td>{dan}</td>\n",
-                            "      <td>[Web, Social, Fiction, Written]</td>\n",
-                            "      <td>CC-BY-4.0</td>\n",
-                            "      <td>Danish Bornholmsk Parallel Corpus. Bornholmsk ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>BibleNLPBitextMining</th>\n",
-                            "      <td>{aoj, ncl, imo, acu, eko, urb, bvd, cab, bsp, ...</td>\n",
-                            "      <td>[Religious, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>Partial Bible translations in 829 languages, a...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>BUCC.v2</th>\n",
-                            "      <td>{deu, cmn, fra, rus, eng}</td>\n",
-                            "      <td>[Written]</td>\n",
-                            "      <td>Unknown</td>\n",
-                            "      <td>BUCC bitext mining dataset</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>DiaBlaBitextMining</th>\n",
-                            "      <td>{fra, eng}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>English-French Parallel Corpus. DiaBLa is an E...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>FloresBitextMining</th>\n",
-                            "      <td>{kin, ita, zul, sin, kbp, khk, ast, ell, shn, ...</td>\n",
-                            "      <td>[Non-fiction, Encyclopaedic, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>FLORES is a benchmark dataset for machine tran...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>IN22GenBitextMining</th>\n",
-                            "      <td>{san, kas, mni, sat, mal, brx, pan, asm, tam, ...</td>\n",
-                            "      <td>[Web, Legal, Government, News, Religious, Non-...</td>\n",
-                            "      <td>CC-BY-4.0</td>\n",
-                            "      <td>IN22-Gen is a n-way parallel general-purpose m...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>IndicGenBenchFloresBitextMining</th>\n",
-                            "      <td>{san, mup, mni, sat, bgc, hne, bho, nep, mal, ...</td>\n",
-                            "      <td>[Web, News, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>Flores-IN dataset is an extension of Flores da...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NollySentiBitextMining</th>\n",
-                            "      <td>{yor, pcm, hau, ibo, eng}</td>\n",
-                            "      <td>[Social, Reviews, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>NollySenti is Nollywood movie reviews for five...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NorwegianCourtsBitextMining</th>\n",
-                            "      <td>{nob, nno}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>Nynorsk and Bokmål parallel corpus from Norweg...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NTREXBitextMining</th>\n",
-                            "      <td>{kin, ita, zul, sin, swa, ell, slk, uig, mal, ...</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>NTREX is a News Test References dataset for Ma...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NusaTranslationBitextMining</th>\n",
-                            "      <td>{bew, bbc, mad, sun, ind, min, bhp, jav, mak, ...</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>NusaTranslation is a parallel dataset for mach...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NusaXBitextMining</th>\n",
-                            "      <td>{bjn, bbc, mad, nij, ace, ind, min, bug, jav, ...</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>NusaX is a parallel dataset for machine transl...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Tatoeba</th>\n",
-                            "      <td>{ita, max, ast, ell, slk, nov, uig, mal, swh, ...</td>\n",
-                            "      <td>[Written]</td>\n",
-                            "      <td>CC BY 2.0</td>\n",
-                            "      <td>1,000 English-aligned sentence pairs for each ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"43\" valign=\"top\">Classification</th>\n",
-                            "      <th>BulgarianStoreReviewSentimentClassfication</th>\n",
-                            "      <td>{bul}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Bulgarian online store review dataset for sent...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>CzechProductReviewSentimentClassification</th>\n",
-                            "      <td>{ces}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>User reviews of products on Czech e-shop Mall....</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>GreekLegalCodeClassification</th>\n",
-                            "      <td>{ell}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Greek Legal Code Dataset for Classification. (...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>DBpediaClassification</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>DBpedia14 is a dataset of English texts from W...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>FinancialPhrasebankClassification</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>cc-by-nc-sa-3.0</td>\n",
-                            "      <td>Polar sentiment dataset of sentences from fina...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PoemSentimentClassification</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC-BY-4.0</td>\n",
-                            "      <td>Poem Sentiment is a sentiment dataset of poem ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ToxicConversationsClassification</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>Collection of comments from the Civil Comments...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>TweetTopicSingleClassification</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Social, News, Written]</td>\n",
-                            "      <td>Other</td>\n",
-                            "      <td>Topic classification dataset on Twitter with 6...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>EstonianValenceClassification</th>\n",
-                            "      <td>{est}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>Dataset containing annotated Estonian news dat...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>FilipinoShopeeReviewsClassification</th>\n",
-                            "      <td>{fil}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>MPL-2.0</td>\n",
-                            "      <td>The Shopee reviews tl 15 dataset is constructe...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>GujaratiNewsClassification</th>\n",
-                            "      <td>{guj}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>A Gujarati dataset for 3-class classification ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SentimentAnalysisHindi</th>\n",
-                            "      <td>{hin}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>Hindi Sentiment Analysis Dataset</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>IndonesianIdClickbaitClassification</th>\n",
-                            "      <td>{ind}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>The CLICK-ID dataset is a collection of Indone...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ItaCaseholdClassification</th>\n",
-                            "      <td>{ita}</td>\n",
-                            "      <td>[Legal, Government, Written]</td>\n",
-                            "      <td>Apache 2.0</td>\n",
-                            "      <td>An Italian Dataset consisting of 1101 pairs of...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>KorSarcasmClassification</th>\n",
-                            "      <td>{kor}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>\\n        The Korean Sarcasm Dataset was creat...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>KurdishSentimentClassification</th>\n",
-                            "      <td>{kur}</td>\n",
-                            "      <td>[Web, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>Kurdish Sentiment Dataset</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MacedonianTweetSentimentClassification</th>\n",
-                            "      <td>{mkd}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 3.0</td>\n",
-                            "      <td>An Macedonian dataset for tweet sentiment clas...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>AfriSentiClassification</th>\n",
-                            "      <td>{yor, kin, pcm, twi, ary, por, tso, arq, hau, ...</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>Creative Commons Attribution 4.0 International...</td>\n",
-                            "      <td>AfriSenti is the largest sentiment analysis da...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>AmazonCounterfactualClassification</th>\n",
-                            "      <td>{deu, eng, jpn}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>A collection of Amazon customer reviews annota...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>CataloniaTweetClassification</th>\n",
-                            "      <td>{spa, cat}</td>\n",
-                            "      <td>[Social, Government, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>This dataset contains two corpora in Spanish a...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>CyrillicTurkicLangClassification</th>\n",
-                            "      <td>{kir, bak, sah, chv, tat, rus, kaz, krc, tyv}</td>\n",
-                            "      <td>[Web, Written]</td>\n",
-                            "      <td>CC BY-NC 4.0 DEED</td>\n",
-                            "      <td>Cyrillic dataset of 8 Turkic languages spoken ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>IndicLangClassification</th>\n",
-                            "      <td>{san, kas, mni, sat, mal, brx, pan, asm, tam, ...</td>\n",
-                            "      <td>[Web, Non-fiction, Written]</td>\n",
-                            "      <td>CC0</td>\n",
-                            "      <td>A language identification test set for native-...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MasakhaNEWSClassification</th>\n",
-                            "      <td>{yor, pcm, lin, run, fra, hau, ibo, amh, som, ...</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>cc-by-nc-4.0</td>\n",
-                            "      <td>MasakhaNEWS is the largest publicly available ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MassiveIntentClassification</th>\n",
-                            "      <td>{afr, ara, ita, pol, hun, rus, cym, dan, jav, ...</td>\n",
-                            "      <td>[Spoken]</td>\n",
-                            "      <td>Apache 2.0</td>\n",
-                            "      <td>MASSIVE: A 1M-Example Multilingual Natural Lan...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MultiHateClassification</th>\n",
-                            "      <td>{ara, deu, nld, por, cmn, ita, pol, fra, hin, ...</td>\n",
-                            "      <td>[Constructed, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Hate speech detection dataset with binary\\n   ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NordicLangClassification</th>\n",
-                            "      <td>{isl, swe, fao, nob, dan, nno}</td>\n",
-                            "      <td>[Encyclopaedic]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>A dataset for Nordic language identification.</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NusaParagraphEmotionClassification</th>\n",
-                            "      <td>{bew, mad, bbc, sun, min, bug, jav, mak, rej, ...</td>\n",
-                            "      <td>[Non-fiction, Fiction, Written]</td>\n",
-                            "      <td>Apache 2.0</td>\n",
-                            "      <td>NusaParagraphEmotionClassification is a multi-...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NusaX-senti</th>\n",
-                            "      <td>{bjn, bbc, mad, nij, ace, ind, min, bug, jav, ...</td>\n",
-                            "      <td>[Reviews, Web, Social, Constructed, Written]</td>\n",
-                            "      <td>CC-BY-SA 4.0</td>\n",
-                            "      <td>NusaX is a high-quality multilingual parallel ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ScalaClassification</th>\n",
-                            "      <td>{swe, nob, dan, nno}</td>\n",
-                            "      <td>[Fiction, News, Non-fiction, Blog, Spoken, Web...</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>ScaLa a linguistic acceptability dataset for t...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SwissJudgementClassification</th>\n",
-                            "      <td>{ita, deu, fra}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>CC-BY-4.0</td>\n",
-                            "      <td>Multilingual, diachronic dataset of Swiss Fede...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>NepaliNewsClassification</th>\n",
-                            "      <td>{nep}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>A Nepali dataset for 7500 news articles</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>OdiaNewsClassification</th>\n",
-                            "      <td>{ory}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>A Odia dataset for 3-class classification of O...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PunjabiNewsClassification</th>\n",
-                            "      <td>{pan}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>A Punjabi dataset for 2-class classification o...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PolEmo2.0-OUT</th>\n",
-                            "      <td>{pol}</td>\n",
-                            "      <td>[Written, Social]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>A collection of Polish online reviews from fou...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PAC</th>\n",
-                            "      <td>{pol}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>cc-by-nc-sa-4.0</td>\n",
-                            "      <td>Polish Paraphrase Corpus</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SinhalaNewsClassification</th>\n",
-                            "      <td>{sin}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>mit</td>\n",
-                            "      <td>This file contains news texts (sentences) belo...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>CSFDSKMovieReviewSentimentClassification</th>\n",
-                            "      <td>{slk}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>The dataset contains 30k user reviews from csf...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SiswatiNewsClassification</th>\n",
-                            "      <td>{ssw}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>Siswati News Classification Dataset</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SlovakMovieReviewSentimentClassification</th>\n",
-                            "      <td>{svk}</td>\n",
-                            "      <td>[Reviews, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>User reviews of movies on the CSFD movie datab...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SwahiliNewsClassification</th>\n",
-                            "      <td>{swa}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>Dataset for Swahili News Classification, categ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>DalajClassification</th>\n",
-                            "      <td>{swe}</td>\n",
-                            "      <td>[Non-fiction, Written]</td>\n",
-                            "      <td>CC-BY-4.0</td>\n",
-                            "      <td>A Swedish dataset for linguistic acceptability...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>TswanaNewsClassification</th>\n",
-                            "      <td>{tsn}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>Tswana News Classification Dataset</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>IsiZuluNewsClassification</th>\n",
-                            "      <td>{zul}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>isiZulu News Classification Dataset</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"17\" valign=\"top\">Clustering</th>\n",
-                            "      <th>WikiCitiesClustering</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>Clustering of Wikipedia articles of cities by ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MasakhaNEWSClusteringS2S</th>\n",
-                            "      <td>{yor, pcm, lin, run, fra, hau, ibo, amh, som, ...</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Clustering of news article headlines from Masa...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RomaniBibleClustering</th>\n",
-                            "      <td>{rom}</td>\n",
-                            "      <td>[Religious, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>Clustering verses from the Bible in Kalderash ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ArXivHierarchicalClusteringP2P</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Academic, Written]</td>\n",
-                            "      <td>CC0</td>\n",
-                            "      <td>Clustering of titles+abstract from arxiv. Clus...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ArXivHierarchicalClusteringS2S</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Academic, Written]</td>\n",
-                            "      <td>CC0</td>\n",
-                            "      <td>Clustering of titles from arxiv. Clustering of...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>BigPatentClustering.v2</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Clustering of documents from the Big Patent da...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>BiorxivClusteringP2P.v2</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Academic, Written]</td>\n",
-                            "      <td>https://www.biorxiv.org/content/about-biorxiv</td>\n",
-                            "      <td>Clustering of titles+abstract from biorxiv acr...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MedrxivClusteringP2P.v2</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Academic, Medical, Written]</td>\n",
-                            "      <td>https://www.medrxiv.org/content/about-medrxiv</td>\n",
-                            "      <td>Clustering of titles+abstract from medrxiv acr...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>StackExchangeClustering.v2</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Web, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>Clustering of titles from 121 stackexchanges. ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>AlloProfClusteringS2S.v2</th>\n",
-                            "      <td>{fra}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>mit</td>\n",
-                            "      <td>Clustering of document titles from Allo Prof d...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>HALClusteringS2S.v2</th>\n",
