MyCVProject_v3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Computer_vision_project.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "cIk-xeIWWKvL",
        "aOOqiajuWnen",
        "i9eQ_esA2oSK",
        "-Kg3I6t94B65",
        "_oiCHz046HNC"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jgc0D92B-cuD",
        "colab_type": "text"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skOfr__v-d07",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing import image as image_utils\n",
        "from keras.applications.imagenet_utils import decode_predictions\n",
        "from keras.applications.imagenet_utils import preprocess_input\n",
        "from keras.applications import VGG16\n",
        "from keras.applications import MobileNet\n",
        "from keras.applications.mobilenet import preprocess_input\n",
        "from lxml import etree\n",
        "import os\n",
        "from skimage import io\n",
        "from skimage.transform import resize\n",
        "from IPython.display import SVG\n",
        "import numpy as np\n",
        "import math\n",
        "from scipy import ndimage\n",
        "import matplotlib.pylab as pl\n",
        "from google.colab import files, drive\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, classification_report, accuracy_score\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, UpSampling2D, Dropout, GlobalAveragePooling2D\n",
        "from keras.models import Model, load_model\n",
        "from keras import backend as K\n",
        "from keras import metrics, initializers\n",
        "from keras.regularizers import l1, l2, l1_l2\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.utils.vis_utils import plot_model, model_to_dot\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "!pip install livelossplot\n",
        "from livelossplot import PlotLossesKeras"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTypFbCalvmj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "drive.mount('/content/drive/')\n",
        "path = \"drive/My Drive/computer_vision/\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cIk-xeIWWKvL",
        "colab_type": "text"
      },
      "source": [
        "# **Load matrices and plot images**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSQmXdZ4OUlA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load matrices\n",
        "xtrain = np.load(path+'xtrain_boat_cat128.npy')#, allow_pickle=True)\n",
        "xval = np.load(path+'xval_boat_cat128.npy')#, allow_pickle=True)\n",
        "ytrain = np.load(path+'ytrain_boat_cat128.npy')#, allow_pickle=True)\n",
        "yval = np.load(path+'yval_boat_cat128.npy')#, allow_pickle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09JDdIrgOL3r",
        "colab_type": "code",
        "outputId": "be024464-e237-46e5-bcfa-6b6d58023178",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        }
      },
      "source": [
        "# plot 2 images of each class\n",
        "pl.figure(1, figsize=(6.4, 3))\n",
        "\n",
        "pl.subplot(1, 4, 1)\n",
        "pl.imshow(xtrain[8])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 1')\n",
        "\n",
        "pl.subplot(1, 4, 2)\n",
        "pl.imshow(xtrain[14])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 2')\n",
        "\n",
        "pl.subplot(1, 4, 3)\n",
        "pl.imshow(xtrain[323])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 1')\n",
        "\n",
        "pl.subplot(1, 4, 4)\n",
        "pl.imshow(xtrain[386])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 2')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Cat 2')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAB9CAYAAABeQj98AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 460.8x216 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqfc7mUWRGqi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plot whole training set\n",
        "pl.figure(1, figsize=(10, 44))\n",
        "for i in range(len(xtrain)): #size 436 if birds and cats\n",
        "    pl.subplot(44, 10, i+1)\n",
        "    pl.imshow(xtrain[i])\n",
        "    pl.axis('off')\n",
        "    pl.title(str(i))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPLkQWWhSamY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plot whole validation set\n",
        "pl.figure(1, figsize=(10, 44))\n",
        "for i in range(len(xval)): #size 432 if boats and cats\n",
        "    pl.subplot(44, 10, i+1)\n",
        "    pl.imshow(xval[i])\n",
        "    pl.axis('off')\n",
        "    pl.title(str(i))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOOqiajuWnen",
        "colab_type": "text"
      },
      "source": [
        "# **Auto-encoders**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qoi2r3G5Zkd8",
        "colab_type": "text"
      },
      "source": [
        "## PCA vs Auto-encoders\n",
        "\n",
        "\n",
        "PCA aim: find a few directions in the input space explaining the variation in the input data; project the data onto those dimentionss\n",
        "\n",
        "--> linear relationship between the data and the learned representation\n",
        "\n",
        "Here 2 PCs used (reduction from dimension 137388 to 2; of course bad, but to be able to plot)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "AE: NN whose outputs are its own inputs; same objective function as PCA\n",
        "\n",
        "Goal = minimize reconstruction error\n",
        "\n",
        "inputs x --> lower dim z = f(Wx) --> reconstruction higher dim ~x = g(Vz)\n",
        "\n",
        "goal --> min sum (x - ~x)^2 \n",
        "\n",
        "         --> = min sum (x - VWx)^2        if f and g are linear\n",
        "         \n",
        "                      => optimal solution is PCA\n",
        "                      \n",
        "2 neurons in hidden layer --> approximate to reducing data to 2 features\n",
        "\n",
        "With linear activation function and a single hidden layer of size p, the weights of the AE span the same vector subspace as the one spanned by the first p components of PCA, and the output of AE is an orthogonal projection onto this subspace\n",
        "\n",
        "/!\\ AE weights != PCs --> in general weights not orthogonal but the PCs can be recovered using Singular Value Decomposition (Plaut, E.,From Principal Subspaces to Principal Components with Linear Autoencoders, Arxiv.org:1804.10253.)\n",
        "\n",
        "--> to compare PCA and AE, compare reconstruction error\n",
        "\n",
        "\n",
        "From visual evaluation here PCA is better than linear AE\n",
        "From reconstruction error comparison here PCA is better than linear AE\n",
        "\n",
        "\n",
        "\n",
        "- the autoencoder is better at reconstructing the original data set than PCA when p is small (but the error converges as p increases) --> for very large data sets this difference will be larger --> means that same accuracy can be achieved with less components than for PCA and hence a smaller data set\n",
        "\n",
        "- in PCA, we can choose the #PCs based on the % of variability they explain; with AE, we don't know how many to keep (can you PCA to determine how many neurons in hidden layer)\n",
        "\n",
        "\n",
        "- better visualization possible with AE than with PCA\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MuQ_pMCR24g6",
        "colab_type": "text"
      },
      "source": [
        "### PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x97nfe_jWp1L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# reshape: 153 x 256*256*3 = 153 x 196608 # cats&birds 528 not 153\n",
        "# if 128 --> 49152\n",
        "xrs = xtrain.reshape(len(xtrain), len(xtrain[0])*len(xtrain[0,0])*len(xtrain[0,0,0]))\n",
        "# scale (mean-center)\n",
        "# scaler = StandardScaler()\n",
        "# scaler.fit(xrs)\n",
        "# xscaled = scaler.transform(xrs)\n",
        "# centering done in decomposition.PCA method BUT NEED TO SCALE FOR LINEAR AUTOENCODER (CAE no, use 3D img)\n",
        "# no need to scale as all same scale (all values represent a pixel)\n",
        "# already between 0 and 1\n",
        "xscaled = xrs\n",
        "xvrs = xval.reshape(len(xval), len(xval[0])*len(xval[0,0])*len(xval[0,0,0]))\n",
        "# use scaler fitted on xtrain because no assumptions about xval\n",
        "# xvscaled = scaler.transform(xvrs)\n",
        "xvscaled = xvrs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHk8nNddgt-4",
        "colab_type": "code",
        "outputId": "48ec5c66-e590-4145-8d44-00268ef9ebd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# PCA\n",
        "pca = PCA()\n",
        "pca_transformed = pca.fit_transform(xscaled)\n",
        "\n",
        "# Get first 2 PCs\n",
        "pca_2pcs = pca_transformed[:,:2]\n",
        "\n",
        "# Plot 2 PCs of PCA\n",
        "plt.figure()\n",
        "lw = 2\n",
        "for i in range(170):\n",
        "  if i==0:\n",
        "    plt.scatter(pca_2pcs[i, 1], pca_2pcs[i, 0], color='navy', alpha=1., lw=lw,\n",
        "                label='boat')\n",
        "  else: # otherwise one point per sample in legend\n",
        "    plt.scatter(pca_2pcs[i, 1], pca_2pcs[i, 0], color='navy', alpha=1., lw=lw)\n",
        "    \n",
        "for i in range(266):\n",
        "  if i==0:\n",
        "    plt.scatter(pca_2pcs[170+i, 1], pca_2pcs[170+i, 0], color='turquoise', alpha=1.,\n",
        "                lw=lw, label='cat')\n",
        "  else:\n",
        "    plt.scatter(pca_2pcs[170+i, 1], pca_2pcs[170+i, 0], color='turquoise', alpha=1.,\n",
        "                lw=lw)\n",
        "   \n",
        "    \n",
        "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
        "plt.title('PCA')  \n",
        "plt.xlabel('PC' + \"1\")\n",
        "plt.ylabel('PC' + \"2\")\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXck4vAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8Ck623sztFj",
        "colab_type": "code",
        "outputId": "f781f445-8fbf-4b6e-ca29-320b49ab4b93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "#print(pca.explained_variance_ratio_) \n",
        "\n",
        "# not fast and big increase in cumsum --> var distributed over a lot of components\n",
        "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
        "plt.xlabel('Number of components')\n",
        "plt.ylabel('Cumulative explained variance')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cumulative explained variance')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nK3xwJgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9eQ_esA2oSK",
        "colab_type": "text"
      },
      "source": [
        "### PCA reconstructions with diff #PCs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDOiAx__0GAJ",
        "colab_type": "code",
        "outputId": "6d714ff3-1f03-4bd7-a594-187682f13de4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Reconstruction with N components\n",
        "pca = PCA(n_components=2) #N\n",
        "pca_transformed = pca.fit_transform(xscaled)\n",
        "inv_pca = pca.inverse_transform(pca_transformed)\n",
        "inv_xtrain = inv_pca.reshape(len(xtrain), len(xtrain[0]), len(xtrain[0,0]), len(xtrain[0,0,0]))\n",
        "\n",
        "print(mean_squared_error(xrs,inv_pca))\n",
        "\n",
        "\n",
        "# plot 2 images of each class\n",
        "pl.figure(1, figsize=(6.4, 4))\n",
        "\n",
        "pl.subplot(2, 4, 1)\n",
        "pl.imshow(xtrain[8])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 1')\n",
        "\n",
        "pl.subplot(2, 4, 2)\n",
        "pl.imshow(xtrain[14])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 2')\n",
        "\n",
        "pl.subplot(2, 4, 3)\n",
        "pl.imshow(xtrain[323])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 1')\n",
        "\n",
        "pl.subplot(2, 4, 4)\n",
        "pl.imshow(xtrain[386])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 2')\n",
        "\n",
        "pl.subplot(2, 4, 5)\n",
        "pl.imshow(inv_xtrain[8])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 1')\n",
        "\n",
        "pl.subplot(2, 4, 6)\n",
        "pl.imshow(inv_xtrain[14])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 2')\n",
        "\n",
        "pl.subplot(2, 4, 7)\n",
        "pl.imshow(inv_xtrain[323])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 1')\n",
        "\n",
        "pl.subplot(2, 4, 8)\n",
        "pl.imshow(inv_xtrain[386])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 2')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.046534553\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Cat 2')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAADzCAYAAAB6+ErUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXmUZdlV3vnb59x733sxR85TZVbW\nJKkGqUqlgUEzJYEQbWlBI7kNLZDN3DaiaWB5GS1MGwPuhm6bxWhwG8wgCRnQssRghCQ0jyWkmlWV\nlUPlnJERkTG88d5z9u4/znmRKRmJoqJUg4i9VqzMiPfeHfa9dw/f9+3zxMzYsi3bsi3bsi17NOae\n7APYsi3bsi3bsqePbSWNLduyLduyLXvUtpU0tmzLtmzLtuxR21bS2LIt27It27JHbVtJY8u2bMu2\nbMsetW0ljS3bsi3bsi171LaVNLZsy7Zsy7bsUdtXZdIQkRMiMhCRrohcEpE/E5GrHsdt3/FlXq9E\n5I/y+0xEXvZ47Pepbk+yz79GRP5KRJZF5KKI/FcR2ft47PvpZiLyT0TkznwdzonIX4jIix7lZ01E\nrvsyr+8VkXeJyNn83qsfr+N+OttX2OevEZGPiMiKiJwXkf8kItOP39H//e2rMmlk+5/MbArYC1wA\nfvkJ3PdHgO8Ezj+B+3wq2JPl83ngN4GrgUPAOvDbT9C+nzImIj8K/Afg54DdwEHg14DXPk67UOC/\nA9/2OG3vaW9PgM9ngX8L7AOeBewHfuFx2vZjMzP7qvsBTgB3XPH7NwMPXfH7LPC7wEXgEeAtgMuv\nXQu8H1gCFoE/AObya79HenAGQBf4ib/jOE4DL3uy/fEPyef5M88F1p9snzzB/p/N/vn2L/OeFwAf\nB1aAc8CvAFV+7UOAAb28nTd8me0U+b1XP9nn/Q/F51ds71uBe57M8/5q7jQAEJEJ4A3AJ6748y+T\nLvg1wEuBNwJvGn8E+HkuZ/argJ8GMLP/FThJrqjN7P9+Ak7haWdPAZ+/BLhv0yfy9LKvBdrAO7/M\neyLwvwM78vu/AfghADN7SX7Pc7Kf//AreKxfLfZk+PzJv7ef7Gz9FaoATpAy9wrQAGeBW/JrHqiB\nG694//cDH/gS23od8Nkv2vYdj/I4/qF1Gk8Fnz8bWAZe/GT75An2/3cA5/+en/kR4J1X/G7AdY/i\nc1udxhPs8/zeVwKXgBuezPMuHlOmeXrY68zsvSLiSfjiB0XkRtJFKkkQydgeIWGFiMhu4JeAFwPT\nJN7n0hN54E9je1J9ngnFvwDebGYf3syJPA1tCdghIoWZhb/tDSJyA/D/As8DJkjB/zNP3CF+1dkT\n5nMR+RrgrcD/bGYPPfZD3rx91cNTZhbN7E9IbeKLSJh5QyJMx3YQOJP//3OkIHeLmc2QCG25cpNf\n8YN+mtuT4XMROQS8F/gZM/u9TZ/E088+DoxIXdqXsl8HPg9cn/38r/hCP2/Z38+eEJ+LyG3Au4B/\nambve4zH+rjZV33SkGSvJSlsHjCzCLwD+FkRmc7B5keB388fmSbBLKsish/48S/a5AUSLv/l9tkS\nkXb+tRKRtoj8g3k4n2if58+8H/gVM/uNx/dsnh5mZqvATwG/KiKvE5EJESlF5NUiMuaBpoE1oCsi\nzwR+8Is282ju7TbQyr9eeZ//g7MnwucicjNJsfYvzOzdj/9ZPAZ7snHBrxDWeILLapt14F7gO654\nfZ4UsC4Cp0gXfqzkuYnUPnaBzwH/B3D6is++lkTMrgA/9mX2b1/0c/WT7ZevVp8D/zr7uHvlz5Pt\nkyfpOnwHcCdJkXMe+DPg6/JrLyFVvV3gw8C/AT5yxWd/gKTwWQFe/yW2/8X3tT3Z5/xk/3wlfU6S\njusX3dv3PZnnK/nAtmzLtmzLtmzL/k77qoentmzLtmzLtuzxs62ksWVbtmVbtmWP2raSxpZt2ZZt\n2ZY9attKGlu2ZVu2ZVv2qG0raWzZlm3Zlm3Zo7anxET4P//5/2wBpeUKRqIU4sZyM5yBiBC84NUQ\nHIZuTHsJHhPFVBHnQA0PjMTjouG9R6XBkDRSYw5nERHBVKhF8TgEwQs4NQJKdOCjxzsj4nAuYmp4\nVzGyiCOi6nEC3gwQBEfjIpjgEaIqhXMEVRQwETxjnaJSlQ5rLP1djDqCilGKAEI0w+cTNUAFogGk\n8/1P/+qfPubZj//nIz1zJD8LAs4lH4qRaglNOxTFBJx5ok/XwmsEhHwomBjeOUwANQxwIqgZzsAE\nRMA0TzVJ8hYiGAamONyGhhMznEu/IyB5G6BYTMeAE5xpkgGKQ0yJGF7SLS0G4ixdcrN0LM4R1fBy\nhU8RzMZnIxv7LxxES6/h0nmowQ+/qPOYfb7wufus3LGH9tQEhSuxlqOohN5aj//yi9/PKw8d4rrv\n/tc4K6m7XfTcOWTUp3TA/G6YmQOJ0H0A23UrTgtEBph6rB7Beg+TgJ/YRlM3/Nkf/xyv/c6fxQhg\nBdQDNI7ww3VoFUQpcJMtrJxCakUbkGYd6zXYqEdReUSn0U7B0YfeyfW3vp560KOQAgsNthYYlIFO\n04eyg5ub4JE7P82hW28D18acEQLopRUsDHHWEFueplrik+/+JK/65u8j+jVMK3rNGgxrbOQopY+v\nIlbuxMQzdcu1m5pxevOrvUF6zl00YumQaGAGBlZCVMOZMCqgXRtaOnww5q97Podu+0HanRZHTq0g\nzlGPhlQSiEGxENl7YA/rK8ssLi7wtV93O4888giz89Ocf+Q4C5dWiEd+i8KUoFAoWFWmWBdqRNND\n4bynrgOlCFGMUtL95x2IpVigko6zMEFlHB9S/JBomKXnKYrgnMOrUUcFB+YEbxCiEYFKHdFBUMOr\n4cXRqNJUKciU6vj1j8S/1e9PiaQRzeOc0RBTkogxBQ01zAuK0gQQcUBEAJXsBEaoVinRAAg0FBRE\ngveUmoLKOCSZGFEcBQ4lghVEM1ouYgbBPIUDryXqoMFAhUYF7zyNBpwVGJHKe2pTwINrKDS9Vw3U\nCaX3NKq4FEUpSBdVMTxC3WgKCDhqtRRc8w3ipEFxGA6Lhrl0UzscCrhNDvKO/SUimBkORTyYSfIT\ngnjIf8RJSiWmiiEU+RylkHSdTJGcfMcJ3SE4SSJzAHGX94cIkrOEicNEMNP8PrexT8wwHJL/dUVK\nAlh6ECzmPYrHb+xpnGTSTi0HjIjhnOR9pqThMNQEERCxlCRENsoS8YbDo6aIXN7+Y7GP3vkZDu07\nyNyu3XTmppmbmSW2S3yr4MLZIStzS7hhxNThh55QN1CPoBUodQBNB2mVRBvgQsBiwFRw3mHdBkYB\nKWtidwWTEjvbAzFCU+MsYKFGRjVohTYRXxaYDNChR6QCiajVRAuIA+pALHtIU7F45hGuuWkNJx0C\nIxxK7Ua4UR/nHETDuiOmDxwiRkHCiOiFMOjThBGgtKyFqufih9/HLS95DYOiRxlDSnqNo65rguvR\n8VOIdPBlF43VpnwO4Mwo8USLxCLdz2aarrMACk7BlZ6pRgkCRS74dl/7UpwPiEyDCAXgfMmwHhAG\n6wgl6+trzMzPc/jwIc6dPcf09BRVUaJSMD09S+eZr+D8g++nioYXIYaGkYVUKLl030sItMsCLG4U\nM4hhCtFJKoCdXC66LBeX4zgp6XnAoFQIFtPnnKDREEsPYpH3N/BKS4RSIXqXir9CKCJ4FSJf+l5/\nSsBT6hsUw6kgFJgIQ02dgYVUTbacQy0FIjOjUFBLYcoTUlKwFIy9NXiD0pQmB6oouUIFKvOMQsQ5\naJtSCaRQCEGMgBAJRJTCNFWa3hFVcSZ0iFRSEENAolIIaHTUZrlKVaI2G8MwAcOZUWsgWKrmxXss\nKsFgEJt0rkYKTgYNZXJOVKRwmEEU0o2Uq4vNmpA6Aie5FRgnVsCLICKIKeNXIPnQiaBiGx1TbhpS\nEnApUTA+PmeIS9ty42TgHELyq3iXfwfnHM75vCOHpSyDc6lgcJJufkEQJ6BpuynpgEixEfxB0XFw\ngPHBpfvtivMHoXDjJOI2/GDjRGKApHullM09Lid/7o9ZuHCG9aUFVi+cZqU3wOp0T7/2G1/Ks7/1\nzfRabVa9stZxDPYeZLR9P8OpXdiwxEJFjIaNlBhBNBUVwzDAAog1aB0gjsACD/eWYK1B+kPEGlwj\nmBgau1hUogxxwyFiAYk1LkSclngNSBMw1XQdojFYHUIzg1pMCcI8pn1aWmFNgSqMuitUxTSiFWoe\nQkFZFCCBQg0rRxQ25OTZY7Smr8LVNaGZwrwjihJQME9Ze5wKPszgQrkpnwOoGVGUWAoaYq4WXOp+\nC4fPJaU0ETSVC9GgFuhMbcfwOOcoK4+LEbWIC4oPjunJCSanJ+n2+5xbPMdw1GN9ZZWVpSXq4YD1\n3jpXXftiKlPUC8Gn3dcKXiJeDO88UaC2iJml+yx37MGnhOG833gIU2fswECiog6CGeaNiBHGnXG0\ntJAPKXGKGkSjMkdLocZSeTh+VF1CQqLLhduXsKdEp2FRcN5oNKAh4LzgxRHMU0qqzGNMkFJUjznQ\nCOYNsRTgMI+5QI3HMEpSwCuIRATVVGmOrKblHJWrklNdJCi5oo6IRmpJAcIrNE4oNeLKgr5BaY4G\no9EMm4kRtUntrneQL3IpHicRM0G9gFpOWoZpZNgEyqIgxtQaqqakJWZEJ5DPV8VIESIlSFVN1btu\ncihTLAfN8U1jOUBmkMZ5IIKlygx0IxE48QmrcQKWKhcnDnM5iTiP03TMCRYSRFOPlboHhQwlJkgr\n4sSlij8fG+TOxo0horwvMsZlxkau2wCXUjqwDGl6L9mvgjlB8kdTtwOVpGcqdW4un4fkmJKOPV0x\nj2BE2ZzP68Or3P/Rt9Hc+I20tk2wpxuZf9ULqK3g0Kv+GesCzaVVYhNxdaCuwHzBSKdptx1hfZFq\nrUDlMMWxi1gbCmdoNNreM900OHWYGE055NzpE9RuhB8qKgEZ1EQdkfrVBuspjZ1DJjqIA40R6SkD\nU7wqVCVOIrFpWOyexuIqTltYyxEGkWAldVXjtaH0U4TlRTi4i4YeUislEww1ECMQBW81RTHPpx5c\n4AUTHXRtRFONqEJBM4KgnlbdoakGtHSSGA3R1t/p17/LvDgUxUeQIiEXguAMmhDBCy2FQIKAzEFl\nEFA67W0srTWou0TpPXWzhlMFixSdFt5XLJxZIMbI/NwEM3MzxBBYv3iRiekJOlXFvpu/jtMPvYwL\nRz4EmoqwWQcheiIRSPd/bYovPUEMF0BUEkxVOGIuzgpz1EWKh2VOdi4YeEEC5AcB7wQfjcYn9MLU\noHTQWDp/D6U6nDNiVNQJzoShAx8tFZJfwp4SSSPgKDVBPKZjKCHgaFFLkwKFVogo0ZQyAkUKtJV5\nrCXEmCJAzK0o0tDEEilqokEhiqdCxHDqic5QUyyk9qxlgqpPwQklRANRvEHEpdJADImRUWm4qKg4\nzBmVFDQudRMaI7UJnQi9IgevEHHOEzTBbg6HlSmYVQhBEi9Qh0hVeJp6hPgWluEQYYzNp/Nzlrid\nzdj4pviCOOgksxmGaMCJg3E14n268QAxvaLTyccmAOkmRJVCBBMhWNqiuNQh2BX3onMJahtXNS7D\nQzKGrrKlwJ0ettxnpGQql98hQl7iIG03o2wpCeQOUHLicZkzaywncrPk49xdwZiHyXvLUJluEhK8\n/+QRyvt2Mj17lL2rsxx+/XcxwFP3G1zpqVd7BOsiUjGioO6uJp4Gz2CtgcJTtyJRSgo/IA5hAqVu\nalbLgkuuIIgiMSJa8f3/5u2cXFyhUwjTtHGN0tSRyWJECC0qMc4vneLA3sM0rT74EgXiMCKjAbHT\noVt4VoYjbn/lWzi62GcYFxn1u1QTE1g7woqjMzVJ01+mUwhz3Zp+HGHe43SFYdkieqGxSagqCin4\npjf/X9x75iF0LdIfrLFrYppdxS6gR62gdUkTHcIQccrMprye7iu1xI9GycUORrBUnKoptXd4cRAi\nhRnmhMI8VWsKH1agr1Raop02itKuU3Lur68yv3uOvXuuojdcZ7C0SDHdZm1thbIqWDi/wHUXVrn9\nNT/Ku37xQ1SFRwRqjbQmp2CwlvhOZ7QDuGhILoiCGA2JMxU1lJTYfMwojCpRBfNCoST41ScO1RAa\nnzpL8nMnQXMBCzq5m870Dgan76csPI1FJEDLJ1TmyxVITwl4qjAIOkoPr6aKEC0wiWiu7gsX8IVR\nesF8gRIpURqM0Hh8VLwVdFBEEu3VLiJRCxwVYo5hjLiQW7jEqGb+IpHciFBoukCVLxJxiqCWiHEh\ntYsSDRWXKtGY2lozxZOCkojQlUBhlloiUiBORGxCC2OMhBgZWURVISiV98SoFFIkkjemIJh4hhTc\nHLlyHvedj9E2OBGXSGVx7nIiyUQ8ZLjMpXPlCohPJCUYlwOtyxKFMUSFpEDucwJIMFImqG1MbluC\nnMaBOkf6L9zHGETi8vGSqkeX9zFmUSS3HhvEu+lGIrCEpSXfjTmV3FVwxf5UvxDLFRIcFyElnU3Y\nQ4sTLJSXWO/3uPV5z8O3A06NqhCaUKOFoz/0aFCGNqQspyEadRMYSYNDKIeCW1snGnTa09TlBNI4\nJERiVEZR0wOlI4L2cSOl3xty7tIlLtY9TIQ4EkbWo6/GubULjBzYSIjRY8MRDGvCVYdZm5omlpM0\nxQS0WtAK+LJEnLC4ukyzsk5TNaA1rckdxNk9rGvNmnOseuNSqw1esPY8vVaDxsAgDmnWejCokV6D\nGw05sXiELotI7ahjH7WGUHWxWFLT35TP0zVMhcSwFEpxFCKJYCsSx1iY4EnPqpaJRzUToihrvTMM\nQyAARaF45ykjeAyn0J6cIITIyWNHcCjt2SkuXVxidrKduE2JdFfOMxoWVJWiqon8VhjWazRWJIi4\nNqxMhaETQVO9looVG5dm6Z71IqgJKg7xkkUa6Rv1JKRtmybYvDCosjiolMRFBvG88DU/xm0v+R7U\nwSiTfNGlZ9wyRP2l7CnRaXg0YaDmEAk0VoAopQaiOKImx8TowCuKYVEpEMRB1ET6mEWQkBUxKQsr\niSOofQq1WhWJ5FKHCqilFq/OAcYp4IXaMikPNDEiGYIZEim5HBQRYxiTyqm2mC6o6hcR1ULMiiNI\nCQOMwhc0MaRLJIKLES+empDIc9LNICI0IVD4RFCpJGJ4s+acJIKMFMCVywFeXCbInQdJyrKx+snc\nuEuxzG+k2iN1cUklJpJwVhn7iZyoXHpIIXE0Gg0Zt9ACLnMJeEn7cymZlLIBz2KSkpmTREaYMwoy\neS35ARKXWu7cIRReLivyEKIp3rsNhZrkAkHMNoh4EfC5aEgxZnNJY1QETg5KfuhXfovolNWLfYZx\nnTrUSHSgFcE7jp46xczsPJ2dcyyefIjtO/agEdRqus2Q2bmd9PrrdF2ffn+Nqu2o+oafdMy02jSD\nSCgcbd/BpmtGFLjVHmd7qyxMtZlvbePC3XcyuXOGEw8c4/qr6tQ5xgGDfbtoD+cYDtYptm1j8fhJ\npNVi1I7Ua20K39DZNs90tQtXN4R2h353wOQostSr0aKHhALnwVUdlmOfqel5qqpiVBQsP7JAv4iE\n7jpaG61aUFdyz2eOEIolbjx4E64w6ii0Wg395eMc4LZN+T34dF87taxC8nhLxWMbIUSF0uGDEgWc\nL2iIlFZx/998jmfc9Aqs8GybmuLs+fNIu43TCqkcE7Mz6HBIURasr5yjLFscOLCX+z7+SQb0aXVa\ndHs9/Mo53vBT7+et//bV1KOGKnfGVRmoY4FPDw5ehJEqXoTS0r1XeiEGaDQroQAnDiEiljoLyaor\nMajE00iKMeMw1KjRw1P7OX7wLW/jrns/zcLDd1JiNJI5UhG8S7HF25fuJ54SncYoY85BQPAUCQmi\nCwQVGoSQQj51NHyM4CsaHNGSokrNCNaAJgFtQSJex4mlCklBWkQjRGNIgycFiKAxw2KCFRm+iKnF\nM6D0QiWeWDq8eCIQQiAXJ4gTmtzONZokqipCHZIMVDNmT64GTKDA0YSGQlzG63PvIBFxX5jLFaPw\nPqkf8BADopsLYIyVGG4MGeVOYUyAiyDebfAsQq5U3JjXSP688ptnFEF9SsKBcXeR+KVCJAferGMb\n3/wZEjOxnDCu6DL85X01OZk55yjFbfBDOJIqTTJ0lW98l2GqmLc93ua4u/CZcHekriWQux6fEo3P\nyhPJcsUNCfAmzGTEL77uday4wMULxkpvjf5qD11pUDdBa26C3mKXshamKCk6HS4trRJsxEimEGco\ngUEzoNdbYRAD7aKiqgu6rQJXlqz1hqwVDb501CJAi9ZgRGt6CteaYnVxlYdO3cXcnhnOL67y0IXz\nDKwmjAYwEFqtCfpxjWbSc+roUZYHPZaXzrB64hIt7bGy1uPk0QdYWuhzbvEizYU+SxdPcH7YY2gD\niqJFn5pRr8uoe4lZ12Gu3eH4XX/D2WPHCeZp1YHCzTIq2jRuyCoFZbukPxzRizVdRrRiH9dvqJcX\nN+n1yyq5Kt9foqniryI0okiZ7nMtE3QsocEFCEQeOnkcqTymBUVhqE/QtqkxGo5YXVhCnGPY79Oa\nnGXX3j1gjvbMFAtLawyHDWur64RgFK2dDNdHSMzPjXqCOerYYJn3bDTi8jMZXE4GMRIdVB6KDAI4\ni18E215R/EguqGMqRhuXIPamvIHn/6MfYzgYcOnieQarD24UyqU5SjOCKRFo9EsjGU+NTkMEDQ2F\nc4zEqLyBedoUCTZyECSAeRxQWsEojBCXQBGJCaYqpUCdoCFQZFy7UcUrKI7ClCZqCsCW6LBRrFPg\nJvEFkiW6hfdoTBwKBr4U4rCHSCvBGe4ylNGSgnWLSCa7E/yRXitMGBEpAVNDvUv4pAgiqUrwzogm\nFHmOAKtxXojBkWR3SrQMI8WYK+HN+fxycL5MDI/xfyNzFJnE1syhxDz34PNnC0mzHePSI/cs6TjH\nXYiRi4HLXZNmqK9AMJ+USh7J3Mj/mAydpNc8SYKoXCaxHWPYzGfILgUGJUF56SHLnc0GkGVjtA9c\n3o6lBFeQNPCFkbq6KyCBTaZpfuOHf5pr/uU/4/P3Hqe7vkrRmqDjCybcNKP10wwulZRTLc701ojL\nnvLMIjdcdwsiQ1RHiXNqT7CyvMR0Z4LQNBC6dLWFF+hrm6KcZHtLWB8NKWJNMyi55NaoRmuUzYj2\nnGdusJ01c+x89n52nXwmy/0eZaNM3nAdZx48xuKps5SzgYUjC8xcdZjnvei5XDp9lne9/ZfZ+6yX\ns2NqFjdVMOiVnD32AEsr55nz5yj27WduvuaaQ4dZXG646voDPHDnZzn6gQ9w4Hm3UJlndWkVpjyD\n0XnKUYummsAPjEvFRSqb5s4PvZfbXvQtaL8mzixy9siDPHeTfjdTTFy671Qxl+Ylhg4mgsMw6jLJ\nTfFG1KRyWqufg/WHVA56hRB1SFEIZbtDZ75kZnqW4WhAbBq27dxJqFdYWlyi7q1TD/s866YbGaz1\nmZiapKkjDz78IN/64/+V3/+lH6AVl5iRSO1g2INi2igsd+kZ6o4uCznGkFV+PVc9NKopzGTuzbs0\nG1aaMaKgQ2DJDCn2waGX8n3f/b30e10eeOghmsUH6J39HCaOFo5aFe89JUZ9RYfyt9lTotNAIyqO\noRo+JO19rYkn6PqAoZSuSkFGlYE0lGWBWgr6Tca3DSVqyJwGhCy3TPoETYQ2CUZ604tvJFrKyEmS\nlwixaEbUSJOHYrxz4B2jpuH5B3fgLKtscuUbTemHGh9ylT6eAssQTDCldAUhhCRsGEv68qCcmtJo\nIp4ai5gGhJKQVTCFCd4VaYYEhUyksUl8PeW9y9twLiuQRKjE4ZxQjgnlwuH8ZWnuWHYL4IrU2ZU4\nPAlGEksJwVlSoTlS1V44jyclm7G6yUxoIUlgLIZI+g5iJwlnLp1QSIK+UqdBksiSklfEEPEIMXVC\nAkVOFmO+BVJX5MbdhBiS2/6k/rK8j0Tel1g+lrStyqVj0U3Oaez6kTdx72dP4NaHDFcWURWm5qe4\nMDyD+RlcewK31COcX+buuz7EOVlivV4nzmzn7MJZlus+R08cwxfGqFMSfUFRzjAkMLtjD1WIxNEK\nzaihNTlDMb8L2zmJdyWumiI4RwgV60ROnb6bUx//LKLG6NISzYFdPHz0CCdPP8gtL/oajtx/hsO3\n3cTtL34uP/tD38fH7ryTpobnv/ylPPTpz/K+P/5D7vrYJxisrzBb7OLmF97BjTfcSCd2qNozhN4K\nv/KWH+ehT9zJs255HpcunOPc+UVcJdSLA4ZNj97SMjPtORq3RqmzDLQi9Kb5jT/4BT564gEuraxy\n54Of2pTPAaoIPo45tPxoiqMyIxRGXRgel1RLmmSnrWB0maVqT1OHSziNGI6pVodKHL3VdU48fJRz\nxx5hbfESg7UV1lZXmZ6cZtQovl3SX+8yv30Xczt3sr6+zMUzj+A7szznFT/E3K1vZqHzXHTu6zmw\ndzYVlQY+l09mY4GiEIrEUTSMuUAgKh31+VlMCEatEQT6CLUFetsOse/2n2Df897Id77xhzh/+jRH\njx4lDrsMVx8iaSKN2iLiUmc1zFDVl/vOuKdE0ohqlBhtXyAegiVFZg3MhRJFEoQEuTR2SJOEkG1X\ngkDp02jXBoxgYE3YaPVEJKmbUESNi6tnCZIq6HEQGVfYhQhYTFxJxj4RR08jqg0iSb6LJlzdi0sc\ngCoaU0eQBt4sy4Qj5gryCB2G4JrEzWiuyBPmCuo9wRokKqZpxiPEkInd3K2o5TL5sdtG0M8JIN18\nUOaHJ7k6Hb9XNhKl19zkiCFZMWaiIGN9ewqwIkkbbjQUliBFk4QZY5oULWqcPH6MD/zpu4nWUJAq\nfHHjbaThqygpQVi+/GOazsxoiUNFKUwuK6XydRTAZxmvSw1rSmSZ3Fa1jYRkeXq9IP8/k/CSOwwj\n+WZTPkfYv3uKZR+Y7myje3aBODCKUNA7d4rQBDq79xDLacrhkKvbezh/9gQaFXpder0eVaypim3I\nyNNcXKUzM8mFB+9lve4hEyWuM0OPBh+HrCycYGA1szO7EApwU7DeZcI76kurLC19nmr3IT5236d5\n/x/9NueOHEMGgXe//bcZ2oj5VBUzAAAgAElEQVSd+w/zwfd9AF90uGrPtQyGgXf96r/nc0fvYXr7\nDvbsOcyFS+eZ2beD2Arc+eA9tHdu59Of/hh//o4/ZN91N3Pw1hcSpytGwyEaC1a7XeoJY8f89Qxd\nQfQFpU6wZCP6sw7Z1cG0z4c++h66yxe5956Lm/I5AF7QPLtgpO5fzIgxzSQUCmhI6ilTMCM4qAfL\ngEd7fTQ0qCqjMKAJNRbSahO+VRBCw6ljx6mqDk0zpBSh3Z5iZmqaejBgeWWZbdu2ISIU3jMzNcNg\nELj+9m/h0LO/ibVmgDloF4JzaS5tzLVFs41h3k7uNtLAq5DZ0DSnlAUytRqUnmrmanZf/3rKqRl2\n7d/PULv0BgMQx9q5uwjDRUpLbLsnzayoT3Gr5AtVjv/jffwUMCdp6rEJATPB+QIxn5h+EiwRQqTW\niIjHqdKQoKM8L0wdmiTxTBpOogWcL/A6VsCk97tcEf/N8QWcCU1Mkt40JhURg2EmWVUZ4yqActfx\nxTxkk8h1Hb+Up7itSLiLRRhDIEkrlRIdTlFJTEljECxQeJ+WIyANKxLSACE5YOp4eZQ8Ue40zUuM\nOZTHbBYpnSTyGsNb4mKiRCokBU9y1e5Iy594w5ym6juPVOu4lc7dj/qxtDWd670f/wA/+cZX8i/f\n8Ap++ttexk9/24t4y7e/iD992x/ynnf+PstnTvDBt/4a7ekqfZ4EY439V2Ss1pEqiYQ2uQRNepcS\nCrLx2fF7SktkIZKUI04E3Lj74LIKRfJQoUDhcgeUt5VRSHSjE9mcyxHj7g98mpldezl1aYGl3hJF\nE7iweJFjZ85w/uRxZqbb+NUFLi4P8Ad2MexHPvOxDxGn25TSYcfkbhoZsHz2BCcuPML0zn284NYX\n0PGKrzyj0YiLFy/Smt3Owetv5O53/imr3TVc5eiuLHP8xP0sX1qjU+xgsDrghq/7Rh75/EnOnDnL\nivW4+oV30F1Z5YbD1/J7v/rvePd//Bn2HbqR1W6f57zgH3H8wY9z87NvYXLbTvbs28k3fNt3ceL4\n/bz3XX/K9qkpzh89wj0f/gSHr38Oew4dQFnhz9/+VpbOX6A90eCATjCa4SJ7r7maU+eOs/3gTubb\nkxSU9EW5bc+t3HX0OJeW1jh2abhJp6dq2sQ2+LVIkrIWhdAOjlLT8+VjTFW+OLQoaXnHtt2TSGuK\nqWqZJgRmp3fSKicQBG0CCHR76+yYn+XBex+k3+/jDKZ2bKdsV+y/+hCzM3PMzs2zd/9+6tGAXTt3\n8fDRBzl46CAHrjrE+opRC3iBmqxkHE9zZ54XZ0RNfIKOO25SUSk+AbNRjaZ9kD0v+xmuveMn6OF5\n/tfeznNuvYULp87gnKddFsxMTMMo0owUnDAxmYq9ALTzPNuXSxpPCU4DhBgUcR6NMVehGWP2Rh0j\nbe+TvNZimiOwhFubaqrAncNpyremihoUpowcl2WUmQRrnHD0XMNUR9PwmMJk0VCasGwpKan4rOZh\nA9Ofbk9lgsgyDp6ye8QoxEMcrzFl4B1FTBV6g+IGI6xdIQpKgr7EfJquFZe2lSv7YJqmM2NSMWnU\nLNdVgileCsrNISX4PI/icWntrpwoxgOFYun3dD6XOYiYB/WiGyuNsuIsJ+bL63MYYo4P/9lfUjhP\nWdQ4cYwaQWNkFHoEq7DFJZxrWDq+wPSunXif6nqHIyTwiXGJ6CV1KJgRyBVPmnRMs4I6VmilCs0n\nKRgRTRwLgkgk4ihECZaqrAIjiMtLhxguV6WSq7piDI2hbOaReee//0Vmb3g2p//8nRy85nYePvUB\n3v3XS+yemGAwWmXRGcPRiJW1Ic941g2sPXKK2geu3X6AiyuXGNqI+e07mNkxy6gf2d+e4tLiaR46\ndor5mRnW63Xq4QijYG77NMceOEKzLbJ+aYG6NcXug4cYhSFLp49z4JqbGdoa/f6A2YP7ue6653H6\n9H0c/+QHuO7ATVQ7d/HZT36UXdsOM3twG3Vc5ezSIjfd+s20d25jvlNw3/2fh/vvZm3U5Z/8wL/g\n1F138aFPvZ89zzzI0aP3UC21eNlLX82OvbNMTM+zcnGBuq6ZOHgt7bntXDxxhFY5xXAlEIuCuX17\nMB/obu/w7b0bWVo6SddWNnejp1uTyhxDURoxRJMQwxk0RLSAIrq8jIYxEKNoTTPbaXPr7d/MdNGw\n/PBdtK7+GvqjBoqCMOrjvafqTDIzMYO4EVddsx8Lgcmd24j1iLXhiJ1793Bx4SKj0ZCdu3ZSNw2d\nuXne9N1vRKMx6HWZfM4buf+uv2K/nGJHaagYvnDUMTFzXiNNJsALTSKaiNKMubpaaGauYs+t3017\nYpLCF6xcvMiLX/IyRt0uS2fO0QRDwoh69RRUFb4zifRG+GgMugmGn6jTAOu4Vv5S9pToNEYaKdIC\nIQRLi/H5LJF0GhBxyUEx0s5yU8XS+LwKpS9SQ5DH4DWTudE08wsJp1M1rCqYdCVxlGYxJWOHYLz5\nH9+eupcsX/PZPeKEaJEmZpVW1NTGkofhTIkxoJaOySPQRIIqjaWEUrRLxBKXYSQVF5E0yUlMiitV\nYp5g1qCoZV23SJoct0zYRt3gax6r5SmLvO5UnjYXQTO/oGQoSZJv0rI2irckbUuqo9T9jJVY3pKM\nWZzhKCmcox71aLVmUddBipJOq6QqHXe967f5xB//Bg984oPEwvPgvZ9JXIWlrk/FaI1VXLlbTPMd\nKclWks5C8swFeQkQJ0opRumMtNRkSkAuD3Wlj3kUhxO3oQJzohRoIhNNL29vY2UR2YDFHqv99w99\nggtnjjPT3sYn/uLtTOy5jsNX7eDk6bvZsXMv6wvrtLdPcezsCY4eu8Cdf/Mp2qaslhVXzc4xatZp\nbZ+gVU4wu22GqZkplpYvccMLX4hNTjPQkoW1ZfZs283qYpeFxUvomtCa3cHU9l34qmRq+zaIQ9ZM\nuelVr+Wev/pj9h24lu5whe17tzEqJ/iLe/6aP/rlf4cMG1p79jNS5YGPfRhnjqld+3jg/hOsrQXq\n1SWmJ3ewd+5qlk4v8ra3/y7tuRmO3H2Eue27efkdr8dm5qCaZP38GmfXz7N3zyHaM9sp4pATxx9g\n996DzM7MsDy8SAw13dXI8rFjFDM76NcrXBiGv9uxj8JqU8pc03gSbKkC4tMSPY1XYlSGHkrxNI1S\nVhVNe5rlhc8jLimoRoMuq2ceITTKaDCiMKAU6jpQ9waMun3aExM4XzI1NUfRajMxMZG41xhpT07Q\nqibYvm0nRVkxPTvHoUPP4Otf/t00M89mpYb1xqjF0WhSaTVuvKBqWqsqioGl2amRpi5/9qpXMzM9\nh1hFiBH1nk67oj8cpsUKEerBEqFZp79yiqbpElxKAIkDVuri8ozUU34i3ItjGNMCXkm7D21JVewo\nY+gSA4pjkOcnMnZDUYBqgDF5JNBYwKtLK2BEo8gKoPFUZI9IpzNN4YTGDHOwHEt+8vfuTivdCozU\n0hozziVoScYcRRrwMZeCvmU9/3hVV49DPeSBAUqDxuLlSesm7W88y1DHGicel6etnaUJzxDrpAjK\ngTsR7AmDlHzMmzGRPJeS5yNsAxJKyq5CXFaSpfenKt1SwE2MfkrU5BvPpSRdWWqfJUZ+73d+lVZ7\nG+1dkwSrWTt7gkHTS/i69Nkxu52V8yew3ohP//Wf8/w7XklJhoYsLRYpYnm63DJsldpyNfCSOBVc\n6s4qESKSl7QUxCLqoBSjUaFAieJTx2CGdxtNTBJCZLLc4/CSlFghi8OS4tlvyuev+uZvYPHIEp9b\n/ySHr7+V4UKf+y+eYMf26ymBoS3xyc/dTxRh8cJRRqeUN3zvD/DWX/oZ3G0vZvuOvdhaZKl7AZxR\nTs/hYuDC0jL9xYvs7mzjwDPn2b3vat73J2/l0C3P4/aXfQPrsWG2U3Hi1AlOPnA3Ezv3cea+T3D4\nWYe4/+7PYFet0FpYYqm3xhQFc3tu4EyzhE5UHP/cx7jx6qspO454/hj9PbspwpBTFx/mhgM3sv3A\nNZQH2vybn3gTN9/6XD770Q/yhu/5EeYPXcsDn/sUixce4uTD56jjiNsPvZSjJ47zjLLFsYdPceCG\nZ9Mi8J6/+m/c+PzbWe83WHeZXjPN/PWzLH76no1VCDZjFVniXQitCMGleycGg1Iox6tqtgt8E2gI\nWFR6ccT+3ROs9/YSRmeJGlhfXqTodGjPThOHIy6eW8RVNZ12ydT8dvZdfZC6O8S5iqnZCS6ePc+O\n3Tsohm1aRcnqpRVmpqdRNfbu38ulxWWuOrifow8/yJ5rX8S504dpuufpTW3HhzUqiXQXPsauqsCK\nQOlhqC0GNkFT7GVq9y3s2Hs9UrYZjFK8Orj3ALM7plk8f5Z2VaHVJEMu0diAQdPjwpH3EXtJraUZ\nGPA5PmKaisYv4/enRNIYT0tjRseXQKDJVXZhjlhYXnfJMgyRq0gHFoUmq5AKKxHChjonoVKWlj32\nacq6McUBrckprBmlSjpkrqSIxMbAS+oyfF4ZVSMiSbJjFJRmNCFJ09Jih4lkt7yiZIgRcQox6aYd\nbkx+YBhePM6VREZ4KdNyJqSOJZjiGsGLT9vTRNxHUZx4Cl8SY42Lm8saImONd5ahOjba37EgIA1Q\nJIVZ4w2v4Ig4geAEp2PCOHV3PktdPcZP/vC306rXmd/9HHphgHMF81ffzNxoSHfpJMNBj34d2bO7\nx8lLPbbZI1SkZOwkDWWW+I2FJMfHjOQloV1aVM45R5S0+uhYbZLeK+Dy0ifiKLK7vFxW1AlA7izG\ns+cuc1Ux3QaUPq0i2pDkuJuxV/5vP8Dq+eNcXFqnKCZ4ZOFh3CfbHDn2N7zjdz/J6toQ/fn/Dz3g\nqM/3uONbv40jDx/j137/v/Cqc8IrXvm1XH94ivd+4E+5dtfVNIM+z3je13LxzClOnD5KEU9x+zOv\n5y0//P28+vVvYvXSRe668wH2H7iW7rnz2MQsrpjk5he8kLd96m5secSOzjy/+bb/jGs6zM+2ee5z\nv55H7v8EM9sqXGuOld4jxIkJ2oNDNIOG8xfOUbWECVqMiiHn7v8cf/SWP+Dl3/Nd2MIZbv3ef86R\nB+7j7rf/LtsbODc4xe13fCerj5zi43/5LkZR2Xbwau7++Hu55ptezPt/8z38Lz/4I7zrT36H6flp\ntmvFxNSAc/eucrS/gm82t/IBwEigtPTVCpq7RkwQbwk6zjMSvmnSLJQB2qcbW3Qk0C/ncHOeWoVB\nb5ltc1eze89uJmZmaHvh5MnjuHKSqqW0yopye8UMST5fTq7jW23azuF8wf4DBzh76ixTU1Oo88xt\n38WlpQvs3rWHffuuTkuVx/189FMf5fbbnseli+eYuel7aJd9Qm+Z8w/fz3D+WZhV3HLrCwg0dKbn\nGHRXWe0OObB7F61JTzMcsGPnblw5garSc5H6UsFw9Rztzk76vfNplQu7vCqCApQJqhuv4/a32VMi\nabgxiCbCKAYqPyaABRNFTBhGSzLbJmJYns5NklDnHBYUtTpBV66ALFfVnIw8qVMRlaT8KXrpOyIa\nBVehOqKN0N+YDFZE83d4ZD4hzTMknkSUHEQchaZk5MdVcl6K3bxg4tEQ8uxCglliTNCV9z5DKONl\nAPICZD4nuhzZ0mJ7aYlv1ZACmt8sPJWORX3iKITL3RhmlEBMa6PTEKjMbwCdSYGSuo+GPEGtScUh\nRKIIuyeVOPlMvHWJq0forfToDmumZ+aY2r6fYAOGg8jZlV1IeYbjp9Py2WO1ksuEklwxVe4szXio\npn+9WpJJMiap85pB5PW8bMzdkBQ0NlZ7QUmRvj/ESN+5EhNhLuMiIHchwZJM9/LVeezWXVigKaeo\n5gNOJ9m1fSdXfdNeHrpwM9fd8TqmKmPZ1jn/yBEOlIcpdu/kP/zkmyl8h+W7P867z74f37mJzxz9\nOK951Wt45rW38Y7f/Y+cv3iWB489zPd/+3cxLNqc7i5w4sHPUA0rrBjxiQsfY9ee/Uz4yJFP3Uu3\nKrjh5a/mt37r51hdukg1NNTVXBq1EBdZv7DKww+fZ9fsDuJMi+WFRTqDFaKfQHbsYrKjLA+F1eUl\nlD6HX3cHn7n/o8y2D3DsE+8iFNuRA9tZnzbavSlG85HmjHH2vgd5xvNvZ0aFS/VJdG3AgWfuotOG\nwwcnmGzv5/7Pf471z6+zvGON2Tjkhj3bNuVzyANxJLWUulS6jTtYlVRuV+SZIp9mH4YhEonUGrDC\nI+UMvk6cqQJrq6tQemIJrSIVITOTk4SY1JSnT59h38G97NixC+c8y8uLTO7qcPLUSQ4euBrD6A+6\nqBqDuuHMwgVm5+cQVXyrwzUH9zFsaqrJWUqNzG6/lmr/LayNpmlZm2LCU1Ul7aINUSl8i+sPX0Wr\ngDCqcWXJqImIdvPaW5Gp+f3sb7+GhYffg5jPi4vm7ygS0hJKToghpkL3S/lz01fkcTAlL/uQlUJ1\naNJUtIGRvscCl1RJ4lxS+uTqPkTN8sy0sFujMVWhMS9JnhUxTbTsoLSv2EAT0pr5pjW/fOdfstrr\n8lMve0PuB/K6Ty4NBaWuAUZxSCeWxLyERSSmYOPz8IAZ3qUbUSWpiEzAG5l0lwQNuSStHSuPvKSK\nNjjDa6qENH13TF7Z1sA8MYYkAd3sRLhcMR/o0kxFJQ6nUBd5eY98bk58amHz52Ku1oIYZQKuUDQr\nVIQWDl89g6Js0YyGtOaeyfaDu2iGfdaGNZ2Dt7H0wBmueeFVfM1zD/Ph//YOzj18L0GSHFbI8j/Y\n+F6VjTWtGA/sJcDPZXkzLsFS3tKihz7/zY1XyVXLPkzf3aFiG6ufOkB8ul6imifMEyTmSN8vgAPZ\nZNErVcme2WnuPnqWCfFYbDi9+ggdVzI/v4O7HvgbZubm2bbvIO2+sLL+EN/yf76ZO7qO+ckpGA2I\noeIV5euZntiOn634hhe9kLMnH2DHxz7Gjuc+h4cWTvGS176ed//671C01+n7hl07b6a47x7W14e0\n/ID3/MKHOX1igbY5Ottn2bfvAF2ndJcX+fCdH+Wmm5/P7oO3otZjMigfPPIxZjsVba1Z+GyXi02f\nPbFFPVWxbl3qYY+dFJzqHKM+vU4xLTQNOD9gpLN85J3vY3b3LJO79/Pxe+9mqVqlc9PzuefTn4EI\nP/WOf8yu578Q4SjH7vkUh6+5Hi1LJib28dwXX7s5p5NUdGJGLDMkk4kNVaPAEyRNXAcByQKceqA8\n54Vfh2pFv79EOTGHbwlz27bzjBtv5P5P30lvfZ7/n7n3DpIsue/8Ppn5XPmqrnbTbrzfmcUa7C4W\ni4UhsQBhSJHgkTyCPEoUqXO6C+nIYATjjkGKpwteMGRCJ50onXQ60YIWhjCEN+uwC2D97qwZ2zPt\nTXX5ejZTf+SrHtwFiVCg+ce+v3p6emqq8r3OX/5+X3f6jntIgwG3rl9ndu4e0iwlSRPq9arVJRV8\n9lpbnD5+lrWtFRzpoTyf3t4OmTb4nke1UmNp/jDfefoZVjeXmZw6zOKRc7QHHWZmpxnu9UnCiGGn\nyz33vYOV7XUmmk2GnT5R6jHqdLn/oXvY3t7ESEnQaFL0yqTG0O+1kXFEe+MNCDdZv/5N6hP3stN/\nPrfIsZhv7mICSU4serOzpxysl1RBSUZGW4WwkejcgCmJE4SyfHxlFNpISw01lnZJbuVrSBFS5Uwb\ny4kOkyTPXsjPilJYr5YsQ0uD1g4GCHe7KCchkRpPWzIbxjrh7kc4aUlgApslkRmUMmTG4jDS2BQs\nIYR120Xj4ObAsCQyKU5mNQHSQJIldsQjJCJLbXeDVa9LY2zGQM4FNzpDZILMvU0FTg846lVC5J2R\nbdlVTlE1UuNpyJQdP+m8Q1KwT0N1zXgcZR+uDI1Q0toYGMEXvvRFZKFgvfsLAcr4aJ3hlco0ig5b\nN9fRgeLuB87z1Uev8M6P/Dh/9lvPErhyf5ZqOw17AlIyYwzXS7K84Gmkss6eQloGmG+w3ZAUllKp\nbadq5O1uylqCAJmxLGjGHwxcdP7aY/W3/exa2SIk1MEKtSMdbq1vM3d0gWoU8MyVa7QGDp5JGMUd\nTpw8zakTp/mTT/wFo/o0teIk7KQoYHVrFc/JmD6+iD+ssre1TKVTZj3p47sei/ecR09Kziydofq6\nx+nf/59QvYyg0kAnIeFgxCvPPkex5HHi/FluDTa58sJ1rr/4HDVHEOoeOjvBTH0Sv1AhFAodGJQo\nMuv2MD4Uo2lmJnZpZIpef4uJzKUhaoyCIn5/QDTIqF5sUsgUfZ3SD2MOlR2u1iVH5xfJkiEL8l5u\ndVcIVp/j1pVlBkmKQLH90jfw/Ar+7BH69TJxr83jz7/M4drUgdYcctNNZUkyAotPGW2sQ4DJcuq2\nRqWAYw8HWsCf//5vc89Df4yI+2xtbrB07m4cZVjfWGPx3EmK5SrXr7xEtVzl7N1vpR+O2N7aYPHw\nIZIoIYkiQj2gXm+yvrtJozFFnMQIoyk16pAZsjRjYrLA3NwiwncYfjFEoBkO+rhYw9Urq9c4JY8Q\nZprmoMvMxAQLS0f55hOPcWv5JseOHkFKqAQFoizFlZIoGRD4ExQcjzgcMNi6RnvtJdrblwhDKwh0\ntD0AZtIgTa530rlA+XtQ+t8URcMyNjXhmN0gLS/Z5JoEkUuAbYKaATKCrAPuBGlOcxVIhLGtYZb7\npiRpiq8cNCb3rc81FGbsJGudLAsoRo5ERR7KCFKTgrGbi8qxEcuc0XiiQyxqCCBLtd3YZIbW0vL7\ndYaRCmEcUlJMKnAVuS2A7ao0IJWiYNJcOKhAZwhHkWWZPQll1k6dNLH0VuVAmpHqNB9rHXTN7ShH\naZuAaO1CdM4as5ujtYcm15vkrDCs8MnNCaiWkir37UekELz+nW/hSp9OexPHdcj8AMf+KGllmoce\nPsO3HnuZ7zx2i3ctvkzBOUYaRziOIY5z/abI36U2IB2U0GRa7wsm7Ww6F+xJa/2gc1KCwOAJQSr1\n/mlqLPxTErTOUI5jO1vASI0da4t8Sjq2pLeMKYlCyOx7qmT//1x+wUUkKV4vYGXjGtXyYa71v007\nizi/UGVPD7m5epM7z9/Ni098je5UjT1ZoDpT58SRExiTMtgesbLxCsotoqYVpUKVneEetdSQDCLi\ndBviGCcRFKpFpAxteqQ03PGDD1L0PIhHTPaKZEcmSUdnCHoZmeoQZlCuTOIWGzjpNmlapVb2cf2j\nlIoV4mGHkZ4hSWLWt1zq1Spb2zscax6i6yesVTeZUB7EKVJLqqUIpcr8wA++ldOHTjKsuOwsr9J/\nrINcanBHc5K4GzO7eIIo7fHyE0/Qal1F7/pU3Um8UpFS8+DJfUoJMmNxKiuXyoErYXE6pH0OMg9E\nkrPslGAgNPGoRxxHqCSmu9diavEYaWZIUodeP+bo8TOsLl9h1B/ieC5ZmqGNItUhlWKF9nCPURxT\nr9TIMk2xWKbT6VCrTpJkIUophnHExvYGC3MLPPzwO/n2M98mGnTxlE+/s8fpU+dJuj3qzWniLKMU\nFIjDIfOHl9htbXPszEmGgx5aSnzjopXA8XyybIRSBi/wmD56F3H/CoNNQ+oodJSCp3DjzBKGsIvj\naWu3E3+PDeZNUTQybXCkBT3HkaiOsO6UJtU5MJrHnUqRdxp163RrxqMEkHlM4X5GgpTWRXas0sIK\n2bRSpFmevicgzgyJCHCzTu5Lb5UYWuc00pwKqmTG+ZkGz62l+SYkESazflHcDjHKdM7wEgqhrC+W\nVNYEEW2TuozJ7GAkvzm2ThiUdNA6I8sFR46RqExbtpaxm53R2YFBWVcqyM0VARLsqE7lxVKSM8Ok\nnfU6+chO5murczaVIwU2x8aCAI42OPGIfm+IV/Qx2hAAnhvQbm/T3ZF86coWpVKZXrrB722d4j3+\nDoNE8tRnP8d97/+QHekxTs+z+gitsdiCtLRbNQbuGDsIy9yRXTC+EfY9j2N2dY5v5OaP5NqO/LXs\n82PpuwIYS/BtobBEiOyATJ61tTVWrt3EUymdeIiRFS4sHWPgpqyvb5OFCUmwgQoKNC+e5s7F03zz\nWy/Q3uvxwtbzVKamqJQrXLjvfqZKNTKTsNcbcGhyEt9XdDt9bq6vMHloCjcC15V4BoQrcesVqtUS\nRd9H+U2OVA+xmSTceWpA3/QpOAW2By3cvQhKNYyTIKTGxSXs7pGi6YRl/MxQUA4Lxw7jKweReuhA\nUau4uKUmwvTZa4X0OxGt9i0c7RKHI166/jxSBMyW6vzoP/qHiFGMGA3YavfQSYeoo1g6dRp3rkYh\ndEiRpIMBr7343IHWHGwutpJYp4Yx8y53vR7b8WTaft9xBCI1bPcEjXKFaBSidZFCzaG9vsz04jGS\ncIRX8EFrVq5dxy0FrK4vc+qOCxw7cYzJ6RlWlq+SOYLJxizScRgO+oBGJ4Jyucow7OO6PkpBtVxh\ndvIQaZoSFCo8880nmD40j69hdW+V3VaH++97mEvPfhsdVfF8Ra1UpFqvcMeZU5SDIoNRH1co3GqN\nLIwZRRGVUgUda5Rw6Y9C9naXiYFEpHgumDQDR9rALWOZnmFuTuqoNzmmoaS0p3sNWa7OTVJtN7D8\nlDA+XSdpipIKqaTVQEhFpFMr2BGWrqq0pX2mWYorVV50xkCvBTxRkjTJEMKQuYJIG2qeS4LGFZJM\nuUijydKcsqtddGxo1Ccx6+vkJF/re6Tt6+gcnFfSYilpns2rjTXag3zGLgSZFihHodPYdklSoU1G\nIgzKKJSynVZq3RZz0aM9JaMt1nKQSxira5HjzkfkSncxPmGPce98OCfyQoLtJjKBNV6U4+ObLWpa\nOWzc+A7oEWkaEw1T0iQjzQHEn/zV/wMXj+eef413PXSExx/d5h0zbV5YXGBtN8WgbP47MGYCGLPP\neraDozENOO9GkCofwuY0YGMFepnIc+SFsaMqcqFi3hVZp4D8tbAsPCVMjqXZriXTmcWbMLdB9e/z\nKjguc8eO0ctCKuvbDHOGFTgAACAASURBVJIe+FVOHlpEJzAYjehtbNPTIyq6zLdefJGTZ4/yyqsv\nMvADkk6LOOwykgMmz7wF6VQIEoF0XbSOmVtaYGZhgZdee4EjC8cpFAKC2LDVs55PhUAQOIL17VW6\nnT64inRPc2PlFQrlCUwS0huNSKKU2cYCW4MdSgWfRqPOoJeyt3uLsj9BGBiCRhPf8ag06ijl0+ls\nsvL6qxidIFVGkin2Bj2aXhPHFVw4cx+OayiVykS9PsNuj2G8RdzXhKnAr/pM+lOWKVgUpJGEwOfI\n/PyB1hzyQqDNvtmfo8aMOXswyIQ1wEyx404lIckMnlvEUQqnMUkxDUHG3HrtEkfPnSPsd5k9cpJW\nexvPCahPzOF6LtkoBOEhpWLYH+FPBIzC4T5JZNBvo4JifujUOG6R5kST7e1NtDHERjI9P02hNEHJ\nFziVAt1eijIOvueys7dNc6ZJv99janERPeqRpCGuVrRGA5quR+D7CAPD0Yjt9TfQoxZCxHhOkTCU\nBErvuyeMHS2EMMTK4oTamH0JwF+7nge+I38Ll0GjUEQywx8zVqSduCdplo9R7KkRZYFMm2glSHP2\nvLa7HTKxHOxUmn3b8STTuFLm8a52czdxhvI8m8Fh4GM/8C5k1EZqQ6gzXGkjEBEao/MQIgVvrGyR\nIPGylFQCRuK6DnGaWkpwnkuhU2u5IXVOFxbjSXmC0Y79uTSnegpFliUgFCoFTYayUziQdr4qhLWD\nx6RIpbD2ZQe4hN5njxhj8I0kk7aE7NtaSWH/PzHODrxdVBzYl4YqmdvIG8GVS5fwqouUqpPoKMZv\nzLK2HEK5wLs+8FaieMiV124y3azxB//hm/zSL/0Qnkh5+P0pl19/AWV+lLFD8NhOxZISxG3wfsyo\nGk8ZIBdb2j84Ku+Ocg2KKyw+ZosO+52r/Yhy3zHAFfkL3K5LOOPM8ttV9Pu+dkY9HN+h5vjMv+UC\nyhV0drrgBpycPcIo7jJYWGB15RqD/ggCw/rqOu+8/2G2b13DbRzi2s03GK1s8tjgScqlCRZmZ1ms\nzbC3HaGnfOLNLq5r2G2tE6eawGhuXH6NIyfvZnNjj2GvTT8ZMlWug59Qn5yEKz1k4hDVJvHMiKBW\nYWAG+C6MwiGnFs9z2HfInJOsvXiZkfJJ44idjTVuPNNjT3dZOHKRURTh6QS/UMZXPkv1mAjJmQsP\nkvW6xPGI3nBI2N2g39WoYo3KtGSuPokaxrTDIemgR2w8qkGJ4TAirk0ebNGx+IWTH7K0Mbn+R+AY\nQyTsJpg6Ei8zpMaQauindU6ePEdQLBAPEvb21qlPLhCUK1aA7DqYLOLUmQv0h0Oq1Qbt7g7VcjWn\n0LtUKyU67R1L6tEZveEIJSSjTp9CSaAjDTOzGANRmGFkikxC7n/b27lx5TKXLr2EUyhx5ux5rl5Z\n5dU3XuPhB97JxsYOCwsLJMOQiakmAo9EjShmQwadNm5zCk9KdDKApM+ou8Jo9zrJcIhXVwy6FjtN\nxO0sSikEaG3JLWZ8PPzrrzdF0cAIktwoi1xRnZmcbyoErlSkWoOy+bUociA7n6Vreyo0mSbLg3WE\nzk8T+TE1Si0WoLWx+duORGsbriQMVEsOa1mAMRJpB5029lFILFRnUwRf3h5RdgK0lAgjUI4gTjN0\nZmmhidFW2axlbjg2VjVbJ1+pHTQpYnxjJKRpYv2Pcgqc1DbqXnuCLLFnfQvaxxYuz0wuIDzAJXPX\nLmGzLrTJH6C8GyPHwoT4rk3UjOFhgxByP5p1PLbSAr746U9TLk9CFqMBVxmkD5MzDYg0N5ZbyEAw\nuVjl/YsP8pu//RmqvuHv/b138fVP/AUvf+0J7njPAxhxu5OS+X0edxLjgCgjxhoOwXhv14x129qO\nBHMyg8o/hDV3s+34viBTkBf1/HE0toM0JstPhFj21AGrhiyWOb60hJc6eLnLceIOSNMIMaFwWw5H\nDx1ls7VG0hlydPIYndEOL1x5heZkQDoaMFFvMr14iGhvAIFgdWWV1sY2d955mmE8Isp2qBYa7EUt\nCqpAJZjg4t0PkrmSuluCeIJ4kDDM9khiF6ldXBWQOYKpCZ/6ifsYRT2azRk6W2t4hRq7G9ts6YRi\nSXFl5RZK+DR8n3DYh0qZe87cSWFW4kaz4PkEUpFlQwKOMDA+SdwnzCKM47Kze5kj86dozAww2vp/\nxWlCWLT3tuqW6IcpYdyjWC1iOLj3lJAWDDc5TVw4ApFp6/dkrC1HpPX+IQ0jqM0e59yFO6iVSwy7\nA4TnU282UYUAo2H28DGiUUi/NyAolVhevszxk2cYDkfsrK/ie5Kt7Q38oEipWKazt013r03gSoyr\nKLoTDInY3d3gxMQdTDQa7O1t0hn0KBRKPPSeR/CLJUaDPjqOee2Fp6lNTBMph0BpHn/0Md713h/A\naEWSZdSak3iFEp1u12J3vo9MDY2JBQqew2p/l0F3B7S2o1bXppRmWPw0FJbsITMrhPxeTfWbomho\nbfZ1Gib3c7IHg/G8MQcsdQ5mGTscwlgKXaw1Mh9fWctyZf2dMo1WFtzRykWbNNdxKEhTMk8hU4t9\nqGIAwxijQGqJUYY40ziZBYnTfN6tlMpHZlaloDMfYzSu40Bud6GwpmepSfLuwwElkVrn7a/9WWlS\n0syak9m9WVkL5FSjFZgss2MrklyQZB15jRybvH//lzAWQIbbp+4x60zfXvr9031+e2BcIDJrJ2LG\n78kYSoFD+/oybq3CoDekObPEGzdShlHIO+49zxNffpKZxhzLl1cYDRNa6y0++CNvZ/lL/5L1lRMk\nesBDD95FVxprLpmf/E1u67FvcZC/IZvQOKZbW9fPcRdi2c/5GEqMMzbYx0tuhzHlAkpbFW53MWNs\njDG8YQ7caUw5VYSWKEfQ1jEqsVjPKA0JtKQ7gqNBAT8o8gPvvGjvs7zAC6+9yJQsIhyXnow5PXeK\ndbFGq79GzwfdN/SjmFLJh0qTpLeCilx6yYiiH5AOhoyUYn7qEFqGpGlMxavRzhyk49ARZcpJj6Jf\nRjsZrphk2GuBKjCK+4y2tolKHumepFY9RDLsUJma4sjUCRw/IskkZlTA8R2kdhh0+wjPpW36DHau\nU588DI5POtzmkD9N1N2Fso8xHukwRDoGkQoKvofYE6RE1FxN6ji4peLBFh2rm0qlwc/y8aoxVhgK\nNpcCbU/XDiAMaSRYOrxEaapGtd6A1KG1tk0vjqj7ARPTs3T22sRxSHNmjlHY5c677mVnd5swHNGo\nN1i9dZ1afYJwFNHtLuM6DvVGhVK5BCgKjmuZVJ6H73u0Wi0Gwz47rS2OHz1Lp9tiYWGJ1y69wNrK\nCmEKvlAMh13KpRJCGzqtFo5SFHwbOau1plqpMBgO2F5bwzM7SL9iA5+SBKk0KTIHbe1YWhs7fnXH\nkdbmNv3jb7reFEUjI0OasdEGpFgAONP/CWMlP/UaY7n5niNzymWefSut6Zybz+mNlDjCPhRG5w6W\nSITRoBxbOIzCFZpWe4BX8NCjzLInUoFEkSnzXUJDqKoiaSbIzdHRxvKKEp0L0bDze4zMVe4KbWJb\nwXGALBf7yVw0ZsbUpH0/KeHkR1vk/vhMYOfrQql8FHQwTx67rvkYCCtEEfJ2splSijSznlNog1T5\niZscnHYsG0rsb8CGV158lcbSAoNul/r0FFdX4Yd+/O2AotXu8fb3PkhvGHLy3kW2N7psX3mdNx79\nFI3F99PrdnCky7BW5DO/+3t88KM/k+/htlhLZZlvY9rkGLDOIF/VnCFlzxL2/eq8DuSvI4WwQHrO\nHRbGjqxswJLZpzErcZuJpYzILVvGDgPf/9Vx+0Tb0JisUJA+HRnS0jGVcpXBcI/TZ0+y2lrj3vN3\nM+xnFIoeQibcd/F+vvzNp7j/7Cnmi1UCx6XWtGK4QqfN5e4Kw+02r155lbpboXlonhPzTYJ+m1U1\n4NDcYcK9Pm7gklVc/G6X9k6LyXNnifZWeNuD97C6soGu1JCuItlrs9ZdpuhP0CjXaIUhFSmZO3uK\nhU4Hf/osyhXI2MOohJ1RDxUL+klM1SQkIiOLQkgl9ek5Rhs38BuTTDcXkamkrdsMIsOot4Eq1PGM\nQ0JEEmpKpRpTiUdrsIov65Y1eMBLZQbjiH0bdJNqtCdxE02IxsX6rYUYZKYYuorFpbMcnj1JuzMg\nNjFHLp7n1o0bnLzjTka9No3pGTrtFlESMhwmLN+4TqFQJokT1lauEcYhw40tyDKa03W6rQ71iRrX\nrt5AuYJGvUmvvUdzYpJw1KfWqPLMM08zO9Vkbf0mjXqD+aUFXNehObPDjetXCeOEl156lgfveytT\nc9NkseHQwixBwQHHZ3dnm6WFJarVKnEzpt8t0d+8RnvnGsIvW8JKpgkNZBp8YX0QjND7gmRX5BZK\n8k2e3Ede3bQWZEJYiXiqbRtpIJM638TTfcaRkHYUJLQhkQaV3WZZJWQ22lVDKnR+MpW2cOSzzSxL\nbDqfydCZxHMDrnU3cLzD+aA8Jc3swukcgFVSEKUZY7ttnUkcZcdiY1l7isz/PzuyUmhwJDoFaRIQ\njh0FGUiNpQlrNMqAFhlZboznZMbGPWqBkSlKOzZq1YAjM5ID2ohIeVuNYIzeXxf7d3Z0Nv7W7djV\nMY5gj/pWiZ2PtoTk0x//OL7RGKVo7Xm8/ZF38NLzrxP1huhY8fAH3sJnPvUknuPzwb/7IIePfoh3\n3bXEr/2Hp7gjW0dIQxLFXFm+RcERhKntIPSYEJU762ZyrMa3zCcE+468AmtwOKbZqhz8NDngOR63\nmbGFQl47rd7LMrWMyTNVpMVS9tPeDkg+eOO165Sn66x+5zqVco0zC4eIY8Hy5cucOnuWXtKj3Ym4\nfPNlphtNZtw6o1AjCx4PnT/Pv3jfL/C/PfYx0izBJJokgaNHT1NV8MZuh3vuuAedjFhZW8PRAyYX\n69TjKq9++9scPXqUuAeO5+JUa9QrFQphD5lW2U07rN+4xSjr4noltq+t0lxYwCtIRlLw0NveAY5H\nLxxxZf1lTHuIDgccOXICHXYxhSrhoIfvephyiYXyFJmrSOOUbn8HU5uktTek5/QYdUNszFiCW63Q\npElQdOgne1Ao0E03EZUy9coJht0dwjg60JoDSCe3BskZgqmwbrZGgSdsnDTaijylEajSWxAO7La2\n2d7ZZWp6mjhNOXriOFIYgqBAlGkajQZGOSAklWKFy2+8ihKG+vQhkp1dvGqFfr9HEttJQrczYmFh\nkWGviwRqlTI6i5BKEA57zC8uoMMQhI2gHg4GVBsN/vCP/ojXr9zi7LlzlCtlqvUpwsGQQzNN4jRm\nujnP9votJicnGaYR6aBPv9fHUYby5Dx+ZZIXv/JbYAShhCAyJG7ODiR3d9Y6j0YGoXIj0L9pPQ98\nR/4WLoFlPmVoPGFvnJE2pCezWCzKcubsLNqS7dGp7QKUlBgl0U4+spIy34QhbwfQY6DU5MaDShLl\ngSdaKkYmZlFOY3SGzlKMFtZ2xDh23JQldiQlnfz0b730UQ7aMST53NRDIlJAK0vvE2ZfyW6kY1Oy\nhB09aSkQIkJiSFVsge40RWlFKiLrp2Tt08hUYimyBobZvnPSgdZ8/+t8MxynghmRU1WN9V+yyza2\n9MjHQzlVVeajIWkMUX8ESuHVjzIKHaK0xx0Xj3Hng2d56yPnae9ZdD/KMkstVhmRyXhAfo3W61+D\nNEMjODw3zQsvvGiBbDlGLbTFfPK5tNE6PyXlgLmx1EGR4y4q785SYdBSM04EdLHJf44AiWXoiZyk\nIPKiMv7a5K69RuYU44MxbtlbWef6l5+HYYovBJeWb5LKkMMnT/LcU8/wlW88hRm2WZqYoTFRyzdX\nTeBISp7PT3/0B/nT//2zeIUSkYzQrkvBSZlYPMX56Vk291pce/U6rbRHZbZMsTlBoRYwP7fISBqG\n3R20GFFqTlOcqDJEwWSZWqVA4sb0OiGjTpu3vuM+Br0ugx60tlfo7m0TxyGeCnC8Cp4r8T3Y3dlC\nFqvceu1bHD9+goUTh5lsTDCQCVvrK6xsL7PZafPqtWdordxia73NgJShGVEol2m4dbSbsUMPXSrQ\nC/sEWREdGpLuFiY0yOEBCR/Y+ygkuFoQGysK1XlXSgaBsc4GJoPYJGh3AgdFEsYEBZ92u43rulQq\nJZIkRBvN9FSTVEiSJGPQH9HpdJidnadab9LZWiPVCWk6IOzuEkd9XL9CuVHFdR2cUoDnBwgpqFTq\njAZ9HNfh5PET9Hpdhv0E1y1QKJdwleC9736I40cXuXr1Ght7PYaDPl7BZ2buEE4QMBoN8TzPkjpi\ncDwfJVJ2V16j21pGiJTa1EkQAt9gx/LjrlwYUiEwjsLkCl6JGXNR/trrTdFpSCHRQiFJLfjoSKR2\nIcvIMJZRlSa2CkqXLM8AVwiEUWSZRkiboKWksjnheTqXmxliMZ512xO0ERLPcVBRiEQxMhGDJGH4\n6udZPP0Iy55P4crTcPwdBL3ruCbj+guPc22tS7Nc5P1vO8tW+Qg73gyjPHZRk1E2EWxcptdaZq/f\nY/7BD0NSIAo0SRxjhGRaDRFJSMlkfPUTf8DcRJNiqcAwHHLsno9gCgm9p75AO4wZVQKqCprVMo2Z\nM6wWFmgJkKkkk/HB1tyAyX2nTH5Kd0SebihstbVhSnnOhxyPiiyQfHtEpEBqjNEEymPYH3L8HReo\nTG3QaJaQWuA4LhjN5//qUd7z4XfwxGef4nMf+xL9OOBzlQqn5qZIqnMMhx/H9xRvf98j/M5v/Cq/\n+e9+38ZwonGEIhW5psKQuwVkVmSYd5NGSTAaD0GsRJ65TJ63You6i2Xfqbz7tMaI4ywNe4lxfge3\nk9FsEdFwAKfbm7MuWWnIbMNj9PpV7v7Qu3n+O8/QKO8yNd9EeQ2WFmfxS2Xa/Q7rGzeoHZpnsLWF\nr4ro11/h8DHNyvJ1GhPTpEWR+6KFFGanKShNrTFBr73DrZsrbO1sEg1HnL14ARlpTK1Gf3uXOOvw\n5He+wbnT90Fnh4pT5+I999Lf7rETb/Daiy8wjByOn51mb1Pw+vU3mJiaYWvtBodP30Ma7nDi1P3I\neglXQzEo8tylF9G+oHtzj+XrN1CzDapa4HhlwrRC5dAkc+USXrFERoSQDlp56F6PQBYx/SGlUpGk\nH5KFGUq6KDGiMTd7kMccgFRapuBIWS2CyCRaaZI8qS9xrEaqkNlnaHVnk+NCsrG9xbxf4PjJkzh+\nAc8vECUhrlOgs7eLlB7tbgu/4NEfDPCkR2tvh2ZzkjjKaDQbtHc7ZGmGcrETCsele6vFzt4uJ0+f\nsBkejovvuKxublEpVRCFEoWiT5ZmjMIhjhewdPoM9eklWms36O3u4s/PkiYjji4dIdIZnuMQholl\njsaGvXCZ3eWvUCgcYiNpM9i9atNJDSAsQUgZS533NaTGxiVn0u4Frvib+4k3RdEIkxDpClwhSQ0E\nRhGaCKUclElJhMUMfOnkjChLqdSk1gVVS1yT2E3AGHSS5NRKSSTy47Iei9UUTtrHM4p7Z29y63Kf\nz3/tWyTTimFawHv5U1TcgM3uiIt+l629Ptd2hkxNlDgswPU8vvbMS5w6ukuBhM6tFp3M4BYceon1\nuh/GmoYn2Xn8D2mWyyxWfK5sbDHsQytLEEbRqJY5cugQzVKBrV4flMulxz9BveqyGw05fewQq2sb\n+IUJrqy1OdZ+Cdd7g6oe0e11iIMi8E+/7zWXCoyxdGaJHfek2OAiixtYPykpBTB2lrUPkhoDxLnK\nQwqBI11W9wrooeKcjHnp0Rd4BcH7fuo9PP31F9jZaPHjH32QF7/4aWbUKikZftxmsDzglSsxUb+P\nKk3R62wwc+gQ2ytXGQ12KJWnLXUas58mOAa+Zb6RCwTjrAw3D1OyRpOAEXn6nmAfHtOabJwCyBir\nuT0C+4/WafyFsd3wQS61vkE4WSCrFVieStn56l8x9baLXN7c4wuvP8ehE4e5tjXDQ/XzlMoVTh8+\ny7XV6zSqU7x+7RKXt66x8toVnvwffofffemLxMOY2uICcWuEEAlhFNFsNKFUpPmWaX7jI/+An/j1\nf8Jz3/42ys3wVAGlBeVhyPve80Hi3SGtLKE2Pcva87eoNOuovTpprcO0H9Df20UAM0snGLR3OXL2\nLo4tTbEb1tnq9ll54xJpN8Grwcor2yRJD1n1OXzhGFPNOcJoxHSzQrPZpNNJcT1F3O2ACTB+gcH2\nLUpTM2iZ4QmP7XYLT7pMHZ5kMIgw2QDVPXieRgL4Gfs54EjrNOBq+0zHWc6ccwVOYlCjFqtbmzSn\nppCOIo5SMkb0O20rCvYCavUamohisUCWZtQbPoNOG8/zGPS7TDSn2Ov3mJiY5NbNq8S