diff --git "a/Gr\303\241ficos.ipynb" "b/Gr\303\241ficos.ipynb"
new file mode 100644
index 0000000..852fc8b
--- /dev/null
+++ "b/Gr\303\241ficos.ipynb"
@@ -0,0 +1,2381 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "09c15044",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números de 1 a 10\n",
+    "exactitud_gpu = [9590, 9671, 9656, 9452, 9529, 9672, 9629, 9653, 9651, 9645]\n",
+    "exactitud_cpu = [9638, 9590, 9489, 9607, 9585, 9578, 9675, 9661, 9640, 9637]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='o', label='Exactitud en GPU')\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='x', label='Exactitud en CPU')\n",
+    "\n",
+    "# Ajustar el rango del eje y\n",
+    "plt.ylim(9000, 10000)\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en GPU vs. Exactitud en CPU')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "db6ed436",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempos de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números de 1 a 10\n",
+    "tiempos_inferencia_gpu = [30.519, 23.426, 30.477, 30.759, 32.441, 32.179, 32.203, 20.931, 21.72, 32.412]\n",
+    "tiempos_inferencia_cpu = [30.374, 30.855, 32.305, 32.2615, 32.551, 31.998, 32.582, 22.036, 21.469, 32.667]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempos_inferencia_gpu, marker='o', label='Tiempo de inferencia en GPU (ms)')\n",
+    "plt.plot(ejecuciones, tiempos_inferencia_cpu, marker='x', label='Tiempo de inferencia en CPU (ms)')\n",
+    "\n",
+    "# Ajustar el rango del eje y\n",
+    "plt.ylim(10, 50)\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (ms)')\n",
+    "plt.title('Tiempo de Inferencia en GPU vs. Tiempo de Inferencia en CPU')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "edbc5e51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempos de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números de 1 a 10\n",
+    "tiempos_entrenamiento_gpu = [548.877, 564.829, 558.464, 557.112, 547.252, 561.476, 561.203, 562.3, 561.0, 547.444]\n",
+    "tiempos_entrenamiento_cpu = [564.503, 562.113, 559.122, 559.299, 571.996, 575.601, 572.583, 570.2, 571, 571.513]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempos_entrenamiento_gpu, marker='o', label='Tiempo de entrenamiento en GPU (s)')\n",
+    "plt.plot(ejecuciones, tiempos_entrenamiento_cpu, marker='x', label='Tiempo de entrenamiento en CPU (s)')\n",
+    "\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en GPU vs. Tiempo de Entrenamiento en CPU')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ce96cd74",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PP2bw4MGcOnWKYcOGcfbs2ZR+XCEhIWzfvp1p06YxefLklOclX2RI5urqipOTU7oJw9OMcQ6Deh6Hhoamuf/WrVupzldPT0/OnTuHoiip9nf58uUXHt/V1RVHR0f0en2GRgmzo1OnTvz2228cOXKEevXqGXXfxjqXhRDmRUq/CyHyta+//poDBw4wd+5cpk+fTqNGjXjzzTdTretIvsr99FXtw4cPc/DgwVT76tGjB4qiMG3atDTHefq59vb26X7ATM+ztk2eYrRnz56U+5JLyj+tTZs2WFpa8v3336eK4dtvv83Q8Xv16oVer2f69OlpHktMTMzw63iRhg0b0r59e3777TfWrFmT5vH4+Pg0a1wyonXr1lhZWTFnzpw0ozdz584lMTGRDh06ZGhfvXv35tChQ8ybN49Hjx6lmkIIpCmhrtVqU0ZMkkuUJyQkcOnSJQICAjL9Wp4lISGBQYMGUbx4cb777jsWLFjAw4cPGTt2bMo26Z3DkPY80Gq1dOvWjXXr1qW0K3ha8vPt7e0BMvTzf9625cqV49ChQ6kqY65fv547d+6k2q5jx47cv3+flStXptwXHR39zGmBT9PpdPTo0YNVq1alm0QGBQW9cB8ZNWHCBOzs7BgyZAgPHz5M8/iLRnifx5jnshDCfMjIlhAiz9q4cSOXLl1Kc3+jRo0oW7YsFy9e5KOPPmLQoEF06dIFUPtU1ahRI2X9DUDnzp1ZvXo13bt3p1OnTvj7+/Pzzz/j6+uban1Gy5Yt6d+/P7Nnz+bq1asp07P27t1Ly5YtU4or1K5dm23btvH1119TvHhxvLy8nlkqunbt2syZM4cZM2bg7e2Nm5sbrVq1ws/Pj9KlSzN06FDGjx+PTqdj3rx5uLq6cvv27ZTnu7q6Mm7cOD777DM6d+5Mx44dOXnyJBs3bnzulKVkzZs3Z8SIEXz22WecOnUKPz8/LC0tuXr1KitWrOC7775LVbQgO/744w/8/Px4+eWX6dKlC61bt8be3p6rV6+ybNkyAgICMtRr62lubm5MnjyZSZMm0axZM7p27YqdnR0HDhxg6dKl+Pn5pfzsX6RXr16MGzeOcePGUaRIkTSjJMOGDSM4OJhWrVpRsmRJbt26xffff0+NGjVSyqLfu3ePSpUqMXDgwJReU88TFhbG4sWL030see3PjBkzOHXqFNu3b8fR0ZFq1aqlvOaePXvSsWNHnJycaNasGTNnziQhIYESJUqwZcuWdHs4ffrpp2zZsoXmzZszfPhwKlWqREBAACtWrGDfvn0UKlSIGjVqoNPp+OKLLwgLC8Pa2ppWrVqlu7avXLlyFCpUiJ9//hlHR0fs7e2pX78+Xl5eDBs2jJUrV9K+fXt69erF9evXWbx4cZr1Sq+//jo//PADAwYM4Pjx43h4eLBo0aIMF4/4/PPP2blzJ/Xr1+f111/H19eX4OBgTpw4wbZt2wgODs7Qfl6kfPnyLFmyhD59+lCxYkVee+01qlevjqIo+Pv7s2TJErRabZoCJxlhzHNZCGFGTFIDUQghsuF5pd9JKkGdmJio1K1bVylZsqQSGhqa6vnJZZ7/+usvRVHUcteffvqp4unpqVhbWys1a9ZU1q9frwwcOFDx9PRM9dzExERl1qxZio+Pj2JlZaW4uroqHTp0UI4fP56yzaVLl5RmzZoptra2qcqvp1f6/cGDB0qnTp0UR0dHBUhV2vn48eNK/fr1FSsrK6V06dLK119/ne4+9Hq9Mm3aNMXDw0OxtbVVWrRooZw7d+6ZZbfTM3fuXKV27dqKra2t4ujoqFStWlWZMGGCcv/+/ZRtPD09lU6dOqV57n/LeD9PdHS08uWXXyp169ZVHBwcFCsrK6V8+fLKqFGjlGvXrqVs96Iy2P+1ePFipUGDBoq9vb1ibW2t+Pj4KNOmTVNiY2Mz9PxkjRs3VgBl2LBhaR5buXKl4ufnp7i5uaX8TEaMGKEEBASkbOPv75+hkvuK8vzS78l/no8fP65YWFgoo0aNSvXc5PO7ePHiKeX07969q3Tv3l0pVKiQ4uzsrLzyyivK/fv3FUCZMmVKquffunVLGTBggOLq6qpYW1srZcuWVUaOHJmqHPqvv/6qlC1bVtHpdKnKr6f38/7nn38UX19fxcLCIk0Z+K+++kopUaKEYm1trTRu3Fg5duxYuvu4deuW0rVrV8XOzk5xcXFR3nnnnZQWBC8q/a4oivLw4UNl5MiRSqlSpRRLS0vF3d1dad26tTJ37tyUbZJLv69YsSLVc5N/bumVr0/PtWvXlDfffFPx9vZWbGxsFFtbW8XHx0d54403lFOnTqXa1lTnshDCPGgUJRtj3kIIIYQQQggh0iVrtoQQQgghhBAiB0iyJYQQQgghhBA5QJItIYQQQgghhMgBkmwJIYQQQgghRA6QZEsIIYQQQgghcoAkW0IIIYQQQgiRA6SpcQYZDAbu37+Po6MjGo3G1OEIIYQQQgghTERRFCIiIihevDha7bPHryTZyqD79+9TqlQpU4chhBBCCCGEMBN37tyhZMmSz3xckq0McnR0BNRvqJOTk4mjEVmRkJDAli1b8PPzw9LS0tThiAJAzjmRm+R8E7lNzjmRm8ztfAsPD6dUqVIpOcKzSLKVQclTB52cnCTZyqMSEhKws7PDycnJLH5JRf4n55zITXK+idwm55zITeZ6vr1oeZEUyBBCCCGEEEKIHCDJlhBCCCGEEELkAEm2hBBCCCGEECIHSLIlhBBCCCGEEDlAki0hhBBCCCGEyAGSbAkhhBBCCCFEDpBkSwghhBBCCCFygCRbQgghhBBCCJEDJNkSQgghhBBCiBwgyZYQQgghhBBC5ABJtoQQQgghhBAiB0iyJYQQQgghhBA5QJItIYQQQgghhMgBFqYOQGSO3qBwxD+YwIhY3BxtqOdVBJ1WY+qwhBBCCCGEEP8hyVYesulcANPWXSAgLDblPg9nG6Z08aV9FQ8TRiaE+C+9QeGwfzDHH2ko6h9MQ283uTAihBBCFDCSbOURm84F8ObiEyj/uf9BWCxvLj7BnH61JOESwkykvjCi44+rx+TCiBBCCFEAyZqtPEBvUJi27kKaRAtIuW/augvoDeltIYTITckXRp4egYYnF0Y2nQswUWRCCCGEyG2SbOUBR/yD03xwe5oCBITFcsQ/OPeCEkKkIRdGhBBCCPE0SbbygMCIZydaWdlOCJEz5MKIEEIIIZ4myVYe4OZoY9TthBA5Qy6MCCGEEOJpkmzlAfW8iuDhbMOz6phpUKsS1vMqkpthCSH+Qy6MCCEKiqcrrh72D5bp0UI8g1QjzAN0Wg1Tuvjy5uITaCDVepDkBGxKF18pKy2EiSVfGHnWVEIN4C4XRoQQeZxUXBUi42RkK49oX8WDOf1q4e6c+oq4u7ONlH0XwkzotBr61Cv9zMcV5MKIECJvk4qrQmSOJFt5SPsqHux7vxVj25QHwMvFnn3vt5JESwgzoSgKu68EAWBnpUvzuIuDFY29XXI7LCGEMAqpuCpE5kmylcfotBq61SwBwL3QGAyKvKEJYS52XArk+K0QrC20bHu3OYuH1GFAeT2/vFaD4s42PIqMZ9KacyjyeyuEyIOk4qoQmSfJVh5UqrAd9lY64hMN3AiKMnU4QgjAYFCYtfkyAIMalaF4IVvqexWhtotCKx83vu9bE51Wwz+n7rP6xD0TRyuEEJknFVeFyDxJtvIgrVZDJQ8nAC4EhJk4GiEEwLoz97n0IAJHawveaF4uzeO1PYukTAH+6J9z3AiKzO0QhRAiW9wcbRhjsZJRutXpPj5Kt5oxFiul4qoQT5FkK4/yLa4mWxcDIkwciRAiQW/gm61XABjerCyF7a3S3e7NFt40KFuE6Hg9o5edJD7RkJthCiFEttTzKoKttRXvWaZNuEbpVvOe5UrsrK2k4qoQT5FkK49KGdm6H27iSIQQK47d5ebjaIraWzG4idczt9NpNXzbuyaF7Cw5dy+cWZsv5WKUQgiRPTqthgMlhvJVQs9UCVdyovVVQk9Kd58qFVeFeIokW3mUb8o0wnBZbC+ECcUm6PluuzqqNbKlNw7Wz29f6O5sw6ye1QH4da8/uy4H5niMQghhDJcehLP3ahDf61/mZ80rvGe5kqvW/VMSrTXOr9HW193UYQphViTZyqMqujui1UBwVDyBEXGmDkeIAmvRwVs8DI+juLMNfes/u8fW09r6FmNgQ08Axq04TZD8DgshzJyiKExbewGDAh2quDPcrxYAlho9Bo2O+RavcCc4hkUHb5o2UCHMjCRbeZSNpY5yrg6ATCUUwlQiYhP4adc1AMa0qYCNZdreWs/yQcdK+Lg78igynvdWnMYgfWmEEGZs07kHHLzxGGsLLRPbV0C7+/OUx7SKng3FfgHgyy1XeBgu1QiFSCbJVh5W6amphEKI3PfbXn9CohMo62rPy7VKZOq5NpY6vu9TExtLLXuuBPH7Pv8cilIIIbInNkHPjH8vAjCiWVlK7XwHYsNQLGy4XaQpAKUDd7DC6Vsi4xL5eN0FU4YrhFmRZCsPS65IKCNbQuS+x5Fx/Lb3BgDvta2IhS7zb6flizkypUtlAGZuvsSZu6HGDFEIIYxi7p4b3AuNwcPZhrct/obzqwAw1HuTk6WHoa87AoC68UdYZPkZ/54NYKesRxUCkGQrT0suknFRRraEyHU/7bpOVLyeKiWc6FAl6wvCX61bio5V3UnQK4xeepLIuEQjRimEENlzPzQmZbr0Bx0rYRWZ1JRda4mhzhDQaDC0nQHNxgPQVHeWdy2WM3nNWWIT9KYKWwizIclWHpY8jdD/cRRR8gFNiFxzPzSGRYduATDOryLabJQ51mg0fNa9GiUK2XLzcTST/zlnrDCFECLbPtt4idgEA/XKFKFLNQ+Ij1YfqPIyOHqotzUaaDUJ2kwFYLTFGgZF/MoP26+aJmghzIgkW3mYq6M1ro7WKApceiDNjYXILbO3XyU+0UA9ryI0r+Ca7f0521ny3as10Gpg9Yl7/H3yrhGiFEKI7DniH8y60/fRaGByF1804ffgwhr1wQZvpX1Ck7HQ8UsAhlpspNSB/3HtQWiuxSuEOZJkK4+TqYRC5K4bQZGsOK4mQxPaVUSjMU7zzjplijCmTQUAJv19jluPo4yyXyGEyAq9QWHq2vMAvFq3NFVKOMORuWBIBM8mULxG+k+s9zrKSz9hQEtv7U4eLBiIkhife4ELYWYk2crjUopkSLIlRK74eusV9AaFVj5u1ClTxKj7HtnSm3peRYiK1zN66UniEw1G3b8QQmTUX0fvcCEgHEcbC8b5VYC4SDi+QH2w4cjnPldT8zVCOv5MgqKjSewu7v/aGxKkHLwomCTZyuNSyr9LRUIhcty5e2GsPxMAqGu1jE2n1fBt7xo421py+m4YX229bPRjCCHEi4RFJ/DlFvX9Z2ybChR1sIZTSyA2DIqUhQrtX7iPovV6s7Xa18QplpR4uIOExb0hXkbsRcEjyVYelzyN8NKDcPTSFFWIHPVV0oePLtWLp4wqG1vxQrZ80aMaAL/svsGeK0E5chwhhHiWb7dfITgqnvJuDvRv6AkGPRyeoz7Y4C3QZuzjY9tuA5jkMIUoxRrLW7tg0ctqwiZEASLJVh7n5WKPjaWW2AQDN2WNhxA55ujNYHZeDkKn1fBu2wo5eqz2Vdzp16A0AO8uP82jyLgcPZ4QQiS7+jCCPw6q1VYnd/HFUqeFK5sg+AbYOEP1Phnel6VOS69XXqNf/ETCFDu4cwgWdoWoxzkVvhBmx6TJVkREBGPGjMHT0xNbW1saNWrE0aNHU21z8eJFunbtirOzM/b29tStW5fbt2+nPN6iRQs0Gk2qf2+88Uaqfdy+fZtOnTphZ2eHm5sb48ePJzExf5RK12k1+LjLVEIhcpKiKMzcdAmAXnVK4eVin+PHnNTJlwrFHHgUGcd7y09jkJFrIUQOUxSFaesuoDcotPUtRtPySdVWD/6k/l97EFg7ZGqfdcsUoULtVvSJn0SoxhkCTsGCThDxwKixC2GuTJpsDRs2jK1bt7Jo0SLOnj2Ln58fbdq04d49tWHe9evXadKkCT4+PuzatYszZ87w0UcfYWNjk2o/r7/+OgEBASn/Zs6cmfKYXq+nU6dOxMfHc+DAARYuXMiCBQuYPHlyrr7WnJSybkuKZAiRI3ZdCeLozRCsLLSMbu2dK8e0sdTxfZ9aWFto2X0liHn7/XPluEKIgmvLhYfsu/YIK52WSZ0qqXfePwW39oHWAuqNyNJ+/9fBhwd2FegRO4koK1cIugjzO0Do7Rc/WYg8zmTJVkxMDKtWrWLmzJk0a9YMb29vpk6dire3N3PmqPOCP/zwQzp27MjMmTOpWbMm5cqVo2vXrri5uaXal52dHe7u7in/nJyerKXYsmULFy5cYPHixdSoUYMOHTowffp0fvzxR+Lj80cp0uS1I1L+XQjjMxgUvtysrtUa2NATD2fbXDt2RXdHPursC8AXmy5x7p6sdRBC5IzYBD0z/r0AwLCmXngWTRrBP5Q0quXbDZxLZGnfhe2tmNixEteVErwUM4lEp9LqtMR5HeDRNSNEL4T5sjDVgRMTE9Hr9WlGqWxtbdm3bx8Gg4F///2XCRMm0K5dO06ePImXlxcffPAB3bp1S/WcP//8k8WLF+Pu7k6XLl346KOPsLOzA+DgwYNUrVqVYsWKpWzfrl073nzzTc6fP0/NmjXTjS8uLo64uCfrJMLD1UQmISGBhIQEY3wLjKaiq/paL9wPN7vYzEny90a+RyIzNpx9wPn74dhb6xjW2DNT548xzrletTzYfTmQrRcDeXvJCda82QB7a5O9dQszJu9xIjvm7r7BneAYijlaM7xJ0ntdeAAW51ahARLrjkD5z7mVmXOua1U3lh8tzJGbMMHxM76ymIIm+BrK/A4k9l0Jbr458KpEfmJu73EZjUOjKIrJFgI0atQIKysrlixZQrFixVi6dCkDBw7E29ub3bt34+HhgZ2dHTNmzKBly5Zs2rSJiRMnsnPnTpo3bw7A3Llz8fT0pHjx4pw5c4b333+fevXqsXr1agCGDx/OrVu32Lx5c8pxo6Ojsbe3Z8OGDXTo0CHd2KZOncq0adPS3L9kyZKURM5cxOnh/SM6FDRMr52Ik5WpIxIif9Ar8PkpHYGxGtqX1NOhlGneLqMSYOYZHaHxGuq5GnjNW/pvCSGMJzQOPjmlI96gob+3njqu6ntdpfsrqPBwHY/tK7CvwqRsH+dBtPpeplc0vF0umNeDZ+Icc5t4nT0HvccTalc228cQIrdER0fTt29fwsLCUs2q+y+TJlvXr19nyJAh7NmzB51OR61atahQoQLHjx9n+/btlChRgj59+rBkyZKU53Tt2hV7e3uWLl2a7j537NhB69atuXbtGuXKlctyspXeyFapUqV49OjRc7+hptL2233cfBzNvIG1aOrtYupwzFJCQgJbt26lbdu2WFpamjockQesOH6XiWsuUNjOkh3vNsUhkyNKxjznjtwMpv+8YxgU+KpnVbpW98jW/kT+I+9xIqveW3GWtWcCqFW6EMuG1UWj0UB8FBY/1EATE0Jij4UoPp3SPC8r59zXW68yZ48/7k7WbBpeGae/+6G9dwzFygF976UopRsa++WJfMLc3uPCw8NxcXF5YbJl0rko5cqVY/fu3URFRREeHo6Hhwe9e/embNmyuLi4YGFhga9v6mHlSpUqsW/fvmfus379+gApyZa7uztHjhxJtc3Dhw8BcHd3f+Z+rK2tsba2TnO/paWlWfyA/6tycWduPo7mamA0rSqZX3zmxFx/hsK8xCbo+X7nDQBGtvSmsEPW12oZ45xrXL4Yo1qV57vtV5my7iJ1vVwoXdS8RtmFeZD3OJEZx28Fs/ZMABoNTOtaBSurpOkxp1ZCTAgULoNF5S6g1T1zH5k5595pW5F/zz3kdnA0PxwKYdKANbC0D5qbe7FY2gte/RO8WxvhlYn8ylze4zIag1n02bK3t8fDw4OQkBA2b97MSy+9hJWVFXXr1uXy5cuptr1y5Qqenp7P3NepU6cA8PBQr/o2bNiQs2fPEhgYmLLN1q1bcXJySpPI5WXJRTKkIqEQxvHn4dsEhMXi4WxDvwbPfs/JTaNaeVO3TGEi4xIZvewkCXqZTiiEyDqDQWHqWrUoRq/apaha0jn5ATiU1MS4/pvPTbQyy8ZSx8cvVQZg/oGbnH9sgNdWQHk/SIyBpa/CxfVGO54QpmbSZGvz5s1s2rQJf39/tm7dSsuWLfHx8WHw4MEAjB8/nr/++otff/2Va9eu8cMPP7Bu3TreeustQJ2GOH36dI4fP87NmzdZu3YtAwYMoFmzZlSrVg0APz8/fH196d+/P6dPn2bz5s1MmjSJkSNHpjtylVdV8nAEpNdWGjs/g90z039s90z1cSH+IzIukR93qhWyRrcuj42l8T5oZIeFTsu3r9bEycaCU3dC+WbrFVOHJITIw1Ycv8PZe2E4Wlswvn3FJw9c3QKPr4G1E9R8zejHbVHRjU5VPdAbFD78+xwGnQ30/hN8XwJ9PCwfAGeWG/24QpiCSZOtsLAwRo4ciY+PDwMGDKBJkyZs3rw5ZViue/fu/Pzzz8ycOZOqVavy22+/sWrVKpo0aQKAlZUV27Ztw8/PDx8fH9577z169OjBunXrUo6h0+lYv349Op2Ohg0b0q9fPwYMGMDHH39sktecU3w91KtRNx5FEZugN3E0ZkSrg52fpE24ds9U7zfi1TqRf/y+15/gqHi8XOx5pXZJU4eTSolCtnzRQ72YNGf3dfZfe2TiiIQQeVF4bAKzktpavNOmPC4OT12APvSj+n+tAWDtmCPHn9zFFwdr9cLRkiO3wcIKesyD6n1B0cPq4XBsfo4cW4jcZNI1W7169aJXr17P3WbIkCEMGTIk3cdKlSrF7t27X3gcT09PNmzYkKUY84piTtYUsbciOCqeKw8jqFaykKlDMg/NJ6j/7/wEbWQQNvG+aPd+CXs+h5YfPnlciCQhUfH8ulddq/Vu2wpY6MxitnUqHap60Ld+aZYcvs3Yv06x8Z2mFHXIPyP1QoicN3vbVR5FxlPW1Z4BDcs8eeDBWfDfAxod1M9aE+OMKOZkwzi/Ckxdd4EvNl2iXWV3XB2t4aUfwcoejv4K68dAQjQ0HJljcQiR08zvU4TIEo1GI1MJ/ysuEi5vhIgHYOOM7uhc/M6PQSeJlniOObuvExmXiK+HE52qmm/Fv486+VLezYHAiDjGrzyDCQvLCiHymGuBkSw4cBOAyZ19sbJ46uPgweQmxl2hUOkcjaN/wzJULeFMRGwinyQ1VEarhY6zoPEY9evNE2HXFyDvcSKPkmQrH/H1KOBFMhQFAi/Cge9hYVeY6aUutD32O8SGAaABFI1WEi2RrgdhsSxM+gAyvl1FtFqNaQN6DlsrHd/3rYmVhZYdlwJTPjgJIcTzKIrC9PUXSDQotPZxo0VFtycPRjyAsyvU2w3fzvFYdFoNn3SvgkYDa07dfzItWqOBNlOhVVJvr12fwtaPJOESeZIkW/lIckXCiwUp2YoNh4vrYO1o+KYK/NQAtkwC/93qIttCnlB3GFRVp6sqgEYxwN9vmDZuYZZm77hKXKKBOp6FaVHR1dThvJCPuxOTOlUC4LMNlzh/P8zEEQkhzN2OS4HsvhKEpU7DpM7/qcp89DcwJEDJelCyTq7EU61kIQYkVXydtObck3XnGg00Gw/tkgpZHfge/n1XrZQoRB4iyVY+klwk42JABAZDPr36oyjw4Bzs+wbmd1JHr/7qBycWQvhdsLAB7zbQ/nN4+zi8cxocisHZ5eib/Y8brn7qfk4vlUqEIpWbj6JYfvQOABPa+6hNPfOA/g08aVOpGPF6A6OWniQ6PtHUIQkhzFRcop7p69XpekOaeOHlYv/kwYQYOPq7ejuX10i9164ibo7W+D+K4ufd11M/2PAt6DIb0MCxebDmTdDL+5zIO0xaIEMYV1lXe6x0WiLjErkTEo1nUfsXPykviAmFG7vg2la4th0iAlI/XqQclG+rJlmejcHqqUavyVUHW36IodFYLoWsoGzUCTTRj2D352o1QplSKIBvtl0h0aDQoqIr9byKmDqcDNNoNMzqWY0O3+3lRlAU09Ze4Iue1UwdlhDCDM3ff5Obj6NxdbRmVKvyqR88vQxigsG5NPh0ztW4nGwsmdzFl7eXnOSnndd5qUaJ1Ilg7YFq0YzVw+HMMkiIgh6/g4UUBhLmT0a28hFLnZYK7g5AHp9KaDDA/VOw50uY1x5mloUVA+HkYjXRsrCF8u2g45cw+iSMPgEdvlATrqcTLQCDPlUxjEQLe/StJquP6axS1nKJgu1iQDhrT98HYJxfxRdsbX4K21vxTe8aaDTw17E7rEt6LUIIkSwwPJbvt18F4P32PjhYP3W9XVGeamI8AnS5fy2+U1UPmlVwJV5v4KM159IW/anaE3ovUv92X1wHy/pCfHSuxylEZkmylc+kFMnIaxUJo4Ph7Er4+034qiLMbQ47psPtg2q/DZcK0GAk9P8b3r8Jry2Heq9DkbLP32/LD9KMXCnVXoUSddQ1XVFBOfeaRJ7x1ZbLKAp0quZBlRLOpg4nSxqWK8rbLb0BmLj6LHeC5UOIEOKJzzddIipeT/VShXi5ZonUD17bDo8ug5Uj1Opvkvg0Gg3TX6qMlYWWfdcepVwAS8WnE/T9Cyzt4No2+PMViIvI/WCFyARJtvKZSnmlIqHBAPeOq+Vcf2sLs8rBqqFweglEBYKlPVTsCJ2+hnfOwNtHof2nUK4VWNpk79gaLXScCWjgzF9w66BRXpLIm47fCmbbxUB0Wg3vtq1g6nCy5Z3W5alVuhARcYmMXnaSBL0sJBdCwMnbIaw+cQ+AqV1801ZaPfiD+n+t/mBjugtOnkXtGZV00Wj6+ouExSSk3ahcK+i3Gqyd4NY++OMl9YKtEGZKkq18xqxHtqIewZnlsOp1+NIbfm2llnO9ewQUA7j5QqNRMGCtOnrVZynUHQqFPY0fS4naT67ebRyvTjcUBY6iKMzcdBmAnrVKUs7VwcQRZY+FTst3r9bE0caCk7dD+W7bVVOHJIQwMYNBYera8wD0qFWSmqULp97g4QW4sVO9EJmDTYwzanjzspRztedRZByzNl9KfyPPhjBwLdgWUS/cLuwCkYG5G6gQGSTJVj5TKan8+/2wWEKj400bjEEPd47Czk9hbkuY5Q2rX4ezyyH6sTpdoVIX6PIdjD0Pbx0EvxlQtjlYWOV8fK2nqFfwHpyF4/Nz/njC7Oy9+ojD/sFY6bS806b8i5+QB5QqYsfnL6sFMn7cdY0D1x+ZOCIhhCmtOnGX03fDcLC24P326axJPfSj+r9PZyhcJldjS4+1hY4Z3aoC8Ofh25y8HZL+hsVrwuANasXhh+dgfgcIu5uLkQqRMZJs5TNONpaUKmILmGgqYWQgnFoKK4eoUwN/bwO7v4D7JwAFilWFJmNh0L/wvj/0Xgy1B4FzydyP1d4FWn2k3t4+HaIe534MwmQURWHWZnVUq18DT4oXsjVxRMbTqZoHr9YthaLA2L9OERxl4gsvIsfpDQqH/YM5/kjDYf9g9Pm1/YfIlIjYBL5IGr0f1cobN6f/TMOPDIQzudfEOKMalivKy7VKoCjw4d/nSHzWlGi3SjB4IziXgsfXYF4HCL6Ru8EK8QJS+j0fquTuxJ3gGC7cD6dROZecPZg+Ee4eTSrLvg0CTqd+3MYZyrZUKwWWaw1OHjkbT2bVHgzHF6hXxXZMhy7fmjoikUs2nXvA2Xth2FvpGNmynKnDMbrJXXw5ejOY60FRTFh5ml8H1MkzvcNE5mw6F8C0dRcICIsFdPxx9RgezjZM6eJL+ypm9p4rctUPO67xKDIOLxd7Bjf2SrvB0d9BH6dOrS9VL/cDfI6JHSux/WIgFwLCWXjwFkObpBM/QNFyasL1x0sQfF1NuAb8A24+uRuwEM8gI1v5kG/SVMKLATlUoSc8AE4sguUDYVZZmN8e9n71JNHyqA5Nx8GQzTD+BvRaCDX7mV+iBWp5246z1NvHF8D9kyYNR+SORL2BL7eoV3uHNvGiqEP+69ViZ2XB931qYaXTsu1iIH8cvGXqkEQO2HQugDcXn0hKtJ54EBbLm4tPsOlcwDOeKfK7G0GRzNvvD8BHnSthZfGfj3wJsXD0N/V2w5FgZhdjXBys+V8HNWH6estlAsJinr1xoVJqwuXmC5EPYEFHtYWMEGZAkq18yNfYFQn1CXBzH2ydAnOawNc+sPZtuLBG7VNlWxiq9IBuP8O4qzBiD7T+CEo3MEmvjkzzbARVXwEU2DBerZQo8rXVJ+9xPSiKQnaWDGv2gvYBeZhvcScmdlQ/rHyy4WLe7r8n0tAbFKatu0B6EwaT75u27oJMKSygZvx7kQS92qi9lU+xtBucXQ7Rj8CpJFR6KfcDzIDedUpR27MwUfF6pq298PyNHYupSxSK11TXhS/sCrcP506gQjyHJFv5UHL592uBEcQnZjFxCLurjvQsew2+8IIFnWD/t/DwLKBRpxw0fx+GboPx16HnPKjRBxzcjPUyclfb6WDloE6JPLPM1NGIHBSXqE+p0vdWi3I42ViaOKKcNbBRGVr7uBGfaGDU0pPExEvlzfziiH9wmhGtpylAQFgsR/ylLHZBs/NyIDsuBWKh1fBRZ9+0GygKHPxJvV1/uNleGNVqNXzSvQo6rYZN5x+w/eLD5z/Broha0bh0I4gLg0Xd4cauXIlViGeRZCsfKlnYFkcbCxL0ClcDMziVMDEObuyGLZPgxwbwTWVY9w5cWg/xEWDnAtV6w8u/wvhr8PoOaDkRStUFrS5nX1BucPKAZuPV21snqyN2Il9acvg290JjKOZkzYCGZUwdTo7TaDTMeqU6bo7WXAuM5OP1L7g6LPKMwIhnJ1pZ2U7kD/GJBqavU3/PBzcuk35Lixs7Ieii2tOy1sBcjjBzfNydGJa0XmvyP+dffMHIxgn6rVL7cSVEwZ+94PLGXIhUiPRJspUPaTSalKmEz123FXJLXRy7tI86evVHVzjwvfoGrNFCyXrQ8kM1sRp3FV6eC9V6qVX88qMGb0FRb4gKUpsti3wnKi6RH3deA2B06/LYWOaDCwUZUMTeim9710CjgaVHbrPhrKzjyQ/cHDPW4D2j24n8YeGBm9x4FIWLgxWjWj+jpcXBpHLvNfuBbaFciy2r3mlTnhKFbLkXGsPsHRnoH2hlB32WqeXs9XHwVz84tyrnAxUiHZJs5VPJRTJSNTdOiIVr22HTRPihLnxXDf59Fy5vUK/+2LtB9b7qlMDx12HYVmg+QZ0yqC0Ap4qFFXSYqd4+/DMEXjRtPMLo5u/351FkPJ5F7ehVp5Spw8lVjbxdeKuFWnXxf6vOcDck2sQRieyIS9Sz4ez9F27n4WxDPa8iuRCRMAdBEXHM3q4mIxPa+aQ/TTrwklo9GA00eCN3A8wiOysLpnatDMCve25w+UEGZu1YWMMrC9Q12YZEWDUMTi7O2UCFSId5TtIV2Za8buvxnYtweJ/6xnpzLyQ89QFLo1NLvXq3UUuzF6taMJKq5/FurV4Ju7QeNk5Q536bWYUmkTWh0fH8skftv/Ju2wpY6greuT6mTQX2X3vMqTuhjFl2imXDG2BRAL8Ped3tx9G8vfQEZ+4+me6sgXQLZfj5FkOnlfewgmLW5ktExCVSraQzPWs/o3/loaS1Wj6doEjeKRDU1rcYfr7F2HLhIZPWnOWv4Q3Rvujc1llC91/A0g5OLIR/RkJ8FNQfkTtBC4GMbOUdOz+D3TPTf2z3TPVxgPhouLqV1v5fstNqLN8FDoGN4+HqZjXRcvSAmv3hlYUw4QYM2QTNxqnl2gt6opWs3SdgYQP+e9SKiyJf+Hn3DSJiE/Fxd6RLteKmDsckLHVavu9TE0drC47dCmH2jmumDklk0qZzAXT6fi9n7oZRyM6S+YPq8nO/Wrg7p54qaG+tTpFdcuQ2h25Iw/aC4MzdUFYcvwvAlC6V009Eoh7Bmb/U2w1H5mJ0xjGla2XsrHQcvRnCyqTX+kJaHXT57knT5o0T1HY1QuQSGdnKK7Q62PmJerv5hCf37/oCdn0K3m1h0ctwaz8kxlIUKKqFBEWHoVR9rH3aqSNYxSrLSM2LFC4DjcfA7s9h8yQo7wdW9qaOSmRDYHgsCw6o/WbG+VV88dXQfKxUETtmdK/CO8tO8cOOqzQqV5QGZYuaOizxAvGJBj7dcJEFB24CUKt0IX7oW4vihWwBaOvrzsFrgWzZexi/pvWpX9aVd/46yYazDxix6Dir32qUfqEEkS8oisLUtedRFOheswS1PQunv+GxeZAYCx41oHTDXI3RGEoUsmVsmwp8suEin268SBvfYhSxt3rxEzUa8JuhVh3e/Tls/xjiIqH1ZPlMJHKcDGXkFc0nqMUqdn4CO2aolXV+baUmWgDXtsL17eqbqFNJqD2IaXYTqRn3C3saLYAmY8C9irypZFSTMVCoNITfhb1fmzoakU3f77hGbIKBWqUL0bpSHm1PYEQv1SjBK7VLYlBg7F+nCImKN3VI4jnuBEfzys8HUhKtEc3K8teIhimJFoBOq6G+VxFquyjU9yqCpYWWr3vVoEapQoTFJDB4/lEeR8aZ6BWInLbm1D1O3A7FzkqX0gg4jcQ4OPKretsMmxhn1KDGZfBxdyQ0OoHPNmRibbVGAy0/UFu9AOz7Gja+L701RY6TZCsvaT5BbTy4ZxYsfRXuHVfv11qCV3P1qs1bh2HsOejyHWGe7YjELnWRDJExlrbQLimRPTAbHl83bTwiy24/jmbpkdsAjG/ngyaPfsAwtqldK1PWxZ6AsFjeX3UGRZHGt+Zoy/kHdJq9l9N3w3C2teS3AXX4oGOlDK05tLHU8dvAOpQsbMvt4GiGLzpObIL0WctvIuMS+WzDJQBGtvSmmNMzqk+eWwVRgeBYHHy75V6ARmap0/Lpy1XRaGDF8bsczuw02cajoVPSNMIjv8DaUWCQ3wuRcyTZymuajXtyW6NVS5u+fxMGroVGo8DNJ+VqVXJFwosBkmxliU9ntU+HPh42TzR1NCKLvtl2hUSDQtPyLjQsJ9PlktlbWzC7T02sdFq2XHjI4sO3TR2SeEp8ooHp6y8wfNFxwmMTqVm6EBveaUob32KZ2o+LgzULBtfF0caC47dCGLfiNAaDJNb5yY87rxEYEUfpInYMTepHlYaiPCn3Xu91tfpuHlardGH61CsNwIdrzhGfmMnRqbrDoNvP6ueoU4th1VBIlBF+kTMk2cprkhvz6axAMcCDs2Cd/jz85F5bFyTZyhqNRi0Fr7WEK5vgymZTRyQy6fKDCNacugeoZZBFalVKOPN+0pSj6esvcOmBvFeYg7sh0fT65SC/71PXGQ5r4sVfwxtS4qlpg5nh7ebIL/1qY6HVsP5MAF9vvWLMcIUJ3XwUxe971fNkUqdKz+4d6L8HHp5Tq/LVHpR7Aeag99v5UNTeimuBkfy690bmd1Cjj1oaXmsJ5/+G5f3VFjlCGJkkW3nJ7pnqGq2WH8JHQU/WcD2jSmFy+ffbwdFExCbkZqT5h0t5aPCmenvj+/JGnMd8ueUyigIdqrhTtaSzqcMxS0Mal6FlRVfiEw2MXnqSmHiZTmNK2y48pNPsfZy6E4qTjQVz+9dmUmdfrCyy9+e6kbcLn75cFYAfdl5j+bE7xghXmNiMfy8SrzfQtLwLbZ836pk8qlWjL9jlj75rznaWTOpcCYDZ269y+3EWegf6vqTOELKwUS+qLnlFLZwhhBFJspVX7J6pJlYtP3xSjfDpohnpJFyF7a3wSCoHfCkjDQBF+ppPAAd3CPGHgz+YOhqRQSdvh7D1wkO0GnjPr4KpwzFbGo2GWa9Ux9XRmisPI5nx7wVTh1QgJejVaoPD/jhGWEwC1UsV4t/RTfGr7G60Y/SqU4q3W3oDMHH1WQ5ce2S0fYvct+dKENsuPsRCq2FKF99nr0d9dFVt/4IG6r+ZqzHmtG41StCoXFHiEg1MXnsua2tPy7eBfqvUSoX+e2BRd4gJNXqsouCSZCuvMOhTJ1rJkhOuZyzuTJlKKEUyss7aEfySqhft/QrCMtjbQ5jUrM2XAXi5Vkm83RxNHI15c3Gw5pteNdBo4M/Dt9l0LsDUIRUo90Jj6PXLQeYmNd0e0tiLFSMaUqqIndGP9W7bCnSpXpxEg8Ibi49zLVAuxOVFCXoDH69XL4wMaFjm+e9xyU2MK7QHF+9ciC73aDQapnergpVOy67LQWw89yBrOyrTBAb8AzbOcPcILOyi9iQTwggk2corWn6QNtFK1nyC+ng6KkmyZRxVX1F7kiREw5ZJpo5GvMC+q484cP0xVjotY9qUN3U4eUKT8i6MaFYOgAkrz3AvNMbEERUMOy49pNPsvZy8HYqjjQU/96vN5C7Znzb4LFqthlk9q1HbszDhsYkMXnCUR1ISPs/54+AtrgVGUtTeinee9x4XHQynlqq382AT44wo5+rAGy3U965p685nfdlEyTow6F+wc4EHZ2B+RwiXC08i+yTZyueSKxJKkYxs0mig4yy1ctH5v+HGblNHJJ5BURRmbVbLIPetX5qShY0/OpBfvedXgeqlChEem8jYZadI1Ev/mZySoDfw2caLDFlwjNDoBKqVdGbD6Ka0r2K8aYPPYmOpY27/2ngWteNOcAyv/3FMSsLnIY8j4/h2m1rkZFy7ijjbWj574+PzITEG3Kuqozf51FstyuFZ1I6H4XHZKwDjXhWGbAKnEvDoMsxvDyG3jBeoKJAk2crnkqcRXn4YIR+cssu9KtQZqt7e+D7opeiIOdp8/iGn74ZhZ6VjZMv8NWUmp1nqtMx+tQYO1hYcuRnMDzuv5d7Bd372zGI/6prVz3Ivlhx2PzSGV+ce4pfd6rTBQY3KsOKNnJk2+CxFHayZN6guzraWnLwdynvLpSR8XvHllstExCZSubgTveqUevaGifFweK56u0HebWKcETaWOmZ0qwLAwgM3OXcvLOs7cykPgzdC4TIQchPmtVfXvQmRRZJs5XOli9hhb6UjPtHAjUdRpg4n72s5EeyKQtBFOPKrqaMR/6E3KHy1RV2rNaSxF66O1iaOKO/xLGrPJ93VDy2zt1/liH9w7hxYq0u/2E9ycSDtM0pa5zE7LwfSafZejt8KwdHagjmv1WJq18pYW+T+6yvn6sAv/WtjqdPw79kAZiX97gjzde5eGMuOqpUkp3atjE77nATq/N8Q+UAt8FSlRy5FaDpNy7vStXpxDApM/Pss+uxcPCjsCYM3gUtFiLgP8zvAg3PGC1YUKJJs5XNarQYfWbdlPHZFoPVk9fauzyAy0LTxiFTWnLzH1cBInG0teb1ZWVOHk2e9VKMEPWqVxKDAmGUnCYvOhVHc9KqrpleFNY9K1Bv4YtMlBs8/Skh0AlVLOLN+dBM6VPUwaVwNyhblix7VAJiz6zrLjkhza3OlKApT155HUaBr9eLULfOcEu6K8qR6br1heb6JcUZN6lwJRxsLztwN48/D2Zz+5+QBgzeAezWICoIFneDuceMEKgoUSbYKgOSphBdl3ZZx1OwPxWtCXDhsm2rqaESS+EQD3yStY3ijebnnr2MQLzTtpcp4udhzPyyW/60+k7WSypnVfIJamnrnJzCtcL5JtB6ExdLn10PM2XUdgIENPVn5ZkM8i9qbODLVy7VK8k5rtcjCh2vOsfdqkIkjEulZe/o+x26FYGup44OOL2jSfmu/WuTBwhZqD8mdAM2Am6MNE9pVBGDWpssEhmezN6a9CwxcByXrQWwo/NEVbu7LfqCiQJFkqwCQIhlGptVBxy/V26f+hDtHTRuPAGDZ0dvcDYnB1dGaQY3KmDqcPM/B2oLZr9bEUqdh47kHLD2SC01w752AsyvU24oBtBZ5PtHafSWIjrP3cvRmCA7WFvzYtxbTXqpikmmDzzOmTXm61yyB3qDw1uITXHkoJeHNSXR8Ip9tUAv/vNWiHB7Ots9/QnIT4+qvgn3RHI7OvPSt70n1ks5ExCUy/d+L2d+hbSHo/zd4NYP4SFjcA65uzf5+RYEhyVYB8HT591y5Ol0QlKwDNfqptzeMe2afM5E7ouMTmb1dLeYwupU3tlbm9UE2r6pa0pkJ7dQr6NPWnc/ZD+BXt8GCzhD9VG8bQ+Kzi2aYuUS9gVmbLzFw3hGCo+Lx9XBi/agmdKpm2mmDz6LRaPi8R1XqlSlCRFwig+cfJTAim6MCwmjm7LrOg/BYSha2ffEU6cfX4fJG9XaDt3I+ODOj02r4pHtVtBpYd/o+e64YYaTW2gH6rlB7lSXGwtI+cOGf7O9XFAiSbBUAFYs5otXA46h4AiOkn4rRtJkC1k4QcApOLjJ1NAXaggM3eRQZR6kitvSuW9rU4eQrQ5t40ayCK3GJBkYvPZkzJcJPLYGlvSEhqYhP3dfVUS1Iv2iGmXsYHkvf3w7z40512mC/BqVZ/VYjyriYx7TBZ7G20PFL/9p4udhzLzSG1xceIyZeLiSZ2p3gaH5Jang9qVMlbCxfcDHp0BxAgfJ+4Foh5wM0Q1VKODOokRcAH/1zzjjvW5Y20HsxVO4OhgRYMQhOL8v+fkW+J8lWAWBrpaOsqwMgUwmNysFNrU4IsG2a2jxS5Lqw6AR+TloLM7ZNhRxrBltQabUavnqlOi4OVlx6EMGnG4wwLSeZosDer2DNm+ooFkDz/0GnL6FGX/XrImXzVMK192oQHb/byxH/YBysLfi+T01mdKv64g/IZqKwvRXzBtWlkJ0lp++GMeavk1IS3sQ++fci8YkGGpUrSrvKL+jDFhOiTm+HAjmq9bR3/Srg7mTDrcfR/GSsNhY6S+jxuzqzRTHA3yPg6G/G2bfIt+RTSQHhKxUJc0bd18HNF2KC1Q+EItfN3Xud8NhEKhRz4KUaJUwdTr7k6mjNV71qAPDHwVtsOf8g+zs16NUpuNs/Vr8uVR9aTISWH6hfN3kXNDoIvgG1B5v9VF29QeHrLZcZMO8Ij6PiqeThxLpRTehSvbipQ8s0Lxd75vavg5VOy+bzD/l80yVTh1Rg7b/2iE3nH6DTapjSpTKaF/XKOr4QEqLBrTKUbZErMZorB2sLpnTxBWDO7utcC4w0zo61Ouj6PdQboX7973uw/zvj7FvkS5JsFRAp67ZkZMu4dBbQIemK+7F5EHDGtPEUMIERsczbdxOA9/wqPr/njMiW5hVcGZ60VmTCqjMEhMVkfWcJsbBiYNIVYQ20/wKGboEW7z/ZpoiXurgfICLgSRJmhgLDY3ntt0PM3nENRYG+9Uvz91uN8DLzaYPPU8+rCLNeUUvCz91zg8WHsllGW2Raot7AtHXnAehXvzQV3R2f/wR9Ahz+Rb3d8K183cQ4o9pXcadlRVcS9AofrTlnvHXrWi10+EK9KASwdTLs/FQdrRfiPyTZKiCSKxJK+fcc4NUUKr+sTinYOEHebHPRjzuuEZOgp0apQvj5FjN1OPneOL+KVC3hTGh0AmOWncpa09CYEFjUHS6uA50VvDIfGryR/rZN3wONFq5sgvunshV7Ttl/7REdZ+/l0I1g7K10fPdqDT7tnnemDT7PSzVK8G5bdc3PlLXn2XVZ+grmpsWHbnHlYSSF7SwZ2zYDa68u/KM24LV3g6qv5HyAeYBGo+Hjl6pgY6nl4I3H/H3ynjF3rq7dTu69ufsL2PyhfAYQaUiyVUAkTyP0fxRFdHyiiaPJh/xmgKUd3D74pHS1yFF3gqNZktSAdUK7ii+eXiOyzcpCy+w+NbG30nHYPzjz6yDC7sK89nD7AFg7Q7/V6mLzZyla7smHxj2zsh54DtAbFL7ZeoV+vx/mUWQ8Pu6OrB3VJN9NZR3VypuXa6kl4d9eclIu2OWS4Kh4vt6q9g18168ihexe0JT46SbGdYeBhXUOR5h3lCpix+ikPnKf/HuR0Oh44x6g6XtPZrgc+hHWjzH7ac8id0myVUC4Olrj6miNosClB9I/xeicS0CzcertLR9BnHyPc9q3266SoFdo7F2URt4upg6nwPBysWd6tyoAfLv9KsduZrAwzMML8FtbCLoEjsVhyEZ1VPhFmo4DNHBpPTw4m/XAjSgwIpb+vx/mu+1XURToU68Ua0Y2plxSIaL8RKPR8PnL1WhQtgiRcYkMWXCUh9ltFCte6KstlwmPTaSShxN962WgwurtQ3D/JOisoe7QnA8wjxnWpCzl3Rx4HBXPF5suG/8A9UdA1x/UkfjjC9TCGfoE4x9H5EmSbBUglaRIRs5q+LZaOS3yQZ6pnJZXXX0Ywd8n7wIwPqkPlMg9L9cqmdIA951lpwiLecGHipv71BGtiPvg6gPDtkKxyhk7mGsFqNJDvW0Gv1cHrj2i43f7OHD9MXZWOr7tXYPPXq6WL6YNPouVhZaf+9WmrKs9AWGxDF14VGZI5KAL98NZmjRqP7WLb8bWoiaPalXvDfZy8em/rCy0zEi6SLT0yG2O38qB6sG1+kOP39S2FWdXwPKBkCjtdoSJk62IiAjGjBmDp6cntra2NGrUiKNHj6ba5uLFi3Tt2hVnZ2fs7e2pW7cut2/fTnk8NjaWkSNHUrRoURwcHOjRowcPHz5MtY/bt2/TqVMn7OzscHNzY/z48SQmFrw/FMlTCWUaSA6xsFYX+gMc+gmCrpg2nnzsqy1XMCjg51uMGqUKmTqcAunjlyrjWdSOe6ExTFx99tkLz8+vUddoxYVB6YYweCM4l8zcwZoljW5dXKuOkJmA3qDw3barSdMG46hYzJG1bzehW838NW3wWQrZWTF/UF2K2Ftx7l44o5dmcc2eeC5FUZi67jwGBTpV86B+2aIvflKwP1z6V71dwMu9P0/9skV5pbb63vPh3+dI0BuMf5AqPdReXDpruPwvLH0V4qONfxyRp5g02Ro2bBhbt25l0aJFnD17Fj8/P9q0acO9e+oCxuvXr9OkSRN8fHzYtWsXZ86c4aOPPsLGxiZlH2PHjmXdunWsWLGC3bt3c//+fV5++eWUx/V6PZ06dSI+Pp4DBw6wcOFCFixYwOTJk3P99ZpacpEMqUiYgyr4QYUOas8gKZaRI07fCWXT+QdoNDCuXUVTh1NgOdpYMvvVmlhoNfx7NoC/jt5Ju9HhX9TGn/p48OkM/f8GuyKZP5hbJfB9Sb1tgrVbQRFxDJx3hG+2qUl+rzolWTOyMd5u+W/a4PN4FrXn1wG1sbLQsu3iQz7514g91wQA/54N4Ih/MDaWWiZ2rJSxJx3+BVCgXGv1d0U80wcdK1HYzpJLDyKYv98/Zw5SsQO8thws7eH6DljcA2Llc1dBZrJkKyYmhlWrVjFz5kyaNWuGt7c3U6dOxdvbmzlz5gDw4Ycf0rFjR2bOnEnNmjUpV64cXbt2xc3NDYCwsDB+//13vv76a1q1akXt2rWZP38+Bw4c4NChQwBs2bKFCxcusHjxYmrUqEGHDh2YPn06P/74I/HxRl4kaeZ8PdSysZcCIuSKZE5q/6l6VevGTnWdiTCqL7eo8+271yxBhWIvKIUsclT1UoUYn5TwTl13nmuBSWsVFQW2TlEvOKCoC/Z7/QGWtlk/WLPx6v/n/4agHFhz8QwHrz+m4+y97Lv2CFtLHV+9Up2ZPatja5V/pw0+T23PInz1SnUA5u33Z+GBm6YNKB+JidfzaVIC+0bzcpQolIHfl9gwOLlIvd1QRrVepIi9FR8kJbHfbL3KvdBstLB4nrIt1ItL1s5qQaA/ukJ0DkxdFHmCyZKtxMRE9Hp9qlEqAFtbW/bt24fBYODff/+lQoUKtGvXDjc3N+rXr8+aNWtStj1+/DgJCQm0adMm5T4fHx9Kly7NwYMHATh48CBVq1alWLEnZaHbtWtHeHg458+fz9kXaWa8XBywsdQSk6Dn1uMoU4eTfxUpC41Hq7c3TZQpBEZ04Poj9l59hKVOw9g2GSiFLHLc603L0rS8C7EJBt5ecpLY2Bj4+w3Y/626QauPoOOXaiPQ7HCvoo6OocCeL7Mb9gsZDArfb7/Ka78dIigijvJuDqx9uzE9amdyCmQ+1KV68ZQke9q68+y49PAFzxAZ8fPu69wPi6VEIVtGNCuXsSed+APiI9W1kOVa52yA+UTPWiWpV6YIMQl6pq7Nwc+BpevDwLVgW0QtXrKgE0TI70pBZGGqAzs6OtKwYUOmT59OpUqVKFasGEuXLuXgwYN4e3sTGBhIZGQkn3/+OTNmzOCLL75g06ZNvPzyy+zcuZPmzZvz4MEDrKysKFSoUKp9FytWjAcPHgDw4MGDVIlW8uPJjz1LXFwccXFPFjaGh6tDwAkJCSQk5N0KMxWKOXDmbjhn74RQqlDBKg2b/HPLlZ9fg1FYnFqCJuw2+r1fY2j2/oufI55LURRmbroEQO86JXF3tDT738VcPedM6Ivulen84wHuPAjkzo9dKR9xBEWjQ9/pW5TqfcBYa2Qbv4vlpfUo51aS2PhdKOptnP3+x+PION5beY791x8D0L1mcaZ29sHOysKsf5a5eb693rg0/kGRrDxxj7eXnGTpsLop64JF5t0LjeHn3dcBeL9deSw0BhISXrCmyJCIxaGf0QCJdUegmGAtel59j5va2YeuPx1k64WHbDxzjzaV3HLmQK6Vof86LJa8jCbwAsqP9UgctivNulXt3i9B0ctnhRcwt/Mto3GYLNkCWLRoEUOGDKFEiRLodDpq1apFnz59OH78OAaD+ibz0ksvMXbsWABq1KjBgQMH+Pnnn2nevHmOxvbZZ58xbdq0NPdv2bIFOzu7HD12TnKI1wJa1u0/BXdyYHFoHrB169ZcOY5Hke7UC/8B9n3LrsfuRFu75spx86uzwRpO3dFhqVWokOjPhg05NN8+B+TWOWdKA4uH0+rWl5SPuEm8xprjXm8TeM8Z7m0w6nHqOdXEI/wkAcvHcdJzuFH3DXAtHP64oiMsQYOlVuEVLwP1bW6za9vtFz/ZTOTW+dbIEs44a7kSBgN+Pci7VfUUsGt4RjP/ipa4RC3eTgqGWyfYkIHTrXjIYeqG3yXOwpEt9xwxBBj3dy0z8uJ7XAt3Ldvua/lg5UkiauixzsGZwXalx9H88hSsYkNRfmrInorTiLJxB6DCgzVUCljNRY+XuRJpup9hXmIu51t0dMZmLpk02SpXrhy7d+8mKiqK8PBwPDw86N27N2XLlsXFxQULCwt8fX1TPadSpUrs27cPAHd3d+Lj4wkNDU01uvXw4UPc3d1Ttjly5EiqfSRXK0zeJj0ffPAB7777bsrX4eHhlCpVCj8/P5yc8u7Vu5DDtzmw/hIJdm507FjL1OHkqoSEBLZu3Urbtm2xtLTM+QMqHTAsOY3u5l5aJ25H3/2PnD9mPqU3KPz440EgkiGNy9LHr7ypQ8qQXD/nTCX4OhZLe6PR3uSR4sQ7mg/4vFN/6jjZvPi5maS57wHz21Iq5CAer34Dhb2Msl+DQWHuXn9+PHQNgwLlXO35vnd1yhfLO0UwTHG+NW+dQK9fj3A9KIpl9wuzdFhd7K1N+tEizznsH8ypg8fQauCb/o3wcc/YWlTd/O8AsGgwgvbNu+VghM+Wl9/jWsbr6fj9fu6GxnLZshz/a5/DBZfC/VB+a4FVTAitr00jcdBGtJc3oDu5Gn2z/+HddBw5M1aff5jb+ZY86+1FzOId0d7eHnt7e0JCQti8eTMzZ87EysqKunXrcvly6oXQV65cwdPTE4DatWtjaWnJ9u3b6dFD7cNy+fJlbt++TcOGDQFo2LAhn3zyCYGBgSmFNbZu3YqTk1OaRO5p1tbWWFunvURnaWlpFj/grKpaqjAAlx5G5OnXkR25+jPs+CX83BjtlQ1ob+0G7zYvfo5I49+T97gSGImjjQVvtSyf587dvP6+8Vx3j8OSVyD6MUqhMvxPmcj+hw5MWHWexcPqZ6xHUGZ41oPyfmiubsHy4Hfw0o/Z3uXjyDjGLj/NnitBALxcswTTu1XJs0lDbp5vRS0tWTC4Ht1/2s/FBxG8u/Icvw6oY/yfez6VqDcwY4P6Oadv/dJULZXBap13jsD946CzQtdgBDoTv7/kxfc4S0tLpneryuAFR1lw8DY965RO6UeaI4qWgZFHYE5jNFGBWP7aTL2/eC10ruXRxQSBU/GcO34+Yi7nW0ZjMGnp982bN7Np0yb8/f3ZunUrLVu2xMfHh8GDBwMwfvx4/vrrL3799VeuXbvGDz/8wLp163jrLbXijrOzM0OHDuXdd99l586dHD9+nMGDB9OwYUMaNGgAgJ+fH76+vvTv35/Tp0+zefNmJk2axMiRI9NNpvK7iu5OaDTwMDyOR5HSbC/HuflAvRHq7Y3vQ2LBqoBpDPGJBr7eqvYse6N5OZztTP8GK5Jc2QILO0P0YyheE82wrUzs1wk7Kx0HbzxOWYNidM0mqP+fXgYhN7O1q6M3g+k0ex97rgRhbaFlZo9qfNWrep5NtEyhVBE7fh1QB2sLLTsuBTJ9vWl6oeVFS4/e4dKDCJxtLXmvbSZGVpKbGFftBQ45tN6oAGjp40aHKu7oDQof/n0WQ05XanZwg5GHgacuRtw/AauGwteV4LvqsOYtOLkYHl+X9jH5hEmTrbCwMEaOHImPjw8DBgygSZMmbN68OSVT7N69Oz///DMzZ86katWq/Pbbb6xatYomTZqk7OObb76hc+fO9OjRg2bNmuHu7s7q1atTHtfpdKxfvx6dTkfDhg3p168fAwYM4OOPP87112sOHKwt8CyirjmT5sa5pMX7YO8Gj6+pzY5Fpiw/dofbwdG4OFgxuHEZU4cjkp1crDbsTIhWq6ANXA8ObpR1dWBa18oAfL31Ciduhxj/2KXqQrlWaj+7fd9kaRcGg8KcXdd5de4hHoTHUtbVnn/ebkyvuqXQaGRUJrNqli7Mt71rALDgwM2c62GUj4RGx/NVUiuLd9tWoLC9VcaeGHILLq5Tb0u592yb3MUXeysdJ26H8texdPoFGtvR3wAFdEk/75L1wKM6aLTqxaNTf8I/I+H7WvCVD6wYDEd+hYfnwVAw19rndSZNtnr16sX169eJi4sjICCAH374AWdn51TbDBkyhKtXrxITE8OpU6d46aWXUj1uY2PDjz/+SHBwMFFRUaxevTrNWixPT082bNhAdHQ0QUFBfPnll1hYFNyrlsnNjSXZyiU2ztA2KbnfPRPC75s2njwkJl7P7O1XAXi7pTd2VgX399ZsKArsnqV+GFD0UL0P9P0LrJ+sbepZuyRdqxdHb1AYvfQk4bE5UDmqeVLVrpN/QmjmPiAFR8UzdOFRvth0Cb1BoVuN4qx7uwk+7nl3Pa456FDVgw86+ADw8foLbL0gZa6f55utVwiNTqBiMUdeq1864088MhcUg9rLqVjlHIuvoPBwtuU9P3VU8fONl3J21s/umbDzE2j5IXwUpP5/94ja1uL9W/DaKmjyLpRqAFpLiHwA51fDhnEwpxHM9IKlfeDA93DvOOhzvwKlyDyTJlvCNJLL8164L8lWrqnWW716lRAFWyebOpo8Y+HBmwRGxFGikC19MvNhROQMgx7+fRd2zlC/bvIudJsDutRTOzUaDTO6V6FUEVvuhsQwcfVZFGNPhyndALyagSHhSU+vDDh2M5hOs/ey83IQVhZaPnu5Kt/0riHTBo1keLOy9KlXGkWB0UtPcvZumKlDMkuXHoSz6NAtAKZ08cVCl8GPY7HhcHyhervByByKruAZ0NCTysWdCItJSGksbXRPJ1rNk6ZCN5+gfr3zEzj8M5RvA22mwNDN8MEddcZAyw/VxNrSDmJD4fIG2DIJfm0Fn5eGP7qpF8Bu7oeE2JyJXWSLJFsFUPLI1gUZ2co9Wi10nAVo4OwK9U1RPFd4bAJzdqlrfsa2rYC1RQ7W5RUvlhADywfAsXmABjrMUj8UPGPKnZONJbNfrYmFVsP6MwGsOH7X+DElj26d+OOFI8YGg8Ivu6/Te+4hAsJiKetiz5q3GtOnXmmZNmhEGo2Gj1+qTNPyLsQk6Bm68Cj3Q2NMHZZZURSFaWsvYFCgfWV3Gnm7ZPzJJxdDfAS4VJCCS0ZkodPySfeqaDSw+uQ9Dlx/ZPyDGPSpE61kyQmXQZ/6fktb8GqqPj7gH/jfbRi2HdpOhwod1FkzCVFwY6d6AWxBR/i8FMxrD9s/hmvbIC7C+K9DZJokWwVQcrWd60FRxCboX7C1MJriNaD2IPX2xgky/P8Cv+65QVhMAt5uDnSvWcLU4RRs0cHq1dNL60FnDb0WQv0X97iqWbow7/pVAGDKP+e5HhRp3LjKNAHPxqCPh/3fPXOzkKh4hv1xjM82qtMGu1QvztpRTVIuPAnjstRp+fG1WlQo5kBgRBxDFhwlIiemkuZRm8494OCNx1hZaPmwU6WMP9Ggh8Nz1NsN3lQv4gmjqVGqEP3qq9WuJ605R1yikT8ftfwgbaKVrPkE9fHn0VlCyTrQeDT0XQYTbsIb+9Wqx5W7g0Mx9b3w9kHY+xUs7qGOfP3SHDZNhIvrIeqxcV+TyBD5TS2A3J1sKGxnid6gcPWhkT/8iOdrPRlsC8PDc0kjBCI9jyLj+H2fusB+nF8FKSNtSqF31Culdw6pV1IHrAHfl174tGRvNCtHY++ixCToGbXkpPE/wCR/eDm+ACIepHn4xO0QOs3ey45LgVhZaPmkexVmv1oDB5k2mKOcbCyZN6guLg7WXHoQwdtLTpKol8X9sQl6ZiRNU3ujWVlKJRWsypBL6yH0NtgWgWqv5lCEBdu4dhVxdbTmRlAUc3ffMHU4z6fVgnsVqPc6vLIA3rsMo05A1++hel8o5Kmu7Qs4BYd+hL9eg1ll4cf6sH4snF0JYfdM/SoKBEm2CiCNRvPUVEKZT5+r7IpAq0nq7Z0zICoHpirkAz/uvEZ0vJ5qJZ1pV/nZzcdFDntwDn5vC48ug1MJGLIZPBtlahdarYave9WgiL0VFwLC+WLj5Rc/KTO8mquLyRNjYf/slLsVReHXPTfo9fNB7ofFUqaoHX+/1YjX6nvKtMFcUrKwHb8PrIONpZbdV4KYsva88dfu5TFz99zgXmgMHs42vNGiXOaefDCpp1ydIWCViSRNZJizrSUfdVZ7sH6/8xo3H0WZOKJM0GigaDmoNQC6z4ExZ2DseXj5N/WccVWL1xB0Sb3Yu2oofOML31aDv9+EE4uk3HwOkWSrgKrkLkUyTKb2YHCvCrFhsH2aqaMxO3dDovnz0G0AxrerKB+MTcV/L8zvABEB4OYLQ7eCWyamPD2lmJMNX75SDYB5+/3ZccmIVeo0miejW8fmQWQgodHxvP7HMT7ZcJFEg0Knah6sG9WEysWdn78vYXTVSxXi29410Wjgz8O3U0asC6L7oTH8tOsaAB90rJS56qp3j8Odw2qFunqv51CEAqBLNQ+alnchPtHAR/+cy9sXCJxLQrVXoPM3an+v8deh92K1uIpHDbXcfOgtOL0E1r6dVG6+IqwYJOXmjUiSrQLqSfl3WTyZ67Q6dY41qFeS7h03bTxmZvb2q8TrDTQsW5QmmVk4Lozn3CpY/DLEhatrogZvBOfsrZtr5VMspU/auBVnCAw3YtWscq2gRB1IjOHh5i/pNHsf2y4GYqXTMr1bFX7oUxNHG2mGbSrtq7jzYUc1Uf9kw0U2nUs73bMg+GzjJWITDNQrU4Qu1Twy9+RDSaNaVXuCo4z25yS1yEsVrCy07L36iPVnAkwdkvHYu0ClLtD+UxixO3W5+dIN1d5fkQ/h/N+py80veVWdOXD3OOhNs/5Sb1A47B/M8UcaDvsHo8/pBtRGJJPWC6inKxIaDApaWROTu0o3UOfcn1kGG8bD0G2y2Bm4FhjJyqSqdePby6iWSRz8CTYnLdT2fQm6zwVLG6Ps+n8dfDh8I5gLAeGMXX6KRUPqG+e9R6NBaT4BzZJeOJ5ZQExcTTyLuvNj31pUKSGjWeZgaBMvbj6OYvGh24z56yR/OTekeqlCpg4r1xzxD2bd6ftoNGoT3Uy9t4XegfNr1NsNpIlxbvBysWdkC2++2XaFj9dfoHlFV5zy4wUbGye13Hz5pMqWCTHqBeBbB9R/d46o5eavbFT/AVjaq43lPRur08pL1FYrJ+agTecCmLbuAgFhsYCOP64ew8PZhildfGlfJZMXLkxAPt0VUOVcHbDSaYmMS+RuiJTlNYm208DKUX1jO/WnqaMxC99svYJBgTaVilGrdGFTh1OwGAxq75bkRKveCOg532iJFoC1hY7ZfWpia6lj/7XH/LLHOAvQw6ITGH6wCGcMXthp4vjUYw/rRjWRRMuMaDQapnapTPMKrsQmGBi68Bh3Q6JNHVau0BsUpq49D8CrdUtn/rw8MldtIF6mKXhUy4EIRXreaFGWsi72BEXE8dVmI681NVeWtmqV1+YT1GJI/7sFw3ao5eYrdgSbQknl5nepvcEWdFIrHv7eDrZNg6vb1F5wRrTpXABvLj6RlGg98SAsljcXn2DTOfMfeZRkq4Cy1GkpX8wBkH5bJuPoDi2S+gRtmwoxoaaMxuTO3Qvj37MBaDQwrl0FU4dTsCTGw98j4MD36tdtpkKHL9Qpr0bm7ebAtK6VAfhqy2VO3g7J1v5O3wml0/d72XoxkJ8MPQBoF7UWJ4NMkTY3FjotP/StiY+7I48i1ZLw4QWgJPxfR+9wISAcRxsLxvll8r0tLvJJE+OG0sQ4N1lb6JjRrQoAfxy6xek7oaYNyBR0llCytlpuvs9SmOAPbx5IW27+ziHY9zX82QO+8EwqN/8BXFyXrUJgeoPCtHUXSG/CYPJ909ZdMPsphZJsFWC+HtLc2OTqvwEuFSH6Eez6zNTRmNTMpCuHL1Uvjo+79D/KNbHhsOQVOLsctBbQ/RdoMvaZzYqN4ZU6JelczYNEg8LoZSez9IFbURTm7/en588HuBsSQ6kitrw14m0oVhVNfCQcmpMDkYvsckwqCe/maM2Vh5GM/PMECfm4JHxYdAJfblHf28a2qUBRB+vM7eDUnxAXBkXKQfl2ORCheJ5G3i50r1kCRYGJf5+V9gVaLRSrnE65+R/UcvOFyzxVbv4n+KsfzCqnlptfNwbOrMhUufkj/sFpRrSepgABYbEc8Q/O5gvLWZJsFWAp67akIqHp6CzVEQR4UvmnADp04zF7rgRhodUwtq2MauWaiIewoKM6JcTSHvr+BdVzvn+PRqPhk+5VKVnYljvBMUz6O3MVv8JiEnhz8QmmrbtAgl6hfWV31o9qSrVShZ9UJjz8M8Rkb9RM5IzihWyZN6gutpY69l59xEdr8njFt+f4dvsVgqPiKe/mQP+Gnpl7skGvfmAFaWJsQhM7VsLJxoLz98NZdOiWqcMxLynl5vur5ebfOQ1jL0CP35PKzSdVsA26BMfnw+phT5WbfwNO/PHccvMZLaQUGGHEgks5QH5zC7BKHskVCSXZMqlyLaFSV3VO/oYJBa7HhaIozEoa1epdtxSeRe1NHFEB8ega/N4GHpwFe1cY/C94t8m1wzvbWvLdqzXRaTWsPX2fVScydrXzzN1QOn+/l03nH2Cp0zCliy9z+tXC2TZp8bpPZ3CrrFZSPPxLDr4CkR1VSjjzfR+1JPyyo3eMtn7PnFx9GMEfB9UP55O7+GKpy+RHrssbIeSmuk6mRl+jxycyxtXRmvc7qD2qvtpyhQfPGWkRqJVrq/ZMKjd/CMbfgN5/quXmi9d8qtz8Ulg76km5+eUD4fBceHCOyNh4Fh26xZdbLjPGYiWjdKvTPdQo3WrGWKzEzdF4a4tzgiRbBVhysnUvNIaw6Pw/b96stfsELGzh1j44n/6bSn6183Igx2+FYG2hZXTr8qYOp2C4c1RtVhx6G4qUhaFb1D+Cuay2Z2HGtlF/5pP/OceNoMhnbqsoCgsP3KTnnIPcCY6hZGFbVrzRiMGNvVJXdtNqofl49fahn9R+dsIstfEtxuSkBrKfb7zEhrPmv9A9oxRF4eP16lqStr7FaFreNfM7SR7VqjMYrOQilCn1qVuamqULERmXyPT1F0wdTt5iXxQqdVbLzQ/fpZab77cKmr6Xutz8hTWwcTz83Bj9Z2Vw/3cQHcKX40Yo71mmTbhG6VbznuVK7KytqOdVxCQvLaMk2SrAnG0tKVlYLdcp67ZMrFBpaPquenvzJHVRdAFgMCjM2nwFgEGNylDMybyvTuULlzfBwi4QEwzFa8GQLWrCZSJvtvCmQdkiRMfrGb3sJNHxiRy8/ph/Tt3j4PXH6A0K4bEJjFxygilrzxOvN+DnW4x/RzWlxrNKh1d6CVx91ETryNxcfT0icwY39mJQozIAjP3rFCeyWTDFXGy98JC9Vx9hpdMyqVMWmoHfPwm39qvrKOsNN36AIlO0Wg2fdKuKTqvh37MB7LwcaOqQ8i4bJ3UWRevJMGQTceNvsq/JHyx1GMAefVWiFGucNVG01Z1gouVS+lrsIF7R8Z7lSv60nEED7QXG6lbwnuVKvk7oSenuU9GZefsi6bNVwPl6OHE3JIYLAeE0LFfU1OEUbI1Gq4uhQ27C3i/VinD53Loz97kYEI6jtQVvNC9n6nDyv+MLYf0YdQFzeT91gbOJr5jrtBq+7V2T9t/t4dy9cOrO2EZUvD7lcRcHKzRAUGQ8FloNH3SsxJDGZZ7fp0irhWbjYdVQOPijWojG2jHnX4zIko86+3InOJrtlwJ5feEx1oxsTKkidqYOK8tiE/TM+PciAMOaemVtavTBpFGtyi+DU3EjRieyyre4E0Mal+HXvf5M/uccW8Y0x9bK+BVbC4o7wdEsOXKb5Ufv8DjKAmiPhbYD7SsWZbh3FFX159DcPgi3D2KVtP62se4CjXXqyOJc3av49pwqfbaE+ZN1W2bE0gbaJVUkPPCDuqYmH0vQG/hmqzqqNbxZWQrbW5k4onxMUWDXF7ButJpo1egHry4xeaKVzN3Zhj51SwOkSrQAHkXGExQZTxE7K1a80ZChTbwy1hC2cnco6q0WyTj6W06ELYxEp9Uwu09NKhd34nFUPIPmHyEsJu9Obf99nz+3g6Mp5mTNyJbemd9B+P0n08kbShNjczKmTQWKO9twJziGH3ZeNXU4eY7eoLDj0kMGzz9Cs1k7mbPrOo+j4nF3smFsmwrs/18rfuhXn2oNWqFJLjc//ga8eRBDhy9RktIWg8aCoR/+nCcSLZBkq8CTioRmpmIH8G4LhgTY9L98XSxjxbG73HwcTVF7KwY38TJ1OPmXPlEdzdr1qfp103Hw0g9qJUwzoTcorDn1/AIZlhYaqpUslPGdanXq6Bao/cPio7IeoMhx9tYW/D6wLu5ONlwPiuLNxceJT8x7ZbYfhMXy4071Qtn/Ovhgb52FCURH5oIhETwbm2QtpXg2e2sLpib1CZy75wZXH0o/v4x4FBnHjzuv0WzmToYsOMbOy0EoCjQt78Iv/Wuz7/2WvNOmfPpLCbRaKOaLNjYEDQb0Ggu0SiK6vbNy/4VkkSRbBVxyr62rgRF58g9bvqPRQPvPQWsJ17bClU2mjihHxCbomb1dvSo4sqU3Dln5QCJeLD4alveH4wvUClCdvoLWH+VoD62seFEvFYCH4XGZ76VSpScU9oLox3D092xEKHKDu7MN8wbVxd5Kx4Hrj5m05myeKwn/+caLRMfrqVW6EN1qlMj8DuKj4Nh89XYDGdUyR36V3WlTqRgJeoUP83HbguxSFIWjN4MZvfQkDT/bzqzNl7kXGoOzrSXDmnixc1wLFg2tT7vK7li8qFLn7pmw8xP0zf7H+hrz0Df7H+z8RL0/D5Bkq4ArWdgWRxsLEvQK1wILRlEGs+fiDY3eVm9v+h8k5L8ys4sO3uJBeCzFnW3oW7+0qcPJn6KD4Y+X4PIGsLCBXoug7jBTR5WujPZIyXQvFZ0FNBun3j4wW00+hVnzLe7ED31rodXA8mN3+WnXdVOHlGHHbwWz5tR9NBqY2rVyxqa7/tepJRAbql4kqNjB6DEK45ja1RdbSx1H/INZefyuqcMxKxGxCSw6eJP23+7llZ8Psvb0fRL0CtVLFeLLV6pzeGJrJnX2xcslg9PYkxItWn6Ioan6fm5oOg5afphnEi5Jtgo4jUYj67bMUdNx4FhcLZZx4HtTR2NUEbEJ/LRLnWYzpk0FbCxlgbHRhdyC3/3g7hG1R8+Af9TSu2Yqoz1SstRLpVpvKOQJUUHqCJ8wey193JiWNFVr1ubLrDt938QRvZjBoDB1rbpwv1ftUpmb8vpkJ3Bojnq7wZvqVFhhlkoWtmNMUtuKTzdcJCQq3sQRmd7FgHA+/PssDT7dzkf/nOfywwhsLLW8WrcU60c14Z+RjelZu2Tm/+Yb9GpildywPlnzCer9Bn36zzMjkmyJlKmEUv7djFg7gN909fber9R+SPnEb3v9CYlOoKyrPS/XysI0G/F8AWfUHlqPr4JTSRiyGUo3MHVUz1XPqwgezjY8axxAA3g422Stl4rOUu3nArD/W0iIyWKUIjf1b1iGIY3VtZzvrTjN8VuZnEKay1Ycv8PZe2E4Wlswvn3FrO3k6mYIvg7WzlDjNeMGKIxuSBMvfNwdCYlO4PONl0wdjknEJepZc/IePeccoMN3e/nz8G2i4vWUc7VnShdfDk9sw+c9qlGlhHPWD9Lyg7SJVrLmE9THzZwkW0KKZJirKj3AswkkxsDmD00djVE8jozjt703AHivbcUXz9MWmXNjN8zvqDaIdKsMw7aCm4+po3ohnVbDlC5qc9v/JlzJX0/p4pv1XirV+4BzKfX7cmJRluMUuevDTpVoU6kY8YkGXv/jOLcem2eRk/DYBGZtvgzAO23K4+JgnbUdHfxR/b/2QPWCmzBrljotn3SvAsBfx+5w9KZ5XxAwpjvB0Xy+8RINP9vBmL9OcexWCBZaDZ2qerDk9fpse7c5gxt74WxrPoWYTEk+6YiUka2LD8Jloac50Wig40zQ6ODiWri+09QRZducXdeJitdTpYQTHaq4mzqc/OXsSljcA+IjoExTGLwhT/XnaV/Fgzn9auHunHqqoLuzDXP61cpeiV8LK2gyVr297xtIjMtGpCK3qCXha1ClhBPBUfEMXnCU0Gjzm641e9tVHkXGU9bVngENy2RtJwFn4OZe9f2+/gijxidyTm3PIvSpVwqAD/8+m68LjekNCtsvPinb/vPu6wQnlW1/t20FDvyvFT++VotG5Vyytl4xH5MSYAJvNwcstBpCoxMICIuleCFbU4ckkhWrDPVeh8M/w8b34c39ZlWyOzPuh8bwx6FbAIzzq4jWzDu+5ykHfoAtSaOflbtD91/AIotX102ofRUP2vq6c8Q/mMCIWNwc1amDWR7RelrNfrDnS4i4DycXQ92h2d+nyHF2VmpJ+O4/7udGUBQjFh1n0dD6WFmYx7Xia4GRLDhwE4DJnX2zHteh5CbG3cC5pFFiE7nj/fY+bD7/kCsPI/l9nz9vtihn6pCM6lFkHH8dvcOSw7e5F/pkGnbT8i70a+BJax83maXyAvLdEdhY6vB2U6csyFRCM9TiA7BzgUeX4fAvpo4my77fcZX4RAP1vIrQvIKrqcPJHwwGdYppcqLV4C3oMS9PJlrJdFoNDcsV5aUaJWhYrqhxEi1QvyepRrfMb4REpK+Ykw2/D6qLg7UFh/2D+d/qM2YxC0NRFKavv0CiQaG1jxstKrplbUcRD9SRaYAGI40XoMgVheys+LBjJQC+236FO8F5v+qpoigc8Q9mVDpl219vmsmy7UKSLaGqJEUyzJdtIWgzVb2963P1D3MecyMokuXH1PK4E9pVlCkGxpAYB6tfh4M/qF+3nQ7tPlUbQIr01RoADu4QdgdOLzF1NCITKnk48eNrtdBpNaw+cY/vd1wzdUjsuBTI7itBWOo0TOrsm/UdHflVbWRfqgGUrG28AEWueblWCRqULUJsgoEpa8+bxcWArIiITeCPgzdp9+0eev1ykHVJZdtrPFW2/cNOmSjbLgBJtkQSXyn/bt5qvAYlaqvrcbZNNXU0mfb11ivoDQqtfNyoUyYLFeVEarFh8GdPOLcStBbw8q/QeLTZNSs2O5Y20Pgd9fber0CfYNp4RKY0r+DKxy+pJeG/3nqFNSfvmSyWuEQ909erpd6HNPHK+ofP+Gg4ltRwu6E0Mc6rNBoNM7pVxVKnYcelQDaff2jqkDLlwv1wJv59lvqfbmfyP+e58jASW0tdStn2NVkt2y4ASbZEkpSKhJJsmSetFjrOAjRweincPmTqiDLs/P0w1p8JANS1WiKbwgPUioP+e8DKAV5bAdV6mTqqvKP2ILB3U9spnPnL1NGITHqtvifDm5UFYMLKMxzxN00FuPn7b3LzcTSujtaMalU+6zs6swxiQtRecD7m2wtPvJi3mwNvNFfXa01de57IuEQTR/R8sQl6/j55lx5zDtBx9l6WHL5N9FNl2w9NbJ39su0CkGRLJEmeRnjrcTQRsXK11yyVqA21+qu3N4zLE438AL5MKoncpXrxlKReZFHQFbVZ8cNzasIweAOUa2XqqPIWKzt1FBDUghl68/5AJNL6X3sf2ld2J15vYPiiY/g/yt2S8IHhsXy//SqgFkdwsM5irbGnmxjXf0OaGOcDI1t6U7qIHQ/CY/lm6xVTh5Ou24+j+WzjRRp9voOxf53m+FNl25e+3kDKtucASbYEAEXsrXB3UksuX34QYeJoxDO1ngI2zvDgLBxfYOpoXujozWB2Xg5Cp9XwbtsKpg4nb7tzBOb5QdhtKFJO7aHlUd3UUeVNdYaAXVEI8VenYoo8RavV8E3vGlQv6UxodAKD5x8hJCr3Cp58sekyUfF6qpcqxMs1s9GY/do2eHQFrBzVapkiz7Ox1DG9m9p7a/5+f87dCzNxRCq9QWHbhYcMmn+E5l/u5JfdNwiOisfDOXXZ9oblisqa6hwgyZZIIVMJ8wB7F2g5Sb29YzpEm28TRUVRmLVJHdXqVaeULKjNjksbYGEXdbpRiTowdCsULmPqqPIuK3toNEq9vWdWnhklFk/YWun4dWAdShSy5ebjaIYvOkZcYs7/HE/eDmHVCbXYz9QuvtlrYXHoqSbGNjLqn180r+BK52oeGBT4cM059AbTFcsIiojjx53XaDZzJ8P+OMauy0Eoilq2fW7/2uyd0JLRrcvj5mTz4p2JLJNkS6RILpIh5d/NXJ0h4FZZ/eC9Y7qpo3mmXVeCOHIzGCsLLaNbe5s6nLzr2Hz46zVIjIUK7WHgWrAvauqo8r66w8C2MDy+Buf/NnU0IgvcHG2YP7gujtYWHL0ZwoSVOVsS3mBQmLr2PAA9apWkZunCWd/Zg3NwYxdotFBvuHECFGbjo86+OFpbcPpOKEuP3M7VYyuKwuEbj3l7yQkaff6kbHshO0uGNyvLrqSy7X5Stj3XyHdZpJDy73mEziKpWAbqB/H7p0waTnoMBiVlrdbAhp54OEuj7ExTFNj5KawfA4oBavaH3n+qozIi+6wdoWFST6PdM9X1MyLPqVDMkTn9amOh1fDPqft8s+1qjh1r1Ym7nL4bhr2VjvfbZ7PYT/JarUpdobBn9oMTZqWYkw3j2qnnyBebLhEYEZvjx3y6bHvvuYdYfyYgpWz7V69U59AHrZnYsRJlZJZJrpNkS6RInkZ4+UEEiXr54GHWyjSGqq8ACmwYb3YfFDecC+D8/XAcrC14s4WMamWaPhHWjoLdX6hfN38fun6vJtrCeOoNV9dAProMF9aYOhqRRU3KuzAjaZ3M7O1XWXX8rtGPERGbwBdJ06JHZXfaVcRDOLtcvd1QmhjnV/0aeFK1hDMRsYl88u/FHDvO+fthfLD6+WXbe0jZdpOSZEuk8Cxih52VjrhEQ65XdxJZ0PZjsLSHu0fMqoR1ot7A11vUKkzDmnpRxN7KxBHlMfFRsKwvnFykTjHq/C20nCg9tHKCjTM0SOpttGeW2V20EBn3ar3SvNlCLbv9v9VnOHj9sVH3/8OOazyKjKNMUTsGNy6TvZ0d+x308VCyLpSqZ5T4hPnRaTV82r0qWg38c+o++64+Mtq+YxP0rD5xl5d/2k+n2ftYeuRJ2fapUrbd7EiyJVJotRqZSpiXOBWH5hPU21snQ6x5/MxWnbjLjUdRFLG3YljTsqYOJ2+JegwLu8LVzWBho04brDPY1FHlb/XfAGsnCLwAl9abOhqRDeP9KtKpqgcJeoURi45xLTDSKPu9ERTJvP3+AEzu4ou1RTZGCBJi4GhSE+MG0sQ4v6ta0pkBDcsA8NE/54hNyF4Rl1uPo/hsw0Uafradd5ef5sTtULVse7UnZdsHSdl2syPJlkilkocjIMlWntHgLSjqDVGBT6acmVBsgp5vk9ZMvNWiXNb7zxREITfV0u73jqmFGwasBZ+Opo4q/7MtpCZcoK7dysECCyJnabUavupVnZqlCxEem8iQBUd5HBmX7f3O+PciCXqFFhVdaeVTLHs7O7Mcoh+Bcyl1vZbI997zq4CbozX+j6L4eff1TD9fb1DYeuEhA+cdofmsXfyy5wYh0QkUd7bhveSy7X2lbLs5k2RLpOLroQ45S0XCPMLCCjokJVmHf4bASyYN58/DtwkIi8XD2YZ+DWTRd4YFnFabFT++Bs6lYcgWKF3f1FEVHA3eBCsHeHgWLm80dTQiG2wsdfw6oA6lithyOzia1/84lq3RhJ2XA9lxKRALrYaPOvtmLzhFgUM/qbfrj5A1mAWEo40lU7pUBuCnnde5EZSxEdfAiFh+2HGVZjN38vofx9h9JQiAZhVcmdu/NnsmtMz++kGRKyTZEqmk9Nq6H56jJXSFEXm3gYqdwJAIGyeY7Mp8ZFwiP+68BsDo1uVlMW5GXd8B8ztC5EMoVhWGbgFXaQCdq+yKPCm/vfsLGd3K41wcrJk/qC5ONhacuB3KuBWnMWSh11F8ooHp6y4AMLhxGcq5OmQvsOvbIeiSmtjXGpC9fYk8pWNVd5pXcCVeb2DSmrMcvP6If07d4+D1x6n6cCmKwqHksu2f7eDLLVfSlG3/Y0g9Kduex8hlFZFKxWKOaDXwOCqeoIg4uWKSV7T/FK5tA//dcOEfqNwt10OYt8+f4Kh4vFzseaV2yVw/fp50ZjmseVNNlL2aQe/FatEGkfsavg2Hf4GAU3B1K1TwM3VEIhu83Rz5uV9tBsw7wvozAZQpap9SijujFh64yY1HUbg4WDGqdfnsB3UwaVSrZn/5PS9gNBoN01+qQquvdnHgejAHrh9OeczD2Ybx7SoSEZvI4kO3uPrUWsOapQvRr74nnap5yAXMPEzSYpGKrZUOr6QeDLJuKw8pXAaajFVvb/4Q4qNz9fAhUfH8uucGAO+2rSBX3F5EUWD/d7D6dTXRqtITXlspH8BMyb4o1B2q3pbRrXyhkbcLn75cFYAfdl5j+bE7GX5uUEQcs7er608ntPPBySabBQcCL6ojW2jUKYSiwLkQEEZiOiOsAWGxvLv8NFPWnudqoFq2vU89tWz7329J2fb8QD4RiTR8iyet25JkK29pMkZd7xN+F/Z9nauHnrP7OhFxifh6ONGpqkeuHjvPMRhg0wdqBUlQR1Re/hUsrE0bl4BGo8DCVi1Scn27qaMRRtCrTinebqn2+pu4+iwHrmWs/PaszZeIiEukWklnehpjpD55rValzlDEK/v7E3mK3qAwLWlK6rNYaDVM6eLL4Q9b89nLUrY9P5FkS6Th6/Fk3ZbIQyxt1emEAPtnQ/CNXDnsg7BYFh64CcD4dhXRaqUa0jMlxsGqIXB4jvq13yfQ7hPQyluxWXBwgzpD1Nu7ZHQrv3i3bQW6VC9OokFhxOLjXAuMeO72Z+6GsiKpMfKULpWz/54WGQSnk3ohNpAmxgXREf9gAsJin7tNokHBx90p+6OowuzIX3iRRnL594syspX3+HSGsi1BHwebJubKIWfvuEpcooE6noVpUdE1V46ZJ8WGweIecP5v0FpCj9+h0dumjkr8V+PRao+zu0fUNZAiz9NqNczqWY3anoWJiE1k0PyjBEWkXxJeURSmrj2PokD3miWo7Vk4+wEcm6e+JxevBaUbZH9/Is8JjHh+opXZ7UTeYvJkKyIigjFjxuDp6YmtrS2NGjXi6NGjKY8PGjQIjUaT6l/79u1T7aNMmTJptvn8889TbXPmzBmaNm2KjY0NpUqVYubMmbny+vKi5IqENx5FER2faOJoRKZoNNBhJmgt4MpGuLIlRw9381EUy4+q6yAmtPeRHh8AOz9T+zU9LTwA5nWAm3tBawX9VkHVnqaJTzyfozvUHqTe/u/PUeRZNpY65vavjWdRO+6GxDyzJPyaU/c4cTsUOysd/+vgk/0DJ8TC0V/V2w1Hqu/RosBxc8xYsbGMbifyFpMnW8OGDWPr1q0sWrSIs2fP4ufnR5s2bbh3717KNu3btycgICDl39KlS9Ps5+OPP061zahRo1IeCw8Px8/PD09PT44fP86sWbOYOnUqc+fOzZXXmNe4Odrg4mCNosDlB8+fbiHMkGsFtW8QwKb31alrOeSbbVdINKjNPut5Fcmx4+QpWh3s/CTlg7pD7D0sFraHwPPq47UHQNnmJgxQvFDjd0BnBbf2w819po5GGElRB2vmDaqLs60lp+6E8u7yUyQkGjh4/TH/nLrHzkuBfLbhIgAjW3pTzBjVeM+thKggcCoBvi9lf38iT6rnVQQPZxuelWprUKsSyt/R/Mmkpd9jYmJYtWoV//zzD82aNQNg6tSprFu3jjlz5jBjxgwArK2tcXd3f+6+HB0dn7nNn3/+SXx8PPPmzcPKyorKlStz6tQpvv76a4YPH27cF5VP+BZ3Ys+VIC4EhFOztBGmUYjc1WwCnFmhrts6+AM0fc/oh7gYEM7a0/cBGOeXuZLK+VrzCer/Oz9BG3KLplf+RqOPUu9rMPLJujphvpyKq32Qjv6mViYs08TUEQkjKefqwC/9a9P/98NsOPuA3Ze3EBWfeoTLxcGKoU2MUMRCUZ6Ue683HHSyFqeg0iUVv3hz8Qk0wNOrQZMTsCldfNHJmud8yaTJVmJiInq9Hhub1FePbG1t2bfvydXEXbt24ebmRuHChWnVqhUzZsygaNGiqZ7z+eefM336dEqXLk3fvn0ZO3YsFhbqyzt48CDNmjXDysoqZft27drxxRdfEBISQuHCaZOJuLg44uKejAiEh6vrlxISEkhISMj+izdzFd3s2XMliPP3QklIKG7qcIwi+edWEH5+6GzRtJqMxdq3UPZ8SaJvT/UDpBHN2nQJRYGOVYpR0c2uYHxfM0JR0JRujNajBrpTi0ku2Ktv+A6GVh+BfJ/yhvpvY3F8IRr/PSTe2ItSyvzX2hSo97hsqF3KiT51S/LHoTtpEi2AR5HxbDsfQLvKxbJ1HI3/biwCz6NY2pFY7bV8+bsv51zGta7owvevVmfGhks8CH/y+dLd2ZoPO/jQuqKLfB9fwNzOt4zGoVEU05ZbatSoEVZWVixZsoRixYqxdOlSBg4ciLe3N5cvX2bZsmXY2dnh5eXF9evXmThxIg4ODhw8eBCdTv0Y8/XXX1OrVi2KFCnCgQMH+OCDDxg8eDBff62Wv/bz88PLy4tffvkl5bgXLlygcuXKXLhwgUqVKqWJa+rUqUybNi3N/UuWLMHOzi6Hvhvm4/gjDX9c1VHGQWFs1bR/jEQeoCg0ufoJRaOucLdQfY57Ga8Kln8EfHvOAi0K/6uhp5it0XadZ9nGBVEqZD+lgvfjEPcw1WN6jQXra8wzUWQiq6rfnk+ZxzsJdKzCQe8Jpg5HGIlBgWkndITGA+lO7FIoZAVTaunJzkBD/etf4R5+mhsubThbakDWdyTyFYMC18M1hCeAkyWUc1KydZ4J04mOjqZv376EhYXh5OT0zO1Mnmxdv36dIUOGsGfPHnQ6HbVq1aJChQocP36cixcvptn+xo0blCtXjm3bttG6det09zlv3jxGjBhBZGQk1tbWWUq20hvZKlWqFI8ePXruNzS/uBYYSYfvD2BnpePkh63yRTnvhIQEtm7dStu2bbG0LCDTOR6cxWJeazSKgcR+a1A8sz8dSlEU+s07xpGbIbxSuwSfdqtshEDzqLgINJfWoT2zDO3tAyl3K5b2KIW90AaeQ6+xQKckom/2PwxNx5kwWJFpobexmFMPjSGRxIEbUUrWNXVEz1Ug3+Oy4LB/MP3mHXvhdouH1KF+VtfQPLqC5S+NUNCQ+OZhKFI2a/sxc3LOidxkbudbeHg4Li4uL0y2TDqNEKBcuXLs3r2bqKgowsPD8fDwoHfv3pQtm/4bU9myZXFxceHatWvPTLbq169PYmIiN2/epGLFiri7u/PwYeorzclfP2udl7W1NdbWaZuMWlpamsUPOKeVd3fG2kJLdLye+xEJeLnYmzokoykoP0MAStVS+wYd/Q2LLRNhxF7QZe/Xfs+VII7cDMFKp2Vs24oF53uZzKCHG7vg9DK4uA4SY5Ie0IBXM6jeB83ja2j2fom+2f9YH+FLZ8cL6PZ8ro7GN5cRkjzDtRxUfxVOLsZi/9fQb6WpI8qQAvUelwWPozNWZfdxdGLWv4/H1AqEmoodsSyW/9e0yjkncpO5nG8ZjcHk1QiT2dvb4+HhQUhICJs3b+all9Kv2nP37l0eP36Mh4fHM/d16tQptFotbm5uADRs2JA9e/akmlu5detWKlasmO56LQEWOi0+7mq/LWlunMe1/BBsi0DgBXXBfzYoisKszZcB6NfAk+KFCtD8wcCLsHUyfFMZFr8MZ5eriVbR8tB6Mow5CwPXQtgd2PsltPwwZSTL0HSc+nN4qkqhyCOavgcaHVzbCveOmzoaYQQ5XoY76rF6MQag4VtZ24cQIt8webK1efNmNm3ahL+/P1u3bqVly5b4+PgwePBgIiMjGT9+PIcOHeLmzZts376dl156CW9vb9q1aweoxS++/fZbTp8+zY0bN/jzzz8ZO3Ys/fr1S0mk+vbti5WVFUOHDuX8+fP89ddffPfdd7z77rumfOlmL7nf1oWAMBNHIrLFroiaDID6YT8yMMu72nTuAWfvhWFvpWNky3JGCtCMRT2CQz/DL83hpwaw/zuICACbQlB3GPy/vfsOj6rM+z/+nknvkJ4gQiCQhBJpiiAdBBbryiOCDVFk1x+KYF0sCIuIoiK2dcWCuy6WZx/LuhYgSkcEDIQivalAIEBIJ3Xm98fJDMYESEKSk5l8XteVi+TMmTPfCUfMJ/d9f+/xS+HeDcYP5M1aGs+xlRnB6vcjWP0fMY7btAbSpYS2geSbjM9XPG9uLVIn6r0Nd+q7xi9iYi6BVlfUtkwRcROmTyPMzs5m6tSpHDp0iNDQUEaOHMmsWbPw8vKitLSULVu28I9//IOsrCxiY2MZOnQoM2fOdE7x8/Hx4aOPPmL69OkUFRURFxfHlClTKgSpkJAQlixZwsSJE+nevTvh4eFMmzZNbd/Po0NMedjSyJbr63Y7pL4H6Wnw7Qy4/vUaX6K0zMYLS4xRrbv6xBEWWHmarVsoLYLdi2Hzh7BnCdjKpxxZPaHdMGNaWfth4HmW9z9w6tmvrSmErqnvg7DlI2Oj8PTNxg/R4rLqtQ13aRGsL9/E+HJtYiwijSBsjRo1ilGjRlX5mJ+fH4sXLz7n87t168YPP/xw3tdJTk5m1apVtaqxqUoqD1s70rWxscuzesCIF+CdIZD2L+gxDi7qcd6nldnsrD+QSUZuITuO5LDveD7N/L0Y38/NFnvb7cYUsc0fwrZP4PSpM4/FdoVLxkCnkRAQbl6NYp7weOj0P8bU0RVzYPRCsyuSCzS8Uwxv3NqNGf/dTnp2ofN4dIgvT13TgeGdzr5U4Zy2fQp5xyAwGjr+sY6qFRFXZnrYksYrsTxsHc0p5GRekfuOZDQVLS+FLrdA2kL4+iFjCpz17DOJF21Lr/SDCMCgxEiCfc1fmFonsg8Zays2fwQn95w5HhRjTB27ZDREVu5WKk1Qv4dg679h55dwdBtEdzK7IrlAwzvFcGWHaOcvlCKDjKmDtd5Y1m6HH8pnDfScAJ7e5z5fRJoEhS05q0AfT1qH+XPwZAE70nPp005hy+UNmW500DuyCTa9D93HVnnaom3p3POvjVS1L8RnGw8ztENU7X/za7aiPON7sPkDOLAK5yQiTz9Iuga6jIG4/sZooIhDRIIxUvHTp7ByDoz6p9kVSR3wsFro1Tasbi52cBUc3Wr8W9J9XN1cU0RcnukNMqRxOzOVUOu23EJgJAwoX1P03YyK0+XKldnszPjv9iqDlsOM/26nzGbqFn0142jX/umf4IV28Pmf4cBKwA6t+8J1r8PDe2DkW9B2kIKWVK3fw8af2/8Dx7abW4s0Pmv/ZvzZ5WajMZGICApbch7OJhkKW+7jsrshIgkKTsKyZyo9vP5AZqWpg79lB9KzC1l/ILMei6wjx3cbDUHmdYZ/Xmc0OSgpgNC2MPAJuH8L3PEldL0VfILMrlYau6gOkHSt8fmqF8ytRRqXE3uNBioAl99jbi0i0qhoGqGck7P9uzoSug8PLxgxB/5xjbHvVrfbIbqz8+GM3LMHrd+q7nkNriDTaHKx+cOK+yL5hhhNLi4ZAxddqi5hUjv9H4EdXxiNEPr/BSLam12RNAbr3jD+bD8cwtuZW4uINCoKW3JOjrC193gehSVl+HppepVbiOtXvv7kM/j6ERj3tTN81PuGn/WhtNjYdDbtA6Ntu618A3OLB7S70ghY7YeDVyOqWVxTdGdIvNpolLHqBbhhvtkVidkKMo1/ewAu1ybGIlKRwpacU3SwL838vcgqKGFvRh6dWoSYXZLUlaFPG8Hkl+9h6/9B8o3AmQ0/j2YXVrluy4LRHrnWG37WFbvdaPSx+SPY9n/GtEiH6GQjYHW+EQIjzKtR3FO/h42wtfXf0P9RCGsCG3zL2aW+Z0xPjupk/CJLROQ3tGZLzslisWhzY3cVcpGxWSvAkiegyNhPzbHhZ1UueMPPupBzBFa/BH+7HN4aCOvfNIJWYBT0vg/u+R7+vAp6/T8FLakfsV2MkVK7DVa9aHY1YqbSYlhfPrrZS5sYi0hlCltyXmqS4cZ63wehbSDvKKx83nl4eKcY5o3uUun06BBf3ri1W8O3fS/Ohy3/C/+8HuZ2gG+nw/Gd4OlrbDZ7yycwZbsxWhfVsWFrk6ap3yPGn5s/gswD5tYi5tn+OeSmQ0CksSZUROR3NI1QzitJYct9efrA8Gfhg1FG2+KutzkXdwf6GP88hAd48+TVHYgMvsANP2vKZoOf1xiNLrb/B4rzzjx2cW9jP6wO1xmNL0Qa2kXdIX4I7P3WGN267jWzK5KGZrfD2vK/98smGP+eioj8jsKWnJejScaOIznY7XYsmibhXtoPM6ZE7V4E3zwCt34KFgvLdmUAMLxzNNd1bdFw9ZzYa7Ro3/wxZP9y5njz1sY6rOSbIDSu4eoROZv+jxpha/OHxjqu5q3Mrkga0s/fQ/pmY4S9x51mVyMijZTClpxX24hAvD2s5BaVcujUaVqG+ptdktS1Yc/AvqXGx86vsCdexbKdxwEYlBhZ/69/+pTRSnvzR3Bo/ZnjPsFG18RLxsDFl2s9hDQuLS+DNgOMDbNXvwTXzDO5IGlQP5RvYnzJaAgIM7cWEWm0FLbkvLw9rbSLCuSnIzn8dCRHYcsdhbWF3pOMVtaLp7Iv+DIOZ53G29NKrzbh9fOaZSWw9zvY/AHs+gbKio3jFiu0HWxME0wYAV5+9fP6InWh/1+MsLXpX9DvIaPxjLi/zP2w8yvjc7V7F5FzUNiSakmKCeanIznsSM9heKdos8uR+tD3AWNkKesXslJeAPrTq00Yft51uLea3Q5Htxivs/XfkH/8zGNRnc60aw+KqrvXFKlPrXpB675wcBWsngdXvWB2RdIQfvg7YIf4KyEiwexqRKQRU9iSalFHwibAOwCGPQ3/voPkgwu4yNKBgQlVt4CvsdyjRjfBzR9CxvYzxwMioPMoYxQrunPdvJZIQ+v/qBG2Nv7D+KVFcKzZFUl9Op1ljGSCscWEiMg5KGxJtTiaZGivLTfX4XpKW/XF++dVPOH5L5ISb6z9tUpOG9NsNn9orAWz24zjHj6QOMIYxWo7GDz0z5C4uNZ9jA6Zv3wPa16BPzxrdkVSnzb+A0ryIbIDtBlodjUi0sjppxyplqRoI2wdzjpN9ukSQvy8TK5I6oXFwtr2j9Dr4BqGe2yAUz9A2ODqP99uh1/WQtoHRrv2ot+E85Y9jYDV8Xrwa17npYuYxmKB/o/A+9dD6gLoM0VTYd1VWQmsK9/E+PL/p6Y9InJeCltSLSH+XrRo5sfhrNPsSM/h8jbqvOSuvjgSwq6yYYz3/Aa+eRTu+R48vc/9pMz9Rqv2zR9C1s9njje7GJJHG926wtrWb+EiZmozAC66zOim+f0rMGyW2RVJfdj+H8g5VD4F+gJG/kWkyVDYkmrrEBvM4azTbD+isOWubDY7y3cfJw5PSj398Ty5B9a9AVfcf+akFXPAVmasVfjpM6PZxS9rzzzuHQQdr4NLboaLe4HV2vBvRKShWSzG2q2FI2HDO3DFZAiMMLsqqUt2+5l275eOBy9fc+sREZegsCXV1iEmmJTtx9Qkw41tT8/heG4Rpd5+eJYWGAdXzDGaWATHwLJnYcVsiEiCNfOgtNA4x2I11i5cMgYSrwJvbQ8gTVD8YIjtBkc2wtpX4cq/ml2R1KVf18PhVGPdaY+7zK5GRFyEwpZUW1J5R8IdCltua9nODAC2xf8JLo6H5c9AcR589QAUnIRf1xknHt9h/BmRZHQS7HyjOrCJWCww4C/wwShY/zb0vl+b3bqTta8ZfyaP0qiliFSbwpZUW8fyjoR7juVRXGrD21PTw9zNsl1G2BqYGAmXPQp5R+HHd2HX12dO8g8zwtUlYyDmEi0QF/mtdkON/y7SN8MPr8PgaWZXJHXh1EHY+aXxuTYxFpEa0E/LUm0XNfcjyMeT4jIb+47nmV2O1LHM/GI2/ZoFwICE8t/aXv0SWMo3NbZYYfSH8OAu+MNzENtFQUvk9xxrt8DoWleQaW49UjfWvWlsX9FmIETV0f6DItIkKGxJtVksFk0ldGMrdx/HbofE6CBiQvyMgyvmgL0MPLyNHzSObQMPtf0XOaeEERDVGYpzYd3fza5GLlRhNmx83/i8173m1iIiLkdhS2pEmxu7rwpTCMEIWstmwcDH4cnjxp/LZhnHReTsLBbo/7Dx+Q9/h9NZppYjF2jj+0ZwDk8wmqCIiNSAwpbUSIfykS11JHQvZTY7K3YfB2BQYmTFoNX/EeOk/o8ocIlUV+I1RgOZomxjCpq4prLSM39/vbSJsYjUnMKW1IhzZCs9B7vdbnI1UlfSfj1FVkEJwb6edG3ZzNhH67dBy8ERuGxlptQp4jKs1t+Mbr0OhfoFlUva+V/I/sVoDJR8k9nViIgLUjdCqZH4yEA8rBayCko4mlN4Zm2PuLRlO41RrX7tI/D0sMLAqWc/+fcBTESq1uF6CH8WTuyG9fOh30NmVyQ1tbZ8E+Med4GX/n8nIjWnkS2pEV8vD+IjAgGt23InzvVaCZEmVyLiRqwe0K/8lxNrX4OiXHPrkZr5dQMcWm80CLp0vNnViIiLUtiSGlOTDPdyLKeQn47kGGv6E7RRp0id6nQDhMXD6VOw4R2zq5Ga+OF148/ON0JQlLm1iIjLUtiSGkuKCQJgx1GFLXewYpcxhTD5omaEB/qYXI2Im7F6QN/y6YPfvwrF+ebWI9WT9Qts/8L4/PJ7zK1FRFyawpbUWIeYEEAjW+5i6U7HFEKNaonUi843QvPWUHACflxgdjVSHeveNPYYjOsH0Z3NrkZEXJjCltSYY2Tr4MkC8opKTa5GLkRxqY3Ve08AWq8lUm88PM+Mbq15GYoLzK1Hzq0oFzb+0/hcmxiLyAVS2JIaCwv0ITrYF4Cd2m/Lpf34cyZ5RaWEB3rTuUWI2eWIuK9LRkPIxZCfARv/YXY1ci6b/gVFORDWDuKvNLsaEXFxCltSK851WwpbLm15+Xqt/u0jsVq1WadIvfHwgr4PGJ+vngclhaaWI2dhK4Mf3jA+v/weY780EZELUO19tl555ZVqX3TSpEm1KkZcR4fYYJbtOs52hS2X5lyvlaj1WiL1rsstsPIFyDkEm96Hy+42uyL5vV1fQ9bP4NccLhljdjUi4gaqHbZeeumlCl8fP36cgoICmjVrBkBWVhb+/v5ERkYqbDUBapLh+n7NLGBvRh4eVgt94xW2ROqdpzf0nQJfPQirX4Jut4OnOoA2KmvL2733uBO8/c2tRUTcQrXHxw8cOOD8mDVrFl26dGHHjh1kZmaSmZnJjh076NatGzNnzqzPeqWRcOy1tfNoLqVlNpOrkdpYXr6RcfeLmxPi72VyNSJNRNfbICgWcg5D2kKzq5HfOpwKv6wFqxdcqlFHEakbtZqM/OSTT/Lqq6+SkJDgPJaQkMBLL73EE088UWfFSePVKtQff28PikptHDypfWNc0bLy9VoDE9WFUKTBePpAn8nG56vmQmmxqeXIb6z9m/FnpxsgOMbcWkTEbdQqbKWnp1NaWrnld1lZGceOHbvgoqTxs1otJEYbTTJ+0lRCl1NYUsb3+8pbvmu9lkjD6nY7BEZB9q+w5SOzq2mals2GFXPOfJ19CLZ/bnzuHWA8LiJSB2oVtgYPHsyf/vQnNm7c6DyWmprKPffcw5AhQ+qsOGncHFMJ1STD9azdf5LCEhsxIb4kRAWZXY5I0+LlB1fcb3y+8gUoKzG3nqbI6gHLZp0JXOvng63UaM//47vG4yIidaBWYevdd98lOjqaHj164OPjg4+PD5dddhlRUVG8/fbbdV2jNFJJMUbY2pGea3IlUlPLy7sQDkiIxGJRy3eRBtd9HAREGJ3vtvyv2dU0Pf0fgYGPG4Hru5mQ+p5xPPsX43j/R0wtT0TcR7W7Ef5WREQEX3/9Nbt372bnzp0AJCYm0r59+zotThq3DuVhSx0JXYvdbj+zXitBUwhFTOHtD73vg5RpsOoFSL4JPGr1v2Sprf6PQGG28f13GPCYgpaI1KkL+pe9ffv2ClhNWGJ0MFYLnMgrIiO3kMggX7NLkmrYfyKfXzIL8PawckV8uNnliDRdPe6CNS9D5n7Y9glccpPZFTUdWb/C6rmw8f0zx6yeMOBR82oSEbdUq2mEd9555zk/aiI3N5fJkyfTqlUr/Pz86N27Nxs2bHA+fscdd2CxWCp8DB8+vMI1MjMzueWWWwgODqZZs2bcdddd5OXlVThny5Yt9O3bF19fX1q2bMmcOXOQC+Pn7UFceACgqYSuZFn5FMKebUIJ8NFv0kVM4xMIve41Pl/5PNjKzK2nKcj6Bf57P7zS1VibZStfL2f1NNZsrdDPBiJSt2oVtk6dOlXhIyMjg6VLl/Lpp5+SlZVVo2uNHz+elJQU3n//fbZu3crQoUMZMmQIhw8fdp4zfPhw0tPTnR8ffvhhhWvccsst/PTTT6SkpPDll1+ycuVKJkyY4Hw8JyeHoUOH0qpVK1JTU3n++eeZPn068+fPr83bl99I0lRCl7Ns15n1WiJissvuBr/mcHIP/PSZ2dW4r1MH4Yv7jJCV+p4Rspq1Mh4b+DhMO3lmDZcCl4jUoVr9Wvuzzyr/D8Fms3HPPffQtm3bal/n9OnTfPLJJ/znP/+hX79+AEyfPp3//ve/vPHGGzz99NMA+Pj4EB0dXeU1duzYwaJFi9iwYQM9evQA4NVXX2XEiBG88MILxMbGsnDhQoqLi3n33Xfx9vamY8eOpKWlMXfu3AqhTGquQ2wwX25JV0dCF5FXVMr6A5mA1muJNAo+QXD5RFj2tDG61fEGsNbq96BSlcwDsOpF2PyhMXIF0GYAhLSETe9XbIbh+HPZrIpfi4hcgDr7F91qtfLAAw/w0ksvVfs5paWllJWV4etbca2Pn58fq1evdn69fPlyIiMjSUhI4J577uHkyZPOx9auXUuzZs2cQQtgyJAhWK1W1q1b5zynX79+eHt7O88ZNmwYu3bt4tSpUzV+r3LGmSYZ2SZXItWxZu8JSsrstA7zp01EoNnliAhAzwngEwLHd8KOL8yuxj1k7ofPJ8Kr3Y1QZSuFNgPhzsVw+38guEXVXQcdXQo1pVNE6kidLtjYt29flZsdn01QUBC9evVi5syZJCUlERUVxYcffsjatWuJj48HjCmEN9xwA3Fxcezbt4/HHnuMP/zhD6xduxYPDw+OHj1KZGTF6VCenp6EhoZy9OhRAI4ePUpcXFyFc6KiopyPNW/evFJtRUVFFBUVOb/OyTFGbkpKSigp0Z4oDu0i/AE4cCKfnPxC/Lwb794kjr+3pvz3991247+Jfu3Cm/T3oaHonpNq8fDHetkEPFY9j33Fc5S2+wNYav67UN1vQOZ+PNbMxbL131jsRmCytRmEre/D2C+61DinpAT6PHTm89/rPeXsj0kFuuekITW2+626ddQqbD3wwAMVvrbb7aSnp/PVV18xduzYGl3r/fff584776RFixZ4eHjQrVs3xowZQ2pqKgCjR492ntu5c2eSk5Np27Yty5cvZ/DgwbUpv1pmz57NjBkzKh1fsmQJ/v7+9fa6rsZuh0AvD/JKLLz32WJaucD+uCkpKWaXYAq7HRZv8QAs+Gcf4Ouv95tdUpPRVO85qT6v0jZcafXFK2M7mz58mvRmPc7/pLNoivdbQOFR2h/7Dy0zv8eCHYBjwcnsir6eUwHxsOU4bPna5CrdV1O858Q8jeV+KygoqNZ5tQpbmzZtqvC11WolIiKCF198scbdCNu2bcuKFSvIz88nJyeHmJgYbrrpJtq0aVPl+W3atCE8PJy9e/cyePBgoqOjycjIqHBOaWkpmZmZznVe0dHRHDt2rMI5jq/PthZs6tSpFUJlTk4OLVu2ZOjQoQQHB9foPbq7/zuRyuq9J2neJpkRl15kdjlnVVJSQkpKCldeeSVeXl5ml9PgdqTnkv3DWvy8rNx742B8vBrvKKS7aOr3nNSMNXgPrJnLpQXLKB3zJNRww/Emeb+d3IPH6rlYdn6CxW4DwNZ2CLa+jxDaohu9TC7P3TXJe05M09juN8est/OpVdhatmxZbZ52TgEBAQQEBHDq1CkWL1581tbshw4d4uTJk8TExADQq1cvsrKySE1NpXv37gAsXboUm81Gz549nec8/vjjlJSUOP9yUlJSSEhIqHIKIRhNOXx8fCod9/LyahR/wY1JxxYhrN57kt0Z+S7xvWmqf4er9hmNMa6IDyfQX3uiNaSmes9JDV1xH2yYj+XYVrwOfAcJf6jVZZrE/XZ8N6ycY+xPVh6yaD8c+j+CtUX3uluQLtXSJO45aTQay/1W3Rpq9e/RoEGDqmzxnpOTw6BBg2p0rcWLF7No0SIOHDhASkoKAwcOJDExkXHjxpGXl8fDDz/MDz/8wMGDB/nuu++47rrriI+PZ9iwYQAkJSUxfPhw7r77btavX8+aNWu49957GT16NLGxsQDcfPPNeHt7c9ddd/HTTz/x8ccf8/LLL1eaDim142ySoY6EjdpytXwXadz8Q41W8AArnjPm/kpFx3fB/90Fr18GW/9tBK2EETBhOdz8MbTobnaFIiIV1Gpka/ny5RQXF1c6XlhYyKpVq2p0rezsbKZOncqhQ4cIDQ1l5MiRzJo1Cy8vL0pLS9myZQv/+Mc/yMrKIjY2lqFDhzJz5swKo04LFy7k3nvvZfDgwVitVkaOHMkrr7zifDwkJIQlS5YwceJEunfvTnh4ONOmTVPb9zriCFs70nOw2exYrTWb+iL1L6ugmNSfjc6bA9TyXaTx6nUvrHsTjmyCvd9CuyvNrqhxyNhh7H/102dQviaLhKuM7oGxXcysTETknGoUtrZs2eL8fPv27c5ufwBlZWUsWrSIFi1a1KiAUaNGMWrUqCof8/PzY/Hixee9RmhoKB988ME5z0lOTq5xEJTqiQsPwMfTSkFxGT9nFhAXHmB2SfI7K/ecwGaH9lGBXNRcDV5EGq2AcLj0Lvj+VVj+LMQPqfHaLbdybLsxyrf9PzhDVuLV0P9RiEk2tTQRkeqoUdjq0qULFosFi8VS5XRBPz8/Xn311TorTlyDp4eVhOggthzKZkd6jsJWI7R8pzGFcKCmEIo0fr0nwfq34PCPsG8pxNdf591G69hPvwlZ5ZKuNUayojubV5eISA3VKGwdOHAAu91OmzZtWL9+PRERZ6YjeXt7ExkZiYeHOpw1RR1igtlyKJvtR3IY0TnG7HLkN2w2O8t3HwdgYKLClkijFxgJPe6EH/5mBI62g5rO6NbRrcZ73vHfM8c6XAf9HoHoTubVJSJSSzUKW61atQLAZrPVSzHiujrEqklGY7X5UBaZ+cUE+XjSvVXV3TdFpJHpPQk2vAO/roMDK6FNf7Mrql/pW4yQtfPL8gMW6Hi9EbKiOphZmYjIBal22Priiy/4wx/+gJeXF1988cU5z7322msvuDBxLb9tkiGNy7JdxqhW3/bheHmoIbKISwiOge5jYf18ozGEu4atI2nG+9v1VfkBC3T8ozFdMDLJzMpEROpEtcPW9ddfz9GjR4mMjOT6668/63kWi4WysrK6qE1cSGJ52ErPLiQzv5jQAG+TKxIHtXwXcVFXTIbU9+Dn1XBwNbTuY3ZFdefIJlj+HOz+pvyABTqNhH4PQ2SiqaWJiNSlaoet304d1DRC+b1AH09ahfnz88kCdqTncEV8uNklCXA8t4gth7IBtXwXcTkhLaDrbfDjO8bojzuErcMbjemCuxcZX1us0Ol/jJAV0d7c2kRE6kGt5hT985//pKioqNLx4uJi/vnPf15wUeKanJsbH9FUwsZiRXljjM4tQogM8jW5GhGpsT5TwOoFB1bALz+YXU3tHUqFhTfCWwONoGWxQvJNMHE9jHxLQUtE3Fatwta4cePIzs6udDw3N5dx48ZdcFHimpK0bqvRWeZs+a5RLRGX1KwldLnZ+HzFHHNrqY1fN8C/RsLbg2DPEiNkXTIGJm6AG+ZDeDuzKxQRqVc16kboYLfbsVTRhvbQoUOEhIRccFHimpwjWwpbjUJJmY2Ve4yRrQFq+S7iuvo+AJv+Bfu+g0M/wkU9zK7o/H5db2zKvO8742uLB1wyGvo+CGFtza1NRKQB1Shsde3a1bmp8eDBg/H0PPP0srIyDhw4wPDhw+u8SHENjvbvezPyKCotw8dTe66ZaePPp8gtLCU0wJtLLmpmdjkiUlvNWxujQWn/MtY73fJvsys6u19+MELW/mXG1xYPo/Z+D0JoG3NrExExQY3ClqMLYVpaGsOGDSMwMND5mLe3N61bt2bkyJF1WqC4jpgQX5r5e5FVUMKeY3l0aqFRTjM5Wr73bx+Bh7WJbIgq4q76PgCbPzCm4h3eCC26mV1RRT9/b4SsAyuMr62eRsjq+yCExplbm4iIiWoUtp566ikAWrduzejRo/Hx8amXosQ1WSwWkqKDWbv/JNvTcxS2TOZYr6UuhCJuIKwtdB4FWz6Clc/DmA/NrshwcA2seNbYeBmMkNXlFiMcNm9tamkiIo1BrRpkdOjQgbS0tErH161bx48//nihNYkLc0wlVEdCcx3OOs2uY7lYLdCvncKWiFvo95DRYGLX15C+2dxaDqyC966G90YYQcvqBd3vgPs2wrWvKGiJiJSrVdiaOHEiv/76a6Xjhw8fZuLEiRdclLguNcloHBwbGXe9uDnNtcG0iHsIb2ds/AvG6FZDs9uNYLVgBPzjaji4yghZPe6ESRvhmpeheauGr0tEpBGrVTfC7du3061b5fniXbt2Zfv27RdclLiu37Z/P1vXSql/y3Ya67UGqQuhiHvp+xBs/T/Y8V849hNEdaz/17TbjbVYy5+DX743jnl4Q7fbjX3AQi6q/xpERFxUrUa2fHx8OHbsWKXj6enpFToUStMTHxmIl4eF3MJSDp06bXY5TVJRaRlr9p4AtF5LxO1EJkLH643P63t0y26HfUvh3eHwz+uMoOXhDZfeDZPS4KoXFbRERM6jVmFr6NChTJ06tcLGxllZWTz22GNceeWVdVacuB5vTyvtIoMATSU0y7r9mZwuKSMyyMc5rVNE3Ei/h40/f/ocMnbW/fXtdtj7HbwzFN7/I/z6A3j4wGV/gvs3w1UvQEiLun9dERE3VKthqBdeeIF+/frRqlUrunbtChjt4KOionj//ffrtEBxPR1ig9mensOO9ByGdYw2u5wmZ1n5eq2BCZGaxinijqI6QtI1xlTClc/D/7xTN9d1hKwVz8KhDcYxT1+j8cUVkyE4pm5eR0SkCalV2GrRogVbtmxh4cKFbN68GT8/P8aNG8eYMWPw8vKq6xrFxTjWbakjoTmWl++vNTBRUwhF3Fa/R4ywte0T6P8oRLSv/bXsdtiTYmyYfLi