--- a
+++ b/Code/All Qiskit ML Demos/02 Classifier & Regressor 85% kkawchak.ipynb
@@ -0,0 +1,1451 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "intense-ecology",
+   "metadata": {},
+   "source": [
+    "# Neural Network Classifier & Regressor\n",
+    "\n",
+    "In this tutorial we show how the `NeuralNetworkClassifier` and `NeuralNetworkRegressor` are used.\n",
+    "Both take as an input a (Quantum) `NeuralNetwork` and leverage it in a specific context.\n",
+    "In both cases we also provide a pre-configured variant for convenience, the Variational Quantum Classifier (`VQC`) and Variational Quantum Regressor (`VQR`). The tutorial is structured as follows:\n",
+    "\n",
+    "\n",
+    "1. [Classification](#Classification) \n",
+    "    * Classification with an `EstimatorQNN`\n",
+    "    * Classification with a `SamplerQNN`\n",
+    "    * Variational Quantum Classifier (`VQC`)\n",
+    "    \n",
+    "    \n",
+    "2. [Regression](#Regression)\n",
+    "    * Regression with an `EstimatorQNN`\n",
+    "    * Variational Quantum Regressor (`VQR`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "functioning-sword",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from IPython.display import clear_output\n",
+    "from qiskit import QuantumCircuit\n",
+    "from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B\n",
+    "from qiskit.circuit import Parameter\n",
+    "from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap, ZFeatureMap\n",
+    "from qiskit.utils import algorithm_globals\n",
+    "\n",
+    "from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier, VQC\n",
+    "from qiskit_machine_learning.algorithms.regressors import NeuralNetworkRegressor, VQR\n",
+    "from qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN\n",
+    "\n",
+    "algorithm_globals.random_seed = 42"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "compact-divide",
+   "metadata": {},
+   "source": [
+    "## Classification\n",
+    "\n",
+    "We prepare a simple classification dataset to illustrate the following algorithms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "short-pierre",
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0fft2denSRa+88op936+//qqDBw+aWB0AAADg3FxdpZkz0//877B+7fWMGen9cHM5EtRnz54tq9UqT09PBQUFafv27Tft27RpU1ksluu2tm3b2vv07Nnzuv2tWrXKiVNBPtGgQQMtWbJEXl5ekiTDMPTyyy+revXq6tatm6KiokyuEAAAAHBOHTpIoaFSuXIZ2/380ts7dDCnrtzE4UH9q6++0pAhQzR27Fjt3LlTtWvXVsuWLXXmzJkb9g8LC1N8fLx927dvn1xdXfXUU09l6NeqVasM/ZYsWeLoU0E+duHCBfn5+ckwDC1ZskT33XefunTpov3795tdGgAAAOB0OnSQYmOljRulxYvTv8bEENIzy+FBffr06erdu7eef/55Va9eXXPmzFGhQoU0f/78G/YvUaKEfHx87Nv69etVqFCh64K6h4dHhn7Fixd39KkgH/Py8tLKlSu1a9cuPfnkkzIMQ1999ZVq1qypp59+mhF2AAAA4F9cXaWmTaWuXdO/crt75jk0qF+5ckU7duxQSEjI/76hi4tCQkK0ZcuWTB1j3rx56tKliwoXLpyhPSIiQqVLl1aVKlXUt29fnT179qbHSElJUVJSUoYNuBN16tRRWFiYdu/erQ4dOsgwDC1fvlwHDhwwuzQAAAAAeYRDg/qff/4pm82mMmXKZGgvU6aMEhISbvv+7du3a9++fXrxxRcztLdq1UqfffaZNmzYoHfeeUc//vijfWmtG5k8ebK8vb3tm7+//52fFCCpdu3a+vrrr7Vnzx4NGTJETz75pH3fN998oz179phYHQAAAIDczGIYhuGog//+++8qV66cNm/erEaNGtnbhw0bph9//FHbtm275ftfeuklbdmyRb/99tst+x07dkwVKlTQ999/r+bNm1+3PyUlRSkpKfbXSUlJ8vf3V2Jion2yMCA7JCcnKyAgQGfOnNGTTz6pMWPGqE6dOmaXBQAAAMBkSUlJ8vb2zlQOdeiIesmSJeXq6qrTp09naD99+rR8fHxu+d5Lly5p6dKl6tWr122/T2BgoEqWLKno6Ogb7vfw8JCXl1eGDXCECxcu6JFHHpHFYtGKFStUt25dtW/fXrt27TK7NAAAAAC5hEODuru7u+rXr68NGzbY29LS0rRhw4YMI+w3snz5cqWkpOiZZ5657fc5efKkzp49K19f37uuGbgbZcqU0ZIlS7R//35169ZNFotFq1atUr169fTEE0+wDjsAAACA23L4rO9DhgzRJ598okWLFikqKkp9+/bVpUuX9Pzzz0uSevTooZEjR173vnnz5ql9+/a65557MrRfvHhRr7/+urZu3arY2Fht2LBB7dq1U8WKFdWyZUtHnw6QKdWqVdOXX36pAwcOqHv37nJxcdE333yjq1evml0aAAAAACfn5uhv0LlzZ/3xxx8aM2aMEhISVKdOHYWHh9snmIuLi5OLS8Z/Lzh06JB++uknrVu37rrjubq66rffftOiRYt0/vx5lS1bVi1atNDEiRPl4eHh6NMBsqRq1ar64osvNHr0aK1bt041a9a075s9e7YaNGighg0bmlghAAAAAGfj0MnknFVWHuIHHCE2NlaVKlVSamqqWrVqpbFjx+qBBx4wuywAAAAADuI0k8kBuDE3Nzc988wzcnV1VXh4uBo1aqSWLVtq8+bNZpcGAAAAwGQEdcAEfn5+WrBggQ4dOqTnn39erq6uWrdunR588EE9+uijOnr0qNklAgAAADAJQR0wUYUKFTR//nwdPnxYvXr1kpubm7Zt26bixYubXRoAAAAAkxDUAScQGBioTz/9VIcPH9Znn32mEiVKSJIMw9DQoUO1adMmkysEAAAAkFMI6oATCQgIUPv27e2vN27cqOnTp+vhhx9Ws2bNFBERYVptAAAAAHIGQR1wYpUqVdLLL7+sAgUKKCIiQs2aNVPTpk21ceNG5cMFGwAAAIB8gaAOODF/f3999NFHOnr0qPr16yd3d3f9+OOPeuSRR/Twww8rISHB7BIBAAAAZDOCOpAL+Pv7a/bs2Tp69KgGDBggDw8PnT59WqVKlTK7NAAAAADZjKAO5CJ+fn764IMPdPToUX3++edydXWVJKWkpKhjx45at24dt8QDAAAAuRxBHciFypUrp4YNG9pfz5s3T2FhYWrZsqUaN26s8PBwAjsAAACQSxHUgTzgySef1KBBg+Tp6amtW7eqdevWatSokdauXUtgBwBkic0mRURIS5akf7XZzK4IAPIfgjqQB/j6+ur9999XTEyMhgwZooIFC2rbtm1q27atgoKCdOnSJbNLBADkAmFhktUqNWsmdeuW/tVqTW8HAOQcgjqQh/j4+Oi9995TTEyMhg4dqoIFC6p06dIqXLiw2aUBAJxcWJjUqZN08mTG9lOn0tsJ6wCQcyxGPrwvNikpSd7e3kpMTJSXl5fZ5QAOc+bMGV28eFGBgYGSpN9//11PPfWUhg0bpieeeEIWi8XkCgEAzsBmSx85/3dIv8Zikfz8pJgY6f/nMQUAZFFWcigj6kAeVrp0aXtIl6Rp06Zp8+bNat++verVq6eVK1fyDDsAQJGRNw/pkmQY0okT6f0AAI5HUAfykTfeeEMjRoxQkSJFtHv3bj355JOqW7euwsLClJaWZnZ5AACTxMdnbz8AwN0hqAP5SMmSJTV58mTFxsbqjTfeUJEiRbRnzx517NhRDz74IGEdAPIpX9/s7QcAuDsEdSAfuueee/TWW28pNjZWo0aNUtGiRdW4cWO5uPzvrwRuiQeA/CM4OP0Z9JtNXWKxSP7+6f0AAI5HUAfysXvuuUeTJk2yB/Zrfv75Z9WsWVPLli1jlB0A8gFXV2nmzPQ//zusX3s9YwYTyQFATiGoA1CJEiVUokQJ++v33ntP+/fvV+fOnVWzZk0tXbpUNpvNxAoBAI7WoYMUGiqVK5ex3c8vvb1DB3PqAoD8iOXZWJ4NuM758+c1a9Ysvf/++zp//rwkqWrVqho9erQ6d+4sV4ZUACDPstnSZ3ePj09/Jj04mJF0AMgOWcmhBHWCOnBTiYmJ9sD+119/SZJatmyp8PBwkysDAAAAchfWUQeQLby9vTV69GjFxsZq0qRJKlGihDp27Gjfn5qaqtTUVBMrBAAAAPIegjqA2/Ly8tKoUaMUGxur5557zt7+2WefqVq1alq0aBGBHQAAAMgmBHUAmVa0aFG5u7vbX8+dO1fR0dHq2bOnqlatqgULFujq1asmVggAAADkfgR1AHfs+++/1zvvvKOSJUvq6NGjeuGFF1S1alXNmzePwA4AwF2y2aSICGnJkvSvLMAC5B8EdQB3rEiRIho2bJhiY2P17rvvqnTp0jp27JhefPFFvfDCC2aXBwBArhUWJlmtUrNmUrdu6V+t1vR2AHkfQR3AXStcuLBee+01HTt2TNOmTVPp0qXVu3dv+/6LFy/qypUrJlYIAEDuERYmdeoknTyZsf3UqfR2wjqQ9xHUAWSbwoULa+jQoTp+/Lgeeughe/uECRNUqVIlffzxxwR2AABuwWaTBg6UbrSA8rW2QYO4DR7I6wjqALKdp6en/c+pqalauXKl4uLi9PLLL6tixYr66KOPlJKSYmKFAAA4p8jI60fS/8kwpBMn0vsByLsI6gAcys3NTXv27NGsWbNUtmxZnThxQv369VPFihU1e/ZsXb582ewSAQBwGvHx2dsPQO5EUAfgcAULFtQrr7yio0eP6oMPPlC5cuV08uRJDRgwQGPHjjW7PAAAnIavb/b2A5A7EdQB5BhPT08NGDBA0dHRmj17tipUqKABAwbY98fHx+vvv/82sUIAAMwVHCz5+UkWy433WyySv396PwB5F0EdQI7z9PRUv379dPjwYfn7+9vbX375ZQUGBmrGjBkEdgBAvuTqKs2cmf7nf4f1a69nzEjvByDvIqgDMI2Ly//+CkpKStKePXuUkJCgwYMHKyAgQNOnT1dycrKJFQIAkPM6dJBCQ6Vy5TK2+/mlt3foYE5dAHKOxTButPhD3paUlCRvb28lJibKy8vL7HIA/L8rV65o0aJFeuutt3T8+HFJUunSpTVs2DC9/PLLKly4sMkVAgCQc2y29Nnd4+PTn0kPDmYkHcjNspJDCeoEdcDpXLlyRZ999pneeustxcbGSpJmz56tfv36mVsYAAAAcIeykkO59R2A03F3d9eLL76ow4cPa968eWrSpIleeOEF+/4DBw7o4sWLJlYIAAAAOA4j6oyoA7mKzWZTjRo19Oeff2ro0KHq37+/ihYtanZZAAAAwC0xog4gzzp58qRSU1P1559/auTIkQoICNDbb7+tpKQks0sDAAAAsgVBHUCuUr58eUVFRWnRokWqVKmSzp49q1GjRikgIEBvvfUWgR0AAAC5HkEdQK7j5uamHj166MCBA/r8889VuXJlnTt3Tm+++aa2bt1qdnkAAADAXcmRoD579mxZrVZ5enoqKChI27dvv2nfhQsXymKxZNg8PT0z9DEMQ2PGjJGvr68KFiyokJAQHTlyxNGnAcDJuLm56ZlnntGBAwf05Zdf6tlnn9Wjjz5q3//jjz/q/Pnz5hUIAAAA3AGHB/WvvvpKQ4YM0dixY7Vz507Vrl1bLVu21JkzZ276Hi8vL8XHx9u3a+spXzN16lTNmjVLc+bM0bZt21S4cGG1bNlSly9fdvTpAHBCrq6u6tatmz777DNZLBZJ0vnz59WuXTtZrVaNGzeOwA4AAIBcw+FBffr06erdu7eef/55Va9eXXPmzFGhQoU0f/78m77HYrHIx8fHvpUpU8a+zzAMzZgxQ2+++abatWunWrVq6bPPPtPvv/+ulStX3vB4KSkpSkpKyrAByNtOnjwpPz8/JSYmavz48bJarRo7dqz++usvs0sDAAAAbsmhQf3KlSvasWOHQkJC/vcNXVwUEhKiLVu23PR9Fy9eVPny5eXv76927dpp//799n0xMTFKSEjIcExvb28FBQXd9JiTJ0+Wt7e3ffP398+GswPgzGrUqKHffvtNy5cvV40aNZSYmKgJEybIarVq9OjRBHYAAAA4LYcG9T///FM2my3DiLgklSlTRgkJCTd8T5UqVTR//nytWrVKX3zxhdLS0tS4cWOdPHlSkuzvy8oxR44cqcTERPt24sSJuz01ALmAi4uLOnXqpD179ig0NFS1atVSUlKSJk+erHPnzpldHgAAAHBDbmYX8G+NGjVSo0aN7K8bN26satWq6eOPP9bEiRPv6JgeHh7y8PDIrhIB5DIuLi7q2LGjnnzySa1atUoHDhxQhQoV7PsXL16sFi1aqGTJkiZWCQAAAKRz6Ih6yZIl5erqqtOnT2doP336tHx8fDJ1jAIFCqhu3bqKjo6WJPv77uaYAPInFxcXPfnkkxo1apS97cCBA3rmmWdktVo1fPhw/fHHHyZWCAAAADg4qLu7u6t+/frasGGDvS0tLU0bNmzIMGp+KzabTXv37pWvr68kKSAgQD4+PhmOmZSUpG3btmX6mABwzcWLF1WnTh1dunRJU6dOldVq1bBhw265MgUAAADgSA6f9X3IkCH65JNPtGjRIkVFRalv3766dOmSnn/+eUlSjx49NHLkSHv/CRMmaN26dTp27Jh27typZ555RsePH9eLL74oKX1G+EGDBmnSpElavXq19u7dqx49eqhs2bJq3769o08HQB7TsGFD7dixQ6tXr1b9+vWVnJysd999VwEBAXrttddY1g0AAAA5zuHPqHfu3Fl//PGHxowZo4SEBNWpU0fh4eH2yeDi4uLk4vK/fy/466+/1Lt3byUkJKh48eKqX7++Nm/erOrVq9v7DBs2TJcuXVKfPn10/vx5NWnSROHh4fL09HT06QDIgywWix5//HE99thjWrt2rcaNG6dff/1VixYt0rhx48wuDwAAAPmMxTAMw+wiclpSUpK8vb2VmJgoLy8vs8sB4GQMw9B3332nv/76S927d7e3TZ06VT169LA/igMAAABkVlZyKEGdoA4gE1atWqX27dvL09NTffr00fDhw1W2bFmzywIAAEAukZUc6vBn1AEgLyhVqpQaN26sy5cva9asWQoMDNSrr76qU6dOmV0aAAAA8hiCOgBkQuPGjfXTTz9p/fr1atKkiVJSUvTBBx+oQoUKGjBggJKTk80uEQAAAHkEQR0AMslisSgkJESbNm3Shg0bFBwcrJSUFG3evFkFCxY0uzwAAADkEQR1AMgii8WiRx55RD/++KN++OEHzZw5UxaLRZJ04cIFDR48WMePHze5SgAAAORWBHUAuEMWi0XNmjVTcHCwvW327NmaMWOGKlWqpJdeekmxsbHmFQgAAIBciaAOANnooYce0iOPPKKrV69q7ty5qlSpknr37q2YmBizSwMAAEAuQVAHgGzUuHFjbdiwQZs2bVLz5s2VmpqqTz/9VJUrV1bv3r2VlpZmdokAAABwcgR1AHCA4OBgff/99/rpp5/06KOPKjU1VRcvXpSLC3/tAgAA4Nb4jREAHOjBBx/UunXrtHnzZk2cONHeHh0dreeff17R0dEmVgcAQN5ns0kREdKSJelfbTazKwJuj6AOADmgUaNGqlixov31W2+9pYULF6pq1ap67rnndOTIEROrAwAgbwoLk6xWqVkzqVu39K9Wa3o74MwI6gBggr59+6pNmzay2Wz67LPPVLVqVT377LM6dOiQ2aUBAJAnhIVJnTpJJ09mbD91Kr2dsA5nRlAHABM0bNhQ3377rbZv367HHntMaWlp+uKLL1S9enX179/f7PIAAMjVbDZp4EDJMK7fd61t0CBug4fzIqgDgIkaNGigb775Rr/88osef/xxpaWlqVSpUmaXBQBArhYZef1I+j8ZhnTiRHo/wBkR1AHACdx///1avXq1duzYoYEDB9rb161bp65du+rAgQMmVgcAQO4SH5+9/YCcRlAHnJwtzaaI2Agt2btEEbERsqVxj1ZeVq9ePRUvXtz+evz48Vq6dKlq1Kihzp07a9++fSZWBwBA7uDrm739gJxGUAecWFhUmKwzrWq2qJm6hXVTs0XNZJ1pVVgUs5/kF//5z3/UsWNHGYahZcuWqWbNmnrqqae0d+9es0sDAMBpBQdLfn6SxXLj/RaL5O+f3g9wRgR1wEmFRYWp07JOOpmU8QGrU0mn1GlZJ8J6PlG7dm2FhoZqz5496tSpkyQpNDRUtWrV0rBhw0yuDgAA5+TqKs2cmf7nf4f1a69nzEjvBzgjgjrghGxpNg0MHyhD109Veq1tUPggboPPR2rVqqXly5dr7969evrpp2WxWFSvXj37fuNG09oCAJCPdegghYZK5cplbPfzS2/v0MGcuoDMsBj58Le7pKQkeXt7KzExUV5eXmaXA1wnIjZCzRY1u22/jc9tVFNrU8cXBKcTFRWlypUry/X/hwJmzZqlH374QWPGjMkQ4AEAyO9stvTZ3ePj059JDw5mJB3myEoOdcuhmgBkQfyFzE1Bmtl+yHuqVatm/3NqaqreffddnTx5UqtWrdLjjz+usWPHqn79+iZWCACAc3B1lZo2NbsKIGu49R1wQr5FMzcFaWb75TbMdJ81bm5uWr9+vbp37y4XFxd98803uv/++/X444/r119/Nbs8AAAAZBFBHXBCwfcGy8/LTxbdeKpSiyzy9/JX8L15b6pSZrq/M1WrVtUXX3yhAwcO6JlnnpGLi4vWrFmjBg0a6K233jK7PAAAAGQBQR1wQq4urprZKn2q0n+H9WuvZ7SaIVeXvPWAFTPd370qVaro888/V1RUlHr06CFXV1e1bt3avj81NdXE6gAAAJAZBHXASXWo1kGhT4eqnFfGqUr9vPwU+nSoOlTLW1OVMtN99qpcubIWLVqkEydOZJhc7tVXX1WrVq20ZcsWE6sDAADArTDrO7O+w8nZ0myKjItU/IV4+Rb1VfC9wXluJF1ipvuckJiYqLJlyyo5OVmS9Oijj2rs2LF68MEHTa4MAAAg78tKDmVEHXByri6uamptqq41u6qptWmeDOkSM93nBG9vb+3du1e9evWyT0DXpEkTPfroo/rpp5/MLg8AAAD/j6AOwCnk95nuc0pgYKA+/fRTHT58WL1795abm5u+//57BQcH6+OPPza7PAAAAIigDsBJ5OeZ7s0QEBCguXPnKjo6Wi+99JKKFy+uDh3+N+/BhQsXTKwOAAAgfyOoA3AK+XWme7OVL19ec+bMUVxcnEqVKmVvb9eunR5++GFt3LhR+XAqEwAAAFMR1AE4jfw2070zKVKkiP3PMTEx+vnnn7Vp0yY98sgjevjhh7VhwwYCOwAAQA5h1ndmfQecTn6Z6d6ZnTx5UlOmTNEnn3yiK1euSJIefPBBjR07ViEhIbJYbvyIAgAAAG4sKzmUoE5QB4CbOnXqlN555x3NnTtXKSkpkqSVK1eqXbt2JlcGAACQu7A8GwAgW5QrV06zZs3SsWPH9Oqrr+q+++5T27Zt7ft///13bokHAADIZgR1AMBtlS1bVjNnztTu3bvl5uYmSbpy5YoaN26sRo0aae3atQR2AACAbEJQBwBk2rWQLkk7d+7UmTNntG3bNrVt21ZBQUH69ttvCewAAAB3iaAOALgjDzzwgGJiYjR06FAVLFhQv/zyix577DE1bNhQ33zzDYEdAADgDjGZHJPJAcBdO3PmjKZNm6bZs2crOTlZkvTrr7+qfv36JlcGAHBWNpsUGSnFx0u+vlJwsOTKIi/Iw5j1/TYI6gDgGH/88Yfee+89HTlyRF9//bW9fd++fapevbpcXLiRCwAghYVJAwdKJ0/+r83PT5o5U+rQwby6AEciqN8GQR0AHMswDPta6wkJCQoICFDlypU1duxYtW/fnsAOAPlYWJjUqZP07xTy/z82FBpKWEfexPJsAABTXQvpkrRr1y4VKFBAv/32mzp27Kg6deooNDRUaWlpJlYIADCDzZY+kn6jocJrbYMGpfcD8rMcCeqzZ8+W1WqVp6engoKCtH379pv2/eSTTxQcHKzixYurePHiCgkJua5/z549ZbFYMmytWrVy9GkAAO5A69atFRsbqzfffFNFixbV3r179dRTT6l27dpavnw5gR0A8pHIyIy3u/+bYUgnTqT3A/Izhwf1r776SkOGDNHYsWO1c+dO1a5dWy1bttSZM2du2D8iIkJdu3bVxo0btWXLFvn7+6tFixY6depUhn6tWrVSfHy8fVuyZImjTwUAcIdKlCihiRMn6vjx4xozZoy8vLy0b98+Pfvsszp9+rTZ5QEAckh8fPb2A/Iqhz+jHhQUpAYNGujDDz+UJKWlpcnf31+vvPKKRowYcdv322w2FS9eXB9++KF69OghKX1E/fz581q5cuUd1cQz6gBgrr/++kszZ85UamqqJk2aZG//4Ycf9PDDD8uVaX8BIE+KiJCaNbt9v40bpaZNHV0NkLOc5hn1K1euaMeOHQoJCfnfN3RxUUhIiLZs2ZKpYyQnJ+vq1asqUaJEhvaIiAiVLl1aVapUUd++fXX27NmbHiMlJUVJSUkZNgCAeYoXL65x48