-                            "      <td>{fra}</td>\n",
-                            "      <td>[Academic, Written]</td>\n",
-                            "      <td>Apache-2.0</td>\n",
-                            "      <td>Clustering of titles from HAL (https://hugging...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SIB200ClusteringS2S</th>\n",
-                            "      <td>{kin, ita, zul, sin, kbp, khk, ast, ell, shn, ...</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>SIB-200 is the largest publicly available topi...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WikiClusteringP2P.v2</th>\n",
-                            "      <td>{sqi, wln, mlt, ilo, lav, min, cat, ces, dan, ...</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>Clustering of wikipedia articles inspired by B...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SNLHierarchicalClusteringP2P</th>\n",
-                            "      <td>{nob}</td>\n",
-                            "      <td>[Encyclopaedic, Non-fiction, Written]</td>\n",
-                            "      <td>CC-BY-NC</td>\n",
-                            "      <td>Webscrabed articles from the Norwegian lexicon...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PlscClusteringP2P.v2</th>\n",
-                            "      <td>{pol}</td>\n",
-                            "      <td>[Academic, Written]</td>\n",
-                            "      <td>cc0-1.0</td>\n",
-                            "      <td>Clustering of Polish article titles+abstracts ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SwednClusteringP2P</th>\n",
-                            "      <td>{swe}</td>\n",
-                            "      <td>[News, Non-fiction, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>The SWE-DN corpus is based on 1,963,576 news a...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>CLSClusteringP2P.v2</th>\n",
-                            "      <td>{cmn}</td>\n",
-                            "      <td>[Academic, Written]</td>\n",
-                            "      <td>Apache-2.0</td>\n",
-                            "      <td>Clustering of titles + abstract from CLS datas...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"17\" valign=\"top\">Retrieval</th>\n",
-                            "      <th>StackOverflowQA</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Programming, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>The dataset is a collection of natural languag...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>TwitterHjerneRetrieval</th>\n",
-                            "      <td>{dan}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>Danish question asked on Twitter with the Hash...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>AILAStatutes</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>This dataset is structured for the task of ide...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ArguAna</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Medical, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>NFCorpus: A Full-Text Learning to Rank Dataset...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>HagridRetrieval</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>apache-2.0</td>\n",
-                            "      <td>HAGRID (Human-in-the-loop Attributable Generat...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LegalBenchCorporateLobbying</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Legal, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>The dataset includes bill titles and bill summ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LEMBPasskeyRetrieval</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Fiction, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>passkey subset of dwzhu/LongEmbed dataset.</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SCIDOCS</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Academic, Written, Non-fiction]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>SciDocs, a new evaluation benchmark consisting...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SpartQA</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>Measuring the ability to retrieve the groundtr...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>TempReasonL1</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>CC BY-SA 3.0</td>\n",
-                            "      <td>Measuring the ability to retrieve the groundtr...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>TRECCOVID</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>TRECCOVID is an ad-hoc search challenge based ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WinoGrande</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>CC BY</td>\n",
-                            "      <td>Measuring the ability to retrieve the groundtr...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>BelebeleRetrieval</th>\n",
-                            "      <td>{kin, ita, zul, sin, khk, ell, shn, slk, mal, ...</td>\n",
-                            "      <td>[Web, News, Written]</td>\n",
-                            "      <td>CC-BY-SA-4.0</td>\n",
-                            "      <td>Belebele is a multiple-choice machine reading ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MLQARetrieval</th>\n",
-                            "      <td>{ara, deu, vie, hin, zho, eng, spa}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>MLQA (MultiLingual Question Answering) is a be...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>StatcanDialogueDatasetRetrieval</th>\n",
-                            "      <td>{fra, eng}</td>\n",
-                            "      <td>[Government, Web, Written]</td>\n",
-                            "      <td>https://huggingface.co/datasets/McGill-NLP/sta...</td>\n",
-                            "      <td>A Dataset for Retrieving Data Tables through C...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WikipediaRetrievalMultilingual</th>\n",
-                            "      <td>{srp, deu, nor, nld, por, swe, fas, ita, hin, ...</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>The dataset is derived from Cohere's wikipedia...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>CovidRetrieval</th>\n",
-                            "      <td>{cmn}</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>COVID-19 news articles</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"3\" valign=\"top\">InstructionRetrieval</th>\n",
-                            "      <th>Core17InstructionRetrieval</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>Measuring retrieval instruction following abil...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>News21InstructionRetrieval</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>Measuring retrieval instruction following abil...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>Robust04InstructionRetrieval</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>MIT</td>\n",
-                            "      <td>Measuring retrieval instruction following abil...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">MultilabelClassification</th>\n",
-                            "      <th>KorHateSpeechMLClassification</th>\n",
-                            "      <td>{kor}</td>\n",
-                            "      <td>[Social, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>\\n        The Korean Multi-label Hate Speech D...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MalteseNewsClassification</th>\n",
-                            "      <td>{mlt}</td>\n",
-                            "      <td>[Constructed, Written]</td>\n",
-                            "      <td>cc-by-nc-sa-4.0</td>\n",
-                            "      <td>A multi-label topic classification dataset for...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>MultiEURLEXMultilabelClassification</th>\n",
-                            "      <td>{est, ita, pol, hun, dan, lit, ell, slk, spa, ...</td>\n",
-                            "      <td>[Legal, Government, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>EU laws in 23 EU languages containing gold lab...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>BrazilianToxicTweetsClassification</th>\n",
-                            "      <td>{por}</td>\n",
-                            "      <td>[Constructed, Written]</td>\n",
-                            "      <td>CC BY-SA 4.0</td>\n",
-                            "      <td>\\n        ToLD-Br is the biggest dataset for t...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>CEDRClassification</th>\n",
-                            "      <td>{rus}</td>\n",
-                            "      <td>[Web, Social, Blog, Written]</td>\n",
-                            "      <td>apache-2.0</td>\n",
-                            "      <td>Classification of sentences by emotions, label...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"11\" valign=\"top\">PairClassification</th>\n",
-                            "      <th>CTKFactsNLI</th>\n",
-                            "      <td>{ces}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>CC-BY-SA-3.0</td>\n",
-                            "      <td>Czech Natural Language Inference dataset of ar...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SprintDuplicateQuestions</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Programming, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>Duplicate questions from the Sprint community.</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>TwitterURLCorpus</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Paraphrase-Pairs of Tweets.</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>ArmenianParaphrasePC</th>\n",
-                            "      <td>{hye}</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>Apache-2.0</td>\n",
-                            "      <td>asparius/Armenian-Paraphrase-PC</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>indonli</th>\n",
-                            "      <td>{ind}</td>\n",
-                            "      <td>[Encyclopaedic, Web, News, Written]</td>\n",
-                            "      <td>CC-BY-SA 4.0</td>\n",
-                            "      <td>IndoNLI is the first human-elicited Natural La...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>OpusparcusPC</th>\n",
-                            "      <td>{deu, swe, fra, rus, eng, fin}</td>\n",
-                            "      <td>[Spoken, Spoken]</td>\n",
-                            "      <td>cc-by-nc-4.0</td>\n",
-                            "      <td>Opusparcus is a paraphrase corpus for six Euro...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PawsXPairClassification</th>\n",
-                            "      <td>{deu, kor, cmn, fra, eng, jpn, spa}</td>\n",
-                            "      <td>[Web, Encyclopaedic, Written]</td>\n",
-                            "      <td>Custom (commercial)</td>\n",
-                            "      <td>{PAWS-X: A Cross-lingual Adversarial Dataset f...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RTE3</th>\n",
-                            "      <td>{fra, deu, eng, ita}</td>\n",
-                            "      <td>[News, Web, Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Recognising Textual Entailment Challenge (RTE-...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>XNLI</th>\n",
-                            "      <td>{tha, ara, deu, vie, fra, hin, rus, zho, swa, ...</td>\n",
-                            "      <td>[Non-fiction, Fiction, Government, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td></td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>PpcPC</th>\n",
-                            "      <td>{pol}</td>\n",
-                            "      <td>[Fiction, Non-fiction, Web, Written, Spoken, S...</td>\n",
-                            "      <td>GPL-3.0</td>\n",
-                            "      <td>Polish Paraphrase Corpus</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>TERRa</th>\n",
-                            "      <td>{rus}</td>\n",
-                            "      <td>[News, Web, Written]</td>\n",
-                            "      <td>mit</td>\n",
-                            "      <td>Textual Entailment Recognition for Russian. Th...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"6\" valign=\"top\">Reranking</th>\n",
-                            "      <th>WebLINXCandidatesReranking</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Academic, Web, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>WebLINX is a large-scale benchmark of 100K int...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>AlloprofReranking</th>\n",
-                            "      <td>{fra}</td>\n",
-                            "      <td>[Web, Academic, Written]</td>\n",
-                            "      <td>CC BY-NC-SA 4.0</td>\n",
-                            "      <td>This dataset was provided by AlloProf, an orga...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>VoyageMMarcoReranking</th>\n",
-                            "      <td>{jpn}</td>\n",
-                            "      <td>[Academic, Non-fiction, Written]</td>\n",
-                            "      <td>CC BY 4.0</td>\n",
-                            "      <td>a hard-negative augmented version of the Japan...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WikipediaRerankingMultilingual</th>\n",
-                            "      <td>{srp, deu, nor, nld, por, swe, fas, ita, hin, ...</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-3.0</td>\n",
-                            "      <td>The dataset is derived from Cohere's wikipedia...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RuBQReranking</th>\n",
-                            "      <td>{rus}</td>\n",
-                            "      <td>[Encyclopaedic, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>Paragraph reranking based on RuBQ 2.0. Give pa...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>T2Reranking</th>\n",
-                            "      <td>{cmn}</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>T2Ranking: A large-scale Chinese Benchmark for...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"16\" valign=\"top\">STS</th>\n",
-                            "      <th>GermanSTSBenchmark</th>\n",
-                            "      <td>{deu}</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Semantic Textual Similarity Benchmark (STSbenc...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SICK-R</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Semantic Textual Similarity SICK-R dataset as ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STS12</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Encyclopaedic, News, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>SemEval-2012 Task 6.</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STS13</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Web, News, Non-fiction, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>SemEval STS 2013 dataset.</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STS14</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Blog, Web, Spoken]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>SemEval STS 2014 dataset. Currently only the E...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STS15</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>[Blog, News, Web, Written, Spoken]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>SemEval STS 2015 dataset</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STSBenchmark</th>\n",
-                            "      <td>{eng}</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>Semantic Textual Similarity Benchmark (STSbenc...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>FaroeseSTS</th>\n",
-                            "      <td>{fao}</td>\n",
-                            "      <td>[News, Web, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Semantic Text Similarity (STS) corpus for Faro...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>FinParaSTS</th>\n",
-                            "      <td>{fin}</td>\n",
-                            "      <td>[News, Subtitles, Written]</td>\n",
-                            "      <td>cc-by-sa-4.0</td>\n",
-                            "      <td>Finnish paraphrase-based semantic similarity c...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>JSICK</th>\n",
-                            "      <td>{jpn}</td>\n",
-                            "      <td>[Web, Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>JSICK is the Japanese NLI and STS dataset by m...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>IndicCrosslingualSTS</th>\n",
-                            "      <td>{ory, tam, mal, guj, hin, tel, mar, kan, eng, ...</td>\n",
-                            "      <td>[News, Non-fiction, Web, Spoken, Government, W...</td>\n",
-                            "      <td>CC0</td>\n",
-                            "      <td>This is a Semantic Textual Similarity testset ...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SemRel24STS</th>\n",
-                            "      <td>{kin, afr, ary, arq, hau, hin, ind, tel, amh, ...</td>\n",
-                            "      <td>[Spoken, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>SemRel2024 is a collection of Semantic Textual...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STS17</th>\n",
-                            "      <td>{ara, deu, kor, nld, ita, fra, eng, tur, spa}</td>\n",
-                            "      <td>[News, Web, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>Semeval-2017 task 1: Semantic textual similari...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STS22.v2</th>\n",
-                            "      <td>{ara, deu, cmn, ita, pol, fra, rus, eng, tur, ...</td>\n",
-                            "      <td>[News, Written]</td>\n",
-                            "      <td>Not specified</td>\n",
-                            "      <td>SemEval 2022 Task 8: Multilingual News Article...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STSES</th>\n",
-                            "      <td>{spa}</td>\n",
-                            "      <td>[Written]</td>\n",
-                            "      <td>cc-by-4.0</td>\n",
-                            "      <td>Spanish test sets from SemEval-2014 (Agirre et...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STSB</th>\n",
-                            "      <td>{cmn}</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>None</td>\n",
-                            "      <td>A Chinese dataset for textual relatedness</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                                                                                             Languages  \\\n",
-                            "Type                     Name                                                                                            \n",
-                            "BitextMining             BornholmBitextMining                                                                    {dan}   \n",
