diff --git a/esp32/neural_network.ipynb b/esp32/neural_network.ipynb
index 2f5797b..9c48489 100644
--- a/esp32/neural_network.ipynb
+++ b/esp32/neural_network.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 276,
+   "execution_count": 173,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 277,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,9 +30,9 @@
     "y_train = torch.tensor(y, dtype=torch.float32).view(-1, 1)\n",
     "\n",
     "model = nn.Sequential(\n",
-    "    nn.Linear(1, 50),\n",
+    "    nn.Linear(1, 16),\n",
     "    nn.ReLU(),\n",
-    "    nn.Linear(50, 1)\n",
+    "    nn.Linear(16, 1)\n",
     ")\n",
     "\n",
     "criterion = nn.MSELoss()\n",
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 278,
+   "execution_count": 175,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 279,
+   "execution_count": 176,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,12 +65,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 280,
+   "execution_count": 177,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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RaM4m8G+q9sJa9DL82ludmPCKVjU8mT+oFY62BjafuMDrv+6j0GTWLrcQwuIGDBiATqdDp9Nha2tL7dq1+eCDDygstOwo7l9//cWHH354W/ver6KkNEmBIzSVnp3PpGnf83XW2/xi+wlNTIfVUY6QgfDaXugxBSrV0jpm6fFtcGU0590rozkr4ftQiPq1aDQnpJonP/Rrga1Bz6rDybz9xwF5hFyIcq5r164kJiZy4sQJRo8ezXvvvccXX3xx3X75+fml9jM9PT1xcXEptfNZGylwhDYUhbxDSzn/TRs+yn6fEP0JFIMdtHoZRuyHxyaCRzWtU1qGwaj2w3p5EwQ0U0dzFr9SbDSnbW0vvnu+GQa9jr/2neOj5dEahxZCWJKdnR1+fn5Uq1aNoUOHEh4ezpIlS4ouK3388ccEBARQr149AOLj43n22Wdxd3fH09OTHj16cPr06aLzmUwmRo0ahbu7O5UqVeLtt9/mfxsX/O8lqry8PN555x0CAwOxs7Ojdu3azJo1i9OnT9OpUycAPDw80Ol0DBgwAACz2cyECROoUaMGDg4OBAcH88cffxT7OStWrKBu3bo4ODjQqVOnYjktSQoccf+YzZCwD6J+RZnWDrs/+lK78ASXseNik8HoRh6A7p+DW2Wtk94fvg1g0FroPA4MtteN5jzc0I+vnlFvpJ69NZZ5205rm1eIskRRID9bm6UUOiA5ODgUjdZERERw7Ngx1qxZw7JlyygoKKBLly64uLiwefNmtm7dirOzM127di065quvvmLu3LnMnj2bLVu2kJqayqJFi0r8mf369ePXX39l0qRJREdHM336dJydnQkMDOTPP/8E4NixYyQmJvLtt98CMGHCBObPn8+0adM4fPgwb7zxBn379mXjxo2AWog9+eSTPPbYY0RFRfHSSy8xZsyYe/58bofMgyPuD7MJfnoKTq0HQAdkKfb8rHQh9Pl3aRpUR9t8WjEYof1oqNsN/n5VLQAXvwKHF8Fj39KzWWXOpV3mi1XHeH/pYap4ONC5/o27lgsh/qUgBz4J0OZn/ycBbJ3u6lBFUYiIiGDVqlW89tprnD9/HicnJ2bOnImtrS0AP/30E2azmZkzZxY1G54zZw7u7u5s2LCBhx9+mIkTJzJ27FiefPJJAKZNm8aqVatu+nOPHz/Ob7/9xpo1awgPDwegZs2aRds9PT0B8PHxwd3dHVBHfD755BPWrl1LWFhY0TFbtmxh+vTpdOjQgalTp1KrVi2++uorAOrVq8fBgwf57LPP7urzuRMygiPuj4O/FxU3F9wa8W3hk7TL+5aApz6tuMXNv/3vaM6JVVdGc37h1Q416d0yELMCw3/Zx6Fz6VqnFUKUsmXLluHs7Iy9vT3dunWjV69evPfeewA0bty4qLgB2L9/PzExMbi4uODs7IyzszOenp7k5uZy8uRJ0tPTSUxMJDQ0tOgYo9FIixYtbvrzo6KiMBgMdOjQ4bYzx8TEkJOTw0MPPVSUw9nZmfnz53Py5EkAoqOji+UAioohS5MRHGF5pgLY8CkAsU3fJHxnCCazwuiH6vJYsEa/YVmjq6M59brD4lchYS8sHoru8GI+7P4159Ius/nEBV6cu4slw9vh5yYdk4W4KRtHdSRFq599hzp16sTUqVOxtbUlICAAo/Ha17OTU/HRoKysLEJCQvj555+vO4+3t/ed50W9JHansrKyAFi+fDmVKxe/tcDOzu6ucpQmKXCE5e3/FS7FYnKoRK+oJpjMCk82q8zwB2trncw6+dSHQWtg2yTYMAFOrMJmWhtmdP6InunVOJaSxSs/7WHhy62lpYMQN6PT3fVlIi04OTlRu/bt/T+xefPmLFy4EB8fH1xdXW+4j7+/Pzt27OCBBx4AoLCwkD179tC8efMb7t+4cWPMZjMbN24sukT1b1dHkEyma3NzNWjQADs7O+Li4m468lO/fn2WLFlSbN327dtv/SZLgVyiEpZVmA8b1UcdfzD3ICXXSItqHkx4qnHRtWNxAwYjtB8FL2+GgOaQl47DitdY7PEtdewziIpP470lh7VOKYTQQJ8+ffDy8qJHjx5s3ryZ2NhYNmzYwOuvv87Zs2cBGDFiBJ9++imLFy/m6NGjvPrqqyXOYVO9enX69+/Piy++yOLFi4vO+dtvvwFQrVo1dDody5Yt4/z582RlZeHi4sKbb77JG2+8wbx58zh58iR79+5l8uTJzJs3D4BXXnmFEydO8NZbb3Hs2DF++eUX5s6da+mPCJACR1javvmQHkeaoRLfpD+Aj4sd3/eVZpK3zSdIHc0Jfw8MtjicieAfm7d4xrCRX3fG8cuOOK0TCiHuM0dHRzZt2kTVqlV58sknqV+/PoMGDSI3N7doRGf06NG88MIL9O/fn7CwMFxcXHjiiSdKPO/UqVN5+umnefXVVwkKCmLw4MFkZ2cDULlyZd5//33GjBmDr68vw4cPB+DDDz/k3XffZcKECdSvX5+uXbuyfPlyatSoAUDVqlX5888/Wbx4McHBwUybNo1PPvnEgp/ONTrlfx+MrwAyMjJwc3MjPT39psN7ohQU5MKkZpCZwLiC/vyidGXhy60JqeapdbKyKeWo+qTVuT0ArDM1ZZx5MN8OeYSQah4ahxNCW7m5ucTGxlKjRg3s7eX+tLKspL/LO/n+lhEcYTl75kBmAomKJwtMD/Luow2kuLkXPkHw4moIfw/FYMuDhihWGN9ixfwvuJiZq3U6IYSwKlLgCMvIz8G8SZ33YHLhEzzSrDr9wsrpzMT3k8EI7d5A9/JmTP7NcdXl8K5pCmenPIo57azW6YQQwmpIgSMswrzzB/Q554k3e7PPszufPCE3FZcqnyAML60hOfQ/5Ck2BOfuonByKOz9sVRmURVCiLJOChxR+vIyyd/4NQDfK0/xTZ9WONjKTcWlzmDEt9s7rH7gd/aZa2NryoIlw+HnpyH9nNbphBBCU1LgiFKXvGYS9gVpnDL70bDbEIL85EZuS3r0wY7MqjuNTwqeIw8biFkL34dBQpTW0YQQQjNS4IhSlZ2eisPu7wFY7zeQPmE1b3GEuFc6nY5Pnm7KP27P0j3vE87Y14O8dFj6ulyuEhVOBXwwuNwprb9DKXBEqdr60/u4ksUpXSBP9Rsh993cJ672Nkzq3YzTuio8kfYGJoM9JO6HUxu0jibEfWFjYwNATk6OxknEvbr6d3j17/RuSasGUWrW7TtK65SFoANT+3dwd77z3ibi7jWr6sGwjrWYtC6GhaZOPM8/sHUi1OqkdTQhLM5gMODu7k5KSgqgToYnv2CVLYqikJOTQ0pKCu7u7hgM93bv5n0pcKZMmcIXX3xBUlISwcHBTJ48mVatWt1w344dO7Jx48br1nfv3p3ly5cDMGDAgKJpoK/q0qULK1euLP3w4rakZudzesmnPKi7TLJjbep07KN1pArptc51WH/sPN8ndKWX3SoMpzZAwj4IaKZ1NCEszs/PD6CoyBFlk7u7e9Hf5b2weIGzcOFCRo0axbRp0wgNDWXixIl06dKFY8eO4ePjc93+f/31F/n5+UV/vnjxIsHBwTzzzDPF9uvatStz5swp+rM1dC6tyD77cwvjzCtABx7d3wO9XP3Ugo1Bzze9gnlkUiZLTa3padgGW7+FZ+ZqHU0Ii9PpdPj7++Pj40NBQYHWccRdsLGxueeRm6ssXuB8/fXXDB48mIEDBwIwbdo0li9fzuzZsxkzZsx1+3t6Fp/pdsGCBTg6Ol5X4NjZ2ZVKhSfu3dL9CdQ6PhMnYx45Xk1wbPio1pEqtNo+LrzTNYjpyx+jp2EbypG/0aWeAk+54VtUDAaDodS+JEXZZdFfs/Pz89mzZ0+x1ut6vZ7w8HAiIyNv6xyzZs2id+/eODkVb3u/YcMGfHx8qFevHkOHDuXixYulml3cnpTMXL5dvJl+htUAOHYZB3LdW3MD2lTHo2ZzNpiC0SlmlG3faR1JCCHuK4sWOBcuXMBkMuHr61tsva+vL0lJSbc8fufOnRw6dIiXXnqp2PquXbsyf/58IiIi+Oyzz9i4cSPdunXDZDLd8Dx5eXlkZGQUW8S9UxSF//x1iD4Ff2KvK8BcpSXUDr/1gcLi9Hodnz/dhDm6HgCY9v4EWec1TiWEEPePVd8oMWvWLBo3bnzdDcm9e/fm8ccfp3HjxvTs2ZNly5axa9cuNmzYcMPzTJgwATc3t6IlMDDwPqQv/1YcTOJw9BH6GCIA0D/4Xxm9sSJVPBzp9PATRJlrYTTnkblJRnGEEBWHRQscLy8vDAYDycnJxdYnJyff8v6Z7OxsFixYwKBBg275c2rWrImXlxcxMTE33D527FjS09OLlvj4+Nt/E+KG0nMKGL/kMMONi7HVFUK1dlCjg9axxP94oU0NVns8B4B+90yUXBm9FEJUDBYtcGxtbQkJCSEiIqJondlsJiIigrCwsBKP/f3338nLy6Nv3763/Dlnz57l4sWL+Pv733C7nZ0drq6uxRZxbz5dGY19djy9jBvUFZ3+I6M3Vsig1/Hkc4M5pfjjZM7i8LLJWkcSQoj7wuKXqEaNGsUPP/zAvHnziI6OZujQoWRnZxc9VdWvXz/Gjh173XGzZs2iZ8+eVKpUqdj6rKws3nrrLbZv387p06eJiIigR48e1K5dmy5dulj67Qhg+6mL/LozntcNizBigpodoXpbrWOJm6jt505sXXUk1PvQTC6mZ2qcSAghLM/iBU6vXr348ssvGTduHE2bNiUqKoqVK1cW3XgcFxdHYmJisWOOHTvGli1bbnh5ymAwcODAAR5//HHq1q3LoEGDCAkJYfPmzTIXzn2QW2DiP4sOUl2XyFPGLerKTv/VNpS4pfZPDeeizhNfUlm9QO7FEUKUfzqlAnYmy8jIwM3NjfT0dLlcdYe+Xn2MSeti+N5hGt2VTVDnYejzu9axxG1IWP4pAbsmcMJcmYv9N9K6lrfWkYQQ4o7cyfe3VT9FJazLieRMpm48SS3dObopm9WVnf6jbShx2wI6DyVX70Qd/TlW/DGHApNZ60hCCGExUuCI26IoCu/+fYgCk8IEj2XoUCDoUelxVJbYu6Fr8SIAj2f/zuwtsRoHEkIIy5ECR9yWZQcS2X4qlSbGeFrlXGmG2vH6m8OFdbNrPxyT3oYW+uNsWruUhLTLWkcSQgiLkAJH3FJ2XiEfL48G4Guff9SVDZ8Av0YaphJ3xcUPfbA6L85AFvPB0iMaBxJCCMuQAkfc0uR1MSRl5NLZPYHaqRtAp5fRmzJM13YECjrCDfs4eWQ364+maB1JCCFKnRQ4okQnz2cxa8spAD7zWKqubPwMeNfTMJW4J1610dVXO76/bFzG+CWHyS24cR83IYQoq6TAETelKArvLTlMgUnhpern8UrcCDoDdHhH62jiXrV9A4Cehq0UpMYzS244FkKUM1LgiJtadTiJzScuYGvQM9rmD3Vl0+egUi1tg4l7VyUEqrfHiIlBxhVMWR9DSkau1qmEEKLUSIEjbii3wMSHy9Qbiz9omoZD/GbQ28ADb2ucTJSatiMB6GNcj01+Op+vOqZtHiGEKEVS4Igbmr01lnNpl/F3teOZjPnqyuYvgEc1bYOJ0lO7M/g2xoFcXjCs4Y89ZzlwNk3rVEIIUSqkwBHXOZ+Zx/frTwLwRcglDPHbwGAL7UdrnEyUKp0O2o4A4GX7NdiRzwdLj1ABu7cIIcohKXDEdb5ec5ysvEKaVHalbdx0dWXIQHCrom0wUfoaPgFuVXExpdHHdhO7z1xi6YHEWx8nhBBWTgocUczRpAwW7ooD4IumKejO7QKjPbQfpXEyYREGI7QZDsAIx1UYMPHpimgu58tj40KIsk0KHFFEURQ+Xh6NWYFuDX2pd2SSuqHlS+Dip204YTnN+oKDJ2655+jjso+E9Fx+2HxK61RCCHFPpMARRTYcP1/0WPj7QWcgMQpsnKDdG1pHE5Zk6wShLwMw2vEfQGH6xpOcz8zTNpcQQtwDKXAEAIUmc1G/qQFtquKz+2t1Q+jL4OSlYTJxX7QaAjaOuKVH088nlux8E5MiTmidSggh7poUOAKAX3fFE5OShYejDSMCoiH5ENi5QpvXtI4m7gdHT2jeD4BRjisA+HVnHKfOZ2mZSggh7poUOIKsvEImrjkOwKjwWjht/Vzd0PpV9YtPVAxhw0BnwD1pGwNrpFFoVvhCJv8TQpRRUuAIZm4+xcXsfGp4OfGc4y64cAzs3SHsVa2jifvJvSo0fhqANxxWoNfBP4eS2Bt3SeNgQghx56TAqeAuZOXxwyb1iZm3wmth3HRl9KbNa2DvpmEyoYkrE/+5xq7g5UbqqgkromXyPyFEmSMFTgX33boYsvNNNK7sRjfzBkg9CY6VIPQVraMJLfg2hDoPg2JmuP1K7G307Dp9ibXRKVonE0KIOyIFTgUWn5rDzzvOADDmoZroro7etB0Jds7aBRPautKE0+nIQl5r6QrAp/9EU2gyaxhKCCHujBQ4Fdg3a45TYFJoV9uLtlkrIS0OnHzUif1ExVWtDVRpCaY8XrJbjYejDSfPZ/P7nrNaJxNCiNsmBU4FFZ2YwaKocwC8E14NNn2pbmg/CmwdNUwmNPevJpx2e+cw6gF/ACZFnCC3QFo4CCHKBilwKqgvVx1DUeCRxv40Tv4bMs6BS4DaVFOIeo9ApTqQl05vQwT+bvYkpufy8444rZMJIcRtkQKnAtp1OpWIoykY9Dre7BQIm79SNzwwGmzstQ0nrINeD21fB8Bm5zRGdqwGwPfrY8jOK9QymRBC3BYpcCoYRVH4fOVRAHq1DKTG6QWQlQxuVaFZP43TCavSpBe4+ENmIk/ZRlKtkiMXs/OZu+201smEEOKWpMCpYDaduMCu05ewM+oZ2T4AtkxUN3R4C4y2mmYTVsZoB62Hqi8jJ/FG59oATN94kvTLBVomE0KIW5ICpwJRFIWvr7RkeKF1NXyi50HOBfCoAcHPaZxOWKWQgWDnBheO85jDfur4OJORW8jMzae0TiaEECWSAqcCWX8shf3xaTjYGBga5gPbJqkbOo4Bg4224YR1sneFli8CYNj6LaMfqgPA7C2xXMzK0zKZEEKU6L4UOFOmTKF69erY29sTGhrKzp07b7rv3Llz0el0xRZ7++I3viqKwrhx4/D398fBwYHw8HBOnDhh6bdRpv179KZ/m+pUOjgLLl8Cr7rQ+BmN0wmrFjoUDHZwdiddXGJpVNmV7HwT0zae1DqZEELclMULnIULFzJq1CjGjx/P3r17CQ4OpkuXLqSk3Hzqd1dXVxITE4uWM2fOFNv++eefM2nSJKZNm8aOHTtwcnKiS5cu5ObmWvrtlFmrjyRz6FwGTrYGXm7lCZFT1A0dx4DeoG04Yd1cfKGpeglTt/VbRj9cD4D5kWdIzpD/5oQQ1sniBc7XX3/N4MGDGThwIA0aNGDatGk4Ojoye/bsmx6j0+nw8/MrWnx9fYu2KYrCxIkT+e9//0uPHj1o0qQJ8+fPJyEhgcWLF1v67ZRJZrPCN1dGbwa2rYFH1HTISwefhtDgCY3TiTKhzeuADk6soqP7eVpU8yCv0MzkdTJyKoSwThYtcPLz89mzZw/h4eHXfqBeT3h4OJGRkTc9Lisri2rVqhEYGEiPHj04fPhw0bbY2FiSkpKKndPNzY3Q0NASz1mR/XMoiaNJmbjYGRkS4gY7pqkbOo1V5zsR4lYq1YIGjwOg2zqJN7uoozgLd8Vz9lKOlsmEEOKGLPrtduHCBUwmU7ERGABfX1+SkpJueEy9evWYPXs2f//9Nz/99BNms5k2bdpw9qzaB+fqcXdyzry8PDIyMootFYXJrDBxrTp6M6h9DVz3ToH8LPAPhqBHNU4nypQr7Rs49AetPXNoU6sSBSaFqRvkXhwhhPWxul/fw8LC6NevH02bNqVDhw789ddfeHt7M3369Ls+54QJE3BzcytaAgMDSzGxdVt2IIETKVm4OdgwqKkj7PxB3dDp/9SeQ0LcrsohUL09mAshcgqvd1afqPptdzwJaZc1DieEEMVZtMDx8vLCYDCQnJxcbH1ycjJ+fn63dQ4bGxuaNWtGTEwMQNFxd3LOsWPHkp6eXrTEx8ff6VspkwpNZr5dq94jMeSBmrjs+g4KL0PlFlDnYY3TiTKp3Uj1n3vn0dpPR2gNTwpMijxRJYSwOhYtcGxtbQkJCSEiIqJondlsJiIigrCwsNs6h8lk4uDBg/j7qx2Na9SogZ+fX7FzZmRksGPHjpue087ODldX12JLRbBkfwKnLmTj4WjDgEY2sPvKjd2d/iOjN+Lu1OoMfo2hIAd2zWREuDqKs2BnPEnp8kSVEMJ6WPwS1ahRo/jhhx+YN28e0dHRDB06lOzsbAYOVLtW9+vXj7Fjxxbt/8EHH7B69WpOnTrF3r176du3L2fOnOGll14C1CesRo4cyUcffcSSJUs4ePAg/fr1IyAggJ49e1r67ZQZJrPCd+vVUa/BD9TEace3YMqDqmFQ60GN04kyS6eDtiPV1zumERboQMvqHuSbzDKKI4SwKkZL/4BevXpx/vx5xo0bR1JSEk2bNmXlypVFNwnHxcWh/9eTPJcuXWLw4MEkJSXh4eFBSEgI27Zto0GDBkX7vP3222RnZzNkyBDS0tJo164dK1euvG5CwIpsxcFETp3Pxs3Bhn719TB9vrpB7r0R96pBT4j4ANLOoIv6hRGdn6DvrB38sjOOVzvWwsdV/jsUQmhPpyiKonWI+y0jIwM3NzfS09PL5eUqs1mh27ebOZacycjwOozMmQx750ONB6D/Uq3jifJg5w+w4k1wr4ry2l6enrGLPWcu8WLbGox7rMGtjxdCiLtwJ9/fVvcUlbh3q48kcyw5E2c7I4PqA/t+Vjd0+q+muUQ50rQPOFaCtDh0R/5mxJUnqn7ecYaUTLkXRwihPSlwyhlFUfhuvfrkVP821XDZ+TUoJqgdDlVDNU4nyg1bRwh9RX29ZSLta1eiaaA7eYVmftgkncaFENqTAqec2XDsPIfOZeBoa2BwfRMcWKhu6PQfbYOJ8qflS2DjBMkH0Z1aV/RE1Y/bz3BBOo0LITQmBU45oigKk670BurbuhruO78CxQz1uquTtAlRmhw9IaS/+nrLRDrW9Sa4ihu5BTKKI4TQnhQ45cjWmIvsi0vDzqjn5fq5cOgvdYOM3ghLaf0q6I1wejO6hL3FRnEuZedrHE4IUZFJgVOOXB29ea5VVSrt/ApQoEEPdWI2ISzBPRAaPa2+3jKRTvV8aODvSk6+iXmRpzWNJoSo2KTAKSd2nLrIzthUbA16htXPhuilgA46jr3lsULck6tNOKOXort4klc71QJgztbTZOUVahhMCFGRSYFTTkxep85a/HSLKnjv+kpd2fhp8KmvYSpRIfg2gDpdAAW2TaJbI39qeDmRfrmAX3fEaZ1OCFFBSYFTDkTFp7El5gJGvY4R9dLh+ErQ6aHDGK2jiYriahPO/b9iyE7hlQ41Afhh8ynyCk3a5RJCVFhS4JQDUzeoozc9mlbGd8+V0Zvg58CrtoapRIVSNQyqtAJTPuyYyhPNquDvZk9KZh5/7jmndTohRAUkBU4ZF5OSyarDyQC8Ue8CnFynPtXywFsaJxMVik53bRRn12xsC7MY3F4dxZm28SSFJrN22YQQFZIUOGXc9I3qfCMPN/Clyr5v1JVN+4BnDQ1TiQqpbjfwqgd56bBnDr1bBeLhaENcag7LDyZqnU4IUcFIgVOGJaRdZnGUOvz/Zt1kOL0ZDLYyeiO0oddD29fV15Hf46g38WJbtdD+fv1JzOYK19dXCKEhKXDKsJmbYykwKbSu4UHdw5PUlc37q3OTCKGFxs+CSwBkJcGBhfQLq46znZFjyZmsO5qidTohRAUiBU4ZdSk7n193qo/g/qdeIsRvB6M9tB+tcTJRoRltIexV9fXWSbjZG+jTuioAUzbEoCgyiiOEuD+kwCmj5kWe5nKBiYb+LjQ+/p26ssUgcPXXNpgQIQPAzg0unoBjyxnUrga2Rj374tLYfipV63RCiApCCpwyKCe/kLnbTgMwPigeXcJesHGEdm9oG0wIADsXaDlIfb1lIj7OdvRqoV42/f7KlAZCCGFpUuCUQQt2xpOWU0B1T3taxk5VV7YaAs7e2gYT4qrWQ8FgB+d2w5mtDHmgJga9js0nLnDwbLrW6YQQFYAUOGVMfqGZmZvVR8M/rHcaXdJBsHW51g9ICGvg7ANNn1dfb5lIoKcjjzVRL59O33RSw2BCiIpCCpwyZsn+BBLSc/F1NtL27Ax1Zeuh4OipbTAh/leb19SWITFrIOkQQx5Qm3CuOJhIfGqOxuGEEOWdFDhliNmsMG2j+tvvx3Vj0J8/CvZuEDZM42RC3EClWlD/cfX1tkk0CHClfR0vzApFo5BCCGEpUuCUIWujk4lJycLdXseDSbPUlWGvgYO7prmEuKmr7RsO/gFpcbzSQR3FWbg7ntTsfO1yCSHKPSlwyghFUZh6ZfTmk1rR6FNPgoMHtH5F42RClCCgGdToAIoJIqfQplYlGga4kltg5sfIM1qnE0KUY1LglBG7z1xiX1wajkYzXS7MU1e2HaE+kiuENbs6irN3PrrLl3j5yijOvMjT5BaYtMslhCjXpMApI2ZsUu9Z+KjaAQzpZ8DJW300XAhrV7MT+DWBghzYOYPujfyo4uFAanY+v+85q3U6IUQ5JQVOGXDyfBZro5OxpYDH0n9SV7Z7A2ydtA0mxO3Q6a6N4uyYjtF0mZfaqU04Z24+hUmacAohLEAKnDJg1pZYFAXGV96NTVYCuPhDixe1jiXE7