8o7Cnr9H44or7IUi/NBMRqa1aL7AKCAhgwoQJdVmLuAl1JDTPgRP5HDiRj5eHhSviw80uR0TqS0wyJIww2sCvehFueLPm17DbjU2Slz8LRzYaxzz9fhOyouq2ZhGRJuiCulls376dX375heLi4grHr7322gsqSlybI2wdOnWa7NMlhPhptLOhODYyvrR1KEG++r6LuLX+jxhha+v/Gp8HX1y959ntsHuRMZJ1ZJNxzNMPLr3LCFmB6mIqIlJXahW29u/fzx//+Ee2bt2KxWLBbrcDONeHlJWV1V2F4nJC/L1o0cyPw1mn2ZmeQ882YWaX1GT8dr2WiLi52K7QbhjsWQyr5sJV8859vt1uhLMVz53ZFNnLHy4dD70nQaCmHouI1LVadSO8//77iYuLIyMjA39/f3766SdWrlxJjx49WL58eR2XKK4oSVMJG1xBcSnr9mcCWq8l0mT0f8T4c/OHkPVz1efY7bDjS3izL3x0sxG0vAKMUazJW2HoTAUtEZF6UquRrbVr17J06VLCw8OxWq1YrVb69OnD7NmzmTRpEps2barrOsXFdIgN5tsdx9QkowF9v/ckxWU2Wob60TYi0OxyRKQh7EmB5m3g1H481swDy2+2X1n+HGTsgJN74dhW45h3IFx2N/S6DwI060BEpL7VKmyVlZURFGTspRQeHs6RI0dISEigVatW7Nq1q04LFNfkWLe146jCVkNRy3eRJsjqAaf2A2DZ8iF+SV3BboN/j4OfPj1znncg9PwT9LoX/ENNKlZEpOmpVdjq1KkTmzdvJi4ujp49ezJnzhy8vb2ZP38+bdq0qesaxQU5wtbuo3mUlNnw8qjVjFWpJrvd7myOofVaIk2IYxrhsllYbKV0+eUdPOc9AwUnjOPeQeUha6JCloiICWoVtp544gny8/MB+Otf/8rVV19N3759CQsL4+OPP67TAsU1XdTcjyAfT3KLStl3PI/E6GCzS3Jru4/lcSS7EB9PK5erIYlI09L/EWO91qZ/EZn7k3HMw8dYk3X5PQpZIiImqlXYGjZsmPPz+Ph4du7cSWZmJs2bN9f0JQHAarWQFBPM+oOZbD+So7BVzxxTCHu1DcPP28PkakSkwV33Ova0D7DYbdgtHlge2gV+zc2uSkSkyavV3K7jx49XOhYaGorFYmHr1q0XXJS4hw6x5eu21JGw3jmmEA5K1BRCkSZpxRwsdhtlFk8s9jJY/5bZFYmICLUMW507d+arr76qdPyFF17gsssuu+CixD0kxRhNVNT+vX5lny7hx59PATCgvcKWSJOzYg4sm0VZv7/wZZd3Kev3F1g2yzguIiKmqlXYeuCBBxg5ciT33HMPp0+f5vDhwwwePJg5c+bwwQcf1HWN4qI6xIQAsP1IjnPja6l7q/ecoMxmp21EABeH+Ztdjog0pPKgxcDHsfV9CMD4c+DjClwiIo1ArdZsPfLII1x55ZXcdtttJCcnk5mZSc+ePdmyZQvR0dF1XaO4qHZRgXhYLZwqKOFYThHRIb5ml+SWftvyXUSaGFuZEaz6PwIlJWeOO7oU2srMqUtERIBahi0wGmN06tSJTz75BICbbrpJQUsq8PXyoG1EALuP5bE9PVthqx7YbHaW7zLWUGq9lkgTNHDq2R9zBC4RETFNraYRrlmzhuTkZPbs2cOWLVt44403uO+++7jppps4depUXdcoLsyx39b2I1q3VR9+OpLDibwiArw96NFa7Z1FREREGpNaha1BgwZx00038cMPP5CUlMT48ePZtGkTv/zyC507d67rGsWFOToSqklG/Vha3oWwT7twvD21cbSIiIhIY1KraYRLliyhf//+FY61bduWNWvWMGvWrDopTNyDo0nGjvRckytxT1qvJSIiItJ41ehX4SNGjCA7O9sZtJ599lmysrKcj586dYoPP/ywTgsU1+Zo/37wZD55RaUmV+NeTuYVsflQFgADFLZEREREGp0aha3FixdTVFTk/PqZZ54hMzPT+XVpaSm7du2qu+rE5YUF+hAV7IPdDruOaiphXVq55zh2u7EuTs1HRERERBqfGoWt3++VpL2TpDqcTTI0lbBOLd1pdCEcmBhhciUiIiIiUhWtqJd6l6SOhHWutMzGyt3lYUtTCEVEREQapRqFLYvFgsViqXRM5FzUkbDupf2aRfbpEkL8vOjSspnZ5YiIiIhIFWrUjdBut3PHHXfg4+MDQGFhIX/+858JCAgAqLCeS8TBMY1w19Ecymx2PKwK6BfK0YWwf/sIPD00QC0iIiLSGNUobI0dO7bC17feemulc26//fYLq0jcTquwAPy9PSgoLuPAiXziIwPNLsnlab2WiIiISONXo7C1YMGC+qpD3JiH1UJCdBCbfslie3qOwtYFOppdyI70HCwW6NdOYUtERESksdL8I2kQHdQko84sL59CeMlFzQgL9DG5GhERERE5G9PDVm5uLpMnT6ZVq1b4+fnRu3dvNmzYUOW5f/7zn7FYLMybN6/C8datWzubdzg+nn322QrnbNmyhb59++Lr60vLli2ZM2dOfb0lqYKjScYONcm4YI71WoMS1YVQREREpDGr0TTC+jB+/Hi2bdvG+++/T2xsLP/6178YMmQI27dvp0WLFs7zPvvsM3744QdiY2OrvM5f//pX7r77bufXQUFBzs9zcnIYOnQoQ4YM4e9//ztbt27lzjvvpFmzZkyYMKH+3pw4Odu/K2xdkOJSG6v3nADU8l1ERESksTN1ZOv06dN88sknzJkzh379+hEfH8/06dOJj4/njTfecJ53+PBh7rvvPhYuXIiXl1eV1woKCiI6Otr54eiQCLBw4UKKi4t599136dixI6NHj2bSpEnMnTu33t+jGBKjg7BY4HhuERm5hWaX47I2HMwkv7iM8EAfOpaPFoqIiIhI42TqyFZpaSllZWX4+vpWOO7n58fq1asBsNls3HbbbTz88MN07NjxrNd69tlnmTlzJhdffDE333wzU6ZMwdPTeHtr166lX79+eHt7O88fNmwYzz33HKdOnaJ58+aVrldUVFShlX1OjjEiU1JSQklJSe3fdBPlZYG4MH/2nyhg26+n6NsuvMFrcPy9ufLf33fbjwLQr10YZWWllJWZXJCckzvcc+I6dL9JQ9M9Jw2psd1v1a3D1LAVFBREr169mDlzJklJSURFRfHhhx+ydu1a4uPjAXjuuefw9PRk0qRJZ73OpEmT6NatG6GhoXz//fdMnTqV9PR058jV0aNHiYuLq/CcqKgo52NVha3Zs2czY8aMSseXLFmCv79/rd9zUxZitwJWPl++gdw9dtPqSElJMe21L9RXaR6AheD8X/n661/MLkeqyZXvOXE9ut+koemek4bUWO63goKCap1n+pqt999/nzvvvJMWLVrg4eFBt27dGDNmDKmpqaSmpvLyyy+zceNGLJazb4T7wAMPOD9PTk7G29ubP/3pT8yePdu5AXNNTZ06tcJ1c3JyaNmyJUOHDiU4WNO3auOXgP1s+nYvtpAWjBiR3OCvX1JSQkpKCldeeeVZp6M2Zr9kFnBs7Wo8rBYm3TiEIF/Xew9Njavfc+JadL9JQ9M9Jw2psd1vjllv52N62Grbti0rVqwgPz+fnJwcYmJiuOmmm2jTpg2rVq0iIyODiy++2Hl+WVkZDz74IPPmzePgwYNVXrNnz56UlpZy8OBBEhISiI6O5tixYxXOcXwdHR1d5TV8fHyqDGpeXl6N4i/YFXVqaYwg7jyWZ+r30FX/DlfvOwVAj1bNCQ3S6KorcdV7TlyT7jdpaLrnpCE1lvutujWY3vrdISAggJiYGE6dOsXixYu57rrruO2229iyZQtpaWnOj9jYWB5++GEWL1581mulpaVhtVqJjDS6tfXq1YuVK1dWmFuZkpJCQkJClVMIpX50LO9IuP94HoUlWmxUU46W7wPV8l1ERETEJZg+srV48WLsdjsJCQns3buXhx9+mMTERMaNG4eXlxdhYWEVzvfy8iI6OpqEhATAaH6xbt06Bg4cSFBQEGvXrmXKlCnceuutziB18803M2PGDO666y4effRRtm3bxssvv8xLL73U4O+3KYsI8iEswJuT+cXsOprLJS2bmV2SyzhdXMbafScBtXwXERERcRWmj2xlZ2czceJEEhMTuf322+nTpw+LFy+u9tCcj48PH330Ef3796djx47MmjWLKVOmMH/+fOc5ISEhLFmyhAMHDtC9e3cefPBBpk2bpj22GpjFYnFubqz9tmrmh/0nKSq10aKZH+2jAs0uR0RERESqwfSRrVGjRjFq1Khqn//7dVrdunXjhx9+OO/zkpOTWbVqVU3LkzrWISaYVXtOsP2IwlZNOKYQDkiIOGezGBERERFpPEwf2ZKmxTGytUMjW9Vmt9tZurN8vZamEIqIiIi4DIUtaVBJMWfCls1m3l5brmTf8TwOnTqNt4eV3vFh53+CiIiIiDQKClvSoNqEB+DtaSW/uIxfMqu3GVxTt2zncQB6tgnF39v0mb8iIiIiUk0KW9KgPD2sJEYHAZpKWF2O9VqD1PJdRERExKUobEmDS4pWR8Lqyi0sYf2BTEDrtURERERcjcKWNDhn+3d1JDyvNXtPUGqzExceQOvwALPLEREREZEaUNiSBqe9tqrPsV5rQEKEyZWIiIiISE0pbEmDc6zZSs8u5FR+scnVNF52u13rtURERERcmMKWNLggXy8uDvUH1CTjXLan55CRW4SflweXxYWaXY6IiIiI1JDClpiiQ4ymEp7PsvKNjK+ID8fH08PkakRERESkphS2xBRat3V+y3YZ67UGJmq9loiIiIgrUtgSUyTFqCPhuZzKL2bTL6cAGKCW7yIiIiIuSWFLTOEY2dqbkUdRaZnJ1TQ+K/ccx2Y3mom0aOZndjkiIiIiUgsKW2KK2BBfQvy8KLXZ2XMsz+xyGh3Hei2NaomIiIi4LoUtMYXFYnE2yVBHworKbHZW7C5fr6X9tURERERclsKWmCZJHQmrtPlQFqcKSgjy9aRbq+ZmlyMiIiIitaSwJaZxdiRUk4wKlpdPIezXPgIvD/0nKiIiIuKq9JOcmOa30wjtdrvJ1TQezpbvWq8lIiIi4tIUtsQ08ZGBeHlYyCks5XDWabPLaRQycgrZejgbgP7ttV5LRERExJUpbIlpvD2txEcGAZpK6LC8vDFG8kUhRAT5mFyNiIiIiFwIhS0xVQc1yahg+S61fBcRERFxFwpbYipHkwy1f4eSMhurdp8AYFCiwpaIiIiIq1PYElMlxZRPI1TY4seDp8gtKiUswJvkFiFmlyMiIiIiF0hhS0zlmEb4a+ZpcgpLTK7GXI4phP3bR2C1WkyuRkREREQulMKWmKqZvzctmvkBsDM91+RqzLXMsV5LUwhFRERE3ILClpguydEk40i2yZWY59CpAnYfy8Nqgf7t1PJdRERExB0obInpOmjdFsvLNzLu3qo5If5eJlcjIiIiInVBYUtM5+hI2JTD1rKdavkuIiIi4m4UtsR0HWKMznu7j+VRUmYzuZqGV1hSxpp9Rsv3gQpbIiIiIm5DYUtMd1FzPwJ9PCkutbH/eL7Z5TS4dQcyKSyxER3s62yFLyIiIiKuT2FLTGe1Wn6z31bTa5LhmEI4MDECi0Ut30VERETchcKWNAqO/bZ2NLH273a7/UzLd00hFBEREXErClvSKDibZBxpWk0yDpzI5+eTBXh5WLgiPtzsckRERESkDilsSaPg3GsrPQe73W5yNQ1nWXnL98viQgn08TS5GhERERGpSwpb0ii0jwrCw2ohM7+YYzlFZpfTYJaXTyFUF0IRERER96OwJY2Cr5cHbSMCANjRRPbbyi8qZd3+TAAGJipsiYiIiLgbhS1pNH47lbApWLP3BMVlNi4O9adNeIDZ5YiIiIhIHVPYkkbD0ZGwqTTJcKzXGpiglu8iIiIi7khhSxoNR0fCpjCN0G63O9drDdAUQhERERG3pLAljYZjGuGBk/nkF5WaXE392nUsl/TsQny9rPRqE2Z2OSIiIiJSDxS2pNEID/QhMsgHux12HnXvzY2X7jRGtXq3DcfXy8PkakRERESkPihsSaPi3NzYzacSLt95Zr2WiIiIiLgnhS1pVBxNMtx53VZ2QQmpv5wCYID21xIRERFxWwpb0qgkNYGOhKv2HqfMZqddZCAtQ/3NLkdERERE6onpYSs3N5fJkyfTqlUr/Pz86N27Nxs2bKjy3D//+c9YLBbmzZtX4XhmZia33HILwcHBNGvWjLvuuou8vLwK52zZsoW+ffvi6+tLy5YtmTNnTn29JbkAjmmEO4/mUGazm1xN/XCs19JGxiIiIiLuzfSwNX78eFJSUnj//ffZunUrQ4cOZciQIRw+fLjCeZ999hk//PADsbGxla5xyy238NNPP5GSksKXX37JypUrmTBhgvPxnJwchg4dSqtWrUhNTeX5559n+vTpzJ8/v97fn9RM67AA/Lw8KCyxcfBkvtnl1Dmbzc6K8v21Bmi9loiIiIhbMzVsnT59mk8++YQ5c+bQr18/4uPjmT59OvHx8bzxxhvO8w4fPsx9993HwoUL8fLyqnCNHTt2sGjRIt5++2169uxJnz59ePXVV/noo484cuQIAAsXLqS4uJh3332Xjh07Mnr0aCZNmsTcuXMb9P3K+XlYLSTGBAHuOZVw6+FsTuYXE+jjSY9WoWaXIyIiIiL1yNSwVVpaSllZGb6+vhWO+/n5sXr1agBsNhu33XYbDz/8MB07dqx0jbVr19KsWTN69OjhPDZkyBCsVivr1q1zntOvXz+8vb2d5wwbNoxdu3Zx6tSp+nhrcgGc67bcsEnGsvKNjPu2C8fb0/SBZRERERGpR55mvnhQUBC9evVi5syZJCUlERUVxYcffsjatWuJj48H4LnnnsPT05NJkyZVeY2jR48SGVlx7YunpyehoaEcPXrUeU5cXFyFc6KiopyPNW/evNJ1i4qKKCoqcn6dk2P84F9SUkJJSUkt37FUR0JkAAA/Hc6q0++141pm/v0t3XkMgL7xYbqPmoDGcM9J06H7TRqa7jlpSI3tfqtuHaaGLYD333+fO++8kxYtWuDh4UG3bt0YM2YMqamppKam8vLLL7Nx40YsFkuD1jV79mxmzJhR6fiSJUvw91cHufqUlQvgSdrBE3z99dd1fv2UlJQ6v2Z15JbA1kMegIXSXzfz9bHNptQhDc+se06aJt1v0tB0z0lDaiz3W0FBQbXOMz1stW3blhUrVpCfn09OTg4xMTHcdNNNtGnThlWrVpGRkcHFF1/sPL+srIwHH3yQefPmcfDgQaKjo8nIyKhwzdLSUjIzM4mOjgYgOjqaY8eOVTjH8bXjnN+bOnUqDzzwgPPrnJwcWrZsydChQwkODq6T9y5VKyguZd5PS8kpsXBZv8GEB/rUyXVLSkpISUnhyiuvrLT2ryF8tukI9h+30SEmiDHX92rw15eGZ/Y9J02L7jdpaLrnpCE1tvvNMevtfEwPWw4BAQEEBARw6tQpFi9ezJw5cxg5ciRDhgypcN6wYcO47bbbGDduHAC9evUiKyuL1NRUunfvDsDSpUux2Wz07NnTec7jjz9OSUmJ8y8nJSWFhISEKqcQAvj4+ODjU/mHfC8vr0bxF+zOQry8iAsLYP+JfPYcP01M88A6vb5Zf4cr954EYFBilO6hJkb/bkhD0v0mDU33nDSkxnK/VbcG08PW4sWLsdvtJCQksHfvXh5++GESExMZN24cXl5ehIWFVTjfy8uL6OhoEhISAEhKSmL48OHcfffd/P3vf6ekpIR7772X0aNHO9vE33zzzcyYMYO77rqLRx99lG3btvHyyy/z0ksvNfj7lepJig1m/4l8dqTn0K+967dILy2zsXK30fJd+2uJiIiINA2mt0PLzs5m4sSJJCYmcvvtt9OnTx8WL15co8S6cOFCEhMTGTx4MCNGjKBPnz4V9tAKCQlhyZIlHDhwgO7du/Pggw8ybdq0CntxSePSwc06Em78JYucwlKa+XvRpWUzs8sRERERkQZg+sjWqFGjGDVqVLXPP3jwYKVjoaGhfPDBB+d8XnJyMqtWrappeWISZ9hyk722HC3f+7ePwMPasM1eRERERMQcpo9siVSlQ6wRtvYdz6OwpMzkai7csp1G2BqYoCmEIiIiIk2FwpY0SpFBPoQFeGOzw+5juWaXc0HSs0+z82guFosxsiUiIiIiTYPCljRKFouFJDeZSrh8l9EYo2vLZjQP8Da5GhERERFpKApb0mg5phK6epOMpZpCKCIiItIkKWxJo+VokrHDhcNWUWkZa/aeANTyXURERKSpUdiSRssxsrUjPRebzW5yNbWz4cApCorLiAjycYZHEREREWkaFLak0WoTHoC3p5W8olJ+PVVgdjm14mj5PjAhAqtavouIiIg0KQpb0mh5elhJiAoCXLdJhlq+i4iIiDRdClvSqLnyuq2DJ/LZfyIfT6uFK9qFm12OiIiIiDQwhS1p1JJiyke2XDBsLS+fQtijdXOCfb1MrkZEREREGprCljRqHWJDANecRrisfH+tQepCKCIiItIkKWxJo5ZYPrJ1JLuQrIJik6upvtPFZazdfxLQei0RERGRpkphSxq1YF8vLg71B1xrKuH3+05QXGqjRTM/4iMDzS5HREREREygsCWNnnPdlgtNJXS2fE+MwGJRy3cRERGRpkhhSxq9DjHl67ZcZGTLbrezbKexXktTCEVERESaLoUtafQ6xDrav+eaXEn17M3I43DWabw9rfRuq5bvIiIiIk2VwpY0eo5phHszcikutZlczfktLd/IuFebMPy8PUyuRkRERETMorAljV6LZn4E+3pSUmZnT0bjH91yrtdKiDC5EhERERExk8KWNHoWi8VlphLmFJbw48FTAAzU/loiIiIiTZrClrgEZ5OMRt6RcM2eE5Ta7LSJCKBVWIDZ5YiIiIiIiRS2xCU427+nZ5tcybmdmUKoUS0RERGRpk5hS1zCb6cR2u12k6upms1mZ9kutXwXEREREYPClriEdpFBeHlYyD5dwpHsQrPLqdL29ByO5xbh7+3BpXHNzS5HREREREymsCUuwdvTSnxk+VTCRrpua1l5y/cr4sPx8VTLdxEREZGmTmFLXIZz3VZjDVvl67UGqQuhiIiIiKCwJS6kQ4xj3VbjC1uZ+cVs+jULgAHaX0tEREREUNgSF+JokrG9EYatlbuPY7dDYnQQMSF+ZpcjIiIiIo2Awpa4DMfI1i+ZBeQWlphcTUXOlu+aQigiIiIi5RS2xGU08/cmNsQXgJ1Hc02u5owym50Vu42W71qvJSIiIiIOClviUpxTCRtRk4y0X7PIKigh2NeTri2bmV2OiIiIiDQSClviUhxTCRtT2HK0fO/XPgJPD/0nJSIiIiIG/WQoLiUppvE1yXCu10rQFEIREREROUNhS1yKYxrhrmO5lJbZTK4GjuUU8tORHCwW6K+W7yIiIiLyGwpb4lJaNvcn0MeT4lIb+0/km10OK3YZjTGSL2pGeKCPydWIiIiISGOisCUuxWq1kBgdBDSOdVtLdzqmEGpUS0REREQqUtgSl+OYSrjD5HVbxaU2Vu89AWi9loiIiIhUprAlLqdDI2mS8ePPmeQVlRIe6E3nFiGm1iIiIiIijY/Clric3+61ZbfbTatjefl6rf7tI7FaLabVISIiIiKNk8KWuJz2UUFYLXAyv5iM3CLT6nCu10rUei0RERERqUxhS1yOr5cHbSMCAfOmEv6aWcDejDw8rBb6xitsiYiIiEhlClvikn47ldAMy8s3Mu5+cXNC/L1MqUFEREREGjeFLXFJSSY3yVhWvl5rgKYQioiIiMhZKGyJS3J0JDSj/XthSRnf7zNavg9KVMt3EREREamawpa4JMfI1oET+RQUlzboa6/df5LCEhsxIb4kRAU16GuLiIiIiOtQ2BKXFBHkQ2SQD3Y77Dya26Cvvby8C+GAhEgsFrV8FxEREZGqmR62cnNzmTx5Mq1atcLPz4/evXuzYcMG5+PTp08nMTGRgIAAmjdvzpAhQ1i3bl2Fa7Ru3RqLxVLh49lnn61wzpYtW+jbty++vr60bNmSOXPmNMj7k/rjXLfVgE0y7Ha7c73WwASt1xIRERGRszM9bI0fP56UlBTef/99tm7dytChQxkyZAiHDx8GoH379rz22mts3bqV1atX07p1a4YOHcrx48crXOevf/0r6enpzo/77rvP+VhOTg5Dhw6lVatWpKam8vzzzzN9+nTmz5/foO9V6pajI2FDrtvafyKfXzIL8PawckV8eIO9roiIiIi4Hk8zX/z06dN88skn/Oc//6Ffv36AMZL13//+lzfeeIOnn36am2++ucJz5s6dyzvvvMOWLVsYPHiw83hQUBDR0dFVvs7ChQspLi7m3Xffxdvbm44dO5KWlsbcuXOZMGFC/b1BqVcdTOhIuKx8CmHPNqEE+Jj6n4+IiIiINHKm/rRYWlpKWVkZvr6+FY77+fmxevXqSucXFxczf/58QkJCuOSSSyo89uyzzzJz5kwuvvhibr75ZqZMmYKnp/H21q5dS79+/fD29naeP2zYMJ577jlOnTpF8+bNK71WUVERRUVFzq9zcowf6EtKSigpKan9m5Y60y7CH4Cd6TkUFhXjYT33+inH39uF/P0t3XEMgL7xYboP5Lzq4p4TqS7db9LQdM9JQ2ps91t16zA1bAUFBdGrVy9mzpxJUlISUVFRfPjhh6xdu5b4+HjneV9++SWjR4+moKCAmJgYUlJSCA8/M4Vr0qRJdOvWjdDQUL7//numTp1Keno6c+fOBeDo0aPExcVVeO2oqCjnY1WFrdmzZzNjxoxKx5csWYK/v3+dvH+5MDY7eFk9OF1i4/3PviHSr3rPS0lJqdXrFZbBugMegAVL+k98/fVPtbqOND21vedEakP3mzQ03XPSkBrL/VZQUFCt8yx2u91ez7Wc0759+7jzzjtZuXIlHh4edOvWjfbt25OamsqOHTsAyM/PJz09nRMnTvDWW2+xdOlS1q1bR2Rk1Xscvfvuu/zpT38iLy8PHx8fhg4dSlxcHG+++abznO3bt9OxY0e2b99OUlJSpWtUNbLVsmVLTpw4QXBwcB1/F6S2/ufNdWw+lM3Lo5IZ0bnqaaQOJSUlpKSkcOWVV+Ll5VXj10rZnsH/+zCNVqH+fDulT21LlibkQu85kZrQ/SYNTfecNKTGdr/l5OQQHh5Odnb2ObOB6YtO2rZty4oVK8jPzycnJ4eYmBhuuukm2rRp4zwnICCA+Ph44uPjufzyy2nXrh3vvPMOU6dOrfKaPXv2pLS0lIMHD5KQkEB0dDTHjh2rcI7j67Ot8/Lx8cHHx6fScS8vr0bxFyyGji1C2Hwom10Z+VxXzb+X2v4drtp3EoCBiZG6B6RG9O+GNCTdb9LQdM9JQ2os91t1azC9G6FDQEAAMTExnDp1isWLF3Pddded9VybzVZh1On30tLSsFqtzpGvXr16sXLlygpzK1NSUkhISKhyCqG4jqQGapJht9tZtrO85Xti1SOqIiIiIiK/ZXrYWrx4MYsWLeLAgQOkpKQwcOBAEhMTGTduHPn5+Tz22GP88MMP/Pzzz6SmpnLnnXdy+PBhbrzxRsBofjFv3jw2b97M/v37WbhwIVOmTOHWW291Bqmbb74Zb29v7rrrLn766Sc+/vhjXn75ZR544AEz37rUAUdHwvpu/74jPZejOYX4elnpGRdar68lIiIiIu7B9GmE2dnZTJ06lUOHDhEaGsrIkSOZNWsWXl5elJWVsXPnTv7xj39w4sQJwsLCuPTSS1m1ahUdO3YEjOl+H330EdOnT6eoqIi4uDimTJlSIUiFhISwZMkSJk6cSPfu3QkPD2fatGlq++4GEqODsFjgWE4RJ/KKCA+sPPWzLizbZbR8v6JtOL5eHvXyGiIiIiLiXkwPW6NGjWLUqFFVPubr68unn356zud369aNH3744byvk5yczKpVq2pVozReAT6etA4L4MCJfHak59C3XUS9vM7y8rClKYQiIiIiUl2mTyMUuVD1PZUwq6CY1J9PATAgoX7CnIiIiIi4H4UtcXkdYsubZBypn7C1cs8JbHZoHxXIRc21x5qIiIiIVI/Clri8DvXckXD5zvIphAmaQigiIiIi1aewJS7P0f593/F8CkvK6vTaNpud5bvV8l1EREREak5hS1xeVLAPoQHelNns7DmWV6fX3nI4m8z8YoJ8POneSnuyiYiIiEj1KWyJy7NYLL+ZSphdp9deWj6FsG/7cLw89J+LiIiIiFSffnoUt5AUEwTUfZMMR8v3AVqvJSIiIiI1pLAlbsHRkXBHem6dXfN4bhFbDhkjZWr5LiIiIiI1pbAlbqFDTAhgdCS02ex1cs0V5Y0xOrcIITLIt06uKSIiIiJNh8KWuIU2EQF4e1rJKyrl0KnTdXLNZc6W7xrVEhEREZGaU9gSt+DlYaV9VCBQN00ySspsrNxjjGwNUMt3EREREakFhS1xG2c6El74uq2NP58it7CU0ABvLrmo2QVfT0RERESaHoUtcRvOsFUHHQmX7TJGtfq3j8DDarng64mIiIhI06OwJW4jKcbRkfDCw9aZlu9aryUiIiIitaOwJW4jqbz9++Gs02QXlNT6OoezTrPzaC5WC/Rrp7AlIiIiIrWjsCVuI9jXi5ahfoDRAr62HKNaXS9uTvMA7zqpTURERESaHoUtcStnmmTUPmwt22ms11LLdxERERG5EApb4laSLrBJRlFpGWv2ngBgoFq+i4iIiMgFUNgSt9LhAptkrNufyemSMiKDfJzXEhERERGpDU+zCxCpSx3Km2TsyciluNSGt2fNfp+wrHy91sCESCwWtXwXERGpLzabjeLiYrPLEBdRUlKCp6cnhYWFlJWV1fvreXl54eHhccHXUdgSt9KimR/Bvp7kFJayNyPPGb6qa3n5/loDE7VeS0REpL4UFxdz4MABbDab2aWIi7Db7URHR/Prr7822C/EmzVrRnR09AW9nsKWuBWLxUJSTDDrDmSyIz2nRmHrwIl8DpzIx8vDwhXx4fVYpYiISNNlt9tJT0/Hw8ODli1bYrVqVYucn81mIy8vj8DAwHq/Z+x2OwUFBWRkGDOeYmJian0thS1xOx1ijbC1PT2HkTV4nqPl+6WtQwny9aqf4kRERJq40tJSCgoKiI2Nxd/f3+xyxEU4pp36+vo2SED38zO2E8rIyCAyMrLWUwr1qwRxOx1q2ZFw6c4z67VERESkfjjW23h7ay9LadwcvwwoKSmp9TUUtsTtJP1mry273V6t5xQUl7Jufyag9VoiIiINQY2opLGri3tUYUvcTruoQDytFrJPl5CeXVit53y/9yTFZTYuau5H24jAeq5QRERERJoChS1xOz6eHsRHGoGpulMJHS3fByWq5buIiIi4FovFwueff14v127dujXz5s2rl2s3BQpb4pYcXQi3V2NzY7vdzjKt1xIREXEpZTY7a/ed5D9ph1m77yRltuotHaitO+64A4vFUulj+PDh9fq6vzV9+nS6dOlS6Xh6ejp/+MMfADh48CAWi4W0tLQGq6u+bNq0iRtvvJGoqCj8/f3p3r07EyZMYPfu3cCZ9+r4CAsLY+jQoWzatMl5jbOFxbN9L+uauhGKW+oQE8ynHGZHNcLW7mN5HMkuxMfTyuVtwhqgOhEREbkQi7alM+O/2yssF4gJ8eWpazowvFPt23Sfz/Dhw1mwYEGFYz4+PvX2etUVHR1tdgl17ssvv2TkyJEMGzaMhQsXEhcXx4EDB/jmm2948skn+fjjj53nfvvtt3Ts2JFDhw4xadIk/vCHP7Bz506aNWtm3hsop5EtcUsdYqo/suWYQtirbRh+3he+U7iIiIjUn0Xb0rnnXxsrrcs+ml3IPf/ayKJt6fX22j4+PkRHR1f4aN68OQDLly/H29ubVatWOc+fM2cOkZGRHDt2zKh90SL69OlDs2bNCAsL4+qrr2bfvn0VXuPQoUOMGTOG0NBQAgIC6NGjB+vWreO9995jxowZbN682TmS89577wEVpxHGxcUB0LVrVywWCwMGDABgwIABTJ48ucJrXX/99dxxxx3OrzMyMrjmmmvw8/MjLi6OhQsXVuv78vbbb5OUlISvry+JiYn87W9/cz7mGH369NNPGThwIP7+/lxyySWsXbv2rNcrKChg3LhxjBgxgi+++IIhQ4YQFxdHjx49eP7553nzzTcrnB8WFkZ0dDQ9evTghRde4NixY6xbt65atdc3jWyJW3J0JPz5ZAG5hSXn3DfLMYVwUKKmEIqIiDQ0u93O6ZKyap1bZrPz1Bc/UdWEQTtgAaZ/sZ0r4sPxsJ5/Dbafl0edrdV2hJnbbruNzZs3s3//fp588kn+/e9/ExUVBUB+fj4PPPAAycnJ5OXlMW3aNP74xz+SlpaG1WolLy+P/v3706JFC7744guio6PZuHEjNpuNm266iW3btrFo0SK+/fZbAEJCQirVsX79ei677DLnaE9NWuzfcccdHDlyhGXLluHl5cWkSZOcG/uezcKFC5k2bRqvvfYaXbt2ZdOmTdx9990EBAQwduxY53mPP/44L7zwAu3atePxxx9nzJgx7N27F0/PynFk8eLFnDhxgkceeaTK1zzXiJVjf6zi4uJqvOP6p7Albql5gDcxIb6kZxey82gul7YOrfK8nMISfvz5FAAD2itsiYiINLTTJWV0mLa4Tq5lB47mFNJ5+pJqnb/9r8Pw967+j8NffvklgYEVuxY/9thjPPbYYwA8/fTTpKSkMGHCBLZt28bYsWO59tprneeOHDmywnPfffddIiIi2L59O506deKDDz7g+PHjbNiwgdBQ42eX+Ph45/mBgYF4enqec9pgRISxhY1jtKe6du/ezTfffMP69eu59NJLAXjnnXdISko65/OeeuopXnzxRW644QbAGFnbvn07b775ZoWw9dBDD3HVVVcBMGPGDDp27MjevXtJTEysdM09e/YAVPnYuWRlZTFz5kwCAwO57LLLavTc+qKwJW6rQ0ww6dmF7EjPOWvYWrX7BGU2O20jArg4TLvYi4iIyNkNHDiQN954o8IxRygCY6PmhQsXkpycTKtWrXjppZcqnLtnzx6mTZvGunXrOHHiBDabDYBffvmFTp06kZaWRteuXStcs6Hs2LEDT09Punfv7jyWmJh4zlGk/Px89u3bx1133cXdd9/tPF5aWlpp1C05Odn5eUyMsa4uIyOjykBV3X1SHXr37o3VaiU/P582bdrw8ccfO0cTzaawJW6rQ2ww3+3MOGf7d8d6LXUhFBERMYeflwfb/zqsWueuP5DJHQs2nPe898ZdymVx5w8sfl41W6sdEBBQYaSpKt9//z0AmZmZZGZmEhAQ4HzsmmuuoVWrVrz11lvExsZis9no1KmTc8qbYwpcfbBarZVCTElJyQVdMy8vD4C33nqLnj17VnjMw6Pi99bL68ySDsfUTUfY/L327dsDsHPnTnr16nXeOj7++GM6dOhAWFhYpXAYHBxMdnZ2pedkZWVVOQ2zrqlBhrit8zXJsNnsLN91HICBWq8lIiJiCovFgr+3Z7U++raLICbEl7OtsrJgdCXs2y6iWter67019+3bx5QpU5zhY+zYsc5AcfLkSXbt2sUTTzzB4MGDSUpK4tSpUxWen5ycTFpaGpmZmVVe39vbm7Kyc69vc6zR+v15ERERpKefaR5SVlbGtm3bnF8nJiZSWlpKamqq89iuXbvIyso662tFRUURGxvL/v37iY+Pr/DhaNRRG0OHDiU8PJw5c+ZU+fjva2rZsiVt27atchQuISGhwnty2LhxozPU1SeFLXFbjiYZu47mUlpW+TcnPx3J4UReEQHeHmedZigiIiKNh4fVwlPXdACoFLgcXz91TYdqNceojaKiIo4ePVrh48SJE4ARXm699VaGDRvGuHHjWLBgAVu2bOHFF18EoHnz5oSFhTF//nz27t3L0qVLeeCBBypcf8yYMURHR3P99dezZs0a9u/fzyeffOLs3Ne6dWsOHDhAWloaJ06coKioqFKNkZGR+Pn5sWjRIo4dO+Yc1Rk0aBBfffUVX331FTt37uSee+6pEFoSEhIYPnw4f/rTn1i3bh2pqamMHz/+vKNtM2bMYPbs2bzyyivs3r2brVu3smDBAubOnVvr73NAQABvv/02X331Fddeey3ffvstBw8eZNOmTTz66KP8+c9/rva1pkyZwldffcWsWbPYsWMH27Zt4/HHH2ft2rXcf//9ta6xuhS2xG1dHOpPgLcHRaU2DpzIr/T40vIuhH3ahePtqf8UREREXMHwTjG8cWs3okN8KxyPDvHljVu71es+W4sWLSImJqbCR58+fQCYNWsWP//8s7MteUxMDPPnz+eJJ55g8+bNWK1WPvroI1JTU+nUqRNTpkzh+eefr3B9b29vlixZQmRkJCNGjKBz5848++yzzil5I0eOZPjw4QwcOJCIiAg+/PDDSjV6enryyiuv8OabbxIbG8t1110HwJ133snYsWO5/fbb6d+/P23atGHgwIEVnrtgwQJiY2Pp378/N9xwAxMmTCAy8tyzf8aPH8/bb7/NggUL6Ny5M/379+e99967oJEtgOuuu47vv/8eLy8vbr75Zjp06MD48ePJzs7m6aefrvZ1evfuzTfffMM333zDFVdcwYABA/j+++/57rvv6NSp0wXVWB0We01XoDVROTk5hISEkJ2dTXBwsNnlSDX9zxvf8+PPp3h5dBdGdIzk66+/ZsSIEXh5eXH962tI+zWLZ2/ozOjLLja7VHFDJSUlFe45kfqk+00aWm3vucLCQg4cOEBcXBy+vr7nf8JZlNnsrD+QSUZuIZFBvlwWF1pvI1piPpvNRk5ODsHBwVitDfNL8nPdq9XNBmqQIW4tKSaYH38+xfYjOYzoeOY3Myfzith8KAuAAWqOISIi4nI8rBZ6tQ0zuwyRc9LcKXFrHWKrbpKxcs9x7HajicbvpyGIiIiIiNQFhS1xa86OhEdyKrQ7XbrT0YUwwpS6RERERMT9KWyJW0uIDsJqgZP5xRzPM/awKC2zsXJ3edjSFEIRERERqScKW+LWfL08aBMRCMDOo7kAbD6UTfbpEkL8vOjSspmJ1YmIiIiIO1PYErfnmEq4I90IWyt2G/th9G8fgaeH/hMQERERkfph+k+aubm5TJ48mVatWuHn50fv3r3ZsGGD8/Hp06eTmJhIQEAAzZs3Z8iQIaxbt67CNTIzM7nlllsIDg6mWbNm3HXXXeTl5VU4Z8uWLfTt2xdfX19atmx51h2pxf04mmQ4wtby8rCl9VoiIiIiUp9MD1vjx48nJSWF999/n61btzJ06FCGDBnC4cOHAWjfvj2vvfYaW7duZfXq1bRu3ZqhQ4dy/Phx5zVuueUWfvrpJ1JSUvjyyy9ZuXIlEyZMcD6ek5PD0KFDadWqFampqTz//PNMnz6d+fPnN/j7lYaX5BjZOppDVhHsOJqLxQL92ilsiYiIiEj9MTVsnT59mk8++YQ5c+bQr18/4uPjmT59OvHx8bzxxhsA3HzzzQwZMoQ2bdrQsWNH5s6dS05ODlu2bAFgx44dLFq0iLfffpuePXvSp08fXn31VT766COOHDkCwMKFCykuLubdd9+lY8eOjB49mkmTJjF37lzT3rs0HMc0wgMnC0jLNDY7vOSiZoQF+phZloiIiIi4OVPDVmlpKWVlZZV2ZPbz82P16tWVzi8uLmb+/PmEhIRwySWXALB27VqaNWtGjx49nOcNGTIEq9XqnG64du1a+vXrh7e3t/OcYcOGsWvXLk6dOlUfb00akYggH8IDvbHbYcmvxi3fv71GtURERMQ9WCwWPv/883q5duvWrZk3b169XLsp8DTzxYOCgujVqxczZ84kKSmJqKgoPvzwQ9auXUt8fLzzvC+//JLRo0dTUFBATEwMKSkphIeHA3D06FEiIyu27/b09CQ0NJSjR486z4mLi6twTlRUlPOx5s2bV6qtqKiIoqIi59c5OcamuCUlJZSUlNTBu5eGsvinY+QUlgKQX2aMbP3rh59pF+HPsI5RZpYmbs7xb4X+zZCGoPtNGlpt77mSkhLsdjs2mw2bzVYfpdWLcePG8c9//rPS8aFDh/LNN980SA0zZszgP//5Dxs3bqxw/PDhwzRv3hybzcbBgwdp27YtqampdOnSpU5e1/H31dA2bdrEc889x6pVq8jMzCQyMpLk5GQmTJjA1VdfjcVicb5fh9DQULp168azzz5L165dAWjTpg33338/999/f4Xrn+376WCz2bDb7ZSUlODh4VHhsere96aGLYD333+fO++8kxYtWuDh4UG3bt0YM2YMqampznMGDhxIWloaJ06c4K233mLUqFGsW7euUsiqS7Nnz2bGjBmVji9ZsgR/f/96e12pW5tPWnh3t2MA1+I8fjK/iHs/SuPO9jYuCbNX/WSROpKSkmJ2CdKE6H6ThlbTe87T05Po6Gjy8vIoLi6u8ev5rn0Ju9VKUc/7Kz3ms+5lLDYbhb2m1Pi651NSUsLgwYN5/fXXK76mj4/zl/L1raioiLKyskqv5+/v7xwocDSJy8/Pr5O6bDYbhYWFDfYeHb7++mvGjRtH//79ef3112nTpg1FRUWsX7+exx9/nC5duhASEuJ8v59//jmJiYkcOXKERx99lBEjRrB+/XpCQkLO+h7O9v10KC4u5vTp06xcuZLS0tIKjxUUFFTrfZgettq2bcuKFSucN0RMTAw33XQTbdq0cZ4TEBBAfHw88fHxXH755bRr14533nmHqVOnEh0dTUZGRoVrlpaWkpmZSXR0NADR0dEcO3aswjmOrx3n/N7UqVN54IEHnF/n5OTQsmVLhg4dSnBwcJ28d6lfZTY7s19cCRRV8agFC/DNMX8euaUfHlZLFeeIXJiSkhJSUlK48sor8fLyMrsccXO636Sh1faeKyws5NdffyUwMLDSUpJq8fPHuvwZfHx8od/DZ46vfB7r2rnYBjyGdz38rObl5UVAQADt2rWr8vHly5czfPhwUlJS6Nu3LwDPP/88L774Ips3byYqKopFixbxzDPPsG3bNjw8PLj88suZN29ehZGZQ4cO8cgjj7BkyRKKiopISkri1VdfZceOHTz33HMAzllZ77zzDnfccQceHh588sknXH/99c6lNv369QOgf//+LF26lEGDBnHJJZfw0ksvOV/rj3/8I82aNWPBggUAZGRkMH78eL777juio6P561//itVqxdfX95w//7799tu89NJLHDhwgNatW3Pfffdxzz33ADhHnv7973/z+uuvs27dOtq1a8ff/vY3evXqVeX18vPzmTRpEiNGjOCTTz4BjNG13NxcevTowcSJE7Hb7VgsFgIDjf1UW7ZsSbt27WjXrh1z586lb9++bN++nWHDhp31Pfj4+ODh4XHW91ZYWIifnx/9+vWrdK9WN3yaHrYcAgICCAgI4NSpUyxevPicrdltNptzil+vXr3IysoiNTWV7t27A7B06VJsNhs9e/Z0nvP4449TUlLi/McgJSWFhISEKqcQgvHN9/Gp3EDBy8tL/xNzET/uO8nRnKqClsEOpGcXselQLr3ahjVcYdLk6N8NaUi636Sh1fSeKysrw2KxYLVasVqtYLdDSfVGCQDofS/YSrAufwZsJdBnCqx+CVY+D/0extr7Xig9Xc3i/cFSvV+4WiwWZ91VGTRoEJMnT2bs2LFs3ryZ/fv3M23aNP79738TExMDGM3hHnjgAZKTk8nLy2PatGmMHDmStLQ0rFYreXl5DBw4kBYtWvDFF18QHR3tnOI2ZswYtm/fzqJFi/j2228BCAkJcdbj+H6uX7+eyy67jG+//ZaOHTvi7e3tPOf39f/+Pd15550cOXKEZcuW4eXlxaRJk8jIyDjn+164cCHTp0/ntddeo2vXrmzatIm7776bwMBAxo4d63zek08+yQsvvEC7du14/PHHueWWW9i7dy+enpXjyLfffsvJkyd59NFHnc93TGP8fS2/f/9g5AowBmDO9t4dx357jd+zWq1YLJYq7/Hq3vOmh63Fixdjt9tJSEhg7969PPzwwyQmJjJu3Djy8/OZNWsW1157LTExMZw4cYLXX3+dw4cPc+ONNwKQlJTE8OHDufvuu/n73/9OSUkJ9957L6NHjyY2NhYwOhrOmDGDu+66i0cffZRt27bx8ssvV0j24n4ycgvr9DwRERGpByUF8Exs7Z678nnj42xfn89jR8A7oNqnf/nll86RFOclHnuMxx57DICnn36alJQUJkyYwLZt2xg7dizXXnut89yRI0dWeO67775LREQE27dvp1OnTnzwwQccP36cDRs2EBoaClChj0FgYKBzGubZREQYTcDCwsLOed7v7d69m2+++Yb169dz6aWXAsbIWVJS0jmf99RTT/Hiiy9yww03ABAXF8f27dt58803GTt2rPO8hx56iKuuugow1kp17NiRvXv3kpiYWGUtAAkJCc5jGzZsYPDgwc6vP/roI66++upKz83KymLmzJkEBgZy2WWXVfft1xvTw1Z2djZTp07l0KFDhIaGMnLkSGbNmoWXlxdlZWXs3LmTf/zjH5w4cYKwsDAuvfRSVq1aRceOHZ3XWLhwIffeey+DBw/GarUycuRIXnnlFefjISEhLFmyhIkTJ9K9e3fCw8OZNm1ahb24xP1EBlVvakJ1zxMREZGmbeDAgc7tiRwcoQjA29ubhQsXkpycTKtWrSr9Yn/Pnj1MmzaNdevWceLECedozS+//EKnTp1IS0uja9euFa7ZUHbs2IGnp6dzphhAYmIizZo1O+tz8vPz2bdvH3fddRd3332383hpaSkhISEVzk1OTnZ+7hjpy8jIqDJsVSU5OZmVK1cSGBhIQkJCpTVUvXv3xmq1kp+fT5s2bfj444+dDfHMZHrYGjVqFKNGjaryMV9fXz799NPzXiM0NJQPPvjgnOckJyezatWqWtUorumyuFBiQnw5ml1IVS0wLEB0iC+XxTX8P2giIiJSzsvfGGGqKcfUQQ9vKCs21m/1qWFjDK+aNT1z9BE4l++//x6AzMxMMjMznVPaAK655hpatWrFW2+9RWxsLDabjU6dOjkbhfj5+dWs/hqwWq3Y7RV/IrrQ7qWO5hRvvfWWc/mOw++79/122p1j+t7ZOhw61sXt2rWLyy+/HDCW+LRp0+as66s+/vhjOnToQFhYWKWAGBwcTHZ2dqXnZGVlVQqFdc3UfbZE6pOH1cJT13QAftuHkApfP3VNBzXHEBERMZPFYkzlq8nH2teNoDXwcXjyuPHnyueN4zW5TjXXa1XXvn37mDJlijN8jB071hkoTp48ya5du3jiiScYPHgwSUlJlfZ7TU5OJi0tjczMzCqv7+3tTVlZ2TlrcOwr+/vzIiIiSE9Pd35dVlbGtm3bnF8nJiZSWlpaoSP4rl27yMrKOutrRUVFERsby/79+53N7Bwfv992qSaGDh1KaGiosyFIdbRs2ZK2bdtWORKXkJBQ4X05bNy4kfbt29e6zupQ2BK3NrxTDG/c2o3okIpTBaNDfHnj1m4M7xRjUmUiIiJSKyvmwLJZRsDq/4hxrP8jxtfLZhmP15OioiKOHj1a4ePEiROAEV5uvfVWhg0bxrhx41iwYAFbtmzhxRdfBIwOgmFhYcyfP5+9e/eydOnSCp2vwWiCER0dzfXXX8+aNWvYv38/n3zyCWvXrgWMDYYPHDjg3BLpt3vCOkRGRuLn58eiRYs4duyYc0Rn0KBBfPXVV3z11Vfs3LmTe+65p0KQSkhIYPjw4fzpT39i3bp1pKamMn78+POOts2YMYPZs2fzyiuvsHv3brZu3cqCBQuYO3durb/PgYGBvP3223z11VdcddVVLF68mP3797Nt2zaef95Yk/f7kbNzmTJlCl999RWzZs1ix44dbNu2jccff5y1a9dW2nurrilsidsb3imG1Y8O4l939uD2dmX8684erH50kIKWiIiIK7KVVQxaDo7AZTv3yM+FWLRoETExMRU++vTpA8CsWbP4+eefefPNNwFjXdL8+fN54okn2Lx5M1arlY8++ojU1FQ6derElClTnMHBwdvbmyVLlhAZGcmIESPo3Lkzzz77rDNYjBw5kuHDhzNw4EAiIiL48MMPK9Xo6enJK6+8wptvvklsbCzXXXcdYHQaHDt2LLfffjv9+/enTZs2DBw4sMJzFyxYQGxsLP379+eGG25gwoQJ593Xdvz48bz99tssWLCAzp07079/f957770LGtkCoy39999/j7+/P7fffjtJSUlcd911LF269KzNMc6md+/efPPNN3zzzTdcccUVDBgwgO+//57vvvuOTp06XVCd52Ox/37yplQpJyeHkJAQsrOztc+WiyopKeHrr79mxIgRaossDUL3nDQk3W/S0Gp7zxUWFnLgwAHi4uJqt8+WNEk2m42cnByCg4PP2qq9rp3rXq1uNtDIloiIiIiISD1Q2BIREREREakHClsiIiIiIiL1QGFLRERERESkHihsiYiIiIiI1AOFLRERERFpcGqILY2dY0PqC+FZB3WIiIiIiFSLl5cXFouF48ePExERgcViMbskcQE2m43i4mIKCwvrvfW73W6nuLiY48ePY7Va8fb2rvW1FLZEREREpMF4eHhw0UUXcejQIQ4ePGh2OeIi7HY7p0+fxs/Pr8ECur+/PxdffPEFhTuFLRERERFpUIGBgbRr146SkhKzSxEXUVJSwsqVK+nXr1+DbNzu4eGBp6fnBQc7hS0RERERaXAeHh54eHiYXYa4CA8PD0pLS/H19W2QsFVX1CBDRERERESkHihsiYiIiIiI1AOFLRERERERkXqgNVvV5NgLIicnx+RKpLZKSkooKCggJyfHpeb6iuvSPScNSfebNDTdc9KQGtv95sgE59svTmGrmnJzcwFo2bKlyZWIiIiIiEhjkJubS0hIyFkft9i1fXe12Gw2jhw5QlBQkDbfc1E5OTm0bNmSX3/9leDgYLPLkSZA95w0JN1v0tB0z0lDamz3m91uJzc3l9jY2HPuw6WRrWqyWq1cdNFFZpchdSA4OLhR/EcqTYfuOWlIut+koemek4bUmO63c41oOahBhoiIiIiISD1Q2BIREREREakHClvSZPj4+PDUU0/h4+NjdinSROiek4ak+00amu45aUiuer+pQYaIiIiIiEg90MiWiIiIiIhIPVDYEhERERERqQcKWyIiIiIiIvVAYUtERERERKQeKGyJ25s9ezaXXnopQUFBREZGcv3117Nr1y6zy5Im4tlnn8VisTB58mSzSxE3dvjwYW699VbCwsLw8/Ojc+fO/Pjjj2aXJW6orKyMJ598kri4OPz8/Gjbti0zZ85E/dakrqxcuZJrrrmG2NhYLBYLn3/+eYXH7XY706ZNIyYmBj8/P4YMGcKePXvMKbYaFLbE7a1YsYKJEyfyww8/kJKSQklJCUOHDiU/P9/s0sTNbdiwgTfffJPk5GSzSxE3durUKa644gq8vLz45ptv2L59Oy+++CLNmzc3uzRxQ8899xxvvPEGr732Gjt27OC5555jzpw5vPrqq2aXJm4iPz+fSy65hNdff73Kx+fMmcMrr7zC3//+d9atW0dAQADDhg2jsLCwgSutHrV+lybn+PHjREZGsmLFCvr162d2OeKm8vLy6NatG3/72994+umn6dKlC/PmzTO7LHFDf/nLX1izZg2rVq0yuxRpAq6++mqioqJ45513nMdGjhyJn58f//rXv0ysTNyRxWLhs88+4/rrrweMUa3Y2FgefPBBHnroIQCys7OJiorivffeY/To0SZWWzWNbEmTk52dDUBoaKjJlYg7mzhxIldddRVDhgwxuxRxc1988QU9evTgxhtvJDIykq5du/LWW2+ZXZa4qd69e/Pdd9+xe/duADZv3szq1av5wx/+YHJl0hQcOHCAo0ePVvh/a0hICD179mTt2rUmVnZ2nmYXINKQbDYbkydP5oorrqBTp05mlyNu6qOPPmLjxo1s2LDB7FKkCdi/fz9vvPEGDzzwAI899hgbNmxg0qRJeHt7M3bsWLPLEzfzl7/8hZycHBITE/Hw8KCsrIxZs2Zxyy23mF2aNAFHjx4FICoqqsLxqKgo52ONjcKWNCkTJ05k27ZtrF692uxSxE39+uuv3H///aSkpODr62t2OdIE2Gw2evTowTPPPANA165d2bZtG3//+98VtqTO/e///i8LFy7kgw8+oGPHjqSlpTF58mRiY2N1v4lUQdMIpcm49957+fLLL1m2bBkXXXSR2eWIm0pNTSUjI4Nu3brh6emJp6cnK1as4JVXXsHT05OysjKzSxQ3ExMTQ4cOHSocS0pK4pdffjGpInFnDz/8MH/5y18YPXo0nTt35rbbbmPKlCnMnj3b7NKkCYiOjgbg2LFjFY4fO3bM+Vhjo7Albs9ut3Pvvffy2WefsXTpUuLi4swuSdzY4MGD2bp1K2lpac6PHj16cMstt5CWloaHh4fZJYqbueKKKyptZ7F7925atWplUkXizgoKCrBaK/746OHhgc1mM6kiaUri4uKIjo7mu+++cx7Lyclh3bp19OrVy8TKzk7TCMXtTZw4kQ8++ID//Oc/BAUFOef0hoSE4OfnZ3J14m6CgoIqrQcMCAggLCxM6wSlXkyZMoXevXvzzDPPMGrUKNavX8/8+fOZP3++2aWJG7rmmmuYNWsWF198MR07dmTTpk3MnTuXO++80+zSxE3k5eWxd+9e59cHDhwgLS2N0NBQLr74YiZPnszTTz9Nu3btiIuL48knnyQ2NtbZsbCxUet3cXsWi6XK4wsWLOCOO+5o2GKkSRowYIBav0u9+vLLL5k6dSp79uwhLi6OBx54gLvvvtvsssQN5ebm8uSTT/LZZ5+RkZFBbGwsY8aMYdq0aXh7e5tdnriB5cuXM3DgwErHx44dy3vvvYfdbuepp55i/vz5ZGVl0adPH/72t7/Rvn17E6o9P4UtERERERGReqA1WyIiIiIiIvVAYUtERERERKQeKGyJiIiIiIjUA4UtERERERGReqCwJSIiIiIiUg8UtkREREREROqBwpaIiIiIiEg9UNgSERExid1uZ+7cufz4449mlyIiIvVAYUtERNxK69atmTdvntllOE2fPp0uXbpU+djs2bNZtGgRl1xyScMWJSIiDcJit9vtZhchIiJSXXfccQf/+Mc/Kh0fNmwYixYt4vjx4wQEBODv729CdZXl5eVRVFREWFhYheMrV65k8uTJLF++nODgYJOqExGR+qSwJSIiLuWOO+7g2LFjLFiwoMJxHx8fmjdvblJVIiIilWkaoYiIuBwfHx+io6MrfDiC1u+nEWZlZTF+/HgiIiIIDg5m0KBBbN68ucL1/vvf/3LppZfi6+tLeHg4f/zjH52PWSwWPv/88wrnN2vWjPfee8/59aFDhxgzZgyhoaEEBATQo0cP1q1bB1SeRmiz2fjrX//KRRddhI+PD126dGHRokXOxw8ePIjFYuHTTz9l4MCB+Pv7c8kll7B27doL/K6JiEhDU9gSERG3duONN5KRkcE333xDamoq3bp1Y/DgwWRmZgLw1Vdf8cc//pERI0awadMmvvvuOy677LJqXz8vL4/+/ftz+PBhvvjiCzZv3swjjzyCzWar8vyXX36ZF198kRdeeIEtW7YwbNgwrr32Wvbs2VPhvMcff5yHHnqItLQ02rdvz5gxYygtLa39N0JERBqcp9kFiIiI1NSXX35JYGBghWOPPfYYjz32WIVjq1evZv369WRkZODj4wPACy+8wOeff87//d//MWHCBGbNmsXo0aOZMWOG83k1aVjxwQcfcPz4cTZs2EBoaCgA8fHxZz3/hRde4NFHH2X06NEAPPfccyxbtox58+bx+uuvO8976KGHuOqqqwCYMWMGHTt2ZO/evSQmJla7NhERMZfCloiIuJyBAwfyxhtvVDjmCDq/tXnzZvLy8io1pzh9+jT79u0DIC0tjbvvvrvWtaSlpdG1a9cqX//3cnJyOHLkCFdccUWF41dccUWlqY3JycnOz2NiYgDIyMhQ2BIRcSEKWyIi4nICAgLOOXrkkJeXR0xMDMuXL6/0WLNmzQDw8/M75zUsFgu/7yVVUlLi/Px8z68tLy+vCjUAZ52aKCIijZPWbImIiNvq1q0bR48exdPTk/j4+Aof4eHhgDGC9N133531GhEREaSnpzu/3rNnDwUFBc6vk5OTSUtLc64BO5fg4GBiY2NZs2ZNheNr1qyhQ4cONX17IiLSyGlkS0REXE5RURFHjx6tcMzT09MZoByGDBlCr169uP7665kzZw7t27fnyJEjzqYYPXr04KmnnmLw4MG0bduW0aNHU1paytdff82jjz4KwKBBg3jttdfo1asXZWVlPProoxVGncaMGcMzzzzD9ddfz+zZs4mJiWHTpk3ExsbSq1evSrU//PDDPPXUU7Rt25YuXbqwYMEC0tLSWLhwYT18p0RExEwKWyIi4nIWLVrkXMfkkJCQwM6dOyscs1gsfP311zz++OOMGzeO48ePEx0dTb9+/YiKigJgwIAB/Pvf/2bmzJk8++yzBAcH069fP+c1XnzxRcaNG0ffvn2JjY3l5ZdfJjU11fm4t7c3S5Ys4cEHH2TEiBGUlpbSoUOHCs0ufmvSpElkZ2fz4IMPkpGRQYcOHfjiiy9o165dXX17RESkkdCmxiIi4lZiYmKYOXMm48ePN7sUERFp4jSyJSIibqGgoIA1a9Zw7NgxOnbsaHY5IiIiapAhIiLuYf78+YwePZrJkydXuVZKRESkoWkaoYiIiIiISD3QyJaIiIiIiEg9UNgSERERERGpBwpbIiIiIiIi9UBhS0REREREpB4obImIiIiIiNQDhS0REREREZF6oLAlIiIiIiJSDxS2RERERERE6oHCloiIiIiISD34/7lMkAnOMU+wAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud para CPU y GPU\n",
+    "ejecuciones = list(range(1, 11))\n",
+    "exactitud_cpu = [9675, 9297, 9540, 9674, 9577, 9630, 9548, 9669, 9555, 9584]\n",
+    "exactitud_gpu = [9623, 9643, 9524, 9550, 9613, 9470, 9505, 9664, 9597, 9580]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5c263d9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia para CPU y GPU\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 20\n",
+    "tiempo_inferencia_cpu = [\n",
+    "    51.919, 44.636, 53.006, 53.006, 51.761,\n",
+    "    52.408, 48.588, 48.668, 51.972, 51.822\n",
+    "]\n",
+    "tiempo_inferencia_gpu = [\n",
+    "    52.083, 44.334, 52.132, 52.177, 51.875,\n",
+    "    49.894, 48.766, 48.737, 49.784, 51.87\n",
+    "]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (segundos)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2a31c09d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento para CPU y GPU\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 20\n",
+    "tiempo_entrenamiento_cpu = [\n",
+    "    886.235, 781.763, 918.454, 918.452, 907.685,\n",
+    "    908.602, 849.307, 848.574, 911.099, 907.219\n",
+    "]\n",
+    "tiempo_entrenamiento_gpu = [\n",
+    "    875.077, 784.811, 920.44, 919.883, 942.762,\n",
+    "    918.752, 871.558, 871.496, 919.484, 942.761\n",
+    "]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (segundos)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e6f893ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [9459, 9327, 9449, 9030, 9247, 9422, 9160, 9424, 9268, 9467]\n",
+    "exactitud_gpu = [9403, 9357, 9410, 9459, 9172, 9424, 9450, 9389, 9335, 9387]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2822fe69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [15.835, 15.829, 15.11, 15.09, 15.836, 16.125, 16.125, 10.687, 10.729, 15.836]\n",
+    "tiempo_inferencia_gpu = [10.717, 13.192, 14.653, 15.122, 15.466, 15.47, 10.167, 10.733, 11.93, 14.7]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0ed906da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [263.789, 263.789, 239.91, 239.965, 245.559, 267.27, 267.271, 175.114, 175.29, 245.561]\n",
+    "tiempo_entrenamiento_gpu = [234.291, 238.56, 237.637, 235.896, 254.432, 254.427, 172.52, 172.01, 238.392, 239.0]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "da45be9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [9388, 9405, 9079, 9459, 9357, 9103, 9342, 9366, 9442, 9331]\n",
+    "exactitud_gpu = [9419, 9414, 9413, 9465, 9448, 9204, 9344, 9347, 9341, 9418]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "01dd2dde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [46.242, 42.224, 48.951, 48.886, 49.832, 50.468, 50.48, 30.9, 32.105, 50.485]\n",
+    "tiempo_inferencia_gpu = [44.85, 35.432, 47.549, 47.231, 49.011, 49.938, 50.023, 31.269, 32.137, 43.782]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "eca212ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [811.15, 825.877, 836.138, 836.206, 822.117, 841.227, 541.138, 579.956, 572.621, 819.69]\n",
+    "tiempo_entrenamiento_gpu = [800.796, 820.056, 839.622, 839.549, 824.735, 839.685, 839.702, 589.182, 584.322, 827.901]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2c30d031",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Mydb53Y4ePcpnn33Ga6+9RlBQEAMHDuTYsWNp83Hdvn2bjRs3Mm7cOMaOHZv2uNSLDKnc3NxwcXHJMGF4UFacw6Cex3fu3Hnk/kuXLqU7X/38/Dh+/DiKoqTb3+nTp596fDc3N5ydnTEajZlqJXwRrVu35rfffmPfvn1Ur149S/edVeeyEMJySel3IUSuM3XqVHbt2sUvv/zC+PHjqV27NoMHD043riP1KveDV7X37t3L7t270+2rU6dOKIrCuHHjHjnOg491dHTM8AtmRh63bWoXo23btqXdl1pS/kFNmzbF2tqa6dOnp4th2rRpmTp+165dMRqNjB8//pF1ycnJmX4eT1OrVi1atGjBb7/9xt9///3I+sTExEfGuGRGkyZNsLGxYcaMGY+03vzyyy8kJyfTsmXLTO2rW7du7Nmzh5kzZ3Lz5s10XQiBR0qo6/X6tBaT1BLlSUlJnDp1irCwsGd+Lo+TlJREv3798Pb25ttvv2X27NlERETwzjvvpG2T0TkMj54Her2e9u3bs3LlyrTpCh6U+nhHR0eATL3+T9q2ePHi7NmzJ11lzFWrVnHlypV027Vq1YrQ0FCWLl2adl9cXNxjuwU+yGAw0KlTJ5YtW5ZhEnnjxo2n7iOz3n//fRwcHOjfvz8RERGPrH9aC++TZOW5LISwTNKyJYTIUdasWcOpU6ceub927doUK1aMkydPMmbMGPr160fbtm0BdZ6qihUrpo2/AWjTpg3Lly+nQ4cOtG7dmpCQEH766ScCAwPTjc9o1KgRvXv35rvvvuPs2bNp3bO2b99Oo0aN0oorVKlShQ0bNjB16lS8vb3x9/d/bKnoKlWqMGPGDD7//HNKlCiBu7s7jRs3plmzZhQpUoQBAwYwcuRIDAYDM2fOxM3NjcuXL6c93s3Njffee49JkybRpk0bWrVqxeHDh1mzZs0TuyylatCgAW+88QaTJk0iKCiIZs2aYW1tzdmzZ1myZAnffvttuqIFL2Lu3Lk0a9aMjh070rZtW5o0aYKjoyNnz55l0aJFhIWFZWqurQe5u7szduxYPv74Y+rXr88rr7yCg4MDu3btYuHChTRr1izttX+arl278t577/Hee+9RoECBR1pJBg4cSGRkJI0bN8bHx4dLly4xffp0KlasmFYW/dq1a5QuXZq+ffumzTX1JFFRUcybNy/Ddaljfz7//HOCgoLYuHEjzs7OlC9fPu05d+7cmVatWuHi4kL9+vWZMmUKSUlJFC5cmHXr1mU4h9PEiRNZt24dDRo0YNCgQZQuXZqwsDCWLFnCjh07yJcvHxUrVsRgMDB58mSioqKwtbWlcePGGY7tK168OPny5eOnn37C2dkZR0dHatSogb+/PwMHDmTp0qW0aNGCrl27cv78eebNm/fIeKXXX3+d77//nj59+nDw4EG8vLz4448/Ml084osvvmDz5s3UqFGD119/ncDAQCIjIzl06BAbNmwgMjIyU/t5mpIlS7JgwQJ69OjBSy+9RM+ePalQoQKKohASEsKCBQvQ6/WPFDjJjKw8l4UQFkqTGohCCPGMnlT6nZQS1MnJyUq1atUUHx8f5c6dO+ken1rm+c8//1QURS13PXHiRMXPz0+xtbVVKlWqpKxatUrp27ev4ufnl+6xycnJypdffqkEBAQoNjY2ipubm9KyZUvl4MGDaducOnVKqV+/vmJvb5+u/HpGpd/Dw8OV1q1bK87OzgqQrrTzwYMHlRo1aig2NjZKkSJFlKlTp2a4D6PRqIwbN07x8vJS7O3tlYYNGyrHjx9/bNntjPzyyy9KlSpVFHt7e8XZ2VkpV66c8v777yuhoaFp2/j5+SmtW7d+5LEPl/F+kri4OOWrr75SqlWrpjg5OSk2NjZKyZIllWHDhinnzp1L2+5pZbAfNm/ePKVmzZqKo6OjYmtrqwQEBCjjxo1T4uPjM/X4VHXq1FEAZeDAgY+sW7p0qdKsWTPF3d097TV54403lLCwsLRtQkJCMlVyX1GeXPo99SP54MGDipWVlTJs2LB0j009v729vdPK6V+9elXp0KGDki9fPsXV1VXp0qWLEhoaqgDKJ598ku7xly5dUvr06aO4ubkptra2SrFixZQhQ4akK4f+66+/KsWKFVMMBkO68usZvd7//POPEhgYqFhZWT1SBv7rr79WChcurNja2ip16tRRDhw4kOE+Ll26pLzyyiuKg4ODUqhQIeXtt99Om4LgaaXfFUVRIiIilCFDhii+vr6KtbW14unpqTRp0kT55Zdf0rZJLf2+ZMmSdI9Nfd0yKl+fkXPnzimDBw9WSpQoodjZ2Sn29vZKQECA8uabbypBQUHpttXqXBZCWB6dorxA+7cQQgghhBBCiAzJmC0hhBBCCCGEyAaSbAkhhBBCCCFENpBkSwghhBBCCCGygSRbQgghhBBCCJENJNkSQgghhBBCiGwgyZYQQgghhBBCZAOZ1DiTTCYToaGhODs7o9PptA5HCCGEEEIIoRFFUYiJicHb2xu9/vHtV5JsZVJoaCi+vr5ahyGEEEIIIYSwEFeuXMHHx+ex6yXZyiRnZ2dA/YO6uLhoHI14HklJSaxbt45mzZphbW2tdTgiD5BzTpiTnG/C3OScE+ZkaedbdHQ0vr6+aTnC40iylUmpXQddXFwk2cqhkpKScHBwwMXFxSL+SUXuJ+ecMCc534S5yTknzMlSz7enDS+SAhlCCCGEEEIIkQ0k2RJCCCGEEEKIbCDJlhBCCCGEEEJkA02TLaPRyJgxY/D398fe3p7ixYszfvx4FEVJ2yYiIoJ+/frh7e2Ng4MDLVq04OzZs+n207BhQ3Q6Xbrbm2++mW6by5cv07p1axwcHHB3d2fkyJEkJyeb5XkKIYQQQggh8h5NC2RMnjyZGTNmMGfOHMqUKcOBAwd47bXXcHV1Zfjw4SiKQvv27bG2tuaff/7BxcWFqVOn0rRpU4KDg3F0dEzb1+uvv85nn32W9ruDg0PastFopHXr1nh6erJr1y7CwsLo06cP1tbWTJw40azPWQghhBBCCJE3aJps7dq1i3bt2tG6dWsAihYtysKFC9m3bx8AZ8+eZc+ePRw/fpwyZcoAMGPGDDw9PVm4cCEDBw5M25eDgwOenp4ZHmfdunUEBwezYcMGPDw8qFixIuPHj+eDDz7g008/xcbGJpufqRBCCCGEECKv0bQbYe3atdm4cSNnzpwB4MiRI+zYsYOWLVsCkJCQAICdnV3aY/R6Pba2tuzYsSPdvubPn0+hQoUoW7Yso0ePJi4uLm3d7t27KVeuHB4eHmn3NW/enOjoaE6cOJFtz08IIYQQQgiRd2nasjVq1Ciio6MJCAjAYDBgNBqZMGECPXv2BCAgIIAiRYowevRofv75ZxwdHfnmm2+4evUqYWFhaft59dVX8fPzw9vbm6NHj/LBBx9w+vRpli9fDkB4eHi6RAtI+z08PDzD2BISEtKSPVAnLgO1xn9SUlLW/RGE2aS+bvL6CXORc06Yk5xvwtzknBPmZGnnW2bj0DTZWrx4MfPnz2fBggWUKVOGoKAgRowYgbe3N3379sXa2prly5czYMAAChQogMFgoGnTprRs2TJdEY1BgwalLZcrVw4vLy+aNGnC+fPnKV68+HPFNmnSJMaNG/fI/evWrUs3HkzkPOvXr9c6BJHHyDknzEnON2Fucs4Jc7KU8+3BXnRPommyNXLkSEaNGkX37t0BNVG6dOkSkyZNom/fvgBUqVKFoKAgoqKiSExMxM3NjRo1alC1atXH7rdGjRoAnDt3juLFi+Pp6Zk2DixVREQEwGPHeY0ePZp333037ffo6Gh8fX1p1qwZLi4uz/+khWaSkpJYv349L7/8skXNPC5yLznnhDnJ+SbMTc45YU6Wdr6l9np7Gk2Trbi4OPT69MPGDAYDJpPpkW1dXV0BtWjGgQMHGD9+/GP3GxQUBICXlxcAtWrVYsKECVy/fh13d3dAzYpdXFwIDAzMcB+2trbY2to+cr+1tbVFvMDi+clrKMxNzjlhTnK+CXOTc06Yk6Wcb5mNQdNkq23btkyYMIEiRYpQpkwZDh8+zNSpU+nfv3/aNkuWLMHNzY0iRYpw7Ngx3n77bdq3b0+zZs0AOH/+PAsWLKBVq1YULFiQo0eP8s4771C/fn3Kly8PQLNmzQgMDKR3795MmTKF8PBwPv74Y4YMGZJhQiWEEEIIIYQQL0rTZGv69OmMGTOGt956i+vXr+Pt7c0bb7zB2LFj07YJCwvj3XffJSIiAi8vL/r06cOYMWPS1tvY2LBhwwamTZtGbGwsvr6+dOrUiY8//jhtG4PBwKpVqxg8eDC1atXC0dGRvn37ppuXSwghhBBCCCGykqbJlrOzM9OmTWPatGmP3Wb48OEMHz78set9fX3ZunXrU4/l5+fH6tWrnydMkQsYTQp7QyI5eFNHwZBIapVwx6DXaR2WEEIIIYTIxTRNtoQwh7XHwxi3MpiwqHjAwNyzB/ByteOTtoG0KOuldXhCCCGEECKX0nRSYyGy29rjYQyedygl0bovPCqewfMOsfZ42GMeKYQQQgghxIuRZEvkWkaTwriVwSgZrEu9b9zKYIymjLYQQgghhBDixUiyJXKtfSGRj7RoPUgBwqLi2RcSab6ghBBCCCFEniHJlsi1rsc8PtF6nu2EEEIIIYR4FpJsiVzL3dkuS7cTQgghhBDiWUiyJXKt6v4F8HK142kF3jeeiuBeotEsMQkhhBBCiLxDki2Raxn0Oj5pG/jU7X7bHkKzaVvZfvaGGaISQgihuc2TYOuUjNdtnaKuF0KILCDJlsjVWpT1Ykavylg9NIGxl6sdP/WqzO99q+LtaseVyHv0/n0f7/4ZRGRsokbRCiGEMAu9ATZPeDTh2jpFvV9v0CYuIUSuI5Mai1yvUYA7iqKWd+9U1EiHxjWoVcIdQ0oCVqNYQb767zRzdl9k+eFrbD59nTFtAulQqTA63dM6IQohhMhxGryv/tw84f7vqYlWo4/urxdCiBckyZbI9c5G3MWogKu9FfU8k6nhXyAt0QJwsrXi01fK0K6iN6OXH+NUeAzvLj7CX4evMaF9OYoUdNAweiGEENniwYRryxegGCXREkJkOelGKHK94LBoAEp7OvOkhqpKRfKzclhdRjZ/CRsrPdvP3qTZtK38su08yUaTmaIVQghhNg3eB3RqogVQsaem4Qghch9JtkSuFxyakmx5uTx1W2uDniGNSvDfiPrUKlaQ+CQTE1efot0POzl+LSq7QxVCCGFO/wxFneI+xYzacOeKZuEIIXIfSbZErnfygZatzPIv5MiC12swpVN5XO2tOREazSvf72DCv8HEJSZnV6hCCCHMZesUOPyHuhzQBuzyQfwd+KkO3L6kZWRCiFxEki2RqymKktaNMOAZki0AnU5H12q+bHi3AW0reGNS4NftITT7ZhvbzkiZeCGEyLFSi2EA6K2hxRcweBfY54f4KPipLkRe0DZGIUSuIMmWyNWu3r5HTHwy1gYdxd0cn2sfbs62TO9RiZn9qlI4nz1Xb9+jz8x9vPNnELfuJmRxxEIIIbKdKRlci6jLVfpCPl9wLQyDd4NDQUiIhlmt4eY5beMUQuR4kmyJXC21VaukuzM2Vi92ujcO8GDdO/V5rU5RdDr46/A1mk7dyrKDV9NKywshhMgB/OpA1GUw2EDdd+/f7+IFb+0BtwCICYXZreDGae3iFLmO0aSw+/wt/gm6xu7ztzCa5PtDbiel30WuljZeKxPFMTLD0daKT9qWoV3FwoxadpRT4TH8b8kR/g6SMvFCCJEjKApsnqguV3lNbdF6kJM79PsX5rwC10/A7NbQZwV4BJo/VpGrrD0exriVwYRFxafd5+VqxydtA2lR1kvDyER2kpYtkaulViIM9M6aZCtVRd98rBxWl/dbvITtA2Xif9oqZeKFEMKind8EV/aAlR3UfSfjbRwLQd+V4FkOYm/AnDYQfsy8cYpcZe3xMAbPO5Qu0QIIj4pn8LxDrD0eplFkIrtJsiVytdRuhIFZ1LL1IGuDnrcalmDtiPrULq6Wif9izSle+X4nx65KmXghhLA4D7ZqVe2vdht8HMeCaouWV0WIuwVz2kJokDmiFLmM0aQwbmUwGXUYTL1v3Mpg6VKYS0myJXKtqHtJXL19D8ieZCuVfyFH5g+swZed1TLxwWHRtPthB5+vkjLxQghhUc5tgGsHwMoe6ox4+vYOBaDPP1C4Kty7DXNfgWsHsz1MkbvsC4l8pEXrQQoQFhXPvpBI8wUlzEaSLZFrnUpp1Sqczx5XB+tsPZZOp6NLVV82/q8Br6SUif9tRwgvT93GltPXs/XYQgghMkFR7pd7rz4QnD0y9zj7fND7L/CtqZaFn9seruzLrihFLnQ95vGJ1vNsJ3IWSbZErhWcxcUxMqOQky3f9ajErH7VKJzPnmt37tFv1n7eXnSYm1ImXgghtHNmLYQeBmvHzLVqPcjOBXotU6sYJkTDHx3g0u5sCVPkPu7Odlm6nchZJNkSuVZ2FcfIjEYB7qx7pz796/ij18E/QaE0nbqVpVImXgghzO/BsVrVX1cLYDwrWyfouQT860PiXZjXCS7uyNo4Ra5U3b8ABRxtnrrd6Yho+Y6QC0myJXKtk+GpxTGcNTm+o60VY9sG8tdbdSjt5cKduCTeW3KEXr/v5dKtWE1iEkKIPOnUvxB+FGycoPbw59+PjSP0+BOKNYKkWJjXGS5sybIwRe50Jy7xsZWKdQ8sf7oimMHzDhF1L8k8gQmzkGRL5EpJRhNnwu8CEOjlqmksFXzzsWJoHT5oEYCtlZ6d527R7JttzNhyniQpEy+EENnLZIItk9TlGm+qVQZfhI0D9FgEJV6G5HuwoJtaeEOIDJhMCv9bcoTo+GQ8XezwcLFNt97T1Y4ZPSszpk0g1gYda0+E0/q77Ry+fFujiEVWk0mNRa50/sZdEo0mnG2t8Mlvr3U4WBv0DG5YnFblPPnwr2PsPHeLyWtPseJIKJM7laO8Tz6tQxRCiNzp1EqIOA62LlBrSNbs09oOus+HxX3hzBpY2AO6zYdSzbJm/yLXmLkzhC2nb2BjpWd2/2qUdHdmX0gk12PicXe2o7p/AQx6tX2rql9+hi48xJXIe3T5aTcftAhgQF1/9HrdU44iLJm0bIlcKXW8VmkvF4t6k/Ir6Mi8ATX4qksF8jlYczIsmvY/7GT8qmBiE6RMvBBCZCmTCTantGrVHKyWcs8qVrbQdS4EtAFjIix6Ve2uKESKo1fvMHntKQDGtAkkwNMFg15HreIFaVexMLWKF0xLtEDtCfPv8Hq0KudJsklhwuqTDJx7gMjYRK2egsgCkmyJXOlkWiVCbcZrPYlOp6NzFR82vNuAdhXVMvG/7wih2Tfb2Cxl4oUQIusE/wU3ToKtK9R8K+v3b2UDXWZDYHswJcHiPhD8T9YfR+Q4MfFJDFt4mCSjQosynvSqUSRTj3Oxs+aHVyvzefuy2Fjp2XTqOq2+3S5zcOVgkmyJXCm17LsWlQgzq5CTLd92r8Ss1+6XiX9t1n6GLZQy8UII8cJMRtjyhbpca4g6X1Z2MFhDp9+hXBcwJcOS1+D4suw5lsgRFEXh47+Pc+lWHIXz2TO5U3l0usz3stHpdPSq6cffb9WhWCFHwqPj6f7Lbr7fdBajSaoV5jSSbIlcR1GU+2XfNS6OkRmNXnJn/bv1GVhXLRO/8kgoTb7eyuIDV6QErBBCPK/jy+HmGbDLBzXfzN5jGaygw89QoQcoRlg2EI4uzt5jCou19OBV/gkKxaDX8W33irg6WD/XfgK9XVg5rC4dKxXGpMBX687Qd+Y+mfw4h5FkK6fYPAm2Tsl43dYp9/ukCyKiE7gdl4RBr6Okh5PW4WSKg40VH7cJ5O8hdQj0ciHqXhLvLz1Kz9/2cvGmlIkXQohnYkyGrSmtWrWHgZ0ZLrzpDdDuB6jUCxQTLB8EQQuy/7jCopy7fpex/5wA4N2XS1G16IuNE3S0tWJqt4p81aUC9tYGdpy7Satvt7Pj7M2sCFeYgSRbOYXeAJsnPJpwbZ2i3q83aBOXBQoOiwKguJsjdtY56+9S3icf/wytw+iWAdhZ69l1/hbNp23jxy3npEy8EEJk1rElcOsc2BeAGm+Y77h6A7SdDlVeAxT4+y04NNd8xxeaik8yMnTBIe4lGaldvCBvNiieZfvuXMWHlcPq8JKHMzfvJtJ75l6++u/0Y+fvEpZDkq2cosH70Oij9AlXaqLV6CN1vQB4oAuh5Y7XehJrg543GhTnvxH1qVuiEAnJJqasPU3b6TsIunJH6/CEEMKyGZNh62R1uc5wsDVzoSS9Htp8A9UHAQqsGAb7fzdvDEITk1af5FR4DAUdbfimW8V0lQazQgl3Z/4ZWoce1YugKPD95nP0+HUPYVH3svQ4ImtJspWTNHgfag1TE6xx+dWfdd+VROshOaE4Rmb4FXTkjwHV+bpLBfI7WHMqPIaOP+5k3MoTUiZeCCEe5+giuB0CDoWg2uvaxKDTQcspUDNlXq9/34W9P2sTizCL/06EM2f3JQC+6loBDxe7bDmOnbWBSR3L8V2PSjjZWrH/4m1afbudTacisuV44sVJspXTeJZVfyopzcY7voGf68N/H8HptRAfpV1sFuJkWAygzrGV0+l0OjqllIlvn1ImftbOi2qZ+FNSJl4IIdIxJt3v/VF3BNhqOG5Xp4PmE6DO2+rva96HXd9rF4/INqF37vH+0qMAvF7Pn0YvuWf7MV+p4M2qYXUpW9iF23FJ9J99gAn/BpOYLN0KLY0kWznNdXVyPHSpL50CYUdg9/ewsBtMLgq/NIR1Y+DMOoiP1ihQbdxNSObiLbWgRG5ItlIVdLJlWvdKzOlfHZ/8KWXiZ+9n6IJD3IiRMvFCCAGoBSnuXAJHd6g6QOto1ISr6Tio9576+7qP1IukItdINpp4e9Fhou4lUd7HlZHNA8x27KKFHFk2uDb9ahcF4NftIXT5eTdXIuPMFoN4Okm2cpKtU2DnN+oYrU9uqz8BSreDyn2hQHG1xSv0MOz6DhZ0UZOvXxvD+rFwdgMkxGj6FLLb6fBoFAU8XGwp5GSrdThZrkEpN9a9U5/X66ll4lcdDaPp1K0s3i9l4oUQeVxyImz7Ul2u+w7YODz1IUaTwu7zt/gn6Bq7z9/KnjmMdDpo/DE0HK3+vuFT2Ppl1h9HaOK7jWfZf/E2TrZWTO9RCRsr8361trUy8OkrZfi5dxVc7Kw4cuUOrb7bzppjYWaNQzyeldYBiEzKqBhG6s/U+4cfgqhrcGknhGyDizvUfuvXDqq3nd+CzgCFK0PRulC0HvjW0LabRRYLzkVdCB/HwcaKj1oH8kqFwoxafpQTodG8v+wofx2+xsSO5fAv5Kh1iEIIYX6H/4CoK+DkCVVfe+rma4+HMW5lMGFR9+cs8nK145O2gbQo65W1sel00HCUWq1w0+ew+XN1AuSGo9R1Ikfadf4m0zefA2BCh7L4FdTu87d5GU/KeLswbOFhDl++w+D5h+hTy48PW5XOcZWZcxtJtnIKkzHjqoOpv5uM6k/XwlC+q3oDiLqqJl0Xt0PIdrV7xdX96m3HN6C3Au/K4F9PTcB8a2bqaqClyumVCJ9FOR9X/hlSh5k7Q5i6/gy7L6hl4t9uUpJB9YthbZCGayFEHpGcANu/VpfrvQvW9k/cfO3xMAbPO8TD7VjhUfEMnneIGb0qZ33CBVB/JOitYcMn6jxgpiRoPEYSrhwoMjaRd/4MQlGga1Uf2lUsrHVI+OR3YPEbtfhq3Wl+3nqBubsvceDibb5/tRLF3HLPhfWcRpKtnKLR6Meve1I1QlcfqNBdvQHcuZySfO1Qk6+oy3B1n3rb/rX6IVC4yv3ky6d6jkq+ckslwsyyMugZVL84Lcp48dHfx9h+9iZf/nealUdCmdSxHJWK5Nc6RCGEyH6H5kL0NXD2VrvVP4HRpDBuZfAjiRaAAuiAcSuDeTnQM8tLdwNq4Q6DNfz3ofq5a0yEl8dLwpWDKIrCe0uOEBGdQHE3Rz59pYzWIaWxNugZ3bI0NYsV5H+LjxAcFk3b6TuY2LGcRSSEeZGml76NRiNjxozB398fe3t7ihcvzvjx49ONPYmIiKBfv354e3vj4OBAixYtOHv2bLr9xMfHM2TIEAoWLIiTkxOdOnUiIiJ9CczLly/TunVrHBwccHd3Z+TIkSQn58Hy2fmKQMVXof2P8M4xePsotPsRKvQAFx/1KtuVPWq/97ntYLIfzGwJmyaoXROTLHcuB6NJ4XR43mnZelCRgg7M7V+dqV0fKBM/YxefrjjBXSkTL4TIzZLu3W/Vqv8/sH5yye19IZHpug4+TAHCouLZFxKZhUE+pNYQaJkybmvXdFg7GmTcbY4xc+dFNp26jo2Vnuk9KuNgY3ltF41ecmf18HpU9y9AbKKRtxcF8f7SI8QlyncCc9P07Jg8eTIzZsxgzpw5lClThgMHDvDaa6/h6urK8OHDURSF9u3bY21tzT///IOLiwtTp06ladOmBAcH4+io9o195513+Pfff1myZAmurq4MHTqUjh07snPnTkBN6lq3bo2npye7du0iLCyMPn36YG1tzcSJE7X8E2gvv596q9RTfaO/fTF9t8OYULi8S71tmwIGG/Cppo73KlpXXX7KB5u5hNyMJT7JhL21QdN+01rR6XR0rOxDw5fc+XxVMMsPX2P2rousOxHO+PZlaVLaQ+sQhRAi6x2cDTFh4OoLlXo/cdNLt2L5bfuFTO32eszjE7IsUWMQGKxg1Tuwd4Y6hqvlFHVSZGGxjl2N4os1JwEY07q0Rfek8XS1Y8HAGny36RzTN51l8YGrHL58hx96VqaUh5kn+87DNE22du3aRbt27WjdujUARYsWZeHChezbtw+As2fPsmfPHo4fP06ZMmoT7YwZM/D09GThwoUMHDiQqKgofv/9dxYsWEDjxo0BmDVrFqVLl2bPnj3UrFmTdevWERwczIYNG/Dw8KBixYqMHz+eDz74gE8//RQbGxtt/gCWRqeDAv7qrXLvlOQrRE26UhOwmDC1AMelnbAVMNiCb/UHkq+qYKVNFcDULoQBXs7Z0/UjhyjgaMPUbhVpX6kwH/19jCuR9xgw5wCty3vxSdtA3J0tIzkWQogXlhh3v5R6vf9l+PmTbDSx6dR15u29zLYzNzK9a7O8V1btr46dXjEc9v+q9i5p/Y0kXBbqbkIywxYeIsmo0LyMB71q+mkd0lNZGfS8+3IpavoX4O0/gzh7/S6vfL+Dca+UoWtVX3SW3n118yS1sExGQ2a2TkmpafCEoTYWQNP/5tq1a7Nx40bOnDkDwJEjR9ixYwctW7YEICFBnT/Izu7+G55er8fW1pYdO3YAcPDgQZKSkmjatGnaNgEBARQpUoTdu3cDsHv3bsqVK4eHx/0r+82bNyc6OpoTJ05k75PMyXQ6KFAMqvSFTr/Cuydh2CFoMw3KdgYnDzAmqEnYlokwuxV8UQTmtFX/AS7tVkvxmkleKo6RGfVLufHfiPq8Ub8YBr2Of4+G0fTrrfy5/7KUiRdC5A4HZsLdiJQu8j3TrboeHc93G89Sb8pmBv1xkG1nbqDTQf2ShcjvYM2TvmI62hqo7l8ge2NPVbmP2rUfndpKt3LY/aJXwmIoisLHfx3j4q04vF3tmNypvOUnKg+oXaIQa96uR72ShYhPMvHBsmOM+DPI8oca6A1q1e3UycpTpVbp1lt+pUVNW7ZGjRpFdHQ0AQEBGAwGjEYjEyZMoGdP9Q0zNWkaPXo0P//8M46OjnzzzTdcvXqVsDB1/oDw8HBsbGzIly9fun17eHgQHh6ets2DiVbq+tR1GUlISEhL9gCio9Uv8klJSSQlJb34k8+pXIpAhV7qTVEg8hz6SzvRXdqB7tIudLHX1bFdIdsAUKzsUXyroxSpg1K0LopXRbUrYjYIDr0DQCl3xwxfo9T78tLrZ62D914uQcsy7nz0zwlOhMbwwbJjLDt4lc/bBUqZ+GyWF885oZ08d74lxmK14xt0QHKd/6EoOpTERPaG3Gb+vitsOHmd5JR5s/I7WNO5cmG6V/OhSAEH/jsRwbBFR9BBhoUyYhOM/LbtHP3rFDXPcynTBZ2iw7DiLXSH52FKTsTYZrrFf5HMS+fc8sPX+DsoFL0Ovu5SDkdrXY573q62en7rVYlfd1zkm43n+CcolKDLd/i2W3nKWGp3yNrvoDcaMWyeAJGX8Y5yQdlyFHZ+hbH+KEy13wGNXofMvv6aJluLFy9m/vz5LFiwgDJlyhAUFMSIESPw9vamb9++WFtbs3z5cgYMGECBAgUwGAw0bdqUli1bZvuV+UmTJjFu3LhH7l+3bh0ODjmnOp95uINtRyjZAaeEUArFnKLQ3ZMUvHsKu+RodCFbIWQrbIVkvQ2RjqW46VSam84B3HHwR9FlzWkYdNEA6LgTcozVN489drv169dnyfFymgFFYKu1jjVX9Oy7eJtW3+2guY+Jxt4KZp6DMc/Jq+ec0EZeOd9KRPxLmbibxNq4s/KSM3sPrmFXhJ6Ie/dbG/ydFep6mKhQMBlr4zmO7znH8ZR1r5XSsfyinjuJ97fPZ6NQwsXEgZsGJq09w5VzJ6nmZq6eAA54+w2mysUZ6I8tJvTqZQ75vYGis+yEC3L/ORdxD746qn7HaOFj5PqJ3azOwR2jfIGhpWHOWQOXIuPo9NNuOhQ1UddDsdCimIGU8uxI6SN/UA3gIpz06siZmEBYvVqzqOLi4jK1nabJ1siRIxk1ahTdu6tlycuVK8elS5eYNGkSffuqpVurVKlCUFAQUVFRJCYm4ubmRo0aNahatSoAnp6eJCYmcufOnXStWxEREXh6eqZtkzoO7MH1qesyMnr0aN59992036Ojo/H19aVZs2a4uFho9m9pFIWkm2fQX9qB7tJOdJd3YhV3C/eY47jHHIcwUKwdUXxroPjVRfGrg+JVQe2//oxuxCQQvXsrOh3069Asw8pASUlJrF+/npdffhlra+useIY5Tlvg7dtxjF1xkh3nbvHvFQNnE5z4vH0glXzzaR1eriPnnDCnPHW+JcRg9cMIAFYWGsCnRxyITzIB4Ghj4JUKXrxa3ZcAz8cXAWgFvG9SOHDpNtdjEnB3tqWqX370Opi09gyzdl1i0QUrGtaqSINSbmZ4UmpUplPV0P01EJ/be/D2cMfY/me1VLwFygvnXEKyiS4/7yXRFENN//xM7Vc114wL7xmXyKjlJ9h0+gZLQwxE27szqX0ZXOwt77XUnbeFRcsBUPTWlOj/CyU0jim119vTaJpsxcXFoX9oEKjBYMBkMj2yraurK6AWzThw4ADjx48H1GTM2tqajRs30qlTJwBOnz7N5cuXqVWrFgC1atViwoQJXL9+HXd3d0C9CuPi4kJgYGCGsdna2mJr++hAW2tr61z7hpItvMuqt1pvgskEN06lFNvYBhd3orsXie7CJriwSd3exgmK1FKLbfjXA88KarWmpzh78w4A/oUccXV88mSWef01LObuyh8DavBPUCifrQrmzPW7dPt1H31q+jGyRQBOtpZXwjany+vnnDCv3H6+3Us0cnbVNMrfi+SCyZOPLgRixESApzM9a/rRoVLhTL+PWQN1Sz1aqXVMmzLcuZfMX4evMWzRUea/XoPK5pq3sFwHsLaFxX3Qn1qB/m8TdJ4FVpZbzCs3n3MT1pzgZHgMBRxt+LZHZexsLfd1eFburtb83q8aM3de5Is1J1kXfJ3gsBim96hkWfN0mkywajgACjp0piSsd33z5HlmzSCz57ym36ratm3LhAkTKFKkCGXKlOHw4cNMnTqV/v37p22zZMkS3NzcKFKkCMeOHePtt9+mffv2NGvWDFCTsAEDBvDuu+9SoEABXFxcGDZsGLVq1aJmzZoANGvWjMDAQHr37s2UKVMIDw/n448/ZsiQIRkmVCKb6PXgEajeagxS/3muB9+vdHhxB8TfgXPr1RuArctDyVf5DPuwn0ypRFhaimNkik6no32lwtQv5cbn/waz/NA15uy+xLrgCMa3K0vTQPXLh9GksC8kkusx8bg721Hdv0CuuaInhMhZzt+4y/w9l1lz8DSrld9BBz+aOtGmoi+9a/pRxS9/lhUs0Ot1TOlcnsjYRLaeuUH/2ftZ+mYtSribqVx2QCvovgD+7AWnVsHiPtB1jmbVfvOq9cERzN51EYCvupTHwyX3VfPV6XQMqOtPtaL5GbrgMJcj4+jy027eb/ESA+sWQ28Jn/lL+8PdCBSDDWsDv6FZ/ivqGC7QPOHKDE2TrenTpzNmzBjeeustrl+/jre3N2+88QZjx45N2yYsLIx3332XiIgIvLy86NOnD2PGjEm3n2+++Qa9Xk+nTp1ISEigefPm/Pjjj2nrDQYDq1atYvDgwdSqVQtHR0f69u3LZ599ZrbnKjKg14NnWfVWM6XlK+J4SvK1Ay7tgPgoOPufegOwdQW/2mryVbQueJYDvUEqET6nAo42TO1akQ6VCvPRX8e5HBnHwLkHaF3Oi/olCzFt49l0k396udrxSdtAWpT10jBqIURekWQ0sT44gnl7LrHr/C0Ahhr+Jb/1XW7bF2X04I8p6JI946itDXpm9KpMj1/3cuTKHXr/vo9lg2vjne/JvSeyTKlm0GMhLHoVzqyBRT2h2zyLmdsytwuLusfIpUcAGFDXn8YBuXuuyvI++Vg1vC6jlx3j32NhTFx9it3nb/F114oUcNSwNW/zFxD8FwCmOu+QGOOMqd57GAwpVQrB4hMunSI1oDMlOjoaV1dXoqKiZMyWuZiMavKVOs/XpV2QEJV+GztX8KvDjxe9WBVdnJF9O9Eo4IFxeA/Mz5CUlMTq1atp1aqV2vSbQ+ZnMJd7iUambTzDb9tDMJoyfltIvb41o1dlSbgy4ZFzTohslJvOt7Coeyzce5lF+69wPUatDKzXQeuSDkwN64N1UjR0+h3Kdc72WCJjE+n80y4u3IilhLsTS9+sRT4HM375vLAFFnSH5HtQrJHa4mVjGYW6ctM596Bko4lXf93LvouRlCvsytLBtbC1svxCJVlBURQW7LvMuJXBJCab8HSx49vuFalRrKA2AS3oBmfWgkMhkt7az+qN2y3me1xmcwOpQSYsl94AXhWg9lB4dRF8EAKDtsDL46Fkc7BxVlu+Tq/mrYTfWW37IQ3+qqFe/dszA8KPg06f4+dnMBd7GwOjW5Zm+eDaWD+m20BqCjZuZfBjEzIhhHgeJpPCtjM3eH3uAep8sYnvNp3jekwChZxsGdqoBNs/aMx0/z1qouUWAGU6mCWuAo42/DGgBp4udpy7fpf+s/dzL9GM82AVawi9loK1I1zYDAu6QmKs+Y6fB03fdI59FyNxtDEwvUelPJNogdqtsGcNP/5+qw7F3BwJj46nx697mL7xrPk/95PuQdhRdbne/8D2oW68Dd7PERfMZSS8yDn0BvCupN7qDAdjMoQfITRoPaf3rKa64TSOCXfU/u2nVqmPsS8AhV6CzRPQR4eDUg/99q9g2xfQ6COLb3rWQlyikaQnvKEqQFhUPPtCIqlVXKMrXUKIXON2bCJLDl5h/t7LXLp1v5RyDf8C9KrpR/MynthY6eHebdiTMkSg4SizXiwrnM+euQOq0+Wn3Ry6fIe35h/klz5VsTaY6Zp10brQeznM66yOcZ7XGXoufvTLp3hhey7cYvqmswBM6FCOonl0PspAbxdWDq3LmL+Ps/zwNb5ef4Y9Ibf4pltF3J3N1JV1/28QEwouPlC1/9O3t1CSbImcy2AFhauw9Zobo5PK0cAvP3Na2KZUOtwBl3bDvUj1BhgO/s4r/K52hXMrDcZECFoIBYtDwRLgUEDTp2MprsfEP32jZ9hOCCEepigKhy7fYf6eS6w6FkZislqF2NnWik5VfOhZowglPR5KJHb/AAnR4F4GSrcze8ylPJyZ2a8qPX/by+bTN/hg2VG+6lzBfAUEitSE3n/BvI5weRfM6wQ9l4KdDG3IKpGxiYxYFIRJgc5VfGhfqbDWIWnK0daKqd0qUrtEIcb8fZyd527R6tvtfNOtIvVKZvN0CPHRsH2qutxotDpWMYdNIp1Kki2R46UWxwjwzg8+pcGnCtR9B4xJEBqUlnwp5zeljTnixkn19iC7fPcTrwLF1eUCxdSfdq5mfEbayuwVK7Nd2RJC5BqxCcn8ExTKH3supVWRBSjj7ULvmn68UtE7w3kSiYtUu4eD+sVLr80oiCp+BfixZ2Ven3uQ5YeuUcjJlg9blTZfAL7VoM8/8Ed7uLJX/dlrOdjnM18MuZSiKLy/9Ajh0fEUc3Nk3CtltA7JYnSu4kNFX1eGzD/M6YgY+szcx1sNi/NO01JYZVfr7u7v1YvlhUpB+e7ZcwwzkWRL5HiPLftusFY/mHyrgcmI7vwmTDoDesUIxZtAviIQeR5uXYDoq2rZ+WsH1dvDHN1SErASULDY/eUCxSxmoHJWqe5fAC9XO8Kj4nlcZ0IvV7UMvBBCZMaZiBjm7bnE8kPXuJuQDICtlZ425b3pVbMIFX3zPbls+67pkHhXrUAb0MZMUWescYAHkzuV570lR/hl2wUKOdkwqH5x8wVQuDL0XQlz26mfV3PbqS1e0jvjhczedZENJ69jY9AzvUclHGXOyXRKuDvzz9A6fLYqmAV7L/PD5vPsC4nk2+6Vsr5CZ+xNtSUboPHHmZpv1ZLl7OhFnmcyKWnJVqD3Y7pSpBTDMNYfxaqYQNo4B2NIHbPVdpq6TWIc3A6BW+fh1rn7SditcxB7HWJvqLcrex7dv7N3SotY8fstYgVLQP6iOXJOFINexydtAxk87xA6yDDhGtsmUObbEkI8UUKykbXHw5m/5zL7Lkam3e9fyJGeNYrQuYpP5qr6xd6EvT+ryw0/hCyaS+tFdK7iw827CXyx5hQTV5+ioKMtnar4mC8Arwr3E66wIJj7CvT+BxxlHO3zOH4tikmrTwHwUevSlPHOO71ZnoWdtYGJHcpRq1hBRi8/xv6Lt2n13Xa+7lKBJqWzsDT+9q/ViyvelaD0K1m3X41IsiVytMuRccQmGrGx0lMso0GsqVUHG32EqfY7sHp1xvMz2DiARxn19rD4aIi8kJKApdwiU5Kye7fVwZsxoeqg5Qfp9ODq81C3xJSf+fws+kpNi7JezOhVmXErg9PNs5UqJj5Zg6iEEDnBlcg4Fuy7zOL9V7gVmwioF3FeLu1Br5p+1C5e8NnGOe38FpJiwasivNQye4J+Dm/UL8bNmAR+2xHC+8uOUsDRhkYB7uYLwLMc9F2lJlrhx2BOW7WLoVM2j6XJZe4mJDNs4WESjSZeDvSgTy0/rUOyeG0reFOusCvDFh7m2LUoBsw5wMC6/rzfIkAtZvMi7lxRC2MANBlrERdXXpTlftsTIhNSW7Ve8nDOuN+wyXi/6uCDAytTqxCaMlG+184FvCuqt4fFRaqJ2K1z6ZOwWxcgMQbuXFZv5zelf5zeSk24CpZ4YGxYyrKLj2bjER7UoqwXLwd6si8kkusx8bg72xF05TaT155mwuqTNC7tTiGnnNdyJ4TIekaTwtYz15m35zKbT18ndQZPDxdbulcrQo/qRfB0fY5xnnev3//i1cgyWrVS6XQ6PmxVmluxifx1+BqD5x9k/sCaVPHLb74gPAKh32o10bp+Aua0gT4rwDl3T8Cblcb+c5yQm7F4udoxpVP5J3dnFWmKFnJk6eBafLHmFLN2XuS3HSHsvxjJ969WxrfACwyv2PKFWsCsaD11XrlcQJItkaMFp3YhfHi8Vqonzb+QFWXfHQqoN5+q6e9XFLXbYbpuiamtYhfUySkjU5Kzsw/t02ALBfzvjwl7sGiHs6dZv2wY9Lp05d2rFs3PiiNhnAyL5vNVwUzrXslssQghLM/Nuwn8uf8KC/Ze5tqde2n31y1RiF41i9CktMeLlUff+S0kxUHhKlCyWRZEnLX0eh1TOpfndlwiW07foP/s/Sx9s9ajlRSzk1speG01zG4DN07B7NZqF0MXmXj+aZYfusryQ9fQ6+Db7pXI72jGyapzAVsrA5+0LUPNYgUZueQIR65G0eq77UzuVJ5W5Z7j/LtxGo4sUJebfGJRF1dehCRbIkdLrUT42PFaWtHpwMldvfnVSr/OZIKYsAySsPMQGQLGBPUD88apR/dr7fhAgY7iDxTtKA4OBbP9jcnaoGdSx3J0+HEnfweF0qmKT/aXfxVCWBRFUdgXEsm8vZdZezyMJKPajOVqb02XKj68WqMIxdycXvxAMeEW26r1IGuDnh97Vqbnb3s5fPkOfWbuY9ng2llfNOBJChaH1/6FOa/ArbMwu5WacLmacRxZDnPhxl0+/vs4AG83KSVFn15A8zKelPF2YfjCwynz0B2id00/PmpdGjvrZ5gPb9PnoJjUIji+1bIvYDOTZEvkaMFPK45hifR6cC2s3oo1SL/OmAxRV+4X6Ejrlnhe7Y6YFKv2zQ8/9uh+bV0fKtTxQOXEZy0LvHmSOmFoBq1/FS/8wiy/MPpdbMpHfx3nvxH1sbcx3+SiQghtxMQn8dfha8zbc4kzEXfT7q/gm49eNYrQtoL3s32xepod30ByPPhUVyvIWjAHGytm9q1G5592cf5GLL1/38vSN2ubt6WkQDHo96/alTDyAsxqBf1WqZV3RToJyUaGLTxMXKKRGv4FGNq4hNYh5Xg++R34841afL3uDD9tPc8fey5x4NJtfni1UuYuvlw7BCdXADq1AmEuIsmWyLFuxyamFW8I8DRjl43sZLBSuxAW8IeH3/uTE+HOpYfGhqV0S4y6AglREHpIvT3MoeADhTqK3V8uUAxsM3gT1D9UQCRVSsGRWvVG43XbjsuRcXy36SwftAjIsj+BEMKynAiNYt6ey/wTdI24RHWcq721gXYVvelV04+yhbOhclt0KByYpS5bcKvWg/I72jB3QA06z1ATrtdm72fB6zUynjcs24Lwuz+G63YIzGoNfVeonykizRdrTnEiNJr8DtZ8272SVNfNItYGPaNaBlCzWAHeXXyEk2HRtJm+g4kdyj19guiNn6k/K3QHdzPOXWcGkmyJHCu1OEaRAg4421lrHI0ZWNlAoZLq7WFJ99QuiGndEs+lFO44D3fDIe6Weruy99HHOnvdT8JSW8RKt1Vb2R5MuB6o7Gjb4H3GeYYz6I+D/LrtAu0qehPgmYNaF4UQTxSfZOTfo2HM23uJw5fvpN1fwt2JXjWK0KGyD6722fi+u32q2qW6SG0o1jD7jpPFCuezZ27/6nT+aTdBV9TuVL/2qfpi49aeVT7f+2O4Is/fH8NV0IxzgVmwDcERzNp5EYCvulR4vsIt4okavuTOmrfrMXzhYfaGRDLizyB2nrvJuHZlMr74ELINLmwGvTU0fMJY+xxKki2RYz21OEZeYm2vVqXyCHx0XULM/cQrrVUs5WfcLXX8WEwYXNrx0AN1atfEzRPU6kDKA5UdgWZlPGlexoP/TkQwevkxlr1Z+9nKOQshLM7Fm7HM33uJJQevcidOreBqpdfRvKwnvWr4UbNYgeyv1hZ1FQ7NUZdzSKvWg0p6ODOzXzV6/raHLadv8MHSo3zVpYJ53x9dvNWEa05buHnmfpfCjC7W5SHhUfGMXHoEgP51/LN2biiRjoeLHQter8l3G8/y3aazLDl4lcNX7vDDq5V56cHeSIoCG8apy1VfU1tncxlJtkSOlZpslZZk68lsndUJML0qPLru3u1Hx4alJmMJ0WrXRFATLZ0e6o9M9/Bxr5Rl57lbHL58h/l7L9G7VtHsfz5CiCyVbDSx8dR15u25xPazN9PuL5zPnh7VfelazRd3ZzNe/d/+9f3Sz/71zHfcLFTFLz8/9qzM63MPsvzwNQo62fBR6wwuhmUnZ8+UMVyvwI2TasLVdyW4581u30aTwtuLDnM7Loky3i580PIlrUPK9Qx6He+8XIoa/gV4+88gzl2/yyvf72DcK2XoVs1XvXBz6l+4dgCsHR75jpFbSLIlciyLrUSYk9jnB58q6u1BigKxN2HTZ3Bobsp9JpjfBXotTdvM09WOkc1f4pMVJ5iy9jTNynji4SJdMoTICSKi41m07wqL9l9OG/+q00GDUm70quFHowB3849luX0JDv2hLufw7kSNAzyY0qk8/1tyhF+3h1DIyZY3Gpi5K5+Tu9qiNbcdRBxP6VK4AjzKmDcOC/D9pnPsDYnE0cbA969WxtZKCjuZS+0ShVjzdj3e+TOI7WdvMmr5MXadv8WEdqVx3jRe3ajmYPV8zYW0nzlViOeQkGzk3HW1GpYkW9lAp4ODs9REq9FH0HKKev+59bBieLpNe9X0o6JvPmISkvl0xQkNghVCZJaiKOw8d5PB8w5S54tNfLPhDGFR8RRwtOHNBsXZ+l4jZr9WnaaBHtoUDdj+FZiS1HFaReuY//hZrFMVH0a3VFuSJq05xdKDV80fhGMhtUXLszzE3VTHcoUdNX8cGtp74RbfbjwDwOcdyuJfyFHjiPKeQk62zHmtOh+0CMCg17HiSCjTp01Up7mxywe1hz91HzmVJFsiRzobcZdkk4KrvTXeMrg16z1QDIMG70P1QRDYXl13aA6s/zRtU4Nex6SO5TDodaw5Hs6G4AhNQhYiLzOaFPaGRHLwpo69IZEYTUq69VFxSfy+I4QmU7fS87e9rDkeTrJJoapffqZ1q8ju0Y0Z1TKAIgUdNHoGqEV+glImNG34oXZxZLE3GhTn9XpqNcAPlh1l0ykN3iMdCqgtWt6V4V6kOpYr9LD549DA7dhERvwZhEmBjpUL06GSzD2mFb1ex+CGxVn8Rk2KuBjoHa/+vx/w7Ydilw1VTS2EJFsiRzqZNl7LOfsHa+dFpvTFMNDp4JXparVCgOC/1cmZU5T2cmFgypeJsf8cJzYh2cwBC5F3rT0eRt3Jm+g18wBzzxroNfMAdSdvYu3xMI5cucPIJUeoMWkD41cFc+FGLI42BnrVLMKat+uxdHBt2lcqbBldqrZ9BaZkdU6tIjW0jiZLjW5Zmo6VCmM0Kbw1/xAHL0WaPwj7/NDnb/CpBvF3YE47uHrQ/HGYkaIojFx6lLCoePwLOTK+XVmtQxJAFb8CrK1/AV/9DSKUfPQ6VoE35x0kKqUoT24jyZbIke5XIsy9V0I01Wj0oxMa27lAtz/Ayl6dv2X71+lWj2hSCt8C9oRGxfP1ujNmDFaIvGvt8TAGzzuUNuYqVVhUPG/OO0S7H3ay5OBV4pNMBHg683n7suz9qCmfty9nWcWFbp2HIwvV5Ua5p1UrlV6vY3Ln8jR6yY34JBP9Zx/gTESM+QOxc4Vey8G3ploAaW47uJzBlCC5xJxdF9lwMgIbg57pPSrhaCulCixCYiwOu78B4FzpIRgNdvx3IoJW323n0OXbGgeX9STZEjmSFMfQiEcZaJ2SZG2ZCBe2pK2ytzHweftyAMzeFcKxq1EaBChE3mE0KYxbGYzylO3aVfBi6Zu1WPN2PXrV9MPJEr9wbp2iVj0t2Qx8qmodTbawNuj5oWdlKhXJR9S9JPr8vo9rd+6ZPxA7F+i1DPzqQmIMzOsIl3aZP45sdiI0iomrTwEwulVA9ky+LZ7PnhkQex3y+1OnyzssG1ybIgUcuHbnHl1/2s0v285jMj3tnS3nkGRL5DiKoqR1I5Q5tjRQqSdU6qVWJ1w2EKLD0lY1KOXGKxW8MSkwavlRko2mJ+xICPEi9oVEPtKilZHu1f2oWtQM82M9r5tn4dhidTmHVyB8GgcbK2b2rUYJdyfCo+Pp8/teImMTzR+IrRP0XAL+DSDxLszrpE4sm0vEJiQzbMFhEo0mmpZ2p1/tolqHJFLFRcLO79TlRh+BwZryPvlYNbwurct7kWxSmLj6FAPm7NfmfyMbSLIlcpxrd+4RHZ+MtUFHCXcnrcPJm1p9BR5lIfYGLO0PxvtjtMa0CcTFzooTodHM2nlRuxiFyOWuxzw90XqW7TSzdbJ68ealVlC4stbRZLv8jjbM7V8dL1c7zt+Ipf/s/cQlajDO1cYBXv1THSOXFAfzu8L5zeaPIxt8suIEF27G4ulix5edK1juhYa8aOc0tQurR1ko2yntbhc7a77vUYkJHcpiY6Vn8+kbtPx2G3sv3AKeXgTIkkmyJXKc1C6EJdydsbGSU1gT1vbQdS7YOMPlXep8XCncnG35sFVpAKauP8OVyDitohQiV8vsRMNmnZD4WV0/BcdS5u5rOErbWMzIO589c/tXJ5+DNUFX7jB43iGStOgJYG0P3RdAyeaQfA8WdIOzG8wfRxb66/BVlh68il4H07pXJL+jjdYhiVTRYbD3Z3W58RjQp/8Op9Pp6FnDj3+G1KGYmyMR0Qn0+HUPwxccos4XGRcBygnkm6rIcYKlC6FlKFgc2n2vLu/8Fk6tTlvVtaov1YsW4F6SkbH/HEdRcs4VKCFyiur+BfB6wtQXOsDL1Y7q/gXMF9Sz2joZUCCgDXhV0Doasyrp4czvfathZ61n65kbvL/0qDbjVKzt1OJHL7UGYwIs6gGn15o/jiwQcjOWj/86DsDwJiWpWaygxhGJdLZNgeR4tUBLqeaP3ay0lwsrh9alY+XCmBRYcTSM8Oj0LfThUfEMnncoRyRckmyJHOfBsu9CY2XaQ43B6vLfb8Lti4BaeWtix7JYG3RsPn2Df49Z/puhEDmNQa/jk7aBGa5L7TT1SdtAbSYnzoyIE3DiL3U5l4/VepwqfvmZ0bMKBr2Ovw5fY+Lqk9pcnLKyhS6zoXRbMCbCn73g5Crzx/ECEpKNDFt4iNhEI9X9CzCscUmtQxIPirwAh+aqy00/UaeUeQJHWyu+7FwBV3vrDNen/peMWxls8V0KJdkSOU5ay5ZUIrQML3+WMm9LFCzuC0nq1acS7s4MblgCUN8Mo+7lzvkzhNBSs0BP3Jwe7Sbl6WrHjF6VaVHWS4OoMmnLF4CiTpjumXfnP2oU4M6XncsD8NuOEH7edkGbQKxsoPMsKNMBTEmwpC+c+FubWJ7DlLWnOX4tmnwO1nzbvaLlXmTIqzZPVOfRK/Ey+NXO1EP2hUQ+8buDgjrNxb4QDeatewaSbIkcJepeElci1VK50o3QQqR+QNvnh7Ag+O/+HDlvNSxOsUKO3IhJYPLaU9rFKCzf5klq+e+MbJ2irheP2HPhFjfuJuJoY+C33pXoU9LIvP5V2fFBY8tOtMKPwckVgC5PjdV6nI6VffgoZazrF2tOseTAFW0CMVhDx9+gXFf1i/HS/vfH1FmwTaci+H1HCABfda6Al6u9xhGJdMKP3z+PmozJ9MNySxEgSbZEjnIqpVWrcD578jnIoFeLkc8XOv6qLh/4HY4uAcDO2sCEDurcWwv2XubARcu++iQ0pDfA5gmPJlxbp6j36w3axGXhFuy7DECHyoVpUMqNKoUUavgXsPyr+lu+UH+W7QjupbWNxUK8Xr8Yg+oXA2DU8mNsCI7QJhCDFXT4CSr2VOc+W/46HPlTm1gyITwqnveWHAWgX+2iNA300Dgi8YhN4wEFynR8prGZuaIIEJJsiRxGxmtZsJIvQ/2R6vLKt+HGaQBqFS9Ilyo+AIxefozEZJl7S2SgwfvqnCubJ8CWyep9qYlWo4/U9SKdW3cT+O9EOADdqxXROJpnEBoEp1aBTg8NpFXrQaNaBNCxcmGMJoUhCw5x8JJGF6j0Bnjle6jcRy3L/9cbcHieNrE8gdGkMOLPw0TGJlLG24XRrQK0Dkk87PIeOLMWdAZo/PEzPTS1CNDjLh3liCJASLIlchipRGjhGo4G//qQFAuL+0BiLAAftipNAUcbzl6/yy/bzmscpLBYDd6Hmm/BlokwLp8kWk+x/NA1kowK5X1cKVvYVetwMi+tVaszuJXSNhYLo9frmNypPI0D3ElINtF/9gHORMRoFQy0+RaqDgAU+GcIHJytTSyP8ePmc+y5EImDjYHpPSphayUt4BZFUWDDOHW5Ui+1ivEzeLAI0MMJV44oApRCki2Ro0hxDAunN0Cn38HJE26cglXvgKKQ39GGMW3UrkLfbTpHyM1YjQMVFunudTi9Rl1WFEAHtYdrGpKlUhSFhSldCHtUz0GtWtcOwpk1Ka1aH2gdjUWyNuj54dXKVC6Sj6h7SfT5fR/X7tzTJhi9Hlp/DdXfUH9f+Tbs+1WbWB6yLySSbzacAWB8u7IUc3PSOCLxiHMb1Lk4reye+/+9RVkvZvSqjOdD01zkiCJAKSTZEjlGktHEmYi7gDoHg7BQTu7QeabaZeDon2lXQttXLEy9koVITDbx0V/HZO4tkV58NMzrBLdDHrhTgZ/qgVEqWT5sb0gkF27G4mBjoG0Fb63DybzUQiflu0OhEtrGYsHsbQzM7FeNku5OhEfH0/v3vUTGJmoTjE4HLSdDraHq76vfg3mdM97WTMVs7sQlMmLRYUwKdKxUmE4pXdWFBTGZYGNKq1b118G18HPvqkVZL3Z80Jh5/avmnCJAD5BkS+QYF27EkphswsnWCt/8DlqHI56kaJ37FYfWfAChQeh0Oj5vXxZbKz27zt9i+aFr2sYoLEdyAvzZE8LVQe7UGAx9VqgJ+60z8EtD9YNbpFmU0qrVrqI3TrZWGkeTSVf2wbn16uvaYKTW0Vi8fA42zB1QHW9XOy7ciOW12fuJTUjWJhidDpp9DnVGqL+fWw9/dEi/jZmK2SiKwvtLjxIaFU/Rgg581j7vThtg0YL/UquO2rpA3XdfeHcGvY4a/gVyThGgB0iyJXKM4LAoQC2Ooc9B/2R5Vu23oVRLMCao87Xcu4NfQUfebqpONPn5v8HaXakVlsOUUu0sZJv6e9X+0PILKNYAus9Xu5tFHIffmqR0LRS3YxNZfVwtjJGjuhBuSWnxqNgDChTTNpYcwsvVnrkDqpPPwZojV+4weP4h7YoM6XTQ9FOonzKG8vwmDPM7AaDf/pXZxlj+secS64IjsDbo+P7VyjnnYkNeYkyCTRPU5drDwMGyC1hkN0m2RI4RHCrFMXIUvR46zIB8ReD2RXVwtaLwer1iBHg6czsuiQn/ntQ6SqElRYE170PwP4AeKvSANt/cX/9Sy/tTCoQegg2fahGlxVl++BqJySbKeLtQLqcUxri0G85vAr3V/aqlIlNKuDszs1817K0NbDtzg/eXHsFk0ujCg04HjT+Chup8ivqLW2l7uB+GbV+YJdEKDo3m85TPjVEtS+eswjB5SdB8iDwPDoWg5mCto9GcJFsixzgZplZkkvFaOYh9fugyBww2aqnn3d9jbdAzsWM5dDpYdugqu87d1DpKoZWtU2D/b4AOOv+uzu3zsHKdoc00dXnnNNj+tRkDtDwPFsboXr0IOl0OaeXfMlH9WakX5C+qaSg5UeUi+fmxV2Ws9Dr+DgplwuqT2o57bfgBNBkLgB4Tit4q2xOtuMRkhi5UW/aaBLjTv07RbD2eeE5J9+5XHK3/HtjKVD2SbIkcQVEUqUSYUxWuDM1Tvmit/wQu76Fykfz0quEHwEd/Hyc+yahhgEIT+3+//wW81Zfq5LaPU/U1dbwIwMbPYO8v2R+fhTp46Tbnrt/F3tpAu4o5pDDGxR1qN1G9NdR7T+tocqxGL7nzZZfyAPy+I4Sftl7QNiDT/fdtnSk521ueP/nnBBduxOLhYsuXXSrknAsNec2+XyEmDFx91W7hQpItkTNERCcQGZuIQa+jlIdcJclxqg2Esp1AMcKS1yD2JiNbvIS7sy0hN2P5YfM5rSMU5hT8D/z7P3W5/vtqpaqnqT3sfungNSMhaEH2xWfBFqS0arWt4IWLnbXG0WSCosDmlKS6ch/I56ttPDlch0o+fNxanUZj8tpTLD5wRZtAUophGOu8S4xtSkW4Hd/cn5A8i/0TdI0lB6+i08G0bpUo4GiTLccRLyg+CnZMVZcbjgIrW23jsRCSbIkc4WRKq1axQo7YWcukhTmOTgdtv4VCpSAmFJYNxMVGz7hXygDw09bznNVq4k5hXiHbYNlAQIEq/aDRh5l/bMPRaqVCUMcABv+THRFarKi4JP49GgaoXQhzhJBtcGmn2pW43v+0jiZXGFivGG80UAuMjF5+jA3BEeYNILXqYKOPMDX8kANF30IxpCQ/Wyaq67PQpVuxfPTXcQCGNS5JreIFs3T/Igvt+h7u3YZCL6nTOwhAki2RQ0gXwlzA1hm6zgVrB7iwGbZ9SYuynjQt7U6SUWH08mPaDfoW5hF2BBa+CsZECGgDraeqiXhm6XRql9RKvUAxwdIB6qSZecTfQddISDYR4OlMJd98WofzdIpyvwJhlX4vNM+OSG9UiwA6VfbBaFIYsuAQ+y9Gmu/gJmO6YhjRDn6YmnymrtMZIDrrpvVITDYxbOFh7iYkU71oAYY3lrnZLNbdG7D7B3W58cdgkCqRqTRNtoxGI2PGjMHf3x97e3uKFy/O+PHj0w36vHv3LkOHDsXHxwd7e3sCAwP56af0g6gbNmyITqdLd3vzzTfTbXP58mVat26Ng4MD7u7ujBw5kuRkjearEM9MKhHmEu6l71eb2/IFuvObGNeuLA42Bg5cus2i/Rp1iRHZL/KCOhFqYgz41YVOvz/ffDx6PbT9DgLbgykJFvVSK93lcg8WxuiRUwpjXNgMl3eDwTZL5tkR9+l0Or7oVI7GAe4kJJsYMHs/p8PN1Dug0ehHimGYqg6Al1qrXcVDtkPC3Sw51Jf/neLo1Shc7a2Z1r0iVgZpI7BY27+GpFjwrgyl22odjUXR9KydPHkyM2bM4Pvvv+fkyZNMnjyZKVOmMH369LRt3n33XdauXcu8efM4efIkI0aMYOjQoaxYsSLdvl5//XXCwsLSblOm3G/GNhqNtG7dmsTERHbt2sWcOXOYPXs2Y8eONdtzFS9GWrZykQrdoXJfQIHlr1NYF8n/mr0EwKQ1J7keE69tfCLr3b0Of3SE2OvgUQ56LABru+ffn96gloQv8TIk34MFXSE0KMvCtUSHr9zhVHgMtlZ62lfMAS1ED47VqjYAXLy0jScXsjbo+eHVylTxy090fDJ9Zu7l6u04bYLR6aDd9+BSWC35vfrFy/tvPn2dX7eHAPBl5/J457N/4X2KbHLnMhz4XV1uMvbZeizkAZomW7t27aJdu3a0bt2aokWL0rlzZ5o1a8a+ffvSbdO3b18aNmxI0aJFGTRoEBUqVEi3DYCDgwOenp5pNxeX+1/K161bR3BwMPPmzaNixYq0bNmS8ePH88MPP5CYKJOqWrrYhGQu3ooFpOx7rtFyCniWg7hbsPQ1+tUoTLnCrsTEJ/PZymCtoxNZKT4a5nWC2yGQzw96LQW7LJgbx8pG7ZbqVwcSouGPDnD91Ivv10It3Ku2arUu74WrQw4ojHFuA1zdD1b2UGeE1tHkWvY2Bn7vW5VSHk5ERCfQZ+Y+7SaLdygAnX5TJyI/sgCO/Pncu4qIjud/i48A0LeWH83KeGZVlCI7bJmsdg/3rw/FG2kdjcXRNNmqXbs2Gzdu5MyZMwAcOXKEHTt20LJly3TbrFixgmvXrqEoCps3b+bMmTM0a9Ys3b7mz59PoUKFKFu2LKNHjyYu7v7Vnd27d1OuXDk8PDzS7mvevDnR0dGcOHEim5+leFGnwmNQFHB3tqWQk1S2yRWs7dQvyraucGUvhk3jmNSxHHodrDoaxubT17WOUGSFpHhY9CqEHwVHN+j9Fzhn4ZcmGwfosQi8K8G9SPijvTqBdi4THZ/EqpTCGK/mhMIYD7dqOXs8eXvxQvI52DCnf3W8Xe24cCOW12btIzZBo2ESfrWhwSh1+d934db5Z96F0aTwzp9BRMYmUtrLhdGtSmdxkCJLXT+lJtcATT7RNhYLpenotVGjRhEdHU1AQAAGgwGj0ciECRPo2bNn2jbTp09n0KBB+Pj4YGVlhV6v59dff6V+/fpp27z66qv4+fnh7e3N0aNH+eCDDzh9+jTLly8HIDw8PF2iBaT9Hh4enmFsCQkJJCQkpP0eHa12Y0tKSiIpKSlr/gAiU45dvQ1AaU/nF/rbpz5WXj8L4eyLrs13WC3rC7u/J8C7Gv1qlWDmrkt8/NcxVg+rjYNNzh5gm6fPOZMRw18D0V/cjmLjSHK3ReBSBLL6b2Gwh25/YjXvFXQ3TqHMeYXk3qtyVbe15QevcC/JSAk3R8p7Oz32fLKU80139j+sQg+hWDuQXGNI1r/m4hGFHKyY2bcK3X/dx5GrUbzxxwF+7lkJG6vsvaae4TlX620MF7agv7wLZUk/kvuueaYS4D9uucCu87dwsDEwrUs5DJhISjJldegiixg2jkevmDCVaoXRo0K2/r9byntcqszGoek3mcWLFzN//nwWLFhAmTJlCAoKYsSIEXh7e9O3b19ATbb27NnDihUr8PPzY9u2bQwZMgRvb2+aNm0KwKBBg9L2Wa5cOby8vGjSpAnnz5+nePHizxXbpEmTGDdu3CP3r1u3DgcHh+fap3g+6y7oAT3WsddZvXr1C+9v/fr1Lx6UyCI6yri1oMSNtSh/vUmVkp/xl4031+7E885vG2hXNHd8wOa5c05RKH91Dv43N2HUWbGnyFBuHr4Gh7OuStnDbD3eom7U5zjduUT8r83ZUfIjEq1y/px8igK/HjUAOso5RrNmzZqnPkbT801RaHD6E/IB5/I3Injrfu1iyYNeKw4/BBvYce4Wvb9fR++SJvRmGD7z8Dln59yVhoaj2IYf5fLM/hz36fmYR6Z3IRqmn1DP9/a+iZzav5Xc2zk458sXe4EGZ1ahoGOLvi4xWfAdLTMs5TP1wV50T6JTHiz9Z2a+vr6MGjWKIUOGpN33+eefM2/ePE6dOsW9e/dwdXXlr7/+onXr1mnbDBw4kKtXr7J27doM9xsbG4uTkxNr166lefPmjB07lhUrVhAUFJS2TUhICMWKFePQoUNUqlTpkX1k1LLl6+vLzZs3040HE9mv8897OXI1imldy9O63PN3QUpKSmL9+vW8/PLLWFvngDEPeYUxCcO8duiv7kPxKMfG2n8wcGEwBr2O5W/WyNEVKPPqOaffNgXD9iko6DB2/A2ldDvzHPjOZazmtkYXE4biWZ7knn+DXc49fwCOXYui4097sbHSs2NkffI7PH4yV0s433SnV2O1tI/amjnkEDjInEjmtu3sTd6Yd5hkk0LfWkX4qOVL2Va98knnnO7sf1gtVpOs5K7zUUo2f+K+ou4l8coPuwmNiueV8l581blszqi6mYcZFnRCH7IVU/nuGNt+n+3Hs4T3uAdFR0dTqFAhoqKinpgbaNqyFRcXh16fvonbYDBgMqlXs1O77D1pm4ykJlVeXmo3klq1ajFhwgSuX7+Ou7s7oGbFLi4uBAYGZrgPW1tbbG0fbfa2tra2iBc4rzCaFE6nTHZbzjd/lvzt5TW0MNbW0GU2/FwPXcQxml6eRutyffn3WBhjVpzkr7fqYDDHpdlslKfOuf2/w3a1Gqyu1ZdYle9svmO7FYc+K2BWS3ThR7Fe0hN6LVfHduVQSw6FAtCqrCfuro6Zeoxm55vJBNu/BEBX402sXaWogRaaBHrxVRcTI/4MYs7uy3i42vNWw+ydnyrDcy6wjToJ+d4ZWK0cBoN3got3ho9XFIWP/zlKaFQ8RQs6MLFTeWxyeDfyXO/CVgjZCnpr9I0+RG/G9xxL+UzNbAyaFsho27YtEyZM4N9//+XixYv89ddfTJ06lQ4dOgDg4uJCgwYNGDlyJFu2bCEkJITZs2czd+7ctG3Onz/P+PHjOXjwIBcvXmTFihX06dOH+vXrU758eQCaNWtGYGAgvXv35siRI/z33398/PHHDBkyJMOESliOkJuxxCeZsLc2ULRg5r5oiBzItbBayhsdHJzNpOIncLaz4ujVKObuvqh1dCKzTvwN//5PXW7wAVR/3fwxuJWC3svV4iuXd8OfvSA54emPs0B3E5L5J0hNtrrnhMIYp1ZCxDGwcYZaQ7WOJk9rX6kwH7dWC0tMWXuaxVrNYfjyOPAsrxawWfa6OiFyBubtvczaE+FYG3RM71EZJ1tJtCyaosDGlKE2VftDfj9t47FwmiZb06dPp3Pnzrz11luULl2a9957jzfeeIPx48enbbNo0SKqVatGz549CQwM5IsvvmDChAlpkxbb2NiwYcMGmjVrRkBAAP/73//o1KkTK1euTNuHwWBg1apVGAwGatWqRa9evejTpw+fffaZ2Z+zeDYnU+bXesnTOce3boinKNFE/YIOuGx8n4l11A/br/47Teide1pGJjLjwlZY/jqgQJXXoOFo7WLxqgA9l4C1A5zfCMsGgjHnTWK/8kgocYlGihVypIZ/Aa3DeTKTCbZ8oS7XHKyWAReaGlivGG82UMetj1p+lPXBEeYPwspW7blg4wSXdqgT3z7kZFg041epU3580CKAcj5ZMDWEyF6n/oVrB8HaEeq/p3U0Fk/TSwfOzs5MmzaNadOmPXYbT09PZs2a9dj1vr6+bN269anH8vPzy5LiCsK8ZDLjPKbB+3BlD1zYQpvTo1jk+wU7r8Qz9p8T/NqnivTft1RhR2BRT3WeldJtofXX2k9qWaQGdJ8PC7rByRWwYhi0+wH0ml5jfCYL96lza/WoXsTyz/3gv+F6sNqiWOstraMRKT5o8RI37yaw9OBVhi44xB8DalDd3Il7weLqe8Jfb8CWSVC0rloiHohLTGbYwsMkJpto9JIbA+r6mzc28exMRtiU0ihSczA4uWsbTw6Qcz51RJ4UHJqSbOXgIgniGegN0PE3cPZCd/MMM1znYKWHDScj+O9ExtM0CI1FXlAnLU6MgaL11NdPb9A6KlXxxtB5FugM6jwwa0ep3V9ygOPXojh6NQobg55OVXy0DufJTMb7rVq1hoB9fm3jEWl0Oh1fdCxHkwB3EpJNDJizn1Ph0eYPpEJ3qNADFJPa0hwXCcC4FcGcu34Xd2dbvupSwfIvKgg4+ifcOAV2+aD2MK2jyREk2RIWLbUbYWlJtvIOJ7e0L8gu5/5hxktBAHyy4gTR8ZYxt4ZIERMBf3SA2BvgUU5tSbK20zqq9Eq3gfYz1OV9P8Omz7WNJ5NSW7WalfGggOPjKxBahOPL4eZpsHOFmm9qHY14iJVBz/evVqaqX35i4pPp8/s+rkRmrmR1lmr1FRQoDtHX4J+hrAi6xp8HrqDTwbTuFSnoJGPoLV5yAmyepC7Xexfs82kaTk4hyZawWDdiErgek4BOBwGeOX++HPEM/GqpA6uBppen0TxfKBHRCXz132mNAxNp4qNgfie4fRHyF4Vey9Qv25aoQjf1ix7A9q9gxzRNw3mauMT7hTFetfTCGMZk2JrSqlV7mOWeA3mcvY2B3/tWo5SHE9djEug7cx+37pq5cIytE3SZBQYbOP0vx/5S/yeHNipB7eKFzBuLeD4HZ0PUZXD2guqDnrq5UEmyJSxWaquWf0FHHKUyUd5TaygEtEFnTORbwzRcuMsfey5x6PJtrSMTSfHqGK3wY+DoBr3/AmcPraN6suqvQ9NP1eUNn6gl6i3UqiNh3E1IpmhBB2oWs/B5qo4vhVvn1K6DNaRVy5K5Olgzt38NCuez58LNWPrP3k9sgpkLx3hVILmJWpzsPebS2TuSt5uUNG8M4vkk3IVt6tQONHgfrO21jScHkWRLWKzU4hilpThG3qTTqQUN8hfFLvYqCwrNBsXEh8uPkWR8/Dx7IpuZjLB8IFzcrpb47rUMChTTOqrMqfsO1H1XXf73f3DkT23jeYwFKV0Iu1Urgt6Sq7Aak2HrZHW59nCwlR4Ils7T1Y45/auT38GaI1ejeHPeQRKTzft++uXtBqw3VsZWl8wkZRpWyRp0aRTPbu8Mtct4gWJQqbfW0eQokmwJi5XasiXFMfIw+3zQZQ4YbCl7dxdv26/lVHgMv20P0TqyvElR1CTl5Eq1K1D3+WqZ9ZykyViollKi/u/BagljC3IyLJqgK3ew0uvobOmFMY7+qRZIcSgoXYpykBLuTszsVw17awPbz97kvSVHMJnMUzhmy+nr/Lw9hPeTBhFv74H17XOw5n2zHFu8gLhI2PmdutzoIzBoP6FwTiLJlrBYUolQAOBdEVqqY0KGs5DqupN8u/EMl2/J1VCz2/IFHJwF6NRJqIs10DqiZ6fTQcspKZXRjLCkH5zfrHVUaRY9UBjDzdmCCwYYk+63atUZoY7HETlGpSL5+al3Faz0OlYcCeWzVcEo2Vyp83p0PP9bfASAtrXKYddtJuj0EDQfji7O1mOLF7TjG0iIVgshlemodTQ5jiRbwiLFJxk5f+MuIHNsCdRJcst1Ra8Y+dn+B5ySbvPR38ey/cuBeMD+3+4XQmj9FZRpr2k4L0Svh1e+V+cEMybColfh8l6to+JeopHlh68B0L2ahRfGCFoAdy6BoztUG6h1NOI5NCjlxldd1Jbp2bsu8uOW89l2LJNJ4Z3FQdyKTSTA05kPW5VW59uqn9KqteoduJV9xxcvIDoU9v2iLjcZk6PmKrQU8hcTFulMRAwmBQo42uBuyVd3hXnodNDmG3ALIL8pkuk237Pz7HVWHAnVOrK84cRf8O976nKDUbnjy7XBCjr9rs7FlRQH87tA2FFNQ/r3WBgx8cn45LenbgkLrs6WnAjbUqo71h0BNg6ahiOeX/tKhRnTJhCAL/87zZ/7L2fLcWZsPc/Oc7ewtzbw/auVsLNOmYuv/kjwqwOJd2Fpf/XcEpZl6xRIjocitaBkM62jyZEk2RIW6cEuhDLJoQDUbkpd54K1I7X0JxhhtZTPVgZzJ04+nLPVha2wfBCgQNX+0HCU1hFlHStb6DYPfGtCQpQ6Z9jNs5qFk9qFsEd1Cy+METRPLf/s5KGeEyJHG1DXn8ENiwMwevkx1mXxBPIHL91m6vozAIxrV4YS7g8UUjFYqV2S7fNDWBBsHJelxxYv6NZ5OPyHutzkE/XCp3hmkmwJi5RaiVC6EIp03F6Ctt8CMNzqb8re28+k1ac0DioXCw1Su9gZE6H0K+pcVbntw9bGEXouVgt9xN2Eue3gTvZc3X+SMxExHLh0G4NeRxdLLoyRnHC/Vave/6T8cy7xfvOX6FrVB5MCwxYeZl9IZJbsNyouieELD2M0KbxSwTvjc9u1MLT7UV3e/T2cWZclxxZZYPNEMCWrLVp+tbSOJseSZEtYJCmOIR6rfJe0q+nfWP/AtgNB7L1wS+OgcqFb52F+Z7V7T9F66tVnvUHrqLKHnSv0Wg6FSkH0NZjzCsRk7dX9p1mY0qrVJMAddxc7sx77mRyaq/6NnL2hcl+toxFZRKfTMbFDOZqWdich2cSAOfvTKgI/L0VRGLX8KNfu3KNIAQcmdCj7+J4qAa3uz9P295sQHfZCxxZZIPyYOo8eQOMx2saSw0myJSyOyaRwKjwGgNKSbImMNJ8EXhUooLvLDzbfMmb5IRKSjVpHlXvERMC8juqcKp7loPsCsLbgBCArOBaCPv9APj+4HaJ2KYzLmqv7TxOfZGT5IbUwRo8aFlwYIyketn+tLtd7N/efE3mMlUHP9B6VqeqXn5j4ZPrO3MeVyOev+rpg32XWHA/HSq9jeo9KONs9pVz4y5+p7zdxt2D56+qcfkI7G8erP8t2Aq/y2saSw0myJSzOldtx3E1IxsZKTzE3R63DEZbI2g66zkWxdaWy/hzd7/zGjGyspJWnxEfBvE5w+yLkLwo9l4FdHrno4eKtJlxOnnA9WP07JMRk+2HXHg8n6l4ShfPZU7+kW7Yf77kdnA0xYeDiA5X7aB2NyAb2NgZ+71uNlzycuR6TQJ+Z+7h1N+GZ93MqPJrPVgYD8EGLACr45nv6g6xsofMssHZUJ03fPvWZjyuyyKXdcPY/0BnUebXEC5FkS1ic1C6EL3k4Y22QU1Q8Rv6i6Dr8BEB/q7Wc3zKfc9fvahxUDpcUDwtfhYhjaknv3n+Bs4fWUZlXAX/o8zfYF4DQQ7CgOyTdy9ZDLkjpQti1qi8GSy2MkXQPdqR8+a3/P/WLsciVXB2smdO/OoXz2RNyM5bXZu/nbkJyph9/L9HI0AWHSUg20fAlNwbU9c/8wQuVVKeWANgySf3SL8xLUe4XKqncGwoW1zaeXEC+yQqLk9pPvLSX81O2FHleQCuU2sMBmGj4melL1srcW8/LZITlA+HSDrBxhl5LoUAxraPShntp6L1c/Ttc2gGL+2RbSerzN+6yLyQSvQ66VrPgwhgHZsLdCHAtAhV7aR2NyGaernbMHVCdAo42HL0axZt/HCQx2ZSpx3626gTnrt/FzdmWr7pUePbKmhV6QPlu6qTjywaarTuvSHF2PVzeDVZ20OADraPJFSTZEhYnrRKhjNcSmaBrMpYE7+o46+7xZsQ4lu89p3VIOY+iwL/vwsmVYLCBHgvU6nx5mXcltUqhlT2cXQd/DcqWMSSp5d4bB7jj5Wqhlf0SY2HHN+pyg5FgZaNtPMIsirs5MbNfNRxsDOw4d5P/LTmCyfTki1krj4SycN8VdDqY1q0ihZyeowVUp4PWX6sXe6Kvwoph6nuUyH4mE2z8TF2uPkjtWi1emCRbwuKkVSL0dtU4EpEjGKyx7T6He9b5Ka2/jG7t+9x8jjEGedqWSep4HHRq1UH/+lpHZBn8aqvzcOmt1YmdV76dpV/6EpKNLD14FVDn1rJY+39Ti6XkL6q2Oog8o6JvPn7qVQUrvY6VR0L5bFXwY3sPXImM48PlxwAY0rAEdV5kYm5bZ+g8U/3fO7VKPQdF9juxXO1GbusCdd/ROppcQ5ItYVHuxCUSGhUPQIB0IxSZ5eKNTbdZmNDRkU2sm/e11hHlHPt+ha2T1eXWX0OZ9pqGY3FKNoXOv4NOr07u+d+HWZZw/XcigttxSXi62NGglIUWxki4CzvVue2o/z4YnlJRTuQ69Uu58XVXtaV79q6L/LD50d4DSUYTQxceJiYhmSp++RnRtOSLH9i7klqhEOC/j9RS5CL7GJNg8wR1ufZwcCigbTy5iCRbwqKkdiH0LWCPy9PKxArxAEOJRkRUVq/EdQj7hoP7dmgcUQ5wfDmsHqkuNxwN1QZoG4+lCmwHr3yvLu/5EbZ8kSW7Te1C2LWaL1aWWgxo3y9qKe4CxdRxNCJPalexMGPbBALw1bozLNp3GaNJYW9IJAdv6hi59BhHrtzBxc6Kb7tXzLrzueZgKNUCjAmwtL/apVVkj8PzIPICOLqpf3eRZSz03V3kVTKZsXgRXm3GcM6lBva6RNzWvM69mNtah2S5LmyB5YMABaoNlIHQT1OpJ7Scoi5v/QJ2ff9Cuwu5Gcuu87fQ6aBbNd8sCDAbxEfDru/U5QajwGClbTxCU/3r+vNWQ7Uy3ejlx6j6+Xp6zTzA3LMG/j0eAUCP6r745HfIuoPqdNDuR3D2gptnYM37WbdvcV/Svfs9HOq9B7ZO2saTy0iyJSzK/eIYMl5LPAe9Hs/X/iCCghRRQrk4a6AMrM5I6GFY1BNMSWqrTcsp6pca8WQ13oDGH6vL6z6Cg3Oee1eL9qutWg1KuVE4n4UWxtj3M9y7DQVLQrnOWkcjLMDI5i9Rp3hBFOB2XNIj63/ZFsLa42FZe1DHgupYUnRq68uxpVm7f6G2YMeEqdVGq76mdTS5jiRbwqKcDFMnEJWy7+J5OeX34GLjH0hSDJSO3ED4xulah2RZbp2HeZ0h8a5aCKPjr6A3aB1VzlHvPXU8A6gFM44ve+ZdJCabWGbphTHio2BXyv9Ow1FyjggATAqcv/HkrnzjVgZjfErVwmfmXw/qp3R5XjlC7e4mskZ81P1qow1HyRx62UCSLWExEpNNnLuuJluB3tKNUDy/GvVb8lehNwAouGMcpsv7NY7IQsSEwx8dIO4meJaHbvPlg/VZ6XTqoP2q/QFF7Yp5eu0z7WLDyQhu3k3E3dmWxgHu2RPni9ozQ/0S5hYAZTpoHY2wEPtCIgmPjn/segUIi4pnX0g2zI3V4AMoUgsSY9TxW9k0912es2u62oLtFgAVumsdTa4kyZawGGevx5BkVHCxs7LcbjUix6jfeyzrlBpYk0zcgt4yMWZ8lNqidecS5PeHXsvATi5qPBedDlp9DeW6gClZnfQ4ZFumH74wpTBGl6o+WFtiYYx7t2H3D+pygw+kVUukuR7z+ETrebZ7JgYr6PQb2OVTu0Jv+izrj5HX3L0Ou39Ulxt/LP/r2cQC3+VFXnV/fi0XdDJ+RLwgz3z23Gz8NSEmD5ziw0hY8ro6YWNelBQPC19V509xdIfef4GThbao5BR6PbSfAS+1UiulLewBVw889WGXb8Wx/exNALpXs9AuhLt/hIRocA+EwPZaRyMsiLuzXZZu98xcfaB9SnKwazqcXZ89x8krtn8NSbFQuAoEtNE6mlxLki1hMe6P15Kr7SJrdKtXlu8KfkyCYo1tyAbY+Y3WIZmfyQjLBsClHepElb2WQQF/raPKHQzW0HkW+DdQx8DN6wThx5/4kD8PqK1a9UoWwrdAFlZtyypxkWoXQlCnA9DL1wRxX3X/Ani52vG4y6E6wMvVjur+2ThHU0BrqD5IXf7rTbV7tHh2dy7DgZnqcpOxUiQpG8m7qLAYwWFRgJR9F1nHoNcxqGt7PjX2A0DZ9PkzdffK8RQFVr0Dp1aBwRa6LwCv8lpHlbtY26l/V5/qEH9HHRN363yGmyYZTSw+oBbGeNVSC2Psmq6OifEsJ1e6xSMMeh2ftFXn23r4q3nq75+0DcSgz+Yv7i+PB49y6vjT5a+rF5XEs9nyBRgT1YtFxRpqHU2uJsmWsAiKoqTrRihEVint5YJr7QEsNdZHp5gwLR2Qd66Ebp4Ah+YAOnWsg389rSPKnWydoOdi9ctf7HWY2w7uXHlks40nr3MjJoFCTjY0Ke2hQaBPEXsL9v6sLkurlniMFmW9mNGrMp6u6bsKerraMaNXZVqU9cr+IKztoPNMsHZQL6DtyIO9Fl7E9VNwZKG63OQTbWPJA+SdVFiEa3fuER2fjJVeRwl3mUxPZK23m5biJ6e3OGXyRR97HZYOAGOy1mFlr72/wLYv1eU2UyHwFW3jye3s86tj4QqWgKgr8Ed7dfD5A1Ln1upcxRcbKwv8+N31rTp+w6uCOhZNiMdoUdaLHR80Zl7/qvQpaWRe/6rs+KCxeRKtVG6loNVX6vLmiXB5r/mOndNtGg+KSW299qmidTS5ngW+24u8KHW8Vgl3J2ytpBqOyFr2NgbGdqjKW0lvc1exU8cvbZ6gdVjZ5/hyWPO+utzww5Qy5SLbOblBn3/A1RdunVO7FN67DcDV23FsPXMDgO7VfLWMMmN3b8C+X9XlRh/J+A3xVAa9jhr+BahSSKGGf4Hs7zqYkYqvqlVBlZSxqSn/b+IJrh5Uu5br9NB4jNbR5AmSbAmLIF0IRXarX8qNchWqMirpdfWOHVPhzH/aBpUdzm9W535CgWqvQ4P3tY4ob3H1URMuR3eIOA7zu0DCXRbvv4KiQO3iBSlayFHrKB+1cxokxalVyUo20zoaITJHp4PWU9XpLKKuwIph6lhV8Xgbx6k/K/QA9wBtY8kjJNkSFkGKYwhzGNMmkO229ZmT/LJ6x/JBcPuStkFlpWuH4M9eYEpSS3a3nCwtFFooWBz6/K3OB3R1P6aFPfhrv1o0o4clFsaICYf9v6nLDT+Uc0bkLHYu6vgtvTWcXHm/wp541IUtELIVDDbQcJTW0eQZkmwJi5DajVCSLZGdCjnZ8mGrACYk9+KoUlytHrekHyQnaB3ai7t1Xm1FSbyrVpfq+ItMUKkljzLQaznYOKG/uI2x8V/i7qCnWRkLLIyxYxokx6sVFUs00ToaIZ5d4crQ9FN1ee3op07BkCcpCmxMmQi6an/IZ4EXfnIpSbaE5qLjk7gcGQfIHFsi+3Wt6ktFfw8GJwznrt4ZQg/Buo+1DuvFRIepBRnibqrFDbrNAytbraMSPlWgxyISdTa8bDjEzPwzsbW0T93o0PstAY1GS6uWyLlqvqV2gTUmwNL+kBirdUSW5dQquHYQrB2h3ntaR5OnWNrbvsiDTqW0anm72pHf0UbjaERup9PpmNihHDcMHgyLf1O9c98vcHyZtoE9r3t3YH5ndYLK/P7Qc6narUZYhLACVRmc+DZJioGyt9bBv+9a1piSHd+oX06L1IJijbSORojnp9dD+xng5Ak3T8Na6SaXxmSEjePV5VpvqcV8hNlIsiU0FxyqjteSVi1hLiXcnRjcsDibTZWYre+g3rliONw8q21gzyrpHix6VS3E4OShlh53ctc6KvGAxfuvstFYiR/yvw/o4OBsWD/GMhKuqKtqPACNZKyWyAUcC6ldqNHBobk59yJaVjuySE1A7fND7WFaR5PnSLIlNJc2XksqEQozeqtRcYq5OTI+riMXHCuqY50W94HEOK1DyxxjMiwbCJd2gq0L9FoGBfy1jko8wGhS+DNlbi3/hr3hle/UFbumw7avNIwsxfavwZgIfnXBv77W0QiRNYo1gPop3eRWjoDIEE3D0VxyAmyZpC7XfQfsXLWNJw+SZEtoLjgspey7tGwJM7K1MjCxQzmMGOh263WS7ArB9WD493+W0erwJIoC/76j9sE32EKPheBZTuuoxEO2nblBaFQ8+RysaV7GEyr3geYT1ZWbP4c9P2kX3J3LcOgPdbnRaO3iECI7NBgFvjUhIVqdfys5UeuItHNglloW39kLqg/SOpo8SZItoalko4nTEdKyJbRRs1hBulb14Qb5GaV/B0WnhyML4PAfWof2ZJs+V7vI6PTQ+XcoWlfriEQGFu5TW7U6VvLBzjqlMmStIdAwJblZ+wEcnqdNcNu+UqcI8G8g54/IfQxW0Ok3tRXn2kHYNF7riLSRcBe2fakuN/gArO21jSePkmRLaOrCzVgSk0042hjwze+gdTgiD/qwVWkKOtqwLNKfPX4pBTNWj4TwY9oG9jh7f4btKV3QWk+F0m21jUdkKCI6no2nrgPQo7pv+pUNPoCaQ9TlFcPgxN/mDe72RQiary43+tC8xxbCXPL5Qrsf1OVd38G5DdrGo4U9M9QqtQWKQaVeWkeTZ2mabBmNRsaMGYO/vz/29vYUL16c8ePHozzQhefu3bsMHToUHx8f7O3tCQwM5Kef0ne9iI+PZ8iQIRQsWBAnJyc6depEREREum0uX75M69atcXBwwN3dnZEjR5KcnGyW5ykeLzhU7UJY2ssFvV4GZwvzy+dgw5g2gQD0O1eXOL8m6pxDi/tAfJTG0T3k+DJY84G63OgjqPqatvGIx1py4ApGk0JVv/yU9HBOv1Kng+YT1G6Fikkde3d2vfmC2/YlmJKheGMoUtN8xxXC3Eq3hWoD1eXlb6gTeOcVcZFqkgnq54XBWtt48jBNk63JkyczY8YMvv/+e06ePMnkyZOZMmUK06dPT9vm3XffZe3atcybN4+TJ08yYsQIhg4dyooVK9K2eeedd1i5ciVLlixh69athIaG0rFjx7T1RqOR1q1bk5iYyK5du5gzZw6zZ89m7NixZn2+4lFp47WkC6HQULuK3tQrWYiEZBiR8CaKqw9EXoB/hljO+K3zm9QvCyhQ7XWoP1LriMRjmEwKi/ZfAaBH9cdMHKrTQZtpUKaj2p3vz15wcWf2B3frPAQtVJcbSquWyAOaTQCPsmoLz19vgMmkdUTmseMbdcyaZzn1fUZoRtNka9euXbRr147WrVtTtGhROnfuTLNmzdi3b1+6bfr27UvDhg0pWrQogwYNokKFCmnbREVF8fvvvzN16lQaN25MlSpVmDVrFrt27WLPnj0ArFu3juDgYObNm0fFihVp2bIl48eP54cffiAxMQ8PmrQAD7ZsCaEVnU7H5+3LYmulZ93FJDaX+xL01nBypdoNQ2vXDsGiXuqX8jIdoOVkKdNtwXacu8nV2/dwsbOidXmvx2+oN0CHn6Fkc7U1dUE39bXOTtu+BMWoTv7qWy17jyWEJbC2g84zwdoBLmyBndO0jij7RYeq80cCNB6rzkEmNGOl5cFr167NL7/8wpkzZyhVqhRHjhxhx44dTJ06Nd02K1asoH///nh7e7NlyxbOnDnDN998A8DBgwdJSkqiadOmaY8JCAigSJEi7N69m5o1a7J7927KlSuHh4dH2jbNmzdn8ODBnDhxgkqVKj0SW0JCAgkJCWm/R0erSUFSUhJJSUlZ/rfIixRFIThM7aZVys0h2/+uqfuX109kxNvFhmGNivPV+rP8b5cVW+uPxWXLGJT1YzB6VkTxefYvpllyzt06h9X8zuiSYjEVrY+xzfdgNKk3YZHm77kIqC2mBkwkJT3ptdJBh98w/Nkd/aWdKPM6ktx7JbgFPPNxn3q+3TqH1dE/0QHJdd9DkfdC8YJyzOdqvmLomk3C6t+3UTZ9jtGn5nO9p+cU+s2TMCTHY/KtibFoQ7D01yeTLO18y2wcmiZbo0aNIjo6moCAAAwGA0ajkQkTJtCzZ8+0baZPn86gQYPw8fHBysoKvV7Pr7/+Sv366pwg4eHh2NjYkC9fvnT79vDwIDw8PG2bBxOt1PWp6zIyadIkxo0b98j969atw8FBCjlkhahEiIy1QofChcM7uXrUPMddv96MYyNEjuJtAi8HA2FxSQw8UJRv8lWn8J19JC3oyZaA8SRaOT99Jxl43nPOLuk29c6MxzrxFnfsi7LTpSfJ6zY+176EeUQnwvqTBkCHd9wFVq++kKnHWbn2obZDGPnjLmCc2YbtpT4iztbj6Q/MwOPOt8oXf8JXMRHmUol9QeEQtPq59i/Ew3LE56qSjyr5a+Jzew+JC3uz+aXxJFs5ah1VlnOMD6fxSbXK6U67JkSuWaNxRFnPUs63uLjMzcupabK1ePFi5s+fz4IFCyhTpgxBQUGMGDECb29v+vbtC6jJ1p49e1ixYgV+fn5s27aNIUOG4O3tna41K6uNHj2ad999N+336OhofH19adasGS4u0uUtK2w5cwMOHqa4mxPt29bJ9uMlJSWxfv16Xn75ZaytZaCoyJhPhTt0+3Uf+24auNhrBt6bumAfeYHmscswdl+kllvPpBc65+KjsPqjLbrEmyj5/XHsu5pmjm7P+GyEuf28LQSTcpaKvq4M7FLj2R58rzHKH69gd+MkTa9NJ7nPKnDxzvTDn3i+3TyD1eHdABTq8jWtPMs/W2z/b+++45us2j+Of5J0T2YXMgrIKEM24gARBASR9fgIorIUVJ4HgUcURVBAZKiIoqKoDH8ynDhAEVBQGbLLagHZq4NdSulK8vsjNBpboJQ2d8f3/XrlRXLfJ3eupIe2V8851xHJQZH7uZrWEvtHrfE7d4h7M37A2nlWsZuSbVn0OGZs2Krfw63/HmZ0OPmqsPW3rFlv12JosjVixAhGjhxJz549AahXrx6HDx9m4sSJ9OnTh0uXLvHCCy+waNEiOnXqBED9+vWJjo7m9ddfp23btoSFhZGens65c+dcRrcSEhIICwsDICwszGUdWNb5rHM58fb2xtvbO9txT0/PQvEFLg72Jjr+IhAVEezWz1RfQ7maZlXL83DzyvzfH4d5cekxlvaci/ecezAf+AXzureg1bPXfc3r7nMZl+CLRxybLAeEYnr0GzxL5f6XbjGGzWbniy3HAXioeeXr/z7jGQKPfguzO2A6cwDPBf+Cfj+Cf7nru0xO/W3164Adat2HZ8XG1xeXyDUUmZ+rnmXggVnwcTvMu7/HvP1TaNLf6KjyT9x2iFkEgLntS5iLwtckDwpLf8ttDIaumEtJScH8j0V7FosF2+VKMVnro67WpnHjxnh6evLzz39NrdmzZw9HjhyhRYsWALRo0YIdO3aQmJjobLN8+XKCgoKIiooqkPcm16ZKhFJYjehQk5BAbw6eusg7Md7Q6Q3HiZWvOhZYFyRrJnw5AI6sBe8gePgrKF2lYF9T8sW6A6c5fDqFQG8P7rtaYYyrCQx1JFxBFeDUXvi/bnDp3I0FlhADuxy/gHHXyBu7lkhRV6ExtHnJcX/p847/H8VF1ubNdf/lqEIohYKhyVbnzp2ZMGECS5Ys4dChQyxatIipU6fSrVs3AIKCgmjVqhUjRoxg1apVHDx4kDlz5vDJJ5842wQHBzNgwACGDx/OypUr2bx5M/369aNFixbceqtj/5B27doRFRXFI488wrZt2/jpp5948cUXGTx4cI6jV+IesZcrEUapEqEUMkE+noy9vw4A7/+6nz8julzeENLu2BMpKa5gXthuh8VDYc8SsHhDr4X6gVmEzN9wBIAuDSPw87qBiSOlKjkSLr9yEL/dUaUw/WLer/frJMAOUV3Un0QAWvwHqrd1VAH9sh+k527tTaF2eC38uQzMHtqsvJAxNNmaPn06//rXv3jqqaeoXbs2zzzzDIMGDWL8+PHONgsXLqRp06b07t2bqKgoJk2axIQJE3jiiSecbd58803uu+8+evToQcuWLQkLC+Prr792nrdYLCxevBiLxUKLFi14+OGHefTRRxk3bpxb36/8JSU9k4OnHb88qOy7FEYd6obRtnYIGVY7z3+9A1uH1xx7tVw8CV/2d4xA5bdfxsPW/3OsC/vXLKhS8GsZJX+cTk5j2S5HwaUr7q11PcrdDI9+Az7BcPQPWNgbMtOu+bRs4ndAzLeACVppVEsEcJRC7/o+BITCyd2wtIj/37DbYcXlom4NH4Gy1YyNR1wYumYrMDCQadOmMW3atCu2CQsLY/bs2Ve9jo+PD++++y7vvvvuFdtUrlyZH35Q5aXCYnf8Bex2KB/oTflAjS5K4WMymRjXpS7r9v/KpsNnWRh9iof+/Ql80Moxxe+XcXBPPv7B5o/34ffL0xXvmwa178u/a0uB+2rLMTKsdm65KZg6EcH5c9GwetD7S/ikKxxY6UjyH5gLluv40b1qkuPfOt0gVNPmRZwCykP3mY7/X1vmQtW7oG4R3fz3z2WOP8p4+ORpXbEULO1yJoaI0RRCKQIiSvnyv3Y1AZj4YyyJXhWgyzuOk2vegt359AecHV/C0ucc9+9+ERr3yZ/rilvY7XYWbjgKQM/8GNX6u4rNoNd8sHjB7sXw7WCw5XKPtRPRjudg0lotkZxUvQvuvFx5+vun4ewhI6PJG5sNfr48I6z5oOuqYCruoWRLDKHiGFJU9LmtCvVvCuZCaibjvo+BOl2h+eVpzN88ceM/nPf/AosuX6/ZQLjzmRu7nrjd+oNnOHDqIv5eFjrfUgC/6FS9Cx6YAyYLbF8IP45wTBu6lqxRrXoPQPma+R+XSHFw1/NwUzNIS3IUJ7IWjg1zc23X15CwA7yD4fahRkcjOVCyJYaIvZxsab2WFHYWs4lXu9XDYjaxeHscK3cnwj3joUITSD0Pn/eBjNS8Xfz4Zlj4MNgyoE536DC52O35UhIsuFwY4/4GFQjwLqDZ+bU6Qbf3ARNs/Ah+vsYU1uNbYO+PjvV/rZ4rmJhEigOLJ/T4yLE+8vgm+OUVoyPKPWvGX/He/l/wK2NsPJIjJVvidlabnd1xFwBNI5SioW6FYPrfXgWAF7/ZSYrN7Bhp8C0NcdHwUx4qP536E+Y9ABkXHSMX3d53LNqWIuXsxXR+3JFVGKNiwb5Y/X/DfVMd91dPhd+nXrntqomXn/MglKtesHGJFHWlK8P90x3310yDfT9ftXmhsfX/4OxB8C8PzZ80Ohq5Av1kF7c7dPoilzKs+HiaiSznb3Q4Irky7J4aVCjly/Fzl3hz+V4oVRG6f+g4uelj2P5F7i+WFAf/1x1STkNEQ3jwU/BQoZii6Kstx0i32qgTEUS9CvlUGONqmvT/qzDLz2Nhw4fZmpiOb3IsmDdZoOWIgo9JpDiI6vLXBseLBsGFBGPjuZb0FPh1iuN+yxHgHWBsPHJFSrbE7bKKY9QMC8Ji1pQpKRr8vDx4pWtdAGatOcTO4+fh5nv+WmP1/dNwcs+1L3TpLHzaHc4fgTLVHNXmvAMLMHIpKHa7nYUbHYUxejWrhMldU0Bvf/qvJOqHZ2DbQpfT5t8mO+7c0ksloEWuR/tXISTKscXHokG5L0ZjhA0z4UIcBFeCxn2NjkauQsmWuF3Wei1NIZSipnWtEDrVD8dqs/PCoh1YbXbH5pFV7nRMB/z80atvPptxCRb0gsQYCAiDRxaBfzn3vQHJV5sOn2VfYjK+nha6NHBzBbDWo6DZIMf9RU9A7PcAlEnei/nASsfGpj5BsHKie+MSKco8feFfs8HD17Hdwtq3jI4oZ5fOweo3HfdbP6+ZEYWcki1xO1UilKLspc5RBPp4sP3YeeauPQRmC/T42JE8ndwNi4flXCnOmunYJ+nIOkfVqIe/cqwTkCJrwXpHYYzOt4QT6OPp3hc3maDDJAirD9jh8z6YDqyiVtzXjvOhdeGP9xz9U0RyL6QW3Ht5dPiXV+DoRmPjycna6ZB6DsrXcqzLlEJNyZa4nfbYkqIsJNCHkffWAuCNZXs4ce4SBIbCv2ZdLs39GWye4/okux0WPw17fgCLN/RaAGF13R+85JvzKRks2REHOKYQGsJshsdXQrmaYLdiWfgg5ZNjsJvMjsItrUdpg1ORvGj0qKNCrC0TvurvGEkqLJIT4Y8Zjvt3j9YfVIoAJVviVqeS00i8kIbJBLXCtE5FiqZeTSvRpHJpLqZbGfPtLux2O1S5HdqMdjRY8j/HhrKXmVdNgK2fAiZHCe8qtxsSt+SfRVuPkZZpo1ZYIA0qljIuEIsHPPE7lKmKyW4FwGS3KdESuREmE3SeBqUqw7kjjjW5udnbzh1+e90xbb1CE8fPEyn0lGyJW2Wt16pS1h//gtqPRqSAmc0mXu1eD0+LiRWxCfy0y1H6m9uehrLVwW6FT+6H1PNUTfwJy9ppl59ph5DaRoUt+cRut7NggwGFMa7EwxueWIMdRxx2s6cSLZEb5RPsmLFg9oCYb2DLXKMjgrOHYdMsx/02Y7QvYxGhZEvcSlMIpbioERrIoJaOSm8vfbeLpNQMx7SuAcsda7JSz+PxfgvqHZ/315M02lAsbD16jj0JF/D2MNO1YQWjw3FY9w4m7FhNHphsGX+VhBaRvLupiSOpAfjxOUiMNTaeVZPAluHYm7FqK2NjkVxTsiVulVUco3a4phBK0fefu6tTpawfCUlpvP7T5bLvfmXg0W/AZMF0MfGvxne9oESrmMgqjHFf/QiCfd1cGCMnv06BlROwthzJ4gazsLYcCSsnKOESyQ8t/gvV2kBmKnzRz1FV1giJsbBtgeN+VgIoRYKSLXGrWFUilGLEx9PChG71APi/Pw6z5chZx4kKjeC+qWTN8LdbvOCu54wJUvJVUmoG328/AUCvZhUNjgZnokXrUdgu7/lmu/MZxyiqEi6RG2c2Q7f3wT8ETsbC0ueNieOXVwA71O4MFRobE4PkiZItcZvUDCv7Tzr2IIoKDzY4GpH8cXv1cnRvVAG7HV74egcZ1subYCYnYgLHtC5run7pLSa+3Xqc1AwbN4cE0LhyaaPDAZs15+mprZ51HLdZjYlLpDgJCIHuHzjub54Nuxa59/WPbYLdi8FkdlQglCJFFQrEbfYmXMBqs1PG34vQIG3AJ8XHi52iWLk7kd3xF/jo94M8afrqr2ldF6K4LzAGy8oJjsaaSlhk2e125hemwhjg2ND0StTXRPJPtbvhjmGOzYS/exoiGrlvr8Sfxzr+veUhKF/TPa8p+UYjW+I2sX9br1UofkkRySdl/L0Y1SkKgPRfJmpaVzG1/dh5YuOS8PIw071RISmMISLu03oU3NQU0s7DVwPAmlHwr7l/JRz8DSxecNfIgn89yXdKtsRtVIlQirMejSpwW7Wy2G1Wvgx6lMw7nmH9wTNsPmVi/cEzWO8coWldRdyCDY7CGB3rhlHKz8vgaETE7Sye0ONjR8XZYxth5asF+3p2O/w8znG/yQAoVQjWicp10zRCcZsYFceQYsxkMjGhWz3aT/s36Yk2XnllBecuZQAWPvlzE+HBPrzU+RE61A03OlTJg+S0TL7bllUYo5LB0YiIYUpXhvvfgi/6OqYURt7pmGJYEGK/hxNbwNMf7vxfwbyGFDiNbIlb2Gx2YuMuAFBbI1tSTEWW86dDVCjA5UTrL/HnU3ny0y0s3RlnRGhyg76LPkFKupWq5f1pFlnG6HBExEh1ukHjvoAdvh4EyYnXesb1s2ZerkAItBgMAeXz/zXELZRsiVscO3uJ5LRMvCxmqpUPMDockQJhtdnZcOhsjueyysCP/T4Gq82eYxspvLKmEPZqWkgKY4iIsdpPhPK14WIiLHoCbLb8vf72hXBqD/iWhtv+k7/XFrdSsiVuERN3HoAaYQF4WtTtpHjacPAM8UmpVzxvB+LOp7Lh4Bn3BSU3bOfx8+w4fh4vi5kejW8yOhwRKQy8/OCB2eDhC/t/hnXT8+/amWmwapLj/h3DwUfb5RRl+q1X3ELFMaQkSLxw5UQrL+2kcMga1WpfN4wy/iqMISKXhdSGDhMd938e59gPKz9smgXnj0JgBDR7PH+uKYZRsiVuEaP1WlIChAT65Gs7Md7FtEy+jc4qjKFKYCLyD437QlRXsGXCl/0h9fyNXS/tAvz2uuP+Xc+Bp++NRigGU7IlbpG1x5ZGtqQ4axZZhvBgH660oscEhAf7qMBCEbJ4+wmS0zKpUtaPFlXLGh2OiBQ2JhN0fgtKVYJzh+H7oY6S7Xn1xwxIOQVlqkGDh/MtTDGOki0pcOdS0jl+7hIAtVX2XYoxi9nES50dmxvnlHDZgZc6R2Exq8BCUbFgw1EAejZTYQwRuQLfUtBjFpgssOtr2Pp/ebtOyhlYe3nt192jwKIdmooDJVtS4LL217qptC9BPp4GRyNSsDrUDWfGw40IC855quDJ5HQ3RyR5FXMiieij5/C0mPiXCmOIyNVUbAptRjvu//AsJO6+/musngppSRBWD6K65W98YhglW1LgsvbX0hRCKSk61A1n9XN382n/Jjx6s5VP+zfhf+1qAPDyd7v4de9JgyOU3Fi40VEY456oUMoFeBscjYgUerc9DVVbQ+Yl+LIfZFzK/XPPH4f1Mx3327wEZv2KXlzoKykFzlmJUFMIpQSxmE00jyxD43J2mkeW4T+tq9Oj0U1YbXb+M28LexMuGB2iXMWldCuLth4HoFezSgZHIyJFgtkM3T4A//KQGAM/jcr9c3+dDNY0qHQbVG9bcDGK2+V6Mujbb7+d64sOGTIkT8FI8RSj4hgimEwmXu1el6NnUthw6Az952zkm8G3a8SkkFqyI44LqZlULOPL7dXKGR2OiBQVgaGOhOvT7rDpY6jaCqK6XP05p/bB1k8d99u+5Ci6IcVGrpOtN9980+XxyZMnSUlJoVSpUgCcO3cOPz8/QkJClGyJU3qmjX2JKvsuAuDtYeH9RxrT7b01HD6dwsBPNjH/8Vvx8bQYHZr8Q9beWj2bVsKsgiYicj2qt4Hbn4Y1b8F3/4WIho5qhVeycgLYrVCjA1S61X1xilvkehrhwYMHnbcJEybQoEEDYmNjOXPmDGfOnCE2NpZGjRoxfvz4goxXiph9iclkWO0E+nhwU2ntFSFSxt+LWX2bEuTjwZYj53j2y+3Yb6RMsOS7vQkX2Hz4LBaziQdUGENE8uLu0VChiWPfrS8HgDUj53Zx2xwVDDE5niPFTp7WbI0ePZrp06dTs2ZN57GaNWvy5ptv8uKLL+ZbcFL0/X0KocomizhUKx/A+w83xsNs4rttJ3jr5z+NDkn+JmtUq23tEEKCtAG1iOSBxRP+9TF4B8GxDbBqYs7tfr48SFHvXxBW133xidvkKdmKi4sjMzMz23Gr1UpCQsINByXFR1ZxDE0hFHF1W/VyvNLV8YN12oo/+Tb6uMERCUBqhpWvt6gwhojkg9JVoPM0x/3fp8KBVa7nD62BfcvB7AGtX3BzcOIueUq22rRpw6BBg9iyZYvz2ObNm3nyySdp21YVVOQvsXGqRChyJT2bVWJQy6oAjPhyO5sPnzE4IvlxZxznL2VQoZQvd95c3uhwRKSoq9sDGvUB7PD1QEi+vPWH3Q4/j3Xcb/QolKlqWIhSsPKUbM2aNYuwsDCaNGmCt7c33t7eNGvWjNDQUD766KP8jlGKKLvdrkqEItfwXIdatIsKJT3TxsBPNnP0TIrRIZVoCzYcBeDBphWxqDCGiOSHDpPArywkJ8A3T4DNBnt/gqPrwcMXvAJg5RWmGUqRl6dkq3z58vzwww/s3r2bL774gi+++ILY2Fh++OEHQkJC8jtGKaJOnE/l/KUMPMwmbg4NMDockULJbDYxrWcD6lYI4vTFdPrP2UhS6hUWUkuB2peYzIaDZzCb4N9NKhodjogUF15+UKe74/6+FbBuOvxyea1WxC2w9m0wqyptcZXr0u85qVGjBjVq1MivWKSYyVqvVT0kAG8PfRMRuRI/Lw8+erQpXd5dzZ+JyQyet4XZfZviYdG+8+608HJhjLtrhRAWrMIYIpKPOr0O54/C3qWwfIzjmMUbjvwBrUdBq2eNjU8KTJ6Srf79+1/1/KxZs/IUjBQvsZpCKJJrYcE+fNynKQ+8v47f/zzFy9/vYnyXuqri6SZpmVa+2nIMUGEMESkgvRbCu83h1B7HY2uaEq0SIE9/Nj179qzLLTExkV9++YWvv/6ac+fO5XOIUlRljWypOIZI7tStEMxbPRtgMsGnfxxh9ppDRodUYvy0K4GzKRmEB/vQqoYKY4hIATCZYMAy4PIf0SxeSrRKgDyNbC1atCjbMZvNxpNPPkm1atVuOCgpHlQcQ+T6tasTxgv31mbCD7G8siSGKuX8uLtWqNFhFXsL1jumEP67SUVN3xSRgrNhJmB3JFrWdPh1ihKuYi7ffqKYzWaGDx/Om2++mevnWK1WRo8eTWRkJL6+vlSrVo3x48djt9udbUwmU4631157zdmmSpUq2c5PmjTJ5bW2b9/OnXfeiY+PDxUrVmTKlCk3/qblii6kZnDkclU17bElcn0euzOSXs0qYrPDf+dvdY4SS8E4eOoi6w6cxmSCfzdVYQwRKSC/ToGVExxTB0efdPy7coLjuBRbN1Qg45/279+f42bHVzJ58mRmzJjB3LlzqVOnDps2baJfv34EBwczZMgQwLGB8t/9+OOPDBgwgB49ergcHzduHI8//rjzcWBgoPN+UlIS7dq1o23btrz//vvs2LGD/v37U6pUKQYOHJiXtyrXsDv+AgDhwT6U9vcyOBqRosVkMjGuS12OnElhzb7TDJi7kW8H305IkIo2FISFGx2jWnfVKE+FUr4GRyMixdLfE62skaysf1dOcH0sxUqekq3hw4e7PLbb7cTFxbFkyRL69OmT6+usXbuWLl260KlTJ8AxQrVgwQI2bNjgbBMWFubynG+//ZbWrVtTtarr5m+BgYHZ2maZN28e6enpzJo1Cy8vL+rUqUN0dDRTp05VslVAnOu1NKolkieeFjPvPdSYbjPWcODkRR77ZBOfDWyBr5cqe+an9EwbX25SYQwRKWA2a87FMLIe26zuj0ncIk/J1tatW10em81mypcvzxtvvHHNSoV/d9tttzFz5kz27t1LjRo12LZtG6tXr2bq1Kk5tk9ISGDJkiXMnTs327lJkyYxfvx4KlWqxEMPPcSwYcPw8HC8vXXr1tGyZUu8vP4aYWnfvj2TJ0/m7NmzlC5dOtv10tLSSEtLcz5OSnIkDxkZGWRkaA+ca9l5/BwANUL9C83nlRVHYYlHir8b7XN+njDz4YY88MF6th87z9CFW3j7wVswa7PdfLN0ZzynL6YTEujNndVKF+nvD/oeJ+6mPncd7njG8W9On9Vtw658TpwKW3/LbRx5SrZWrlyZl6dlM3LkSJKSkqhVqxYWiwWr1cqECRPo3bt3ju3nzp1LYGAg3bt3dzk+ZMgQGjVqRJkyZVi7di3PP/88cXFxzqQtPj6eyMhIl+eEhoY6z+WUbE2cOJGxY8dmO75s2TL8/Pzy9H5Lkj92WwATqXH7+OGHP40Ox8Xy5cuNDkFKmBvtc49EwrsxFn6KSWTwzJ/oXMmWT5HJuzFmwEyDoEss+2mp0eHkC32PE3dTnxN3Kiz9LSUlJVft8pRs3X333Xz99deUKlXK5XhSUhJdu3bll19+ydV1Pv/8c+bNm8f8+fOdU/uGDh1KREREjtMRZ82aRe/evfHxcV238PdpjfXr18fLy4tBgwYxceJEvL29r/8NAs8//7zLdZOSkqhYsSLt2rUjKEhT464m02pjxMZfABu9O7aictnCkZxmZGSwfPly7rnnHjw9PY0OR0qA/OxzN209wbNf72TFcTNtm9WjR6MK+RRlyXXkTAp7163GZIKRD7akYunC8b0qr/Q9TtxNfU7cqbD1t6xZb9eSp2Rr1apVpKenZzuemprK77//nuvrjBgxgpEjR9KzZ08A6tWrx+HDh5k4cWK2ZOv3339nz549fPbZZ9e8bvPmzcnMzOTQoUPUrFmTsLAwEhISXNpkPb7SOi9vb+8cEzVPT89C8QUuzA6euUB6pg1/LwtVQ4IK3ZQnfQ3F3fKjz/27WWWOnE3lnZX7GP1dDFXKB3Jr1bL5FGHJ9NVWRwGmO6qXo2pIsMHR5B99jxN3U58Tdyos/S23MVxXsrV9+3bn/ZiYGOLj452PrVYrS5cupUKF3P+1NSUlBbPZtfq8xWLBZss+Rebjjz+mcePG3HLLLde8bnR0NGazmZCQEABatGjBqFGjyMjIcH4wy5cvp2bNmjlOIZQbE3t5f61a4YUv0RIpyobfU4ODpy+yZHscT3y6mUVP3U5kOX+jwyqSMqw2Pr9cGOMhFcYQEZECcl3JVoMGDZz7WN19993Zzvv6+jJ9+vRcX69z585MmDCBSpUqUadOHbZu3crUqVOzFdlISkriiy++4I033sh2jXXr1rF+/Xpat25NYGAg69atY9iwYTz88MPOROqhhx5i7NixDBgwgOeee46dO3fy1ltvXdeeYJJ7qkQoUjDMZhNvPHALx89eIvroOfrP2ciip26jlJ+2V7heP8cmcio5jXIB3rSN0qbRIiJSMK4r2Tp48CB2u52qVauyYcMGypcv7zzn5eVFSEgIFkvuyxJPnz6d0aNH89RTT5GYmEhERASDBg1izJgxLu0WLlyI3W6nV69e2a7h7e3NwoULefnll0lLSyMyMpJhw4a5rLcKDg5m2bJlDB48mMaNG1OuXDnGjBmjsu8FJObyyFZUhJItkfzm42nhw0eb0PXdNRw8dZEnPt3MJ/2b4+WRb3vUlwgLNjj21nqgyU14WvTZiYhIwbiuZKty5coAOU7zy4vAwECmTZvGtGnTrtpu4MCBV0yMGjVqxB9//HHN16pfv/51rSeTvLHb7c6Rrdoa2RIpEOUDvfm4bxP+NWMdfxw4w4vf7GByj/qYTJq2mxtHz6Tw258nAejZtKLB0YiISHGW62Tru+++495778XT05Pvvvvuqm3vv//+Gw5MiqaTF9I4fTEdswlqhgYaHY5IsVUrLIjpDzVkwJyNfL7pGFXLB/BEq2pGh1UkfL7pKHY73F69LJXLas2biIgUnFwnW127diU+Pp6QkBC6du16xXYmkwmrVbtgl1S7Lk8hrFo+AF+v3E8pFZHr17pmCGPui+Ll72OYvHQ3Vcr606FuzhVWxSHTauPzTUcB6KXCGCIiUsByPVHdZrM5q/vZbLYr3pRolWwqjiHiXn1vj+TRFpWx22HoZ1vZcey80SEVaiv3nCQhKY0y/l7co8IYIiJSwPK0KviTTz4hLS0t2/H09HQ++eSTGw5Kiq6ssu9aryXiPmPui6JVjfKkZtgYMHcjcecvGR1SoZVVGONfjW/C20Oj7yIiUrDylGz169eP8+ez//X0woUL9OvX74aDkqJLlQhF3M/DYuadhxpSMzSQxAtpDJiziYtpmUaHVeicOHeJVXsSARXGEBER98hTsmW323OsenXs2DGCg4NvOCgpmlLSMzl46iKgaYQi7hbo48nHfZtQLsCLmLgknl64FavNbnRYhcrnm45is8OtVctQtXyA0eGIiEgJcF2l3xs2bOjc1LhNmzZ4ePz1dKvVysGDB+nQoUO+BylFw+74C9jtUC7Am/KB3kaHI1Li3FTaj5mPNqHnzD9YEZvIxB9iefG+KKPDKhSsNjufb1RhDBERca/rSrayqhBGR0fTvn17AgL++sugl5cXVapUoUePHvkaoBQdsZpCKGK4RpVK88YDt/DfBVv5aPVBqpYP4KHmSi5+23uSE+dTKeXnSfs6qtgoIiLucV3J1ksvvQRAlSpV6NmzJ97eGr2Qv6gSoUjh0PmWCA6eusjU5XsZ/e1OKpXx446byxkdlqHmXy6M0aPRTfh4qjCGiIi4R57WbEVFRREdHZ3t+Pr169m0adONxiRFlIpjiBQe/727Ot0aVsBqs/PkvM3sS7xgdEiGiT+fyi+7HYUxejVTYQwREXGfPCVbgwcP5ujRo9mOHz9+nMGDB99wUFL0WG129sQ7fpmLCg80OBoRMZlMTOpRj6ZVSnMhNZP+czZxOjn7lh0lwRebjmK12WlapTTVQ/T9SURE3CdPyVZMTAyNGjXKdrxhw4bExMTccFBS9Bw+fZGUdCs+nmYiy6nKl0hh4O1h4YNHmlCpjB9HzqQw6P82k5ZZsjaet9nsLFRhDBERMUieki1vb28SEhKyHY+Li3OpUCglR9YUwpphQVjM2bcFEBFjlPH3YlbfpgT6eLDp8FlGfrUDu73klIT/fd8pjp+7RJCPBx3rhRsdjoiIlDB5SrbatWvH888/77Kx8blz53jhhRe455578i04KTr+Ko6hKToihU31kABm9G6MxWxi0dbjTP9ln9Ehuc2C9Y7CGN1VGENERAyQp2Tr9ddf5+jRo1SuXJnWrVvTunVrIiMjiY+P54033sjvGKUIcJZ9VyVCkULpjpvLMb5LXQCmLt/L99tOGBxRwUu8kMqKWMcsjJ4qjCEiIgbI05y/ChUqsH37dubNm8e2bdvw9fWlX79+9OrVC09Pz/yOUYoAVSIUKfweal6JAyeT+Wj1Qf73xTYqlPalUaXSRodVYL7cfIxMm51GlUpRK0zfm0RExP3yvMDK39+fgQMH5mcsUkSdTk4jISkNk8mxZktECq/nO9bm0OkUVsQmMPCTTSx66nYqlvEzOqx8Z7PZWbjBURijpwpjiIiIQW6omkVMTAxHjhwhPT3d5fj9999/Q0FJ0RIb5yj5XrmMHwHeKpAiUphZzCbe6tmAB95fR0xcEo/N3cSXT7Yg0Kd4zUpYu/80R86kEOjtwX31VRhDRESMkaffjA8cOEC3bt3YsWMHJpPJWdnKZHJUobNaS1Zp4ZIuJs5RKEVTCEWKBn9vDz7u24Qu76xhT8IF/jN/Kx/3aYKHJU/LeAulBRsdhTG6NqyAn5f+CCQiIsbI00/Wp59+msjISBITE/Hz82PXrl389ttvNGnShFWrVuVziFLY/VWJUMmWSFERHuzLx32a4utp4de9Jxm3uPjskXgqOY1lu+IBFcYQERFj5SnZWrduHePGjaNcuXKYzWbMZjN33HEHEydOZMiQIfkdoxRyWcUxaivZEilS6t0UzJsPNsBkgk/WHWbOmoNGh5Qvvtp8jAyrnVtuCqZORLDR4YiISAmWp2TLarUSGOjYT6lcuXKcOOEoIVy5cmX27NmTf9FJoZeaYWX/yYuAphGKFEUd6obxXIdaAIxbHMPK3YkGR3Rj7HY7Czc6CmP0UmEMERExWJ6Srbp167Jt2zYAmjdvzpQpU1izZg3jxo2jatWq+RqgFG5/JiRjtdkp7edJWJCP0eGISB4MalmVfze5CZsd/rtgK7vjk4wOKc/+OHCGg6cu4u9lofMtEUaHIyIiJVyekq0XX3wRm80GwLhx4zh48CB33nknP/zwA2+//Xa+BiiF29+LY2QVSBGRosVkMvFK13q0qFqW5LRMBszZROKFVKPDypMFGxyFMe5vUAF/VUcVERGD5eknUfv27Z33q1evzu7duzlz5gylS5fWL9wlTFbZ99raX0ukSPPyMDPj4UZ0f28tB05dZOAnm1k48FZ8PC1Gh5ZrZy6ms3SnozDGQ5pCKCIihUCeRrZOnjyZ7ViZMmUwmUzs2LHjhoOSosNZiVDrtUSKvFJ+Xnzctyml/DyJPnqO/32+DZvNbnRYufb1lmOkW23UrRBEvZtUGENERIyXp2SrXr16LFmyJNvx119/nWbNmt1wUFI02Gx2ZyVCJVsixUNkOX8+eLgxnhYTS3bEMXX5XqNDyhW73e6cQtizqUa1RESkcMhTsjV8+HB69OjBk08+yaVLlzh+/Dht2rRhypQpzJ8/P79jlELq2NlLJKdl4mUxU618gNHhiEg+aV61LBO71wfgnZX7+GrzMYMjuraNh86y/+RFfD0tdGmgwhgiIlI45CnZevbZZ1m3bh2///479evXp379+nh7e7N9+3a6deuW3zFKIZU1qnVzaACeljx1JREppP7V+CaeuqsaACO/3s6Gg2cMjujqFmYVxrglgkAfT4OjERERccjzb8jVq1enbt26HDp0iKSkJB588EHCwsLyMzYp5JxTCLWZsUix9Ey7mnSsF0aG1c6g/9vEoVMXjQ4pR+dS0lm8Iw6Ans0qGhyNiIjIX/KUbK1Zs4b69evz559/sn37dmbMmMF///tfHnzwQc6ePZvfMUohpeIYIsWb2WzijQcacMtNwZxNyaD/3I2cT8kwOqxsFm09TnqmjVphgTSoWMrocERERJzylGzdfffdPPjgg/zxxx/Url2bxx57jK1bt3LkyBHq1auX3zFKIRV7eWSrtka2RIotXy8LHz7ahIhgHw6cvMiT8zaTYbUZHZaT3W5n4YajADzUvJK2HxERkUIlT8nWsmXLmDRpEp6ef82Lr1atGmvWrGHQoEH5FpwUXudTMjh+7hKgZEukuAsJ8uGjPk3x97Kwdv9pRn+zE7u9cJSE33LkHHsSLuDjaaZLgwpGhyMiIuLiupKtjh07cv78eVq1agXApEmTOHfunPP82bNnWbBgQb4GKIVT1nqtm0r7EuyrxegixV1URBDTH2qI2QQLNx7lw98PGB0SgLPce6d6EfpeJCIihc51JVs//fQTaWlpzsevvvoqZ878VaEqMzOTPXv25F90UmjFaAqhSIlzd61QXuwUBcDEH3fz0654Q+M5fymDxdtPAPBQcxXGEBGRwue6kq1/ThspLNNIxP1iVYlQpETqd3sVHr61EnY7DF0Yzc7j5w2L5bvo46Rm2KgRGkCjSqUNi0NERORKtDmS5IkqEYqUTCaTiZc71+HOm8txKcPKgLkbiT+f6vY47HY789Y7phD2bKrCGCIiUjhdV7JlMpmy/UDTD7iSJz3Txp+JFwCNbImURB4WM+/2bsTNIQEkJKUxYO5GUtIz3RrDtmPn2R1/AS8PM90bqTCGiIgUTh7X09hut9O3b1+8vb0BSE1N5YknnsDf3x/AZT2XFF/7EpPJsNoJ9PHgptK+RocjIgYI8vFkVt+mdH13DbtOJDF0YTTvP9wYs9k9f4Bb6CyMEU4pPy+3vKaIiMj1uq6RrT59+hASEkJwcDDBwcE8/PDDREREOB+HhITw6KOPFlSsUkj8fX8tjWyKlFwVy/gx89HGeHmYWRaTwOSlu93yuhdSM/hum6MwRq9mldzymiIiInlxXSNbs2fPLqg4pAiJUXEMEbmsceUyvPav+jy9MJoPfjtAZDl/ehZwAvTdthOkpFupVt6fplVUGENERAovQwtkWK1WRo8eTWRkJL6+vlSrVo3x48e7VDnMWif2z9trr73mbHPmzBl69+5NUFAQpUqVYsCAASQnJ7u81vbt27nzzjvx8fGhYsWKTJkyxW3vs7hxFsdQsiUiQJcGFRja9mYAXvxmJ2v2nSrQ18vaW6tXMxXGEBGRws3QZGvy5MnMmDGDd955h9jYWCZPnsyUKVOYPn26s01cXJzLbdasWZhMJnr06OFs07t3b3bt2sXy5ctZvHgxv/32GwMHDnSeT0pKol27dlSuXJnNmzfz2muv8fLLLzNz5ky3vt/iwG63ExuvSoQi4urpNjfTpUEEmTY7T366mX2Jydd+Uh7sOHaenceT8LKY6d7opgJ5DRERkfxyXdMI89vatWvp0qULnTp1AqBKlSosWLCADRs2ONuEhYW5POfbb7+ldevWVK1aFYDY2FiWLl3Kxo0badKkCQDTp0+nY8eOvP7660RERDBv3jzS09OZNWsWXl5e1KlTh+joaKZOneqSlMm1xZ1P5VxKBh5mE9VDAowOR0QKCZPJxOQe9Tl29hKbD59lwNyNLHrqdsr452/xigUbHaNa7euG5fu1RURE8puhydZtt93GzJkz2bt3LzVq1GDbtm2sXr2aqVOn5tg+ISGBJUuWMHfuXOexdevWUapUKWeiBdC2bVvMZjPr16+nW7durFu3jpYtW+Ll9dcP5vbt2zN58mTOnj1L6dLZ5/ynpaW5VFdMSnKM5mRkZJCRkXHD772o2n70DADVyvtjwUZGhs3giHIv6+tWkr9+4l4lrc9ZgHd71udfH6zn8OkUBn6ykTl9m+DtkT+TKC6mZfLt1uMA/LtRRIn5XHOrpPU3MZ76nLhTYetvuY3D0GRr5MiRJCUlUatWLSwWC1arlQkTJtC7d+8c28+dO5fAwEC6d+/uPBYfH09ISIhLOw8PD8qUKUN8fLyzTWRkpEub0NBQ57mckq2JEycyduzYbMeXLVuGn5/f9b3RYuSnYybAQqA1iR9++MHocPJk+fLlRocgJUxJ63OPVIY3L1jYdPgc/d5bRu9qNvJjadW6BBMX0y2U87FzOvYPfnBP8cMip6T1NzGe+py4U2HpbykpKblqZ2iy9fnnnzNv3jzmz5/vnNo3dOhQIiIi6NOnT7b2s2bNonfv3vj4+BR4bM8//zzDhw93Pk5KSqJixYq0a9eOoKCSu1bphwXRQCJtm9Si4+1VDI7m+mRkZLB8+XLuuecePD09jQ5HSoCS3OdubnCKx/9vKxtPmrnzlho82arqDV/z4w/+AJLo17IGne6MvGb7kqYk9zcxhvqcuFNh629Zs96uxdBka8SIEYwcOZKePXsCUK9ePQ4fPszEiROzJVu///47e/bs4bPPPnM5HhYWRmJiosuxzMxMzpw541zvFRYWRkJCgkubrMf/XBOWxdvb27l58995enoWii+wUXYnOBa917updJH9HEr611DcryT2ubtrh/Py/emM/mYnU1fso2pIIPfVj8jz9XadOM/2Y0l4Wkw82Kxyifs8r0dJ7G9iLPU5cafC0t9yG4Oh1QhTUlIwm11DsFgs2GzZ1wF9/PHHNG7cmFtuucXleIsWLTh37hybN292Hvvll1+w2Ww0b97c2ea3335zmVu5fPlyatasmeMUQsnZhdQMDp92DJnWVtl3EbmGR26tTP/bHSNQ//t8G1uPnM3ztRZuOApAu6gwygVk/0OYiIhIYWRostW5c2cmTJjAkiVLOHToEIsWLWLq1Kl069bNpV1SUhJffPEFjz32WLZr1K5dmw4dOvD444+zYcMG1qxZw3/+8x969uxJRITjr6gPPfQQXl5eDBgwgF27dvHZZ5/x1ltvuUwTlGvbE38BgLAgH1UBE5FcGdWpNm1qhZCWaePxTzZz7Gzu5rj/3aV0K99cLozRq4A3TBYREclPhiZb06dP51//+hdPPfUUtWvX5plnnmHQoEGMHz/epd3ChQux2+306tUrx+vMmzePWrVq0aZNGzp27Mgdd9zhsodWcHAwy5Yt4+DBgzRu3Jj//e9/jBkzRmXfr1NMnPbXEpHrYzGbeKtXQ2qFBXIqOY3H5m7iQur1VZJavP0EF9IyqVjGl9uqlS2gSEVERPKfoWu2AgMDmTZtGtOmTbtqu4EDB141MSpTpgzz58+/6jXq16/P77//npcw5bKYE5eTLU0hFJHrEODtway+Teny7hp2x19gyIKtfPhoEzwsuft734INjr21ejathNmcD2UNRURE3MTQkS0pWrJGtrReS0SuV0QpXz56tAk+nmZW7jnJK0tic/W8PfEX2HLkHB5mEw80uamAoxQREclfSrYkVzKtNueaLU0jFJG8uKViKab+uwEAc9Ye4pN1h675nKxRrTa1QwgJLPhtP0RERPKTki3JlYOnLpKWacPPy0LlMiV3U2cRuTEd64XzbIeaALz83S5W7Um8YtvUDCtfbzkGqDCGiIgUTUq2JFeyphDWCgvUmgkRuSFPtqrGA41vwmaH/8zf6hw1/6cfd8aRlJpJhVK+3HlzeTdHKSIicuOUbEmuqBKhiOQXk8nEhG71aB5ZhuS0TPrP2cjJC2nZ2i1Y79hbq2fTilj0Rx4RESmClGxJrvxViTDY4EhEpDjw8jDz/sONiSznz/Fzlxj4f5tIzbA6z+9LvMCGQ2cwm+CBJhUNjFRERCTvlGzJNdnt9r+SLY1siUg+Ke3vxcd9mhDs68nWI+cY8eV2Mq021u0/7axW2LpmCGHBKowhIiJFk6H7bEnRcPJCGqcvpmM2Qc3QQKPDEZFipGr5AGY83IhHP97A99tOsHJ3Islpmc7zW46cZenOODrUDTcwShERkbzRyJZcU9Z6rchy/vh6WQyORkSKm9uqlXNWG/x7ogVwLiWDJz/dwtKdcUaEJiIickOUbMk1/VUcQ+u1RCT/WW12VsQm5HjOfvnfsd/HYLXZc2wjIiJSWCnZkmvKWq9VO1xTCEUk/204eIa486lXPG8H4s6nsuHgGfcFJSIikg+UbMk1xWaNbIWrOIaI5L/EC1dOtPLSTkREpLBQsiVXlZKeyYFTFwFVIhSRghESmLtqg7ltJyIiUlgo2ZKr2hN/AbsdygV46xcdESkQzSLLEB7sw5W2LTYB4cE+NIss486wREREbpiSLbmqrOIYWq8lIgXFYjbxUucogGwJV9bjlzpHYTFfKR0TEREpnJRsyVU512tpCqGIFKAOdcOZ8XCjbBsYhwX7MOPhRtpnS0REiiRtaixXlVWJUMUxRKSgdagbzj1RYWw4eIbEC6mEBDqmDmpES0REiiolW3JFNpud3fEXACVbIuIeFrOJFtXKGh2GiIhIvtA0Qrmiw2dSSEm34u1hJrKcv9HhiIiIiIgUKUq25IqyphDWCgvEw6KuIiIiIiJyPfQbtFxRTNx5QMUxRERERETyQsmWXFHWyFZtrdcSEREREbluSrbkimLjVBxDRERERCSvlGxJjk4npxGflApALSVbIiIiIiLXTcmW5ChrVKtyWT8CvLVDgIiIiIjI9VKyJTlyFsfQqJaIiIiISJ4o2ZIcab2WiIiIiMiNUbIlOcqqRKiy7yIiIiIieaNkS7JJzbCy72QyoLLvIiIiIiJ5pWRLstmXmIzVZqeUnyfhwT5GhyMiIiIiUiQp2ZJsnFMIw4MwmUwGRyMiIiIiUjQp2ZJsYuIcyZamEIqIiIiI5J2SLcnm7yNbIiIiIiKSN0q2xIXdbic2TpUIRURERERulJItcXHs7CUupGXiZTFTrXyA0eGIiIiIiBRZSrbExa7LUwirhwTg5aHuISIiIiKSV/ptWlxoCqGIiIiISP5QsiUusioRqjiGiIiIiMiNUbIlLrIqEarsu4iIiIjIjVGyJU7nUzI4fu4SoJEtEREREZEbpWRLnGLjHaNaFUr5EuznaXA0IiIiIiJFm6HJltVqZfTo0URGRuLr60u1atUYP348drvdpV1sbCz3338/wcHB+Pv707RpU44cOeI8f9ddd2EymVxuTzzxhMs1jhw5QqdOnfDz8yMkJIQRI0aQmZnplvdZVDg3M1ZxDBERERGRG+Zh5ItPnjyZGTNmMHfuXOrUqcOmTZvo168fwcHBDBkyBID9+/dzxx13MGDAAMaOHUtQUBC7du3Cx8fH5VqPP/4448aNcz728/Nz3rdarXTq1ImwsDDWrl1LXFwcjz76KJ6enrz66qvuebNFQFZxDK3XEhERERG5cYYmW2vXrqVLly506tQJgCpVqrBgwQI2bNjgbDNq1Cg6duzIlClTnMeqVauW7Vp+fn6EhYXl+DrLli0jJiaGFStWEBoaSoMGDRg/fjzPPfccL7/8Ml5eXvn8zoqmWFUiFBERERHJN4YmW7fddhszZ85k79691KhRg23btrF69WqmTp0KgM1mY8mSJTz77LO0b9+erVu3EhkZyfPPP0/Xrl1drjVv3jw+/fRTwsLC6Ny5M6NHj3aObq1bt4569eoRGhrqbN++fXuefPJJdu3aRcOGDbPFlpaWRlpamvNxUpIjEcnIyCAjIyO/PwrDpWfa2JtwAYAaIb7F8j1mvafi+N6kcFKfE3dSfxN3U58Tdyps/S23cRiabI0cOZKkpCRq1aqFxWLBarUyYcIEevfuDUBiYiLJyclMmjSJV155hcmTJ7N06VK6d+/OypUradWqFQAPPfQQlStXJiIigu3bt/Pcc8+xZ88evv76awDi4+NdEi3A+Tg+Pj7H2CZOnMjYsWOzHV+2bJnLFMXi4vhFyLB64GOxs33tKnaYjI6o4CxfvtzoEKSEUZ8Td1J/E3dTnxN3Kiz9LSUlJVftDE22Pv/8c+bNm8f8+fOpU6cO0dHRDB06lIiICPr06YPNZgOgS5cuDBs2DIAGDRqwdu1a3n//fWeyNXDgQOc169WrR3h4OG3atGH//v05TjnMjeeff57hw4c7HyclJVGxYkXatWtHUFDxm2a3aOsJ2L6TujeVplOnZkaHUyAyMjJYvnw599xzD56eqrYoBU99TtxJ/U3cTX1O3Kmw9besWW/XYmiyNWLECEaOHEnPnj0BR6J0+PBhJk6cSJ8+fShXrhweHh5ERUW5PK927dqsXr36itdt3rw5APv27aNatWqEhYW5rAMDSEhIALjiOi9vb2+8vb2zHff09CwUX+D8tjfxIgB1K5Qqlu/v74rr11AKL/U5cSf1N3E39Tlxp8LS33Ibg6Gl31NSUjCbXUOwWCzOES0vLy+aNm3Knj17XNrs3buXypUrX/G60dHRAISHhwPQokULduzYQWJiorPN8uXLCQoKypbIlVQxKo4hIiIiIpKvDB3Z6ty5MxMmTKBSpUrUqVOHrVu3MnXqVPr37+9sM2LECB588EFatmxJ69atWbp0Kd9//z2rVq0CHKXh58+fT8eOHSlbtizbt29n2LBhtGzZkvr16wPQrl07oqKieOSRR5gyZQrx8fG8+OKLDB48OMfRq5LGbrer7LuIiIiISD4zNNmaPn06o0eP5qmnniIxMZGIiAgGDRrEmDFjnG26devG+++/z8SJExkyZAg1a9bkq6++4o477gAco18rVqxg2rRpXLx4kYoVK9KjRw9efPFF5zUsFguLFy/mySefpEWLFvj7+9OnTx+XfblKsvikVM6lZGAxm7g5NMDocEREREREigVDk63AwECmTZvGtGnTrtquf//+LqNdf1exYkV+/fXXa75W5cqV+eGHH/ISZrEXc8IxqlW9fAA+nhaDoxERERERKR4MXbMlhUNWslU7PNDgSEREREREig8lW/JXcYwIrdcSEREREckvSraEWGclwmCDIxERERERKT6UbJVwyWmZHDrt2AFb0whFRERERPKPkq0SbvflUa3QIG/KBqgMvoiIiIhIflGyVcLFajNjEREREZECoWSrhFNxDBERERGRgqFkq4TLKvuu4hgiIiIiIvlLyVYJlmm1sTv+AqDiGCIiIiIi+U3JVgl26PRF0jJt+HlZqFzW3+hwRERERESKFSVbJdiuy1MIa4UFYjGbDI5GRERERKR4UbJVgmUVx6itSoQiIiIiIvlOyVYJFhvnWK+lSoQiIiIiIvlPyVYJ9lclQiVbIiIiIiL5TclWCZV4IZVTyWmYTVArTMmWiIiIiEh+U7JVQmWNalUp54+vl8XgaEREREREih8lWyWUc72WphCKiIiIiBQIJVslVFYlQhXHEBEREREpGEq2SqiYE+cBlX0XERERESkoSrZKoEvpVg6eughAHSVbIiIiIiIFQslWCbQn4QI2O5QL8KJ8oLfR4YiIiIiIFEtKtkqgrEqEtcODMJlMBkcjIiIiIlI8KdkqgWLiHOu1VIlQRERERKTgKNkqgZxl31WJUERERESkwCjZKmFsNjuxWWXfNbIlIiIiIlJglGyVMIfPpJCSbsXLw0xkOX+jwxERERERKbaUbJUwWcUxaoUF4mHRl19EREREpKDot+0SRlMIRURERETcQ8lWCROTlWypOIaIiIiISIFSslXC/H2PLRERERERKThKtkqQMxfTiU9KBRxrtkREREREpOAo2SpBstZrVS7rR6CPp8HRiIiIiIgUb0q2ShDnFMIwTSEUERERESloSrZKEBXHEBERERFxHyVbJYjKvouIiIiIuI+SrRIiNcPKvsRkQCNbIiIiIiLuoGSrhNiXmEymzU6wryfhwT5GhyMiIiIiUuwp2SohYv42hdBkMhkcjYiIiIhI8adkq4TIqkSoKYQiIiIiIu6hZKuEyBrZqq3iGCIiIiIibmFosmW1Whk9ejSRkZH4+vpSrVo1xo8fj91ud2kXGxvL/fffT3BwMP7+/jRt2pQjR444z6empjJ48GDKli1LQEAAPXr0ICEhweUaR44coVOnTvj5+RESEsKIESPIzMx0y/s0mt1uVyVCERERERE38zDyxSdPnsyMGTOYO3cuderUYdOmTfTr14/g4GCGDBkCwP79+7njjjsYMGAAY8eOJSgoiF27duHj81eRh2HDhrFkyRK++OILgoOD+c9//kP37t1Zs2YN4EjqOnXqRFhYGGvXriUuLo5HH30UT09PXn31VUPeuzsdO3uJC6mZeFpMVA8JMDocEREREZESwdBka+3atXTp0oVOnToBUKVKFRYsWMCGDRucbUaNGkXHjh2ZMmWK81i1atWc98+fP8/HH3/M/PnzufvuuwGYPXs2tWvX5o8//uDWW29l2bJlxMTEsGLFCkJDQ2nQoAHjx4/nueee4+WXX8bLy8tN79gYWVMIbw4JxMtDM0dFRERERNzB0N+8b7vtNn7++Wf27t0LwLZt21i9ejX33nsvADabjSVLllCjRg3at29PSEgIzZs355tvvnFeY/PmzWRkZNC2bVvnsVq1alGpUiXWrVsHwLp166hXrx6hoaHONu3btycpKYldu3a54Z0aK6s4htZriYiIiIi4j6EjWyNHjiQpKYlatWphsViwWq1MmDCB3r17A5CYmEhycjKTJk3ilVdeYfLkySxdupTu3buzcuVKWrVqRXx8PF5eXpQqVcrl2qGhocTHxwMQHx/vkmhlnc86l5O0tDTS0tKcj5OSHAlLRkYGGRkZ+fL+3SXmxHkAaob6F7nY81PWey/Jn4G4l/qcuJP6m7ib+py4U2Hrb7mNw9Bk6/PPP2fevHnMnz+fOnXqEB0dzdChQ4mIiKBPnz7YbDYAunTpwrBhwwBo0KABa9eu5f3336dVq1YFFtvEiRMZO3ZstuPLli3Dz8+vwF63IGw+YAFMJB2O4YdzxX8k71qWL19udAhSwqjPiTupv4m7qc+JOxWW/paSkpKrdoYmWyNGjGDkyJH07NkTgHr16nH48GEmTpxInz59KFeuHB4eHkRFRbk8r3bt2qxevRqAsLAw0tPTOXfunMvoVkJCAmFhYc42f18HlnU+61xOnn/+eYYPH+58nJSURMWKFWnXrh1BQUVnOl7SpQzOrFsJQN+ubQn29TQ4IuNkZGSwfPly7rnnHjw9S+7nIO6jPifupP4m7qY+J+5U2Ppb1qy3azE02UpJScFsdl02ZrFYnCNaXl5eNG3alD179ri02bt3L5UrVwagcePGeHp68vPPP9OjRw8A9uzZw5EjR2jRogUALVq0YMKECSQmJhISEgI4suKgoKBsiVwWb29vvL29sx339PQsFF/g3PrzqKMjVCjlS7mgojUiV1CK2tdQij71OXEn9TdxN/U5cafC0t9yG4OhyVbnzp2ZMGEClSpVok6dOmzdupWpU6fSv39/Z5sRI0bw4IMP0rJlS1q3bs3SpUv5/vvvWbVqFQDBwcEMGDCA4cOHU6ZMGYKCgvjvf/9LixYtuPXWWwFo164dUVFRPPLII0yZMoX4+HhefPFFBg8enGNCVZzEajNjERERERFDGJpsTZ8+ndGjR/PUU0+RmJhIREQEgwYNYsyYMc423bp14/3332fixIkMGTKEmjVr8tVXX3HHHXc427z55puYzWZ69OhBWloa7du357333nOet1gsLF68mCeffJIWLVrg7+9Pnz59GDdunFvfrxGyKhFGRSjZEhERERFxJ0OTrcDAQKZNm8a0adOu2q5///4uo13/5OPjw7vvvsu77757xTaVK1fmhx9+yGuoRVbWHltR4YEGRyIiIiIiUrJoh9tiLMNq48+EZACiwoMNjkZEREREpGRRslWM7T+ZTLrVRqC3BzeV9jU6HBERERGREkXJVjGWtV6rVnggZrPJ4GhEREREREoWJVvFmLM4hioRioiIiIi4nZKtYiw2XpUIRURERESMomSrmLLb7X8b2VJxDBERERERd1OyVUzFJ6VyNiUDi9nEzaEBRocjIiIiIlLiKNkqpmIv769Vrbw/Pp4Wg6MRERERESl5lGwVUyqOISIiIiJiLCVbxVTM5ZGt2kq2REREREQMoWSrmHKObKkSoYiIiIiIIZRsFUPJaZkcPpMCaGRLRERERMQoSraKoT3xSdjtEBrkTbkAb6PDEREREREpkZRsFUNZUwg1qiUiIiIiYhwlW8VQTNwFQJUIRURERESMpGSrGMqqRKjiGCIiIiIixlGyVcxkWm3sVtl3ERERERHDKdkqZg6dvkhapg1fTwtVyvobHY6IiIiISImlZKuYyVqvVSs8EIvZZHA0IiIiIiIll5KtYsa5mbGmEIqIiIiIGErJVjETo/VaIiIiIiKFgpKtYiZWlQhFRERERAoFJVvFSOKFVE5eSMNkglphgUaHIyIiIiJSoinZKkZiLxfHiCzrj5+Xh8HRiIiIiIiUbEq2ipGs4hi1NYVQRERERMRwSraKEed6LRXHEBERERExnJKtYiRGxTFERERERAoNJVvFxKV0KwdOJgMa2RIRERERKQxURaGY2JNwAZsdyvp7ERLobXQ4IiIiIldls9lIT083OgwpIjIyMvDw8CA1NRWr1Vrgr+fp6YnFYrnh6yjZKib+vr+WyWQyOBoRERGRK0tPT+fgwYPYbDajQ5Eiwm63ExYWxtGjR932u26pUqUICwu7oddTslVMOCsRagqhiIiIFGJ2u524uDgsFgsVK1bEbNaqFrk2m81GcnIyAQEBBd5n7HY7KSkpJCYmAhAeHp7naynZKiZiVIlQREREioDMzExSUlKIiIjAz8/P6HCkiMiadurj4+OWBN3X1xeAxMREQkJC8jylUH9KKAZsNju7VYlQREREioCs9TZeXl4GRyJydVl/DMjIyMjzNZRsFQNHzqRwMd2Kl4eZquX8jQ5HRERE5Jq0xlwKu/zoo0q2ioGsKYQ1QwPxsOhLKiIiIiJSGOg382IgqziG1muJiIiIlDwmk4lvvvmmQK5dpUoVpk2bViDXLgmUbBUDsVqvJSIiIiWM1WZn3f7TfBt9nHX7T2O12Qv09fr27YvJZMp269ChQ4G+7t+9/PLLNGjQINvxuLg47r33XgAOHTqEyWQiOjrabXEVlK1bt/LAAw8QGhqKn58fjRs3ZuDAgezduxf4671m3cqWLUu7du3YunWr8xpXShav9FnmN1UjLAayphGq7LuIiIiUBEt3xjH2+xjizqc6j4UH+/BS5yg61M17me5r6dChA7Nnz3Y55u3tXWCvl1thYWFGh5DvFi9eTI8ePWjfvj3z5s0jMjKSgwcP8uOPPzJ69Gg+++wzZ9sVK1ZQp04djh07xpAhQ7j33nvZvXs3pUqVMu4NXKaRrSLu7MV05zeaWuGBBkcjIiIiUrCW7ozjyU+3uCRaAPHnU3ny0y0s3RlXYK/t7e1NWFiYy6106dIArFq1Ci8vL37//Xdn+ylTphASEkJCQoIj9qVLueOOOyhVqhRly5blvvvuY//+/S6vcezYMXr16kWZMmXw9/enSZMmrF+/njlz5jB27Fi2bdvmHMmZM2cO4DqNMDIyEoCGDRtiMpm46667ALjrrrsYOnSoy2t17dqVvn37Oh8nJibSuXNnfH19iYyMZN68ebn6XD766CNq166Nj48PtWrV4r333nOeyxp9+vrrr2ndujV+fn7ccsstrFu37orXS0lJoV+/fnTs2JHvvvuOtm3bEhkZSZMmTXjttdf44IMPXNqXLVuWsLAwmjRpwuuvv05CQgLr16/PVewFTSNbRVzWFMJKZfwI8vE0OBoRERGR62O327mUYc1VW6vNzkvf7SKnCYN2wAS8/F0Mt1cvh8V87Upyvp6WfKuKmJXMPPLII2zbto0DBw4wevRovvjiC0JDQwG4ePEiw4cPp379+iQnJzNmzBi6detGdHQ0ZrOZ5ORkWrVqRYUKFfjuu+8ICwtjy5Yt2Gw2HnzwQXbu3MnSpUtZsWIFAMHBwdni2LBhA82aNXOO9lxPif2+ffty4sQJVq5ciaenJ0OGDHFu7Hsl8+bNY8yYMbzzzjs0bNiQrVu38vjjj+Pv70+fPn2c7UaNGsXrr7/OzTffzKhRo+jVqxf79u3DwyN7OvLTTz9x6tQpnn322Rxf82ojVln7Y6Wnp+fiHRc8JVtFnDYzFhERkaLsUoaVqDE/5cu17EB8Uir1Xl6Wq/Yx49rj55X7X4cXL15MQECAy7EXXniBF154AYBXXnmF5cuXM3DgQHbu3EmfPn24//77nW179Ojh8txZs2ZRvnx5YmJiqFu3LvPnz+fkyZNs3LiRMmXKAFC9enVn+4CAADw8PK46bbB8+fLAX6M9ubV3715+/PFHNmzYQNOmTQH4+OOPqV279lWf99JLL/HGG2/QvXt3wDGyFhMTwwcffOCSbD3zzDN06tQJgLFjx1KnTh327dtHrVq1sl3zzz//BMjx3NWcO3eO8ePHExAQQLNmza7ruQXF0GmEVquV0aNHExkZia+vL9WqVWP8+PHY7X/9vSKnxYj/XIhYpUqVbG0mTZrk0mb79u3ceeed+Pj4ULFiRaZMmeKW91jQsioRar2WiIiISMFq3bo10dHRLrcnnnjCed7Ly4t58+bx1VdfkZqayptvvuny/D///JNevXpRtWpVgoKCqFKlCgBHjhwBIDo6moYNGzoTLXeKjY3Fw8ODxo0bO4/VqlXrqqNIFy9eZP/+/QwYMICAgADn7ZVXXsk2PbJ+/frO++HhjnV1Vxo1+3sukBu33XYbAQEBlC5dmm3btvHZZ585RxONZujI1uTJk5kxYwZz586lTp06bNq0iX79+hEcHMyQIUOc7f65GDGnhYjjxo3j8ccfdz4ODPxr/VJSUhLt2rWjbdu2vP/+++zYsYP+/ftTqlQpBg4cWEDvzj1iVIlQREREijBfTwsx49rnqu2Gg2foO3vjNdvN6deUZpHXTlh8PS25et0s/v7+LiNNOVm7di0AZ86c4cyZM/j7+zvPde7cmcqVK/Phhx8SERGBzWajbt26zilvWVPgCoLZbM6WxGRkZNzQNZOTkwH48MMPad68ucs5i8X1s/X0/Gu5S9bUTZvNluN1a9SoAcDu3btp0aLFNeP47LPPiIqKomzZstmSw6CgIM6fP5/tOefOnctxGmZ+MzTZWrt2LV26dHEOKVapUoUFCxawYcMGl3ZZixGvJjAw8Ipt5s2bR3p6OrNmzcLLy4s6deoQHR3N1KlTi3SylZZpZV+io5Mr2RIREZGiyGQy5Xoq3503lyc82If486k5rtsyAWHBPtx5c/lcrdnKb/v372fYsGF8+OGHfPbZZ/Tp04cVK1ZgNps5ffo0e/bs4cMPP+TOO+8EYPXq1S7Pr1+/Ph999BFnzpzJcXTLy8sLq/Xq69uy1mj9s1358uWJi/ureIjVamXnzp20bt0acIxiZWZmsnnzZuc0wj179nDu3LkrvlZoaCgREREcOHCA3r17XzWu69GuXTvKlSvHlClTWLRoUbbz586dc0mqKlasSLVq1XK8Vs2aNdm8eXO241u2bKFmzZr5FvOVGDqN8LbbbuPnn3921srftm0bq1evdu4TkGXVqlWEhIRQs2ZNnnzySU6fPp3tWpMmTaJs2bI0bNiQ1157jczMTOe5devW0bJlS5cFgu3bt2fPnj2cPXu2gN5dwfszIZlMm50gHw8ign2MDkdERESkQFnMJl7qHAU4Equ/y3r8UueoAku00tLSiI+Pd7mdOnUKcCQvDz/8MO3bt6dfv37Mnj2b7du388YbbwBQunRpypYty8yZM9m3bx+//PILw4cPd7l+r169CAsLo2vXrqxZs4YDBw7w1VdfOSv3ValShYMHDxIdHc2pU6dIS0vLFmNISAi+vr4sXbqUhIQE56jO3XffzZIlS1iyZAm7d+/mySefdEmkatasSYcOHRg0aBDr169n8+bNPPbYY9ccbRs7diwTJ07k7bffZu/evezYsYPZs2czderUPH/O/v7+fPTRRyxZsoT777+fFStWcOjQIbZu3cpzzz3nMnXzWoYNG8aSJUuYMGECsbGx7Ny5k1GjRrFu3TqefvrpPMeYW4aObI0cOZKkpCRq1aqFxWLBarUyYcIEl8y4Q4cOdO/encjISPbv388LL7zAvffey7p165zDk0OGDKFRo0aUKVOGtWvX8vzzzxMXF+f8IsfHxzvLYGbJmscZHx/vLNn5d2lpaS4dOCnJMV0vIyPjhodc88uOY45EsXZ4oEtyKTnL+roVlq+fFH/qc+JO6m/ibnntcxkZGdjtdmw22xWnkV1Nu6hQ3n2oIeMWxxKf9Ff597BgH0Z3qk27qNA8Xfda7HY7S5cuda43ylKzZk1iYmJ45ZVXOHz4MN999x02m43Q0FDef/99evfuTdu2bbnllluYP38+Q4cOpW7dutSsWZNp06Zx9913Oz8LDw8Pli5dyjPPPEPHjh3JzMwkKiqK6dOnY7PZ6NatG1999RWtW7fm3LlzfPzxx87S7VnXMJvNTJs2jVdeeYUxY8Zw55138ssvv9C3b1+io6N59NFH8fDwYOjQodx1113OrwU4CmI8/vjjtGrVitDQUMaNG8fRo0dd2vxT//798fHx4Y033mDEiBH4+/tTr149hgwZ4vI1/uf9fx77p86dO7N69WomTZrEQw89RFJSEhUqVKBNmzaMGzfuitf+p1tvvZUlS5bwyiuv8MYbb2A2m6lXrx7Lly8nKirqqn3FZrNht9vJyMjINi0yt/3eZL/eFWj5aOHChYwYMYLXXnvNObVv6NChTJ061aV6yd8dOHCAatWqsWLFCtq0aZNjm1mzZjFo0CCSk5Px9vamXbt2REZGutTkj4mJoU6dOsTExORYZeXll19m7Nix2Y7Pnz8fPz+/PL7j/PX1QTO/xptpFW6je5X8/6YiIiIikt+yqulVrFjxusqS/5PVZmfL0SROXUynnL8XjSoGGTJ1UIqv9PR0jh49Snx8fLaBjZSUFB566CHOnz9PUNCVl/MYOrI1YsQIRo4cSc+ePQGoV68ehw8fZuLEiVdMtqpWrUq5cuXYt2/fFZOt5s2bk5mZyaFDh6hZsyZhYWHOzeSyZD2+0jqv559/3mVoNykpiYoVK9KuXburfqDuNO/jjcBZOraoR8eGFYwOp9DLyMhg+fLl3HPPPS6LNEUKivqcuJP6m7hbXvtcamoqR48eJSAgAB+fG1sG0aZUwRc4kMLBbrdz4cIFAgMD821vtGtJTU3F19eXli1bZuurWbPersXQZCslJQWz2XXZmMViuepw3rFjxzh9+nS2Idy/y9oYLiQkBIAWLVowatQoMjIynN8Mli9fTs2aNXOcQgiOohw5VT309PQsFD/E7HY7sfEXAKh7U+lCEVNRUVi+hlJyqM+JO6m/ibtdb5+zWq2YTCbMZnO23wNFriQrP8jqO+5gNpsxmUw59vHc9nlDe3jnzp2ZMGECS5Ys4dChQyxatIipU6fSrVs3wFFOcsSIEfzxxx8cOnSIn3/+mS5dulC9enXat3eUCF23bh3Tpk1z7tQ9b948hg0bxsMPP+xMpB566CG8vLwYMGAAu3bt4rPPPuOtt97KtiixKDl29hIXUjPxtJi4OSTw2k8QERERERG3MnRka/r06YwePZqnnnqKxMREIiIiGDRoEGPGjAEco1zbt29n7ty5nDt3joiICNq1a8f48eOdo07e3t4sXLiQl19+mbS0NCIjIxk2bJhLIhUcHMyyZcsYPHgwjRs3ply5cowZM6ZIl32Pvby/VvWQQLw89FchEREREZHCxtBkKzAwkGnTpjFt2rQcz/v6+vLTTz9d9RqNGjXijz/+uOZr1a9fn99//z0vYRZKWZsZ1w7XqJaIiIiISGGkIZEiKuaEI9mKCi8cxTpERERERMSVkq0iKjb+crIVoWRLRERERKQwUrJVBJ2/lMHRM5cAjWyJiIiIiBRWSraKoN2X12tFBPtQyi/vmwGKiIiIiEjBUbJVxFhtdn7YEQdAWLAPVpvd4IhERERExEgmk4lvvvmmQK5dpUqVKxazk2tTslWELN0Zxx2Tf2HuusMAbDlyjjsm/8LSnXEGRyYiIiJSvPXt2xeTyZTt1qFDB7fF8PLLL9OgQYNsx+Pi4rj33nsBOHToECaTiejoaLfFVVC2bt3Kgw8+SHh4OL6+vtSrV4/OnTvz/fffY7c7Bhyy3m/WrWzZsrRr146tW7c6r3OlhPFKn2d+UrJVRCzdGceTn24h7nyqy/H486k8+ekWJVwiIiJSMqycCL9Oyfncr1Mc5wtIhw4diIuLc7ktWLCgwF4vt8LCwpx70BYX3377LbfeeivJycnMnTuXXbt28eWXX9K1a1defPFFzp8/79J+xYoVxMXF8dNPP5GcnMy9997LuXPnjAn+b5RsFQFWm52x38eQ04TBrGNjv4/RlEIREREp/swWWDkhe8L16xTHcbOlwF7a29ubsLAwl1vp0qUBWLVqFV5eXi77uk6ZMoWQkBASEhIAWLp0KXfccQelSpWibNmy3Hfffezfv9/lNY4dO0avXr0oU6YM/v7+NGnShPXr1zNnzhzGjh3Ltm3bnKM4c+bMAVynEUZGRgLQsGFDTCYTd911FwB33XUXQ4cOdXmtrl270rdvX+fjxMREOnfujK+vL5GRkcybNy9Xn8tHH31E7dq18fHxoVatWrz33nvOc1kjT19//TWtW7fGz8+PW265hXXr1l3xehcvXmTAgAF06tSJJUuW0K5dO6pWrUrNmjUZMGAA27ZtIzg42OU5ZcuWJSwsjCZNmvD666+TkJDA+vXrcxV/QTJ0U2PJnQ0Hz2Qb0fo7OxB3PpUNB8/QolpZ9wUmIiIicqPsdshIyX37FoPBmu5IrKzpcMcwWP0m/PYatBzhOJ9+MXfX8vQDkylvcf9DVjLzyCOPsG3bNg4cOMDo0aP54osvCA0NBRxJxPDhw6lfvz7JycmMGTOGbt26ER0djdlsJjk5mVatWlGhQgW+++47wsLC2LJlCzabjQcffJCdO3eydOlSVqxYAZAt4QDYsGEDzZo1Y8WKFdSpUwcvr9wXU+vbty8nTpxg5cqVeHp6MmTIEBITE6/6nHnz5jFmzBjeeecdGjZsyNatW3n88cfx9/enT58+znajRo3i9ddf5+abb2bUqFH06tWLffv24eGRPR1ZtmwZp0+f5tlnn73i65qu8nXz9fUFID09/VpvucAp2SoCEi9cOdHKSzsRERGRQiMjBV6NyNtzf3vNcbvS42t54QR4+ee6+eLFiwkICHC9xAsv8MILLwDwyiuvsHz5cgYOHMjOnTvp06cP999/v7Ntjx49XJ47a9YsypcvT0xMDHXr1mX+/PmcPHmSjRs3UqZMGQCqV6/ubB8QEICHhwdhYWFXjLF8+fLAXyM9ubV3715+/PFHNmzYQNOmTQH4+OOPqV279lWf99JLL/HGG2/QvXt3wDGyFhMTwwcffOCSbD3zzDN06tQJgLFjx1KnTh327dtHrVq1cowFoGbNms5jGzdupE2bNs7HCxcu5L777sv23HPnzjF+/HgCAgJo1qxZbt9+gVGyVQSEBPrkazsRERERuX6tW7dmxowZLseykiIALy8v5s2bR/369alcuTJvvvmmS9s///yTMWPGsH79ek6dOoXNZgPgyJEj1K1bl+joaBo2bOhyTXeJjY3Fw8ODxo0bO4/VqlWLUqVKXfE5Fy9eZP/+/QwYMIDHH3/ceTwzMzPbqFv9+vWd98PDwwHHtMWckq2c1K9fn99++42AgABq1qxJZmamy/nbbrsNs9nMxYsXqVq1Kp999plzRNFISraKgGaRZQgP9iH+fGqO67ZMOMrAN4t0/39MERERkRvi6ecYYbpeWVMHLV6O6YQtRzimFF7va18Hf39/l5GmnKxduxaAM2fOcObMGfz9/xo569y5M5UrV+bDDz8kIiICm81G3bp1ndPdsqa/FQSz2eys4JclIyPjhq6ZnJwMwIcffkjz5s1dzlksrmvnPD09nfezpgBmJZv/dPPNNwOwZ88ebr31VsCxXq5q1aoEBQXl+JzPPvuMqKgoypYtmy1BDAoKylZQAxyjYDlNxcxPKpBRBFjMJl7qHAU4Equ/y3r8UucoLOb8mXMsIiIi4jYmk2Mq3/Xc1r3rSLRaj4LRJx3//vaa4/j1XCef1mtl2b9/P8OGDXMmH3369HEmFKdPn2bPnj28+OKLtGnThtq1a3P27FmX59evX5/o6GjOnDmT4/W9vLywWq1XjSFrjdY/25UvX564uL+qV1utVnbu3Ol8XKtWLTIzM9m8ebPz2J49e65a0S80NJSIiAgOHDhA9erVXW5ZhTryol27dpQpU4bJkyfn+jkVK1akWrVqOY7E1axZ0+V9ZdmyZQs1atTIc5y5oWSriOhQN5wZDzciLNh1qmBYsA8zHm5Eh7rhBkUmIiIi4kZZVQdbj4JWlwsotHrW8TinKoX5KC0tjfj4eJfbqVOnAEfy8vDDD9O+fXv69evH7Nmz2b59O2+88QYApUuXpmzZssycOZN9+/bxyy+/MHz4cJfr9+rVi7CwMLp27cqaNWs4cOAAX331lbNyX5UqVTh48CDR0dGcOnWKtLS0bDGGhITg6+vL0qVLSUhIcI7o3H333SxZsoQlS5awe/dunnzySZdEqmbNmnTo0IFBgwaxfv16Nm/ezGOPPXbN0baxY8cyceJE3n77bfbu3cuOHTuYPXs2U6dOzfPnHBAQwEcffcSSJUvo1KkTP/30EwcOHGDnzp289ppjTd4/R86uZtiwYSxZsoQJEyYQGxvLzp07GTVqFOvWrePpp5/Oc5y5oWSrCOlQN5zVz93Ngsdv5a2eDVjw+K2sfu5uJVoiIiJSctisrolWlqyEy3b1kZ8bsXTpUsLDw11ud9xxBwATJkzg8OHDfPDBB4BjXdLMmTN58cUX2bZtG2azmYULF7J582bq1q3LsGHDnIlDFi8vL5YtW0ZISAgdO3akXr16TJo0yZlY9OjRgw4dOtC6dWvKly+f4x5fHh4evP3223zwwQdERETQpUsXAPr370+fPn149NFHadWqFVWrVqV169Yuz509ezYRERG0atWK7t27M3DgQEJCQq76mTz22GN89NFHzJ49m3r16tGqVSvmzJlzQyNbAN26dWPt2rX4+fnx6KOPUrt2bbp06cIvv/xyxeIYV3Lbbbfx448/8uOPP3L77bdz1113sXbtWn7++Wfq1q17Q3Fei8n+z8mbkqOkpCSCg4M5f/78FeeKSuGWkZHBDz/8QMeOHV3mDYsUFPU5cSf1N3G3vPa51NRUDh48SGRkJD4+Ku4luWOz2UhKSiIoKAiz2T3jRVfrq7nNDTSyJSIiIiIiUgCUbImIiIiIiBQAJVsiIiIiIiIFQMmWiIiIiIhIAVCyJSIiIiIiUgCUbImIiIiI26kgthR2WRtS3wiPfIhDRERERCRXPD09MZlMnDx5kvLly2MymYwOSYoAm81Geno6qampBV763W63k56ezsmTJzGbzXh5eeX5Wkq2RERERMRtLBYLN910E8eOHePQoUNGhyNFhN1u59KlS/j6+rotQffz86NSpUo3lNwp2RIRERERtwoICODmm28mIyPD6FCkiMjIyOC3336jZcuWbtm43WKx4OHhccOJnZItEREREXE7i8WCxWIxOgwpIiwWC5mZmfj4+Lgl2covKpAhIiIiIiJSAJRsiYiIiIiIFAAlWyIiIiIiIgVAa7ZyKWsviKSkJIMjkbzKyMggJSWFpKSkIjXXV4ou9TlxJ/U3cTf1OXGnwtbfsnKCa+0Xp2Qrly5cuABAxYoVDY5EREREREQKgwsXLhAcHHzF8ya7tu/OFZvNxokTJwgMDNTme0VUUlISFStW5OjRowQFBRkdjpQA6nPiTupv4m7qc+JOha2/2e12Lly4QERExFX34dLIVi6ZzWZuuukmo8OQfBAUFFQo/pNKyaE+J+6k/ibupj4n7lSY+tvVRrSyqECGiIiIiIhIAVCyJSIiIiIiUgCUbEmJ4e3tzUsvvYS3t7fRoUgJoT4n7qT+Ju6mPifuVFT7mwpkiIiIiIiIFACNbImIiIiIiBQAJVsiIiIiIiIFQMmWiIiIiIhIAVCyJSIiIiIiUgCUbEmxN3HiRJo2bUpgYCAhISF07dqVPXv2GB2WlBCTJk3CZDIxdOhQo0ORYuz48eM8/PDDlC1bFl9fX+rVq8emTZuMDkuKIavVyujRo4mMjMTX15dq1aoxfvx4VG9N8stvv/1G586diYiIwGQy8c0337ict9vtjBkzhvDwcHx9fWnbti1//vmnMcHmgpItKfZ+/fVXBg8ezB9//MHy5cvJyMigXbt2XLx40ejQpJjbuHEjH3zwAfXr1zc6FCnGzp49y+23346npyc//vgjMTExvPHGG5QuXdro0KQYmjx5MjNmzOCdd94hNjaWyZMnM2XKFKZPn250aFJMXLx4kVtuuYV33303x/NTpkzh7bff5v3332f9+vX4+/vTvn17UlNT3Rxp7qj0u5Q4J0+eJCQkhF9//ZWWLVsaHY4UU8nJyTRq1Ij33nuPV155hQYNGjBt2jSjw5JiaOTIkaxZs4bff//d6FCkBLjvvvsIDQ3l448/dh7r0aMHvr6+fPrppwZGJsWRyWRi0aJFdO3aFXCMakVERPC///2PZ555BoDz588TGhrKnDlz6Nmzp4HR5kwjW1LinD9/HoAyZcoYHIkUZ4MHD6ZTp060bdvW6FCkmPvuu+9o0qQJDzzwACEhITRs2JAPP/zQ6LCkmLrtttv4+eef2bt3LwDbtm1j9erV3HvvvQZHJiXBwYMHiY+Pd/nZGhwcTPPmzVm3bp2BkV2Zh9EBiLiTzWZj6NCh3H777dStW9focKSYWrhwIVu2bGHjxo1GhyIlwIEDB5gxYwbDhw/nhRdeYOPGjQwZMgQvLy/69OljdHhSzIwcOZKkpCRq1aqFxWLBarUyYcIEevfubXRoUgLEx8cDEBoa6nI8NDTUea6wUbIlJcrgwYPZuXMnq1evNjoUKaaOHj3K008/zfLly/Hx8TE6HCkBbDYbTZo04dVXXwWgYcOG7Ny5k/fff1/JluS7zz//nHnz5jF//nzq1KlDdHQ0Q4cOJSIiQv1NJAeaRiglxn/+8x8WL17MypUruemmm4wOR4qpzZs3k5iYSKNGjfDw8MDDw4Nff/2Vt99+Gw8PD6xWq9EhSjETHh5OVFSUy7HatWtz5MgRgyKS4mzEiBGMHDmSnj17Uq9ePR555BGGDRvGxIkTjQ5NSoCwsDAAEhISXI4nJCQ4zxU2Srak2LPb7fznP/9h0aJF/PLLL0RGRhodkhRjbdq0YceOHURHRztvTZo0oXfv3kRHR2OxWIwOUYqZ22+/Pdt2Fnv37qVy5coGRSTFWUpKCmaz66+PFosFm81mUERSkkRGRhIWFsbPP//sPJaUlMT69etp0aKFgZFdmaYRSrE3ePBg5s+fz7fffktgYKBzTm9wcDC+vr4GRyfFTWBgYLb1gP7+/pQtW1brBKVADBs2jNtuu41XX32Vf//732zYsIGZM2cyc+ZMo0OTYqhz585MmDCBSpUqUadOHbZu3crUqVPp37+/0aFJMZGcnMy+ffucjw8ePEh0dDRlypShUqVKDB06lFdeeYWbb76ZyMhIRo8eTUREhLNiYWGj0u9S7JlMphyPz549m759+7o3GCmR7rrrLpV+lwK1ePFinn/+ef78808iIyMZPnw4jz/+uNFhSTF04cIFRo8ezaJFi0hMTCQiIoJevXoxZswYvLy8jA5PioFVq1bRunXrbMf79OnDnDlzsNvtvPTSS8ycOZNz585xxx138N5771GjRg0Dor02JVsiIiIiIiIFQGu2RERERERECoCSLRERERERkQKgZEtERERERKQAKNkSEREREREpAEq2RERERERECoCSLRERERERkQKgZEtERERERKQAKNkSERExiN1uZ+rUqWzatMnoUEREpAAo2RIRkWKlSpUqTJs2zegwnF5++WUaNGiQ47mJEyeydOlSbrnlFvcGJSIibmGy2+12o4MQERHJrb59+zJ37txsx9u3b8/SpUs5efIk/v7++Pn5GRBddsnJyaSlpVG2bFmX47/99htDhw5l1apVBAUFGRSdiIgUJCVbIiJSpPTt25eEhARmz57tctzb25vSpUsbFJWIiEh2mkYoIiJFjre3N2FhYS63rETrn9MIz507x2OPPUb58uUJCgri7rvvZtu2bS7X+/7772natCk+Pj6UK1eObt26Oc+ZTCa++eYbl/alSpVizpw5zsfHjh2jV69elClTBn9/f5o0acL69euB7NMIbTYb48aN46abbsLb25sGDRqwdOlS5/lDhw5hMpn4+uuvad26NX5+ftxyyy2sW7fuBj81ERFxNyVbIiJSrD3wwAMkJiby448/snnzZho1akSbNm04c+YMAEuWLKFbt2507NiRrVu38vPPP9OsWbNcXz85OZlWrVpx/PhxvvvuO7Zt28azzz6LzWbLsf1bb73FG2+8weuvv8727dtp3749999/P3/++adLu1GjRvHMM88QHR1NjRo16NWrF5mZmXn/IERExO08jA5ARETkei1evJiAgACXYy+88AIvvPCCy7HVq1ezYcMGEhMT8fb2BuD111/nm2++4csvv2TgwIFMmDCBnj17MnbsWOfzrqdgxfz58zl58iQbN26kTJkyAFSvXv2K7V9//XWee+45evbsCcDkyZNZuXIl06ZN491333W2e+aZZ+jUqRMAY8eOpU6dOuzbt49atWrlOjYRETGWki0RESlyWrduzYwZM1yOZSU6f7dt2zaSk5OzFae4dOkS+/fvByA6OprHH388z7FER0fTsGHDHF//n5KSkjhx4gS33367y/Hbb78929TG+vXrO++Hh4cDkJiYqGRLRKQIUbIlIiJFjr+//1VHj7IkJycTHh7OqlWrsp0rVaoUAL6+vle9hslk4p+1pDIyMpz3r/X8vPL09HSJAbji1EQRESmctGZLRESKrUaNGhEfH4+HhwfVq1d3uZUrVw5wjCD9/PPPV7xG+fLliYuLcz7+888/SUlJcT6uX78+0dHRzjVgVxMUFERERARr1qxxOb5mzRqioqKu9+2JiEghp5EtEREpctLS0oiPj3c55uHh4UygsrRt25YWLVrQtWtXpkyZQo0aNThx4oSzKEaTJk146aWXaNOmDdWqVaNnz55kZmbyww8/8NxzzwFw9913884779CiRQusVivPPfecy6hTr169ePXVV+natSsTJ04kPDycrVu3EhERQYsWLbLFPmLECF566SWqVatGgwYNmD17NtHR0cybN68APikRETGSki0RESlyli5d6lzHlKVmzZrs3r3b5ZjJZOKHH35g1KhR9OvXj5MnTxIWFkbLli0JDQ0F4K677uKLL75g/PjxTJo0iaCgIFq2bOm8xhtvvEG/fv248847iYiI4K233mLz5s3O815eXixbtoz//e9/dOzYkczMTKKiolyKXfzdkCFDOH/+PP/73/9ITEwkKiqK7777jptvvjm/Ph4RESkktKmxiIgUK+Hh4YwfP57HHnvM6FBERKSE08iWiIgUCykpKaxZs4aEhATq1KljdDgiIiIqkCEiIsXDzJkz6dmzJ0OHDs1xrZSIiIi7aRqhiIiIiIhIAdDIloiIiIiISAFQsiUiIiIiIlIAlGyJiIiIiIgUACVbIiIiIiIiBUDJloiIiIiISAFQsiUiIiIiIlIAlGyJiIiIiIgUACVbIiIiIiIiBUDJloiIiIiISAH4fxuxMPLW3KHBAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [8535, 8940, 8892, 8772, 8683, 8873, 8904, 8821, 8924, 8868]\n",
+    "exactitud_gpu = [8910, 8891, 8880, 8781, 8864, 8784, 8933, 8859, 8756, 8899]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d61eb09b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [13.158, 9.212, 13.883, 13.883, 13.91, 13.868, 13.868, 8.373, 9.612, 13.91]\n",
+    "tiempo_inferencia_gpu = [13.059, 9.238, 13.629, 13.64, 13.489, 14.059, 14.059, 10.268, 10.26, 9.041]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "462159ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [196.989, 199.003, 212.854, 212.919, 226.58, 220.31, 220.295, 153.684, 152.979, 226.57]\n",
+    "tiempo_entrenamiento_gpu = [189.87, 191.979, 216.392, 216.321, 200.981, 217.668, 217.561, 157.549, 157.561, 203.165]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6a9bec85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [8738, 8844, 8869, 8703, 8634, 8895, 8793, 8841, 8830, 8896]\n",
+    "exactitud_gpu = [8938, 8784, 8664, 8929, 8856, 8698, 8938, 8898, 8939, 8846]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "bb1dbcab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [40.42, 33.473, 40.478, 40.484, 40.076, 42.181, 42.178, 28.527, 29.068, 40.082]\n",