ZlCOnbt29X8+bNVaNGDS1evFg2HlAEgDwnODh9dvd/TGWSgcUi+fun9wPyM4cG9T///FM2m01lypTJ0F6mTBklJCRk6hjDhw9X2bJlM4T9Vq1a6bPPPtOGDRv0zjvv6Mcff1Tr1q1v+kvd5MmT5e3tbd/8/f3v/KQAAA4RGxur4sWL6+DBg+revbvuu+8+ffHFF0pNTTW7NABANnF1TV+CTbo+rF97PWMG66kDTj3r+5QpU7R06VKtWLFCnp6e9vYuXbroiSeeUM2aNdW+fXutWbNGv/zyiyIiIm54nJEjRyoxMdG+nThxIofOAACQWU8//bRiY2M1adIklShRQocOHdKzzz6r6tWr67PPPiOwA0Ae0aFD+hJs5cplbPfzY2k24BqHBvWSJUvK1dX1uomCTp8+LR8fn1u+d9q0aZoyZYrWrVunWrVq3bJvYGCgSpYsqejo6Bvu9/DwkJeXV4YNAOB8vLy8NGrUKMXExOitt95SiRIldOTIEY0aNYpb4QEgD+nQQYqNTX8WffHi9K8xMYR04BqHBnV3d3fVr19fGzZssLelpaVpw4YNatSo0U3fN3XqVE2cOFHh4eG6//77b/t9Tp48qbNnz8rX1zdb6gYAmMvLy0tvvPGGYmNjNXnyZE2aNEkeHh6SpNTUVC1dupQRdgDI5Vxd0yeM69o1/Su3uwP/4/Bb34cMGaJPPvlEixYtUlRUlPr27atLly7p+eeflyT16NFDI0eOtPd/5513NHr0aM2fP19Wq1UJCQlKSEjQxYsXJUkXL17U66+/rq1btyo2NlYbNmxQu3btVLFiRbVs2dLRpwMAyEFFixbViBEj9Nxzz9nblixZoq5du6pKlSqaP3++rl69amKFAAAA2c/hQb1z586aNm2axowZozp16mj37t0KDw+3TzAXFxen+H8slPjRRx/pypUr6tSpk3x9fe3btGnTJEmurq767bff9MQTT6hy5crq1auX6tevr8jISPtoCwAg7zIMQ6VLl9axY8fUq1cvValSRZ9++imBHQAA5BkOX0fdGbGOOgDkbsnJyZozZ46mTp1qnwelfPnyGjVqlHr16iUXF6eeKxUAAORDTrOOOgAAjlCoUCENGTJEx44d0/Tp0+Xj46Pjx4/r888/l+Vmi/MCAADkEgR1AECuVahQIQ0ePFjHjh3TjBkz9NZbb9mD+tmzZ/XRRx8pJSXF5CoBAACyhqAOAMj1ChYsqIEDByo4ONje9v7776tfv36qUKGCZs+ercuXL5tYIQAAQOYR1AEAeVJAQIDKlSunU6dOacCAAapQoYI++OADAjsAAHB6BHUAQJ7Uq1cvRUdH68MPP5Sfn59+//13vfrqqwoMDNR//vMfs8sDAAC4KYI6ACDP8vT0VP/+/RUdHa3//Oc/8vf3V3x8vHbt2mV2aQAAADdFUAcA5HkeHh7q27evjhw5ojlz5uiNN96w79u7d6/ef/99JScnm1ghAADA/7COOuuoA0C+1qlTJ3399dcqU6aMXn/9db388ssqXLiw2WUBAIA8hnXUAQDIpDZt2iggIECnT5/Wa6+9psDAQE2bNk2XLl0yuzQAAJBPEdQBAPnaCy+8oEOHDmnevHkKDAzUmTNn9PrrrysgIEBz5841uzwAAJAPEdQBAPlegQIF9MILL+jgwYNasGCBKlSooD/++EMXLlwwuzQAAJAP8Yw6z6gDAP4lNTVVS5cu1ZNPPml/Xn3NmjX67bffNGDAAH52AACALMtKDiWo88sWAOA20tLSVLt2be3bt0/FixfXkCFD9Oqrr/IzBAAAZBqTyQEAkM1GjBihKlWq6K+//tLo0aNVvnx5TZgwQYmJiWaXBgAA8hiCOgAAt+Hi4qLu3btr//79+vLLL1W1alWdP39eY8eOldVq1cKFC80uEQAA5CEEdQAAMsnV1VXdunXTvn37tGTJElWvXl3nz59XqVKlzC4NAADkITyjzvOFAIA7lJaWpvDwcLVu3VoWi0WSNGPGDJ07d06DBw9W8eLFTa4QAAA4CyaTuw2COgDAES5cuKDy5cvrr7/+kpeXl1599VUNHjxYJUqUMLs0AABgMiaTAwDABIULF9Ynn3yimjVrKikpSZMmTZLVatWoUaN09uxZs8sDAAC5BEEdAIBs4uLioo4dO2r37t36+uuvVbt2bV24cEFvv/22rFarQkNDzS4RAADkAgR1AACymYuLizp06KCdO3dqxYoVqlOnjpKTk1WzZk2zSwMAALkAQR0AAAdxcXFR+/bttXPnTm3btk1VqlSx7+vbt6+GDRumM2fOmFghAABwRkwmx2RyAIAcduzYMVWqVElpaWkqVKiQ+vbtq9dff11lypQxuzQAAOAgTCYHAIATCwgI0OrVq9WgQQMlJyfrvffeU0BAgIYOHaqEhASzywMAACYjqAMAkMMsFovatm2rbdu2ae3atQoKCtLff/+t6dOnKyAgQOvXrze7RAAAYCKCOgAAJrFYLGrdurW2bNmi8PBwPfDAAypYsKCCgoLsfdLS0kysEAAAmIGgDgCAySwWi1q2bKnNmzdr586d9ufWDMNQ06ZN9corr+jUqVMmVwkAAHIKQR0AACdhsVhktVrtr3/66SdFRkbqww8/VGBgoAYMGKCTJ0+aVyAAAMgRBHUAAJxUkyZN9P333ys4OFhXrlzR7NmzVaFCBfXr108nTpwwuzwAAOAgBHUAAJyUxWJR8+bN9eOPP+qHH37Qww8/rCtXruijjz5ShQoVtGvXLrNLBAAADkBQBwDAyVksFjVr1kwRERHauHGjmjZtqipVqqh27dr2PpcvXzaxQgAAkJ0I6gAA5CJNmzbVxo0btWnTJrm4pP8YT05OVqVKldSnTx/FxsaaWyAAALhrBHUAAHKh4sWL2//8zTff6OTJk/rkk09UqVIlvfjiizp27JiJ1QEAgLtBUAcAIJfr3Lmzfv75Z7Vo0UKpqamaN2+eKleurBdeeEFHjx41uzwAAJBFBHUAAPKAxo0b67///a82b96sli1bymazacGCBapatSprsAMAkMsQ1AEAyEMaNWqk8PBwbd26Va1bt9YTTzyhcuXK2ff/+eefJlYHAAAyg6AOAEAeFBQUpLVr12rx4sX2ttjYWPn5+enZZ5/VoUOHTKwOAADcCkEdAIA8zMPDw/7nNWvWKCUlRV988YWqV6+u7t276+DBgyZWBwAAboSgDgBAPjFgwAD9+uuveuKJJ5SWlqbFixerevXq6tatm6KioswuDwAA/D+COgAA+Uj9+vW1atUq7dixQ+3atZNhGFqyZIkaNWqk5ORks8sDAAAiqAMAkC/Vq1dPK1eu1K5du/Tkk09qwIABKlSokCTJMAxFR0ebXCEAAPlXjgT12bNny2q1ytPTU0FBQdq+ffst+y9fvlxVq1aVp6enatasqbVr12bYbxiGxowZI19fXxUsWFAhISE6cuSII08BAIA8qU6dOgoLC9PEiRPtbRs3blSlSpX01FNPae/evSZWBwBA/uTwoP7VV19pyJAhGjt2rHbu3KnatWurZcuWOnPmzA37b968WV27dlWvXr20a9cutW/fXu3bt9e+ffvsfaZOnapZs2Zpzpw52rZtmwoXLqyWLVvq8uXLjj4dAADyJIvFYv/zzz//LEkKDQ1VrVq11LFjR+3Zs8es0gAAyHcshmEYjvwGQUFBatCggT788ENJUlpamvz9/fXKK69oxIgR1/Xv3LmzLl26pDVr1tjbHnjgAdWpU0dz5syRYRgqW7ashg4dqtdee02SlJiYqDJlymjhwoXq0qXLbWtKSkqSt7e3EhMT5eXllU1nCgBA3rF3715NnDhRoaGhuvarwpNPPqkxY8aoTp065hYHAEAulJUc6tAR9StXrmjHjh0KCQn53zd0cVFISIi2bNlyw/ds2bIlQ39Jatmypb1/TEyMEhISMvTx9vZWUFDQTY+ZkpKipKSkDBsAALi5mjVratmyZdq7d686d+4si8WiFStWqHPnzkpLSzO7PAAA8jSHBvU///xTNptNZcqUydBepkwZJSQk3PA9CQkJt+x/7WtWjjl58mR5e3vbN39//zs6HwAA8pv77rtPS5cu1b59+9S1a1eNHj1aLi7pvz6kpKRo586dJlcIAEDeky9mfR85cqQSExPt24kTJ8wuCQCAXKV69epavHixnnnmGXvbvHnzVL9+fT3++OP69ddfTawOAIC8xaFBvWTJknJ1ddXp06cztJ8+fVo+Pj43fI+Pj88t+1/7mpVjenh4yMvLK8MGAADuzvHjx+Xi4qI1a9aoQYMGatu27W1XdgEAALfn0KDu7u6u+vXra8OGDfa2tLQ0bdiwQY0aNbrhexo1apShvyStX7/e3j8gIEA+Pj4Z+iQlJWnbtm03PSYAAMh+77zzjqKiotSjRw+5uLho7dq1CgoKUuvWrbV161azywMAINdy+K3vQ4YM0SeffKJFixYpKipKffv21aVLl/T8889Lknr06KGRI0fa+w8cOFDh4eF67733dPDgQY0bN06//vqrBgwYICl9+ZhBgwZp0qRJWr16tfbu3asePXqobNmyat++vaNPBwAA/EPlypW1aNEiHTp0SD179pSrq6vCw8MzrMsOAACyxs3R36Bz5876448/NGbMGCUkJKhOnToKDw+3TwYXFxdnn5RGkho3bqzFixfrzTff1BtvvKFKlSpp5cqVqlGjhr3PsGHDdOnSJfXp00fnz59XkyZNFB4eLk9PT0efDnBLtjSbIuMiFX8hXr5FfRV8b7BcXVzNLgsAHK5ixYpasGCB3nzzTb399tt66aWX7Pvi4+N17NgxPfjggyZWCABA7uHwddSdEeuowxHCosI0MHygTiadtLf5eflpZquZ6lCtg4mVAYC5Bg4cqFmzZql58+YaO3asgoODzS4JAIAc5zTrqAP5RVhUmDot65QhpEvSqaRT6rSsk8KiwkyqDADMZxiG3NzctGHDBj300EN65JFH9OOPP5pdFgAATougDtwlW5pNA8MHytD1N6dcaxsUPki2NFtOlwYATmHWrFk6cuSI+vTpowIFCmjjxo1q2rSpmjVrpk2bNpldHgAAToegDtylyLjI60bS/8mQoRNJJxQZF5mDVQGAc7Farfr444915MgRvfzyyypQoIAiIiK0cuVKs0sDAMDpENSBuxR/IT5b+wFAXla+fHl99NFHOnr0qAYMGKBhw4bZ9+3evVs//PCD8uH0OQAAZEBQB+6Sb1HfbO0HAPmBv7+/PvjgA/n4+Njbhg8frubNm+uhhx7S999/T2AHAORbBHXgLgXfGyw/Lz9ZZLnhfoss8vfyV/C9zHIMADeTmpqqqlWrysPDQz/99JMeffRRNWnSROvWrSOwAwDyHYI6cJdcXVw1s9VMSbourF97PaPVDNZTB4BbcHNz08yZM3Xs2DENHDhQnp6e2rx5s1q2bKnGjRszSzwAIF8hqAPZoEO1Dgp9OlTlvMplaPfz8lPo06Gsow4AmVS2bFnNmDFDx44d06BBg+Tp6amtW7fq8OHDZpcGAECOsRj58H6yrCw0D2SFLc2myLhIxV+Il29RXwXfG8xIOgDchYSEBM2ZM0dvvPGG3N3dJUnffvutDMNQ27ZtZbHc+LEjAACcTVZyKEGdoI5sQkgHAMe7evWqqlatqmPHjql+/foaO3asHnvsMQI7AMDpZSWHcus7kA3CosJknWlVs0XN1C2sm5otaibrTKvCosLMLg0A8pQrV66oY8eOKlSokHbs2KEnnnhC999/v1atWsWkcwCAPIOgDtylsKgwdVrWSSeTTmZoP5V0Sp2WdSKsA0A2Kly4sKZOnarY2FgNHz5chQsX1s6dO9W+fXvVq1dPkZGRZpcIAMBdI6gDd8GWZtPA8IEydP0ozrW2QeGDZEuz5XRpAJCnlSpVSlOmTFFsbKxGjBihIkWKaPfu3YyqAwDyBII6cBci4yKvG0n/J0OGTiSdUGQcIzwA4AglS5bU5MmTFRsbq48//lgPPfSQfd/s2bP19ddfKy0tzcQKAQDIOoI6cBfiL8Rnaz8AwJ2555571KdPH/vrP//8U8OHD1enTp1Uu3ZtLV++nMCOPM1mkyIipCVL0r/auJkPyNUI6sBd8C3qm639AADZo0CBAhoyZIi8vLy0b98+Pf3006pVq5aWLVtGYEeeExYmWa1Ss2ZSt27pX63W9HYAuRNBHbgLwfcGy8/LTxbdeFkgiyzy9/JX8L3BOVwZAORv3t7emjBhgmJjYzV27Fh5e3tr//796ty5s2rWrKlffvnF7BKBbBEWJnXqJJ3815N4p06ltxPWgdyJoA7cBVcXV81sNVOSrgvr117PaDWD9dQBwCTFixfXuHHjFBsbq/Hjx6tYsWI6cuSIfHx8zC4NuGs2mzRwoHSjORSvtQ0axG3wQG5EUAfuUodqHRT6dKjKeZXL0O7n5afQp0PVoVoHkyoDAFxTrFgxjRkzRrGxsVq5cqX8/f3t+wYPHqwvvvhCqampJlYIZF1k5PUj6f9kGNKJE+n9AOQuFiMfrmOSlJQkb29vJSYmysvLy+xykEfY0myKjItU/IV4+Rb1VfC9wYykA4CT27Vrl+rVqydJqlSpkt58801169ZNbm5uJlcG3N6SJenPpN/O4sVS166OrwfArWUlhzKiDmQTVxdXNbU2VdeaXdXU2pSQDgC5QMWKFfX222/rnnvu0ZEjR/Tcc8+pWrVqWrhwISPscHq+mZyrNrP9ADgPRtQZUQcAIN+7cOGCZs+erWnTpuns2bOSpMDAQH3zzTeqXr26ydUBN2azpc/ufurUjZ9Tt1gkPz8pJkZyZfwAMB0j6gAAAFlQtGhRjRgxQrGxsXrnnXdUsmRJJScnKyAgwOzSgJtydZVmps9pK8u/FqC59nrGDEI6kBsR1AEAAP5fkSJFNGzYMMXGxmrNmjUqWLCgJMlms6lt27b69NNPdeXKFZOrBP6nQwcpNFQql3FOW/n5pbd3YE5bIFfi1ndufQcAALexfPlyPf3005Kk8uXL64033lDPnj3l7u5ucmVAOpstfXb3+Pj0Z9KDgxlJB5xNVnIoQZ2gDgAAbiM5OVkff/yx3nnnHZ0+fVqSdO+99+qNN97Q888/T2AHANwWQf02COoAAOBOJCcna+7cuXrnnXeUkJAgSfL399fWrVtVtmxZk6sDADgzJpMDAABwgEKFCmnQoEE6duyYZs6cKV9fX5UrV06+/1j/Kh+OgQAAshlBHQAAIIsKFiyoV199VceOHdOSJUtk+f8pts+fP6+aNWvqww8/1OXLl02uEgCQWxHUAQAA7pCnp6esVqv99dy5c7V//3698sorqlChgmbNmqW///7bvAIBALkSQR0AACCbDBw4UP/5z3/k7++v33//XQMHDlSFChU0c+ZMAjsAINOYTI7J5AAAQDZLSUnRwoUL9fbbbysuLk6S5Ofnp0OHDqlQoUImVwcAMAOTyQEAAJjIw8NDL730ko4cOaK5c+fKarWqefPmGUL61atXTawQAODMCOoAAAAO4u7urt69e+vw4cN6//337e0HDhzQvffeq3fffVeXLl0ysUIAgDMiqAMAADhYgQIFVLx4cfvrOXPmKCEhQcOGDVNAQICmTp2qixcvmlghAMCZENQBAABy2Hvvvaf58+erQoUK+uOPPzR8+HAFBARoypQpunDhgtnlAQBMxmRyTCYHAABMkpqaqi+//FKTJk1SdHS0JKlKlSo6cOCAXFwYTwGAvITJ5AAAAHIBNzc3Pffcc4qKitJnn32mSpUqqWfPnvaQbhgGI+wAkA8R1AEAAEzm5uamZ599VgcOHNDgwYPt7atXr1b58uU1ceJEJSYmmlghACAnEdQBAACchJubmzw8POyvFy9erL/++ktjxoyR1WrV+PHjdf78efMKBADkCII6AACAk1q8eLGWLFmi6tWr6/z58xo3bpysVqvGjRtHYAeAPIygDgAA4KRcXV3VpUsX7d27V1999ZXuu+8+JSYmavz48Wrfvr3Z5QEAHMShQf3cuXPq3r27vLy8VKxYMfXq1euWa4SeO3dOr7zyiqpUqaKCBQvq3nvv1auvvnrdM1kWi+W6benSpY48FQAAANO4uLjo6aef1m+//ably5erRo0aGjRokH3/pUuXdO7cOfMKBABkK4cG9e7du2v//v1av3691qxZo02bNqlPnz437f/777/r999/17Rp07Rv3z4tXLhQ4eHh6tWr13V9FyxYoPj4ePvGvyoDwI3Z0myKiI3Qkr1LFBEbIVuazeySANwhFxcXderUSXv27FG7du3s7R9++KGsVqtGjRqls2fPmlghACA7OGwd9aioKFWvXl2//PKL7r//fklSeHi42rRpo5MnT6ps2bKZOs7y5cv1zDPP6NKlS3Jzc0sv2mLRihUr7jics446gPwiLCpMA8MH6mTSSXubn5efZraaqQ7VOphYGYDs1KJFC61fv16SVKRIEQ0YMEBDhw5VyZIlTa4MAHCNU6yjvmXLFhUrVswe0iUpJCRELi4u2rZtW6aPc+0kroX0a/r376+SJUuqYcOGmj9/vm717w0pKSlKSkrKsAFAXhcWFaZOyzplCOmSdCrplDot66SwqDCTKgOQ3cLDw7VixQrVqVNHFy9e1JQpU2S1WjV8+HD98ccfZpcHAMgihwX1hIQElS5dOkObm5ubSpQooYSEhEwd488//9TEiROvu11+woQJWrZsmdavX6+OHTuqX79++uCDD256nMmTJ8vb29u++fv7Z/2EACAXsaXZNDB8oAxd/4+Y19oGhQ/iNnggj3BxcVH79u21c+dOrVq1SnXr1tWlS5c0depUDR061OzyAABZlOWgPmLEiBtO5vbP7eDBg3ddWFJSktq2bavq1atr3LhxGfaNHj1aDz74oOrWravhw4dr2LBhevfdd296rJEjRyoxMdG+nThx4q7rAwBnFhkXed1I+j8ZMnQi6YQi4yJzsCoAjmaxWPTEE09ox44dWr16tRo0aKDhw4fb98fHx+v06dMmVggAyAy323fJaOjQoerZs+ct+wQGBsrHx0dnzpzJ0J6amqpz587Jx8fnlu+/cOGCWrVqpaJFi2rFihUqUKDALfsHBQVp4sSJSklJkYeHx3X7PTw8btgOAHlV/IX4bO0HIHexWCx6/PHH9fjjj2doHzVqlJYuXaqXX35Zw4YNu+3vZAAAc2Q5qJcqVUqlSpW6bb9GjRrp/Pnz2rFjh+rXry9J+uGHH5SWlqagoKCbvi8pKUktW7aUh4eHVq9eLU9Pz9t+r927d6t48eKEcQD4f75FfbO1H4DcLzU1VYcOHdLff/+t999/Xx999JE9sPv68neBs7DZpMhIKT5e8vWVgoMlV1ezqwKQ0xz2jHq1atXUqlUr9e7dW9u3b9fPP/+sAQMGqEuXLvYZ30+dOqWqVatq+/btktJDeosWLXTp0iXNmzdPSUlJSkhIUEJCgmy29Ocov/nmG3366afat2+foqOj9dFHH+ntt9/WK6+84qhTAYBcJ/jeYPl5+ckiyw33W2SRv5e/gu8NzuHKAJjFzc1NP/30k7777js98MADunz5smbMmKHAwEANHDhQv//+u9kl5nthYZLVKjVrJnXrlv7Vak1vB5C/OHQd9S+//FJVq1ZV8+bN1aZNGzVp0kRz586177969aoOHTqk5ORkSdLOnTu1bds27d27VxUrVpSvr699u/ZceYECBTR79mw1atRIderU0ccff6zp06dr7NixjjwVAMhVXF1cNbPVTEm6Lqxfez2j1Qy5ujBMA+QnFotFrVq10ubNm/Xf//5XjRo10uXLlzVr1qwMv6Mh54WFSZ06SSf/Nb3IqVPp7YR1IH9x2Drqzox11AHkFzdaR93fy18zWs1gHXUAMgxD33//vaZPn67FixerePHikqR9+/apWLFi8vPzM7nC/MFmSx85/3dIv8Zikfz8pJgYboMHcrOs5FCCOkEdQB5nS7MpMi5S8Rfi5VvUV8H3BjOSDuCmDMPQgw8+qB07dujFF1/UiBEjWNrWwSIi0m9zv52NG6WmTR1dDQBHyUoOdeit7wAA87m6uKqptam61uyqptamhHQAt5SUlCR3d3dduXJF//nPf1ShQgX17dtXcXFxZpeWZ8VncgGOzPYDkPsR1AEAAGDn7e2tiIgIbdy4UU2bNtXVq1c1Z84cVaxYUS+99JKOHz9udol5TmYn3WdyfiD/IKgDAADgOk2bNtXGjRv1448/6pFHHtHVq1c1d+5cbdmyxezS8pzg4PRn0C03XqhDFovk75/eD0D+QFAHAADATT300EPasGGDIiMj1atXLz311FP2fd9//72OHTtmYnV5g6urNDN9oY7rwvq11zNmMJEckJ8Q1AEAAHBbTZo00aeffirX/0+Lly9fVo8ePVS5cmW98MILOnr0qMkV5m4dOkihoVK5chnb/fzS2zuwUAeQrxDUAQAAkGVnz55VrVq1ZLPZtGDBAlWpUkU9e/ZUdHS02aXlWh06SLGx6bO7L16c/jUmhpAO5Ecsz8bybAAAAHds69atGj9+vMLDwyVJLi4u6t69uyZMmCCr1WpucQDgRFieDQAAADnigQce0HfffaetW7eqTZs2SktL0+eff65z586ZXRoA5FoEdQAAANy1oKAgffvtt9q+fbsmTZqkevXq2fctWrRIBw8eNLE6AMhduPWdW98BAAAc5sSJE6pYsaKuXr2qLl26aPTo0apWrZrZZQFAjuPWdwAAADiFq1evqk2bNjIMQ0uWLNF9992nrl276sCBA2aXBgBOi6AOAAAAhwkMDNSKFSu0a9cuPfnkkzIMQ0uXLlWNGjXUuXNnnThxwuwSAcDpENQBAADgcHXq1FFYWJh2796tjh07yjAMrVmzRp6enmaXBgBOh6AOAACAHFO7dm2FhoZqz549mjt3rkqVKmXfN378eO3Zs8fE6gDAOTCZHJPJAQAAmG7Tpk16+OGHJUnt27fXmDFjVLduXZOrAoDsw2RyAAAAyFV8fX3VuXNnWSwWrVy5UvXq1VP79u21c+dOs0sDgBxHUAcAAIDpKlWqpKVLl2rfvn3q2rWrLBaLVq1apfr16+uJJ57QmTNnzC4RAHIMQR0AAABOo3r16lq8eLEOHDig7t27y8XFRfv27VPx4sXNLg0AcgxBHQAAAE6natWq+uKLL3TgwAEtWLBABQoUkCRduXJFL7zwgrZv325yhQDgOAR1AAAAOK0qVarYJ5mTpEWLFmnBggUKCgpSmzZttG3bNhOrAwDHIKgDAAAg12jWrJl69uwpV1dXfffdd3rggQfUqlUrbdmyxezSACDbENQBAACQa1SsWFELFizQoUOH9Pzzz8vV1VX//e9/1bhxY7Vo0ULJyclmlwgAd42gDgAAgFynQoUKmj9/vg4fPqxevXrJzc1NklSoUCGTKwOAu0dQBwAAQK4VGBioTz/9VEeOHNGsWbPs7QkJCWrTpo1+/PFHE6sDgDtDUAcAAECuZ7VaVbVqVfvradOm6bvvvlPTpk3VtGlTRUREmFccAGQRQR0AAAB5zquvvqqXX35ZBQoU0I8//qhmzZrp4Ycf1g8//CDDMMwuDwBuiaAOAACAPOfee+/VRx99pKNHj6pfv35yd3fXpk2b1Lx5c7Vs2VJpaWlmlwgAN0VQBwAAQJ7l7++v2bNn6+jRo+rfv7/c3d1VpUoVubjwazAA58XfUAAAAMjz/Pz89OGHH+rYsWMaPXq0vf2XX37Rgw8+qHXr1nFLPACnQVAHAABAvlGuXDmVLl3a/nry5MnavHmzWrZsqcaNGys8PJzADsB0BHUAAADkW7Nnz9agQYPk6emprVu3qnXr1mrUqJG+++47AjsA0xDUAQAAkG/5+vrq/fffV0xMjAYPHqyCBQtq27ZtatOmjTp27Gh2eQDyKYI6AAAA8j0fHx9Nnz5dMTExGjp0qAoWLKjmzZvb96elpTHCDiDHENQBAACA/1emTBlNmzZNsbGx6tWrl7198eLFuv/++7V69WoCOwCHI6gDAAAA/1K6dGl5enraX7///vvauXOn2rVrp/r162vlypUEdgAOQ1AHAAAAbuO///2vRo4cqSJFimjXrl168sknVbduXa1YsUJpaWlmlwcgjyGoAwAAALdRsmRJvf3224qNjdUbb7yhokWLas+ePerQoYP69OljdnkA8hiCOgAAAJBJ99xzj9566y3FxsbqzTfflJeXl7p27Wrff/nyZUbYAdw1gjoAAACQRSVKlNDEiRMVFxenRx55xN4+adIk1apVS1999ZVsNpuJFQLIzQjqAAAAwB3y9vaWxWKRJKWmpurzzz/X/v371aVLF9WsWVNLly4lsAPIMoI6AAAAkA3c3Ny0Z88ejRs3TsWKFVNUVJS6du2qGjVqaPHixQR2AJnm0KB+7tw5de/eXV5eXipWrJh69eqlixcv3vI9TZs2lcViybC9/PLLGfrExcWpbdu2KlSokEqXLq3XX39dqampjjwVAAAA4LaKFSumsWPHKjY2VhMmTFDx4sV18OBBde/eXWPGjDG7PAC5hEODevfu3bV//36tX79ea9as0aZNmzI1K2bv3r0VHx9v36ZOnWrfZ7PZ1LZtW125ckWbN2/WokWLtHDhQv7iAwAAgNPw9vbW6NGjFRsbq0mTJqlMmTLq3bu3ff+5c+cYaAJwUxbDMAxHHDgqKkrVq1fXL7/8ovvvv1+SFB4erjZt2ujkyZMqW7bsDd/XtGlT1alTRzNmzLjh/u+++06PPfaYfv/9d5UpU0aSNGfOHA0fPlx//PGH3N3dr3tPSkqKUlJS7K+TkpLk7++vxMREeXl53eWZAgAAALd29epVFShQwP76qaee0u7du/Xmm2+qe/fucnNzM7E6ADkhKSlJ3t7emcqhDhtR37Jli4oVK2YP6ZIUEhIiFxcXbdu27Zbv/fLLL1WyZEnVqFFDI0eOVHJycobj1qxZ0x7SJally5ZKSkrS/v37b3i8yZMny9vb2775+/vf5dkBAAAAmffPkH7+/Hn9+OOPio6OVs+ePVW1alUtWLBAV69eNbFCAM7EYUE9ISFBpUuXztDm5uamEiVKKCEh4abv69atm7744gtt3LhRI0eO1Oeff65nnnkmw3H/GdIl2V/f7LgjR45UYmKifTtx4sSdnhYAAABwV4oVK6Zjx47pnXfeUcmSJXX06FG98MILqlq1qubPn09gB5D1oD5ixIjrJnv793bw4ME7LqhPnz5q2bKlatasqe7du+uzzz7TihUrdPTo0Ts+poeHh7y8vDJsAAAAgFmKFCmiYcOGKTY2Vu+++65Kly6tY8eOqVevXvrkk0/MLg+AybL8MMzQoUPVs2fPW/YJDAyUj4+Pzpw5k6E9NTVV586dk4+PT6a/X1BQkCQpOjpaFSpUkI+Pj7Zv356hz+nTpyUpS8cFAAAAzFa4cGG99tpr6tevn+bMmaNFixbpueees+8/duyY/Pz8bjgPE4C8K8tBvVSpUipVqtRt+zVq1Ejnz5/Xjh07VL9+fUnSDz/8oLS0NHv4zozdu3dLknx9fe3Hfeutt3TmzBn7rfXr16+Xl5eXqlevnsWzAQAAAMxXqFAhDRkyRIMHD5bFYpEkpaWlqV27dkpKStLIkSP1/PPPy8PDw+RKAeQEhz2jXq1aNbVq1Uq9e/fW9u3b9fPPP2vAgAHq0qWLfcb3U6dOqWrVqvYR8qNHj2rixInasWOHYmNjtXr1avXo0UMPPfSQatWqJUlq0aKFqlevrmeffVZ79uzRf//7X7355pvq378/f3EBAJyaLc2miNgILdm7RBGxEbKl2cwuCYCTuRbSJSk2NlZnz55VXFyc+vbtq0qVKumjjz7KsJoRgLzJoeuof/nll6pataqaN2+uNm3aqEmTJpo7d659/9WrV3Xo0CH7rO7u7u76/vvv1aJFC1WtWlVDhw5Vx44d9c0339jf4+rqqjVr1sjV1VWNGjXSM888ox49emjChAmOPBUAAO5KWFSYrDOtaraombqFdVOzRc1knWlVWFSY2aUBcFKBgYE6evSoZs6cKV9fX504cUL9+vVTxYoVNXv2bF2+fNnsEgE4iMPWUXdmWVm/DgCAuxUWFaZOyzrJUMYfuRalj5yFPh2qDtU6mFEagFzi8uXL+vTTTzVlyhSdOnVKkrR27Vq1bt3a5MoAZFZWcihBnaAOAHAgW5pN1plWnUw6ecP9Flnk5+WnmIExcnVxzeHqAOQ2ly9f1rx58/T9998rLCzMfqv8L7/8oho1aqhgwYImVwjgZrKSQx166zsAAPldZFzkTUO6JBkydCLphCLjInOwKgC5laenp/r3768VK1bYQ3pSUpJatmypwMBAzZgxQ3///bfJVQK4WwR1AAAcKP5CfLb2A4B/i46OVpEiRZSQkKDBgwcrMDBQ77//vn0eKAC5D0EdAAAH8i3qm639AODf6tWrp+joaH388ccqX768EhISNGTIEAUGBuq9997TpUuXzC4RQBYR1AEAcKDge4Pl5+Vnnzju3yyyyN/LX8H3BudwZQDyEnd3d/Xp00eHDx/WJ598IqvVqtOnT2vYsGE6efLmj98AcE4EdQAAHMjVxVUzW82UpOvC+rXXM1rNYCI5ANnC3d1dL774og4fPqxPP/1Ur732mqpUqWLfv2bNGl28eNHECgFkBrO+M+s7ACAHhEWFaWD4wAwTy/l7+WtGqxkszQYgRxw+fFjVqlVTiRIl9Nprr6l///4qUqSI2WUB+QbLs90GQR0AYAZbmk2RcZGKvxAv36K+Cr43mJF0ADkmMjJSL7zwgqKjoyVJ99xzj4YOHaoBAwaoaNGiJlcH5H0E9dsgqAMAACA/Sk1N1ZIlSzRp0iQdPnxYklSiRAkNGTJEgwYNUuHChU2uEMi7WEcdAAAAwHXc3Nz07LPP6sCBA/riiy9UpUoVnTt3Tu+9955sNpvZ5QH4fwR1AAAAIJ9xdXVV9+7dtX//fn355ZeaOnWqfYTPMAzNmTNH58+fN7dIIB/j1ndufQcAAEAecrfzYaxdu1Zt27aVt7e3Bg0apIEDB6p48eIOrBjIH7j1HQAAAMiHwqLCZJ1pVbNFzdQtrJuaLWom60yrwqLCMn0Md3d3Va9eXYmJiRo/frysVqvGjBmjc+fOObByAP/EiDoj6gCAXI7Z5AFI6SG907JOMpTx13uLLJKk0KdDM70cZFpamr7++mtNmDBB+/btkyQVLVpUr776qt588015enpmb/FAPsCs77dBUAcA5BU3Wp/dz8tPM1vNZH12IB+xpdlknWnN8HfBP1lkkZ+Xn2IGxmTpH/LS0tK0YsUKjR8/Xnv37lWNGjW0Z88eubhwYy6QVdz6DgBAPnBt9Ozfv5ifSjqlTss6ZelWVwC5W2Rc5E1DuiQZMnQi6YQi4yKzdFwXFxd17NhRu3fv1tdff63p06fbQ3pycrLGjx+vP//8865qB3A9gjoAALmQLc2mgeEDr7vFVZK9bVD4INnSWG4JyA/iL8Rna79/c3FxUYcOHfToo4/a2+bMmaNx48bJarVqxIgR+uOPP+7o2ACuR1AHACAXctToGYDcybeob7b2y4waNWqoXr16unTpkt555x0FBARo2LBhOnPmTLZ9DyC/IqgDAJALOXr0DEDuEnxvsPy8/OwTx/2bRRb5e/kr+N7gbPueLVq00K+//qpvvvlG9evX16VLl/Tuu+8qICBAr7/+utLS0rLtewH5DUEdAIBcyIzRMwDOy9XFVTNbzZSk68L6tdczWs3I9hUhLBaLHnvsMf3yyy9as2aNGjRooOTkZB07dowJ54C7wKcHAIBcyIzRMwDOrUO1Dgp9OlTlvMplaPfz8svS0mx3wmKxqG3bttq2bZvWrl2riRMn2vfFxsZq8ODBio/nDh8gs1iejeXZAAC51LVZ3yVlmFTuTtZMBpB32NJsioyLVPyFePkW9VXwvcHZPpKeFX369NEnn3wiT09P9enTR8OHD1fZsmVNqwcwC8uzAQCQD5g5egbAebm6uKqptam61uyqptampoZ0SXr66afVuHFjXb58WbNmzVJgYKBeeeUVnTp1ytS6AGfGiDoj6gCAXM7ZRs8A4N8Mw9CGDRs0btw4/fzzz5Ikd3d3DRkyRJMnTza5OiBnZCWHuuVQTQAAwEGujZ4BgLOyWCwKCQlR8+bN9cMPP2j8+PGKjIxkwjngJvhkAAAAAMgRFotFzZs3148//qgffvhBQ4YMse+LiIhQ3759FRcXZ2KFgHMgqAMAAADIURaLRc2aNdM999xjbxs7dqzmzJmjihUr6qWXXtLx48dNrBAwF0EdAAAAgOkmTpyoRx55RFevXtXcuXNVsWJF9e7dWzExMWaXBuQ4gjoAAAAA0z300EPasGGDNm3apObNmys1NVWffvqpKleurPHjx5tdHpCjCOoAAAAAnEZwcLC+//57/fTTT3r00UeVmpqqgIAAs8sCchRBHQAAAIDTefDBB7Vu3Tpt3bpV3bp1s7d/8sknev755xUdHW1idYBjsY4666gDAAAAuUJqaqoCAwN14sQJubq6qnv37nrzzTdVqVIls0sDbisrOZQRdQAAAAC5gpubm0JDQ9WmTRvZbDZ99tlnqlq1qp599lkdOnTI7PKAbENQBwAAAJBrNGzYUN9++622b9+uxx57TGlpafriiy9UvXp1TZ8+3ezygGxBUAcAAACQ6zRo0EDffPONfv31Vz3xxBNKS0tTkyZN7Pvz4RO+yEMI6gAAAAByrfr162vVqlU6fPiwGjZsaG8fMmSIunTpov3795tYHXBnCOoAAAAAcr1/Tih3/vx5ffzxx/rqq69Us2ZNPf3009q3b5+J1QFZQ1AHAAAAkKcUK1ZMW7ZsUYcOHWQYhpYvX66aNWvqqaee0t69e80uD7gtgjoAAACAPKd27dr6+uuvtWfPHnXq1EmSFBoaqlq1amnBggUmVwfcGkEdAAAAQJ5Vq1YtLV++XL/99pueeuopFSpUSK1bt7bvv3LlionVATdGUAcAAACQ59WsWVPLli3T8ePH5ePjY29v166d2rdvr127dplYHZCRQ4P6uXPn1L17d3l5ealYsWLq1auXLl68eNP+sbGxslgsN9yWL19u73ej/UuXLnXkqQAAAADIA0qWLGn/c3R0tNatW6dVq1apXr16ateunXbs2GFidUA6hwb17t27a//+/Vq/fr3WrFmjTZs2qU+fPjft7+/vr/j4+Azb+PHjVaRIkQy3p0jSggULMvRr3769I08FAAAAQB5TsWJF7d+/X927d5eLi4tWr16t+++/X48//rh+/fVXs8tDPmYxDMNwxIGjoqJUvXp1/fLLL7r//vslSeHh4WrTpo1OnjypsmXLZuo4devWVb169TRv3rz/FW2xaMWKFXcczpOSkuTt7a3ExER5eXnd0TEAAAAA5B2HDh3SW2+9pS+//FJpaWmSpDVr1qht27YmV4a8Iis51GEj6lu2bFGxYsXsIV2SQkJC5OLiom3btmXqGDt27NDu3bvVq1ev6/b1799fJUuWVMOGDTV//nzd6t8bUlJSlJSUlGEDAAAAgGuqVKmizz77TFFRUerRo4cCAwP16KOP2vefP3/evOKQ7zgsqCckJKh06dIZ2tzc3FSiRAklJCRk6hjz5s1TtWrV1Lhx4wztEyZM0LJly7R+/Xp17NhR/fr10wcffHDT40yePFne3t72zd/fP+snBAAAACDPq1y5shYtWqR9+/bJ3d1dknT16lXVr19fLVu21ObNm02uEPlBloP6iBEjbjrh27Xt4MGDd13Y33//rcWLF99wNH306NF68MEHVbduXQ0fPlzDhg3Tu+++e9NjjRw5UomJifbtxIkTd10fAAAAgLyrYMGC9j9v27ZNx48f17p16/Tggw+qRYsW+vnnn02sDnldloP60KFDFRUVdcstMDBQPj4+OnPmTIb3pqam6ty5cxmWQ7iZ0NBQJScnq0ePHrftGxQUpJMnTyolJeWG+z08POTl5ZVhAwAAAIDMaNKkiQ4fPqxevXrJzc1N69evV5MmTRQSEqLIyEizy0Me5PDJ5H799VfVr19fkrRu3Tq1atUqU5PJNW3aVCVLllRoaOhtv9dbb72l9957T+fOnctUbUwmBwAAAOBOxMTEaPLkyVqwYIFSU1MlSTt37lTdunVNrgzOzikmk6tWrZpatWql3r17a/v27fr55581YMAAdenSxR7ST506papVq2r79u0Z3hsdHa1NmzbpxRdfvO6433zzjT799FPt27dP0dHR+uijj/T222/rlVdecdSpAAAAAIAkKSAgQHPnztWRI0fUp08ftWjRIkNIP378uInVIa9wc+TBv/zySw0YMEDNmzeXi4uLOnbsqFmzZtn3X716VYcOHVJycnKG982fP19+fn5q0aLFdccsUKCAZs+ercGDB8swDFWsWFHTp09X7969HXkqAAAAAGBntVr18ccf25dyk6Q///xT9913n+6//36NHTtWTZs2lcViMbFK5FYOu/XdmXHrOwAAAIDstmrVKj399NO6cuWKJCk4OFhjx47VI488QmCHc9z6DgAAAAD5Sbt27RQdHa3+/fvL3d1dkZGRCgkJUXBwsL7//nvlwzFS3CFG1BlRBwAAAJDNTp06pXfeeUdz585VSkqKChQooOPHj8vX19fs0mASRtQBAAAAwETlypXTrFmzdOzYMQ0cOFD9+/fPENJ37NjBCDtuihF1RtQBAAAA5KBdu3apXr16atiwocaOHavWrVvzDHs+wIg6AAAAADipvXv3qmDBgtq+fbvatm2roKAgrVmzhhF22BHUAQAAACAH9ejRQzExMRo6dKgKFiyoX375RY8//rgaNGigb775hsAObn3n1ncAAAAAZjlz5oymTZum2bNnKzk5WT4+Pjp27JgKFixodmnIZtz6DgAAAAC5QOnSpTV16lTFxsZq+PDhGjdunD2k22w2fffdd4yw50MEdQAAAAAwWalSpTRlyhS99NJL9rbQ0FC1adNGdevWVVhYmNLS0kysEDmJoA4AAAAATujs2bMqUqSI9uzZo44dO6pu3br6+uuvCez5AEEdAAAAAJxQv379FBsbq1GjRqlo0aL67bff1KlTJ9WuXVvLly/nlvg8jKAOAAAAAE7qnnvu0aRJkxQbG6vRo0fLy8tL+/bt03vvvWd2aXAggjoAAAAAOLkSJUpowoQJio2N1dixYzVp0iRZLBZJ0vnz5/XVV1/JZrOZXCWyC0EdAADkabY0myJiI7Rk7xJFxEbIlsYvsgByr+LFi2vcuHEKCQmxt82cOVNdunRRzZo1tXjxYgJ7HkBQBwAAeVZYVJisM61qtqiZuoV1U7NFzWSdaVVYVJjZpQFAtilWrJiKFy+uqKgode/eXffdd5++/PJLAnsuRlAHAAB5UlhUmDot66STSScztJ9KOqVOyzoR1gHkGQMHDlRsbKwmTZqkEiVK6NChQ3rmmWdUvXp1LV682OzycAcI6gAAIM+xpdk0MHygDF0/I/K1tkHhg7gNHkCe4eXlpVGjRik2NlZvv/227rnnHh0+fFjr1q0zuzTcAYI6AADIcyLjIq8bSf8nQ4ZOJJ1QZFxkDlYFAI5XtGhRjRw5UjExMZo8ebJGjRpl33fw4EEtWLBAV69eNbFCZAZBHQAA5DnxF+KztR8A5DZFixbViBEjVKlSJXvbhAkT9MILL6hKlSqaN28egd2JEdQBAECe41vUN1v7AUBe0KBBA5UuXVoxMTF68cUXVblyZX366ae6cuWK2aXhXwjqAAAgzwm+N1h+Xn6yyHLD/RZZ5O/lr+B7g3O4MgAwz+DBg3Xs2DFNmzZNpUuXVmxsrHr37q3KlSsz6ZyTIagDAIA8x9XFVTNbzZSk68L6tdczWs2Qq4trjtcGAGYqXLiwhg4dqpiYGE2fPl1lypTR8ePHFRcXZ3Zp+AeLYRjXT4eaxyUlJcnb21uJiYny8vIyuxwAAO6ILc2myLhIxV+Il29RXwXfG0zw/JewqDANDB+YYWI5fy9/zWg1Qx2qdTCxMgBwDsnJyZo3b5569uypokWLSpLWr1+v6OhovfDCC/Lw8DC5wrwjKzmUoE5QBwDkQjcKoH5efprZaiYB9F/4Bw0AyDzDMFS/fn3t2rVLfn5+GjFihHr16iVPT0+zS8v1COq3QVAHAORmYVFh6rSs03VrhF+7pTv06VDCOgDgjqSmpurjjz/W5MmTderUKUlSuXLlNGLECL344osE9ruQlRzKM+oAAOQitjSbBoYPvC6kS7K3DQofJFuaLadLAwDkAW5uburfv7+io6M1e/Zs+fn56dSpU3rllVdUoUIFLVu2zOwS8wWCOgAAuUhkXGSG293/zZChE0knFBkXmYNVAQDyGk9PT/Xr10/R0dH66KOP5O/vr99//13u7u5ml5YvENQBAMhF4i/EZ2s/AABuxcPDQy+//LKOHDmixYsXq127dvZ9c+fO1fTp05WcnGxihXkTQR0AgFzEt6hvtvYDACAzPDw81LVrV1ks6fOhXLx4UaNGjdLQoUMVEBCg9957T5cuXTK5yryDoA4AQC4SfG+w/Lz8rlsb/BqLLPL38lfwvcE5XBkAID/x8PDQlClTZLVadebMGb322msKCAjQu+++S2DPBgR1AAByEVcXV81sNVOSrgvr117PaDWD5ccAAA5VoEAB9erVS4cPH9a8efMUGBioP/74Q8OGDZPVatU333xjdom5GkEdAIBcpkO1Dgp9OlTlvMplaPfz8mNpNgBAjipQoIBeeOEFHTx4UAsWLFCFChV09uxZBQYGml1arsY66qyjDgDIpWxpNkXGRSr+Qrx8i/oq+N5gRtIBAKZKTU1VZGSkmjVrZm977bXXVKJECQ0YMCBf56+s5FCCej7+HwUAAAAAHCkmJkaVKlWSzWZTiRIlNGTIEL3yyiv5ModlJYdy6zsAAAAAwCH8/f21cOFCVa5cWefOndObb74pq9WqiRMnKjEx0ezynBZBHQAAAADgEG5ubnrmmWd04MABffnll6pSpYr++usvjRkzRlarVREREWaX6JQI6gAAAAAAh3J1dVW3bt20f/9+LV68WNWqVVNaWppq165t75MPn8q+KYI6AAAAACBHuLq6qmvXrtq7d682b96s4sWLS0oP6W3bttWYMWP0119/mVyl+QjqAAAAAIAc5erqqvvuu8/+OjIyUt99950mTpwoq9Wq0aNH69y5cyZWaC6COgAAAADAVE2aNFFoaKhq1aqlpKQkTZo0SVarVaNGjdLZs2fNLi/HEdQBAAAAAKZycXFRx44dtWvXLoWFhal27dq6cOGC3n77bVmtVv32229ml5ijHBbU33rrLTVu3FiFChVSsWLFMvUewzA0ZswY+fr6qmDBggoJCdGRI0cy9Dl37py6d+8uLy8vFStWTL169dLFixcdcAYAAAAAgJzk4uKiJ598Ujt37tSKFStUt25d+fn5ZbhNPjU11cQKc4bDgvqVK1f01FNPqW/fvpl+z9SpUzVr1izNmTNH27ZtU+HChdWyZUtdvnzZ3qd79+7av3+/1q9frzVr1mjTpk3q06ePI04BAAAAAGACFxcXtW/fXjt27NCGDRvk6uoqSbp8+bKqV6+uYcOG6cyZMyZX6TgWw8Fz4C9cuFCDBg3S+fPnb9nPMAyVLVtWQ4cO1WuvvSZJSkxMVJkyZbRw4UJ16dJFUVFRql69un755Rfdf//9kqTw8HC1adNGJ0+eVNmyZTNVU1JSkry9vZWYmCgvL6+7Oj8AAAAAQM744osv9Oyzz0qSChUqpL59++r1119XmTJlTK7s9rKSQ53mGfWYmBglJCQoJCTE3ubt7a2goCBt2bJFkrRlyxYVK1bMHtIlKSQkRC4uLtq2bdtNj52SkqKkpKQMGwAAAAAgd+nevbvWrFmjBg0aKDk5We+9954CAgJ06tQps0vLVk4T1BMSEiTpun8JKVOmjH1fQkKCSpcunWG/m5ubSpQoYe9zI5MnT5a3t7d98/f3z+bqAQAAAACOZrFY1LZtW23btk1r165Vw4YN1bRpU5UrV87s0rJVloL6iBEjZLFYbrkdPHjQUbXesZEjRyoxMdG+nThxwuySAAAAAAB3yGKxqHXr1tq6dauWLl1qdjnZzi0rnYcOHaqePXvesk9gYOAdFeLj4yNJOn36tHx9fe3tp0+fVp06dex9/j1hQGpqqs6dO2d//414eHjIw8PjjuoCAAAAADgni8WSJ+cdy1JQL1WqlEqVKuWQQgICAuTj46MNGzbYg3lSUpK2bdtmnzm+UaNGOn/+vHbs2KH69etLkn744QelpaUpKCjIIXUBAAAAAJCTHPaMelxcnHbv3q24uDjZbDbt3r1bu3fvzrDmedWqVbVixQpJ6f8SMmjQIE2aNEmrV6/W3r171aNHD5UtW1bt27eXJFWrVk2tWrVS7969tX37dv38888aMGCAunTpkukZ3wEAAAAAcGZZGlHPijFjxmjRokX213Xr1pUkbdy4UU2bNpUkHTp0SImJifY+w4YN06VLl9SnTx+dP39eTZo0UXh4uDw9Pe19vvzySw0YMEDNmzeXi4uLOnbsqFmzZjnqNAAAAAAAyFEOX0fdGbGOOgAAAAAgJ+XKddQBAAAAAABBHQAAAAAAp0JQBwAAAADAiRDUAQAAAABwIgR1AAAAAACcCEEdAAAAAAAnQlAHAAAAAMCJENQBAAAAAHAiBHUAAAAAAJwIQR0AAAAAACdCUAcAAAAAwIkQ1AEAAAAAcCIEdQAAAAAAnIib2QWYwTAMSVJSUpLJlQAAAAAA8oNr+fNaHr2VfBnUL1y4IEny9/c3uRIAAAAAQH5y4cIFeXt737KPxchMnM9j0tLS9Pvvv6to0aKyWCxml3NTSUlJ8vf314kTJ+Tl5WV2ObgJrlPuwHVyflyj3IHrlDtwnZwf1yh34DrlDrnlOhmGoQsXLqhs2bJycbn1U+j5ckTdxcVFfn5+ZpeRaV5eXk79PxzScZ1yB66T8+Ma5Q5cp9yB6+T8uEa5A9cpd8gN1+l2I+nXMJkcAAAAAABOhKAOAAAAAIATIag7MQ8PD40dO1YeHh5ml4Jb4DrlDlwn58c1yh24TrkD18n5cY1yB65T7pAXr1O+nEwOAAAAAABnxYg6AAAAAABOhKAOAAAAAIATIagDAAAAAOBECOoAAAAAADgRgjoAAAAAAE6EoG6yt956S40bN1ahQoVUrFixTL3HMAyNGTNGvr6+KliwoEJCQnTkyJEMfc6dO6fu3bvLy8tLxYoVU69evXTx4kUHnEHel9X/lrGxsbJYLDfcli9fbu93o/1Lly7NiVPKk+7k//mmTZtedw1efvnlDH3i4uLUtm1bFSpUSKVLl9brr7+u1NRUR55KnpbV63Tu3Dm98sorqlKligoWLKh7771Xr776qhITEzP04/N0d2bPni2r1SpPT08FBQVp+/btt+y/fPlyVa1aVZ6enqpZs6bWrl2bYX9mfk4ha7JyjT755BMFBwerePHiKl68uEJCQq7r37Nnz+s+M61atXL0aeR5WblOCxcuvO4aeHp6ZujDZ8kxsnKdbvS7gsViUdu2be19+Dxlr02bNunxxx9X2bJlZbFYtHLlytu+JyIiQvXq1ZOHh4cqVqyohQsXXtcnqz/rTGfAVGPGjDGmT59uDBkyxPD29s7Ue6ZMmWJ4e3sbK1euNPbs2WM88cQTRkBAgPH333/b+7Rq1cqoXbu2sXXrViMyMtKoWLGi0bVrVwedRd6W1f+WqampRnx8fIZt/PjxRpEiRYwLFy7Y+0kyFixYkKHfP68hsuZO/p9/+OGHjd69e2e4BomJifb9qampRo0aNYyQkBBj165dxtq1a42SJUsaI0eOdPTp5FlZvU579+41OnToYKxevdqIjo42NmzYYFSqVMno2LFjhn58nu7c0qVLDXd3d2P+/PnG/v37jd69exvFihUzTp8+fcP+P//8s+Hq6mpMnTrVOHDggPHmm28aBQoUMPbu3Wvvk5mfU8i8rF6jbt26GbNnzzZ27dplREVFGT179jS8vb2NkydP2vs899xzRqtWrTJ8Zs6dO5dTp5QnZfU6LViwwPDy8spwDRISEjL04bOU/bJ6nc6ePZvhGu3bt89wdXU1FixYYO/D5yl7rV271hg1apQRFhZmSDJWrFhxy/7Hjh0zChUqZAwZMsQ4cOCA8cEHHxiurq5GeHi4vU9Wr7szIKg7iQULFmQqqKelpRk+Pj7Gu+++a287f/684eHhYSxZssQwDMM4cOCAIcn45Zdf7H2+++47w2KxGKdOncr22vOy7PpvWadOHeOFF17I0JaZv3iQOXd6nR5++GFj4MCBN92/du1aw8XFJcMvTh999JHh5eVlpKSkZEvt+Ul2fZ6WLVtmuLu7G1evXrW38Xm6cw0bNjT69+9vf22z2YyyZcsakydPvmH/p59+2mjbtm2GtqCgIOOll14yDCNzP6eQNVm9Rv+WmppqFC1a1Fi0aJG97bnnnjPatWuX3aXma1m9Trf73Y/PkmPc7efp/fffN4oWLWpcvHjR3sbnyXEy8/N92LBhxn333ZehrXPnzkbLli3tr+/2upuBW99zmZiYGCUkJCgkJMTe5u3traCgIG3ZskWStGXLFhUrVkz333+/vU9ISIhcXFy0bdu2HK85N8uO/5Y7duzQ7t271atXr+v29e/fXyVLllTDhg01f/58GYaRbbXnJ3dznb788kuVLFlSNWrU0MiRI5WcnJzhuDVr1lSZMmXsbS1btlRSUpL279+f/SeSx2XX302JiYny8vKSm5tbhnY+T1l35coV7dixI8PPFBcXF4WEhNh/pvzbli1bMvSX0j8X1/pn5ucUMu9OrtG/JScn6+rVqypRokSG9oiICJUuXVpVqlRR3759dfbs2WytPT+50+t08eJFlS9fXv7+/mrXrl2Gny18lrJfdnye5s2bpy5duqhw4cIZ2vk8med2P5ey47qbwe32XeBMEhISJClDcLj2+tq+hIQElS5dOsN+Nzc3lShRwt4HmZMd/y3nzZunatWqqXHjxhnaJ0yYoEceeUSFChXSunXr1K9fP128eFGvvvpqttWfX9zpderWrZvKly+vsmXL6rffftPw4cN16NAhhYWF2Y97o8/atX3Imuz4PP3555+aOHGi+vTpk6Gdz9Od+fPPP2Wz2W74//nBgwdv+J6bfS7++TPoWtvN+iDz7uQa/dvw4cNVtmzZDL+ktmrVSh06dFBAQICOHj2qN954Q61bt9aWLVvk6uqareeQH9zJdapSpYrmz5+vWrVqKTExUdOmTVPjxo21f/9++fn58VlygLv9PG3fvl379u3TvHnzMrTzeTLXzX4uJSUl6e+//9Zff/1113+PmoGg7gAjRozQO++8c8s+UVFRqlq1ag5VhH/L7DW6W3///bcWL16s0aNHX7fvn21169bVpUuX9O677xIs/sHR1+mfYa9mzZry9fVV8+bNdfToUVWoUOGOj5vf5NTnKSkpSW3btlX16tU1bty4DPv4PAE3NmXKFC1dulQREREZJirr0qWL/c81a9ZUrVq1VKFCBUVERKh58+ZmlJrvNGrUSI0aNbK/bty4sapVq6aPP/5YEydONLEy3My8efNUs2ZNNWzYMEM7nyc4AkHdAYYOHaqePXvesk9gYOAdHdvHx0eSdPr0afn6+trbT58+rTp16tj7nDlzJsP7UlNTde7cOfv787vMXqO7/W8ZGhqq5ORk9ejR47Z9g4KCNHHiRKWkpMjDw+O2/fODnLpO1wQFBUmSoqOjVaFCBfn4+Fw3I+jp06clic/SP+TEdbpw4YJatWqlokWLasWKFSpQoMAt+/N5ypySJUvK1dXV/v/1NadPn77pNfHx8bll/8z8nELm3ck1umbatGmaMmWKvv/+e9WqVeuWfQMDA1WyZElFR0cTLO7A3VynawoUKKC6desqOjpaEp8lR7ib63Tp0iUtXbpUEyZMuO334fOUs272c8nLy0sFCxaUq6vrXX8+zcAz6g5QqlQpVa1a9Zabu7v7HR07ICBAPj4+2rBhg70tKSlJ27Zts/+rbKNGjXT+/Hnt2LHD3ueHH35QWlqaPYjkd5m9Rnf733LevHl64oknVKpUqdv23b17t4oXL06o+Iecuk7X7N69W5LsvxA1atRIe/fuzRAu169fLy8vL1WvXj17TjIPcPR1SkpKUosWLeTu7q7Vq1dft3zRjfB5yhx3d3fVr18/w8+UtLQ0bdiwIcNI3z81atQoQ38p/XNxrX9mfk4h8+7kGknS1KlTNXHiRIWHh2eYF+JmTp48qbNnz2YIhMi8O71O/2Sz2bR37177NeCzlP3u5jotX75cKSkpeuaZZ277ffg85azb/VzKjs+nKcyezS6/O378uLFr1y778l27du0ydu3alWEZrypVqhhhYWH211OmTDGKFStmrFq1yvjtt9+Mdu3a3XB5trp16xrbtm0zfvrpJ6NSpUosz3aHbvff8uTJk0aVKlWMbdu2ZXjfkSNHDIvFYnz33XfXHXP16tXGJ598Yuzdu9c4cuSI8Z///McoVKiQMWbMGIefT16V1esUHR1tTJgwwfj111+NmJgYY9WqVUZgYKDx0EMP2d9zbXm2Fi1aGLt37zbCw8ONUqVKsTzbXcjqdUpMTDSCgoKMmjVrGtHR0RmWvklNTTUMg8/T3Vq6dKnh4eFhLFy40Dhw4IDRp08fo1ixYvbVDp599lljxIgR9v4///yz4ebmZkybNs2Iiooyxo4de8Pl2W73cwqZl9VrNGXKFMPd3d0IDQ3N8Jm59rvFhQsXjNdee83YsmWLERMTY3z//fdGvXr1jEqVKhmXL1825Rzzgqxep/Hjxxv//e9/jaNHjxo7duwwunTpYnh6ehr79++39+GzlP2yep2uadKkidG5c+fr2vk8Zb8LFy7YM5EkY/r06cauXbuM48ePG4ZhGCNGjDCeffZZe/9ry7O9/vrrRlRUlDF79uwbLs92q+vujAjqJnvuuecMSddtGzdutPfR/68PfE1aWpoxevRoo0yZMoaHh4fRvHlz49ChQxmOe/bsWaNr165GkSJFDC8vL+P555/PEP6Rebf7bxkTE3PdNTMMwxg5cqTh7+9v2Gy264753XffGXXq1DGKFCliFC5c2Khdu7YxZ86cG/ZF5mT1OsXFxRkPPfSQUaJECcPDw8OoWLGi8frrr2dYR90wDCM2NtZo3bq1UbBgQaNkyZLG0KFDMywLhqzJ6nXauHHjDf+OlGTExMQYhsHnKTt88MEHxr333mu4u7sbDRs2NLZu3Wrf9/DDDxvPPfdchv7Lli0zKleubLi7uxv33Xef8e2332bYn5mfU8iarFyj8uXL3/AzM3bsWMMwDCM5Odlo0aKFUapUKaNAgQJG+fLljd69ezv1L6y5RVau06BBg+x9y5QpY7Rp08bYuXNnhuPxWXKMrP6dd/DgQUOSsW7duuuOxecp+93sZ/+16/Lcc88ZDz/88HXvqVOnjuHu7m4EBgZmyE7X3Oq6OyOLYbB+DQAAAAAAzoJn1AEAAAAAcCIEdQAAAAAAnAhBHQAAAAAAJ0JQBwAAAADAiRDUAQAAAABwIgR1AAAAAACcCEEdAAAAAAAnQlAHAAAAAMCJENQBAAAAAHAiBHUAAAAAAJwIQR0AAAAAACfyf8IkfuOBSO5UAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_inputs = 2\n",
+    "num_samples = 20\n",
+    "X = 2 * algorithm_globals.random.random([num_samples, num_inputs]) - 1\n",
+    "y01 = 1 * (np.sum(X, axis=1) >= 0)  # in { 0,  1}\n",
+    "y = 2 * y01 - 1  # in {-1, +1}\n",
+    "y_one_hot = np.zeros((num_samples, 2))\n",
+    "for i in range(num_samples):\n",
+    "    y_one_hot[i, y01[i]] = 1\n",
+    "\n",
+    "for x, y_target in zip(X, y):\n",
+    "    if y_target == 1:\n",
+    "        plt.plot(x[0], x[1], \"bo\")\n",
+    "    else:\n",
+    "        plt.plot(x[0], x[1], \"go\")\n",
+    "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "religious-history",
+   "metadata": {},
+   "source": [
+    "### Classification with an `EstimatorQNN`\n",
+    "\n",
+    "First we show how an `EstimatorQNN` can be used for classification within a `NeuralNetworkClassifier`. In this context, the `EstimatorQNN` is expected to return one-dimensional output in $[-1, +1]$. This only works for binary classification and we assign the two classes to $\\{-1, +1\\}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "recognized-musician",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 705.35x200.667 with 1 Axes>"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# construct QNN\n",
+    "qc = QuantumCircuit(2)\n",
+    "feature_map = ZZFeatureMap(2, reps=1)\n",
+    "ansatz = RealAmplitudes(2)\n",
+    "qc.compose(feature_map, inplace=True)\n",
+    "qc.compose(ansatz, inplace=True)\n",
+    "qc.draw(output=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "formed-animal",
+   "metadata": {},
+   "source": [
+    "Create a quantum neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "determined-hands",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estimator_qnn = EstimatorQNN(\n",
+    "    circuit=qc, input_params=feature_map.parameters, weight_params=ansatz.parameters\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "acute-casting",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.13662181]])"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# QNN maps inputs to [-1, +1]\n",
+    "estimator_qnn.forward(X[0, :], algorithm_globals.random.random(estimator_qnn.num_weights))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "stone-holiday",
+   "metadata": {},
+   "source": [
+    "We will add a callback function called `callback_graph`. This will be called for each iteration of the optimizer and will be passed two parameters: the current weights and the value of the objective function at those weights. For our function, we append the value of the objective function to an array so we can plot iteration versus objective function value and update the graph with each iteration. However, you can do whatever you want with a callback function as long as it gets the two parameters mentioned passed. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "similar-controversy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# callback function that draws a live plot when the .fit() method is called\n",
+    "def callback_graph(weights, obj_func_eval):\n",
+    "    clear_output(wait=True)\n",
+    "    objective_func_vals.append(obj_func_eval)\n",
+    "    plt.title(\"Objective function value against iteration\")\n",
+    "    plt.xlabel(\"Iteration\")\n",
+    "    plt.ylabel(\"Objective function value\")\n",
+    "    plt.plot(range(len(objective_func_vals)), objective_func_vals)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "lesser-receiver",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# construct neural network classifier\n",
+    "estimator_classifier = NeuralNetworkClassifier(\n",
+    "    estimator_qnn, optimizer=COBYLA(maxiter=60), callback=callback_graph\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "adopted-editor",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.85"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create empty array for callback to store evaluations of the objective function\n",
+    "objective_func_vals = []\n",
+    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
+    "\n",
+    "# fit classifier to data\n",
+    "estimator_classifier.fit(X, y)\n",
+    "\n",
+    "# return to default figsize\n",
+    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
+    "\n",
+    "# score classifier\n",
+    "estimator_classifier.score(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "civilian-analysis",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# evaluate data points\n",
+    "y_predict = estimator_classifier.predict(X)\n",
+    "\n",
+    "# plot results\n",
+    "# red == wrongly classified\n",
+    "for x, y_target, y_p in zip(X, y, y_predict):\n",
+    "    if y_target == 1:\n",
+    "        plt.plot(x[0], x[1], \"bo\")\n",
+    "    else:\n",
+    "        plt.plot(x[0], x[1], \"go\")\n",
+    "    if y_target != y_p:\n",
+    "        plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n",
+    "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "japanese-seattle",
+   "metadata": {},
+   "source": [
+    "Now, when the model is trained, we can explore the weights of the neural network. Please note, the number of weights is defined by ansatz."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "offshore-basket",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.7089176 , 0.11520665, 0.17393497, 0.05920832, 1.63006754,\n",
+       "       0.50302721, 1.06888149, 0.91627614])"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "estimator_classifier.weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "determined-standing",
+   "metadata": {},
+   "source": [
+    "### Classification with a `SamplerQNN`\n",
+    "\n",
+    "Next we show how a `SamplerQNN` can be used for classification within a `NeuralNetworkClassifier`. In this context, the `SamplerQNN` is expected to return $d$-dimensional probability vector as output, where $d$ denotes the number of classes. \n",
+    "The underlying `Sampler` primitive returns quasi-distributions of bit strings and we just need to define a mapping from the measured bitstrings to the different classes. For binary classification we use the parity mapping."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "discrete-factor",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 621.739x200.667 with 1 Axes>"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# construct feature map\n",
+    "feature_map = ZFeatureMap(num_inputs, reps=4)\n",
+    "\n",
+    "# construct ansatz\n",
+    "ansatz = RealAmplitudes(num_inputs, reps=1)\n",
+    "\n",
+    "# construct quantum circuit\n",
+    "qc = QuantumCircuit(num_inputs)\n",
+    "qc.append(feature_map, range(num_inputs))\n",
+    "qc.append(ansatz, range(num_inputs))\n",
+    "qc.decompose().draw(output=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "young-sensitivity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# parity maps bitstrings to 0 or 1\n",
+    "def parity(x):\n",
+    "    return \"{:b}\".format(x).count(\"1\") % 2\n",
+    "\n",
+    "\n",
+    "output_shape = 2  # corresponds to the number of classes, possible outcomes of the (parity) mapping."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "statutory-mercury",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# construct QNN\n",
+    "sampler_qnn = SamplerQNN(\n",
+    "    circuit=qc,\n",
+    "    input_params=feature_map.parameters,\n",
+    "    weight_params=ansatz.parameters,\n",
+    "    interpret=parity,\n",
