diff --git "a/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\350\241\200\345\216\213\345\201\245\345\272\267\347\233\221\346\216\247\351\242\204\346\265\213 - \350\204\232\350\270\217\345\256\236\345\234\260\345\271\262/README.md" "b/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\350\241\200\345\216\213\345\201\245\345\272\267\347\233\221\346\216\247\351\242\204\346\265\213 - \350\204\232\350\270\217\345\256\236\345\234\260\345\271\262/README.md"
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+++ "b/2 \350\200\201\345\271\264\344\272\272\347\232\204\350\277\234\347\250\213\346\231\272\350\203\275\350\257\212\347\226\227/\350\200\201\344\272\272\350\241\200\345\216\213\345\201\245\345\272\267\347\233\221\346\216\247\351\242\204\346\265\213 - \350\204\232\350\270\217\345\256\236\345\234\260\345\271\262/README.md"	
@@ -0,0 +1,46 @@
+# 老人血压健康监控预测
+
+## 作品介绍
+
+关于老年人血压控制“高一点”还是“低一点”的话题，一直都是人们争论的焦点。在临床上，正常成人收缩压为90-139毫米汞柱，
+
+无人陪伴、远在家乡、健康意识落后等问题普遍存在于这一代中老年群体中，利用此作品对老年人的血压健康进行监控预测，给老年人的健康保健服务带来有力的保障。
+
+利用AWS SegaMaker的内置算法DeepAR进行机器学习，同时利用aws部署模型，调用线上模型，预测老人的后两天血压。
+
+## 作品截图
+
+- 全部数据以及训练数据
+<p>
+  <img src="./images/1.png" width="450" style="display:inline;" alt="">
+</p>
+
+- 训练数据与测试数据展示
+<p>
+  <img src="./images/2.png" width="450" style="display:inline;" alt="">
+</p>
+
+- 构建模型
+<p>
+  <img src="./images/3.png" width="450" style="display:inline;" alt="">
+</p>
+
+- 预测结果
+<p>
+  <img src="./images/4.png" width="450" style="display:inline;" alt="">
+</p>
+
+<p>
+  <img src="./images/5.png" width="450" style="display:inline;" alt="">
+</p>
+
+## 安装、编译指南
+- 安装、运行
+直接在aws的segamker服务中的笔记本实例中新建实例，把lpr.ipynb复制粘贴进去即可运行
+
+## 团队介绍
+本团队为一个人，在校学习过Python，联系邮箱：1067537312@qq.com
+
+## 使用到的 AWS 技术
+- s3
+- sagemaker
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@@ -0,0 +1,1327 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 导入相关包\n",
+    "import time\n",
+    "import numpy as np\n",
+    "np.random.seed(1)\n",
+    "import pandas as pd\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import boto3\n",
+    "import s3fs\n",
+    "import sagemaker\n",
+    "from sagemaker import get_execution_role"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 设置s3存储数据位置\n",
+    "prefix = 'lpr/Elderly-deepar'\n",
+    "\n",
+    "sagemaker_session = sagemaker.Session()\n",
+    "role = get_execution_role()\n",
+    "bucket = sagemaker_session.default_bucket()\n",
+    "\n",
+    "s3_data_path = \"{}/{}/data\".format(bucket, prefix)\n",
+    "s3_output_path = \"{}/{}/output\".format(bucket, prefix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "'get_image_uri' method will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 使用内置算法forecasting-deepar进行数据预测\n",
+    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
+    "image_name = get_image_uri(boto3.Session().region_name, 'forecasting-deepar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freq = 'H'\n",
+    "prediction_length = 48"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "context_length = 72"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t0 = '2020-09-01 00:00:00'\n",
+    "data_length = 400\n",
+    "num_ts = 200\n",
+    "period = 24"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 生成数据\n",
+    "time_series = []\n",
+    "for k in range(num_ts):\n",
+    "    level = 150 * np.random.rand()\n",
+    "    seas_amplitude = (1 + 0.3*np.random.rand()) * level\n",
+    "    sig = 0.005 * level # 噪点\n",
+    "    time_ticks = np.array(range(data_length))\n",
+    "    source = level + seas_amplitude*np.sin(time_ticks/period/2000)\n",
+    "    noise = sig*np.random.randn(data_length)\n",
+    "    data = source + noise\n",
+    "    index = pd.date_range(start=t0, freq=freq, periods=data_length)\n",
+    "    time_series.append(pd.Series(data=data, index=index))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2020-09-01 00:00:00    114.684729\n",
+       "2020-09-01 01:00:00    113.980506\n",
+       "2020-09-01 02:00:00    114.323596\n",
+       "2020-09-01 03:00:00    114.995357\n",
+       "2020-09-01 04:00:00    114.730439\n",
+       "                          ...    \n",
+       "2020-09-17 11:00:00    115.510475\n",
+       "2020-09-17 12:00:00    115.503586\n",
+       "2020-09-17 13:00:00    115.775045\n",
+       "2020-09-17 14:00:00    116.208944\n",
+       "2020-09-17 15:00:00    115.405255\n",
+       "Freq: H, Length: 400, dtype: float64"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_series[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 模拟老人的收缩压\n",
+    "time_series[0].plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 进行训练的数据 切走后面48小时的数据\n",
+    "time_series_training = []\n",
+    "for ts in time_series:\n",
+    "    time_series_training.append(ts[:-prediction_length])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "time_series_training[0].plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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942Jes3zMTepitDqqbmaYkCBbMFG/njhnfNDoZAOVK10vc6bzfXaKTvxonI1LaeDTR6/AC8hfOPirgEr6OouFcclaVlMvIEWpeiGeE94LqHXr1iQlJXGyl4t0OBy0bt26Tq95zNIaJw7OrdOrhqgJ22Kv4PYtXXi8ij9pq1EAgObWAtoeVL+hpSsD40fB5G3X8+TtHjXgKcoRFpiV1/l1aRrfGgNZZ3ZmRpCC6FJKlhun00jk09nvGXS4ZSkA+9OHQ4vj7zbd9Mhi1tqf40b3WJJlNHqQz0+XlMYILFTPZ2FRFyY588EriiHQCOylAvK3ARi+KqCSJ/2SXVeZF1DN7qka6/+JLwCsVqtP6oUQtUeslsgpRv3nlvFmU2I2ppT0adu4vqfyj8I0NL6yvUj20eHAI5X2/7TTJD5ddYgl9uiAtoetnlQpqbIRs/I9XkWmKbnUNZ4MGcWDlYw9IfMB9lmacLf2CJohsVl8VyS3YXKr9iQA2xTfJWiQ620uUDZwrT2m0nuoDQrtsWTLSJbYHy89ZhqZKKpa+t5wF/G57RWP4ZruvGd/nh3HYqFDWeGYABuA1+sAFZCXsCjSjFKdv2c3IEvf70jO5bddKfynS+UBqd5YlKordirtKYSYLoRIFUJs8zp2nRBiuxDCFEL09evfUwixqrh9qxAiQG8hhHhRCHFECLGp+F91CweFqAM228/gO7VqKoW64soP/uDqD/+s72n84+iSsoB+yh4MWbUff8mi5A5iA/jJOIsP9Sv43BiMU/Xo4k0pec4ym2etcyode57lItaanTlT7ETXA1U5mm4QRQEW9AAD6WEZywzjEpz2JlW6j79LRsPu3OJ+kre0azGkR1Dpfh4+RlEuZyk7ecL6JZeZvwGQ3cD3oVPzL+BbQSCYvw2gJG+QKUEzZKl9Zv7Wo9w+c53PuX/uS+eDpfsqvKdqrP9VsgHMBPxN/9uAqwGfxN1CCAswBxgjpTwNTxRI4B7Tw9tSyl7F/xZUfcoh6op+hb/Tzaz/AiPeLLQ9yQ+25+p7GseF9Yeyapz/RZhu1pmnsr1pxQXVC906Y2av54o9z5LgGIFI2xXQ5z7tAd7Vr+Y+9QdO1TzthqHTROSTJSt3M/1GHYqBwhf2V9ALcgPatbwMtjjuZJ/jZmSBb3bc96yTuE39Gc0VmDPreGCYkmRieM+4mo6uucQ54zEVq08f3d6YC1wT6OWcwl6lAzP0oeRbmgSM442PG6hfm2b6BoJ5bxA8O4LyvwMjPvmLCYsqzlakiKrrgCoVAFLKFXhqAHsf2ymlDDaLi4AtUsrNxf0ypJR1E9ERotY5aO3ELv5Z6rUuSiKnKwcq73iCsT8pmXemTGbd9pqkIoMNTa/iWveLlQYP/bT5KAu3HyMlz+MtpAXxGR+uLmGJ/TG+MQayLspTKcvUXHRTDpEiq6B609wsNvoy3P0sLjUsoNktbMwzzuJXoze6YvNpu1xdzQvW2TQ4Wjd5s9omfs8W+2jaiWM0Ig8HrsAndhT2y1ZkE8kP1kuYY1zIgrW7WLortbRPYCqIsjH8hbrhZwT2bndpRkAaaP+x/cf3d4BRa1MAVJNTASmEWCSE2CCEeKKCvvcJIbYUq5hCCt1/INF6CsPl/Mo71iI5GSmsmTQKZ2F+0Pbh7me51f140LYTGVfaQWbbxmNJXluj8w1T8qXtJXof/bzCfi2OLSHBMYK9ZmvinPFkRwYmQHvNOo3WIp1z1O3YijfwpmrnAtcEvjAGVzqXH7Q7ucc6j1Xmaegi0MzoVsK4X3uAO7THMS1lBViklPR1fsSz2u3kRnSo9Dq1QXZYW/bK1iy3P8Imx93sctyG4fYt1m4W5jDXOo6nLXNpJrJYYn+cdxL+S8Kc+0r7VGQDCNgB+BmBvVu9jcIl5DsDo6/dXmP4u/L65xaqiNoWABZgADCy+O9VQohgxd8/AjoCvYCjwFvlDSiEuEsIsU4Ise5k9/T5p7He1peZXFGn1zzy5xf0z5xH0palQdtXmaexzOxdp3OqC3RhY6HRj4LwVjU6v8+xL+mv7MYpAp+4vdFUj/tiAZ4U5MFsAL8ZvXheu4WvjfNooycAYCB41jKXidaPKp3LJ1zJTrUzA5St6EUFAe0ul4sYcrDj9nn6NSWk05C5xoXk2ZtXep3aILXR6YxxP8Rr2nCypeezkZqf+qkghXPV7dxtmc8Y10wAFhj9OSyblXYJTPmMV5vvZ+xvA/Be8J16oAooL4gAKHKX7dz8YzbqUwAkAcullOlSykJgAdDHv5OUMkVKaUgpTeBjoFzfMinlVCllXyll36ZNA0PHQxw/hhb+RFdZt+qWY20u4TLXq+TFBHxtANhsv4MvbS/V6ZzqAqXgGBera1HcgTrzKiFNfjN6sSG64oLqKTFnEueMp6uSSIJjBBFHVgb0uV17glnGUK5Xl9G5yBNVarqdNBG5HJKVe6RMNy5FsdiYY3sNmZsU0K5k7GGd43/sdtwKXu2mofGx9U1GqwuQ7uAp1msbw5Sk0ZgpxuX0cn1MnDMe3dbQp48rsi1nO9+jq3M6B9QOvK1dw+9mDwq90tUFqIC8nuv9NTjewkI3pY+wcGpGgDDJdQaaUQt8BIDvBfzdbiuitgXAIqCnECK82CB8HhCQTEYI4Z20/Co8RuUQ/zB2WLuxTdatDcBAwUDB82wQSENRSH+lZnryfzI5jbrzgPs+ChwtK+8chD9iruN27YkqG5ELZfEOQKo+x6WUDFY2kuAYwW6zDT81vxcA05lHL+UAB2Xl9QbsRgG77T241vU8RWGB/QttMXymn89X+iB0a1nEr2EYDFK2MtY6h6ZJv1bpPv4unQ/Fs9t+M61II5ZMGlAU6LaJylGiKcLBl2HXs9jsy2j1Zy5TyuwUFReEKT8bqCmljw6/yG0E+P977wBKHu6L3GXHAncAFdywH1VxA/0MWAV0FkIkCSFGCyGuEkIkAWcD84UQiwCklFnARGAtsAnYIKVHiSyE+MTLZfSNYhfRLcD5wMNVn3KIuiLCzOde8XWdXrNZ0iIW2p+iQfKqoO1XuV7itpPQBqAUZTLJ9j6N09bU6HzDlHxm/T8GHi0/Bz9A2yMLSHCMIEU2Js4ZT3Ij352WrmvMtE0AoLeyF70kN729EQNc7/CtUXFqEEPX2WwbzRXmEtbJLrgDvcBx2hrztH4nj+tj0K1lwVRSsdHHNZnHtbtIjepZldv+26Q3OIXVZjf+cDzIX4772O4YjVmY5dNH5qcx0/o6r1umogrJz/an6aQks9gs84APlgqi5EG8okhgw5Q+Sd9+35tOWp5vOg/vHYDD6hHYBa6yRT/ABlCNHUClgWBSyuHlNH1XTv85eFxB/Y/f4fU6eJ24arBu+iPEJv9Km+eqnvgoRPVYazmDvaaDJ2tw7r7NK8nY9Qdn3lC9sxXNozPWLQ2Ctm+UnUBWI9TxBEG68lho9IOo7jU6/7yUWZyt7uA7tRI3ULtHjVpYjg1A0zT+NHrytTGIViKdfpk/Ar0xhcIzlnhaiEzgunLH1wyTV7WRWBufyvnKRsyiboCvSkV3FtKKNPIIxzDLFjJDSgoI4ytjML3CKo56f+WnHRxML2D6rf0q7FcZRxqewQvaPVxl/sHtlp9pLdKRLl+7hZqbxGB1MwCd8nLIlw52yHYkyTKVtH8qCCQIz58KK4J5BEDZ+/eD+Ph77wAcVpVCt0FhBTaA6qSCOGFzAel5qYTp5VfuCfH3GVk4h84cqtG59m9u5cydr1b7vMQ2/+VC1xvkxPQK2p7gGMksa3mFBuuGQ7s2UFRQuzpqR14CF6trMczK8+0HQ0fle+McVjX+b4X9jjXpS5wznk4imQTHCNok/uDTrgkbt2hPMc88h/+oG+lY4Fn4TGc+TUU2W8yKVYKaFEwzLkNYHcywTcCaHhhnEHZ0NX84HmSL407UzLIFz3TlM8P6Oneo8xFFWQHneTNt5UFW7EqusE9VMKUkiyimG5cwwDWJOGc8WgNfNVxe4270ck7hFOcsdiqn8IZ+A98aA4kTx0r7BHoBlaXACAwEKxMWhimpTGmX570DKE4cV1iRCqgebQB1xo/N7uEGdWJ9T+OkZoOlF9tluxqdO8j9NnHOudU+zwTCcINeflbLQerWGs2pNnC5nLT7/HyOzby5VsdNbnYeD7jvq1KytWAsaXIjD2n3BRgcyyO9OI9NEb5eQ5phcqpILC3a/kGT4h1cUQb9lD2V2oQ0TacxuWQ2OJX/ul4mt1HXgD7ZER35WL+Uqfpl6F6pKEzDIE4c4znrXDoc+SHgPG8SHCPY5/j7/wd9Ej4mwTGCZmQRJ44SRUGADcBAJZtINCxMVoezzuzMLepiLlbLXHaD2QBEOW3ewsLwswEEw38HAPjuAPx2cf+KHcC5md/wmDm9vqdxciNNnrXMRfqHuVflVBQop7hGRTRL/o159udomPx70PaLXePrNQ4go0jyonYzvzYpTzNaM4RWwCTb+7RI+6NG5+umZJb1NS479mGF/TolfkOCYwROaSfOGc/OxoN92o3cFH6wjQWgpcgoVRFp4S04y/ke842KE7QZeSlsdIyhr/MPNstOFCnhAX3y7LGM00fxqj4St70sBMi0RXKh+00ect/DwUbnVHidVUY30mTDCvtUhaSI0/nJOIs1jntZZn+ULY47Ie+YTx81N5Gp1rd4zzoJt7OQBfZn6Koc5m3t2tI+/gVhDClLbQD+hnlvgWCaEtMEq1r+b8V7B2AvFQC1swM4YZPBdSrYSGf5z0pUdrKxRu3NIc3CMxLUyrv78I3tBc5Q9lJRge1g2Fyerb8zLNAP3DBM9smW7JJtqjmbmpGUVUikw0rDsLLUABn5bmYaF3OlqJm3TnnY8w7zs9GP3OiBnFmD869Im8IgdStfWc6vsF92A0+AVXk2AN2ElWZ3PjWG0kEkMyz3M+BsDKHypPVzeol9wDXlju9Swnleu4W2EZ0YqqxFyWsNNPPpI505dBDJZMkITKNscTNMiYHK9+YAOjjiKryP4ZonHcjBINlGq8OhqDP4VLOy2uzKSHUJXZXD4PYNQrTlHOAi1bPW9DN3k0cYu2Qb0rxsG1pAQZgyT6BAI7Dp9dpjAwizqmhGcPWf7w6gRAX0z4wDqDOSbXHkEvh0EaL2eNj5IZ1FYhWKAAYSKzwLeXV3DwfaXMV5rolkNzotoE1zF7HPcTMfW+tG9Tfg9aVc/I5Puiuciet53zqJxlm1q4aKzNnDJeraoOmIg3Eku4hZqxJK3xeKcGbrF7Ks4VUVnpfaqBdxzni6iMMkOEbQP3GaT7vT3oQ7tcdYafagt7KPbu7i+yzKIpYsVpgVe+e41XBmGUMRjkim2N4mMm1DQJ/YxEX8Zn+MjY4x2NK8PMALM5hlfY071Z+wFaUGnOeNwCQcJ053zWwmJZimTj4O5hhDuMQ9njhnPM4oXzVXetOz6OacTkfnbL43BvCcdhtfGedxiVLmseXv6gllC7+/DaBEWNgsiscNFAizlf+I5S0A7JZAARCsqE9VOWEFwA/RdzDE/KC+p3FSs1w9i52yLUY16saWUGJQK6/GanmYEmLIQQQJBNKlwJSCIWrd7Py+sb3Atfm+BULcmUkMU1fzcOqztXqtvS2v4EH3PahegWBTlu9nT0pwY/O8zck8/8P2UvXATw1HMFa/vfJC4tIEJCl4VC/Zim9SM83wLKwJjhGcKpJ4KuxFAJT8FM5Rd7DGDNTpl1DkNtDcLpqRhbtBSy51vUpKzNkB/ZIbncHb2jVM0K4nQWtEgcuzwJmmQQPh5FlrPKcfCSzM4s1Bxyh2OG6nMPvv1Q4edOgD9thvoQUZdBMJNCIv4DM0pEIhDgxUxuvD2S3bcrP6CxdYNpX28d8B+J7vnwvI83uyW5RSLyBrBc77ea4yAVCS6tlbBeTy2wFUp5j8CasCGpoxm/+QiCcpaYjjQRF2XrDOokh7DWxVKzbuj2HKam1Jm6es4Bv7S2w5Gg74LjaatHCB+03ailRmGCZKdSJeasAZyl7aCt8n0W0R5zLV/SStIhSq7+NUPrqu867tQ1anmcDFuHWTuQuX890inYWv3hXQv9/uN0lwxJORuwMcrTBMyTTrBKypLYHyF8/OiV+Q4BjPa9pw4pzxXMuax/QAACAASURBVNuwtc8vSE3bxXzb057XmKU5Z4oatqef88NS1ZE/v6/dwPTvFnDjoB6scdzLooKJ7JBxuCyB35sMe2veNYrVSN8l8WBuGA8PORU9LIZr3C8xVFlDn8ZnECg6yvjV6E0rkU6EaSWwokHV2R/Vn91Jqaxy3F96bGP6Uu5fIXjusq7ERjmwZ+/jPeskAMZpI1lofwqA88R0lhef4+8F5E15OwC7RcUwiw3GFfxENN03cAx84wD8I4GrIwBO2B1AnHMnVyjBDYUhaoc1ogdjtVsxgiT0qoz5tqdJcIzADJIPviLCizyufdmRnQPaNF0nRTbmd7NHaYDS8SRe/w9TdN/UChkFbpabpzPPXbv5iGKyNrPQ6MeWWI8bp9Pt4hfbE/yf5eOg/WNzPaoZt6nyyk87uHD/eC5QN3LEUrF9JC3Ko1orKE5j4G8DcAsrO2U7hrn+j0+MS7nH6bm+iYWHLN+wzB48ZlPfvYgZtgm88/tRntZG44yKY5iyCnve4YC+lvxkOokkYsjhLeuHdEj81nON4oVrkdmfI/aKk8HdoT3OJe7Xyafi3EfeHMku4j9vLSM5u6j02L7IfozXh/OEdid/mV0ASE06iGXrF2zf7vmMw7P3cLm6msvV1ax23E+2bMAaszPSWharEkwFVIK/cChZoO0lKiApK0zh7B85DBXnAqpOKckTVgActHfGLU/YDcwJweva6x4bgFL9z9mKZ4tqaFUrUl7C7tbXc45zEjlRgVkqzZwkdjhu5w3rVAy9vDITtccz+h18Ylzmc6zboTm8ZJlBM9chdK325tAkZwfnKxtxCs+i4s5Nxy409prBk8N91v41LnaNx2WJZNrKg6TKRnykX87CyPINtAApkd2Jc8ZzlrKTBMcIrj06wac9P7wt92gPEd6uL/0iMuhheGpCi4JU2oqUcr2AjrQYwh3uRwk3C/nMuADFEcn7tveITvsroG/ng7P41f4E6xz/4xp1JZcmvkXqkYMc+uIxPre9wmh1PhHOoxXeh4JJFAUUFRVW2M+bQ4cPE5uxhkPJZV4+QndhovClcT43uJ8nzhlPIXbetn1Ek2RP8ZekFhdyinMW7Z1zmK5fzCPa//jSGMyd5hel42gVPHX7J3criQOwWZRiI3DFOfx9BEDxywJvLyC/lN7+RueKOGEFwHeNbqGXNq3yjiFqzE/iPHbLNph69Q1tF7knEOeMx7BWz1CvS2inpGB1ZgS0udUIUmUjrlVXoDuDp4uuNaRkue0hblN/9jkcUXCIWyy/sMT+ODmZf0//7M2GViN4SruTRgWe5HtOJZz73PczxRgWNB98pmjILtkWt2GS4BjBaMvPvK4Pr7CYCIDU3djQOCCbY0rBEdV3x1CyeDw2tDO7uj/CDXI8AGpeMgPVbeUagbNFQzqLRL6xv0QHkYwa7imicij2woC+yxxDeEG7hbHarfxonM2cuFf5ac47DEiZSzhOxlrnMvjYzPLvwTQ54BjFFsedWA9X3W025sD3fGYbR5N935QeG5r0Lqvt99FOHKOP2ENjclFcHjtMns2T+M6QAg0LEoWX9ZtJlM24WV1MX2Nz2edWgZ3M30VUMyVWVaCIYjdQKSu0lLn94gbAdwfgrQ4C393IH/vS2ZFcfoLBE/YR+vqMyVysZgJX1vdUTloyaMQr1mlkFT0FEVXfantT3QpXLdP/5DPbODYcs+HJFl6G2xrJja6Xaaek8KESmGOmNjFMk3ZKKmPEPOD90uNv2+5mnGswPcRBHtT/nv7ZG92UvGb9hE3pucBVOLGxzDyd3so+jiQfIa6N70L9nyNTeM0xh33HPMX0EmVTJlvfplG6A/ip3Ot0PzyLPY4PeEUbRQfXXM5tEI13REN48iqW2h7FmT0Dm6UJruKFraBJN3o7J2OU88wYkb2LbKx8qg/hN/tjrCn8iv2yVWlJSW82a61ZbQ5FSphtXMR14a1xFaXxjTGAR7X/MVDZSu8mp5abItit68w3BtCYPISjbbn36k9GY4/wyreXJajbHjWIv1JVltvL6ih/bk7hJvdTXBbp+f5FZm7jDcsUwoSLj/Qr+M72Anahcab1G0r2NxU9dQcYlU2JRVFQFeGxkakCIeCTm/tyx6x1Aee7vZ7wS20AXjuA/Wn5tGzoIDnHWTx+2bkjPwncgXlzwu4AWmkJXKP+HuBm+Nz3W3n0y83lnBWiOqyVXXlaG40epKpTZSyxPUqCYwSGs3opE6IKDgKQ2rhvQJvbrVGAg7Vm56CFRgAO7VzPX+/dgvtvlhTUTJisD2Om7lsNNc+pkxvejh/Nc8jUrOWcXX3apv/Or2YfVjb3RLe6CvP5r/ons23jydkb+JR7Wp7H/lVkiSLOGc8B2YKL1bXstQTaTrxJjDoDACeeSlwuPwOiU2nAZtkR1RHBadnLeUu8gzRNTKFwt+Un1tjvDTpul5SfeNDyLR/pV/CoewxEteJqZQUNcgNz2zTO3krvyBxakMGb1skMSPucbVoLVho9CMfF72ZPjtgDVYClczQEj2j3cJv2JOnWyrOTlpDW6HTinPEkxQ4uPba7QT8+4Soe0+7mF8OTGE8zJC1EBtYCjxoqKmcn11uWc7m6mgX2Z1jX+iYejpnis9BWzwhsYlEEqqKURgIrQnBht1j6xwXWQvb2MDKDuJbuPpZHj9ZlMQkV2SP8OWEFwHb76QCYfjfbY91znL315KwZW9dMl89zqkhCWqr/tJ2DR5dtalXX0QJsbT2Cvs6PyA0LNGZa07ay2XEXz1rmYriLAtoL8rKxfX4tGanJHM4MLERSHTTDZLw+go8M34I4j7o+4C7rz3QWh8nLqthXvTo0y9vBucp28hWP14yauo1x1um8q1/NQUdggrg3mk1goOtt8ovTRx+UzXlDu54fwiu2ASRF9CDOGc9FyjoSHCN4JtPXnTWrYVce0u5DNu5AlJFJN3EITdNQ85LpLBKJN4LVd4Lfoocz2v0YrUUaC8z+WMMjmGibTMv0QOE1vvAFXjPfYZXjfq5VVzA0bQa95C7etn3EcvtD3K7+TLQz0Hhcgks3AEk0OWiF2RXerzeWvCMMUjajO8u+k6pRSJhq8LVxHndqjxHnjCdVieUN68fEHVsIwJ5WVxPnnEtX53Qm68PIazWQc8KTeNwoM9D7q3m88d4duHWT1DwXFlWgKsXJ4MwyL6BgHnO+RmDP3xIBkFXgJjXPRY9WZQLg32EDaHAjHZxzMPwyQ2YQRZYM3HaGqD7xXMx+2RLDqL6x82r3yx4bgKN6RXwMKeimJOAoCtSvFzqas8Nsx22WRcj8wPa96S6Gu59lnDYSjb/3dG4UZLHafi/Xqct8jjc3U+jOPhbZnyIycVnQc2vC8pajeUUbRZu8jQDkNIjjbvfDzNKHkGEGZkZNlQ1JlLG4NI0ExwgesHzPh8aVlXqACK2QBhSxVbbngNmcbUoXn/aSxcaiCva0vYEL3G/hkgrW3MOcr27mF/OMoOOm04iGooCv7S9zgbIRuyOMga632dncNzmdUzP4n/sB5je7ixe1m/nWGMD7rV7nGvV3iqSNfWZrnrfO5qK08tNa6xmHSHCMZL3jf8Qd/KLcfiXcOmMN98VvoGXSAmbZXqfNoTIbwNVH3uQ7HqOzOMwAZSvR5NAs1xOctqfhuQDFcTCCIhyM10eQFdOXpu5ETpc7S8fx30l54/20PnXFfuZvOYoiBKoQxcngyryALEFSQmh+yeOgTBDsLo4TOa1Vw4A+VeGEtQHclT2RKy0aJpf6HO8gjvpk6QtRcw7LprxqncKxvHuhcQ3jAKrhkgbQKvNPZtleZ02Kin+huEJbDPe7n6KdOMZER0zAuXm6gkvaGKBuReYPAqIC+lQVTQqaiywet3wJlHnK3Ku+QL9mNubs68VFUb0oLxnx5qVfYbE34LRzLi2nh9/1DMnT1ngOZJ4PjCJfiWKR2Y/ByiZsGY2A9mQWuLGogr0peVyWNYt4x1w2HHoPgN1ma961vk+7rEIo9U4P5KzEj3nYMYfXtRv5j3si7aMa4J1Srdnhn1lpfw2lcCE21bM8uHWTnKZ96emcihUDaZoIxffZsU3OBg5TxI/G2bxve4/D7lEkylgKlbKHsVynRs8XFwM9OKV5HDMPtAADLrLFUmj24TezN9OMS+mn76J3u7YEKgE98QafffcdfZRLiKAILaJfhfECAAl7tmDB4OCF/6HXrokUqGXf5bVRF5Jf0JZFxb79AFPkeG5wjaWfvSMAMZkbeMLyPdHkstrsikXpyfJWd/H10UspiQf398TxxntB3l5skM0ocNM2OhxTeryAStJZBAsIcweJAyj5WxIo2LV5FBZFoJuyTEhUQRCcsAIgxkhlsGULRbobrGU66u+MATQU+bxRj3M7WdhqdOAJ404esDWq9rkr7Q/QWqRzJH8rNKq6oS4mz1PtK7Hp4AAjoKEVIZBslh3RlcCAJJmxn+ess7lMXcPunEuAqhcW3/T+SNROF9Dj4tsB0CwRvKl58t4/5j0H08QeHsn35pmcoTYLMpKH05cXl784p2q5kLplLGaZ0YttsWM4EzDz0+kp9jPV+hYbk64iL+cs+ry2CjtuBiubmGLzZFrNsLclzhnPp9bx/Ff9kxmWWwmeSNvD3qhzOPvonNKawP5RpAW2aFab3Rhob0CbnD+YbJ2Cln86BnZuURfzqPVrdONaLIrN57z/ZH6OsKRyn/YAvxp9eDoqhhvUT2iSYwAefX5KjpN24hhPWT4jOWw87cQx7lO/x5HRhteMMxmgbqUxuayVXWhqC14T+ODvn/GhbSqnO6eSQwS3WyuvWLfM/igA0+ybiHPG83B4S7om7qN5m05sC+/PFktHkorsnKtsZ5i6mgLTSlslhZjcHcAZROdsY4i6ALvQuYFlrDvWkd3iXJ+HG/9gLG+8BUBChpf6qWQH4OUFFFwF5F032dcGsOtYHlEOC7FRdo9bqbuspGRFQqmEE1YFtM7WH10qGH4FnC9X/6SP2FtPszq5+NbyDG1FarVdOQEOmp4fsObM55J3f+fXHVVzmVzX+hZ6OqeSbwtcXCOTV7LWcQ/3W77HLAp0bXMcXctl6hqGu58lr3H5KQuC0Sv9J6K2lmWX1QyT942reN/wza3zmjmRwQULOE0cRMk7Uu54cc65dHTOrvL1mxfsopeyn4LiOIBGR5bxo30sD2r3sftoDpFvt2e0Op9R6q9Msb3Dh/oV9HN+SIrDswBuk3G8qN3MF/aKbQAHGnhyAV2l/kGCYwTxrvt82pMb9uYxbQxqRDRhZiFx4hi6qwhbbgK9lP1M1odhSHhz0W56vrio9LzJUQ8yVtxHK5HOKrMb9rBIXrd+TPuMst2ImnWA5fZHuERdSx9zO8vtj3CdZQXn5f1EZyWRN6wfs9ExhlvURcQW7Q86/2W2wQx3P0su4TQlC6WgcjtMghnLIqMv4XkHuFBZz3mrR9N8mkeVZdfyaGxx8oM5gCf0u+nmmsER0ZwJ1qn0TPd4U21sPZLOrln0cU5mkn4lhQ1PoUfWL4yjLBWNSzdKE7X5U6KTl1JyOKPMNqUWP7FLCSUbqmBZQStUAR3Lo0vzKIQQpbuHkut5u4qWR1VKQk4XQqQKIbZ5HbtOCLFdCGF6lXksaesphFhV3L5ViMCacEKIJkKIX4QQe4v/NvbvUxnfh11NJ9ccTJuvvj9ZxpBFzdQVIXyZbFxOsowJELJV4SbtGeKc8eRYYojPvJGwdYFpivOcGh8u2+ezVdWloJ+ymwaFiQH9syNOYbnRkwct36JkJwS0746+gAGud1ljdsFdTRtAd/klkzt45ZbK2M96+91cpKz16Rcts2kgnXxne56uiV9WMKLAqEYO1Xmx9/CRfjmdcj1ue0mN+3O7+zE22vuxS7Zln9mSsda5jLXO4St9ED8b/UmjIUZRNgmOEdxr+ZGZxsWVxgGoWh4NyWeV2Y0VRg+WSl+dfsnTplVVSG8zhIvdr1PkaI4jJ4EL1I0sNPp76javmMgHxsul5x2TjQgPC+cz2zhGW37GZhEMcL/PmuYjSvvowsJ8oz/L+rxLo9OG8Io2iq/0Qbwa/RozbBNYaZzGCqMHL1k/5YrM4DYAYbj5zDaOg45RrHXcyyVH3q30sx3sfpu7tUeIO7aYT2xvkSyjeU67jSKXzk0pr/NO0bP0FPu5SFlLDDl0LfD8Hyxs4ilcaBR/JoXWRkzUr8fZpAtRWhrdxMHSazg1kwa24AqVku93er7bp5i7qojSOIBSG4Cfas2mFgeL+S38ZvHOYc+xPDo396x3tuJEcSVCokirXABURQU0E48j9CyvY9vwJOGZ4t2xuBD8HOAmKeVmIUQ0EMyC+BSwREo5XgjxVPH7atUOfCLn/0iw2JHmRT7HG+DkIjXQlzZE9ZBSssdszXu29zmUczM0r5nHu1OXrDM747QFuuut2JPOGwt3858uzejS3KOvb5O1mgdsb7E6FWCQT/88Rwue0e4jTk9hXGRcwHg5uhWntHGjuhRLXjsg0E5QHm3NJCKcZU9fbjWMaJHHOOs04MXS46OMF7i1VRzfJ4XTuVHPcvXPCY4RHJVNAE8ksZQSl26WFvTwRzclD1i+IyO7O3AXWWoMv5l9uDLsMEmuGC50v8lVyu90VpIYY5lHnHKMfsoeVh5+AID9ZgvesEzh9LwkIDADZwlDj7zPWMc8PtCv4GbtaWwWhdu82k89FM8a+1Rs5vbSzJMu3SSt+SC6OacTQRGGYRAunLQTKR71hRD0yV+BYW/AioIejLH8hAudVCWGQqXMgO1s0Ip7tYf4pFNfhCOCaYbHPtLD7WCyPozfzR78Yfagp76fXi1jCZZso4PbU2FsjdmZhUZ/IiJ7E9wsTenn7in0UsjmpldwSkI8v5s9+My4gNF5LpZHXoZTZPGjObb0nLfMJ7na9SKtFY8DQ+uMlTxrWUyOrQXNZQJWtR/rWo1i8r5zKNmnODXDk9EziPNZyRO5v2eaqgiMEhtA8TGLnwrIYVVwG56cTA5F9bEBJOc4yXPpnFosAGIi7KTlucpUQBWopUqodAcgpVwBZPod2yml3B2k+0XAFinl5uJ+GVLKYGLov8Cnxa8/pQbRXA3MfEZYlmK4fP3MfzLPYqJ+bTlnhagqpinZK1vzqHsMelj1PHkA1trHkOAYgTVtG0PU9dhdmQF9Wu7/wtPnSNlTdstcT43nfbFBats684nAyXYZhxYkNqFx2joesXzFOOt07JnBvp7ls0B9hPsP3F363uVoxovazcw1fCNZPUE8gvW2/hwSrX2yMvrjqZ8LbrebiyYu59JJ5eeuOjPzB5abpxPfzKOvtuYlcobYzS3mt8yyvc7Dlq/4zhzIBP16Hnb/j37KHgAOhHt82/MIY4i6nuVqxSbRDVEX4JJWMqVH4Lp106ciVaa9Db8afbDYbDTJ3cl06xso6bsxpeQGdSlrHPdiFmbjklbaKmnoxSk5bi2axcXaEl7Ub2GM+yGsVjs3Kr/RImdj6dglC6GqChRT51SRyFvWD7kpbyrT9EtpL47Rggy2yI4cLke3P7JoDj8b/bje/QIzzEvYYa84PbWzMJ9l9kf50T6WbDWavq7J5MswnrB8TlpKEpsd/fjDMZj73ffxsX4paTKKHDOM1iKdLrmrAGiat5Pr1eUMZRWjLEuISf2jVH9fgksvfwdQ0i8py+O6/O6NvfjirrNQhGcH4CkgH9wLqCRNdIkaqGQsQ0oOFauTOsZ4hOzs0f0Z3r+NlwCoexvAqYAUQiwSQmwQQjxRTr9YKeVRgOK/5VvTymG5bRBF0oZ0+/qZj1Hn0V0kVHc4H/Jzs8h7oTlbln79t8Y5kTF0NwvtTxErMtFqkAl0vekJSNINkye1O0mICDRNRhQHCUl32ZPR763upKtzOvnWwICYZkcW8qfjAW5VFyIK0wPaO6UuZJi6in7OD0iNqbhyVTCSHKeUvtYMk5nGxbzj9TAhpWSGOo7u6T/TXT3M1o2r6Pb8Ip8FtNCtsz05hzhnPHFOT1bOTfM+YGTWBxxKKz8kv1XRXrooiRQWJ2nrlPwD39hf4seWnuRrD1q+40nLZ9yoLuUey4+sNrvS0/kxB+weW8dq8zRe1Ucw2+qxAeiGyX8/+IMVe9J8rrMnrDedXZ8yQl3CXvtNbLPfjttV9hva3/AsntHvwGKxYldMYkQOmquQ8Ow9nKNs523tGnTVwTzzbG52P4m7OCvfGMvLLGj9IK1FGntlKxRV5XExm86Zv5WObU/dzDLbwzRJ34C16BiL7U9yjbqSAcZa2ohU/s86g1WO+xml/kJbZ3ABfo/5BG/q12NDo5Mtmyh3xbalQqeLw2ZTJuvDiM7dyRBlHU9b47nH8iPKvl8J17NoTA7z5TmM00fRzzWZQzKWSbb3GZjjKUu5suXt9DOm8W7Dx5moXYsW1Z7OmUuYbH0b0/AssqU7gCCULMglKRvObB/NmR2isRTvADyBYJ6+Fj8voDBriQAosSN4jpumJKvAI3yjIzwG/ZgIO1EOa2lMQm2pgKqDBRgA9AMKgSVCiPVSyiU1HVAIcRdwF0DbtmXeJAtsQ5mYeQ5rwny3+ftkK4rw9VCoLkmZhXQRRaz9awU9zz++uwmXbjBt5UHuGNChVIf3T8BE8LZ2DZlEId1FVNelcoz2MGjwQVR3HrbcyYHUAvBz2d10yn3csrMf78aWLdYGgsHKZqLyooCOPv2PNTydb4wBjLXOZXvGZcApPu3fxPyPpUcuIo1GaKJ6NoA4ZzyXndqi1KznOLaOLfbR3Ol+jBI1jmmahAk3LqnzlPs9Otn3kyYbYuj7sFg937kv1yby6s++hdCz84u41bKYa9UVuF0XYbMHBtbNinmIiPSv6Zm3AjiDjU0uZUpCLG2i2vKA+z7GWObxP8s8AOboFzDXuJBC7DgKjpTW741zxtOqeNefnZnOlNSRTPnsRga9ML70OjYtm2hyWGT2oxH5FODgBt0sTfKsGSY2VUEIgd6iD1e7xzEjqhtRBxdwlrKJCfoNjFTsDFeXcqpIxGU+RgMgSW/IGQ2aMcv2OiuMHsBdXKG8xwXN4yj533Ur4eyRnWhvj4QGMbyijaSTSGaL7MC39heZqF1LT2U/j1m+JCl7E3CTz2fk1Ax2OGNK7zeVJqSktwM8GVs3fv0GpO6k9z1l9oMCwhnk9tgJvkj7mjtt3zJLH8IW2YHT7H0Yk/40VtPJC8r1tJBp/GF2Z4BrJQBvNXqOmXg8vyyKwBneiknG1fQNb0WYnktLcQzDNFBU1WMDsHsW65IUDyWUvC5J6VDyO1cUgW742wD8VUDBdwCmhMxCT6bdxg3Kvuve167KDqC2BUASsFxKmQ4ghFgA9AH8BUCKEKKFlPKoEKIFUK4pX0o5FZgK0Ldv39JPdVzeWLZbmmJK38jEAuzcp1ZcULoyLGFRnO18D6utGaP/1kiVs2HXfpYumkffFtfSv3PNCrAfD0yhskO242PbRPZlXQVtY2s0jttdxF9mV9yOUwLaXEoYycSgyTLB1z57NU/a3mVVuhsY4tM/y9GO17Xb+US/jLGNfaNjb5qygt8P5hJjsXGrXIgjpyHQApezEKvVjqKWb5A1TUknkUR0kY7n6wpF1sZEiSIm296mxBFUl3Ct+0Ueb9WZ3zKbcF36h5wqEjGkLP0hxR6axzTxBQMd28iQkUASv4Rfxky3ygXKRq4sLCI6iAAwTMltloXIvGbAw6Sqzdlo6cWZ7oO4hItL3a9yobKBC5QNjLIsIUoUcoW6ij+OXU6WjCBLRvCKZToXOLcAe1EsFpYavThg+u6krj82kfGO5XyqD2GseSeGKbnK64Hp3IQPudnyE3AAh8XzmTk1g/yWF3GT6xOaiDwMQ8NA0Eqk43LmQ4MmXKkvpL3rXDaanRikerzj89RGFHn5gBREduBh7V6+jO6KagtnWnGm1ThxlPf0K/naGMQk42q6qomcFtWYN/0+o/S0FK5WVrDNjCNWZPJ+2F1YwhvTo7i9+Z45tHAfArwEgFunjUihtUjn16Y3o6Ru46iM5mvjPMKckaQ0uAZMjS+0l0rPecX8H8Nc/4fV8IjFUzOW8JiyhmxjEPdap1OY/RG7Wl3L7dt6sAsVK54HufBiFVC4TfWp4lXyRO72ygIKHjdQU/pGAvsbgUt2FSWxAIaXG2hWQbEACC/7//MWALXiBVRNFgE9hRDhxQbh84AdQfr9CNxS/PoWoNortsBkpPorsshXt7zE6MPz+q01qmJVgsw7xo2W32joSq7xGFUl7Og6vrK/jJp14LhfqzoYhkGSbMqD7ntwRVS/Bu8W+2gSHCOISV7Gf9U/ccvABTgu8QcSHCOITCoru9g2x2MP2BobWOhHcWbSTGSxS7bB5WcDmJB8Mzvst3OzbTkvWmcRmbEFaZrYx7dg7Ye3BYzle686v9qf4JmkMhtAXoN2PK7dxWzjQh74bCPp+a7SVLyqIkiK6EGWjGCfbOXj7XP2oQ8ZqHoc5qKFxz6Vlu/iT7M7r+g3kWsE351elPUZf5jdmRTzPAAN8/bQV91L75RvecP6MRcqG/jVPINn9dE84L6XK9RVHJHRrAo/j96uqQgk5yjb+V7xPBAZlgZ0Uo4wWPo6RCyPHMZhsylHZAzhxU+X3iUFk8K7sFB4ImAjtHRmWV8jKnkFppRcpq5mpf1ByE1mr9mazkoSRtYRTMPgJXUaXfL+5AntLm52e/w5rpJL6JBTloysZCFUFYGCQQ9xgHes7/OI5Ws+0S9lqLqWjuIIB0Q7DlsCY0cKkncx0TaZt/Tr6OeazOaIgWy2ltkAxraaRl/LN74nJW/kd/vDfGYbR6ZownXuF8kjjNHqAsJSNrDB3peNYWdzr/EoE7TrOWQ2I91sQCuRweACTybY2IJdXMgaBre1cLa6g06RRqm/vtsw+XJdIkVug/DixTrcTxWkl+4AigVAsZqnZLGWSK9AMD8bgN8OQHoZoZsLtQAAIABJREFUgTML3EQ6LD7BY6oiMKXnoaYqKqCquIF+BqwCOgshkoQQo4UQVwkhkoCzgflCiEXFk8sCJgJrgU3ABinl/OJxPvFyGR0PDBFC7MXzmDeeavKz5QIKCEO6fNMCP2+dTSdxpML83JWSf4wHLd9xh/lVzceohL0bV3Bg21+l6ipbVnC/5/rCLMrjZ/vTRIs83NbqR9SWpAwoJIxH3GNIsHcJ6NM4x7NQaqJsUVzc6j46OWdRYAkMPuuU9B3L7I9yjfo7ljzffPFT9WGECxfnKNvo5ZzCoeYXoxmSpcbpfHm04oRhRnHRmk1hZeXYdcPkK2MwE/XrWLj5EO/9shPdmccXtpc5Je1XOspEPjYu4yb30z7b/c86v8sD7nvp6pxeagO48dib3KV61Dd5RcEL5LRyJ9BOpOCSnu38wJQ5vGRM4q+GQwF41/o+EyyTuVVdxDnKdoqkjf+43mKzxbMALjb7MlG/jk+ExwYgpWS92Zld0nch3WbvzSD3u9xpWcB88QC77TdjppXFzWyJHMgHFs+zmd1qIUIUoWsakZlbuUhZzzhtBJqtEX+Y3RnufpZCeywuXXKG8yO2tbuZaJFLvvQI59HGV/TILtv8Rx39g5X2B4jI2onFncc8+3Ncqf5JF3GYaJHLC9bZLLE/zo3qUk5xbsOf/+fuvMOkqLp1/6vYPTlH0pAkSZYgoKCoKAIqYgIMgGQFMQLChwERA4KKIiqYEBUFBUREUVBRoqCAJEEGGGBynunuSvv+UTM9AcZwznfOvd9dz+MjU71rV3V39dp7rfdd78oKu4DegRf41WlKGD4aKXkkGBnB1xWjCMWs8gdHs0t5dUsG+500ppmjqF+8m77yz0xWVzFTW8aNOQuJMrOJJ59NUldesa+nt7GAdCuOhdpLXO9396VfJo9jsPoKna4Zgf+RMzRq1ZkmuZtZoj3HW5sO8PAne7EcEYwAaoPBlY6/8v+VTj7orAVBDKB2IVhtDKAqBSQoKDeIDau5oahMIdlC/Kk8RXD8Xw0QQtxWx0uf1jF+GS4VtPbxu6v9Ow84v6rU37Sv1N68EejC9+H1axzf6zShmFBM06yTcrf9w6dpe3A+IbMyzylpByiKbsMmuz1NpP+5CKD56oEAHLhyM4usgbSOuOB/7FqrdmfQKiWSVil/35E7is4z5q1u5yijFPhnpRoPmBN4wJzAtKiWjFefpDDrGNCvxphtTSZx59E+PBXbqeq6SPSXdxBXKuNyCqrsWHRP9lrHeF5bzO68tkD74GsSAkdIvBg9lcJSmwAKhiMYYbq70Xl/cq+27CHNv5xLGsQH89UxJ7/msOc+XrQG87D2ERuyxmDb03CEjCRB/+IPmaV/RaEIwynvA16Xx5Cl1meN07PG/B6jkH7es9xvfcLvx5dAg5r6OAAvRz9EStEaLi79CujGmqjbybdzaZ7ambsOPcR96kpuUt1Iaal1NQOMp5AQJJcdJr2inWEH/2JEZdFRyVluVjbxhHlHjeuEGzkkUsAn9qVoIRGYvhKuUKqomqblBB2UHpXMYOMJZsW0pnXuBtrK+3ncuoPb1DCmqB8TI5Xil8bgsxzyiEINiWSp9hzFhAJTuDPkRTokJQQruv1aNFvt1rTzRiJ5IphtDqOhlM0pkcC3ngeZbo6il7yPUcpnlJfEAqNr3HuxKXNCJPOV/hAXyKf5pbg74UYOcDMAi0/fWMGndKuvF72xCKOsjAGO27zzk/xFTNF/YK3dnaXWNSSGRfNg4VME1HD8Un+SlDN8bXfmejZjI3On5wU249YBVDpWb4j7WemOj1ipkLLiPJLJI4dowip2/tXBYK8mB3fvAdtBV+Xgbt8tBHMqqLSuH6oNAnsr5tr2Rx6N4kKricFBfplRI/3jzllVC/B/AwT+X7NF5Q+xXW2CI/oArgxw1obnyRAJzNSWUVg+F0LPX06+df8RuqkBLNs+J+cGblg3ybwXHzrnitn+e+yGwONYKIw0A/zgtCX1POqX/y67f8WvgCB97oC/HFtptuJhn2jMMv1pDuZdCc3+2f3JODhI2IEydjoXYId0Okc3xy95ySWqhqBf88ItDNMXsjW3FBhYY3y2tzGLrZtYYffhoWosH8d2SBdJvGH3JxAwGaOsI6pAIuCP5Up5FyfFn5PMbCG4QDpFaqAIcKOAktD6eCSThzVXbKxUjsLSwrjNnMETqW3IM+rRpHArOSKahGopoE5ZH9Nf/4Ku8mGKRQi2c5a7A/cxuCk0Pf4+rZXz1yZYtsMtymaiy2RgJiflVPJD4pl/WVMeyR/A9bs70lU6yAj1S0aqX6JjMlz9hj3F7TnoNMREYar6Adc624FMbMXLl/bF57z3u/Oe40Xvbj61e7I4bAyHSkroqVfVeAzJeJrxxm/AvuAGym86nEi9lhFbE0iSCnBMP0VE0Fo6gVWaR0ALMFJZT0IgjBMiicaSG50F5LAahIzCyJY8ZI3jy+iGyJoe7LbWSjrBfPNGPre7sdzuy0WeXBIjPNQuHZSzD3CTshljyDL2ZB7g63SLvKISnql4/VetPe3NX3Eq+kUP8q8hUS3giFmfNCmTpeFjOFEC2SKaPaI5KZaXD0KHEur1ssT/CGgwR1vCE+btDDCewudxfUOnvLVcaB+l+p41PfVa7vq5MfNKtrPNO5ee/heDqZ/qEYBHVYK7d8Ny8FRz8C4NlJqVwHVEALPW/MbR7NJqBWFuBJAYURNPqpz+7y4A/+/QTv6hlUqhDFW+hQpVyPWfvUfXwi84IurzkDkGQzp/82qAhdb1NPe/iyXOLbsGUPOPMlFdTQJFNSh+/07bI5qzTzQh5czXLNfnIP4HO1yle4eyvqLR9981x7IoEBFMNCZRdp72jH9lBz0jSPcOo8XpVQxVN5EtV9USCCEYv+xnzD1uHUD8yS+DrzWtqITdkXRu4Kn5cqgv5XBM1COgVMlT+P2lLNHncauyiUa+/UzXPiA+/2eskmze0F+oIfR13vfqL+UrzyM8kz0+eCw/vBkTjEm8Yg0izb+crTEDgz8+RZYoDU8jknK+cjpjq2EYlsPUlXvpmPUpXWWXwhgp+SgoN7AdQWxKY+ZaQ8nUz6+LNLTkLX5y2vCv6KcBaFS6jzbiKJoi07elC8DvEK0Yb97HNHMUw9Vv+M5ux0fqIK4x5tJAyqG5fJolwpWvtvUoTFSe1t6scZ3V4UPY5VzAEadBxU5V1NADOhDSiW+0PgB4FIkP9Sfx7lvGJz9n0Fv+lU2eB1AKjvE9negg/4GWvR+z6Cz/0t4jqewwU8wJDDfcZ22AtZELi6vwncpcuCpLKJKgq3SQl7SXmaYuZ7E9gNuUTVwo/cFZtQGn5JqRPUBs5g88p71OWlpjOl41nBPh7dgptw2+/kjYbNL8ywnYLrXySWs4reRTbPI8wFv6cxSaMlPN0fiEh+vlLXQM7GCn2pnDoZ2ZKE1nhjmCQ04DskU0qVIew0wXT0j2H6OT+K3GvVSmak6Ft2OqeTd5RBJS4fi91SIAj1oVARiWU4PpF5SDrsYCUurAAMBV/rSrYQAFZWadEYDliP8rLKD/NftEvprG9nEkwwXavqEbe4xoZqtL2eh0xlTq1rDvLf9KmpSJ6fSnpCRAVIhW44vRik8wTl1LcymD0pJBRET+czG0v7Jv9Ac4LpI5EfEQ3YHUjHVA27867W/ZqZ+/pMHaW8i67SuSWrg72njp74mSBa0sm3We6Uw178bQo/56fC1bYffmdnUj+Woi9xr3oFT7QVuO4Jf9v3Gt9j0oUOap2qWuqTeFWzNuZKx67jUvOrOMez0fcL8xDm9xNOD2y/VbEgvM25iufUAz+xit/UuZktKGFDWK96wr2Cua8Nw5s1WZUyF18VVIfyrryk3L4QunO1843QjDh2z5cUqy+VT/F6U5k8n1JzPRnMR3TntuEoKf9/zMDb/ew9PWIAQS25xWFBDJN6cO8JH+BMXqTBYDJb7zy2qkWBn4pdAgI2po0esE1DDgLuLyd/O+9hQBNHY5LcjGfR4ft+6g3GkC+PnY7s1PTmu+Fx25O2BhO4K9TlMCaFRvEfOr1pE3jUYc9tzJoaLfae/9mf0Z70JjNy21NfRyTvrLGQ3IioyNSmz2Nu7gJzTFYoY5gqEhyfwuNeLGwCwmRbVGDounnf8Nnm/YjSlNPBT5XH76jeZaCoobApMBSMpYz07P4/hLvkSOaMQKz5MA7HUaE0k507QPAJjjTKa8xMOqhWvZ2/AO/jWgNbIssS12MA8GGvNDuPtsJFqZ+MxMoA8AYUYuEdgELJtiv8kpkcgepxmNpbOMNe6nhW8f8XIJ92ufUCpCCBF+7jYbE+dEsVHuRL5tsMy+koukQzyrvU6SUwgs4uOEezhgFVNV0QD1c7fwnraI79Vn+NC+3L1+JQ20mg/3agrlFWyccxeAqkrgStNqZSRCa6STlBpicPllBrFhNenOQQzg//cU0GapKysC7dkQ6So+Rlp5REjlfON0okiEYRl+4PydrO5XP6GNfIKson9x/fyvuKxbR+bcUOV8zyZeyrvGeOZpr3Em5/T/yALQVD5LU87ystaAF60bSAtv/W+bO6vMpsxpgFVmkQQMCjxJnojk73dPBUuL4ElzGAIJ2V/EP5FVAJhpjWSmNZLbIxpxpzoCT+5PgFvdawbK2eq9l5NOAu39rzM9quqzF0JwnbyFpOJyoKag257ofnyVofKC/ho7c1MBN9fuFyqv2wNIVEq59LrhPP1xGQYqfjzMtFx1zz9bACxvDGn+5XRIiA4uAGnpK/jDM5vx5mQW6wv4LmswtniMYhGKrOhckPctk/VFAJwp2IFtWyBkskQMP4sql1tSUoxAIik6gsOewez+/S649FxE4onQ6bQo28BVvnVAL54PnUxypJd2uA5FlUx6yr9xufILi61r6ep/BR8eehi72eadDcBngZ4owuLCWRv4flQDntKW8Ig5Gst2grnlWPMsKZSxxO6PN/wCNpc1pKu3CiS3HFGDVTJenskUawkj1A0UiVCmm6O4WY/iaXkRpapGmXwb4ZagmDA8IaH0vqAq0rsvcj6KqnLvoWwua5lIqZ7MV/ZF9PGEo2genjSHUU/Ko4RQdngnMtq4n/7KdgZbX9DS+Z3j2UksO53MmUsaUz8mlAJTocSbGsTt+ue9QwP/z4D7Ha8sHwFeyC7pwekT6dytfMFI40EKKmpYpgWeoIN+iG1OK14Mn0JJQTYvl86mwEkjIHqRqJzgU/sSblc3ouDQzlzKMaqqv6ubikWoFEArz6a+VEyGSAju1qsDuR5VDi6Ihn2eBaCiEriufgDVcUyvKgeZaOUVqp8xYbUjAKnie3Tw/w0a6H/sArDCvJev1I444hIAbrDXM0DbwkPmGJ7TXudEwT2QeH7g8gP7cmbLb3Fq1+ds9T7AtN8fp/ru27Qd1joXsybQgxVqKucGo/996x+Yg4zDdWUn+d5pgR7W5t82d15cZ4YYz7A80nVEe0VTVP5ZY3dbDeUXpxkrPY+zL7cHtYuy/so8GNjISIFC9jpNML0tgp+wabhMmG+cThQRjmmLYGvPNgWbGK6/yrb8m4Gbasx5VGnMOtGPjYFO3Jt0cRBT8AcCxFBCwnWzadGhIRNXjSUpvy9WeU/6y9s49VcYgCNoIZ2ksf8MOz/bQ6cB48iJaIksCRbrCwBI97SgoTeeO82pzE9uz6ksi+4inJ1OC1qpoZR7FKIlmznaEkxULpTT8QmdbZG/McKYyWcp7VnC9YR7z68fZDkOA5StNPXlA09zTKTiCXUdV2lSF0YYs2glnWS6+j5j1XUol0+nx9axRBjZbHLao2ExSl3PjcoPrLEvxtYWscLuwwmRRLHfCrJFHix6mube3/nG7siquCmsO32W16rhT/dlTkWxfcBPgOuA4uxiPrN7MN28m/pSDiJQxgmpHm2lgxQUn0JYKmOVtUQYjYGqBcBRPBzKyGH821vYO/t6cqLa8qg1ip/CE1BkJVgH0F46il/obHI68LVzEb2jyynLPUkL2WX4+P0+IJT6OT9wvZoNFcv0Twk3c6S0W7B470upF1eLLRh+PxT8wUPaChdfI496Ui7T7PH0c74jjmKsyAbszw/nGXE7zWOSebFsEmjwqLaceeYQnnNuxhYuNtMn/2Mu8efjstpdO5vSl4eNBN7M/YRx+nraBZYQ5nHdaaUzr/z8aqSAqlM2q8lBByuBa2MA1dNJmlKjDgAIUnmDc1ac7zj/Ji2g/1ftNIkMVzaiFLuqkTts19mdFXHcZ0zA76l7x/q+3Zc0//tsKG7M7049WiR6OZN+mO0fuWzU8JzdTFM/IAzf3yqm+K/YAZHGftGEtpmf8Zo2H9s8Pz3wv2KSv5DO0mGcQDHCcdisT+Go946/PrGaOZZBAI2xxhSKYv754rTfM4qj3ju4LPNtRqpfclip2hWbahhp/uXsdFqQ7h1KkxMr8D2ezB9PdaZp6S4CQuO75JHnzBlW+DttworcTlhyVXTnZB9kj3ccDXO/R1ZkHlA/pl7BdpScg7yqv8Raz5+3CBVluWzwTGV+yYN0+eVRjm96m8zwNtxlPMSrFRjAKrtXUNJakWWwfERQzmvWQAxPDBYKplBpIWdwoZwOgA+9qsOWLLHMO5RftfOr9U/2vcI2pzXjI9yq1U6BnTQ2j1ZcTwIkDopGFSqr73PXpa34KnIws81hjDXvp4t8mChcHGmQshUzJJ5dzgWs1B+j7GyVrMLb3tv50u7CHqcZoaqMioVhVD17WzyXsiW0Cuyc68xjkLKVdJFMZ/kIX3keQc87wCrlarrJh4jN/BE17wjTtA+IMGvKc1xpfM33nvv41TOGIp9ZAwOQJDcVu1B7kSe1t1hkD2ScspZu0kHy9RR2iZZkiRhWeR6DbLfzVpf81dzhVJEPc8IvYGu1jdtDziTS/Msp9yZyKvEymvnfZY1nJus8j/K6Ph/Mcj60LqOAcHooh7ha3sGP/iakh7VjgvoYk4yJ7HaacUokkkQBj6gfYATKSTbSaebUrNOpdPKb9MuYbroEx8p0Te0IoC4MQK6mBlqlBVSLBVRtvEeVz1F7VWqNrx4B/H+dAnqPATTmJFTUAWwwO3LKfoiF2kt8aF+OodZNebxR+QEvButPDmKSlE9L3x5yNv5Kt4zl+MruIaT4OKPU9URQjpyTDM171znXf9V+9dzNNqc1m+Om0T3nYzqnvw7M/7fM3fTgIlZ63ub3I37sCx4kTc7CLzQ02znnganL5KITfO6ZwWRjAsZ/oSHMErs/49S1nFCbMNGYREk1KQnLFtSXcrhRccXRCkIasd7pSlEgjP0pD3Db2Vu5XT5Xf2hYzgtYAt5TehJRpAFpAJRp8Txm3sHV8W4ara31HrenNqVPRAjPmreQQ9SfpoBEhRbOcuty1jtdGZN0CVaewQ9OOzY7HYiilHpntqFmHmadfojS3MfwXjyQZkdTXPqpI4jO2o5XCtA38ByNpUz2OY3JIpYPz/7ESn0OoWVvE+VVCJSXnPceEpxcYqWQoJN82HiV9MIewM1EFfzGZ/oMLFS+s9vxmj0IRZHZF3Ep35zORsViqX0N3znt8aNzXKRwhSM4LlJ4x76KnrZO5R5/l9KRT836HPCOZFu2w3PelexMfwI6uXn6b7xXEqIpVJbE+eVwvjE60kP+jdbSCaYY4xkenkaRbDGo/EmGxl1CeEwCt/rfYk1yTWG2K/1fkSS5PXvLzxykyclv+MUzD8e/CykylXd0l7+z3WlJpOTjQc2tu3nefITGchl5RPCoOZKbPC4I/nzkI3hFgEpYO97MorV9iMqK8STrDBJhBEwHwxZYqGy/9ktOHtjJW4cU2sl/kEgBE5XV7AooTNFXcr8xjkgrgg1KO846ftYYPekt/8ok9VO6ywfxlZeyJPZ+inxmjWrV5NytfKQ/z2vWVDY5bkxXWQcgy7UjAHeXb9hOUGEVKiIAUdEPoOKU2v1gqkcAuipTm5NyTmqqGgbwZ0KFwfF/OeL/UfuRdvQILGR1jLuz7CAfQ8ZhlX0JJSIEy18KnB+8vE9dSX0pl1uL6nOrmEmP5G40kzL47LjCA7LGkeSBzNgVwlf6w+wpGET10O/fZVFSOf2UXWyQ4nnGvJV6ERf9ZWu7v2unUvtReORHfIk9aYjMoMAcckQUX/mtc3KGdZnpjWeGOQKPZKKU5wP/TApirnUbc63buCw0gaHKVJoUraWS1mkXZ7LFM5lMEUMr/1ImR3Vgrunyq28QcIP8Aw1L8oCacg+vaHfSINTiGetRtuVFUJkKKFNjeNu+mqtjXAVJoWjYAnxyGK/aLrj5bLVdVm0zIuoHi7YAbleiaHdiAce8ixhuTGOZ/nTwta/tTkRoIVzVJpmVfQpot3Uyp3LWE3AkLBFCkQhnYzWNfUtIGMJLvKoxr3wGwicB56qC3qc+ykX+77jZvxLow3j5X/RIbUBXQFJUZBw6yEe4SD5CPbkASbqOKKmc6+QtvKi7hMlfjGY8bI6lHA/X5OxntT6TCeYkWstVchCpZjoByWCBNZjkuEvYclamZXhVkZ5jGaiequjqtajJ9Cp/l4e1jygWoUw2J3KTHsPL1pMcUhIplq9FMQV+PIR4aj5bj8c9R+GJfQxWfqC77aEgpBGf2r0YrLsMrifM20mUCtGw2K5P4JbATK5XtnCFfyMd9J8BGBiYzdVKNI4jyPUr1I+piuy7537CaPEpcC+ObbNRuw802J/9BRF5+UxRV5KWNg8zujkHDm7nVeVF0uQsTjiJ/HrBPTx2+iLe1Z8mPfc4htOeRPUoy6wruVtZR6gUoEXgHbZp0TjOiXMcrSRLCCSi/BnUI4LTJAQjgOopoEqHb9quHHgNDECRKlhAVWqgtZ/R6hjA+QiJSq3xSrUFoNj///ECsEmM5iO1N47oAcBkdRWt5ROMNu7nDf0FDuQMhmb1znvuU+YwFmiv8KH+JJ/aPdkt9abQk8IOpxW242pv/yFSaBZ4j7kJ7ers+/rfsb6B5/BiclnJXn4WTfGF/lkjv39mOZFtuct4nJfDm2MLOCAaEUKAojLf314ALD2SPU5z1nmmsye3A7UB2b+ycMoxUfEE8jkqUjmgda3ggoBVUXn7kX0ZPrxYtsPNyiYyRAKdC/cxXF/EjoKBQM201VajGWqDGLpnvMzdyR2rRMb8pSRQgFdxfyFj5NUk5neExG4MlH/itIjHMK/Eo5//vVdiAIOVHxirruPHrGWcjOhEB6EHnf9y6zI0bKZao1kcX6EXFJnGYnsAV3hi+CMykZY47PKO5w8nmSZyJqZQWB27lwfNaXwf1YCfogeQXxbgfAk12xH0ln+lm+WmfQ7bqXQISXXfX3xrhhhPUV/K4QVtEQNlF84fmvcSF+kbWWH1JlIqp5+yk8X6fLY7LfHJb7DUvpoTIgmrWkvBJ31zqO85y89Oc35ImMZCO5GZoVUFd/MKJlPkSwVcGQSvqnCj8j1Pm7fxrn0lTaSzECjmV6UNTaxDZBT+Tni5yT3KajxcDFTRc2VFJUPE87J1A83lOE5HdWSOFcJNIW4Tp6W2SwroIh0iU0lhu2jFdqsV/WMh+8QhGknZqNiYZYUMmP0195lLOR55K1R0C96bdAOvnm7Ku7hCa+9Z/RmqfINpGEQV7Gey+imF0lxCrTIGyFsZbT5AB/ko3eWDhEXGclA04n5zAhfFN2XOsdGgwn3qKl61BvGWebX72VsO1xe+g40M9Ai+t4KkHtxjzORjZQ5DdZObjVl4NRlZqskC8miVtEwHw3KIDKkm3Ha+lpC1HHp1GmigQkyusvcvnFs5XH0BKPkbC8B/LAawl2bcrnyNVuhKKLxmuUVOATQ35RBRdz/Y9U43WgTe5XZjKj6h07hoG23OrmKdZzrCV0Rq9g88ob6NzN+jUv1X7Jiox28ijSvylvOk+hZOLVnr/44p/nwulX9F8udjmwbztEUc9I7EyNjz1ydXmGP4ULAZYTxEflynvz6hlv3iGcNh713cWfAS49W1bJGrltFAaApp/uUccBqR7h1Kx/Q3eVZ7g+X6HJqU/cIZEcu6pAk15hOOQ0v/Hhp6SskkrgYGEHlqEzu9E4kqdfO0d/A5TYq2EpG1nZf1hazyPEZ58bn9CCpNLjnNBs9UxqrrAIjO3sHJ8HaMMB/mZet6mvrf44hoQF9lNzpmkKsdiLmA561bCHgT8RkW+RWd6JrImQAVzrcCA1Akfk/qz0qr53nuAJ6y5vGLaMatutvk/UpnC6kB9/1UYgAZIpGbjVn0kN1Wk7tiB3CvcQ9zrdu4WtlJK+kEAN3kQ5RpsXxuX8x6fRoxpzYC4CsrYZ48gvetvvzktCFUkwjBj1VNDnq13p/dkVU9EEaVvEpT+SwZIoFW0knWeR4lPHs3S/Xb6Cnvp/WhF4k+sYEHtY8JqVV439v3Nfu9d/O5Pp1iX6AGBgDQT97Bq9oC5mpv8Il0BROU1Vwu76ZUT2CXaMkvoimfemYReXozY833uFrZSafyKi5bSXgaP9hu2i9gSzxlDadN4C3yo1qzN+Vm0vzvo4XHEVZ+koX6y9SXcjjsNCBHRJNEHgPlnzglEsgMa8kE71zGGvfxrd2B4yKZOKmYGep7WEVnSLTOkmrXVAWodNQLzBuZXyEZ7lEVV+eoegqoQlDPtMS5ILBcPQXknlO7LjW0WlFZJa+/OlOo9gKgBjEAQbHPDFYn12X/sRHA6/YgmsgZSIaLAfzoXMgwYxrv60+7fTu1uqULxipryRVRrHQu5VntdTIKQglXAgBYaigRpX/QS/kJHzpxmdcAf914+p+YY9uke4eyzu7KW1H38qLvdgZmzKOkoodshLdql3DdKz+SVeRn2/S/r5xxwbF3eFdfwt7jfpzm93KjsoVMEUOefK7Gfl2m5v/OGs9MxhhT8P/JZ1mXvWgN5gHtE3ar7Xm7pAvlnmrdtiyHBlIWoysc7pmwVswzh5AmZ/HedSdRAAAgAElEQVRt0sOsyz7LkGqdpADKy0pYps7mp9JJ3K4UE1doUNlsPDusBY+aIxkX7UZ8V6tLuTwlie6xsNwYh43Mg45ep5iFEygL3vNWpzW31u+LKPTzi9OUbU5rYikmDD8r7UtZrc+kPG8+0A9ZlpBwsB2HVieWESvl0ti/jEvlfRwTqWSIBN45vZ61+qtoxnpiQxUoy0M4zjkSJDGimBD87o7QcZivvMS2glHAAEKLj7FRfxCBxOd2dz6V3T7FJ6Mu4nMnnjD8vGgNJl9E0E/sZIY1kpnoZIsoFtsDaB/mIgDpz/SghdOe+dYQdnnHsz2zFQe9T7P1xBgqibKfKVfRIboK8ymSo9jmtOJe9VPyRQRjjCkMj26N42QywJjDl9JUcsv20yzwLgfDaqZcu/pcWeVGcjaZR94n3lfCAc8bKOIUoAQZVt/Z7YihNFh1/ZI/kvrKCQ45DXjIHEPv8Ha8ZAletQaRpLSkUrEp2siku/Qbjn0NgYCPZlIGWSIWv+lU6O5I6JqCndCaKwLP0lfeQ2P5LDcoP/KbGMnL+kI+tPpwymzENrUVx52GbHC60l/exjBlIzcoWzhbmsv8yAcJ96hcUu29xeb9zCp9Fg+a4/hDuJGapsjIklRjF++p2MFXdvWqjgHIkoQQbiV65eMg144A9KrxlYQUTZbxUyWsV92qS0EU+01iwnTKDB912X/sArDLuYA+1nw+jHVZANcoOzghknjduha/8ICvkLr6zExUVxMplRMwNG43ptI5uSOtlZO8m9mI+2SdbclDGXWkG79qI9hT9O+XaDBtCw9wrbKDt4lnqnk3jcKbs+RjV1TqzTur2iz3O/taxb/+/gLwR9JV5B/dQXH8pdRTw7gi8Cy5IoonpL/H5RdCcMKJ53VzNDFSKVp5FvDngmrgUuZkyd0BvWwP5mV7MM0jwxklv8DlgXdxu4iCkn+EHzxTyBMRNPO/y6iIC1hsJ4AN1wjBYPl7mpc0pbrWT5EhMcqYwdCGvXj82DVsz5MB1xHmeerxvn0FkyJcSQNNVbAcQYkczSrHbSs5wam7P4A/qmkNDOBaNY5LT85mnGctD5njWKy74PwpJ4G9ojH1KvpQx2f9yHHvXRzMXMFJwvCJeAQy3zlV910uPFgihvqqh0vzVvCItoDS0qsIj6y5HN3hzKS32MYYazmWcxlXBeYxvH5bugOSqiGQaC6fZoq8krbiLDCQSKeEkcqXzNSW8Wbrt/EktWNoRS+CkLM72emdyHBjGo3D3YUyTiqmu3yAT6RLeNa8mbYx7ZlrD6d+RJcg/iSbPjxyFWC/InQojfNDeE57HYCh5qMM0WN5L3ATm5ULGRCYjR8dZA1NrbnbfCX5Kb47eJr71Y+J15shyz7es69gtOK6nZnmXcRSQkM5m/d4lJsDM+mvbKd7+Wa6am7ZVZ/APNrqSXyhT+cNuz8xnap6SrTJ3cAo/RUC1r1YhWfY6HH7T/14+jVSi/w8pH6LKvdH84RyVNRntTqT7yMHMr3BN8xqfwF91zzHl/pUdufEErDSSFYPssgaxD3qas4Qz4WBpayMaoHt/HYOBiDLKqUihJbSScqFh0ziCPeqKLLkRgGSK/JWhQE457CAgrt120HirzGASlqnpsrg7lfPswAQvF5pwKJJfFiwE9n57D92Adiv3cFr9kAc4e4HZqnvckok8Ig5ho89T7A7swe1xcQq7W7jAZbqz7FQf5nF1rUcpCv5WgqHnIbYhh/TctBVha7iXW5LbEivf3BfjiNoMv0LZg5ozahe548cLKFwRWA+oQToUbaNfU4qhZ4utMpag09oVOY4AW5SviPhH1bx5oS3YJI5jcdDm2FLMkdFPSIpp7S0GEjlsTW/4TNsnhly/nZ6W47mMuqjozSRWvCt50F25TQH/hqjaPboeq5olcSbd15ELMX40Ykyy8gkljelG5leMc623Nzkq9YgLFRMW3C3so69ThN6Fu5guP4auwqvoKIPEACFAdjmtObOuDQuthZxc0qroNNyygupRw7eitB4mLOa8Pz6eKO6MKgizPeVXgicv7NZJQYwVv2cwcoWVue9x4GIXiRnfhd0/i9Z15Mq5TPbHMbrcS4eYkQ0Yr55I71DUvgxPJnefEO6dyinRRz1pDz8QmNp+CaeNRPYFxpJSWpPZh0qYLTPJrwWSc2yBRfJR+gvdmJWMHisimZHIroJVxnPEU0J7+pzaVnRjLxP9rt01z7gBXMIAy9qS/MmTWlZsg19+0IC4jFetG7glEjAst3Pu2vgFfZ6RrPR8zBHnVR2Rc9kmSxzS7U6gE+M8RzNvBRwo9Fk4wQPqiu403iE7U5LOkm/I/uasIluxEtn0bFIlgoYqO2mdsMfRSZIlb1Ja403VOFVO5axFb0Z3rNdEL+H2M8etSM7RCt2WK24NTGEZ/IuIVEqJIZSRHEmr9kDqdfuMoZ0q+qZcSzlGl44HMtbjoxPi2KWeSej1XUYtkNy8S9cqWxEkiQ8jo8b5B+4xZhJ35a9mHdte4QQHKced5kP0yOhDTNyh4MKd6vredu6irf0oWC4efcRJa/j2AkQlLWD0sROjDCnsdszhiMJV9Fx3Jt4VCW4AVIVGaNaD2jTdghYdo0UUGWqyLRFsB9AbRZQ9YjBXw0DqPqMzx8BFPlMhOActdDa9h+JAQgh2OR0YKTyJSH57o5njHk/TeRMwiUfo437yYlqX+f5O0QrLgws5Q7jERQc2hT/QJvc9XzqmQWlWbTK+YLp8ruE6Crl/xADKCkpoZ+8gw++/qnOMZYtOCWSOCwacmfZW0xSP0UxiplcuoCpZTUJiz86bUh3/hkDR/Pl0F/ehlqegxMoZbiykb3e0TQ57pbax+x4jva/zKrzfOXgGrZ4JtFcOs1wYxpZCT3qHFvdrpO3UHJoMwC7POM54B3Jk/653Keu4pMK0T6A4sjmpPmXUyAiSPcO5aqT85ihvc8Kz5M09e3jgNOID5LuD47ffDib/OzTXCLvJVb1UyzHEKim9dTs5Ef86J2MV3Ed3dXWJlqUbqf+2a95SV/Ip55ZKGfqxj/UwmNs8ExlsOKmLBpkb+L3sI5MNicyzxxCK/9SDjkN6SXvIwx/cJdlRTXiRftG/KGplJs2Z4QbgdST8gDY4bTEb1dKAMvIKe15x+5Hvllz3yUch3eU2Rx0GtJPvILp9zFE+Y44/0n3+6i4XiERDDKe4o5wNyo8nNCPycYEXrIHQ5j7jKgyqJJNkZbAKvsSvvPcT+rJtdiO4B7lMyKlchZa1/Gt0xEFmzjFB4GqVpWvS4M5HFP1XfUv+4wkqZBsEU19KYdVnseIyd7Ky9KtXKf8xCrPY7ysvcwwqUrPqdIuLv2G3d5xvKHPo6ysFMsRNcQXB8k/8oq2gGfUN9gqdWCsspZB8o8EPLH8LFrwpdOFTz2zaHLyE+ZbQ8hOrPkc+sPqs0O0whIyPimUd+x+9Aq8RHpMD75OHkNP6W0APHYJ8/VFbn2G7qYWJUlisGeH2x85rCkTw+dzl/Ewq+xeHBYNSNADPK6+hZZ7iDg7lyinoMa1K533ZPMe9qfcgEetZAC5/1U6+j+LAKpz9oMYgFQ7p19tATCrnqXgHOeMd/8uLHerj2PD6tZEg//QCEAIV9CtmXYGySjDcQQ/Oxdwm/EoH+hPMc8cQqledyPz6er7/Ow0Z4PTlZnqMkqKywip2D1a3lhiy4+TygHGSiuJzK7PP9HoKck5yWJ9Ab9IrYBh5x1jleWS7h3KarsHD3tmMN+exrjsJ5ihPkCsncP91cY+bI7FQfpLVVKfYWNYDlGhGq1OfcQd+pvszDAQzUcyW3uLg04Djod3pjuQQj4xUt3ic3rZGepLuSzW57tA+d/EACrpiPAgT1lDeVRdzjqpJy8Y1xGqVF3PsgUNpSwmqW5Rz5HQTrxvJNNP2cUHEZP5Ma+UayUX5M0tDXDXWzvpJ+/gPX0BR60+DJO/on5BFTPpSGRPPjVNntHdc8ZHLCQtPpTOcSavHI7Ai8FtYXXLbUtGGT6hM8caym9OGtcmdUMuLuMPkcqvdjNiKeYKZTer7EtYps8hUNgCGl2MIoGOiWVZ3HLmGYqkQtL8y7lIOkQuUaSLFF49u5Yv9Q/RpH7EhMgkUEhRYQHUr8qz246DJlkokoMtBGZ5Ac9ri9lelABchl52lu2eCQgkPrQvY6tUkfqKbM3njoIHI7hzzEvtw2QjmieVGEpEKAuswbSOaI6vvJSblO/Y56Txsd2b7zz3s/NMT1Y48zhxqhtUcKqW2f24NaZKsO43pQUpzlGW6M+RJWK503iE22I7Y9tZzLVuZar2IR5MrvQuZUutz7WVz6VydpUPo514DH9kGhPUTwFXLfQl3a3h3Wy3J1IqC2oBvV7ehCRlJz86FzLJuIfGYd3QsPDWqmGJNLLoI/+CZVyC6SumtZROukgmYDkELCfolJXIZPoGnuMGZQvJ5b9RmRl4gPcxNInPzQ78qDbjkJPEZqcDtyibuIodDFK28kfJTTzmfZjWSZFcUe3aUXl7+VyfziPmaMqjqxhyN13UgO5N4li3132PlSwg0xbnLgBSZQqo7gig+g7/vCCwUjsCcP8uqGgXWVsrqLb9R0YAtuPwm0jjSuM5CuM74veXM0L5Er/QecUahIGG4quzyyQjlfUs1hdwp7KBkeaDvJH4KN8nDmWyMQFLCeHzhDGM9L5AT2c39Uv3/aN7y1XiyRMR7NY71znGctwv6TrlJ07Yccw2h7M+dCBr7e4sMq6poUA6V3uDEcq5u6vaNnDBN3R6wqXuHY6/ki/srhyO64sRksSlgfncbMwi3eO2ZbxAzkDHrHOujPALyRWRvGldQ6qUh7fs9N967+vtLswwRzDzs/0ssa+lSeB9lljX0lE+yrdyFasnNHMn33umECcVk+Zfzs8hPVjr9OAecxLFpsIQ5TvaFrtcedNXyqvaAsaqnzMk8C9Cky/gHukjWhRsrrpfvTFr5b5BYFWt4FcXyDFscdqy0elMqVK3oF1xTBtaBd7mPfsqdosLKFTjuOHM83yhT2OYspHd3nEMVrbQQjrFXtEUqWIXGZH3K0e8dxJ19ntOS8mkC1d+fJdoSbpwMZNiKZzjIhlFVUm0zrDTO4GQP2p+n5aQuNmYRYGIYCpvEdBj6BV4kTP1XakERdXxC51kqYD71FU87HPTUuFOCferH3PYexeeEjdaqNwxxmdtZbd3HD/YbckPb4GvpIBkqYDfRX1CCfCUOZTimAt5Sx/KzojLq74bpwSvVEUfXK9czgvWEFKlfDrKR/nOaU+5Hs9m7iZc8nFpYD69jReCSpjV7aN600nzL2eqeTeb9d78Ed6J96iSJJ9mjuJJczgSgiecl7jNeJQ3rP60Lf2RqdqHrPXM4CenDdmmh9+9d9Amc1WN+RvmbeFt/Vmc8nz0rL184ZnOAe9Imp/8mLa56xmDq+ap6zpnRRz3qKupX/JL8PxZsc/SSM6mU/Zn9DM28rT6BrEUM0X9hCZk0DHwOqcTLjmHvw+A5iFPRNJRPkqcKAwenjmgNVe2Tgru0oMsoAoQuHYlcOVrch11AIossXXa5XRvEhsEgWukgOqoAyj4d0UAkiQtxe26nC2EuLDi2E3AY7hbsK5CuH3nJElKAw4ClbXn24QQ484z52O43R5yKg5NF0J88Vf3UmmOFSDdO4xnzZsRogu+kgKmaCtpZ//Bs9YtbPBMZfuZJlT2d61uQgiuNuayXp/G49o7qKbFCdGWHL0eZ0UGwigLflHTIl4gOkTj/MS981uBIXN9YDGdk2M4V8zANUOLpLv/ZSIkH32lzewXiZxVO3OX9TaHRCoB80q8urty95N3crW8E1j0p9e9r+R5WugZwCCyQ5oww7yPSd40bEnlpEgilmKkcjeMfdUahIFaZ3nbydALuSjwGinksdV7Lzty6rEzvTkdGkTXCD9r23hzCtGUcMmOD2gnJXFcpJBoF1EshfKEdQdPVhRjWY4Ljj5pDQcElr+ce5VVfO+0o19JOhO0pewp6QVMRpTl0l/ZAcCZ0FYkxcVyufIKvVPqB9kgWnkWTdWc4H1cH1iDVujF8Haiv7ybLBGDWl6PSvXQ2uY4guZSBrO1pXSTD7Gk6F22RVxJ54IzPKUtBQiylF60BrM4Og0AO6Iez5q30DM0jeV6KkNYQLp3KGdFLClSPgUinBe9y1jutOAaICKhITPMEXTwtKqG8sCZQhekuyjkDP2sbZQ7MhkiAUIqgIKIJHobCwjBz2f6vwjFVRRtm72ai9U1vGjdwI2hLsMroWA3K/THybIm8rx5E3lEYlsGJWoyXQLvcMwzjMGeLeSISLZGz2R7tIq/1OHikwW0TY1kuzKa7WdH4jb2c+UcRmvv0sX/KsWE0k06iFaewifOZURQTlPpDClSPg2tw9QumJRliRTy2GBfRBL16BoRyxqlIRMrXv/AdokNR0U9Ej0SW502bHXaMKZeHM+c6UCsVEx9tRCp1OY582Y6xtVM62Yl9+WG3Sov69EUhjdhrDGF8eoafOg0KvuZRMeVcNYlwUBlK4MDj3Fdo2uD9SNEN+S2zEe5PKkjU84OARW6yIc56qTyUfxEKCzFsBxmBuYRKOxCdRwsENuKh8yx7PBOZHtBA6hVLVT5O6l0+AHLwbRFDQygOmWzKgI416EnRXqJCtGCGIB2njmqjweC/YLj/gID+DspoLeBhcC71Y7tx6V0LD7P+GNCiL9T1TRfCFG77/PfMgeFVXYvxqjrOJJzE+WJvRgdeIxVnsdYaV/CSONBro7tRbfznGs5gqOiPq0Cb9FVPkR3+QBdi78myR9ghmcBx4t6c2nuSi42c1kTOeofawFZ2Ue4Vt6GoV1Z5xjTdsgkjkwBa5wZrFZ6sMZfjynKClAgv3AM+8tDmL3uIP3tKxiubKxzrl9OFXL9Kz/yuBpJQHIXjRBfJjfIP+AtD0f4QrhF2cQz2hvsPNUf6EGKlE9feTdwfo2cdulL2er5kHuMe7klMJMuRgvkNx5iS8v+NG9/MQPapZ73vEHyj7SQTzFRXRM8lici8GByYWApsxyBpkjkxnagdeAt7lA2kO4dxr6znWir7eYBPuGg0YQtdhuWxT/Ka4AZmsg1gac5I+L4cHIvt42eJ5ISq