diff --git a/.idea/misc.xml b/.idea/misc.xml
index 7becee8..300ab96 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.14 (nn)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.14 (nn)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="nn" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file
diff --git a/.idea/nn.iml b/.idea/nn.iml
index 83fcc1a..9d32d5c 100644
--- a/.idea/nn.iml
+++ b/.idea/nn.iml
@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/.venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.14 (nn)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="nn" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>
\ No newline at end of file
diff --git a/plot.ipynb b/plot.ipynb
new file mode 100644
index 0000000..ec623ff
--- /dev/null
+++ b/plot.ipynb
@@ -0,0 +1,371 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-03-15T08:05:44.505817091Z",
+     "start_time": "2026-03-15T08:05:44.465878069Z"
+    }
+   },
+   "source": [
+    "import matplotlib\n",
+    "import numpy as np\n",
+    "\n",
+    "print(matplotlib.__version__)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.10.8\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-15T08:05:45.746695680Z",
+     "start_time": "2026-03-15T08:05:45.737336298Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "from matplotlib import pyplot as plt",
+   "id": "5c2717897baa0e77",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-15T08:05:46.610556088Z",
+     "start_time": "2026-03-15T08:05:46.592296562Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "xpoints = np.array([0,6])\n",
+    "ypoints = np.array([0,100])"
+   ],
+   "id": "d6552163a60278f6",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-15T08:06:00.671077133Z",
+     "start_time": "2026-03-15T08:06:00.570020691Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "plt.plot(xpoints,ypoints)",
+   "id": "a250a770f3e18991",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f670479bcb0>]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-15T08:24:13.926301561Z",
+     "start_time": "2026-03-15T08:24:13.864690943Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "xpoints = np.array([1, 8])\n",
+    "ypoints = np.array([3, 10])\n",
+    "\n",
+    "plt.plot(xpoints, ypoints)\n",
+    "plt.show()"
+   ],
+   "id": "b8f1cd82eb99aa44",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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wGO3e8JPpWPAwlA8AwE3zKeGnHuGDdfcTj0uS9vywUbOGxyj99BnDyeCJKB8AgJtSue4tCp04WlXq11VBQYFWvv2BVr77oayCAtPR4KEoHwCAG9aq+6N6YkSYfEuWUNqp05o5LFr7fvrFdCx4OMoHAOC6+ZYsqScjh6hl186SpF3rf9Ds8FG6cPac4WQoDCgfAIDrUrVhPYUkxKty3VtUkJ+vL956T199MEOWZZmOhkKC8gEAuGb39Oym7sP+KZ8SfjqfclIzh0bpwC+bTcdCIUP5AAD8Jb8Af/WKGqbmXTpKknZ8u15zRsTq4vlUw8lQGFE+AAB/qvqtDRWSGK+KtWsqPy9Pyya/o7UfzeYyC24Y5QMAcFX393lSXYe8Km9fX509dlwzh0bp0ObfTMdCIUf5AAD8QQlHKT0VE66mHR+SJP225hvNjRytzLQ0w8lQFFA+AACXqXn7bQpJjFP5GtWVl5urz9+Yqm9nfmw6FooQygcA4JI2wb312Osvy9vHR2eOJispbKSObNthOhaKGMoHAEAlAwPVJ26Egh5qK0navOIrzYsZq6z0C4aToSiifABAMVfrjtsVkhCnctWqKi8nR4sT3tS6uZ+YjoUizO70J7TbFRsbq/379ysjI0N79+5VZGSks5cBANwkm82mdk/3198/fEflqlXV6cNH9WbwcxQPuJzTz3wMGzZML774ogYMGKBt27apZcuWmj59ulJTUzVlyhRnLwcAuAEBZUqrz+iRavzA/ZKkTctXav6occq+mGE4GYoDp5eP++67T4sWLdKyZcskSYcOHVLfvn111113OXspAMANqHNnUwVPiFWZypWUm52tz8ZN0vcLFpmOhWLE6Zdd1q9fr/bt26tBgwaSpDvuuEOtW7fW8uXLr7i/r6+vHA7HZQMAcD6bzab2AwfoxQ/eUpnKlXTywCFN7vc3igeMsJw5NpvNGjt2rJWfn2/l5ORY+fn51vDhw6+6f3R0tHUlDofDqbkYhmGK85QqV9Z6/p1J1sStG6yJWzdYfcdEWb4lSxrPxRSdcTgc13P8du7ivXv3tg4fPmz17t3bCgoKsoKDg63Tp09boaGhV9zf19fXcjgcl6ZatWqUD4ZhGCdOvZbNrajVi62JWzdYY39cY7Xq/qjxTEzRG6Pl4/Dhw9ZLL7102baIiAhrx44drgjPMAzDXGVsdrvVcdCzVsKv31kTt26whiycZVWuV8d4LqZozvUcv51+w6m/v78KCgou25afny+73em3lwAArsJRvpz6jxulBve0lCT98OkSLRw7UblZ2YaTAS74tMuSJUsUERGhw4cPa9u2bWrevLlef/11TZs2zdlLAQCuoME9rdR/XIwc5cspOyNDn8Ql6OfPvzAdC7iMU0+7lCpVypo0aZJ18OBBKyMjw9q7d68VFxdn+fj4OP20DcMwDPP/x+7lZXV65XkrYfM6a+LWDdbgT5KsSnVqG8/FFI+5nuO37fcvPIbD4VBaWpoCAwOVnp5uOg4AFAqBlSoqePwo1WvZXJK0ft5CLZowWXnZXGaBe1zP8Zu/7QIAhdytre9R39FRKlWurLIuXNT8UeP06xerTMcCroryAQCFlN3bS51feUEPPRsiSTq6fZeShkTq9OGjhpMBf47yAQCFUJkqlRU8IVZ1mt8hSfpuzgItSZyivJwcw8mAv0b5AIBCpnHb1uo7eqT8SwcqMy1dH0eP0dZVX5uOBVwzygcAFBJe3t7q8tqLajegnyTp8NbtSho6UmePHjOcDLg+lA8AKATKVa+q4Alxqn3H7ZKktUlztfSNqcrPyzOcDLh+lA8A8HBN2rdV79gIlQx0KCM1TXMj47Tt6+9MxwJuGOUDADyUl4+Puoa9otb9ekmSDm7eqplDonTu+AnDyYCbQ/kAAA9UvmYNhSTGqWbjWyVJX01L0vIp/1FBXr7hZMDNo3wAgIdp+kh7PRUTrhKlAnTx3HnNjojVzm83mI4FOA3lAwA8hLefn7oN/Yfue6qHJGn/z79q5rAopaacMpwMcC7KBwB4gIq31FJoYryqNWqggoICffX+DH357/dVkM9lFhQ9lA8AMOzORzuqZ9Qw+fn7K/3MWc0OH6XdG340HQtwGcoHABjiU8JPPYa/rruf7CpJ2vvjz5o5LFrpp88YTga4FuUDAAyoXPcWhSTGq2qDeiooKNDKd6Zp5X+myyooMB0NcDnKBwC4WatuXdRjRJj8/Esq7dRpzRoeo70//mw6FuA2lA8AcBPfkiX1RESYWnXrIknatf4HzR4xShfOnDOcDHAvygcAuEGVBvUUmhivynVvUUF+vr6Y+p6+en+GLMsyHQ1wO8oHALjY3U92VY/hr8unhJ9SU05p5rAo7f/5V9OxAGMoHwDgIn7+/uoZPUx3dukoSdrx7XrNiYjTxXPnzQYDDKN8AIALVL+1oUIS41Wxdk3l5+Vp+Zvv6OsPZ3OZBRDlAwCc7r7eT6jb0H/I29dX546f0MwhUTq4eavpWIDHoHwAgJOUKBWgp0aNUNOOD0mStq35VnMi45WZlmY4GeBZKB8A4AQ1Gt+q0InxKl+juvJyc7V00r/1TdJc07EAj0T5AICb1Kb/U3ps8N/l7eOjM0ePKWnISB35bbvpWIDHonwAwA0qGehQ79gINWnfVpK0ZeUafRw9RlnpFwwnAzwb5QMAbkCtO25XyIQ4lateVXk5OVqcOEXr5iwwHQsoFCgfAHAdbDab2ob2VZd/vCgvH2+dPnxUM8IilLxjt+loQKFB+QCAa+RfOlB9R0epcdv7JUmblq/U/FHjlH0xw3AyoHChfADANajT/A4FT4hVmSqVlZudrc/G/0vfz//MdCygUKJ8AMCfsNlsevDZEHX6+3Py8vbWyQOHNCMsUsd37zUdDSi0KB8AcBWlypVVvzFRanT/PZKkjUuW65O4BOVkZhpOBhRulA8AuIJ6LZur//hRKl2ponIys7RwzET9+NnnpmMBRQLlAwD+F5vdrg7PP62Og56V3ctLJ/Yd0IzBEUrZd8B0NKDIoHwAwO8c5cup/7hRanBPS0nSjws/18KxE5WTmWU4GVC0UD4AQFKDe1qp39hoBVYor+yMDH0Sl6CfP//CdCygSKJ8ACjW7F5e6vji39T+uQGy2+06tnuvksIidfLAIdPRgCLL7uwnPHDggCzL+sO89dZbzl4KAG5KYKWKGvT+FD38wjOy2+3aMP8zTe43kOIBuJjTz3y0atVKXl5elx4HBQVp1apVmj9/vrOXAoAb1uj+e9RvTJRKlSurrIsXNT9mnH79YpXpWECx4PTycfr06cseDx8+XHv37tXatWudvRQAXDe7t5c6vfy82g8MlSQl79itGWEROn34qOFkQPHh0ns+fHx8FBwcrDfeeOOq+/j6+srPz+/SY4fD4cpIAIqxMpUrKTghTnWa3yFJ+m7OAi1JnKK8nBzDyYDixaXlo3v37ipTpow+/PDDq+4THh6umJgYV8YAADVu21p94iMVUKa0MtMvaF70GG1ZucZ0LKBYskmyXPXkX3zxhXJyctS1a9er7nOlMx/JyckKDAxUenq6q6IBKCa8vL3V5bUX1W5AP0nS4d+2K2nISJ09esxwMqBocTgcSktLu6bjt8vOfNSqVUsdOnTQE0888af75eTkKIdTngBcoGy1KgpJiFftO26XJK1Nmqulk/6t/Nxcw8mA4s1l5eOZZ57RyZMntXTpUlctAQBXFfRQW/WOGyH/wEBlpKVpbmS8tq351nQsAHJR+bDZbHrmmWf00UcfKT8/3xVLAMAVefn46PHBf1eb/k9Jkg5u3qqZQ6J07vgJw8kA/JdLykeHDh1Uu3ZtTZs2zRVPDwBXVL5GdYUkxqnm7bdJktZMm6llU95RQR7/CAI8iUvKx8qVK2Wz2Vzx1ABwRXd0fEhPxYSrpKOULp47rzkRcdrx7XrTsQBcAX/bBUCh5u3rq65DXtX9fZ6UJO3/+VfNHBal1JRThpMBuBrKB4BCq0LtmgpNjFf1WxtKkla995G+nPqeCrjXDPBolA8AhdKdj3ZUz6hh8vP3V/qZs5ozIla71v9gOhaAa0D5AFCo+JTwU4/hr+vuJ//nlxfu/fFnzRoeo7RTp//iJwF4CsoHgEKjUp3aCp04WlUb1FNBQYFW/We6VrwzTVZBgeloAK4D5QNAodCyaxc9EREmP/+SSjt1WrOGx2jvjz+bjgXgBlA+AHg035Il9ETEELXq1kWStHvDj5oVHqMLZ84ZTgbgRlE+AHisKg3qKTQxXpXr3qKC/Hx9MfU9ffVBEpdZgEKO8gHAI939xOPqET5YPiX8lJpySjOHRWn/z7+ajgXACSgfADyKn7+/ekYN1Z2PPiJJ2vHdBs0ZEauL586bDQbAaSgfADxGtUYNFJoYr4q31FJ+Xp6WT/mPvp4+S5ZlmY4GwIkoHwA8wr1P9VC3of+Qj5+fzh0/oZlDonRw81bTsQC4AOUDgFElSgWoV0y4mj3SXpK0bc23mjsyXhmpaYaTAXAVygcAY2o0vlUhiXGqULOG8nPz9Pm/puqbGXNNxwLgYpQPAEa07tdLj4e9Im8fH51NPq6kIZE6vHW76VgA3IDyAcCtSgY61Ds2Qk3at5UkbVn1teZFj1FmWrrhZADchfIBwG1qNWmskIR4lateVXk5OVqcOEXr5iwwHQuAm1E+ALhF29C+evS1l+Tl463Th48qaUikjm7fZToWAAMoHwBcyr90oPrEj9Tt7VpLkn79YpXmjxqnrAsXDScDYArlA4DL3NLsDoUkxKpMlcrKzc7WovGTtWH+QtOxABhG+QDgdDabTQ8+G6xOf39eXt7eOnngkGaERer47r2mowHwAJQPAE5VqlxZ9R0dpVtb3yNJ+vnzL/RJXIKyMzIMJwPgKSgfAJymbsvmCh4/SqUrVVROZpYWjn1DPy5cYjoWAA9D+QBw02x2u9o/N0CPvPg32b28dGLfASWFRerE3v2mowHwQJQPADfFUb6c+o2LUcN7WkmSfvzscy0cM1E5mVmGkwHwVJQPADeswd0t1W9cjAIrlFd2RqY+iU/Qz0uWm44FwMNRPgBcN5vdro4v/k0dnn9adrtdx/fs04zBETp54JDpaAAKAcoHgOsSWLGC+o8fpfqt7pQkbVjwmT4b9y/lZWcbTgagsKB8ALhmje67W/3GRqtUubLKunhRC0aN16blK03HAlDIUD4A/CW7l5c6/f15tR8YKklK3rFbM4ZE6vShI4aTASiMKB8A/lSZypUUPCFWde5sKklaN/cTLU54U3k5OYaTASisKB8Aruq2Nvep75goBZQprcz0C5oXM1ZbVnxlOhaAQo7yAeAPvLy91eUfL6rd0/0kSUe27VBS2EidOZpsOBmAooDyAeAyZatVUciEONVuGiRJ+ibpY30+aaryc3MNJwNQVFA+AFwS9NAD6h0XIf/AQGWkpenjkaP121ffmI4FoIihfACQl4+PHnv9ZT0Q3FuSdGjzb0oaMlLnjp8wnAxAUUT