+    "    output_shape=output_shape,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "hybrid-orlando",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# construct classifier\n",
+    "sampler_classifier = NeuralNetworkClassifier(\n",
+    "    neural_network=sampler_qnn, optimizer=COBYLA(maxiter=30), callback=callback_graph\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "adult-newman",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.5"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create empty array for callback to store evaluations of the objective function\n",
+    "objective_func_vals = []\n",
+    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
+    "\n",
+    "# fit classifier to data\n",
+    "sampler_classifier.fit(X, y01)\n",
+    "\n",
+    "# return to default figsize\n",
+    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
+    "\n",
+    "# score classifier\n",
+    "sampler_classifier.score(X, y01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "angry-bulgarian",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# evaluate data points\n",
+    "y_predict = sampler_classifier.predict(X)\n",
+    "\n",
+    "# plot results\n",
+    "# red == wrongly classified\n",
+    "for x, y_target, y_p in zip(X, y01, y_predict):\n",
+    "    if y_target == 1:\n",
+    "        plt.plot(x[0], x[1], \"bo\")\n",
+    "    else:\n",
+    "        plt.plot(x[0], x[1], \"go\")\n",
+    "    if y_target != y_p:\n",
+    "        plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n",
+    "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "assisted-individual",
+   "metadata": {},
+   "source": [
+    "Again, once the model is trained we can take a look at the weights. As we set `reps=1` explicitly in our ansatz, we can see less parameters than in the previous model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "indonesian-bulletin",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.13689409, 0.52108839, 1.41795412, 1.71954365])"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampler_classifier.weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "champion-approval",
+   "metadata": {},
+   "source": [
+    "### Variational Quantum Classifier (`VQC`)\n",
+    "\n",
+    "The `VQC` is a special variant of the `NeuralNetworkClassifier` with a `SamplerQNN`. It applies a parity mapping (or extensions to multiple classes) to map from the bitstring to the classification, which results in a probability vector, which is interpreted as a one-hot encoded result. By default, it applies this the `CrossEntropyLoss` function that expects labels given in one-hot encoded format and will return predictions in that format too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "legislative-dublin",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# construct feature map, ansatz, and optimizer\n",
+    "feature_map = ZFeatureMap(num_inputs, reps=4)\n",
+    "ansatz = RealAmplitudes(num_inputs, reps=1)\n",
+    "\n",
+    "# construct variational quantum classifier\n",
+    "vqc = VQC(\n",
+    "    feature_map=feature_map,\n",
+    "    ansatz=ansatz,\n",
+    "    loss=\"cross_entropy\",\n",
+    "    optimizer=COBYLA(maxiter=30),\n",
+    "    callback=callback_graph,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "geographic-adjustment",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.6"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create empty array for callback to store evaluations of the objective function\n",
+    "objective_func_vals = []\n",
+    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
+    "\n",
+    "# fit classifier to data\n",
+    "vqc.fit(X, y_one_hot)\n",
+    "\n",
+    "# return to default figsize\n",
+    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
+    "\n",
+    "# score classifier\n",
+    "vqc.score(X, y_one_hot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "stopped-heavy",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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N7LGZPMlYv349Y8aMYerUqYSGhtKkSRM6d+780P81BwQEEB0dbXidOnUKS0tLoyXOAXx9fY3qrV271tS3UiS5uLhw+PBhmjRpwvXr1+nTpw8vvPACMTEx5g5NCCGKBw+PtO0HZmz2ruaNi51LhrlxUunQ4Wrninc1b60g/X/imzbN40BzzuRJxueff87QoUMZPHgwDRs2ZMmSJZQqVYoVK1ZkWr98+fI4OTkZXrt27aJUqVIZkgwbGxujeuUKQMZWWDVt2pQjR47w4YcfYmVlxaZNm3Bzc2P16tXSqiGEEKb25JNp299+q83ieV+OJ+H75pvMr2smJk0yEhMTOX78OD4+PmlvaGGBj48PISEh2brG8uXL6dOnD6VLlzYqDwoKolKlStSrV48333yT69evP/QaCQkJxMXFGb2EMWtra6ZOncqxY8fw8PDgxo0bDB8+nBs3bpg7NCGEKNrq1IF27bTts2dh3Tqjw9mehO/IkbRhq66u8Mwzpo78kaxMefFr166h1+upXLmyUXnlypU5c+bMI88/cuQIp06dYvny5Ublvr6++Pn5UbNmTf7880/ee+89unTpQkhICJaWGecLmDlzJh+lzoQmstSkSROOHDnC7NmzqVGjhtGkXUopw4gUIYQQeWjsWPjlF217xAht4TNXV8NhvwZ+9KzX8+GT8MXFwcCBaa0go0dr82aYmUln/Lxy5QpVq1bl4MGDeHl5GconTJjAL7/88sjVQl9//XVCQkI4efJklvX++usvateuze7du+n4wBhj0FoyEhISDPtxcXG4uroWixk/88rWrVtZsmQJX331FVWrVn30CUIIIXKmd2/4/nttu04dCAzUlnd/lGvXoGdPOHhQ22/eXFu7xERJRoGZ8dPR0RFLS0uuXr1qVH716lWcnJyyPPfOnTusW7eOIUOGPPJ9atWqhaOjI+fOncv0uI2NDXZ2dkYvkX3JycmMGjWKbdu24ebmhr+/v/TVEEKIvPbll1pyAdqy7k2awNy5cOdO5vWTkuC777S5MVITjPLltbIC0IoBJk4yrK2tad68OXv27DGUpaSksGfPHqOWjcx8//33JCQk8PLLLz/yfS5dusT169dxds7eUB+RM1ZWVvz000+0atWK2NhYXn31Vbp06UJU+qFSQoicUwouXoRdu2DrVti3D/7919xRCXNxdIS9e7X1R0BLLt55B6pWhf79tdVVV66E+fO1BdSqV4dXXoHU0ZoVK8Lu3VC/vtluIQNlYuvWrVM2NjZq5cqV6vTp02rYsGHKwcFBxcTEKKWUeuWVV9TEiRMznNe2bVv10ksvZSi/deuWGjdunAoJCVHnz59Xu3fvVs2aNVN169ZV9+7dy1ZMsbGxClCxsbGPd3PFTFJSkpo9e7aysbFRgCpbtqz66quvVEpKirlDE6JwiYhQavhwpSpVUkpLNYxfNWsqNWWKUpcumTtSYQ5xcUq98Ubm/zYe9urZU6no6HwJLyffoSZPMpRSasGCBapatWrK2tpatWrVSh06dMhwrF27dmrgwIFG9c+cOaMAtXPnzgzXio+PV506dVIVK1ZUJUqUUNWrV1dDhw41JC3ZIUnG44mMjFStW7dWgALU/v37zR2SEIVDTr88rK2V+vhjpZKSzB25MIfjx5V69VWlSpbM/N+HlZVSfn5K7d6tVD7+Zy8n36Gy1Lv0z8gVvV7PvHnziIyMZNmyZeYOR4iC79w58PWFP/9MKytVCry9tWfvZcvC9evaYlkHDxpPCe3tDVu2FIgZHIUZJCVBRASEh2uPUGxttUciTZpAyZL5Hk5OvkMlyZAkI89ER0fz9ttv87///Y+aNWuaOxwhCo6oKGjTJm3K6NKlYepUGDoUHBwy1r90Cb74Quv0l5KilXl6wp492rlCmFGBGV0iipd33nmHTZs24e7uzqJFi0hJ/eUoRHGWkqJ1zktNMNzc4ORJGD8+8wQDwMVF6+T3669aZz7Qppt+9918CVmIvCJJhsgzM2bM4KmnnuLOnTsMHz6cDh068Gf6pmEhiqNly9ImWapeXRtBUqtW9s718tJaL1KbxBctggMHTBOnECYgSYbIM3Xq1GHfvn0sWLCA0qVL88svv9C4cWPmz58vrRqieEpJgc8+S9tfsSKtZeI+fYqeoAtBrA1fS9CFIPQpDyzP7e4Os2al7ae/nhAFnPTJkD4ZJvHXX3/x2muvsW/fPkBbKO+dd94xc1RC5LM9eyB17aZnnoGdO40OB0QGMCpwFJfi0pb3drFzYZ7vvLT1KACSk7VWkCtXwMJC6+NRpUp+3IEQGUifDGF2tWrVYvfu3SxevJjGjRszbNgwc4ckRP5LfUwCMHiw0aGAyAB6behllGAAXI67TK8NvQiIDEgrtLKCAQO07ZQUeWQiCg1JMoTJWFhY8MYbbxAaGmpYRTclJYXRo0dz9uxZM0cnRD4IDU3bTjfLsT5Fz6jAUSgyNiSnlo0OHG386CT9LMnprytEASZJhjC59CvjLlmyhHnz5tGkSRM+++wz9Hp9FmcKUcilnyK8enXDZvDF4AwtGOkpFFFxUQRfDE4rTN9ZNHUaaSEKOEkyRL7q3r07nTt3JiEhgQkTJtCmTRtOnz5t7rCEMA2dLm07Xefn6FvR2TrdqF76hNxCfnWLwkH+pYp8Va1aNbZv387y5cuxs7PjyJEjNG3alFmzZpGcnGzu8ITIWy4uadvpHhE6l83eYo5G9SIj07ZdXR83MiHyhSQZIt/pdDpeffVVIiIi6Nq1K4mJiUyaNIkhQ4aYOzQh8lbz5mnb6TqBelfzxsXOBR26TE4CHTpc7VzxruadVrh/f+bXFaIAkyRDmI2Liws//fQTK1euxNHRkVGjRpk7JCHyVufOadtffaUtawVYWlgyz3ceQIZEI3V/ru9cLC3u92e6fRtWr9a2S5aEtm1NG7cQeUSSDGFWOp2OgQMH8vfff9OsWTNDub+/PydPnjRjZELkgWbNoFUrbTssDFauNBzya+DHxt4bqWpX1egUFzsXNvbeaDxPxuTJEBenbffrB/b2po1biDwik3HJZFwFTnh4OM2bN0cpxQcffMCkSZOwtrY2d1hC5M62bdCtm7ZtZwfBwdC4seGwPkVP8MVgom9F41zWGe9q3mktGAA//gjPPae1gtjYaOuePPFE/t5DNjzyPkSRIauwPoIkGQVbTEwMb775Jps3bwagSZMm+Pv707RpU/MGJkRuDRwI33yjbZcvrz368PXN+hyltHVPhg/XlvoGbUrxceNMG2suZHvmUlEkyIyfolBzcnIiICCAtWvXUqFCBX777TdatWrFlClTSExMNHd4QuTcokXQurW2feMGdOmiPfY4csTQT8MgJQW2b4eOHeH119MSjP79YcyY/I07G3I0c6kodqQlQ1oyCrR//vmH4cOH8/333wPQsmVLQkJCjCb4EqJQiIuDl16CwEDj8ooVwcMDypaFa9e0vhup/S9SvfUWzJ8PBezfvT5FT415NR46sZgOHS52LpwfdV4enRQh0pIhioxKlSqxYcMGNmzYQMWKFXnppZckwRCFk52d1j9j6VIoVy6t/N9/YdcuCAjQhqmmTzCqV9fOWbSowCUYkMuZS0WxYmXuAITIjhdffJEOHTrg4OBgKDt69ChKKVql9t4XoqDT6WDYMHj5ZVi3Dr7/Ho4d01owUrm4aCNSBgzQOoxaFdxf07mauVQUKwX3X68QD6hQoYJh++7du7z88sucO3eOcePG8dFHH2Fra2vG6ITIgVKl4NVXtZdScP06JCRA6dKQLpEu6HI1c6koVuRxiSiUEhMTadmyJSkpKXz66ac0bdqUkJAQc4clRM7pdODoCFWrFqoEA3I5c6koViTJEIWSvb093333HVu2bMHJyYkzZ87w5JNPMm7cOO7evWvu8IQoFnI8c6kodiTJEIVajx49iIiIYMCAASil+N///oeHhwdXr141d2hCFAs5mrlUFDsyhFWGsBYZP//8M8OGDcPd3Z3t27ej02XehCuEyHsy42fxITN+PoIkGUXXzZs3uXfvHk5OTgDExsZy8uRJvL3lmbAQQuSFAjdPxqJFi6hRowa2trZ4enpy5MiRh9ZduXIlOp3O6PXgqAGlFFOmTMHZ2ZmSJUvi4+PDH3/8YerbEIWAg4ODIcEAGDt2LE899RQjRozg9u3bZoxMCCGKH5MnGevXr2fMmDFMnTqV0NBQmjRpQufOnfnnn38eeo6dnR3R0dGG199//210/NNPP2X+/PksWbKEw4cPU7p0aTp37sy9e/dMfTuiEElJScHCQvsnvnDhQho3bsy+ffvMHJUQQhQfJk8yPv/8c4YOHcrgwYNp2LAhS5YsoVSpUqxYseKh5+h0OpycnAyvypUrG44ppZg7dy4ffPABPXv2pHHjxnzzzTdcuXLFsKDWgxISEoiLizN6iaLPwsKCr776ih07dlCtWjXOnz9Phw4deOutt7h165a5wxNCiCLPpElGYmIix48fx8fHJ+0NLSzw8fHJck6D27dvU716dVxdXenZsycRERGGY+fPnycmJsbomvb29nh6ej70mjNnzsTe3t7wcnV1zYO7E4VFp06dCA8P54033gBg8eLFuLu7c+LECTNHJoQQRZtJk4xr166h1+uNWiIAKleuTExMTKbn1KtXjxUrVrBlyxa+++47UlJSaNOmDZcuafPjp56Xk2tOmjSJ2NhYwysqKupxb00UMnZ2dixevJg9e/ZQo0YN4uPjJdkUQggTK3DTint5eeHl5WXYb9OmDQ0aNGDp0qVMnz49V9e0sbHBxsYmr0IUhViHDh0IDw/n7NmzODo6AtojuBMnTtCsWTMzRyeEEEWLSVsyHB0dsbS0zDAx0tWrV41GAGSlRIkSNG3alHPnzgEYznuca4rirUyZMjRv3tywv2HDBpo3b85rr71GbGysGSMTQoiixaRJhrW1Nc2bN2fPnj2GspSUFPbs2WPUWpEVvV5PeHg4zs7aAjs1a9bEycnJ6JpxcXEcPnw429cUIr3IyEh0Oh3Lly/Hzc2Nbdu2mTskIYQoEkw+umTMmDEsW7aMVatWERkZyZtvvsmdO3cYPHgwAAMGDGDSpEmG+tOmTWPnzp389ddfhIaG8vLLL/P333/z2muvAdrIk9GjRzNjxgx+/PFHwsPDGTBgAFWqVOG5554z9e2IIujDDz9k//791K1bl8uXL9OtWzcGDRrEf//9Z+7QhBCiUDN5n4yXXnqJf//9lylTphATE4OHhweBgYGGjpsXL140zGUA8N9//zF06FBiYmIoV64czZs35+DBgzRs2NBQZ8KECdy5c4dhw4Zx8+ZN2rZtS2BgoCz1LXKtbdu2hIWFMXnyZL744gtWrVrFzp07WbVqFc8884y5wxNCiEJJphWXacXzRlISnD8P9+5BqVJQsyZYFs51Cw4ePMirr77K2bNn2b17Nx07djR3SEIIUWAUuGnFRRH133/wxRfQujWULQv16kGTJlC3LtjZgbc3LF0KhWw67zZt2nDixAl++OEHowTj4sWLZoxKCCEKH0kyRM7p9Vpy4eICY8bA4cOQkGBcJz4efv0V3ngDXF1hxQooRI1mJUuWxM8vbYnqv//+Gzc3N/r168e1a9fMGJkQQhQekmSInPnvP+jYUUsu4uPTyp94Anr3hqFDwc8PatRIO3bzJgwZAs8/b3xOIbJv3z7i4+NZu3Ytbm5u/PDDD+YOSQiRQ/oUPUEXglgbvpagC0HoU/TmDqnIkz4Z0icj++LioEMHOH48rWzoUHjnHWjQIGP90FCYMwfWrk0r69gRfv4Z8mJyNKXgzz/h2DG4eBFSUqBCBWjaFNzd8+Y90jl69CiDBw82THP/4osvsnDhQipVqpSn7yOEyHsBkQGMChzFpbhLhjIXOxfm+c7Dr4FfFmeKB+XkO1SSDEkysm/AAPj2W227YkXYuBGeeurR523dCv37Q+qiZOPGwWef5T6OW7dg2TJYvBjuT9KWgZ0dDBwII0ZofUTySEJCAtOnT2fWrFno9XocHR358ssvefHFF/PsPYQQeSsgMoBeG3qhMP6606EDYGPvjZJo5IB0/BR5b9u2tATD3h6CgrKXYAA8+yzs2AHW1tr+55/DkSO5i2PnTmjYEMaOfXiCAVqry4IF0KgRfPIJJCfn7v0eYGNjw4wZMzh8+DDu7u5cu3aN0NDQPLm2ECLv6VP0jAoclSHBAAxlowNHy6MTE5EkQ2TPxx+nbc+dq33Rp6PXa3nH2rXan/oHf169vOCjj7TtlBSYNSvnMcyZA507w6W05k7at9eu+/33sGkTzJsHfftCyZLa8cREeP996N49T/uDNG/enGPHjjF37lymTp1qKI+NjaUYNg4KUWAFXww2ekTyIIUiKi6K4IvB+RhV8SGPS+RxyaOdPKkNTQWtr8Nvv4FOZzgcEACjRhl/97u4aN/3fulbIJOSoHp1iI4GCwutH0XVqtmLYfFieOuttH0fH62lon79zOvfvKklMp99piU1AF27ao9uLEyTWycnJ9OmTRuqVKnC4sWLDVPhCyHMZ234WvoF9HtkvTV+a+jr3jcfIir85HGJyFvp1onhtdcyJBi9ehknGACXL2vlAQHpCkuUgPvTyZOSAr/8kr33j4zUOpemmjZNe2zysAQDwMFBSzL27dPm8ADtkc+CBdl7z1w4evQoYWFhbNmyBTc3N7799ltp1RDCzJzLZi/Zz249kTOSZIhHSz+axNvbsKnXay0YmX2PppaNHv3Ao5O2bTO/blZefz1tHo5Ro2DyZKNEJ0tPPaU9Rkk1aZKWAZmAl5cXx48fp3nz5vz3338MGDCAZ599lssmej8hxKN5V/PGxc7F0MnzQTp0uNq54l3NO9Pj4vFIkiEeLTo6bTvdSI3g4IwtGOkpBVFRWj2DJ57I/LoPc/x42gXq1oWZMzNUeWR/kI4d4e23te27d7WRKSbi7u7OoUOH+OSTT7C2tubnn3/Gzc0Nf39/adUQwgwsLSyZ5zsPIEOikbo/13culhaFcxmEgk6SDPFo6b8c07UgZCdHyFAvfQtEdr50/f3TtsePT+vQeV9AgDbv19NPQ79+2p81ajzwmAZg4sS0tVRMPPuolZUVkyZNIjQ0lFatWhEbG8uSJUvQZ8h+hBD5wa+BHxt7b6SqnXEfMBc7Fxm+amImX4VVFAFOTmnb584ZOoFmt1+jUb0//sj8ug8TEqL9aWEBL71kdCi1P8iD+UJqf5CNG9N1PHVx0Uai7NmjNa9cuZL9Tqe55ObmxoEDB/j888/p3r07Vlbaj1tSUhJWVlbosvvIRwjx2Pwa+NGzXk+CLwYTfSsa57LOeFfzlhYME5OWDPFozZqlbR84YNj09ta+ux/2XanTacuWeKd/1HnwYNp28+ZZv69eD+Hh2na9etoEW+kO5bg/SMuWadsnTmT93nnEysqKCRMm0DDdkN/Jkyfj6+srC64Jkc8sLSxpX6M9fd370r5Ge0kw8oEkGeLROnRI2/76a8O3uKWlNkwVMiYaqftz56Zb8V2vT3v8odM9ejKvu3e1Ya8AVaoYHcpVf5D014iNzfq9TeTatWssWrSInTt34ubmxtKlS6WvhhCiyJIkQzxas2bQooW2feIErF9vOOTnpz2WePDJg4vLA48rABYu1L75Abp1g2rVsn5fq3RP8+7dMzqUq/4gd++mbZcokb0L5DFHR0eOHz/Ok08+ye3bt3njjTfw8fHh/PnzZolHCCFMSZIMkT3vvZe2/fbbRlN6+/nBhQvalBRr1mh/nj//QIJx4oQ2fDTVu+8++j1tbdNaH06dMno2kqv+IKmPXgBq187eBUzgiSee4JdffmHu3LmULFmSvXv34u7uzqJFi0hJnThMCCGKAJnxU2b8zL7evbXpu0Fruti8Oa2FIyv79mk9MW/c0PaHD8/+pFg9e8KPP2rbhw6BpyegPXmpUUPr5JnZv2CdTmtNOX/+/uOa5GRtttErV7RWjFu38nyV1tw4d+4cQ4YMYf/+/ZQpU4YzZ85Q1cQdUoUQ4nHIjJ/CNJYu1RYcA+3bvXVrbaGyv//OvP7ZszBsmNanIzXBePJJmD07++/Zo0fa9sKFhs0c9wfZvFlLMAC6dCkQCQZAnTp12LdvHwsWLGDevHlGCUYxzP+FEEWMtGRIS0bO/POPtqpq+lVULSzAw0N72dtrCUVoqPHjCdDWGwkISJvmOzvi47VWk5s3tf2gIGjXznA4s3VTXF21BMPwuCYuTkuOUvuDbN8Ovr7Zj8EMdu/ezfTp0/n666+pm4dL1QshxOPKyXeoJBmSZORcUpK2LsiMGdoqp49SqpQ2U+fw4blbnGzu3LS1S6pU0YaM1KplOKzXa0XR0VofDG/vdC0YiYnw4otpj1w6dIDdu7M/LbkZKKVo3Lgxp06domTJknz88ceMHDkSS0sZbidEKn2KXua8MBNJMh5Bkow8EhOjDWldtw5OnzbuHGFpCY0bw8svw6BBUL587t8nJUWbynP/fm3fyUkbCvuo1ogLF7T3Tl2IrWxZbUXZGjVyH0s+uXDhAkOHDmX37t2Ati6Kv78/9erVM3NkQphfQGQAowJHGS3h7mLnwjzfeTJ7Zz6QJOMRJMkwgdu34ffftaGmpUppk2c9MAX4Y/n3X23GztOn08q6d4c339Tm2yhTRitLTtYe06xcCcuXw507WrmtLfz8s/GcHwWcUoqvv/6asWPHcuvWLWxsbJg+fTpjxoyRVg1RbAVEBtBrQy8Uxl9dqeuQyDThplfgOn4uWrSIGjVqYGtri6enJ0fSP89/wLJly/D29qZcuXKUK1cOHx+fDPUHDRqETqczevkW8GfsRV6ZMtp8Gm3aaH0z8jLBAKhYUWuReOaZtLKfftLm27Cz01on6tbVtps1g/nz0xIMV1ftEUkhSjAAdDodQ4cO5dSpU3Tq1ImEhAQmTJjAjh07zB2aEGahT9EzKnBUhgQDMJSNDhyNPkXWCSooTJ5krF+/njFjxjB16lRCQ0Np0qQJnTt35p9//sm0flBQEH379mXfvn2EhITg6upKp06dMiyX7evrS3R0tOG1du1aU9+KMDdHR9ixQ1tF1cUlrVwpbYTLuXPGE27Z2mr9QE6d0ka1FFLVqlUjMDCQ5cuXM3jwYLp06WLukIQwi+CLwUaPSB6kUETFRRF8MfihdUT+MvnjEk9PT1q2bMnC+8MPU1JScHV1ZcSIEUycOPGR5+v1esqVK8fChQsZMGAAoLVk3Lx5k82bN+cqJnlcUgQkJ8PWrdpIkdBQLcnQ67VEpGlTaNsW+vd/vL4gBdy1a9cYOHAgs2fPplHq0GIhirC14WvpF9DvkfXW+K2hr3vffIioeMrJd6hJV2FNTEzk+PHjTEo306OFhQU+Pj6EpK6u+Qjx8fEkJSVR/oEvi6CgICpVqkS5cuXo0KEDM2bMoEKFCpleIyEhgYSEBMN+XFxcLu5GFChWVvD889qrmJo4cSLbtm1j9+7dTJkyhQkTJlDCTNOlC5EfnMtmb6rf7NYTpmfSxyXXrl1Dr9dTuXJlo/LKlSsTExOTrWu8++67VKlSBR8fH0OZr68v33zzDXv27GH27Nn88ssvdOnSBb0+8+dwM2fOxN7e3vBydXXN/U0JUUBMmzaNZ599lsTERD744ANat27NyZMnzR2WECbjXc0bFzsXQyfPB+nQ4Wrninc170yPi/xXoGf8nDVrFuvWrWPTpk3Y2toayvv06UOPHj1wd3fnueee46effuLo0aMEBQVlep1JkyYRGxtreEWlTsokRCFWpUoVtmzZwnfffUe5cuUIDQ2lRYsWfPTRRyRmZ/4SIQoZSwtL5vlqU/0+mGik7s/1nSvzZRQgJk0yHB0dsbS05OrVq0blV69excnJKctz58yZw6xZs9i5cyeNGzfOsm6tWrVwdHTkXLpFu9KzsbHBzs7O6CVEUaDT6ejfvz+nT5/mueeeIykpiQ8//JBPPvnE3KEJYRJ+DfzY2HsjVe2M1/hxsXOR4asFkEn7ZFhbW9O8eXP27NnDc889B2gdP/fs2cPw4cMfet6nn37Kxx9/zI4dO2iRjQW4Ll26xPXr13HO7tKcQhQxTk5OBAQEsH79embPns07qTOkClEE+TXwo2e9njLjZyFg8tEl69evZ+DAgSxdupRWrVoxd+5cNmzYwJkzZ6hcuTIDBgygatWqzJw5E4DZs2czZcoU1qxZw5Pphh2WKVOGMmXKcPv2bT766CNeeOEFnJyc+PPPP5kwYQK3bt0iPDwcm2wsfCWjS0RRppRCd3/adKUUo0ePZsCAATRv3tzMkQkhioICNRnXSy+9xJw5c5gyZQoeHh6EhYURGBho6Ax68eJFoqOjDfUXL15MYmIivXr1wtnZ2fCaM2cOAJaWlpw8eZIePXrwxBNPMGTIEJo3b05wcHC2EgwhijpdunVZvvnmG+bPn4+npyfvv/++0SgrIYQwNZlWXFoyRBH277//MmLECNavXw+Am5sb/v7+tGzZ0syRCSEKqwLVkiGEMJ+KFSuybt06Nm7cSKVKlYiIiKB169ZMnDiRe/fumTs8IUQRJ0mGEMXACy+8QEREBP369SMlJYXZs2fz0ksvmTssIUQRJ0mGEMWEo6Mjq1evZvPmzVSpUoV3333X3CEJIYo46ZMhfTJEMZSQkGDUUXrVqlXUqVPHaESXEEJkRvpkCCGylD7B+P3333njjTfw9vbmnXfeIT4+3oyRCSGKEkkyhCjmKlWqRN++fVFKMXfuXBo3bsz+/fvNHZYQogiQJEOIYs7BwYEVK1awbds2XFxc+PPPP2nXrh0jRozg9u3b5g5PCFGISZIhhACgS5cunDp1itdeew2AhQsX0rJlS1lsTQiRa5JkCCEM7O3tWbZsGTt27MDV1ZV+/fphbW1t7rCEEIWUSRdIE0IUTp06deLUqVOULFnSUHbixAmuX7+Oj4+PGSMTQhQm0pIhhMiUnZ0dJUqUACAxMZEBAwbwzDPP8PrrrxMXF2fm6IQQhYEkGUKIR0pOTqZdu3YAfPXVVzRq1IidO3eaOSohREEnSYYQ4pFKlSrFwoUL2bdvHzVr1iQqKorOnTvz2muvERsba+7whBAFlCQZQohsa9++PeHh4YwcORKA5cuX4+bmxoULF8wbmBCiQJIkQwiRI6VLl2bevHns37+fOnXqUKdOHapVq2busIQQBZCMLhFC5Iq3tze//fYbsbGxWFho/1+5c+cO+/fvp0uXLmaOTghREEhLhhAi10qVKoWzs7Nhf9KkSXTt2pVXXnmFGzdumDEyIURBIEmGECJPKKUoWbIkFhYWfPfddzRs2JDNmzebOywhhBlJkiGEyBM6nY7Zs2dz4MAB6tevz9WrV3n++efp168f165dM3d4QggzkCRDCJGnWrduzYkTJ5g4cSIWFhasXbsWNzc3fv31V3OHJoTIZ5JkCCHynK2tLTNnzuTQoUO4ubmRlJRE7dq1zR2WECKfSZIhhDCZli1bcvz4cXbv3m3UQTQkJASllBkjE0LkB0kyhBAmZWNjQ7NmzQz7P/74I23atOHFF1/k6tWrZoxMCGFqkmSIrCUlwaVL8PffcOuWuaMRRcD58+exsrLihx9+wM3NjbVr10qrhhBFlCQZIqNLl2DqVGjVCsqUAVdXqFED7OzgiSdgyBA4dAjki0HkwqhRozh69CgeHh5cv36dfv368fzzzxMdHW3u0IQQeSxfkoxFixZRo0YNbG1t8fT05MiRI1nW//7776lfvz62tra4u7uzbds2o+NKKaZMmYKzszMlS5bEx8eHP/74w5S3UDzcugVvv60lFNOmwdGjkJhoXOePP2DFCvDyAm9viIw0S6iicPPw8ODIkSNMmzaNEiVKsGXLFtzc3Pjhhx/MHZoQIg+ZPMlYv349Y8aMYerUqYSGhtKkSRM6d+7MP//8k2n9gwcP0rdvX4YMGcKJEyd47rnneO655zh16pShzqeffsr8+fNZsmQJhw8fpnTp0nTu3Jl79+6Z+naKrvBwaNwYvvwS9HqtTKeDevXg+efhxRfB0xNsbdPOOXAAmjaF5cvNE7Mo1EqUKMHkyZM5duwYzZo147///sPGxsbcYQkh8pIysVatWqm3337bsK/X61WVKlXUzJkzM63fu3dv1a1bN6MyT09P9frrryullEpJSVFOTk7qs88+Mxy/efOmsrGxUWvXrs30mvfu3VOxsbGGV1RUlAJUbGzs495e0RAerlS5ckppD0CUKlVKqcmTlbp0KWPd+HilVq5Uqm7dtPqg1Jdf5n/coshITExUmzdvNio7d+6cSklJMVNEQoiHiY2NzfZ3qElbMhITEzl+/Dg+Pj6GMgsLC3x8fAgJCcn0nJCQEKP6AJ07dzbUP3/+PDExMUZ17O3t8fT0fOg1Z86cib29veHl6ur6uLdWdMTHay0V//2n7bdoobVqTJsGVatmrF+yJAwcCL/9pj1aSTV8uNZPQ4hcKFGiBD179jTsX7lyhRYtWtCtWzeioqLMGJkQ4nGYNMm4du0aer2eypUrG5VXrlyZmJiYTM+JiYnJsn7qnzm55qRJk4iNjTW85JdWOh98AOfOadvNm8PevVCr1qPPK1kSFiyA8eO1/ZQUGDwYEhJMF6soNg4fPszdu3fZvn07jRo1Yvny5TICRYhCqFiMLrGxscHOzs7oJYBr12DRIm3b1hbWrIGyZY2q6BP1BO1IYO3qFIKC0rprAFqfjU8+gZYttf0zZ2DjxnwJXRRtzz//PCdOnMDT05O4uDhee+01fH19uXjxorlDE0LkgEmTDEdHRywtLTNMuHP16lWcnJwyPcfJySnL+ql/5uSa4iH8/dNGj7z9tjY8NTkZAgKgXz8CnN+mhs0Vnva1od/LFjz9NNSwv0HAJ5Fpw1etrOB//0u75uLF+X8fokhq0KABBw4c4LPPPsPW1padO3fSqFEjli1bZu7QhBDZZNIkw9ramubNm7Nnzx5DWUpKCnv27MHLyyvTc7y8vIzqA+zatctQv2bNmjg5ORnViYuL4/Dhww+9pniInTvTtocNg/XrteGrL7xAwNp79IpZwCWM+2VcvuNAr/frEVB7vDbEFaBtW6hfX9s+eFAm7RJ5xtLSknHjxvHbb7/x5JNPcuvWLaORZkKIAs7UvVDXrVunbGxs1MqVK9Xp06fVsGHDlIODg4qJiVFKKfXKK6+oiRMnGuofOHBAWVlZqTlz5qjIyEg1depUVaJECRUeHm6oM2vWLOXg4KC2bNmiTp48qXr27Klq1qyp7t69m62YctIztshKSUkbUVK5slK9extGiiRjoVy4qEBvNIAk9aVDr1z5WyXrrJT66CPtWsOGpVXYv9/cdyeKoOTkZLV06VJ1+/ZtQ9mNGzeUXq83Y1RCFD85+Q61MnUS89JLL/Hvv/8yZcoUYmJi8PDwIDAw0NBx8+LFi1hYpDWotGnThjVr1vDBBx/w3nvvUbduXTZv3kyjRo0MdSZMmMCdO3cYNmwYN2/epG3btgQGBmKbfg4HkbW7d9NGlCQkwIYNhkPBrcZx6cjDR+AoLIiiGsHqSdpPnQo3b2rzaaS6dMlEQYvizNLSkmHDhhn2U1JS6NmzJ5aWlixfvpxa2emwLITIVzqlil+X7bi4OOzt7YmNjS2+nUDv3NGmDE+vbFn4+mvW6nvTr9+jL7GGvvRlnbbTty+sXattr15Nti4gxGM4efIkXl5exMfHU6pUKWbNmsXbb79t9J8WIUTey8l3qPw0FlclSxrP3lm6NOzeDb17k25F7iw5j38lbSf9dNCVKuVNjEJkoXHjxoSHh9O+fXvi4+MZOXIk7du351zqkGwhhNlJklFcWVhAiRJp+5MnawuioS1J4uKijVDNjE6nrZnmPbMrDBigFaZf46RpUxMFLYSxWrVqsWfPHr788ktKly5NcHAwjRs35osvvkBvNN5aCGEOkmQUVydOGI8CSU42bFpaak8/snqQNneuVo///c84WWnYECpUyPNwhXgYCwsL3nzzTU6dOkXHjh25e/cuK1eulCRDiAJAkozi6oGVbfniC7i/aF1AAMyZ8/BTx40DP7/7OxUqGD8e6dgxb+MUIptq1KjBrl27WLp0Kf7+/lhbWwOQlJQkCYcQZiJJRnF1/Ljx/vXrMGQI+oRkRo3KuhVj3bp0M38uXQqXL6cdrFgxz0MVIrt0Oh3Dhg2jWbNmhrKZM2fStm1bIiMjzRhZ8aRP0RN0IYi14WsJuhCEPkWSveJGkozi6u+/tT91OnB01LZ/+ongTtMfOQI1KgqC9yttSvL0i6QBPDATqxDmdOvWLRYsWMChQ4do2rQps2fPJjndo0FhOgGRAdSYV4OnVz1Nv4B+PL3qaWrMq0FAZIC5QxP5SJKM4iq1KcLKSluz5H6/iuj9v2fr9OhRs7SVV1NSMr+uEAVA2bJlCQ0NpUuXLiQkJDBx4kTatGlDRESEuUMr0gIiA+i1oReX4oz/x3I57jK9NvSSRKMYkSSjuErtnJmUBO7usGULlC6NM9HZOt05fEfaznPPZbyuEAWEq6srP//8M/7+/tjb23P06FGaNWvGJ598Iq0aJqBP0TMqcBSKjM9cU8tGB46WRyfFhCQZxVX6YaaHD0OXLhAejnd7K1yIQkdKpqfpSMGVi3gTDFWrws8/p61b8uB1hSggdDodgwYN4vTp03Tv3p3ExESmT58uq7qaQPDF4AwtGOkpFFFxUQRfDM7HqIS5SJJRXLVtm7b9zTfanzVrYrl3F/M+igV0GRKN1P25TyzGcsXX8Pvv0KkTfPfd/Qo6kEXqRAFWpUoVfvzxR7799lvmzp1rNBV5MZz82CSib2WvNTS79UThJklGcdWtGzg5adubN6eNNtHp8JvSiI0/6KjqYjwbl0vlJDauTcbv7EwYPBhKlYKvv05bq6R7d6hSJf/uQYhc0Ol0vPzyy7z++uuGsgMHDtCiRQvCwsLMF1gR4Vw2e1MGZ7eeKNwkySiuSpSA0aO17ZQUGDjQaHIuPz+4cEHHvn1av9B9++D8ZRv8+linXeP332H8+LT9sWPzJ3Yh8tj48eMJDQ2lZcuWTJ06lcT0M9iKHPGu5o2LnQs6Mp8yWIcOVztXvKt553NkwhwkySjOxowBDw9tOyJC65fx77+Gw5aW0L69Nvtn+/b3Z/hMdfIkdOgAt29r+0OHQrt2+RS4EHlr06ZN+Pn5kZyczLRp02jRogWhoaHmDqtQsrSwZJ7vPIAMiUbq/lzfuVhaWGY4VxQ9kmQUZyVKaM0U5cpp+wcOaNOCr1ypLf+emf/+g2nToGXLtEm4mjTJeopQIQq4ypUrs3HjRtavX4+joyPh4eG0atWKDz74gISH/SyIh/Jr4MfG3hupalfVqNzFzoWNvTfi18DvIWeKokaWei+uS72nd+IE+PoaphUHtJk7n3lGSyDKltVaOI4fh1274O7dtHrNmkFgoMz0KYqMf//9l+HDh7NhwwYA1q9fT+/evc0cVeGkT9ETfDGY6FvROJd1xruat7RgFAE5+Q6VJEOSDM3Vq/Dmm7BpU/bqW1ho/TE+/NB4yXghiogffviBrVu34u/vj+5hSxILUQzl5DtUHpcITeXK8MMPsH8/vPQS2NhkXq9cORg5Ek6fhlmzJMEQRdYLL7zAypUrDQnGzZs3eeaZZzh06JCZIxOi8LAydwCiANHpwNtbeyUmap1Bz57V+meUKQONG0Pt2lorhhDFzEcffcTu3bvZu3cvY8aMYdq0aZQsWdLcYQlRoMnjEnlcIoTIhhs3bvDOO+/wzf3J65544gn8/f1p06aNmSMTIn/J4xIhhMhj5cuXZ9WqVWzdupUqVarw+++/07ZtW8aMGUN8fLy5wxOiQJIkQwghcqB79+5EREQwePBglFJ88cUXTJ482dxhCVEgSZIhhBA55ODgwIoVK9i2bRvNmzfnvffeM3dIQhRIkmQIIUQudenShaNHj1KhQgVAW2Rt1KhRBAUFmTcwIQoIkyYZN27coH///tjZ2eHg4MCQIUO4nToN9UPqjxgxgnr16lGyZEmqVavGyJEjiY2NNaqn0+kyvNatW2fKWxFCiEyln0Pjhx9+YP78+Tz99NO8/fbbWf6+E6I4MGmS0b9/fyIiIti1axc//fQT+/fvZ9iwYQ+tf+XKFa5cucKcOXM4deoUK1euJDAwkCFDhmSo6+/vT3R0tOH13HPPmfBOhHh8ej0EBcHatdqfer25IxJ5rVOnTobVXb/88ksaNWrEnj17zByVEGakTOT06dMKUEePHjWUbd++Xel0OnX58uVsX2fDhg3K2tpaJSUlGcoAtWnTplzHFhsbqwAVGxub62sIkRM//KCUi4tSkPZycdHKRdGze/duVb16dQUoQL3++uvy+0YUGTn5DjVZS0ZISAgODg60aNHCUObj44OFhQWHDx/O9nVSx+FaWRnPG/b222/j6OhIq1atWLFiBSqL6T4SEhKIi4szegmRXwICoFcvuHTJuPzyZa08IMA8cQnT6dixI+Hh4bz11lsALF26lB49epg5KiHyn8mSjJiYGCpVqmRUZmVlRfny5YmJicnWNa5du8b06dMzPGKZNm0aGzZsYNeuXbzwwgu89dZbLFiw4KHXmTlzJvb29oaXq6trzm9IiFzQ62HUKK3t4kGpZaNHy6OToqhs2bIsWrSIvXv3Urt2baZMmWLukITIdzmeVnzixInMnj07yzqRkZG5DihVXFwc3bp1o2HDhnz44YdGx9KPSW/atCl37tzhs88+Y+TIkZlea9KkSYwZM8bo2pJoiPwQHJyxBSM9pSAqSqvXvn2+hSXy0dNPP01kZCQlSpQwlH333XdUqFCBLl26mDGyvCOrrYqHyXGSMXbsWAYNGpRlnVq1auHk5MQ/6ZcOB5KTk7lx4wZOTk5Znn/r1i18fX0pW7YsmzZtMvrhzIynpyfTp08nISEBm0wW9rKxscm0XAhTi47O23qicEr/O+zChQu88cYb3Llzh0GDBvH5559Trlw5M0b3eAIiAxgVOIpLcWnZtIudC/N85+HXwM+MkYmCIMePSypWrEj9+vWzfFlbW+Pl5cXNmzc5fvy44dy9e/eSkpKCp6fnQ68fFxdHp06dsLa25scff8Q2G6t8hoWFUa5cOUkkRIHj7Jy39UThV6lSJYYNG4ZOp2PlypW4ubmxdetWc4eVKwGRAfTa0MsowQC4HHeZXht6ERApHY6KO5MukNalSxeuXr3KkiVLSEpKYvDgwbRo0YI1a9YAcPnyZTp27Mg333xDq1atDAlGfHw8mzZtonTp0oZrVaxYEUtLS7Zu3crVq1dp3bo1tra27Nq1i3HjxjFu3Dg++uijbMUlC6SJ/KLXQ40aWifPzH7SdDpwcYHz58FSWpeLlYMHDzJ48GB+//13AF5++WXmzZtH+fLlzRxZ9uhT9NSYVyNDgpFKhw4XOxfOjzovj06KmAKzQNrq1aupX78+HTt2pGvXrrRt25avvvrKcDwpKYmzZ88aFhcKDQ3l8OHDhIeHU6dOHZydnQ2vqKgoQGt2XLRoEV5eXnh4eLB06VI+//xzpk6daspbESJXLC1h3jxtO92cTUb7c+dKglEctWnThrCwMMaNG4eFhQXfffcdjRs35s6dO+YOLVuCLwY/NMEAUCii4qIIvhicj1GJgkaWepeWDJEPAgK0USbpO4G6umoJhp88ti72Dh06xODBg3n++ef55JNPzB1OtqwNX0u/gH6PrLfGbw193fvmQ0Qiv+TkOzTHHT+FEDnn5wc9e2qjSKKjtT4Y3t7SgiE0rVu35sSJE0ZTlEdERHD27Fn8CmgW6lw2ex2JsltPFE2SZAiRTywtZZiqeLj0ndyTk5MZNGgQx44do3fv3ixcuJCKFSuaMbqMvKt542LnwuW4yygyNoin9snwruZthuhEQSGrsAohRAGTkpJC586dsbS0ZMOGDTRs2JDvv//e3GEZsbSwZJ6v1uFIh3GHo9T9ub5zpdNnMSdJhhBCFDDW1tbMmDGDw4cP4+7uzrVr1+jduze9evXi6tWr5g7PwK+BHxt7b6SqXVWjchc7Fzb23ijzZAjp+CkdP4UQBVliYiIff/wxn3zyCcnJyVSoUIH9+/fTsGFDc4dmIDN+Fi85+Q6VJEOSDCFEIRAWFsagQYOwtbXlwIEDWEqvYWEmBWaeDCGEEHnDw8ODo0ePsmnTJkOCce/ePX744YcsV6EWwpwkyRBCiEKiRIkSOKebg37q1Kn06tWLHj16cOXKFTNGJkTmJMkQQohCqnz58pQoUYKffvoJNzc3Vq1aJa0aokCRJEMIIQqpd999l9DQUFq0aMHNmzcZNGgQ3bp149Klh0/3LUR+kiRDCCEKsUaNGhESEsKsWbOwtrZm+/btuLm5ERgYaO7QhJAkQwghCjsrKyveffddwsLC8PT0RClVoIa4iuJLkgwhhCgiGjRowIEDBwgODqZatWqG8l9++UX6agizkCRDCCGKEEtLS5o0aWLY37VrF+3bt+eZZ57hwoUL5gtMFEuSZAghRBF25coVSpYsyZ49e2jUqBGLFy8mJSXF3GGJYkKSDCGEKMIGDhzIyZMn8fb25s6dO7z11lt07NiRv/76y9yhiWJAkgwhhCji6tSpQ1BQEPPnz6dUqVIEBQXh7u7OihUrzB2aKOIkyRBCiGLAwsKCESNGcPLkSdq3b098fDxly5Y1d1iiiJMkQwghipHatWuzZ88efv75Z1588UVD+e+//45erzdjZKIokiRDCCGKGQsLC7p27WrY//fff3nyySd56qmnOHv2rBkjE0WNJBlCCFHM/fbbb9y7d4+DBw/i4eHBnDlzpFVD5AlJMoQQopjz8fHh1KlTPPPMM9y7d4/x48fTtm1bIiMjzR2aKOQkyRBCCEH16tXZsWMHy5Ytw87OjkOHDtG0aVNmz54ts4WKXJMkQwghBAA6nY7XXnuNU6dO4evrS0JCAn/++Sc6nc7coYlCysrcAQghhChYXF1d2bZtG2vWrKF79+6G8uvXr2Nvb4+VlXx1iOwxaUvGjRs36N+/P3Z2djg4ODBkyBBu376d5Tnt27dHp9MZvd544w2jOhcvXqRbt26UKlWKSpUqMX78eJKTk015K0IIUazodDr69++Pvb09AEop+vbti6enJydPnjRzdKKwMGmS0b9/fyIiIti1axc//fQT+/fvZ9iwYY88b+jQoURHRxten376qeGYXq+nW7duJCYmcvDgQVatWsXKlSuZMmWKKW9FCCGKtb/++otjx44RGhpKixYtmD59OklJSeYOSxRwOmWiHj2RkZE0bNiQo0eP0qJFCwACAwPp2rUrly5dokqVKpme1759ezw8PJg7d26mx7dv30737t25cuUKlStXBmDJkiW8++67/Pvvv1hbW2c4JyEhgYSEBMN+XFwcrq6uxMbGYmdn95h3KoQQxUN0dDRvvvkmW7ZsAcDDwwN/f388PDzMG5jIV3Fxcdjb22frO9RkLRkhISE4ODgYEgzQhklZWFhw+PDhLM9dvXo1jo6ONGrUiEmTJhEfH290XXd3d0OCAdC5c2fi4uKIiIjI9HozZ87E3t7e8HJ1dX3MuxNCiOLH2dmZTZs2sWbNGsqXL09YWBgtW7Zk6tSpJCYmmjs8UQCZLMmIiYmhUqVKRmVWVlaUL1+emJiYh57Xr18/vvvuO/bt28ekSZP49ttvefnll42umz7BAAz7D7vupEmTiI2NNbyioqJye1tCCFGs6XQ6+vbty+nTp/Hz8yM5OZn169fL5F0iUznuIjxx4kRmz56dZZ3HmcAlfZ8Nd3d3nJ2d6dixI3/++Se1a9fO1TVtbGywsbHJdUxCiDR6PQQHQ3Q0ODuDtzdYWpo7KpHfKleuzMaNG/n++++pVq0aJUuWBLR+c8nJyfI7VwC5SDLGjh3LoEGDsqxTq1YtnJyc+Oeff4zKk5OTuXHjBk5OTtl+P09PTwDOnTtH7dq1cXJy4siRI0Z1rl69CpCj6wohci4gAEaNgkuX0spcXGDePPDzM19cwjx0Oh29e/c2Kvv8889ZtWoV/v7+tGzZ0kyRiYIix0lGxYoVqVix4iPreXl5cfPmTY4fP07z5s0B2Lt3LykpKYbEITvCwsIA7Vlg6nU//vhj/vnnH8PjmF27dmFnZ0fDhg1zeDdCiOwKCIBeveDBruKXL2vlGzdKolHcJSQksGjRIv7++29at27NhAkTmDpiBLYREfDnn5CUBA4O0KQJNGwIMt9G0adMyNfXVzVt2lQdPnxY/frrr6pu3bqqb9++huOXLl1S9erVU4cPH1ZKKXXu3Dk1bdo0dezYMXX+/Hm1ZcsWVatWLfXUU08ZzklOTlaNGjVSnTp1UmFhYSowMFBVrFhRTZo0KdtxxcbGKkDFxsbm3c0KUYQlJyvl4qKUlmJkfOl0Srm6avVE8fbvv/+qvn36KEABqgGoQ5n9o7GzU2r4cKXOnDF3yCKHcvIdatJ5MlavXk39+vXp2LEjXbt2pW3btnz11VeG40lJSZw9e9YwesTa2prdu3fTqVMn6tevz9ixY3nhhRfYunWr4RxLS0t++uknLC0t8fLy4uWXX2bAgAFMmzbNlLciRLEWHGz8iORBSkFUlFZPFG+OMTGs+eMPNgGVgUigDTABuJu+YlwcLFwIbm4weTLI6JQiyWTzZBRkORnjK4SAtWuhX79H11uzBvr2NX08ooD68Ufo3Rvuz0t0AxhlZ8d3cXGUsLQk7OOPaWhhAUePwk8/wd10aceTT2plDg5mCV1kX06+Q+WBmBDike53icqzeqII2rVL65yTOgto/fqUX7iQbzt0oPdPP3Hx4kUavv22obr++nUs582DmTMhORkOHIBu3WDPHrC1NdNNiLwmq7AKIR7J21sbRfKwxTh1OnB11eqJYuj6dXjllbQEo18/OHECOnYEnY5nn32Wt9MlGMeOHaNR27YEP/MMHDwIqYMJDh6Ejz4yww0IU5EkQwjxSJaW2jBVyJhopO7PnSvzZRRbEyfC/akE8PWFb77JsjVi8uTJnDlzhnbt2jHqu++4s2kTlCihHfz0UwgPz4egRX6QJEMIkS1+ftow1apVjctdXGT4arH2779aUgFgZwfLl2fINvV6CArS+vYEBcHq1esYMmQISinmz59P4wEDCEqd2TklBRYsyNdbEKYjHT+l46cQOZKTGT9ldtBi4IsvYMwYbXvcOPjsM6PDWU3gVrr0DoYOHWpY6uGtEiWYnZREmVKl4No1uD+LqChYCsQCaUKIosnSEtq310aRtG//8KQhIABq1ICnn9Ye0T/9tLYfEJB/sYp8cPBg2vYrrxgdSp3A7cHhz6kTuN2505lTp04ZlpP4MimJDQDx8XB/IkZRuEmSIYTIc4/6cpFEowhJTQZKldLmvLhPr9daMDJrK08tGz0aSpe2Y+nSpezatYsBnp4MevC6olCTJEMIkaey++Uii3YWETduaH86Oxs1a+V0AjcfHx9Wvfee4UvpdnQ07du3Z9euXaaJW+QLSTKEEHlKZgctZlLXH7k/AVeq6OjsnW5UL901Pj1wgF9++YVOnToxdOhQYmNjHzNQYQ6SZAgh8lSuvlxE4VWzpvbnpUtw86ahOFcTuJ06ZdicMHAgI0aMAODrr7+mUaNGBAYGPmawIr9JkiGEyFMyO2gxc3+VbQD27jVs5moCtz17DJtl2rZl/vz5/PLLL9SuXZtLly7RpUsXXn31VW6mS2ZEwSZJhhAiT8nsoMVM9+5p24sWGTZzPIHbb79pU4sDNGhgaCF56qmnOHnyJO+88w46nQ5/f3/Gjx+f9/chTEKSDCFEnpLZQYuZzp2hVi1te+9e+P57w6FsT+Cm10O6acd56y2jfzylSpXi888/Jzg4GC8vL6ZPn26imxF5TSbjksm4hDCJzCZhcnXVEgyZHbSIWbcubfndcuVg/35o1MhwOMtJ2ZSCCRNgzhxtv3ZtOHlSGxKbCaUUunQJyDvvvEP79u3p2bOnKe5MZCIn36GSZEiSIYTJyIyfxYRSxhOglC+vTS/+3HNZnxcbq2Wiq1Zp+zod/PJLtp+lbd++na5duwLQr18/5s+fT4UKFXJ5EyK7ZMZPIUSBkN3ZQUUhp9OBvz+0bKnt37gBzz+vLd2+fTvcu2dc/9IlmD0bGjZMSzAAlizJUWed9u3bM2HCBCwsLFizZg0NGzYkQGZ6K1CkJUNaMoQQIm/ExWlTi//4o3G5lRXUrautRXLlCsTEGB8vW1ZLMPr1y9XbHjlyhMGDB3P69GkAXnrpJRYsWEDF1CXkRZ6SlgwhhBD5z84ONm/WVmV1cUkrT06GyEgIDTVOMHQ66NFDmx8jlwkGQKtWrQgNDeW9997D0tKS9evX06lTJ4rh/6ELHGnJkJYMIYTIe8nJ8NNPsGULHD8O585BUhLY24OHB3h5waBBWkfPPHT8+HEGDx7MzJkz6datW55eW2ik4+cjSJIhhBBFl16vxzJdB6B169YB2mMU3cMmcBHZJo9LhBBCFFvpE4wrV67wxhtv0LdvX1544QViHuwPIkxKkgwhhBBFlqOjI2PGjMHKyopNmzbh5ubG6tWrpb9GPpEkQwghRJFlbW3NlClTOHbsGE2bNuXGjRu8/PLL9OzZkytXrpg7vCLPpEnGjRs36N+/P3Z2djg4ODBkyBBu37790PoXLlxAp9Nl+vo+3VS1mR1PfeYmhBBCPKhJkyYcPnyYGTNmUKJECbZu3Yq7uzv//fefuUMr0kza8bNLly5ER0ezdOlSkpKSGDx4MC1btmTNmjWZ1tfr9fz7779GZV999RWfffYZ0dHRlClTRgv6/iI5vr6+hnoODg7Y2tpmKy7p+CmEEMXXqVOnGDx4MG3btuWLL74wdziFToEYXRIZGUnDhg05evQoLVq0ACAwMJCuXbty6dIlqlSpkq3rNG3alGbNmrF8+fK0oHU6Nm3axHOPmrL2ISTJEEKI4i05OZnk5GTDf07/+OMPgoODGTx4sIxAeYQCMbokJCQEBwcHQ4IB4OPjg4WFBYcPH87WNY4fP05YWBhDhgzJcOztt9/G0dGRVq1asWLFiiw78SQkJBAXF2f0EkIIUXxZWVkZEoyUlBReffVVhgwZQpcuXYiKijJzdEWHyZKMmJgYKlWqZFRmZWVF+fLlsz2EaPny5TRo0IA2bdoYlU+bNo0NGzawa9cuXnjhBd566y0WLFjw0OvMnDkTe3t7w8vV1TXnNySEEKJIUkrRo0cPbGxs2LFjB25ubixbtkxGoOSBHCcZEydOfGjnzNTXmTNnHjuwu3fvsmbNmkxbMSZPnsyTTz5J06ZNeffdd5kwYQKfffbZQ681adIkYmNjDS/JUoUQQqSytLRk/PjxhIWF4eXlxa1btxg2bBidOnXi77//Nnd4hZpVTk8YO3YsgwYNyrJOrVq1cHJy4p9//jEqT05O5saNGzg5OT3yfTZu3Eh8fDwDBgx4ZF1PT0+mT59OQkICNjY2GY7b2NhkWi6EEEKkql+/PsHBwcyfP5/333+f3bt306hRI/bt22f06F9kX46TjIoVK2ZrZTsvLy9u3rzJ8ePHad68OQB79+4lJSUFT0/PR56/fPlyevToka33CgsLo1y5cpJICCGEeCyWlpa88847dO/enSFDhhAXF0eTJk3MHVahZbI+GQ0aNMDX15ehQ4dy5MgRDhw4wPDhw+nTp49hZMnly5epX78+R44cMTr33Llz7N+/n9deey3Ddbdu3crXX3/NqVOnOHfuHIsXL+aTTz5hxIgRproVIYQQxUzdunUJCgpix44dlChRAoDExES+/fZbUlJSzBxd4WHSybhWr15N/fr16dixI127dqVt27Z89dVXhuNJSUmcPXuW+Ph4o/NWrFiBi4sLnTp1ynDNEiVKsGjRIry8vPDw8GDp0qV8/vnnTJ061ZS3IoQQopixsLCgcuXKhv2ZM2cyYMAAnn76ac6dO2fGyAoPWYVV5skQQgiRDV999RVjxozhzp07lCxZkpkzZzJixAgsLIrXCh0FYp4MIYQQoigZNmwY4eHhdOjQgbt37zJ69Gieeuopfv/9d3OHVmBJkiGEEEJkU82aNdm9ezdLliyhTJkyHDhwgCZNmhitryXSSJIhhBBC5IBOp+P111/n1KlTPPPMM+h0Ojw8PMwdVoEkSYYQQgiRC9WrV2fHjh0cPXqUunXrGsr37NlDcnKyGSMrOCTJEEIIIXJJp9Ph5uZm2A8ODuaZZ56hTZs2REREmDGygkGSDCFEsaHXQ1AQrF2r/anXmzsiUdRcu3YNOzs7jh49SrNmzZg5c2axbtWQJEMIUSwEBECNGvD009Cvn/ZnjRpauRB55fnnnyciIoLu3buTmJjIe++9R+vWrQkPDzd3aGYhSYYQosgLCIBeveDSJePyy5e1ckk0RF6qWrUqP/74I99++y3lypUzLK/xxRdfmDu0fCdJhhCiSNPrYdQoyGzawdSy0aPl0YnIWzqdjpdffpmIiAh69OhBUlIS5cuXN3dY+U6SDCFEkRYcnLEFIz2lICpKqydEXnN2dmbz5s3s3r3baFXxM2fOkJiYaMbI8ockGUKIIi06Om/rCZFTOp2Ojh07otPpAPjvv//o2LEjLVu2JDQ01MzRmZYkGUIIkygoIzmcnfO2nhCP6+zZsyQmJnLy5ElatWrF5MmTSUhIMHdYJiFJhhAizxWkkRze3uDiAvf/E5mBTgeurlo9IfJD69atOX36NL1790av1zNjxgyaN2/OsWPHzB1anpMkQwiRpwraSA5LS5g3T9t+MNFI3Z87V6snRH6pWLEi69ev5/vvv6dixYpERETg6enJpEmTSElJMXd4eUaSDCFEnimoIzn8/GDjRqha1bjcxUUr9/PL33iESNWrVy9Onz5Nnz59SElJISoqqkgtHa9TKrNfB0VbXFwc9vb2xMbGYmdnZ+5whCgygoK0RyOPsm8ftG9v6mgy0uu1USTR0VofDG9vacEQBceWLVto27YtFSpUAODGjRuUKlUKW1tbM0dmLCffoVb5FJMQohgo6CM5LC3Nk9wIkR09e/Y0bCulGDx4MGfPnsXf3x8vLy8zRpZ7RadNRghhdjKSQ4i8ER0dzdGjRzl79ixPPvkkY8eOJT4+3txh5ZgkGUKIPCMjOYTIG1WqVCEiIoJBgwahlOLzzz/Hw8ODX3/91dyh5YgkGUKIPCMjOYTIO+XKlcPf35+ff/6ZqlWr8scff/DUU08xatSoQtOqIUmGECJPyUgOIfJW165diYiIYMiQISil2Lp1K4VlzIaMLpHRJUKYhIzkECLv7dixg1KlSuF9/5ljSkoKd+/epXTp0vkWQ06+Q6UlQwhhEqkjOfr21f6UBEOIx9e5c2dDggGwePFiGjVqxN69e80Y1cNJkiGEEEIUQnq9nsWLF3PhwgU6duzIm2++ya1bt8wdlhFJMoQQQohCyNLSkpCQEN58800AlixZQqNGjdi1a5eZI0tjsiTj448/pk2bNpQqVQoHB4dsnaOUYsqUKTg7O1OyZEl8fHz4448/jOrcuHGD/v37Y2dnh4ODA0OGDOH27dsmuAMhhBCiYCtbtixffvkle/fupWbNmly8eJFOnToxbNgwYmNjzR2e6ZKMxMREXnzxRUOGlR2ffvop8+fPZ8mSJRw+fJjSpUvTuXNn7t27Z6jTv39/IiIi2LVrFz/99BP79+9n2LBhprgFIYQQolB4+umnOXnyJMOHDwdgxYoV/Pnnn2aOKh9Gl6xcuZLRo0dz8+bNLOsppahSpQpjx45l3LhxAMTGxlK5cmVWrlxJnz59iIyMpGHDhhw9epQWLVoAEBgYSNeuXbl06RJVqlTJ9NoJCQkkJCQY9uPi4nB1dZXRJUIIIYqc/fv3ExYWxsiRI01y/UI5uuT8+fPExMTg4+NjKLO3t8fT05OQkBAAQkJCcHBwMCQYAD4+PlhYWHD48OGHXnvmzJnY29sbXq6urqa7ESGEEMKMnnrqKZMlGDlVYJKMmJgYACpXrmxUXrlyZcOxmJgYKlWqZHTcysqK8uXLG+pkZtKkScTGxhpeUVFReRy9EEIIIR6UoyRj4sSJ6HS6LF9nzpwxVay5ZmNjg52dndFLCCGEEKaVo6Xex44dy6BBg7KsU6tWrVwF4uTkBMDVq1dxTrdE49WrV/Hw8DDU+eeff4zOS05O5saNG4bzhRBCCFEw5CjJqFixIhUrVjRJIDVr1sTJyYk9e/YYkoq4uDgOHz5sGKHi5eXFzZs3OX78OM2bNwdg7969pKSk4OnpaZK4hBBCCJE7JuuTcfHiRcLCwrh48SJ6vZ6wsDDCwsKM5rSoX78+mzZtAkCn0zF69GhmzJjBjz/+SHh4OAMGDKBKlSo899xzADRo0ABfX1+GDh3KkSNHOHDgAMOHD6dPnz4PHVkihBBCCPPIUUtGTkyZMoVVq1YZ9ps2bQrAvn37aN++PQBnz541mixkwoQJ3Llzh2HDhnHz5k3atm1LYGAgtra2hjqrV69m+PDhdOzYEQsLC1544QXmz59vqtsQQgghRC7JKqzSCVQIIYTItkI5T4YQQgghihZJMoQQQghhEpJkCCGEEMIkJMkQQgghhElIkiGEEEIIkzDZENaCLHVATVxcnJkjEUIIIQqX1O/O7AxOLZZJxq1btwBkNVYhhBAil27duoW9vX2WdYrlPBkpKSlcuXKFsmXLotPp8uSacXFxuLq6EhUVVWTm3pB7Khzkngq+onY/IPdUWJjinpRS3Lp1iypVqmBhkXWvi2LZkmFhYYGLi4tJrl0UV3mVeyoc5J4KvqJ2PyD3VFjk9T09qgUjlXT8FEIIIYRJSJIhhBBCCJOQJCOP2NjYMHXqVGxsbMwdSp6Reyoc5J4KvqJ2PyD3VFiY+56KZcdPIYQQQpietGQIIYQQwiQkyRBCCCGESUiSIYQQQgiTkCRDCCGEECYhSYYQQgghTEKSjBz4+OOPadOmDaVKlcLBwSFb5yilmDJlCs7OzpQsWRIfHx/++OMPozo3btygf//+2NnZ4eDgwJAhQ7h9+7YJ7sBYTt/3woUL6HS6TF/ff/+9oV5mx9etW2fy+4Hc/V22b98+Q7xvvPGGUZ2LFy/SrVs3SpUqRaVKlRg/fjzJycmmvBWDnN7TjRs3GDFiBPXq1aNkyZJUq1aNkSNHEhsba1QvPz+nRYsWUaNGDWxtbfH09OTIkSNZ1v/++++pX78+tra2uLu7s23bNqPj2fm5MrWc3NOyZcvw9vamXLlylCtXDh8fnwz1Bw0alOHz8PX1NfVtGMnJPa1cuTJDvLa2tkZ1CtvnlNnvAp1OR7du3Qx1zPk57d+/n2effZYqVaqg0+nYvHnzI88JCgqiWbNm2NjYUKdOHVauXJmhTk5/PnNEiWybMmWK+vzzz9WYMWOUvb19ts6ZNWuWsre3V5s3b1a//fab6tGjh6pZs6a6e/euoY6vr69q0qSJOnTokAoODlZ16tRRffv2NdFdpMnp+yYnJ6vo6Gij10cffaTKlCmjbt26ZagHKH9/f6N66e/XlHLzd9muXTs1dOhQo3hjY2MNx5OTk1WjRo2Uj4+POnHihNq2bZtydHRUkyZNMvXtKKVyfk/h4eHKz89P/fjjj+rcuXNqz549qm7duuqFF14wqpdfn9O6deuUtbW1WrFihYqIiFBDhw5VDg4O6urVq5nWP3DggLK0tFSffvqpOn36tPrggw9UiRIlVHh4uKFOdn6uTCmn99SvXz+1aNEideLECRUZGakGDRqk7O3t1aVLlwx1Bg4cqHx9fY0+jxs3buTL/SiV83vy9/dXdnZ2RvHGxMQY1Slsn9P169eN7ufUqVPK0tJS+fv7G+qY83Patm2bev/991VAQIAC1KZNm7Ks/9dff6lSpUqpMWPGqNOnT6sFCxYoS0tLFRgYaKiT07+jnJIkIxf8/f2zlWSkpKQoJycn9dlnnxnKbt68qWxsbNTatWuVUkqdPn1aAero0aOGOtu3b1c6nU5dvnw5z2NPlVfv6+HhoV599VWjsuz84zeF3N5Tu3bt1KhRox56fNu2bcrCwsLoF+jixYuVnZ2dSkhIyJPYHyavPqcNGzYoa2trlZSUZCjLr8+pVatW6u233zbs6/V6VaVKFTVz5sxM6/fu3Vt169bNqMzT01O9/vrrSqns/VyZWk7v6UHJycmqbNmyatWqVYaygQMHqp49e+Z1qNmW03t61O/BovA5ffHFF6ps2bLq9u3bhjJzf06psvPzO2HCBOXm5mZU9tJLL6nOnTsb9h/37+hR5HGJCZ0/f56YmBh8fHwMZfb29nh6ehISEgJASEgIDg4OtGjRwlDHx8cHCwsLDh8+bLLY8uJ9jx8/TlhYGEOGDMlw7O2338bR0ZFWrVqxYsUKVD7M+fY497R69WocHR1p1KgRkyZNIj4+3ui67u7uVK5c2VDWuXNn4uLiiIiIyPsbSSev/n3ExsZiZ2eHlZXxmoim/pwSExM5fvy40c+AhYUFPj4+hp+BB4WEhBjVB+3vO7V+dn6uTCk39/Sg+Ph4kpKSKF++vFF5UFAQlSpVol69erz55ptcv349T2N/mNze0+3bt6levTqurq707NnT6OehKHxOy5cvp0+fPpQuXdqo3FyfU0496mcpL/6OHqVYrsKaX2JiYgCMvpxS91OPxcTEUKlSJaPjVlZWlC9f3lDHVLE97vsuX76cBg0a0KZNG6PyadOm0aFDB0qVKsXOnTt56623uH37NiNHjsyz+DOT23vq168f1atXp0qVKpw8eZJ3332Xs2fPEhAQYLhuZp9h6jFTyovP6dq1a0yfPp1hw4YZlefH53Tt2jX0en2mf39nzpzJ9JyH/X2n/5lJLXtYHVPKzT096N1336VKlSpGv9x9fX3x8/OjZs2a/Pnnn7z33nt06dKFkJAQLC0t8/QeHpSbe6pXrx4rVqygcePGxMbGMmfOHNq0aUNERAQuLi6F/nM6cuQIp06dYvny5Ubl5vyccuphP0txcXHcvXuX//7777H/LT9KsU8yJk6cyOzZs7OsExkZSf369fMposeT3ft5XHfv3mXNmjVMnjw5w7H0ZU2bNuXOnTt89tlnuf7yMvU9pf/ydXd3x9nZmY4dO/Lnn39Su3btXF83K/n1OcXFxdGtWzcaNmzIhx9+aHQsrz8nkT2zZs1i3bp1BAUFGXWU7NOnj2Hb3d2dxo0bU7t2bYKCgujYsaM5Qs2Sl5cXXl5ehv02bdrQoEEDli5dyvTp080YWd5Yvnw57u7utGrVyqi8sH1O5lbsk4yxY8cyaNCgLOvUqlUrV9d2cnIC4OrVqzg7OxvKr169ioeHh6HOP//8Y3RecnIyN27cMJyfE9m9n8d9340bNxIfH8+AAQMeWdfT05Pp06eTkJCQq0V68uue0scLcO7cOWrXro2Tk1OG3tZXr14FyNVnBPlzT7du3cLX15eyZcuyadMmSpQokWX9x/2cMuPo6IilpaXh7yvV1atXHxq/k5NTlvWz83NlSrm5p1Rz5sxh1qxZ7N69m8aNG2dZt1atWjg6OnLu3DmTf3k9zj2lKlGiBE2bNuXcuXNA4f6c7ty5w7p165g2bdoj3yc/P6ecetjPkp2dHSVLlsTS0vKxP/dHypOeHcVMTjt+zpkzx1AWGxubacfPY8eOGers2LEj3zp+5vZ927Vrl2G0wsPMmDFDlStXLtexZlde/V3++uuvClC//fabUiqt42f63tZLly5VdnZ26t69e3l3A5nI7T3Fxsaq1q1bq3bt2qk7d+5k671M9Tm1atVKDR8+3LCv1+tV1apVs+z42b17d6MyLy+vDB0/s/q5MrWc3pNSSs2ePVvZ2dmpkJCQbL1HVFSU0ul0asuWLY8db3bk5p7SS05OVvXq1VPvvPOOUqrwfk5Kab/jbWxs1LVr1x75Hvn9OaUimx0/GzVqZFTWt2/fDB0/H+dzf2SceXKVYuLvv/9WJ06cMAzbPHHihDpx4oTR8M169eqpgIAAw/6sWbOUg4OD2rJlizp58qTq2bNnpkNYmzZtqg4fPqx+/fVXVbdu3XwbwprV+166dEnVq1dPHT582Oi8P/74Q+l0OrV9+/YM1/zxxx/VsmXLVHh4uPrjjz/Ul19+qUqVKqWmTJli8vtRKuf3dO7cOTVt2jR17Ngxdf78ebVlyxZVq1Yt9dRTTxnOSR3C2qlTJxUWFqYCAwNVxYoV83UIa07uKTY2Vnl6eip3d3d17tw5o6F2ycnJSqn8/ZzWrVunbGxs1MqVK9Xp06fVsGHDlIODg2G0ziuvvKImTpxoqH/gwAFlZWWl5syZoyIjI9XUqVMzHcL6qJ8rU8rpPc2aNUtZW1urjRs3Gn0eqb87bt26pcaNG6dCQkLU+fPn1e7du1WzZs1U3bp1TZ7I5vaePvroI7Vjxw71559/quPHj6s+ffooW1tbFRERYXTfhelzStW2bVv10ksvZSg39+d069Ytw/cOoD7//HN14sQJ9ffffyullJo4caJ65ZVXDPVTh7COHz9eRUZGqkWLFmU6hDWrv6PHJUlGDgwcOFABGV779u0z1OH+3AOpUlJS1OTJk1XlypWVjY2N6tixozp79qzRda9fv6769u2rypQpo+zs7NTgwYONEhdTedT7nj9/PsP9KaXUpEmTlKurq9Lr9RmuuX37duXh4aHKlCmjSpcurZo0aaKWLFmSaV1TyOk9Xbx4UT311FOqfPnyysbGRtWpU0eNHz/eaJ4MpZS6cOGC6tKliypZsqRydHRUY8eONRoOWpDuad++fZn+OwXU+fPnlVL5/zktWLBAVatWTVlbW6tWrVqpQ4cOGY61a9dODRw40Kj+hg0b1BNPPKGsra2Vm5ub+vnnn42OZ+fnytRyck/Vq1fP9POYOnWqUkqp+Ph41alTJ1WxYkVVokQJVb16dTV06NA8+0VvinsaPXq0oW7lypVV165dVWhoqNH1CtvnpJRSZ86cUYDauXNnhmuZ+3N62M926j0MHDhQtWvXLsM5Hh4eytraWtWqVcvo+ylVVn9Hj0unVD6MLRRCCCFEsSPzZAghhBDCJCTJEEIIIYRJSJIhhBBCCJOQJEMIIYQQJiFJhhBCCCFMQpIMIYQQQpiEJBlCCCGEMAlJMoQQQghhEpJkCCGEEMIkJMkQQgghhElIkiGEEEIIk/g/y/31spx4AjEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# evaluate data points\n",