+rR7XP6de4U24HbAehq7sS0vdjZJxmjfwbAjiyNuoBsb95vfF3BIAG4MGctS8LHscaCy509LLf70lI6yS3qZiIpC/7IREQyr9rX0T68IeXGIddpAymSW3Pwtd2ZMksOhuyRkdEsd64kRqrJqMrfs4ZP9Xm8n/I43Y4O5OvSPG5TviHKnwjUD/7IfXjpZzxL13qxrABOJ/bmnkOCL50u3BTippQURcUvVHK1FJbbfdntHcf2k1MpbzSBZlIGiiTY6VzAAacRCcJkSOf6BNY+zDeLIzk1ZBa/WMNpFdczCLBfnBZN/JFiSvESSwkfeZ5kR5bKZOsW9uojuVP9GoD95rmEgk7F3/K89zG+sLvyUuA+bDsStZq/ulX5ll7yfi6SD3Ov/BSjlC/wo2Pro/lFNAMB3+v3c6yoBSPssSyMr1lCZ4UlsEc0x5Y0StUYNjhd2GB0YWR0Y05zKemUswHQVYVntDeYb96Ix3Nj8PyOyjEypQxKQq/j3uhXyM7KpF/EHxSUBQjXJeaobxKdNZQwUUwpNWW8FVmiiDCGGdMYXu/crVSl46/052UBq8ZxqIoALFtUwwBqzlN5vq4qVTTQ8wDJlVb5rOSUuDSh/zYILIT4HsivdeygEOJwHaf8j5sjKbxiXccZEY9k+Sg3bA6JhgwzpvGq/hJNpLMU6efvBmb4y5mrvk43+SA/ORdyibyf7mUbaV7iNiLxh9UnIXCSZk46/f1fcH2Ru+7ZlsXWxRM5dbQqJVQWsEibuo5l204Ej0Wc2swr+kvcVLacukzKO0q6dyjztEX0NebRSMpmdsm/GBR4kifM2ym1NV7/YAV3ZM5hqXU1rQPuLnRfxrkNar759isWai/SWMokpQJ87HD2I+bri+h05gPkkjM8o73BBvsifgqv0EyRCoiQ6qaGGUImRcpnpedxLGROF5vcr33CmUPbuGe52yD9fPaS/krQ+a+zu1IsQlliXcMEczKp5GJV7GBM26GhlMVD6goAvvRezcDAbFbalzBJPMBw81HMisXMljQOikY8fkuvYEvLgWyhdW5VGuW7iGt52VvV0H1O3NM8GzOLDXF3cm3gKW43pnIm7k+ENsxyckQkk4x76B+Yw+b4oXjMQrJEDK/Zg0iUCpikruIT+1Je0xcE0y2KJAinHMcK8FLpg8RIpaT5l3NN4GkuDzzPw9ZYeuatZIXs8p9kWaJeGEinttW4/KmCAD50UlJd3VlReJKntSXElLg/MTVQyEHPXfzmGcEU9RNShZveLI9qxtdOZ0IJoEjuc+FL6sww81GyNHeReda8mczIdtg5RxivrnV7JNt9uFP9mpSszQxuG08fdT8xUglniwO8ZV9DXkzVQtn71gfYd9MWdnnGs1R/ltuMRzkZ1xNHwAspz7InzOXIzYn61zkfa0OfK+LWQjrFe76JXHfqWb6wq76nudqbDFC2ccSpTxg+ZmrLeEpbSlr5Ph5WP6SzdJgXPONZxgBC8eOple8OC+RwpbwLy1eMKMulvXQUDwZ+y66RtpEVhdnmMEIlP93TqlKBHQK7eFx7h8jydE5pjdkuWrEldQQ+PFxo7OUKZTee0gxG2o+yo17NqvSwwkOs0WcQEBoi4tzIsvbOvCzgPvu1tYDABYErR9eOACr/1hWZgFVVVFjXdZomhBPhUfnyN7cYMTb8f58F1FiSpD2SJH0nSdIlfzLuHkmS9kqStFSSpH9UaWQ7DqdEIgOMp8hL6IqVd5zx6hqyRAzPmzch4+ApyzjvuabfxyBlK+/rTzNF/ZgxxhSeiZ/D1/HDGWvch62GsiRmMk/HPE5T6ygtDbc59Z4Dh7j47DKOfValC/P7vu3MVN9j046qvOKumP4cceoR8MSdc+1KM2Q3L3ej8gNnRBwL7etYYA9hr2jKUvsaih0PLUt30l/ezlrPDG5SvuNYTikDF27hp2N5NeZKMM8wQNnOe/aV9A24AdWvsdew3LqMn2OvpTyqKRf7X2ayOZF9mgtmD5C3clwk19ntbHd4H7JFNB9ZfWghZxDqz2GE8RAJFJHuHUrxmfND0qvtHsw1b2W6OYpZ5gjaBd7kVft6mkun+ck7CcPnsk0STn/D954p6Fik+ZezSe7BPtGEB8zx/B6IYYjyHReVVjQT9xdxnbyFMH9VJea11lf0LPo8+PdhtSU/h1QxRDRFxnYEeXIMx7Vm/OC0+1NSQGF8J7oEXmON04MDIo08OY7ROXNYps9hsrKSjZ6H6aX8hheTrXZrZI+LAYQU/cF+790kZnzNr6IZxyswgIOiUbA4KFtO4IhURQd+NmolE04+QHFh1ff4pdGWGZFP07z8F+aqr1MY2phu/oUU13N/PrKqU0A4YVKAyeoqJha4qrUhdgmz1HfY6x2N4nPnq3QOabk/sNs7js1OB7LDWyEKM+gn72SBdSN/xFzKLPNOSqJbERIaRoMZe9ntNEfL/IUECvFStcBLskyIKCdMCtBKPsVWpw1FUhS/e27nSu9h/ujhtkMsF+c6mq8auOqcj1l38ho3si+8Fx+qVf2QHzTH8rA5mjipmFHWhww1prPIGkjzop+YoK5hpedx/ghpR8BfzgHvSJJyay6cCYW/8Ib+AnLRSWJPb2K151+s1mdyecZrXFHwMYMDVfJt9aRcLpX3kpZYtQCcajoUgIZnv+Jq/zoWai/SMsriYfVDkov30jXwKr8lDcK0nXNSn0INIVdE0Un+nXCnmNo2rJsLpNeLdokJZca5EUDllE61SuAGsaFUt0r3rlcLnf4sAgjzqNzSpYrWGxv6308B/RM7CzQUQuRJktQZ+EySpDZCiNqf0CLgSUBU/H8enD9lLknSGCoI4Q0b/h/y3jM8qrLr+/7tOpPeE1KAAKH33hGkKqiIhSJIU0FQkaZgQUVQL3tBQEARKyAKKKhIVTpIFwg9lPTeZ3Z9P0zaEMp13c99vO97Hc/6kmT23ueemcyc6zzX+hfPm2qX5nPWOZrX9FFYdjsouMbT8jp8cbPMuJu9zqfZmxoI9Kg2nksOoLf7Qw45n2SKvJYLVgzpZj3SfeIo4hq2Kx/N8PzD19acxd4L2ewGDuU4mOhaxD0tGpb5D0HOpWOMl38j3Kcp4PlgZ2gq/bR3eDg87jpHW0/kl+hsuiozwrWIcCGfkdJm9luNSdRrMVVew0GrISVFLRgqb2ez1Zb+4kFmyqvJuHwvv6gvUHj1DUgYWDGeantqfW8oy0i0agHDSHPU4S3jcUY6amEik0oYoRTgLPWsCt4wHiHbDuTuKjjlqpEuRNDBvZBgCjnqnMChoja0VQ9TbHsSV0FuZrVrbNtmiv4U/pTQX/ybxuJljll1iRWy0ZB5Tn+c58s+bhoKKXYor+mjkTDxc2cwTf6ZjWYn+oqHmKH8wPGidsAshMIUPlIXcii3AeV11k+j3+RyvsXGsnsHZR9B9a9ErvQt2YDgyidLasEAOYNUQ8ZZEgbU5kZhmDb1hGS+UN6htpjBh4Wf8qvPvTQo3sxUxdNInK+PoKV4kSXGQDr7ewiGtl8E8/RH6BTQkFf1WGYJX5LkHEG6HUyUkEee7cfL4iccUDqXUeAgqOdkRn/XhKEXihnS1rNIOJdRRIOoAEJcyXSQjnFWN0knFKUs0Ug+AXRxL0DBYIs6g3zZww+onbmDDvJ2lhv9Gez0cBwCCs6yTn2Jc9pw3tCHU4IDDBfXwjsy2P0Fm6f2IOvnV2hf+DU7Al4EPMS5d5QlnE7uzkHnZvanzQKqGBRFNiHe9R0iFt3EEygFKp+Zg6gZ1AJHQBgPyztwl17h+h6AJArUFVL4x4pnv96Ku/1qcFhtzDNlx9eYnkw1piEAACAASURBVPMz7WD8fQLZU1KPPVYzptWMY/7FBIKFIupJ6RQaBbwhDOfuUG9PirwaXRjons/b/vFcC4FF2kwGqwcwxQAauI+CUDlRrqvxDDl1A70UrfxDInnQPYcERyPeSh8BEoSYB7EQOYRHIM/l1vhKep38nGFApWieGVyXj40hrHK8zsmCu8DLdw1Gd4lndJd4Tqd61F8rSkBeUhBVJ3LPz4RIf3Y934vvD1zh0+0XKjwGql6n3GIHAPDUnQlkFbnRTMvLUexG8b+aAGzbdlPGUbNt+5AgCBfwYK7+vu689PLfBUFYCmzgJmHb9hJgCUC7du1sAFNU+cbozdPyWhIz+pBfuxt93W+z2fEcZ+w4RmvP0y288w3VNTXDIpsgGrmW00y4RDvxLM2KNxBgOXhDfY/E3PY8nL8SU/HjRNRjFVpAyVfOk4cfIUGVDJ6NZiea24EEq5V9gqjMPfQSMzCsGzccfzh0lfc3nwWCEGyYpyznHf1hLtrRTJE9KIfd52OZok2mFCdHxXo8J68iM/cCzcQkzqT+BVQmgAvBnXjg4isMk7bTUPR4IwSWXuMhaQdBJb0Qih2MlDYzRtqEmRUI3IOIxcvK17iLxuAMqb5TefDam4xTE5lvjGSwey7BvqE85nLztdmXg1YjXlYTqlXTLRvuFfeQIF7jGXmd17Frdjjd3B8zrQy7fzW0C+PcC5gsreMz5wekl4QRJWfzjLwOty2zwezI6rBX+QpwBcTTy/0eL0RX0vFUH38KMz3IC9MwmJ8/m6M+DwL9AWjoPkmnkq1QAilCFDFqOvvSdKhs/3lFYOYhtjpmVvzdI3s1r/k8z29GAP9YdVhvdiFIKGa4tI0gobIHIPqGscwcSCO/OmjmMa5Knl1GuQTyOrMrBZbiheWOb9iS/XYanXNdbDieQlyILwsLp3DNry+JTSYx6p9WfFmUyWhpEz5aAhBe8SXXkblD+5C7Y2rQDsiN7MgT2lR2Ws0ZUp4sFAeZdgDJSk2Wmw0553yUfVcmcSXAg8LydcikBrVg9eU7iLErfQAec7xDhF8oa7Nr0iX8xg1NAZtv1Df5K+0JHjWG8mJYY5paOQyS17JLq/7eNs37k1ccs/nS6Mcnxv1YejA+VRBGY6TfaS2ep7t4nFnCqzwqbcIPN5Y8nRN2XbBhfcmHIGdxnzaPgcHefsqCTwgn7TroooNcOZLtVmvOOrvQ2D+ADHsQoX5qBXVr/VPV6ZyRiou24jkyi2rwdPgXXE5OZla9nqw++hcX3UG8rSxFTe2OJFioovduWRThtF2Lh90v80JU9f5HOZqnfOV+ox6A96aiciKPC/FlRr+GTO3ToMIfwJtB7C0ncX0E+6p8OKw1ADnFt/YZ+V9NAIIgRAA5tm2bgiDUBeoDF29wXrRt26llf96Pp6n8b4clO1ls3ushdpgalg1X7EhGa8+zQv0XL+ljyVJu3AMw867ynrKQFUZ//rYbMVlcT2SpgaR7JqfiwAQijFQsMZDWRX/SVv8d6Mvjl6Yx15nCytwlZS8LTqYV0d69mGeiEyrWPt2zVjJZPcTm9BRgYbX71734LUlOT6lmldGT9q5Ped6xhglsoK1rEf2kvwkpqkGKbTFb+Y4vjLto6P6Kz4Nrc9rsQVhEN6+1hsPWmCBvwBcXzYQkbNuma/o3dFR+5mD6ZeT8x5mnLOcLaxBJjjbMBaKFbCwE3PqNoaCXpdqMEH9npTqPu91vcNUMo7N4ih1WKzbRgYyC6j0AU9f4WF1Q8feXRj96i0dYY/Zgj9WUWkI6uuYCnOimRwtoprIa3ZZ4X3iELa7GTJPXsMrsyXG7Hp3LvHFN0aNvb6uVLN5W2mEalO4HenElp5gX9OcY3aiSpflljRdJPevifmk3s31fw517jXsjut1k+gcMN1esCF4yxpFsh9O2Vk0C8tJIEf35wryLHuIxpso/stbsxvvKInCNhKCaiIJFCAVYrkIOOCaxyPSYx9QS0lEwuGDH8lHRd9QykgAP1NIpS/QWD3H4QDofFkQiiyJvyHXx8Yut2M47cs7wmrKCs64hQENESyfJ6SlXfGQMocS4B2iLEVCTnVbzsh6A56VYIQmM0Z/nPikMP5KYr4+gXlAHQlJ38qq8Fj+hG7ocwAh5O8czD0CZU0CeXx1yS20SzT60DPZezap6IUnOEVyyohiqv0KbwJYIlCCJAqGapzT3s9y/GmM+0u1hLIeLhRxyPsmx5PYEaumAp4/2quLpr+00m6EKBnOVFQCsLhzAPPlrNlqd2BA+nqN5yQRRhOO62cqp5XC3uA+KG6AWptJOSMTwa0+hy6gmvHajiHCazFa+50vNn98DH+C47SQipg4b2r7Cs+1qErDsXc6W1GeoNoeXYr3VcJ0Fl1invsxc41Fkv5trbJUnz6KyHoAq33jyrt78Fbxq/V6e5VVRQFL1BFA1bnv8lkc9T+R7PK7L4YIgXANewdMU/gSP/9tGQRCO2rbdH0/NZa4gCAZgAhNt284pG2cZsLgMMvq2IAit8JSAkoAJ/AdhmSa5tj8Paq/wbkQ7YvMvMFVewxqzB/P0R1AwCCq6xI0kjI3SQrqIpxik7uMH8w6mG09SM6gmXRwXeeNSXybJ/sz3eY74cF9GGL9QizNYpskn5hDeFhdQO/svYCgAjxUsYL9UG8Oq3Jq+5TOD8cVvUyT4Vbs3QLtrKyp+Hyrv4EVjHL/I/fmrtCnZBPG92Zu70nLpLp2gt3iEexz7GKfNIF9qyQx9Im8FeZPSIrRk+kmHeF5/nEn6FA6bFrtCh7AvQ6Ik5B56hzannWsRqn8ICk7AI3m7yuxJP+nGpjk/O++lh/0jh636tBbP49ZzeUKfhoFMknMEv5+fBnd4M4ktYKXRkzN2TYrwYZPZnlcZA8BgcRc/qHPp+b7F2pcepd61tex0zMWyBeq7v0aVRDQsXjTGA/Cg9CcJxT5AJyjO4iFpB76uOMCT1Bu6jtLKWottLeZMRil7rabMSqjck8iSwAfGgywwBpNqBVNiBdJL9daFyipyM2f9P/zrgRZkRHRimPZRxbE4wnircCqnlNpkCn7cI+0lUChhhdGPzWZbBpTxANSSDI44J7Lnymy2mG1Il2PAgIhajTiRnA+GxQViKZEdtC4bWxQFPlU+RnEbSE6bjWYHntGeZmpsY+rnHuA95StSAt6ktWsx30Y2L3s9Mpl2EBFCPlPkn0hLOQIMwccq4j1lMXdLByg17wGUivJA0/w/+cj5Br3c7+Hj35SY9NXcJ+3B16FS7BPDHH00g4LrV7zmruIJinMzKSAeJ96rRkXwTF51xHROCU2pp4tcco5k/7VZiN0fp6NrAX4+1dnqe2LH8+j5nnT1vcZ+d0OCg2ogCsVMLTv+jDYZE4lXlK8YoG/jEW02rYXzdC04SGd5KyPZyjT/P4kVjvGDYy7XincDlQS6gMJzLFQ/5mR2JxJSd7DGsYQt2mACC5K5QE1stQ54AW69o1btBPbVn86A7sMRf1zGk8oeFL1VhT94a+kjOgeGAWlezVsAUfEhg2BaChfw4ear7PLJ+sYloKoJ4NYTtXKTEtD1PIBq110vL3pd3DYB2LY9/CaH1l7/gG3bP0IZ+6L6sceq/D7qdve9ZRSlc9I5nln6Y1h2W9SiZCbKG5CwWGH2Y4P6IonpJVyvTQJQEFCPAe6PuOAcxUh5K1uELmRacaTIcUhcQSzJRjc9WkCJtR5ldGIHjmkWq7Uu/E4LHq7RoKK01NC6yIPKJtZnxgGNsG2bxAKF8fpMBgTVKJMq846PGqxg8+Ez5Nl+1BQyeVTaTKLSifMlMUyVf2Cb2Zru2TsZJm+hu/tDfna8xEx5FelX4/hdfY9z+R8AlVvhqLJV1kvyN3QQEzHsB0lW6/CT8RB3KzUwkMkiiFYODdntQY88pz/BFTuSHoY3xNU0DLR5sdTWR9HN/BgHGmecYygyffBXS1ltePY5eml1bSJLkJhlPIEDjQeknXQTT7DHakpdIRVFMPjMGMiHykJ2767JhUybGCuOV43RONCoY6UxWN7N92Yvhko7mCT/zKmSZgxbMoB67kTeUZZwrKA95eJwR+s/xfCL/Uk0bXIvHKSNcJ76Uf0rnkvXwt94RvmWNWYPThk5SKKFb7EvUI+9F7Lxc0jkp10m+tQXXGz1JJYVQh0hlc3qTGTB4q3i+XwqjaS+ncQ4fuCSFUXxhAP0XzSWleadDCyrt+MbxBx9NE0C2/CC0Zz5jlUsdL7F4fpLEBqKNPhzMpOsefzhP4iqX6LR4nxC3cm8oXxOe/Esvrjxc8j45WXRWDjDLk0nl0DUMv8CSZZp716EgMUexzNc8m9NDSAs5ygtpQP8aHbjHsWzg3WWpvGrOptjWj9PjwwBUS9kb/iDjD/dhnNOHxpn/so4ZQX7fCrLXne7fqWNtROccCjjQ6p6QCsBkRVieXf5niSoKIgP9AdoHNKcGKeTqfIaDujdKDfoKQ/bMmgsXKbIJ46vS+JoJAQg+woVCeBny8OwcekKql80u4ti2E1zAmvV5dXEmgRRTEc7hVyhlLn6KJ4I9E7i7shW9HW/zauhzfgn1IdFF8K537eAzFKbJuYp8o1bT46CKNLpEQ966dH8xSDBNVcu5XwRVRYRizNYo75KcfYUyh3oAKzAWNaYPXhHWUKq8TxVvZCrRvkK/ESy5zsTG1IpY161fn+bedy7BHSTJHKjUP5PdwD/fwxD8eczYyAz5VWcTGtHTt3edHV9xG7nFDLtIJ7QptEurNkNt/yaYWEi0dT1OQlCMl2cSXQr+RFBCGe8+g4ncpoxtfQr9PymSI09HLajSRnEC6mk2yG47Mq3bIQ9nx/tqQS4PdWsjLSrPOT6kV/ojHET/Z5c248rtueYRh5zlK/5WAhAla4yUf6FKfJaVho9GSO9RjIRvKqP5k1lGVklGTQSr6Inb6DcAg/gVGgfFp4PZZi0jQZiMoZWSnjpJYZKu3GWdEAulBgn/cad+iVa6IeB0WTZQbyrLMZObw9RlWyJnOw0InDxL2Up/cS/+cIcwF3uN3Gh8pr8JVut1sx1jaKpHEuTE/sIiE4gMtzTkDQtm0HiXhLEZJ6VvRmbx606/Gx2oZV4gXnb/+FvuzEf8Daz5W/5Xp0PgNuWmSj/AnisGXdoLYkrWcsaszN/Ch/xVlTlf9PfxwkIFLoM6iT/zJeODfiqUyuOx7ov0EBM5gXxe85YcTQUr7E3fSLQi+FLPUiS1Xfk8LLyDSczexJWarHdMb3i+vsKvmWE9RpJvo25mOvPFqsdv6kqdYQ0Aiip+OJKqj9fmf2ZosYD58hSosEEUXHg9onkO7M36aJfNQTJVTWB/aVx/ObuSCyZbHHM4ELOK1yOu4eRB+J5syST8dJGVKMNEIAglKtLinR2L+DRWrXpArgimjNWm8khqz6DZQ9sVlIcJNvhnJPqsdysRaJjNEeuDuX3Gk9WaMtnBzbhe6MXCVSahm+Mm87044NpLyYyKNzbiU2pgkB52V7EhfxmjDIf41+hzfEVDYbJO1Cd1UuujbM2Mdsxl6+sR0mnPYLLwNffv+L4iVf7cf7oXyT8tpB5zOIRaQt1hFQsaT5n7FoIAkzLXU9zeS1N3ct5ys/bmlR0+HPOjkMTfchSanBQbEl8TBw/Z6YgSyJ94yJvAAO5cUyJ/oZTSSl8HlS5uJpmrUDOdeCyFSTZe6qUBIGtZhuGWK/yWeDNlXLLV/xHr+YREeCgSXTlrtu7BHTridqrCXwLMbjrw0Mkc3D5Jsf/KxOAKfuxrExiQLBMbGzSCWGcNoMv1HeZrk3k2k0sDNXME3yifMy7xsMcsxOYJGyhtpaKUIZwyQtpTriVTbFVSMs6wSxQPkLcUpsdjtX8bTXgRM448ksakVnkQjMt+hnvMDYqnjuB5NMHma18zyh5M+cyOwLVuQDtM3/ifaen3PCl0Y/2roVM89vDcPkXmro+p490hDNWHH7RzVic9hq/mh1o5v6CGaG1uGrsIyi4k5cykS17VmA+gkY78Sz5rmIGZC6njfInx7KPQt5E5ihf84XPZL4vbsfHpkW8mEYxPqiWiGXZpOSXEhfiS4YZQHvXt0yVf2SK/BO9pSP0cr/HJTua5uIlmtpJbLI6kHD1B8LXrKSzvohl47vRpV44Vmk+C8rMTMDDnO0tHWan1YJNZjs+VhZwyYpikfoRp61avGA8xgR5I5esKD4yHmCd1Y0X5W/YbTVlh9Wad5XFPCj9RSPhCvOMUQhqJTwuxn2J2fK3lGQ3Zlf4wyzKasWKKu/Jt0ETeSu1LUPDL9G+a1+GrD/FgIj2tCzywFjXml0RSzxJtMCvNhSd5bRVk1n642QTRP2QYKKzriI6YvnG7IssCsj+ofxg3sE85QskazYgIwsQRQ4+RUkcdTzOl8oE4vO+47vYjpi2zXxjJIvEjwkrtIE/Kp5fN/E4yaKbvVZTNGS2ma2pHRhTsZ0Pyz3GcOVb0sxKVaiLDk8P4H39QYJKBwDNsP0i+NtqiB+lFTVkOSCSx/XptBedRJDGPGMkCUGdaJa2lrriRaAfEhbD5e0k5p2hHBklBdXgkl3CJTOauwK90VKqCEnOEWTbAcz2nYep+OFAQxIgLCSYI10+pVfr6nLlPpqHea5IAvucT5PrCiDdrEm5tHmAU8ERGMVJOx5bkCtY1+sKH+FDZSHrra5cqD2UT67W8cBTZe/JTtULuE/chVwQQ2BRMh2lCwQ44yl0GfipUoUW0L8TeUoU52wRscpEX8e+xhWzJiP1F1kQ09rrfLU4hZ8dLzFffwSnj8/1w1VE1eTfvX64l9SD9B/sABzyjev+t0scgiCw/4U+CC/e+Ph/ZQKwTAMDkZHaC7wW2Yb4nDO8KH/Ld2ZvXtFHYyASmb0faF392tJ8mgiXWanO45hVj098p1GCLz18kzh9NYsJij9PCy9yb2wMfYL8iHQYHMk2+El7kg/URdj523h6YTBGzhU+kLbwq9gRy/J8YXZaLRjnXsLLAb9iV9FUP5VSgK8qER/uR/+cbyseHyP/wfvGgxwN6Mnu3BA0FNJrD6JB0gai/aMZIB1kgHSQJHcNCq26vGs8xnPXNejC3VfpJR3jbX0oj2nT2a4GsyH4UdbnxqNFdqZvZGdauJbSKb42f6Rn8S9XCe8oS3hbH0pnv3gWbD/P+5vPsmNGTzIK3QwWd3PNDuewlUC2HUg38QQ9OM5UfRIONIZJ26jfqAUHxVhKTshczCz2JABB5nPjLs7ZsZTYDjZZ7fnE9IAfu4vHqSum8YQ2lSXqB0RIJ9gleQwiB2pvUlLWm5hvjATgAfEvTlh10GyJ2kI6I6StOErrAp7dRpiRRlfpD67kTSJDjOSs4r3Czi01OWXHE953CE1aRHNkvUFXNYKrl87SELhf2s1PoS+zTHuW++VwciJrMEqrpKL4W/78Ko5hg/YQY5QLXCQWRerPGbsma81ujCxbbYtGMfudT/Fn+hjWmV3Jd3qIXIosQpkK6EGrPnUdahUhYXhC+5oE1eNk96PZjcXGvbwc3ZbIpD0sUBZyym8201zL2BFciSQr7wFMU9ZwPj0deAiHVcJS9T06iadB8BCVyssDXYu38YNzAZ1cn6D4Nadn6q+Ecg6AQv86zNFH80CVib6OmcQkaR1/WO1w2N6SC+VwxTChkFRnAkGlVzjjfIy/U/4FTKR1v5HcKHZHDmPChc48FGdzPFdGFiE0OLIKmBKswDg6CIlcMI4zSptFA+EqfQuO00nawyBxH2sjR9NG+JZvnW9iiA95je9TmsZH6kIOZzekQ/pWhvMXJ3INdioLOGom4M6/B25owFk9BhasYoq6A0n/nXJby9eC5pJdpAGuag1lQfUhyw6kpXgB5/U6QVWiagJoW9ub7lQVwy9wu1LO/6wJfLv4r0wAYv5ljjgnMkWbhGW3xlGSynBpG24UfjDv4Dl5FQ1yNgLVZIhID2vPQ9p7JDlHEC3lsE7K5bThwzUpjmAhCbk4vYIHAPBLs4/5uozp+5urA33r16TL6Y8Yp/zKJTuaBeonrMvyAZpxJr2AkLAoVvpPQJFEHiy7590f/4UPbk6/9QDTwxfxz+VMinGSICQzQtpGTO1H+POKylPyWhp1ep3+KQvYnjuE/u63WKR8yGz5e/KvZbBVXcNfxUsod8ICSMj3yE5Pl1fjJ5RiWIO4osSzxfShoxSKgUQBfkSobpoKSRS6ujBdm8o5O5bWuoXrxHpWqT+Qk9UI+/JxPlQXkmjVZID2L4AK9MlZK5aLdgwNlQzqjHqP3PwCJp2aQXCOBdTGlH143RiFgMUYaRMPSDv5zWxPE/EygUIJT2lPM0Laxh6zCToyAjYfGg8gYdFF/Ic+4mEWGfcwSf6ZsfImjlgJ3K/NpYv4D9+pb3CquB/lTX13fB8abV7Bt/6NqFG0mY5kUNUwp1/e9yx1/MA51fPe9JGOEFwkcNzdnAGubwhxykyxfIkX0lAKLmM66lJTSGenw1NGeqV0Os/oT9HeoXCvtJpzdjqKKDBG+p1fzM6MljwJQFZ9ma2Pp0ZANz4wHHxkbibJOY1LV5dQIgdx1jGK0foszgT1oOoU+WHICwSm7qG5cJFu4j+sphA/h4wtuokTrnCiqJAifFGqrEbvZCmFLoMvepTQopUnnfgXJdFaPM0msx3lHRDZLGGzOpPDRnte1MfhEHRULZ/lwU+Ro2j8DNTO3snDygr+kZ6uGL9u6QmGK6t5jtWczK0JxFUcE0Sxogcwyj6F7k7jX/owOoTc2idaMFy0FM4jOFt7dH9M6OJXHXb8hD4NMSgBK6Iun5/PpnbNhrxwPJIg0cV0h5sUweIjYwhTFG9SkxVWj17u95gR0ZVjWZF8XtSFoQEh7DKb0106wRmjOl/lZvFw3jIQIUMrpLye75BFol3n+UxdjJH3L8pBCACCbzg7rRZMktZXI2NVjao1+Nqh3sAQ6RYooOvjZiig2zWBbxf/lQlAd4Tyvv4gc5SvOZGaQEbCQMa5F3DUOQELkSXSUCwc1bvUUGGQ3cK1lJpCJgPts3R0badIqcPT6gf8nRrLMvsL8krGAk2oEeQkiCLChXwu21G4TIErdiQ/W12ZoU9ktfoaAWXN1TaZ62kiiuwS+1f4nwIsV94mRCgEHiDb9CWzDMngQGeWspJ//O8mV7zIJOlnxCbL2HvwMerfMZYzyy4zW3+cxeoH7NE16omp5F/7hqoEt2NRQ5h3vg7DpG20FC5gFecQV5rIMOkIuJrgzLeYIP1C+wJ/5juWcj6vH6fsWnypvE3WFV+auo/SUUzkVGk6cuYpLFugjpDKm/JSNlntudP9LgI2T8tr+dNsyaLwfqwHZAyeU1axLycGuBfLsrlb3EdD8SpTymSe31A+B+CA1ZDdZjPChHx2WK14z3gYgDflpQyS9hEglFJg+zBO9sg7LDXu5pwdyzBpGz+Z3Wnv+pTFEZWr0gAfz0RQ6NLpmfU9YfoFqLCchztdWwkSSogUPY23T6QPOZrxMFsd8diIRAb74Zd/llnKSg5mNyFOvMDOKj2Ax7Wv6WZ9SGCwL8+nFrJfaMkOSSRSyMNfcFV84UVF4XuzN0PkWCAZrd0E9p4SaNdxMIlnz7DUHEiKHUb8dUiMfJ+abDB7E+DsTxdtDwvVD8kv7UtWvT702eLH2MI0npQOoAqVAsTl96zfaSDhZWxRK6Quj2izOW3VrkgAqqJy1o7lMI1ZZTbnoGMil1J6cVR9soL0lx9Yn++NXrQUK6WCs+sNoc+RIFoIFxkdWrnAuD4eKlmJbeRznzmXFsE3l9gGaJ+1ltccC/ipZAZ7hDhsW8Af73KJmn+RT5WPed+cxoyxwzBMmzWHr3HRjkFBINpOZ4r8ExO0qdXGlxQfLtnRaKIPaVI0ZxUfiuMa8pwRAAZMq9WggrR5u5hWew0Hzl7jp4DKSX5EyXcE2yfJIYBQ1VtVUxThK6Mfv5sd2HgLpE3Vkk+t61i+VZvAt0oicD0P4N/vAdwu/isTgKkGstwcQAfxNBYitm1TgB+Pa9NYqr5PprMRX+a3wrTsam9QSMZeFiuLeVEfx0k7ninmfupaRxGKjpJr+3NBaUBdwSCozMO2uetvjpXpzCwRHoL8OgQKF+gqeqgLD2uv8HBkHL2BTsVbsSQHzfJOoZollPPbeknHKLA9H/w7izbws9Oj7LnAuI92rkUsiO7EvSNiOJ50B21lmc7j3wNgnjyHdDuE1u4lDAyOJi3lCoEBHbw0TgvlEGbIqwkQSmgpXCC9OIcHcj+nuXKYc4X1Kch7ktnK96wJeo8nrkzlSVOhgXCNdDsETfRnj9KZoKKLhDqD2BpwH4+6WzJb/o4J8kaGs52OrgWkE0pj4Qp5oj+XZM8XQfEJpKHrS6bENvU02wtTWah+XPG8ntcfp794kNN2LQ5YjVmh/ou9ZhOeltdxzKrHGTuO4fJ29luNWK335EerB5OkdVy0Y/jd6sA3yny6SSdpJZxnlvEEguKsGDtQKGaO/BVqisHqkCfIFfO8VAkf0F8nykpnVYwH5viI/To9IhvT4NLX7HGsZEdpH+KyPOMlRt9LTOpWjlgJTNWfpNh24hAtGguXcTpb8YXZC5+yiXO71YqZ8irAI8csCwJxQgaxhRmcdDxPYuH7tH3Mc8wIrMk7xjBWqXOxs2OBysZ4C/MkkeIZTvr3ITU7jF/NjvQJCiOkbEKuXfA3Y5SVmPKnFdccsx8CJ6TpiZSXKCSfIE5btfEVKoXKZNXJZP1ZaqsmMWTxtvkIdYOa8UDacgw1GOiMj57PcHk7F0pSKEe2BAcHc96O47wdx1j/6v2zJOcIdFtiTsSX7DmfiT8lyNxaKFHDk6gtRxC7HM96XkdOB8o5EQCm4s9Rux4lZc1yRYLwgtN8rrzDj3YvAuKeYaw2kyt2dXtXxSjhIWkHzvwAoouuIlFAgLOy5FNNwvkWUSyHcM3WEMTKvkEYueTgx3R9/MBSXAAAIABJREFUEmsivOHXsiuXjY4XeKdsMfPvRHSw0+tv7xLQrcOrCXwDU5n/afx3JgBDwwc34/WZzIpoRf2cE8yVl7PC7M8L+nicYRHcW7CLnNxORIR5bzlFdyG1hTS+V+dTiA9LIz7iUHIpd/pfJjGtiA5GBM9rr7Ksvgc/HBAUximrNhvMjgx2HkYrOscz5kiO2/VYobzFcrM/puXZLk/zfYN6Yb7cmf0dll1Scc/FxiCu2pHMBx5xrap4/Cl5Pd8ZvVEUhToJ7anTxJuBeb+0Cz/BzRrzDkq1SF43RvFYYB2vc8ILT9NbOsLnxl2MNZ5jXWA8XwVNwH3tOHpAIwZE92Sk6wuejmrGH8fOM7oon8/V95ilP0azwFYclUP5Vo9nQ2AcmUXnaS2cR8Rmi9kaX9z0lQ5RU8jgJX0c8WIafbQtQFcUWcKNim4K/JOcz95TebiMwZy3YtBQ2GS1Z5XZC4A+DYLZp13jiKMDcZfmsUx9r+L5z9AncLUMFbVMGIJmWgwR/2K31ZyjdgKthfOMkX7H4WoIZbbuAQ6Z4dI2DuR1JlXoQp7qjQ556q7WvL7hFIF+nonyrFiX5nIEbYoTiRFyaKKfYnfISyxOiqKbYXMp4k7Ga5VIjjg7g98cs9lYNIv71ZXsFNoCAzhiJaBgUu5sIAqwy/Esf+XdxXdmb5oFVCJIPGxNm+1mK+J8Y7wQab2KNtJO3cIx/SCXZZVNZnvuDYpCcRWwWPmAP9zdaeT+ksQqJaB0wogiG8mqJO8ptpvv1Pk0Eq8CYwHPhCAIMMDYymznl3SxPsf2qU9rbQVaGVO5IKgBc/TRjAqoVHWNchhMlX/gkhWN4tWxqAxFMCnyrUkQSfzpnMaxzM+AYTc8F2B3yGCmXWrHjIgazDg7gRAK8Y9qStUOg4lERzGRI1ZqxWNhhYl0kI7Q3T6BFDyHu8X9dJFOUtUiFEA1i3hHWcL+rGjuytmEv5aJVtSU044x6MjszX2HqnDWW8Wg3K+ZoW5GMvcAnkXO95HT+CPLI1pwfTIRFQfZdgD1hNTrh7ppXI8G824C36YHIP/PUEC3i//KBKDknOeAczITtGex7FY4StIYKO2nEF9+NrvwkKOYOepCLqYOrpYALkfeySgtsqK2vVIysCyby2Itagk70LKTAAgP8HwIfOp2ZIDmEYA7EjEawxboUbSSV5WvuGpF8J6ymJ1ZxUBLit0mvk6V30NGkFbgKqOLwb+MYQRTxMtujQfED8gt0dGQaSok8YD0F05XJ6A6m/AxfQbPyat4RfkKMSOKuY59fK17I4uaZnpMYMbLv3HZjkQ3+3FZqs0BK4AEwZ++tkgpToIVg5bCeQrccYzVZnLGqkmCbjKgdCOvqVsRhC30SFlOI2U3DYRk7nS/SxphnHKMxVdwEyXkEi3k4F/qAF5BEgWekX8iNucOxn8iECwUccb2rIbGSb8xQdrAKrMn7cQzNLLi6TRxIVFZxSxcH849SfPJx48FxmCKbR/uFXfTXjzDYuVRnmUZD8l/ccyqy33aPPqKf7NUfZ/zpUMp730EBofRSviKe9Q46pfsRrMEqMJDHd+tjpcf853CIaKKC5nvmEpi7mPEBvvQUwqluXCJ8OyDpIe0I4Ys9jifodRWedd4mAnaswzwi6a5mIRke75kD0o7OWxVkqcEUWSmMRHFtwXfZQWzPKyyvembc5Ik5yNM0KZyLeQerx7Aptin2JIdTmh0E3q5PuOQ1QA/hwRKAPFCGqJejC15rxYHO5aSmu/iUEDl6tyh5VJbvMpOsxnlqouCILBZnck/dl1mGk8Qqpbgp2Uxy/ECLWKC6QFE5h5jrrKCJP3xirEi/JWK0t3l4qFcj2sv7wFM088QKp7ndf0R+lUhkt0oVD2fdkIiPo64Ct2fAf7ecFHfwGBGa8/TqEElYSup1hCm/R1MsKSxQRRIIYwtZhtGXze+GBBJV9dHTI7qwOGseHStlCnBUeywWnKXdBA/++aud9fHPTlfggj5ZuVuyqFIdBePM1v+Hkfx11QloQmOAM7aNeknHvy373F93IoJfH34VNHs+k94ALeL/8oEoPlG8bo+kjeUzzmeEk5KgyE84v7U485EMRmR0+h96R2mpBRQp4lV4RQFHrw6QE/xS+TSDMa6/6aVeZBr7pY8ry7gQPJh7lJziZTWAMFEBjipKaTjQEdVIygp1TGQ+N7oxcvGWBYrH+BreLRfxrlW4HB14S+xbcV9AIZKO3hLWUZaRnsydSeleLb64UI+05U1nHePv+Hr3Gs1Zbo+kZXqPA5akcQJWfS5+glUcTr4M3IUr11txUBpHz3FYwgFV0koPUFd6Qy6VhP/XI2npZ+oU9Sb9Y45/JGxkJ1Wc7aqM7h8eTTFxjnaiWc5m32OxqWHKLJ9UAWdodIOTtm16e1+F6eg8aj0B4esBlyJGlYBuZwsredwrg8vKefoLx5kiv4UncVTPFqmEf+8shKAvwv6AGOoE+7HwB6dGHHuJRxo/EtZQjMhiQQxhWw7gDs5QazsMV1x2wr3iHv43epAK9dnfBdWKU4miCJ1IgK5mFnMy/krMEWVW5HJ59iLOZ/Vk+XFHqSMbloEFl1iurKGvdkhBLhS2ON8FQAfQWOmvIpG7hV0DotjmjaRU2ozfgccaNWc1NbbPWkhBAG5XqxL278GHxlDuGBH0/y6lZ/pG8VS814eCo1DSTvMZHk9svQxSCoPi+/RzjjABOUX4K6Ka8q/6F46MP4RPOx+mUt2NFWnoWM0YIfRlF+sLvxpTyczrQm/65NwlqGlXJEteVV/lMcDwyuuCQgMYYD7LRKEZJ4PvLHfA0DHgk3Ul/+gjXsJd14HF70+emav4k3HN/xZEERdwUbFIADvRmjdGmFMemwCLWtWTq6S6DHEySwTK+4rHibZrt48lmWFTIJ5beMZosNCiAqMxie8Fk/qU0GH92rd3Bf8+piZsJFt/1xhm0/lQqx33o+0kVdyxq5FA9W7dyGJAvP0R/ATXOz4N8ZvUyu42mP/CRHMv4oOhpcl5P+NCUB3BPON2Ydu4gl0wYFtg4bMRO1ZFqsfsskeTJF4lC67X+do5Dpat6q0Z4xJ3cLnyje8oTzH+ZI4Yo0/qG0fRSw5yUWrBtvstvRiM6HBnn9YiI9cgQ5ZUvoiVlEGtcST1BNSMJB5TJ/JwLBo+gH321tJdAUTV7yHwKKLgMfW8A35cwptH3LcCqOttcxyeibGf+nDaO/6lG8ibgxVe0paSx0xlQ7uhdQP8udMoQ8Rfi29vIcKpGAmyL9QS8iggXCNq0UZjClaSgPlLKmuCK7mPcV0ZQ2Hw8YxVptJR2JoLRzmgh1DoRTB73J/HK4sogWRJ+TXMV1ZjLF/rVDBbOFaQqodRoKQTKBQQopU+ZFpx9c8HFubKSm9UQWTRWqlnMIE7Vn6S3+TYYeQVmNcBSHfX9JpLFxGwWCw5JngP9MH8YPZk6nBf3GxQGS91Y1N6nM0FK+x0ezAZP1ZJNnb2/R+x0HCUnbwftAMgnxkbuUs9KQ8l5YRtXgl821ExaBYD6JuhsYFK5q/Yh+jVd4W9phNeFp/GgOJIKGYFsIFfBx1+cnqQVCZN0GqHcYYeZPX2PXEVDoUH+I7x/ucy/oW6pdN2v4RfGA8yDZ1GmlpnaAKU6G2dpZnpF8olidxTm3MBk2okMIN8VPpaRxjEPupqiW1q/R+cEKheRXKFhCqw4ckuwZ+13k7zBUngZVLvJDKYuVRIoJieOnaR+gF3YGWdO7YGbVGI2JrVE6qgiiSaNci0a7FCz4BXB8VWkQ1NzIruSshFCBjVDuvaqQrHhirERjHNodnP/x3Zh+4rjXbsa735B5SeIZV6lxW0w+4i0n6MxTZvlXoj56QMflceYfu0j/MyRuN5NeaAGd7BCxsRDrVu7kk+/WhyX5kE+RVl/cVXJy3Y3lcn86u4Div8yXDxR+O5/nYuBHf3zsSXx9ww4n6P5GCqJoAqi40/q9EAVlaKRFCHhP1qUyJaE6T7MO8LS9liTmQGfoEBkQ3J9c+xT6rCYrszdKTjBLChHw+0V/BT83jm5rreTL5LvoGXONsts5luw7fit054ethLAqiyFGrLpfsaNqW7ibYncQofRr3SXtYq85hvj4Cw4pCMyzauD9jRkIDIhO/IaVErGjWLjcHcNquRdeVzzNL+a3iuTyvrGSz1Qb5JoSVcfJvhApFzNQnUqKZfGwOYUhALFV1NOIKjnGXeIBNVntGabP5IrQFy/2epTTzMrpvFHfH9Gb4wbr8WrM52608mudn8YNjLk9oU4kP6sFJIY3f9Of4MSAet5FF/zZNSBab8vlhjTbiOe6XdtNRPM3rxii6iyfoXLKdcllmSZLQTYsZ+gSChSKiyOW8HYsbhW1WGzZZnlry+MDKbX+IO4XfHLMr/v5GvJddWl0AtgXcy7G8PO4Xd7LJasc6sxv9pYM8Lm1A0ZoDlRNTgppLhHGWi64A6obcXIwL4JpUk2jdSUMu4y+WkmMH833AbE5mp9BKtzgd3o8P9cqSUTPhEt+qb7KrOI6DjmdZxV1AP3ZazUmzQ3muytg/iC9wwtWcZeZAulcxBpEEAQWDn60uRAS0okuVaxoU7qOzsoYD6QpXBI3iKrX+Z62vyUZmoPIFe6tckyJEEWOne60aFcHmF8eLZeqjj1U+Lgk8LG3hOWU1/dQ11HWE0tf+iCzLQ8ySJbHapAsedc6GwlUUoedN30vTJ4JYYRfbHdNJzP4BEm4sughwLvpe4i+3ZHlsa57VJ1ObNBwh3W6hzuOJgKJLtBcTaWYnAa+zfs7oG3pXyJh0lzxgjOHSdrTCRPyU0VxyjuS0VYvY4H/fz3tQ5jJecGxAtE5TnmD3xIzlyyRPH+t6HoAoyxTYvkQKedcPVS1uJLkO3pP+7XoAflUTwP/tOwBn9kl2OZ5ltPY8tt0cR2kG3aQTZBDMFrMtamhNOg15iqfWHGOh7f0GnY68m3eO1CNJHAGiZ7tp2jYXhFo0EbaiGTJ2mHdtc7A2z/MzLoYjV/No6drO88pKim0HryvLOZt9mRLNIwnrq8rsC7ufg0U5jCi7fp7xCK2F87xrfMY3Rm9eN0ZhItJGOMd90h5UbRDgz/UxW3+MB6W/eFtZQqwrj3jHNd40vKWW2uX8gkMwuFfay0azE4bVkyShJolWMKG2ygA8jbYQf5U24jnyikIZqc3mtFWLaE1jrPtb4pWzmNY6XjI+RXT1Y8jIycQf8KSZ8pXfXqsJD0p/UVRaCREcz3pCchrxh1WXKCGX1bbnyzJe+pVZ8nd8ZfSjt3SY+tJ9lOvLq2G12W814podzhJjELZ/NJP09dQRUlkrz+Iz5X36S39zyqrN3dqbJNthfKx+ylX3RKpqIBW2mcgjpzrQz9hDg/C63Er0q7t9EDU3nD7au4T7qxS4DNoKIXQQt1ErWyEtpi8R5HLQOZkMO5jV5h1M0J5ldEAEEUI+d3gsr+kjHiYLbwG9V4QnyZVj2WZE07WKjICj6BrnnI8yXZtITpg3S/Zc/Aj2n08nNqoPEfkr8RUqJ4gA2aAYDfm6SWNM4FLOphdxxlEJJZQFkyghj8NWghcybIU5m2zRyRRtEjUCcgjU3NzjnscztevT66bvUqU6Z75ZAteVasp7AG/qSbQXE3ldH8ngoFuXgGbf3YjG0YH0bBjJZKkHJZrJwwFxt7wGIL3W3XTZpRCo2vwOBPkoNzxPcfjSzf0hY6RNnLFr4ojoQUtRZH/Tl4lscisvqurRN/sbEKDUrkQ2ORSRB8S/eFL+GdXcBVT2ZaQyk54mws1EFm4f3ozeW5/rXQKqTEa3Sxy3fQ7/R1f/fxSl/rV4Tn+cD5RPOZoscq3RIwx31yDJOcIjPmYNoX1wIbscU0i+OgeaV7aPynkAYyJWk5aeysTcHbwh/MoJvTOvqYsB+N0aRNVt6u/Pej5MX+y6hGZYRAm5LDf685YxnDeVZfhZxZTkpjFXXk506ThOi7GYZuWKJYhifnK8yqfGvbxjDEXCwkKgtpjO0/I60swbe/Naje5h+qmm/OqYzWm7DtFCDqNTXgUqPXfXRUxkTsYd9BSP8YD0F0pOX1pqh2kuJeM2gwjNKWaavBbV6srn6nvsyevCV9ajrFDeotHRa6RaIbSSLnAs8yjtOM1l3QN3qyck01C4ShvXYnwFNw9Jf7LB7ERK3OSKEtQQezNXC/KZrWRzn7SHydozDJT2VZi4T5Q9MNiDWkPKoX++AUEM1eYQQgHzlS/wFf3oqWwHoGFuCS2kv3lTH04NIYde4hE2Wp3Y6mrDr8He6KfoIE9N9iX5GzLyWwHeln1V4xl9OacLGvA1E4kMcJJdXEDNkpPMUlayL7+YSHKZ4vAgkyKFPJ6S19PCtYQxIXV4WnuKK85GrMcjXWtfB9jbLnVBMgDc3qxM3xDe1h/mpB1Pl+tw4rJPMB8aD/JSeGPCGncHvVKT59e4aThzvmKYuZGqcMlyNm7VHoCiOLnf/RrJdjgHqox/TGrKES2S9VY3NpfMoUgLZTXPVvQAbha93e9QT0jhA2f1xcibQ5ojiwI1z65guLyWlq4lDPa/+eofPIuhEWXOWI3lVFQjkwDrxgq0VUMSBFIIJ59bSzmIokC/Di0Ye3QqbxnDSffxJJeOD8247T2uj+cbb2H9kascq1Lrb527hQHqYjaZ7YhRvUlokijwuDYdEZv9//HdKscoj9sxgav+724n8PafxH9lAtAcYfxg3kFv8QiGFED57vAp7WkWqJ/wT/ZR5Fot2Wc1IUzx3uo2Sl3P1+o6FqnvkiYLBGu7qSecoqv7NEesBBKtmvgq3pTtRjU8H9oWRbuorx2msXgah6DjRmWaPok7giN4tSiXQdJeLhqDGJTxM0+49wDHAdjmmE6KHcoeqylPS2uZrqwB4G19KB1cn7Ih6MarooWPtCG3uBkd3vCsxsZIvxPjE0fbKufki0EMlbbTXjxDjJBNUnE6T5V+Rk0lhRLbwbG8Z7hbWk+R/R5zfGeTVWrTWTxJUafpTNl9DgGbMdImQiyFntoHzIxvyB3AT+orBAkl1Hd9RY4dSG0hHR/BTUaVD+0I389oGerk49wBFNsOPi3jAVi2wFj9OXqJRyjChzb1K5uZfqpEc+EikUIud0sH2OH3MFMKJ7Hdas3kiCS+LmzDD+YdHHJM5EHJgwYaqb+IKHl/VAO0DBYrH/CB8SBh0f1uWVaY6TuX0OLzLFY+II/WBMpHaVKQyh9mWzZGTaevtp1tVhue0ScDECNk00C4hqpI/GJ1IbzMwtNH0BgoeNsS1hWS6Wj8zUzn1yTlb4MYz39H8glioTmYk46xHEsZBlRyJCLcSbwgf0uYNZU7G9X1Gi/QR6GBcImmQqHX47/lDgIn2ORSjhoXJZFMgggQSrzOXeYcS0FRCvWFa3zrPwbR1vlceQczbxxVWeTXx53durF05yVUh6PaseEdPBP5D677mX2ihseD4DY9gKrxqTWPGmoW+zOGALdenQcWnmOT+hxfCfcCA256niAIvHxfa+oe+IZ+wt801s5wI/mXfyskBy4ciFUSrJ+vk71mE2bqT3DY6b0jEgSBrY4ZLDMGAv8zcWMvItht5vSqK335FsSz//g5/K+N9P9i2FoxdYQ0puiTeSK8Ga0y9/GBspwFxmCe0SYzOrg+YkDEDfXzsXSc6LySNZ0o6zIr4rYzJrEDvQKSuewSuGjHMCC8Bvfd4L71So5T197GXfobPCn/zFZ1OpP1KZhWOLk+tejlXsLyuu3JSs3iilBEfcC2LNabXcmK6kqHnDNMsddUjPecsoo9VlOkmxhXKJJIiF/lyuNLcwA9fSO80NAN/h/2zjs8iuL/46/Zcne59B5IQu+9g4CK0hUQLKggTRE7IAoiShEpKvYuiNhQUbEiYEEUEJAiTYrU0AIkgZB+bXd+f1wq6SGIfH+8nsdHsjs7O3uXzM58yvuTvoGe6iYOmlW4yTWNl8M78rz1cbJSTpOl2Okf043bd7Rklz2I+ICWWM7+zqeW2ewO+4L1ptcss85szBv2OsBfuaJTb3huYID6B5O0hdQSJ3ja9gh9s76hefpqcmSZdVUhMcPNONe9JBBME3GI/TKaDGysMxvzu+lt92NIXrSJpip8b83b8aRFtefbeK+fZmtgV5YdPckNyhq+NjoTJ6MYqv7E/eo3aO625CRAAdg0QXVxkhR88fUpvt4vQKIahXAdooZ2khNqBlVFPDOUBzjsUantNtke1pu5u+rnOg/bK3uYpc/nH0c3DlgH87o5BOjGMqMdm0Vdpufr+03PU8TLEF42bqS/b95zKoAvWXxo9CAsqFWB8VRL+I3u2g/sOxoJFMz9uKNDNX71mUqN1gUryh1XqxJtxBeIaAP42TIBH+Eif4y8rioM037kfvVbbrUtxZOexACRzFkKF/LJz+O9G/Joz/qF4tXzo/kG0lAc5k3rq8SlNQLK5mh9yecBqqdtQQ/tnS+GrWjsmfHUV44xgQ+A2SW2FYqCTdN4WlnAoZSrKCkvoSSuP/kGT1kXo5BbrBB3g34MWe+NiNKK+EyysKKKkpPhSiJ/n6VlAhe87v/5DsAvcQu/Wh9loHMyUjbG6jxDS7GfPsp6/pQNMX0jcj8kt1nQebQ5vD8f7m/BHqf3FyWn3S6zGl2UX7CabjS1aHnX32qMZcSJ/jQSe7hbWwrAk/pCUpI3kOmcA4CvReP3sN58fbQZQwCPhKc8w3i0cT0it+7HmaLT3DkXFzodlZ30UjeiyTvJST45F00RvKS/QTtlD06pM82zoMD5K89+SxVxhirqGep7juIxTQ7JaOJlKBjQJ/v5FSForMdzigwGOifzZFgDeugf85BYxAFZFVf6HF7XX8WaehdQiz8iBzM3vm+uD+ADX5ObXKs5kpn3QrrF9S1pThtfmo2JFYm8Y/ZBonCX+gMDtDX8ZLYhWiQRbu9SYMzrjEbsl1V5z+jNo7W78Nj2aTQUR/hWeZ5Fluk0EYc4KiOY4fL6Smbr80k0CsoZWkKr0dv1LDcpq6jh+Ac450Wfj07Gn+wHerme5aF6dRhx7HrsqNysLKX22XgOB92aW//4N6M5cTKSVzwDGBRZFVVIOjT02rrbKv/gT8HV9rP6/exJ92GnrMlNfnkvIt11lp22u5jqHoYjpGOBa07XvZnZW+K5ss6tnBtJXyfCnzrdzy24CaPD5rPjeAp7zznuI1xeyet8x57JmIxNOcMYzxiqmCc56cqgr2sWL5YSFqkoAqtSstklyHmSlsp+5rgHcnMpJqD8nAjvzKLk+tzlX7PUtskx19DB8Rp+NpVfytD3aO0r3nf1RKl6e6kvl+K4KukzEJDfZRgZYON+9RtuUNeSvwxrDjoegih7rsG5FFXPtyyUVumsXGOotJ7+RdIC6jLa9YDX3HM8lUMN72LwnzHst95Be3M3qjEYq9vgT+v97I97EDrkFb7wGBJdUXi4xnccOZXEXWfW8Kr+GauN9jyrzwPg59N3AK0K3VdXFdyGpJ26lzc9/XjN058nrZ8TIj2oJ/9itjaPAHcdVEXk5gHk+Bw0VeFwg7upv6onD11blxMpDgK3LmeEugxDmIXulYMQgqZKHAkymJbKfh5LnACsyT0/P+QRDiT3paWyn1HaEvwS69LZswW3koaJICrxNBO1H9HoQa+Mb6il/05b51sotkCeVd8kmDSaEseq+N+IFoc5ZXhNDwtHtudAYgaN35qPHQdjlU0sN9pyovrY3IiWzp71JHt0YrWd3Kr9xnj3KG5WV9Fe2QPAQH4HwPR9qcAz3e727gBevb0lXRtFYvveWwfA99QLtFX2MMc9kBiRSCd9H5+5r+FL42rWnjPZ5CTGzNLn81dKOuRK7xXmDsdnxKkBrDGbEmz3vsDaeTZzv/4dxzPqE3MiiwesXidnljWEQe4VvO4ZgH9gCExLyZ1UMrGiUPC72qi34bhMQ8dTIENTWH2Z6R7EFrMOPc75ew2LiuUdoy99YkoWU8uPKkSRE0Yv5zMkS78Cdujt1pbsy4KfRAe+Sn0Gp8vBTUwpkExUUUIyD3K3tpT+zukMtAeXfkE2/ZqE0eTAu2QeakuBgvNFoAjBSUJzw29L43pW8ZtowtEiqpKVlcnNV/Hxn0c4lO8FGGXzMEH/HKcsehw9nc9Ru0pwoQS1slKeMND8FLUbqShlKQn5HtAHSJBSNsk+dgswDa88Y7vsMo8IIWoAu4F/si9fL6UsJMkphAgBFuEVIokDBkopk8s6aKctjO/MTlxrbkFoobkhYuPc9/Gq5Q3+Sd6DGtmWX42WBNgKbqXbnfyEq8VaDtadj39wKH5ZPxIt4ugq/+JXswVn8UOxhBV1W2qnbWCK8i3NlYMkyCCysPGSPpLq/r48mrqfa9UtuFWDq0+8xyi+BOLwZKV47cDHx9Jm4ERqRYUwoGU0f23fxuptmXR1Pc+KIpxu+enPS6S7PN7VboC9gMBtivCnl7qRPso6/EUW+zNOMNF4mxBLKgB/nB3B9eqPqKrC9urDePNkQ65WtmGVrRljmU5mWjLDtZ/IslZnqOsFXqvutaEG2S20rm4hAx8y8CFaJqCKoyTm235ODplD4LGVLLDMYYXRkjn6XADOSl9GucbRSf0bgWS0WnDiWTr6SgJ8NGKCvSadoa7HSJDB3Bqs8nVSNL8aLfjWOpmm4iimMOjnmlnITGbTFObrc3jFcyO+McNyq7QVxcyAqdRJWM58fQ4i9X7m6W/RXf2Ltz19+CXwAQarq1hptuQx993UCPPntYzu+JPFw+ek/18x8iXSnAXt3rXlEYZqyxipLeO0cyc5KpqaxYeFRjd22e5kfXwS5DMc1Yv0589JXYkMKJjtWxKfn+zltStRsBqbG5WAc3wAS/wHcuz0fhrpcXwdOJSs9J18qM/GJ206UHzxkrLgqnYlXZxeh7laihZQfnrUCyRAX8Sfwg3cXWJb37RDrLN91tWZAAAgAElEQVQ+yLvczLlVxopiuN/btDqzlCaO/ZT2cikOoagIUfD7DgkKYrXRhM+NLrxWxDVfPNo/VzGgIpTHB1Dgun/ZBPQ+8DrwYb5jfwM3QgENrhwOSCkL72ELMhFYIaV8RggxMfvnx8owFgCEK40G4ggT3XczNLQh7RLW8Lr+IS8Zt3KfawwPBtVCt9l53HM34wML6ud7pMAUKiM6ebei763xZejWBjRWj5Bi+nBMhnNHZDX6F3HfiKxDXK2uo5vzeaboH7LRei/3qM9jmHb2h17N7c432RBRiyTfuhwwOzACMFD41OhGrH9dLJrCja28E4RP5gnGal+xwWxQat3OHK//YvMqmlsCeSjfuVYZq+mhbMSFTifns0yPasksNJxOBx5Uro5oxx2HunNIUVFCaxPIr96wyszrOG6txYHUDDa5G/CgpQ6wv1Dt0w/12RgoLKn6Gk0Tn6ZeyjpyZJl1VWGXWZ3RrgfYLavzh9mE/bIqqdKXrbIOGzzedqPPeZ5GVQtGgmj1upN07CwHg6rwiRFNX2Utvxot2W9vxgjnQh7WvkQ1rgLyzE+qqhAs0knFjo+t5JXoWT2MLKwEK+k4pJMAcYaRrkfYbVYj1DDZGHI9n+xrghU3ioAb1D8Yqv6MEAWjSdrUKJxvMM05B6E4eM49kHtsec+lCvAnk7c8fYkJLrybLM/kD3Bcq0a050ih4yusObvbvExoXVW4S1vK7cpKHvT5nlOmg5vV3ylHfZRiaVYjgg7Kbp7R3yXB0RUoPaoHvPIdiaO20SKksKjbudicp6kiznCv/ByYU3p7VfC8/g7rUiTQr0zjOZfrj73EdOuX5H/BCkVliHsSQJEvgBphRdf9LisFwznLfl1lmoBK7UlKuQpvEfj8x3ZLKf8p5pKycAN5qZEfQJHzbbEEntrAcutE6orjmKZEd5+lvjhGb3UjWViRPsG5k6rbKLhlXx1yC4/rE3N/ztmG7TSq0UXZSguxv1gv+56aQ2jlnIsPTvqrawkXqdxnfsoDqS/lFn32s2rEhXVhuscbGeBWfJjlGUxSeEGBrcyodtzpepQuyrZSHUBT5DvE2QaxzDIRxVMw67Nn+jc0VI7SUDlCCKl4TEmcjCJOieWQrILDyFtpVFG9csbDXRMQQbG0F3+z1zqEadr72FP28o7+IiEZ+wv0f5W6g2vUbVgUSV91HZGOA7nnemct4Qb1D/40GxIhkvnI6M4qszmtlb3M05/njf7V2PBE4UpR5/Le8LZserI7PsLFL5ZHmaR/QmtlL2t8urHA04sx2lcI6S503RDTW5g+0rG/0Ln8dHD9yUkZwlBmkhTTlT6uWfxituYKdRc3pn2KaUqiSeIf23AmOF7lVL07GOKaWGKfObzucx/3u8fwptEfzZbnpFYx+NP2IBnSRnJoaeuh0plUdT6t1S+LPHdMFtyxjj0zgwHqGsaLsVQxjmOgcKNrOp4qFYyQyYdVU6nTtiePuO7FN7DsmbYA4VVrYLXZS22XHtWeto43uVWUlN+dxw2uJSzw9GRnTMUcwADtE4v+bGdo81lkmV7kufNFq6gJ6DyTvwr0VWk95VFTCLEFSAWelFKuLqJNpJTyBICU8oQQovRlQTYpp0+RvuYdxrnu5S3Ly+yLP8z+Rg8wbFNNdlrv5IiIAGMkihLIJuu97Dl8O/Bc7vWecySio8+s5239XX4xWzFD9zpYf008BcwqdG9L9kr8RnU1L7hvZr5xHVN9luNvpBJzfBnPaT9h065DVRSkBNOUub6Ac780VVVoKg5xs/p7qc8cIxI4bETQUDnCS6njgLwMx9mBU0k4tp86Ip4n9Y+xnFK4Tv6NqVsQZNLodCR11K3AdUQ7DzJLn8+NzmmoVjt3ZH2KRRgM135iRVIVosUpDFHQxFHX8SEW3Mw4u5yVZgtOVh+RK7vWyLWDK9QDRIskhmk/87T7Dm5Wf6ehchSAHa79RPgX75w9l2tOzqeOEs8bnn5EiWTqKceYb1zHfKM3O62F5Qn8FSczlAWsTwkjv27OufTJ/Io0zcNmtQMWVWDFxTXKVvorawh1u6geb3CrzzbWhw+kQY/RdKvdpMT+8rPT0owDMglfssi/eVJVnVnu29kla3B9JazYdCGxFBFx0sX5ApnSViAPYI+9DT+mVuMPS2veT36ZIfoReruexVYZWwBgZP8eOK7vWmyG6/miKoJEgrI1nkqnuWc7mcLBQZ+y+yTOZUabdXz852H2nHO8mo+TWFdChfstifKogYJXkdTlMSvVB1B5PXk5AVSTUrYExgGfCCHKtkcsBiHEKCHEJiHEpsTERJb9uJTObCGRIFYZzUjWq+T6AGaJkTRUjmBLiwPgB9mJtBP72fTiTbn99Tg1j5nuvG2lzUinhjjJ8/o7fGV0ZonRngxb0c6kKqnbmaXN43btV2JEEpnY+DxgBK/aH8CeeZzW6j4URdAm/mN2WkdwNCmZn9ZuYJ91CLXjvyvQlzUrAYtwM9A1pdTP4HG/GVzteplRrof5Qi+4xU2VPnRQdjNZ/4iWyn58sk4yU7zDY3zAHH0u9VLX0U94ncZatfbc7RpHtEhCM928Gz6Bvs4ZLDY6s9XWjl6uZ/FEFVyt1o8OoUvTWvibydQUJwskocyLmsJysy3DtJ95zdOfyfrHNFSOkigD6O+cTkZY2cW4AOJCu/CkewQ/GW1or+zmVueXrLQ8jIpZZKjsRPERH3m6cajaTUX0lscrIVPYZNbjOfkSoSl/873lCd62vMwS8wru0p8lSa/KfqUmHe6fR2ydpgghypxhWduM4wX9LXba7irgBFZUhX0yho8ts2kqz43dKT/vHu7BOs+thY7bcWYXG8pjfUhffjLb0EQc5MeQIfxituYzy9P4VuJEdqEmfwCftMPstg7nNnNpmdq/HPgYv5itCcuKq/A9m8YE0rF2Yd9fxwlfE/7Eua+FyqGAGFwZ2uc48f+ziWBSSid4g42llJuFEAeAesCmc5qeEkJUyV79VwGK/c2UUs4F5gK0adNGnvGvzwz3YCy4ecozlFtD6tI5YQXz9E95Xb2Tu7IeYVJALABzxAgediygs/u33P6yhA1TyduGxkf35I51VagnjnJW+pFAMGMji5a59Xcl0FTdwi2uqTynz+WAdTD3qF+Tair8Fj6Y7+KvYStwxr8+C41u2H5bQcr2JcznOuoFFqyeZHGeZpT6A1vMkiV1AewW7xf/k9mW3aoP4/OduyrrF2ooB/jTbMhY9/2Mj2rIE9tCiAq0E382iwbV6rMhPZUdQGBwCDEikan6R6SYo8nwiWaH1HjEfT9dlQggodAf9pKHvEk7c76XxB7eQ8zZzeRorFtUhe+Mjuwyq7PDrMUpGcx+GU2K9GW3rI5pLd+7PzG4BR8bdq5T1vOXWZcjfldRP2MTj2mfocjecE5mqCokZ/Ej0lqyEz1LD8xWOTXQFYkTndGuB9huaYnLMFkT1JetaVdyS4m9FM2DGa9RRTnCLM8gJp3j7E6TPrzt6cuQRqWp35TOMUstYlwHCx1fap1EprQC9+Ue01WFe9Ul9DQ385T9G9LMg7RT9mCzlG1FfbHRPGn4CBdD5HfkFN8piUDSmaXPZ0NqNPmzp8vDDS2iuaFFdKHjmn7hPjNRDjlo8M4DKVnuAtUGz5dK3QEIIcKF8AqbCCFqAXWBwr+1Xi2DnOipYcC3Zb1HgOLgKmU78y0vECsSMKVEc2dQVSRxDZvQMZDZE4+mChYbV/Ko+95cU8zywNt52Z5XPjBHTW+vjKWXuoH2YnexiTAJ1a6jvfNN4mUoHZTdqEJyS/pCJmfMxuk2c7fYp8I6MMszmPDkv3hI+4ZXPQNID2lSoC93eBMecd9HK2Vfqc88zPExcbZBfG2ZgtVdMO54QNbXxNpNAgZ/gMS7RTwqI0ixRnGccDI9eeGDoXYLJgqPue9GswfR0LOHONsgxqiLqZW6gfn6HOyupCLH4CPcdFG3EeTKK4DRKW05N6mr2WLWpZ5ylMXGlaw3G3GFsss7VmfpQln5seFgs/UeHtYW01jEsTfoKhZ4ejJM/bFI0asXleGclX4EO0rWY2ntWEe8DGWqz+M4IlvT1zWL78xOdLHt41H3XG/luApqqrwXcB+3uybzfhHOx48tswGJr39hKeDy8mzN9+ji+02h45vazOGfjgUdpbedepHB2gqmqGOJ8hwnWfpxq2sKlsCyx+1fTFzhzWjpeJtB2otlat/MtY1vjY4cj+1zgUd24ShLItjUvo0JtutEnEfk0bmUJQz0U7zCOGFCiGPAVLxO4dfwVo34QQixVUrZE2+x2ulCCA9gAPdKKc9k9/Mu8HZ2yOgzwOdCiLuAI1D2xVds+jauUnfwqqc/c/UXOXZkA383mcCdW+uyVbmHLF3HaY4G/PjWHIPUDbbK2rgcmfjYffGYZoHJJDJ5M/P1l/jTbMAk/VMAVp2UUGCd7SWnKtCj2hfMcA/mE6MrM8PW485Mpn3CZ7Q2jwBdc/t3GIKfjVZkYSlgHgCv/a+JcoguytZSnzkoO8yvpbKfRe4HyR/p8JDvHOpF+PJi/XBe1l9HOdGL29XD2M1o2qjHqZ2q0kueAnrgY9WYqH3KR0Z3NF3jijSvtPHD+mJWpycQJJKxFmMnrpm6iZ1mDRJjb8jNA4hxHeA6dQUmgge1b3nJfRMDtd+IFqcB2J12gJKkB86l2YkvCRVp/Ga2QMMgQpzlTaM/bxr9OaQWHlekcpbJ+sdsTmtD/oIw53JN+jK6aSd5WO2JripUJYk2yl5aqydowVaiT76Ib9YRYFWZx5rDIWsD4uRxwpSMQuemeIZzylaTQnHQFWB4x+qcTCk8gbfpM6rQsUMBbVmdYGOLtRkjTs9jmGUjnZ2vXlCzTWWiCEEyAeiibBNdrHGEuuIY+2yF/USXCmVZfvRqEkWvJlGcTi85o7s8lPoCkFLeXsypQjXXpZSLgcVFtEVKOTLfv08DpYeHFMGJ6B6wexoC+MVszagzX5K+8ggwlrnW4UxwvsbxzJNAKD+qV9HN/I3+6lpSnE587L7cnvgKijudHD0SzXQRKJKZpH/KQk9XfEUWPvaiC2IEp+3nef1tBiirmWf0IRMbq8MHsyHjDNOcC/HHuzpueHwx+6yz+N1xA22VPRyy3cGWE29A07y6UJrjDALJw+77+aGUZ/6qyljuSryVK5SdNLIkMjnfuUxpwdDsCCFoohzmSOZRZuvz2eVsSiN9B9sdTfCVeaFtT+ljkZ5kdCFYHXMPk0904k51Get9evCNqwZbg4peJfrILMJFMmeVvO3nspgxpCUe4TZ1JW96+uXWEDglg7jTNYEZoUXXOSiO+JAreNydRpq0M077Av/UHxhu/YbrXbOLtMn3lqtZYbTEjCn5V+ndyCdov/9lHnW8hl/6k6y1eQNTp4e9z0Onb2KWvg6h+RSR+lc6tTwHeczyEnWUeM5NRht6/2Rign2KvrCctK5esuR1fvaGdmX1XhtN5D5Wh91O9dNpfGmZhlUtm2P7YmPJjCfONoiXjWFAt1LbLw+4mc3JPlzrOA4UX8zmv0x5VD3PVwI6P5dcJrBL9WW6ewhZWPjKuJKqIolQ0nhff5a5+miGpj3GLF+vE/dj621Ucx3AZeqEaF67f4oShKLm2fWSq3TmNtdMaokTnJV+nCGAGZFNiry3zUyng7KL/q6neUF/i3tt3zNeWYVhShYG3UOS5uJ7ICWwAe8YfThlvZqjhndl2DCoYG1S3ZPOIHUF28zSa5badRWByTqzMdtNtcALoK9zCQEZtYCW9DFf4Mawqkw81IK20eFsPJBIYFgUGW7B2uz2LfTD3OL+HEV9Ho9PGHtlLBM9o6hq2gBHsavEY+FXkrznd8LTdgPeHApdVXjT058PyeI4YRyWkewzo0nGn0OyCljKFyedGlifTw2DnspG9skYUvwb0YLPmKAtAgoX33YrVpJkIJHWkmPqDd2XZPwJU5xommCHWYNPjK7Yg2sgOMYye19Omy6KW+mUxODUd2mq7GOGci/naro2iQ6sQI/nj64KHtS+ob17P6/7LibebEy4moZSidEjFxLd8IY632j+VKb2kWYCU/UP2JrekXO1lS4VyhMGer4S0AXuW2k9/Uv4J++in7qW2fp8wkUKD7rH8LXZiSCRTkdjE2GkIKzeiUdXBW96+vGUZygujzcf4HO/wXwSkJeJ6LW9eUXg+qjr6KJsLdbL7qjSjs7OV9kha1FPOQ5A98QPeMU1FafHzJVsTQlpzvOeWwl0HGeE9iMfG91wBRU0hcigmszyDKaucqzUZ74q9TsO2e7gY30mAWbBhOlB7q9plu6NtNUVBYdHkkAwbmsoCQST6VEK2BeT7LV5W3rTLmIydhJnG8QIdRndnL/woT4bazHPbpNZtBN7cstfAjQ/u4LB6s8clFVoLOJYZrTlL1mPLspWVlnGoLtSS322/PgYqfxjHcZ92ndUEwmcDGnLR0YPeitFC+7+rF3DIRmFn+Nkif02y1jPcRnG/KDREFKbvq5ZfGp0pbXtOK/pr9Pm0FsVXlUtCr6H65yz+EEvPWP13+Ka+LkMUP9ghvVhIpxHOWpGMFafXPqF/xGMkLo0c8xjqP5CmdoHmWf5zWjOmbBLc/KH8iWC/b/eAUQl/kEL5QAz3YOYr88hRKRxt+sR+htPs1Ebg11PJc2YBPjwVuYjBFmS2GjWwzg7AILrYpiygJM3JHk7n+gz2CrrcL/mDdVcfyoKKFynNydLdqY2n8fcd/O7fhWP61twEsrA5Ll4NF+gY/YXJIl17OMzTxeOyXC0c74zVRU0FnHUFCVPXgDW7EV5jEhijXIf5Fur9tfeoFtMBO2AGeJN3AkN8VMziXQ3o6q6jVhnerbvwhsd0cG9gQjhzSOonuYNzpqqf8ROWYMMxaeQ2mQOoY4jxMlI3OF5YtSh7hN00VZyUFblSX0h73p6c6v6G/7ZJQr3ZxwFSi4akp/qCSuxCjd/+lzFpqp3cE1wEJM8N/OOciu7imrPCSbqn7HbcSPQrNh+26T/Si91B48rt2BRFVqJvcSIJLpfO4Wle39hzJkFdEw9Cqwo81hzOGGrTbyEWCWl9Mb/EieC2/Dy4RR2KfUZevpT7rQsoa/tk4s9rDIjhCAVX3yVsmVLB5pnqSFOst+ouDLnxaY8c/r5loEscN9K6+lf4q+o2zhoRtFV3cJBWYVwkcIHlmcQAhb7DsRXONGyo09WWrtyWvrTU9mE2+l1pI5Ons3dZ1/O7U8VkhCRxv3ad7zv6cGnnmtw+xYOBwOwO07xiv46g7UVBJOGR7OzOeImpogHCPScJjjb1l79xFLibIOp5omjs/o3/9iGE3R6c4G+NFcaLjRe8BQvYpbDP7G3UcPxCWPdDzDVMxwzX3azS6oIzWvSihaJhDqPMF3/gLbpvzJN/5Ae5hqambtz229vMpFxAd7Qur+rDKSz8xU+9nRlpnsQI8VTxY5Bx8QunFjy+QA2xY5gjxlLV20b8zzXMVJbhr/I4rgMpZvzOVzBpYe45ud0eDsedd/D8Zq3MG9oG2qeWs5G631UzXYqn0usPME/ZgyuqNZFns/h86oT+FvWYHja21hcyXxlncarltfRLVbULhN4yj2E3xzlG2sO1d0HWGZ9nDcdT5Te+F/iZGgHfjTa0tDYw+bIWzgmw3nS8/rFHlaZsWQlEmcbxFBPke7EQuy3N2eh0RUMxwUeWeWTM5eXx6xTifP/pbcDyMLCy56biRUJfGN0IhMbEsFH+iw+sE7kttPBvO7rdZgt97uBK9J/4nezOWG+3pXoCSUKRc+zTWdEtOIm12yiRRKp0pdUfHk7vOjUfavw0FQcZIRrPM/q85jo+YxpYi0eUzLTNo56kX50BTIC6/GS+ybW62252ViKG5Um/rEF+lKkk/7qH+yUNUp9Zh+LigU322Utthp1mCxlrnj0MGMxIeltgSaMtjxNtN3CuKS+DKxem1En+uFQ7FQLCiTHmjq8WxuGdvVO4qbVP3ty8O52IkqIEskIrEWKDKWqK28y1lVvSKndbueYS2WXWZ29Moaz0o/jhKNYyucAzfKrxpfG1dwovN+P014VC24eEF8Agwu1dyi+HJERVCkiQig/UrWRIIMJU/3QNY1tZi0WG1cyHWgWHcRkoxNWw11E3Ffp9Dm7kCriDDNt4/mvvAIsiuAR7XNqu5L4zP4FP5utqKlfOn/qqvRmo3c315TS0kusO4579E9YlXnjhRzWBUERAkPKcvkAKtMEdMntAKKSN/GA9g3j9c9xYOER931sMuuhY9DW9Sd1xHEUzTs96kLwlHsoL3tuwpW9al5gG8ry4LxoHFURSBSOyQhuUP+gl7KhWB+AGlaLa10vstJsmVsMuvOphXwmH8PhNnLzALKC6/OKcRPSlckt2iqWmh2QAQV3FYpvBHM9fYgqKLNUJHXOrmWvbRifWmZQlSQ8nryt7l3yG+qmbQS84lKbj6aSTAD1q1XlLP44TLXAL5eiiNxU8tjkDcTZBjFE/Ykx6mJmmi9THD5mBo3EYaxmnhZRneTV3K0tJVMLopk4xGqzGTtlTbqpf7HbOhytnD4AP6d35dfhtDfePSWsJZ8aXWlVKEHfyxG9JtvNWkhHyeaXxtk+gGVhI9D8QrjBNYMPjZ7ezyDYxgrro4zRvirXWHP4MfJuejmf4Q/b1RW6/kLQNP5zuqlbeFEdQZjzKFvMOiyJqIxg1H8HMyCaBo4FjLSULgQH0LbnIJq53qNBi5I0Yf+b5JhzyjOlV6YJ6NJZFmRT5/RK6ivHeM49kIWWWdQW8dzhmsQQprIyczzR+lFSzFmAhclnJxFr2ctGswF6QnOocRWGaRaY4P3O7uEby2R2mdUYpHlr025Lak1RqoIWVcGCm9n6PB5wjeZAyJU8YP2bfTKGKY7ncaS0BVqgCrDiYrD6C695+rPFrIOmFMzeUxVBXeUYehnK6lmsXhNPEOmstY0mJaMXZMtVtDIWMKJ6dToAj7repoG+g4+5nmZhzRip/kA9cQzDWQVvikZBAtyJADytv0+6tLFWKf4PyGK6SCIQu29e0RN/I5mr1PUcoBljLHP5xHMtg7Rfc89rmQnkyCOXhZAUr2/C3+1NRlMVwTOe23nXNrxQKjlAHfMgY/Qv2eE4V2+0II3S/uB6bSVPqyOxqAr9lLWk4Atcj1AU9vq1J9peMS15S1R9Tu9Kpb4s/UX+b3E2tAXPuQeyVa3L4NPLuUt/j/G2vhd7WGVGSnBgxaOUrR5AqxphbJ9VshzIfxVFAYzs/5eRyjQBXXI7gGVR9/KBpzuPal8QIDLQhcFo7SuEgN98vXHOIjuMbK1fdw7LSDoqf2O4vD6A2elPMjDpjdz+VEXBn0wGaSuZ57mOBZ6emH5Fa6ZbcTJXf5Gb1DUEiAzQbOyKuI7xxv34ynR8cAEQkbCGf2zDaS4O0E3ZzE7bXdhTCipWKqaTLGllkXFNqc+cHn0VNRyf8KB7NBPcd+PK5xwzTJm7og8UadRTjnOrbR0+nnSe1BfSX11DTaPoTNm4Kr1o4XiHBZ6e3Ocey6sBjxQ7BlUVSEQBZ/aBmAF4pEI1TrHA0zN38o8zI7na+SKugLI7gAFSw1oy2vUgKwO9UUpRJ39jq/Vu6lBYBhmg621juTVyCXXrlZxvsCx2HP+YsfQ5vQBdgVctr/OB5dnc8x3Gf8VVD7xVrrHmUNuewUbbAzx9dlKFrr8QpIU04Q+zCXXMQ/xdpT+KkPRMWXSxh1VmRKZ3JzjCc+mMuaLk7M4vVhjoJbcDyDR13jd68ZPZhnTpw83qKlLwZYE6k898J7E4oSofZYeBbgzowS1Jb7LYuIqwwObUmPgDk7RYQsLyVqWu0Ib0dc0hjBTS8SETG4tDio7Nt+oaVcRpxrtH8bS2ANvZ+TwvNmCYkjs9jzM8pgbdAVdgLZ5138avZgtGa1+xx6hGc7+CK0wVSTf1L3bJ0idJu0XDn0xvnL0Rw8PZKonSNHlU+YTw1N5AAx5lHImOB5jYMYZ+wdVo4niXLKw0iwwtnLUHDGhbi1UHU3lqj1eVo2UJceI12/Rk+olYnqtbcLId6JpCWFh19qSeZbNZjz0ylhTpRyJBmGVcweVg2MP5zuxID+GVTnD5VkXHwzDzG+DOQu2bRAey6L6SC4wDoOockRH46sGoqsoGsz4/GB0o3uVddupIr/Lpj56WlZLxWxlYVMmT+sf4Sje/WHvzldEZ3afMgrsXHeH2LuA6GptLaXnpU5nmnIpwye0A6p9dRU/fAzw3YQzbZB0me+4kXobhQqelYwOtlb0oqnfisSqSB92jed/oSXpGJver37DE6MD6sLztoqYomCgkEMxN6ioGKKuLrQdgsfrQ0/UcXxhd+NBnKJtq3kvrk4v4TR+D02NgzXaiugOr85bRjwx86KVsZLXRFNW3oHa6avXlK6MzdkqPXAjOOMAO20iWWCZRXZzE4/KmghuGhzvVZUSne00nswY0ZdaApozs2hRd00jHjoFarH54oI/Oe8PboimCmdp87kt7o8h2ABH+Nl4f1Aq7JW/NEJG0gXu17/FVPbQWe/nLrMt+GUNv9U/ibIMQGUXrChWHb8Yx4myDuPKsVxoqK6Qhi4xrqC6Pl6ufc6mbuoETMpRNkV7FkYGuqawOqRyHYUCdK+jhfJaXPP8dE0TsqV9pq+zlXbMftvTD/Gi05XBUz4s9rDLj9o+htuMj7rc9c7GHcsHJydEpzw6gMrmkdgDxp1NolfIzoeZBooNms9zyGA2UowxzPcZDTGRJ+hNU1/fhFK8CKiMSnqGevo6/zLqYR90M1D+nm/kXnytdcvu0pcXxs2U8+2VVeqteZ+qBlAEQW7i8tBCCANKZqb+HveldtLl+IEsWzWW7rMUiy9OcPTMUqIeGxJcsXtVfY4bnDlaaLRgnCvsAaoqT+FC6rofNxxtN40bjd+s4Die1gPDWeFBp4PyQCeCu0vsAACAASURBVDXrcwXQvVHeLkOXTu5Xv6G9soeE9GZAx6I7BzbrIwkUGXyvlk8PU3eepoe6mWOeXdxpeYtvjY7ZBbSznzHjFFCrzP3ZsxO6otxek4+iCKZ7hlItwF4BlZ48aqVvpL/2Pc+rXhPXkoc6V5pEQ1R4GJlYCaSwFtDFIiu4ITPdg1gnGtEz8TdGWV7nQ630cOP/ClWDfYgK8mNyv/JJiVyK5KzNLtYL4JLaAQS7TzE3fBJjsuPYG2QXHrlD/RkhYIuvVxBMkd6In60BXdgrY2isHCLN0IkzI2ml7KfnqXm5fSqqjg0XvdWNvOXpyzue61H8iq4JDPC2/jJ91fX4eLyRJ4cjrmW8+x4kguxEYALObGOn7S5aKfvpq65ji+1eLFkFV8MqkIadtWbRshP50cPqUMPxCaNc4xjjup94w2si2RTnzQouaoWvYTBB/5yr1e1EmiUnm31CT4a7xvNlSGFhsZI44dvA+yyKwoee7rmT/z4zmo6OV8kMKl9sfVZoI+51jeV7P++LKDRhPTutI2gmz0+PfVW1hzgsI+iU6E2GahIdSJC9cmR+LZrCGutYllnLVkHs38ATXIvtZm1qyOMcsnhlyKulb7vIoyo7Vk3lj4nXcm2Dihd5v1TImfgvliXoktoBJCshZBkKUvOu3oa7xlNbxBMi0pjL03wdOIV3T9bmu2wT0G57G0Yqk5ntvh2PXyfGuKoyVfuAQJ8auX3KoFiudr1EIOlkYsOJhV7+xcvmmghmu2+nXozX4awqgixs3OaazIxY72RuBMQywz2YX8xWTNU+5CejNa18CmrjK6pCB2UXu81qpT63TROEkEoSgXxrdqZTloUOpsmWDx7ha8vfHE+ZRI5Gfw6azZ96jg9wo3JtjagiYoDyeJPbSDM9dCunVsxZPYI+zhk0DWzC2uPRrDGbsFtWIyQknPjTCjUiyqeF07hmDMvNdnx8rXf35fGtgobJTe4l5Ne7Ly+aqrBHVsNivTB28D8bPYEeWIXyFUi8cOjC4Gn9PVLxZQnTWGxcSYjf//5keimSZwK6OPe/pF4ADmHjitTluEwBdEZv0Iv5u05xh/ozKSKJplkbqKIeQGR/mqYpGep6jL1mDL3TExirfclCoytXh19JjgXYoqqYKCQTwB3qz95IF7X4ohJ3uL3pPq9kh5I2iv+a9dbX6ep8PlcqQvpF8a5xPQomHZTdvOy5iXa2wkVLVhtN8VC6RK9NOPjLdi9b1SY8kjWc9BM+uFyhPKR54+WdWQcKXaNrKi68L0KbpeR7KIrgHf1FRHJtoOzFS4LT9vGI9gXbZCTtld1sNBtwVEbSv3kdvu1Rv8z95BBktxD3zPW5P7uDarHI6EJjPbHcfeUnNmUjp2QwjogLYwdvP3DCBem3ooSc2U495ThPukfgcXtYabRgQEjFMp0vc2HJzQO47AMoHU26uDpjOYaiA5OZN7QNz774LAeSgpikjOPz1GnU1v4G4a3t2zfpXdrpS9lm1ibrZCO6aV/RRdnKMiVvPRyou1ljHU2GtFE/W5gtyfEwUPTKvAqnmaZ/gF/yw0A0Gfaq7Der8oVlOmfPTgNi0RSoLY7znj6Hx90jWWs25qEiFtdVxWkSKL2OaUBAMJtaPUu11j35dN61xB3sgEt25nbnNGor8bSofisdzrlGVQRjtK+4Tf2VdalDoQSx41/lSELVFL7Ry1e5KlzL4hp1GxlaAmP1efxstKK7+her4x8CZpSrr6JQFcEUzwga+Pmz/Dz6iUzbRV/1R+aJc/U6/zcxg2sw1T2MlUYLhqdt5m7La2yUhctJXubik7Pyv1ixQJeUDyDSOMnEgGd5ISIvOqBf1jcMVX9CCMFBewvcMm+1u8unFX+ZdYkWSRwU1fje6EAL5SCdTn6Q28bf1x8dD/WVY7zuuYHXPP3RSigs8Zz+Dj3VTViycw1OhV3B456RJMkA1GxNnjCRwgrreKorCdys/s4G2wNopqtQXwkyiENm2ao0tel3LxHRNXk7cCxfWfriMiTXq38yXXu/2D3Efeq3VBFnCCjFQblQ6cedrkdZFV4+QeTu19/CplbP0KrnED7xXEN39S8Aap85H5dtHv6nt7HXOoR2nr/Oq5+/Im8iiUAaJZRWeeF/A+EfxSEZRYxI4sou3dmstqBRnZoXe1iXKYKKRgEtGNGWXx85/+zzslQEew/oAyRIKZtkH7sFmAY0BNplV/nKf001YBcwTUr5fBF9vg9cDeTk8A+XUpZaGitRhBFjgqrlxZc/GzydjvELeMN4iqWh03nlVDOWZJ87pVbhKnUHD7hGE693ZYu7NcnSnwB7nr1cURWucL6OD06c6HjQuNOn+BfASUJ529OH2lW9uwhVVTgqIxnqfpwPqrYDIDQihqfdd7DGbMIEbRF/GI1ppxeubtRIOcxBWb4CFqcir2RXfCqezBSixGlOyBDUk1soSnWzDR+T7jAYVa1WiWU1Fmr9OWU6ua2cPgBNt9Cm330kpDl4x+jLz2Yb/jZrcE/nZows/fJSMf2isAiDnq6fgLEV7seQgvVmI6y2SycW/nzQheRZfR5xZhQNWj4KLX+/2EO6TDHkJoKVcyl+Tf3K+V0uy23fB3qdc+xv4EaKr6H3ErCslH7HSylbZP9Xel1EIAMf+mV+TYesvNs6ND+OyAgSRBiNMv7kZuW33HMpSjDDXRNYYzYhMOMw47XPeNvTlyPhXQr0a6KQgQ+3qSu5R/0erRgtIIAn5H084xmUG3lTO34Jm633EEFyrg9AKArzjev4R1ajhjjJblkNTSv8rv2g2Uc0v31aWR49lwa2ZKqc/QtP+mmuVzdQSzmJ+3RckW3V7N+q0koBWjD51vIk3RI/LNdYcu9jOLlS2cEJGUoiwUhLyUXay4rpX5VPPdecd4hEZPoeUqWdU0Elq4b+r+CTGU8VcYY1ZYgwu8zFJUfYTVwkI1BZSkKuEkLUOOfYbijacSGE6I+3EHylB0ZbpYPrXMtJzGwIjAPgCscaDkg7s/UH+TBlJnWUvOzBq1O/4ybL+/xqtCAo00UrbRfXKNv4Te1UoN+F+kw6qTtzfzZF8QlR9cRxxukfEXD2KSCCLN8YEmQQX1ieIjOtLmTHgnS17WGufJq73OPZZ0YzsojP6rmbm5f7M2iTvJSHtHfZ7zuSGo6FCCSfdik6xv8ecxH3275k+dk5QL1i+1zkfoCqSgLfWm8r93gAdMPJDH0Bf5oNaK/sYcuhe+Cq5yrUV35URfC4ZyStA4PpVHrzYgnJPEhn9Xe+lu7zHtOlgOofzhPuO1lnNqqQwull/j1yfQAXyQlQqT4AIYQv8BiUKct+phBiuxDiJSHKVv05Rp7kNstrLIrJq250bcZShmleH8ApW23SZF6CT5ylDmuNRlhx84vRmtc9N9BIOUzz+IIaIzlFUF7x3MgL7ptRSpAXHq8s5Fp1KzpeRc7ksNZMco9kv4xGs+bd+0H1