diff --git a/appendix-D/01_main-chapter-code/appendix-D.ipynb b/appendix-D/01_main-chapter-code/appendix-D.ipynb
index 9b59a22..1c34f13 100644
--- a/appendix-D/01_main-chapter-code/appendix-D.ipynb
+++ b/appendix-D/01_main-chapter-code/appendix-D.ipynb
@@ -47,7 +47,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch version: 2.2.2\n"
+      "torch version: 2.2.1\n"
      ]
     }
    ],
@@ -224,7 +224,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x300 with 1 Axes>"
       ]
@@ -241,7 +241,7 @@
     "plt.xlabel(\"Step\")\n",
     "total_training_steps = len(train_loader) * n_epochs\n",
     "plt.plot(range(total_training_steps), track_lrs)\n",
-    "#plt.tight_layout(); plt.savefig(\"1.pdf\")\n",
+    "plt.tight_layout(); plt.savefig(\"1.pdf\")\n",
     "plt.show()"
    ]
   },
@@ -310,7 +310,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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UREREhOWaVqslIiKC+Pj4Kp+Jj4+3Kg8QGRlpKZ+amkpGRoZVGYPBQFhYmKWMt7c3119/PZ9++imFhYWUl5ezdOlSWrZsSUhIyGXjLSkpwWg0Wr2aCkuXdpC0mtWg0WiY8K8OfDiqDzpHLXEpWYxYFk+WsfjqDwsh7JJqyTk7OxuTyYSvr6/VdV9f38t2L2dkZFyxfOXXK5XRaDT8/PPPJCcn4+HhgV6v5+233yY2NpZmzZpdNt558+ZhMBgsrzZt2tSswnZsx/EcQLq01XZnD3++nNAfbzdnDpwxErVITrYSorFSfUJYQ1MUhSlTptCyZUt+++03EhMTiYqK4u677+bs2bOXfW7WrFnk5eVZXqdOnWrAqNWTX1xmWcoj65vV1yewGd8+XjGTOz2vmAcWb+e3o+fUDksIUcdUS84+Pj44ODiQmZlpdT0zMxM/P78qn/Hz87ti+cqvVyqzefNmNmzYwJo1axg4cCB9+vThww8/xMXFhVWrVl02Xp1Oh6enp9WrKdh5IgeTWaGttysBXrK+2RYEervyzeQB9AtqTn5JOeNW7OS7PXL0pBCNiWrJ2dnZmZCQEOLi4izXzGYzcXFxhIeHV/lMeHi4VXmATZs2WcoHBQXh5+dnVcZoNJKQkGApU1RUBFSMb/+TVqvFbJbtEv+XpUtbWs02xcvVmc/G9+OeXgGUmxWmr93Dym2paoclhKgjqnZrR0dH89FHH7Fq1SoOHTrE5MmTKSwsZNy4cQCMGTOGWbNmWcpPmzaN2NhYFixYQEpKCi+99BK7du1i6tSpQMV48vTp03n11VdZv349+/fvZ8yYMQQEBBAVFQVUJPhmzZoxduxY9u7dy5EjR3jmmWdITU1lyJAhDf5nYOss+2nLeLPN0Tk68O6IYB4e0A5FgZe+P8jbPx2WtdBCNAKOav7wESNGcO7cOebMmUNGRgbBwcHExsZaJnSlpaVZtXAHDBjA6tWreeGFF5g9ezadOnUiJiaG7t27W8rMnDmTwsJCJkyYQG5uLoMGDSI2Nha9Xg9UdKfHxsby/PPPc/PNN1NWVka3bt347rvv6NWrV8P+Adi4vItl/JEu4822TKvVMPfurni7ObNg0xEWbj7G+cJSXrm3Ow5a2clNCHulUeRjdq0YjUYMBgN5eXmNdvz554OZPPrpLtr7uLH56RvVDkdcxRcJJ3kh5gCKAnf28OOdEcHoHB3UDkuIRq2+ckGTm60tqs+yvlm6tO3CqLC2LPp3H5wdtGzcn8EjK3dSUFKudlhCiFqQ5Cwu65+HXQj7cGcPf1aM64ubswPbjp1n5LIdnC+QYyeFsDeSnEWVcotKOZRRsQuaHHZhXwZ29OHLCf1p7ubM/jN5DFsaT0ae7CYmhD2R5CyqlJCag6JAx5butPTQqx2OqKGerb34elI4rbxcOH6ukOFL4zmVU6R2WEKIapLkLKr0d5e2tJrtVfsW7qyd2J+23q6k5RQxfGk8qdmFaoclhKgGSc6iSjv+mgwW3t5H5UjEtWjdzJWvJobToYUbZ/OKGb40niOZsh+3ELZOkrO4RE5hKSl/HaggLWf75+upZ+3EcDr7eXAuv4QHl+2w7JcuhLBNkpzFJRL+ajVf7+uBt7tO5WhEXfBx17FmQn96tjaQU1jKvz/aQXLaBbXDEkJchiRncYnKLm1pNTcuXq7OfP5oGKFtm2EsLuehjxMsH8SEELZFkrO4ROXmI7KfduPjqXdi1SP9GNDBm8JSE2NXJMqRk0LYIEnOwkp2QQlHMgsACAuS5NwYuekc+eThvtx4fQuKy8w8umoXW49IghbCltQqOZeXl/Pzzz+zdOlS8vMrJg6lp6dTUFBQp8GJhpfw1xGRnf08aObmrHI0or7onRxYOjqEiC6+lJSbeexTSdBC2JIaJ+eTJ0/So0cP7r33XqZMmcK5cxX/Q7/xxhs8/fTTdR6gaFjxx7MB6dJuCnSODnw4qg8RXVpKghbCxtQ4OU+bNo3Q0FAuXLiAi4uL5fp9991HXFxcnQYnGp7l/GbZT7tJcHbU8uGoEKsELWPQQqivxsn5t99+44UXXsDZ2brLs127dpw5c6bOAhMNLyu/mD/PFaLRyHhzU/K/CfrRVZKghVBbjZOz2WzGZDJdcv306dN4eHjUSVBCHTv+Gm/u6u+JwdVJ5WhEQ5IELYRtqXFyvu2223j33Xct32s0GgoKCpg7dy533nlnXcYmGph0aTdtkqCFsB01Ts4LFixg27ZtdO3aleLiYv79739burTfeOON+ohRNJCE43J+c1NXVYL+/Wi22mEJ0eRoFEVRavpQeXk5a9euZe/evRQUFNCnTx9GjRplNUGssTMajRgMBvLy8vD09FQ7nGuWaSwm7P/FodXAnrm34amXbu2mrLTczONfJPHzoSz0TlpWjetHmHxoE+IS9ZULapyct27dyoABA3B0dLS6Xl5ezvbt2/nXv/5VZ8HZssaWnGOSzzB97R56tjawfuogtcMRNqC03MzEz3bxy+FzuDk78NmjYfQJbKZ2WELYlPrKBTXu1r7pppvIycm55HpeXh433XRTnQQlGt4O6dIW/8PZUcvih0L+3urzk0Q5zUqIBlLj5KwoChqN5pLr58+fx83NrU6CEg3Psp+2JGfxD3onBz4eG0rfds3ILy5n9PIEDmfIedBC1DfHqxepcP/99wMVs7MffvhhdLq/jxI0mUzs27ePAQMG1H2Eot6l517k5PkiHLQaQttJt6Ww5upcsRf3Q8sT2Xsql1Ef72DtxHA6tHBXOzQhGq1qt5wNBgMGgwFFUfDw8LB8bzAY8PPzY8KECXz++ef1GauoJ5Vd2t1bGfCQiWCiCh56Jz4d14+u/p5kF5Qy6qME0s4XqR2WEI1WtVvOK1asACp2Anv66aelC7sRkfXNojoMrk58Nr4fDy7bwdGsAv798Q6+mhhOgFfTWaUhREOp8Zjz3LlzJTE3MnJ+s6gub3cdXzwaRjtvV05fuMi/P9pBlrFY7bCEaHSq3XL+p6+//pqvvvqKtLQ0SktLre7t3r27TgITDeNUThGnL1zEUashtK2MN4ura+mpZ/Vj/Rm+NJ4T54sY9XECayb0x9tdd/WHhRDVUuOW88KFCxk3bhy+vr4kJyfTr18/vL29OX78OHfccUd9xCjqUeV4c8/WBtx0tfqsJpqgAC8XvnysP36eeo5mFfDQ8kRyi0qv/qAQolpqnJw//PBDli1bxvvvv4+zszMzZ85k06ZNPPnkk+TlyRpIeyNd2qK22jR3ZfVjYfi46zh01sjYTxLJLy5TOywhGoUaJ+e0tDTLkikXFxfy8yvWPI4ePZovv/yybqMT9UpRFBL+OolKNh8RtdG+hTtfPBpGM1cn9p7O47FPd1FcdumpdUKImqlxcvbz87PsEBYYGMiOHTsASE1NpRbbdAsVncq5yJncizg5aAht21ztcISdut7Pg8/Gh+Guc2TH8Rymrt5NmcmsdlhC2LUaJ+ebb76Z9evXAzBu3Dieeuopbr31VkaMGMF9991X4wAWLVpEu3bt0Ov1hIWFkZiYeMXy69ato3Pnzuj1enr06MHGjRut7iuKwpw5c/D398fFxYWIiAiOHj16yfv83//9H2FhYbi4uNCsWTOioqJqHLu9iz9ecdpQcBsvXJwdVI5G2LPurQwsHxuKzlHLz4eymPn1Psxm+bAuRG3VODkvW7aM559/HoApU6bwySef0KVLF1555RUWL15co/dau3Yt0dHRzJ07l927d9OrVy8iIyPJysqqsvz27dsZOXIk48ePJzk5maioKKKiojhw4IClzPz581m4cCFLliwhISEBNzc3IiMjKS7+e7nHf//7X0aPHs24cePYu3cv27Zt49///ndN/yjs3g7p0hZ1KKy9N4sf6oOjVsO3yWd46fs/pDdNiNpSaqCsrEx5+eWXlVOnTtXkscvq16+fMmXKFMv3JpNJCQgIUObNm1dl+eHDhytDhgyxuhYWFqZMnDhRURRFMZvNip+fn/Lmm29a7ufm5io6nU758ssvLXVo1aqV8vHHH19T7Hl5eQqg5OXlXdP7qMVsNithr/2stH12g7Lt6Dm1wxGNSEzyaaXdcxuUts9uUN76MUXtcISoV/WVC2rUcnZ0dGT+/PmUl5df84eC0tJSkpKSiIiIsFzTarVEREQQHx9f5TPx8fFW5QEiIyMt5VNTU8nIyLAqYzAYCAsLs5TZvXs3Z86cQavV0rt3b/z9/bnjjjusWt9VKSkpwWg0Wr3s2YnzRWQYi3F20NJH1jeLOnRvcCv+c293AN7ffIyPfzuuckRC2J8ad2vfcsst/Prrr9f8g7OzszGZTPj6+lpd9/X1JSMjo8pnMjIyrli+8uuVyhw/XvGL4qWXXuKFF15gw4YNNGvWjBtvvLHKozArzZs3z2o/8TZt2tSgtrancn1zcKAXeicZbxZ166H+bXkm8noAXv2/Q3y185TKEQlhX2q868Qdd9zBc889x/79+wkJCblkK8977rmnzoKrD2ZzxSzS559/nqFDhwIV+4a3bt2adevWMXHixCqfmzVrFtHR0ZbvjUajXSdo2U9b1LfHb+yA8WIZS7ce57lv9uGud+TOHv5qhyWEXahxcn788ccBePvtty+5p9FoMJmqt8bRx8cHBwcHMjMzra5nZmbi5+dX5TN+fn5XLF/5NTMzE39/f6sywcHBAJbrXbt2tdzX6XS0b9+etLS0y8ar0+msjsm0Z4qiyOYjot5pNBqeu6MzxuIyvkw8xbQ1ybjpHBl8XQu1QxPC5tW4W9tsNl/2Vd3EDODs7ExISAhxcXFW7x0XF0d4eHiVz4SHh1uVB9i0aZOlfFBQEH5+flZljEYjCQkJljIhISHodDoOHz5sKVNWVsaJEydo27ZtteO3Z8ezCzmXX4Kzo5bgNl5qhyMaMY1Gw6tRPRjS058yk8Kkz5JIOnn54SMhRIUaJ+e6FB0dzUcffcSqVas4dOgQkydPprCwkHHjxgEwZswYZs2aZSk/bdo0YmNjWbBgASkpKbz00kvs2rWLqVOnAhW/CKZPn86rr77K+vXr2b9/P2PGjCEgIMCyjtnT05NJkyYxd+5cfvrpJw4fPszkyZMBGDZsWMP+Aaiksks7JLCZjDeLeueg1fDO8GAGX9eCi2UmHl6xk4Pp9j2hUoj6pupJByNGjODcuXPMmTOHjIwMgoODiY2NtUzoSktLQ6v9+/PDgAEDWL16NS+88AKzZ8+mU6dOxMTE0L17d0uZmTNnUlhYyIQJE8jNzWXQoEHExsai1+stZd58800cHR0ZPXo0Fy9eJCwsjM2bN9OsWdOYtSxd2qKhOTtqWfJQCGM+SWDniQuM+SSBdZMGEOQjx88KURWNosguAbVhNBoxGAzk5eXh6empdjjVpigKfV/7meyCUr6aGE6/INm2UzScvItljFy2g4NnjbTycuHryeH4G1zUDkuIWquvXKBqt7ZoeMeyCsguKEXvpKVXG4Pa4YgmxuDixKfj+9Hex40zuRd56OMEcgrlqEkh/pck5yamsks7tG1zdI4y3iwano+7js8eDSPAoOfPc4U8vCKRgpJr39hIiMakxsn5f3fJqnzl5+dTWiqfgG1d5eYj/dtLd7ZQTysvFz57NIzmbs7sO53HBDlqUggrNU7OXl5eNGvW7JKXl5cXLi4utG3blrlz51o2+xC2w2xWLIddyGQwobYOLdxZNa4fbs4ObP/zPNPWJFMuR00KAdQiOa9cuZKAgABmz55NTEwMMTExzJ49m1atWrF48WImTJjAwoULef311+sjXnENjmTlk1NYiouTAz1be6kdjhD0aG3go7GhODtq+fGPTGZ/u19OshKCWiylWrVqFQsWLGD48OGWa3fffTc9evRg6dKlxMXFERgYyGuvvcbs2bPrNFhxbXb8tb45tF0znBxkuoGwDQM6+PD+yN5M/jyJr3adppmrM7Pu7KJ2WEKoqsa/obdv307v3r0vud67d2/LyU+DBg264laYQh2yvlnYqshufrw+tCcAS7ceZ8mvf6ockRDqqnFybtOmDcuXL7/k+vLlyy0HQZw/f77JbOhhL8xmhYTUv8ab5bALYYOGh7bh+b9azK//kMKaRPmAL5quGndrv/XWWwwbNowffviBvn37ArBr1y5SUlL4+uuvAdi5cycjRoyo20jFNUnJyCe3qAw3Zwe6t5L1zcI2Pfav9uQUlbJ4y5/M/nY/Bhcn7pCTrEQTVOPkfM8995CSksLSpUs5cuQIUHGMZExMDO3atQOw7FUtbEdll3bfoOYy3ixs2szI68ktKv3rJKs9eOidGNTJR+2whGhQtdpbOygoSGZj2xk5v1nYi8qTrPIulrFxfwYTPtvF6sf6ywlqokmpVXLOzc0lMTGRrKysS9Yzjxkzpk4CE3XHZFZITK3cfESSs7B9DloN74wIJr94F78dzebhFYl8PSmcji091A5NiAZR4+T8/fffM2rUKAoKCvD09ESj0VjuaTQaSc426NBZI8bicjx0jnQLsJ9DOkTTpnN0YMlDIfz74wT2nsrloY8T+XpyOK2buaodmhD1rsaDjzNmzOCRRx6hoKCA3NxcLly4YHnl5Mgh6raosku7X1BzHGW8WdgRN50jKx/uS6eW7mQYixmzPJHsghK1wxKi3tX4N/WZM2d48skncXWVT6/24u/9tKVLW9ifZm7OfDY+jFZeLhzPrjgoI7+4TO2whKhXNU7OkZGR7Nq1qz5iEfWg3GQmMVX20xb2zc+g57Px/fB2c+bAGSOPrpKDMkTjVuMx5yFDhvDMM89w8OBBevTogZOTk9X9e+65p86CE9fuj3Qj+SXleOod6eIv483CfrVv4c6qR/oxctkOElJzmLo6mSUP9ZGhGtEoaZQa7jKv1V7+fwSNRoPJ1DQ+zRqNRgwGA3l5eXh62m7SW/rrn8z7IYWILr58PDZU7XCEuGY7jp9n7CeJlJSbGdqnNW8+0BOtVnP1B4WoB/WVC2r8kdNsNl/21VQSsz2R/bRFY9O/vTeL/t0HB62G/+4+zWsbD8lJVqLRkf6gRqzMZGan7KctGqGIrr7M/+ugjOW/p/LhFjkoQzQu1RpzXrhwIRMmTECv17Nw4cIrln3yySfrJDBx7Q6cyaOw1ISXqxOd/WTzBtG4DA1pTe7FMv6z4SBv/ngYg4sTD/Vvq3ZYQtSJaiXnd955h1GjRqHX63nnnXcuW06j0UhytiGVXdphQc1lTE40SuMHBZFbVMr7m4/x4ncH8HJ14q6eAWqHJcQ1q1ZyTk1NrfK/hW2T/bRFUxB963XkFJbyRUIaT62tOChj8HUt1A5LiGsiY86NVJnJzK4TFwDoL5PBRCOm0Wh45d7u3NXTnzKTwqTPktiddkHtsIS4JjVe52wymVi5ciVxcXFVHnyxefPmOgtO1N6+07lcLDPR3M2Z6+SwANHIOWg1vD08GGNxOVuPnGPcip18NTGc62WuhbBTNW45T5s2jWnTpmEymejevTu9evWyegnbUNml3b+9jDeLpsHZUcuSh/rQJ9CLvItljF6ewKmcIrXDEqJWatxyXrNmDV999RV33nlnfcQj6ki87KctmiBXZ0c+ebgvI5bu4HBmPg8tT+DrSQNo4aFTOzQhaqTGLWdnZ2c6duxYH7GIOlJSbiLpZMWYm0wGE02Nl6szn47vR5vmLpw8X8SYTxLJuygHZQj7UqsjI9977z3ZkceG7T2VR3GZGR93Zzq2dFc7HCEanK+nns8eCcPHXcehs0YeXbWTi6Wyg6GwHzXu1v7999/55Zdf+OGHH+jWrdslB1988803dRacqJ3K8eaw9t5oNDLeLJqmdj5ufPpIP0Ysi2fniQtMWb2bpaNDcJKDMoQdqPG/Ui8vL+677z4GDx6Mj48PBoPB6iXUV3l+s3Rpi6aua4AnnzzcF52jls0pWcz8eh9ms/T6CdtXo+RcXl7OTTfdxLx581ixYkWVr9pYtGgR7dq1Q6/XExYWRmJi4hXLr1u3js6dO6PX6+nRowcbN260uq8oCnPmzMHf3x8XFxciIiI4evRole9VUlJCcHAwGo2GPXv21Cp+W1JcZiLprzWectiFENC3XXMWP9QHR62Gb5PP8MqGgzIsJ2xejZKzo6MjkyZNoqSkpM4CWLt2LdHR0cydO5fdu3fTq1cvIiMjycrKqrL89u3bGTlyJOPHjyc5OZmoqCiioqI4cOCApcz8+fNZuHAhS5YsISEhATc3NyIjIykuLr7k/WbOnElAQOPZ7i85LZfScjMtPHS093FTOxwhbMLNnX15a1jFUs+V20+wMO6YyhEJcRVKDQ0ePFj59ttva/rYZfXr10+ZMmWK5XuTyaQEBAQo8+bNq7L88OHDlSFDhlhdCwsLUyZOnKgoiqKYzWbFz89PefPNNy33c3NzFZ1Op3z55ZdWz23cuFHp3Lmz8scffyiAkpycXO248/LyFEDJy8ur9jMN4e2fDittn92gPLF6t9qhCGFzVvx+XGn77Aal7bMblFXbU9UORzQC9ZULajwh7PHHH2fGjBmcPn2akJAQ3NysW2c9e/as9nuVlpaSlJTErFmzLNe0Wi0RERHEx8dX+Ux8fDzR0dFW1yIjI4mJiQEq9v7OyMggIiLCct9gMBAWFkZ8fDwPPvggAJmZmTz22GPExMTg6up61VhLSkqsegyMRmO169mQ5PxmIS7v4YFBXCgq4724o8xd/wcGFyfuDW6ldlhCXKLGybkyuf3z9CmNRoOiKGg0Gkym6i9XyM7OxmQy4evra3Xd19eXlJSUKp/JyMiosnxGRoblfuW1y5VRFIWHH36YSZMmERoayokTJ64a67x583j55ZerVS+1FJeZ2JOWC8jmI0JczvSITuQWlbIq/iQzvtqLp4sTN13fUu2whLBS4+TcGE6lev/998nPz7dqsV/NrFmzrFrsRqORNm3a1Ed4tbb75AVKTWb8PPW08756b4AQTZFGo2Hu3d3IvVjGd3vSmfx5Ep+PDyO0XXO1QxPCosbJuW3bujvM3MfHBwcHBzIzM62uZ2Zm4ufnV+Uzfn5+Vyxf+TUzMxN/f3+rMsHBwUDF4Rzx8fHodNZb+oWGhjJq1ChWrVp1yc/V6XSXlLc1/+zSlvXNQlyeVqvhrWG9yLtYxpbD53hk5U7WTgyni7+n2qEJAVzDkZEHDx4kNjaW9evXW71qwtnZmZCQEOLi4izXzGYzcXFxhIeHV/lMeHi4VXmATZs2WcoHBQXh5+dnVcZoNJKQkGAps3DhQvbu3cuePXvYs2ePZSnW2rVree2112pUB1vyz8MuhBBX5uSgZfGoEELbNsNYXM6YTxI5eb5Q7bCEAGrRcj5+/Dj33Xcf+/fvt4w1A5aWWk3GnAGio6MZO3YsoaGh9OvXj3fffZfCwkLGjRsHwJgxY2jVqhXz5s0DKk7FGjx4MAsWLGDIkCGsWbOGXbt2sWzZMksc06dP59VXX6VTp04EBQXx4osvEhAQQFRUFACBgYFWMbi7V2xx2aFDB1q3bl3TPxKbUFRazt7TuQCEt/dRNxgh7ISLswPLH+7LiKXxpGTkM3p5Il9PCqelp17t0EQTV6sjI4OCgsjKysLV1ZU//viDrVu3EhoaypYtW2ocwIgRI3jrrbeYM2cOwcHB7Nmzh9jYWMuErrS0NM6ePWspP2DAAFavXs2yZcvo1asXX3/9NTExMXTv3t1SZubMmTzxxBNMmDCBvn37UlBQQGxsLHp94/0fLunkBcpMCq28XGjT3EXtcISwGwYXJz4d34+23q6k5VQclJFbVKp2WKKJ0yhKzbbK8fHxYfPmzfTs2RODwUBiYiLXX389mzdvZsaMGSQnJ9dXrDbFaDRiMBjIy8vD01P9car5sSl8uOVP7u/TireHB6sdjhB2J+18EQ8s2U5Wfgk9Wxv4/NEwPPVOV39QNGn1lQtq3HI2mUx4eHgAFYk6PT0dqJgodvjw4ToLTNSM7KctxLUJ9Hbli0fDaO7mzL7TeYxbsZPCknK1wxJNVI2Tc/fu3dm7dy8AYWFhzJ8/n23btvHKK6/Qvn37Og9QXF1hSTn7TucBsr5ZiGvRydeDz8b3w1PvSNLJCzy6ahfFZXLUpGh4NU7OL7zwAmazGYBXXnmF1NRUbrjhBjZu3MjChQvrPEBxdTtP5FBuVmjdzIU2zWV9sxDXoluAgU/Hh+GucyT++HkmfJZESbkkaNGwapycIyMjuf/++wHo2LEjKSkpZGdnk5WVxc0331znAYqr23E8B5AubSHqSnAbL1aM64uLkwNbj5xj6upkykxmtcMSTUit1zkfO3aMH3/8kYsXL9K8uayrVZPspy1E3evbrjkfjw3F2VHLpoOZPLV2DyY5C1o0kBon5/Pnz3PLLbdw3XXXceedd1qWOY0fP54ZM2bUeYDiyvKLyzhwRsabhagPAzv6sPShEJwcNGzYd5Znvt6LWRK0aAA1Ts5PPfUUTk5OpKWlWZ3mNGLECGJjY+s0OHF1u05cwGRWaOvtSoCXrG8Woq7d1Lkl74/sg4NWwze7z/DCdweo4QpUIWqsxsn5p59+4o033rhkJ61OnTpx8uTJOgtMVE+8LKESot7d3t2Pt4f3QqOB1Qlp/GfDIUnQol7VODkXFhZWef5xTk6OzR8M0Rj9vZ+2JGch6tO9wa14Y2jFefWfbEvlzR8PS4IW9abGyfmGG27g008/tXyv0Wgwm83Mnz+fm266qU6DE1eWd7GMP9IrxptlMpgQ9W94aBv+c283AD7c8ifvxR1VOSLRWNX44Iv58+dzyy23sGvXLkpLS5k5cyZ//PEHOTk5bNu2rT5iFJexMzUHswLtfdzwlY36hWgQo8PbUVJu5tX/O8S7Px9Fg4ZpEZ3UDks0MrXaIezIkSMMGjSIe++9l8LCQu6//36Sk5Pp0KFDfcQoLqNyvDlMurSFaFCP3tCe5+7oDMA7Px/hvZ+lBS3qVo1bzgAGg4Hnn3/e6trp06eZMGGC5ehGUf92yPpmIVQzaXBFY+T1H1J45+cjANKCFnWm1puQ/K/z58+zfPnyuno7cRW5RaUcPGsEoH972QRGCDVMGtxBWtCiXtRZchYNKyE1B0WBDi3caOkh481CqEUStKgPkpztlHRpC2E7JEGLuibJ2U5Vrm8Ob++jciRCCJAELepWtSeEVZ5EdTm5ubnXGouoppzCUlIy8gEIk/FmIWzG/04SMysK0yM6odFoVI5M2JtqJ2eDwXDV+2PGjLnmgMTVJaZWtJqv83XHx112ZRPClvwzQb8Xd5TiMhPP3dFZErSokWon5xUrVtRnHKIG/u7SlvFmIWzRpMEdcHbQ8sqGgyzdepyLZSZeursbWq0kaFE9MuZshyo3H5H9tIWwXY8MCuL/3dcDjQY+jT/JrG/2y3nQotokOduZ7IISjmQWALIzmBC27t9hgSwY1gutBtbuOkX0V3soN5nVDkvYAUnOdibheA4Anf08aO7mrHI0Qoirub9Pa94f2QdHrYbv9qQzdXUypeWSoMWVSXK2M/HHswHp0hbCngzp6c+Sh0JwdtAS+0cGEz/bRXGZSe2whA2T5GxndvzVcpbNR4SwLxFdfVn+cCh6Jy2/HD7HIyt3UlhSrnZYwkZJcrYjWfnFHMsqQKOBsCBZ3yyEvbmhUwtWjeuHm7MD2/88z6iPE7hQWKp2WMIGSXK2I5Wt5i5+nni5ynizEPYorL03nz8ahperE3tO5TJsaTzpuRfVDkvYGEnOdkT20xaicegd2Ix1E8PxN+g5llXAA4u3cyyrQO2whA2R5GxHdsjmI0I0Gp18Pfh68gDat3AjPa+YYUu2s/dUrtphCRshydlOZBqLOZ5diFYDfWW8WYhGoZWXC19PGkCv1gYuFJUx8qMd/Hb0nNphCRsgydlOVHZpdwswYHBxUjkaIURdae7mzBeP9WdQRx+KSk08snInG/alqx2WUJlNJOdFixbRrl079Ho9YWFhJCYmXrH8unXr6Ny5M3q9nh49erBx40ar+4qiMGfOHPz9/XFxcSEiIoKjR/8+vu3EiROMHz+eoKAgXFxc6NChA3PnzqW01HZnTVr205bxZiEaHXedI8sfDmVIT3/KTApPfJnMZztOqh2WUJHqyXnt2rVER0czd+5cdu/eTa9evYiMjCQrK6vK8tu3b2fkyJGMHz+e5ORkoqKiiIqK4sCBA5Yy8+fPZ+HChSxZsoSEhATc3NyIjIykuLgYgJSUFMxmM0uXLuWPP/7gnXfeYcmSJcyePbtB6lwbf++nLV3aQjRGOkcHFj7Ym4f6B6Io8GLMAd78MQWz7MfdJGkURVH1bz4sLIy+ffvywQcfAGA2m2nTpg1PPPEEzz333CXlR4wYQWFhIRs2bLBc69+/P8HBwSxZsgRFUQgICGDGjBk8/fTTAOTl5eHr68vKlSt58MEHq4zjzTffZPHixRw/frxacRuNRgwGA3l5eXh6eta02jWSnnuRAa9vxkGrYc+cW/HQS7e2EI2Voii8F3eUd3+u6O27u1cAbz7QE72Tg8qRiarUVy5QteVcWlpKUlISERERlmtarZaIiAji4+OrfCY+Pt6qPEBkZKSlfGpqKhkZGVZlDAYDYWFhl31PqEjgzZtfvlVaUlKC0Wi0ejWUyvHm7q0MkpiFaOQ0Gg3TI67jzQd64qjV8P3edEYvl81KmhpVk3N2djYmkwlfX1+r676+vmRkZFT5TEZGxhXLV36tyXseO3aM999/n4kTJ1421nnz5mEwGCyvNm3aXLlydahyvFm6tIVoOoaFtmHVI/3w0Duy88QF7l+8nRPZhWqHJRqI6mPOajtz5gy33347w4YN47HHHrtsuVmzZpGXl2d5nTp1qsFi3JEq65uFaIoGdvThm8kDaOXlQmp2Ifd9uI2kkzlqhyUagKrJ2cfHBwcHBzIzM62uZ2Zm4ufnV+Uzfn5+Vyxf+bU675mens5NN93EgAEDWLZs2RVj1el0eHp6Wr0awukLRZzKuYiDVkPfdtJyFqKp6eTrwbdTBtDTshY6QZZaNQGqJmdnZ2dCQkKIi4uzXDObzcTFxREeHl7lM+Hh4VblATZt2mQpHxQUhJ+fn1UZo9FIQkKC1XueOXOGG2+8kZCQEFasWIFWa5udCJVd2j1bG3DTOaocjRBCDS099KyZ0J9bu/pSWm5m6upkPtxyDJXn84p6pHpGio6O5qOPPmLVqlUcOnSIyZMnU1hYyLhx4wAYM2YMs2bNspSfNm0asbGxLFiwgJSUFF566SV27drF1KlTgb8mU0yfzquvvsr69evZv38/Y8aMISAggKioKODvxBwYGMhbb73FuXPnyMjIuOyYtJosR0RKl7YQTZqrsyNLHgrhkYFBAMyPPcyMr/bKudCNlOpNsREjRnDu3DnmzJlDRkYGwcHBxMbGWiZ0paWlWbVqBwwYwOrVq3nhhReYPXs2nTp1IiYmhu7du1vKzJw5k8LCQiZMmEBubi6DBg0iNjYWvV4PVLS0jx07xrFjx2jdurVVPLb0SVRRFDnsQghh4aDVMOfurrT1duWVDQf5JvkMf2YXsmx0CL6eerXDE3VI9XXO9qoh1jmnnS/iX2/+gpODhr1zb8PVWfXPUkIIG7HtWDZTVu8mt6iMlh46lo0JJbiNl9phNTmNcp2zuLLKVnOv1l6SmIUQVgZ29OG7KQO5ztedrPwShi+N55vdp9UOS9QRSc42LF66tIUQV9DW241vHh9IRJeKiWLRX+3l5e//oMxkVjs0cY0kOdsoRVH+sfmIJGchRNXcdY4sGx3CEzd3BGDFthOMXLaDTGOxypGJayHJ2UadPF9EhrEYZwctIW2bqR2OEMKGabUaZtx2PctGh+Chc2TXyQsMWfib5QO+sD+SnG1UZZd2cKCXbHgvhKiW27r58f0Tg+js50F2QSkPLU9g6a9/2tQqFFE9kpxtlHRpCyFqo52PG98+PpD7e7fCZFaY90MKEz5LIrdIDs6wJ5KcbZDV+mZJzkKIGnJxdmDB8F78J6o7Tg4aNh3M5M73fmPnCdmX215IcrZBx7MLycovwdlRS+9AL7XDEULYIY1Gw+j+bfn28YEE+biRnlfMiKXxLIw7isks3dy2TpKzDars0u4j481CiGvUvZWB758YxP29W2FW4O1NRxj1sczmtnWSnG3Q313aPipHIoRoDNx1jrw9IpgFw3rh6uzAjuM5RL67lY37z6odmrgMSc42pmK8+a/DLmTzESFEHRoa0poNTwyiW4AnuUVlPP7FbqavSSavqEzt0MT/kORsY45lFZBdUILOUUuvNga1wxFCNDLtW7jz7eMDmXpTR7QaiNmTTuS7W/nt6Dm1QxP/IMnZxlR2aYe2a4bOUcabhRB1z9lRy9OR1/P15AG083Ylw1jM6OWJzPnuAIUl5WqHJ5DkbHPiZQmVEKKB9AlsxsZpN/BQ/0AAPo0/yW3vbGXL4SyVIxOSnG2I2fz3eLNsPiKEaAiuzo68GtWDTx/pRysvF87kXuThFTt5au0ecgpl4xK1SHK2IUezCsgpLMXFyYGerb3UDkcI0YT867oW/PTUv3hkYBAaDXybfIaIt3/luz1nZPtPFUhytiHxf2YDFePNzo7yVyOEaFhuOkfm3N2VbyYP4HpfD3IKS5m2Zg8PLU/gaGa+2uE1KZIBbEjleLN0aQsh1NQ7sBnfPzGIGbdeh7Ojlm3HznPHe7/x6oaD5BfLsquGIMnZRpjNCgmpsr5ZCGEbnB21PHFLJ35+ajC3dvWl3Kzw8e+p3PTWr/w36TRm2QK0XklythEpGfnkFpXh5uxAj1ayvlkIYRsCvV35aEwoK8f1JcjHjeyCEmas28t9H26T86LrkSRnGxFvWd/cHCcH+WsRQtiWG69vSez0G3j29s64Ojuw93QeIz/awbgViaRkGNUOr9GRLGAjLPtpS5e2EMJG6RwdmHxjB3595iZG92+Lo1bDL4fPccd7vzHjq72cyb2odoiNhiRnG2AyKyTI5iNCCDvRwkPHf6K6syl6MEN6+KMo8N/dp7nxzV+Y9c1+TuUUqR2i3ZPkbAMOnTViLC7HXedItwBPtcMRQohqCfJxY9GoPsRMGUh4e2/KTApfJqZx41tbeHrdXlKzC9UO0W5JcrYBlV3a/YKa4yjjzUIIOxPcxosvJ/Tnq4nh3NDJB5NZ4euk09yyYAtPfpnM/tN5aododxzVDkBgmfEoXdpCCHvWL6g5n40PY3faBT7YfIzNKVms35vO+r3phLZtxriBQUR285VGSDVIclZZuclMYqrspy2EaDz6BDbjk4f7cuBMHh/9dpz/23eWXScvsOvkBQIMekaHt2N4aGu83XVqh2qzNIpsmlorRqMRg8FAXl4enp61HyfedzqXez7YhqfekeQ5t+Gg1dRhlEIIob5MYzGf7zjJFwlplsM0HLUabunSkmEhbbjx+hZ225quq1zwv6TlrLLKLu1+Qd6SmIUQjZKvp54Zt13PlJs6sn5POp8nnGTf6Tx+/COTH//IxMddx9A+rbg3uBVd/D3QaOR3oSRnlf29n3ZzlSMRQoj6pXdyYHjfNgzv24aUDCNf7zrNt8lnyC4oYenW4yzdepy23q7c3s2P27v70au1F9om2miRbu1aqouujHKTmV4v/0RhqYn/e3IQ3QJk204hRNNSZjLzS0oW/919mi2Hz1FSbrbc8zfouaVLSwZ1bEF4B28MLk4qRlq1+urWtolO/kWLFtGuXTv0ej1hYWEkJiZesfy6devo3Lkzer2eHj16sHHjRqv7iqIwZ84c/P39cXFxISIigqNHj1qVycnJYdSoUXh6euLl5cX48eMpKCio87pdyf4zeRSWmjC4ONHFT9Y3CyGaHicHLbd182Pp6FB2v3grH47qw929AnBzduBsXjGf70hj0udJ9H7lJ6IWbeOtHw+z/Vh2oz8dS/Vu7bVr1xIdHc2SJUsICwvj3XffJTIyksOHD9OyZctLym/fvp2RI0cyb9487rrrLlavXk1UVBS7d++me/fuAMyfP5+FCxeyatUqgoKCePHFF4mMjOTgwYPo9XoARo0axdmzZ9m0aRNlZWWMGzeOCRMmsHr16gare2WXdlhQ8ybbdSOEEJXcdI7c2cOfO3v4U1xmYtuxbLYeOcfvx7L581whe07lsudULh/8cgyNBtr7uNGrtRc9Wxvo2caLji3d8dTbXuu6NlTv1g4LC6Nv37588MEHAJjNZtq0acMTTzzBc889d0n5ESNGUFhYyIYNGyzX+vfvT3BwMEuWLEFRFAICApgxYwZPP/00AHl5efj6+rJy5UoefPBBDh06RNeuXdm5cyehoaEAxMbGcuedd3L69GkCAgKuGndddGWM+SSRrUfOMffurowbGFSr9xBCiKbgbN5Fth07z7Zj2SSm5lx2H28fdx3tfdwI8nEjqIUb/gY9Ldx1+Hjo8HZzppmrc502hhrlbO3S0lKSkpKYNWuW5ZpWqyUiIoL4+Pgqn4mPjyc6OtrqWmRkJDExMQCkpqaSkZFBRESE5b7BYCAsLIz4+HgefPBB4uPj8fLysiRmgIiICLRaLQkJCdx3332X/NySkhJKSkos3xuN134Ky9O3XUf/9s256fpLewiEEEL8zd/gwgMhrXkgpDUA2QUl7Dudy95Teew7ncuBdCPn8kvILqh4JZ7IqfJ9HLQaDC5OuDg54OrsQP/23vwnqntDVqVaVE3O2dnZmEwmfH19ra77+vqSkpJS5TMZGRlVls/IyLDcr7x2pTL/22Xu6OhI8+bNLWX+17x583j55ZerWbPq6dnai56tver0PYUQoinwcddxc2dfbu789+/6/OIyTmQXcTy7gNTsQk5kF5Jp/DthXygqw2RWLGutAdp6u6oR/lWpPuZsL2bNmmXVYjcajbRp00bFiIQQQvyTh96JHq0N9Ghd9cqXMpOZnMJS8i6WUVRqoqikHE8bnAEOKidnHx8fHBwcyMzMtLqemZmJn59flc/4+fldsXzl18zMTPz9/a3KBAcHW8pkZWVZvUd5eTk5OTmX/bk6nQ6dTraaE0IIe+XkoMXXU4+vp17tUK5K1aVUzs7OhISEEBcXZ7lmNpuJi4sjPDy8ymfCw8OtygNs2rTJUj4oKAg/Pz+rMkajkYSEBEuZ8PBwcnNzSUpKspTZvHkzZrOZsLCwOqufEEIIUSuKytasWaPodDpl5cqVysGDB5UJEyYoXl5eSkZGhqIoijJ69Gjlueees5Tftm2b4ujoqLz11lvKoUOHlLlz5ypOTk7K/v37LWVef/11xcvLS/nuu++Uffv2Kffee68SFBSkXLx40VLm9ttvV3r37q0kJCQov//+u9KpUydl5MiR1Y47Ly9PAZS8vLw6+FMQQghhj+orF6g+5jxixAjOnTvHnDlzyMjIIDg4mNjYWMuErrS0NLTavxv4AwYMYPXq1bzwwgvMnj2bTp06ERMTY1njDDBz5kwKCwuZMGECubm5DBo0iNjYWMsaZ4AvvviCqVOncsstt6DVahk6dCgLFy5suIoLIYQQl6H6Omd7VV9r24QQQtiPRr19pxBCCCH+JslZCCGEsDGSnIUQQggbo/qEMHtVOVRfF9t4CiGEsE+VOaCup29Jcq6l/Px8ANklTAghBPn5+RgMVe9MVhsyW7uWzGYz6enpeHh4oNHU7oSTyi1AT5061WhmfEudbF9jqw80vjo1tvpA46tTZX3S0tLQaDQEBARYLfu9VtJyriWtVkvr1q3r5L08PT0bxT/Wf5I62b7GVh9ofHVqbPWBxlcng8FQL/WRCWFCCCGEjZHkLIQQQtgYSc4q0ul0zJ07t1GddiV1sn2NrT7Q+OrU2OoDja9O9V0fmRAmhBBC2BhpOQshhBA2RpKzEEIIYWMkOQshhBA2RpKzEEIIYWMkOato0aJFtGvXDr1eT1hYGImJiWqHVC3z5s2jb9++eHh40LJlS6Kiojh8+LBVmeLiYqZMmYK3tzfu7u4MHTqUzMxMlSKumddffx2NRsP06dMt1+yxPmfOnOGhhx7C29sbFxcXevTowa5duyz3FUVhzpw5+Pv74+LiQkREBEePHlUx4iszmUy8+OKLBAUF4eLiQocOHfjPf/5jtaexrddp69at3H333QQEBKDRaIiJibG6X534c3JyGDVqFJ6ennh5eTF+/HgKCgoasBZ/u1J9ysrKePbZZ+nRowdubm4EBAQwZswY0tPTrd7DluoDV/87+qdJkyah0Wh49913ra7XRZ0kOatk7dq1REdHM3fuXHbv3k2vXr2IjIwkKytL7dCu6tdff2XKlCns2LGDTZs2UVZWxm233UZhYaGlzFNPPcX333/PunXr+PXXX0lPT+f+++9XMerq2blzJ0uXLqVnz55W1+2tPhcuXGDgwIE4OTnxww8/cPDgQRYsWECzZs0sZebPn8/ChQtZsmQJCQkJuLm5ERkZSXFxsYqRX94bb7zB4sWL+eCDDzh06BBvvPEG8+fP5/3337eUsfU6FRYW0qtXLxYtWlTl/erEP2rUKP744w82bdrEhg0b2Lp1KxMmTGioKli5Un2KiorYvXs3L774Irt37+abb77h8OHD3HPPPVblbKk+cPW/o0rffvstO3bsICAg4JJ7dVInRaiiX79+ypQpUyzfm0wmJSAgQJk3b56KUdVOVlaWAii//vqroiiKkpubqzg5OSnr1q2zlDl06JACKPHx8WqFeVX5+flKp06dlE2bNimDBw9Wpk2bpiiKfdbn2WefVQYNGnTZ+2azWfHz81PefPNNy7Xc3FxFp9MpX375ZUOEWGNDhgxRHnnkEatr999/vzJq1ChFUeyvToDy7bffWr6vTvwHDx5UAGXnzp2WMj/88IOi0WiUM2fONFjsVfnf+lQlMTFRAZSTJ08qimLb9VGUy9fp9OnTSqtWrZQDBw4obdu2Vd555x3Lvbqqk7ScVVBaWkpSUhIRERGWa1qtloiICOLj41WMrHby8vIAaN68OQBJSUmUlZVZ1a9z584EBgbadP2mTJnCkCFDrOIG+6zP+vXrCQ0NZdiwYbRs2ZLevXvz0UcfWe6npqaSkZFhVSeDwUBYWJjN1mnAgAHExcVx5MgRAPbu3cvvv//OHXfcAdhnnf6pOvHHx8fj5eVFaGiopUxERARarZaEhIQGj7mm8vLy0Gg0eHl5AfZZH7PZzOjRo3nmmWfo1q3bJffrqk5y8IUKsrOzMZlM+Pr6Wl339fUlJSVFpahqx2w2M336dAYOHEj37t0ByMjIwNnZ2fI/YCVfX18yMjJUiPLq1qxZw+7du9m5c+cl9+yxPsePH2fx4sVER0cze/Zsdu7cyZNPPomzszNjx461xF3Vv0FbrdNzzz2H0Wikc+fOODg4YDKZeO211xg1ahSAXdbpn6oTf0ZGBi1btrS67+joSPPmzW2+jsXFxTz77LOMHDnSclCEPdbnjTfewNHRkSeffLLK+3VVJ0nO4ppMmTKFAwcO8Pvvv6sdSq2dOnWKadOmsWnTJvR6vdrh1Amz2UxoaCj/7//9PwB69+7NgQMHWLJkCWPHjlU5utr56quv+OKLL1i9ejXdunVjz549TJ8+nYCAALutU1NRVlbG8OHDURSFxYsXqx1OrSUlJfHee++xe/fuWh8VXF3Sra0CHx8fHBwcLpntm5mZiZ+fn0pR1dzUqVPZsGEDv/zyi9XxmX5+fpSWlpKbm2tV3lbrl5SURFZWFn369MHR0RFHR0d+/fVXFi5ciKOjI76+vnZVHwB/f3+6du1qda1Lly6kpaUBWOK2p3+DzzzzDM899xwPPvggPXr0YPTo0Tz11FPMmzcPsM86/VN14vfz87tk0mh5eTk5OTk2W8fKxHzy5Ek2bdpkdbyivdXnt99+Iysri8DAQMvvipMnTzJjxgzatWsH1F2dJDmrwNnZmZCQEOLi4izXzGYzcXFxhIeHqxhZ9SiKwtSpU/n222/ZvHkzQUFBVvdDQkJwcnKyqt/hw4dJS0uzyfrdcsst7N+/nz179lheoaGhjBo1yvLf9lQfgIEDB16yvO3IkSO0bdsWgKCgIPz8/KzqZDQaSUhIsNk6FRUVXXKYvYODA2azGbDPOv1TdeIPDw8nNzeXpKQkS5nNmzdjNpsJCwtr8JivpjIxHz16lJ9//hlvb2+r+/ZWn9GjR7Nv3z6r3xUBAQE888wz/Pjjj0Ad1qn289jEtVizZo2i0+mUlStXKgcPHlQmTJigeHl5KRkZGWqHdlWTJ09WDAaDsmXLFuXs2bOWV1FRkaXMpEmTlMDAQGXz5s3Krl27lPDwcCU8PFzFqGvmn7O1FcX+6pOYmKg4Ojoqr732mnL06FHliy++UFxdXZXPP//cUub1119XvLy8lO+++07Zt2+fcu+99ypBQUHKxYsXVYz88saOHau0atVK2bBhg5Kamqp88803io+PjzJz5kxLGVuvU35+vpKcnKwkJycrgPL2228rycnJltnL1Yn/9ttvV3r37q0kJCQov//+u9KpUydl5MiRNlef0tJS5Z577lFat26t7Nmzx+p3RUlJiU3W52p1qsr/ztZWlLqpkyRnFb3//vtKYGCg4uzsrPTr10/ZsWOH2iFVC1Dla8WKFZYyFy9eVB5//HGlWbNmiqurq3LfffcpZ8+eVS/oGvrf5GyP9fn++++V7t27KzqdTuncubOybNkyq/tms1l58cUXFV9fX0Wn0ym33HKLcvjwYZWivTqj0ahMmzZNCQwMVPR6vdK+fXvl+eeft/pFb+t1+uWXX6r8f2fs2LGKolQv/vPnzysjR45U3N3dFU9PT2XcuHFKfn6+CrW5cn1SU1Mv+7vil19+scn6XK1OVakqOddFneTISCGEEMLGyJizEEIIYWMkOQshhBA2RpKzEEIIYWMkOQshhBA2RpKzEEIIYWMkOQshhBA2RpKzEEIIYWMkOQshhBA2RpKzEAKAc+fOMXnyZAIDA9HpdPj5+REZGcm2bdsA0Gg0xMTEqBukEE2EHBkphABg6NChlJaWsmrVKtq3b09mZiZxcXGcP39e7dCEaHJk+04hBLm5uTRr1owtW7YwePDgS+63a9eOkydPWr5v27YtJ06cAOC7777j5Zdf5uDBg5azlZ9//nkcHSs++2s0Gj788EPWr1/Pli1b8Pf3Z/78+TzwwAMNUjch7JF0awshcHd3x93dnZiYGEpKSi65v3PnTgBWrFjB2bNnLd//9ttvjBkzhmnTpnHw4EGWLl3KypUree2116yef/HFFxk6dCh79+5l1KhRPPjggxw6dKj+KyaEnZKWsxACgP/+97889thjXLx4kT59+jB48GAefPBBevbsCVS0gL/99luioqIsz0RERHDLLbcwa9Ysy7XPP/+cmTNnkp6ebnlu0qRJLF682FKmf//+9OnThw8//LBhKieEnZGWsxACqBhzTk9PZ/369dx+++1s2bKFPn36sHLlyss+s3fvXl555RVLy9vd3Z3HHnuMs2fPUlRUZCkXHh5u9Vx4eLi0nIW4ApkQJoSw0Ov13Hrrrdx66628+OKLPProo8ydO5eHH364yvIFBQW8/PLL3H///VW+lxCidqTlLIS4rK5du1JYWAiAk5MTJpPJ6n6fPn04fPgwHTt2vOSl1f7962XHjh1Wz+3YsYMuXbrUfwWEsFPSchZCcP78eYYNG8YjjzxCz5498fDwYNeuXcyfP597770XqJixHRcXx8CBA9HpdDRr1ow5c+Zw1113ERgYyAMPPIBWq2Xv3r0cOHCAV1991fL+69atIzQ0lEGDBvHFF1+QmJjI8uXL1aquEDZPJoQJISgpKeGll17ip59+4s8//6SsrIw2bdowbNgwZs+ejYuLC99//z3R0dGcOHGCVq1aWZZS/fjjj7zyyiskJyfj5ORE586defTRR3nssceAiglhixYtIiYmhq1bt+Lv788bb7zB8OHDVayxELZNkrMQol5VNctbCHFlMuYshBBC2BhJzkIIIYSNkQlhQoh6JSNnQtSctJyFEEIIGyPJWQghhLAxkpyFEEIIGyPJWQghhLAxkpyFEEIIGyPJWQghhLAxkpyFEEIIGyPJWQghhLAxkpyFEEIIG/P/Ac1/dd9vNGhpAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 500x300 with 1 Axes>"