+    "y_predict = vqc.predict(X)\n",
+    "\n",
+    "# plot results\n",
+    "# red == wrongly classified\n",
+    "for x, y_target, y_p in zip(X, y_one_hot, y_predict):\n",
+    "    if y_target[0] == 1:\n",
+    "        plt.plot(x[0], x[1], \"bo\")\n",
+    "    else:\n",
+    "        plt.plot(x[0], x[1], \"go\")\n",
+    "    if not np.all(y_target == y_p):\n",
+    "        plt.scatter(x[0], x[1], s=200, facecolors=\"none\", edgecolors=\"r\", linewidths=2)\n",
+    "plt.plot([-1, 1], [1, -1], \"--\", color=\"black\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "grave-testament",
+   "metadata": {},
+   "source": [
+    "### Multiple classes with VQC\n",
+    "In this section we generate an artificial dataset that contains samples of three classes and show how to train a model to classify this dataset. This example shows how to tackle more interesting problems in machine learning. Of course, for a sake of short training time we prepare a tiny dataset. We employ `make_classification` from SciKit-Learn to generate a dataset. There 10 samples in the dataset, 2 features, that means we can still have a nice plot of the dataset, as well as no redundant features, these are features are generated as a combinations of the other features. Also, we have 3 different classes in the dataset, each classes one kind of centroid and we set class separation to `2.0`, a slight increase from the default value of `1.0` to ease the classification problem.\n",
+    "\n",
+    "Once the dataset is generated we scale the features into the range `[0, 1]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "plastic-dividend",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "X, y = make_classification(\n",
+    "    n_samples=10,\n",
+    "    n_features=2,\n",
+    "    n_classes=3,\n",
+    "    n_redundant=0,\n",
+    "    n_clusters_per_class=1,\n",
+    "    class_sep=2.0,\n",
+    "    random_state=algorithm_globals.random_seed,\n",
+    ")\n",
+    "X = MinMaxScaler().fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "forced-disclosure",
+   "metadata": {},
+   "source": [
+    "Let's see how our dataset looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "premier-drill",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f470868f070>"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:, 0], X[:, 1], c=y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deadly-response",
+   "metadata": {},
+   "source": [
+    "We also transform labels and make them categorical."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "exposed-bailey",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'C']\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_cat = np.empty(y.shape, dtype=str)\n",
+    "y_cat[y == 0] = \"A\"\n",
+    "y_cat[y == 1] = \"B\"\n",
+    "y_cat[y == 2] = \"C\"\n",
+    "print(y_cat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "instructional-headquarters",
+   "metadata": {},
+   "source": [
+    "We create an instance of `VQC` similar to the previous example, but in this case we pass a minimal set of parameters. Instead of feature map and ansatz we pass just the number of qubits that is equal to the number of features in the dataset, an optimizer with a low number of iteration to reduce training time, a quantum instance, and a callback to observe progress."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "latin-result",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vqc = VQC(\n",
+    "    num_qubits=2,\n",
+    "    optimizer=COBYLA(maxiter=30),\n",
+    "    callback=callback_graph,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "proper-bookmark",
+   "metadata": {},
+   "source": [
+    "Start the training process in the same way as in previous examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "reported-pioneer",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.9"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create empty array for callback to store evaluations of the objective function\n",
+    "objective_func_vals = []\n",
+    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
+    "\n",
+    "# fit classifier to data\n",
+    "vqc.fit(X, y_cat)\n",
+    "\n",
+    "# return to default figsize\n",
+    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
+    "\n",
+    "# score classifier\n",
+    "vqc.score(X, y_cat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "weighted-renaissance",
+   "metadata": {},
+   "source": [
+    "Despite we had the low number of iterations, we achieved quite a good score. Let see the output of the `predict` method and compare the output with the ground truth."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "employed-patient",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted labels: ['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'B']\n",
+      "Ground truth:     ['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'C']\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict = vqc.predict(X)\n",
+    "print(f\"Predicted labels: {predict}\")\n",
+    "print(f\"Ground truth:     {y_cat}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "guided-secret",
+   "metadata": {},
+   "source": [
+    "## Regression\n",
+    "\n",
+    "We prepare a simple regression dataset to illustrate the following algorithms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "iraqi-flavor",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_samples = 20\n",
+    "eps = 0.2\n",
+    "lb, ub = -np.pi, np.pi\n",
+    "X_ = np.linspace(lb, ub, num=50).reshape(50, 1)\n",
+    "f = lambda x: np.sin(x)\n",
+    "\n",
+    "X = (ub - lb) * algorithm_globals.random.random([num_samples, 1]) + lb\n",
+    "y = f(X[:, 0]) + eps * (2 * algorithm_globals.random.random(num_samples) - 1)\n",
+    "\n",
+    "plt.plot(X_, f(X_), \"r--\")\n",
+    "plt.plot(X, y, \"bo\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "talented-capitol",
+   "metadata": {},
+   "source": [
+    "### Regression with an `EstimatorQNN`\n",
+    "\n",
+    "Here we restrict to regression with an `EstimatorQNN` that returns values in $[-1, +1]$. More complex and also multi-dimensional models could be constructed, also based on `SamplerQNN` but that exceeds the scope of this tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "perfect-kelly",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# construct simple feature map\n",
+    "param_x = Parameter(\"x\")\n",
+    "feature_map = QuantumCircuit(1, name=\"fm\")\n",
+    "feature_map.ry(param_x, 0)\n",
+    "\n",
+    "# construct simple ansatz\n",
+    "param_y = Parameter(\"y\")\n",
+    "ansatz = QuantumCircuit(1, name=\"vf\")\n",
+    "ansatz.ry(param_y, 0)\n",
+    "\n",
+    "# construct a circuit\n",
+    "qc = QuantumCircuit(1)\n",
+    "qc.compose(feature_map, inplace=True)\n",
+    "qc.compose(ansatz, inplace=True)\n",
+    "\n",
+    "# construct QNN\n",
+    "regression_estimator_qnn = EstimatorQNN(\n",
+    "    circuit=qc, input_params=feature_map.parameters, weight_params=ansatz.parameters\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "velvet-marks",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# construct the regressor from the neural network\n",
+    "regressor = NeuralNetworkRegressor(\n",
+    "    neural_network=regression_estimator_qnn,\n",
+    "    loss=\"squared_error\",\n",
+    "    optimizer=L_BFGS_B(maxiter=5),\n",
+    "    callback=callback_graph,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "working-mongolia",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.9769994291935522"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create empty array for callback to store evaluations of the objective function\n",
+    "objective_func_vals = []\n",
+    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
+    "\n",
+    "# fit to data\n",
+    "regressor.fit(X, y)\n",
+    "\n",
+    "# return to default figsize\n",
+    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
+    "\n",
+    "# score the result\n",
+    "regressor.score(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "diverse-conservative",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot target function\n",
+    "plt.plot(X_, f(X_), \"r--\")\n",
+    "\n",
+    "# plot data\n",
+    "plt.plot(X, y, \"bo\")\n",
+    "\n",
+    "# plot fitted line\n",
+    "y_ = regressor.predict(X_)\n",
+    "plt.plot(X_, y_, \"g-\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "false-india",
+   "metadata": {},
+   "source": [
+    "Similarly to the classification models, we can obtain an array of trained weights by querying a corresponding property of the model. In this model we have only one parameter defined as `param_y` above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "terminal-turner",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.58870599])"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regressor.weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "offensive-legislation",
+   "metadata": {},
+   "source": [
+    "### Regression with the Variational Quantum Regressor (`VQR`)\n",
+    "\n",
+    "Similar to the `VQC` for classification, the `VQR` is a special variant of the `NeuralNetworkRegressor` with a `EstimatorQNN`. By default it considers the `L2Loss` function to minimize the mean squared error between predictions and targets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "offensive-entry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vqr = VQR(\n",
+    "    feature_map=feature_map,\n",
+    "    ansatz=ansatz,\n",
+    "    optimizer=L_BFGS_B(maxiter=5),\n",
+    "    callback=callback_graph,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "cooperative-helmet",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.9769955693935384"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create empty array for callback to store evaluations of the objective function\n",
+    "objective_func_vals = []\n",
+    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
+    "\n",
+    "# fit regressor\n",
+    "vqr.fit(X, y)\n",
+    "\n",
+    "# return to default figsize\n",
+    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
+    "\n",
+    "# score result\n",
+    "vqr.score(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "genetic-cambridge",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot target function\n",
+    "plt.plot(X_, f(X_), \"r--\")\n",
+    "\n",
+    "# plot data\n",
+    "plt.plot(X, y, \"bo\")\n",
+    "\n",
+    "# plot fitted line\n",
+    "y_ = vqr.predict(X_)\n",
+    "plt.plot(X_, y_, \"g-\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "backed-visit",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>0.44.1</td></tr><tr><td><code>qiskit-terra</code></td><td>0.25.1</td></tr><tr><td><code>qiskit_machine_learning</code></td><td>0.6.1</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.10.8</td></tr><tr><td>Python compiler</td><td>GCC 10.4.0</td></tr><tr><td>Python build</td><td>main, Nov 22 2022 08:26:04</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>31.14303207397461</td></tr><tr><td colspan='2'>Tue Oct 03 20:18:51 2023 UTC</td></tr></table>"
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+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2023.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+      ],
+      "text/plain": [
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+   "source": [
+    "import qiskit.tools.jupyter\n",
+    "\n",
+    "%qiskit_version_table\n",
+    "%qiskit_copyright"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
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