5AIq58jWqKyQxTjVvv02StGb6LC17820V5OUbTgagqLK74kmrVaumpKQknT59WhkZGdqyZYtatGjhiqUA3IQ7Oj6kf877UDVvv00Xz6fq/ZcG6/M33qJ4AHApp5/5KFOmjNatW6c1a9aoc+fOOnXqlBo0aKBz5845eykAN8jb11ddh7yq+/s8KUk68MtmzRwapfMpJw0nA1AcOL18DBs2TEeOHNGzzz57advBgwedvQyAG1ShVg2FJo5W9dsaSpJWvfeRvpz6ngryOdsBwD2cftmla9eu2rhxo+bNm6eUlBT98ssvGjhw4FX39/X1lcPhuGwAuEbzzg/rn/M+VPXbGir9zFm9+8JrWv7mOxQPAG5nOXMyMzOtzMxMa/To0VazZs2s5557zsrIyLBCQ0OvuH90dLR1JQ6Hw6m5GKY4j08JP6tX9HBr4tYN1sStG6wXp021AitWMJ6LYZiiMw6H45qP37bfv3Ca7Oxsbdy4Uffff/+lbZMnT1arVq103333/WF/X19f+fn5XXrscDiUnJyswMBApaenOzMaUCxVqlNboRNHq2qDeiooKNCq/0zXyv9M52wHAKdyOBxKS0u7puO30+/5OH78uLZv337Zth07dujJJ5+84v45OTnK4Q9UAS7RsmsXPRERJj//kko7fUazh8dozw8bTccCUMw5vXysW7dOjRo1umxbw4YNdejQIWcvBeAqfEuW0BMRYWrV7VFJ0u7vf9Ls4TFKP3PWcDIAcEH5mDRpktavX6/w8HDNmzdPd911l55//nk9//zzzl4KwBVUqV9XIYnxqlKvjgry8/Xlv9/X6vdnyCooMB0NAC5x+k0njz76qLVlyxYrMzPT2r59uzVw4ECX3LDCMMzlc1ePx61xP31tTdy6wYpatdiq27K58UwMwxSPMXrD6c26nhtWAPwPP39/PTlyiFo81kmStOO7DZozIlYXz503GwxAsWH0hlMA7lW1YX2FJsarUp3ays/L0/Ip/9HX02fJsjzq3xUAcAnlAyjE7u3VQ92G/UM+fn46fyJFSUOidPDXLaZjAcCfonwAhVCJUgHqFT1czTp1kCRt+/o7zY2MU0ZqmuFkAPDXKB9AIVOjcSOFJMarQs0ays/N0+f/mqpvZsw1HQsArhnlAyhEWvfrqccHvyJvX1+dTT6upCGROrx1+1//IAB4EMoHUAiUDHToqVEjdEeHdpKkravX6uOo0cpM4xNhAAofygfg4Wo1aazgCXEqX6Oa8nJytGTiFH03e4HpWABwwygfgAd7ILSPHnvtZXn5eOv0kaNKCovU0e27TMcCgJtC+QA8kH/pQPWJH6nb27WWJP365WrNjxmrrAsXDScDgJtH+QA8zC1Nmyg4IVZlq1ZRbna2Fo2frA3zF5qOBQBOQ/kAPITNZlO7Z/qr8ysvyMvbW6cOHtaMsEgd27XHdDQAcCrKB+ABAsqWUd8xUbqt9b2SpF+WfqkFsROUnZFhOBkAOB/lAzCsbotmCh4fq9KVKyo3K1ufjpmoHxcuMR0LAFyG8gEYYrPb1X5gqB55aaDsXl5K2X9QMwZH6MTe/aajAYBLUT4AA0qVL6v+40ap4T2tJEk/LVqqT0cnKiczy3AyAHA9ygfgZg3ubql+42IUWKG8sjMy9enoRG1cvMx0LABwG8oH4CY2u10dBz2rDi88I7vdruN79mnG4AidPHDIdDQAcCvKB+AGgRUrqP+4GNW/q4Uk6fsFi/TZ+EnKzco2nAwA3I/yAbhYo/vuVt8xUXKUL6esixe1IHaCNi1bYToWABhD+QBcxO7lpUdefk4dnhsgSUreuVszwiJ1+tARw8kAwCzKB+ACpStXVPD4WNVt0UyStG7uJ1qc8KbycnLMBgMAD0D5AJzstjb3qe/okQooW0aZ6Rc0f9Q4bf5ytelYAOAxKB+Ak9i9vdTl1Rf14DP9JUlHtu1QUthInTmabDgZAHgWygfgBGWrVlFIQpxqNw2SJH0z82N9/sZU5efmGk4GAJ6H8gHcpKCHHlDvuAj5BwYqIy1NH48co9++Wms6FgB4LMoHcIO8vL312Ot/1wMhvSVJh7ZsU9KQSJ07dsJwMgDwbJQP4AaUq1FNIQlxqhXUWJL09YeztWzy28rPyzOcDAA8H+UDuE53PPygnho1QiUdpXTxfKrmRMRpxzfrTMcCgEKD8gFcI29fX3Ud8qru7/OkJOnAL5s1c2iUzqecNJwMAAoXygdwDSrUqqHQxNGqfltDSdLq92foi6nvqiAv33AyACh8KB/AX2je+WH1jB6mEgEBunD2nGaPiNWudd+bjgUAhRblA7gKbz8/dR/+mu7t2V2StG/jJs0cFq20k6fMBgOAQo7yAVxBpTq1FZIYr2oN66ugoECr3v1QK9+ZpoJ8LrMAwM2ifAD/R4vHO+vJyCHy8y+ptNNnNDt8lPZ8/5PpWABQZFA+gN/5liyhHiMG667uj0mS9ny/UbOGRyv9zFnDyQCgaKF8AJIq16uj0MR4ValfVwX5+fry7Q+0+r2PZBUUmI4GAEUO5QPF3l09HleP8NflW7KEUk+e0sxh0dq/cZPpWABQZNmd/YTR0dGyLOuy2bFjh7OXAW6an7+/+o2NVu/YEfItWUI7v/teb/QaQPEAABdzyZmP3377TR06dLj0OI+/dwEPU7VhfYUmxqtSndrKz8vTF2+9qzXTZsqyLNPRAKDIc0n5yMvLU0pKiiueGrhp9/Tqru7DXpOPn5/On0jRzKFROrBpi+lYAFBsuKR8NGjQQMnJycrKytKGDRsUHh6uI0eOXHFfX19f+fn5XXrscDhcEQmQX4C/nooJV7NO/3NWbvvadZoTEauM1DTDyQCgeHH6PR8//PCDnn76aXXq1Ekvvvii6tSpo2+//ValSpW64v7h4eFKS0u7NMnJyc6OBKj6bQ31+ryP1KxTB+Xn5mlxwpua9soQigcAGGCT5NKL3KVLl9ahQ4f0+uuva9q0aX/4/pXOfCQnJyswMFDp6emujIZionW/nnp88Cvy9vXV2eTjSho6Uoe3bDMdCwCKFIfDobS0tGs6frv8o7apqanavXu36tevf8Xv5+TkKCcnx9UxUAyVcJRS71EjdMfDD0qStq5eq4+jRiszjVILACa5vHwEBASoXr16SkpKcvVSwCU1gxorJCFO5WtUU15urpYkTtF3s+ebjgUAkAvKR0JCgpYsWaJDhw6pWrVqGjVqlPLz8zVnzhxnLwVc0QOhffTYay/Ly8dbp48cVVLYSB3dvtN0LADA75xePmrUqKE5c+aofPnyOnXqlL777jvdc889On36tLOXAi5TMjBQfeMjdfuDbSRJm1d8pXnRY5R14aLhZACA/83p5aNv377OfkrgL93StImCE2JVtmoV5WZna9GEydowb6HpWACAK+Bvu6BQs9lsavd0P3V+dZC8vL116uBhzQiL1LFde0xHAwBcBeUDhVZA2TLqO3qkbmtznyTpl2UrtGDUeGVnZBhOBgD4M5QPFEp1WzRT8PhYla5cUblZ2Vo4dqJ++HSJ6VgAgGtA+UChYrPZ9NDAUHV6+TnZvbyUsv+gZoRF6sSefaajAQCuEeUDhUap8mXVb0y0Gt13tyTpp0XL9OnoROVkZhpOBgC4HpQPFAr172qh/uNiFFixgrIzMrVwTKJ+WrTMdCwAwA2gfMCj2ex2dRz0rDq88IzsdruO79mnpLBIpew/aDoaAOAGUT7gsQIrVlD/cTGqf1cLSdL3Cxbps/GTlJuVbTgZAOBmUD7gkRree5f6jY2Wo3w5ZV28qAWxE7Rp2QrTsQAATkD5gEexe3npkZcG6qGBobLb7UreuVszwiJ1+tAR09EAAE5C+YDHKF25ooLHx6pui2aSpPUff6pFEyYrLyfHbDAAgFNRPuARbm1zr/qNjlJA2TLKunBR82LGavOXq03HAgC4AOUDRtm9vdTllUF68NlgSdKR7TuVNDhSZ44mG04GAHAVygeMKVu1ioITYnVL0yaSpG9nzdOSiW8pPzfXcDIAgCtRPmDE7Q+2UZ+4SPmXDlRmWro+jhqtravXmo4FAHADygfcysvbW4+9/nc9ENJbknRoyzbNHDpSZ5OPG04GAHAXygfcplyNagpJiFOtoMaSpK8/nK1lk99Wfl6e4WQAAHeifMAtmnRop96xESrpKKWL51M1NzJe29d+ZzoWAMAAygdcytvXV4+HvaLWfXtKkg5s2qKZQ6N0/kSK4WQAAFMoH3CZCrVqKCQhXjUaN5IkffXBDC1/610V5OUbTgYAMInyAZdo1qmDekUPV4lSAbpw9pxmj4jVrnXfm44FAPAAlA84lbefn7oPe0339uouSdq3cZNmDotW2slTZoMBADwG5QNOU6lObYUkxqtaw/oqKCjQ6vc+0oq3P1BBPpdZAAD/H+UDTtHisU56cuQQ+fn7K/3MWc0aHqM93/9kOhYAwANRPnBTfEuWUI/wwbqrx2OSpD3fb9Ss8Bilnz5jOBkAwFNRPnDDKtero9DEeFWpX1cF+fla8c40rXr3Q1kFBaajAQA8GOUDN+Su7o+px4jB8i1ZQqknT2nWsGjt27jJdCwAQCFA+cB18S1ZUk+OHKKWj3eWJO1a971mj4jVhbPnDCcDABQWlA9cs6oN6yk0cbQq1amt/Lw8ffHWe1ozLUmWZZmOBgAoRCgfuCb39Oqu7sNek4+fn86nnNTMISN1YNMW07EAAIUQ5QN/yi/AX72ih6t554clSdvXrtPcyDhdPJ9qOBkAoLCifOCqqt/WUCEJ8apYu6byc/O0bPLbWjtjDpdZAAA3hfKBK7q/b091DXtF3r6+OnvsuJKGjNThLdtMxwIAFAGUD1ymhKOUnooJV9OOD0mSfvtqreaOHKPMtDTDyQAARQXlA5fUDGqskIQ4la9RTXm5ufp84lv6dtY807EAAEUM5QOSpAdC+ujRf74kbx8fnTmarKSwkTqybYfpWACAIojyUcyVDAxUn/gIBT34gCRp84qvNC96jLIuXDScDABQVNldvcCwYcNkWZYmTZrk6qVwnWo3DdLgBR8p6MEHlJeTo0/iEzRjcATFAwDgUi4989GyZUu98MIL2rx5syuXwXWy2Wxq93Q/dX51kLy8vXXq0BElhUUqeedu09EAAMWAy858BAQEaNasWXruued07hx/98NTBJQprb9NTdRjr/9dXt7e+mXZCk166mmKBwDAbVxWPqZOnaqlS5dq9erVf7qfr6+vHA7HZQPXqHNnU72+YIZua3OfcrOyNS9mrGYNi1Z2RobpaACAYsQll1169+6tO++8U61atfrLfcPDwxUTE+OKGPidzWbTQwND1enl52T38tLJA4c0IyxCx3fvMx0NAFAMOf3MR40aNTR58mT1799f2dnZf7n/2LFjFRgYeGmqV6/u7EjFWqnyZfXcO5PU5dVBsnt5aePi5ZrU+xmKBwDAGJskp/6hjm7duumzzz5TXl7epW3e3t4qKChQQUGB/Pz8VFBQcNWfdzgcSktLU2BgoNLT050Zrdip1+pOBY8fpcCKFZSTmaVPRyfop0XLTMcCABRB13P8dvpll9WrVysoKOiybdOnT9fOnTs1fvz4Py0ecA6b3a6Hn39aDw96VnYvLx3fs09JYZFK2X/QdDQAAJxfPi5cuKBt2y7/A2QXL17UmTNn/rAdzueoUF79x8Wowd0tJUk/fLJYC8e9odysv74EBgCAO/AbTouQhve2Ur+xMXKUL6fsjAwtiB2vX5auMB0LAIDLuKV8PPjgg+5Yptiye3mp40t/U/uBA2S323Vs1x7NCIvUqYOHTUcDAOAPOPNRyJWuXFH9x49SvRbNJUnrP/5UixLeVN41fNIIAAATKB+F2K1t7lW/0VEKKFtGWRcuan7MWP365Z//UjcAAEyjfBRCdm8vdXllkB58NliSdGT7TiWFjdSZI0cNJwMA4K9RPgqZMlUqKyQhTrc0ayJJ+nbWPC2Z+Jbyc3MNJwMA4NpQPgqR29u1Vp/4kfIvHajMtHR9HDVaW1evNR0LAIDrQvkoBLy8vfXoP19S29C+kqTDW7craUikziYfN5wMAIDrR/nwcOWqV1VIQrxqNWksSVo7Y46WTvq38v/Xr68HAKAwoXx4sCbt26p3bIRKBjqUkZqmuZFx2vb1d6ZjAQBwUygfHsjb11ePD/67WvfrJUk6+OtWJQ0ZqfMnUgwnAwDg5lE+PEz5mjUUmhivGo0bSZK+mpak5VP+o4K8fMPJAABwDsqHB2n2SHv1iglXiVIBunD2nOZExGrnd9+bjgUAgFNRPjyAt5+fug39h+57qockad/PmzRzaLTSTp4ynAwAAOejfBhW8ZZaCk2MV7VGDVRQUKDV732kFW9/oIJ8LrMAAIomyodBdz72iHqOHCo/f3+lnzmr2eEx2r3hJ9OxAABwKcqHAT4l/NQjfLDufuJxSdKeHzZq1vAYpZ8+YzgZAACuR/lws8p1b1HoxNGqUr+uCgoKtPLtD7Ty3Q9lFRSYjgYAgFtQPtyoVfdH9cSIMPmWLKG0U6c1c1i09v30i+lYAAC4FeXDDXxLltSTkUPUsmtnSdKudd9r9ohYXTh7znAyAADcj/LhYlUb1lNo4mhVqlNbBfn5+uKt9/TVBzNkWZbpaAAAGEH5cKF7enZT92H/lE8JP51POamZQ6N04JfNpmMBAGAU5cMF/AL81StqmJp36ShJ2v7NOs2NiNPF86mGkwEAYB7lw8mq39pQIYnxqli7pvJz87TszXe09qPZXGYBAOB3lA8nur/Pk+o65FV5+/rq7LHjmjk0Soc2/2Y6FgAAHoXy4QQlHKX0VEy4mnZ8SJL025pvNDdytDLT0gwnAwDA81A+blLN229TSGKcyteorrzcXH3+xlR9O/Nj07EAAPBYlI+b0Ca4tx57/WV5+/jozNFkJYWN1JFtO0zHAgDAo1E+bkDJwED1iY9Q0IMPSJI2r/hK82LGKiv9guFkAAB4PsrHdardNEjBE2JVrlpV5eXkaHHCm1o39xPTsQAAKDQoH9fIZrOp7YB+6vLqIHn5eOvUoSNKCotU8s7dpqMBAFCoUD6uQUCZ0uozeqQaP3C/JGnTshWaHzte2RczDCcDAKDwoXz8hTp3NlXwhFiVqVxJuVnZ+mz8JH2/YJHpWAAAFFqUj6uw2Wx66G+heuTlgfLy9tbJA4c0IyxCx3fvMx0NAIBCjfJxBaXKlVW/MVFqdP89kqSNi5frk/gE5WRmGk4GAEDhR/n4P+q1bK7+40epdKWKysnM0qdjEvXTZ0tNxwIAoMigfPzOZrfr4eef1sODnpXdy0sn9u7XjLBIpew7YDoaAABFCuVDkqN8OfUfN0oN7mkpSfrh0yVaOHaicrOyDScDAKDoKfblo8E9rdR/XIwc5cspOyNDn8Ql6OfPvzAdCwCAIsvu7CccNGiQNm/erNTUVKWmpmr9+vXq1KmTs5e5aXYvL3V65Xk9/59/yVG+nI7t2qN/9XmW4gEAgIs5/czH0aNHNXz4cO3Zs0c2m00DBgzQokWL1Lx5c23fvt3Zy92QwEoVFTxhlOq1aC5JWj9voRZNmKy8bC6zAADgajZJlqsXOXPmjIYMGaJp06b95b4Oh0NpaWkKDAxUenq607Pc2voe9R0dpVLlyirrwkXNHzVOv36xyunrAABQnFzP8dul93zY7Xb16tVLAQEB2rBhwxX38fX1lZ+f36XHDofDNVm8vdT5lRf00LMhkqSj23dpRlikzhw56pL1AADAlbmkfAQFBWnDhg0qUaKELly4oB49emjHjh1X3Dc8PFwxMTGuiHGZ29u2vlQ8vps9X0smvqW8nByXrwsAAC7nkssuPj4+qlWrlkqXLq2ePXtq4MCBatu27RULyJXOfCQnJ7vksssTEWHa88NGbV31tVOfFwCA4u56Lru45Z6PlStXat++fRo0aNBf7uvqez4AAIDzXc/x2+kftb3iInb7ZWc3AABA8eX0ez7GjBmj5cuX6/Dhw3I4HOrXr5/atWunRx55xNlLAQCAQsjp5aNSpUqaMWOGqlatqtTUVG3ZskWPPPKIVq3i46wAAMAF5WPgwIHOfkoAAFCEuOWeDwAAgP+ifAAAALeifAAAALeifAAAALeifAAAALeifAAAALeifAAAALeifAAAALeifAAAALdy+m84dRaHw2E6AgAAuEbXc9z2uPLx3/DJycmGkwAAgOvlcDiUnp7+p/vYJFnuiXPtqlWr9pfBb4TD4VBycrKqV6/ukuf3dMX99Uu8B8X99Uu8B7z+4v36Jde+Bw6HQ8eOHfvL/TzuzIekawp+M9LT04vtf3QSr1/iPSjur1/iPeD1F+/XL7nmPbjW5+OGUwAA4FaUDwAA4FbFqnxkZ2crJiZG2dnZpqMYUdxfv8R7UNxfv8R7wOsv3q9f8oz3wCNvOAUAAEVXsTrzAQAAzKN8AAAAt6J8AAAAt6J8AAAAtyoW5aNNmzZavHixkpOTZVmWunXrZjqSWw0fPlw//vij0tLSlJKSooULF6phw4amY7nNoEGDtHnzZqWmpio1NVXr169Xp06dTMcyZtiwYbIsS5MmTTIdxW2io6NlWdZls2PHDtOx3K5atWpKSkrS6dOnlZGRoS1btqhFixamY7nFgQMH/vDfgGVZeuutt0xHcwu73a7Y2Fjt379fGRkZ2rt3ryIjI43l8cjfcOpsAQEB2rx5s6ZNm6aFCxeajuN2bdu21dSpU/XTTz/J29tbY8aM0YoVK9S4cWNlZGSYjudyR48e1fDhw7Vnzx7ZbDYNGDBAixYtUvPmzbV9+3bT8dyqZcuWeuGFF7R582bTUdzut99+U4cOHS49zsvLM5jG/cqUKaN169ZpzZo16ty5s06dOqUGDRro3LlzpqO5RatWreTl5XXpcVBQkFatWqX58+cbTOU+w4YN04svvqgBAwZo27ZtatmypaZPn67U1FRNmTLFSCarOI1lWVa3bt2M5zA5FSpUsCzLstq0aWM8i6k5c+aM9eyzzxrP4c4JCAiwdu3aZbVv395as2aNNWnSJOOZ3DXR0dHWpk2bjOcwOWPHjrW++eYb4zk8ZSZNmmTt2bPHeA53zZIlS6z333//sm0LFiywkpKSjOQpFpddcLnSpUtLks6ePWs4ifvZ7Xb17t1bAQEB2rBhg+k4bjV16lQtXbpUq1evNh3FiAYNGig5OVn79u3TzJkzVbNmTdOR3Kpr167auHGj5s2bp5SUFP3yyy8aOHCg6VhG+Pj4KDg4WNOmTTMdxW3Wr1+v9u3bq0GDBpKkO+64Q61bt9by5cuNZTLeyNw5xf3Mh81ms5YsWWJ9++23xrO4c4KCgqz09HQrNzfXOnfunNW5c2fjmdw5vXv3trZs2WL5+flZkordmY9OnTpZPXv2tJo0aWJ17NjRWrdunXXw4EGrVKlSxrO5azIzM63MzExr9OjRVrNmzaznnnvOysjIsEJDQ41nc/f06tXLys3NtapWrWo8i7vGZrNZY8eOtfLz862cnBwrPz/fGj58uMlM5t8Ud05xLx///ve/rQMHDljVq1c3nsWd4+PjY9WrV8+68847rTFjxlgnT560brvtNuO53DE1atSwTpw4YTVp0uTStuJWPv7vlC5d2jp//nyxuvSWnZ1trVu37rJtkydPttavX288m7vniy++sBYvXmw8hzund+/e1uHDh63evXtbQUFBVnBwsHX69GmT5dP8m+LOKc7lY8qUKdbhw4etW265xXgW07Ny5UrrnXfeMZ7DHdOtWzfLsiwrNzf30liWZeXn51u5ubmW3W43ntHE/Pjjj9aYMWOM53DXHDx40Hrvvfcu2zZo0CDr6NGjxrO5c2rVqmXl5eVZXbt2NZ7FnXP48GHrpZdeumxbRESEtWPHDiN5isWnXSBNmTJFPXr0ULt27XTw4EHTcYyz2+3y8/MzHcMtVq9eraCgoMu2TZ8+XTt37tT48eNVUFBgKJk5AQEBqlevnpKSkkxHcZt169apUaNGl21r2LChDh06ZCiRGc8884xOnjyppUuXmo7iVv7+/n/4fz0/P192u7lbP403MldPQECA1bRpU6tp06aWZVnWa6+9ZjVt2tSqWbOm8WzumKlTp1rnzp2zHnjgAaty5cqXpkSJEsazuWPGjBljtWnTxqpdu7YVFBRkjRkzxsrPz7c6dOhgPJupKW6XXRISEqwHHnjAql27tnXvvfdaK1assE6ePGlVqFDBeDZ3TcuWLa2cnBwrPDzcqlevntW3b1/rwoULVr9+/Yxnc9fYbDbr4MGD1tixY41ncfdMnz7dOnLkiNWlSxerdu3aVvfu3a2TJ09a48aNM5XJ/Jvi6mnbtq11JdOnTzeezR1zNQMGDDCezR3z/vvvWwcOHLCysrKslJQUa+XKlcW6eEjFr3zMmTPHSk5OtrKysqwjR45Yc+bMserWrWs8l7vn0UcftbZs2WJlZmZa27dvtwYOHGg8kzvn4YcftizLsho0aGA8i7unVKlS1qRJk6yDBw9aGRkZ1t69e624uDjLx8fHSB7b718AAAC4Bb/nAwAAuBXlAwAAuBXlAwAAuBXlAwAAuBXlAwAAuBXlAwAAuBXlAwAAuBXlAwAAuBXlAwAAuBXlAwAAuBXlAwAAuBXlAwAAuNX/A4KpaYUaU9/wAAAAAElFTkSuQmCC"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-15T08:24:34.963847853Z",
+     "start_time": "2026-03-15T08:24:34.861332504Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "xpoints = np.array([1, 8])\n",
+    "ypoints = np.array([3, 10])\n",
+    "\n",
+    "plt.plot(xpoints, ypoints, 'o')\n",
+    "plt.show()"
+   ],
+   "id": "5be4ed26e6b0c8a5",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": "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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-15T08:24:54.674718435Z",
+     "start_time": "2026-03-15T08:24:54.597481975Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "xpoints = np.array([1, 2, 6, 8])\n",
+    "ypoints = np.array([3, 8, 1, 10])\n",
+    "\n",
+    "plt.plot(xpoints, ypoints)\n",
+    "plt.show()"
+   ],
+   "id": "7b294572af58fede",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-15T08:25:18.777976769Z",
+     "start_time": "2026-03-15T08:25:18.708436940Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ypoints = np.array([3, 10])\n",
+    "\n",
+    "plt.plot(ypoints)\n",
+    "plt.show()"
+   ],
+   "id": "c4267318eada5db3",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-15T08:26:01.816626634Z",
+     "start_time": "2026-03-15T08:26:01.741990644Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "x = np.arange(0,4*np.pi,0.1)   # start,stop,step\n",
+    "y = np.sin(x)\n",
+    "z = np.cos(x)\n",
+    "plt.plot(x,y,x,z)\n",
+    "plt.show()"
+   ],
+   "id": "7e91ab6501b1a517",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-15T08:27:42.004727940Z",
+     "start_time": "2026-03-15T08:27:41.889455094Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "x = np.array([1, 2, 3, 4])\n",
+    "y = np.array([1, 4, 9, 16])\n",
+    "plt.plot(x, y)\n",
+    "\n",
+    "plt.xlabel(\"x - label\")\n",
+    "plt.ylabel(\"y - label\")"
+   ],
+   "id": "c03d076cb254bba9",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'y - label')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-15T08:29:04.176192729Z",
+     "start_time": "2026-03-15T08:29:04.084352055Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "x = np.array([1, 2, 3, 4])\n",
+    "y = np.array([1, 4, 9, 16])\n",
+    "plt.plot(x, y)\n",
+    "\n",
+    "plt.title(\"tittle\")\n",
+    "plt.xlabel(\"x - label\")\n",
+    "plt.ylabel(\"y - label\")\n",
+    "\n",
+    "plt.show()"
+   ],
+   "id": "a4eac88a69fc262d",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "ef99a3200e9fb29e"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/test.ipynb b/test.ipynb
index e2910e6..f27c542 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -6,65 +6,40 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:53.937066295Z",
-     "start_time": "2026-03-15T06:40:52.962818496Z"
+     "end_time": "2026-03-19T05:45:23.532371843Z",
+     "start_time": "2026-03-19T05:45:23.227305138Z"
     }
    },
    "source": [
     "import torch\n",
     "import numpy\n",
-    "import pandas\n",
-    "from sympy.physics.control.control_plots import matplotlib\n",
-    "from torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook import batched_powerSGD_hook\n",
-    "\n"
+    "import pandas"
+   ],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'torch'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mModuleNotFoundError\u001B[39m                       Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[1]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mtorch\u001B[39;00m\n\u001B[32m      2\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mnumpy\u001B[39;00m\n\u001B[32m      3\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mpandas\u001B[39;00m\n",
+      "\u001B[31mModuleNotFoundError\u001B[39m: No module named 'torch'"
+     ]
+    }
    ],
-   "outputs": [],
    "execution_count": 1
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:53.993520411Z",
-     "start_time": "2026-03-15T06:40:53.939990190Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "torch.randn(3,4,2)",
    "id": "3e141a42d342fa96",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[[ 0.5509, -1.6216],\n",
-       "         [ 0.1083,  0.4464],\n",
-       "         [ 1.8819,  0.4029],\n",
-       "         [-0.0733,  2.6961]],\n",
-       "\n",
-       "        [[-2.0316,  0.7172],\n",
-       "         [-0.3774,  0.5248],\n",
-       "         [-0.0134,  0.3256],\n",
-       "         [ 0.3433,  0.1697]],\n",
-       "\n",
-       "        [[ 1.1434,  0.6595],\n",
-       "         [ 0.2386, -0.6560],\n",
-       "         [ 1.3177, -0.6876],\n",
-       "         [-1.0916, -0.6199]]])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 2
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:54.023331390Z",
-     "start_time": "2026-03-15T06:40:53.996610811Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "X = torch.arange(12, dtype=torch.float32).reshape((3,4))\n",
@@ -72,35 +47,11 @@
     "torch.cat((X, Y), dim=0), torch.cat((X, Y), dim=1)"
    ],
    "id": "8ae20ae68abbf32f",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(tensor([[ 0.,  1.,  2.,  3.],\n",
-       "         [ 4.,  5.,  6.,  7.],\n",
-       "         [ 8.,  9., 10., 11.],\n",