-                            "                         BibleNLPBitextMining                        {aoj, ncl, imo, acu, eko, urb, bvd, cab, bsp, ...   \n",
-                            "                         BUCC.v2                                                             {deu, cmn, fra, rus, eng}   \n",
-                            "                         DiaBlaBitextMining                                                                 {fra, eng}   \n",
-                            "                         FloresBitextMining                          {kin, ita, zul, sin, kbp, khk, ast, ell, shn, ...   \n",
-                            "                         IN22GenBitextMining                         {san, kas, mni, sat, mal, brx, pan, asm, tam, ...   \n",
-                            "                         IndicGenBenchFloresBitextMining             {san, mup, mni, sat, bgc, hne, bho, nep, mal, ...   \n",
-                            "                         NollySentiBitextMining                                              {yor, pcm, hau, ibo, eng}   \n",
-                            "                         NorwegianCourtsBitextMining                                                        {nob, nno}   \n",
-                            "                         NTREXBitextMining                           {kin, ita, zul, sin, swa, ell, slk, uig, mal, ...   \n",
-                            "                         NusaTranslationBitextMining                 {bew, bbc, mad, sun, ind, min, bhp, jav, mak, ...   \n",
-                            "                         NusaXBitextMining                           {bjn, bbc, mad, nij, ace, ind, min, bug, jav, ...   \n",
-                            "                         Tatoeba                                     {ita, max, ast, ell, slk, nov, uig, mal, swh, ...   \n",
-                            "Classification           BulgarianStoreReviewSentimentClassfication                                              {bul}   \n",
-                            "                         CzechProductReviewSentimentClassification                                               {ces}   \n",
-                            "                         GreekLegalCodeClassification                                                            {ell}   \n",
-                            "                         DBpediaClassification                                                                   {eng}   \n",
-                            "                         FinancialPhrasebankClassification                                                       {eng}   \n",
-                            "                         PoemSentimentClassification                                                             {eng}   \n",
-                            "                         ToxicConversationsClassification                                                        {eng}   \n",
-                            "                         TweetTopicSingleClassification                                                          {eng}   \n",
-                            "                         EstonianValenceClassification                                                           {est}   \n",
-                            "                         FilipinoShopeeReviewsClassification                                                     {fil}   \n",
-                            "                         GujaratiNewsClassification                                                              {guj}   \n",
-                            "                         SentimentAnalysisHindi                                                                  {hin}   \n",
-                            "                         IndonesianIdClickbaitClassification                                                     {ind}   \n",
-                            "                         ItaCaseholdClassification                                                               {ita}   \n",
-                            "                         KorSarcasmClassification                                                                {kor}   \n",
-                            "                         KurdishSentimentClassification                                                          {kur}   \n",
-                            "                         MacedonianTweetSentimentClassification                                                  {mkd}   \n",
-                            "                         AfriSentiClassification                     {yor, kin, pcm, twi, ary, por, tso, arq, hau, ...   \n",
-                            "                         AmazonCounterfactualClassification                                            {deu, eng, jpn}   \n",
-                            "                         CataloniaTweetClassification                                                       {spa, cat}   \n",
-                            "                         CyrillicTurkicLangClassification                {kir, bak, sah, chv, tat, rus, kaz, krc, tyv}   \n",
-                            "                         IndicLangClassification                     {san, kas, mni, sat, mal, brx, pan, asm, tam, ...   \n",
-                            "                         MasakhaNEWSClassification                   {yor, pcm, lin, run, fra, hau, ibo, amh, som, ...   \n",
-                            "                         MassiveIntentClassification                 {afr, ara, ita, pol, hun, rus, cym, dan, jav, ...   \n",
-                            "                         MultiHateClassification                     {ara, deu, nld, por, cmn, ita, pol, fra, hin, ...   \n",
-                            "                         NordicLangClassification                                       {isl, swe, fao, nob, dan, nno}   \n",
-                            "                         NusaParagraphEmotionClassification          {bew, mad, bbc, sun, min, bug, jav, mak, rej, ...   \n",
-                            "                         NusaX-senti                                 {bjn, bbc, mad, nij, ace, ind, min, bug, jav, ...   \n",
-                            "                         ScalaClassification                                                      {swe, nob, dan, nno}   \n",
-                            "                         SwissJudgementClassification                                                  {ita, deu, fra}   \n",
-                            "                         NepaliNewsClassification                                                                {nep}   \n",
-                            "                         OdiaNewsClassification                                                                  {ory}   \n",
-                            "                         PunjabiNewsClassification                                                               {pan}   \n",
-                            "                         PolEmo2.0-OUT                                                                           {pol}   \n",
-                            "                         PAC                                                                                     {pol}   \n",
-                            "                         SinhalaNewsClassification                                                               {sin}   \n",
-                            "                         CSFDSKMovieReviewSentimentClassification                                                {slk}   \n",
-                            "                         SiswatiNewsClassification                                                               {ssw}   \n",
-                            "                         SlovakMovieReviewSentimentClassification                                                {svk}   \n",
-                            "                         SwahiliNewsClassification                                                               {swa}   \n",
-                            "                         DalajClassification                                                                     {swe}   \n",
-                            "                         TswanaNewsClassification                                                                {tsn}   \n",
-                            "                         IsiZuluNewsClassification                                                               {zul}   \n",
-                            "Clustering               WikiCitiesClustering                                                                    {eng}   \n",
-                            "                         MasakhaNEWSClusteringS2S                    {yor, pcm, lin, run, fra, hau, ibo, amh, som, ...   \n",
-                            "                         RomaniBibleClustering                                                                   {rom}   \n",
-                            "                         ArXivHierarchicalClusteringP2P                                                          {eng}   \n",
-                            "                         ArXivHierarchicalClusteringS2S                                                          {eng}   \n",
-                            "                         BigPatentClustering.v2                                                                  {eng}   \n",
-                            "                         BiorxivClusteringP2P.v2                                                                 {eng}   \n",
-                            "                         MedrxivClusteringP2P.v2                                                                 {eng}   \n",
-                            "                         StackExchangeClustering.v2                                                              {eng}   \n",
-                            "                         AlloProfClusteringS2S.v2                                                                {fra}   \n",
-                            "                         HALClusteringS2S.v2                                                                     {fra}   \n",
-                            "                         SIB200ClusteringS2S                         {kin, ita, zul, sin, kbp, khk, ast, ell, shn, ...   \n",
-                            "                         WikiClusteringP2P.v2                        {sqi, wln, mlt, ilo, lav, min, cat, ces, dan, ...   \n",
-                            "                         SNLHierarchicalClusteringP2P                                                            {nob}   \n",
-                            "                         PlscClusteringP2P.v2                                                                    {pol}   \n",
-                            "                         SwednClusteringP2P                                                                      {swe}   \n",
-                            "                         CLSClusteringP2P.v2                                                                     {cmn}   \n",
-                            "Retrieval                StackOverflowQA                                                                         {eng}   \n",
-                            "                         TwitterHjerneRetrieval                                                                  {dan}   \n",
-                            "                         AILAStatutes                                                                            {eng}   \n",
-                            "                         ArguAna                                                                                 {eng}   \n",
-                            "                         HagridRetrieval                                                                         {eng}   \n",
-                            "                         LegalBenchCorporateLobbying                                                             {eng}   \n",
-                            "                         LEMBPasskeyRetrieval                                                                    {eng}   \n",
-                            "                         SCIDOCS                                                                                 {eng}   \n",
-                            "                         SpartQA                                                                                 {eng}   \n",
-                            "                         TempReasonL1                                                                            {eng}   \n",
-                            "                         TRECCOVID                                                                               {eng}   \n",
-                            "                         WinoGrande                                                                              {eng}   \n",
-                            "                         BelebeleRetrieval                           {kin, ita, zul, sin, khk, ell, shn, slk, mal, ...   \n",
-                            "                         MLQARetrieval                                             {ara, deu, vie, hin, zho, eng, spa}   \n",
-                            "                         StatcanDialogueDatasetRetrieval                                                    {fra, eng}   \n",
-                            "                         WikipediaRetrievalMultilingual              {srp, deu, nor, nld, por, swe, fas, ita, hin, ...   \n",
-                            "                         CovidRetrieval                                                                          {cmn}   \n",
-                            "InstructionRetrieval     Core17InstructionRetrieval                                                              {eng}   \n",
-                            "                         News21InstructionRetrieval                                                              {eng}   \n",
-                            "                         Robust04InstructionRetrieval                                                            {eng}   \n",
-                            "MultilabelClassification KorHateSpeechMLClassification                                                           {kor}   \n",
-                            "                         MalteseNewsClassification                                                               {mlt}   \n",
-                            "                         MultiEURLEXMultilabelClassification         {est, ita, pol, hun, dan, lit, ell, slk, spa, ...   \n",
-                            "                         BrazilianToxicTweetsClassification                                                      {por}   \n",
-                            "                         CEDRClassification                                                                      {rus}   \n",
-                            "PairClassification       CTKFactsNLI                                                                             {ces}   \n",
-                            "                         SprintDuplicateQuestions                                                                {eng}   \n",
-                            "                         TwitterURLCorpus                                                                        {eng}   \n",
-                            "                         ArmenianParaphrasePC                                                                    {hye}   \n",
-                            "                         indonli                                                                                 {ind}   \n",
-                            "                         OpusparcusPC                                                   {deu, swe, fra, rus, eng, fin}   \n",
-                            "                         PawsXPairClassification                                   {deu, kor, cmn, fra, eng, jpn, spa}   \n",
-                            "                         RTE3                                                                     {fra, deu, eng, ita}   \n",
-                            "                         XNLI                                        {tha, ara, deu, vie, fra, hin, rus, zho, swa, ...   \n",
-                            "                         PpcPC                                                                                   {pol}   \n",
-                            "                         TERRa                                                                                   {rus}   \n",
-                            "Reranking                WebLINXCandidatesReranking                                                              {eng}   \n",
-                            "                         AlloprofReranking                                                                       {fra}   \n",
-                            "                         VoyageMMarcoReranking                                                                   {jpn}   \n",
-                            "                         WikipediaRerankingMultilingual              {srp, deu, nor, nld, por, swe, fas, ita, hin, ...   \n",
-                            "                         RuBQReranking                                                                           {rus}   \n",
-                            "                         T2Reranking                                                                             {cmn}   \n",
-                            "STS                      GermanSTSBenchmark                                                                      {deu}   \n",
-                            "                         SICK-R                                                                                  {eng}   \n",
-                            "                         STS12                                                                                   {eng}   \n",
-                            "                         STS13                                                                                   {eng}   \n",
-                            "                         STS14                                                                                   {eng}   \n",
-                            "                         STS15                                                                                   {eng}   \n",
-                            "                         STSBenchmark                                                                            {eng}   \n",
-                            "                         FaroeseSTS                                                                              {fao}   \n",
-                            "                         FinParaSTS                                                                              {fin}   \n",
-                            "                         JSICK                                                                                   {jpn}   \n",
-                            "                         IndicCrosslingualSTS                        {ory, tam, mal, guj, hin, tel, mar, kan, eng, ...   \n",