       ]
@@ -324,7 +324,7 @@
     "plt.ylabel(\"Learning rate\")\n",
     "plt.xlabel(\"Step\")\n",
     "plt.plot(range(total_training_steps), track_lrs)\n",
-    "#plt.tight_layout(); plt.savefig(\"2.pdf\")\n",
+    "plt.tight_layout(); plt.savefig(\"2.pdf\")\n",
     "plt.show()"
    ]
   },
@@ -428,7 +428,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor(0.0373)\n"
+      "tensor(0.0411)\n"
      ]
     }
    ],
@@ -464,7 +464,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor(0.0166)\n"
+      "tensor(0.0185)\n"
      ]
     }
    ],
@@ -579,47 +579,47 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ep 1 (Iter 000000): Train loss 10.914, Val loss 10.940\n",
-      "Ep 1 (Iter 000005): Train loss 8.415, Val loss 8.846\n",
+      "Ep 1 (Iter 000000): Train loss 10.934, Val loss 10.939\n",
+      "Ep 1 (Iter 000005): Train loss 8.529, Val loss 8.843\n",
       "Every effort moves you,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,\n",
-      "Ep 2 (Iter 000010): Train loss 6.291, Val loss 6.838\n",
-      "Ep 2 (Iter 000015): Train loss 6.057, Val loss 6.834\n",
+      "Ep 2 (Iter 000010): Train loss 6.400, Val loss 6.825\n",
+      "Ep 2 (Iter 000015): Train loss 6.116, Val loss 6.861\n",
       "Every effort moves you,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,\n",
-      "Ep 3 (Iter 000020): Train loss 6.630, Val loss 7.308\n",
-      "Ep 3 (Iter 000025): Train loss 5.847, Val loss 6.838\n",
-      "Every effort moves you, I, I, I, I, I, I, I, I, I had, I, I, I, I, I, I, I had, I, I, I, I, I had, I had, I\n",
-      "Ep 4 (Iter 000030): Train loss 8.879, Val loss 9.904\n",
-      "Ep 4 (Iter 000035): Train loss 5.453, Val loss 6.866\n",
+      "Ep 3 (Iter 000020): Train loss 6.033, Val loss 6.904\n",
+      "Ep 3 (Iter 000025): Train loss 5.915, Val loss 6.851\n",
+      "Every effort moves you, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the\n",
+      "Ep 4 (Iter 000030): Train loss 5.375, Val loss 6.815\n",
+      "Ep 4 (Iter 000035): Train loss 5.001, Val loss 6.701\n",
       "Every effort moves you.                                                 \n",
-      "Ep 5 (Iter 000040): Train loss 5.454, Val loss 6.880\n",
-      "Every effort moves you. \"I the the the the the the the the the the a-- the, and I had been, and I had been, and I had been his the picture, and I had been, and, and I had been, and I\n",
-      "Ep 6 (Iter 000045): Train loss 5.279, Val loss 6.642\n",
-      "Ep 6 (Iter 000050): Train loss 4.472, Val loss 6.542\n",
-      "Every effort moves you, and, and in the picture--I to me--as of the picture. Gisburn, and, and, and to see of the picture--as of the picture--as, and, and, and, and, and, and\n",
-      "Ep 7 (Iter 000055): Train loss 3.858, Val loss 6.370\n",
-      "Ep 7 (Iter 000060): Train loss 2.623, Val loss 6.249\n",
-      "Every effort moves you know the fact that a little of the picture to the fact of the last of the house of the fact of the fact of the fact of Jack's it.    \"Oh, I had the donkey. \"Oh, I was\n",
-      "Ep 8 (Iter 000065): Train loss 1.856, Val loss 6.311\n",
-      "Ep 8 (Iter 000070): Train loss 2.269, Val loss 6.284\n",
-      "Every effort moves you know,\" was not that my dear, the picture, and Mrs.                                    \n",
-      "Ep 9 (Iter 000075): Train loss 1.585, Val loss 6.299\n",
-      "Ep 9 (Iter 000080): Train loss 1.382, Val loss 6.319\n",
-      "Every effort moves you?\"  \"Yes--quite insensible to the irony. Gisburn. Gisburn's past!  \"Oh, and the moment--as Jack himself, and; and as I had been the man of the hour. \n",
-      "Ep 10 (Iter 000085): Train loss 1.089, Val loss 6.391\n",
-      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back his head to look up at the honour being _mine_--because he had always his\n",
-      "Ep 11 (Iter 000090): Train loss 0.874, Val loss 6.409\n",
-      "Ep 11 (Iter 000095): Train loss 0.432, Val loss 6.413\n",
+      "Ep 5 (Iter 000040): Train loss 4.993, Val loss 6.641\n",
+      "Every effort moves you of the                                                \n",
+      "Ep 6 (Iter 000045): Train loss 3.872, Val loss 6.508\n",
+      "Ep 6 (Iter 000050): Train loss 3.636, Val loss 6.466\n",
+      "Every effort moves you know to see and in spite of his pictures--I looked up the end of his pictures--and by me to see it was.                       \n",
+      "Ep 7 (Iter 000055): Train loss 3.803, Val loss 6.266\n",
+      "Ep 7 (Iter 000060): Train loss 1.846, Val loss 6.334\n",
+      "Every effort moves you know it was his pictures--I must have to the fact the picture--had not till, when I felt to me to have that, in the moment--as Jack himself, as once one had been the fact, and his own he was his\n",
+      "Ep 8 (Iter 000065): Train loss 1.565, Val loss 6.278\n",
+      "Ep 8 (Iter 000070): Train loss 2.342, Val loss 6.286\n",
+      "Every effort moves you know,\" was one of the axioms he said--as such--had not to my work, and in fact, and that, in the moment--as Jack himself, as once one had longed to say: \"Be dissatisfied with your\n",
+      "Ep 9 (Iter 000075): Train loss 1.043, Val loss 6.323\n",
+      "Ep 9 (Iter 000080): Train loss 0.780, Val loss 6.375\n",
+      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  \"Oh, I felt him back his head to look up at the honour being _mine_--because he had always his\n",
+      "Ep 10 (Iter 000085): Train loss 0.493, Val loss 6.467\n",
+      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"     \"Oh, and I looked at the donkey again. I saw that, and down the room, when I\n",
+      "Ep 11 (Iter 000090): Train loss 0.434, Val loss 6.547\n",
+      "Ep 11 (Iter 000095): Train loss 0.250, Val loss 6.602\n",
+      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"         He placed them at my elbow and as I turned, my eye fell on a small picture\n",
+      "Ep 12 (Iter 000100): Train loss 0.242, Val loss 6.631\n",
+      "Ep 12 (Iter 000105): Train loss 0.237, Val loss 6.630\n",
       "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back his head to look up at the sketch of the donkey. \"There were days when I\n",
-      "Ep 12 (Iter 000100): Train loss 0.389, Val loss 6.485\n",
-      "Ep 12 (Iter 000105): Train loss 0.405, Val loss 6.521\n",
+      "Ep 13 (Iter 000110): Train loss 0.144, Val loss 6.664\n",
+      "Ep 13 (Iter 000115): Train loss 0.118, Val loss 6.677\n",
       "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back his head to look up at the sketch of the donkey. \"There were days when I\n",
-      "Ep 13 (Iter 000110): Train loss 0.293, Val loss 6.543\n",
-      "Ep 13 (Iter 000115): Train loss 0.204, Val loss 6.558\n",
+      "Ep 14 (Iter 000120): Train loss 0.109, Val loss 6.686\n",
+      "Ep 14 (Iter 000125): Train loss 0.109, Val loss 6.698\n",
       "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back his head to look up at the sketch of the donkey. \"There were days when I\n",
-      "Ep 14 (Iter 000120): Train loss 0.239, Val loss 6.576\n",
-      "Ep 14 (Iter 000125): Train loss 0.183, Val loss 6.580\n",
-      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back his head to look up at the sketch of the donkey. \"There were days when I\n",
-      "Ep 15 (Iter 000130): Train loss 0.165, Val loss 6.580\n",
+      "Ep 15 (Iter 000130): Train loss 0.101, Val loss 6.707\n",
       "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back his head to look up at the sketch of the donkey. \"There were days when I\n"
      ]
     }
@@ -668,7 +668,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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TOSz48Swqa7RCp0YIMSdMQH5+fmzmzJn891qtlrm6urKIiIh648eOHcuCgoL0jqlUKjZ9+nTGGGM6nY4plUq2cuVKvr2oqIhJpVK2fft2xhhjaWlpDABLTk7mYw4ePMg4jmPXr19njDG2e/duJhaLmVar5WP27dvHOI5jVVVVDe6fRqNhAJhGo2nwOY/D69+mMI95UezrY1eETqVZ0+l0bO3hTOY5P4p5zItiI788zm4WlQudFiHEyBqrFgg2Aq+qqkJKSgoCAgL4YyKRCAEBAYiPj6/3nPj4eL14AAgMDOTjs7KyoFar9WLkcjlUKhUfEx8fD4VCAV9fXz4mICAAIpEIiYmJAIDevXtDJBJhy5Yt0Gq10Gg0+N///oeAgABYWpr/oi+6C5tp4DgOrz/VDlsm94Hc2hKnc4rw3JrjSP6TtpoRQh5MsAKen58PrVYLZ2dnvePOzs5Qq9X1nqNWq+8bX/f1QTFOTk567WKxGA4ODnyMl5cXfv31V7z//vuQSqVQKBS4du0adu3add8+VVZWori4WO9limgK3bQ81dEJ+97oh05Ke+SXViJ4YwK+if8TjNFWM0LIvQm+iM0UqdVqTJs2DZMmTUJycjKOHDkCiUSCF1988b6/VCMiIiCXy/mXm5vbY8y6YcqqalBwh/aAmxqPlrb48fW+eK67C2p0DIv3XsC7359FRTVdFyeE1E+wAu7o6AgLCwvk5ubqHc/NzYVSqaz3HKVSed/4uq8Pivn3IrmamhoUFhbyMWvXroVcLseKFSvg4+ODgQMH4ttvv0VMTAw/zV6fBQsWQKPR8K+cnJwH/Rgeu+t/jb5pD7jpsZGIsSbYB+8/2wkiDvg+5Rpe3pSA/NJKoVMjhJggwQq4RCJB7969ERMTwx/T6XSIiYmBv79/vef4+/vrxQNAdHQ0H+/l5QWlUqkXU1xcjMTERD7G398fRUVFSElJ4WNiY2Oh0+mgUqkAAGVlZRCJ9H80FhYWfI73IpVKIZPJ9F6mhh5iYto4jkPYwCfwzRQVZFZipGYXYeSXJ5CuNs3LMYQQARl1SZyBduzYwaRSKYuMjGRpaWksLCyMKRQKplarGWOMTZgwgc2fP5+PP3HiBBOLxWzVqlXs4sWLLDw8nFlaWrJz587xMcuWLWMKhYLt3buXnT17lo0cOZJ5eXmx8vK/V/cOGzaM+fj4sMTERHb8+HHWvn17FhwczLfHxMQwjuPYkiVL2KVLl1hKSgoLDAxkHh4erKysrMH9M8VV6N/EZTGPeVHs1a3JDw4mgrqcV8IGrYhlHvOiWJdFB1nMRbXQKRFCHkJj1QJBCzhjjK1Zs4a5u7sziUTC/Pz8WEJCAt82aNAgNmnSJL34Xbt2sQ4dOjCJRMK6du3K9u/fr9eu0+nYokWLmLOzM5NKpWzIkCEsIyNDL6agoIAFBwczOzs7JpPJWGhoKCspKdGL2b59O/Px8WG2trasVatWbMSIEezixYsG9c0UC/jH+9OYx7wo9p9954VOhTRAYWklG7chjnnMi2Je82u3/ul0OqHTIoQYoLFqAccYLXVtLMXFxZDL5dBoNCYznT5zWyr2n7uJRc91wdT+XkKnQxqgqkaHxXvPY0dy7ZqKYD93fDiyKywtaA0qIeagsWoB/QZoZmgPuPmRiEWIGOONhUGdwXHA9qRsTPq/JGjKqoVOjRAiICrgzUzdIjY3WsRmVjiOw6sD2mLTBF/YSiwQ90cBRn91AlcL7gidGiFEIFTAm5F/7gFvTSNwsxTQxRnfz+iL1gprXMm/gzFfxeF0TpHQaRFCBEAFvBmp2wMuoz3gZq2ziww/zeyLbq1lKLhThfEb4xGdlvvgEwkhTQoV8GaE9oA3HU72VtgZ5o+nOrZCRbUO0/93Ev+L/1PotAghjxEV8GaEFrA1LbZSMb6e6IvxfdygY8CivRew7GA6dDraWEJIc0AFvBmhEXjTI7aoXaH+9jMdAADrj/yBOTtP07PFCWkGqIA3I/QUsqaJ4zi8OaQ9PnmpB8QiDvvO3MDEzbTNjJCmjgp4M5JDU+hN2gu922BLaB/YScVIzCrEi+vjcKOoXOi0CCGNhAp4M0JT6E3fgPatsPs1fyhlVsjMK8WL6+JwOa9U6LQIIY2ACngzcaeyBoW0B7xZ6Owiww+v90XbVra4oanAS+vjcCr7ttBpEUKM7KEKeE1NDX777Tds2LABJSUlAIAbN26gtJT+0jdV14toD3hz0lphje9f64sebeS4XVaNlzcl4silW0KnRQgxIoML+NWrV+Ht7Y2RI0di5syZuHWr9pfC8uXL8c477xg9QWIcf28ho+nz5sLBVoLvpj2JAe0dUV6txdTIZOw9fV3otAghRmJwAZ89ezZ8fX1x+/ZtWFv/PRU7evRoxMTEGDU5Yjy0Ar15spWKsXlSHzzfwxU1OobZO05jy4ksodMihBiB2NATjh07hri4OEgkEr3jnp6euH6d/ro3VbSArfmSiEX4YlxPONhYYmv8VSz5OQ2Fd6ow95kO4DhO6PQIIQ/J4BG4TqeDVnv3TSKuXbsGe3t7oyRFjI/uwta8iUQc/jOiK3/DlzWxl/H+T+ehpbu2EWK2DC7gQ4cOxeeff85/z3EcSktLER4ejmeffdaYuREjoil0UnfDl49He0P013PF5+w8jWqtTujUCCEPweAp9E8++QSBgYHo0qULKioq8PLLLyMzMxOOjo7Yvn17Y+RIjICm0Emdl1XukFtbYvaOU/j5zA2UV9Xgy5d7wcrSQujUCCEGMLiAt2nTBmfOnMHOnTtx5swZlJaWYurUqQgJCdFb1EZMB+0BJ/8W1N0FNhILvPZtCn67mIcpkcnYNNEXtlKDfyUQQgTCMcYMugh29OhR9O3bF2Kx/j/0mpoaxMXFYeDAgUZN0JwVFxdDLpdDo9FAJpMJlsel3BIM/ewoZFZinP1PoGB5ENMT/0cBXt2ajDtVWvi4KxA52Q9yG7pPACHG1Fi1wOBr4IMHD0ZhYeFdxzUaDQYPHmyUpIhx0R5wci/+T7TEtmlPQm5tiVPZRRi/KQH5pZVCp0UIaQCDCzhjrN6tJwUFBbC1tTVKUsS4aAEbuZ+ebgrsnP4kHO2kuHizGGM3xNNDUAgxAw2+4DVmzBgAtStZJ0+eDKlUyrdptVqcPXsWffv2NX6G5JHVFXA3BxqBk/p1Usqwa/qTeOXrRFy5dQcvrY/Hd9NU8GhJf5QTYqoaPAKXy+WQy+VgjMHe3p7/Xi6XQ6lUIiwsDN9++21j5koeEu0BJw3RtpUdds/oC8+WNrheVI6X1sfjUm6J0GkRQu6hwSPwLVu2AKi949o777xD0+VmhLaQkYZqrbDGrtf8MXFzEtLVJRi7IR7fTPFD9zYKoVMjhPyLwdfAw8PDqXibGboGTgzhZG+FHWFPooebAkV/Pcks+c+7F64SQoT1UJs+v//+e+zatQvZ2dmoqqrSa0tNTTVKYsQ4aA84eRgKGwm2varC1MhkJGYVYuLmJGye7Iu+TzgKnRoh5C8Gj8BXr16N0NBQODs749SpU/Dz80PLli1x5coVDB8+vDFyJI+gbvQtt7aEzIr295KGs5OKsXWKH/840tAtyThKzxQnxGQYXMC/+uorbNy4EWvWrIFEIsF7772H6OhozJo1CxqNpjFyJI+AFrCRR2FlaYFNE33xdCcnVNbo8OrWk4hNzxU6LUIIHqKAZ2dn89vFrK2tUVJSu0p1woQJdC90E0TXv8mjsrK0wPpXemNoF2dUaXWY/r8U/HJBLXRahDR7BhdwpVLJ34nN3d0dCQkJAICsrCwYeFdW8hjQXdiIMUjEIqwN6YWg7i6o1jLM3JaK/WdvCp0WIc2awQX86aefxr59+wAAoaGheOutt/DMM89g3LhxGD16tNETJI+GRuDEWCwtRPhiXE+M6umKGh3Dm9tTsff0daHTIqTZMriAb9y4ER988AEAYObMmfi///s/dO7cGR9++CHWrVtncAJr166Fp6cnrKysoFKpkJSUdN/43bt3o1OnTrCysoK3tzcOHDig184Yw+LFi+Hi4gJra2sEBAQgMzNTL6awsBAhISGQyWRQKBSYOnUqSktL73qfVatWoUOHDpBKpWjdujWWLl1qcP+ERnvAiTGJLUT4ZGxPvNi7DXQMmLPzNL5PuSZ0WoQ0T8wA1dXVbMmSJSwnJ8eQ0+5px44dTCKRsP/7v/9jFy5cYNOmTWMKhYLl5ubWG3/ixAlmYWHBVqxYwdLS0tjChQuZpaUlO3fuHB+zbNkyJpfL2Z49e9iZM2fYiBEjmJeXFysvL+djhg0bxnr06MESEhLYsWPHWLt27VhwcLDeZ7355pusY8eObO/evezKlSvs5MmT7NdffzWofxqNhgFgGo3GoPOMqeeSX5jHvCiWdkO4HEjTo9Xq2IIfzzKPeVHMc34U+y7xqtApEWKyGqsWGFTAGWPM1taWZWVlGeXD/fz82MyZM/nvtVotc3V1ZREREfXGjx07lgUFBekdU6lUbPr06YwxxnQ6HVMqlWzlypV8e1FREZNKpWz79u2MMcbS0tIYAJacnMzHHDx4kHEcx65fv87HiMVilp6e/kj9E7qAl1RUM495UcxjXhTTlFcJkgNpunQ6HQvfe57/b2xrXJbQKRFikhqrFhg8hT5kyBAcOXLkkUf+VVVVSElJQUBAAH9MJBIhICAA8fHx9Z4THx+vFw8AgYGBfHxWVhbUarVejFwuh0ql4mPi4+OhUCjg6+vLxwQEBEAkEiExMREA8PPPP6Nt27aIioqCl5cXPD098eqrr9b7GNV/qqysRHFxsd5LSNdpDzhpRBzHIfz5Lpg2wAsAsHjvBXx97IrAWRHSfBh8J7bhw4dj/vz5OHfuHHr37n3XbVVHjBjRoPfJz8+HVquFs7Oz3nFnZ2ekp6fXe45ara43Xq1W8+11x+4X4+TkpNcuFovh4ODAx1y5cgVXr17F7t278c0330Cr1eKtt97Ciy++iNjY2Hv2KSIiAkuWLHlQ1x8b2gNOGhvHcXj/2c61q9QP/4GP9l+EjjGEDXxC6NQIafIMLuCvv/46AODTTz+9q43jOGi12kfPSmA6nQ6VlZX45ptv0KFDBwDA5s2b0bt3b2RkZKBjx471nrdgwQLMnTuX/764uBhubm6PJef60Ap08jhwHId3hnaEhUiE1TGZ+PhA7R/gVMQJaVwGT6HrdLp7vgwp3o6OjrCwsEBurv5dnXJzc6FUKus9R6lU3je+7uuDYvLy8vTaa2pqUFhYyMe4uLhALBbzxRsAOnfuDKD2Rjb3IpVKIZPJ9F5Coj3g5HHhOA5zn+mA2UPaAwA+PpCODUf+EDgrQpo2gwu4sUgkEvTu3RsxMTH8MZ1Oh5iYGPj7+9d7jr+/v148AERHR/PxXl5eUCqVejHFxcVITEzkY/z9/VFUVISUlBQ+JjY2FjqdDiqVCgDQr18/1NTU4I8//v4FdOnSJQCAh4fHo3T7saIROHnc3nqmA+YE1BbxiIPpWE9FnJDGY9QlcQbasWMHk0qlLDIykqWlpbGwsDCmUCiYWq1mjDE2YcIENn/+fD7+xIkTTCwWs1WrVrGLFy+y8PDwereRKRQKtnfvXnb27Fk2cuTIereR+fj4sMTERHb8+HHWvn17vW1kWq2W9erViw0cOJClpqaykydPMpVKxZ555hmD+if0KvTnVh9jHvOi2K8X1IJ8Pmm+Po++xK9O/+rwZaHTIURQJrONzNjWrFnD3N3dmUQiYX5+fiwhIYFvGzRoEJs0aZJe/K5du1iHDh2YRCJhXbt2Zfv379dr1+l0bNGiRczZ2ZlJpVI2ZMgQlpGRoRdTUFDAgoODmZ2dHZPJZCw0NJSVlJToxVy/fp2NGTOG2dnZMWdnZzZ58mRWUFBgUN+ELuC0B5wI6Yvf/i7iaw9nCp0OIYJprFrAMUY3MG8sxcXFkMvl0Gg0j/16eGllDbqF/wIAOPefobCnbWREAGtiMvFJdO3lp3cDO2Lm4HYCZ0TI49dYtUCwa+CkcdXtAVfYWFLxJoJ5c0h7vP1M7WLQlb9kYO3hywJnREjTYfA2snvdnITjOEilUkgkkkdOijw62gNOTMWbQ9qD44BVv17Cyl8ywBjDG0+3FzotQsyewQVcoVCA47h7trdp0waTJ09GeHg4RCIa4AuFX4GuoC1kRHhvPN0eHMdh5S8ZWPXrJTBWW9gJIQ/P4AIeGRmJDz74AJMnT4afnx8AICkpCVu3bsXChQtx69YtrFq1ClKpFO+//77REyYNQyNwYmrqrn+v/CWDvy5ORZyQh2dwAd+6dSs++eQTjB07lj/2/PPPw9vbGxs2bEBMTAzc3d2xdOlSKuACyimkPeDE9Mwc3A4cB6w4VFvEGYBZVMQJeSgGz3HHxcXBx8fnruM+Pj78A0P69+9/3zuWkcZ3rYjuwkZM0+tPtcO8YZ0AAJ9GX8IXv2UKnBEh5sngAu7m5obNmzffdXzz5s38fb8LCgrQokWLR8+OPDT+GrgDjcCJ6Znx1BOYP7y2iH/22yV8/tslgTMixPwYPIW+atUqvPTSSzh48CD69OkDADh58iTS09Px/fffAwCSk5Mxbtw442ZKGqykohpFZdUAgNYKKuDENL026AlwqL3l6ue/ZYKx2luxEkIaxuACPmLECKSnp2PDhg38/cGHDx+OPXv2wNPTEwAwY8YMoyZJDHO9iPaAE/MwfdAT4Ljah598EVM7lU5FnJCGMbiAA7UPDVm2bJmxcyFGco0WsBEzUvfYUSrihBjmoQp4UVERkpKSkJeXB51Op9c2ceJEoyRGHh6/hYz2gBMzETbwCXDgsPTARSrihDSQwQX8559/RkhICEpLSyGTyfRu6sJxHBVwE0CPESXmaNrAtgDAF3EG4K2A9ve9cRQhzZnBq9DffvttTJkyBaWlpSgqKsLt27f5V2FhYWPkSAxEBZyYq2kD2+KDZzsDAFbHZOKz3zJBz1sipH4GF/Dr169j1qxZsLGh6VlTRXvAiTmbNrAtFgZRESfkQQwu4IGBgTh58mRj5EKMhPaAE3P36gAq4oQ8iMHXwIOCgvDuu+8iLS0N3t7esLTU36Y0YsQIoyVHDEd7wElT8eqA2mviH+2/iNUxmQBjeOuZDnRNnJC/GFzAp02bBgD48MMP72rjOA5arfbRsyIPjfaAk6ZEr4jH1j5LnIo4IbUMLuD/3jZGTAvtASdNDRVxQupHD+xuYmgPOGmK9K6Jx17Gp9GX6Jo4afYaNAJfvXo1wsLCYGVlhdWrV983dtasWUZJjDwc2kJGmqp/jsTX/DUSn0sjcdKMNaiAf/bZZwgJCYGVlRU+++yze8ZxHEcFXGBUwElTRkWckL81qIBnZWXV+7+J6anbA+7mQFPopGl6dUBbcByH/0alUREnzRpdA29i/h6BUwEnTdfU/l5Y9FwXAMCa2Mv45Fe6Jk6aH4NXoWu1WkRGRiImJqbeh5nExsYaLTliGL094DSFTpq4qf29AAD/jUrDl4drR+JvD6WROGk+DC7gs2fPRmRkJIKCgtCtWzf6x2JC6kbfLWwsYSd9qAfNEWJWpvb3AgfgQyripBky+Lf8jh07sGvXLjz77LONkQ95BDR9TpqjKX+NxOuKOAPDO0M7UhEnTZ7B18AlEgnatWvXGLmQR8TvAafpc9LMTOnvhcV/XRNfe/gPrPo1g66JkybvoR4n+sUXX9A/DhNEW8hIczalvxfCn6ciTpoPg6fQjx8/jsOHD+PgwYPo2rXrXQ8z+fHHH42WHDHM3yNwmkInzVNov9rp9CU/p2Ht4T/AGPBuIE2nk6bJ4AKuUCgwevToxsiFPCIagROiX8S/+v0PAFTESdNkUAGvqanB4MGDMXToUCiVysbKiTwkWsRGSC0q4qQ5MOgauFgsxmuvvYbKykqjJrF27Vp4enrCysoKKpUKSUlJ943fvXs3OnXqBCsrK3h7e+PAgQN67YwxLF68GC4uLrC2tkZAQAAyMzP1YgoLCxESEgKZTAaFQoGpU6eitLS03s+7fPky7O3toVAoHqmfjam4ohqactoDTkid0H5e+M9f18S/+v0PrPyFromTpsXgRWx+fn44deqU0RLYuXMn5s6di/DwcKSmpqJHjx4IDAxEXl5evfFxcXEIDg7G1KlTcerUKYwaNQqjRo3C+fPn+ZgVK1Zg9erVWL9+PRITE2Fra4vAwEBUVFTwMSEhIbhw4QKio6MRFRWFo0ePIiws7K7Pq66uRnBwMAYMGGC0PjeG67QHnJC7TP5XEV92KJ2KOGk6mIF27tzJ2rZty9asWcPi4uLYmTNn9F6G8vPzYzNnzuS/12q1zNXVlUVERNQbP3bsWBYUFKR3TKVSsenTpzPGGNPpdEypVLKVK1fy7UVFRUwqlbLt27czxhhLS0tjAFhycjIfc/DgQcZxHLt+/bree7/33nvslVdeYVu2bGFyudygvmk0GgaAaTQag857GL9eUDOPeVHsudXHGv2zCDE3kSeymMe8KOYxL4r9Z995ptPphE6JNCONVQsMHoGPHz8eWVlZmDVrFvr164eePXvCx8eH/2qIqqoqpKSkICAggD8mEokQEBCA+Pj4es+Jj4/XiweAwMBAPj4rKwtqtVovRi6XQ6VS8THx8fFQKBTw9fXlYwICAiASiZCYmMgfi42Nxe7du7F27doG9aeyshLFxcV6r8eF9oATcm+T+nrio1HdAABbTvyJRXvPQ6ejkTgxbwbPtRrzaWT5+fnQarVwdnbWO+7s7Iz09PR6z1Gr1fXGq9Vqvr3u2P1inJyc9NrFYjEcHBz4mIKCAkyePBnffvstZDJZg/oTERGBJUuWNCjW2GgFOiH398qTHpBYiDDvx7P4NiEbNVqGj0d7QySihW3EPBlcwD08PBojD5Mzbdo0vPzyyxg4cGCDz1mwYAHmzp3Lf19cXAw3N7fGSO8utAeckAcb28cNlmIOb+86gx3JOajS6rDyxR6woCJOzNBDr3ZKS0tDdnY2qqqq9I6PGDGiwe/h6OgICwsL5Obm6h3Pzc295zY1pVJ53/i6r7m5uXBxcdGL6dmzJx/z70VyNTU1KCws5M+PjY3Fvn37sGrVKgC1K9t1Oh3EYjE2btyIKVOm3JWbVCqFVCptaPeNikbghDTMaJ82EItEmLPzNH5MvY5qLcNnY3tAbEFPVybmxeACfuXKFYwePRrnzp0Dx3H8is66/ZVarbbB7yWRSNC7d2/ExMRg1KhRAACdToeYmBi88cYb9Z7j7++PmJgYzJkzhz8WHR0Nf39/AICXlxeUSiViYmL4gl1cXIzExETMmDGDf4+ioiKkpKSgd+/eAGoLtk6ng0qlAlB7nfyffdm7dy+WL1+OuLg4tG7dusF9fFxoDzghDfd8D1dYWnB447tT+PnMDdRodfhivA8kYirixIwYuurtueeeYyNHjmS3bt1idnZ2LC0tjR07doz5+fmxo0ePGryKbseOHUwqlbLIyEiWlpbGwsLCmEKhYGq1mjHG2IQJE9j8+fP5+BMnTjCxWMxWrVrFLl68yMLDw5mlpSU7d+4cH7Ns2TKmUCjY3r172dmzZ9nIkSOZl5cXKy8v52OGDRvGfHx8WGJiIjt+/Dhr3749Cw4OvmeeprwKXVNexa+wLamobtTPIqQpib6gZu3fP8A85kWxqZFJrKK6RuiUSBPUWLXA4ALesmVLfruYTCZj6enpjDHGYmJiWM+ePR8qiTVr1jB3d3cmkUiYn58fS0hI4NsGDRrEJk2apBe/a9cu1qFDByaRSFjXrl3Z/v379dp1Oh1btGgRc3Z2ZlKplA0ZMoRlZGToxRQUFLDg4GBmZ2fHZDIZCw0NZSUlJffM0ZQLeNoNDfOYF8V6LvmlUT+HkKbocHou6/BBbRGfuDmRlVdRESfG1Vi1gGPMsLsatGjRAqmpqfDy8sITTzyBr7/+GoMHD8Yff/wBb29vlJWVNcZEgVkqLi6GXC6HRqNp8Er2hxGdlotp35yEd2s5fn6zf6N9DiFN1YnL+Zi6NRkV1Tr0b+eITRN9YS2xEDot0kQ0Vi0w+IJPt27dcObMGQCASqXCihUrcOLECXz44Ydo27at0RIjDZdTSHvACXkU/do5IjLUDzYSCxy/nI9JW5JQWlkjdFqE3JfBBXzhwoXQ6XQAgA8//BBZWVkYMGAADhw4gNWrVxs9QfJgdQvY3BxoARshD+vJti3xv6l+sJeKkZRViJCvE1FUVvXgEwkRiMEFPDAwEGPGjAEAtGvXDunp6cjPz0deXh6efvppoydIHozuwkaIcfT2cMC2aSq0sLHEmZwijNuQgLySigefSIgAHnrPxOXLl/HLL7+gvLwcDg4OxsyJGIj2gBNiPN3bKLBzuj9a2UuRkVuCcRsScL2oXOi0CLmLwQW8oKAAQ4YMQYcOHfDss8/i5s2bAICpU6fi7bffNnqC5MHoLmyEGFcHZ3vsnu6P1gprZOXfwUvr4nDlVv2PGyZEKAYX8LfeeguWlpbIzs6Gjc3fBWPcuHE4dOiQUZMjD6Ypr0ZxRe1im9YKGoETYiyejrb4foY/2rayxQ1NBcZuSMDFm4/vAUWEPIjBBfzXX3/F8uXL0aZNG73j7du3x9WrV42WGGmYuueAO9hKYEvPASfEqFzk1tg13R+dXWTIL63E+I0JOJV9W+i0CAHwEAX8zp07eiPvOoWFhYLdB7w5owVshDQuRzspdkx7Er3cFdCUV+OVrxMR/0eB0GkRYngBHzBgAL755hv+e47joNPpsGLFCgwePNioyZEHowVshDQ+uY0l/jdVhX7tWuJOlRaTtyThcHreg08kpBEZXMBXrFiBjRs3Yvjw4aiqqsJ7772Hbt264ejRo1i+fHlj5Ejugx5iQsjjYSsVY/OkPgjo7ITKGh2mfXMSUWdvCJ0WacYe6k5sly5dQv/+/TFy5EjcuXMHY8aMwalTp/DEE080Ro7kPmgKnZDHx8rSAute6Y0RPVxRo2OYtf0UdiRlC50WaaYeatWTXC7HBx98oHfs2rVrCAsLw8aNG42SGGkYmkIn5PGytBDhs3E9YSu1wPakHMz/8RwK7lTh9aee4B+rTMjjYLSH3xYUFGDz5s3GejvSQLQHnJDHz0LE4ePR3nj9qdpZx5W/ZOC/UReh0xn0bChCHgk9vd6M0R5wQoTDcRzeG9YJC4M6AwD+70QW5u46jWqtTuDMSHNBBdyM0R5wQoT36oC2+GxcD4hFHPacvoFp35xEWRU9yYw0PirgZowWsBFiGkb7tMGmSb6wshTh94xbCPk6Ebfv0JPMSONq8LCt7glk91JUVPSouRAD0QI2QkzH4I5O2Pbqk5gSmYxT2UV4aUM8vpniB1e6vEUaSYNH4HK5/L4vDw8PTJw4sTFzJf9Ce8AJMS29PVpg92v+UMqscDmvFC+ui8PlvBKh0yJNVINH4Fu2bGnMPMhDoCl0QkxPB2d7/PB6X0zYnIgrt+5gzFdx2DTRF6q2LYVOjTQxdA3cjOXQFDohJqm1whrfv9YXvdwVKK6owYTNSfj5DN21jRgXFXAzRnvACTFdDrYSfDftSQzrqkSVVoc3t5/CxqN/gDHaK06Mgwq4mdKUV6OE9oATYtKsLC2wNqQXQvt5AgA+PpCO/+y7AC3d8IUYARVwM1U3+m5Je8AJMWkWIg7hz3flb/iyNf4qXvs2BeVVWoEzI+aOCriZoi1khJiXVwe0xdqXe0EiFiE6LRfBmxJQUFopdFrEjFEBN1O0hYwQ8xPU3QXbXlVBbm2J0zlFeGFdHK7cKhU6LWKmqICbKdpCRoh56uPpgB9m9EWbFtb4s6AMo7+KQ9zlfKHTImaICriZoil0QsxXOyc7/PR6P/RyV0BTXo2J/5eE7xLpueLEMFTAzRRNoRNi3lrZS/HdtCcxsqcranQM7/90Dh/+nEYr1EmDUQE3UzSFToj5s7K0wOfjeuLtZzoAqH0k6bRvTqKkolrgzIg5oAJuhvT2gFMBJ8SscRyHN4e0x9qXe8HKUoTY9Dy8uC4eOYVlQqdGTBwVcDP0zz3gNhLaA05IUxDU3QW7pvvDyV6KjNwSjFp7AilXC4VOi5gwKuBmiBawEdI0dW+jwN43+qGrqwwFd6oQvDERO5NpcRupn0kU8LVr18LT0xNWVlZQqVRISkq6b/zu3bvRqVMnWFlZwdvbGwcOHNBrZ4xh8eLFcHFxgbW1NQICApCZmakXU1hYiJCQEMhkMigUCkydOhWlpX/vx/z9998xcuRIuLi4wNbWFj179sS2bduM1+lHQAvYCGm6XOTW2P2aP38P9Xk/nMMHP51DVY1O6NSIiRG8gO/cuRNz585FeHg4UlNT0aNHDwQGBiIvL6/e+Li4OAQHB2Pq1Kk4deoURo0ahVGjRuH8+fN8zIoVK7B69WqsX78eiYmJsLW1RWBgICoqKviYkJAQXLhwAdHR0YiKisLRo0cRFham9zndu3fHDz/8gLNnzyI0NBQTJ05EVFRU4/0wGogWsBHStNlIxPgqpBfeGdoBHAdsS8xG8KYE5BVXPPhk0nwwgfn5+bGZM2fy32u1Wubq6soiIiLqjR87diwLCgrSO6ZSqdj06dMZY4zpdDqmVCrZypUr+faioiImlUrZ9u3bGWOMpaWlMQAsOTmZjzl48CDjOI5dv379nrk+++yzLDQ0tMF902g0DADTaDQNPqchXt2azDzmRbFv4rKM+r6EENMTezGXdQs/xDzmRbE+H0Wzk38WCp0SMVBj1QJBR+BVVVVISUlBQEAAf0wkEiEgIADx8fH1nhMfH68XDwCBgYF8fFZWFtRqtV6MXC6HSqXiY+Lj46FQKODr68vHBAQEQCQSITEx8Z75ajQaODg43LO9srISxcXFeq/GQFPohDQfgzs5Yd8b/dHeyQ55JZUYvzEe2xKvCp0WMQGCFvD8/HxotVo4OzvrHXd2doZara73HLVafd/4uq8PinFyctJrF4vFcHBwuOfn7tq1C8nJyQgNDb1nfyIiIiCXy/mXm5vbPWMfBU2hE9K8eDnaYs/MfnjWW4lqLcMHP53Hgh/PorKGnmjWnAl+DdwcHD58GKGhodi0aRO6du16z7gFCxZAo9Hwr5ycHKPnQnvACWmebKVirH25F+YN6wSOA7Yn5WDshgT+D3rS/AhawB0dHWFhYYHc3Fy947m5uVAqlfWeo1Qq7xtf9/VBMf9eJFdTU4PCwsK7PvfIkSN4/vnn8dlnn2HixIn37Y9UKoVMJtN7GVvdzR1oDzghzQ/HcZjx1BOIDPWD3NoSZ3KKELT6OKLTch98MmlyBC3gEokEvXv3RkxMDH9Mp9MhJiYG/v7+9Z7j7++vFw8A0dHRfLyXlxeUSqVeTHFxMRITE/kYf39/FBUVISUlhY+JjY2FTqeDSqXij/3+++8ICgrC8uXL9VaoC4n2gBNCBnVohag3+6OHW+3DUKZ9cxIfRaXRVrPmxqhL4h7Cjh07mFQqZZGRkSwtLY2FhYUxhULB1Go1Y4yxCRMmsPnz5/PxJ06cYGKxmK1atYpdvHiRhYeHM0tLS3bu3Dk+ZtmyZUyhULC9e/eys2fPspEjRzIvLy9WXl7OxwwbNoz5+PiwxMREdvz4cda+fXsWHBzMt8fGxjIbGxu2YMECdvPmTf5VUFDQ4L41xsrDTUf/YB7zotjr36YY7T0JIeapslrLPvz5AvOYF8U85kWxEV8eZ9kFd4ROi/xLY61CF7yAM8bYmjVrmLu7O5NIJMzPz48lJCTwbYMGDWKTJk3Si9+1axfr0KEDk0gkrGvXrmz//v167Tqdji1atIg5OzszqVTKhgwZwjIyMvRiCgoKWHBwMLOzs2MymYyFhoaykpISvn3SpEkMwF2vQYMGNbhfjfF/Wvje88xjXhT7eH+a0d6TEGLefjl/k3n/tdXMO/wQ++X8TaFTIv/QWAWcY4zRs+saSXFxMeRyOTQajdGuh7+69SR+u5iL/47sign+nkZ5T0KI+cspLMOb20/hdE4RAGBKPy/MH94JEjGtVRZaY9QCgFahmx1+C5kD7QEnhPzNzcEGu6b7Y9oALwC1jyZ9cX0crtwqfcCZxFxRATcjjDFc/2sRmxstYiOE/ItELMIHQV3w9URfyK0tcfaaBkGrj+O7xGzQZGvTQwXcjBSX16Ck8q894AoagRNC6hfQxRmH5gxAv3YtUV6txfs/ncO0b1JQUFopdGrEiKiAm5Gcv6bPHe0ksJZYCJwNIcSUucit8b8pKiwM6gyJhQi/XcxF4OfHcDi9/gdFEfNDBdyM1O0Bb033QCeENIBIxOHVAW2xZ2Y/dHC2Q35pJUIjkzH/h7MoqagWOj3yiKiAmxG6Bzoh5GF0cZVh3xv9MaWfFzgO2JGcg8DPjuJY5i2hUyOPgAq4GaG7sBFCHpaVpQUWP98FO6Y9CXcHG9zQVGDC5iQs+PEcjcbNFBVwM0KPESWEPCpV25Y4NGcAJvf1BABsT8rGsM+P4fcMujZubqiAmxGaQieEGIONRIz/jOiK7dOehJuDNa4XlWPylmS8uf0UbpXQSnVzQQXcTNAecEKIsfk/0RKHZg/Eq/29IOKAn8/cwJBPfseOpGzodLRv3NRRATcTtAecENIYbKViLHyuC/bO7I9urWUorqjB/B/PYfzGBGTmlgidHrkPKuBmgvaAE0Iak3cbOfa83g8LgzrD2tICSX8WYvgXx/BRVBotcjNRVMDNBO0BJ4Q0NrGFCK8OaIvouQPxTBdn1OgYvj6ehcGrjuD7lGs0rW5iqICbCVrARgh5XNq0sMGmib6IDO2Dto62yC+txDu7z+CF9XE4e61I6PTIX6iAmwnaA04Iedye6uiEQ3MGYsHwTrCVWOBUdhFGfHkCs3ecQk5hmdDpNXtUwM3E3yNwmkInhDw+ErEI0wc9gdh3nsIYn9YAgL2nb2DIJ0ewdH8aisqqBM6w+aICbiZoBE4IEZKzzAqfjuuJqDf7o1+7lqjS6rDpWBYGrfwdG4/+gfIqrdApNjtUwM0AY4wv4LQHnBAipG6t5fh2qgqRoX3QSWkPTXk1Pj6QjoErD+P/jmehopoK+eNCBdwMaMqrUUp7wAkhJoLjODzV0Qn7Zw3Ayhe7o00La9wqqcSHUWkYtPIwtsb9SYX8MaACbgbqRt+0B5wQYkosRBxe8nVD7NtP4ePR3mitsEZucSXC913A4FW/Y/PxLNz5a/BBjI8KuBmoW8BGe8AJIaZIIhbhZZU7Yt8ZhI9GdYOL3Ao3NRX4b1Qa+i6LxSe/ZiC/lO6xbmxUwM0ALWAjhJgDqdgCrzzpgd/frR2ReznaQlNejTWxl9FvWSw++Okc/rhVKnSaTYZY6ATIg/29gI1G4IQQ0ycVW+BllTvG9XFDdJoa645cwZmcImxLzMa2xGz0b+eICf4eGNLJCWILGkc+LCrgZoDuwkYIMUcWIg7DurkgsKsSSVmF2HTsCmLS83D8cj6OX86Hi9wKL/u5Y5yfG5zsrYRO1+xQATcDNIVOCDFnHMdB1bYlVG1bIqewDN8lZWNncg5uairwSfQlfB6TiYHtHfFC7zYI6OwMK0tarNsQVMBN3D/3gNNd2Agh5s7NwQbzhnXCnID2OHDuJv4XfxWp2UU4nHELhzNuwd5KjOe6u+KFXq3Ry70FRCJO6JRNFhVwE/fPPeA0AieENBVSsQVG+7TBaJ82+ONWKX5KvY4fU6/hhqYC25OysT0pG84yKQK7KjGsqxJ+Xg50vfxfOMYYPR+ukRQXF0Mul0Oj0UAmkz3Ue5y/rsFza47D0U6KkwsDjJwhIYSYDp2OIeFKAb5PvYboC7ko+cce8hY2lnimizMGd3RC3yccIbexFDBTwxijFtSHRuAmjhawEUKaC5GIQ992jujbzhGVNVrEXS7AofNq/Jqmxu2yauw6eQ27Tl6DiAN6uikwoH0rDOzgiO5tFLBshqNzKuAmjhawEUKaI6nYAoM7OWFwJycs1XZD0p+FiE7LxbHMfFzOK0VqdhFSs4vwRUwmrCxF6N5Ggd4eLdDbvQV6ebSAg61E6C40OirgJo4WsBFCmjuxhQh9n3BE3yccAQA3ispxPDMfRzNv4fjlfBSVVSMpqxBJWYX8OW4O1ujoLEMnpT06/vXycrRtUiN1k+jJ2rVr4enpCSsrK6hUKiQlJd03fvfu3ejUqROsrKzg7e2NAwcO6LUzxrB48WK4uLjA2toaAQEByMzM1IspLCxESEgIZDIZFAoFpk6ditJS/TsEnT17FgMGDICVlRXc3NywYsUK43TYADSFTggh+lwV1hjbxw1fvtwLqQufwW9zB2HFC90xztcN7Z3sAAA5heX47WIuvjx8GW9uP4Whnx1F50WHMGBFLII3JuC9789gdUwmfki5ht8z8nAmpwg5hWUoqaiGuSwNE3wEvnPnTsydOxfr16+HSqXC559/jsDAQGRkZMDJyemu+Li4OAQHByMiIgLPPfccvvvuO4waNQqpqano1q0bAGDFihVYvXo1tm7dCi8vLyxatAiBgYFIS0uDlVXtzQJCQkJw8+ZNREdHo7q6GqGhoQgLC8N3330HoHbRwdChQxEQEID169fj3LlzmDJlChQKBcLCwh7bz4em0Akh5N5EIg7tnOzQzskOY/u4AQA0ZdVIu1mMDHUxMnJLkK4uwSV1Ce5UaZFTWI6cwnLEX7n3e1pacLCVimEltoC1xAJhA9si2M/9MfWo4QRfha5SqdCnTx98+eWXAACdTgc3Nze8+eabmD9//l3x48aNw507dxAVFcUfe/LJJ9GzZ0+sX78ejDG4urri7bffxjvvvAMA0Gg0cHZ2RmRkJMaPH4+LFy+iS5cuSE5Ohq+vLwDg0KFDePbZZ3Ht2jW4urpi3bp1+OCDD6BWqyGR1F5LmT9/Pvbs2YP09PQG9e1RVx4yxuD9n19RWlmD3+YOQru//rIkhBBiGJ2OQV1cgetF5bh2uww5hbVfrxeVo/BONYrKqnC7rAoV1bq7zl0Y1BmvDmj70J/dJFehV1VVISUlBQsWLOCPiUQiBAQEID4+vt5z4uPjMXfuXL1jgYGB2LNnDwAgKysLarUaAQF/b7mSy+VQqVSIj4/H+PHjER8fD4VCwRdvAAgICIBIJEJiYiJGjx6N+Ph4DBw4kC/edZ+zfPly3L59Gy1atLgrt8rKSlRW/v3EneLiYsN+IPXYEfYkrt0uh5sDjcAJIeRhiUQcXBXWcFVYo4+nwz3jyqu0uF1WhbIqLSqqtSiv1prsDKigBTw/Px9arRbOzs56x52dne85ylWr1fXGq9Vqvr3u2P1i/j09LxaL4eDgoBfj5eV113vUtdVXwCMiIrBkyZJ7d9hAHMehW2s5urWWG+09CSGE3Ju1xALWEtMs2P9mEovYmooFCxZAo9Hwr5ycHKFTIoQQ0kQJWsAdHR1hYWGB3NxcveO5ublQKpX1nqNUKu8bX/f1QTF5eXl67TU1NSgsLNSLqe89/vkZ/yaVSiGTyfRehBBCSGMQtIBLJBL07t0bMTEx/DGdToeYmBj4+/vXe46/v79ePABER0fz8V5eXlAqlXoxxcXFSExM5GP8/f1RVFSElJQUPiY2NhY6nQ4qlYqPOXr0KKqrq/U+p2PHjvVOnxNCCCGPFRPYjh07mFQqZZGRkSwtLY2FhYUxhULB1Go1Y4yxCRMmsPnz5/PxJ06cYGKxmK1atYpdvHiRhYeHM0tLS3bu3Dk+ZtmyZUyhULC9e/eys2fPspEjRzIvLy9WXl7OxwwbNoz5+PiwxMREdvz4cda+fXsWHBzMtxcVFTFnZ2c2YcIEdv78ebZjxw5mY2PDNmzY0OC+aTQaBoBpNJpH+RERQggxY41VCwQv4IwxtmbNGubu7s4kEgnz8/NjCQkJfNugQYPYpEmT9OJ37drFOnTowCQSCevatSvbv3+/XrtOp2OLFi1izs7OTCqVsiFDhrCMjAy9mIKCAhYcHMzs7OyYTCZjoaGhrKSkRC/mzJkzrH///kwqlbLWrVuzZcuWGdQvKuCEEEIaqxYIvg+8KWusvX+EEELMR2PVAlqFTgghhJghwW+l2pTVTW4Y44YuhBBCzFNdDTD2hDcV8EZUUlICAHBzcxM4E0IIIUIrKSmBXG68G3PRNfBGpNPpcOPGDdjb24PjuId6j+LiYri5uSEnJ6fJXEdvan1qav0Bml6fmlp/AOqTOajrT3Z2NjiOg6urK0Qi4125phF4IxKJRGjTpo1R3qsp3himqfWpqfUHaHp9amr9AahP5kAulzdKf2gRGyGEEGKGqIATQgghZogKuImTSqUIDw+HVCoVOhWjaWp9amr9AZpen5pafwDqkzlo7P7QIjZCCCHEDNEInBBCCDFDVMAJIYQQM0QFnBBCCDFDVMAJIYQQM0QF3MStXbsWnp6esLKygkqlQlJSktApNUhERAT69OkDe3t7ODk5YdSoUcjIyNCLqaiowMyZM9GyZUvY2dnhhRdeQG5urkAZG2bZsmXgOA5z5szhj5ljf65fv45XXnkFLVu2hLW1Nby9vXHy5Em+nTGGxYsXw8XFBdbW1ggICEBmZqaAGd+fVqvFokWL4OXlBWtrazzxxBP473//q3cPalPu09GjR/H888/D1dUVHMdhz549eu0Nyb2wsBAhISGQyWRQKBSYOnUqSktLH2Mv9N2vT9XV1Zg3bx68vb1ha2sLV1dXTJw4ETdu3NB7D3Pq07+99tpr4DgOn3/+ud5xY/SJCrgJ27lzJ+bOnYvw8HCkpqaiR48eCAwMRF5entCpPdCRI0cwc+ZMJCQkIDo6GtXV1Rg6dCju3LnDx7z11lv4+eefsXv3bhw5cgQ3btzAmDFjBMy6YZKTk7FhwwZ0795d77i59ef27dvo168fLC0tcfDgQaSlpeGTTz5BixYt+JgVK1Zg9erVWL9+PRITE2Fra4vAwEBUVFQImPm9LV++HOvWrcOXX36JixcvYvny5VixYgXWrFnDx5hyn+7cuYMePXpg7dq19bY3JPeQkBBcuHAB0dHRiIqKwtGjRxEWFva4unCX+/WprKwMqampWLRoEVJTU/Hjjz8iIyMDI0aM0Iszpz79008//YSEhAS4urre1WaUPhn16eLEqPz8/NjMmTP577VaLXN1dWURERECZvVw8vLyGAB25MgRxhhjRUVFzNLSku3evZuPuXjxIgPA4uPjhUrzgUpKSlj79u1ZdHQ0GzRoEJs9ezZjzDz7M2/ePNa/f/97tut0OqZUKtnKlSv5Y0VFRUwqlbLt27c/jhQNFhQUxKZMmaJ3bMyYMSwkJIQxZl59AsB++ukn/vuG5J6WlsYAsOTkZD7m4MGDjOM4dv369ceW+738u0/1SUpKYgDY1atXGWPm26dr166x1q1bs/PnzzMPDw/22Wef8W3G6hONwE1UVVUVUlJSEBAQwB8TiUQICAhAfHy8gJk9HI1GAwBwcHAAAKSkpKC6ulqvf506dYK7u7tJ92/mzJkICgrSyxswz/7s27cPvr6+eOmll+Dk5AQfHx9s2rSJb8/KyoJardbrk1wuh0qlMtk+9e3bFzExMbh06RIA4MyZMzh+/DiGDx8OwDz7VKchucfHx0OhUMDX15ePCQgIgEgkQmJi4mPP+WFoNBpwHAeFQgHAPPuk0+kwYcIEvPvuu+jatetd7cbqEz3MxETl5+dDq9XC2dlZ77izszPS09MFyurh6HQ6zJkzB/369UO3bt0AAGq1GhKJhP9HWsfZ2RlqtVqALB9sx44dSE1NRXJy8l1t5tifK1euYN26dZg7dy7ef/99JCcnY9asWZBIJJg0aRKfd33/DZpqn+bPn4/i4mJ06tQJFhYW0Gq1WLp0KUJCQgDALPtUpyG5q9VqODk56bWLxWI4ODiYfP+A2nUk8+bNQ3BwMP/wD3Ps0/LlyyEWizFr1qx6243VJyrgpNHNnDkT58+fx/Hjx4VO5aHl5ORg9uzZiI6OhpWVldDpGIVOp4Ovry8+/vhjAICPjw/Onz+P9evXY9KkSQJn93B27dqFbdu24bvvvkPXrl1x+vRpzJkzB66urmbbp+aiuroaY8eOBWMM69atEzqdh5aSkoIvvvgCqampD/0Y6YaiKXQT5ejoCAsLi7tWMefm5kKpVAqUleHeeOMNREVF4fDhw3qPVlUqlaiqqkJRUZFevKn2LyUlBXl5eejVqxfEYjHEYjGOHDmC1atXQywWw9nZ2az6AwAuLi7o0qWL3rHOnTsjOzsbAPi8zem/wXfffRfz58/H+PHj4e3tjQkTJuCtt95CREQEAPPsU52G5K5UKu9a5FpTU4PCwkKT7l9d8b569Sqio6P1Hr1pbn06duwY8vLy4O7uzv+uuHr1Kt5++214enoCMF6fqICbKIlEgt69eyMmJoY/ptPpEBMTA39/fwEzaxjGGN544w389NNPiI2NhZeXl1577969YWlpqde/jIwMZGdnm2T/hgwZgnPnzuH06dP8y9fXFyEhIfz/Nqf+AEC/fv3u2tp36dIleHh4AAC8vLygVCr1+lRcXIzExEST7VNZWRlEIv1faxYWFtDpdADMs091GpK7v78/ioqKkJKSwsfExsZCp9NBpVI99pwboq54Z2Zm4rfffkPLli312s2tTxMmTMDZs2f1fle4urri3XffxS+//ALAiH16+LV3pLHt2LGDSaVSFhkZydLS0lhYWBhTKBRMrVYLndoDzZgxg8nlcvb777+zmzdv8q+ysjI+5rXXXmPu7u4sNjaWnTx5kvn7+zN/f38BszbMP1ehM2Z+/UlKSmJisZgtXbqUZWZmsm3btjEbGxv27bff8jHLli1jCoWC7d27l509e5aNHDmSeXl5sfLycgEzv7dJkyax1q1bs6ioKJaVlcV+/PFH5ujoyN577z0+xpT7VFJSwk6dOsVOnTrFALBPP/2UnTp1il+R3ZDchw0bxnx8fFhiYiI7fvw4a9++PQsODhaqS/ftU1VVFRsxYgRr06YNO336tN7visrKSrPsU33+vQqdMeP0iQq4iVuzZg1zd3dnEomE+fn5sYSEBKFTahAA9b62bNnCx5SXl7PXX3+dtWjRgtnY2LDRo0ezmzdvCpe0gf5dwM2xPz///DPr1q0bk0qlrFOnTmzjxo167Tqdji1atIg5OzszqVTKhgwZwjIyMgTK9sGKi4vZ7Nmzmbu7O7OysmJt27ZlH3zwgV4xMOU+HT58uN5/N5MmTWKMNSz3goICFhwczOzs7JhMJmOhoaGspKREgN7Uul+fsrKy7vm74vDhw2bZp/rUV8CN0Sd6nCghhBBihugaOCGEEGKGqIATQgghZogKOCGEEGKGqIATQgghZogKOCGEEGKGqIATQgghZogKOCGEEGKGqIATQgghZogKOCHknm7duoUZM2bA3d0dUqkUSqUSgYGBOHHiBACA4zjs2bNH2CQJaabocaKEkHt64YUXUFVVha1bt6Jt27bIzc1FTEwMCgoKhE6NkGaPRuCEkHoVFRXh2LFjWL58OQYPHgwPDw/4+flhwYIFGDFiBP9oxNGjR4PjOP57ANi7dy969eoFKysrtG3bFkuWLEFNTQ3fznEc1q1bh+HDh8Pa2hpt27bF999/z7dXVVXhjTfegIuLC6ysrODh4cE/EpQQUosKOCGkXnZ2drCzs8OePXtQWVl5V3tycjIAYMuWLbh58yb//bFjxzBx4kTMnj0baWlp2LBhAyIjI7F06VK98xctWoQXXngBZ86cQUhICMaPH4+LFy8CAFavXo19+/Zh165dyMjIwLZt2/T+QCCEAPQwE0LIPf3www+YNm0aysvL0atXLwwaNAjjx49H9+7dAdSOpH/66SeMGjWKPycgIABDhgzBggUL+GPffvst3nvvPdy4cYM/77XXXsO6dev4mCeffBK9evXCV199hVmzZuHChQv47bffwHHc4+ksIWaGRuCEkHt64YUXcOPGDezbtw/Dhg3D77//jl69eiEyMvKe55w5cwYffvghP4K3s7PDtGnTcPPmTZSVlfFx/v7+euf5+/vzI/DJkyfj9OnT6NixI2bNmoVff/21UfpHiDmjAk4IuS8rKys888wzWLRoEeLi4jB58mSEh4ffM760tBRLlizB6dOn+de5c+eQmZkJKyurBn1mr169kJWVhf/+978oLy/H2LFj8eKLLxqrS4Q0CVTACSEG6dKlC+7cuQMAsLS0hFar1Wvv1asXMjIy0K5du7teItHfv3ISEhL0zktISEDnzp3572UyGcaNG4dNmzZh586d+OGHH1BYWNiIPSPEvNA2MkJIvQoKCvDSSy9hypQp6N69O+zt7XHy5EmsWLECI0eOBAB4enoiJiYG/fr1g1QqRYsWLbB48WI899xzcHd3x4svvgiRSIQzZ87g/Pnz+Oijj/j33717N3x9fdG/f39s27YNSUlJ2Lx5MwDg008/hYuLC3x8fCASibB7924olUooFAohfhSEmCZGCCH1qKioYPPnz2e9evVicrmc2djYsI4dO7KFCxeysrIyxhhj+/btY+3atWNisZh5eHjw5x46dIj17duXWVtbM5lMxvz8/NjGjRv5dgBs7dq17JlnnmFSqZR5enqynTt38u0bN25kPXv2ZLa2tkwmk7EhQ4aw1NTUx9Z3QswBrUInhDx29a1eJ4QYhq6BE0IIIWaICjghhBBihmgRGyHksaMrd4Q8OhqBE0IIIWaICjghhBBihqiAE0IIIWaICjghhBBihqiAE0IIIWaICjghhBBihqiAE0IIIWaICjghhBBihqiAE0IIIWbo/wFVGCrhrpUbVgAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 500x300 with 1 Axes>"
       ]
@@ -695,15 +695,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "445d8155-6eae-4b50-a381-d0820ebc27cc",
    "metadata": {
     "scrolled": true
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/jg/tpqyh1fd5js5wsr1d138k3n40000gn/T/ipykernel_34986/3589549395.py:5: UserWarning: The figure layout has changed to tight\n",
+      "  plt.tight_layout(); plt.savefig(\"3.pdf\")\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x300 with 2 Axes>"
       ]
@@ -717,7 +725,7 @@
     "\n",
     "epochs_tensor = torch.linspace(1, n_epochs, len(train_losses))\n",
     "plot_losses(epochs_tensor, tokens_seen, train_losses, val_losses)\n",
-    "# plt.tight_layout(); plt.savefig(\"3.pdf\")\n",
+    "plt.tight_layout(); plt.savefig(\"3.pdf\")\n",
     "plt.show()"
    ]
   },
@@ -747,7 +755,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/appendix-D/01_main-chapter-code/previous_chapters.py b/appendix-D/01_main-chapter-code/previous_chapters.py
index f7a02cd..c9e6683 100644
--- a/appendix-D/01_main-chapter-code/previous_chapters.py
+++ b/appendix-D/01_main-chapter-code/previous_chapters.py
@@ -152,7 +152,6 @@ class FeedForward(nn.Module):
             nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
             GELU(),
             nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
-            nn.Dropout(cfg["drop_rate"])
         )
 
     def forward(self, x):
diff --git a/ch04/01_main-chapter-code/ch04.ipynb b/ch04/01_main-chapter-code/ch04.ipynb
index a74c7b8..3afbb54 100644
--- a/ch04/01_main-chapter-code/ch04.ipynb
+++ b/ch04/01_main-chapter-code/ch04.ipynb
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "5ed66875-1f24-445d-add6-006aae3c5707",
    "metadata": {},
    "outputs": [],
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "619c2eed-f8ea-4ff5-92c3-feda0f29b227",
    "metadata": {},
    "outputs": [],
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "794b6b6c-d36f-411e-a7db-8ac566a87fee",
    "metadata": {},
    "outputs": [
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "009238cd-0160-4834-979c-309710986bb0",
    "metadata": {},
    "outputs": [
@@ -263,7 +263,7 @@
       "tensor([[[-1.2034,  0.3201, -0.7130,  ..., -1.5548, -0.2390, -0.4667],\n",
       "         [-0.1192,  0.4539, -0.4432,  ...,  0.2392,  1.3469,  1.2430],\n",
       "         [ 0.5307,  1.6720, -0.4695,  ...,  1.1966,  0.0111,  0.5835],\n",
-      "         [ 0.0139,  1.6755, -0.3388,  ...,  1.1586, -0.0435, -1.0400]],\n",
+      "         [ 0.0139,  1.6754, -0.3388,  ...,  1.1586, -0.0435, -1.0400]],\n",
       "\n",
       "        [[-1.0908,  0.1798, -0.9484,  ..., -1.6047,  0.2439, -0.4530],\n",
       "         [-0.7860,  0.5581, -0.0610,  ...,  0.4835, -0.0077,  1.6621],\n",
@@ -318,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "79e1b463-dc3f-44ac-9cdb-9d5b6f64eb9d",
    "metadata": {},
    "outputs": [
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "9888f79e-8e69-44aa-8a19-cd34292adbf5",
    "metadata": {},
    "outputs": [
@@ -404,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "9a1d1bb9-3341-4c9a-bc2a-d2489bf89cda",
    "metadata": {},
    "outputs": [
@@ -417,11 +417,11 @@
       "        [-0.0189,  0.1121, -1.0876,  1.5173,  0.5647, -1.0876]],\n",
       "       grad_fn=<DivBackward0>)\n",
       "Mean:\n",
-      " tensor([[2.9802e-08],\n",
-      "        [3.9736e-08]], grad_fn=<MeanBackward1>)\n",
+      " tensor([[-5.9605e-08],\n",
+      "        [ 1.9868e-08]], grad_fn=<MeanBackward1>)\n",
       "Variance:\n",
-      " tensor([[1.],\n",
-      "        [1.]], grad_fn=<VarBackward0>)\n"
+      " tensor([[1.0000],\n",
+      "        [1.0000]], grad_fn=<VarBackward0>)\n"
      ]
     }
    ],
@@ -445,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "3e06c34b-c68a-4b36-afbe-b30eda4eca39",
    "metadata": {},
    "outputs": [
@@ -454,11 +454,11 @@
      "output_type": "stream",
      "text": [
       "Mean:\n",
-      " tensor([[    0.0000],\n",
-      "        [    0.0000]], grad_fn=<MeanBackward1>)\n",
+      " tensor([[    -0.0000],\n",
+      "        [     0.0000]], grad_fn=<MeanBackward1>)\n",
       "Variance:\n",
-      " tensor([[1.],\n",
-      "        [1.]], grad_fn=<VarBackward0>)\n"
+      " tensor([[1.0000],\n",
+      "        [1.0000]], grad_fn=<VarBackward0>)\n"
      ]
     }
    ],
@@ -479,7 +479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "3333a305-aa3d-460a-bcce-b80662d464d9",
    "metadata": {},
    "outputs": [],
@@ -521,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "23b1000a-e613-4b43-bd90-e54deed8d292",
    "metadata": {},
    "outputs": [],
@@ -532,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "94c12de2-1cab-46e0-a099-e2e470353bff",
    "metadata": {},
    "outputs": [
@@ -597,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "f84694b7-95f3-4323-b6d6-0a73df278e82",
    "metadata": {},
    "outputs": [],
@@ -615,13 +615,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "fc5487d2-2576-4118-80a7-56c4caac2e71",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x300 with 2 Axes>"
       ]
@@ -665,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "9275c879-b148-4579-a107-86827ca14d4d",
    "metadata": {},
    "outputs": [],
@@ -677,7 +677,6 @@
     "            nn.Linear(cfg[\"emb_dim\"], 4 * cfg[\"emb_dim\"]),\n",
     "            GELU(),\n",
     "            nn.Linear(4 * cfg[\"emb_dim\"], cfg[\"emb_dim\"]),\n",
-    "            nn.Dropout(cfg[\"drop_rate\"])\n",
     "        )\n",
     "\n",
     "    def forward(self, x):\n",
@@ -686,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "7c4976e2-0261-418e-b042-c5be98c2ccaf",
    "metadata": {},
    "outputs": [
@@ -707,12 +706,12 @@
    "id": "fdcaacfa-3cfc-4c9e-b668-b71a2753145a",
    "metadata": {},
    "source": [
-    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch04_compressed/09.webp\" width=\"400px\">"
+    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch04_compressed/09.webp?12\" width=\"400px\">"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "928e7f7c-d0b1-499f-8d07-4cadb428a6f9",
    "metadata": {},
    "outputs": [
@@ -781,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "05473938-799c-49fd-86d4-8ed65f94fee6",
    "metadata": {},
    "outputs": [],
@@ -839,7 +838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "c75f43cc-6923-4018-b980-26023086572c",
    "metadata": {},
    "outputs": [
@@ -848,10 +847,10 @@
      "output_type": "stream",
      "text": [
       "layers.0.0.weight has gradient mean of 0.00020173587836325169\n",
-      "layers.1.0.weight has gradient mean of 0.0001201116101583466\n",
-      "layers.2.0.weight has gradient mean of 0.0007152041653171182\n",
-      "layers.3.0.weight has gradient mean of 0.001398873864673078\n",
-      "layers.4.0.weight has gradient mean of 0.005049646366387606\n"
+      "layers.1.0.weight has gradient mean of 0.00012011159560643137\n",
+      "layers.2.0.weight has gradient mean of 0.0007152039906941354\n",
+      "layers.3.0.weight has gradient mean of 0.0013988736318424344\n",
+      "layers.4.0.weight has gradient mean of 0.005049645435065031\n"
      ]
     }
    ],
@@ -877,7 +876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "11b7c0c2-f9dd-4dd5-b096-a05c48c5f6d6",
    "metadata": {},
    "outputs": [
@@ -886,10 +885,10 @@
      "output_type": "stream",
      "text": [
       "layers.0.0.weight has gradient mean of 0.22169792652130127\n",
-      "layers.1.0.weight has gradient mean of 0.20694105327129364\n",
+      "layers.1.0.weight has gradient mean of 0.20694106817245483\n",
       "layers.2.0.weight has gradient mean of 0.32896995544433594\n",
-      "layers.3.0.weight has gradient mean of 0.2665732502937317\n",
-      "layers.4.0.weight has gradient mean of 1.3258541822433472\n"
+      "layers.3.0.weight has gradient mean of 0.2665732204914093\n",
+      "layers.4.0.weight has gradient mean of 1.3258540630340576\n"
      ]
     }
    ],
@@ -930,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "0e1e8176-e5e3-4152-b1aa-0bbd7891dfd9",
    "metadata": {},
    "outputs": [],
@@ -976,7 +975,7 @@
    "id": "36b64d16-94a6-4d13-8c85-9494c50478a9",
    "metadata": {},
    "source": [
-    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch04_compressed/13.webp\" width=\"400px\">"
+    "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch04_compressed/13.webp?123\" width=\"400px\">"
    ]
   },
   {
@@ -990,7 +989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "3fb45a63-b1f3-4b08-b525-dafbc8228405",
    "metadata": {},
    "outputs": [
@@ -1099,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "ef94fd9c-4e9d-470d-8f8e-dd23d1bb1f64",
    "metadata": {},
    "outputs": [
@@ -1112,15 +1111,15 @@
       "        [6109, 1110, 6622,  257]])\n",
       "\n",
       "Output shape: torch.Size([2, 4, 50257])\n",
-      "tensor([[[ 0.6525,  0.5753,  0.0174,  ...,  0.2988,  0.1441,  0.0032],\n",
-      "         [ 0.0839, -0.6789, -0.6605,  ..., -0.2912,  0.4267, -0.2696],\n",
-      "         [ 0.8440,  0.1894,  0.0708,  ...,  0.0982, -0.2183,  0.0920],\n",
-      "         [-0.7958,  0.5066,  0.0209,  ...,  0.7497,  0.3233, -0.1251]],\n",
+      "tensor([[[ 0.3613,  0.4222, -0.0711,  ...,  0.3483,  0.4661, -0.2838],\n",
+      "         [-0.1792, -0.5660, -0.9485,  ...,  0.0477,  0.5181, -0.3168],\n",
+      "         [ 0.7120,  0.0332,  0.1085,  ...,  0.1018, -0.4327, -0.2553],\n",
+      "         [-1.0076,  0.3418, -0.1190,  ...,  0.7195,  0.4023,  0.0532]],\n",
       "\n",
-      "        [[ 0.0181,  0.2606, -0.3022,  ...,  0.2940,  0.1998, -0.6246],\n",
-      "         [ 0.0596,  0.3041, -0.0293,  ...,  0.6796, -0.1226,  0.1303],\n",
-      "         [ 1.1895,  1.0891,  0.0237,  ...,  0.8299,  0.1794, -0.2250],\n",
-      "         [ 0.5457,  0.1861,  0.3872,  ...,  1.3537, -0.4062, -0.0268]]],\n",
+      "        [[-0.2564,  0.0900,  0.0335,  ...,  0.2659,  0.4454, -0.6806],\n",
+      "         [ 0.1230,  0.3653, -0.2074,  ...,  0.7705,  0.2710,  0.2246],\n",
+      "         [ 1.0558,  1.0318, -0.2800,  ...,  0.6936,  0.3205, -0.3178],\n",
+      "         [-0.1565,  0.3926,  0.3288,  ...,  1.2630, -0.1858,  0.0388]]],\n",
       "       grad_fn=<UnsafeViewBackward0>)\n"
      ]
     }
@@ -1146,7 +1145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 26,
    "id": "84fb8be4-9d3b-402b-b3da-86b663aac33a",
    "metadata": {},
    "outputs": [
@@ -1178,7 +1177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 27,
    "id": "e3b43233-e9b8-4f5a-b72b-a263ec686982",
    "metadata": {},
    "outputs": [
@@ -1207,7 +1206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 28,
    "id": "95a22e02-50d3-48b3-a4e0-d9863343c164",
    "metadata": {},
    "outputs": [
@@ -1236,7 +1235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 29,
    "id": "5131a752-fab8-4d70-a600-e29870b33528",
    "metadata": {},
    "outputs": [
@@ -1331,7 +1330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 30,
    "id": "c9b428a9-8764-4b36-80cd-7d4e00595ba6",
    "metadata": {},
    "outputs": [],
@@ -1385,42 +1384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "bb3ffc8e-f95f-4a24-a978-939b8953ea3e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([-1.4929,  4.4812, -1.6093], grad_fn=<SliceBackward0>)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "tensor([    0.0000,     0.0012,     0.0000,  ...,     0.0000,     0.0000,\n",
-       "            0.0000], grad_fn=<SoftmaxBackward0>)"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b = logits[0, -1, :]\n",
-    "b[0] = -1.4929\n",
-    "b[1] = 4.4812\n",
-    "b[2] = -1.6093\n",
-    "\n",
-    "print(b[:3])\n",
-    "torch.softmax(b, dim=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 31,