-       "         [ 2.,  1.,  4.,  3.],\n",
-       "         [ 1.,  2.,  3.,  4.],\n",
-       "         [ 4.,  3.,  2.,  1.]]),\n",
-       " tensor([[ 0.,  1.,  2.,  3.,  2.,  1.,  4.,  3.],\n",
-       "         [ 4.,  5.,  6.,  7.,  1.,  2.,  3.,  4.],\n",
-       "         [ 8.,  9., 10., 11.,  4.,  3.,  2.,  1.]]))"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 3
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:54.081323347Z",
-     "start_time": "2026-03-15T06:40:54.028230344Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "a = torch.arange(3).reshape((3, 1))\n",
@@ -109,55 +60,19 @@
     "a+b"
    ],
    "id": "2960a1ded2cdd5a4",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[0, 1],\n",
-       "        [1, 2],\n",
-       "        [2, 3]])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 4
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:54.538103014Z",
-     "start_time": "2026-03-15T06:40:54.136512769Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "X[-1], X[1:3]\n",
    "id": "69c2ec23ab6ae97c",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(tensor([ 8.,  9., 10., 11.]),\n",
-       " tensor([[ 4.,  5.,  6.,  7.],\n",
-       "         [ 8.,  9., 10., 11.]]))"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 5
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:54.691375092Z",
-     "start_time": "2026-03-15T06:40:54.563788479Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "A = X.numpy()\n",
@@ -165,27 +80,11 @@
     "type(A), type(B)"
    ],
    "id": "b8d779a1bc7e4b1a",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(numpy.ndarray, torch.Tensor)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 6
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:54.732824469Z",
-     "start_time": "2026-03-15T06:40:54.714530988Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import os\n",
@@ -201,15 +100,10 @@
    ],
    "id": "82be028b0f1dd1e3",
    "outputs": [],
-   "execution_count": 7
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:54.811604177Z",
-     "start_time": "2026-03-15T06:40:54.734220773Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import pandas as pd\n",
@@ -217,28 +111,11 @@
     "print(data)\n"
    ],
    "id": "ddd789a2656899d1",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   NumRooms Alley   Price\n",
-      "0       NaN  Pave  127500\n",
-      "1       2.0   NaN  106000\n",
-      "2       4.0   NaN  178100\n",
-      "3       NaN   NaN  140000\n"
-     ]
-    }
-   ],
-   "execution_count": 8
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:54.853161280Z",
-     "start_time": "2026-03-15T06:40:54.813874506Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "inputs, outputs = data.iloc[:, 0:2], data.iloc[:, 2]\n",
@@ -250,33 +127,11 @@
     "print(inputs)\n"
    ],
    "id": "e98fcc3bd4f067cf",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   NumRooms  Alley_Pave  Alley_nan\n",
-      "0       NaN        True      False\n",
-      "1       2.0       False       True\n",
-      "2       4.0       False       True\n",
-      "3       NaN       False       True\n",
-      "   NumRooms  Alley_Pave  Alley_nan\n",
-      "0       3.0        True      False\n",
-      "1       2.0       False       True\n",
-      "2       4.0       False       True\n",
-      "3       3.0       False       True\n"
-     ]
-    }
-   ],
-   "execution_count": 9
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:54.885351255Z",
-     "start_time": "2026-03-15T06:40:54.855249881Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "X = torch.tensor(inputs.to_numpy(dtype=float))\n",
@@ -284,119 +139,41 @@
     "X, y\n"
    ],
    "id": "8ff0f7b40f0e4996",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(tensor([[3., 1., 0.],\n",
-       "         [2., 0., 1.],\n",
-       "         [4., 0., 1.],\n",
-       "         [3., 0., 1.]], dtype=torch.float64),\n",
-       " tensor([127500., 106000., 178100., 140000.], dtype=torch.float64))"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 10
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:54.918539225Z",
-     "start_time": "2026-03-15T06:40:54.887053932Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "B=torch.tensor([[1,2,3],[2,0,4],[3,4,5]])\n",
     "B"
    ],
    "id": "91a6e0da442b95a0",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[1, 2, 3],\n",
-       "        [2, 0, 4],\n",
-       "        [3, 4, 5]])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 11
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.091544877Z",
-     "start_time": "2026-03-15T06:40:54.972764187Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "B==B.T",
    "id": "297e6a678fb19be7",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[True, True, True],\n",
-       "        [True, True, True],\n",
-       "        [True, True, True]])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 12
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.318112438Z",
-     "start_time": "2026-03-15T06:40:55.208719181Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "X=torch.arange(24).reshape(2,3,4)\n",
     "X"
    ],
    "id": "24e864b336beb58b",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[[ 0,  1,  2,  3],\n",
-       "         [ 4,  5,  6,  7],\n",
-       "         [ 8,  9, 10, 11]],\n",
-       "\n",
-       "        [[12, 13, 14, 15],\n",
-       "         [16, 17, 18, 19],\n",
-       "         [20, 21, 22, 23]]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 13
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.396075402Z",
-     "start_time": "2026-03-15T06:40:55.345582838Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "A = torch.arange(20, dtype=torch.float32).reshape(5, 4)\n",
@@ -407,19 +184,8 @@
     "id(A),id(B)"
    ],
    "id": "ee0905479b1dbc2b",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(140556050244048, 140556050244432)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 14
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -428,40 +194,15 @@
    "id": "136459f5efe765cf"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.435745645Z",
-     "start_time": "2026-03-15T06:40:55.410554891Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "A*B",
    "id": "f576b0df17cc0e98",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  0.,   1.,   4.,   9.],\n",
-       "        [ 16.,  25.,  36.,  49.],\n",
-       "        [ 64.,  81., 100., 121.],\n",
-       "        [144., 169., 196., 225.],\n",
-       "        [256., 289., 324., 361.]])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 15
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.547870490Z",
-     "start_time": "2026-03-15T06:40:55.455797448Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "a=2\n",
@@ -469,34 +210,11 @@
     "a+X,(a*X).shape"
    ],
    "id": "b2373af1d7f2a45",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(tensor([[[ 2,  3,  4,  5],\n",
-       "          [ 6,  7,  8,  9],\n",
-       "          [10, 11, 12, 13]],\n",
-       " \n",
-       "         [[14, 15, 16, 17],\n",
-       "          [18, 19, 20, 21],\n",
-       "          [22, 23, 24, 25]]]),\n",
-       " torch.Size([2, 3, 4]))"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 16
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.646393885Z",
-     "start_time": "2026-03-15T06:40:55.571643582Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "print(A)\n",
@@ -505,65 +223,22 @@
     "A_sum_axis0,A_sum_axis1"
    ],
    "id": "2b50246e1ca8a3bc",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[ 0.,  1.,  2.,  3.],\n",
-      "        [ 4.,  5.,  6.,  7.],\n",
-      "        [ 8.,  9., 10., 11.],\n",
-      "        [12., 13., 14., 15.],\n",
-      "        [16., 17., 18., 19.]])\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(tensor([40., 45., 50., 55.]), tensor([ 6., 22., 38., 54., 70.]))"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 17