avfAzyqw+VU2PcTz7YMxN3RhjMXc1h1OEnrdEKIckgKHCt3ISuPP/ecxY58nsn5TV3ZfjTYOGgbTIg7YTBC2HD19bbvcDQo9GtdDYDpG09KE04hRKmTAsfK/Rh5hrxCM29V2obt5WRwrQLN+2kdS4g716wvOHpBehwcXkS/NtWxM+rZfzadHbHShFMIUbqkwLFil/NN/Lj9DA7k8kLhn+rKDm+B0U7bYELcDRsHCL0yrcHWb/FysuXpkCrAtZvohRCitEiBY8X+3HuW1Ox8XnfZgF3eRfUehqZ9tI4lxN1rOQhsnCD5EMRE8FL7muh0sO5oCseTM7VOJ4QoR+5LgTNlyhSqV6+Ovb09oaGh7Ny586b7/vDDD7Rv3x4PDw88PDwIDw+/bv8BAwag0+mKLV27drX027ivTGaFWVticeIyA1miruzwDhhstA0mxL1w9ISQAerrrROp4eVE14Z+gIziCCFKl8ULnIULFzJq1CjGjx/P3r17CQ4OpkuXLqSkpNxw/w0bNvDcc8+xfv16IiMjCQwM5OGHH+bcuXPF9uvatSuJiYlFy6+//mrpt3JfrY1OJvZCNkPt12BfkAaVakPjZ7WOJcS9C3sV9EY4vRnO7mHIAzUB+DvqHEnpuRqHE0KUFxYvcL7++msGDx7MwIEDadCgAdOmTcPR0ZHZs2ffcP+ff/6ZV199laZNmxIUFMTMmTMxm81EREQU28/Ozg4/P7+ixcPDw9Jv5b76YdMpXMnmJcNydUXHseqTKEKUdW5VrhXrW7+hWVUPWtXwpMCkMGdbrLbZhBDlhkULnPz8fPbs2UN4ePi1H6jXEx4eTmRk5G2dIycnh4KCAjw9PYut37BhAz4+PtSrV4+hQ4dy8eLFm54jLy+PjIyMYos123PmErvPXGKIzT/YmzLBuz40fFLrWEKUnrYj1H9GL4MLJxjcXh3F+XVHHNl5hRoGE0KUFxYtcC5cuIDJZMLX17fYel9fX5KSbm9yr3feeYeAgIBiRVLXrl2ZP38+ERERfPbZZ2zcuJFu3bphMt24r82ECRNwc3MrWgIDA+/+Td0HMzefwp1MXrJZqa7oNBb0cj+4KEd8gqBuN0CBbZPoHORD9UqOZOQW8oe0bxBClAKr/tb89NNPWbBgAYsWLcLe3r5ofe/evXn88cdp3LgxPXv2ZNmyZezatYsNGzbc8Dxjx44lPT29aImPj79P7+DOnbmYzcrDSbxsXIa9OQf8GkPQY1rHEqL0XW3fsH8B+uxkBl1p3zB7a6y0bxBC3DOLFjheXl4YDAaSk5OLrU9OTsbPz6/EY7/88ks+/fRTVq9eTZMmTUrct2bNmnh5eRETE3PD7XZ2dri6uhZbrNXMzbFUUtJ50WaNuqLjf2T0RpRPVVtDYGsw5cP273kqpApuDmr7hrXRybc+XgghSmDRb05bW1tCQkKK3SB89YbhsLCwmx73+eef8+GHH7Jy5UpatGhxy59z9uxZLl68iL+/f6nk1oraXTmeV4xLsFNyIaA51OumdSwhLOfqvTi75+BozqZPaFUAZm2Wm42FEPfG4kMDo0aN4ocffmDevHlER0czdOhQsrOzGThwIAD9+vVj7NixRft/9tlnvPvuu8yePZvq1auTlJREUlISWVlZAGRlZfHWW2+xfft2Tp8+TUREBD169KB27dp06dLF0m/Hon7afgbXgou8YLxSEHb6P7UTsxDlVd2u4B0EeRmwezb921THxqBj5+lU9senaZ1OCFGGWbzA6dWrF19++SXjxo2jadOmREVFsXLlyqIbj+Pi4khMTCzaf+rUqeTn5/P000/j7+9ftHz55ZcAGAwGDhw4wOOPP07dunUZNGgQISEhbN68GTu7stvCILfAxPzI0wwzLsaOfAgMhdqdtY4lhGXp9dDmdfX19qn4OsBjwQEAzNoiozhCiLunUypgG9+MjAzc3NxIT0+3mvtxftsVz8Q/17HBfhS2FEK/JVCzg9axhLC8wnyY1BQyzsFjkzjs35NHJm3BoNex+e1OBLg7aJ1QCGEl7uT7W+5etQKKojB7ayzDjYvV4qZ6eyluRMVhtIXWr6qvt02ioZ8zbWpVwmRWmLvttKbRhBBllxQ4VmDbyYtkJ8fwjGGjuqLT/2kbSIj7LaQ/2LvBxRg4upyX2quPjP+6I44smfhPCHEXpMCxArO2xPK6YRE2OhPUehCq3fwJMyHKJTsXaDlYfb11Ih3reFPT24nMvEJ+22W981YJIayXFDgaO3U+izPH9vGkYbO6QkZvREUV+goY7eHcHvRxW2XiPyHEPZECR2Nztp5mtPF3DDoF6j0CVW49748Q5ZKzNzTto77eOpEnm1XBw9GGs5cus/rw7bV2EUKIq6TA0VB6TgFH92ygu2EnCjro/K7WkYTQVpvXQKeHmLU4pB6hb+tqAMyUR8aFEHdIChwN/borjhH8ov4huDf41Nc2kBBa86wBDXqqr7d+ywth1bA16Nlz5hJ74y5pGk0IUbZIgaORApOZI1v+pp3hMCa9DbqOY295jBAVwtX2DYf+wqcwmcebysR/Qog7JwWORlYeTGRQ3o8AKCEvgkc1jRMJYSUCmkLNjqCYIPK7opuN/zmYSHxqjqbRhBBlhxQ4Gjmy7ieC9afI1ztg7PCW1nGEsC5tR6r/3Psj9V3zaVfbC7MC82TiPyHEbZICRwN7Ys/zdNocAApavao+PSKEuKZmR/APhsLLsHMGg65M/LdgVzyZuQXaZhNClAlS4GggeuU0aukTyTa44dRxpNZxhLA+Ot21UZydM+hQzZHaPs5k5RWyUCb+E0LcBilw7rNzF1LpnDQbgMyWI8DeOpp9CmF1GvQAjxpw+RL6qJ94sa06ijN322mZ+E8IcUtS4Nxn0X9/jb8ulQsGb/w6D9M6jhDWS29Q58UBiPyOJ4N9ZOI/IcRtkwLnPspOT6VFvHrvTUrzUWBjr3EiIaxc0+fByRvS47E/9rdM/CeEuG1S4NxHMX9PwJ0szuiqENRlsNZxhLB+Ng5qjypQJ/4LrYqNQceeM5eIik/TNJoQwrpJgXOfmDOSqXtqPgCxwW+gN9ponEiIMqLlILB1hpTD+CRv5rFgmfhPCHFrUuDcJ2eXfIgDuRykFi279tc6jhBlh4MHhAxQX2+dWDTx34qDiSSkXdYulxDCqkmBcz9cOk1AzK8AHAx6Ayd7Gb0R4o60fhX0NnBmKw1Nx2ld0xOTWWFe5GmtkwkhrJQUOPdB2ooPMFLIZnNjOnZ9Wus4QpQ9bpWhybPq660TGdSuJgC/7ogjO69Qw2BCCGslBY6lJR/B9cRfAERWH0aAu4PGgYQoo6424Ty6nM5eaVSv5EhGbiF/7j2rbS4hhFWSAsfC8la/hx6F5aZWhId31TqOEGWXdz2o1x1Q0EdO4sUr9+LM3hKLWSb+E0L8DylwLCluB3YnV1Go6Fnh/RLNq3ponUiIsu1q+4b9C3m6jgFXeyOnL+YQcTRF01hCCOsjBY6lKArmNeMB+N3Uga4d2mscSIhyoGooVA0DcwGOe2fwXGhVAGZtOaVxMCGEtZECx1Ji1qKPjyRPsWGB43N0a+SndSIhyoerozi75zCgmQcGvY7tp1I5nJCuaSwhhHWRAscSzGaUte8BMNf0MN3btsBokI9aiFJR52Hwrg/5mfif+IXujf0BmfhPCFGcfOtawuG/0CUfIkNxYK7uCXq3rKp1IiHKD73+2hNV26fyUmu1wFm6P4GUjFwNgwkhrIkUOKXNVADrPgJgRuGjPNSiPm6OMrGfEKWq8dPgWgWyUwi++A8tqnlQYFL4cfsZrZMJIayEFDilbe98uBTLecWV2aZuDGxbQ+tEQpQ/BhsIG6a+3jaJQW3VUdKftp8ht8CkYTAhhLWQAqc05efAxs8AmFz4BGFBVanh5aRxKCHKqeb9wN4dUk/xsH43VTwcuJRTwF97z2mdTAhhBe5LgTNlyhSqV6+Ovb09oaGh7Ny5s8T9f//9d4KCgrC3t6dx48asWLGi2HZFURg3bhz+/v44ODgQHh7OiRMnLPkWbs+OaZCVzFnFm19NnYuaAgohLMDOGVoNBsCwbSIDwqoBMHtrLIoiE/8JUdFZvMBZuHAho0aNYvz48ezdu5fg4GC6dOlCSsqNJ+batm0bzz33HIMGDWLfvn307NmTnj17cujQoaJ9Pv/8cyZNmsS0adPYsWMHTk5OdOnShdxcjW8wrNmBc+4t+LLgGWr5eRBWq5K2eYQo71q9DEZ7SNjH8z6ncbYzEpOSxcbj57VOJoTQmE6x8K86oaGhtGzZku+++w4As9lMYGAgr732GmPGjLlu/169epGdnc2yZcuK1rVu3ZqmTZsybdo0FEUhICCA0aNH8+abbwKQnp6Or68vc+fOpXfv3rfMlJGRgZubG+np6bi6upbSO4UCk5kHPltHYkYunz8dzLMtAkvt3EKIm1g+GnbNhFoP8oH7x8zeGkv7Ol78OChU62RCVEhZeYVsP3mRB4N80Ot1pXruO/n+tugITn5+Pnv27CE8PPzaD9TrCQ8PJzIy8obHREZGFtsfoEuXLkX7x8bGkpSUVGwfNzc3QkNDb3rOvLw8MjIyii2WsPJQEokZeXg52/F4cIBFfoYQ4n+EDQedHk6uY0jdLPQ62HziAseTM7VOJkSF9PvueF6av5uX5u/WNIdFC5wLFy5gMpnw9fUttt7X15ekpKQbHpOUlFTi/lf/eSfnnDBhAm5ubkVLYKBlRlZyC0x4OdvSJ7Qa9jYGi/wMIcT/8KwBDZ8AwO/gdB5uoM4aPlsm/hPivjOZFeZuOw3Ag0E+mmapEE9RjR07lvT09KIlPj7eIj/nmRaBbHnnQYY8UNMi5xdC3MTVif8O/8WrTY0A/LXvHBez8jQMJUTFs+5oCmcu5uDmYMOTzStrmsWiBY6XlxcGg4Hk5ORi65OTk/Hzu3FvJj8/vxL3v/rPOzmnnZ0drq6uxRZLsbcx4GRntNj5hRA34B8MtR4ExUzjuB8JruJGfqGZn3fEaZ1MiArl6sjpc62q4mir7XehRQscW1tbQkJCiIiIKFpnNpuJiIggLCzshseEhYUV2x9gzZo1RfvXqFEDPz+/YvtkZGSwY8eOm55TCFEBXGnCqdv3E6+0dANgfuQZ8gpl4j8h7ofDCelEnrqIQa+jf5tqWsex/CWqUaNG8cMPPzBv3jyio6MZOnQo2dnZDBw4EIB+/foxduzYov1HjBjBypUr+eqrrzh69Cjvvfceu3fvZvjw4QDodDpGjhzJRx99xJIlSzh48CD9+vUjICCAnj17WvrtCCGsVY0HIKAZFF7m4ay/8Xez50JWHkuiErROJkSFMGfraQC6N/bH381B2zDchwKnV69efPnll4wbN46mTZsSFRXFypUri24SjouLIzExsWj/Nm3a8MsvvzBjxgyCg4P5448/WLx4MY0aNSra5+233+a1115jyJAhtGzZkqysLFauXIm9vb2l344QwlrpdEWjOIZdP/BiS/UGx1lbZOI/ISztfOa1XyZebFtd2zBXWHweHGtkqXlwhBAaM5vguxaQeorLD35E8zV1uFxg4peXQmlT20vrdEKUWxPXHmfi2hM0q+rOolfbWuznWM08OEIIcV/pDdDmNQAcdk/j2ebqgwez5JFxISwmr9DET9vPAPCiFTWYlgJHCFG+BD8PTj6QcZZhXvvQ6SDiaAqnzmdpnUyIcmnp/kQuZOXj72ZP10Y3fppZC1LgCCHKFxt7aP0KAD4HphNeT700dfUGSCFE6VEUpejR8H5h1bExWE9ZYT1JhBCitLQYBLYucD6aUTXUuXD+2HOWtJx8jYMJUb7siE3lSGIG9jZ6nmtlXf0XpcARQpQ/Du7QYgAAQSdnUd/fVb3ZeKdM/CdEabp6f9tTzavg7mircZripMARQpRPrV8FvQ26uEjeapAOwPxtZygwmTUOJkT5cOZiNmuj1a4CA63k0fB/kwJHCFE+uQZAcC8AOpz/GS9nO5IycllxMPEWBwohbsfcbadRFOhQ15vaPi7XNmSdhxVvQeaNG2DfL1LgCCHKrzYjAB2G4ysYEayO3MjEf0Lcu8zcAn7ffRaAQe3+59HwTV/Azhnwx4saJLtGChwhRPnlXReCHgHgmby/sDPqOXA2nd1nLmkcTIiy7bfdZ8nKK6S2jzPt6/xrEs3UWNg9W33d4R1twl0hBY4Qony70r7B/sgfDGhkA8CszTLxnxB3y2RWmLtN/W/oxbY10Ol01zau/xjMBVCrM9TsoFFClRQ4QojyLbAlVGsL5gJesVsNwOojScRdzNE4mBBl09roZOJTL+PuaMMTzSpf25C4Hw7+rr4Of0+TbP8mBY4QovxrOwIAj+if6VrbHrMCc7bJKI4Qd+PqxH7Pt6qKg63h2oa176n/bPws+De5/8H+hxQ4Qojyr87D4NMA8rN4p9IWAH7bFU9GboHGwYQoWw6dS2dHbCpGvY4Xwqpd23ByPZxcB3obePD/tAv4L1LgCCHKP52uaBSnesyPNPS2ITvfxG+74jUOJkTZMnurOnrTvbE//m4O6kqz+droTctB4FFdk2z/SwocIUTF0OgpcAtEl32e96oeANT+VIUy8Z8QtyUlM5el+xMAePHfj4YfWQSJUWp7lAfe0ibcDUiBI4SoGAw2EDYMgJCEn/ByNHAu7TKrjyRrHEyIsuGnyDMUmBSaVXWnaaC7utJUABEfqq/bvg5OXjc9/n6TAkcIUXE07wcOHugvxTK+9kngWi8dIcTN5RaY+HH7GQBealfz2oY9c+FSLDj5qO1RrIgUOEKIisPWCVoNAaBr+kJsDTr2nLlEVHyatrmEsHJ/7T3HpZwCqng40KWhr7oyLws2fqa+7vgO2DlrF/AGpMARQlQsrV4GowM2yfsZVUvtlSOjOELcnNmsMGvLKQAGtq2B0XCldIicAtnnwbMmNO+vYcIbkwJHCFGxOFWC5i8A0Nf0FwArDiZyLu2ylqmEsFobj5/n5PlsXOyMPNuiiroy6zxsm6S+fvBd9R43KyMFjhCi4gkbDjoDzuc281yVVExmhfnbTmudSgirNPPK6E3vVoG42F8pZDZ9AflZENAMGvTULlwJpMARQlQ8HtWg0ZMAjHBYAcAvO+PIzivUMpUQVudwQjpbYy5i0Ovo36a6uvLfDTXD3we9dZYS1plKCCEs7crEf75nV9LWM4PM3EL+2HNW41BCWJer96d1a+RHFQ9HdaUVNdQsiRQ4QoiKya8x1OqMTjEzrtI6AOZsjcVsVjQOJoR1SM64NrHfS+2vPBpuZQ01SyIFjhCi4mo3EoC6iUuoYZ/D6Ys5RBxN0TaTEFZifuRpCkwKLat7XJvYr6ih5jNW0VCzJFLgCCEqrurtIaA5usJcPgrYClD0OKwQFVlOfiE/74gDYNDVif3+3VCzk3U01CyJFDhCiIpLpysaxQm7+Beu+jy2n0rl0Ll0bXMJobE/954jLaeAqp6OPNTA9/qGmp41SjzeGkiBI4So2IIeBc9a6PPSea/ybgBmy8R/ogIzm5Wi/wZebFsdg14HRxZbZUPNkkiBI4So2PQGtUkg8GjOX9hQyNIDCaRk5GocTAhtRBxNIfZCNi72Rp5pEXiloeYH6kYra6hZEilwhBCiSW9w9sU2O5HXfaIoMCnMjzyjdSohNDFzs3of2vOhVXGyM1p1Q82SWLTASU1NpU+fPri6uuLu7s6gQYPIysoqcf/XXnuNevXq4eDgQNWqVXn99ddJTy9+PVyn0123LFiwwJJvRQhRntnYQ+uhAAxgCTrM/LzjDLkFJo2DCXF/HTybzo7YVIx6HQPaVC/eULPD21bXULMkFi1w+vTpw+HDh1mzZg3Lli1j06ZNDBky5Kb7JyQkkJCQwJdffsmhQ4eYO3cuK1euZNCgQdftO2fOHBITE4uWnj17WvCdCCHKvRYvgp0rLhkxPOt6hEs5Bfy195zWqYS4r64+RfhoE3/83RyKN9QMGaBtuDukUxTFIrNaRUdH06BBA3bt2kWLFi0AWLlyJd27d+fs2bMEBATc1nl+//13+vbtS3Z2NkajUQ2t07Fo0aK7LmoyMjJwc3MjPT0dV1fXuzqHEKIcWjMOtn5LkltTWie/TW0fZ9a88QA6nU7rZEJYXGL6Zdp/tp5Cs8LS4e1o7J4Pk5qqPaeenlPU3kRLd/L9bbERnMjISNzd3YuKG4Dw8HD0ej07duy47fNcfRNXi5urhg0bhpeXF61atWL27NmUVKfl5eWRkZFRbBFCiOuEDgWDLX7pUbS3iyEmJYuNx89rnUqI+2LetjMUmhVCa3jSuIobbP7S6htqlsRiBU5SUhI+Pj7F1hmNRjw9PUlKSrqtc1y4cIEPP/zwustaH3zwAb/99htr1qzhqaee4tVXX2Xy5Mk3Pc+ECRNwc3MrWgIDA+/8DQkhyj9Xf2jSC4B33VcD13rxCFGeZecV8ssO9cb6l9rXVBtq7pqlbrTihpoluePEY8aMueFNvv9ejh49es/BMjIyeOSRR2jQoAHvvfdesW3vvvsubdu2pVmzZrzzzju8/fbbfPHFFzc919ixY0lPTy9a4uPj7zmfEKKcajsC0FE3fQv19GfZfOICx5IytU4lhEX9vjuejNxCqldypHOQT5lpqFmSOy5wRo8eTXR0dIlLzZo18fPzIyWleE+XwsJCUlNT8fPzK/FnZGZm0rVrV1xcXFi0aBE2NjYl7h8aGsrZs2fJy8u74XY7OztcXV2LLUIIcUNedSDoEQDeqxQByMR/onwrNJmZeeXf8UHta6JPPvCvhprjNUx2b4y33qU4b29vvL29b7lfWFgYaWlp7Nmzh5CQEADWrVuH2WwmNDT0psdlZGTQpUsX7OzsWLJkCfb29rf8WVFRUXh4eGBnZ3f7b0QIIW6m3RtwdBmhWRH48SiLovS81bUeXs7y/xhR/qw4lMTZS5ep5GTLMyFVYMFIdUPjZ8A/WNNs98JiF9Xq169P165dGTx4MDt37mTr1q0MHz6c3r17Fz1Bde7cOYKCgti5cyegFjcPP/ww2dnZzJo1i4yMDJKSkkhKSsJkUuejWLp0KTNnzuTQoUPExMQwdepUPvnkE1577TVLvRUhREVTpQVUa4deKWSMxzryC838vD1O61RClDpFUZi+8SQA/cKqYx+/uUw11CyJRe8a+vnnnwkKCqJz5850796ddu3aMWPGjKLtBQUFHDt2jJycHAD27t3Ljh07OHjwILVr18bf379ouXrfjI2NDVOmTCEsLIymTZsyffp0vv76a8aPL7vDaEIIK3SlCeejBatwJYsft58hr1Am/hPly7aTFzmckIGDjYF+rQNhzZXv0jLSULMkFpsHx5rJPDhCiFtSFJjWDpIPMc3wPJ9mP8oXTzdRe/MIUU68MGsHm09cYECb6rxX8xj8MVBtqDkiyip7TlnFPDhCCFGm6XRXnqiC/vp/sCOfWVtiS5xzS4iy5EhCBptPXECvg0FhVcpkQ82SSIEjhBA30/BJcKuKQ8ElnrfdzNGkTLadvKh1KiFKxYxN6r03jzQJIDD2tzLZULMkUuAIIcTNGIzQZjgAr9n/gwET0zed0jiUEPfu7KUclh5IBGBomG+ZbahZEilwhBCiJM36goMnnvkJPGLcxabj5zl0Ll3rVELck9lbTmMyK7StXYkGp38ssw01SyIFjhBClMTWCVqp7WLecvoHUGQUR5Rp6TkFLNilTnswrJU7bJukbnjwXTCUPLFuWSIFjhBC3EqrIWB0IDDvBO30h1h+IIEzF7O1TiXEXflpxxly8k0E+bkQdnZ2mW6oWRIpcIQQ4lacKkHzfgCMdV2JWYEfNssojih7cgtMzNl6GoBRLWzR7Z6tbgh/r0w21CxJ+Xo3QghhKWHDQGegYe4+GulO8dvus5zPvHH/OyGs1aJ957iQlUeAmz3hST9caaj5INTsqHW0UicFjhBC3A6PatDoKQDGuK4iv9DMnK3ShFOUHWazwg9X7h97Ozgf/aE/1A3h72kXyoKkwBFCiNt1ZeK/tnlbqapL5sftZ8jMLdA4lBC3Z010MqcuZONqb+TR89PVlWW8oWZJpMARQojb5dcIaj+EDjNvOa8iM7eQX3ZIE05h/RRFYeoGdWK//9RPwRi7vlw01CyJFDhCCHEnrjTh7G5ahxfpzNoSK004hdWLPHWRqPg07I3wVOoP6spy0FCzJFLgCCHEnajWFiq3wGDOZ7jTWlIy81i095zWqYQo0ffr1dGbD2rHYJO8H2ydof2bGqeyLClwhBDiTuh0RaM4z+lW48RlZmw6hcksTTiFdYqKT2NLzAXs9SaevHTlsfA2r4Ozt7bBLEwKHCGEuFP1HoFKtbErzORF+42cupDN6sNJWqcS4oa+Xx8DwMdV92JMPw1O3uq0B+WcFDhCCHGn9Hr1N2BgiO1KbChk2saTKIqM4gjrcjw5k9VHknHS5dIj/Sd1ZYd3yk1DzZJIgSOEEHcjuDc4++GSn8IzttvYfzadyJMXtU4lRDFXn5ya4L8Z4+Xy11CzJFLgCCHE3TDaQeuhALzh8A86zHx/5ctECGsQn5rDkv0JeJLBI5m/qSvLWUPNkkiBI4QQd6vFQLBzxTvvDF0M+9gSc4G9cZe0TiUEANM3ncRkVpjgtRJDQTb4Ny13DTVLIgWOEELcLXs3aPEiAGPcVgHw3boYLRMJAUBKRi6/7T5LoC6Zh3KWqysfer/cNdQsScV5p0IIYQmth4LBluo5h2ilP8q6oykcOpeudSpRwc3aEkt+oZmP3f5GX44bapZEChwhhLgXLn4Q/BwA4zzWADKKI7SVlpPPT9vP0FB3mgdyN6gry2lDzZJIgSOEEPeqzeuAjkbZkdTTx7PycBLHkjK1TiUqqHnbzpCdb+J9pyvdwstxQ82SSIEjhBD3yqs21H8MgPcrRQAwZb2M4oj7LyuvkDnbYmmjP0SLwr3lvqFmSaTAEUKI0nClfUNo1joCuMCyAwmcOp+lbSZR4cyPPE16Th7j7ReqK1q8WK4bapZEChwhhCgNlUOgent0SiHv+2zArCDz4oj7KjuvkB82naK7fif1zCfVhpoPvKV1LM1IgSOEEKXlyijOgzkrcSOLRfvOEZ+ao20mUWH8uP0MmTmXGWv3u7qiAjTULIkUOEIIUVpqdQbfxhgKc3jXZwsms8LUjTKKIywvJ18dveltWE8VJbHCNNQsiRQ4QghRWnQ6aDsCgB55y7Anjz92nyUx/bLGwUR59/P2OC5nZ/CGzSJ1RQVpqFkSKXCEEKI0NXwC3Ktik5fKWz67yDeZmb7xlNapRDl2Od/E9E0nGWRYQSXSwKNGhWmoWRKLFjipqan06dMHV1dX3N3dGTRoEFlZJT9V0LFjR3Q6XbHllVdeKbZPXFwcjzzyCI6Ojvj4+PDWW29RWFhoybcihBC3x2CEsNcA6GNeigETv+6MIyUjV+Ngorz6eccZzFkXGGqzTF3RueI01CyJRQucPn36cPjwYdasWcOyZcvYtGkTQ4YMueVxgwcPJjExsWj5/PPPi7aZTCYeeeQR8vPz2bZtG/PmzWPu3LmMGzfOkm9FCCFuX7O+4FgJ+6x4hvkeIq9QOo0Ly8gtMDF90yleMy7CkdwrDTWf0DqWVbBYgRMdHc3KlSuZOXMmoaGhtGvXjsmTJ7NgwQISEhJKPNbR0RE/P7+ixdXVtWjb6tWrOXLkCD/99BNNmzalW7dufPjhh0yZMoX8/HxLvR0hhLh9to7Q6mUAhuiXAgq/7IiTe3FEqftlRxz2WXH0NaoTTFa0hpolsdinEBkZibu7Oy1atChaFx4ejl6vZ8eOHSUe+/PPP+Pl5UWjRo0YO3YsOTnXHrOMjIykcePG+Pr6Fq3r0qULGRkZHD58+Ibny8vLIyMjo9gihBAW1Wow2DjifOkILwWcJt9kltmNRanKLTAxbeNJRhn/wIbCCtlQsyQWK3CSkpLw8fEpts5oNOLp6UlSUtJNj3v++ef56aefWL9+PWPHjuXHH3+kb9++xc777+IGKPrzzc47YcIE3NzcipbAwMC7fVtCCHF7HD2heX8AXrNV741YuCues5dkXhxROhbsjMM76xhPGLaqKypgQ82S3HGBM2bMmOtuAv7f5ejRo3cdaMiQIXTp0oXGjRvTp08f5s+fz6JFizh58u6vX48dO5b09PSiJT4+/q7PJYQQty1sGOiNuCVF0ifwIgUmRUZxRKnILTAxdeNJ3jYuUFc0erpCNtQsifFODxg9ejQDBgwocZ+aNWvi5+dHSkpKsfWFhYWkpqbi5+d32z8vNDQUgJiYGGrVqoWfnx87d+4stk9ycjLATc9rZ2eHnZ3dbf9MIYQoFe6B6hfPgQWMdlzBz7zA77vPMrRDbapWctQ6nSjDfow8Q62sPXSwPYCit0H34H+1jmR17ngEx9vbm6CgoBIXW1tbwsLCSEtLY8+ePUXHrlu3DrPZXFS03I6oqCgA/P39AQgLC+PgwYPFiqc1a9bg6upKgwYN7vTtCCGEZbV9HQDPMyt5pkYehWaFyetOaBxKlGVZeYVM23CCMcZfAdBV4IaaJbHYPTj169ena9euDB48mJ07d7J161aGDx9O7969CQgIAODcuXMEBQUVjcicPHmSDz/8kD179nD69GmWLFlCv379eOCBB2jSpAkADz/8MA0aNOCFF15g//79rFq1iv/+978MGzZMRmmEENbHtyHUeRhQeMd1NQB/7TtH7IVsbXOJMmv2llha526hiT4WpYI31CyJRZ8l+/nnnwkKCqJz5850796ddu3aMWPGjKLtBQUFHDt2rOgpKVtbW9auXcvDDz9MUFAQo0eP5qmnnmLp0qVFxxgMBpYtW4bBYCAsLIy+ffvSr18/PvjgA0u+FSGEuHttRwLgFfMXPWsbMZkVJkXIKI64c2k5+czZdJw3jQsB0FXwhpol0SmKomgd4n7LyMjAzc2N9PT0YnPsCCGERSgKzHoIzu4iJfhVWu1oh04Ha954gNo+LlqnE2XIZyuPkrl5Gh/ZzEFx8kb3elSF6jl1J9/fMhuQEEJYmk5XNIrjc/RnHgtyRlHg6zXHtc0lypSUzFx+23qUEca/ANBJQ80SSYEjhBD3Q73u4FUX8tJ5128nOh2sOJjE/vg0rZOJMuL79Sd53rwUb106ikeNonmWxI1JgSOEEPeDXg9t1CeqfA7P4ulg9b6Jz1fd/bxhouI4l3aZf3Yc4mWjOmmkrvO7YLTVOJV1kwJHCCHulybPgos/ZCYytvJBbA16tsZcZPOJ81onE1ZucsQJXtH9ibNOGmreLilwhBDifjHaQetXAfCMmkqf0CoAfL7yGGZzhXveQ9ym2AvZRO7ZSx/DWnWFNNS8LfIJCSHE/RQyAOzc4OIJ3giMwcnWwMFz6fxz6OY9+kTF9sWqo4w0/IatziQNNe+AFDhCCHE/2btCy0EAuO6ZwuD26gy0X64+RoHJrGUyYYX2xl3izKHt0lDzLkiBI4QQ91voK2Cwg7O7GFI9mUpOtsReyOb33We1TiasiKIoTFgRLQ0175IUOEIIcb+5+ELT5wBw3DmZ4Q/WBmDi2uNczjdpmUxYkTVHkrGJ20wHg9pQE2moeUekwBFCCC20eR3QwYnV9KmRRRUPB1Iy85i9NVbrZMIKFJrMfPpPNO9cGb2Rhpp3TgocIYTQQqVa0OBxAGy3T+bNh+sB8P36GM5n5mmZTFiBhbvjCUpdR7D+FIqtkzTUvAtS4AghhFautG/g4B88Xq2Q4CpuZOebpIVDBZedV8ik1dG8VdRQc4Q01LwLUuAIIYRWKjeHGg+AYkK/Yyr/fbQBAAt3xXEsKVPjcEIrP2w+xUO5K6mhT0Zx8oawYVpHKpOkwBFCCC1dHcXZO4+WPtCtkR9mBT5eEa1pLKGNlMxcftp0RBpqlgIpcIQQQku1HgS/JlCQAzt/YEy3IGwMOjYdP8+GYylapxP32ZerjvGcSRpqlgYpcIQQQks6HbQdob7eOZ1qLjoGtKkOwMfLoymUyf8qjINn04nYc0QaapYSKXCEEEJrDXqCezXIuQj7fmJ4pzq4O9pwIiWLhbvjtU4n7gNFUXh/6WGGGRZLQ81SIgWOEEJozWCENq+pryMn42anY2TnOgB8vfo4mbkFGoYT98PSA4kkxR2jr2GNuiL8PWmoeY/k0xNCCGvQrC84ekFaHBxeRJ/W1ajp5cTF7Hwmr4vROp2woMv5Jj5dEc1o4+9qQ82anaBWJ61jlXlS4AghhDWwcYDQl9XX6z/GxpTLu4+pj43P3hJLTIo8Nl5eTd90Eo+Mo9JQs5RJgSOEENYi9BVwCYBLsbDhEzrV8yG8vi+FZoX3lhxBURStE4pSlpB2mWkbTxZvqBnQVNNM5YUUOEIIYS3sXeGxierryClwdg/jHm2ArVHPlpgLrDyUpGk8Ufom/HOUZqaD0lDTAqTAEUIIa1K3CzTpBYoZ/h5GVTcDr3SoBcCHy45It/FyZGvMBZbuP8dY46+ANNQsbVLgCCGEten6KTh5w/lo2PwVQzvUorK7AwnpuXy/QW44Lg/yCk28u/gQ3fU7aKI/BbbO0lCzlEmBI4QQ1sbRE7p/ob7e/BUOqUd490qfqukbT3H6QraG4URp+GHTKeIupDPG9jd1RZvXpKFmKZMCRwghrFGDnhD0KJgL4e/hdKlfifZ1vMg3mXl/6WG54bgMi0/NYfK6GHob1lOVJHW0ThpqljopcIQQwhrpdPDIV2DvDolR6CK/473HG2Jj0LH+2Hn+kRuOyyRFURi/5DCGwhzetFusruzwDti5aJqrPJICRwghrJWLH3SdoL5eP4FaukRe7VgbgPFLDpN+WWY4LmtWH0lm3dEUhtj8g7v5EkhDTYuRAkcIIaxZ8HNQOxxMefD3cF7tWIOa3k6cz8zjs5VHtU4n7kBOfiHvLzmMJxkMtV2urpSGmhYjBY4QQlgznQ4enag+ZRO/Hbu9s/nkicYA/LIjjl2nU7XNJ27bl6uOk5CeyxinZdiZcsA/WBpqWpBFC5zU1FT69OmDq6sr7u7uDBo0iKysrJvuf/r0aXQ63Q2X33//vWi/G21fsGCBJd+KEEJoxz0QHnpffb32fVp7ZNG7ZSAAY/86SF6hzI1j7fbGXWLOtliq6FJ4Wlmlrgx/XxpqWpBFP9k+ffpw+PBh1qxZw7Jly9i0aRNDhgy56f6BgYEkJiYWW95//32cnZ3p1q1bsX3nzJlTbL+ePXta8q0IIYS2Ql6Eam2hIBuWvs7YrkF4OdsSk5LFtA2ntE4nSpBXaOKdPw6gKPCtz3L05gJpqHkfWKzAiY6OZuXKlcycOZPQ0FDatWvH5MmTWbBgAQkJCTc8xmAw4OfnV2xZtGgRzz77LM7OzsX2dXd3L7afvb29pd6KEEJoT6+HxyeD0R5ObcDt2ALGPdYQgCnrY4hJufnouNDWlPUnOZGSRRunc4Skr1FXSkNNi7NYgRMZGYm7uzstWrQoWhceHo5er2fHjh23dY49e/YQFRXFoEGDrts2bNgwvLy8aNWqFbNnzy5xToi8vDwyMjKKLUIIUeZUqgWd/k99veq/PFYDOtbzJt9k5s3f91NoMmubT1wnOjGD79ers09P9FqirpSGmveFxQqcpKQkfHx8iq0zGo14enqSlHR78zfMmjWL+vXr06ZNm2LrP/jgA3777TfWrFnDU089xauvvsrkyZNvep4JEybg5uZWtAQGBt75GxJCCGsQNgwqh0BeOrplo/ikZyNc7IxExacxY7NcqrImhSYz7/x5gEKzwus1EvBJ3gzSUPO+ueMCZ8yYMTe9EfjqcvTovT+6ePnyZX755Zcbjt68++67tG3blmbNmvHOO+/w9ttv88UXX9z0XGPHjiU9Pb1oiY+Pv+d8QgihCb0BekxRvyiP/0PA2RWMe0xt4zBxzQmOJWVqHFBcNX3TKQ6cTcfF3sBr5p/UlS0GSkPN++SOC5zRo0cTHR1d4lKzZk38/PxISUkpdmxhYSGpqan4+fnd8uf88ccf5OTk0K9fv1vuGxoaytmzZ8nLy7vhdjs7O1xdXYstQghRZvnUv9aY8Z+3eTrIns5BPuSbzIz6LYoCuVSluUPn0vlmzXEAZoScwyY56kpDzbe1DVaBGO/0AG9vb7y9b90QLCwsjLS0NPbs2UNISAgA69atw2w2ExoaesvjZ82axeOPP35bPysqKgoPDw/s7Oxu/QaEEKI8aPcGHPkbUg6jW/kOE578noe+2cThhAymrI9hZHhdrRNWWLkFJt5YGEWhWaF7Ay9ax165JCUNNe8ri92DU79+fbp27crgwYPZuXMnW7duZfjw4fTu3ZuAgAAAzp07R1BQEDt37ix2bExMDJs2beKll1667rxLly5l5syZHDp0iJiYGKZOnconn3zCa6+9Zqm3IoQQ1sdoCz2ngM4Ah/7EJyGCD3qoT1V9ty6Gg2fTNQ5YcX2x6hgnUrLwcrbji5pR6FJPSUNNDVh0Hpyff/6ZoKAgOnfuTPfu3WnXrh0zZswo2l5QUMCxY8fIyckpdtzs2bOpUqUKDz/88HXntLGxYcqUKYSFhdG0aVOmT5/O119/zfjx4y35VoQQwvoENFNHBQCWjeLxeo50b+xHoVlhxIJ9ZOcVapuvAtoWc4FZW2IB+KpHTZwiv1Q3SEPN+06nlPR8dTmVkZGBm5sb6enpcj+OEKJsK7gM09rBxRho1pdL4d/Q7dvNJGXk8myLKnz+dLDWCSuM9MsFdJu4iYT0XJ5rVZUJlVbC+o/AozoM2yU9p0rBnXx/yxzRQghRltk4wOPfATrY9xMeSVv4pldTdDr4bfdZluy/8cSqonQpisLYvw6QkJ5LtUqO/LeTN2z9Vt34oDTU1IIUOEIIUdZVC4NWV9rgLBlBWBU7hneqDcD//XWQ+NScEg4WpeGnHXGsOJiEUa/j297NcNo+EfIz1YaaDZ/UOl6FJAWOEEKUB53HgXtVSI+DiPcZ0bkOzau6k5lXyOsL9smj4xZ0OCGdD5cdAWBMtyCaOqfBrpnqRmmoqRn51IUQojywc4bHrlwS2TkD49kdfNu7GS52RvbFpfHl6mPa5iunsvIKGf7LPvILzXQO8mFQuxqw7mOQhpqakwJHCCHKi1oPQrO+6uslwwl00fHpU00AmL7xFCsPJWoYrvxRFIX/W3SQ2AvZBLjZ8+UzweiSDsLB39UdpKGmpqTAEUKI8uThj8HZT32qasMEHmnir44qAG/+foCT56XreGmZs/U0f0clYNDrmPRcMzycbCHifUCBRk9JQ02NSYEjhBDliYM7PPqN+nrbZDi3lzHdgmhV3ZOsvEJe+XGPzI9TCradvMDHK6IB+E/3+rSo7gmnNkLMWtAbpaGmFZACRwghypug7uoIgmKGv4djoxTyXZ9m+LjYcSIli3f+PEAFnAKt1JxLu8zwX/ZhMis80awyL7atDooCa69MONviRfCsqWlGIQWOEEKUT90+B8dKkHIYtnyDj4s9U/o0x6jXsexAIlPWx2idsEzKLTDx8o+7Sc3Op1FlVyY82RidTgdHFkPCPmmoaUWkwBFCiPLIyUstcgA2fQHJR2hZ3ZP3r/Sr+nL1cZYdkEkA74TZrPDWHwc4dC4DTydbpvUNwd7GAKYCiPhA3UkaaloNKXCEEKK8avQU1OuuPrL89zAwFdIntBovtlVvOh79236i4tO0zViGfLn6GEv3J2DU6/ju+WZU8XBUN+ydD9JQ0+pIgSOEEOWVTgePfA12bpCwF7Z/D8D/PVKfB4N8yCs089K83ZxLu6xxUOv3y444vt9wEoBPn2pCm1pe6oa8LNjwqfr6gbeloaYVkQJHCCHKM1d/6PKR+nr9x3DxZNFjzUF+LlzIymPA7J2k5eRrm9OKbTiWwrt/HwJgROc6PB1S5drG7VMhO0VtqBkyQJN84sakwBFCiPKu2QtQsyMU5sLfw8FsxtnOyKwBLfF1VZ+sGjBnlzw+fgP749MY9vNeTGaFJ5tXZmR4nWsbsy9IQ00rJgWOEEKUdzodPDYJbJwgbhvsngVAZXcHfhwUirujDVHxabzy0x7yCk0ah7Uex5Iy6T9nJ9n5JtrUqsSnTzZRn5i6atOX0lDTikmBI4QQFYFHNQi/Mk/L2vcgLQ6Aur4uzBnQEkdbA5tPXGDUwv2YzDJHzukL2fSdtYO0nAKCA92Z0a8FtsZ/fWVeOi0NNa2c/I0IIURF0XIwBLaG/CxYOkKdnA5oVtWD6S+EYGPQsfxgIm/+XrGLnHNpl+kzcwfnM/MI8nNh3sCWONsZi++0/hNpqGnlpMARQoiKQq+HHt+BwQ5OroOoX4o2ta/jzeTnmmHU61i07xxvLIyi0GTWMKw2zlzM5tlpkZxLu0wNLyfmD2qFu+P/3FuTeAAO/Ka+loaaVksKHCGEqEi86kCnserrVWMhM6loU9dG/nz3vDrb8ZL9CYxYEEVBBSpyYlKyeHa6WtzU9HLil8Gh+LjYX7+jNNQsE6TAEUKIiibsNfBvCrnpsHx00aUqgK6N/Jja99rlqpd/3ENOfvl/uio6MYPeMyJJzsijrq8zC15ujb+bw/U7SkPNMkMKHCGEqGgMRvVSld4IR5fB4UXFNj/UwJcZL7TAzqhn3dEUnvthBxez8jQKa3mbT5znmWmRXMjKp4G/KwuGhN145EYaapYpUuAIIURF5NcY2o9WX694C7IvFtvcKciHXwa3xt3Rhv3xaTw9LZIzF7M1CGpZv+2OZ+CcXWTlFRJaw5NfB7fG0+km89lIQ80yRQocIYSoqNq/Cd71IecCrBxz3eaQah788UobKrs7EHshmx5TtrL5xHkNgpY+k1nhi1VHefuPAxSaFXo0DWD+oFa4Odrc5IACiPhQfS0NNcsEKXCEEKKiMtpCjymg08PB3+DYyut2qe3jzKJX2xAc6E5aTgH9Z+9k+saTKErZfYw8NTufAXN2MmW92ltqWKdaTOzVFDuj4eYH7Z0PqSfB0UsaapYRUuAIIURFViUEWr+qvl72hnrj8f/wcbVn4ZDWPBNSBbMCE/45yqs/7y2T/aui4tN4bPIWNp+4gIONgW97N+WtLkHFZyj+X/9uqNnhHWmoWUZIgSOEEBVdp/9Tb5jNTIDV795wF3sbA58/3YQPezTEqNfxz6Ekuk7czLaYC/c57N0pMJn5Zs1xnpq6rWiOm8XD2tKjaeVbHywNNcskKXCEEKKis3WEx79TX++dB6c23HA3nU7HC2HV+XNoG2p6OZGUkcvzM3fwwdIjZFlxo84TyZk8+f02vo04gcms8EgTf/4e3pZ6frcxEpN+VhpqllE6pSxfSL1LGRkZuLm5kZ6ejqurq9ZxhBDCOiwbpTbidK8Gr0aCrdNNd83JL+Tj5dH8vEPtaeXnas+7jzage2O/ki/33EfZeYVMXhfDrC2nKDApuDnY8GHPRjweHHDrgy+dhh0z1IIvPwsCmsFL66TnlMbu5PtbChwpcIQQQpWbAd+HQcZZCB0K3T695SEbjqUw7u/DxKXmANCuthfvdA2icRU3S6e9qUKTmcVRCXy56hhJGbkAdA7y4ZMnG+PreoP5ba5SFDizVb0kdWwFKFdmcfaqC71/UWeBFpqSAucWpMARQoibOLEWfn4K0MGLq6Bq6C0PyS0w8f2Gk0zbcJL8K60dujb04/XOdWgQcP/+H1toMrP8YCLfRpzg1Hl1zp6qno6Mf6wBnev7lnBgHhz6E7Z/D0kHr62v9aB6A3atzjJyYyXu5PvbYn9jH3/8MW3atMHR0RF3d/fbOkZRFMaNG4e/vz8ODg6Eh4dz4sSJYvukpqbSp08fXF1dcXd3Z9CgQWRlZVngHQghRAVUJxyCnwcUWDIcCnJveYi9jYFRD9VlzagHeKJZZXQ6WHk4ie6TNtN7RiSrDidZtHFnanY+32+IocMXGxixIIpT57PxcLRhTLcgVr/xwM2Lm8xkWD8BvmkIi4eqxY3RAUIGwqs74IVFUOchKW7KKIuN4IwfPx53d3fOnj3LrFmzSEtLu+Uxn332GRMmTGDevHnUqFGDd999l4MHD3LkyBHs7dVhxW7dupGYmMj06dMpKChg4MCBtGzZkl9++eUWZ79GRnCEEKIEOakwJVR9csivCTTsqY5m+AXf1pf98eRMJkWc4J9DSZjM6ldMJSdbujf259Em/jSv5oGN4d6KhvScAiKOJrPsQCKbT5ynwKT+HE8nWwa2qc7AdjVwtjPe+OCEKNgxTR21MV151N21MrQaDM37g6PnPWUTlmNVl6jmzp3LyJEjb1ngKIpCQEAAo0eP5s033wQgPT0dX19f5s6dS+/evYmOjqZBgwbs2rWLFi1aALBy5Uq6d+/O2bNnCQi4jRvHkAJHCCFuKWYt/Po8mP7Vg8qxEtTspBY7tR4EV/8ST5GQdpkft59h4a54UrOvzZnjZGugVQ1PWlT3JMjPhXp+Lvi52mO8SdGTW2Di5PksTiRncTghnchTFzmckPHvHqE0ruxG/zbVebSJP/Y2N5iwz2yCo8vVwubM1mvrq7SC1kOh/mNguMksxsJq3Mn3903K2/svNjaWpKQkwsPDi9a5ubkRGhpKZGQkvXv3JjIyEnd396LiBiA8PBy9Xs+OHTt44oknbnjuvLw88vKu/UeakZFhuTcihBDlQe1weH0vHPsHTq6H2E2QcxEO/aEuAD4NrhU71dqATfHu2wHuDrzTNYhRD9Vla8wFluxPYP3RFC7lFLD+2HnWH7vW9kGnAw9HW9wdbTDodOh0kFtgJjU7/6aPoNf2ceaRxv48FuxPbZ+bPPJ9OQ32/Qg7Z0Ca+sQXeiM0fEK9kbpKyL1+UsJKWU2Bk5SUBICvb/Frpb6+vkXbkpKS8PHxKbbdaDTi6elZtM+NTJgwgffff7+UEwshRDnnVkW9bNNqsNqL6ewuOLkOYiLUppMpR9Ql8jsw2KlFTu3OasHj00CtWgAbg56O9XzoWM8Hs1khOimDbTEXOZSQzrGkTE6ez6LApJCanV9spKdYFAcb6vo6U9fXhZbVPQmrVankJ6IunlRHa/b9DAVXmoQ6eEKLgdDyJXC9vRF/UXbdUYEzZswYPvvssxL3iY6OJigo6J5ClbaxY8cyatSooj9nZGQQGBioYSIhhChjDDZqAVOtDTz4X/U+nVMb4GSEOsKTcQ5OrVcXAGe/a6M7NTsWNafU63U0DHCjYcC1x8hNZrW4uZCVR1pOQVGfK1ujHk8nWyo52eHqYLz1/DqKombaPhVOrLq23ru+ehmqybPXjTKJ8uuOCpzRo0czYMCAEvepWbPmXQXx8/MDIDk5GX//a9d1k5OTadq0adE+KSkpxY4rLCwkNTW16PgbsbOzw87O7q5yCSGEuAFHT2j0pLooClw4ro7snFwHp7dAVhLs/0VdAPyDrxU8ga2LzQhs0OvwdrHD2+Uu/z9dcBkOLITt0+B89LX1dbtC6CtqgWUlkw+K++eOChxvb2+8vS3TIr5GjRr4+fkRERFRVNBkZGSwY8cOhg4dCkBYWBhpaWns2bOHkBD1uum6deswm82Eht56rgYhhBAWoNOBdz11CXtVfbQ8frta7Jxcpz5+nbhfXbZ8AzZOUL2dWuzU7gyVat9dAZKRADt/gD1z4XKqus7GCZr1gVYvg1ftUn2bomyx2D04cXFxpKamEhcXh8lkIioqCoDatWvj7OwMQFBQEBMmTOCJJ55Ap9MxcuRIPvroI+rUqVP0mHhAQAA9e/YEoH79+nTt2pXBgwczbdo0CgoKGD58OL17977tJ6iEEEJYmI29OmpSsyM89IE638ypDdcKnuwU9RLS1ctIboFQq5M6oV7NDuDgUfL5z+5WL0MdWQzmKzcgu1dVi5pmfcHB3WJvTZQdFntMfMCAAcybN++69evXr6djx47qD9fpmDNnTtFlL0VRGD9+PDNmzCAtLY127drx/fffU7du3aLjU1NTGT58OEuXLkWv1/PUU08xadKkoqLpdshj4kIIoRGzGVIOX7tZOS7y2lw0ADo9BDS/drNy5RZgMKo3OUcvUQubs7uu7V+trXp/Tb3uoL/B4+GiXLGqeXCskRQ4QghhJfJz4My2Kzcrr4PzR4tvt3NVG11eOAGZCeo6gy00ehpav6Le2yMqjDI5D44QQogKyNZRbQ9R58ocaOnnrl3KOrUeLl+C2I3qNidv9RHvFi+Cs8/NzykEUuAIIYSwJm6VofkL6mI2QWIUnIkE90D1qSijPBErbo8UOEIIIayT3gCVQ9RFiDskLVKFEEIIUe5IgSOEEEKIckcKHCGEEEKUO1LgCCGEEKLckQJHCCGEEOWOFDhCCCGEKHekwBFCCCFEuSMFjhBCCCHKHSlwhBBCCFHuSIEjhBBCiHJHChwhhBBClDtS4AghhBCi3JECRwghhBDlToXsJq4oCgAZGRkaJxFCCCHE7br6vX31e7wkFbLAyczMBCAwMFDjJEIIIYS4U5mZmbi5uZW4j065nTKonDGbzSQkJODi4oJOpyvVc2dkZBAYGEh8fDyurq6leu6yTj6bksnnUzL5fEomn8/NyWdTsrL0+SiKQmZmJgEBAej1Jd9lUyFHcPR6PVWqVLHoz3B1dbX6f1G0Ip9NyeTzKZl8PiWTz+fm5LMpWVn5fG41cnOV3GQshBBCiHJHChwhhBBClDtS4JQyOzs7xo8fj52dndZRrI58NiWTz6dk8vmUTD6fm5PPpmTl9fOpkDcZCyGEEKJ8kxEcIYQQQpQ7UuAIIYQQotyRAkcIIYQQ5Y4UOEIIIYQod6TAKUVTpkyhevXq2NvbExoays6dO7WOZDU2bdrEY489RkBAADqdjsWLF2sdyWpMmDCBli1b4uLigo+PDz179uTYsWNax7IaU6dOpUmTJkWTkIWFhfHPP/9oHcsqffrpp+h0OkaOHKl1FKvw3nvvodPpii1BQUFax7Iq586do2/fvlSqVAkHBwcaN27M7t27tY5VKqTAKSULFy5k1KhRjB8/nr179xIcHEyXLl1ISUnROppVyM7OJjg4mClTpmgdxeps3LiRYcOGsX37dtasWUNBQQEPP/ww2dnZWkezClWqVOHTTz9lz5497N69mwcffJAePXpw+PBhraNZlV27djF9+nSaNGmidRSr0rBhQxITE4uWLVu2aB3Jaly6dIm2bdtiY2PDP//8w5EjR/jqq6/w8PDQOlrpUESpaNWqlTJs2LCiP5tMJiUgIECZMGGChqmsE6AsWrRI6xhWKyUlRQGUjRs3ah3Fanl4eCgzZ87UOobVyMzMVOrUqaOsWbNG6dChgzJixAitI1mF8ePHK8HBwVrHsFrvvPOO0q5dO61jWIyM4JSC/Px89uzZQ3h4eNE6vV5PeHg4kZGRGiYTZVF6ejoAnp6eGiexPiaTiQULFpCdnU1YWJjWcazGsGHDeOSRR4r9P0ioTpw4QUBAADVr1qRPnz7ExcVpHclqLFmyhBYtWvDMM8/g4+NDs2bN+OGHH7SOVWqkwCkFFy5cwGQy4evrW2y9r68vSUlJGqUSZZHZbGbkyJG0bduWRo0aaR3Hahw8eBBnZ2fs7Ox45ZVXWLRoEQ0aNNA6llVYsGABe/fuZcKECVpHsTqhoaHMnTuXlStXMnXqVGJjY2nfvj2ZmZlaR7MKp06dYurUqdSpU4dVq1YxdOhQXn/9debNm6d1tFJRIbuJC2Gthg0bxqFDh+Q+gf9Rr149oqKiSE9P548//qB///5s3Lixwhc58fHxjBgxgjVr1mBvb691HKvTrVu3otdNmjQhNDSUatWq8dtvvzFo0CANk1kHs9lMixYt+OSTTwBo1qwZhw4dYtq0afTv31/jdPdORnBKgZeXFwaDgeTk5GLrk5OT8fPz0yiVKGuGDx/OsmXLWL9+PVWqVNE6jlWxtbWldu3ahISEMGHCBIKDg/n222+1jqW5PXv2kJKSQvPmzTEajRiNRjZu3MikSZMwGo2YTCatI1oVd3d36tatS0xMjNZRrIK/v/91vyTUr1+/3FzGkwKnFNja2hISEkJERETROrPZTEREhNwnIG5JURSGDx/OokWLWLduHTVq1NA6ktUzm83k5eVpHUNznTt35uDBg0RFRRUtLVq0oE+fPkRFRWEwGLSOaFWysrI4efIk/v7+WkexCm3btr1uSorjx49TrVo1jRKVLrlEVUpGjRpF//79adGiBa1atWLixIlkZ2czcOBAraNZhaysrGK/NcXGxhIVFYWnpydVq1bVMJn2hg0bxi+//MLff/+Ni4tL0X1bbm5uODg4aJxOe2PHjqVbt25UrVqVzMxMfvnlFzZs2MCqVau0jqY5FxeX6+7VcnJyolKlSnIPF/Dmm2/y2GOPUa1aNRISEhg/fjwGg4HnnntO62hW4Y033qBNmzZ88sknPPvss+zcuZMZM2YwY8YMraOVDq0f4ypPJk+erFStWlWxtbVVWrVqpWzfvl3rSFZj/fr1CnDd0r9/f62jae5GnwugzJkzR+toVuHFF19UqlWrptja2ire3t5K586dldWrV2sdy2rJY+LX9OrVS/H391dsbW2VypUrK7169VJiYmK0jmVVli5dqjRq1Eixs7NTgoKClBkzZmgdqdToFEVRNKqthBBCCCEsQu7BEUIIIUS5IwWOEEIIIcodKXCEEEIIUe5IgSOEEEKIckcKHCGEEEKUO1LgCCGEEKLckQJHCCGEEOWOFDhCCCGEKHekwBFCCCFEuSMFjhBCCCHKHSlwhBBCCFHuSIEjhBBCiHLn/wGgvkXu0uMHsAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 281,