-                            "                         SemRel24STS                                 {kin, afr, ary, arq, hau, hin, ind, tel, amh, ...   \n",
-                            "                         STS17                                           {ara, deu, kor, nld, ita, fra, eng, tur, spa}   \n",
-                            "                         STS22.v2                                    {ara, deu, cmn, ita, pol, fra, rus, eng, tur, ...   \n",
-                            "                         STSES                                                                                   {spa}   \n",
-                            "                         STSB                                                                                    {cmn}   \n",
-                            "\n",
-                            "                                                                                                               Domains  \\\n",
-                            "Type                     Name                                                                                            \n",
-                            "BitextMining             BornholmBitextMining                                          [Web, Social, Fiction, Written]   \n",
-                            "                         BibleNLPBitextMining                                                     [Religious, Written]   \n",
-                            "                         BUCC.v2                                                                             [Written]   \n",
-                            "                         DiaBlaBitextMining                                                          [Social, Written]   \n",
-                            "                         FloresBitextMining                                      [Non-fiction, Encyclopaedic, Written]   \n",
-                            "                         IN22GenBitextMining                         [Web, Legal, Government, News, Religious, Non-...   \n",
-                            "                         IndicGenBenchFloresBitextMining                                          [Web, News, Written]   \n",
-                            "                         NollySentiBitextMining                                             [Social, Reviews, Written]   \n",
-                            "                         NorwegianCourtsBitextMining                                                  [Legal, Written]   \n",
-                            "                         NTREXBitextMining                                                             [News, Written]   \n",
-                            "                         NusaTranslationBitextMining                                                 [Social, Written]   \n",
-                            "                         NusaXBitextMining                                                          [Reviews, Written]   \n",
-                            "                         Tatoeba                                                                             [Written]   \n",
-                            "Classification           BulgarianStoreReviewSentimentClassfication                                 [Reviews, Written]   \n",
-                            "                         CzechProductReviewSentimentClassification                                  [Reviews, Written]   \n",
-                            "                         GreekLegalCodeClassification                                                 [Legal, Written]   \n",
-                            "                         DBpediaClassification                                                [Encyclopaedic, Written]   \n",
-                            "                         FinancialPhrasebankClassification                                             [News, Written]   \n",
-                            "                         PoemSentimentClassification                                                [Reviews, Written]   \n",
-                            "                         ToxicConversationsClassification                                            [Social, Written]   \n",
-                            "                         TweetTopicSingleClassification                                        [Social, News, Written]   \n",
-                            "                         EstonianValenceClassification                                                 [News, Written]   \n",
-                            "                         FilipinoShopeeReviewsClassification                                         [Social, Written]   \n",
-                            "                         GujaratiNewsClassification                                                    [News, Written]   \n",
-                            "                         SentimentAnalysisHindi                                                     [Reviews, Written]   \n",
-                            "                         IndonesianIdClickbaitClassification                                           [News, Written]   \n",
-                            "                         ItaCaseholdClassification                                        [Legal, Government, Written]   \n",
-                            "                         KorSarcasmClassification                                                    [Social, Written]   \n",
-                            "                         KurdishSentimentClassification                                                 [Web, Written]   \n",
-                            "                         MacedonianTweetSentimentClassification                                      [Social, Written]   \n",
-                            "                         AfriSentiClassification                                                     [Social, Written]   \n",
-                            "                         AmazonCounterfactualClassification                                         [Reviews, Written]   \n",
-                            "                         CataloniaTweetClassification                                    [Social, Government, Written]   \n",
-                            "                         CyrillicTurkicLangClassification                                               [Web, Written]   \n",
-                            "                         IndicLangClassification                                           [Web, Non-fiction, Written]   \n",
-                            "                         MasakhaNEWSClassification                                                     [News, Written]   \n",
-                            "                         MassiveIntentClassification                                                          [Spoken]   \n",
-                            "                         MultiHateClassification                                                [Constructed, Written]   \n",
-                            "                         NordicLangClassification                                                      [Encyclopaedic]   \n",
-                            "                         NusaParagraphEmotionClassification                            [Non-fiction, Fiction, Written]   \n",
-                            "                         NusaX-senti                                      [Reviews, Web, Social, Constructed, Written]   \n",
-                            "                         ScalaClassification                         [Fiction, News, Non-fiction, Blog, Spoken, Web...   \n",
-                            "                         SwissJudgementClassification                                                 [Legal, Written]   \n",
-                            "                         NepaliNewsClassification                                                      [News, Written]   \n",
-                            "                         OdiaNewsClassification                                                        [News, Written]   \n",
-                            "                         PunjabiNewsClassification                                                     [News, Written]   \n",
-                            "                         PolEmo2.0-OUT                                                               [Written, Social]   \n",
-                            "                         PAC                                                                          [Legal, Written]   \n",
-                            "                         SinhalaNewsClassification                                                     [News, Written]   \n",
-                            "                         CSFDSKMovieReviewSentimentClassification                                   [Reviews, Written]   \n",
-                            "                         SiswatiNewsClassification                                                     [News, Written]   \n",
-                            "                         SlovakMovieReviewSentimentClassification                                   [Reviews, Written]   \n",
-                            "                         SwahiliNewsClassification                                                     [News, Written]   \n",
-                            "                         DalajClassification                                                    [Non-fiction, Written]   \n",
-                            "                         TswanaNewsClassification                                                      [News, Written]   \n",
-                            "                         IsiZuluNewsClassification                                                     [News, Written]   \n",
-                            "Clustering               WikiCitiesClustering                                                 [Encyclopaedic, Written]   \n",
-                            "                         MasakhaNEWSClusteringS2S                                                                 None   \n",
-                            "                         RomaniBibleClustering                                                    [Religious, Written]   \n",
-                            "                         ArXivHierarchicalClusteringP2P                                            [Academic, Written]   \n",
-                            "                         ArXivHierarchicalClusteringS2S                                            [Academic, Written]   \n",
-                            "                         BigPatentClustering.v2                                                       [Legal, Written]   \n",
-                            "                         BiorxivClusteringP2P.v2                                                   [Academic, Written]   \n",
-                            "                         MedrxivClusteringP2P.v2                                          [Academic, Medical, Written]   \n",
-                            "                         StackExchangeClustering.v2                                                     [Web, Written]   \n",
-                            "                         AlloProfClusteringS2S.v2                                             [Encyclopaedic, Written]   \n",
-                            "                         HALClusteringS2S.v2                                                       [Academic, Written]   \n",
-                            "                         SIB200ClusteringS2S                                                           [News, Written]   \n",
-                            "                         WikiClusteringP2P.v2                                                 [Encyclopaedic, Written]   \n",
-                            "                         SNLHierarchicalClusteringP2P                            [Encyclopaedic, Non-fiction, Written]   \n",
-                            "                         PlscClusteringP2P.v2                                                      [Academic, Written]   \n",
-                            "                         SwednClusteringP2P                                               [News, Non-fiction, Written]   \n",
-                            "                         CLSClusteringP2P.v2                                                       [Academic, Written]   \n",
-                            "Retrieval                StackOverflowQA                                                        [Programming, Written]   \n",
-                            "                         TwitterHjerneRetrieval                                                      [Social, Written]   \n",
-                            "                         AILAStatutes                                                                 [Legal, Written]   \n",
-                            "                         ArguAna                                                                    [Medical, Written]   \n",
-                            "                         HagridRetrieval                                                      [Encyclopaedic, Written]   \n",
-                            "                         LegalBenchCorporateLobbying                                                  [Legal, Written]   \n",
-                            "                         LEMBPasskeyRetrieval                                                       [Fiction, Written]   \n",
-                            "                         SCIDOCS                                                      [Academic, Written, Non-fiction]   \n",
-                            "                         SpartQA                                                              [Encyclopaedic, Written]   \n",
-                            "                         TempReasonL1                                                         [Encyclopaedic, Written]   \n",
-                            "                         TRECCOVID                                                                                None   \n",
-                            "                         WinoGrande                                                           [Encyclopaedic, Written]   \n",
-                            "                         BelebeleRetrieval                                                        [Web, News, Written]   \n",
-                            "                         MLQARetrieval                                                        [Encyclopaedic, Written]   \n",
-                            "                         StatcanDialogueDatasetRetrieval                                    [Government, Web, Written]   \n",
-                            "                         WikipediaRetrievalMultilingual                                       [Encyclopaedic, Written]   \n",
-                            "                         CovidRetrieval                                                                           None   \n",
-                            "InstructionRetrieval     Core17InstructionRetrieval                                                    [News, Written]   \n",
-                            "                         News21InstructionRetrieval                                                    [News, Written]   \n",
-                            "                         Robust04InstructionRetrieval                                                  [News, Written]   \n",
-                            "MultilabelClassification KorHateSpeechMLClassification                                               [Social, Written]   \n",
-                            "                         MalteseNewsClassification                                              [Constructed, Written]   \n",
-                            "                         MultiEURLEXMultilabelClassification                              [Legal, Government, Written]   \n",
-                            "                         BrazilianToxicTweetsClassification                                     [Constructed, Written]   \n",
-                            "                         CEDRClassification                                               [Web, Social, Blog, Written]   \n",
-                            "PairClassification       CTKFactsNLI                                                                   [News, Written]   \n",
-                            "                         SprintDuplicateQuestions                                               [Programming, Written]   \n",
-                            "                         TwitterURLCorpus                                                                         None   \n",
-                            "                         ArmenianParaphrasePC                                                          [News, Written]   \n",
-                            "                         indonli                                                   [Encyclopaedic, Web, News, Written]   \n",
-                            "                         OpusparcusPC                                                                 [Spoken, Spoken]   \n",
-                            "                         PawsXPairClassification                                         [Web, Encyclopaedic, Written]   \n",
-                            "                         RTE3                                                      [News, Web, Encyclopaedic, Written]   \n",
-                            "                         XNLI                                              [Non-fiction, Fiction, Government, Written]   \n",
-                            "                         PpcPC                                       [Fiction, Non-fiction, Web, Written, Spoken, S...   \n",
-                            "                         TERRa                                                                    [News, Web, Written]   \n",
-                            "Reranking                WebLINXCandidatesReranking                                           [Academic, Web, Written]   \n",
-                            "                         AlloprofReranking                                                    [Web, Academic, Written]   \n",
-                            "                         VoyageMMarcoReranking                                        [Academic, Non-fiction, Written]   \n",
-                            "                         WikipediaRerankingMultilingual                                       [Encyclopaedic, Written]   \n",
-                            "                         RuBQReranking                                                        [Encyclopaedic, Written]   \n",
-                            "                         T2Reranking                                                                              None   \n",
-                            "STS                      GermanSTSBenchmark                                                                       None   \n",
-                            "                         SICK-R                                                                                   None   \n",
-                            "                         STS12                                                          [Encyclopaedic, News, Written]   \n",