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.715281021Z",
-     "start_time": "2026-03-15T06:40:55.664043259Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "x=torch.arange(4,dtype=torch.float32)\n",
     "torch.mv(A,x)"
    ],
    "id": "3195464dfeb554ed",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([ 14.,  38.,  62.,  86., 110.])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 18
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.752354225Z",
-     "start_time": "2026-03-15T06:40:55.717824570Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import time\n",
@@ -584,49 +259,19 @@
     "fun()\n"
    ],
    "id": "ebda8c74ead3e42b",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I am silly\n",
-      "执行时间: 0.000183秒\n"
-     ]
-    }
-   ],
-   "execution_count": 19
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.822293071Z",
-     "start_time": "2026-03-15T06:40:55.771678228Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "torch.norm(torch.ones((4, 9)))",
    "id": "3343cc0c01d0161c",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(6.)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 20
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.831732982Z",
-     "start_time": "2026-03-15T06:40:55.824867487Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "x =torch.arange(4.0,requires_grad=True)\n",
@@ -634,69 +279,32 @@
    ],
    "id": "674e2416e9417cfe",
    "outputs": [],
-   "execution_count": 21
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.870751082Z",
-     "start_time": "2026-03-15T06:40:55.832780649Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "y=2*torch.dot(x,x)\n",
     "y"
    ],
    "id": "66c0febebcf98cde",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(28., grad_fn=<MulBackward0>)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 22
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.897976833Z",
-     "start_time": "2026-03-15T06:40:55.872431759Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "y.backward()\n",
     "x.grad"
    ],
    "id": "825f2ce6c46ca4a8",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([ 0.,  4.,  8., 12.])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 23
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.913347196Z",
-     "start_time": "2026-03-15T06:40:55.901720461Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "x.grad.zero_()\n",
@@ -705,27 +313,11 @@
     "x.grad\n"
    ],
    "id": "df399463515e9d3c",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([1., 1., 1., 1.])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 24
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:55.985375574Z",
-     "start_time": "2026-03-15T06:40:55.929676346Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# 对非标量调用backward需要传入一个gradient参数，该参数指定微分函数关于self的梯度。\n",
@@ -738,58 +330,19 @@
     "x.grad"
    ],
    "id": "f9207619bd4b3de8",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([0., 1., 4., 9.], grad_fn=<MulBackward0>)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "tensor([0., 2., 4., 6.])"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 25
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.018025749Z",
-     "start_time": "2026-03-15T06:40:55.987257926Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "torch.ones(len(x))",
    "id": "409c14c230570859",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([1., 1., 1., 1.])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 26
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.046029736Z",
-     "start_time": "2026-03-15T06:40:56.019720336Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "x.grad.zero_()\n",
@@ -800,27 +353,11 @@
     "x.grad==u"
    ],
    "id": "521b948fe0683b12",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([True, True, True, True])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 27
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.086132664Z",
-     "start_time": "2026-03-15T06:40:56.049185612Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "x.grad.zero_()\n",
@@ -828,27 +365,11 @@
     "x.grad==2*x"
    ],
    "id": "b040beecf0632315",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([True, True, True, True])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 28
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.121364505Z",
-     "start_time": "2026-03-15T06:40:56.091095688Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "from torch.distributions import multinomial\n",
@@ -856,51 +377,19 @@
     "fair_probs"
    ],
    "id": "4e6ec763dbea5aa3",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([1., 1., 1., 1., 1., 1.])"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 29
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.150868516Z",
-     "start_time": "2026-03-15T06:40:56.126763544Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "multinomial.Multinomial(1, fair_probs).sample()",
    "id": "f12d5e85bc6ab595",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([0., 1., 0., 0., 0., 0.])"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 30
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.197988031Z",
-     "start_time": "2026-03-15T06:40:56.152490623Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "counts = multinomial.Multinomial(10, fair_probs).sample((500,))\n",
@@ -909,27 +398,11 @@
     "cum_counts.size()"
    ],
    "id": "b02f43376fd6f1fe",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([500, 6])"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 31
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.313824234Z",
-     "start_time": "2026-03-15T06:40:56.200143209Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -960,30 +433,11 @@
     "#plt.savefig('dice_probability.png', bbox_inches='tight')"
    ],
    "id": "8b80daa4edd0b066",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x450 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 32
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.335068964Z",
-     "start_time": "2026-03-15T06:40:56.317850591Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import numpy as np\n",
@@ -1011,15 +465,10 @@
    ],
    "id": "4bdbb4999907154a",
    "outputs": [],
-   "execution_count": 33
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.450342214Z",
-     "start_time": "2026-03-15T06:40:56.336961011Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "n = 10000\n",
@@ -1032,27 +481,11 @@
     "f'{timer.stop():.5f} sec'"
    ],
    "id": "c6f71622e2cc578a",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0.03117 sec'"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 34
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.498587786Z",
-     "start_time": "2026-03-15T06:40:56.454031052Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "timer.start()\n",
@@ -1060,27 +493,11 @@
     "f'{timer.stop():.5f} sec'"
    ],
    "id": "2578c79b1214a79f",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0.00041 sec'"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 35
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.507308186Z",
-     "start_time": "2026-03-15T06:40:56.500119417Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import math\n",
@@ -1090,15 +507,10 @@
    ],
    "id": "fd17fdbe38a5f79",
    "outputs": [],
-   "execution_count": 36
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.533559761Z",
-     "start_time": "2026-03-15T06:40:56.508247056Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "from matplotlib_inline import backend_inline\n",
@@ -1150,15 +562,10 @@
    ],
    "id": "82158a69cba14da0",
    "outputs": [],
-   "execution_count": 37
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.625694875Z",
-     "start_time": "2026-03-15T06:40:56.535032024Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# 再次使用numpy进行可视化\n",
@@ -1170,30 +577,11 @@