a1QhuUP9mRXWR8vyeGXin8g+DHJNIstlAoK3h7SlQ62iAxBvw1v03UcpuVDGIr0/I12PsCuysABeWVB8g/jcczXtlT0kqeH41S6cRFcRfJL3EWcbTGfn6vPq50BAO7KwEp3yvy8tAKD7+JMoA3MVay/z36UiWkCVSWVHAT0FvCSlTC8lrOlx4CRgAebifWlML6qhEGIUMAogJCqamoaJns+k8Wr4NDrGvcFLrmn8XnUmz525km+zz5kSOqq76O+czlZZBysufHAR6VuwPOOj1qkkpWbgwRsSWrwkGvwjanLCE0DNCG/NAFWBLbIud7nHsyI0L+v1y+BRvBN/gP7qH+yW1WlZ0qdRDjJ8Y1lrZuJJPsIE7TMCM8OBoh3J7dxz8ZiSZ6s1LbHP7y3Xc9DMoH4Jpq+SUBWVN4wb+NrszKezJlB8Gl35kNnKox2dq4CKJ1o5hZUVRkv8beGlN/4fQFfhZf1NNpt14fIe4D/NxU4Eq+wooPbAc0KIOLxeu0lCiAfPbSSlPCG9OIEFQLviOpRSzpVStpFStskUdkZ5PqFJ+rrc84Zm55CM4pgSzW1dWvPIbb1zzx3Q6jLcNYF9MpqWYh/jtC94xXMjhwMK2oL9fCy40Bms/sJj2qclPuA76u1M9IzK9RNEn/iFHda7qCXi8bfmvU+v73Udy8122HFwSJYt0qcsBLpO0knZASlHGKn+gJ/jRLFtPaZXfqI0H4ANJxut99Hm1BcVGpOKSVdlC1myTBu5MiN8Q1lsXEmmWrxTviwEOE5gopDuWz510ksVi3RjF07+kbGlN77MReV/SgtISpkrzSiEmAakSykL1aITQlSRUp4Q3m1Cf7xO5VKxyyxukr/wT2aek7FN5irisPCa7R5WhtiJDbHnnmud9Qd3W15lldGU1spefIWT1so+NmQVtPE/6H6ffrayFQRpzj5G6Auxp74KhOD0i8WGi88sMwiyDctt17FOGHHPXE/bicn4CBeVVauoTtKvLLS8wC9+G6nn/IgvYovX8J+lzWOQtpINqYuAostcAryZMY5wkUKWT8VeVKoimKJ/xBqjMTCmQn0U2a+Ax90juSI4gtJFs4sn0BlPV3UjvxtZpTf+H0C3+jDePYrtZq1Kica6zIUjJ/DuP7sDEEJ8CqwD6gshjgkh7hJCDBBCHAOuAH4QQvxYhn6WCiFyYh4XCiF2ADuAMMqYNVRTxNPW9RZ/1Hgg99gV6SsYof5Y5Aeo17qSx9x3k6h5M3Mnu4fTRtlLk/gvC7Szq14b+ZuefkxxDyvcUT6GyG+5Uv0794vLCGnEI+772GDWx2IprC8zTvuSLyyVITzs5XBUD252TiHN8N7LUkLoZo64naWUcoCf6gMY5XqYY5HFZ0CXhKIofGV0ppWyv/TG5UDPPMle2zCuzvyp9MYlcEavgoKJvzO+kkb230ZVVZzSQrBIv9hDuUwp/Od9AFLK4rKDipKYz3/dtHN+vi7fvys00xyUVQmQXvmGHN6NmkLHvc/xXOZUoGC885C+3TnaqROPf7WDdQdPE0A60eI0ml+7Ato431V9mJHbBmb/JIp2RmSzSW2Ow21QIzu1XhGC78yOfGd2pE8R7a3CTbL0p7KUWJz2KDbJBow48w9PaB9jc9YHgops29I5F4DFVYvPAgZYql3LUTOLDuUNRs5GKAove27iY7pRscKKRaP4eLWEmjk2nlc/6WoA3xtXEGatLO/Ef59XLa/zm9Gcyz6A/zY5vtLLmcBlIBMr07QPqJGWb0JQLfwjY9ivFi7ioiqCGmG+WDSFa5QtPKwt5hnPbcTZCurUTOzdgB6Notg2pSe7p5+b8lCQJZbe3Od+ODcPICJhNXusw2gmCuvxADikzilZutxDWfF3nuIaZQv2lH0MUldgM0pf5ZXmA4jynORv653UO1XxTNnuyiasonLDLBWrP98YHTmjnZ8PxeJJx4YbU7eX3vh/iDOcn+/kMhceNTcM9LIcdKkEkMnN6irCsw7lHmuZsQoFyQKf4cVed3vqeyywzGGE9iPv6s8TkbqzwPmqQT7MHdqGQLuOTynCae3NrSyyTEfL8JoTXH4x2ISbCZYvi2z/bexjDHNXnlRw9Ol1LLDM4ZBvcxo7F6CEFx/fP1+fQ5xtEH6OkuWgn3PPxE84cNqLLoVZFibrC3lCW1jh64tCFfCk+06+DbvrvPrx1i7ejtX472j2X2juc43hPU/Ji5nLXHzUiyUDms0lJQZXTZziaudHjK7RMbe8SYv01XRVd7BeFF/MxO0TzkdJ3VhodGO5dSJBoRWfCLp7fqe9soeDbm8hF2dQHV719Edag+hcRPvPRnWo1Lf7qSrXcMMmwZUEAqlYtOLf4W2UfwCw2UoufjJXvZUzmSY9Iioev/+t0ZGOys7SG5YD1ZPB37aRfJl6HyUVtCmN/gPv5IWfOjGlc6PKG9x/nLbXjWBolYDSG17mopIzNximLKXlheGSegEckFUJw7fApPdJ1SfpcGYqM9KnAiuLvO7vaoN459BBgu066WPjaBNQcZPMH5ZOmI40qmXHlKuK4EXPQOqF+hUZ/1LZWzuPTyjbZB0GJG9lmvY9FqMjYCuybXPnuwBsDSrZhPIzHUkynVxXwTwAgDmegQSSSWXKrakW73PVcZQpSKxYQnwtzBxQci7E/xp3dr4s/nYpkCMHLeXFeQFcUiYgFzpPaQuISNuVe0woCjtlDf7RGxR7XaCPzs3q7zxoLsTvPCZ/gKCWN3CPexyhwd5+AhI2cdA6mE7i/CapsuLrTKSnspGI1J30V/+gFPM+ULoPoLm5izjbIKITKq7i2VPZRKQ4U+Hri0LVrXxvdOCEtexlJS9zmUuJnLgL4/ILoHSCSaO/upYgI2+iaZa+Cl8cLPIdXOx1TVJ+43n9Ha6T5y9TPPraumx+shsRAd7VaYZpRRGSFl55pAtOWPJW3rG8xFafdrRwzsNiL77q1lLL48TZBmEtZWU/yXwbAI/f+fgAPmaa9kHpDcuBCkxxD+f3sPLJVF/mMpcKykU2AV1SL4AocYYbXU+RWbN77rH66RsZpK0o0dSi+UfxldGZfs6nz3sMiiII9cvLeE1Uw/nY05W4wLbn3XdZSI7qRG/nbI5Kb2CpXsLkHiMSgDyto+J4kSHc43oYR2jFbeRLjXZlqm5WHlRFsMV2L9ee+axS+73MZf4r5DiBL9IG4NLyAeyX0aTLSOyWvGF/FzuejoknmZIyBVhR5HVmbAfGuS+Mt71xnZpc47mLT66qHAG00pDWQHbL6vRKW0sny0aEuL7Yts2c8wGIK6XP32Ur0k0Pw8pZDyA/M92DsQlXMd9Axch5cUU54yqx18tc5r9Dzg7AvEhvgEvqBSCQPK0tINDTDLJLKapCsMFsQB2LRnFq74E+Oveq36FiAsVPmBWhZphvgTq2FxofVxJ9lHXUyTpME7G9xLbzh7Vh27GUEtsAdJdrecn2MrtOL4Y6JZWOKZ6e6iYSZNEJaefDcrMdhm9jmlV6z5e5zMXnsgmoHISKVPqpa7FrZu6xxqmrCBcpfO93c/HXmUlM1D9jvP75vzHMC0pA6l5et7zGCrUTA7RCMksF6NowknHdi88TyOE+4c1hkH4VrzI0Rf+I6fqCCl9fHDPlCLaEFpVjfZnLXPrkbLov1g7gknoBBJHO3e5HsIXVyD0Wm/E3N6urSqyo4xsYwueeq7nVObnYNpcKGf/X3p3HSVHeeRz/fLunZ4ZLLkFGDtGIKCaKihpdxXhFQrIqica4Roniumo8ojEe6xE1uomaSHbdja5GgajxeGmMGjFekcUVRNHIFVTAoBlBkQyCHMNcv/xRNdoM0zPdXT3TU/Tv/Xr1a7qfrv7Nr2a66ql6nqrnGbAvR26+hUVNw9scBygXNzR+j7PqLsL65D9a5vON+7DCCj/Uwh8TF3HER1MLHte5rqBXZTC9bTLPYViiilUT0BIbwsamkfSo+Lyz8cWh51G3YiGXr7kaeL7Vz/Xq1YdLG/6tk7LsWKroyTIbzDm1zzM0sRo4MnLMl5v2pLHJ+GGECuWq+jNIqYFoU7dsbW1ZP8rbuY/Bubi6+huj2Klfd47cvTjzVceqAqikjutTUylPfj64ciIhXmwazc6VPcg05FkiIa4qu5dqG0Ch+wA6W0X9GiYkXmLfxnn0V21BYp6m6fy48l7e/3QmDMp9mkoI+gA2sfVoqFHtePVidmx/MediqXe3FOcfOaJovz9WFcAAfcJXE3O3uORzt09eYp0+4vleF3F6G589s+xpliS2HjAubrpvqGZy+e1Mqr+Ulf3GMr0AMb+dnAFAsnvrU0tm47rUNDZZOfDzAmTknOsMsaoAKqnjuvrT+EVa2aBNSxibfIUX2xlyYe2Fyxha0fqQCXFS128Pxm6ezCrrw8g2xgHKxZX1k+ivdVzfM/8KYFbjKMrUmHlqN+dclxOrCuAdG0pt05gtKoA5Q88k9d5MLlp9DZB5XprefbeNseAT5d1433bgB2WPULWpB/BPkWO+YSPAxE8j9AFcWP99EhhzImfjnOsssaoA+rCes8ueBD4f+TMh8VTjgezUrX/GPoBtSXnDOk5KvsjYxHw2NA0vSMzLyx7k7LInWVu3FMhv4vQ7zvl60aa1c87lJ6tDPkn3SFolfT7imaQTJS2S1CRpTCufGSZpvaRLMsTcWdIcSUskPSSp3R7EAfqEsYktb37aZc3L7K73eanHV7NZldhLbVrNTam7mNowjikDCzPPwKHh3zRVmf+EKfvt1Jd9hxVu4hvnXMfL9px/KtBydomFwDeBTCOsTQaebiPmTcBkMxsBrAHanfVDGNMbtxxyoV/t+xyZ/DOiSINpdDLrM5yDam/j2ab9CnYfwKX1Z3Fm3Q9JVbQ9b4BzbtuS1R7EzGYCNS3KFpvZ260tL+l44F2g1RlCFFzGcwTQPI3WNOD49vJ4y4ZyX+OWQxUsGHYKH1sfvr86q3nlYy+ZKmcl/bmq7D6O+rQwM/C+bUN5sWn0Z9NcOudKQ8FvP5PUA7gMuK6NxfoDn5hZQ/i6GhjcXuwq1XB88uUtyhISDzYezqvdDs0z43hJNW3ku8nn+EpyHn0aPylIzJtSd7Gs8tSizUvqnCuOjrj/+DqCpp22ZitvbU/TahuOpLMkzZU0tzcb2btiy/ltd6qZxT6JpbzWLfrVMHFQVreBG1JTuL3hWF4YfHZBYg7d9xjeKtujILGcc/HREVcBHQicIOlmoA/QJKnWzNJHLlsN9JFUFp4FDAFWtBbMzO4E7gT44h672eFn3bzF+z3rPmbvxCKesfoOWJWuRz0HsF/t7aynGycX6D6AAyacDxPOL0gs51x8FPwMwMwONbPhZjYc+CXwHy12/lgwAeaLQPMQnhOBx9uLXdljO7YfNGyLsqVDJrDJKjh9zX8WIv0ur6wsxd/pzW2p2xizphD3ATvnSlW2l4E+AMwGRkqqljRJ0gRJ1cBBwFOSMt+F9Xmc6ZKah3a5DLhY0lKCPoG781mBZEJMaRzHK93H5vPx2EnSwKTkdA5LzKPSCjMWkHOuNGXVBGRmmSZlfaydz13b4vX4tOfvQvSRA6pq5tAn8RZPlsd7kLdsJa2Bq1P38dP6k+k+5DvkN32Lc87FbD6A1nSrX8uYxNuUN20qdiqdIlnenb1q72JK4zjKC9QH4JwrTbHfg3wweByNluSkddOKnUqnKEsmaCTB1NRNjKiZUex0nHMxFvsKQBK/ajyWWd1Kow8gkRDnlj3Owcm/kFJT+x9wzrkMYl8B7PD3uRyWmM+7ZbsWO5VOc1byKf674Tg+HHJMsVNxzsVYrEYDbU1500ZGJJbxjG0odiqdZkzjPaxvSPBz7wNwzkUQ+z3IqqqvUEYT4zcUZlycOKhKfMKD5T9hUM3rxU7FORdjsa8Akglxa8MJzK4ojT4AgDMST7J/4h3KymJ/AuecK6LYVwD9av7M0YnX+TBZVexUOs3RzOE3DUezYdD+xU7FORdjsT+ELGuqY1TiPZ5pWlfsVDrNMcm7WVVbz2+9D8A5F0Hs9yBrBx1Md2o5bNMLxU6l03xJS/l9+dX0/nRJsVNxzsVY7CuAhOCGhu8yq/KQYqfSaU5ums7oxLuUpSqLnYpzLsZiXwH0XrOAYxOzWKdexU6l0+xuy3ikcSz0L517H5xzhRf7PoAExhcSK+jV9GmxU+k0p/W4g3dXb+BPSZ/ByzmXv9ifAWwcsA99Wc/+da8UO5VOM7ZxNtPLr6By8+pip+Kci7HYVwCJBFxVfwazKkpjTmCA8fXPMSrxHqnyimKn4pyLsdhXAD3XvMW3y2bQZKXTHNLL1vOHxgMp77l9sVNxzsVYuxWApHskrZK0MK3sREmLJDVJGpNWfoCkN8PHPEkTMsScKumvacuOznsFEmKYVtGjhPoALu97K+fVX+jzATjnIslmDzIVGNeibCHwTWBmK+VjzGx0+Jn/lZSpo/lHZjY6fLyZQ85b2Nx/FDuwhj3rF7a/8Dbi67V/4LnyH/lw0M65SNq9CsjMZkoa3qJsMQRj8bco35j2shKwyBm2IyG4uP4cmir24KSO/mVdxCGbX2JE4gNIpYqdinMuxgrehiDpQEmLgAXA2WbWkGHRGyXNlzRZUt69md3WLmNi2bN036Lu2bbVJrozw/YpdhrOuZgreAVgZnPMbE9gf+AKSa3drnoFsHu4TD/gskzxJJ0laa6kuR9//PHWv09JBqmG7k2lMx/ALwb8hAt0RbHTcM7FXIf1IobNRBuAL7by3koLbAamAAe0EedOMxtjZmMGDBiw1fv12w2hSjUMb/hrAbPv2k5aN4X7ubLYaTjnYq6gFYCknZs7fSXtBIwElreyXFX4U8DxBJ3Hef7OMs6tu4DZ5QflGyJ2vlT7Bl/CB4JzzkWTzWWgDwCzgZGSqiVNkjRBUjVwEPCUpGfCxQ8B5kl6E3gMONfMVodxpkvaMVzufkkLCPoJtgduyHcFUuurObfsCbZv3Lp5aFu1snwnXk/sVew0nHMxl81VQCdneOuxVpa9F7g3Q5zxac+PyDbB9iiRpL/WUWmbChWyy3t8+DUs+3g9jxQ7EedcrMV+MLjG8t5UqYaBTauKnUqnueobe1DX4PcAOOeiif2tpJaq4F/rLmZ26svFTqXT9KpM0b+njwPknIsm9hVAalMNl5Q9zPCG5cVOxTnnYiX2FYApwXbaSDmbi52Kc87FSuwrACWSVKmGniV0I5hzzhVC7DuBK3v2ZWLdZVQN3bvYqTjnXKzEvgIYNrAPp353Egfu0q/YqTjnXKzEvgIAOGrUDsVOwTnnYif2fQDOOefy4xWAc86VKK8AnHOuRHkF4JxzJcorAOecK1FeATjnXInyCsA550qUVwDOOVeiZGbFziFrkj4F3i5gyN7A2hKK1xExPZ7HK3ZMj9e+kWbWa6tSM4vNA5hb4Hh3llK8OOTo8bbteHHIcVuMl2nfWepNQE+WWLyOiOnxPF6xY3q8PMWtCWiuO51wNQAACZZJREFUmY0pdh7OORcnmfadcTsDuLPYCTjnXAy1uu+MVQVgZnlXAJLGSXpb0lJJl4dl94dlCyXdIylVgJh3S5onab6kRyT1jBIv7b3bJK0vQH5TJf1V0pvhY3TEeJJ0o6R3JC2WdEHEeC+l5bZC0u8jxjtS0hthvP+XtGvEeEeE8RZKmiYp6xF1w+/YKkkL08r6SXpO0pLwZ9+I8U6UtEhSk6SczpYzxLtF0lvh9/kxSX0ixvtJGOtNSc9K2jFqjmnvXSLJJG0fMcdrJX2Q9j0cHzU/SeeH36VFkm6OmN9Dabktl/Rme3Ey7jsL3cHTFR9AElgG7AKUA/OAUcB4QOHjAeCcAsTcLm2ZW4HLo8QL3xsD3AusL0B+U4ETCvg3PB34DZAIlxsYdX3TlnkUOC1ifu8Ae4TLnAtMjRjvb8Bu4TLXA5Ny+BuOBfYFFqaV3dz8HQEuB26KGG8PYCQwAxiT4/+4tXhfBcrC5zcVIL/07eMC4I6oOYblQ4FngPeA7SPmeC1wSa7bSBvxDgeeBypy2UbaWt+0938BXJNPrmZduBM4w9HXeeHrnGp54ABgqZm9a2Z1wIPAcWY23ULAq8CQAsRcF+YqoBuQbSdLq/EkJYFbgEtzyC1jvBxjZBPvHOB6M2sCMLNVhchPUi/gCCDbM4BM8QzYLlymN7AiQrxvAZvN7J1wmefCsqyY2UygpkXxccC08Pk04Pgo8cxssZnldal0hnjPmllD+PIVcthGMsRbl/ayB9lvHxljhiYTbCOFipeXDPHOAX5mZpvDZbLdRtrML9zHfJvg4DUvXbICCHd6/wN8jeCo62RJo4CXgaMIavlcDCY4cmtWHZY1/74UcCrwx0LElDQF+BDYHbgtYrzzgCfMbGUOubWZH3BjeBo+WVJFxHhfAE6SNFfS05JGFCA/gAnACy12GPnEOxOYLqma4H/8swjxBgGptKaVEwiOPKPYofl/G/4cGDFeRzoDeDpqkLDJ8G/AKcA1BYh3LPCBmc2LGivNeeE2ck8uzXIZ7AYcKmmOpP+TtH8hEgQOBT4ysyX5BuiSFQCZj67/bGbL84inVsrSjxR+Bcw0s5cKEdPMTgd2BBYDJ0WIVwGcSPaVSDb5XUFQMe0P9AMuixivAqi14AqDu4B7IsZrdjK5HdlkincRMN7MhgBTCJrl8o3XBHwHmCzpVeBToKGV5bY5kq4kWNf7o8YysyvNbGgY67yIeXUHrqQAFUma2wkObEYDKwmaWaIoA/oCXwZ+BDwcHr1Hles2spWuWgG0d3SYq2q2PFIbQtgUIOnHwADg4kLFBDCzRuAhsm8iaC3ecmBXYKmk5UB3SUuj5GdmK8NWr80EO8QDosQLyx8Nyx4D9ooYD0n9w7yeyjJWpnirgL3NbE5Y9hBwcJT8zGy2mR1qZgcAM4G8j75CH0mqAgh/Zt080FkkTQS+AZwSNpcWym/JoQktgy8AOwPzwm1kCPCGpEH5BjSzj8ysMWzWvIvst5FMqoHfhdvdqwQHErk0YW8lvPjgmwTf6bx11QqgvaPDXL0GjJC0s6RygqO4JySdCRwDnNzchl2AmLvCZ+1z/wy8FSHe781skJkNN7PhwEYzy/Yqlkz5Ne9sRNDevNXVFLnEI2ijPyJc5jCCTtco8SA46/mDmdVmGauteL0l7RYuczTBWVne8SQNBAibzi4D7sghx9Y8AUwMn08EHo8Yr6AkjSNYz2PNbGMB4qU3ER5L9ttHq8xsgZkNTNtGqoF9zezDCDlWpb2cQPbbSCafbSPhd7EcWB0x5lHAW2ZWHSlKvr3HHfkADgKeSXt9BXBF2uvl5NDTH35mPMHOaRlwZVjWEL5+M3zk1JveMiZBhfoysIDgS3M/aVc95JNji/ezvgqojXX+U1p+9wE9I8brQ3CkvgCYTXDEHWl9Ca5gGZfH96a1/CaEuc0L4+4SMd4tBJXI28APcszvAYImhXqCHdUkoD/wAsGZxAtAv4jxJoTPNwMfpW9HecZbSnA23ryNZH3VToZ4j4bfvfkEd7QOjvo3bPH+cnK7Cqi1HO8NvzPzCSroqojxysNtbSHwBnBE1PUluJrv7Fy3kZaPLnkncHh68w5wJPABwdHYv5jZovD95QSXuEWtRZ1zrmR1ySYgCy47O4/gut7FwMNmtkjSBeHVHEOA+ZJ+Xcw8nXMuzrrkGYBzzrmO1yXPAJxzznU8rwCcc65EdakKQNKV4WBJzYNFHVjsnJxzbluV9UiGHU3SQQQ3m+xrZpvDsX7Ki5yWc85ts7rSGUAVsNo+HzBptZmtkLRfOH7G65KeSbuRaYakX0qapWBo3qh36znnXEnpShXAs8BQBePK/0rSYeEgbbcRDF+8H8E4MzemfaaHmR1MMMxvtmPQOOecows1AZnZekn7EYxwdzjBGBc3AF8EngvHTkoS3BXX7IHwszMlbSepj5l90rmZO+dcPHWZCgA+G0BtBjBD0gLg+8AiMzso00faee2ccy6DLtMEJGlki4GiRhPcBTwg7CBGUkrSnmnLnBSWHwKsNbO1nZawc87FXFc6A+gJ3KZgztEGgkGoziKYzPi/JPUmyPeXwKLwM2skzSKY8emMzk/ZOefiK7ZDQUiaQTBv59xi5+Kcc3HUZZqAnHPOda7YngE455yLxs8AnHOuRBW1ApA0VNKLkhaHYwBdGJb3k/ScpCXhz75h+SnhOEHzwzuA906LNU7S25KWSrq8WOvknHNxUdQmoHBYhyoze0NSL+B1gnlqvwfUmNnPwp15XzO7TNLBwGIzWyPpa8C1ZnagpCTBDGJHE0yb9hrBPL9/KcZ6OedcHBT1DMDMVprZG+HzTwmu+x8MHAdMCxebRlApYGazzGxNWP4KwcxgAAcAS83sXTOrAx4MYzjnnMugy/QBSBoO7APMAXYws5UQVBLAwFY+Mgl4Onw+mGDi6mbVYZlzzrkMusSNYJJ6Ao8CPzCzdeG4P20tfzhBBXBIc1Eri/nlTc4514ainwGEI34+CtxvZr8Liz9KG/a5CliVtvxewK+B48zs72FxNTA0LewQYEVH5+6cc3FW7KuABNxN0LF7a9pbTwATw+cTgcfD5YcBvwNONbN30pZ/DRghaWdJ5cB3whjOOecyKPZVQIcALwELgKaw+N8J+gEeBoYB7wMnmlmNpF8D3wLeC5dtMLMxYazxBOMEJYF7zCx93gDnnHMt+J3AzjlXooreB+Ccc644vAJwzrkS5RWAc86VKK8AnHOuRHkF4JxzJcorAOecK1FeATjnXInyCsA550rUPwA5TFB9QzIdkgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "time_series[0].plot(label='test')\n",
+    "time_series_training[0].plot(label='train', ls=':')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def series_to_obj(ts, cat=None):\n",
+    "    obj = {\"start\": str(ts.index[0]), \"target\": list(ts)}\n",
+    "    if cat is not None:\n",
+    "        obj[\"cat\"] = cat\n",
+    "    return obj\n",
+    "\n",
+    "def series_to_jsonline(ts, cat=None):\n",
+    "    return json.dumps(series_to_obj(ts, cat))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoding = \"utf-8\"\n",
+    "s3filesystem = s3fs.S3FileSystem()\n",
+    "\n",
+    "# 将训练数据和测试数据存到s3\n",
+    "with s3filesystem.open(s3_data_path + \"/train/train.json\", 'wb') as fp:\n",
+    "    for ts in time_series_training:\n",
+    "        fp.write(series_to_jsonline(ts).encode(encoding))\n",
+    "        fp.write('\\n'.encode(encoding))\n",
+    "\n",
+    "with s3filesystem.open(s3_data_path + \"/test/test.json\", 'wb') as fp:\n",
+    "    for ts in time_series:\n",
+    "        fp.write(series_to_jsonline(ts).encode(encoding))\n",
+    "        fp.write('\\n'.encode(encoding))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Parameter image_name will be renamed to image_uri in SageMaker Python SDK v2.\n"
+     ]
+    }
+   ],
+   "source": [
+    "estimator = sagemaker.estimator.Estimator(\n",
+    "    sagemaker_session=sagemaker_session,\n",
+    "    image_name=image_name,\n",
+    "    role=role,\n",
+    "    train_instance_count=1,\n",
+    "    train_instance_type='ml.c4.xlarge',\n",
+    "    base_job_name='lpr-deepar',\n",
+    "    output_path=\"s3://\" + s3_output_path\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hyperparameters = {\n",
+    "    \"time_freq\": freq,\n",
+    "    \"context_length\": str(context_length),\n",
+    "    \"prediction_length\": str(prediction_length),\n",
+    "    \"num_cells\": \"40\",\n",
+    "    \"num_layers\": \"3\",\n",
+    "    \"likelihood\": \"gaussian\",\n",
+    "    \"epochs\": \"20\",\n",
+    "    \"mini_batch_size\": \"32\",\n",
+    "    \"learning_rate\": \"0.014\",\n",
+    "    \"dropout_rate\": \"0.05\",\n",
+    "    \"early_stopping_patience\": \"10\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estimator.set_hyperparameters(**hyperparameters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\n",
+      "'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2020-09-15 09:59:27 Starting - Starting the training job...\n",
+      "2020-09-15 09:59:34 Starting - Launching requested ML instances......\n",
+      "2020-09-15 10:00:36 Starting - Preparing the instances for training.........\n",
+      "2020-09-15 10:02:12 Downloading - Downloading input data...\n",
+      "2020-09-15 10:02:42 Training - Downloading the training image.\u001b[34mArguments: train\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-input.json: {u'num_dynamic_feat': u'auto', u'dropout_rate': u'0.10', u'mini_batch_size': u'128', u'test_quantiles': u'[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]', u'_tuning_objective_metric': u'', u'_num_gpus': u'auto', u'num_eval_samples': u'100', u'learning_rate': u'0.001', u'num_cells': u'40', u'num_layers': u'2', u'embedding_dimension': u'10', u'_kvstore': u'auto', u'_num_kv_servers': u'auto', u'cardinality': u'auto', u'likelihood': u'student-t', u'early_stopping_patience': u''}\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'dropout_rate': u'0.05', u'learning_rate': u'0.014', u'num_cells': u'40', u'prediction_length': u'48', u'epochs': u'20', u'time_freq': u'H', u'context_length': u'72', u'num_layers': u'3', u'mini_batch_size': u'32', u'likelihood': u'gaussian', u'early_stopping_patience': u'10'}\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Final configuration: {u'dropout_rate': u'0.05', u'test_quantiles': u'[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]', u'_tuning_objective_metric': u'', u'num_eval_samples': u'100', u'learning_rate': u'0.014', u'num_layers': u'3', u'epochs': u'20', u'embedding_dimension': u'10', u'num_cells': u'40', u'_num_kv_servers': u'auto', u'mini_batch_size': u'32', u'likelihood': u'gaussian', u'num_dynamic_feat': u'auto', u'cardinality': u'auto', u'_num_gpus': u'auto', u'prediction_length': u'48', u'time_freq': u'H', u'context_length': u'72', u'_kvstore': u'auto', u'early_stopping_patience': u'10'}\u001b[0m\n",
+      "\u001b[34mProcess 1 is a worker.\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Detected entry point for worker worker\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Using early stopping with patience 10\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] [cardinality=auto] `cat` field was NOT found in the file `/opt/ml/input/data/train/train.json` and will NOT be used for training.\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] [num_dynamic_feat=auto] `dynamic_feat` field was NOT found in the file `/opt/ml/input/data/train/train.json` and will NOT be used for training.\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Training set statistics:\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Real time series\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] number of time series: 200\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] number of observations: 70400\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] mean target length: 352\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] min/mean/max target: 2.7915456295/76.1075677906/151.775772095\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] mean abs(target): 76.1075677906\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] contains missing values: no\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Small number of time series. Doing 2 passes over dataset with prob 0.8 per epoch.\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Test set statistics:\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Real time series\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] number of time series: 200\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] number of observations: 80000\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] mean target length: 400\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] min/mean/max target: 2.7915456295/76.1509840103/152.21762085\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] mean abs(target): 76.1509840103\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] contains missing values: no\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] nvidia-smi took: 0.0251801013947 secs to identify 0 gpus\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Number of GPUs being used: 0\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:57 INFO 139774100076352] Create Store: local\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"get_graph.time\": {\"count\": 1, \"max\": 347.1407890319824, \"sum\": 347.1407890319824, \"min\": 347.1407890319824}}, \"EndTime\": 1600164178.271435, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164177.923482}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:58 INFO 139774100076352] Number of GPUs being used: 0\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"initialize.time\": {\"count\": 1, \"max\": 859.8160743713379, \"sum\": 859.8160743713379, \"min\": 859.8160743713379}}, \"EndTime\": 1600164178.783418, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164178.271513}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:59 INFO 139774100076352] Epoch[0] Batch[0] avg_epoch_loss=4.609416\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:59 INFO 139774100076352] #quality_metric: host=algo-1, epoch=0, batch=0 train loss <loss>=4.60941553116\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:59 INFO 139774100076352] Epoch[0] Batch[5] avg_epoch_loss=5.606053\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:59 INFO 139774100076352] #quality_metric: host=algo-1, epoch=0, batch=5 train loss <loss>=5.60605303446\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:02:59 INFO 139774100076352] Epoch[0] Batch [5]#011Speed: 235.29 samples/sec#011loss=5.606053\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:00 INFO 139774100076352] processed a total of 320 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"epochs\": {\"count\": 1, \"max\": 20, \"sum\": 20.0, \"min\": 20}, \"update.time\": {\"count\": 1, \"max\": 1561.9840621948242, \"sum\": 1561.9840621948242, \"min\": 1561.9840621948242}}, \"EndTime\": 1600164180.345551, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164178.783479}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:00 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=204.850324863 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:00 INFO 139774100076352] #progress_metric: host=algo-1, completed 5 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:00 INFO 139774100076352] #quality_metric: host=algo-1, epoch=0, train loss <loss>=5.3249232769\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:00 INFO 139774100076352] best epoch loss so far\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:00 INFO 139774100076352] Saved checkpoint to \"/opt/ml/model/state_849957fa-ce6f-4d4d-8fd1-3f0b2baef61f-0000.params\"\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 