+   "execution_count": 178,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,10 +107,14 @@
     "channels = 1\n",
     "\n",
     "def convert_torch_to_onnx(model, onnx_model_path = 'model.onnx'):\n",
+    "\n",
+    "    dummy_input = torch.tensor(np.linspace(0, 2 * np.pi, 1, dtype=np.float32).reshape(-1, 1))\n",
+    "\n",
+    "\n",
     "    sample_input = torch.rand((batch_size, channels, *data_size))\n",
     "    torch.onnx.export(\n",
     "        model,\n",
-    "        sample_input, \n",
+    "        dummy_input, \n",
     "        onnx_model_path,\n",
     "        verbose=False,\n",
     "        input_names=['input'],\n",
@@ -124,12 +128,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 282,
+   "execution_count": 179,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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RaM4m8G+q9sJa9DL82ludmPCKVjU8mT+oFY62BjafuMDrv+6j0GTWLrcQwuIGDBiATqdDp9Nha2tL7dq1+eCDDygstOwo7l9//cWHH354W/ver6KkNEmBIzSVnp3PpGnf83XW2/xi+wlNTIfVUY6QgfDaXugxBSrV0jpm6fFtcGU0590rozkr4ftQiPq1aDQnpJonP/Rrga1Bz6rDybz9xwF5hFyIcq5r164kJiZy4sQJRo8ezXvvvccXX3xx3X75+fml9jM9PT1xcXEptfNZGylwhDYUhbxDSzn/TRs+yn6fEP0JFIMdtHoZRuyHxyaCRzWtU1qGwaj2w3p5EwQ0U0dzFr9SbDSnbW0vvnu+GQa9jr/2neOj5dEahxZCWJKdnR1+fn5Uq1aNoUOHEh4ezpIlS4ouK3388ccEBARQr149AOLj43n22Wdxd3fH09OTHj16cPr06aLzmUwmRo0ahbu7O5UqVeLtt9/mfxsX/O8lqry8PN555x0CAwOxs7Ojdu3azJo1i9OnT9OpUycAPDw80Ol0DBgwAACz2cyECROoUaMGDg4OBAcH88cffxT7OStWrKBu3bo4ODjQqVOnYjktSQoccf+YzZCwD6J+RZnWDrs/+lK78ASXseNik8HoRh6A7p+DW2Wtk94fvg1g0FroPA4MtteN5jzc0I+vnlFvpJ69NZZ5205rm1eIskRRID9bm6UUOiA5ODgUjdZERERw7Ngx1qxZw7JlyygoKKBLly64uLiwefNmtm7dirOzM127di065quvvmLu3LnMnj2bLVu2kJqayqJFi0r8mf369ePXX39l0qRJREdHM336dJydnQkMDOTPP/8E4NixYyQmJvLtt98CMGHCBObPn8+0adM4fPgwb7zxBn379mXjxo2AWog9+eSTPPbYY0RFRfHSSy8xZsyYe/58bofMgyPuD7MJfnoKTq0HQAdkKfb8rHQh9Pl3aRpUR9t8WjEYof1oqNsN/n5VLQAXvwKHF8Fj39KzWWXOpV3mi1XHeH/pYap4ONC5/o27lgsh/qUgBz4J0OZn/ycBbJ3u6lBFUYiIiGDVqlW89tprnD9/HicnJ2bOnImtrS0AP/30E2azmZkzZxY1G54zZw7u7u5s2LCBhx9+mIkTJzJ27FiefPJJAKZNm8aqVatu+nOPHz/Ob7/9xpo1awgPDwegZs2aRds9PT0B8PHxwd3dHVBHfD755BPWrl1LWFhY0TFbtmxh+vTpdOjQgalTp1KrVi2++uorAOrVq8fBgwf57LPP7urzuRMygiPuj4O/FxU3F9wa8W3hk7TL+5aApz6tuMXNv/3vaM6JVVdGc37h1Q416d0yELMCw3/Zx6Fz6VqnFUKUsmXLluHs7Iy9vT3dunWjV69evPfeewA0bty4qLgB2L9/PzExMbi4uODs7IyzszOenp7k5uZy8uRJ0tPTSUxMJDQ0tOgYo9FIixYtbvrzo6KiMBgMdOjQ4bYzx8TEkJOTw0MPPVSUw9nZmfnz53Py5EkAoqOji+UAioohS5MRHGF5pgLY8CkAsU3fJHxnCCazwuiH6vJYsEa/YVmjq6M59brD4lchYS8sHoru8GI+7P4159Ius/nEBV6cu4slw9vh5yYdk4W4KRtHdSRFq599hzp16sTUqVOxtbUlICAAo/Ha17OTU/HRoKysLEJCQvj555+vO4+3t/ed50W9JHansrKyAFi+fDmVKxe/tcDOzu6ucpQmKXCE5e3/FS7FYnKoRK+oJpjMCk82q8zwB2trncw6+dSHQWtg2yTYMAFOrMJmWhtmdP6InunVOJaSxSs/7WHhy62lpYMQN6PT3fVlIi04OTlRu/bt/T+xefPmLFy4EB8fH1xdXW+4j7+/Pzt27OCBBx4AoLCwkD179tC8efMb7t+4cWPMZjMbN24sukT1b1dHkEyma3NzNWjQADs7O+Li4m468lO/fn2WLFlSbN327dtv/SZLgVyiEpZVmA8b1UcdfzD3ICXXSItqHkx4qnHRtWNxAwYjtB8FL2+GgOaQl47DitdY7PEtdewziIpP470lh7VOKYTQQJ8+ffDy8qJHjx5s3ryZ2NhYNmzYwOuvv87Zs2cBGDFiBJ9++imLFy/m6NGjvPrqqyXOYVO9enX69+/Piy++yOLFi4vO+dtvvwFQrVo1dDody5Yt4/z582RlZeHi4sKbb77JG2+8wbx58zh58iR79+5l8uTJzJs3D4BXXnmFEydO8NZbb3Hs2DF++eUX5s6da+mPCJACR1javvmQHkeaoRLfpD+Aj4sd3/eVZpK3zSdIHc0Jfw8MtjicieAfm7d4xrCRX3fG8cuOOK0TCiHuM0dHRzZt2kTVqlV58sknqV+/PoMGDSI3N7doRGf06NG88MIL9O/fn7CwMFxcXHjiiSdKPO/UqVN5+umnefXVVwkKCmLw4MFkZ2cDULlyZd5//33GjBmDr68vw4cPB+DDDz/k3XffZcKECdSvX5+uXbuyfPlyatSoAUDVqlX5888/Wbx4McHBwUybNo1PPvnEgp/ONTrlfx+MrwAyMjJwc3MjPT39psN7ohQU5MKkZpCZwLiC/vyidGXhy60JqeapdbKyKeWo+qTVuT0ArDM1ZZx5MN8OeYSQah4ahxNCW7m5ucTGxlKjRg3s7eX+tLKspL/LO/n+lhEcYTl75kBmAomKJwtMD/Luow2kuLkXPkHw4moIfw/FYMuDhihWGN9ixfwvuJiZq3U6IYSwKlLgCMvIz8G8SZ33YHLhEzzSrDr9wsrpzMT3k8EI7d5A9/JmTP7NcdXl8K5pCmenPIo57azW6YQQwmpIgSMswrzzB/Q554k3e7PPszufPCE3FZcqnyAML60hOfQ/5Ck2BOfuonByKOz9sVRmURVCiLJOChxR+vIyyd/4NQDfK0/xTZ9WONjKTcWlzmDEt9s7rH7gd/aZa2NryoIlw+HnpyH9nNbphBBCU1LgiFKXvGYS9gVpnDL70bDbEIL85EZuS3r0wY7MqjuNTwqeIw8biFkL34dBQpTW0YQQQjNS4IhSlZ2eisPu7wFY7zeQPmE1b3GEuFc6nY5Pnm7KP27P0j3vE87Y14O8dFj6ulyuEhVOBXwwuNwprb9DKXBEqdr60/u4ksUpXSBP9Rsh993cJ672Nkzq3YzTuio8kfYGJoM9JO6HUxu0jibEfWFjYwNATk6OxknEvbr6d3j17/RuSasGUWrW7TtK65SFoANT+3dwd77z3ibi7jWr6sGwjrWYtC6GhaZOPM8/sHUi1OqkdTQhLM5gMODu7k5KSgqgToYnv2CVLYqikJOTQ0pKCu7u7hgM93bv5n0pcKZMmcIXX3xBUlISwcHBTJ48mVatWt1w344dO7Jx48br1nfv3p3ly5cDMGDAgKJpoK/q0qULK1euLP3w4rakZudzesmnPKi7TLJjbep07KN1pArptc51WH/sPN8ndKWX3SoMpzZAwj4IaKZ1NCEszs/PD6CoyBFlk7u7e9Hf5b2weIGzcOFCRo0axbRp0wgNDWXixIl06dKFY8eO4ePjc93+f/31F/n5+UV/vnjxIsHBwTzzzDPF9uvatStz5swp+rM1dC6tyD77cwvjzCtABx7d3wO9XP3Ugo1Bzze9gnlkUiZLTa3padgGW7+FZ+ZqHU0Ii9PpdPj7++Pj40NBQYHWccRdsLGxueeRm6ssXuB8/fXXDB48mIEDBwIwbdo0li9fzuzZsxkzZsx1+3t6Fp/pdsGCBTg6Ol5X4NjZ2ZVKhSfu3dL9CdQ6PhMnYx45Xk1wbPio1pEqtNo+LrzTNYjpyx+jp2EbypG/0aWeAk+54VtUDAaDodS+JEXZZdFfs/Pz89mzZ0+x1ut6vZ7w8HAiIyNv6xyzZs2id+/eODkVb3u/YcMGfHx8qFevHkOHDuXixYulml3cnpTMXL5dvJl+htUAOHYZB3LdW3MD2lTHo2ZzNpiC0SlmlG3faR1JCCHuK4sWOBcuXMBkMuHr61tsva+vL0lJSbc8fufOnRw6dIiXXnqp2PquXbsyf/58IiIi+Oyzz9i4cSPdunXDZDLd8Dx5eXlkZGQUW8S9UxSF//x1iD4Ff2KvK8BcpSXUDr/1gcLi9Hodnz/dhDm6HgCY9v4EWec1TiWEEPePVd8oMWvWLBo3bnzdDcm9e/fm8ccfp3HjxvTs2ZNly5axa9cuNmzYcMPzTJgwATc3t6IlMDDwPqQv/1YcTOJw9BH6GCIA0D/4Xxm9sSJVPBzp9PATRJlrYTTnkblJRnGEEBWHRQscLy8vDAYDycnJxdYnJyff8v6Z7OxsFixYwKBBg275c2rWrImXlxcxMTE33D527FjS09OLlvj4+Nt/E+KG0nMKGL/kMMONi7HVFUK1dlCjg9axxP94oU0NVns8B4B+90yUXBm9FEJUDBYtcGxtbQkJCSEiIqJondlsJiIigrCwsBKP/f3338nLy6Nv3763/Dlnz57l4sWL+Pv733C7nZ0drq6uxRZxbz5dGY19djy9jBvUFZ3+I6M3Vsig1/Hkc4M5pfjjZM7i8LLJWkcSQoj7wuKXqEaNGsUPP/zAvHnziI6OZujQoWRnZxc9VdWvXz/Gjh173XGzZs2iZ8+eVKpUqdj6rKws3nrrLbZv387p06eJiIigR48e1K5dmy5dulj67Qhg+6mL/LozntcNizBigpodoXpbrWOJm6jt505sXXUk1PvQTC6mZ2qcSAghLM/iBU6vXr348ssvGTduHE2bNiUqKoqVK1cW3XgcFxdHYmJisWOOHTvGli1bbnh5ymAwcODAAR5//HHq1q3LoEGDCAkJYfPmzTIXzn2QW2DiP4sOUl2XyFPGLerKTv/VNpS4pfZPDeeizhNfUlm9QO7FEUKUfzqlAnYmy8jIwM3NjfT0dLlcdYe+Xn2MSeti+N5hGt2VTVDnYejzu9axxG1IWP4pAbsmcMJcmYv9N9K6lrfWkYQQ4o7cyfe3VT9FJazLieRMpm48SS3dObopm9WVnf6jbShx2wI6DyVX70Qd/TlW/DGHApNZ60hCCGExUuCI26IoCu/+fYgCk8IEj2XoUCDoUelxVJbYu6Fr8SIAj2f/zuwtsRoHEkIIy5ECR9yWZQcS2X4qlSbGeFrlXGmG2vH6m8OFdbNrPxyT3oYW+uNsWruUhLTLWkcSQgiLkAJH3FJ2XiEfL48G4Guff9SVDZ8Av0YaphJ3xcUPfbA6L85AFvPB0iMaBxJCCMuQAkfc0uR1MSRl5NLZPYHaqRtAp5fRmzJM13YECjrCDfs4eWQ364+maB1JCCFKnRQ4okQnz2cxa8spAD7zWKqubPwMeNfTMJW4J1610dVXO76/bFzG+CWHyS24cR83IYQoq6TAETelKArvLTlMgUnhpern8UrcCDoDdHhH62jiXrV9A4Cehq0UpMYzS244FkKUM1LgiJtadTiJzScuYGvQM9rmD3Vl0+egUi1tg4l7VyUEqrfHiIlBxhVMWR9DSkau1qmEEKLUSIEjbii3wMSHy9Qbiz9omoZD/GbQ28ADb2ucTJSatiMB6GNcj01+Op+vOqZtHiGEKEVS4Igbmr01lnNpl/F3teOZjPnqyuYvgEc1bYOJ0lO7M/g2xoFcXjCs4Y89ZzlwNk3rVEIIUSqkwBHXOZ+Zx/frTwLwRcglDPHbwGAL7UdrnEyUKp0O2o4A4GX7NdiRzwdLj1ABu7cIIcohKXDEdb5ec5ysvEKaVHalbdx0dWXIQHCrom0wUfoaPgFuVXExpdHHdhO7z1xi6YHEWx8nhBBWTgocUczRpAwW7ooD4IumKejO7QKjPbQfpXEyYREGI7QZDsAIx1UYMPHpimgu58tj40KIsk0KHFFEURQ+Xh6NWYFuDX2pd2SSuqHlS+Dip204YTnN+oKDJ2655+jjso+E9Fx+2HxK61RCCHFPpMARRTYcP1/0WPj7QWcgMQpsnKDdG1pHE5Zk6wShLwMw2vEfQGH6xpOcz8zTNpcQQtwDKXAEAIUmc1G/qQFtquKz+2t1Q+jL4OSlYTJxX7QaAjaOuKVH088nlux8E5MiTmidSggh7poUOAKAX3fFE5OShYejDSMCoiH5ENi5QpvXtI4m7gdHT2jeD4BRjisA+HVnHKfOZ2mZSggh7poUOIKsvEImrjkOwKjwWjht/Vzd0PpV9YtPVAxhw0BnwD1pGwNrpFFoVvhCJv8TQpRRUuAIZm4+xcXsfGp4OfGc4y64cAzs3SHsVa2jifvJvSo0fhqANxxWoNfBP4eS2Bt3SeNgQghx56TAqeAuZOXxwyb1iZm3wmth3HRl9KbNa2DvpmEyoYkrE/+5xq7g5UbqqgkromXyPyFEmSMFTgX33boYsvNNNK7sRjfzBkg9CY6VIPQVraMJLfg2hDoPg2JmuP1K7G307Dp9ibXRKVonE0KIOyIFTgUWn5rDzzvOADDmoZroro7etB0Jds7aBRPautKE0+nIQl5r6QrAp/9EU2gyaxhKCCHujBQ4Fdg3a45TYFJoV9uLtlkrIS0OnHzUif1ExVWtDVRpCaY8XrJbjYejDSfPZ/P7nrNaJxNCiNsmBU4FFZ2YwaKocwC8E14NNn2pbmg/CmwdNUwmNPevJpx2e+cw6gF/ACZFnCC3QFo4CCHKBilwKqgvVx1DUeCRxv40Tv4bMs6BS4DaVFOIeo9ApTqQl05vQwT+bvYkpufy8444rZMJIcRtkQKnAtp1OpWIoykY9Dre7BQIm79SNzwwGmzstQ0nrINeD21fB8Bm5zRGdqwGwPfrY8jOK9QymRBC3BYpcCoYRVH4fOVRAHq1DKTG6QWQlQxuVaFZP43TCavSpBe4+ENmIk/ZRlKtkiMXs/OZu+201smEEOKWpMCpYDaduMCu05ewM+oZ2T4AtkxUN3R4C4y2mmYTVsZoB62Hqi8jJ/FG59oATN94kvTLBVomE0KIW5ICpwJRFIWvr7RkeKF1NXyi50HOBfCoAcHPaZxOWKWQgWDnBheO85jDfur4OJORW8jMzae0TiaEECWSAqcCWX8shf3xaTjYGBga5gPbJqkbOo4Bg4224YR1sneFli8CYNj6LaMfqgPA7C2xXMzK0zKZEEKU6L4UOFOmTKF69erY29sTGhrKzp07b7rv3Llz0el0xRZ7++I3viqKwrhx4/D398fBwYHw8HBOnDhh6bdRpv179KZ/m+pUOjgLLl8Cr7rQ+BmN0wmrFjoUDHZwdiddXGJpVNmV7HwT0zae1DqZEELclMULnIULFzJq1CjGjx/P3r17CQ4OpkuXLqSk3Hzqd1dXVxITE4uWM2fOFNv++eefM2nSJKZNm8aOHTtwcnKiS5cu5ObmWvrtlFmrjyRz6FwGTrYGXm7lCZFT1A0dx4DeoG04Yd1cfKGpeglTt/VbRj9cD4D5kWdIzpD/5oQQ1sniBc7XX3/N4MGDGThwIA0aNGDatGk4Ojoye/bsmx6j0+nw8/MrWnx9fYu2KYrCxIkT+e9//0uPHj1o0qQJ8+fPJyEhgcWLF1v67ZRJZrPCN1dGbwa2rYFH1HTISwefhtDgCY3TiTKhzeuADk6soqP7eVpU8yCv0MzkdTJyKoSwThYtcPLz89mzZw/h4eHXfqBeT3h4OJGRkTc9Lisri2rVqhEYGEiPHj04fPhw0bbY2FiSkpKKndPNzY3Q0NASz1mR/XMoiaNJmbjYGRkS4gY7pqkbOo1V5zsR4lYq1YIGjwOg2zqJN7uoozgLd8Vz9lKOlsmEEOKGLPrtduHCBUwmU7ERGABfX1+SkpJueEy9evWYPXs2f//9Nz/99BNms5k2bdpw9qzaB+fqcXdyzry8PDIyMootFYXJrDBxrTp6M6h9DVz3ToH8LPAPhqBHNU4nypQr7Rs49AetPXNoU6sSBSaFqRvkXhwhhPWxul/fw8LC6NevH02bNqVDhw789ddfeHt7M3369Ls+54QJE3BzcytaAgMDSzGxdVt2IIETKVm4OdgwqKkj7PxB3dDp/9SeQ0LcrsohUL09mAshcgqvd1afqPptdzwJaZc1DieEEMVZtMDx8vLCYDCQnJxcbH1ycjJ+fn63dQ4bGxuaNWtGTEwMQNFxd3LOsWPHkp6eXrTEx8ff6VspkwpNZr5dq94jMeSBmrjs+g4KL0PlFlDnYY3TiTKp3Uj1n3vn0dpPR2gNTwpMijxRJYSwOhYtcGxtbQkJCSEiIqJondlsJiIigrCwsNs6h8lk4uDBg/j7qx2Na9SogZ+fX7FzZmRksGPHjpue087ODldX12JLRbBkfwKnLmTj4WjDgEY2sPvKjd2d/iOjN+Lu1OoMfo2hIAd2zWREuDqKs2BnPEnp8kSVEMJ6WPwS1ahRo/jhhx+YN28e0dHRDB06lOzsbAYOVLtW9+vXj7Fjxxbt/8EHH7B69WpOnTrF3r176du3L2fOnOGll14C1CesRo4cyUcffcSSJUs4ePAg/fr1IyAggJ49e1r67ZQZJrPCd+vVUa/BD9TEace3YMqDqmFQ60GN04kyS6eDtiPV1zumERboQMvqHuSbzDKKI4SwKkZL/4BevXpx/vx5xo0bR1JSEk2bNmXlypVFNwnHxcWh/9eTPJcuXWLw4MEkJSXh4eFBSEgI27Zto0GDBkX7vP3222RnZzNkyBDS0tJo164dK1euvG5CwIpsxcFETp3Pxs3Bhn719TB9vrpB7r0R96pBT4j4ANLOoIv6hRGdn6DvrB38sjOOVzvWwsdV/jsUQmhPpyiKonWI+y0jIwM3NzfS09PL5eUqs1mh27ebOZacycjwOozMmQx750ONB6D/Uq3jifJg5w+w4k1wr4ry2l6enrGLPWcu8WLbGox7rMGtjxdCiLtwJ9/fVvcUlbh3q48kcyw5E2c7I4PqA/t+Vjd0+q+muUQ50rQPOFaCtDh0R/5mxJUnqn7ecYaUTLkXRwihPSlwyhlFUfhuvfrkVP821XDZ+TUoJqgdDlVDNU4nyg1bRwh9RX29ZSLta1eiaaA7eYVmftgkncaFENqTAqec2XDsPIfOZeBoa2BwfRMcWKhu6PQfbYOJ8qflS2DjBMkH0Z1aV/RE1Y/bz3BBOo0LITQmBU45oigKk670BurbuhruO78CxQz1uquTtAlRmhw9IaS/+nrLRDrW9Sa4ihu5BTKKI4TQnhQ45cjWmIvsi0vDzqjn5fq5cOgvdYOM3ghLaf0q6I1wejO6hL3FRnEuZedrHE4IUZFJgVOOXB29ea5VVSrt/ApQoEEPdWI2ISzBPRAaPa2+3jKRTvV8aODvSk6+iXmRpzWNJoSo2KTAKSd2nLrIzthUbA16htXPhuilgA46jr3lsULck6tNOKOXort4klc71QJgztbTZOUVahhMCFGRSYFTTkxep85a/HSLKnjv+kpd2fhp8KmvYSpRIfg2gDpdAAW2TaJbI39qeDmRfrmAX3fEaZ1OCFFBSYFTDkTFp7El5gJGvY4R9dLh+ErQ6aHDGK2jiYriahPO/b9iyE7hlQ41Afhh8ynyCk3a5RJCVFhS4JQDUzeoozc9mlbGd8+V0Zvg58CrtoapRIVSNQyqtAJTPuyYyhPNquDvZk9KZh5/7jmndTohRAUkBU4ZF5OSyarDyQC8Ue8CnFynPtXywFsaJxMVik53bRRn12xsC7MY3F4dxZm28SSFJrN22YQQFZIUOGXc9I3qfCMPN/Clyr5v1JVN+4BnDQ1TiQqpbjfwqgd56bBnDr1bBeLhaENcag7LDyZqnU4IUcFIgVOGJaRdZnGUOvz/Zt1kOL0ZDLYyeiO0oddD29fV15Hf46g38WJbtdD+fv1JzOYK19dXCKEhKXDKsJmbYykwKbSu4UHdw5PUlc37q3OTCKGFxs+CSwBkJcGBhfQLq46znZFjyZmsO5qidTohRAUiBU4ZdSk7n193qo/g/qdeIsRvB6M9tB+tcTJRoRltIexV9fXWSbjZG+jTuioAUzbEoCgyiiOEuD+kwCmj5kWe5nKBiYb+LjQ+/p26ssUgcPXXNpgQIQPAzg0unoBjyxnUrga2Rj374tLYfipV63RCiApCCpwyKCe/kLnbTgMwPigeXcJesHGEdm9oG0wIADsXaDlIfb1lIj7OdvRqoV42/f7KlAZCCGFpUuCUQQt2xpOWU0B1T3taxk5VV7YaAs7e2