     "legend=[f'mean {mu}, std {sigma}' for mu, sigma in params])"
    ],
    "id": "f69ac10ebc3d13d8",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
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-   "execution_count": 38
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.659870613Z",
-     "start_time": "2026-03-15T06:40:56.640905156Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "#注意一下matmul做向量乘上矩阵的时候不用考虑转置的情况\n",
@@ -1206,15 +594,10 @@
    ],
    "id": "7ed837bdd2b3a26d",
    "outputs": [],
-   "execution_count": 39
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.700642098Z",
-     "start_time": "2026-03-15T06:40:56.665034974Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "true_w = torch.tensor([2, -3.4])\n",
@@ -1223,55 +606,21 @@
    ],
    "id": "5ec2e204a6fd5cb2",
    "outputs": [],
-   "execution_count": 40
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.766436205Z",
-     "start_time": "2026-03-15T06:40:56.704907868Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "set_figsize()\n",
     "plt.scatter(features[:, (1)].detach().numpy(), labels.detach().numpy(), 1)"
    ],
    "id": "38213d46b3d9900d",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fd5c02b8830>"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 350x250 with 1 Axes>"
-      ],
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-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 41
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.788076510Z",
-     "start_time": "2026-03-15T06:40:56.778166680Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "w=torch.normal(0,0.01,size=(2,1),requires_grad=True)\n",
@@ -1292,15 +641,10 @@
    ],
    "id": "12166e1bc3ddd695",
    "outputs": [],
-   "execution_count": 42
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.811093546Z",
-     "start_time": "2026-03-15T06:40:56.788825920Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import random\n",
@@ -1316,15 +660,10 @@
    ],
    "id": "f3b7ee9f326bc687",
    "outputs": [],
-   "execution_count": 43
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:56.833447562Z",
-     "start_time": "2026-03-15T06:40:56.813045993Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "batch_size =10\n",
@@ -1333,43 +672,11 @@
     "    break"
    ],
    "id": "f386e12d65afff2e",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[-1.8455, -0.9126],\n",
-      "        [-0.0299,  1.6530],\n",
-      "        [-2.3513,  0.6457],\n",
-      "        [ 0.1707,  0.8342],\n",
-      "        [ 0.2096, -0.4362],\n",
-      "        [ 0.6160,  1.7403],\n",
-      "        [ 0.4242,  0.0484],\n",
-      "        [-1.4459,  0.7434],\n",
-      "        [ 0.5302, -0.5594],\n",
-      "        [-0.5957, -1.5179]]) \n",
-      " tensor([[ 3.6056],\n",
-      "        [-1.4872],\n",
-      "        [-2.6965],\n",
-      "        [ 1.6980],\n",
-      "        [ 6.1001],\n",
-      "        [-0.4750],\n",
-      "        [ 4.9009],\n",
-      "        [-1.2270],\n",
-      "        [ 7.1486],\n",
-      "        [ 8.1680]])\n"
-     ]
-    }
-   ],
-   "execution_count": 44
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:57.028635227Z",
-     "start_time": "2026-03-15T06:40:56.852110495Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "for epoch in range(num_epochs):\n",
@@ -1382,68 +689,22 @@
     "        print(f'epoch {epoch+1}, train loss: {float(train_l.mean()):3f}')"
    ],
    "id": "8888ab6adcec36f1",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 1, train loss: 0.030682\n",
-      "epoch 2, train loss: 0.000100\n",
-      "epoch 3, train loss: 0.000049\n",
-      "epoch 4, train loss: 0.000049\n",
-      "epoch 5, train loss: 0.000049\n",
-      "epoch 6, train loss: 0.000049\n",
-      "epoch 7, train loss: 0.000049\n",
-      "epoch 8, train loss: 0.000049\n",
-      "epoch 9, train loss: 0.000049\n",
-      "epoch 10, train loss: 0.000049\n",
-      "epoch 11, train loss: 0.000049\n",
-      "epoch 12, train loss: 0.000049\n",
-      "epoch 13, train loss: 0.000049\n",
-      "epoch 14, train loss: 0.000049\n",
-      "epoch 15, train loss: 0.000049\n",
-      "epoch 16, train loss: 0.000049\n",
-      "epoch 17, train loss: 0.000049\n",
-      "epoch 18, train loss: 0.000049\n",
-      "epoch 19, train loss: 0.000049\n",
-      "epoch 20, train loss: 0.000049\n"
-     ]
-    }
-   ],
-   "execution_count": 45
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:57.085057122Z",
-     "start_time": "2026-03-15T06:40:57.045310900Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "print(f'w的估计误差: {true_w - w.reshape(true_w.shape)}')\n",
     "print(f'b的估计误差: {true_b - b}')"
    ],
    "id": "8199439fa7f26309",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "w的估计误差: tensor([-0.0002,  0.0003], grad_fn=<SubBackward0>)\n",
-      "b的估计误差: tensor([0.0002], grad_fn=<RsubBackward1>)\n"
-     ]
-    }
-   ],
-   "execution_count": 46
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:57.146334186Z",
-     "start_time": "2026-03-15T06:40:57.086581923Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "from torch.utils import data\n",
@@ -1458,15 +719,10 @@
    ],
    "id": "560d537dcbb5a335",
    "outputs": [],
-   "execution_count": 47
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:57.187559751Z",
-     "start_time": "2026-03-15T06:40:57.147857709Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "from torch import nn\n",
@@ -1475,27 +731,11 @@
     "net[0].bias.data.fill_(0)"
    ],
    "id": "c54fe059d6fd20de",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([0.])"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 48
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:57.523547926Z",
-     "start_time": "2026-03-15T06:40:57.191697380Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "loss = nn.MSELoss()\n",
@@ -1511,26 +751,11 @@
     "    print(f'epoch {epoch + 1}, loss {l:f}')\n"
    ],
    "id": "e8a44851125b7cc6",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 1, loss 0.599957\n",
-      "epoch 2, loss 0.011503\n",
-      "epoch 3, loss 0.000325\n"
-     ]
-    }
-   ],
-   "execution_count": 49
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:57.637796611Z",
-     "start_time": "2026-03-15T06:40:57.539577276Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import torchvision\n",
@@ -1541,66 +766,29 @@
    ],
    "id": "bd4e8a65ccd03177",
    "outputs": [],
-   "execution_count": 50
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:57.669099255Z",
-     "start_time": "2026-03-15T06:40:57.639295575Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "use_svg_display()\n",
     "len(mnist_train),len(mnist_test)"
    ],
    "id": "ed2c915af7f6a76a",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(60000, 10000)"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 51
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:57.717512337Z",
-     "start_time": "2026-03-15T06:40:57.672873756Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "mnist_train[0][0].shape",
    "id": "4df1cbc292aa5981",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 28, 28])"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 52
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:57.739205016Z",
-     "start_time": "2026-03-15T06:40:57.719179425Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def get_fashion_mnist_labels(labels):\n",
@@ -1610,15 +798,10 @@
    ],
    "id": "332f3d6da0bafbe8",
    "outputs": [],
-   "execution_count": 53
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:57.764139878Z",
-     "start_time": "2026-03-15T06:40:57.754725053Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def show_images(imgs, num_rows, num_cols, titles=None, scale=1): #@save\n",
@@ -1641,15 +824,10 @@
    ],
    "id": "c83202fe9b0ab487",
    "outputs": [],
-   "execution_count": 54
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:40:57.957479055Z",