-                            "                         STS13                                                       [Web, News, Non-fiction, Written]   \n",
-                            "                         STS14                                                                     [Blog, Web, Spoken]   \n",
-                            "                         STS15                                                      [Blog, News, Web, Written, Spoken]   \n",
-                            "                         STSBenchmark                                                                             None   \n",
-                            "                         FaroeseSTS                                                               [News, Web, Written]   \n",
-                            "                         FinParaSTS                                                         [News, Subtitles, Written]   \n",
-                            "                         JSICK                                                                          [Web, Written]   \n",
-                            "                         IndicCrosslingualSTS                        [News, Non-fiction, Web, Spoken, Government, W...   \n",
-                            "                         SemRel24STS                                                                 [Spoken, Written]   \n",
-                            "                         STS17                                                                    [News, Web, Written]   \n",
-                            "                         STS22.v2                                                                      [News, Written]   \n",
-                            "                         STSES                                                                               [Written]   \n",
-                            "                         STSB                                                                                     None   \n",
-                            "\n",
-                            "                                                                                                               License  \\\n",
-                            "Type                     Name                                                                                            \n",
-                            "BitextMining             BornholmBitextMining                                                                CC-BY-4.0   \n",
-                            "                         BibleNLPBitextMining                                                             CC-BY-SA-4.0   \n",
-                            "                         BUCC.v2                                                                               Unknown   \n",
-                            "                         DiaBlaBitextMining                                                            CC BY-NC-SA 4.0   \n",
-                            "                         FloresBitextMining                                                               CC BY-SA 4.0   \n",
-                            "                         IN22GenBitextMining                                                                 CC-BY-4.0   \n",
-                            "                         IndicGenBenchFloresBitextMining                                                  CC-BY-SA-4.0   \n",
-                            "                         NollySentiBitextMining                                                           CC BY-SA 4.0   \n",
-                            "                         NorwegianCourtsBitextMining                                                         CC BY 4.0   \n",
-                            "                         NTREXBitextMining                                                                CC-BY-SA-4.0   \n",
-                            "                         NusaTranslationBitextMining                                                      CC BY-SA 4.0   \n",
-                            "                         NusaXBitextMining                                                                CC BY-SA 4.0   \n",
-                            "                         Tatoeba                                                                             CC BY 2.0   \n",
-                            "Classification           BulgarianStoreReviewSentimentClassfication                                          cc-by-4.0   \n",
-                            "                         CzechProductReviewSentimentClassification                                     CC BY-NC-SA 4.0   \n",
-                            "                         GreekLegalCodeClassification                                                        cc-by-4.0   \n",
-                            "                         DBpediaClassification                                                            cc-by-sa-3.0   \n",
-                            "                         FinancialPhrasebankClassification                                             cc-by-nc-sa-3.0   \n",
-                            "                         PoemSentimentClassification                                                         CC-BY-4.0   \n",
-                            "                         ToxicConversationsClassification                                                    CC BY 4.0   \n",
-                            "                         TweetTopicSingleClassification                                                          Other   \n",
-                            "                         EstonianValenceClassification                                                       CC BY 4.0   \n",
-                            "                         FilipinoShopeeReviewsClassification                                                   MPL-2.0   \n",
-                            "                         GujaratiNewsClassification                                                                MIT   \n",
-                            "                         SentimentAnalysisHindi                                                        CC BY-NC-SA 4.0   \n",
-                            "                         IndonesianIdClickbaitClassification                                                 cc-by-4.0   \n",
-                            "                         ItaCaseholdClassification                                                          Apache 2.0   \n",
-                            "                         KorSarcasmClassification                                                                  MIT   \n",
-                            "                         KurdishSentimentClassification                                                      CC BY 4.0   \n",
-                            "                         MacedonianTweetSentimentClassification                                        CC BY-NC-SA 3.0   \n",
-                            "                         AfriSentiClassification                     Creative Commons Attribution 4.0 International...   \n",
-                            "                         AmazonCounterfactualClassification                                                  CC BY 4.0   \n",
-                            "                         CataloniaTweetClassification                                                     cc-by-sa-4.0   \n",
-                            "                         CyrillicTurkicLangClassification                                            CC BY-NC 4.0 DEED   \n",
-                            "                         IndicLangClassification                                                                   CC0   \n",
-                            "                         MasakhaNEWSClassification                                                        cc-by-nc-4.0   \n",
-                            "                         MassiveIntentClassification                                                        Apache 2.0   \n",
-                            "                         MultiHateClassification                                                             cc-by-4.0   \n",
-                            "                         NordicLangClassification                                                         cc-by-sa-3.0   \n",
-                            "                         NusaParagraphEmotionClassification                                                 Apache 2.0   \n",
-                            "                         NusaX-senti                                                                      CC-BY-SA 4.0   \n",
-                            "                         ScalaClassification                                                              CC BY-SA 4.0   \n",
-                            "                         SwissJudgementClassification                                                        CC-BY-4.0   \n",
-                            "                         NepaliNewsClassification                                                         CC BY-SA 4.0   \n",
-                            "                         OdiaNewsClassification                                                                    MIT   \n",
-                            "                         PunjabiNewsClassification                                                                 MIT   \n",
-                            "                         PolEmo2.0-OUT                                                                    cc-by-sa-4.0   \n",
-                            "                         PAC                                                                           cc-by-nc-sa-4.0   \n",
-                            "                         SinhalaNewsClassification                                                                 mit   \n",
-                            "                         CSFDSKMovieReviewSentimentClassification                                         CC-BY-SA-4.0   \n",
-                            "                         SiswatiNewsClassification                                                        CC-BY-SA-4.0   \n",
-                            "                         SlovakMovieReviewSentimentClassification                                      CC BY-NC-SA 4.0   \n",
-                            "                         SwahiliNewsClassification                                                     CC BY-NC-SA 4.0   \n",
-                            "                         DalajClassification                                                                 CC-BY-4.0   \n",
-                            "                         TswanaNewsClassification                                                         CC-BY-SA-4.0   \n",
-                            "                         IsiZuluNewsClassification                                                        CC-BY-SA-4.0   \n",
-                            "Clustering               WikiCitiesClustering                                                             cc-by-sa-4.0   \n",
-                            "                         MasakhaNEWSClusteringS2S                                                                 None   \n",
-                            "                         RomaniBibleClustering                                                                     MIT   \n",
-                            "                         ArXivHierarchicalClusteringP2P                                                            CC0   \n",
-                            "                         ArXivHierarchicalClusteringS2S                                                            CC0   \n",
-                            "                         BigPatentClustering.v2                                                              cc-by-4.0   \n",
-                            "                         BiorxivClusteringP2P.v2                         https://www.biorxiv.org/content/about-biorxiv   \n",
-                            "                         MedrxivClusteringP2P.v2                         https://www.medrxiv.org/content/about-medrxiv   \n",
-                            "                         StackExchangeClustering.v2                                                      Not specified   \n",
-                            "                         AlloProfClusteringS2S.v2                                                                  mit   \n",
-                            "                         HALClusteringS2S.v2                                                                Apache-2.0   \n",
-                            "                         SIB200ClusteringS2S                                                              cc-by-sa-4.0   \n",
-                            "                         WikiClusteringP2P.v2                                                             cc-by-sa-3.0   \n",
-                            "                         SNLHierarchicalClusteringP2P                                                         CC-BY-NC   \n",
-                            "                         PlscClusteringP2P.v2                                                                  cc0-1.0   \n",
-                            "                         SwednClusteringP2P                                                                  cc-by-4.0   \n",
-                            "                         CLSClusteringP2P.v2                                                                Apache-2.0   \n",
-                            "Retrieval                StackOverflowQA                                                                           MIT   \n",
-                            "                         TwitterHjerneRetrieval                                                              CC BY 4.0   \n",
-                            "                         AILAStatutes                                                                        CC BY 4.0   \n",
-                            "                         ArguAna                                                                          cc-by-sa-4.0   \n",
-                            "                         HagridRetrieval                                                                    apache-2.0   \n",
-                            "                         LegalBenchCorporateLobbying                                                         CC BY 4.0   \n",
-                            "                         LEMBPasskeyRetrieval                                                            Not specified   \n",
-                            "                         SCIDOCS                                                                          cc-by-sa-4.0   \n",
-                            "                         SpartQA                                                                                   MIT   \n",
-                            "                         TempReasonL1                                                                     CC BY-SA 3.0   \n",
-                            "                         TRECCOVID                                                                                None   \n",
-                            "                         WinoGrande                                                                              CC BY   \n",
-                            "                         BelebeleRetrieval                                                                CC-BY-SA-4.0   \n",
-                            "                         MLQARetrieval                                                                    cc-by-sa-3.0   \n",
-                            "                         StatcanDialogueDatasetRetrieval             https://huggingface.co/datasets/McGill-NLP/sta...   \n",
-                            "                         WikipediaRetrievalMultilingual                                                   cc-by-sa-3.0   \n",
-                            "                         CovidRetrieval                                                                           None   \n",
-                            "InstructionRetrieval     Core17InstructionRetrieval                                                                MIT   \n",
-                            "                         News21InstructionRetrieval                                                                MIT   \n",
-                            "                         Robust04InstructionRetrieval                                                              MIT   \n",
-                            "MultilabelClassification KorHateSpeechMLClassification                                                    cc-by-sa-4.0   \n",
-                            "                         MalteseNewsClassification                                                     cc-by-nc-sa-4.0   \n",
-                            "                         MultiEURLEXMultilabelClassification                                              CC BY-SA 4.0   \n",
-                            "                         BrazilianToxicTweetsClassification                                               CC BY-SA 4.0   \n",
-                            "                         CEDRClassification                                                                 apache-2.0   \n",
-                            "PairClassification       CTKFactsNLI                                                                      CC-BY-SA-3.0   \n",
-                            "                         SprintDuplicateQuestions                                                        Not specified   \n",
-                            "                         TwitterURLCorpus                                                                         None   \n",
-                            "                         ArmenianParaphrasePC                                                               Apache-2.0   \n",
-                            "                         indonli                                                                          CC-BY-SA 4.0   \n",
-                            "                         OpusparcusPC                                                                     cc-by-nc-4.0   \n",
-                            "                         PawsXPairClassification                                                   Custom (commercial)   \n",
-                            "                         RTE3                                                                                cc-by-4.0   \n",
-                            "                         XNLI                                                                            Not specified   \n",
-                            "                         PpcPC                                                                                 GPL-3.0   \n",
-                            "                         TERRa                                                                                     mit   \n",