9Ni+88jp/\n+Md/gkaijGFxbooH3v4Av/iLv0hw8iRTzUm6m5usLl+hUgxYXJwni0MwhvZ2i7PnjvN7zz3D7Nw8\n/UEXKWD5xlVKxSK15iylimJva51by1dJzJDy5B20lp9E64Ruexewj7c0hkha519y519X2CiC8dHp\ne02/3xRFY2KwQuoU8KXCFBukWQIYHB3hSIVIu7z+9F+x9PafsurgfKPzMhihEcohzTQFYYhzj+8M\nQFhLcS/N7SGkpjbY5djJCW68ep3lnXVu3FjhLSebvHZzl7dcmKOzsc7usM/h6YBhGjFKDcfqFbpZ\nTEWG9IcxSzMT9PpDAkdx5FCVfiIYDSO8os/N3Q6n5mfY6bRJRhkrYZdra3B0usBUuUjBhydvbHK4\nUsB1AsK4TclxyDzNAxdO0h50uDeY5clXb3HfxQs8/8olZkpFXmq1aI9ilqZqCKMoDEcHXvexo6z9\nQ/4wiNuZ2GPbEIxVVY9/OpcGgdDozAoZlTAcnhoSZQ46jol1xo999F18+k++xvt+7CEuvXidNAtw\nVYme9pk8cpZRv8uM61NuVImiPW5eeoGdnV1OVWeQWvKpP/ssP/1z/7llZ+VCyrHFvBh3EGasUh+z\nncx32ZCwz+waA/aMeSFj0sF3UYbzIZUdP37XGo1HUwed5d64fA2ncJKdr3+L4MIio2dfZt0xLJ07\nwcV3v5vtYZcbO7s89fH/hcojb+Vsoco9U8cYZBEzh+d5faSZqPnEzoh/9WP/kH/2u/+GQTSiViiR\nSkllqkyvtYLnBvyvP/KPWNJXOX78HCu3LhGGCVmasNUeoEctNm9dYfHIHQRFh729LZyCy/Zum6qK\nKMUl/GKJvptRq5coFANKp4+RacPqzgpGQyGoMTsxCfM+29ublBZ82ptd5qeboBR7u3s0qlUa5RpR\nWsUxe0gHZOCTDLroOMM3AW4GQerRcwyVQKFNiU53wChLKauAgX+wjhrAByKRjyIdhZNaP7JMGoQ2\nFIwdjafajmtckzIctCkHPmp6lnanRXN6mjgcId0y3V47Z+ZJ6vUpEIZBv4cRhmKxRK/X4ebNG7gC\nqtUqr3znOzz2+Je4dGOH2ckCwzBi0Dcsr2+y/Oef4q333sWZc+dRyqE2M4lyJUFgJQPFYoEgKFKo\nFLh+/QblwKcQOIT9mOFoiJSK9dV1vFKVkiqSpBmlWp0s0dxYfx7peQxb6zYH3FJoSPMgpyADM/6V\n0gIh7bRBJJrvZbP2piganSvfouK4dMMM40vSVOAEhmIh4MZaG4xk0lO0n/gDDk+WWWn16A4Semmf\nxXKVlf6IpeYUulhh4tS9JLJEdumvaNSbbG2vMTQxFeNT8n3+8a/9c37t1/85UrpsZwELUw1Wd1qc\nnC1y14W7eLHXRokUE8K/+Nf/HcIt8JGP/Dz1kkeY+UzXPeIwsT5RccIra9vMNqaplMq0hm2migHd\n7jZV6SF8iXJLRErRixPaSY9GXGCpUmKzNSI2bTZbQx48Octmp8+LL10HoXk1snGqX3n6EqRDokBQ\nKnjEccz1jRZnZiboJQcrGmMaqpWb53BYHvqkMqtbsJwDa1yYSZNTfi2rTOZgvsw34kGY8tp1wz1v\nv8jl12+hfI/Pf+Jp5o9N4PqKxlSZL/zZVznUiFB+gFAOQcEnzlLSNEPIMhfveoAnPvVFDv+TUxw9\nf4Fv/+X/SeAX+MhP/wQWobCdj436vf0Zxn5U489l/hMQ7zZT7PZvgsgB9v1/gy0wcPu1yJWxQuT6\njwNiGsHhaXQ3Ilxf5cSHH+K5+EkW5+e48tIb3DMzRXJjj5X117nnpx5hZiC5vLfBl1PBrRefxosT\noivbXBWK6ekGye4Wv/oTP8WR5hIJI8qlOm+/c5annnyD9fYec2qIPPZ2tEjQngNGYvo9JosBeCmt\noeD5W8/hdqHUqNCoTXHvxdPcuHmd00dPExUyRDiJ1gLfKdPtbCOFy87yLjthyu71b5BJSSEoUZlr\n0JgoUfEdTt95Fw4Z0hgUPkMZYehSKkGaSYTn45kasYFYeZbBowXdfoegPIHe6ZHqmIIv0dLB4YCU\nNcCxdDqkhFinpBoyz9qFhAp8IxgnbistaA+7nG3OU52dplwpMxra036pNkU46lvXZyPYa23SH/QJ\nPAclC0zMNkiGMadOneG5576FF49YeeUKX3/027y+NcF/89NnecsH34trfLaGe3z8zz/Hs998iV/+\n1d/g/gffzdziFMVSjUa1RhAEaAHFYoGjJ44z99oVHDIuX16mMj1FWSomJid58tGvcfzUHdRrdZTr\nMX/kGDtPPUnqeCSiQOfWc9byHItdBoFHfxBREspifZmVHGhjjUpHwiA964H3N67nge/I38K10Jjg\n+ZUbKAN1ETAcZFSMpKehqBKOzc+y0unijDK6xByqFZDCMB80SbSD6LRROqSuNN1nPsXizARxzSFJ\nugxHQypBkb50uNVa51//y9+irAxFP2CilrK+HbI7GECUMjU9w61Bn4YXsDuI0JmhWCjwX/zCL/CV\nT/45U46gL4b0QkklMQyTDBOUWNnZ5b6jR6jUKmwPh0QjQbHs8sZmm/pMiUkpmApKLG+2uZp0qTqC\ngUgIBzHHZ5s8qRFwBAAAIABJREFUtbpH03UZxC2EUgSRIA4EDy4dISrFPH3pBk4MRgXocIDvQS87\n2K0TxmaoWwNFi1toDI7RJFKjcK34Uag8vtUCxlLoXLA49oayrsSD0YAP/9wjPPfkS1y86zhLC0t0\nel3WVzf51jdeZHdjnYUpSRpp/KBE4Dr0R7mXv9SEa0/RXLqfV19/GSEkWklO3PMeBsMti1cYY90A\nvssCBOz3Vd5Z7lN0x8KS8WWwcZ35ri9ysaAaz3dRlrKbYyBjwFuIMbHagu/ygEh4f6Q5ee4Y1Tsu\nIIMCH/jHP8dysUAQSG48/goDd8BdH/wQq5eustYe0VxaRHsZU9Vpqnt9PpPG9HXIYC+jMTeLcGuM\nRgMyF3avvMGhpRFXt1fRiWJNaX713/wKItZ42lAul9hKhriBT0XV2PWWKadN2umARuATqoSbm9u4\nXom9LMEfSG68fplSvU6BHUJXsLG5wq2NK5i+x6jkc/LwMY4uzJEASS/Emy4T9gYMRn20a3DxcIRg\nZrpKL5QM+l2qExNkyYiip3BLNdLeiGHaw5WCzq0VStU5+o5hSrm0+7sckFkO2IhltPVQcADtSpzU\n9stuHr6VCessERuDlhW8apVyULQZG8D2xhaNqSlMEiJdh96gz2A4IO30ODQ/Sxx16O6lzDSn2dpa\nZaJWYnrmHFH8DCc++F8TPfo/8vB/9l6c2ixGuOx1CrzjkR/ECRRf/8sXuP7ai0xOvgu/WEeJGOk6\nuNIw6HRYX1vnjhPHGKVDzt1xEdd1aTQncZTm4sW76A9C+v0BjYmALI2o1WtcWb5EPHgDIW1n4eWk\noWEYoZS0hABhXaylsb/fqczNSnMTw7/pelMUjetrW5yarNGODFE0wi0FRFqztrZFtVbkiVevMlWv\nM+h3KWuJVyhSDgJ0nFJ2M4xwCRPo7SXsxIr1GztMVcoM9JBMOZg0pOKkHJ6skbkJrlticzDg+Nx7\nubTyV3zovQ/yjcde4gvPPMsXH32eODXEGj556p0UymWGgzbGKALP44G33Y2MQ1rhkIlSiaoJ6Lkx\nG+1NCtUSVzb6DDPN0lAxU6gyIwN24xHSN2hfQxKR6hLFgsepQxX22ikPLDTYaocMRx5ZmvEr/9VP\n8u8/8yVe2d2g0SvS8KuEnqbfanPX8SVWdzvU/IPN1x2ZK2SFHbtkBhxx+xfJGItviFxjbdtVu2la\nVleOGeQdwNNPv8Tn/+IxHF+i5TEe/+o3uf/dd3Px/hM4UiDMGR7/xMcolQoElUmyJMbxCqikSzyM\nKc6+jZP33c+XP/s5qiWHoDDFaDBCr+zi+4IkySwDTjgokWG0sp2BwKr6sVTazIwDusCqLRVg9lMf\nMyNQIvfXUVbzI7BpgwKdg962lc+0wRG3I28Peh0/epqp0/O4gcv1R5/jsqeZDB3q5w/zSv9F3v+2\nH+bbzz7HHcfPcaOyg9twefFPvsaJv/swv3/fz9JptWj4Be47d4EfOl/nfR9+iGR3i9/+nz/OyvQ0\nzz2+xfZujyPNMj/7f/87ykNDYWECx3fZXNmmKMscPX2StTcuM+c1CSYmmJqLufTCJQwbDDtPsDhz\nntrGHkG1gtco4QqfzIFBb5MAwZ0XHgDlYTLFyq3XCUeGQBhqhRKDOMWtlJnyA0qVMkpnhKHDVq9N\nqVTGE5o0zuiFI5JIYHQCJiUbaQItKc7PkZqE6X7CXj9jsjbLMDs4e0phKbepbScRQhIrTUHbuOPY\n2C400wKEy/TSBQqeYn17nUqlTDKKqEw1GfYHxGGMCjKSMKJRb7Dd2mFzbY0sSRiNGsjUUKpVOHH6\nDtLUcPj0eb70D/4On/jjn0dX57nRGpEh+dgn/5LDh8/ygx/6AElnh5//+/+Uz3z8k5y+605c10dK\nwbDTQggHqSSliQrtG7ucPDrPmRMneebxL3HPnRdAK65cf4HjZ+4gTUMct8R2u4PSLlHfQaQCx5Fo\nlSE01glcCjwgyl2fMdjY61ztoBxJ/Gb3nmp1d4hNFZ1YHUXJi0iMQ6NWpiAETjXAkFINAg6VCjge\nuIUirXabkc7ohT3umasjPMFox6A9j93+kExonnr2WR795hf573/pV2i3BH09xHVdjjYcfurvf4Qf\n/pmH2Lm5xoMPPUhj6Tg/83d+khdf/jz/7Jf+LUma0el2kdLhvjtn+PAPv5sPfviHqEYj/ttf+116\ngx6NSkqQ+HTxaLUSFmeqhL0+s3WH1Xaf7sqA3X7IwlQA0kEqj6k6lFxJb6NDW48YRBWONIss1KaQ\nrsv/+xefI1AOAgfftSFMi40CTsnDODBd95mtVg+05mNLQpmPXVQOEijGitAM6/s0ttIwCJPH36Jz\nMNx2G0ma0ent4RQqjFpthl3DD3zwYb7y6S/xgZ/5Ebq7XR773JMcmqqRhDFZluEUfUxq8L0Sm9td\npBcThyMKJUsFFMUSqjdkOOry73/n/+Jnf+G/RAiNILXvUFjXMcuytiJEg6XTZiZDGQuOy1wBrr6L\nuiuM/dx5viJ2CEcu/db5mApU7qirc6bVQW1EqJfpvrpFy3VoVJt48xVG6xvMBT7zjXN89dlHOfGu\nt7F6fYPKZJln/+KLTC3WWfuzx3nk9BHMyQUev3KDX/7hRzDlDNNq89k//QI/9o6jfOzSLT57bY3M\nkZR6mmde/ipfCSZoc53F4/cy39EcP38Xg06Xcm2S4WAP/IzZrIJ3/1uIVjvcFA7ZoEXp3CGe+eqj\nBFMlprwqc8fPkEqPazdfIPCm6Lf3yLKAUafH6uYW9951JyPdIWhMsnprDZTANT0WG8dI6x7h9RZh\no4/rB+jhNs1+i9NhSqiHPFppEHdizEydsD+ktbtOUKpQLEukCfD/FjLCRQqhMPhYqql1mBBELjiJ\nBgeM1mghidOYpZOnKRQqFCpVXOFSLAcM+0PKpRK4ioJXZtDZRUhFqVBkZ2+H6eY0ru+ToojSjFE0\nYGFuiTCKOLoUov0JerFmqz3g6JmLXHzbv6S9+v+wvePwIz/5czz52G/ym7/16/zRn34SxwsgS9AY\n/MCnWq2SxhmLhw/T6WfcWr3B6bvvRZuUUdhndv4Qzz77GPe+9Z1E0R7VSpn5mTrPfHpEIY+Y0NIS\nQmKlKeEQ6Sz/XbcGr2OMRog8DfN7zGLfFEVjYmKCousRDWMGSUQ/FRQCzbAXY3zJVLFONx5gPMVW\nuEfSc7mxdY1UGqaLJR5YOkHPi+lt93GKBeJBROBCuVygWa7wjT/5JDJ10K6hECv63SGL734///aX\nf4GgUuTrz1+h6hU4MRdQOvcwH/3pj/Kljx8nTBNSFI9/9o/5vc+8xq/9+sd4/FtriM4WwikiXYGO\nDWFZUtIaE42IjEspCLi62yPNBLthh4tLs8RJzEKpxJ3zFQZhhskUQbPM0WCS0HFgFNJKNXUdsjRZ\noJOEVDOHjBhfaIrCsUFN0jDSsLLbPtCa58OdfUBZ5p5emclwlOWsi3xLHbu8MC4WuU5DSitCcn2f\nb3zhFT7wow/R3hvx5b98grpfZLqxxyvf+Ev8UpHjxyTJqGBZWAWbcSJzmxetQaUFhrHNGdne2EZ5\nDqnw8L2UsNffT+IbJ41Z5pPe97+yHydXeGOLnDRjZfd/jH3s/3zusYWBsbWIfZkxBgIiTzW8HfTp\nft9rblptNtIBM8cPkxpshrMPvWsbuCenaa5nrL56FX9th5J7hre8/2Fat7bwnrlGc6bKd775BvfO\nnOONrQEPLszS37nK9mZKtRkRaYXjKpLRkCtpyN0vr3LtxBCvrwkv+qy7CTevv8TO1Vf+P+beM9qy\nqzzTfebKa+dwcq6kUpUCklBGIEAmGAzGGLAxBuPUNmDA1wl32/e6jfva7evhxqltwDiB2wmM1Y0w\nAgkhQBKipFKVVKXK51Q6+Zyd9155zdk/1j4lfrgZd3T1D61/teucGmPP2nvO+X3f+z4vd91wB7P2\nBOlWg81Ri5rQYb6EWT9AZ9vnxIllqpN16mOzSBXS7mzR3Fwm8fM0O1s0uk3Knsntb34lgzAlNzXC\n5kab8089R27EJW1rVMbGUCMVrBLY06PEfouo1WFaB/PCedqGRtdLGWyewbj1BqzIwqpYVGt10m5M\noPuEMkaEVy/4UHrWkkzkUJpNFj5kZeO8Ya6OIk0UsRRsL59nbvcu0kTR7rVwLJdISbygj2Hn6A86\nSEy8QY8kUTi6jWVaaIaJJiQqTiCWWJZFuVJFs/Mk4TZWsUIswNBMyu0TWOO72WhcYP/MODLVuXjp\nAt5ggJ3PZxJ3YdBtNtlqtNm39xq219Y5vnKEimsSJxGGYaJIsTSdiZFxHNfENHKEk6Mc+fYhbLNA\nFMe4yMxvJMFKINITlK5hJJnAJBUKTdcRaZoZH8mkuP+r50VxaHhRQi/xMGPoDxLGJsr02gOqxTwX\nNjvYtkPHD+j1ImZmCvQ7KfVSEct2qToGg6iJF8AgjKnrglJe0A8NNlptnjj6ZX7hh9+D5pRI4pgk\nERQsOH2hyd9+7jC2DR9662380WcPwxG4p1kj5uusXzzM/Q88RBKnGI6LhsAsF2k3Iw5U86A59BId\nK0jQQoXUPXppdhtXusFY3qZguCyplO1Wn6Kbsai+eXaT2eoI3SCgahs4hkHa9Tmxtk1tvAyDhFze\npJrLQSJRKZgjNo4h8WUEacxkIY/4bu6b/1/PCxuoEAox/CSYaMPsCq7kc6ciez3L4x6a4IbOdV3T\ncG2oqGd4/Bsm3a0+b3znnZw7ts720vM4yqPT7mc47P6AKApZu5CAFJiajWk42WbXdXDcPkYqeeTB\nLzA/dw0r6fMkfkSnuU6rt0GpOIqmZ54JpEIbqujSYbbxFce6GNZQQwQJDCurHYc7V869DLkuBGkG\nNctYVEN4o06Wga4PFWVXW2msr/c4uHeGoOKgeimtI8+ziZ/d+C576M0e1y3sInrFPMFgAALMbz+D\nc+oif/30CWpSsrbc4Nrlf2Xhlb/KM5fPsXDwIIcvfoubZ/dy5MQaE/kJTq+t8vhnvsyNf/JBGvEG\neeXQ2dpkEEYs3HI3ZzSfM60zkFOsHzrM3C03Y+dtKtJnbLzK9PQCN3cV7a2LSD/myPkTSHeK+tQk\ntbk5Llzcot/ucvzkMWruOMuLF0n9lFLBITAtdk9Nkp+u02peJmrreBe2CAPFnCvwz51laeUs/bUm\nl5bP8q2ViMnHnuJdf/j7iDAhsQp4pS7lyKbne2z2m1e15tl/eBYRLXWBTDNysUaKVApTZNVHnGS5\nN0EKC3v2Z6w5mRIrSd4qUMznkUlC3oJId6nUKmxtbKHCkF2zu7h06QJuPkfesVDCwI8UYRgyOTbK\n4pnzNDcEXtpAJj7/8Ojf8OX/9Pe85f3fhy108oUKpkhZ2+wQhhGu4xKlMY5j4odJZiMIfVKlc+2+\na9CkoNXeoNXZJIkiCqUi1ZEJdN3A1A3SWHDnK17JkUf+FhE1iABbDWOZDUUsJEYis3TOZBhqlqZI\nY5g/kHIlQfPfel4Uh8Z2t4vpQCl1KLiCdq+N6bp0ghBfprTaA0YqRXRtwFY7wnZNypZDoxcRaB59\nKbCRQEo5b/Lc8hbVfIE4UvzcD/48KUX6fifLjBh2sB955DHe89538fS3DzO9Zxevf02O+7/4COg5\ngqjL73z0V3n4a08SBAEYBrquk0sjioU8xckq9aRNgEV7q0mcJOAlVF2X5Y1N4lqFuuOQK8dcTxmR\n16k4BkEkuW1ilL4vGau79MKIXhxQytvcsGecwaCDlc/T9j1yuQLn2huM20UcV8cQOpZl0xtI4tRD\nqavM0xDZDTodgvi+Y2SRzSwEV/IHTLKbtjaUq2b4QHVF4x1GCbe/6Sd4+LNPMD7Z49zjXyGXr1Cu\nzXN5uUHqpxjSIo0MEAFaCnM3X0++WKXXbWPlchipIJE5Uqlx+Bvf5j0f/gU2L54mTgLSKOT+v/s8\nP/Te9+Dm8sMo2KFKShMZVXg4/N4h32oZondYK2XHxE44F+xIcBn+0k7uuBi2rsQVw6MuXhiwX+1o\nY+aWXaweW0WuXGCqnmOzYJEzCsgLKxSsGrk7rqPbi7FMxfLlLQZr51k4u8Gjp4+jeh7rqU+YbHPX\nwTcj5qapnlZc8tdxfRtbpuyqFTmzHvKymRrrcUjvzCVueNcb2bhwBrnVx6oU2NpYw5idQ4gm/nZC\neSRP/tlnmWj2uK9Wp3d+FVvFXNpuoGSbfj+ks34JXaXkCmVip8z+PWVO7bqPSiiRuYhbVi+THNyF\n1ruIPvparDzEnS6xVAStAdJIMVaOM6UJvn3qCJeWLzFVm2NT90nTiMPPP8/3dXrk8y6D5ja5cpHU\nsWHgUXXKV7nqO0iZTOmXpUMnw89IVommIoP2CaWIIgsMPbvBGy7b25ch1rBTJ0u2jA3KlRGSWJHL\nF6kUSoRhwOTUNFHgs7WxxejIKBd7p6jVXKjBB979kzz2dJN7Xl/hfGONpQsXSA2PuO+RL7loZAiP\nnFNg8cwJ9u3fj2649JopXt/HsUw000KTMd32ADunsbW9zuzsOIXZKYSukcsXaGxuojSB7bh0On2+\n710f4YFPvic7LKRCZaFDWMNvsSQjXCfa8LuggZYOPVnfxZP0ojg0fN+n1VXMz9U43+xw7YRDLzbR\njJib56bpdD2anT6FvEUaxXi9lNGKyUzJ5NR6l3LO4lLoU8u5NH1Jvl7Aa8G5rSZ3jhWojZucWezw\nqut34Romg0FAUhjnLXfv4fCTh8kpn3K9BmQ3j37QIVQhKkkxTQuh64RxSJpIiiVoJAajiUAkEdte\nQDOGbsejkMYkukbBUDgmeJ7PWK6E1BRbrS5oLkVLYVtkgU+JSSB7dKMswyMIgaBBpVhASyPGzSKG\noaEHCUnOJE5iHD3ClwamcXWHxg5mZScpcaeNo5RCDY1vuoJ0R1WVpXNf8SsoslAbTUAQR6AbGHpM\nrVij2++w0nAxhKJYN+n32qgkJfUtjGSA0GNWnjtDnKbM3HANhtIIQw9lQiIjOp116rUSURJh5Q0i\nZbF+6Sm++LkcP/zu92SZ7JkX/Yq3QmgSpbIZhbhixBuqpXb83jt9WvXCQbIzz8jOx52hd1a26+x4\nVrI14CrZU3LUQVShOLWAv9Qm9Hz27p3hfG3AytoGk2ccrLE88myfvGtRmp1nsrjGqw/exGdWHuHl\n19zAN08f4z/+y1m+9g5YPLnEqZPr7C16WGoTaZZIxTIDLG6ZMVj++hHCH3g1qqlICwX63QHp2XOM\nJh4ylqi/+BqNxiXWIp9yIuhOGpxdaTMxZaPaI2w11tg1V2JlWyefl1iqi9Hs0e33KH3rv6KmrsGd\nLnHx2UV2rdUZ+CmNbxynevN1jFVzbBV2YfW38I+vIdefYWm0hN1NafpdVp49xWJfst0f4ExNIaOQ\nnhI0kjaVIEHzJImI6CX9q1pzIItS2OGM6ZnEV8jsc5IIhaE0IqFIhUE/KZNEfhafYKZUiyNors72\ndoPpyUlUktLrtZmcnCEJfaSmYxoGoR+gGxZpHDPwPSanxun3+hQKVV79xtfwwQ//PK9546/RWm7h\nSsXeu24EqbFrzy4000Aqh2TQZ3NlDakLSDNMTrFYpdVoc/LkaaYnp9C1mLAXZkw7ZZLEEhOD9cuX\neOKJhzh+7DhvePOPUXAVbqWGihVK10jtrDUlNJ2AjLqhUklIBuXUpUY0dGAZuo58sUtuXdfENG0u\nNRuEseD0VpeKY2MrMMt9xkYd8m4NQwWYCHw/YanVpNeP0S2DViLJxQZnL7cZK8SUiwZbrRYVzSKS\nOR589jled+BaTCm46DVQaHzoIx9hOj5HruTgeRGPfP0wuq5h5eHc4Wf4lQ80+dkP/Ayf+MRf4asY\n27D56uMP8Acf+xMWrt1FcvQyl7aaiMTE225xcG6UlY5P1E54/mKDsm3xhtv20m0PsHTFeNEG08Tz\nFeN5gyBOmBpVqKhMKiRmIkkKYGpVojhEt3QCExwDHNOgGfu0Bz1EomMYKd3kKnMGBBlCRM8Af4Jh\nap1gmE+irtzSDTGsz4YtqUzzTebOF4JOz0dGOoa+SRhO4Mld3PSy/VgODLyEk0fOEXZ6aG4FgU7i\nbaCnCY5I2Dy9Qum2UYJBF9U5hK4MvM6AMd3C73mUK1WEGCBUnXMnTgyhgsMhixAZZXjHpKinCJkd\nZIKURAlMkSHUtcyilx0mYiefIwOiDNltSKGuHCXySkWSlRq6Uhkd+Sqe7aVN3GunGLR8VN0lfmad\npeUnmL/jAEZ1hHCkROq32XPNS9j2u3TOn+fy0ipfPvociUz45qlTHJgepRBKmk8+zLNPPUNxO8HM\nl/jm8xGdTY9px+VS12NS5VlPF5EPP8z3P7aCyqdsbq1QsKosPnQIOxzQ9yxWN9psRj2MUDAi8ixU\nE/xlwbqxxMNtH7ndJU4DZCK5ZXaM+cSk3D7HrpExOieeYeloF8stEp3xaUce1+1fYPH++zkjJbmc\nQ8X1aAwMeoMeT367x+WuYC2O8H1JqBIsXSeNGnzpn/8Rt2Syf99ttEf7RAONQtWhVLj6SiNBZXak\n4aVBUxANo393ev2kkAQKtzKP7RTpdAc4cUi9UGN1c4O5qTHydh6tZpKGCZsbq5QqFTRNI01TTMci\nSqDVbVGtVVk6v0S5UGLpzPPc+5rv4zf/nxbVUoHv/Z7b+PrXjrAo++w+MEctX0WXCe/74C/zV3/6\nW2xtrBP1A9yci2HqNJYbTE3N4LWbNJqb1OojnD1xDIVGvlyh70VsrV/k+9/2/WgipdGCT3/mC7zj\nba/lJ3/mQxkxXM+CQtQQEa0pgaWyHHCNbF0SLYtBTlMJKsV+sVcaKk7ZbG8zPlKlUBX0egIrMdEd\nHRklWGaKFQ7odgJOBx5Kg4WcyVw1T6PfZ6OrCFxJ3jfIlQSXGi2cnGKyYvNLv/h+fvGj/4FPfurt\nPHZilp//yIcJA8l//5s/xQ36HKxrHDl+npvmS9y1/wYefeIQM2MFTpxa4o//9Fe4fO4Qn3vwGVqd\nJj/3vl9l8fwpXta9nsndLyW8/CBz+6dotBoEQUjOsrh5t03Otnn5nS/h2aOnsEdq+LEiiCTlnIuT\nL5HKgNG6hR8mBCIgDAVaziJvazi6gWXnEGHA2EQBx9IJexETqcLdN8OgH1CwbaLo6polGU9KolSM\npltoahhmpWsIPcNz6GQRsBriSom/06aSKoPpCTROHTvDI5/5bczyy7i0bXHfG65n8fw6pmExNZ9H\nCB3TyaGrHHriIfJzyME6xVyeRqI4++RTOOU81eteB5e/gCbAGKsQpRGREJh2HkWKP2jz9W89xr13\n3YUmsymDoUE8lNOqVGExjLFVAmPYwsrkwkO68dAdLofViqYUicg2En2IERE7nDJBBklEI9XEVVCn\nsifx+5z++llG9tn0ThtMvmIWM4YLlzqMT9c499ijmAsTXG6tMNfLsbzZ5jWvvxNh+KxdGOGhkyd5\n1zt+mFNHTnLsiROMzs1iF7c4t9EhX3RpEVPSJfsmSjx+ucNktUj141/kL4KYSd3lfNpjV26T7kBn\ntdnmmrk6qedxbzWHVkk42UtoRhoWXRrtHHZkMjlicLGTIgydohGwZ0ow2LK41PUp+YpCQbHejlhS\nHU5cjji7eoEff/udOMJis5/yd194glOddRpJgpARumHR7vXQtAy97xQcupsxXzr0JSZqY5zdXiae\nmiBJyhTHClSExr977fdd1bpbZAmVDhqJJtGUjiEzUUcy3EQjBKFWpTi9m1KpSBgGSCloDXrMzi8Q\n9rt0TY/R0ghuwaVaqeLkbAZhQNGwCEOfIAiYn53n2LGj3H73PfhewMb6KvWRJb7njW/g7d//Vj76\nx/+e3Kstju2e4uD+/bhIPvbxP+HQA8+i6y6N5TW6gx6FUoEkiKmUSnj9HhoG45OTXDx7DsPSmZuc\nRbcdPvoLbya2JvjRD/wY3toSWhzxyc88yelTJ1m7cJG73vxrPPml3yMnwwzCKtJMmi4EUuPKd1tX\nApVmhAehie+aC/qiODQ6nmSkkqdYzZHGEZaM6PX7OGaJ1e2IQi6hjUGlmGch8UhMm6Jls+FF5OoO\nkykc2/KZnamz0Wwy71bYTn3a7QDNbvHxj/9nHnvyFKN7TB764v1IGaMGOn2VohmKrdUVBs01pg+8\nlI/0lgGH0UKJXvMC73zL2/iXh47y13/7aX72Ax/ke+59OZWqYLxcZDGVfM89d/Ke197Gb/zhZ7nr\nZXeyeOIwN80UaC+f5pbdRYJ+j66MMRGUiy6anYC0CEIPkx5FW8dwdYI4IfAgLAtIbCJPwzRCmls9\nEj0PXkCYCNIwIg7CqyauZi0cDU0ZGb9pqGFPpcQQmbY9Hh4QmU8jMwdljKcs70RmsiNWly/jIDGL\neWKg1W+yZ1+Vpw8tMb1rP3e+Yh+mqfHg55/h7lfcwOp6h81TG7TaLUZnZ9la6hL1fXobF0nSlJKl\nYQCOaqD8IhoQJhp2rsbiqRN8732vJAyirG0k5ZBBpWW5IOKF+JiYnUyMYStt+LpkRzWTzXZ0lc1A\nlOJKfrhOpqL6zpCn78SL/O88F46uMPKKOc498DD3/eyPsH66RVQwCcdyLP7LAxRunGf9y6fZ86q7\nCCwP3w+49Mwa1+3bz3OHHmXP5Cyf+8IRLjRP8OBXW6yqPr/+6mtwOpKHNkO6KqQibE5uNCk6Di1v\nwOmtHrWcRVf1cS0IrQKebHHXvKDpScZyIVNFgx4uG6s9lJZwrV5nO+4RmzGXe4p2HHONa7IgoNtN\nccfqWKnHqZbL3iDhcD/kBkMx57jUr82zvNWESKOYg7e/9aX81qcepJbTaPUEYRiBMEilyMyjukt+\nRGLmcugz46i56xgdmaBYTmg4FXorK1e15pBVjhZZkIqSQ23Ed5g41RA1E0QpeWGgpwbm0PNTK5eR\naUqcxuR1A6EUSZpiWjpBmEIqsQsuYRhg2xbr/T61eo1CYYTzlxdZmF/gwtIlDtxwgP/42x/loa88\nwE23vpRCcFQpAAAgAElEQVRb9+2jXjCQseT8N7cwgJqREiQxjmWSpAlhFGIYFmmcMjU3xeqlS1iO\nhW5BfXwKkYAxMsXc/A10zTwTe/dC5GOKJ+h1PYLEp7l+noIjUJEYxjcrTKHhyRRHEyQqY3OhZRXH\nMCGb5MUuubWSGKG5rKw0KFcKNPwB+/eNYAwiIik5t96hUqvR2lzD1wR+PyTvm5xd6vDS/eM8fWGb\negnG8zZev0Slmife1jDrU3ziv32Jy4vrrLS22Tuzm8e/9TVG5+bJGwVuvnaGjeaACc2nWK/x2Km/\noj5Sw3BsDn3rCV5176t474/cS96y+PCHPozXbPHwVx/hN//9+/jGQ//KK/aOceLIE3xmscGt104y\nmi5SmaswWlGsbTXZaqakmqBatCjZRTzPI4oVhpPjc18+ipQ61+3exVK3wXLDI/C2qeQr2GaO8eky\nmpXjXx5ep1x0mZmdItEL4NgorYgnLD54FWuukWRoCeRw81SkUsMa4uIVAl0kWTNIShAauqYN8zQS\nlDDYiUF66vAhYgw0U0Esef6ps9zxqpuZna8jEo1YE0S9hGTQwR0ps79a5oZbdvHgf/sXDt53D4+e\nPUuSJKTRSSZn97CyvEgce5j5WeIozvDzWCAkW2ubfPLP/4Z3vfuHrpj4dC27SWbKDzl0hJvoIs56\n18JAiSzMyshwvZnZj2EDSrwwu8hGGllmh0If/vxwbHiVwML8BGwdX2f6tjv44ie+yORojnsOHiSe\nu4bzd7yUVqwxojV44oH7aW+s8rYPvZ+1L5/kwrFzBKbHSquHFkAifVSliOqnfPrwgEEQsxYOqORy\nxAbYKseAmKLQkWFEYBgIEZMzcqy1txgvuJimzTvGDRYTnY0O6G6Xe3f3eWTJJBJrzOcq3GiU8ISN\nra1SK/q0c3VK+Rq1nMbeG25DP7bJQAyohMc57Rv0VJ/2U5KJlwsWDr6M/fvuxJDL/NTN2/zm408i\nRUQYSZTMgIH6hEvunuuQUyMUJndj520kCYudJfon1+ivb2FXrj4jXCdrpwYK8ppOIFOErmVO9lRi\nkP2d7RYZnz4AdoKpivQby2j1MRSKqZl5PN/HDzxGRjPyruNY2O4YSdBnfGKCQbfL+PgI6+uS1tYy\n9XqV08+fYGJ2hqWli9xx5x3s37OPz/3jP/KJz/wlK1sbmKHGPbfeihHMsXLxGGL1AnGUEochtdE6\nwcBju9FhafEspVodq9+ks9GkcGuFf/r87yNqt3Dbq3+KH3/z9/PjP3M3C9Pj/P7vvYNH7z+D5do0\nG+eBEaRaReggJCQa2FKgxVl72lbZ8FsTIvM9CTFM9fy3nxfFoVGcyHPjbJ3DZza4dkxn4IywuTkg\nl8uhuYLpfJmVrTZd1yYeBOimQd8PmJ/Os7S+zEzVxohhql5kbXObkdFR3njnQR48m/LWl89x+rpx\npnTBlw5d5C8++lN84+gl7v7eN/Pe9/4cN92yh/rBPZxYi5iYnKZQLbB45Bn210vceO0M//w/nmZz\nbRXh5LCLZXq9Nq962Uu477WvJ4ha/MDbfwm9UOPQhk19YgphaAQbLm75djSzQtzYRE9q+M1tnHyO\nMA3w+wHWwTk0y2LTj2Guhu+soEyLnpujkUpadZdwELDw6hspVmoEvZC0YGF2+0hTp2j87/sFgKHe\nVA4riSzlTmnpEFwmYMirUVdiVzNQR9ae0pFqyGISCm3QJ/R75KWNNDLcxrnnz9PZDijdPUIxbxFp\nKa99+2tRKTz3zAVecscCe647yIXjp0hVitIF3XaNO++7Das2AalC1006nW3cQhnDtfC7bdIYLpw+\nh6ln5bQmsxTHLJUvzUzgApSMMkCEyAbh2o7ZREmUPvRyyJ3ZdpZ5kuWmD02PEoSWDmcfQ0XVVcKn\nxuYOIJ48g36wwtxsHlms83ijwUJ8jlh2mbxhLye/cJapO/eRr7isffkJdk+kbKSS/dYMY7M99ho6\nj61JfvZVe3nszGkefGYTQ08wU51B32M9NUHXMBT4SVYZRXFE3rWYMRSRpjMmUoqJ4umgze76OEHc\nZj2K2NgcJR97pIbBatClWoDdRpVRp8iT3YCx6QVCbKJY0B208Fo+x1vn0HIGd+sC5ips9Ef4gbf+\nDEmyguhepm8HlG/exQ+sPs9jLcGlVoBpWNhlC246gD1eRMu7mN0Ghb37OPbFL6JKJUaSHLVRl07S\nu6o1h6wFkwowDR0ZSkxdy27SKmtnkoAKwZ68DWGE5OwKUiaMjc0TK4WmJP3ugEIpj+M4JGmCZbsI\no4AmJLlSkVa7RRgGGFaOyalpDMvAMCzGpifptNrk3DzHjzzHdTcc5H0f/Dne/qM/TOSFaJpJHASc\nOv4czz76BfxeShp3iCOBTAWBH9Jud5iencMLAvKFErWJcYSl0fL7jFRnefjrn8IkYHuzQ61UZffe\na8jbmxTtPINBiIjXslNTpSihMGTWmgI9Qwlpw8uUzJh3Ks2EL/+r50VxaPze+97Ipz/3OIZr8ONv\nvo//7y+/RNnKsX+qzGpngEg0JsdH2TXq4vV8tj0P2ylQcCUOeZ7fbvOB97wMRzMo2ALLAW+wgm3v\no+oW2b7wHHZ1Btut8ZWj66z3Eu4+f5pffs/t/NfPHuUVN11HE8mps6c4sGuOesHk5IVtsAWt3iaP\nfu0feO1rfpyB1+ZTf/b/sry9wd7SFG//7YdYePlraIQahrLwkxhDKYRtMfBCdKuBTkrUaxLpCSKJ\n0F0HC7DcAlGvhx8qahWNoJBHs7UMc6Eb6BE46DQHPYLBAMNyMZSgGw6wPJvB/4m41+HGL4aKIWPH\n1Ca/40Y9HDirYU57Nkx+QUEFsHn2aXTHobV6iPLULURKp7HeQwidJx45imlppBpZRroSyMGA9fPH\nQQnk5ipSBmh6TKoUIxNTnDx+HE0XJFGKiYkms2TEYqGClCH9foOt7QYj1SpKU1fSAzWRzWOk2pEG\nyyGMMMt93SHaCrmTNghS7pgD1bB18YIJ8AoIcXg4iqvsCfYuLlK4fhS13OXU8TVe9+F7OfXYIZZO\nLrPVXGH1/kcYv/UWdiUxm5rDhbNneMf3vJF//uuLrIXbXLi4zsJt13Ft0UVYk5xYOUK73UPXTRKZ\nYBoGUZRiGALbtIi0rDMtFFRMl0AT5AwJusCKTZKozzFvhYKr00wEUobkcjok0BM2C4mDbRkshjpl\n4dJoRhRminTNPlrfYHwqRNfrdL0uS6nPHlmjVplgfeU8c+MzeKUWG0dPYGgBiQ1jqcS9aw/cfQN2\nZOIUy/S9AcVKme2tFq0LDYrLFlF7i9VeSG5PBdu1r2rNAaQGIpQoV2SfZZFhcDQJqIx2qw/jjG2r\nSK5cZ9BuUKkWUCpB1y1008C0bKIoQekx+VwelXj4Uqdol8lZLpEfZD4izaTf7ZOmHSrVGiA4c/Ik\nOddhYWEBUdGplmsETkwYRHT6AWY+x02vej3fuP8hfH9AoVTED0KanRYzs9NcvnwBVxigRZRLVTSZ\nkrPzNJIucc+n194kDX1ilQ2ywzChUCyw57pbOfnoMxhG1k0wr+QjiywbXdfQY0miSSIFhi7QlSJ6\nsbenPvX5b0ES8YOvPsjx88sUaxX8fpszGz6Xu4Jeb0CxPMmJRkxlapYg8dHjGigLYdvY4w6/87iJ\nrqf0uwLbyWOhM1fUKVfKPHemy9Y8rEUGsjiDDAb8wdeWuWnc4id/6JU8d/ZZCqUF4lRy8JoFHvzi\nIr6E9dUVvn3oORZmFvjUp/4DTz9+nE9+6q/4w9/9DfxQ8pWP/SjfOHSOP/vGMrpmYBmKIFSkaUgc\nRhRLZWI/QIqM7dTpNTAVKNNAkzEqidlub2BoAr/bQmnZJmfqLl1TYth5CuUKSOi3W5i5MRzLRiOm\ntb5+dYuuhrhx7QVp6s6fhcwkpvowCV3JF1ozcihpZccXoRS79xewrVGEM87S+gqJctCtPFImOJZN\nGgpUCv32NknaRdc1ykWHnJtjM2ghtQFaHHH9a36QqflpLMvCtDRQMYkGQsWkXp9ibZSgEaEZJf7T\nL/0YH/vUf/8OxAmQZtJaQ2ioNMQQBlIfHhpKu+Ik1644yxlmbgyluZoYKsd24ocVupYN/Rm28K7q\nKdepaymNuXHufsfb2X7mMusXN9BEn9d84L0sP38IbWwv5z79Dwz2zjA3W6Wg5XndvW/i2W89yh/+\nzE/glnQ+8Ft/yi/9+WFAYRoWc6NVNro9ojgiFRoYKa3YY8TMNtwoSdnwPHa7BTwiVOBySe+xYOcY\nLbq0/JBpPeVUqGj4PQIl2VVymKxptMMORatCWiszOZInTi8RtDTODfr0B6ukvYiolCMamBxb6RK4\n53h9sI+ly8dZWTpL1O6TmjbdfJ6L8yGOm6OiDJLNBvrMPtzJGK0Tk5+fQkQp+994N+tbHndNVtie\ndKj8H1HcKpSdZars5MSbiSDWyXJXEPR9F91yaTe3GZucxM7ZKF2jkCujYSKzEwbLMbAdi163i1so\nUcrn6HYamJpFrlDC9/p0u11K5Qpry8vMzM6jo9h77QGWTp/i4a9+hamJaXbt2sXM7BRp5DM2WmBk\n9HZ6zQ5tP+Hpx59mbs889VoNr+vTbJ9lpFRldXWN5YtneOWrXoXvd/CBaqGMn4RD9LlkLGdRGp+m\nsXoJN19i90tezvHH/548PVLEEN6YkIqMZq1QCF2RINB1SBKJbjAUmvzbz4vi0Dgbz9NLbb7+ONTH\niuRr17Es1igVKrizAjMIMfI5yqGiJWLckoZt63hJTMHKkfgDohSMXB5dj5FpSuDaeP46RfsmVjsh\npTTEtAxITaK8g3AFE6MxobAYKRUZLef5NhpHz67R9wZASqLl8AY9/KDBh3/uv3DPG97EuOqw3tvi\ntTffit/u8srbDvC+3/5HpifHkZaDMAR6DHHUIw180iim44cUR8rkrRx+EpM01knyJaIooOrWERY4\n5TJlt0g/8DBydYphj8iUpJtdrJEq9YV5etsNauMzGLaGbuWvas3VC2cFkN1Gd5zWauj4Bl5gMu38\n3Hf8fqbQzf6hVJeYus7CRMriYoMkHmC4JdJYx++sZTf/OMSyTKJBh153g55M0TTJwnX7SPyIgdfD\nETZ+v4Op6Zw49CCV6T3UJhcouzlkGuEULKK+h1Y/gIaGTkKq9Cs4CF1IJJI4TTHNYZ78d7xvTVNI\nmbJj9lPD15TUrxj4NCWRQqCLFISOPjxU5FUmatxy/T6OnTrNVL3K2aMnWXvyKPN37INSjqc+/68Y\n87OUjh6ndfuNhEeP0tzc5tTsAW4/eC0/8c6biAOfwZlF9lbGOWSsktN0RF7g2iaSzLBmWxppkiCS\nhIJlX6mwEIpWHGKlOqYZ0UtirOKA9iCik5pYmk4zHpAKgR3rTFtwph2iU2IrEjBosqCX8KKEyNCx\nu1tYgYGu5YiSHu1Yp9eJscoDOtsdirrGpt8m7YZopmSuVORbZxKMgk9v+RKl8Tm2VRsj1lFpiGoJ\nrHoFu6oYX5jHO7NN48tH0fctXNWaw9CYKXSEzKSnQmZYfFNCJDMFnukYmXBCSwi9Poal43sDdMBx\noJCvEschhg6GZpC6gnDQRyuXqdRGaTe20QVYtkmxUMTrdyjmciydX2T37n00t7cZGR/H0iZ4+sgz\nFIp51taWue6GG+n1B5hmSq6YY37vNXTWNwiDCN+LQFPoGOiGgZKCgwdewuryKrv27GarmTC/12HC\nmiVFIvtt4iBgvj5OKBKK5QrPHztKpb6XuPVMZsoVAi3Ty6N0iJREJxODREphk0E9je9SabwoMsKT\niQPkJ+bZdeP1VCf3oDkWC7t24Tg2fVJSYSBNE0+FGEqiFW0anR5anKIKJqJcYm5hGsd20VyTSn0M\nI4oo2QWKM2WSxMMXFQpOAeXYSCm4ve7y8T9/iD/6g3+i25IsLXq8/62vY75sMDo3T6oks5O7Eali\npL6bQ88/yv/17tt43Vtez4E9NyEV+CpAJDHHPvthrHqBkakZ0l6IRGMQ9Glv9YlSSXGkCF6AEXnc\nOllGJH3W1lq45Qp0t4jbXWwzTz8OsCslDBXgxyFrF8/RjT0Ggz5aBEF7wPbaJS499xznD3/pqtZc\n7GACVDaUVEq+gAZPsxwJUpl5E6QaEjIzWaqWDkt7shQwTZjoqkTg9RCpZGIkhNYpwqVvEi4fxvCb\nyM5FjLBFun2KihUxGKxx3e0HmLp2FqFZFEcn6S2fZH3jEotnzzMIPA7c80bGJudRYUwoU3qbazTX\nVsmXq8go5oGvfHnI/ZcYUiJUilKQxgmWYSDQ2JnnaUqiK4lIs4pBV0PQoUyz11SKLiU62XxEH+Ki\nUZJUpEhtmFF+VWsec9vdNzGn5dm9d4obX3oHe687SM4z0AKbPbkxBhf77N69m9u/963cvCX51h/f\nz+apZ3n62SX+y298ga+e3uTmvSO8++4Fao7FS6t1gn6Pa2oj5HM2URiBElRdl47Us+G/pjHuGOia\nSaBpRKZOqjS2Y8VmpEjR2Igk+VRmMyHNIvA0RooaW5pGr9Om7fV4/MI5CnoFM/Q505Vc7gm8MCGQ\nMGLECEdxqbPFmVOH+fsHvkhvu89mbxu90CYv4Ja5CKcD6eUWl488R2/1LOZyB+/0MvQCcrpG3Nfo\nnT7GVlHi7tvFSqN1VWueLXx2PzJVNqtCy3JZlFDEStHyFeQPUDDyFEo1ut0muVwe17TwOk1cN08a\nh5DGWJaNH3po6OgGDPoeSRIThD4C8Ppd/MAnlZAfqWEYEA7a1KplhCbY2txgpFbBdYvM7bmG1vY2\nlWqNwPM59uwz9JtbHD92lChJOfzcMba2N1Eqot/ZJFdUKBFgmBpryytYbYWr6ZxprnLgnoNUr7uF\nsT3Xcu2N7+R1b3oHExPjXLhwkqVzJ0iT7O5noIiSHbGIwtBEZnAUmckvU5Upvkuh8eKoNCwlSaKY\nrQuX6Uc9CrkCSioSleBaZQqFPJdOP0V1dC9R0CNqNnByDqGE3olzVCZGObu6hm5YVEYmifsNomhA\nX4e4H/GB9x/k77+6hhe79LwmI5URXndQcvbaMc6dXead993GzdfdyMf+9O8Ym7X59R99OT/9u5e5\nbf8I8/N7aLU2EEIwt/cW5nYdZOBtsXFhi3x9ClNz0aIIpdk0NtZIZYfdEyWMyhSnN3tEXh/TKOFt\nDQgqZZ6+sEkw0Mm7JuHGJfRanjhU+J018vUJOqcXEcU8wnWYn7kGYeVoNFeJdHAqeUzDRh938DYu\nXdWaCwVCplleNilSM1AyzWSrSiI0/YrxR5ION9BhG0cM2U5kWRqXL/no4sJQcSUwiyX2veRawKDb\n2yBOEnLuLJoQWLk95AzJdDhPb9Al8LrkcmXCQZcoFpxdOk8lZ7GydBHbMAmtlDQJM5mj41IYmaTT\nWCeXL/Kthx7Bsky+995XDDcGhVCCo6cXufGaGUzTRt95P+xUTpmHA+QwT2NnUiOvzHbUlZm5uIJb\nyfLCr05ye+LhI8zvm+LcagN/c4ObbruJpWfPMHPDDIML0Dx9Aa2k8euXBxRsk8+OVOiPlrnvB97A\n3/7Zx7kQt+md7vDnX3yEQt7BkDZPbTaJopAVL0IAedtBF4q6Y9AKs5lGnCasdyOmx8qZG7+bUKnY\nnB/o7MrneSro0ujEWLrGvnqFCUNhSFjpFvFkl0HqE0cp+RGbM/0uZQJEqiEKAb5QBKHLmW6TzV42\nS2lsNblhtsTqRkjqpFzaHGDnquSSKgE+Sa7AqEoIfPALEeLgCJZR5FJjg/JIHT0tYI1WSSoBUxNX\nf6+VKlPYJUIhLA1NKsLhZ9nRNAJpk1hV4u4GbsdhdmEfYZRQH5+iP+jQ7/fJFQukYULf97A0A9dy\niHSB7VikQUwxVySWMYVijfX1iwgM+q029dokmi5YW1tFF7Brz24c16VQLmCaBZI4otXYotFoMD2/\nQOh79D2fpcVFbDtHt9MhSSPq+QLjkzNsbm4g0w5nLl6imviIcMCb9l3PNW8ps2/XHsZzGtfUHQ68\n9ABJGnPx+W+QtyJMMzPpSgSukX2jkZJ0KEtPDB0rUSQqxRA6yXcJv3pRHBqB55PoCklEzjTx+y0s\n18Ex8gR+iyT1KOQms1aDmUNZPkrqpNLHdHVEmqCjCActWvEATQk0C/qDPm1PMm3MIP3TaJUS9aKN\nYcC/PvAE7UurFA2DT/7TQ/zInYJcziKNS2wuHePa0SojoyXufvnLScJtDM1GM0wsK49pzpPqkCYp\nSbzNkWe+jd7ZJI/AKI8TxibrvkDDwx2dJona1ObmCLoddMtm4GhYdNCLEyjDoVCx6bQ3MWUKU+N4\nzSauFeOFCuIExzDpLp2hNDNP2uuBSKlfe8tVrbmpMkAbYuj7lslw+Jtkcw6RDHMgtSFCZLhhDv0O\nUqVDOqhEpSnCVGiGRegNCFsNRqZ2EQU9RGpRzFcwDR00ncgfYLkug04Ht1yh12qi6YI0SXCcIgMv\nwCrk6HT6JKmCNEXTdKIUbMPM3KtIDNMgCHye/MZjvO7el6EpbdiHklQcN8PQk0VmakLPNn2lkCKL\n+FRkY47MwzH8ggwPiyt+DqGG7zyLfNWuEhI5t3s3p86fxrUt9APzLG0sMjk2j6PyzN9xEyOGw/v7\nAX/0u5+gub3JLbt2cfu9B9hebzA9VqbrxbT76wShxDFS9o2V8bYCer4iTEIKxRwFRxGmEssWdAfR\nMB8986Fc7Peo5B1sBzbihCRIKTkpUT/bIpqez4blsE3ImF4g0do0hSKKIUlS6vUShdTH9BwsPWQU\nk4iAJEmw0jIiaWNZKe0oQjM1dMeFpsQcSYnIk7fb6CgSIyLUctiNhGiwTMGbZTu8zFavTy9Zo7hX\no7gBI9UpHNyrWnNgyClLMYYJdYgMyKej4SUKN18mcvJIw0ETFlLTGQx66KTYhTLImO7WJsVqlVyu\ngq4JgsQHZdDv97GsoTFOCpIkopDP0x+EFEtVOp1tTNNmemaatdVV4jTG3/Zx7AKGCChVR+i2tmk1\nGxRr42xvbCA0jSi1qFgCP0npttoU9SLNxiaFQpmNjU1Gq2XmXnEfDx0+RDte5KW33kjUWOSH/90f\n8t7Xvon6yBiGbuEaJoYuSKREF4pICUxNZfEECKQOIhUoJYmUxJEQ69/9c/6iODT8KETTdQrVEcJU\nQ497WOQIky5JGmHioDRBkiYYlknsmQySATnXImiv0YgGWIZB0N3CntqLDH3cKMYPA17xtp/mtpfc\nyOKJM5iaTr5U4qY77ubTX30WC8n//VOv5NnTfd70jtex/D8GzM7VmTk4z5G//DV+6ud/h7X1Jj/6\n9rv544//NWdOrXLmzBKWbbJrZpIT584D2QU8l3e595X3cONLX83fPvAkUyN1/vwX3wJ+n37U4M7b\nX41hGdz2nv8MVp6p6THajRDf79PrtnAcl8iPMGJFkiRoqkIKeJurlEdGwbAImtuUpmcJVi4j9KtD\nRidkJaqQYmjsg0RmoSzacFONIWtfaVk7S2o7UlWZpeupzPy362Xv5cwz32Tm+pdz+dwS1ZEJvPZz\n6KaB62q4bp7ttQtY5ToT9TE2ty5jOg5er42jG1lLTCpWLy0SpJJ9u/dw5uQJAGQaEXoBpqETKxth\npJRLVUzLBKWI/QDTtpFBNKx9oFqysJRkx8klSYcHghrObjK/iVCKVGVfmAyVMhz0QyYsVlcExtmi\nXeUcvL+rCCf7vOenf5lvHvoal5XJIJfgPb9Ic7uBMVZipT/gnw4dZsLKsdps87F3/gTzd96ONiVx\nn17l8//8KUzLYKvVodntEKc6mqYYL5WpFQS+B2XXodePmCjmEIZFozPANix020BLXcJgE7c6RqkS\ncbQfMGo7dDsdam6RVuiREzENx6DZBeFqBL6PaVkkSnBqs0NZd5kzLJJch3aacLyn8GUOy9DJFzQa\nsUG+mSDbbbq1BoNBnpXuGSxhIGYsqknAloK5cp9qYQprzsGICsyP7yffEKyRIAcduoNVNtcuw7uv\nbt3FUP0XahmIT0NgiUxybeRqeEkNTctEA3HsYWoahVyeKIqwooAolmi6jmWamJpBmPQwDIck9nBL\n49hWVs16Xg87X8DUDPqdZZqbq1RH64yOzdDttpmdmqTTG3Dg4PVsb63Q9TXam9uMjo4zv2cBHYu4\n3+Jtb3srre6A5dUVBq117FwNXyhEmLLWOE/JddGETc/3+dCHPsxzT36Nb3z7DF9/5Cv87I/8JL/w\nkV+hmC9y4vhxPG+bqqGQGaoKO1KEZAY+tGEapYAkldnljYxHl36XVuyL4tBwCgaJl7C2eAwzX6Vc\nrdLpXsLKFbDNHIkGaAZRv4uwbXSRUKmP0tleRcvVscsVauUqmlUi9z+Ze68wy67yXPcdM8+VU+Xq\nqq7OUiugCBISAkQUSBbJBgzGJCeMNxsHsPc5xmz87OPH9nbCx9sH2xjbm2CSQYDJIAlJCCGp1VLn\nru7qrhxWrRxmGmOci1mSfeHtfR73udC4q4u6WP+qmv+Y//997+dnSFyL/dk+NdvmdS/+RfKlDG+1\n7iQOIrSUDKKE337jh7j5Nb/D7T/z0/zDz/5fvOV3/4S7XvUC7nj1S8jWxtlc+QmioE8QDfjqVx7m\ny1++H8uyEaZEY/GVz/3f7HnOK1FKkcvlGRmf4Py5JQbFdfysy9BSXLZ3FuEY2AJOLxxn3+7LMGWH\nmy7bz1tv8Dj43LuxzB7Pf9dHwZAM+12kTijlxwmjHlJrnJxP2GljlQoYhkm/2SRBkbXyl1RzLTVS\nJ1jCeEZWKnTK6JGk+PMUVSOQMv3ZkAot9M7NVaXuaQEz44Lm5CTj1QKrS4IbbtnNsScTzh09gjk4\niW1bmLZLrt/CiptEgUCIPqZpY5RL2BjMn3sCwzDptVc4eOtPcOyJYyRuGZ1oLN8nCQNsW2FqjTRN\nNtfW8f0MpjD5b3/wp3zwl38BpRMMLM6vbFHM57FMMHWqelGaf0Wv1U9bNtLPlRpSUE8b+XbeWHYc\nLIChgZUAACAASURBVDvj33+VHf4fPFunt9hz4EbOrJxh+3SLq24/gD3ocXa1TtRYo2/0ef0HPkoS\nm9jFLOXJaT75qY/zd1/4PP/ldz9IJnyAiuuhoojxaoUwSsi5sMctcHRrhd1ihI4/4EyvR8XMcqUb\nsdSXFEyXTNZntdNEFgykbVAbtugqhYpNlkUfTEE/CCj4Jo5pUytoVvoRUSdOES0qYbtbpz/s42eh\n7AyJTJt25BKGXbYHm5iOgZX4FKwQhcV81EVulYjcPJvGNn7sURjbTcdyOFCeInT7LLcFxmafYrmA\n6MQcX7vAdG2KeMxjdmqO3uGZS6o5O9+zApyUj0Ms9TO5GomzC6u4jyAR5LwMOdtnY3UFxsewLY9E\nabJZD8uykZgMgw6Ol8U0DTwrTxwNiIKIYRhQrlRIujGRVji+g1IGhumwUd/At01sL0dGatrtFkko\nGZ8cI5/JkcsXkFGEkgl79u0iiWNkEqVqS8/iB48+zmX7JyAOKZVrrPW3qZRGQduEQcALX/WTvOQu\nh17v14iiIUkcsHhhjfkTR5CDBmZJY6SzWEJb44qUeitJfU5CpHQIjUaZglj9+0TnZ0XT6K1t0xt2\nKNZmyZVL9Ot1tAlOkmD4VTr9DYrlcRIETrlKL9wkV62ytXCEbGGSYH2R9foWSgRoVcbyslxx3WXc\ndmgUYRrkCgVkEoOpcc0MwhCMeO30ATlscc8X/wbbsTEtCxCoJGC4AwS0LY+zFx9FKclLXno73/vu\nQxiGoDixiyuvvIrNzS22tjZZWbqAIeA973kby8EhljaadM2Aj//ZX3F8fo2ZyRpvfK3kgz/zWq4+\n4FIbm2Vz6zwf+vN/xvSyDAcNCtNT2FEWZSYEjT6unUHJGKdSJYeg71oMej1ytk+rvnxJNZc6wUz1\npik+YGcfoZ6GFWpQIvVsGIbxzOIsvYMbO5S3dPbRr29jC58nThxj36G9YPocumqG6clJjtyzhJaK\n2CnR5ADNC4/gCQvbkQhhkKwnxInEkAntdhPdzjHsJbTadUpjBbTjpLkGSqJVwqDfxzRNeu0+KuoT\nhglrK+fQ+t3pI14opkeLKCHSRb6xQ6jF+BfZ7DO7jPQzaJ22EaF31DU6NTU+7d9IU6r+JWP8P3pu\nesW1ZCJNc7BF8bDN/KOnMEqj1C67kuFGi1Mf/TiZQpHtzW02ggEWK3idXeh+jw+/50M89OiPmA86\nvOuWvTww32OsoDnTizjV6eBlqqwzxDSyjBcGLPQGXJmzmAzy1MYNTm70ULFmvdHEdmwmJrJ02yGO\nk9BuBWT9DEGSsCtjkLMMNgcKU0k810pZRLEk7CmKhSwGCZbh0moIGmaPtjIwTBcZaaQ1ZKAsXGWw\nRpTCN5tg7a/Q2uWyp1DETXwaVsIgirlyepyLxIjtkKFfYO6GqxhGJu52SL27Sj8w4VWXVHZskeZ8\np7eB9CJgmCZxotGJJLFz2CpV3YU6YbJcIegOiJ0E0zTZbDco5EsUShZhFGA6NnEUEWJgGAaGEBSK\nRZCafD6fyr5tl431Zfr9LrO79zCMIwbDPraXIYxCquNjBGGIa9n0Om3GJicJw5C4FzMcDtBCYBsW\nR448xj2fu4eTc/uYmKgxs3sXUxMjBFurjGYKZHIFBu0mpcoY7VaT9dVllIpZXKyzurxIrAWRNPAs\niVIaTwsSA2LTwJHphUjuoHbUzj4vBX4+yyW3djHH7J45WmtLdC5uYI9Oons9EtMiCJvEvSGx3cLN\nuYTdDbLFUbbmT2ALh6DdYWLPXtrdOs3lJgbQ21hkZbWIOljBwuM/v/cjXNNZoX34Bmx7iQ/86m9j\nJor/+tHfp4/LpJ2mXguRji7gaZleWrivfPlRDMPgm9/4LqVClvGxKv3tdZ588iiWZeE4No7nIrTg\nNXfdjistNH1+/OiD/PwvvJtEgtYRiYLKtM3m4jLb611G3DHONrO4ok1t/7XEQY9Yd6hMHSRMYoSM\nIEzorC3SNy2KtXHCrTUcN0c0vESnrFJpOpeWzzCbJGAIEy1lOjIyQGmFUimE3FAGylCIHXc4pMvn\njXP3YIgxRkWV00957Dm4mygQnDu9STdzG4VKzIte8jyE7eJ6d3D/Nx5izGsRJRGGhDjoog2TfdUR\nuhv3MnNghm98dRVHreHnD+AUS/gjI3S7XZABcRAzOTFBYiSY3R653CiN7pBq3uPiRpc4VoyQpOFM\nYscxvhOulH63qRFwx6oIO27vtCXInUW4kTZN2LGYX9oSHODkwz9gev/1JItdOj2PW++6jXPzT7Gy\ntMbYC29g7sZr+Ps7fo3ZWVhe68NxyO2tMzLicuVNt/L9c2f5iUKZbx/dZLnTYvzQNOsL21QKWYoZ\nzfHlDnm/z65KnrJlM+gbDKKAJ9Zigm6EMAwM08IQJvVGTD0McB0LJQTdfogwBHvKecpOliebfWw/\noR9ptCEwTINYabxYUi26LEc22XwfM3bRckAQxumlwyvw2MUOWcslecGV6MkMs3aB3tQYbqtHM1dC\nR108zyFf2EtbeZT7PQoHRwmWlklGTEa8Ct6VGbrH1hm7Ys8l130gFB6gRRoVbBrGznLcRGRmUVJj\nOz65XIFsNk8cRQjDxjIdwqBHxrbQwHDYJ58vYAkHbaY7NKljwjgmqA8pl2sM+gN81yGbL+B28iRh\nRHO7iZfNYNoeIyNVoighny8gkwTLtMgkMeFwiO2YFCfmCPQyZgDffuBHnHn8ONlCkVtmM5x46kcc\nXb3I0twsJcfntuddlRJ7Q83jjz3BwoXzrC4tMTM3Q6mY5cjyCkEsU9+R0kgTIpWaWy2x40tC4CiN\nYaVw2MBM/8+Rz/LxVK+5SXfxNDIRTMwepNNpYSeK7fZFqiOzVKbn6HW36bVamFFKnwxVmvJVyJdY\nW1pjbGIEuTvHsN+ktGuCZrdNzu3z1HyDX3zXG3HlNkKbGPoAw1YdOw9/9vsf5e8+/v6UXKf+NcM0\nDePB0GglWFpZRWsFmEgJf/Fnv8Vdb3kfjutx+80385a33cEvve8jnDj6XebnF3jpK99Ec+MJbr3t\nlVhxQqPbYZho1lY3aK5sccMLb+XYuSP8zB89gLBtTLNIv7NBWN8mVxlh4cH7KE1NYDk+Tm6EpDqG\nDAfYno/j+0CEZzuXVHMtFU9HmsY6XYIbQiBJUDpVE6UtQadzT3ZSKRSkKPL0d5WGM/NdXFuj9SqW\neYy1lUPM7ZthrX6Gu996K//8mQf5xlfPcOdrruSe//ktXNdFGgOEiuh2YoQRY9s+cRDiJoLNzpBw\nMCR0biCXq9DdXiKWBpbpY1omG9sraMfFdx38YoF4sMX/+etv5y//+nNoNLtGiilixDSQidpxjCc7\n+Us6bQhPo89FuiQVGDtvH5oUpm3ydLKT1jIN8LrEnca0M0PeyVHfV8U61SSrCrTCPmYzoV/UNBsN\nbv/LDzG9v8TF8wsc/fQ3eOxr98E5zZFTTbRUXDBtIt8hbsBTF5rYrkPO0nQGPSZqBdRQEGlFa9Dn\nu11NFMboHXl1LuMjpSaIQ5aGQ0zTZhgOGS3l2e4MKGV9zjcUVUdxodHHMROSHXlmFEmiqIvO2Izk\nsygr4cy2Rzfq02gNMCyBWSxg33QlE1MFEjeHHUeQM3Gzo2i/Rpjp42pNwagROSbKyeFm+9h6jqiz\nBrOTXF6bYiluEjZiEtOnXe9cWtFJOUspOcBMLxDAUClw9qK9ETzDI+dniVWAYRYpVUfothr0B20M\nJFa+jGMa2IZDHCcgumAIfMfDMlxyfgHbyyCjPtKSYNjkixVqYUySJAx7XYzIJE4ihsMQ2xDpCEom\nmIaJVBrbEsgEoliDlafZWOPb37yX5+wf5+7dWfZds5cX33AlwcopNnsx51ZX+MevbXHTi+/A8308\n36a+fpHLr7iafqfB9sZFJkZybPoCw+SZ2GJjB8RoIJBKY2rN0NwRh9hAnApA0lSyf/s8K5pGZdd+\numsruKbGLpaJLqxQGZ/CjSr0Og2SOCaTySJMF9vyCaIQQ5m4UmNnMuQ19GWIKyVOtoDsN9FhjXav\nyNSYYjRvoxnFdxwsy0aZBnlP0NzeQuxQViQxWpv/EsYDOEKQ2Baz06OcX1oijgRXXXE5hw9cya+9\n7eXcfMt/xfTy/ODxH7O2eJSg12Z2epqxQpZOo4uUEKuYVm+AqWz279tHd3KMs6cfY3V5E+GW8a0M\nnaVTFHftJ+qHFIs1YgTVyV302y3CcEDQaRAN+wyDGDTEKDKliUuquVbpgvhpSgYaEp3szP0tNDFC\nW2hSo5tm5y3kacSGIXfUVZDEMa4do4WJjGMeufcxzp9cx7MF93//JFdce4B8weab//wYSI+rr9/D\n2rFVbMdDZLIIM8FKAizDJnIEMpForbAtkJhoy0MmA+JBG69YQisT3/WIk5is45BkxrA6XT7xxU9z\n90t+Iv3HSGQ6VjN2Fvf6GVvizudWz3zwFIOuMGQqP4w1CLEjwRV6Z68jucSVBkFWsr2xwolvP4i9\nt8z05jQnvneUO97wRpY3zrG5vMXz3vYqHvvOD3EiyV0ffg9/+Z0fIkiot7YIhhFBlEU4WUAQm5qr\nJqa4UG8wamd4YqPOeC5LuydJQlDaBPUvkOtBP8b3LXzfIwkjHMei25MMhwlKQzbjcOVUgUcXW6gk\nojGIUVqgkii9oVsWjmWz2OyxJ+ex2WnTH4TkRrIUnrcHf3qMnDdFkJM4hkN5fAylckQZHzfs4eV8\n7GwGpQ0IErycpizHwOkwGJ9ECI+FlUWyTo3YTqhM2LS6g0srOjtEYyUwLQiVwtZg2Q4DI4vn5Ymi\niMR18XGJ45AgGOBmcvhoBoMe/V4fJRICO6BSqdLvDijkChhCYNs2waBHohTDQZdisYTQiv6gQ6lQ\nIDYNhuEQJQVRt0uU8ZEGlIwSpp2+9YXDAZZlk8tXiKXCNm1yuSxRv88Pf/QEL3vFboKNeXKlQ5RG\nRsgUXWpVg2995lEKpSJxEGG7JrXaGNWaTxxaDIc+wjKo5gwyAkKhMZVGGQLHMFIhzI4E15KpyMXC\nTJurluhnuyNcDiNKI2No02EQBpSn9pAocAs2sXLwfEiSGEMpejLGsx2yeReEQZAkdNp1asUa0jEJ\nWk2ibp0zzQsU3/oK8l6MqQ2cUoVwmGBKhRxE/Kf3v58b942xeew4Vd+mmC/QavR44B//grve95eU\nqyXa7VWSuMeXPvNhqmOH6bT7VAp52sGAu37yHSSJQqJ4+S0vo99tEYdDut0GDz34TSwvh5vLcubE\nQ2yutrjxmhv47d/7Pb7+5R/wd3/1mwyEQ7O5Qda0CYVB7+JFlOxxfikg5/nMP/x9xg9cS3vzHJZb\nIl8aQ3Y6GLaFEXXJjVQvqeaxjBCGQmrS3AwUBkaa3aVCDMtE6ygdW0mBMAykVjszT41WVvruoTXa\n1MRRCEJgmjb5/r2wvYeZg8/n8iv28NQPv8mZkxPc+frn8/lP3stjD58jDGd4w9tuZ9hewcxVUEHI\n8R/dR7E2y+rCKVzbpd1usnLhPH4mQ6HgYloaUyrK42MMEk0yDImjCNt2yFVG+fE3v8ZcZQ8HD86S\nc12kYaCSdKln7kR7PhPY9K+OLTSRViRRguv7KbL76Wzxf9UpLnWn4VTHiet1rr3tCpbnz9K1mhy8\n7WayYyUy7SwH77yJ1kqf6154A4N6THt9lfc+9CXWVlbZ3Frn8T/8BFGzy9joLl7xR/8Hg5VtFj/5\nZe66fJJvrpzEMgWN4YAwjNJFre+ihsaOARKUThgEkvFclvpAYg0NHN9gEMf4rklfxtx3bhvtxNiu\nRa7gAhprdBKnkEcUmijKeOUM3dooczIHbkTimYzmpjBzFlK55MOYtuiSmZkhrvfoqCHlqSrdzTp+\nsQiDCLOSxdrustXdorJnP5bVQQ00STZHo71NsNLBnRxhYuTS3qif/t4Mg2celBECnQjcYolYC3Kl\nMuFgSOLaTEzMEUZD8nmfCInqxURxQHerwVhtikG/QzZXIIxDok5EJpNNMTPREN8vMQgkhmng50vE\n2kb0uxjCQ5gGQRjwg3u/x113v4kvfe5zHDh0GceePModP/Ea1jfq1BJNpjRGsVTAvuwQP/MzbybY\naPA793wOoS/w8V8ZJVcYIzM6gbGd4RUHz5NzTFabPRw3g+ObHD/6GLXaNLYlETIGUk+KaaRKSa0U\nWoIwxU52zA5eRYOp07/7CHbUIf/2eVY0jSjoErWbaLtCr7NBpVSj3+kgZUx5YjdB0MPO5vGLFVwS\nwk6TGMWwuU02X2BqcorNtsTpLuLnRsmXxvizX7iOYdDCVCb4Ar+5iWE4SE/w9fuO8ocf+xhHTtzL\nc65+Mb3uANfIcPBlL+JT/+2X+cTvvY9zWwmHn38zl++Z4uCuGaQyyOcKhDrg3JkjhFMHOPrEKS47\ncBW12RLN7XWisE8tV8N2BUEc8Wu/9CE+/Q9fIF/Mcv7oD3jvu97ML779NRxf6PDHn3+coltiGPQo\njkyTdJpYFAjXT1O5+hakGZO0N+jWmxQmCriVKl4UEYZDdK+H0JdGua33EwwBUwWbtX5IFAuqWSed\n8CuBkgmmodDCIozjFGQmdli3IvV5QEyiDa697TXMH/kBg1aTRCvCMKC2SyBXHuD4xiNYpsNU9hSP\nfe0Md7/lZ6E35Ev/dD/BsI808rTXt3FMSNy9DM0Z8nkH17MoVQoMNhcYtgI6WwGzB68iClsMe12K\n4wfQOqGQr9DYroOW1Kp72Npa4PCVexnEEZ5pksQhju08owhLM0EMDJ1GxColiEhwsPHVACzYOLPI\n5MQckU2K8BYCtUPiuqSanzjOxR+d5o7/dCd2psz2kXOc+Nopir9RZd2Aq0cnOfrY46y1vNT06OYJ\n7R4661ApHuQ1H/tdTGFQLuc5efQ0bVtR+vU3cvR8m2uu/wVuiXt85T0fJlxcxnc9qlmDTWkRBGlz\nN2wT03MYFm0qt9+CO+5TdYoYu0qYsQ8yYt/YLMvdDbKuxLPzCGmwcXGZ7MYGi5kIRwvytTEsy2KY\nKKSh8YYxbq6IyFqodkCTAf7EHBfvewJ7rsruyTk6SZNSOUs/Cuhc6LD3ihxnbU1pbJTNYRN/OSQz\nYTAqfOySxSkd0VmaRw3GLqnmwI77G0xpYCBJ0GhvF0oJCpkcTjaL7WXIZHI0ttbwcrk0H14JcoUK\nUTBgOAg5f/YpqmOTeG6WkbFRXNujoxWu62M6GQaDFlobuNksaJd+p0kYhERhj3CQMHfVjczsvYwg\niHjNm96CUpLde/chTIc4aZNo0j2HbZGxLX711z7I5voGUa/F577/PTbOd8ha05iqzsmzT/H8d/w6\nrWadXNZnY20dx7WoVcap1oqEg20Wl44gTIVjGcRobKExRLqX1GqHWygVtgGINO71aTSQeLYDCw1D\nYJUnSbp1arUxdCLJVyuEUUQcxegkIWMKZBASrC+i8z4Z4ZLYCbbvs3LuBP1QUyvmQUj6rWUMfSNC\nCaShsBOHxNLYhkYnFg9/4/PkxRJf+NZR7rptlTvf+CaCrQaPfPPjFIsjPO/O1+L5BaSR4oKFEKmi\nKJY4SnPosusxhMW+yyZQRpu4bzM1Nonrl/nzv/pr/uSP/4bt+jYWJmeOPYztRjT6m+ltPRY8cmSJ\ngrZo1DcolkfYmD+JW8pRGalhO4fwqjnK8RT1xga7rrgVmdUYvQF+rkZp1GIYjNLf2ry0mieDHUCh\njSM1wUDS0wmWDcOhRCLZN5Vjvt5BhCbKCHBsmzBM8CyLIImxbRMtDCrVUQ7f/Cp+/I1PI5SkUB5H\nRxEJEqFTE5UhQRPz1U9/i/37dlG1FvjGFwNefvdLKI1UGTYlnfZpZi6bwbUkcRyza9/1LJ94DKVD\ntJEKDXrDIV6uSM6JaUcZut0upusyXNtgobXJxYWTzC9K3vG2F6fmPMsilqlhUcl0FGXIhJgU2GYI\nidQmphXTDwdMxB7H58+TqIRCvoZXKiCFgdbxv+OR/f92FhcDrn/vXRy7f5Eg3yfohFR2V9l8+CKF\nayZYeuIMuaGgPVxl16H9tFfaGO2YjOXSKxh0T18gzNicX+hSCDV7b97P9okWuVumSBqp1+euP/8D\nYMCnXvvzNPsWUZQ8E0mqZUryHXSGiCfn6Z8XbCmwq1P0yz57qiWWrRyNtXXU3C4meiZH1Ba1vfvp\nFMe43O6wPlmgmtjUF9cZKU6SK2r6wyGOtpAGbHk247rC5qnT7L35CjQRG3EfZ2vAKgHlycuw90rO\n9Xvoros7VWZURDTHNWpLc7y+gLbbzIQ1ztY3GXNLl1j1f8FiJLbGiFMOl87PkM/vJYglRqTQKqDd\nCyjlywjbgURjuzaDbkAQBCgRUShUaHdakE2fWY12h2K5jIojlGUhtSRfKBCFMYkZIyVoJJZtoYSB\nbTkgFdliDtO0MQ0Dt1ohGCbs2XMAYVsYGjrdPq5lMzE9hRCC2YOzvH7zMsqz0ww7LU5cXOOjX/wx\nX/y5/4JhWWidYNguEyNT9LtBCgp1fbqbS1T8dBSVRh+k0wJXQvx0tIB4WlSmsISBUop/CT3+t8+z\nommgFYQBlpNHSoUUCZa2MUzBoLNFtjjB5vl5wmCAPzKBbjdwikWGjQ799U1uuWIPczUPQw3I2Am2\n4fHg939IMTfAzZTJlfNkPId+t8ETKz3e9bMvYk1W+eFDn+LII0/yqtf/FI+ePsZLbr8bZcZ0egNW\nl89gKoVjOZTKVQzLQUtFuTrCwvl5PvvFr/D3n/gc9a0WMpHM7Zrh7W9+I+/8+Tfy06+/CxmFYCSE\nMiRJIrSMiJOI7c0tXnTdKHN7Jvjsw1sM+g0KGYF0ijieiW8X6S6vMFAmGTdL69yT5CbnaA46VHZN\nk3QjiAOKU1OXVPJDs3kunljgz/7HP/KcW2+BMVhdEpBk0EmAMiQPnNZYto0WinDpAXTHY+nRJ/iF\nyX289h/+nA/+zT+hRYxfcdjaWGD3dTcRtOqsLpxia3Md1/WpTk5iWUPanW0808YNz7DxpIdtZ8kG\nqzxxzwKtTgvXyVFX13DeazE7lScJIrKeQyIthBpQrGQxjSGuW8Y0BOfOHiebrSKEg2U74Eiay3WM\njMc7X7WPy2sGP3zqDCe3eghLkTeLDK2IgudjWhaGMkmEIgkNbj1Q4+c+8N+xjAyjRQu7MsXESotm\nY5M73/HTyHYP07GoVS9tJHjrc6/j61++j7nMYW6940a6nTbLJ5dQuyT71mJ6+/cxqJ8jI6osNzbx\npKK8azfmahu72WUwMYYUBqq5jTc6ydF7H6VcnKLYt+kIm7OJpGS06G32eOtDn2LQ6eFNZ1i+5wj3\n/sH/JFsURNM+45ddixVL/CkLy6mRS2A7X+RwkqFZisjtupH2iRMc319hqj3FIB5gZiWLXYvnlq9i\ndX0FvyoJbJvEEYSBSS1fgkHAVMmhuNvClgXc/Ayddp/9RkyjNkpemDhlhT7vIy1BrAMunrpApTrE\nbmsGlQpXlYucthyKZo5dpf00k8wl1RwgMQT2DnPMEppYmIigQcfagzfiopCUqxPpLd+1SaKIMOii\nRAavUMJwTKIoxMyW8KI+cRCwfHGJRERYps3+a56LVCGrG+t0Oj06jS2snI9vZLBMF0MqGs0N5k8/\nzq6Zg1iGYGm5wVh5BGyPJIlYDxMy+QxJqKiNVFOO1XBIqVzmmptuZ2bQRtS3SHyfdZnnTz72N+ye\nPYAnAtbWFykXq3Q6MUHcpbO+QS5XJbvDNLUU6V5SqnRMtzN5kju9IdWEiGdiEEytUMazPE9DJiFS\na0xpUhyfABlRr28gDChP72FjdYGRkRkG8Rbt9RVENMSxTDK+h5l3sONVnHCcfYcnqOQzYAgSDFzb\nwDQdyuUiri3YbtpcoU0CWeOWvYf4wVf/gmK1hAxj9u6aYWP5LJXKOEXbozh7iCSMwDIIFNzz9a/x\nhc/fw/33P7Wj51dUq1VuveFGvvilv2fQaxAnCZoIpIFCoqWBoRISmYLGO80GQdAjFgU8T9FqLpKx\n8+y76gUMtcIXgvX1ZUzTRcRdhsMuxd37EGFMkMvQq9fROPiVEsHw0lQlr3/br9DaWMbJZHjoyUfY\ndfgF9KMhr3jD3cTaQcYGw+42EUMmRvZiFt/EytoGxnaLE699O8e+fYxybQzIMjdzkCuvuRmvkGNj\nvQ5hH8NyUFqRCIHSBqZjM3/ffQzqdTrLJ1htbzPsC+LeMu1OEyn7yOQxFlY8GuUxIiVx+6d54Wve\nzUNf+X+I4pgwMTGEIDFMhDbRMkCJPknsEIcBwnAojR5gsPheMqUt7tyX8MZbX0W/fTd3fvgL/Nw7\n38NDJxZIdA+VJAhlY9mCk8tnue21L6LV7xHFEbs3GwT7DlIwJvnh8Se4dmoXt19/kJzrXVLN1xsX\nefXdL2OwvsZn/+4ziFqOimGz8fUnyb3htej6BrLjsdo7i7s1YPqmm1leXmPtwgpl32KQdNlutRFD\nF39znclds8SmhXY8HFOSkaCCmGw5T2TZBK4ipzPsf+nLmX3ZCylv9Xhq8SlE16GbNHDH96KHTZy2\nS9lps1Yaohsheb3MoekZnuoNaC4tEldcEilwyyUWwi4NEZK3RlhYOMH45Dhe2aLdjYjiFuPV3awt\nLFMuuvi0WNZdTFNQ3PLZKmsyLZeR0IVimY3+GjMjkN1VYTNJ8PpD2tk88YrNZiaBfowngkuqOYAD\naAt2wAAobZJEHUi6CG3guQ5xEKaRp3GqonPzJRIZ49g2llEml03ot1v0wxjhZyiXpimVPLYbLRbO\nn8RybZRSRFFMrlBhMOgjvYggCNhudKnmK6wuXEQpwcHLr2bl7FnyzymzMf8Eh666kX5vC9NwWF45\nS7lSJVEJrmkhlaJaG2Vhe4NYJjimzYtf9yauuuZa/IxH3GkT9NrUKpMkcYTn2Kh8ASkHOHR2MEFp\nsqWwDUhSqb0NWBpiAQZp3o2QKVEBYyet839xxKUu9/7/OFfc8TZdqNWwdYyRz9Ne2cBxLcJeEIaN\nOgAAIABJREFUj9AxMYSPaXoU8pLmegtPRwyiAY5SRMBvvO0afv03/5y//eMPkM96uK6F59mYQoOb\nwzJsHNtjcXuRlR89wU2v/RW+9b1vcN+D3+ORh09y4eIKnu2SKIHvOliOQbfbY7w6ykihwAfe9z7u\nfsdPocJtNlfXcDNZDCGIkxhLCyQSZZoYWqNUhFISrU2kionjAVHUY2FlkVApVpY6YOUxHQNbW3zp\nZIIRSoZqSLcZszH/ICN7b6I6Mcq582cYm9hFe22JTKHEIApwcj5EEG8scOwbn/4P63k+8oUf6I1m\nE0tYDIIgXRx32tz1ghsYRNvcd6pDqVql1emQjUMaA4NrJkocKGzxlve8h29/5Vt87N4j9BIHpWNs\np5R6VmwHFQ/oBm2QICxBzc1ixB3Ks5fxwPt/ktnD19Ia2cVv3PE83v2JL/DEN79GNlNgbGqC5/3c\nr9LfusiX/+D3efOH/xTpSIzYxHVtpLCQUZ8wCHFzLtuPH2O7uckP//lLXPfSlzN+8DC7r7yB8z9+\niNLcfhAhnuczacNCfUAxn6MX99H9VZzCJImCvJ1jz94pHnzkcWoTu2h3OozWxkkSRZx00FrgCJNG\nt06SbPPJD/zmf7jmL/qt39F7XnUlgT/FSE6wtbBA4Zox1u+/QHHWY6xZYG24zVNLR3nFu17LxqkN\n6vMLVKix4UZUzZDsxAyt9RZRpkO/b1AtjLJ+7iTTE7spdAd083mGIya2aVDO1VCqj2PnUYki2WwQ\nOBZ9JMXugH6jTj9J0cbN7gCr0cc+XOPisUWuvWIfOXIstS8yCEJE4JEcyNA9skrthsOsPfwoo7Oz\nROe20PtLWNqmvb5KoS/JXH6IQRjiBRFraxewS0UOHbyMzY0tRMbBDyRBN6Akspypr5DxCuy5rEJn\nM8RzXIZZH68REBcFreVV7v+lj1ySbu0jbxVaG2CTItEtUaZXejnaLmD5BYqVUQCyvoPCwDFtTMck\nDGOyuQyOl0crQa6YpV/foN3rU8hk6A66bKwuUiiUyWXyTO0+yHp9A8+0mdu3l7NnzqC1xBQ2Wd9l\nevcsju1jWg5aKCrV6k5jAC+TvlEN+gGGrTEx6bQ7WI7D2tISb77pei6fnWbPXJH3/s03yWVsorBL\nY+0CYRhRKVc5cfIJuu0Grucjgz4P/dNvYLHjKLCNdH+hU8UUWuyw2HgmEkBq0iAz0uXlH349+Tfr\n/qx40xCOxebKGtVKmf72OVQkKRSmWZo/y+Hnv5Tzpx7HdXPgF/FLFdqrp1GtJjLroVXE/PGT/OFv\n/zRPzK/ytW/8iN0jBe56w8286+2/x0c+8h5uefF1fP+ez5Mt1jg8dYiNc8u88y3v4N3v/kWEGNLq\nNKDfI5YC23Z3mESgZbITUWfQ2lgAFLbnoFUqQ0UrpCFQSYKWMTESuTM3VzoiDPucOfck3Y6mHg4w\nKbPZCHH8DPmCx6ErD/DLuyPanYSVrQEPnjrBMLyC+uIJlGEQbC3TGUQkloGtI3rrda5+7i1stFYY\nikvDiNhISr6i2dlmNOtwbnWeB/7679hf+21eeutNXL93jk6SUM3sxzFt/umfv8KdL3kh8xcWWVsf\n8snPfpGffPXdfOcH3yWXyQEp9iTpQjGfwcmYSK0ItUu72eLA3AQXLzzGdb/6UVzP4Xq3zsL4IXrL\nyyjDYXpuhrf/7NsZxqswWiH/rp/lkc98nGavQaFQo7GxhlPI81u/9G6+d+o4F7clG8sLOBmLuSuu\nw8mP0V6tc2Lru0iZML15kbNbm1xz650YGR+vv4qTLZKRBezCNMK0QScoYbLZ8ZieuwLHypL1qyQ7\nueiO74AyyPgW5dE9SBleUs333HEYujEzuwxoSqw9JR77+LcxE4vLr7sFtX8X+pE217/kdurHtxjU\nA+bG9jEsW2S2Vin1xjh27DhTY+NcONtgcjWicyPYkzM0L7ZolUOcjmakNEFvo812coFkwsfu9hA5\nizCOqPVCkvEc6+tDhhsN8lOjZPMe2nZx600MbTM9NcvZ5VUix6G41EWMeTTm5xn195H1S1hrdTI6\nR7w1IKiYeLZgY3WJsclx5GaEKtiMNiPONfuohsPUCw6zfbEO/Q56TdPIClQ7ojgCxbndeHbEyiOb\n+LMedVuy8dQF5nbPItdDdpVGL6nmAIaZGjy1YabsNDs12iVWBsP26PV75P0MZiZLzvExhYXjWs+E\nkJmmpNEd4nQFiQYvm6Hb7WFYBrtmDxDFAREmFy6exs+UGKqYs4uL9IcBY6NlSoUi586do9Pd5urr\nXkAcJWTzOYbtPma5SCQTBo0GxXwBKRSGMukP+sSJotvd5Pz5k5w3HVpLdd7/t5/BcQy2NjfxTc32\nZpMw7lEsFEHGVEbGScIB8/OPY1kmWu1EICdPo/4hNjS20sgdma3UElMZmKTSXND8O9OpZ0fT0LGk\nmHXBUjhGFrtisLl1kXypzMrCWUQ0RGaLzB8/QrFcREsTJ5MhDga4vsMglPzpJx7ija++ljOnzvP2\nN72La666ivGxaToNSRBoAumjBi6iFjF3eIR2bxWaCsuydjzBDo5toElSiB0GhplSMbVIzX5aGCmz\nSKcoAnZYTakXANACw1AoFe1kVcc45hhT00Wi9U2OPPYUk1Mz9EKPsjNCJhikprGciUWOcLifbn8V\nQwoM12R87kokirztYNgm5YpkaXUZ1WljZS5t1ru41cIUIXtndvPK6y7n3seeJPvLozzWjDj6tYcR\njkfGcfiHD/4KypDIRPOf/Y+g4xAHzR/+94/yx3/yF6AE6mk8h1JoKw1wuuqmn+TJhz7L2BU/xdaT\nn0JaFpYQuHaNbtTEFBIh09pals25+RU++JsfonDdO3nRSw/wuU//D4zuGgLFulhNvTSbm/zi+38L\niLFMByENDMNGGxGLp07u+C0MhCF5RBkoW/OjL3+BKFHoRBLLGN/xUIZA7HgOEhly7eveybXPey6t\n4QDHcYmjCC/j0qyvUC7NsrVVp1SWCHFpirVAaxaOLCJHyrTa29xo72P2ra/m5PxFzpxZImcrwmye\nXKjwixWKWY/G0oD+Wo/hwgqDW6tMtaYZLG8zOL/GxC//FBc+/z2yM+Ns9tfJaR9pD1k4VidbqjG2\ne4QgMgmKJoaGasYnFg3C5XXKUwVk2+f8ibPMHJwmNzJJ5QW3sNaYZ16HFAtj+JZga18Wuxex+6XX\nc/H0UZzRvVjDHrlykUyuwFRWsrUyZLiY0N7o4OzPk7nY4Phqk7GxPNWXXIt1Zpue36fV6uIqB68y\nSnZ/kdWVBv3586iJMcgGbDU6FL05rjg0w6DdwhCCujb/94X935ynqcYqTlAChCUxLIHn2Xh+BpUk\nO2opg36nTb5YwrIzqCQiky0Q9Dt4hmY47GG7DqZSSNdJMfsCSuUa3V6XJDLQWpB1HTI5j/1zV3Pi\n6OOcOnECzze55tqb0UlEqGKSdoLlWIjAIe4PCaIo9YsohSVS1/pmfZNhvcXKhQ20jHjjW34O2zbp\ntJrYpkUU99h3YD9PHT/GMOiTSE3OsDFch8HmadAqNewaGpROBRFKpYl9Buk4bof+EJoKocA0DGKd\nyu//V+dZ0TQyGY8w6GMOA2xD015fJlMo0t5uECQxOddFKIfa+B5MxyBJVhFSEccBw3hIb6vEK2+Y\nYaxg8A9/+vO4foaLZ+b55F+8HbdQI5dTvOW1rySTq7Byeim9ZUqV4ohE+uBX8EyIj2GYyCR9oGkE\nSqVGrzSsaAc2oVN+k8BAyRTQppIEZYAaDpEi5PEnjzM1cy1SC+ZmJpmdneXChRUOl0ZwfZNTSyuM\nlAsYloHUJq4FN+zP8t2ehWmZdBpNdGeLxiAgU67ilyqgY0SUIN1LyxmwVIO1rQ69VsTjj5/CzSkO\njBapjU3zyJPzrC6e4tHvnKbf3Uy13YYJvTUgizJCrGSINNKZq/F0TcKQm3/6P9FcW2f+5A/J1PbS\n766SmB5xuI03+zqKjmRsRDG5/3qWzz6IicX8Ew9x25vfQ6fdo92Gr3ziU7z6He/F9/Pc8/efI1z+\nNrGOEaQpZomhkLFCabkTAG2k8ETShm6iibBQkUKRYJDmYQilCcMEpVL7ZkRq/rv3Y3/Ewav/FsNw\nGAx7hEmPbk+SL07THbYQBnSHA2zz0jwDdruPtSVJlGaXP8mDT5yk2WlRmXM5/snvcOsb7mZqagxp\nmYQiQdkmvaLGjDQTlx1O80hyGY6tH2XmFTew9MgTBLksG2eO4GVLbK82Mbw8UgZcOH2co/cO8PwS\ne2+8jqztcvS+hylPHGLuJftw+k0euP9RrrrrFur1AYPtBc4TkF1sk91/JWMHy3QHPcaHBt2xKmY7\nZLI0SxyHOJUJMAZEoc3m2hptKbn88Bx5x8DSLuMzo1y15zCPLR7hyAOP4Gc0xV3jKMMkmCmwOxjh\nwlMXyObLDI0+Fx5/jOte+lK2Wy062xsEfh613WcwjLGCC5dUc4DYEpjqadK/QRyEJP0hrqXRBmRz\nOXAsur0W5WwJpMY0LGzfJxp20VpjCQuRd5AJWKaD60psO0cYtpFa4noevm9RqY2iELTrTR64/7ME\n/QE33PIywu4KF84ex7I8HMdhs9HjtlteyJmnTrBrbi/9wRDHNlleWWekUmFlaQknW+TUqbNMzU3y\nK+/7da694kriJMFAk807XFxogTHCwcsPE3R7fOfr9/C6N7yVdq9Dr72MLQXKSAPTlJFm49iGILY0\nWgsMmYI8U3uz2iEDaUz1NKbz3z7PiqYRdHtE/TYi4/K2V1zLenMaP1umubFBqZSwui05u9RgcfUC\nbsbBcxy0sNEdRZzEJDJBKot4GFNvdijHgvMXt7jxOROYaFQiMERMEPao7qmitZG+LRgq1e3vZC1o\nndrtE5k6ZNMku7R4AoHUGoF6hmGkRJq+MAxa9FotkmRIL86TaB+Nxi/sphP0CYcx/a5FIDuE/YQL\nFx6jUCrx0b/8K6ZmnoPje7jFMTbqdZIwQAsfR9p4hQz52esYNFrYOQcLH0xJR2uS+tYl1Xxm4gBX\nXF5gaWMbOexTrBW5YaLKZ+//OnZxgsO151IdnaFUeB25bIFKzubHR+/FdscYGy2yunmBJPbxnSxC\nW0RsMGiv0OvF7JueYNfVBbQ0SGRIqfga3IzP9kaLMBpgYCHUSSZ3F1EkuJWr6bQeRcmEfN7kwFWS\nduPbuNkMz3+BpD24BmEKYtJZbxxEOI6DZ3iEMiZbzPPUfUfR9Nl95fVASDCM8ctVJvZdhSWt9GaZ\nrSCTPnEcIWyJSFwst4RpOzQ7TZKkjW3auNkKCTFKB/QHm+TyNWwS+pfI+5Lk2PeGK9BWk1PH1yhk\nHSZrik4/4pYXvRJ//xjHfniM7OWzuGGPOLaIgh5hp02m5tP/cQ9rOCRZXWTQ9elZPaZffiOl3hQb\nj/4If7KEN4D1dgedxLz4hpexFK2z+NSTbDbWuP3ut9GPGvzoq/cxON/l0Jvv4OLiOao1l6gb0/3R\nPIU7r0cnAWeX1pnwLJp9iTL+X/beO1qS477v/VR1mJ6efHPejF1swCKDIMEkgGKSGEyJVLIkSkei\nLNtPfnqy35GeX7Bli0+W/GyZsmxT1NGxlSWKoiAmWQRJJCIR2AWwWGy6u/fuzXMnp57pUPX+6NnF\nmkekV1gQgezPnnv2zp3q7upvVf2+1d3VVdu4+w+Qq5lEHUlaGGx2FXntMZjfQ95vEpolSAd4K23O\n1ssMUiFOw2Lf7UfY7oZkohZvuOUQT1a2ePTpvyGodJgaHyNzx/Vkd2lqrTXSlouFYPvURcb2j8JK\nn8yh669Jc4BUJIaLdCl8IpAjSKeEYUA6lUYbEkOYOJk0fe0z6o5ipNKgfGwnjdIa0zQJlSJlaTw/\nIJ8r0ut7mKk0/V4HKU0i7XH27HOknCyO5VAameK2t9/CJ//g9yHqcfTWN3P4+uuwrCwzoU+gFbv3\n70cgsZ0UlVqT0dEJgqBPrlDkq/d/iWAQ8sefuZcPvufdXFy6yNFbbkJLSbfrkctk4dJqlVLw/T/2\n0+hgQHv1FL2Gj+NMEA62MaUmFApLa/z4rnq8vowQ8XNfIZDDhQVCMVz+4NW+CNM/eP9t1OseEoWt\n+zhCsdmo8Pn7vkAxXeQNd76ZUdfD2ruD8vYW9WaZjONgpmzcVJ5BENJs+OjZPH907xNEpoWrHSan\n4dkHN/jxH7oDIR24NE5fvzB1xqXxyJfmIDK0gdI+QhlgWugojMXUIHUKZXl0OlW8doN0GCKdUcZL\nIyzXahw7scqt+25lfGQELMGJ8xforlZwchJ/0OPkuQ0e/NJ9dFt9upbNO975I5RKKZa3e1ysNvF8\nzdjCHggU64tPkstlubB4Ea+9yfjsTrr9DnsO3oFhOUzvnL8mzet+xPZmhd72BqXpGRwUf/7UKWZ2\n3UW30mSgFHZKUm0sU6nVOdE1OLTvCAajhJZg4C+h/RaV5gZjuWlShk3Vk8xPjGJISa9tsum1kIbA\njwK8ZkQub5IapDGETbXVxRQBWihyExPoYIAUGUzLxFIafJNmL8IXaYRjI6WJGwIOpB0brUNSboZU\n0MNXETe/5fUo0yCb34udzjCSn6cx2CQIQtxMj3RqGl/Fa4pn81PxhHNa0m1uoP0GjmFgpUuMjexk\ns7rIeGGSSCryhSmUH+CpkFT62m4JTh2YpFLu0lk0yE/uZnvrOMvPLmHWKuz+F/+cvBlx48EDLAU9\nfJnD0h5q4FNp1FFWhNpsUo/qjBbzbJ09S4sqVtfCGc/SvrhMEEpGpm/EnRrDDAtsFTP4az79sIpc\n77G0eoGg2SefziCPjpO3BN3JMbyyh845XPf626i1mjRamnFl8dBzp3BdTX5+J7nJOt2liG63wWDc\nYlRLMjNjpAYR7a6gWtuiJ2Dkhp2EtQFue4vOtMmE36B7YRUPwb1PHuPozW8he+MhBsKkWfXRtkPn\n2TMcHSuy6pj41Q5Bt0Nxc4r+wgz92rU9RwIIhcYgNg6pBKGRZrQ0h5FKEQYhGTtHpCVS2tiGi+8P\nsL02YRSQzeZRWmGm06hBQOT3yGbzBIMBpmkSqIB9+w5ycXmRra0yCkE+LfAHPXbsmuexhx7ETAk2\nLpY5fPttXDjxNWbnD5PLp+i1mxBG2LkcMlJ0Oj1U1Kfb6VLerGBYNu0g5MH7PsN7734rB48eZf3C\nMmNTBcIoJJdxSWVcOrUyYdjHDAWRaVCpnsMxB0wfuBtv7RmkPUF98xG0jpdEECbIACI5nCJeKqLh\nHKSmBi0uvavxt/OqMI2//tJTnL54mk7bx2s2eM+P/hD7pub4rY/+Ms0+TI6k2dzq86sf+zeUNzeY\nm53gxoN7OXTdDP/xY59kKz/Dl55YZrlj89PvPUKLIr//wBqfOx7Q2QhY2uhx5MAYg148f5LSUTyF\nRhSvxiaEwDBtQt+n1lxChJpCdhIpLKKBT8a1GFiCzz31CL/0K/+Fzc4kpUKWsWAT0fZYfO457r3v\nj9mzcz+f/syn+KkP/yCr2016W+uc39AUZye47/6THL7+MO9/749RlpqC0lxo+iy3Ap67/7+z59bv\n5rqbd9NptlhfO8fuN7wDr9tn8yt/iSBD2O4xcfAIftRCKR9tFq5Jc8O2KaQd+v0Gm1trjB++g2zd\nw/P69FWTlJunkJrm8OgeVtc28PxLI6LW2TU9wz0/8CFOnDvD7tFRnl29wNLKBfYW9mKbGS6ce473\nvP17eOSZp+j0mghhMWj2iawIFQncXIYbd13H+tomlUYTyzQwMzadTotU0YXIIPIj7JQCMYnRrxEO\nLEwTwiCEMCA/NkIUmmDkyNujhFGTkdwuekEFMz1GU3dI5aYwgy5ajCGzE6QtqFUj+vU+2WIBx8wi\nRgoYlsQ1TbpehYgcxfxePL+NoU1C7aNDRbdbZjKz/5o0v3huiSPZWZ5K11g/uUjp8B729tJMzglc\nBc89foKMIzA8hz0LBVSpxFY6RW5qipOLW0xe7+IvCrQ5IOM0CQoOPfMizz6wxhszO6nuW8DdP0Wv\ns0Kr2cbKC8xWkcZGDXH9LKWREvYEOHrA6a9dILKmyWUKPP3UM4yOaYz3v5PomTKy34WjB7nO2oPK\nZIlWapz80uPMHt1DL+9hhopeKkN/o8741BgZu0B+bxZRqdM4sUZpOkUnNYIdRJglh/FZk37PxN67\ng9KIy5NPPIfumaisQ/piyOt3zeHYBR48cYp9cwWy4xN00xZRDrzGtc8ubCAItcIQoEiTK0whpcIw\nIBI2rXaD0dFxmu0Khm0znZlFCQOkAmFgaJNIBbi2SceXaIZLEIcRtm1z+vQJBn0fw7JJWw7l8hZT\n01OsLJ3HMiWl/ARqGv7kd/4tO3ffQbv5CLMLc0Q6zfraMY7ccDOV6jbHHnyMndfvYnu7Sbmyyenj\nJ1m/cBqBiZYujXqDsb2zcTuRJkYuT9Af4KZdLi5dpDg2y/kzJ1i68AhFS7D+9L0YaZNsbo5Iwpu/\n79f5qz/5ZQqDBr4JgQEpPZyzWsTL4RpRfDVivNqH3C4cukHXWk2+623fw9HdO7jr1ts5v75OoPqM\nFAsEgy7FtMtWN+LJZ1b55B/9O2ypGR3JMTma5pkzHRqtGrP7bseijOFOk7Z6TFouxWKGfWMh55sD\nHvjSMd72nrt5wx3fyyCqkDWajEw4HD1wgBMnj7NV8fil33iQSphias8kE9vPUWnW2Th9kZAgXnRd\n2uD7/MLP/1MW5nPs2z/Lw195jH0H97O80eWJp8/h25p2ucXC/E4WduyiLzX1RoeZQoGxQppPfuU4\n9X4VW43hzqRJkcMPAwZK0PY71E48yv4b30qYMTn70BeZOnw7aQH1doex4gSNzVUMy+bU5z7+ooci\n/qNPfEF3WmuMjO0AaaGVoNnaIpcpgR8S4uFrEz/qMZkbJVsYodloEvhtBIqJvMvO2VGqlSYX1pYI\nUVSqFZysRbvb4cie6+n4Daq1PvVmA0GAVJKFyXnsnEspleKrx45TyhVRKqLRbrNzZpI9+/awtbmO\nNlz6wYBOq4WdcpmcnKLv1fD6klxhDB0E9LXAMl3eeOQAT15cI2tajKQdbMdkuzWg0+sShk3yGTi3\nFpKyDW6dLXF2q85bb9jLo6eXqfX7uE6BLA1OLG5x202H2Cxf5N03vQFf9Gh7LfbM7eDCRo0vfu0p\nfvXDH3zRmv/o/Z/WtpMi57RpCoszf/Q8d/z0LXSaEb2Bz0jGoA1UT66xEYXMOhk8v07b81iwTNbX\nq5AWbJ5YxGi1aA5a3Pi+72P9+BkCf4OJu9+BicQKBeeWjjG92Sf3va9D1ntsnGtSTTfYOT5Ob21A\n7YnTTH3PrWgRwsVt2hfbpF83ie2nqC6tsP8dd9BYadFob9E4s82bP/AWVqsN3Db0CzbaUVRPdynl\nBJM7Z6he6LB26hRvuvMom46HnRoDW+GHfYqhYMlrkvUVHZVmfizPxZUN7DDEiyKcwKa+0cDem6W6\nskGhOM5IMWTGi3iy5fPkP/7oNQ25/Vc/LrWIl25ESoeBczOMvIFUJkO+MEY6nWWg+vHEjFrjpDO4\n+Tx5N4cQknQmh448IBU/w1QDLNOg2+0QKEU08PG8LkoYzM7OcPbUSQa+R6fTIuOWOHLjrTzw5b9m\nbXmJfC7NgeuO4hYKqMhEWgZB0KeyusF6tcr6+ga3HL2Zankt7tS1m3z10Se5813vZufOBcZLGVTo\n46RsLMdlbmGeoOfRrG9y9tnHSGeLrK8dZ8fuN/DVP/mHGFIhpcYuLODXlgmFgZXP4lU7CCO+NY8A\nFcW3uNAaQwtQ8G8/+7fPWviqMI09t92jf+Ynf5xIRRzaNc9EAVqdAYIu/cCh3/c4vVKjHqUoZBya\nq1tsdhu4TpZHn3ycreXzGNk8YWOLsYnZuAJIyUTe5uDsOM8+e4abb72OfTvSmBG47jhN6eJmJsnn\nMohoQCGbplq+gJHOgg4ob2xz/6NPc25jwOr2Gt3lJd54+x2MjJb4qY/8PU6cO0NeRlTrAYXRLI8+\ntspb7j7Ml061yKbTjOVyKKU5fX4d0hn2z0/y9IUlHCGxUg6r1RaV5QsYYYhRmmZsbopOy0fqPpmR\nSapbFdIpB616RNogUhGWKRAqxSCoIXzB8T/7dy+6MX3ktz+vg26dbHEMKRwcy8J2bCzbZHnpGaRZ\nolgoEgQBke/hpHM0GmX6/TbTkwts1+sMBj4516DXbuD7EZlshkZ5mUgIhA+VdoORbJrAsFG+jzAC\nlAoxDItB2MN18oRhgGvlaXeauNkixdEZgtCn22kyms2xa8ccM/k8J7e22dhax3Uimt0e3/W6t9Bq\ndrl15wJPb3Xo9Wu865YjPH3yLJUwi7QVbz68l4dPrxMMPO7cu5O/fuoYEyOTtJTCCn3ybo7tdgeh\nTVQ44MCOceZH80gE5XYfx4D6YEC1UqcWmBApfvE9R1+05h/69B/pTHGEqrFNc3GRg/fcjbHdQDop\natU60pD0Wm1C3yM1OkZjq0GvU6NW62L6Pr1em0yvy3Vvv52tZxdZff4ie950I1mh+fIXH+fNR47g\nzc7gb69RzUoKKkNhZpz1v/wq1s4x1IRChHkWdha5cPoYo1O3U1m9SGYyQ/eRZSbuOYp9vsqDZ5/l\nprfejneyjBdUKZZ2kb9ujpW1Ko3GCjsyYzhTORrrFbQFZg/6pQwz2QxlC/K+ou81ee++oxi2y/3n\n1xB5xeR4lupqjTGzwMOPnGbiYJbudgsvUyI9WGM0m6PnlrCCkPLjp7jrXe/gS3/+GCf+869fk2n8\n65+QGi0QWoI0UIXvwpy4DTudIZXJEgYRbioF0sQU4ORcbMPAEALLzWCbDinLIBKSKBxQKkxRrq5i\nCckgiPB6HQSSXq9HFAVEQYht25w79wz54jhpJ0ulcpGR3CheYPCFz/8Bu+aOMDo3jilNnFSG8+fO\n8MxTT/NDP/ITTMxMI7SmvbyKYkDDU5wpr/P6W24hnYJdu3ZhGJJU2sF1MgwGfXq9BttXJWhbAAAg\nAElEQVSrZ1g88QTrz/0hTtrlPT9zL5/+D2/D0CGRANuZx/O2sJXP1E3fz3Nf/QsyTrzGCEIgIk0o\nNHL4dviv/dWr2DT+/W//pj66c4GxUp5e36cbNgkHHZ44WaXctegFikBY7C7k2AbWzp/h5LkN6hvL\nGF6LwsGb0YGPEQq81iYZU1IYLbK8skbJBsdNgzTwWzWkdBjNppEO7Jsv4vmC227ezePHV3jvPYc4\n/uwG49MRUVRgImvS8TUnzyxTLLicujDg6K4RxmYnuLjuEWnBzmmTzz+3xQ3zM1zsKXJugWMnznHP\nXbdy//Hn2bdjnMp6mdrWFsXxnWw02/RqLcbnx+n36yyfWMQaK2F4IWHkIbN5DNuhVy0zPref+sYq\ndspAqYhUqkBmapRupY5SEc/9xcdedGP63//gIS1kfLsuiCK6/QGGVPT6PQyhMKWJ0CHNdhttSqQK\n8AY+GdclDHyEIRFK0et0CHwPw7JImSatVpPRXIFGp4yVkWTdcfxIoMMIIXpIwyYMIjZWV5CYWI6N\nbTpggI4CtDSwpQlaEvl9FBrbzlMsTeBLMFUEMiKfm6WnIiK/Rbl8kb17XkeoBhimT7fTIRr02LHn\nFixhIIx4niBNRD5XwOv1SVspel4HaZk4ZoogaGEiyGQdCqk0FzaWUXaenG1iGxbdUDFZKvC/3HP4\nRWt+zyd+TTera/RP93nn//FO2k2LtcVFyheqzN+8F+VrMBWDSotUCvxmAzEzQ0pF1NcWefLPn+D6\nokPu3W+ERpPtSNKPTFLBAP/8BawC7Np7HeeDPq2tDuHFdUZLkvHb3siZLz+NEHUiX9FvZ7j59p0M\nsiari8uM7JqnvbXMwt3fTeuxM7Tbxxl583uQ1RrbF5fIGtP0cgGu6TA4tUzWTDN18EZsa5vKWp/y\nhMfY6F7we0TtDq50aXuClZMn2fG2G+mXq8yUxmn1PPLSpF7ZYtfUKKpQRPqKrpDUGi2069BfWsXp\nZ+j0GhR22+we28F/evdPXduVxocNLRBEMoNlSKLx92O4C5TGpgiIkJEC08CxzHhmCiHJ5bLkckVs\nJw0qINBgSLBNh0hJhIhQSjFod6l361hCEKqIbqdDGCmE0vjhgF179vLs8SeRhsnIaImVi2cRSjI2\nMkIYmGxtblLKj7L70I24diq+JRYprJRDZWOZ8ZEpuhq+/OADjGZsbrjxKKMjJZx0mkgFrCwvMZI1\nUVrQ2lrj5LEH6Hc6nLqwyL7xdaxckXf8xG/zmY99EEsIAh2ilMSMr71w52+nsfLocH2ZeDZooniR\nzl/7zN9uGq+KZxpz45M0+mVEs0cqLblY6XH29Danyw0e/vxnyczsolPZZufOnRy//wvc/IGfJWWb\nTM/uo16uUj9zCnt0nNnJvQS6y+j0bkbtBqPjhzj26OPsmdqB0B6puXGihsdaucaN83s4s7rN3I4S\nJ8+ss12u89H/8jnuuvN2drmTfOXR57nrdYdJjWcpP72BJs1zp57l4L538PF7H2F6dAJPuHx102Xp\nieM8FD6F19tmbu/rGZ+b4StnVoiUyb1/+oek8nNMjYxCBOunnmJsYQdLFy9QyIwzd+AooRHhGBnG\nipM8/cyXsRCkjQypVJ7pHTsJTJPW2iqmDNB9jZ2xCILUNWm+UT4FOqLVWMNxR9CkGPhryEhgWTaG\nNUIqO4JtWWQyLqbpUq6sIJQkZTqgLbShcfIOduBjqAhfDZicnafdKpMujKII6fbrIOI1OuKBCB0E\nBjsO3YlUAYEfIISJCnwMBJX1C7R0n0w2A6ksOvTp95u0PEUkNKaRJW1l6IeKlOngm7Bnzx1YqTSD\nTojCJO1OgaPp+336wsbzPCw7hWtJfC8gl83geQNEyqbRqpA2LUDQ6VQQ23206WKn8qQJqAcNSplR\ntLapNa9tSoub5+ZY37GLyfcJjj+0zFhpklalCkENP/RxuhF1emRHLAqjo7SqFiotWD23xsXP3se7\nfvHnaJS3qdQrcGGdzNQcbnODioinGu/WKyyVy9Q2OyxML1Db1ad88iwHVwdUJ23mjrwRMZKmu1pj\n4bZbOP38GW64boEvf/Jh0rqNqlex3Rxj6YMUQpvKdptorcPIBxfIVge4bTh3pE/vuTaz0wanTvq4\nk5rp9EHMsEtzo45TLNHSmpG0oPTW29hqemRH8myUG6RsRdkLmMwXWRqEcLGDTUTasRkr5IGIbiZH\nS3YojRRpnvNYubZqHqPjleEdPCLlEkQDctkC7X4L13bj5QDCiMgQpCwHy7YQQtIb9MGQZFJplAop\n5Ip0GrW4Nw4M+n2kY5NRaUZKIywunkVKiRkpthvbvPHNd/PlL9yLbaeYm9uBk82wY/4Ip08+ikUG\nYfcpTU4xMzuPIQTNTovRkRG6/SbptMPY5AS9Tge3NIFlCZZWV9mxawcD3yOVdpkdGWVirIBjF9hY\nPkl10MW0c/Q7j/CWt72dytN/SdSs8bnf/BA77/xHrDz0G1hKEEpFX8QTdnbWnmRm9z2snfsbTC0R\nWhEC+pssHvOquNL4q0//nl6v+DzwyFdZvbDME888RW5qN0KYeLUKprTY8bq3xytSnT9BfmIHzvg0\nIzOTBK0eg0HA0omHGZveTfn8McZ2H6Vd2yKTytDaWInfsK3WCPqKlZXnyWaKeJtnmNx9E6NjGYp2\nilx6hEAMiKTAsR1UqLFdher3MRyTjeaA2rlNbnvTIRa3WvR8kzsXRrhQ6zExO0+l0qNWXqMwvYf8\nmM2jjxyncuEsb/ngj5AOfTYqZVJS4CnN5voWY8UxBhZ0ai3a20vM7TiCF4TYtozHVEtIWzbaNJDa\nQA0ihGkQRH365W1kLsVT/+3FX7Z/6Fd/RyuhSQkXy01jm5K0nabX9QEfw87Q6qzT91voUGBaGWxp\nkHbSYJrYVoZGq0K3vY1jpej2evR6LQzT5sB1h1hdXwGh8Zp1Mm6JTK7EwGuRHx3BMEzqnS4i1CA0\nqZRLp98lnUqTMqx4tUAzTbuzSa9bJl/YgWHnMQkwrDRaG/R6Tdy0DdpCmhbFYpHq9jYpN4PSim5j\ng0E4iF/sMgT5VI4gkgg7RTjoIaIITIEKukgrRTaTJvBDhDSRpkshY2PYOar1GpGCKPAwHJdP/NQ7\nXrTmN/3sz+pbPvK9iGZIvbXGxOgUi6dP095oY9sGI7vGsS2b8soGIrCphWUKIkOw3Wbs1imsTYWe\nKrL68BnKVpl+x8MMOoxnJ2iPuJiVBo3zFYTwuefnf4xWU3DxzBJTB+boNjXNM0+RykZMZ/YQzJXo\ndEK6lQbttSWCaogetHjXL/wge5cFn9u+QDvoINoNcjcdprdWY1Du8fz9X+a7f/EjVNfKoEK85RaZ\ndkjh5jGaGJhK0wxCpB9SNKEbKcaMEdq2ItRdlGFCF1K+j28VyOcDtjuKCXeGnD3A7oREGZuUk6e+\ntYLMpvnT91/blcb/+2FDawSmtJBGkWD0bfjOAtlCjrSTRxsMZ501kIYkm8nGg00E5LJFMpk0dipL\n32ujgzBeBllphNC0mk1M26bbaRFFilazieukqVcqtNsVMvkiqWyBo0du49knH0akDIQ0CPsRtmky\nPr2DXDZHs94gl8vgdZsU8/k4TRgR9vuki2N0/D6f+9S93H77EUZGC+TSKYJIMjU2zfLSowjpUi63\nmJ7bzV/+1g/i2j123vrTHLv/40zkDLSGuz7wWzz4Bz9DmNIYxMscxEsbw+79d3P+ufvoW2D78Vv0\nv/4Nbk+9Kq40/ujTf45R2MfzVY/VtQYycnAyOTq1Lvn5Q4zNziFlGiPlMr33BmS2hAgiWqsbmI5F\nKptlx96bsKVg/u4P0PECQiUYHx3l9W99HaeeOs747AQZN8uuyQy90MCbmWf13An8oEg4Os8dh0Z4\n5Omz3HZ4Ae1mWTm7yVhunCYDIkvQaq8yv28v6cjCa0c0Gm1OixSLlSZjk1NEUtLEJ6huEDLF7l37\niXo9Vp8/Q3HEpVAao9Gr0i136bYaFJ0sQaPP6OQCeIr6dpn05AjKtNlYfIa0M4Ecz+PkcgxaVSLD\nxhhoDEfR8epkczPXpPnh/TexWd6k024iwxAj5TKIFD2/iTfwSKUDosgjCmG0MENr0MKxRwhFh0Hb\no6OqBH4vno2z3yOIAjK5EoeO3MzK0mmUGuDYDrI4RT5XoNupE0R91i8uEwnIuFmC0EMr8DsdrHQ2\nXoNcKHrdFlK2sSwTS2ZplNcYLSgCU2NKAykMioUckTKJgpDCSIHadg0k1GvbFAp5Jqd2YpppokEP\nbZgIAaZpMeh5mCNj1LY3sRyB1/OZmp7DEFla3Q5Z1+TM0jFMuUCv1mVsdIJadQM3ZRNc4zQih37i\n7YQXK7RsGJmYZfHcFq1enzFM+q5Lq99Hn28Ryh4Zy2LKKnDu2JOgTGSjSJ0OxfNbhCOaHQduJ2Mr\nnv/Tr3K+u0HuTETWddn7ltvI7pxj/dkVqstlNh47zszc96EJaQ62ya0VED8wwtbTZ2htb+NvnWPP\n9/wgqbERes+f41Mf/2OmZwo4PZdeaNCttOn1L2JkWwQ+3PH3P0T17CZ9GaHVgMD0yN2yn41qBWlJ\nRvYs4CyuMpAW/SBgJO/Sr/cJex7CSJH3BWdrXbIjKYyCgB6M54q0G1u0jYCoo7BritBcIq8LzKWv\nbZJIiAO8KUAFEcpM0/QiJsYyOG4W28yA8OP3EiKItE+nFZDJuRjSZDDokXWzdNp10k4OTZdIKbSK\nCFXI3OxONrfXkUIjLZtMLsOg18LN57Acm1Ar9u49zMb6WXxhYvUDzLzENFOUJmfi1QQHXUxL0+u3\nyBRL9Ac9Bu0+2VyOzOgoWsDG8xe47fU30m9WUNksy5uLFIujXOxtcOD676I98Oj3jjE9N4OVP0Cn\n8RTbj32C0VIJmd2D0TjGo5/6CDptxIswiQihBSYQaM3ZM19mYu4QtY3nwdCoV/vKfUZpD5/9/P1M\nzCzwhnveRphKsbHVQVw4xY4bb6N87gyZqQL4EULZOBkXaZn06mWU0vSqDQI9IJ2dZrvepb++yO6D\n11F0s2ytNRCOi4zSLK1UKJVKBEGdfqfC+7/v7fSbPrgF/ut9T3BgfpztgaZa3uDR58+wo9dHBn12\n79rFwd1jWF5ExjWYnR7l8MGdiMhgtb7BdqvL2MgE06M38ZnPfQ6FIFcqsXBgN7XNClu1HuHqKt16\njUx2hpnpeaRhY2cUg7BCt7WM4xYwDAPVa9JZPIOzP4ffFoRolDcgN1Ki53ngCUw3hb7GWW6feeZ+\nfKmwpYvfDTAaHUKi+JnPyK54fi09hWt06PXaiCgiXYBqrYfSEm8QgYow0fG08TJFyjZYWj6P4+Sw\nBgrL0bSrNQj6KKkIVICTcxmEYTwlixRIQ2KaikjV6XTqZPNFtPQIQ4dsYYyMW8Ab+Ph+gyjwsSOX\nYiGPYaaoVC/g+5pOVRP0Wly353qU0HgBKN1DRO34bV8V0up6+P0OW1uLFPOTeGGXsdw86ewo5eom\nmm2mxufRWnD4wJ2sb25TKZ9CiDaFwjRZt0ir3bgmzdVmD7w6UZABR+HKiCCE5oKJqteZVZOsjARY\n3jztYgen4RM0qtS0Jt/aT3HFYyPSZMMGa8uLWKkxZg9NUehmMKIu2eld1J5dR48U6Q06QIebfuGD\nXLzvGXJTNt7SFjs+fDdn7nuY7ladeuAjlzRBaGCVt+OedLdPu5xnafFpZt50G9ffcoSU47C2XaAg\nt/Fdm2I6hekL1r74MNX1CiMLC9idFqFTYrDSIPQDwn4IkxM0B12qdsCO0Gal2aFvaGZ2Z+Irie0y\nAzHK6vlT6JzAiQpQChktlaj7mqwqcLZevSbNgXi1PiEwnBxGuI4wbbyBTyg75DIG2pCYSiMMjWmm\nsCwDYTjxIkVK4fkephD4fj++/aQB08LEot6sEIUBYahIuwZRaNJTFilpok2FpRXL507QaVW4/c67\neeqxr7B37igbFy+SMhWGkHiDPoOehzQEjhPidbt0mk2KY5NEkU+302Zspkij0gAd0PcbZLNpZEqT\ntnMsLT5JpdYl0gbHHn8Yy3BYqxtkJyLsoI70z/H6H/+v3Pe7PwZmxPW3foQLj308filw+O63pRSV\nrZNkS3voNS8g1Dce6vyqMI0LlQGHbnw9fTQXtlqEITRWF1nYe4BWrUbQi+i162Syo0RRRN7N0GzU\nKEzM0mv3yLma0BGE3T4LC3M0RzOYUR+sFM36Ok5xjGY/ZHZujpTqs+m5yMI0j52tUXQLyPYm1++a\nIZ0b5QtffpKJhV0cPHQjSiiWltqoC2UCC4JWF1XtkrbypLIGzz6zzNjEDi48/hgbu4+QzZW44XVv\nYm29ztb5M8xNzeNHAlfakHYxhaTTa+NtVpncdR2jpVmefOBviNpNJvYcpH5+hezoKNe/9X14QQft\nh2BYGBlYPf04mewC7ahNdnScjdPPXZPmTm6KgpNm0GmRSTkgIrx+k0JxBNVXpHKCSrXK2MgOHEJa\n7TrrlSUK2RK25dLdWMQ2UkRKAwFB1EF7AxxdxGtbtDtNRvUo+dwUg6COUh4Zt4SKJKYRYVmaIJCY\nlkQpiTBdtG7S6tQo5tP0fUHPq9Ko13DsNNlCgXbXi6dXaTeRtoWbmSbSZTa2TpLLTHFy8VmEYaKD\nLhEaS6QwLRulIzyvRqRCDOnQCyIcN812fYuB18Yf9MGUnGusI4RChz4YadKZDpVam1rjHK4zyq6d\nt1+T5qbyKSNgoKh5G0Q5zcggy9Z6E68WUF5dw9rnkhnPQiCobtbYsf8oYxdOI/BpW22sQZ3S7qMU\nBpJ2ps3JE09w5PDN+Nl9bD1/jlx3CcO9AVUZ0POr9DoGI/M7MLMSwytTirKEe6/Dmt6g99Qp9rz7\nTi7e/1WkHBCurLL7ru9h6o6dbC/tQy9WWA079B84jxl16e0ssE90WduOiE4us/78cd7yf/4zFk8u\n4vYV3UEFKSG9d55weYVi2qfZM3D9gHKnR8pSROGA7qpNZFbIuRl0ysLMjLO+epZyf43MOYe6dw4v\n32HZnmRs9CVYuc8A00yD32KgQiIpcTNZTNPG830MQ2FJGyUV0SAk445jaI0X+KRMES/dECikobGc\nDHaqgIg8nLRLWN/GMA1cx6XX7dJuVrHTafqDDpHXw7SzmIbEsFM8+8QDdPptThx7mOv33cjAj+h4\nmwgzx9jYFK1WDe37OE6eyIlQ9LFklmxaE6gO2rXwdYpcJo9AUJycZ2F2gVPPHsPXbean52jVt5k8\n/FYqF5/GMLpEUmA2Ozz8ez/Gh/7F43z6V2/h7BMfZ8/tP8mZxz8Rr9InIIpAhIJe63w8X/qr/eW+\n0E/R7HZxXZu+0hy6bj/PGxGWWyJoNRk7sJelpx5i/M7vRhHSrG3Sarbp9JpEhiCMJLRtBn6PQauL\nNlJ0pKLW3iZTzGClU0SmhnSW8nqVrWqLlA7RGZf2yiazB3aytqXItrd43V038eiTp9AETE7NcMOR\nvWyu1enWG/jSwV5fY+amKZbPbbB/3zwrlToDd5rB+gbXv3GK58+ts3P3FNofoVH3yE6OU7IsWr0B\nlj1D325gGg79bo/1fo/rj95Fu1tHaoPxvTP0OwGk07j5NPXNMqJaIYx8pMxgT+Up2JO0ttbZedNN\n16R5Op3FDwImZnYTBAGV+grpwhgaiZETbGwukk5PU6uvIrTCSefxPZN6vYIhLHKZAv2BT6QG5Irz\nzLkj+GGPZrtOv7NOPpdHodGDFq6bpdc3CCINxIvWKy0R+IRRRGniEINuA79vYtsmgcqiFQhpMDG5\ngO/1abeqSGnSbJSxrAxROyCTL5LL5JmY2ENlexUVBBh2iuagRdpw6Q16qEEdQw0wnXFKpQl8z8MP\nPZRvQARGqoSTFtiGgy0EQeQToRCIePVG38OwNJXNJbbWl4E7XrTmXqs2nGFAEPbzCBHQqa2DNEnN\npBmdmWDxudMYe1zCQQtlG5Sr26jrrsNZTxMVS5SwuXDqa4h+B6/pkRrfw9r5KjP7p9h56/V4xyBl\nWNQ2DaZG97K1+AzByCiN+x9hpB/SfPAJzlaW8M+uI6enSe2YYv/COK16BfXOt5Ha7vDUw8/QOXWC\nUilH5fMXsFQXMT1DYTFCT43j+c9gHpzi0O0/yfqxU1Sqm6TaGqXrrD/6ALmxOZz5WVJhDsPrx+uy\n9NqoXEQmMDFExEm1zY7eDDrdIOVpJvOTZGcFYRjy7KeOMzlisZLdYLC0ck31HMAwM0SDLpguppjE\nFWkG3T7C1aTSWWxDknKycTszTKLAJ1cYxxCteNZrZeGFDUZyU4Rhn2DQwTAl3WYVKSWBN8C0TNA+\nmUwR3+9CoOkPfG4+ephHH/oiKdNGWiYpmceWEWvri/QH/Xh2WaeLIyHnZtlaW2JqZhY3G89g0apt\nYEpJu7WBEBbZTJZ8pkQYDdCtFqfLj9IPQmbG56iXN8DJsPLkfyeyXHpqQFEpQitCR4I/+5e3s2vP\nD7B89o85+/jvkMlPM2huxOs3C40ONEoCloDgG+v5qngQnpCQkJDw2uDapkpNSEhISPiOIjGNhISE\nhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISr\nJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGN\nhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISE\nhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISrJjGNhISEhISr5tvSNIQQS0II\nTwjREULUhRCfFULMv4T7vuebfG8LIT45TKeFEG95KY77aucV1vx1Qoi/EULUhBDbQog/E0JMvxTH\nfq0hhPghIcTXhuWwIYT4vBDirqvcVgsh9n6T76eFEPcKIdaHaXe+VPl+LfMt1vzdQoiHhBANIcSm\nEOITQojcS5f7vzvflqYx5Hu11llgGtgCPvYyHvsh4EeAzZfxmK8GXinNS8DHgZ3ADqAN/O7LdOxX\nDUKInwf+PfArwCSwAPwW8N6X6BAK+ALwgZdof695XgbNC8C/AmaA64FZ4Ndeon2/OLTW33Y/wBJw\nzxWf3wWcueJzAfhvwDawDPxzQA6/2wN8CagCFeAPgOLwu98jbjge0AH+2f8kH6vAW15pPb6TNB9u\nczPQfqU1eZn1Lwz1+f5vkuZ24BGgAWwAvwnYw+8eADTQHe7nQ99kP+Yw7c5X+ry/UzS/Yn9/D3j2\nlTzvb+crDQCEEC7wIeDRK/78MeIC3w28GfhR4MOXNgE+ygvOPg/8PwBa678PXGTYo9Za/5uX4RRe\nc7wKNH8T8Nw1n8hrizsBB/iLb5ImAv5XYGyY/m7gZwG01m8apjk61PlPvoV5/XbhldD8la/br7Rb\nf4t6AEvEzt0AAmAdODL8zgB84OAV6T8CfOUb7Ot9wLGv2/c9V5mP77QrjVeD5jcANeCNr7QmL7P+\nPwxs/h23+SfAX1zxWQN7r2K75ErjZdZ8mPZtQB247pU8b/NFOc1rg/dprb8ohDCI7y/eL4Q4SFxI\nFvEtkkssE98rRAgxCfwG8EYgR/zcp/5yZvw1zCuq+fCB4ueBn9NaP3gtJ/IapAqMCSFMrXX4tyUQ\nQlwH/H/ArYBLHPyffPmy+G3Hy6a5EOJ1wB8C36e1PvPis3ztfNvfntJaR1rrTxFfJt5FfM88IH5g\neokFYG34+68QB7kjWus88QNtceUuv+WZfo3zSmguhNgBfBH4Za31713zSbz2eAQYEF+lfSP+E3AK\n2DfU+Zf4H3VO+LvxsmguhLgJuBf4Ca31fS8yry8Z3/amIWLeSzzC5nmtdQT8KfCvhRC5YbD5eeD3\nh5vkiG+zNIUQs8A//bpdbhHfl/9mx0wJIZzhR1sI4QghvmMa58ut+XCbLwG/qbX+zy/t2bw20Fo3\ngf8L+I9CiPcJIVwhhCWEeKcQ4tJzoBzQAjpCiAPAP/i63VxN3XaA1PDjlfX8O46XQ3MhxGHiEWv/\nWGv9Vy/9WbwIXun7gt+ie41LvDDapg2cAH74iu9LxAFrG1ghLvhLI3kOEV8+doDjwP8GrF6x7XuJ\nH8w2gF/4JsfXX/ez85XW5dtVc+D/HmrcufLnldbkFSqHHwa+RjwiZxP4LPD64XdvIu71doAHgX8J\nPHTFtj9DPMKnAXzwG+z/6+u1fqXP+ZX++VZqTjx0XH1d3X7ulTxfMcxYQkJCQkLC/5Rv+9tTCQkJ\nCQkvHYlpJCQkJCRcNYlpJCQkJCRcNYlpJCQkJCRcNa+Kl/t+7uf/iTakgTQk6OEgZiEQUiK0iv8g\nBGiNEAJDShCCS8/whZTx10ojpYEQ8SZKa6SUKKWRQ3sUQiCEGKaVgI6HgCiFIWT8SQiEkKDj30Eg\nDYlWCiEEUgiUVkhpoLRGRSoeRyLi/UdRdDmdkAKBQKn4xJRScUaG6dGXhqHo+HBcMaINUCq6In28\ngVYKrTUf/ZWPvuhhvF989CltWyksyyKKIogipGlgmhYCjZCAkEgRZ0pKA0OawywL4gQgpEAKidY6\n1giwpIkf+EgpkZLhKBDB5UEXQ43QGqVBDrXWAoSQCBnXA8uK8yKFQMpYQ8M0QEMYhahIo1SENEyU\nUshL9UXKy+UihLisuVaXdB1qfUVZaQ1hFMXlrzVCQBQphAClNCqKEEJy5w27X7Tmv/fJv9SWaSEN\nA9BIrdFSYpomOgrj8xYCOTyCYRhIaV6q+ghpxKNX0BiGidYKIeO8S2mgojCu03ElGrYZkJcahBBo\nrVBRrGlcfsbwf3n5mJfO3zSNy7pqBZGKNddohJCoYXswpQAp0fpS/Y6Pg451RojL7VFrjdIqbhNa\no5RCq7gc4/quhuWhiMIIreED7777moar//APfUCbpoVhmvE+lUbKuI2bpoHSUU1c7UMAACAASURB\nVJxnFWEal2KLRkUapEAgkaYRt2ENpmEM96wxpEEQhcP2HmsupYy3jc+aIAhBa2zbIlIKKSBSGjF8\nXUMjMEwTPdRaqQilNKZlDXVQcRtBI02DKAhBSLSKhnHpUvCBKIri9kisbRRppOByPbk08P9SfAGQ\n8oV6ooffCyH57U/87t+q+6vCNARxUNA6LpRLgX344XIaMWxNWuvLDUEghpVzuE3sOHGlhSsCRiyO\nIDaI4ZZoBGJY2EiBiiKkMIhfLZBDA4qDxgut99Ix4gB+2QiIA6C4dEBhoFSEQMQxX4PQGj2sAEII\nomED0koP8y/iQBXpy2kg1kQNg25csV445ovBNCyklIRhgGGYSGlgmoJQKYSOK6kUcRDRahgAVIQW\noLVAGnEwNqUBxA3hUtCKdDQMzAqt5RXnIBFooq8bsBfnQYIWSFMOg6BGRX7cWEwDreNyjwN4iFI6\nDgBoNAECQRQpLNMgDIM4n5frxtA8tMKQkiCK/4811EjDIIqiuFMgJUor0OIK4xAY0rjmtzoNw8Qw\nDCIVxQZnGLG+w86QEHHZxtXs0pDLOCDEPqDQehjEuWQYl4L0sJOhdVwHL7UfOexo6GE9Z2iWwzwo\nFWAYFvCCQYrhP4btMTaDiGE1uGw+cXPUIOJA98Lfh/m6sp4rFYdQpWLTkfKytyFeeHszNvm4PV4y\nxmtHXK47L6DRKKKIYbtWsWGKS+mG5qjjgIuK8yyvKAchJEEYxCFKKYQRG1EUxm0+NiSBlGpYxkNT\nlAamIVD6UmzTLxi+fqHToiMV6x4pIgKU1ljYRFGIhtiwggCIO9JaxR0jJWPDuFQ+Qhrx70Nz0Cii\nSxrLFzrFlwo4rjHfWPdXh2kMrwaEIYceIS4X2OUO9rBngtbDwh1eeQxPWg978pcaWvzXYWUcptMM\n25B4QbzLHkBcUMawN8cVBnXJjC71AC9VsvjXS9YcH0xHikhFcVDSL/ToLreOYU8yGh437hVcYZSX\n3wEcBothg1LDigRyaHrXKnpcUQ3TRBpxhgQCQzDsNV5hYlLGwfuymYv4ik6KYZCLK+0lvU3TvKwp\nl3wchld8EkNo0HHQNg0JhhEHmss66riHJAXxYePentZhHOiHAdEw4h7V/8/c13XJkdtKBkBmteR7\n9///0fWoK0kC+xABsuQzo+NV6aHz2B6P1F2VySTxEQgEvH7PGC3y9cTeM3wkx1rYeyjjGNDKqOzl\n+WovNUWVa87ttH97yY3GlxlEZdWpgKkMrOvrU5la7QnjeVAmVvfqxgi4Nf2M1t31DjMBb74NIfTv\nfLVnr6UWfz++G3KfQ54fzwSa9gZ0NpSh1PdV8Ff3vVbu7Ij7pjJUZ7YJBhNcDgYmXmdawce7V6EN\nifo+LkXEQhidHMOH2PZlOzFvaMpkLcv4c693Nyw5o9YcaQlkKHJPrDkAd+1tfuKjd0QGEk3nOWlK\ntrFmNj3GxMICLLFi7TM0x9hB0Bxrx7EZJued6HoH5r7371QGYk5nPOfzBAlWQasDMAYAv7AxX6Km\nkbF+zjL00AUhlLHdP18HvwwxsCEpVypcUJFvQ3CMl5dLqdQd52ciy9Axks4MRp7IHb1a3U/dg70e\nV24OHtJQvHLgpteUsA7+zwf4/FBFnHj5ZBrN99c85kJUFDkT6cfAlnHf8MZrVCz4wOxERAWJMPqR\n8ZPjbjJ8LscSawomWkDGfpTMIGzz8uy2Mx0dxuaKomn0Cs6sCC1XMOID0JqhNVdUroPdaZyu60K/\nGlrznf2gomYU/Me4ICIQayFf9tHvXtznCgIiXzLn2quCR8vZ1p9F7IjX5FTpSI8hLpdRAQiQe4/t\nICMBs3x5VBqKymoiC3qSA3/JdPcZseNYMgMZ67xvZTKu7y/n4c73yPfx81nmJwORseGf1H5gNvL+\nZl+xdIbxkxHNBGIF921rQjyY6eLF/kQuwdG+M8GyU8zIlSWC+9xf7MGO9o3vKpWZFdxawS5s/zAs\nBU2ikj0G1OUsKhuLiBPI2IF/zezsbTPAXoJXAK+WKAvCT2U1MbG0Jv90fYlMAzgbzWT0KyNwrz/H\nibgENeQL9GAvL6pSX2YY9MLe7KQeesEGptGF3aK+ApWVVMSpg8i0Ralb6h5OpkLbpc81KJIW/lle\nrZxHVpp71qAwx3q2iqr56AYIi2Wt5r31bl5ZnTIsbejr6sBa2/DW/XZXhiNjYYKvyrpWNBYZQPDv\nynhtWM0c7XpgzoGrdaQn5joRvtfhMcOYgdaujT9X9GbaIwsNc04GGC/GjLhw08HQAfaGOOeEkEdl\nekrZKxOszzSTQzRHIGErXhz4714KYlQPq+e9ett7uTXfmH9rvm86LdHdEct29uZWz6kIVTg993zq\nPRvMOmHSchDAPjOv2XNEwu3UNE4kfIxRBQ2AwZvOnKBYl4HizzYFJaeOtINCBX16GZjrJQMAP9Pc\nj0F9e9X3IwLIjd0ncq9BJmuacIaFY0w5lVpTPmfBQsyGA80NKaNMx8RovdZojonr0RGhepJsyM5O\nMrEWkQmZFQTkQOXgax/UeZpzavkCD38wCypXkIE5AXdg5YHFGNzSLq5JeCuV9e7PLjgSAWqO/v31\nZZyGeZ3d2nQHC69j4YA2YuwMgoVSWtD63dpsFVGZ2Y44+dmGKhRlJjz8xSDIs2QZl5OlpHBAprry\nPpbCws/iWxk4OQArbLecGgyExir6+DnTOBmW7sgABJCKJuwlw/rdy13FN3BzBgLNGqOMTKwMdFwy\nCsB937h6346T616FS9YlTItuFamVIZ+nVsCaKWELFvmZgay5MGPs33Ekci0QZGrImAgkmnUkArEC\n9KkiRAhX99Z2VEks/XiLtULEBMF9293HhiSbv2RTIjGIC6Dc6b01d1exUUarWdvZI7Mh25nwWpMG\nqrKfyuiSB34JWqk1zwTcS7X8ZW3WVETZtmFjpBuCiF4+IIG1A5tFY+rcJ7GqzleOixAj6zJ8U8DL\nOcwiIdjersfJ4ac1rwDptYQh1/TWmvM+WNTPBGsreaJ9KFqvbHPNONkrgN5cayyn0DpWBCpLjnAF\nYA2OU3eE1qP1hjnXflbaZhX6FdG7mQgHZ395A9ZUUIzczoZbXZl1MIs6QS4AM7SypcEMORbtk2nP\nQWcwVI99sTQ8ky/w7d9dXwKeAvhiWQzVhndtTBPEoZSZxexzKLZjyZcPejHQ5x3m3gxV6Ds4/Mlw\navPTGDJq4AayDQ+stfaCxwo5kjhwlpwRcpcUecdWtZXX+1OUoPvOjH2w9trUQSpnUhH5W+udO91G\nimEB7GyoNcYTIRYMD9TcmQAqQNci14Z80czh2gQEK1U2COGnZUSFkTcayS4IyoXtE/+X8alUbP+D\ndxCCNxO2C7kAi69p/CkaRxq+UNH7LIay0wSLmIJ1kGKvKWJ7d80t+cy9NdYJoDVDrbkcRBZ8+QJJ\nnHj5fJ7eI1LBPmo/CZqDodmpO8FZi4Ky2GKW8a9M0B/PIaFEwXSvPy+4NsXwAV7Oj4KxAicz4wR0\nKoBXrLX/Tx5cHTjB0jmX7625bnDfP4u9ZECxZuaqzR3GVwT3xy4eO/cSYIRAkXLmvCK5v9bLnmIg\nk1hr7r3XZLjdyNrrrcNwnFKt5Yr1QnR5hctyvwsScFLZSWo/s7aVwT3O2sSivcyCXcs20ekULEn7\ntXTve1f+7fUlMg1CGdz55W3PYfEdwZ66R7EP6rTYdiqGUycAimZmFSDvQ/DqeDJeFkkvu7IT3kse\ngwcWeiOXGE6vG9y2ly4ckqyRg5XWoV61CSJUmNTPbkeYIjsc9hfsBWd9sxjeWsMSm2hDbw7MuVjk\nhmOtgEPRWeuEaSLROxSNO5pBRIbYVFmYgWcrdjRb9MG0ww4qbLaKn83FAsnEWIHL2g5SmyCcKp6S\n5lxFcbLeovYNAFexOaZYUVCBFoYQAaIZNqvHlc5drSnqNCzQ4c21NvPuncubKYMzNLHEyJxjPWdn\nooq4K9PJTDQHLHPvo9rThAiPsd7GpQqcBekB23AUGaCM5dW7HOvUz/I+Wmexei6OijiMqpNRJIBU\ndlqFVjpyZS0oQ8z94a3YRTzzPGKHzs7MvPYXgF8Yr//2qufcAanpWbW+VTdaL4FJa8y+Yi3kC+vI\njI7YLNBaOXbVG2cIBjeYB8w7PIGICciJ0kGmWIaBOjJXcwSABhJDLBNzLlJzzcl2a4T8QmfAuSnA\nwNaUgRZFem2bBzAIke9hITwmump8AESlrn36a7fwJTKNE6UcY1IlMLOXugWqaJxnk+Il89ABsxPa\n74UwRdAVH5cjcj8R8CnCn5+vPwdOoBu5drTLAxI/FaZ+KnnXPyoCT9VCit5WmDoq2s9N6+MtcmO6\nVyRoPzm0370SxLuLPihLzgissZ7hdoprLGDH3nwVF5n57mEYY6FwU1fEuuE6fWlz35kjUtxwLRfX\nvNaG97jGoAGfa/d2MGJaiEzBT+eetvGJFNRG55LK4qAD26roqPXYVFemHcpy7RjK1x/+7UtOEYzu\nWHvLnQm4n30P1HMeBtdP+1p7cy3tk/0ZfjJGZc/u/nMWr8AqX/YwVOdJReXIZGa5o9KFzIUlWGUH\nPS91uU2J3u9HWZ2KyAr5lblpL8XL/sMr+QX7fL9/FQ8s9F/ZGGPtKmPu2hbRB/aOpHotHC/QYq5N\n1IGJAVYZV8FyBRkJIuj9ZBgAsAtsGx4B63IKICvb8d7pXOLYv+bnHRdho+j45rZZoubVzya2nDLc\n1nQ+3Vj8N0drHZe+q2rI3v7ZvnyJTKMuRwUxtcmUxvWm4qHhJWAALQ1QWJw7m3XAP5JjUertjrDK\nBnZgrej0pNcr46d0ESCuySKWoixlPPVFaYJJCirQre2IwA3sbWg0SGIDmbuibwBqUkv9clF2GY2W\nkywXWeno71+9NT5zCgpEwNulSJWG42oN2Xhwz8bVUzsj8ohgg2AkrusUUaElbK0j7sCaEwbHXIPr\nLF56rDiFvrRt6L3xgKwFjPuJMYawdWZHay028RUma8w2xmRanooga/2bnXeYa52oygyEABKtd4x7\nYC41eVVFxZRtvAtPKTBqOBlA7fPXYrIJlF5iJkERboJMHgeplD81r27KMHaNgLCdscfA1JthFV2W\n42BWsiK2cZtrYa3AnBMO3w5o7aDIYKk/r6y6DlRlS5Xxb7h3AQaEce8tZSJNPTKbnp4Bt7az1z9B\nL2cG1GBBB7zmwnVdiOS+bF29SIraW2uohlVkwKwDyvgfjwf3lZ61Fb3VOvM5rfVPAVkFmwVbI2HW\nEMrAYcwiC9Iz4pgiCIgI0hxzMGhyd6TOUCahrN47HZ7OhoUCBlTmuXYfiRng2l+nifkFwqsA6x+u\nr+E0Ct/NKjUeBgKpcPSUVtCSORc2lFL5odLVZxxqWgBYqj0omrTkZ2rTQ9nBWuKd76wvdzpZnmot\nFbWs6HbQAS/GSTU28S92FABFBsCGfyqTAMA0N8o4JCGynU6bgrSFYmW8S/+cY+KjfWxH6sZ7ci8n\nwiKegRhrb31DBmbFy2d6HVkNcMJfQxGoGywOtTaDNN9VNNbk7z4eH2R0ZAImZojqGOO+FSgkwha7\nooPPPwdZIN5Jbo61ePiS6zkX8dw5E//613esULR2NUXwff/MWgtr6pAKc84IPNdSULjD8t++quO4\n6KTQfQLAqZspkNhQFOm2IWd5umGq/0TZtLID2mbbMF0576VaToBwYbuuXVeZwrUxRW5Y1bCa3B9w\nGXMXbp9QaUQBkgq0QeivmESPx8dOzwsBrQDE5eQzcgeCKWo7G9ZQv/D+pRt1ZdNXf+wCvnXbGfHl\nF2LOnfmtOTZd3PySPQhRbeks1gq4JeDcR9boINeqGun5fnPsTLPqegf60zrOakj2Fwq6EfqSfZlD\n9Qtzse3o9FfchNta4+9sR5S41DtFQgU3RcwFIlGH6g7IFr38+39eX8JpVIq8DWJBNDoY+5yA6SST\nkRc6KrC9ZTGcXi8eqoS13EVlKxI0qohLPnl1o2YGHEyfA46SOShYiDx7OQVJUZxqJCG0eq4q6p5i\natUryKL6mTn1c8pfdQw3w/y5dvve5djZWUZiYTE6EhxGw6FMxA4v3MzQ7TjoiEAu9VPAEblQ/B8a\nqVNfCosNR0Qc2ut93zRsc4qFZogFYE0Vs1WYRcLkeHNjyXT2VYy0gm8slKlorSNZU5gLQMmOiDlT\nMI4Rcw9BX+6OrsxuzfW204CB7xtQABRgW1lBAQVbYK9vroXwVM3jGOmSP4moQMl2RhrGjuQ6HwUR\n1e+7GSNsMdpMkCmy0fnq83yzmviu6mdRn1kPBQGE3tDUYEb7zKBiFakkU0hCAbipwMlhtn4qiB/2\nzvteg3Zbsj/KjNxMDst2VtfRVKzmSrXe9R4ge0NpmgZDlqSL8/5XVAZcQctRrVh6FpcBr4yxwMi5\nFpAMonpnLTEt0cBMfQ1miNv5eTXjnTpFrVRgwdE27F1EnXqOSghXHCYhIAgrTJnfzyoX/3l9CacB\nQNEcC4IRgfBq5MKOZNyqszfUuX0cBzcff2jTZwsngtFJbO6zIeYUnknsfZYWUiQcxPZj0+qUAVST\nl+CjyiooSbHk5BxmsV9ok8MAGA37NhDFrMZOWd2q89tBGRP8TWqriOxXocB/ud78hyJB/XvTRm+N\nz/RxPZBIxBj4eHygCvm7uCwnO+dEOF1+xEQVB5uaHCMSa4yNpUOR2vN+nogpcuPDKxLtcTEDaQ3Q\neiz4NgAGpdigDEZr7JL9fn1ghdDrXEhQuqMkX9y4fmNORX0qDt6fu8fHjRIrXO/jzN9ackEykQlv\nlV1DcFHuNWEvDqPCfj0EMwUhTjlwMyhYwV7TpfvLMix5WDVs2KSszY7kXcw0wXAzBq7W5LxdDs2A\nEIsOhJsSgHknQuDM+B6PC0D1GPH9QA6o4BEaLQZKkeqBWbUn5EYi9nf9EeUDaE9rf7sryFNtoPdG\nlpgUKT4enXtSsCz0nOWYe+tEGZxQaizWHno5WASa3k2QfoW+CTG5HWMz2pdiXLk3xGTBHMYsogLS\nR2eQc8+FvunNyYZbsUnrfkjnzW23KihMqQZEVL5X7QEumzqRsO0wftWT9CWcxoamXoJ1bvSK4GU4\nzcUiKtztMOerB+BAViqSG1kyr8VHZNLTK7CLJcZTki2k0GKzoDKOk6CDUtSE2I4pFNGW9Y3FzTCx\ncPVr44fMcOowxjkwFpgrNme7XlkmP7t0hgqTXG8eqGNUTvNQpW0xid83d6yYO9tYc8Cc0e0ck2uE\nil6BOVUDkJHPCIw18Hh8yAkegbreO2YG2SX6u3KOEcAYU3jsqyhbohhTMSZmsqFt5Q0Gg4RezA0f\nHw/cnwPIQBgwB9fSdejmmng8HhiDBvJ+fnKPrMVoWWsdclb+EjH+9ppbHeRXIvahDJsZvPeN98OA\nXHRsrPdMZaZ6X8kMy4yQhMtYjTXx8fGxM7oUucC05xD/oVsVKVZOwC7Coo4E63er/DjhmKjghvWJ\nGGMXvh/XA1OU5YxTLIfRCRYkNMWeutfApuHiJTgTJGx2ztE7V0GBpQNXNUtCN4DSIsJF4wYKnqls\nxB2AnF5F6LIFzelIi8iA+kwhDIcmm4JOFVo2ZZrVyyJqr1WNDbKDlS0mlQxQkiIKWCMdj+vCmIS5\n2ktLAQvnjgDrJGtNUukNgup5r3OWbljR8PVn/7SP334jf+D6GbqU0Q31QCjCK4y1YJ6qLVS0bwCq\nIXDjg0hxzc8/ibcfSmJBJwqwle4dTjRSm04bLV7YEvveTd27drq5M4sZlTtyqkgKigDzxWAsYecR\np4O6noPOVIcnqjj+5prrMJcMRKoo+PnjB4rQVdH5jFKkTZSkB8Rg08vacEJEaAML2luJNSbWnLjv\nISiAxn2OAeBVyK7qI7y3coxr3IxUq2lTEVKtOfBSoJf66rwnjU7ZX6vnDu0dKYK6CrFbyiZRLnvN\n8QIT4u1MI5ByuqF/oxOdozKa6vY9znXpn6YI52DkO6xA4vzeUp1gTdbx9udlCmsv2Q/sCJoNo7Hv\ngYHDRFS/AA8XO/rV0LO5/rSuYqsd3a9dE1ExO9TbNOc8+zzEQsShxlMY9ASDvxLO+6+vyjLswIDV\nr7Pp7VVYeaHZVzBUDqD1viHnwxRLOZmsmAtqgJAz9y2lUwlPsSxLSsZUwDZwzxkODL6ZXlJkqLfu\nMFytem6qQP7C9tM7q2w0kn0ise0mXloNyi4FTgDzz8v5JTKNV4Xbokg2EDePMP0MG68YFZuE0mrT\n83NYaM2XDktJKmhzs2h4MpRmhjR1CoORCOEnsZ10OEpwrDD0MrSQ2GBlAK9iet5IpywxRkDR+loq\ndkIGpIywxPe8IosQRJHC+xXBlWd7F18HueqRiRwTMQc3eGsY40ZJlK/J/w8Der9omOhRCGsEZSBa\na5hjqngXiGWY4wlvF+5xy4ixKF5c8yWm0yUIBqn+DnM0Bxw0qCkBOWaCFxv21gQlWxLSswBgu/lz\nSq766h1jTYxJ5swso7qWClvcM1vNVyqlXhLZwrItuSfeveacoOdUb0nvNFxzCvZwzDl3I5ix0IaJ\n2itVFxAZoNg/CcwMKgb3B+77BmsxgatzDxYMM+dA74RZOhxLgRSb3wyziugoKZtaiwUzZmrmbcM0\nhPx811nMiYsTN3ekGt9ihb4XMnK+O53L4cQLuQUVpLx59e5bSaIkNwhnV5d7ScIH1uST90enHTBX\nB7nuU9IbRat1IQdlhAnPBnp3XNpDK5ekYgLcyUQU/OoADI9H55lrQlLU20KhSdmXCq4UJKQcYNtI\nBd/FnBPNuJd7c2Qoo0DBmqS7Dzl1S8JTY4xdK9ksz39az7ffyB+7aA0Z0J86QIUJBmfjDMo7sj/A\nnSyDWAF4g2lZT7NUblVPQFj9WsgXAbBmBW/xt5EJhOt7Q0VQUjBN/2tlUFA1BzmSPEVJUiF9O4VQ\n5FhFUERx50+xdmVBbycDW4pIKtKwomm+udyE64BMGp6KaJYcd/cOKyeQ1OZfk0aW0M6NSsczFwrv\nM3eM+0lDsAK9X5jq83g+Aw9RdNlMFvj88e+dEZAP3/C8B/73f/8HiUCGpC0yMFeit84sKPXzydjZ\nzfG8b3x8fAMPONAU6aYZfjyffB6UbAy/dI4J7441E5ETqUDFUFHzgqmD+N3r1LFid4BzDU/xuAkO\nWSvRUhHmAvrVMe4p0kFlwSdKryAAa5LWOmkg11xo3TFnbMc97idrUcUABDDmxPfv35nVwRBh25Fa\n7f/qkeKbhplhjIHr8QAAxALcY0u2lANOQA6gJGBSkB92tlKwDoK/762K0u9d7mXkHa0FENW8x6xp\nzonH46PivS1pNOaAe3vp1KfkiRsky0GUo/cuNGNt5wMUvO4sbkcAyTXMLBaVCVIcO5vs3bCS3xkq\nricMa5KabhHoj4uwVQUxeXSsunovqpGQf12ChZTVObC6bUSnNc5jOcHvV9eeyv0/+/AUL4oF0jg2\nMhkhFES0VSktFdmbItAKxlN4LpnWbMYJJuiVZkMZvwnPVxHvpN+pZrDqneAmd0WCmwlkciYAiglG\nHFvfhWpmkzvIQzcsGOosx2FHFIVyu5HMP5C25y6A8WsXPr59bBhizqlUNzaUQZ740vwNlGvkBq7n\nkFmsA4NMwTyBoc7qlm0X20hCkMJmAuNeeDwedJbKCtITaw10iSKi594HqcjUzJEuOjYO44zd1Mai\nd/CAb8mXKKE8GZBc7Pbtji2+aJXdvU8+oEQMLdNaATgFIhM4czbsFDIZ+cZ+V6dx9aX7HjoP+vxq\noluLMvKzKK2LQUxlFJWJEete6Fffqqp8i3RUJdTHwAwotYRyuBC8UmelOqTdjtyON9eYg6P3ZlZn\nJA4UU+QF7aI/QT6oq4YbFZ++3jlrd7Yb77w6rFUPuB6X1lOIQ1NAYdhBZhnbBOSU6+y+MCjBGpDX\nu1Rtak6+886ZBBJqlIoEeF9VS4tYnF9TdsENFXYUalnO12vEg7I6ZGrmhl7wi00lC09QXb3a+Oea\nxpdwGiuqCe6IFIY2Xj1sdV1XF20VT2s4UhX2tlpuatKby7sDKnCWkakMocwy5IhMEAI2LFXDYEp5\nEoqszcW8QKpfITc1lmd7YoH69eslK3kt4FdXJ4Rp8p5UYJbODTvkwa5RvdV3C4TP51Mcb6hYxvti\nuqtC5fNzwwiM3LnhHi0x7vtEgXqG3Q2fyTkDa+LjerwIpqlGEccofj7nburKwE9EgDmeiJDybiae\nn3+RzQIWyLugnevqgh9YwF1z4fHtA+MeKCXi3TSHoynEiXHs9VgLR0bCBUmAkURBL0NsqvfWnEeu\ngp3KrGKx+7pqENSIioOVm2GOgZKx9nipJ+g1VA3q6hepnwqVIhM5q/7Hxj1AFGdlGiG4bo4n90Qj\npLHGYNPnZNG6KZijim2xogJzBPp17b4DAIAGP4XkSTINayqYs9fJffqdl+Cx+pbmm2sOQDDrYJZQ\nBBB5R28N/XrsAIXZ9qlNhiYqMkAEkGd4VOQkm0pOsFQFpla+9Q6k7Yw+xkAkYb4mFpY5JzeuNWhf\nVHOInEAS2opkn0VkcK/r/Kw5gdbwuC42ZEagAftMV59aCOlgUMKs/QSxvp8vdYaWam7/dH0Jp0HD\nqBoFuXhIdfxOHBYCKZ7VL2GCTUhfqxCpcF/bEdfasEJWZmGa4maHOgdge97qBI0kJIBd1yiBQm7q\nclg0lYy7lyQ1QiwHFifnrmGgoKmCzoqKkom0RASEMc79UrlCO7ADZGzeWnGtZ9v4smGOG+FVWxGO\nnep/eO3r0CFZOuypgqk56xpzCYITfrw01a0Jrpj5xBqkD8YKhDnmmOi94TkmuqKwDMO8n5pqRi/P\nqO5VQM+QIG2zakiwJDw2Dxe9Mr+CHzBlkIzMnmYpJhU7trkHeXhYG/sD0i2K+KpplOq+E+HKJozx\nJUBKOBO9OHUDl6ZQLCzBo2l0wjsgrya8lQfOVVQ5R03royOdc+HqwFzBxFgSLAAAIABJREFUfQ7A\nsmHNIUlwGvih0b0iUTOTd1F4pU+GglnUIHuyIhE3GoAoZWIuQ2++SQCv2UrJqVTA9O7llpLJEDPL\nyc5jNqCeo8pviI5pwJdiCDDsbNXnkYnHx4POZyW8l9SI4563GlCPUgGSZBhzh0lnjecn8LgYHFzS\ngotMNMNmDJrVJMnqzwghJZU9JZ43YWKongTZpRAN3eLoY9XvFfHGmqvhlOfZi/7+C47Ul3AaG18W\nRlpQD2GOAAT9i7jB36kMoaKTZNuU4/yuwVACatwQPFlb/pekTpwGPezMZkVKTvkUt+pee+uoSXqK\ni/i/cnwlY8HvUJZwnnYb3tyGFVv2vL7jlRmmtqAd8b9gQb99Na0LB//IuKAKkdVcWLIDZ5CROVlN\n1KYCIScViWuozK4lCY+lUcZxnPwm3M+BTUM24HlPNHHar+5bVG7NAQhW4jxw38Y+M4ip94YZXCvO\ngp7qBbAtG4EE0h05VLAVtdIBjDFQedxcE1j8864xmFGF/Dcubi81atX+0B5AJqXPq0i6WVaJzNov\npzGyPoOMwPoGBSEKYw4D8EA993Ns+NWMc0u8GR1HYzTKNZ/KWNSFryiU/ufATFW4dhW8i0BS9Fo+\nOAvyG37VKN1NCtD5q9zIjZl9xp+REeFWFoxdfQ3qto51qPe1zwvKOjN+gEvjfl37rqjO/WqCVhlo\nWhos1z6fNa8nwzBzIdKU+eTubSk6O1BOs8n58t5bu9QMiF0Txb43GvmlAOcwQnk/gNQzTJXWYHBy\nr9K5Ym8HoTpmhowA/tm+fAmnwSjq1DFgtnn5Cb4hM86v5vQvMTgAzfQmC6GiwUrpK7Ogca+mnqLU\n1XvN7RBq5nSJg8014eFoDXqpJihDCaiM+gpFCnFYULAaasPiNuGqI0W9ufbAcVrtTPTakHU5VMOm\nwxVc8s41J6MKd2N6HFSAhSKO5l2pKot7V3OMcdPw3Cy4tWqMcpOBF4w3q7eBv2NJeYtUCm5IhAbJ\nfP71F9fcDbCGTHazRhpyPtlslImeZGl9+6Y+izExbahB68I9yqmfblZrjnGzn2RORW0rpQvErtwm\nyQWWXxbwUvyNTMycyhRtDwv63YvwkSE90RqDHNoXGvRmzrraCrTe8GidtaUOPJ/3ES4ElJXWfb4M\n2zGOxQUMcyVy1kjQFI0W+PzxF/egMBeLqus4cg7+LAzAUIBw9nP1mVApVTDq7vXhvh5jyFEpG1yV\ndajz+uovpJOXRj55lTnunUm+u+YAVIznZ1e07jB4L90lKtcm6Ixrih0i8OgdSJJtEsD1+EDNknen\nvMfVOEp4TWa8rXUy/NL29zV3WJc9qn4OCD5PMt/KhkVOAA3Nmc2McePqjvtm9tl7Z41p0jlRtFJ2\nIQT3WirUrHoZv7fJFpZqdCR07usdh+7hy8NTOOlTltEugwrsSXHV9xDSGAIk+wB6eUUqpfSIrCxG\na6I/WxFoWfLIp9CewtV3A5VV34BesiLDKG/8kq4WZRCvcXuyXG0mplJlUDvjOZ3jdSihe/zPm7YK\nO+w80ztXPVcktX4SwHU1ZQqsGTCCBCImnhI8HfeAdz53PG89nMFR2cgx2isIYPfW8Hze8KyhMxD8\nlhjzRkm39OvitDGcQVfs3DeMEahpcGtMQibGjl5ODXyJ0vQl476xoUrVqRKGVBdsOQYAG4rKZB90\nioHEJKDE696DSkKQK3tt1o4ST39I9WQAuRbuZEPYUC1jLTXpKTiqqYKEVEp5ORHOBstx34RWBG2N\nMcWCGzQWqqdgGzHWqaoQOycDluUNiGQwEQbrnLpYWfBuXjOjICXAtV1nPkNldgyeAjNMUW6A0u5A\nFfjLXhUN/92r5EC2orIpSIhDw3c5h0QAM2U7OMLg4ce5LO1Hwzm/boaZp9cmEnDtu3qfdbmbpPYZ\nrKwVuxZSvTlVQE/UbI3EmEt7hlWlOSdp4EiiFJFIYwa91uJAtRCBoRzCOqSD6vuAMtrmruFgLgjx\nn/f613EakSzo8W2w9R/YENB+SaW6qjS/GqHMEo6mWoLLOFWdBIqsxFDBqS8UbFXyHhQYpad1saVC\nBWnkYXaRTq0dHYkmFox5tfOnhNlYeCp8mPeiqQ7JiGeVr0h+95JejOBvZBUz63437Pb7V0Rp3VDO\ngHUDCbmZIcANWJz+SOCW0muPw/CpyL73C+uWDELBayj8dMigd8w1sFbi+XwK/jvSMX/99Re+fTzI\nprouDgPS4SmhN+RR+2UBXHIgCZEjFvrjosBbwSiDkM91XbDmyiAl7LZAeGamqL3k38da5LIr01uR\nW2rldy+O4gxJUbAI/4GLODYEr6rO0XtHRmKsKbj0yM7b4qTE1vo2MvxPwhDozpG6NAqS81gT97i3\n/E5TsHLfz02Bhox7M0qSGBoSBlfNLyPhV9fEubkbBudiX0LBS9rIcoqGRNVRyvlhQypZheLaSwWj\nFNPsDziNzETrEuUrJ1XBQkHLOKKjJQvkzj1DBPBF/doYAFDQk+jgdV0iSxBipYOy0+6gQJN9KqdG\nWwOfSOV1+KMJvQBqSFrGE4/HA8ADsMSc1FUj7EvyQQlXhpABUue5d2uYXO/sZxq3WHruJz5FChKT\n7ftF/e5LOI2CXPgKD231jLIsjF8vy8WGeclGKhMorfvNN8apgxhe8F3ICUG6Lxs/VkHpBctckT/h\n2SUXYM5I0Dq7io8GvQpXsjIuuGr/Q9hmNQ/WfTBKgO6xIhnNnqiRpFVYe7vpiYXpKkD31jDH2Eyq\nMQY+Pr6JfitqamGkfmpJDMIM9/P5wlJx3PeNx8cHPAPPzyf5+A7M+6m1ZcQ11sIFOq8xWJ+gBEUg\n18K379/Q+4VxD1hrsDnIRXdTAxvgkgBfW1H3AwCw3JGilsI7ZVqUtY4x4XNIKpu1GUPTXGVTDxAP\nfu8XLKfEDn//UkKFKUdAKvAgq8+o2UXF0cT9vM8M6Qg0ETLKMGckZhAyrR6jtU7j2RxjTy6c43PP\n6Zhr7oznNGIyCxuDbKiu2Qozal667TrLfT9hMLiKtqW/ZKCse5jBxHJMBNVeX6YnxrpVQBfrU6we\nbE2xYpUxsJp/QEbE3anZFDzfRbYxyAFESg7dfvodItAytrypTdkuinQJwpR8uQdXurTRIkOZcMCc\nAUlvhqoRXv2BWFMsLX4/a39qNkVqLPMLBOgsyjdUhldacdTsq8y1q5g+lzIUgyAzSGuPie/ulDe9\ntxnbmf7d9SWcBm+vdJWEr9VVWG3WdDDbbILqrAaqYO5oWZ7yVSLENqwjaHUvbKLgq1C6WqiQmEPq\n9qz5yhkLMHaL7kxFqXkored35k4Va6oWsorEO194eX5sp1AyA/u+dGATDFh21+wbF1U+ZWRqHRRJ\nEc6gcF1FLnNOFcLFojLnxLaYOOHUkRWvaHGqwQwwzPFJxlJyMt+SYCCQlDlPNRUp/b8eDxqkq+8I\nrwrrY8hBFDaPGk6j+RJbAJHOilTGhY/HtedUmPkeYpTV9GTCiLP6WCS/EetddIrvWXh4wciZpMxa\nVIYTu2k1VhVODS6IKVYeYUJ9agUfZsY5C3sOBzOSNScW2EhXxWc3YI2Sh5Gh2fTfkFwIPy/1aWOo\npycX2WWAzoZE8fwoHBQhhfRd38EOWXG1zxeq3yhiKtDSImeyZvIn4Kn93ccO7Bk7UUJ+obUsynfu\nvqC5a3RFE+bvMMCFaP3YlHFYSYaQDVdOcAsltk6yhY4Nx+s+WEMTZF6oRGpNXGuSmezV0PquPISD\n5p3z4PXcbh0Tc9eTSinuZ8FT23snkIi5Nmz9T9eXcBoAsAfI73tNLZgOW+GRsJ+MdOuU8S4MroYF\nlRNgRlCQpO3aSGlRbSyVob6iH2KITRr0gO2O5gSQGhTfWv9Ja6rmYOdmUVVmwyJbNdZUt2exlCi7\nMNV8s1B9Csx3+DsMOlQ8zV9HAv/NVWNmU1FTOeWQJSCjuQTvqrfCkDGwZiPNsDWsXPt+bC05GmYM\nazCS7I8HWgamYItXTrxF4Pm8kQk8n594fNBB98au8G6GG7eeHXh8cywzPJ9PZY5AXwsmIbaMxON6\niJvONcuZWODfrTnhraO3hh8/nng8VAPJatSKPSGQjVWhCPl02P7uVVpHKfZTNWxGFNE2ZTiWxoTS\neVguLGsA+IwF1cI7LEvXiT0ezArIIHMk7jVRGlZbvy0Sz0U2zu7mNhdMN8j2C02NSyrtmtt2Gm6M\nVM3ZmZ5Z4pY0tpYvInhF/PDGDHROPB6qQZ3a9zbaRfBY0PyPP+E0WtOMCdKPbbO6DNfFG/CmEC4B\n9K6AZ6pQXb1audfSBp2vGfu/Ety3PNtiYK6qPzEzrv1UKBlpzonLOfzLBAlWMfq6LozBLHCuBSyO\nPYYg8mrQ5OeTbdh633AyVANxb/oc1VMT+5+xmVNkv5kRxvuVdfkSTqNotZbGaKcKTSpmFJ2tZhDH\njF3IXSPEAnI01SC4sdWR6Y4YgotaA8W16Xq5AdZO+1AmWhLUm163aoELH8R2WoDYGdU0BDAd0ItN\njavl51Z0u15ouOo9icQMbsSi86VcB/S5pQDKj3rvNGVFzi89JQrlMVWDWHPhejy0zhQddHfcY2J2\nqfbmonHvTj2qWJiDDWR3LFwfH1jPgfF8omiUkJNh89bN1NkdH48HXKygJUz+KSrm1TphDhlGhOQ9\nzPH88UT7yD1D+87nZtHR8JLpxe7fdqLvFfhLAnEoLSXzzexStViRfLzPnhIcZsLMzex0SucR9+v9\ngrlhzsAYN1rrDGJ8CpvX8B8nxsOalCLzNXE9PlgXWsze6PDUtRyhqYlHO6o5u47XYDPkkMG/IKO+\nghnbpFTMgiFzqr5IhzA1B5t7K7HHzyrejjkQYJb8Yyr6Nd9SIaWqkMLyoTMf632v8cqYhDlqymEx\nNA/lnkElgv0vNWq1OUcn9OvaWcfuanfDWAtN+mdFEeY8lsrGyNgzb4ghluLHRadirGmSncX9UPXV\nOcmOm2OouZCEhNIac03PdD96YTUzhDXAxmCKLg0oqXucbvaC4JCk9mZuK/iP15dwGm5VOBJNz8vb\nHfVXV8e3glUU7u8vNMTy/GVkS+vGtwFmFMSI6rCdqsNyc9uVstUGqElzTAsW4H3fA4AXKKyMvTZ+\n3ZXewTKgaH31F3SO4tMjt8OEHEfhaUVJrM9612lwClzumc+9dxpzM0JOPAGYauxyp8hZCdLVc68p\nNdhF2mKskp8G0NqeztZ7x19//YCBg5U+Hg/NGOD3WFLrp/cL3knbnMF3dz+f6N+d0FXiFHn1jsuZ\neG8vTWi5ufYRC129AdTw6ZhTPSKCteCk52Y7PQ1zLkqOy7C919qnTLecQ3JNqkax1BFehsTSthHi\nxEJ1EMOwYkrmhdALO3hparIcEgK9NXx+3gA0zrUdBmDtpzUnpjsAx1ih6NXJvPooWRn1TCg61UkB\nFut553zxSslwX404OmtzrAHU0CsOJrMtcklGpD45fKMDf4Ip2FunE15sjCzKPgy7GZeigeyVaBp4\nZeZol+qV5qp76F0CPL8qYpsINIDvjMIEhWX6JvoUFARgz+guUopWj1mNoEpE4HE9dl1oU8QB1UVl\nb6pOZYLtAfVg1Z5Xw6cgfTPfDYmFJrggtTRmLf+4nm+/kT9wnUU8E9+q9lDOJKpvQ/r+HMGoNAtS\nqrW+I4CtXDtehM8iN5wEVDQg/RevLKU6YW1LUEDeXFTqc/CyQKR6jvo3wlKhTuqqExwnkjuaWjtC\nEDSle2tielW6VZlHbUh/E54yRZcwFsyqgB3zdL5SGZXZz9Aa5ZqIxb6LrqE+W65AUE5qnd3FvjH+\n3VxswmvEXrBmEGufU3Mj6BTzHqrjNKx5Y4E4/VoLawzJfKjDtgGT6SDmoJPrrclBm6iUifvmdyOB\nFQPmba85qfKBR2dEGTFA/bJSbtV8D3vPbZiBvUbd6WDF63/FjzOTBX7Vg7gvJ6wZ1rwxnEapmlGn\naLhVEIXZrvXNOeX4+H2E2QjzUaKczrrG5pKNQye75MzXit2jZAhlBzKCmcAEVk72QeisuqLbe0x1\nG6teBN6bgQ7IoHko897QKAOl1Axrzgt/93J3GWgg0zXPQ7Tl3rW3Xujakj8hkyg1F5y1JfOOUmQ2\nN0ABFunPtEucMV+Cg2A2CELLTRmzGeeKZFQwyvqRede98My36wKVDmz3hxCeVKCpuLK97CM323Nj\nYiXax6VZN/r7NPT+wBxzF/PdSd++esN9z12n+bvrazgNyFAVNr6LhMdo1+XCrRMJKzVSHSRk7OJV\nMasqBViah1vpV3vBhvnPmiwGRhDuWrgUHxqAAZeafOaODg6zywANb9c+UF/JjtDE3KlIuPBfc8Ai\ndvKYhg0ZvcqoQ9lPbJ73G2uuukUIpM5ZEMlxrEsRSG+igg415/lCu/rWlzJvCKkBc+g9I99YjNRX\nLKxJmfPuqT8PPO8nbvUO2JpIa/j2jWs35oK3jnU/8f1f/9r48FyBloGxG5oMYQ1dc7+v3sQaIuwy\nxtw1GxjT+94cEcT6vXE64OkrOFFwJvfHHHw2u6631hyogmgCTlIF2UMvhcmUAqzLsS8V7dcRWyRN\n+ULE3JEykUXOLmni5i+AfS44c7nvMejYAZjYWg8WDTFVZ1pz4Pv3b/q53EPAWDucsEmWlGs0aNsF\nekFR61Y2Slgkf6ozTna+Z6K020pzrmqXERzgtGIB7/sM9aMA8Esw96nhFexT8v476wNgcFzKLkz/\nbWZY6CioJ7JtRe2rV62E/TeeNdZY3eVplIk3g3uxm2jYs+yNnGUNZCtZ9oLWWr8YZGaioUlg1TTK\neG459WIoeiOkDw/WaqonysHJgiZWnhlJRJFbu+qfri/hNBJQG3xVYITbqyYgG4sqFO5of0dV0qCq\nUbFKGfcEPBn1M3REbYD5Qsm1l94DFCqUwvoM0MjQglTone3IieTJJCrXO+3+gibUG7LknVjzIG+8\najE7W9FYWlgxMQR9vaSf715VlNsDcbBlsMihX9I2WlyNahJypfW0bDQmczBiGbOmhCWgjuuYIbii\n474HYg6MPI2FrTXcY6J1Zj/FHmveAHc874EZlc4bljsQk++/Nbw2UHDNHL0a+JSyE100WAPuOURn\nPdTqVDTH/KJ6CAARplFzst+5WJvLHVV7bewKF/Mo0Uap0tbhV9c9axCuIVY14119PBHIJP24BC3N\nmElsGZzIDbutlWiegkdJNW79QqyF+55YcfYs0mCaN9PFHKogqCCSvllsgqUy97rWrA0AanLVmhv2\n/gZemIg6O38AncJhybGBbeXSXHS+jwqOCrW4eme250C7Osc0ZynhOtyTzKRJCLApkASKESbSiM40\nVCvhUoq442VlDNfj4hyZJOW5kIe+Fb5N9SmiML1VjSZf4HCDqVcshWqY0dYUhGlwwBM9GWS01uDa\ni7x3fpf7gdD+7voSTiMKl04Nw4GB40IplcxFVJdwVip+Cp1lbFOLDkBRnO0GvDIOp7vjZBUbZhL+\nmploasI/LyE1t0F9Gi+zeF91jJCpFvzcjsVNNNDCnyPU0FcfaPq9l4yqaJmLKekSJXN3oL7ZM/Dj\nx1Mdq5xxnEaIKrfBDAAL816AAfc8vSRjUEK8KwqbY21HCySad4z7LziAW2q4hLCodurOz5hzEjv/\n/h0wKst+fv4Ft4a5BO/B0SzRncye6/GBlOT2ysTn58D16GyqujgkyhWNsTP2rP24b0QyYp+D75lU\nXw6War26dPkeKD9x7YBj5Htr/nxWc93ashTIIYMFBTc06pEv0aY5lpUacANiYc2UMYH6ZqqYnEgL\nQbSS6SbnjrIiUN0QjGodkAote1LWuFG9ByUw6TvDIbvsHoNNb3a6rc0bmjETfGgIk4F1qtRnVaOs\nWZFGBBH50TUL7XdSwhMj/xlb/28vN8PSeW3eYU11O61XSaP/pK3Wr20PiLwtODQrI5kZlOPbPT0g\nxN4FrdqiPVohPTOYpFcoaU55msRaVggV79dPjbfJEbcHe48I86o2BNbF+AIYpM71Oje+0BDeDxlt\nUDMymW9L9OqxarAWxR1/Rfr4Ek6Dh1KRgFJGnSEAVQQvg2qlelBx/cGFCx5JE8QTOyMBsIuQxUne\nMgWvTTUKe4oixzVXsUrfuL1/baAsvNe2H7AE0hIO3/WRiv62I6rEJE7GU5lQ9YEE25TPjhKsVQSA\n315zr4Y8buCK9uZcWJLQuOcqVA01mOc1AFlrqYP6SJwgC281wCnzMp5k5cxx49GLXFodrywwt9Zw\n9YaxFrHhmtAorPeWcff9/g5bLaSq21oj9BELaXRoqV6QNEc3Fg/v5xNeNY1kt3frlyL2chrCix37\n/78rnudyfDBDYqkrm1lZTVqbWnN9ITId3orxr9rRmpooxzA9l5pZge2gSz12jUFaepxaXu37akir\nPd7czhmAbbonIVSX6KA0jeS03RxphGIMjc5B8t47K03RrPXZRf9tIgJEhDrubZNKKgf7E1epFtQw\nJTow7iEyL7lnoeBgBzlJAcKa7uktWMerCL8mhRYZJxMxtUeahowJxnPw97pTDLVpMNyclPJAfZa3\nzeoaeofX9cCS1A/PTCPVWtB6QjL3oM0aHK2JDaGVc5TcfVrZGF71bjQdDmZF5Pn760s4jW0ILXdd\noRrtHFWY9q3TsnHfdjwywJ+p3obS6QGAtOrOfKGu7g5upW7OrMTbi54PqtZy7nLz7PcEr8PAALQZ\nVASGsptNzd0RgphH5SxUECnsl1hpCSlyMzc1ItEgl3P5/Wvcn6iBL/eaeFws0PUGdDNSPaFCfRqu\nTmfcilqsdN6NneRkeyxEAO75AhXOPXwpMzBmAnDc90DfczC4RvcIeH/gwzkEakVgKNdsFvDrgfF8\nblyXcgmERcwNWIF+SU5hDsokqB7VPDGNuO8uzBoIu+BFO8yAiIFYoaxmUouqoM43rqXGK8vEmknK\nquoy3m0bmRT9tAmGbCqKLsGIXdTZNNMY3WOEIhMjWPNxQbdzJmCOsebOekPZ9dTPNTVDZgRGsLHN\nDWiPx6Y2AwDkjK/eYGJ8uSLbOUjDzRQ9XczFWEMOwzcM13a9j+esDJtflzJ1Izz65poDPCvudoZp\nCVbtnfRaFrkDFtSgYpaarGeooNGuB99HIRNJRhIkGyIcar+rGRMfD+5v1lPJwurWWVeKRDPHx4PC\nkgFTFpqnW1s9ZdWEG5pdQhbXxf0iGzOTwoaRqTkepTJdjNBD67baA2v9BMnVdxs4AfWfri/hNHZG\nIG+/KahJTK43pzYSKqOkMV0TSKehT8FCVVNsbqfDdevjMCJYGuwSQ3xx19CdCKTa6quYXgOEqhBb\n2cKaQw01VTRmdFCbKkyFSzB63R3oex7Ia2RHo1VZUEk37FGeMrhAZULvSyvESiQoGbEmmUxFj+d8\n8sCPz8/thMvI1Mbm+WOWUcN/WgrLBY3+fd94aHgMSQtBNdB1A4Yty3HPiXY9sMZNlVWxWMyAR3Pc\nzyeWNzS91/v5icfV1b3aMN3w6FQWNfWfcM6xEqAgXl+d2ObA1Whw7xlkc2HC+0OzxxkFU222y8m/\nb76qobS68GuAl5ljZjXb3Qr4RM01E90bgpwoFQEwsEJWDYA1qPse+Li6HDFnsMfKvRZ8DSfSr7Nw\n30PRJou9NSe+BZ3KXLekOGqEQNtZZXMDWmNGL0ZeMYbYOW6AsX4y1yKDTIGgtb5TCmuNTkzsrArS\n3r8ULEYSmtod93NnrFFQ5lpCMQKODjOngxTqsBTd994QK3F9+06hSe1NSqeEzgnPiG9yTCJS830y\ncU9Oo6TMSA2qwq7H9s7JpTwnge50/LkW0gLXxYANaWhOSr/JofXuZyYIgJjq9nblJiomZYhizAQd\nH49viDXxK1DwSziN04Pw0sMAFNCLmjnB5iQaujEGN4AKeO4HInGIvgcZwBdYobp7WfRMJv2RWKDe\nkS3sl8dMQul2hHq92h6+BLFLRCIsmUTBCiVdXY/C6LbSyYJWzqGwnRYfTLL6TnAypBT97k1NnpAw\nX1bEBBmeYJNYyWsQ/x/oXZgoEs/7id4vptrCyQ3EU5tmJz9/cOrfkj4XC3nkf69IzJsOac6FXIFH\nqhM7gN5oWBCaDyF5mZg3ltU8D9+SMk19M5GxewHSWJRsXmw6x70ZcqnvFs48KUXO7A9A5Kby2gpQ\nQt3xrvbULgZn/pT+VwMeQON1XQ+sOdCvC/cYMDvS6L03RNiuU1S22vxixqd62R5nazRkbPDjz861\nMDPRBSPNXFt2JV/WHFbGdm2lhbMfHTDWvpYK8d4vzJi4qoCLxFQQR7hYUzorGOyVHZUCwlGI5fwI\n++ns/u5Va55JaXqISp2qYVgaPh4fcrJig4n3wKa8o+dUQ9My6TjmOkHgDhjlrKlb1VHDsqpZ2Myx\ncslhMOhqrimarWEGxyOXbH4vcUfEhvLDgKYx0pWl1b46ja2yc+r29yzaMVDjYD1LuomAFZUiGuwX\n9uVrOI2XB0ZBSq+RNeQAJEhHmWwc+ZCE5mooVH7F3iV/EbmQyW5aQ6rbt+oStqOdFES11oT3C8QE\n1y4Mbm388tTQQTNG0q9YbPVm1KS5wnlrE1eHdTkD2KnfVMRTXU6lfFl9JL/ShvlvrtIAyjizGPa0\nQDcOnWkdz+cTnqlRpQ3P5xA0yN6HDI6YRLDe4Ncl+Wg5+1xYYyLmE9YaYg7MFbifn+j9ASgLybXw\n+de/0foDEa4o3+mK18RDjXuzIJZ5YyalGJ4/fmC44Xo8cN/APSauawK9Y4CRtLtwdkjf6ZJIppQF\nYgY+1+fulDXx5+8xdTDt7T4NCNKDotbKjhXrKDAyjPsJxMKtiWz3pExIazUsSlTMJGUbmw4dO9LM\nOZCLPS2xFovgYj9x6zCL+fzxSRkRaV6Rusxs9rrYoZ+mOdpQP0Nz3PeT7753wKh42xdp42Ss1r7P\nbfiXsP+UdtkYE5iCsABAhu6OUwT+EzIiNCWvARlrCdVkSJib59VdBz88AAAgAElEQVS9I3Lh0owZ\nU62l9Jq6GUyIhbeOLjnyoSbFEksFSvKDsGJl8QZJlljDpuYuwraPi4FZa2ewUnMWz71EE+OMwc7K\nIsHzXN9XLEMkOBQKRE8eD9ZKU06bdcSuyY+BkYd1+qua6ZdwGqgIBoSUrFA1QT7IU8yGSbpA0e2K\n0/tgooanFrkoaaTGuvBXdjaHvGwEJ2iRGtjO5mjV/BVbV2fec39e1r2qeF/4eDk7siIYzTPbOJ24\ntYGAA82V9HmKcmmtqZmwejwgwbwyMe+eJqN0izV4o1IEnM/++eTgpJkg1VUyEat0jBL4vNmYdrlj\n3BQhZCGbs43NDJ/jRsRErMDz85NY8hp4jsLWJ6A5HFOy3Y6EpaSfV2CNhW8fF3LciNbwaJRGWFOz\nUJbWLhJ235j3JwJiJo0nEjyIj48a1MW/Gz9+wFvH1gtb7Au5JYFxj7mbHGHktZdQ4O9eVUAtCfTk\n6QXALncDDzOpsNT1QmkxhUKSBD66tIR08NcUswrAWBM2KAEzx61plhSI7I2yE+yrYfbXm2/qb2XV\nUz+7kmvOegkzrdAe3kHLHGTdmSOasqUkjNavn+U7MFkfatXgFwHvHSMTsIaRkvjWmufLOXnnukrr\nKisra/DrNNwmGxMESUoexxyPbhIsNYoBpuZOJB2CmUuxVkQcBTG3iB8xmHFc/TqwmyA96kU12QNH\nKW3zHsjsst6ZAc6J3tkjFBrtsObgmZXd3A4chlLXroAhlmqPOjNU+a1gFAqqc0vtGCjP/0/X13Aa\nyf+xpkWRjEgV8kp5khv7dRQjSIMDtkHOciKhiXpSSN2Sy3ZmPZeOk1n1eiQ7UHcazr/f6V8BMYoU\nDS/NV9aE/Zc0hhJuS2HU0ltKsa3MttPLSk/kPMpZpaQkCpqTF4G97TCw7z8ArOcT1i6sGfo+fum6\nhyYl0sBdrSM8t25W4fLu5PaU9Eql4SjHn6QG/ng+EWNgRKJZwsOwLGG50C4RDoLR6pjk0jd3/Hgm\nEAvX4wNxETJyYGsKUebBsAzEbZupQCnpilgAurLCYhGxEc4fnc7eDgRRQor1/jd77w90JxeePMat\n7uKaAsdDHJMzS0r63yWUV93JGWw0rKJPQSrWum73BFs1x74K3zTCiYBqJBdnO1TgMubaGc8s6DIT\nkQ1mKlSLKkwFBsEcK2DtZOSBIqtXVqM+oCR8m8U01HcjAWvVVFrabtC7eFe8Bft9HwRCAKWMahdz\nzox9P3MOMu0g22BFsJRIqmTPKzhlgNO35P3jQY2qe8wN90UQnL66+sdS6rtNFGyvzF+MRXd075TO\nry7wxEYrVtVa5XRgJXYqaRcvK1GUf37HWmfQVb0bGHZD6N7/hav/zfU1nAbRQ9UMJN0AvtoSU0OK\nE17yFJk7hXMrznQVvicARpFrF3/EaliSYJfGVUFDNRsbjVzuKsRVB24lPY4qdlOJk0Vhg+kgEvni\n77x2eALVDwIkiF9uB2PYkXMCO12EWFUCljeDh4XU91a82DgeQemC+ynjslh/iNyb2Q2ai04nfN8D\nTQZ4puG6GmJQ5DDB0ZWEpgIjACw2990rMG8286HJicQA4HjExRS9UdYhk6qt1cj07dGx1pCcwiDu\nf98wvwFvGDOw5r0Dgvu+8XhcGGMx+0H1KXDut+vgTGWSrV2AYESXcR7KFOcEPh6Pt501359hYnBf\n3J/cMnOegVERko6QdlByVv2YE9JpxQyguyRdtOZjPHkOMnBPACJWfN4DqW7ye0qKRU2CFz6wpmig\npTWUVa5b8MeFmUO9HFR9nrsngYrQsQabMFXbYO2riC3MWiojMjOsxcmPDhpokwF3BSAh8kdm4uNx\nwfz9mgY1vhRkunESIhi8eZFgzNAkunldD6S0u4pdZjCYq3Y3J7495GjAXh+D4du3j71nr+vCx4fY\nbl0wr4KDEqpUNIg5krUr0c9J2GobiuXtGSDJjykCCMe3aixzzY1BkQdOoM0YNpCg4q0BWOB7MdlU\n6uvFVqD4VS3pSzgN4qQBDyD0wJUqr2pwAhcuApt1tEp+uYpzYj9VRFGLfSKNM4KSjBMWbl3/jRdo\nqeikzCxqmKn8Qi2oUdMHhk035QtijaBGNVaxz1XQrcLznDVytv5MkaA2R5VeeKWgOfUmvFkgHKLJ\nVgoLGLwBMQObLZXAXImGhd5Z9L4/n+rWZTR8fz5hUdRQYN7EdusgWQT+/df/hSHRE/BLA34ySLmV\nFlJmIubAU1lDNTixuJ2IHwvfvtNYsVi44BGYy9AvRy4WyccYjJx6xz2YNV2txsIyg4lcyly+091P\n1mmuZjLA944GYWSMPcf9NpNn7gCm2HngPo+FJfgygkw8BhSEtJ6fn4yMjX0P4/MJdDFfumONgh00\nSyE4kQ+ZaCnGlAIUGsCGBdbUxj2RahAr2ZyaEvjj8xPX1TlP22XekzPivbHJsAKeVBYys+bipHTG\nGKitdeZns19qIIOz5wFgaPiYBaDmGNwSZXz3mpJBgeyBW8NmLCbp2E19O7aRCAZ5l/mWOIIosA81\n9rkg7Huu3Yw4BRX2boJsmc19/9f/bISk8q0xTyOwO4vmlIHhvJ4gM5gBzK3eIisJF9VTUcQYqRm0\nEhPVKGqo3to59Y/qEwCqKdQdSNqVIrFEJlZ88UyD2jB8qdVAtiEh3Tt7L16m55mJh180T0Xuux2f\nvRybvfDyWd7Uyg954JRAmD7J3EXFwynOCypbygDMGB3VRYpsbmNTdZbd6h9JBVUZusLSa8D7qzMq\nYbJi+RRuWTMicr1gyr95iQcAdpiyhrDkpFksZleyzbkdC7NpZXfXA+P+BAQB5ZrI1jDGQE+SDcjC\nYrbSjLpHBukvyalEcDZ5bA2jABq35XU98Hz+QAQIOSka5zyUYt2wWWpMFi/NjPOUsWCWG1p6LHY2\nkxnU0SWOmcKs15p7QmB/JUBYNUM1rc371xIU2qsfRdH7mus0ehrhIUN107MBcc2p/owAEMBi8NR7\nBRh5mD/NdzZbEtyKRfFobMrTQMD9jiChzNq3AHafBmc7qQ6g4KYkNCh+eZiJRAK6elHoxLeSQtQe\nm1jW2bnfiwGX4BwIQ/zCcP3/XO4MNLY90fltBtgiC4qUWgarvXdUN/UKEgH4a2RKruAQqiVH2Ztz\nKBgOe2mOgdYbYtEJcOZG1TyZ5RdkHaHpkKigGbDmuAQ/ptPRsfH0P9haMVHV/TPiofYONvOrmirJ\n/go8rq6+n7pnnHe3JXT+/voSTgPAxr5f/oT/yAR1pXwbf1OXNaDDJdiA/RHCZKOaWKSvBMAUIhcO\nn4nNVGAnOrOcI21sx+jz2ygpbrsEoXoylUglb7hzkoKTqqHGYGKvBGbhzDpAZ7wk02UTbllTCFW1\n1osFfvlW/5srE1MbN+Rg788fKLXLaoiKTEqR64HNDI+r7z+LCIwkE+nzkzOgrXWkkRY7xw8OZqrU\nXN33rdFgclgQiPNHyUKDRIAMXNeFpnkQ4x7wh2M+F1IdzGZBei6A5z1YRHTnHO3F+/Hm+PHXX/wZ\nsX/MDNfjOxvYWu0nvufP+7nrMpkGDweJKe8ZMVJfK5sMTBDeMbAeU3twCh4sLSyAgc4S86wMjhvw\n/LxlAF21TaMDX4HJFGML8cEkPmjQyBfJpxT91xsAzTAHM+W5AkV9TWg4FxJYizs9ROU1kkLWCmm1\nAT8+fwBg45nJILd+cc1foBrI0as/k5piIFvsV53J/+1lMtgUuHRc3feEvCYnsmeIg46rmbMeqYy3\n9wfuJ/WorDV445qvRToyvKBvvunHxyUExXB9PJARuB6cr46k/lkkdvYRqn1QrpzrPCXSdV0fGM8f\n7BdxpxSJ2R5lzBHYvvu7eisCh+ygVHtdTY3F8RxzqGGaLKqaZEgI64vLiBSckzXyNcWMyuKGlw7R\ni4x0nV8pM1aPw5pr67XAUqwFFcvru9y2zaWnV7MazRW7NXfnc6jIFPvfTUUmL8OuyjDh+9hRjeEM\nfKnDx4znQFFJPEs8bGoPRcTORPbvbBUuIF9ojL97RQbWTGLMingNIHZeRdnyisBmeWQEfnxStdY0\nn2HNiW8fF/rVyPrRykYGvHPQjveOzx+f20BH5Fb3+vF5swD5/MS3//M/uEcix2CUFbE5+xkTtkhU\nSDTO0e4d1ljm3muSi9TgZCa37sR80ACMycFQ7FTnvuu9U1E32KcSAGYuUYsvxBi4+jfpf72x5ors\n1irmF4v9QwVUM7KnigCSq+jZhNWWaKIZC89x49ujK0vnHHvGusqa3WCNE+ECVQfkewEE/bSOXJNK\nvyuBNfekwsLGC95YEbB2sabROjOlciAAg5AxsZK1gsgjBbMi0B/MkqbOWlMvSFPNI53yLStiS6s3\n1R/evgxiFFHt1g3HYZUKr95PGmAZWFr3tBItNDweD+S2Tew/Wmviurh35mCGthKak2504tKNyzgR\nfHOSeCakr7ekFCGqOqR0QPYzxQpHdZKLJuuyc631XRgnJOXqOeKZaMam2lSthCoIpsxctVjBYMy+\na27H319fwmkA4J1qU9eAFC+DGrn16fmjYlxYRa4mugX/L7uzA57CJkOZQtUaVuxB7ADETHrRkDk3\nJaipDkds1hZMqbSiccJbABZk6GI3yuySij7Z8lCIC15jRiFM1byWYhfPSwsLgpQqYv/t5RZ3fAyR\nMrMMkmQWFMmuYpghMW8a9+aO7Owqb+5INzyHBOC6nLdUWKcm9v34/NThofO8xwRy7YiTcEbRUWUs\ng+JsV2OjWQUWsI4VRc9cbCq8SDesWfEAC/QcuHUTVkkK9C0NGppr8uCInTLmUtQs1l7ljs3VZPde\n1Fsd/8wuyXbhK1WmHGc2eO6zkIqIG7KE/Yyww3PQifVeWZBtAgESGPdNY7hSxAbwvTYnrg+Ixl29\nPwymotQTFOmGpDdWBPWyxNgKFYhpyPh5zQStiuFV2RIZbk2TMpuYb7bPSVHiC0pjoCXK+ptXSQ25\nnzO3/5m5e2BgcmZpmjPhqh2Y6pGVYR1JIjdsWisdHccmTAVZFHE89uUwNqGG12JLltAgYbBSx+B5\nT4lpNqz7BgCs4PmBEZ5lwsQMrSj8KxmE1pAu2ksxDkVrxkZG+F0mW9p+MXrhSziNwr3LIIYinT1B\nC6eZbRt/Yf7luq3wP7rM7TzcoGhFsMrunXAApHdiscmuBpJUJFyRVm1sOpCdovCS5hTZUHnqE1kH\nTlmQNmzNDEFBQ3m6vrdR0ZS5NOHNOkTA2azvolM/fvygouecGJgsBq9FnvmciLXweFzqqD/YZ8F9\nxH+B56dUWmMhEXg4M4vPzyfMGr5//45///v/wkRFvD+fnDnigCXT5TXXXov7HnCvqD/IcZ8DJOgE\nci0KLKoXIyLweHS0ruK784D1ppnVySZBwpJJuYRIKcEySssIWHcoTNlDcHyrARjQpXD6xlWKvyuC\nhft5g1MN1ZMRges62lCR1D9i9CiVYwfGrc72TbIQ3Pe8AXM8JO1dVN5537smV0EWB3xxH63Bfhlz\nrq811zyTgn+D659OzD0ZLLA2WIPGsOENhliBCGXoSOQ0TOlswTlNziVPH0EVWNK7NSAKBae8D0+t\ntdBaBRViZspYn8K3RiAkNlkhge3kTbU8FR9FdSYCMoO9Sd+/f8PzfiIT+Ph4bIIJZ40bxjhQ9D04\nlvda67BFNQgKRpSle99ZUiRwq4ETmPvdZwJXYxOsoTIovo8Ow8gmBY25nUo5BXPsOohXs29C2eYX\nZ0+Viy2+OO1jUc0A+r/zMwJogeSLPo07XMVABUlipIimCzs87bU0XjICoShi1y7qhej2UrUQs9xO\nrAw6XwIOdGWVUuJ4blSiIYaDOd1VYNdRaiZx9ZMEwIYzOz0dm3//Gh385rUygZmKqvgMh6+OSq/U\ngDR3I6MlIxc2o51mNTg22yw3K8XwnOyobuaEL+bAXLY7+F+FAymwttCvb4jnvdlTpx+kUwU3eYiL\nHLGicdwl6jMAiJuO1tDSt0wGWVl0Vim8urWGVsVvUUxbidUpgMmiZL9xpd4hKZJqPBU/viak0EFI\nYZUpBd9HxKbF+suab+kZORkXJFud52vOQypJ0tRDhuN1rgX1npTpBOsViAS6+PtFzkiNB26uvlPB\nqJUFK6IuRmJKcSENSDt1O4r8OXZjmbKfctTVyBhvQoIAfrIvfOAq5pecBgvxlSHvwKiccWjks+Ae\n071RimQh4zT7FawTEUAkwqoLn/fgRqfoXhln2Q7askx1+efRuUp9n5k01VBwOhSslo0pRlVB2Kks\nuvSleI/VgU+HzzrKT7C/HRbW311fxGkInhBMsVbuBqe6WNCBcP/iVksvR8/rrWEl8eGM3F7X/P+1\n94XNcuQ4jiClrOfZ//9TL/balSnxPgCQ0nPTDq+rPzhiUxe309Njv1elVIokCICxsmQ/BLKkYgUp\nGyZuBoIrBDUY3fAD5MdmhpO1JaTUVqpsbYaSfE4VqLCZDSlxDjMaajdqbJgqMvm7fHEKrkoPwflw\nVTHbjACuq3CBluilIHWNieu6sGYdgM/oOt84Ne0uAVznia+vF87zxBx0Dz0OqnD/+6+/0Goi0xc8\nvZUwma1dajqGLq7Ijv/z3/9NFb6ETW/RYckZUMZ2fUdE4l//+oK5/hXM/t7f/8L8YhDAtbHf3tsN\n9w3iu7qYPaLWFyJnHbSVEXvMzkf7jUDVpQAZuK5CBI3onNWegjqqjFFfonMSqvNEyXm+hWVfOGui\nbsOA3ufJS17DsMzUKlXQo65bFV9AdryvE0drJA8gcBVwZEp1HLgmEDVWxbiyIgwUEn99f8vEb6zL\n6k5N98WICExBjZmq0Bf8PDBGrCYu4KTxsxUKvBQNy5dOga7KvTqs/s0aDy0iTRO81cSAaeqpHf0g\nqWW5Bw8cra8+KKJUnfDOSlFaA644GLh6b+gtMOd9giSWqDlbw18nRxADqiZeNDnM7GS9VaiKnstA\nsqMLqlQfpKZRWiajjd5ZlgzPQYKJ9R5/t/6IoGHK4Zqspyyf+b0PGxb0wzLdfGQ+LM6y3oPfc1Eq\nyZhw3yAEbS0hi5kIijy7QbYzAQK/W1U+9PLokyDw49TAxWKZtUwV3egsAB5xWbqEFz7qaqpc0cTW\njgAL53Tl88lyJjQWI632IXXFZZZWTaDYHxi6zGp8x7je6Mchd1BZT5wnelHghznRoxDzRFRHzhNf\nLTHO78z0ayDBSXBD2WjviSMSR+u4QkOsMlF14Xi9cIlhtYJqcP8OCeavUVLdkubZW65LorUmlk7I\nX2ygvV7rrGUEvr5eDBydfy+7A3hD+/AC8wvs7Dn0f92Dg5IiCJ5KFPIwaUOB4/0GXi/Y9aDJigL2\nKmNTAlEDiAaMEz0T85IP1fSEvoaCHRdKDCFCcIQu6PZ7HJxQ1wIImURC+8XMOCgQbPx8HF60tVEt\nJUxsbV3C7jGIOCi9Dt9HT4cEiMn/bOzory6nWKEzR5NCJoVdpJVsbV3WvfWl/eqvY1UfKI+H5WyT\nAPtdiVyfveRoYcJLEE5YlctU36b3JsLNbRSx/k7p3QRCotPA11fikumk75GoxsmV+rlsE0kcnX4/\nSA3GFESne9a9qlDCxpuWfdMmwsjfrT8iaKBqwzkRixZoiwE4AwhL7DZ33BXBijT84wuKuuszADag\nbRmQyr6ghjovHZboR6MAyheKH7pxQ34GY8ruS9Qqg22Otsdc1mI42A1T+JWgJ/rwRBkECeAGmaWy\nImf9/8SekwlDda7HSSLc/CZ81eTVlKL6zTHkZxDa44brei9/otfrhfN8L5irtYbrJLUUNTEvTn6j\nElUCyBIFs2k+c3QgxVnXMz9eXwqYto/XNLn3Wy+8Pysz7JaEx1wpHQd9hRKB43jpeY3VwIWUt8R7\nRbWENB9FVlV9Sj4wzKaZ0lPzKNyMRvGyyMw1eOu6BhYdvAw+hPj/t2dznQvGBMREOt9Umg+q29tx\nKMhO/HWSBNBCjVxdHQX/6txUXVfdUK9vjlWlZzQMUGti23z7FmXyd6GwSRzgzAooGVyfOByImiYX\nUpj4s4z3V5cby9YprH7AmDinTU9NxZalfmFVAq0fiEGYL3TpN6nZgUKqguVseghN2LDdmIZxTcRg\nNes9CRFfTCq4O0NEbOTBPbXrPFn99oYpaLL3JDmkHURlQGg5I5c7QIi4UNJkmMlIm5OJjp2g/OyO\n+SOCRpWHGgU59tNNsi2OY0Y21iwCZuHkds/aPlX2bppjq2zbotXtSsX4K6sBMkxWM7wCl0Z7VhXO\nWbdyXEsPy9z18xJ8VuNGuzUzpBbm6+9DWG1oephyoTmXqLB0QfgSyEhh2KycXIF8suc0XCOTZazD\nPnYfqCfebxoJnufFkZX+zumA8X1lM21eGinJQzdONndrEAO7BvHa7MfK9HtjFj8Hx79Ga/h+/oXz\n6xswC0c/+DKeyhT1TOcYeF8DX8cLNQZG0uZkDg66mRP7ZdGeDj3H79dARyGPjnnRzZhT2Phitn4o\nSwNewV7NnYzw23u+GDWgYAu5qruVRLTA+/1eolT7AU0lOT1op1JVbJwquXKWOFY1IxEZisSBzFVZ\nR2L3DmSfPsbEdwDzvNCPWhdYSSfE+7vwvi7qOK4Lc4raebHJeo2hkcjKXKswwV4Mz7ky+uuUJsT9\nFzeABf+oX9IlDPx01RyI7EuTdZ7npuFP2f048UStavSaIBHj3D2IANCPY4nsIKgZIl04mZpSy09V\nDJwceCxSztT89TEmvr9PHL1hFIkgrTVkbyQoRPJOqSCUDPXlWgeiKPSdk1WH909EktT5aWKqkSUK\n2cpwNHJX+RxHUNjaclG7/279EUGjq3RV5wt+fEsVHcT7mFmVsoBcmU9K+Af9e8M/e7gT1HTsS0wH\nKKMPO+UGLhuuFWmoZlAx40hjRLJxhjIHVhCetcC9rv0525bmO6EExNgJjvd0dWHzQl9YUbLp1t93\nrwFBtsMnK7ODlXmhTmZRtgph87VRH5F0x6T9e9PAGQXfDCQa4Y9iwIsIBjcEWg+MN4BODDzbASTQ\nauCcfMGydVq0tETrL5xj4ngd8LziOQmTcC87nwMK43pjXgPvmjhaAEcgoy/NRWan7xEY0HoArXWy\nc8R8OeJYkE82Ti5EkMvfe5NHE3+fk5JPVhOvs6pQ8oHKvq1cyaobaP1g0jPnEjsCG56dkz0KN/qF\nauhypLtHZVPfry2igx0VzAgqYDk4Q/3E0MjZRPE5SgPC2+bCdZ4YeaElcBwdVW1VKHvKpTNo6QEO\nqr6rgOgHPaaWtYc1VliJolEAQrifq/CP44D7b6gpIVsoSPUlnOSdS/hzQ2hDsF0hjxcAOh1EpsgJ\nA1+vF9/JJMw2AcTFHmcGhXwB3i+999XzuQZt0L994yyPV3vd7oqQ66xMBlErGaMpKHs0r9fX0tY4\nKW4t0dEYGOWGPEWqmRVoRxdbaq5Kh3qt2MnqT2DBPyJoAIC56mlcEw4cvig1sGVyQ1OH0px6swRY\nSkN9qM2YsIGam8nNPQ/iSxjy+LfsvuYW/C3CFnShFstQTI9VigXt0HZE3ym2l/6PjWtdEsJ9iYq0\n9cCN61ryvwr0BWv9UPP85ipUpYRgwHwPjOtacNScdJL1KM5CCJZIpCsRM3tgCibhofP7Wx/QmR2z\nIe41m4RHC8wgBTSCQ1++v9+IdrCCq8KRgfNU+Y8QxKA+0MylsehHAyC32gLOc+DbtxsTTDTna5z4\nai+4acjHSVz7FMXYkM95nlK001wwjrZ7XL+744LrfSmy8TtWBriYeHMu2C6n4U1pB1IjQMsQawDR\nRKmEzsgUg00Xjf6+9RJNthlzarRo5vp8Leg8jNyT+ab6goRLCENms2eRNBXXRL7Evb19WfYwmMnz\n4zEoHJk4NTOlS1g55NRAVpLIIv9ApQF4b8UADKx9DKeorWnynliMOhuKxrDymvAVP9ulpJIWN4E1\nwrmA/vXF+fQrKAYdKSLYC5oWXE7CSm6ARyzx4XUBAxRpcpgOtUwQVF2IZWtkOA3le2XfeSgGzllY\nyvcg0wh2J+DznesOu1s2/fv6I4KG7h1F0driLv2v7EOzUWPxG8JVRjErneaGq9RVxt4ECVVBTKGN\n11GzDcygOrNQq+oYsqNIPRCL66ggv1VCxah8HG1XNgA8mMmYuV++9eKsUMDvVIHN0pCSlgfOGa4t\nHPTA69O3iVBMlBpsZcbUAOdqF8dKSonOaqgQQ2yRbPQ8ap4voLJ/7bcs6gvCf/m8zpqyX7iEr+sy\nk4rYNN4arPaiJVlCmrLXMzEhHrz2O1vDNYCZxdkDr29SMLNK6p0skgh6BNmpddaJr9eB7+8T/euF\nU5x5zyCPghhMQ1bgH8JTS5QasLs5wP7cFH5PqAdyEADcMl8isEveW4Idrmusn20X3TXPPDZV295W\nNEgESswqCsSYbV4nGVSmn5+DLgFubCdvFHBWNYdDpajr2Ug+8DvUMnH9RcKDqxkmQZwvfr5P5NEx\nRqHmqYauUAG9HUt39eGyLcq9ooECYdOcipasIoxWBIB5XUCjwSP7lKx4D1PTX/o5i0UZeB259Foj\nGxLAERoXXeyPZWPllapg++sLqjO3Gh8899eYeL3onmv43X5hAPtjaRqtJgDaFikQiJfOD9wXJQmk\nEORO+BDu6xaBRO9/Hxr+iKABKbLtt1PlKgLry7v3ECp9l/hkAuX5ABCtTpgqIQWbVbgHYR0Ibk1n\nyfcdlEz9vS5Mqclr0o3Ff3diU327M5ewIrmtqshVzvQHVDbIO9s8aj0zZ4WlbyAaH9kO+mXFA/Wp\nYeF1DpWkbUFohu72ICnTBfml59uWDqqA9MJEJu2wJ5u6tLm+EP3QniYrPxAb5qAm7j+Fd8SvdcKF\nMTccr0PCM10dcyw9CIqw5hwDdV1o7cDr9cK7OLa3BuETaJwlKdYAmgbnOCNHMbicb1Q29H5gnu9F\n5S41IA/ZeX+y5uTwHHsARVm1C8KeVXwGKwMGzvepzLhtEqVewTAAACAASURBVEDtOdK8/Mi820LS\nxLz15KoK50WFe/g9qqAH0+yCtWLNKKkxcJVmUWOfTT4n9sBi0qOqH517F6J7Nr5vU3ubAVJsV8mw\n4Y9xXZDBFOb7zf2G3n0UjvgniM4gNKWEyw4Nr1dfe9N7Rw3PBlc1iEDKRBHF79P6sXoUiBRVWpP0\nstHzKzajs3uGS01EFloeCyUxPMRMX4iDkkQBDIASg6MF4dbzQorOy2TAvQreUa13VvxgUlAAjnit\ncxX67qm+hXVZ4yK0ZSt+KMH4u/VHBA3ek8qswctSVzjsd0QIX1ipIqehEWDDWM7mZk20aOuBm5pp\nK4FV4ilAFfiCzboJBQG4p+Jqw7AMai6KbMSdMSVkMQSBKDgklD0q+7MK3opZRN6aiGqfoBZG7AoF\nwNKLfLK4Dx1mjvDnGq7hKE5miKaw0luI9ivMaPoaK1mC8dhf8hTCnPzuHlYTI/B+/1/CWo3meNEa\nzvcbvfEl5HURZE9NUyHtzaT9b4lI/j2/WCXadVWhBVlYs6kirVoDmTAuVVhQtiVOvUL37mNQlVvF\nuRVmuny05wH1ILY5JsTNb61xrsatclueaqmgJw796nEBq6IbIhw46Sq9IJHBxjOCI0N11sYYHISF\noOhOFzktNdjP8iyVOcZqVBu2aKlqMVVBSyWdeSyIJjJwXrTKt59cqRe3XKDVyDUzkhcoYM1E+7C6\nw/ppsfZpabb8nCW2c1XlgUiL/QUy7hi0Sy4JchQwi6yAU2cxJYSdGAy8uito7TEx4OSWa7/bGq6k\nM+8AVwVBd0yCiURor/TMHIhWNVyFFpy4KUf0JRkIV1PqqzDhZcLnXtrP1h8RNPaFa9bGUNWgRpgy\ncygrq/X3dNnGxnihhwNhsFba2gJ9DKsqRYvTxT0ZYJe4ZzvqCge2GEq9EztnunDw772fcao7dxZh\n2I2XghwvVQ4CdhLFqnQ8R4RpkgMOg9Kn7HXSUnkJ937g/T5Biq9oobokj6MtLcfX14uN+8Y5yjWB\nV0u8BR1lJ8SXS6jFC9qkhbccPUNUv/cVyDk4/1tkiPN8U/g2BrpEgCgrccEmOYBzcljOeZ6sAOdc\ntEjO41Bg7rywUpj1KKrT5xykLerFSWW+/fVa9vBUyg5pfuqnJm6/snhGNzx5DtFpa4rZtUkettjP\n1hY0FpiqnLACMyswVbQAalyCpRgwLlULTbNCxiwcGAs3R4X2PPE+33i9vlhl6eIYCiKZkJmgnq2+\nzxiikIowcF6nLmgAepfGYCCfRQdcJk3Xws67GuV37yT3ENiv+my5bxGhmRXad5NubORHuEh/WkI5\n7+MQJFsqh3oXnAVWyaXfQ1ZVIrMjwOrksjo7CkeILTitv2Klm7AbRKDmhf6vb+whjS3+ZZ/V9OcE\ncqzq8tU7h1qVAx/tcHp0Qo/uUZR/H1fvhzRbhA9bNrSa+8//h/VHBI17/8JZoS/JsnpXf4IMk91c\n4pXMl2eJ4mL/eYR52TtT5AvhS5l0xi5Of2s3w7gQe0lwQiE0Z1mye+HQQMibZ4t3GAT252wtVxYF\nHiuMGreASVsFwxNp+GoFqA2OLFPBzzZ9BbLzPNF74nyTacGeiQ0dJfrjdBz0FymIyBcib4ypoocS\nn1DDW89iXieJAzWQxWlsYzLsHZ2ZJiGqorGgzsDRu4YjTbQGvF4vYvnXiVGcef0SNXYpcq9YJpeZ\nXYOYTs57B6m+OE8qriNI71XTtSW59zXsbsv9/jo4RtXi0U+WL5wAluBtXtdy+53Y/SD+Be55a+w7\nmTFnMhN0NvyfpeA8Lk2Bm0xaXkeDRweTUnmhH7QHGRcHN11zig1l3Lyph8fe1kSShZYAli4klxDx\nUkO/iW3HgMSMetZb7rZM5ibmDX+/a4R4wpcrQ20I+JPFWR21qtWuaXWAIe0uyn+pMhU8lWZOFiL6\n0mUdUt8vZqOSv6+vL44MmJzPHsASy6WqX0DQ7eo9aLJnNgy91v14MbFM4JonaqofEbUmTLLKjGWJ\njpKID7YjEn1e4zaO45Ajt1mQ6qFg4HUc2mv1Ys7xU1HlnxE0ECoV6xZANhVx/RlhnaS6uqKofeCq\nFu+cCtQ7y4SlZpkNklatbrsSHyoEOfRzMCCQRsdMgdYPqhZuDetl6HcNNdpULqat1/dlYJjlx1Xq\nV2BXHLGDDwR5sWq59XR+c1Hcw8qgdywblMDG+1P+S1VBd099b1diEfTtua6B19E3BFS0G+fHFlw4\n+GKQ5ieO/PBzkNdTMbtE02S+4HCmiGA/IxZ4xWFQdQl/nTi+fUOBViXfpO0A+HNHcdY1hDkb4vSz\n8IhLyBgOse3yr0kmEqeyfeaD5O85pq1u+BkXgaLq9sK7Iol1tmy1Q+cBVkfRnIHeKL2gSHBqtDGP\njtmHkxY3JcJIE+kjQoGJ78yIQun5M1aJ2VTUZIw58fV6wZqM1rqm1vGdGnNwRPC0nqGWs3Ms3N9i\nRagPg7XnCSVN8bn3FH9tEvPXRR3Y1RSABYsaOu7Nnluu4ngvzHFqXsgeN006v6rXCM6OCQ7HqjHX\nZ7BIz2y0KjMKsSqeMTS/Zhb661gz4mvWaqCHlN+o7Xk19LsB2Q5pmqZV4UtQfLsjQ7AsqzxXM5qf\n8hN6+R8RNAAseqTLLQnwVWoz43UI2T2NXX/QoVGZXE1S1dy/mLUjq0tfYZoLa61tWWCIrEBYqBD7\ngQw3ouo2poQv4ansq9QbYHYx16VUpU8uiKJggaHcX9LZuZpzTCeA2JCFipiN0f3m4qEmI+OU7Xdk\n4DovZWK1mErvS9lOSolfhePVOXN7Cs4DaI8eyhylEL5sGd1sk8ChS5cEYS7bEaQgGga89JyqJXo2\nqcULAK1AEI103veFo7+QIh8gxeIyywcq5VG4LlZxq29VDQ1T8FsgDrK8rsE5z10mdpUToz6fYRIo\nXJoTYjiGfaOhKqLWpbIcVUONVGCxYgCsKvw8PeKVwZqiSWbE2czoC6BM16rbBS4xrJlLC/7lRTYx\nMc5bH02Ggudgv3BrKigMCyvw7wmezvlAoS7TQy9S30EIktURk601qEu09k+1Md4rV0Gu9OgUsUch\nOMg5cCNiPROIlVk388QqIJHsPQ1rXgJ/fT/XKNxc/l78fl9fL9jVQegY75/WWA1VYdRgNVH0iyol\njHb/NmklMpFNKEeSAn1eMlCEHTM2G/SaHF+dSmJ912U4AHac59sbhp+xM/+IoGGY5PYvCB8ElC2t\nVta6NB0w7PEC9Szsm7TuVr1I/rPuC5gKu4LPVENO7JCErNaD88FdyXgaYHhkbMulZ3A1FNhMLao1\nefCprA29MFBl4eCm7BKm8NW2TQlfdIY3wqbuv72M6152pI1SYHCTTRdKkQpskzmE2Tncj6GDOipw\nHAf3OxJnDfUSOmpe8ECj3iiaY2b6XpYxrXX5HF38roNzkMmHbmR6BV9KGrfJRUD9EV/0AJA1MOeJ\nFgdex4sYsrLo93mi947j9cJALmqroZHIQEOTXobizVkcx/kpVuKZCyZa/HD+UFLOsyJoNxbh/rW8\nTFllbuJFVBD2WTYzfh8oiGy59RJjXKjlsJt0H1alwXeJwQjtkGFk4Pv3k+8YSLGmf1tb/SOe5aE9\nTo4ujc3wq0nTv/Y6MAIM3oxwa8/Zc0g5C0CXcPtHggZTjdh9k/DeMhh2BdoRN0PTuvcnExGEYu1K\n3Ezbn1s7wbG7hPhSs8EZHKbGOk+66crEcIrs0ZSw2WaGHl6ER62puU5Sxjc6shNO3hkiS7RN/V/G\nnJDHljoZtt+HzxmpjetOwg3K/0/rjwgaiC2qYV7Ia78QyDQN9EfYZsa2jjZEZdNAD1USWXexIZyd\n6/6XwKtAZXITBruhCb6QGiGrFzKbmT+1mC7REzhZBbD044u1jcFKmVPu5ra+aYReoPWZ7EmTCo65\n8etbtVHjswuskBjz3NVVFCe5zYmap3j954IU5hyIyd4PP44m6gU/c29smIfEgV//+i9aJlRRnQzg\ndbxY3WRHVuH17b9WZkdatbLUDJoNdjbxWhTQv4i1g9AhsqsHAlFxGYxiEgyOCMwE6hqILPSky2dm\nk3KZosv24vjRS8wjElISo4AuqCVURX667HtmSxhj6FWJOU8gAud1rqrWAdhcQlfbnO6IhaeH1N/H\n68UKrlgtFDaUaIfb1g+gW/MTiwSCsvdSIrKUKcuOone+D+1ATfpK8WwQhlFpDWDvuSvHqffOpJZE\nInrXheyzZeO+LearOTFi3vqAv78M/XTBd5kAgnoULDg7lbCo8ijCNGiiz180zPS4AhjGFtQZ6oUg\nAtleusEYrI+jr3/uLTHVmxrjQvQND00Ar9fBKjFSm8FKgmgA7wP2i3RvKcHkLHPqmJZkYIxNJc6m\nx0RYjKafWKQjlGa31ATGTVD8H9YfETSoxKz1spgFxf8iczOpRaGL1YFwlhkO/O+mS5Yu2wKxRW+2\nvXzcMrFQsEViKDD4Z/4w+AmsWDbnwbOVt+p8vwhqKt2+j0cwojY05eiem+MFN9GdKTrw85+NOWPR\nBX930UuHNtdmcZEGDEyw2uop7UYVehybSigWUCZhh2uI7RMBdt4K1/svtAzkq3N867hZewPoSebS\n+zzZWI9iX2RYLwJeXJm45kAp0/o6vkRLTAAH0NUzmbRaaEdb0NWsos+Vs/bk8x5VSIsKrwuVTVAd\nL4Km8/W+BjLkqosbnfF39zxieUg5kx2e+a4muwbIiWZuiq2zdga6RGCZRkQIkijU+7vOOYVbNScp\nr8qu29HQVG1d6id0e6SVGrKCyM45qd9AoB9dmiJQjJmx5slQjNq1v9ZT5WqiA/z317TtOD8vgkEk\ndNHK6Q/nVcgURIRc7/Unq+nib+o1Rot1x5gCXLJv8cUcgUUXj5YLKqIQl9W2Yec5J1rRk8pBLhub\n7dH7punzF3H2eAGzaHpoaKojUR5Uhd1zuMYlEaKIDxJxIpkwGEQLVZRrvnwGus7tGLtCKTBgLr8z\nPSnTkVnF/+GNcCU6LHdvH5aX5cZaoewpnJI40grzNZsKxmbVyGJjeuIcYvgIQiKGyIxoyFOptUbB\n4NpQ1zxYKUsp8/dM6RWoIFqhympjovx+tRpaC1qDm+qlqor/PpuDCPxmyl8Lay8+REpk2paL9VKi\nlpaoqGQYbXZHT/llNbkNF0v9GZohUpx5cWm4EvsDE3/99R0Ijs2cKrvnmIS7ZnHk6qvjfL9RICWW\nArMLV+0LTakB3ueJQgKT2gMzcno71jMPgAaU8gwao3C0pOWL4ATOSKcuYkzOy3bA8IXHPsxYzdz8\nkOh8aYgS5lwvuC1BFpSpBMNH3A1vn8UMYEgsiarNyik1tjHx/fu5dUv6XWOIjBHb+HNePPOe211z\nYCi5mW6pBJleQCyYFUXCQE/SVHm5SQjK0oM0WyXL162RTzcBJYmNrC4TUAqFfiTmIETZNPTs0+Uk\n08p4U2eXO4QqJVdcAKExMuHVowM2ajAZvAlfxrpfqshS8ljgOflut2XGWOj9JWo4RcGIXCLKAkhN\nnhvCHNOiw1iApvtWVIILWppT+hhCh8t9QN89e/IRGS0Ji3jnQlZatycek4O/W39E0ABuEEWZqy6M\nVf/fzW8rOzH9wngON/+cB9oUmLwgY0Eo2bYViE2/qqCXQayRUeulcHBwCUgUiU3qoaYYdQAQNFU/\n4I50jOVDhD6z6xYHpVQ02j5BVjDrEHkf1mXioPMZ1nufMBbKbjJJ27RraURDNJc7dM8ECte8FABp\nFf3tdUgApu/jbBpWd2ug0/vkZRSga25o9OlFKOS8hnpZiRlNWXAQOiv1VsQKEfIHiLCKGnTRBecz\n8DOITFFUXo+q1bQP6GiBtFCa6vGFOY62XEwzDfl8DpMYVpiMxJzJkAF1TmA78uUdhJ2QmOFUCsqE\nYkXJli0G9My+feM5mcLIp3Dv62RFvDQgCJznpbMHBvdlhX+r2vX+GdKbZQjZ+pKhfldqgqDPN51b\ndQtxD4JsvExZwuh3926hK9C/Xmtg2T8RNCA1t/tFc4ydnNZcvSZuwuTnnnK/LaC1A63R9K/7XV49\nIAZPSL1+tL56Ddm36ty6k3GDwAxXV2s3XUQJ1mPfck0UvFhNJlTpZ6DBxAHdeYIaoybh57idOTlI\nZ8QKagH+jBDUf57SW7UDd5rPv68/ImhsCMYwkumw4r4YJoGgKzf7VHL6xTPOGLCPk9TWdcvoi5jm\nDOzyPtT8E17kDIsrpC5n1UC7nm3RkBruPpe6fFMojXOGyv4JZjqk8ppBsQOJs8lrYNH5SnOL5zVW\n8OJl99klRlyTcyggaMHQWRP25OrNytSmzB1gELbZXraGmYM+X5Nme1ttzyB0ZHLYDoqBoB/LmM4v\nZ8TE0dW3aYLzZGtfud2Imy7PGtNShnVWKAbV/jfyproCwkvVhqfx9ZAdzDjxfZw4Xi+8339hzq7h\nWITsTJH82I5+enaDZ4Iwa1l7HhsOBLAGhsEwkrQ8UOM0RO2scSmJIRkdheX0zK0l3NE084F77cpF\nA5SKbB4dt8Xa8Z9v+qzXOX6ATV0zu2Ky6rjJ+t5U98tz1yOASZLJe1LYd75P1NzW3k4MMn9O/fzV\nxXiligK5eldMw+iFtQg1OsMZuSaJZgsNGZODgqtQw+Jla3vuSU/OUqd63g1uekr5c0RrCJ0tkxTs\nOME+RCLh983VDKSnOmEpwaxakCaC32VcDFq9seKYqk78+TzcqmLqfpX4byUUP79d/oigcW8Oc/OV\neWFfp2w6CetrvPTu1FbDFz7EPs4oZ0z6aeXyb0Wq9WD2RK+dYTHLMrsBanDvALZsQ1bVsJBc/T9V\nFrP2xezvnVu8WPqcU8NbChD+C9jCePdY1GT/aM+5Jw5sJVYYM5dD2bt/W+hlEDOjAdCh9CyMpS/Q\nflTRV4q8b1JZv18nPPAF+jNUaqdUuYlxDZzCkrPpUkxWJF1l85wlgWfDmO9FR+69oWUnC6y1ZfOC\nyWfoSrElg84MKfNVzlcVXq8D86LrceilMwX7B4bf7+z5jeZIOnmuCiLC9EwAxptDf1b3ZgKYgtDc\nVwoEoEa48fiJwQwSmmEiLce6gGObX2Zvt/+tBMORLVdDo055DJQIUZvj+e0BNpHHIqBAWbU+m45p\n07tWgxj+NVU5FiTwcyC1ZTcDyz9Ra4gPKG3X5Mz4BEyYWRWbHCnKPQ0FwYToy2XEitGSd5DhNQad\nghhQen6lxKhK44YjALTV7/S5ysVGS1yYtBHJbjnG2v9xyg9MVX+LBivrnXyVgqRFuuHvl7neMQTZ\njnMULlNtAfXDHOT+8/ojgoYQDWZQMGTC/4Xl1YAdZQFnfSHoaTc5d68AsNkeR7zaxBDwvGhn6+uC\nj1iW3C152RCXTSmYVcWEegCLD106+C4RLTYUA0MByLAI8dypBy25Zu2XNx3bxtxQHIwQua79UZj0\ne5vOTOg4DsSwnQLhiQw230InkJe3xUKBjg50XrzwEKdrSnthFbwV5aTTnhd/lxXIFQn2Cnf2XSAV\nmoK9xLevr6UkzsmZAVkKmBKeIQ90aTT4ck18vci0yuNFpXfSS6v3xPf3GygOJ/r2+kJV4tUDf73f\nmHKQ5UyWwsIRpBOK+rQrG5vWPfQMBUfmuiRF7oitE7A4FRFINWazeFGPSZUxAFFtN42bbKm+kg4H\nC2fLWTshcB+L1exW1pO2OddBTLolIjPRy1DhJCSI3SBuGXjbq0ywx6xatOxXb5wEeQ00NW1r1kpA\nYol2P++Ep/pU9GYyLFXY5pdjXagAlntD6mUk8hCaT4Jbsslq+pK1SwZ7piXU5JIoGKHxwnNDkJzj\nDbEOac2TY+BSQ375kE3N9DgOzBq4BCd6GFvrZmgdq9p5vQ4yri5NG8yJnsciAdnzbkzetRNyQ8DY\nupCfwLF/RNCYBZr1mTHiusPZiQ6wISZrCFZDXBVKhioPYF1wdW2ZPxBqfJvUCwCyHl/Zzc7GpjBC\nXkbKjCOAUB2kudZAqXFYRhZ0trbewlkgys3PWjRVHwz+DqypYqZVxu1z+ef/jN3wS3uu5tcYxFjb\nzAUPDGtWIvg9B+ee523PA8B5nWiD3+HoDaUg/n5/h2eT+DNz7CtV4Zkd13XifdII75yXGJuqGi/q\nOL6fp95N2klXkG0SnfqCMScaCKXZuqL1F65iVdQ6TRBHTbSDEMTX60VPsgH9ncAYiVdv60WJSPQX\n7TDGghIL/eszGxHawRufjh9cc6cqYPI7eOHzyhG4dKt42jRpI1Hq3ZzjwrxKiQ3HxG7xJLPbOccy\nNVzOBcGgTNv0vudQA+pvxD7Heas6sCuNTMKOLXIlGLPmmr/9OjrmYHI1Lj7rMfY4WbKLGi3wp/yU\n5oZGP12ZDGjWKTipyaRtjC38PXjJ/VKIQu6khnv7w08W5FXrviC0zJ4EEGhBR4JSvxPr/ort6lCF\nmqcqTQZMVs9Y79DUJc/Eei5GnA0mCZnlgoVH0QrmGoEazkT5OdpNQ0NqLysbatUoYswfv+gP688I\nGnOuBhzEZkLEOqhs/hAfNH0ytTlGUZzdOxoEyKC5Y1xmp9xZWm7yuZJmryT3XxIEUHB8ihXR15JW\nhNAXYRIVBAoefmi7KVblwOMsxmp4rGzRd4pFW6lDkfgPn+F/uK7zzexFdOQ49LJPNjW3YpQsqBDG\nGwqSlwbMTPD7Oouxsn57KHHvCoIl3t/1iJiVjvX7OgIltlAjo23SE6rU9DU+3lvD+6SanRP2Cu04\nVhbdsiE7k48pRsOqQoO2GYT/6OmFOfec7sCCGExOMONtfqiNGRcxfNMyoxMiLGeO6l+kzxN89ry3\nEm3Vtl+pmriuuv0cvwtb+8SEZMMN44av87jxQjNUycFO2yttwpYZhsAIF5pG6vO74detc+KzD8wa\n6/0qQPYaIkwEE7DlxSa9FeHNz21Exhzo9IMHwIsypF9ZkzVvfcha7yBWBUxVFWD7cPcToCSXpINr\nEyfcdC+TQtj/SOlRFr05E57X4WdKqVFTIGJQ4EXO++X1+lKyc2FN+Qz1QB3EwFEO9vgaLAlpj8KH\nuuHnSWGvnyUBjT88aEAPwCWqYShAF6kiA8s+gXaK2tkVkRcOfOP53y58q86XH75/Q4lxYNpfTb+m\nzAwCwKIhGkcWW8UPZw7MUWi9YcxcwSmg2dkuUTMRcwdDwl67otqTxHLvB/QAw0OcQpDAZxcYIKdR\n0C6i5mDGrz02zfT1egl2G+j9a33OIRZPtq6BLbUM2aBgAAXCRFA4WBPxeuF9ngv66r3J10pQSTuE\no5MpQvZZkuEm6DAEObmp2fR3xxz4dhDSMsTXxRip5CV4jYvjOiM1+4Gwz+BNgQjNEgfEAtJkPUGW\nH60wJXmfyT27BCsoZG6dgJ1+Oa99K5qdXc4p8Z+gjJL+wpXuqlTHubj6voR00yOjs08IO/ua+vtv\n/bWVuG1K+JyTZp8OziBRgX0R3MgEOztevQuIzB721zIpZS7Yef7k8vofbDyrN0ObeosCQeNF7ZUn\nT0YZvrZmR/Bp5qK1Ki7fxIfBflrxn1HybBDMVkHK9iXih524C+xvhaq1SCBmYYZGugZWMKVlvfyn\n5IxtK3Uz7RY64fuKlxtCENwUJKqrkmiJqLeld3VGrHP5n9afETSKzou8L51Z+YABLhfW8UnxBZrg\nIFNoxLn2cKO6lZTG2BevWZduObtcVYDk+c5OK5BZu3kOrD/HWLbZKG7wurG2lJtxz3SxGvg2UHRT\nzg9qSneyDiDMlNk05E+FZpBjZqDEbtGchNiYtAWR4+IoVD0qVFHIdMfPz/fJ7y4IaxV9KzMdkLRg\nPcfwbIluHrv8sMQMOt9v0pUFl7Fh7qapnpMca22nwCmEGwKcwctrnEMBQZcpCpiBa54Sp01Vp/p5\n5d7M5vR/ylgDihYbOiOGHN1YNZPFw7xaYzC2yMujeEPB7bqGEhMmTTyIP1azOz1ipmvfo7t53tRI\n2dYbzlMjRxPSSihJUVDwzAUHPT5z/XPsYUcDhXEx8Zn+joAqmLH2M5HAqHWp+zytqzg+3XMGY8hq\nhRUZln4rF/y0A3jKiHTODdUB0JjjEnszVsAw1DXn4H7dgklVLAjRJppDrr7O6v3eLAjJz6wMAeZ6\nHk5y32VhJMkRAaA830bPawz2AYVz8SzdPrdyWSVFQNd3bklx6N+tPyNo6ABNlaxu1lDJOBdrZRfs\nhpVUcYTVum5Y++c6Y8OCXFYDfeXwtTj5UX4lsbJknrDGXonEUz+oLu9QFmR7cgsuNSkg5DCZ7WZa\nU9dq4HZg9H28VOrzJ+MHFtWn5nmlLNqUSgB4vb7WAJgFV5j1pfK3ryFCTZfQgBXxacWTqoI0bRjG\neXkh9eOFa/5fXZ4TEX3vtSuqWfQ5SkOSzKYKQBOFFnOwFxZB+uCyotBUuN6WIK1qyjHCWSczODJZ\nEimBIjLlAaVgp7dnjLkcS397KbhdqsKgfYnMpXOBoA57QxGik0eTLN4Xvi3FdNw+Fp8BLxb7HWHS\nWG+c31kxjLkqrxAcV8UMt2lwVYYrXKyhPVOV33rFhPn7DI1BI8ntmzDIbQdUVRAvRxn+3GHY2hQm\nA0qeEBjnP0C5nROoBg9ju8bA0TsvdL1TDnakmwq7KUOihlwFDUftxGO9r/JZ011lkWOicM3J/p2T\n4Ng+WNzDUC+ogZYy1Bxlp1knyTyiCYdtZbBYb0MjjR1UfIZ8D/2wlIxn6A4pPV+9MwFR23+SIP0Z\nQQO3Mk+Xqlkbix3lzB2CmlRi8ULKvT/OeFquCxpRq7m4jMEWLuxifj/olrYH0Asijrq8c+E3yVTe\nSl94Acx7lhSqdPh3HBvW3A/+oZV929AwY0/q4n64MoE45CH/q99ftkBYUxKDjKksZ1aFKl42KOA8\n37TCXlVW/SBEM+sqcuK81G+SkpjP9/SmqAEtR9F2M5p0pjplAicqZpUgpGusCWqoiS7/oOh00/XP\nWeV+FTUyEZiiPFaR1YaayEq0Q7DkBIYuR8I3yjzL2j3H+QAABqpJREFUBAhCNJ+t2JoB7eGYtAEX\nLgjAehis4V/e84QrhPUQRXWmUzHvjAHKXKhkFp4q4aJ/lqBdBSknvP657D+w8p9jUGAGcM/Uvo+W\nS/7FLHpT21d/a3IwV865xiMnEv1wtW9YypR4/ofp5Bn4+JxDe8bvJ9ilQJhw2i2iFgkB4HtPUSrv\nhn+3eF82R2FL9FoN+w0zDtLRhUTY66mUTHXv002X5hojknYvvbP/1YOJXNe7YxKxBX6AFDpVQLRV\nIVEE2jWBsly88H6B4XrT37n550Xo+Gc2RX9M0FjlIbmz62JyqbDtxed6+CmMcU43g4SRrk0AlpFY\nbSUuS15tvLMIX/W34IXlwXLLrvjf4HGNd+DMdgo1LXJayrMNFcTWdpQuisC2kHZQMsUSawt+bHLi\nw0qjqZzepAD+IltMkD8OzHMKW1djunUaEQLA8EeR4GvBfoEIWkksvjp4kWUUMXAFUULE4oWbfppY\nVWJquiIi0PMQO044c4ZK9MS83gtW4hwQ4vaG9QKwLweghi6CMzJaerKano3+eVwbKvCF98nymfCI\nVsQWsLX05UM4kMPINkx1Xdd+F+ImwppTPQAJPwU/XZTCw4PGTNGs2wWydDmqLCDmIaYpsKBZYTg7\nFVvLkJW8xEglbSvqZAQrtgWZQboaoKwi15uTofn0+nv3CXP4cL/XchWk5MsGj9YTla3pRUXN2EzJ\nqtqmftDfvz3PBS9NukUvCBm59mmNaLgFxKmEZEsJAIjKbxt3X3/rdyFgEkIYAFalPcegjB+3c3yr\nFmxNworR9kvA7qlxP6zT2L2A/3/9EUGD2Qyte9eXUKaT5Qzhx3KpoEFEfjDK4F1mukKxUpU/TFCM\nKbDYGZarANyaSc0undgZOZtTuSlpAQhUhk4lsnnPN3R1VwDzr+1/9isULjx9uKu2uRngG/ZzjQaA\n79+/a9KemByhTAWBsO02oKzdF+jAeXE4EjBxXcxourK1gvpGUpofx7GM7QKeXEaNhDH84cu6tcXg\nyO5Jc4n2ytUYjbU3yQqiLFhStXmjNPvZVriha549oShnirzPau0FCpCShBWY4KTeD9jK+3fXeb5/\nMLlcFO0I2YqD0BKggEY18phvLHpnQX0wvRO+XDKRcyKlgzArsG6Q6IZGNguKwWlToysTEFV2fV1t\nF98zZzE3bZKSBPgilqvxSsiFyfuHmZLL6vQ/BIbJvlRryWD04ZpTFSp0Wc9b8xgkafCjsYdlN4ZR\nxazfkJNTurBLgi79y8GgFuMOTgJ1V0yp0cnYY/UyxoXlXaezSJ1LW0xNAHrf+ftaPxCGtgBs5+9c\n94v7Lr4xjU6UgrBHWeswwTRdf+YF4fzN+iOChiO+P2xVoVLRNP1FANNaGU1TlhQMCEPBxazIKuLy\nQ35Es3xR8H83H9qZfxpychaH+GFAE3tFubNgZ9DKCA07tQhMveAhfHl//B1AiG+PTWsszrRYAXLy\n0K6GvH8Ifvo8f30t8V0tbDQlECqVp2abOFMiVIQ1WyNWRXFR+yAGyqkRq6a27iKugBpb8QsavDmw\nzxmY88J18ZJuEpxx1GvKiZYVWvYOa2MyYrmA0ifW1Y5EXe4TpCztJQa0fsfOwSvzrNLFyv7H0Tru\nzsq/veUSSJqsUTVBpwhhBtimeTwjtaCTyOmEnf8pTr0dDMZ16SIinba08QRYdO6S5/bVu4IAIcgy\n1VU0z5gct8szKiseBPLwtD3NX1Ht5aQHMCeJ/70JNbDAjZn2bS+qMATfmHxicWuKWfQptRwQzJfb\n+K+wzSERrjKxguGl3hdAzdBVskYRlZ7PhBXgsn1xzqhnnH27CSyvr2RyMq57hc99MxzEIm2Pu95k\niY1WZOui9/4b/bf4s1gBNtS8NDGT1euCAZXI0bCSXlPc/2vfsz857PFPPJRnPetZz3rW/471D4GG\nz3rWs571rP8N6wkaz3rWs571rF9eT9B41rOe9axn/fJ6gsaznvWsZz3rl9cTNJ71rGc961m/vJ6g\n8axnPetZz/rl9QSNZz3rWc961i+vJ2g861nPetazfnk9QeNZz3rWs571y+sJGs961rOe9axfXk/Q\neNaznvWsZ/3yeoLGs571rGc965fXEzSe9axnPetZv7yeoPGsZz3rWc/65fUEjWc961nPetYvrydo\nPOtZz3rWs355PUHjWc961rOe9cvrCRrPetaznvWsX15P0HjWs571rGf98nqCxrOe9axnPeuX1xM0\nnvWsZz3rWb+8nqDxrGc961nP+uX1BI1nPetZz3rWL6//B6rZBG+JV/t7AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 460.8x288 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqIJDN5Q2wmA",
        "colab_type": "text"
      },
      "source": [
        "### Linear AE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8-SXc0kwMdg",
        "colab_type": "code",
        "outputId": "6ac1b59f-a02f-479a-8bba-95a420c5ae60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        }
      },
      "source": [
        "# Create an AE and fit it with the data using 2 neurons in the hidden layer (to compare with 2 PCs)\n",
        "# --> fully-connected\n",
        "# --> use linear activation function\n",
        "input_dim = xscaled.shape[1] # size*size*3\n",
        "encoding_dim = 2  \n",
        "input_img = Input(shape=(input_dim,))\n",
        "encoded = Dense(encoding_dim, activation='linear')(input_img)\n",
        "decoded = Dense(input_dim, activation='linear')(encoded)\n",
        "autoencoder = Model(input_img, decoded)\n",
        "autoencoder.compile(optimizer='adam', loss='mse')\n",
        "print(autoencoder.summary())\n",
        "\n",
        "history = autoencoder.fit(xscaled, xscaled,\n",
        "                epochs=20,\n",
        "                batch_size=16,\n",
        "                shuffle=True,\n",
        "                validation_data=(xvscaled, xvscaled),\n",
        "                         verbose=0)\n",
        "\n",
        "# Plot the loss --> MSE\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model train vs validation loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper right')\n",
        "plt.show()\n",
        "\n",
        "# Use the encoded layer to encode the training input\n",
        "encoder = Model(input_img, encoded)\n",
        "encoded_input = Input(shape=(encoding_dim,))\n",
        "decoder_layer = autoencoder.layers[-1]\n",
        "decoder = Model(encoded_input, decoder_layer(encoded_input))\n",
        "encoded_data = encoder.predict(xscaled)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         (None, 49152)             0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 2)                 98306     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 49152)             147456    \n",
            "=================================================================\n",
            "Total params: 245,762\n",
            "Trainable params: 245,762\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "poub5PD3yzxl",
        "colab_type": "code",
        "outputId": "ebe7923b-b96b-49e8-8c00-65ddcbcdfefb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Get first 2 features --> weight vectors\n",
        "ae_weights = encoded_data[:,:2] # normally already shape (153, 2) with 2 neurons\n",
        "\n",
        "# Plot the 2 weight vectors of AE\n",
        "plt.figure()\n",
        "lw = 2\n",
        "for i in range(170):\n",
        "  if i==0:\n",
        "    plt.scatter(ae_weights[i, 0], ae_weights[i, 1], color='navy', alpha=1., lw=lw,\n",
        "                label='boat')\n",
        "  else: # otherwise one point per sample in legend\n",
        "    plt.scatter(ae_weights[i, 0], ae_weights[i, 1], color='navy', alpha=1., lw=lw)\n",
        "    \n",
        "for i in range(266):\n",
        "  if i==0:\n",
        "    plt.scatter(ae_weights[170+i, 0], ae_weights[170+i, 1], color='turquoise', alpha=1.,\n",
        "                lw=lw, label='cat')\n",
        "  else:\n",
        "    plt.scatter(ae_weights[170+i, 0], ae_weights[170+i, 1], color='turquoise', alpha=1.,\n",
        "                lw=lw)\n",
        "   \n",
        "    \n",
        "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
        "plt.title('Linear AE')  \n",
        "plt.xlabel('AE' + \"1\")\n",
        "plt.ylabel('AE' + \"2\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmJlZJicKMzPL5ERhZmaZ/gdH0u+z/tXLOgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ib4Q-fYXplQB",
        "colab_type": "text"
      },
      "source": [
        "Linear AE very similar to PCA --> same subspace spanned"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Kg3I6t94B65",
        "colab_type": "text"
      },
      "source": [
        "### Reconstruction AE diff #neurons"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XjBb3AW4Ffs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create an AE and fit it with the data using N neurons in the hidden layer (to compare with N PCs)\n",
        "# --> fully-connected\n",
        "# --> use linear activation function\n",
        "input_dim = xscaled.shape[1] # size*size*3\n",
        "encoding_dim = 436  # N\n",
        "input_img = Input(shape=(input_dim,))\n",
        "encoded = Dense(encoding_dim, activation='linear')(input_img)\n",
        "decoded = Dense(input_dim, activation='linear')(encoded)\n",
        "autoencoder = Model(input_img, decoded)\n",
        "autoencoder.compile(optimizer='adam', loss='mse')\n",
        "print(autoencoder.summary())\n",
        "\n",
        "history = autoencoder.fit(xscaled, xscaled,\n",
        "                epochs=50,\n",
        "                batch_size=16,\n",
        "                shuffle=True,\n",
        "                validation_data=(xvscaled, xvscaled),\n",
        "                verbose = 0)\n",
        "\n",
        "# Plot the loss --> MSE\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model train vs validation loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper right')\n",
        "plt.show()\n",
        "\n",
        "# Use the encoded layer to encode the training input\n",
        "encoder = Model(input_img, encoded)\n",
        "encoded_input = Input(shape=(encoding_dim,))\n",
        "decoder_layer = autoencoder.layers[-1]\n",
        "decoder = Model(encoded_input, decoder_layer(encoded_input))\n",
        "encoded_data = encoder.predict(xscaled)\n",
        "\n",
        "decoded_data = autoencoder.predict(xscaled)\n",
        "decoded_imgs = decoded_data.reshape(len(xtrain), len(xtrain[0]), len(xtrain[0,0]), len(xtrain[0,0,0]))\n",
        "\n",
        "print(mean_squared_error(xrs,decoded_data))\n",
        "\n",
        "# plot 2 \"good\" images of each class\n",
        "pl.figure(1, figsize=(6.4, 4))\n",
        "\n",
        "pl.subplot(2, 4, 1)\n",
        "pl.imshow(xtrain[8])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 1')\n",
        "\n",
        "pl.subplot(2, 4, 2)\n",
        "pl.imshow(xtrain[14])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 2')\n",
        "\n",
        "pl.subplot(2, 4, 3)\n",
        "pl.imshow(xtrain[323])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 1')\n",
        "\n",
        "pl.subplot(2, 4, 4)\n",
        "pl.imshow(xtrain[386])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 2')\n",
        "\n",
        "pl.subplot(2, 4, 5)\n",
        "pl.imshow(decoded_imgs[8])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 1')\n",
        "\n",
        "pl.subplot(2, 4, 6)\n",
        "pl.imshow(decoded_imgs[14])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 2')\n",
        "\n",
        "pl.subplot(2, 4, 7)\n",
        "pl.imshow(decoded_imgs[323])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 1')\n",
        "\n",
        "pl.subplot(2, 4, 8)\n",
        "pl.imshow(decoded_imgs[386])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 2')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_oiCHz046HNC",
        "colab_type": "text"
      },
      "source": [
        "## Non-linear and convolutional AE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5A3N6li2lrZk",
        "colab_type": "code",
        "outputId": "41082bd6-ba3c-4dc5-b9d6-518af6ab5c4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1688
        }
      },
      "source": [
        "# Convolutional AE: use images in 3D, not flattened\n",
        "# AE1\n",
        "# size code --> /4\n",
        "\n",
        "input_img = Input(shape=(len(xtrain[0]),len(xtrain[0,0]),len(xtrain[0,0,0]))) \n",
        "\n",
        "# 196608 dimensions if 256\n",
        "# 49152 if 128\n",
        "\n",
        "net = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
        "#net = Dropout(0.25)(net)\n",
        "net = MaxPooling2D((2, 2), padding='same')(net)\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(net)\n",
        "encoded = MaxPooling2D((2, 2), padding='same')(net)\n",
        "\n",
        "# \"code\" layer: at this point the representation is \n",
        "  # (32, 32, 8) --> 8192 dimensions \n",
        "\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n",
        "net = UpSampling2D((2, 2))(net)\n",
        "net = Conv2D(16, (3, 3), activation='relu', padding='same')(net)\n",
        "net = UpSampling2D((2, 2))(net)\n",
        "decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(net)\n",
        "\n",
        "autoencoder = Model(input_img, decoded)\n",
        "autoencoder.compile(optimizer='adam', loss='mse')\n",
        "print(autoencoder.summary())\n",
        "\n",
        "#plot_model(autoencoder, to_file=path+'autoencoder.png', show_shapes=True)\n",
        "SVG(model_to_dot(autoencoder).create(prog='dot', format='svg'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0617 15:28:54.908169 139886258935680 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0617 15:28:54.926352 139886258935680 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0617 15:28:54.931329 139886258935680 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0617 15:28:54.950908 139886258935680 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "W0617 15:28:54.988506 139886258935680 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
            "\n",
            "W0617 15:28:55.026180 139886258935680 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 128, 128, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 128, 128, 16)      448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 64, 64, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 64, 64, 8)         1160      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 32, 32, 8)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 32, 32, 8)         584       \n",
            "_________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2 (None, 64, 64, 8)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 64, 64, 16)        1168      \n",
            "_________________________________________________________________\n",
            "up_sampling2d_2 (UpSampling2 (None, 128, 128, 16)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 128, 128, 3)       435       \n",
            "=================================================================\n",
            "Total params: 3,795\n",
            "Trainable params: 3,795\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg height=\"702pt\" viewBox=\"0.00 0.00 229.00 702.00\" width=\"229pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 698)\">\n<title>G</title>\n<polygon fill=\"#ffffff\" points=\"-4,4 -4,-698 225,-698 225,4 -4,4\" stroke=\"transparent\"/>\n<!-- 139884848136032 -->\n<g class=\"node\" id=\"node1\">\n<title>139884848136032</title>\n<polygon fill=\"none\" points=\"44,-657.5 44,-693.5 177,-693.5 177,-657.5 44,-657.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-671.8\">input_1: InputLayer</text>\n</g>\n<!-- 139884848135976 -->\n<g class=\"node\" id=\"node2\">\n<title>139884848135976</title>\n<polygon fill=\"none\" points=\"44,-584.5 44,-620.5 177,-620.5 177,-584.5 44,-584.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-598.8\">conv2d_1: Conv2D</text>\n</g>\n<!-- 139884848136032&#45;&gt;139884848135976 -->\n<g class=\"edge\" id=\"edge1\">\n<title>139884848136032-&gt;139884848135976</title>\n<path d=\"M110.5,-657.4551C110.5,-649.3828 110.5,-639.6764 110.5,-630.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"114.0001,-630.5903 110.5,-620.5904 107.0001,-630.5904 114.0001,-630.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139884848189792 -->\n<g class=\"node\" id=\"node3\">\n<title>139884848189792</title>\n<polygon fill=\"none\" points=\"0,-511.5 0,-547.5 221,-547.5 221,-511.5 0,-511.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-525.8\">max_pooling2d_1: MaxPooling2D</text>\n</g>\n<!-- 139884848135976&#45;&gt;139884848189792 -->\n<g class=\"edge\" id=\"edge2\">\n<title>139884848135976-&gt;139884848189792</title>\n<path d=\"M110.5,-584.4551C110.5,-576.3828 110.5,-566.6764 110.5,-557.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"114.0001,-557.5903 110.5,-547.5904 107.0001,-557.5904 114.0001,-557.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139884848190576 -->\n<g class=\"node\" id=\"node4\">\n<title>139884848190576</title>\n<polygon fill=\"none\" points=\"44,-438.5 44,-474.5 177,-474.5 177,-438.5 44,-438.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-452.8\">conv2d_2: Conv2D</text>\n</g>\n<!-- 139884848189792&#45;&gt;139884848190576 -->\n<g class=\"edge\" id=\"edge3\">\n<title>139884848189792-&gt;139884848190576</title>\n<path d=\"M110.5,-511.4551C110.5,-503.3828 110.5,-493.6764 110.5,-484.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"114.0001,-484.5903 110.5,-474.5904 107.0001,-484.5904 114.0001,-484.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139884848190352 -->\n<g class=\"node\" id=\"node5\">\n<title>139884848190352</title>\n<polygon fill=\"none\" points=\"0,-365.5 0,-401.5 221,-401.5 221,-365.5 0,-365.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-379.8\">max_pooling2d_2: MaxPooling2D</text>\n</g>\n<!-- 139884848190576&#45;&gt;139884848190352 -->\n<g class=\"edge\" id=\"edge4\">\n<title>139884848190576-&gt;139884848190352</title>\n<path d=\"M110.5,-438.4551C110.5,-430.3828 110.5,-420.6764 110.5,-411.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"114.0001,-411.5903 110.5,-401.5904 107.0001,-411.5904 114.0001,-411.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139884847787928 -->\n<g class=\"node\" id=\"node6\">\n<title>139884847787928</title>\n<polygon fill=\"none\" points=\"44,-292.5 44,-328.5 177,-328.5 177,-292.5 44,-292.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-306.8\">conv2d_3: Conv2D</text>\n</g>\n<!-- 139884848190352&#45;&gt;139884847787928 -->\n<g class=\"edge\" id=\"edge5\">\n<title>139884848190352-&gt;139884847787928</title>\n<path d=\"M110.5,-365.4551C110.5,-357.3828 110.5,-347.6764 110.5,-338.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"114.0001,-338.5903 110.5,-328.5904 107.0001,-338.5904 114.0001,-338.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139884847787872 -->\n<g class=\"node\" id=\"node7\">\n<title>139884847787872</title>\n<polygon fill=\"none\" points=\"0,-219.5 0,-255.5 221,-255.5 221,-219.5 0,-219.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-233.8\">up_sampling2d_1: UpSampling2D</text>\n</g>\n<!-- 139884847787928&#45;&gt;139884847787872 -->\n<g class=\"edge\" id=\"edge6\">\n<title>139884847787928-&gt;139884847787872</title>\n<path d=\"M110.5,-292.4551C110.5,-284.3828 110.5,-274.6764 110.5,-265.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"114.0001,-265.5903 110.5,-255.5904 107.0001,-265.5904 114.0001,-265.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139884838975416 -->\n<g class=\"node\" id=\"node8\">\n<title>139884838975416</title>\n<polygon fill=\"none\" points=\"44,-146.5 44,-182.5 177,-182.5 177,-146.5 44,-146.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-160.8\">conv2d_4: Conv2D</text>\n</g>\n<!-- 139884847787872&#45;&gt;139884838975416 -->\n<g class=\"edge\" id=\"edge7\">\n<title>139884847787872-&gt;139884838975416</title>\n<path d=\"M110.5,-219.4551C110.5,-211.3828 110.5,-201.6764 110.5,-192.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"114.0001,-192.5903 110.5,-182.5904 107.0001,-192.5904 114.0001,-192.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139884839049144 -->\n<g class=\"node\" id=\"node9\">\n<title>139884839049144</title>\n<polygon fill=\"none\" points=\"0,-73.5 0,-109.5 221,-109.5 221,-73.5 0,-73.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-87.8\">up_sampling2d_2: UpSampling2D</text>\n</g>\n<!-- 139884838975416&#45;&gt;139884839049144 -->\n<g class=\"edge\" id=\"edge8\">\n<title>139884838975416-&gt;139884839049144</title>\n<path d=\"M110.5,-146.4551C110.5,-138.3828 110.5,-128.6764 110.5,-119.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"114.0001,-119.5903 110.5,-109.5904 107.0001,-119.5904 114.0001,-119.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139884839136336 -->\n<g class=\"node\" id=\"node10\">\n<title>139884839136336</title>\n<polygon fill=\"none\" points=\"44,-.5 44,-36.5 177,-36.5 177,-.5 44,-.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-14.8\">conv2d_5: Conv2D</text>\n</g>\n<!-- 139884839049144&#45;&gt;139884839136336 -->\n<g class=\"edge\" id=\"edge9\">\n<title>139884839049144-&gt;139884839136336</title>\n<path d=\"M110.5,-73.4551C110.5,-65.3828 110.5,-55.6764 110.5,-46.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"114.0001,-46.5903 110.5,-36.5904 107.0001,-46.5904 114.0001,-46.5903\" stroke=\"#000000\"/>\n</g>\n</g>\n</svg>"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNYeRTH5l1lv",
        "colab_type": "code",
        "outputId": "6358031a-f812-4c90-b6bc-91c54f1f5ccc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        }
      },
      "source": [
        "history = autoencoder.fit(xtrain, xtrain,\n",
        "                epochs=50,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, xval),\n",
        "                         verbose=0,\n",
        "                         callbacks=[PlotLossesKeras()])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb8AAAE1CAYAAAB3OO7MAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIgMOQo/EREZcv5/2iLQGmzbVXAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mse (cost function):\n",
            "training   (min:    0.007, max:    0.075, cur:    0.007)\n",
            "validation (min:    0.015, max:    0.071, cur:    0.017)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QV6-VdYDl64u",
        "colab_type": "code",
        "outputId": "7059562e-0cc8-4e13-f455-eb0665440801",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "decoded_imgs = autoencoder.predict(xtrain)\n",
        "decoded_rs = decoded_imgs.reshape(len(xtrain), len(xtrain[0])*len(xtrain[0,0])*len(xtrain[0,0,0]))\n",
        "\n",
        "print(mean_squared_error(xrs,decoded_rs))\n",
        "\n",
        "#decoded_imgs = autoencoder.predict(xval)\n",
        "#decoded_vrs = decoded_imgs.reshape(len(xval), len(xval[0])*len(xval[0,0])*len(xval[0,0,0]))\n",
        "\n",
        "#print(mean_squared_error(xvrs,decoded_rs))\n",
        "# --> same error on validation set (from previous plot, we see that the two errors converge)\n",
        "\n",
        "\n",
        "# plot 2 images of each class\n",
        "pl.figure(1, figsize=(6.4, 4))\n",
        "\n",
        "pl.subplot(2, 4, 1)\n",
        "pl.imshow(xtrain[8])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 1')\n",
        "\n",
        "pl.subplot(2, 4, 2)\n",
        "pl.imshow(xtrain[14])\n",
        "pl.axis('off')\n",
        "pl.title('Boat 2')\n",
        "\n",
        "pl.subplot(2, 4, 3)\n",
        "pl.imshow(xtrain[323])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 1')\n",
        "\n",
        "pl.subplot(2, 4, 4)\n",
        "pl.imshow(xtrain[386])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 2')\n",
        "\n",
        "pl.subplot(2, 4, 5)\n",
        "pl.imshow(decoded_imgs[8])\n",
        "pl.axis('off')\n",
        "pl.title('Bird 1')\n",
        "\n",
        "pl.subplot(2, 4, 6)\n",
        "pl.imshow(decoded_imgs[14])\n",
        "pl.axis('off')\n",
        "pl.title('Bird 2')\n",
        "\n",
        "pl.subplot(2, 4, 7)\n",
        "pl.imshow(decoded_imgs[323])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 1')\n",
        "\n",
        "pl.subplot(2, 4, 8)\n",
        "pl.imshow(decoded_imgs[386])\n",
        "pl.axis('off')\n",