-     "start_time": "2026-03-15T06:40:57.765366722Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "X, y = next(iter(data.DataLoader(mnist_train, batch_size=18)))\n",
@@ -1657,37 +835,11 @@
     "show_images(X.reshape(18, 28, 28), 2, 9, titles=get_fashion_mnist_labels(y));"
    ],
    "id": "cf4abd8370d55416",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([18, 1, 28, 28])\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 900x200 with 18 Axes>"
-      ],
-      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"524.634446pt\" height=\"137.375483pt\" viewBox=\"0 0 524.634446 137.375483\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n <metadata>\n  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2026-03-15T14:40:57.897836</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.10.8, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linejoin: round; 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-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 55
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:00.262791420Z",
-     "start_time": "2026-03-15T06:40:57.970609683Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "batch_size = 256\n",
@@ -1703,27 +855,11 @@
     "f'{timer.stop():.2f} sec'"
    ],
    "id": "552769fbffc16142",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'2.26 sec'"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 56
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:00.481904106Z",
-     "start_time": "2026-03-15T06:41:00.308644956Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def load_data_fashion_mnist(batch_size, resize=None):\n",
@@ -1743,15 +879,10 @@
    ],
    "id": "aa81880abd86cae6",
    "outputs": [],
-   "execution_count": 57
+   "execution_count": null
   },
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-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:02.218194285Z",
-     "start_time": "2026-03-15T06:41:00.632922086Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "train_iter, test_iter = load_data_fashion_mnist(32, resize=64)\n",
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     "    break"
    ],
    "id": "4248a103f745154",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([32, 1, 64, 64]) torch.float32 torch.Size([32]) torch.int64\n"
-     ]
-    }
-   ],
-   "execution_count": 58
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:02.319582689Z",
-     "start_time": "2026-03-15T06:41:02.252523696Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "from IPython import display\n",
@@ -1791,29 +909,11 @@
     "X.sum(0, keepdim=True), X.sum(1, keepdim=True)\n"
    ],
    "id": "94c52f0cca88ef48",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(tensor([[5., 7., 9.]]),\n",
-       " tensor([[ 6.],\n",
-       "         [15.]]))"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 59
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:02.375197243Z",
-     "start_time": "2026-03-15T06:41:02.320989852Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def softmax(X):\n",
@@ -1825,29 +925,11 @@
     "X_prob, X_prob.sum(1)"
    ],
    "id": "c4ab34373c5a664e",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(tensor([[0.4360, 0.2195, 0.2044, 0.0829, 0.0571],\n",
-       "         [0.0678, 0.3243, 0.2988, 0.0572, 0.2519]]),\n",
-       " tensor([1.0000, 1.0000]))"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 60
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:02.400574302Z",
-     "start_time": "2026-03-15T06:41:02.379951465Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def net(X):\n",
@@ -1855,15 +937,10 @@
    ],
    "id": "6eacc53b2b9738af",
    "outputs": [],
-   "execution_count": 61
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:02.433540648Z",
-     "start_time": "2026-03-15T06:41:02.402525751Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "y = torch.tensor([0, 2])\n",
@@ -1871,27 +948,11 @@
     "y_hat[[0, 1], y]"
    ],
    "id": "698449b4dafb545c",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([0.1000, 0.5000])"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 62
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:02.473364236Z",
-     "start_time": "2026-03-15T06:41:02.438405972Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def cross_entropy(y_hat, y):\n",
@@ -1899,27 +960,11 @@
     "cross_entropy(y_hat, y)"
    ],
    "id": "1720369fc8568c8c",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([2.3026, 0.6931])"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 63
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:02.495413592Z",
-     "start_time": "2026-03-15T06:41:02.475628534Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def accuracy(y_hat, y): #@save\n",
@@ -1932,27 +977,11 @@
     "accuracy(y_hat, y)/len(y)"
    ],
    "id": "e65719500a64ed87",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 64
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:02.505437086Z",
-     "start_time": "2026-03-15T06:41:02.497345583Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "class Accumulator: #@save\n",
@@ -1968,15 +997,10 @@
    ],
    "id": "f1eebb35ff2e9fea",
    "outputs": [],
-   "execution_count": 65
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:02.526181989Z",
-     "start_time": "2026-03-15T06:41:02.506819558Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def evaluate_accuracy(net, data_iter): #@save\n",
@@ -1991,39 +1015,18 @@
    ],
    "id": "bc2beb5f2d6afe7e",
    "outputs": [],
-   "execution_count": 66
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:06.805858093Z",
-     "start_time": "2026-03-15T06:41:02.527858694Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "evaluate_accuracy(net, test_iter)",
    "id": "65bcfb7e40c1a98b",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0498"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 67
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:06.843120784Z",
-     "start_time": "2026-03-15T06:41:06.827746920Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def train_epoch_ch3(net, train_iter, loss, updater): #@save\n",
@@ -2052,15 +1055,10 @@
    ],
    "id": "2faf1dcc6c023a53",
    "outputs": [],
-   "execution_count": 68
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:06.897158404Z",
-     "start_time": "2026-03-15T06:41:06.851017516Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "class Animator: #@save\n",
@@ -2104,15 +1102,10 @@
    ],
    "id": "7cd5367ab43c5e5f",
    "outputs": [],
-   "execution_count": 69
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:06.913685898Z",
-     "start_time": "2026-03-15T06:41:06.898876289Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater): #@save\n",
@@ -2130,15 +1123,10 @@
    ],
    "id": "b02a143c75fad40",
    "outputs": [],
-   "execution_count": 70
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:06.945935582Z",
-     "start_time": "2026-03-15T06:41:06.916434775Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "lr = 0.1\n",
@@ -2147,59 +1135,21 @@
    ],
    "id": "6a97b70779276b61",
    "outputs": [],
-   "execution_count": 71
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:41:54.907637364Z",
-     "start_time": "2026-03-15T06:41:06.946861739Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "num_epochs = 10\n",
-    "train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, updater)"
+    "#train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, updater)"
    ],
    "id": "df3cceb72faee402",