-                            "Reranking                WebLINXCandidatesReranking                                                    CC BY-NC-SA 4.0   \n",
-                            "                         AlloprofReranking                                                             CC BY-NC-SA 4.0   \n",
-                            "                         VoyageMMarcoReranking                                                               CC BY 4.0   \n",
-                            "                         WikipediaRerankingMultilingual                                                   cc-by-sa-3.0   \n",
-                            "                         RuBQReranking                                                                    cc-by-sa-4.0   \n",
-                            "                         T2Reranking                                                                              None   \n",
-                            "STS                      GermanSTSBenchmark                                                                       None   \n",
-                            "                         SICK-R                                                                                   None   \n",
-                            "                         STS12                                                                           Not specified   \n",
-                            "                         STS13                                                                           Not specified   \n",
-                            "                         STS14                                                                           Not specified   \n",
-                            "                         STS15                                                                           Not specified   \n",
-                            "                         STSBenchmark                                                                             None   \n",
-                            "                         FaroeseSTS                                                                          cc-by-4.0   \n",
-                            "                         FinParaSTS                                                                       cc-by-sa-4.0   \n",
-                            "                         JSICK                                                                               cc-by-4.0   \n",
-                            "                         IndicCrosslingualSTS                                                                      CC0   \n",
-                            "                         SemRel24STS                                                                     Not specified   \n",
-                            "                         STS17                                                                           Not specified   \n",
-                            "                         STS22.v2                                                                        Not specified   \n",
-                            "                         STSES                                                                               cc-by-4.0   \n",
-                            "                         STSB                                                                                     None   \n",
-                            "\n",
-                            "                                                                                                           Description  \n",
-                            "Type                     Name                                                                                           \n",
-                            "BitextMining             BornholmBitextMining                        Danish Bornholmsk Parallel Corpus. Bornholmsk ...  \n",
-                            "                         BibleNLPBitextMining                        Partial Bible translations in 829 languages, a...  \n",
-                            "                         BUCC.v2                                                            BUCC bitext mining dataset  \n",
-                            "                         DiaBlaBitextMining                          English-French Parallel Corpus. DiaBLa is an E...  \n",
-                            "                         FloresBitextMining                          FLORES is a benchmark dataset for machine tran...  \n",
-                            "                         IN22GenBitextMining                         IN22-Gen is a n-way parallel general-purpose m...  \n",
-                            "                         IndicGenBenchFloresBitextMining             Flores-IN dataset is an extension of Flores da...  \n",
-                            "                         NollySentiBitextMining                      NollySenti is Nollywood movie reviews for five...  \n",
-                            "                         NorwegianCourtsBitextMining                 Nynorsk and Bokmål parallel corpus from Norweg...  \n",
-                            "                         NTREXBitextMining                           NTREX is a News Test References dataset for Ma...  \n",
-                            "                         NusaTranslationBitextMining                 NusaTranslation is a parallel dataset for mach...  \n",
-                            "                         NusaXBitextMining                           NusaX is a parallel dataset for machine transl...  \n",
-                            "                         Tatoeba                                     1,000 English-aligned sentence pairs for each ...  \n",
-                            "Classification           BulgarianStoreReviewSentimentClassfication  Bulgarian online store review dataset for sent...  \n",
-                            "                         CzechProductReviewSentimentClassification   User reviews of products on Czech e-shop Mall....  \n",
-                            "                         GreekLegalCodeClassification                Greek Legal Code Dataset for Classification. (...  \n",
-                            "                         DBpediaClassification                       DBpedia14 is a dataset of English texts from W...  \n",
-                            "                         FinancialPhrasebankClassification           Polar sentiment dataset of sentences from fina...  \n",
-                            "                         PoemSentimentClassification                 Poem Sentiment is a sentiment dataset of poem ...  \n",
-                            "                         ToxicConversationsClassification            Collection of comments from the Civil Comments...  \n",
-                            "                         TweetTopicSingleClassification              Topic classification dataset on Twitter with 6...  \n",
-                            "                         EstonianValenceClassification               Dataset containing annotated Estonian news dat...  \n",
-                            "                         FilipinoShopeeReviewsClassification         The Shopee reviews tl 15 dataset is constructe...  \n",
-                            "                         GujaratiNewsClassification                  A Gujarati dataset for 3-class classification ...  \n",
-                            "                         SentimentAnalysisHindi                                       Hindi Sentiment Analysis Dataset  \n",
-                            "                         IndonesianIdClickbaitClassification         The CLICK-ID dataset is a collection of Indone...  \n",
-                            "                         ItaCaseholdClassification                   An Italian Dataset consisting of 1101 pairs of...  \n",
-                            "                         KorSarcasmClassification                    \\n        The Korean Sarcasm Dataset was creat...  \n",
-                            "                         KurdishSentimentClassification                                      Kurdish Sentiment Dataset  \n",
-                            "                         MacedonianTweetSentimentClassification      An Macedonian dataset for tweet sentiment clas...  \n",
-                            "                         AfriSentiClassification                     AfriSenti is the largest sentiment analysis da...  \n",
-                            "                         AmazonCounterfactualClassification          A collection of Amazon customer reviews annota...  \n",
-                            "                         CataloniaTweetClassification                This dataset contains two corpora in Spanish a...  \n",
-                            "                         CyrillicTurkicLangClassification            Cyrillic dataset of 8 Turkic languages spoken ...  \n",
-                            "                         IndicLangClassification                     A language identification test set for native-...  \n",
-                            "                         MasakhaNEWSClassification                   MasakhaNEWS is the largest publicly available ...  \n",
-                            "                         MassiveIntentClassification                 MASSIVE: A 1M-Example Multilingual Natural Lan...  \n",
-                            "                         MultiHateClassification                     Hate speech detection dataset with binary\\n   ...  \n",
-                            "                         NordicLangClassification                        A dataset for Nordic language identification.  \n",
-                            "                         NusaParagraphEmotionClassification          NusaParagraphEmotionClassification is a multi-...  \n",
-                            "                         NusaX-senti                                 NusaX is a high-quality multilingual parallel ...  \n",
-                            "                         ScalaClassification                         ScaLa a linguistic acceptability dataset for t...  \n",
-                            "                         SwissJudgementClassification                Multilingual, diachronic dataset of Swiss Fede...  \n",
-                            "                         NepaliNewsClassification                             A Nepali dataset for 7500 news articles   \n",
-                            "                         OdiaNewsClassification                      A Odia dataset for 3-class classification of O...  \n",
-                            "                         PunjabiNewsClassification                   A Punjabi dataset for 2-class classification o...  \n",
-                            "                         PolEmo2.0-OUT                               A collection of Polish online reviews from fou...  \n",
-                            "                         PAC                                                                  Polish Paraphrase Corpus  \n",
-                            "                         SinhalaNewsClassification                   This file contains news texts (sentences) belo...  \n",
-                            "                         CSFDSKMovieReviewSentimentClassification    The dataset contains 30k user reviews from csf...  \n",
-                            "                         SiswatiNewsClassification                                 Siswati News Classification Dataset  \n",
-                            "                         SlovakMovieReviewSentimentClassification    User reviews of movies on the CSFD movie datab...  \n",
-                            "                         SwahiliNewsClassification                   Dataset for Swahili News Classification, categ...  \n",
-                            "                         DalajClassification                         A Swedish dataset for linguistic acceptability...  \n",
-                            "                         TswanaNewsClassification                                   Tswana News Classification Dataset  \n",
-                            "                         IsiZuluNewsClassification                                 isiZulu News Classification Dataset  \n",
-                            "Clustering               WikiCitiesClustering                        Clustering of Wikipedia articles of cities by ...  \n",
-                            "                         MasakhaNEWSClusteringS2S                    Clustering of news article headlines from Masa...  \n",
-                            "                         RomaniBibleClustering                       Clustering verses from the Bible in Kalderash ...  \n",
-                            "                         ArXivHierarchicalClusteringP2P              Clustering of titles+abstract from arxiv. Clus...  \n",
-                            "                         ArXivHierarchicalClusteringS2S              Clustering of titles from arxiv. Clustering of...  \n",
-                            "                         BigPatentClustering.v2                      Clustering of documents from the Big Patent da...  \n",
-                            "                         BiorxivClusteringP2P.v2                     Clustering of titles+abstract from biorxiv acr...  \n",
-                            "                         MedrxivClusteringP2P.v2                     Clustering of titles+abstract from medrxiv acr...  \n",
-                            "                         StackExchangeClustering.v2                  Clustering of titles from 121 stackexchanges. ...  \n",
-                            "                         AlloProfClusteringS2S.v2                    Clustering of document titles from Allo Prof d...  \n",
-                            "                         HALClusteringS2S.v2                         Clustering of titles from HAL (https://hugging...  \n",
-                            "                         SIB200ClusteringS2S                         SIB-200 is the largest publicly available topi...  \n",
-                            "                         WikiClusteringP2P.v2                        Clustering of wikipedia articles inspired by B...  \n",
-                            "                         SNLHierarchicalClusteringP2P                Webscrabed articles from the Norwegian lexicon...  \n",
-                            "                         PlscClusteringP2P.v2                        Clustering of Polish article titles+abstracts ...  \n",
-                            "                         SwednClusteringP2P                          The SWE-DN corpus is based on 1,963,576 news a...  \n",
-                            "                         CLSClusteringP2P.v2                         Clustering of titles + abstract from CLS datas...  \n",
-                            "Retrieval                StackOverflowQA                             The dataset is a collection of natural languag...  \n",
-                            "                         TwitterHjerneRetrieval                      Danish question asked on Twitter with the Hash...  \n",
-                            "                         AILAStatutes                                This dataset is structured for the task of ide...  \n",
-                            "                         ArguAna                                     NFCorpus: A Full-Text Learning to Rank Dataset...  \n",
-                            "                         HagridRetrieval                             HAGRID (Human-in-the-loop Attributable Generat...  \n",
-                            "                         LegalBenchCorporateLobbying                 The dataset includes bill titles and bill summ...  \n",
-                            "                         LEMBPasskeyRetrieval                               passkey subset of dwzhu/LongEmbed dataset.  \n",
-                            "                         SCIDOCS                                     SciDocs, a new evaluation benchmark consisting...  \n",
-                            "                         SpartQA                                     Measuring the ability to retrieve the groundtr...  \n",
-                            "                         TempReasonL1                                Measuring the ability to retrieve the groundtr...  \n",
-                            "                         TRECCOVID                                   TRECCOVID is an ad-hoc search challenge based ...  \n",
-                            "                         WinoGrande                                  Measuring the ability to retrieve the groundtr...  \n",
-                            "                         BelebeleRetrieval                           Belebele is a multiple-choice machine reading ...  \n",
-                            "                         MLQARetrieval                               MLQA (MultiLingual Question Answering) is a be...  \n",
-                            "                         StatcanDialogueDatasetRetrieval             A Dataset for Retrieving Data Tables through C...  \n",
-                            "                         WikipediaRetrievalMultilingual              The dataset is derived from Cohere's wikipedia...  \n",
-                            "                         CovidRetrieval                                                         COVID-19 news articles  \n",
-                            "InstructionRetrieval     Core17InstructionRetrieval                  Measuring retrieval instruction following abil...  \n",
-                            "                         News21InstructionRetrieval                  Measuring retrieval instruction following abil...  \n",
-                            "                         Robust04InstructionRetrieval                Measuring retrieval instruction following abil...  \n",
-                            "MultilabelClassification KorHateSpeechMLClassification               \\n        The Korean Multi-label Hate Speech D...  \n",
-                            "                         MalteseNewsClassification                   A multi-label topic classification dataset for...  \n",
-                            "                         MultiEURLEXMultilabelClassification         EU laws in 23 EU languages containing gold lab...  \n",
-                            "                         BrazilianToxicTweetsClassification          \\n        ToLD-Br is the biggest dataset for t...  \n",
-                            "                         CEDRClassification                          Classification of sentences by emotions, label...  \n",
-                            "PairClassification       CTKFactsNLI                                 Czech Natural Language Inference dataset of ar...  \n",
-                            "                         SprintDuplicateQuestions                       Duplicate questions from the Sprint community.  \n",
-                            "                         TwitterURLCorpus                                                  Paraphrase-Pairs of Tweets.  \n",
