diff --git a/Untitled.ipynb b/tf_sheet.ipynb
similarity index 66%
rename from Untitled.ipynb
rename to tf_sheet.ipynb
index 7cfd947..2909d14 100644
--- a/Untitled.ipynb
+++ b/tf_sheet.ipynb
@@ -4,17 +4,56 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "02d39a02-17f2-4307-8d41-6903c289e9d2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-29T19:26:44.051696Z",
+     "start_time": "2024-12-29T19:26:42.404515Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eager Execution Enabled: True\n",
+      "Available Devices:\n",
+      "PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')\n",
+      "PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "from tensorflow.python.framework.ops import EagerTensor\n",
+    "print(\"Eager Execution Enabled:\", tf.executing_eagerly())\n",
+    "print(\"Available Devices:\")\n",
+    "for device in tf.config.list_physical_devices():\n",
+    "    print(device)\n",
+    "\n",
+    "tf.debugging.set_log_device_placement(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5a948f66-bc82-4e98-bb2c-d4e6a5d6c353",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tensorflow as tf"
+    "%load_ext tensorboard\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "793e06aa-0230-433f-a92a-6c32846bc444",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-29T19:27:50.307150Z",
+     "start_time": "2024-12-29T19:27:50.304200Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -22,6 +61,17 @@
      "text": [
       "()\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-29 23:09:19.737726: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M2\n",
+      "2024-12-29 23:09:19.737750: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n",
+      "2024-12-29 23:09:19.737756: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n",
+      "2024-12-29 23:09:19.737769: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
+      "2024-12-29 23:09:19.737778: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
+     ]
     }
    ],
    "source": [
@@ -31,9 +81,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "4ea11dda-ab38-4e21-97e4-c318c0790ec7",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-29T19:27:53.630687Z",
+     "start_time": "2024-12-29T19:27:53.627165Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -50,9 +105,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "a3394f67-a3fc-4c26-887e-70c315703d65",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-29T19:27:55.934553Z",
+     "start_time": "2024-12-29T19:27:55.925292Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -77,9 +137,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "e3b108ce-d5ec-421f-a655-5b9c6ecc299d",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-29T19:27:58.162658Z",
+     "start_time": "2024-12-29T19:27:58.158969Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -96,9 +161,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "d33d85e8-43e8-4a20-aa31-6303b774d0da",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-29T19:28:00.310769Z",
+     "start_time": "2024-12-29T19:28:00.307345Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import tensorflow as tf\n",
@@ -113,9 +183,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "6ed0c75b-a964-48df-a9dd-b6e0a8dcbafa",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-29T19:28:02.677382Z",
+     "start_time": "2024-12-29T19:28:02.673991Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -131,9 +206,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "45bdd81b-c86d-4abc-804f-3d43a60382be",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-29T19:29:26.646642Z",
+     "start_time": "2024-12-29T19:29:26.635782Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -176,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "0ac17c30-2312-40a2-9df9-66d088e1a6b5",
    "metadata": {},
    "outputs": [
@@ -184,7 +264,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-0.24501422 -0.5116961  -0.44979703]]\n",
+      "[[ 1.1468321 -1.8221927  1.7510278]]\n",
       "[[4 5]]\n"
      ]
     }
@@ -215,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "id": "b3c566e4-6643-4af4-a13f-ed69c6ac259f",
    "metadata": {},
    "outputs": [],
@@ -229,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "id": "28ad88bb-f378-47b0-aaf9-87c43b1260f1",
    "metadata": {},
    "outputs": [
@@ -237,7 +317,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-1.6479137\n"
+      "-1.2997332\n"
      ]
     }
    ],
@@ -274,18 +354,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "id": "0841827f-1770-4b6c-97e7-01c1aedde23b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Trace already enabled\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "logdir = \"logs/graph_demo\"\n",
     "writer = tf.summary.create_file_writer(logdir)\n",
@@ -298,14 +370,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "id": "bb359e92-3724-4e0b-a247-1138000553c6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Reusing TensorBoard on port 6006 (pid 36911), started 0:00:18 ago. (Use '!kill 36911' to kill it.)"
+       "Reusing TensorBoard on port 6006 (pid 85848), started 2:35:02 ago. (Use '!kill 85848' to kill it.)"
       ]
      },
      "metadata": {},
@@ -315,11 +387,11 @@
      "data": {
       "text/html": [
        "\n",
-       "      <iframe id=\"tensorboard-frame-794b75c9ae2164bd\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
+       "      <iframe id=\"tensorboard-frame-e7c10ccffd3661a5\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
        "      </iframe>\n",
        "      <script>\n",
        "        (function() {\n",
-       "          const frame = document.getElementById(\"tensorboard-frame-794b75c9ae2164bd\");\n",
+       "          const frame = document.getElementById(\"tensorboard-frame-e7c10ccffd3661a5\");\n",
        "          const url = new URL(\"/\", window.location);\n",
        "          const port = 6006;\n",
        "          if (port) {\n",
@@ -342,10 +414,47 @@
     "%tensorboard --logdir logs/graph_demo\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "id": "2a6b9053-c6fb-436b-9d57-9a8a0ce56459",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-29T23:59:13.685350Z",
+     "start_time": "2024-12-29T23:59:13.534620Z"
+    }
+   },
+   "source": [
+    "with tf.device('/GPU:0'):  # Explicitly use Metal GPU\n",
+    "    a = tf.constant([[1.0, 2.0], [3.0, 4.0]])\n",
+    "    b = tf.constant([[1.0, 1.0], [0.0, 1.0]])\n",
+    "    c = tf.matmul(a, b)\n",
+    "    print(c)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tf.Tensor(\n",
+      "[[1. 3.]\n",
+      " [3. 7.]], shape=(2, 2), dtype=float32)\n"
+     ]
+    }
+   ],
+   "execution_count": 25
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2425d80b-c1c4-4cb3-86ea-3ca72e7b1fa7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6c253276-6b56-4cff-a81b-c863939a035c",
+   "id": "f0215ac3-e4cb-46c5-88bd-4b44a4f79917",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -353,9 +462,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (gym-env-py3.10)",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "gym-env-py3.10"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -367,7 +476,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.16"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,