47.77884483337402, \"sum\": 47.77884483337402, \"min\": 47.77884483337402}}, \"EndTime\": 1600164180.394063, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164180.345643}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:00 INFO 139774100076352] Epoch[1] Batch[0] avg_epoch_loss=4.983928\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:00 INFO 139774100076352] #quality_metric: host=algo-1, epoch=1, batch=0 train loss <loss>=4.98392772675\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:01 INFO 139774100076352] Epoch[1] Batch[5] avg_epoch_loss=4.143058\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:01 INFO 139774100076352] #quality_metric: host=algo-1, epoch=1, batch=5 train loss <loss>=4.14305798213\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:01 INFO 139774100076352] Epoch[1] Batch [5]#011Speed: 232.61 samples/sec#011loss=4.143058\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:01 INFO 139774100076352] processed a total of 309 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1447.788953781128, \"sum\": 1447.788953781128, \"min\": 1447.788953781128}}, \"EndTime\": 1600164181.842036, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164180.394134}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:01 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=213.401591637 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:01 INFO 139774100076352] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:01 INFO 139774100076352] #quality_metric: host=algo-1, epoch=1, train loss <loss>=3.67150185108\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:01 INFO 139774100076352] best epoch loss so far\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:01 INFO 139774100076352] Saved checkpoint to \"/opt/ml/model/state_f5f484e2-d530-4b75-9942-c12eb41620b5-0000.params\"\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 40.87686538696289, \"sum\": 40.87686538696289, \"min\": 40.87686538696289}}, \"EndTime\": 1600164181.883737, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164181.842136}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:02 INFO 139774100076352] Epoch[2] Batch[0] avg_epoch_loss=2.205648\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:02 INFO 139774100076352] #quality_metric: host=algo-1, epoch=2, batch=0 train loss <loss>=2.20564818382\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:02 INFO 139774100076352] Epoch[2] Batch[5] avg_epoch_loss=4.012246\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:02 INFO 139774100076352] #quality_metric: host=algo-1, epoch=2, batch=5 train loss <loss>=4.01224648952\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:02 INFO 139774100076352] Epoch[2] Batch [5]#011Speed: 178.73 samples/sec#011loss=4.012246\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:03 INFO 139774100076352] Epoch[2] Batch[10] avg_epoch_loss=3.663141\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:03 INFO 139774100076352] #quality_metric: host=algo-1, epoch=2, batch=10 train loss <loss>=3.24421515465\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:03 INFO 139774100076352] Epoch[2] Batch [10]#011Speed: 166.61 samples/sec#011loss=3.244215\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:03 INFO 139774100076352] processed a total of 327 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 2073.482036590576, \"sum\": 2073.482036590576, \"min\": 2073.482036590576}}, \"EndTime\": 1600164183.957351, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164181.883809}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:03 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=157.696301878 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:03 INFO 139774100076352] #progress_metric: host=algo-1, completed 15 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:03 INFO 139774100076352] #quality_metric: host=algo-1, epoch=2, train loss <loss>=3.66314133731\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:03 INFO 139774100076352] best epoch loss so far\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:04 INFO 139774100076352] Saved checkpoint to \"/opt/ml/model/state_74adc656-7f0d-46ca-b6c5-6f8858b22dcd-0000.params\"\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 44.35920715332031, \"sum\": 44.35920715332031, \"min\": 44.35920715332031}}, \"EndTime\": 1600164184.002531, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164183.957436}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:04 INFO 139774100076352] Epoch[3] Batch[0] avg_epoch_loss=3.230448\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:04 INFO 139774100076352] #quality_metric: host=algo-1, epoch=3, batch=0 train loss <loss>=3.23044753075\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:04 INFO 139774100076352] Epoch[3] Batch[5] avg_epoch_loss=3.444484\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:04 INFO 139774100076352] #quality_metric: host=algo-1, epoch=3, batch=5 train loss <loss>=3.4444839557\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:04 INFO 139774100076352] Epoch[3] Batch [5]#011Speed: 236.13 samples/sec#011loss=3.444484\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:05 INFO 139774100076352] processed a total of 302 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1477.283000946045, \"sum\": 1477.283000946045, \"min\": 1477.283000946045}}, \"EndTime\": 1600164185.479974, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164184.002626}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:05 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=204.412456594 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:05 INFO 139774100076352] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:05 INFO 139774100076352] #quality_metric: host=algo-1, epoch=3, train loss <loss>=3.37664489746\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:05 INFO 139774100076352] best epoch loss so far\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:05 INFO 139774100076352] Saved checkpoint to \"/opt/ml/model/state_aa79f08f-8522-4b61-b0de-a70a5a363d31-0000.params\"\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 43.12491416931152, \"sum\": 43.12491416931152, \"min\": 43.12491416931152}}, \"EndTime\": 1600164185.523735, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164185.480055}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:05 INFO 139774100076352] Epoch[4] Batch[0] avg_epoch_loss=3.022581\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:05 INFO 139774100076352] #quality_metric: host=algo-1, epoch=4, batch=0 train loss <loss>=3.02258086205\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:06 INFO 139774100076352] Epoch[4] Batch[5] avg_epoch_loss=2.871891\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:06 INFO 139774100076352] #quality_metric: host=algo-1, epoch=4, batch=5 train loss <loss>=2.87189054489\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:06 INFO 139774100076352] Epoch[4] Batch [5]#011Speed: 242.23 samples/sec#011loss=2.871891\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:07 INFO 139774100076352] Epoch[4] Batch[10] avg_epoch_loss=2.615662\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:07 INFO 139774100076352] #quality_metric: host=algo-1, epoch=4, batch=10 train loss <loss>=2.30818743706\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:07 INFO 139774100076352] Epoch[4] Batch [10]#011Speed: 237.39 samples/sec#011loss=2.308187\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:07 INFO 139774100076352] processed a total of 339 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1555.2778244018555, \"sum\": 1555.2778244018555, \"min\": 1555.2778244018555}}, \"EndTime\": 1600164187.079144, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164185.523806}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:07 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=217.94994691 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:07 INFO 139774100076352] #progress_metric: host=algo-1, completed 25 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:07 INFO 139774100076352] #quality_metric: host=algo-1, epoch=4, train loss <loss>=2.61566185951\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:07 INFO 139774100076352] best epoch loss so far\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:07 INFO 139774100076352] Saved checkpoint to \"/opt/ml/model/state_7b71d08c-9498-4081-a1dd-08ecea120881-0000.params\"\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 53.009033203125, \"sum\": 53.009033203125, \"min\": 53.009033203125}}, \"EndTime\": 1600164187.132795, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164187.079229}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:07 INFO 139774100076352] Epoch[5] Batch[0] avg_epoch_loss=2.340788\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:07 INFO 139774100076352] #quality_metric: host=algo-1, epoch=5, batch=0 train loss <loss>=2.34078788757\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] Epoch[5] Batch[5] avg_epoch_loss=2.246251\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] #quality_metric: host=algo-1, epoch=5, batch=5 train loss <loss>=2.24625080824\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] Epoch[5] Batch [5]#011Speed: 235.54 samples/sec#011loss=2.246251\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] Epoch[5] Batch[10] avg_epoch_loss=2.044126\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] #quality_metric: host=algo-1, epoch=5, batch=10 train loss <loss>=1.80157606602\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] Epoch[5] Batch [10]#011Speed: 234.35 samples/sec#011loss=1.801576\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] processed a total of 329 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1591.2930965423584, \"sum\": 1591.2930965423584, \"min\": 1591.2930965423584}}, \"EndTime\": 1600164188.724231, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164187.132876}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=206.735692524 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] #quality_metric: host=algo-1, epoch=5, train loss <loss>=2.04412592541\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] best epoch loss so far\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] Saved checkpoint to \"/opt/ml/model/state_eb107c62-065f-48c8-8302-c8cc2a40fea8-0000.params\"\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 43.84016990661621, \"sum\": 43.84016990661621, \"min\": 43.84016990661621}}, \"EndTime\": 1600164188.768604, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164188.724308}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] Epoch[6] Batch[0] avg_epoch_loss=1.334702\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:08 INFO 139774100076352] #quality_metric: host=algo-1, epoch=6, batch=0 train loss <loss>=1.33470249176\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:09 INFO 139774100076352] Epoch[6] Batch[5] avg_epoch_loss=1.707769\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:09 INFO 139774100076352] #quality_metric: host=algo-1, epoch=6, batch=5 train loss <loss>=1.7077690959\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:09 INFO 139774100076352] Epoch[6] Batch [5]#011Speed: 235.51 samples/sec#011loss=1.707769\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:10 INFO 139774100076352] processed a total of 314 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1441.230058670044, \"sum\": 1441.230058670044, \"min\": 1441.230058670044}}, \"EndTime\": 1600164190.209965, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164188.768672}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:10 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=217.85539457 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:10 INFO 139774100076352] #progress_metric: host=algo-1, completed 35 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:10 INFO 139774100076352] #quality_metric: host=algo-1, epoch=6, train loss <loss>=1.98426616192\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:10 INFO 139774100076352] best epoch loss so far\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:10 INFO 139774100076352] Saved checkpoint to \"/opt/ml/model/state_c0e8c875-05c5-4507-b644-e8ed6217ac27-0000.params\"\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 40.612220764160156, \"sum\": 40.612220764160156, \"min\": 40.612220764160156}}, \"EndTime\": 1600164190.251234, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164190.210021}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:10 INFO 139774100076352] Epoch[7] Batch[0] avg_epoch_loss=2.592846\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:10 INFO 139774100076352] #quality_metric: host=algo-1, epoch=7, batch=0 train loss <loss>=2.59284591675\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:11 INFO 139774100076352] Epoch[7] Batch[5] avg_epoch_loss=2.217547\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:11 INFO 139774100076352] #quality_metric: host=algo-1, epoch=7, batch=5 train loss <loss>=2.2175471584\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:11 INFO 139774100076352] Epoch[7] Batch [5]#011Speed: 245.81 samples/sec#011loss=2.217547\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:11 INFO 139774100076352] Epoch[7] Batch[10] avg_epoch_loss=2.064843\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:11 INFO 139774100076352] #quality_metric: host=algo-1, epoch=7, batch=10 train loss <loss>=1.88159809113\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:11 INFO 139774100076352] Epoch[7] Batch [10]#011Speed: 232.09 samples/sec#011loss=1.881598\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:11 INFO 139774100076352] processed a total of 330 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1570.3539848327637, \"sum\": 1570.3539848327637, \"min\": 1570.3539848327637}}, \"EndTime\": 1600164191.821718, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164190.251301}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:11 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=210.129505341 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:11 INFO 139774100076352] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:11 INFO 139774100076352] #quality_metric: host=algo-1, epoch=7, train loss <loss>=2.06484303691\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:11 INFO 139774100076352] loss did not improve\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:12 INFO 139774100076352] Epoch[8] Batch[0] avg_epoch_loss=1.843123\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:12 INFO 139774100076352] #quality_metric: host=algo-1, epoch=8, batch=0 train loss <loss>=1.84312307835\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:12 INFO 139774100076352] Epoch[8] Batch[5] avg_epoch_loss=1.647901\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:12 INFO 139774100076352] #quality_metric: host=algo-1, epoch=8, batch=5 train loss <loss>=1.6479006211\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:12 INFO 139774100076352] Epoch[8] Batch [5]#011Speed: 243.10 samples/sec#011loss=1.647901\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:13 INFO 139774100076352] processed a total of 282 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1286.8261337280273, \"sum\": 1286.8261337280273, \"min\": 1286.8261337280273}}, \"EndTime\": 1600164193.109025, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164191.821789}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:13 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=219.127828085 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:13 INFO 139774100076352] #progress_metric: host=algo-1, completed 45 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:13 INFO 139774100076352] #quality_metric: host=algo-1, epoch=8, train loss <loss>=1.61567240291\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:13 INFO 139774100076352] best epoch loss so far\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:13 INFO 139774100076352] Saved checkpoint to \"/opt/ml/model/state_d7f306f2-fcd5-4be7-991a-316dc1073588-0000.params\"\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 41.35608673095703, \"sum\": 41.35608673095703, \"min\": 41.35608673095703}}, \"EndTime\": 1600164193.150989, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164193.109081}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:13 INFO 139774100076352] Epoch[9] Batch[0] avg_epoch_loss=1.172302\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:13 INFO 139774100076352] #quality_metric: host=algo-1, epoch=9, batch=0 train loss <loss>=1.1723023653\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:14 INFO 139774100076352] Epoch[9] Batch[5] avg_epoch_loss=3.327431\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:14 INFO 139774100076352] #quality_metric: host=algo-1, epoch=9, batch=5 train loss <loss>=3.32743132114\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:14 INFO 139774100076352] Epoch[9] Batch [5]#011Speed: 240.46 samples/sec#011loss=3.327431\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:14 INFO 139774100076352] processed a total of 292 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1464.9930000305176, \"sum\": 1464.9930000305176, \"min\": 1464.9930000305176}}, \"EndTime\": 1600164194.616104, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164193.151059}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:14 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=199.304605821 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:14 INFO 139774100076352] #progress_metric: host=algo-1, completed 50 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:14 INFO 139774100076352] #quality_metric: host=algo-1, epoch=9, train loss <loss>=2.96129029989\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:14 INFO 139774100076352] loss did not improve\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:14 INFO 139774100076352] Epoch[10] Batch[0] avg_epoch_loss=2.762169\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:14 INFO 139774100076352] #quality_metric: host=algo-1, epoch=10, batch=0 train loss <loss>=2.76216936111\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:15 INFO 139774100076352] Epoch[10] Batch[5] avg_epoch_loss=2.738133\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:15 INFO 139774100076352] #quality_metric: host=algo-1, epoch=10, batch=5 train loss <loss>=2.73813331127\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:15 INFO 139774100076352] Epoch[10] Batch [5]#011Speed: 238.97 samples/sec#011loss=2.738133\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:16 INFO 139774100076352] processed a total of 315 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1419.5780754089355, \"sum\": 1419.5780754089355, \"min\": 1419.5780754089355}}, \"EndTime\": 1600164196.036253, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164194.616173}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:16 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=221.881602196 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:16 INFO 139774100076352] #progress_metric: host=algo-1, completed 55 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:16 INFO 139774100076352] #quality_metric: host=algo-1, epoch=10, train loss <loss>=2.76047251225\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:16 INFO 139774100076352] loss did not improve\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:16 INFO 139774100076352] Epoch[11] Batch[0] avg_epoch_loss=2.991304\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:16 INFO 139774100076352] #quality_metric: host=algo-1, epoch=11, batch=0 train loss <loss>=2.99130415916\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:16 INFO 139774100076352] Epoch[11] Batch[5] avg_epoch_loss=2.657235\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:16 INFO 139774100076352] #quality_metric: host=algo-1, epoch=11, batch=5 train loss <loss>=2.65723490715\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:16 INFO 139774100076352] Epoch[11] Batch [5]#011Speed: 245.02 samples/sec#011loss=2.657235\u001b[0m\n",