-                            "                         ArmenianParaphrasePC                                          asparius/Armenian-Paraphrase-PC  \n",
-                            "                         indonli                                     IndoNLI is the first human-elicited Natural La...  \n",
-                            "                         OpusparcusPC                                Opusparcus is a paraphrase corpus for six Euro...  \n",
-                            "                         PawsXPairClassification                     {PAWS-X: A Cross-lingual Adversarial Dataset f...  \n",
-                            "                         RTE3                                        Recognising Textual Entailment Challenge (RTE-...  \n",
-                            "                         XNLI                                                                                           \n",
-                            "                         PpcPC                                                                Polish Paraphrase Corpus  \n",
-                            "                         TERRa                                       Textual Entailment Recognition for Russian. Th...  \n",
-                            "Reranking                WebLINXCandidatesReranking                  WebLINX is a large-scale benchmark of 100K int...  \n",
-                            "                         AlloprofReranking                           This dataset was provided by AlloProf, an orga...  \n",
-                            "                         VoyageMMarcoReranking                       a hard-negative augmented version of the Japan...  \n",
-                            "                         WikipediaRerankingMultilingual              The dataset is derived from Cohere's wikipedia...  \n",
-                            "                         RuBQReranking                               Paragraph reranking based on RuBQ 2.0. Give pa...  \n",
-                            "                         T2Reranking                                 T2Ranking: A large-scale Chinese Benchmark for...  \n",
-                            "STS                      GermanSTSBenchmark                          Semantic Textual Similarity Benchmark (STSbenc...  \n",
-                            "                         SICK-R                                      Semantic Textual Similarity SICK-R dataset as ...  \n",
-                            "                         STS12                                                                    SemEval-2012 Task 6.  \n",
-                            "                         STS13                                                               SemEval STS 2013 dataset.  \n",
-                            "                         STS14                                       SemEval STS 2014 dataset. Currently only the E...  \n",
-                            "                         STS15                                                                SemEval STS 2015 dataset  \n",
-                            "                         STSBenchmark                                Semantic Textual Similarity Benchmark (STSbenc...  \n",
-                            "                         FaroeseSTS                                  Semantic Text Similarity (STS) corpus for Faro...  \n",
-                            "                         FinParaSTS                                  Finnish paraphrase-based semantic similarity c...  \n",
-                            "                         JSICK                                       JSICK is the Japanese NLI and STS dataset by m...  \n",
-                            "                         IndicCrosslingualSTS                        This is a Semantic Textual Similarity testset ...  \n",
-                            "                         SemRel24STS                                 SemRel2024 is a collection of Semantic Textual...  \n",
-                            "                         STS17                                       Semeval-2017 task 1: Semantic textual similari...  \n",
-                            "                         STS22.v2                                    SemEval 2022 Task 8: Multilingual News Article...  \n",
-                            "                         STSES                                       Spanish test sets from SemEval-2014 (Agirre et...  \n",
-                            "                         STSB                                                A Chinese dataset for textual relatedness  "
-                        ]
-                    },
-                    "execution_count": 24,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# create a dataframe with tasks\n",
-                "import pandas as pd\n",
-                "\n",
-                "data = []\n",
-                "\n",
-                "for t in tasks:\n",
-                "    data.append(\n",
-                "        {\n",
-                "            \"Name\": t.metadata.name,\n",
-                "            \"Type\": t.metadata.type,\n",
-                "            \"Languages\": set(t.metadata.languages),\n",
-                "            \"Domains\": t.metadata.domains,\n",
-                "            \"License\": t.metadata.license,\n",
-                "            \"Description\": t.metadata.description,\n",
-                "        }\n",
-                "    )\n",
-                "\n",
-                "tasks_df = pd.DataFrame(data)\n",
-                "# tasks_df\n",
-                "\n",
-                "# print all rows\n",
-                "pd.set_option(\"display.max_rows\", 140)\n",
-                "_tasks_df = tasks_df.set_index([\"Type\", \"Name\"], inplace=False)\n",
-                "_tasks_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 25,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "(131, 4)"
-                        ]
-                    },
-                    "execution_count": 25,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "_tasks_df.shape"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 26,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "tasks_df.to_csv(\"mult_tasks.csv\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Benchmark Performance"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 29,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# It is possible to start the notebok from here:\n",
-                "import pandas as pd\n",
-                "\n",
-                "import mteb\n",
-                "\n",
-                "_df = pd.read_csv(\"mult_tasks.csv\")\n",
-                "task_names = _df[\"Name\"].tolist()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 30,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/au561649/.virtualenvs/mteb/lib/python3.8/site-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
-                        "  warnings.warn(\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Getting revision for sentence-transformers/all-MiniLM-L12-v2\n",
-                        "Getting revision for sentence-transformers/all-mpnet-base-v2\n"
-                    ]
-                }
-            ],
-            "source": [
-                "def get_models():\n",
-                "    model_names = [\n",
-                "        \"sentence-transformers/all-MiniLM-L6-v2\",\n",
-                "        \"sentence-transformers/all-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\",\n",
-                "        \"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/all-mpnet-base-v2\",\n",
-                "        \"sentence-transformers/LaBSE\",\n",
-                "        \"intfloat/multilingual-e5-large-instruct\",\n",
-                "        \"intfloat/e5-mistral-7b-instruct\",\n",
-                "        \"GritLM/GritLM-7B\",\n",
-                "        \"GritLM/GritLM-8x7B\",\n",
-                "        \"intfloat/multilingual-e5-small\",\n",
-                "        \"intfloat/multilingual-e5-base\",\n",
-                "        \"intfloat/multilingual-e5-large\",\n",
-                "        # additional models\n",
-                "        # \"Alibaba-NLP/gte-multilingual-base\",\n",
-                "        # \"BAAI/bge-m3\",\n",
-                "    ]\n",
-                "    models: list[mteb.ModelMeta] = [mteb.get_model_meta(name) for name in model_names]\n",
-                "\n",
-                "    # get missing revisions - Assuming we are using the latest revision\n",
-                "    for model in models:\n",
-                "        if model.revision is None:\n",
-                "            print(f\"Getting revision for {model.name}\")\n",
-                "            encoder = model.load_model()\n",
-                "            model.revision = encoder.model_card_data.base_model_revision  # type: ignore\n",
-                "\n",
-                "    return models\n",
-                "\n",
-                "\n",
-                "models = get_models()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 31,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# load task results for the specified models from mteb/results repository\n",
-                "task_names += [\"MIRACLRetrievalHardNegatives\"]\n",
-                "mteb_results = mteb.load_results(models=models, tasks=task_names, download_latest=False)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 22,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# import mteb.task_selection as task_selection\n",
-                "# test = task_selection.results_to_dataframe(mteb_results, drop_na=False)\n",
-                "\n",
-                "\n",
-                "# # columsn with NA\n",
-                "# test[test.columns[test.isna().any()]].loc[[\"BAAI/bge-m3\", \"Alibaba-NLP/gte-multilingual-base\"]]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 33,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from collections import defaultdict\n",
-                "\n",
-                "import mteb.task_aggregation as task_aggregation\n",
-                "\n",
-                "mean = task_aggregation.mean(mteb_results)\n",
-                "weighted_mean = task_aggregation.task_category_weighted_mean(mteb_results)\n",
-                "borda = task_aggregation.borda_count(mteb_results)\n",
-                "\n",
-                "category_mean = defaultdict(list)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 34,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import pandas as pd\n",
-                "\n",
-                "data = []\n",
-                "for model_name, revisions in borda.items():\n",
-                "    for rev, avg_score in revisions.items():\n",
-                "        total_eval_time = sum(\n",
-                "            res.evaluation_time for res in mteb_results[model_name][rev]\n",
-                "        )\n",
-                "\n",
-                "        data.append(\n",
-                "            {\n",
-                "                \"model\": model_name,\n",
-                "                \"revision\": rev,\n",
-                "                **mean[model_name][rev],\n",
-                "                **weighted_mean[model_name][rev],\n",
-                "                **avg_score,\n",
-                "                \"Total Evaluation time (hours)\": total_eval_time / 3600,\n",
-                "            }\n",
-                "        )\n",
-                "\n",
-                "df = pd.DataFrame(data)\n",
-                "df = df.sort_values(\"borda_count\", ascending=False)\n",
-                "# round\n",
-                "df = df.round(3)\n",
-                "df\n",
-                "\n",
-                "df.to_csv(\"mult_results.csv\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 37,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\begin{tabular}{llrrrrrrrrrr}\n",
-                        "\\toprule\n",
-                        " & Rank (Borda Count) & mean & mean (weighted by task type) & mean (BitextMining) & mean (PairClassification) & mean (Classification) & mean (STS) & mean (Retrieval) & mean (MultilabelClassification) & mean (Clustering) & mean (Reranking) \\\\\n",
-                        "model &  &  &  &  &  &  &  &  &  &  &  \\\\\n",
-                        "\\midrule\n",
-                        "multilingual-e5-large-instruct & 1 (1244) & 63.4 & 55.3 & 80.1 & 81.2 & 65.0 & 76.7 & 58.0 & 22.9 & 51.5 & 63.0 \\\\\n",
-                        "GritLM-7B & 2 (1119) & 60.9 & 53.6 & 70.5 & 80.2 & 61.9 & 73.2 & 59.1 & 21.2 & 50.4 & 62.8 \\\\\n",
-                        "e5-mistral-7b-instruct & 3 (1100) & 60.2 & 53.1 & 70.6 & 81.4 & 60.3 & 73.9 & 55.4 & 22.2 & 51.4 & 63.4 \\\\\n",
-                        "multilingual-e5-large & 4 (980) & 58.7 & 51.5 & 71.7 & 79.3 & 59.9 & 73.4 & 55.0 & 21.3 & 43.1 & 62.6 \\\\\n",
-                        "multilingual-e5-base & 5 (811) & 57.1 & 50.0 & 69.4 & 77.6 & 58.2 & 71.2 & 53.6 & 20.2 & 42.8 & 59.9 \\\\\n",
-                        "paraphrase-multilingual-mpnet-base-v2 & 6 (698) & 52.0 & 45.2 & 52.1 & 81.6 & 55.1 & 69.5 & 39.3 & 16.4 & 41.2 & 53.2 \\\\\n",
-                        "multilingual-e5-small & 7 (654) & 55.6 & 48.8 & 67.5 & 76.8 & 56.5 & 69.9 & 50.2 & 19.1 & 41.8 & 60.2 \\\\\n",
-                        "LaBSE & 8 (589) & 52.1 & 45.8 & 76.3 & 76.1 & 54.6 & 65.2 & 32.9 & 20.1 & 39.4 & 50.4 \\\\\n",
-                        "paraphrase-multilingual-MiniLM-L12-v2 & 9 (475) & 48.8 & 42.5 & 44.5 & 79.4 & 51.7 & 66.4 & 36.2 & 14.9 & 39.6 & 51.0 \\\\\n",
-                        "all-mpnet-base-v2 & 10 (398) & 42.4 & 36.2 & 21.2 & 71.0 & 47.0 & 57.1 & 32.8 & 16.3 & 41.1 & 42.1 \\\\\n",
-                        "all-MiniLM-L12-v2 & 11 (355) & 42.1 & 36.2 & 22.9 & 71.9 & 46.8 & 56.6 & 32.4 & 14.6 & 36.8 & 44.3 \\\\\n",
-                        "all-MiniLM-L6-v2 & 12 (290) & 41.5 & 35.2 & 20.1 & 71.3 & 46.3 & 55.6 & 33.1 & 15.1 & 38.3 & 40.0 \\\\\n",
-                        "\\bottomrule\n",
-                        "\\end{tabular}\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "latex_df = df.drop(columns=[\"revision\"])\n",
-                "latex_df[\"model\"] = [name.split(\"/\")[1] for name in latex_df[\"model\"]]\n",
-                "latex_df = latex_df.set_index(\"model\")\n",
-                "\n",
-                "avg_cols = [\n",
-                "    \"mean\",\n",
-                "    \"mean (BitextMining)\",\n",
-                "    \"mean (PairClassification)\",\n",
-                "    \"mean (Classification)\",\n",
-                "    \"mean (STS)\",\n",
-                "    \"mean (Retrieval)\",\n",
-                "    \"mean (MultilabelClassification)\",\n",
-                "    \"mean (Clustering)\",\n",
-                "    \"mean (Reranking)\",\n",
-                "    \"mean (InstructionRetrieval)\",\n",
-                "    \"mean (weighted by task type)\",\n",
-                "]\n",
-                "\n",
-                "borda_col_name = \"borda_count\"\n",
-                "\n",
-                "# multiply by 100 to get percentage values and round to 2 decimal places\n",
-                "latex_df[avg_cols] = latex_df[avg_cols] * 100\n",
-                "\n",
-                "latex_df[\"Rank (Borda Count)\"] = [\n",
-                "    f\"{rank} ({borda:.0f})\"\n",
-                "    for rank, borda in zip(range(1, len(latex_df) + 1), latex_df[borda_col_name])\n",
-                "]\n",
-                "latex_df = latex_df.drop(columns=[borda_col_name])\n",
-                "\n",
-                "# column order and rename\n",
-                "cols = [\n",
-                "    \"Rank (Borda Count)\",\n",
-                "    \"mean\",\n",
-                "    \"mean (weighted by task type)\",\n",
-                "    \"mean (BitextMining)\",\n",
-                "    \"mean (PairClassification)\",\n",
-                "    \"mean (Classification)\",\n",
-                "    \"mean (STS)\",\n",
-                "    \"mean (Retrieval)\",\n",
-                "    \"mean (MultilabelClassification)\",\n",
-                "    \"mean (Clustering)\",\n",
-                "    \"mean (Reranking)\",\n",
-                "]\n",
-                "\n",
-                "latex_df = latex_df[cols]\n",
-                "\n",
-                "table_latex = latex_df.to_latex(index=True, float_format=\"%.1f\")\n",
-                "\n",
-                "\n",
-                "print(table_latex)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 36,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>Rank (Borda Count)</th>\n",
-                            "      <th>mean</th>\n",
-                            "      <th>mean (weighted by task type)</th>\n",
-                            "      <th>mean (BitextMining)</th>\n",
-                            "      <th>mean (PairClassification)</th>\n",
-                            "      <th>mean (Classification)</th>\n",
-                            "      <th>mean (STS)</th>\n",
-                            "      <th>mean (Retrieval)</th>\n",
-                            "      <th>mean (MultilabelClassification)</th>\n",
-                            "      <th>mean (Clustering)</th>\n",
-                            "      <th>mean (Reranking)</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>multilingual-e5-large-instruct</th>\n",
-                            "      <td>1 (1244)</td>\n",
-                            "      <td>63.4</td>\n",
-                            "      <td>55.3</td>\n",
-                            "      <td>80.1</td>\n",
-                            "      <td>81.2</td>\n",
-                            "      <td>65.0</td>\n",
-                            "      <td>76.7</td>\n",
-                            "      <td>58.0</td>\n",
-                            "      <td>22.9</td>\n",
-                            "      <td>51.5</td>\n",
-                            "      <td>63.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>GritLM-7B</th>\n",
-                            "      <td>2 (1119)</td>\n",
-                            "      <td>60.9</td>\n",
-                            "      <td>53.6</td>\n",
-                            "      <td>70.5</td>\n",
-                            "      <td>80.2</td>\n",
-                            "      <td>61.9</td>\n",
-                            "      <td>73.2</td>\n",
-                            "      <td>59.1</td>\n",
-                            "      <td>21.2</td>\n",
-                            "      <td>50.4</td>\n",
-                            "      <td>62.8</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>e5-mistral-7b-instruct</th>\n",
-                            "      <td>3 (1100)</td>\n",
-                            "      <td>60.2</td>\n",
-                            "      <td>53.1</td>\n",
-                            "      <td>70.6</td>\n",
-                            "      <td>81.4</td>\n",
-                            "      <td>60.3</td>\n",
-                            "      <td>73.9</td>\n",
-                            "      <td>55.4</td>\n",
-                            "      <td>22.2</td>\n",
-                            "      <td>51.4</td>\n",