+    "tiempo_inferencia_gpu = [40.688, 23.754, 41.593, 41.589, 39.119, 36.526, 36.504, 28.894, 28.836, 31.03]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "112283f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [688.38, 695.094, 713.97, 713.75, 741.775, 732.558, 732.552, 514.614, 512.516, 741.785]\n",
+    "tiempo_entrenamiento_gpu = [678.552, 689.337, 743.371, 743.38, 688.34, 713.465, 713.715, 515.651, 514.873, 690.612]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "38612090",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [9514, 9461, 9569, 9335, 9421, 9432, 9443, 9495, 9115, 9532]\n",
+    "exactitud_gpu = [9521, 9540, 9498, 9337, 9273, 9361, 9554, 9403, 9538, 9513]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f82ce5bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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y3N3d6dSpE3fu3LH8BQUF4eLikq4LV82aNWnWrJnlfpMmTQC1W1S5cuXSTU9bQc0sbQEHc9eX5ORkNm/eDMC6deswGAy88sorVsu9/vrrKIqS5S+IBw8eJCwsjOeff95qDNCIESNwd3dPt+8BAQHUqFHDat/NXbxy0n0trZo1a9KqVSvLfS8vL6pXr57hc5GWoiisXLmSXr16oSiKVUxdunQhKiqKw4cPWy0zfPhwHB0dLfePHj3KuXPnePrpp7l7965l+bi4ODp06MCOHTvStSY+//zzVvdbtWrF3bt3LV0M16xZg8lkYvLkyenGc2VV8c5gMFheA5PJRHh4OKmpqTRs2DDdfjxo06ZNREZGMnjwYKvnwWAw0KRJkwxfm4z242HPuXkfXV1ds5zvQaNGjcLLyws/Pz969OhBXFwcixcvztWKjzqdjieffJJ169YRGxtrmf7bb79RpkwZWrZsCdwfa/O///0vXUtzfsrp+6lDhw5W3WHN54z+/ftbvR6ZnUtsbGwYO3as5b6dnR1jx44lLCyMQ4cOAeq5xMfHh8GDB1vms7W15ZVXXiE2Npbt27dnuj+bN28mOTmZl19+2eo4f+211zLc91atWlGyZEmrfe/YsSNGo5EdO3Zkup2sdOzYkcqVK1vu161bFzc3t4ce1+Hh4WzZssXSKm2O5+7du3Tp0oVz585x7do1q2WeffZZq9bbvHgPrly5Ep1OZ9VTwSyrc4m9vb3l3GM0Grl79y4uLi5Ur179oeeSVatWYTKZGDhwoNV++Pj4ULVq1XT74eLiYtX6ZGdnR+PGjbN9Lnmwguy6devw8vKy/GVUyfHHH3/Ey8sLb29vmjRpwq5du5gwYUKGx5oQeUEKWgiRi86dO0dUVBTe3t4ZPm4eLG2WNoECLAmLv79/htMfHLel1+upVKmS1bRq1aoBWLqZXL58GT8/v3RfeAMCAiyPZ8b8WNWqVa2mm0tmp3Xu3DnOnDmDl5dXhut6cN+z68HnCKBkyZLpnosH3b59m8jISObNm5dpd7YHY6pYsaLV/XPnzgFq0pWZqKgoSpYsmWm85sciIiJwc3PjwoUL6PV6atasmWX8GVm8eDGzZs3i7NmzVl/8H4z7Qeb9MH8xf5Cbm5vVffPYjQf342HPuXk9MTExWc73oMmTJ9OqVSsMBgOenp4EBASkK3qRGwYNGsScOXP4888/efrpp4mNjWXdunWMHTvW8mW0TZs29O/fn6lTpzJ79mzatm1Lnz59ePrpp/O1MEBO30+Pey7x8/NLV3Qh7bmkadOmXL58mapVq6b7UeBxziVeXl5W7x9Q9/348eMF5lxy/vx5FEXh/fff5/333880pjJlyljuZ3Yuyc334IULF/Dz86NUqVJZxv8g8zitb775hpCQEKtxXA/rOnfu3DkURUn3OprZ2tpa3S9btmy6RK9kyZIcP348y+2YP6/S/hAC0KJFC0sRixkzZqQb+wrwxBNPWC7P4OrqSq1atR6poEhBu7yDKDwkuRIiF5lMJry9vVm2bFmGjz/4YZnZuJTMpivZLEChBZPJRJ06dfj8888zfPzBL3nZ9ajPhblF6Zlnnsk0Oapbt67V/bStVmnXMWPGDOrXr5/hOh78ZTWvXrulS5cyYsQI+vTpw5tvvom3tzcGg4Fp06ZZxihlxrwfS5YswcfHJ93jDyYyjzpeqkaNGgCcOHGCPn36ZHu5OnXq0LFjx0wfd3BwANQxPxmJj4+3zJOVpk2bUqFCBX7//Xeefvpp/vrrLxISEqzGMJmvp7V3717++usvNm7cyKhRo5g1axZ79+59rGux5URO309F7VzSqVMnJk6cmOHj5qQvpx73XPLGG2/QpUuXDOepUqWK1f3MziV5/R7Mjk8//ZT333+fUaNG8dFHH1GqVCn0ej2vvfbaQwuGmEwmdDod69evzzDG3Dofms8lJ0+epF69epbpXl5elnPF0qVLM1y2bNmyWZ5PQG29y+p8AmTrnCJERiS5EiIXVa5cmc2bN9OiRYt0H655wWQycfHiRasvG//99x+ApYtQ+fLl2bx5MzExMVatV2fPnrU8nhnzY+fOnbP6xTUlJYWQkBCrD73KlStz7NgxOnToUCB+8fPy8sLV1RWj0fjQD9rMmLsQubm5PfI6MlqnyWTi9OnTmSZsGVmxYgWVKlVi1apVVs9vRl2CMtomgLe3d67tR0ZatmxJyZIl+eWXX3jnnXdy7Qui+TgMDg7O8PHg4OBsX+h14MCBfPHFF0RHR/Pbb79RoUIFmjZtmm6+pk2b0rRpUz755BN+/vlnhgwZwq+//ppv1z7K7/fT9evX05UMz+hccvz4cUwmk1XrVU7PJWlbvW/fvp2u5ahy5crExsbm6bGaE+Z4bW1tH/tckpvvwcqVK7Nx40bCw8Nz1Hq1YsUK2rVrl+76UJGRkXh6ej50m4qiULFixUdOcrOjW7duGAwGli1bxpAhQ3J9/eXLl8/yfGKeR4hHIWOuhMhFAwcOxGg08tFHH6V7LDU1NV0lpdzw1VdfWW4risJXX32Fra0tHTp0AKB79+4YjUar+QBmz56NTqfL8rofDRs2xMvLi++++47k5GTL9EWLFqXbl4EDB3Lt2jXmz5+fbj0JCQnpKoLlNYPBQP/+/Vm5ciUnT55M97j52lhZCQoKonLlysycOTNd95TsruNBffr0Qa/X8+GHH6b7lTirX3PNiUraefbt28eePXseus0uXbrg5ubGp59+muE4okfZj4w4OTnx1ltvcebMGd56660M92fp0qUZVlnMiq+vL/Xr12fp0qXpjrtDhw6xd+/ebF+/ZtCgQSQlJbF48WI2bNjAwIEDrR6PiIhIF7c5CU5bAv3ChQsPbTF8HPn9fkpNTeX777+33E9OTub777/Hy8uLoKAgQD2X3Lx5k99++81qublz5+Li4mIpY56Rjh07Ymtry9y5c62e34yqdg4cOJA9e/awcePGdI9FRkaSmpr6KLv4yLy9vWnbti3ff/89N27cSPd4dt4/efEe7N+/P4qipKtGCg8/lzz4+PLly9ONG8tIv379MBgMTJ06Nd06FEWxVF58XOXKlWPUqFGsX78+3WdX2u09qu7du7N3717LeEKzyMhIli1bRv369TNsYRQiO6TlSohc1KZNG8aOHcu0adM4evQonTt3xtbWlnPnzrF8+XK++OILy8VSc4ODgwMbNmxg+PDhNGnShPXr17N27VreeecdSxfEXr160a5dO959910uXbpEvXr1+Pvvv/njjz947bXXrAZ4P8jW1paPP/6YsWPH0r59ewYNGkRISAgLFy5MN+Zq6NCh/P777zz//PNs3bqVFi1aYDQaOXv2LL///jsbN27M1QIF2fF///d/bN26lSZNmvDss89Ss2ZNwsPDOXz4MJs3byY8PDzL5fV6PT/88APdunWjVq1ajBw5kjJlynDt2jW2bt2Km5sbf/31V45iqlKlCu+++y4fffQRrVq1ol+/ftjb23PgwAH8/PyYNm1ahsv17NmTVatW0bdvX3r06EFISAjfffcdNWvWzDDxS8vNzY1vv/2WoUOH0qBBA5566im8vLwIDQ1l7dq1tGjRItMvMDn15ptvcurUKWbNmsXWrVsZMGAAPj4+3Lx5kzVr1rB//352796d4/V+/vnndOnShfr16zNixAj8/Pw4c+YM8+bNw9fXN9sXG27QoIHlNUhKSkpX1nzx4sV888039O3bl8qVKxMTE8P8+fNxc3Oje/fulvnMP148WEI7t+T3+8nPz4/PPvuMS5cuUa1aNX777TeOHj3KvHnzLONonnvuOb7//ntGjBjBoUOHqFChAitWrGDXrl3MmTMny0Im5us0TZs2jZ49e9K9e3eOHDnC+vXr07WWvPnmm/z555/07NmTESNGEBQURFxcHCdOnGDFihVcunTpoS0sue3rr7+mZcuW1KlTh2effZZKlSpx69Yt9uzZw9WrVx96jai8eA+2a9eOoUOH8uWXX3Lu3Dm6du2KyWRi586dtGvXzqrYUVo9e/bkww8/ZOTIkTRv3pwTJ06wbNmydOf0jFSuXJmPP/6Yt99+m0uXLtGnTx9cXV0JCQlh9erVPPfcc7zxxhs52o/MzJkzh5CQEF5++WV+/fVXevXqhbe3N3fu3GHXrl389ddfVK9e/ZHWPWnSJJYvX07r1q0ZO3YsNWrU4Pr16yxatIgbN26wcOHCXNkHUUzlX2FCIQqnnJRiN5s3b54SFBSkODo6Kq6urkqdOnWUiRMnKtevX7fMU758eaVHjx7pliWDErEZlRo3ly6/cOGC0rlzZ8XJyUkpXbq0MmXKlHRldmNiYpTx48crfn5+iq2trVK1alVlxowZVuV6s/LNN98oFStWVOzt7ZWGDRsqO3bsSFc6WlHUssyfffaZUqtWLcXe3l4pWbKkEhQUpEydOlWJiorKchuZlWLP6Dl6cNuZlWJXFEW5deuW8tJLLyn+/v6Kra2t4uPjo3To0EGZN2+eZZ6Hlfo+cuSI0q9fP8XDw0Oxt7dXypcvrwwcOFD5559/LPOYS7GbS+CbmUszpy19rSiKsmDBAiUwMNDyPLVp00bZtGlTpvtoMpmUTz/9VClfvrxib2+vBAYGKv/73/8yPQYzsnXrVqVLly6Ku7u74uDgoFSuXFkZMWKEcvDgQcs8Gb0Oafcvu1asWKF07txZKVWqlGJjY6P4+voqgwYNUrZt22YVT1bP+4P27t2r9OzZUylZsqRiY2OjlClTRhkzZoxy9erVbMelKIry7rvvKoBSpUqVdI8dPnxYGTx4sFKuXDnF3t5e8fb2Vnr27Gn1HCmKemxm93k3y0kpdkXJ/vspu+cMRcn4OTeXLj948KDSrFkzxcHBQSlfvrzy1VdfpYvz1q1bysiRIxVPT0/Fzs5OqVOnjrJw4cJs7b/RaFSmTp2q+Pr6Ko6Ojkrbtm2VkydPpisVrijqOevtt99WqlSpotjZ2Smenp5K8+bNlZkzZ1pKw2cms1LsGZXefnDbWR2TFy5cUIYNG6b4+Pgotra2SpkyZZSePXsqK1assMxjfr9nVso/t9+DqampyowZM5QaNWoodnZ2ipeXl9KtWzfl0KFDme5jYmKi8vrrr1tehxYtWih79uzJ8BjMzMqVK5WWLVsqzs7OirOzs1KjRg3lpZdeUoKDgy3zZPQ6mPcvu++d1NRUZeHChUr79u0t5xJPT0+lQ4cOynfffackJCRYzZ/Z65yRq1evKmPGjFHKlCmj2NjYKKVKlVJ69uyp7N27N1vLC5EZnaIU4FGtQohMjRgxghUrVjy01UIIIbLStm1b7ty5k2H3WSGEEDkjY66EEEIIIYQQIhdIciWEEEIIIYQQuUCSKyGEEEIIIYTIBTLmSgghhBBCCCFygbRcCSGEEEIIIUQukORKCCGEEEIIIXKBXEQ4AyaTievXr+Pq6opOp9M6HCGEEEIIIYRGFEUhJiYGPz8/9Pqs26YkucrA9evX8ff31zoMIYQQQgghRAFx5coVypYtm+U8klxlwNXVFVCfQDc3N42jEY8qJSWFv//+m86dO2Nra6t1OKKIk+NN5Dc55kR+kuNN5LeCdMxFR0fj7+9vyRGyIslVBsxdAd3c3CS5KsRSUlJwcnLCzc1N8zelKPrkeBP5TY45kZ/keBP5rSAec9kZLiQFLYQQQgghhBAiF0hyJYQQQgghhBC5QJIrIYQQQgghhMgFMuZKCCGEEEWG0WgkJSVF6zCKnJSUFGxsbEhMTMRoNGodjigG8vOYs7W1xWAw5Mq6JLkSQgghRKGnKAo3b94kMjJS61CKJEVR8PHx4cqVK3INUJEv8vuYK1GiBD4+Po+9LUmuhBBCCFHomRMrb29vnJycJAHIZSaTidjYWFxcXB56EVUhckN+HXOKohAfH09YWBgAvr6+j7U+Sa6EEEIIUagZjUZLYuXh4aF1OEWSyWQiOTkZBwcHSa5EvsjPY87R0RGAsLAwvL29H6uLoLw7hBBCCFGomcdYOTk5aRyJEKKwMp8/HnfMpiRXQgghhCgSpCugEOJR5db5Q9PkaseOHfTq1Qs/Pz90Oh1r1qyxelyn02X4N2PGjEzX+cEHH6Sbv0aNGnm8J0IIIYQQQojiTtPkKi4ujnr16vH1119n+PiNGzes/hYsWIBOp6N///5ZrrdWrVpWy/377795Eb4QQgghRJ4ZMWIEffr00TqMXJXRj+n5Yd68efj7+6PX65kzZ06+bz8niuLrnttat27Nzz//nK15mzZtysqVK/M4ovs0Ta66devGxx9/TN++fTN83MfHx+rvjz/+oF27dlSqVCnL9drY2Fgt5+npmRfhCyGEEKIIMZoU9ly4yx9Hr7Hnwl2MJiXPtpVZ7xzz3wcffMAXX3zBokWL8iyGwujSpUvodDqOHj2a7WWio6MZN24cb731FteuXeO5557LuwBzQUF/3bdu3Ur37t3x8PDAycmJmjVr8vrrr3Pt2jUAtm3bZnUsly5dmv79+3Px4kXLOjJLsrOTWP7555/cunWLp556Klvxvvfee0yaNAmTyZTtfXwchWbM1a1bt1i7di2jR49+6Lznzp3Dz8+PSpUqMWTIEEJDQ/MhQiGEEEIUVhtO3qDlZ1sYPH8vr/56lMHz99Lysy1sOHkjT7aXtofNnDlzcHNzs5r2xhtv4O7uTokSJfJk+8VJaGgoKSkp9OjRA19f30cufJJfF6cuyK/7999/T8eOHfHx8WHlypWcPn2a7777jqioKGbNmmU1b3BwMNevX2f58uWcOnWKXr165crFgL/88ktGjhyZ7QqC3bp1IyYmhvXr1z/2trOj0CRXixcvxtXVlX79+mU5X5MmTVi0aBEbNmzg22+/JSQkhFatWhETE5PpMklJSURHR1v9gfom0vIvMSmZf/+7xapDofz73y0Sk5I1j6mw/RWE11H+is+fHG/yl99/cszd/1MUBZPJ9Eh/645f54Wlh7kRlWj1/eBmVCIvLD3MuuPXH3ndmf15e3tb/lxdXdHpdFbTnJycGD58OE888YRlmdTUVD799FMqVqyIo6Mj9erV4/fff7c8vmXLFnQ6HevXrycwMBBHR0fat2/PzZs3Wbt2LQEBAbi5uTF48GBiY2Mty7Vt25aXXnqJl156CXd3dzw9PXnvvfcwGo2WecLDw3n++ectrRVdu3YlODg4y30MDg6mdevWODg4ULNmTTZu3AhgNc/ly5d58sknKVGiBKVKlaJ3795cvHgxy/WmXYd5nzdt2kTDhg1xcnKiefPmnDlzBpPJxIIFC6hTpw4AlSpVQqfTWda/evVqGjRogIODA5UqVeKDDz4gOTnZsm6dTsfXX39Nr169cHZ25uOPP872cvPmzaNPnz44OTlRtWpV1qxZY7UPJ06coEePHri5ueHq6kqrVq04d+4cJpMp3eu+bt06WrZsSYkSJfDw8KBHjx6WeTP7y+6xktnzltFfaGgor7zyCi+//DI//PADrVu3ply5crRs2ZJ58+bx3nvvWb1Gnp6elC5dmpYtW/Lee+9x+vRp/vvvv3SvYdo/RVEs72VFUVuO0763b926xZYtW+jRo4dlmtFoZMqUKZQrVw57e3v8/Px4+eWXrV6Pbt268csvvzz0fakoSpbn2+woNNe5WrBgAUOGDMHBwSHL+bp162a5XbduXZo0aUL58uX5/fffM231mjZtGlOnTk03/e+//9asrOuxuzpWXdITmXy/ckkJO4V+FUzU88i7bgpF0aZNm7QOQRQjcryJ/CbH3P3hALGxsSQnJwPqF7LElId3AzKaFD748xQZfbKap33w1ynqetth0D+8mpiDrT7HVccSExNRFMXy465ZSkoKqamplukzZ85k+fLlzJw5k8qVK7N7926GDRuGs7MzLVq0ID4+HoApU6Ywbdo0nJycGDlyJAMGDMDe3p7vv/+e2NhYhg4dysyZM3nttdcASE1N5aeffuKZZ55h8+bNHDlyhPHjx+Pl5cXw4cMBGDZsGBcvXmTZsmW4uroydepUunfvzt69e7G1tU23TyaTib59++Lt7c2mTZuIjo5m4sSJACQkJBAdHU1KSgpdunShUaNGrF27FhsbG2bOnEnXrl35999/sbOzS7fe2NhYQB23Hx0dbdnnd955h6lTp+Lh4cGECRMYMWIEGzdupFu3bqxZs4Y+ffrwzz//UKZMGdzd3dmwYQPDhw/ns88+o1mzZoSEhPDaa6+RlJTEW2+9Zdne1KlTmTJlCh999BEGgyFHy02dOpXJkyczb948hg4dyvHjxylZsiTXr1+nTZs2tGzZkj/++ANXV1f27dtHZGSk5XlJ+7rfuXOHsWPHUqtWLeLi4vj000/p06cPO3fuzLT1JrvHSmbPW0aWLl1KcnIyzz//fLpjFUCv11u9JjExMeniCw8PtyxrPg7SenDfzesx27RpE05OTpQpU8Yyzx9//MHs2bP58ccfqVGjBmFhYZw8edJqHXXq1GHOnDkZxm2WnJxMQkICO3bsIDU11eox8z5lR6FIrnbu3ElwcDC//fZbjpctUaIE1apV4/z585nO8/bbbzNhwgTL/ejoaPz9/encuTNubm6PFPPj2HjqFgv3HEt3oo9K1rHwPwNzn6pHl1ql8z2uwiYlJYVNmzbRqVOnDE/8QuQmOd5EfpNj7r7ExESuXLmCi4uL5UfY+ORUAj/LncQzLCaZlnP2ZWvekx90wskuZ1+vHBwc0Ol06b5z2NraYmNjg5ubG0lJScyePZu///6bZs2aAeqPyIcOHWLp0qV069bN8oPwJ598QocOHQAYM2YM77zzDufOnbOMWR8wYAB79uxh8uTJgJqc+vv789VXX6HT6QgKCuLChQt8//33vPzyy5w7d47169ezYcMGOnbsiE6n45dffqF8+fJs2bKFJ598Mt0+/f3335w7d46///4bPz8/QB1n06NHDxwdHXFzc2Pp0qUALFq0yJKQLlmyhFKlSnH48GE6d+6cbr0uLi4AODs74+bmZtnnTz/91LLP77zzDr169cLOzg43Nzf8/f0BqFChAhUqVADg888/Z9KkSYwdO9byXMbExDBp0iQ++eQTy/aefvppXnjhBcv98ePHZ2u5kSNHMmrUKABmzJjB999/z5kzZ+jatSufffYZJUqUYPny5Zb3boMGDTJ83QGeeeYZq+dg8eLFlC5dmqtXr1K7du10z1FOjpXMnreMGjOuXLmCm5sb1apVS/dYWuZ1u7q6Wrq7fvvtt5QpU4agoCBL0mw+DtJKu++KohATE2Np2QW4ffs2pUuXtuo2eefOHXx9fenduze2trbUqlWLdu3aWa23UqVKXLt2DRcXl0wT0sTERBwdHS2trWlllZQ9qFAkVz/++CNBQUHUq1cvx8vGxsZy4cIFhg4dmuk89vb22Nvbp5tua2ub7x9YRpPCJ+uDM/0FTQd8sj6YbnXLZOsXNKHN6yiKLzneRH6TYw6MRiM6nQ69Xm/54pTd8Ri5LW0MOVkm7X8zc0EAvV7PxYsXiY+Pp0uXLlbzJCcnExgYaLXd+vXrW277+Pjg5ORElSpVLMv4+Phw4MABq+01bdoUg8Fgud+8eXM+//xzFEUhODgYGxsbGjZsaInHy8uL6tWrExwcnOH+BgcH4+/vT9myZS3TWrRoYfUcnThxgvPnz+Pu7m61bGJiIiEhIRmuN+1zldk+lylTBlC/dJcrVy7dMgDHjh1j165dfPrpp5Z1G41GEhMTSUxMtCQIjRo1sooju8vVq1fPspw5ybhz5w56vZ5jx47RqlWrDL97gvXrDmotgcmTJ7Nv3z7u3Llj6VZ39epV6tatm275Rz1WHnzeMovtYce3+fFy5cqhKArx8fHUq1ePlStXWiUtGb1X0u67eT/TbjMxMREHBwer5QYOHMgXX3xBlSpV6Nq1K927d6dXr17Y2NxPc5ydnTGZTKSkpODo6Jhp3DqdLsNzak7OsZomV7GxsVYtSiEhIRw9epRSpUpZXtTo6GiWL1+ebpCcWYcOHejbty/jxo0D4I033qBXr16UL1+e69evM2XKFAwGA4MHD877HcoF+0PC0/X5TksBbkQlsj8knGaVPfIvMCGEEKIQcbQ1cPrDLg+db39IOCMWHnjofItGNqJxxVLZ2m5eMHeHW7t2reVLsNmDX9LTfhE0f1lMS6fT5VvltKzExsYSFBTEsmXL0j3m5eWVo3U9uM9AlvsYGxvL1KlTMxzLnzYBcHZ2fqTlsnrOM/tynxnz99r58+fj5+eHyWSidu3ali6wD3qcYwUyf96qVatGVFQUN27cwNfX96Fx79y5Ezc3N8u4wrRcXV2JiopKt0xkZGS6ZDstT09PIiIirKb5+/sTHBzM5s2b2bRpEy+++CIzZsxg+/btlv0LDw/H2dk5x8/9o9A0uTp48KBVs525a97w4cMtJSh//fVXFEXJNDm6cOECd+7csdy/evUqgwcP5u7du3h5edGyZUv27t2b4zepVsJiMk+sHmU+IYQQojjS6XTZ6p7XqqoXvu4O3IxKzLDXiA7wcXegVVUvTXuM1KxZE3t7e0JDQ2nTpk2ur3/fPutuj3v37qVq1aoYDAYCAgJITU3l4MGDdOrUCYC7d+8SHBxMzZo1M1xfQEAAV65csfoivnfvXqt5GjRowG+//Ya3t3e+D8No0KABwcHBVi16eblcWnXr1mXx4sWkpKQ8tEXE/DzPnz+fVq1aATz0+q15dawMGDCASZMmMX36dGbPnp3u8cjISKvuehUrVsy06mH16tU5dOiQZUwfqC2Ax44dY8yYMZnGEBgYyM2bN4mIiKBkyZKW6Y6OjvTq1YtevXrx0ksvUaNGDU6cOGHpbnny5EkCAwNzuMePRtPkqm3btpZKIJl57rnnsrwewaVLl6zu//rrr7kRmma8XbMu2JHT+YQQQgiROYNex5ReNXlh6WF0YJVgmVOpKb1qat4V39XVlTfeeIPx48djMplo2bIlUVFR7Nq1Czc3N6svqY8iNDSUCRMmMHbsWA4fPszcuXMtvYaqVq1K7969ee211/j+++9xd3dn0qRJlClThieeeCLD9XXs2JFq1aoxfPhwZsyYQXR0NO+++67VPEOGDGHGjBk88cQTfPjhh5QtW5bLly+zatUqJk6caNWlMLdNnjyZnj17Uq5cOQYMGGDprnfy5Ek+/vjjXF8urXHjxjF37lyeeuop3n77bdzd3dm7dy+NGzemevXqVvOWLFkSDw8P5s2bh6+vL6GhoUyaNCnL9efVseLv78/s2bMZN24c0dHRDBs2jAoVKnD16lV++uknXFxcMu1p9qAJEyYwevRoatSoQadOnYiLi2Pu3LlEREQ8NLny9PRk165d9OzZE1DH7BmNRpo0aYKTkxNLly7F0dGR8uXLW5bbuXNnhmP48kKhKcVeXDSuWApfdwcyO4XrAF93h2x1TRBCCCHEw3Wt7cu3zzTAx936h0sfdwe+faYBXWs/vAtUfvjoo494//33mTZtGgEBAXTt2pW1a9dSsWLFx173sGHDSEhIoHHjxrz00ku8+uqrVj9uL1iwgHr16tG7d2+aNWuGoiisW7cu05YXvV7P6tWrLescM2aMVcEHUAsf7Nixg3LlytGvXz8CAgIYPXo0iYmJed6S1aVLF/73v//x999/06hRI5o2bcrs2bOtvpDn5nJpeXh4sGXLFmJjY2nTpg1BQUHMnz8/w+dSr9fz66+/cujQIWrXrs348eOZMWPGQ7eRV8fKiy++yN9//821a9fo27cvNWrUYMyYMbi5ufHGG29kez2DBw/mhx9+YMGCBQQFBdG1a1du3rzJjh07KF0686JtBoOBkSNHWnUlLVGiBPPnz6dFixbUrVuXzZs389dff+HhoQ6fuXbtGrt372bkyJGPvuM5oFMe1nRUDEVHR+Pu7k5UVJQm1QI3nLzBC0sPAxn/glaQTvQFWUpKCuvWraN79+7FfrC3yHtyvIn8JsfcfeYCCBUrVnzoJVuyYjQp7A8JJywmEW9X9YdMrVus8kPbtm2pX78+c+bMyXQek8lEdHQ0bm5umhULEcVLZsfczZs3qVWrFocPH85WUvvWW28RERHBvHnzspwvq/NITnIDeXcUQJn9glbK2U4SKyGEECKPGPQ6mlX24In6ZWhW2aNYJFZCFDY+Pj78+OOPhIaGZmt+b29vPvroozyO6r5CUYq9OOpa25dONX3YHxLOx2tPc+p6NOPaV5HESgghhBBCFGt9+vTJ9ryvv/563gWSAUmuCjDzL2jta3hz6no0Z2/EPHwhIYQQQogc2LZtm9YhCFFkSLfAQiDAV+3befpG9q8OLYQQQgghhMhfklwVAjXvJVfBt2JINWp/0T8hhBBCCCFEepJcFQLlSjnhbGcgOdXExTtxWocjhBBCCCGEyIAkV4WAXq+jhrlr4HXpGiiEEEIIIURBJMlVIVFTxl0JIYQQQghRoElyVUjU9FOTqzOSXAkhhBBCCFEgSXJVSNRM0y1QURSNoxFCCCFEXhsxYkSOrudTGOh0OtasWZPv2503bx7+/v7o9XrmzJmT79vPiaL4uueF1q1b8/PPP2dr3qZNm7Jy5co8jkglyVUhUd3HFb0O7sYlExaTpHU4QgghhHgMOp0uy78PPviAL774gkWLFmkdaoFy6dIldDodR48ezfYy0dHRjBs3jrfeeotr167x3HPP5V2AuaCgv+5bt26lZ8+eeHl54eDgQOXKlRk0aBA7duywzLNt2zar47l06dL079+fixcvWubJLNHOTnL5559/cuvWLZ566qlsxfzee+8xadIkTKa8r7otyVUh4WBroJKXCyBFLYQQQohctXUabJ+e8WPbp6uP57IbN25Y/ubMmYObm5vVtDfeeAN3d3dKlCiR69subkJDQ0lJSaFHjx74+vri5OT0SOtJSUnJ5cgyVpBf92+++YYOHTrg4eHBb7/9RnBwMKtXr6Z58+aMHz8+3fzBwcFcv36d5cuXc+rUKXr16oXRaHzsOL788ktGjhyJXp+9VKZbt27ExMSwfv36x972w0hyVYhIUQshhBAiD+gNsPWT9AnW9unqdL0h1zfp4+Nj+XN3d0en01lNc3FxSfcLvslkYtq0aVSsWBFHR0fq1avHihUrLI+bWws2btxIYGAgjo6OtG/fnrCwMNavX09AQABubm48/fTTxMfHW5Zr27Yt48aNY9y4cbi7u+Pp6cn7779vNQwhIiKC559/Hg8PD5ycnOjWrRvnzp3Lch/PnTtH69atcXBwoGbNmmzatCndPFeuXGHgwIGUKFGCUqVK8cQTT3Dp0qVsP4/mff7nn39o2LAhTk5ONG/enODgYAAWLVpEnTp1AKhUqRI6nc6y/j/++IMGDRrg4OBApUqVmDp1KqmpqZZ163Q6vv32W3r37o2zszOffPJJtpf74Ycf6Nu3L05OTlStWpU///zTKu5Tp07Rs2dP3NzccHV1pVWrVly4cAFI33KzYcMGWrZsSYkSJfDw8KBnz56WeTOT3WMls+ctI6Ghobz22mu89tprLF68mPbt21O+fHnq1q3Lq6++ysGDB9Mt4+3tja+vL61bt2by5MmcPn2a8+fPZxn7w9y+fZstW7bQq1cvyzRFUfjggw8oV64c9vb2+Pn58corr1geNxgMdO/enV9//fWxtp0dklwVIgGSXAkhhBDZoyiQHJe9v2YvQes31URqy8fqtC0fq/dbv6k+nt115eG46GnTpvHTTz/x3XffcerUKcaPH88zzzzD9u3breb74IMP+Oqrr9i9e7cleZkzZw4///wza9eu5e+//2bu3LlWyyxevBgbGxv279/PF198weeff84PP/xgeXzkyJEcPXqUNWvWsGfPHhRFoXv37pm25phMJvr164ednR379u3ju+++46233rKaJyUlhS5duuDq6srOnTvZtWsXLi4udO3aleTk5Bw9N++++y6zZs3i4MGD2NjYMGrUKAAGDRrE5s2bAdi/fz83btzA39+fnTt3MmzYMF599VVOnz7N999/z6JFiywJVNrnsm/fvpw4cYJRo0Zle7mpU6cycOBAjh8/Tvfu3RkyZAjh4eEAXLt2jdatW2Nvb8+WLVs4dOgQo0aNskrQ0oqLi2PChAkcPHiQf/75B71eT9++fbPs4pbdYyWz5y0jK1euJCUlhYkTJ2b4uE6ny3RZAEdHR4Acv7YP+vfff3FyciIgIMAqttmzZ/P9999z7tw51qxZY0mqzRo3bszOnTsfa9vZooh0oqKiFECJiorSOhQr24LDlPJv/U9pN2Or1qEUCsnJycqaNWuU5ORkrUMRxYAcbyK/yTF3X0JCgnL69GklISHh/sSkWEWZ4pb/f0mxOY5/4cKFiru7e7rpw4cPV5544glFURQlMTFRcXJyUnbv3m01z+jRo5XBgwcriqIoW7duVQBl8+bNlsenTZumAMqFCxcs08aOHat06dLFcr9NmzZKQECAYjKZLNPeeustJSAgQFEURfnvv/8UQNmwYYNiNBoVRVGUO3fuKI6Ojsrvv/+e4T5t3LhRsbGxUa5du2aZtn79egVQVq9erSiKoixZskSpXr261XaTkpIUR0dHZePGjRmuNyQkRAGUI0eOZLrPa9euVQDL8XDkyBEFUEJCQizzdOjQQfn000+t1r1kyRLF19fXch9QXnvtNat5srvce++9Z7kfGxurAMr69esVRVGUt99+W6lYsWKm7920r3tGbt++rQDKiRMnMnz8UY+VB5+3Bz3//POKm5ub1bQVK1Yozs7Olr/jx49brT8iIkJRFEW5fv260rx5c6VMmTJKUlKSoiiK1bGQ0f4bjUYlIiLCcsyZzZ49W6lUqZLVtFmzZinVqlXL8nz4xx9/KHq9Pt36zDI8j9yTk9zAJu/TN5FbzN0CQ+7GEZ+cipOdvHxCCCFEcXD+/Hni4+Pp1KmT1fTk5GQCAwOtptWtW9dyu3Tp0jg5OVGpUiWrafv377dapmnTplYtD82aNWPWrFkYjUbOnDmDjY0NDRs2tDzu4eFB9erVOXPmTIbxnjlzBn9/f/z8/KzWmdaxY8c4f/48rq6uVtMTExMf2u3tQWn32dfXF4CwsDDKlSuX4fzHjh1j165dVi1ORqORxMRE4uPjLeOy0u5zTpZLG4+zszNubm6EhYUBcPToUVq1aoWtrW229u3cuXNMnjyZffv2cefOHUuLVWhoKLVr1043/6MeK9l53h5snerSpQtHjx7l2rVrtG3bNt14qrJly6IoCvHx8dSrV4+VK1diZ2eXrf3OTEJCAg4ODlbTnnzySebMmUOlSpXo2rUr3bt3p1evXtjY3P+u7OjoiMlkIikpydKKlhfk23kh4uVqj5erPbdjkjh7M4YG5UpqHZIQQghRMNk6wTvXc7bMv7Nhxwww2IExWe0S2DL9IP2HbjcPxMbGArB27VrKlClj9Zi9vb11CGm+tOt0unRf4nU6Xb5UTXuY2NhYgoKCWLZsWbrHvLy8crSuB/cZyHIfY2NjmTp1Kv369Uv3WNov7s7Ozo+0XFbPeU6/2Pfq1Yvy5cszf/58/Pz8MJlM1K5dO9PudY9zrEDmz1vVqlWJiori5s2b+Pj4AODi4kKVKlWskpi0du7ciZubG97e3umSaFdXV6KiotItExkZibu7e4brA/D09CQiIsJqmr+/P8HBwWzevJlNmzbx4osvMmPGDLZv327Zx/DwcJydnfM0sQJJrgqdAF83bsfc5vT1aEmuhBBCiMzodGDn/PD5zLZPVxOrdu9Cm4n3i1kY7NT7GqtZsyb29vaEhobSpk2bXF//vn37rO7v3buXqlWrYjAYCAgIIDU1lYMHD1paQ+7evUtwcDA1a9bMcH0BAQFcuXKFGzduWFpE9u7dazVPgwYN+O233/D29sbNzS3X9ykrDRo0IDg4mCpVquTLcmnVrVuXxYsXk5KS8tDWK/PzPH/+fFq1agWoY46yklfHyoABA5g0aRKfffYZs2fPztYyFStWzLTyYfXq1Tl06BDDhw+3TDMajRw7dowxY8Zkus7AwEBu3rxJREQEJUve/y7s6OhIr1696NWrFy+99BI1atTgxIkTNGjQAICTJ0+ma7nLC5JcFTI1fd3Y8d9tKWohhBBC5BZzImVOrOD+/62fWN/XiKurK2+88Qbjx4/HZDLRsmVLoqKi2LVrF25ublZfUB9FaGgoEyZMYOzYsRw+fJi5c+cya9YsQG2x6N27N6+99hrff/897u7uTJo0iTJlyvDEE09kuL6OHTtSrVo1hg8fzowZM4iOjubdd9+1mmfIkCHMmDGDJ554gg8//JCyZcty+fJlVq1axcSJEylbtuxj7VNWJk+eTM+ePSlXrhwDBgxAr9dz7NgxTp48yccff5zry6U1btw45s6dy1NPPcXbb7+Nu7s7e/fupXHjxlSvXt1q3pIlS+Lh4cG8efPw9fUlNDSUSZMmZbn+vDpWypUrx6xZs3j11VcJDw9nxIgRVKxYkfDwcJYuXQqoVfmya8KECYwePZoaNWrQqVMn4uLimDt3LhEREQ9Nrjw9Pdm1axc9e/YE1KqQRqORJk2a4OTkxNKlS3F0dKR8+fKW5Xbu3Ennzp0fad9zQqoFFjI1/dRfds5IciWEEELkDpPROrEyazNRnW56/Ovy5IaPPvqI999/n2nTphEQEEDXrl1Zu3YtFStWfOx1Dxs2jISEBBo3bsxLL73Eq6++anWx3QULFlCvXj169+5Ns2bNUBSFdevWZdryotfrWb16tWWdY8aMSVdRz8nJiR07dlCuXDn69etHQEAAo0ePJjExMc9bsrp06cL//vc//v77bxo1akTTpk2ZPXu21Zfx3FwuLQ8PD7Zs2UJsbCxt2rQhKCiI+fPnZ/hc6vV6fv31Vw4dOkTt2rUZP348M2bMeOg28upYefnll/n777+5ffs2AwYMoGrVqnTv3p2QkBA2bNiQrkJfVgYPHswPP/zAggULCAoKomvXrty8eZMdO3ZQunTpTJczGAyMHDnSqjtpiRIlmD9/Pi1atKBu3bps3ryZv/76Cw8PD0Ct0Lh7925Gjhz56DufTTpFycOaoYVUdHQ07u7uREVF5Xsz9cOcD4ul4+fbcbQ1cHJqFwz6rMteFmcpKSmsW7eO7t27Z3vQqBCPSo43kd/kmLsvMTGRkJAQKlasmG6gu3i4tm3bUr9+febMmZPpPCaTiejoaNzc3LJ94VYhHkdWx9zNmzepVasWhw8fzlZi+9ZbbxEREcG8efMynSer80hOcgN5dxQyFT2dcbDVk5Bi5NLdOK3DEUIIIYQQIl/5+Pjw448/Ehoamq35vb29+eijj/I4KpWMuSpkDHod1X3cOHYlkjM3oqns5aJ1SEIIIYQQQuSrPn36ZHve119/Pe8CeYAkV4VQTV81uTp9PZqedf0evoAQQgghRCa2bdumdQhCFBnSLbAQqumrXidAKgYKIYQQQghRcEhyVQiZKwaevi7JlRBCCGEmNbqEEI8qt84fklwVQtV93NDpICwmiTuxSVqHI4QQQmjKXC0xPj5e40iEEIWV+fzxuNVXZcxVIeRib0MFD2dC7sRx5kY0rap6aR2SEEIIoRmDwUCJEiUICwsD1Osn6XRyqZLcZDKZSE5OJjExUUqxi3yRX8ecoijEx8cTFhZGiRIlcnQh5IxIclVIBfi6EnInjtPXJbkSQgghfHx8ACwJlshdiqKQkJCAo6OjJK4iX+T3MVeiRAnLeeRxSHJVSNX0dWPdiZuckaIWQgghBDqdDl9fX7y9vUlJSdE6nCInJSWFHTt20Lp162J/0WqRP/LzmLO1tX3sFiszSa4KKUtRC0muhBBCCAuDwZBrX5LEfQaDgdTUVBwcHCS5EvmisB5z0mm2kArwVZOrC7fjSEwxahyNEEIIIYQQQpKrQsrHzYGSTrYYTQr/3YrROhwhhBBCCCGKPUmuCimdTmfpGijjroQQQgghhNCeJFeFWE1fuZiwEEIIIYQQBYUUtCjEzOOupKiFyC1Gk8L+kHDCYhLxdnWgccVSGPRSclcIIYQQIjskuSrE7ncLjMFkUtDLl2DxGDacvMHUv05zIyrRMs3X3YEpvWrStbavhpEJIYQQQhQO0i2wEKvs5YKdQU9sUipXIxK0DkcUYhtO3uCFpYetEiuAm1GJvLD0MBtO3tAoMiGEEEKIwkOSq0LM1qCnamkXAE7fiNI4GlFYGU0KU/86jZLBY+ZpU/86jdGU0RxCCCGEEMJMkqtCTopaiMe1PyQ8XYtVWgpwIyqR3w+GcjUinpjEFBRFEi0hhBBCiAfJmKtCrqafGxyC0zfkWlfi0YTFZJ5YpfX2qpOW23oduDna4n7vz83h3n/zfUebdI+Z/1wdbLAxyO86QgghhCh6JLkqiLZOA70B2kxM/9j26WAyQru3gfstV3KtK/GovF0dsjWfu6MtCSlGklNNmBSIjE8hMj7lkbbpYm9jScbcHGyskq+0SVvaRM083d7G8EjbzEtGk8K+kHAO3dHhERJOsyreUmVR5I0cfD4IIYTIf5JcFUR6A2z9RL2d9gN0+3R1ert3LZNq3EuurkUmEBmfTAknu/yMVBQBjSuWwtfdgZtRiRmOu9IBPu4O/PtWewx6HYkpRqITUohK8xedmEJUfApRCan375sfu/cXlZBCXLIRgNikVGKTUrkWmfNCLPY2+kyTsYcla052BnS63E16rKssGvjp3EGpsijyTtrPh+bj70/P4PNBCCFE/pPkqiAyJ1TmD9BmL8GuL2D7Z+oHZ5qEy93RlrIlHbkakcCZGzE0q+yhQcCiMDPodUzpVZMXlh5O95g5DZnSq6alJcbB1oCDrQFvt+y1eKWVYjSpyVZiqnVy9uD/ROvkLSo+hZikVBQFklJNhMUkERaTlOPt2+h1aboumrstPqTlzOF+d8YHL3dgrrL4YFJqrrL47TMNJMESuSvN54PeaARqot85E3b8X7rPByGEEPlPkquCKm2CZU6yMvngrOnrxtWIBE7fiJbkSjySrrV9+frpBrz0s3Wi4JPLLTC2Bj0eLvZ4uNjneFmTSSEmKTVdIhaVLiFLTddiFpWQQqpJIdWkEB6XTHhcco63r9OBq72NVVJ2JDQy0yqLOtQqi51q+kgXQZG72kyE2FsYdvwfvdCjxySJlRBCFBCSXBVkbSbCtmmgmECnz/SDM8DXjb9P35KKgeKxVC3tgoLa7e7/+tfFx82BxhVLFZjEQK/XWVqT/HO4rKIoJKQYrVrCMmo9i84wWUshMcWEokB0YirRidm7rpy5yuL+kHD50UPkrquH4MRyAPSYUPS26CSxEkKIAkGSq4Js+3Q1sQL1//bpGbdc+d0rxy5FLcRjOHolEoB6ZUvQN7CMtsHkMp1Oh5OdDU52Nvi6O+Z4+aRU470E7H6r2JazYSzZe/mhy2a3GqMQ2XJ5NywbCMn3K8TqTCmZfj4IIYTIX1IPuaAyD05uOR7LyJetn6jTH2CuGHg+LIbkVFM+BimKkuNX1QtR1y3rrnEkBY+9jQFvVweqeLsQVL4k7Wp4071O9rpKZrcaoxAPdWErLOlnSaxMVToBoNi7Z/r5IIQQIn9JclUQpa361PED8KuvTg/oneEHaNmSjrg62JBiVDgfFpvv4Yqi4fjVSADq+ZfQNI7CwlxlMatOk24ONjSuWCrfYhJFWPAG+HkQpN7rktp6IsZ+P5JicEKXFAV1n5IESwghCgBJrgoik9F6cHKltup/Oxd1usloNbtOpyPAV7oGikeXlGq0HDv1ypbQNphCwlxlEcg0wYpOTGXFoSv5F5Qomk6tgd+GgDEJPKpCm7eg/btg68SVks3VeVLiM/x8EEIIkb8kuSqI2r1t3XfenFxd3Aat38zwApFyMWHxOM7eiCHFqFDSyRb/Ujkfk1Rcda3ty7fPNMDH3brrn6+7Ax0DvAGYtOoEfxy9pkV4oig49iusGAmmVKg9AF7cA+3esTx8ybOdeiN4HTQYLhcQFqKg2Dot85bk7dPVx0WRJMlVYeDfFGwcIOY63Pkvw1ksRS2kYqB4BOYugXXLlsj1i+wWdV1r+/LvW+1ZOqohw6oaWTqqIf++1Z75wxryTNNyKApM+P0YG0/d1DpUUdgcXAirn1cLGgUOhX7zwGBrNUuMoz+mMo3U5OvoMo0CFUKkY77g94MJlnnoh96gTVwiz0lyVRjYOkC5purti9synKVmmm6BipLRlXeEyNzRK2oxi3pSzOKRGPQ6mlQsRZCnQpN75et1Oh0f9q5N/wZlMZoUXv75CNv/u611qKKw2Pst/O81QIHGz0GvLzP9MmYKHKbeOLwYTFLUSIgCoc1Etavu1k9g+UiIu2M9pl6qexZZmiZXO3bsoFevXvj5+aHT6VizZo3V4zqdLsO/GTNmZLner7/+mgoVKuDg4ECTJk3Yv39/Hu5FPknbNTADVbxdsNHriEpI4XqUlH4WOZO25UrkHr1ex2f969Cjji/JRhPP/XSQvRfvah2WKOh2zIQNk9TbLV6FbtNBn/nHtVLzCbB3h4hLELI9f2IUQjxcm4lQtTOcWgUzKktiVUxomlzFxcVRr149vv766wwfv3HjhtXfggUL0Ol09O/fP9N1/vbbb0yYMIEpU6Zw+PBh6tWrR5cuXQgLC8ur3cgf5uQqZCcYU9M97GBroIq3CwBnpGugyIHYpFTO31arTNb1l5ar3GZj0DN7UH3a1/AmKdXE6EUHOBwaoXVYoiBSFPjnI9jykXq/7TvQcSo8rKuurRPUHajePrQoT0MUQuSAyQR3zllPqztIm1hEvtE0uerWrRsff/wxffv2zfBxHx8fq78//viDdu3aUalSpUzX+fnnn/Pss88ycuRIatasyXfffYeTkxMLFizIq93IHz51wbGken2T64cznKWmVAwUj+DE1SgUBfzcHeSaTHnEzkbPN0Ma0KKKB3HJRkYs2M+p61FahyUKEkWBje/Czpnq/U4fQdu3Hp5YmQUNV/+f/R/EFvIfE4UoKs5vgogQ62nftVJbmUWRVWjGXN26dYu1a9cyevToTOdJTk7m0KFDdOzY0TJNr9fTsWNH9uzZkx9h5h29ASq2UW9n0jXQUo5dWq5EDkiXwPzhYGtg/rCGNCxfkujEVIb+uJ9zt2K0DksUBCYTrJ0Ae+/14ug+E1q8krN1+NSBMg2lsIUQBcn6t9T//k1gwllwLAVJUWqCFR6S9bKi0LLROoDsWrx4Ma6urvTr1y/Tee7cuYPRaKR06dJW00uXLs3Zs2czXS4pKYmkpCTL/ehoNTlJSUkhJSXlMSPPPbryrbA5vQbThS0Ym49P93g1bycATt+IKlBxa8X8HMhzkbWj97qo1fZzlefqMWTneLPVwbxn6jN80SFOXItmyA/7+HlMI8qXcsqvMEVBY0rF8L9X0Z/4DQUdxh5zUOoPgWy8Fx885nT1h2Jz7SDKocWkNn4RdIXm91NRCMhnas7oN76NISIEBUjt/Q04esKzO7CZ3xpdQjjK961IHb0FSlbUOtQCqyAdczmJodAkVwsWLGDIkCE4OOR+t6Vp06YxderUdNP//vtvnJwKzpcepySFTgBX9rPxr1UYDdbPRWwKgA2h4Qms+nMdDoXm1c1bmzZt0jqEAm3feQOgI+HaWdatO6N1OIVedo63wb5wJ9zAjZgkBn6zk5drGSllnw/BiQJFp6QSdOk7ykTux4Sew+XHcu16Sbi+LkfrMR9zBqMTXfSO2EaEsP/3WdxxrZUXYYtiTj5Ts6f12c2UBG66N2D/7lPAKQDsK39AuzPvYJ8UQ+r8Luyq+jZx9qWzXFdxVxCOufj4+GzPWyi+fu/cuZPg4GB+++23LOfz9PTEYDBw69Ytq+m3bt3Cx8cn0+XefvttJkyYYLkfHR2Nv78/nTt3xs3N7fGCz2XK9bnoIy/TNcANpUrHdI9/+d92bkUnUa5eMxqWL6lBhAVHSkoKmzZtolOnTtja2j58gWLoblwy4Xu2ATC6b0dcHeR5elQ5Pd7adUji6R8OEHI3nsWX3fh5dCO8XCXDKjZSEzGsGo0+cj+K3hZTvx+pV7079XKwioyOOb3tHji0gGa2ZzF2fzNvYhfFknym5kBiNDYn1bGPXj3eo3vF1taPd+iIsqwvjneC6XDlc1KfWQOlKud/nAVcQTrmzL3asqNQJFc//vgjQUFB1KuX9ceOnZ0dQUFB/PPPP/Tp0wcAk8nEP//8w7hx4zJdzt7eHnv79F9qbG1tNX8x06nUFg4vxib0Xwjolu7hWn7u3IoO47+weJpV8c7/+AqgAvk6FhBnbqpdAit7OVPKteC00hZm2T3efEva8vNzTXnyuz1cuhvPiMWH+PW5ZpRytsuHKIWmkuNh+VC4uBVsHNANWoZN1fQ/lmWX1THXaBQcWoA+eB36pEhw8cqdmIW4Rz5Ts+HQ75ASB141sKnaPn1hmpJlYMT/YHFvdLfPYLvkCfW+Z1Vt4i3gCsIxl5Pta9ohOzY2lqNHj3L06FEAQkJCOHr0KKGhoZZ5oqOjWb58OWPGjMlwHR06dOCrr76y3J8wYQLz589n8eLFnDlzhhdeeIG4uDhGjhyZp/uSb8wl2S9szfBhc8XAM1IxUGTD0SuRANSTYhaa8HV35OcxTSntZs9/t2IZtmAfUQna9y0XeSgpBpb2VxMrW2cYsgIeI7FKx6cOlAkCUwoc+zn31iuEyB6TCfbPU283fi7zip8u3jD8L/CuCbE3YVFPuP1f/sUp8oymydXBgwcJDAwkMDAQUBOjwMBAJk+ebJnn119/RVEUBg8enOE6Lly4wJ07dyz3Bw0axMyZM5k8eTL169fn6NGjbNiwIV2Ri0LLXDEw7BTE3Er3cE0/Kccusu9+pUC5vpVWynk4sWxMUzyc7Th5LZqRC/cTl5T+WnaiCEiIgJ+egNDdYO8Gw9ZAxVa5v52gEer/Q4vUEu9CiPxzfjOEX1Qv7F3vqazndfG6l2DVUhOsxT3hdnD+xCnyjKbJVdu2bVEUJd3fokWLLPM899xzxMfH4+6e8Ze/S5cu8cEHH1hNGzduHJcvXyYpKYl9+/bRpEmTPNyLfObsoV7zCiBkR7qHzeXYz96MIdVoys/IRCGjKArHr6rXWqrnX0LbYIq5Kt4uLBndBHdHWw6HRjJm8UESU4xahyVyU9wdWNwLrh1Sr1k4/E/wb5w326rVD+xc1S94l3bmzTaEEBnb9536v8FQsHN++PzOnmqCVboOxN5SW7DCMq9wLQo+qdNaGJm7BmZwvavypZxwsjOQnGoi5E5cvoYlCperEQncjUvGRq+zJOVCOzX93Fg8qjEu9jbsuXiXF5YeIjlVfiApEmJuwsLucPMEOHvDiHXgF5h327N3gbpPqrcPLsy77QghrN05Bxf+AXTQKOPhLBly9lB/cPGpA3FhagtWmFTvLawkuSqM0iZXD3T50Kf5oixdA0VWzK1WNXxdcbA1aByNAKjvX4IFIxrhYKtna/BtXv31iLRAF3aRV2BhN7gTDG5lYOR6KF0z77dr7hp45i+11UwIkffMY62qd4NSObx+lVMpGPan2jsp7rbagnXrdO7HKPKcJFeFUfnmYLCH6Ktw90K6hwN8XQE4fV2SK5E583grKWZRsDSuWIp5QxtiZ9Cz/uRNJq44jskk42YKpbsX1MQq/CKUKA8j14FnlfzZtm89tXXMlAJHpbCFEHkuMfr+e63xc4+2DqdSMOwP8K0P8XfUFqxbp3ItRJE/JLkqjGwdody9cWQX01cNrOmrjk+TliuRFakUWHC1rubFV08HYtDrWHXkGu/9cRJFChMULreD1a6AUVfAo4raYlWyQv7GIIUthMg/R3+G5FjwrH6/h9GjcCqlFrvxC4T4u2oL1s0TuRWlyAeSXBVWWYy7slQMvB4tX8hEhowmhZPX1G6Bdf2lUmBB1LmWD7MH1Ueng5/3hfLx2jPyfi4sbp5QE6vYm2oVsJHrwb1M/sdRewDYuUD4Bbj0b/5vX4jiwmSC/d+rt5tkUX49uxxLwtA14NcAEsLVYjg3jj92mCJ/SHJVWJmTq5CdYLKuKla9tCt6HdyNS+Z2TFL+xyYKvIu3Y4lLNuJkZ6Cqt6vW4YhM9K7nx2f91eqgP/4bwueb5BooBd7VQ7Coh9qlx7e+emFQF40u6G7vAnXuFbY4tEibGIQoDi78c7/8et2HlF/PLscSagtWmYbqZRwW94LrR3Nn3SJPSXJVWPnWBwd3SIqC60esHnK0M1DRUy3/eUq6BooMmLsE1vZzx6B/zF/YRJ4a2NCfD5+oBcDcLef5Ztt5jSMSmbq8W72OVWIU+DdRq385ldI2Jkthiz8h7q6moQhRZKUtv27vknvrdXCHoaugbCNIjFTPLw985xMFjyRXhZXeABVbq7czGnflp3b1OiPJlciAuVKgXDy4cBjWrAKTutUAYPqGYBbtCtE4IpHOha2wpB8kx0CFVvDMKvWLkdb86qs/xhmT4dgvWkcjRNFz55x64eCcll/PLgd39XxStvH9BOva4dzfjsg1klwVZpZxV9vTPVTT9/64KyEeZKkUKBcPLjSeb1OZVzpUBeCDv07z+4ErGkckLII3wM+DIDUBqnSCIctz99frxyWFLYTIO/vnq/+rdc15+fXscnBTW7D8m6ot4z/1US9ILgokSa4Ks0rt1P9X9kGy9QWDLUUtpOVKPCAp1Wg5LqRSYOEyvmNVnm2lfni/teo4fxy9pnFEglOr4bchYEyCGj3hqWVqRdeCpM4AsHWGu+fg8i6toxGi6EiMhqPL1NtNHrH8enbZu8IzK6Bcc3VIyE994OrBvN2meCSSXBVmpSqBu7/a3SN0j9VD5mtdhdyJIz45VYvoRAF19kYMKUaFkk62+JcqYF8CRZZ0Oh3vdA9gSJNyKApM+P0YG0/d1Dqs4uvYr7BiFJhS1cIRTy4GG3uto0rP3lVNsEAKWwiRm479kqb8eru83569q9oyXr4FJEXDkr5w5UDeb1fkiCRXhZlOB5XaqLcfKMnu7eqAp4s9igLBN2PyPzZRYJm7BNYtWwLd45aLFflOp9Px0RO16degDEaTwss/H2H7f7e1Dqv4ObgQVj8PigkCh0Lf78Fgo3VUmTN3DTz9B8SHaxqKEEWCyQT7crH8enbZu6gJVoVWaRKs/fmzbZEtklwVduZfSrK63pV0DRRpHL2iFrOoJ8UsCi29Xsf0/nXpUceXZKOJ5346yN6LUgku3+z5Bv73GqBA47HQ60u1yFBB5hcIPnWlsIUQueXCP+o15HKz/Hp22TnD07+pCVZyjJpghe7N3xhEpiS5Kuwq3mu5unkC4u5YPWTuGihFLURaaVuuROFlY9Aze1B92tfwJinVxOhFBzgSGqF1WEXfjpmw8W31dotXodtnoC8EH6U6nRS2ECI3mVutAp/RpoCNnTM8/btaOTo5Vq1Wenl3/sch0ikEnwgiSy5eULqOejvEumqguWKglGMXZrFJqZy/HQtAXX9puSrs7Gz0fDOkAS2qeBCXbGT4gv2cuh6ldVhFk6LAPx/Blo/U+23fgY5T868rUG6o86Ra2OLOf+nG6QohcuDOeTi/CdBB4zwov55ddk4w+De1enRKHCwdAJekaI3WJLkqCjIZd1XrXrfAszdjMJrkV0oBJ65GoSjg5+6At6uD1uGIXOBga2D+sIY0LF+S6MRUhv64n/NhMs4yVykKbHwHds5U73f6CNq+VbgSK1DLOdfpr96WwhZCPLoD5vLrXdTiYlqyc4LBv6rDRFLiYNkAuPSvtjEVc5JcFQXmcVcXtll19ajo6YKDrZ74ZCOX78ZlvKwoVqRLYNHkZGfDgpGNqFPGnfC4ZJ6ev0/e87nFZIL/jYe936j3u8+EFq9oG9PjMHcNPLVGClsI8SiSYuCIufz6WG1jMbN1hMG/QOUOkBIPy56EkB1aR1VsSXJVFJRvBnpbiAqF8IuWyQa9juql1XFXZ27IL9kCjl+9V8xCLh5c5Lg52PLTqMZUL+1KWEwST8/fx7XIBK3DKtyMqfDHi3BoIaCD3l9B42e1jurx+DUAnzrqdbmO/6Z1NEIUPkd/UYtIeFbLn/Lr2WXrCE/9DFU63kuwBsLF7Q9fTuQ6Sa6KAjtn8G+i3n6ga+D9ioEyDkPA0SuRgFQKLKpKOtuxZExjKnk6cy0ygWd+2EdYTKLWYRVOqcmwcrRaWU9ngP4/QIOhWkf1+KSwhRCPzmSC/fcKWTTOx/Lr2WXrAIOWQdXOkJoAPw+EC1u1jqrYkeSqqKjUVv3/YHJ1r6iFVAwUd2OTLC0ZtSW5KrK8XR1YOqYJZUo4EnInjmd+2EdEXLLWYRUuKYnw+zA4vQYMdjDwp/sX4S0K6jwJtk5w+6yUbxYiJy5sgbvnwd4N6g3WOpqM2TrAoKVQrSukJsIvT6lxi3wjyVVRYU6uQnaAyWiZHOAr17oSKnOXwMpezrg52GocjchLfiUc+eXZppR2s+e/W7EMW7Cf6MQUrcMqHJLj1S8j/60HGwd46hcI6Kl1VLnLwR1q91NvS2ELIbJvv8bl17PLxl79Uah6dzXB+vkpOL9Z66iKDUmuigq/QPVCdomRcOOYZXKNe8nVregk7sYmaRScKAjudwksoWkcIn+U83Bi2ZimeDjbceJaFCMXHiAuKVXrsAq2xGhY2h8ublVLlg9ZAVU7ah1V3ggaqf4/tVoKWwiRHXcvwLm/AR000rD8enbZ2MOTi6F6D3WM5S9Pw7lNWkdVLEhyVVQYbKBiK/V2mq6BLvY2VPBwAqSoRXF3v1KgdAksLqp4u7BkdBPcHW05dDmCZ386SGKK8eELFkcJEbCkD4TuVn+oGrbm/jm1KCoTBKVr3yts8bvW0QhR8O2/V369amfwqKxtLNllYwdPLoIaPdX3+q9Pw39/ax1VkSfJVVGS2bgrKWpR7CmKIpUCi6mafm4sHtUYZzsDuy/c5YWlh0hONWkdVsESdwcW94Jrh8CxFAz/E/wbax1V3pLCFkJkX1IMHFmq3i4o5dezy5xgBfQGYzL8NgSCN2gdVZEmyVVRYk6uQvdCyv0SzAE+anIlLVfF19WIBO7GJWOj11nG4Ynio75/CRaMaISDrZ6twbd59dcjpBolwQIg+gYs7A43T4CzN4xYC371tY4qf9QdCDaOcPsMXNmvdTRCFFzm8useVQtW+fXsMtjCgAVQ84l7CdYzELxe66iKLEmuihKPKuBWRm36Dd1jmWxpuZKKgcWWudWqhq8rDrYGjaMRWmhSyYN5QxtiZ9Cz/uRNJq44jslUzFsrIkNhYTe4E6yeO0euh9I1tY4q/zi4Q+3+6m0pbCFExkwm2D9Pvd1kLOgL6Vdngy30/xFq9QVTCvw2FM6u1TqqIqmQHiEiQzpdhl0DzcnV+duxMt6imDKPt5JiFsVb62pefPV0IAa9jlVHrvHeHydRimt3sLsX1BariBAoUR5GrgPPKlpHlf/MXQNPrVLHnQkhrF3cAnfPgZ0r1HtK62gej8EW+v2g/qhiSlEvOXHmL62jKnIkuSpqMkiufNwcKOFki9GkcO5WrCZhCW1JpUBh1rmWD7MH1Ueng5/3hfLJ2jPFL8EKO6smVlFX1Bb/keuhZAWto9JG2YbgXUst13x8udbRCFHw7LvXahX4DNi7ahtLbjDYQN956vXuTKmwfASc/lPrqIoUSa6Kmopt1P83jkPcXQB0Op3lYsJn5HpXxY7RpHDymtotsK6/VAoU0LueH5/1rwvAD/+GMHvTfxpHlI9uHIdF3SH2pppUjFwP7mW0jko7VoUtFkphCyHSSlt+vfGzWkeTeww20Pd7qDvofoJ1ao3WURUZklwVNa6lwbsmoMClHZbJNeViwsXWxduxxCUbcbIzUNW7CPzqJnLFwIb+fPhELQC+3HKeb7dd0DiifHD1ECzuCfF3wbc+jPgfuHhrHZX26g5UL5gcdhquHtA6GiEKjgM/AApU7VR4yq9nl94Afb6Fuk+BYoQVo+DkKq2jKhIkuSqKshh3JUUtih9zl8Dafu4Y9DptgxEFyrBmFZjUrQYAn204y6JdIRpHlIcu74afnoDEKPBvopZbdyqldVQFg2MJqNVPvS2FLYRQFeby69mlN0Cfb6De02qCtXIMnFihdVSFniRXRVEGyVVAmm6BxW58RTFnrhQoFw8WGXm+TWVeaa8Wcvjgr9P8fuCKxhHlgQtbYEk/tZRyxdbwzCq1Up64z9w18OQqSIjUMhIhCoZjv0JStDous1J7raPJO3oDPPEV1H9GTbBWPSvjLx+TJFdFUfkWoLeBiEsQrv4SXdnLBTuDnpikVK5GJGS9vChSLJUC5eLBIhPjO1VjTMuKALy16jh/HL2mcUS5KHg9/DwIUhOgSid4+newd9E6qoLHvzF4BajP0wn5YiWKubTl1xsX4vLr2aU3QO+5EDgUFBOsfg6O/651VIVWET9aiil7FyjbWL0dsh0AOxs9VUurXyhOSdfAYiMp1WgZZyeVAkVmdDod7/YIYEiTcigKTPj9GH+fuql1WI/v1Gr1YpnGZKjRE55aBraOWkdVMOl00HCkevugFLYQxdzFrXDnP7X8ev3BWkeTP/R66PUlNBh2L8Eaq7beiRyT5KqoyqJroBS1KD7O3oghxahQ0skW/1LypVJkTqfT8dETtenXoAxGk8K4n4+w/b/bWof16I7+og7QNqWqJYefXAw29lpHVbBZClucgmuHtI5GCO2YW60ChxSN8uvZpddDzy/UbsKKCVY/D0d/1jqqQkeSq6LKklxtV5u3QcqxF0PmLoF1y5ZAp5NiFiJrer2O6f3r0qOOL8lGE2OXHGTfxbtah5VzBxfAmufVLweBQ9WSwwYbraMq+BxLQq2+6u1DC7WNRQit3L0A/21Ubzd+TttYtKDXQ4/Z0HAUoMCaF+HIMq2jKlQkuSqqyjRQm7MTwuHmcUAqBhZHR6+oxSzqSTELkU02Bj2zB9WnfQ1vElNMjFp0gCOhEVqHlX17voH/jVdvNx6rdnPRG7SNqTBJW9giMUrTUITQhLn8epUiWH49u/R66PE5NBoDKPDHS3B4idZRFRqSXBVVBluo0FK9fa9roLlb4LXIBKLiUzQKTOSntC1XQmSXnY2eb4Y0oHllD+KSjQxfsJ9T1wvBF+0dM2Dj2+rtFq9Bt8+K/kD03ObfBLxqQEq8FLYQxU9SbJry689rG4vWdDroPlP9kQoF/hwHhxZrHVWhIJ86RdkD467cHW0pU0Idd3PmprReFXWxSamcvx0LQF1/abkSOeNga2D+sIYElS9JdGIqQ3/cz/mwGK3DypiiwD8fwpaP1ftt34GOH6hfDkTO6HT3W68OLpLCFqJ4OfaLWn69VGWoXITLr2eXTqf+SNXkBfX+X6+oBW9EliS5KsrMyVXoHkhJBKRrYHFy4moUigJ+7g54uzpoHY4ohJztbVg4shF1yrgTHpfM0/P3cflunNZhWVMU2PgO7Jyl3u/0EbR9SxKrx1F3EBjs4dYJuHZY62iEyB+Kcr+QRZNiUH49u3Q66DoNmr6o3v/fa3DgR01DKujkyCnKvKqDiw+kJsKVfcD9ohZSMbDoky6BIje4Odjy06jGVC/tSlhMEk/P38f1yAJyrTyTSR1ftfcb9X73mdDiFW1jKgqcSkGtPuptKWwhiou05dfrFZPy69ml00GXT6HZOPX+2gmwf762MRVgklwVZTpduq6BlnLs0nJV5B2/eq+YhVw8WDymks52LBnTmEqezlyLTGDID/sIi0nUNihjKqx54d6Xfx088TU0flbbmIoSS2GLlZAonxeiGNh3r9Wq/tPg4KZtLAWRTgedP4bmL6v3171x/zkTViS5Kuoqt1P/30uuat3rFng+LJbkVJNGQYn8cPRKJCCVAkXu8HZ1YOmYJpQp4UjInTiG/rCfiLhkbYJJTYaVo+H4r6AzQP8fIPAZbWIpqso1A8/qUthCFA/hF+G/Dert4lh+Pbt0OrXrdYtX1fvr34S932kbUwEkyVVRV7GN+v/6EUiIoGxJR1ztbUg2mrhwr9iBKHruxiZx7V7XrdqSXIlc4lfCkZ+fbUJpN3uCb8UwbMF+ohPzufJoSiL8PhROrwGDHQz8CeoMyN8YioO0hS0OLZTCFqJo228uv94RPKtoHU3BptNBx6nQ8t4lLza8pV4CQ1hIclXUufmqZXVRIGQnOp2OAClqUeSZuwRW9nLGzcFW42hEUVLew5llY5rg4WzHiWtRjFx4gLik1PzZeHIc/DJI/YXZxgGe+gUCeubPtoujek+phS1unlB/oBOiKJLy6zmn00GHKdDqdfX+xrdhz9faxlSASHJVHFjGXW0F7he1OCNFLYqs+10CS2gahyiaqni7smR0E9wcbDh0OYJnfzpIYooxbzeaGA1LB6hdnG2dYcgKqNoxb7dZ3DmVgppPqLcPLdI0FCHyzPFfISnqXvn1DlpHU3jodND+fWg9Ub2/8R3YPVfbmAoISa6KgweKWkjFwKLvfqVA6RIo8kZNPzcWj2qMs52B3Rfu8uKyw3k3jjM+HJb0gdDdYO8Ow9ZAxVZ5sy1hzdw18MQKSCqg1zkT4lEpyv2iDI2fk/LrOaXTQft3oc0k9f7f78G/czQNqSCQo6g4KN9CHfQdfhEiLt+/1tWNaBTpR1/kKIoilQJFvggsV5IFIxrhYKtny9kwXvvtCKnGXE6wYm/D4t5w7RA4loLhf4J/49zdhshc+ebgURVS4qSwhSh6Lm6DO8Fg56JWCRSPpt3b0PZt9fbmKbDzc23j0ZgkV8WBgxuUbajeDtlOFW8XDHodkfEp3IjSuJyyyHVXIxK4G5eMjV5nKb0vRF5pUsmDeUMbYmfQs+7ETSauOI7JlEs/2kTfgEU91IvZOnvDiLXgVz931i2yx6qwxSItIxEi9+37Xv0v5dcfX9tJ0O5d9fY/U2HHzMdandGksC8knEN3dOwLCceYW58r+UCSq+IiTddAB1sDVbxcABl3VRSZW61q+LriYGvQOBpRHLSu5sVXTwdi0OtYdeQa7/9x8vFbxSNDYWE39VdltzIwcj2Urpk7AYucqTdYrcx445gUthBFR3iIlF/PbW0mQvv31NtbPoLtMx5pNRtO3qDlZ1t4ZsFBfjpn4JkFB2n52RY2nLyRi8HmHUmuigtLcrUdTKb7XQOlYmCRYx5vJcUsRH7qXMuH2YPqo9PBsn2hfLL2zKMnWHcvwIJuEBECJcqriZWUR9aOswcE9FZvS+uVKCoO3Cu/XrkDeFbVOpqio/Wb0GGyenvrx7DtsxwtvuHkDV5Yejhdz6qbUYm8sPRwoUiwJLkqLso0VCtsxd+BsFNS1KIIk0qBQiu96/nxWb+6APzwbwizN/2X85WEnYWF3SH6qjrWZ9QGKFk+lyMVOdZwpPpfCluIoiApFg4vUW9L+fXc1+p16PiBenvbp7B1WrYWM5oUpv51mox+ljNPm/rX6QLfRVCSq+LCxg4qtFRvX9xmGYsj3QKLFqNJ4eQ1tVtgXX+pFCjy38BG/kztXQuAL7ec59ttFzKeces02D7detqN47CoO8TeBCcvGLkO3PzyOGKRLeVbgEcVSI6Fkyu1jkaIx3P8t3vl1yupFw4Wua/leOj0oXp7+//B1k8fejHy/SHhWdYCUIAbUYnsDwnPxUBznyRXxUmacVcBvq4AXLobT2x+XQBU5LkLt2OJSzbiZGegqrer1uGIYmp48wq81bUGAJ9tOMvi3ZfSz6Q3wNZP7idYVw/C4p4Qf1e93+AZcPHOn4DFw0lhC1FUKArsl/Lr+aLFq9D5Y/X29s/Uc34WCVZYTPaKrGV3Pq3IEVWcmJOrS7vwcAAfNwcAzkrrVZFx7F6XwNp+7hj0Om2DEcXaC20r80p7dZzUlD9P8fuBK9YztJmoVpba+gn88RL89AQkqq2uVl1KRMFR72m1sMX1I3D9qNbRCPFoQrbD7bNSfj2/NH8Zunyq3t4xA/75MNMEy9vVIVurzO58WtE0udqxYwe9evXCz88PnU7HmjVr0s1z5swZevfujbu7O87OzjRq1IjQ0NBM17lo0SJ0Op3Vn4NDwX4R8o13gFrOODUBruy3tF7JuKuiw1wpUC4eLAqC8Z2qMaZlRQDeWnWcP49dt56h5QSo1g2OLFW7mwG0euP+YGhRsDh7QEAv9fbhxdrGIsSjMpdfrzcYHOSzMl80ewm63its8e/nsPmDDBMsJzsDWf0urAN83R1oXLFUnoSZWzRNruLi4qhXrx5ff/11ho9fuHCBli1bUqNGDbZt28bx48d5//33H5osubm5cePGDcvf5cuX8yL8wkens+oaaK4YKOOuio5j5kqBcvFgUQDodDre7RHAkCblUBQY/9tR/j51U/1QPfM/+LYZ/Lf+/gIGO+jwvnYBi4czdw08vlwtCiBEYRJxCYLvnXOk/Hr+avo8dLtXmn3XHNg02SrB2hYcxuD5e8msVoU555rSq2aB75ljo+XGu3XrRrdu3TJ9/N1336V79+5Mn35/0HPlypUful6dToePj0+uxFjkVGoLJ35Xk6smYwApx15UJKUaLYmyVAoUBYVOp+OjJ2qTkGxk1ZFr/PjLrzTx+QP3O4fVGWwdISVBTayMyeoYrDYTtQ1aZK5CK7UIQPhFtbBF0HCtIxIi+/bPx1J+3aua1tEUP02eU3/oX/cG7P4SFBN0/pjfDl7hndUnMZoUWlTxoH+DsszYGGxV3MLH3YEpvWrStbavhjuQPZomV1kxmUysXbuWiRMn0qVLF44cOULFihV5++236dOnT5bLxsbGUr58eUwmEw0aNODTTz+lVq1amc6flJREUlKS5X50tPoFNSUlhZSUlFzZnwKjXAtsAeX6YWq4JwNw9mYMCYlJ2BiK1hA882tX5F7DTJy8GkWKUaGkky0+rjbFZr8LiuJ2vOXUpy1tGBb6NfXjdsEdMBoc0JVpgD50N5frvMqhCmMIuvQD5bd+gtFoxNTqDa1DLvC0Oub09Ydi2DIV08GFGOvKmJXiotCf45LjsDmyBB2QGjQapbDuR2EXOAK9yYRhw0TY8xUHL93hrZBegI6+9X35+Ila2Nno6V7Lm70XbrNlzyHaNwuiaWUvDHqdZsdfTrarUx75Ko+5S6fTsXr1akvidPPmTXx9fXFycuLjjz+mXbt2bNiwgXfeeYetW7fSpk2bDNezZ88ezp07R926dYmKimLmzJns2LGDU6dOUbZs2QyX+eCDD5g6dWq66T///DNOTk65to8FRfvTb+GadIO9FV5l2H+NSTbpeLteKj5Fb1eLlZ03dawIMRBQwsTzASatwxECAPuUSGrcWE25u9vRY8KIjt9S2xKPE2Ns1vK1aQAzkvtZ5n/TbhUv6Vdwxrcf//n00S5wkSm7lGi6nHoVvWJkW/UPiXKqoHVIQjxU+TtbqH9lEbF23vxTczroitYPyoVNudtbCLy6CIAFqV3Z6z2E7uUUdAW0x198fDxPP/00UVFRuLm5ZTlvjpKryMhIVq9ezc6dO7l8+TLx8fF4eXkRGBhIly5daN68+SMH/WBydf36dcqUKcPgwYP5+eefLfP17t0bZ2dnfvnll2ytNyUlhYCAAAYPHsxHH32U4TwZtVz5+/tz586dhz6BhZF+w1sYDv2IMWg0A0L7ceRKFJ8/WYdedQt+U2tOpKSksGnTJjp16oStra3W4eS5iatOsvrIdV5qW4nXOlTROpxip7gdbw+VFIN+71fo932LLiUeAFPVriS0eocxG+JpEjoPo6JnrrGf1WI64GXDKnrW9qbigA81CLzw0PKYM6wajf7MHxgbjMRkHkchirRCfY5TFGzmt0J3+yzGTh9jaiwXDtZSbFIqr/x6jDIhy/k/2x8AMDZ8FlPnT0mbXRWkYy46OhpPT89sJVfZ6hZ4/fp1Jk+ezLJly/Dz86Nx48bUr18fR0dHwsPD2bp1KzNnzqR8+fJMmTKFQYMGPfZOeHp6YmNjQ82aNa2mBwQE8O+//2Z7Pba2tgQGBnL+/PlM57G3t8fe3j7DZbV+MfNElfZw6EcMl3ZQy38kR65EERwWR7+iuK8U4dfxASevqd1ZG5QvVSz2t6AqLsdbpowp6nWQtv0fxN9Rp5VtBJ0+Ql++Gc7A90NTCPo4ghRj+t/2FGCusR/LLznwr8GmwA9cLgg0OeYajYYzf2A4uQJD10/Azjl/ty80UyjPcRfvlV+3dcYQNAxDYYu/CAmLTmTEwoOcvhGNo20nhjeqSMCB9zAcnI9BB3SfwYPNVwXhmMvJ9rOVXAUGBjJ8+HAOHTqULtkxS0hIYM2aNcyZM4crV67wxhuP11/ezs6ORo0aERwcbDX9v//+o3z58tlej9Fo5MSJE3Tv3v2x4ilSKrZSm8PvnqNhnQSWIkUtCrvYpFTO31Yrd9WVYhZCC4oCp9eo1zAJv6hO86gCHaao5bvTfFieuh6dYWJlWRVwIyqR/SHhNKvskbdxi0djVdhiFTQYqnVEQmTOXH69vpRf19K5WzGMWHiAa5EJeLrYsWBEIwLKdgVfd/jzZTgwXy1y0X1mob64c7aSq9OnT+PhkfUHnKOjI4MHD2bw4MHcvXs3WxuPjY21alEKCQnh6NGjlCpVinLlyvHmm28yaNAgWrdubRlz9ddff7Ft2zbLMsOGDaNMmTJMmzYNgA8//JCmTZtSpUoVIiMjmTFjBpcvX2bMmDHZiqlYcHCHMkFw9QCBxqNAGc7ciNE6KvEYTlyNQlHAz90BL9f0rbBC5KlL/6plda8dUu87e0PbSdBgGBjS/9oXFpOYblpGsjuf0IBeDw2Gw+YpakulJFeioIq4dP+SD1J+XTP7Lt7l2Z8OEp2YSiVPZxaNbEw5j3uD/RsMVX/0/+MlOPgjXD8MY7akX8n26WAyQru38zf4HMpWWviwxOpR5z948CCBgYEEBgYCMGHCBAIDA5k8Wb2AZN++ffnuu++YPn06derU4YcffmDlypW0bNnSso7Q0FBu3LhhuR8REcGzzz5LQEAA3bt3Jzo6mt27d2fa4lZs3bveVZnwfeh1cCc2Sb7IFGLH5fpWQgu3TsOygbCoh5pY2TpD27fhlSNqt7EMEisAb9fsXdg9u/MJjdQfAnpbuHYQbp7QOhohMnbgB7U1pHJ78KqudTTF0l/HrjP0x/1EJ6YSVL4kK19ofj+xMgscAn2+VW9fPwLz26mvm9n26bD1E9Ab8i/wR5TjNrfFixezdu1ay/2JEydSokQJmjdvnuOL9bZt2xZFUdL9LVq0yDLPqFGjOHfuHAkJCRw9epQnnnjCah3btm2zmn/27NlcvnyZpKQkbt68ydq1ay3Jm0jjXnJlc2k7Fe8d4NI1sPAyXzxYugSKfBF1Tf2F8bsWcG4j6AzQaAy8elRtsbJ3yXLxxhVL4evuQFajqbxc7WlcsVSuhi1ymYsX1Oih3j60WNtYhMhIchwc/km93XistrEUQ4qiMG/HBV7+5QjJRhNda/mwbEwTSjrbZbxA/cHQdx6ggxtHMSzoCIoJ/c6ZamLV7t1CcR3EHCdXn376KY6OjoBa9vzrr79m+vTpeHp6Mn78+FwPUOSRso3A1gnibtPBQ+3GKV0DC69jV6IAqFdW+pKLPJQYBZs/gLkN4MhS9VfFgN7w0n7oMQtcvLO1GoNex5Ream+CzBKs6IQU/nf8eu7ELfJO0Aj1//Hf1C+yQhQkx39Xz1slK0LVzlpHU6wYTQpT/zrNp+vOAjCyRQW+HtIAB9uHtDzVGwT95gM69DeP0/voSAw7/q/QJFbwCMnVlStXqFJFLfO8Zs0a+vfvz3PPPce0adPYuXNnrgco8oiNPZRXS+e3tjkFwOkb0nJVGN2JTeJaZAI6HdSW5ErkhdQk2PMNfFEf/p0NqYlQrjmM3gyDloBnzkv/d63ty7fPNMDH3brrX2k3e6p6u5CUauLVX4/yzuoTJKYYc2lHRK6r2AZKVoCkaDi1WutohLhPUe4Xsmj8bKEukFDYJKYYeXHZIRbtvgTAez0CmNKrVvarv9Z9Evr/gALoUFD0toUmsYJsFrRIy8XFhbt371KuXDn+/vtvJkyYAICDgwMJCQm5HqDIQ5XawvnNBCQcAhpy+nqU1hGJR2Aeb1XJ0xk3BykvK3KRyQQnV8KWDyEyVJ3mWR06TYVqXdOVy82prrV96VTTh/0h4YTFJOLt6kDjiqVQFIUv/znH3K3n+XlfKEdCI/n66UAqeWXd3VBowFzY4p+pamGLwGe0jkgI1aWdcPuMOha0/hCtoyk2wuOSGbP4AIdDI7Gz0TN7YH16PMp1VMMvogNMOgN6U4o65qqQJFg5Tq46derEmDFjCAwM5L///rOUOD916hQVKlTI7fhEXro37qrk7QPYMpqQO3EkJBtxtCv4gwXFffe7BJbQNhBRtFzYqlaCu3FMve/qqxarqD8EDDn+6MiUQa/LoNy6jgmdq9OoYile+/UoZ25E02vuv0zrX5fe9fxybdsil9Qfoo6HuHoAbp4En9paRySEdfl1xxKahlJcXL4bx4iFBwi5E4e7oy3zhzV8tLGz94pXGFtP4n8xNenpehrD1k/UxwpBgpXjNtKvv/6aZs2acfv2bVauXGmpDHjo0CEGDx6c6wGKPORdC5w80afE08b5EiYFgm/JuKvCRioFilx14zgs6QtL+qiJlZ0rtH8fXj4MQcNzNbF6mFZVvVj3aiuaVCxFXLKRV345It0ECyLX0vcLWxyWwhaiAIi4DMHr1NtSfj1fHLsSSb9vdhNyJ44yJRxZ+UKzx0qsaPcuplbqNXNNrd5Qx1xt/UR9vIDL8adkiRIl+Oqrr9JNnzp1aq4EJPKRXg+V2sDJlfRw/o/NcVU4fT2a+vIlvdBQFIVjV9WWq7oy3ko8jshQ2PKJWpgARS2x3Wg0tH4TnD01C6u0mwPLxjSRboIFXdAIOP0HHPsNOk4FO6eHLiJEnjGXX6/UTsqv54N/ztxi3M9HSEgxUruMGwuGN8Lb7REvpWEy3i9ekZJyf7q5xcpU8H9cy1bLVWhoaI5Weu3atUcKRmigUjsAGpnUrj+nb8i4q8LkakQC4XHJ2Oh1BPi6aR2OKIziw2HjuzC3IRz/FVCgdn8YdwC6faZpYmVmY9AzoXN1fhrVGA9nO0s3wT+PSTXBAqNiWyhRHpKi4PQajYMRxVpy/P3y602k/HpeW7r3Ms/+dJCEFCNtqnnx63PNHj2xAvUCwZl1/WszscBfQBiymVw1atSIsWPHcuDAgUzniYqKYv78+dSuXZuVK1fmWoAij90bd+UXdxoX4qUceyFz/F6rVYCv28PLmwqRVkoi7PoCvqwPe74CYxJUaAXPboUBC6BURa0jTCejboLvSjfBgkGvV7uNglrYQgitnPgdEiPVKpZSfj3PKIrC9A1neW/NSUwKDGrozw/DG+Jin39dxwuqbD0Dp0+f5pNPPqFTp044ODgQFBSEn58fDg4OREREcPr0aU6dOkWDBg2YPn26pciFKARK+EOpyujDL9BUf4bdN1wxmRT02S2XKTR1/+LB0iVQZJPJqHb92/IJRF9Vp3nXUisAVun42BUA89qD3QSX7QvlsHQTLBjqPwNbP4Ur++DWaShdU+uIRHGTtvx6o2dBLz865oXkVBMTVxxjzVG198D4jtV4pUMVdAX88yO/ZKvlysPDg88//5wbN27w1VdfUbVqVe7cucO5c+cAGDJkCIcOHWLPnj2SWBVG91qvWtucIj7ZyOXweG3jEdl27EokIJUCRTYoCpzbBN+1gjUvqImVW1no8y08vxOqdirwiZWZdBMsoFxLQ/Vu6m1pvRJauPQvhJ0GWye5LEAeiU5MYcTC/aw5eh0bvY7pA+ryaseqklilkaO2O0dHRwYMGMCAAQPyKh6hhUpt4eCPtLM9Bclw+no0FT2dtY5KPITRpHDy2r0y7FKERGTl2mHYNFm97guAgzu0el2tomXrqG1sj8HcTfCVX46wLyRc/X/xLu/3rCndZLUSNALO/KWO3+v4gRS2EPlr33fq/3pSfj0v3IhKYOTCA5y9GYOznYFvnwmidTUvrcMqcORy1QIqtgJ0+BuvUJpwztyI1joikQ0XbscSl2zEyc5AFW/pDiUyEB4CK0bB/HZqYmWwg2bj4JWj0OLVQp1YmZm7Cb7cvgo6HSzbF0rfb3Zz8Xas1qEVT5Xag3s5SIxSqwcKkV8iQ6X8eh46ezOavl/v5uzNGLxd7fn9+WaSWGVCkisBjiXBLxCAFvqTnJbkqlAwdwms7eeOQcbIibTi7sL6SfBVIzi5EtBB3afg5UPQ5RNweoRrjxRgNgY9r0s3wYJBr4egYept6Roo8pOl/Hpb8K6hdTRFyq7zd3jy2z3cjE6kircLq15sTi0/GeudGUmuhOreuKsWhpOcvi7JVWFgrhRYz19OcOKe5HjYMVOtALjvWzClQOX2MHYH9PseSpTTOsI8JdUEC4j6z4DOAFf2QtgZraMRxUFyPBy6dwHrxlJ+PTetOnyVEQv3E5OUSuOKpVj5fHPKlpTuvlmR5Eqo7iVXLfUnuRmtXjtJFGz3KwWW0DQOUQAYU9UvFnMbwJaPICkafOrC0DUwdDX41tU6wnyTWTfBkDtxWodWfLj5pilssVjbWETxcGK5Wn69RHmo1kXraIoERVH4eut5Jvx+jBSjQs+6viwZ3Rh3J1utQyvwJLkSKv8mYONIaV0kVXTXZNxVAZeUarS8RlIpsBhTFDi7Dr5rAX+9AjE31Napfj/Ac9uhcjutI9RERt0Ee365U7oJ5qegker/Y79ASoK2sYiiLW359cbPSfn1XJBqNPHumpPM2BgMwNjWlfjyqUDsbeS5zY5HvtLX6dOnCQ0NJTnZuoWjd+/ejx2U0ICtA5RvBhe20FJ/ktPXO9GiiqfWUYlMnL0RQ4pRoaSTLf6lCn9RAvEIrh6Ev9+H0N3qfceS0PpNaDQGbOy1ja2AkGqCGqrcTi1sERUKp/+EeoO0jkgUVZd3QdgpKb+eS+KTU3n55yP8czYMnQ4+6FWL4c0raB1WoZLj5OrixYv07duXEydOoNPpUBQFwFLf3miUvu2FVqW295KrE6yVlqsCLW2XQLm2RDFz5zz8MxXO/Knet3GApi9Ai9ek9HAGzN0Ev/jnHF+luejwN0MayCUn8pLeAA2GwdaP1cIWklyJvGIpv/6UnAMf0+2YJMYsPsCxq1HY2+j54qlAutb20TqsQifH3QJfffVVKlasSFhYGE5OTpw6dYodO3bQsGFDtm3blgchinxzb9xVU/0Z/rserm0sIkvHrtwrZlFWilkUG7FhsPZ1+KaJmljp9GrhgJcPq9cTki8VmTJ3E1w80rqb4F/STTBvBQ5RC1uE7obbwVpHI4qiyFA4u1a9LeXXH8vF27H0/3Y3x65GUdLJlp+fbSqJ1SPKcXK1Z88ePvzwQzw9PdHr9ej1elq2bMm0adN45ZVX8iJGkV9K18HoUAoXXSLOd45Jha0C7Pi9liu5eHAxkBQL2/4PvgxUSw2bUqFqF3h+F/T5GtzLaB1hodG6mnU1wZelmmDecvODal3V21KWXeSFAz+q5dcrtgHvAK2jKbQOXQ6n/7e7CQ2Pp1wpJ1a+0Jyg8iW1DqvQynFyZTQacXV1BcDT05Pr19Vf/sqXL09wsPwyVajp9egrtwGgGSc4HyYX4SyIYpNSOX/vAqlSKbAIM6aoydSXgbBtGiTHgl8DGLEWhvwOpWtqHWGhJNUE81nQCPX/0Z8hJVHTUEQRk5IAh+9Vo2zyvLaxFGIbTt7k6fn7iIhPoV5Zd1a92JxKXi5ah1Wo5Ti5ql27NseOHQOgSZMmTJ8+nV27dvHhhx9SqVKlXA9Q5C9d2utdybirAunE1SgUBfzcHfBylcIFRY6iqAUAvmmqdgOMC4OSFeHJRfDsFqjQUusICz3pJpiPqnQAt7JqmWzzOEEhcsOJ5ZAQoVZIlfLrj2TRrhBeWHaIpFQTHQO8+eW5pni6yPeKx5Xj5Oq9997DZDIB8OGHHxISEkKrVq1Yt24dX375Za4HKPLZveQqUHee81duaBuLyJB0CSzCLu+BHzvD70Ph7nlw8oRuM+Cl/VCrL0jxklwl3QTzgbmwBUjXQJF7pPz6YzGZFD5Ze5oP/jqNosCQJuX47pkgnOweuYi4SCPHz2KXLvd/HahSpQpnz54lPDyckiVLStWyoqBkBWKd/HGJv4IudDfQWOuIxAPk4sFF0O1g2DwVgu8NzLZ1gmbjoPnL4OCmbWxFnFQTzAcNhsL2/1NLZt/+D7yqaR2RKOwu74ZbJ6X8+iNITDHy+vJjrD2u/oA+sWt1XmhTWb7D56JcuYhwqVKl5EUpQlLKtwagbMQ+S6l9UXBIpcAiJPoG/PmK2gUweK1aWS1oBLxyBNq/K4lVPpFugnksbWEL8xgZIR6Hufx63UHqNf5EtkTGJzNswX7WHr+BrUHH7EH1eLFtFfkOn8uy1XLVr18/Fi1ahJubG/369cty3lWrVuVKYEI7LjU7wZllNDKd4GpEAv6lnLQOSdxzJzaJa5EJ6HRQW5KrwisxGnZ9AXu+htQEdVqNntBhivyqryFzN8GXfznC/pBwXv7lCHvlosO5I2gEBK9TC1u0f1+9cL0QjyLyCpz9n3pbyq9n29WIeEYsPMD5sFhc7W34fmgQzat4ah1WkZSt5Mrd3d2S1bq7yxe6os62chtM6Kihv8LWkAv4l6qjdUjiHvN4q0qezrg52GobjMi51GQ4uAB2TIf4u+q0so2h80dQrqm2sQlA7Sb48wPdBI+ERvK1dBN8PFU6glsZiL6mfjGuM0DriERhddBcfr21VE3NppPXohi56AC3Y5LwcXNg0ahG1PCRnhF5JVvJ1cKFCzO8LYoop1Jcc6iGf2IwiWf/gSBJrgqK+10CS2gbiMgZRYFTq+CfDyHikjrNoyp0nKK2WEmXjALF3E2wUYVSjP/tKKfvdRP8v/516VXPT+vwCidzYYtt09TCFpJciUeRknC/MIqUX8+W7f/d5sWlh4hLNlLDx5WFIxvh6+6odVhFWo7HXIWEhHDu3Ll008+dO8elS5dyIyZRAISXbg6A+41dGkci0pJKgYVQyA6Y3w5WjFITK5fS0HM2vLgXAnpJYlWAmbsJNpZqgrkj8BnQ6eHSTriT/nuEEA91YkWa8utdtY6mwPv9wBVGLTpAXLKR5pU9+P35ZpJY5YMcJ1cjRoxg9+7d6abv27ePESNG5EZMogCwrdoOgCqxh9Rf3YXmFEXh2FW15apubo+32joNtk/P+LHt09XHRXpZPW9r31AvALy4F1w/AnYu0O5dePkwNBwFBil5WxiYuwmOa3f/osP95KLDj8a9LFTtrN6Wsuwip9KWX2/0rJRfz4KiKMze9B8TVx7HaFLoG1iGRSMby3CCfJLj5OrIkSO0aNEi3fSmTZty9OjR3IhJFABl6rQjSbHFm7vEXDujdTgCuBqRQHhcMjZ6HQG+udxXWm+ArZ+kTxS2T1eny4dYxjJ63qKvwXet4MB8CL8Iehv1i8ArR6HNRLB30Sxc8WhsDHre6HK/muDpG9H0mvuvVBN8FEEj1P9Hf4bUJE1DEYXM5d1w6wTYOEr59SykGE1MXHGcL/5RW4dfaleZzwfWw84mVwqEi2zI8U+nOp2OmJiYdNOjoqIwGqWrRFHh7u7GAUMAjUzHuXN8I65lZdCo1o7fa7UK8HXL/cplbSaq/7d+AknRUL27+uXnyBIIHAoVWqofbMJahZbq87P1E/ThIdS8EYXN16PAlKo+XrMPdJgMHpU1DVPkDqkmmAuqdAJXP4i5Dmf+krFXIvv232u1qjcInEppG0sBFZuUyovLDrPjv9vodfBRn9oMaVJe67CKnRwnV61bt2batGn88ssvGAzqh4nRaGTatGm0bNky1wMU2gl1b0yjiOPoQ7YB47UOp9i7f/HgPKrY2WYiJMXC7i9g99z7048sUf9ElgzHfqaq+Y57OXhyIZRtqGVIIg+YuwnO2XyOr7dJNcEcM9iohS22/58UthDZF3UVzpjLr4/VNpYCKiw6kZGLDnDqejSOtga+ejqQDgGltQ6rWMpxcvXZZ5/RunVrqlevTqtWrQDYuXMn0dHRbNmyJdcDFNpJ9G8FET/gffcAGFNljIjGjl2JBPK4UqCNfZo7OvCoknfbKmKUu+fQAYreBt1rx6VQRRFm7ibYqOL9aoK95v7LtH51pJpgdgQ+o16O4NJOuHMePOU8Ix7iwI+gGKFCKym/noHzYTEMX3CAa5EJeDjbsWBEIyl8paEcf1uuWbMmx48f56uvvuLYsWM4OjoybNgwxo0bR6lS0kxblHhUbUTkMWdKmOLUAfn+jbQOqdgymhROXrtXhj2vTpgpCbDnXouV3kbt2lZ34P0ugyJz26ej2/oJRp0NBlMq7Jghz1sx0KaaF+teacUrv0o3wRwp4a92Dzy3EQ4vVq/zJkRmpPx6lvZdvMuzPx0kOjGVip7OLBrZiPIe0oqupUdqivDz8+PTTz/N7VhEAVPTryS7TLXoYdiP8cJWDJJcaebC7Vjiko042Rmo4p1HBRFWjFI/xOzdYeJF+PdzdQwWSKKQlXtFP4ytJ/G/mJr0dD2NQZ63YsPHXboJPpKgEWpydXQZtH/vgVZzIdI4uRISwtXu1tW7aR1NgfK/49eZ8Nsxko0mGpQrwQ/DG1HK2U7rsIq9R0quIiMj2b9/P2FhYZhMJqvHhg0bliuBCe2VLenIIn09erCfpOB/cGorXxS1Yu4SWNvPHYM+D7qbbfsMgtept9tMVLuApi1yYZ4urJmrKbZ7F1Pz8bBuHaZWb6jjUeV5Kzakm+AjqNoZXH0h5gacXQu1+2kdkSiIFAX2fafebjxGKtfeoygKP+wM4ZN1ajXnLrVK88VTgdJiXkDkOLn666+/GDJkCLGxsbi5uaFLM65Ap9NJclWE6PU6bns1gzvzcbh5UC12IGWkNWGuFFjPP4+KWdw9r/63c4EGQ+9PNycGJqkEmiGTUb12VZuJkJJyf7o8b8VSRt0E94Xc5b0e0k0wHYONWmlzx3Q4tFCSK5Gx0D1w01x+fejD5y8GjCaFj/53mkW7LwEwonkF3u9ZM29+eBWPJMdF719//XVGjRpFbGwskZGRRERE/H97dx4eVXn+f/x9ZrKHrEBIwpqALGHfZdeiIipura1rXRDRaq212lb9Vktbtfqt/my/WgXcq9ZWW3dFKZVNWWQJi4BCCISEhIQt+zozvz9OJiEmgQyZyZmZfF7XlStnzpw5c5McJnPPcz/30/B19OhRX8QoFuraexAHnN2xuerMFzmxRGOnwHjfPEHFYfP7mB9DxHcSuBm/hLPv883zBrqz72t9ZEo/t07JXSboXnT4tbVadLhVY64DDMheCUeyrI5G/JF70eARP1T7daCq1sFPXt/YkFg9cMEQHpqjxMrfeJxc5eXlceeddxIVFeWLeMTPDEmNZbVzmHlj73JLY+msqusc7MwvAXzUKfDQDsj6Lxg2mKgWtyLt5S4TfPnGCSRq0eHWxfeBAeeY25tesTYW8T/FueZaaKC/TcDR8hquXryWT78+RJjdxv9dNZp509ObVJCJf/A4uZo1axYbNmzwRSzihzJS4viiPrly7f3c4mg6p135pdQ6XCREhdI7MdL7T7D2r+b3wRdBQj/vn1+kk3KXCU5IS6Ssuo6f/n0z//PuNqpqVS7aYOwN5vfNr0NdjaWhiJ/Z8OIJ7deHWh2NpXKOVPD9Z79kU85xYiNC+NvcCZrP6cc8nnN14YUXcu+997Jjxw6GDx9OaGhok/svvvhirwUn1jujRxfWYSZXxqGvoawQuiRZHFXncmJJoNc/oSorgq3/NLcn3e7dc4tIk26CT3++h9fW5rBpv7oJNhg4C7okQ1kBfPMRDL3M6ojEH9RWwoaXzO1OPmq15cBx5r7yFYfLaugZH8nLN47njB4xVoclJ+FxcjVv3jwAfve73zW7zzAMHA59IhdMIkLtJHRP4etjfRlq22/Wxg//gdVhdSpbDtQ3s+jlg2YWG14ARzX0HAu9J3r//CKiboInYw81516t/F9zLSMlVwIntF/vDQM7b/v1ZTsPcccbm6msdTA0NZaXbhhPUmyE1WHJKXhcFuh0Olv9UmIVnDJSTpx3pdLAjra1fuTK64sH11bB+sXm9pk/AdVti/iUygRbMbq+scXe5XB0r9XRiNVcrsZGFuNvNjtLdkKvr9vPvFc3UFnrYPrA7vxj/iQlVgHC4+TqRFVVVd6KQ/xYRmpsw7wrspabL3zSIcqq69hTVAb4oFPgtrfMLoGxvSDjEu+eW0RadGI3QWjsJrivM3cTTOgLA2aa25tetTYWsV7OWijYarZfH9P5lvdxuVz876e7eOCd7ThdcMXYXrxw/Ti6hHfOJDMQeZxcORwOfv/739OzZ0+6dOnC3r3mp0y/+c1veOGFF7weoFgvIyWO9c7B1BACJbn6ZLEDbcstxuWC1LgIuseEe+/ELhesecbcnniLWZojIh3CXSb4yk2N3QQv6uzdBBsaW7ymxhad3Xp3+/UrOl379Zo6J3f/cwvPfG4uTXDXOWfw+A9GEGpv11iIdDCPf1sPP/wwL7/8Mo8//jhhYWEN+4cNG8bzzz/v1eDEPwxJiaGKcDY4B5o7sv5rbUCdiM9KAvd+DkU7ITQaxlzv3XOLSJs0lAn2U5kgA8+HLj2gvAi+/cTqaMQqxXmw431ze0LnamRRUlXLjS+v553NedhtBo9/fwR3nTNQrdYDkMfJ1auvvsqiRYu45pprsNsbV5wfOXIku3bt8mpw4h+6dgmnR2w4qx1a76qj+WzxYPeo1ehrIdLL5xaRNkuOi+CNeSoTxB5qvh5BY5c46Xzc7df7ToXkYVZH02Hyiyv54XNr+GLPEaLD7Lxw/Th+OL631WHJaTqtRYQHDBjQbL/T6aS2ttYrQYn/yUg5Yd5V9ipwdsJPVi3gk06Bhbtgz38AA8681XvnFZHTojLBeu75NXs/h6PZ1sYiHa+2CjZ2vvbruwpKuOyZL9lVUEr3mHD+MX8SZw3SkjeBzOPkKiMjg1WrVjXb//bbbzN69GivBCX+JyM1lm2udCptXaC6GA5mWh1S0DtcVk3e8UoMA4Z5M7lqWDT4QkhM9955RaRdOn2ZYEI/6P89c1uNLTqf7f+CiiNm+/VBF1gdTYf4cs9hrnh2DQUlVQxI6sI7P5nMsJ4+WHZFOpTHydWDDz7IHXfcwWOPPYbT6eTf//438+bN4+GHH+bBBx/0RYziB4akxOLERmbICHOHWrL7nHu+VXq3aGIjvNRwovwwbP2Hua1Fg0X8jrtM8Paz+wOdsEzwxMYWDlXDdBouF6x7ztweP7dTtF9/Z3Mu17+0ntLqOiakJfKvWyfTKyHK6rDECzxOri655BI++OAD/vOf/xAdHc2DDz7Izp07+eCDDzj33HN9EaP4gYyUWAA+qxxs7tC8K59rLAmM995JN7wIdVWQMgr6TPLeeUXEa0LsNu6dNbhZmeCHWxvLBB1OF+uyj7LxsMG67KM4nEGyRMagCyA6CcoL4Rs1tug0Dqyrb78eEXRNlhxOF2uyjvBeZh5rso5Q53DyzOd7+Pk/tlDrcHHhiBRevWkCcVHq2hssPPpooK6ujkceeYSbbrqJpUuX+iom8UN9u0YTFWZnee1QHrJjvhDWVECYPmXxFa93Cqyrblw0eNIdWjRYxM+5ywTv/Ptm1u87yh1vbGbt3iNM6JfIo5/sIr+4CrDz6u4NpMRF8NCcDM4flmJ12O3jbmyx+knY+DJkXGx1RNIR3IsGDw+u9utLtuez4IMd9f9XTVFhdipqzFLfedPSuG/2EGw2/T0OJh6NXIWEhPD4449TV1fnq3jET9ltBoOSY8h2JVMRmQKOGshZY3VYQcvlcrEl1xy5GuGt+Vbb3jY/DY5JhaGXeuecIuJTLZUJ3vlmZpM3awAFxVXc9tomlmzPtyJM73I3tsj6LxzbZ2ko0gGK82DHe+Z2EDWyWLI9n9te29Ts/6o7sfrhuF48cGGGEqsg5HFZ4MyZM1mxYoUvYhE/Z5YGGuzpMtbcodJAn8k9VsnR8hpCbAZD6ksy28XlamxkoUWDRQKKu0zwxevHtTrg7C4KXPDBjsAvEUxMg/SzARds+pvV0YivNbRfnwLJw62OxiscThcLPtjByf4nrtp9OPD/r0qLPE6uZs+eza9//Wvuuece/v73v/P+++83+fLEypUrmTNnDqmpqRiGwbvvvtvsmJ07d3LxxRcTFxdHdHQ048ePJycn56Tnfeuttxg8eDAREREMHz6cjz/+2KO4pGUZqeab/NXO+hc/JVc+s7V+1GpISiwRofZTHN0G2Svg0HYIjWqcMC4iASUyLATXSd6LuYD84irWZx/tsJh8Ro0tOofaKrP8E4Jq1Gp99tFmI1bfFTT/V6UZj9ux/OQnPwHgySefbHafYRg4HG1vGVteXs7IkSO56aabuPzyy5vdn5WVxdSpU5k7dy4LFiwgNjaWr7/+moiIiFbP+eWXX3LVVVfx6KOPctFFF/HGG29w6aWXsmnTJoYN6zwL0vmCu6nFe8UD+AmYk0/LD0N0N0vjCkaNiwd7qSRwTf2o1ahrIDLBO+cUkQ5VWHryN2ueHufXBl0A0d2hrAC+XQJD5lgdkfjC1/+GisMQ2wsGXWh1NF7Tqf6vSjMeJ1dOp9NrTz579mxmz57d6v0PPPAAF1xwAY8//njDvv79+5/0nH/+8585//zzuffeewH4/e9/z9KlS3n66ad57rnnvBN4JzUoOQbDgG/KIqntnUFo0Q5zRGTY960OLehsOXAc8FKnwKJvYfenmIsG39b+84mIJZJiWv9g8XSO82shYeaHQV88ZY5sKLkKPi5XYyOLIGu/3qn+r0oz7bqSq6qqTjqK1B5Op5OPPvqIX/7yl8yaNYvNmzeTlpbGfffdx6WXXtrq49asWcPdd9/dZN+sWbNaLDl0q66uprq6uuF2SUkJALW1tdTWqhzBLdSAtK5R7D1cQX7CBPoU7cC55784BvlnNyf37y7QfocOp4vteWZZ4NCU6HbHb1vzDHbAecYsHLF9IMB+HoEiUK83CRyje8WQHBvOoZLqFudyGEByXDije8UEx3U44mpCv3gK155l1BVlQXwfqyPq1Lz9GmfkrickPxNXSAR1I64Oqr9N7v+rBSXVLd4fdP9XfcSf/q56EoPHyZXD4eCRRx7hueee49ChQ3z77bekp6fzm9/8hn79+jF37lxPT9miwsJCysrK+OMf/8gf/vAHHnvsMZYsWcLll1/O559/zowZM1p8XEFBAT169Giyr0ePHhQUFLT6XI8++igLFixotv+zzz4jKkqtxk8U57IBNj461I3bgKodS1jKR37d1jvQlg3Ir4DymhDCbC6+3bCKPe340YbWlXLe9jcA+NI1miOaf+hzgXa9SWC5INngxRL3dOkTXxxcuIDZPSr4dEnwrA81uUsG3ct2sPft37Ir9QdWhyN47zVubPZf6QXkxE4gc/k6r5zTn5ybZPC3EhtN/59CsP5f9SV/+LtaUVHR5mM9Tq4efvhhXnnlFR5//HHmzZvXsH/YsGE89dRTXkuu3OWHl1xyCT//+c8BGDVqFF9++SXPPfdcq8nV6bjvvvuajHaVlJTQu3dvzjvvPGJjvdCpLYjkRO9l83/2sLvb2bhK/0xUzWEumJwBCWlWh9ZMbW0tS5cu5dxzzyU0NHC64/1rUx5s+ZoRvRO46MIJ7TqX7Yv/h91Vg6vHcCb+8G6/ToIDXaBebxJYLgDGfH2IP3y8q8mn4jbD4IkrRnDR8GTrgvMBY0cNvHMzA8vXkX7+IrAFT+lYoPHqa1xJPiFbNgCQetkCUoOkS+CJCtfshz3fYDcMHCd0okmJi+CB2YOZNbTHSR4t4F9/V91VbW3h8avUq6++yqJFi5g5cya33nprw/6RI0eya9cuT0/Xqm7duhESEkJGRkaT/UOGDGH16tWtPi45OZlDhw412Xfo0CGSk1v/gxMeHk54eHiz/aGhoZb/Mv3NsN5mM4StRQ6M3hNg/xeE5qyGpIEWR9a6QPs9bs8vBWB0n4T2xV1XAxteAMCYfAehYWHeCE9OIdCuNwk8F43qxewRPVmzp5BPVqzj4/wIjlXUUuNwBd+1N/QS+PTXGGWHCM3+LwwOnqYHgcorr3FbXgVnHfSdQmjvMd4JzI/UOpy8/KXZ2fq3l2QwoHsMhaVVJMVEMCEtEbvWtvKIP/xd9eT5PW7FnpeXx4ABA5rtdzqdXq2JDAsLY/z48XzzzTdN9n/77bf07du31cdNmjSJZcuWNdm3dOlSJk2a5LXYOrOh9R0D9xaVUdt3urlTLdm9amvD4sHx7TvR1/82O211SYahzbtxikjgstsMJqYlMiHJxfzpZuXAwpV7cQbbujkhYTD6GnPb3bJbAltdNWx4ydyecIu1sfjIR1vzyTteSbcuYVwxtjeT+nflklE9mdS/qxKrTsDj5CojI4NVq1Y12//2228zevRoj85VVlZGZmYmmZmZAGRnZ5OZmdmwjtW9997LP/7xDxYvXsyePXt4+umn+eCDDxrawQP8+Mc/5r777mu4/bOf/YwlS5bwxBNPsGvXLn7729+yYcMG7rjjDk//qdKC7jHhdOsShtMF+2LHmzuzV4Kz7S34pXXVdQ525ptDz+3qFOhywZqnze0J88w3KCISlH44thcxESHsLSrnPzsPnfoBgWbM9eb33Uvh+AFrY5H22+5uv94TBl9kdTRe53K5eG5FFgA3TO7nnbUqJaB4nFw9+OCD3HHHHTz22GM4nU7+/e9/M2/ePB5++GEefPBBj861YcMGRo8e3ZCU3X333YwePbrhPJdddhnPPfccjz/+OMOHD+f555/nX//6F1OnTm04R05ODvn5+Q23J0+ezBtvvMGiRYsYOXIkb7/9Nu+++67WuPISwzAYUj96tbEuDcJjofKYueaVtNvO/FJqHS4SokLpnRh5+ifatxoKtkFIJIy7yXsBiojfiYkI4dozzYqOhSv3WhyND3TtD2nTARds/pvV0Uh7uFywrn5ZnCBrv+62cvdhdhWUEhVmb/h/KZ2Lx8nVJZdcwgcffMB//vMfoqOjefDBB9m5cycffPAB5557rkfnOuuss3C5XM2+Xn755YZjbrrpJnbv3k1lZSWZmZlccsklTc6xfPnyJscDXHHFFXzzzTdUV1ezfft2LrjgAk//mXIS7sWEvy6ogH7TzJ0qDfSKrQ2LB8djtKf5xJpnzO+jroKoxPYHJiJ+7cbJ/Qiz29i4/xgb9h21OhzvG3uD+X3T38BRZ2ko0g65X0F+JtjDYcwNVkfjEwvrR62uHN+H+ChVjXRGbUqu/vKXv1BVZa4inZOTw9SpU1m6dCmFhYVUVFSwevVqzjvvPJ8GKv4jI9VMrnbkl0D6WebOrM+tCyiIbDlgzrca2Svu9E9yeA98u8TcPvMnJz9WRIJCUmwEl43uCQTp6NXgiyCqK5QehN2fWR2NnC73qNXwKyC6q7Wx+MDW3ON8mXUEu81g7jT/66IsHaNNydXdd9/d0IIwLS2NoqIinwYl/s1dFrgrvwRnWn1L/Jy1UFtpYVTBYUv9yNXI3vGnf5J1zwIuOGMWdDvDG2GJSACYNz0dgKU7DrGnsMziaLwsJBxGXW1uq7FFYCrJhx3vmdsTg7ORhfuDjYtHptIzvh2l/RLQ2pRcpaam8q9//Yv9+/fjcrnIzc0lJyenxS8JfundogkLsVFe4yDH6AkxqeCoNhMsOW1l1XVkFZlviE67U2DFUcg0Fw1m0u3eCUxEAsKApC6cm2GunbM4GEev3GVke9TYIiBteNFsv95nMqSMtDoar9t/pJxPtpk9AG6p/6BDOqc2JVf/8z//w1133UV6ejqGYTB+/HjS0tKafPXr14+0NA2BdgYhdhuDk2MA2FFQ2lgaqHlX7bIttxiXC1LjIuge03zdtTbZ+DLUVkCPYfUTwEWkM7l1hvmm7p3NeRwqqbI4Gi/rNsCc5+tywubXrI5GPFFXDRvr268H6ajV86uycbpgxsDuDRU+0jm1Kbm65ZZbOHz4MFu2bMHlcrF06VI2bdrU5Gvz5s1s2rTJ1/GKn3A3tdh54rwrJVft0u6SwLoaWL/I3J50O7SnIYaIBKSxfRMZ1zeBGoeTl77YZ3U43udubLFZjS0CytfvQHlR0LZfP1JWzT83mKOp82do1Kqza3MPzJiYGIYNG8ZLL73ElClTCA8/zU/WJSi4P5XZcbAEJtXPu8rfYpalqTvdaTmxU+Bp2fEulOZDdBIM+763whKRADN/Rn82vLqB19fu5/az+xMTEWp1SN4zZA5EJkJJHuz5Dww63+qI5FRObL8+7iawB9H1WO+VNfuprnMyolcck9KDr1GHeMbjVuzXX3894eHh1NTUtDj3SjqHJh0DY5Kh+xDAZS4oLKelXZ0CmywafIs5+VtEOqWZg5Po3z2a0uo6/r4+yP4uq7FF4MndAAc3m+3X3SOPQaSipo5X1+wDYP70/u1bRkWCgsfJ1e7du5k2bRqRkZH07dtXc646Kfecq/ziKo6V16g0sJ0Ol1WTd7wSw4Bhp5Nc7f/SHDkMidCiwSKdnM1mMH96fwBeXL2PmjqnxRF5mfsN+u5PoTjP0lCkDZq0X+9mbSw+8M+vDnC8opY+iVGcPyzZ6nDED3icXN1www3YbDY+/PBDNm7cqDlXnVRMRCh9EqMAzbvyBndJYHq3aGJPp4Rn7V/N7yOvDMq1Q0TEM5eMTiUpJpyCkire33LQ6nC8q9sZ0HeqGlsEgpJ8s2QdgrKRRZ3DyeJV2YC5FILdplEr8WDOlVtmZiYbN25k8ODBvohHAkhGSiw5RyvYkV/C5AlTwBYCx7Lh2D5I6Gd1eAGloSTwdJpZHMmCXR+Z21o0WESA8BA7N05J47Elu1i0MovLR/fEFkxv/MbeAPtXw6ZXYfo9YLNbHZG0ZONL9e3XJwVl+/WPtuWTd7ySrtFhXDG2l9XhiJ/weOQqIyODw4cP+yIWCTAN864OlkB4DPQab96h0SuPNXQKPJ1mFusWAi4YcC50H+TNsEQkgF09sQ9dwkP49lAZy78ttDoc7xoyByIToCTXbGwh/qeuGjbUt1+fEHyjVi6Xi4UrzPXkrp/cj4hQJfhi8ji5euyxx/jlL3/J8uXLOXLkCCUlJU2+pPNwt2PfkV//e1dp4GlxuVxszTVHrkZ4Ot+q8nhjWcwkjVqJSKO4yFCuntgHgOdWBNmiwqERMFKNLfza1+9CeSHEpJrJcJBZvecwO/JLiAy1c92Zfa0OR/yIx8nVOeecw9q1a5k5cyZJSUkkJCSQkJBAfHw8CQkJvohR/NSQ+pGrPYVlVNc5TkiuVoAzyCZQ+1DusUqOltcQajc8X3hw0ytQWw5JGZB+tm8CFJGAdeOUfoTaDdZnH2VTzjGrw/Gusdeb379dAiVBNq8sGLgbWYyfG5Tt192jVj8a35uE6DCLoxF/4vGcq88//9wXcUgASo2LIC4ylOLKWnYfKmNYz7EQ1gUqj8KhbUFZX+0L7pLAwcmxnpUVOGrrSwIx51qp/auIfEdKXCSXjOrJ2xtzWbRiL89dN9bqkLyn+yDoOwX2f2GO4M/4pdURiVvuBji4KWjbr2/PK2b1nsPYbQZzp6pTtjTlcXI1Y8YMX8QhAcgwDDJSYlmz9wg78ksY1jMO+k01P0Xcu1zJVRuddkngjvfMhTSju5stbkVEWnDL9HTe3pjLpzsK2FtURnr3LlaH5D1jbzCTq02vwrRfqLGFv2hov/6DoGy/vnClOWp10YgUetd3ThZxa3NytXXr1jYdN2LEiNMORgLPkPrkaueJ867cydWUn1kZWsDYcuA44GGnQJcL1jxjbo+/2Zx/ICLSgoE9Ypg5OIlluwpZvCqbRy8fbnVI3jPkYoi4F4oPQNZ/4YxzrY5ISgvM+VYQlI0sDhyt4KOtZhnqLdPTLY5G/FGbk6tRo0ZhGAYul6vVYwzDwOFweCUwCQxNOgZC47yr/Wugtkpv+k/B4XSxLa++DbsnnQIPrGssuRg31zfBiUjQmD+jP8t2FfKvTbncfe5AuseEWx2Sd4RGwKirzbX+Nr6s5MofbHgJnLXQ+0xIHWV1NF73/Kq9OF0w7YxuDE31sOJEOoU2J1fZ2dm+jEMC1IkdA10uF0b3wdClB5Qdgtz1kDbd4gj9W1ZRGRU1DqLC7AxI8qBUZ83T5vcRP4Qu3X0TnIgEjfH9EhjdJ57NOcd55ct93DMriJZtGHO9mVx984m5aG1sitURdV51NbDhRXM7CBcNPlpewz82HADg1hn9LY5G/FWbuwX27du3TV/SuQxI6kKo3aC0qo6845VmUwW1ZG8zd0ngsJ5xbV/Z/Wh246LBk273TWAiElQMw2B+fQnTq2v2UV5dZ3FEXpQ02Fyk1uVoXJpCrLHj3fr26ylmyWaQeXXNPqpqnQzrGcvk/l2tDkf8lMet2EVOFBZiY0BSDHBiaWB9S/AsdZY8lcbFgz0oLVi3EFxO6P89SBrim8BEJOicm5FMWrdoSqrqePOrA1aH413ujnSbXgWnpidYJojbr1fWOHjly30AzJ/eH0MdeqUVSq6k3ZovJlzfUfLgZqgMsnVVvKyxU2B82x5QVQyb/2Zua9RKRDxgtxnMm2aOXr2wai+1jiBajzDjEoiIg+IcfbBnldwNkLcR7GEw5garo/G6tzYe4FhFLb0TI5k9LNnqcMSPKbmSdmvW1CI2FboNAlyQvcq6wPxcdZ2jocviqLZ2Ctz0KtSUQffB0H+m74ITkaB0+ZiedOsSxsHiKj7cGkQL74ZGwsirzO2NL1kbS2flXndx2A+Cbi5wncPJ4lVm+/V509IJsevts7ROV4e025AUsyxwZ0FJ407Nuzqlnfml1DpcJESF0ish8tQPcNRp0WARaZeIUDs3TjEXPV24Yu9JOwAHHHdp4DefmO3ApeOUHoKv3zG3g7CRxSfbCzhwtJKEqFCuGNvb6nDEz51WclVXV8d//vMfFi5cSGlpKQAHDx6krKzMq8FJYHCXBR44WklxZa25U8nVKW2tn281old822q3d75vruUS1dXsEigichqundiXqDA7uwpKWfFtkdXheE/SELP9txpbdLyN7vbrEyF1tNXReJXL5WLhyiwArp/cj8gwLVQtJ+dxcrV//36GDx/OJZdcwu23305RkfnC/Nhjj3HPPfd4PUDxf/FRYfSMN0dedrnnXfWbAoYdjmbB8RwLo/NfWw7Ur2/V1pLAtX81v4+/2SyBERE5DXFRoVw1oQ9gjl4FlYbGFq+AM4jmlPmzJu3X51sbiw98mXWE7XklRITa+PGkflaHIwHA4+TqZz/7GePGjePYsWNERja+wbvssstYtmyZV4OTwDGkfvTKPYeIiDjoOdbc3rvCoqj8m0edAg+sh9yvzInC42/2bWAiEvRumppGiM1gzd4jDaPoQWHopebfn+M5sFeNLTrEjvfMtS2DtP36cyvMUasfjetNYnSYxdFIIPA4uVq1ahX/8z//Q1hY0wusX79+5OXleS0wCSwZ9fOuGjoGgkoDT6Ksuo6sIrOMtk2dAtc8Y34f/kPokuS7wESkU+gZH8mckakALFwZRKNXoZEw4kpze+PLlobSabjbr48LvvbrOw6WsGr3YWwG3FzfaVPkVDxOrpxOJw5H8zUkcnNziYmJ8UpQEngaOga2llypPKOJbbnFuFzmG5zuMeEnP/jYfnO+FcCkn/g+OBHpFG6pX1T4k2357D9SbnE0XjT2evP7Nx+bjRbEd3I3Qt4Gs6rCXZIZRBbVz7W6cEQqvROjLI5GAoXHydV5553HU0891XDbMAzKysp46KGHuOCCC7wZmwSQjBSztO3bgrLGtVN6jYfQaKg4DIU7LIzO/2xpaGbRhpLA9YvMRYPTz4IeQ30al4h0HkNSYpkxsDtOFzy/KtvqcLynx1DoNQGcdZCpxhY+td7dfv37Qdd+PfdYBR9szQdg/nSNWknbeZxcPfHEE3zxxRdkZGRQVVXF1Vdf3VAS+Nhjj/kiRgkAvRIiiQkPocbhZG9R/SegIWFmYwtQ7ft3nNgp8KSqSmDjK+b2pDt8GpOIdD7zZ5hvGv+54QBHyqotjsaL3KMoG9XYwmdKD8H2f5vbE4Kv/foLq7NxOF1MHdCNYT3b8EGoSD2Pk6tevXqxZcsW7r//fn7+858zevRo/vjHP7J582aSkjQXpLOy2QwGN8y7Km68Q/OuWtTYKfAUL