gYT4qrWQ8FgB+d2w5mtDHmgJga9js0nLnDwbLrW6YQQFYAUOGVMfqGZmZvVR8M/rHcaXdJBsHW51g9ICGvg7ANNn1dfb5lIoKcjjzVRL59O33RSw2BCiIpCCpwyZsn+BBLSc/F1NtL27Ax1Zeuh4OipbTAh/leb19SWITFrIOkQQx5Qm3CuOJhIfGqOxuGEEOWdFDhliNmsMG2j+tvvx3Vj0J8/CvZuEDZM42RC3EClWlD/cfX1tkk0CHClfR0vzApFo5BCCGEpUuCUIWujk4lJycLdXseDSbPUlWGvgYO7prmEuKmr7RsO/gFpcbzSQR3FWbg7ntTsfO1yCSHKPSlwyghFUZh6ZfTmk1rR6FNPgoMHtH5F42RClCCgGdToAIoJIqfQplYlGga4kltg5sfIM1qnE0KUY1LglBG7z1xiX1wajkYzXS7MU1e2HaE+kiuENbs6irN3PrrLl3j5yijOvMjT5BaYtMslhCjXpMApI2ZsUu9Z+KjaAQzpZ8DJW300XAhrV7MT+DWBghzYOYPujfyo4uFAanY+v+85q3U6IUQ5JQVOGXDyfBZro5OxpYDH0n9SV7Z7A2ydtA0mxO3Q6a6N4uyYjtF0mZfaqU04Z24+hUmacAohLEAKnDJg1pZYFAXGV96NTVYCuPhDixe1jiXE7avfAzyqw+VU2PcTz7YMxN3RhjMXc1h1OEnrdEKIckgKHCt3ISuPP/ecxY58nsn5TV3ZfjTYOGgbTIg7YTBC2HD19bbvcDQo9GtdDYDpG09KE04hRKmTAsfK/Rh5hrxCM29V2obt5WRwrQLN+2kdS4g716wvOHpBehwcXkS/NtWxM+rZfzadHbHShFMIUbqkwLFil/NN/Lj9DA7k8kLhn+rKDm+B0U7bYELcDRsHCL0yrcHWb/FysuXpkCrAtZvohRCitEiBY8X+3HuW1Ox8XnfZgF3eRfUehqZ9tI4lxN1rOQhsnCD5EMRE8FL7muh0sO5oCseTM7VOJ4QoR+5LgTNlyhSqV6+Ovb09oaGh7Ny586b7/vDDD7Rv3x4PDw88PDwIDw+/bv8BAwag0+mKLV27drX027ivTGaFWVticeIyA1miruzwDhhstA0mxL1w9ISQAerrrROp4eVE14Z+gIziCCFKl8ULnIULFzJq1CjGjx/P3r17CQ4OpkuXLqSkpNxw/w0bNvDcc8+xfv16IiMjCQwM5OGHH+bcuXPF9uvatSuJiYlFy6+//mrpt3JfrY1OJvZCNkPt12BfkAaVakPjZ7WOJcS9C3sV9EY4vRnO7mHIAzUB+DvqHEnpuRqHE0KUFxYvcL7++msGDx7MwIEDadCgAdOmTcPR0ZHZs2ffcP+ff/6ZV199laZNmxIUFMTMmTMxm81EREQU28/Ozg4/P7+ixcPDw9Jv5b76YdMpXMnmJcNydUXHseqTKEKUdW5VrhXrW7+hWVUPWtXwpMCkMGdbrLbZhBDlhkULnPz8fPbs2UN4ePi1H6jXEx4eTmRk5G2dIycnh4KCAjw9PYut37BhAz4+PtSrV4+hQ4dy8eLFm54jLy+PjIyMYos123PmErvPXGKIzT/YmzLBuz40fFLrWEKUnrYj1H9GL4MLJxjcXh3F+XVHHNl5hRoGE0KUFxYtcC5cuIDJZMLX17fYel9fX5KSbm9yr3feeYeAgIBiRVLXrl2ZP38+ERERfPbZZ2zcuJFu3bphMt24r82ECRNwc3MrWgIDA+/+Td0HMzefwp1MXrJZqa7oNBb0cj+4KEd8gqBuN0CBbZPoHORD9UqOZOQW8oe0bxBClAKr/tb89NNPWbBgAYsWLcLe3r5ofe/evXn88cdp3LgxPXv2ZNmyZezatYsNGzbc8Dxjx44lPT29aImPj79P7+DOnbmYzcrDSbxsXIa9OQf8GkPQY1rHEqL0XW3fsH8B+uxkBl1p3zB7a6y0bxBC3DOLFjheXl4YDAaSk5OLrU9OTsbPz6/EY7/88ks+/fRTVq9eTZMmTUrct2bNmnh5eRETE3PD7XZ2dri6uhZbrNXMzbFUUtJ50WaNuqLjf2T0RpRPVVtDYGsw5cP273kqpApuDmr7hrXRybc+XgghSmDRb05bW1tCQkKK3SB89YbhsLCwmx73+eef8+GHH7Jy5UpatGhxy59z9uxZLl68iL+/f6nk1oraXTmeV4xLsFNyIaA51OumdSwhLOfqvTi75+BozqZPaFUAZm2Wm42FEPfG4kMDo0aN4ocffmDevHlER0czdOhQsrOzGThwIAD9+vVj7NixRft/9tlnvPvuu8yePZvq1auTlJREUlISWVlZAGRlZfHWW2+xfft2Tp8+TUREBD169KB27dp06dLF0m/Hon7afgbXgou8YLxSEHb6P7UTsxDlVd2u4B0EeRmwezb921THxqBj5+lU9senaZ1OCFGGWbzA6dWrF19++SXjxo2jadOmREVFsXLlyqIbj+Pi4khMTCzaf+rUqeTn5/P000/j7+9ftHz55ZcAGAwGDhw4wOOPP07dunUZNGgQISEhbN68GTu7stvCILfAxPzI0wwzLsaOfAgMhdqdtY4lhGXp9dDmdfX19qn4OsBjwQEAzNoiozhCiLunUypgG9+MjAzc3NxIT0+3mvtxftsVz8Q/17HBfhS2FEK/JVCzg9axhLC8wnyY1BQyzsFjkzjs35NHJm3BoNex+e1OBLg7aJ1QCGEl7uT7W+5etQKKojB7ayzDjYvV4qZ6eyluRMVhtIXWr6qvt02ioZ8zbWpVwmRWmLvttKbRhBBllxQ4VmDbyYtkJ8fwjGGjuqLT/2kbSIj7LaQ/2LvBxRg4upyX2quPjP+6I44smfhPCHEXpMCxArO2xPK6YRE2OhPUehCq3fwJMyHKJTsXaDlYfb11Ih3reFPT24nMvEJ+22W981YJIayXFDgaO3U+izPH9vGkYbO6QkZvREUV+goY7eHcHvRxW2XiPyHEPZECR2Nztp5mtPF3DDoF6j0CVW49748Q5ZKzNzTto77eOpEnm1XBw9GGs5cus/rw7bV2EUKIq6TA0VB6TgFH92ygu2EnCjro/K7WkYTQVpvXQKeHmLU4pB6hb+tqAMyUR8aFEHdIChwN/borjhH8ov4huDf41Nc2kBBa86wBDXqqr7d+ywth1bA16Nlz5hJ74y5pGk0IUbZIgaORApOZI1v+pp3hMCa9DbqOY295jBAVwtX2DYf+wqcwmcebysR/Qog7JwWORlYeTGRQ3o8AKCEvgkc1jRMJYSUCmkLNjqCYIPK7opuN/zmYSHxqjqbRhBBlhxQ4Gjmy7ieC9afI1ztg7PCW1nGEsC5tR6r/3Psj9V3zaVfbC7MC82TiPyHEbZICRwN7Ys/zdNocAApavao+PSKEuKZmR/APhsLLsHMGg65M/LdgVzyZuQXaZhNClAlS4GggeuU0aukTyTa44dRxpNZxhLA+Ot21UZydM+hQzZHaPs5k5RWyUCb+E0LcBilw7rNzF1LpnDQbgMyWI8DeOpp9CmF1GvQAjxpw+RL6qJ94sa06ijN322mZ+E8IcUtS4Nxn0X9/jb8ulQsGb/w6D9M6jhDWS29Q58UBiPyOJ4N9ZOI/IcRtkwLnPspOT6VFvHrvTUrzUWBjr3EiIaxc0+fByRvS47E/9rdM/CeEuG1S4NxHMX9PwJ0szuiqENRlsNZxhLB+Ng5qjypQJ/4LrYqNQceeM5eIik/TNJoQwrpJgXOfmDOSqXtqPgCxwW+gN9ponEiIMqLlILB1hpTD+CRv5rFgmfhPCHFrUuDcJ2eXfIgDuRykFi279tc6jhBlh4MHhAxQX2+dWDTx34qDiSSkXdYulxDCqkmBcz9cOk1AzK8AHAx6Ayd7Gb0R4o60fhX0NnBmKw1Nx2ld0xOTWWFe5GmtkwkhrJQUOPdB2ooPMFLIZnNjOnZ9Wus4QpQ9bpWhybPq660TGdSuJgC/7ogjO69Qw2BCCGslBY6lJR/B9cRfAERWH0aAu4PGgYQoo6424Ty6nM5eaVSv5EhGbiF/7j2rbS4hhFWSAsfC8la/hx6F5aZWhId31TqOEGWXdz2o1x1Q0EdO4sUr9+LM3hKLWSb+E0L8DylwLCluB3YnV1Go6Fnh/RLNq3ponUiIsu1q+4b9C3m6jgFXeyOnL+YQcTRF01hCCOsjBY6lKArmNeMB+N3Uga4d2mscSIhyoGooVA0DcwGOe2fwXGhVAGZtOaVxMCGEtZECx1Ji1qKPjyRPsWGB43N0a+SndSIhyoerozi75zCgmQcGvY7tp1I5nJCuaSwhhHWRAscSzGaUte8BMNf0MN3btsBokI9aiFJR52Hwrg/5mfif+IXujf0BmfhPCFGcfOtawuG/0CUfIkNxYK7uCXq3rKp1IiHKD73+2hNV26fyUmu1wFm6P4GUjFwNgwkhrIkUOKXNVADrPgJgRuGjPNSiPm6OMrGfEKWq8dPgWgWyUwi++A8tqnlQYFL4cfsZrZMJIayEFDilbe98uBTLecWV2aZuDGxbQ+tEQpQ/BhsIG6a+3jaJQW3VUdKftp8ht8CkYTAhhLWQAqc05efAxs8AmFz4BGFBVanh5aRxKCHKqeb9wN4dUk/xsH43VTwcuJRTwF97z2mdTAhhBe5LgTNlyhSqV6+Ovb09oaGh7Ny5s8T9f//9d4KCgrC3t6dx48asWLGi2HZFURg3bhz+/v44ODgQHh7OiRMnLPkWbs+OaZCVzFnFm19NnYuaAgohLMDOGVoNBsCwbSIDwqoBMHtrLIoiE/8JUdFZvMBZuHAho0aNYvz48ezdu5fg4GC6dOlCSsqNJ+batm0bzz33HIMGDWLfvn307NmTnj17cujQoaJ9Pv/8cyZNmsS0adPYsWMHTk5OdOnShdxcjW8wrNmBc+4t+LLgGWr5eRBWq5K2eYQo71q9DEZ7SNjH8z6ncbYzEpOSxcbj57VOJoTQmE6x8K86oaGhtGzZku+++w4As9lMYGAgr732GmPGjLlu/169epGdnc2yZcuK1rVu3ZqmTZsybdo0FEUhICCA0aNH8+abbwKQnp6Or68vc+fOpXfv3rfMlJGRgZubG+np6bi6upbSO4UCk5kHPltHYkYunz8dzLMtAkvt3EKIm1g+GnbNhFoP8oH7x8zeGkv7Ol78OChU62RCVEhZeYVsP3mRB4N80Ot1pXruO/n+tugITn5+Pnv27CE8PPzaD9TrCQ8PJzIy8obHREZGFtsfoEuXLkX7x8bGkpSUVGwfNzc3QkNDb3rOvLw8MjIyii2WsPJQEokZeXg52/F4cIBFfoYQ4n+EDQedHk6uY0jdLPQ62HziAseTM7VOJkSF9PvueF6av5uX5u/WNIdFC5wLFy5gMpnw9fUttt7X15ekpKQbHpOUlFTi/lf/eSfnnDBhAm5ubkVLYKBlRlZyC0x4OdvSJ7Qa9jYGi/wMIcT/8KwBDZ8AwO/gdB5uoM4aPlsm/hPivjOZFeZuOw3Ag0E+mmapEE9RjR07lvT09KIlPj7eIj/nmRaBbHnnQYY8UNMi5xdC3MTVif8O/8WrTY0A/LXvHBez8jQMJUTFs+5oCmcu5uDmYMOTzStrmsWiBY6XlxcGg4Hk5ORi65OTk/Hzu3FvJj8/vxL3v/rPOzmnnZ0drq6uxRZLsbcx4GRntNj5hRA34B8MtR4ExUzjuB8JruJGfqGZn3fEaZ1MiArl6sjpc62q4mir7XehRQscW1tbQkJCiIiIKFpnNpuJiIggLCzshseEhYUV2x9gzZo1RfvXqFEDPz+/YvtkZGSwY8eOm55TCFEBXGnCqdv3E6+0dANgfuQZ8gpl4j8h7ofDCelEnrqIQa+jf5tqWsex/CWqUaNG8cMPPzBv3jyio6MZOnQo2dnZDBw4EIB+/foxduzYov1HjBjBypUr+eqrrzh69Cjvvfceu3fvZvjw4QDodDpGjhzJRx99xJIlSzh48CD9+vUjICCAnj17WvrtCCGsVY0HIKAZFF7m4ay/8Xez50JWHkuiErROJkSFMGfraQC6N/bH381B2zDchwKnV69efPnll4wbN46mTZsSFRXFypUri24SjouLIzExsWj/Nm3a8MsvvzBjxgyCg4P5448/WLx4MY0aNSra5+233+a1115jyJAhtGzZkqysLFauXIm9vb2l344QwlrpdEWjOIZdP/BiS/UGx1lbZOI/ISztfOa1XyZebFtd2zBXWHweHGtkqXlwhBAaM5vguxaQeorLD35E8zV1uFxg4peXQmlT20vrdEKUWxPXHmfi2hM0q+rOolfbWuznWM08OEIIcV/pDdDmNQAcdk/j2ebqgwez5JFxISwmr9DET9vPAPCiFTWYlgJHCFG+BD8PTj6QcZZhXvvQ6SDiaAqnzmdpnUyIcmnp/kQuZOXj72ZP10Y3fppZC1LgCCHKFxt7aP0KAD4HphNeT700dfUGSCFE6VEUpejR8H5h1bExWE9ZYT1JhBCitLQYBLYucD6aUTXUuXD+2HOWtJx8jYMJUb7siE3lSGIG9jZ6nmtlXf0XpcARQpQ/Du7QYgAAQSdnUd/fVb3ZeKdM/CdEabp6f9tTzavg7mircZripMARQpRPrV8FvQ26uEjeapAOwPxtZygwmTUOJkT5cOZiNmuj1a4CA63k0fB/kwJHCFE+uQZAcC8AOpz/GS9nO5IycllxMPEWBwohbsfcbadRFOhQ15vaPi7XNmSdhxVvQeaNG2DfL1LgCCHKrzYjAB2G4ysYEayO3MjEf0Lcu8zcAn7ffRaAQe3+59HwTV/Azhnwx4saJLtGChwhRPnlXReCHgHgmby/sDPqOXA2nd1nLmkcTIiy7bfdZ8nKK6S2jzPt6/xrEs3UWNg9W33d4R1twl0hBY4Qony70r7B/sgfDGhkA8CszTLxnxB3y2RWmLtN/W/oxbY10Ol01zau/xjMBVCrM9TsoFFClRQ4QojyLbAlVGsL5gJesVsNwOojScRdzNE4mBBl09roZOJTL+PuaMMTzSpf25C4Hw7+rr4Of0+TbP8mBY4QovxrOwIAj+if6VrbHrMCc7bJKI4Qd+PqxH7Pt6qKg63h2oa176n/bPws+De5/8H+hxQ4Qojyr87D4NMA8rN4p9IWAH7bFU9GboHGwYQoWw6dS2dHbCpGvY4Xwqpd23ByPZxcB3obePD/tAv4L1LgCCHKP52uaBSnesyPNPS2ITvfxG+74jUOJkTZMnurOnrTvbE//m4O6kqz+droTctB4FFdk2z/SwocIUTF0OgpcAtEl32e96oeANT+VIUy8Z8QtyUlM5el+xMAePHfj4YfWQSJUWp7lAfe0ibcDUiBI4SoGAw2EDYMgJCEn/ByNHAu7TKrjyRrHEyIsuGnyDMUmBSaVXWnaaC7utJUABEfqq/bvg5OXjc9/n6TAkcIUXE07wcOHugvxTK+9kngWi8dIcTN5RaY+HH7GQBealfz2oY9c+FSLDj5qO1RrIgUOEKIisPWCVoNAaBr+kJsDTr2nLlEVHyatrmEsHJ/7T3HpZwCqng40KWhr7oyLws2fqa+7vgO2DlrF/AGpMARQlQsrV4GowM2yfsZVUvtlSOjOELcnNmsMGvLKQAGtq2B0XCldIicAtnnwbMmNO+vYcIbkwJHCFGxOFWC5i8A0Nf0FwArDiZyLu2ylqmEsFobj5/n5PlsXOyMPNuiiroy6zxsm6S+fvBd9R43KyMFjhCi4gkbDjoDzuc281yVVExmhfnbTmudSgirNPPK6E3vVoG42F8pZDZ9AflZENAMGvTULlwJpMARQlQ8HtWg0ZMAjHBYAcAvO+PIzivUMpUQVudwQjpbYy5i0Ovo36a6uvLfDTXD3we9dZYS1plKCCEs7crEf75nV9LWM4PM3EL+2HNW41BCWJer96d1a+RHFQ9HdaUVNdQsiRQ4QoiKya8x1OqMTjEzrtI6AOZsjcVsVjQOJoR1SM64NrHfS+2vPBpuZQ01SyIFjhCi4mo3EoC6iUuoYZ/D6Ys5RBxN0TaTEFZifuRpCkwKLat7XJvYr6ih5jNW0VCzJFLgCCEqrurtIaA5usJcPgrYClD0OKwQFVlOfiE/74gDYNDVif3+3VCzk3U01CyJFDhCiIpLpysaxQm7+Beu+jy2n0rl0Ll0bXMJobE/954jLaeAqp6OPNTA9/qGmp41SjzeGkiBI4So2IIeBc9a6PPSea/ybgBmy8R/ogIzm5Wi/wZebFsdg14HRxZbZUPNkkiBI4So2PQGtUkg8GjOX9hQyNIDCaRk5GocTAhtRBxNIfZCNi72Rp5pEXiloeYH6kYra6hZEilwhBCiSW9w9sU2O5HXfaIoMCnMjzyjdSohNDFzs3of2vOhVXGyM1p1Q82SWLTASU1NpU+fPri6uuLu7s6gQYPIysoqcf/XXnuNevXq4eDgQNWqVXn99ddJTy9+PVyn0123LFiwwJJvRQhRntnYQ+uhAAxgCTrM/LzjDLkFJo2DCXF/HTybzo7YVIx6HQPaVC/eULPD21bXULMkFi1w+vTpw+HDh1mzZg3Lli1j06ZNDBky5Kb7JyQkkJCQwJdffsmhQ4eYO3cuK1euZNCgQdftO2fOHBITE4uWnj17WvCdCCHKvRYvgp0rLhkxPOt6hEs5Bfy195zWqYS4r64+RfhoE3/83RyKN9QMGaBtuDukUxTFIrNaRUdH06BBA3bt2kWLFi0AWLlyJd27d+fs2bMEBATc1nl+//13+vbtS3Z2NkajUQ2t07Fo0aK7LmoyMjJwc3MjPT0dV1fXuzqHEKIcWjMOtn5LkltTWie/TW0fZ9a88QA6nU7rZEJYXGL6Zdp/tp5Cs8LS4e1o7J4Pk5qqPaeenlPU3kRLd/L9bbERnMjISNzd3YuKG4Dw8HD0ej07duy47fNcfRNXi5urhg0bhpeXF61atWL27NmUVKfl5eWRkZFRbBFCiOuEDgWDLX7pUbS3iyEmJYuNx89rnUqI+2LetjMUmhVCa3jSuIobbP7S6htqlsRiBU5SUhI+Pj7F1hmNRjw9PUlKSrqtc1y4cIEPP/zwustaH3zwAb/99htr1qzhqaee4tVXX2Xy5Mk3Pc+ECRNwc3MrWgIDA+/8DQkhyj9Xf2jSC4B33VcD13rxCFGeZecV8ssO9cb6l9rXVBtq7pqlbrTihpoluePEY8aMueFNvv9ejh49es/BMjIyeOSRR2jQoAHvvfdesW3vvvsubdu2pVmzZrzzzju8/fbbfPHFFzc919ixY0lPTy9a4uPj7zmfEKKcajsC0FE3fQv19GfZfOICx5IytU4lhEX9vjuejNxCqldypHOQT5lpqFmSOy5wRo8eTXR0dIlLzZo18fPzIyWleE+XwsJCUlNT8fPzK/FnZGZm0rVrV1xcXFi0aBE2NjYl7h8aGsrZs2fJy8u74XY7OztcXV2LLUIIcUNedSDoEQDeqxQByMR/onwrNJmZeeXf8UHta6JPPvCvhprjNUx2b4y33qU4b29vvL29b7lfWFgYaWlp7Nmzh5CQEADWrVuH2WwmNDT0psdlZGTQpUsX7OzsWLJkCfb29rf8WVFRUXh4eGBnZ3f7b0QIIW6m3RtwdBmhWRH48SiLovS81bUeXs7y/xhR/qw4lMTZS5ep5GTLMyFVYMFIdUPjZ8A/WNNs98JiF9Xq169P165dGTx4MDt37mTr1q0MHz6c3r17Fz1Bde7cOYKCgti5cyegFjcPP/ww2dnZzJo1i4yMDJKSkkhKSsJkUuejWLp0KTNnzuTQoUPExMQwdepUPvnkE1577TVLvRUhREVTpQVUa4deKWSMxzryC838vD1O61RClDpFUZi+8SQA/cKqYx+/uUw11CyJRe8a+vnnnwkKCqJz5850796ddu3aMWPGjKLtBQUFHDt2jJycHAD27t3Ljh07OHjwILVr18bf379ouXrfjI2NDVOmTCEsLIymTZsyffp0vv76a8aPL7vDaEIIK3SlCeejBatwJYsft58hr1Am/hPly7aTFzmckIGDjYF+rQNhzZXv0jLSULMkFpsHx5rJPDhCiFtSFJjWDpIPMc3wPJ9mP8oXTzdRe/MIUU68MGsHm09cYECb6rxX8xj8MVBtqDkiyip7TlnFPDhCCFGm6XRXnqiC/vp/sCOfWVtiS5xzS4iy5EhCBptPXECvg0FhVcpkQ82SSIEjhBA30/BJcKuKQ8ElnrfdzNGkTLadvKh1KiFKxYxN6r03jzQJIDD2tzLZULMkUuAIIcTNGIzQZjgAr9n/gwET0zed0jiUEPfu7KUclh5IBGBomG+ZbahZEilwhBCiJM36goMnnvkJPGLcxabj5zl0Ll3rVELck9lbTmMyK7StXYkGp38ssw01SyIFjhBClMTWCVqp7WLecvoHUGQUR5Rp6TkFLNilTnswrJU7bJukbnjwXTCUPLFuWSIFjhBC3EqrIWB0IDDvBO30h1h+IIEzF7O1TiXEXflpxxly8k0E+bkQdnZ2mW6oWRIpcIQQ4lacKkHzfgCMdV2JWYEfNssojih7cgtMzNl6GoBRLWzR7Z6tbgh/r0w21CxJ+Xo3QghhKWHDQGegYe4+GulO8dvus5zPvHH/OyGs1aJ957iQlUeAmz3hST9caaj5INTsqHW0UicFjhBC3A6PatDoKQDGuK4iv9DMnK3ShFOUHWazwg9X7h97Ozgf/aE/1A3h72kXyoKkwBFCiNt1ZeK/tnlbqapL5sftZ8jMLdA4lBC3Z010MqcuZONqb+TR89PVlWW8oWZJpMARQojb5dcIaj+EDjNvOa8iM7eQX3ZIE05h/RRFYeoGdWK//9RPwRi7vlw01CyJFDhCCHEnrjTh7G5ahxfpzNoSK004hdWLPHWRqPg07I3wVOoP6spy0FCzJFLgCCHEnajWFiq3wGDOZ7jTWlIy81i095zWqYQo0ffr1dGbD2rHYJO8H2ydof2bGqeyLClwhBDiTuh0RaM4z+lW48RlZmw6hcksTTiFdYqKT2NLzAXs9SaevHTlsfA2r4Ozt7bBLEwKHCGEuFP1HoFKtbErzORF+42cupDN6sNJWqcS4oa+Xx8DwMdV92JMPw1O3uq0B+WcFDhCCHGn9Hr1N2BgiO1KbChk2saTKIqM4gjrcjw5k9VHknHS5dIj/Sd1ZYd3yk1DzZJIgSOEEHcjuDc4++GSn8IzttvYfzadyJMXtU4lRDFXn5ya4L8Z4+Xy11CzJFLgCCHE3TDaQeuhALzh8A86zHx/5ctECGsQn5rDkv0JeJLBI5m/qSvLWUPNkkiBI4QQd6vFQLBzxTvvDF0M+9gSc4G9cZe0TiUEANM3ncRkVpjgtRJDQTb4Ny13DTVLIgWOEELcLXs3aPEiAGPcVgHw3boYLRMJAUBKRi6/7T5LoC6Zh3KWqysfer/cNdQsScV5p0IIYQmth4LBluo5h2ilP8q6oykcOpeudSpRwc3aEkt+oZmP3f5GX44bapZEChwhhLgXLn4Q/BwA4zzWADKKI7SVlpPPT9vP0FB3mgdyN6gry2lDzZJIgSOEEPeqzeuAjkbZkdTTx7PycBLHkjK1TiUqqHnbzpCdb+J9pyvdwstxQ82SSIEjhBD3yqs21H8MgPcrRQAwZb2M4oj7LyuvkDnbYmmjP0SLwr3lvqFmSaTAEUKI0nClfUNo1joCuMCyAwmcOp+lbSZR4cyPPE16Th7j7ReqK1q8WK4bapZEChwhhCgNlUOgent0SiHv+2zArCDz4oj7KjuvkB82naK7fif1zCfVhpoPvKV1LM1IgSOEEKXlyijOgzkrcSOLRfvOEZ+ao20mUWH8uP0MmTmXGWv3u7qiAjTULIkUOEIIUVpqdQbfxhgKc3jXZwsms8LUjTKKIywvJ18dveltWE8VJbHCNNQsiRQ4QghRWnQ6aDsCgB55y7Anjz92nyUx/bLGwUR59/P2OC5nZ/CGzSJ1RQVpqFkSKXCEEKI0NXwC3Ktik5fKWz67yDeZmb7xlNapRDl2Od/E9E0nGWRYQSXSwKNGhWmoWRKLFjipqan06dMHV1dX3N3dGTRoEFlZJT9V0LFjR3Q6XbHllVdeKbZPXFwcjzzyCI6Ojvj4+PDWW29RWFhoybcihBC3x2CEsNcA6GNeigETv+6MIyUjV+Ngorz6eccZzFkXGGqzTF3RueI01CyJRQucPn36cPjwYdasWcOyZcvYtGkTQ4YMueVxgwcPJjExsWj5/PPPi7aZTCYeeeQR8vPz2bZtG/PmzWPu3LmMGzfOkm9FCCFuX7O+4FgJ+6x4hvkeIq9QOo0Ly8gtMDF90yleMy7CkdwrDTWf0DqWVbBYgRMdHc3KlSuZOXMmoaGhtGvXjsmTJ7NgwQISEhJKPNbR0RE/P7+ixdXVtWjb6tWrOXLkCD/99BNNmzalW7dufPjhh0yZMoX8/HxLvR0hhLh9to7Q6mUAhuiXAgq/7IiTe3FEqftlRxz2WXH0NaoTTFa0hpolsdinEBkZibu7Oy1atChaFx4ejl6vZ8eOHSUe+/PPP+Pl5UWjRo0YO3YsOTnXHrOMjIykcePG+Pr6Fq3r0qULGRkZHD58+Ibny8vLIyMjo9gihBAW1Wow2DjifOkILwWcJt9kltmNRanKLTAxbeNJRhn/wIbCCtlQsyQWK3CSkpLw8fEpts5oNOLp6UlSUtJNj3v++ef56aefWL9+PWPHjuXHH3+kb9++xc777+IGKPrzzc47YcIE3NzcipbAwMC7fVtCCHF7HD2heX8AXrNV741YuCues5dkXhxROhbsjMM76xhPGLaqKypgQ82S3HGBM2bMmOtuAv7f5ejRo3cdaMiQIXTp0oXGjRvTp08f5s+fz6JFizh58u6vX48dO5b09PSiJT4+/q7PJYQQty1sGOiNuCVF0ifwIgUmRUZxRKnILTAxdeNJ3jYuUFc0erpCNtQsifFODxg9ejQDBgwocZ+aNWvi5+dHSkpKsfWFhYWkpqbi5+d32z8vNDQUgJiYGGrVqoWfnx87d+4stk9ycjLATc9rZ2eHnZ3dbf9MIYQoFe6B6hfPgQWMdlzBz7zA77vPMrRDbapWctQ6nSjDfow8Q62sPXSwPYCit0H34H+1jmR17ngEx9vbm6CgoBIXW1tbwsLCSEtLY8+ePUXHrlu3DrPZXFS03I6oqCgA/P39AQgLC+PgwYPFiqc1a9bg6upKgwYN7vTtCCGEZbV9HQDPMyt5pkYehWaFyetOaBxKlGVZeYVM23CCMcZfAdBV4IaaJbHYPTj169ena9euDB48mJ07d7J161aGDx9O7969CQgIAODcuXMEBQUVjcicPHmSDz/8kD179nD69GmWLFlCv379eOCBB2jSpAkADz/8MA0aNOCFF15g//79rFq1iv/+978MGzZMRmmEENbHtyHUeRhQeMd1NQB/7TtH7IVsbXOJMmv2llha526hiT4WpYI31CyJRZ8l+/nnnwkKCqJz5850796ddu3aMWPGjKLtBQUFHDt2rOgpKVtbW9auXcvDDz9MUFAQo0eP5qmnnmLp0qVFxxgMBpYtW4bBYCAsLIy+ffvSr18/PvjgA0u+FSGEuHttRwLgFfMXPWsbMZkVJkXIKI64c2k5+czZdJw3jQsB0FXwhpol0SmKomgd4n7LyMjAzc2N9PT0YnPsCCGERSgKzHoIzu4iJfhVWu1oh04Ha954gNo+LlqnE2XIZyuPkrl5Gh/ZzEFx8kb3elSF6jl1J9/fMhuQEEJYmk5XNIrjc/RnHgtyRlHg6zXHtc0lypSUzFx+23qUEca/ANBJQ80SSYEjhBD3Q73u4FUX8tJ5128nOh2sOJjE/vg0rZOJMuL79Sd53rwUb106ikeNonmWxI1JgSOEEPeDXg9t1CeqfA7P4ulg9b6Jz1fd/bxhouI4l3aZf3Yc4mWjOmmkrvO7YLTVOJV1kwJHCCHulybPgos/ZCYytvJBbA16tsZcZPOJ81onE1ZucsQJXtH9ibNOGmreLilwhBDifjHaQetXAfCMmkqf0CoAfL7yGGZzhXveQ9ym2AvZRO7ZSx/DWnWFNNS8LfIJCSHE/RQyAOzc4OIJ3giMwcnWwMFz6fxz6OY9+kTF9sWqo4w0/IatziQNNe+AFDhCCHE/2btCy0EAuO6ZwuD26gy0X64+RoHJrGUyYYX2xl3izKHt0lDzLkiBI4QQ91voK2Cwg7O7GFI9mUpOtsReyOb33We1TiasiKIoTFgRLQ0175IUOEIIcb+5+ELT5wBw3DmZ4Q/WBmDi2uNczjdpmUxYkTVHkrGJ20wHg9pQE2moeUekwBFCCC20eR3QwYnV9KmRRRUPB1Iy85i9NVbrZMIKFJrMfPpPNO9cGb2Rhpp3TgocIYTQQqVa0OBxAGy3T+bNh+sB8P36GM5n5mmZTFiBhbvjCUpdR7D+FIqtkzTUvAtS4AghhFautG/g4B88Xq2Q4CpuZOebpIVDBZedV8ik1dG8VdRQc4Q01LwLUuAIIYRWKjeHGg+AYkK/Yyr/fbQBAAt3xXEsKVPjcEIrP2w+xUO5K6mhT0Zx8oawYVpHKpOkwBFCCC1dHcXZO4+WPtCtkR9mBT5eEa1pLKGNlMxcftp0RBpqlgIpcIQQQku1HgS/JlCQAzt/YEy3IGwMOjYdP8+GYylapxP32ZerjvGcSRpqlgYpcIQQQks6HbQdob7eOZ1qLjoGtKkOwMfLoymUyf8qjINn04nYc0QaapYSKXCEEEJrDXqCezXIuQj7fmJ4pzq4O9pwIiWLhbvjtU4n7gNFUXh/6WGGGRZLQ81SIgWOEEJozWCENq+pryMn42anY2TnOgB8vfo4mbkFGoYT98PSA4kkxR2jr2GNuiL8PWmoeY/k0xNCCGvQrC84ekFaHBxeRJ/W1ajp5cTF7Hwmr4vROp2woMv5Jj5dEc1o4+9qQ82anaBWJ61jlXlS4AghhDWwcYDQl9XX6z/GxpTLu4+pj43P3hJLTIo8Nl5eTd90Eo+Mo9JQs5RJgSOEENYi9BVwCYBLsbDhEzrV8yG8vi+FZoX3lhxBURStE4pSlpB2mWkbTxZvqBnQVNNM5YUUOEIIYS3sXeGxierryClwdg/jHm2ArVHPlpgLrDyUpGk8Ufom/HOUZqaD0lDTAqTAEUIIa1K3CzTpBYoZ/h5GVTcDr3SoBcCHy45It/FyZGvMBZbuP8dY46+ANNQsbVLgCCGEten6KTh5w/lo2PwVQzvUorK7AwnpuXy/QW44Lg/yCk28u/gQ3fU7aKI/BbbO0lCzlEmBI4QQ1sbRE7p/ob7e/BUOqUd490qfqukbT3H6QraG4URp+GHTKeIupDPG9jd1RZvXpKFmKZMCRwghrFGDnhD0KJgL4e/hdKlfifZ1vMg3mXl/6WG54bgMi0/NYfK6GHob1lOVJHW0ThpqljopcIQQwhrpdPDIV2DvDolR6CK/473HG2Jj0LH+2Hn+kRuOyyRFURi/5DCGwhzetFusruzwDti5aJqrPJICRwghrJWLH3SdoL5eP4FaukRe7VgbgPFLDpN+WWY4LmtWH0lm3dEUhtj8g7v5EkhDTYuRAkcIIaxZ8HNQOxxMefD3cF7tWIOa3k6cz8zjs5VHtU4n7kBOfiHvLzmMJxkMtV2urpSGmhYjBY4QQlgznQ4enag+ZRO/Hbu9s/nkicYA/LIjjl2nU7XNJ27bl6uOk5CeyxinZdiZcsA/WBpqWpBFC5zU1FT69OmDq6sr7u7uDBo0iKysrJvuf/r0aXQ63Q2X33//vWi/G21fsGCBJd+KEEJoxz0QHnpffb32fVp7ZNG7ZSAAY/86SF6hzI1j7fbGXWLOtliq6FJ4Wlmlrgx/XxpqWpBFP9k+ffpw+PBh1qxZw7Jly9i0aRNDhgy56f6BgYEkJiYWW95//32cnZ3p1q1bsX3nzJlTbL+ePXta8q0IIYS2Ql6Eam2hIBuWvs7YrkF4OdsSk5LFtA2ntE4nSpBXaOKdPw6gKPCtz3L05gJpqHkfWKzAiY6OZuXKlcycOZPQ0FDatWvH5MmTWbBgAQkJCTc8xmAw4OfnV2xZtGgRzz77LM7OzsX2dXd3L7afvb29pd6KEEJoT6+HxyeD0R5ObcDt2ALGPdYQgCnrY4hJufnouNDWlPUnOZGSRRunc4Skr1FXSkNNi7NYgRMZGYm7uzstWrQoWhceHo5er2fHjh23dY49e/YQFRXFoEGDrts2bNgwvLy8aNWqFbNnzy5xToi8vDwyMjKKLUIIUeZUqgWd/k99veq/PFYDOtbzJt9k5s3f91NoMmubT1wnOjGD79ers09P9FqirpSGmveFxQqcpKQkfHx8iq0zGo14enqSlHR78zfMmjWL+vXr06ZNm2LrP/jgA3777TfWrFnDU089xauvvsrkyZNvep4JEybg5uZWtAQGBt75GxJCCGsQNgwqh0BeOrplo/ikZyNc7IxExacxY7NcqrImhSYz7/x5gEKzwus1EvBJ3gzSUPO+ueMCZ8yYMTe9EfjqcvTovT+6ePnyZX755Zcbjt68++67tG3blmbNmvHOO+/w9ttv88UXX9z0XGPHjiU9Pb1oiY+Pv+d8QgihCb0BekxRvyiP/0PA2RWMe0xt4zBxzQmOJWVqHFBcNX3TKQ6cTcfF3sBr5p/UlS0GSkPN++SOC5zRo0cTHR1d4lKzZk38/PxISUkpdmxhYSGpqan4+fnd8uf88ccf5OTk0K9fv1vuGxoaytmzZ8nLy7vhdjs7O1xdXYstQghRZvnUv9aY8Z+3eTrIns5BPuSbzIz6LYoCuVSluUPn0vlmzXEAZoScwyY56kpDzbe1DVaBGO/0AG9vb7y9b90QLCwsjLS0NPbs2UNISAgA69atw2w2ExoaesvjZ82axeOPP35bPysqKgoPDw/s7Oxu/QaEEKI8aPcGHPkbUg6jW/kOE578noe+2cThhAymrI9hZHhdrRNWWLkFJt5YGEWhWaF7Ay9ax165JCUNNe8ri92DU79+fbp27crgwYPZuXMnW7duZfjw4fTu3ZuAgAAAzp07R1BQEDt37ix2bExMDJs2beKll1667rxLly5l5syZHDp0iJiYGKZOnconn3zCa6+9Zqm3IoQQ1sdoCz2ngM4Ah/7EJyGCD3qoT1V9ty6Gg2fTNQ5YcX2x6hgnUrLwcrbji5pR6FJPSUNNDVh0Hpyff/6ZoKAgOnfuTPfu3WnXrh0zZswo2l5QUMCxY8fIyckpdtzs2bOpUqUKDz/88HXntLGxYcqUKYSFhdG0aVOmT5/O119/zfjx4y35VoQQwvoENFNHBQCWjeLxeo50b+xHoVlhxIJ9ZOcVapuvAtoWc4FZW2IB+KpHTZwiv1Q3SEPN+06nlPR8dTmVkZGBm5sb6enpcj+OEKJsK7gM09rBxRho1pdL4d/Q7dvNJGXk8myLKnz+dLDWCSuM9MsFdJu4iYT0XJ5rVZUJlVbC+o/AozoM2yU9p0rBnXx/yxzRQghRltk4wOPfATrY9xMeSVv4pldTdDr4bfdZluy/8cSqonQpisLYvw6QkJ5LtUqO/LeTN2z9Vt34oDTU1IIUOEIIUdZVC4NWV9rgLBlBWBU7hneqDcD//XWQ+NScEg4WpeGnHXGsOJiEUa/j297NcNo+EfIz1YaaDZ/UOl6FJAWOEEKUB53HgXtVSI+DiPcZ0bkOzau6k5lXyOsL9smj4xZ0OCGdD5cdAWBMtyCaOqfBrpnqRmmoqRn51IUQojywc4bHrlwS2TkD49kdfNu7GS52RvbFpfHl6mPa5iunsvIKGf7LPvILzXQO8mFQuxqw7mOQhpqakwJHCCHKi1oPQrO+6uslwwl00fHpU00AmL7xFCsPJWoYrvxRFIX/W3SQ2AvZBLjZ8+UzweiSDsLB39UdpKGmpqTAEUKI8uThj8HZT32qasMEHmnir44qAG/+foCT56XreGmZs/U0f0clYNDrmPRcMzycbCHifUCBRk9JQ02NSYEjhBDliYM7PPqN+nrbZDi3lzHdgmhV3ZOsvEJe+XGPzI9TCradvMDHK6IB+E/3+rSo7gmnNkLMWtAbpaGmFZACRwghypug7uoIgmKGv4djoxTyXZ9m+LjYcSIli3f+PEAFnAKt1JxLu8zwX/ZhMis80awyL7atDooCa69MONviRfCsqWlGIQWOEEKUT90+B8dKkHIYtnyDj4s9U/o0x6jXsexAIlPWx2idsEzKLTDx8o+7Sc3Op1FlVyY82RidTgdHFkPCPmmoaUWkwBFCiPLIyUstcgA2fQHJR2hZ3ZP3r/Sr+nL1cZYdkEkA74TZrPDWHwc4dC4DTydbpvUNwd7GAKYCiPhA3UkaaloNKXCEEKK8avQU1OuuPrL89zAwFdIntBovtlVvOh79236i4tO0zViGfLn6GEv3J2DU6/ju+WZU8XBUN+ydD9JQ0+pIgSOEEOWVTgePfA12bpCwF7Z/D8D/PVKfB4N8yCs089K83ZxLu6xxUOv3y444vt9wEoBPn2pCm1pe6oa8LNjwqfr6gbeloaYVkQJHCCHKM1d/6PKR+nr9x3DxZNFjzUF+LlzIymPA7J2k5eRrm9OKbTiWwrt/HwJgROc6PB1S5drG7VMhO0VtqBkyQJN84sakwBFCiPKu2QtQsyMU5sLfw8FsxtnOyKwBLfF1VZ+sGjBnlzw+fgP749MY9vNeTGaFJ5tXZmR4nWsbsy9IQ00rJgWOEEKUdzodPDYJbJwgbhvsngVAZXcHfhwUirujDVHxabzy0x7yCk0ah7Uex5Iy6T9nJ9n5JtrUqsSnTzZRn5i6atOX0lDTikmBI4QQFYFHNQi/Mk/L2vcgLQ6Aur4uzBnQEkdbA5tPXGDUwv2YzDJHzukL2fSdtYO0nAKCA92Z0a8FtsZ/fWVeOi0NNa2c/I0IIURF0XIwBLaG/CxYOkKdnA5oVtWD6S+EYGPQsfxgIm/+XrGLnHNpl+kzcwfnM/MI8nNh3sCWONsZi++0/hNpqGnlpMARQoiKQq+HHt+BwQ5OroOoX4o2ta/jzeTnmmHU61i07xxvLIyi0GTWMKw2zlzM5tlpkZxLu0wNLyfmD2qFu+P/3FuTeAAO/Ka+loaaVksKHCGEqEi86kCnserrVWMhM6loU9dG/nz3vDrb8ZL9CYxYEEVBBSpyYlKyeHa6WtzU9HLil8Gh+LjYX7+jNNQsE6TAEUKIiibsNfBvCrnpsHx00aUqgK6N/Jja99rlqpd/3ENOfvl/uio6MYPeMyJJzsijrq8zC15ujb+bw/U7SkPNMkMKHCGEqGgMRvVSld4IR5fB4UXFNj/UwJcZL7TAzqhn3dEUnvthBxez8jQKa3mbT5znmWmRXMjKp4G/KwuGhN145EYaapYpUuAIIURF5NcY2o9WX694C7IvFtvcKciHXwa3xt3Rhv3xaTw9LZIzF7M1CGpZv+2OZ+CcXWTlFRJaw5NfB7fG0+km89lIQ80yRQocIYSoqNq/Cd71IecCrBxz3eaQah788UobKrs7EHshmx5TtrL5xHkNgpY+k1nhi1VHefuPAxSaFXo0DWD+oFa4Odrc5IACiPhQfS0NNcsEKXCEEKKiMtpCjymg08PB3+DYyut2qe3jzKJX2xAc6E5aTgH9Z+9k+saTKErZfYw8NTufAXN2MmW92ltqWKdaTOzVFDuj4eYH7Z0PqSfB0UsaapYRUuAIIURFViUEWr+qvl72hnrj8f/wcbVn4ZDWPBNSBbMCE/45yqs/7y2T/aui4tN4bPIWNp+4gIONgW97N+WtLkHFZyj+X/9uqNnhHWmoWUZIgSOEEBVdp/9Tb5jNTIDV795wF3sbA58/3YQPezTEqNfxz6Ekuk7czLaYC/c57N0pMJn5Zs1xnpq6rWiOm8XD2tKjaeVbHywNNcskKXCEEKKis3WEx79TX++dB6c23HA3nU7HC2HV+XNoG2p6OZGUkcvzM3fwwdIjZFlxo84TyZk8+f02vo04gcms8EgTf/4e3pZ6frcxEpN+VhpqllE6pSxfSL1LGRkZuLm5kZ6ejqurq9ZxhBDCOiwbpTbidK8Gr0aCrdNNd83JL+Tj5dH8vEPtaeXnas+7jzage2O/ki/33EfZeYVMXhfDrC2nKDApuDnY8GHPRjweHHDrgy+dhh0z1IIvPwsCmsFL66TnlMbu5PtbChwpcIQQQpWbAd+HQcZZCB0K3T695SEbjqUw7u/DxKXmANCuthfvdA2icRU3S6e9qUKTmcVRCXy56hhJGbkAdA7y4ZMnG+PreoP5ba5SFDizVb0kdWwFKFdmcfaqC71/UWeBFpqSAucWpMARQoibOLEWfn4K0MGLq6Bq6C0PyS0w8f2Gk0zbcJL8K60dujb04/XOdWgQcP/+H1toMrP8YCLfRpzg1Hl1zp6qno6Mf6wBnev7lnBgHhz6E7Z/D0kHr62v9aB6A3atzjJyYyXu5PvbYn9jH3/8MW3atMHR0RF3d/fbOkZRFMaNG4e/vz8ODg6Eh4dz4sSJYvukpqbSp08fXF1dcXd3Z9CgQWRlZVngHQghRAVUJxyCnwcUWDIcCnJveYi9jYFRD9VlzagHeKJZZXQ6WHk4ie6TNtN7RiSrDidZtHFnanY+32+IocMXGxixIIpT57PxcLRhTLcgVr/xwM2Lm8xkWD8BvmkIi4eqxY3RAUIGwqs74IVFUOchKW7KKIuN4IwfPx53d3fOnj3LrFmzSEtLu+Uxn332GRMmTGDevHnUqFGDd999l4MHD3LkyBHs7dVhxW7dupGYmMj06dMpKChg4MCBtGzZkl9++eUWZ79GRnCEEKIEOakwJVR9csivCTTsqY5m+AXf1pf98eRMJkWc4J9DSZjM6ldMJSdbujf259Em/jSv5oGN4d6KhvScAiKOJrPsQCKbT5ynwKT+HE8nWwa2qc7AdjVwtjPe+OCEKNgxTR21MV151N21MrQaDM37g6PnPWUTlmNVl6jmzp3LyJEjb1ngKIpCQEAAo0eP5s033wQgPT0dX19f5s6dS+/evYmOjqZBgwbs2rWLFi1aALBy5Uq6d+/O2bNnCQi4jRvHkAJHCCFuKWYt/Po8mP7Vg8qxEtTspBY7tR4EV/8ST5GQdpkft59h4a54UrOvzZnjZGugVQ1PWlT3JMjPhXp+Lvi52mO8SdGTW2Di5PksTiRncTghnchTFzmckPHvHqE0ruxG/zbVebSJP/Y2N5iwz2yCo8vVwubM1mvrq7SC1kOh/mNguMksxsJq3Mn3903K2/svNjaWpKQkwsPDi9a5ubkRGhpKZGQkvXv3JjIyEnd396LiBiA8PBy9Xs+OHTt44oknbnjuvLw88vKu/UeakZFhuTcihBDlQe1weH0vHPsHTq6H2E2QcxEO/aEuAD4NrhU71dqATfHu2wHuDrzTNYhRD9Vla8wFluxPYP3RFC7lFLD+2HnWH7vW9kGnAw9HW9wdbTDodOh0kFtgJjU7/6aPoNf2ceaRxv48FuxPbZ+bPPJ9OQ32/Qg7Z0Ca+sQXeiM0fEK9kbpKyL1+UsJKWU2Bk5SUBICvb/Frpb6+vkXbkpKS8PHxKbbdaDTi6elZtM+NTJgwgffff7+UEwshRDnnVkW9bNNqsNqL6ewuOLkOYiLUppMpR9Ql8jsw2KlFTu3OasHj00CtWgAbg56O9XzoWM8Hs1khOimDbTEXOZSQzrGkTE6ez6LApJCanV9spKdYFAcb6vo6U9fXhZbVPQmrVankJ6IunlRHa/b9DAVXmoQ6eEKLgdDyJXC9vRF/UXbdUYEzZswYPvvssxL3iY6OJigo6J5ClbaxY8cyatSooj9nZGQQGBioYSIhhChjDDZqAVOtDTz4X/U+nVMb4GSEOsKTcQ5OrVcXAGe/a6M7NTsWNafU63U0DHCjYcC1x8hNZrW4uZCVR1pOQVGfK1ujHk8nWyo52eHqYLz1/DqKombaPhVOrLq23ru+ehmqybPXjTKJ8uuOCpzRo0czYMCAEvepWbPmXQXx8/MDIDk5GX//a9d1k5OTadq0adE+KSkpxY4rLCwkNTW16PgbsbOzw87O7q5yCSGEuAFHT2j0pLooClw4ro7snFwHp7dAVhLs/0VdAPyDrxU8ga2LzQhs0OvwdrHD2+Uu/z9dcBkOLITt0+B89LX1dbtC6CtqgWUlkw+K++eOChxvb2+8vS3TIr5GjRr4+fkRERFRVNBkZGSwY8cOhg4dCkBYWBhpaWns2bOHkBD1uum6deswm82Eht56rgYhhBAWoNOBdz11CXtVfbQ8frta7Jxcpz5+nbhfXbZ8AzZOUL2dWuzU7gyVat9dAZKRADt/gD1z4XKqus7GCZr1gVYvg1ftUn2bomyx2D04cXFxpKamEhcXh8lkIioqCoDatWvj7OwMQFBQEBMmTOCJJ55Ap9MxcuRIPvroI+rUqVP0mHhAQAA9e/YEoH79+nTt2pXBgwczbdo0CgoKGD58OL17977tJ6iEEEJYmI29OmpSsyM89IE638ypDdcKnuwU9RLS1ctIboFQq5M6oV7NDuDgUfL5z+5WL0MdWQzmKzcgu1dVi5pmfcHB3WJvTZQdFntMfMCAAcybN++69evXr6djx47qD9fpmDNnTtFlL0VRGD9+PDNmzCAtLY127drx/fffU7du3aLjU1NTGT58OEuXLkWv1/PUU08xadKkoqLpdshj4kIIoRGzGVIOX7tZOS7y2lw0ADo9BDS/drNy5RZgMKo3OUcvUQubs7uu7V+trXp/Tb3uoL/B4+GiXLGqeXCskRQ4QghhJfJz4My2Kzcrr4PzR4tvt3NVG11eOAGZCeo6gy00ehpav6Le2yMqjDI5D44QQogKyNZRbQ9R58ocaOnnrl3KOrUeLl+C2I3qNidv9RHvFi+Cs8/NzykEUuAIIYSwJm6VofkL6mI2QWIUnIkE90D1qSijPBErbo8UOEIIIayT3gCVQ9RFiDskLVKFEEIIUe5IgSOEEEKIckcKHCGEEEKUO1LgCCGEEKLckQJHCCGEEOWOFDhCCCGEKHekwBFCCCFEuSMFjhBCCCHKHSlwhBBCCFHuSIEjhBBCiHJHChwhhBBClDtS4AghhBCi3JECRwghhBDlToXsJq4oCgAZGRkaJxFCCCHE7br6vX31e7wkFbLAyczMBCAwMFDjJEIIIYS4U5mZmbi5uZW4j065nTKonDGbzSQkJODi4oJOpyvVc2dkZBAYGEh8fDyurq6leu6yTj6bksnnUzL5fEomn8/NyWdTsrL0+SiKQmZmJgEBAej1Jd9lUyFHcPR6PVWqVLHoz3B1dbX6f1G0Ip9NyeTzKZl8PiWTz+fm5LMpWVn5fG41cnOV3GQshBBCiHJHChwhhBBClDtS4JQyOzs7xo8fj52dndZRrI58NiWTz6dk8vmUTD6fm5PPpmTl9fOpkDcZCyGEEKJ8kxEcIYQQQpQ7UuAIIYQQotyRAkcIIYQQ5Y4UOEIIIYQod6TAKUVTpkyhevXq2NvbExoays6dO7WOZDU2bdrEY489RkBAADqdjsWLF2sdyWpMmDCBli1b4uLigo+PDz179uTYsWNax7IaU6dOpUmTJkWTkIWFhfHPP/9oHcsqffrpp+h0OkaOHKl1FKvw3nvvodPpii1BQUFax7Iq586do2/fvlSqVAkHBwcaN27M7t27tY5VKqTAKSULFy5k1KhRjB8/nr179xIcHEyXLl1ISUnROppVyM7OJjg4mClTpmgdxeps3LiRYcOGsX37dtasWUNBQQEPP/ww2dnZWkezClWqVOHTTz9lz5497N69mwcffJAePXpw+PBhraNZlV27djF9+nSaNGmidRSr0rBhQxITE4uWLVu2aB3Jaly6dIm2bdtiY2PDP//8w5EjR/jqq6/w8PDQOlrpUESpaNWqlTJs2LCiP5tMJiUgIECZMGGChqmsE6AsWrRI6xhWKyUlRQGUjRs3ah3Fanl4eCgzZ87UOobVyMzMVOrUqaOsWbNG6dChgzJixAitI1mF8ePHK8HBwVrHsFrvvPOO0q5dO61jWIyM4JSC/Px89uzZQ3h4eNE6vV5PeHg4kZGRGiYTZVF6ejoAnp6eGiexPiaTiQULFpCdnU1YWJjWcazGsGHDeOSRR4r9P0ioTpw4QUBAADVr1qRPnz7ExcVpHclqLFmyhBYtWvDMM8/g4+NDs2bN+OGHH7SOVWqkwCkFFy5cwGQy4evrW2y9r68vSUlJGqUSZZHZbGbkyJG0bduWRo0aaR3Hahw8eBBnZ2fs7Ox45ZVXWLRoEQ0aNNA6llVYsGABe/fuZcKECVpHsTqhoaHMnTuXlStXMnXqVGJjY2nfvj2ZmZlaR7MKp06dYurUqdSpU4dVq1YxdOhQXn/9debNm6d1tFJRIbuJC2Gthg0bxqFDh+Q+gf9Rr149oqKiSE9P548//qB///5s3Lixwhc58fHxjBgxgjVr1mBvb691HKvTrVu3otdNmjQhNDSUatWq8dtvvzFo0CANk1kHs9lMixYt+OSTTwBo1qwZhw4dYtq0afTv31/jdPdORnBKgZeXFwaDgeTk5GLrk5OT8fPz0yiVKGuGDx/OsmXLWL9+PVWqVNE6jlWxtbWldu3ahISEMGHCBIKDg/n222+1jqW5PXv2kJKSQvPmzTEajRiNRjZu3MikSZMwGo2YTCatI1oVd3d36tatS0xMjNZRrIK/v/91vyTUr1+/3FzGkwKnFNja2hISEkJERETROrPZTEREhNwnIG5JURSGDx/OokWLWLduHTVq1NA6ktUzm83k5eVpHUNznTt35uDBg0RFRRUtLVq0oE+fPkRFRWEwGLSOaFWysrI4efIk/v7+WkexCm3btr1uSorjx49TrVo1jRKVLrlEVUpGjRpF//79adGiBa1atWLixIlkZ2czcOBAraNZhaysrGK/NcXGxhIVFYWnpydVq1bVMJn2hg0bxi+//MLff/+Ni4tL0X1bbm5uODg4aJxOe2PHjqVbt25UrVqVzMxMfvnlFzZs2MCqVau0jqY5FxeX6+7VcnJyolKlSnIPF/Dmm2/y2GOPUa1aNRISEhg/fjwGg4HnnntO62hW4Y033qBNmzZ88sknPPvss+zcuZMZM2YwY8YMraOVDq0f4ypPJk+erFStWlWxtbVVWrVqpWzfvl3rSFZj/fr1CnDd0r9/f62jae5GnwugzJkzR+toVuHFF19UqlWrptja2ire3t5K586dldWrV2sdy2rJY+LX9OrVS/H391dsbW2VypUrK7169VJiYmK0jmVVli5dqjRq1Eixs7NTgoKClBkzZmgdqdToFEVRNKqthBBCCCEsQu7BEUIIIUS5IwWOEEIIIcodKXCEEEIIUe5IgSOEEEKIckcKHCGEEEKUO1LgCCGEEKLckQJHCCGEEOWOFDhCCCGEKHekwBFCCCFEuSMFjhBCCCHKHSlwhBBCCFHuSIEjhBBCiHLn/wGgvkXu0uMHsAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -144,13 +148,13 @@