+      "\n",
+      "2020-09-15 10:03:22 Training - Training image download completed. Training in progress.\u001b[34m[09/15/2020 10:03:17 INFO 139774100076352] processed a total of 306 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1413.3579730987549, \"sum\": 1413.3579730987549, \"min\": 1413.3579730987549}}, \"EndTime\": 1600164197.450208, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164196.036317}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:17 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=216.485281966 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:17 INFO 139774100076352] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:17 INFO 139774100076352] #quality_metric: host=algo-1, epoch=11, train loss <loss>=2.26395412683\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:17 INFO 139774100076352] loss did not improve\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:17 INFO 139774100076352] Epoch[12] Batch[0] avg_epoch_loss=1.665831\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:17 INFO 139774100076352] #quality_metric: host=algo-1, epoch=12, batch=0 train loss <loss>=1.66583108902\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:18 INFO 139774100076352] Epoch[12] Batch[5] avg_epoch_loss=1.989155\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:18 INFO 139774100076352] #quality_metric: host=algo-1, epoch=12, batch=5 train loss <loss>=1.98915479581\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:18 INFO 139774100076352] Epoch[12] Batch [5]#011Speed: 245.47 samples/sec#011loss=1.989155\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] Epoch[12] Batch[10] avg_epoch_loss=1.903293\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] #quality_metric: host=algo-1, epoch=12, batch=10 train loss <loss>=1.800259161\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] Epoch[12] Batch [10]#011Speed: 227.98 samples/sec#011loss=1.800259\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] processed a total of 327 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1578.9520740509033, \"sum\": 1578.9520740509033, \"min\": 1578.9520740509033}}, \"EndTime\": 1600164199.029793, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164197.450303}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=207.086280829 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] #progress_metric: host=algo-1, completed 65 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] #quality_metric: host=algo-1, epoch=12, train loss <loss>=1.90329314362\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] loss did not improve\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] Epoch[13] Batch[0] avg_epoch_loss=1.288209\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] #quality_metric: host=algo-1, epoch=13, batch=0 train loss <loss>=1.28820860386\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] Epoch[13] Batch[5] avg_epoch_loss=1.478354\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] #quality_metric: host=algo-1, epoch=13, batch=5 train loss <loss>=1.47835389773\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:19 INFO 139774100076352] Epoch[13] Batch [5]#011Speed: 244.98 samples/sec#011loss=1.478354\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:20 INFO 139774100076352] Epoch[13] Batch[10] avg_epoch_loss=1.437386\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:20 INFO 139774100076352] #quality_metric: host=algo-1, epoch=13, batch=10 train loss <loss>=1.38822498322\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:20 INFO 139774100076352] Epoch[13] Batch [10]#011Speed: 234.40 samples/sec#011loss=1.388225\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:20 INFO 139774100076352] processed a total of 349 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1566.511869430542, \"sum\": 1566.511869430542, \"min\": 1566.511869430542}}, \"EndTime\": 1600164200.596754, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164199.029861}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:20 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=222.773730084 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:20 INFO 139774100076352] #progress_metric: host=algo-1, completed 70 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:20 INFO 139774100076352] #quality_metric: host=algo-1, epoch=13, train loss <loss>=1.43738620931\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:20 INFO 139774100076352] best epoch loss so far\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:20 INFO 139774100076352] Saved checkpoint to \"/opt/ml/model/state_719cb6a8-7c92-485e-afe6-eecdc94f8c58-0000.params\"\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 41.65506362915039, \"sum\": 41.65506362915039, \"min\": 41.65506362915039}}, \"EndTime\": 1600164200.639076, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164200.596821}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:20 INFO 139774100076352] Epoch[14] Batch[0] avg_epoch_loss=1.866858\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:20 INFO 139774100076352] #quality_metric: host=algo-1, epoch=14, batch=0 train loss <loss>=1.86685776711\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:21 INFO 139774100076352] Epoch[14] Batch[5] avg_epoch_loss=1.649016\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:21 INFO 139774100076352] #quality_metric: host=algo-1, epoch=14, batch=5 train loss <loss>=1.64901594321\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:21 INFO 139774100076352] Epoch[14] Batch [5]#011Speed: 247.60 samples/sec#011loss=1.649016\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:22 INFO 139774100076352] processed a total of 291 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1403.4569263458252, \"sum\": 1403.4569263458252, \"min\": 1403.4569263458252}}, \"EndTime\": 1600164202.042676, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164200.639156}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:22 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=207.329485966 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:22 INFO 139774100076352] #progress_metric: host=algo-1, completed 75 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:22 INFO 139774100076352] #quality_metric: host=algo-1, epoch=14, train loss <loss>=1.53826291561\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:22 INFO 139774100076352] loss did not improve\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:22 INFO 139774100076352] Epoch[15] Batch[0] avg_epoch_loss=1.243294\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:22 INFO 139774100076352] #quality_metric: host=algo-1, epoch=15, batch=0 train loss <loss>=1.24329388142\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:22 INFO 139774100076352] Epoch[15] Batch[5] avg_epoch_loss=1.501441\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:22 INFO 139774100076352] #quality_metric: host=algo-1, epoch=15, batch=5 train loss <loss>=1.50144104163\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:22 INFO 139774100076352] Epoch[15] Batch [5]#011Speed: 231.19 samples/sec#011loss=1.501441\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:23 INFO 139774100076352] processed a total of 296 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1445.6939697265625, \"sum\": 1445.6939697265625, \"min\": 1445.6939697265625}}, \"EndTime\": 1600164203.488986, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164202.042749}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:23 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=204.732218387 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:23 INFO 139774100076352] #progress_metric: host=algo-1, completed 80 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:23 INFO 139774100076352] #quality_metric: host=algo-1, epoch=15, train loss <loss>=1.26670198739\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:23 INFO 139774100076352] best epoch loss so far\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:23 INFO 139774100076352] Saved checkpoint to \"/opt/ml/model/state_760b118d-dafa-4e5e-bd0c-25f8ca7043e6-0000.params\"\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"state.serialize.time\": {\"count\": 1, \"max\": 41.96286201477051, \"sum\": 41.96286201477051, \"min\": 41.96286201477051}}, \"EndTime\": 1600164203.531599, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164203.48905}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:23 INFO 139774100076352] Epoch[16] Batch[0] avg_epoch_loss=4.818616\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:23 INFO 139774100076352] #quality_metric: host=algo-1, epoch=16, batch=0 train loss <loss>=4.81861639023\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:24 INFO 139774100076352] Epoch[16] Batch[5] avg_epoch_loss=1.935913\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:24 INFO 139774100076352] #quality_metric: host=algo-1, epoch=16, batch=5 train loss <loss>=1.93591250976\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:24 INFO 139774100076352] Epoch[16] Batch [5]#011Speed: 244.93 samples/sec#011loss=1.935913\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:24 INFO 139774100076352] processed a total of 312 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1439.7499561309814, \"sum\": 1439.7499561309814, \"min\": 1439.7499561309814}}, \"EndTime\": 1600164204.971469, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164203.531666}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:24 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=216.689691076 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:24 INFO 139774100076352] #progress_metric: host=algo-1, completed 85 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:24 INFO 139774100076352] #quality_metric: host=algo-1, epoch=16, train loss <loss>=1.75793412924\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:24 INFO 139774100076352] loss did not improve\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:25 INFO 139774100076352] Epoch[17] Batch[0] avg_epoch_loss=1.198596\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:25 INFO 139774100076352] #quality_metric: host=algo-1, epoch=17, batch=0 train loss <loss>=1.19859588146\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:25 INFO 139774100076352] Epoch[17] Batch[5] avg_epoch_loss=1.455770\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:25 INFO 139774100076352] #quality_metric: host=algo-1, epoch=17, batch=5 train loss <loss>=1.45576999585\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:25 INFO 139774100076352] Epoch[17] Batch [5]#011Speed: 246.41 samples/sec#011loss=1.455770\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:26 INFO 139774100076352] Epoch[17] Batch[10] avg_epoch_loss=1.445060\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:26 INFO 139774100076352] #quality_metric: host=algo-1, epoch=17, batch=10 train loss <loss>=1.43220837116\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:26 INFO 139774100076352] Epoch[17] Batch [10]#011Speed: 238.39 samples/sec#011loss=1.432208\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:26 INFO 139774100076352] processed a total of 336 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1541.9280529022217, \"sum\": 1541.9280529022217, \"min\": 1541.9280529022217}}, \"EndTime\": 1600164206.513996, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164204.971533}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:26 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=217.892625948 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:26 INFO 139774100076352] #progress_metric: host=algo-1, completed 90 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:26 INFO 139774100076352] #quality_metric: host=algo-1, epoch=17, train loss <loss>=1.44506016645\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:26 INFO 139774100076352] loss did not improve\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:26 INFO 139774100076352] Epoch[18] Batch[0] avg_epoch_loss=1.525126\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:26 INFO 139774100076352] #quality_metric: host=algo-1, epoch=18, batch=0 train loss <loss>=1.52512633801\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:27 INFO 139774100076352] Epoch[18] Batch[5] avg_epoch_loss=1.413618\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:27 INFO 139774100076352] #quality_metric: host=algo-1, epoch=18, batch=5 train loss <loss>=1.41361786922\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:27 INFO 139774100076352] Epoch[18] Batch [5]#011Speed: 242.78 samples/sec#011loss=1.413618\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:28 INFO 139774100076352] processed a total of 309 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1493.6730861663818, \"sum\": 1493.6730861663818, \"min\": 1493.6730861663818}}, \"EndTime\": 1600164208.00824, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164206.514075}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:28 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=206.857487244 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:28 INFO 139774100076352] #progress_metric: host=algo-1, completed 95 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:28 INFO 139774100076352] #quality_metric: host=algo-1, epoch=18, train loss <loss>=1.346801579\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:28 INFO 139774100076352] loss did not improve\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:28 INFO 139774100076352] Epoch[19] Batch[0] avg_epoch_loss=1.272043\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:28 INFO 139774100076352] #quality_metric: host=algo-1, epoch=19, batch=0 train loss <loss>=1.27204275131\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:28 INFO 139774100076352] Epoch[19] Batch[5] avg_epoch_loss=2.839922\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:28 INFO 139774100076352] #quality_metric: host=algo-1, epoch=19, batch=5 train loss <loss>=2.83992232879\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:28 INFO 139774100076352] Epoch[19] Batch [5]#011Speed: 234.58 samples/sec#011loss=2.839922\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] processed a total of 281 examples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"update.time\": {\"count\": 1, \"max\": 1321.9850063323975, \"sum\": 1321.9850063323975, \"min\": 1321.9850063323975}}, \"EndTime\": 1600164209.330816, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164208.008309}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] #throughput_metric: host=algo-1, train throughput=212.540782207 records/second\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] #progress_metric: host=algo-1, completed 100 % of epochs\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] #quality_metric: host=algo-1, epoch=19, train loss <loss>=2.35883384281\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] loss did not improve\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] Final loss: 1.26670198739 (occurred at epoch 15)\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] #quality_metric: host=algo-1, train final_loss <loss>=1.26670198739\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] Worker algo-1 finished training.\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 WARNING 139774100076352] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] All workers finished. Serializing model for prediction.\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"get_graph.time\": {\"count\": 1, \"max\": 430.7820796966553, \"sum\": 430.7820796966553, \"min\": 430.7820796966553}}, \"EndTime\": 1600164209.762674, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164209.330892}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] Number of GPUs being used: 0\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"finalize.time\": {\"count\": 1, \"max\": 620.6891536712646, \"sum\": 620.6891536712646, \"min\": 620.6891536712646}}, \"EndTime\": 1600164209.952543, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164209.76274}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] Serializing to /opt/ml/model/model_algo-1\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] Saved checkpoint to \"/opt/ml/model/model_algo-1-0000.params\"\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"model.serialize.time\": {\"count\": 1, \"max\": 32.28497505187988, \"sum\": 32.28497505187988, \"min\": 32.28497505187988}}, \"EndTime\": 1600164209.984944, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164209.952613}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] Successfully serialized the model for prediction.\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:29 INFO 139774100076352] Evaluating model accuracy on testset using 100 samples\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"model.bind.time\": {\"count\": 1, \"max\": 0.04601478576660156, \"sum\": 0.04601478576660156, \"min\": 0.04601478576660156}}, \"EndTime\": 1600164209.985764, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164209.984998}\n",
+      "\u001b[0m\n",
+      "\n",
+      "2020-09-15 10:03:54 Uploading - Uploading generated training model\n",
+      "2020-09-15 10:03:54 Completed - Training job completed\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"model.score.time\": {\"count\": 1, \"max\": 13970.802068710327, \"sum\": 13970.802068710327, \"min\": 13970.802068710327}}, \"EndTime\": 1600164223.956515, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164209.985826}\n",
+      "\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, RMSE): 1.01795614289\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, mean_absolute_QuantileLoss): 7088.556153827243\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, mean_wQuantileLoss): 0.009656040627419368\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, wQuantileLoss[0.1]): 0.00986153816132739\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, wQuantileLoss[0.2]): 0.014234286045650737\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, wQuantileLoss[0.3]): 0.015296243592794394\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, wQuantileLoss[0.4]): 0.01353600906502284\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, wQuantileLoss[0.5]): 0.009266148196134411\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, wQuantileLoss[0.6]): 0.005305637382400745\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, wQuantileLoss[0.7]): 0.0055087606023424525\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, wQuantileLoss[0.8]): 0.0073578722886909\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #test_score (algo-1, wQuantileLoss[0.9]): 0.00653787031241043\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #quality_metric: host=algo-1, test mean_wQuantileLoss <loss>=0.00965604062742\u001b[0m\n",
+      "\u001b[34m[09/15/2020 10:03:43 INFO 139774100076352] #quality_metric: host=algo-1, test RMSE <loss>=1.01795614289\u001b[0m\n",
+      "\u001b[34m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 46315.71412086487, \"sum\": 46315.71412086487, \"min\": 46315.71412086487}, \"setuptime\": {\"count\": 1, \"max\": 8.629083633422852, \"sum\": 8.629083633422852, \"min\": 8.629083633422852}}, \"EndTime\": 1600164224.002674, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/DeepAR\"}, \"StartTime\": 1600164223.956579}\n",
+      "\u001b[0m\n",
+      "Training seconds: 102\n",
+      "Billable seconds: 102\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 进行训练任务\n",
+    "data_channels = {\n",
+    "    \"train\": \"s3://{}/train/\".format(s3_data_path),\n",
+    "    \"test\": \"s3://{}/test/\".format(s3_data_path)\n",
+    "}\n",
+    "\n",
+    "estimator.fit(inputs=data_channels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---------------!"