9ibX4OaUug2UIsGi4jXTUrvyohecVTXOXllzX6rw/GeoZdBeBwc3w/Zy62OJjhtfNlsv95rAvQcY3U0XnWsvIY31x8AGj+AEGmrkNN6UEgI1157rbdjkQCXkRLLV/uOseNgCZe5l7lwJ1f7v4S6agg5xfyiTuBwWTV5xysxDBh+sk/DnA5Y96y5feZPwKY1v0XEuwzDYP70/tz+xiZeXbOPW2ekExV2Wm8N/EtYlLke4FeLzSSg//esjii41NXAhhfM7SBsv/7a2v1U1jrISIll6oBuVocjAaZNr6Dvv/9+m0948cXB14ZT2sbd1GJnfmnjzqQMiO4O5UVmK/F+Uy2Kzn+4SwLTu0UTE3GSzkq7PjTbCUcmwsgrOyY4Eel0zh+WTJ/EKHKOVvDPrw5ww5Q0q0PyjnE3msnVro+grFCdVr1p5/tm+/UuyZBxidXReFVVrYOXv9wHmKNWhmFYG5AEnDYlV5deemmT24Zh4HK5mu0DWuwkKJ2De62rHfkluFwu85owDHP0attbZmmgkqu2Lx7sbr8+fq4WDRYRn7HbDOZNT+c3725n8apsrj2zLyH2IBgp7zHUbKyU+xVkvg5Tf251RMHD3X59fPC1X397Yy5HymvoGR/JhcNTrA5HAlCbXj2dTmfD12effcaoUaP45JNPOH78OMePH+eTTz5hzJgxLFmyxNfxih8b2CMGu83gaHkNh0pOmBiteVdNNC4eHN/6Qbkb4MA6sIVq0WAR8bkrxvaia3QYeccr+WhbvtXheI8aW3idcXBT46L2QdZ+3eF0sXiVue7bvGlpwfEhg3Q4j6+au+66iz//+c/MmjWL2NhYYmNjmTVrFk8++SR33nmnL2KUABERaqd/92jgO00t0maY3/M2QlVxC4/sPFwuF1tzzZ/BSduwNywafAXEJHdAZCLSmUWE2vnxpH4ALFq5t1l1SsAaehmEx8KxbNi30upogoJtw/PmxtDLg67U8tOvC9h/pIL4qFB+OL631eFIgPI4ucrKyiI+Pr7Z/ri4OPbt2+eFkCSQZaS0MO8qvjd0HWCu1bRvtUWR+YfcY5UcLa8h1G40lFE2c/wA7HjP3NaiwSLSQX48qS+RoXa+PljCF3uOWB2Od4RFm40twGxsIe0SXluM8fU75o2JwdV+3eVysXCFuWjwjyf1C47GLmIJj5Or8ePHc/fdd3PoUOOq54cOHeLee+9lwoQJXg1OAk/DvKuDJU3vUGkg0FgSODg5lohQe8sHrV8ILgekTYfk4R0XnIh0agnRYfyo/tP6hSuzLI7Gi9ylazs/hLIiS0MJOJ8/Ciseb7jZ98jnGM5acy7bnmXm/UFi7d6jbMktJjzExvWT+lodjgQwj5OrF198kfz8fPr06cOAAQMYMGAAffr0IS8vjxdeeMEXMUoAcXcM3JH/3eTqbPN7VudeTPiUJYHVpbDxVXP7zNs7KCoREdPcqWnYbQardh9me16QlHEnD4eeY801mTJftzqawGKzw+cPmwmWo4a0ov+a+2N7mvttrXxIGIDcHyj8cFxvunbRsjFy+jwe8xwwYABbt25l6dKl7Nq1C4AhQ4ZwzjnnqF2lNIxc7TtSTnl1HdHh9ZdYv6lg2ODIbijOhbheFkZpnS0HjgMn6RS4+XWoLjbLKM84r8PiEhEB6J0YxYXDU3h/y0EWrdzLX64afeoHBYKxN5jzfje9ApPv1LqBLXE6oabMnBvt/koeDkMuhs8fxr7zA0LrjuMKjcbY8S6c/QDM+KXVUXvFzvwSln9ThM2Am6cFyVIEYpnTKig1DIPzzjuP887Tmz9pqluXcJJiwiksrWZXQSlj+yaYd0TGQ+oYyNsAe1fA6GssjdMKDqeLbfWfBLfYKbDJosG36Y+/iFjilunpvL/lIB9ty+feWYPonRhldUjtN/RyWHI/HN0L+1ZB+gyrI/I+pxOqS8yvExOktn5Vl5hzo1thK9gKgFFbHlSJFcDilWaHwNnDU+jbNdriaCTQabaeeF1GaiyF3xSxI7+kMbkCc95V3gZz3lUnTK6yisqoqHEQFWZnQFKX5gd88zEc2weRCTDyqg6PT0QEYFjPOKad0Y1Vuw/zwupsfnvxUKtDar8v/w+6nQEHN5mNLU5MrlY8bn64dfZ9loUHmDGcMjE6yf3VJYAXujzawyAirtmXa8d7GC4nLnsYRhAlVnnHK3l/y0EA5k9PtzgaCQZKrsTrMlJiWf5NUctNLVb9yUyuXC5zgeFOxF0SOKxnHHZbC//2NX81v4+90exwJSJikfnT+7Nq92He/CqHO2eeQWJ0mNUhtY/NbiZWADs/gPLDEN3NTKw+f9gciWkvR119cnS87QnRiV81pad8ijYJiWgxOSIizmxL32RffPNjQiOan3PF4xhfv4PDCMHuqDF/bkGSYL24Ops6p4vJ/bsy4mTrT4q0kZIr8Tp3U4ud321q0XsChERCeSEU7oQeGRZEZ53GxYNbaGaRtwlyvjQXDZ4QXO1tRSTwTBnQlaGpsXx9sIS/rdnPz845w+qQ2sedCHz+cH1jizegrqoxsZrxS6irOWHk6HjbkqITR5pqyrwTa2hUG5KilhKkWPO4lpKj9qhPQB3Tf82HpRlcFLMD++cPm/cFeIJVXFHL39fnADB/Rn+Lo5FgoeRKvM7d1GJXQQkOp6txlCYkHPpOhqxl5uhVJ0uuGjsFxje/c239qNWwyyE2peOCEhFpgWEY3DI9nZ+9mckra/Yxf0Z668tHBIoZv4SDm80S7KW/MfdFdYMNL8Lq/we1Fd55nrAubUiI4hoTohMTpPBYCPGjUcITRvack38OH3+Mc9o92O31XQQhoBOs19btp6LGweDkGKaf0c3qcCRInFZy5XA4ePfdd9m5cycAQ4cO5eKLLzb/s0mn169rNJGhdiprHWQfLm86vyj9rMbkqhMtkFtd52gYyRv13U6BxXngXpTxzM7zMxER/3bh8BQeX/INeccreWtjLtedGQRr/1y+GB7t2Xi74nDzY8JiPEiKvjOCFB4D9tAO++f4nNPROLJXW9u4351QOR3WxOUFVbUOXvoiG4D5M9LV8Vq8xuPkas+ePVx44YXk5uYyaNAgAB599FF69+7NRx99RP/+Glbt7Ow2g8EpMWzOOc7O/JLmyRXAvtXgqA2uP0InsTO/lFqHi4SoUHolRDa9c/0icNZB36mQOsqS+EREvivEbmPetDR++8EOFq/cy9UT+rQ8XzSQuKsEbCHm6+6oa2Di/Kbld0G0dlO7nazJRwCPWAH8e1Meh8tqSI2L4KIRqVaHI0HE417Pd955J+np6Rw4cIBNmzaxadMmcnJySEtL48477/RFjBKA3KWBzRYT7jHMLMOoLYfcDRZEZo2t9fOtRvSKb/rpWHUZbHzJ3J6kRYNFxL/8cHxv4qNCyTlawZLtBVaH0z4nNq948Ij5PfN1+PZTSOhndmpVYtUpOJwuFq8y26/PnZZOqF1Ln4j3eHw1rVixgscff5zExMSGfV27duWPf/wjK1as8GpwErgy3MnVdzsG2myNLXD3ft7BUVlny4H69a2+WxK45e/mROjEdBh4fscHJiJyElFhIfx4Uj8AnluRhcvlhVbfVjgxsXKPuMz4pXn784fN+6XTWLqjgOzD5cRFhnLl+N5WhyNBxuPkKjw8nNLS5u1Cy8rKCAvzo0mYYil3x8BmI1fQWBq4d3mHxWO1FjsFOp2NJSpn/kSLBouIX7p+Ul/CQ2xsyytmzd4jVodzek6cO3Qid4IVwHOHxDMul4tnV5ijVted2ZfocPV2E+/y+N3cRRddxC233MK6detwuVy4XC7Wrl3LrbfeysUXX+yLGCUADU6OwTCgqLSaotLqpne6k6vcDWar2yBXVl1HVpHZordJp8Bvl8DRvWadvxYNFhE/1bVLOD8cZ366v7D+TWnAOfu+1ucIzfil9QsIS4dZn32ULQeOExZi4/rJ/awOR4KQx8nVX/7yF/r378+kSZOIiIggIiKCKVOmMGDAAP785z/7IkYJQFFhIaR1NRfCbbbeVXwfswzO5YD9X1gQXcfalluMywU94yPpHhPeeMeaZ8zvY2+E8C4tP1hExA/cPC0NmwErvi1q/pouEkAWrjQ/IPjB2F5N/yaLeInHyVV8fDzvvfce33zzDW+99RZvv/0233zzDe+88w5xcS0sjiqd1hCVBgKNJYEjTiwJPJgJ+1ebHau0aLCI+Lm+XaOZPdxcg2/RygAdvZJO75uCUv67qxDDgHnT0q0OR4LUaU/yOOOMM5gzZw4XXXQRAwYM8GZMEiTcTS1a/JSzEyVXJ3YKbOCeazX0Mojr2ewxIiL+Zv50883oB1sOkne80uJoRDzn/mDg/KHJpHWLtjgaCVanlVy98MILDBs2rKEscNiwYTz//PMen2flypXMmTOH1NRUDMPg3XffbXL/DTfcgGEYTb7OP//kHdV++9vfNnvM4MGDPY5N2q/VjoEA/aYBBhTtgpL8jg2sgzV2CqwfuSo5CNv/ZW5r0WARCRAjesUzKb0rdU4XL67OtjocEY/kF1fyXmYeALdM16iV+I7HydWDDz7Iz372M+bMmcNbb73FW2+9xZw5c/j5z3/Ogw8+6NG5ysvLGTlyJM8880yrx5x//vnk5+c3fP39738/5XmHDh3a5DGrV6/2KC7xDnfHwKyiMqpqv9OJKSqxccHc7OBt4X+4rJq845UYBgzvWZ9crV9sLl7ZZzL0HGNtgCIiHpg/w3xT+vf1ORRX1FocjUjbvbg6mzqni4lpiYzuk2B1OBLEPO4/+eyzz7J48WKuuqqxu9nFF1/MiBEj+OlPf8rvfve7Np9r9uzZzJ49+6THhIeHk5yc7FGMISEhHj9GvC8pJpyu0WEcKa/hm4LS5ms8pZ8FBzebpYEjr7QgQt9zlwSmd4smJiIUaspPWDRYo1YiElhmDOzO4OQYdhWU8tq6/dx+tqYFiP8rrqzljXU5ANw6o7/F0Uiw83jkqra2lnHjxjXbP3bsWOrq6rwS1ImWL19OUlISgwYN4rbbbuPIkVOvsbF7925SU1NJT0/nmmuuIScnx+txyakZhtEwenXSeVdZn0OgLkx5Cs0WD97yd6g8Bgn9YNAFlsUlInI6DMNoGL166Yvs5lUJIn7o9XX7Ka9xMKhHDGcN6m51OBLkPB65uu6663j22Wd58sknm+xftGgR11xzjdcCA7Mk8PLLLyctLY2srCzuv/9+Zs+ezZo1a7Db7S0+ZuLEibz88ssMGjSI/Px8FixYwLRp09i+fTsxMTEtPqa6uprq6sa1mEpKzESgtraW2lqVPbTHwKRoVu0+zPa849TWpjS9M2UsISERGGUF1OZ/Dd0HefW53b87K3+HmTnHABiWEkNtTTUha/6KATjG34LT4QSH07LYxLv84XqTzsWqa27WkO6kxEWQX1zFW1/lcOX4Xh36/GKNQH2Nq6518FL9HMG5U/r6ZCBAfMOfrjlPYjBcLs+GDH7605/y6quv0rt3b84880wA1q1bR05ODj/+8Y8JDQ1tOPa7CdhJAzEM3nnnHS699NJWj9m7dy/9+/fnP//5DzNnzmzTeY8fP07fvn158sknmTt3bovH/Pa3v2XBggXN9r/xxhtERUW16XmkZRuKDP62x05ajIu7hjX/hHPSnsdJKt3Otp7XsjfpPAsi9B2XCx7YYKe8zuDnw+qY6NzMmXv/H7X2KD4b+v+os0daHaKIyGlZnm/wzj473SNc3D/Kgc2wOiKRlq05ZPDmXjvxYS5+M9pByGn3yZbOrKKigquvvpri4mJiY2NPeqzHI1fbt29nzBhzEn5WVhYA3bp1o1u3bmzfvr3hOMPw/itteno63bp1Y8+ePW1OruLj4xk4cCB79uxp9Zj77ruPu+++u+F2SUkJvXv35rzzzjvlD1BO7oxDZfzt6S8prAnh/PPPw/adv8C2NXvgv9sZGlXE4Au8WyZXW1vL0qVLOffcc5sk/R0l91gl5WtXEWo3mHv5LKL+uRgA2/gbOW/m9zs8HvEtq6836XysvOZmVNfx3ydWUlRZR2i/scwa2qNDn186XiC+xjmdLp76yxdABbd9bxAXT+lndUjiAX+65txVbW3hcXL1+eefe/oQr8nNzeXIkSOkpKSc+uB6ZWVlZGVlcd1117V6THh4OOHhzVfpDg0NtfyXGegGpsQRFmKjvNpBQVktfbt+Z12JATPhv7/Dtv9LbDbA7v2ft1W/x68LigAYnBxLl+JvYd8qMOzYz7wNu66roKXXDeloVlxz8aGhXHtmX575PIvnv9jPhSN7+uRDVfE/gfQa9+nXBWQfqSAmIoRrJqURGurx217xA/5wzXny/JYOjpaVlZGZmUlmZiYA2dnZZGZmkpOTQ1lZGffeey9r165l3759LFu2jEsuuYQBAwYwa9ashnPMnDmTp59+uuH2Pffcw4oVK9i3bx9ffvkll112GXa7vUl3Q+k4IXYbg3qYc91aXO8qeQREJkBNKeRt6uDofGtrrtnMYkSvOFj7rLkz4xKI721hVCIi3nH95H6EhdjIPHCcr/YdszockSZcLhfPrTArrK47sy9dwpVYScfwOLmqqqrif//3f7ngggsYN24cY8aMafLliQ0bNjB69GhGjx4NwN13383o0aN58MEHsdvtbN26lYsvvpiBAwcyd+5cxo4dy6pVq5qMMmVlZXH48OGG27m5uVx11VUMGjSIH/7wh3Tt2pW1a9fSvbu6w1jFvZhwix0DbTZIm2Fu713ecUF1gC0HjgMwsXstbHvL3DnpDusCEhHxoqSYCL4/xmxmsbD+TayIv9iw/xibc44TZrdxg8oBpQN5nMbPnTuXzz77jB/84AdMmDChXWUAZ511Fifrp/Hpp5+e8hz79u1rcvvNN9887XjEN9zt2He0lFyB2ZJ9x7tmcnXWrzoqLJ9yOF1syzNHriYffRectdB7IvQaa21gIiJeNG9aGm9+lcOyXYV8e6iUgT1a7sor0tHcCf/3x/YkKSbC4mikM/E4ufrwww/5+OOPmTJlii/ikSA0pH7kqsWyQGhc7yp3PVSXQXiXjgnMh7KKyqiocZAQ5qDrzr+ZOyfdbm1QIiJelt69C7MyklnydQGLVu7lT1eMtDokEXYfKuU/OwsxDLh5WrrV4Ugn43FZYM+ePVtdL0qkJYNTzOvlYHEVxytqmh+QmAbxfcFZB/u/7ODofMNdEnhrwlcYlUchvg8MvsjaoEREfMC9qPB7mXnkF1daHI0ILFq5F4DzMnrQv3vgf2ArgcXj5OqJJ57gV7/6Ffv37/dFPBKEYiNC6ZNorhd20tJAgL3WdaP0pi25xzFw8v3q98wdE28DW8sLX4uIBLLRfRKYkJZIrcPFS1/sszoc6eQKiqt4NzMPgPkz+lscjXRGHidX48aNo6qqivT0dGJiYkhMTGzyJdKSISkn6RgI0P9s83uQNLXYmlvMDNtWulXth7AYGH2t1SGJiPjMrfWjV2+sy6G4stbiaKQze+mLbGodLib0S2RMnwSrw5FOyOM5V1dddRV5eXk88sgj9OjRQ+taSJtkpMTx6deHWh+56jcdMKBwB5QegpjAXZCyus7BzvwSXrR/bO4Yez1EaDFqEQleZw1MYmCPLnx7qIw31uVw21kaMZCOV1JVy+vrcoDGclWRjuZxcvXll1+yZs0aRo7UpFVpO3fHwJ35pS0fEN0VUkZA/hbIXgEjftiB0XnXzvxS0p37mRa6HZdhw5hwi9UhiYj4lM1mMG9aOve+vZWXvsjmpqn9CA9RKbR0rL+vy6Gsuo4zkrpw9qAkq8ORTsrjssDBgwdTWakJq+IZd3K1p7CUmjpnywc1zLta3iEx+crW3OPMtX8CgDHkYkjoa3FEIiK+d8monvSIDaewtJr3Nh+0OhzpZKrrHLz4RTYAt0xPx2ZTZZVYw+Pk6o9//CO/+MUvWL58OUeOHKGkpKTJl0hLUuMiiI0IodbhYndhK6NXJyZXJ1n/zN/tyd7LJfYvzBtqvy4inURYiI25U9MAWLgyC6czcF/HJfC8l3mQQyXV9IgN55JRPa0ORzoxj5Or888/nzVr1jBz5kySkpJISEggISGB+Ph4EhI0cVBaZhhG42LCrTW16DMJ7OFQkgdH9nRgdN41YN8/CDfqKO46CnpPsDocEZEOc9WEPsSEh5BVVM6yXYVWhyOdhNPpami/PndqGmEhHr+9FfEaj+dcff55cLTKlo6XkRLH2r1HW593FRoJfSZC9kpz9KrbGR0anzeUlpZwQfXHYIChUSsR6WRiIkK55sy+PLcii4Ursjg3I3CbE0ng+O+uQvYUlhETHsJVE/pYHY50ch4nVzNmzPBFHNIJNLRjzy9u/aD0sxqTqwnzOiQubzr0xd8YYJRQQHeSR19udTgiIh3uxin9eHF1Nhv2H2PDvqOM66dlWsS3Fq7MAuCaM/sSExFqcTTS2Z3WuOmqVau49tprmTx5Mnl55kJtf/vb31i9erVXg5PgcmJZoKu1OVXueVfZq8BR1zGBeYvLReLW5wFY3fX7YPf4swsRkYDXIzaCy0abc14W1pdqifjKxv1H+WrfMcLsNm6c0s/qcEQ8T67+9a9/MWvWLCIjI9m0aRPV1dUAFBcX88gjj3g9QAkeZyTFEGo3KKmq42BxVcsHpYyCiHioLoaDmzsyvPbLWkZixV7KXBEUD7nK6mhERCwzb7q5xtDSHYfYU1hmcTQSzBauMBP4y0b3pEdshMXRiJxGcvWHP/yB5557jsWLFxMa2jj0OmXKFDZt2uTV4CS4hIXYGJBUXxrYWlMLmx3SppvbgdaSfc1fAfin4yyGpPWyOBgREesMSOrSMN9qsUavxEf2FJaxdOchoDGhF7Gax8nVN998w/Tp05vtj4uL4/jx496ISYJYw7yr1pIrCMz1rgp3QtYyHC6Dlx2zGN4zzuqIREQsNb/+ze47m/MoLGmlWkGkHZ5ftReXC87N6MGApC5WhyMCnEZylZyczJ49zdtkr169mvR0fWogJ5eRUj/v6lRNLQAOrIOact8H5Q1rzVGrz5zjCO2Wrgm1ItLpjeuXyNi+CdQ4nLz05T6rw5EgU1hSxb83mfP+b52h95/iPzxOrubNm8fPfvYz1q1bh2EYHDx4kNdff5177rmH2267zRcxShBxN7VotR07QGI6xPUBZy3sX9NBkbVDWRFs+QcAz9ddwMje8dbGIyLiJ9yjV6+t3U9pVa3F0UgweenLfdQ4nIzrm8DYvupIKf7D4+Tq17/+NVdffTUzZ86krKyM6dOnc/PNNzN//nx++tOf+iJGCSLukaucoxWUtPaH1jAgvb7l/94AWFdtw4vgqGZv2CA2ugYysle81RGJiPiFc4b0IL17NKVVdby5/oDV4UiQKK2q5bW1+wGYP6O/xdGINOVxcmUYBg888ABHjx5l+/btrF27lqKiIn7/+9/7Ij4JMvFRYaTGmd18dp1s9Kph3tUK3wfVHrVV8NViABbVzgYMRvTSfCsREQCbzWgYvXphdTY1dU6LI5Jg8Ob6A5RW1dG/ezQzBydZHY5IE6e1zhVAWFgYGRkZTJgwgS5dNIlQ2q6xNPAkTS3S6keuDm0zy+781fa3obyIui6pvF05hlC7wZD60TkREYFLR/eke0w4BSVVvL/loNXhSICrqXPywupsAOZP74/NZlgckUhTbVrl9PLLL+fll18mNjaWyy+//KTH/vvf//ZKYBK8MlJi+c/OwpN3DOzSHXoMN5Or7BUw/AcdF2BbuVwN7de/7Xc1dYdDGJ4cS0So3eLARET8R3iInZumpPHYkl0sWpnF5aN76g2xnLb3txykoKSKpJhwLhmdanU4Is20aeQqLi4OwzAatk/2JXIqQxo6Bp4kuQL/n3e1dzkUfg2h0XwSNgtAJYEiIi24emIfuoSH8O2hMpZ/W2h1OBKgnE4Xi1ZmAXDT1DTCQ/RhpvifNo1cvfTSS/zud7/jnnvu4aWXXvJ1TBLk3GWB3xwqpc7hJMTeSo6ffjaseRqylpujRIaffdK55hnz++hrWXfAAaBOgSIiLYiLDOXqiX1YtHIvz63Yy/cG97A6JAlAy78t5NtDZXQJD+HqiX2sDkekRW2ec7VgwQLKysp8GYt0Er0TougSHkJNnZO9h0+yjlXfSWAPg5JcOLq34wJsi6JvYM9SwMAx4Va255nrdqlToIhIy26c0o8Qm8H67KNszjlmdTgSgJ5bYb4XuGZiH2K1nqT4qTYnVy6Xy5dxSCdisxkMSYkBOPm8q7Bo6D3R3Pa30sD6RYMZfCFZju5U1DiICrNrhXgRkVakxEVyyaieACxa6WcfmInf25RzjPXZRwm1G9w4Jc3qcERa5VG3QMPfyrIkYHk+72q5bwPyRPkR2PKmuX3mT8g8cByAYT3jsGuStohIq26pb8u+5OsCsk9WuSDyHYvqR60uHdWT5PolXUT8kUfJ1cCBA0lMTDzpl0hbuBcTPmk7djDnXQFkrwSnw8dRtdGGF6GuClJGQd/JbM09DsBINbMQETmpQckxfG9wEi4XLF6l0Stpm71FZXy6owBoTNBF/FWbGlq4LViwQB0BxSvcTS12HCzB5XK1PiqaMgrC46CqGPIzoefYDouxRXXVDYsGM+l2MAy25tbPt1IzCxGRU5o/PZ3/7irk7Y25/PycgXSPCbc6JPFzi1dl43LBOUOSOKNHjNXhiJyUR8nVlVdeSVKSVsKW9hvYIwabAUfKaygsraZHbCtD/PYQSJsGuz40SwOtTq62/wvKDkFMKmRcSnWdo2H0Tc0sRERObUJaIqN6x5N54DivfLmPe2YNsjok8WOFpVX8a1MuAPNn9Lc4GpFTa3NZoOZbiTdFhNrp391s/nDSphYA6WeZ362ed3XCosFMmAchYezML6XW4SIhKpReCZHWxiciEgAMw+DWGWZp16tr9lFeXWdxROLPXvlyHzV1Tsb0iWdc3wSrwxE5JXULFMs0lAaect7VWeb3nLVQU+HboE4meyUc2gahUTD2BoDG+Va94/UBhIhIG52bkUxat2hKqup486sDVocjfqqsuo6/rdkPwC3T++vvrASENidXTqdTJYHiVRlt7RjYdQDE9gRHDeSs6YDIWuFuvz7qaogym7e4OwWOUEmgiEib2W0GN08z22m/sGovtQ6nxRGJP3pzfQ4lVXWkd4vm3AwtPC2BwaNugSLe5G7HvvNUZYGGYX1p4OHd8O0SwICJtzXsbmhmoU6BIiIe+f6YXnTrEsbB4io+2ppvdTjiZ2odTl5YnQ3AvOnpWupEAoaSK7GMO7nKPlJORc0pau7dLdmtSq7WPmt+H3g+dBsAQGlVLVlFZYBGrkREPBURaueGyf0AeG5FlqYfSBMfbDlIfnEV3bqEc9nonlaHI9JmSq7EMt1jwkmKCcflgl0FpSc/2L2YcMFWcxHfjlRxFDLfMLcn3d6we1teMS4X9IyPVCthEZHTcO2ZfYkKs7OroJSVuw9bHY74CZfLxcL6RYNvnNKPiFC7xRGJtJ2SK7GUe/TqlB0DuyRB0lBzO3uFj6P6jo0vQV0lJA+HflMbdrtLAkeoJFBE5LTER4Vx5fg+ACxckWVxNOIvln9bxDeHSokOs3PtxL5WhyPiESVXYqk2dwwEa+Zd1dXAukXm9qQ7zPlf9U7sFCgiIqdn7rQ07DaDL7OONLyuSufmTrSvmtCHuKhQi6MR8YySK7GUu2PgTn9Nrr5+B8oKoEsyDL28yV1bDmjkSkSkvXrGR3LxyFQAFq7ca3E0YrXMA8dZu/coITaDm6amWR2OiMeUXIml3CNXu/JLcThPMZm572SwhcDx/XA02/fBuVyw5mlzu37RYLfDZdXkHa/EMGB4TyVXIiLtcct0c1HhT7bls/9IucXRiJUWrTRHrS4elUpqfKTF0Yh4TsmVWKpf12giQm1U1jrYd6o/qOFdoNcEc7sjRq/2f2E20AiJhHE3NbnLXbrSv3sXYiJUsiAi0h5DUmKZMbA7Thc8v6oDPjwTv7TvcDmfbC8AGhNukUCj5EosZbcZDE7209LANc+Y30dd1bBosFumSgJFRLxqfv2b6X9uOMCRsmqLoxErLF61F5cLzh7UveG9gUigUXIllmtoanGqjoHQmFxlrwCnw3dBHcmCbz4xt8/8SbO7G5pZaH0rERGvmNS/K8N7xlFd5+TVNfutDkc6WFFpNW9tzAVg/oz+FkcjcvqUXInlGtqxt2XkqucYCIuBymNmyZ6vrH0WcMEZs6DbGU3ucrlcDW3Y1SlQRMQ7DMNg/gxz9OrVNftOvbi8BJVX1+yjps7JyN7xTExLPPUDRPyUkiuxXEZb17oCsIdC2jRz21elgZXHIPN1c3tS81Gr3GOVHC2vIdRuMCQlxjcxiIh0QucPTaZPYhTHKmp5a0Ou1eFIBymvrmsYrbx1ejrGCcueiAQaJVdiucHJMRgGFJZWc7gtdfa+nne18WWorYAewyBtRrO7t9SXBA5OjiU8RKvGi4h4S4jdxrxpZvvtxav2UudwWhyRdIR/fHWA4spa+nWN4ryhyVaHI9IuSq7EctHhIaR1jQY8bGqxfw3UVno3GEdt46LBZ/6kyaLBbo0lgWpmISLibT8Y25vE6DByj1XycX3nOAletQ4nL6w2O0TOm56O3aZRKwlsSq7ELwzxpDSw20CISQFHNRxY591Avn4XSg9CdBIM/0GLh2QeOA7ACDWzEBHxusgwO9dP6gfAwhVZuFynWANRAtpHW/PJO15Jty5hfH9ML6vDEWk3JVfiF9wdA9s0cmUYvikNdLlgbX379QnzICS82SEOp4vtefUjV0quRER84seT+hIZaufrgyV8seeI1eGIj7hcLp5bYS4afMPkfkSEqtReAp+SK/ELGZ50DATfJFc5a+DgZgiJaLZosFtWURkVNQ6iwuwMSOrivecWEZEGCdFh/Gh8bwAWrsyyOBrxlZW7D7OroJSoMDvXntnX6nBEvELJlfgFd1lgVlE5VbVtWL/K3WjiYCZUHPVOEO5Fg0f8CKK7tXiIuyRwWM841YWLiPjQ3Klp2AxYtftwQ8WABJdF9YnzleP7EB8VZnE0It6h5Er8Qo/YcBKjw3A4XXx7qPTUD4hNge6DARdkr2x/AEf3wq6PzO0WFg12a1w8WM0sRER8qXdiFBeOSAXMzoESXLblFvPFniPYbQZz6ztEigQDJVfiFwzDaCgNbNO8K/BuaeC6hYALBpwDSYNbPUyLB4uIdJz5081FhT/cms+BoxUWRyPe5C73vHhkKj3jIy2ORsR7lFyJ33A3tWhTx0CA9LPN7+1NriqPw6a/mduTbm/1sOo6R0Pip2YWIiK+N6xnHFMHdMPhdDW065bAl3Okgo+35QNwS30CLRIslFyJ3xiSEgN40NSi3xQw7HAsG47tO/0n3vQq1JZDUkZjwtaCnfml1DpcJESF0itBn7KJiHSE+TPMN9//+OoAx8prLI5GvOH51XtxumDGwO4Nc65FgoWSK/EbGSnmPKad+aU4nW1Y1yQ8BnqNN7f3rji9J3XU1ZcE0uqiwW4N8616x2Oc5DgREfGeqQO6kZESS2Wtg7+t3W91ONJOR8qq+eeGA0Bj4iwSTCxNrlauXMmcOXNITU3FMAzefffdJvffcMMNGIbR5Ov8888/5XmfeeYZ+vXrR0REBBMnTmT9+vU++heIN6V3jyYsxEZZdR25xyrb+KCzzO+nWxq48z0oyYXo7jD8ipMeqsWDRUQ6nmEYDW/CX/5yX9s6yorfenXNfqpqnYzoFcek9K5WhyPidZYmV+Xl5YwcOZJnnnmm1WPOP/988vPzG77+/ve/n/Sc//jHP7j77rt56KGH2LRpEyNHjmTWrFkUFhZ6O3zxslC7jYE9zLWjduS3se2uO7nKXgFOp2dP6HI1tl8ffzOERpz08IZmFuoUKCLSoS4cnkLP+EiOltfw1sZcq8OR01RRU8era/YBMH96f1WBSFCyNLmaPXs2f/jDH7jssstaPSY8PJzk5OSGr4SEhJOe88knn2TevHnceOONZGRk8NxzzxEVFcWLL77o7fDFBxoWE25rU4te4yCsC1QcgUPbPXuyA+shbyPYw2Hc3JMeWlpVS1ZRGaCRKxGRjhZitzGvvl334pV7cbSldFz8zlsbcjlWUUufxCjOH5ZsdTgiPhFidQCnsnz5cpKSkkhISOB73/sef/jDH+jateVh5JqaGjZu3Mh9993XsM9ms3HOOeewZs2aVp+jurqa6urqhtslJeYb+9raWmpra730L5G2GFQ/cvX1weI2/+ztfSZh27MUx55lOLsNadjvfnxr57F/+X/YAOewH+AIj4eTPF/m/qO4XJAaF0F8hE3XhTRzqutNxNs62zV32ahknvrPbnKOVvDRllxm6815h2rv9VbncLK4vv36TVP64nTU4VSFp5yEP73GeRKDXydX559/PpdffjlpaWlkZWVx//33M3v2bNasWYPdbm92/OHDh3E4HPTo0aPJ/h49erBr165Wn+fRRx9lwYIFzfZ/9tlnREVFtf8fIm1WXAIQwqa9hXz88cdtekx6ZXeGA4fX/4u1R5tPjl26dGmzfVHVRZyz60MAllcPpfQUz7UszwDsdLdXtDku6Zxaut5EfKkzXXMTE218mmfjTx9uwbl/08l6EImPnO71tumwQe5xO11CXEQf2sbHH2/zcmQSrPzhNa6iou3r7Pl1cnXllVc2bA8fPpwRI0bQv39/li9fzsyZM732PPfddx933313w+2SkhJ69+7NeeedR2ysWoR2pNKqWv7y9eccqzGYfNa5xEeFnvpBhWmw+A2SKvdwwXnfgxBz7lRtbS1Lly7l3HPPJTS06XlsSx/AwIUz/Wymff+WUz7FJ29uAQ5x3rhBXKCV5KUFJ7veRHyhM15zE8uqWf7EKnLKnXTLOJOJaYlWh9RptOd6c7lcLHx2LVDK3OkDuPTs/r4JUoKKP73Guava2sKvk6vvSk9Pp1u3buzZs6fF5Kpbt27Y7XYOHTrUZP+hQ4dITm69fCA8PJzw8PBm+0NDQy3/ZXY2iaGh9E6M5MDRSvYcrmRS/zaMHKYOhy49MMoOEVqwGdKmN7m72e+xqhgy3wDANukObG34HW/LM/9Tje6bqGtCTkqvG9LROtM1l5wQyhXjevHa2hye/2I/Uwf2OPWDxKtO53pbvfswO/JLiQy1c8OU9E5zvYp3+MNrnCfPH1DrXOXm5nLkyBFSUlJavD8sLIyxY8eybNmyhn1Op5Nly5YxadKkjgpT2qmhqUVbFxM2DM9asm/6G9SUQvfBMODUI6CHy6rJO16JYcDwnuoUKCJipZunpmMzYPk3Rexs698JsdTC+rlWPxrfm4ToMIujEfEtS5OrsrIyMjMzyczMBCA7O5vMzExycnIoKyvj3nvvZe3atezbt49ly5ZxySWXMGDAAGbNmtVwjpkzZ/L000833L777rtZvHgxr7zyCjt37uS2226jvLycG2+8saP/eXKahnjaMRDanlw1WTT4tpMuGuzmXjy4f/cuxETo0zYRESv16xbN7GHmh6yLV+61OBo5le15xazafRi7zWDuVJXVS/CzNLnasGEDo0ePZvTo0YCZGI0ePZoHH3wQu93O1q1bufjiixk4cCBz585l7NixrFq1qkkJX1ZWFocPH264/aMf/Yg//elPPPjgg4waNYrMzEyWLFnSrMmF+C+PR64A0maY3w9uhspjrR+36wMozoGorjDiR206deYBc32rEVrfSkTEL9wy3Wxe9P6Wg+Qdb+Oi82KJRfUJ8EUjUuidqCZhEvwsnXN11lln4XK1vlbFp59+espz7Nu3r9m+O+64gzvuuKM9oYmFMlLN5GpPYSk1dU7CQtrwGUBcT+g2EA5/C/tWw5A5LR+35q/m93FzITSyTfG4R65Gan0rERG/MLJ3PJPSu7Jm7xFeXJ3Nby7KsDokacGBoxV8tC0faEyIRYJdQM25ks6hZ3wksREh1Dpc7Cksa/sDT1UaeOAryF0P9jAYf3ObTulyudiaa45cjewd3/ZYRETEp26ZYb5Z//v6HIorrF8HR5p7YXU2DqeLaWd0Y2iqqj+kc1ByJX7HMIzGeVeelAaeKrla+4z5ffgVENO2MtHcY5UcLa8h1G4wJCWm7bGIiIhPnTWwO4N6xFBR4+C1dfutDke+42h5DW9+lQPArTPUel06DyVX4pfcpYEedYLqNxUMGxzZA8cPNL3veA7seM/cPvMnbT7llvqSwMHJsYSHNF+4WkRErGEYBvPrR69e+mIfVbUOiyOSE/1tzX6qap0M6xnL5P5drQ5HpMMouRK/lHE6HQMj4qDnWHM7e0XT+9YtBJfTbHyRPKzNp2wsCVQ5g4iIv5kzMpXUuAgOl1XzzuY8q8ORepU1Dl5Zsw+A+dP7Y7ShM69IsFByJX7JPXK1I7/kpE1PmnGXBmZ93rivuhQ2vWpuT/Ks0UnmgeMAjFAzCxERvxNqt3FTfXvvxSv34nB68PdCfObtjQc4Wl5D78RIZg9LtjockQ6l5Er80oCkLoTYDIorazlYXNX2B54478rlBMC25Q2oLoGuZ8CAc9p8KofTxfa8+pErJVciIn7pygl9iI0IYe/hcpbuOGR1OJ1encPJ4lXZAMyblk6IXW81pXPRFS9+KTzEzoCkLgDs9KQ0sNcECI2CisNQuBNcTmxfLTLvm/QTsLX9ks8qKqOixkFUWGMsIiLiX7qEh3DdpL4APLciy7NqB/G6JV8XkHO0goSoUK4Y29vqcEQ6nJIr8Vsnlga2WUgY9J0CgG3fClKKN2Ic3w+RCTDiSo+e310SOKxnHHab6sVFRPzV9ZP7ERZiI/PAcb7ad5KF5MWnXC4XC1eYiwZfP7kfkWFqBCWdj5Ir8Vun1dTi80cbygGN7JX0L1xi7h83F9Y8bd7fRo2LB6uZhYiIP0uKieD7Y3oBsHBFlsXRdF5rso6wLa+YiFAbP57Uz+pwRCyh5Er8lju52lngQXJls0PWMgCM7OV0Ld+NyxYKjhr4/GHz/jbS4sEiIoFj3rQ0DAOW7Spk96FSq8PplJ5baY5a/WhcbxKjwyyORsQaSq7Eb7kXEt5/pILSqtq2PWjGL+Gs+wEwnHUAuLoNgi//Amc/YN7fBtV1joY1ttTMQkTE/6V378J5GeYC8Yvq3+RLx9lxsISV3xZhM+DmaelWhyNiGSVX4rcSosNIjYsAYFeBB59CnvUrSMpouGkr3O5RYgWwM7+UWoeLhKhQeiVEtv25RUTEMvNn9Afg3cw8CjzpNCvttmilWY554YhUeidGWRyNiHWUXIlfG3I6864Apv2iYdNlD/MosQLYUt/MYmTveC1+KCISIMb0SWBCv0RqHS5e+iLb6nA6jdxjFXywNR+A+dM1aiWdm5Ir8WvujoE7PekYCHDE/ATNYYRgOGpgxeMePXxLfTMLLR4sIhJY5s8w39y/vi6HkraWlEu7vLA6G4fTxdQB3RjWU02gpHNTciV+raFjoCfJ1YrHYfkjOKb/mg9HvYhj+q/NZhYeJFgNzSzUKVBEJKCcPSiJM5K6UFZdxxvrcqwOJ+gdK6/hzfUHgMbEVqQzU3Ilfs1dFriroJQ6h/PUD1jxuJlInf0Azmn3AJjfz36gzQlWaVUtWUVlgEauREQCjc1mcEt9adqLq7OprnNYHFFwe23tfiprHWSkxDJ1QDerwxGxnJIr8Wt9EqOIDrNTU+ck+3D5qR/gdLTcvGLGL839zlP/kd2WV4zLBT3jI+keE36akYuIiFUuGdWTHrHhFJZW897mg1aHE7Sqah28/OU+wBy10hxlESVX4udsNqOxqUVbSgPPvq/15hUzfmnefwruksARKgkUEQlIYSE25k5NA2DhyiycTpfFEQWntzfmcqS8hp7xkVwwPMXqcET8gpIr8XvuphYedww8TSd2ChQRkcB01YQ+xISHkFVUzrJdhVaHE3QcTheLV5nrid08LY1Qu95SioCSKwkAHo1ceYFGrkREAl9MRChXn9kHaFyDSbzn068L2H+kgvioUH40vrfV4Yj4DSVX4vcyTljryuXybWlHUWk1eccrMQwYrnayIiIB7aYpaYTaDb7ad4yN+49aHU7QcLlcLFxhJqw/PrMvUWEhFkck4j+UXInfG5Qcg82AI+U1FJVW+/S5ttavb9W/exdiIkJ9+lwiIuJbPWIjuGx0TwAWrthrcTTBY+3eo2zJLSY8xMaPJ/ezOhwRv6LkSvxeRKid9O5dAPjax6WBW1QSKCISVNxt2ZfuPMSewjKLowkOC+vLLK8Y14tuXdRVV+RESq4kILhLA3f6OLlyj1yN1PpWIiJBYUBSDOcM6YHLBc+v0uhVe+0qKGX5N0XYDLh5qhYNFvkuJVcSEDqiY6DL5VKnQBGRIHTrDDMJ+NfGXD7Zls97mXmsyTqCQy3a28ThdLEu+ygbDxv8cck3AMwelkK/btEWRybifzQDUQJCRgd0DMw9VsmxilpC7QZDUmJ89jwiItKxxvVLJL17NHuLyrnt9U0N+1PiInhoTgbnD9MaTa1Zsj2fBR/sIL+4CrADZmMQNX0SaZlGriQguNuxZx8up6KmzifPsaW+JHBwcizhIXafPIeIiHS8Jdvz2VtU3mx/QXEVt722iSXb8y2Iyv8t2Z7Pba9tqk+smnpsyS793ERaoORKAkL3mHC6x4TjcsE3BaU+eY7GkkB9GiciEiwcThcLPtjR4n3uosAFH+xQieB3uH9uJ/up6Ocm0pzKAiVgZKTEsqK0iB35JYzuk+D18zd2Coz3+rlFRMQa67OPtjjy4uYC8ourmPzHZUSGqmrBrbLWwaGS1pc/cf/c1mcfZVL/rh0XmIifU3IlAWNISiwrvi3ySVMLh9PF9jwzuVKnQBGR4FFY2npidaKTJRLSurb+fEU6CyVXEjDcHQN90Y59T2EZFTUOosLsDEjq4vXzi4iINZJiItp03IKLMximJg0NtucV89D7LZdTnqitP1+RzkLJlQQMd8fAXQWlOJwu7DbDa+d2N7MY1jPOq+cVERFrTUhLJCUugoLiqhbnDxlAclwE157ZT6//JxjVO4HnVuw95c9tQlpiR4cm4tfU0EICRlq3aCJCbVTUONh/pHnXp/ZoXDxYn1qKiAQTu83goTkZgJkQnMh9+6E5GUqsvkM/N5HTo+RKAobdZjAo2TfrXW05UD/fSosHi4gEnfOHpfDstWNIjmtawpYcF8Gz147ROlet0M9NxHMqC5SAkpESy5YDx9mZX8JFI1K9cs7qOge7CsxkTc0sRESC0/nDUjg3I5n12UcpLK0iKcYsadPIy8m5f25r9hTy2ap1nDdtIpMGJOnnJtIKJVcSUNxNLbzZMXBnfim1DhcJUaH0Soj02nlFRMS/2G2G2oafBrvNYGJaIkd2upiohFTkpFQWKAElIyUG8G5ZYOPiwfEYhv5giIiIiMjpUXIlAWVQciyGYa5HcqTMO2uSuDsFavFgEREREWkPJVcSULqEh9CvazRglvN5w9Zcs5nFqN7qFCgiIiIip0/JlQQc93pXO/KL232u0qpasorKAI1ciYiIiEj7KLmSgDPEPe/KC00ttuUV43JBz/hIunUJb/f5RERERKTzUnIlAcfdMdAbZYHuksCRKgkUERERkXZSciUBJyPFTIT2FJVRVeto17ncnQJVEigiIiIi7aXkSgJOj9hwEqJCcThd7D5U1q5zuUeuRvTSyJWIiIiItI+SKwk4hmGcUBp4+vOuikqryTteiWHA8J5KrkRERESkfZRcSUBq7Bh4+snV1vr1rfp370JMRKg3whIRERGRTkzJlQQk98hVezoGblFJoIiIiIh4kZIrCUhDUhrLAl0u12mdwz1yNap3vJeiEhEREZHOTMmVBKT+3bsQZrdRWl1H7rFKjx/vcrnUKVBEREREvErJlQSkULuNgcldAPj6NEoDc49VcqyillC70bAosYiIiIhIeyi5koA1JPn0m1psqS8JHJISS3iI3ZthiYiIiEgnpeRKAlZ72rE3lgSqmYWIiIiIeIeSKwlYDe3YT6MssLFTYLw3QxIRERGRTkzJlQSsIfUjV3nHKymuqG3z4xxOF9vzzORKnQJFRERExFuUXEnAio0IpVdCJODZvKs9hWVU1DiICrPTv3sXX4UnIiIiIp2MpcnVypUrmTNnDqmpqRiGwbvvvtvqsbfeeiuGYfDUU0+d9Jy//e1vMQyjydfgwYO9G7j4jYwUz+dduZtZDOsZh91m+CIsEREREemELE2uysvLGTlyJM8888xJj3vnnXdYu3YtqampbTrv0KFDyc/Pb/havXq1N8IVP+RuauHJyJUWDxYRERERXwix8slnz57N7NmzT3pMXl4eP/3pT/n000+58MIL23TekJAQkpOTvRGi+LnTaWqx5YC7mYU6BYqIiIiI91iaXJ2K0+nkuuuu495772Xo0KFtftzu3btJTU0lIiKCSZMm8eijj9KnT59Wj6+urqa6urrhdkmJ+Ua9traW2tq2N0qQjndG9ygAdheWUl5ZTVhI42Cs+3d34u+wus7JrgLz95uRHK3fr3hNS9ebiC/pmpOOpOtNOpo/XXOexODXydVjjz1GSEgId955Z5sfM3HiRF5++WUGDRpEfn4+CxYsYNq0aWzfvp2YmJgWH/Poo4+yYMGCZvs/++wzoqKiTjt+8T2XCyLtdiod8Mo7S+gZ3fyYpUuXNmzvL4VaRwjRIS62frmcbZpyJV524vUm0hF0zUlH0vUmHc0frrmKioo2H+u3ydXGjRv585//zKZNmzCMtr8DPrHMcMSIEUycOJG+ffvyz3/+k7lz57b4mPvuu4+777674XZJSQm9e/fmvPPOIzY29vT/EdIh3ij4ivX7jtFtwCguGN04L6+2tpalS5dy7rnnEhoaCsDf1ubA9l2MS+vOhReOsSpkCUItXW8ivqRrTjqSrjfpaP50zbmr2trCb5OrVatWUVhY2KScz+Fw8Itf/IKnnnqKffv2tek88fHxDBw4kD179rR6THh4OOHh4c32h4aGWv7LlFPLSI1j/b5jfFNY3uLv68Tf4/b8UgBG9knQ71Z8Qq8b0tF0zUlH0vUmHc0frjlPnt9vk6vrrruOc845p8m+WbNmcd1113HjjTe2+TxlZWVkZWVx3XXXeTtE8RPujoFtace+Nde9eLCaWYiIiIiId1maXJWVlTUZUcrOziYzM5PExET69OlD165dmxwfGhpKcnIygwYNatg3c+ZMLrvsMu644w4A7rnnHubMmUPfvn05ePAgDz30EHa7nauuuqpj/lHS4Ro6BuaX4HK5Wi0jLa2qJauoDIARveI7KjwRERER6SQsTa42bNjA2Wef3XDbPe/p+uuv5+WXX27TObKysjh8+HDD7dzcXK666iqOHDlC9+7dmTp1KmvXrqV79+5ejV38xxk9uhBiMzheUUt+cRWp8ZEtHrctrxiXC3rGR9KtS/MyUBERERGR9rA0uTrrrLNwuVxtPr6leVbf3ffmm2+2MyoJNOEhdgYkdWFXQSk780taTa7cJYEjVRIoIiIiIj5gO/UhIv6vLYsJbzlwHFBJoIiIiIj4hpIrCQruphY7TtLUwj1yNaKXRq5ERERExPuUXElQGJJy8uSqqLSavOOVGAYM76nkSkRERES8T8mVBAV3crX/SAVl1XXN7t+aexyA/t27EBOh9TlERERExPuUXElQSIwOIyUuAoBdLYxebVFJoIiIiIj4mJIrCRoZJykNdI9cjeod34ERiYiIiEhnouRKgoa7NHDnd5Irl8ulToEiIiIi4nNKriRoNHQM/E479tzjlRyrqCXUbjAkJcaK0ERERESkE1ByJUHDXRa4q6CUOoezYf+2XDPZGpISS3iI3ZLYRERERCT4KbmSoNEnMYroMDvVdU6yD5c37N+ap2YWIiIiIuJ7Sq4kaNhsBoNbaGqxNc/c1nwrEREREfElJVcSVL7bMdDpgq/r52CpU6CIiIiI+JKSKwkq321qUVAJFTUOosLs9O/excrQRERERCTIKbmSoNLYjr0UgJwyA4BhPeOw2wzL4hIRERGR4KfkSoLKoB4x2Aw4XFZNUWl1Q3KlkkARERER8TUlVxJUIsPspNeX/+0sKG1IrtQpUERERER8TcmVBB13aeCWA8UcrDD3jVSnQBERERHxMSVXEnTcHQPf3XIQh8sgISqUXgmRFkclIiIiIsFOyZUEHXfHwJyjlQD0SYjE6bIyIhERERHpDJRcSdApKK5qcntLXglTH/svS7bnWxSRiIiIiHQGSq4kqCzZns+v/7W12f6C4ipue22TEiwRERER8RklVxI0HE4XCz7YQUsVgO59Cz7YgUM1giIiIiLiA0quJGiszz5K/ndKAk/kAvKLq1iffbTjghIRERGRTkPJlQSNwtLWE6vTOU5ERERExBNKriRoJMVEePU4ERERERFPKLmSoDEhLZGUuAiMVu43gJS4CCakJXZkWCIiIiLSSSi5kqBhtxk8NCcDoFmC5b790JwM7LbW0i8RERERkdOn5EqCyvnDUnj22jEkxzUt/UuOi+DZa8dw/rAUiyITERERkWAXYnUAIt52/rAUzs1IZs2eQj5btY7zpk1k0oAkjViJiIiIiE8puZKgZLcZTExL5MhOFxPTEpVYiYiIiIjPqSxQRERERETEC5RciYiIiIiIeIGSKxERERERES9QciUiIiIiIuIFSq5ERERERES8QMmViIiIiIiIFyi5EhERERER8QIlVyIiIiIiIl6g5EpERERERMQLlFyJiIiIiIh4gZIrERERERERL1ByJSIiIiIi4gVKrkRERERERLwgxOoA/JHL5QKgpKTE4kikPWpra6moqKCkpITQ0FCrw5Egp+tNOpquOelIut6ko/nTNefOCdw5wskouWpBaWkpAL1797Y4EhERERER8QelpaXExcWd9BjD1ZYUrJNxOp0cPHiQmJgYDMOwOhw5TSUlJfTu3ZsDBw4QGxtrdTgS5HS9SUfTNScdSdebdDR/uuZcLhelpaWkpqZis518VpVGrlpgs9no1auX1WGIl8TGxlr+n1I6D11v0tF0zUlH0vUmHc1frrlTjVi5qaGFiIiIiIiIFyi5EhERERER8QIlVxK0wsPDeeihhwgPD7c6FOkEdL1JR9M1Jx1J15t0tEC95tTQQkRERERExAs0ciUiIiIiIuIFSq5ERERERES8QMmViIiIiIiIFyi5EhERERER8QIlVxJUHn30UcaPH09MTAxJSUlceumlfPPNN1aHJZ3IH//4RwzD4K677rI6FAlSeXl5XHvttXTt2pXIyEiGDx/Ohg0brA5LgpTD4eA3v/kNaWlpREZG0r9/f37/+9+jfmjiLStXrmTOnDmkpqZiGAbvvvtuk/tdLhcPPvggKSkpREZGcs4557B7925rgm0DJVcSVFasWMHtt9/O2rVrWbp0KbW1tZx33nmUl5dbHZp0Al999RULFy5kxIgRVociQerYsWNMmTKF0NBQPvnkE3bs2METTzxBQkKC1aFJkHrsscd49tlnefrpp9m5cyePPfYYjz/+OP/3f/9ndWgSJMrLyxk5ciTPPPNMi/c//vjj/OUvf+G5555j3bp1REdHM2vWLKqqqjo40rZRK3YJakVFRSQlJbFixQqmT59udTgSxMrKyhgzZgx//etf+cMf/sCoUaN46qmnrA5Lgsyvf/1rvvjiC1atWmV1KNJJXHTRRfTo0YMXXnihYd/3v/99IiMjee211yyMTIKRYRi88847XHrppYA5apWamsovfvEL7rnnHgCKi4vp0aMHL7/8MldeeaWF0bZMI1cS1IqLiwFITEy0OBIJdrfffjsXXngh55xzjtWhSBB7//33GTduHFdccQVJSUmMHj2axYsXWx2WBLHJkyezbNkyvv32WwC2bNnC6tWrmT17tsWRSWeQnZ1NQUFBk7+tcXFxTJw4kTVr1lgYWetCrA5AxFecTid33XUXU6ZMYdiwYVaHI0HszTffZNOmTXz11VdWhyJBbu/evTz77LPcfffd3H///Xz11VfceeedhIWFcf3111sdngShX//615SUlDB48GDsdjsOh4OHH36Ya665xurQpBMoKCgAoEePHk329+jRo+E+f6PkSoLW7bffzvbt21m9erXVoUgQO3DgAD/72c9YunQpERERVocjQc7pdDJu3DgeeeQRAEaPHs327dt57rnnlFyJT/zzn//k9ddf54033mDo0KFkZmZy1113kZqaqmtOpAUqC5SgdMcdd/Dhhx/y+eef06tXL6vDkSC2ceNGCgsLGTNmDCEhIYSEhLBixQr+8pe/EBISgsPhsDpECSIpKSlkZGQ02TdkyBBycnIsikiC3b333suvf/1rrrzySoYPH851113Hz3/+cx599FGrQ5NOIDk5GYBDhw412X/o0KGG+/yNkisJKi6XizvuuIN33nmH//73v6SlpVkdkgS5mTNnsm3bNjIzMxu+xo0bxzXXXENmZiZ2u93qECWITJkypdnyEt9++y19+/a1KCIJdhUVFdhsTd8u2u12nE6nRRFJZ5KWlkZycjLLli1r2FdSUsK6deuYNGmShZG1TmWBElRuv/123njjDd577z1iYmIa6nHj4uKIjIy0ODoJRjExMc3m9EVHR9O1a1fN9ROv+/nPf87kyZN55JFH+OEPf8j69etZtGgRixYtsjo0CVJz5szh4Ycfpk+fPgwdOpTNmzfz5JNPctNNN1kdmgSJsrIy9uzZ03A7OzubzMxMEhMT6dOnD3fddRd/+MMfOOOMM0hLS+M3v/kNqampDR0F/Y1asUtQMQyjxf0vvfQSN9xwQ8cGI53WWWedpVbs4jMffvgh9913H7t37yYtLY27776befPmWR2WBKnS0lJ+85vf8M4771BYWEhqaipXXXUVDz74IGFhYVaHJ0Fg+fLlnH322c32X3/99bz88su4XC4eeughFi1axPHjx5k6dSp//etfGThwoAXRnpqSKxERERERES/QnCsREREREREvUHIlIiIiIiLiBUquREREREREvEDJlYiIiIiIiBcouRIREREREfECJVciIiIiIiJeoORKRERERETEC5RciYiIdBCXy8WTTz7Jhg0brA5FRER8QMmViIgEtH79+vHUU09ZHUaD3/72t4waNarF+x599FGWLFnCyJEjOzYoERHpEIbL5XJZHYSIiEhrbrjhBl555ZVm+2fNmsWSJUsoKioiOjqaqKgoC6JrrqysjOrqarp27dpk/8qVK7nrrrtYvnw5sbGxFkUnIiK+pORKRET82g033MChQ4d46aWXmuwPDw8nISHBoqhERESaU1mgiIj4vfDwcJKTk5t8uROr75YFHj9+nJtvvpnu3bsTGxvL9773PbZs2dLkfB988AHjx48nIiKCbt26cdlllzXcZxgG7777bpPj4+Pjefnllxtu5+bmctVVV5GYmEh0dDTjxo1j3bp1QPOyQKfTye9+9zt69epFeHg4o0aNYsmSJQ3379u3D8Mw+Pe//83ZZ59NVFQUI0eOZM2aNe38qYmISEdTciUiIkHliiuuoLCwkE8++YSNGzcyZswYZs6cydGjRwH46KOPuOyyy7jgggvYvHkzy5YtY8KECW0+f1lZGTNmzCAvL4/333+fLVu28Mtf/hKn09ni8X/+85954okn+NOf/sTWrVuZNWsWF198Mbt3725y3AMPPMA999xDZmYmAwcO5KqrrqKuru70fxAiItLhQqwOQERE5FQ+/PBDunTp0mTf/fffz/33399k3+rVq1m/fj2FhYWEh4cD8Kc//Yl3332Xt99+m1tuuYWHH36YK6+8kgULFjQ8zpMGE2+88QZFRUV89dVXJCYmAjBgwIBWj//Tn/7Er371K6688koAHnvsMT7//HOeeuopnnnmmYbj7rnnHi688EIAFixYwNChQ9mzZw+DBw9uc2wiImItJVciIuL3zj77bJ599tkm+9yJzYm2bNlCWVlZs2YSlZWVZGVlAZCZmcm8efNOO5bMzExGjx7d4vN/V0lJCQcPHmTKlClN9k+ZMqVZqeKIESMatlNSUgAoLCxUciUiEkCUXImIiN+Ljo4+6eiQW1lZGSkpKSxfvrzZffHx8QBERkae9ByGYfDdXk+1tbUN26d6/OkKDQ1tEgPQaqmhiIj4J825EhGRoDFmzBgKCgoICQlhwIABTb66desGmCNEy5Yta/Uc3bt3Jz8/v+H27t27qaioaLg9YsQIMjMzG+ZwnUxsbCypqal88cUXTfZ/8cUXZGRkePrPExERP6eRKxER8XvV1dUUFBQ02RcSEtKQMLmdc845TJo0iUsvvZTHH3+cgQMHcvDgwYYmFuPGjeOhhx5i5syZ9O/fnyuvvJK6ujo+/vhjfvWrXwHwve99j6effppJkybhcDj41a9+1WRU6aqrruKRRx7h0ksv5dFHHyUlJYXNmzeTmprKpEmTmsV+77338tBDD9G/f39GjRrFSy+9RGZmJq+//roPflIiImIlJVciIuL3lixZ0jAPyW3QoEHs2rWryT7DMPj444954IEHuPHGGykqKiI5OZnp06fTo0cPAM466yzeeustfv/73/PHP/6R2NhYpk+f3nCOJ554ghtvvJFp06aRmprKn//8ZzZu3Nhwf1hYGJ999hm/+MUvuOCCC6irqyMjI6NJc4oT3XnnnRQXF/OLX/yCwsJCMjIyeP/99znjjDO89eMRERE/oUWERUQkoKWkpPD73/+em2++2epQRESkk9PIlYiIBKSKigq++OILDh06xNChQ60OR0RERA0tREQkMC1atIgrr7ySu+66q8W5TiIiIh1NZYEiIiIiIiJeoJErERERERERL1ByJSIiIiIi4gVKrkRERERERLxAyZWIiIiIiIgXKLkSERERERHxAiVXIiIiIiIiXqDkSkRERERExAuUXImIiIiIiHiBkisREREREREv+P/F5biFfb6CsgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [17.467, 14.27, 17.13, 17.091, 16.468, 16.021, 14.727, 14.727, 16.02, 16.545]\n",
+    "tiempo_inferencia_gpu = [17.095, 14.855, 16.199, 16.199, 16.523, 17.095, 15.601, 15.638, 17.03, 16.439]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "747504b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+vOLh4aFoNBplzpw55vX79u1TAOW5556rdOzu3btbJEOvv/66Aig///yzoij1PwdHjx6tuLu7W7z37tmzR9HpdBafAYcPH1Z0Op0yffp0i/vv2rVLcXFxqbT+YqbHU9WPm5ubeTtTEtSiRQuLc8L0pXbx4sXmddV9Tpn24evrq2RkZFjcNmjQICUxMVG5cOGCeZ3RaFT69OmjtG/fvlK8tXnPvfi9TlEU5f7771c8PT0tjtO/f38FUGbNmmVeZ3rttVqtsmnTJvN60+dixf8fW5yPl3rvPnPmTKVz0aRLly5KcHCwcu7cOfO6HTt2KFqtVpkwYUKl7Suy5rMSUFxdXS0+p3fs2KEAyowZM2o8TteuXRV/f/9K6/Py8qq9UGl6f/3yyy+VM2fOKCdPnlR+/fVXpW3btopGo1H+/vtvi+0kkWqcpGufcFrz58/Hz8+PIUOGcPbsWfNP9+7d8fb2ZvXq1Rbbx8XFcfnll5v/7tWrF6A2o7dp06bS+opVc0weeugh87KpG0BxcbG5K8TSpUvR6XQ8/PDDFvebOnUqiqKwbNmyah/PP//8Q0ZGBv/6178s+tPfeeed+Pn5VXrsnTp1IjY21uKxm7oEXPzYq+Lu7s7KlSsr/bz66quVtr3vvvssqgL169cPg8HAkSNHLnkck4kTJ1r0s1+5ciVZWVmMHz/e4jHodDp69epV5WO4eIxMv379Kr1OHh4e5uXz58+TnZ1Nv379quyKNWjQIIvueabX/sYbb8THx6fS+qrOCZO6nI/9+vUz/x0UFETHjh1rPIbJqlWrKC4uZsqUKWi15W/TEydOxNfXt9K4o4oURWHhwoWMGjUKRVEsYh02bBjZ2dmVnqu77rrL4pw0xV1TrDk5OQAWz2NtrFixgqCgIIKCgkhKSmL+/PncfvvtvPbaa1bt51LGjh1LSUkJixYtsjh2VlYWY8eONa/z9/fnr7/+4uTJkzY9vjXq8r9SsbCFv78/HTt2xMvLizFjxpjXd+zYEX9//ypfx/vuu89iHOADDzyAi4sLS5cuBep3DhoMBn777TdGjx5t8d7bqVMnhg0bZrHtokWLMBqNjBkzxuKxh4SE0L59+1q91wF88MEHld7rqno/Hjt2LAEBAea/a3OuX+zGG280d1UDyMzM5Pfff2fMmDHk5uaaH8O5c+cYNmwYBw8e5MSJExb7qM17bsX3OtN++/XrR0FBAfv27bPYn7e3N+PGjTP/bXrtO3XqZH5/g9q919nrvbsqp06dYvv27dx5550EBgaa13fu3JkhQ4aYz8fqWPtZOXjwYGJiYiyO4+vre8lYc3Jy8Pb2rrT+P//5j/n9LCgoiFtuuaXSNnfffTdBQUGEhYUxYsQI8vPz+eabbxwypk/YnhSbEE7r4MGDZGdnExwcXOXtGRkZFn9X/MAGzMlJRERElesvHg+g1WqJjo62WNehQwcAc9/xI0eOEBYWVunLo6kqV02Jh+m29u3bW6w3lUSt6ODBg+zdu9fiw7qiix97VXQ6HYMHD77kdlD5uTN90ahqzER1Lq6YdfDgQaC8P/jFfH19Lf52d3ev9HgDAgIqxbBkyRJeeukltm/fbtEHvqrysPU9Jy5+PPU5H6Hqx1MV07nSsWNHi/Wurq5ER0fXeJ6dOXOGrKwsPv30U4tqedbEWpvX3/T65ebmVrtNVXr16sVLL72ERqPB09OTTp064e/vb9U+oOrXu6KkpCRiY2OZO3cu99xzDwBz586lZcuWFufk66+/zh133EFERATdu3dn+PDhTJgwodL/pD3Z4n/Fz8+P1q1bV3pe/Pz8qnwdL34f8vb2JjQ01OK9Dup+DhYWFlY6hml/Fb8cHzx4EEVRqtwWal/0pWfPnrX6YmqP97pDhw6hKArPPPMMzzzzTJX3ycjIIDw83Ko4du/ezX//+19+//1384ULk+zsbIu/q3vt6/peB7Z/765KdecZqJ+rv/32W43Fi6z9rKzr+7KPjw/nzp2rtP7BBx80T9VQXXGnZ599ln79+qHT6WjZsiWdOnXCxUW+fjcV8koKp2U0GgkODuaHH36o8vaL3zirqzpU3XqllsUhHMFoNJKYmMjbb79d5e0XfzjWly2eo4pXTwFz6fDvvvuOkJCQSttf/EFSm6pR69ev59prr+XKK6/kww8/JDQ0FL1ez1dffVVpUH1N+6zL47XV+Wjv8870vN92223ccccdVW7TuXNni7/rEquvry9hYWEkJydbFV/Lli0vmeC7u7tbzD9VUUFBgXmbSxk7dizTp0/n7Nmz+Pj48MsvvzB+/HiLc2/MmDHmuaxWrFjBG2+8wWuvvcaiRYtqNXDcFmz1v9JY3+s0Gg3Lli2rMv6qWgHqw57vdY899lilFjeTi8uwXyqOrKws+vfvj6+vLy+88AIxMTG4u7uzdetWnnzyyUpTM9j6vQ5s+95tL9Z+Vtb19Y+NjWX79u2cOHHCIiHu0KGD+YJrde9JiYmJNb7nme5X03teXed/FPYniZRwWjExMaxatYorrrii0geXPRiNRlJTU81vigAHDhwAMHcPi4yMZNWqVeTm5lq0Spm6WURGRla7f9NtBw8etLjSV1JSQlpaGklJSeZ1MTEx7Nixg0GDBjnNRHzWxmHqPhEcHFzrlrFLWbhwIe7u7vz2228Wc5589dVXNtl/TexxPlb3nJrOlf3791u0jBQXF5OWllbj8xkUFISPjw8Gg8Fmz3t1Ro4cyaeffsrGjRstutXWV2RkpLkS2sX2799v3uZSxo4dy7Rp01i4cCGtWrUiJyfHovuTSWhoKA8++CAPPvggGRkZdOvWjenTpzdYImWP/5VLOXjwIAMHDjT/nZeXx6lTpxg+fDhQ/3PQw8PD3LJRken1M4mJiUFRFKKioizeex3J2vc60/Oj1+tt9vqtWbOGc+fOsWjRIq688krz+oao9GaP87E273UX27dvHy1btqxxKo2G+qwcOXIkc+bM4YcffuCJJ56w6b5reg5M62vzficcQ8ZICac1ZswYDAYDL774YqXbSktLa12C1xozZ840LyuKwsyZM9Hr9QwaNAiA4cOHYzAYLLYDeOedd9BoNDV+8erRowdBQUF8/PHHFBcXm9d//fXXlR7LmDFjOHHiBJ999lml/RQWFpKfn1+Xh1cvXl5eVj3nw4YNw9fXl5dffpmSkpJKt19c6rc2dDodGo3Govzv4cOH+emnn6zel7XscT6aviBcfN/Bgwfj6urK+++/b3Gl9IsvviA7O5sRI0ZUu0+dTseNN97IwoULq2wtqsvzXp0nnngCLy8v7r33Xk6fPl3p9pSUFN577z2r9zt8+HCOHz9e6XUtKiri888/Jzg4mG7dul1yP506dSIxMZG5c+cyd+5cQkNDLb6UGgyGSl2kgoODCQsLs+g2evbsWfbt22duDbM1e/yvXMqnn35qcayPPvqI0tJS83tYfc/BYcOG8dNPP3H06FHz+r179/Lbb79ZbHvDDTeg0+mYNm1apVYBRVGq7E5lb9X9X1YnODiYAQMG8Mknn3Dq1KlKt9f1vQ4sW0qKi4v58MMPrd6XtexxPnp6egKVn9PQ0FC6dOnCN998Y3FbcnIyK1asMCf21Wmoz8oxY8YQFxfHiy++yKZNm6rcpq4tv6bn4Pvvv6/0/GzZsoVNmzY12EUdYT1pkRJOq3///tx///288sorbN++naFDh6LX6zl48CDz58/nvffeM09yZwvu7u4sX76cO+64g169erFs2TJ+/fVXnn76aXO3rVGjRjFw4ED+85//cPjwYZKSklixYgU///wzU6ZMsRjEejG9Xs9LL73E/fffz1VXXcXYsWNJS0vjq6++qjQe4/bbb2fevHn861//YvXq1VxxxRUYDAb27dvHvHnz+O233y45HqC0tJTvv/++ytuuv/56qyfM7d69Ox999BEvvfQS7dq1Izg4uNo+9KB2/froo4+4/fbb6datG+PGjSMoKIijR4/y66+/csUVV1RKSC9lxIgRvP3221x99dXccsstZGRk8MEHH9CuXTt27txp1b6sZY/zsUuXLuh0Ol577TWys7Nxc3PjqquuIjg4mKeeeopp06Zx9dVXc+2117J//34+/PBDLrvssktOtPzqq6+yevVqevXqxcSJE4mLiyMzM5OtW7eyatUqMjMz6/NUmMXExDBr1izGjh1Lp06dmDBhAgkJCRQXF/Pnn38yf/587rzzTqv3e9999/Hll19y8803c/fdd9O1a1fOnTvH3LlzSU5O5ttvv631BKhjx47l2Wefxd3dnXvuuceicEJubi6tW7fmpptuIikpCW9vb1atWsXff//NW2+9Zd5u5syZTJs2jdWrV1c5uW592eN/5VKKi4sZNGgQY8aMMZ9bffv25dprrwXUVqX6nIPTpk1j+fLl9OvXjwcffJDS0lJmzJhBfHy8xf9qTEwML730Ek899RSHDx9m9OjR+Pj4kJaWxo8//sh9993HY489dsnHs2zZskoFGAD69Olj9Xi37t27A/Dwww8zbNgwdDpdlS2ZFX3wwQf07duXxMREJk6cSHR0NKdPn2bjxo0cP36cHTt2WBVDnz59CAgI4I477uDhhx9Go9Hw3XffNUg3TXucjx4eHsTFxTF37lw6dOhAYGAgCQkJJCQk8MYbb3DNNddw+eWXc88991BYWMiMGTPw8/OzmGuqKrb4rKwNvV7Pjz/+yLBhw+jbty833HCDed6rEydO8Msvv3D06NEaLzDU5O2332bYsGF06dKFO++8k7CwMPbu3cunn35KaGioTNzrzBquQKAQ1s8jpShqefDu3bsrHh4eio+Pj5KYmKg88cQTysmTJ83bVFcuGahU2thUwvaNN96wOLaXl5eSkpJinpOiVatWynPPPWdR+ldR1NKwjzzyiBIWFqbo9Xqlffv2yhtvvGFRyrYmH374oRIVFaW4ubkpPXr0UNatW6f079+/Umnx4uJi5bXXXlPi4+MVNzc3JSAgQOnevbsybdo0ixKrVamp/DkVytyayt6ayrCamMqxVizdnJ6erowYMULx8fGxKNde3T4q7mvYsGGKn5+f4u7ursTExCh33nmn8s8//1jE6+XlVem+pnlsKvriiy+U9u3bK25ubkpsbKzy1VdfVbldbV/7io+3YvlZe5yPVb3On332mRIdHW0uC13xOZ85c6YSGxur6PV6pVWrVsoDDzxwyXl1TE6fPq1MmjRJiYiIUPR6vRISEqIMGjRI+fTTT2t83IpS/jxdqsS4yYEDB5SJEycqbdu2VVxdXRUfHx/liiuuUGbMmGFRprmmsuYXO3/+vPLII48oUVFRil6vV3x9fZWBAwcqy5Ytq9X9TQ4ePGg+7zds2GBxW1FRkfL4448rSUlJio+Pj+Ll5aUkJSVVmrvMdH5VVcq8OtbOI6Uo9ftf6d+/vxIfH19p/cXPuenYa9euVe677z4lICBA8fb2Vm699VaL8tMm9TkH165dq3Tv3l1xdXVVoqOjlY8//rjK/1VFUZSFCxcqffv2Vby8vBQvLy8lNjZWmTRpkrJ///4aj1FT+fOK53B1//uKolQqy11aWqpMnjxZCQoKUjQajTnemvahKOrccBMmTFBCQkIUvV6vhIeHKyNHjlQWLFhQKd7avOf+8ccfSu/evRUPDw8lLCxMeeKJJ8zlyytuV9vXvuLjrfjeaI/zsarX+c8//zSfDxc/56tWrVKuuOIKxcPDQ/H19VVGjRql7Nmzp9J+q1Lbz8qqPhMURX2e7rjjjlodKysrS3nhhReUrl27Kt7e3oqrq6sSERGh3HTTTRYl9BXl0mXNL7Zp0yZl5MiRSkBAgOLi4qKEh4cr9957r1VzV4mGp1EUJx6FKkQDufPOO1mwYAF5eXmODkUIIezm66+/5q677uLvv/+W8stCCFFPMkZKCCGEEEIIIawkiZQQQgghhBBCWEkSKSGEEEIIIYSwkoyREkIIIYQQQggrSYuUEEIIIYQQQlhJEikhhBBCCCGEsJJMyAsYjUZOnjyJj48PGo3G0eEIIYQQQgghHERRFHJzcwkLC7OYyP1ikkgBJ0+eJCIiwtFhCCGEEEIIIZzEsWPHaN26dbW3SyIF+Pj4AOqT5evr6+BoRF2UlJSwYsUKhg4dil6vd3Q4ohmQc040JDnfREOTc040JGc733JycoiIiDDnCNWRRArM3fl8fX0lkWqkSkpK8PT0xNfX1yn+AUXTJ+ecaEhyvomGJuecaEjOer5dasiPFJsQQgghhBBCCCtJIiWEEEIIIYQQVpJESgghhBBCCCGsJImUEEIIIYQQQlhJEikhhBBCCCGEsJIkUkIIIYQQQghhJUmkhBBCCCGEEMJKkkgJIYQQQgghhJUkkRJCCCGEEEIIK0kiJYQQQgghhBBWkkRKCCGEEEIIIawkiZQQQgghhBBCWEkSKSGEEEIIIYSwkiRSQgghhBBCCGElSaScwepXYO3rVd+29nX1diGEEEIIIYTTkETKGWh1sHp65WRq7evqeq3OMXEJIYQQQoiayQXxZsvF0QEIoP8T6u/V09XfVz4O695Q/x74n/LbhRBCNB+rX1EvpFX1GbD2dTAaYOBTDR+XEMKS6YI4WP6/mi6ID/yPY+ISdieJlLPo/wTkpqv/cKtfBhRJooQQojmr+OWszyPl6+XLmRDOpeIF8QvZkHAjHFolF8SbAUmknMkV/4Z/vgAU0LnKP54QQjRnFb6caQ0GIA7t+jdh3avy5UwIZ9P/CVCMsOYV2DhTXSf/p02eJFLOZOfc8mVDsXrVUf4BhRCi+Sr7DNCtns61aNBIbwUhnFdEL8u/Y0c6Jg7RYKTYhLMwddUISVT/juxTdQEKIYQQzUv0QBRAg4KiqWbMlBDC8VY+Y/n3V9dA4XnHxCIahCRSzqBif/deD6jrjAb1b0mmhBCi+SrIhO9vQFP2p0YxwJrXHBqSEKIKv78E6bvU5fFzwM0PLmTBZ4PU73SiSZJEyhmYkqb+T0Dbvuq6E1vg8knqevkHFEKI5sdohM8HQ1EOirs/pVp3df2al+UCmxDOZO3rarVlgIAo6HA13LkYtC6QmQLfjHJsfMJuJJFyBgOfKu+qERAJfm3AWApHN6nrpbytEEI0P9/foH4J0+gove0nTvpfpq4P6yq9FYRwJkaDmkABJI0DjQZCk+C6D9V1R/6APb84Lj5hN5JIOSNTq9ThDY6NQwghhGMc/gNS16jLI9+BVgkcC7xC/ftcKlz5hPRWEMJZdL8Tso6oy53HlK9PGgu9H1SXf3oAMvY1eGjCviSRciIGo8LGlHNs1cYDoEgiJYQQzU/eGVhwN6BA57HQbQIAZ71jUXxbQ1E2BHeS3gpCOItd89XS5xG9ITDa8rYhL0DbflCcB3NuUeeZEk2GJFJOYnnyKfq+9jvjP9vEw5u8ATAc38LK7YccHJkQQogGYzTAonshLx1adoQRb6vdhAA0WoyJZVe7d8x2XIxCCEum6WuSxla+TaeHm74C39ZqV91F96njH0WTIImUE1iefIoHvt/KqewLABxXgjiutMQFA9/Nm8/y5FMOjlAIIUSDWPem2qVP7wljvgU3b4ubzYnUof9B7umGj08IYSl9F5xOBp0rxF9f9TbeQTD2O9C5wYHlsFYqbzYVkkg5mMGoMG3xHpSL1m8yxgHQS7uHaYv3YDBevIUQQogmJXUNrHlFXR7xNgTHVt6mRTtofRkoBrU7kRDCsXbMUX93GAYeAdVvF95NHe8IsPZV2LfU/rEJu5NEysE2p2WaW6Iq2mTsBEBv7V5OZV9gc1pmQ4cmhBCioeSmw8J7AQW63g5dxle/bVLZbaYvcEIIxzAaYNcCdTmphv9Zk663Qs/71OVF98GZA/aLTTQISaQcLCO3chIF5S1SnTWpeHKh2u2EEEI0coZSWHAP5J+BVgkw/I2at4+/Xu1GdHpX+QSgQoiGl7pGHc/oEQjthtTuPsNehjZ9oDgX5t4KF3LsGqKwL0mkHCzYx73K9aZxUnqNge7aA9VuJ4QQopFb8zIc2QCu3nDzN6D3qHl7z0DoeI26LK1SQjiOqchEwg3g4lq7++j0MOYb8AmDswfUsuhSfKLRkkTKwXpGBRLq546mittMrVKDPQ7QMyqwYQMTQghhfwdXwvq31OVr34eW7Wp3P1M3op3z1BYtIUTDKsqDvYvV5c7jrLuvd3BZ8QlX2Lek/D1ANDqSSDmYTqvhuVFqwnRxMmUaJ3WtXyo6bVWplhBCiEYr+7g6TgLgsnsh4cba37fdYPBsCfkZkPK7feITQlRv72IoKYDAGGjdw/r7t+4BI8oSqNXT4cBvto1PNAhJpJzA1QmhfHRbN0L8LLvvpXh1BSAgK1m98iGEEKJpMJTA/LugMBNCk9RxE9bQ6SHxZnV5xyzbxyeEqNnOsm61SePK53qzVrcJ0KNs8u2FE+Fcis3CEw1DEikncXVCKBuevIoZ49XkSauB76feDH5twFgKx/5ycIRCCCFs5n/T4PhmcPNTx0W5uFm/D1Nlv31LoTDLpuEJIWqQfQJS16rLncfUb19XvwYRvaAoG+bcAkW59Y9PNBhJpJyITqthZOdQAjz1GBVIOZMHbfuqNx7e4NjghBBC2Ma+X+HPGery6A8gMKpu+wnpDMFxYCiC3T/aLj4hRM12zQcUtfpeQNv67cvFVZ182zsEzuyDnx4EReYObSwkkXIyGo2G+DA/AHafzJFESgghmpLzh9UqXQC9H4ROo+q+L41G5pQSoqEpSnm1vqSxttmnT4hafEKrh72/wIZ3bLNfYXeSSDmh+DBfAHafzC5PpE5ulXFSQgjRmJUWqeOiLmRDeA8YPK3+++w8BjRaOLZJxlcI0RDSd0HGHtC5Qdxo2+03omf5HHL/ewEOrrLdvoXdSCLlhOLMiVQOBESCv4yTEkKIRm/FM+pFMXd/uPmr2s87UxOfEIi5Sl02XSUXQtiPqfW349Xg4W/bffe4C7rdgVp84m7ITLXt/oXNSSLlhExd+/adysVgVKBtP/UG6d4nhBCN0+4fYfMn6vL1n6gXyGzF3L1vtkzsKYQ9GUrLxkdR/n9na8PfUFusL2TDnNugON8+xxE2IYmUE4pq6YWHXkdhiYG0s1JwQgghGrVzKfDzZHX5iinqlWxbih0Bbr6QdRSObrTtvoUQ5VLXqHO3ebZQ53KzBxc3dbyUVzBk7IafH5LiE05MEiknpNNq6BTqA5R174u8Qr1BxkkJIUTjUnIB5t8BxbnQ5nK46hnbH0PvAXHXqcsyp5QQ9rNjtvo74UZ1Ljd78Q1TK/lpXWD3ovIqn8LpSCLlpCwq98k4KSGEaJyW/586ON2zBdz0Jehc7HOcLreov3f/DMUF9jmGEM1ZUa46dQGok/DaW+TlcPWr6vKq5yDld/sfU1hNEiknZVG5D2SclBBCNDY758GWrwAN3PCZepXZXiJ6g3+k2vJl+rInhLCdPb9AaSG0aA9h3RrmmJfdC11uA8UIC+5Wp08QTkUSKSdVsUVKURQZJyWEEI3JmQOweIq6fOXj0G6QfY+n1VoWnRBC2NbOsmp9SWPVOdwagkYDI96CsK5QeL6s+IS0ODsTSaScVIcQb1y0GrIKSjiZfUHGSQkhRGNRXADzJkBJvtqbYMD/NcxxTZODpq6GnFMNc0whmoPs45C2Xl1OHNOwx9a7w9jvwbMlnN4Fix+W4hNORBIpJ+XmoqNdsDcAySeyZZyUEEI0FksfgzN7wbsV3PgFaHUNc9zAaLWghWKEXfMa5phCNAc75wEKRPZVv481NL/WMOYb0OjU8uubPmz4GESVJJFyYgnhFQpOQIVxUusdFJEQQogabfsetv8AGq2aRPm0atjjmwbBb58tV62FsAVFKZ/s2tTq6wht+8Kwl9XlFc9A6lrHxSLMJJFyYqaCE3vMBSdknJQQQjit07vh18fU5YFPQ1S/ho8h/nrQuaktYqd2NPzxhWhqTu2AM/vAxb18mgFH6XU/dB4HigEW3KXOHSccShIpJ2ZRAh3Kx0mdkHFSQgjhVIpyYd4dalWvmEHQd6pj4nD3UyfoBSk6IYQt7CgrMtHxGvX/y5E0Ghj1LoR0hoJzMPc2KCl0bEzNnCRSTsw0Ke+p7Atk5heXj5NSDHBsk4OjE0IIAahdfxZPgXMHwSdMLXWudeDHq2lOqV3zwVDiuDiEaOwMpZC8QF02VcV0NL0HjPsBPALV1rIlj0g3XgeSRMqJ+bjradvCE5D5pIQQwmlt+Ur9sqXRwc1fgVcLx8YTPVAtdFFwDg6udGwsQjRmKb9D/hm1Yl7MVY6Oppx/G7j5a3Us5o7ZsPlTR0fUbEki5eQqde+TcVJCCOE8Tm6HZU+qy4Ofhza9HRmNSucCiTery9K9T4i6M/3/JN4EOr1jY7lYdH8Y8qK6vPwp+V7oIJJIObm4soITMk5KCCGczIVsmH8HGIqhwzXQZ7KjIypn6oZ0YDkUZDo2FiEaowvZsH+pumyqhulsLp+kXjRRDOoYzezjjo6o2ZFEysnFmxOpsq59Mk5KCCEcT1Hg54fg/GHwawOjP1QHgjuLkAQISVSTvN2LHB2NEI3Pnl+g9AK07AihXRwdTdU0Ghj1PrRKhIKzMPd2KLng6KiaFUmknJypa1/a2Xzyi0rVlTJOSgghHOuvT2DvL6DVq2MVPAMdHVFlplap7dK9TwirVZw7ypkuklzM1RPGfQ8eAXByK/w6VYpPNCBJpJxckI8bwT5uKArsS5dxUkII4XDHt8CK/6rLQ1+C1t0dG091Em9WC2Cc+AfOHnR0NEI0HllH4fB6dTlxjGNjqY2AtnDTl2rxie3fwz9fODqiZkMSqUYgXsZJCSGEcyjIhPl3grEEOl2rTpDprLyDod1gdVmKTghRezvnqb/b9gP/CMfGUlsxV6kFb0AtgHNko0PDaS4kkWoETN37kk/IOCkhhHAYoxF+egCyj0JAFFw307m7/AB0Kevet2OuGr8QomaKUqFbn5MWmahOn4ch/nowlsK8CZBz0tERNXmSSDUCCeEXtUiBjJMSQoiGtnGGWgVP56aOi3L3c3REl9bhGjXOnOPlXZWEENU7uRXOHgAXd7XVuTHRaOC6DyA4HvIz1GSqtMjRUTVpkkg1AqYWqQOncykuLbuiKOOkhBCi4RzdBKumqcvXvAphXRwaTq3p3SH+BnVZuvcJcWk7ylqjYkeCu69jY6kLVy+1+IS7Hxz/G5Y94eiImjRJpBqB1gEe+Lq7UGJQOJiRq66UcVJCCNEw8s/C/LvU7tQJN0H3uxwdkXVM1fv2/CKfF0LUxFACyQvV5cbWra+iwGi48UtAA1u+hn++cnRETZYkUo2ARqOpPDGvjJMSQgj7Mxph0X2QexJatIdR7zr/uKiLRfRUv1iV5MPexY6ORgjndeh/6nxMXsEQPdDR0dRP+8Ew6Bl1eenjcGyzY+NpohyaSL3yyitcdtll+Pj4EBwczOjRo9m/f7/FNunp6dx+++2EhITg5eVFt27dWLhwocU2mZmZ3Hrrrfj6+uLv788999xDXl7Tuupm6t63R8ZJCSFEw9nwFqT8D1w8YMw34Obj6Iisp9GUt0pJ9z4hqmf6/0i8CXQujo3FFvo+qo7zMpaok/Xmpjs6oibHoYnU2rVrmTRpEps2bWLlypWUlJQwdOhQ8vPzzdtMmDCB/fv388svv7Br1y5uuOEGxowZw7Zt28zb3HrrrezevZuVK1eyZMkS1q1bx3333eeIh2Q35SXQs8tXyjgpIYSwn7R1sPpldXnEm9Aq3rHx1EfnservtHWQfdyxsQjhjAqzYP8ydbkxd+urSKOB0R9CUCzkpZcVnyh2dFRNikMTqeXLl3PnnXcSHx9PUlISX3/9NUePHmXLli3mbf78808mT55Mz549iY6O5r///S/+/v7mbfbu3cvy5cv5/PPP6dWrF3379mXGjBnMmTOHkyebTtnHii1SRmPZjNWmRErGSQkhhG3lnoYF94BihC63QtfbHB1R/QRElvViqFDaWQhRbs/PYCiCoE4Q0tnR0diOmw+MmwVufnDsL1j+f46OqElxqjFS2dlqa0tgYKB5XZ8+fZg7dy6ZmZkYjUbmzJnDhQsXGDBgAAAbN27E39+fHj16mO8zePBgtFotf/31V4PGb08xQV64uWjJLzZwJLNAXenfBvwjZZyUEELYktEAC+9RywcHx8HwNx0dkW2YrrJvn63OlSOEKGeeO2ps4xsHeSktYuDGzwAN/PMFbP3W0RE1GU7TAdRoNDJlyhSuuOIKEhISzOvnzZvH2LFjadGiBS4uLnh6evLjjz/Srl07QB1DFRwcbLEvFxcXAgMDSU+vui9oUVERRUXldfVzctRxRyUlJZSUlNj6odlMx1be7DyRw86jmbT2cwVA1+YKtFlHMKSuwxjZ38EROo7pdXPm1080LXLONV3ata+gO7weRe9F6fVfgEYPDn6dbXK+tR+Oi4sHmnMHKT2yGSW8m42iE01Rs3qPyzqK/sgfKGgo7XSDw//f7SLqKrRXPolu3asov07FENjRqd4DnO18q20cTpNITZo0ieTkZDZssBzv88wzz5CVlcWqVato2bIlP/30E2PGjGH9+vUkJibW6VivvPIK06ZNq7R+xYoVeHp61mmfDcG7RAtoWbxhOxxT55OKyPKmG5C9fQnrC7s7MjynsHLlSkeHIJoZOeealqCcXVye8jYAW8Ju58Tmg8BBxwZVQX3Pt24+XYk4/yfHlrzGzog7bBSVaMqaw3tch/Sf6QSc9e7Enxu2A9sdG5C9KLH09OtGaPZWSn4Yy9qOL1Ckd66JxZ3lfCsoKKjVdhpFcXz7/kMPPcTPP//MunXriIqKMq9PSUmhXbt2JCcnEx9fPsh38ODBtGvXjo8//pgvv/ySqVOncv78efPtpaWluLu7M3/+fK6//vpKx6uqRSoiIoKzZ8/i6+u8k6/N2nyM5xbvpV+7Fnx5R1nSlH0M/cyuKBodpY+lgKu3Y4N0kJKSElauXMmQIUPQ6/WODkc0A3LONUE5p3D5YgCagnMYut6Bcfhbjo7IzFbnmyZ1DS6zb0LxCKD04WRwcbNhlKIpaTbvcYqCy8e90WSmUDpqJkrnJlJoojpFubh8NRTNuYMYI3pjuPVH0Dn+9XW28y0nJ4eWLVuSnZ1dY27g0BYpRVGYPHkyP/74I2vWrLFIoqA8G9RqLYdy6XQ6jEa1Rebyyy8nKyuLLVu20L27mlz8/vvvGI1GevXqVeVx3dzccHOr/OGh1+ud4sWrTueIAAD2nMrFxcUFjUYDLaPBPxJN1hH0p7ZAu8EOjtKxnP01FE2PnHNNhKEEfr4PCs5BSCK64a+jc8LXtd7nW/urwCcMTe5J9Gm/Q9y1tgtONElN/j3u+D+QmQIuHrgkjIam/FgB9IEwfjZ8OhDtsU1of38ehr/u6KjMnOV8q20MDi02MWnSJL7//ntmzZqFj48P6enppKenU1hYCEBsbCzt2rXj/vvvZ/PmzaSkpPDWW2+xcuVKRo8eDUCnTp24+uqrmThxIps3b+aPP/7goYceYty4cYSFhTnw0dlep1BfdFoN5/KLOZ1T3qIm80kJIUQ9/f4iHN0Irj5w8zegd3d0RPah1UHnMeryjjmOjUUIZ2D6P+g0qnHOE1cXLdvDDZ+qy5s/ge2zHBtPI+bQROqjjz4iOzubAQMGEBoaav6ZO1etnKLX61m6dClBQUGMGjWKzp078+233/LNN98wfPhw835++OEHYmNjGTRoEMOHD6dv3758+umnjnpYduOu1xET5AVUM59U2noHRCWEEI3c/uXwx3vq8nUz1QpXTZlpct6Dv0H+WcfGIoQjlRZD8kJ1OWmsY2NpaLHDoX9ZKfTFU+Dktho3F1VzeNe+S2nfvj0LFy6scZvAwEBmzWoe2XR8mB8HTuex+2QOgzq1Ule2vUL9fXIbFOU2nysqQghRX1lH4cf71eWe90P8aIeG0yCCYyGsq/qZkbwQet3v6IiEcIxDq6AwE7xbQdQAR0fT8Po/Cae2w4HlMOc2uH8teLV0dFSNilPNIyUuLT5MHfBm0SJVcT6po01n7iwhhLCr0mKYfxdcyIKwbjD0RUdH1HBMrVLSpUc0Zztmq78Tbwad0xSybjhardrFr0U7yDkO8+8EQ6mjo2pUJJFqZOLMiVSO5Q3mcVLSvU8IIWpl1XNw4h9w94Obv25eFewSbgKti3o1OmOvo6MRouEVnldbYqB8surmyN0Pxv6gVn0+vB5WPuvoiBoVSaQamfhQtd7/8fOFZBdUmCzMNE5KCk4IIcSl7fkFNn2oLo/+GAIiHRtPQ/NqAe2Hqcumq/JCNCe7fwJDMQTHQ0jd5iVtMoJjYfRH6vKmD2DnPMfG04hIItXI+HnqaR3gAcDuUxULTlw0TkoIIUTVMlPh54fU5T6T1UHXzVGXsu59O+eB0eDYWIRoaDvVwmbNrshEdeKuhX6Pqcu/PAyndjg2nkZCEqlGyDROak/F7n0yTkoIIS6t5II6DqAoGyJ6waDnHB2R47QfCh4BkHsKUtc4OhohGk5mmjrdARp1fJRQDXwa2g2B0kK1+ET+OUdH5PQkkWqE4sPU7n0yTkoIIaz029PqlVaPQLjpS9A5fuJHh3FxU8dKgcwpJZoXU9e16AHg27TmHK0XrQ5u/AwCoiD7KCy4S4pPXIIkUo1QlZX7QMZJCSFETXYtgH++UJdv+Az8Wjs2Hmdgqt63dzFcyKl5WyGaAkWBnWUXDppzkYnqeATAuFmg94K0tfC/aY6OyKlJItUImVqkDmXkUVhcoV+7jJMSQoiqnT0Ii/+tLvebCu0HOzYeZxHeDVp2ULvy7P3F0dEIYX/H/1bHSeo9IXako6NxTq3iYPQH6vKf75dPWiwqkUSqEWrl60YLL1eMCuxLl3FSQghRo+ICmHcHFOdBZF8Y8LSjI3IeGk35VfntUr1PNAOmbqydrgU3b8fG4szir4crpqjLPz8E6ckODcdZSSLVCGk0GuLDZZyUEELUyrInIGM3eAXBTV80z4k3a9J5LKCBIxvg/BFHRyOE/ZQWw+5F6rJU67u0Qc9CzFVQUgBzb4WCTEdH5HQkkWqk4qudmFfGSQkhhNn2WbDtO0ADN34OPiGOjsj5+LWGqCvVZVNJaCGaooMr1Il4fUIhqr+jo3F+Wh3c+IXa2+n8YVh4j0yVcBFJpBqp8hLoFxeckHFSQggBQMZeWPKoujzgKbVCl6hal1vU3ztmq4PxhWiKTJNPJ96kJgni0jwDYdwP4OIBKb/D7y86OiKnIolUI2UqOLEvPZdSg7H8BhknJYQQUJSnjosqLYTogXDlY46OyLnFjlSrdGWmwrHNjo5GCNsryIQDv6nLpmqVonZCEuG6meryhndg94+OjceJSCLVSEUGeuLt5kJRqZGUM/mWN8o4KSFEc6YosOQROLtf7cJzw2dy9flS3Lwh7jp1eYcUnRBN0O4fwVgCrRKhVbyjo2l8Em+CPpPV5Z8mwek9jo3HSUgi1UhptRo6hfoAMp+UEEJY2PoN7JoHmrL+/d5Bjo6ocTBV79u9CEouODYWIWzNVK1PikzU3aDn1bFlJfkw5xZ1vFkzJ4lUI2bq3le54ISMkxJCNFOndsLSJ9TlQc+Uvx+KS2vbD3xbw4VsOLDM0dEIYTvnUuD4ZtBoIfFmR0fTeOlc4KavwK8NnE+DhRObffEJSaQasThz5b6LWqRknJQQojm6kAPz7wBDEbQfCn3+7eiIGhettvxqvcwpJZqSnfPU39EDpXJnfXm1gHHfg4s7HFoJa15xdEQOJYlUI1ZeuS8H5eIqSzJOSgjRnCgK/DJZLZbg2xqu/0RNDIR1TIPwD62CvAzHxiKELSgK7DR16xvn2FiaitAkGPW+urzuDdi72LHxOJB8yjRi7YN90Os05Fwo5fj5Qssbo0yJlIyTEkI0A39/Dnt+Aq0L3Py1WrJXWK9lewjvofZo2DXf0dEIUX/H/lLnQNJ7QewIR0fTdCSNhd4Pqss//gvO7HdsPA4iiVQj5uqipUOragpORMo4KSFEM3FiCyx/Sl0e8gJEXObYeBq7LmWtUlK9TzQFpiITcdeBq5djY2lqhryg9oAqzlOLT1zIvvR9mhhJpBo5U/e+5BMXFZzwj4CAtjJOSlS2+hVY+3rVt619Xb1dVE2eO+dTeB7m36mWNY4dWX6FVNRd/A2gc4X0XZCe7OhohKi70qLyOY+kWp/t6fRq8Qnf1nDuECy6H4zGS9+vCZFEqpFLCDdV7qviKoC5DLqMkxIVaHWwenrlhGDt6+p6mW+nevLcORdFUeczyTqqFti57gPQaBwdVePnGQgdrlaXpVVKNGYHfoMLWeATVj52XNiWdxCM/Q50bmq1z3XVXGxsolwcHYCon3hz5b6cyje27QfbvpdxUsJS/7LS0Kung7EU2vSG7bPU8RCJN6szmO93ptLHTvTFOKQzJI6B1dPRZuzHnb5o178J616Fgf8pf25Fw9j4Aez/VW09uflr8PB3dERNR9J42PuLWu1s8DS17LEQjY2pW1/nm+VClz2Fd4OR78DPD6pV/EI6Q+xwR0fVIOSdsZGLDfFFo4GM3CLO5BYR5ONWfuPF46TcfBwTpHA+FZOpinbNlwHmtaTbvYBhLFD/iB4ICTc6NqDm5thmWPWcujzsZfWDXNhO+yHg2QLyMyB1tfq3EI1JQSYcXKEud5ZqfXbX9VY4tR02fwo/3g8Tf1eL1zRxkkg1cl5uLkS19CL1TD67T2YzoGNw+Y2mcVLnD8PRTfJBKCz1m1ohkdJAeHeHhlM15dKbOIhyYisaU3ypq2FGNwiOU8fpdBqltuxJNzP7yD9XNi6qVB3Pc9m9jo6o6dHp1Rbqvz5WW6zl80M0NskL1bGTIZ2hVZyjo2kehr2sjqs8+qdafOLe/4G7r6OjsitJpJqA+DC/skQqxzKRAnWc1PnD6jgp+SAUFf0yucIfCnQYJl3Tamvt62hObMGo0aFVDBAQBdnHIGOP+rPudXXMTqdR6k/rnjKnka0YjerVzpwTEBgDo96ThNVeksaridS+X6EwS7pOisZlh8wd1eB0ehjzDXzSH84egJ8egDHfNenPv6b7yJqRihPzVtJW5pMSVVj7Omz/QV3ueps6vqeqIgqisrLCEoYr/4/FXb7CcOX/wfk06POwOgls7Eh1xvesI7BxJnw5DN6OhSWPQMrvYChx9CNo3P54Bw6tVJ/jMd80+audDhWaBEGdwFCkztElRGNx9hCc+Ac0Oki4ydHRNC/ewWXFJ1xh3xLY8JajI7IrSaSagPKCE1VU7jOPk9oOF6pItETzY6owp/dU/46/Xm2JkmTq0kzP3cD/YOz3GID6e+B/YMPbavW4cT/AE6nqVbjEMeDmC3mn4Z8v4bvr4Y0YtUTs3iVQXODgB9TIHN4Av7+kLl/zutp9UtiPRlM+p9R2qd4nGpGdc9XfMVeBTyvHxtIcte4BI8oSqN+nw4EVjo3HjiSRagLiw9QS6IfPFZB74aKr3RXnkzom80kJwGiALrdASQF4BEBUf3W9KZkyGhwbnzMzGqquznfxc+fqBXHXwo2fweMpcOtC6H4neAWpExbunANzb1WTqrm3qZXRCrMa+tE0LnkZsOAeUIzqwPFuExwdUfOQOAY0Wji2CTJTHR2NEJdmNKrvsSDd+hyp2wTocTegwMJ74VyKoyOyC0mkmoBAL1dC/dwB2Hsqt/IGMp+UqGjgU2qTO6jjd3T68tv6P6HeLqo28Knqx5FV99y5uEL7wepYnqn74a5l6qSxfm3UZHbvYlg0Ed5oB9/fCP98pSYNopzRoH4Q56VDUCyMfFvGRTUU31C1KiWUjzkRwpkd26T2DnD1gY7NowS307r6NYjoBUXZMOdWKMpzdEQ2J4lUE1Fj9z4ZJyUqMpTCnl/U5fjrHRtLc6PVQWQfuPoVmLIT7lsL/R6Dlh3V6lKHVsGSKfBmB/jyGtj4ofqFoLlb9wakrVW7o978jdriJxpOUln3vh2z1av9QjgzU8Ifdx24ejo2lubOxRXGfAveIXBmrzrPlOK81XjrQhKpJiKurHtflRPzyjgpUdHhdVCYqc4R0/ZKR0fTfGk0ENYFBj0DD22GSX/DoGchrCugqOVjf3sK3k2ET65Uk4kz+x0ddcNLWQ1rXlWXR74DwbGOjac5ih2hXt3POgpHNzo6GiGqV3IBdv+kLieNdWgoooxPiFp8QquHPT/DH+86OiKbkkSqiTC1SCWfqKJFSsZJiYqSF6m/O10LOpkBwWkEdVDn9rpvDUxJVrtERPZVx6ec2qEWWfigJ8zoAaumwYmtTe7KXiU5p9QufShqf3sZ7+AYrp4Qf526vEOKTggndmC52o3Mt7X6/imcQ0RPGP6Gurxqmtr7oomQRKqJSAhXW6QOZeRRVFpFsQAZJyVALb29d7G6LN36nJd/BPT+F9z1K0w9AKPeh/ZD1St65w6qFQI/GwjvJMCyJ9Vuu02tSIihFBbeAwVnoVWCWqVPOE7SLerv3T9JtUnhvEzd+jqPadJzFzVKPe6CbncACiy4u8kUr5GzrIkI83PH31NPqVHhQHoVg/lknJQASF0LF7LU6nGmLp/CuXkHQfc74Nb58EQK3PiF2vdf7wU5x9UJU78eoY6r+mWyWma2tMjRUdff6ulw5A9w9VbHRek9HB1R89bmcvBvA8W5sH+po6MRorL8s+occyCt185q+BsQ3kOtXjvnNijOd3RE9SaJVBOh0WhkPilxabt/VH/HXSfd+hojdz9IvEkdvPtECoybrbYUuPurLTdbv4VZN8PrMWqp8N0/Nc4qSQdXqq1uANe+Dy3bOTYeoV7dNxWd2D7LsbEIUZXkRWAshdAuENTR0dGIqri4qeOlvIIhYzf8/FCj76IuiVQTEl9TwQkZJyVKi2GfdOtrMvQeEDscrv8IHj8EE36Gy+5VqyMV50LyAph/hzpX1ezx6pffgkyHhmwwKmxMOcfP20+wMeUcBmMVH6DZx9Vy8KA+noQbGzZIUb3OZYP3U1er49eEcCam8XvSGuXcfMPUi4FaF9i9CP6c4eiI6kUuSTchNbZIgTpO6vxhdZxU+yENF5hwDqmr1eZ071ZqNx3RdOj0ED1A/bnmDTixBfb+ov6cP6x2xdq/FDQ69X2g0yiIHanOEdRAliefYtriPZzKvmBeF+rnznOj4rg6oSwOQwnMvwsKz6tXlYe93GDxiVpoEQMRvdV5enbNgyv+7eiIhFCdPQgnt6rvcQk3OToacSmRl8PVr8LSx2DVcxCSCG0aZ3EQaZFqQkyJ1N5TuVVf6ZVxUs1bxW59Wp1jYxH2o9VCxGUw9EV4eDv86w/o/39qwQbFoM7HtPQxeDsWPh8Cf7xn90G/y5NP8cD3Wy2SKID07As88P1WlieXtW6seh6ObwY3P7j5a7UbiHAupqv922c3+i45ogkxFZloN1gdVyqc32X3QqtEUIyw4C7IOmJ5+9rXYfUrjonNCpJINSFRLb3x0OsoLDGQdraKAXwyTqr5Ki2Cfb+qy9Ktr/nQaCAkAQY+BQ/8AZO3wpAXoPVl6u3HN8PKZ+H9rvDRFep8TenJNv2CbDAqTFu8h6r2aFo3bfEeDHuXwMaZ6orRH0BglM1iEDYUfz3o3NTJNU/tcHQ0QqiTRO+cpy7L3FGNh0YDHYery4XncVlwJzpjWaGkta+rBYcawUVf6drXhOi0GmJDfdh2NIvdJ7NpF+xtuYFpnNT5w+o4Kene13yk/A5FOeATqnbNEc1Tixi1O9YV/4ack2pyvXex2kp9Oln9WfMKBESp3f86jVIrLFlZRvhsXhH703PZeyqH9QfPVGqJqkgBdDlHMS56Bh1A70nqcYVz8vBXx+bt/lFtBQjr4uiIRHN39E/IPgpuvuVfzEXjcNXTUJIPG2eiOb2LpOKv0K5PgXWvwsD/QP8nHB3hJUki1cTEh/my7WgWe07mcF2X8MobyDip5sk0CW/caJlbQ6h8w6DnRPWnIFOdyHLvYjj0PzifBn++r/54h0CnkWpyE3mFOh6rzIUSA4cy8tiXnsu+UznsP53L3lO5nM2rffl1V0r4QP8++pJc9uk6srB4LL32nOayqED8PPSX3oFoeEm3qInUrvlqF1KdvE7CgUzd+uKuk2kSGqNh09VpWbZ9T8T5P2Hdn40miQJJpJqcGiv3gTpOatv3Mk6qOSkpLJ/3Rbr1iap4BkKXW9Sfojx1Lpa9S+DAb5CXDn9/Dn9/TrGrPykBfVmr680vuR3Zf660yvGYGg1EBnoSG+KLl5uOhVtPVHvop11+IEmbynnFm7vzJ3Hyz+N89udxtBr1/ax3dCCXx7SgR9tAfN3lC7tTiLlKLV+cnwGHVkHHaxwdkWiuSgphz8/qslTra7QMo2ai3T4bjWLAqNGh9Hsc5+/Up5JEqokxFZxIPpmNoihoNBrLDS4eJ+Xu27ABioZ36H9QnAe+4eVjY4SoRo7ixn6v/uxr3Y2DmgdxO7ae2PNrGcDftCjOotPpJXRiCbcrbqzWdWGDW29Ot7qSyLBQYkN8iA31pUMrbzxd1Y8Xg1Hhz5RzpGdfqDROaoR2E3e6rABAGf0RT2q6syk1k02p50g7m8+uE9nsOpHNZ+vT0GogMdyP3tEt6B3TgsvaBuLtJh9hDqFzgc5j1DFt22dJIiUcZ/9Stdu6XwS06ePoaEQdLE8+xdEfn+c+xUCxosMVA59O/xdtrn++vKKrE5NPoSamQysfdFoNWQUlnMy+QLj/Rc3cMk6q+TFV64u/Xrr1CbNSg5G0s/lqt7z0HPadymVfei4nsgov2rI90B533d2MCjjKKP0WuhVswLvoNCN1fzFS+QvOfAQ+A6D1KAgcDq7lHy06rYbnRsXxwPdb0VBeYKKt5hSv6j8DILXjRKK7Xst1YO6SnJ59gU2p59iUeo6Nqec4cq6AHcez2XE8m0/WpaLTasyJ1eUxLegRGYCXJFYNJ2mcmkgdWK52DfUMdHREojnaMVf93XmsfL41QsuTT7Fn9n95VL+At0puYobhBibrFjGVObw9uxTGv+T0yZR86jQx7nod7YO92Zeey+4T2ZUTKVC798k4qeahpBD2L1OXpVtfs6QoCmfyith3KlctAFGWNB06k0dxqbHK+4T5udOxrHUpNsSH2BBfooO80Ou0pp3CyW3qmKq9i+HcQTi4Qv3R/Fu9MtxplDq2yq81V5/5mhXdCpiQMoBT2Rdwo5gP9e/joymkwDOM6FYBlWII8XNndNdwRndVE6uTWYUWidWxzEK2H8ti+7EsPl6bgotWQ+fW5YlV98gAc6uYsIOQRLV08eld6qSal93r6IhEc5N3Ru1aCtKtrxEyGBWO/vi8RRIFmH9P1S/g0x9dMMR9jE6rqWlXDiWfMk1QfJifmkidzGFofEjlDdr2g23fyTip5uDgCrUijl8EhHd3dDTCzgqLDRzMyDW3Lu1Lz2F/ei7n8our3N7LVUfHEB86hvjSKdSHjq3UpMnP8xJjkTQaCO+m/gx+Ds7sL5sAeLFaEvvIBvVn+ZMQ1hXc/WifuoY/BnjzV8S9hK57krZHjqDoPfEsOFmrYgVh/h7c0K01N3RrDcDx8wVsSs1kY4qaXJ3IKmTr0Sy2Hs3iwzUp6HUaklr7mxOrbm0C8HBtLL3uG4mkcbBilzrYXxIp0dCSF6pz44V1g5btHR2NsNLmtEwKiop5SylPokxMf+tKi9mclsnlMS0cEWKtSCLVBMWH+bJwa00FJ2ScVLNh7tY3Wv3yK5oEo1Hh+PlC9pYlSqaueYfP5VPVXNxaDbRt6WVuXTL9bh3ggdYWV/qCOkLQ43Dl43D+COxboharOLpRbbkyxbHmZS5v9YtaZh3QlBTUuTpT6wBPburuyU3d1cTqWGYBG1PPsSlFbbE6lX2Bf46c558j55m5+hCuOi1dIvzpHR1I77LEyl0viVW9dB6jzkN2/G84e1C+zIqGtWO2+ltao5ya0ahwKucCqWfySMnII+VMPiln8th9Mofs0puqvZ8pmXovt/rpM5yBJFJNkKngxJ6T2VVv4NdanSfmfJqMk2rKivPVqmsA8TfUvK1wWtkFJWqilF7eynQgPZf8YkOV27fwciU21IeOrXyJDfWhU4gv7Vt5N1zSEBAJl09Sf/IyyueqSlsHxhJzEgXYtMRtRKAnEYGejOkRgaIoHM0sULsBliVWp3OK2Hw4k82HM3n/90O4umjpGlHeYtUlwl8SK2t5B0O7wXDwN7VVatAzjo5INBdn9sOp7aB1gYQbHR2NQO0RkXZWTZJSzuSRWpYwpZ7Jp7Ck6s+r2gj2cbdhlLYniVQTFFeWSJ3MvsD5/GICvFwrb9S2r5pIpa2TRKqpOrgCSgrAP1LtXnURg1Fhc1omGbkXCPZxp2dUoFP3Q27qSgxGUs/ksy89h72nctlfljxVN5mtq05L+1be5S1MoWorU5CPWwNHXgPvYOhxl/pTmKWekz/eD4oRdK52mydEo9EQ2cKLyBZejL2sDYqicPhceWK1KfUcGblF/JWWyV9pmbz3v4O4uWjp1iaAy2Na0Du6BUkRfri5SGJ1SUnj1ERq51w1MZYB/6IhmOaOajcEvFo6NpZmRFEUzuQWcahCopRyJp+UjDxOZheiVNEjAsBFq6FtSy+iW3oRE+xNTJA3kS08eWjWVjJyiipVdAXQoI6V7Rnl3IVsJJFqgnzc9US28OTIuQJ2n8yhb/sq3mRknFTTZ5qEN/76St36liefYtriPRZf0kP93HluVJzTV8hxBgajwl9pmWw5q6FFWiaXtwuudRKqKAqnc4rKW5lOqb9TzuRRYqj6Uyjc34NOZYlSxxAfOoX60LaFFy66RvSl1cNfLXJjSqIMxbD29QaZdFGj0RDV0ouoll6M76kmVqln8yskVpmczStiY1khCwB3vZbukQFcHq0mVp1b++Pq0oie74bScTi4+UH2MXVcXNSVjo5INHVGI+ycpy4njXVsLE1UUamBI+cK1O54ZYmSqXUpt6i02vv5e+qJCfImJsiLmCBvosuWIwI9y4sVVTDt2vhKFV1BTaIAnhsV5/QXeCWRaqLiw3zLEqnsahKpsnFSp7bLOKmmqChPvfoPlar1LU8+xQPfb610BSg9+wIPfL+Vj27rJslUDSyTUB3fHvyn2iS0oLiU/em5ZeOYcs3JU1ZBSZX79nFzKauWV1YAIsSHDiE+TWMi2rWvw+rp5d35TH9Dg89gr9Foyj7svbm1VySKopByJo+NqZlsKmuxOpdfzB+HzvHHITWx8tDr6NE2QJ3HKroFnVv7VfnFoNnRu0PC9bDla9g+WxIpYX9HNkDOcTWB7yBzmNVHZn6x2qqUYdkd72hmQZXjbUEdc9sm0LMsUVITJlMrU2BVPaBqcHVCKB/d1q3Shd2QRnRhVxKpJio+zI+lu9KrLzhRcZzU0U3QYWjDBijs68ByKL2gvsahSebVBqPCtMV7qmxGN63770/JtA7wxNVFi1YDWo0GrUaDTqtBo1HnBTKt05b9rSm73bR9+TZUnhS6EaspCf3X91u5/8oo3PUu5mp5RzILquzqoNNqiG7pVda65KtWywv1Idzfo0k9X2YXJ1FQ/ttByVRFGo2GdsE+tAv24fbeamJ1MCPPoivg+YIS1h88y/qDZwHwdNXRo21gWYtVIInhfo2rhdCWkm5RE6k9P8OIN8HVy9ERiabMNHdU/Gg1kRc1KjUYOZpZQMqZ/LIWpvKCD9Vd1AP1wp5louRFdFmXPFt2e746IZQhcSFsPJTBivV/MbRfL6t6eTiaJFJNlGmc1O7qCk5A+Tipw+slkWpqTNX6Em6w6Na3OS2z2jE3Jmfzihk5w3ZdPjUa0JkSL21ZoqW5KCkrS8J05uUKiZvmom1MiZspyauwvmLSZ10SSOX7muIo2xYNfLnhcI1J6Cfr0irdFuTjVlYlr7xrXrvgBiz+4AyMhqoLS5j+NtZ9ILI9aDQaOrTyoUMrHyZc3hajUeFARm5Za1Umm9LOkVVQwroDZ1h34AyglpK/LCrQ3BUwPsy3+SRWET0hMBoyU9XCIlJFTdhLcYGasIOcZxfJLiwp74pX1sqUejafI+fyq+02DmrX8YqJUkyQF+2CvAnycWuwC3s6rYZeUYGc26vQq5GN17YqkTIajaxdu5b169dz5MgRCgoKCAoKomvXrgwePJiIiAh7xSmsZKrcl3o2n4Li0qonppRxUk1TUS4cXKkuX9StL6OWZUR93HToXXQYjApGRcFoVDAqYFAUFEUpW1+7cBQFShUFUMC5vi/bRf8OLbmyQzCdQnzoGOJDC28nKv7gKAOfqv42B7ZE1ZZWqykr6uHLnVdEYTQq7D+da26t+istk+zCEtbsP8Oa/Wpi5ePmYpFYxYX5NqovB1bRaCBpvNq6uGO2fMEV9rN/KRTngn8biOjt6GganMGocDKrkEMVEiVTSfGzeUXV3s9DryO6QqJk6toc1dJL5terp1olUoWFhbz11lt89NFHZGZm0qVLF8LCwvDw8ODQoUP89NNPTJw4kaFDh/Lss8/Su3fzO7mdTbCPO0E+bpzJLWLvqRy6R1ZR9UTGSTVN+5eBoQhatINWCRY31baM6KcTLqvVBHjGskRLTbCokHipSdfFSZhpe2PZtkrZfY1GytaXJ2nly+W3lydy1JjgVYzLvI352Bcdv8KxjWV/m/ZV8XGlnsnjj5Rzl3xObujWmuu6hNfqeRaNk1aroVOoL51Cfbm7bxQGo8K+9Bxz4Yq/0s6Re6GU3/dl8Pu+DAB83F3oFRVoHmMVF+pb6zm86lPcpMF0HqsmUqlrIfu42n1cCFszVevrPM4pK0TaqhpuflFphfLh5a1MaWfzKSo1Vnu/EF/38u54QWqFvOggb0J93W0zZ6CopFaJVIcOHbj88sv57LPPGDJkCHp95YHPR44cYdasWYwbN47//Oc/TJw40ebBCuvEh/myZv8Zdp+sJpGScVJNk3kS3srV+npGBRLq50569gWblBvVajVo0TSLPsIbU87VKpFy9jkvhO3ptBriw/yID/Pj3n7RGIwKe0/lmFusNqdlknuhlFV7M1i1V02s/Dz09KzQYhUb4lPlFx1rips4VEAkRPZVCwHsnAf9HnV0RKKpycuAlN/VZSds9bS2Gq6iKJzKvlChjHh5wYeauuC76rREtfQiJtjLouBDVEsvfJpCYaJGplbff1asWEGnTp1q3CYyMpKnnnqKxx57jKNHj9okOFE/CWF+aiJ1opqCEyDjpJqaC9lwaJW6XMUkvDqthudGxfHA91sr3daYyo06gq2TUNF06bQaEsL9SAj3Y+KV0ZQajOypkFj9ffg82YUlrNxzmpV7TgNq2eBepsQqpgUdgn1YsSe9cVXYTBqnJlI7ZkPfRypdyBGiXnYtAMUA4T2gRYyjo7FwqWq4/xnRiRA/d1Iy8kk9W54wFVQzsTpAS2/XSl3xYoK8CQ/wkM9oJ1KrROpSSVRFer2emBjnOsGbK9M4qd2naio4IeOkmpT9y9T5eVp2hOCq/29N5UYfmrWN0goDnRpTuVFHqJiENuY5L0TDc9Fp6dzan86t/bm/fwylBiPJJysmVplkFZTw2+7T/LZbTawCPPUUFhuqLW6iAaYt3sOQuBDnOefiroOlj8PZA3ByK4R3d3REoinZMVv97WStUbWphvvSr3urvK+LVkObFp4VEqXycUz+ntaVEheOYXWPnOXLl+Pt7U3fvn0B+OCDD/jss8+Ii4vjgw8+ICAgwOZBirqJD/MD4EB6HiUGY9Vznsg4qaalhm59FfWKamFOol4anUBMkHed+3I3J01hzgvheC46LV0i/OkS4c8DA2IoMRjZdSLbnFj9c/g852soSwzqF7RT2RfYnJZZq/GMDcLdFzqNhF3z1TmlJJEStpKxF9J3glZfZW8LR6pNNVyA9sFedIkIKG9lCvamTTUT1YrGw+pX7/HHHycnR+0qtmvXLqZOncrw4cNJS0vj0UelT7QziQj0wMfdhWKDkYOn86reyDROSjGq46RE41V4Hg79T12OH13jpn+lqWN9OrTy5rbekVwe00KSqFq6OiGUDU9exfd392BCewPf392DDU9eJUmUqDO9Tku3NgFMGtiO7+7pxY7nhjJlcPta3be2lTgbTNJ49XfyAigtdmwsoukwFZloPxS8nOTCQZna/g8+dFV73rg5iQcGxDA0PoSYIG9JopoAq1/BtLQ04uLiAFi4cCEjR47k5Zdf5oMPPmDZsmU2D1DUnUajIS60lvNJgTpOSjRe+5aCsQSCOlXbrc9kY1nRhMujnesDqbEwzXnRvWXjm/NCOD9XFy29omr3v+l0xU2iB4BPqHph5+Bvjo5GNAVGg1rABCBprGNjqUJt/wed7n9V2ITViZSrqysFBQUArFq1iqFD1QIFgYGB5pYq4TxM3ft2n6yp4EQ/9beMk2rcKk7CewkbU8sSKWfpEiSEsGAqblJTih7qjMVNtDroPEZdNrUiCFEfh9dD7klw94MOVzs6mkp6RgUS4Fl9tTwNTvq/KmzC6kSqb9++PProo7z44ots3ryZESNGAHDgwAFat5Z5I5yNqeDEnhoTqYvGSYnGpyATUlery3Gja9z0bF4RB8q6evas5VVvIUTDMhU3AapNpv7vmljnbA01de878BvkX3rKACFqtGOu+jv+BnBxvgnOz+UVUWKoeoZ6KUTU9FmdSM2cORMXFxcWLFjARx99RHi4OvHksmXLuPpq57tS0NzFh5clUqdyMBqr/keXcVJNwL4lYCxVJ+AN6lDjpn+lZgIQG+JDoJdUBRLCWZmKm4T4WXYJMn0f+/OQkyYpwZ0gtIva1Th5oaOjEY1ZcT7s/UVddrJqfaBO/v7ovB3kFZUS7u9BiK9lohfi5+580xQIm7K6al+bNm1YsmRJpfXvvPOOTQISthUT5I2ri5a8olKOZhbQtqVX1RvKfFKNm7la3+hLbrox9Swg3fqEaAyuTghlSFwIGw9lsGL9Xwzt1ws0Wm7/cjNz/zlG3/YtGZUU5ugwK0sar/Zy2DELet3n6GhEY7XvVyjOg4C2ENHL0dFU8tn6VDYcOouHXsc3d/ckqqUXm9Myyci9QLCPu1TDbQZq1SKVn59v1U6t3V7Yj16nJTbEB5BxUk1W/jlIXasu16IsrBSaEKJxubi4Sd/2QUwa0A6Apxft4lhmgYMjrELiTaB1gZPbIGOfo6MRjZVpnF3ncU43wfOOY1m88dt+QO261y7YG51Ww+UxLbiuS7hUw20mapVItWvXjldffZVTp05Vu42iKKxcuZJrrrmG999/32YBivozjZNKrrFyn4yTarT2LVZnew/pfMnZ3jNyLpByJh+NhlpXBRNCOJ8pg9vTPTKA3KJSJs/eRonB6OiQLHm1VEtVQ/lEqkJYIze9fOyvqYCJk8grKuXhOeqk9iMSQxl7WYSjQxIOUqtEas2aNfz9999ERUXRq1cvJk2axPTp03nrrbf473//yw033EBYWBh33303o0aN4oknnrB33MIKcbWp3CfjpBqvipPwXsKmNHV8VFyoL341VBkSQjg3F52W98Z1wdfdhe3HsnhrxQFHh1SZqejEznlqCWshrLFrgfqdpHXPS14kbGjP/pTMkXMFhPt78PINiWicrLVMNJxaJVIdO3Zk4cKFHDhwgDFjxnDixAkWLFjAZ599xpo1awgPD+ezzz7j8OHDPPjgg+h0OnvHLayQYK7cl42iVFNwAmQ+qcYo7wykrVOXazM+Srr1CdFktA7w5LUbOwPw8doU1h044+CILtJhGLj7q6Wr09Y6OhrR2Ji69TnZ3FGLth5n0bYTaDXw3rgu+HnIRcnmzKpiE23atGHq1KlMnTrVXvEIO4gN8UWrgbN5xWTkFtHKt5pJ4dr2g23fyTipxmTvL+oVu9AuEBh9yc03yfxRQjQp1ySGckuvNsz66yiPztvBsn/3I8jHSUpEu7ipY6X+/lz9UhxzlaMjEo3F6d1wehdo9bUa+9tQDp/N55mfkgGYMrgDPdrK3FDNndXlz0Xj4+GqIybIG4DdMk6qabFiEt707Auknc1Hq4HLZGJAIZqMZ0fG0bGVD2fzinh03vbqp7pwBFP3vr2LoSjXsbGIxsPUGtVhGHg6x+dVcamRf8/ZRn6xgZ5RgUwa2M7RIQknIIlUM2EqOLH7hIyTajJyT8ORP9TlS0zCC+WtUQnhfvi6S1cEIZoKd72OGbd0xV2vZf3Bs3y2PtXRIZUL7w4t2kNJAez52dHRiMbAaIBd89VlJ5o76q2V+9lxPBs/Dz3vju0iFfkEIIlUsxFfm4ITAFGmMugyTsrpmbr1hXeHgMhLbi7jo4Roujq08uHZkfEAvPHbfrYfy3JsQCYaTfmXYVMrgxA1SVsLuafAI6C88qODrT94hk/WqhcoXruxM2H+Hg6OSDgLSaSaCXOL1KkauvaBzCfVmFhRrQ9gY1mLVG8ZHyVEkzS+ZwQjEkMpNSo8PHsbuRdKHB2SqvNYQKNeoDt/xNHRCGe3Y676O/4GdZydg6ldZncAcFvvNlydEOLgiIQzkUSqmYgrS6SOZRaSXVjDh2tkxXFSl0i6hOPknIIjf6rLtejWdyKrkKOZBei0Gi6TwbFCNEkajYaXb0gk3N+Do5kF/OfH5JortTYU/4jy3g475zk2FuHcivLU8XTgFN36jEaFx+bv4ExuER1aefPfEXGODkk4mTolUllZWbz11lvce++93HvvvbzzzjtkZ8uXbmfm7+lKeFlT9J4a55MKV6u/yTgp57b3F0BR59fwv/REgKZufYnhfni7WVWsUwjRiPh56Hl/fFd0Wg2/7DjJ/C3HHR2SKukW9feO2eAMyZ1wTvuWQEm++j2k9WWOjoav/jzMmv1ncHPRMmN8N9z1Mr2PsGR1IvXPP/8QExPDO++8Q2ZmJpmZmbz99tvExMSwdetWe8QobMTcva+myn0g80k1BlZ265Oy50I0H90jA3h0SAcAnvt5N4cy8hwcEdBpFOi9IDMFjv/t6GiEszKNo+s8Th1f50DJJ7J5bdk+AP47Mo6OIT4OjUc4J6sTqUceeYRrr72Ww4cPs2jRIhYtWkRaWhojR45kypQpdghR2Iqp4ESNLVIg46ScXfYJOLpRXY67rlZ3kUITQjQv/+ofQ5+YFhSWGJg8exsXSgyODcjNG+KuVZe3z3JsLMI55Zwqn7i58xiHhpJfVMrDs7dRbDAyNK4Vt/Vq49B4hPOqU4vUk08+iYtLefcgFxcXnnjiCf755x+bBidsy9QilXypFinzOKkdMk7KGZlKCEf0VrtiXsKxzAJOZBXiotXQo22AnYMTQjgDnVbDO2O7EOjlyt5TObxadmXdoUxjXnYvgpILjo1FOJ9d89VhBRG9ITDKoaFMW7yb1LP5hPi689qNndE4uHVMOC+rEylfX1+OHj1aaf2xY8fw8bGu2fOVV17hsssuw8fHh+DgYEaPHs3+/fvNtx8+fBiNRlPlz/z5883bHT16lBEjRuDp6UlwcDCPP/44paWl1j60Ji8+XE2kUs7k13x1UsZJOTcrJuGF8taopAh/PF1lfJQQzUUrX3feujkJgK//PMzKPacdG1DbK8G3tXqB7sAyx8YinI+pW5+Di0ws3nGSef8cR6OBd8d1IcDL1aHxCOdmdSI1duxY7rnnHubOncuxY8c4duwYc+bM4d5772X8+PFW7Wvt2rVMmjSJTZs2sXLlSkpKShg6dCj5+fkAREREcOrUKYufadOm4e3tzTXXXAOAwWBgxIgRFBcX8+eff/LNN9/w9ddf8+yzz1r70Jq8EF93Wni5YjAq7Eu/xAzzMk7KOWUdg+ObAQ10urZWdzGPj5JufUI0OwNjg7mnr3p1//EFOziVXei4YLTa8i5bMqeUqCh9F2TsBp0rxI92WBjHMgt4etEuAB4a2I7e8rkpLsHqy9NvvvkmGo2GCRMmmFt99Ho9DzzwAK+++qpV+1q+fLnF319//TXBwcFs2bKFK6+8Ep1OR0iIZb3+H3/8kTFjxuDt7Q3AihUr2LNnD6tWraJVq1Z06dKFF198kSeffJLnn38eV1e5kmCi0WiIC/Nl/cGz7D6ZTZcI/+o3btsPtn4r46ScjalbX2Qf8A295OaKopjnj5JCE0I0T09c3ZG/0s6RfCKHKXO2M2tib3RaB3VVShoPG96GgyshLwO8gx0Th3AupsS6w9XqRLwOUGow8u8528gtKqVbG3/+Pai9Q+IQjYvVLVKurq689957nD9/nu3bt7N9+3YyMzN55513cHOr38RpphLqgYFVz3OzZcsWtm/fzj333GNet3HjRhITE2nVqpV53bBhw8jJyWH37t31iqcpMhWc2H2pghMyTso57V6k/q5ltb4j5wo4lX0BV52Wbm1kfJQQzZGbi44Z47vh5arjr7RMZv5+yHHBBHWA8O6gGGDXAsfFIZyHoVQdHwUO7db33v8OsvVoFj7uLrw3risuOplqVVya1S1Sd999N++99x4+Pj4kJiaa1+fn5zN58mS+/PLLOgViNBqZMmUKV1xxBQkJCVVu88UXX9CpUyf69OljXpeenm6RRAHmv9PT06vcT1FREUVFRea/c3LUpKKkpISSEieZCd5OYlt5AZB8Iqvmx+oZjEtAFJrzaZSmbkBpP7SBIqwb02Np0q9f1lH0J7agaLSUtr8GavFYNxzMAKBza19cNEZKSoz2jrLZaBbnnHAa9T3fWvu58vyoTjy+MJn3/neAyyL9uMxBxWe0CWPRndiCsn0WpT0mOiQGcWkN9R6nSfkdl7zTKB6BlLYdUKvPNlv7Ky2TmavVCwwvXRtHiI9e3tsbmLN9ptY2DqsTqW+++YZXX321UmGJwsJCvv322zonUpMmTSI5OZkNG6ruSlZYWMisWbN45pln6rT/il555RWmTZtWaf2KFSvw9PSs9/6d2ZlCABf2nshm8a9L0dXQuyNJ24a2pJG25jv2HGwcxTtWrlzp6BDspt3pX4kHznp15M91W2p1n0UHtICWFoZMli5datf4mqumfM4J51Of880VuKyllr/Pannwu8080dmAl952sdWWvtSLqzU6tKd3sWHhx+R4SGlpZ2bv97huhz8mAkjz6sau31bZ9VhVyS+B13bqUBQNvYONcGwrS481eBiijLN8phYUFNRqu1onUjk5OSiKgqIo5Obm4u7ubr7NYDCwdOlSgoPr1tf5oYceYsmSJaxbt47WrVtXuc2CBQsoKChgwoQJFutDQkLYvHmzxbrTp0+bb6vKU089xaOPPmrx2CIiIhg6dCi+vr51egyNhdGo8O6e38kvNhDb40rat/KudltNcj78vJYY3SnaDh/egFFar6SkhJUrVzJkyBD0egd8M2gAui/fBiCw790M737p10NRFKYnrwOKuH1oT3pFVd1lVtRNczjnhPOw1fl2ZVEpoz/cxJHMAlbnh/HB+CTHlHYuXgb7l3Cl30mMg//V8McXl9Qg73FFubi8p77+bUY+TkR4d/scpxqKovDgrO1kF58huqUnn9zfW6rbOoizfaaaeqtdSq3PFn9/f3Pp8Q4dOlS6XaPRVNnKUxNFUZg8eTI//vgja9asISqq+nkDvvjiC6699lqCgoIs1l9++eVMnz6djIwMcyK3cuVKfH19iYuLq3Jfbm5uVY7n0uv1TvHi2VunUF/+OXKe/WfyiWtdQ9eO6P4AaNN3ojUUgLtfA0VYd032NcxMhVPbQaNFl3A9ulo8xpQzeWTkFuHqoqVHVEv0ep3942yGmuw5J5xSfc+3AL2embd044aP/mDl3gzmbjnJ7Ze3tV2AtdX1Fti/BN3uheiGvgg6+fLqrOz6Hrd7OZQUQGAMLpG9oIGT+u82HmbVvjO46rTMuKUbfl4eDXp8UZmzfKbWNoZav3OtXr0aRVG46qqrWLhwoUVBCFdXVyIjIwkLC7MqyEmTJjFr1ix+/vlnfHx8zGOa/Pz88PAoP5kPHTrEunXrquyaNHToUOLi4rj99tt5/fXXSU9P57///S+TJk2qd/GLpio+TE2kdp/I4fquNWxomk8qM1WdT6rDsAaLUVxk90/q77b9wDuoxk1NTPNHdW8TgLskUUKIMomt/Xjy6lhe+nUvL/66l+6RgcSFNXBvjHZDwLMF5J2G1NXQfkjDHl84B/PcUeMbPInal57Di7/uBeD/rok1F+MSwhq1TqT691dbJ9LS0oiIiECrrX81k48++giAAQMGWKz/6quvuPPOO81/f/nll7Ru3ZqhQysXPNDpdCxZsoQHHniAyy+/HC8vL+644w5eeOGFesfXVNW6ch+o80llpqrzSUki5ThWTsILmMueyzwYQoiL3dM3ij9TzvH7vgwmz97K4sl9G7ZLk4srJNwEmz+BHbMlkWqOsk9A2jp12TS/WAMpLDbw8OxtFJcauSo2mLuuaNugxxdNh9XvmpGRkWRlZbF582YyMjIwGi2rgF08hqkmiqLUaruXX36Zl19+ucaYZCB97ZmuPO4+mY2iKDX3j5f5pBzvXAqk7wSNDmJH1eouiqLwl8wfJYSohkaj4Y2bOnPNe+tJOZPPtF/28NpNnRs2iC7j1URq36/qNBuNoPu4sKFd8wAF2vSBgMgGPfRLv+7hwOk8gnzceOOmzo4ZJyiaBKsTqcWLF3PrrbeSl5eHr6+vxclnmqhXOLcOrXzQ6zTkXCjl+PlCIgJrqFR48XxS8kHX8EytUdH9wat2SdGhjDzO5hXjrteSFCGvmRCishbebrw7rgu3fv4Xc/85Rt/2LRmVZF0X/XoJ7QJBsXBmn9p9ufsdDXds4ViKAjvmqssNPHfU8uRT/PDXUTQaeGdMF1p4yzAQUXdW98+bOnUqd999N3l5eWRlZXH+/HnzT2Zmpj1iFDbm6qKlfbBavn73yUtMtmsaJ6UY1XFSouGZEqlaTsIL5d36ekQG4uYi46OEEFXrE9OSSQPaAfD0ol0cy6xdyV+b0GjUsTGgdu8TzUf6TjizF3RuEHddgx32ZFYhTy7cBcD9V8bQt33LBju2aJqsTqROnDjBww8/3OTnW2rqEsJN3ftqOU4K1HFSomGdOQCnk0HrArEja303U6GJ3tFS8lwIUbMpg9vTPTKA3KJSJs/eRomhASfu7jwGNFo4ulEdjyuaB1NrVMdrwMO/QQ5pMCpMmbOd7MISklr7MXVo5QrUQljL6kRq2LBh/PPPP/aIRTQg6wpO9FN/yziphrfnJ/V39EDwrF1SZDQq/JWmtg7L+CghxKW46LS8N64Lvu4ubD+WxVsrDjTcwX3DIHqAumz6ci2aNkMp7JqvLjdgt76Zvx9i8+FMvN1ceH98V/S6+hdNE8LqMVIjRozg8ccfZ8+ePSQmJlaqs37ttdfaLDhhP/EVCk5ckoyTcpw6dOs7kJFLZn4xnq46Orf2t09cQogmpXWAJ6/d2JkHftjKx2tTuKJdC/q1r91UC/WWNB5Sfle79w34vwYvgy0aWOpqyM9Qy9+3G9wgh/zncCbv/U+9QPDi6HgiW3g1yHFF02d1IjVx4kSAKsuLazQaDAZD/aMSdtcp1BeNBk7nFHE2r4iWNQ22lPmkHCNjH2TsAa0eYofX+m6mbn092gbKFTchRK1dkxjKLb3aMOuvozwydwfL/t2PIJ8GGIgfOxJcvSHriNrFL7KP/Y8pHMc0d1TCTaCz/8Sr2QUl/HvOdowK3NA1nOu7trb7MUXzYfW3LKPRWO2PJFGNh5ebC1FlV2RknJSTMrVGxVwFHgG1vpuMjxJC1NWzI+Po0Mqbs3lFTJ2/A6OxdtOU1IurJ8SNVpel6ETTdiFHLXcPkDTW7odTFIWnftzJiaxC2rbw5IXRCXY/pmhe6nW5+sKFC7aKQzhAnDXd+2ScVMNSlDpNwmsxPkom4hVCWMldr2PmLd1wc9Gy7sAZPt/QQAUgupRV79v9E5QUNswxRcPb+wuUFkKL9hDWze6Hm/v3MZbuSsdFq+G9cV3xdmvASadFs2B1ImUwGHjxxRcJDw/H29ub1FT1TfaZZ57hiy++sHmAwn6sKzhR1iJlGicl7CtjL5zdDzpXtapRLe1NzyG7sARvNxcSw2UsmxDCeh1a+fDsqDgAXl++nx3Hsux/0DZ9wK8NFFVosRBNj6lbX9I4u4+FO5SRy/OLdwPw+LCOJEX42/V4onmyOpGaPn06X3/9Na+//jqurq7m9QkJCXz++ec2DU7Yl6ngxJ7aJFK+YRAYo84ndWSjnSMT5taodoOtKu5h6tZ3WdsAXGR8lBCijm7p2YZrEkIoNSpMnr2N3Asl9j2gVltewU269zVNWcfKe7V0HmPXQ10oMfDQrG1cKDHSr31LJvaLtuvxRPNl9Tetb7/9lk8//ZRbb70Vna58os+kpCT27dtn0+CEfZkSqbSz+eQVlV76DjJOqmEoCuxepC5bUa0PYFPZRLxS9lwIUR8ajYZXb+hMuL8HRzML+O9PySiKncdLmRKplN8hN92+xxINb9c8QIHIvuDfxq6HenXZPval59LCy5W3xiSh1UolSGEfdZqQt127dpXWG41GSkrsfMVK2FQLbzdCfN0B2HtK5pNyGqeT4dwhdcZ3K7r1GSqMj+ot46OEEPXk56nn/fFd0Gk1/Lz9JAu2HLfvAVvEQEQvtefDznn2PZZoWIpSPk+YneeOWrXnNF//eRiAN29OItjH3a7HE82b1YlUXFwc69dXbpFYsGABXbt2tUlQouGYWqWST9Sm4ETZfFLpO6Ewy35BNXembn3th4CbT63vtudkDrkXSvFxdzGPfxNCiProHhnII4PbA/Dsz7tJOZNn3wMmlRWd2DFb/fItmoZT29Vxvy7uEHed3Q5zOucCjy/YAcA9faMYGBtst2MJAXVIpJ599lkeeughXnvtNYxGI4sWLWLixIlMnz6dZ5991h4xCjsqn5jXynFSRzfZObJmqmK1Piu79W1MPQtAr6hAdNKNQQhhIw8MaMfl0S0oLDEwedY2ikrtONVJ/PVqa3zGHvWinWgaTK1RHYeDu69dDmEwKjwydzvnC0qID/Plias72uU4QlRkdSJ13XXXsXjxYlatWoWXlxfPPvsse/fuZfHixQwZMsQeMQo7ig+3onIfyDgpe0vfqU587OIOHa626q7l80dJtz4hhO3otBreHdeFQC9X9pzK4ZWldhwP7eFfPgG5qcKbaNwMJbBrvrpsx259n6xL4c+Uc3jodbw/vituLrpL30mIeqpTWa9+/fqxcuVKMjIyKCgoYMOGDQwdOtTWsYkGYGqROng6t3ZXGWWclH0llxWZaD8U3LxrfbdSg5G/D58HJJESQtheK1933ry5MwBf/3mYlXtO2+9gpu59O+epX8JF45byOxScBa8gdYJ5O9h29DxvrTgAwLTr4okJqv3npxD1IfWRm7lwfw/8PPSUGhUOnq5F33cZJ2U/dZyEFyD5ZA55RaX4eeiJC7VPtwkhRPN2VWwr7ukbBcDjC3ZwKttOE+fGDFK/dBechUP/s88xRMMxtSwm3AQ6vc13n3OhhIfnbMNgVBjZOZSbu7e2+TGEqE6tEqnAwEDOnlXHXwQEBBAYGFjtj2hcNBpNhXFStSg4IeOk7OfkNsg6AnpPtUXKCqZufb2iAqXMqxDCbp64uiMJ4b5kFZQwZc52DEY7FITQuUBi2TxDO2bZfv+i4VzILp9gOWmszXevKAr//TGZY5mFtA7wYPr1iWjsPNGvEBW51Gajd955Bx8ftXrYu+++a894hAPEh/nyZ8o568ZJZaao46Q6WjeOR9TA1BrVYRi4ell1140yf5QQogG4ueiYMb4bI99fz19pmcz8/RD/LqvqZ1NdxsOmD2D/Mig8Dx4Btj+GsL89P4OhCIJiIbSLzXe/cOsJftlxEp1Ww3vjuuLnYfsWLyFqUqtE6o477qhyWTQNplLZtU+k+sHWb2SclC0pCuz+SV22slpficHIP4fV+aMkkRJC2FtUSy9eHJ3Ao/N28N7/DnB5TAt6Rtm4R0pIIrRKUOfVS14El91j2/2LhmGq1td5LNi4pSj1TB7P/pwMwCOD29M9UpJt0fDqPEYqIyOD5ORkdu7cafEjGh9T1769p3Jq101DxknZ3oktkH0U9F5Wd+vbeTyLgmIDAZ56OgTXft4pIYSoqxu6teaGruEYFZgyZxtZBcW2P4h5Timp3tcoZR2FIxsADXQeY9NdF5caeXjONgqKDfSODuSBAe1sun8hasvqRGrLli0kJCQQGhpK586d6dKli/lHJuRtnKKDvHHXaykoNnD4XP6l7yDjpGzP1K2v4zWg97DqrptS1dao3tEtZHyUEKLBvDA6gbYtPDmZfYEnFuxEsfUEuok3g0YHxzfD2UO23bewv51lrVFt+4KfbQtAvPHbPpJP5ODvqefdsV1l7kThMFYnUnfffTcdOnTgzz//JDU1lbS0NPNPamqqPWIUdqbTaogNsWJiXpD5pGzJaKxztz4oLzQh3fqEEA3J282FGeO7oddpWLHnNN9vOmLbA/i0gnaD1OWd0irVqChKebc+U8uijaw9cIbP1qcB8PqNnQnxc7fp/oWwhtWJVGpqKq+//jq9evWibdu2REZGWvyIxsmqyn0g80nZ0ol/IOc4uPpAu8FW3bWo1MA/R8rGR8n8UUKIBpbY2o8nr44F4MVf97L3VC0vxtWWaQLXHXPUi06icTi5Fc4dBBcPiLvWZrs9k1vE1HnbAZhweSRD40Nstm8h6sLqRGrQoEHs2LHDHrEIBzIXnDhR2xYpGSdlM6ZJeGOHg966K2s7jmVzocRIS29X2gXLBIRCiIZ3T98oBnYMorjUyOTZ2ygoLrXdzjsOBzc/yD4GR/6w3X6FfZlao2JHgJttxu4ajQpT5+/gbF4xsSE+PD28k032K0R91KpqX0Wff/45d9xxB8nJySQkJKDXW5aavPZa2115EA2nYouUoiiXnofBNE4qM0UdJyVl0OvGaIQ9P6nLdejWt6ms7Hmv6BYyd4YQwiE0Gg1v3pzENe+t51BGHi8s3sOrN3a2zc71HhA/Wq0Uu2M2RPWzzX6F/RhKIHmBumxqUbSBL/9IY92BM7i5aJkxvivuep3N9i1EXVmdSG3cuJE//viDZcuWVbpNo9FgMBhsEphoWB1DfNBpNZwvKOFU9gXC/GtR8EDmk6q/Y39B7ilw84WYq6y+u3l8lHTrE0I4UAtvN94d24Vbv/iLOX8f44p2LRmVFGabnXe5RU2k9vwMw9+wep490cAOrYKCc+AVDNEDbbLLXcezeW35PgCeHRVH+1ZSoVY4B6u79k2ePJnbbruNU6dOYTQaLX4kiWq83PU62pd1DbNqPimQcVL1YarWFzsCXNysuuuFEgNbjp4HpNCEEMLx+rRryYMDYgB4etEujmUW2GbHEb0gIAqK82DvEtvsU9iPqVx94s2gs/p6fSX5RaU8PGcbJQaFq+NDuKVnm3rvUwhbsTqROnfuHI888gitWrWyRzzCgeKsLjgh46TqxWhQr7BCnbr1bTuaRXGpkSAfN6JbyhVaIYTjTRncgW5t/MktKmXy7G2UGGxQIEKjqTCn1Kz670/YT2EW7C/rsZQ01ia7fO6X3aSdzSfUz51Xb0yUbuzCqVidSN1www2sXr3aHrEIBzMXnKhti5TMJ1U/RzdCXjq4+9Wp+4NpfNTlMj5KCOEk9Dot743rio+7C9uPZfH2ygO22bHpS3nqWsg+YZt9Ctvb8xMYiiA4DkLqP07u5+0nWLDlOFoNvDu2C/6ervWPUQgbsrrNtUOHDjz11FNs2LCBxMTESsUmHn74YZsFJxqWqeDEntomUiDjpOrD3K1vFLhY/+GwMVXmjxJCOJ+IQE9eu7EzD/6wlY/XpnBFTEv6tm9Zv50GtIXIK9TKfTvnQr9HbRKrsDFTtb7OY9WWxHo4eq6A//yYDMDkq9rTS8YCCydUp6p93t7erF27lrVr11rcptFoJJFqxExd+05kFXI+v5gAr1p8uW/bTx0ELOOkrFPPbn0XSgxsP5oFSKEJIYTzGZ4YyviebZi9+SiPzNvOsn/3o6W3deNAK0kapyZSO+ZA30fq/UVd2Nj5w3D0T0Cjjo+qhxKDkYfnbCOvqJQekQFMvqqdTUIUwtas7tqXlpZW7U9qaqo9YhQNxNddT5tATwD21HZSRRknVTdH/oD8M+ARANH9rb77liPnKTYYCfVzJ7KFpx0CFEKI+nl2ZBwdWnmXTaK6A6NRqd8O40aDizuc3a9O+Cqcy8556u+oK8EvvF67emflAbYfy8LX3YV3x3XBRWf111UhGoScmcJCvLUFJ2ScVN2YJuHtNAp0+pq3rYKp7HlvGR8lhHBSHq46Zt7SDTcXLWsPnOGLDWn126G7L8SOVJdNleGEc1CU8tfEVBikjv48dJaP1qYA8OqNnWkdIBcLhfOqU13K48eP88svv3D06FGKi4stbnv77bdtEphwjPgwX5Ylp9e+4ATIOClrGUph7y/qch269YFloQkhhHBWHVr58OyoOP7zYzKvLd9Hz6hAkiL8677DLuPVyV53LYCh0+s0vlTYwYkt6vcAvad6gbCOMvOLmTJ3O4oC43tGMDwx1IZBCmF7VidS//vf/7j22muJjo5m3759JCQkcPjwYRRFoVu3bvaIUTQgqyv3QYVxUuvtFFUTc3i9OlmhRyC0vdLquxcUl7LjeBYghSaEEM7vlp5t2HDwLMuS05k8exu/PtwXH3frW+IBtcKpd4ha8fTgCug00rbBiroxtUbFjgQ37zrtQlEUnliwg4zcItoFe/PsyHgbBiiEfVjdte+pp57iscceY9euXbi7u7Nw4UKOHTtG//79ufnm+g0uFI5n6tqXciaPguLS2t2pbV/19ykZJ1Urpmp9cdfWabLCfw6fp8SgEO7vQUSgdHkQQjg3jUbDqzd0Jtzfg6OZBfz3p2QUpY7jpbQ66DxGXd4x23ZBirorLVZbCaFec0d9u/EIq/Zm4Oqi5f1xXfFw1dkoQCHsx+pEau/evUyYMAEAFxcXCgsL8fb25oUXXuC1116zeYCiYQX7utPS2w1Fgb2ncmt3J99QaNEOUNS5kUT1DCWwd7G6XMdufaay572lW58QopHw89Tz/vgu6LQaft5+kgVbjtd9Z6YxOAd+g/xztglQ1N2hlVB4Xm0pjBpQp13sOZnD9KV7AXj6mlhzFWEhnJ3ViZSXl5d5XFRoaCgpKSnm286ePWu7yITDJISb5pOqZcEJKG+VkjLoNUtbC4WZ4BUEkX3rtItNMn+UEKIR6h4ZyCOD2wPw7M+7STmTV7cdtYqD0CQwlkDyQhtGKOrE1K0v8aY69bIoLDYwefZWikuNDIoN5o4+bW0bnxB2ZHUi1bt3bzZsUL8sDx8+nKlTpzJ9+nTuvvtuevfubfMARcMrr9xn5TgpkHFSl2Lq1tepbt368opK2XlcTXAlkRJCNDYPDGjH5dEtKCwxMHnWNopKDXXbkalVSrr3OVbheTiwXF1OGlenXbywZA8pZ/IJ9nHjjZuTpBKtaFSsTqTefvttevXqBcC0adMYNGgQc+fOpW3btnzxxRc2D1A0vDoVnIgsm09KxklVr7QY9i5Rl+vYre/vw5kYjAptAj0J9/ewYXBCCGF/Oq2Gd8d1IdDLlT2ncnhl6b667SjhJtC6qPNJndlv2yBF7e3+EQzF0CoBQhKtvvvSXaeYvfkoGg28O1Y9L4RoTKxOpKKjo+ncuTOgdvP7+OOP2blzJwsXLiQyMtLmAYqGZ2qR2p+eS4nBWLs7yTipS0tbCxeywLsVRPap0y42meePCrRhYEII0XBa+brz5s3q94iv/zzMqj2nrd+JdxC0G6IuS6uU4+yYq/7ubH2RiRNZhfzfwp0APNA/hj7tWtoyMiEaRJ0n5C0uLub48eMcPXrU4kc0fhEBnvi4uVBsMHIow4o+7DJOqmamSXjjrlMrT9XBRhkfJYRoAq6KbcXdV0QB8PiCHaRnX7B+J11M3fvmgrGOXQRF3WWmwrFNoNFConVVm0sNRqbM2UbOhVK6RPjzyJAOdgpSCPuyOpE6cOAA/fr1w8PDg8jISKKiooiKiqJt27ZERUXZI0bRwLRaDZ1knJRtlRbBvl/V5Tp268u5UELyibLxUdFy5U4I0bg9eU1H4sN8OV9Qwr/nbMNgtLIkeoerwd0fck9C2jq7xChqsHOe+juqv9orxQozfj/E34fP4+3mwvvjuqLX1fm6vhAOZfWZe9ddd6HValmyZAlbtmxh69atbN26lW3btrF161Z7xCgcoLzghBWV+2ScVPVSVkNRtloeNqJuRVn+TsvEqEBUSy9C/NxtHKAQQjQsNxcdM8Z3xdNVx19pmXyw+pB1O3Bxg4Qb1WXp3tewFKW8Wp+p8EctbU7LZMbvBwGYfn0CbVrIfIii8bK6bNj27dvZsmULsbGx9ohHOIk6FZwwjZM6d0gdJ9XxGjtF1wiZqvXFjwZt3a68bUyR+aOEEE1LdJA3L16XwNT5O3h31QEuj2nBZW2tGAOaNB7++UKdn68oF9x87BesKHf8bzifBnov6DSy1nfLKihmypxtGBW4sVtrrusSbscghbA/q7/RxcXFyXxRzYCpRWrvyRyM1nS3kHFSlZVcgP1L1eU6duuDihPxSqEJIUTTcWP31lzfNRyjAv+evY2sguLa37l1D/UCXkkB7PnFfkEKS6YWwE6jwNWrVndRFIX/W7iLk9kXiGrpxbTr4u0YoBANw+pE6rXXXuOJJ55gzZo1nDt3jpycHIsf0TS0C/bG1UVLblEpx84X1P6OMk6qspT/QVEO+IZD65512kV2QQl7Tqn/X5dLi5QQool5cXQCbVt4cjL7Ak8u3Imi1PICnkZTPn+RdO9rGKVF5cWTrJg7atbmoyzfnY5ep+H9cV3xdrN+LkUhnI3VidTgwYPZtGkTgwYNIjg4mICAAAICAvD39ycgIMAeMQoH0Ou0dGyldpFIPiHzSdWLqVtf3Og6d+v7K+0cigIxQV4E+8r4KCFE0+Lt5sKM8d3Q6zT8tvs03/9lRRXgzmVf5g+vhyypHmx3B1eoU3n4hELUlbW6y4HTubyweA8ATwyLJbG1nx0DFKLhWH05YPXq1faIQzih+DBfdp3IZvfJbEZ0rmVFHhknZamkEPYvU5dt0K1Pyp4LIZqqxNZ+PHl1LC/9upcXl+zhsrYBxIb4XvqO/hFqb4jD62HnXLjycfsH25yZikwk3lyrqTwulBh4ePY2ikqNXNkhiHv6SoVn0XRYnUj179/fHnEIJxQf7gd/H7Ou4ASo46TOHVLHSTX3ROrQKijOA78ItS9/HUmhCSFEc3D3FVH8cegsq/ef4aFZ21j8UF88XGsx716XW9REavts6PeY2uVP2F5BJhz4TV2uZbe+l5fuZV96Li29XXnr5iS0WnltRNNRp35G69ev57bbbqNPnz6cOHECgO+++44NG6TAQFMSX5e5pEDGSVVUsVpfHT/Yz+cXsy89F5BESgjRtGm1Gt68OYlgHzcOZeTxwpLdtbtjp1Gg94TMFDj+j32DbM52LwJjCYQkQqtLF4tYuec03248AsBbY7oQ5ONm7wiFaFBWJ1ILFy5k2LBheHh4sHXrVoqKigDIzs7m5ZdftnmAwnE6hfii1cDZvCIycqyYdV7GSamKC2D/cnW5Ht36/kpTW6M6tPKmpbd8CAkhmrYW3m68O7YLGg3M3nyMJTtPXvpObj7Q6Vp1eccs+wbYnO2Yq/7ufOnWqPTsCzy+YAcAE/tF0b9DkD0jE8IhrE6kXnrpJT7++GM+++wz9Hq9ef0VV1whE/I2MR6uOqKDvIE6zieFoo6Taq4OroCSfPBvA2Hd6rwbU7c+qdYnhGgu+rRryYMDYgB4auEujmXWonqsqatZ8kK1spywrXMpcHwzaLSQeFONmxqMClPmbiOroITEcD8eHyZzj4qmyepEav/+/Vx5ZeUqLX5+fmRlZdkiJuFEyrv3ZVt3R5lPqkK3vuvr1V9fCk0IIZqjKYM70K2NP7lFpUyevY0Sg7HmO0RdqU4zcSG7vMiPsJ2dZa1R0QPBJ6TGTT9em8Km1Ew8XXW8P74rri51q1grhLOz+swOCQnh0KFDldZv2LCB6OhomwQlnIeMk6qj4vzyAbn16NZ3Nq+IA6fzAOgZJYmUEKL50Ou0vDeuKz7uLmw/lsXbKw/UfAetDjqPUZdNleWEbShKeSKVNL7GTbccOW9+rV64LoGolrWbsFeIxsjqRGrixIn8+9//5q+//kKj0XDy5El++OEHHnvsMR544AF7xCgcKD5MnevB6kSquY+TOrAcSgshIApCu9R5N3+lZgIQG+JDoJerjYITQojGISLQk9du7AyorRwbDp6t+Q6mL/mHVkLeGTtH14wc+wvOHwZXb4gdUe1mORdK+PecbRiMCtd1CePGbuENF6MQDmB1IvV///d/3HLLLQwaNIi8vDyuvPJK7r33Xu6//34mT55sjxiFA5lapI5mFpBzoaT2d2zu46Rs1q1P/dIg3fqEEM3V8MRQxvdsg6LAI/O2czavhvFPQR3VManGUkhe0HBBNnU7Zqu/O10Lrp5VbqIoCk8v2sXx84VEBHrw0ugENFKGXjRxViVSBoOB9evXM2nSJDIzM0lOTmbTpk2cOXOGF1980V4xCgfy93Ql3N8DgD11mU8Kmt84qaJcOLhSXa5Htz6QQhNCCAHw7Mg4OrTy5kxuEVPn7cBoVKrfuMst6u/tUr3PJkovlF8crGHuqPlbjrNk5ylctBreH9cVH3d9tdsK0VRYlUjpdDqGDh3K+fPncXV1JS4ujp49e+Lt7W2v+IQTiJNxUtY58Jv6wRMYo861UUcZORdIOZOPRgO9ZHyUEKIZ83DVMWN8N9xctKw9cIYvNqRVv3HCjaDVQ/pOOF3LeahEtTSHVqoFPHzDyz/XL5JyJo/nflaf60eHdqBrm4CGDFEIh7G6a19CQgKpqan2iEU4KXPBiRNWVu6zGCd13sZROTHTlbuEG+rVrW9Tmjo+Ki7UFz9PubInhGjeOob48MzIOABe/20fO49nVb2hZyB0GKYum7qkiTrT7pqnLiTeDNrKXxuLSg08PHsbhSUG+sS04F9XxjRwhEI4Tp3mkXrsscdYsmQJp06dIicnx+JHND11LjhRcZzUkWYyTupCjnTrE0IIO7m1VxuuSQihxKAwefY2cqsbu2sqOrFzHhhKGy7AJsa1NFdtkYJqu/W9vnw/u0/mEOCp552xXdBqZVyUaD6sTqSGDx/Ojh07uPbaa2ndujUBAQEEBATg7+9PQIA05TZFCeFqi9ShM3lcKDFYd+fmNk5q/zIwFEHLDhAcV69dbZL5o4QQwoJGo+HVGzoT7u/BkXMFPPNTMopSxXipk9vBxQPyTkPqGsvb1r4Oq19piHAbvfDzf6ExlkJoEgR3qnT76v0Z5m6Wb96cRCtf94YOUQiHcrH2DqtXr7ZHHMKJhfi6E+jlSmZ+MfvTc0mK8K/9ndv2gy1fN59xUjaq1peefYG0s/loNXBZVKCNghNCiMbPz1PPe+O6MPbTTfy0/SR92wdxU/fWlhu5uKpTUIDava/9YHV57euwejoM/E/DBt1Itc78Q13oXLk1KiP3Ao/N2wHAnX3aMqhTq4YMTQinYHUiFRUVRURERKWSloqicOzYMZsFJpyHRqMhPsyX9QfPsvtkjpWJVFmLVPoudZyURxNutSzMgpT/qcv17NZnao1KCPfDVyofCSGEhR5tA5kyqD1vrTzAsz8n062NP9FBFQpf9X8Cck7Clq9gz09w4W3465PyJKr/Ew6L3amtfkWd2Lj/E3DuEIEFKSgaHZrEm9Qk1GiAgU9hNCpMnbeDc/nFxIb48H/XxDo6ciEcok6J1KlTpwgODrZYn5mZSVRUFAaDlV2/RKMQZ06krCw44RMCLdrDuYPqOKnY4fYJ0BnsXwqGYgjqVGUXCGvI+CghhKjZgwPb8UfKWTalZjJ59jYWPdgHNxdd+QYj34G9v0DBOXg1ElCgRQf1wt78u0DrUvajq7Bc1d+12cYe9ylbbsi5mLQ6NdkEtMUXAFCiB6LZ8rVFS95n61NZf/As7notM2/pirteV90ehWjSrE6kFEWpcoK1vLw83N2lb2xTVeeCE6C2Sp07qI6TasqJVMVuffW0saxFqreMjxJCiCrptBreHduVa95bx+6TOby6bB/PjYov30CjgX5T4bengbJxVOcOqD+NiUZbzwTNyr8jesHq6Whd1RY+Re9p0ZK341gWb/y2H4D/b+++45o81/+Bf56EFbaILAXBgQKKiDgQa7VWpY46alutSrWtPd/+tE6sdrirqFXraI92He1p9bQ9p2qte9RdxD0QJ4I4QFRkRFZInt8fMdHIDCQkwOf9evGSPHly50p41Fzc93Xds/oHoZmbgynfHSKTqnAiNXnyZADqZV4zZsyAre3Tna2VSiXi4uIQEhJi8ADJPGhaoF9Oy4ZSJUKqT1ce3y7q5RW1uU4q7xGQ+Jf6+6CBVRrqTmYeUjJyIZUIaO/L+igiotJ4ONlg6Rtt8M66k1h7NBldmrnq1urk56j/lFgAqiLA/xWgWQ/1EjVV0TNf+t4u5xxRqf+YoqrkFymq1KsdlIXGf0OfIRTKIQKQXt6iTaLkBUUY/8sZFKlE9GntgaHtvas1JiJzU+FE6syZMwDUM1IXLlyAlZWV9j4rKyu0adMG0dHRho+QzIJffTvYWkmRW6jEjftyNHfX4zdQdaFO6vI29X+EbkFAgxZVGkqzrK91QyfYW+s9aUxEVKe81NId70T44V9HkxD933PYMaErPJxs1DU9B2Oe1kRpGk00DDXPGimV6kkCZsCETue2Uq9xxWP/hCCqIEqtIDx5v2ZujsfNh7lo6CxDzKDgElcoEdUlFf6UpunWN3r0aKxYsQKOjo5GC4rMj0QiIMDTEaduPsLFu9n6JVJ1oU5Kuwlv1Zf1se05EZF+pr3SAnFJD3HxbjYm/noGG/wPQXJggW5jCc2fT2qAzC6ZkkgASACpGTQYOrgYgqiCUrCAVFkIHFyMTY5vYeOZO5AIwIqhIdwongiV2Edq7dq1TKLqKM3yPr0bTgC1ez+p3Iyn+5QEGqA+io0miIj0Ym0hxaphbWFrJcWxGxk4ceN+yd35XvxIfVzFxlilejJzp+w6HVtD/gVl1+nA/vm4vWkOAGBCD3+Ecdk5EYBKNJt4/PgxFi5ciH379iE9PR0qle6a3hs3bhgsODIvTxOpSjacqK11Upf+VC+F8GgNuDar0lC3MnJxJzMPFhIBYb61cAkkEZGRNGlgj3kDWmHKf89h2LXu+PWlcISqRBxPykB6Tj7cHGzQwc8FUnObiTInT5IoVbdPEOs1CqcOx8HRfxTuym7hw7z1cHO1xpCXauGqEqJK0juReu+993Dw4EGMHDkSnp6eXB9bh2g698XfySq1e2OpanOdlCG79T2ZjWrj7QxbK9ZHERHp47V2jXDk+gNsOnMH7//7JKwsJLiXXaC939PJBrP6ByKylacJozRjKiWuBY5HVGwYUrNOApDi39dOA+iLh1YKjA5y06/ZFFEtp/cntR07dmDbtm2IiIgwRjxkxpq728NCIiA7vwi3H+XB28W2/Adp1NY6qccPgKRD6u8NkEhp66O4rI+IqFLmDWyFI9fu4768eJe7tKx8fPDzaaweEcpkqgQ7G4zCB7tOQ0R+sfuWFQ6Ev3coIk0QF5G50rtGql69enBx4drYusjaQgr/J00mKr28D6hddVKX/lR3WfIMAVyaVGkoURS1+0ex0QQRUeXILKUopZG4ZjcpzPkzAUqVWMpZdZNSJWLOnwko7V0RwPeN6Hl6z0jNmzcPM2fOxI8//qizlxTVDUFejkhIzUbC3SxEtvLQ78G1sU7q4kb1nwaYjbr5MBepWfmwkkoQ6lOLlj4SEVWj40kZeFjCbJSGCCA1Kx/j/3MaXs6y6gvMzN3NzENqVvGZKA3N+3Y8KYO/7CN6Qu9EaunSpUhMTIS7uzt8fX1haanb/vL06dMGC47MT5CXI/57qoozUrWlTkqe/nR2rYqb8ALQzkaFeDtDZiWt8nhERHVRek7pycCztl1IM3IktVNF31+iukDvRGrgwIFGCINqiqCG6oYTlUqkalud1KUt6h3nvUKBer5VHk5TH9WJv+kjIqo0NwebCp3Xv40nZ6SecTczD3+eSy33vIq+v0R1gd6J1KxZs4wRB9UQAZ6OEAQgLTsfD+UFqG9vrd8Avl3UiVTykZqfSF3crP6z1eAqDyWKIvePIiIygA5+LvB0skFaVn6J9T4CAA8nGyx/sy070D1DqRJxMvlRue9bBz/WyRNpVLjZxPHjx6FUlr6BXUFBAX777TeDBEXmy97aAr717QBUteFEDa+Tykl7uqwvcECVh7vx4DHScwpgZSFBWx/nKo9HRFRXSSUCZvUPBKD+8P8sze1Z/QOZRD2H7xuR/iqcSIWHh+Phw4fa246Ojjqb72ZmZmLYsGF6PXlMTAzat28PBwcHuLm5YeDAgbhy5Uqx82JjY/HSSy/Bzs4Ojo6O6Nq1K/Ly8rT3Z2RkYPjw4XB0dISzszPeffddyOVyvWKhigus6sa8wNM6qZoqYQsAEWjUHnD2qfJwmtmodj71YGPJ+igioqqIbOWJ1SNC4eGkuwzNw8mGrc/LwPeNSD8VXtonimKZt0s7VpaDBw9i7NixaN++PYqKivDJJ5+gV69eSEhIgJ2detYjNjYWkZGR+Pjjj7Fq1SpYWFjg3LlzkEie5oDDhw9Hamoq9uzZA4VCgdGjR+P999/Hhg0b9IqHKibIyxHbzqfi4t0s/R9cW+qkDLgJL/C00UQnLusjIjKIyFae6BnogeNJGUjPyYebg3pZGmdUyqZ532Kvp2P34Tj0eqEjwptxI16ikuhdI1UWQdDvL9nOnTt1bq9btw5ubm44deoUunbtCgCYNGkSxo8fj+nTp2vPa9Gihfb7S5cuYefOnThx4gTCwsIAAKtWrUKfPn2wZMkSeHl5VfblUCmCvNQNJxIqMyMF1Pw6qey7QEqs+nsDLOsTRRFx3D+KiMjgpBKB/65WglQioKOfCx5eEtGRySdRqQyaSFVVVpZ6hkOz4W96ejri4uIwfPhwdO7cGYmJiWjZsiXmz5+PLl3US8RiY2Ph7OysTaIA4OWXX4ZEIkFcXBwGDSo+Y1BQUICCggLt7exsdUKgUCigUCiM9vpqixYN1F2Okh4+xiN5Huyt9buMBO9wWJxaCzHpEIoM9H5rfm7V8fOTxG+CFCJUjTpCaesOVPE5r6XL8UBeCBtLCQI97HgN1hDVec0R8Xqj6sZrjqqTuV1vFY1Dr0/ACQkJSEtT77sgiiIuX76srUV68OCBniHqUqlUmDhxIiIiItCqVSsA0NZgzZ49G0uWLEFISAj+/e9/o0ePHoiPj0fz5s2RlpYGNzc33RdlYQEXFxdtrM+LiYnBnDlzih3fvXs3NxmuICdLKbIUAtZu2o2mjvo91lqRj0gAuBePPVv+C4WFncHi2rNnj8HGKk2Xq2tRH8BFsTlubN9e5fEOpwkApPCxLcK+3TvLPZ/MS3Vcc0QavN6ouvGao+pkLtdbbm5uhc7TK5Hq0aOHTh1Uv379AKiX9ImiqPfSvmeNHTsW8fHxOHLkiPaYSqUCAPzjH//A6NGjAQBt27bFvn378K9//QsxMTGVeq6PP/4YkydP1t7Ozs6Gt7c3evXqBUdHPbOCOmrTw9M4cPUBnHxboU8n/ZstiKkrITy8jl4t7CC2qPryPoVCgT179qBnz57FNok2qOw7sDxzDSIEtBw8DS0dq154u/0/ZwGko2+YP/p0a1Ll8ah6VNs1RwReb1T9eM1RdTK3602zWq08FU6kkpKSKh1MecaNG4etW7fi0KFDaNSokfa4p6f6Q2pgYKDO+QEBAUhJSQEAeHh4ID09Xef+oqIiZGRkwMPDo8Tns7a2hrV18f2PLC0tzeKHVxMEN3LGgasPcDlNXrn3zPcF4OF1WNw+BrSqep2RhtF/hle3AQAEn3BY1q96tz6VSsSJm5kAgC7+DXj91UD8d4OqE683qm685qg6mcv1VtEYKpxINW7cuNLBlEYURXz44YfYtGkTDhw4AD8/P537fX194eXlVawl+tWrV/HKK68AULdlz8zMxKlTp9CuXTsAwF9//QWVSoWOHTsaPGZSC3zScKJSLdABdcOJU2tr3n5Smm59BtiEFwCupucg43EhbK2kCG7kbJAxiYiIiMj4TNpsYuzYsdiwYQP++OMPODg4aGuanJycIJPJIAgCpk6dilmzZqFNmzYICQnBjz/+iMuXL+N///sfAPXsVGRkJMaMGYM1a9ZAoVBg3LhxGDp0KDv2GVHQk72krqXnoLBIBSuLCm9JpqbdTyoeyM0AbGvATumZKcDtEwAEIOBVgwyp2T8qzNcFllI930MiIiIiMhmTJlKrV68GAHTr1k3n+Nq1azFq1CgAwMSJE5Gfn49JkyYhIyMDbdq0wZ49e9C0aVPt+evXr8e4cePQo0cPSCQSvPbaa1i5cmV1vYw6qVE9GZxklsjKU+DqvRy0auik3wDP7ieVEgu07GucQA3p4mb1n75dAAd3gwypSaTCuX8UERERUY1i0kSqohv4Tp8+XWcfqee5uLhw891qJggCAj0dEXvjIRLuZuufSAHP7SdVExIpzSa8Aw0ynEolIi4pAwDQqUkNmJEjIiIiIi2uJaJK0yzvu3g3q3ID+L2g/rMm1Ek9SgbungYEicGW9V1Ky0ZWngL21hZoXZlElIiIiIhMplKJVFFREfbu3YtvvvkGOTk5AIC7d+9q95SiuiGooSaRqmTDicbP1UmZM+2yvhcAe7cyT60ozbK+9r71YMH6KCIiIqIaRe+lfTdv3kRkZCRSUlJQUFCAnj17wsHBAYsWLUJBQQHWrFljjDjJDAU96dx3KTUbKpUIiUTPfcQc3AFXf+DBVfOvk7q4Uf1n0CCDDXnsxpP6qKasjyIiIiKqafT+NfiECRMQFhaGR48eQSaTaY8PGjQI+/btM2hwZN6auNrB2kKCx4VKJD98XLlBNN37ko+UfZ4pPUwEUs8BghQI6G+QIZU69VFMpIiIiIhqGr0TqcOHD+Ozzz6DlZWVznFfX1/cuXPHYIGR+bOQStDSs4rL+7SJlBnXSSVsVv/p1xWwczXMkHezkZNfBAcbC+3MHhERERHVHHonUiqVCkqlstjx27dvw8HBwSBBUc2haTgRX9mGEzWhTsrAm/ACQOyNBwCAjn4ukOq7JJKIiIiITE7vRKpXr15Yvny59rYgCJDL5Zg1axb69OljyNioBtAkUgmVnZHS1ElBVNdJmZsH14G0C4DEAmjZz2DDahpNcFkfERERUc2kdyK1dOlSHD16FIGBgcjPz8dbb72lXda3aNEiY8RIZqzVk2VpF+9mV3hfsGLMuU5KMxvVpBtga5i9noqUKpxIfgSAiRQRERFRTaV3175GjRrh3Llz+OWXX3D+/HnI5XK8++67GD58uE7zCaobWng4QCoRkPG4EGnZ+fB0qsQ14NsFOPkv86yT0m7Ca7huffF3syEvKIKTzBKBT2rMiIiIiKhm0TuRAgALCwuMGDHC0LFQDWRjKUWzBva4ci8HF+9kVy6Rer5OykAzP1V2/wqQfhGQWBq0NbtmWV9HPxf9W8YTERERkVmoUCK1ZcuWCg/46quvVjoYqpmCvBzVidTdbLwc6K7/AOa6n5RmE96mLwGyegYbNpb7RxERERHVeBVKpAYOHKhzWxCEYvUwgqD+zXpJHf2odgv0csTGM3dwsbKd+wD18r4HV9V1UmaTSBl+E16FUoWTyeruhEykiIiIiGquCjWbUKlU2q/du3cjJCQEO3bsQGZmJjIzM7Fjxw6EhoZi586dxo6XzFDQMw0nKs3c9pNKvwTcvwxIrYAWrxhs2PO3M5FbqEQ9W0v4u3G7ACIiIqKaSu8aqYkTJ2LNmjXo0qWL9ljv3r1ha2uL999/H5cuXTJogGT+Ap+0QL+TmYfM3EI421qV84gSmFudlKbJRNMegMzZYMMeu6GejerUpD7ro4iIiIhqML3bnycmJsLZ2bnYcScnJyQnJxsgJKppnGSW8HZRN5moFftJiaJRNuEFnjaa4LI+IiIioppN70Sqffv2mDx5Mu7du6c9du/ePUydOhUdOnQwaHBUcwR5GnJ5n4n3k0pPUNdrSa0B/0iDDVtQpMTJm0/qo7h/FBEREVGNpnci9a9//Qupqanw8fFBs2bN0KxZM/j4+ODOnTv44YcfjBEj1QBBT5b3VbnhBGD6Oqn4J00mmvcEbAy3z9O5W1nIV6jgam+FZm72BhuXiIiIiKqf3jVSzZo1w/nz57Fnzx5cvnwZABAQEICXX35Z27mP6p6ghuqEI74qM1LmUCf17LI+A3brA4BjT9qed2xSn39XiIiIiGq4Sm3IKwgCevXqhV69ehk6HqqhNJ37btyXI69QCZmVVP9BzGE/qbQLQEYiYGFj0GV9wDP1UVzWR0RERFTj6b20j6gkbg7WcLW3hkoELqUZoE4qyUTL+zSzUc17AdaGW36Xr1DiVMojAGw0QURERFQbMJEigxAE4Zk6qRracMKIy/rOpGSisEiFBg7WaOJqZ9CxiYiIiKj6MZEig9EkUglVaTihqZO696ROqjqlngUeJQEWMsC/t0GH1tRHhbM+ioiIiKhWYCJFBqOpk6rSjNSz+0nd/NswgVWUZjbKvzdgZdhZo9gb3D+KiIiIqDapVLMJpVKJzZs349KlSwCAoKAgvPrqq5BKK9FggGoNzYzU5bQcKJQqWEormaf7dlE3nEg+AgT0M2CEZTDiJrz5CiXOpmQCYKMJIiIiotpC70+6169fR2BgIKKiorBx40Zs3LgRI0aMQFBQEBITE40RI9UQPi62sLe2QGGRCon35ZUfyBR1UndPA5kpgKUd0KynQYc+dfMRCpUqeDrZoHF9W4OOTURERESmoXciNX78eDRp0gS3bt3C6dOncfr0aaSkpMDPzw/jx483RoxUQ0gkAgI9nzScuGOA/aSqs05Kswlvi0jAyrDJjqbteSfWRxERERHVGnonUgcPHsTixYvh4vJ0s9T69etj4cKFOHjwoEGDo5on0BCd+6q7TkoUgYub1d8buFsfoNtogoiIiIhqB70TKWtra+Tk5BQ7LpfLYWVlZZCgqOZ62gK9Cp37AMD3BfWf1bG87/ZJIPs2YGVv8GV9uYVFOHc7EwAbTRARERHVJnonUv369cP777+PuLg4iKIIURRx7Ngx/N///R9effVVY8RINYimc1/C3WyoVGLlB6rOOilNk4kWfQBLG4MOfTL5ERRKEQ2dZfB2YX0UERERUW2hdyK1cuVKNG3aFOHh4bCxsYGNjQ0iIiLQrFkzrFixwhgxUg3S3N0eVlIJcgqKcOtRbuUH8q2mOimVCkjYrP7eCMv6NG3PO3FZHxEREVGtonf7c2dnZ/zxxx+4du0aLl26BEEQEBAQgGbNmhkjPqphLKUS+HvYI/5ONi7ezUbj+pXcj8neDXBtATy4oq6TMlYb9NvHgew7gLUj0PQlgw9/jPtHEREREdVKldpHCgCaN2+uTZ7YiYye1crL6UkilYU+rT0rP5BvF3UiZcz9pIy4rE9eUITzt9W1YkykiIiIiGqXSu2Y+sMPP6BVq1bapX2tWrXC999/b+jYqIYKMkTnPsD4dVIq1dNufQbehBcATiRnQKkS4eNii4bOMoOPT0RERESmo/eM1MyZM7Fs2TJ8+OGHCA8PBwDExsZi0qRJSElJwdy5cw0eJNUsgU8aThgskdLUSdm6lH2+vm4dA+RpgLUT0KS7YccGcEy7f5SB4yYiIiIik9M7kVq9ejW+++47DBs2THvs1VdfRXBwMD788EMmUoQATwcIAnA/pwDpOflwc6jkkjlj10lpNuEN6AdYGL51P+ujiIiIiGovvZf2KRQKhIWFFTverl07FBUVGSQoqtlsrSzQxFXdZMJsl/eplEDCH+rvjdCtLztfgQt3ntRHNXE1+PhEREREZFp6J1IjR47E6tWrix3/9ttvMXz4cIMERTXfs/tJVYmxEqmbfwOP0wEbZ6BJN8OODeBEUgZUIuDnagcPJ8M2sSAiIiIi06tU174ffvgBu3fvRqdOnQAAcXFxSElJQVRUFCZPnqw9b9myZYaJkmqcIC9HbDl3FxfvZlVtIGPVSWm69QX0B6SWhhnzGbGJ3D+KiIiIqDbTO5GKj49HaGgoACAxMREA4OrqCldXV8THx2vPY0v0ui3IUA0njFEnpSwCLm1Rf2+EZX3AsxvxstEEERERUW2kdyK1f/9+Y8RBtYymBfrNh7nIzlfA0aYKsz6G3k/q5hHg8X1A5gL4da36eM/JylUgIVWdQIZzRoqIiIioVqrUPlJE5alnZwWvJ7VBl8ytTsrIy/rikh5CFIGmDezg5sj6KCIiIqLaSO8Zqfz8fKxatQr79+9Heno6VCqVzv2nT582WHBUswV6OeFuVj7i72ajY1VmZgxZJ6UsAhKeLOszwia8wNNlfWx7TkRERFR76Z1Ivfvuu9i9ezeGDBmCDh06sBaKShXk5Yi9l+5VveGEIeukkg8BeRmArSvQuEvV4ioFG00QERER1X56J1Jbt27F9u3bERERYYx4qBZp1dBALdABw9VJaZb1Bb4KSCvVtLJMjx4X4nJaDgAmUkRERES1md41Ug0bNoSDg4MxYqFaRtNw4lq6HPkKZdUGM0SdlFIBXPrzSXDG6dYXl6SejfJ3t4ervbVRnoOIiIiITE/vRGrp0qWYNm0abt68aYx4qBbxdLJBPVtLKFUirt7Lqdpg2jqpC+o6qcq4cRDIewTYuQGNjTOjqlnWx259RERERLWb3olUWFgY8vPz0aRJEzg4OMDFxUXni0hDEATD7ycFADePVm4M7bK+AYBEWrV4SsFGE0RERER1g95FIsOGDcOdO3ewYMECuLu7s9kElSnIyxFHrj+oesMJ4Lk6qf76PbaoELhs3GV9D+QFuHpPDgDo4MdEioiIiKg20zuR+vvvvxEbG4s2bdoYIx6qZQKf1ElVeUYKUCdSJ3+oXJ3Ujf1AfhZg7wH4dKp6LCWIu6FectjSwwEudlZGeQ4iIiIiMg96L+1r2bIl8vLyjBEL1UKapX2XU3OgVIlVG+z5/aT0US3L+h4A4LI+IiIiorpA70Rq4cKFmDJlCg4cOICHDx8iOztb54voWX6udpBZSpGnUCLpgbxqg1W2TqqoALi8Tf29kTbhBdhogoiIiKgu0XtpX2RkJACgR48eOsdFUYQgCFAqq9jmmmoVqURAgKcDTqdk4uLdbDRzq2Lr/MrUSSX+BRRkAw5eQKMOVXv+UqRn5yPx/mMIAtCR9VFEREREtZ7eidT+/fuNEQfVYkFeTtpEakBIw6oNVpk6Kc2yvqCBgETvSdgKOZakXmoY6OkIJ1tLozwHEREREZkPvROpF1980RhxUC2m2Zg3/o6BOvcBT+ukbMtpua/IBy5vfxKIcbr1AVzWR0RERFTXVOrX84cPH8aIESPQuXNn3LlzBwDw008/4ciRSnRTo1rv2b2kRLGKDSfs3YAGLdXfV6RO6vpeoDAHcPIGGrWv2nOX4Rj3jyIiIiKqU/ROpH7//Xf07t0bMpkMp0+fRkFBAQAgKysLCxYsMHiAVPP5e9jDQiIgK0+BO5kG6PiomZWqyPK+Z7v1GWnPs7SsfCQ9eAyJALT346bURERERHWB3onU559/jjVr1uC7776DpeXTWpCIiAicPn3aoMFR7WBtIUVzd3WTCYPtJwWUn0gp8oArO9TfBxmvW59mNqpVQyc42rA+ioiIiKgu0DuRunLlCrp27VrsuJOTEzIzMw0RE9VCQYbcmLdxBfeTurYHUDwGnHyAhqFVf95SsD6KiIiIqO7RO5Hy8PDA9evXix0/cuQImjRpYpCgqPbRJFIJdw3QcMK+QcXqpC5ufPLkA422rA8AYp/MSHVifRQRERFRnaF3IjVmzBhMmDABcXFxEAQBd+/exfr16xEdHY0PPvjAGDFSLfBswwmDKG95X+Fj4Oou9fdG3IT3TmYeUjJyIZUIaO/L+igiIiKiukLv9ufTp0+HSqVCjx49kJubi65du8La2hrR0dH48MMPjREj1QIBnuoaqdSsfGQ8LoSLnVXVBvTtApz4vvRE6tpuQJEL1PMFPEOq9lxl0Czra93QCfbWev91IiIiIqIaSu8ZKUEQ8OmnnyIjIwPx8fE4duwY7t+/j3nz5hkjPqolHGws4VvfFgBw0RDL+8qrk9JuwjvIqMv62PaciIiIqG6q1D5SAGBlZYXAwEB06NAB9vb2hoyJaimDLu8rq06qQA5c3f3kSY23CS/ARhNEREREdVWF1iINHjwY69atg6OjIwYPLrveZOPGjQYJjGqfQC9HbLuQatg6qfuX1cv7mkU+PX51J1CUB7g0BTyCDfNcJbiVkYs7mXmwkAgI861ntOchIiIiIvNToUTKyckJwpPlUU5OTkYNiGqvpy3QDbC0Dyi9TqqalvVpZqPaeDvD1or1UURERER1SYU+/a1duxZz585FdHQ01q5da+yYqJbSLO1LevAYjwuKYFfV5gwl1UkV5Kj3jwKMvqxPWx/FZX1EREREdU6Fa6TmzJkDuVxuzFiolmvgYA03B2uIInAp1bB1UkJKrPrPa7sAZQFQvzngHlT15yiFKIra/aPYaIKIiIio7qlwIiWKojHjoDqiVUPj7CclpKgbTkgSNquPG3lZ382HuUjNyoeVVIJQH9ZHEREREdU1enXtE4z4wZTqBqPUSQGQ3DwKC2UuhBt/qY8bcRNeANrZqBBvZ8ispEZ9LiIiIiIyP3oVqfj7+5ebTGVklLCnD9ETTxMpA81IPamTEtIvwsfyEARloXq5n1uAYcYvhaY+qhOX9RERERHVSXolUnPmzGHXPqoSTcOJq/dyUFikgpVFpbcyA/bHABKpOnG6fxkt0zY/eZJBwMHFgEoJdP+46kE/RxRF7h9FREREVMfplUgNHToUbm5uxoqF6oBG9WRwtLFAdn4RrqXnaBOrSpFIgf3zgYbtAACWylz18dwM4Pg3QPdPDRBxcTcePEZ6TgGsLCRo6+NslOcgIiIiIvNW4ekA1keRIQiCgEBDLe978SN1snTnlPaQaNfgaRL14kdVG78Umtmodj71YGPJ+igiIiKiuohd+6jaaWahEgxRJ/XiR0DERO1N4fF9oyZRwNNGE524rI+IiIiozqpwIqVSqbisjwzC4J37es6BCPWMqSixNGoSJYoi4rh/FBEREVGdV4VKf6LKeXZGSqUywEznwcUQIEIlWEBQKdSNJozkerocD+SFsLGUoI03G68QERER1VV6NZsgMoSmDexgbSHB40Ilbmbkws/VrvKDHVwM7J8PZdfp2JoTiH4OCZDun6++zwgzU5plfWGNXWBtwfooIiIiorqKM1JU7SykErT0cAAAxN+pwvK+J0kUun8K1QvRAKD+s/un6uNGmJnSNJro1MTF4GMTERERUc3BRIpMIvDJ8r4qde5TKUtuLKHp5qdSViHCEp5OJSIuSb3hNOujiIiIiOo2Lu0jk2jV0AANJ8rabNcIy/qupucg43EhbK2kCG7kbPDxiYiIiKjmMOmMVExMDNq3bw8HBwe4ublh4MCBuHLlis453bp1gyAIOl//93//p3NOSkoK+vbtC1tbW7i5uWHq1KkoKiqqzpdCenq24URNaa2vWdYX5usCSyknc4mIiIjqMpPOSB08eBBjx45F+/btUVRUhE8++QS9evVCQkIC7OyeNiAYM2YM5s6dq71ta2ur/V6pVKJv377w8PDA33//jdTUVERFRcHS0hILFiyo1tdDFdfSwwFSiYCHjwtxL7sAHk42pg6pXJpEKpz7RxERERHVeSZNpHbu3Klze926dXBzc8OpU6fQtWtX7XFbW1t4eHiUOMbu3buRkJCAvXv3wt3dHSEhIZg3bx6mTZuG2bNnw8rKyqivgSrHxlKKpg3scPWeHBfvZpl9IvVsfRQbTRARERGRWdVIZWWp62VcXHQ/qK5fvx4///wzPDw80L9/f8yYMUM7KxUbG4vWrVvD3d1de37v3r3xwQcf4OLFi2jbtm2x5ykoKEBBQYH2dna2uuGBQqGAQqEw+OuikgV4OODqPTnO33qErs2qlpxofm7G+vklpGYjK08BO2spWrrZ8joho19zRM/i9UbVjdccVSdzu94qGofZJFIqlQoTJ05EREQEWrVqpT3+1ltvoXHjxvDy8sL58+cxbdo0XLlyBRs3bgQApKWl6SRRALS309LSSnyumJgYzJkzp9jx3bt36ywbJOMSMgUAUuw/ew1N8q6Ue35F7NmzxyDjPG//XXWsjWUK7N61s9zzqe4w1jVHVBJeb1TdeM1RdTKX6y03N7dC55lNIjV27FjEx8fjyJEjOsfff/997fetW7eGp6cnevTogcTERDRt2rRSz/Xxxx9j8uTJ2tvZ2dnw9vZGr1694OjoWLkXQHpzuZGBzWtPIkNliz59upb/gDIoFArs2bMHPXv2hKWlpYEifOqPn88AuI/+HVuiTxdfg49PNY+xrzmiZ/F6o+rGa46qk7ldb5rVauUxi0Rq3Lhx2Lp1Kw4dOoRGjRqVeW7Hjh0BANevX0fTpk3h4eGB48eP65xz7949ACi1rsra2hrW1tbFjltaWprFD6+uCPZWL+e7nZmPXAXgZFv1994YP0OlSsSJm48AABHNG/AaIR38d4OqE683qm685qg6mcv1VtEYTNrDWRRFjBs3Dps2bcJff/0FPz+/ch9z9uxZAICnpycAIDw8HBcuXEB6err2nD179sDR0RGBgYFGiZsMw8nWEo3qyQAAF1OrsJ+UkSXczUZOfhEcbCy0bduJiIiIqG4zaSI1duxY/Pzzz9iwYQMcHByQlpaGtLQ05OXlAQASExMxb948nDp1CsnJydiyZQuioqLQtWtXBAcHAwB69eqFwMBAjBw5EufOncOuXbvw2WefYezYsSXOOpF5CfJSL6VMuFuxKVRTiL3xAADQ0c8FUolg4miIiIiIyByYNJFavXo1srKy0K1bN3h6emq/fv31VwCAlZUV9u7di169eqFly5aYMmUKXnvtNfz555/aMaRSKbZu3QqpVIrw8HCMGDECUVFROvtOkfnSzPDE3zHfGSnN/lGduH8UERERET1h0hopURTLvN/b2xsHDx4sd5zGjRtj+/bthgqLqpFmRuqimc5IFSlVOJGsro9iIkVEREREGiadkSJq1VA9I5V4X468QqWJoyku/m425AVFcJJZItCTHR2JiIiISI2JFJmUm4M1XO2toBKBy2nmNyulWdbX0c8FEtZHEREREdETTKTIpARBQOCTOilzXN4Xe0OdSIU35bI+IiIiInqKiRSZnLnWSSmUKpxMzgDARIqIiIiIdDGRIpN72gLdvDr3nb+didxCJerZWsLfzcHU4RARERGRGWEiRSanaYF+OS0HRUqViaN56tgN9WxUpyb1WR9FRERERDqYSJHJNXaxhb21BQqKVEi8/9jU4WhpGk1wWR8RERERPY+JFJmcRCIgwFO9dO6imSzvKyhS4uTNJ/VR3D+KiIiIiJ7DRIrMQpCZde47dysL+QoVXO2t0MzN3tThEBEREZGZYSJFZiFQ27nPPGakjj1pe96xSX0IAuujiIiIiEgXEykyC8+2QBdF0cTRPFMfxWV9RERERFQCJlJkFpq7OcBSKiAnvwi3MvJMGku+QolTKY8AsNEEEREREZWMiRSZBSsLCVp4mEfDiTMpmSgsUqGBgzWauNqZNBYiIiIiMk9MpMhsBHmaR8MJTX1UOOujiIiIiKgUTKTIbAQ1NI+GE7E3uH8UEREREZWNiRSZjWcbTphKvkKJsymZANhogoiIiIhKx0SKzEZLD0cIApCeU4D7OQUmieHUzUcoVKrg6WSDxvVtTRIDEREREZk/JlJkNuysLeD3pLmDqZb3adqed2J9FBERERGVgYkUmZUgL9M2nHi20QQRERERUWmYSJFZ0dRJJZggkcotLMK525kA2GiCiIiIiMrGRIrMytOGE9W/tO9k8iMolCIaOsvg7cL6KCIiIiIqHRMpMiuapX3JD3ORk6+o1ufWtD3vxGV9RERERFQOJlJkVlzsrODpZAOg+pf3HeP+UURERERUQUykyOyYYj8peUERzt9WLydkIkVERERE5WEiRWbHFJ37TiRnQKkS4eNii4bOsmp7XiIiIiKqmZhIkdkxRcOJY4lse05EREREFcdEisxOUEP1jNT1dDkKipTV8pya+qhOTV2q5fmIiIiIqGZjIkVmx8vJBs62lihSibiaJjf682XnK3DhzpP6qCauRn8+IiIiIqr5mEiR2REEoVqX951IyoBKBPxc7eDxpGMgEREREVFZmEiRWarOhhOxidw/ioiIiIj0w0SKzFJ1zkg93YiX9VFEREREVDFMpMgsaRKpS6k5UKpEoz1PVq4CCanqWS927CMiIiKiimIiRWbJz9UeMksp8hRKJD14bLTniUt6CFEEmjawg5sj66OIiIiIqGKYSJFZkkoEtPR0AGDc5X2aZX3hTTkbRUREREQVx0SKzNbTOinjNZxgowkiIiIiqgwmUmS2nnbuM86M1KPHhbiclgOAiRQRERER6YeJFJmtVs+0QBdFwzeciEtSz0b5u9vD1d7a4OMTERERUe3FRIrMlr+HPSwkAjJzFbiblW/w8TXL+titj4iIiIj0xUSKzJa1hRTN3OwBABfvGH55HxtNEBEREVFlMZEisxb0zPI+Q3ogL8DVe3IAQAc/JlJEREREpB8mUmTWjNW5L+5GBgCgpYcDXOysDDo2EREREdV+TKTIrGkSqQQDd+6LvfEAAJf1EREREVHlMJEisxb4JJG6m5WPR48LDTYuG00QERERUVUwkSKz5mBjicb1bQEYbnlfenY+Eu8/hiAAHVkfRURERESVwESKzN7TOinDLO87lqSujwr0dISTraVBxiQiIiKiuoWJFJk9Q3fu47I+IiIiIqoqJlJk9jR1UvGGmpHi/lFEREREVEVMpMjsaZb2JT14jMcFRVUaKy0rH0kPHkMiAO39XAwRHhERERHVQUykyOy5OdjAzcEaoghcTqva8j7NbFSrhk5wtGF9FBERERFVDhMpqhEMtTEv66OIiIiIyBCYSFGNoG04caeKidSTGalOrI8iIiIioipgIkU1gnZGKrXyDSfuZOYhJSMXUomA9r6sjyIiIiKiymMiRTWCZkbqapocCqWqUmNolvW1bugEe2sLg8VGRERERHUPEymqEbxdZHCwsUChUoVr9+SVGoNtz4mIiIjIUJhIUY0gCAICPTUNJyq3vI+NJoiIiIjIUJhIUY2hbThRic59tzJycSczDxYSAWG+9QwdGhERERHVMUykqMbQNJxIqEQipZmNauPtDFsr1kcRERERUdXwEyXVGEENnyRSqdlQqURIJEKFH6utj+KyPiKiKlOpVCgsLDR1GGRECoUCFhYWyM/Ph1KpNHU4VMtV9/VmaWkJqVRa5XGYSFGN0bSBPawsJJAXFOFmRi78XO0q9DhRFLX7R7HRBBFR1RQWFiIpKQkqVeU6qFLNIIoiPDw8cOvWLQhCxX9xSVQZprjenJ2d4eHhUaXnYyJFNYalVIKWHg44fzsLF+9mVTiRuvkwF6lZ+bCSShDqw/ooIqLKEkURqampkEql8Pb2hkTCCoHaSqVSQS6Xw97enj9nMrrqvN5EUURubi7S09MBAJ6enpUei4kU1ShBXk5PEqls9Av2qtBjNLNRId7OkFlVfRqXiKiuKioqQm5uLry8vGBra2vqcMiINMs3bWxsmEiR0VX39SaTyQAA6enpcHNzq/QyP/7NoBpF03BCn859mvqoTlzWR0RUJZraBSsrKxNHQkRUNZpfBikUikqPwUSKapSnnfuyIIpiueeLosj9o4iIDIw1M0RU0xni3zEmUlSjtPRwhEQAHsgLkZ5TUO75Nx48RnpOAawsJGjr42z8AImIiIioTmAiRTWKzEqKpg3sAQAX72aVe75mNqqdTz3YWLI+ioiIihs1ahQGDhxo6jAMShAEbN682dRh1Brr1q2Ds7OzqcOokwoLC9GsWTP8/fffFTrX19cXJ0+erIbImEhRDaStk7pTfp2UptFEJy7rIyIyG0qVetn1H2fvIDbxIZSq8pdqV5YgCGV+zZ49GytWrMC6deuMFkNNlJycjHr16kEqlRZ7z44dO1bhcbp164aJEycaL9Bq8uabb+Lq1asGHfPAgQMQBAGZmZkGHdfQfv/9d3Tr1g1OTk6wt7dHcHAw5s6di4yMDADqJFNzbUgkEjRq1AijR4/WdsVLTk6GIAg4e/ZssbErcn2sWbMGfn5+6Ny5c7mxWllZITo6GtOmTdP7dVYGu/ZRjRPk5YTNZ++W23BCFEXEcf8oIiKzsjM+FXP+TEBqVr72mKeTDWb1D0Rkq8q3IS5Namqq9vtff/0VM2fOxJUrV7TH7O3tYW9vb/DnrS12796N1q1b6xyrX9+w/6eKogilUgkLC/P9WCqTybSd3uqSTz/9FIsWLcKkSZOwYMECeHl54dq1a1izZg1++uknTJgwAQDg6OiIK1euQKVS4dy5cxg9ejTu3r2LXbt2Ven5RVHEV199hblz51b4McOHD8eUKVNw8eJFBAUFVen5y8MZKapxtDNSqWUv7bueLscDeSFsLCVo4+1UHaEREVEZdsan4oOfT+skUQCQlpWPD34+jZ3xqaU8svI8PDy0X05OThAEQeeYvb19saV9KpUKMTEx8PPzg0wmQ5s2bfC///1Pe79mJmHXrl1o27YtZDIZXnrpJaSnp2PHjh0ICAiAo6Mj3nrrLeTm5mof161bN4wbNw7jxo2Dk5MTXF1dMWPGDJ3mSY8ePUJUVBTq1asHW1tbvPLKK7h27VqZr/HatWvo2rUrbGxsEBgYiD179hQ759atW3jjjTfg7OwMFxcXDBgwAMnJyeW+f/Xr19d5vzw8PGBpaQkAmD17NkJCQvDTTz/B19cXTk5OGDp0KHJycgCol0wePHgQK1as0M5YJCcna9+/HTt2oF27drC2tsaRI0cq/L7v27cPYWFhsLW1RefOnXUS48TERAwYMADu7u6wt7dH+/btsXfvXp3X5Ovri88//xxRUVGwt7dH48aNsWXLFty/fx8DBgzQzro8uzyspKV9f/zxB0JDQ2FjY4MmTZpgzpw5KCoq0t4vCAK+//57DBo0CLa2tmjevDm2bNkCQD1L0717dwBAvXr1IAgCRo0aBQAoKCjA+PHj4ebmBhsbG3Tp0gUnTpwo8+dUUFCA6OhoNGzYEHZ2dujYsSMOHDhQLP5du3YhICAA9vb2iIyM1PlFw/OOHz+OBQsWYOnSpfjiiy/QuXNn+Pr6omfPnvj999/x9ttv67xWDw8PeHl54ZVXXsH48eOxd+9e5OXllRl3eU6dOoXExET07dtXe6ywsBDjxo2Dp6cnbGxs0LhxY8TExGjvr1evHiIiIvDLL79U6bkrgokU1TiBTxKpWxl5yMorvWWlZllfWGMXWFuwPoqIyNBEUURuYVGFvnLyFZi15SJKWsSnOTZ7SwJy8hUVGq8inVsrKyYmBv/+97+xZs0aXLx4EZMmTcKIESNw8OBBnfNmz56Nr776Cn///bc2UVm+fDk2bNiAbdu2Yffu3Vi1apXOY3788UdYWFjg+PHjWLFiBZYtW4bvv/9ee/+oUaNw8uRJbNmyBbGxsRBFEX369Cm1RbNKpcLgwYNhZWWFuLg4rFmzptiyJoVCgd69e8PBwQGHDx/G0aNHtR+kCwsLq/ReJSYmYvPmzdi6dSu2bt2KgwcPYuHChQCAFStWIDw8HGPGjEFqaipSU1Ph7e2tfez06dOxcOFCXLp0CcHBwRV+3z/99FMsXboUJ0+ehIWFBd555x3tfXK5HH369MG+fftw5swZREZGon///khJSdEZ48svv0RERATOnDmDvn37YuTIkYiKisKIESNw+vRpNG3aFFFRUaVeZ4cPH0ZUVBQmTJiAhIQEfPPNN1i3bh3mz5+vc96cOXPwxhtv4Pz58+jTpw+GDx+OjIwMeHt74/fffwcAXLlyBampqVixYgUA4KOPPsLvv/+OH3/8EadPn0azZs3Qu3dv7VK6kowbNw6xsbH45ZdfcP78ebz++uuIjIzUScJzc3OxZMkS/PTTTzh06BBSUlIQHR1d6pjr16+Hvb09/t//+38l3l9WzZhMJoNKpdJJLCvj8OHD8Pf3h4ODg/bYypUrsWXLFvz222+4cuUK1q9fD19fX53HdejQAYcPH67Sc1eE+c6hEpXC2dYKDZ1luJOZh4S72aUu29M0mujUxKU6wyMiqjPyFEoEzqza0h0NEUBadj5az95dofMT5vaGrZXhP8YUFBRgwYIF2Lt3L8LDwwEATZo0wZEjR/DNN9/gxRdf1J77+eefIyIiAgDw7rvv4uOPP0ZiYiKaNGkCABgyZAj279+vk9h4e3vjyy+/hCAIaNGiBS5cuIAvv/wSY8aMwbVr17BlyxYcPXpUWw+yfv16eHt7Y/PmzXj99deLxbt3715cvnwZu3btgpeXeqP6BQsW4JVXXtGe8+uvv0KlUuH777/Xtnxeu3YtnJ2dceDAAfTq1avU96NLly7FNkiVy+Xa71UqFdatW6f9oDty5Ejs27cP8+fPh5OTE6ysrGBrawsPD49iY8+dOxc9e/bU+32fP3++9vb06dPRt29f5Ofnw8bGBm3atEGbNm20586bNw+bNm3Cli1bMG7cOO3xPn364B//+AcAYObMmVi9ejXat2+vfY+nTZuG8PBw3Lt3r8TY58yZg+nTp2tnZZo0aYJ58+bho48+wqxZs7TnjRo1CsOGDQOg/rmsXLkSx48fR2RkJFxc1J9P3NzctEnJ48ePsXr1aqxbt077M/zuu++wZ88e/PDDD5g6dWqxWFJSUrB27VqkpKRor4Ho6Gjs3LkTa9euxYIFCwCoE+o1a9agadOmANTJV1lL5q5du4YmTZpoZyArSrP0LywsDA4ODnj48KFej3/WzZs3ta9JIyUlBc2bN0eXLl0gCAIaN25c7HFeXl64efNmpZ+3ophIUY0U5OWIO5l5uHg3q8RESqUSEZek/s0N66OIiKiirl+/jtzcXO0HfI3CwkK0bdtW51hwcLD2e3d3d9ja2mqTKM2x48eP6zymU6dOOvvXhIeHY+nSpVAqlbh06RIsLCzQsWNH7f3169dHixYtcOnSpRLjvXTpEry9vXU+bGoSEY1z587h+vXrOr/VB4D8/HwkJiaWOK7Gf/7znzLrTHx9fXXG9fT01DYZKE9YWJj2+8q+756e6rq69PR0+Pj4QC6XY/bs2di2bRtSU1NRVFSEvLy8YjNSz//sAOjUgmmOpaenl5hInTt3DkePHtWZgVIqlcjPz0dubq52s9dnn8fOzg6Ojo5lvj+JiYlQKBTaBB0ALC0t0aFDh1KvgQsXLkCpVMLf31/neEFBgU49m62trTaJAsr/Wekz65uVlQV7e3uoVCrk5+ejS5cuOjOtlZWXlwcbGxudY6NGjULPnj3RokULREZGol+/fsV+GSCTyXSW1RoLEymqkYK8nLA74V6pDSeupucg43EhbK2kCG7kXL3BERHVETJLKRLm9q7QuceTMjBqbdl1HgCwbnR7dPArfyWBzEhbWmhmW7Zt24aGDRvq3Gdtba1z+9nf1AuCUOw394IgQKVSGSVOfcjlcrRr1w7r168vdl+DBg3KfKy3tzeaNWtW6v1Vec12dnY6MQKVe98BaJ8zOjoae/bswZIlS9CsWTPIZDIMGTKk2BLGksYoa9znyeVyzJkzB4MHDy5237Mf/KvjmpDL5ZBKpTh16hSkUt2/F882UikplrKSJX9/fxw5cgQKhaLcWSkHBwecPn0aEokEnp6eOo05HB3VJRlZWcVr2zMzM+HkVHodu6urKy5cuKBzLDQ0FElJSdixYwf27t2LN954Ay+//LJOPV1GRka517YhMJGiGqlVwycNJ0rZS0qzrC/M1wWWUpYCEhEZgyAIFV5e90LzBvB0skFaVn6JdVICAA8nG7zQvAGkEqGEM6pHYGAgrK2tkZKSorOczFDi4uJ0bh87dgzNmzeHVCpFQEAAioqKEBcXp13a9/DhQ1y5cgWBgYEljhcQEIBbt24hNTVVOzvzfHvy0NBQ/Prrr3Bzc9N+qK0uVlZWUCqV5Z5nqPf96NGjGDVqFAYNGgRAnWRUpKmGvkJDQ3HlypUyk8zyWFlZAYDO+9O0aVNYWVnh6NGj2iVrCoUCJ06cKLVNeNu2baFUKpGeno4XXnih0vE876233sLKlSvxz3/+U9ud71mZmZnaJYkSiaTU98LFxQWurq44deqUzs82Ozsb169fLzaT9qy2bdti9erVEEVRZybX0dERb775Jt58800MGTIEkZGRyMjI0C6XjI+PLzaTaQxMpKhGCvJS//Yi8f5j5CuUeP73kppEKpz7RxERmQWpRMCs/oH44OfTEACdZErz8WhW/0CTJlGA+jfr0dHRmDRpElQqFbp06YKsrCwcPXoUjo6OOp3KKiMlJQWTJ0/GP/7xD5w+fRqrVq3C0qVLAQDNmzfHgAEDMGbMGHzzzTdwcHDA9OnT0bBhQwwYMKDE8V5++WX4+/vj7bffxhdffIHs7Gx8+umnOucMHz4cX3zxBQYMGIC5c+eiUaNGuHnzJjZu3IiPPvoIjRo1KjXehw8fIi0tTeeYs7NzseVWpfH19UVcXBySk5Nhb2+v/aD7PEO9782bN8fGjRvRv39/CIKAGTNmGGVWcObMmejXrx98fHwwZMgQSCQSnDt3DvHx8fj8888rNEbjxo0hCAK2bt2KPn36QCaTwd7eHh988AGmTp0KFxcX+Pj4YPHixcjNzcW7775b4jj+/v4YPnw4oqKisHTpUrRt2xb379/Hvn37EBwcrNPxTh8dO3bERx99hClTpuDOnTsYNGgQvLy8cP36daxZswZdunQpMcEqyeTJk7FgwQK4u7ujU6dOePjwIebNm4cGDRqUOKun0b17d8jlcly8eBGtWrUCACxbtgyenp5o27YtJBIJ/vvf/8LDw0On+cXhw4cxb968Sr1uffBX9VQjuTtao76dFZQqEZfTcnTue7Y+io0miIjMR2QrT6weEQoPJ90P4R5ONlg9ItQo+0hVxrx58zBjxgzExMQgICAAkZGR2LZtG/z8/Ko8dlRUFPLy8tChQweMHTsWEyZMwPvvv6+9f+3atWjXrh369euH8PBwiKKI7du3l7q0SiKRYNOmTdox33vvvWKd42xtbXHo0CH4+Phg8ODBCAgIwLvvvov8/PxyZ6h69eoFT09Pna/NmzdX+PVGR0dDKpUiMDAQDRo0KFar9CxDvO/Lli1DvXr10LlzZ/Tv3x+9e/dGaGhohR9fUb1798bWrVuxe/dutG/fHp06dcKXX35ZYuOD0jRs2FDbtMLd3V3bDGPhwoV47bXXMHLkSISGhuL69evYtWsX6tWrV+pYa9euRVRUFKZMmYIWLVpg4MCBOHHiBHx8fKr0OhctWoQNGzYgLi4OvXv3RlBQECZPnozg4GC9fqmgacKxaNEiBAcH47XXXoOdnR32799f5v5c9evXx6BBg3SWpTo4OGDx4sUICwtD+/btkZycjO3bt2ubosTGxiIrKwtDhgyp/AuvIEE0Zv/QGiI7OxtOTk7Iysqq9ilvqryRP8Th8LUHmD+oFd4I9cL27dvRp08fXL2fi74rj8De2gJnZ/aEBZf2kREoFArtNadvRyMifZnL9Zafn4+kpCT4+flVeEaiJEqViONJGUjPyYebgw06+LmYfCaqOnTr1g0hISFYvny5qUMpl0qlQnZ2NhwdHYt17SMytLKut/Pnz6Nnz55ITEys0ObZb775Jtq0aYNPPvmkzPPK+vesorkB/2ZQjaVZ3vd8wwnNsr72vvWYRBERmSGpREB40/oYENIQ4U3r14kkiogqJzg4GIsWLUJSUlK55xYWFqJ169aYNGlSNUTGGimqwYK8NA0ndBOpY0824mXbcyIiIqKab9SoURU6z8rKCp999plxg3kGEymqsTSJ1OXUbBQp1YWkSp36KCZSRERkPg4cOGDqEIjIgLjuiWos3/p2sLOSoqBIhaQH6k3XLqXmICe/CA42Ftqlf0REREREhmbSRComJgbt27eHg4MD3NzcMHDgQFy5cqXEc0VRxCuvvAJBEIp1i0lJSUHfvn1ha2sLNzc3TJ06FUVFRdXwCsiUJBIBAZ7qWamEVPXyvmNPZqM61pHCZSIiIiIyDZMmUgcPHsTYsWNx7Ngx7NmzBwqFAr169cLjx4+Lnbt8+XKdjbg0lEol+vbti8LCQvz999/48ccfsW7dOsycObM6XgKZmGZ5X0KqugX6MS7rIyIiIqJqYNIaqZ07d+rcXrduHdzc3HDq1Cl07dpVe/zs2bNYunQpTp48qd21W2P37t1ISEjA3r174e7ujpCQEMybNw/Tpk3D7NmztbtGU+2k7dyXmo1W7sDJm48AMJEiIiIiIuMyq2YTWVlZAKCz63Vubi7eeustfP311/Dw8Cj2mNjYWLRu3Rru7u7aY71798YHH3yAixcvom3btsUeU1BQgIKCAu3t7Gz1sjCFQgGFQmGw10PG5+9mC0BdG3XLDnhcoISTzALNXWX8WZJRaa4vXmdUHczlelMoFBBFESqVCiqVyqSxkHFpthnV/LyJjMkU15tKpYIoilAoFJBKpTr3VfTfWrNJpFQqFSZOnIiIiAi0atVKe3zSpEno3LkzBgwYUOLj0tLSdJIoANrbaWlpJT4mJiYGc+bMKXZ89+7dsLW1rexLIBMoUgFSQYrs/CIcv69eqepjU4idO3eYODKqK/bs2WPqEKgOMfX1ZmFhAQ8PD8jlchQWFpo0FqoeOTk5pg6B6pDqvN4KCwuRl5eHQ4cOFeutkJubW6ExzCaRGjt2LOLj43HkyBHtsS1btuCvv/7CmTNnDPpcH3/8MSZPnqy9nZ2dDW9vb/Tq1avM3YvJPH1/829cSpPjWLq6hq5/p5bo09nXtEFRradQKLBnzx707NkTlpaWpg6Hajlzud7y8/Nx69Yt2Nvbw8bGxmRxGNro0aORmZmJTZs2mToUg5FKpfj9998xcODASj1eFEXk5OTAwcGhxBr1umbdunWYPHkyMjIyTB1KrVTe9VZYWIhWrVph3bp16Ny5c5ljFRYWomXLlvjtt98QFhZW6nn5+fmQyWTo2rVrsX/PNKvVymMWidS4ceOwdetWHDp0CI0aNdIe/+uvv5CYmAhnZ2ed81977TW88MILOHDgADw8PHD8+HGd++/duwcAJS4FBABra2tYW1sXO25packPRDXMzvhUJD/MAwAoRfVfvG8PJ8Onvj0iW3mW9VAig+C/G1SdTH29KZVKCIIAiUQCiaRm7KBSXhIwa9YsrFy5EqIo1pjXVFFV+TnduHEDTZs2LfG+2NhYdOrUqULjdOvWDSEhIVi+fHml4jAXw4YNQ79+/Qx6jRw4cADdu3fHo0ePin3WNSe///47vv76a5w5cwb5+fnw8fFBREQEPvzwQ20Jzbp16zB69GgA6r9zXl5e6NmzJxYtWgQ3NzckJyfDz88PZ86cQUhIiM743bp1Q5s2bTBnzhztvy/P+/bbb+Hn54cuXbqUG6+NjQ2io6Px8ccfY9++faWeJ5FIIAhCif+uVvTfWZP+iyGKIsaNG4dNmzbhr7/+gp+fn87906dPx/nz53H27FntFwB8+eWXWLt2LQAgPDwcFy5cQHp6uvZxe/bsgaOjIwIDA6vttVD12xmfig9+Po08hVLn+EN5IT74+TR2xqeaKDIiIirR/hjg4OKS7zu4WH2/gaWmpmq/li9fDkdHR51j0dHRcHJyMusPsqa0e/dunfcrNTUV7dq1M+hziKJo9tvWyGQyuLm5mTqMajdt2jS8+eabCAkJwZYtW3DlyhVs2LABTZo0wccff6xzrubv1u3bt/Hdd99hx44dGDlyZJVjEEURX331Fd59990KP2b48OE4cuQILl68WOXnL4tJE6mxY8fi559/xoYNG+Dg4IC0tDSkpaUhL089w+Dh4YFWrVrpfAGAj4+PNunq1asXAgMDMXLkSJw7dw67du3CZ599hrFjx5Y460S1g1IlYs6fCRBLuE9zbM6fCVCqSjqDiIhMQiIF9s8vnkwdXKw+LpGW/Lgq8PDw0H45OTlBEASdY/b29hg1apTOEjiVSoWYmBj4+flBJpOhTZs2+N///qe9/8CBAxAEAbt27ULbtm0hk8nw0ksvIT09HTt27EBAQAAcHR3x1ltv6dRadOvWDePGjcO4cePg5OQEV1dXzJgxQ1toDwCPHj1CVFQU6tWrB1tbW7zyyiu4du1ama/x2rVr2uVJgYGBJdbS3bp1C2+88QacnZ3h4uKCAQMGIDk5udz3r379+jrvl4eHh/a39bNnz0ZISAh++ukn+Pr6wsnJCUOHDtXWuYwaNQoHDx7EihUrIAgCBEFAcnKy9v3bsWMH2rVrB2traxw5cqTC7/u+ffsQFhYGW1tbdO7cWWcP0sTERAwYMADu7u6wt7dH+/btsXfvXp3X5Ovri88//xxRUVGwt7dH48aNsWXLFty/fx8DBgyAvb09goODcfLkSe1j1q1bVyzZ/uOPPxAaGgobGxs0adIEc+bM0UkIBUHA999/j0GDBsHW1hbNmzfHli1bAADJycno3r07AKBevXoQBAGjRo0CoG6KNn78eLi5ucHGxgZdunTBiRMnyvw5FRQUIDo6Gg0bNoSdnR06duyIAwcOFIt/165dCAgIgL29PSIjI5GaWvovnY8dO4bFixdj2bJlWLZsGV544QX4+PigXbt2+Oyzz7Bjh249uubvlpeXF1555RWMHz8ee/fu1X6ur6xTp04hMTERffv21R4rLCzEuHHj4OnpCRsbGzRu3BgxMU9/EVOvXj1ERETgl19+qdJzl8ekidTq1auRlZWFbt26wdPTU/v166+/VngMqVSKrVu3QiqVIjw8HCNGjEBUVBTmzp1rxMjJ1I4nZSA1K7/U+0UAqVn5OJ7EtcxEREYjikDh44p/hY8Fuk5VJ01/fa4+9tfn6ttdp6rvr+hYovF+URYTE4N///vfWLNmDS5evIhJkyZhxIgROHjwoM55s2fPxldffYW///5bm6gsX74cGzZswLZt27B7926sWrVK5zE//vgjLCwscPz4caxYsQLLli3D999/r71/1KhROHnyJLZs2YLY2FiIoog+ffqU2kVMpVJh8ODBsLKyQlxcHNasWYNp06bpnKNQKNC7d284ODjg8OHDOHr0qPaDdFWbhiQmJmLz5s3YunUrtm7dioMHD2LhwoUAgBUrViA8PBxjxozRzmZ5e3trHzt9+nQsXLgQly5dQnBwcIXf908//VS7LY6FhQXeeecd7X1yuRx9+vTBvn37cObMGURGRqJ///5ISUnRGePLL79EREQEzpw5g759+2LkyJGIiorCiBEjcPr0aTRt2hRRUVE6Se6zDh8+jKioKEyYMAEJCQn45ptvsG7dOsyfP1/nvDlz5uCNN97A+fPn0adPHwwfPhwZGRnw9vbG77//DgC4cuUKUlNTsWLFCgDARx99hN9//x0//vgjTp8+jWbNmqF3795l1meNGzcOsbGx+OWXX3D+/Hm8/vrriIyM1EnCc3NzsWTJEvz00084dOgQUlJSEB0dXeqY//nPf2Bvb4//9//+X4n3l7dsViaTQaVSVXm28fDhw/D394eDg4P22MqVK7Flyxb89ttvuHLlCtavXw9fX1+dx3Xo0AGHDx+u0nOXSyQxKytLBCBmZWWZOhSqoM1nbouNp20t92vzmdumDpVqqcLCQnHz5s1iYWGhqUOhOsBcrre8vDwxISFBzMvLUx8okIviLEfTfBXI9Y5/7dq1opOTU7Hjb7/9tjhgwABRFEUxPz9ftLW1Ff/++2+dc959911x2LBhoiiK4v79+0UA4t69e7X3x8TEiADExMRE7bF//OMfYu/evbW3X3zxRTEgIEBUqVTaY9OmTRMDAgJEURTFq1evigDEo0ePau9/8OCBKJPJxN9++63E17Rr1y7RwsJCvHPnjvbYjh07RADipk2bRFEUxZ9++kls0aKFzvMWFBSIMplM3LVrV4njJiYmigBEmUwm2tnZ6XxpzJo1S7S1tRWzs7O1x6ZOnSp27NhR5zVPmDBBZ2zN+7d582btscq+79u2bRMBPL0mSxAUFCSuWrVKe7tx48biiBEjtLdTU1NFAOKMGTO0x2JjY0UAYmpqqiiKxa+dHj16iAsWLNB5np9++kn09PTU3gYgfvbZZ9rbcrlcBCDu2LFD5/U8evRI5xxLS0tx/fr12mOFhYWil5eXuHjx4hJf382bN0WpVKpzDWhi/Pjjj7XxAxCvX7+uvf/rr78W3d3dSxxTFEUxMjJSDA4O1jm2dOlSnWshMzOzxPfn6tWror+/vxgWFiaKoigmJSWJAMQzZ84Ue54XX3xRHD9+vPjo0SNRqVQWu3/ChAniSy+9pHPsww8/FF966SWda/p5K1asEH19fUu9v9i/Z8+oaG5gFs0miPTl5lCxblEVPY+IiAgArl+/jtzcXPTs2VPneGFhYbG9KYODg7Xfu7u7w9bWFk2aNNE59nxDrE6dOun8Jj88PBxLly6FUqnEpUuXYGFhgY4dO2rvr1+/Plq0aIFLly6VGO+lS5fg7e0NLy8vnTGfde7cOVy/fl3nN/qAumtZYmJiieNq/Oc//0FQUFCp9/v6+uqM6+npqVO3XpZnO6pV9n339FQ3lkpPT4ePjw/kcjlmz56Nbdu2ITU1FUVFRcjLyys2I/X8zw4AWrduXexYenp6ic3Lzp07h6NHj+rMQCmVSuTn5yM3N1e7nc6zz2NnZwdHR8cy35/ExEQoFApERERoj1laWqJDhw6lXgMXLlyAUqmEv7+/zvGCggLUr19fe9vW1langYg+PyuNd955B6+++iri4uIwYsQInRm7rKws2NvbQ6VSIT8/H126dNGZba2svLy8Yl31Ro0ahZ49e6JFixaIjIxEv3790KtXL51zZDJZhduYVxYTKaqROvi5wNPJBmlZ+SXWSQkAPJxs0MHPpYR7iYjIICxtgU/u6v+4I18Ch74ApFaAslC9rK/LJP2f2wjkcjkAYNu2bWjYsKHOfc/XXj/b2UvT/etZgiCYxWa2crkc7dq1w/r164vd16BBgzIf6+3tjWbNmpV6f1Ves52dnU6MQOXedwDa54yOjsaePXuwZMkSNGvWDDKZDEOGDCm2hLGkMcoa93lyuRxz5szB4MGDi9337If+6rgm5HI5pFIpTp06VWxjWXt7+zJjEctYItu8eXMcOXIECoVC+1hnZ2c4Ozvj9u3bxc53cHDA6dOnIZFI4OnpCZlMpr1Ps71QVlZWscdlZmbCycmp1DhcXV1x4cIFnWOhoaFISkrCjh07sHfvXrzxxht4+eWXdWrqMjIyyr2+q4qJFNVIUomAWf0D8cHPpyEAOsmU5vd8s/oHQirh3hdEREYjCICVXfnnPevgYnUS1f1T4MWPnjaakFqpb5tYYGAgrK2tkZKSghdffNHg48fFxencPnbsGJo3bw6pVIqAgAAUFRUhLi5Ou1fOw4cPceXKlVI7EQcEBODWrVtITU3Vzs4cO3ZM55zQ0FD8+uuvcHNzq/b9Mq2srKBUKss9z1Dv+9GjRzFq1CgMGjQIgDrJqEhTDX2FhobiypUrZSaZ5bGysgIAnfenadOmsLKywtGjR9G4cWMA6hq3EydOYOLEiSWO07ZtWyiVSqSnp+OFF16odDzPGzZsGFatWoV//vOfmDBhQrnnSySSUt8PFxcXuLq64tSpUzo/3+zsbFy/fh3Nmzcvddy2bdti9erVEEVRZzbX0dERb775Jt58800MGTIEkZGRyMjIgIuL+pfo8fHxxWYzDY2JFNVYka08sXpEKOb8maDTeMLDyQaz+gdyHykiInOjSZo0SRTw9M/983Vvm4iDgwOio6MxadIkqFQqdOnSBVlZWTh69CgcHR3x9ttvV2n8lJQUTJ48Gf/4xz9w+vRprFq1CkuXLgWgngEYMGAAxowZg2+++QYODg6YPn06GjZsiAEDBpQ43ssvvwx/f3+8/fbb+OKLL5CdnY1PP/1U55zhw4fjiy++wIABAzB37lw0atQIN2/exMaNG/HRRx/p7OH5vIcPHyItLU3nmLOzc4U3ZPb19UVcXBySk5Nhb2+v/ZD7PEO9782bN8fGjRvRv39/CIKAGTNmGGVWcObMmejXrx98fHwwZMgQSCQSnDt3DvHx8fj8888rNEbjxo0hCAK2bt2KPn36QCaTwd7eHh988AGmTp0KFxcX+Pj4YPHixcjNzS21/be/vz+GDx+OqKgoLF26FG3btsX9+/exb98+BAcH63S700d4eDimTJmCKVOm4ObNmxg8eDC8vb2RmpqKH374odQ9n0ozefJkLFiwAO7u7ujUqRMePnyIefPmoUGDBhg8eHCpDVW6d+8OuVyOixcvajt4L1u2DJ6enmjbti0kEgn++9//wsPDQ6ez4uHDhzFv3rxKvfaKYiJFNVpkK0/0DPRA7PV07D4ch14vdER4MzfORBERmSOVUjeJ0tDcVpU/c1EdNB/uYmJicOPGDTg7OyM0NBSffPJJlceOiopCXl4eOnToAKlUigkTJuD999/X3r927VpMmDAB/fr1Q2FhIbp27Yrt27eXukGoRCLBpk2b8O6776JDhw7w9fXFypUrERkZqT3H1tYWhw4dwrRp0zB48GDk5OSgYcOG6NGjR7kzVM/XnQDquqmhQ4dW6PVGR0fj7bffRmBgIPLy8pCUlFTquYZ435ctW4Z33nkHnTt3hqurK6ZNm4bs7OwKP76ievfuja1bt2Lu3LlYtGgRLC0t0bJlS7z33nsVHqNhw4aYM2cOpk+fjtGjRyMqKgrr1q3DwoULoVKpMHLkSOTk5CAsLAy7du1CvXr1Sh1r7dq1+PzzzzFlyhTcuXMHrq6u6NSpE/r161el17lkyRJ06NABq1evxr/+9S/k5ubC3d0dXbt2RWxsrF4znB999BHs7e2xaNEiJCYmwsXFBREREdi/fz9kMlmpiVT9+vUxaNAgrF+/Xtvi3MHBAYsXL8a1a9cglUrRvn17bN++XZvYxcbGIisrC0OGDKnS6y+PIJa1OLKOyM7OhpOTE7Kysqp9ypsMQ6FQYPv27ejTp0+Fd6Mmqgpec1SdzOV6y8/PR1JSEvz8/Co8I0FPdevWDSEhIVi+fLmpQymXSqVCdnY2HB0d9Zp1IKqM8q638+fPo2fPnkhMTNSp+yrNm2++iTZt2pSZhJf171lFcwP+zSAiIiIiIrMVHByMRYsWlTmjqVFYWIjWrVtj0iQ9G9hUApf2ERERERGRWRs1alSFzrOyssJnn31m3GCeYCJFREREVA0OHDhg6hCIyIC4tI+IiIiIiEhPTKSIiIiIiIj0xESKiIiI9MKGv0RU0xlifzHWSBEREVGFWFpaQhAE3L9/Hw0aNIAgcM++2kqlUqGwsBD5+flsf05GV53XmyiKKCwsxP379yGRSGBlZVXpsZhIERERUYVIpVI0atQIt2/fRnJysqnDISMSRRF5eXmQyWRMmMnoTHG92drawsfHp0qJGxMpIiIiqjB7e3s0b94cCoXC1KGQESkUChw6dAhdu3blpuNkdNV9vUmlUlhYWFQ5aWMiRURERHqRSqWQSqWmDoOMSCqVoqioCDY2NkykyOhq6vXGRa9ERERERER6YiJFRERERESkJyZSREREREREemKNFJ7uh5GdnW3iSKiyFAoFcnNzkZ2dXaPW1lLNxWuOqhOvN6puvOaoOpnb9abJCcrbM4+JFICcnBwAgLe3t4kjISIiIiIic5CTkwMnJ6dS7xdEbk8OlUqFu3fvwsHBgXsl1FDZ2dnw9vbGrVu34OjoaOpwqA7gNUfVidcbVTdec1SdzO16E0UROTk58PLyKnOfKc5IAZBIJGjUqJGpwyADcHR0NIu/gFR38Jqj6sTrjaobrzmqTuZ0vZU1E6XBZhNERERERER6YiJFRERERESkJyZSVCtYW1tj1qxZsLa2NnUoVEfwmqPqxOuNqhuvOapONfV6Y7MJIiIiIiIiPXFGioiIiIiISE9MpIiIiIiIiPTERIqIiIiIiEhPTKSIiIiIiIj0xESKarSYmBi0b98eDg4OcHNzw8CBA3HlyhVTh0V1xMKFCyEIAiZOnGjqUKgWu3PnDkaMGIH69etDJpOhdevWOHnypKnDolpIqVRixowZ8PPzg0wmQ9OmTTFv3jywLxkZyqFDh9C/f394eXlBEARs3rxZ535RFDFz5kx4enpCJpPh5ZdfxrVr10wTbAUwkaIa7eDBgxg7diyOHTuGPXv2QKFQoFevXnj8+LGpQ6Na7sSJE/jmm28QHBxs6lCoFnv06BEiIiJgaWmJHTt2ICEhAUuXLkW9evVMHRrVQosWLcLq1avx1Vdf4dKlS1i0aBEWL16MVatWmTo0qiUeP36MNm3a4Ouvvy7x/sWLF2PlypVYs2YN4uLiYGdnh969eyM/P7+aI60Ytj+nWuX+/ftwc3PDwYMH0bVrV1OHQ7WUXC5HaGgo/vnPf+Lzzz9HSEgIli9fbuqwqBaaPn06jh49isOHD5s6FKoD+vXrB3d3d/zwww/aY6+99hpkMhl+/vlnE0ZGtZEgCNi0aRMGDhwIQD0b5eXlhSlTpiA6OhoAkJWVBXd3d6xbtw5Dhw41YbQl44wU1SpZWVkAABcXFxNHQrXZ2LFj0bdvX7z88sumDoVquS1btiAsLAyvv/463Nzc0LZtW3z33XemDotqqc6dO2Pfvn24evUqAODcuXM4cuQIXnnlFRNHRnVBUlIS0tLSdP5vdXJyQseOHREbG2vCyEpnYeoAiAxFpVJh4sSJiIiIQKtWrUwdDtVSv/zyC06fPo0TJ06YOhSqA27cuIHVq1dj8uTJ+OSTT3DixAmMHz8eVlZWePvtt00dHtUy06dPR3Z2Nlq2bAmpVAqlUon58+dj+PDhpg6N6oC0tDQAgLu7u85xd3d37X3mhokU1Rpjx45FfHw8jhw5YupQqJa6desWJkyYgD179sDGxsbU4VAdoFKpEBYWhgULFgAA2rZti/j4eKxZs4aJFBncb7/9hvXr12PDhg0ICgrC2bNnMXHiRHh5efF6IyoBl/ZRrTBu3Dhs3boV+/fvR6NGjUwdDtVSp06dQnp6OkJDQ2FhYQELCwscPHgQK1euhIWFBZRKpalDpFrG09MTgYGBOscCAgKQkpJiooioNps6dSqmT5+OoUOHonXr1hg5ciQmTZqEmJgYU4dGdYCHhwcA4N69ezrH7927p73P3DCRohpNFEWMGzcOmzZtwl9//QU/Pz9Th0S1WI8ePXDhwgWcPXtW+xUWFobhw4fj7NmzkEqlpg6RapmIiIhiWzpcvXoVjRs3NlFEVJvl5uZCItH9aCiVSqFSqUwUEdUlfn5+8PDwwL59+7THsrOzERcXh/DwcBNGVjou7aMabezYsdiwYQP++OMPODg4aNfQOjk5QSaTmTg6qm0cHByK1d/Z2dmhfv36rMsjo5g0aRI6d+6MBQsW4I033sDx48fx7bff4ttvvzV1aFQL9e/fH/Pnz4ePjw+CgoJw5swZLFu2DO+8846pQ6NaQi6X4/r169rbSUlJOHv2LFxcXODj44OJEyfi888/R/PmzeHn54cZM2bAy8tL29nP3LD9OdVogiCUeHzt2rUYNWpU9QZDdVK3bt3Y/pyMauvWrfj4449x7do1+Pn5YfLkyRgzZoypw6JaKCcnBzNmzMCmTZuQnp4OLy8vDBs2DDNnzoSVlZWpw6Na4MCBA+jevXux42+//TbWrVsHURQxa9YsfPvtt8jMzESXLl3wz3/+E/7+/iaItnxMpIiIiIiIiPTEGikiIiIiIiI9MZEiIiIiIiLSExMpIiIiIiIiPTGRIiIiIiIi0hMTKSIiIiIiIj0xkSIiIiIiItITEykiIiIiIiI9MZEiIiIyAlEUsWzZMpw8edLUoRARkREwkSIiohrD19cXy5cvN3UYWrNnz0ZISEiJ98XExGDnzp1o06ZN9QZFRETVQhBFUTR1EERERAAwatQo/Pjjj8WO9+7dGzt37sT9+/dhZ2cHW1tbE0RXnFwuR0FBAerXr69z/NChQ5g4cSIOHDgAR0dHE0VHRETGxESKiIjMxqhRo3Dv3j2sXbtW57i1tTXq1atnoqiIiIiK49I+IiIyK9bW1vDw8ND50iRRzy/ty8zMxHvvvYcGDRrA0dERL730Es6dO6cz3p9//on27dvDxsYGrq6uGDRokPY+QRCwefNmnfOdnZ2xbt067e3bt29j2LBhcHFxgZ2dHcLCwhAXFweg+NI+lUqFuXPnolGjRrC2tkZISAh27typvT85ORmCIGDjxo3o3r07bG1t0aZNG8TGxlbxXSMiourGRIqIiGqs119/Henp6dixYwdOnTqF0NBQ9OjRAxkZGQCAbdu2YdCgQejTpw/OnDmDffv2oUOHDhUeXy6X48UXX8SdO3ewZcsWnDt3Dh999BFUKlWJ569YsQJLly7FkiVLcP78efTu3Ruvvvoqrl27pnPep59+iujoaJw9exb+/v4YNmwYioqKKv9GEBFRtbMwdQBERETP2rp1K+zt7XWOffLJJ/jkk090jh05cgTHjx9Heno6rK2tAQBLlizB5s2b8b///Q/vv/8+5s+fj6FDh2LOnDnax+nT/GHDhg24f/8+Tpw4ARcXFwBAs2bNSj1/yZIlmDZtGoYOHQoAWLRoEfbv34/ly5fj66+/1p4XHR2Nvn37AgDmzJmDoKAgXL9+HS1btqxwbEREZFpMpIiIyKx0794dq1ev1jmmSWKede7cOcjl8mKNHvLy8pCYmAgAOHv2LMaMGVPpWM6ePYu2bduW+PzPy87Oxt27dxEREaFzPCIiothyw+DgYO33np6eAID09HQmUkRENQgTKSIiMit2dnZlzvpoyOVyeHp64sCBA8Xuc3Z2BgDIZLIyxxAEAc/3XFIoFNrvy3t8ZVlaWurEAKDU5YJERGSeWCNFREQ1UmhoKNLS0mBhYYFmzZrpfLm6ugJQz/zs27ev1DEaNGiA1NRU7e1r164hNzdXezs4OBhnz57V1lyVxdHREV5eXjh69KjO8aNHjyIwMFDfl0dERGaOM1JERGRWCgoKkJaWpnPMwsJCmxxpvPzyywgPD8fAgQOxePFi+Pv74+7du9oGE2FhYZg1axZ69OiBpk2bYujQoSgqKsL27dsxbdo0AMBLL72Er776CuHh4VAqlZg2bZrObNGwYcOwYMECDBw4EDExMfD09MSZM2fg5eWF8PDwYrFPnToVs2bNQtOmTRESEoK1a9fi7NmzWL9+vRHeKSIiMiUmUkREZFZ27typrRvSaNGiBS5fvqxzTBAEbN++HZ9++ilGjx6N+/fvw8PDA127doW7uzsAoFu3bvjvf/+LefPmYeHChXB0dETXrl21YyxduhSjR4/GCy+8AC8vL6xYsQKnTp3S3m9lZYXdu3djypQp6NOnD4qKihAYGKjTOOJZ48ePR1ZWFqZMmYL09HQEBgZiy5YtaN68uaHeHiIiMhPckJeIiGoMT09PzJs3D++9956pQyEiojqOM1JERGT2cnNzcfToUdy7dw9BQUGmDoeIiIjNJoiIyPx9++23GDp0KCZOnFhibRIREVF149I+IiIiIiIiPXFGioiIiIiISE9MpIiIiIiIiPTERIqIiIiIiEhPTKSIiIiIiIj0xESKiIiIiIhIT0ykiIiIiIiI9MREioiIiIiISE9MpIiIiIiIiPTERIqIiIiIiEhP/x9PrXYpyJbF4AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [284.26, 239.341, 273.38, 273.295, 274.83, 272.66, 260.874, 260.874, 272.749, 274.086]\n",
+    "tiempo_entrenamiento_gpu = [285.547, 251.118, 276.689, 276.688, 273.938, 285.323, 261.994, 261.86, 285.496, 274.691]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ba228d66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [9511, 9485, 9500, 9510, 9540, 9589, 9480, 9503, 9448, 9554]\n",
+    "exactitud_gpu = [9420, 9503, 9558, 9411, 9472, 9531, 9532, 9587, 9323, 9484]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9bf2cb80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [51.919, 44.636, 53.006, 53.006, 51.761, 52.408, 48.588, 48.668, 51.972, 51.822]\n",
+    "tiempo_inferencia_gpu = [52.083, 44.334, 52.132, 52.177, 51.875, 49.894, 48.766, 48.737, 49.784, 51.87]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "53a09406",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [886.235, 781.763, 918.454, 918.452, 907.685, 908.602, 849.307, 848.574, 911.099, 907.219]\n",
+    "tiempo_entrenamiento_gpu = [875.077, 784.811, 920.44, 919.883, 942.762, 918.752, 871.558, 871.496, 919.484, 942.761]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d5febb4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [9176, 9093, 9205, 9107, 9148, 9115, 9145, 9237, 9144, 8951]\n",
+    "exactitud_gpu = [9090, 9166, 9249, 9154, 9083, 9180, 9100, 9163, 9132, 9122]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "813f3768",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [6.096, 7.602, 9.022, 9.022, 8.824, 8.817, 7.527, 7.516, 8.798, 8.824]\n",
+    "tiempo_inferencia_gpu = [5.821, 7.625, 8.507, 8.569, 8.919, 8.934, 7.516, 7.511, 8.934, 8.919]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9e1e71bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [131.332, 111.552, 129.261, 129.163, 130.462, 127.84, 112.905, 112.906, 127.983, 130.459]\n",
+    "tiempo_entrenamiento_gpu = [135.0, 111.16, 125.4, 125.27, 129.674, 129.88, 111.763, 111.762, 129.879, 129.674]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4b2c818d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [9320, 9200, 9121, 9142, 9253, 9197, 9230, 9302, 9180, 9146]\n",
+    "exactitud_gpu = [9133, 9284, 9278, 9235, 8708, 9123, 9177, 9139, 9170, 9194]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1cc77d94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [21.918, 18.698, 21.646, 21.677, 21.5, 21.845, 18.694, 18.7052, 21.803, 21.469]\n",
+    "tiempo_inferencia_gpu = [21.581, 18.502, 21.507, 21.28, 22.15, 21.376, 18.254, 18.229, 21.372, 22.164]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "662390da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [333.392, 311.051, 356.358, 356.003, 358.609, 363.542, 310.19, 310.18, 363.413, 358.644]\n",
+    "tiempo_entrenamiento_gpu = [329.895, 304.568, 353.362, 354.196, 364.563, 360.09, 296.87, 297.283, 360.048, 364.44]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "4a0bac97",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [8463, 8941, 8901, 8931, 8611, 8818, 8837, 8835, 8844, 8673]\n",
+    "exactitud_gpu = [8744, 8954, 8900, 8595, 8850, 8853, 8925, 8735, 8617, 8912]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "27900af7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [8.392, 7.043, 7.669, 7.666, 7.581, 7.81, 7.03, 7.03, 7.79, 7.577]\n",
+    "tiempo_inferencia_gpu = [4.427, 7.028, 8.012, 8.021, 7.699, 7.988, 6.71, 6.703, 7.993, 7.7]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f30c95a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [100.836, 100.182, 108.688, 108.733, 112.214, 112.462, 100.006, 100.004, 112.472, 112.176]\n",
+    "tiempo_entrenamiento_gpu = [86.438, 98.306, 112.468, 112.46, 111.97, 111.936, 95.044, 94.934, 111.937, 111.968]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "0ba268a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 12\n",
+    "exactitud_cpu = [8939, 8897, 8800, 8900, 8920, 8947, 8820, 8788, 8920, 8559]\n",
+    "exactitud_gpu = [8964, 8946, 8964, 8946, 8964, 8946, 8964, 8946, 8964, 8946]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "0f94a836",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 12\n",
+    "tiempo_inferencia_cpu = [16.734, 15.352, 18.342, 18.332, 17.308, 17.6, 15.176, 15.176, 17.655, 17.309]\n",
+    "tiempo_inferencia_gpu = [15.546, 15.533, 10.456, 15.639, 10.456, 15.639,15.546, 15.533, 10.456, 15.639]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "550a8670",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 12\n",
+    "tiempo_entrenamiento_cpu = [251.586, 258.968, 299.264, 298.437, 300.302, 297.073, 248.295, 248.317, 297.113, 300.15]\n",