    "id": "3d7e3e94-df0f-4c0f-a6a1-423f500ac1d3",
    "metadata": {},
    "outputs": [
@@ -1445,7 +1409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 32,
    "id": "a72a9b60-de66-44cf-b2f9-1e638934ada4",
    "metadata": {},
    "outputs": [
@@ -1482,7 +1446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 33,
    "id": "053d99f6-5710-4446-8d52-117fb34ea9f6",
    "metadata": {},
    "outputs": [
@@ -1525,7 +1489,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/ch04/01_main-chapter-code/exercise-solutions.ipynb b/ch04/01_main-chapter-code/exercise-solutions.ipynb
index 74c213f..72d8c3a 100644
--- a/ch04/01_main-chapter-code/exercise-solutions.ipynb
+++ b/ch04/01_main-chapter-code/exercise-solutions.ipynb
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "id": "5fee2cf5-61c3-4167-81b5-44ea155bbaf2",
    "metadata": {},
    "outputs": [],
@@ -265,7 +265,6 @@
     "    \"n_heads\": 12,\n",
     "    \"n_layers\": 12,\n",
     "    \"drop_rate_emb\": 0.1,    # NEW: dropout for embedding layers\n",
-    "    \"drop_rate_ffn\": 0.1,    # NEW: dropout for feed forward module\n",
     "    \"drop_rate_attn\": 0.1,   # NEW: dropout for multi-head attention  \n",
     "    \"drop_rate_resid\": 0.1,   # NEW: dropout for residual connections  \n",
     "    \"qkv_bias\": False\n",
@@ -274,26 +273,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "5aa1b0c1-d78a-48fc-ad08-4802458b43f7",
    "metadata": {},
    "outputs": [],
    "source": [
     "import torch.nn as nn\n",
-    "from gpt import MultiHeadAttention, LayerNorm, GELU\n",
-    "\n",
-    "class FeedForward(nn.Module):\n",
-    "    def __init__(self, cfg):\n",
-    "        super().__init__()\n",
-    "        self.layers = nn.Sequential(\n",
-    "            nn.Linear(cfg[\"emb_dim\"], 4 * cfg[\"emb_dim\"]),\n",
-    "            GELU(),\n",
-    "            nn.Linear(4 * cfg[\"emb_dim\"], cfg[\"emb_dim\"]),\n",
-    "            nn.Dropout(cfg[\"drop_rate_ffn\"]) # NEW: dropout for feed forward module\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        return self.layers(x)\n",
+    "from gpt import MultiHeadAttention, LayerNorm, GELU, FeedForward\n",
     "\n",
     "\n",
     "class TransformerBlock(nn.Module):\n",
@@ -356,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "1d013d32-c275-4f42-be21-9010f1537227",
    "metadata": {},
    "outputs": [],
@@ -384,7 +370,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/ch04/01_main-chapter-code/gpt.py b/ch04/01_main-chapter-code/gpt.py
index 9d459d5..037e8d6 100644
--- a/ch04/01_main-chapter-code/gpt.py
+++ b/ch04/01_main-chapter-code/gpt.py
@@ -144,7 +144,6 @@ class FeedForward(nn.Module):
             nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
             GELU(),
             nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
-            nn.Dropout(cfg["drop_rate"])
         )
 
     def forward(self, x):
diff --git a/ch05/01_main-chapter-code/ch05.ipynb b/ch05/01_main-chapter-code/ch05.ipynb
index 2def63d..bad9771 100644
--- a/ch05/01_main-chapter-code/ch05.ipynb
+++ b/ch05/01_main-chapter-code/ch05.ipynb
@@ -31,10 +31,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "matplotlib version: 3.8.2\n",
-      "numpy version: 1.26.0\n",
+      "matplotlib version: 3.7.2\n",
+      "numpy version: 1.25.2\n",
       "tiktoken version: 0.5.1\n",
-      "torch version: 2.2.2\n",
+      "torch version: 2.2.1\n",
       "tensorflow version: 2.15.0\n"
      ]
     }
@@ -1263,34 +1263,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ep 1 (Step 000000): Train loss 9.558, Val loss 9.856\n",
-      "Ep 1 (Step 000005): Train loss 7.651, Val loss 8.051\n",
+      "Ep 1 (Step 000000): Train loss 9.781, Val loss 9.933\n",
+      "Ep 1 (Step 000005): Train loss 8.111, Val loss 8.339\n",
       "Every effort moves you,,,,,,,,,,,,.                                     \n",
-      "Ep 2 (Step 000010): Train loss 6.421, Val loss 6.812\n",
-      "Ep 2 (Step 000015): Train loss 5.913, Val loss 6.559\n",
-      "Every effort moves you, and, and,, and,,,,, and, and,,,, and,,,, and, and,, and,,,, and,, and,,,,, and,,,,,,\n",
-      "Ep 3 (Step 000020): Train loss 5.680, Val loss 6.490\n",
-      "Ep 3 (Step 000025): Train loss 5.557, Val loss 6.602\n",
-      "Every effort moves you, and, and the picture.                             \", and, and the, and the, and, and,\n",
-      "Ep 4 (Step 000030): Train loss 5.204, Val loss 6.508\n",
-      "Ep 4 (Step 000035): Train loss 4.865, Val loss 6.420\n",
-      "Every effort moves you, and I had a a a--I to the picture. \"I. I had the picture. \"I had the picture. I had the the picture. I had the the picture. \"I had the picture. \"\n",
-      "Ep 5 (Step 000040): Train loss 4.332, Val loss 6.328\n",
-      "Every effort moves you, I was a and I was a little to the picture.                                     \n",
-      "Ep 6 (Step 000045): Train loss 4.295, Val loss 6.221\n",
-      "Ep 6 (Step 000050): Train loss 3.268, Val loss 6.184\n",
-      "Every effort moves you know to see the end of the Riv I felt--the a little of the last: \"                               \n",
-      "Ep 7 (Step 000055): Train loss 2.880, Val loss 6.129\n",
-      "Ep 7 (Step 000060): Train loss 2.820, Val loss 6.194\n",
-      "Every effort moves you know the fact of a little a--I was his painting.                                     \n",
-      "Ep 8 (Step 000065): Train loss 2.260, Val loss 6.236\n",
-      "Ep 8 (Step 000070): Train loss 1.754, Val loss 6.260\n",
-      "Every effort moves you know,\" was one of the picture for nothing--I turned Mrs.                                    \n",
-      "Ep 9 (Step 000075): Train loss 1.447, Val loss 6.319\n",
-      "Ep 9 (Step 000080): Train loss 1.120, Val loss 6.310\n",
-      "Every effort moves you?\"               \"I looked--and me.\"         He placed them at my elbow and I looked up his pictures--because he's I had\n",
-      "Ep 10 (Step 000085): Train loss 0.762, Val loss 6.372\n",
-      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back his head to look up at the sketch of the donkey. \"There were days when I\n"
+      "Ep 2 (Step 000010): Train loss 6.661, Val loss 7.048\n",
+      "Ep 2 (Step 000015): Train loss 5.961, Val loss 6.616\n",
+      "Every effort moves you, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and,, and, and,\n",
+      "Ep 3 (Step 000020): Train loss 5.726, Val loss 6.600\n",
+      "Ep 3 (Step 000025): Train loss 5.201, Val loss 6.348\n",
+      "Every effort moves you, and I had been.                                            \n",
+      "Ep 4 (Step 000030): Train loss 4.417, Val loss 6.278\n",
+      "Ep 4 (Step 000035): Train loss 4.069, Val loss 6.226\n",
+      "Every effort moves you know the                          \"I he had the donkey and I had the and I had the donkey and down the room, I had\n",
+      "Ep 5 (Step 000040): Train loss 3.732, Val loss 6.160\n",
+      "Every effort moves you know it was not that the picture--I had the fact by the last I had been--his, and in the            \"Oh, and he said, and down the room, and in\n",
+      "Ep 6 (Step 000045): Train loss 2.850, Val loss 6.179\n",
+      "Ep 6 (Step 000050): Train loss 2.427, Val loss 6.141\n",
+      "Every effort moves you know,\" was one of the picture. The--I had a little of a little: \"Yes, and in fact, and in the picture was, and I had been at my elbow and as his pictures, and down the room, I had\n",
+      "Ep 7 (Step 000055): Train loss 2.104, Val loss 6.134\n",
+      "Ep 7 (Step 000060): Train loss 1.882, Val loss 6.233\n",
+      "Every effort moves you know,\" was one of the picture for nothing--I told Mrs.  \"I was no--as! The women had been, in the moment--as Jack himself, as once one had been the donkey, and were, and in his\n",
+      "Ep 8 (Step 000065): Train loss 1.320, Val loss 6.238\n",
+      "Ep 8 (Step 000070): Train loss 0.985, Val loss 6.242\n",
+      "Every effort moves you know,\" was one of the axioms he had been the tips of a self-confident moustache, I felt to see a smile behind his close grayish beard--as if he had the donkey. \"strongest,\" as his\n",
+      "Ep 9 (Step 000075): Train loss 0.717, Val loss 6.293\n",
+      "Ep 9 (Step 000080): Train loss 0.541, Val loss 6.393\n",
+      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back the window-curtains, I had the donkey. \"There were days when I\n",
+      "Ep 10 (Step 000085): Train loss 0.391, Val loss 6.452\n",
+      "Every effort moves you know,\" was one of the axioms he laid down across the Sevres and silver of an exquisitely appointed luncheon-table, when, on a later day, I had again run over from Monte Carlo; and Mrs. Gis\n"
      ]
     }
    ],
@@ -1298,7 +1298,7 @@
     "torch.manual_seed(123)\n",
     "model = GPTModel(GPT_CONFIG_124M)\n",
     "model.to(device)\n",
-    "optimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=0.1)\n",
+    "optimizer = torch.optim.AdamW(model.parameters(), lr=0.0005, weight_decay=0.1)\n",
     "\n",
     "num_epochs = 10\n",
     "train_losses, val_losses, tokens_seen = train_model_simple(\n",
@@ -1323,7 +1323,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x300 with 2 Axes>"
       ]
@@ -1427,11 +1427,7 @@
      "output_type": "stream",
      "text": [
       "Output text:\n",
-      " Every effort moves you?\"\n",
-      "\n",
-      "\"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"\n",
-      "\n",
-      "\n"
+      " Every effort moves you know,\" was one of the axioms he laid down across the Sevres and silver of an exquisitely appointed lun\n"
      ]
     }
    ],
@@ -1621,13 +1617,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 34,
    "id": "2e66e613-4aca-4296-a984-ddd0d80c6578",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 500x300 with 1 Axes>"
       ]
@@ -1890,7 +1886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 59,
    "id": "aa2a0d7d-0457-42d1-ab9d-bd67683e7ed8",
    "metadata": {},
    "outputs": [
@@ -1899,7 +1895,7 @@
      "output_type": "stream",
      "text": [
       "Output text:\n",
-      " Every effort moves you?\",\" was down surprise. It is to face watching me by his painting him back to my work\n"
+      " Every effort moves you stand to work on surprise, a one of us had gone with random-\n"
      ]
     }
    ],
@@ -1909,10 +1905,10 @@
     "token_ids = generate(\n",
     "    model=model,\n",
     "    idx=text_to_token_ids(\"Every effort moves you\", tokenizer),\n",
-    "    max_new_tokens=20,\n",
+    "    max_new_tokens=15,\n",
     "    context_size=GPT_CONFIG_124M[\"ctx_len\"],\n",
-    "    top_k=10,\n",
-    "    temperature=1.5\n",
+    "    top_k=25,\n",
+    "    temperature=1.4\n",
     ")\n",
     "\n",
     "print(\"Output text:\\n\", token_ids_to_text(token_ids, tokenizer))"