-                            "      <td>63.4</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>multilingual-e5-large</th>\n",
-                            "      <td>4 (980)</td>\n",
-                            "      <td>58.7</td>\n",
-                            "      <td>51.5</td>\n",
-                            "      <td>71.7</td>\n",
-                            "      <td>79.3</td>\n",
-                            "      <td>59.9</td>\n",
-                            "      <td>73.4</td>\n",
-                            "      <td>55.0</td>\n",
-                            "      <td>21.3</td>\n",
-                            "      <td>43.1</td>\n",
-                            "      <td>62.6</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>multilingual-e5-base</th>\n",
-                            "      <td>5 (811)</td>\n",
-                            "      <td>57.1</td>\n",
-                            "      <td>50.0</td>\n",
-                            "      <td>69.4</td>\n",
-                            "      <td>77.6</td>\n",
-                            "      <td>58.2</td>\n",
-                            "      <td>71.2</td>\n",
-                            "      <td>53.6</td>\n",
-                            "      <td>20.2</td>\n",
-                            "      <td>42.8</td>\n",
-                            "      <td>59.9</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>paraphrase-multilingual-mpnet-base-v2</th>\n",
-                            "      <td>6 (698)</td>\n",
-                            "      <td>52.0</td>\n",
-                            "      <td>45.2</td>\n",
-                            "      <td>52.1</td>\n",
-                            "      <td>81.6</td>\n",
-                            "      <td>55.1</td>\n",
-                            "      <td>69.5</td>\n",
-                            "      <td>39.3</td>\n",
-                            "      <td>16.4</td>\n",
-                            "      <td>41.2</td>\n",
-                            "      <td>53.2</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>multilingual-e5-small</th>\n",
-                            "      <td>7 (654)</td>\n",
-                            "      <td>55.6</td>\n",
-                            "      <td>48.8</td>\n",
-                            "      <td>67.5</td>\n",
-                            "      <td>76.8</td>\n",
-                            "      <td>56.5</td>\n",
-                            "      <td>69.9</td>\n",
-                            "      <td>50.2</td>\n",
-                            "      <td>19.1</td>\n",
-                            "      <td>41.8</td>\n",
-                            "      <td>60.2</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LaBSE</th>\n",
-                            "      <td>8 (589)</td>\n",
-                            "      <td>52.1</td>\n",
-                            "      <td>45.8</td>\n",
-                            "      <td>76.3</td>\n",
-                            "      <td>76.1</td>\n",
-                            "      <td>54.6</td>\n",
-                            "      <td>65.2</td>\n",
-                            "      <td>32.9</td>\n",
-                            "      <td>20.1</td>\n",
-                            "      <td>39.4</td>\n",
-                            "      <td>50.4</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>paraphrase-multilingual-MiniLM-L12-v2</th>\n",
-                            "      <td>9 (475)</td>\n",
-                            "      <td>48.8</td>\n",
-                            "      <td>42.5</td>\n",
-                            "      <td>44.5</td>\n",
-                            "      <td>79.4</td>\n",
-                            "      <td>51.7</td>\n",
-                            "      <td>66.4</td>\n",
-                            "      <td>36.2</td>\n",
-                            "      <td>14.9</td>\n",
-                            "      <td>39.6</td>\n",
-                            "      <td>51.0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>all-mpnet-base-v2</th>\n",
-                            "      <td>10 (398)</td>\n",
-                            "      <td>42.4</td>\n",
-                            "      <td>36.2</td>\n",
-                            "      <td>21.2</td>\n",
-                            "      <td>71.0</td>\n",
-                            "      <td>47.0</td>\n",
-                            "      <td>57.1</td>\n",
-                            "      <td>32.8</td>\n",
-                            "      <td>16.3</td>\n",
-                            "      <td>41.1</td>\n",
-                            "      <td>42.1</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>all-MiniLM-L12-v2</th>\n",
-                            "      <td>11 (355)</td>\n",
-                            "      <td>42.1</td>\n",
-                            "      <td>36.2</td>\n",
-                            "      <td>22.9</td>\n",
-                            "      <td>71.9</td>\n",
-                            "      <td>46.8</td>\n",
-                            "      <td>56.6</td>\n",
-                            "      <td>32.4</td>\n",
-                            "      <td>14.6</td>\n",
-                            "      <td>36.8</td>\n",
-                            "      <td>44.3</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>all-MiniLM-L6-v2</th>\n",
-                            "      <td>12 (290)</td>\n",
-                            "      <td>41.5</td>\n",
-                            "      <td>35.2</td>\n",
-                            "      <td>20.1</td>\n",
-                            "      <td>71.3</td>\n",
-                            "      <td>46.3</td>\n",
-                            "      <td>55.6</td>\n",
-                            "      <td>33.1</td>\n",
-                            "      <td>15.1</td>\n",
-                            "      <td>38.3</td>\n",
-                            "      <td>40.0</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                      Rank (Borda Count)  mean  \\\n",
-                            "model                                                            \n",
-                            "multilingual-e5-large-instruct                  1 (1244)  63.4   \n",
-                            "GritLM-7B                                       2 (1119)  60.9   \n",
-                            "e5-mistral-7b-instruct                          3 (1100)  60.2   \n",
-                            "multilingual-e5-large                            4 (980)  58.7   \n",
-                            "multilingual-e5-base                             5 (811)  57.1   \n",
-                            "paraphrase-multilingual-mpnet-base-v2            6 (698)  52.0   \n",
-                            "multilingual-e5-small                            7 (654)  55.6   \n",
-                            "LaBSE                                            8 (589)  52.1   \n",
-                            "paraphrase-multilingual-MiniLM-L12-v2            9 (475)  48.8   \n",
-                            "all-mpnet-base-v2                               10 (398)  42.4   \n",
-                            "all-MiniLM-L12-v2                               11 (355)  42.1   \n",
-                            "all-MiniLM-L6-v2                                12 (290)  41.5   \n",
-                            "\n",
-                            "                                       mean (weighted by task type)  \\\n",
-                            "model                                                                 \n",
-                            "multilingual-e5-large-instruct                                 55.3   \n",
-                            "GritLM-7B                                                      53.6   \n",
-                            "e5-mistral-7b-instruct                                         53.1   \n",
-                            "multilingual-e5-large                                          51.5   \n",
-                            "multilingual-e5-base                                           50.0   \n",
-                            "paraphrase-multilingual-mpnet-base-v2                          45.2   \n",
-                            "multilingual-e5-small                                          48.8   \n",
-                            "LaBSE                                                          45.8   \n",
-                            "paraphrase-multilingual-MiniLM-L12-v2                          42.5   \n",
-                            "all-mpnet-base-v2                                              36.2   \n",
-                            "all-MiniLM-L12-v2                                              36.2   \n",
-                            "all-MiniLM-L6-v2                                               35.2   \n",
-                            "\n",
-                            "                                       mean (BitextMining)  \\\n",
-                            "model                                                        \n",
-                            "multilingual-e5-large-instruct                        80.1   \n",
-                            "GritLM-7B                                             70.5   \n",
-                            "e5-mistral-7b-instruct                                70.6   \n",
-                            "multilingual-e5-large                                 71.7   \n",
-                            "multilingual-e5-base                                  69.4   \n",
-                            "paraphrase-multilingual-mpnet-base-v2                 52.1   \n",
-                            "multilingual-e5-small                                 67.5   \n",
-                            "LaBSE                                                 76.3   \n",
-                            "paraphrase-multilingual-MiniLM-L12-v2                 44.5   \n",
-                            "all-mpnet-base-v2                                     21.2   \n",
-                            "all-MiniLM-L12-v2                                     22.9   \n",
-                            "all-MiniLM-L6-v2                                      20.1   \n",
-                            "\n",
-                            "                                       mean (PairClassification)  \\\n",
-                            "model                                                              \n",
-                            "multilingual-e5-large-instruct                              81.2   \n",
-                            "GritLM-7B                                                   80.2   \n",
-                            "e5-mistral-7b-instruct                                      81.4   \n",
-                            "multilingual-e5-large                                       79.3   \n",
-                            "multilingual-e5-base                                        77.6   \n",
-                            "paraphrase-multilingual-mpnet-base-v2                       81.6   \n",
-                            "multilingual-e5-small                                       76.8   \n",
-                            "LaBSE                                                       76.1   \n",
-                            "paraphrase-multilingual-MiniLM-L12-v2                       79.4   \n",
-                            "all-mpnet-base-v2                                           71.0   \n",
-                            "all-MiniLM-L12-v2                                           71.9   \n",
-                            "all-MiniLM-L6-v2                                            71.3   \n",
-                            "\n",
-                            "                                       mean (Classification)  mean (STS)  \\\n",
-                            "model                                                                      \n",
-                            "multilingual-e5-large-instruct                          65.0        76.7   \n",
-                            "GritLM-7B                                               61.9        73.2   \n",
-                            "e5-mistral-7b-instruct                                  60.3        73.9   \n",
-                            "multilingual-e5-large                                   59.9        73.4   \n",
-                            "multilingual-e5-base                                    58.2        71.2   \n",
-                            "paraphrase-multilingual-mpnet-base-v2                   55.1        69.5   \n",
-                            "multilingual-e5-small                                   56.5        69.9   \n",
-                            "LaBSE                                                   54.6        65.2   \n",
-                            "paraphrase-multilingual-MiniLM-L12-v2                   51.7        66.4   \n",
-                            "all-mpnet-base-v2                                       47.0        57.1   \n",
-                            "all-MiniLM-L12-v2                                       46.8        56.6   \n",
-                            "all-MiniLM-L6-v2                                        46.3        55.6   \n",
-                            "\n",
-                            "                                       mean (Retrieval)  \\\n",
-                            "model                                                     \n",
-                            "multilingual-e5-large-instruct                     58.0   \n",
-                            "GritLM-7B                                          59.1   \n",
-                            "e5-mistral-7b-instruct                             55.4   \n",
-                            "multilingual-e5-large                              55.0   \n",
-                            "multilingual-e5-base                               53.6   \n",
-                            "paraphrase-multilingual-mpnet-base-v2              39.3   \n",
-                            "multilingual-e5-small                              50.2   \n",
-                            "LaBSE                                              32.9   \n",
-                            "paraphrase-multilingual-MiniLM-L12-v2              36.2   \n",
-                            "all-mpnet-base-v2                                  32.8   \n",
-                            "all-MiniLM-L12-v2                                  32.4   \n",
-                            "all-MiniLM-L6-v2                                   33.1   \n",
-                            "\n",
-                            "                                       mean (MultilabelClassification)  \\\n",
-                            "model                                                                    \n",
-                            "multilingual-e5-large-instruct                                    22.9   \n",
-                            "GritLM-7B                                                         21.2   \n",
-                            "e5-mistral-7b-instruct                                            22.2   \n",
-                            "multilingual-e5-large                                             21.3   \n",
-                            "multilingual-e5-base                                              20.2   \n",
-                            "paraphrase-multilingual-mpnet-base-v2                             16.4   \n",
-                            "multilingual-e5-small                                             19.1   \n",
-                            "LaBSE                                                             20.1   \n",
-                            "paraphrase-multilingual-MiniLM-L12-v2                             14.9   \n",
-                            "all-mpnet-base-v2                                                 16.3   \n",
-                            "all-MiniLM-L12-v2                                                 14.6   \n",
-                            "all-MiniLM-L6-v2                                                  15.1   \n",
-                            "\n",
-                            "                                       mean (Clustering)  mean (Reranking)  \n",
-                            "model                                                                       \n",
-                            "multilingual-e5-large-instruct                      51.5              63.0  \n",
-                            "GritLM-7B                                           50.4              62.8  \n",
-                            "e5-mistral-7b-instruct                              51.4              63.4  \n",
-                            "multilingual-e5-large                               43.1              62.6  \n",
-                            "multilingual-e5-base                                42.8              59.9  \n",
-                            "paraphrase-multilingual-mpnet-base-v2               41.2              53.2  \n",
-                            "multilingual-e5-small                               41.8              60.2  \n",
-                            "LaBSE                                               39.4              50.4  \n",
-                            "paraphrase-multilingual-MiniLM-L12-v2               39.6              51.0  \n",
-                            "all-mpnet-base-v2                                   41.1              42.1  \n",
-                            "all-MiniLM-L12-v2                                   36.8              44.3  \n",
-                            "all-MiniLM-L6-v2                                    38.3              40.0  "
-                        ]
-                    },
-                    "execution_count": 36,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "latex_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 32,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "132"
-                        ]
-                    },
-                    "execution_count": 32,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "from collections import Counter\n",
-                "\n",
-                "sum(Counter([mteb.get_task(task).metadata.type for task in task_names]).values())"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 40,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\\begin{tabular}{lllll}\n",
-                        "\\toprule\n",
-                        " &  & languages & domains & license \\\\\n",
-                        "type & name &  &  &  \\\\\n",
-                        "\\midrule\n",
-                        "Retrieval & MIRACLRetrievalHardNegatives \\cite{10.1162/tacl_a_00595} & ['ara', 'ben', 'deu', ...] & ['Encyclopaedic', 'Written'] & cc-by-sa-4.0 \\\\\n",
-                        "\\cline{1-5}\n",
-                        "\\bottomrule\n",
-                        "\\end{tabular}\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(mteb.get_tasks(tasks=[\"MIRACLRetrievalHardNegatives\"]).to_latex())"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "mteb",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.8.19"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}