     "\n",
     "    onnx_y_pred = np.zeros(y.shape)\n",
     "    for idx, _x in enumerate(x):\n",
-    "        x_train_np_reshaped = np.array([[[[_x]]]])\n",
+    "        x_train_np_reshaped = np.array([[_x]])\n",
     "\n",
     "        ort_value = ort.OrtValue.ortvalue_from_numpy(x_train_np_reshaped)\n",
     "\n",
     "        input_name = ort_session.get_inputs()[0].name\n",
     "        inputs = {input_name: ort_value.numpy()}\n",
-    "        outputs = ort_session.run(None, inputs)[0][0][0][0][0]\n",
+    "        outputs = ort_session.run(None, inputs)[0][0][0]\n",
     "        onnx_y_pred[idx] = outputs\n",
     "\n",
     "    plot_pred(x, y, onnx_y_pred)\n",
@@ -167,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 283,
+   "execution_count": 180,
    "metadata": {},
    "outputs": [
     {
@@ -204,45 +208,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 287,
+   "execution_count": 181,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Cannot set tensor: Got value of type INT8 but expected type FLOAT32 for input 0, name: serving_default_input:0 ",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[287], line 17\u001b[0m\n\u001b[0;32m     14\u001b[0m         f\u001b[38;5;241m.\u001b[39mwrite(tflite_model)\n\u001b[0;32m     16\u001b[0m tflite_model_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel.tflite\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m---> 17\u001b[0m \u001b[43mconvert_tensorflow_to_tensorflow_lite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtf_model_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtflite_model_path\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[1;32mIn[287], line 11\u001b[0m, in \u001b[0;36mconvert_tensorflow_to_tensorflow_lite\u001b[1;34m(tf_model_path, tflite_model_path)\u001b[0m\n\u001b[0;32m      9\u001b[0m converter\u001b[38;5;241m.\u001b[39minference_input_type \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mint8\n\u001b[0;32m     10\u001b[0m converter\u001b[38;5;241m.\u001b[39minference_output_type \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mint8\n\u001b[1;32m---> 11\u001b[0m tflite_model \u001b[38;5;241m=\u001b[39m \u001b[43mconverter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(tflite_model_path, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m     14\u001b[0m     f\u001b[38;5;241m.\u001b[39mwrite(tflite_model)\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\lite.py:1065\u001b[0m, in \u001b[0;36m_export_metrics.<locals>.wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1062\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(convert_func)\n\u001b[0;32m   1063\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1064\u001b[0m   \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m-> 1065\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_and_export_metrics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconvert_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\lite.py:1042\u001b[0m, in \u001b[0;36mTFLiteConverterBase._convert_and_export_metrics\u001b[1;34m(self, convert_func, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1040\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_save_conversion_params_metric()\n\u001b[0;32m   1041\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mprocess_time()\n\u001b[1;32m-> 1042\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1043\u001b[0m elapsed_time_ms \u001b[38;5;241m=\u001b[39m (time\u001b[38;5;241m.\u001b[39mprocess_time() \u001b[38;5;241m-\u001b[39m start_time) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000\u001b[39m\n\u001b[0;32m   1044\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\lite.py:1390\u001b[0m, in \u001b[0;36mTFLiteSavedModelConverterV2.convert\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1384\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1385\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_debug_info \u001b[38;5;241m=\u001b[39m _get_debug_info(\n\u001b[0;32m   1386\u001b[0m       _convert_debug_info_func(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_trackable_obj\u001b[38;5;241m.\u001b[39mgraph_debug_info),\n\u001b[0;32m   1387\u001b[0m       graph_def,\n\u001b[0;32m   1388\u001b[0m   )\n\u001b[1;32m-> 1390\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_from_saved_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph_def\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\lite.py:1257\u001b[0m, in \u001b[0;36mTFLiteConverterBaseV2._convert_from_saved_model\u001b[1;34m(self, graph_def)\u001b[0m\n\u001b[0;32m   1254\u001b[0m converter_kwargs\u001b[38;5;241m.\u001b[39mupdate(quant_mode\u001b[38;5;241m.\u001b[39mconverter_flags())\n\u001b[0;32m   1256\u001b[0m result \u001b[38;5;241m=\u001b[39m _convert_saved_model(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconverter_kwargs)\n\u001b[1;32m-> 1257\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimize_tflite_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1258\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquant_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquant_io\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexperimental_new_quantizer\u001b[49m\n\u001b[0;32m   1259\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\convert_phase.py:215\u001b[0m, in \u001b[0;36mconvert_phase.<locals>.actual_decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    213\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[0;32m    214\u001b[0m   report_error_message(\u001b[38;5;28mstr\u001b[39m(error))\n\u001b[1;32m--> 215\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m error \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\convert_phase.py:205\u001b[0m, in \u001b[0;36mconvert_phase.<locals>.actual_decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    202\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m    203\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    204\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 205\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    206\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m ConverterError \u001b[38;5;28;01mas\u001b[39;00m converter_error:\n\u001b[0;32m    207\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m converter_error\u001b[38;5;241m.\u001b[39merrors:\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\lite.py:991\u001b[0m, in \u001b[0;36mTFLiteConverterBase._optimize_tflite_model\u001b[1;34m(self, model, quant_mode, quant_io)\u001b[0m\n\u001b[0;32m    989\u001b[0m   q_allow_float \u001b[38;5;241m=\u001b[39m quant_mode\u001b[38;5;241m.\u001b[39mis_allow_float()\n\u001b[0;32m    990\u001b[0m   q_variable_quantization \u001b[38;5;241m=\u001b[39m quant_mode\u001b[38;5;241m.\u001b[39menable_mlir_variable_quantization\n\u001b[1;32m--> 991\u001b[0m   model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_quantize\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    992\u001b[0m \u001b[43m      \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    993\u001b[0m \u001b[43m      \u001b[49m\u001b[43mq_in_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    994\u001b[0m \u001b[43m      \u001b[49m\u001b[43mq_out_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    995\u001b[0m \u001b[43m      \u001b[49m\u001b[43mq_activations_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    996\u001b[0m \u001b[43m      \u001b[49m\u001b[43mq_bias_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    997\u001b[0m \u001b[43m      \u001b[49m\u001b[43mq_allow_float\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    998\u001b[0m \u001b[43m      \u001b[49m\u001b[43mq_variable_quantization\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    999\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1001\u001b[0m m_in_type \u001b[38;5;241m=\u001b[39m in_type \u001b[38;5;28;01mif\u001b[39;00m in_type \u001b[38;5;28;01melse\u001b[39;00m _dtypes\u001b[38;5;241m.\u001b[39mfloat32\n\u001b[0;32m   1002\u001b[0m m_out_type \u001b[38;5;241m=\u001b[39m out_type \u001b[38;5;28;01mif\u001b[39;00m out_type \u001b[38;5;28;01melse\u001b[39;00m _dtypes\u001b[38;5;241m.\u001b[39mfloat32\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\lite.py:710\u001b[0m, in \u001b[0;36mTFLiteConverterBase._quantize\u001b[1;34m(self, result, input_type, output_type, activations_type, bias_type, allow_float, enable_variable_quantization)\u001b[0m\n\u001b[0;32m    706\u001b[0m calibrate_quantize \u001b[38;5;241m=\u001b[39m _calibrator\u001b[38;5;241m.\u001b[39mCalibrator(\n\u001b[0;32m    707\u001b[0m     result, custom_op_registerers_by_name, custom_op_registerers_by_func\n\u001b[0;32m    708\u001b[0m )\n\u001b[0;32m    709\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_experimental_calibrate_only \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_new_quantizer:\n\u001b[1;32m--> 710\u001b[0m   calibrated \u001b[38;5;241m=\u001b[39m \u001b[43mcalibrate_quantize\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalibrate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    711\u001b[0m \u001b[43m      \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrepresentative_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minput_gen\u001b[49m\n\u001b[0;32m    712\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    714\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_experimental_calibrate_only:\n\u001b[0;32m    715\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m calibrated\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\convert_phase.py:215\u001b[0m, in \u001b[0;36mconvert_phase.<locals>.actual_decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    213\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[0;32m    214\u001b[0m   report_error_message(\u001b[38;5;28mstr\u001b[39m(error))\n\u001b[1;32m--> 215\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m error \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\convert_phase.py:205\u001b[0m, in \u001b[0;36mconvert_phase.