+    "tiempo_entrenamiento_gpu = [252.469, 252.465, 176.054, 255.036, 285.562, 252.036, 200.003, 258.036, 182.523, 255.036]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "86043236",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [9518, 9655, 9546, 9612, 9624, 9603, 9661, 9633, 9545, 9659]\n",
+    "exactitud_gpu = [9616, 9605, 9616, 9606, 9684, 9659, 9205, 9582, 9568, 9632]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "3b7301cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [28.122, 28.243, 28.566, 28.102, 28.095, 27.415, 27.479, 28.843, 28.837, 15.676]\n",
+    "tiempo_inferencia_gpu = [101.03, 28.583, 28.524, 28.646, 28.761, 91.693, 28.355, 30.677, 30.677, 15.999]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "650dc3bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [486.576, 485.602, 505.469, 486.97, 486.976, 481.844, 481.564, 480.338, 480.32, 283.615]\n",
+    "tiempo_entrenamiento_gpu = [1818.157, 506.249, 509.052, 497.716, 497.039, 1826.225, 508.983, 502.729, 502.825, 287.526]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "78de2211",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [9573, 9596, 9628, 9612, 9657, 9570, 9634, 9634, 9653, 9612]\n",
+    "exactitud_gpu = [9463, 9616, 9580, 9635, 9636, 9472, 9662, 9586, 9628, 9637]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "20069102",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [101.889, 101.814, 97.975, 97.771, 96.442, 100.963, 100.61, 100.143, 99.979, 53.692]\n",
+    "tiempo_inferencia_gpu = [14.46, 100.076, 102.194, 106.921, 106.971, 9.604, 95.138, 106.025, 106.12, 57.616]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "3f018d40",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [1792.243, 1792.046, 1795.466, 1758.339, 1765.855, 1770.342, 1771.619, 1805.684, 1806.816, 1013.849]\n",
+    "tiempo_entrenamiento_gpu = [234.047, 1824.474, 1877.544, 1833.155, 1832.856, 212.42, 1812.238, 1823.627, 1823.116, 1058.987]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "22ac2876",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [9510, 9410, 9468, 9469, 9295, 9443, 9449, 9497, 9381, 9467]\n",
+    "exactitud_gpu = [9376, 9429, 9411, 9388, 9451, 9428, 9415, 9441, 9423, 9462]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "dbcd236b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [13.352, 13.352, 15.97, 13.453, 13.453, 13.197, 13.175, 14.361, 14.361, 7.79]\n",
+    "tiempo_inferencia_gpu = [42.51, 14.46, 16.336, 14.515, 14.515, 36.795, 13.665, 14.698, 14.698, 8.018]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "e441685f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CqVTqnQOuXbsmlEqlmDlzpt7zT506JWxtbQ2mF6R7PYX9ODg4SMvpkqAqVaro7RO6L7W//fabNK2o85RuHe7u7iIpKUlvXqdOnUSDBg1EVlaWNE2j0YjWrVuLWrVqGcRbkmNuwWOdEEK8/vrrwtnZWW877du3FwDEjz/+KE3TffY2Njbi0KFD0nTdeTH//48p9scnHbvv3LljsC/qNG7cWPj4+Ih79+5J006cOCFsbGxETEyMwfL5GXOuBCDs7e31ztMnTpwQAMTixYuL3U6TJk2Ep6enwfT09PQiL1Tqjq///e9/xZ07d8StW7fE5s2bRWhoqFAoFOLo0aN6yzGRqpjYtI8s1tq1a+Hh4YEuXbrg7t270k9kZCRcXV2xa9cuveUjIiIQFRUl/d2yZUsA2tvowcHBBtPzV83RGTVqlPRY1wwgJydHagqxZcsWKJVKjB49Wu9548aNgxACW7duLfL1HDt2DElJSfjPf/6j155+6NCh8PDwMHjt4eHhqFu3rt5r1zUJKPjaC+Po6IgdO3YY/MyZM8dg2ddee02vKlC7du2gVqtx/fr1J25HZ8SIEXrt7Hfs2IGUlBS89NJLeq9BqVSiZcuWhb6Ggn1k2rVrZ/A5OTk5SY/v37+P1NRUtGvXrtCmWJ06ddJrnqf77Pv27Qs3NzeD6YXtEzql2R/btWsn/V2tWjXUqVOn2G3o7Ny5Ezk5OXjnnXdgY/P4MD1ixAi4u7sb9DvKTwiBdevWoVevXhBC6MUaHR2N1NRUg/fqlVde0dsndXEXF2taWhoA6L2PJbF9+3ZUq1YN1apVQ6NGjbB27VoMHjwYc+fONWo9TzJgwADk5uZi/fr1ettOSUnBgAEDpGmenp44fPgwbt26ZdLtG6M0/yv5C1t4enqiTp06cHFxQf/+/aXpderUgaenZ6Gf42uvvabXD/CNN96Ara0ttmzZAqBs+6Barca2bdvw3HPP6R17w8PDER0drbfs+vXrodFo0L9/f73X7ufnh1q1apXoWAcAS5cuNTjWFXY8HjBgALy8vKS/S7KvF9S3b1+pqRoAJCcn488//0T//v3x4MED6TXcu3cP0dHRuHTpEm7evKm3jpIcc/Mf63TrbdeuHTIyMnD+/Hm99bm6uuLFF1+U/tZ99uHh4dLxDSjZsc5cx+7CJCQk4J9//sHQoUPh7e0tTW/YsCG6dOki7Y9FMfZc2blzZ9SoUUNvO+7u7k+MNS0tDa6urgbTP/jgA+l4Vq1aNbz88ssGywwbNgzVqlVDQEAAevTogYcPH+Lbb7+VpU8fmR6LTZDFunTpElJTU+Hj41Po/KSkJL2/85+wAUjJiUqlKnR6wf4ANjY2CAsL05tWu3ZtAJDajl+/fh0BAQEGXx51VbmKSzx082rVqqU3XVcSNb9Lly7h3Llzeifr/Aq+9sIolUp07tz5icsBhu+d7otGYX0milKwYtalS5cAPG4PXpC7u7ve346Ojgav18vLyyCGTZs2YcaMGfjnn3/02sAXVh62rPtEwddTlv0RKPz1FEa3r9SpU0dvur29PcLCwordz+7cuYOUlBR89dVXetXyjIm1JJ+/7vN78OBBkcsUpmXLlpgxYwYUCgWcnZ0RHh4OT09Po9YBFP5559eoUSPUrVsXa9aswfDhwwEAa9asQdWqVfX2yXnz5mHIkCFQqVSIjIxE9+7dERMTY/A/aU6m+F/x8PBAUFCQwfvi4eFR6OdY8Djk6uoKf39/vWMdUPp9MDMz02AbuvXl/3J86dIlCCEKXRYoedGXFi1alOiLqTmOdZcvX4YQAh999BE++uijQp+TlJSEwMBAo+I4c+YMPvzwQ/z555/ShQud1NRUvb+L+uxLe6wDTH/sLkxR+xmgPa9u27at2OJFxp4rS3tcdnNzw7179wymv/nmm9JQDUUVd5o8eTLatWsHpVKJqlWrIjw8HLa2/PptLfhJksXSaDTw8fHBDz/8UOj8ggfOoqoOFTVdlLA4hBw0Gg0aNGiABQsWFDq/4MmxrEzxHuW/egpAKh2+evVq+Pn5GSxf8ERSkqpRe/fuxbPPPounnnoKX3zxBfz9/WFnZ4eVK1cadKovbp2leb2m2h/Nvd/p3vdBgwZhyJAhhS7TsGFDvb9LE6u7uzsCAgJw+vRpo+KrWrXqExN8R0dHvfGn8svIyJCWeZIBAwZg5syZuHv3Ltzc3LBx40a89NJLevte//79pbGstm/fjk8++QRz587F+vXrS9Rx3BRM9b9SUY91CoUCW7duLTT+wu4ClIU5j3Xjx483uOOmU7AM+5PiSElJQfv27eHu7o5p06ahRo0acHR0RGxsLN59912DoRlMfawDTHvsNhdjz5Wl/fzr1q2Lf/75Bzdv3tRLiGvXri1dcC3qmNSgQYNij3m65xV3zCvt+I9kfkykyGLVqFEDO3fuRJs2bQxOXOag0Wjw77//SgdFALh48SIASM3DQkJCsHPnTjx48EDvrpSumUVISEiR69fNu3Tpkt6VvtzcXFy9ehWNGjWSptWoUQMnTpxAp06dLGYgPmPj0DWf8PHxKfGdsSdZt24dHB0dsW3bNr0xT1auXGmS9RfHHPtjUe+pbl+5cOGC3p2RnJwcXL16tdj3s1q1anBzc4NarTbZ+16Unj174quvvsLBgwf1mtWWVUhIiFQJraALFy5IyzzJgAEDMHXqVKxbtw6+vr5IS0vTa/6k4+/vjzfffBNvvvkmkpKS0LRpU8ycObPcEilz/K88yaVLl/D0009Lf6enpyMhIQHdu3cHUPZ90MnJSbqzkZ/u89OpUaMGhBCoXr263rFXTsYe63Tvj52dnck+v927d+PevXtYv349nnrqKWl6eVR6M8f+WJJjXUHnz59H1apVix1Ko7zOlT179sRPP/2EH374ARMnTjTpuot7D3TTS3K8I3mwjxRZrP79+0OtVmP69OkG8/Ly8kpcgtcYS5YskR4LIbBkyRLY2dmhU6dOAIDu3btDrVbrLQcACxcuhEKhKPaLV7NmzVCtWjUsX74cOTk50vRVq1YZvJb+/fvj5s2b+Prrrw3Wk5mZiYcPH5bm5ZWJi4uLUe95dHQ03N3dMWvWLOTm5hrML1jqtySUSiUUCoVe+d9r165hw4YNRq/LWObYH3VfEAo+t3PnzrC3t8fnn3+ud6V0xYoVSE1NRY8ePYpcp1KpRN++fbFu3bpC7xaV5n0vysSJE+Hi4oJXX30Vt2/fNph/5coVLFq0yOj1du/eHTdu3DD4XLOzs/HNN9/Ax8cHTZs2feJ6wsPD0aBBA6xZswZr1qyBv7+/3pdStVpt0ETKx8cHAQEBes1G7969i/Pnz0t3w0zNHP8rT/LVV1/pbWvZsmXIy8uTjmFl3Qejo6OxYcMGxMXFSdPPnTuHbdu26S3bp08fKJVKTJ061eCugBCi0OZU5lbU/2VRfHx80KFDB3z55ZdISEgwmF/aYx2gf6ckJycHX3zxhdHrMpY59kdnZ2cAhu+pv78/GjdujG+//VZv3unTp7F9+3YpsS9KeZ0r+/fvj4iICEyfPh2HDh0qdJnS3vnVvQfff/+9wftz/PhxHDp0qNwu6pDxeEeKLFb79u3x+uuvY/bs2fjnn3/QtWtX2NnZ4dKlS1i7di0WLVokDXJnCo6Ojvj9998xZMgQtGzZElu3bsXmzZvx/vvvS822evXqhaeffhoffPABrl27hkaNGmH79u349ddf8c477+h1Yi3Izs4OM2bMwOuvv46OHTtiwIABuHr1KlauXGnQH2Pw4MH4+eef8Z///Ae7du1CmzZtoFarcf78efz888/Ytm3bE/sD5OXl4fvvvy903vPPP2/0gLmRkZFYtmwZZsyYgZo1a8LHx6fINvSAtunXsmXLMHjwYDRt2hQvvvgiqlWrhri4OGzevBlt2rQxSEifpEePHliwYAG6deuGl19+GUlJSVi6dClq1qyJkydPGrUuY5ljf2zcuDGUSiXmzp2L1NRUODg4oGPHjvDx8cGkSZMwdepUdOvWDc8++ywuXLiAL774As2bN3/iQMtz5szBrl270LJlS4wYMQIRERFITk5GbGwsdu7cieTk5LK8FZIaNWrgxx9/xIABAxAeHo6YmBjUr18fOTk5OHDgANauXYuhQ4cavd7XXnsN//3vf/HCCy9g2LBhaNKkCe7du4c1a9bg9OnT+O6770o8AOqAAQMwefJkODo6Yvjw4XqFEx48eICgoCD069cPjRo1gqurK3bu3ImjR49i/vz50nJLlizB1KlTsWvXrkIH1y0rc/yvPElOTg46deqE/v37S/tW27Zt8eyzzwLQ3lUqyz44depU/P7772jXrh3efPNN5OXlYfHixahXr57e/2qNGjUwY8YMTJo0CdeuXcNzzz0HNzc3XL16Fb/88gtee+01jB8//omvZ+vWrQYFGACgdevWRvd3i4yMBACMHj0a0dHRUCqVhd7JzG/p0qVo27YtGjRogBEjRiAsLAy3b9/GwYMHcePGDZw4ccKoGFq3bg0vLy8MGTIEo0ePhkKhwOrVq8ulmaY59kcnJydERERgzZo1qF27Nry9vVG/fn3Ur18fn3zyCZ555hlERUVh+PDhyMzMxOLFi+Hh4aE31lRhTHGuLAk7Ozv88ssviI6ORtu2bdGnTx9p3KubN29i48aNiIuLK/YCQ3EWLFiA6OhoNG7cGEOHDkVAQADOnTuHr776Cv7+/hy415KVX4FAIuPHkRJCWx48MjJSODk5CTc3N9GgQQMxceJEcevWLWmZosolAzAobawrYfvJJ5/obdvFxUVcuXJFGpPC19dXfPzxx3qlf4XQloYdM2aMCAgIEHZ2dqJWrVrik08+0StlW5wvvvhCVK9eXTg4OIhmzZqJv/76S7Rv396gtHhOTo6YO3euqFevnnBwcBBeXl4iMjJSTJ06Va/EamGKK3+OfGVudWVvdWVYdXTlWPOXbk5MTBQ9evQQbm5ueuXai1pH/nVFR0cLDw8P4ejoKGrUqCGGDh0qjh07phevi4uLwXN149jkt2LFClGrVi3h4OAg6tatK1auXFnociX97PO/3vzlZ82xPxb2OX/99dciLCxMKgud/z1fsmSJqFu3rrCzsxO+vr7ijTfeeOK4Ojq3b98WI0eOFCqVStjZ2Qk/Pz/RqVMn8dVXXxX7uoV4/D49qcS4zsWLF8WIESNEaGiosLe3F25ubqJNmzZi8eLFemWaiytrXtD9+/fFmDFjRPXq1YWdnZ1wd3cXTz/9tNi6dWuJnq9z6dIlab/ft2+f3rzs7GwxYcIE0ahRI+Hm5iZcXFxEo0aNDMYu0+1fhZUyL4qx40gJUbb/lfbt24t69eoZTC/4nuu2vWfPHvHaa68JLy8v4erqKgYOHKhXflqnLPvgnj17RGRkpLC3txdhYWFi+fLlhf6vCiHEunXrRNu2bYWLi4twcXERdevWFSNHjhQXLlwodhvFlT/Pvw8X9b8vhDAoy52XlyfeeustUa1aNaFQKKR4i1uHENqx4WJiYoSfn5+ws7MTgYGBomfPnuJ///ufQbwlOebu379ftGrVSjg5OYmAgAAxceJEqXx5/uVK+tnnf735j43m2B8L+5wPHDgg7Q8F3/OdO3eKNm3aCCcnJ+Hu7i569eolzp49a7DewpT0XFnYOUEI7fs0ZMiQEm0rJSVFTJs2TTRp0kS4uroKe3t7oVKpRL9+/fRK6Avx5LLmBR06dEj07NlTeHl5CVtbWxEYGCheffVVo8auovKnEMKCe6ESlZOhQ4fif//7H9LT0+UOhYjIbFatWoVXXnkFR48eZfllIqIyYh8pIiIiIiIiIzGRIiIiIiIiMhITKSIiIiIiIiOxjxQREREREZGReEeKiIiIiIjISEykiIiIiIiIjMQBeQFoNBrcunULbm5uUCgUcodDREREREQyEULgwYMHCAgI0BvIvSAmUgBu3boFlUoldxhERERERGQh4uPjERQUVOR8JlIA3NzcAGjfLHd3d5mjodLIzc3F9u3b0bVrV9jZ2ckdDlUC3OeoPHF/o/LGfY7Kk6Xtb2lpaVCpVFKOUBQmUoDUnM/d3Z2JVAWVm5sLZ2dnuLu7W8Q/IFk/7nNUnri/UXnjPkflyVL3tyd1+WGxCSIiIiIiIiMxkSIiIiIiIjISEykiIiIiIiIjsY8UERERGUWj0SAnJ0fuMMiMcnNzYWtri6ysLKjVarnDIStX3vubnZ0dlEplmdfDRIqIiIhKLCcnB1evXoVGo5E7FDIjIQT8/PwQHx/PMTbJ7OTY3zw9PeHn51em7TGRIiIiohIRQiAhIQFKpRIqlarYgSqpYtNoNEhPT4erqys/ZzK78tzfhBDIyMhAUlISAMDf37/U62IiRURERCWSl5eHjIwMBAQEwNnZWe5wyIx0zTcdHR2ZSJHZlff+5uTkBABISkqCj49PqZv58T+DiIiISkTXd8He3l7mSIiIykZ3MSg3N7fU62AiRUREREZhnxkiquhMcRxjIkVERERERGQkJlJERERUqQ0dOhTPPfec3GGYlEKhwIYNG+QOw2qsWrUKnp6ecodRKeXk5KBmzZo4cOBAiZYNDQ3FsWPHyiEyJlJERERUztQagYNX7uHXf27i4JV7UGuE2balUCiK/ZkyZQoWLVqEVatWmS2GiujatWvw8vKCUqk0eM8OHTpU4vV06NAB77zzjvkCLScDBgzAxYsXTbrO3bt3Q6FQICUlxaTrNbV169ahQ4cO8PDwgKurKxo2bIhp06YhOTkZgDbJ1O0bNjY2CAoKwiuvvCJVxbt27RoUCgX++ecfg3WXZP9Yvnw5qlevjtatWz8xVnt7e4wfPx7vvvuu0a+zNFi1j4iIiMrN76cTMPW3s0hIzZKm+Xs44uNeEehWv/RliIuSkJAgPV6zZg0mT56MCxcuSNNcXV3h6upq8u1ai+3bt6NBgwZ606pUqWLSbQghoFarYWtruV9LnZycpEpvlckHH3yAuXPnYsyYMZg1axYCAgJw6dIlLF++HKtXr8bbb78NAHB3d8eFCxeg0Whw4sQJvPLKK7h16xa2bdtWpu0LIbBkyRJMmzatxM8ZOHAgxo0bhzNnzqBevXpl2v6T8I4UERERlYvfTyfgje9j9ZIoAEhMzcIb38fi99MJRTyz9Pz8/KQfDw8PKBQKvWmurq4GTfs0Gg1mz56N6tWrw8nJCY0aNcL//vc/ab7uTsK2bdvQpEkTODk5oWPHjkhKSsLWrVsRHh4Od3d3vPzyy8jIyJCe16FDB4waNQqjRo2Ch4cHqlatio8++ghCPL4jd//+fcTExMDLywvOzs545plncOnSpWJf46VLl/DUU0/B0dERERER2LFjh8Ey8fHx6N+/Pzw9PeHt7Y3evXvj2rVrT3z/qlSpovd++fn5wc7ODgAwZcoUNG7cGKtXr0ZoaCg8PDzw4osv4sGDBwC0TSb37NmDRYsWSXcsrl27Jr1/W7duRWRkJBwcHLBv374Sv+9//PEHmjVrBmdnZ7Ru3VovMb5y5Qp69+4NX19fuLq6onnz5ti5c6feawoNDcWMGTMQExMDV1dXhISEYOPGjbhz5w569+4t3XXJ3zyssKZ9v/76K5o2bQpHR0eEhYVh6tSpyMvLk+YrFAp88803eP755+Hs7IxatWph48aNALR3aZ5++mkAgJeXFxQKBYYOHQoAyM7OxujRo+Hj4wNHR0e0bdsWR48eLfZzys7Oxvjx4xEYGAgXFxe0bNkSu3fvNoh/27ZtCA8Ph6urK7p166Z3oaGgI0eOYNasWZg/fz4++eQTtG7dGqGhoejSpQvWrVuHIUOG6L1WPz8/BAQE4JlnnsHo0aOxc+dOZGZmFhv3kxw/fhxXrlxBjx49pGk5OTkYNWoU/P394ejoiJCQEMyePVua7+XlhTZt2uCnn34q07ZLgomUJdg1G9gzr/B5e+Zp5xMREVkYIQQycvJK9PMgKxcfbzyDwhrx6aZN2XgWD7JyS7S+/MmHqc2ePRvfffcdli9fjjNnzmDMmDEYNGgQ9uzZo7fclClTsGTJEhw4cEBKVD777DP8+OOP2Lx5M7Zv347FixfrPefbb7+Fra0tjhw5gkWLFmHBggX45ptvpPlDhw7FsWPHsHHjRhw8eBBCCHTv3r3IEs0ajQZ9+vSBvb09Dh8+jOXLlxs0a8rNzUV0dDTc3Nywd+9e7N+/X/oinZOTU6b36sqVK9iwYQM2bdqETZs2Yc+ePZgzZw4AYNGiRYiKisKIESOQkJCAhIQEqFQq6bnvvfce5syZg3PnzqFhw4Ylft8/+OADzJ8/H8eOHYOtrS2GDRsmzUtPT0f37t3xxx9/4O+//0a3bt3Qq1cvxMXF6a1j4cKFaNOmDf7++2/06NEDgwcPRkxMDAYNGoTY2FjUqFEDMTExRe5ne/fuRUxMDN5++22cPXsWX375JVatWoWZM2fqLTd16lT0798fJ0+eRPfu3TFw4EAkJydDpVJh3bp1AIALFy4gISEBixYtAgBMnDgR69atw7fffovY2FjUrFkT0dHRUlO6wowaNQoHDx7ETz/9hJMnT+KFF15At27d9JLwjIwMfPrpp1i9ejX++usvxMXFYfz48UWu84cffoCrqyvefPPNQucX12fMyckJGo1GL7Esjb1796J27dpwc3OTpn3++efYuHEjfv75Z1y4cAE//PADQkND9Z7XokUL7N27t0zbLgnLvYdamdgogV2P/vHaT3w8fc887fSnP5AnLiIiomJk5qoRMblsTXd0BIDEtCw0mLK9RMufnRYNZ3vTf43Jzs7GrFmzsHPnTkRFRQEAwsLCsG/fPnz55Zdo3769tOyMGTPQpk0bAMDw4cMxadIkXLlyBWFhYQCAfv36YdeuXXqJjUqlwsKFC6FQKFCnTh2cOnUKCxcuxIgRI3Dp0iVs3LgR+/fvl/qD/PDDD1CpVNiwYQNeeOEFg3h37tyJ8+fPY9u2bQgICAAAzJo1C88884y0zJo1a6DRaPDNN99IJZ9XrlwJT09P7N69G127di3y/Wjbtq3BAKnp6enSY41Gg1WrVklfdAcPHow//vgDM2fOhIeHB+zt7eHs7Aw/Pz+DdU+bNg1dunQx+n2fOXOm9Pd7772HHj16ICsrC46OjmjUqBEaNWokLTt9+nT88ssv2LhxI0aNGiVN7969O15//XUAwOTJk7Fs2TI0b95ceo/fffddREVF4fbt24XGPnXqVLz33nvSXZmwsDBMnz4dEydOxMcffywtN3ToULz00ksAtJ/L559/jiNHjqBbt27w9vYGAPj4+EhJycOHD7Fs2TKsWrVK+gy//vpr7NixAytWrMCECRMMYomLi8PKlSsRFxcn7QPjx4/H77//jpUrV2LWrFkAtAn18uXLUaNGDQDa5Ku4JnOXLl1CWFiYdAeypHRN/5o1awY3Nzfcu3fPqOfnd/36dek16cTFxaFWrVpo27YtFAoFQkJCDJ4XEBCA69evl3q7JcVEyhLokiddMvXUBOCvTx4nUfmTKyIiIjKby5cvIyMjQ/qCr5OTk4MmTZroTWvYsKH02NfXF87OzlISpZt25MgRvee0atVKb/yaqKgozJ8/H2q1GufOnYOtrS1atmwpza9SpQrq1KmDc+fOFRrvuXPnoFKp9L5s6hIRnRMnTuDy5ct6V/UBICsrC1euXCl0vTr/93//V2w/k9DQUL31+vv7S0UGnqRZs2bS49K+7/7+2n51SUlJCA4ORnp6OqZMmYLNmzcjISEBeXl5yMzMNLgjVfCzA6DXF0w3LSkpqdBE6sSJE9i/f7/eHSi1Wo2srCxkZGRIg73m346Liwvc3d2LfX+uXLmC3NxcKUEHADs7O7Ro0aLIfeDUqVNQq9WoXbu23vTs7Gy9/mzOzs5SEgU8+bMy5q5vamoqXF1dodFokJWVhbZt2+rdaS2tzMxMODo66k0bOnQounTpgjp16qBbt27o2bOnwcUAJycnvWa15sJEylK0nwg8SNQmT7tmARBMooiIyKI52Slxdlp0iZY9cjUZQ1cW388DAFa90hwtqnuXaNvmoLvbsnnzZgQGBurNc3Bw0Ps7/5V6hUJhcOVeoVBAo9GYJU5jpKenIzIyEj/88IPBvGrVqhX7XJVKhZo1axY5vyyv2cXFRS9GoHTvOwBpm+PHj8eOHTvw6aefombNmnByckK/fv0MmjAWto7i1ltQeno6pk6dij59+hjMy//Fvzz2ifT0dCiVShw/fhxKpf7/Rf5CKoXFUlyyVLt2bezbtw+5ublPvCvl5uaG2NhY2NjYwN/fX68wh7u7OwBtslVQSkoKPDw8ilxv1apVcerUKb1pTZs2xdWrV7F161bs3LkT/fv3R+fOnfX60yUnJz9x3zYFJlKWpM1o4NgKAAJQ2jOJIiIii6ZQKErcvK5drWrw93BEYmpWof2kFAD8PBzRrlY1KG0UhSxRPiIiIuDg4IC4uDi95mSmcvjwYb2/Dx06hFq1akGpVCI8PBx5eXk4fPiw1LTv3r17uHDhAiIiIgpdX3h4OOLj45GQkCDdnSlYnrxp06ZYs2YNfHx8pC+15cXe3h5qtfqJy5nqfd+/fz+GDh2K559/HoA2yShJUQ1jNW3aFBcuXCg2yXwSe3t7ANB7f2rUqAF7e3vs379farKWm5uLo0ePFlkmvEmTJlCr1UhKSkK7du1KHU9BL7/8Mj7//HN88cUXUnW+/FJSUqQmiTY2NkW+F97e3qhatSqOHz+u99mmpaXh8uXLBnfS8mvSpAmWLVsGIYTenVx3d3cMGDAAAwYMQL9+/dCtWzckJydLzSVPnz5tcCfTHJhIWZITax4/Vudo+0gxmSIiIiugtFHg414ReOP7WCgAvWRK9/Xo414RsiZRgPbK+vjx4zFmzBhoNBq0bdsWqamp2L9/P9zd3fUqlZVGXFwcxo4di9dffx2xsbFYvHgx5s+fDwCoVasWevfujREjRuDLL7+Em5sb3nvvPQQGBqJ3796Frq9z586oXbs2hgwZgk8++QRpaWn44AP9vtUDBw7EJ598gt69e2PatGkICgrC9evXsX79ekycOBFBQUFFxnvv3j0kJibqTfP09DRoblWU0NBQHD58GNeuXYOrq6v0RbcgU73vtWrVwvr169GrVy8oFAp89NFHZrkrOHnyZPTs2RPBwcHo168fbGxscOLECZw+fRozZswo0TpCQkKgUCiwadMmdO/eHU5OTnB1dcUbb7yBCRMmwNvbG8HBwZg3bx4yMjIwfPjwQtdTu3ZtDBw4EDExMZg/fz6aNGmCO3fu4I8//kDDhg31Kt4Zo2XLlpg4cSLGjRuHmzdv4vnnn0dAQAAuX76M5cuXo23btoUmWIUZO3YsZs2aBV9fX7Rq1Qr37t3D9OnTUa1atULv6uk8/fTTSE9Px5kzZ1C/fn0AwIIFC+Dv748mTZrAxsYGa9euhZ+fn17xi71792L69Omlet3GYNU+S7FnHrB7FlC1jvbvsKe1zfyKquZHRERUwXSr749lg5rCz0P/S7ifhyOWDWpqlnGkSmP69On46KOPMHv2bISHh6Nbt27YvHkzqlevXuZ1x8TEIDMzEy1atMDIkSPx9ttv47XXXpPmr1y5EpGRkejZsyeioqIghMCWLVuKbFplY2ODX375RVrnq6++alA5ztnZGX/99ReCg4PRp08fhIeHY/jw4cjKynriHaquXbvC399f72fDhg0lfr3jx4+HUqlEREQEqlWrZtBXKT9TvO8LFiyAl5cXWrdujV69eiE6OhpNmzYt8fNLKjo6Gps2bcL27dvRvHlztGrVCgsXLiy08EFRAgMDpaIVvr6+UjGMOXPmoG/fvhg8eDCaNm2Ky5cvY9u2bfDy8ipyXStXrkRMTAzGjRuHOnXq4LnnnsPRo0cRHBxcptc5d+5c/Pjjjzh8+DCio6NRr149jB07Fg0bNjTqooKuCMfcuXPRsGFD9O3bFy4uLti1a1ex43NVqVIFzz//vF6zVDc3N8ybNw/NmjVD8+bNce3aNWzZskUqinLw4EGkpqaiX79+pX/hJaQQ5qwfWkGkpaXBw8MDqamp5X7LG4B+dT5bR2DHR0CdHkBAYxacKKHc3Fxs2bIF3bt3N7q6DFFpcJ+j8mQp+1tWVhauXr2K6tWrl/iORGHUGoEjV5OR9CALPm6OaFHdW/Y7UeWhQ4cOaNy4MT777DO5Q3kijUaDtLQ0uLu7G1TtIzK14va3kydPokuXLrhy5UqJBs8eMGAAGjVqhPfff7/Y5Yo7npU0N2DTPkugUT9OluIetZ2OPwy8+MPj+URERFZCaaNAVI0qT16QiCq9hg0bYu7cubh69apeZcXC5OTkoEGDBhgzZky5xMZEyhI8PenxY/9G2kITGXeB5H95J4qIiIiIKrWhQ4eWaDl7e3t8+OGH5g0mHyZSlsbOEfBvDNw4AsQfAarUeOJTiIiIyPLt3r1b7hCIyITY6NUSqVpof8cfLn45IiIiIiKSBRMpS6R6NKJ5/JHilyMiIiIiIlkwkbJEujtSSWeBrDR5YyEiIiIiIgNMpCyRmx/gGQxAADePyR0NEREREREVwETKUrF5HxERERGRxWIiZamkRIoFJ4iIKqVds7UDthdmzzztfCIikg0TKUul6yd14xgH5CUiqoxslMCumYbJ1J552uk2SnniskJDhw7Fc889J3cYJqVQKLBhwwa5w7Aaq1atgqenp9xhVFo5OTmoWbMmDhw4UKJlQ0NDceyY+bvHMJGyVD71ADsXIDsNuHNe7miIiKi8tZ8IPP0BsGsmbHZNh1f6Jdjs/VSbRD39AQdsLyGFQlHsz5QpU7Bo0SKsWrVK7lAtyrVr1+Dl5QWlUmnwnh06dKjE6+nQoQPeeecd8wVaTgYMGICLFy+adJ27d++GQqFASkqKSddrauvWrUPHjh3h5eUFJycn1KlTB8OGDcPff/8tLbNq1Spp/7CxsUFQUBBeeeUVJCUlAdDuTwqFAv/884/B+jt06IAxY8YUG8Py5ctRvXp1tG7d+onx2tvbY/z48Xj33XeNe6GlwAF5LZXSFgiKBK7+pW3e51tP7oiIiKi8PUqWlLtm4ikAuISKnUTtmq29k1ZY/HvmaVtgPD3JpJtMSEiQHq9ZswaTJ0/GhQsXpGmurq5wdXU16Tatyfbt29GgQQO9aVWqVDHpNoQQUKvVsLW13K+lTk5OcHJykjuMcvfuu+9i/vz5GD16NKZOnYqQkBDcuXMHW7duxaRJk/D7779Ly7q7u+PChQvQaDQ4ceIEXnnlFdy6dQvbtm0rUwxCCCxZsgTTpk0r8XMGDhyIcePG4cyZM6hXz3zfoXlHypJJ/aSOyhsHERHJp+0YiEcPhcKm4iZRgCzNFf38/KQfDw8PKBQKvWmurq4GTfs0Gg1mz56N6tWrw8nJCY0aNcL//vc/ab7uTsK2bdvQpEkTODk5oWPHjkhKSsLWrVsRHh4Od3d3vPzyy8jIyJCe16FDB4waNQqjRo2Ch4cHqlatio8++ghCCGmZ+/fvIyYmBl5eXnB2dsYzzzyDS5cuFfsaL126hKeeegqOjo6IiIjAjh07DJaJj49H//794enpCW9vb/Tu3RvXrl174vtXpUoVvffLz88PdnZ2AIApU6agcePGWL16NUJDQ+Hh4YEXX3wRDx48AKBtMrlnzx4sWrRIultx7do16f3bunUrIiMj4eDggH379pX4ff/jjz/QrFkzODs7o3Xr1nqJ8ZUrV9C7d2/4+vrC1dUVzZs3x86dO/VeU2hoKGbMmIGYmBi4uroiJCQEGzduxJ07d9C7d2+4urqiYcOGek3DCmva9+uvv6Jp06ZwdHREWFgYpk6diry8PGm+QqHAN998g+effx7Ozs6oVasWNm7cCEB7h+bpp58GAHh5eUGhUGDo0KEAgOzsbIwePRo+Pj5wdHRE27ZtcfRo8d8Fs7OzMX78eAQGBsLFxQUtW7bE7t27DeLftm0bwsPD4erqim7duuldaCjo0KFDmDdvHhYsWIAFCxagXbt2CA4ORmRkJD788ENs3bpVb3nd/1ZAQACeeeYZjB49Gjt37kRmZmaxsT/J8ePHceXKFfTo0UOalpOTg1GjRsHf3x+Ojo4ICQnB7NmP+416eXmhTZs2+Omnn8q07SdhImXJWHCCiIi2TIDi0UOF0BRdgEIOQgA5D0v+EzUSeGqCNmn6c4Z22p8ztH8/NUE7v6Trypd8mNrs2bPx3XffYfny5Thz5gzGjBmDQYMGYc+ePXrLTZkyBUuWLMGBAwekROWzzz7Djz/+iM2bN2P79u1YvHix3nO+/fZb2Nra4siRI1i0aBEWLFiAb775Rpo/dOhQHDt2DBs3bsTBgwchhED37t2Rm5tbaKwajQZ9+vSBvb09Dh8+jOXLlxs0acrNzUV0dDTc3Nywd+9e7N+/X/oinZOTU6b36sqVK9iwYQM2bdqETZs2Yc+ePZgzZw4AYNGiRYiKisKIESOQkJCAhIQEqFQq6bnvvfce5syZg3PnzqFhw4Ylft8/+OADzJ8/H8eOHYOtrS2GDRsmzUtPT0f37t3xxx9/4O+//0a3bt3Qq1cvxMXF6a1j4cKFaNOmDf7++2/06NEDgwcPRkxMDAYNGoTY2FjUqFEDMTExeklufnv37kVMTAzefvttnD17Fl9++SVWrVqFmTNn6i03depU9O/fHydPnkT37t0xcOBAJCcnQ6VSYd26dQCACxcuICEhAYsWLQIATJw4EevWrcO3336L2NhY1KxZE9HR0UhOTi7ycxg1ahQOHjyIn376CSdPnsQLL7yAbt266SXhGRkZ+PTTT7F69Wr89ddfiIuLw/jx44tc5//93//B1dUVb775ZqHzFQpFodN1nJycoNFo9JLL0ti7dy9q164NNzc3adrnn3+OjRs34ueff8aFCxfwww8/IDQ0VO95LVq0wN69e8u07ScSJFJTUwUAkZqaKnco+jKShfjYXfuTfkfuaCxaTk6O2LBhg8jJyZE7FKokuM9Rudg99/F54GN3oZnipX28e64s4WRmZoqzZ8+KzMxM7YTsdL34yvUnO93o+FeuXCk8PDwMpg8ZMkT07t1bCCFEVlaWcHZ2FgcOHNBbZvjw4eKll14SQgixa9cuAUDs3LlTmj979mwBQFy5ckWa9vrrr4vo6Gjp7/bt24vw8HCh0Wikae+++64IDw8XQghx8eJFAUDs379fmn/37l3h5OQkfv7550Jf07Zt24Stra24efOmNG3r1q0CgPjll1+EEEKsXr1a1KlTR2+72dnZwsnJSWzbtq3Q9V65ckUAEE5OTsLFxUXvR+fjjz8Wzs7OIi0tTZo2YcIE0bJlS73X/Pbbb+utW/f+bdiwQZpW2vd98+bNAsDjfbIQ9erVE4sXL5b+DgkJEYMGDZL+TkhIEADERx99JE07ePCgACASEhKEEIb7TqdOncSsWbP0trN69Wrh7+8v/Q1AfPjhh9Lf6enpAoDYunWr3uu5f/++3jJ2dnbihx9+kKbl5OSIgIAAMW/evEJf3/Xr14VSqdTbB3QxTpo0SYofgLh8+bI0f+nSpcLX17fQdQohRLdu3UTDhg31ps2fP19vX0hJSSn0/bl48aKoXbu2aNasmRBCiKtXrwoA4u+//zbYTvv27cXo0aPF/fv3hVqtNpj/9ttvi44dO+pNe+utt0THjh319umCFi1aJEJDQ4ucb3A8y6ekuYHlNkYlwMkLqFoHuHtBO55U3e5yR0REROVF19ytam3grraTu0KogaZDtdOBit3Mz0JdvnwZGRkZ6NKli970nJwcNGnSRG9aw4YNpce+vr5wdnZGWFiY3rQjR/THg2zVqpXelfyoqCjMnz8farUa586dg62tLVq2bCnNr1KlCurUqYNz584VGu+5c+egUqkQEBCgt878Tpw4gcuXL+td0QeArKwsXLlypdD16vzf//1fsX1MQkND9dbr7+8vFRh4kmbNmkmPS/u++/v7AwCSkpIQHByM9PR0TJkyBZs3b0ZCQgLy8vKQmZlpcEeq4GcHQK8vmG5aUlIS/Pz8DGI/ceIE9u/fr3cHSq1WIysrCxkZGXB2djbYjouLC9zd3Yt9f65cuYLc3Fy0adNGmmZnZ4cWLVoUuQ+cOnUKarUatWvX1puenZ2t15/N2dkZNWrUkP425rPSGTZsGJ599lkcPnwYgwYN0rtjl5qaCldXV2g0GmRlZaFt27Z6d1tLKzMzE46OjnrThg4dii5duqBOnTro1q0bevbsia5du+ot4+TkpNe01hyYSFk6VYtHidRhJlJERJWJRg10eB848iUAINPOC06594GqtbQFJyxhaAw7Z+D9W8Y/b99C4K9PAKU9oM7RNutrW3zVrkK3bQbp6ekAgM2bNyMwMFBvnoODg34Ij/oKAdpmTvn/1k3TaDRmidMY6enpiIyMxA8//GAwr1q1asU+V6VSoWbNmkXOL8trdnFx0YsRKN37DkDa5vjx47Fjxw58+umnqFmzJpycnNCvXz+DJoyFraO49RaUnp6OqVOnok+fPgbz8n/pL499Ij09HUqlEsePH4dSqd/PMH8hlcJiEcU0ka1Vqxb27duH3Nxc6bmenp7w9PTEjRs3DJZ3c3NDbGwsbGxs4O/vr1ecw93dHYA22SooJSUFHh4eRcZRtWpVnDp1Sm9a06ZNcfXqVWzduhU7d+5E//790blzZ70+dcnJyU/cv8uKiZSlU7UE/l6tvSNFRESVx9OTgDsXgN2zIGwdca3q0whPWA/cOAL0/07u6LQUCsDe5cnL5bdnnjaJ0lUf1N15U9pbxB22iIgIODg4IC4uDu3btzf5+g8f1u/3fOjQIdSqVQtKpRLh4eHIy8vD4cOHpTLP9+7dw4ULFxAREVHo+sLDwxEfH4+EhATp7kzB8uRNmzbFmjVr4OPjI32hLS/29vZQq5+c9Jvqfd+/fz+GDh2K559/HoA2yShJUQ1jNW3aFBcuXCg2yXwSe3t7ANB7f2rUqAF7e3vs378fISEhALR93I4ePVpkGfkmTZpArVYjKSkJ7dq1K3U8Bb300ktYvHgxvvjiC7z99ttPXN7GxqbI98Pb2xtVq1bF8ePH9T7ftLQ0XL58GbVq1SpyvU2aNMGyZcsghNC7m+vu7o4BAwZgwIAB6NevH7p164bk5GR4e3sDAE6fPm1wN9PUmEhZOl3BiVuxQF4OYGsvbzxERFR+4g4CAERAU9xzqKudFn9EW2jhCR29LZIuacpfwl3320KaK7q5uWH8+PEYM2YMNBoN2rZti9TUVOzfvx/u7u4YMmRImdYfFxeHsWPH4vXXX0dsbCwWL16M+fPnA9DeAejduzdGjBiBL7/8Em5ubnjvvfcQGBiI3r17F7q+zp07o3bt2hgyZAg++eQTpKWl4YMPPtBbZuDAgfjkk0/Qu3dvTJs2DUFBQbh+/TrWr1+PiRMnIigoqMh47927h8TERL1pnp6eBk2tihIaGorDhw/j2rVrcHV1lb7kFmSq971WrVpYv349evXqBYVCgY8++sgsdwUnT56Mnj17Ijg4GP369YONjQ1OnDiB06dPY8aMGSVaR0hICBQKBTZt2oTu3bvDyckJrq6ueOONNzBhwgR4e3sjODgY8+bNQ0ZGBoYPH17oemrXro2BAwciJiYG8+fPR5MmTXDnzh388ccfaNiwoV61O2NERUVh3LhxGDduHK5fv44+ffpApVIhISEBK1askMaMKqmxY8di1qxZ8PX1RatWrXDv3j1Mnz4d1apVQ58+fYosqPL0008jPT0dZ86cQf369QEACxYsgL+/P5o0aQIbGxusXbsWfn5+epUV9+7di+nTp5fqtZcUq/ZZuio1tX2l8rKA26eevDwREVmP648SKVUUUpyrQ9jYAg8SgFTDZjUVgkZd+DhYusGHLaG5IoDp06fjo48+wuzZsxEeHo5u3bph8+bNqF69epnXHRMTg8zMTLRo0QIjR47E22+/jddee02av3LlSkRGRqJnz56IioqCEAJbtmwxaJalY2Njg19++UVa56uvvmpQOc7Z2Rl//fUXgoOD0adPH4SHh2P48OHIysp64h2qrl27wt/fX+9nw4YNJX6948ePh1KpREREBKpVq2bQVyk/U7zvCxYsgJeXF1q3bo1evXohOjoaTZs2LfHzSyo6OhqbNm3C9u3b0bx5c7Rq1QoLFy6U7iKVRGBgIKZOnYr33nsPvr6+GDVqFABgzpw56Nu3LwYPHoymTZvi8uXL2LZtG7y8vIpc18qVKxETE4Nx48ahTp06eO6553D06FEEBweX6XV++umn+PHHH/H333+jZ8+eqFWrFl544QVoNBocPHjQqDucEydOxMcff4y5c+eiYcOG6Nu3L1xcXLBr165ix+iqUqUKnn/+eb2mqW5ubpg3bx6aNWuG5s2b49q1a9iyZYuU2B08eBCpqano169f6V98CShEcY0jK4m0tDR4eHggNTW13G95l8gP/YFL24Buc4BWb8gdjUXKzc3Fli1b0L179yJPNkSmxH2OysVnDYCUOOS9+DM2X8hCr8QFsEn4B+i7Amhg3i8IhcnKysLVq1dRvXr1Et+RoMc6dOiAxo0b47PPPpM7lCfSaDRIS0uDu7u7UXcdiErjSfvbyZMn0aVLF1y5cqVEA2gPGDAAjRo1wvvvv1/kMsUdz0qaG8j6nxEaGioN0Jb/Z+TIkQC0L3DkyJGoUqUKXF1d0bdvX9y+fVtvHXFxcejRowecnZ3h4+ODCRMmlLlevcVRNdf+5nhSRESVR+pNICUOUNhABGnPAyKohXbeDQ7UTkSVR8OGDTF37lxcvXr1icvm5OSgQYMGGDPGyAI2pSBrInX06FFpgLaEhARpJO4XXngBADBmzBj89ttvWLt2Lfbs2YNbt27pVUdRq9Xo0aMHcnJycODAAXz77bdYtWoVJk+eLMvrMRtpYF4WnCAiqjQe9Y+CXwPAQVteWpdQ8cIaEVU2Q4cO1StRXxR7e3t8+OGHxTYXNBVZi00ULEk4Z84c1KhRA+3bt0dqaipWrFiBH3/8ER07dgSgbf8ZHh6OQ4cOoVWrVti+fTvOnj2LnTt3wtfXF40bN8b06dPx7rvvYsqUKVI1lAovoCmgUAJpN7Xt4j2K7hRKRERWQpdIBT8eE0gEPkqkEk8BORmAvXlKgJN57N69W+4QiMiELKbRa05ODr7//nsMGzYMCoUCx48fR25uLjp37iwtU7duXQQHB+PgQe3J5eDBg2jQoIE0aBqg7fyXlpaGM2fOlPtrMBsHV8BPW6WEVyGJiCqJuEclrPMlUnAPBNwCAE0ecOtveeIiIiIAFlT+fMOGDUhJScHQoUMBAImJibC3t9crYwhoR5rWleFMTEzUS6J083XzipKdnY3s7Gzp77S0NADazuNFlV6Um01gcygTTkB9/RA0dZ6VOxyLo/vcLPXzI+vDfY7MKisVtrfPQAEgN6DZ4/0tLw/KwGawOb8R6usHoQlsUa5h5eXlQQgBtVptEQPNkvnoapEJIfhZk9nJsb+p1WoIIZCXl2dwLi/pud1iEqkVK1bgmWeeQUBAgNm3NXv2bEydOtVg+vbt2+HsbJnNJAKT7dAMQNqZnfgrr63c4VgsXT87ovLCfY7MwSf1BKIgkO7giz/+Oi5N37FjB8IeuKIBgKTYTTiSUvQgluZgY2MDf39/pKam8iJCJfHgwQO5Q6BKpDz3twcPHuDhw4f4888/UbCIeUZGRonWYRGJ1PXr17Fz506sX79emubn54ecnBykpKTo3ZW6ffs2/Pz8pGWOHNEvwKCr6qdbpjCTJk3C2LFjpb/T0tKgUqnQtWtXyyx/DgCpDYAly+CZFYfuXToAdpaZ8MklNzcXO3bsQJcuXViKmsoF9zkyJ5tdx4F/Aec6ndC9e3e9/c0+yQdY9SP8cuPQ/ZlnynVgXiEEbt68iYcPH7IstpUTQuDhw4dwcXGBoiIO/kwVSnnub0IIZGRk4MGDB/D390fjxo0NltG1VnsSi0ikVq5cCR8fH72RlyMjI2FnZ4c//vgDffv2BQBcuHABcXFxiIrSthePiorCzJkzkZSUBB8fHwDaq3Xu7u6IiIgocnsODg5wcHAwmG5nZ2e5X4iqVAdc/aBIT4Rd0mkgtI3cEVkki/4MySpxnyOzeNQf1ia0NWzy7V92dnawDWoKKB2gyLgHuwfxQJUa5RpaYGAgrl69ivj4+HLdLpUvIQQyMzPh5OTERIrMTo79zcvLC35+foVur6TnddkTKY1Gg5UrV2LIkCGwtX0cjoeHB4YPH46xY8fC29sb7u7ueOuttxAVFYVWrVoB0I62HRERgcGDB2PevHlITEzEhx9+iJEjRxaaKFVoCgWgagGc26g9wTKRIiKyTrlZwK1Y7eOQ1obzbR2AgMbac0H8kXJPpOzt7VGrVi3k5OSU63apfOXm5uKvv/7CU089xYtFZHblvb/Z2dlBqVSWeT2yJ1I7d+5EXFwchg0bZjBv4cKFsLGxQd++fZGdnY3o6Gh88cUX0nylUolNmzbhjTfeQFRUFFxcXDBkyBBMmzatPF9C+VG1fJRIcTwpIiKrdSsWUOcALj6Ad1jhy6haaBOpG0eAxi+Vb3zQ9pVydHQs9+1S+VEqlcjLy4OjoyMTKTK7irq/yZ5Ide3a1aCDl46joyOWLl2KpUuXFvn8kJAQbNmyxVzhWRZpYN7DgBDl2i6eiIjKyfUD2t/BrYo+zgc9qtbHC2tERLJhL9GKxL8hoHQAMpOBe1fkjoaIiMxBN35UYc36dFSPEqmks0BWyTpFExGRaTGRqkhsHYCAJtrHN3gVkojI6mjUjwdezz8Qb0FufoBnCCA0wM3jRS9HRERmw0SqotFdhdSdaImIyHrcPgNkpwH2roBv/eKX1Z0Pbhw1f1xERGSAiVRFo2K7eCIiq6Vr1qdqASif0I05f79ZIiIqd0ykKhpdB+Okc0BmiqyhEBGRicXpCk0U0z9KJ6i59nf8UUCjMV9MRERUKCZSFY2bL+AVCkAAN4/JHQ0REZmKEI/vSAW3evLyvvUBO2cgOxW4e9G8sRERkQEmUhWR1JyDzfuIiKzG/WvAgwTAxg4Iavbk5ZW2QGCk9jGb9xERlTsmUhURC04QEVmfuIPa3wFNADunkj1HKjjBC2tEROWNiVRFpLsjdeO4tlQuERFVfLpEqiTN+nQ4MC8RkWyYSFVEPhHa0rg5D7RFJ4iIqOK7/iiRKm4g3oJ0BSfuXgQykk0fExERFYmJVEVko2S7eCIia5J+B7h3SftY1+qgJFyqAFVqaR/fYAEiIqLyxESqomLBCSIi6xH/qFpftXDA2du457KfFBGRLJhIVVQciJGIyHpIzfqijH8uCxAREcmCiVRFpSuNe/8qkJ4kbyxERFQ20kC8pUikdAUnbhwH1Hmmi4mIiIrFRKqicvLUNgEB2LyPiKgiy04HEk5qH5cmkapWF3BwB3IfAklnTRsbEREViYlURcZ28UREFd+No4BQAx4qwFNl/PNtbB63UmDzPiKicsNEqiJjwQkiooqvNONHFSSNL3i07PEQEVGJMJGqyHR3pG7GAnk58sZCRESlIyVSpWjWp6MbT4p3pIiIyg0TqYqsSk3AyQtQZwOJJ+WOhoiIjKXOfTz+kzED8RYU1AyAArh/jQWIiIjKCROpikyhYBl0IqKKLOEkkJsBOHoCVeuUfj2OHoAPCxAREZUnJlIVHccPISKquPKXPbcp4ymZBYiIiMoVE6mKLn/BCSHkjYWIiIxTloF4C2IBIiKicsVEqqILaAoolMCDBCD1htzREBFRSQlhmkITOrqBeW/9zQJERETlgIlURWfvDPg31D5m8z4ioorj7kUgMxmwdQT8G5d9fVVqAE7eQF4WkHiq7OsjIqJiMZGyBrqrkGzOQURUcVx/1D8qsBlga1/29SkU7DdLRFSOmEhZA544iYgqnrhD2t+m6B+lw4ITRETlhomUNdB1ME48BeQ8lDcWIiIqmfwV+0yFLRSIiMoNEylr4BEEuAUAQg3cjJU7GiIiepLUm0BKHKCwAYKam269gY8KEKXdZAEiIiIzYyJlDdgunoioYtFV6/NrADi6m2699i6AX33tY96VIiIyKyZS1kLXvO/GUXnjICKiJ5PKnrc2/bp5PiAiKhdMpKyFNBDjYQ7MS0Rk6XSFJoJbmX7dQWyhQERUHphIWQu/BtqxSDLvA/cuyx0NEREVJTMFuH1G+zjEHHekHiVSCSeB3EzTr5+IiAAwkbIetvZAQBPtY16FJCKyXPGHAQjAuwbg6mP69XsGA65+gCYXuPWP6ddPREQAmEhZFxacICKyfFL/KBOWPc9PoQBUjyoBcjwpIiKzYSJlTaR+UjxxEhFZrOuPEilTDsRbEM8HRERmx0TKmug6GN85r+0rRUREliU3C7j1aLw/c92RAvQLTrAAERGRWTCRsiau1QDvMO3jG8fljYWIiAzdigXUOYCLz+PjtTn4NwKU9sDDO8D9a+bbDhFRJcZEytrkL4NORESW5foB7e+QKG1fJnOxc9QmUwCb9xERmQkTKWsT9KiDMRMpIiLLY+5CE/lJA/MykSIiMgcmUtZGd+K8eRxQ58kbCxERPaZRP747VB6JFC+sERGZFRMpa+MTDti7ATnpQNJZuaMhIiKd22eA7DTtMdq3vvm3p7uwdvsMkJ1u/u0REVUyTKSsjY0SCGqmfcyrkERElkPXrE/VHFDamn977v6AhwoQGm0rBSIiMikmUtaI44cQEVkeqX9U6/Lbpm6gdvaTIiIyOSZS1ognTiIiyyJE+QzEWxAvrBERmQ0TKWsU1AyAQjt2yIPbckdDRET3rwHpiYCNHRAYWX7blQpOHAE0mvLbLhFRJcBEyho5egA+EdrHvCtFRCQ/XbO+gCaAnVP5bdevAWDrBGSlAPcul992iYgqASZS1krFsrdERBYj/0C85UlpBwQ21T7m+YCIyKSYSFkrtosnIrIccYe0v8tj/KiC2G+WiMgsmEhZK10idetvIC9b3liIiCqz9DvAvUvax7pjc3kKepRI8cIaEZFJMZGyVt5hgHMVQJ0DJJyQOxoiospL1z/KJwJw9i7/7evuSN05D2SmlP/2iYisFBMpa6VQsHkfEZElkJr1tZJn+y5VtRfXAODGMXliICKyQkykrJnuKiQ7GBMRySfuUaGJ8hyItyDdhTX2kyIiMhkmUtZMuiN1WDsYJBERla/sdCDhpPaxXHekgHzjSfHCGhGRqTCRsmYBTQAbWyD9NpASJ3c0RESVz42jgFADHirAUyVfHNIdqeOARi1fHEREVoSJlDWzcwL8Gmofs58UEVH50xWakKPseX4+4YC9G5DzAEg6J28sRERWgomUtcvfvI+IiMqXlEjJ2KwPAGyUQFCk9jHPB0REJsFEytqx4AQRkTzUuY+r5IXIWGhCR2red1TeOIiIrITsidTNmzcxaNAgVKlSBU5OTmjQoAGOHXtcnlUIgcmTJ8Pf3x9OTk7o3LkzLl26pLeO5ORkDBw4EO7u7vD09MTw4cORnp5e3i/FMulOnLfPaDs9ExFR+Ug4AeRmAE5eQNU6ckeTb2BeXlgjIjIFWROp+/fvo02bNrCzs8PWrVtx9uxZzJ8/H15eXtIy8+bNw+eff47ly5fj8OHDcHFxQXR0NLKysqRlBg4ciDNnzmDHjh3YtGkT/vrrL7z22mtyvCTL4xEIuAdpOzvfipU7GiKiykPXrE/VCrCR/bolENRM+zv5X+DhXXljISKyArZybnzu3LlQqVRYuXKlNK169erSYyEEPvvsM3z44Yfo3bs3AOC7776Dr68vNmzYgBdffBHnzp3D77//jqNHj6JZM+1JYvHixejevTs+/fRTBAQElO+LskSqFsCZG9qrkNWfkjsaIqLK4fqjRCpE5kITOk6eQLW6wJ3z2gJEdbvLHRERUYUm6yWyjRs3olmzZnjhhRfg4+ODJk2a4Ouvv5bmX716FYmJiejcubM0zcPDAy1btsTBg9oT1MGDB+Hp6SklUQDQuXNn2NjY4PBhNl8AkK+fFCv3ERGVC40mX6EJC+gfpaM7H3BgXiKiMpP1jtS///6LZcuWYezYsXj//fdx9OhRjB49Gvb29hgyZAgSExMBAL6+vnrP8/X1leYlJibCx8dHb76trS28vb2lZQrKzs5Gdna29HdaWhoAIDc3F7m5uSZ7fZZC4d8UtgBE/BHk5WQDCgtoYmJius/NGj8/skzc56hYdy/CLjMZwtYJedUigDLuJ6ba3xT+kbDFd9DEHYKa+y4Vg8c4Kk+Wtr+VNA5ZEymNRoNmzZph1qxZAIAmTZrg9OnTWL58OYYMGWK27c6ePRtTp041mL59+3Y4OzubbbtyUYg8dFfYwzYrBX/9sgLpjoFyh2Q2O3bskDsEqmS4z1FhQu7uQmMAdx1DcWDbTpOtt6z7m2tWJjoB0MQfx9bNGyEUsn4NoAqAxzgqT5ayv2VkZJRoOVmPoP7+/oiIiNCbFh4ejnXr1gEA/Pz8AAC3b9+Gv7+/tMzt27fRuHFjaZmkpCS9deTl5SE5OVl6fkGTJk3C2LFjpb/T0tKgUqnQtWtXuLu7l/l1WSKb5K+BuINoH+YE0dj62sXn5uZix44d6NKlC+zs7OQOhyoB7nNUHOWvvwHxgHfj7ujevuzHXJPtb0IDcW0ubDPvo3sTFURAkzLHRtaJxzgqT5a2v+laqz2JrIlUmzZtcOHCBb1pFy9eREhICABt4Qk/Pz/88ccfUuKUlpaGw4cP44033gAAREVFISUlBcePH0dkpHawwT///BMajQYtW7YsdLsODg5wcHAwmG5nZ2cRH55ZBLcC4g7C9tZxoPkrckdjNlb9GZJF4j5Hhbqh7aOrDG0DpQn3D5Psb0HNgUvbYZv4NxDSwjSBkdXiMY7Kk6XsbyWNQdbOMmPGjMGhQ4cwa9YsXL58GT/++CO++uorjBw5EgCgUCjwzjvvYMaMGdi4cSNOnTqFmJgYBAQE4LnnngOgvYPVrVs3jBgxAkeOHMH+/fsxatQovPjii6zYl59uPCkWnCAiMq/Um0BKnLY/qsoCExUO1E5EZBKy3pFq3rw5fvnlF0yaNAnTpk1D9erV8dlnn2HgwIHSMhMnTsTDhw/x2muvISUlBW3btsXvv/8OR0dHaZkffvgBo0aNQqdOnWBjY4O+ffvi888/l+MlWS7dQIx3LwAZyYCzt7zxEBFZK121Pr8GgIObvLEUJoiVXImITEH2XqY9e/ZEz549i5yvUCgwbdo0TJs2rchlvL298eOPP5ojPOvhUgXwrgEkXwFuHANqd5U7IiIi62SJZc/zC4zU3i1LjQfSbgHubL1BRFQa1lcHm4omNe9jcw4iIrOxtIF4C3JwBXzraR/zrhQRUakxkapM2C6eiMi8Mu8DSWe1j4MtNJECHl9Yu3FU3jiIiCowJlKVie7EefM4oM6TNxYiImsUfwSA0DaldvV54uKyCeKFNSKismIiVZlUqws4uAO5GUDSGbmjISKyPtcPaH9barM+HV0LhYQTQG6WvLEQEVVQTKQqExsb7fghANvFExGZQ9wh7W9LbtYHAF6hgEs1QJ2jTaaIiMhoTKQqGxacICIyj9ws4Fas9rGlJ1IKRb5+UrywRkRUGkykKhuV7o4UEykiIpO6eVx7h8fVF/AOkzuaJ2MBIiKiMjFqHCmNRoM9e/Zg7969uH79OjIyMlCtWjU0adIEnTt3hkqlMlecZCqBzQAogJQ4IC0BcPeXOyIiIusgjR/VSnvHx9LlH5hXiIoRMxGRBSnRHanMzEzMmDEDKpUK3bt3x9atW5GSkgKlUonLly/j448/RvXq1dG9e3ccOnTI3DFTWTi6Px4/hM05iIhMx9IH4i0ooDFgYwek39ZeXCMiIqOU6I5U7dq1ERUVha+//hpdunSBnZ2dwTLXr1/Hjz/+iBdffBEffPABRowYYfJgyURULYDbp7VXISN6yx0NEVHFp1E/LuJj6RX7dOycAP+G2iaJ8UcArxC5IyIiqlBKdEdq+/bt+Pnnn9G9e/dCkygACAkJwaRJk3Dp0iV07NjRpEGSiUkFJ3hHiojIJG6fAbLTAHs3wLe+3NGUHAtOEBGVWokSqfDw8BKv0M7ODjVq1Ch1QFQOpPFD/uH4IUREpqBr1qdqAdgo5Y3FGEEsQEREVFpGV+37/fffsW/fPunvpUuXonHjxnj55Zdx//59kwZHZuJVneOHEBGZktQ/qoI069PR3ZFKPA3kPJQ3FiKiCsboRGrChAlIS0sDAJw6dQrjxo1D9+7dcfXqVYwdO9bkAZIZKBT5qjXxKiQRUZkIAVx/lEhVlP5ROh6BgHsgINTAzVi5oyEiqlCMTqSuXr2KiIgIAMC6devQs2dPzJo1C0uXLsXWrVtNHiCZCccPISIyjftXgfREbQW8wEi5ozGe7nzAflJEREYxOpGyt7dHRkYGAGDnzp3o2rUrAMDb21u6U0UVQP6CE0LIGwsRUUUW92jYj4Am2kp4FU3+8aSIiKjEjBqQFwDatm2LsWPHok2bNjhy5AjWrFkDALh48SKCgoJMHiCZiW78kIdJwP1rgHd1uSMiIqqYrh/Q/q5ozfp0Cl5Y48C8REQlYvQdqSVLlsDW1hb/+9//sGzZMgQGBgIAtm7dim7dupk8QDITOyfAv5H28Y2j8sZCRFSRVbSBeAvyawDYOgKZycC9K3JHQ0RUYRh9Ryo4OBibNm0ymL5w4UKTBETlSNUSuHlM20+qYX+5oyEiqnjS7wD3Lmsf6/oaVTS29tpmiXEHteeDqjXljoiIqEIo0R2phw+NK4lq7PIkExacICIqG93dKJ8IwNlb3ljKggUniIiMVqJEqmbNmpgzZw4SEhKKXEYIgR07duCZZ57B559/brIAyYx0J87bZ4DsB/LGQkRUEVXU8aMKYsEJIiKjlahp3+7du/H+++9jypQpaNSoEZo1a4aAgAA4Ojri/v37OHv2LA4ePAhbW1tMmjQJr7/+urnjJlNwDwA8VEBqPHDzOBDWQe6IiIgqFmtJpHQX1pLOAVmpgKOHvPEQEVUAJUqk6tSpg3Xr1iEuLg5r167F3r17ceDAAWRmZqJq1apo0qQJvv76azzzzDNQKpXmjplMSdVCm0jFH2EiRURkjOx0IOGk9nFFrdin4+oDeIVqq7jeOAbU7CR3REREFs+oYhPBwcEYN24cxo0bZ654qLypWgKn17GfFBGRsW4cAYQa8AgGPKxg+A9Vy0eJ1FEmUkREJWB0+XOyMlIH46OARiNvLEREFYluIN7gVvLGYSpBzbW/eWGNiKhEmEhVdr71ATtnbZv4uxfljoaIqOKo6APxFqQbmPfGMV5YIyIqASZSlZ3SDgiM1D7mVUgiopJR52oTDqDiF5rQ8YkA7FyA7DTgznm5oyEisnhMpChfcw6WvSUiKpGEE0BeJuDkBVStI3c0pqG0BYIeXVjjeFJERE/ERIoeN+fgHSkiopLRNesLjgJsrOhUKp0PmEgRET2JUVX7dFJSUrBixQqcO3cOAFCvXj0MGzYMHh4cd6JC0t2RuncJeHgPcKkibzxERJbO2gpN6EgD8/LCGhHRkxh9Ge3YsWOoUaMGFi5ciOTkZCQnJ2PBggWoUaMGYmNjzREjmZtLFaBKLe3jG0fljYWIyNJpNPkG4m0tbyymFtRM+/veZe2FNSIiKpLRidSYMWPw7LPP4tq1a1i/fj3Wr1+Pq1evomfPnnjnnXfMECKVC6laE5tzEBEV6+5FIDMZsHUC/BvJHY1pOXsDVWtrH/PCGhFRsUp1R+rdd9+Fre3jVoG2traYOHEijh07ZtLgqBzpxpNiu3giouLp7kYFNQNs7eWNxRyk8QV5PiAiKo7RiZS7uzvi4uIMpsfHx8PNzc0kQZEMdHekbh7XlvUlIqLCSc36rKTseUFBvLBGRFQSRidSAwYMwPDhw7FmzRrEx8cjPj4eP/30E1599VW89NJL5oiRykPV2oCjB5CbAdw+LXc0RESW6/qjRMpaBuItSO/CWp68sRARWTCjq/Z9+umnUCgUiImJQV6e9gBrZ2eHN954A3PmzDF5gFRObGy01fsu79RehQxoIndERESWJ/UGkBoHKGweVzy1NroLa1mp2gtrAY3ljoiIyCIZfUfK3t4eixYtwv379/HPP//gn3/+QXJyMhYuXAgHBwdzxEjlheNJEREVT1f23K8h4GClzdlt8iWJLDhBRFQkoxOpYcOG4cGDB3B2dkaDBg3QoEEDODs74+HDhxg2bJg5YqTywoITRETF0/WPCrGysucFcTwpIqInMjqR+vbbb5GZmWkwPTMzE999951JgiKZBEZqm6ukxgNpt+SOhojI8uj6R1nbQLwFqZhIERE9SYkTqbS0NKSmpkIIgQcPHiAtLU36uX//PrZs2QIfHx9zxkrm5uAG+NbTPuZdKSIifZn3gaSz2sfWWrFPR3dhLSUOeJAodzRERBapxMUmPD09oVAooFAoULt2bYP5CoUCU6dONWlwJANVSyDxlDaRqvec3NEQEVmO+CMABOBdA3C18guHju6AT4S22ET8ESDiWbkjIiKyOCVOpHbt2gUhBDp27Ih169bB29tbmmdvb4+QkBAEBASYJUgqR6qWwNFv2JyDiKig6we0v6217HlBqhbaROoGEykiosKUOJFq3749AODq1atQqVSwsTG6exVVBLpKTQkngNxMwM5J3niIiCyFNBCvlRea0AlqARz7L5t6ExEVwehxpEJCQpCSkoIjR44gKSkJGo1Gb35MTIzJgiMZeIUCLj7AwyTg1j+V58orEVFxcjOBm7Hax9ZeaEJHV3Di1j9AXjZgyyFOiIjyMzqR+u233zBw4ECkp6fD3d0dCoVCmqcbqJcqMIVCe/I8v0nbvI+JFBGRNonS5AKuvoB3mNzRlA/vMMC5CpBxD0g4CaisdABiIqJSMrp93rhx4zBs2DCkp6cjJSUF9+/fl36Sk5PNESOVN93AvByIkYhIK+5R/6jgKO0Fp8pAoch3PmDzPiKigoxOpG7evInRo0fD2dnZHPGQJdCdOOMPA0LIGwsRkSWIO6T9be1lzwvS9ZtlASIiIgNGJ1LR0dE4duyYOWIhS+HfCFDaAw/vAPevyh0NEZG8NOrHBRcqW3Nn6cLaEV5YIyIqwOg+Uj169MCECRNw9uxZNGjQAHZ2dnrzn32WJVIrPDtHwL+xtilH/JHK0x+AiKgwt08D2WmAvRvgW1/uaMpXQBPAxhZ4kACk3gA8VXJHRERkMYxOpEaMGAEAmDZtmsE8hUIBtVpd9qhIfqoWjxKpw0CjF+WOhohIPrpmfaoWgI1S3ljKm70z4NcAuPW39nzARIqISGJ00z6NRlPkD5MoK6Ire8vxQ4iosqtsA/EWxAJERESFKtOoullZWaaKgyxN0KNE6vYZICtN3liIiOQiRL5CE5VkIN6CWHCCiKhQRidSarUa06dPR2BgIFxdXfHvv/8CAD766COsWLHC5AGSTNz9Ac9gAAK4eVzuaIiI5HH/KpCeCNjYAYFN5Y5GHro7UomngJwMeWMhIrIgRidSM2fOxKpVqzBv3jzY29tL0+vXr49vvvnGpMGRzPJXayIiqoyuH9T+DmwK2DnJG4tcPIIAN39Ak6ftK0VERABKkUh99913+OqrrzBw4EAolY873TZq1Ajnz583aXAks/zjSRERVUZxjxKp4FbyxiEnheJxv1kOzEtEJCnVgLw1a9Y0mK7RaJCbm2uSoMhCSCfOo4BGI28sRERykBKpSto/SieIBYiIiAoyOpGKiIjA3r17Dab/73//Q5MmTUwSFFkIn3qAnYt2/JQ7vNtIRJVMehJw77L2cXBLeWORGwfmJSIyYHQiNXnyZIwaNQpz586FRqPB+vXrMWLECMycOROTJ082al1TpkyBQqHQ+6lbt640PysrCyNHjkSVKlXg6uqKvn374vbt23rriIuLQ48ePeDs7AwfHx9MmDABeXl5xr4sKozS9nHnajbvI6LKRletzycCcPKSNxa5+TcElPZAxl0g+V+5oyEisghGJ1K9e/fGb7/9hp07d8LFxQWTJ0/GuXPn8Ntvv6FLly5GB1CvXj0kJCRIP/v27ZPmjRkzBr/99hvWrl2LPXv24NatW+jTp480X61Wo0ePHsjJycGBAwfw7bffYtWqVUYndFQMFpwgospKatZXScePys/WAQh41OqE40kREQEAbEvzpHbt2mHHjh2mCcDWFn5+fgbTU1NTsWLFCvz444/o2LEjAGDlypUIDw/HoUOH0KpVK2zfvh1nz57Fzp074evri8aNG2P69Ol49913MWXKFL2qglRK0kCMTKSIqJKRBuKt5P2jdFQttK0T4g8DjV6UOxoiItmVaUBeU7h06RICAgIQFhaGgQMHIi4uDgBw/Phx5ObmonPnztKydevWRXBwMA4e1F4lPHjwIBo0aABfX19pmejoaKSlpeHMmTPl+0KsVVAz7e97l4GH9+SNhYiovGQ/ABJPah9X5op9+bHgBBGRnhLdkfL29sbFixdRtWpVeHl5QaFQFLlscnJyiTfesmVLrFq1CnXq1EFCQgKmTp2Kdu3a4fTp00hMTIS9vT08PT31nuPr64vExEQAQGJiol4SpZuvm1eU7OxsZGdnS3+npaUBAHJzc1l5sCA7N9hWrQ3F3YvIu3YAonY3uSMqlO5z4+dH5YX7nHVTXDsEW6GB8FAhz9kXkPlztoj9za8J7ACIpLPIS08GHNzki4XMziL2Oao0LG1/K2kcJUqkFi5cCDc37QHzs88+K3VQBT3zzDPS44YNG6Jly5YICQnBzz//DCcn8w18OHv2bEydOtVg+vbt2+Hs7Gy27VZUjYU/QnAR//71E85dtuwy6KZqckpUUtznrFOdhPWoC+CGjQqxW7bIHY5E7v2ts31VuOTcxdFfluGOe31ZY6HyIfc+R5WLpexvGRkZJVquRInUkCFDCn1sap6enqhduzYuX76MLl26ICcnBykpKXp3pW7fvi31qfLz88ORI/pNDHRV/Qrrd6UzadIkjB07Vvo7LS0NKpUKXbt2hbu7uwlfkXVQ/JMMbN6Dmg73UL17d7nDKVRubi527NiBLl26wM7OTu5wqBLgPmfdlN9/BQAIaNUHfk3lP+5Zyv6mzPkVOLMOLQNtoGkn//tC5mMp+xxVDpa2v+laqz1JqYpNAEBSUhKSkpKgKTBQa8OGDUu7SqSnp+PKlSsYPHgwIiMjYWdnhz/++AN9+/YFAFy4cAFxcXGIitJWUIqKisLMmTORlJQEHx8fANpM1t3dHREREUVux8HBAQ4ODgbT7ezsLOLDszih2o7WNrf+ho0NAKXlvkf8DKm8cZ+zQnk5wM3jAABl9XZQWtDnK/v+FtwKOLMOylvHLOp9IfORfZ+jSsVS9reSxmB0InX8+HEMGTIE586dgygwKJ9CoYBarS7xusaPH49evXohJCQEt27dwscffwylUomXXnoJHh4eGD58OMaOHQtvb2+4u7vjrbfeQlRUFFq10nb87dq1KyIiIjB48GDMmzcPiYmJ+PDDDzFy5MhCEyUqpSq1AEdPICtF2/k6MFLuiIiIzCfxJJCXCTh5A9XqyB2NZVE9Kjhx4yig0UB7dY2IqHIyOpEaNmwYateujRUrVsDX17fYwhNPcuPGDbz00ku4d+8eqlWrhrZt2+LQoUOoVq0aAG3fLBsbG/Tt2xfZ2dmIjo7GF198IT1fqVRi06ZNeOONNxAVFQUXFxcMGTIE06ZNK3VMVAgbG+3J89J2IP4oEykism66sufBrYAynOOskm99wM4ZyEoF7l4EfOrKHRERkWyMTqT+/fdfrFu3DjVr1izzxn/66adi5zs6OmLp0qVYunRpkcuEhIRgiwV1BLZaUiJ1GGj1H7mjISIyHw7EWzSlrfZi2rW92vEFmUgRUSVm9D35Tp064cSJE+aIhSyZbmBejh9CRNZMowHiDmkfM5EqXFBz7e/4w/LGQUQkM6PvSH3zzTcYMmQITp8+jfr16xt0xnr22WdNFhxZkICmgEIJpN0AUm8AHkFyR0REZHp3LwKZyYCtE+DfSO5oLBMvrBERAShFInXw4EHs378fW7duNZhnbLEJqkAcXAHfetpO2PFHmEgRkXWKe9Q/KqgZYGsvbyyWSndH6u5FICMZcPaWNx4iIpkY3bTvrbfewqBBg5CQkACNRqP3wyTKyvEqJBFZOzbrezKXKkCVR/2kbxyTNxYiIhkZnUjdu3cPY8aMga+vrzniIUsmJVJsF09EVur6o0ITIUykiqU7H9zghTUiqryMTqT69OmDXbt2mSMWsnS68UMSTwK5mfLGQkRkaqk3gNQ4bX9QXfM1KhwLThARGd9Hqnbt2pg0aRL27duHBg0aGBSbGD16tMmCIwvjGQy4+gHpicCtv4GQ1nJHRERkOrpmfX4NAAc3eWOxdLo7UjdjAXWetiw6EVElU6qqfa6urtizZw/27NmjN0+hUDCRsmYKhfau1LmN2quQTKSIyJroBuLlse3JqtUFHNyB7DQg6Szg31DuiIiIyp3RidTVq1fNEQdVFKqWjxIptosnIivDQhMlZ2OjrWx45U9tPykmUkRUCRndR4oqOV0/qfjDgBDyxkJEZCqZ97V3VgAguJW8sVQUQbrzAS+sEVHlVKpGzTdu3MDGjRsRFxeHnJwcvXkLFiwwSWBkofwbAUp7IOMekPwvUKWG3BEREZVd3GEAQlvW29VH7mgqBhUTKSKq3IxOpP744w88++yzCAsLw/nz51G/fn1cu3YNQgg0bdrUHDGSJbF1AAKaaO9IxR9mIkVE1iHuUdlzNusruaBmABTA/atAehITUCKqdIxu2jdp0iSMHz8ep06dgqOjI9atW4f4+Hi0b98eL7zwgjliJEvDq5BEZG2YSBnP0QPwCdc+5vmAiCohoxOpc+fOISYmBgBga2uLzMxMuLq6Ytq0aZg7d67JAyQLJA3MyxMnEVmB3ExtGW+AA/EaS3dhjQPzElElZHQi5eLiIvWL8vf3x5UrV6R5d+/eNV1kZLl0HYyTzgJZqfLGQkRUVjdjAU0u4OoLeFWXO5qKhQUniKgSMzqRatWqFfbt2wcA6N69O8aNG4eZM2di2LBhaNWKlY4qBTdfwCsUgABuHJM7GiKisol7NH5UcJR2vDwqOV0LhVt/A3k5xS9LRGRljE6kFixYgJYttQfOqVOnolOnTlizZg1CQ0OxYsUKkwdIFopXIYnIWlx/1D+KA/Ear0oNwMkbyMsCEk/JHQ0RUbkyumpfWFiY9NjFxQXLly83aUBUQahaAKd+1lbuIyKqqDTqxxeEOH6U8RQK7fng4u/aflJBkXJHRERUbko1jhQA5OTkICkpCRqNRm96cHBwmYOiCkDXnOPGMe0XERulvPEQEZXG7dNAzgPAwR3wrS93NBVTUHNtIhV/GGj1htzREBGVG6MTqYsXL2L48OE4cOCA3nQhBBQKBdRqtcmCIwvmEwHYu2q/gNw5D/jWkzsiIiLjxR3S/la14AWh0pIquR6VNw4ionJmdCL1yiuvwNbWFps2bYK/vz8U7JhbOSltgcBI4Ooe7VVIJlJEVBFd1xWaYLO+UgtsCiiUQNoNIPUm4BEod0REROXC6ETqn3/+wfHjx1G3bl1zxEMViarlo0TqCNBsmNzREBEZR4h8A/Gy0ESp2bsAfvWBhBPaflIez8sdERFRuTC6al9ERATHiyItqTkHC04QUQV0/yqQfhtQ2mvvsFPpsZIrEVVCRidSc+fOxcSJE7F7927cu3cPaWlpej9UieiqMyX/C6TfkTcWIiJj6cqeBzQB7BzljaWi44U1IqqEjG7a17lzZwBAp06d9Kaz2EQl5OQFVKurLTZx4whQt4fcERERlVz+gXipbFSP7kglnARyMwE7J3njISIqB0YnUrt27TJHHFRRqVpoE6n4w0ykiKhi0VXs40C8ZecZDLj6aptK3voHCGFySkTWz+hEqn379uaIgyoqVUsg9juWvSWiiiU9Cbh3GYDi8d0UKj3dwLznftO2UGAiRUSVgNF9pABg7969GDRoEFq3bo2bN28CAFavXo19+/aZNDiqAHTt4m/FAnk58sZCRFRSump9PhHaZspUdiw4QUSVjNGJ1Lp16xAdHQ0nJyfExsYiOzsbAJCamopZs2aZPECycFVqar+E5GUBiafkjoaIqGR0zfo4fpTpSAUnjmhLyxMRWTmjE6kZM2Zg+fLl+Prrr2FnZydNb9OmDWJjY00aHFUACgWrNRFRxaMbiJf9o0zHvxFgYwc8TALuX5M7GiIiszM6kbpw4QKeeuopg+keHh5ISUkxRUxU0QQ11/5mIkVEFUH2AyDxpPYxK/aZjp0jENBY+/gG+80SkfUzOpHy8/PD5cuXDabv27cPYWFhJgmKKpj8d6TYnIOILN2No4DQAB7BgEeg3NFYF6mfFC+sEZH1MzqRGjFiBN5++20cPnwYCoUCt27dwg8//IDx48fjjTfeMEeMZOkCmwIKJfAgAUi9IXc0RETF0w3Ey8pypqdiIkVElYfR5c/fe+89aDQadOrUCRkZGXjqqafg4OCA8ePH46233jJHjGTp7F0AvwZAwj/asreeKrkjIiIqmq5iH5v1mZ6uhcLtM0B2OuDgKm88RERmZNQdKbVajb1792LkyJFITk7G6dOncejQIdy5cwfTp083V4xUEeSv1kREZKnycoAbx7SPmUiZnrs/4KHSNp28eVzuaIiIzMqoREqpVKJr1664f/8+7O3tERERgRYtWsDVlVecKj025yCiiiDhBJCXCTh5A9XqyB2NddKdD27wwhoRWTej+0jVr18f//77rzlioYpMd0cq4SSQ81DeWIiIipK/WZ9CIW8s1ooD8xJRJVGqcaTGjx+PTZs2ISEhAWlpaXo/VEl5BAFu/oBQA7f+ljsaIqLCSYkUB+I1G+mO1FFAo5E3FiIiMzK62ET37t0BAM8++ywU+a7mCSGgUCigVqtNFx1VHAqF9uR59ldt877QtnJHRESkT6N5nEhxIF7z8WsA2DoBmfeBe5eBarXljoiIyCyMTqR27dpljjjIGqhaPkqk2JyDiCzQ3YvaL/e2ToB/I7mjsV5KO+2wGNf3a/tJMZEiIitldCJVvXp1qFQqvbtRgPaOVHx8vMkCowoof+U+Idj/gIgsS9wB7e+gZtov+2Q+Qc21iVT8YaDJILmjISIyC6P7SFWvXh137twxmJ6cnIzq1aubJCiqoPwaAkoHIDMZuHdF7miIiPRdZ7O+ciNdWDsqbxxERGZkdCKl6wtVUHp6OhwdHU0SFFVQtvba5hwAy6ATkeWJO6T9zUIT5hfUXPv7zjkgM0XWUIiIzKXETfvGjh0LAFAoFPjoo4/g7OwszVOr1Th8+DAaN25s8gCpglG10Hbmjj8MNBkodzRERFqpN4DUOEChfFyem8zHtRrgHQYk/6sdALlWZ7kjIiIyuRInUn//rS1pLYTAqVOnYG9vL82zt7dHo0aNMH78eNNHSBVL/n5SRESWQnc3yr8h4MBB5MuFquWjROoIEykiskolTqR01fpeeeUVLFq0CO7u7mYLiiow3ZVeXXMOJ085oyEi0rr+qNBEcJS8cVQmQc2BE//Hpt5EZLWM7iO1cuVKJlFUNNdqgNejoiM3jskbCxGRjjQQLxOpcqNroXDjOKDhGJNEZH2MLn/+8OFDzJkzB3/88QeSkpKgKTBq+b///muy4KiCUrUE7l9lcw4isgyZ94Gks9rHTKTKj084YO8G5DwAks4BfvXljoiIyKSMTqReffVV7NmzB4MHD4a/v3+hFfyoklO1AE7+xOYcRGQZ4h4di6rU1N41p/JhowSCIoF/d2svrDGRIiIrY3QitXXrVmzevBlt2rQxRzxkDaTmHMe0zTlslPLGQ0SVWxz7R8kmqIU2kYo/AjQbJnc0REQmZXQfKS8vL3h7e5sjFrIWUnOO9MfNaYiI5KKr2MeBeMsfK7kSkRUzOpGaPn06Jk+ejIyMDHPEQ9bARgkENdM+ZvM+IpJTbiZwM1b7mAPxlr+gSO3v5CvAw7vyxkJEZGJGN+2bP38+rly5Al9fX4SGhsLOzk5vfmxsrMmCowpM1QL4d5f2KmTzV+WOhogqq5vHAU0u4Or3uKIolR8nL6BaXeDOeeDGUaDOM3JHRERkMkYnUs8995wZwiCro3o0nhTvSBGRnHRlz0OiABZHkkdQc20iFX+YiRQRWRWjE6mPP/7YHHGQtQlsBkAB3L8GpCcBrj5yR0REldF1jh8lO1VL4O/V7CdFRFanxH2kjhw5ArW66AH1srOz8fPPP5skKLICTp7aohMAT55EJA+N+vHxh4mUfHQFJ27GAupceWMhIjKhEidSUVFRuHfvnvS3u7u73uC7KSkpeOmll0wbHVVsbN5HRHK6fVo7GKyDO+BbT+5oKq8qNQFHTyAvE0g8JXc0REQmU+JESghR7N9FTSupOXPmQKFQ4J133pGmZWVlYeTIkahSpQpcXV3Rt29f3L59W+95cXFx6NGjB5ydneHj44MJEyYgLy+v1HGQCbHsLRHJSdesT9WC49nJycbm8YW1G0fljYWIyISMLn9eHEUpO/IePXoUX375JRo2bKg3fcyYMfjtt9+wdu1a7NmzB7du3UKfPn2k+Wq1Gj169EBOTg4OHDiAb7/9FqtWrcLkyZPL9DrIRHSJ1K2/gbxseWMhosonjv2jLEYQWygQkfUxaSJVGunp6Rg4cCC+/vpreHl5SdNTU1OxYsUKLFiwAB07dkRkZCRWrlyJAwcO4NAh7eCK27dvx9mzZ/H999+jcePGeOaZZzB9+nQsXboUOTk5cr0k0vEOA5yrAOpsIOGk3NEQUWUiBBMpSyI19eYdKSKyHkYlUmfPnsXJkydx8uRJCCFw/vx56e8zZ86UKoCRI0eiR48e6Ny5s97048ePIzc3V2963bp1ERwcjIMHtSfHgwcPokGDBvD19ZWWiY6ORlpaWqnjIRNSKHgVkojkkfwvkH4bUNoDgZFyR0OBkYDCBkiNA9IS5I6GiMgkjCp/3qlTJ71+UD179gSgbdInhDC6ad9PP/2E2NhYHD1qeIUqMTER9vb28PT01Jvu6+uLxMREaZn8SZRuvm5eUbKzs5Gd/bipWVpaGgAgNzcXubmsKGRKNoHNoLy4FZq4w1A3f91s29F9bvz8qLxwn7Nsiqv7YQtA498EaiiBCv45Vfj9zcYBtj71oLh9CnnXD0LU7SV3RPQEFX6fowrF0va3ksZR4kTq6tWrpQ6mMPHx8Xj77bexY8cOODo6mnTdTzJ79mxMnTrVYPr27dvh7OxcrrFYuyrparQFkH1lL7Zv3mz2ATF37Nhh1vUTFcR9zjI1vr4WIQCu5FTF2S1b5A7HZCry/tZQXQ3VAVz7aw3O/MviHxVFRd7nqOKxlP0tIyOjRMuVOJEKCQkpdTCFOX78OJKSktC0aVNpmlqtxl9//YUlS5Zg27ZtyMnJQUpKit5dqdu3b8PPzw8A4OfnhyNH9CvC6ar66ZYpzKRJkzB27Fjp77S0NKhUKnTt2hXu7u6meHmkk9sB4tN5cMq9j+5tGwIeKvNsJjcXO3bsQJcuXWBnZ2eWbRDlx33OstkumwIAqN5hIEJrdZU3GBOwhv1NcSod2PgnwuzvIqR7d7nDoSewhn2OKg5L2990rdWexKimfabUqVMnnDqlP57EK6+8grp16+Ldd9+FSqWCnZ0d/vjjD/Tt2xcAcOHCBcTFxSEqSttxOCoqCjNnzkRSUhJ8fHwAaDNZd3d3REREFLltBwcHODg4GEy3s7OziA/Pqth5AH4NgVuxsEuIBaqGmXdz/AypnHGfs0DpSdo+UlDAtnprwIo+nwq9v4Vqz902iSdhAzVgV76tUah0KvQ+RxWOpexvJY1BtkTKzc0N9evX15vm4uKCKlWqSNOHDx+OsWPHwtvbG+7u7njrrbcQFRWFVq1aAQC6du2KiIgIDB48GPPmzUNiYiI+/PBDjBw5stBEiWSiagncitUWnGjQT+5oiMja6ar1+UQATl7FL0vlx6s64FINeHgHSDgBBLeUOyIiojKRvfx5cRYuXIiePXuib9++eOqpp+Dn54f169dL85VKJTZt2gSlUomoqCgMGjQIMTExmDZtmoxRkwFVc+1vVu4jovKgG4g3hGXPLYpC8Xh8wRscqJ2IKj7Z7kgVZvfu3Xp/Ozo6YunSpVi6dGmRzwkJCcEWK+pIbJV0J87E00DOQ8DeRd54iMi6cfwoyxXUHDi/6dGFtbfkjoaIqExKdUcqLy8PO3fuxJdffokHDx4AAG7duoX09HSTBkdWwiMIcA8EhBq4GSt3NERkzbIfAImPBgBnImV5dBfW4o9oB00mIqrAjE6krl+/jgYNGqB3794YOXIk7ty5AwCYO3cuxo8fb/IAyUqoODAvEZWDG0cBoQE8gwGPQLmjoYICGgM2ttrBklPi5I6GiKhMjE6k3n77bTRr1gz379+Hk5OTNP3555/HH3/8YdLgyIrkvwpJRGQu19msz6LZOQH+jbSPbxyVNxYiojIyOpHau3cvPvzwQ9jb2+tNDw0Nxc2bN00WGFkZ3R2pG0cAjUbeWIjIerF/lOULYgsFIrIORidSGo0GarXaYPqNGzfg5uZmkqDICvk1BGydgMz7wL3LckdDRNYoLwe4cUz7OKS1vLFQ0aSm3myhQEQVm9GJVNeuXfHZZ59JfysUCqSnp+Pjjz9Gd45UTkVR2gEBTbSPeRWSiMwh4QSQlwk4eQNVa8sdDRVFl0glntJWciUiqqCMTqTmz5+P/fv3IyIiAllZWXj55ZelZn1z5841R4xkLVhwgojMKe6A9ndwlHbMIrJMrORKRFbC6HGkgoKCcOLECfz00084efIk0tPTMXz4cAwcOFCv+ASRAWkgRnYwJiIziDuk/c2BeC1fUHPg7E1tv9nq7eSOhoioVEo1IK+trS0GDRpk6ljI2unuSN05r+0r5eQlbzxEZD00GhaaqEhULYGzG9hPiogqtBIlUhs3bizxCp999tlSB0NWzqUq4F0DSL6i7RBeq4vcERGRtbh7QXuBxs75cXltslwFB+ZlU0wiqoBKlEg999xzen8rFAqIAiOSKx4dBAur6EckUbXUJlLxh5lIEZHp6O5GBTXTFrchy+bXALB1BDKTgXtXgKo15Y6IiMhoJSo2odFopJ/t27ejcePG2Lp1K1JSUpCSkoKtW7eiadOm+P33380dL1V0LDhBRObAgXgrFlv7x5Vcb7B5HxFVTEb3kXrnnXewfPlytG3bVpoWHR0NZ2dnvPbaazh37pxJAyQrIw3MexxQ5wHKUnXTIyLSx/5RFU9Qc+3nFn8YaPyy3NEQERnN6PLnV65cgaenp8F0Dw8PXLt2zQQhkVWrVhdwcAdyHwJJZ+SOhoisQUo8kBoPKJTaL+dUMUj9pFjJlYgqJqMTqebNm2Ps2LG4ffu2NO327duYMGECWrRoYdLgyArZKLV9GABWayIi09CVPfdvCDi4yhsLlZyuhULSWSArVd5YiIhKwehE6r///S8SEhIQHByMmjVrombNmggODsbNmzexYsUKc8RI1iZ/tSYiorKSmvW1ljcOMo6rD+AVCkAAN4/LHQ0RkdGM7qBSs2ZNnDx5Ejt27MD58+cBAOHh4ejcubNUuY+oWCw4QUSmJCVSreSNg4wX1AK4f017Ya1GR7mjISIySql6+isUCnTt2hVdu3Y1dTxUGQQ2A6AAUq4DDxIBNz+5IyKiiiojWds0DGChiYpI1QI49TMvrBFRhWR00z6iMnN0B3zraR+zeR8RlYXuGFKlFuBaTd5YyHi6pt43jgEajbyxEBEZiYkUyUNXWYtXIYmoLOIOaH+zWV/F5BMB2LkA2WnAnfNyR0NEZBQmUiQPFpwgIlPQDcQbwkITFZLSFgiK1D7mwLxEVMEwkSJ56ApOJPwD5GXLGgoRVVC5mcCtv7WP2T+q4grSFSBiIkVEFUupik2o1Wps2LAB586dAwDUq1cPzz77LJRKpUmDIyvmHQY4VwUy7gIJJx4nVkREJXXzOKDJBVz9HpXRpgqJLRSIqIIy+o7U5cuXERERgZiYGKxfvx7r16/HoEGDUK9ePVy5csUcMZI1UijynTzZT4qISkFq1helPaZQxaQbpP3eJW0VRiKiCsLoRGr06NEICwtDfHw8YmNjERsbi7i4OFSvXh2jR482R4xkrTieFBGVBQfitQ7O3kDV2trHN47KGwsRkRGMbtq3Z88eHDp0CN7e3tK0KlWqYM6cOWjTpo1JgyMrl785hxC8okxEJadRP24Kxop9FV9QC+DuRe2FtdrRckdDRFQiRt+RcnBwwIMHDwymp6enw97e3iRBUSUR0BiwsQXSb2sH5yUiKqnbp4GcB4BDvnHpqOJSseAEEVU8RidSPXv2xGuvvYbDhw9DCAEhBA4dOoT//Oc/ePbZZ80RI1krOyfAv5H2MU+eRGQMXf8oVUvAhoWOKjxdInXzOKDOkzcWIqISMjqR+vzzz1GjRg1ERUXB0dERjo6OaNOmDWrWrIlFixaZI0ayZqzWRESlwYF4rUvVOoCDB5Cbob3bSERUARjdR8rT0xO//vorLl26hHPnzkGhUCA8PBw1a9Y0R3xk7VQtgENfsOAEEZWcEEDcIe1jDsRrHWxsAFVz4PJObcGJgMZyR0RE9ESlGkcKAGrVqiUlTwoWCaDS0g3EePs0kJ0OOLjKGw8RWb7kf7V9K5X2QEBTuaMhUwlqoU2k4g8DLUbIHQ0R0RMZ3bQPAFasWIH69etLTfvq16+Pb775xtSxUWXgEQh4qACh0baNJyJ6El3Z84CmgJ2jvLGQ6bDgBBFVMEbfkZo8eTIWLFiAt956C1FRUQCAgwcPYsyYMYiLi8O0adNMHiRZOVULIDVee/IMay93NERk6eLyDcRL1iMwEoBCW8X1wW3AzVfuiIiIimV0IrVs2TJ8/fXXeOmll6Rpzz77LBo2bIi33nqLiRQZL6gFcHod+0kRUcnoKvYFM5GyKo6PStnfPg3cOAKE95I7IiKiYhndtC83NxfNmjUzmB4ZGYm8PJYspVLQNee4cQTQaOSNhYgs24PbQPIVAIrHVT/JegQ11/7mhTUiqgCMTqQGDx6MZcuWGUz/6quvMHDgQJMERZWMXwPA1gnISgXuXZI7GiKyZPGPqvX51gOcPGUNhcxAGhLjqLxxEBGVQKmq9q1YsQLbt29Hq1ba8TsOHz6MuLg4xMTEYOzYsdJyCxYsME2UZN2Udtq28df3aa9CVqsjd0REZKmkZn0cP8oq6Voo3PobyMsGbB3kjYeIqBhGJ1KnT59G06bacrNXrlwBAFStWhVVq1bF6dOPB9FjSXQyiqrF40SqaYzc0RCRpZIG4mX/KKvkHQY4VwEy7gEJJ7VjSxERWSijE6ldu3aZIw6q7KTmHCx7S0RFyH4AJJ7SPmYiZZ0UCm0Bootbtf1mmUgRkQUr1ThSRCan62B89yKQkSxvLERkmeKPaMec8wzWjkFH1kkaT4oFJ4jIshl9RyorKwuLFy/Grl27kJSUBE2BKmuxsbEmC44qEZcqQJVa2mITN44CtaPljoiILE3co0ITwa3ljYPMK38LBSG0d6mIiCyQ0YnU8OHDsX37dvTr1w8tWrRgXygyHVULbSIVf5iJFBEZ4kC8lUNAE8DGFniQAKTeADxVckdERFQooxOpTZs2YcuWLWjTpo054qHKTNUC+OcH9pMiIkN5Odq71QD7R1k7e2ftsBi3/tb2k2IiRUQWyug+UoGBgXBzczNHLFTZ6Zpz3DwOqDm4MxHlk3ACyMsCnLyBqrXljobMLUjXT4oX1ojIchmdSM2fPx/vvvsurl+/bo54qDKrWgdw8AByM4Dbp5+8PBFVHvnLnrNJufVTMZEiIstndCLVrFkzZGVlISwsDG5ubvD29tb7ISo1G5vHpW558iSi/K6zf1SlokukEk8CORnyxkJEVASj+0i99NJLuHnzJmbNmgVfX18WmyDTUrUELu/UFpxo+Zrc0RCRJdBogHhW7KtUPFSAm7+24MStv4FQ9ssmIstjdCJ14MABHDx4EI0aNTJHPFTZsTkHERV09wKQeR+wcwb8G8odDZUHhUI7vuC5jdqCE0ykiMgCGd20r27dusjMzDRHLERAYCSgsAFS44C0BLmjISJLcP1R/6igZoDSTt5YqPzkH0+KiMgCGZ1IzZkzB+PGjcPu3btx7949pKWl6f0QlYmDG+BTT/v4Bk+eRAQOxFtZ5W+hIIS8sRARFcLopn3dunUDAHTq1ElvuhACCoUCarXaNJFR5aVqAdw+pT15RvSWOxoikptuIN7gVvLGQeXLvxGgtAcy7gLJ/wJVasgdERGRHqMTqV27dpkjDqLHVC2BYyu0BSeIqHJLiQdS4wGFUttnhioPWwcgoIn2XHDjKBMpIrI4RidS7du3N0ccRI/pmnPc+gfIzQLsHGUNh4hkpGvW598IcHCVNxYqf0HNtYlU/GGg0YtyR0NEpMfoPlIAsHfvXgwaNAitW7fGzZs3AQCrV6/Gvn37TBocVVJeoYCLD6DJBRL+kTsaIpJT/oF4qfKRCk4clTcOIqJCGJ1IrVu3DtHR0XByckJsbCyys7MBAKmpqZg1a5bJA6RKSKHI18mYzfuIKjXdHSkOxFs56c4FSWeA7AfyxkJEVIDRidSMGTOwfPlyfP3117Cze1yGtk2bNoiNjTVpcFSJcTwpIspIBpLOah/zjlTl5OYHeAYDQgPcPC53NEREeoxOpC5cuICnnnrKYLqHhwdSUlJMEROR/vghLHtLVDnp7khXqQW4VJU3FpJPEC+sEZFlMjqR8vPzw+XLlw2m79u3D2FhYUata9myZWjYsCHc3d3h7u6OqKgobN26VZqflZWFkSNHokqVKnB1dUXfvn1x+/ZtvXXExcWhR48ecHZ2ho+PDyZMmIC8vDxjXxZZGv/GgI0d8DAJuH9N7miISA66suds1le5SRfW2NSbiCyL0YnUiBEj8Pbbb+Pw4cNQKBS4desWfvjhB4wfPx5vvPGGUesKCgrCnDlzcPz4cRw7dgwdO3ZE7969cebMGQDAmDFj8Ntvv2Ht2rXYs2cPbt26hT59+kjPV6vV6NGjB3JycnDgwAF8++23WLVqFSZPnmzsyyJLY+cIBDTWPuZVSKLK6bpu/CgmUpWa6lHZ+xtHAY1G3liIiPIxuvz5e++9B41Gg06dOiEjIwNPPfUUHBwcMH78eLz11ltGratXr156f8+cORPLli3DoUOHEBQUhBUrVuDHH39Ex44dAQArV65EeHg4Dh06hFatWmH79u04e/Ysdu7cCV9fXzRu3BjTp0/Hu+++iylTpsDe3t7Yl0eWRNVSe+KMPww0GiB3NERUnnIzgVt/ax8zkarcfOsDds5AVipw9yLgU1fuiIiIAJTijpRCocAHH3yA5ORknD59GocOHcKdO3cwffr0MgWiVqvx008/4eHDh4iKisLx48eRm5uLzp07S8vUrVv3/9u77/ioqvz/4687k0nvQBokEOmhBwQjNhQBdVEUdXHVRdey+sXdRazYEBVZ+epaUdctoKv81vJFV1kXRZYiilSDUgWkQxJaeptk5vfHzUwyJEACIXeSvJ+PRx6Tuffmzic3N8m87zn3HFJSUli+3LxKuXz5cvr06UN8fLx3m5EjR5Kfn+9t1ZJmTANOiLRe+9aYUyBEJJpTIkjrZXdAUrr5+V79PxAR/9HgFimPwMBA0tLSTruAH3/8kYyMDEpLSwkPD+fjjz8mLS2NzMxMAgMDiY6O9tk+Pj6erKwsALKysnxClGe9Z93xlJWVeYdtB8jPzwfA6XTidDpP+3uSRpIwAAfgztlAReERCIo47qaen5t+ftJUdM6dWbYdy7ADrg6DqdR9r63+fLO1H4R91zJcu76jss8NVpfTKrT2c06alr+db/Wto15B6pprrmH27NlERkb63KNUl7lz59brhT26d+9OZmYmeXl5fPTRR4wfP54lS5Y0aB8NNX36dKZOnVpr+ZdffkloaOgZfW1pmEsD2xJafohVH7/BwcjeJ91+wYIFTVCVSDWdc2dGxrbPiAPWF0Sy4/PPrS7Hb7TW8y0+z+AcoGjLIv5r0/nQlFrrOSfW8Jfzrbi4uF7b1StIRUVFYRiG9/PGFBgYSJcuXQAYOHAgq1at4uWXX+aXv/wl5eXl5Obm+rRKZWdnk5CQAJgjCK5c6dvM7xnVz7NNXSZPnsykSZO8z/Pz80lOTmbEiBFERkY21rcmjcBe/glsmMuQ9jZc519+3O2cTicLFizg0ksv9ZnfTORM0Tl3BrkqCHjBHLyo56jb6Bl/8osoLV2rP9+Kh8CLLxJRup/Lh2VASIzVFbV4rf6ckyblb+ebp7faydQrSM2aNYunnnqK+++/n1mzZp1WYSfjcrkoKytj4MCBOBwOFi5cyNixYwFzDqvdu3eTkWHeeJyRkcG0adPIyckhLi4OMJNsZGTkCbsdBgUFERQUVGu5w+Hwix+e1JCSARvmYt+/Gns9fjb6GUpT0zl3BuzfAOVFEBSJI6kv2OxWV+Q3Wu35FpUAbbrA4W04stdB10utrqjVaLXnnFjCX863+tZQ78Empk6dSmFh4SkXVJfJkyezdOlSdu7cyY8//sjkyZNZvHgxN954I1FRUdx2221MmjSJRYsWsWbNGm699VYyMjI455xzABgxYgRpaWncfPPNrFu3ji+++ILHHnuMCRMm1BmUpBnyDjihYW9FWo3d35mPyUMUoqSad2JezSclIv6h3oNNuN3uRn/xnJwcfv3rX3PgwAGioqLo27cvX3zxBZdeal5pevHFF7HZbIwdO5aysjJGjhzJ66+/7v16u93OvHnzuPvuu8nIyCAsLIzx48fz1FNPNXqtYhHPsLdleXBoC8T1tLoiETnTdn9rPmoiXqkpeTCsm6ORXEXEbzRo1D7PfVKN5W9/+9sJ1wcHBzNz5kxmzpx53G06duzI57oRueWyB0D7gbDza/MqpIKUSMvmdmsiXqmbp4fCvjVQWWH+fxARsVCD/gp169btpGHqyJEjp1WQSC3JQ6qC1EoYeIvV1YjImXTkZyjKAXtg9dxBIgDtekBQJJTlQ85GSOxrdUUi0so1KEhNnTq10UftEzmpZPWLF2k1dle1RrUfCI5ga2sR/2Kzm+fFz4vMiXkVpETEYg0KUuPGjfOOjifSZDqcbT4e3gZFhyGsjbX1iMiZ4+3Wd461dYh/Sh5iBqk9K+Hs262uRkRauXqP2tfY90eJ1FtoLLTtZn6+d5W1tYjImeVpkUo519o6xD95eyhowAkRsV69g9SZGLVPpN7UvU+k5SvIhiPbAaP6d16kpg6DAAOO7oDCg1ZXIyKtXL2DlMvlUrc+sU7yEPNRVyFFWi5Pa1R8LwiJtrQU8VPBUdWjt+7V/wMRsVa9g5SIpTxBat8aqHRaW4uInBmeiXg17LmciOe+WfVQEBGLKUhJ89CmKwRHQ0UJZP1odTUiciZ4JuLVQBNyIt4eCrpnVkSspSAlzYPNVuMqpLpziLQ4ZQXVF0k6aqAJOQHP/XP710JFubW1iEirpiAlzYfnKqT6xYu0PHtWgtsF0R0hMsnqasSftekCITFQUaoeCiJiKQUpaT407K1Iy+Ud9lz3R8lJGAZ0qPp/oAtrImIhBSlpPtoPBMMGeXsgb5/V1YhIY/IMNNFRQUrqQVNiiIgfUJCS5iMoHOJ7m5/rKqRIy1FRXj3ZtibilfrwBikNOCEi1lGQkuZF80mJtDwHMs37XULbQNuuVlcjzUFSOhh2yN+rHgoiYhkFKWlevEFK3TlEWoya90cZhrW1SPMQFA4J6qEgItZSkJLmJblqCPQD68BZYm0tItI4dmmgCTkFHTQAkYhYS0FKmpfojhAeD64K2J9pdTUicrpcLo3YJ6dGXb1FxGIKUtK8GIZGaxJpSQ5tgdJccIRCYl+rq5HmxKeHQqm1tYhIq6QgJc2PrkKKtBy7vjUfOwwCu8PaWqR58fZQcJoDloiINDEFKWl+ag444XZbW4uInB5vtz4Ney4NZBjQoapVSj0URMQCClLS/CT2A3sgFB+CIz9bXU3zs2g6LJlR97olM8z1Ik1FE/HK6VAPBRGxkIKUND8BQZA0wPxc/zwbzmaHRdNqh6klM8zlNrs1dUnrk7sH8vaY8wG1H2R1NdIcJdcYuU89FESkiSlISfOk7hyn7sIHYdijvmHKE6KGPWquF2kKntaoxH7mvEAiDZXYH2wOKMqBozutrkZEWhkFKWmePN059q6yto7m6uzbcfW/ERZNw/1kDCyahqv/TTD4Tqsrk9Zkd9VAEx11f5ScIkewGcRB/w9EpMkFWF2AyCnxdOfI3gCl+WAPsbYef1VZAYe3QfZ681h5HvP3ea+iGLgAsGW+C5nvQmR7iOsJcWkQ38t8bNfd7FIp0pi8E/GeY20d0rwlD4F9q80eCn2vt7oaEWlFFKSkeYpIMIe+zd1l/gNNOd/qiqxXdBiyf6wKTFWhKWczVJbVuXmeO5Qoo5hKt4HdcJPvDiHSKIH8febHtq+qNzbs0Kazb7iKT4PoTmBTw7acguIjcHCT+bkm4pXTkTwYvpupe2ZFpMkpSEnzlTzEDFJ7VrauIFVRDoe3mmEpq0ZwKsyqe3tHmBl64nvhiuvN3V+V0ad4Jfc4/sULzmt5tfIafmefy32Oj3jNeRU/hg7mjUtDsB3cCNkbIWcDlObBoZ/Mj42f1Nh3KLTrYe4/rpfZkhXfC8LjmuRQSDPmub+xbTcIa2ttLdK8eXsorIeyQt1vJyJNRkFKmq/kwfDjBy13wAm3GwpzanfLO7jFnICyLjGpZpCJ7w0Jvc3Pa7Qardh+mG7FD/qEKMD7eJ/jI14odrCizQwyBt9WXUfBgepQlb0RcjaadTiLYf9a86Om0La1w1W7HnqDI9U8E/GqW5+crsgkiEo2R4DcvxZSL7C6IhFpJRSkpPnyDjixGlyV1tZyuirK4OBm3255WevNubLqEhRZFZg8H30grgcERdTatLzCxYa9R1m7O5fP1u3nIsPlE6I8PM/thosJc9aQnhJD94QIuidE0iMhgtSzLsbRdXj1F1RWmPN45WyAnE1m3Tkb4cgOs+4dS82PmmI61QhXVUGrTRew609Rq+MZsU8T8Upj6HC2GaT2rFCQEpEmo3cv0nzFpZnd1sry4dAWq6upH0/rTtb6Gi1NG8wuc+66wqBhBg1PK5MnOEWngGHU+RI5BaWs3ZXL2t1HWbvrKD/sy6O8wuVdn8m1xy3PG64qnHy1KYevNuV41znsBp3bhVeFqwh6JETQPSGZpLSuGL2urt5JeZEZCnM2+bZieYYnProTtvy7ent7ILTt7huu4tPMQS+O8z1KM+csgf3fm59rIl5pDMlDYMNc2KOR+0Sk6ShISfNlD4AOA2HHUoy9q4B2Vlfky1lS3VJTs3teydG6tw+O9u2SF98L2vWEwNDjv0Sli80HCli7+yhrdh1l7e6j7D1aUmu7mFAH6Skx9E+JZtY3OzlaVE5dU1caQFxkEC9e35+tOYVszipgS1Y+P2UXUlhWweasAjZnFfh8TURwAN3ja4arSLrH9yWq/UDfnRcdMlusfLoIbgJnUdUgGT/CjzW2D4qqEa48g1z0hJCY4x4PaSb2rja7p0YkmoPGiJyu5Kq5BfeuBJdLg+CISJNQkJLmLXkI7FiKbd8qsF9uTQ1ut9mlpGZgyloPR7aD21V7e8MObbv6dsuL72X28z9JC8zhwjLW7q5ubVq3N5dSp+9rGAZ0j48gvWMM6SkxDOwYQ6c2oRhV++4aF87d767FAJ8w5XnlqVf24twubTm3S/UAAG63m71HS/gpu6AqXJkf2w8WUlBawepdR1m9yzcgJkQG1whX5keX5KEE1ex243JB3u7qcOVpxTq8FcryYM935kdNEUm1w1Xb7uZ8MtI8eLv1ZajVURpHQl8ICDEvVB3eBu26WV2RiLQCClLSvFXdJ2XsXQkdmyBIlReZb/ZrjpaXvcF801+X0DZVXfKqWpkSetf7TX+ly82WrAJvaFq7+yg7DxfX2i4yOIABVYEpPSWGfslRRAQ7jrvfUb0TeeOmdKZ+tpEDeaXe5QlRwUwZncao3om1vsYwDJJjQ0mODeWSnvHVh6PCxc+HCtmS5Ruw9uWWkJVfSlZ+KUt+Oujd3m4zSG0bZgas+Ai6JUTQI6Edyd0uw9ajxs+vogwObTWPdc0BLvL2QMF+8+N4w7N7hmaPSzMH39CVaf/jmYhXw55LY7E7IGmAeW7tXakgdaoWTQebHS58sPa6JTPM+5GHTW76uvydjlurpSAlzVuHQQAYR34mMCm/8fbrcplDq3tbmapamo7sgLo6xdkc5qS1PgNA9Ibw+Hpfcc8tLud7T2vT7qNk7s6lqLz2fVNd48JJT4khvWM0AzvGcFbbcGy2hl3VH9U7kUvTEli54wg5BaXERQQzODUWewP3Exhgo0dCJD0SIrmqxvL8Uidba7ReeR7zSpxsyylkW04h/+aAd/vQQDtd481wVd2K1ZU2Cb2B66p3XJpnzo1VM1xlb4DS3BMPz14zXGl4dmtVVlTP96P7o6QxJQ82g9SeFTDgJquraZ5sdlg0zfz83Hurly+ZYS4f9qg1dfm7msetZpjScWvxFKSkeQuJMd8oH9xMbPG2U9tHaX7VG/Ia3fJyNkJ5Yd3bh8fXGPih6rFtNwgIrPdLulxuth0sZO2u6nubth8sqv1SQQEMSIlmQEoM6SnRDEiOISr0+K1NDWG3GWR0btMo+zpWZLCDgR1jGdgx1rvM7XaTnV/G5qx8b8vVluwCtuYUUlxeybo9uazbk+uzn7bhQfRIiKBbfHUXwW4JgwhJGUKNHUNBlm+4qs/w7J5h2T3hqj7Ds+uq46mpedyy15u/W0FR5rHXcZPG4plPSgNOnDrP37ZF09h/MI8NR3sz4OPHSNn4Jpw3CQbfCSW5lpbolwbfad4XvWgaew8eZe1Zd9F/1yxS1r1ohqi6/mdIi6AgJc2X581Z8mAzSBVurV5X15szV6U5YpxPt7z1ZstTXeyBVZPN1hgAIq4XhDd8UIv8UieZ3tamXL7ffZSC0opa253VNqy6m17HaLrGRTS4lchfGYZBQlQwCVHBXNS9ukWootLFzsPFVeEq32y9yi5g95FiDhWWsWxbGcu2HaqxH+gYG+ozNHv3hAg6nXUJ9i41hmd3VZrDs2dv8O0ieORnc3j2nV+bHzVFd6wRrqpasNp0MbsNga7Wnqqax80zRH/yYPj6BR03aTwdqoLUwU3mm/2QaCuraX4qymDvKrZl5RJKOzquf41HAPZVrV/2J/NDTqjD+tdp/+PrGAa8ZR9HSpubGWV1UXLGKEhJ8+V5c9b9CgBiiqpapDxvagfcBCv+XN3SlLPJbKGoS2R73y558b3Ne27sDW/9cbvd/HyoyHtf09pdufyUU4D7mB6BIQ47/ZOjSe8YTXpKDANSYogNq3+rVksRYLfRJS6cLnHhXNG3+v6s4vIKfsouZEtWPluyCtmSbbZkHSosZ+fhYnYeLuaLDdne7YMCzP3UHD2wR0IycWldMHqNqX7B8uLq4dk9XQNzNkJhthmqc3fBls+rt7cHmi2OcWlmK1af62HRNGyVlUAatq+fh6V/1FXHE6lxlZt2PczPXc7qEKXjJo0hvB3EnmVeLNm3GmpeWJHaKp2wby3sXAo7vja7RFaU0sXquloAwzA7S3xdksqyd9fyxk3pdd5/LM2f4XYf+/au9cnPzycqKoq8vDwiIyOtLkcawhOagErDgRGTgu3I9uNvHxBS3aWr5rxMobHH/5qTKCqrYN2eXO8Q5N/vySW32Flru5TYUNJTzPuaBqTE0CMhggC7BkJoqEOFZTXuuzLD1U/ZhZQ4656UOTrUQff4GkOzJ0TQLT689oAcRYeruwV6wlXOpuN38cS8W84A3EGRGEH623Ey7rJ8jLLqexldFz2C7aKHLKyo+XA6nXz++edcfvnlOByN0723RZr7W/jhn3DhQzDsEaur8S+uSjiQaYamnV/DruXm9BM1HCaabyp7EkIZl9rXUu4OINCo4EXnNbxeOYb4yGCWPDCsxfSUaAyVLjcX/u8isvJLmWD/hHsdc3G5DWyGmwq3jQnOP/BDxPkse+hiHbcT8Le/cfXNBmqRkubtwgfNyz6Ln8XudppDjntEpfjOyRTf27xaabOf8su53W52Hyn23te0dlcum7PycR1zOSIowEa/DtEMqGptSk+JoV1E0Cm/rlRrGx5E2y5BDK0xPLvLZf5ctmRXjxy4OSufHYeKyC12smLHEVbsOOKzn/bRIT5Ds/dIiOSslKE4Us+v3sjthtzdNcLVJgp2ryMk/2cCqPQOGW+U5ZsTQ8sJ1XwLUe4O4MLlg5jS9oCu1ErjSR5sBinPgCatmctl9sjY+bUZnnZ9U+vvlDMohoNtzmZHeDqLyrrz1y2B/M7+Mfc5PuIF57W8WnkNv7PP5T7HR7iw8Wr+NXSf8lWDBzhqyVwuNxUuN7+zf8q9jrm84LyWP1eOZl7gI3Sz7eN1x0s8UFDCyh39z9h9yWIdBSlp/i56CPfSGRiuCtyGHeOWf5v3twRHnfauS8or+WFvLmt355qtTbuPcriovNZ27aNDquZtMoNTz8RIAgPU2tRUbDaDTm3D6NQ2jJG9ErzLS52VbMsp9A5s4WnFys4vY19uCftyS1i4Oce7vcNu0LlduDdcmRMNt6N9t1EY3S9j/voD3L16LX+wf8REx1ycbjsOo5J3Ky7h/cphTL68B+d2bltXia3at9sPMf3zzfzSvoibAhZ6r3JfVziHu9+9Rt1epPF4BpzYu9psgTmNC2fNjdvlonjfBop/Woxt51IislYQ6PSdmqOQMFa6e7KsoifLXWlsLk3GnVf9v8oTmjwhCvA+3uf4qPr5sVcPW7m6jttl5X/kX47H6W3fyZ8C3+SHzFjorBb4lkZBSpq/JWaIqjQCsLsrzKtvpzCssmfS2bW7j3qHId+4P5+KY/5hBNpt9G4f6Z3sNr1jDPGRmgzWHwU77PRuH0Xv9r6hOre4/JhwZX4UllWwuarbYE0RQQF0jQ9nc1YB99jnMrHqqmPNq7U57hjuW5bGkox+6r5RQ6XLzaTZi7jOlslNAQtrHTeAqZ8Fc2lago6bnL64NAgMh/ICs2tuQm+rKzptbrebgrIKcvJLyckvI7vAfMzJL8V9eBuJR1bRuWgtfSvW09bII6zG1xa6g1nl6s5yVxrfunqx0d0JF2ZwCg200ykymHYRQcRHBlNZ6cK+2eUTBjw8z+2Gi1dvGEB6x5im+vb93tpdR9n+4Ue1jlsldkY7n+EjpjLQvpW+Pz4LbW1wwf2aiLwFUZDyI5Uu92nP69PqVN0jtavPH/hb7iBui15Nx7rmcqhDqbOSDfvzzG56u8zglFNQVmu7+Mgg72S3A1Ji6N0+kqCA1nOVsyWKDg1kyFltGHJWdTcLt9vNvtySWpMLbz9YSEFZBWt35570aq27ELo9Vlrna7Zm9Tluv/xzCB3bhBEZEkBEsIPI4AAigx01njuICA4gMsR8dOj+QqmLzQ7tB8KOJebEvH4cpNxuN/klFeQUlJKdX0ZOQSk5BWVk55uPOfnVz0udLgA6GDmca9tAhm0jV9k2kmAcrd6hASXuQDLpzvqg/uyKTKcwtg/tosKIiwjmjsgg4iKCiYs0g1N4kO9bwEqXm/Oeu4msvLr/hr1WeQ0JUcEs65Oo9yY1JEQGc97ndR83NzbGOp/koYB/cbf7A1j0jDnv4YhnFKZaCAUpPzF//QGmfraRAzV+EROjgpkyOk1dXo6nKkS9ZR/Hs6vMeYXeYQiPhI3jzjrC1IG8Em9gWrv7KBv25VNe6fLZZYDNoFdSZI0hyGNIigrG0B+8Fs8wDDrEhNIhJpRLesZ7l5dXuNhxqIj3VuzCvurkV2ulNrtx8uO2etdRVu86WteX1ynEYfcJVp7wFVEVviKPeX5sGAsLtDeb3+tKl5sVO46w5pBBmx1HyOgSpzeyJ5I8xAxSe1ZSmX5rk1+gdLvd5JU4veHIG5JqPHpalcoqTvw3I5HDXG7bQEbARs4N2ER7DvqsrzQcHG3Tn9L2Q7F1vpCozkPICAvjVKa6ttsMpoxO4+5315qD6NRY5zliU0an6dw7xomOm8ngf0vHMHTgWfRd/0dY/po5ufzol1tV19OWSqP2Yf2offPXH+Dud9fW+uXz/KnS/QN12/b+I3z6QzavHPPmzMC8An5+l1h+7Po/rNl9lO93HWV/HVeL2oYHmfc1VbU49e0QRbBDf9iktuXbD3PDX7476XZ/+fVAzu506qNAtjSrdh7hjnfWnHS7287rRNvwYApKneSXOikorSC/pOqxxvOi8rpHZ2womwERnmB1TGuXJ4TVfF4zkHmWNcV9kLrIdgq2LoD3rqUovCPDnS822rFzu90cLXZWh6M6Wo5yCsrIKSij/CQBqabI4ADiI82Woi4hRaS71tO9NJPk3NWEFe323dgWYLa4pV4Anc437wlzhDT4ezkRnXOn5njHLTk2lJVVgx29N2grQzdMBbcL0sbANX+BgNY37UldmuuofQpSWBukzKb0//r84h2rTXggb9w4sNGvAjXVxdgz8TKVLje//ceaOgd+OB67zaBHQoS3m156SgzJsSHN5qq0WMvzu5qVV1rHFUfzPE+ICtYQt8do7ONWUemisKyCgtIK8qqClhm+qh5LKnzDWB2hzFnZOP/2ggJsx4QtM3xFHiec+baOBRAWGHDC0c90ke0UlRyF5zoBkF76Jkeo/r9e17FzudwcLS6v3XLk082ujIMFZbV6MZxIdKiDuKr7jzz3IcVFmN3r4j3d7AIKCd77bfXIeoe2+O7EsEHSADM0pZ4PyedAUPjpHJ16qXS5Wb4thy+/XsGI84eoFbSe6rpFwwCe/GwD7yzfBcAbA/YwastjGC6nOdfZ9f+AwFBrC/cDzTVIqWufxVbuOHLCEAVwuLCc6/+8vIkqalnSk6O5JC2e9JQY+iVHERqoU15Ojbq9nJrGPm4BdhvRoYFEhwaSfAr1uN1uyipc5JeY4avu1q+agezYgFZBYVkFAGUVLg4WmG+wT4VhmAOZmAHLtzUsPDiAuWv31hk+Pcse+Xg9wQF2bxireU3IqHEJ69hrRcZxnxz/64796dS8AHWi/RsneLF67/+4+6u73kqXQQQdOIu9DLBtZaFroHcbz7H7wz8z6Z6wnUNVLUjHDip0IjGhjlrhyBuSqgJSu4iguns3lByFXd/Cpq9hx1LI2XDsdw4JfapbnDpmNMoItA1ltxkMSY3l8CY3Q3S/dr3ZbUadQ5xPvbIXUSEOXv3vNu7+PpnpfWcw7udHMLZ9Be9eA79635Kfs5w+vau0WE5B/W5MbxMWSFiQtT8ud53/0q1RVFbBkaLak94ea/zQTlzVv30TVCStwajeibxxU3qt7hsJ6vZyQv503AzDINhhJ9hhJ+4UOyBUutwU1gxhPmHsmPBVVncoK6904XZTFeYq2Jdb0uA6jhSVc8vsVaf2TbRwfwzowlkBexl4TJDyKKtw8cNe36HB24QFEudtNQrydrerOUBDu/CghnXpLCswJ77dudQMTgd+oNZdNHFp1S1OHYee1gTx4p8Mw+C+Ed2JCnHwzL83MfmHeI72fJ6790/G2L0c3h4NN82FME2f0dwoSFksLqJ+w2a/9qt0TeRWQ33vV6nv8RWpr1G9E7k0LUHdXhrIc9xawsikdptBVKiDqNBT735S6qw84b1ga3Yd5cuN2SfdT/voEKJCHD5vzU/UY7/mqmMvjvmuO/7+3Md9cvyvO7aietdxgut3x9t/SXkla8q7MY7FpNu2HvfrbzsvldH9koiLCKJtQwPS8ZQXw57vzG56O5bC/u/Bfcx9fW26mqEp9QLoeB6Etzv915Vm4fbzzyIy2MHDc39gxqYYcrs9z+RDj2AcWAezLoObP4EoXfxtThSkLDY4NZbEqOCT3j8wOFVXqGrScRMrqdvLqTlet5fWyNsqFlH3+uXbD9crSD1/XT8d02Ms336Yx/66E4B+xnYCqKCijrc7w3vG0z85+vRezFkKe1eZoWnn11UTAR/TWyKmU1WL04XQ6TyIVMt1a3b92cmEBwfwh39+z1s/hXO003M8F/AEtkM/wd9Hwa8/gTadrS5T6klBymK67+LU6LiJSEumi0WnbnBqLCURqeSWhRFtFNHD2M1691ne9ad17CrKYf9aMzjtWAp7VkLlMffIRXYwW5tSzzcDVPSp3M0nLdnlfRIJCwrgrn+s4cOdIeR3eJbXY6ZiP/pzdZiK72V1mVIPmtHQD3juH0iI8u2GlhAVrFGZTkDHTURaKs/FIqhjIIaqR10sqpvdZvDElb353tUFgIE1uvc1+NhVVpitTF//Cf5xNTzXEf4+EhZNM1ugKssgPAH6XAejX4Hffw/3roer34D+v1KIkuO6sFs73r19MBHBAXyx18HNrqlUtOsFRTkw63LYo/sfmwO1SPmJlnT/QFPS/Soi0lL50yAdzc2o3on8tOki2LCOgbafeLtyJFCPY+eqhKwfq4cj3/UtlBf4bhPa1uyil3o+dLoA2nZtuvlEpEUZ2DGW9+/M4Nd/X8G32eVc3eYRPkp8kaADq+Gdq+CGOXDWRVaXKSegIOVHdP/AqdH9KiLSUuli0SlaNJ1ujoMAjIjczcuX9q++QPn1/8KiShg2GVwuOLjJDE07v4ady6A013dfwdFmcOpUNUBEux5gU4ceaRxpSZF8eNe53PTXFfx4uITLnPcyr8ObhO79Gt67Dq6bDT2usLpMOQ4FKRERET+mi0WnwGaHzDmAQXDRPq46ywaRbWDxc7D4Weg2Cj4Ybwan4kO+XxsYAR3PrR5ZL763uT+RMyS1bRgf3Z3BTX9dwfaDRQyr+B++7BRG1M758P7NMOZ16DfO6jKlDgpSIiIi0rJc+KD5uGia+bh0BuxeUT0B7k/zq7d1hELKOdUj6yX2A7veHknTSowK4YPfZjB+1krW78vnwh3jWdAljHbb/w8+/q05J9ngO6wuU46hvxQiIiLS8lz4IGz5jznK3uq/Vy+3B0HyYDM0pZ4PSekQEGhdnSJV2oQHMeeOc7j97dWs3HGE87eM5aueoXTY+g/4/H4oy4fzJumePD+iTr4iIiLSMl3xQvXnhh3Gz4OHd8Mt8+DCB8yWKIUo8SORwQ7e+c1gLu4RR2kFXLThMrb0+B9z5cKn4KspJ56pWpqUpUFq+vTpnH322URERBAXF8eYMWPYsmWLzzalpaVMmDCBNm3aEB4eztixY8nO9p2kcPfu3VxxxRWEhoYSFxfHAw88QEVFRVN+KyIiIuJvtn1lPtoDwV0Ju5eDI/jEXyNisWCHnT/fPJAr+yVR4YJR685jbY/7zZXfvAzzJpojTIrlLA1SS5YsYcKECXz33XcsWLAAp9PJiBEjKCoq8m5z77338tlnn/Hhhx+yZMkS9u/fzzXXXONdX1lZyRVXXEF5eTnffvstb7/9NrNnz+aJJ56w4lsSERERf7BkhnmP1LBH4fGD5uOiaeZyET/nsNt48Zf9uXFICm43XJOZzqLuT4BhgzWzYe4dUOm0usxWz9J7pObPn+/zfPbs2cTFxbFmzRouuOAC8vLy+Nvf/sacOXO4+OKLAZg1axY9e/bku+++45xzzuHLL79k48aNfPXVV8THx9O/f3+efvppHnroIZ588kkCA9VkLyIi0qrUDFGegSeOHYDC81zET9ltBs+M6U1UiIPXF2/n1nU9eKnPM1y1fQrG+v+DskK4/m1whFhdaqvlV/dI5eXlARAbGwvAmjVrcDqdDB8+3LtNjx49SElJYfny5QAsX76cPn36EB8f791m5MiR5Ofns2HDhiasXkRERPyCq9I3RHlc+KC5XN2ipJkwDIMHR/Xg4ct6ADDxx0680/FZ3AEhsPULeHcslOZbXGXr5Tej9rlcLiZOnMjQoUPp3bs3AFlZWQQGBhIdHe2zbXx8PFlZWd5taoYoz3rPurqUlZVRVlbmfZ6fb56ATqcTp1PNpM2R5+emn580FZ1z0pR0vjXQeVX3k9R1vM699/jrxEvnnH+57dwUwhw2nvhsI1M2tedwl+nce/BxjF3f4Jr9Cypv+ABC21hd5inzt/OtvnX4TZCaMGEC69evZ9myZWf8taZPn87UqVNrLf/yyy8JDQ09468vZ86CBQusLkFaGZ1z0pR0vklT0znnPyKBX3cx+Mc2G69si2NH5IM8b59BUNY6it4YxredH6Q0MNbqMk+Lv5xvxcXF9drOL4LUPffcw7x581i6dCkdOnTwLk9ISKC8vJzc3FyfVqns7GwSEhK826xcudJnf55R/TzbHGvy5MlMmjTJ+zw/P5/k5GRGjBhBZGRkY31b0oScTicLFizg0ksvxeFwWF2OtAI656Qp6XyTpqZzzj9dDpz/00Hu+ec6PsvvjNH+eV4qe5KIwv2M2PciFb/6P4jpZHWZDeZv55unt9rJWBqk3G43v/vd7/j4449ZvHgxqampPusHDhyIw+Fg4cKFjB07FoAtW7awe/duMjIyAMjIyGDatGnk5OQQFxcHmGk2MjKStLS0Ol83KCiIoKCgWssdDodf/PDk1OlnKE1N55w0JZ1v0tR0zvmf4b2SeOc3wdw2exWf7ougMOEZ/hL9DPbcHTje+QXc/DHE1/0e2N/5y/lW3xosHWxiwoQJvPvuu8yZM4eIiAiysrLIysqipKQEgKioKG677TYmTZrEokWLWLNmDbfeeisZGRmcc845AIwYMYK0tDRuvvlm1q1bxxdffMFjjz3GhAkT6gxLIiIiIiLN2eDUWP7fnefQJiyQ/2aF8EvnFJxte0JhFsy+HPausbrEVsHSIPXGG2+Ql5fHRRddRGJiovfj/fff927z4osv8otf/IKxY8dywQUXkJCQwNy5c73r7XY78+bNw263k5GRwU033cSvf/1rnnrqKSu+JRERERGRM653+yg+uCuDpKhgVh8OZHTBZErjB0LJUXjnStix1OoSWzzLu/adTHBwMDNnzmTmzJnH3aZjx458/vnnjVmaiIiIiIhf69wunA/vPpeb/7qCzYeKuLRiIvOT3iRs/zfw7rXmPFPdL7O6zBbLr+aREhERERGR+msfHcIHd2WQlhjJniI7F+y/m6PJl0JlGfzzRvjhQ6tLbLEUpEREREREmrG24UH8vzvPYVDHGA6X2jh/53iyO10F7kqYewes+qvVJbZIClIiIiIiIs1cVIiDf9w2hAu7taPQaeP8rdez86xfAW74933w9Z+sLrHFUZASEREREWkBQgLt/OXXg7iiTyLllQYXb7qCDV3uMFcunAoLpkA9xiiQ+lGQEhERERFpIQIDbLxywwDGnZ2My21wxfphrOg80Vz5zUvw70ngcllZYouhICUiIiIi0oLYbQbTr+nDby84C4BfbhjMF2c9ghsDVv8dPr4TKp0WV9n8KUiJiIiIiLQwhmHw8GU9eGBkdwB+u7E3H3Z6ErctAH78EN6/GZylFlfZvClIiYiIiIi0QIZhMGFYF54e0xvDgAc3d+Uv7afhDgiGn/4D710LZQVWl9lsKUiJiIiIiLRgN5/TkZd+2R+7zeDZrcn8b9tpuAPDYefX8PaVUHzE6hKbJQUpEREREZEW7qr+7Xnr5oEEBdh4fWcij0Y+izskFvavhVmXQ/4Bq0tsdhSkRERERERagUt6xvP2bwYTHhTAnL1tuSdoGq7wBDi4CWaNgqM7rS6xWVGQEhERERFpJc45qw1z7hhCTKiDf2dFMZ6nqIjqaIaov4+CnM1Wl9hsKEiJiIiIiLQifTtE88FvM0iIDObrQ+FcW/4E5bHdoeAAzLoM9q21usRmQUFKRERERKSV6RofwYd3ZdCxTSiZR0O4PH8yJXH9oeSIOQDFzmVWl+j3FKRERERERFqh5NhQPrwrgx4JEWwrDOTinEkUJGZAeQG8OxZ++sLqEv2agpSIiIiISCsVFxHM+3dmkJ4SzYHSAM7f9z8caX8JVJTCP38FP35kdYl+S0FKRERERKQViwp18O7tQzi/a1tyy+2ct/NW9if/AlwV8H+3w+pZVpfolxSkRERERERaudDAAP46fhCjeiVQXGnj/G3j2N7xl4Ab5k2Eb162ukS/oyAlIiIiIiIEBdh57VcDuHZgByrdNi7ZciU/dPqNuXLBE7DwKXC7rS3SjyhIiYiIiIgIAAF2GzPG9uU3Q1MBgys3D2dZxwnmyq9fgM/vB5fL0hr9hYKUiIiIiIh42WwGj/+iJ/cO7wbATVuGMi/lAdwYsOqv8MldUOm0uErrKUiJiIiIiIgPwzD4w/CuTBmdBsA9Pw1gTvvHcBt2+OF9+GA8OEstrtJaClIiIiIiIlKnW4em8sJ1/bDbDB7d3pPX45/EbQ+CLf+GOddBWaHVJVpGQUpERERERI5r7MAOvH5jOoF2G/+7szPPxjyDOzAMdiyFd66C4iNWl2gJBSkRERERETmhkb0SmHXr2YQG2vnL3vY8FPYMruAY2LcaZv8CCrKtLrHJKUiJiIiIiMhJDe3SlvduH0JUiIMPDsRzV8DTVIbFQ84G+PtIOLrL6hKblIKUiIiIiIjUy4CUGD74bQZxEUF8eSiWm1xPUhGZAkd3wN9HwcEtVpfYZBSkRERERESk3ronRPDhXRkkx4aw/GgUY0oeoyymGxTsh1mXwf5Mq0tsEgpSIiIiIiLSIB3bhPHRXefSNS6c9QXhjMp9iOK2faH4MLw9GnZ9a3WJZ5yClIiIiIiINFh8ZDAf/DaDfh2i2FESwkU595IXPwTK8uEfV8PWBVaXeEYpSImIiIiIyCmJCQvkvTvOIeOsNuSUB3HevgkcTLwIKkrh/42D9XOtLvGMUZASEREREZFTFh4UwKxbz+bStHgKKgI4b9dt7Gl/Gbgq4P9ug7XvWF3iGaEgJSIiIiIipyXYYeeNG9O5ZkB7ylx2Ltx+I1s6XAtuF3z6O/j2NatLbHQKUiIiIiIictoC7Daev64f4zM64sLGyG1Xs6bDr82VXz4K/50Gbre1RTYiBSkREREREWkUNpvBk1f24vcXdwEMxm4byaL2d5krl86A/zwELpelNTYWBSkREREREWk0hmEwaUR3HruiJ2Bw6/YL+Dhxkrly5Z/hX/8DlRWW1tgYFKRERERERKTR3X7+WcwY2xebAffuGMTs+Mm4DTus+3/w4XioKLO6xNOiICUiIiIiImfE9WcnM/NX6TjsBk/u6sNLbR7HbQ+EzfNg5mAoK6z9RUtmwKLpTV9sAylIiYiIiIjIGXNZn0T+Nv5sQhx2Xt7bjakRT+K2OeDoTjNMleRWb7xkBiyaBja7VeXWm4KUiIiIiIicURd0a8e7tw8mMjiA2VmduDfsj7gCgiF/H+7Xh7A5O5/9nz5thqhhj8KFD1pd8kkpSImIiIiIyBk3sGMs7/82g7bhQXxyMJEbeZYiggksPcyD++6h448v85Z9HPPb3Gx1qfWiICUiIiIiIk2iZ2IkH96VQWxoIMsLE7i87FncbjAMqHQbTC+6krvfXcv89QesLvWkFKRERERERKTJpMSGEmA3ALjS9q03RNkNN/fY5wIw9bONVLr8e/JeBSkREREREWkyK3ccIaegjN/Z53Kf4yNecF5L57L3eMF5Lfc5PuIe+1wO5JWycscRq0s9oQCrCxARERERkdYjp6DUJ0S9WnkNgPfxPsdHVdv1t6rEelGQEhERERGRJhMXEcwOw+UTojw8z+2Gi7iIYCvKqzcFKRERERERaTKDU2OZFHYTWXmlda5/rfIaEqKC+V1qbBNX1jC6R0pERERERJqM3WYwZXQaAMYx6zzPp4xOw247dq1/UZASEREREZEmNap3Im/clE5ClG/3vYSoYN64KZ1RvRMtqqz+1LVPRERERESa3KjeiVyalsDybTl8+fUKRpw/hIwucX7fEuWhICUiIiIiIpaw2wyGpMZyeJObIamxzSZEgbr2iYiIiIiINJiClIiIiIiISAMpSImIiIiIiDSQgpSIiIiIiEgDKUiJiIiIiIg0kIKUiIiIiIhIA1kapJYuXcro0aNJSkrCMAw++eQTn/Vut5snnniCxMREQkJCGD58OFu3bvXZ5siRI9x4441ERkYSHR3NbbfdRmFhYRN+FyIiIiIi0tpYGqSKioro168fM2fOrHP9jBkzeOWVV3jzzTdZsWIFYWFhjBw5ktLSUu82N954Ixs2bGDBggXMmzePpUuXcueddzbVtyAiIiIiIq2QpRPyXnbZZVx22WV1rnO73bz00ks89thjXHXVVQC88847xMfH88knnzBu3Dg2bdrE/PnzWbVqFYMGDQLg1Vdf5fLLL+f5558nKSmpyb4XERERERFpPfz2HqkdO3aQlZXF8OHDvcuioqIYMmQIy5cvB2D58uVER0d7QxTA8OHDsdlsrFixoslrFhERERGR1sHSFqkTycrKAiA+Pt5neXx8vHddVlYWcXFxPusDAgKIjY31blOXsrIyysrKvM/z8/MBcDqdOJ3ORqlfmpbn56afnzQVnXPSlHS+SVPTOSdNyd/Ot/rW4bdB6kyaPn06U6dOrbX8yy+/JDQ01IKKpLEsWLDA6hKkldE5J01J55s0NZ1z0pT85XwrLi6u13Z+G6QSEhIAyM7OJjEx0bs8Ozub/v37e7fJycnx+bqKigqOHDni/fq6TJ48mUmTJnmf5+fnk5yczIgRI4iMjGzE70KaitPpZMGCBVx66aU4HA6ry5FWQOecNCWdb9LUdM5JU/K3883TW+1k/DZIpaamkpCQwMKFC73BKT8/nxUrVnD33XcDkJGRQW5uLmvWrGHgwIEA/Pe//8XlcjFkyJDj7jsoKIigoCDvc7fbDUBJSYlf/PCk4ZxOJ8XFxZSUlFBRUWF1OdIK6JyTpqTzTZqazjlpSv52vpWUlADVGeF4LA1ShYWFbNu2zft8x44dZGZmEhsbS0pKChMnTuSZZ56ha9eupKam8vjjj5OUlMSYMWMA6NmzJ6NGjeKOO+7gzTffxOl0cs899zBu3LgGjdhXUFAAQHJycqN+fyIiIiIi0jwVFBQQFRV13PWG+2RR6wxavHgxw4YNq7V8/PjxzJ49G7fbzZQpU3jrrbfIzc3lvPPO4/XXX6dbt27ebY8cOcI999zDZ599hs1mY+zYsbzyyiuEh4fXuw6Xy8X+/fuJiIjAMIxG+d6kaXm6Z+7Zs0fdM6VJ6JyTpqTzTZqazjlpSv52vrndbgoKCkhKSsJmO/4g55YGKZHGkp+fT1RUFHl5eX7xCygtn845aUo636Sp6ZyTptRczze/nUdKRERERETEXylIiYiIiIiINJCClLQIQUFBTJkyxWc0RpEzSeecNCWdb9LUdM5JU2qu55vukRIREREREWkgtUiJiIiIiIg0kIKUiIiIiIhIAylIiYiIiIiINJCClIiIiIiISAMpSEmzNn36dM4++2wiIiKIi4tjzJgxbNmyxeqypJX44x//iGEYTJw40epSpAXbt28fN910E23atCEkJIQ+ffqwevVqq8uSFqiyspLHH3+c1NRUQkJC6Ny5M08//TQal0way9KlSxk9ejRJSUkYhsEnn3zis97tdvPEE0+QmJhISEgIw4cPZ+vWrdYUWw8KUtKsLVmyhAkTJvDdd9+xYMECnE4nI0aMoKioyOrSpIVbtWoVf/7zn+nbt6/VpUgLdvToUYYOHYrD4eA///kPGzdu5IUXXiAmJsbq0qQFeu6553jjjTd47bXX2LRpE8899xwzZszg1Vdftbo0aSGKioro168fM2fOrHP9jBkzeOWVV3jzzTdZsWIFYWFhjBw5ktLS0iautH40/Lm0KAcPHiQuLo4lS5ZwwQUXWF2OtFCFhYWkp6fz+uuv88wzz9C/f39eeuklq8uSFujhhx/mm2++4euvv7a6FGkFfvGLXxAfH8/f/vY377KxY8cSEhLCu+++a2Fl0hIZhsHHH3/MmDFjALM1Kikpifvuu4/7778fgLy8POLj45k9ezbjxo2zsNq6qUVKWpS8vDwAYmNjLa5EWrIJEyZwxRVXMHz4cKtLkRbu008/ZdCgQVx33XXExcUxYMAA/vKXv1hdlrRQ5557LgsXLuSnn34CYN26dSxbtozLLrvM4sqkNdixYwdZWVk+/1ujoqIYMmQIy5cvt7Cy4wuwugCRxuJyuZg4cSJDhw6ld+/eVpcjLdQ///lP1q5dy6pVq6wuRVqBn3/+mTfeeINJkybxyCOPsGrVKn7/+98TGBjI+PHjrS5PWpiHH36Y/Px8evTogd1up7KykmnTpnHjjTdaXZq0AllZWQDEx8f7LI+Pj/eu8zcKUtJiTJgwgfXr17Ns2TKrS5EWas+ePfzhD39gwYIFBAcHW12OtAIul4tBgwbx7LPPAjBgwADWr1/Pm2++qSAlje6DDz7gvffeY86cOfTq1YvMzEwmTpxIUlKSzjeROqhrn7QI99xzD/PmzWPRokV06NDB6nKkhVqzZg05OTmkp6cTEBBAQEAAS5Ys4ZVXXiEgIIDKykqrS5QWJjExkbS0NJ9lPXv2ZPfu3RZVJC3ZAw88wMMPP8y4cePo06cPN998M/feey/Tp0+3ujRpBRISEgDIzs72WZ6dne1d528UpKRZc7vd3HPPPXz88cf897//JTU11eqSpAW75JJL+PHHH8nMzPR+DBo0iBtvvJHMzEzsdrvVJUoLM3To0FpTOvz000907NjRooqkJSsuLsZm831raLfbcblcFlUkrUlqaioJCQksXLjQuyw/P58VK1aQkZFhYWXHp6590qxNmDCBOXPm8K9//YuIiAhvH9qoqChCQkIsrk5amoiIiFr334WFhdGmTRvdlydnxL333su5557Ls88+y/XXX8/KlSt56623eOutt6wuTVqg0aNHM23aNFJSUujVqxfff/89f/rTn/jNb35jdWnSQhQWFrJt2zbv8x07dpCZmUlsbCwpKSlMnDiRZ555hq5du5Kamsrjjz9OUlKSd2Q/f6Phz6VZMwyjzuWzZs3illtuadpipFW66KKLNPy5nFHz5s1j8uTJbN26ldTUVCZNmsQdd9xhdVnSAhUUFPD444/z8ccfk5OTQ1JSEjfccANPPPEEgYGBVpcnLcDixYsZNmxYreXjx49n9uzZuN1upkyZwltvvUVubi7nnXcer7/+Ot26dbOg2pNTkBIREREREWkg3SMlIiIiIiLSQApSIiIiIiIiDaQgJSIiIiIi0kAKUiIiIiIiIg2kICUiIiIiItJAClIiIiIiIiINpCAlIiIiIiLSQApSIiIiZ4Db7eZPf/oTq1evtroUERE5AxSkRESk2ejUqRMvvfSS1WV4Pfnkk/Tv37/OddOnT2f+/Pn069evaYsSEZEmYbjdbrfVRYiIiADccsstvP3227WWjxw5kvnz53Pw4EHCwsIIDQ21oLraCgsLKSsro02bNj7Lly5dysSJE1m8eDGRkZEWVSciImeSgpSIiPiNW265hezsbGbNmuWzPCgoiJiYGIuqEhERqU1d+0RExK8EBQWRkJDg8+EJUcd27cvNzeX222+nXbt2REZGcvHFF7Nu3Tqf/X322WecffbZBAcH07ZtW66++mrvOsMw+OSTT3y2j46OZvbs2d7ne/fu5YYbbiA2NpawsDAGDRrEihUrgNpd+1wuF0899RQdOnQgKCiI/v37M3/+fO/6nTt3YhgGc+fOZdiwYYSGhtKvXz+WL19+mkdNRESamoKUiIg0W9dddx05OTn85z//Yc2aNaSnp3PJJZdw5MgRAP79739z9dVXc/nll/P999+zcOFCBg8eXO/9FxYWcuGFF7Jv3z4+/fRT1q1bx4MPPojL5apz+5dffpkXXniB559/nh9++IGRI0dy5ZVXsnXrVp/tHn30Ue6//34yMzPp1q0bN9xwAxUVFad+IEREpMkFWF2AiIhITfPmzSM8PNxn2SOPPMIjjzzis2zZsmWsXLmSnJwcgoKCAHj++ef55JNP+Oijj7jzzjuZNm0a48aNY+rUqd6va8jgD3PmzOHgwYOsWrWK2NhYALp06XLc7Z9//nkeeughxo0bB8Bzzz3HokWLeOmll5g5c6Z3u/vvv58rrrgCgKlTp9KrVy+2bdtGjx496l2biIhYS0FKRET8yrBhw3jjjTd8lnlCTE3r1q2jsLCw1kAPJSUlbN++HYDMzEzuuOOOU64lMzOTAQMG1Pn6x8rPz2f//v0MHTrUZ/nQoUNrdTfs27ev9/PExEQAcnJyFKRERJoRBSkREfErYWFhJ2z18SgsLCQxMZHFixfXWhcdHQ1ASEjICfdhGAbHjrnkdDq9n5/s60+Vw+HwqQE4bndBERHxT7pHSkREmqX09HSysrIICAigS5cuPh9t27YFzJafhQsXHncf7dq148CBA97nW7dupbi42Pu8b9++ZGZmeu+5OpHIyEiSkpL45ptvfJZ/8803pKWlNfTbExERP6cWKRER8StlZWVkZWX5LAsICPCGI4/hw4eTkZHBmDFjmDFjBt26dWP//v3eASYGDRrElClTuOSSS+jcuTPjxo2joqKCzz//nIceegiAiy++mNdee42MjAwqKyt56KGHfFqLbrjhBp599lnGjBnD9OnTSUxM5PvvvycpKYmMjIxatT/wwANMmTKFzp07079/f2bNmkVmZibvvffeGThSIiJiJQUpERHxK/Pnz/feN+TRvXt3Nm/e7LPMMAw+//xzHn30UW699VYOHjxIQkICF1xwAfHx8QBcdNFFfPjhhzz99NP88Y9/JDIykgsuuMC7jxdeeIFbb72V888/n6SkJF5++WXWrFnjXR8YGMiXX37Jfffdx+WXX05FRQVpaWk+A0fU9Pvf/568vDzuu+8+cnJySEtL49NPP6Vr166NdXhERMRPaEJeERFpNhITE3n66ae5/fbbrS5FRERaObVIiYiI3ysuLuabb74hOzubXr16WV2OiIiIBpsQERH/99ZbbzFu3DgmTpxY571JIiIiTU1d+0RERERERBpILVIiIiIiIiINpCAlIiIiIiLSQApSIiIiIiIiDaQgJSIiIiIi0kAKUiIiIiIiIg2kICUiIiIiItJAClIiIiIiIiINpCAlIiIiIiLSQApSIiIiIiIiDfT/Aeq4IsaGqdSNAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [223.427, 223.415, 249.186, 228.997, 229.001, 222.041, 221.91, 233.348, 233.347, 125.792]\n",
+    "tiempo_entrenamiento_gpu = [743.35, 234.043, 254.83, 234.694, 234.695, 720.628, 208.575, 237.899, 237.9, 129.915]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "f8685a35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [9463, 9389, 9453, 9449, 9423, 9384, 9432, 9417, 9460, 9375]\n",
+    "exactitud_gpu = [8590, 9419, 9377, 9442, 9468, 8888, 9442, 9400, 9402, 9452]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "949d1b04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [40.826, 40.853, 48.48, 42.392, 42.344, 40.675, 40.869, 43.766, 43.711, 24.485]\n",
+    "tiempo_inferencia_gpu = [12.276, 42.531, 49.947, 42.691, 42.708, 7.874, 41.844, 42.613, 42.555, 24.684]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "27d195ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [720.205, 720.161, 813.375, 722.162, 722.207, 725.24, 724.081, 720.608, 720.551, 428.981]\n",
+    "tiempo_entrenamiento_gpu = [192.655, 743.32, 836.534, 748.373, 748.336, 176.378, 715.832, 743.79, 743.858, 438.196]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "256a54a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [8979, 8993, 8985, 8963, 8972, 8960, 8892, 8952, 8930, 8913]\n",
+    "exactitud_gpu = [8920, 8913, 8841, 8946, 8901, 9018, 8907, 8881, 8953, 8884]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "107a731d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [12.992, 12.989, 14.676, 11.954, 11.96, 37.122, 12.036, 12.661, 12.66, 6.689]\n",
+    "tiempo_inferencia_gpu = [35.2182, 12.277, 15.047, 12.791, 12.802, 28.628, 11.795, 12.364, 12.366, 6.697]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "c28be9e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [187.377, 187.42, 220.285, 186.519, 186.51, 617.958, 184.905, 187.492, 187.498, 105.068]\n",
+    "tiempo_entrenamiento_gpu = [620.974, 192.708, 225.799, 194.857, 194.855, 605.446, 173.443, 193.72, 193.664, 405.464]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "9f776fd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de exactitud\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "exactitud_cpu = [9624, 8980, 8974, 8889, 8917, 9599, 8906, 8863, 8622, 8948]\n",
+    "exactitud_gpu = [8879, 8874, 8943, 8778, 8642, 8961, 8938, 8901, 8943, 8961]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, exactitud_cpu, marker='o', label='Exactitud en CPU')\n",
+    "plt.plot(ejecuciones, exactitud_gpu, marker='x', label='Exactitud en GPU')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Exactitud')\n",
+    "plt.title('Exactitud en CPU vs. Exactitud en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "5642b4b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de inferencia en milisegundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_inferencia_cpu = [28.556, 35.71, 40.95, 35.16, 35.15, 28.539, 37.124, 36.417, 36.421, 20.61]\n",
+    "tiempo_inferencia_gpu = [35.187, 33.868, 35.986, 35.984, 35.529, 34.864, 34.884, 20.33,  35.984, 34.864]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_cpu, marker='o', label='Tiempo de Inferencia en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_inferencia_gpu, marker='x', label='Tiempo de Inferencia en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Inferencia (s)')\n",
+    "plt.title('Tiempo de Inferencia en CPU vs. Tiempo de Inferencia en GPU ')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "98022c52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datos de tiempo de entrenamiento en segundos\n",
+    "ejecuciones = list(range(1, 11))  # Números del 1 al 10\n",
+    "tiempo_entrenamiento_cpu = [506.494, 628.93, 694.698, 612.912, 612.889, 507.855, 618.086, 623.247, 623.215, 350.935]\n",
+    "tiempo_entrenamiento_gpu = [621.025, 615.615, 626.285, 626.281, 603.774, 624.132, 624.055, 349.676, 626.281, 624.055]\n",
+    "\n",
+    "# Crear el gráfico de líneas\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_cpu, marker='o', label='Tiempo de Entrenamiento en CPU (s)')\n",
+    "plt.plot(ejecuciones, tiempo_entrenamiento_gpu, marker='x', label='Tiempo de Entrenamiento en GPU (s)')\n",
+    "\n",
+    "# Etiquetas y título\n",
+    "plt.xlabel('Ejecución')\n",
+    "plt.ylabel('Tiempo de Entrenamiento (s)')\n",
+    "plt.title('Tiempo de Entrenamiento en CPU vs. Tiempo de Entrenamiento en GPU (TFG2-Cpu_1024_100 y TFG2-Gpu_1024_100)')\n",
+    "\n",
+    "# Mostrar leyenda\n",
+    "plt.legend()\n",
+    "\n",
+    "# Mostrar el gráfico\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a175d617",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/IPUTFG.ipynb b/IPUTFG.ipynb
new file mode 100644
index 0000000..3aa5350
--- /dev/null
+++ b/IPUTFG.ipynb
@@ -0,0 +1,247 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f714b90e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mERROR: Could not find a version that satisfies the requirement poptorch (from versions: none)\u001b[0m\u001b[31m\r\n",
+      "\u001b[0m\u001b[31mERROR: No matching distribution found for poptorch\u001b[0m\u001b[31m\r\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install poptorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cfd476c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'poptorch'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;241m,\u001b[39m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnn\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpopart\u001b[39;00m\u001b[38;5;241m,\u001b[39m \u001b[38;5;21;01mpoptorch\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msnntorch\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msnn\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msnntorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctional\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mSF\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'poptorch'"
+     ]
+    }
+   ],
+   "source": [
+    "import torch, torch.nn as nn\n",
+    "import popart, poptorch\n",
+    "import snntorch as snn\n",
+    "import snntorch.functional as SF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cdbfc95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "batch_size = 128\n",
+    "data_path='/data/mnist'\n",
+    "\n",
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)\n",
+    "\n",
+    "# Train using full precision 32-flt\n",
+    "opts = poptorch.Options()\n",
+    "opts.Precision.halfFloatCasting(poptorch.HalfFloatCastingBehavior.HalfUpcastToFloat)\n",
+    "\n",
+    "# Create DataLoaders\n",
+    "train_loader = poptorch.DataLoader(options=opts, dataset=mnist_train, batch_size=batch_size, shuffle=True, num_workers=20)\n",
+    "test_loader = poptorch.DataLoader(options=opts, dataset=mnist_test, batch_size=batch_size, shuffle=True, num_workers=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca06da51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_steps = 25\n",
+    "beta = 0.9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ca4a94e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Model(torch.nn.Module):\n",
+    "def __init__(self):\n",
+    "    super().__init__()\n",
+    "\n",
+    "    num_inputs = 784\n",
+    "    num_hidden = 1000\n",
+    "    num_outputs = 10\n",
+    "\n",
+    "    self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "    self.lif1 = snn.Leaky(beta=beta)\n",
+    "    self.fc2 = nn.Linear(num_hidden, num_output)\n",
+    "    self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    # Cross-Entropy Spike Count Loss\n",
+    "    self.loss_fn = SF.ce_count_loss()\n",
+    "\n",
+    "def forward(self, x, labels=None):\n",
+    "    mem1 = self.lif1.init_leaky()\n",
+    "    mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "    spk2_rec = []\n",
+    "    mem2_rec = []\n",
+    "\n",
+    "    for step in range(num_steps):\n",
+    "        cur1 = self.fc1(x.view(batch_size,-1))\n",
+    "        spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "        cur2 = self.fc2(spk1)\n",
+    "        spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "\n",
+    "        spk2_rec.append(spk2)\n",
+    "        mem2_rec.append(mem2)\n",
+    "\n",
+    "    spk2_rec = torch.stack(spk2_rec)\n",
+    "    mem2_rec = torch.stack(mem2_rec)\n",
+    "\n",
+    "    if self.training:\n",
+    "        return spk2_rec, poptorch.identity_loss(self.loss_fn(mem2_rec, labels), \"none\")\n",
+    "    return spk2_rec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3a02ba6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "self.lif1 = snn.Leaky(beta=beta)\n",
+    "self.fc2 = nn.Linear(num_hidden, num_output)\n",
+    "self.lif2 = snn.Leaky(beta=beta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba8b3a05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from snntorch import surrogate\n",
+    "\n",
+    "self.lif1 = snn.Leaky(beta=beta, spike_grad = surrogate.fast_sigmoid())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d8c3d12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "self.loss_fn = SF.ce_count_loss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71c22d0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mem1 = self.lif1.init_leaky()\n",
+    "mem2 = self.lif2.init_leaky()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94b42c2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for step in range(num_steps):\n",
+    "    cur1 = self.fc1(x.view(batch_size,-1))\n",
+    "    spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "    cur2 = self.fc2(spk1)\n",
+    "    spk2, mem2 = self.lif2(cur2, mem2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24a3d65a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = Model()\n",
+    "optimizer = poptorch.optim.Adam(net.parameters(), lr=0.001, betas=(0.9, 0.999))\n",
+    "\n",
+    "poptorch_model = poptorch.trainingModel(net, options=opts, optimizer=optimizer)\n",
+    "\n",
+    "epochs = 10\n",
+    "for epoch in tqdm(range(epochs), desc=\"epochs\"):\n",
+    "    correct = 0.0\n",
+    "\n",
+    "    for i, (data, labels) in enumerate(train_loader):\n",
+    "        output, loss = poptorch_model(data, labels)\n",
+    "\n",
+    "        if i % 250 == 0:\n",
+    "            _, pred = output.sum(dim=0).max(1)\n",
+    "            correct = (labels == pred).sum().item()/len(labels)\n",
+    "\n",
+    "            # Accuracy on a single batch\n",
+    "            print(\"Accuracy: \", correct)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Cpu_1024_100.ipynb b/TFG-Cpu_1024_100.ipynb
new file mode 100644
index 0000000..2bc07a0
--- /dev/null
+++ b/TFG-Cpu_1024_100.ipynb
@@ -0,0 +1,540 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_cpu_1024_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Cpu_1024_25.ipynb b/TFG-Cpu_1024_25.ipynb
new file mode 100644
index 0000000..fbb41dd
--- /dev/null
+++ b/TFG-Cpu_1024_25.ipynb
@@ -0,0 +1,539 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_cpu_1024_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Cpu_256_100.ipynb b/TFG-Cpu_256_100.ipynb
new file mode 100644
index 0000000..647650f
--- /dev/null
+++ b/TFG-Cpu_256_100.ipynb
@@ -0,0 +1,540 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_cpu_256_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Cpu_256_25.ipynb b/TFG-Cpu_256_25.ipynb
new file mode 100644
index 0000000..4b67771
--- /dev/null
+++ b/TFG-Cpu_256_25.ipynb
@@ -0,0 +1,539 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_cpu_256_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Cpu_32_100.ipynb b/TFG-Cpu_32_100.ipynb
new file mode 100644
index 0000000..e8362e8
--- /dev/null
+++ b/TFG-Cpu_32_100.ipynb
@@ -0,0 +1,541 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_cpu_32_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Cpu_32_25.ipynb b/TFG-Cpu_32_25.ipynb
new file mode 100644
index 0000000..d72229e
--- /dev/null
+++ b/TFG-Cpu_32_25.ipynb
@@ -0,0 +1,474 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_cpu_32_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Gpu_1024_100.ipynb b/TFG-Gpu_1024_100.ipynb
new file mode 100644
index 0000000..03f48a7
--- /dev/null
+++ b/TFG-Gpu_1024_100.ipynb
@@ -0,0 +1,540 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_gpu_1024_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Gpu_1024_25.ipynb b/TFG-Gpu_1024_25.ipynb
new file mode 100644
index 0000000..8d272cf
--- /dev/null
+++ b/TFG-Gpu_1024_25.ipynb
@@ -0,0 +1,541 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_gpu_1024_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Gpu_256_100.ipynb b/TFG-Gpu_256_100.ipynb
new file mode 100644
index 0000000..c468ddb
--- /dev/null
+++ b/TFG-Gpu_256_100.ipynb
@@ -0,0 +1,554 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 256 100\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'DataLoader' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdispositivo\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m 256 100\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# drop_last switched to False to keep all samples\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m test_loader \u001b[38;5;241m=\u001b[39m \u001b[43mDataLoader\u001b[49m(mnist_test, batch_size\u001b[38;5;241m=\u001b[39mbatch_size, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, drop_last\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Guardar el modelo entrenado\u001b[39;00m\n\u001b[1;32m     11\u001b[0m torch\u001b[38;5;241m.\u001b[39msave(net\u001b[38;5;241m.\u001b[39mstate_dict(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodelo_entrenado.pth\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'DataLoader' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_gpu_256_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f77d929b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Gpu_256_25.ipynb b/TFG-Gpu_256_25.ipynb
new file mode 100644
index 0000000..f40b1d2
--- /dev/null
+++ b/TFG-Gpu_256_25.ipynb
@@ -0,0 +1,475 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_gpu_256_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Gpu_32_100.ipynb b/TFG-Gpu_32_100.ipynb
new file mode 100644
index 0000000..91585f3
--- /dev/null
+++ b/TFG-Gpu_32_100.ipynb
@@ -0,0 +1,540 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_gpu_32_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG-Gpu_32_25.ipynb b/TFG-Gpu_32_25.ipynb
new file mode 100644
index 0000000..8803e1e
--- /dev/null
+++ b/TFG-Gpu_32_25.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'torch' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m32\u001b[39m\n\u001b[1;32m      3\u001b[0m data_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../datos\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 5\u001b[0m dtype \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241m.\u001b[39mfloat\n\u001b[1;32m      6\u001b[0m dispositivo \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      8\u001b[0m device \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_available() \u001b[38;5;28;01melse\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), 'modelo_entrenado_gpu_32_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG.ipynb b/TFG.ipynb
new file mode 100644
index 0000000..73d704a
--- /dev/null
+++ b/TFG.ipynb
@@ -0,0 +1,555 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bb636ef6",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install snntorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.95\n",
+    "\n",
+    "# Definición de la red\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden, num_outputs)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk2_rec = []\n",
+    "        mem2_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            spk2_rec.append(spk2)\n",
+    "            mem2_rec.append(mem2)\n",
+    "\n",
+    "        return torch.stack(spk2_rec, dim=0), torch.stack(mem2_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    print_batch_accuracy(data, targets, train=True)\n",
+    "    print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    #print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'net' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 11\u001b[0m\n\u001b[1;32m      9\u001b[0m test_loader \u001b[38;5;241m=\u001b[39m DataLoader(mnist_test, batch_size\u001b[38;5;241m=\u001b[39mbatch_size, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, drop_last\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Guardar el modelo entrenado\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m torch\u001b[38;5;241m.\u001b[39msave(\u001b[43mnet\u001b[49m\u001b[38;5;241m.\u001b[39mstate_dict(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodelo_entrenado.pth\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[1;32m     14\u001b[0m   net\u001b[38;5;241m.\u001b[39meval()\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'net' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(dispositivo)\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "\n",
+    "# Nombre del archivo de guardado\n",
+    "nombre_archivo = 'modelo_entrenado.pth'\n",
+    "\n",
+    "# Ruta completa al archivo en la carpeta \"modelos\"\n",
+    "ruta_guardado = os.path.join('modelos', nombre_archivo)\n",
+    "\n",
+    "# Guardar el modelo\n",
+    "torch.save(net.state_dict(), ruta_guardado)\n",
+    "#torch.save(net.state_dict(), 'modelo_entrenado.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Cpu_1024_100.ipynb b/TFG1-Cpu_1024_100.ipynb
new file mode 100644
index 0000000..f5f07f2
--- /dev/null
+++ b/TFG1-Cpu_1024_100.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_cpu_1024_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Cpu_1024_25.ipynb b/TFG1-Cpu_1024_25.ipynb
new file mode 100644
index 0000000..9537acd
--- /dev/null
+++ b/TFG1-Cpu_1024_25.ipynb
@@ -0,0 +1,551 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_cpu_1024_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Cpu_256_100.ipynb b/TFG1-Cpu_256_100.ipynb
new file mode 100644
index 0000000..0379773
--- /dev/null
+++ b/TFG1-Cpu_256_100.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_cpu_256_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Cpu_256_25.ipynb b/TFG1-Cpu_256_25.ipynb
new file mode 100644
index 0000000..2eeb1fd
--- /dev/null
+++ b/TFG1-Cpu_256_25.ipynb
@@ -0,0 +1,551 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_cpu_256_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Cpu_32_100.ipynb b/TFG1-Cpu_32_100.ipynb
new file mode 100644
index 0000000..1d89d31
--- /dev/null
+++ b/TFG1-Cpu_32_100.ipynb
@@ -0,0 +1,553 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_cpu_32_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Cpu_32_25.ipynb b/TFG1-Cpu_32_25.ipynb
new file mode 100644
index 0000000..26c6669
--- /dev/null
+++ b/TFG1-Cpu_32_25.ipynb
@@ -0,0 +1,486 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_cpu_32_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Gpu_1024_100.ipynb b/TFG1-Gpu_1024_100.ipynb
new file mode 100644
index 0000000..9495bef
--- /dev/null
+++ b/TFG1-Gpu_1024_100.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_gpu_1024_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Gpu_1024_25.ipynb b/TFG1-Gpu_1024_25.ipynb
new file mode 100644
index 0000000..c2b2112
--- /dev/null
+++ b/TFG1-Gpu_1024_25.ipynb
@@ -0,0 +1,553 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_gpu_1024_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Gpu_256_100.ipynb b/TFG1-Gpu_256_100.ipynb
new file mode 100644
index 0000000..b409119
--- /dev/null
+++ b/TFG1-Gpu_256_100.ipynb
@@ -0,0 +1,566 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 256 100\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'DataLoader' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdispositivo\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m 256 100\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# drop_last switched to False to keep all samples\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m test_loader \u001b[38;5;241m=\u001b[39m \u001b[43mDataLoader\u001b[49m(mnist_test, batch_size\u001b[38;5;241m=\u001b[39mbatch_size, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, drop_last\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Guardar el modelo entrenado\u001b[39;00m\n\u001b[1;32m     11\u001b[0m torch\u001b[38;5;241m.\u001b[39msave(net\u001b[38;5;241m.\u001b[39mstate_dict(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodelo_entrenado.pth\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'DataLoader' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_gpu_256_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f77d929b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Gpu_256_25.ipynb b/TFG1-Gpu_256_25.ipynb
new file mode 100644
index 0000000..5753685
--- /dev/null
+++ b/TFG1-Gpu_256_25.ipynb
@@ -0,0 +1,487 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_gpu_256_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Gpu_32_100.ipynb b/TFG1-Gpu_32_100.ipynb
new file mode 100644
index 0000000..bc5f7b2
--- /dev/null
+++ b/TFG1-Gpu_32_100.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_gpu_32_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG1-Gpu_32_25.ipynb b/TFG1-Gpu_32_25.ipynb
new file mode 100644
index 0000000..cf66d4f
--- /dev/null
+++ b/TFG1-Gpu_32_25.ipynb
@@ -0,0 +1,564 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'torch' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m32\u001b[39m\n\u001b[1;32m      3\u001b[0m data_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../datos\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 5\u001b[0m dtype \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241m.\u001b[39mfloat\n\u001b[1;32m      6\u001b[0m dispositivo \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      8\u001b[0m device \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_available() \u001b[38;5;28;01melse\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 128\n",
+    "num_hidden3 = 64  # Agregamos una tercera capa oculta diferente\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9  # Cambiamos el valor de beta\n",
+    "\n",
+    "# Definición de la red modificada\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)  # Agregamos una tercera capa oculta\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)  # Agregamos una capa final diferente\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '1modelo_entrenado_gpu_32_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Cpu_1024_100.ipynb b/TFG2-Cpu_1024_100.ipynb
new file mode 100644
index 0000000..82ad9d0
--- /dev/null
+++ b/TFG2-Cpu_1024_100.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_cpu_1024_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Cpu_1024_25.ipynb b/TFG2-Cpu_1024_25.ipynb
new file mode 100644
index 0000000..02aea3a
--- /dev/null
+++ b/TFG2-Cpu_1024_25.ipynb
@@ -0,0 +1,551 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_cpu_1024_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Cpu_256_100.ipynb b/TFG2-Cpu_256_100.ipynb
new file mode 100644
index 0000000..cfa12b7
--- /dev/null
+++ b/TFG2-Cpu_256_100.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_cpu_256_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Cpu_256_25.ipynb b/TFG2-Cpu_256_25.ipynb
new file mode 100644
index 0000000..e65d135
--- /dev/null
+++ b/TFG2-Cpu_256_25.ipynb
@@ -0,0 +1,551 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_cpu_256_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Cpu_32_100.ipynb b/TFG2-Cpu_32_100.ipynb
new file mode 100644
index 0000000..54dacd5
--- /dev/null
+++ b/TFG2-Cpu_32_100.ipynb
@@ -0,0 +1,553 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_cpu_32_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Cpu_32_25.ipynb b/TFG2-Cpu_32_25.ipynb
new file mode 100644
index 0000000..9a7d67f
--- /dev/null
+++ b/TFG2-Cpu_32_25.ipynb
@@ -0,0 +1,486 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cpu\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_cpu_32_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Gpu_1024_100.ipynb b/TFG2-Gpu_1024_100.ipynb
new file mode 100644
index 0000000..35e5acd
--- /dev/null
+++ b/TFG2-Gpu_1024_100.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_gpu_1024_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Gpu_1024_25.ipynb b/TFG2-Gpu_1024_25.ipynb
new file mode 100644
index 0000000..15952a4
--- /dev/null
+++ b/TFG2-Gpu_1024_25.ipynb
@@ -0,0 +1,553 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 1024\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 1024 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_gpu_1024_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Gpu_256_100.ipynb b/TFG2-Gpu_256_100.ipynb
new file mode 100644
index 0000000..dd72c63
--- /dev/null
+++ b/TFG2-Gpu_256_100.ipynb
@@ -0,0 +1,566 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 256 100\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'DataLoader' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdispositivo\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m 256 100\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# drop_last switched to False to keep all samples\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m test_loader \u001b[38;5;241m=\u001b[39m \u001b[43mDataLoader\u001b[49m(mnist_test, batch_size\u001b[38;5;241m=\u001b[39mbatch_size, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, drop_last\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Guardar el modelo entrenado\u001b[39;00m\n\u001b[1;32m     11\u001b[0m torch\u001b[38;5;241m.\u001b[39msave(net\u001b[38;5;241m.\u001b[39mstate_dict(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodelo_entrenado.pth\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'DataLoader' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_gpu_256_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f77d929b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Gpu_256_25.ipynb b/TFG2-Gpu_256_25.ipynb
new file mode 100644
index 0000000..fdeb119
--- /dev/null
+++ b/TFG2-Gpu_256_25.ipynb
@@ -0,0 +1,487 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 256\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "        \n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 256 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_gpu_256_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Gpu_32_100.ipynb b/TFG2-Gpu_32_100.ipynb
new file mode 100644
index 0000000..11e7ce6
--- /dev/null
+++ b/TFG2-Gpu_32_100.ipynb
@@ -0,0 +1,551 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 100\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 100\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_gpu_32_100.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TFG2-Gpu_32_25.ipynb b/TFG2-Gpu_32_25.ipynb
new file mode 100644
index 0000000..4adce8c
--- /dev/null
+++ b/TFG2-Gpu_32_25.ipynb
@@ -0,0 +1,564 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bb636ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip uninstall powerapi\n",
+    "#!pip install powerapi\n",
+    "#!pip install torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f13e29e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import snntorch as snn\n",
+    "from snntorch import spikeplot as splt\n",
+    "from snntorch import spikegen\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0702785c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leaky neuron model, overriding the backward pass with a custom function\n",
+    "class LeakySurrogate(nn.Module):\n",
+    "  def __init__(self, beta, threshold=1.0):\n",
+    "      super(LeakySurrogate, self).__init__()\n",
+    "\n",
+    "      # initialize decay rate beta and threshold\n",
+    "      self.beta = beta\n",
+    "      self.threshold = threshold\n",
+    "      self.spike_gradient = self.ATan.apply\n",
+    "  \n",
+    "  # the forward function is called each time we call Leaky\n",
+    "  def forward(self, input_, mem):\n",
+    "    spk = self.spike_gradient((mem-self.threshold))  # call the Heaviside function\n",
+    "    reset = (self.beta * spk * self.threshold).detach() # remove reset from computational graph\n",
+    "    mem = self.beta * mem + input_ - reset # Eq (1)\n",
+    "    return spk, mem\n",
+    "\n",
+    "  # Forward pass: Heaviside function\n",
+    "  # Backward pass: Override Dirac Delta with the ArcTan function\n",
+    "  @staticmethod\n",
+    "  class ATan(torch.autograd.Function):\n",
+    "      @staticmethod\n",
+    "      def forward(ctx, mem):\n",
+    "          spk = (mem > 0).float() # Heaviside on the forward pass: Eq(2)\n",
+    "          ctx.save_for_backward(mem)  # store the membrane for use in the backward pass\n",
+    "          return spk\n",
+    "\n",
+    "      @staticmethod\n",
+    "      def backward(ctx, grad_output):\n",
+    "          (mem,) = ctx.saved_tensors  # retrieve the membrane potential \n",
+    "          grad = 1 / (1 + (np.pi * mem).pow_(2)) * grad_output # Eqn 5\n",
+    "          return grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a08a0b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = LeakySurrogate(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9439e055",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lif1 = snn.Leaky(beta=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7bed8abd",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'torch' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m32\u001b[39m\n\u001b[1;32m      3\u001b[0m data_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../datos\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 5\u001b[0m dtype \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241m.\u001b[39mfloat\n\u001b[1;32m      6\u001b[0m dispositivo \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      8\u001b[0m device \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcuda\u001b[38;5;241m.\u001b[39mis_available() \u001b[38;5;28;01melse\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# dataloader arguments\n",
+    "batch_size = 32\n",
+    "data_path='../datos'\n",
+    "\n",
+    "dtype = torch.float\n",
+    "dispositivo = \"cuda\"\n",
+    "\n",
+    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "#device = torch.device(\"cpu\")\n",
+    "#device = torch_ipu.IPUDevice()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e172902b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a transform\n",
+    "transform = transforms.Compose([\n",
+    "            transforms.Resize((28, 28)),\n",
+    "            transforms.Grayscale(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize((0,), (1,))])\n",
+    "\n",
+    "mnist_train = datasets.MNIST(data_path, train=True, download=True, transform=transform)\n",
+    "mnist_test = datasets.MNIST(data_path, train=False, download=True, transform=transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f459bf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create DataLoaders\n",
+    "train_loader = DataLoader(mnist_train, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3b098564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parámetros de la red\n",
+    "num_inputs = 28 * 28\n",
+    "num_hidden1 = 256\n",
+    "num_hidden2 = 64\n",
+    "num_hidden3 = 128\n",
+    "num_outputs = 10\n",
+    "\n",
+    "# Parámetros temporales\n",
+    "num_steps = 25\n",
+    "beta = 0.9\n",
+    "\n",
+    "# Definición de una arquitectura muy distinta\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Inicialización de capas\n",
+    "        self.fc1 = nn.Linear(num_inputs, num_hidden1)\n",
+    "        self.lif1 = snn.Leaky(beta=beta)\n",
+    "        self.fc2 = nn.Linear(num_hidden1, num_hidden2)\n",
+    "        self.lif2 = snn.Leaky(beta=beta)\n",
+    "        self.fc3 = nn.Linear(num_hidden2, num_hidden3)\n",
+    "        self.lif3 = snn.Leaky(beta=beta)\n",
+    "        self.fc4 = nn.Linear(num_hidden3, num_outputs)\n",
+    "        self.lif4 = snn.Leaky(beta=beta)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        # Inicialización de estados ocultos en t=0\n",
+    "        mem1 = self.lif1.init_leaky()\n",
+    "        mem2 = self.lif2.init_leaky()\n",
+    "        mem3 = self.lif3.init_leaky()\n",
+    "        mem4 = self.lif4.init_leaky()\n",
+    "\n",
+    "        # Registro de actividad de disparo\n",
+    "        spk4_rec = []\n",
+    "        mem4_rec = []\n",
+    "\n",
+    "        for step in range(num_steps):\n",
+    "            cur1 = self.fc1(x)\n",
+    "            spk1, mem1 = self.lif1(cur1, mem1)\n",
+    "            cur2 = self.fc2(spk1)\n",
+    "            spk2, mem2 = self.lif2(cur2, mem2)\n",
+    "            cur3 = self.fc3(spk2)\n",
+    "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+    "            cur4 = self.fc4(spk3)\n",
+    "            spk4, mem4 = self.lif4(cur4, mem4)\n",
+    "            spk4_rec.append(spk4)\n",
+    "            mem4_rec.append(mem4)\n",
+    "\n",
+    "        return torch.stack(spk4_rec, dim=0), torch.stack(mem4_rec, dim=0)\n",
+    "\n",
+    "        \n",
+    "# Load the network onto CUDA if available\n",
+    "net = Net().to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7e40a115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pass data into the network, sum the spikes over time\n",
+    "# and compare the neuron with the highest number of spikes\n",
+    "# with the target\n",
+    "\n",
+    "def print_batch_accuracy(data, targets, train=False):\n",
+    "    output, _ = net(data.view(batch_size, -1))\n",
+    "    _, idx = output.sum(dim=0).max(1)\n",
+    "    acc = np.mean((targets == idx).detach().cpu().numpy())\n",
+    "\n",
+    "def train_printer(\n",
+    "    data, targets, epoch,\n",
+    "    counter, iter_counter,\n",
+    "        loss_hist, test_loss_hist, test_data, test_targets):\n",
+    "    #print(f\"Epoch {epoch}, Iteration {iter_counter}\")\n",
+    "    #print(f\"Train Set Loss: {loss_hist[counter]:.2f}\")\n",
+    "    #print(f\"Test Set Loss: {test_loss_hist[counter]:.2f}\")\n",
+    "    #print_batch_accuracy(data, targets, train=True)\n",
+    "    #print_batch_accuracy(test_data, test_targets, train=False)\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2ba337c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d193116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(net.parameters(), lr=5e-4, betas=(0.9, 0.999))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35bc1e3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, targets = next(iter(train_loader))\n",
+    "data = data.to(device)\n",
+    "targets = targets.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23641ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([25, 1024, 10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "#print(mem_rec.size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96f6f0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 58.938\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "987d83e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set accuracy for a single minibatch: 12.40%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "31f4b7d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear previously stored gradients\n",
+    "optimizer.zero_grad()\n",
+    "\n",
+    "# calculate the gradients\n",
+    "loss_val.backward()\n",
+    "\n",
+    "# weight update\n",
+    "optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "47b9b467",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training loss: 54.836\n",
+      "Train set accuracy for a single minibatch: 18.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# calculate new network outputs using the same data\n",
+    "spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "# initialize the total loss value\n",
+    "loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "\n",
+    "# sum loss at every step\n",
+    "for step in range(num_steps):\n",
+    "  loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "#print(f\"Training loss: {loss_val.item():.3f}\")\n",
+    "#print_batch_accuracy(data, targets, train=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f1a273dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0, Iteration 0\n",
+      "Train Set Loss: 56.03\n",
+      "Test Set Loss: 53.13\n",
+      "Train set accuracy for a single minibatch: 31.84%\n",
+      "Test set accuracy for a single minibatch: 28.61%\n",
+      "\n",
+      "\n",
+      "Epoch 0, Iteration 50\n",
+      "Train Set Loss: 14.94\n",
+      "Test Set Loss: 13.88\n",
+      "Train set accuracy for a single minibatch: 88.18%\n",
+      "Test set accuracy for a single minibatch: 88.28%\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 1\n",
+    "loss_hist = []\n",
+    "test_loss_hist = []\n",
+    "counter = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo de entrenamiento\n",
+    "start_train_time = time.time()\n",
+    "\n",
+    "# Outer training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    \n",
+    "    iter_counter = 0\n",
+    "    train_batch = iter(train_loader)\n",
+    "\n",
+    "    # Minibatch training loop\n",
+    "    for data, targets in train_batch:\n",
+    "        \n",
+    "        data = data.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "        \n",
+    "        # forward pass\n",
+    "        net.train()\n",
+    "        spk_rec, mem_rec = net(data.view(batch_size, -1))\n",
+    "\n",
+    "        # initialize the loss & sum over time\n",
+    "        loss_val = torch.zeros((1), dtype=dtype, device=device)\n",
+    "        for step in range(num_steps):\n",
+    "            loss_val += loss(mem_rec[step], targets)\n",
+    "\n",
+    "        # Gradient calculation + weight update\n",
+    "        optimizer.zero_grad()\n",
+    "        loss_val.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # Store loss history for future plotting\n",
+    "        loss_hist.append(loss_val.item())\n",
+    "\n",
+    "        # Test set\n",
+    "        with torch.no_grad():\n",
+    "            net.eval()\n",
+    "            test_data, test_targets = next(iter(test_loader))\n",
+    "            test_data = test_data.to(device)\n",
+    "            test_targets = test_targets.to(device)\n",
+    "\n",
+    "            # Test set forward pass\n",
+    "            test_spk, test_mem = net(test_data.view(batch_size, -1))\n",
+    "\n",
+    "            # Test set loss\n",
+    "            test_loss = torch.zeros((1), dtype=dtype, device=device)\n",
+    "            for step in range(num_steps):\n",
+    "                test_loss += loss(test_mem[step], test_targets)\n",
+    "            test_loss_hist.append(test_loss.item())\n",
+    "\n",
+    "            # Print train/test loss/accuracy\n",
+    "            if counter % 50 == 0:\n",
+    "                train_printer(\n",
+    "                    data, targets, epoch,\n",
+    "                    counter, iter_counter,\n",
+    "                    loss_hist, test_loss_hist,\n",
+    "                    test_data, test_targets)\n",
+    "            counter += 1\n",
+    "            iter_counter +=1\n",
+    "            \n",
+    "end_train_time = time.time()\n",
+    "total_train_time = end_train_time - start_train_time\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dba6cd66",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "Total correctly classified test set images: 8908/10000\n",
+      "Test Set Accuracy: 89.08%\n",
+      "Tiempo total: 1.8388621807098389 segundos\n",
+      "Tiempo total de entrenamiento: 23.53195571899414 segundos\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "correct = 0\n",
+    "\n",
+    "# Empezamos a medir el tiempo\n",
+    "start_time = time.time()\n",
+    "print(f\"{dispositivo} 32 25\")\n",
+    "\n",
+    "# drop_last switched to False to keep all samples\n",
+    "test_loader = DataLoader(mnist_test, batch_size=batch_size, shuffle=True, drop_last=False)\n",
+    "# Guardar el modelo entrenado\n",
+    "torch.save(net.state_dict(), '2modelo_entrenado_gpu_32_25.pth')\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "  net.eval()\n",
+    "  for data, targets in test_loader:\n",
+    "\n",
+    "    data = data.to(device)\n",
+    "    targets = targets.to(device)\n",
+    "    \n",
+    "    # forward pass\n",
+    "    test_spk, _ = net(data.view(data.size(0), -1))\n",
+    "\n",
+    "    # calculate total accuracy\n",
+    "    _, predicted = test_spk.sum(dim=0).max(1)\n",
+    "    total += targets.size(0)\n",
+    "    correct += (predicted == targets).sum().item()\n",
+    "\n",
+    "end_time = time.time()\n",
+    "total_time = end_time - start_time\n",
+    "     \n",
+    "print(f\"Total correctly classified test set images: {correct}/{total}\")\n",
+    "print(f\"Test Set Accuracy: {100 * correct / total:.2f}%\")\n",
+    "\n",
+    "print(f\"Tiempo total: {total_time} segundos\")\n",
+    "print(f\"Tiempo total de entrenamiento: {total_train_time} segundos\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..11a8fde
--- /dev/null
+++ b/main.py
@@ -0,0 +1,28 @@
+import subprocess
+import time
+
+# Inicia el proceso que se va a medir
+process = subprocess.Popen(['python', 'pruebagpu.py'])
+
+# Espera a que el proceso se inicie completamente
+time.sleep(5)
+
+# Inicia la medición del consumo de energía de la GPU
+nvidia_dmon = subprocess.Popen(['nvidia-smi', 'dmon', '-s', 'u'], stdout=subprocess.PIPE)
+
+# Espera a que el proceso termine
+process.wait()
+
+# Detiene la medición del consumo de energía de la GPU
+subprocess.Popen(['nvidia-smi', 'dmon', '-f', 'output.csv', '-s', 'p'], stdout=subprocess.PIPE)
+
+# Mata los subprocesos
+try:
+    process.kill()
+except OSError:
+    pass
+
+try:
+    nvidia_dmon.kill()
+except OSError:
+    pass
diff --git a/modelo_entrenado.pth b/modelo_entrenado.pth
new file mode 100644
index 0000000..a5a752f
Binary files /dev/null and b/modelo_entrenado.pth differ