@@ -1946,7 +1942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 60,
    "id": "3d67d869-ac04-4382-bcfb-c96d1ca80d47",
    "metadata": {},
    "outputs": [],
@@ -1964,7 +1960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 61,
    "id": "9d57d914-60a3-47f1-b499-5352f4c457cb",
    "metadata": {},
    "outputs": [],
@@ -1985,7 +1981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 62,
    "id": "bbd175bb-edf4-450e-a6de-d3e8913c6532",
    "metadata": {},
    "outputs": [],
@@ -2000,7 +1996,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 63,
    "id": "8a0c7295-c822-43bf-9286-c45abc542868",
    "metadata": {},
    "outputs": [],
@@ -2010,7 +2006,7 @@
     "model = GPTModel(GPT_CONFIG_124M)\n",
     "model.load_state_dict(checkpoint[\"model_state_dict\"])\n",
     "\n",
-    "optimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=0.1)\n",
+    "optimizer = torch.optim.AdamW(model.parameters(), lr=0.0005, weight_decay=0.1)\n",
     "optimizer.load_state_dict(checkpoint[\"optimizer_state_dict\"])\n",
     "model.train();"
    ]
@@ -2053,7 +2049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 64,
    "id": "fb9fdf02-972a-444e-bf65-8ffcaaf30ce8",
    "metadata": {},
    "outputs": [],
@@ -2063,7 +2059,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 65,
    "id": "a0747edc-559c-44ef-a93f-079d60227e3f",
    "metadata": {},
    "outputs": [
@@ -2072,7 +2068,7 @@
      "output_type": "stream",
      "text": [
       "TensorFlow version: 2.15.0\n",
-      "tqdm version: 4.66.1\n"
+      "tqdm version: 4.66.2\n"
      ]
     }
    ],
@@ -2083,7 +2079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 66,
    "id": "c5bc89eb-4d39-4287-9b0c-e459ebe7f5ed",
    "metadata": {},
    "outputs": [],
@@ -2102,7 +2098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 67,
    "id": "76271dd7-108d-4f5b-9c01-6ae0aac4b395",
    "metadata": {},
    "outputs": [
@@ -2126,7 +2122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 68,
    "id": "b1a31951-d971-4a6e-9c43-11ee1168ec6a",
    "metadata": {},
    "outputs": [
@@ -2144,7 +2140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 69,
    "id": "857c8331-130e-46ba-921d-fa35d7a73cfe",
    "metadata": {},
    "outputs": [
@@ -2190,7 +2186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 70,
    "id": "9fef90dd-0654-4667-844f-08e28339ef7d",
    "metadata": {},
    "outputs": [],
@@ -2223,7 +2219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 71,
    "id": "f9a92229-c002-49a6-8cfb-248297ad8296",
    "metadata": {},
    "outputs": [],
@@ -2236,7 +2232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 72,
    "id": "f22d5d95-ca5a-425c-a9ec-fc432a12d4e9",
    "metadata": {},
    "outputs": [],
@@ -2318,7 +2314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 73,
    "id": "1f690253-f845-4347-b7b6-43fabbd2affa",
    "metadata": {},
    "outputs": [
@@ -2407,7 +2403,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/ch05/01_main-chapter-code/exercise-solutions.ipynb b/ch05/01_main-chapter-code/exercise-solutions.ipynb
index 227c302..1c38151 100644
--- a/ch05/01_main-chapter-code/exercise-solutions.ipynb
+++ b/ch05/01_main-chapter-code/exercise-solutions.ipynb
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "id": "a61a4034-797a-4635-bf42-ddfff1b07125",
    "metadata": {},
    "outputs": [],
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "ee95a272-b852-43b4-9827-ea7e1dbd5724",
    "metadata": {},
    "outputs": [],
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "id": "4ab43658-3240-484a-9072-a40a0ed85be6",
    "metadata": {},
    "outputs": [
@@ -273,11 +273,7 @@
      "output_type": "stream",
      "text": [
       "Output text:\n",
-      " Every effort moves you?\"\n",
-      "\n",
-      "\"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"\n",
-      "\n",
-      "\n"
+      " Every effort moves you know,\" was one of the axioms he laid down across the Sevres and silver of an exquisitely appointed lun\n"
      ]
     }
    ],
@@ -298,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "id": "ebb22d06-393a-42d3-ab64-66646d33b39b",
    "metadata": {},
    "outputs": [
@@ -307,11 +303,7 @@
      "output_type": "stream",
      "text": [
       "Output text:\n",
-      " Every effort moves you?\"\n",
-      "\n",
-      "\"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"\n",
-      "\n",
-      "\n"
+      " Every effort moves you know,\" was one of the axioms he laid down across the Sevres and silver of an exquisitely appointed lun\n"
      ]
     }
    ],
@@ -332,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "75469f24-47cc-458d-a200-fe64c648131d",
    "metadata": {},
    "outputs": [
@@ -341,11 +333,7 @@
      "output_type": "stream",
      "text": [
       "Output text:\n",
-      " Every effort moves you?\"\n",
-      "\n",
-      "\"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"\n",
-      "\n",
-      "\n"
+      " Every effort moves you know,\" was one of the axioms he laid down across the Sevres and silver of an exquisitely appointed lun\n"
      ]
     }
    ],
@@ -412,7 +400,7 @@
     "model = GPTModel(GPT_CONFIG_124M)\n",
     "model.load_state_dict(checkpoint[\"model_state_dict\"])\n",
     "\n",
-    "optimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=0.1)\n",
+    "optimizer = torch.optim.AdamW(model.parameters(), lr=0.0004, weight_decay=0.1)\n",
     "optimizer.load_state_dict(checkpoint[\"optimizer_state_dict\"])\n",
     "model.to(device)\n",
     "model.train();"
@@ -497,9 +485,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ep 1 (Step 000000): Train loss 0.523, Val loss 6.445\n",
-      "Ep 1 (Step 000005): Train loss 0.422, Val loss 6.541\n",
-      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  \"Oh, and I remember getting off a prodigious phrase about the sketch of the donkey. \"There were days when I\n"
+      "Ep 1 (Step 000000): Train loss 0.271, Val loss 6.545\n",
+      "Ep 1 (Step 000005): Train loss 0.244, Val loss 6.614\n",
+      "Every effort moves you?\"  \"Yes--quite insensible to the irony. She wanted him vindicated--and by me!\"  He laughed again, and threw back his head to look up at the sketch of the donkey. \"There were days when I\n"
      ]
     }
    ],
@@ -558,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "id": "68d162d6-bbb9-4d6d-82ee-1c410694f872",
    "metadata": {},
    "outputs": [],
@@ -586,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "id": "d8373461-7dad-47da-a489-3e23f0799b23",
    "metadata": {},
    "outputs": [
@@ -612,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
    "id": "cdd44873-d6c2-4471-a20f-f639b09fdcd3",
    "metadata": {},
    "outputs": [],
@@ -637,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "id": "c7d562e4-33f6-4611-9b75-6ad1cb441d3b",
    "metadata": {},
    "outputs": [],
@@ -652,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "id": "46eda9ea-ccb0-46ee-931b-3c07502b2544",
    "metadata": {},
    "outputs": [],
@@ -705,7 +693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "id": "4e3574a2-687d-47a2-a2f6-457fe9d595f1",
    "metadata": {},
    "outputs": [
@@ -713,8 +701,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training loss: 3.754748503367106\n",
-      "Validation loss: 3.559617757797241\n"
+      "Training loss: 3.7547483444213867\n",
+      "Validation loss: 3.5596189498901367\n"
      ]
     }
    ],
@@ -739,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "id": "1a79a4b6-fe8f-40c2-a018-e731dcf391b3",
    "metadata": {},
    "outputs": [
@@ -754,8 +742,8 @@
       "File already exists and is up-to-date: gpt2/1558M/model.ckpt.index\n",
       "File already exists and is up-to-date: gpt2/1558M/model.ckpt.meta\n",
       "File already exists and is up-to-date: gpt2/1558M/vocab.bpe\n",
-      "Training loss: 3.3046312597062855\n",
-      "Validation loss: 3.119514226913452\n"
+      "Training loss: 3.3046313656700983\n",
+      "Validation loss: 3.1195149421691895\n"
      ]
     }
    ],
@@ -812,7 +800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 19,
    "id": "31e0972b-e85e-4904-a0f5-24c3eacd5fa2",
    "metadata": {},
    "outputs": [],
@@ -838,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "id": "b641ee88-f9d4-43ec-a787-e34199eed356",
    "metadata": {},
    "outputs": [
@@ -882,7 +870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 21,
    "id": "c98f56f4-98fc-43b4-9ee5-726e9d17c73f",
    "metadata": {},
    "outputs": [],
@@ -893,7 +881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 22,
    "id": "b1f7853c-6e81-4f1f-a1d0-61e2c7d33a20",
    "metadata": {},
    "outputs": [
diff --git a/ch05/01_main-chapter-code/previous_chapters.py b/ch05/01_main-chapter-code/previous_chapters.py
index 996d0bb..a0a40b8 100644
--- a/ch05/01_main-chapter-code/previous_chapters.py
+++ b/ch05/01_main-chapter-code/previous_chapters.py
@@ -144,7 +144,6 @@ class FeedForward(nn.Module):
             nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
             GELU(),
             nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
-            nn.Dropout(cfg["drop_rate"])
         )
 
     def forward(self, x):
diff --git a/ch05/02_alternative_weight_loading/previous_chapters.py b/ch05/02_alternative_weight_loading/previous_chapters.py
index d772e86..d45661e 100644
--- a/ch05/02_alternative_weight_loading/previous_chapters.py
+++ b/ch05/02_alternative_weight_loading/previous_chapters.py
@@ -144,7 +144,6 @@ class FeedForward(nn.Module):
             nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
             GELU(),
             nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
-            nn.Dropout(cfg["drop_rate"])
         )
 
     def forward(self, x):
diff --git a/ch05/03_bonus_pretraining_on_gutenberg/previous_chapters.py b/ch05/03_bonus_pretraining_on_gutenberg/previous_chapters.py
index 46fd9d2..60d1b27 100644
--- a/ch05/03_bonus_pretraining_on_gutenberg/previous_chapters.py
+++ b/ch05/03_bonus_pretraining_on_gutenberg/previous_chapters.py
@@ -146,7 +146,6 @@ class FeedForward(nn.Module):
             nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
             GELU(),
             nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
-            nn.Dropout(cfg["drop_rate"])
         )
 
     def forward(self, x):
diff --git a/ch05/05_bonus_hparam_tuning/previous_chapters.py b/ch05/05_bonus_hparam_tuning/previous_chapters.py
index 1091fa3..7f3142e 100644
--- a/ch05/05_bonus_hparam_tuning/previous_chapters.py
+++ b/ch05/05_bonus_hparam_tuning/previous_chapters.py
@@ -149,7 +149,6 @@ class FeedForward(nn.Module):
             nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
             GELU(),
             nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
-            nn.Dropout(cfg["drop_rate"])
         )
 
     def forward(self, x):