<locals>.actual_decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    202\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m    203\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    204\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 205\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    206\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m ConverterError \u001b[38;5;28;01mas\u001b[39;00m converter_error:\n\u001b[0;32m    207\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m converter_error\u001b[38;5;241m.\u001b[39merrors:\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\optimize\\calibrator.py:254\u001b[0m, in \u001b[0;36mCalibrator.calibrate\u001b[1;34m(self, dataset_gen)\u001b[0m\n\u001b[0;32m    244\u001b[0m \u001b[38;5;129m@convert_phase\u001b[39m(Component\u001b[38;5;241m.\u001b[39mOPTIMIZE_TFLITE_MODEL, SubComponent\u001b[38;5;241m.\u001b[39mCALIBRATE)\n\u001b[0;32m    245\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcalibrate\u001b[39m(\u001b[38;5;28mself\u001b[39m, dataset_gen):\n\u001b[0;32m    246\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calibrates the model with specified generator.\u001b[39;00m\n\u001b[0;32m    247\u001b[0m \n\u001b[0;32m    248\u001b[0m \u001b[38;5;124;03m  Returns:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    252\u001b[0m \u001b[38;5;124;03m    dataset_gen: A generator that generates calibration samples.\u001b[39;00m\n\u001b[0;32m    253\u001b[0m \u001b[38;5;124;03m  \"\"\"\u001b[39;00m\n\u001b[1;32m--> 254\u001b[0m   \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_feed_tensors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset_gen\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresize_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    255\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_calibrator\u001b[38;5;241m.\u001b[39mCalibrate()\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\tensorflow\\lite\\python\\optimize\\calibrator.py:152\u001b[0m, in \u001b[0;36mCalibrator._feed_tensors\u001b[1;34m(self, dataset_gen, resize_input)\u001b[0m\n\u001b[0;32m    150\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_calibrator\u001b[38;5;241m.\u001b[39mFeedTensor(input_array, signature_key)\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 152\u001b[0m   \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_calibrator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFeedTensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_array\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[1;31mValueError\u001b[0m: Cannot set tensor: Got value of type INT8 but expected type FLOAT32 for input 0, name: serving_default_input:0 "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def convert_tensorflow_to_tensorflow_lite(tf_model_path = 'model_tf', tflite_model_path = 'model.tflite'):\n",
     "    def representative_dataset_gen():\n",
-    "        for i in range(1000):  # Generate 100 samples\n",
+    "        for i in range(1000):  # Generate 1000 samples\n",
     "            yield [np.array([x[i]], dtype=np.float32)] \n",
     "    converter = tf.lite.TFLiteConverter.from_saved_model(tf_model_path)\n",
     "    converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
     "    converter.representative_dataset = representative_dataset_gen\n",
-    "    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
-    "    converter.inference_input_type = tf.int8\n",
-    "    converter.inference_output_type = tf.int8\n",
+    "    # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
+    "    # converter.inference_input_type = tf.int8\n",
+    "    # converter.inference_output_type = tf.int8\n",
     "    tflite_model = converter.convert()\n",
     "\n",
     "    with open(tflite_model_path, 'wb') as f:\n",
@@ -254,12 +233,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 285,
+   "execution_count": 182,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -277,11 +256,11 @@
     "    \n",
     "    tf_lite_y_pred = np.zeros(y.shape)\n",
     "    for idx, _x in enumerate(x):\n",
-    "        input_data = np.array([[[[_x]]]])\n",
+    "        input_data = np.array([[_x]])\n",
     "\n",
     "        interpreter.set_tensor(input_details[0]['index'], input_data)\n",
     "        interpreter.invoke()\n",
-    "        output_data = interpreter.get_tensor(output_details[0]['index'])[0][0][0][0]\n",
+    "        output_data = interpreter.get_tensor(output_details[0]['index'])[0][0]\n",
     "        tf_lite_y_pred[idx] = output_data\n",
     "\n",
     "    plot_pred(x, y, tf_lite_y_pred)\n",
@@ -298,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 286,
+   "execution_count": 183,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -319,6 +298,193 @@
     "\n",
     "save_tflite_model_as_c_header()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = bytes([0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00, \n",
+    "  0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00, \n",
+    "  0x08, 0x00, 0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, \n",
+    "  0x80, 0x00, 0x00, 0x00, 0xd8, 0x00, 0x00, 0x00, 0x3c, 0x02, 0x00, 0x00, \n",
+    "  0x4c, 0x02, 0x00, 0x00, 0xf0, 0x06, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, \n",
+    "  0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x76, 0xfd, 0xff, 0xff, \n",
+    "  0x0c, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, \n",
+    "  0x0f, 0x00, 0x00, 0x00, 0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 0x67, 0x5f, \n",
+    "  0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x00, 0x01, 0x00, 0x00, 0x00, \n",
+    "  0x04, 0x00, 0x00, 0x00, 0x9c, 0xff, 0xff, 0xff, 0x08, 0x00, 0x00, 0x00, \n",
+    "  0x04, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x6f, 0x75, 0x74, 0x70, \n",
+    "  0x75, 0x74, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, \n",
+    "  0x46, 0xfe, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, \n",
+    "  0x69, 0x6e, 0x70, 0x75, 0x74, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, \n",
+    "  0x34, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xdc, 0xff, 0xff, 0xff, \n",
+    "  0x0b, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, \n",
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+    "  0x0c, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, \n",
+    "  0x00, 0x00, 0x00, 0x09, 0x0c, 0x00, 0x0c, 0x00, 0x0b, 0x00, 0x00, 0x00, \n",
+    "  0x00, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x72, 0x00, 0x00, 0x00, \n",
+    "  0x00, 0x00, 0x00, 0x72\n",
+    "  ])\n",
+    "\n",
+    "file_name = 'model.tflite'\n",
+    "\n",
+    "# Write the data to a file\n",
+    "with open(file_name, 'wb') as file:\n",
+    "    file.write(data)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
   }
  ],
  "metadata": {
diff --git a/esp32/src/NeuralNetwork.cpp b/esp32/src/NeuralNetwork.cpp
index b4238ca..20a553c 100644
--- a/esp32/src/NeuralNetwork.cpp
+++ b/esp32/src/NeuralNetwork.cpp
@@ -49,10 +49,7 @@ NeuralNetwork::NeuralNetwork(const void *model_data, const int kArenaSize) : _kA
     output = interpreter->output(0);
 }
 
-void NeuralNetwork::setInput(float value) {
-    int8_t x_quantized = value / input->params.scale + input->params.zero_point;
-    input->data.int8[0] = x_quantized;
-}
+void NeuralNetwork::setInput(float value) { input->data.f[0] = value; }
 
 float NeuralNetwork::predict(float value) {
     setInput(value);
@@ -66,9 +63,5 @@ float NeuralNetwork::predict() {
         return -1;
     }
 
-    int8_t y_quantized = output->data.int8[0];
-
-    float y = (y_quantized - output->params.zero_point) * output->params.scale;
-
-    return y;
+    return output->data.f[0];
 }
\ No newline at end of file
diff --git a/esp32/src/model.h b/esp32/src/model.h
index 5e2e2ca..025c34a 100644
--- a/esp32/src/model.h
+++ b/esp32/src/model.h
@@ -1,134 +1,162 @@
 alignas(16) const unsigned char g_model[] = {
-    0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00, 0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10,
-    0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x98, 0x00,
-    0x00, 0x00, 0xc8, 0x00, 0x00, 0x00, 0x1c, 0x03, 0x00, 0x00, 0x2c, 0x03, 0x00, 0x00, 0x30, 0x09, 0x00, 0x00, 0x03,
-    0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x60, 0xf7, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00,
-    0x18, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x73, 0x65, 0x72,
-    0x76, 0x65, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 0x67, 0x5f, 0x64, 0x65,
-    0x66, 0x61, 0x75, 0x6c, 0x74, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xbc, 0xff, 0xff, 0xff, 0x09,
-    0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x34, 0x00,
-    0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x76, 0xfd, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00,
-    0x00, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32, 0x5f, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x00, 0x00, 0x00, 0x01, 0x00,
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-    0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x6d, 0x69, 0x6e, 0x5f, 0x72, 0x75, 0x6e, 0x74,
-    0x69, 0x6d, 0x65, 0x5f, 0x76, 0x65, 0x72, 0x73, 0x69, 0x6f, 0x6e, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x50, 0x02, 0x00,
-    0x00, 0x48, 0x02, 0x00, 0x00, 0x34, 0x02, 0x00, 0x00, 0xdc, 0x01, 0x00, 0x00, 0x8c, 0x01, 0x00, 0x00, 0x6c, 0x01,
-    0x00, 0x00, 0x5c, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00, 0x24,
-    0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xfa, 0xfd, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
-    0x31, 0x2e, 0x35, 0x2e, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xfd, 0xff,
-    0xff, 0x88, 0xfd, 0xff, 0xff, 0x8c, 0xfd, 0xff, 0xff, 0x22, 0xfe, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00,
-    0x00, 0x00, 0x21, 0xa5, 0x8b, 0xca, 0x5e, 0x1d, 0xce, 0x42, 0x9d, 0xce, 0x1f, 0xb0, 0xdf, 0x54, 0x2f, 0x81, 0x3e,
-    0xfe, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0xee, 0xfc, 0x00, 0xec, 0x05, 0x17, 0xef, 0xec,
-    0xe6, 0xf8, 0x03, 0x01, 0x00, 0xfa, 0xf8, 0xf5, 0xdc, 0xeb, 0x27, 0x14, 0xf1, 0xde, 0xe2, 0xdb, 0xf0, 0xde, 0x31,
-    0x06, 0x02, 0xe6, 0xee, 0xf9, 0x00, 0x16, 0x07, 0xe0, 0xfe, 0xff, 0xe9, 0x06, 0xe7, 0xef, 0x81, 0x1b, 0x18, 0xea,
-    0xc9, 0x01, 0x0f, 0x00, 0xda, 0xf7, 0x0e, 0xec, 0x13, 0x1f, 0x04, 0x13, 0xb4, 0xe6, 0xfd, 0x06, 0xb9, 0xe0, 0x0d,
-    0xec, 0xf0, 0xde, 0xeb, 0xf7, 0x05, 0x26, 0x1a, 0xe4, 0x6f, 0x1a, 0xea, 0x1e, 0x35, 0xdf, 0x1a, 0xf3, 0xf1, 0x19,
-    0x0f, 0x03, 0x1b, 0xe1, 0xde, 0x13, 0xf6, 0x19, 0xff, 0xf6, 0x1b, 0x18, 0xf0, 0x1c, 0xda, 0x1b, 0x1b, 0x20, 0xe5,
-    0x1a, 0xf5, 0xff, 0x96, 0x0b, 0x00, 0x01, 0xcd, 0xde, 0x0d, 0xf6, 0x16, 0xe3, 0xed, 0xfc, 0x0e, 0xe9, 0xfa, 0xeb,
-    0x5c, 0xfc, 0x1d, 0x02, 0x5b, 0xe2, 0xe1, 0xf5, 0x15, 0xec, 0xf4, 0x00, 0x13, 0x05, 0xec, 0x0c, 0x1d, 0x14, 0x0e,
-    0xe7, 0x0b, 0xf4, 0x19, 0x00, 0xd7, 0x05, 0x27, 0x02, 0x15, 0xea, 0xea, 0x02, 0x9b, 0x00, 0x0c, 0xfa, 0xe8, 0xea,
-    0xfd, 0x00, 0x14, 0xfd, 0x0b, 0x02, 0xef, 0xee, 0x06, 0xee, 0x01, 0x0d, 0x06, 0xe6, 0xf7, 0x11, 0xf7, 0x09, 0xf8,
-    0xf1, 0x21, 0xff, 0x0e, 0xf3, 0xec, 0x12, 0x26, 0x1d, 0xf2, 0xe9, 0x28, 0x18, 0xe0, 0xfb, 0xf3, 0xf4, 0x05, 0x1d,
-    0x1d, 0xfb, 0xfd, 0x1e, 0xfc, 0x11, 0xe8, 0x07, 0x09, 0x03, 0x12, 0xf2, 0x36, 0xfb, 0xdc, 0x1c, 0xf9, 0xef, 0xf3,
-    0xe7, 0x6f, 0x0c, 0x1d, 0x00, 0x45, 0xfd, 0x0e, 0xf0, 0x0b, 0x19, 0x1a, 0xfa, 0xe0, 0x19, 0x1f, 0x13, 0x36, 0x1c,
-    0x12, 0xeb, 0x3b, 0x0c, 0xb4, 0xcb, 0xe6, 0x13, 0xfa, 0xeb, 0xf1, 0x06, 0x1c, 0xfa, 0x18, 0xe5, 0xeb, 0xcb, 0x0c,
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-    0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09};
-
-const int g_model_len = sizeof(g_model);
\ No newline at end of file
+  0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00, 
+  0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00, 
+  0x08, 0x00, 0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 
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+  0x00, 0x00, 0x00, 0x72
+};
\ No newline at end of file