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mKeyboardInterrupt\u001B[39m                         Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[72]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m      1\u001B[39m num_epochs = \u001B[32m10\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m \u001B[43mtrain_ch3\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnet\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_iter\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_iter\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcross_entropy\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnum_epochs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mupdater\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[70]\u001B[39m\u001B[32m, line 6\u001B[39m, in \u001B[36mtrain_ch3\u001B[39m\u001B[34m(net, train_iter, test_iter, loss, num_epochs, updater)\u001B[39m\n\u001B[32m      3\u001B[39m animator = Animator(xlabel=\u001B[33m'\u001B[39m\u001B[33mepoch\u001B[39m\u001B[33m'\u001B[39m, xlim=[\u001B[32m1\u001B[39m, num_epochs], ylim=[\u001B[32m0.3\u001B[39m, \u001B[32m0.9\u001B[39m],\n\u001B[32m      4\u001B[39m     legend=[\u001B[33m'\u001B[39m\u001B[33mtrain loss\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33mtrain acc\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33mtest acc\u001B[39m\u001B[33m'\u001B[39m])\n\u001B[32m      5\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m epoch \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(num_epochs):\n\u001B[32m----> \u001B[39m\u001B[32m6\u001B[39m     train_metrics = \u001B[43mtrain_epoch_ch3\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnet\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_iter\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mloss\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mupdater\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m      7\u001B[39m     test_acc = evaluate_accuracy(net, test_iter)\n\u001B[32m      8\u001B[39m     animator.add(epoch + \u001B[32m1\u001B[39m, train_metrics + (test_acc,))\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[68]\u001B[39m\u001B[32m, line 10\u001B[39m, in \u001B[36mtrain_epoch_ch3\u001B[39m\u001B[34m(net, train_iter, loss, updater)\u001B[39m\n\u001B[32m      7\u001B[39m metric = Accumulator(\u001B[32m3\u001B[39m)\n\u001B[32m      8\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m X, y \u001B[38;5;129;01min\u001B[39;00m train_iter:\n\u001B[32m      9\u001B[39m \u001B[38;5;66;03m# 计算梯度并更新参数\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m10\u001B[39m     y_hat = \u001B[43mnet\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     11\u001B[39m     l = loss(y_hat, y)\n\u001B[32m     12\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(updater, torch.optim.Optimizer):\n\u001B[32m     13\u001B[39m \u001B[38;5;66;03m# 使用PyTorch内置的优化器和损失函数\u001B[39;00m\n",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[61]\u001B[39m\u001B[32m, line 2\u001B[39m, in \u001B[36mnet\u001B[39m\u001B[34m(X)\u001B[39m\n\u001B[32m      1\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mnet\u001B[39m(X):\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m softmax(\u001B[43mtorch\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmatmul\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m.\u001B[49m\u001B[43mreshape\u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[43m-\u001B[49m\u001B[32;43m1\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mW\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mW\u001B[49m\u001B[43m)\u001B[49m + b)\n",
-      "\u001B[31mKeyboardInterrupt\u001B[39m: "
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-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 72
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:42:03.186617720Z",
-     "start_time": "2026-03-15T06:42:01.659427155Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "def predict_ch3(net, test_iter, n=6): #@save\n",
@@ -2214,30 +1164,11 @@
     "predict_ch3(net, test_iter)\n"
    ],
    "id": "94f6177bfb40eece",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 600x100 with 6 Axes>"
-      ],
-      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"357.008839pt\" height=\"90.784071pt\" viewBox=\"0 0 357.008839 90.784071\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n <metadata>\n  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2026-03-15T14:42:03.146128</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.10.8, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: 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-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    }
-   ],
-   "execution_count": 73
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-03-15T06:42:05.791711771Z",
-     "start_time": "2026-03-15T06:42:05.741066616Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "batch_size = 256\n",
@@ -2245,7 +1176,7 @@
    ],
    "id": "4a0bfd0479ec7386",
    "outputs": [],
-   "execution_count": 74
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -2269,10 +1200,142 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "source": "",
+   "source": [
+    "x = torch.arange(-8.0, 8.0, 0.1, requires_grad=True)\n",
+    "y = torch.relu(x)\n",
+    "plot(x.detach(), y.detach(), 'x', 'relu(x)', figsize=(5, 2.5))"
+   ],
    "id": "c25dd146307f58e0",
    "outputs": [],
    "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "y.backward(torch.ones_like(x), retain_graph=True)\n",
+    "plot(x.detach(), x.grad, 'x', 'grad of relu', figsize=(5, 2.5))"
+   ],
+   "id": "f96acd2015dccb38",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "y = torch.sigmoid(x)\n",
+    "plot(x.detach(), y.detach(), 'x', 'sigmoid(x)', figsize=(5, 2.5))"
+   ],
+   "id": "74013cea59cd8be3",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "x.grad.data.zero_()\n",
+    "y.backward(torch.ones_like(x),retain_graph=True)\n",
+    "plot(x.detach(), x.grad, 'x', 'grad of sigmoid', figsize=(5, 2.5))"
+   ],
+   "id": "6a0b4f529bf9cc5c",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "batch_size = 256\n",
+    "train_iter, test_iter = load_data_fashion_mnist(batch_size)"
+   ],
+   "id": "f1de998439b1b9f",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "num_inputs,num_outputs,num_hiddens = 784, 10, 256\n",
+    "W1=nn.Parameter(torch.randn(num_inputs,num_hiddens,requires_grad=True)*0.01)\n",
+    "b1=nn.Parameter(torch.zeros(num_hiddens,requires_grad=True))\n",
+    "W2 = nn.Parameter(torch.randn(num_hiddens,num_outputs,requires_grad=True)*0.01)\n",
+    "b2=nn.Parameter(torch.zeros(num_outputs,requires_grad=True))\n",
+    "params=[W1,b1,W2,b2]\n"
+   ],
+   "id": "adcea8cd4ee792a8",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "def relu(X):\n",
+    "    a = torch.zeros_like(X)\n",
+    "    return torch.max(X,a)\n",
+    "def net(X):\n",
+    "    X = X.reshape((-1,num_inputs))\n",
+    "    H = relu(X@W1+b1)\n",
+    "    return (H@W2+b2)\n",
+    "loss = nn.CrossEntropyLoss(reduction='none')\n",
+    "num_epochs,lr=10,0.05\n",
+    "updater=torch.optim.SGD(params,lr=lr)\n",
+    "#train_ch3(net,train_iter,test_iter,loss,num_epochs,updater)\n"
+   ],
+   "id": "cfd81a0f16d0c573",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "predict_ch3(net, test_iter)",
+   "id": "f2ed2e9cee14c28a",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "net = nn.Sequential(nn.Flatten(),\n",
+    "                    nn.Linear(784,256),\n",
+    "                    nn.ReLU(),\n",
+    "                    nn.Linear(256,10))\n",
+    "def init_weights(m):\n",
+    "    if type(m) == nn.Linear:\n",
+    "        nn.init.normal_(m.weight,std=0.01)\n",
+    "\n",
+    "net.apply(init_weights)\n"
+   ],
+   "id": "9e4ed6d103380bc7",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "batch_size,lr, num_epochs=256,0.1,10\n",
+    "loss = nn.CrossEntropyLoss(reduction='none')\n",
+    "trainer = torch.optim.SGD(net.parameters(),lr=lr)\n",
+    "train_iter, test_iter = load_data_fashion_mnist(batch_size)\n",
+    "#train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)"
+   ],
+   "id": "52d71c77c4f51e90",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "",
+   "id": "94706c936b4be3e1",
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {