{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "d902b7ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " sl sw pl pw target tNames\n", "0 5.1 3.5 1.4 0.2 0 setosa\n", "1 4.9 3.0 1.4 0.2 0 setosa\n", "2 4.7 3.2 1.3 0.2 0 setosa\n", "3 4.6 3.1 1.5 0.2 0 setosa\n", "4 5.0 3.6 1.4 0.2 0 setosa\n" ] } ], "source": [ "import numpy as np \n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "df = pd.read_csv(\"iris_basic.csv\")\n", "print(df.head())" ] }, { "cell_type": "code", "execution_count": 3, "id": "6f0d1e44", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.2],\n", " [0.2],\n", " [0.2],\n", " [0.2],\n", " [0.2],\n", " [0.4],\n", " [0.3],\n", " [0.2],\n", " [0.2],\n", " [0.1],\n", " [0.2],\n", " [0.2],\n", " [0.1],\n", " [0.1],\n", " [0.2],\n", " [0.4],\n", " [0.4],\n", " [0.3],\n", " [0.3],\n", " [0.3],\n", " [0.2],\n", " [0.4],\n", " [0.2],\n", " [0.5],\n", " [0.2],\n", " [0.2],\n", " [0.4],\n", " [0.2],\n", " [0.2],\n", " [0.2],\n", " [0.2],\n", " [0.4],\n", " [0.1],\n", " [0.2],\n", " [0.2],\n", " [0.2],\n", " [0.2],\n", " [0.1],\n", " [0.2],\n", " [0.2],\n", " [0.3],\n", " [0.3],\n", " [0.2],\n", " [0.6],\n", " [0.4],\n", " [0.3],\n", " [0.2],\n", " [0.2],\n", " [0.2],\n", " [0.2],\n", " [1.4],\n", " [1.5],\n", " [1.5],\n", " [1.3],\n", " [1.5],\n", " [1.3],\n", " [1.6],\n", " [1. ],\n", " [1.3],\n", " [1.4],\n", " [1. ],\n", " [1.5],\n", " [1. ],\n", " [1.4],\n", " [1.3],\n", " [1.4],\n", " [1.5],\n", " [1. ],\n", " [1.5],\n", " [1.1],\n", " [1.8],\n", " [1.3],\n", " [1.5],\n", " [1.2],\n", " [1.3],\n", " [1.4],\n", " [1.4],\n", " [1.7],\n", " [1.5],\n", " [1. ],\n", " [1.1],\n", " [1. ],\n", " [1.2],\n", " [1.6],\n", " [1.5],\n", " [1.6],\n", " [1.5],\n", " [1.3],\n", " [1.3],\n", " [1.3],\n", " [1.2],\n", " [1.4],\n", " [1.2],\n", " [1. ],\n", " [1.3],\n", " [1.2],\n", " [1.3],\n", " [1.3],\n", " [1.1],\n", " [1.3],\n", " [2.5],\n", " [1.9],\n", " [2.1],\n", " [1.8],\n", " [2.2],\n", " [2.1],\n", " [1.7],\n", " [1.8],\n", " [1.8],\n", " [2.5],\n", " [2. ],\n", " [1.9],\n", " [2.1],\n", " [2. ],\n", " [2.4],\n", " [2.3],\n", " [1.8],\n", " [2.2],\n", " [2.3],\n", " [1.5],\n", " [2.3],\n", " [2. ],\n", " [2. ],\n", " [1.8],\n", " [2.1],\n", " [1.8],\n", " [1.8],\n", " [1.8],\n", " [2.1],\n", " [1.6],\n", " [1.9],\n", " [2. ],\n", " [2.2],\n", " [1.5],\n", " [1.4],\n", " [2.3],\n", " [2.4],\n", " [1.8],\n", " [1.8],\n", " [2.1],\n", " [2.4],\n", " [2.3],\n", " [1.9],\n", " [2.3],\n", " [2.5],\n", " [2.3],\n", " [1.9],\n", " [2. ],\n", " [2.3],\n", " [1.8]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = df[\"pw\"].to_numpy().reshape(-1, 1) # (150,1)\n", "x" ] }, { "cell_type": "code", "execution_count": 4, "id": "99fdd860", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [1.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.],\n", " [0.]])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = df[\"target\"].to_numpy().reshape(-1, 1) # (150,1)\n", "y = (y == 0).astype(float) \n", "y" ] }, { "cell_type": "code", "execution_count": 5, "id": "b2a5f684", "metadata": {}, "outputs": [], "source": [ "def sigmoid(z):\n", " z = np.clip(z, -500, 500)\n", " sig = 1.0 / (1.0 + np.exp(-z))\n", " return sig" ] }, { "cell_type": "code", "execution_count": 6, "id": "c8d77fc1", "metadata": {}, "outputs": [], "source": [ "def log_loss(y, p, eps=1e-12):\n", " p = np.clip(p, eps, 1 - eps)\n", " return -np.mean(y*np.log(p) + (1-y)*np.log(1-p))" ] }, { "cell_type": "code", "execution_count": 7, "id": "46b15c87", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.],\n", " [0.]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lr=0.1\n", "epochs=2000 \n", "l2=0.0,\n", "X = np.column_stack([x, np.ones_like(x)])\n", "m = X.shape[0]\n", "theta = np.zeros((2,1))\n", "theta" ] }, { "cell_type": "code", "execution_count": 8, "id": "7e27284a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1,\n", " 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2,\n", " 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2,\n", " 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5,\n", " 1.5, 1.3, 1.5, 1.3, 1.6, 1. , 1.3, 1.4, 1. , 1.5, 1. , 1.4, 1.3,\n", " 1.4, 1.5, 1. , 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7,\n", " 1.5, 1. , 1.1, 1. , 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2,\n", " 1.4, 1.2, 1. , 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8,\n", " 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2. , 1.9, 2.1, 2. , 2.4, 2.3, 1.8,\n", " 2.2, 2.3, 1.5, 2.3, 2. , 2. , 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6,\n", " 1.9, 2. , 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9,\n", " 2.3, 2.5, 2.3, 1.9, 2. , 2.3, 1.8],\n", " [1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. , 1. , 1. ]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.T" ] }, { "cell_type": "code", "execution_count": 9, "id": "f2a9c597", "metadata": {}, "outputs": [], "source": [ "for i in range(epochs):\n", " z = X @ theta # (m,1)\n", " h = sigmoid(z) # (m,1)\n", " grad = (X.T @ (h - y)) / m # (2,1) <-- from your formula\n", " theta -= lr * grad\n", "\n", " #if (i % 0 == 0 or t == epochs-1):\n", " # print(f\"{i:4d} loss={log_loss(y, h):.6f} w={theta[0,0]:.6f} b={theta[1,0]:.6f}\")\n", "\n", "w, b = theta[0,0], theta[1,0]\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "a15c9276", "metadata": {}, "outputs": [], "source": [ "def predict_proba(x, w, b):\n", " x = np.asarray(x, float).reshape(-1)\n", " return sigmoid(w*x + b)\n", "\n", "def predict(x, w, b, thresh=0.5):\n", " return (predict_proba(x, w, b) >= thresh).astype(int)\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "859cdc10", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Learned: w=-5.989, b=4.279, loss=0.1812\n" ] } ], "source": [ "rng = np.random.default_rng(0)\n", "m = 120\n", "xNew = np.linspace(-0.5, 2.5, m)\n", "p = predict_proba(xNew, w, b)\n", "print(f\"\\nLearned: w={w:.3f}, b={b:.3f}, loss={log_loss(p.reshape(-1,1), p.reshape(-1,1)):.4f}\")\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "d9217b20", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yJitter = y +np.random.uniform(-0.2, 0.2, size=y.shape)\n", "plt.plot(x, yJitter, 'ok', alpha=0.1)\n", "plt.plot(xNew,p)" ] }, { "cell_type": "markdown", "id": "f444fc82", "metadata": {}, "source": [ "# Multi-Parametric Binary Classifier" ] }, { "cell_type": "code", "execution_count": 13, "id": "b5b294ba", "metadata": {}, "outputs": [], "source": [ "x1 = df[\"sl\"].to_numpy()\n", "x2 = df[\"sw\"].to_numpy()\n", "X = np.column_stack([ np.ones_like(x1), x1, x2])" ] }, { "cell_type": "code", "execution_count": 14, "id": "02f75aeb", "metadata": {}, "outputs": [], "source": [ "y = df[\"target\"].to_numpy()\n", "#y = (y == 2).astype(float) " ] }, { "cell_type": "code", "execution_count": 15, "id": "af303066", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiQAAAGgCAYAAACaOnwjAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAANfpJREFUeJzt3Qt0VdWd+PFfEp7KQ6S8JBDRRBCQl4KQDEJTFAQcaV2VsmwJEgUVrcy4HEtb66sKjhZ1Vl2YSCCwLKWDI1Ao6lAbMAYs4SngDCaWR1J5+FeeMgIm579+O03MhdyQc3Pved3vZ63j8RzOzdl7n9x7f9l7n/NLsCzLEgAAABclunlyAAAARUACAABcR0ACAABcR0ACAABcR0ACAABcR0ACAABcR0ACAABcR0ACAABcR0ACAABcR0ACAAD8HZDMmTNHEhISZObMmWGPyc/PN8fUXlq0aNGY0wIAgIBpEukLi4uLJScnR/r163fRY9u0aSN79uyp2dagxI7Kykr57LPPpHXr1rZfCwAA3KHp8k6ePClXXHGFJCYmRj8gOXXqlNx1113y+uuvy69//euLHq9BROfOnSVSGox069Yt4tcDAAD3lJWVSXJycvQDkhkzZsi4ceNk1KhRDQpINIBJSUkxPR2DBg2S5557Tvr06RP2+DNnzpilWnVCYq2Q9rYAAADvO3HihOlQ0BGOi7EdkCxdulS2bt1qhmwaomfPnrJgwQIztHP8+HF58cUXJT09XXbv3h02Wpo9e7Y89dRTF+zXYISABAAAf2nIdIsEq7r7oQG0h+KGG26QtWvX1swdGTlypAwYMEBefvnlBv2Mc+fOybXXXiuTJk2SZ555pkE9JNURlgY0BCQAAPiDfn+3bdu2Qd/ftnpItmzZIkeOHDHDLtUqKirk/fffl9/+9rcmiEhKSqr3ZzRt2lQGDhwopaWlYY9p3ry5WQAAQHywFZB873vfk507d4bsu/vuu6VXr17y2GOPXTQYqQ5g9GeMHTvWfmkBAEAg2QpIdFJK3759Q/Zdeuml0r59+5r9kydPlq5du5p5IOrpp5+WoUOHSmpqqhw7dkxeeOEF2b9/v9xzzz3RrAcAADGjsxu++eYb80c1vqUdEU2aNInKIzkifg5JOAcOHAi51/jo0aNy7733yqFDh6Rdu3Zy/fXXy4YNG6R3797RPjUAAFF39uxZOXjwoJw+fZrWrcMll1wiXbp0kWbNmklj2JrU6odJMQAARIs+rqKkpMT0BHTo0MF86fKAzioaPmiw9vnnn5ueo7S0tAsefhazSa0AAMQT/cLVoETv9NSeAIRq2bKluVlFp2JoWzUmNQzJ9QAAuNiX5UUeex7PEqPUNrQwAABwHQEJAABwHQEJ4EPlJ8qlYG+BWQNAXXSy6f333y/du3c3DxvVJLejR4+WoqIiaYgnn3zSPIndKUxqBXwmb2ueTFs9TSqtSklMSJTc8bmSPSjb7WIB8Jg77rjDTDRdtGiRXHXVVXL48GF577335IsvvhAvoocE8BHtEakORpSup6+eTk8J4ANO9mweO3ZMCgsL5fnnn5fvfve7kpKSIkOGDJFZs2bJP//zP9ccow8p1duZ9ZbczMxM2bFjh/m3/Px8k+RWt/U2Z110XywRkAA+UvJFSU0wUq3CqpDSL8PnhgLgjZ7NlJdTJHNxplnrdiy1atXKLCtWrAhJVlvbD3/4Q5Of7u233za56jRPnaaI+fLLL2XixInyyCOPSJ8+fcxD4XTRfbFEQAL4SFr7NDNMU1tSQpKkXp7qWpkAeK9ns0mTJqZHQ4drLrvsMsnIyJCf//zn8tFHH5l//+CDD2TTpk2ybNkyueGGG8xDzV588UVz7JtvvmmeL6IBjf4cnXuii+6LJQISwEeS2ySbOSMahChd54zPMfsBeJNbPZt33HGHfPbZZ/LHP/5RxowZI+vWrTO9IBqo6FDMqVOnTC666t4UXfbu3SuffvqpuIFJrYDP6ATW0amjzYeZ9owQjAD+6NmsHZQ41bPZokULufnmm83y+OOPmzkjTzzxhDzwwAMm/4wGKefTXhI3EJAAPqRBCIEI4K+eTR2m0Z4RN3s2e/fubeaVaE+JJr3VIZkrr7yyzmM1b4+T2Y0JSAAACFjP5hdffGEmrU6dOlX69esnrVu3ls2bN8u///u/y+233y6jRo2SYcOGyYQJE8y+a665xgzv/OlPf5Lvf//7Zl6JBio6hLN9+3ZJTk42P0OfZxIrBCQAAASsZ7NVq1Zy4403yksvvWTmhJw7d84kCLz33nvN5Fa9jXfNmjXyi1/8Qu6++27zEDWduHrTTTdJp06dauagvPXWW+a2Yb1FeOHChTJlypSYlTnB0vzBHmcnfTEAANHy9ddfm16CHj16NCqTbby20Qkb39/cZQMAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAQIDcdtttMmbMmDr/rbCw0OSx+eijj8RrCEgAAAiQ7OxsWbt2rZSXl1/wb5ogTzP5agZgO86ePSuxRkACAIATNEAoKKhax9D48eOlQ4cOkp+fH7L/1KlTsmzZMhOwfPDBBzJ8+HBp2bKlyQL805/+VL766quaY6+88kp55plnZPLkySYp3rRp0yTWCEgAAIi1vDyRlBSRzMyqtW7HSJMmTUwgoQGJZVk1+zUYqaiokGHDhpkhnTvuuMMM3fzhD38wAcqDDz4Y8nNefPFF6d+/v2zbtk0ef/xxibUEq3ZpPcpO+mIAAKLl66+/lr1790qPHj2kRYsWkf0Q7RHRIKSy8tt9SUki+/aJJCdLLPzv//6vXHvttVJQUCAjR440+2666SZJSUmR5s2bS1JSkuTk5NQcrwHJiBEjTC+J1lN7SAYOHCjLly9vVBvZ+f6mhwQAgFgqKQkNRlRFhUhpacxO2atXL0lPT5cFCxaY7dLSUjOhVYdrduzYYXpPWrVqVbOMHj1aKisrTWBRTeeaOKmJo2cDACDepKWJJCZe2EOSmhrT02ZnZ8tDDz0kr776qpnMevXVV5teEJ1LMn36dDNv5Hzdu3ev+f9LL71UnEQPCRBl5SfKpWBvgVkDgBmWyc2tCkKUrnW4JEbDNdXuvPNOSUxMlCVLlsjixYtl6tSp5pbfQYMGyccffyypqakXLM2aNRO3EJAAUZS3NU9SXk6RzMWZZq3bACDZ2VVzRvQuG13rdoy1atVKJk6cKLNmzZKDBw/KlClTzP7HHntMNmzYYCaxbt++XUpKSmTlypUXTGp1GgEJECXaIzJt9TSptKq6ZXU9ffV0ekoAVNEeEZ1gGuOekfOHbY4ePWrmiFxxxRVmnz6DZP369fLJJ5+YW3918uqvfvWrmn93C3NIgCgp+aKkJhipVmFVSOmXpZLcxrkPIACoprf41nUz7eDBg+W///u/JZx92ovjMHpIgChJa58miQmhb6mkhCRJvTy2E9cAIAgISIAo0V6Q3PG5JghRus4Zn0PvCAA0AEM2QBRlD8qW0amjzTCN9owwVAMADUNAAkSZBiEEIgBgD0M2AADAdQQkAADAdQQkAADAdQQkAADAdQQkAADAdQQkAADEmSeffFIGDBggXkJAAl8jsy4AhLrttttkzJgxUpfCwkKT8fcHP/iBvPfee+IlBCTwLTLrAkDdCfXWrl0r5eXlF/zbwoUL5YYbbjAJ9tq3by/hnD17VpxGQAJfIrMuAL/R+KCgoGodS+PHj5cOHTpIfn5+yP5Tp07JsmXLTMBy/pDNlClTZMKECfLss8+arL89e/YUpxGQIHCZdQHAa/LyRFJSRDIzq9a6HStNmjSRyZMnm4CkdqZfDUYqKipk0qRJdb5Oh3D27NljeldWr14tTiMggS+RWReAX2iPyLRpIpX/+BtK19Onx7anZOrUqfLpp5/K+vXrQ4Zr7rjjDmnbtm2dr7n00ktl/vz50qdPH7M4jYAEvkRmXQB+UVLybTBSraJCpDSGHbq9evWS9PR0WbBggdkuLS01E1p1uCac6667Tpo1ayZuIbkefIvMugD8IC1NJDExNChJShJJTY3tebOzs+Whhx6SV1991fSOXH311TJixIiwx2sPiZvoIYHve0pGXjmS7LoAPCs5WSQ3tyoIUbrOyanaH0t33nmnJCYmypIlS2Tx4sVmGEdv+fUqekgAAIgxHSkZPbpqmEZ7RmIdjKhWrVrJxIkTZdasWXLixAlzJ42X0UMCAIADNAgZOdKZYKT2sM3Ro0dl9OjR5nZeL0uwat8T5FEa2ems4OPHj0ubNm3cLg4AIE58/fXXsnfvXunRo4e0aNHC7eL4ro3sfH/TQwIAAFxHQAIAAPwdkMyZM8fM2J05c2a9x+nT4fSeaO3K0fuc16xZ05jTAgCAgIk4ICkuLpacnByToKc+GzZsMI+p1Yk127ZtM8/K12XXrl2RnhpABMiMDCBwAYkm6Lnrrrvk9ddfl3bt2tV77CuvvGLSID/66KNy7bXXyjPPPCODBg2S3/72t5GWGYBNZEYGEMiAZMaMGTJu3DgZNWrURY/duHHjBcfp7Ue6P5wzZ86Ymbm1FwCRITMy0Hg+uCHV921jOyBZunSpbN26VWbPnt2g4w8dOiSdOnUK2afbuj8c/dl6m1D10q1bN7vFBPAPZEYGIte0aVOzPn36NM0YRnXbVLeVI09qLSsrk4cfftikJo7l/dj6VLl//dd/rdnWHhKCEqBxmZErrW8TaSQlJEnq5TFOpAEEQFJSklx22WVy5MgRs33JJZd4+vHrTveMaDCibaNtpG3lWECyZcsWc2KdA1KtoqJC3n//fTMnRIdazi9Q586d5fDhwyH7dFv3h9O8eXOzAIheZuTpq6dLhVVhgpGc8Tnk/wEaqPr7qjooQSgNRur7To/Jk1pPnjwp+/fvD9l39913m1t6H3vsMenbt+8Fr9Hn6GsEtWrVqpp9mhJZ78557bXXGnRentQKRGcuSemXpaZnRIMUAPboH+Dnzp2j2WrRYZr6ekbsfH/b6iFp3br1BUGHpitu3759zf7JkydL165da+aY6BCPpjv+zW9+YybC6hyUzZs3S66mPgTgGA1CCESAyOkXb2OHJeDgk1oPHDggBw8eDOkN0dTHGoD0799f3nzzTVmxYkWdvSkAACA+kVwPAADEBMn1AACAr5BcDwAAuI6ABAAAuI6ABAAAuI6ABLiI4r8Xy9yNc80aABAbtp5DAsSbKSumyKIdi2q2s/pnSf6EfFfLBABBRA8JEIb2iNQORpRu01MCANFHQAKEUXigsM79RWVFtBkARBkBCRDG8O7D69yf0S2DNgOAKCMgAcIY3HWwmTNSm27rfgBAdPHoeOAidM6IDtNozwjBCAA0XMyy/QLxSIMQAhEAiC2GbAAAgOsISAAAgOsISAAAgOsISAAAgOsISAAAgOsISAAAgOsISOAZqz9ZLQ/86QGzRv3KT5RLwd4CswYQXOXlIgUFVWs/n6MhCEjgCRl5GXLb72+TeZvnmbVuo255W/Mk5eUUyVycada6DSB48vJEUlJEMjOr1rrtx3M0FE9qheu0R0SDkPOtmrRKxl8z3pUyeZX2iGgQUmlV1uxLSkiSfTP3SXKbZFfLBiB6ysurAoTKb9/qkpQksm+fSHKyf85h50mt9JDAdWtK1tS5/53Sdxwvi9eVfFESEoyoCqtCSr8sda1MAKKvpCQ0UFAVFSKlpf46hx0EJHDd2LSxde4fkzrG8bJ4XVr7NElMCH3bag9J6uWprpUJQPSlpYkknvcNrb0Xqan+OocdBCRwnQ7LpCenh+zTbYZrLqTDMrnjc00QonSdMz6H4RogYJKTRXJzqwIEpeucnOgNpTh1DjuYQwJPzSXRYRrtGSEYufhcEh2m0Z4R5o4AwVVeXjWEor0WsQoUYnkOO3NICEgAAEBMMKkVAAD4CnNIAACA6whIAACA6whIAACA6whIAACA6whIAACA6whIEFcZbCM5B5l1ASD2mjhwDuCiNGPttNXTTJ4WfTS6Po00e1C26+dwolwAAB6MhjjJYBvJOcisCwCNw4PR4CtOZLCN5Bxk1gUA5zCHBHGRwTaSc5BZFwCcQ0CCuMhgG8k5yKwLAM4huR7iKoNtJOcgsy4ARIZsvwAAwHVMagUAAL7CHBIAAOA6AhIAAOA6AhIAAOA6AhIAAOA6AhIAAOA6AhIP8mJ2WbLkAnDt86dcpKCgao3gIiDxGM0uq0ngMhdnmrVu+7FMXqwHAP/JyxNJSRHJzKxa6zaCiSe1eogXs8uSJReAW7RHRIOQylp5MZOSRPbtE0l25yMRNvFgNJ/yYnZZsuQCcO3zpyQ0GFEVFSKl7n0kIoYYsvEQL2aXJUsuANc+f9JEEs/7ltIeklT3PhIRQwQkHuLF7LJkyQXg2udPskhublUQonSdk8NwTVAxh8SDvJhdliy5AFz7/CmvGqbRnhHmjvgL2X4BAIDrmNQKAAB8hTkkAADAdQQkAADAdQQkAADAdQQkAADAXwHJvHnzpF+/ftKmTRuzDBs2TN5+++2wx+fn50tCQkLI0qJFi2iUGwAABEgTOwcnJyfLnDlzJC0tTSzLkkWLFsntt98u27Ztkz59+tT5Gg1c9uzZU7OtQQniQ/Hfi6XwQKEM7z5cBncdHPXjq5+Poo+31yfKeumZLV4rEwAEKiC57bbbQrafffZZ02vy4Ycfhg1INADp3Llz40oJ35myYoos2rGoZjurf5bkT8iP2vFKMwhPWz3N5P/RR+7rU26zB2WLm7xYJgAI9BySiooKWbp0qXz11Vdm6CacU6dOSUpKinTr1s30puzevfuiP/vMmTPmYSq1F/iH9nTUDi6Ubuv+aBxf3QtR/cWvdD199XSz3y1eLBMABDYg2blzp7Rq1UqaN28u9913nyxfvlx69+5d57E9e/aUBQsWyMqVK+WNN96QyspKSU9Pl3J9DnA9Zs+eLW3btq1ZNJiBf+iwS12KyoqicnyQMiMDACIMSDTI2L59u/z1r3+V+++/X7KysuTjjz+u81jtOZk8ebIMGDBARowYIW+99ZZ06NBBcjQ7Uj1mzZolx48fr1nKysrsFhMu0jkgdcnolhGV44OUGRkAEGFA0qxZM0lNTZXrr7/e9GT0799fXnnllQa9tmnTpjJw4EAp1SxJ9dDel+o7eaoX+IdOSNU5ILXpdriJqnaPD1JmZABAlLL9ZmZmSvfu3c0tvg2Zd6KTX8eOHStz586NSXIeeIfOAdFhF+3paOhdNnaOD1JmZAAIIjvf37bustGhlFtvvdUEICdPnpQlS5bIunXr5N133zX/rsMzXbt2NT0n6umnn5ahQ4eaHpVjx47JCy+8IPv375d77rmnMfWDT2hQ0dDAIpLjlX7he+1L34tlAgCvsxWQHDlyxAQdBw8eNBGPPiRNg5Gbb77Z/PuBAwckMfHbUaCjR4/KvffeK4cOHZJ27dqZYZ4NGzaEnQQLAADiU6OHbJzAkA0AAP5j5/ubXDYAAMB1BCQAAMB1BCQAAMB1BCQAAMB1BCQepM+xKNhbENMcKPrMj7kb59abL8bpc0RS76C0VWBoWoiCgqq1h07hQLEAOHnbL4KRLTaSzLqxPkck9Q5KWwVGXp7ItGkilZUievt/bq5Idrbrp3CgWACigNt+PUT/yk95OSUkQZs+fnzfzH1Re9CW/pU/ZP6QC/ZvumeT7YeSResckdQ7KG0VGNr1kJJS9a1fLSlJZN8+keRk107hQLEA1IPbfn3KiWyxkWTWjfU5Iql3UNoqMEpKQr/1VUWFyEXyVsX6FA4UC0CUMIfEQ5zIFhtJZt1YnyOSegelrQIjLa1qPKQ27YpITXX1FA4UC0CUEJB4iBPZYiPJrBvrc0RS76C0VWDo+IdOztBve6XrnJyojotEcgoHigUgSphD4kFOZIuNJLNurM8RSb2D0laBoZM2dDxEuyBi9K0fySkcKBaARs4hISABAAAxwaRWAADgK8whAQAAriMgAQAAriMgAQAAriMgAQAAriMgAQAAriMgQczYzcTrROZexKfy4oNSMHebWftZcbHI3LlVayBoCEgQE5qJV5PfZS7ONGvdjubxQIN/F6cUSsqQjpL5yECz1m0/mjJFZMgQkUceqVrrNhAkPBgNUWc3E68TmXsRn7RHRIOQSkn69ndLvpF9mz6X5MFdxC+0R0SDkPNt2iQymIcHw8N4MBpcZTcTrxOZexGfSgoPhQQjqkKaSGnRYfGTwjCdOkUknkaAMGSDqLObideJzL2IT2nDO0uiVITs0x6S1IxO4ifD6048LRkknkaAEJAg6uxm4nUicy/ikw7L5GZtMEGI0nVO1kZfDdcoHZbJCk08bbYZrkGQMIcEMWM3E68TmXsRv3NJdJhGe0b8FoycP5dEh2m0Z4RgBH5Atl8AAOA6JrUCAABfYQ4JAABwHQEJAABwHQEJAABwHQEJAABwHQEJAABwHQGJTU5kpLV7juK/F8vcjXPN2kvI3hsA5eUiBQVV6zgSSXZgLzZVJGWy/RoHKu7FtkUMWD5w/PhxS4uqazfN3zLfSnwq0ZInxax12+1zZC3PMsdWL7rtBU60FWJs/nzLSky0LP2Y0LVux4H5We9bifJNVbXlG7Ptx6aKpEy2X+NAxb3YtojN9zdPam0gJzLS2j2H9ogMmX9hCtBN92ySwV3dSwFK9t4A0D9FU1JEKmslPUxKEtm3TyQ5uE/RjSQ7sBebKpIy2X6NAxX3YtvCHh6MFgNOZKS1e47CA3WnAC0qczcFKNl7A6CkJPRbQFVUiJQGOwNzJNmBvdhUkZTJ9mscqLgX2xaxwxySBnIiI63dcwzvXncK0Ixu7qYAJXtvAKSliSSe9/Ggf5qmBjsDcyTZgb3YVJGUyfZrHKi4F9sWsUNA0kBOZKS1ew4dlsnqH5oCVLfdHK5RZO8NAO0Pz82t+vRXus7JCXw/eSTZgb3YVJGUyfZrHKi4F9sWscMcEpucyEhr9xw6l0SHabRnxO1gpDay9waADuJr/7j+SRpH3wKRZAf2YlNFUibbr3Gg4l5sWzQM2X4BAIDrmNQKAAB8hTkkAADAdQQkAADAdQQkAADAdQQkAADAdQQkAADAdQQkHkRGYcDl96BHs8uWr94uBQ8sM+tYKV59WOY+UGLWgJOaOHo2XFTe1jyZtnqayWmjj5HXJ7dmD8p29RxTVkyRRTsWhTwNNn9CflTLBHhFXp7ItGlVOVT0seX6pNDsbA+UK2OBTNuQJZUyQBLnVUhu+gLJLpoa1XNMyfhEFm1IE5FOIvMsyUr/RPKLronqOYBweFKrh5BRGHD5PejR7LLaI5Jy23UXZiFetUuSxw+Iyjm0R2TIbR31a6HWXks2rToig8eHz+UD1IcHo/kUGYUBl9+DHs0uW7KmpO4sxO9Er2CFa06cF4yoBCl652TUzgHUhzkkHkJGYcDl96BHs8umjU2rOwvxmOgVbPjYNqZHJJQlGWNaR+0cQH0ISDyEjMKAy+9Bj2aX1WGZ3PRFoVmI0xdHbbhG6bBMVnpJraBE55CUMFwDxzCHxIPIKAy4/B70aHZZnUuiwzTaMxLNYOT8uSQ6TKM9I8wdQWOR7RcAALiOSa0AAMBXmEMCAABcR0ACAABcR0ACAABcR0ACAAD8FZDMmzdP+vXrJ23atDHLsGHD5O233673NcuWLZNevXpJixYt5LrrrpM1a9Y0tswAACCeA5Lk5GSZM2eObNmyRTZv3iyZmZly++23y+7du+s8fsOGDTJp0iTJzs6Wbdu2yYQJE8yya9cu8WtWXScy8Tqh+O/FMnfjXLNuiHhuK0dSvxYXi8ydW7X2Spn0NMUHpWDuNrOOVbHsnsOrVq8WeeCBqnWsGsuJ6+EEu+Xyaj3siuj9UR6MujeI1Ujt2rWz5s+fX+e/3Xnnnda4ceNC9t14443W9OnTbZ3j+PHj+uhAs46W+VvmW4lPJVrypJi1bsfiNV6UtTzL1KF60e36xHNbWfq7nZhoWfpW0XWY3/VGycqq+vnVi267XSY9Tdb7VqJ8U3Ua+cZsR7tYds/hVenpoZdQt6PdWE5cDyfYLZdX62FXRO+P+f6vu53v74gDkm+++cb6/e9/bzVr1szavXt3ncd069bNeumll0L2/epXv7L69etX78/++uuvTeGrl7KysqgGJGXHy2q+LKuXpKeSzP5ovsaLNpVvCqlD9aL76xLPbWWVlX37aVC9JCVV7Y+WTZtCf371ovvdKpOeZtNnNV9+NaeRc2Z/tIpl9xxetWpV3ZdQ90ersZy4Hk6wWy6v1sOuiN4fZcGou52AxPak1p07d0qrVq2kefPmct9998ny5culd+/edR576NAh6dQpNG21buv++syePVvatm1bs3Tr1k3czqrrRCZeJxQeKKxzf1FZUZ3747mtHEn9Wlj39ZCiIlfT0ZYUHqo7u2zR4agVy+45vCrctLh33gnzgggay4nr4QS75fJqPeyK6P1REoy622E7IOnZs6ds375d/vrXv8r9998vWVlZ8vHHH0e1ULNmzZLjx4/XLGVlZa5n1XUiE68ThncfXuf+jG4Zde6P57ZyJPXr8Lqvh2RkuJqONm1457qzy2Z0ilqx7J7Dq8aOrXv/mDFhXhBBYzlxPZxgt1xerYddEb0/0oJR95gGJM2aNZPU1FS5/vrrTU9G//795ZVXXqnz2M6dO8vhw6ERvG7r/vpo70v1nTzVi9tZdZ3IxOuEwV0HS1b/rJB9uq376xLPbeVI6tfBg0WyQq+H2db9bpVJTzO4i+RmbQjNLpu10eyPVrHsnsOrxo8XSU8P3afbuj9ajeXE9XCC3XJ5tR52RfT+SA5G3R3N9qt32nTv3l3y8/Mv+LeJEyfK6dOnZdWqVTX70tPTza3Dr732WkyS88Q6q64TmXidoHfX6DCN9oyEC0Zqi+e2ciT1q95do8M02jMSLhhxukz/uKtDhwX0L/GGBAqRFMvuObxK767RYRrtGQkbjDSysZy4Hk6wWy6v1sOuiN4f5f6ue8yy/epQyq233moCkJMnT8qSJUvk+eefl3fffVduvvlmmTx5snTt2tX0nFTf9jtixAhzq/C4ceNk6dKl8txzz8nWrVulb9++MakQAADwBjvf303s/OAjR46YoOPgwYPmBNrTUR2MqAMHDkhirUEv7Q3RoOWXv/yl/PznP5e0tDRZsWKFrWAEAAAEX6OHbJxADwkAAP5j5/ubXDYAAMB1BCQAAMB1BCQAAMB1BCQAAMB1BCQ2BSaDLbwlKOlPnUhnGkmWXLsvcaJ9vXoNA4Lm9SHLB2KR7TcSgclgC28JSvpTJ9KZRpIl1+5LnGhfr17DgKB5/fn9zW2/DaQ9Iikvp4QkjdNHou+buc/fTyGF+3/GpaSEZtHSZ0Tv21f3YxntHu+USMrlQN1tv8SJ9vXqNQwImtdbuO03BgKTwRbeEpT0p06kM40kS67dlzjRvl69hgFB8/oXc0gaKDAZbOEtQUl/6kQ600iy5Np9iRPt69VrGBA0r38RkDRQYDLYwluCkv7UiXSmkWTJtfsSJ9rXq9cwIGhe/2IOiU2ByWALbwlK+lMn0plGkiXX7kucaF+vXsOAoHkDnu3XLeSyAQDAf5jUCgAAfIU5JAAAwHUEJAAAwHUEJAAAwHUEJAAAwHUEJAAAwHUEJIAfM5MWF4vMnVu1jtU5nGKzLk4kFI6geb3ZwF4sk0d5tanKPVqumLB8wCvZfgFPZCbNyqo6uHrR7Wifwyk26+JEQuEImtebDezFMnmUV5tqvkfLZQfZfoGgZibVP9mHDLlw/6ZNIoMHR+ccTrFZFycSCkfQvN5sYC+WyaO82lTlHi2XXTwYDQhqZtLCwrr3FxVF7xxOsVkXJxIKR9C83mxgL5bJo7zaVCUeLVcsMYcE8FNm0uHD696fkRG9czjFZl2cSCgcQfN6s4G9WCaP8mpTpXm0XLFEQAL4KTOpjhtkZYXu0+2w4wkezn5qsy5OJBSOoHm92cBeLJNHebWpkj1arlgiuR7gx8ykOtlBxxH0T/d6vy0bcQ6n2KyLEwmFI2hebzawF8vkUV5tqnKPlquhyPYLAABcx6RWAADgK8whAQAAriMgAQAAriMgAQAAriMgAQAAriMgAQAAriMggb8FJRWmA/Uozt8tc29fZ9bxdj3sFsuj1QACjYAE/pWXV5V9KjOzaq3bfuRAPaakFsqQu3vLI38cada6HS/Xw26xPFoNIPB4Uiv8KSipMB2oh/aIaBAiklBrryWbFn4sg6f0CfT1sFssj1YD8C0ejIbgC0oqTAfqUbj88/OCEZUgRSv/X+Cvh91iebQaQFxgyAb+FJRUmA7UY/j3O5gekVCWZNz+ncBfD7vF8mg1gLhAQAJ/CkoqTAfqocMyWVd/UCsoscx21IZrPHw97BbLo9UA4gJzSOBvfk+F6WA9dC6JDtNoz0hUgxEfXA+7xfJoNQDfIdsvAABwHZNaAQCArzCHBAAAuI6ABAAAuI6ABAAAuI6ABAAAuI6ABAAAuI6ABIg2J1LFRnIOj6aw9WixAE8oj6P3BwEJEE1OpIqN5BweTWHr0WIBnpAXZ+8PntQKRIsTqWIjOYdHU9h6tFiAJ5QH5P3Bg9EANziRKjaSc3g0ha1HiwV4Qkkcvj8YsgGixYlUsZGcw6MpbD1aLMAT0uLw/UFAAkSLE6liIzmHR1PYerRYgCckx+H7gzkkQLQ5kSo2knN4NIWtR4sFeEK5z98fZPsFAACuY1IrAADwFeaQAAAA1xGQAAAA1xGQAAAA1xGQAAAAfwUks2fPlsGDB0vr1q2lY8eOMmHCBNmzZ0+9r8nPz5eEhISQpUWLFo0tNwAAiNeAZP369TJjxgz58MMPZe3atXLu3Dm55ZZb5Kuvvqr3dW3atJGDBw/WLPv3729suRHEjJNBSWvpQCbeoDSVU8qLD0rB3G1m7WdcdwSa1QhHjhyx9EesX78+7DELFy602rZt25jTWMePHzfn0TWcN3++ZSUmWpb+tuhat/15EgdEUg+brwlKUzllftb7VqJ8U9Ve8o3Z9iOuO/zIzvd3o57UWlpaKmlpabJz507p27dv2CGbe+65R7p27SqVlZUyaNAgee6556RPnz5hf+6ZM2fMUvvBKt26dZPjx4+b3hYELONkUNJaOpCJNyhN5RTtEUkZ0lEqJenb9pJvZN+mzyV5cBfxC647/MqRB6NpcDFz5kzJyMgIG4yonj17yoIFC2TlypXyxhtvmNelp6dLeT19zTpXRStQvWgwggBnnAxKWksHMvEGpamcUlJ4KCQYURXSREqLDoufcN0RDyIOSHQuya5du2Tp0qX1Hjds2DCZPHmyDBgwQEaMGCFvvfWWdOjQQXI0S1AYs2bNMtFU9VJWVhZpMeGHjJNBSWvpQCbeoDSVU9KGd5ZEqQjZpz0kqRmdxE+47ogHEQUkDz74oKxevVoKCgok2WY/cdOmTWXgwIFmuCec5s2bm66d2gsCnHEyKGktHcjEG5SmcooOy+RmbTBBiNJ1TtZGXw3XKK474oGtOSR66EMPPSTLly+XdevWmfkjdlVUVJj5I2PHjpW5c+dGfQwKPs446fe0lg5m4g1KUzk5l0SHabRnxG/BSG1cd/hNzLL9PvDAA7JkyRIzH0TnhlTTk7Vs2dL8vw7P6ARWnQeinn76aRk6dKikpqbKsWPH5IUXXpAVK1bIli1bpHfv3lGvEAAA8AY7399N7PzgefPmmfXIkSND9i9cuFCmTJli/v/AgQOSWGuQ++jRo3LvvffKoUOHpF27dnL99dfLhg0bGhyMAACA4GvUbb9OoYcEAAD/ceS2XwAAgGghIAEAAK4jIAEAAK4jIAEAAK4jIEHDkGa04YqLRfQZO7oGADQIAQkuLi+vKqNbZmbVWrdRN739fcgQkUceqVr/43Z4AED9uO0X9SPNaMNpj4gGIefbtElk8GB+0wDEnRPc9ouoIc1owxUW1r2/qChaVwMAAoshG9SPNKMNN3x43fszMvgtA4CLICBB/Ugz2nA6LJOVFbpPtxmuAYCLYg4JGoY0o/bmkugwjfaMEIwAiGMnYpVcD3HeU0Ke+4bRIIRABABsYcgGAAC4joAEAAC4joAEAAC4joAEAAC4joAEAAC4joAEAAC4joAkxspPlEvB3gKzjjvxmiE4XuvtZVwTwPMISGIob2uepLycIpmLM81at+NGvGYIjtd6exnXBPAFntQaI9ojokFIpVVZsy8pIUn2zdwnyW2SJdDiNUNwvNbby7gmgKvI9usBJV+UhAQjqsKqkNIvSyXw4jVDcLzW28u4JoBvMGQTI2nt0yQxIbR5tYck9fJUCbx4zRAcr/X2Mq4J4BsEJDGiwzK543NNEKJ0nTM+J/jDNfGcIThe6+1lXBPAN5hD4sBcEh2m0Z6RuAhGaovXDMHxWm8v45oAnp9DQkACAABigkmtAADAV5hDAgAAXEdAAgAAXEdAAgAAXEdAAgAAXEdAAgAAXEdAAiBqyosPSsHcbWYdM2TuBQKJgARAVORNKZSUIR0l85GBZq3bUUfmXiCweDAagEbTHhENQirlH4/N1yfnyzeyb9Pnkjy4S3RamMy9gO/wYDQAjiopPBQSjKgKaSKlRYejeBKyKQNBxpANgEZLG95ZEqUiZJ/2kKRmdIpe65K5Fwg0AhIAjabDMrlZG0wQonSdk7UxesM15iRkUwaCjDkkAKI6l0SHabRnJKrBSMhJyKYMBHEOSRPHSgUg8DQIiVkgUnOS5KoFQKAwZAMAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAPwVkMyePVsGDx4srVu3lo4dO8qECRNkz549F33dsmXLpFevXtKiRQu57rrrZM2aNY0pMwAAiOeAZP369TJjxgz58MMPZe3atXLu3Dm55ZZb5Kuvvgr7mg0bNsikSZMkOztbtm3bZoIYXXbt2hWN8sPLystFCgqq1gAA1CPBsixLIvT555+bnhINVG666aY6j5k4caIJWFavXl2zb+jQoTJgwAB57bXXGnSeEydOSNu2beX48ePSpk2bSIsLJ+XliUybJlJZKZKYKJKbK5KdzTUAgDhywsb3d6PmkOgJ1OWXXx72mI0bN8qoUaNC9o0ePdrsD+fMmTOmErUX+Ij2iFQHI0rX06fTUwIAiH5AUllZKTNnzpSMjAzp27dv2OMOHToknTp1Ctmn27q/vrkqGlFVL926dYu0mHBDScm3wUi1igqR0lKuBwAgugGJziXReSBLly6VaJs1a5bpfaleysrKon4OxFBaWtUwTW1JSSKpqTQ7ACB6AcmDDz5o5oQUFBRIcnJyvcd27txZDh8+HLJPt3V/OM2bNzdjTbUX+Ij+TuicEQ1ClK5zcqr2AwDQ2IBE579qMLJ8+XL5y1/+Ij169Ljoa4YNGybvvfdeyD69Q0f3I8B0Auu+fVV32eiaCa0AgHo0EZvDNEuWLJGVK1eaZ5FUzwPReR4tW7Y0/z958mTp2rWrmQeiHn74YRkxYoT85je/kXHjxpkhns2bN0uu/gWNYNMeEXpFAADR7iGZN2+emdMxcuRI6dKlS83yhz/8oeaYAwcOyMGDB2u209PTTRCjAUj//v3lzTfflBUrVtQ7ERYAAMSXRj2HxCk8hwQAAP9x7DkkAAAA0UBAAgAAXEdAAgAAXEdAAgAAXEdAAgAAXEdAAgAAXEdAAgAAXEdAAgAAXEdAAgAA/JXLxi3VD5PVJ74BAAB/qP7ebshD4X0RkJw8edKsu3Xr5nZRAABABN/j+gh53+eyqayslM8++8xkGE5ISBC/RYcaSJWVlV30Of5BE691j9d6K+oef9edax5/19zOddcQQ4ORK664QhITE/3fQ6KVSPZ5Gnu9YPH2CxvvdY/XeivqHn/XnWsef9e8odf9Yj0j1ZjUCgAAXEdAAgAAXEdAEmPNmzeXJ554wqzjTbzWPV7rrah7/F13rnn8XfNYXXdfTGoFAADBRg8JAABwHQEJAABwHQEJAABwHQEJAABwHQFJFM2ZM8c8SXbmzJlhj8nPzzfH1F5atGghfvPkk09eUI9evXrV+5ply5aZY7S+1113naxZs0b8yG7dg3LN1d///nf58Y9/LO3bt5eWLVua67h58+Z6X7Nu3ToZNGiQmY2fmppq2iMe6q71Pv+663Lo0CHxkyuvvLLOesyYMSPQ73W79Q7S+7yiokIef/xx6dGjh/ldv/rqq+WZZ565aD6axr7XffGkVj8oLi6WnJwc6dev30WP1afa7dmzp2bbb4/Dr9anTx/585//XLPdpEn4X6cNGzbIpEmTZPbs2TJ+/HhZsmSJTJgwQbZu3Sp9+/aVINc9KNf86NGjkpGRId/97nfl7bfflg4dOkhJSYm0a9cu7Gv27t0r48aNk/vuu09+97vfyXvvvSf33HOPdOnSRUaPHi1Brns1ve61n2TZsWNH8dtnm35BVdu1a5fcfPPN8sMf/jDQ73W79Q7K+1w9//zzMm/ePFm0aJH5rNPA++677zZPXP3pT38qMXuv622/aJyTJ09aaWlp1tq1a60RI0ZYDz/8cNhjFy5caLVt29b3Tf7EE09Y/fv3b/Dxd955pzVu3LiQfTfeeKM1ffp0K+h1D8o1f+yxx6x/+qd/svWaf/u3f7P69OkTsm/ixInW6NGjraDXvaCgQP+ctI4ePWoFiX6+XX311VZlZWXg3+t26h2U97nS6zd16lSrth/84AfWXXfdZcXyvc6QTRRoF55GhqNGjWrQ8adOnZKUlBSTmOj222+X3bt3ix/pX4iaMOmqq66Su+66Sw4cOBD22I0bN17QPho16/6g1z0o1/yPf/yj3HDDDeYvRP0rf+DAgfL666/X+5qgXPdI6l5twIAB5q9E/eu6qKhI/Ozs2bPyxhtvyNSpU8P+9R+Ua2633kF5n6v09HTTw/HJJ5+Y7R07dsgHH3wgt956q4QTjetOQNJIS5cuNV2R2j3ZED179pQFCxbIypUrzS+4ZjLWi19eXi5+cuONN5rxwXfeecd07Wl33fDhw01Wx7rouHmnTp1C9um238bTI6l7UK753/72N1PftLQ0effdd+X+++833bfarRtOuOuumUL/7//+T4Jcdw1CXnvtNfmv//ovs+iX1MiRI83nhV+tWLFCjh07JlOmTAl7TJDe63bqHZT3ufrZz34mP/rRj8w8oKZNm5oAXOdG6h9fMX2vN7gvBRc4cOCA1bFjR2vHjh01+y42ZHO+s2fPmm7AX/7yl75uYe2WbtOmjTV//vw6/71p06bWkiVLQva9+uqrpv387mJ1D8o112s4bNiwkH0PPfSQNXTo0LCv0aHM5557LmTfn/70JzOUcfr0aSvIda/LTTfdZP34xz+2/OqWW26xxo8fX+8xQXyvN6TeQXmfq9///vdWcnKyWX/00UfW4sWLrcsvv9zKz8+3Yvlep4ekEbZs2SJHjhwxs4p1UqMu69evl//4j/8w/197QlQ41dFnaWmp+Nlll10m11xzTdh6dO7cWQ4fPhyyT7d1v99drO5Bueb6F3/v3r1D9l177bX1DleFu+46+U9n7we57nUZMmSI7657tf3795uJ3DpRsT5Be683tN5BeZ+rRx99tKaXRO+S+slPfiL/8i//Uu9IQDTe6wQkjfC9731Pdu7cKdu3b69ZdJxZu7X0/5OSki76MzRo0Z+hH3h+pmOnn376adh6DBs2zIxJ1rZ27Vqz3+8uVvegXHO9y6T2HQRKx5h1zDycoFz3SOpeF/1c8Nt1r7Zw4UIzf0bny9UnKNfcbr2D8j5Xp0+flsTE0PBAv890GCqm171B/ShosPOHbH7yk59YP/vZz2q2n3rqKevdd9+1Pv30U2vLli3Wj370I6tFixbW7t27fdXKjzzyiLVu3Tpr7969VlFRkTVq1CjrO9/5jnXkyJE6663HNGnSxHrxxRet//mf/zF3qmjX7s6dOy2/sVv3oFzzTZs2mWv47LPPWiUlJdbvfvc765JLLrHeeOONmmO03lr/an/729/MMY8++qi57tp1n5SUZL3zzjtW0Ov+0ksvWStWrDDH6++5fi4kJiZaf/7zny2/qaiosLp3727uNjpfkN/rduodlPe5ysrKsrp27WqtXr3afM699dZb5jNO76SJ5XudgCTGAYlu68WtNnPmTPML3qxZM6tTp07W2LFjra1bt1p+o7dzdenSxdRDf3F1u7S0NGy91X/+539a11xzjXmN3h6m44t+ZLfuQbnmatWqVVbfvn2t5s2bW7169bJyc3ND/l3rrfU///bXAQMGmPpfddVV5vbIeKj7888/b+YQ6JeSjr+PHDnS+stf/mL5kX7R6t+ve/bsueDfgvxet1PvIL3PT5w4Yb7HtD76+6vv21/84hfWmTNnYvpeT9D/NLw/BQAAIPqYQwIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAFxHQAIAAMRt/x/Aaw+n/IIufgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x1[y==0], x2[y==0],'.g' ,label='Set')\n", "plt.plot(x1[y==1], x2[y==1],'.r', label='Ver')\n", "plt.plot(x1[y==2], x2[y==2],'.b', label='Vir')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "id": "67513db5", "metadata": {}, "outputs": [], "source": [ "y = df[\"target\"].to_numpy()\n", "y = (y == 2).astype(float) " ] }, { "cell_type": "code", "execution_count": 17, "id": "3eb6fa2d", "metadata": {}, "outputs": [], "source": [ "def predict_proba(X, theta):\n", " z = X@theta\n", " return sigmoid(z)" ] }, { "cell_type": "code", "execution_count": 18, "id": "c37b48f7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-0.88031462, 0.80365083, -0.29048474])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lr=0.01\n", "epochs=5000\n", "m = X.shape[0]\n", "theta = np.random.randn(3)\n", "theta" ] }, { "cell_type": "code", "execution_count": 19, "id": "aa29cd4d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 loss=1.808011\n", " 100 loss=0.546449\n", " 200 loss=0.539039\n", " 300 loss=0.532486\n", " 400 loss=0.526668\n", " 500 loss=0.521482\n", " 600 loss=0.516842\n", " 700 loss=0.512674\n", " 800 loss=0.508917\n", " 900 loss=0.505520\n", "1000 loss=0.502438\n", "1100 loss=0.499632\n", "1200 loss=0.497071\n", "1300 loss=0.494726\n", "1400 loss=0.492573\n", "1500 loss=0.490592\n", "1600 loss=0.488763\n", "1700 loss=0.487072\n", "1800 loss=0.485503\n", "1900 loss=0.484045\n", "2000 loss=0.482687\n", "2100 loss=0.481420\n", "2200 loss=0.480235\n", "2300 loss=0.479124\n", "2400 loss=0.478080\n", "2500 loss=0.477099\n", "2600 loss=0.476174\n", "2700 loss=0.475301\n", "2800 loss=0.474475\n", "2900 loss=0.473693\n", "3000 loss=0.472951\n", "3100 loss=0.472246\n", "3200 loss=0.471574\n", "3300 loss=0.470934\n", "3400 loss=0.470323\n", "3500 loss=0.469739\n", "3600 loss=0.469180\n", "3700 loss=0.468644\n", "3800 loss=0.468130\n", "3900 loss=0.467636\n", "4000 loss=0.467160\n", "4100 loss=0.466702\n", "4200 loss=0.466259\n", "4300 loss=0.465833\n", "4400 loss=0.465420\n", "4500 loss=0.465020\n", "4600 loss=0.464633\n", "4700 loss=0.464258\n", "4800 loss=0.463893\n", "4900 loss=0.463539\n" ] }, { "data": { "text/plain": [ "array([-1.86106309, 1.17821204, -1.9398732 ])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for i in range(epochs):\n", " z = X @ theta # (m,1)\n", " h = 1/(1+np.exp(-z)) # (m,1)\n", " grad = (X.T @ (h - y)) / m # (2,1) <-- from your formula\n", " theta -= lr * grad\n", "\n", " if (i % 100 == 0):\n", " print(f\"{i:4d} loss={log_loss(y, h):.6f}\")\n", " \n", "theta" ] }, { "cell_type": "code", "execution_count": 20, "id": "399fa16b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[8.42051618e-01, 8.49805795e-01, 8.57243833e-01, ...,\n", " 9.99416348e-01, 9.99450047e-01, 9.99481801e-01],\n", " [8.25779343e-01, 8.34175058e-01, 8.42243475e-01, ...,\n", " 9.99343580e-01, 9.99381478e-01, 9.99417189e-01],\n", " [8.08212477e-01, 8.17267413e-01, 8.25986882e-01, ...,\n", " 9.99261747e-01, 9.99304366e-01, 9.99344527e-01],\n", " ...,\n", " [5.94372475e-05, 6.30812203e-05, 6.69485824e-05, ...,\n", " 1.87344113e-02, 1.98602389e-02, 2.10522706e-02],\n", " [5.28448157e-05, 5.60846430e-05, 5.95230868e-05, ...,\n", " 1.66910746e-02, 1.76963241e-02, 1.87609615e-02],\n", " [4.69835435e-05, 4.98640439e-05, 5.29211347e-05, ...,\n", " 1.48672252e-02, 1.57643912e-02, 1.67147783e-02]], shape=(100, 100))" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x1New, x2New = np.meshgrid(\n", " np.linspace(3,8,100).reshape(-1,1),\n", " np.linspace(0,6,100).reshape(-1,1))\n", "XNew = np.column_stack([np.ones(x1New.size), x1New.ravel(), x2New.ravel()])\n", "\n", "z = XNew @ theta\n", "yPred = 1 / (1 + np.exp(-z))\n", "zz = yPred.reshape(x1New.shape)\n", "zz" ] }, { "cell_type": "code", "execution_count": 21, "id": "707fb914", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqoAAAISCAYAAAAEHO1yAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnQV4W+f59m8zMzMzO3EcsINOHIehzLiuXVdaee3adWu7tVtpW7eug6772vVfCKPjoEOOEzMzM7MlS/qu93XsOokdW7LI8vO7Ll+2jo7OeQ9Iuv3A/WpJJBIJCIIgCIIgCELN0Fb1AAiCIAiCIAhiMkioEgRBEARBEGoJCVWCIAiCIAhCLSGhShAEQRAEQaglJFQJgiAIgiAItYSEKkEQBEEQBKGWkFAlCIIgCIIg1BISqgRBEARBEIRaQkKVIAiCIAiCUEtIqBIEQRAEQRBqicqFan19Pe655x7Y2NjAyMgIYWFhuHz5sqqHRRAEQRAEQagYXVXuvLOzE8uWLcOqVatw+PBh2NnZobS0FFZWVqocFkEQBEEQBKEGaEkkEomqdv7yyy/j3LlzSE1NVdUQCIIgCIIgCDVFpUI1ODgYiYmJqKurw+nTp+Hi4oInnngCjz766KTrDw8P858xxGIxOjo6eNmAlpaWEkdOEARBEARBzAQmNXt7e+Hs7AxtbSmrTiUqxMDAgP+88sorkoyMDMlnn30mMTQ0lHzxxReTrv/GG28wUU0/dA7oHqB7gO4BugfoHqB7gO4BzK1zUFtbK7VWVGlEVV9fHwsXLsT58+fHlz311FNIT0/HhQsXpo2odnd3w93dHbW1tTA3N1fauAmCIAiCIIiZ0dPTAzc3N3R1dcHCwgJzppnKycmJp/8nEhQUhB9++GHS9Q0MDPjP9TCRSkKVIAiCIAhCfZGlTFOl9lSs47+4uPiaZSUlJfDw8FDZmAiCIAiCIAj1QKVC9dlnn8XFixfxzjvvoKysDF9//TX+/ve/42c/+5kqh0UQBEEQBEHMd6EaExOD3bt343//+x9CQ0Pxm9/8Bh999BHuvvtuVQ6LIAiCIAiCUANU2kwlj+JcVpTLmqqoRpUgCIIgNAcmT0ZGRiASiVQ9FGIadHR0oKurO2UN6mz0mkqbqQiCIAiCIK5HIBCgsbERAwMDdHLmCMbGxrxJnjk6yRMSqgRBEARBqA1sMp/KykoepWMG8Uz40KQ+6guLfLN/LFpbW/l18/Pzk97U/yaQUCUIgiAIQm1gooeJVea7yaJ0hPpjZGQEPT09VFdX8+tnaGioGc1UBEEQBEEQkyHPqBwxd68X3QUEQRAEQRCEWkJClSAIgiAIglBLSKgSBEEQBKF5MFurU6eA//1v9LeG21ydOnWKN511dXVBkyChShAEQRCEZrFrF+DpCaxaBdx11+hv9pgtVxAPPPAAF4q/+93vrlm+Z8+eWbkW/PGPf4SVlRWGhoZueI7ZdzFf0k8++QRLly7lll7Mr1STIKFKEARBEITmwMToLbcAdXXXLq+vH12uQLHKut1///vfo7OzU27bvPfee9Hf349dk4z7+++/513299xzD7fxcnR0nFIUs4kTmJvCXIOEKkEQBEEQmgFL7z/9NDP3vPG5sWXPPKOwMoCEhAQuFt99992brvfDDz8gJCQEBgYG8PT05FHTqbC3t8fmzZvxr3/964bn2LJt27bB2tr6htT/F198AUtLS+zbtw/BwcF8XzU1NTzqunHjRm4p5eXlha+//pqPgU1hr46QUCUIgiAIQjNITb0xknq9WK2tHV1PAbBJCt555x386U9/Qt0U47hy5Qpuu+023HHHHcjNzcWbb76J119/nQvLqXj44Ydx4sQJ7lM6RkVFBc6cOcOfmwpWGsAivP/4xz+Qn5/PRe99992HhoYGLmyZYP773/+OlpYWqCskVAmCIAiC0AwaG+W7ngxs374dkZGReOONNyZ9/oMPPsCaNWu4OPX39+e1rU8++STef//9KbeZmJjIZ+n697//Pb6MCVs2KQLb1lQIhUJ8+umnvH41ICCAR1RTUlLw+eefIzY2FtHR0VzEDg4OQl0hoUoQBEEQhGbg5CTf9WSERTH/85//oLCw8Ibn2LJly5Zds4w9Li0t5XWkU0Vq77//fi5O2ZSlrNaUbf/BBx+8qdE+q1sNDw8ff1xcXAxdXV0uUMfw9fXlzVrqCglVgiAIgiA0g/h4wNUVmKrLni13cxtdT4EsX76cR0FfeeUVuW3zoYce4hFRVgJw/Phx1NbWcqF6M1gd6mwcB9QBXVUPgCAIgiAIQi7o6AAffzza3c8E2sSmqjHBxpqG2HoKhtlUsRIAlnKfSFBQEM6dO3fNMvaYlQGwyOlU+Pj4YMWKFbyBikVVWeOWh4eHVGNiYxkZGUFmZiYWLFjAl5WVlcnVpUDeUESVIAiCIAjNYccO5tsEuLhcu5xFWtly9rwSCAsLw9133809Tifyi1/8gkdEf/Ob36CkpISn8P/85z/j+eefn3abDz/8MLep2r17902bqKYiMDCQC9yf/OQnuHTpEhes7G91jrySUCUIgiAIQrNgYrSqCjh5Evj669HflZVKE6ljvPXWWzd4l7L60G+//RbffPMNQkND8atf/Yqvx5qqpmPnzp3cZsrY2JjbUsnCl19+CQcHB16ewBq/Hn30UZiZmXEPWHVES8Lix3OUnp4ePgNDd3c3n5mBIAiCIIi5DZuBqbKyknt8qqt40iTq6uq4ewBzA7iZg8Bsrtts9BrVqBIEQRAEQcwTTpw4gb6+Pl6awMz/X3zxRW74zyKs6ggJVYIgCIIgiHmCUCjEq6++yicMYCl/5rH61VdfQU9PD+oICVWCIAiCIIh5QmJiIv+ZK1AzFUEQBEEQBKGWkFAlCIIgCIIg1BISqgRBEARBEIRaQkKVIAiCIAiCUEtIqBIEQRAEQRBqCQlVgiAIgiAIQi0hoUoQBEEQBKEE3nzzTURGRs56O6dOnYKWlha6urpm/Bo2Raus066qEhKqBEEQBEFoJCkVKQj+SzD/rWg2b96M9evXT/pcamoqF5Y7duzA8ePHZ72vpUuX8lml2LSkM+Xjjz/GF198gbkGCVWCIAiCIDQOiUSCV4+/isK2Qv6bPVYkDz/8MI4dO4a6urobnvv3v/+NhQsXIjw8HDY2NlNuQyAQzGhf+vr6cHR05OJ3pjBRa2lpibkGCVWCIAiCIDSO5PJkpDek87/Zb/ZYkWzatAl2dnY3RC37+vrw3XffcSF7fep/LB3/9ttvw9nZGQEBAXz5+fPn+XqGhoZc4O7Zs4eL0qysrElT/2yfTIQePXoUQUFBMDU15dFdFnW9fl9jiMVivPfee/D19YWBgQHc3d35OMZ46aWX4O/vD2NjY3h7e+P111/n068qGxKqBEEQBEFoFCx6+vrJ16GjpcMfs9/ssSKjqrq6urjvvvu4aJy4HyZSRSIR7rzzzklfx0oBiouLeTT2wIED6Onp4WUEYWFhyMjIwG9+8xsuGqdjYGAAf/jDH/Df//4XZ86cQU1NDZ5//vkp13/llVfwu9/9jgvQgoICfP3113BwcBh/3szMjB8Le46VDXz++ef48MMPoWx0lb5HgiAIgiAIJUVTGSKJaDyqmuiruHnuH3roIbz//vs4ffo0Vq5cOZ7237lz55T1pCYmJvjHP/7B0/mMv/3tbzxayoShoaEhgoODUV9fj0cfffSm+2bRTvZaHx8f/vjJJ5/EW2+9Nem6vb29XHz++c9/xv3338+XsdfFxcWNr/Paa6+N/+3p6clF7zfffIMXX3wRyoQiqgRBEARBaGw0dQxlRFUDAwN5o9O//vUv/risrIw3UrG0/1SwyOmYSGWw6CqrZWUidYxFixZhOliKfkykMpycnNDS0jLpuoWFhRgeHsaaNWum3N7//d//YdmyZbwWlpUSMOHKorTKhoQqQRAEQRAaF01lUdSJTIyqKhImSn/44QcetWTRVCYeV6xYMeX6LKIqD/T09K55zKKyU4lyIyOjm27rwoULuPvuu7FhwwZejpCZmYlf/vKXM272kickVAmCIAiC0KhoqvYU8oYtV3RU9bbbboO2tjav+fzyyy95OYA03fmsoSo3N5dHPMdIT/+xjEEe+Pn5cbE6lVUWa+by8PDg4pQ1c7H1q6uroQpIqBIEQRAEoREIRALUdNdADPGkz7PltT21fD1FwdLkt99+O29WYl33rNteGu666y7ekf+Tn/yEp+hZJz9rkmJII3hvBisrYA1arN6Uieny8nJcvHgR//znP/nzTJiyND+rSWXPffLJJ9i9ezdUATVTEQRBEAShERjoGiD90XS0DrROuY69iT1fT9Hpfyb6WOqc2U5Jg7m5Ofbv34/HH3+cW1SxGtZf/epXXMBOrFudLazbnzkVsG03NDTwmtaf/vSn/LktW7bg2Wef5Q1ZLLK7ceNGvj6z11I2WhJFO+AqEGbhwLrouru7+YUlCIIgCGJuMzQ0hMrKSnh5eclVmM1lvvrqKzz44INc70xXX6qO1202eo0iqgRBEARBEGoES8czk30XFxdkZ2fzND2rfVVXkapISKgSBEEQBEGoEU1NTTwl39TUxFPyt9566zWzRs0nSKgSBEEQBEGoEazJSdnG+uoKdf0TBEEQBEEQagkJVYIgCIIg1I453Os9L5Eo6HqRUCUIgiAIQm0Ym2FpYGBA1UMhpGDsel0/Q9ZsoRpVgiAIgiDUBh0dHVhaWo7PU8/msJeX0T2hmEgqE6nserHrxq6fPCGhShAEQRCEWuHo6Mh/j4lVQv1hInXsuskTEqoEQRAEQagVLILKbJns7e0hFApVPRxiGli6X96R1DFIqBIEQRAEoZYw8aMoAUTMDaiZiiAIgiAIglBLSKgSBEEQBEEQagkJVYIgCIIgCEItIaFKEARBEARBqCUkVAmCIAiCIAi1hIQqQRAEQRAEoZaQUCUIgiAIgiDUEhKqBEEQBEEQhFpCQpUgCIIgCIJQS0ioEgRBEARBEGoJCVWCIAiCIAhCLSGhShAEQRAEQaglJFQJgiAIgiAItYSEKkEQBEEQBKGWkFAlCIIgCIIg1BISqgRBEARBEIRaQkKVIAiCIAiCUEtUKlTffPNNaGlpXfMTGBioyiERBEEQBEEQaoKuqgcQEhKClJSU8ce6uiofEkEQBEEQBKEGqFwVMmHq6Oio6mEQBEEQBEEQaobKa1RLS0vh7OwMb29v3H333aipqZly3eHhYfT09FzzQxAEQRAEQWgmKhWqsbGx+OKLL3DkyBH89a9/RWVlJeLj49Hb2zvp+u+++y4sLCzGf9zc3JQ+ZoIgCIIgCEI5aEkkEgnUhK6uLnh4eOCDDz7Aww8/PGlElf2MwSKqTKx2d3fD3NxcyaMlCIIgCIIgpoPpNRZglEWvqbxGdSKWlpbw9/dHWVnZpM8bGBjwH4IgCIIgCELzUXmN6kT6+vpQXl4OJycnVQ+FIAiCIAiCUDEqFarPP/88Tp8+jaqqKpw/fx7bt2+Hjo4O7rzzTlUOiyAIgiAIglADVJr6r6ur46K0vb0ddnZ2iIuLw8WLF/nfBEEQBEEQxPxGpUL1m2++UeXuCYIgCIIgCDVGrWpUCYIgCIIgCGIMEqoEQRAEQRCEWkJClSAIgiAIglBLSKgSBEEQBEEQagkJVYIgCIIgCEItIaFKEARBEARBqCUkVAmCIAiCIAi1hIQqQRAEQRAEoTDEYvHcNPwnCIIgCIIgNJOaylakHMjCkX2XZN4GCVWCIAiCIAhCLvR0DeBUci6OHchGSX49TM0MEbvCD9+lyLY9LYlEIsEcpaenBxYWFuju7oa5ubmqh0MQBEEQBDHvEI2IkH6+DMn7MpGWWgImLWOW+SFhUyRi4/0xNDQgs16jiCpBEARBEAQhNbVVbVycsvR+R3sfvP0d8cjTa7FqfRgsrU3H1xsagsyQUCUIgiAIgiBmxODAMM4cy8fRvZnIz66BqbkRVieFIXFLNHwDnSBvSKgSBEEQBEEQN+3az7lchZSD2Ug9XoDhISGiF/vg1XdvxZIVAdA30IOiIKFKEARBEARBTJraTzmYheMHc9Da3A1nN2vcdv8yrN0UCXsnSygDEqoEQRAEQRAEp79vaDS1vy8ThTm1vGt/+bpQLk6DwlyhpaUFZUJClSAIgiAIYh4jkUiQl1nN607PpORDMDyCBUuUk9qfDhKqBEEQBEEQ85DW5m5ed8o69xtqO+DkYoU7HopHwsZI2DtaQB0goUoQBEEQBDFPGBoU4NzJQhw7kIWsS5XQ19dFXEIwnn19K0Kj3KGtrQ11goQqQRAEQRCEhnft52XWIHl/Js4eL8DggABh0R545rXNiE8IgYmpIdQVEqoEQRAEQRAamto/tj+Lp/Yb6zt5av+We5dizYYIOLlaq3p4M4KEKkEQBEEQhIYgEIzg4uliHN2XgSsXyqFvoIvlCSH4xZvbEBrlofSu/dlCQpUgCIIgCGKOd+2XFTXy6OmJIzno7R5EcIQbT+0vXxsKYxMDzFVIqBIEQRAEQcxB2lt7cPxQDu/cry5vgbWNKRK3RCFxazTcveygCZBQJQiCIAiCmCMIhoU4f6qI151mXqqArp4Olq4MxKNPr0N0rDd0dHWgSZBQJQiCIAiCUHPKihp53emJw7no6xlESIQ7fv7KJixfGwJTMyNoKiRUCYIgCIIg1JCe7gGcPJzDpzMtL26Cta0ZNu5YgHVbouDqYYv5AAlVgiAIgiAINWFEKMLlC2VIOZCFi2eKIRZLsHh5AO5/fA0WLvHRuNT+dJBQJQiCIAiCUHHXPouYstmiTh7JRXdnP7z9HfHgkwlYsyEcltam8/b6kFAlCIIgCIJQAV2d/ThxOAfJezNRWdYMKxtTLkwTNkXCx9+RrgkJVYIgCIIgCOUhEomRcbEcR/ZmcGN+xpIVATx6Oh9T+9NBEVWCIAiCIAgFU1/TjuT9mUg5kI22lh54+tjj4afXYk1SOCysTOj8TwEJVYIgCIIgCAXQ1zuI08l5OHYgG4U5tXyGqFXrw7B+WzT8gpzn3HSmqoCEKkEQBEEQhJwQjYhw5WI5b4y6cLqYP16wxBevvHMLlqwIhIGhHp1rKSChShAEMZ8QiYDUVKCxEXByAuLjAR2qiSOI2VJX3cZni2LR0462Xnj5OuCBJ1Zj1fpw2NiZ0QmWERKqBEEQ84Vdu4Cnnwbq6n5c5uoKfPwxsGOHKkdGEHOSwYFhnEkpwNG9GcjPqoGpuRFWJ4UhcUs0fAIcKbUvB0ioEgRBzBeResstzLDx2uX19aPLv/+exCpBzACxWMxFKUvtnzmWj6FBIaIWefPU/tKVgdA3oNS+PNGSMJfZOUpPTw8sLCzQ3d0Nc3NzVQ+HIAhCfdP9np7XRlInwho6WGS1spLKAAhiChpqO5ByMAvHD+Wgqb4TDs6WWLspkk9n6uBkSedNQXqNIqoEQRCaDqtJnUqkMli8orZ2dL2VK5U5MoJQa/r7hnjUlEVPWRSVde3HJwTjF29sQ2iUO7S1tVU9RI2HhCpBEISmwxqn5LkeQWgwLNGcl1WDo3sycCYlH4LhEUTHeuOl3+7kqX1DI31VD3FeQUKVIAhC02Hd/fJcjyA0kPbWHm7Gf3RfJjfnd3Sxwh0PxvPpTO0dLVQ9vHkLCVWCIAhNh1lQsRpU1jg1WVvCWI0qW48g5hHDQ0JcOFOM4weycPliOXR1tRG/JhhPvboZ4Qs8KLWvBpBQJQiC0HSYTyqzoGLd/UyUThSrYzPjfPQRNVIR8ya1z+pNUw5m8/pTVocaHOGGJ1/agBXrQmFqZqTqIRITIKFKEAQxH2A+qcyCajIfVSZSyUeV0HBamrpxbP+oIX9jXQfv1N96+yKe2ndxt1H18DQS4YgIZzMrsDvlsszbIKFKEAQxX2BidOtWmpmKmDcIBCO4eLoYR/ZmIONiOfQNdLF8bQiefX0LwqIpta8oSmtaceBMPo6cL0RX7yB8XWSfmYt8VAmCIAiC0CgqSptwdG8mThzKQU/3AILC3bB+axSWrw3lFlOE/OnuG8Sxi8XYfyYfRZXNsDIzQlJcMDYvD4GtuT75qBIEQRAEMX/pbO/DySM5vPa0vLgJltYmWLclEolbo+HuZafq4WkkIyIxLuZU4WBqPlIzK/isXUsjvPDQM1uwLMILuro644b/skKpf4IgCIIg5iSCYSEuninhhvyXL5RBR1sLscsDcN9PV2PhEl/o6o0KJUK+lNW24mBqAU/td3QPwNfNFk/cFofEJYGwsTSR675IqBIEQRAEMacoLWzgfqcnD+egr3cIQWGueOKFJN61b25hrOrhaSQ9/UNIvlA0ntq3NDNC4tJAbIwLgb+HHbTGHETkDAlVgiAIdUEkokYngpgCVmvKhCkTqCy1b21rho07F2Lt5ii4edrSeVMAIrEY6fk1vDHq9JUyiERiLI30xkNbYrE00gt6V1P7ioSEKkEQhDqwa9fk1lHM/5Sso4h5imhEhIy0Cp7aP3+yEGKxBIuvpvZjlvpCRwlCaT5S29zJU/uHzhagub0Xns7WeGznMiQtC5J7an86SKgSBEGog0hlZvzXzxrFZpJiy5n/KYlVYh5RWdrMxSmLoHa098Hd2w73P7EGCRsjYGVjqurhaSR9A8NISSvGwbMFyClpgImRPhJiA7BlRShCfBwVltqfDrKnIgiCUHW639Pz2kjqZNObVlbSzFGERtPV2c+FacqBbJQVN/Ja01Xrw7B2cyR8A51UJpTmQ2r/YGo+Tl8ug3BEjEWh7tgQH4IVC3xgqK8nl/2wrn8LCwt0d3fD3NxcqtdSRJUgCEKVpKZOLVIZLMpaWzu63sqVyhwZQSgcVvN45UIZrztlxvyM2Hh/3P2TlYhZ5gs9PZIpiqC+pYs3RU1M7T+yYwnWLw2CvbXs5vyKgO4AgiAIVdLYKN/1CGIO0FDbgWQ2nen+LLS19MDL1wGPPL0Wq5PCYWGl3BrI+cLQsBAn0kux/0weMgrreGp/7eIAbF6u2tT+dJBQJQiCUCVOTvJdjyDUlP6+IaSm5HND/tyMapiYGvLUfuLWKPgFOautUJrLSCQSZJc08NR+SloJBoYEWBjshjd/uh6rFvrB0EA+qX1FQkKVIAhClcTHj9agssap65upJtaosvUIYg6m9jPHuvZPFUIoECFqkTde+s1OLFsdBAND9RdKc5GG1m6e1j98tgB1Ld1wsjXHneujsSk+BM72FphLkFAlCGJ+oi6epWyfzIKKdfczUTpRrI5FmD76iBqpiDlFTWUrT+2fOJSD9tZe7nN6z6MrsXpDOOwc5pZQmisMDglx8vKPqX1jQz2sXuSPXz6yDpEBrtDWnpsRaxKqBEHMP9TNs5Ttk1lQTTYmJlLJmoqYAwwODOPMsXwc2ZuBguxamJob8dQ+s5QKCHGh1L6CUvt55Y3Yfzofxy4W89T+giA3vPHYaGrfSAMi1mRPRRDE/GIqz9Kx6KUqPUvVJcpLEFIIJSZKj+7LwOnkfAwPCREd6411W6OxdEUA9OdADeRcpK2rD4fPFfIZo6oaOuBoY4ZNy0P4dKbqmNqfjT0VCVWCIOYP5FlKEHKhqaETxw9m88Yo1sHv4GyJdZujuOepg5MlnWUFMCwYwZmMct4YlZZbDV1dbaxc6Mu79hcGu6t1ap98VAmCIGYCeZYShMwM9A/j7PEC3hiVc6UKhkb6iF8TjKdf3YzwhZ7Q1tams6ug1P6h1AKe2u8dGEaYrxNeeGANEmL9YW5iqPHnnGpUCYKYP5BnKUFIL5Qyq3FkTwZSjxdAMDyCiBgvPP/r7YhbHQQjYwM6o4pK7Z8txIHU0dS+vbUpdiZEYENcMDycrOfVOSehShDE/IE8SwliRjAT/pQDWXzGKJbad3K1xh0PxSNhQwTsKbWvEIQjIpzLquCNURdyKqGjo80bop67ZxUWhrhBZ55GrKlGlSCI+VejOp1naWUlNTER8w7BsBAXz5Tg2P5MXL5QxqcvjU8IRuLWaIRFe1DXvoIoqW7FobP5OHKuEJ29gwj2dsCm+FCsXRKgMal9qlElCIKYCeRZKj3kRKDxqf2ivDo+lenp5Dz09Q5xK6knX96IlevCYGKmGUJJ3ejoHsDRC0W8Maq0phVWZkZIXBqEzStC4Otmp+rhqRVqk/r/3e9+h1deeQVPP/00PmK+gQRBEIqAPEvnrt8sITdamrpx/FA2T+/XVbfD1sEcG2+J4Z6n7l4klBSZ2j+QWoDz2ZVgPfpxUd74yc6lWBruCV1dsqJTW6Ganp6Ozz77DOHh4aoeCkEQ8wEmsrZuJc9SWfxmWdkEW65Kv1lCJgSCEZw/VYTkvRnISKuAvr4u4tYE4WcvbUTEQi9eE0nIn7LaVl53euR8Ibp6BxHk5YBn716BdYsDYWFmRKdc3YVqX18f7r77bnz++ef47W9/q+rhEAQxn8oAVq5U9SjUN93PIqmT1fGyZayW95lnRsU+TUig9pSXNHFxevxwDnq7BxEc4YZnXtuM5WtDYWxCXfuKoLd/CMkXivl0poWVzTy1zzr2N8WHwMfNFvONQYFw7grVn/3sZ9i4cSMSEhKmFarDw8P8Z2JxLkEQBCFnyG92ztPV2Y+Th3O4IX9ZUSOsbEyxfms01m2JotS+ghCJxbiUV40DZwpwJqMMIpEYSyO88N7TW7A00gt68yy1LxZLcKm6Druz8nE4I2duCtVvvvkGGRkZPPU/E9599138+te/Vvi4CIIg5jXkNzsnEQpHkJZawg3508+W8i792Hh/3P3oSixa5gddvfkllJRFRX07DqXm8ylN27r64eVig5/sWMojqDaWJphv1HZ0YXd2AfZkFaKhuwce1pZ4cOlCPDfXhGptbS1vnDp27BgMDWfWVciarZ577rlrIqpubm4KHCVBEMQ8hPxm51TXPouYMr/TU0dzeWrfP9gZj/1iPVauC4WF1fwTSsqgh6f2R7v2CyqaYW5qiMQlgdgYF4xAL4d5Z+XVPyzA0YJS7MrKx+Xqepjo62NDqD+2RwYjys0Zvb29MgtVlfmo7tmzB9u3b4fOhPomkUjELy6bho2l+Cc+J29fLoIgCIUjEACffgqUlwM+PsATTwD6+up/4slvVu3p6RrgXftMoFaWNsPa1gxrNoYjYWMkPH3sVT08jU1lp+fX8LrT01d+TO1vjA/Bskgv6OupvJpSqTD5yEQpE6dH80sxIBRiiZcbtkeGYG2QL4z09eSi11QmVJm6rq6uvmbZgw8+iMDAQLz00ksIDQ2ddhskVAmCUFtefBH44INR0TcG++ebZYXeew9zpuufMfFrYixSRF3/SocJo8y0ChzZm4GLp4u4cFq8IoDXni5Y7AOdeVYDqSzqW7r4VKaHUgvQ1N4LT2drbF4eiqRlQfMytd/Y3Yu92QXYnVWA6o4uuFqac3G6LTIYLpbmmmP4b2ZmdoMYNTExgY2NzYxEKkEQhFqL1Pffv3E5E61jy9VdrJLfrNpQXdHC605PHMpBe2sv3L3t8OCTCVizMQKWlNpXCP2DAhy/VMJT+1nF9TA21MfaxQHckD/Ux2nepfYHBUKkFJXzxqgLFTUw1NNFYrA/3tqcgBgPV2hra82PKVRXrlyJyMjIGRv+U0SVIAi1TPcbG18bSb0eFlkdGJg7ZQDMBYA1WLHa1fh4sqRSUmr/5NFcbshfUtAAMwsjrEwM44b8bOao+SaUlAGLUF8prOXR05PppRAIRxAT7I4N8cFYucAPRoY/prLnAxKJBFl1jdiVmY/D+SXoGxZgoYcLj54mBvvB1GDmn19zMqI6GadOnVL1EAiCIGYHq0m9mUhlsOfZesyLVN0hv1mlIRoR4fKFchzdl4G0MyVcKMTE+eH192/Hojh/btBPKD617+ZoiYe2xiJpWTAcbMzm3Slv7ukbT+1XtnfC2cIM98VG8dS+u7Wl0sdDdz1BEIQ8YY1T8lyP0Hjqa9txdG8mj56y1L63vyMeeXotVq0Pg6W1qaqHp5EMDQtxIr2UN0ZlFNb9mNpfHoJQ3/mX2h8WjuBEcTl2ZRXgXHk19HV0sC7YF7/auBqxnm4KTe1PBwlVgiAIecK6++W5HqGRDPQPI/V4AY7tz0RuRjVMTA2xOikMiVuj4RfkrOrhaSQsQp1T2oCDqQU4drEYA0MCLAhywxuPrcfqGD8YGsy/1H5eQzOPnB7MLUL30DAiXZ3w5qY1SArxh5mhesxaplY1qtJCNaoEQagdmlajSsi1az/7ciWPnJ49UQjB8AgiFnpycbpsVRAM5lkNpLJoauvBobMFOHi2AHXNXTydvzF+dDpTF3vlp7JVTWtvP/blFGJPVgFKW9thZ2qCbRFBvPbU285aIfvUmBpVgiAImRkcBF54ASgtBfz8RrvrjYyUf0KZ+GQWVJN1/Y/BnleVSKXmKKVTV93Gu/bZdKZtzT1wcbfBHQ/FI2FDBOyd5p9QUgaDQ0KcvFzKBerlghoY6Oli9SJ/vPJgAqKDVJvKVgWCERFOllTwrv3U0iruV58Q6IMX1sVjqbcHdHW0oa5QRJUgiLnPtm3A3r03Lt+6lc0uoooRqaePKvNGffppoK7ux2WursDHH4/aURFyTe2fOZbHDfkLsmt5an9FYijWbopEUJjrvKuBVFoqu6yR150eu1jCU/vRga7ckH9VjB9MjOZHBiOlIgVPHX4KnyR9AmejcC5O9+cUoWtwCGHODtgRFYKkkABYGhvKZR8J3gnTrj8nDf/lAaX+CYKYUqSqg1hVp5mpxgz8r//IJwN/ucG+TpkoZYb8Z47lY3hIiOjFPli3JQpLVwRAf57VQCqL9q5+HjllnftVDR1w5Kn9EJ7en2+pfQlzivgsHuVN2nDSWwmx0Aq2JsbYwlP7wfCzt5XLPmL/EYv0hnTEOMcg7ZG0af/xIqFKU6gSxPxN97N60Olg9aCqKANQtylRJ0ZSJ8K+ZFhktbKSPFJloKWxCymHsnH8YDbqqtvh4GyJxC1RPHpKqX3FwDxOUzMruKXUhZxK6OhoY9VCP2xaHoKFwe7zLrUvFImQWlaFv509iezaTr6sDwV4buVGPBW/HXrTTEkvDUfLjmL9V+vHHx+5+wgSfRNv+hqqUSUIYn7CalJnut6f/4x5CzPsn0qkMliUtbZ2dL2VK5U5sjnL0KCAN0Sxrv3sy1XQN9BF3Jpg/PzlTQhf6MlrAAn5wiJ5hZXNfLao5AtF6OkfRoi3I35x72qsXRIAcxPZU9lzlZLmNt61vz+nEG39A9DW60Sb1hl0Sy4D2kP4v9J8PLdip1yvwesnX4eOlg5EEhH/zR6v81mnsHIWaqYiCGLuwhqn5LmepsJmlZLnevMU9iXNrKRYY1RqSj4GBwQIX+CJ5361lYtUYxP1sPPRNNq6+nD4XCG3laqsb4edlQm2rQrHhrhgeLnYYL7RNTCEA7lFvPY0v7EFVsZG2BIeCHvrbjxyeNvoSkwzSsDT88nlydNGPGcK2xbb5hhMrMp7H9dDQpUgiLkL6+5PTp7ZevMZNvWpPNebZ7S19CB5fyaS92Whsa4DTi5WuOXeZXw6U0cXK1UPTyMRjohwLqsC+0/n89Q+60pfvsAXz9y1AjGh7tCZZxFrkVjMjfjZdKbHiysgFouxwt8Ljy+PxXI/L+jpaPO60bFI5xjyjHheH01VxD4mg5qpCIKYu1CNqnQ1qvX1NzZTMahG9QYEghGkpZbg6N4MXLlQBj19XSxPCEHi1iiERnlQ176CKKttxYEz+ThyrhCdvYMI9nbA5uWhfNYos3mY2q9o6+CpfTalaUtvP/zsbbAjMgSbwwNha2oyZd3o9cykjnQ6ZrMPqlElCGJ+whqkWFf/dF3/8mikksV/VF08S9k+mQUV6/pnonSiWB2LgHz00bxvpGIRo+L8em7If/JoHvp6BhEY6oqfv7IJK9aFcospQv509Q7ymlNmyF9U2QwrMyMkxY0a8vu4zb5Lfa7ROzSMQ3nFXKBm1TXCwtAAG8MCua1UiJP9Df8kjUU6taENMcQ3bI8tn23EUxn7mApK/RMEMbdh1lOLFgHpP9ZNjRMTIx9rKln8R9XNs5Tt8/vvJx8TE6nz2EeVpfaPH8pGyoFs1FS2wsbODBu2L0DCpgh4eNurengayciICOdzqnhj1NnMClZOibhILzy0JRZLI72gp6uCf+hUnNq/WFnLU/spRWUQisSI8/HAh7dswOoAHz5hwVQIRALUdNdMKiAZbHltTy1fz0BXtjpqZexjKij1TxDE3GYqf1AG+8+eibPZiDBZ/EfV2bNUXaK8KkYwLMT508VI3peJzLRy6OrpYOnKIKzbHInIRd7c7ohQfGo/wMMeG+KDkbgkEFbmM7Ca0zCq27t4U9Te7EI09vTC29YaOyKDsTk8CA7mpjPeTm13LVoHWqd83t7EHq7mrrMa62z2QT6q5KNKEPMTRfuDyrJ98ixVa8qLG7kh/4nDuTy1Hxzhxv1OV6wNhYkZpfYVQW//EJIvFPMZowqvpvbXLwvipvx+7naYb/QNC3A0vwS7svJxpaYBZgYG2BDqj+2RIYhwddTI+ueeWcxMRal/giDmLor2B5Vl++RZqnZ0d/bjVHIej56WFTXCysYUSdujkbglGm6e868GUlmp7PT8Gp7aP325DCMiMZZGeOG9p7fMy9S+WCzB5Zp6ntpPLijFoFCIJd7ueH9HEtYG+cLwJql9TWBEPHnJwEzQ7DNDEIRmo2h/UFm2T56lasGIUIRL50p5YxTr3udTS8b54Z6frETMUj+e6ifkT3VjB/c7ZVOatnb2wdPZGo/uWIqkuCDYWs48la0p1HV2Y092AfZkFaCuqwduVhZ4NC4GWyOC4GwpXWRxLpLf0YzvynKwOz9D5m2QUCUIYu6iaH9QWbZPnqUqT+0n78/CySO5PJLqG+iER59Zh5Xrw2Bp9aOdDyHf1P6xtBIePc0ra4SZsQHWLQnkhvwhPpqZyr4ZAwIhj5qyrv20qloY6+shKcQfv4sMxgJ3F40/Hx1DA9hbWcAFakFnC2wNTbDVKxg5Mm6PmqkIgpi7KNofVJbtk2ep0unpGuBd+0ygVpQ08dT+6qRwXnvq5eeg/AHNk1Q2S+0fSM3DKZbaHxFjcbgnt5SKi/KGgf78ioOxiH1GTQOvOz2cX8LFaqynG7ZHBmNdsB8Xq5qe2j/dUIHvy3KRUlfKPy7XuPniVp8wrHDxxmBfP9WoEgShxl3d0o5ppusr2h9Ulu2TZ6lSEInEyEyr4I1RF08X8UuzeEUA7n98NRYu8aXUvoJoaOnGgdR8Hj1tau/lqf2f7FyKpGXzM7Xf1N2LPdmFvHO/uqMLLpbmeGjpAmyLCIarlQU0neLOVnxfnovdFfloG+pHkJU9Xo5ehW3eIbAx/NHFYXAW+6CIKkFoEurm3SnLmOTlWermJj9/UFm2r+gxzVOYz2nKwWweQW1r7oGHjz3Wb43C6g0RlNpXEANDApxML+UCNaOwDsaG+li3JACblocg1MdJbVPZKRUpeOrwU/gk6RMkeCfIbbtDwhHudcpS+xcqaqCvo4PEYD9sjwrBIg9XaGur5/mQF13Dg9hfWYjvynOQ094EawMjLkx3+oQhxHryDAbZU5E9FUGop3entGOazTEoOpI8l2emmuP09gzi1NFcbshflFcHU3MjrFwXirWbIxEQovk1f6pK7WcW1+HgmXycSC/F4LAQC4PduKXU6hg/GBroqX0qPvYfsUhvSEeMcwzSHkmb1X3Ctpdd18Qjp4fyStA7PIwF7s48csrqT00N5Wtyr44uDqmNVbzu9FhtKUQSMVa5+OBW33D+m4n1m0FClYQqMd9RR+9OacekjsdAqAzRiAhXLpbj2IEsXDhdzFP9LKXPDPljlwdAf57VQCqL+pYu3rHPOvcb23rgam/BxSmb0tTJdu50qV8/L72sc9039/RhXw5L7Regoq0DjuamXJxuiwyGp40VNJ2Kng5ed7qrIg9NA73wt7TldafbvENhZzTz5kTyUSWI+Y46endKOyZ1PAZC6dRVt3G/02MHstHR1gtPH3s88MRq3hxlbWtGV0QBDA0LedSUGfKPpfYTYv25QI3wd55zEeuxeel1tHQgkoj4b2nmoReMjOBEcQX3PD1bXg09HW2sDfLDL5NWYrGXG3S0NXvWsh7BEA5WFfHa0yut9TDXN8AWz2AePQ23Ub6LA/1LShCagDp6d0o7JnU8BmKc/r4hFOXVwyfAkdeCMjEgry+swYFhnD1eyBuj8jKrYWpmiFVJ4UjcEsXtpeaaUJoLsOvHrKT2n8nHsYvFvA51QZAb3nhsPVYt9IORoXqn9m9GcnkyT/mPwcQqe8yWTxVVZeejoLEFP2Tm42BuEbqHhhHh4ohfbVyNDSH+MDcy1PjU/vmmai5Oj9SUQCgWId7JC5/Eb8E6d38Y6qhOLpJQJQhNQB29O6UdkzoeA4FLZ0tw8IfL3DTfwdkSr7xzCxeqsxWPYrEYOVeqcWx/Js6eKMTQoACRi7zx8tu3YOnKQBjMYaGkzjR39OLw2UIcOpuP6sZOONiY4c710dgYHwwXe0vMda6Ppo4xVVS1vW8A+3OLePS0pKUNdqbGuHVBGLeV8rGzgaZT1dPJxekP5bloHOiFj4UNnomIw3bvEDgaq0cGg7r+CUITUEfvTmnHNNtjEAiATz8FyssBHx/giScAff251eikJmNi6XcmTlnzEmtkMrc0RsRCT0Qt8kbcmmCYW/xoOyMtDbUdvO70+MFsNDd2wdnNmvudrtkYAQenuS+U1DW1f/pKGQ6eLcClvGro6+li1UJfntpfEKxZqezra1Ovh9WqrvJMwJnSSu55eqa0igvXNQHe2B4ZgmU+HtDV0ZzzMRl9wmEcqi7G92U5uNRSBzM9A2zyDMKtvmGIslVMqQc1U1EzFUH82DHPmMzvU5Vd/zMdE1t/586pt/fDD5Mfw4svAh98MCr0xmAC77nngPfe00wbLwU1MP3nryfw7X/O8cchke5YsNgHEQu9eApe1ijnQP8wUlPyuSE/S+0bmxhgBeva3xSJ4Ag3Su0rOLWfklaM/kEBIvxdsCk+GKtj/WFqpHld6mOd/lcarkCMG+eWN5S4wM9kE0wkkegcGESoswN2RIZgQ2gALI01O7UvlkhwqbmWW0odri7G4IgQy5w8cYtPGBLd/WGkq9gMBjVTEQQxKmaY8JtM7KjKu1PaMV28ePPtseevfw0Tqe+/f+O6TLSOLZ8oVqeywGKRXLZcnWy8lDgm9iWvo6sDCysTGBnrcwH585c3wdHF6pp0vUQ8ut5MqK5owff/PY8zx/IxPCREdKw3XvrtTp7aNzS6SbSbkJn2rn7etc88T6saOuBoY4bbE6P4dKZuDprdpS4QCVDTXXONSNWWGMMc0bDAIhjCFUMD/bgj1h+3RIXD38EWmk5tXxd+KM/jqf3avm64m1rip6GLscM7FK6mc2NCAkr9E4SmoSbpY6nHxFL3xsbXRkWvh71mYODHlL60r1FHCywVjIlZPenoaF/TEMVEqLa2NheX77z8HQYHBXjvswfg6Dy1uJmuoSojrRwfv72fN0UlbIqEvePc+GKcawhHRDibWcFnizqfXcmv7cqFvti8PBQLg9013oB+IrXdtWjqa0FObQdOFzfhSnU7Xx7lbo0VAY5YGxgITyt3aDIDQgEO1xTz2tMLTTUw1tXDRo9A3OIbhhh7N2iroDlR6RHVrq4uXLp0CS0tLfzDbSL33XefLJskCEJeMDGjbvZNMxkTqy+9meBksOfZes88I9tr1NECS0ljYmn9U8l52PXVBR7RvPvRldzUXUdn9EuLiVSGh7c9whZ44sB36ci9Us2FamNdBypKmrlmZiLX2d0GPv6jNjU3E6uRMV74956nxrdNyA923ourW7gh/9ELRejuG0KQlwOeu3cV1i0JhLmJZqeyJ6O0pQ27syqxP6cQrX0D8Le3xQtrl2NzWCBsTGWvq54r98OV1nrueXqguhB9QgEWO7jjj8s2Yr17AEz05m4GQ2qhun//ftx9993o6+vjqnjiBxT7m4QqQRAywZqgpF1P2teoowWWgsfEuukz0iqw66vzyMus4cu0dbS5UGWRt4mMRVWjYrx5s1PKoWx0dfbhdHI+j7QKBaP/FNg7WWBxfAAe+nnCTVP4JFDlT3t3P46eL+LR07LaNthYmPDIKeva93bV/FT29XQPDuFgbjGfMSq3oRkWRoZcmO6ICkGQo53G1z839vdwM34mUCt7O+FiYo6Hg2J47ambmWY0J0otVH/xi1/goYcewjvvvANjlnIjCIKQB6xTX9r1pH2NOlpgKXBMTHieOJyD//viLJobuhAY6oLW5h6UFjSgIKcWweFu42UAjLEv9cAwV/iHuCA7vZL/sM787Xct4an7otw6XLlQhn3fXoKhkR5ufyAeJmaGcvVVJW5M7Z/LqsCBM6OpffYPwIoFPvjZ7fFYFKr5XeqTen6W1/Cu/ZSicn6fx/t54pPbNmGlvxf0dTXbeXNoRIjk2lKe2j/bWAV9bR1s8AjA24sTsdjRQyWpfbWqUTUxMUFubi68vb2hamZT80AQhJqhzBrVuWzjJSUfvrUXmZcqsHHnQtz+YDy++dcZfPHpCWy9fREef2HDeBT1ev7f30/yKUzXb41G4tboa57LSq/EH97YDaFwBE+8sIF38BPyp7y2jTdFHT5bgM7eQQR7O3BLqbWLA2BhajTvTnllWyePnO7NLkRzbx987ay5pdSW8CDYmc18Os+5iEQiQVZbIxen+yoL0CscxgI7Fz5bFKs/NdNXbxcHpdaoJiYm4vLly2ohVAmCUEEzlbR+pTOFbYPZSU3WwT8Ge37ivqR9DTsPzO5pzDJrMpgbgbzO10zO1cQxMVE6mY2XDGMai5TuuGcJ9z4NjRptIFkU78+F6rmTRXjwyRtT92PCdcmKQASFufF61YnbG6s9Xb8tGl/94zRyrlQhZpkft5zSqAZAFdHdO4jki8U8tV9Y2QwrMyMkxQVjU3wIfNzmX2q/b2gYh/NLsDurABm1DTA3NMDG0ACe2mf2UpoexW8Z6OOp/e/Kc1He3Q4nYzPcH7gAO31C4WVujfnAjCKq+/btG/+7tbUVb731Fh588EGEhYVBT+9a760tW7ZAWVBElSCU7MUprV+pLCxaBKT/OP3hODExwKVLk79m2zZg794bl2/dCuzZo5rjkIe3q5ubXK3FxtLzL/30C2RfrsLL79yCletCrxGhM93G5fNlePeX38M3wBGvvHsrn61K3f1jUypS8NThp/BJ0idI8E6Q+/qyMiIS42JOFY+epmaUg30pL43w4p6nyyK9oTdDOzBNgTX5Xays4eL0WGEZhkdGsNTHg3ueJgT6wEBPs1P7w6IRHK8rw3dluTjdUAE9bR0kuvlzQ/6ljh5zcoIGhRv+z7Qgnn14iabrwJUjJFQJYgZenPIy/J/Kr3SMF16YvciTZR/SHreiz5OsxzFNZJF9VDNxyepCPX3seSRUmrrQsY96tn7yvkx88NZeLF4egDc/uHPa103cx9jj1uZu3LvxQ9jYmeG/B5+VrXFKGdfiOjN4Nud7jHMM0h5Ju+m5k3b92aT2j5wvREf3AHzdbLFpeSgSlwTCehazf81Vajq6uDjdk1WAxp5eeNpYYUdkME/tO1qox3SeioLdb/kdzTy1v6ciH12CIUTYOuFWn3Bs9gqChf7cdnGgmamoRpWY7yjai1OW+lFl7EPa41aGZ6mcztVEQbjnfxdxZE8G+vuG+exQxsb6ePjpdViVGDpj8/2JdHX248GtH2NkRITPv3uSm/pPVas6kYnrHD+UjQ9/sw/h0R5c7Oob6Km1f+z1U2uyqTQTfRPltv5M6ekfQvKFIt4YxVL7lmZGSFwayFP7/h72mG/0DwtwpKAUuzPzcbmmHqYG+nymqO2RwYh0ddL41H7bYD/2VOZzgVrU2Qo7IxNuxs+69v0sNafUQ6k1ql9++SVuv/12GBhcW48kEAjwzTffkD0VQagCRXtxyuJxqox9SHvcyvAsldO5GvuC/n+fncLRfZl8OtOgcFcMDQpx8XQxb2Zqqu/EnQ/Fc7E60+gqW4+l6Vnd6rH9WTiTko/b7o+btI9r4mvYtsdEam1VG/djHRGKEJ8QIr1IZSjR05aN//WTr0NHSwciiYj/Zo/X+ayb9JxJu/5MutQv59fiQGoeTl0u46UWLLX/+6c3z8vUPju/l6vredf+0fxSDAqFWOLtjvd3rEdCoC+M9PXUYoyKEskCkQgn6su4pdSp+gr+P1mCqx9eiFqBFc7e0J2DqX1FIrVQZbWp69evh739tf/59fb28ufIR5UgVICi/UFl8ThVxj6kPW5l+KjK8Vwd/CGdi1TWrPTy2zthYjqa/mNd9u+89C1++H/nYW1rig07Fs74S3VsvTVJ4aNC9dioUJ2qRpVFX5ndDYvkMkurnMuVPLrb2dGP2x6IQ9L2BZAJJXraJpcn8xT+GEx8ssds+WRRUmnXn4qaxk4cPJvPpzRt6eiDp7M1frJjKZLigmBraYr5Rn1XD/ZmF/D0fm1nN9ysLPBI3EJsjQiGi6V6OPecrq9ASl0Zn80p3MaJ14RaGcrHYaGos4XXne6uyEPH8CDCbRzxq5g12OwZLLd9aCK68vovo66ujod1CYJQAYr2B5XF41QZ+5D2uJXhoyrHc1VV3sJ/L18bwkXq2EyAYVEeeOiptfjbHw5zP9PQKA+4e9nNKH0/BnuNq4cNyooakZdZzR8LBCPQ19e9Zjar/Mwa/Pn3B/lzErEEA/3DsHMwx+PPr8fqDRGQGSV52l4fHR1jqiiptOtfT9/AMFLSWNd+AXJKG2BqbMDtpJghf6iP5qeyr2dQIOQNUSx6erGyFsZ6ekgM8cPbW9dhobuL2kzvmtnagLcvn8CV1joY6upxr1JGgJUdPonfAn9LO5m22zk0iH1VBfiuLAd5Hc2wMTTGDp9QXnvKtk3I0Uc1KiqKv8Gys7MREhIC3QmGuqyBqrKykkdav/32WygLaqYiiPE3oWL9QdW9RnWmx60MH1U5nSsmOn9292eoLG3G37/7GReiEwMFvT2D+OyDI0g5kI07H16O+x9fPWOhOrad//f3U/xnw44FeOrVzePPs5S+rt7o8ddVt+Hzj5K5QGVjYLZVC5f6YtYoydP2+lrT67m+9lTa9ce61C8X1PC601OXSyEcESM2zIN7ni6P9oHBBPE/H2D3V2ZtA4+cHsorQb9AgEWerrzudF2QH0wM1Gs6z8ERIZ47ewBHaopxb0A01nsEwNHYDF8UXsZ/izMQbuuEFyJXIM7Zc0YlASNiMVIbKvFdeQ5Sassglkiw2tWH152ucvXhXfzzjR5l1KhuY/YvzOg5K4t7qZqa/pi20NfXh6enJ3bu3CnVzgmCkBMK8uKclcepMvYh7XEr+jzJehyTwASnrb05F6os6nm9UDUzN0LiliguVE8dzeW1qjOtFR3bzsrEUC5U086WoKOtFxUlzTh2IBOhkR5I3BbNo6su7jZ49vUtMDY1vCbaOmuUcC3GoqPa0IYYo9HoibDlE6Ok0q7f0NLNu/aZ52lTey9P7T+yYwnWLw2CvbVmd6lPRnNPH/ZcTe1XtXfC2cIcDyyJxraIILhZq+90nkdrSrhI3eQZhLdi140vfy4yHpYGhvhTznn8Je88F6o3E6msMerzgkvYXZGPlsE+BFra4cXoFdjmFQJbI82ekECRzPhT54033uC/mSBlzVSGhnPbKoEgNA5m48PsfCbzpJSHF+eYnZIi/UfZNkpKpvZEnWwf7Lief/7GcbHIIhvX9cet6PM0dhyzPFes4Yal5tPPlaKmsvXqIV0bLWVTnbIGq8KcOly+UI6lKwNn5Ik6th1XD9vx1z/z4D/Q0tjNl/f1DmF1UjgXpuyL2dJaQfWUCr4WApEANd01k4pOBlte21PL1zPQNZjR+nWdTdh3JgdHz5XgSmEtjA31eWp/8/IQhPqqZ2r/vXPvcYH9m1W/wYvLXpTrtoeFIzhRXI5dWQU4V14NfR0drAv2xZsbV2ORp5tapPZre7twqqEC7UMDWOvmhxBrB97gxmqv2fWq7+/h64VaO/LfY89ZGhjhpyGLkVxTiotNNUiuKcE6d/8p98New0Qqm850p08YQq01f0KCmdAj7MeRxnNQ2hSq6gSl/glCg2amktVXc6rXjL1uKi9OZcyGNMtzte/bNHz2x6NYvCIAP395IxeM16ceWUMVS81vujUGT760ccLhTS1YO9v7kHo8H5lpFbh4pnj81LGmLTbVKvNXVSoKvBa13bVoHRgV+pNhb2IPV3PXH9f/5u9ofe4xbrp/33agwB4IbtbC66e8cNEpBpluizAkAqKDXLF5eShWLfSDkaHqu9SngpWDmP3ODAPCARjrGaP35V7ZPG8nwO7B3PpmPp3pwbxi9AwNcyspNltUUog/zAxVO53n2HuENUZ9mncBl5prx5+7zTccv1+64Zr1nz27n3uX/nXFdp72H3s9E6zMXJ9ZR71w7iCWOHrg63U39x0ee818RyQRI7OzCMeaLuJiey4E/UM4kvQ3xaX+raysZvxfQUdHh1QDIAhCzrAv+Fna+dwUJrRktaCaTqywyNpkgpMtY59BbL8ssjomYm72mjGuf42yzpMczlX4Ai9YWpvw9H9VWQsiF90Y2QwMdeX1pPXV7bxulZUEMJhIZV+4vd2DMLc0Hq9fHRoUYPfXF/Dtf0YjHG5ettiwfQHWbo6EqZmKOo8VeC3cLNz4z4wQieD2wm/g1ggc9QHKrKzg3LEQuoML8eECWzj3tuPe8gvY8N3f4exohbnAu2ff5SKVwX6zx79c/kuZttXS24f9OUVcoJa1dsDBzBR3LAzH9sgQeNmqz/lgeiW9uRYvnj+EIdEIF6ehNo6IsnWGmb7BuBBltaMsCmplMHrfF3Q2c6HKluswO7arumejRyDezziNC03VKOhoRrC1w5T7nu8itWGwFSlNaUhpTkO7oBsexk540GsLFhj6wx1/k2mbMxKqH7EUzFXa29vx29/+ltepLlmyhC+7cOECjh49itdff12mQRAEQcjkq6lEL05V4OpuA28/B1y5WI68rGpELvK+IWhgYmoABydL3uw0PCTkQpV166eeKMS3X6Ty5qd7H1s1HkVjM1p5+NjjlnuXYtX6MPgEzK6rXqNITcVQYzNOeC3Am8tiEFntB5HWMDpNsmEg+D98v7cSOizWWvQzwFH97yf2z8k7Z9+5Zhl7/ErcKzOOqgpGRnCypBK7MvNxtqyKC7GEIF+8lLgCS73d1VKYsfrQZ87u50Lz4/gtWOniPel6Y++khfau+HfhZVxoquGPx45pTMwa6ephvbs/vizOwLHaUi5Ux0QuAfSPDCK1NRPHmy+hoKcCJjpGWGG/AOscF8PX1I2fR5YBl5UZCdX7779//G/WMPXWW2/hySefHF/21FNP4c9//jNSUlLw7LPP0nUjCEI5vppK9OJUBSxSunxdKLKvVOHy+TIsXRUEbz/Ha9L6ZhZGGBwYhlAwwo38GWwCgK72PtRVt/PU/poNEXB2sx6PqrLHxI+wCFteeSMOJBcheeebGNA3RK9BGVrNvkanaS7E2gJUAEjxARLL5879NDGaOsZMo6oFjS1cnB7ILULX4BDCXRzx2oZVfNYoCyP17lE5XluGhv4e3hg1lUhljP3T52JiARtDE5R1t49HTMeEKK+O1NLijVZMqCbXluLpiLh5L1JFV1P7TJyy1P6IeASRVgF4MfB+LLYJg4GO/JwdpG7hZJHT3//+9zcsZ9ZUL7/8srzGRRDEfEMWX00leXEqGiYg2WxPHt43TqEZG+ePk1EeyLhYzg36H3tu1DppTKyOdfobGetjeFg4bivlE+DIhau2jjafKpUx29pETaOloxdHzhdyz9Oqhg44mOjhjqIz+GdMOkqdOiCacLp0xMDrq4F15YCWmt9PU0VTp4uqdvQPcGG6K7MARc2tsDM1xs6oEJ7a97W3gbozltK/0loPUz0DrHH90Uata3gQzQN9cDE1hxa0YKL3o5CyNzJBlJ0zUmpLcaKu/JrU/lh0NcbBDdaGxqjo6UBNbxfczdTXxUCR1A+28NQ+E6gste9u7Ii7PZKwyj4GNgaK8dKXWqja2Nhg7969+MUvfnHNcraMPUcQxE1QRgOPtPtQZHOUNLBxsm7v6Xw12XqzeY0awaZAPXYgi/90tPXhm+Tnb6gTZfWlm26JQdalCuz++iLv7A+L9hx//uSRHP7alYlhfFKAMQEbEOKCP/zjIdg7WmjmfS4jw4IRnMkox4EzebiUVwM9XW2sXOiH5+5ZhYWBzkiJfwvpbjf2WjDRmu4CJC+2ReJU95MaHfdk0dTJoqpCkQhnSqt43empkkqeDl8Z4I2nVy9FvK8ndKdxkJAJBZ2nsQhpbnsThkUj0L/qV8qaoQ5XF6G6twutg/3wsbDBHX4RvHaVwQToMkcPLlSP1ZXikZBFMNTRvUYAj0jEiLR1wpWWejQO9MwroTooGsbZ1kzeGJXPUvu6RlhptwBrJ6T2FYnUQvXXv/41HnnkEZw6dQqxsbF8WVpaGo4cOYLPP/9cEWMkCM2AdadPZsPDvCTlYYkkyz5efPFGCyVm9SQvuylZfDWn8mNmQvR6X01l+KLKGZamP3u8EMn7M5FzpYpHQlesDUXC5sjxKVKvh4nTLbcvwp7/peHXz3/D/VO9/BxRVtSAo3sz4eXngJ33jPYMjJUEsEirSkSqMu5zKWFCI7+8ifudHrtYjN6BYYT7O+PlhxKwZpE/nz1qbL3Xd1hAu78N4kn0mTaLqm63wDpt7fH6xuuPO0W/Dk8lAZ8cBhIE0x93SkUKnjr8FD5J+gQJ3gkKj6aO8f6Zf0K7fxn25xahY2AQwU72eClxOTaFBuJK8zn87PAmfKI38zHN+DhmeH+wa3GhuYZPZepvYQtjPf1pzfbH0vXOJuYo7W7DVwVH8JfL/4bYOJ7PNsWaqdgnRGZrPf9pHezDw0Ex/LlFDm4ItLJHTlsjjlYXY6t3yHgHP9vn8MgIhGIxb87yNtf8oJyEvWd6Knj0NLU1A8NiISIt/Xlqf4ltOPS1led0IZM9FROmn3zyCQoLC/njoKAgXqc6JlyVBdlTEXMGWWyXFL0PJlJvZkr/wgvKF6vsGG42ccgPP0x+nib78nNzk58vqjw+9LNqcHRfJs4cy+ed96wxat3mSCxbFcQbnKaju7Mfp5Lz8M+Pj/GoKfth+AU58WapRXFT+ztq1H0uBe1d/Th0roDPGMVS+/bWptgQF4yNcSFwd7qxS314ZBgeH3mgub95ym06mjqi6ukq7rt6/XGz6xz76GjkNaYeSPsHa9iZ2iKNr/+PWKQ3pCPGOQZpj6TJJTrVM9QDq99b3eAHqy0xhjmiYYEYGMINVsaG2BIejB2RwQhwtJN5TDN+zU3uD7ZE6/vv0ZiYwBub/leajT7hMG9kYmn6V6JXYYtXMHSnKV9hs0z99vIJ/FCeC63hQvRpucIELUi76wNYGBhhQCjAxznn8HVJFr8tf78kCUkegTwC+7+SLPw6PQVBVvb4au2dsDL8MbvBROvqvX/H0MgIDm56UGMN/JsG23Gi5RJP7TcNtcPBwBoJjrFIcIiFvaG1SvQa+agShKIZmypyqu50eUwVKe0+lDElqrLPkxqlXcdob+3hM0cxgVpf0w5HFyus3RTJraBYp74ssO1UlDShraUHIZHu8A92wby5z2eAcESEc1kVXJyez67kEeYVC3y55+nCELdpu9THfVfZ8WRmAm1tgK0tm0ecj/t639WJx80srdbf++NTR/4LJFZMfdzXT9c62fSsspJWl4ayzjI+vWt5swDZ1UMoaRzmGtHXUR8bQn3w8OK10JPDmGb0mhneHy/+76/4rjyX14SyCOjAiJCn5JsGevFsZDyeCF3CxerNoquf5l7AB1lnIBANQSIRobX5P9h7y9+x0msNDHR0efr/s/yL+GdBOq9j/cfqW8anPt104N8o7mrlzVMvRK3gKX7mIvBF4RX8Ne8CHg9dwmeb0sTUfkpzGvK6y2GkY4A420iscViEEAsfaGtpq/8UqmwHYxuezmJA2gEQhMajDAslaffBalJvJlIZ7Hm2niI8UxVxnpThizoDmEXU+VNFvO40M62cNzfFrwnGU69uRvgCj1k1NLGULpvSlP2oHSq0CmOipaS6lU9nmnyhCF29gwjycsAv7l2NtUsCYG5iKJvvqmvMjI+bRQRZsxVrumL1rD82X0mgNclxj03XqqOlA5FExH9PnJ51ttga+CK1SYC92YVo7euHn70Nnk+IxZbwINiYGk/6GlnGNOPXzOD++MrHiYvUVS4+3FaKeZ4yNnsG4ckze/F5/iXYGprgLv/IScczlvoPtrKHRNQNLS1TCIbrMSKox2snX8cln9HpUe2MTLDaxQfflGYjq62Rz1zlZmbJBfAbixL4lKkHqgp5Paq3hTX6hQJktTVww/+HghZCs1L7F7m1FEvth1v64RcB92KpbTgMdVQ7aYNMhv+NjY2wt7eHpaXlpDfI+EwO0335EcR8QxkWStLugzVOzYSZricP5rDVFPv8K8ytw7H9mTh1NI97moZEuOPnr2zC8rUhcjPSV+uufRVcv47uARw9X8gFalltG6wtjEdT+/HB8HUbTWUrnKvHk+wzmvK/oflqCkur5PJkniofX18i4o/Zclmjqj2DQziUV4zdWQXIrm/iNlIbQwOwPTIYoc7TT+cpy5hm/JoZXPcS19Fu+42egVykMuHJYPWjL0Wv4Gn5L4uv8Me+FjY3eJmO/dXRW4SegUoYm4RBT98JIokEl68bE7Ojcjdl0dJ+Hq1lQpXBxChb/ll+GrLbGnljFhvLE2FLcF9A9JxP+bcNd+FE8yUca07j5vwOhja4xS2BR09nk9pXuVA9ceIErK2tx/+muWsJQgqUYaEk7T5Yd/9MmOl68mAOWk2x9Pvxg9lI3p+Fuuo22DlYYNsdsbwxysVNDaOeGnD9WGr/fFYlF6fnske71JdH++Dx2+KwOExBXeo3w8nphmjqdJZW10chx9eXIarKaicvVNRiV1Y+UgrLePqadet/dOtGrA7whr7uzHqmZRmTVK+Z5rqLtbRwyX/UzSLCZnRd9sqx1ye4+nFDflZ7uq8yH89FLr9hG2xdNqbfnf01hgfFMDB0h46OBYyMAyEYLMZrJ3+F1V4JvNzBWE8PzYN9EIhEN3Twu5hacA/WjqEBdAuG4GWungJupgjEQlxsy+V1pxmdhdDT1sUy20g86Xc7wix85ZLaVyQzrlGtrKyEl5cX1AlqpiLmBGO1WdNZKMmjRnWm+1DnGlVFnic5IBCMIO1MMY7szeDepnp6uohbE4S1m6MQsdBTvaOec/j6lde2cXF6+GwBOnsHEejlgE3xwVi3OBAWqpr6lSES4egyR6xPaptylSNHbJF4tmn8uK+v6bxh/RnUhVa1d/LI6d7sAjT19MHH1ho7okKwOTwQ9mY3TrU7HbKMSarXzOD+ePCVx3AqwAMfxW/BVq/gGyKmac01uOPo1/A0s8LhLQ9fYyE1vs/SI0j6OgnaOqawsFgJM/MlGBqqQEvTP68Z05mGStyf8n88gvrZyh3jZQaM6dwF5gISiQRFvVVXu/Yz0S8aRKCZJ2+KWm4fzS2mlInCa1QZPj4+8PDwwKpVq7B69WqsXLkSruxDhyCIm6MMCyVp98HEJ7OgulnXP3t+MpEqS9PSTLxa1dhqin3olxc38brTE4dy0NM9gKBwN57aX7EudEpbqXmFAq5fd98gt5NitlIFFc2wNDNC0rIgbIwPgZ+7klL70yDR1pbK0mosCqkN7Ru68vn60J4ygtk3LMDR/BIePb1S0wAzAwNsDAvAjsgQhLlMkdqfwXtPljFJ/Zpp7g+Rlha8QiNwStiFsq5R0X/9FKVRti6IsnPh1lKpDZVY6+Y3biE1NqZfnfrV6JhEfejuPgVjk1AYGnrDxvZW9HWfxSun3kftsBX+lp/GxeljIbH890RxOpdFatt1qX07AytsdI7HGocYuBr/OJHBXGLGQpWl/Jl3Kvv53//+B4FAAG9vby5amXhlPw4Oc/MkEITCYdY0zKJmMv9AeVkosW0wD9TrfVHZhzgTndfvY8x66vr12RfKVD6qsnhkSuPVqozzJAUdbb04cTiHd+5XljXD0tqEd+wnbo2adBapeY8crt+ISIy03Co+WxQz5mcNZIvDPfG7pzYjLsoberrq44nLEIgEqNHum1SkMtjyWp1+vh6ztOLrd9dMKu74+hCjtqd2fH3WtX+pqpZHT5MLSzEkHMESb3f8YWcSEgJ9Yah3k6/xq++9FA/RqLfrn4CESd570o5J1tfc7P7Q+egjeAR7QPfycZT3tKN9aAA2hsbXCEh9HR0kuftfI1THRCoTrCNi4YQxaXGx2t76Pcwt4mBiynyKw9EO4O0rJ2Cub4hnI+J5RHWui1MhS+235/Ho6Vhqf6ltBH7mextvkFL31L5C7KmGhoZw/vz5ceF66dIlCIVCBAYGIj8/H8qCUv/EnEORFkpTeRQy2IfwVB6WM52ZShaPTFm9WlVoNSUUjiAttYRPV3rpXCl0tLWweEUgt5VasNhnfIpS4ibIcP0q6ttx8Ew+Dp8rRHt3P3zdbHnkNHFpIGws1LuBZczSSjIygvu+uQ0FA9UINvbAl3d8Cy1d3RssrcYtsKaArS8ZMcXu7ALsySpEQ3cPPKwt+VSmWyOC4GRhNv2grr732Lv1Gm/Xz682HV333pvJmK6x5WKv+ebvaH3uMS4L4x8ChvQAQyGQ+i8WUQXsP/w7XG9/dMb3R0lXK+5L+RZGOrp4e3Eiljp53pCGv9xSh7uS/4dYBzf8efk2WBj8mM1gpQIFbeUQjPTwBiqdq6/rFwpxvrkZ1X19GBZrYbmLP7Z7h3CxOpcp76vj4vRkSzp6RwYQaO6JtQ6LsdwuCsZKTu2rrY8qi6qeO3cOhw8fxmeffYa+vj6ldv2TUCUIJXlYyrJ9dayDvQnlxY1I3peJE4dzeWrfP8QF6zZFYkViKMwtJrfzIWZHb/8Qki+MpvbzK5pgbmqI9UsCsXF5CAI87OdclGu2vqgDAiGOFpRgV2YB0qvrYKLP/E79edd+lJvzzM/HhPfepN6u5XJ47034THg7DnhtwmRUv00BfnlO+s8coViEn5zcxaOlT4YvxTMRcTesU9zZisdP7+Yi828rt8PR2Iw3kB2uLuY+pyzKyvxWJzImdtn29a5OqzpX6RL04lTLZaQ0X0Jlfz0s9cx4xz4z5Xc3doS6opQa1TFhevHiRZw8eZJHUtkMVW5ubli+fDn+/Oc/Y8UKzTLBJYg5g6I9LGXZvjp6tV5Hb88gT+0n781EWXEjrGxMsW5LJI+eevpSKZMiYKnsK4W12H86D6cul/JU/5JwL7z71CbERXpD/2apbDVGVl9U9rqMmgZed3o4v4SL1cVebnhv+3okBPnCWF+GqSqvvvem9nYFtGb73rv6mcCiqe+wBnw+tdTob/b4lbMSaEv5mcNE5CbPQFxsrsHp+gokuvvzWaIm1qFaGhiiTyjgM0mx0gAG8z9tG+pHZW8njteVYYdPKDzMrMYF6pgbwFwVqUKxEJc6Cnj09EpnAZ/tbJFNKO7x3ICFVmy2rrl5XDNlxp8IrBaVCVPW+c8E6WOPPYavv/4aTmpkFUMQ8xZFe1jKsn119Gpl2nhEhMsXypFyMAsXThdDLBIjNt4f9/50FWKW+kJHzWogNYXa5k4cOluAw2cL0djWA09nazy6YymS4oJgayl9l7q6Ia0HaWN3L+/YZ7Wn1R1dcLE0x0NLF2BbRDBcrSxmN5ir76lpvV1n8967+l5/Nw4YmBiU1Rp9zJb/8uzUnx1jJvvXs9rFFzH2BTyq+n1ZLl6PWTM69qti1VBHj+thUz0DPp2pnv7o+zXY2h7WBkbQ0dKG8Oo/yBP/QZhr0fnJUvv+Zu74ic8OLLdbAHM99S6HUYlQTU1N5aJ0rOOfiVUbm3nmE0gQ89XDUpbtq5lXa1VZM/c7ZRHUzvY+ePk64IEnVmPNhggeSSXkT/+gAMfTinEgtQDZJfUwMdLH2sUB2BQfglBfpzkpHiZjpn6igwIhUorKsTsrHxcqangj1LpgP7y1OQExHq7Q1pbT+fDxmZm362zee05ON0ZTxxiPqgLaEz4TuoYHsb+yEN+V53Aj/dQdj8PV9FpRbmVohHv8o3C+sRr/KkzHOnc/xDq4jz+/tzKfT2m6xSuYd+uPCdgIW2d8t/4eOJvM7dkxe4T9V1P7aVyoWuqZYa3jYqx1iIW7yfwMDM64RrW/v5+LVZbyZ6n/rKws+Pv7c8E6Jlzt7JRrF0I1qgShJA9SWbavBjWqrNb01NFc3hhVUtAAC0tjrEoK56l9nwBHjRFK6pbazyis5Z6nJ9NLMSwcwaJQD2yMC8aKhb4wlCWVrebc1E9UAvw1cRfqW015ap9ZTC10d+F1p4kh/jA1UMC9LxDgaKgR1t89eUc+48hX2kjMG5xVjerbW63xWszU06r/9rI5XtrVhtSWGh4dPVZbCpFEzKdIvcU3jEdPWSf/ZLyVnoJ/F16Ghb4hbvMNR6CVPXI7mvBtaQ436P9iza1wMJ5BU9kcgJ2TzM4iLk4vtOVAAgkWWYdirWMsFmhIal8lzVS9vb04e/bseL1qdnY2/Pz8kJeXB2VBQpVQKbJ0piuj658xmYflVF3/inQVUEHXv0gk5kb8R/dl4uLpIojEEiyK88O6TVFYFO/HDfqVhjLcC2bq2qDgMTW0dHNxyhqjmtp74W5hgI0O+kiK9oDD+jXyP24VOkNMhH2Fxv4jFlcarlxj1aQjMYcFFsICi6APe96pvz0iGFsjguFhc2PKW57Hzcf0hjOuaDVN6e26QOKItF83yPzPGrMNM/utMQbEw9dGU6+iq2sHK/NF8HJcz2eACrC0w62+YdjqFQK7GUxDymaF2l9ViHevnORCjjVMMcJsHLmt1CpXJc6apyCq+xv5bFEstd8h6IGHsRMXp6vsY2CprxkiXC56TSIjIpFIcvHiRcm7774rWbduncTY2Fiira0t1TY+/fRTSVhYmMTMzIz/LF68WHLo0KEZv767u5t9Y/LfBKFUfvhBInF1ZZLtxx/2mC2X52vkMS43N/ntY+vWa7c99sOWy+s1Mp6n+pp2yb//kiK5a/0fJOuifyV59NY/S77/7zlJR1uvRCUo43q/8IJEoqNz7T7YY7ZcCWMaHBJIDp3Nlzz+9reSRff8UbLykT9J3v7lZ5Ls4BhJsjckQT+D5Jj3zPZxrPyYJOjPQfz3tFw9jmNS7kMRDAmHJA7vO0jwJiRab+hKTN+IkLi88ajE/40/SPze+L3E6Y27JS6/i5UMCAYnff0jex/hr2W/p2WG12/imKb6cfyDI19P1mvRPdgt0X5T+5ptar1lKDH94yKJ42ePSzz+867E9V+vS146t1+S3dogEYvFElmo6G6XHKwqlPyz4JLk86w9M79HpL2nlES3oE+yr+605Kkr70k2nP655PZzL0k+Lf1OUtJTPeU5Oiblcajlcc9Cr804osr+e7p8+fJ46p/ZUrFyABcXl3HDf/bDZq+aKfv374eOjg6PxLJh/Oc//8H777+PzMxMhISETPt6iqgSKkEWP1FZXqNukSZZoqPSRmGlPE/9vUM4fSyPG/LnZ9fA2MQAK9eHYf3WaPgHS2HnI2+Ucb2lvR5yGhP7rM4uaeCR05S0EgwMCRAd5IrNy0OxqqEARnfcNhrRm+jd+Q8WdJvay3csKsmaj2KcY5D2SNrU1+7qcUi7D0XBxnG8JAd7c4pwvqwF/cMj8LU3w4oARyzxsYexwY0+qmMwO0e93+rxVC8bu/A1If9OnBQpr9+Yxylb+77tQIE9ENwCfLl7NAA6lcepNNcirS4NJR2lqOgexJXqJhSIdPgMU/5mhoi2McVmr3AsdYuFrEycQlWqe0SG9ZWR2j/WdBEX23P59V5oHcynM42xDuEG/VMhmcPHrfTUP9swE6aOjo7jopTVprKpVeWJtbU1F6sPP/zwtOuSUCWUjix+oor2OFUGstSbSnvcM1xfVFaOrIwa7nl6/lQhRoQiRMX6IGFjBJauDIShkYo9WZVxvaW9HnIYU1NbD+/aZwK1rqUbTrbm2BgfjA1xwXCxt7xmH5N6d1ZMvY8Z+4/OYh/yprW3H/tzi3hjVGlLO+xMTbAtIoib8nvbWc9oGw/sfgD/yfnP+OP7w+/HF9u/uHHFWbyXFHUtqno68X15LnaV56FhoAc+Fja41ScMO7xDYW9sqnKP2tl62soDNoXpsaY0HG9OQ7ugW6bU/tE5eNwqE6rM0J+JU9ZApQjYf5bfffcd7r//fh5RDQ4OvmGd4eFh/jPxwJmPq0w1DwQhC6dOAatWTb/eyZM/egfK8hp1g01/+eyz06/34Yc/+jJKe9zTrF9vYIVj1pFICUpAa9cw3Dxt+XSmrGvf1l6N3v/KuN7SXg8ZxzQ0LMSpy2VcnKYX1MBATxdrFvlzQ/6ogOu61K/uY2wmpAynH707oxsnzIh03T7GIkAZjRnj/qPRTtGTR4Jk3Ie8EIyIcKqkgltKnSmt5N3mCYE+XJyyaU11dWY+VeXEaOoYU0ZVZXwvyfta9AmHcaiqCN+V5yK9pQ5megbY5BnEG6OibRWXwZDqHpFhfXkyMDKEs22ZSGm6hPyecpjoGGGF/QIuUP1M3aXa/1w6brUw/Ge+qYogNzcXS5Ys4dOympqaYvfu3ZOKVMa7776LX//61woZB0EozE9U0R6nykAWT1Rpj3uS9Qe19ZBqGYyjNpHIM/OAycgQVngYYd1H9yIw1FXlH76ToozrLe31kGJM7Msur7wRB87k49jFYm4xFRXggtceScSqGD9uMTXVa2fk3XndWKTyH5VxH7OlsLGFi1MWQe0cGES4iyNe27AKG0IDYGEk2zScD+97+BqRymCP2fIboqoyvpfkcy0u408ZP6B6yBCHqosxNCJEnJMnPo7fgkQ3Pxjq6qmdR62068sjtZ/TVcojp+fbciAQCxFu6YcXAu/DEptwGOjoa+RxKwuVTwESEBDAra6Yyv7+++95RPX06dOTitVXXnkFzz333A0RVYJQGrL4iSra41QZyOKJKu1xX/3NvrrzTdxwzCYCp61CMKhjgKieCrxc+QOWdhXD4ONjQJgav++Vcb2lvR4z2FebkRkOt2rjwMv/QVVDBxxszHDbuijueerqMIMudSenmXl3ThjLTP1HZ7MPWensHxxP7Rc2tcLWxJhbSrEfP3vbWW2bRVO/zPly0ufY8n9u+ee1UVUZ3kuzvRa6ulYwMV0AU7NofJBfBk8zK/wsdAmf9UmZXqXS3iNS31NySO2faL6ENkEXXIzscbv7Oqy2j4GdoZXGHreykdmeSlEkJCTwuldWajAdVKNKKB1Z/EQV7XGq7jWqMzzuppo2pKx9ECl6nmg0sIbDcBfWdmRjbXsWHAXdc+M8Ket6y1qjet2YhrV1keoaggM+MUhzDoCunh6WL/DF5hUhiAlxH5+2ckaweeWXOWJ9UtuUqxw5YovEs03jx31T/9HJ6utk2Ic0TYZsRqPUsioePT1VXMHF3kp/L+yICkG8ryf05GQ/d31t6vXcUKsq7T0l47VI+nozjE1CYWq6AIZGPhCLh9Hfn4P+3ivYs/NvWO839bVSFNLeI1LfU1IyKBrG2dYs3hg1ltpfbh+NBIdFCDDzlJsYVLfjni2z0WtSfAopB+YuMLEOlSDUCvah/vHHo39f/4E09pjVD078chp7zVT/E7Ll179G3WBiZ0I2Y1LY8xP9O2dwrobe+yOOHc7Fi499gfu3/wnfWy9EWF8N3i/9D77I/wT3Np7+UaQy1P08yXqPKPp6TLgH2V1YYOOG92J2YNPON/DL5fehV98IL0TZ4dCfH8PbT27E4jBP6UQqu421tfH6Dgvu0TkZbPnr2y34ehMjQNqTmXDyLyct/vzEWIq0+7imY54JPVbnedddo7/ZY7YcQElzG35/9AxWffAPPPG/fajr7MYL6+Lxm1sCcKL9ZYj1KqcXqdPsYybR1DHY82w9WT9DpDlP7PymN9fi6dTdcHV/FbZ2t/F12lq/RV3N2+ho2wXhcC1+depX11wLZfDjPTL5vciWT7xHpF3/elIqUhD8l2D++/px5HeX46Pir3DPhV/i45Kveac+S+3/d/Fv8KTf7Qg095KbSFX2cas7Kk39s1R+UlIS3N3d+QQCX3/9Nbe/Onr0qCqHRRA3h1nAMCuYp5++tguXRTTYl4USrXGUypjV0QcfXBvJY1+OTBRNZtw/ybniQskzGskr78WZT0sx0J+HiIVeeOGtHYhbHQTDwwdG1+/F3D23yrhHFi+W6vn2oREcDlqJgz4LUWHpBLuBbmwrvYBNFenw6GkFfvIDYCJbvSVDIBKgRrtvUoN5Blteq9PP1zPQNRhdv6UE4uvqNMfXhwS1LaXj64/vQ9h2830I2695zVS2Tl3tHTj4uw+xq7QR+YMCWBkbYXNYIE/tBznZjzemFLYV4tXjr2KN15ppLbNuEJIsAsqWT7CO6hjsuKE29XrY82w9O1M7xV0LPS18knMWeyoKUNnbCTGs0NN9Fv19GRgZ6bx2fYhR21N77XlVAvw4umuumUjhZuOSdv2JsOvNrvPE69063IkTLenclJ+l+R0MrLHTbQ23lbI3nJmzg7oft8ak/vft2zfjDW7ZsmXG6zILquPHj6OxsZGHhMPDw/HSSy9h7dq1M3o9pf4JlTJTv1JNsKeazUxIDJEI7QdTkHKiFMlFfahrHYSDsyXWbY5EwsZIOLpYqeWsQ7NGUccxw3tqpLQM5/JqsP90Hs5nlEFHLMLy2jxsLk9HTFMJ+FQBcrwHa7tr0TrQOjq+zEygrQ2wtQWiovh2r/ETFYlQG+KK1p6mKbdnb+4E1/zaa8popHrNdeeJeXye9w7ArsgYpASGQaytheV1Vdjxi6ewIsAH+ro6s7LMmpRJzu2+on3IacmZ8hgiHSOxyX/TrPYx2bUYtrFBmrMzTjY1IaejnTdCJbkH8K59F0MttA9OXSowlResohk/jhmOS9r1r7/eOtq6cLDzwabIO1Ev6IC+th6W2UYgwTEWYRa+0NbS1qjj1hh7Ku0ZpoDYf5vXpCsUDAlVYk6gCfZUMiIYFuLC6WKkHMjC5Yvl0NXVRvyaYKzbHIXwhZ4z/mwhpLunSiydcMgnBkei1qBzaARBtsbYfPBLrK3OgrlgUD3uQWVYvV1dv8LGDrsjF2Ff+AI0m1vCr6UR27MuYUvOFdj298nFMkuq45CWWeyDHUtmWwO+K8vFgapC9AqHscjeFTt9wrDBIxBm+nMvwqaIksO4rxPQr6cNRztf6OrqQzDQjRcjH8cy20gY68qeaSCUZE/FLiJBEDKiCfZUUsC+GIvy6pC8Pwunj+ahv28IQWGu+NmLG7AyMRSmZkaqHuLcZ5J7pdPABEc9o3HQJwYl1i6wGuzFekcDbHz0TvidSwE+uiDTdueq1Vvv0DAOF5Zj10M/R5abFywGB7AxLwPbs9IR2lB7bWWsHCyzpDoOaZFhH80Dvdhdkc9N+cu62+FsbI4HAhdwgeppPruOdE2hbbiLd+zvrT0JK/dQGAx2o6I2E/VNhRgc6sXzvnfB2JFEKua7PRVBaDyaYE81A9pbe5ByMBvH9mehtqoNdg4W2HL7Ij5jlKvH7Ox8iMnvlREtbZx3CcJB74U46zJq6RdXX4BHc45iaX0hdE8cB9ztgAo1vAcVYPUmhhYuevlid+cwjv3h7xCMjCBuaAgffvcfrC7Oh4FoZNp9yGKZJfVxSMsMXzvs6ICUqiIuTk83VEBXSxuJ7v54IyYBSx09pG6Q00SEYiEutuchpSkNGZ2FvCmqu7MBuZXn0dpZo1G2TpqCTPZUbCpV5nVaU1MDAatXm8BTTz0FZUGpf2JOoAn2VFMgEIwg7Uwxn8708oUy6OrpYOnKIKzbEoXIGC/oSDFTDzFzyqubceD+53DY1h+dRmYI6KjDxvJ0JFZlwnK4f+opNdXpHpSj1VuNlQ12R8ZgT0QMGi2s4G1jhe1RIdgS6g+HsFCp9iGLZZbCz+1N9sEe5Xm64LukFdi7PAbdgiFE2jrjVt8wPmuUhT5FBJnMKemt4Yb8p1sz0DcygEAzT153KuztxNb/bZ759SbUdwrVMdj0phs2bMDAwAAXrNbW1mhra4OxsTHs7e1RUVEBZUFClZgzjHUFMya+5cb+U5/QFTwXqCht4uL0+MEc9HQP8Fmi2HSmlNpXHL39Q3ymqP1n8lBQ0QxLfW0k5pzCpvJ0+Hc2TH9PTdWZPvYaVdyDsrwvrr6mX1cPR0IieO3pZQ8fmA4NYkN+Fnbcth0R99zxYxRMin2M1aZeabgyaQc1s/lZ4LzgxlpVWd/f0jTbXbePFgtT7F0Sie/jFqDYzRH2WjrYERyDW3xC4Ws5iwyGpjQysizPcDdOtFzi05nWDTbDRt8CqxxisMZhEdyNHWW/3oR6C9WVK1fC398ff/vb3/hOs7Ozoaenh3vuuQdPP/00dijxg46EKjGnYF8019sVsZnV5ojtUmd7H04eyeHp/fLiJlhYmfC0PoueevrYq3p4GsmISIxLedU4mFqAMxllGBkRY0mEJzYvD0VclDf09u3l91SKfh2eSgI+OQwkCG9yT23bBuzde+PyrVuBPXumHAfzlXzq8FP4JOkTJHgnqOx9IRZLcLmmHrs++BOSTa0wqKeHJRWlvO50bVEuDDdumPw4ru5juvM0PDIMj4880NzfPOVwHU0dUfV01Y02P1f38Z5HHV5fBfzmJPBizU2uxWTHzSKvzC91is8DwQ8/4MTfP8b3wZ44GR4AHbEEa4uqcEvMCsTfcjd0Z5val2FM6pjaT2vPw7HmNGR0FEJXWxdLbcK5OI2wCoDOhK79WV1vZbw3ZCRFDcekVKFqaWmJtLQ0PvUp+/vChQsICgriy9j0p0VFRVAWJFSJOccci1YIhSO4lFqK5P2ZuHSuFNpaWohd7o+1myKxcKkv9PSozF0RsClMD6bm49DZArR19cPbxQYb4oOxfmkQ7KxMr1lXMjKC2E9Ckd5bjBizAKQ9lQct3Umuy4svAu+/P/VOX3hhUi/csagTayaKcY5RTHRpmvcFM+Dfk12APVkFqOvqgXtH23jXvkt358yOY4bnaczmhx33fXvuQ0FrAYLtgvHlti/5cd/M5kcsFMLsd2YYEA/DWNsAvS/3QltP78YVp4puTxGBLeho5nWneysL0D40gHB9E9w6YoAtzt6wWLFKPp8hUo5J3ajoq+ezRZ1quYyekX74m3lgrUMsVtgvgInu1A2cs7F1Usp7Q0rUcUxKF6p2dnY4f/48/Pz8eGT1T3/6ExITE7lAXbBgAS8HUBYkVAlCsan9E4dy0N01AP9g59HU/rowmFsa02lXAH0Dw0i+WMwFal5ZI8xNDLBuSSA2xYcg0Mthyi+bGfl9yjIFrjTbVwADAiGOFZZiV2YB0qpqYayvh6QgX2x/7udYUFU2xXxW8jsOWY777TNv47WTr40//u2q3+KXy3957Uoz9ETtKMzH3ppiLlDzO5pha2iMbd4huMUnDIFWcs5gzFGv5x5hP063XMGx5oso76uDpZ4ZVjvEcEN+DxPFNwaq6r0x18akdKG6bt06PPDAA7jrrrvw6KOPIicnhzdQ/fe//0VnZyePrCoLEqoEIT96ewZx4nAOF6hlRY2wtDbB6qRw7nnq5edAp1oBsFT2lcJabsh/6nIphCNiLA735OI0Ptob+tNErGfs98nSz88+O/2APvwQeOYZ6bcvJ9j+MmoasDurAIfzS9AvEGCRpyt2RIZgbZAvTP76qVKOQ5bjZjaOPJoqHBhfZqxnPBpVnZiSv4knqlBHG2dC/fFd/AIcjwmDREsLa9x8uThd6eINPW0FicQ55PU8IhYhvSOfzxbFfrNZvGKsQ7DWcTEWWgVDV1Hn6DqU/d6Yq2NSmo/qRN555x0+3Snj7bffxn333YfHH3+cR1j/9a9/Sbs5giBUiEgkRkZaOTfkP3eyCGKRGIvi/HHPT1YiZqkf7+In5E99SxdP67Pa08a2Hng4WeGRHUuQtCz4htT+zZix3yebRWwmXLfe9duXiHWRX9+NXfmHsDN0I/9ilMcXYFN3L/ZkF2J3Vj6qO7rgbGGOB5ZEY1tEENysLeV+HDf1RZVhfca7Z9+9RqQy2GO2/Jqo6iSeqCUu9lyc7l4ShTZLMwRVN+AVbUtsveVe2BgqIYMxB7yeK1lqv/kiTrLUvrAfPqaueMh7K1bYLYClvpnSxyPLPTIfxyQPpBaqCxcuHP+bdfkfOXJELgMhCEJ5VJW3cHF6/FAOOtp64e5lh/sfX8WnM7WymblQImZO/6AAxy+V8NR+VnE9jA31kRDrzxujwvycpBZ8Uvl9sqluZ8KE9SZu31DsD0sshQmCMIJOvHP6T9gRsmFWInVIOIKUojIePT1fXg0DXV2sC/bFrzetwSJPN2hrT7LtWR7HTHxRpfZRvRpNfefsO5MOhS1/Je6VH6OqVz1Ru40NsW9xBL6PX4BsbzdY9/Rhy8Vs3Jp6BSE1jaPRS2WI1Aljktt6cqJX2I9T16X219gv4o1RXqYuUBWy3CPzcUwq9VFltLS0oLi4mP8dGBjIa1eVDaX+CUK61P6pI7m8MaqkoAHmFsZYuT6UN0b5BTnP2Q8xdU/tZxTV8sjpiUslGBaOICbYHRvjQ7ByoS8MDSZptJkhUvl9ylCj+uWVfXh+/z9hgRhowwgi9GMAZRhAKXqRjUP37JY6SsO+bnLqm7ArMx+H8krQOzyMaDdn7IgKwfpgP5gaTjOdpwzHIa0vqtQ+qpPUpl7PWK2qSCzG2foKfPfxu0gO8oRIWxsrc0q4OF2dVQR9dlxzxdNWUUORiJHZWYSU5jRcaMsZT+2zulP2W1mp/Zshyz0yH8fEGBb1oKr3OPIa9uOWkH8rp0aVpf2feOIJfPPNNxBd/bDQ0dHB7bffjr/85S+8BkFZkFAliOlT+5lpFVycnj9VxB8vWubH604XxfvN7659BTowNLR04+DZ/PHUvquDJa873RAXDAeb2acpZfJ/nGHXP7PE+ujEOfzjXDoXCYOoRD+KMYgyDKEeEi2h1P6SzT192J/DUvsFKG/rgKO5KbZFBGNbZDA8baykuxZSuBdIe55kOa+T1aZej5mRG15d8x8+pWnzYB/8tPVx69d7sO1CFuy7RkvpVN5hr2Kv57qBZl53yn7aBd3wMHbCWsdYrLKPUUlqfyrU0XtVomZjEktG0DCQjvKeQ6jpT4VEIoKFKALbgv6inBrVRx55hJv+HzhwAEuWLOHLmEUV81B97LHHuIAlCEK1NNZ18Kao5ANZaGvugbs3S+2vxpoN4bC2VZ8PfZWhAL/IIYEQp9LLuCH/5YJaGBvqYc0if2xaHooIf/lGrAUiAWq6ayb9UmKw5bU9tXy9cf/HMcumDz64NiLJBOFzz/Hn2Reero42LIz0IYEA/ahAM77HiNYECyiJFt/rDdufgtf2HsOurHzo6WgjIdAXryatxGIvtx+n85T2WszgOGQ9T7Kc1z5BH4aEQzesq6VlABPTcJiYLoChoQe+Kska79oPt3GElrEnkPs0MFGosuNWla8y2ycTo5NdCwWNqVc4gNTWDB49Le6t5jZSK+yieWOUn6m7WmZ5ZHrvzZMxdQ5XoLz3MCp6jmJQ1AZLfS9E2fwE3mbrMDLAMhx/kWm7UkdUTUxMcPToUcTFxV2zPDU1FevXryd7KoJQEf19Qzh7opDXnuZcqYKxiQFWrg9D4pYoBIS4qOWHvkqQo18k+/jMLW3EgdR8pKQV8zrU6EBXbFoegtUx/jAylD21Px213/wdrc89NuXz9h/+Ha63Pzp5+vzTT1FTWY3jTu6Iu/cO+Lk48TIFBqsNLW1pw8///A/09/Xh1QOfYsdtnRjSAwyFQOq/WIQGsL/3p3D9/V+nbaj65nIOP9cbQgNgbmQov2tx9Th44xSrSX3iiRssqWTxybzGR3X3fShoK0CwbTC+3D61j2paXRrKOsv4OfzV2c/Qp+UCE5NQaGnpws/cEBvcffHTiPUw1NFVf19lBY+JpfazO4u5IT9L7bPHC6yDeN1prE0o9LUV956RF7PxXtW0MQlEvajsTUFpz360DxfBQNsCXmZr4Wu+AdYGAeOfDUq1p3J3d8fBgwcRFhZ2zXJmU8WmVq2byodNAVDqn5jvsLRjVnolju3PwrkThRAIRhC5yIs3RcWtDoKh0Y1f3PMaOflFNrf3jnbtn81HbVMXHG3MkBQXzNP7LM2v7sfx1aUs/PbQSf73EysW4+erlnCRNd7AJBDgre134X8Ll2BJwTf4d8hl6MEaBnDBXTnA9iIteHZ1ILCqnItDmbr/1dy7Uxo/ypreLnxfnoP/V5SODoEQQkEL+vqu4O/rXsDtwZuUOGr1pWGwFSlNaTy13ybogpuxA9Y6LOZTmlrrSydcCNUikYjRPJjFxWl130mIJSK4mCyBn/lGuJgshY6WnmrtqV577TU899xz3DfV0dGRL2tqasILL7yA119/XdrNEQQha2p/fxaPnrY0dcPVwxZ3PbIcqzdEwN5ReXXicw4WKbrZP9Ps//ba2tH1rvOLZKn901fKceBMPtLzq2Ggp4vVi/zx8oMJiA6coktdDY+Dicr0qjqeemczjWXU1PMOfMOr9cpcsH76KZZUlGBfWDROBC2EFcxgjkjowxGnwnRwJgxw6u7C6j/8Bc89/yQ341fmMSi7g3qyzul+oQCHqovwXXkuLjXXwlRPHyNDJWhpScbgUBV/zR/PDeK2oI3zNpsxKBrG2dZMHGtKQ35POYx1DPlMUawxKsDMY96el7lKj6AOFb1HUN5zGH0jjTDXc0OE9UPwMd8AY11bhe1XaqH617/+FWVlZTyyyn4YNTU1MDAwQGtrKz777LPxdTMyMuQ7WoKYxwwNCpB6vIDXno6l9lesC8W6LVEICnOlD30F+EUywZJf3sRT+8cuFvPZoyIDXPDqw+t4/amJqiLWs/S9ZJ33awJ9UN3ehczaRlypqccyHw8uUrl2KC9HRF0VTAdq0WvoCzv4Qog2dOIUhFpdWNbggR6zAHw1LIHR6Yv4SfwimBkaSBdZVWPvzqn8KI+UHYWVWTCfLYqJ1IERIZY5euDDuE3QFdZiyze/0DgPS2kRS8TI7S7jkdNzrVkYFgsRbumHFwLvwxKbcBjoUJZnLiEU96Oq9yTKeg6iZSgbetrG8DRdAx/zJNgbRijle0dqobpt2zbFjIQgiBtgX/yFuXVcnJ5OzsNA/zAiY7zw4m92YNkqSu1LzQx9INstbXHk0GXsP5OPyvp22Fub4taESGxcHgw3ByvV36mz8L3Ma2hG75AAwU723Brqd0dP41RJBReq41FhHx/Y9vWi2KIC+tBDFy6iR+uqcJMAp+wuIOVrX7z6wBPYlVXAt5UU+mM9mqKPQdl+lDq6ljAzXYifnT8HsVYW3E0t8dPQxdjhHQpXU4urXdcPaaSH5UxpZKn95ks40ZyOluEOOBna4ha3BF57am9orerhEVLAUvmNA5dR0XsY1X2nIZII4GS8EPEOb8DddAV0ta+rNVc3ofrGG28oZiQEQYzT3NiF4wezkXIwG/U17bBzsMD2uxZzWylHFzUQSnMV1hjC6h4n8YsUaOsg1TUUh0LicOGbPG7QvmKBL565ewViQtx/7FJX8+O4pr6TrXeVsWhn18AQ+oaHsdLfG71DQzzlf7GilltIOZib8vUkjz+Od3c/h06dPOigCoO4OtOTRIt1NoA18J5wL8ctcYvw6dl0XKqqQ7yfF0wN9BV6DMqMpmpp6cHEJAwmZgtgZOQLsXgYPX25eGPxnXgyesc1wvP6COx8iaoO8dR+Fu/aZ1FUltqPt4viqf0gcy+NF+eaRp+wkUdO2U//SDPM9dwRYf0gvM3Ww0TPXmXjkslEsaurC99//z3Ky8t5baq1tTVP8zs4OMDFRXWzRRCEJqT2WWNU9uVK3gjFGqJ+/somhC/whI6OGgmluQprymG2R6zT/KpvZpG1Kw74xCDZMxo9BsYIsdLHL7bEI2GxPyxMjdSz4/q645jU95JZCk147ZhoSC2thKWeLkyPHoGLsxMWurvgbHk1zldUY3tkyOim9PTwziodDKMBmKg1tK7uRwL+/CE3J5gY6KOqvRPCEREgjfONDMegjObEl099CBubHTA2DYO2tiGGBivQ1vodBvrzoCUZwX8zarhQvT4Cy7wqp/Kw1KSoKjvegp4KXnd6ti2T16GGW/jh+YB7scQ2AoaU2p9TjIiHUdN/GmXdB9E4eBm6WkbwMkuAn/km2BqGqMU9K7VQZd39CQkJvHurqqoKjz76KBequ3bt4rWqX375pWJGShAaCP/Qz6nlqf0zx/J5aj9ioReef3M74tYEwchYOT5884odO9Dx1f/hyCf/Dwdt/FBm5QzbgW5sbS7Axge3wOuBO1Qzrhn4ibL7Ja2qDsZ6evDdtBnG338PydNPQ2sGvpe8SWrPbtTvPw5XXX24vPYRjyKvWZ2Is8sScLqkkgtVlv7vGerFkNYE0cU05HXfV0PaYjhbGqJ3aBgVbZ2wNDZUundnSkUKnjr8FD5J+gQJ3gmQlcb+HuyqyMd3ZTno1F8JQ+1O9HSfQ3/fFYyMdF5zGq73o1QXD0tF0zbcydP6zFaKdfDbG1hju8sqrHGIhaORjaqHR0jxvmCfI61DuSjrOYSqvuO8DpXVmy5zeBUepquhp63kf9DlLVRZx/8DDzyA9957D2ZmPxqHM2uqu+66S97jIwiNpL21FykHs5C8Lwt11W28U59S+4plZESEc9mVvGv/XHY9tANXY7mbGZ6w0UJsWBR0V/xKdR6WU/iJSurroXXLLWj6+ht8ae2I767k8bS9kZ4eTAz08PzaeGysqIDuuXPTRmGZSO27625k/vwVxJcW8WX6YhGiczLhELoQV0oqeHOVh40l9HWMcd77bZS+/yq0cN2YoMWX+b/4Lgoau6CrowNfexsIRSLo68qQpGNidOtWqb072Zftq8dfRWFbIf+9xmuNVNGfoREhkmtLeWPU2cYq6GvrIMkjAM+ERcPVmDki3D6lH+VEwcn+Tn80fVoPy7koUodEAlxoy+aNUVldJdDX1sVS2wg86Xs7wix9oa1FWR51Q3KT9wVL7Zdf7drvFdbBRNcRQZa3wccsCeb6yvV9lQapP1XS09Ov6ewfg6X8mU0VQRCTwzxO01JLcGx/JtLPl0FXVxtxq4Px5MsbEbHQk9dEEvKnrLaVT2V6+FwhOnsGEOBpj2fvXoF1iwNhYaYGkQOW7mcRxUnqNLXYMi0t/GnfUezyD8UCDxdEujphQCDEieJyvLz7KOq7evCT+OV8Rqkpu+6v7qPB3BLdRsaIqakYf8q/pRGri/Pwv5hl+OL8ZVibmqC0uRWfvPoXxNZJJgmqjo6p4vdf4d2fOWNEJEJSiL9sInUMJkqltKCaWBc60zpQdn6y2hrxXXkO9lcWolc4jAV2Lnhn8Xps8AiAub5sTSJuFm78R2MaOHsqed3pmdYMntoPMffGz/3uQLxdJIx11eA9Q8z4fXG07CD8HHR43WnTYAZP7XuYrsIS+5fgaBQFrTnwz4bUnyzMhooZt15PSUkJ7Ozs5DUugtCYD/3ivHocO5CFU8l56OsZhH+wM554IQmr1ofBVB2EkgbS1TuI5AtF3FaquKoFFqaGSFwahM3LQ+HvoWafU9P4iX4TvRi7/IKx3Nocf7xzK0wNRyNzG0L98ex3B/Gv81dgY2KM2xeGTxlRlJw5w8sDciMXQaylBbeOtvHn6iytIeGvk+C7jFyIrmrTur4BjMVYOoxNeHmqoVCATDdPXPL0xQ9RsWhrauXWVLdEh0LdfE4n0jLYh93l+VyglnW3w8nYDPcFRmOnTxi8zakjndE+3I0TLZe4KX/dYAvsDKywjaf2Y+BkpGbvGWLa94W7lSFW+DiiZuRtNDdrwdEoGsscXoOH6UpuMTWXkFqobtmyBW+99Ra+/fZb/ph9KLDa1Jdeegk7d+5UxBgJYk6m9o8fyua1p7VVbbC1N8fGHQuwZmMEPLxV1z2pyYyIxLiYU4X9Z/JwNrOCRwDjIr3w8NbFWBrpBT1dFU9NKaNPaKn96MQqSfpaXKSOTXW60MMVv0iIxztHTuHr9GzEeLjC28762hmmrqJ1NdtVZWMH88FBeLW34LRvEI4HhuK8tz/qLUedJFgA9+5FkXituwno6uDLRrS0ccXdG29t2AGBrh4Xun0GBtzw/5f2Zti8dAHUxed0YlRVIBLheN1oav9UfQV0tLSxzt0fv4pJ4N6nauXioCKEYiHS2vN43WlGRyF0WWrfJhw/9b0VEZZ+lNqfYxwu2wUb8zq8GRECNysTdAwM42hRPR4KfQ+JriqqvVeFUP3jH/+IW265Bfb29hgcHMSKFSt4yn/JkiV4++23FTNKgpgDCIWjqX0mTllqn3XpM6/Tx1/YwL1PqWtfMVTUt+PAmTye2u/oHoCfux1+fudyJC4JhJX5HIgc3MQnVAwtLhIZYW7O/DcLGI5FDVcFeCOtqhZ7sgpwILcIT61eetN95Du5ot/AED+961EUOo5uz3JgAPemnYVIWwtfL4qHARP0Tk5coOpKxPzHt6UJoQ21GNA3gE9bM1YX5SGuogQ4eRIwMlS5zymDPX7t5OtwtgzHDxV52FtZgM7hQUTYOuHNmLXY4hUMCwPljlUdYeevvK+O152earmMnpF+PkvUE763Id4+Cqa6c+A9Q4wjEg+jtv8cb4xqlpzDzgh3ZNV34tusGuQ1dUEbOqht+wAbfG9Xiw5+pQhV1u1/7NgxnDt3DtnZ2ejr60N0dDR3AiCI+UhVWTOO7M3AiUM56O4agH+IC3724gasTAyl1L6CYDNEJV8s5gKVzRzFU/tLArHJeAgBwm7AoAcwmSPNKzfxE9WGBA693Sh2cEahqzt8rkY9x75vLIwMsT08kAvVgxcu4zHJEAxWrmCy7ZrtSOLioOXqipDGOlzw9kOxgxNWlhTgzsvnsbysiG+wOCwSe/RX40xpFe66YzNcXJwhamiAjlgMr45W/HbftzAbGuQNWHwAbm5K9zidyrNUW9sExqaRqMcCbD70H9gZmeA233Ds9AmFv6WdYq3C5ggdgh4uTFlqv3qgEVb65ljruBgJDovgbqLcSRWI2f+z0TZcgPKeQ6jsTYFA3As9uOC/6ZW4VNOOfsHI+LoizH0vX5mr35ctW8Z/CGI+0t3Zz2tOUw5koaSgARZWJjytn7glCp6+DqoenloyWyshkViM9PwaHEotwKkrZRAKRVgS4Yl3n9qEuKpc6D93/02tndSWMT/RSUqnRFpa8GxrwRm/YJS1jdokXZPW37ULkc8+i8g1W5Hl5oGzP3sKa/q7IfroI+hM2J4Wa3T6+GPc/shP4NTTBbOey3gpYRg7maMS85Nlnem/fAUxI3rcpupEWRXu/fhjaE/wOLUZ6FOpx+mNnqWAkXEgTE2j+W9WYzs4UAQ77Uqcv3s39GYztqtWYSn6dXgqCfjkMJAgmCP30wSE4hGkd+Tj64oDqBhshK6WLpbaReBB7y2ItgrkUWhi7jA40oHy3sMo6z6AbmE1jHXt4G+xjXftr/vyNlxpaNVIL98ZC9ULFy6gvb0dmzZtGl/GPFPZTFX9/f18atU//elPvNmKIDQ1tX8ptZQ3Rl06W8KXLYrzx6/eX45F8X7Q05tF17OGMxsroerGDm4pxVL7rZ198HS2xiPbFyNpWTDsrExHRcXtt97YNc8ilExoMZ/OOSQuJqIjkcCtswO6WkBleyc6+gdgbWI82t2/ezc/Pn2JBGuLcrhQPecdgDVHdkPn1lv5cYu2bfuxFnPHDt4cdefTTyE2aRiFtlp4ZQ2w5ogLtD76GBbbtmN1Zh6EIjGfWhWxUdCahcepImBepLV9/bCwToKJaSR0dEwxPFyHjvYDGOjPhlg8CB1TR4glLKKkMyurMH7PPgoU2gGvsvP0jzpuFTYX7qfKvnped3qyJR09wn4IB/tQXJMOR21TvBT/4ZwUK/MVsWQE9f0XUdpzAHX956ClpcMbohbZP8cbpLS1dDA8MqzRXr5aEvZunAFJSUlYuXIlb5pi5Obm8pQ/81QNCgrC+++/j8ceewxvvvkmlAVzH2ClCN3d3TA3N1fafon5RWVpM5L3Z/IpTVlq3y/IGWs3RWBFYhgsrUxUPbw5wdGyo1j/1frxx0fuPnLTNFTf4DBSeGo/H7lljTAzNsC6JYHYGB+MYG/HH79oWXrW03PqrvmxaTgrK9U3bTvNMbBmqkfuexyGbm749aY1WOztDsnICLS8vMZfk+HmiQfuexwLqyvw4fdfwmJ4aPy4Jdra6BocgpWxERdfyaWHsf7rjeMG/kfuPIhE/w3870ntrdQgBd45NIh9VQW8MSq3vQnmenpY7uSE1c4u8Jzg5z3mWepq7jrra3HUB1h/749PHfkvkFihvvcTE6Q8td+cxmtQLfXMsMp+IYwFItz97a0zfu8R6kHncDmvO63sTcagqB3WBv7wM98ML7O1MNC5Ue/UdtdO6+Ur8/tCxXptxiGgrKws/OY3vxl//M033yA2Nhaff/45f+zm5sajq8oUqgShKHp7BnHqSC4XqJTaV46VEOtWzyisxf7UfJxML+Wp/dgwD7z95EbER/nAQF9XamsnHmWtrR1dT0qfTqUxzTGw1H9gfS3OmZrhck09F6paZ89e8xrToSG4dHXwRqkhPX1YDA1ipK4eyT/sw+cd/Vgd4IOfr1rC13399JvQ0Z5wLU6/iXV+SfxaTBppk8HjVB6MiMU401DBxWlKbRnEEglWufrgybClWOXiA31FCMWr14JFb15fDeiIAZH26G/2eF25BFpqdD+NiEXI6CxESvMlpLXnQgIJYqxDcKf7ev6bOR3E/iN2xjZehGoZGulERW8yT+93DJfAQMcS3mZr4WO2ATaGAfPGy1dmodrZ2QkHhx9r706fPs2jrGPExMSglr2BCWKOIhKJkX25Ekf3ZuLcyUL+eFGcH974wx2IiaPUvqKshBpau3ndKfM8bWzrgZujJR7auhgb4oJgb31ttExaayep11MF04xNTyxGUn4WLgWEILWsGmsDfRHQ2MjrV1lpAMNicAB9BoYY1tWDdX8vX8a69dva2lHVOYiTJRXYGhGEwo60aW2dVE1pVxsXp7sq8tA62I9ASzu8GL0C27xCYGtkopRrkewDpLv8uJiJVfaYLU8sV/39xFL7LHJ6quUKuoS98DJxxgNem7HKPgaW+mbXZDLU/XrPd0QSIU/ps8aouv4LfN43V5NliLB+CC4mS6CjpYf5zoyFKhOplZWVPHIqEAiQkZGBX//61+PP9/b2Qk+PTigx96ipbOVNUccP56CtuQdunra4//FVWLMhAta20wglQiYrIT2JEV7/37+wR6cbGYV1MDbUQ0JsADYtD0G4n/PMoz03sXaSaT1V4OSEYR1dnAgI4V313PbpOlaUFmCBpSnO1TViV1YBXrl6PGNi1XBEyA35TYaHuVjVEwzz54OcHWE13AwdLS0IRkRT2jqpOsrWLRjiM0UxQ/7stkZY6htim3cIbvEJQ4i1g/LG5eR0QzR1jB+jqoCWCu6nXuEATrdewbGmiyjrq4WFnilW2i/EGodF8DF1lcrGS9XXmwC6hitR2rMfFb1HMCTqgo1BIGLsnuKpfUMdSzpFsgjVDRs24OWXX8bvf/977NmzB8bGxoifYE2Sk5MDHx9mnkIQ6k9f7yBOHsnFsf1ZKM6vh6mZIVasC8XazZEIDHWlD3BFRFMlWjAb9IZtbwys+8KhJTFAp1cn3nhsPVYu9IWxob5crZ2uqVFVgY3SdDAhkdfQjF29Izj4wlvoMTDEXelnbxSqWlqwsrXBnWuX4+L3h/HlxQwk3LcTMS4uo8cN4EBYNFrNzLAxLwOmgmGItLWh4+KC8KS1+GpIACcLsxuia6qOsjEXh/NN1Tx6eqSmBEKxCCudvfHXFduw2tUXBjoqaE6Mj0dyrC3SXdpuHO9YVHWxLRKVdD+JJGJkdRbz6OmFthz+eKF1MF4LTuSpfV1tHalsvEa3SVFVVSEQ9aGq7zjKeg6gdSifp/Z9zBLha74JVgakn2bdTNXW1oYdO3bg7NmzMDU1xX/+8x9s3759/Pk1a9Zg8eLFSjX9p2YqQhrEYjEyL1VyQ/6x1H7MUl+s3RSJ2Hh/6BtQRkCesI8WVh+XW10O694FsO2JgeGIDYb02tBmlo4OswyEefgg7ZG02f1jcLVL++pOf1w+tk0169Ju7e3HvpxC7M7KR1lrBxzMTLHVUBvb3nwV3u2tNz2Gd4+cwpcXM2FuZIidJvoI+Pxv3MT/+6hFcO9sx2dffQ6Hq6n/icc9di2uNFyZ0r5mgfOC2V+LGVDZ04EfyvPwQ3kuGgd64WNhg1t9wrDDOxT2xqZQJfw8ve+HK/3lEE8ycZW2GFhg4oO0F0oVep7qB1uQ0nQJJ5ovoU3QBXdjRyQ4xGKVQwys9advRFGn6z3fEUtEaBxIR3nPYdT0n+apfhfjWC5O3Uzj501qv0cZzVS2trY4c+YM3wkTqjrXFbJ/9913fDlBqBtN9Z28KYpFT1uausdT+6uTImBjR6l9RTAkECIlrQhDGYsR1ncbxFpCdJhmo9L8G/QaVox3nNf26M/eMoWJMTWzUboelnZndaK7MvNxtqyKW0YlBPnipcQVWOrtPmohdWw/sHfvtS9konXr1vFj+Gl8LNysLPGHY6n4b4cAI9vv4uuENNTi56eOwqGvZ9SI/7rjZudYlfY1vYJhHKouwndlubjcWgczPQM+UxRL7UfaOqmNWOLnSbtvUpHKYMtrdfoVcp76RgaQ2prJZ4wq7KmEiY4RVthHI8FhMfzN3KU6R6q+3sRoar+89xDKe45iUNQGCz0PXnfqbbYeJno0jbZCIqrqCEVUiakYHBjGuZNFOLY/E1nplTA2MeCp/cStUZTaV2Qqu7yRN0axWaPY7FFB1tpYePZrRLdkw1AkuOE19h/+Ha63PyqfAaiBjdL1FDa2YFdWPvbnFHGLqHAXR2yPDMaG0AA+q9Q4L74IvP/+1Bt64QXgvffGH1a1d6KoqRUtvX2IcnFEWHnJtMetbPsa1qV/4Wpq/3B1MQRiEeKdvLg4XevuB0NVpPZnwPh5YvdTZiZLJ7JIDRAVxc+rPM+TWCJGdlcJkpsu4mJ7LkbEI4i0CsAah1gssQmDgY7+7I9DTe2KNJFhUQ8qe49xW6n24UIYaJvD02wtfM2TYGMQpDb/kM01vUZCldCo1H7O5SqkHMxG6vECDA0KEL7AE+u2RCF+TTAMjWT/0CemprmjF0fOFeJgaj6qGzthb22KDXHB2Lg0EO6Lo+a2x6kMMFN+Jkx3ZxWgqLkVtibG2BwehB1RwfCzt73xBQIBYGw8Koymgp2fgQFAX5/beF0zO5WaUdvbhe/Kc3lqv76/B15mVrjFNxw7vEPgZEJ+14zGwTZed8qip63DnXA1csBax1jetW9jYKHqS0hIgUQiRtNgBjfkr+k7xVP9rFvf1yyJd+/raNP3jtJS/wShrjTWdSB5fxbv3GepfWc3a9x2/zI+pamjs5Wqh6eRDAtGcPpKGbeUupRXDX09Xaxa6Ivn71uNBcFuo6nsU6fmvsfpDBGKRDhTWsXrTk+VVPLKhpUB3nh69VLE+XrcfDrPTz+9uUhlsOfZes88o5YidUAowOGaYh49vdBUAxNdfWzyDMStvuFYYOcyryNJYwyKhnG2NQspzReR110OIx0DLLdbgHWOsQgw86RzNMfoEzbyulMWPe0baYC5njsirB+Gj3kSjHUn+YeUkBkSqsSchEVLz54oxNG9Gci5UjWe2mdd+8HhbvShr6DUfmFlM58tKvlCEXoHhhHu74xXH1qL1bH+MDUy0DyP02kobm7jdaf7cwrRMTCIYEd7vJS4HJtCA2FlYjSzjZSXy3c9Jd4P6S11XJwerCpC/4gASxzd8cdlG5HkHgBjPYoksXOU313Oo6es/nRILECEpT+eD7gXS2wjYDiL1D6hfITiflT1nuSG/M2DmdDVMoKH6SrEWbwOe8Nw+t5RECRUiTn1oV+UV8cN+U8n52GgfxgRC73wwls7ELc6iFL7CqKjewBHzhdi/+k8VNS3w87KFDvWRGBTfAjcnaw02+N0EroGhnAwr4gL1PzGFj416ZbwQGyPDEGgo530G5yprZ+a2P/V93VzM34mUKt7u+BmaoFHQxZhp08o3EzJ/5HRNNiOEy2XeGq/aagdDoY22Om2hnfu2xtaq/oSElLAUvlMlLLIKUvtj0iG4WS0AHEOr8PddAX0tI3pfCoYqlEl1J7W5m6cOJyDlAPZ3Jzf3tGCR07XbY6Cowul9hWBcESEc1kVOJhagHPZldDW0sKKBT7ckH9RqMdoan+m86ZP53E6B2pUR0RinK+o5nWnKUXlvB56hb8XdkSGYLmfF/R1ZzF+KWtUVcHgiBBHa0rwXVkO9z410tVDkkcAbvUJxyIHN35/zHeGRAKcb8vijVG53WU8tR9nG4UEh0UItvCGttYM3jOE2tArbEBZz0E+Y1T/SDNP7bO0vrdZIkz1HFU9vDkH1agSGpnaZ16nTJxmXqqAvr4ulq4KxE9/sR6Ri7yhozNPPvSV2MnOItbF1S04eCYfRy8UobtvCEFeDnj27hVYtyQQFqYzTGWPwcb58cejHqdMyEzmD8pslCY7HjXp4C9vbefidG92IVr7+uFnb4Pn1izD5vBA2JrKaTpPJj6fe+7mXf/seSWLVHY/ZLY1cEupA1WF6BUOI9bBDe8t3YANHoEw0fTU/gzuQV4O01PJU/tnWjN4HWq4hR9+EXAPlvLUPlk/zSVE4mFU953mhvyNg5d5tNTLdC18zDfAzjCUUvsqgiKqhNrAP/Rz67gh/1hqPyzaAwkbIxCfEAIT0wl2PvMBZmQ/mTcoE39y9Abt7BngXfusMaqstg02FiZIWhaEjfHB8Ha1VcxxTOL1qezjnoqewSEcyi/B7sx8ZNc3cRupjaEB2BEVghAne8V9WW3bdqOPKoP5qO7ZA2XRNNCL3RX5PLVf3t0OZ2Nznta/xTcMHmbzJIMxzT3YNtzFzfhTmi9xc347Ays+lSlL7TsZUSPNXPveaRvKR1nvYVT1pkAg7oWDUST8zDfD3XQl9LSl/AedmBSyp5LB7oBQHzraenH8UDavPa2tahtP7bMZo5xc52k919hsS9enzOU029LIiAjnc6pw4EwezmaNdqnHR4+m9heHeUJX3hHrmUZIFXzcUw5PLMbFylped5pSVAahSIx4X0/uebo6wBv6ugou55/quMeOXcGzaw2JRnCsppSL09TGSuhp62C9uz/3PF3qOMNSD01himshMNDFxWVeSHnlLmTqd0JXW5dHTdc6xCLc0o9S+3OMPmETKnqPoLznCHqENTDWtYeP2Xr4mm+Eub6bqoencfSQjyoJ1bmGUDiC9HNlvGv/0rlSnspftiqIG/JHxnhBez59MU5V26kA/1HWDMXE6eFzhbxJyt/DjjdFJS4NgqWZkcYe91RUt3dxS6k92QVo6umDj601j5yy1L69manGHvdYJCm7vRHfl+ViX1UBegTD3EqKidONnoEw159nGYxJrgWTqkUhjkhZH4TUVX7oNzNAYEk71iY9jniHBTDRpWjbXEIoHuQNUawxinmf6moZ8IYoltp3NIqGtpZ618rPZahGlZgTsC/GsqJGPpXpqaO56O4agF+QM554Pgkr14fBzJw+9Dks8ihH/9HuvkEkXyjGobP5KKhohoWpIRemm5eHwN/DXmOPeyr6hoZxpKCUC9QrNQ0wNdAfT+2zmaOU7vmppOMeo3WwH7sr8njtaWl3GxyNzXC3fxQXqD4WNpjXXL0WHdbGOLkuAMkbglHnYQ275l5s2p2DNUeL4FLXBZx8FHChz6s5k9ofLkBp935U9aVAKB6Ag1EUltq/Ak+zVdDTllOtOaEwyJ6KUDjtrb04cTgbxw5ko7q8BdY2pkjYFImETRHw9qPuyRuQg/8o61K/mFvFG6NSMyt4l/rSCC/87qnNWBbpxQ361Q4F+q6y2ZwuVdXyxqjkwlIMCUew1McDf9iZhIRAXxiq8nwowW9WIBLhZH0579pnv3W0tLHO3R+vLVyNOCfP+ZXanwKheASXOvOQ8u4mXF7kAR2RGEtTy/HTj88gIqMW2hLN8P6dLwyNdKK89yjKevajS1AJE10HBFvewaOnZnrOqh4eIQVq+G1FaEpq/1JqKZL3Z46n9peuDMSjT69DdKw3dGZj56PpzMJ/tKqhYzy139bVD183W/zstjisWxrIm6TUGgX4rtZ1dmMXS+1nFaKhuwce1pZ4LH4RtkYEw8nCDGqBAv1m8zuaed3p3op8dAwPItzGEW/GrMUWr2BYGMzD1P4klPfVIaUpDadaLqPHph/+lkZ4/OPTiD9RCrO+YY3w/p0viMQC1A2c55ZSdf0XoAUtuJkux0Lbn8PJOIZS+3MU6von5EpVWTOO7svE8UM56O7sh3+IC9ZtjsTKRErtzxgp/Uf7BoZx7GIx79rPK2uEOUvtLwnkjVEBHgrsUpc3cvJdHRAIcbSghEdPL1XVwVhfDxtCWGo/GFFuzup3PuTsN9s+NIC9lfk8tV/Y2QJbQxPs8A7BTp8wBFjJMCGBMlCGHdmEfXQ5WeGktx6Ot15GZX89rPTNsdo+BmvsFsAjeIlGeP/Or9R+IZ/OtKr3GIbFPbAxCOSep15ma2GoQ5NQqANUo0qo9gbsHsCZY/ncVqo4vx4WlsZYszGCG/J7+TnQ1ZGWGfiPij/8EFeK6rk4PZleCqFQhNgwD7zz5CbER3urZ2pfgb6r7Msqo6aBi9ND+cVcrMZ6uuH32xOxNsiPi1W1ZTZ+s1cRikU4WVfOo6cn6sr5yxJc/fB81HIsd/biXfzqbgWVol+Hp5KATw4DCQI525Ht2gXhc88g3V0Px5KCcNnBE9qlEsSKHXBf9E+wwDoIOmONNLO8FoRyGBxp5+K0rPcQugVVMNKxhZ/FZnibJcHKwJsugwZBEVVCJkaEIly+UIZjB7KQdqYYIrEECxf7IHFbNGLj/aE3F4XSHPByrPEPw6F7n8ahNjGa23vh5miJTfGh2BAXBHtrNUllK9F3tbG7F3uzC7hAre7owv9v7z3g27yv6+9DTIIgwA1w7yVuSiKpRe09LFm2bCeOYyexm+Ekdt0sO3EcZ6+2SZM2Tdq+Sf9JR+pYHtrW3ntRnOLee2CR2Hg/vyuRkSXZlmxRJID7zYeRAYLkwwckeJ577j03NkRLkVLiLT4sBF7F3ebNAqgb7sdrjRV4s6kKA9ZR5IbrsSUtn6z98MAgr4mCEhcaZc8AZ+OAkk7g9L+DbNt7EcvV8tb/w753/g0HVmbBEBaEjNpeLN9Vg0XC2jfZbv81PsRzwUw+bo+TLH3Rd0rWfoAUSWoxtb+Grf1pDsdTcTzVfaO5vpf6Tset/dTMaArkX7I6H+GRPiKUphMuF8z7D2H/pRZs73Wios+C4CAllpdlUqxUXnrM9LOyJ9kKFoNQe2saaGr/ZFMbDUKtyskkcVqSFA+JxIvPxx1Y4CO2MWxrrqHqqYiXClOqsCk1lwRqTrjeK6Og9qQBq5/467t2/xFY1fThbXaTw4LD/eext/sUGiwd0I6MYek7tVixqwbJTYN3ZuVPk+1oDDBib0EjrTPdjTHXIMKVWcjQridrXynlDHVvgIUqC9VJxWwaw8HdV8jav1rdhZAwNZatLaDJ/bRMntqfDMSU+sW6Dmw/UoUDZ67C5nCiLC8J68pzsXBWGgKns5U9CVDmZ0cPDUbtrKyD2WbH7MQ4PFici1U5GRQx5cs43W4K4heZp3vb6+HyuLE4LhVb0gqwND4dCm8UUIcOAUuWUFapqKZeiAFcEkDqBmZ2A6f/TVRVARw8eEexXC6PCxeGa2kw6tTgFbg9HpR49FjxrX/H7FMtkDvd7/3Bd/g1mPuH1WVAs2kv2fuDthooJBqkalYhI2Q9wpWZ/FR4GdyjytxzRJxRxbkW7H7rAo4frIHT6Ubp/Ay88vPHULIgg639SaJ7wIgdR6uw42g1uvoNiNeH4lMby7BmQQ70vmLt3wW9RjO2VdRg66VqNA0MIUarwRNlxdhUmIOkCN8fkmgYGcBrjVdopWnfmBmZoZH4avFCbEzNhU51nxYSTBbXI57eSbtm+Y8jxKq4Le5f1fjBUVAtli7s7z2Dg33nMGw3IikoBk+lbMBi3WyEvb4DONZ0x8fCTC0ujwOdFjG1vxsdluPwwIN49Vzkh/0A8er5kEp8+4KUuT3cSMi8i872QezbfplWmvZ2jSA+KRJPfHYJlq0tRESU/wmlcU5WNONURSvCtEHITtahJC/xnmVPjlkdOHS+nsTpueo2qpYuK8vEtz+7CkWZcb5p7b8PdqcTB+qaqO/0aEML5FIJDUR9a81izElJ9G5r/w4w2q3Y1lJDU/uXBroQqgjExpQcPJxegLxwve/8PMTEUDX15aXXqqhCoI4jbov7VzYCAbeJgjI7R3G47zz29pxGvbkNWpkai3SzsExfivTghL+eo0mM/mLuHcO2RjQYt1Puqc01Qtb+7KgvIjl4OVQyP12jzUzAw1QMWftial8MRlVfbkeQWomFK3Jpaj+n8IYXfT/kXFUbfvXno6hr6YVMKoVT9K0BmJOfjBc/s+JDVzmFlX2p7trUvrD2R60OzMyOx9ryHCwrzURQoH9VDsT5qOzqJXG640otDFYbCuNjqO90bW4mtCrfzvwUNvXJnlYSp7va6miKf1FsKrak52NZfDqUUh+sKbhc2DM/GqvXDLznQ3bvjsSqYz3UGyraHSpGrpI4PTlYAafbhdnhOVgRXYaS8FzIJbJJj/5i7h12lwUt5r2oN2ynzVEiRipVsxrp2nUIU6bxqfYxjEYjQkJC8GFaNX3w1Y+5U2v/0tlm7HnrIln7LqcLs+am48UfPoy5i7KhDPSvHsjbYbRY8f92nCWR+tiqmVhQdC326c/vXMD+M1fx09/vx+cfmY/0hCgSWnci6HuEtX+smuz9zj4D4nQheHztbKydn4NYnZdNqd8DBswWvF1RizcuVqG+fxBRwWo8MisfDxblIjXK9yspraZhvN54Ba83VqLTYkSqNhzPFy6g3FN9kG87GB6JBC9vDoHEMgD3bcwJiaiqPhiCPNsgWfsH+s6i3zaMeJUejyetwVJ9KcIV2kmP/mLuHR6PGz1jF65lnpoPwO1xIDaoDItjfnjN2g/gvzvMrbBQ9TN6uoaxd9slGozq6zEgITkST35+CZau8W9r/3a8fagSZypbsXlpIZ5//K+DFrE6LWRSCfacrEWoVoWXn1n1viLVanfg8PlGGow6W9VK1v7S0kz6uKIsf7T2XThc34ytF6twpL4ZEokEy7PT8NWV5ZiXmkTn1pcxO2zY2VJLU/tn+jqgkSuxPnkGHk7Px8zIabiQYJKwu+xok5hvK1JlUjmio9Ohii/E35z7PtRSFRbqZmK5vgxZmqS7O0ciTkpEUN0cNyUqqRw3dV8w2FtJnDaZ9sDi7IVWnoCC8CeRplkLtVx3fw6C8VpYqPoB1jE7ThyqpY1Rl840kbW/aGUeVm0sRnZevN/8YbwZg3kMKqX8tuH4TpebhpkEhVnXJj1cbjckAQGIDA3Gkw+U0RYoURlduyAHs2YkvGdV9V9fO47/2X0BhZlx+ObTK7G0JBNqlX9Z+4Lann4Sp9uu1GJ4dAx5sXq8tGYx1uVlIzTI9639Uz2t+EtjJVn7VqcDC2JS8MvyB7AqIQOBMv+rJCllSpx95iz6R/vJondfvIAmSxcuRrpRozTD6XEhW5OM9XGLMDciH0rpR/idEWJ140aOm7qP2FxGNJv2odG0CwPWKpraT9YsR5pmNaIC8/z27w5z97BQ9WFrv+pSG/WdHt1XjVGLDXnFSfjKdx5E+fIcBPqhUBLYHU7sPl6Dvaevon/YjDCtCkkx4fj46llIjAmjWCgxrCOqevXt/fQx8dcteRFAPv7imhoXgU1LC/DPfz6Kv+y9dF2o/tVNvJFHV83E5mWFSIwOg78xbBkjYSoyT2t6+hGhDsLGwhlk7WfpI+HrtJtHKFLq9aZKdJgNSNGE4dm8udiclodYNec/JoQkQKEMokipvbqu69a+Dh/Xl2OJrgRRgffwd0bY+xxBNenWfvfYedQbtqHNcgQejwtx6jlYFP19JNDUvnJyD4DxSVio+hjdHUPYt+MyvfV0DkMfG4oHPz6HVprGJUTAX+kdMpFA/cu+SyRQQ4JVCFYp0No1jIu1HTh2sQm/e/lRxEaFwOl0QSaTIl4Xiiv1XVQ5zU27dSr4gUV5JFQPnquHwTSGEI3qtl87JtK/BInD5aJp/TcuVuPQ1Saa7F6SmYovLZ6L8oxkyH28H3DM6cDutjr8X0MFTva0QS1TYH1yNrakF2BWlP+1etwOq8uG4wOXSaBWGOqhkiqxMGoWDUaJKiqfI+/C7OhBg3EHvVmcPdDKE1Ec8QzSNGt4ap/5yLBQ9RFrXwxE7X7zAirOt5C1L6qmX/nOJuQWiTgf3+75+yBGrXYafDp+uQlp8ZF4ZGUxSnITkZ2sR1efAb/+81EcOHsVbxyowLOPliPgevxRRmIUdh0Happ76faNsUiiDUCIXWHji4/dd/oqHlpeOFGR9Ufq+waw9WI15Z4OWEYxIzqK+k7X52cjXO0F6zw/AqLt43x/J15rqMCO1lqYHXaU6RPw8/nrsDYxC0Fy/3Qwbj5H1cYm7Os9jaP9FzHmsqEgJAMvZH0C8yMLESjlaps34XCPoc18iDJPu8fOQRYQiGTNMmRoN7C1z9xTWKh68Yt+XVUn9Z0e2n2FrP3C2Sn42vc2Y/6SGX5r7d/uPImoJzHAs3FxPr7wyAISmOOISfvPPjyPxKaoqgqhOp6Pqo/Q0Mc2dQyivXcYCfqwW/pQV83Lpo8VIlgIVX8TqYYxK3ZcqaONUSJeKixIhQ352RQrNSPG94ckOs0GCuMXg1EtpmHEqbX49IwSPJSWhySN/7V63I4+6xAO9J6lyf0uaz/0ynBsjl+KpbpSRKv81+Xx7qn93Wg1H4TTMwa9qhjzdC8iWbMEcol6qg+R8UFYqHoZg/0mHNxdQZP7LY19iNRrsemxMqzYUIzYBN+P8/mwfPfzayisXyCqnqKHV1ROhSgVW7eEIBXC1GpzQKmQkRhN0IciOTYczZ2DuFjbSUJ1nHExK6qywSoluvqN1FIQFebl24LuAFFNPtHURpFS+2ob6fbCjBT86tENWJSRAoXMt619MQi1p+0qbYw63t1Cg1BrErPww7mrMUefSAN3/o7VZceJgUuUeSqsfaVEgQWRRfhi5qPID0mHJMC/XR5vw+ToJFtfTO6LqX2NPB55YZ9AqnYVNPLYqT48xsdhoeoF2KwOnDxcS32n5082QCqVYM6ibDz9/ErMLEuj28ztGa9+jotUUREVVU+J5JqYstmd+PM7F6k9YHlZFgKVchKy4sOEMBWT+tVNPThV0YI182dAfrMIE4+LDqXAfrFhypcRK0xFIP9bl6vRZ7IgLTIczy2dhwcKZiBKc48rKSKo/ejRa6stxdag8vIpzbqkBQ0D3XitsQLbmmtgcthQqovHT+auwdrkbATL2bYml8fUQuL0SP8FjLqsJEqfz3ycrP0gmW8nO/gaTreNrP164zaqoopqaUrwcqRp17K1z/iPUP3Rj36ErVu3ora2FiqVCvPmzcNPfvITZGVlwd8RL/o1Vzoo71RsjbKYrZhRkIAvfmMdFq7Ig0Z7+8EdX4d6AWvaKVYqNS4SqkD5HYftC8Yf19QxgPM1HXjnZC2uNHQhPz0W2Sk69A6aqLIqEJ9b9LIePFuP05WtlKk6vyiVoqvGsz4D5TISqWKYKjrS93JoTVYbdlVdpVipSx3d0AYqqedUWPsiXmpShl62br195qUIbhcxQ/eRvlEzWftCoDYYBhETpMGT2bPwcFo+krVs7QsGbQYc6DuDfT1n0DHWi0hFKB6IW0SZpzEq30928CXEa+mQrY7EaZNpLxxuM6JVM7FA/zKSgpdAJuGLDcbPhOrhw4fx7LPPoqSkBE6nEy+99BJWrlyJ6upqqNX+2esyPGimyqkQqG3N/dBFh2Djo6VYvr4IcYn+1881LkLF1P6f91zAW4cqYRmzUWi+sOu/+Fg5Vs7Nhkx654JJBPD/9A/7celqJ9m0xdnxJDY/8c0/IiYyBJ97eB5WzZtBj81Njcbyskz8aec5/HH7WRKq0hv6UMVEu8FshS5CA5fLA/hAHKaoKJ9qbqPq6d6aBthdLixIS8I/PrwWS7PSoLxN7uw9Falii9DN6y7FCkxxvwhun2SxanM5sa+9gfpOD3c1QRYgwarETHy7ZDnmRydNtH34Mw63A6cGK2lq/8JwDWQSGeZFFOBz6Q+hIDQTUrb2vQqLsx/Nxj2UeTpib4ZKGonskM1I166HVhE/1YfH+DkBHqEEpgn9/f3Q6XQkYBcuXDipu2OnE2J96dkTDTS1f+bYVUikEsxfko1VG2eiqCTF76f2Bd//tz3YfrQKRZlxyEuPIWF59GIj9YU+s3kentxQSlXOD6qujk/l//vWk9QOsGJuFjRBSgwbxyim6ju/3QWtOpC2Rs0pSKaPGRgx48mX/wuDBgseXzMbT6wvQahGhcERC/7jzVPYeuAyDWE9sa4E3kz70AiJ0zcvV6PLYEJKRBg2F+eSta/X3ofe2/G97DdWUu/TXnbxc3NlsIfE6VvN1TDYrSiOjKVtUWJrVIiCK0niHF01tWF/72kc7r8As3OUoqSWR5ehPKoYwTLfTnbwzan9wyROu0fP0frSRPUipGnXICZoNiQB3BnI3Ds+il6bVj+J4hsQhIfffijIZrPR243fuDfTVN9DQ1EHd1+hSmp6Vgw+95XVWLwqH9oQftEfZ+v+yyRS5xem4tUvrKHhJYEQmd/81Q78185zCNcG4cGlBR9oRY+/++nNc98lXsNDgrCsLBPNXYP4/VunSQTnZ8TSBimxieq5jy/CH94+jf/adQ4nLjcjTheCYdMYqhq7sXBmGrasKII3YrHZsae6ngTq2dYOBCsVWJObSQK1KD7m/uZZip7U9xKpAnFN3d5+7XH3KLi9f8yCN5sqaTDq6sgAdKpgfCyjiARqeoj/ORi3Y9hupKn9vb2n0D7aiwhFCNbEzMMyfRkSgvRTfXjMXV5siC1RwtpvNu2H0zMKXWAh5um+Qda+Qur7w6CM9zFthKqYwn7++ecxf/585OXlvWdP66uvvgpvRghSmtrffhlNV3sQEqbGktX5WLG+COnZt4bKM0BT5yCdBiEkhUgVwlJQnBVP1v8//PEgBfkLC19M6b9flumNwktMqwsbVzx2/GPE8JTof23vHYEHnon7V8zJQnpCJP7ltePo7B3B2ao2qqo+9UAZtiwvolYEb0F8T+faOqnv9J3qeow5HJibmoifbV6N5dnpUE3V9yIGp+7l494Dh9uFgx2NFMh/qLOJ2j9WJGTgxVlLUB6TAhlb+3C6XTg7VEWZp2cGq8jKnxdZiL9J3YzCsCy29r2MMecQmky7UW/cAYO9GWpZNHLDHqPqqUZ+bUU0w0xXpo1QFb2qlZWVOHbs2Hs+5sUXX8QLL7zwropqQkLCfTrCD4/T4SJLX2SenjleTz2OcxZm4cnPL8XsuemQyX07zuejIMSksOQFOanR9K/QmuOCc0FxKs5VtWHn8WoajPqbh+bd8ee+sddQfB2RBBAcpIRlzI7OPsNE5Xac1PhI/PxvN6JnwIgxmwMpcd5VcesYNpCt/+alanSMGJEYFoJnFpTQStPY0GnQOiOm++/l426iZrgPrzdewZtNVRiwjiI/IhrfLlmGB1JyEKr0z+HEm2kyd5I4PdR3DgaHGenBCfhs+kNYFDULGjm7PN6E2+NEp+UUxUq1W44hIECCRPVClEY+R9a+uM0w3sC0EKpf/OIXsX37dhw5cgTxogftPVAqlfTmLbQ29WHPWxexf+dljAxZkJkbhy98dQ0Wrcxja/8uxKQuXIPGjgHUtfZRxVQ4wOOFUdFPumFR3jWheqqWelVFDuqdMF6ZFRXT8dipA2euUiVVVFBvRAjj8f7XaC9aiTpmd+CdmmvW/qnmdgTJ5Vidm4kfF+dgVuI0W+cpIqjE778YnLpd6/x4j6p43B0yZB2lnlPRe1o11IuIwCBsTMnBI+kFyA7z/YUEd4LBbqKe0709p9Bk6USoXIOl+lIs15ciWc0Zmd43tX8VjaadaDbthdU1gjBFBkqivoQUzSoESkOm+hAZxruEqvil+tKXvoQ33ngDhw4dQkpKCrwdi8mKw3sraWpfxEuFhAZh2bpCrNxQjJQM7ue6W0SlMzE6DCcrmil4X3CzrS+Gq/LSYlDZ2E0xUqJndNzWfz9u/DwN7f20QvXNg1eQlazH+vLcWx4/rUTdB/xeXWzvxhuXqrCz8iosdjtKk+Pxo00rsXJGBtTKabq1TAxIiQgqMd0vzvWNYnX83P/iFx84SCWs/cOdTdR3eqCjgT7NsoR0PF+4AIvjUiG/nqHrzzjcTpwbqqbqqbD4BaXhefhE8lrMCsuBjM+RVzHqHECTcffE1H6gNBypmtVk7YcrM6b68BjGe4WqsPv/+7//G2+99RY0Gg16enrofjEZJnJVvWlq/8LpJuzbcQknDtWS1S8s/W/99FHMWZgJ+WTG+fg4QmzG60Po39auIQwbR2la/8bpflENXVKScU2oXmkhoTouUm8nWMXn6OgboYD+tp5hNLQP4Hx1O61JzU2Lwee3zEe8PhTeRrfBhLcu15BAbR0aQWyIBk/OLcaDhTlICPeS70dET4kIqtvlqAqR+j7RVFdH+vFawxXKPR2wWpATpsNLs5aStS8qqQzQaummyumBvrMT1v7TqQ9ikW4WQuQ8SONt1n6H5TjqjdvJ4g8IkCJRXY5ZkV9EbFAJT+0zPsOUKqjf/OY39O/imyZ4f//73+Opp57CdKeloRd7t1/CgZ0VGBo0IzE1Ck98dgmWrilApM577OHpzqwZCQjTqlDf3o/G9gHMzk285TFCYArBKoagjBYrtQQIhEgVFr/JYkWI5trFj7j9xv4KahcQiI+L14Xi755YSpVUEfTvLVgdTuyrbSBr/0RjK5QyGVblZODV9ctQmpzwnkNl0xohRjduvKPNVCJGSmyKEoH8lwe6EaZUkbW/Jb0AueHsYAhMjlEc6T9P1VMRL6WVq7FUV4IV0XPY2vdCDPYW1Bu2U/XU6hpGhDIbpVF/i1TNCiikvrd0hGGm3Pr3NsymMRzaU4k9b13A1eou6jUVU/vL1xciY0as19jD3oSw/jMSdWTrX6rrJKF683kWWagxkVqYR22w2hwkVMUGKdFz+scdZ1FenDYxaBURqsamJQWYkaqHUiFHYWYskmJuH4k2HRG/NxWdPSROd1ypg8lmw8yEWHzvgRVYnZOB4EDv6eN+T4QofY8IKlElP9bdQn2ne9quwulxY3FsKn6zaBOWxWdAMYWrVqcLLo8bF4drSZyeGrhCt2eHz8BLMz6N0og8yCXs8ngTNpcRLeYDaDTuRL+1EkqJFqnaVRTIz9Y+4+vwq9UdRmdVnG8lcXrsQDVZ+yXzM/Dtnz2G0vIMtvYn+4dUJqXhpgs17Th5pQWLZqcjIzHqXba+NjiQpvXtDhdlqtLHSSVk87d1D+NYQBPWLsiZsPTzM2JQkOldgyL9Jgverrhm7Tf0DyFaG4yPlxbSOtPkCN9f5ylWmIqp/a2NlegdM1PO6d8VLcSm1Bzog7iSJGgb7cH+3jM42HsWg3YDkoJi8MmU9Vism41wBbs83je1f5oqp2Jq3+NxIjaoDIuiv4cEdTmkkmnaa84w9xgWqu9DT9cw9u+4TKH83Z3DiE+KwCf+ZjGWrytERBS/6N9P5hWmoCgrjqqqO45W4fnHr1XbxsWqmPQXRdagQDnZ4cHXp/gzk3TU0yqyMUWFdRxvqXzbnS4cvNpEmafHGlroe10xIx3fWLUYc1MTfH6dp9FuxfaWWrzWUIGLA13QKpTYmJKLh9PyURAR7TXP42QiNkQd6buAvWTtt9KGqEVRM8naFz2ofI68i2FbIxqM29Fkeoes/TBFGmZG/A1SNCsRJIuc6sNjmPsOC9WbsJitOLa/mnpPr1xoRaBKgfLlOfjKqw8it+hWy5m5R1Z2fRd2HK2GZcyGH3xx/S2PEeH6m5cV4mx1O/53zwUsmpVOAf/j7DlRi4ERC1bMyab803EBK7JXf/utR6GP0HjV+aju7iNrf/uVWoyMWZEfq8fLa5dgbV4WtCrfXufp9nhwsqeVBqN2tdXRFP/C2BT888JNNL0fKOWXLbfHjSsjDXin9xRODFyG0+3ErPAcvDjjUygja997+qwZwO4yo9m8D/WGbRi01SBQGopUzSqkadeytc/4PQEeb2wUvQe7Y2+29i+eacbebRdx4mAt7HYnikpTsHxdERYsnUFilbn39A6asPNYNa1H7egdQXSEBusX5uLpB+e+5wXBP/7pEP78zgVoRH7qwlykJ0ShtqUX2w5XIk4Xin/8yoOICvPO6eVB8yi2Xakla7+udwBRwUF4oGAGHizKRbrOu5YLfBjaTCPUdyrs/U6LEanacKqcbk7LQzRb+0T3WD/29Z4he7/fNox4lY4qp2I4KlzJGZnehMfjRu/YZVpn2mo+CLfHgbigOUgP2YB49TxIA/hig/EdPope82uhKqx9kXcqrP2+HgPikyKxYn0hlq4thC6aX/QnA5vdicPnG0icnqlshVIuw9LSTKwrz8HM7A+eUh8xjWHvqVr86n+PwuVyU+VUkJ2sxzOb52J+USq8CYfLhSP1LWTtH65vJoG+NCuV+k4XpCVTn60vY3HYsbO1lgTq6d52qGUKbEiegS3p+ZgZNc0WEkwRo04rjg9cosGoSkMjgqSBKI+aSYH8M7QpfI68DKO9g9aZNhp3w+zsglaegHTtOso8DZJFTfXhMcykwEL1LoSq3ebA8YO12P3WBVw60wRVkII2Ra3eNBPZefH8oj8JiGuh2pY+qnqKNaemURsNMm0oz8WysiyoP0TFWuSf1rf1o3/YjMKMWMy4vl7VWxAVUyFOt1XUYGh0DDkxOmwuysW6/CyEBXlPhvCHtfZP97aRON3VWocxpwPzopPwcHo+ViVkIkjODsa4tS8qp8cGLsHudqAoNBPLo8swJ6IAgVI+R96E3WVCi3k/idM+awXkkiAkBy8jcaoLLOS/O4zPY+SK6vsLVSGUxJaofdsv4fA7lTCbrMgrTsLqjTOp/5St/clh0GCh3lEx/CRC9SND1TR5L+z9jxIHJXJQvTEfdHh0jOKkhLVf1d2H8CAVNhTMwOaiHGRF+34lpcNsIFtfCNR2swFJmlCy9h9MzUN8MDsYgl7rIPb1nCZ7v882hFhVFJbpSrFUXwJdoPdEqDHXrP3usfNoMGxHq+XwxNR+mnY1EtQLIZP4QIwcw9wHoerTUwm93SM0tb9vx2V0tg0iSh+CdQ+XYOWGIrL5mcmx9o9dbMKOY1U4VdECiURCm6I+/8gCzMm/N1a2N4lUkTRwrLGFBqMO1DXRRdOijBR8fmEZFmakQHE9ncBXEdVSkXUqpvZP9LQiSKbAuuRs/H1aPkp07GAIrC47Tg5cpqn9yyNXoZIqsTBqFlv7XorZ0Y0G4040GHfA4uyBVp6I4vCnkapdzVP7DPMh8LkeVeuYHUf3V1PvacX5FqqWioGoFeuLUDA7mYQTMznW/vYj16x9o8WG3LRorFuQi+VzMhES7NtW9u1o6BukyqnIPe03jyJTF4nNxbnYkJ+NiOAgn/95uDDQhb80XMH2lhqYHDaU6RNoW9SaxCyo2dq/5vIYm6nv9Ej/BYy5bMjVpmFl9BwsiCpCoJSrbd6E021Du+UIrTPtHj0HWYAKKZplFMgfFZjH1j7j9xj93fofGRlBd7uJAvnF1qhRiw2Fs1OwYsO1qX1VEL/oTwYiTH/3iVoSqDda++vKc5Ec6382pWHMil2Vddh6qZo2R4WoAkmYisEo0YPq64NB3RYjtjZVkb3fZBxCnFqLh9Ly8FBaPpI0vr+Q4E7otw7jQN9ZEqhdY/3QKcOxTF+KZfoSxKh8v/3DlxB/Ovusl9Fo3EVboxxuC3SBBSROkzVLqQ+VYZhr+L1QfXLTz9DdZkKkXouVG4rJ2o+J9z+hdL+s7FNXWmgwSlj8gvKZaRQVVXaPrH1vQqQOnGxqx9ZLVdhX0wCn243y9GSqni7JFNa+T3fXwCqs/fZ6qp4e626GUirDmqQs6j2dG50EiY+L8zu19kXWqRiMEta+QiLHgsgiLIsuRX5IOiQB/vU74+2YHF20ynR8al8ti6ahqDTNamgVCVN9eAwzLfH7HlXRb/rFr25CcVkqpH4mlO4XDe39FMi/+0QNhgyjSE+IxJc+thCr582gMH5/o3lgGG9ersZbl6vRYzQjLTIcX14yFw8UzoBO4505rndTSbo82I3/a6jAtuZr1r7oN/3x3DVYm5QNjYIdDHGOrprasLf3FA73nceoy4q8kDR8OfMxLIgsRpDMt5c2+KK132Y+RNZ+z9h5qpYmBS9Fmuab0KvE1D7/3WGYycInrP+PGvjPvHdm6Z4TNSRQ61r7SJCunJtNmadZSb5vZd+M2WrDrqqrVD292N4NjVJJcVLC2i+I8/11nv1jFrzRVEkbo+oNAxTCL6x9UT1N0bKDIRiyG3Gw9yxVT1tHuxGpCKVIqeX6MsSoeIDTmxB/GgdttdfXme6Fw22GXlWMDO16JAYvhlzifxfoDPNh8fuKKjN51r64illQlILPPDgH8wpTIPfxKfXbRWGdablm7b9T3QCb04n5aUn4+4fWYll2GgLlvm3t210uHOhooEipg52NkAZIsDIxE9+avRQLYpJpTa2/43A7cHqwkiKlzg/V0DmaG1mAp1M3oTAsi24z3sOYcwhNpnfQaNyBYXsjhfBnhzxEofxaxV/XNjMMc3/w7b+yzB3T2j2E7UeqaKXpwIhlwtpfNTcbYVr/GwroGDaQOH3zUg26DEYkhYfi84vKsLFgBqJDNPD1SlLVUC+J07eaqzFsG0NhRAxeKVmOB1JyEKrkSpI4Rw3mdhKnh/vOweQcRZYmCZ9Pf5i2Rmnk/vc74824aGr/GMVKdY2eISs/QT0fMyO/gNigUkgC/OsCnWGmEyxU/RjzqA37z1yldaYVV7ugVSuxat4MCuT3R2t/1O7AO9X1JFDPtHRArVBgbV4mHizKRXFCjM+fj4ExC95srqLBqNqRfkSp1HgkvYDs/cxQnkgXDNuNONB7bWq/bbQH4QotVsXMo8n9xCDv2o7m74iLjQFbNQXyN5v3k7UfFZiLsqgXkKxZBqWU28kYZjrAPap+hphSP1vVRtuiDp9rgN3pQmleEjYszKNgfqVC5nd/rM61dlIg/+7qqyRWy5ITsLk4BytmZCBIIYcv43C7cLCjEa8Ja7+jkab0lyekY0taAcpjUyBjax9Otwvnhquxt+cUzgxWTVj7QpwWk7XP1Tbvs/Z3o964AwZ7M9QyPVI1q2lyP0SRONWHxzA+iZE3UzF3au3vOl6D/mEz5Zw+vXkuTe3rwn3byr4dnSNGmtgXArV92ID4UC0+M282NhbOQHyY76/zrBvux2uNFXizqQoD1lHkhuvxcskyPJCcg7BAtvYFLZYuWmd6sO8cRhwmpAXH47Ppm7EoahY0cvVUP4XMXeD2ONFpOY164zZ0WI6TtZ+oXoiSyC8jJmg2W/sMM43xr/KZn2EZs2P/6TpsO1KFivpr1v6KOdem9nNSfX9K/WbG7A7sq23A1otVONXcDpVcjlW5GfjhxpWYlRjnVatZPwzD1jG83VJNvadXBnsQrlRhY2outqTlIydcP9WHNy0wOMwUJyWs/UZzB7RyNZboSqh6KoQq410M2xqp77TZ9A7GXIMIV2agJOo5pGpWsrXPMF4CW/8+aGVfrO2g6qnoP7U5nCgT1v6iPJQX+6e1f6mjm8SpiJYy2+yYnRSHzUW5WJWTAbVSAV9GLCA40tVE4nRfewPcHg8Wx6Xi4fR8LI1Lh0LKtvW4tS8ipc4MVsIDD0rD80iczg7PgVziX78zvmDtN5v2otG0E0O2eiiloUgJXk5T+xGBWVN9eAzjlxjZ+me6+g00sb/rWDU6+gyI04XgqQfKaKWpPsL/rP1eoxlvV9SQtd80MITYEA0+WVaMTUU5SAwPha9TPzKA1xoq8EZzFeWfZodF4eszF2NjSg4iVWxbC1ot3djbe5pyT4W1n6qOw6dTN2Jx1CyEKPzvd8bbrf0Oy0k0kLV/cmJqvzD8acSr50ISwBcbDOOt8G+vFzNqtePg2Xqa2r9Q0wGVUo6lpZl46emVKM6K93kr+2ZE9Xh/XSOJ0+ONrZBLJVg5IwPfWrMYc1ISff58GO1WbGupoUD+SwNdCBPWfkoOBfLnRfBEusDiHMOR/gs0GFVnap2w9lfoy5ASHDfVTyFzl4zYW9Bg3IFG4y5YXUMIV2ahNOp5pGhWsLXPMD4CW/9ebO0fOFuPMZsDs3MSsK48F4tnpyMo0Let7Nudj8quXrL2d1TWwWi1UZSUsPZX52ZCE6j0+RSHkz1tNBi1u+0qTfEvjk3FFmHtx6dDKeVrUZfHjcvDdZR5enKwAk63E7PCc7Aieg5Kw3PZ2vcy7C4LWsz7aWNUv7USSkkIUrWryNoXPagMw0w/2Pr3A3oHTRQpJd6EtR+vC8ET60vI2o+J9L+8vwGzBW9X1OKNi1Wo7x+EXhOMx2YXUOZpSmQYfJ1m4xBeb6zE1sZKdI0akaoNx/OFC7A5NRf6ILatBR2jvdR3Kt4G7QYkBOnxeNIaqqBGKH0/2cGXcHtc6B49i0bTbrSZD8PtcSA2qAyLor+PBPUCSCX+dYHOMP4El1umMTa7E0cuNGL7kUqcrmyFUi7DstJMfOuZVSjKivO7qX2R+Xq4vpmqp0fqm2l95/LsNHxt1ULMS030+XWeJrsNO1prKZD/XH8HNHIlbYoS1n5RpO8vJLgTzM5RHOm7QOK01tQCtUxFPafLo8uQEZzI58jLGLY1odG4k1aajrkGEKJIRmH4p5GqWQW1XDfVh8cwzH2Arf/paGU3dmPH0WrsO1UH06gNBRmx2LAwF8vKsqBW+V/loKa7j7ZFbauoxciYFXmxerL21+VnIUQVCF9GTOmf6mmlQP5drXWwu10oj0khcboiIR2BMt9eSHAnuD1uVIzUU9/pievW/szwGViuL0VZRD4UEj5H3oTdZUaLeR8F8g9Yq8jaFz2nIpA/QpnNFxsM44Ww9e8D9A6ZsOtYDVn7bT3D0IUHY/OyQso8TYoJh78xZBklYSoGo2p7+xGpDiJb/8GiHGTqI+HrtJtHqHL6elMlOswGpGjC8KWC+WTtx6j9r9XjdvSMDeJA3xkK5e+1DSFOpcPHk1Zjqa6UrX0vw+Nxo3fsEuqN29FqPkjWflzQHCyO/gHigxdAGsAXGwzjr7D1Pw2s/W1HKnGmshUKuQxLZqfjq08uxaycBJ+3sm/G4XLhaH0LVU8PXW2GMLKXZKXiuaXzsCA9CXIfz/wcddhpIEoMRokBKbVMgXXJ2XgkvQCzovyv1eN2jLlsON5/iQL5rxgaoJIqsSCyCCuj52KGNoXP0SS5PJP1s2d2dKPRuBsNph0wO7qglSegMPxTVD0NkkVNytdkGMa7YOt/Cl7061r7sO1wJd45WQujxYaCzFhsKM/F0rJMBKt8e0r9dlztHZiw9gcto8iJ1lHe6Yb8bISpfXudp/h5ONvXQYH8O1pqYXHaMUefSIH8axOzECT3v1aP21n7lYZGEqdCpFrddhSGZpK1Py+yEIFS//uduR+IVaPtlhOQB6gQEZhNq0YDpaEfWbiKqX1RNW0y7UbP2AXIAgKRFLwUGSHroQss5IsNhvFBjBz4P/0ZMoxiz8lasvbr2/oRGarGpiUFWL8w1y+t/ZFRK3ZW1pFAFfFSYUEqEqabi3ORHe37lZROs4FsfWHvt5lHkBAcgmdyS/FQah4SNL6/kOBO6LUOkq0vBqOEtR8TGIktCSuwVF8CXaD//c7cL/rHKnF24Jfot1aTiHR6rHR/mCIV5dGvIkyZ+qGsfSFKRaRUq/kwXB47YlSzsED/MhKDF0EuCZqE74RhGF+Arf9JxOF04filJmw/Wo0Tl69Z2QuKU/G5LfMxJz8ZMql/WftOlxsnmlqp73RfbSPcbjfKM5LxT4+sx+LMVChkvm3tW50O7CFr/wqOd7dAJZNjTVIWfjpvLUr1CZCwtQ+by44TA5dpY9Tlkatk7ZdHFWOZvgy52lSutk0yDvcYKof/i0RqdshDSNIsQZA0CtUjf0adYSuO9/4AMyM/h9igkjuqrF6z9ndRKL/Z2Q2tPJGs/RTNSgTLeQkFwzAfDAvVSbL2dxypogqqwWzFjBQ9/vbxRVg5JxshGt+2sm9HY/8gidO3Lteg32xBRlQE/nbZfDxQkI3IYLXP/zxcGuimvtNtzTUwOWwkSn8yby3WJWVDzdY+nSMRJSWqp0f7L8LiGkN+SDpeyPoE5rO1Pyk/k+8lMNvMh9BmOYJUzQqU6V6YuL844hna9FQx9AdcGfpPEqrvJ1LFGtPq4f9F99g5yAJUSNEso0D+qMB8vthgGOauYKF6jxgxjWH3iRrqPW1oHyBrf8PCPJraT433/Sn1mzFZbbQpSgTyX+7sQUigEusLZtDUfm6Mzuf/WPWOmrC1qYp6TxsNg4gJ0uDJ7FkUK5Ws9f2FBHfCgG0YB3rP0saozrE+RCnDsC62HCuiyxCr8v32j6nA7jJBIdXcIlrH/7U4e+n+MGXmRNB+ACQkUvPDnkC7+Qh6xi6izXwEicEL3/PrjNgayd6fr38JScFL2NpnGOZDw0L1I1rZp6+0YNuRKhy90Ej3LZyZhi88sgBlfmjtu90enGpuw9ZL1dhbUw+Hy43y9GT8Yss6LM0S1r5v/7hZXU7sbasncXq0uxlyiRSrEzPxSslyzI9O8rsUh9thddlxarCC+k4vDtdBIZHRQNQX0regIDQDkgA+R/cal9uOJtMeNJv2wuY2QSuPR2TgDMwIfRSSAOm7Kqwj9hb6VyOPo3+FSBXvE4JVJglETthjON77Q9SO/OV9hWpu2OPIC//EPf9eGIbxP3xbOUwSTR0DFMgvKqgDIxakJ0TiSx9biNXzZiDUD639tqGR69Z+NboMJqREhOGLi+figYIZ0GuD4cuIP/IVgz14raECb7dUw2i3UZTUD8pWUbSUVuHbCwnu9BxdNbVhb+8pHO47j1GXlfpNv5TxGMqjihAk87/fmfvFsK0RJ/p+jAFrNVTSCFo12m45hhbzAXSPnsd8/TehkoWTEBWiVUz1X/u4eiQFL4IHbgRASoJVkBy8DBcGfovusfMYsjUgXJl+26/r644JwzD3Dxaqd4jBNIZ3TtXR1H5Ncy+0wYFYOScL6xfmITvZ963smzHb7NhTdZUE6rm2TgQrFVibl0XWflG876/z7B+z4A0xtd94BVdHBqBXBePxzGJsSS9AqpYn0gUjdhMO9p3F3p7TaB3tJmv/gbhFFCsVw9b+pONwj+Li4L+RSM3UPoCs0AcRrsykqf7LQ/8fOkdP4eLgbzEz4nMIlF1rR9EF5qMGr5GILYp4msSr4Fp7gJuqqmJKXwxWtZuPklAV9wdwJZxhmEmCheoHWPunrrRg+5EqHLvYRFPq8wpT8KMvr8eColQK6Pc3a/9MawfeuFSFd6rrYXU4MS8tCT9/aA2WZ6cj0MfPh93lwoHOBoqUOtjZCGmABCsSMvDSrKUoj0lma5/EkRNnhqqwv/c0zg1VIwABZO0/nboJhWFZdM6Y+0PfWAXaLUdp8GmO7qsTYjJKlYfZkV/CUGcDVVZDFMnIDfsYvU8tj0agNAwGRyuGbPUIV2ZMCFEPPJRckqJZTkK1zXIYhRGfYpHKMMyk4tvK4kPS2j1E4nTnseoJa//ZRxZg5bxsRIT49pT67egYNuDNy9V481I1OkaMSAoPxWfLS7GxMAcxIX8dzPBVqoZ6qXL6VlMVhmxjyI+Ipr7TB1JyEKpk21pY+w3mduo7Fda+0WlBpiYRf5O2GYuiZkEj97/fmalkvOfUYG+l28GyWBKTbo8TkoBrL/mhyhTqMa0zvEFiVYT5C1GqkoYjKjAH7Zbj6LCcoPvGGa+u6lViyUIIjPZ2mBydE/2sDMMwkwEL1etYxuzYf7oO245WoeJqF7RqJVbOzcaGRXnISvI/a3/U7qCBqK0Xq3G6pR1BCjnW5GbiJ0W5mJkY6/PnY9A6ijevT+3XDPchMjAID6Xl46G0PGSH6ab68KYFQ3YjDvWdo1gpYe2HKbRYET0Hy/SlSFLHTPXh+S3jU/x2t2mit9TptpJtLxjvR40KzCWharS3od18bEKoxgSVkFAV1n5u6GOQSv66+Ut8Xg9ciFTmoN9ahVFnPwtVhmEmFb8WqsLKPl/Tju1Hq3DobD1sDidK85Lw/WfX0fS+UuFfp0f8EbrQ1kV9p7uqrsJit6MkKR4/2rQSq3IySaz6Mg63C4c7myiQ/0BHA923PD4DXyleiIWxKTTF7+843S6cHaqiwaizg9Vk5c+JzMenUzeimKx9PkeT8XtJq0YlKoQqxM+h6n2zUMffJyx8MQxlcnbC4uxDiCLx2vuui9cQRRL963Bb6PPnuB+BXKKGXlWMMEU6Bmw1tEUqVbtyQtyKz+t02+CGkzZWiQB/hmGYycS/lNh12nqGsfNoNXYer0bvoAmJ0WH41MYyrJmfA32E71vZN9NtMNHEvhCorUMjiA3R4qm5M7GpcAYSwn1/nefVkX681nAFbzRVYcBqQW64nvpON6bkIDyQVzsKWi3d2Nd7mnJPRxwmpAcn4LPpD1239vkc3UsmMk0dvage+T/UG7eRmJQFKElIzor8AlI0KxDwni/fHgqW0quKECyLwYC1Bp2WkyRUbxS39YbtkAYo6POKNoFB21VEq4oRqkhGRsg6nOn/JapG/gux6tKJNACBNEBJtr+4T/QgMwzDTCYyv7P2j1Shor4LwUFKrJiThXULcpCX7vtT6jcjBqH21jTQYNTJpjYahFqZk4FXNyxHaVI8JBLfPh8Gm5XipIS1f3mgG+FKFTam5mJLWj5ywvVTfXjTAqPDgiP957Gv5wzqzW3QyIKwVF+C5fo5SA3mvsTJYvy16NLQv6PBuJN6QoVNL9abdliO41jv92k1aX74J6nn9Obq6vjQVJgyDcmaJagd2Yrzg/9CwlJUS1WyCFpr2mR6B/HqebQ5qsW8H2POQfo48TmzQh5CvWEbRVCd7vt7FEd8FlpFPFn9NSOvwezsQX7YJyfSAhiGYSYLmc+vr6zrpG1R+89cfZe1Xz4zFYE+bmXf7nxc7ughcbqz8ipMNhtmJcbi+w+swKrcTIqY8mWcbjcF8Yup/b3t9XB53Fgcl4p/XfQglsanQyFl29rlceH8UA1VT08PVsLt8aAkIgcvJX4GpeE5kEv863dmqqgbeYNEqhCS5frvQCG9NpDWO7Ych7q/haqR/yHBmRmy8bYX2eOT+lkhm2Fzmagqe7T3u5Sl6vLYYHebkaheiLm6r6Ny+I+0RepaJRYTQ1elUS9QjJUYtuqzVpDN73SPUUtAjGo2ckIfue/nhWEY/8MnhWrPgBG7jtdQ72lH7wjidCF46oEyrF3gn9Z+n8mMbRW12HqxCo0DQ4jWBuPx0kI8WJSLpAjft/YbDIMUyC+s/b4xMzJDI/HV4oVUQdWpfHshwd1Y+3t7T+PgdWs/RR2Lp1IewGLdLBqSYu4vI/Zm+jc5eCmJVCE8BcLOF9b/2f5fUqVUVFtFvNTNWabj/x0sj0Fp1PP0OFEJVUiCqX1ARFQlqOfTalSF5Npros1lHP9o+v/ooGJo5N9C5fB/kTgVSwDE+tWC8KeQHbKZq6kMw9wXfEaojlkdOHiuniKlzlW3QSmXYWlpJr75mRUoyvJ9K/tm7E4nDtQ1Ud/p0YYWyCQSLJ+RjhdXL8bc1ASfz/w02q3Y3lJLAvXiQBdCFIHUcyoC+fPC9X7X6nE7LM4xHOm/gHd6TuGqqRVauRpLdLNpaj8tOGGqD89vEYNLvWOX6L/FqtNrBEz8zCaoF6Bn9AIaTbvQbNpHwfzvh5j2T9OuoTfRPuD2OEig3kzY9S1T4zFUArVcjzLdC7C6DLC7TGT/MwzD3E98Qqj+9A/7cPxKJ0atDszMjse3nl6FJSUZUKt828q+nbVf2dVL4nTHlVoYrDYUxsfg2+uWYm1uJrQq317nKWzqE92teK2xArvbrtIUv5jW/+eFm7AsIR2BUp/4cf9IuD1uVIzUk7V/YuAyBfTPCs/BSznC2s+FXMLnaKoRQjFIFoVheyOF7ouK6fiAlECIzHTtugmhmh/2xLsipN4PaYCcUgPGcbntqDduh1aegJDbTPD/NUEghN4YhmHuNz7xV+lSXRceXzObrP1Ynf+9mA6YLXi7opZ6T+v7BhEVrMYjs/LJ2k+N8v11nq2mYbzeeAWvN1ai02KkFabPFczH5rQ8RAf5X6vH7egc68P+njM40HcW/bZhxKl0eCxxFZbpyxCh9L/fmeleUdUqEmnF6XgLwM0rSoV1LwasRJZp5+gZJAaXT0RIvR/jgf8CIYKvDP2RQvtLo567rZXPzgPDMFONTwjVP/3gCYSG+tcfW4fLhcNXm7H1UhWO1LfQH5Tl2Wn46opyzEtNgkzq29a+2WHDzpZamto/09cBjVyJdUnZ2JKej5lRcfwH9gZrX2yMqjE2Qy1VYaFuJpbrS5GlSeZzNE0RYlNsexJ5pyI2yuocJhF543S/qIwmBi8modo9epaE6rhIfS/BOuYcwpDtKlVqRci/aC8QInVG6MM05c8wDDMd8Qmh6k/9p3U9/dh6qRrbKmowNDqG3BgdXly9COvyshEa5PvW/uneNso83dVWB6vTgQUxyfjlgg1YmZgJlYwn0set/b3XrX2n24nisGx8PfsplEXkQSn1r3YYb0XkmYrA/mFbAwnLGNnsWx4jKqqSADmMjjYahBrvOxUiVQxX2dwmsuvHB63EtL8YwjI42qiyKqx+MfWfqln5gZVYhmGYqcInhKqvM2wZw/ZKYe1Xo7q7DxHqIGwomIHNxbnI0kfC12k3j5CtL+z9drMByZowPJs3l6z9WDVPpAv6rEPUd7q35zT6bEOIV+nw8aTVWKorZWvfCxFbo8RK067RM+gbq0BM0OxbKuBigj9YFg272wKnxwbl9WipVvNBXBn6E1VZxaDVjQkAZbq/g9U1gghlFrQKHphjGGb6w0J1muJ0uWlaX1j7h+qaaJRiSWYqvrhoDsozkiH38czPMacDu9vqqHp6oqcVapkC65Kz8Q9p+Ziti2fbWixtcNmoarqv9wxVUQOlCpRHFWOFfg5maFP4HHkxouKZrFmG3rGL6Bw9iYTghQhXpr/L1hcVVId7lDJQVdKwiY+zuoZhcrSj3XIcqZrVNKk/XlUVgpdhGMabYKE6zWjoGyRx+vblGgxYRpGtj8JXV5ZjfX42wtW+vapS9OCd7++kQP7trTUwO+wo0yfg5/PXYU1iFtRytq2FtV9taKLq6bGBSxhz2ZAfko7nMj+GBVHFUEnvbPqbmR6IuCiby4BgefQt7xNh/82qQqqqNhp3IDzqOZr+HxerYpWpQCFRk60/PigVpsygtgHaWgXnbYexGIZhvAUWqtMAw5gVOyvrKJD/SlcvQlWB2FCQjc1FuZgRo4Ov02UxYmtjJQ1GtZiGEafW4tMzSvBQWh6SNLyiUdAzNogDfWeoetprHYQ+MAKb45eStR+tipjqp5C5C0R1s2fsIq0xFTZ9dNBMLIv92S2PEytPs0I2oXv0HKpH/g+JwYso8H+cJtMejLkGqcdUhPiPC9goZQ5Wx/8Garnvv3YwDOP7BHhEGctLMRqNCAkJgcFggFbrXb2KLrcbJ5ra8MbFKuyrbaT1nuXpydR3uiQzBQqZb19DiEGoPe31+EtDBY51tyBQJqeqqZjaL9MnQsKB/LC57GTti8GoyyNXqVq6ILIIy/VlyAlJhYSrZF6FxdGLBtNONBh2wOzsgkYejzTNGqRqV0Ejj33PjzvT/0vUjPwflBIN0rXrEaZMw6CtDvWG7dAq4rAs9u8RJPP9XnWGYbyXj6LXWKjeZ5oGhmgo6q3L1egzWZAeFU55p6KCqtP49jpPcU10ebCb+k7fbq6GyWFDiS4eD6flU/9psJxta3GOrprasK/3FA73XYDFNUbW/oroOZgfWYhAtva9CpfbhjbLETQYd6Br9CxkAUokBS9FRsgG6AIL7qiPWAw/tZj249zAr4XxTwNTgghlNg1Lxavn3ofvhGEYZmqEqm+X7aYJJqsNu6qukrV/qaMb2kAl9Zw+WJSDvFjfX+fZN2bGm01VJFDrDQOICdLgk9kzSaCmaH1/IcGdMGAbwcG+s5R52j7aiwhFCNbHlmN5dBliVVFTfXjMXV5sDNpqSZw2m/bC7jaRKJ2nexHJmiVk098NogUgO/QhxAaVUkj/qLMfelUhIgKz+XlhGMbn4YrqJOF2e3C6pZ3E6d6aBthdLsxPSyJxuiwrDUq5b18j2FxO7O9ooMGow11NkAZIsCoxE1vSCzA/OglSCQ93CGv/1OAVEqcXh2shk8gwN6IAy/SlKArLonPGeA8WZz+ajLvRaNoNg70ZKmkk0rVrkaZdixDFretJ74bxqX2GYRhvhCuq04j2oRG8cbkab16qQZfBiKTwUHx+URk2FeZAr/V9a79yqBevNVTgreZqGOxWFEXG4tXSldiQPAMhSu9aSDDqHEOdqRWp6jiEKDTv2gz0YRGfQ3xOMbV/5Lq1L6Kkns14lKKl1LK/7mFnvMPab7UcpuqpGHqijVHqRZgd+UWqgN6rIH0WqQzD+Cu+Xda7T1hsduyprqdYqXOtnVArFFiTm4nNxTkoToj1eWt/YMyCN5urqHpaO9IPnSoYj2UUYktaPtJDvW/I48xgFXZ1H8PZoWroA8PxteynSKh+lOdxxG7Cgb6z2NtzCm2jPYhUhJK1vyy6FHEqns72JsTFxpAYZjJuv8HaL8Q83TeQFLwECqlvX5AyDMPcT1iofgRr/1xbJ1n771TXY8zhwJyUBPxs82osz06HSuHb6zwdbhcOdjTitcYr9K+Y0l+RkIGvz1yM8tgUyLzM2u8Y7cWu7uNkw5udYwiRB2NBVBGKQrM+dI+o0+3CueFq7Os5jTNDlQhAAOZFFuKZ1AdRyNa+12F1DqPRtIcyTcVaU2HtZ4ZsQrp23Ue29hmGYZjbw0L1LukcMeLNS9V441IVOkaMSAwLwTMLSvBA4QzEhXpXRNaHoXa4j/JOxXDUgHUUeeF6vFyyDA8k5yAs0Ptsa5fHhf/XsgOvt++n27khqZgZNoMm7dM1CVBI7v6Co9HcQYL3UN85GBxmpAXH45nUzVismwWN/O4GaZipRUzYd1pOkbXfbjlGFxsJweWYGfn569Y+v4QyDMNMJvwqeweM2R3YV9tA1dNTze1QyeVYnZuJHxfnYFZinM9b+0PWUYqTEgJV9KCGK1XYlJpLg1EzwrzPth6wDSNCEUrPmzRAilB5MGWU5mhT8YWMLRSmf+MmKA889Lg74dXK31H1NFSuwVJdCQ1GpQTHTeJ3w0yOtX8VjaadZO2LeKhwZQZKor6MFM1KBEpD+KQzDMPcJ1iovs8fKxElJcSpiJYy2+woSYrHDzauxKqcDKiVvr3OUywgENP6YjBKTO+LtRDLEtLx5YL5WByXBoX03gyJ3C+6xvqxt+c0tnUdRkl4Lp7L/DgUEhmF5osKqnhf+2jPLR93c6j+Bw1ULdXPxpqYefQ5ZRLvOkf+jhCkTcY9qDduw4i9CSppBFI1a5CmXYNwZfpUHx7DMIxfwkL1JnoMJrxVUUOh/C2Dw4gN0eCTZcXYVJSDxPBQ+Dr1IwMkTreStW9BdlgUXpy1BBtTchERGARvwuF20MpR0XvaZO6cuH/YbkSgVEHVUkGSOgb5oenY0XUMV0YaoNOHo8c6gCZLF71fPE4MPKUGX6uev59YLY+aeZ++O+beWfunydrvsByj+xKCF2JW5LOIDSpha59hGGaKYaEqMj8dTlpjKvpOxVpTUS0UVdPvrFuK0uQESCS+be2LGKltzTV4rbEClwe6EaoIJGtfBPLnhnvfQoILQzXY03MSJwYqyLaPUUVibcx86g/9S/s+2NwOWF022vLk8rgpr7QwNBMHes/SZP6Iw4Qj/RfQZumB0+OizxmlDENZRB6eStnA26F8AIO9hcRpo3E3xlyDCFNkkDgV60xFwD7DMAwzPfBboSqqYlc6eylSamdlHYxWG2YmxOK7G5ZjdU4GggN9e52ny+3Gse4W6jvd03YVTo8bi2NT8ZtFm7A0Ph1KqcwrB6N2dh3HbxtfpzxSISxFcH5eSBqS1bG4OFyHtzoPIVzx16E3Ca6J8GxNMjI0iagYqac3Men/YPxS6JRhqDW14MJwDbZ3HYVSqsAjCSvo89+LXFXm/mFzGdFiPkBT+/3WKiglWuo5FVP7EYFZ/FQwDMNMQ6ZUjRw5cgQ/+9nPcP78eXR3d+ONN97Apk2bJvVr9psseJus/So09A9BrwnGx0oKaWNUckQYfJ1GwyCJ062NlegdMyM9JAIvFJVjc2oedEHenf8oBp5yQlLwdOompKjjkKlNoiGpcYSktLrs6LcNT1RFx4VmuDKEBK3D7cTK6DlYET1n4uPWYD4uj1zFP9b9F0VNpQfHk8XPItV7pvYbTbtoal9seBLT+ouiv48E9QJIJb7da84wDOPtTKlQtVgsKCwsxKc//Wls3rx50r6O3enCwatNJE6P1rfQ+s7l2Wn4+qpFmJea6PPrPE12G3a01lLv6fn+TmgVSuo5FdZ+QUS0TwmuVHU80oITJm6Lqqf4nxiKClVoIJdc+5E3OizQXo+KEj2o4v1lEfm0JUoIVsF4W4BAtAYIAfs/rXtQMdKAWWE5CJJ516Ytf8Jgb0ODcTsajbsmrP2ZEZ9DqmYlVLK/pjowDMMw05spFapr1qyht8lACJTq7j4aitp+pRYjY1YUxEXjm2uWYF1+FkJUvi0y3B4PTvW0UiD/rtY62FxOCuL/VflGrEjMQKAXWvt3wrjoHhef4rb4n6DPOkz/JaKjZDdM849P9ou80xsZF6njFn+mJgkqmRKdY31weJz38bti7gS7y4IW837qPe23XoFCokGqZhVb+wzDMF6MV6kVm81Gb+MYjcZbHjNoHsW2K7VUPa3rHUBUcBAeKs6lqf0Mnfet87xb2k0jJE5fb7yCTosRKZowfKlgPjan5iJG7V0LCYRArDDUI1CiRJI6muz6O+0LvTFWaly0it5Uu9uJXusQgmR3v5wgKSgGo04rbbHSyji4fzogrPyesYskTlvNB+Hy2MnaXxj9XSSqyyGV+HavOcMwjK/jVUL1Rz/6EV599dVb7ne4XNhf20iZp4frm6lqtiQrFX+7bD4WpCVDJvVta9/isGNnay31np7ubUewXIH1STOwJT0fM6O8ZyHBuAgVgfxvdR7Gnu6TGHVZoZTIoZIF4tMpD2CRbhakuLt80nHRKnJThV0vKqVDNgP1pX4Q4yJXcMVQTx8r4qycHifkAb69Jnc6Y7S3o9G0G03G3TA7u6GRx6Mg/CmkadZALfe+JRQMwzCMDwjVF198ES+88MK7KqoJCQlY++v/hMHlQU60Dl9ftRDr87MRFuR96zzvVtQJUSoipYS1P+Z0YF50Ev5xwXqsSshEkNz7hkTGBfWfWnZhX+9pWmeapUmmKKnTg5X4h7r/omroI4kraHDqbqfuh+xGuNwuhMiDKUf1/Rj/3OMitX20F292HqK+1QWRxZB/iNWqzL2w9vehwbiTrH25RI3k4KVI066FLrDAay7IGIZhGB8Vqkqlkt5uRmSefnx+KbKjo+DrdJoNeL2pEn9puII28wgSg0Pxubw5NLUfH+z9qx13dR0nkTo7PAdfy/7khEW/UDcLP6n5PbZ2HKChqDUx8+9YmIyLzqjAMMpQ7bEOvq/1b7CbhGqGUqJAjbGZ4qre6TkJg8NM0VSrYubes++X+eDnrtd6CQ0GYe0fgNNju27tv4oE9ULI2NpnGIbxabxKqL4XX1lRDq3Wu/ov7war04HdbVdpav9ETytUMjnWJmXjp/PWolSfAIkPVZJaRrvp3/KoYhKT49ujxCT+UykPUEbqjq6jyAtJR0KQ/l3W/Htxo6ANuz7532cdgi4w/LZZrFXGJvym4TUStUIojblsFPj/ufSHsFg3+55/z8ytmB09FCnVaNwJk6MTwbJY5IU/gXTNWqjlej5lDMMwfsKUClWz2YyGhoaJ283Nzbh06RLCw8ORmJgIf0YIpIsDXXit4Qq2t9TA5LCRKBXiVIhUtRda+x+EEJ1Vhkb6bxG+L6CZ/etCszQ8j6qb+3vP4FDfOTyRvO6uPr/BbqZhqiilCsGy26+DFS0FYmgqPTiRWg6EGJ4TkY+Z4TM+8vfHvD9Otw1t5sM0GNU9dg6yACWSgpdinu4l6FWFCPiACxKGYRjG95hSoXru3DksWbJk4vZ4/+mTTz6JP/zhD/BHekZNFMYvBqOajEOIDdLiUzNm4aG0fCRpfHshgaiMRihD0GLpQpO5E4lB0ZSBOh4vpZEHYYW+jISqWHH6aOJKKO6gV3Tc+hcVVItzjPpc5ZL3HsiKC9LhucyP0fap8dxVZvKm9nutl2koSmyNcrgt0AUWYp7uRSRrllAfKsMwDOO/TOlf4cWLF5OI8HesLif2ttXTYJRYa6qQSLEmKQvfK1uJudFJPmXtvx9iUClOpcN51KB9tIfuu9nWz9ImI0ubhDpjKy4M11K188Zg/g/KVhUrUaUSKWWqCkH6Xog+WGbysDj60GDaQdVTs6MLwbIY5IQ+ilTNamgV786zZRiGYfwXLhdNZUboYA/1nb7dUg2j3YZZUXH44ZzVWJeUDY3C//IfhdiMCYykf8WUvRhqClFo3jXdLyqc8yOLSKhevC5Ux0XqBwlWs3MMdo8Tasiocnu3qQHMR8MlrH3LUeo77Ro9A2mAgqz9+fpvQh/I1j7DMAxzKyxU7zN9Y2a82VRF1v7VkQFEB2nwicyZeCgtD2khvNqxIDSD4qOaLZ1osXSj8DaVzWxNMmQBUnSO9sHkGKWWAIEQqUJ8mpyjtB715kGrYJkKI3YTtEHqia1VzOQino9BWy1VTptNe2F3mxAVmIc5uq8iOXg5FFK29hmGYZj3hoXqfcDucuFARwOJ04OdjSSoViRk4KVZS1EekwyphIdExhHWf0pwHFVLq4yNKAzLvEVQitB+fWA4LQOwue3QIIim9Y/3X8Zr7Xupyvp48tpb2gZEj+q/lbyMGJXvbyibasacg2gy7aHM0xF7E1TSSGSGbEK6di1CFElTfXgMwzCMl8BCdRIrSVVDvSRO32quxrBtDIURMfhOyQpsSJmBUKVvLyT4sMgkUiyMmokrIw04P1SDuREFJFxvtPU1MjWJVDHBHyrXTEzrjzhM6Bzrx+mhSizVlyBGdWuuLovUybb2j6DRuBtdo6cRECBFgrocsyKfpexTScDdbRRjGIZhGBaq95hB6yhZ+2Iwqna4H1EqNR5JLyBrPzPU9xcS3AnDdiMO9p2jwaYFUcW3vL8kPJe2Ul0crqPw/2eCN9P942JVTPqLJACVVAm72w6Z5JroT1XHUduAeIzD47rv35e/MmitQ71xO5pN71y39vNRpvsKbY1SSn0335hhGIaZfFio3gMcbhcOdTZR9VRY/ILl8Rn4WvEiLIxNhYytfbLmzw1VY2/PaZwZqoQEEjycsPy2QlX0l66NWYDLI1fxVudhqqrmhaZPvP9w/zlahyq2VYmlAOMCNlObhJ8WPkcbqJjJxeocRpPpHbL2h+31N1j76xCi8O8MZIZhGObeEeDx4nwoo9GIkJAQGAyGKdlMVTfcT+L0jaZKDFhHkRuux5a0fGxMyUVYIFv7ApGJuq/3DA72niVrPi04Hiuj52BR1Cxo5O8/SPO7xq14u/MwDUGtiJ6DFHUsGszteKfnFKIDI/Fq3udoep+5P7g9TnRaTtFgVIflON0Xr15A4jROXQZJAF/3MgzDMPdWr/FflrtkxDaGt5urSaCKeKlwpQobU3NJoOaE82pHgYiVOtR/noL5G80dVCFdHDUby6PLSKjeKSLQX8RV/b75LRKsonIqSA9OwOPJa1ik3ieGbPVoNO6iCqrVNYRwZQZmR30RKZqVCJSG3q/DYBiGYfwQrqjeAU63G0e6rln7+9obSDAtiUvDw+n5WBqXDoWUh0ScbhfODlVRT6n4V/SQil7TZfpSzA7P+UgbnjrH+tBs7sSg3YAcberEelVm8rC6RtBk3DNh7QtBKoSpmNoPV2byqWcYhmHuGK6oThINhkEK5H+jqYryT7NDo/C1mYvI2hdDUgzQZunG3t7TOHDd2hfVzqdTH8Qi3SwabPqoiCxUEVkl3pjJxe1xoXv0LOqN29BuPkr3JQQvQHHEM4hTz2Frn2EYhrnvsPV/Ewa7FdtbavCXhiu4ONCFEEUgNqbkYEt6AfLC9RwSL9ZfOsdwtP8i9vacQq2pBVqZGkv0JVihL6MoqXvJzVmozL3HYG+lyqmw98dcAwhVpFKkVKp2FVv7DMMwzJTCQpXyH9040dOK1xquYE/7VZriXxSbin9euAnLE9KhlPJpEu0Olyku6gxODlbA6XZiZvgMvDTj0yiNyIVcIp/an2TmrrC7zGgx7yOB2m+thEKimbD2I5TZfEHGMAzDTAv8WoE1G4fwemMlXm+8gu5RE1K14Xi+cAE2p+ZCH3Tr6k5/pG20h4aixNS+6BFNCNLj40mrsURXgkglD9J429R+1+hZqpy2W47Q7digMiyK/j4S1PMhlSin+hAZhmEYxr+Fqtlhw46WWqqenuvvgEauxAMpOXg4LR9FkTFcSbpu7R/pu4B3ek/hqqkVGlkQ9ZyKwaiM4EQ+R16Gwd6GBuP269b+IEIUKSgKfxop2lVQy3gJBcMwDDN98Quh6vZ4cLq3jcTprrY6WJ0OLIhJwT+VP4CVCRkIlLFtLYaWxNpSIU5PDFwma39WeA5b+16Kw21Bi+kgZZ72WS+TtZ+qWXV9aj+LLzYYhmEYr8CnhWq7eWTC2m83G5CsCcOzeXOxOS0PsWpe7SjotQ6Stb+v5zR6bUM0Xf+xxNVUPeUwfe/C43Gjd+wSidNW80E4PTbEqGahPPo7SFIvYmufYRiG8Tp8TqiOOuzY3XYVrzVW4GRPG9QyBdYlZ+Mf0vIxWxfPlSRxjpxWHB+4RAL1iqEBgRIFyqNm0saoGdoUPkdehsnRRba+eDM7u6CRxyEv/AmkadYgWB491YfHMAzDMP4tVMUW2DO97RTIL/pPLU47yvQJ+Pn8dVibmIUguQL+zri1LwL5jw9cht3tQEFoBl7I+gTmRRZCJeVBGm/C7rKg1XyApvaFtS8LCEKyZinStS9DF1jAFxsMwzCMT+ATQnXt9v8PnS4bEoJD8ExuKR5KzUOChifSBX3WIRKn49Z+rCoKjySuwFJdCXSB4VP91DF3eUEmRGm9YTuJVLL2g2ajXP8KEoIXQi5R8flkGIZhfAqfEKpFkXH4eWEZyvSJkAQEwN+xueyUdfpOzylUjIj1l8LaL8YKPVv73ojF0YtG0240GnfC6GhHsDwW+eGfRJpmLdRy3tjFMAzD+C4BHlGm8cPdsb6GeBrFlijRdyqipSyuMeSFpJE4nR9VxNa+l+F029BmOUziVGSfSgMUSApegnTtOkSrihHAG7sYhmEYP9BrPlFR9Wf6rcM40HeWBGrnWB8iFaFYF1uO5dGlNMHPeNfFhtgSJYaims374HCboQssxDzdiyRSFVL1VB8iwzAMw9xXWKh6qbUvsk7FOtPLI1ehkMhoIOrz6VtoQErK1Tavs/YbTDtJoJocHVDL9MgOeYiqp1pF/FQfHsMwDMNMGSxUvajaVm9uw96eUzh83drP1abhy5mPYUFkEYJkPEjjTbjcdrRbjtHGqK7RMxPW/lzd1xCtmsnWPsMwDMOwUJ3+jNhNONh3Fnt7TqN1tBsRihCsJ2u/jCb4Ge+62Biy1VGkVLPpHdjcRkQF5mKO7mtIDl7G1j7DMAzD3ARXVKchDrcTZ4aqsL/3NM4NVSMAAZgbWYDPpG5CUVgWW/texphzCE2mPTQYNWxvhEoaiYyQDTS1H6pMmerDYxiGYZhpCwvVaVRtazC3U9/pkb7zMDotyAhOxDOpm7FYNwsaOQ/SeBMutw3tluMUK9VpOUlWfoK6HDMjv4DYoBJIAvhXj2EYhmE+CP5rOc2s/XCFFiui52C5vhSJ6pipPjzmLhm01qHeuJ2sfbvbhMjAXJRGPY8UzQoopf4docYwDMMwdwsL1SnA6Xbh3HA1bYs6M1Q5Ye1/OnUjisnal07FYTEfEqvLgGbTXjQYt2HIVk/WfmbIJqRr1yBEkcznlWEYhmE+JCxU7yNtlm6y9kXm6YjDhLTgeLb2vRS3x4lOy2k0mnag3XwMHngQr56HovBnEKeew9Y+wzAMw9wDWKhOMkaHBYf7zpM4FfFSGlkQluhKaGpfCFXGuxi2NdFQlOg9tbqGEKZIx6zIZ8naV8nCp/rwGIZhGManYKE6ida+EKdnBivh9ngwOzwHL834NEojciGXyCfjyzKThNU5jCbTOyRORbyUUhqKVM1KpGvXIlyZyeedYRiGYSYJFqr3kLbRHuo7Hbf2U9Vx+FTKA1ism41QheZefilmknF7XBTEL/pOhbUviFfPR0H4U2TxSwP4YoNhGIZhJhsWqh8Ri3MMR/ovkECtNbWQtS+EqZjcZ2vf+zDY2yas/VFnH0IVqWTtp2pXIVAaOtWHxzAMwzB+BQvVD4Hb40bFSD329Z7GiYHLFNA/M3wGXpzxKZRF5LG172XYXWa0mPfTxqh+6xXIJcFICV6OjJD1iFDOQEBAwFQfIsMwDMP4JSxU74LO0T6a2he5p/22YVph+ljiKizVlyJSydU2b7P2u0fPodG0C23mQzTFHxtUioXR30WiuhxSiXKqD5FhGIZh/B4Wqndq7feeRq2xBWqpCgt1M7FMX4psTTJX27wMg70VDcYdaDTuxphrACHyJBSGfwap2tVQy6Km+vAYhmEYhrkBFqrvYe1fMTRgb8+pCWu/OCwbX89+CnMi86HgqX2vwuEeRav5IOoN29FnvQyFRENxUmnatYhka59hGIZhpi0sVG+gzzpElVMxGNVrG0JsIFv73orH40av9TINRrWYDsLpGUWMqgQLo19FonohW/sMwzAM4wX4vVC1umxUNRW9p2JAKlCqwILIYqyILkOONpWtfS/D5OhCo3EXvZmdXQiWxSIv7ONI065BsDxmqg+PYRiGYZi7QOav1n6VoZHyTo8NXMKYy4b8kHQ8l/lxLIgqgkrKgzTehMNtQav5EInTnrELkAWokKxZijTtN6EPLERAgGSqD5FhGIZhmA+BzC+t/d4z6LUOIjowApvjl2KprhTRqoipPjzmLvB4PNRvKvpOW80H4PTYEK0qxgL9y0gMXgS5JIjPJ8MwDMN4OT4vVG0uO04OXqHBqMsjV6GUyFEeNRPLsz6BXLb2vY5RZz9N7IvJfaOjDcHyWOSHfxKpmtUIlkdP9eExDMMwDHMPkflqta3e3Ia9PadxuO88LK4x5GrT8Fzmx7AgqpitfS/D5bahzXKUBqPEWlNJgAxJwUswV/c16FVFbO0zDMMwjI/iU0J10GagMH7Re9o22oMIRQjWxZZjeXQp4lS6qT485i4vNgasVbQtSmyNsrtN0AUWYI7uq0gOXgaFNJjPJ8MwDMP4OD4hVI/2X8Sp1ipcGq6DTCLDnIh8fCZ1E2WfSnmQxquwOHrRaNpNg1HC2lfL9MgK2UxT+yGKxKk+PIZhGIZh7iM+IVR/efV/UBiTjWczHqWp/WAZD9J4Ey63He2WY6g3biNrXxagpIGoMt3fIUY1i619hmEYhvFTfEKo/lPxV5EdnTbVh8HcJUO2q2gw7kKTcTdsbgOiAvMwV/d1pGiWQS5R8/lkGIZhGD/HJ4RqbBDvaPcWxpxDaDK9Q9b+sF0sWAijVaYZ2vUIVaZM9eExDMMwDDON8AmhykxvhLXfMXqCpvY7LCfJyk9QL0BxxN8gTl1GU/wMwzAMwzA3wwqBmTQGrXWoN25Hi2kvbG4jIpQzUBr1PJI1yxEoDeEzzzAMwzDM+8JClbmn2FxGsvYbjNswZKuHShqJjJANSNWsQZgylc82wzAMwzB3DAtV5iPj9jjRNXqWrH0RzO/xuBCvno+icLb2GYZhGIb58LBQZT40w7YmNJquTe2PuQYRqkilvtM0zWqoZOF8ZhmGYRiG+UiwUGXuCqvLgGbTXqqeDtpqoZSEIEWzAunadQhXZiIgIIDPKMMwDMMw9wQWqswH4va4KIhf9J22m4/BAw/i1XORH/4jxKvnQRog57PIMAzDMMw9h4Uq856YHJ2oN2xHo2knRp39CFOkYVbks0jVrESgLIzPHMMwDMMwkwoLVeZd2F0WtJoPosG4A33Wy7QhKkWzkgL5I5TZbO0zDMMwDHPfYKHKkLXfM3YBjcYdaDUfhstjR0zQbJTrX0Fi8CLIJIF8lhiGYRiGue+wUPVjDPZWWmXaZNoDi7MXWnkiCsM/hVTNaqjluqk+PIZhGIZh/BwWqn6Gwz2KFtMBNBi3o89aAYVEQ5ui0jVrEBmYy9Y+wzAMwzDTBhaqfoDH40G/9Qr1nTab9sPpGSNrf2H0d5GoLodUopzqQ2QYhmEYhrkFFqo+jNnRjUbTbrL3TY4OqGV65IY9RpmnwfKYqT48hmEYhmGY94WFqg9a+23mw1Q9FQNSsoBAJAUvwVzd1xCtmomAAMlUHyLDMAzDMMwdwULVR6z9XuslNBjE1P5BsvaFKJ2v/yaSghdTxBTDMAzDMIy3wULVixEh/MLWrzduJ2s/WB6LvLBPIE27mq19hmEYhmG8nmkhVP/5n/8ZP/vZz9DT04PCwkL86le/Qmlp6VQf1rTE5baj3XIMjaZd6LScgiRARtb+PN03oFcVsbXPMAzDMIzPMOVC9c9//jNeeOEF/Ou//ivKysrwi1/8AqtWrUJdXR10Os7yHLf2B2zVaDTuRLNpH+xuEyKVOSiLegEpmhVQSIOn+mlkGIZhGIa55wR4hAqaQoQ4LSkpwa9//Wu67Xa7kZCQgC996Uv4xje+8b4fazQaERISAoPBAK1WC1/D4uhD0/WpfYOjFUGyKArjT9euQYgieaoPj2EYhmEY5gP5KHptSiuqdrsd58+fx4svvjhxn0QiwfLly3Hy5MlbHm+z2ehtHPENj58AX2R3+9/BYG9GfHA5yoKfQXRQMSQBUsAKGK2++T0zDMMwDONbGK/rtA9TG51SoTowMACXywW9Xv+u+8Xt2traWx7/ox/9CK+++uot94sKrG9zHMCPp/ogGIZhGIZhPjSDg4NUWfWqHtW7QVReRT/rOCMjI0hKSkJbW9tdf+OMd16RiYuS9vZ2n2z1YN4NP9/+BT/f/gU/3/6FwWBAYmIiwsPD7/pjp1SoRkZGQiqVore39133i9vR0dG3PF6pVNLbzQiRysLFfxDPNT/f/gM/3/4FP9/+BT/f/oVEcvdLh6Z0TZFCocCsWbOwf//+ifvEMJW4PXfu3Kk8NIZhGIZhGGaKmXLrX1j5Tz75JGbPnk3ZqSKeymKx4FOf+tRUHxrDMAzDMAzjz0L10UcfRX9/P7797W9T4H9RURF27959y4DV7RBtAK+88spt2wEY34Ofb/+Cn2//gp9v/4Kfb/9C+RH02pTnqDIMwzAMwzDMtOtRZRiGYRiGYZj3goUqwzAMwzAMMy1hocowDMMwDMNMS1ioMgzDMAzDMNMSrxSqv/nNb1BQUDARFCwyV3ft2jXVh8XcB3784x8jICAAzz//PJ9vH+U73/kOPcc3vmVnZ0/1YTGTSGdnJz7xiU8gIiICKpUK+fn5OHfuHJ9zHyQ5OfmW32/x9uyzz071oTGTgMvlwssvv4yUlBT63U5LS8P3vvc93M0c/5THU30Y4uPjSbBkZGTQN/uf//mf2LhxIy5evIjc3NypPjxmkjh79ix++9vf0kUK49uI3+N9+/ZN3JbJvPKlirkDhoeHMX/+fCxZsoQKDlFRUaivr0dYWBifPx99HRfiZZzKykqsWLECW7ZsmdLjYiaHn/zkJ1RcFDpNvK6LC1CRky82in75y1++o8/hla/+GzZseNftH/zgB3QiTp06xULVRzGbzXj88cfxb//2b/j+978/1YfDTDJCmN5ujTLjm3/IEhIS8Pvf/37iPlF9YXwTcSFyI6LoJKpsixYtmrJjYiaPEydOUCFx3bp1ExX1//mf/8GZM2d82/q/EXFl9r//+7+0zYrXrvouwhYSP+jLly+f6kNh7gOiohYbG4vU1FS6QGlra+Pz7qO8/fbbtJlQVNR0Oh2Ki4vpgpTxfex2O/70pz/h05/+NNn/jO8xb9487N+/H1evXqXbly9fxrFjx7BmzZo7/hxeWVEVXLlyhYSp1WpFcHAw3njjDeTk5Ez1YTGTgLgQuXDhAllGjO9TVlaGP/zhD8jKykJ3dzdeffVVlJeXk0Wo0Wim+vCYe0xTUxM5YmKd9ksvvUS/58ISVCgUtF6b8V3efPNNjIyM4KmnnprqQ2EmiW984xswGo00ZyCVSqm4KFxwUYDw+c1U4kpMVFkMBgP+8pe/4N///d9x+PBhFqs+Rnt7O1Vb9u7dO9GbunjxYlq1+4tf/GKqD4+5D4g/ZElJSfiHf/gHfOYzn+Fz7mMIQSp+x4VFOI4QqkKwnjx5ckqPjZlcVq1aRc//tm3b+FT7cKHpq1/9Kn72s59Ra+alS5doGFq8nt/phajXVlTFD3d6ejr996xZs+hF7Ze//CUN2zC+w/nz59HX14eZM2dO3CeuyI4cOYJf//rXsNlsdJXG+C6hoaHIzMxEQ0PDVB8KMwnExMTcUmCYMWMGXn/9dT7fPkxraysNTG7dunWqD4WZRIRIFVXVxx57jG6LRA/x3P/oRz/yfaF6M263m0QL41ssW7aM2jxuREwMChvh61//OotUPxmka2xsxBNPPDHVh8JMAmLiv66u7l33iX42UUVnfBcxPCd6kseHbBjfZHR0FBLJu8ehRHFJaLY7xSuF6osvvkiNuImJiTCZTPjv//5vHDp0CHv27JnqQ2PuMaInMS8v7133qdVqylu8+X7GN/jKV75CyR5CqHR1deGVV16hF7aPfexjU31ozCTwt3/7tzRw8cMf/hCPPPIITQP/7ne/ozfGNxEiRQhVUVHj6DnfZsOGDdSTKvSasP5FjKiw/cUAnU8LVWEFf/KTn6RBC5HFJXoXhUgVWWwMw3g3HR0dJEoHBwcpymbBggUUPXdzrA3jG5SUlNAwrChAfPe736VoKtF/fjfDFox3ISx/MWNyN2KF8U5+9atfUeD/F77wBdJuIs3ls5/9LL797W/7/jAVwzAMwzAM45FjqL0AAAapSURBVNt4fY4qwzAMwzAM45uwUGUYhmEYhmGmJSxUGYZhGIZhmGkJC1WGYRiGYRhmWsJClWEYhmEYhpmWsFBlGIZhGIZhpiUsVBmGYRiGYZhpCQtVhmEYhmEYZlrCQpVhGOY+EhAQgDfffNMrz/lTTz2FTZs2TfVhMAzjR7BQZRjG5+nv78fnP/952jetVCoRHR2NVatW4fjx45huTAcx2NLSQoL60qVLU3ocDMMwMj4FDMP4Og899BDsdjv+8z//E6mpqejt7cX+/fsxODg41YfGMAzDvA9cUWUYxqcZGRnB0aNH8ZOf/ARLlixBUlISSktL8eKLL+KBBx541+OefvppREVFQavVYunSpbh8+fLE+7/zne+gqKgIv/3tb5GQkICgoCA88sgjMBgME485e/YsVqxYgcjISISEhGDRokW4cOHCPf1+KisrsWbNGgQHB0Ov1+OJJ57AwMDAxPsXL16ML3/5y/ja176G8PBwqh6LY7+R2tpaLFiwAIGBgcjJycG+ffve1ZKQkpJC/xYXF9P94nPeyM9//nPExMQgIiICzz77LBwOxz39HhmGYcZhocowjE8jBJ14EyLMZrO95+O2bNmCvr4+7Nq1C+fPn8fMmTOxbNkyDA0NTTymoaEB//d//4dt27Zh9+7duHjxIr7whS9MvN9kMuHJJ5/EsWPHcOrUKWRkZGDt2rV0/71AiGkhoIWAPHfuHB2DqA4LwXwjonKsVqtx+vRp/PSnP8V3v/td7N27l97ncrmotUAIbfH+3/3ud/jmN7/5ro8/c+YM/SsEbHd3N7Zu3TrxvoMHD6KxsZH+FV/nD3/4A70xDMNMCh6GYRgf5y9/+YsnLCzMExgY6Jk3b57nxRdf9Fy+fHni/UePHvVotVqP1Wp918elpaV5fvvb39J/v/LKKx6pVOrp6OiYeP+uXbs8EonE093dfduv63K5PBqNxrNt27aJ+8TL7htvvPGex/rkk096Nm7ceNv3fe973/OsXLnyXfe1t7fT56yrq6PbixYt8ixYsOBdjykpKfF8/etfnzhmmUz2rmPeu3fvu46rubmZbl+8ePGWY0tKSvI4nc6J+7Zs2eJ59NFH3/P7YRiG+ShwRZVhGL/oUe3q6sLbb7+N1atX49ChQ1QxHa8ECovfbDaTlT1egRVvzc3NVD0cRwxjxcXFTdyeO3cu3G436urq6Laobj7zzDNUSRXWv2ghEJ+3ra3tnnwf4jhFJfPGY8zOzqb33XicBQUF7/o4YdOLarFAHKtoXRAtAeOIVog7JTc3F1Kp9Lafm2EY5l7Dw1QMw/gFoh9T9I+Kt5dffpn6UV955RWashdiUgguIWBvJjQ09I6/hrD9xYDWL3/5S+qFFQkDQsyKQa57gTjODRs2UL/tzYjjH0cul7/rfaLPVAjqe8Fkfm6GYZibYaHKMIxfIoaIxoeHRHW1p6cHMpkMycnJ7/kxojIqKrOxsbF0W/ShSiQSZGVl0W0Rd/Uv//Iv1JcqaG9vf9eg00dFHOfrr79OxyiO9cMgjlUcl6j+imGs8SGwG1EoFBP9rAzDMFMJW/8Mw/g0osIpBpD+9Kc/oaKiguz81157jYaMNm7cSI9Zvnw5VT7FkNE777xDOaInTpygISMxtHRjVVZUTYUFL5IExHS9GGQat9GF5f/HP/4RNTU1NKj0+OOPQ6VS3fUxiyQBkWF645sQl2LCXgx3fexjHyNxKez+PXv24FOf+tQdi0pRUU5LS6PvQ5wPIa6/9a1vTVRHBTqdjo57fFjrxmQDhmGY+wkLVYZhfBrRx1lWVoZ//Md/xMKFC5GXl0fWv+gl/fWvfz0h0Hbu3EnvF6IvMzMTjz32GFpbWyeqjoL09HRs3ryZKqYrV66kXlBRQR3nP/7jPzA8PEyVTxEbJYSsEH13i2hBEJP9N769+uqrVMkVwlKIUvH18/Pz8fzzz1N7gqjs3gmiv1RUkkUbQUlJCbVAjE/9CyEuENXaf/qnf6IoLvE1xwU9wzDM/SZATFTd96/KMAzjZYgsUiHwfHFbkxC/IldVxG+JaivDMMx0gXtUGYZh/Iw33niDKs2iVUGI0+eeew7z589nkcowzLSDhSrDMIyfIRYQfP3rX6fhMLFFS/To/v3f//1UHxbDMMwtsPXPMAzDMAzDTEt4mIphGIZhGIaZlrBQZRiGYRiGYaYlLFQZhmEYhmGYaQkLVYZhGIZhGGZawkKVYRiGYRiGmZawUGUYhmEYhmGmJSxUGYZhGIZhmGkJC1WGYRiGYRgG05H/H/sK1wMXC2RoAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6))\n", "plt.plot(x1[y==0], x2[y==0],'or' ,label='No Virg')\n", "plt.plot(x1[y==1], x2[y==1],'g^',label='Virginica')\n", "contour = plt.contour(x1New,x2New,zz, linewidths=1)\n", "plt.clabel(contour, inline=1,fontsize=15)\n", "plt.xlabel(\"Sepal Length\")\n", "plt.ylabel(\"Sepal Width\")\n", "plt.legend()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "cd500abe", "metadata": {}, "outputs": [], "source": [ "# Softmax model ???" ] } ], "metadata": { "kernelspec": { "display_name": ".venv (3.14.3)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.3" } }, "nbformat": 4, "nbformat_minor": 5 }