+     ]
+    }
+   ],
+   "source": [
+    "# 创建终端节点\n",
+    "job_name = estimator.latest_training_job.name\n",
+    "\n",
+    "endpoint_name = sagemaker_session.endpoint_from_job(\n",
+    "    job_name=job_name,\n",
+    "    initial_instance_count=1,\n",
+    "    instance_type='ml.m4.xlarge',\n",
+    "    deployment_image=image_name,\n",
+    "    role=role\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DeepARPredictor(sagemaker.predictor.RealTimePredictor):\n",
+    "\n",
+    "    def set_prediction_parameters(self, freq, prediction_length):\n",
+    "        \"\"\"设置时间频率和预测长度参数。 调用此方法\n",
+    "         必须在使用`predict`之前。\n",
+    "        \n",
+    "        参数:\n",
+    "        freq -- 指示时间频率的字符串\n",
+    "        prediction_length -- 整数，预计时间点数\n",
+    "        \"\"\"\n",
+    "        self.freq = freq\n",
+    "        self.prediction_length = prediction_length\n",
+    "        \n",
+    "    def predict(self, ts, cat=None, encoding=\"utf-8\", num_samples=100, quantiles=[\"0.1\", \"0.5\", \"0.9\"]):\n",
+    "        \"\"\"请求对“ ts”中列出的时间序列进行预测\n",
+    "        \n",
+    "        参数:\n",
+    "        ts -- pandas.Series对象列表，可预测的时间序列\n",
+    "        cat -- 整数列表（默认值：无）\n",
+    "        encoding -- 字符串，用于请求的编码（默认值：“ utf-8”）\n",
+    "        num_samples -- 整数，在预测时要计算的样本数（默认值：100）\n",
+    "        quantiles -- 指定要计算的分位数的字符串列表（默认：[“ 0.1”，“ 0.5”，“ 0.9”]）\n",
+    "        \n",
+    "        返回: pandas.DataFrame对象的列表，每个对象都包含预测\n",
+    "        \"\"\"\n",
+    "        prediction_times = [x.index[-1]+pd.Timedelta(1, unit=self.freq) for x in ts]\n",
+    "        req = self.__encode_request(ts, cat, encoding, num_samples, quantiles)\n",
+    "        res = super(DeepARPredictor, self).predict(req)\n",
+    "        return self.__decode_response(res, prediction_times, encoding)\n",
+    "    \n",
+    "    def __encode_request(self, ts, cat, encoding, num_samples, quantiles):\n",
+    "        instances = [series_to_obj(ts[k], cat[k] if cat else None) for k in range(len(ts))]\n",
+    "        configuration = {\"num_samples\": num_samples, \"output_types\": [\"quantiles\"], \"quantiles\": quantiles}\n",
+    "        http_request_data = {\"instances\": instances, \"configuration\": configuration}\n",
+    "        return json.dumps(http_request_data).encode(encoding)\n",
+    "    \n",
+    "    def __decode_response(self, response, prediction_times, encoding):\n",
+    "        response_data = json.loads(response.decode(encoding))\n",
+    "        list_of_df = []\n",
+    "        for k in range(len(prediction_times)):\n",
+    "            prediction_index = pd.date_range(start=prediction_times[k], freq=self.freq, periods=self.prediction_length)\n",
+    "            list_of_df.append(pd.DataFrame(data=response_data['predictions'][k]['quantiles'], index=prediction_index))\n",
+    "        return list_of_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictor = DeepARPredictor(\n",
+    "    endpoint=endpoint_name,\n",
+    "    sagemaker_session=sagemaker_session,\n",
+    "    content_type=\"application/json\"\n",
+    ")\n",
+    "predictor.set_prediction_parameters(freq, prediction_length)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 取前5组数据进行预测\n",
+    "len(time_series_training[:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 预览前5组数据\n",
+    "plt.figure()\n",
+    "for i in range(5):\n",
+    "    ##将小图分成2行2列,第三个参数表示第n个图\n",
+    "    plt.subplot(2,3,i+1)\n",
+    "    #设置小图的x,y坐标\n",
+    "    plt.plot(time_series_training[i])\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_of_df = predictor.predict(time_series_training[:5])\n",
+    "actual_data = time_series[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[                            0.1         0.9         0.5\n",
+       " 2020-09-15 16:00:00  109.618805  119.650276  114.841759\n",
+       " 2020-09-15 17:00:00  111.111977  118.695213  115.363564\n",
+       " 2020-09-15 18:00:00  109.537338  119.537781  114.257874\n",
+       " 2020-09-15 19:00:00  109.425247  119.731613  114.290581\n",
+       " 2020-09-15 20:00:00  110.602982  119.915047  114.681694\n",
+       " 2020-09-15 21:00:00  111.008171  120.078873  115.048393\n",
+       " 2020-09-15 22:00:00  111.331848  120.017532  115.589294\n",
+       " 2020-09-15 23:00:00  110.501633  118.785423  114.782433\n",
+       " 2020-09-16 00:00:00  110.571609  119.482300  115.228546\n",
+       " 2020-09-16 01:00:00  110.391556  119.484863  115.048759\n",
+       " 2020-09-16 02:00:00  110.076904  120.262993  114.556152\n",
+       " 2020-09-16 03:00:00  110.491127  120.094200  115.147926\n",
+       " 2020-09-16 04:00:00  109.796982  119.284714  115.364822\n",
+       " 2020-09-16 05:00:00  111.950104  120.621643  116.070404\n",
+       " 2020-09-16 06:00:00  108.803909  119.831520  114.552940\n",
+       " 2020-09-16 07:00:00  110.617897  120.466530  114.612549\n",
+       " 2020-09-16 08:00:00  109.857491  118.712448  114.803268\n",
+       " 2020-09-16 09:00:00  110.402191  119.315781  115.124908\n",
+       " 2020-09-16 10:00:00  111.028496  120.281654  115.116783\n",
+       " 2020-09-16 11:00:00  110.028702  119.195190  114.249252\n",
+       " 2020-09-16 12:00:00  109.781090  118.575119  114.376358\n",
+       " 2020-09-16 13:00:00  110.068558  120.145935  115.025726\n",
+       " 2020-09-16 14:00:00  109.921738  118.725723  114.638123\n",
+       " 2020-09-16 15:00:00  110.425896  119.248360  115.057846\n",
+       " 2020-09-16 16:00:00  110.992088  120.073753  114.678986\n",
+       " 2020-09-16 17:00:00  110.322105  119.785461  115.275116\n",
+       " 2020-09-16 18:00:00  110.462090  119.272675  115.161819\n",
+       " 2020-09-16 19:00:00  109.669914  118.881378  114.809090\n",
+       " 2020-09-16 20:00:00  109.742851  120.375130  114.476501\n",
+       " 2020-09-16 21:00:00  110.261559  120.444717  115.561821\n",
+       " 2020-09-16 22:00:00  110.876373  121.063248  115.382797\n",
+       " 2020-09-16 23:00:00  111.017235  119.921555  114.483864\n",
+       " 2020-09-17 00:00:00  110.639282  120.866600  115.431343\n",
+       " 2020-09-17 01:00:00  109.365822  119.247177  114.523628\n",
+       " 2020-09-17 02:00:00  109.798615  121.147041  114.805618\n",
+       " 2020-09-17 03:00:00  111.604942  120.014542  115.780228\n",
+       " 2020-09-17 04:00:00  110.658623  120.539124  115.778725\n",
+       " 2020-09-17 05:00:00  110.450569  119.322258  114.507935\n",
+       " 2020-09-17 06:00:00  109.956635  119.592346  115.094307\n",
+       " 2020-09-17 07:00:00  110.336235  118.194244  114.745316\n",
+       " 2020-09-17 08:00:00  109.180244  119.175537  114.559784\n",
+       " 2020-09-17 09:00:00  110.742485  119.101898  115.366631\n",
+       " 2020-09-17 10:00:00  109.920067  118.870430  114.921715\n",
+       " 2020-09-17 11:00:00  110.837166  119.038002  115.440475\n",
+       " 2020-09-17 12:00:00  110.470886  119.325500  114.807281\n",
+       " 2020-09-17 13:00:00  110.594421  119.964134  114.541946\n",
+       " 2020-09-17 14:00:00  109.914558  119.863434  115.173767\n",
+       " 2020-09-17 15:00:00  110.906570  119.077904  115.059555,\n",
+       "                            0.1         0.9        0.5\n",
+       " 2020-09-15 16:00:00  91.272865   99.838135  94.886261\n",
+       " 2020-09-15 17:00:00  90.974121   98.493584  95.034927\n",
+       " 2020-09-15 18:00:00  90.281677   99.355759  94.284477\n",
+       " 2020-09-15 19:00:00  91.420128   99.798279  95.099472\n",
+       " 2020-09-15 20:00:00  90.872910   98.720726  95.160797\n",
+       " 2020-09-15 21:00:00  90.741562   99.078682  95.335739\n",
+       " 2020-09-15 22:00:00  91.520401   99.029434  95.787216\n",
+       " 2020-09-15 23:00:00  92.106133   98.716232  95.349609\n",
+       " 2020-09-16 00:00:00  92.703079   99.132378  95.284172\n",
+       " 2020-09-16 01:00:00  91.433632   99.429970  95.284546\n",
+       " 2020-09-16 02:00:00  90.625633   98.298363  94.195351\n",
+       " 2020-09-16 03:00:00  91.824234   99.124557  95.901115\n",
+       " 2020-09-16 04:00:00  90.416862  100.068787  95.380768\n",
+       " 2020-09-16 05:00:00  91.313469   99.384521  95.500481\n",
+       " 2020-09-16 06:00:00  91.697327   98.638626  95.608200\n",
+       " 2020-09-16 07:00:00  91.787132   98.400276  94.994141\n",
+       " 2020-09-16 08:00:00  90.988945   99.380692  95.345528\n",
+       " 2020-09-16 09:00:00  90.132980   99.561729  95.040405\n",
+       " 2020-09-16 10:00:00  91.112007   98.902824  94.476212\n",
+       " 2020-09-16 11:00:00  90.931053   99.453682  95.310959\n",
+       " 2020-09-16 12:00:00  90.911201   99.674530  95.140182\n",
+       " 2020-09-16 13:00:00  92.076401   99.066940  95.506660\n",
+       " 2020-09-16 14:00:00  91.651100   98.706932  95.597343\n",
+       " 2020-09-16 15:00:00  91.165527   99.167877  94.838974\n",
+       " 2020-09-16 16:00:00  91.586021  100.185143  96.490456\n",
+       " 2020-09-16 17:00:00  91.685226   98.749947  94.994476\n",
+       " 2020-09-16 18:00:00  90.394394   98.357925  94.373955\n",
+       " 2020-09-16 19:00:00  91.787560   99.139771  95.383171\n",
+       " 2020-09-16 20:00:00  91.947327  100.102959  95.027550\n",
+       " 2020-09-16 21:00:00  91.192696   99.583672  95.572334\n",
+       " 2020-09-16 22:00:00  91.429886   99.586906  95.561707\n",
+       " 2020-09-16 23:00:00  90.899742   98.588028  95.864449\n",
+       " 2020-09-17 00:00:00  91.154091   98.948982  95.186897\n",
+       " 2020-09-17 01:00:00  91.386261   99.663406  95.630203\n",
+       " 2020-09-17 02:00:00  91.345818   99.864487  94.956535\n",
+       " 2020-09-17 03:00:00  92.168854   99.873543  95.604195\n",
+       " 2020-09-17 04:00:00  90.797409   99.531601  95.381355\n",
+       " 2020-09-17 05:00:00  90.835655   99.428589  95.331818\n",
+       " 2020-09-17 06:00:00  91.717659   98.728287  94.912689\n",
+       " 2020-09-17 07:00:00  91.289597   98.728798  94.776237\n",
+       " 2020-09-17 08:00:00  91.503471   99.108788  95.328728\n",
+       " 2020-09-17 09:00:00  91.405655   99.419281  95.160286\n",
+       " 2020-09-17 10:00:00  91.399017   98.733078  95.155220\n",
+       " 2020-09-17 11:00:00  90.615532   99.446556  94.949783\n",
+       " 2020-09-17 12:00:00  91.625389   99.145515  95.363251\n",
+       " 2020-09-17 13:00:00  90.667992   98.600693  95.258850\n",
+       " 2020-09-17 14:00:00  90.634872   99.293808  95.532974\n",
+       " 2020-09-17 15:00:00  90.972137   99.636017  95.248833,\n",
+       "                            0.1        0.9        0.5\n",
+       " 2020-09-15 16:00:00  62.102448  66.856758  64.323036\n",
+       " 2020-09-15 17:00:00  61.634785  66.872543  64.493401\n",
+       " 2020-09-15 18:00:00  61.884991  67.133781  64.337051\n",
+       " 2020-09-15 19:00:00  62.222343  67.425301  64.264725\n",
+       " 2020-09-15 20:00:00  61.542671  67.198677  64.168434\n",
+       " 2020-09-15 21:00:00  61.860317  67.238014  64.664986\n",
+       " 2020-09-15 22:00:00  61.800045  67.739037  64.171188\n",
+       " 2020-09-15 23:00:00  62.065620  66.740799  64.392982\n",
+       " 2020-09-16 00:00:00  61.485508  67.047226  64.241852\n",
+       " 2020-09-16 01:00:00  61.837700  66.570160  64.279488\n",
+       " 2020-09-16 02:00:00  62.331516  66.751396  64.586952\n",
+       " 2020-09-16 03:00:00  62.286655  67.529541  64.586121\n",
+       " 2020-09-16 04:00:00  62.630943  67.276535  64.848328\n",
+       " 2020-09-16 05:00:00  61.895821  67.302917  63.859287\n",
+       " 2020-09-16 06:00:00  62.130970  67.445808  64.638504\n",
+       " 2020-09-16 07:00:00  62.511555  67.153252  64.458191\n",
+       " 2020-09-16 08:00:00  61.606632  67.047729  64.624207\n",
+       " 2020-09-16 09:00:00  61.967865  66.984451  64.333412\n",
+       " 2020-09-16 10:00:00  61.494579  67.340919  64.500443\n",
+       " 2020-09-16 11:00:00  61.436188  67.864067  64.214439\n",
+       " 2020-09-16 12:00:00  62.476612  66.708405  64.392853\n",
+       " 2020-09-16 13:00:00  62.338127  67.380539  64.684006\n",
+       " 2020-09-16 14:00:00  61.196812  66.887337  64.367065\n",
+       " 2020-09-16 15:00:00  62.126705  67.462852  64.973175\n",
+       " 2020-09-16 16:00:00  61.765999  67.048050  64.477547\n",
+       " 2020-09-16 17:00:00  61.941418  67.607910  64.240379\n",
+       " 2020-09-16 18:00:00  62.285069  67.000961  64.699966\n",
+       " 2020-09-16 19:00:00  62.275024  67.551804  64.691437\n",
+       " 2020-09-16 20:00:00  61.918640  66.559219  64.479477\n",
+       " 2020-09-16 21:00:00  61.715504  68.109123  64.704277\n",
+       " 2020-09-16 22:00:00  61.707474  66.782745  64.345848\n",
+       " 2020-09-16 23:00:00  62.042046  66.916924  64.599777\n",
+       " 2020-09-17 00:00:00  61.884125  67.550644  65.162216\n",
+       " 2020-09-17 01:00:00  61.356304  67.200371  64.268639\n",
+       " 2020-09-17 02:00:00  61.885319  67.285583  64.687515\n",
+       " 2020-09-17 03:00:00  61.524887  67.442192  64.815643\n",
+       " 2020-09-17 04:00:00  62.257374  67.166504  64.776619\n",
+       " 2020-09-17 05:00:00  62.019100  67.372803  64.623283\n",
+       " 2020-09-17 06:00:00  61.588409  66.773376  63.873104\n",
+       " 2020-09-17 07:00:00  61.766220  67.263336  64.548981\n",
+       " 2020-09-17 08:00:00  61.520828  67.144081  64.362030\n",
+       " 2020-09-17 09:00:00  62.020000  67.725632  64.949348\n",
+       " 2020-09-17 10:00:00  61.770298  66.844551  64.200645\n",
+       " 2020-09-17 11:00:00  61.675903  66.875099  64.376320\n",
+       " 2020-09-17 12:00:00  62.113510  66.956245  64.623116\n",
+       " 2020-09-17 13:00:00  61.815826  68.051842  64.431190\n",
+       " 2020-09-17 14:00:00  62.411579  66.772499  64.462563\n",
+       " 2020-09-17 15:00:00  62.024609  67.483261  64.322075,\n",
+       "                            0.1        0.9        0.5\n",
+       " 2020-09-15 16:00:00  81.133087  89.151382  85.541504\n",
+       " 2020-09-15 17:00:00  82.738190  88.759811  86.023544\n",
+       " 2020-09-15 18:00:00  81.813896  89.708504  86.413078\n",
+       " 2020-09-15 19:00:00  82.750298  88.881134  85.472267\n",
+       " 2020-09-15 20:00:00  82.626923  89.964455  85.432762\n",
+       " 2020-09-15 21:00:00  82.204575  89.279099  85.459290\n",
+       " 2020-09-15 22:00:00  83.299500  89.806755  86.380692\n",
+       " 2020-09-15 23:00:00  81.192360  89.072876  84.893936\n",
+       " 2020-09-16 00:00:00  82.586266  88.425667  85.377701\n",
+       " 2020-09-16 01:00:00  80.238220  89.188889  85.268265\n",
+       " 2020-09-16 02:00:00  82.084839  88.901772  85.759880\n",
+       " 2020-09-16 03:00:00  81.579811  90.118393  85.535835\n",
+       " 2020-09-16 04:00:00  81.140961  89.500381  85.710236\n",
+       " 2020-09-16 05:00:00  81.219200  88.308044  84.957039\n",
+       " 2020-09-16 06:00:00  82.740578  89.321228  85.739815\n",
+       " 2020-09-16 07:00:00  81.842712  89.307571  85.629494\n",
+       " 2020-09-16 08:00:00  81.522247  88.938164  85.152321\n",
+       " 2020-09-16 09:00:00  81.655914  89.016182  85.709373\n",
+       " 2020-09-16 10:00:00  81.797180  89.239845  85.174522\n",
+       " 2020-09-16 11:00:00  81.983109  89.025146  85.425591\n",
+       " 2020-09-16 12:00:00  82.624702  88.975578  85.243835\n",
+       " 2020-09-16 13:00:00  82.152283  89.050430  85.592072\n",
+       " 2020-09-16 14:00:00  81.960854  89.554924  85.136810\n",
+       " 2020-09-16 15:00:00  81.652466  88.940392  85.386108\n",
+       " 2020-09-16 16:00:00  82.442390  89.453613  85.725456\n",
+       " 2020-09-16 17:00:00  82.547211  88.401505  85.722862\n",
+       " 2020-09-16 18:00:00  82.026115  89.890770  85.463417\n",
+       " 2020-09-16 19:00:00  81.232605  88.930878  84.183426\n",
+       " 2020-09-16 20:00:00  81.501610  89.305092  85.419739\n",
+       " 2020-09-16 21:00:00  82.095695  89.406776  85.777664\n",
+       " 2020-09-16 22:00:00  82.271568  88.250626  84.781586\n",
+       " 2020-09-16 23:00:00  82.271393  89.199226  85.305489\n",
+       " 2020-09-17 00:00:00  81.403404  89.124031  85.410004\n",
+       " 2020-09-17 01:00:00  81.609520  89.289352  85.788506\n",
+       " 2020-09-17 02:00:00  81.975082  89.168404  85.889023\n",
+       " 2020-09-17 03:00:00  82.165199  88.591385  85.671745\n",
+       " 2020-09-17 04:00:00  81.527184  88.800179  85.631058\n",
+       " 2020-09-17 05:00:00  82.187256  89.064445  85.599716\n",
+       " 2020-09-17 06:00:00  82.538933  88.950218  85.906281\n",
+       " 2020-09-17 07:00:00  82.633774  88.485680  85.729515\n",
+       " 2020-09-17 08:00:00  82.824356  89.059921  85.672318\n",
+       " 2020-09-17 09:00:00  81.017876  89.122879  85.727478\n",
+       " 2020-09-17 10:00:00  82.387016  88.880913  85.489738\n",
+       " 2020-09-17 11:00:00  82.087502  88.808563  85.549744\n",
+       " 2020-09-17 12:00:00  82.186325  89.071533  85.786232\n",
+       " 2020-09-17 13:00:00  82.396126  89.362213  85.761658\n",
+       " 2020-09-17 14:00:00  82.691109  88.778908  85.745979\n",
+       " 2020-09-17 15:00:00  82.604836  89.032127  85.332718,\n",
+       "                             0.1         0.9         0.5\n",
+       " 2020-09-15 16:00:00  121.186768  131.323181  126.361275\n",
+       " 2020-09-15 17:00:00  122.553299  131.998871  127.295135\n",
+       " 2020-09-15 18:00:00  121.776726  132.769318  126.932396\n",
+       " 2020-09-15 19:00:00  120.583817  132.045242  125.643120\n",
+       " 2020-09-15 20:00:00  120.518471  130.731201  126.463753\n",
+       " 2020-09-15 21:00:00  122.132080  131.987732  126.324623\n",
+       " 2020-09-15 22:00:00  122.616646  132.855148  127.569702\n",
+       " 2020-09-15 23:00:00  122.249130  131.726471  127.513321\n",
+       " 2020-09-16 00:00:00  122.169029  131.769135  126.637695\n",
+       " 2020-09-16 01:00:00  121.339203  132.367035  126.942795\n",
+       " 2020-09-16 02:00:00  122.718300  132.115341  127.501312\n",
+       " 2020-09-16 03:00:00  121.198517  132.200485  126.102959\n",
+       " 2020-09-16 04:00:00  122.883461  132.660767  127.250229\n",
+       " 2020-09-16 05:00:00  121.117683  132.626755  126.265404\n",
+       " 2020-09-16 06:00:00  121.296875  132.191742  126.552307\n",
+       " 2020-09-16 07:00:00  121.448112  132.356934  126.699074\n",
+       " 2020-09-16 08:00:00  121.833481  131.575211  126.848785\n",
+       " 2020-09-16 09:00:00  121.560188  131.186096  126.133064\n",
+       " 2020-09-16 10:00:00  121.788589  132.246902  127.100456\n",
+       " 2020-09-16 11:00:00  121.742043  131.650818  126.300896\n",
+       " 2020-09-16 12:00:00  122.045815  131.924011  126.050308\n",
+       " 2020-09-16 13:00:00  121.729599  132.348419  126.635529\n",
+       " 2020-09-16 14:00:00  120.601143  133.354614  127.244659\n",
+       " 2020-09-16 15:00:00  120.710693  132.580368  126.786339\n",
+       " 2020-09-16 16:00:00  121.707405  133.305298  126.423691\n",
+       " 2020-09-16 17:00:00  121.956337  132.886749  127.242065\n",
+       " 2020-09-16 18:00:00  122.515442  131.237747  127.014160\n",
+       " 2020-09-16 19:00:00  121.680542  133.405426  127.565514\n",
+       " 2020-09-16 20:00:00  121.074203  133.637878  126.341621\n",
+       " 2020-09-16 21:00:00  122.218819  132.801956  126.542580\n",
+       " 2020-09-16 22:00:00  121.073036  132.041595  126.363197\n",
+       " 2020-09-16 23:00:00  122.538132  132.874893  127.717720\n",
+       " 2020-09-17 00:00:00  122.689064  133.183502  127.709328\n",
+       " 2020-09-17 01:00:00  121.993019  133.299469  127.440529\n",
+       " 2020-09-17 02:00:00  121.965515  131.344971  126.323502\n",
+       " 2020-09-17 03:00:00  121.824501  131.690002  127.107895\n",
+       " 2020-09-17 04:00:00  120.688774  133.552612  126.463936\n",
+       " 2020-09-17 05:00:00  122.769028  132.833496  126.481674\n",
+       " 2020-09-17 06:00:00  122.418198  131.443085  126.786728\n",
+       " 2020-09-17 07:00:00  121.310158  132.658539  126.798981\n",
+       " 2020-09-17 08:00:00  121.412941  133.170776  126.678116\n",
+       " 2020-09-17 09:00:00  121.634186  132.191788  126.135117\n",
+       " 2020-09-17 10:00:00  121.719208  133.003754  126.769066\n",
+       " 2020-09-17 11:00:00  120.994202  131.309769  126.506546\n",
+       " 2020-09-17 12:00:00  122.390976  131.443802  127.552299\n",
+       " 2020-09-17 13:00:00  121.594604  131.216919  126.582878\n",
+       " 2020-09-17 14:00:00  121.276276  132.523666  126.569000\n",
+       " 2020-09-17 15:00:00  121.573792  131.740265  127.010307]"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_of_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2020-09-01 00:00:00    114.684729\n",
+       " 2020-09-01 01:00:00    113.980506\n",
+       " 2020-09-01 02:00:00    114.323596\n",
+       " 2020-09-01 03:00:00    114.995357\n",
+       " 2020-09-01 04:00:00    114.730439\n",
+       "                           ...    \n",
+       " 2020-09-17 11:00:00    115.510475\n",
+       " 2020-09-17 12:00:00    115.503586\n",
+       " 2020-09-17 13:00:00    115.775045\n",
+       " 2020-09-17 14:00:00    116.208944\n",
+       " 2020-09-17 15:00:00    115.405255\n",
+       " Freq: H, Length: 400, dtype: float64,\n",
+       " 2020-09-01 00:00:00    94.509951\n",
+       " 2020-09-01 01:00:00    94.867177\n",
+       " 2020-09-01 02:00:00    94.808135\n",
+       " 2020-09-01 03:00:00    94.327173\n",
+       " 2020-09-01 04:00:00    95.301840\n",
+       "                          ...    \n",
+       " 2020-09-17 11:00:00    95.773236\n",
+       " 2020-09-17 12:00:00    95.175815\n",
+       " 2020-09-17 13:00:00    96.200919\n",
+       " 2020-09-17 14:00:00    95.609396\n",
+       " 2020-09-17 15:00:00    96.011424\n",
+       " Freq: H, Length: 400, dtype: float64,\n",
+       " 2020-09-01 00:00:00    63.892876\n",
+       " 2020-09-01 01:00:00    64.813673\n",
+       " 2020-09-01 02:00:00    64.767470\n",
+       " 2020-09-01 03:00:00    64.777741\n",
+       " 2020-09-01 04:00:00    64.290959\n",
+       "                          ...    \n",
+       " 2020-09-17 11:00:00    64.929124\n",
+       " 2020-09-17 12:00:00    65.284277\n",
+       " 2020-09-17 13:00:00    64.719387\n",
+       " 2020-09-17 14:00:00    64.920175\n",
+       " 2020-09-17 15:00:00    64.931953\n",
+       " Freq: H, Length: 400, dtype: float64,\n",
+       " 2020-09-01 00:00:00    85.197118\n",
+       " 2020-09-01 01:00:00    85.502920\n",
+       " 2020-09-01 02:00:00    84.998443\n",
+       " 2020-09-01 03:00:00    85.893374\n",
+       " 2020-09-01 04:00:00    84.310592\n",
+       "                          ...    \n",
+       " 2020-09-17 11:00:00    85.898729\n",
+       " 2020-09-17 12:00:00    86.424445\n",
+       " 2020-09-17 13:00:00    85.552700\n",
+       " 2020-09-17 14:00:00    85.906816\n",
+       " 2020-09-17 15:00:00    86.035640\n",
+       " Freq: H, Length: 400, dtype: float64,\n",
+       " 2020-09-01 00:00:00    127.058569\n",
+       " 2020-09-01 01:00:00    127.594104\n",
+       " 2020-09-01 02:00:00    127.091324\n",
+       " 2020-09-01 03:00:00    126.696417\n",
+       " 2020-09-01 04:00:00    126.244158\n",
+       "                           ...    \n",
+       " 2020-09-17 11:00:00    127.457563\n",
+       " 2020-09-17 12:00:00    127.144558\n",
+       " 2020-09-17 13:00:00    127.725714\n",
+       " 2020-09-17 14:00:00    128.822689\n",
+       " 2020-09-17 15:00:00    127.556642\n",
+       " Freq: H, Length: 400, dtype: float64]"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "actual_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for k in range(len(list_of_df)):\n",
+    "    plt.figure(figsize=(12,6))\n",
+    "    actual_data[k][-prediction_length-context_length:].plot(label='target')\n",
+    "    p10 = list_of_df[k]['0.1']\n",
+    "    p90 = list_of_df[k]['0.9']\n",
+    "    plt.fill_between(p10.index, p10, p90, color='y', alpha=0.5, label='80% confidence interval')\n",
+    "    list_of_df[k]['0.5'].plot(label='prediction median')\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sagemaker_session.delete_endpoint(endpoint_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_amazonei_mxnet_p36",
+   "language": "python",
+   "name": "conda_amazonei_mxnet_p36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}