        "pl.title('Cat 2')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0167772\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Cat 2')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAADzCAYAAAB6+ErUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXmUZdlV3vnb59x733sxR85TZVbW\nJKkGqUqlgUEzJYEQbWlBI7kNLZDN3DaiaWB5GS1MGwPuhm6bxWhwG8wgCRnQssRghCQ0jyWkmlWV\nlUPlnJERkTG88d5z9u4/znmRKRmJoqJUg4i9VqzMiPfeHfa9dw/f9+3zxMzYsi3bsi3bsi17NOae\n7APYsi3bsi3bsqePbSWNLduyLduyLXvUtpU0tmzLtmzLtuxR21bS2LIt27It27JHbVtJY8u2bMu2\nbMsetW0ljS3bsi3bsi171LaVNLZsy7Zsy7bsUdtXZdIQkRMiMhCRrohcEpE/E5GrHsdt3/FlXq9E\n5I/y+0xEXvZ47Pepbk+yz79GRP5KRJZF5KKI/FcR2ft47PvpZiLyT0TkznwdzonIX4jIix7lZ01E\nrvsyr+8VkXeJyNn83qsfr+N+OttX2OevEZGPiMiKiJwXkf8kItOP39H//e2rMmlk+5/MbArYC1wA\nfvkJ3PdHgO8Ezj+B+3wq2JPl83ngN4GrgUPAOvDbT9C+nzImIj8K/Afg54DdwEHg14DXPk67UOC/\nA9/2OG3vaW9PgM9ngX8L7AOeBewHfuFx2vZjMzP7qvsBTgB3XPH7NwMPXfH7LPC7wEXgEeAtgMuv\nXQu8H1gCFoE/AObya79HenAGQBf4ib/jOE4DL3uy/fEPyef5M88F1p9snzzB/p/N/vn2L/OeFwAf\nB1aAc8CvAFV+7UOAAb28nTd8me0U+b1XP9nn/Q/F51ds71uBe57M8/5q7jQAEJEJ4A3AJ6748y+T\nLvg1wEuBNwJvGn8E+HkuZ/argJ8GMLP/FThJrqjN7P9+Ak7haWdPAZ+/BLhv0yfy9LKvBdrAO7/M\neyLwvwM78vu/AfghADN7SX7Pc7Kf//AreKxfLfZk+PzJv7ef7Gz9FaoATpAy9wrQAGeBW/JrHqiB\nG694//cDH/gS23od8Nkv2vYdj/I4/qF1Gk8Fnz8bWAZe/GT75An2/3cA5/+en/kR4J1X/G7AdY/i\nc1udxhPs8/zeVwKXgBuezPMuHlOmeXrY68zsvSLiSfjiB0XkRtJFKkkQydgeIWGFiMhu4JeAFwPT\nJN7n0hN54E9je1J9ngnFvwDebGYf3syJPA1tCdghIoWZhb/tDSJyA/D/As8DJkjB/zNP3CF+1dkT\n5nMR+RrgrcD/bGYPPfZD3rx91cNTZhbN7E9IbeKLSJh5QyJMx3YQOJP//3OkIHeLmc2QCG25cpNf\n8YN+mtuT4XMROQS8F/gZM/u9TZ/E088+DoxIXdqXsl8HPg9cn/38r/hCP2/Z38+eEJ+LyG3Au4B/\nambve4zH+rjZV33SkGSvJSlsHjCzCLwD+FkRmc7B5keB388fmSbBLKsish/48S/a5AUSLv/l9tkS\nkXb+tRKRtoj8g3k4n2if58+8H/gVM/uNx/dsnh5mZqvATwG/KiKvE5EJESlF5NUiMuaBpoE1oCsi\nzwR+8Is282ju7TbQyr9eeZ//g7MnwucicjNJsfYvzOzdj/9ZPAZ7snHBrxDWeILLapt14F7gO654\nfZ4UsC4Cp0gXfqzkuYnUPnaBzwH/B3D6is++lkTMrgA/9mX2b1/0c/WT7ZevVp8D/zr7uHvlz5Pt\nkyfpOnwHcCdJkXMe+DPg6/JrLyFVvV3gw8C/AT5yxWd/gKTwWQFe/yW2/8X3tT3Z5/xk/3wlfU6S\njusX3dv3PZnnK/nAtmzLtmzLtmzL/k77qoentmzLtmzLtuzxs62ksWVbtmVbtmWP2raSxpZt2ZZt\n2ZY9attKGlu2ZVu2ZVv2qG0raWzZlm3Zlm3Zo7anxET4P//5/2wBpeUKRqIU4sZyM5yBiBC84NUQ\nHIZuTHsJHhPFVBHnQA0PjMTjouG9R6XBkDRSYw5nERHBVKhF8TgEwQs4NQJKdOCjxzsj4nAuYmp4\nVzGyiCOi6nEC3gwQBEfjIpjgEaIqhXMEVRQwETxjnaJSlQ5rLP1djDqCilGKAEI0w+cTNUAFogGk\n8/1P/+qfPubZj//nIz1zJD8LAs4lH4qRaglNOxTFBJx5ok/XwmsEhHwomBjeOUwANQxwIqgZzsAE\nRMA0TzVJ8hYiGAamONyGhhMznEu/IyB5G6BYTMeAE5xpkgGKQ0yJGF7SLS0G4ixdcrN0LM4R1fBy\nhU8RzMZnIxv7LxxES6/h0nmowQ+/qPOYfb7wufus3LGH9tQEhSuxlqOohN5aj//yi9/PKw8d4rrv\n/tc4K6m7XfTcOWTUp3TA/G6YmQOJ0H0A23UrTgtEBph6rB7Beg+TgJ/YRlM3/Nkf/xyv/c6fxQhg\nBdQDNI7ww3VoFUQpcJMtrJxCakUbkGYd6zXYqEdReUSn0U7B0YfeyfW3vp560KOQAgsNthYYlIFO\n04eyg5ub4JE7P82hW28D18acEQLopRUsDHHWEFueplrik+/+JK/65u8j+jVMK3rNGgxrbOQopY+v\nIlbuxMQzdcu1m5pxevOrvUF6zl00YumQaGAGBlZCVMOZMCqgXRtaOnww5q97Podu+0HanRZHTq0g\nzlGPhlQSiEGxENl7YA/rK8ssLi7wtV93O4888giz89Ocf+Q4C5dWiEd+i8KUoFAoWFWmWBdqRNND\n4bynrgOlCFGMUtL95x2IpVigko6zMEFlHB9S/JBomKXnKYrgnMOrUUcFB+YEbxCiEYFKHdFBUMOr\n4cXRqNJUKciU6vj1j8S/1e9PiaQRzeOc0RBTkogxBQ01zAuK0gQQcUBEAJXsBEaoVinRAAg0FBRE\ngveUmoLKOCSZGFEcBQ4lghVEM1ouYgbBPIUDryXqoMFAhUYF7zyNBpwVGJHKe2pTwINrKDS9Vw3U\nCaX3NKq4FEUpSBdVMTxC3WgKCDhqtRRc8w3ipEFxGA6Lhrl0UzscCrhNDvKO/SUimBkORTyYSfIT\ngnjIf8RJSiWmiiEU+RylkHSdTJGcfMcJ3SE4SSJzAHGX94cIkrOEicNEMNP8PrexT8wwHJL/dUVK\nAlh6ECzmPYrHb+xpnGTSTi0HjIjhnOR9pqThMNQEERCxlCRENsoS8YbDo6aIXN7+Y7GP3vkZDu07\nyNyu3XTmppmbmSW2S3yr4MLZIStzS7hhxNThh55QN1CPoBUodQBNB2mVRBvgQsBiwFRw3mHdBkYB\nKWtidwWTEjvbAzFCU+MsYKFGRjVohTYRXxaYDNChR6QCiajVRAuIA+pALHtIU7F45hGuuWkNJx0C\nIxxK7Ua4UR/nHETDuiOmDxwiRkHCiOiFMOjThBGgtKyFqufih9/HLS95DYOiRxlDSnqNo65rguvR\n8VOIdPBlF43VpnwO4Mwo8USLxCLdz2aarrMACk7BlZ6pRgkCRS74dl/7UpwPiEyDCAXgfMmwHhAG\n6wgl6+trzMzPc/jwIc6dPcf09BRVUaJSMD09S+eZr+D8g++nioYXIYaGkYVUKLl030sItMsCLG4U\nM4hhCtFJKoCdXC66LBeX4zgp6XnAoFQIFtPnnKDREEsPYpH3N/BKS4RSIXqXir9CKCJ4FSJf+l5/\nSsBT6hsUw6kgFJgIQ02dgYVUTbacQy0FIjOjUFBLYcoTUlKwFIy9NXiD0pQmB6oouUIFKvOMQsQ5\naJtSCaRQCEGMgBAJRJTCNFWa3hFVcSZ0iFRSEENAolIIaHTUZrlKVaI2G8MwAcOZUWsgWKrmxXss\nKsFgEJt0rkYKTgYNZXJOVKRwmEEU0o2Uq4vNmpA6Aie5FRgnVsCLICKIKeNXIPnQiaBiGx1TbhpS\nEnApUTA+PmeIS9ty42TgHELyq3iXfwfnHM75vCOHpSyDc6lgcJJufkEQJ6BpuynpgEixEfxB0XFw\ngPHBpfvtivMHoXDjJOI2/GDjRGKApHullM09Lid/7o9ZuHCG9aUFVi+cZqU3wOp0T7/2G1/Ks7/1\nzfRabVa9stZxDPYeZLR9P8OpXdiwxEJFjIaNlBhBNBUVwzDAAog1aB0gjsACD/eWYK1B+kPEGlwj\nmBgau1hUogxxwyFiAYk1LkSclngNSBMw1XQdojFYHUIzg1pMCcI8pn1aWmFNgSqMuitUxTSiFWoe\nQkFZFCCBQg0rRxQ25OTZY7Smr8LVNaGZwrwjihJQME9Ze5wKPszgQrkpnwOoGVGUWAoaYq4WXOp+\nC4fPJaU0ETSVC9GgFuhMbcfwOOcoK4+LEbWIC4oPjunJCSanJ+n2+5xbPMdw1GN9ZZWVpSXq4YD1\n3jpXXftiKlPUC8Gn3dcKXiJeDO88UaC2iJml+yx37MGnhOG833gIU2fswECiog6CGeaNiBHGnXG0\ntJAPKXGKGkSjMkdLocZSeTh+VF1CQqLLhduXsKdEp2FRcN5oNKAh4LzgxRHMU0qqzGNMkFJUjznQ\nCOYNsRTgMI+5QI3HMEpSwCuIRATVVGmOrKblHJWrklNdJCi5oo6IRmpJAcIrNE4oNeLKgr5BaY4G\no9EMm4kRtUntrneQL3IpHicRM0G9gFpOWoZpZNgEyqIgxtQaqqakJWZEJ5DPV8VIESIlSFVN1btu\ncihTLAfN8U1jOUBmkMZ5IIKlygx0IxE48QmrcQKWKhcnDnM5iTiP03TMCRYSRFOPlboHhQwlJkgr\n4sSlij8fG+TOxo0horwvMsZlxkau2wCXUjqwDGl6L9mvgjlB8kdTtwOVpGcqdW4un4fkmJKOPV0x\nj2BE2ZzP68Or3P/Rt9Hc+I20tk2wpxuZf9ULqK3g0Kv+GesCzaVVYhNxdaCuwHzBSKdptx1hfZFq\nrUDlMMWxi1gbCmdoNNreM900OHWYGE055NzpE9RuhB8qKgEZ1EQdkfrVBuspjZ1DJjqIA40R6SkD\nU7wqVCVOIrFpWOyexuIqTltYyxEGkWAldVXjtaH0U4TlRTi4i4YeUislEww1ECMQBW81RTHPpx5c\n4AUTHXRtRFONqEJBM4KgnlbdoakGtHSSGA3R1t/p17/LvDgUxUeQIiEXguAMmhDBCy2FQIKAzEFl\nEFA67W0srTWou0TpPXWzhlMFixSdFt5XLJxZIMbI/NwEM3MzxBBYv3iRiekJOlXFvpu/jtMPvYwL\nRz4EmoqwWQcheiIRSPd/bYovPUEMF0BUEkxVOGIuzgpz1EWKh2VOdi4YeEEC5AcB7wQfjcYn9MLU\noHTQWDp/D6U6nDNiVNQJzoShAx8tFZJfwp4SSSPgKDVBPKZjKCHgaFFLkwKFVogo0ZQyAkUKtJV5\nrCXEmCJAzK0o0tDEEilqokEhiqdCxHDqic5QUyyk9qxlgqpPwQklRANRvEHEpdJADImRUWm4qKg4\nzBmVFDQudRMaI7UJnQi9IgevEHHOEzTBbg6HlSmYVQhBEi9Qh0hVeJp6hPgWluEQYYzNp/Nzlrid\nzdj4pviCOOgksxmGaMCJg3E14n268QAxvaLTyccmAOkmRJVCBBMhWNqiuNQh2BX3onMJahtXNS7D\nQzKGrrKlwJ0ettxnpGQql98hQl7iIG03o2wpCeQOUHLicZkzaywncrPk49xdwZiHyXvLUJluEhK8\n/+QRyvt2Mj17lL2rsxx+/XcxwFP3G1zpqVd7BOsiUjGioO6uJp4Gz2CtgcJTtyJRSgo/IA5hAqVu\nalbLgkuuIIgiMSJa8f3/5u2cXFyhUwjTtHGN0tSRyWJECC0qMc4vneLA3sM0rT74EgXiMCKjAbHT\noVt4VoYjbn/lWzi62GcYFxn1u1QTE1g7woqjMzVJ01+mUwhz3Zp+HGHe43SFYdkieqGxSagqCin4\npjf/X9x75iF0LdIfrLFrYppdxS6gR62gdUkTHcIQccrMprye7iu1xI9GycUORrBUnKoptXd4cRAi\nhRnmhMI8VWsKH1agr1Raop02itKuU3Lur68yv3uOvXuuojdcZ7C0SDHdZm1thbIqWDi/wHUXVrn9\nNT/Ku37xQ1SFRwRqjbQmp2CwlvhOZ7QDuGhILoiCGA2JMxU1lJTYfMwojCpRBfNCoST41ScO1RAa\nnzpL8nMnQXMBCzq5m870Dgan76csPI1FJEDLJ1TmyxVITwl4qjAIOkoPr6aKEC0wiWiu7gsX8IVR\nesF8gRIpURqM0Hh8VLwVdFBEEu3VLiJRCxwVYo5hjLiQW7jEqGb+IpHciFBoukCVLxJxiqCWiHEh\ntYsSDRWXKtGY2lozxZOCkojQlUBhlloiUiBORGxCC2OMhBgZWURVISiV98SoFFIkkjemIJh4hhTc\nHLlyHvedj9E2OBGXSGVx7nIiyUQ8ZLjMpXPlCohPJCUYlwOtyxKFMUSFpEDucwJIMFImqG1MbluC\nnMaBOkf6L9zHGETi8vGSqkeX9zFmUSS3HhvEu+lGIrCEpSXfjTmV3FVwxf5UvxDLFRIcFyElnU3Y\nQ4sTLJSXWO/3uPV5z8O3A06NqhCaUKOFoz/0aFCGNqQspyEadRMYSYNDKIeCW1snGnTa09TlBNI4\nJERiVEZR0wOlI4L2cSOl3xty7tIlLtY9TIQ4EkbWo6/GubULjBzYSIjRY8MRDGvCVYdZm5omlpM0\nxQS0WtAK+LJEnLC4ukyzsk5TNaA1rckdxNk9rGvNmnOseuNSqw1esPY8vVaDxsAgDmnWejCokV6D\nGw05sXiELotI7ahjH7WGUHWxWFLT35TP0zVMhcSwFEpxFCKJYCsSx1iY4EnPqpaJRzUToihrvTMM\nQyAARaF45ykjeAyn0J6cIITIyWNHcCjt2SkuXVxidrKduE2JdFfOMxoWVJWiqon8VhjWazRWJIi4\nNqxMhaETQVO9looVG5dm6Z71IqgJKg7xkkUa6Rv1JKRtmybYvDCosjiolMRFBvG88DU/xm0v+R7U\nwSiTfNGlZ9wyRP2l7CnRaXg0YaDmEAk0VoAopQaiOKImx8TowCuKYVEpEMRB1ET6mEWQkBUxKQsr\niSOofQq1WhWJ5FKHCqilFq/OAcYp4IXaMikPNDEiGYIZEim5HBQRYxiTyqm2mC6o6hcR1ULMiiNI\nCQOMwhc0MaRLJIKLES+empDIc9LNICI0IVD4RFCpJGJ4s+acJIKMFMCVywFeXCbInQdJyrKx+snc\nuEuxzG+k2iN1cUklJpJwVhn7iZyoXHpIIXE0Gg0Zt9ACLnMJeEn7cymZlLIBz2KSkpmTREaYMwoy\neS35ARKXWu7cIRReLivyEKIp3rsNhZrkAkHMNoh4EfC5aEgxZnNJY1QETg5KfuhXfovolNWLfYZx\nnTrUSHSgFcE7jp46xczsPJ2dcyyefIjtO/agEdRqus2Q2bmd9PrrdF2ffn+Nqu2o+oafdMy02jSD\nSCgcbd/BpmtGFLjVHmd7qyxMtZlvbePC3XcyuXOGEw8c4/qr6tQ5xgGDfbtoD+cYDtYptm1j8fhJ\npNVi1I7Ua20K39DZNs90tQtXN4R2h353wOQostSr0aKHhALnwVUdlmOfqel5qqpiVBQsP7JAv4iE\n7jpaG61aUFdyz2eOEIolbjx4E64w6ii0Wg395eMc4LZN+T34dF87taxC8nhLxWMbIUSF0uGDEgWc\nL2iIlFZx/998jmfc9Aqs8GybmuLs+fNIu43TCqkcE7Mz6HBIURasr5yjLFscOLCX+z7+SQb0aXVa\ndHs9/Mo53vBT7+et//bV1KOGKnfGVRmoY4FPDw5ehJEqXoTS0r1XeiEGaDQroQAnDiEiljoLyaor\nMajE00iKMeMw1KjRw1P7OX7wLW/jrns/zcLDd1JiNJI5UhG8S7HF25fuJ54SncYoY85BQPAUCQmi\nCwQVGoSQQj51NHyM4CsaHNGSokrNCNaAJgFtQSJex4mlCklBWkQjRGNIgycFiKAxw2KCFRm+iKnF\nM6D0QiWeWDq8eCIQQiAXJ4gTmtzONZokqipCHZIMVDNmT64GTKDA0YSGQlzG63PvIBFxX5jLFaPw\nPqkf8BADopsLYIyVGG4MGeVOYUyAiyDebfAsQq5U3JjXSP688ptnFEF9SsKBcXeR+KVCJAferGMb\n3/wZEjOxnDCu6DL85X01OZk55yjFbfBDOJIqTTJ0lW98l2GqmLc93ua4u/CZcHekriWQux6fEo3P\nyhPJcsUNCfAmzGTEL77uday4wMULxkpvjf5qD11pUDdBa26C3mKXshamKCk6HS4trRJsxEimEGco\ngUEzoNdbYRAD7aKiqgu6rQJXlqz1hqwVDb501CJAi9ZgRGt6CteaYnVxlYdO3cXcnhnOL67y0IXz\nDKwmjAYwEFqtCfpxjWbSc+roUZYHPZaXzrB64hIt7bGy1uPk0QdYWuhzbvEizYU+SxdPcH7YY2gD\niqJFn5pRr8uoe4lZ12Gu3eH4XX/D2WPHCeZp1YHCzTIq2jRuyCoFZbukPxzRizVdRrRiH9dvqJcX\nN+n1yyq5Kt9foqniryI0okiZ7nMtE3QsocEFCEQeOnkcqTymBUVhqE/QtqkxGo5YXVhCnGPY79Oa\nnGXX3j1gjvbMFAtLawyHDWur64RgFK2dDNdHSMzPjXqCOerYYJn3bDTi8jMZXE4GMRIdVB6KDAI4\ni18E215R/EguqGMqRhuXIPamvIHn/6MfYzgYcOnieQarD24UyqU5SjOCKRFo9EsjGU+NTkMEDQ2F\nc4zEqLyBedoUCTZyECSAeRxQWsEojBCXQBGJCaYqpUCdoCFQZFy7UcUrKI7ClCZqCsCW6LBRrFPg\nJvEFkiW6hfdoTBwKBr4U4rCHSCvBGe4ylNGSgnWLSCa7E/yRXitMGBEpAVNDvUv4pAgiqUrwzogm\nFHmOAKtxXojBkWR3SrQMI8WYK+HN+fxycL5MDI/xfyNzFJnE1syhxDz34PNnC0mzHePSI/cs6TjH\nXYiRi4HLXZNmqK9AMJ+USh7J3Mj/mAydpNc8SYKoXCaxHWPYzGfILgUGJUF56SHLnc0GkGVjtA9c\n3o6lBFeQNPCFkbq6KyCBTaZpfuOHf5pr/uU/4/P3Hqe7vkrRmqDjCybcNKP10wwulZRTLc701ojL\nnvLMIjdcdwsiQ1RHiXNqT7CyvMR0Z4LQNBC6dLWFF+hrm6KcZHtLWB8NKWJNMyi55NaoRmuUzYj2\nnGdusJ01c+x89n52nXwmy/0eZaNM3nAdZx48xuKps5SzgYUjC8xcdZjnvei5XDp9lne9/ZfZ+6yX\ns2NqFjdVMOiVnD32AEsr55nz5yj27WduvuaaQ4dZXG646voDPHDnZzn6gQ9w4Hm3UJlndWkVpjyD\n0XnKUYummsAPjEvFRSqb5s4PvZfbXvQtaL8mzixy9siDPHeTfjdTTFy671Qxl+Ylhg4mgsMw6jLJ\nTfFG1KRyWqufg/WHVA56hRB1SFEIZbtDZ75kZnqW4WhAbBq27dxJqFdYWlyi7q1TD/s866YbGaz1\nmZiapKkjDz78IN/64/+V3/+lH6AVl5iRSO1g2INi2igsd+kZ6o4uCznGkFV+PVc9NKopzGTuzbs0\nG1aaMaKgQ2DJDCn2waGX8n3f/b30e10eeOghmsUH6J39HCaOFo5aFe89JUZ9RYfyt9lTotNAIyqO\noRo+JO19rYkn6PqAoZSuSkFGlYE0lGWBWgr6Tca3DSVqyJwGhCy3TPoETYQ2CUZ604tvJFrKyEmS\nlwixaEbUSJOHYrxz4B2jpuH5B3fgLKtscuUbTemHGh9ylT6eAssQTDCldAUhhCRsGEv68qCcmtJo\nIp4ai5gGhJKQVTCFCd4VaYYEhUyksUl8PeW9y9twLiuQRKjE4ZxQjgnlwuH8ZWnuWHYL4IrU2ZU4\nPAlGEksJwVlSoTlS1V44jyclm7G6yUxoIUlgLIZI+g5iJwlnLp1QSIK+UqdBksiSklfEEPEIMXVC\nAkVOFmO+BVJX5MbdhBiS2/6k/rK8j0Tel1g+lrStyqVj0U3Oaez6kTdx72dP4NaHDFcWURWm5qe4\nMDyD+RlcewK31COcX+buuz7EOVlivV4nzmzn7MJZlus+R08cwxfGqFMSfUFRzjAkMLtjD1WIxNEK\nzaihNTlDMb8L2zmJdyWumiI4RwgV60ROnb6bUx//LKLG6NISzYFdPHz0CCdPP8gtL/oajtx/hsO3\n3cTtL34uP/tD38fH7ryTpobnv/ylPPTpz/K+P/5D7vrYJxisrzBb7OLmF97BjTfcSCd2qNozhN4K\nv/KWH+ehT9zJs255HpcunOPc+UVcJdSLA4ZNj97SMjPtORq3RqmzDLQi9Kb5jT/4BT564gEuraxy\n54Of2pTPAaoIPo45tPxoiqMyIxRGXRgel1RLmmSnrWB0maVqT1OHSziNGI6pVodKHL3VdU48fJRz\nxx5hbfESg7UV1lZXmZ6cZtQovl3SX+8yv30Xczt3sr6+zMUzj+A7szznFT/E3K1vZqHzXHTu6zmw\ndzYVlQY+l09mY4GiEIrEUTSMuUAgKh31+VlMCEatEQT6CLUFetsOse/2n2Df897Id77xhzh/+jRH\njx4lDrsMVx8iaSKN2iLiUmc1zFDVl/vOuKdE0ohqlBhtXyAegiVFZg3MhRJFEoQEuTR2SJOEkG1X\ngkDp02jXBoxgYE3YaPVEJKmbUESNi6tnCZIq6HEQGVfYhQhYTFxJxj4RR08jqg0iSb6LJlzdi0sc\ngCoaU0eQBt4sy4Qj5gryCB2G4JrEzWiuyBPmCuo9wRokKqZpxiPEkInd3K2o5TL5sdtG0M8JIN18\nUOaHJ7k6Hb9XNhKl19zkiCFZMWaiIGN9ewqwIkkbbjQUliBFk4QZY5oULWqcPH6MD/zpu4nWUJAq\nfHHjbaThqygpQVi+/GOazsxoiUNFKUwuK6XydRTAZxmvSw1rSmSZ3Fa1jYRkeXq9IP8/k/CSOwwj\n+WZTPkfYv3uKZR+Y7myje3aBODCKUNA7d4rQBDq79xDLacrhkKvbezh/9gQaFXpder0eVaypim3I\nyNNcXKUzM8mFB+9lve4hEyWuM0OPBh+HrCycYGA1szO7EApwU7DeZcI76kurLC19nmr3IT5236d5\n/x/9NueOHEMGgXe//bcZ2oj5VBUzAAAgAElEQVSd+w/zwfd9AF90uGrPtQyGgXf96r/nc0fvYXr7\nDvbsOcyFS+eZ2beD2Arc+eA9tHdu59Of/hh//o4/ZN91N3Pw1hcSpytGwyEaC1a7XeoJY8f89Qxd\nQfQFpU6wZCP6sw7Z1cG0z4c++h66yxe5956Lm/I5AF7QPLtgpO5fzIgxzSQUCmhI6ilTMCM4qAfL\ngEd7fTQ0qCqjMKAJNRbSahO+VRBCw6ljx6mqDk0zpBSh3Z5iZmqaejBgeWWZbdu2ISIU3jMzNcNg\nELj+9m/h0LO/ibVmgDloF4JzaS5tzLVFs41h3k7uNtLAq5DZ0DSnlAUytRqUnmrmanZf/3rKqRl2\n7d/PULv0BgMQx9q5uwjDRUpLbLsnzayoT3Gr5AtVjv/jffwUMCdp6rEJATPB+QIxn5h+EiwRQqTW\niIjHqdKQoKM8L0wdmiTxTBpOogWcL/A6VsCk97tcEf/N8QWcCU1Mkt40JhURg2EmWVUZ4yqActfx\nxTxkk8h1Hb+Up7itSLiLRRhDIEkrlRIdTlFJTEljECxQeJ+WIyANKxLSACE5YOp4eZQ8Ue40zUuM\nOZTHbBYpnSTyGsNb4mKiRCokBU9y1e5Iy594w5ym6juPVOu4lc7dj/qxtDWd670f/wA/+cZX8i/f\n8Ap++ttexk9/24t4y7e/iD992x/ynnf+PstnTvDBt/4a7ekqfZ4EY439V2Ss1pEqiYQ2uQRNepcS\nCrLx2fF7SktkIZKUI04E3Lj74LIKRfJQoUDhcgeUt5VRSHSjE9mcyxHj7g98mpldezl1aYGl3hJF\nE7iweJFjZ85w/uRxZqbb+NUFLi4P8Ad2MexHPvOxDxGn25TSYcfkbhoZsHz2BCcuPML0zn284NYX\n0PGKrzyj0YiLFy/Smt3Owetv5O53/imr3TVc5eiuLHP8xP0sX1qjU+xgsDrghq/7Rh75/EnOnDnL\nivW4+oV30F1Z5YbD1/J7v/rvePd//Bn2HbqR1W6f57zgH3H8wY9z87NvYXLbTvbs28k3fNt3ceL4\n/bz3XX/K9qkpzh89wj0f/gSHr38Oew4dQFnhz9/+VpbOX6A90eCATjCa4SJ7r7maU+eOs/3gTubb\nkxSU9EW5bc+t3HX0OJeW1jh2abhJp6dq2sQ2+LVIkrIWhdAOjlLT8+VjTFW+OLQoaXnHtt2TSGuK\nqWqZJgRmp3fSKicQBG0CCHR76+yYn+XBex+k3+/jDKZ2bKdsV+y/+hCzM3PMzs2zd/9+6tGAXTt3\n8fDRBzl46CAHrjrE+opRC3iBmqxkHE9zZ54XZ0RNfIKOO25SUSk+AbNRjaZ9kD0v+xmuveMn6OF5\n/tfeznNuvYULp87gnKddFsxMTMMo0owUnDAxmYq9ALTzPNuXSxpPCU4DhBgUcR6NMVehGWP2Rh0j\nbe+TvNZimiOwhFubaqrAncNpyremihoUpowcl2WUmQRrnHD0XMNUR9PwmMJk0VCasGwpKan4rOZh\nA9Ofbk9lgsgyDp6ye8QoxEMcrzFl4B1FTBV6g+IGI6xdIQpKgr7EfJquFZe2lSv7YJqmM2NSMWnU\nLNdVgileCsrNISX4PI/icWntrpwoxgOFYun3dD6XOYiYB/WiGyuNsuIsJ+bL63MYYo4P/9lfUjhP\nWdQ4cYwaQWNkFHoEq7DFJZxrWDq+wPSunXif6nqHIyTwiXGJ6CV1KJgRyBVPmnRMs4I6VmilCs0n\nKRgRTRwLgkgk4ihECZaqrAIjiMtLhxguV6WSq7piDI2hbOaReee//0Vmb3g2p//8nRy85nYePvUB\n3v3XS+yemGAwWmXRGcPRiJW1Ic941g2sPXKK2geu3X6AiyuXGNqI+e07mNkxy6gf2d+e4tLiaR46\ndor5mRnW63Xq4QijYG77NMceOEKzLbJ+aYG6NcXug4cYhSFLp49z4JqbGdoa/f6A2YP7ue6653H6\n9H0c/+QHuO7ATVQ7d/HZT36UXdsOM3twG3Vc5ezSIjfd+s20d25jvlNw3/2fh/vvZm3U5Z/8wL/g\n1F138aFPvZ89zzzI0aP3UC21eNlLX82OvbNMTM+zcnGBuq6ZOHgt7bntXDxxhFY5xXAlEIuCuX17\nMB/obu/w7b0bWVo6SddWNnejp1uTyhxDURoxRJMQwxk0RLSAIrq8jIYxEKNoTTPbaXPr7d/MdNGw\n/PBdtK7+GvqjBoqCMOrjvafqTDIzMYO4EVddsx8Lgcmd24j1iLXhiJ1793Bx4SKj0ZCdu3ZSNw2d\nuXne9N1vRKMx6HWZfM4buf+uv2K/nGJHaagYvnDUMTFzXiNNJsALTSKaiNKMubpaaGauYs+t3017\nYpLCF6xcvMiLX/IyRt0uS2fO0QRDwoh69RRUFb4zifRG+GgMugmGn6jTAOu4Vv5S9pToNEYaKdIC\nIQRLi/H5LJF0GhBxyUEx0s5yU8XS+LwKpS9SQ5DH4DWTudE08wsJp1M1rCqYdCVxlGYxJWOHYLz5\nH9+eupcsX/PZPeKEaJEmZpVW1NTGkofhTIkxoJaOySPQRIIqjaWEUrRLxBKXYSQVF5E0yUlMiitV\nYp5g1qCoZV23SJoct0zYRt3gax6r5SmLvO5UnjYXQTO/oGQoSZJv0rI2irckbUuqo9T9jJVY3pKM\nWZzhKCmcox71aLVmUddBipJOq6QqHXe967f5xB//Bg984oPEwvPgvZ9JXIWlrk/FaI1VXLlbTPMd\nKclWks5C8swFeQkQJ0opRumMtNRkSkAuD3Wlj3kUhxO3oQJzohRoIhNNL29vY2UR2YDFHqv99w99\nggtnjjPT3sYn/uLtTOy5jsNX7eDk6bvZsXMv6wvrtLdPcezsCY4eu8Cdf/Mp2qaslhVXzc4xatZp\nbZ+gVU4wu22GqZkplpYvccMLX4hNTjPQkoW1ZfZs283qYpeFxUvomtCa3cHU9l34qmRq+zaIQ9ZM\nuelVr+Wev/pj9h24lu5whe17tzEqJ/iLe/6aP/rlf4cMG1p79jNS5YGPfRhnjqld+3jg/hOsrQXq\n1SWmJ3ewd+5qlk4v8ra3/y7tuRmO3H2Eue27efkdr8dm5qCaZP38GmfXz7N3zyHaM9sp4pATxx9g\n996DzM7MsDy8SAw13dXI8rFjFDM76NcrXBiGv9uxj8JqU8pc03gSbKkC4tMSPY1XYlSGHkrxNI1S\nVhVNe5rlhc8jLimoRoMuq2ceITTKaDCiMKAU6jpQ9waMun3aExM4XzI1NUfRajMxMZG41xhpT07Q\nqibYvm0nRVkxPTvHoUPP4Otf/t00M89mpYb1xqjF0WhSaTVuvKBqWqsqioGl2amRpi5/9qpXMzM9\nh1hFiBH1nk67oj8cpsUKEerBEqFZp79yiqbpElxKAIkDVuri8ozUU34i3ItjGNMCXkm7D21JVewo\nY+gSA4pjkOcnMnZDUYBqgDF5JNBYwKtLK2BEo8gKoPFUZI9IpzNN4YTGDHOwHEt+8vfuTivdCozU\n0hozziVoScYcRRrwMZeCvmU9/3hVV49DPeSBAUqDxuLlSesm7W88y1DHGicel6etnaUJzxDrpAjK\ngTsR7AmDlHzMmzGRPJeS5yNsAxJKyq5CXFaSpfenKt1SwE2MfkrU5BvPpSRdWWqfJUZ+73d+lVZ7\nG+1dkwSrWTt7gkHTS/i69Nkxu52V8yew3ohP//Wf8/w7XklJhoYsLRYpYnm63DJsldpyNfCSOBVc\n6s4qESKSl7QUxCLqoBSjUaFAieJTx2CGdxtNTBJCZLLc4/CSlFghi8OS4tlvyuev+uZvYPHIEp9b\n/ySHr7+V4UKf+y+eYMf26ymBoS3xyc/dTxRh8cJRRqeUN3zvD/DWX/oZ3G0vZvuOvdhaZKl7AZxR\nTs/hYuDC0jL9xYvs7mzjwDPn2b3vat73J2/l0C3P4/aXfQPrsWG2U3Hi1AlOPnA3Ezv3cea+T3D4\nWYe4/+7PYFet0FpYYqm3xhQFc3tu4EyzhE5UHP/cx7jx6qspO454/hj9PbspwpBTFx/mhgM3sv3A\nNZQH2vybn3gTN9/6XD770Q/yhu/5EeYPXcsDn/sUixce4uTD56jjiNsPvZSjJ47zjLLFsYdPceCG\nZ9Mi8J6/+m/c+PzbWe83WHeZXjPN/PWzLH76no1VCDZjFVniXQitCMGleycGg1Iox6tqtgt8E2gI\nWFR6ccT+3ROs9/YSRmeJGlhfXqTodGjPThOHIy6eW8RVNZ12ydT8dvZdfZC6O8S5iqnZCS6ePc+O\n3Tsohm1aRcnqpRVmpqdRNfbu38ulxWWuOrifow8/yJ5rX8S504dpuufpTW3HhzUqiXQXPsauqsCK\nQOlhqC0GNkFT7GVq9y3s2Hs9UrYZjFK8Orj3ALM7plk8f5Z2VaHVJEMu0diAQdPjwpH3EXtJraUZ\nGPA5PmKaisYv4/enRNIYT0tjRseXQKDJVXZhjlhYXnfJMgyRq0gHFoUmq5AKKxHChjonoVKWlj32\nacq6McUBrckprBmlSjpkrqSIxMbAS+oyfF4ZVSMiSbJjFJRmNCFJ09Jih4lkt7yiZIgRcQox6aYd\nbkx+YBhePM6VREZ4KdNyJqSOJZjiGsGLT9vTRNxHUZx4Cl8SY42Lm8saImONd5ahOjba37EgIA1Q\nJIVZ4w2v4Ig4geAEp2PCOHV3PktdPcZP/vC306rXmd/9HHphgHMF81ffzNxoSHfpJMNBj34d2bO7\nx8lLPbbZI1SkZOwkDWWW+I2FJMfHjOQloV1aVM45R5S0+uhYbZLeK+Dy0ifiKLK7vFxW1AlA7izG\ns+cuc1Ux3QaUPq0i2pDkuJuxV/5vP8Dq+eNcXFqnKCZ4ZOFh3CfbHDn2N7zjdz/J6toQ/fn/Dz3g\nqM/3uONbv40jDx/j137/v/Cqc8IrXvm1XH94ivd+4E+5dtfVNIM+z3je13LxzClOnD5KEU9x+zOv\n5y0//P28+vVvYvXSRe668wH2H7iW7rnz2MQsrpjk5he8kLd96m5secSOzjy/+bb/jGs6zM+2ee5z\nv55H7v8EM9sqXGuOld4jxIkJ2oNDNIOG8xfOUbWECVqMiiHn7v8cf/SWP+Dl3/Nd2MIZbv3ef86R\nB+7j7rf/LtsbODc4xe13fCerj5zi43/5LkZR2Xbwau7++Hu55ptezPt/8z38Lz/4I7zrT36H6flp\ntmvFxNSAc/eucrS/gm82t/IBwEigtPTVCpq7RkwQbwk6zjMSvmnSLJQB2qcbW3Qk0C/ncHOeWoVB\nb5ltc1eze89uJmZmaHvh5MnjuHKSqqW0yopye8UMST5fTq7jW23azuF8wf4DBzh76ixTU1Oo88xt\n38WlpQvs3rWHffuuTkuVx/189FMf5fbbnseli+eYuel7aJd9Qm+Z8w/fz3D+WZhV3HLrCwg0dKbn\nGHRXWe0OObB7F61JTzMcsGPnblw5garSc5H6UsFw9Rztzk76vfNplQu7vCqCApQJqhuv4/a32VMi\nabgxiCbCKAYqPyaABRNFTBhGSzLbJmJYns5NklDnHBYUtTpBV66ALFfVnIw8qVMRlaT8KXrpOyIa\nBVehOqKN0N+YDFZE83d4ZD4hzTMknkSUHEQchaZk5MdVcl6K3bxg4tEQ8uxCglliTNCV9z5DKONl\nAPICZD4nuhzZ0mJ7aYlv1ZACmt8sPJWORX3iKITL3RhmlEBMa6PTEKjMbwCdSYGSuo+GPEGtScUh\nRKIIuyeVOPlMvHWJq0forfToDmumZ+aY2r6fYAOGg8jZlV1IeYbjp9Py2WO1ksuEklwxVe4szXio\npn+9WpJJMiap85pB5PW8bMzdkBQ0NlZ7QUmRvj/ESN+5EhNhLuMiIHchwZJM9/LVeezWXVigKaeo\n5gNOJ9m1fSdXfdNeHrpwM9fd8TqmKmPZ1jn/yBEOlIcpdu/kP/zkmyl8h+W7P867z74f37mJzxz9\nOK951Wt45rW38Y7f/Y+cv3iWB489zPd/+3cxLNqc7i5w4sHPUA0rrBjxiQsfY9ee/Uz4yJFP3Uu3\nKrjh5a/mt37r51hdukg1NNTVXBq1EBdZv7DKww+fZ9fsDuJMi+WFRTqDFaKfQHbsYrKjLA+F1eUl\nlD6HX3cHn7n/o8y2D3DsE+8iFNuRA9tZnzbavSlG85HmjHH2vgd5xvNvZ0aFS/VJdG3AgWfuotOG\nwwcnmGzv5/7Pf471z6+zvGON2Tjkhj3bNuVzyANxJLWUulS6jTtYlVRuV+SZIp9mH4YhEonUGrDC\nI+UMvk6cqQJrq6tQemIJrSIVITOTk4SY1JSnT59h38G97NixC+c8y8uLTO7qcPLUSQ4euBrD6A+6\nqBqDuuHMwgVm5+cQVXyrwzUH9zFsaqrJWUqNzG6/lmr/LayNpmlZm2LCU1Ul7aINUSl8i+sPX0Wr\ngDCqcWXJqImIdvPaW5Gp+f3sb7+GhYffg5jPi4vm7ygS0hJKToghpkL3S/lz01fkcTAlL/uQlUJ1\naNJUtIGRvscCl1RJ4lxS+uTqPkTN8sy0sFujMVWhMS9JnhUxTbTsoLSv2EAT0pr5pjW/fOdfstrr\n8lMve0PuB/K6Ty4NBaWuAUZxSCeWxLyERSSmYOPz8IAZ3qUbUSWpiEzAG5l0lwQNuSStHSuPvKSK\nNjjDa6qENH13TF7Z1sA8MYYkAd3sRLhcMR/o0kxFJQ6nUBd5eY98bk58amHz52Ku1oIYZQKuUDQr\nVIQWDl89g6Js0YyGtOaeyfaDu2iGfdaGNZ2Dt7H0wBmueeFVfM1zD/Ph//YOzj18L0GSHFbI8j/Y\n+F6VjTWtGA/sJcDPZXkzLsFS3tKihz7/zY1XyVXLPkzf3aFiG6ufOkB8ul6imifMEyTmSN8vgAPZ\nZNErVcme2WnuPnqWCfFYbDi9+ggdVzI/v4O7HvgbZubm2bbvIO2+sLL+EN/yf76ZO7qO+ckpGA2I\noeIV5euZntiOn634hhe9kLMnH2DHxz7Gjuc+h4cWTvGS176ed//671C01+n7hl07b6a47x7W14e0\n/ID3/MKHOX1igbY5Ottn2bfvAF2ndJcX+fCdH+Wmm5/P7oO3otZjMigfPPIxZjsVba1Z+GyXi02f\nPbFFPVWxbl3qYY+dFJzqHKM+vU4xLTQNOD9gpLN85J3vY3b3LJO79/Pxe+9mqVqlc9PzuefTn4EI\nP/WOf8yu578Q4SjH7vkUh6+5Hi1LJib28dwXX7s5p5NUdGJGLDMkk4kNVaPAEyRNXAcByQKceqA8\n54Vfh2pFv79EOTGHbwlz27bzjBtv5P5P30lvfZ7/n7n3DpIsue/8Ppn5XPmqrnbTbrzfmcUa7C4W\ni4UhsQBhSJHgkTyCPEoUqXO6C+nIYATjjkGKpwteMGRCJ50onXQ60YIWhjCEN+uwC2D97qwZ2zPt\nTXX5ejZTf+SrHtwFiVCg+ce+v3p6emqq8r3OX/5+X3f6jntIgwG3rl9ndu4e0iwlSRPq9arVJRV8\n9lpbnD5+lrWtFRzpoTyf3t4OmTb4nke1UmNp/jDfefoZVjeXmZw6zOKRc7QHHWZmpxnu9UnCiGGn\nyz33vYOV7XUmmk2GnT5R6jHqdLn/oXvY3t7ESEnQaFL0yqTG0O+1kXFEe+MNCDdZv/5N6hP3stN/\nPrfIsZhv7mICSU4serOzpxysl1RBSUZGW4WwkejcgCmJE4SyfHxlFNpISw01lnZJbuVrSBFS5Uwb\ny4kOkyTPXsjPilJYr5YsQ0uD1g4GCHe7KCchkRpPWzIbxjrh7kc4aUlgApslkRmUMmTG4jDS2BQs\nIYR120Xj4ObAsCQyKU5mNQHSQJIldsQjJCJLbXeDVa9LY2zGQM4FNzpDZILMvU0FTg846lVC5J2R\nbdlVTlE1UuNpyJQdP+m8Q1KwT0N1zXgcZR+uDI1Q0toYGMEXvvRFZKFgvfsLAcr4aJ3hlco0ig5b\nN9fRgeLuB87z1Uev8M6P/Dh/9lvPErhyf5ZqOw17AlIyYwzXS7K84Gmkss6eQloGmG+w3ZAUllKp\nbadq5O1uylqCAJmxLGjGHwxcdP7aY/W3/exa2SIk1MEKtSMdbq1vM3d0gWoU8MyVa7QGDp5JGMUd\nTpw8zakTp/mTT/wFo/o0teIk7KQoYHVrFc/JmD6+iD+ssre1TKVTZj3p47sei/ecR09Kziydofq6\nx+nf/59QvYyg0kAnIeFgxCvPPkex5HHi/FluDTa58sJ1rr/4HDVHEOoeOjvBTH0Sv1AhFAodGJQo\nMuv2MD4Uo2lmJnZpZIpef4uJzKUhaoyCIn5/QDTIqF5sUsgUfZ3SD2MOlR2u1iVH5xfJkiEL8l5u\ndVcIVp/j1pVlBkmKQLH90jfw/Ar+7BH69TJxr83jz7/M4drUgdYcctNNZUkyAotPGW2sQ4DJcuq2\nRqWAYw8HWsCf//5vc89Df4yI+2xtbrB07m4cZVjfWGPx3EmK5SrXr7xEtVzl7N1vpR+O2N7aYPHw\nIZIoIYkiQj2gXm+yvrtJozFFnMQIoyk16pAZsjRjYrLA3NwiwncYfjFEoBkO+rhYw9Urq9c4JY8Q\nZprmoMvMxAQLS0f55hOPcWv5JseOHkFKqAQFoizFlZIoGRD4ExQcjzgcMNi6RnvtJdrblwhDKwh0\ntD0AZtIgTa530rlA+XtQ+t8URcMyNjXhmN0gLS/Z5JoEkUuAbYKaATKCrAPuBGlOcxVIhLGtYZb7\npiRpiq8cNCb3rc81FGbsJGudLAsoRo5ERR7KCFKTgrGbi8qxEcuc0XiiQyxqCCBLtd3YZIbW0vL7\ndYaRCmEcUlJMKnAVuS2A7ao0IJWiYNJcOKhAZwhHkWWZPQll1k6dNLH0VuVAmpHqNB9rHXTN7ShH\naZuAaO1CdM4as5ujtYcm15vkrDCs8MnNCaiWkir37UekELz+nW/hSp9OexPHdcj8AMf+KGllmoce\nPsO3HnuZ7zx2i3ctvkzBOUYaRziOIY5z/abI36U2IB2U0GRa7wsm7Ww6F+xJa/2gc1KCwOAJQSr1\n/mlqLPxTErTOUI5jO1vASI0da4t8Sjq2pLeMKYlCyOx7qmT//1x+wUUkKV4vYGXjGtXyYa71v007\nizi/UGVPD7m5epM7z9/Ni098je5UjT1ZoDpT58SRExiTMtgesbLxCsotoqYVpUKVneEetdSQDCLi\ndBviGCcRFKpFpAxteqQ03PGDD1L0PIhHTPaKZEcmSUdnCHoZmeoQZlCuTOIWGzjpNmlapVb2cf2j\nlIoV4mGHkZ4hSWLWt1zq1Spb2zscax6i6yesVTeZUB7EKVJLqqUIpcr8wA++ldOHTjKsuOwsr9J/\nrINcanBHc5K4GzO7eIIo7fHyE0/Qal1F7/pU3Um8UpFS8+DJfUoJMmNxKiuXyoErYXE6pH0OMg9E\nkrPslGAgNPGoRxxHqCSmu9diavEYaWZIUodeP+bo8TOsLl9h1B/ieC5ZmqGNItUhlWKF9nCPURxT\nr9TIMk2xWKbT6VCrTpJkIUophnHExvYGC3MLPPzwO/n2M98mGnTxlE+/s8fpU+dJuj3qzWniLKMU\nFIjDIfOHl9htbXPszEmGgx5aSnzjopXA8XyybIRSBi/wmD56F3H/CoNNQ+oodJSCp3DjzBKGsIvj\naWu3E3+PDeZNUTQybXCkBT3HkaiOsO6UJtU5MJrHnUqRdxp163RrxqMEkHlM4X5GgpTWRXas0sIK\n2bRSpFmevicgzgyJCHCzTu5Lb5UYWuc00pwKqmTG+ZkGz62l+SYkESazflHcDjHKdM7wEgqhrC+W\nVNYEEW2TuozJ7GAkvzm2ThiUdNA6I8sFR46RqExbtpaxm53R2YFBWVcqyM0VARLsqE7lxVKSM8Ok\nnfU6+chO5murczaVIwU2x8aCAI42OPGIfm+IV/Qx2hAAnhvQbm/T3ZF86coWpVKZXrrB722d4j3+\nDoNE8tRnP8d97/+QHekxTs+z+gitsdiCtLRbNQbuGDsIy9yRXTC+EfY9j2N2dY5v5OaP5NqO/LXs\n82PpuwIYS/BtobBEiOyATJ61tTVWrt3EUymdeIiRFS4sHWPgpqyvb5OFCUmwgQoKNC+e5s7F03zz\nWy/Q3uvxwtbzVKamqJQrXLjvfqZKNTKTsNcbcGhyEt9XdDt9bq6vMHloCjcC15V4BoQrcesVqtUS\nRd9H+U2OVA+xmSTceWpA3/QpOAW2By3cvQhKNYyTIKTGxSXs7pGi6YRl/MxQUA4Lxw7jKweReuhA\nUau4uKUmwvTZa4X0OxGt9i0c7RKHI166/jxSBMyW6vzoP/qHiFGMGA3YavfQSYeoo1g6dRp3rkYh\ndEiRpIMBr7343IHWHGwutpJYp4Yx8y53vR7b8WTaft9xBCI1bPcEjXKFaBSidZFCzaG9vsz04jGS\ncIRX8EFrVq5dxy0FrK4vc+qOCxw7cYzJ6RlWlq+SOYLJxizScRgO+oBGJ4Jyucow7OO6PkpBtVxh\ndvIQaZoSFCo8880nmD40j69hdW+V3VaH++97mEvPfhsdVfF8Ra1UpFqvcMeZU5SDIoNRH1co3GqN\nLIwZRRGVUgUda5Rw6Y9C9naXiYFEpHgumDQDR9rALWOZnmFuTuqoNzmmoaS0p3sNWa7OTVJtN7D8\nlDA+XSdpipIKqaTVQEhFpFMr2BGWrqq0pX2mWYorVV50xkCvBTxRkjTJEMKQuYJIG2qeS4LGFZJM\nuUijydKcsqtddGxo1Ccx6+vkJF/re6Tt6+gcnFfSYilpns2rjTXag3zGLgSZFihHodPYdklSoU1G\nIgzKKJSynVZq3RZz0aM9JaMt1nKQSxira5HjzkfkSncxPmGPce98OCfyQoLtJjKBNV6U4+ObLWpa\nOWzc+A7oEWkaEw1T0iQjzQHEn/zV/wMXj+eef413PXSExx/d5h0zbV5YXGBtN8WgbP47MGYCGLPP\neraDozENOO9GkCofwuY0YGMFepnIc+SFsaMqcqFi3hVZp4D8tbAsPCVMjqXZriXTmcWbMLdB9e/z\nKjguc8eO0ctCKuvbDHOGFTgAACAASURBVJIe+FVOHlpEJzAYjehtbNPTIyq6zLdefJGTZ4/yyqsv\nMvADkk6LOOwykgMmz7wF6VQIEoF0XbSOmVtaYGZhgZdee4EjC8cpFAKC2LDVs55PhUAQOIL17VW6\nnT64inRPc2PlFQrlCUwS0huNSKKU2cYCW4MdSgWfRqPOoJeyt3uLsj9BGBiCRhPf8ag06ijl0+ls\nsvL6qxidIFVGkin2Bj2aXhPHFVw4cx+OayiVykS9PsNuj2G8RdzXhKnAr/pM+lOWKVgUpJGEwOfI\n/PyB1hzyQqDNvtmfo8aMOXswyIQ1wEyx404lIckMnlvEUQqnMUkxDUHG3HrtEkfPnSPsd5k9cpJW\nexvPCahPzOF6LtkoBOEhpWLYH+FPBIzC4T5JZNBvo4JifujUOG6R5kST7e1NtDHERjI9P02hNEHJ\nFziVAt1eijIOvueys7dNc6ZJv99janERPeqRpCGuVrRGA5quR+D7CAPD0Yjt9TfQoxZCxHhOkTCU\nBErvuyeMHS2EMMTK4oTamH0JwF+7nge+I38Ll0GjUEQywx8zVqSduCdplo9R7KkRZYFMm2glSHP2\nvLa7HTKxHOxUmn3b8STTuFLm8a52czdxhvI8m8Fh4GM/8C5k1EZqQ6gzXGkjEBEao/MQIgVvrGyR\nIPGylFQCRuK6DnGaWkpwnkuhU2u5IXVOFxbjSXmC0Y79uTSnegpFliUgFCoFTYayUziQdr4qhLWD\nx6RIpbD2ZQe4hN5njxhj8I0kk7aE7NtaSWH/PzHODrxdVBzYl4YqmdvIG8GVS5fwqouUqpPoKMZv\nzLK2HEK5wLs+8FaieMiV124y3azxB//hm/zSL/0Qnkh5+P0pl19/AWV+lLFD8NhOxZISxG3wfsyo\nGk8ZIBdb2j84Ku+Ocg2KKyw+ZosO+52r/Yhy3zHAFfkL3K5LOOPM8ttV9Pu+dkY9HN+h5vjMv+UC\nyhV0drrgBpycPcIo7jJYWGB15RqD/ggCw/rqOu+8/2G2b13DbRzi2s03GK1s8tjgScqlCRZmZ1ms\nzbC3HaGnfOLNLq5r2G2tE6eawGhuXH6NIyfvZnNjj2GvTT8ZMlWug59Qn5yEKz1k4hDVJvHMiKBW\nYWAG+C6MwiGnFs9z2HfInJOsvXiZkfJJ44idjTVuPNNjT3dZOHKRURTh6QS/UMZXPkv1mAjJmQsP\nkvW6xPGI3nBI2N2g39WoYo3KtGSuPokaxrTDIemgR2w8qkGJ4TAirk0ebNGx+IWTH7K0Mbn+R+AY\nQyTsJpg6Ei8zpMaQauindU6ePEdQLBAPEvb21qlPLhCUK1aA7DqYLOLUmQv0h0Oq1Qbt7g7VcjWn\n0LtUKyU67R1L6tEZveEIJSSjTp9CSaAjDTOzGANRmGFkikxC7n/b27lx5TKXLr2EUyhx5ux5rl5Z\n5dU3XuPhB97JxsYOCwsLJMOQiakmAo9EjShmQwadNm5zCk9KdDKApM+ou8Jo9zrJcIhXVwy6FjtN\nxO0sSikEaG3JLWZ8PPzrrzdF0cAIktwoi1xRnZmcbyoErlSkWoOy+bUociA7n6Vreyo0mSbLg3WE\nzk8T+TE1Si0WoLWx+duORGsbriQMVEsOa1mAMRJpB5029lFILFRnUwRf3h5RdgK0lAgjUI4gTjN0\nZmmhidFW2axlbjg2VjVbJ1+pHTQpYnxjJKRpYv2Pcgqc1DbqXnuCLLFnfQvaxxYuz0wuIDzAJXPX\nLmGzLrTJH6C8GyPHwoT4rk3UjOFhgxByP5p1PLbSAr746U9TLk9CFqMBVxmkD5MzDYg0N5ZbyEAw\nuVjl/YsP8pu//RmqvuHv/b138fVP/AUvf+0J7njPAxhxu5OS+X0edxLjgCgjxhoOwXhv14x129qO\nBHMyg8o/hDV3s+34viBTkBf1/HE0toM0JstPhFj21AGrhiyWOb60hJc6eLnLceIOSNMIMaFwWw5H\nDx1ls7VG0hlydPIYndEOL1x5heZkQDoaMFFvMr14iGhvAIFgdWWV1sY2d955mmE8Isp2qBYa7EUt\nCqpAJZjg4t0PkrmSuluCeIJ4kDDM9khiF6ldXBWQOYKpCZ/6ifsYRT2azRk6W2t4hRq7G9ts6YRi\nSXFl5RZK+DR8n3DYh0qZe87cSWFW4kaz4PkEUpFlQwKOMDA+SdwnzCKM47Kze5kj86dozAww2vp/\nxWlCWLT3tuqW6IcpYdyjWC1iOLj3lJAWDDc5TVw4ApFp6/dkrC1HpPX+IQ0jqM0e59yFO6iVSwy7\nA4TnU282UYUAo2H28DGiUUi/NyAolVhevszxk2cYDkfsrK/ie5Kt7Q38oEipWKazt013r03gSoyr\nKLoTDInY3d3gxMQdTDQa7O1t0hn0KBRKPPSeR/CLJUaDPjqOee2Fp6lNTBMph0BpHn/0Md713h/A\naEWSZdSak3iFEp1u12J3vo9MDY2JBQqew2p/l0F3B7S2o1bXppRmWPw0FJbsITMrhPxeTfWbomho\nbfZ1Gib3c7IHg/G8MQcsdQ5mGTscwlgKXaw1Mh9fWctyZf2dMo1WFtzRykWbNNdxKEhTMk8hU4t9\nqGIAwxijQGqJUYY40ziZBYnTfN6tlMpHZlaloDMfYzSu40Bud6GwpmepSfLuwwElkVrn7a/9WWlS\n0syak9m9WVkL5FSjFZgss2MrklyQZB15jRybvH//lzAWQIbbp+4x60zfXvr9031+e2BcIDJrJ2LG\n78kYSoFD+/oybq3CoDekObPEGzdShlHIO+49zxNffpKZxhzLl1cYDRNa6y0++CNvZ/lL/5L1lRMk\nesBDD95FVxprLpmf/E1u67FvcZC/IZvQOKZbW9fPcRdi2c/5GEqMMzbYx0tuhzHlAkpbFW53MWNs\njDG8YQ7caUw5VYSWKEfQ1jEqsVjPKA0JtKQ7gqNBAT8o8gPvvGjvs7zAC6+9yJQsIhyXnow5PXeK\ndbFGq79GzwfdN/SjmFLJh0qTpLeCilx6yYiiH5AOhoyUYn7qEFqGpGlMxavRzhyk49ARZcpJj6Jf\nRjsZrphk2GuBKjCK+4y2tolKHumepFY9RDLsUJma4sjUCRw/IskkZlTA8R2kdhh0+wjPpW36DHau\nU588DI5POtzmkD9N1N2Fso8xHukwRDoGkQoKvofYE6RE1FxN6ji4peLBFh2rm0qlwc/y8aoxVhgK\nNpcCbU/XDiAMaSRYOrxEaapGtd6A1KG1tk0vjqj7ARPTs3T22sRxSHNmjlHY5c677mVnd5swHNGo\nN1i9dZ1afYJwFNHtLuM6DvVGhVK5BCgKjmuZVJ6H73u0Wi0Gwz47rS2OHz1Lp9tiYWGJ1y69wNrK\nCmEKvlAMh13KpRJCGzqtFo5SFHwbOau1plqpMBgO2F5bwzM7SL9iA5+SBKk0KTIHbe1YWhs7fnXH\nkdbmNv3jb7reFEUjI0OasdEGpFgAONP/CWMlP/UaY7n5niNzymWefSut6Zybz+mNlDjCPhRG5w6W\nSITRoBxbOIzCFZpWe4BX8NCjzLInUoFEkSnzXUJDqKoiaSbIzdHRxvKKEp0L0bDze4zMVe4KbWJb\nwXGALBf7yVw0ZsbUpH0/KeHkR1vk/vhMYOfrQql8FHQwTx67rvkYCCtEEfJ2splSijSznlNog1T5\niZscnHYsG0rsb8CGV158lcbSAoNul/r0FFdX4Yd+/O2AotXu8fb3PkhvGHLy3kW2N7psX3mdNx79\nFI3F99PrdnCky7BW5DO/+3t88KM/k+/htlhLZZlvY9rkGLDOIF/VnCFlzxL2/eq8DuSvI4WwQHrO\nHRbGjqxswJLZpzErcZuJpYzILVvGDgPf/9Vx+0Tb0JisUJA+HRnS0jGVcpXBcI/TZ0+y2lrj3vN3\nM+xnFIoeQibcd/F+vvzNp7j/7Cnmi1UCx6XWtGK4QqfN5e4Kw+02r155lbpboXlonhPzTYJ+m1U1\n4NDcYcK9Pm7gklVc/G6X9k6LyXNnifZWeNuD97C6soGu1JCuItlrs9ZdpuhP0CjXaIUhFSmZO3uK\nhU4Hf/osyhXI2MOohJ1RDxUL+klM1SQkIiOLQkgl9ek5Rhs38BuTTDcXkamkrdsMIsOot4Eq1PGM\nQ0JEEmpKpRpTiUdrsIov65Y1eMBLZQbjiH0bdJNqtCdxE02IxsX6rYUYZKYYuorFpbMcnj1JuzMg\nNjFHLp7n1o0bnLzjTka9No3pGTrtFlESMhwmLN+4TqFQJokT1lauEcYhw40tyDKa03W6rQ71iRrX\nrt5AuYJGvUmvvUdzYpJw1KfWqPLMM08zO9Vkbf0mjXqD+aUFXNehObPDjetXCeOEl156lgfveytT\nc9NkseHQwixBwQHHZ3dnm6WFJarVKnEzpt8t0d+8RnvnGsIvW8JKpgkNZBp8YX0QjND7gmRX5BZK\n8k2e3Ede3bQWZEJYiXiqbRtpIJM638TTfcaRkHYUJLQhkQaV3WZZJWQ22lVDKnR+MpW2cOSzzSxL\nbDqfydCZxHMDrnU3cLzD+aA8Jc3swukcgFVSEKUZY7ttnUkcZcdiY1l7isz/PzuyUmhwJDoFaRIQ\njh0FGUiNpQlrNMqAFhlZboznZMbGPWqBkSlKOzZq1YAjM5ID2ohIeVuNYIzeXxf7d3Z0Nv7W7djV\nMY5gj/pWiZ2PtoTk0x//OL7RGKVo7Xm8/ZF38NLzrxP1huhY8fAH3sJnPvUknuPzwb/7IIePfoh3\n3bXEr/2Hp7gjW0dIQxLFXFm+RcERhKntIPSYEJU762ZyrMa3zCcE+468AmtwOKbZqhz8NDngOR63\nmbGFQl47rd7LMrWMyTNVpMVS9tPeDkg+eOO165Sn66x+5zqVco0zC4eIY8Hy5cucOnuWXtKj3Ym4\nfPNlphtNZtw6o1AjCx4PnT/Pv3jfL/C/PfYx0izBJJokgaNHT1NV8MZuh3vuuAedjFhZW8PRAyYX\n69TjKq9++9scPXqUuAeO5+JUa9QrFQphD5lW2U07rN+4xSjr4noltq+t0lxYwCtIRlLw0NveAY5H\nLxxxZf1lTHuIDgccOXICHXYxhSrhoIfvephyiYXyFJmrSOOUbn8HU5uktTek5/QYdUNszFiCW63Q\npElQdOgne1Ao0E03EZUy9coJht0dwjg60JoDSCe3BskZgqmwbrZGgSdsnDTaijylEajSWxAO7La2\n2d7ZZWp6mjhNOXriOFIYgqBAlGkajQZGOSAklWKFy2+8ihKG+vQhkp1dvGqFfr9HEttJQrczYmFh\nkWGviwRqlTI6i5BKEA57zC8uoMMQhI2gHg4GVBsN/vCP/ojXr9zi7LlzlCtlqvUpwsGQQzNN4jRm\nujnP9votJicnGaYR6aBPv9fHUYby5Dx+ZZIXv/JbYAShhCAyJG7ODiR3d9Y6j0YGoXIj0L9pPQ98\nR/4WLoFlPmVoPGFvnJE2pCezWCzKcubsLNqS7dGp7QKUlBgl0U4+spIy34QhbwfQY6DU5MaDShLl\ngSdaKkYmZlFOY3SGzlKMFtZ2xDh23JQldiQlnfz0b730UQ7aMST53NRDIlJAK0vvE2ZfyW6kY1Oy\nhB09aSkQIkJiSFVsge40RWlFKiLrp2Tt08hUYimyBobZvnPSgdZ8/+t8MxynghmRU1WN9V+yyza2\n9MjHQzlVVeajIWkMUX8ESuHVjzIKHaK0xx0Xj3Hng2d56yPnae9ZdD/KMkstVhmRyXhAfo3W61+D\nNEMjODw3zQsvvGiBbDlGLbTFfPK5tNE6PyXlgLmx1EGR4y4q785SYdBSM04EdLHJf44AiWXoiZyk\nIPKiMv7a5K69RuYU44MxbtlbWef6l5+HYYovBJeWb5LKkMMnT/LcU8/wlW88hRm2WZqYoTFRyzdX\nTeBISp7PT3/0B/nT//2zeIUSkYzQrkvBSZlYPMX56Vk291pce/U6rbRHZbZMsTlBoRYwP7fISBqG\n3R20GFFqTlOcqDJEwWSZWqVA4sb0OiGjTpu3vuM+Br0ugx60tlfo7m0TxyGeCnC8Cp4r8T3Y3dlC\nFqvceu1bHD9+goUTh5lsTDCQCVvrK6xsL7PZafPqtWdordxia73NgJShGVEol2m4dbSbsUMPXSrQ\nC/sEWREdGpLuFiY0yOEBCR/Y+ygkuFoQGysK1XlXSgaBsc4GJoPYJGh3AgdFEsYEBZ92u43rulQq\nJZIkRBvN9FSTVEiSJGPQH9HpdJidnadab9LZWiPVCWk6IOzuEkd9XL9CuVHFdR2cUoDnBwgpqFTq\njAZ9HNfh5PET9Hpdhv0E1y1QKJdwleC9736I40cXuXr1Ght7PYaDPl7BZ2buEE4QMBoN8TzPkjpi\ncDwfJVJ2V16j21pGiJTa1EkQAt9gx/LjrlwYUiEwjsLkCl6JGXNR/trrTdFpSCHRQiFJLfjoSKR2\nIcvIMJZRlSa2CkqXLM8AVwiEUWSZRkiboKWksjnheTqXmxliMZ512xO0ERLPcVBRiEQxMhGDJGH4\n6udZPP0Iy55P4crTcPwdBL3ruCbj+guPc22tS7Nc5P1vO8tW+Qg73gyjPHZRk1E2EWxcptdaZq/f\nY/7BD0NSIAo0SRxjhGRaDRFJSMlkfPUTf8DcRJNiqcAwHHLsno9gCgm9p75AO4wZVQKqCprVMo2Z\nM6wWFmgJkKkkk/HB1tyAyX2nTH5Kd0SebihstbVhSnnOhxyPiiyQfHtEpEBqjNEEymPYH3L8HReo\nTG3QaJaQWuA4LhjN5//qUd7z4XfwxGef4nMf+xL9OOBzlQqn5qZIqnMMhx/H9xRvf98j/M5v/Cq/\n+e9+38ZwonGEIhW5psKQuwVkVmSYd5NGSTAaD0GsRJ65TJ63You6i2Xfqbz7tMaI4ywNe4lxfge3\nk9FsEdFwAKfbm7MuWWnIbMNj9PpV7v7Qu3n+O8/QKO8yNd9EeQ2WFmfxS2Xa/Q7rGzeoHZpnsLWF\nr4ro11/h8DHNyvJ1GhPTpEWR+6KFFGanKShNrTFBr73DrZsrbO1sEg1HnL14ARlpTK1Gf3uXOOvw\n5He+wbnT90Fnh4pT5+I999Lf7rETb/Daiy8wjByOn51mb1Pw+vU3mJiaYWvtBodP30Ma7nDi1P3I\neglXQzEo8tylF9G+oHtzj+XrN1CzDapa4HhlwrRC5dAkc+USXrFERoSQDlp56F6PQBYx/SGlUpGk\nH5KFGUq6KDGiMTd7kMccgFRapuBIWS2CyCRaaZI8qS9xrEaqkNlnaHVnk+NCsrG9xbxf4PjJkzh+\nAc8vECUhrlOgs7eLlB7tbgu/4NEfDPCkR2tvh2ZzkjjKaDQbtHc7ZGmGcrETCsele6vFzt4uJ0+f\nsBkejovvuKxublEpVRCFEoWiT5ZmjMIhjhewdPoM9eklWms36O3u4s/PkiYjji4dIdIZnuMQholl\njsaGvXCZ3eWvUCgcYiNpM9i9atNJDSAsQUgZS533NaTGxiVn0u4Frvib+4k3RdEIkxDpClwhSQ0E\nRhGaCKUclElJhMUMfOnkjChLqdSk1gVVS1yT2E3AGHSS5NRKSSTy47Iei9UUTtrHM4p7Z29y63Kf\nz3/tWyTTimFawHv5U1TcgM3uiIt+l629Ptd2hkxNlDgswPU8vvbMS5w6ukuBhM6tFp3M4BYceon1\nuh/GmoYn2Xn8D2mWyyxWfK5sbDHsQytLEEbRqJY5cugQzVKBrV4flMulxz9BveqyGw05fewQq2sb\n+IUJrqy1OdZ+Cdd7g6oe0e11iIMi8E+/7zWXCoyxdGaJHfek2OAiixtYPykpBTB2lrUPkhoDxLnK\nQwqBI11W9wrooeKcjHnp0Rd4BcH7fuo9PP31F9jZaPHjH32QF7/4aWbUKikZftxmsDzglSsxUb+P\nKk3R62wwc+gQ2ytXGQ12KJWnLXUas58mOAa+Zb6RCwTjrAw3D1OyRpOAEXn6nmAfHtOabJwCyBir\nuT0C+4/WafyFsd3wQS61vkE4WSCrFVieStn56l8x9baLXN7c4wuvP8ehE4e5tjXDQ/XzlMoVTh8+\ny7XV6zSqU7x+7RKXt66x8toVnvwffofffemLxMOY2uICcWuEEAlhFNFsNKFUpPmWaX7jI/+An/j1\nf8Jz3/42ys3wVAGlBeVhyPve80Hi3SGtLKE2Pcva87eoNOuovTpprcO0H9Df20UAM0snGLR3OXL2\nLo4tTbEb1tnq9ll54xJpN8Grwcor2yRJD1n1OXzhGFPNOcJoxHSzQrPZpNNJcT1F3O2ACTB+gcH2\nLUpTM2iZ4QmP7XYLT7pMHZ5kMIgw2QDVPXieRgL4Gfs54EjrNOBq+0zHWc6ccwVOYlCjFqtbmzSn\nppCOIo5SMkb0O20rCvYCavUamohisUCWZtQbPoNOG8/zGPS7TDSn2Ov3mJiY5NbNq8S9Ni+88jp/\n+Md/gkaijGFxbooH3v4Av/iLv0hw8iRTzUm6m5usLl+hUgxYXJwni0MwhvZ2i7PnjvN7zz3D7Nw8\n/UEXKWD5xlVKxSK15iylimJva51by1dJzJDy5B20lp9E64Ruexewj7c0hkha519y519X2CiC8dHp\ne02/3xRFY2KwQuoU8KXCFBukWQIYHB3hSIVIu7z+9F+x9PafsurgfKPzMhihEcohzTQFYYhzj+8M\nQFhLcS/N7SGkpjbY5djJCW68ep3lnXVu3FjhLSebvHZzl7dcmKOzsc7usM/h6YBhGjFKDcfqFbpZ\nTEWG9IcxSzMT9PpDAkdx5FCVfiIYDSO8os/N3Q6n5mfY6bRJRhkrYZdra3B0usBUuUjBhydvbHK4\nUsB1AsK4TclxyDzNAxdO0h50uDeY5clXb3HfxQs8/8olZkpFXmq1aI9ilqZqCKMoDEcHXvexo6z9\nQ/4wiNuZ2GPbEIxVVY9/OpcGgdDozAoZlTAcnhoSZQ46jol1xo999F18+k++xvt+7CEuvXidNAtw\nVYme9pk8cpZRv8uM61NuVImiPW5eeoGdnV1OVWeQWvKpP/ssP/1z/7llZ+VCyrHFvBh3EGasUh+z\nncx32ZCwz+waA/aMeSFj0sF3UYbzIZUdP37XGo1HUwed5d64fA2ncJKdr3+L4MIio2dfZt0xLJ07\nwcV3v5vtYZcbO7s89fH/hcojb+Vsoco9U8cYZBEzh+d5faSZqPnEzoh/9WP/kH/2u/+GQTSiViiR\nSkllqkyvtYLnBvyvP/KPWNJXOX78HCu3LhGGCVmasNUeoEctNm9dYfHIHQRFh729LZyCy/Zum6qK\nKMUl/GKJvptRq5coFANKp4+RacPqzgpGQyGoMTsxCfM+29ublBZ82ptd5qeboBR7u3s0qlUa5RpR\nWsUxe0gHZOCTDLroOMM3AW4GQerRcwyVQKFNiU53wChLKauAgX+wjhrAByKRjyIdhZNaP7JMGoQ2\nFIwdjafajmtckzIctCkHPmp6lnanRXN6mjgcId0y3V47Z+ZJ6vUpEIZBv4cRhmKxRK/X4ebNG7gC\nqtUqr3znOzz2+Je4dGOH2ckCwzBi0Dcsr2+y/Oef4q333sWZc+dRyqE2M4lyJUFgJQPFYoEgKFKo\nFLh+/QblwKcQOIT9mOFoiJSK9dV1vFKVkiqSpBmlWp0s0dxYfx7peQxb6zYH3FJoSPMgpyADM/6V\n0gIh7bRBJJrvZbP2piganSvfouK4dMMM40vSVOAEhmIh4MZaG4xk0lO0n/gDDk+WWWn16A4Semmf\nxXKVlf6IpeYUulhh4tS9JLJEdumvaNSbbG2vMTQxFeNT8n3+8a/9c37t1/85UrpsZwELUw1Wd1qc\nnC1y14W7eLHXRokUE8K/+Nf/HcIt8JGP/Dz1kkeY+UzXPeIwsT5RccIra9vMNqaplMq0hm2migHd\n7jZV6SF8iXJLRErRixPaSY9GXGCpUmKzNSI2bTZbQx48Octmp8+LL10HoXk1snGqX3n6EqRDokBQ\nKnjEccz1jRZnZiboJQcrGmMaqpWb53BYHvqkMqtbsJwDa1yYSZNTfi2rTOZgvsw34kGY8tp1wz1v\nv8jl12+hfI/Pf+Jp5o9N4PqKxlSZL/zZVznUiFB+gFAOQcEnzlLSNEPIMhfveoAnPvVFDv+TUxw9\nf4Fv/+X/SeAX+MhP/wQWobCdj436vf0Zxn5U489l/hMQ7zZT7PZvgsgB9v1/gy0wcPu1yJWxQuT6\njwNiGsHhaXQ3Ilxf5cSHH+K5+EkW5+e48tIb3DMzRXJjj5X117nnpx5hZiC5vLfBl1PBrRefxosT\noivbXBWK6ekGye4Wv/oTP8WR5hIJI8qlOm+/c5annnyD9fYec2qIPPZ2tEjQngNGYvo9JosBeCmt\noeD5W8/hdqHUqNCoTXHvxdPcuHmd00dPExUyRDiJ1gLfKdPtbCOFy87yLjthyu71b5BJSSEoUZlr\n0JgoUfEdTt95Fw4Z0hgUPkMZYehSKkGaSYTn45kasYFYeZbBowXdfoegPIHe6ZHqmIIv0dLB4YCU\nNcCxdDqkhFinpBoyz9qFhAp8IxgnbistaA+7nG3OU52dplwpMxra036pNkU46lvXZyPYa23SH/QJ\nPAclC0zMNkiGMadOneG5576FF49YeeUKX3/027y+NcF/89NnecsH34trfLaGe3z8zz/Hs998iV/+\n1d/g/gffzdziFMVSjUa1RhAEaAHFYoGjJ44z99oVHDIuX16mMj1FWSomJid58tGvcfzUHdRrdZTr\nMX/kGDtPPUnqeCSiQOfWc9byHItdBoFHfxBREspifZmVHGhjjUpHwiA964H3N67nge/I38K10Jjg\n+ZUbKAN1ETAcZFSMpKehqBKOzc+y0unijDK6xByqFZDCMB80SbSD6LRROqSuNN1nPsXizARxzSFJ\nugxHQypBkb50uNVa51//y9+irAxFP2CilrK+HbI7GECUMjU9w61Bn4YXsDuI0JmhWCjwX/zCL/CV\nT/45U46gL4b0QkklMQyTDBOUWNnZ5b6jR6jUKmwPh0QjQbHs8sZmm/pMiUkpmApKLG+2uZp0qTqC\ngUgIBzHHZ5s8qRFwBAAAIABJREFUtbpH03UZxC2EUgSRIA4EDy4dISrFPH3pBk4MRgXocIDvQS87\n2K0TxmaoWwNFi1toDI7RJFKjcK34Uag8vtUCxlLoXLA49oayrsSD0YAP/9wjPPfkS1y86zhLC0t0\nel3WVzf51jdeZHdjnYUpSRpp/KBE4Dr0R7mXv9SEa0/RXLqfV19/GSEkWklO3PMeBsMti1cYY90A\nvssCBOz3Vd5Z7lN0x8KS8WWwcZ35ri9ysaAaz3dRlrKbYyBjwFuIMbHagu/ygEh4f6Q5ee4Y1Tsu\nIIMCH/jHP8dysUAQSG48/goDd8BdH/wQq5eustYe0VxaRHsZU9Vpqnt9PpPG9HXIYC+jMTeLcGuM\nRgMyF3avvMGhpRFXt1fRiWJNaX713/wKItZ42lAul9hKhriBT0XV2PWWKadN2umARuATqoSbm9u4\nXom9LMEfSG68fplSvU6BHUJXsLG5wq2NK5i+x6jkc/LwMY4uzJEASS/Emy4T9gYMRn20a3DxcIRg\nZrpKL5QM+l2qExNkyYiip3BLNdLeiGHaw5WCzq0VStU5+o5hSrm0+7sckFkO2IhltPVQcADtSpzU\n9stuHr6VCessERuDlhW8apVyULQZG8D2xhaNqSlMEiJdh96gz2A4IO30ODQ/Sxx16O6lzDSn2dpa\nZaJWYnrmHFH8DCc++F8TPfo/8vB/9l6c2ixGuOx1CrzjkR/ECRRf/8sXuP7ai0xOvgu/WEeJGOk6\nuNIw6HRYX1vnjhPHGKVDzt1xEdd1aTQncZTm4sW76A9C+v0BjYmALI2o1WtcWb5EPHgDIW1n4eWk\noWEYoZS0hABhXaylsb/fqczNSnMTw7/pelMUjetrW5yarNGODFE0wi0FRFqztrZFtVbkiVevMlWv\nM+h3KWuJVyhSDgJ0nFJ2M4xwCRPo7SXsxIr1GztMVcoM9JBMOZg0pOKkHJ6skbkJrlticzDg+Nx7\nubTyV3zovQ/yjcde4gvPPMsXH32eODXEGj556p0UymWGgzbGKALP44G33Y2MQ1rhkIlSiaoJ6Lkx\nG+1NCtUSVzb6DDPN0lAxU6gyIwN24xHSN2hfQxKR6hLFgsepQxX22ikPLDTYaocMRx5ZmvEr/9VP\n8u8/8yVe2d2g0SvS8KuEnqbfanPX8SVWdzvU/IPN1x2ZK2SFHbtkBhxx+xfJGItviFxjbdtVu2la\nVleOGeQdwNNPv8Tn/+IxHF+i5TEe/+o3uf/dd3Px/hM4UiDMGR7/xMcolQoElUmyJMbxCqikSzyM\nKc6+jZP33c+XP/s5qiWHoDDFaDBCr+zi+4IkySwDTjgokWG0sp2BwKr6sVTazIwDusCqLRVg9lMf\nMyNQIvfXUVbzI7BpgwKdg962lc+0wRG3I28Peh0/epqp0/O4gcv1R5/jsqeZDB3q5w/zSv9F3v+2\nH+bbzz7HHcfPcaOyg9twefFPvsaJv/swv3/fz9JptWj4Be47d4EfOl/nfR9+iGR3i9/+nz/OyvQ0\nzz2+xfZujyPNMj/7f/87ykNDYWECx3fZXNmmKMscPX2StTcuM+c1CSYmmJqLufTCJQwbDDtPsDhz\nntrGHkG1gtco4QqfzIFBb5MAwZ0XHgDlYTLFyq3XCUeGQBhqhRKDOMWtlJnyA0qVMkpnhKHDVq9N\nqVTGE5o0zuiFI5JIYHQCJiUbaQItKc7PkZqE6X7CXj9jsjbLMDs4e0phKbepbScRQhIrTUHbuOPY\n2C400wKEy/TSBQqeYn17nUqlTDKKqEw1GfYHxGGMCjKSMKJRb7Dd2mFzbY0sSRiNGsjUUKpVOHH6\nDtLUcPj0eb70D/4On/jjn0dX57nRGpEh+dgn/5LDh8/ygx/6AElnh5//+/+Uz3z8k5y+605c10dK\nwbDTQggHqSSliQrtG7ucPDrPmRMneebxL3HPnRdAK65cf4HjZ+4gTUMct8R2u4PSLlHfQaQCx5Fo\nlSE01glcCjwgyl2fMdjY61ztoBxJ/Gb3nmp1d4hNFZ1YHUXJi0iMQ6NWpiAETjXAkFINAg6VCjge\nuIUirXabkc7ohT3umasjPMFox6A9j93+kExonnr2WR795hf573/pV2i3BH09xHVdjjYcfurvf4Qf\n/pmH2Lm5xoMPPUhj6Tg/83d+khdf/jz/7Jf+LUma0el2kdLhvjtn+PAPv5sPfviHqEYj/ttf+116\ngx6NSkqQ+HTxaLUSFmeqhL0+s3WH1Xaf7sqA3X7IwlQA0kEqj6k6lFxJb6NDW48YRBWONIss1KaQ\nrsv/+xefI1AOAgfftSFMi40CTsnDODBd95mtVg+05mNLQpmPXVQOEijGitAM6/s0ttIwCJPH36Jz\nMNx2G0ma0ent4RQqjFpthl3DD3zwYb7y6S/xgZ/5Ebq7XR773JMcmqqRhDFZluEUfUxq8L0Sm9td\npBcThyMKJUsFFMUSqjdkOOry73/n/+Jnf+G/RAiNILXvUFjXMcuytiJEg6XTZiZDGQuOy1wBrr6L\nuiuM/dx5viJ2CEcu/db5mApU7qirc6bVQW1EqJfpvrpFy3VoVJt48xVG6xvMBT7zjXN89dlHOfGu\nt7F6fYPKZJln/+KLTC3WWfuzx3nk9BHMyQUev3KDX/7hRzDlDNNq89k//QI/9o6jfOzSLT57bY3M\nkZR6mmde/ipfCSZoc53F4/cy39EcP38Xg06Xcm2S4WAP/IzZrIJ3/1uIVjvcFA7ZoEXp3CGe+eqj\nBFMlprwqc8fPkEqPazdfIPCm6Lf3yLKAUafH6uYW9951JyPdIWhMsnprDZTANT0WG8dI6x7h9RZh\no4/rB+jhNs1+i9NhSqiHPFppEHdizEydsD+ktbtOUKpQLEukCfD/FjLCRQqhMPhYqql1mBBELjiJ\nBgeM1mghidOYpZOnKRQqFCpVXOFSLAcM+0PKpRK4ioJXZtDZRUhFqVBkZ2+H6eY0ru+ToojSjFE0\nYGFuiTCKOLoUov0JerFmqz3g6JmLXHzbv6S9+v+wvePwIz/5czz52G/ym7/16/zRn34SxwsgS9AY\n/MCnWq2SxhmLhw/T6WfcWr3B6bvvRZuUUdhndv4Qzz77GPe+9Z1E0R7VSpn5mTrPfHpEIY+Y0NIS\nQmKlKeEQ6Sz/XbcGr2OMRog8DfN7zGLfFEVjYmKCousRDWMGSUQ/FRQCzbAXY3zJVLFONx5gPMVW\nuEfSc7mxdY1UGqaLJR5YOkHPi+lt93GKBeJBROBCuVygWa7wjT/5JDJ10K6hECv63SGL734///aX\nf4GgUuTrz1+h6hU4MRdQOvcwH/3pj/Kljx8nTBNSFI9/9o/5vc+8xq/9+sd4/FtriM4WwikiXYGO\nDWFZUtIaE42IjEspCLi62yPNBLthh4tLs8RJzEKpxJ3zFQZhhskUQbPM0WCS0HFgFNJKNXUdsjRZ\noJOEVDOHjBhfaIrCsUFN0jDSsLLbPtCa58OdfUBZ5p5emclwlOWsi3xLHbu8MC4WuU5DSitCcn2f\nb3zhFT7wow/R3hvx5b98grpfZLqxxyvf+Ev8UpHjxyTJqGBZWAWbcSJzmxetQaUFhrHNGdne2EZ5\nDqnw8L2UsNffT+IbJ41Z5pPe97+yHydXeGOLnDRjZfd/jH3s/3zusYWBsbWIfZkxBgIiTzW8HfTp\nft9rblptNtIBM8cPkxpshrMPvWsbuCenaa5nrL56FX9th5J7hre8/2Fat7bwnrlGc6bKd775BvfO\nnOONrQEPLszS37nK9mZKtRkRaYXjKpLRkCtpyN0vr3LtxBCvrwkv+qy7CTevv8TO1Vf+P+beM9qy\nqzzTfebKa+dwcq6kUpUCklBGIEAmGAzGGLAxBuPUNmDA1wl32/e6jfva7evhxqltwDiB2wmM1Y0w\nAgkhQBKipFKVVKXK51Q6+Zyd9155zdk/1j4lfrgZd3T1D61/teucGmPP2nvO+X3f+z4vd91wB7P2\nBOlWg81Ri5rQYb6EWT9AZ9vnxIllqpN16mOzSBXS7mzR3Fwm8fM0O1s0uk3Knsntb34lgzAlNzXC\n5kab8089R27EJW1rVMbGUCMVrBLY06PEfouo1WFaB/PCedqGRtdLGWyewbj1BqzIwqpYVGt10m5M\noPuEMkaEVy/4UHrWkkzkUJpNFj5kZeO8Ya6OIk0UsRRsL59nbvcu0kTR7rVwLJdISbygj2Hn6A86\nSEy8QY8kUTi6jWVaaIaJJiQqTiCWWJZFuVJFs/Mk4TZWsUIswNBMyu0TWOO72WhcYP/MODLVuXjp\nAt5ggJ3PZxJ3YdBtNtlqtNm39xq219Y5vnKEimsSJxGGYaJIsTSdiZFxHNfENHKEk6Mc+fYhbLNA\nFMe4yMxvJMFKINITlK5hJJnAJBUKTdcRaZoZH8mkuP+r50VxaHhRQi/xMGPoDxLGJsr02gOqxTwX\nNjvYtkPHD+j1ImZmCvQ7KfVSEct2qToGg6iJF8AgjKnrglJe0A8NNlptnjj6ZX7hh9+D5pRI4pgk\nERQsOH2hyd9+7jC2DR9662380WcPwxG4p1kj5uusXzzM/Q88RBKnGI6LhsAsF2k3Iw5U86A59BId\nK0jQQoXUPXppdhtXusFY3qZguCyplO1Wn6Kbsai+eXaT2eoI3SCgahs4hkHa9Tmxtk1tvAyDhFze\npJrLQSJRKZgjNo4h8WUEacxkIY/4bu6b/1/PCxuoEAox/CSYaMPsCq7kc6ciez3L4x6a4IbOdV3T\ncG2oqGd4/Bsm3a0+b3znnZw7ts720vM4yqPT7mc47P6AKApZu5CAFJiajWk42WbXdXDcPkYqeeTB\nLzA/dw0r6fMkfkSnuU6rt0GpOIqmZ54JpEIbqujSYbbxFce6GNZQQwQJDCurHYc7V869DLkuBGkG\nNctYVEN4o06Wga4PFWVXW2msr/c4uHeGoOKgeimtI8+ziZ/d+C576M0e1y3sInrFPMFgAALMbz+D\nc+oif/30CWpSsrbc4Nrlf2Xhlb/KM5fPsXDwIIcvfoubZ/dy5MQaE/kJTq+t8vhnvsyNf/JBGvEG\neeXQ2dpkEEYs3HI3ZzSfM60zkFOsHzrM3C03Y+dtKtJnbLzK9PQCN3cV7a2LSD/myPkTSHeK+tQk\ntbk5Llzcot/ucvzkMWruOMuLF0n9lFLBITAtdk9Nkp+u02peJmrreBe2CAPFnCvwz51laeUs/bUm\nl5bP8q2ViMnHnuJdf/j7iDAhsQp4pS7lyKbne2z2m1e15tl/eBYRLXWBTDNysUaKVApTZNVHnGS5\nN0EKC3v2Z6w5mRIrSd4qUMznkUlC3oJId6nUKmxtbKHCkF2zu7h06QJuPkfesVDCwI8UYRgyOTbK\n4pnzNDcEXtpAJj7/8Ojf8OX/9Pe85f3fhy108oUKpkhZ2+wQhhGu4xKlMY5j4odJZiMIfVKlc+2+\na9CkoNXeoNXZJIkiCqUi1ZEJdN3A1A3SWHDnK17JkUf+FhE1iABbDWOZDUUsJEYis3TOZBhqlqZI\nY5g/kHIlQfPfel4Uh8Z2t4vpQCl1KLiCdq+N6bp0ghBfprTaA0YqRXRtwFY7wnZNypZDoxcRaB59\nKbCRQEo5b/Lc8hbVfIE4UvzcD/48KUX6fifLjBh2sB955DHe89538fS3DzO9Zxevf02O+7/4COg5\ngqjL73z0V3n4a08SBAEYBrquk0sjioU8xckq9aRNgEV7q0mcJOAlVF2X5Y1N4lqFuuOQK8dcTxmR\n16k4BkEkuW1ilL4vGau79MKIXhxQytvcsGecwaCDlc/T9j1yuQLn2huM20UcV8cQOpZl0xtI4tRD\nqavM0xDZDTodgvi+Y2SRzSwEV/IHTLKbtjaUq2b4QHVF4x1GCbe/6Sd4+LNPMD7Z49zjXyGXr1Cu\nzXN5uUHqpxjSIo0MEAFaCnM3X0++WKXXbWPlchipIJE5Uqlx+Bvf5j0f/gU2L54mTgLSKOT+v/s8\nP/Te9+Dm8sMo2KFKShMZVXg4/N4h32oZondYK2XHxE44F+xIcBn+0k7uuBi2rsQVw6MuXhiwX+1o\nY+aWXaweW0WuXGCqnmOzYJEzCsgLKxSsGrk7rqPbi7FMxfLlLQZr51k4u8Gjp4+jeh7rqU+YbHPX\nwTcj5qapnlZc8tdxfRtbpuyqFTmzHvKymRrrcUjvzCVueNcb2bhwBrnVx6oU2NpYw5idQ4gm/nZC\neSRP/tlnmWj2uK9Wp3d+FVvFXNpuoGSbfj+ks34JXaXkCmVip8z+PWVO7bqPSiiRuYhbVi+THNyF\n1ruIPvparDzEnS6xVAStAdJIMVaOM6UJvn3qCJeWLzFVm2NT90nTiMPPP8/3dXrk8y6D5ja5cpHU\nsWHgUXXKV7nqO0iZTOmXpUMnw89IVommIoP2CaWIIgsMPbvBGy7b25ch1rBTJ0u2jA3KlRGSWJHL\nF6kUSoRhwOTUNFHgs7WxxejIKBd7p6jVXKjBB979kzz2dJN7Xl/hfGONpQsXSA2PuO+RL7loZAiP\nnFNg8cwJ9u3fj2649JopXt/HsUw000KTMd32ADunsbW9zuzsOIXZKYSukcsXaGxuojSB7bh0On2+\n710f4YFPvic7LKRCZaFDWMNvsSQjXCfa8LuggZYOPVnfxZP0ojg0fN+n1VXMz9U43+xw7YRDLzbR\njJib56bpdD2anT6FvEUaxXi9lNGKyUzJ5NR6l3LO4lLoU8u5NH1Jvl7Aa8G5rSZ3jhWojZucWezw\nqut34Romg0FAUhjnLXfv4fCTh8kpn3K9BmQ3j37QIVQhKkkxTQuh64RxSJpIiiVoJAajiUAkEdte\nQDOGbsejkMYkukbBUDgmeJ7PWK6E1BRbrS5oLkVLYVtkgU+JSSB7dKMswyMIgaBBpVhASyPGzSKG\noaEHCUnOJE5iHD3ClwamcXWHxg5mZScpcaeNo5RCDY1vuoJ0R1WVpXNf8SsoslAbTUAQR6AbGHpM\nrVij2++w0nAxhKJYN+n32qgkJfUtjGSA0GNWnjtDnKbM3HANhtIIQw9lQiIjOp116rUSURJh5Q0i\nZbF+6Sm++LkcP/zu92SZ7JkX/Yq3QmgSpbIZhbhixBuqpXb83jt9WvXCQbIzz8jOx52hd1a26+x4\nVrI14CrZU3LUQVShOLWAv9Qm9Hz27p3hfG3AytoGk2ccrLE88myfvGtRmp1nsrjGqw/exGdWHuHl\n19zAN08f4z/+y1m+9g5YPLnEqZPr7C16WGoTaZZIxTIDLG6ZMVj++hHCH3g1qqlICwX63QHp2XOM\nJh4ylqi/+BqNxiXWIp9yIuhOGpxdaTMxZaPaI2w11tg1V2JlWyefl1iqi9Hs0e33KH3rv6KmrsGd\nLnHx2UV2rdUZ+CmNbxynevN1jFVzbBV2YfW38I+vIdefYWm0hN1NafpdVp49xWJfst0f4ExNIaOQ\nnhI0kjaVIEHzJImI6CX9q1pzIItS2OGM6ZnEV8jsc5IIhaE0IqFIhUE/KZNEfhafYKZUiyNors72\ndoPpyUlUktLrtZmcnCEJfaSmYxoGoR+gGxZpHDPwPSanxun3+hQKVV79xtfwwQ//PK9546/RWm7h\nSsXeu24EqbFrzy4000Aqh2TQZ3NlDakLSDNMTrFYpdVoc/LkaaYnp9C1mLAXZkw7ZZLEEhOD9cuX\neOKJhzh+7DhvePOPUXAVbqWGihVK10jtrDUlNJ2AjLqhUklIBuXUpUY0dGAZuo58sUtuXdfENG0u\nNRuEseD0VpeKY2MrMMt9xkYd8m4NQwWYCHw/YanVpNeP0S2DViLJxQZnL7cZK8SUiwZbrRYVzSKS\nOR589jled+BaTCm46DVQaHzoIx9hOj5HruTgeRGPfP0wuq5h5eHc4Wf4lQ80+dkP/Ayf+MRf4asY\n27D56uMP8Acf+xMWrt1FcvQyl7aaiMTE225xcG6UlY5P1E54/mKDsm3xhtv20m0PsHTFeNEG08Tz\nFeN5gyBOmBpVqKhMKiRmIkkKYGpVojhEt3QCExwDHNOgGfu0Bz1EomMYKd3kKnMGBBlCRM8Af4Jh\nap1gmE+irtzSDTGsz4YtqUzzTebOF4JOz0dGOoa+SRhO4Mld3PSy/VgODLyEk0fOEXZ6aG4FgU7i\nbaCnCY5I2Dy9Qum2UYJBF9U5hK4MvM6AMd3C73mUK1WEGCBUnXMnTgyhgsMhixAZZXjHpKinCJkd\nZIKURAlMkSHUtcyilx0mYiefIwOiDNltSKGuHCXySkWSlRq6Uhkd+Sqe7aVN3GunGLR8VN0lfmad\npeUnmL/jAEZ1hHCkROq32XPNS9j2u3TOn+fy0ipfPvociUz45qlTHJgepRBKmk8+zLNPPUNxO8HM\nl/jm8xGdTY9px+VS12NS5VlPF5EPP8z3P7aCyqdsbq1QsKosPnQIOxzQ9yxWN9psRj2MUDAi8ixU\nE/xlwbqxxMNtH7ndJU4DZCK5ZXaM+cSk3D7HrpExOieeYeloF8stEp3xaUce1+1fYPH++zkjJbmc\nQ8X1aAwMeoMeT367x+WuYC2O8H1JqBIsXSeNGnzpn/8Rt2Syf99ttEf7RAONQtWhVLj6SiNBZXak\n4aVBUxANo393ev2kkAQKtzKP7RTpdAc4cUi9UGN1c4O5qTHydh6tZpKGCZsbq5QqFTRNI01TTMci\nSqDVbVGtVVk6v0S5UGLpzPPc+5rv4zf/nxbVUoHv/Z7b+PrXjrAo++w+MEctX0WXCe/74C/zV3/6\nW2xtrBP1A9yci2HqNJYbTE3N4LWbNJqb1OojnD1xDIVGvlyh70VsrV/k+9/2/WgipdGCT3/mC7zj\nba/lJ3/mQxkxXM+CQtQQEa0pgaWyHHCNbF0SLYtBTlMJKsV+sVcaKk7ZbG8zPlKlUBX0egIrMdEd\nHRklWGaKFQ7odgJOBx5Kg4WcyVw1T6PfZ6OrCFxJ3jfIlQSXGi2cnGKyYvNLv/h+fvGj/4FPfurt\nPHZilp//yIcJA8l//5s/xQ36HKxrHDl+npvmS9y1/wYefeIQM2MFTpxa4o//9Fe4fO4Qn3vwGVqd\nJj/3vl9l8fwpXta9nsndLyW8/CBz+6dotBoEQUjOsrh5t03Otnn5nS/h2aOnsEdq+LEiiCTlnIuT\nL5HKgNG6hR8mBCIgDAVaziJvazi6gWXnEGHA2EQBx9IJexETqcLdN8OgH1CwbaLo6polGU9KolSM\npltoahhmpWsIPcNz6GQRsBriSom/06aSKoPpCTROHTvDI5/5bczyy7i0bXHfG65n8fw6pmExNZ9H\nCB3TyaGrHHriIfJzyME6xVyeRqI4++RTOOU81eteB5e/gCbAGKsQpRGREJh2HkWKP2jz9W89xr13\n3YUmsymDoUE8lNOqVGExjLFVAmPYwsrkwkO68dAdLofViqYUicg2En2IERE7nDJBBklEI9XEVVCn\nsifx+5z++llG9tn0ThtMvmIWM4YLlzqMT9c499ijmAsTXG6tMNfLsbzZ5jWvvxNh+KxdGOGhkyd5\n1zt+mFNHTnLsiROMzs1iF7c4t9EhX3RpEVPSJfsmSjx+ucNktUj141/kL4KYSd3lfNpjV26T7kBn\ntdnmmrk6qedxbzWHVkk42UtoRhoWXRrtHHZkMjlicLGTIgydohGwZ0ow2LK41PUp+YpCQbHejlhS\nHU5cjji7eoEff/udOMJis5/yd194glOddRpJgpARumHR7vXQtAy97xQcupsxXzr0JSZqY5zdXiae\nmiBJyhTHClSExr977fdd1bpbZAmVDhqJJtGUjiEzUUcy3EQjBKFWpTi9m1KpSBgGSCloDXrMzi8Q\n9rt0TY/R0ghuwaVaqeLkbAZhQNGwCEOfIAiYn53n2LGj3H73PfhewMb6KvWRJb7njW/g7d//Vj76\nx/+e3Kstju2e4uD+/bhIPvbxP+HQA8+i6y6N5TW6gx6FUoEkiKmUSnj9HhoG45OTXDx7DsPSmZuc\nRbcdPvoLbya2JvjRD/wY3toSWhzxyc88yelTJ1m7cJG73vxrPPml3yMnwwzCKtJMmi4EUuPKd1tX\nApVmhAehie+aC/qiODQ6nmSkkqdYzZHGEZaM6PX7OGaJ1e2IQi6hjUGlmGch8UhMm6Jls+FF5OoO\nkykc2/KZnamz0Wwy71bYTn3a7QDNbvHxj/9nHnvyFKN7TB764v1IGaMGOn2VohmKrdUVBs01pg+8\nlI/0lgGH0UKJXvMC73zL2/iXh47y13/7aX72Ax/ke+59OZWqYLxcZDGVfM89d/Ke197Gb/zhZ7nr\nZXeyeOIwN80UaC+f5pbdRYJ+j66MMRGUiy6anYC0CEIPkx5FW8dwdYI4IfAgLAtIbCJPwzRCmls9\nEj0PXkCYCNIwIg7CqyauZi0cDU0ZGb9pqGFPpcQQmbY9Hh4QmU8jMwdljKcs70RmsiNWly/jIDGL\neWKg1W+yZ1+Vpw8tMb1rP3e+Yh+mqfHg55/h7lfcwOp6h81TG7TaLUZnZ9la6hL1fXobF0nSlJKl\nYQCOaqD8IhoQJhp2rsbiqRN8732vJAyirG0k5ZBBpWW5IOKF+JiYnUyMYStt+LpkRzWTzXZ0lc1A\nlOJKfrhOpqL6zpCn78SL/O88F46uMPKKOc498DD3/eyPsH66RVQwCcdyLP7LAxRunGf9y6fZ86q7\nCCwP3w+49Mwa1+3bz3OHHmXP5Cyf+8IRLjRP8OBXW6yqPr/+6mtwOpKHNkO6KqQibE5uNCk6Di1v\nwOmtHrWcRVf1cS0IrQKebHHXvKDpScZyIVNFgx4uG6s9lJZwrV5nO+4RmzGXe4p2HHONa7IgoNtN\nccfqWKnHqZbL3iDhcD/kBkMx57jUr82zvNWESKOYg7e/9aX81qcepJbTaPUEYRiBMEilyMyjukt+\nRGLmcugz46i56xgdmaBYTmg4FXorK1e15pBVjhZZkIqSQ23Ed5g41RA1E0QpeWGgpwbm0PNTK5eR\naUqcxuR1A6EUSZpiWjpBmEIqsQsuYRhg2xbr/T61eo1CYYTzlxdZmF/gwtIlDtxwgP/42x/loa88\nwE23vpRCcFQpAAAgAElEQVRb9+2jXjCQseT8N7cwgJqREiQxjmWSpAlhFGIYFmmcMjU3xeqlS1iO\nhW5BfXwKkYAxMsXc/A10zTwTe/dC5GOKJ+h1PYLEp7l+noIjUJEYxjcrTKHhyRRHEyQqY3OhZRXH\nMCGb5MUuubWSGKG5rKw0KFcKNPwB+/eNYAwiIik5t96hUqvR2lzD1wR+PyTvm5xd6vDS/eM8fWGb\negnG8zZev0Slmife1jDrU3ziv32Jy4vrrLS22Tuzm8e/9TVG5+bJGwVuvnaGjeaACc2nWK/x2Km/\noj5Sw3BsDn3rCV5176t474/cS96y+PCHPozXbPHwVx/hN//9+/jGQ//KK/aOceLIE3xmscGt104y\nmi5SmaswWlGsbTXZaqakmqBatCjZRTzPI4oVhpPjc18+ipQ61+3exVK3wXLDI/C2qeQr2GaO8eky\nmpXjXx5ep1x0mZmdItEL4NgorYgnLD54FWuukWRoCeRw81SkUsMa4uIVAl0kWTNIShAauqYN8zQS\nlDDYiUF66vAhYgw0U0Esef6ps9zxqpuZna8jEo1YE0S9hGTQwR0ps79a5oZbdvHgf/sXDt53D4+e\nPUuSJKTRSSZn97CyvEgce5j5WeIozvDzWCAkW2ubfPLP/4Z3vfuHrpj4dC27SWbKDzl0hJvoIs56\n18JAiSzMyshwvZnZj2EDSrwwu8hGGllmh0If/vxwbHiVwML8BGwdX2f6tjv44ie+yORojnsOHiSe\nu4bzd7yUVqwxojV44oH7aW+s8rYPvZ+1L5/kwrFzBKbHSquHFkAifVSliOqnfPrwgEEQsxYOqORy\nxAbYKseAmKLQkWFEYBgIEZMzcqy1txgvuJimzTvGDRYTnY0O6G6Xe3f3eWTJJBJrzOcq3GiU8ISN\nra1SK/q0c3VK+Rq1nMbeG25DP7bJQAyohMc57Rv0VJ/2U5KJlwsWDr6M/fvuxJDL/NTN2/zm408i\nRUQYSZTMgIH6hEvunuuQUyMUJndj520kCYudJfon1+ivb2FXrj4jXCdrpwYK8ppOIFOErmVO9lRi\nkP2d7RYZnz4AdoKpivQby2j1MRSKqZl5PN/HDzxGRjPyruNY2O4YSdBnfGKCQbfL+PgI6+uS1tYy\n9XqV08+fYGJ2hqWli9xx5x3s37OPz/3jP/KJz/wlK1sbmKHGPbfeihHMsXLxGGL1AnGUEochtdE6\nwcBju9FhafEspVodq9+ks9GkcGuFf/r87yNqt3Dbq3+KH3/z9/PjP3M3C9Pj/P7vvYNH7z+D5do0\nG+eBEaRaReggJCQa2FKgxVl72lbZ8FsTIvM9CTFM9fy3nxfFoVGcyHPjbJ3DZza4dkxn4IywuTkg\nl8uhuYLpfJmVrTZd1yYeBOimQd8PmJ/Os7S+zEzVxohhql5kbXObkdFR3njnQR48m/LWl89x+rpx\npnTBlw5d5C8++lN84+gl7v7eN/Pe9/4cN92yh/rBPZxYi5iYnKZQLbB45Bn210vceO0M//w/nmZz\nbRXh5LCLZXq9Nq962Uu477WvJ4ha/MDbfwm9UOPQhk19YgphaAQbLm75djSzQtzYRE9q+M1tnHyO\nMA3w+wHWwTk0y2LTj2Guhu+soEyLnpujkUpadZdwELDw6hspVmoEvZC0YGF2+0hTp2j87/sFgKHe\nVA4riSzlTmnpEFwmYMirUVdiVzNQR9ae0pFqyGISCm3QJ/R75KWNNDLcxrnnz9PZDijdPUIxbxFp\nKa99+2tRKTz3zAVecscCe647yIXjp0hVitIF3XaNO++7Das2AalC1006nW3cQhnDtfC7bdIYLpw+\nh6ln5bQmsxTHLJUvzUzgApSMMkCEyAbh2o7ZREmUPvRyyJ3ZdpZ5kuWmD02PEoSWDmcfQ0XVVcKn\nxuYOIJ48g36wwtxsHlms83ijwUJ8jlh2mbxhLye/cJapO/eRr7isffkJdk+kbKSS/dYMY7M99ho6\nj61JfvZVe3nszGkefGYTQ08wU51B32M9NUHXMBT4SVYZRXFE3rWYMRSRpjMmUoqJ4umgze76OEHc\nZj2K2NgcJR97pIbBatClWoDdRpVRp8iT3YCx6QVCbKJY0B208Fo+x1vn0HIGd+sC5ips9Ef4gbf+\nDEmyguhepm8HlG/exQ+sPs9jLcGlVoBpWNhlC246gD1eRMu7mN0Ghb37OPbFL6JKJUaSHLVRl07S\nu6o1h6wFkwowDR0ZSkxdy27SKmtnkoAKwZ68DWGE5OwKUiaMjc0TK4WmJP3ugEIpj+M4JGmCZbsI\no4AmJLlSkVa7RRgGGFaOyalpDMvAMCzGpifptNrk3DzHjzzHdTcc5H0f/Dne/qM/TOSFaJpJHASc\nOv4czz76BfxeShp3iCOBTAWBH9Jud5iencMLAvKFErWJcYSl0fL7jFRnefjrn8IkYHuzQ61UZffe\na8jbmxTtPINBiIjXslNTpSihMGTWmgI9Qwlpw8uUzJh3Ks2EL/+r50VxaPze+97Ipz/3OIZr8ONv\nvo//7y+/RNnKsX+qzGpngEg0JsdH2TXq4vV8tj0P2ylQcCUOeZ7fbvOB97wMRzMo2ALLAW+wgm3v\no+oW2b7wHHZ1Btut8ZWj66z3Eu4+f5pffs/t/NfPHuUVN11HE8mps6c4sGuOesHk5IVtsAWt3iaP\nfu0feO1rfpyB1+ZTf/b/sry9wd7SFG//7YdYePlraIQahrLwkxhDKYRtMfBCdKuBTkrUaxLpCSKJ\n0F0HC7DcAlGvhx8qahWNoJBHs7UMc6Eb6BE46DQHPYLBAMNyMZSgGw6wPJvB/4m41+HGL4aKIWPH\n1Ca/40Y9HDirYU57Nkx+QUEFsHn2aXTHobV6iPLULURKp7HeQwidJx45imlppBpZRroSyMGA9fPH\nQQnk5ipSBmh6TKoUIxNTnDx+HE0XJFGKiYkms2TEYqGClCH9foOt7QYj1SpKU1fSAzWRzWOk2pEG\nyyGMMMt93SHaCrmTNghS7pgD1bB18YIJ8AoIcXg4iqvsCfYuLlK4fhS13OXU8TVe9+F7OfXYIZZO\nLrPVXGH1/kcYv/UWdiUxm5rDhbNneMf3vJF//uuLrIXbXLi4zsJt13Ft0UVYk5xYOUK73UPXTRKZ\nYBoGUZRiGALbtIi0rDMtFFRMl0AT5AwJusCKTZKozzFvhYKr00wEUobkcjok0BM2C4mDbRkshjpl\n4dJoRhRminTNPlrfYHwqRNfrdL0uS6nPHlmjVplgfeU8c+MzeKUWG0dPYGgBiQ1jqcS9aw/cfQN2\nZOIUy/S9AcVKme2tFq0LDYrLFlF7i9VeSG5PBdu1r2rNAaQGIpQoV2SfZZFhcDQJqIx2qw/jjG2r\nSK5cZ9BuUKkWUCpB1y1008C0bKIoQekx+VwelXj4Uqdol8lZLpEfZD4izaTf7ZOmHSrVGiA4c/Ik\nOddhYWEBUdGplmsETkwYRHT6AWY+x02vej3fuP8hfH9AoVTED0KanRYzs9NcvnwBVxigRZRLVTSZ\nkrPzNJIucc+n194kDX1ilQ2ywzChUCyw57pbOfnoMxhG1k0wr+QjiywbXdfQY0miSSIFhi7QlSJ6\nsbenPvX5b0ES8YOvPsjx88sUaxX8fpszGz6Xu4Jeb0CxPMmJRkxlapYg8dHjGigLYdvY4w6/87iJ\nrqf0uwLbyWOhM1fUKVfKPHemy9Y8rEUGsjiDDAb8wdeWuWnc4id/6JU8d/ZZCqUF4lRy8JoFHvzi\nIr6E9dUVvn3oORZmFvjUp/4DTz9+nE9+6q/4w9/9DfxQ8pWP/SjfOHSOP/vGMrpmYBmKIFSkaUgc\nRhRLZWI/QIqM7dTpNTAVKNNAkzEqidlub2BoAr/bQmnZJmfqLl1TYth5CuUKSOi3W5i5MRzLRiOm\ntb5+dYuuhrhx7QVp6s6fhcwkpvowCV3JF1ozcihpZccXoRS79xewrVGEM87S+gqJctCtPFImOJZN\nGgpUCv32NknaRdc1ykWHnJtjM2ghtQFaHHH9a36QqflpLMvCtDRQMYkGQsWkXp9ibZSgEaEZJf7T\nL/0YH/vUf/8OxAmQZtJaQ2ioNMQQBlIfHhpKu+Ik1644yxlmbgyluZoYKsd24ocVupYN/Rm28K7q\nKdepaymNuXHufsfb2X7mMusXN9BEn9d84L0sP38IbWwv5z79Dwz2zjA3W6Wg5XndvW/i2W89yh/+\nzE/glnQ+8Ft/yi/9+WFAYRoWc6NVNro9ojgiFRoYKa3YY8TMNtwoSdnwPHa7BTwiVOBySe+xYOcY\nLbq0/JBpPeVUqGj4PQIl2VVymKxptMMORatCWiszOZInTi8RtDTODfr0B6ukvYiolCMamBxb6RK4\n53h9sI+ly8dZWTpL1O6TmjbdfJ6L8yGOm6OiDJLNBvrMPtzJGK0Tk5+fQkQp+994N+tbHndNVtie\ndKj8H1HcKpSdZars5MSbiSDWyXJXEPR9F91yaTe3GZucxM7ZKF2jkCujYSKzEwbLMbAdi163i1so\nUcrn6HYamJpFrlDC9/p0u11K5Qpry8vMzM6jo9h77QGWTp/i4a9+hamJaXbt2sXM7BRp5DM2WmBk\n9HZ6zQ5tP+Hpx59mbs889VoNr+vTbJ9lpFRldXWN5YtneOWrXoXvd/CBaqGMn4RD9LlkLGdRGp+m\nsXoJN19i90tezvHH/548PVLEEN6YkIqMZq1QCF2RINB1SBKJbjAUmvzbz4vi0Dgbz9NLbb7+ONTH\niuRr17Es1igVKrizAjMIMfI5yqGiJWLckoZt63hJTMHKkfgDohSMXB5dj5FpSuDaeP46RfsmVjsh\npTTEtAxITaK8g3AFE6MxobAYKRUZLef5NhpHz67R9wZASqLl8AY9/KDBh3/uv3DPG97EuOqw3tvi\ntTffit/u8srbDvC+3/5HpifHkZaDMAR6DHHUIw180iim44cUR8rkrRx+EpM01knyJaIooOrWERY4\n5TJlt0g/8DBydYphj8iUpJtdrJEq9YV5etsNauMzGLaGbuWvas3VC2cFkN1Gd5zWauj4Bl5gMu38\n3Hf8fqbQzf6hVJeYus7CRMriYoMkHmC4JdJYx++sZTf/OMSyTKJBh153g55M0TTJwnX7SPyIgdfD\nETZ+v4Op6Zw49CCV6T3UJhcouzlkGuEULKK+h1Y/gIaGTkKq9Cs4CF1IJJI4TTHNYZ78d7xvTVNI\nmbJj9lPD15TUrxj4NCWRQqCLFISOPjxU5FUmatxy/T6OnTrNVL3K2aMnWXvyKPN37INSjqc+/68Y\n87OUjh6ndfuNhEeP0tzc5tTsAW4/eC0/8c6biAOfwZlF9lbGOWSsktN0RF7g2iaSzLBmWxppkiCS\nhIJlX6mwEIpWHGKlOqYZ0UtirOKA9iCik5pYmk4zHpAKgR3rTFtwph2iU2IrEjBosqCX8KKEyNCx\nu1tYgYGu5YiSHu1Yp9eJscoDOtsdirrGpt8m7YZopmSuVORbZxKMgk9v+RKl8Tm2VRsj1lFpiGoJ\nrHoFu6oYX5jHO7NN48tH0fctXNWaw9CYKXSEzKSnQmZYfFNCJDMFnukYmXBCSwi9Poal43sDdMBx\noJCvEschhg6GZpC6gnDQRyuXqdRGaTe20QVYtkmxUMTrdyjmciydX2T37n00t7cZGR/H0iZ4+sgz\nFIp51taWue6GG+n1B5hmSq6YY37vNXTWNwiDCN+LQFPoGOiGgZKCgwdewuryKrv27GarmTC/12HC\nmiVFIvtt4iBgvj5OKBKK5QrPHztKpb6XuPVMZsoVAi3Ty6N0iJREJxODREphk0E9je9SabwoMsKT\niQPkJ+bZdeP1VCf3oDkWC7t24Tg2fVJSYSBNE0+FGEqiFW0anR5anKIKJqJcYm5hGsd20VyTSn0M\nI4oo2QWKM2WSxMMXFQpOAeXYSCm4ve7y8T9/iD/6g3+i25IsLXq8/62vY75sMDo3T6oks5O7Eali\npL6bQ88/yv/17tt43Vtez4E9NyEV+CpAJDHHPvthrHqBkakZ0l6IRGMQ9Glv9YlSSXGkCF6AEXnc\nOllGJH3W1lq45Qp0t4jbXWwzTz8OsCslDBXgxyFrF8/RjT0Ggz5aBEF7wPbaJS499xznD3/pqtZc\n7GACVDaUVEq+gAZPsxwJUpl5E6QaEjIzWaqWDkt7shQwTZjoqkTg9RCpZGIkhNYpwqVvEi4fxvCb\nyM5FjLBFun2KihUxGKxx3e0HmLp2FqFZFEcn6S2fZH3jEotnzzMIPA7c80bGJudRYUwoU3qbazTX\nVsmXq8go5oGvfHnI/ZcYUiJUilKQxgmWYSDQ2JnnaUqiK4lIs4pBV0PQoUyz11SKLiU62XxEH+Ki\nUZJUpEhtmFF+VWsec9vdNzGn5dm9d4obX3oHe687SM4z0AKbPbkxBhf77N69m9u/963cvCX51h/f\nz+apZ3n62SX+y298ga+e3uTmvSO8++4Fao7FS6t1gn6Pa2oj5HM2URiBElRdl47Us+G/pjHuGOia\nSaBpRKZOqjS2Y8VmpEjR2Igk+VRmMyHNIvA0RooaW5pGr9Om7fV4/MI5CnoFM/Q505Vc7gm8MCGQ\nMGLECEdxqbPFmVOH+fsHvkhvu89mbxu90CYv4Ja5CKcD6eUWl488R2/1LOZyB+/0MvQCcrpG3Nfo\nnT7GVlHi7tvFSqN1VWueLXx2PzJVNqtCy3JZlFDEStHyFeQPUDDyFEo1ut0muVwe17TwOk1cN08a\nh5DGWJaNH3po6OgGDPoeSRIThD4C8Ppd/MAnlZAfqWEYEA7a1KplhCbY2txgpFbBdYvM7bmG1vY2\nlWqNwPM59uwz9JtbHD92lChJOfzcMba2N1Eqot/ZJFdUKBFgmBpryytYbYWr6ZxprnLgnoNUr7uF\nsT3Xcu2N7+R1b3oHExPjXLhwkqVzJ0iT7O5noIiSHbGIwtBEZnAUmckvU5Upvkuh8eKoNCwlSaKY\nrQuX6Uc9CrkCSioSleBaZQqFPJdOP0V1dC9R0CNqNnByDqGE3olzVCZGObu6hm5YVEYmifsNomhA\nX4e4H/GB9x/k77+6hhe79LwmI5URXndQcvbaMc6dXead993GzdfdyMf+9O8Ym7X59R99OT/9u5e5\nbf8I8/N7aLU2EEIwt/cW5nYdZOBtsXFhi3x9ClNz0aIIpdk0NtZIZYfdEyWMyhSnN3tEXh/TKOFt\nDQgqZZ6+sEkw0Mm7JuHGJfRanjhU+J018vUJOqcXEcU8wnWYn7kGYeVoNFeJdHAqeUzDRh938DYu\nXdWaCwVCplleNilSM1AyzWSrSiI0/YrxR5ION9BhG0cM2U5kWRqXL/no4sJQcSUwiyX2veRawKDb\n2yBOEnLuLJoQWLk95AzJdDhPb9Al8LrkcmXCQZcoFpxdOk8lZ7GydBHbMAmtlDQJM5mj41IYmaTT\nWCeXL/Kthx7Bsky+995XDDcGhVCCo6cXufGaGUzTRt95P+xUTpmHA+QwT2NnUiOvzHbUlZm5uIJb\nyfLCr05ye+LhI8zvm+LcagN/c4ObbruJpWfPMHPDDIML0Dx9Aa2k8euXBxRsk8+OVOiPlrnvB97A\n3/7Zx7kQt+md7vDnX3yEQt7BkDZPbTaJopAVL0IAedtBF4q6Y9AKs5lGnCasdyOmx8qZG7+bUKnY\nnB/o7MrneSro0ujEWLrGvnqFCUNhSFjpFvFkl0HqE0cp+RGbM/0uZQJEqiEKAb5QBKHLmW6TzV42\nS2lsNblhtsTqRkjqpFzaHGDnquSSKgE+Sa7AqEoIfPALEeLgCJZR5FJjg/JIHT0tYI1WSSoBUxNX\nf6+VKlPYJUIhLA1NKsLhZ9nRNAJpk1hV4u4GbsdhdmEfYZRQH5+iP+jQ7/fJFQukYULf97A0A9dy\niHSB7VikQUwxVySWMYVijfX1iwgM+q029dokmi5YW1tFF7Brz24c16VQLmCaBZI4otXYotFoMD2/\nQOh79D2fpcVFbDtHt9MhSSPq+QLjkzNsbm4g0w5nLl6imviIcMCb9l3PNW8ps2/XHsZzGtfUHQ68\n9ABJGnPx+W+QtyJMMzPpSgSukX2jkZJ0KEtPDB0rUSQqxRA6yXcJv3pRHBqB55PoCklEzjTx+y0s\n18Ex8gR+iyT1KOQms1aDmUNZPkrqpNLHdHVEmqCjCActWvEATQk0C/qDPm1PMm3MIP3TaJUS9aKN\nYcC/PvAE7UurFA2DT/7TQ/zInYJcziKNS2wuHePa0SojoyXufvnLScJtDM1GM0wsK49pzpPqkCYp\nSbzNkWe+jd7ZJI/AKI8TxibrvkDDwx2dJona1ObmCLoddMtm4GhYdNCLEyjDoVCx6bQ3MWUKU+N4\nzSauFeOFCuIExzDpLp2hNDNP2uuBSKlfe8tVrbmpMkAbYuj7lslw+Jtkcw6RDHMgtSFCZLhhDv0O\nUqVDOqhEpSnCVGiGRegNCFsNRqZ2EQU9RGpRzFcwDR00ncgfYLkug04Ht1yh12qi6YI0SXCcIgMv\nwCrk6HT6JKmCNEXTdKIUbMPM3KtIDNMgCHye/MZjvO7el6EpbdiHklQcN8PQk0VmakLPNn2lkCKL\n+FRkY47MwzH8ggwPiyt+DqGG7zyLfNWuEhI5t3s3p86fxrUt9APzLG0sMjk2j6PyzN9xEyOGw/v7\nAX/0u5+gub3JLbt2cfu9B9hebzA9VqbrxbT76wShxDFS9o2V8bYCer4iTEIKxRwFRxGmEssWdAfR\nMB8986Fc7Peo5B1sBzbihCRIKTkpUT/bIpqez4blsE3ImF4g0do0hSKKIUlS6vUShdTH9BwsPWQU\nk4iAJEmw0jIiaWNZKe0oQjM1dMeFpsQcSYnIk7fb6CgSIyLUctiNhGiwTMGbZTu8zFavTy9Zo7hX\no7gBI9UpHNyrWnNgyClLMYYJdYgMyKej4SUKN18mcvJIw0ETFlLTGQx66KTYhTLImO7WJsVqlVyu\ngq4JgsQHZdDv97GsoTFOCpIkopDP0x+EFEtVOp1tTNNmemaatdVV4jTG3/Zx7AKGCChVR+i2tmk1\nGxRr42xvbCA0jSi1qFgCP0npttoU9SLNxiaFQpmNjU1Gq2XmXnEfDx0+RDte5KW33kjUWOSH/90f\n8t7Xvon6yBiGbuEaJoYuSKREF4pICUxNZfEECKQOIhUoJYmUxJEQ69/9c/6iODT8KETTdQrVEcJU\nQ497WOQIky5JGmHioDRBkiYYlknsmQySATnXImiv0YgGWIZB0N3CntqLDH3cKMYPA17xtp/mtpfc\nyOKJM5iaTr5U4qY77ubTX30WC8n//VOv5NnTfd70jtex/D8GzM7VmTk4z5G//DV+6ud/h7X1Jj/6\n9rv544//NWdOrXLmzBKWbbJrZpIT584D2QU8l3e595X3cONLX83fPvAkUyN1/vwX3wJ+n37U4M7b\nX41hGdz2nv8MVp6p6THajRDf79PrtnAcl8iPMGJFkiRoqkIKeJurlEdGwbAImtuUpmcJVi4j9KtD\nRidkJaqQYmjsg0RmoSzacFONIWtfaVk7S2o7UlWZpeupzPy362Xv5cwz32Tm+pdz+dwS1ZEJvPZz\n6KaB62q4bp7ttQtY5ToT9TE2ty5jOg5er42jG1lLTCpWLy0SpJJ9u/dw5uQJAGQaEXoBpqETKxth\npJRLVUzLBKWI/QDTtpFBNKx9oFqysJRkx8klSYcHghrObjK/iVCKVGVfmAyVMhz0QyYsVlcExtmi\nXeUcvL+rCCf7vOenf5lvHvoal5XJIJfgPb9Ic7uBMVZipT/gnw4dZsLKsdps87F3/gTzd96ONiVx\nn17l8//8KUzLYKvVodntEKc6mqYYL5WpFQS+B2XXodePmCjmEIZFozPANix020BLXcJgE7c6RqkS\ncbQfMGo7dDsdam6RVuiREzENx6DZBeFqBL6PaVkkSnBqs0NZd5kzLJJch3aacLyn8GUOy9DJFzQa\nsUG+mSDbbbq1BoNBnpXuGSxhIGYsqknAloK5cp9qYQprzsGICsyP7yffEKyRIAcduoNVNtcuw7uv\nbt3FUP0XahmIT0NgiUxybeRqeEkNTctEA3HsYWoahVyeKIqwooAolmi6jmWamJpBmPQwDIck9nBL\n49hWVs16Xg87X8DUDPqdZZqbq1RH64yOzdDttpmdmqTTG3Dg4PVsb63Q9TXam9uMjo4zv2cBHYu4\n3+Jtb3srre6A5dUVBq117FwNXyhEmLLWOE/JddGETc/3+dCHPsxzT36Nb3z7DF9/5Cv87I/8JL/w\nkV+hmC9y4vhxPG+bqqGQGaoKO1KEZAY+tGEapYAkldnljYxHl36XVuyL4tBwCgaJl7C2eAwzX6Vc\nrdLpXsLKFbDNHIkGaAZRv4uwbXSRUKmP0tleRcvVscsVauUqmlUi9z+Ze68wy67yXPcdM8+VU+Xq\nqq7OUiugCBISAkQUSBbJBgzGJCeMNxsHsPc5xmz87OPH9nbCx9sH2xjbm2CSQYDJIAlJCCGp1VLn\nru7qrhxWrRxmGmOci1mSfeHtfR73udC4q4u6WP+qmv+Y//997+dnSFyL/dk+NdvmdS/+RfKlDG+1\n7iQOIrSUDKKE337jh7j5Nb/D7T/z0/zDz/5fvOV3/4S7XvUC7nj1S8jWxtlc+QmioE8QDfjqVx7m\ny1++H8uyEaZEY/GVz/3f7HnOK1FKkcvlGRmf4Py5JQbFdfysy9BSXLZ3FuEY2AJOLxxn3+7LMGWH\nmy7bz1tv8Dj43LuxzB7Pf9dHwZAM+12kTijlxwmjHlJrnJxP2GljlQoYhkm/2SRBkbXyl1RzLTVS\nJ1jCeEZWKnTK6JGk+PMUVSOQMv3ZkAot9M7NVaXuaQEz44Lm5CTj1QKrS4IbbtnNsScTzh09gjk4\niW1bmLZLrt/CiptEgUCIPqZpY5RL2BjMn3sCwzDptVc4eOtPcOyJYyRuGZ1oLN8nCQNsW2FqjTRN\nNtfW8f0MpjD5b3/wp3zwl38BpRMMLM6vbFHM57FMMHWqelGaf0Wv1U9bNtLPlRpSUE8b+XbeWHYc\nLIChgZUAACAASURBVDvj33+VHf4fPFunt9hz4EbOrJxh+3SLq24/gD3ocXa1TtRYo2/0ef0HPkoS\nm9jFLOXJaT75qY/zd1/4PP/ldz9IJnyAiuuhoojxaoUwSsi5sMctcHRrhd1ihI4/4EyvR8XMcqUb\nsdSXFEyXTNZntdNEFgykbVAbtugqhYpNlkUfTEE/CCj4Jo5pUytoVvoRUSdOES0qYbtbpz/s42eh\n7AyJTJt25BKGXbYHm5iOgZX4FKwQhcV81EVulYjcPJvGNn7sURjbTcdyOFCeInT7LLcFxmafYrmA\n6MQcX7vAdG2KeMxjdmqO3uGZS6o5O9+zApyUj0Ms9TO5GomzC6u4jyAR5LwMOdtnY3UFxsewLY9E\nabJZD8uykZgMgw6Ol8U0DTwrTxwNiIKIYRhQrlRIujGRVji+g1IGhumwUd/At01sL0dGatrtFkko\nGZ8cI5/JkcsXkFGEkgl79u0iiWNkEqVqS8/iB48+zmX7JyAOKZVrrPW3qZRGQduEQcALX/WTvOQu\nh17v14iiIUkcsHhhjfkTR5CDBmZJY6SzWEJb44qUeitJfU5CpHQIjUaZglj9+0TnZ0XT6K1t0xt2\nKNZmyZVL9Ot1tAlOkmD4VTr9DYrlcRIETrlKL9wkV62ytXCEbGGSYH2R9foWSgRoVcbyslxx3WXc\ndmgUYRrkCgVkEoOpcc0MwhCMeO30ATlscc8X/wbbsTEtCxCoJGC4AwS0LY+zFx9FKclLXno73/vu\nQxiGoDixiyuvvIrNzS22tjZZWbqAIeA973kby8EhljaadM2Aj//ZX3F8fo2ZyRpvfK3kgz/zWq4+\n4FIbm2Vz6zwf+vN/xvSyDAcNCtNT2FEWZSYEjT6unUHJGKdSJYeg71oMej1ytk+rvnxJNZc6wUz1\npik+YGcfoZ6GFWpQIvVsGIbxzOIsvYMbO5S3dPbRr29jC58nThxj36G9YPocumqG6clJjtyzhJaK\n2CnR5ADNC4/gCQvbkQhhkKwnxInEkAntdhPdzjHsJbTadUpjBbTjpLkGSqJVwqDfxzRNeu0+KuoT\nhglrK+fQ+t3pI14opkeLKCHSRb6xQ6jF+BfZ7DO7jPQzaJ22EaF31DU6NTU+7d9IU6r+JWP8P3pu\nesW1ZCJNc7BF8bDN/KOnMEqj1C67kuFGi1Mf/TiZQpHtzW02ggEWK3idXeh+jw+/50M89OiPmA86\nvOuWvTww32OsoDnTizjV6eBlqqwzxDSyjBcGLPQGXJmzmAzy1MYNTm70ULFmvdHEdmwmJrJ02yGO\nk9BuBWT9DEGSsCtjkLMMNgcKU0k810pZRLEk7CmKhSwGCZbh0moIGmaPtjIwTBcZaaQ1ZKAsXGWw\nRpTCN5tg7a/Q2uWyp1DETXwaVsIgirlyepyLxIjtkKFfYO6GqxhGJu52SL27Sj8w4VWXVHZskeZ8\np7eB9CJgmCZxotGJJLFz2CpV3YU6YbJcIegOiJ0E0zTZbDco5EsUShZhFGA6NnEUEWJgGAaGEBSK\nRZCafD6fyr5tl431Zfr9LrO79zCMIwbDPraXIYxCquNjBGGIa9n0Om3GJicJw5C4FzMcDtBCYBsW\nR448xj2fu4eTc/uYmKgxs3sXUxMjBFurjGYKZHIFBu0mpcoY7VaT9dVllIpZXKyzurxIrAWRNPAs\niVIaTwsSA2LTwJHphUjuoHbUzj4vBX4+yyW3djHH7J45WmtLdC5uYI9Oons9EtMiCJvEvSGx3cLN\nuYTdDbLFUbbmT2ALh6DdYWLPXtrdOs3lJgbQ21hkZbWIOljBwuM/v/cjXNNZoX34Bmx7iQ/86m9j\nJor/+tHfp4/LpJ2mXguRji7gaZleWrivfPlRDMPgm9/4LqVClvGxKv3tdZ588iiWZeE4No7nIrTg\nNXfdjistNH1+/OiD/PwvvJtEgtYRiYLKtM3m4jLb611G3DHONrO4ok1t/7XEQY9Yd6hMHSRMYoSM\nIEzorC3SNy2KtXHCrTUcN0c0vESnrFJpOpeWzzCbJGAIEy1lOjIyQGmFUimE3FAGylCIHXc4pMvn\njXP3YIgxRkWV00957Dm4mygQnDu9STdzG4VKzIte8jyE7eJ6d3D/Nx5izGsRJRGGhDjoog2TfdUR\nuhv3MnNghm98dRVHreHnD+AUS/gjI3S7XZABcRAzOTFBYiSY3R653CiN7pBq3uPiRpc4VoyQpOFM\nYscxvhOulH63qRFwx6oIO27vtCXInUW4kTZN2LGYX9oSHODkwz9gev/1JItdOj2PW++6jXPzT7Gy\ntMbYC29g7sZr+Ps7fo3ZWVhe68NxyO2tMzLicuVNt/L9c2f5iUKZbx/dZLnTYvzQNOsL21QKWYoZ\nzfHlDnm/z65KnrJlM+gbDKKAJ9Zigm6EMAwM08IQJvVGTD0McB0LJQTdfogwBHvKecpOliebfWw/\noR9ptCEwTINYabxYUi26LEc22XwfM3bRckAQxumlwyvw2MUOWcslecGV6MkMs3aB3tQYbqtHM1dC\nR108zyFf2EtbeZT7PQoHRwmWlklGTEa8Ct6VGbrH1hm7Ys8l130gFB6gRRoVbBrGznLcRGRmUVJj\nOz65XIFsNk8cRQjDxjIdwqBHxrbQwHDYJ58vYAkHbaY7NKljwjgmqA8pl2sM+gN81yGbL+B28iRh\nRHO7iZfNYNoeIyNVoighny8gkwTLtMgkMeFwiO2YFCfmCPQyZgDffuBHnHn8ONlCkVtmM5x46kcc\nXb3I0twsJcfntuddlRJ7Q83jjz3BwoXzrC4tMTM3Q6mY5cjyCkEsU9+R0kgTIpWaWy2x40tC4CiN\nYaVw2MBM/8+Rz/LxVK+5SXfxNDIRTMwepNNpYSeK7fZFqiOzVKbn6HW36bVamFFKnwxVmvJVyJdY\nW1pjbGIEuTvHsN+ktGuCZrdNzu3z1HyDX3zXG3HlNkKbGPoAw1YdOw9/9vsf5e8+/v6UXKf+NcM0\nDePB0GglWFpZRWsFmEgJf/Fnv8Vdb3kfjutx+80385a33cEvve8jnDj6XebnF3jpK99Ec+MJbr3t\nlVhxQqPbYZho1lY3aK5sccMLb+XYuSP8zB89gLBtTLNIv7NBWN8mVxlh4cH7KE1NYDk+Tm6EpDqG\nDAfYno/j+0CEZzuXVHMtFU9HmsY6XYIbQiBJUDpVE6UtQadzT3ZSKRSkKPL0d5WGM/NdXFuj9SqW\neYy1lUPM7ZthrX6Gu996K//8mQf5xlfPcOdrruSe//ktXNdFGgOEiuh2YoQRY9s+cRDiJoLNzpBw\nMCR0biCXq9DdXiKWBpbpY1omG9sraMfFdx38YoF4sMX/+etv5y//+nNoNLtGiilixDSQidpxjCc7\n+Us6bQhPo89FuiQVGDtvH5oUpm3ydLKT1jIN8LrEnca0M0PeyVHfV8U61SSrCrTCPmYzoV/UNBsN\nbv/LDzG9v8TF8wsc/fQ3eOxr98E5zZFTTbRUXDBtIt8hbsBTF5rYrkPO0nQGPSZqBdRQEGlFa9Dn\nu11NFMboHXl1LuMjpSaIQ5aGQ0zTZhgOGS3l2e4MKGV9zjcUVUdxodHHMROSHXlmFEmiqIvO2Izk\nsygr4cy2Rzfq02gNMCyBWSxg33QlE1MFEjeHHUeQM3Gzo2i/Rpjp42pNwagROSbKyeFm+9h6jqiz\nBrOTXF6bYiluEjZiEtOnXe9cWtFJOUspOcBMLxDAUClw9qK9ETzDI+dniVWAYRYpVUfothr0B20M\nJFa+jGMa2IZDHCcgumAIfMfDMlxyfgHbyyCjPtKSYNjkixVqYUySJAx7XYzIJE4ihsMQ2xDpCEom\nmIaJVBrbEsgEoliDlafZWOPb37yX5+wf5+7dWfZds5cX33AlwcopNnsx51ZX+MevbXHTi+/A8308\n36a+fpHLr7iafqfB9sZFJkZybPoCw+SZ2GJjB8RoIJBKY2rN0NwRh9hAnApA0lSyf/s8K5pGZdd+\numsruKbGLpaJLqxQGZ/CjSr0Og2SOCaTySJMF9vyCaIQQ5m4UmNnMuQ19GWIKyVOtoDsN9FhjXav\nyNSYYjRvoxnFdxwsy0aZBnlP0NzeQuxQViQxWpv/EsYDOEKQ2Baz06OcX1oijgRXXXE5hw9cya+9\n7eXcfMt/xfTy/ODxH7O2eJSg12Z2epqxQpZOo4uUEKuYVm+AqWz279tHd3KMs6cfY3V5E+GW8a0M\nnaVTFHftJ+qHFIs1YgTVyV302y3CcEDQaRAN+wyDGDTEKDKliUuquVbpgvhpSgYaEp3szP0tNDFC\nW2hSo5tm5y3kacSGIXfUVZDEMa4do4WJjGMeufcxzp9cx7MF93//JFdce4B8weab//wYSI+rr9/D\n2rFVbMdDZLIIM8FKAizDJnIEMpForbAtkJhoy0MmA+JBG69YQisT3/WIk5is45BkxrA6XT7xxU9z\n90t+Iv3HSGQ6VjN2Fvf6GVvizudWz3zwFIOuMGQqP4w1CLEjwRV6Z68jucSVBkFWsr2xwolvP4i9\nt8z05jQnvneUO97wRpY3zrG5vMXz3vYqHvvOD3EiyV0ffg9/+Z0fIkiot7YIhhFBlEU4WUAQm5qr\nJqa4UG8wamd4YqPOeC5LuydJQlDaBPUvkOtBP8b3LXzfIwkjHMei25MMhwlKQzbjcOVUgUcXW6gk\nojGIUVqgkii9oVsWjmWz2OyxJ+ex2WnTH4TkRrIUnrcHf3qMnDdFkJM4hkN5fAylckQZHzfs4eV8\n7GwGpQ0IErycpizHwOkwGJ9ECI+FlUWyTo3YTqhM2LS6g0srOjtEYyUwLQiVwtZg2Q4DI4vn5Ymi\niMR18XGJ45AgGOBmcvhoBoMe/V4fJRICO6BSqdLvDijkChhCYNs2waBHohTDQZdisYTQiv6gQ6lQ\nIDYNhuEQJQVRt0uU8ZEGlIwSpp2+9YXDAZZlk8tXiKXCNm1yuSxRv88Pf/QEL3vFboKNeXKlQ5RG\nRsgUXWpVg2995lEKpSJxEGG7JrXaGNWaTxxaDIc+wjKo5gwyAkKhMZVGGQLHMFIhzI4E15KpyMXC\nTJurluhnuyNcDiNKI2No02EQBpSn9pAocAs2sXLwfEiSGEMpejLGsx2yeReEQZAkdNp1asUa0jEJ\nWk2ibp0zzQsU3/oK8l6MqQ2cUoVwmGBKhRxE/Kf3v58b942xeew4Vd+mmC/QavR44B//grve95eU\nqyXa7VWSuMeXPvNhqmOH6bT7VAp52sGAu37yHSSJQqJ4+S0vo99tEYdDut0GDz34TSwvh5vLcubE\nQ2yutrjxmhv47d/7Pb7+5R/wd3/1mwyEQ7O5Qda0CYVB7+JFlOxxfikg5/nMP/x9xg9cS3vzHJZb\nIl8aQ3Y6GLaFEXXJjVQvqeaxjBCGQmrS3AwUBkaa3aVCDMtE6ygdW0mBMAykVjszT41WVvruoTXa\n1MRRCEJgmjb5/r2wvYeZg8/n8iv28NQPv8mZkxPc+frn8/lP3stjD58jDGd4w9tuZ9hewcxVUEHI\n8R/dR7E2y+rCKVzbpd1usnLhPH4mQ6HgYloaUyrK42MMEk0yDImjCNt2yFVG+fE3v8ZcZQ8HD86S\nc12kYaCSdKln7kR7PhPY9K+OLTSRViRRguv7KbL76Wzxf9UpLnWn4VTHiet1rr3tCpbnz9K1mhy8\n7WayYyUy7SwH77yJ1kqf6154A4N6THt9lfc+9CXWVlbZ3Frn8T/8BFGzy9joLl7xR/8Hg5VtFj/5\nZe66fJJvrpzEMgWN4YAwjNJFre+ihsaOARKUThgEkvFclvpAYg0NHN9gEMf4rklfxtx3bhvtxNiu\nRa7gAhprdBKnkEcUmijKeOUM3dooczIHbkTimYzmpjBzFlK55MOYtuiSmZkhrvfoqCHlqSrdzTp+\nsQiDCLOSxdrustXdorJnP5bVQQ00STZHo71NsNLBnRxhYuTS3qif/t4Mg2celBECnQjcYolYC3Kl\nMuFgSOLaTEzMEUZD8nmfCInqxURxQHerwVhtikG/QzZXIIxDok5EJpNNMTPREN8vMQgkhmng50vE\n2kb0uxjCQ5gGQRjwg3u/x113v4kvfe5zHDh0GceePModP/Ea1jfq1BJNpjRGsVTAvuwQP/MzbybY\naPA793wOoS/w8V8ZJVcYIzM6gbGd4RUHz5NzTFabPRw3g+ObHD/6GLXaNLYlETIGUk+KaaRKSa0U\nWoIwxU52zA5eRYOp07/7CHbUIf/2eVY0jSjoErWbaLtCr7NBpVSj3+kgZUx5YjdB0MPO5vGLFVwS\nwk6TGMWwuU02X2BqcorNtsTpLuLnRsmXxvizX7iOYdDCVCb4Ar+5iWE4SE/w9fuO8ocf+xhHTtzL\nc65+Mb3uANfIcPBlL+JT/+2X+cTvvY9zWwmHn38zl++Z4uCuGaQyyOcKhDrg3JkjhFMHOPrEKS47\ncBW12RLN7XWisE8tV8N2BUEc8Wu/9CE+/Q9fIF/Mcv7oD3jvu97ML779NRxf6PDHn3+coltiGPQo\njkyTdJpYFAjXT1O5+hakGZO0N+jWmxQmCriVKl4UEYZDdK+H0JdGua33EwwBUwWbtX5IFAuqWSed\n8CuBkgmmodDCIozjFGQmdli3IvV5QEyiDa697TXMH/kBg1aTRCvCMKC2SyBXHuD4xiNYpsNU9hSP\nfe0Md7/lZ6E35Ev/dD/BsI808rTXt3FMSNy9DM0Z8nkH17MoVQoMNhcYtgI6WwGzB68iClsMe12K\n4wfQOqGQr9DYroOW1Kp72Npa4PCVexnEEZ5pksQhju08owhLM0EMDJ1GxColiEhwsPHVACzYOLPI\n5MQckU2K8BYCtUPiuqSanzjOxR+d5o7/dCd2psz2kXOc+Nopir9RZd2Aq0cnOfrY46y1vNT06OYJ\n7R4661ApHuQ1H/tdTGFQLuc5efQ0bVtR+vU3cvR8m2uu/wVuiXt85T0fJlxcxnc9qlmDTWkRBGlz\nN2wT03MYFm0qt9+CO+5TdYoYu0qYsQ8yYt/YLMvdDbKuxLPzCGmwcXGZ7MYGi5kIRwvytTEsy2KY\nKKSh8YYxbq6IyFqodkCTAf7EHBfvewJ7rsruyTk6SZNSOUs/Cuhc6LD3ihxnbU1pbJTNYRN/OSQz\nYTAqfOySxSkd0VmaRw3GLqnmwI77G0xpYCBJ0GhvF0oJCpkcTjaL7WXIZHI0ttbwcrk0H14JcoUK\nUTBgOAg5f/YpqmOTeG6WkbFRXNujoxWu62M6GQaDFlobuNksaJd+p0kYhERhj3CQMHfVjczsvYwg\niHjNm96CUpLde/chTIc4aZNo0j2HbZGxLX711z7I5voGUa/F577/PTbOd8ha05iqzsmzT/H8d/w6\nrWadXNZnY20dx7WoVcap1oqEg20Wl44gTIVjGcRobKExRLqX1GqHWygVtgGINO71aTSQeLYDCw1D\nYJUnSbp1arUxdCLJVyuEUUQcxegkIWMKZBASrC+i8z4Z4ZLYCbbvs3LuBP1QUyvmQUj6rWUMfSNC\nCaShsBOHxNLYhkYnFg9/4/PkxRJf+NZR7rptlTvf+CaCrQaPfPPjFIsjPO/O1+L5BaSR4oKFEKmi\nKJY4SnPosusxhMW+yyZQRpu4bzM1Nonrl/nzv/pr/uSP/4bt+jYWJmeOPYztRjT6m+ltPRY8cmSJ\ngrZo1DcolkfYmD+JW8pRGalhO4fwqjnK8RT1xga7rrgVmdUYvQF+rkZp1GIYjNLf2ry0mieDHUCh\njSM1wUDS0wmWDcOhRCLZN5Vjvt5BhCbKCHBsmzBM8CyLIImxbRMtDCrVUQ7f/Cp+/I1PI5SkUB5H\nRxEJEqFTE5UhQRPz1U9/i/37dlG1FvjGFwNefvdLKI1UGTYlnfZpZi6bwbUkcRyza9/1LJ94DKVD\ntJEKDXrDIV6uSM6JaUcZut0upusyXNtgobXJxYWTzC9K3vG2F6fmPMsilqlhUcl0FGXIhJgU2GYI\nidQmphXTDwdMxB7H58+TqIRCvoZXKiCFgdbxv+OR/f92FhcDrn/vXRy7f5Eg3yfohFR2V9l8+CKF\nayZYeuIMuaGgPVxl16H9tFfaGO2YjOXSKxh0T18gzNicX+hSCDV7b97P9okWuVumSBqp1+euP/8D\nYMCnXvvzNPsWUZQ8E0mqZUryHXSGiCfn6Z8XbCmwq1P0yz57qiWWrRyNtXXU3C4meiZH1Ba1vfvp\nFMe43O6wPlmgmtjUF9cZKU6SK2r6wyGOtpAGbHk247rC5qnT7L35CjQRG3EfZ2vAKgHlycuw90rO\n9Xvoros7VWZURDTHNWpLc7y+gLbbzIQ1ztY3GXNLl1j1f8FiJLbGiFMOl87PkM/vJYglRqTQKqDd\nCyjlywjbgURjuzaDbkAQBCgRUShUaHdakE2fWY12h2K5jIojlGUhtSRfKBCFMYkZIyVoJJZtoYSB\nbTkgFdliDtO0MQ0Dt1ohGCbs2XMAYVsYGjrdPq5lMzE9hRCC2YOzvH7zMsqz0ww7LU5cXOOjX/wx\nX/y5/4JhWWidYNguEyNT9LtBCgp1fbqbS1T8dBSVRh+k0wJXQvx0tIB4WlSmsISBUop/CT3+t8+z\nommgFYQBlpNHSoUUCZa2MUzBoLNFtjjB5vl5wmCAPzKBbjdwikWGjQ799U1uuWIPczUPQw3I2Am2\n4fHg939IMTfAzZTJlfNkPId+t8ETKz3e9bMvYk1W+eFDn+LII0/yqtf/FI+ePsZLbr8bZcZ0egNW\nl89gKoVjOZTKVQzLQUtFuTrCwvl5PvvFr/D3n/gc9a0WMpHM7Zrh7W9+I+/8+Tfy06+/CxmFYCSE\nMiRJIrSMiJOI7c0tXnTdKHN7Jvjsw1sM+g0KGYF0ijieiW8X6S6vMFAmGTdL69yT5CbnaA46VHZN\nk3QjiAOKU1OXVPJDs3kunljgz/7HP/KcW2+BMVhdEpBk0EmAMiQPnNZYto0WinDpAXTHY+nRJ/iF\nyX289h/+nA/+zT+hRYxfcdjaWGD3dTcRtOqsLpxia3Md1/WpTk5iWUPanW0808YNz7DxpIdtZ8kG\nqzxxzwKtTgvXyVFX13DeazE7lScJIrKeQyIthBpQrGQxjSGuW8Y0BOfOHiebrSKEg2U74Eiay3WM\njMc7X7WPy2sGP3zqDCe3eghLkTeLDK2IgudjWhaGMkmEIgkNbj1Q4+c+8N+xjAyjRQu7MsXESotm\nY5M73/HTyHYP07GoVS9tJHjrc6/j61++j7nMYW6940a6nTbLJ5dQuyT71mJ6+/cxqJ8jI6osNzbx\npKK8azfmahu72WUwMYYUBqq5jTc6ydF7H6VcnKLYt+kIm7OJpGS06G32eOtDn2LQ6eFNZ1i+5wj3\n/sH/JFsURNM+45ddixVL/CkLy6mRS2A7X+RwkqFZisjtupH2iRMc319hqj3FIB5gZiWLXYvnlq9i\ndX0FvyoJbJvEEYSBSS1fgkHAVMmhuNvClgXc/Ayddp/9RkyjNkpemDhlhT7vIy1BrAMunrpApTrE\nbmsGlQpXlYucthyKZo5dpf00k8wl1RwgMQT2DnPMEppYmIigQcfagzfiopCUqxPpLd+1SaKIMOii\nRAavUMJwTKIoxMyW8KI+cRCwfHGJRERYps3+a56LVCGrG+t0Oj06jS2snI9vZLBMF0MqGs0N5k8/\nzq6Zg1iGYGm5wVh5BGyPJIlYDxMy+QxJqKiNVFOO1XBIqVzmmptuZ2bQRtS3SHyfdZnnTz72N+ye\nPYAnAtbWFykXq3Q6MUHcpbO+QS5XJbvDNLUU6V5SqnRMtzN5kju9IdWEiGdiEEytUMazPE9DJiFS\na0xpUhyfABlRr28gDChP72FjdYGRkRkG8Rbt9RVENMSxTDK+h5l3sONVnHCcfYcnqOQzYAgSDFzb\nwDQdyuUiri3YbtpcoU0CWeOWvYf4wVf/gmK1hAxj9u6aYWP5LJXKOEXbozh7iCSMwDIIFNzz9a/x\nhc/fw/33P7Wj51dUq1VuveFGvvilv2fQaxAnCZoIpIFCoqWBoRISmYLGO80GQdAjFgU8T9FqLpKx\n8+y76gUMtcIXgvX1ZUzTRcRdhsMuxd37EGFMkMvQq9fROPiVEsHw0lQlr3/br9DaWMbJZHjoyUfY\ndfgF9KMhr3jD3cTaQcYGw+42EUMmRvZiFt/EytoGxnaLE699O8e+fYxybQzIMjdzkCuvuRmvkGNj\nvQ5hH8NyUFqRCIHSBqZjM3/ffQzqdTrLJ1htbzPsC+LeMu1OEyn7yOQxFlY8GuUxIiVx+6d54Wve\nzUNf+X+I4pgwMTGEIDFMhDbRMkCJPknsEIcBwnAojR5gsPheMqUt7tyX8MZbX0W/fTd3fvgL/Nw7\n38NDJxZIdA+VJAhlY9mCk8tnue21L6LV7xHFEbs3GwT7DlIwJvnh8Se4dmoXt19/kJzrXVLN1xsX\nefXdL2OwvsZn/+4ziFqOimGz8fUnyb3htej6BrLjsdo7i7s1YPqmm1leXmPtwgpl32KQdNlutRFD\nF39znclds8SmhXY8HFOSkaCCmGw5T2TZBK4ipzPsf+nLmX3ZCylv9Xhq8SlE16GbNHDH96KHTZy2\nS9lps1Yaohsheb3MoekZnuoNaC4tEldcEilwyyUWwi4NEZK3RlhYOMH45Dhe2aLdjYjiFuPV3awt\nLFMuuvi0WNZdTFNQ3PLZKmsyLZeR0IVimY3+GjMjkN1VYTNJ8PpD2tk88YrNZiaBfowngkuqOYAD\naAt2wAAobZJEHUi6CG3guQ5xEKaRp3GqonPzJRIZ49g2llEml03ot1v0wxjhZyiXpimVPLYbLRbO\nn8RybZRSRFFMrlBhMOgjvYggCNhudKnmK6wuXEQpwcHLr2bl7FnyzymzMf8Eh666kX5vC9NwWF45\nS7lSJVEJrmkhlaJaG2Vhe4NYJjimzYtf9yauuuZa/IxH3GkT9NrUKpMkcYTn2Kh8ASkHOHR2MEFp\nsqWwDUhSqb0NWBpiAQZp3o2QKVEBYyet839xxKUu9/7/OFfc8TZdqNWwdYyRz9Ne2cBxLcJeEIaN\nOgAAIABJREFUj9AxMYSPaXoU8pLmegtPRwyiAY5SRMBvvO0afv03/5y//eMPkM96uK6F59mYQoOb\nwzJsHNtjcXuRlR89wU2v/RW+9b1vcN+D3+ORh09y4eIKnu2SKIHvOliOQbfbY7w6ykihwAfe9z7u\nfsdPocJtNlfXcDNZDCGIkxhLCyQSZZoYWqNUhFISrU2kionjAVHUY2FlkVApVpY6YOUxHQNbW3zp\nZIIRSoZqSLcZszH/ICN7b6I6Mcq582cYm9hFe22JTKHEIApwcj5EEG8scOwbn/4P63k+8oUf6I1m\nE0tYDIIgXRx32tz1ghsYRNvcd6pDqVql1emQjUMaA4NrJkocKGzxlve8h29/5Vt87N4j9BIHpWNs\np5R6VmwHFQ/oBm2QICxBzc1ixB3Ks5fxwPt/ktnD19Ia2cVv3PE83v2JL/DEN79GNlNgbGqC5/3c\nr9LfusiX/+D3efOH/xTpSIzYxHVtpLCQUZ8wCHFzLtuPH2O7uckP//lLXPfSlzN+8DC7r7yB8z9+\niNLcfhAhnuczacNCfUAxn6MX99H9VZzCJImCvJ1jz94pHnzkcWoTu2h3OozWxkkSRZx00FrgCJNG\nt06SbPPJD/zmf7jmL/qt39F7XnUlgT/FSE6wtbBA4Zox1u+/QHHWY6xZYG24zVNLR3nFu17LxqkN\n6vMLVKix4UZUzZDsxAyt9RZRpkO/b1AtjLJ+7iTTE7spdAd083mGIya2aVDO1VCqj2PnUYki2WwQ\nOBZ9JMXugH6jTj9J0cbN7gCr0cc+XOPisUWuvWIfOXIstS8yCEJE4JEcyNA9skrthsOsPfwoo7Oz\nROe20PtLWNqmvb5KoS/JXH6IQRjiBRFraxewS0UOHbyMzY0tRMbBDyRBN6Akspypr5DxCuy5rEJn\nM8RzXIZZH68REBcFreVV7v+lj1ySbu0jbxVaG2CTItEtUaZXejnaLmD5BYqVUQCyvoPCwDFtTMck\nDGOyuQyOl0crQa6YpV/foN3rU8hk6A66bKwuUiiUyWXyTO0+yHp9A8+0mdu3l7NnzqC1xBQ2Wd9l\nevcsju1jWg5aKCrV6k5jAC+TvlEN+gGGrTEx6bQ7WI7D2tISb77pei6fnWbPXJH3/s03yWVsorBL\nY+0CYRhRKVc5cfIJuu0Grucjgz4P/dNvYLHjKLCNdH+hU8UUWuyw2HgmEkBq0iAz0uXlH349+Tfr\n/qx40xCOxebKGtVKmf72OVQkKRSmWZo/y+Hnv5Tzpx7HdXPgF/FLFdqrp1GtJjLroVXE/PGT/OFv\n/zRPzK/ytW/8iN0jBe56w8286+2/x0c+8h5uefF1fP+ez5Mt1jg8dYiNc8u88y3v4N3v/kWEGNLq\nNKDfI5YC23Z3mESgZbITUWfQ2lgAFLbnoFUqQ0UrpCFQSYKWMTESuTM3VzoiDPucOfck3Y6mHg4w\nKbPZCHH8DPmCx6ErD/DLuyPanYSVrQEPnjrBMLyC+uIJlGEQbC3TGUQkloGtI3rrda5+7i1stFYY\nikvDiNhISr6i2dlmNOtwbnWeB/7679hf+21eeutNXL93jk6SUM3sxzFt/umfv8KdL3kh8xcWWVsf\n8snPfpGffPXdfOcH3yWXyQEp9iTpQjGfwcmYSK0ItUu72eLA3AQXLzzGdb/6UVzP4Xq3zsL4IXrL\nyyjDYXpuhrf/7NsZxqswWiH/rp/lkc98nGavQaFQo7GxhlPI81u/9G6+d+o4F7clG8sLOBmLuSuu\nw8mP0V6tc2Lru0iZML15kbNbm1xz650YGR+vv4qTLZKRBezCNMK0QScoYbLZ8ZieuwLHypL1qyQ7\nueiO74AyyPgW5dE9SBleUs333HEYujEzuwxoSqw9JR77+LcxE4vLr7sFtX8X+pE217/kdurHtxjU\nA+bG9jEsW2S2Vin1xjh27DhTY+NcONtgcjWicyPYkzM0L7ZolUOcjmakNEFvo812coFkwsfu9hA5\nizCOqPVCkvEc6+tDhhsN8lOjZPMe2nZx600MbTM9NcvZ5VUix6G41EWMeTTm5xn195H1S1hrdTI6\nR7w1IKiYeLZgY3WJsclx5GaEKtiMNiPONfuohsPUCw6zfbEO/Q56TdPIClQ7ojgCxbndeHbEyiOb\n+LMedVuy8dQF5nbPItdDdpVGL6nmAIaZGjy1YabsNDs12iVWBsP26PV75P0MZiZLzvExhYXjWs+E\nkJmmpNEd4nQFiQYvm6Hb7WFYBrtmDxDFAREmFy6exs+UGKqYs4uL9IcBY6NlSoUi586do9Pd5urr\nXkAcJWTzOYbtPma5SCQTBo0GxXwBKRSGMukP+sSJotvd5Pz5k5w3HVpLdd7/t5/BcQy2NjfxTc32\nZpMw7lEsFEHGVEbGScIB8/OPY1kmWu1EICdPo/4hNjS20sgdma3UElMZmKTSXND8O9OpZ0fT0LGk\nmHXBUjhGFrtisLl1kXypzMrCWUQ0RGaLzB8/QrFcREsTJ5MhDga4vsMglPzpJx7ija++ljOnzvP2\nN72La666ivGxaToNSRBoAumjBi6iFjF3eIR2bxWaCsuydjzBDo5toElSiB0GhplSMbVIzX5aGCmz\nSKcoAnZYTakXANACw1AoFe1kVcc45hhT00Wi9U2OPPYUk1Mz9EKPsjNCJhikprGciUWOcLifbn8V\nQwoM12R87kokirztYNgm5YpkaXUZ1WljZS5t1ru41cIUIXtndvPK6y7n3seeJPvLozzWjDj6tYcR\njkfGcfiHD/4KypDIRPOf/Y+g4xAHzR/+94/yx3/yF6AE6mk8h1JoKw1wuuqmn+TJhz7L2BU/xdaT\nn0JaFpYQuHaNbtTEFBIh09pals25+RU++JsfonDdO3nRSw/wuU//D4zuGgLFulhNvTSbm/zi+38L\niLFMByENDMNGGxGLp07u+C0MhCF5RBkoW/OjL3+BKFHoRBLLGN/xUIZA7HgOEhly7eveybXPey6t\n4QDHcYmjCC/j0qyvUC7NsrVVp1SWCHFpirVAaxaOLCJHyrTa29xo72P2ra/m5PxFzpxZImcrwmye\nXKjwixWKWY/G0oD+Wo/hwgqDW6tMtaYZLG8zOL/GxC//FBc+/z2yM+Ns9tfJaR9pD1k4VidbqjG2\ne4QgMgmKJoaGasYnFg3C5XXKUwVk2+f8ibPMHJwmNzJJ5QW3sNaYZ16HFAtj+JZga18Wuxex+6XX\nc/H0UZzRvVjDHrlykUyuwFRWsrUyZLiY0N7o4OzPk7nY4Phqk7GxPNWXXIt1Zpue36fV6uIqB68y\nSnZ/kdWVBv3586iJMcgGbDU6FL05rjg0w6DdwhCCujb/94X935ynqcYqTlAChCUxLIHn2Xh+BpUk\nO2opg36nTb5YwrIzqCQiky0Q9Dt4hmY47GG7DqZSSNdJMfsCSuUa3V6XJDLQWpB1HTI5j/1zV3Pi\n6OOcOnECzze55tqb0UlEqGKSdoLlWIjAIe4PCaIo9YsohSVS1/pmfZNhvcXKhQ20jHjjW34O2zbp\ntJrYpkUU99h3YD9PHT/GMOiTSE3OsDFch8HmadAqNewaGpROBRFKpYl9Buk4bof+EJoKocA0DGKd\nyu//V+dZ0TQyGY8w6GMOA2xD015fJlMo0t5uECQxOddFKIfa+B5MxyBJVhFSEccBw3hIb6vEK2+Y\nYaxg8A9/+vO4foaLZ+b55F+8HbdQI5dTvOW1rySTq7Byeim9ZUqV4ohE+uBX8EyIj2GYyCR9oGkE\nSqVGrzSsaAc2oVN+k8BAyRTQppIEZYAaDpEi5PEnjzM1cy1SC+ZmJpmdneXChRUOl0ZwfZNTSyuM\nlAsYloHUJq4FN+zP8t2ehWmZdBpNdGeLxiAgU67ilyqgY0SUIN1LyxmwVIO1rQ69VsTjj5/CzSkO\njBapjU3zyJPzrC6e4tHvnKbf3Uy13YYJvTUgizJCrGSINNKZq/F0TcKQm3/6P9FcW2f+5A/J1PbS\n766SmB5xuI03+zqKjmRsRDG5/3qWzz6IicX8Ew9x25vfQ6fdo92Gr3ziU7z6He/F9/Pc8/efI1z+\nNrGOEaQpZomhkLFCabkTAG2k8ETShm6iibBQkUKRYJDmYQilCcMEpVL7ZkRq/rv3Y3/Ewav/FsNw\nGAx7hEmPbk+SL07THbYQBnSHA2zz0jwDdruPtSVJlGaXP8mDT5yk2WlRmXM5/snvcOsb7mZqagxp\nmYQiQdkmvaLGjDQTlx1O80hyGY6tH2XmFTew9MgTBLksG2eO4GVLbK82Mbw8UgZcOH2co/cO8PwS\ne2+8jqztcvS+hylPHGLuJftw+k0euP9RrrrrFur1AYPtBc4TkF1sk91/JWMHy3QHPcaHBt2xKmY7\nZLI0SxyHOJUJMAZEoc3m2hptKbn88Bx5x8DSLuMzo1y15zCPLR7hyAOP4Gc0xV3jKMMkmCmwOxjh\nwlMXyObLDI0+Fx5/jOte+lK2Wy062xsEfh613WcwjLGCC5dUc4DYEpjqadK/QRyEJP0hrqXRBmRz\nOXAsur0W5WwJpMY0LGzfJxp20VpjCQuRd5AJWKaD60psO0cYtpFa4noevm9RqY2iELTrTR64/7ME\n/QE33PIywu4KF84ex7I8HMdhs9HjtlteyJmnTrBrbi/9wRDHNlleWWekUmFlaQknW+TUqbNMzU3y\nK+/7da694kriJMFAk807XFxogTHCwcsPE3R7fOfr9/C6N7yVdq9Dr72MLQXKSAPTlJFm49iGILY0\nWgsMmYI8U3uz2iEDaUz1NKbz3z7PiqYRdHtE/TYi4/K2V1zLenMaP1umubFBqZSwui05u9RgcfUC\nbsbBcxy0sNEdRZzEJDJBKot4GFNvdijHgvMXt7jxOROYaFQiMERMEPao7qmitZG+LRgq1e3vZC1o\nndrtE5k6ZNMku7R4AoHUGoF6hmGkRJq+MAxa9FotkmRIL86TaB+Nxi/sphP0CYcx/a5FIDuE/YQL\nFx6jUCrx0b/8K6ZmnoPje7jFMTbqdZIwQAsfR9p4hQz52esYNFrYOQcLH0xJR2uS+tYl1Xxm4gBX\nXF5gaWMbOexTrBW5YaLKZ+//OnZxgsO151IdnaFUeB25bIFKzubHR+/FdscYGy2yunmBJPbxnSxC\nW0RsMGiv0OvF7JueYNfVBbQ0SGRIqfga3IzP9kaLMBpgYCHUSSZ3F1EkuJWr6bQeRcmEfN7kwFWS\nduPbuNkMz3+BpD24BmEKYtJZbxxEOI6DZ3iEMiZbzPPUfUfR9Nl95fVASDCM8ctVJvZdhSWt9GaZ\nrSCTPnEcIWyJSFwst4RpOzQ7TZKkjW3auNkKCTFKB/QHm+TyNWwS+pfI+5Lk2PeGK9BWk1PH1yhk\nHSZrik4/4pYXvRJ//xjHfniM7OWzuGGPOLaIgh5hp02m5tP/cQ9rOCRZXWTQ9elZPaZffiOl3hQb\nj/4If7KEN4D1dgedxLz4hpexFK2z+NSTbDbWuP3ut9GPGvzoq/cxON/l0Jvv4OLiOao1l6gb0/3R\nPIU7r0cnAWeX1pnwLJp9iTL+X/bePFqS667z/NzYM3LPfPtWu6pUi0u7LO9GMt7ASwM2W2NjGgwM\n3XOGoacHZnq6aZgGGnq6GWgWYw700GCgwRjhjQbZlmRZG9pLpdpe1Xv19ny5b7HfO39kyS7Jkiir\nZG2Ozzl53ovIGzd+8c2I+N1748bvt427/wD5pkHS18gInc2BpKA8gvk9FMIOsVGGTIS30uN0q0Zg\nxzhtk303HGF7EJNNurz+2kM8UN/inkf+jqjeZ2p8jOyNV5LbpWh218iYLiaC7RPnGdtfhRWf7KEr\nL0tzADsRF5J0SUIS0CpoThldh4ydQekaujBwshl8FVJ1q+h2BmSI5WSQSmEYBrGU2KbCCyMK+RJD\n38OwM/jDPppmkCiP06cfx3ZyOKZDuTLF9W+/lr/44/8GyZCj172Zw1degWnmmIlDIiXZvX8/Ag3L\nsak3O1SrE0SRT75Y4iu3f4EoiPnTT9/KB97zbs4vnefotVejNI3BwCOfzcGT2So1wfd86MdQUUBv\n9QTDdojjTBAH2xiaIhYSUynC0aj6KL+MEKPnvkKgXUgsEIsL6Q9e7kmYfuL919NqeWhILOXjCMlm\nu87nbvs8pUyJ19/0Zqquh7l3B7XtLVqdGlnHwbAtXLtAEMV02iFqtsAnbr2fxDBxlcPkNDx25wYf\n/v4bEZoDT87TV18LnfHkfOQnYxDpSkeqECF1MExUEo/EVKApG2l69PsNvF6bTByjOVXGyxWWm00e\nOrbKdfuuY7xSAVNw7Ow5Bqt1nLxGGAw5fmaDO79wG4Ouz8C0eMc7f5By2WZ5e8j5RgcvVIwt7IFI\nsr74APl8jnOL5/F6m4zP7mTg99lz8EZ002F65/xlad4KE7Y36wy3NyhPz+Ag+csHTzCz6w0M6h0C\nKbFsjUZ7mXqzxbGBzqF9R9CpEpuCIFxChV3qnQ3G8tPYukXD05ifqKJrGsOewabXRdMFYRLhdRLy\nBQM7yKALi0Z3gCEilJDkJyZQUYAmshimgSkVhAadYUIoMgjHQtMM3BhwIONYKBVju1nsaEgoE655\ny+uQhk6usBcrk6VSmKcdbBJFMW52SMaeJpSjnOK5wtQo4JzSGHQ2UGEbR9cxM2XGKjvZbCwyXpwk\n0SSF4hQyjPBkjJ25vCHBqQOT1GsD+os6hcndbG89zPJjSxjNOrt//v+kYCRcdfAAS9GQUMtjKg8Z\nhNTbLaSZIDc7tJIW1VKBrdOn6dLAHJg44zl655eJYo3K9FW4U2MYcZGtUpZwLcSPG2jrQ5ZWzxF1\nfAqZLNrRcQqmYDA5hlfzUHmHK153Pc1uh3ZXMS5Nvvz4CVxXUZjfSX6yxWApYTBoE4ybVJVGdmYM\nO0joDQSN5hZDAZXX7CRuBri9LfrTBhNhm8G5VTwEtz7wEEeveQu5qw4RCINOI0RZDv3HTnF0rMSq\nYxA2+kSDPqXNKfyFGfzm5T1HAoiFQmfkODQpiPUM1fIcum0TRzFZK0+iNDTNwtJdwjDA8nrESUQu\nV0AqiZHJIIOIJBySyxWIggDDMIhkxL59Bzm/vMjWVg2JoJARhMGQHbvmuffLd2LYgo3zNQ7fcD3n\njv0Ds/OHyRdshr0OxAlWPo+WSPr9ITLxGfQH1Dbr6KZFL4q587ZP896b38rBo0dZP7fM2FSROInJ\nZ13srEu/WSOOfYxYkBg69cYZHCNg+sDNeGuPolkTtDbvRqlRSgRhgBZBol0IEa9JkgsxSA0FSjz5\nrsYz87JwGn/7hQc5ef4k/V6I12nznh/6fvZNzfFbv/QLdHyYrGTY3PL5ld/4D9Q2N5ibneCqg3s5\ndMUM/+U3/oKtwgxfuH+Z5b7Fj733CF1K/Lc71vjswxH9jYiljSFHDowRDEfxk6RKRiE0klE2NiEE\numERhyHNzhIiVhRzk2jCJAlCsq5JYAo+++Dd/Ny//102+5OUiznGok1Ez2Px8ce59bY/Zc/O/Xzq\n05/kR3/4+1jd7jDcWufshqI0O8Fttx/n8JWHef97P0RNUxSl4lwnZLkb8fjt/4M91307V1yzm36n\ny/raGXa//h14A5/NL/01gixxb8jEwSOESRcpQ5RRvCzNdcuimHHw/TabW2uMH76RXMvD83x82cF2\nCxTtaQ5X97C6toEXPjkjap1d0zPc8r0f5NiZU+yuVnls9RxLK+fYW9yLZWQ5d+Zx3vP27+DuRx+k\nP+wghEnQ8UnMBJkI3HyWq3ZdwfraJvV2B9PQMbIW/X4Xu+RCopOECZYtQUyi+03iwMQwII5iiCMK\nYxWS2AA9T8GqEicdKvldDKM6RmaMjupj56cwogFKjKHlJsiY0Gwk+C2fXKmIY+QQlSK6qeEaBgOv\nTkKeUmEvXthDVwaxClGxZDCoMZndf1manz+zxJHcLA9mmqwfX6R8eA97hxkm5wSuhMfvO0bWEeie\nw56FIrJcZitjk5+a4vjiFpNXuoSLAmUEZJ0OUdFhaJznsTvWeGN2J419C7j7pxj2V+h2epgFgdEt\n0d5oIq6cpVwpY02AowJO/sM5EnOafLbIIw8+SnVMob//nSSP1tD8ARw9yBXmHmQ2R7LS5PgX7mP2\n6B6GBQ8jlgztLP5Gi/GpMbJWkcLeHKLeon1sjfK0Td+uYEUJRtlhfNbAHxpYe3dQrrg8cP/jqKGB\nzDlkzse8btccjlXkzmMn2DdXJDc+wSBjkuTBa19+dGEdQawkugBJhnxxCk2T6DokwqLba1OtjtPp\n1dEti+nsLFLooEkQOroySGSEaxn0Qw3FhRTEcYJlWZw8eYzAD9FNi4zpUKttMTU9xcrSWUxDo1yY\nQE7Dn/3+f2Tn7hvpde5mdmGORGVYX3uII6+5hnpjm4fuvJedV+5ie7tDrb7JyYePs37uJAIDpbm0\nW23G9s6OrhPNQM8XiPwAN+Nyfuk8pbFZzp46xtK5uymZgvVHbkXPGOTycyQavPm7f42/+bNfoBi0\nCQ2IdLDVhZjVYpQOV09GvRH95T7lduHQa1Sz2+Hb3vYdHN29gzdcdwNn19eJpE+lVCQKBpQyLluD\nhAceXeUvPvGfsDRFtZJnsprh0VN92t0ms/tuwKSG7k6TMYdMmi6lUpZ9YzFnOwF3fOEh3vaem3n9\njd9JkNTJ6R0qEw5HDxzg2PGH2ap7/Nyv30k9tpnaM8nE9uPUOy02Tp4nJholXdcsCEN+5qf/JQvz\nefbtn+WuL93LvoP7Wd4YcP8jZwgtRa/WZWF+Jws7duFrila7z0yxyFgxw1986WFafgNLjuHOZLDJ\nE8YRgRT0wj7NY/ew/6q3EmcNTn/575k6fAMZAa1en7HSBO3NVXTT4sRnP/a8pyL+1Mc/r/rdNSpj\nO0AzUVLQ6W6Rz5YhjInxCJVBmAyZzFfJFSt02h2isIdAMlFw2TlbpVHvcG5tiRhJvVHHyZn0Bn2O\n7LmSftim0fRpddoIIjSpsTA5j5V3Kds2X3noYcr5ElImtHs9ds5MsmffHrY211G6ix8F9LtdLNtl\ncnIK32vi+Rr54hgqivCVwDRc3njkAA+cXyNnmFQyDpZjsN0N6A8HxHGHQhbOrMXYls51s2VOb7V4\n62v2cs/JZZq+j+sUydHm2OIW1199iM3aed599esJxZCe12XP3A7ObTT5+394kF/54Q88b81/6PZP\nKcuxyTs9OsLk1Cee4MYfu5Z+J2EYhFSyOj2gcXyNjSRm1snihS16nseCabC+3oCMYPPYInq3Syfo\nctX7vpv1h08RhRtM3PwODDTMWHBm6SGmN33y3/latNaQjTMdGpk2O8fHGa4FNO8/ydR3XIcSMZzf\npne+R+a1k1ihTWNphf3vuJH2Spd2b4v2qW3e/F1vYbXRxu2BX7RQjqRxckA5L5jcOUPjXJ+1Eyd4\n001H2XQ8LHsMLEkY+5RiwZLXIRdK+jLD/FiB8ysbWHGMlyQ4kUVro421N0djZYNiaZxKKWbGS3ig\nG/LAP/+ly5py+4sf1pQYpW5E0xwC5xqovB47m6VQHCOTyRFIfxSYUSmcTBa3UKDg5hFCI5PNoxIP\nsEfPMGWAaegMBn0iKUmCEM8bIIXO7OwMp08cJwg9+v0uWbfMkauu444v/i1ry0sU8hkOXHEUt1hE\nJgaaqRNFPvXVDdYbDdbXN7j26DU0amujRl2vw1fueYCb3vVudu5cYLycRcYhjm1hOi5zC/NEQ49O\na5PTj91LJldife1hdux+PV/5s/8JXZNomsIqLhA2l4mFjlnI4TX6CH00NI8AmYyGuFAKXQmQ8B8/\n88xRC18WTmPP9beoH/+RD5PIhEO75pkoQrcfIBjgRw6+73FypUkrsSlmHTqrW2wO2rhOjnseuI+t\n5bPouQJxe4uxidnRCaBpTBQsDs6O89hjp7jmuivYtyODkYDrjtPRXNzsJIV8FpEEFHMZGrVz6Jkc\nqIjaxja33/MIZzYCVrfXGCwv8cYbbqRSLfOjH/0nHDtzioKW0GhFFKs57rl3lbfcfJgvnOiSy2QY\ny+eRUnHy7Dpksuyfn+SRc0s4QsO0HVYbXerL59DjGL08zdjcFP1uiKZ8spVJGlt1MraDkkMSpZPI\nBNMQCGkTRE1EKHj4v/+n530xffT3PqeiQYtcaQxNODimieVYmJbB8tKjaEaZUrFEFEUkoYeTydNu\n1/D9HtOTC2y3WgRBSN7VGfbahGFCNpelXVsmEQIRQr3XppLLEOkWMgwReoSUMbpuEsRDXKdAHEe4\nZoFev4ObK1GqzhDFIYN+h2ouz64dc8wUChzf2mZjax3XSegMhnzba99CtzPgup0LPLLVZ+g3ede1\nR3jk+GnqcQ7Nkrz58F7uOrlOFHjctHcnf/vgQ0xUJulKiRmHFNw8270+QhnIOODAjnHmqwU0BLWe\nj6NDKwho1Fs0IwMSyc++5+jz1vyDn/qEypYqNPRtOouLHLzlZvTtNppj02y00HSNYbdHHHrY1THa\nW22G/SbN5gAjDBkOe2SHA654+w1sPbbI6hPn2fOmq8gJxRf//j7efOQI3uwM4fYajZxGUWYpzoyz\n/tdfwdw5hpyQiLjAws4S504+RHXqBuqr58lOZhncvczELUexzja48/RjXP3WG/CO1/CiBqXyLgpX\nzLGy1qDdXmFHdgxnKk97vY4ywRiCX84yk8tSM6EQSnyvw3v3HUW3XG4/u4YoSCbHczRWm4wZRe66\n+yQTB3MMtrt42TKZYI1qLs/QLWNGMbX7TvCGd72DL/zlvRz7nV+7LKfxf39EUyiBUBpoOrL4bRgT\n12NlstjZHHGU4No2aAaGACfvYuk6uhCYbhbLcLBNnURoJHFAuThFrbGKKTSCKMEb9hFoDIdDkiQi\niWIsy+LMmUcplMbJODnq9fNU8lW8SOfzn/tjds0doTo3jqEZOHaWs2dO8eiDj/D9P/gRJmamEUrR\nW15FEtD2JKdq67zu2mvJ2LBr1y50XcPOOLhOliDwGQ7bbK+eYvHY/aw//ic4GZf3/PjjD8feAAAg\nAElEQVStfOr/fRu6ikkEWM48nreFJUOmrv4eHv/KX5F1RjlGEAKRKGKh0C68Hf6rf/Mydhr/+fd+\nUx3ducBYucDQDxnEHeKgz/3HG9QGJsNIEgmT3cU828Da2VMcP7NBa2MZ3etSPHgNKgrRY4HX3SRr\naBSrJZZX1ihb4LgZ0HTCbhNNc6jmMmgO7Jsv4YWC66/ZzX0Pr/DeWw7x8GMbjE8nJEmRiZxBP1Qc\nP7VMqehy4lzA0V0VxmYnOL/ukSjBzmmDzz2+xWvmZzg/lOTdIg8dO8Mtb7iO2x9+gn07xqmv12hu\nbVEa38lGp8ew2WV8fhzfb7F8bBFzrIzuxcSJh5YroFsOw0aN8bn9tDZWsWwdKRNsu0h2qsqg3kLK\nhMf/6jee98X0r/74y0poo+G6KEkY+AG6Jhn6Q3QhMTQDoWI6vR7K0NBkhBeEZF2XOAoRuoaQkmG/\nTxR66KaJbRh0ux2q+SLtfg0zq5FzxwkTgYoThBii6RZxlLCxuoKGgelYWIYDOqgkQmk6lmaA0khC\nH4nCsgqUyhOEGhgyAS2hkJ9lKBOSsEutdp69e15LLAN0I2TQ75MEQ3bsuRZT6Ah9FCdIkVDIF/GG\nPhnTZuj10UwDx7CJoi4GgmzOoWhnOLexjLQK5C0DSzcZxJLJcpF/ccvh5635LR//VdVprOGf9Hnn\n//FOeh2TtcVFaucazF+zFxkqMCRBvYttQ9hpI2ZmsGVCa22RB/7yfq4sOeTf/UZod9hONPzEwI4C\nwrPnMIuwa+8VnI18ult94vPrVMsa49e/kVNffAQhWiShxO9lueaGnQQ5g9XFZSq75ultLbNw87fT\nvfcUvd7DVN78HrRGk+3zS+T0aYb5CNdwCE4skzMyTB28Csvcpr7mU5vwGKvuhXBI0uvjai49T7By\n/Dg73nYVfq3BTHmc7tCjoBm06lvsmqoiiyW0UDIQGs12F+U6+EurOH6W/rBNcbfF7rEd/Pa7f/Ty\neho/rCuBINGymLpGMv5+dHeB8tgUEQlaIsHQcUxjFJlCaOTzOfL5EpaTARkRKdA1sAyHRGoIkSCl\nJOgNaA1amEIQy4RBv0+cSIRUhHHArj17eezhB9B0g0q1zMr50wipMVapEEcGW5ublAtVdh+6Ctey\nR0NiicS0Heoby4xXphgo+OKdd1DNWrzmqqNUK2WcTIZERqwsL1HJGUgl6G6tcfyhO/D7fU6cW2Tf\n+DpmvsQ7PvJ7fPo3PoApBJGKkVLDGPW9cOdvoL1yz4X8MqNo0CSjJJ2/+ulndhovi2cac+OTtP0a\nojPEzmicrw85fXKbk7U2d33uM2RndtGvb7Nz504evv3zXPNdP4ltGUzP7qNVa9A6dQKrOs7s5F4i\nNaA6vZuq1aY6foiH7rmPPVM7EMrDnhsnaXus1ZpcNb+HU6vbzO0oc/zUOtu1Fr/0u5/lDTfdwC53\nki/d8wRveO1h7PEctUc2UGR4/MRjHNz3Dj52691MVyfwhMtXNl2W7n+YL8cP4g23mdv7OsbnZvjS\nqRUSaXDrn/8JdmGOqUoVElg/8SBjCztYOn+OYnacuQNHifUER88yVprkkUe/iIkgo2ex7QLTO3YS\nGQbdtVUMLUL5CitrEkX2ZWm+UTsBKqHbXsNxKyhsgnANLRGYpoVuVrBzFSzTJJt1MQyXWn0FITVs\nwwFlonSFU3CwohBdJoQyYHJ2nl63RqZYRRIz8FsgRjk6RhMR+gh0dhy6CU1GRGGEEAYyCtER1NfP\n0VU+2VwW7BwqDvH9Dl1PkgiFoefImFn8WGIbDqEBe/bciGlnCPoxEoOMOwWOwg99fGHheR6mZeOa\nGqEXkc9l8bwAYVu0u3UyhgkI+v06YttHGS6WXSBDRCtqU85WUcqi2bm8kBbXzM2xvmMXk+8TPPzl\nZcbKk3TrDYiahHGIM0hoMSRXMSlWq3QbJjIjWD2zxvnP3Ma7fvZ/pl3bpt6qw7l1slNzuJ0N6mIU\nanzQqrNUq9Hc7LMwvUBzl0/t+GkOrgY0Ji3mjrwRUckwWG2ycP21nHziFK+5YoEv/sVdZFQP2Wpg\nuXnGMgcpxhb17R7JWp/KBxbINQLcHpw54jN8vMfstM6J4yHupGI6cxAjHtDZaOGUynSVopIRlN96\nPVsdj1ylwEatjW1Jal7EZKHEUhDD+T4WCRnHYqxYABIG2TxdrU+5UqJzxmPl8k7zEWqUGd7BI5Eu\nURKQzxXp+V1cyx2lA4gTEl1gmw6mZSKExjDwQdfI2hmkjCnmS/TbzVFrHAh8H82xyMoMlXKFxcXT\naJqGkUi229u88c0388XP34pl2czN7cDJZdkxf4STx+/BJIuwfMqTU8zMzqMLQaffpVqpMPA7ZDIO\nY5MTDPt93PIEpilYWl1lx64dBKGHnXGZrVSZGCviWEU2lo/TCAYYVh6/fzdvedvbqT/y1ySdJp/9\nzQ+y86afYuXLv44pBbEm8cUoYGd/7QFmdt/C2pm/w1AaQkliQD1H8piXRU/jbz71R2q9HnLH3V9h\n9dwy9z/6IPmp3Qhh4DXrGJrJjte+fZSR6uwxChM7cManqcxMEnWHBEHE0rG7GJveTe3sQ4ztPkqv\nuUXWztLdWBm9YdtoEvmSlZUnyGVLeJunmNx9NdWxLCXLJp+pEImARBM4loOMFZYrkb6P7hhsdAKa\nZza5/k2HWNzqMgwNblqocK45ZGJ2nnp9SLO2RnF6D4Uxi3vufpj6udO85QM/SCYO2ajXsDWBJxWb\n61uMlcYITOg3u/S2l5jbcQQvirEsbTSnWoOMaaEMHU3pyCBBGDpR4uPXttHyNg/+f8+/2/7BX/l9\nJYXCFi6mm8EyNDJWhuEgBEJ0K0u3v44fdlGxwDCzWJpOxsmAYWCZWdrdOoPeNo5pMxgOGQ676IbF\ngSsOsbq+AkLhdVpk3TLZfJnA61KoVtB1g1Z/gIgVCIVtu/T9ARk7g62bo2yBRoZef5PhoEahuAPd\nKmAQoZsZlNIZDju4GQuUiWaYlEolGtvb2G4WqSSD9gZBHIxe7NIFBTtPlGgIyyYOhogkAUMgowGa\naZPLZojCGKEZaIZLMWuhW3karSaJhCTy0B2Xj//oO5635lf/5E+qaz/6nYhOTKu7xkR1isWTJ+lt\n9LAsncqucSzToraygYgsmnGNosgSbfcYu24Kc1Oipkqs3nWKmlnD73sYUZ/x3AS9iotRb9M+W0eI\nkFt++kN0O4Lzp5aYOjDHoKPonHoQO5cwnd1DNFem348Z1Nv01paIGjEq6PKun/k+9i4LPrt9jl7U\nR/Ta5K8+zHCtSVAb8sTtX+Tbf/ajNNZqIGO85S7ZXkzxmjE66BhS0YlitDCmZMAgkYzpFXqWJFYD\npG7AAOwwJDSLFAoR233JhDtD3gqw+jFJ1sJ2CrS2VtByGf78/ZfX0/jlH9aVQmBoJppeIqq+jdBZ\nIFfMk3EKKJ0LUWd1NF0jl82NJpsIyOdKZLMZLDuH7/VQUTxKgywVQii6nQ6GZTHod0kSSbfTwXUy\ntOp1er062UIJO1fk6JHreeyBuxC2jtB0Yj/BMgzGp3eQz+XptNrk81m8QYdSoTAqEyfEvk+mNEY/\n9PnsJ2/lhhuOUKkWyWdsokRjamya5aV7EJpLrdZlem43f/1b34drDdl53Y/x0O0fYyKvoxS84bt+\nizv/+MeJbYXOKM3BKLUx7N5/M2cfvw3fBCscvUX/a88yPPWy6Gl84lN/iV7cxxMNj9W1Nlri4GTz\n9JsDCvOHGJudQ9My6LbL9N7XoOXKiCihu7qB4ZjYuRw79l6NpQnmb/4u+l5ELAXj1Sqve+trOfHg\nw4zPTpB1c+yazDKMdbyZeVbPHCOMSsTVeW48VOHuR05z/eEFlJtj5fQmY/lxOgQkpqDbW2V+314y\niYnXS2i3e5wUNov1DmOTUySaRoeQqLFBzBS7d+0nGQ5ZfeIUpYpLsTxGe9hgUBsw6LYpOTmitk91\ncgE8SWu7RmaygjQsNhYfJeNMoI0XcPJ5gm6DRLfQA4XuSPpei1x+5rI0P7z/ajZrm/R7HbQ4Rrdd\ngkQyDDt4gYediUgSjySGanGGbtDFsSrEok/Q8+jLBlE4HEXj9IdESUQ2X+bQkWtYWTqJlAGO5aCV\npijkiwz6LaLEZ/38MomArJsjij2UhLDfx8zkRjnIhWQ46KJpPUzTwNRytGtrVIuSyFAYmo4mdErF\nPIk0SKKYYqVIc7sJGrSa2xSLBSandmIYGZJgiNINhADDMAmGHkZljOb2JqYj8IYhU9Nz6CJHd9An\n5xqcWnoIQ1tg2BwwVp2g2djAtS2iywwjcugjbyc+X6drQWVilsUzW3SHPmMY+K5L1/dRZ7vE2pCs\naTJlFjnz0AMgDbR2iRZ9Sme3iCuKHQduIGtJnvjzr3B2sEH+VELOddn7luvJ7Zxj/bEVGss1Nu59\nmJm570YR0wm2ya8VEd9bYeuRU3S3twm3zrDnO74Pe6zC8IkzfPJjf8r0TBFn6DKMdQb1HkP/PHqu\nSxTCjf/0gzROb+JrCUoGRIZH/tr9bDTqaKZGZc8CzuIqgWbiRxGVgovf8omHHkK3KYSC080BuYqN\nXhQwhPF8iV57i54ekfQlVlMSG0sUVJG5zOUFiYTRDd4QIKMEaWToeAkTY1kcN4dlZEGEo/cSEkhU\nSL8bkc276JpBEAzJuTn6vRYZJ49iQCIlSibEMmZudieb2+toQqGZFtl8lmDYxS3kMR2LWEn27j3M\nxvppQmFg+hFGQcMwbMqTM6NsgsEAw1QM/S7ZUhk/GBL0fHL5PNlqFSVg44lzXP+6q/A7dWQux/Lm\nIqVSlfPDDQ5c+W30Ag9/+BDTczOYhQP02w+yfe/HqZbLaLk96O2HuOeTH0Vl9FESJpEglMAAIqU4\nfeqLTMwdornxBOgK+XLP3KeX9/CZz93OxMwCr7/lbcS2zcZWH3HuBDuuup7amVNkp4oQJghp4WRd\nNNNg2KohpWLYaBOpgExumu3WAH99kd0Hr6Dk5thaayMcFy3JsLRSp1wuE0Ut/H6d93/32/E7IbhF\n/utt93NgfpztQNGobXDPE6fYMfTRIp/du3ZxcPcYppeQdXVmp6scPrgTkeistjbY7g4Yq0wwXb2a\nT3/2s0gE+XKZhQO7aW7W2WoOiVdXGbSaZHMzzEzPo+kWVlYSxHUG3WUct4iu68hhh/7iKZz9ecKe\nIEYhvYB8pczQ88ATGK6Nuswot48+ejuhJrE0l3AQobf7xCSjZz6VXaP4WmoKV+8zHPYQSUKmCI3m\nEKk0vCABmWCgRmHjNRvb0llaPovj5DEDiekoeo0mRD5Sk0Qywsm7BHE8CsmiCTRdwzAkiWzR77fI\nFUoozSOOHXLFMbJuES8ICcM2SRRiJS6lYgHdsKk3zhGGin5DEQ27XLHnSqRQeBFINUQkvdHbvjKm\nO/AI/T5bW4uUCpN48YCx/DyZXJVaYxPFNlPj8yglOHzgJtY3t6nXTiBEj2JxmpxbottrX5bmcnMI\nXoskyoIjcbWEKIbOgoFstZiVk6xUIkxvnl6pj9MOidoNmkpR6O6ntOKxkShycZu15UVMe4zZQ1MU\nB1n0ZEBuehfNx9ZRlRLDoA/0ufpnPsD52x4lP2XhLW2x44dv5tRtdzHYatGKQrQlRRTrmLXtUUt6\n4NOrFVhafISZN13PldcewXYc1raLFLVtQteilLExQsHa399FY71OZWEBq98ldsoEK23iMCL2Y5ic\noBMMaFgRO2KLlU4fX1fM7M6OehLbNQJRZfXsCVRe4CRFKMdUy2VaoSIni5xuNS5Lc2CUrU8IdCeP\nHq8jDAsvCIm1PvmsjtI1DKkQusIwbExTR+jOKEmRlHihhyEEYeiPhp8UYJgYmLQ6dZI4Io4lGVcn\niQ2G0sTWDJQhMZVk+cwx+t06N9x0Mw/e+yX2zh1l4/x5bEOiCw0v8AmGHpoucJwYbzCg3+lQGpsk\nSUIG/R5jMyXa9TaoCD9sk8tl0GxFxsqztPgA9eaAROk8dN9dmLrDWksnN5FgRS208Ayv+/B/5bY/\n+BAYCVde91HO3fux0UuBF979NqWkvnWcXHkPw845hHz2qc4vC6dxrh5w6KrX4aM4t9UljqG9usjC\n3gN0m02iYcKw1yKbq5IkCQU3S6fdpDgxy7A3JO8qYkcQD3wWFuboVLMYiQ+mTae1jlMao+PHzM7N\nYUufTc9FK05z7+kmJbeI1tvkyl0zZPJVPv/FB5hY2MXBQ1chhWRpqYc8VyMyIeoOkI0BGbOAndN5\n7NFlxiZ2cO6+e9nYfYRcvsxrXvsm1tZbbJ09xdzUPGEicDULMi6G0OgPe3ibDSZ3XUG1PMsDd/wd\nSa/DxJ6DtM6ukKtWufKt78OL+qgwBt1Ez8LqyfvI5hboJT1y1XE2Tj5+WZo7+SmKToag3yVrOyAS\nPL9DsVRB+hI7L6g3GoxVduAQ0+21WK8vUcyVsUyXwcYilm6TSAVEREkf5QU4qoTXM+n1O1RVlUJ+\niiBqIaVH1i0jEw1DTzBNRRRpGKaGlBrCcFGqQ7ffpFTI4IeCodeg3WriWBlyxSK9gTcKr9LroFkm\nbnaaRNXY2DpOPjvF8cXHELqBigYkKExhY5gWUiV4XpNExuiawzBKcNwM260tAq9HGPhgaJxpryOE\nRMUh6Bky2T71Zo9m+wyuU2XXzhsuS3NDhtQQEEia3gZJXlEJcmytd/CaEbXVNcx9LtnxHESCxmaT\nHfuPMnbuJIKQntnDDFqUdx+lGGj0sj2OH7ufI4evIcztY+uJM+QHS+jua5D1gGHYYNjXqczvwMhp\n6F6NcpIj3nsF5vQGwwdPsOfdN3H+9q+gaQHxyiq73/AdTN24k+2lfajFOqtxH/+OsxjJgOHOIvvE\ngLXthOT4MutPPMxb/vX/xuLxRVxfMgjqaBpk9s4TL69QyoR0hjpuGFHrD7FNSRIHDFYtEqNO3s2i\nbBMjO8766mlq/hrZMw4t7wxeoc+yNclY9QXI3KeDYWQg7BLImETTcLM5DMPCC0N0XWJqFlKTJEFM\n1h1HVwovCrENMUrdEEk0XWE6WSy7iEg8nIxL3NpGN3Rcx2U4GNDrNLAyGfygT+INMawchq6hWzaP\n3X8Hfb/HsYfu4sp9VxGECX1vE2HkGRubotttosIQxymQOAkSH1PLkcsoItlHuSahsslnCwgEpcl5\nFmYXOPHYQ4Sqx/z0HN3WNpOH30r9/CPo+oBEExidPnf90Yf44M/fx6d+5VpO3/8x9tzwI5y67+Oj\nLH0CkgRELBh2z47ipb/cX+6LQ5vOYIDrWvhSceiK/TyhJ5humajbYezAXpYe/DLjN307kphOc5Nu\np0d/2CHRBXGiQc8iCIcE3QFKt+lrkmZvm2wpi5mxSQwFmRy19QZbjS62ilFZl97KJrMHdrK2Jcn1\ntnjtG67mngdOoIiYnJrhNUf2srnWYtBqE2oO1voaM1dPsXxmg/375lmptwjcaYL1Da584xRPnFln\n5+4pVFih3fLITY5TNk26wwDTmsG32hi6gz8Ysu4PufLoG+gNWmhKZ3zvDH4/gkwGt5ChtVlDNOrE\nSYimZbGmChStSbpb6+y8+urL0jyTyRFGERMzu4miiHprhUxxDIWGnhdsbC6SyUzTbK0ilMTJFAg9\ng1arji5M8tkifhCSyIB8aZ45t0IYD+n0Wvj9dQr5AhKFCrq4bo6hrxMlChglrZdKQxASJwnliUME\ngzahb2BZBpHMoSQITWdicoHQ8+l1G2iaQaddwzSzJL2IbKFEPltgYmIP9e1VZBShWzadoEtGdxkG\nQ2TQQpcBhjNOuTxB6HmEsYcMdUhAt8s4GYGlO1hCECUhCRKBGGVvDD10U1HfXGJrfRm48Xlr7nWb\nFyIMCGK/gBAR/eY6aAb2TIbqzASLj59E3+MSB12kpVNrbCOvuAJnPUNSKlPG4tyJf0D4fbyOhz2+\nh7WzDWb2T7HzuivxHgJbN2lu6kxV97K1+ChRpUr79rup+DGdO+/ndH2J8PQ62vQ09o4p9i+M023V\nke98G/Z2nwfvepT+iWOUy3nqnzuHKQeI6RmKiwlqahwvfBTj4BSHbvgR1h86Qb2xid1TSNVi/Z47\nyI/N4czPYsd5dM8f5WUZ9pD5hGxkoIuE43KbHcMZVKaN7SkmC5PkZgVxHPPYJx9msmKyktsgWFq5\nrPMcQDeyJMEADBdDTOKKDMHAR7gKO5PD0jVsJze6znSDJArJF8fRRXcU9VqaeHGbSn6KOPaJgj66\noTHoNNA0jcgLMEwDVEg2WyIMBxAp/CDkmqOHuefLf49tWGimga0VsLSEtfVF/MAfRZd1Bjga5N0c\nW2tLTM3M4uZGESy6zQ0MTaPX3UAIk1w2RyFbJk4CVLfLydo9+FHMzPgcrdoGOFlWHvgfJKbLUAaU\npCQ2E1Qi+O//7gZ27flelk//Kafv+32yhWmCzsYof7NQqEghNcAUED27ni+LB+EpKSkpKa8MLi9U\nakpKSkrKtxSp00hJSUlJuWRSp5GSkpKScsmkTiMlJSUl5ZJJnUZKSkpKyiWTOo2UlJSUlEsmdRop\nKSkpKZdM6jRSUlJSUi6Z1GmkpKSkpFwyqdNISUlJSblkUqeRkpKSknLJpE4jJSUlJeWSSZ1GSkpK\nSsolkzqNlJSUlJRLJnUaKSkpKSmXTOo0UlJSUlIumdRppKSkpKRcMqnTSElJSUm5ZFKnkZKSkpJy\nyaROIyUlJSXlkkmdRkpKSkrKJZM6jZSUlJSUSyZ1GikpKSkpl0zqNFJSUlJSLpnUaaSkpKSkXDLf\n0k5DCPE7Qoh//Q1uo4QQe79ZNr3aSTVPSXll86p2GkKIJSGEJ4ToCyFaQojPCCHmn/xeKfXjSqlf\neAH39wEhxFeEEEMhxJdeqHpfSbwEmv+aEOK0EKInhDghhPihF6ruVyJCiO8XQvzDBf03hBCfE0K8\n4RK3fU7nLISYFkLcKoRYv1B25wtl9yuZb7Lm7xZCfFkI0RZCbAohPi6EyL9w1n/jvKqdxgW+UymV\nA6aBLeA3LmUjIYTxPPbVBP4z8MvPY9tXEy+m5gPgO4Ei8CHg14UQr3se9bziEUL8NKPz798Dk8AC\n8FvAe1+gXUjg88B3vUD1veJ5ETQvAr8IzABXArPAr75AdT8/lFKv2g+wBNxy0fK7gFMXLf8h8IsX\n/n8LsAr8K2AT+KML6/8lsAGsAx8BFLD3H9nvPwO+9FIf/7eS5hfVfyvwv77UOrwEuheBPvA9z1Hm\nBuBuoH1B398ErAvf3XFB58GFej74HPUYF8rufKmP+1tF84vq+yfAYy/lcX8r9DQAEEK4wAeBe56j\n2BRQAXYAPyaEeAfwM8DbgH3ALd9sO19NvNiaCyEywPXA48/X5lcwNwEO8FfPUSYB/hdg7EL5m4Gf\nBFBKvelCmaNKqZxS6s++iba+WngpNH8TL/H5/XyGA15pfEoIEQNZYBt4+3OUlcC/UUoFMHpGAfyB\nUurYheV/C3zfN9fcVwUvlea/AzwC/O3ztPuVTBWoK6XiZyuglHrgosUlIcTvAm9mNLyS8o3zomou\nhHgboyHYG7/RbV9IvhV6Gu9TSpUYtQh+CrhdCDH1LGW3lVL+RcszwMpFy8vfJBtfbbzomgshfhU4\nDHxAXejHf4vRAMae67mQEOIKIcSnLzxQ7TIahx970Sx89fGiaS6EeC3wJ8B3K6VOPW+LXwC+FZwG\nAEqpRCn1SUbdxWeb2fD0m80GMH/R8sI3w7ZXKy+W5kKInwfeCXy7Uqr7fGx9FXA3EADve44yvw2c\nAPYppQrAzwHiRbDt1cqLorkQ4mpGz+o+opS67Xna+oLxLeM0xIj3AmXgiUvc7M+BDwshDl4Yn/83\n/8g+dCGEw2jYTxNCOEII87IMfwXzImn+s8D3M3r43rgsg1/BKKU6wP8F/BchxPuEEK4QwhRCvFMI\n8R8uFMsDXaAvhDgA/MTTqtkCdj/Xfi6c3/aFRfvC8rckL4bmQojDjGas/XOl1N+88EfxPHipZyB8\nMz+MZvJ4jGYm9IBjwA9c9P0f8rSZPM9Qx//OaGbPPzqTB/jwhe8v/vzhS63Dq1xzxai117/o83Mv\ntQ4vof4/APwDoxk5m8BngNdd+O5NjFq9feBO4N8BX75o2x9n1NNrMxrmeza9n/J5qY/5pf58MzUH\n/oDRc7+Lz+/HX8rjFRcMS0lJSUlJ+Uf5lhmeSklJSUm5fFKnkZKSkpJyyaROIyUlJSXlkkmdRkpK\nSkrKJfOyeCP8e37gXypfthDKRlMKw9AAcWECv/raTP6nz25WF//52pdCPHWTJyd9iCe/fOZqRv+I\ni/5+rcavrn6KPS8ET+5HgXp6xQoMTSAMDaEZKH+IITTagUZxIssnfvuXn/cc+3/6L35H6WMJtlnB\nQKGLrx3hM5l3sW4XT564WNOLDuVZEU+p9GkbiKeXFV+//umVX6IC4uv+4ak/5VcnBoEuBMLUMCwH\n3R+QixO2NQdVkvw//+wDz1vzX/qdv1Szcz4WFSyhoSUJdW9IpEGn3sKKfCYnCpTHp1FKkfgBStPQ\nTQPT0CGOkFISKYXQBLqmoRsWCA2BQiUxSioMkaAbGgpIEomUEqUEEoEQYvT7JQmmrtB0jViBkhJk\ngpQChIamC2QSI5OEyB+ilETTTXTLRtM0pAQpQRMSVEIU+pAkIAS6mUEzDHTDBBRxlJDEAXEUEAyH\nIBMQGk6ugOXYICVxlCAFBFGC1m2RiUPW9DwbCH7xoz9xWe+SvPOdt6ho0EeGHoZM0E3xlN8bBJo2\n0kYo8PyEGIWpW4xPjpMtlDEMC7QCCRFxkhDHyehc0XQs28TQNNA0dGIUCscw0BMPTUo2NtboDbr0\nBi0MTaCjYZkWCRIpIVHqOe49T96/Ll7+2oonD+Mpa55yX3ymKhWaEMgLfQYpFYmU6ELHQCN2TMr5\nCn/+mb97Rt1fFk7DtCCIMygcpCaIdOOZ7u3PwTfr/aTncDDwtV/z6caqCz/TM0JBhIoAACAASURB\nVN+Hn3kXTzqNp5UfOQyBISBGwzNMusLF8fRLPopnolTIE1gVpJYlFgHJV49BPMV5iaf991Tz1Fed\ngLio5Nc54YuP5xuw8Rs7B55h+2cy4Mn14qkX48U2W6aG0DV02yAQNh3NIBhUmXWTy7InZyUU9Awi\nsdD1iMgqkonzyGGEioYMlYaXZCmQwbB0kkQjimNkooMmCCMNKQVJLHEzClPX0HQTIXQUIBXEMmLo\nx6PGhgaGrrB0HSk0pDJACKRMiGOPMJIYsT7SWUk0XYFmkihQMrngRGKiJAYktmVhmCaa0EikRMYh\n6kIZGQckMsaxTWzHQGg2UgiklMQyIJERkphYJCgRoWsC3QRNQKzJr4ZAtIwYt6ITJhmmwgyV/PRl\naQ6QDDbR/ADpe2iAFuujs3x0GoyOXwiUVCgEiR+joWO6GYRIsPN5NDNLYtpEoYeME2QsUUohNQ1h\nWyhDw9AFumWDpjD1BOlr+IFHJHsMvA5xt4suAEMjsQxIRtePpv7xc/0pPu7iK/TrGj4XfauenBM9\n0lkTAiEgjiVKMWpY6CNHqSJJEkokkFgGXvjsN66XhdOwMjomORJhogmBZtgXRHwW1/t0Lm6+qqev\nf5bifL0H//ryF7eun7b6aS2Vpzazn9p0/qozEE8pdKHEU8s8pYcEo5MMiYbEyRawMPAsEPrl3VEz\nJRtRLBLoWaLQwyT5mn3P1Ot5lla/urDuqc6UZ9T+Unzo06u5VL5q+tOvoGfY87N1dJ4k0XUUGmQd\nTF2SwSAyBYH+rCGGLgnVP4sRLaDMcTQLJooFvJxOu+sRBnUMNGbKGaqu8/8z925PkiPLmd/PPSIA\nZNalp3tmzplD7pLaXZNetA8yPUhv+v/NZDLpQWu21C4pkntIzpwzM91VlRcAEeGuhwAysy7d08Ma\nmXW0VXVlAomMCAT88rn7F8QQGBXuxwmRQArQpUCtxpQPDDZxJVtSCk3QG5g4Gec4zjBlVJzNRths\nOiT0uPZUBzNjthGbJygzEhzB6eIA2lFccS+gjilsaoeIsL2+odvegiilGvO4w2vG5pE5RBznqh/Y\n3twiccNcodbKCFAFamIkYjYRg3JzfU3st9SSKbNRqhHCFTf6hsM48mCZsLVXzTmAuNHRnrXeoPPz\ncyfQhCaANW8DA4nQReXN1YbvvvoG7QceauAQIqXMTLPh1lZOnzpSjPSdsO0Tqk4iowyMFtiHLQeO\nmEe0FgKwjSC2CPFVea3r5FNraDFIL8XMioD46bWcnmFbtEYQIUl7TsbiHIoh1Rk6PV23oIjDjCP1\n4/P+RSiNGITgUL25rSbeXMWPOlgX0/pUEJ/0zMtCdT3cfpa75fJI7r/wLW1lvXSh07GXbvmF6JVn\nV3x6xpNPtlbxZvVhhGjU6ISpUuvrPI2rPiJqSFLcBUWfjP/jSkl+YXmfZ+MFRfPyiS8qlNeoxXVe\nn/X1GRR2Pr4+dCEEsoO4UAOkztGxEEt+RY/gww8/se86+j98jZnQfy30oUNwpquOTpXbtKULuljB\nvgg0P0GVijK6U0ql1EIqDmoLxOCtDEyFilOqkYoymKAL/CgOeJOKrgUrFckVUUElAAF1obqCCeZC\nUykKrgRXzAWpDqY4AffQlJaBuaJEggdcQcxQCRiRJo0d0wAhgkSCBEIAUqZaxUulpA40Ywg6vc67\nAwgYgYq6oQ6BM+B8aUDK8n7AQJQkMPSRTUpITOyNBVKqVLdFfgguhqvjIlR3aim4Z9QKFSOFQOwj\nplCqkwRSUMQNEbBqJ6XxUcPqwqC8RCRO7/n5//V4MT8pDLEmX50mUyqAOV6MoG2tmSqCIEEvZuZ5\n+yKUhiKYO1Yr0tYi8oIQf2aoXwYvnsIjT3TIeYEsuC7rRMs57vGRQkcRcJeL61x4BzThcv7ul7yj\n1Yv4TFvbz6pE3BeBUFEpTZHmQq7HX77OJ1rqekRB3UiiSAwXWRFPViS8qFQ/3ZbZlsez4Y9/0e7H\n4687fY/76zGqX9vcQRSKM80ZqzNXqWNQJ9j0qkv/X//n/8Hf/8M7/vp/Svzl7/+Kt98Etm86blOC\n339LLXsOQdjtRqrBnCdKzpg7Ko7GCKLsizEaHGohHh8AxTUiVMQrpcKxKLUYRwlMAZQZZ26wRDDM\nhFx7prliJaMqWKcYkGulmuMmWBXmHHFgT2FTDjhOHifMHVfFq3KcA6UYD8AxZbqkKJVaK8fZKJYo\n2Zim5sVpiUxq9NNEIJPzyDzPjIeRfnMDKSEpIvX1SkNpgs7E6VSJQVHWJ/nCaFsgnBgFC4IHJadI\nTgmNHVTBfdcUqQacJpCDhhYTcZinmVwm8rgjhkgMA1ff/p5v+kQ97tjd7SAKQ6dEBVHBJmt9kcf9\nedoeeRLtj5MMcm/eQhNj7XgQSAhDaOvhx8mowHCz4RZBrRJqM0jNnSEpQRTpA+WpkXzRvgil0QSD\nrWqST1apP8NB4GP6+VILy4Jhu1gT8tKC7XL67e3fC1/tFzL0Egk7y9aX+ru8exn3OF3cL36fR3B6\n7zLQtQbJTn87tTpqr3uYRBfPwh13e6T4ngzjNO7PUHcnWNHdTn1vlo+csON2teb+quqCqS8B2ifa\n5elaeNnL+ZRi+Vz/5eKeLPMs3mxsvAWUK6+Dp37884/cW+Drhx38rhAVgrbrX93cksfI/rBnX/a4\nQa1OmSfca4tPWI9KaJajC1aMMR/b+g6JoBGRirujCBIiqpF5rmDG7BUqxCQEFUqp5JyxWtCgTLmC\nFGp1ZPEGRBQzp2THxgkT2rWmGdEI0oRy1YCpM1XhOBXmKoQ6t9gHTnWlulJEqVPFPHOc78BAKajW\nphgd0EgVxUvB7fVGw7rsRKTFNOQSDloMQAcRX4wjOaERiCIaEAntnQXiUW3XSzEQgxJUUAUKWHWO\nx4mUKttNz/X1NcVnGDqm9yCjsZOZG4UQFNXmyT0yNZ/anXCSUito4etzwwXtiJ8eHToNqDWDPKuT\nY0Bi4PbdW/7iJuHTxJ8+3PHwMFPMSNLWPJfP4Qvty1AacHYtToIFHvV8ldaPYB55ZIg2uO8ZuH1x\nbV8mud3wJx1oiuSXaFXWz639eRQM9xdk1NMxXPzhZ+/nLJUvNNylme5rn8+L/HVNEF1+ZLVyXjrt\nU47qxXiWh219qGoFvFkw5s2mExYI5KR5HQ2gKk2p+IVqulBWz/SDr39cvPepvn3y9foFsg4XpAWR\nRQX1NeMI/BPW1+e0oe9JsqUfbumvtmiAmitSKl6bwZKr46VgNIuxQQzWsl3cAcO9Yjh4pdZF/boQ\ngqDSFN3ZYGowlVlhrta+h4DFFpcopWJAcKF4g7Fc2z0RFFRQjTiZXAtStClRXwKrNAirgT7LGKxd\nN6yZW4sFLd6OVytUq9QiWKkEcVJo66YPAiq4NChH7PUxjVWuPDL4Lg4tM3UB/5zsBk7GhIAvz5+o\ntlijKrELhKCIKIrjKosSaEliboIOA8m3dNfXhHSPW2YqzlVsGldFT9BWi5PIo749WvLPluD5jfX8\nFbhOwZkqjEXIKbLdBKTrePvuDbfXkTLek45KGgWr2mA7fT5HT9sXozTkYoouxZe8dKc/9vriP1/S\n57gQiG6GV1sE2yoMF929Cr3mt7YjJ2uXs1B9lG761Jn8XHv8YswvWhOngV84KJfL+/VtVRT66Oel\nL3g8tx/rhMhZAcGSoWOVYnayjtRbFlATNCA4qoE+AAbVmrhbdaXZ+sA+0RqnyOXar8/xNB4rxef3\nbXl3uZ4ulqlrswSbYvvE13xGe/PuO+Lbr/ndH77hdvsNeYKf9nd4mZFaqLWwm2fEKggYAZMmvdQX\nCBejmKPePPOyKBsJTl2gnJhC8ySbeU2pRi3OXDJW2zWiK/NcOM6lBdNDInQDQTpqKZhbE2IIhIQH\n4zBP5MmIqrhEgjc8vuSCmzUPOAnZG/RRFqWxpqc6Tq7OZE6plXkulDzh7iQ3wLjqA/2bdxAimYK/\nHp3iJFMWD+0EA13cz1VRrM9bNcfMoBinqI4oQRWJkZh6Qgxs+0T05hWKT00mpEgQpVjBg3P1zbfc\n6jcMwxV6yOwPPzHeHXkoLdieUot5KFD8vM7k0Qhe8PgXJbw+X7Ks2yjNex9C4qEW7t1J2y1/+d23\nXN9c8+/+h7/CPrxnn4/kyZizgwkpSlN+sj7HL7cvRmlcAjbufoo7nGANLidr+f8lGb3O+YsPuC8C\n6EJYG9gJspLV51subgtodeGZeHtX7PxdZ2vYn/dx7edHxiyPVsLzTr/oUTifhvA+o63u+vnnuSP0\n0mdO3/9U2UnzIgQ5BdygwS+6aKMYmuXajOSKuLW6nJPyFeqi7EX0rCyeQWfC4+SDi35fvn2pbOXx\n2y8Ocb330qzo80fb/DxNFfi17atv3tJ98zuuumtUjd1+x/vxiJRKCOBuzKUiVtr3RyXEiFfBrQlg\nRFrNRb0YiQAotVoLPnvLt5dlTOZtQ5NShVrbM1BqYZozuzmzDS3ITYiYB6obY56YrLTpCAEzw4qR\nfYaYMIvMXrFqTCUD7dwute8w8VONyDgWzCqo4aVQZqN6S/3MpcFpuRpWC26V23kmLSbvbvoNsqee\n/P/owIWBuMLTthwzawrXvEFncoKTlNAlUoh0QVH1BgOV5jWKtkByLYaVBkNeDwPdu2/5/vYNNT/w\n4AfmJbkAF5IILoLLGdZ+5mmsbemrQYsrLXCkVcdEmrfjwuxwTIGCsH17zZs//IGvbq+5uvmKP/7z\n99yNR/a5MOKkZR0F4Zkp/LR9EUrjFMRZoIzLModm6X9c6ja5cDHIRZivTVhNdQEJaGhCK4SWq21e\n8boEooJefO7CTRRp2PtiQVu15ejaw6WfH5vsk+Xw3Fx/pCueaBxf+rEG6deYhn/quz6zPcJ35SwY\n2xdfXPtCk5z/vFjQ0uywKAFDKWZMeSYEJSik6GyHZkV1fc9UlVxhPBTmsXA4fkCvNrg3azCkDSLS\nFPNlH/x5f57+9URHvKgZPqowns3P479fSsz4te2/+x//F66uNnQJfnj/PcdyJGokxI6YBtyceZwo\n4xEDNpvE9WbAHKac8ZoXa3IpzDJv8JEoMQWKCdXWoGqr3cjmFHOqt8yfYoU8O1OeOIyF3XHPVZ7Y\nTIWr6zeoFQ7jkd205zhNWDWGvidYZXy4p5SJmBKzXFM9UIuRKaShI8XEdXV8v8eqUZkxg3FuKarm\ntQWOF9jSBUwCWGGaKyXPTIeJoj+wvU2UfMN42L1y1lv7mOH81LNHhRAUQkBEmKbCXAvJnZgSfdfj\nNbS6B3esQgqBvlM8KHPdE8VxUeZqHKfM/mhEFUi3vPnurxArsJvQoHRRqdmYijWl5M2zbl7lIn8u\noeq1eavvCA6gJIXZhYow+5K4kK7p3tzw1c0tf/3v/oLvfvdviREO4x3/+H7H8a7gxenFSWmp11Hn\nlwDpL0JpLKbzY6t6ebovPZBzexIOfa6GeSRKvCl0UUG8uW5BmzIxWjUkCEq7cbKcf7qKNLftUkVc\n/tUWnj/rZpv+s7S5DPTKE5foc1TASbf4Z37gF9oKKa1FP2tY5iNm+OPPLv+rNotQpaX4FZzZCtuh\nQ4OwFecqAeb00YgB5qB0OrAT+OGPP7Ab92joueoHbmLX7tEKr6zz+kKfngoC4azvXlIal5d57twt\nD+YKYywehy+Wl8pHpuVXtN//5V8j+chhfs9xdEZRNiEQvIJkcGM3Hnj4cEfJmeurDnn3hloCh3Fq\n2W6i9F2C4hiFqBENEEXQmPDaLGKjJRZYMao5VYRSYoNdysw4Zw5TZXc/czyM9H3GCYQYKHNhrhPj\ndMSKsek6oji7Dzv2465VR/eV2G1awkMQNuGKPvaoFfbzkalAqRkxiBGQZn2X6hQ3BCOEQGpDwfLc\nBOxhx9//6e/YXgWG7ppUDq+c9Zfac7G4JmmEoHRRyCHgKky5kktFzeg6oaZA8co0zyxuGDVHUgqo\nwTwbuQpmghMxU/a5omNhkza8+fZrYpzxfGTOhSDC8cM9xzEjOL0IMbWguF7a/KsnZL6Ky6W1ZPmW\niNA8+RqvqO5cffMNb7/799y++4rff3tNF2/Y7e/4l3/5I//y44/Uw0jKzejoZE1F/uV1/kUojZfl\nny/W5wtNfoXM9LNAl4vr+ZoZc7KwZYFOnJOguvx29yXguFj/FxLrJav/+TuPBeBT/fiCXnzxCr9Z\nW+Xj8vdl7oG81J+PdGadO1EhAMGdpM4mQjUI+yNzntBiaAzklPB+Q2dCChN5PjJPla6Hq77HxZbL\nCyJ2gTMvANYvpOC+qEged/nxsYsT5BLyuvAuPudB+qxWDPMMlundmYjMNRPcEF1jFIU8ZfI8Mwdj\nLluqQTFrXoZCdAevmEsDoQTMmqXYHvwWrMYdKw0qdGnvuUEtFcsNbqplJtcZ98rd3cDV0IM7Nc/U\nacKrk2nW71wq42Sk6oSoRAcVX54bw61SzahTxkwxaYq/1goL1GWurb7DIJgT+4AFb5aaGNWMaT+i\nlbY/4CsLKj+7+fl++3rzVVFZ6imsIlTw9uOs2YFGLka10Op6SovjWctnbwF9q5RizFSKdFjYEq5u\niLk2b39/oGah1taBZog5CkvCwyo2zlHURXe0UgBpsZCiEWJCNteIC8PNN1x9dUO62RJiaDHdUrAi\nSGlxKFsMImiezeeQEX4RSuPkZZyezNVGv3SVLqSYXwoHeXTMnwgAx09YMNL0srtRS13S5lo5vUoL\nBlrJC30CiLQTxFuQb8UaWbyOpw7OZc9PbbFg1vPXtLiPe0pPrufP3nr297+mXaYbPP3/8sVL/XwM\n3SigVAQPLWAcK/RRyA8Tf/9f/hP/5W/+hvsPP7J7uCPe3PL23V/wH//X/w2Jzs8/fk8ulc2bt9y8\nvW7UGLQCMpc1nfBlZfExZfB8rL9w7KlbIiuTpzxSHK+NaTz89CeszwRrAdAgkamOuEMh0cVIiAFS\npeaRw2zM5bpZrCGQUiKGiAYl15lSC6VWpBpzHZsA0kDoI1Ecq4Xj/gCpQ0LjOppL5ni8Zy4ztSru\nlTEfyHVEOpD0FWrOcfcTD4cdZpVhs6WLiXEu7KYDyYSbW4UY0cU1z+OMSeHD4UCeH5ChQ7s3RAlM\n93tKOSB9hw7XqBjVcksASFdIiITtli4Jcc6Mfs+UK/00sXllmvOpfexBk0VfLfbiGttQDYTQ0fcJ\n94rbjJUJ8YyoL3LEwZZiP6vtw9oyqIarDd2QGIZI33VIClQV2NzQqXKtEa1OEMeycVcKdpyZq9GV\nBn+rnhXEug5VW6C6VMgGaGwwVt8h6YruasvV7/8tGhO3v/+Ot2/eoGkL7JjLkdkyloRh04o3pSgB\nW4r/OHsxn1jqX4TSOG8leL5xq8KQy0wZOJnB5zjHR/yUS+jnstACX5RGRmJoHsMikII0t7lizXKi\nA9pNe5R6+gRWeqpA/PSunLu8HDkpjxfgrMej8dNnfQ2KXeZhv7Ktw9ZFQLbenr/z2fmXBy5eBBWK\nQXEjeSPA65Ki88Txp/f8zX/9r/zdf/rfOT58oM5NyfzY/Wd+/Jc7rq+v8bxn880fIHaUYsRaW2Zb\nOBnLj+Lgz5bCL43z4jOX53/yuRBOyQF+mqvPST3+dPv5w49tnnRowrZTJq9Yrbz9akN300j+jhIo\nKNWNPDt9FG43G7rNQIwJ9coUjJJhdMHMOU4TITghJAZaVXKtmanu0eCtKjnRqsSHQpSEzpFxH4nz\nAcR5N1wRauVwv+fnH//E/cMd7s7126+53vbMNZErSBDEDMuZGCtSYZpHzAq7acTU6MUZbMJU2O0P\nZNuRLLENkSDgXhjHA9WcFHqSBrQbKEPPpg8Uy0z7e8xeV4V/aqtHfDKXXsACFjiyWiMijJuOzdWG\nKAXqSDlCrRkRiKFrntNSUCmiaFDiwhV3velxnJQim35LXCrgzSpBoYsJXFCc7e49D+WIyo5pHFua\ndTU2cYGOl4dekZbqrMqMklWoaQOqDG/e0F+9YfPmlrff/YHUbYjDltArUZ05O4fdnsNuh0wznfji\nWxit3LIlTHxOfs2XoTTW/09BXk7exHkQq+BfTWB/JJLPPsnjR/sxTcdZVLRsFEAiTgu8Kkt+n7Xj\noou34mve+rmPjwtgLhTYC9LopMCeuAprfz6l/s4Vn+vnV7/0hZN/ZVucoEfW9NPjH/9k+9+XhTzV\nCtosp+DGuD/wsN8xHnaYjSAV6a6RMlLrzOGnf8CndwQyOtyy+aYuRFvGkhR/CjFcTt2/RnA/9aQe\nqfTlzaex/6ew1G8BT+VpYtLMvkwNyugnzByrTtcVutgGejxMTFNGgjEeR7wXYor0Dkm8rV0NhKSo\nNyF1sD0i1jiqloEZsljMSohK0gguJDuCG2rKh1o4TPct/jFdUy2yP+y42z3wcLgjiLKxtxiKSo9w\nbFCLjQQNBDO0Gsf8QM6ZrEJKG4IEqo/kIoy6pLrnTM5HNHZUt0Yb4jNFElsNuCsSlRACXlrw3+y3\ng6eeOpTPjtG8DmsSGmIgdv1C6JfJbmQzNCiq/RITbAJeROhCpE8JlZaRiSghBjpNS/qzI0EIIRH6\ngSXVjdtvv2Mej0zhPXf3HyjThItRxAkqJFrRowqU4hSkxS9iRIZtSwG+vWW4/Yr+5h3d1S0x9Ujs\nQSqIYW6UaeI47TmOe0ouSDbUGtvAisd+LOfosn0RSuOUGcQqHJvGOFnYp18X0vKC1uN0mdO5XOBU\nq9XY0tB0SRcqBWzOhORIaDiul4kT34+v9QUNjlpzl93OmR8nQSNP+nD6tY7hsYQ/ExhefOZjNvQv\n4Vf/ynZyd08pt48t6U+uHVlU3eKhmUPxSsIJAuOh8vM//Ynvv/9vjLv39N2m0ZbEPzA/fE+ZRvJ0\nh1vBqxCHI1Gdr646tkmx2ipYnTN2+wid8st+nHv7fB5fHos8e+WPXJKT87munYv5ek3rNhsM4Tjt\nKRPsDjNzbvxPx/0VPww7yvHA3e6ObJWYOnZ7Z0gd128nvr65oUsdKhWNQhcHhqEniBL6gkkmeiLM\nxrGOlFIQVYYQGUSJg9BH5ciG6VCYx4n3Hx74b//wL5R54o9//MCQbpk9szsemOcjm82Gd75Bw4Zh\ns+E4ZaZSqflAnzZ0JOo8cdzfMc0T27fvuLl6i+bKOP1EsYqKUCWQi3E8HJDBmpUbhWEYCN1AQqnz\n3KgvYqTOyuyO/ga1fXBhIPlzA8mX9bxmb5ZFEeJO30VwJ9eZ/QSzQ+gCV11LPOi0I49HXJwQI0M/\nICqUkgFFW/VqW8dmxJBIUdh0w1IT5tz8m3/P9eYN+x9+oHz/d+ze/8Q8VcZqrfpfnBiaJ7D3jGlA\nYqLbDFx/+x2h6+luvuL65oZue0XXdY0dO4CrY65Uc3IpjNPI7uGB+TgipZEnhqVOqxXXvmR6P25f\nhNI4GdHr68dHz1YnF7pAztGDU6D7sWty+sAJnZKmVUWUbJU8ZzbRuOoHFEezkOeJudL2LAjS6BS8\nQVqN6uIsQFrV7nr9p/7C0jP3C2H21AVZxvGky3KqTziP+SQWHx96VTspjqW7+lRvPXm9znazmhqo\nZeZgFS8jXVA2Ubm3kX/6p3/khz/+v+wPme3mmtQwCe7njgJIbGmNUxEOx8o0PhDd6BGyCNmawNOX\nVu8j7XZ+cdLdTzXM06m/fL0G2J9cd61jWfmIPqNQ9hfbd3/1Hwg68XAoHPd7/nSYefjwM3MeOZSZ\nu/sju3GH1AlBGeh4fzgS45EHKg9js+4xYdvD1bClT/dtz4t5pOsbbcRuv+fDwx21Gv3Q8ebqDUMM\nxEFRA7eZ+92Bf/ppx9/+3f/NP//9H7E6E+QfGbZvSdueqJtG3CjK/uEek8Dt0Likai6UXaamnxkl\nsttn7j8cyGUkz5Xdh0yp4NMHWKq95yIUa3Qlu66xWHcpQIEQ9uyLkPPMcfczed4zV6e6/TYetTxZ\nEk9czDVLqXirezBz1Jpi0+jEEDAcs5lSjCo9NyJ0qeN60zMqzDk3jy4lYggECRSzFkzX9VkRas0U\nU0Y3Yl6AEx0YwzUPw0wdvkauCqEviDYyS1OHLqEaiIeZ2QPSDfTXN9x884aQOvrtLX3q0JSIsVHg\nd6qNF8uVqk6XjD4awQrBClRvJJgiuJ1jJ7805V+E0njcXgQRPnmmwCfcqrP082VBII7VVnjjSzBK\nBTQKjcjUcLfH37yk5+LGiwrgdB7PhE/7+JMiRb84/6VrfKK9tkbjo+0TJvmFaH4Eqi3wKyB0Ki3A\ni2LzRM0TEgIhXhO7gNeB3iB1RkgBD1fIvhJjxEul5krpnWoXKOTH+vfSLXjxlshJm8iT8/zi2KPr\nyeO1dfrjldPe3dzQS4/1iW7YMXb3QGWaN+ScGacJiwX1gEik77aIt1ibilCrQYU6V0IByXBwx3Jh\nmme2m57gcPdwz4f793h1+s1A3U2EEIlJUReCVO73B37803t++vFHxsMIYsQgxLIgGgvfUnWYjxPo\nnjAGDlNhXjiholUwYX/M7PZHcpk47jPmI3M1VEZUhU2XqBXKwnGVuxkRaVZ8bph9XVJ0p+OOOeeF\nhub1caTPaX7xs76xVoE7i+Foi4nqjltdjMgGQUkIjVhxCQo4Dqon4oITvY4qaqGxTC17d6wixWqD\nKZFIiBtUjdRvmpchxqYfCCGgsdAhEDu6YUAloizcWEuGnNWW+GPSCmjbexU7MfPCGtA80Yb4c3v7\nY+3LURprp59gy6fDvzCil9CLl437pbI2CCEJQ5e52bSqTTsaLpVCC1CxuG3VvNG2X8IXL32zX8Rj\n1pvD+TMn18/PYv/TOuN8J58N5TeoCFe5KO6Ty+ygR6b4+onTZ9ddzlxADbpOkCJ0QehF2H3Ysfvw\nPfP4ARsN3tyCRDT13Hz3O0Ic6Ice7wfu7jN22KMRpnpkKsNCbNcjgaUI0Z+P/4lUPyc+POYjawef\nYE8XHz17rhdaShqHE7JAF7IkC7xSgt0MtyR1tpueY+xxCVy5MpWJuVagVZWnEAAAIABJREFUMNuI\nlgkNEffIw8MRUSOFSI1d22FPjIM7u2lmGidynhHtuI4RMWNXCg/WqMVTNn4cf8bnmflYEWtFg+Ph\njp9/+pl8/0DQgGhgu+3pNwNxGOi21ydvcj8dGKtx78I4ZnLJBIUftdnoBWMaZ6xWfJ4xb7vwtUJE\npesi1kj+QSMSOhRh03d0aU8thZwLVgulZMRa2vZs9cQm8Nr2NHn/6eq+hJGFZlSSDTFHiKgvJI5d\nKwJum1QZZS7U3LKPssBxrqTqpJiIKYG01FlUWi2NLhiFOE6rRyomlG6AzRYfrsmlEAzCZouqkNQJ\n/UCMgdvbgA5tt44yGqIVYmDYRrBWkGjalG3xgpVCzc7x/p79+zuO9w/U40yStsUJ1oxQ+4jcfal9\nOUrj1NOz5bcWVj05oR325w/xSbQ8EqjtqW/nLrUfKqSuo9ZMr5krKn1QjqXwMM9QI2dywAuptEBg\nsrxucQ9OqbmX1ugjb+LUk1/jI1xCUSvZm5/gttf6Gpd5JE9GyuO5PgvTk5xectjb9phOHwPBE8wz\nk028v3vP4bin5gl3MGkpBhI7hpt3dMOW7dUtmjpsK8w//RMxVfI8k/vaxh7bd64U1i9XqZ+n/Cm8\n9GSwL47spXMe6SK5mCN5Lnh+bZuPR2qM5Aq7w5GcrUEYJvSaCKHjukbMBwxnLE6KRorC1dUGT1vc\nreHkolSB2RSrlV6V7eaK4jBN7yjjoeHsaWDa7yjTA7spgzvDdkt92NH/6WekDyCRGCNvroaGyQ89\nKW2YvTHcpui4KiU73XEil4JTFkYYoXNjWLairQu3G75a4xCHAQh4rW3DpVpxc7QHouKSsJqpQC0L\nj1IAPPAbhTRO6/e0zh89q34hZQTMKXNhOhwZ73YM4YpcKqKRbb8lxtCyoWjV7rXMDUIPinmlLhsR\nRg0nudNYJULzsK20NRs6RANxk/h6c83m6oqpZOZxpJbMIWdUnUEi9JVsMAw9N1+9w4pyzwO17hEg\nqJ7kgkpjsFBlqcWZOBzvudv9zP3ujmk80psRvdHF/1r788tRGkt71P9LK1uenrVSacsvStB235pa\ndWkLOWhjq4wCHZUOZ/RWhJPdMBr1xUUO8AnnbvK8QVhrkd+l1fpZY3vxnbP986iy+Rks/yt8yY+0\nU9bU5c+nzr/4+0TmJwLSKnurR47TkToXdg87ajFAEQmYRKo1azl0PRoiVZpLLkNHTB3BZqyOZPeF\naTUiYoi0YOhaWPmSuyEXvz6OIr1E4nLhYVxecZmPR4JG/Nk+XL+2HY4jIQammtgfR2avZBNcAuKN\ngkVmpRahuHGcM5VAqAGvkT4JaCSlRN8HQoyQtlBb8d/QR6o4ngPBbkAhEJhvN9Ryw3HMLT2z35LH\nB96+uWEzgIaBIXZsekgx4CGBO0UC5hDUEAnM2dkfx7YBlGeEChpIoniZ23apsqTDW8HKjLmThgHi\nBs+FaT4wT4VqRh8gpESuTkza6k6GiHijGp+nQv9a944L2XHxv3CGQBv3o59ie9WcOVdCLoy5kOdM\nzrUF7lNPiomUEuhCbEiDeXSRKRoUDXHhT18JcZpbLysjsBsaQktR7zs2fWSL8PDmhof3PeO8BOSp\nzARYhPyAE4NjAiklCqHRsdSCrISgC7FiikKujfGilplaMjVn6tRqe9akgIaQ/IIAuGhfnNIA2iDk\nsQB4ZKOfMKBm6T758LOXq8di7ieW29hFUpfotCClkanlXCmuVCISO2QJYDlL0dTCP1VrPdWJyKJQ\nnqUGv3gHzkrwZcl2uU3T+vulEx/7Bf+atnJPNaLB81VfPPfyD28B87ZfhmC1oimi4twd9hyPIz/9\n+M/MZcJDIKQBl45pOhKuA6nr8So87I8UMtxs6FSw6cD9Tz/z9vrfMAwdsetBJ7DKsm/ck7jV41Tl\nRwWHl+9/ag4+45gsa2fl6XpNm6YjJRdK3TLnGbpIvxlwM8ZcmHJhLIU8NX6zORdUjDlGRmCYZ1SV\n6+u3XG0HboYN3bChoox1YZ91wXUiel0yYiqmCdEb9KYNKAVlnK/Ybt7yZrhlLPXkHURpmTaOnTLr\n+r4nBGWcjd1xbMdl2cRH6sLj1uh53Erbe2SpE8Gd2A1o7FuUoOa2X7itzLc0eGYulFoQMqIJ08jD\nhw+vXOXn9tybftwCjZZdgi41C0IIkZvrLVGEKWcOc2TzVhm2A4O2VP3ZrGVfitB3G7aboQnsoI0f\n7FHMbKn98aZYG7O0EjWgIRJvtvz+d7/HpiPHac/hMLb0W7e2m2Kt5OOekm9JXcebNzeUI8DCNOxG\niKlliSJEM8p8xMYDdTqg5UAoE7FkpDRITUPjq7qco9OLj7QvRmn8ouH8ESH7fHQv2JpP4BxhsQpC\nC2IZSjWhLkWdiFxwHy2feZaC8VIPPm7n/vJYLg+9Fnz6zPZrnsgLK81p1aO2xGZ02byl5kKZMjWP\nS8CtYbYsVeOyWHRm7cclLAFXw2omz7ndGxFiUMpy3y69rpNK/gzI6fUC59Ld+y1aszRb5bagsRHf\nNRpyKLW2DDZbgqxtg+dWjEqhGA0D7ydK7SnVCW1fpUZyJ80oKnmmlNJoKMzwWgh6zo6bBOZayKWV\ndk05NyPAKp2GhUHBiaFtQNRUZlgc3HXvAMXcFky8sb+eSC/XDbjw07ks+L2ooKST0kAgmBJCRmtG\ntSN1GyxErBj+yt0SP+uuyAWkE1Y+tkBKHf2mR4loCNhUsdIGvBqUQYTYSMGIKRJCXArylvvdJuS0\nbm2hXK/F0FBhMUINxdyJXcew3eIKtTbG51omhEb4aNWptRBxQkqI9Q0GrKV59+umV2aUWilTpswz\ntRSg1Z7pQqTma12Gn+/U5yz1L0Jp+JMuL+DTk7NeGM7pI34W9hefO1mgfhbGKm1RiIGbMJbAXYWg\n8GEWDkUoC9Wx2op2Lum2tsQxrEFTp+DRixqvvXepeM6F6a3j8uTc9aQmKBen1pe/3R7XE76ynYLf\ny8+ld/rp71g8wGVP6pILddNRzLl72HH/0088fPjAfGxBzLS9IXTXWNhATIyHHSoR6TpSVPohgBoy\nT1QOeFRSnwhdwGdf+HvklCTxouf1iQ6/BEl96iNrQP1yLpoi49XwVNxuiVKQzS3buSeEti+GmzHO\nE7UE3m87jvtDqwY3bwoEo9a2x4WqUsSYs/FBKtd5jwehGzqkKpaXGovjhFkl16blQ1CiNEFf8oyE\ngBPJvmGqB3KZiCngadMUhlQsBMJyr1wi7gUtLcpgLky54NZUQq9xYdvdoNGIbiw7UZNSREKiGsuO\nkwK14l5JKTa23FopJROZCGmghoBvMrWEj87nr2lnmPEMYq6CfdXTmVZE56JsrrdcvXvHm7df47Mw\n1ZmajapKlaYsGiNuT6JBtX1KpBRxaxxi5hUxRRe681IrtRZKaQJ9o9piH26QZ8zhKibCt98y5om7\n/Z78cM/dwwd29+/J40QtymY/Icy8SVfosMWtUvLELDNBwWl087vDkf3xgemw53jYUw4jMhdirW2j\nJjmP/1Qnd4J5P/5QfRFKY9UZj6yThYjrsWB9MpAVk7wQ3i9xVZ0s1QV3BCilEahlS4yxWU9zFLwT\nnIBJo5J2XzIpTvETOwsWPSu5c3bXx9ME18+dvInTAhZWk+1UcX6aCWclR2s06X767GvbZaHT49k9\nz91jHq3lMZPmvldzKpUYnL4LVCtM05E8H1vK4OaK22/+Ak9b6lyAsW3/GYygirqh80jJM7VUvEyg\nineB4aajPnRYyRRv6YhrQeUj2f1xff3iHL00bZeQ4NlCbAfWWI/gr86eurq5JvUdw/VbmCam6UCe\njkvcIKK+pd8Ejm/Kkg7ZUfc7ShkZpyPOQtMSEkGVeR65qzMizg1v0ATZatv4yGujQg9KVEWjEGLL\n2be5UdO5BkJM9D4QirDdbhiuvm5jztNpgyfzJaakSkqJ6kapRugDuBIChNgRVAkhnYwQXSy2mBKi\ngVIqNR/bboEqbYMlXxxRAnih5IUG3gtl1Ui/RVs1xhMSVBFOG1xlawFr6TrevP2ab3/3HTe3v2d6\nONB1me0Whu0NKfWnrVax5jGusVVbDMu2z3qFBQZ2d3KecazxqmGtbkwhCi1OVDK1ZjRFtinQdRtG\n6djf3XO4v2M8TIQQuLp5QENk2FyxTW1oKUZElkI9QLx5Jk0illZjshSSnmIZcrGHx6UA+IV1/kUo\njVVMruR0Z2tvzTd6oghWYXshQJ7CT1y+vywYkWYFuBnVG5JXAlRTJIImJTmIKx4WigB3WnpcXYJY\ndk6fXfa1FrGT1oZL7+bCg1o8IvwMrZwD0IvyWWCB1t1WcAMNBmoFf83qXJk2X92eeBePYbbnc7l6\nTUGlZV6ElqaborSMEkCsET6GkIhpYHu1hbBhjpm55FbUp9pw6yLUnJeiKWcaJ+o0Q6lsA2RVsrTx\nNzui0b08UnEf0wK/fiouJmL1WC8yp34LT0MDSZVtDOCJUpRxWRNuAgGGEEl9IqZI6jbsFOYsuBai\nKDEGUkzUYsjUkjbcnJQLnTjFKxK0QSUKHUIQpQvSdugzJ0mDnFwCNQA5UYOzHTq22x4cqlTIlVIr\n2etCTNiCvG5OwNmElmUYZFFIMbRMQudi9mhwTxAiAbNWk+PQ9ht3X9Ja27NmXqkuuLcq6teSRD5t\n62N6rqFozYAsjdEgdYmvvnrDzfUbQkhNSnbKEAb6vqdLaYGLGvO1LhaGOYg1A6+UtkuhLNc87WAY\nzoaPuC17cjfWXKsFK5k4RKJGutDjKVO9cYsdp4mYemYz+rrsimkLtBYE0Yi6gCzZWerESFsP3lKY\nqRVZkk3wy+2HPn+evwylsaSuniztRVq6n93IVVRAE16P9reQy4OXlv+yN7UsG7/jLXvAawvSLZZb\nih1ikGLDkYsZVUFWnFgL4gsHzlIZLqLUUxXlsrkTsnBULRaa2dkLQhahcx6Jcg5GC45Jgyqa56u4\ntqIckbIIsdL49G26GPC/rglr7YGfdrhdld2lMmGdY4Sga8kTxNCEQkS4iULt2u5jYhWVigxv0O0b\npN8QuzcEc9LBKGWPiyNxwoPjYUuxCtPET9+P/PEf/4HjceJqGJA8cZxHqgi9tLqGvHZnHQSP+/oo\nM3f1RPnICc8elLPSX39MLufqdQJsnmeO+yPZILnwMGf2xxFoFmqsQuxgEzpi7LgaBmKtHEalHg5U\nH1GPfP3mFi89D/sj+7F5wqUaZdnjoYsd18MATTVg5gRd0jJpyqsVwURyydh8pCCIx1akBgsUW1qN\njIRTZhEpEjwRFKI2y9ahZQwtSiOXtp1rKbUFxa2lDseg9DHhOVMxKkuqelv9p7lPIWBBKQt9z2ub\nXPxcvnmy46TtP1Ja7SRx6Lj++h3p+oZ9gRJ6wnDDm7jl+uqaPkWCNxr4ucKSH9zqTKYWkD6OzUhC\nnBRX10sRK6dYwlSnxjvmA2IQDLxOaGmU8XGzaZXm3QYv1tCREMAr6nXZhji0/0WIlMadZYXqhtRM\nr4JFIdWKTwXP9ZHxc0JInljan5IuX4TSUDUCbZtJlVa8JKs5AJzurPgJbRN5Ad1eLJfTZ2iub7Ni\nDLdlW0qpaMhEUTqMpFNTECYYuZX7S3sQgjb3Ppig2uIYZs1nbkV/DmFRYOaEuHZjCfieNd2Cyzsi\n7cFsOLkvqXi0B0daJbrowjq5Vqc7EB2XQp8q+sqU20fQywrFXLx/aqurtr7vsvKsNfI1B8+FPE3M\nhwN5GgldD9uv6DY3dKlHOrDZKLStP1NX2d52bK4H+s2Gn+5vOEx/5v7DPX/7n/8ffvx5pKjy1bZR\nhW82m3bfg6AmT32Nx8N65Bq1X5f7QfsvCKH16Lrd6wnd/Q08jfuff+Zuf4Sff6CPPTn1qFuLL1hb\n+8GNihDNMW+0NnsKP+1G8v4HtAt8/e0bNt0txQOejDJnxikzl0Nbj6aEmAh4qxUIS1bVqmRjAkKD\nGF2YKszV8MPIVEADSLVTDKWLEaExvDarVum6HtW2Dt29pY+qLjBJ836OZSKXzDRWuij0fSTFHkQR\niQSthNg8nka0UMETFsJJazcKjle205r29eXZu8dPGZB486L7rmezeBP7+UgUJcS+ZU11aamsD41q\npEjbS722PdHnmjFzxlpo4W0hRqOLkb7ftkLMUAka6GKgS4nbN1eMx8qY65J1aHRdJGqg71rlNxpw\nBJtbtmdSZ9NFNkMT4c1AVajLbo01czxORDJSK9UK2SrFnUDblwUX6vpsPIGnPvWUfBlKA0cXWnJZ\nHvIVF10xGpdLwQXI6o2sg26K5YRNywowKOItRVa87cWMg0qgUyOqgecmBKvhecayUFWIkpAQUS9o\nnVB3Qoh4aN9ZvWIKLtYoAMzQ0rit3M6b0Z9vwcph1cBcobnmrksRma2xC2vUALTgu1ldqAcCLkry\nwms9jbU/l/UZJ9js8owTBcd5DGuWTAPMWibIbsxMh0weZyCgsV+wXmuxCAVigweVxnmTJLK1yrEX\n5tQRcEoZmaYDH96PbDRxc7Vt8Q+RRdjY2o3LaX1xlZ/Fwvn48xTtx2dfCphLaOq3mO06Tuz3O2Q3\nM/VXTbGmluLpZqwsAmZtg6VSWnqrEoiuzPOMmSLV0KSN48g7qIIzUb3ty53rUoPkQkiVlJqCOJML\nNaFRF6/eFjvfrDbL1qQFhGm2WmCl7lzhY1sES0AXA6htVOStL8WxXMnFKAVKmUECUgSVxifd4GJb\n+MVaUrOptv2tFWxhHVgCHv8/NX/0W1ljWkZxo7gjFQgLtE1YKsFlwQi0GZJ1oRiivS+0ivBAIIVW\ng9JJZONCrD1KodNGv96FxJtrBe142I8tC46Wndh4zwIx9qQuElNY0habYRmCEoOCO65xgRydsoRg\nbc5Um2GaMVvqjNrtf7yeL5Ea+MXF/kUoDbyQQlosG7C4YKjQqicdXLTlUEsTpA2GkhPB2KlSeoGl\nVFtJfVTHSm5WfjD6LgBtb95YJigT5eEILuz3Ix/e/8w+Z+a58PXb35O6nmAjPn4AhOurd/RfbWnu\nqC0KQsi5LMH1jGoLFAuCppVCWVofrVVoru6sn0jNYoMC3FCsYamL8jGr7bxuQGKg5Ep8JWP0pcv+\nyNN4dt4ZIlyFs8OpbsFKZtzD3fv3/Hz3Iw/7D1Sn8fn3jbYZFVQj0l2h+r7tU313z3F/ZPSfmMtM\nH5R6s4HNhkrh/Y/fM/S39JueW2370TnN8jRb+b8uBvMLY33+4vNgj9UqXelWXtPudg/8+PAz4/Ge\nvt+gXzk3V1tijIi3xIorUXpngZzmlvaKEbzw8PAn3DLv//SO+m6ghp60GQjDwKZXQnV8Hhn3D9zt\njwRzRm1MsiLtPgRtsJSm2HbzCxC7HhelzFPL0jJv7Kq09VfWWRBv+0UEJXRta8ZaK2WqzNOh7dDn\ntP2+3cjSBJoEI8ZIaISvIKEZD9LIAkW0xas04Jqo2radjSkh/Aaxu4+2RRHCklTc6r4a7FQJ1Yju\nSAzNOhfI84yZkVLHyjHltsQkvOBWAKFLEalQzRj//IH9lPl+d8/DYcYpKJlvv/uG4WrLu6+/Zhhu\nubqeyYeB4AG3BiVKDAzbK643byizMx/3DWIvFVVt8KAIaFjiI8Zsihdj//CeOv2Ml4njOJ/JN7UZ\ngwsYy69Ny/wilEadP6BxS+dGlEYdoAvSXmppikBbcEhozKqtCnNJx5OWiSAqi0PoDcoyqPOMknFR\neoU3qSckxY4T4+4DdeG8sRj5cCj8/P5nUijc/O6KeBsYusjD9++Z7/6FSqRSuYm3iDglT2SDSli2\neSzUksFaFaiHQBcTGgJBG32AmVFyK24qpbQgVQzE0Bgtm4CuiCyC0prrmedC9UTqte2h3b1Ogl2m\n266Fc2eb7rmpcerbEjxbze9slXGq/PThnuPhQC6G9ltC2uLaoV7BDRUn1hkoTGPm+H4H84HURbqr\nAa2FTiPeRwzj/s93vL1xwtdfoarU7NRa0WgLfQUnKPLjUYrzq6dg5lNi/cef8xPXlF2O/fOm9qNt\nv9tzOBw5HGbGCdQemMdWlNcSiJRxKMy5cV91EW5vtiAFy0f+/Ocf2N994Id//sB//J+Nb/7iD7y7\nHlCHgUJVo8aZP8vENO+ZHQ6yoS8thdxE6ZZ4iURI6qg629RRQ8S6SKlz85A1rGUiCMs8ePulOFoz\nVpzjXDjcH3g47Ml5ptaCxkTqE1fbRIiBvruij+15FQJzMWpxcD0JbA1L8NtCE4ChKY5GkPS6tmYo\nnkhhTh5qu6cqC5KxQG2lVHKuJHOS6LI/vDQDqcxIiHSlbbwUUsBqJlDx2GRQi3FG8ph5//OOf/yb\nv2X3cM993vNwuIfJ6DTy9h//iX4Y6Lt/y3//HwbeDjfYt19j99P/x9x7NUeSZXl+v3OVu0cEAkDK\nEtM93WuzI3Zs12jzQDN+dxrfKIy7Q+4uuWOzHNXd1aVSQIVw9yv5cDwAZJbqnqyHvGZZQAWQSLjH\n9XvUX+D7QOeV/LdaD2y2WxKFXGdireqrUSqmCM0Yuj7QmqPGAtOO0hq7uwPzeAPzSLw94rJ2Mszy\nvj6QZd8rNX7iln8UQWO/u0V8IUkB46ErS0oCJSZqUcp99QExcm+9Kicmqhoe3h/O2tWq1FLI80w1\nFWMFg8M5wRbY73Yc7nbUHBFj8P1AqA0phdA1PtmueHJxSdcHvnj9BW9rBcmUOpFjB1TKNC5Dc0+j\n6hAxRmWMo9LfxSewFrGOkhOtZuU2FDWhcc5DdYirnBwMVSxP+9C1VmptpGkmVX2onO3oy4er8jyu\nNuCnk433tbPE6HA8lkoqlSwC1uJ8rzaUFVLN2JS1Wkp7ap0hR3I50MqeWje46pb7By42kmukeGSe\nDCXPuFY5lkJtBm/BiWVBa/7E7/3w2/7Q9/zo6/cQ8D8qEfvBlUuhUCitagWdJuokOg9oKogXWyY7\nPeZWAXzvEVOUaX99y+svv2Scv6BtzvlzZoaXLzANchopJlNbI+5GIhFvHNYEWsvMFUpKJN/wrULK\nBFtx1uKtKPLNGDrbaSJhvfq6VQ3WpakbYM2FKgWYASGVTJJEMo28mJa5rqPrApt+pR4T3uOtAkhS\ngyyZZnXYTuMeCHIattclI74nC/4M6/t/jFZP9yjNE0qvcd+yY2kdqqifUGrRNjIgYvDNKR/DgBOH\nWG1JG6OJZDrueX3zipvDHS2Ac5kSA2IqV/tb6v6Wv//NP7D5bMPm8hmX7ozYq8FW13tcMgzdwLA+\nJ9bKPE5Y66nW6Hvh9eK8c4jpSS0uAa4wzSPj8YiJEyT1zuDhaoGHkeXphft29Y/c948iaNy+fUWx\nBwoZbxzSD9hlqBHnRClFD3bvtZqoFcRirF3aPgWaEELAmIAI5JTIJTOlEd9ZhjAg/ZpjUmr+1f6a\n/e2BmiJd8KzPznB2S9811q7yYuj5D59fElYD9e2WafyKlFWArNYErRDjSCwFMR5nlp7xAptDBCuB\n0MDUBi1TUlTVyVnF3UQEi8W0QokzeUGu+GA106val8y1kubIXAytGAiRD+U8PcB94WTA9I5MRnvn\ng/6dR3/XWUGshRBIMeI6y7DdIlYHg/hKFXSwWmfSPJJ2bynHO6QlOluw2xXOaRZaK7RkqC3RqsF3\nhRYT82FHmnbUFkACpioKR1v0PxA2fmjD/1CU+Z4erixgdt2FS6XxgQeYGI9zAR8q0iypaetRZJGX\nz20BPagkR+0sNQk1z7z+4iu+/JffcP36FeOcMf/n/8589Zr0J7+g1ca3r98wEWlecL5j+/IZz55/\nwvOn57ScmNLELk/EUkglk1MFCs5ZBhcwYugHPaC8s3ShW2TvK6llnU/UyhyTVshjxneBWkXFDjcG\nakffB1b9luAdvbf3shyQtfdfCx6LrXLvkFmrogRPQobNyP17+6Eikff3/lF2dPr0ZL50evWkb1ZF\nVI13aYuWrMRKjCWXBqWSFmHF2QjGQu8t3gWVL8dhnGecZq7fXvHm5jXjOPP8kyf8zf/wa3y64Pb6\nDf/3v/yGcdzzn/72b+kvBv79f/hLnm2e0RMQ7xR6awubzcCz5y9wqwC24Z3Dh45KI+aMlEZOhf4s\n0IolFxhj4vbuwLw/0tXE0LS1qGRRyN+Tc8oPfP7++iiCRq2FWmeyFAwFMUKz+muXrPR4MaJtG7Nk\nKGKoRQdlciI0tISRCCg1v9REyZkQDN4kSj1yGJsexEkH0rU2mvpX4TtLt+7w5cDxkBjvJnKsmOLw\nrcOiWaA9+eueuBMl36Ny6omIJ6D58AKnLY1aEq2URY9/wUqj0D2tQJTgZ4wgDlpplJxUrmNp8Zja\nkGp+EgX0h65TwPgOFu30gGkfaHlxaekseuFizAL7E7yz2mYLTmc9S/vQGgu5QE16LTnr93faunDS\ntBUnDfCERRBOJ5Aju3FknhK278DqTOM0h7mH1J6Il+/+6t9ZbRnMfOcguh/YLP/zM2W37y/lKwRt\nsTVDtQ5nNfl5XO7VnIBKxFLmQpoO3F7fUIu2jYJrbJzQ5T3Hm99Rc+btqz1TKTTrOFut2VxcQBYs\nYLqBKo0uqOtbbkAd1aWuFVJWTSuJHV4SpglZCrVCqUWh4EUlNGosOiOUirGn5M3hRPvpq1XHEBxG\nLNbKPb+lNZUxsYCTBV4uyhqvDUqpC4m2PtyInwN9sPyoRx/effnUqjm9InKPBBM5mTLVZfi/pBBN\nq3+AFhvOgmseaYv9rgNcD9bTrINmAcewPuPJp08wsWMyAfelwcwQb/fsr6+ZDzva+glVPLSArXo+\nmWZwPuB9R+h6QjCE3uMs1Jag6gw2lGVvG0tbWPZt4YKYU+XUFrRYO0FxHta7eJIffgg+iqCheimR\nWjNVrOrYlwXDXaoGhSoLQkMP07ooap4argLUqNl5o5GLBhwR4WJwrJqn3F1zcxyhatXy9HJNEocf\nAsNqjXXnpJs7Drs7/pe//0/8z/K3yuTMCdcKvXM8eRr5/JcvNdDMIJtIAAAgAElEQVTFAikrbK3o\n0C+nRM1VmefN0moiw6I0mRVhBUhrBGOxokOp0uDe4KkZLIqSqMtg0lAZnMc7izHgPxiKuKC3vmcG\n9sMIo+WAblUffiM0q+0TcBg8iF9IDarXY53RjVsatc5UEuICPgRlr+aM6QKteuggXGxwXU8na7h7\nxdXb1/Sbp3w+DARf8cuhbsQsisWngPF9c5h3ZxoP5/LD4/E4WJ5eOfW6tbp4sBz+UMitMQ5rg3Jc\nxNBcwHudeYFVmHaeSVElKHJJlHTkeLjjenyF9Y1hM/Ci9/z1v/uM882a6fqKw2Hk7e0NhylRLezP\nz1nFl1ymTMmRZjtC6NmcewVSNMs0j+RSaWkmjjMtZXaHyMGBt47QeT3QK/jBa1Y9TRznabEyXeyQ\nbSD4JTkQgzcdFp2HpCWdbbVSSkRqpZiGlAwlQ4kgjZqFOSYVv0RwoV/Y/+8mAz/H+u5Pe3RULomQ\nD7o/jeiQO4nOVb21iFnAAVHnktM4YU0ld4FuGOjOzln7FecXT5h3B9bdN2rJawMvf/krPtn+CRwa\nt7byxNyACG/yDd9+8y/cfn0BZ0+pbVAdvDGRcqFS6VY9Z1bPvGBg6Fes+w3lcMU8zUzskPUZne2U\nmW8W4mwq2KbzmSZqpWzkAbb+r1kfR9CgIbVArvfSIc0qacUviCgRsEsQKLkSU8IYxVW3k2NVS/og\n6CQdaY3VekVAOBxm8nGHyQXXdWzWA33n8MbQ9R1Dv2Yuwrwy3M1niLliPBxJKZJSonOOOgjnObPf\nRW1FmROk1twzzVnY27WhUgJ5cctaMnaD4ESzzuAsxi0oMDE4VBM/WCGYha2uABftN3fatpqptA8c\nED5uNWkrpj0Mwn/wR2smYxaZcFmIWzFVjmNmPiRSyuAtNTdKTLgp6cA1zUjJiwtdw4hVMEHNi+RB\noGRDVyqD95yfBb58HRl3V5wP1/SfvqT3DcmFnBtiHXa5o/yQJ4C8FxDeSaXa/ZD3+26O8NCyO7Xx\nzAdGjVYNqUBZlE0xlly1GrBGsGIxpqgCAMLQW8bpgIsjrhy57ATCwF//1Uv+5i8+oaTG1/GKNzGy\nlZHdFNmnxP7NHU+efs6WNb25IEkm+IrtO1wQfBDW3oNvNFPZEYmpkttEBNWCigdaLUgVrFL/KTFR\n4kipWe1ca8I6D9lTFwXoo1RKlxc+hz67KSYgYhFcS9hWaTkSxxGxDlpP5ywEbdCb3mMMzG5px/7M\n694W+hFcvzYoTTT16Xvcouxbiwr9tdawiw6a1IUMWCqpZlKDOmVyOUA/MFRh1a1Zby+4/PQJ29cv\nKBE+ef45nTljd3fN/s0d6ewpdljR9ZXD9Q2/+eff85cvf83m5TnWBUpTl0QjlfWqows6++ocOLfC\nWs9sDdE3VRt2QtdZhu1AP68w3tKaDvVrU/4NjUX+6LTz78usB6WLn1gfRdBQlrMeuvV0BbXdK+nJ\nknCc2gu1NGpKyh71BsTSWNp1C0LABlVK3ThLTZkxqwJr5z3eOB0eHjPYjM+NKQkZwQ+e8/yU22cj\nx/YNZlRNe9t3hM2KsFIp6iYNJzoIFrSMbzTEgF2G2NpK08s5HUCGh5tuAKmPdO1FR1QBYZlvkUWo\ni+Jov2RzuVZ+Dvy6PPoD379d2nuvn2RPWlPmfCqZOWdijMz1SK4TBk+riZIyx3mPb4VaIzUnak6I\nWNogi6aR0FqhtkRMwhj3uNTTmXPqVJkPO97Mr0nyZ5zZDomQcqKYhl9gpOWHKqPv/PLvfe0Pujfy\no/fnj1lNRJV7BbCa7JRlqIq0eyUATMIC3ghznmhxR4t7bFPDq8+HMy5lxVRnfK2EVFlVcLFA1PnD\neHfLbr+nOxw51oTrKn1b43PPunUEA04yjYIzBuk8zjSk6aFomJG5qHpCLGCg5QZtxlABS6lJ++nL\nzWo0bPaIc0qIRdu1tWVMU0BLrXsoCoM/3tzRvKV1W/z5JcE5bA1UG8im4rBk++GChT/Vqz8VquU0\nlHcOa5TQWEtWSZXWKM3fOzpaEaoxWKtzDwTmmghxJuZENQ0bBtbDEzabc/JcsDZwSJm3hyu+2H3F\n1P8JftjwtNxhd1fsrt5yFXechY5gAyVHpqWLEqzDInTWLQmGUCWDC3ggBM96PbDuBqb9GfNmZt0N\nJKW7k2kKH/5Oc1YvXt5/6UfWRxE0cmnqIZwXNdkGZfFFPiV3csoMlj5jq0oMyqI4cDVo116/GJUG\nccbSmuGYj4yxUavHZ0eykOfI8W6ixpFW1QwlV2F9sWXYPuGXn/4FKSaO1nGcdlw8ecrl5TnPL84p\nWM2oS6YVzZrqgmFzTXBeNBN2Th+weoJkaCXSquLZa86LZMpCFhK5DyzStH3kraFZg7d2MY5SKWb7\ngW/dPTdDfixoPN5Mizrw0muutdBEK74xzszTRB4Pmolmi/iOHDPH3RUt7hCqZq8lakA3EeO0t1gW\nDao5FnZ3IHbFXe+53t1xd7dHvnnLYXfLWQAnntL0cHPi9DCriwAcJ1bJj1z36evfudhHVYk8KP/e\n+2j8DKOOhtz3x2Wpnk/bIqN+zoW4EP2EHBPH/Y797Vv21ze4HFltBlbditoah7my22eOcyNqB1eT\np6bOgHfHI12emKaETyO5jXgfKHWDyRnXZsRV1kOgt46zTYcXA6WpvLpkpnniOO7Ub5rA2qsEuCwS\nH61lVN5GE74ghsF7nHVYq4PvKokaj+SUuLv9ijpHphT5/Vc3zM2xevKUX642rFdrgqyZEYpEJDgk\nhQ+86w/rMXl1gTZwQhJVlBFiEMQGEEspjSnP2n6mYW2HWE0MnRUEi3crlLhYSfUApVBzJE+RRsGe\ney43L4hdJo/C33/1D/zmH/+R/++Lf+LJX73gxeolz//0V3zx/77my9s3/NPrt/zqzwKm85pQot0V\na/XZs84wxRG/JLLbyy20xrrvudz0+OBoTweG/gm//PNfMu+/pN2pnEtbBoEfCi74KIJGWozkc11S\nwwJitUGgsNplmLZk7eb+4lUYTJoOnL13uOXhn8fEsYzcGrc8aJbULGNNmNzo1w6/CsytMt5NpJa4\nvd3T3Y08fTnx4rNfM4SObC37mIhp1oeiCxgbyDlz2E9MY2WcRiQXBIMp4INjWDVWIRCERXZDZapz\nrVQWL99aaGXhwFt5SHlaoVaLwSy6NUr2Os6qjyVWlP/wgeukJPXQqmrfPXEftXNOrGwt1QEj9N7S\nWUNvhVYnSt4DBhuea/8XiPNMLRN5Pii2vYBg8F2HDw26hniPN0JwAQMccyEWQ6uwv73ii6+/JOfE\n87MniAl4AbtQXAuNwr0/2ncCx/vDve/K7n93nZBkVR7dnz/wvv7QqrXcVxalymIwJSBVDy1pWDFY\n42i1EtORed6xv73h7etbbJnJMXN7c2Dd7bi5OfLN7sDuMBJrpfeejEK2lZFs2PaOtXfYeqTGPUQo\ncc/xcCDGPdPuhs3KsupX1CcXrLy+v3UemXd3THHk+uaOWjL9as3T8wu6rtN2khhiLkzTSFxUiE16\nzmAghEBnCiVnxuMd++OOw3Hkzeu3jNNILonf/PMdd7Fy9vyG889+hfdrejsy257mFxmV9jO1p+Sd\nD0v++TC7qiIUMVjjMaGnWUOlkVKmLN2EvOjAGcwyKBdFSxlLzQWmGZdnyrjj5vYVxwKlObaXFxzH\nHd++/ZLXX/+OV7fXlChszczzjeWT8Ix/Oh64vtnxT19+yf9kE6tgSfsKueCcoRX1MxGxNFnh+54n\nmwvWMqmx193Eb1/9XxQbCJ2j6wf+4lefM3/5KVf59xymmd6oLJJFnR5/KMn6qX3+UQSNe42mpoeA\nCMp2tAbnnKKJWlNTFrRP2hZ4nvIYTj0gIReFbx7nmRgjGIdxlt4PajNaKjSHxWODR5KQXKNlqLkw\njkcOhwC1qtpo55RdXhqtKJbeWK8qJgtuO05xEYsDUw1d9giN3nvt6y6zjFpUwNCIuec4NLPobFnL\nA1a8Lm+cavHTGqWiLNC28Dd+dqbsqaTju20d3j14m7AMDRczIXvKhJYg5DwuBIoUfAi00FFyo6VZ\nkWNF4ceq7aVoFCxY0cG5dXYxxHHLDCtxPIwc1xOxz4QQllaepv/t0c7/8XDwPV/9nggjj7/2Q9/3\nr1wqemgeyeEs/+ZSRRrvcBhqzjoXyokUI3NMmJI5jJHxOJOmRJ4jeU6kOdGKVubWGqxRQEVrRQOT\nNCqJlGZFKM1HDje3HMdb7t6+Ztcb1qsB4p61XzSO5olxt2OaJ65udtSS2WxGbKl0XSA4JbGlXDhO\nR1K5Hw8QvCWEjkim5MJ+f6d/ppm7/Z5xHkm5crPbs5szNVjiHJnjDAZm5xa5kfazqdy+/1NO6cNJ\nW6OdJEtEjadEadOcnC3vOSMnIdRTEDJmQREq+KUWJe9OxxGGgX7oOdtuwBSO+z0t50WPSomBPliQ\nzBwT01TY3yh3rOFpHlXbroaTN8kJSdmW5G0cR9I8c/PVG15988/sDGyfnLM+P6czDTsMuM4jU1za\n/I823b9yfRRBI6VMKUJeku3eeIJ1OGdYbzoVTBOYFw2VnCs5Qc6GsshqqylQYpwr8fTAtULoOrrQ\n0W9XGOm5O4xY6dhePmHTW6bxgFtdc3sAPzmwBh+EMFTOLwZy6nDek1PheDdyOMx0QyBOmaRnnUqG\nVM1cjKlUUXetXDJ+2WgZizhLZz3rzYBzlq6z5JhUxx+BVlXCYI4gjiYLA54K06zyDl7fMls/rNd7\nasG0Rx/fqTgevvP+s8e2sNYI1jmyGGycIU6qo9UaXbdiWJ9Ra8HJhDWZnHuM85QYFbLrHeVkYXnI\n+K4Seo+3omQzI3jvsUEY943ff/GKeAebv9zy9NlKh3o5gyhgWk7B7PG6l0e4PyK+vwL5zonyQDR7\nbPP6oXavCttcMnkM5VRcArYWGhYvlc4Y9QgvE7QJsRA6A8ljxfL117fYlrjNjau7kcMx4g0YZ2mm\nUubK3e0dhm+53G5ZrTpaPXC4uSGWzG6KHO+umeaR49srHa4Gz6tgORsUzTXGyt3dnpgy4xQxApfr\njvnFDcEaVl4VVhPCOEXi/SG2p6Qj1gQ4jpSc+frLNxzHI8eUeXU7McaZVCqvb0fm2ri72/N3/+03\nfPJsz+XZhrZ5SegrZh+p7QP1cu6XfOf9FrQ1WFqjVbDO4buADb0mliIED2WBeavbJ1hnCN6h3Y2g\nyWjOlDozHyfm40Txb3i2/gxnVrizSwyNr77+LVEq4WzF09XAs/OXPAlrvvnvX/Pl21vGqXD1uy+p\n4w67ESRFbBMQi7XLLGVpjbVamfYjr7/5gv3dNf/lv/4dv/v6K/Jc+cWTDRfPP+Hf/9vPcLkhEihm\nJhlNWp2oh8b9w/A9D8KPbfWPImjMMTHPahpSjMUZwdZKS6o22zq1KJSkQ2OLAet08FbBVFWUzKXq\nkLRWfDcQnGU4G1g9W7Nen1ETmDYhwGrwXDw/x9Rznn76gilGtp9eM88jfd+zGs6Yux2Hbo3BMh2P\n5JhYucazTz5DxHK5XdNtHZfbLXPUfiYVfOgYvGW97jFVVS9zM1jv8cGzHnqsdXjnmGSCBfFlUGez\nhse4QDUeWHgsElRK3TtiypA+sNI4ZUxLmb7kW9/3jcBy4Datmgoq9xKccEyFm7dvuXn7ihgTrTSs\ncYSuA2sVOYWh5IT348KetVgU7knaE+cDpRZ867HbLa5zhCHg+w5/OGMywuHumtep8Pb4nLO2ZU0g\nFYUwW6dcAbWffYSkkodg8X3rB4MGcPI1MfIQd+Q7UemPXUIplVwzDUMRg2nlHtYtrVF9ovRrWss4\n3zi7HCjpkrZ7ip0rq97ztkb2r0ayqUxJ+QOpVo4pMcbEPCfSm7fsb2558+YLhvUKJ4U8zsrEFnCm\nYmrDtQXSnipjbtSkUOmYheMcyVnZ/ip7r54nzRpc1fbMmCv7/cS4cApolbUP+K6n3B2Y58RXr17z\nZn9QyZExk40CKU4Ahkrlbvca54VcJmxq+I3HHIq62v0M6/2i8fSx1aZ2u8Zhu0A3rOi7YUFU1cWa\nV9vJ3jqc8zjn6HwAMfjQadCwmTLPREnkWtQdsRYwmWYdLgz4sGG92bDeeDZhjZ8z3/zmd/z9P/4X\nDvsjpcK4v6WmAy0GZXyXpjQPUCWMmvVpLZmxVq4P11zv3pL6AuuBxoHbcsTEa1J5RjKNaBzZKBtf\nHm16WVCHj/Kqh6//yFb/KIKGob5DoCm5EFGBEEsipYa1CwNYDA0VrVv26L1QoepRCc44+vWK4Dx9\n3zEYhwfmnBa3uUaZKjZm1iJsVwE2Pb1bs7s5kGuhMZFNRnqDC544T5SFYW5cZb0auLQDVu5YBzgk\nQ5ozUmEYlODUDYEYJ0pZWL420MQyqxcLYy2MUWcdpSrUSmUbBG8srVp1uiuQqgHjsNUwp0abfyYZ\nkdPh+D6CAni/HyMnWY1F58uIIZfKfr/nMO+JuSBN0WzWGMSC6QzdeqAUlVhokrHW40UouWO6mUl3\nt6rGaQVSwpdGhyqDemfAC9OkKLZ5jpQSofhFKcBiG1hZJKLb497aUsHJ42t4L8H63oDxoMP1rpTF\nh/WnUq7EpJpjtQkVh12kYlpJ+nkpVEZMLWxC5TwE6FfUbiCbSj84dvuJ8TjhTSPFRivClDJTruSk\nc5McI8RInI9M+4OS72rFOIP3RgmvyAJb5l5hOZWCmEpuqnumyYy2Eb21BGPonMU7Q4yVFAvznJlm\nfbamboQy46tHnBBTZa6NcSocU2aKmWLNIuJpscbh/YAxgVKFeS5Im9UTfQSXjh90z4Ef6MZoilQa\nZFBJEO/pO62Ic1MV3ia6z7VlrjMM/WMQMdpmOvmC+44mozJYWqPEjK0JJ4XOVc7OHEYuqOLYzh03\nV6+5Szuu724WCwVhsJ5WMjVlFSU+ySmh3Yu2SCa1opIxZU6UGNkOnulsw0Sjc4XOO4IH3wXEqe97\nW2Co71Tbwn2S9YemRB9F0DhbrWlo66mzyiymafY7RX0YtG/uMKKS5LkszlkVKjqMclboVit8cKzO\nNmrLaJxG5ePElPMiU5F5e3PFzfUrvExY6+iHLbbbkOPE9d2eN2+/xQznEM745MVz3mSVSt5uN/zV\nLz7j2dmWw7c3fHHt2OWDwmCL42x1Bs5QO0+2Hc31NKOM2rlBSpVYMiKqbR+zGi8pTzBTawYxDMWr\njlDMi/Joxnc9zhmmqWHyhx1g9/SedhK9flxpPPrZ73yq+D/TKlbUiKm1yvG4Y7y7U1ltZ0jVkueR\nmiyWQjcMlKJzCGql6z1n/QBS+PqwYx9nmmSk7jmGN6xX55TthmaFFiyGjlYUanv75htebTu8vMSI\nx4rgqfeEpVoeeZHJTz8I36Fe3Petl0GptPt79aF1xjwnxjiR44RNDjoh+ECrGVMjQiWmyPE4Q8mk\noXKWE2W/4/Z2BNto0pPnA22cyK2xz5lYGoeonJmFTkcrUZFZudFKohhDZy1G4CwYLgQ60T58y0It\njak0jjmSq2CcoZOGFzDO0QXLZvBs1yuG4BlC47CfiFOEmIizqoG5As83G54//4S+zOyOE/NupMwV\n9kcm2xTcIo6h3xLOzlltL9lsn+veno/MU8PMMB0EP1594F1/iBkndvppmeW9bq1RjeD6jrBZE1Zn\nVOuX+dbCEBezEDOtIsOMxxjBWa8KBs6R1o3DNFGizkePhwMpVLoQ8C7wq88+w3FBTIZxl5hrohws\nnzw9g/EzajP84pefkhMck+CNYLy9d1DMtWFDh5tnCoU8RY7HwmEXyVNl1TtWw4YXZc2T5xdY42mh\nw1q3EKDVRdAZIfEurfXdBOrHd/pHETTW6xXiAlMueKMGLG1BUuUGuYE0i21eI3guS2Q2GGvpvMcZ\nw2oVcKFHgmHoA27oEfHEu1tyrWQczihK5RBnrr99RRzvmHMidAPrsxfUOXHY3XB3vObZ5/+G87Pn\nvHj5KePdjmNMbM6f8vlnv+as77i6PfLNOPHF12+gFoYu8PzlEygGmQvGqIRxq42UCmlpVaWib1db\nmNNUyCWrpzBaDqdF3TYmNaOvOdG1gA+W47zIun/gukePtBOK6l1z3eWbvvO5iLogmuUwLTUpjNRo\nYK9pT5xuqc7jjRrQWKkYMjEWWi6YpNIpdzdvGaeMdRlfoaZCHGfSfMR5oQsr1mFAcoSU2F3f8NWr\njiH0PL94ge8CTh6G8Mt0Q3/R5WLk0f++f0nfWe0UbB6QZfcz6w+MGsYITqq6qrW6tDwMzRjaDC0X\ncpkoKUEppDqzG19xd/eK3fGgiLVcWFvIxnGXEndjVoJlOQWMZZTzKGIKKvWi2adj0zlWQVuHDiHm\nqm2oKTLWSq6NDmHdBZWHsR3GC/22w59dLEPcAkmQkPGdo7MeamN7ecbzz3/Bk6efMsw71nPkV5Ph\nbfk95fqGMgrVO0I/0F/8KavLJwyDx3crasrM85E5HampcbiLhLz7sJv+3joN67WRuXCFlvsVfGAY\nBkLfk0y3EF8F6/0i4S73P+NeBvn0QYxyJYYVzjpK0s6EyZEWhN4bnm+esh1eIA1iLnz+y5eMxyP1\nf/wrvv7yjqurHc+fOhiEXKIiswRyisx5ppaC9wEnnjpnju3APsFurrz65o7QNZ4+CTz59DlPnz/D\nBovv7+iDxzqhJU2y338A/tht/VEEDazFePUyNqJQvlrUSKk2NYTBWLBuOeTykgXq8MoEp2VcCIsp\nkCEUgyk6bI7NLsSk02ZpiymJenXYZjBFIDdijKSYIRnKXGhrwax6ZPDKofCWFBy5d5Rhi1/1KnuQ\nG0bc4pHiIKogXMo6fMq5kNEeal44J61YxDRFQxUle+nBplIPih/XASkGGo6CIS8aNx+2HlcV7f7/\n5Z2vvf9X2pJ9PRymtFMGUzGyMPbjSE0HoKNRKEXlzvN0ZD4cKbkyG4+YxjxF9SBoKo/tPNBm4qwt\nPWcMtqkOVWNinir724l5O2Mu672pVls8NozCBr5n/Ti88N3X20PN/s59+bBlDHoQeK/tDQfGLj31\nGkkxUUvCkfV9z+r13HUd222Pkca6C3QN7ubIVCpTXPy271u3D9dwCp9tIYO0trSAl/3fWqOKwn91\n36pe9P1uMEZ1jIyQxdCKUDNLIie0AgarAnpAKxWRgFRDSg2OhTFmYqqU5hdUksX5sPT4PdZ7xHpM\nU8MubIfkeeGuqlrsz7Heef/a+x2rZUefzhhrtatBUVDE4uMjizHUieC6NC8XaR1Fkxgj2MVESpnk\nj7oirlKqJxjDqvM8sZbRO6xbUe0GOXvFyiZqSuR2RGxPyToDy1F19LwzSEP3yzSRUiNnQ5wLxjYE\ni1s7XO8JboW3nnDieJ2AF+9t5j+2Z/FRBA3vAs15WgFvLFUc0BBjKEUtKcXpwwbgFrVMMeCcxzvt\nKdZS2O9uiCkSnxhWrmPYeFIujGNkFkPOjdIqQQTbOTrpKK3hfYdfB1IZqVLJJROnHamec/Hpv+Pl\nMXE8Hnj6yWests9Yna35/M/OMcNT/PmX3FxfU8vEXCpzyRjTwAm5qQNfcZ5mFnHFRR9LIbiLK5q0\nhTCkonZagTRMyEgt93BMDLS50YgffuNPhMl3tk37wV2k/c+2PCRLa0sa1grOClYq0jJ5mjneWIoR\n5t01dY5gjOoqzRM1F6yz2K6ntkxY9YQ+sD7v1WUuT8TpeA8hlTRBnWmtcHdzy34aWa88n/ziE1YG\nyJWUZoxVBax0fyVtmbE8hIvHgeP9IqqdZiD3lcoDjv/nwNsa0XmXuE5Rd8ZB0sP2eHMgHg+Im3my\nNmAiIR1YDYHPzj/jL3/xKRbovOXNb37Pf377O45zYk75QThSHrJhEf2NT45tCiBB7XlzI6GEWCsw\nlUYqlXkJPGLVPyPVRk6Z434mN5hWgSYe7w29q+Q5EmvFOoNrhtwK+ynx+uqGXXaU4zXjPHGVJqI3\ntM7hTY/drAj9Cr/qsF2POK0+aCrTXsNElUqaJzr5eYLG43VfQRglArMQhY3zGOexLiA2LMx3BXbo\n9+vHEw7r9Dzcm4JJwzlLrepLUmshp0wro8oYHxJxmBj6jovtGU82l9St0G/WbJ8Jz69vGa9+Szwk\nriUjJgJGz5Q0q5BlMxAzh/2Bq6s31KSK2s1VbGfpVh3n3cAQBpztcQuM2NSGlLbI3t/fCU77+jsd\nhh9ZH0XQqOIoeNX8E6MieOgb2swiyeEs4r1WHwUVPRMQ52nOUYCc9uxLVOc4Y+iMBwJzauzSTJKG\ndQNiHNIKwTa6YBHnCN0KI4Gy+IK3gg4OjfD02UBoLzjsJj59ecH6mWE7WM7OLlkNHrzlN6awP8D1\nzcSiuoBpgt5ig3OCuI5WC6acho5ZmbbiEKu9RhHBOK+GKkadAeHUbtDDq+no7gPv+rtkuB/u2T/K\nXZs+GO1RqmaNYDHqWV4ytWUd5tqRiiEfdqTpCKhcSCtZjaWMwzTNOvtg6IaBlTdYqWBnTAc2BdoE\nJh8QovpCpJla4XA8Lv4OCclK8jTG0InBCot+00OL6eH4/+HZxEnRVy/tQQP0AVDyYYHDoPITwVlM\nM2SxmFpUv8gmigMbdFZEKrg8s71YcXm55U/9JYNUmqncfvkts1UJcazVPSI8UNd5mOecKo26KC3U\nBkkp6DijwX5u6EytKsmNZShbS6O2wmGOlNaw0hj2E8FbkitQCjkXpKrU9pQrr25Hvnx9zSoa6njN\nFCNjalCUuBh8pyZLBWWT13rP6FeP8U5VZE2h7zLh5xIsfDTfuj8gT7mAyFJRWRBtj58et/ZIB0ju\n7RvlQbrqxJtYfro51SALjLfIya2zEOvMjGOqBgmFi64t7SbHWeeQ9ZpXtyv2463OIFzAGkuRqvIu\nGGpuTIeR4+7A3XwkNgN4utDTNYOJi/Ktr8qRakKqmVgrjkU769E9+b5o8VMaVB9F0Gi2Y6yeY1VW\ndcMr47I9qqWqYJK+gXUZ+AlCEpUgb60pAsn1dN6w8ZaVbfsBpDwAACAASURBVEibGdvMcZ6oneP5\nxYZgDe3ujrurTPAzT4cVlxtPI8ChcBDhKI5hveV8e8bLi2f81S8+R6qwsoWL7glD8FgfGOjZ3ex5\nswHvOu52jTzekOlxdkFXGAdFMC0p5HKO5KJ+H361QhzIgpoC9f5tsiDDRB0MT2KIzTRiirgfaML8\n4Td9+c8CkfkhLT55568sVQYos9taldGeJ6bxyLS/puWESGXwWjUkJ1Rn1MimGYxfY6zFd93SqhA2\nqzXONm3HGHWZuxgChsA83ZDSAVMnzQbDGa05jndXvLp5RZNCZwNGvIpbSlv0vOpDOd7MexfyU4f/\nEjCaykifUGMfenyVnEkx0Rw477F4PSCtY3vxBLcdkbZjkxOpFNp0ZDAvedo9Y1g9Y3f1Ww7jntdj\nRtzAZqjkqtlvyjpXYpEnkdO+aagXBE2BIEAwlmxUz6wTw5jrPfpKWzGLBpYIpaCIr9YwktgfR7pg\nqbbcH4wAh5TYHyfifmb1+47L/QGbpwUNZsnZLG20yLSPGDOxsh7jB0r2+KaWx4pYUn0l7x3uJ9+r\nn14Ph+TpQL+Pi2o0JQIugA9ksdRl79Qm96RjU1WJwi5McL23atXUFpKTLPJG1ErNqmunevAG7CLu\nWaJ6pBxG5io4H9hWJTOP8ciYMm/f3pBLQkJHCJ4QPF3ncFLJMXO9v+Nmv+MwZ3y/ZcCw2V0j01tu\nroVDLVz4nqfnA1+3ojya0tgagzeCfZRQnD57f2//WKz+KIKG+IEWhYIgTbSHz6Og0dSoXnVu5B6a\nK8YisujG14pxDefXeCOUCDORN8cdMR3wprFaD/zp+RnOCa8Pt3x1t8fWI2EfWUfwK2HlHdvNmpQN\nLz+55LNPL/nlJ2f88sklQmM8XDPbzJQT435ifzvy7Zuvub16Ta6e5gy2CzhnGFaGYdPjXKAmy3F/\nQ5x3HG6vKSVScAo/bQ3BEFEtKwcYHzRILA6EJc8saoi0UqnyYUHjFADuW02P348f+Vsimi1baxYV\nUM36D/s7jtcKCOg3K4w4vPO4zTlpGFDZYad9YateC7SKs+CtoZSZcTrcVyFN1AbXilBzpLYZJx1u\nOMNVoY4zr6++gZp4+uwlZ8MKa1XX6+Spbqw6P39H7ZbHRfn3XW/7nj88+vivW7kUjlPCefBiECvg\nEt4VNsOGTV0jI7TdK3b5yLGMVOuJbsVutvzd77/izdVr/uGLr7g9ZFI62dJqL71VPQBOM9q2JAYa\nONoiVV4Y5f4cI9XCfk7EnIml4ptK9uMMqTTmXFStuTaiMRxj0kG+VxJirYZUYX+MHKbIXOHbNzfk\nVLhce4XsugYYShWm45G5grEB32e6XKA5shRlsJPAelwQWrWI/FzkPvR5wmggEM26sSp6Kb4D46mL\nRL1TggZk8zAHWNpaIssc43EShc4r28LvKK1imypa994iOMSrL4axHiuNnEZqmdnRQDxzmjjMicNU\nmWvB1khHpgtC7zukOSIV4wTpIFSLwROc49hZ5jmRa6TvOrbdmpUzwMhcZqY507wGDAPvVBv3JPE/\ncH0UQUN9sFnsJJTdXZcDiqX8q1W9phG5N0AxRqimgShs1YhqBRXTiK3RcqQcEnVqdAa8rbQ2YUQo\ndSbXmZoLc6xMc6L5iJgO1wthBWe9ZTsIIRvsfk+m0Y4jxujmSKNnfHPF7vqW+fZIdQFxl7hVpQuW\n7eWWi+eXeNfhvOHNK2ivZtLkKLWRilVInbWKRFo8TFtbAuMik3IybVLCmdH22s8ymm3vfrrcb+B7\nN9HpJSsqE65oK+UF1JpV7tw7urDGO7sw2hN2sbtFAojqVokRqIvceC1aSRa9B7VUBUKgBCRplbp0\n5FwwNJsoLbK/S/Q18mSV6bYGTKUlHT7q0Bd9r+5Hwj96ed+51ndCxM+R8YrDGNW6r6bhTAPpoHh6\naXRSNKvfR+qYMcXjnKHaQG1qv5pyJc6FOWXlEzXl2Jz02E6/+NKkWg655Q40rdLz4kXTimpepaqu\nfLXqgLyI8jUKjZg1YJwuXyV/NFU3IqrPVBaUX9Vnt2X1maFa3cMofLy0SqksMxjttbelBVQWEU8V\n8zSINYsC8off9/erTI2lCrJpoh7bZlGQBrkHVQgLC/w+WHAv2vkud6c93OtlTnLSptKfsZBEjcV5\ns0gkLf8cjVozuSkcX4wn9AMtW6zLOtiOBZMrVpTPFaxCfaXL2K6jVqHfbbBmxXo443zVsRmsVsgp\nIVXVLJd/bpmjvrMx76/hDzlWPoqgkWumqPiQDnWMLHBUlqGx3GvBAAjLoSDqRCbW6dzD6vc0LHHa\nk2pkvH2DlJHgHetekOMeKZY2F+wSqd3ioFaikJ1l6HrKKtDHgrna8dv/7X/lb7/6B768nbga95jz\nDdLgqzeV69sbrm92tBTpNxs++fO/4eUvXnA2rHj+J5/w65ef0a1WrMIZX3z5Nb/rfsdvzW8pRRVi\nfbggxZl43EFRhIQYR0OzZc1edLZhnEe8wwaw7QMht++ggxao7all9ejDO0u0r28w9+2L5iym6/Gr\nFd3lJ7gQOD/f4s3Afj9xuP2aPM+4MLC5/ATvDIVIGo/okBEdHkolTRNxmljVwjge9NBIcSEyJao4\nnAcrnjrNvPr97zh014TB8OLzF7hmiHWktaKDX+eWQ+d7cIbff3m8W7g/Qol9J4r88atbremMghp6\nu/icp4TERE0jh7sj//Kb/8Rv//EfKLXw+ctn/FXfE8420D2j9udkd8exFKacdD7WFhKsyL3ETKsn\njTZw1j64PC5cllLVebIsLO+8+KLUptl3E/V9n9PiT9Me2hW1qQ6a+kk05lQ5LMQ+RHDG4J3HOEeu\nqhSbZsMxwpwMRhxhtcL5ntCv9JmuqpGlu7AsN9s+flM+fJ36Ucsgu6LIMZzDeIcNA8ZZSmukFLE5\nY63OybSjASL2nh1uTjdkCcRilPDqnKO1SjSekmclIGdNDJ1zeBsWBWC3JMQqv66BPNOvBy7aJeN4\npKRb8nHH1dtr9jfnyKLL5ga4CGd0w3OGsz/FWMsvP/sMOf6ePhh+dfGEi9Wam1fXXN/cEeMMIlgv\nWLfs6PLQktKxzgNg/adu+UcRNFKcKbWj1GW67zr9xWt7QCsYp+XIMsEqWTcaOeKspyHYrF/rOkd3\nsaVROXtWSa/eqk1ljYgUnPOcv3jCr23Fd41PLy94+nxL8ReMrTJ49Vqe50pP4z/+x//G//Gf/46r\nQySnyrbvsFYYi5b3JVecMVRJPHWNv/43n3K28XSbLc88DF7ACJ8/3bD1n3L+slO9rWnk7XXh628T\nOc0q1uc92I5ak2aHRnWtrLO4zoNxiCu0+WeA3C4lXqvlIUvlNCx+GBzDqTrXwze3phIHWb0YztYD\n6fwCVoWuH9hsVsyT0+9pHtdZXBhwmw1IhTGpjWjKzKVijcG4itSF9DjP7K5u8StDk4K1Qq2iwao0\nRArHmJneJA42cvHkkunf7unEkeaI9fZB/2Nx5HqMmPrRs18ai22cVnWnQXprH+zct9puOe8arWSC\ngYiKl5VOeP3tyN03v+d3//wlN1c7Wqt4Or79u7es9g55KRxHzzh7RReKCtBRixL4GjjvFG1mnfIP\nFphtXq4nV3WIbDQditLuK49T9S7LVdfl+/OpsqiQUmGcZ1LOmGyZBMZU2cfIlNXr2xhhjA0Ohbsx\nghiqaUTjaSYQtmvCeo0LHW5YYaw+S3KSBRKhGYcNQk0V234O9NRC/FzIwLST/J/B+KCmS5sV4j3V\nCLGoYq23YL3FLqrUp1aucD8sW3zPsyZ11qm5lveIC+QYaVRyqxixON/R9QPeGpzzNFlsY62lNw4x\nHdZ44tnAOB747T9+w/WrV7x++y2mu8bSeLrZcnG54axb8eknT/jFrz/DhRU5fYY5PoE4cpgKsWR+\n99t/4fXNnjYnnGkLwo77hPH9rtQ7LasfuZsfRdBQS1NHLUWHTE5lRQRgUZHUM06DiCYkhdoqtUaa\nCQpvk4a1Bes6nl4+o+sDT+Kat2PibnfEmMLaOrb9hrM+UM/O2a4dL15+yub8jG77grEJm66x7i1v\nvrpjf7jlv8f/h5vjzBSTMtCbSp57a2lSEbF0NjB0PRed40JWvHSWKJ5SM/M8gdPe4/n2DGcGYk3s\nb6+4evWKGCNjynTrDc55ah2IUcWoTGsK1ZXTEaYl/Hc8vf/IdUJ91KUtIaa9d7A+7vqfPtM/pRRS\nzthkMKXialtMg8C5/5+5N2mSI0vy/H76NjPzJRYgE5WVVVnTMyRFKMI5UXiYA4UXfm5+BB5nmiJk\nL9NdWYUEEojNFzN7i/Kgzz3QM93VM4U6pB0ykQAyItzc/KnqX/+LY9jf4vxIXoXl9kwIal5g+3tY\nZ4v+PHtwPXe6VQIQA3g1WnUuZ6TtQCCKp6qgtVLzQtWKrj0xrZ44zI/k5YSTkSVXhiB4dV8MU/pF\nQfwvPg5fLEWv/3HdlH5JueWr7/mYEiknIwS4SpRA2o40zTyHI6VB7Znh2qC0yj/89AeafGJXV17y\nkdkVfPQ2QbRmjZMaA8lhZnTiAw2lFsiXV/DFFGmHp5FJaqvXQuKwDtpyMtr1/X6dSfs00uGNqpC1\ns676OGJ5556KoBfNVYgQN7gYiNOeOO3w0aJuJQwmjxBPo+LFm12OF5rLeIlfdc+/vK7vs2JoBg4X\nIzJY9jbeCDjaGqWaZjpIRJ25Lrf++kFx2sOzOpzunBrc5gSH6Tz4IpJYEGIIpH4O+Jiu9xIXSONo\nMc545uJhXnj59JkPHz/w/uePbG6PTF6Y1kyYD7i7gfl8TyiVYQAvieYSzVXW9UhumZ9PD5xapoqQ\nsOfDGP9fvqf/0vUvl41fRNG47iwwhk3N67VoeB+vB5rBNP46equqvbnnF1opOFGSFkp0vPvmB97d\nfsu78C2/12c+fHDMbeXtNzt+/d0bxvQ9zcHN3ci7d98xbRPj7i3NjYwxMIWBu+17ng6fWWXb/aMg\nhsDbmx13+wHGLafWUBybNHGz3fLbH77DiWUoD9G6wHPNyAC7zS3juGfceGqufPjjZ/746Q88Pn3m\nVJQkIy4OOAZKWazjVwxuAVoPitea/9Tt/G+961YwpNpUU79QvF7QjC/6cvP2Kqg0sjbWxeE8zPnE\naT7wcjhwWhdqqbz7za/Zpr159GxMRBnDwLi7YX0Scn4xKLH7USEZ7x3bGHDOQm6ohuEGb3x6VKk5\nU/RgzwkYo8SBL8rL02fKtAcZLG+9KNVlBHMjvZhsixODMvsruyw0+QI/lx4fzMVy327AX2CtIdRq\nMQCCYxo23E0TSmX+puDqwtIOnIYN6zrjEf7up5/48XNj+sMDKSTWdSWoJ4mnukbBUuWcM/hExGwu\nijaqvO5xWh+gpENTpV4mjXoB13HeXWmiudjfsVtw6awvxAJPcwOlNVbNFPWmcfCBabMl3b4lpGQU\ncxHiZodM9/joGYbBXBvEWbKlRLxTvPMoFY81ST4oFDtE/xKXflE81YykcNETxgmdtqRxpIWIcwEV\nqM0cp0ttjGKwkGp7NcVsrT8Pcv19jxVNyxkf8fFMa5ncGq42EH81PHTeXCwUNUuVzQ4nSs2V+Xzk\nx/ef+b//+q/54+9/ZD7P/PabG+Ju4hwK739cGAfh81yZpl9xc/ctrQ6cXx6o55mPTy/M5cz7Ty+0\nRQkNpiBmCYO9/quT9ZUw0f9T+uf+l86e8t5GwOiMl9wk4PoHVsTU3heIwGghgk8DIuaBhBYagq+G\nDS9PgcOnn9g44VfvvuP+/juCi5QQ+e2vv2d3c8N5gacc0ewYs2c5OTbLEzG+sIrwtCh/8/jM89Mz\nfjvxb3/9K1I5c7O/5/sffuBu94bbX39D3d0R49Rh88abux1LEQorb5wgw0BplaWuaJ5ZV89pLsxz\n4T9/+Jmnc0FECWlg7ZOMuEpV6SwHg5Fa6/qIYGJB95XolPM22VXfKLXSWiNclK+XBd4XoGfTRq6Z\nqo65VeoQUTcgOHRZWI5nXh4+sE5PPO4G3nwTiCmwXUeWuiB4aoMiyqp9+T953O4NujwT40r0iSgm\nimo5U8qZXMxivjVFgpk2ihR8VDYdXz4/f+L//ZsfefPdPb/79V8xhBG0MS9nKxbi8cE0PlGivR4u\ndaGgWnHBY+Ic+/CrigXruEZTB3rpMv/8q5TKnK3gh9o46WKwm3hkvOXtbwem27fMP/xIWU68PJ75\n/NMfKOuJw9NC9CfjgrTA6BMVWPWiSRFKq6xzQ5eZnLMVJ71urHDi+gEnPWkSwBG83ccUAjhPbspy\nhbIczhuDyPuACwkXPFk81QckRoYw4ccdcZjY3r4lThMSRjPLc4443RB6Jx/8JSLVHiz7EUz5r1rw\n2HI++EpzpffHX38Zq6k3CiK0ECAMuGkL0w6XBqRPeSFEvPfmd5YLq8yE4qkxYqF+HjpkZR8Rd90J\nXDQf4yYZi2o90YpR7ZdqcKy2jJTWxbzGJMtnM0JtLfJcC+dWOJ1tEo9eeDdEfrNPvBkC/3F5Jq+O\nz58e+enTR7IETnXPy88P6HLkcZ5Zyszz4ZnzckJKZlsqUo055lyHXK/N4T/997/2lP8iioZzVjTU\nB2PaqO9B9fSlcO8MLyKaXuHNRAxoFlTi8CbfF2g1o6UgseG3E5PcIsPIZrcjpoHn55nTYQE3MAzP\nDCEwO7NscCrUtfL0+MDz0zP7t3t++He/440/sL39Nd/+7t9wu7tn/+2v4OYtYdrT5ko7nUhx5fPH\nF2iNnO3QybWyVKG0mbU6Ds8z5zlzPL5QL8FOPRUCNb1CqxWldKgA44BfNQN89VJWL+rhCxxxVbp+\nyQ654LevTCbQzj034oFROys5n5iPz9Qy8/nn96TxG9x0S5NrPUVrP8W8Q6JlQ7hxh/hKch4v3uyk\n+/JfqxVMxZg3HrkuJkOA4CNQjK778sKyn+z/DQLNofk6T1xv2GWwuPyO8fDtdTvnX/UddGuOPmHI\n148ZV4qgafG66CtXJJhnV0wb/Dix3W+peWG4eWFdYT19IrsDWlcQxcUOV1ZzFK61Umul1NKN7TKl\nW5B4cZ0B1IvGRRKsrz5jzhurK8RI6Bn0UrtZowjOB7xzpBhJaSIGg1+8OHww7n/Y3hGHDdPtHSGO\nEAIhJkAIacB5g3lehWNiDwWvz13vg18hQ77wEfvKy57fL595863zIaC9EbukMusX31m1UUtGtFFy\nsaLipLMB+2el77/oX985h4onxAgt0KrZFbXSqNms4ZGG1v6qg0N8NSdiMfJCjIlhmIgxUEs1rcYY\nGbeJ/XlirkKcIiI2+flQ8b6i3vQtDZvCXbcDUCfXz+LXXr+couE8TUxP6bzrIThi8IJcqFTdZkyq\nsXeo+FZoWSwetpilRRLwpeFLZhDY7m8p2w0aR8YwUUvh55dP/OPnM1u9QVIkJMf88MThuTKGgHMJ\nLZ9ZW+U//K//nm//j/+d3VCJ0x1VJpIb2d7cwjAyeofQOJwOvH945HR44GWFzz894dqB5iCrR9LC\nem58/PDC8XTk/ftnygqOaJS/plQtfYdTsKQB13FTuw2q3Vf/K5P7tBRqyTT9bLTINLIi0MzOxF0w\nQAyzraqs84wTZUiOIEJAGJNjUcd5nTm/HDi/FNbnF9rRc/Pd7xg2N5SlUVpG20KrBY9j2OzwcWD3\n5i1B9uhyQA4zywJFVtZFWV7OLPOZh48/s+aVOBY2bkdMjqBKEKjaeD6dKO8fERG+/fa3JGfPT+ti\nN+cq1+x5B6Uoper14NSmTC2Thmh3tS04LdSs+MHTgqDF4fLXHWC5rtRS7NByZsa5aiE2g200eBNg\nbd5ArYzDgaqe0/zM+fEjazlhOfQVLZmWzctLa2E9n1hOJ/KywvnYDT/pVvtg6X32e94HQkqvcEm0\nZm0aJvvZSkXywmCcZXwccd6Ch0IabOnrHEMaTZEeE8PNLTEOpLQxOu0XlFpwSOi7SeT65JrPHPTH\nrudxdOPFppZr/RfJ09ALt4Gqak4NIeLSgN/s0GGDhNjZZV2LFL0Ru9aVknN3oD6wrw2mAY/vBA5v\nAkE192dL3VSSc2j0aIt9Id5ZolowzrWytGZT7+oJreGKYz9Ext3I3Xd3fPc//Y8Ed+Lxw0/MpbCe\nVtL+hl//6i3Fe+TmDfv9yGYbGBr4XaQmqIsQ10YQJWrBoyRnDDu7G1yX3fpP7tL17fiT1y+iaFwi\nW3Mr9kOH6XVk8h2r9YFreMZFr1XN56jmFa0Vr4XBe7YxMcZA9Eo+naCtqESkCcd8JrfMuVYYBEbP\neLslhMZ6quTwwhqEOz+wuYvc+IG/evcd++23uLShZs+Hh0fWZcU9vSc7h7Qj05AoAs/P52sc52F9\nonjDMMfNnmkfca4ylxOH8xOn08/k1vDeW9ccQNThh9HU0a30btcUzeJ6N+PoQq4//1oOz7zMC4fD\nAXykbt/gollAJ2cdn0E31m2WopyPzzhfuNnv0WzWCISBsP2WcfiGkH6mLS8spyc+/+FvWU4v7N5+\nS5OI4ljOkZoPlLJSloU0bAk+sN8MuEUtb+Go5KVyOH2A8Ehezpw+f6C2TEgvOD/ShoG5mjdVGEZ0\nhcPygIsnfvz9Pa58Q/SB7WbEDyPOe1KKHcsOFFGaFGpptJIpa+b8/EKIqUMUViy9CtPm3nyIgtDq\n131cbOlpu6nchCLFPImC4FLC5cZzm687uyKN/fe/YcfvqPlAPS82PThLPNR1JS9HWm3kdWZZFtan\nJ14+/4H89Alq5unhgXldOiwFIKQwsL19Rxp3+M3WNAPiSJstSLSuGMUFg2tEItoa0SshDj1LxZIb\nzfArElPqim7fGx65cAm6N5P0Qq7Ui9WqeFsiG6+eWjNVi8FHGCU9/EWS+77w5VKlCmZ3ngbCuEWH\nDd7b67bsDKPOOjG7lGUxp+pzOaItM64DdVtsh7Pb4nyHPK0q2UKfbKJBbzR51IpRTMFcuUO60pxL\nEXAVrw5iYNq94bv9hv/tP/yffNp9z3/8T/8X7fjAS2s8nhQ33fB2HJnefc+b/S03uy3zSSk+Ur2Q\nnNF3KRW6xiNiuppLlbCCYfPUP9cK/fLZU1cWT8WhffF7Wcx2SINuo0Ezm4hWoGaazkiqSFXGNHDr\nHfvdxH6KjFFodaVSCS5SizC3E2vJtCDstjv2YcvGOVxr1NHDmzuqCnsXeLObSCGxCfc4N1Bq43le\nef9w4HBYmPWZUgOyPLLfDgxDIC+OZbEgFu9hnKwg7DeR7TBwKAveK81lVBTXnO001Iiu3kdiGCh0\nEWPV68GNCBIs68C1r3vrHp6OPJ1PPB6fQBJuDYzThhAjS49hrbrgogmf1qycTgvBZ3wcOR5Xmouc\nzzNxELbbDWkaKTqjtSLR0Vyh1CMStojz1KWQ10ezgV8VcMzPT7jF4ctMaZmlFErOZsMgHqV0eMw8\nlhSDHtuy0jyI82hzRrX1Kw+Pn7mbAtOwYRoCLtv0tnZ9hJp3qekMnJAznM+Zw+MzKmLWMGMiBc8g\nAb9rxFJx1bGuX1eoLxBR0YqokKUzBFUJCkoj50xu9ucOx7RJeD8gMZL9uX8dK0DaKi1vbaISyGuh\nvH3m6e2e+ac/Mq8nZhrlxQqL9iVn3N4w3b9l2tzgt1vr/UUIacTFCcRdi4JzjqqO2ipBGikOfRLu\n+qHg8CHZnsIJTZ19L+gw0yv7z3LGzAoH5BpW9CpkNT8r7Z/7UntB+Ytc3Qa9/1qcvyY+qu97U6WL\n9+xnF4TgPKtztv9TZVkrVTLiF0JopHHEO/PKq0Cu9lo8YtBQ/34X9wUvQhDPZIIjWmvMa2O9iB4R\nfPRs/MT//MNvecyO95/+E48/PlKcctpMhO0tdR9Iwx1DHAjegSzYGWlwGVp7LMNlN2w/iX5xP17/\n/d83Qf8iigZgWK1zV2Wrc7EzEXxf6XdRTm3U9djZVYUYPN/c7RmHke/f3HCTzBf/m1tPCp7oKiEE\nkhOOa+Ph8YWlNAiBnRP84YEf//NCdMJ+8vzu9gZtnnU+o+vCqo3H1Tx1DqeFn5eZh08vvMwHKiui\nFT8vDG1gyFuGtGXcv2O388RhS2hLX8J5qmaiU8btnm2r3N994rwmam1d7JMQDXitplLVhPhKq7nD\nBGbbnAYI7etcbp8eH/j98bNlSbuEnCp3t7ekcUCbcF4Wajuz2Y1dB+N4OT7h24m1nJnrwLSceHhZ\nYdzy5q9+R/URLWekvTDt3yFxZJg8USIKPH/+zPMCMXqGYUJEmD//kUM5E0bH/u09d7ffwU3j6emB\nEitKYtzuERfwIRDiYN157AwnD2srrO3EefE8v3zg57EypQ3SKmv+A6t44rQz7H4Y8dEW5VWtEC7L\ngffv/8DpfEbGLW9v7kgxMYhyq4Hd7ZnhHGnt6wWV3gtOQ1+qeC6ZDrUW80CCTie3XHmdIYTVlNvZ\n4JtccieHqMG3KNMwEJIw7PZs799xfvcDa17Y/fC/MB8OrBnqPCM0ps2GYXdLTAFJI1o6+UAcPiYu\nvlXibX9kYHhDaBAtnKiuK6UZVTYGiw1Q7Wp/SZ240UWWVyNFg4nkC+1Fc61Pzba7an1Rf9lpOP8X\nYk+JddTXmSdECIPtTH3AhUi4UPqbUrIVUp8Co2ypWqllpjXItXKcZ7wv4CKbTSWEZNN0tjhWVwrB\ni0VQ98ZvzZUlV4JUNAmDj6jrTtEtgYtUdejqad6z+8bs43/3+3/P8f0fOGhlt9nzb374gbth4v7b\nd4QAZZ1Z52fQgheoZaauM7Wsrw4I3fz1osfRf6ZWyH/1i3/++kUUjYv8/sITV2cQjK0ytEMy0j1S\nrMOirYD5C43TwLTdcPvtPTdJ8d4xpEZ0njRYspZXR1vUurHcQAI5nzkfjqy8EAXyzYZERDUyn2Zc\ny5ZaNjvW7DjNM/Oy0NYF0cIweYbgSfvIfjuyHTf4sMelDdttIo4bdD7QauV8zKxLZskVhoTkxLC9\noyVnUFc1znwrjTxjRIDmbOkMXGS54ly/V1/HKhFnLgDjOQAAIABJREFUTqsOwVmYJPaRsuWJLYTN\n2dSL4rwQvXRr9wJdlR8jTDeJKbylpYFWZ2R9ZBrv0RAZBiE0R6mm8nbHiA+QhgFFKW2GZUFaZBoS\n97f3gGNlZakncIFhYw2ECx7XBM0VLc48pjBzuJgCaesZRo8LDh+E5Xjk+emRuQlhWnA+MOx3xHHF\ndWpYLSbQuoR7uVyo60ruosf4ckR8oh0LLn1l0TAfildLigsNWGzhbywcUxZjSBat2fK0CeiFcl1K\nFy8ah/Jirmg2CtZBD1uPp6DDPbuaWbJSjie0VaYpWAEVpeGpDsu3r9phnFfCgHQSwIUIYLuSLu6s\nFk/QfOvPg4IPqIQv/i68UjgvVOdXns6FemvfzNukgXTbDfdKDf3K60ud80XRLV+038ZIlOsy+zLd\nO+cIg+V9eG9Na2vtypwq68oqig4YzNupuCrNwJ8LNbfv1EqprFJZc8G53jRc2FdqItbWjG4uTogx\n4jd7mgvk3CjHleQD0yYZ5FobKoW6mnu0qD0nrXYDy16v3UXs2hvwf1Iw/jTD9r+6fhFFw1EJTihq\n3vatGbbpnCdEc4sVB61lNC9oXaDMOKk4PzCGzP3o2TrhPgrbFO0NzguyZHCTCapY2SRHHBI6bnn8\n8Pd8/vSRTw8HgnjevvmOvICLkbnYfgXn2DVL78oe6gjDNJAk8u4uspsC+5gY48gQBsQnxCWSc1Qi\nn4+J+Tzz4cMzj/NCVRiYuJ/2DP/D7zjlxrzMzEvl5fFAPi+UdUZbpLloI3VtqJbe+TlUw/UD+ede\nIo6QEsNmQxBHTULzdhhs9wPb0bMuBdVMyMrgAy0JSxPmw8yUTnhxbGLi+2mLbka+/+ad+SmVUx/H\nzRWUtXBaTpTjA07ucENgnyaqnnn48MByWqA09m/2/PDue2JIrEvm3P5ICJ4hvDEaJFDzmVYqax2o\nJVNaZh0dbtgyTYn7zYaggubCHx5+4qeHz5Qm+Olg0NNxZjuOiPNEF/DR0TzUsMENSgue81px6wpt\n5fTpM8Pi4XzDN/uvm+40F2PguEB03feoNUQaqtk6fGeai0YvGKVQpe8C+kGQSzayiBN8P9jPR3tP\n1QkDkTDt2cbIm9tETJ06Op+prVhIUFkppTDPC3MQaiu2axBrDEz813ob0dk+mOBQW6XSUA9oYym5\nW943xCs+dpM/rVxwKu2OsULrjEgQtRhnMDaSCLikeOcIEWSpRqn/ykv11f7EAxICLg24NKDO4MqA\nMcFM2e16wbJM8MH5frCak0PJmaVPg4fTiaUEYqmMaTJfe+dM57F2exStqMB5hafjQknNikDr3yMG\nwDRTVReqCtGbz5qjIsOOA4njXLhtjvtvNryd9vg4sNaFuqwc15kmxv6r60KZT+RlQZqlM15iF15Z\ng1+cHxcsscOXVt//5Rv/iyga83ymiDdGS67kuhpGHRLT9s4cKBN96VehFKRmlEpTx/HxhK+eESDB\nwUeiruRlwaWRYXNiGMxM7+5mQlJif/+G863wcHvHz4cPQGS8/y3vvv01zkfWUvEUnDi2mz3juAUX\nWFVoNeNUuZ0M9hptawKYc87plDnowlJOPB9mlnnhKZ95Os9Go906xinx9vZ7sgwsS+V8yvyUPnI8\nPPH08DPzko1GWTKlLHgRXAnQnWVb+boFoZNASBNbF0kuctJImQs4x2afCE15/umBv/u7v6EsCy4O\ntDJT5jOn04GQ9ky7O4Zxy3p+pJRMiBNp2rDbbplSMp1Hy5R5RX3j+eUzyzITlxHut0j1HH4+8vzh\nMyKRMP2ej3+faep5eH4m+DNBMzn/A3k502qhrnOneQi4yxrPdRqose1i8maPgbLWjomnCe9svySt\n9Ex35e7dd9z/6jds3vyacbc340sFbQVPJO0GdBhZ1sbj6esmjZwXSl4Ig9FYo3PUdjHGA/FCELOK\nUYFSGlWyFeBir8MEiKYn0aoGQTTlnO0Qw3visGFwhVgT6hp59eZ11lXMQYQQPNKg6oI4h5dEHBy1\nQssFdL12qiLOqMytmruyiOle1CYkbfWVrlrsGXLed+q0mj16U3utTvGxv5baYKUfzh0Sc6HD1PUL\nHcTXXv1AFECtEPiU8HEwGByhacNh3lIhRLy76DD6MapGjTe0zKGs/QAuPf0wU4iM3qbiWpTjcqC1\nBfFiyvOUUK8UGrk2fHfhdJ1trBSqeuqqrNpIriI1g1fcfo93gbf/7ne8vX/DpIFj6dYwl/hoMdZl\nWY4s5wM5rzjVbjD6X5LP+8x32Rv/d9zpX0TRuCRkudqrci29SymmztQKa0ZL99tBuHDra62sdWFu\nnlVueMnKMSu6nshlZRIY1sQgkb2rTHHDEALfxMDw/T3n2z0vj1uaT4Tbd+xv3yLOk1tD1gW8MKYt\n4+YWxbE2y+zW1ki+r/OW2ewYirnrHpeFdV055pnzWcmlsDpYBbIqW1GiD9yMHgkjc1QeZGa7GSht\ngBdHXYTalfKCvtqpSOe3f+XYrj28JbjYtS6WaojzlByo58LPHx/5/P4jJc/dWTfT8spynvFhYTks\n+BTIywu1FnyaiOOO025PGiacGr6qLSNeKKcjWjM1wbMcaeXMfDhTlowCP//4kZieIUTUjRRRJM+c\njw+s52cT2/UPmsEarkM5jlnkep98N42TEFHtC85wojhnS/48G+bezAeKFtlO79jdmIV7aZkmQvRm\nMEf0NErPpfjzr1pLX/o2+zD3g/aC+HtsMnP98baFrLtiRa2zrzyXE8CerVYLy2I/n7hAa47WMzO8\nBJAzjQt91SCQQqHUxtIPdsFiSp0K6l1/Prpjqxr9tNaO8zuHI9hntHUoxtnfoblez80QUNSos+3C\n3mog/ZBrRam1IF6IRMQLvgm1NERM0Kl/AXjq8hW+xPe9D8b2uuxtXo8Ug6RCh9guVh/XL9Qhq+6V\nZXRhewbpLhZyITUUOyucNpIocdwQ3WvKaM6LQXHNNBZ4oWmgFZsEnp+PlHIizy9sNyNpHPlm+w1R\nPa4qWRdzmAYkeCgWwdvyao4S5iwJYl6w9XITpBcqDIq0SVI7rPavF5BfRNHYb2+IfuC0CE4bWe5N\nER08abNHi1KPjzQpmJmFqWovmK9vyqied/sBp4G5wpwSaGF7u2czbW3sjDBu77kdtmzHid1+z/2+\nMb/5oY+sG7v5LlFatfHOO5wocYw0FXxVVCPURq0Ly7LwcnzhuK7kqlQZWeczx+PK8/GFLAOKUP0A\noVJXOM+Z1s64T5EhVYoElnNjWU7mjCkBFzOUiG8mzAnBG60xBLxrRvP7iquJUFSo6llbBQmkDgMe\n5xdOxyPP64ofPBIizm3wriFa2e6VJgFx5heEm8xIMHoIkDVTFqPrtrIiZMtM10YIgdIKjy8PtJJp\nwVxPcxHm9Uz14GNk/+aeti7kg02Ul0ZCnMM731XKVjRaNRqnirmsOm/5BSGarcalm3MiaM1kp1Rp\ntgBtnvJy4vT4M2n8K6LLnJfFsOgIbVnxtnDC6dfZt4hYwSjrmbUVqquUWg0qVCBiGTJOrsWk26RC\nqwgVrw2PJeaZoG+llULOJ1Q9IiutLCxlIYTEvBwppRBTZUoj4oW6ZNsHibf71moHJSxlUsUR0sTV\nUl3E2O41Gnwmlo1iAsxKLRnf7X1aE4JFVOBw9uxf9h2YQ7J2v6yci5mUqreo4NaoNZMrFDWbe+/+\nApY5vb1uDSpGu5Y04JNl3TjnUWe4v+sFJSXLsym94NnXUZw34oG/GCzWQs6VUpV17UFh3nfiS2FZ\nC+VwJPQGJOz2+Cac88Kx9KW8+E5n9ngXqdkYpX//4yNPj5/4h08/EqfI7bjj3e2d5a4Dy6omlBUh\nhYgjU2iUfCLPR7w0UgoEbVQnZLUMeNea7XR4FbBe9+JfUHL/pesXUTQ20w3TEIhbT3AO2d0gEvDB\nk6YNbWk8/OR4/PhH1vNCqWe0rHgqwQVcdYQWGcLAOGwY1LEfHKLKt3f37EajEcbkzRMpRlYHz9kh\nRFbdoAVEBtpSEa8050CG3j0U/PlgnUhtaBFazszzkZfjmY9Pn1hyxcdAipY9WCq8nDJrK9aFAacl\nMx8WzvmICJTyE957mvPM2fP88plaC2sRfJoIBPtQ54tIzR5I5637+5orxIjkuePkhZASKYyA0urK\nMDS++82vuN8I6hoSJlJ0JG86jqWuVK2spdGyLWsrpUML5uXfaiPn1bytxOjEgkNrpfQDdLvfUE5H\n1rUyqzDdvmEc99zsdizzA/PWc5oc9XSDwReR2N2ArTgIpRRyMaV1TIkYk0WHJrMP8cGEWGDAQimm\nMak+on5DCBPpZkNtM7VlltyhwVyJeAKROkNMXyeoNIqnER9arpzraupr8UwF4tgYB6GIdEFlZTmf\n0bpS+3KT1tB8JueV2rQHkBlLKYTY95yCakabsiyFdS7UokQEcY3j05FcKpIi0Q84zR1Gss8DXSkt\nvC6inRfb112ew+7FVKs34kpXRKsaqVmwaVa7u2rTCwxnAt4G8MXkphVqK7R8porHBeuaS/u6e369\n+iShYBY2Lprl+RfL7wvN3/ueh9FP0Caei1Tcwsv4Qt0g5GKaprVk1jabHY9LoI2iysu5GHEmHbi9\nvcWHRHQRiZ1GrpVcVrR4M5E8w3FZ+du//3ve//xHDscHvru/4+52yy4k1rURouCDR3zXWqiD5siq\n5DyzrjNDhN0wMFGJwFwba1ZagVrq9fXZHuuVW/Gn9hnwCykajUrVAd/tg8OwYZgGXAxM2w2smTlv\neHz0sAhO7aB1aj421kgopxO40ijOI6Ubso0W5+hDxzJV8Si5OLMsVrE9RWv40CgKyTfwQjDvC1sC\n6opKZWmOfK60snI8z5wOM+eDZWPXoMhoPvzrWljPK+duE6ICy5zJ68x5XqA11tmyM4p4comc59kE\nP+JxKYD0FLDWkO7q+2WQytdcEi3gZ73sKnHGfOkfjiAjm+0NQ6g0Zx/26DzJm0fRVFeDcRRyVnJe\nqGU1yKh1y+ja8FmoJRj8EiKuC7oqPThLjamlwYz3hsHsEsLgqQwk36hthwbTaHjvCcETUsJ5izqt\ntVKaJZqlFI1x4h0hJEI/AL33nfLqWLOaGt4FkIAPibSxZSREYgJXxSw4ognciuvY/tfcczFMXLHD\nvvbwsYtauzZoxaYpg8+aZYk0KxilZAtOWjJlXXuwEVemjzi5xrWqEbVoXAKzLJoUMT1EK6YjEq9m\nc9EaqC1x7Yd1V9hM+t4IxzXiVcT1KVwITg1P7wegqKUHXgqDeIdT172OOuzV8fQL+tT61zX0pH+/\nvxBz6tJBK/2miLfowutrlat9iK1pXgvCJUzp6h7QbWUuTr8i7Trx0uz/bdoQX3q7ftkHQckr82kl\nDo44efzlLkmh1IVWHbXBOleWdeE8L6zHhXyaqTeFJpXcMpSB5M1+BN8zObjAmT2Dp1rwkgSHcxBQ\nYv+kZ+QKFXJ9j//pruNPXb+IonF4tsAkYkTGPdtd5O3ujhRH3r57g5fAULcc3r/wnKE1hyeRxDEM\ngULj4Xjk+a//P5ZlQQnc3G8RUd69ect+t2MzJd7e7bj/9hvGacLFrUEeGO2/1kaeV0q25XpZM+34\nSMmZ83zm8eEjaym04ik4cjVMuGiG6NlOkRgTaTgBjuNh4aenB2Y1b584REoTshbO8wulFM7rirqp\n721Gcp2NcTQMRvdDjT5XVjPtK4XmQ0+2+7oOLMXAkAaaKKUV3BAYxgFEzOpCK9vNiGv3gGVuazGB\nl3PmEVW12sRUlZILJZv4LM9n1pppVJwEW+YCXgIxDSCei/fSuQ0Ev2UUwQ+mnB/Gyd7zNKK1UW8E\nr6t1qhXwnmEYzUnVmTOr854QPckHpFvHt776Eeev9McQA02C7Yq0oS6gIhTE8P0Q2Gw9RQsOMYgr\nCsyZEL+OfCA+4CQAloUucSR6tayFcbBo27ZSinXsDSX3FEJE+/teOL8cKOWIeI8Ee1Z8n6xciBbj\nWhuidmiE5PHBKKUmcIRcCkQBSdS60Eoxq5Ro2g9xDloxNbcWfI04n4xmC9Tac97F4WLoQUWG7Yua\nhsaiK4zCXbztc1QvwkDBB4P9TFCnXbNgja4XTDfyr3S9/413vhdR8xeLacCnARei2bf3gtGqNTOl\nlK6d6uaKF68u7aaHXZCo2utsaCbOFdtlWFE2coDNK45cF54Pz6THyHncsF33DGOywlUzz48PrBVK\ni5QKWQuPhwOH0wt5PeODxcautTG3gg/KkLaEznHLpTHPK/Oyci6NpSoZg9dKUwbXiCKWCx88i4OS\n9VWIyReM3H/lbv4iisZ+P3EIG4bbDXdv7vnm7R1x3DJpYosnV9gOiV//6g3jreN0PrGNlVwc56cX\nPr7/bMyaYss27zzys3GYt5sP3N/tmbY7fnO35Ve/O3OzveFuuzdjNVfRUilUXk4nzi8vPJ4rTx+e\n+PDxPTmvnF4eOZ/OCDCkCdlMZvAXIngYx8Rmu2GMFaGxZMdaGsc5Ux04V9mstola18K6mLOs3f7O\nFnFDxxi7GKoWaLacJFjiGaq0/kC3+pX2CiWbclshINYptmZ0z6aUBqoXIZotPGupdGEAWgXUU3JB\nxDDSXAdarSyzkKugzVGq5UQjphammT2DdiijOIU0EoNj3EzsNjtCDAjdWgU1d9T+aJc108QxpC3D\nNNr06ISYzCHZiS2cSzPGTu6v40K7JEaGIeGdUVhzadRqBWTyAR8cY4q0Voiu4YeIRgj1SF2+7p5r\nqZSi12kpTht8p0KmYAfTOmeWOdNqxUXf7XIErYo2j1Y7IBpCCkYf930qiN2grtRGXgqK4JMRTbQo\n+bSCM1o1dHuPmqFPMviIiWyN4JFLoeYVpNKoSIWaM6oQNXYfJyuGPoS+6+CqIHLOdhohGBREP3Qv\nJpQ4IVxyKZriWqOp74tZU6K3L+3p/9z7jk0Z0hf7PqUeuRAveMx1cm/NPpu5FFBvjghcNA6OYEPH\n688lENXowlKEsq4WXlVz/3s2mTcyRTKn5ciyVl5OBS+mJJ+PM4fTE1KF5oJNo952Q9u7gfU84UKC\n0qh1xsVolN8lm/eeVubDwqfPB14+PHJ4OTMvBQmO3BzHWnDad2cijIMjbRNzbsyzNb9aL3iDaXXc\nn7DR/kUUjbff/YbBbbn/1Ru+3e0Yh8jp3Kht5vFp5azCUz3jbzbskuLdQJITtSnH8ydO59lYV06R\nMNAcUC0OU7OHxSorYWH9SdhOR+5vnxmmW5Lv0Zg0jocTz4cHnk+Vx/mZn5cTrS6s9cxCta4nWNCQ\nSMQPCcUCb9ZsmcaVzNyEgqM4s6KWpiyl0HJlyZm1Caq+Y6kOs8jAcFYFUTvwbFR3FkTv7CFtvUPX\nr/wwnZeZNa+s1WATzRXagjihVstyvq7HOq1SWw8xR3sedGPNC86ZrTrNDARnrTQxpk/DYaYKcFGN\nGVRiWQLFOWNseE8VkxlyMWd0YItZoXRoaHXGFsG7Dhs5gseYJ64fSl7Qi5NAe1UXA0iIxM1I9J6S\nV8q8IK0itXVWDB3i8WbIGAc0wOIyS/g6nUbOZlioqJl0ije2UmvQKabaxDKja7HpoN/7ctlhUNEY\n8JpMz9Azq21RXe0ZbGaOqGoGjcHZ8nMVg0zKRQVGt+5oai7E8moe6uhU2VpBKt47mlRKK1bH1VNK\n7tvlTGjDdQksfeltNFp7ZOz9vxiR9hsSXQ8Uk/6zK4jvJIBXl9m/xKWq1xwN6dODivsCvr8UBoMP\nmzaqCl71iv2/ijLp0Jbi1YgZKo0m3XNLLaumY7KEKTGsSpCCw4wLl/Pp2jid5jNrnYk+GIPOe6L3\n3N29YZMiJTyzcQOtOs5rZkyWb55rZikrtRaWPPN8fOLh+MBhPZNbYfSOKdjkXbJhlg2DGL3rbr3O\nIoOlvb5PFwbYv3T9IorGd7/9LTlOvHnzhi2B95/+yD/++MDT4cjhdODcBK2OaQyINGpxnBbheDzx\ncjhQNCAEhmlinG6MQqpGRXTOMfqB1Dyn55l/PHzE8ZkQHGncM42OabOxJLSSqS1DU/ZDJH73jloy\neb3hNBe0NmKcCLu9pa4NG2rxnOYXwyCXheY9kvYIAe8jta2IoRvkpRoNTwZjoWC6gibmKut6KEtt\nmCLbeRujNRBoHVpQo8d95T0/PD3ykgu5mWajBL367lzwXRvrtWOytiC/XK1ncOTVnG9j8IDrdgnN\n2DPOFortwl5RqNkOIZyluulFESyOUhqH4xlxq+HOfQmq4q8i1lwsQzpWz3IlCBibhV6OfHdFjtEj\nzowKne8LWPXERWnBOvYlt+6wKrQ5s0plSQ6nzWzCXUSisGZvCv2vuHKeWUtGxeNqQ9eClkbJK14K\nPgUg2oTnjQ5NzdSaWVabirQZy8bpBKKUpfbPhDHzXDD4reZKA4IraPC0C5TaAf4QE0EENBttUwDx\nXXgYOgHEvLudt6U4vWkQF3ASoRZqzmRt+Kg4Z+QVU0FrX6w6m6a+uA/izYNLfOxsJMBXasXMRZvi\nghBDwZO+6p5fv+dl2S3GulOcwZdNe0yA66mF5pFw2W3UZuFrX74CmyC0/1qgN0Y1N3N1UHv9wXu8\nBNzgaWMyKBJhWSvnNZOr0aSdwM00MQ2JlPaETWI7jgzTW6QVnp/+wPHlhabCcZ6RdEaaOTnUcqLU\nwvl85uHwwuPxhbxktCj3U+LNbkspA4eXI+vJomdLbowNaGpsw05XN0a1vc4/dcL8IoqGddiF+fNH\nlhL4/OmZpw/veTw+8XjK5FJpVTnvdtb1VggSKFkIMtCifZjFJ8MmuzlGU0HUHHou0YplMXFYbY3o\nV/KYKDdKcIFIQ50SvJKSsUKqKI6RWhdUzNtFug9PXY32mHNjPWXrLtJgi0GtlNKuS0pUqZcJ42L8\n4i7xrUazlJ5Q58zuzD5wwXWYyA7Qa7fzlVCvNssHKM2apkLhkqPx2lthPyfdPqH20x+69Ua7cuml\ndtuEzuRQ6Z0dfZK4LBP1glsb28Z5Z6+9t3ANtV2DcrVgsEJ5cd61X9ZWyTlfi0atZiNvCmsrELkE\nfMDEZv3QKN0tRZyjlULJC01MIFXWBS+NUKzTjjiqJFyCZalE93Vdr3MBJ5mKaS7MRy1T15Xmqt1p\nd5nK+usttkui22hLtxtXrZYC2Oz90Vpoorj2+lxd/iG+M3+KwV2uU6u1VcqqaBUUD+oNdvQAPfrV\nCRKC7aEar+w4sXvdWu1ddYOgFk/cI4Qve2eQnkDZgSL1Pc9Cu6us2YqrOMvg0EqMggQl/YXiXnuE\n2VXhrr2JuST7XpfgF9V0//POSL3uhy+L8NbvrTUc9qz2c7irzmxqFKmo93107nt4JzhX8X4wUrIo\nY+okjuAIg1nXT4NDNRKXkXA4GoyYM2WdySQCcm3eJIAbByOIXHYrNfQGRAg+UN1q8jeFXC4E667V\ncBbfe5k1fvGU26eHn/n0dOTv/vb/4eV44pSV+fBCpeHi1g4WAlnb1QMn7u7QYcTLN4xqo31wxsQB\n44OLmA+Rc9EWnirkNttYN88gCy9lYCcQXSCI3cTQcW3pKvWCR1tAXaT6EVqkqCAUmjiyC8w0qhrX\nPJXVIIJiqXzS4yJbiK9hR71z1masFnOT6h40zZgSxhIajU1SMyVbjCwqX20jYlB5pXTwqEntoiyB\ny/6ktivWax84vVIw8QrNHHel472X2FAvgvP2tZwqtXf+VmCqKZ+j0UObXrq/1y7nEspje5baD0bD\nZP1gVFvfpzLtMBj+wmhRmloAUa7V+P9qC1URQcvCeaZTQC8gmcE0pdiyPRYrMlFch1g8pwWmr9QM\njDd7BmCu2fQlWljLQqkz0iqBjHPNCiMCeeH88ohWK2wSNragnoVWDK7qWi0TPbZqYWSDvS+mfrYg\nJpV+sONI4wgtk88L8/kMMYEPpBSoJXOuxXZbaqSHGEZrekrudCwTRZZ17bBZhpBwEnDe0bpL9UUs\np9IbjM7sMV+zhteeQtl3YooSpg1jDITQyLp8tTEnvDKdLotsgH/aeV2W3L24XVhQ9KboylS8RLu+\n2nBYY1M7/OXMo008UpW6WmiWtPBFAFbAOyGNymbcmB9atzP34qlqu0KqNVXee3bjnjUemduJ83wi\npIi2TPIR8RW8cn9zx6qReFqZP/wN87zy07IwH49sBsemXQqXLexbK6zdYsSEpDahi1rPUP9E1fhF\nFI2PP/3E73/6xMePP5HnzFxt+eq8YwjBKm8Y0GiOm+I942QWALIdQHsORzOhkWK0WulU26gGcfim\nPWxloUbjzYdxMM6/cz3SFOasnHLDd1Wrdx60K1ZDMlsT5KrWHkLCb3c0oKinlYxDCRc6nlhgkHNq\noUqdpiDUvlzm6nVjh1lBqyKl4aqJC6U1nIes4Fq5nM9/9jXebtg8Q6wz3jmqtI7ld4aNitF/+3Rh\nuDNcsF+thsfWYsrsL43uVKVbeghoo3k7EGp9nRR8twW/1Bq5dLWXD3LXtoi6roy3e5mGYOwt9Erh\ndM7jve8mhFYArimPF1v9LmYqxdLt6PYd3tnrCFKJwaAv1ymjDge14JbC8OWG98+89m927OaFkM84\nJ5ybUTOzGrVWasGr9rRGYVkLy/mMsBKSENUjTchtJbdCxNyanXOQAq3aJDIkE8baHyteKs0pLpg4\nLaVEPp1RCkVWXDQm2jQFypwtP7y8agGis8lIfLV9XhDQSiUToxIlEqMQvOLVkggVMWYRYv5LUlFR\nqqgVFS04otFDsXhjUZssxmgMsEhj/dqbjk1vUR2DV1Iyc9Np2jIOk02cQLnsD8VZs+Y93tnzEXoz\nJRe/D/rnWE3V7hWyCh7L/VE8qxNOLXd4z9x/qQIFkhOmaWLaTESfzBAUo/9nVdPuaMGVxdh73rEf\nBhyFXGak5u7J5km9EOx8QqdK2G34PUKuyvp0xB0caZPYvd1TYiQ6T602cZSiVwQmOMcuBaoXYhAO\ncfwX7+cvomgs+cAsmRYSbkz4mkyo5Y2CGbxuCsyDAAAHsElEQVQZEGZxJswJAY83DrVPODXWh7b1\nujy70OuCB5otH2sQ1EWkKEGzRVXGiI+jFeFa7MGuYpkGYjTC0FfcBt80m17E0RaDp6oILo524BVl\n7iwu0ddyXUvriXt0JgkYHmP6AxFHE2djJZFWM0ZUXXA0vBRcsmmpVghfCU+N0TH6RKsZaa4zmww5\n88FmtctyzDjqF5Wyzd+tal/a2iL/Glbf6ZTXgnDdLsqVcqlVuq6r9dyGy5Shr9OGXLANf13Eg9CK\nt4Oyzz5y4d03U34LgmvuquLVrkqmv5RWPa38/+2dQZMctw1GH0CyZ2dXsmT7kBxyyTmV//+fXIkj\nS9ZOd5MAcwB35YuqpmovrjJelW46zLLZDRL48EGWQ27c8SCelRCn8ynELbc4Op4RUY7zymZvu2lU\nYtCOd2OWUJbZKEwr2NnXHh2UssW8D1OGC8yKniA1doSZYyNufSprtGipq2N9JVuWbHcMCyNKXrZc\nqKfO7pxnNHpVlyiGmoTEeVikUtH4qGDRD2KVbbugaMxE8Xg+VSp+OOfcmbVGc2XV1YcgUV9ZuZ5h\ncz0biVTajPRs2LkM5jiwOSh1cPtaKPP3N605wKXGgXGWwawSgpkCouGFFQWduLW6OFPjnwtxsFud\n1IX6OiPjJZSZOE6MZp6EVD1EsIO50pkhkZmcPjFTyqZc5bq8vE5MhCJR2xgetknqxhjhbOA43gQx\nXX5hJ53CRaIG68O47c90H4xSuLRH5rVT2jrYqSPVQ7yz9rlbpKT6y5yVItCip8NFYojTd/fxnwD3\nwuPjB+zjxjzh3J7Ytkor0exnbpzHjWqOlOiHkDmRPni1SpnR0OLdQ7fflipphiul2YwPAqFsmBa5\nUtEYHVrmDP8oKzF9a/jSnkMfJ/28wVS2B+HxxwuihXEa+xEv1HYJ2V0MJQt/GfUojE2I+c09HoTW\n9vqRfJE/0gpli3m/s8DZQ0I89s/QdwqD6/v3eGnI2Lm+e9uaH/sNbY8hx+wF1+hdYMYtQoqgtaJj\nBbVXbXrIJbtHncmnUqQif6xFGEzxlUtVynI19WnrAy7MFaF8QplhVeGmK/McBUMtYdznMrE1Q316\npfgaR0vMW1GtDFfUI9XlE+YKxnHU+2aFrVtj2yIfP2wsVdhSu7w0drij68UVKcsT6sbF//emNf/0\n3/+w28bRBQ7Frxt6EbwrdobLbBvC1gpFYOigXq4RrP3E+9eQExuMHqmT2qC2mNM9bSm1u4GcIR09\nzlUjiTGnWhvSjlDd9IEfDsQ6qA/2/YwOZ1pIjOcZNZG2sdUnti1qLn2c9HmhlIaL8uXTL/TzGaPy\n+O5HLo8Xrg8XRAr9dPo5Vl1veWgh1DpWZ7kwdsOOA+m/8VkL2sCG8GF7ftOaA+jliX52dgZ9F25f\nOpefnY6h1WPIlMQtLm7Zcc+ESJ+ar2yWhrosCubwkq7qHWDVqPpg+GA/9qjfUfDJ6gy/cRq0VuOQ\n0w0thalKK5EKFW3gBVv1zYmj4lxaRfQa0vQW0/8et439+aT7ZP/yO8MOnp6Ef/37n4zPv/Hr1fjy\n+ZkicDuc+qQUCR+1sxcOZHm2CbNsPJSCIewWTc7f408RNN7/sDHOaGazHg+yNVB1pHRsOmedeB9o\nMUpbhahpCMdrkcp1YkQTUaklHgIT8U4Vf202mvOl4A3bNniocQk2HVQsrtBlRvQVwqJi5QFLu9F0\no5aKF0O2gaO01hHidz0ua+gYSj+WoqhjEi9OqXUFDWGWCBqlHLS2gQimg5Me/RJ64OVEp/NDG6gO\nLu/g7x9/etOa2/iEtMlVBNeDBzmxcUZaUNctLg65wCqcr9/ky4HT1ijO+Hte5JWTWXy9WEItEvPf\nJ7iOb0oq5ipavwzaWTUMXrJ3z6zjIFPBygq4qktIACplFWYryLfi/VwNcTEBUpEZ1hAyY2qaanTS\nDiz2y7rf+Uu9RsKKvpXCxxp+T59s8L6/rRB+3H5h1kcuDw7jKx/qwNUwHRzzxhgDxbi2AwUOO9gf\nxrq1OtJPfBjDBodER7k2o7bG1hquBi2kw+5GH51j9tUTBE3bsjK5UWXG/6+Dh+sz7WLUbaPajul4\ntc6QGY2dgtLkEw/lI1oaWm48bY7Uistkbp8Zc8e986AHF648yQWk0hsMjjhZe9SS5pzUqnEwmJOj\n7vTZMdl5KIXCxIvyt8uHN605wPX9e76ejtgD0NhrCXt3j4bGMENor2KMqNvFAUfWDfulJuf8sZMc\nmB6/dU50pd+WrCD2oExMQiEZB9SOCrjv9C6IxcF4SmFSERlLyAH4CaMsAQS00mgXpRJp8d4HfR2G\n9XimWadV4+P1J27zyv7xxrBfue0nv5rzzrYYYYvyRSpdPex8EEapSH2HCvR25fHpH99dT3mrHUWS\nJEny1+HtVaYkSZLkL0MGjSRJkuRuMmgkSZIkd5NBI0mSJLmbDBpJkiTJ3WTQSJIkSe4mg0aSJEly\nNxk0kiRJkrvJoJEkSZLcTQaNJEmS5G4yaCRJkiR3k0EjSZIkuZsMGkmSJMndZNBIkiRJ7iaDRpIk\nSXI3GTSSJEmSu8mgkSRJktxNBo0kSZLkbjJoJEmSJHeTQSNJkiS5mwwaSZIkyd1k0EiSJEnuJoNG\nkiRJcjf/B41drbLDZiBQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 460.8x288 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VH41Iqg8l-0j",
        "colab_type": "code",
        "outputId": "b5826a90-fbb4-40a1-bdd1-15235ed3d18f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        }
      },
      "source": [
        "# Show encoded representations\n",
        "encoder = Model(input_img, encoded)\n",
        "encoded_imgs = encoder.predict(xtrain)\n",
        "pl.figure(1, figsize=(6.4, 10))\n",
        "\n",
        "s = int(len(xtrain[0])/4)\n",
        "print(s)\n",
        "\n",
        "pl.subplot(1, 4, 1)\n",
        "pl.imshow(encoded_imgs[8].reshape(s, s * 8).T)\n",
        "pl.axis('off')\n",
        "pl.title('Boat 1')\n",
        "\n",
        "pl.subplot(1, 4, 2)\n",
        "pl.imshow(encoded_imgs[14].reshape(s, s * 8).T)\n",
        "pl.axis('off')\n",
        "pl.title('Boat 2')\n",
        "\n",
        "pl.subplot(1, 4, 3)\n",
        "pl.imshow(encoded_imgs[323].reshape(s, s * 8).T)\n",
        "pl.axis('off')\n",
        "pl.title('Cat 1')\n",
        "\n",
        "pl.subplot(1, 4, 4)\n",
        "pl.imshow(encoded_imgs[286].reshape(s, s * 8).T)\n",
        "pl.axis('off')\n",
        "pl.title('Cat 2')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Cat 2')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAJOCAYAAABV+mvlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQvowUBRFUYjo/wDvz+13X6f4PAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 460.8x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ExuXk-3XrFIm",
        "colab_type": "text"
      },
      "source": [
        "### To do\n",
        "\n",
        "TO DO if we want: greedy layer-wise training (see ANN exercise session 3)\n",
        "\n",
        "1) Train 1st layer as AE with 1 hidden layer\n",
        "\n",
        "2) Predict encoder part with training data \n",
        "          \n",
        "          features = encoder.predict(xtrain)\n",
        "          \n",
        "3) Train 2nd layer as AE with 1 hidden layer, and use features as input\n",
        "\n",
        "4) Predict encoder part\n",
        "\n",
        "5) Train 3rd layer as AE with 1 hidden layer, and use features obtained in step 4\n",
        "\n",
        "etc.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LE6hDQ8s-qbX",
        "colab_type": "text"
      },
      "source": [
        "# Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYcB9jgfP8X2",
        "colab_type": "text"
      },
      "source": [
        "### Classifier from auto-encoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuosDUH3-p8G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Classifier using AE with 2 conv layers in encoder\n",
        "# size code --> /4\n",
        "\n",
        "input_img = Input(shape=(len(xtrain[0]),len(xtrain[0,0]),len(xtrain[0,0,0]))) \n",
        "# 196608 dimensions if 256\n",
        "# 49152 if 128\n",
        "\n",
        "\n",
        "net = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
        "net = MaxPooling2D((2, 2), padding='same')(net)\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(net)\n",
        "encoded = MaxPooling2D((2, 2), padding='same')(net)\n",
        "\n",
        "# \"code\" layer: at this point the representation is \n",
        "  # (32, 32, 8) --> 8192 dimensions if 256\n",
        "  # (16, 16, 8) --> 2018 if 128\n",
        "\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n",
        "net = UpSampling2D((2, 2))(net)\n",
        "net = Conv2D(16, (3, 3), activation='relu', padding='same')(net)\n",
        "net = UpSampling2D((2, 2))(net)\n",
        "decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(net)\n",
        "\n",
        "autoencoder = Model(input_img, decoded)\n",
        "autoencoder.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "history = autoencoder.fit(xtrain, xtrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, xval),\n",
        "                verbose=0)\n",
        "\n",
        "# \n",
        "encoder = Model(input_img, encoded)\n",
        "encoded_imgs = encoder.predict(xtrain)\n",
        "#encoder.save(path+'AE1model.h5')\n",
        "#encoder.save_weights(path+'AE1weights.h5')\n",
        "\n",
        "encoder_frozen = load_model(path+'AE1modelDeep2.h5')\n",
        "for layer in encoder_frozen.layers:\n",
        "  layer.trainable = False\n",
        "  \n",
        "  \n",
        "net = Flatten()(encoder_frozen.output)\n",
        "classOut = Dense(2, activation='sigmoid')(net) #  simple logistic regression\n",
        "  # not softmax because we could have a multi-label image, with both sheeps and cows\n",
        "  # softmax --> only one label\n",
        "  # probably no images with both cats and boats? --> try softmax\n",
        "classifier = Model(encoder_frozen.input, classOut)\n",
        "print(classifier.summary())\n",
        "\n",
        "#plot_model(classifier, to_file=path+'classifier.png', show_shapes=True)\n",
        "SVG(model_to_dot(classifier).create(prog='dot', format='svg'))\n",
        "\n",
        "#autoencoder = Model(input_img, classOut)\n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "\n",
        "history = classifier.fit(xtrain, ytrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, yval),\n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n",
        "\n",
        "#classifier.save(path+'classifierAdamSig.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZKnxRGpOQBu8",
        "colab_type": "text"
      },
      "source": [
        "### Fine-tuning of classifier from auto-encoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGICwyaINejA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fine-tuning: retrain whole classifier when unfreezing the encoder part\n",
        "\n",
        "classifier = load_model(path+'classifierAdamSig.h5')\n",
        "                      \n",
        "for layer in classifier.layers:\n",
        "    layer.trainable = True\n",
        "    \n",
        "    \n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "history = classifier.fit(xtrain, ytrain, #x, y,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, yval), #validation_split=0.1,\n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n",
        "\n",
        "#classifier.save(path+'classifierAdamSigTuned.h5')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxS7cJWPE7KX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Show encoded representations of the same 4 images as previously\n",
        "encoder = load_model(path+'AE1model.h5')\n",
        "encoded_imgs = encoder.predict(xtrain)\n",
        "pl.figure(1, figsize=(6.4, 10))\n",
        "\n",
        "s = int(len(xtrain[0])/4)\n",
        "print(s)\n",
        "\n",
        "pl.subplot(1, 4, 1)\n",
        "pl.imshow(encoded_imgs[8].reshape(s, s * 8).T)\n",
        "pl.axis('off')\n",
        "pl.title('Boat 1')\n",
        "\n",
        "pl.subplot(1, 4, 2)\n",
        "pl.imshow(encoded_imgs[14].reshape(s, s * 8).T)\n",
        "pl.axis('off')\n",
        "pl.title('Boat 2')\n",
        "\n",
        "pl.subplot(1, 4, 3)\n",
        "pl.imshow(encoded_imgs[323].reshape(s, s * 8).T)\n",
        "pl.axis('off')\n",
        "pl.title('Cat 1')\n",
        "\n",
        "pl.subplot(1, 4, 4)\n",
        "pl.imshow(encoded_imgs[286].reshape(s, s * 8).T)\n",
        "pl.axis('off')\n",
        "pl.title('Cat 2')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8puk1dn6QHSq",
        "colab_type": "text"
      },
      "source": [
        "### Classifier trained from scratch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxZWsDI-SX_N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# From scratch: same architectures but initial random weights (i.e. not from auto-encoder training)\n",
        "\n",
        "\n",
        "#encoder_randomw = load_model(path+'AE1model.h5')\n",
        "#for layer in encoder_randomw.layers:\n",
        "#  layer.trainable = True\n",
        "#  if hasattr(layer, 'kernel_initializer'):\n",
        "#            layer.kernel_initializer = 'random_uniform'\n",
        "#            layer.bias_initializer = 'zeros'\n",
        "# then flatten and dense --> same\n",
        "\n",
        "input_img = Input(shape=(len(xtrain[0]),len(xtrain[0,0]),len(xtrain[0,0,0]))) \n",
        "\n",
        "net = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
        "net = MaxPooling2D((2, 2), padding='same')(net)\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(net)\n",
        "encoded = MaxPooling2D((2, 2), padding='same')(net)\n",
        "  \n",
        "net = Flatten()(encoded)\n",
        "classOut = Dense(2, activation='sigmoid')(net) \n",
        "classifier = Model(input_img, classOut)\n",
        "print(classifier.summary())\n",
        "\n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "\n",
        "history = classifier.fit(xtrain, ytrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, yval),\n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n",
        "\n",
        "#classifier.save(path+'classifierAdamSigScratch.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7l1fgsIQLpI",
        "colab_type": "text"
      },
      "source": [
        "### Classifier from greedy layer-wise trained auto-encoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hScm9LibNPpx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Greedy layer-wise training of the auto-encoder\n",
        "\n",
        "input_img = Input(shape=(len(xtrain[0]),len(xtrain[0,0]),len(xtrain[0,0,0]))) \n",
        "\n",
        "net = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
        "encoded1 = MaxPooling2D((2, 2), padding='same')(net)\n",
        "\n",
        "\n",
        "net = Conv2D(16, (3, 3), activation='relu', padding='same')(encoded1)\n",
        "net = UpSampling2D((2, 2))(net)\n",
        "decoded1 = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(net)\n",
        "\n",
        "autoencoder1 = Model(input_img, decoded1)\n",
        "autoencoder1.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "\n",
        "print(autoencoder1.summary())\n",
        "\n",
        "history = autoencoder1.fit(xtrain, xtrain,\n",
        "                epochs=50,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, xval),\n",
        "                verbose=0)\n",
        "\n",
        "encoder1 = Model(input_img, encoded1)\n",
        "encoded_imgs1 = encoder1.predict(xtrain)\n",
        "\n",
        "print(encoded_imgs1.shape)\n",
        "\n",
        "\n",
        "encoded_imgs1val = encoder1.predict(xval)\n",
        "\n",
        "#input2 = Input(shape=(64,64,16)) \n",
        "\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(input2)\n",
        "encoded2 = MaxPooling2D((2, 2), padding='same')(net)\n",
        "\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded2)\n",
        "net = UpSampling2D((2, 2))(net)\n",
        "decoded2 = Conv2D(16, (3, 3), activation='sigmoid', padding='same')(net)\n",
        "\n",
        "autoencoder2 = Model(input2, decoded2)\n",
        "autoencoder2.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "\n",
        "print(autoencoder2.summary())\n",
        "\n",
        "history = autoencoder2.fit(encoded_imgs1, encoded_imgs1,\n",
        "                epochs=50,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(encoded_imgs1val, encoded_imgs1val),\n",
        "                verbose=0)\n",
        "\n",
        "encoder2 = Model(input2, encoded)\n",
        "encoded_imgs2 = encoder2.predict(encoded_imgs1)\n",
        "\n",
        "print(encoded_imgs2.shape)\n",
        "\n",
        "\n",
        "for layer in encoder1.layers:\n",
        "  layer.trainable = False\n",
        "  \n",
        "for layer in encoder2.layers:\n",
        "  layer.trainable = False\n",
        "  \n",
        "  \n",
        "#encoded_imgs2 = encoder2.predict(encoder1.predict(xtrain))\n",
        "\n",
        "net = Flatten()(encoder2.output)\n",
        "classOut = Dense(2, activation='sigmoid')(net) \n",
        "classifier = Model(encoder2.input, classOut)\n",
        "print(classifier.summary())\n",
        "\n",
        "SVG(model_to_dot(classifier).create(prog='dot', format='svg'))\n",
        "\n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "\n",
        "history = classifier.fit(encoder1.predict(xtrain), ytrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(encoder1.predict(xval), yval),\n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n",
        "\n",
        "#classifier.save(path+'greedyClassifier.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Y8keWXNQSBA",
        "colab_type": "text"
      },
      "source": [
        "### Deeper CNN classifiers (no auto-encoder i.e. from scratch)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iY2btKhGiuaE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Deeper CNN\n",
        "\n",
        "input_img = Input(shape=(len(xtrain[0]),len(xtrain[0,0]),len(xtrain[0,0,0]))) \n",
        "\n",
        "net = Conv2D(64, (5, 5), activation='relu', padding='same')(input_img)\n",
        "net = MaxPooling2D((4, 4), padding='same')(net)\n",
        "net = Conv2D(128, (3, 3), activation='relu', padding='same')(net)\n",
        "net = MaxPooling2D((2, 2), padding='same')(net)\n",
        "net = Conv2D(256, (3, 3), activation='relu', padding='same')(net)\n",
        "net = Conv2D(256, (3, 3), activation='relu', padding='same')(net)\n",
        "net = Conv2D(128, (3, 3), activation='relu', padding='same')(net)\n",
        "encoded = MaxPooling2D((2, 2), padding='same')(net)\n",
        "\n",
        "net = Flatten()(encoded)\n",
        "classOut = Dense(2, activation='sigmoid')(net)\n",
        "classifier = Model(input_img, classOut)\n",
        "print(classifier.summary())\n",
        "\n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "\n",
        "history = classifier.fit(xtrain, ytrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, yval),\n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n",
        "\n",
        "#classifier.save(path+'classifierDeep.h5')\n",
        "#plot_model(classifier, to_file=path+'classifierDeep.png', show_shapes=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nyaM4J9YQaBL",
        "colab_type": "text"
      },
      "source": [
        "## Trying to decrease overfitting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "reG7rsHbQey_",
        "colab_type": "text"
      },
      "source": [
        "### More training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YwsPWyorYX2l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# More training data, less validation: 80-20% split\n",
        "\n",
        "encoder_frozen = load_model(path+'AE1model.h5')\n",
        "for layer in encoder_frozen.layers:\n",
        "  layer.trainable = False\n",
        "  \n",
        "  \n",
        "net = Flatten()(encoder_frozen.output)\n",
        "classOut = Dense(2, activation='sigmoid')(net)\n",
        "classifier = Model(encoder_frozen.input, classOut)\n",
        "\n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "x = np.append(xtrain, xval, axis=0)\n",
        "y = np.append(ytrain, yval, axis=0)\n",
        "\n",
        "# 80% of the 436+432 training data, 20% validation data\n",
        "\n",
        "history = classifier.fit(x, y,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_split=0.2,\n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MC55c6GqQiF_",
        "colab_type": "text"
      },
      "source": [
        "### L1 & L2 regularization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXDcBNNbx4g_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# L1 regularization\n",
        "\n",
        "encoder_frozen = load_model(path+'AE1model.h5')\n",
        "for layer in encoder_frozen.layers:\n",
        "  layer.trainable = False\n",
        "  if hasattr(layer, 'activity_regularizer'):\n",
        "            layer.activity_regularizer=l1(0.05) # 0.01 0.05 0.1\n",
        "  \n",
        "  \n",
        "net = Flatten()(encoder_frozen.output)\n",
        "classOut = Dense(2, activation='sigmoid', activity_regularizer=l1(0.05))(net) # 0.01 0.05 0.1\n",
        "classifier = Model(encoder_frozen.input, classOut)\n",
        "\n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "x = np.append(xtrain, xval, axis=0)\n",
        "y = np.append(ytrain, yval, axis=0)\n",
        "\n",
        "\n",
        "history = classifier.fit(xtrain, ytrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, yval), \n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n",
        "\n",
        "#classifier.save(path+'classifierAdamSigL1.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OjgpxJnM-8Pd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# L2 regularization\n",
        "\n",
        "encoder_frozen = load_model(path+'AE1model.h5')\n",
        "for layer in encoder_frozen.layers:\n",
        "  layer.trainable = False\n",
        "  if hasattr(layer, 'activity_regularizer'):\n",
        "            layer.activity_regularizer=l2(0.05) # 0.01 0.05 0.1\n",
        "  \n",
        "  \n",
        "net = Flatten()(encoder_frozen.output)\n",
        "classOut = Dense(2, activation='sigmoid', activity_regularizer=l2(0.05))(net) # 0.01 0.05 0.1\n",
        "classifier = Model(encoder_frozen.input, classOut)\n",
        "\n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "x = np.append(xtrain, xval, axis=0)\n",
        "y = np.append(ytrain, yval, axis=0)\n",
        "\n",
        "history = classifier.fit(xtrain, ytrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, yval), \n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n",
        "\n",
        "#classifier.save(path+'classifierAdamSigL2.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6qZxxSc_EoJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# L1 & L2 regularization\n",
        "\n",
        "encoder_frozen = load_model(path+'AE1model.h5')\n",
        "for layer in encoder_frozen.layers:\n",
        "  layer.trainable = False\n",
        "  if hasattr(layer, 'activity_regularizer'):\n",
        "            layer.activity_regularizer=l1_l2(0.02, 0.02)\n",
        "  \n",
        "  \n",
        "net = Flatten()(encoder_frozen.output)\n",
        "classOut = Dense(2, activation='sigmoid', activity_regularizer=l1_l2(0.02, 0.02))(net)\n",
        "classifier = Model(encoder_frozen.input, classOut)\n",
        "\n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "x = np.append(xtrain, xval, axis=0)\n",
        "y = np.append(ytrain, yval, axis=0)\n",
        "\n",
        "history = classifier.fit(xtrain, ytrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, yval), \n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n",
        "\n",
        "#classifier.save(path+'classifierAdamSigL1L2.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNmsCy13Qmwf",
        "colab_type": "text"
      },
      "source": [
        "### Dropout & Batch normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NL98TTATGEZe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Add Dropout layers in encoder \n",
        "# to do: check reconstruction\n",
        "# --> autoencoder not symmetric anymore --> dropout in decoder as well?\n",
        "\n",
        "input_img = Input(shape=(len(xtrain[0]),len(xtrain[0,0]),len(xtrain[0,0,0]))) \n",
        "\n",
        "net = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
        "net = MaxPooling2D((2, 2), padding='same')(net)\n",
        "net = Dropout(0.4)(net)\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(net)\n",
        "encoded = MaxPooling2D((2, 2), padding='same')(net)\n",
        "\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n",
        "net = UpSampling2D((2, 2))(net)\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(net)\n",
        "net = UpSampling2D((2, 2))(net)\n",
        "decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(net)\n",
        "\n",
        "autoencoder = Model(input_img, decoded)\n",
        "autoencoder.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "history = autoencoder.fit(xtrain, xtrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, xval),\n",
        "                verbose=0)\n",
        "\n",
        "encoder = Model(input_img, encoded)\n",
        "encoded_imgs = encoder.predict(xtrain)\n",
        "encoder.save(path+'AE1model_dropout.h5')\n",
        "\n",
        "for layer in encoder.layers:\n",
        "  layer.trainable = False\n",
        "\n",
        "net = Flatten()(encoder.output)\n",
        "classOut = Dense(2, activation='sigmoid')(net)\n",
        "classifier = Model(encoder.input, classOut)\n",
        "\n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "history = classifier.fit(xtrain, ytrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, xval),\n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n",
        "\n",
        "#classifier.save(path+'classifier_dropout.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYoFd2qMJcfQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Batch normalization \n",
        "\n",
        "input_img = Input(shape=(len(xtrain[0]),len(xtrain[0,0]),len(xtrain[0,0,0]))) \n",
        "\n",
        "\n",
        "net = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
        "net = BatchNormalization()(net)\n",
        "net = MaxPooling2D((2, 2), padding='same')(net)\n",
        "#net = Dropout(0.4)(net)\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(net)\n",
        "net = BatchNormalization()(net)\n",
        "encoded = MaxPooling2D((2, 2), padding='same')(net)\n",
        "\n",
        "net = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n",
        "net = UpSampling2D((2, 2))(net)\n",
        "net = Conv2D(16, (3, 3), activation='relu', padding='same')(net)\n",
        "net = UpSampling2D((2, 2))(net)\n",
        "decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(net)\n",
        "\n",
        "autoencoder = Model(input_img, decoded)\n",
        "autoencoder.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "history = autoencoder.fit(xtrain, xtrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, xval),\n",
        "                verbose=0)\n",
        "\n",
        " \n",
        "encoder = Model(input_img, encoded)\n",
        "encoded_imgs = encoder.predict(xtrain)\n",
        "encoder.save(path+'AE1model_batchnorm.h5')\n",
        "\n",
        "#autoencoder = Model(input_img, classOut)\n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "history = classifier.fit(xtrain, ytrain,\n",
        "                epochs=100,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xval, yval),\n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n",
        "\n",
        "classifier.save(path+'classifier_batchnorm.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nh5wcQgzQq6C",
        "colab_type": "text"
      },
      "source": [
        "### Data augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uWQ-sI_zfjU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xtrainIN = np.load(path+'xtrainIN.npy')#, allow_pickle=True)\n",
        "ytrainIN = np.load(path+'ytrainIN.npy')#, allow_pickle=True)\n",
        "xvalIN = np.load(path+'xvalIN.npy')#, allow_pickle=True)\n",
        "yvalIN = np.load(path+'yvalIN.npy')#, allow_pickle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mf9kmNx3kIbJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data augmentation\n",
        "encoder_frozen = load_model(path+'AE1model.h5')\n",
        "for layer in encoder_frozen.layers:\n",
        "  layer.trainable = False\n",
        "  \n",
        "  \n",
        "net = Flatten()(encoder_frozen.output)\n",
        "classOut = Dense(2, activation='sigmoid')(net)\n",
        "classifier = Model(encoder_frozen.input, classOut)\n",
        "print(classifier.summary())\n",
        "\n",
        "classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "\n",
        "#history = classifier.fit(xtrain, ytrain,\n",
        " #               epochs=100,\n",
        "  #              batch_size=32,\n",
        "   #             shuffle=True,\n",
        "    #            validation_data=(xval, yval),\n",
        "     #           verbose=0,\n",
        "      #          callbacks=[PlotLossesKeras()])\n",
        "\n",
        "#classifier.save(path+'classifierAdamSig.h5')\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "    featurewise_center=True,\n",
        "    #featurewise_std_normalization=True,\n",
        "    rotation_range=20,\n",
        "    shear_range=0.15,\n",
        "    zoom_range=0.15,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    horizontal_flip=True)\n",
        "\n",
        "# compute quantities required for featurewise normalization\n",
        "# (std, mean, and principal components if ZCA whitening is applied)\n",
        "#datagen.fit(xtrain)\n",
        "\n",
        "# fits the model on batches with real-time data augmentation:\n",
        "history = classifier.fit_generator(datagen.flow(xtrain, ytrain, batch_size=32), validation_data=(xval,yval),\n",
        "                    steps_per_epoch=len(xtrain)/32, epochs=100, validation_steps=len(xval)/32,\n",
        "                callbacks=[PlotLossesKeras()])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DNyPU-9ZQyIM",
        "colab_type": "text"
      },
      "source": [
        "### Using pre-trained networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHTRuvWRQxJD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Transfer learning using ImageNet --> CNN\n",
        "\n",
        "#base_model=obileNet(weights='imagenet',include_top=False) #imports the mobilenet model and discards the last 1000 neuron layer.\n",
        "model = VGG16(weights=\"imagenet\")\n",
        "net=model.output\n",
        "#net=GlobalAveragePooling2D()(net)\n",
        "#net=Dense(1024,activation='relu')(net) #we add dense layers so that the model can learn more complex functions and classify for better results.\n",
        "#net=Dense(1024,activation='relu')(net) #dense layer 2\n",
        "#net=Dense(512,activation='relu')(net) #dense layer 3\n",
        "preds=Dense(2,activation='sigmoid')(net) #final layer with sigmoid activation #120\n",
        "\n",
        "imgNetModel=Model(inputs=model.input,outputs=preds)\n",
        "\n",
        "print(imgNetModel.summary())\n",
        "\n",
        "# if we want all layers except dense layers not trainable\n",
        "#for layer in imgNetModel.layers:\n",
        "#    layer.trainable=False\n",
        "# if we want to set the first 20 layers of the network to be non-trainable\n",
        "#for layer in imgNetModel.layers[:20]:\n",
        "#    layer.trainable=False\n",
        "for layer in imgNetModel.layers:#[20:]:\n",
        "    layer.trainable=True\n",
        "\n",
        "imgNetModel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "history = imgNetModel.fit(xtrainIN, ytrainIN,\n",
        "                epochs=50,\n",
        "                batch_size=32,\n",
        "                shuffle=True,\n",
        "                validation_data=(xvalIN, yvalIN),\n",
        "                verbose=0,\n",
        "                callbacks=[PlotLossesKeras()])\n",
        "\n",
        "preds = imgNetModel.predict(preprocess_input(xvalIN))\n",
        "print(preds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DzJ13PUvQ5Lc",
        "colab_type": "text"
      },
      "source": [
        "Pre-trained network... not working: 60\\% accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2zhR1Ey9MpaP",
        "colab_type": "text"
      },
      "source": [
        "TO DO:\n",
        "- try pre-trained network\n",
        "- try sparse auto-encoder?\n",
        "- try CNN\n",
        "- try RBM?\n",
        "- GAN? can it be used for classification\n",
        "- Variational autoencoder?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4OEF90yoXQx",
        "colab_type": "text"
      },
      "source": [
        "# Image Segmentation\n",
        "\n",
        "\n",
        "good link for most know techniques\n",
        "\n",
        "https://medium.com/@arthur_ouaknine/review-of-deep-learning-algorithms-for-image-semantic-segmentation-509a600f7b57"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbatZ9ZecNE_",
        "colab_type": "text"
      },
      "source": [
        "## Load data and Images visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "POFjlZuHcQa_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xtrain = np.load(path+'x_train_seg_all224.npy')\n",
        "xval = np.load(path+'x_val_seg_all224.npy')\n",
        "yval = np.load(path+'y_val_seg_all224.npy')\n",
        "ytrain = np.load(path+'y_train_seg_all224.npy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lvycxm25e4o_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plot 2 \"good\" images of each class\n",
        "ratio = 15\n",
        "pl.figure(1, figsize=(1*ratio, 4*ratio))\n",
        "\n",
        "pl.subplot(1, 4, 1)\n",
        "pl.imshow(xtrain[0])\n",
        "pl.axis('off')\n",
        "\n",
        "\n",
        "pl.subplot(1, 4, 2)\n",
        "pl.imshow(xtrain[1])\n",
        "pl.axis('off')\n",
        "\n",
        "\n",
        "pl.subplot(1, 4, 3)\n",
        "pl.imshow(xtrain[2])\n",
        "pl.axis('off')\n",
        "\n",
        "\n",
        "pl.subplot(1, 4, 4)\n",
        "pl.imshow(xtrain[3])\n",
        "pl.axis('off')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-x7oasUoe31w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plot 2 \"good\" images of each class\n",
        "ratio = 15\n",
        "pl.figure(1, figsize=(1*ratio, 4*ratio))\n",
        "\n",
        "pl.subplot(1, 4, 1)\n",
        "pl.imshow(ytrain[0])\n",
        "pl.axis('off')\n",
        "\n",
        "\n",
        "pl.subplot(1, 4, 2)\n",
        "pl.imshow(ytrain[1])\n",
        "pl.axis('off')\n",
        "\n",
        "\n",
        "pl.subplot(1, 4, 3)\n",
        "pl.imshow(ytrain[2])\n",
        "pl.axis('off')\n",
        "\n",
        "\n",
        "pl.subplot(1, 4, 4)\n",
        "pl.imshow(ytrain[3])\n",
        "pl.axis('off')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wr1QUasRolzS",
        "colab_type": "text"
      },
      "source": [
        "## Image pre processing\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTfMNP1aoyIi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# preprocess segmentation to binary images\n",
        "\n",
        "ytrain_bin = np.zeros((ytrain.shape[0],ytrain.shape[1],ytrain.shape[2],2))\n",
        "ytrain_bin_plot = np.zeros((ytrain.shape[0],ytrain.shape[1],ytrain.shape[2]))\n",
        "yval_bin = np.zeros((yval.shape[0],yval.shape[1],yval.shape[2],2))\n",
        "yval_bin_plot = np.zeros((yval.shape[0],yval.shape[1],yval.shape[2]))\n",
        "\n",
        "for i in range(ytrain.shape[0]):\n",
        "  for j in range(ytrain.shape[1]):\n",
        "    for k in range(ytrain.shape[2]):\n",
        "      if ytrain[i][j][k][0] ==  0.0 and ytrain[i][j][k][1] ==  0.0: \n",
        "        ytrain_bin[i][j][k] = [1, 0]\n",
        "        ytrain_bin_plot[i][j][k] = 1\n",
        "      else:\n",
        "        ytrain_bin[i][j][k] = [0, 1]\n",
        "        \n",
        "for i in range(yval.shape[0]):\n",
        "  for j in range(yval.shape[1]):\n",
        "    for k in range(yval.shape[2]):\n",
        "      if yval[i][j][k][0] ==  0.0 and yval[i][j][k][1] ==  0.0: \n",
        "        yval_bin[i][j][k] = [1, 0]\n",
        "        yval_bin_plot[i][j][k] = 1\n",
        "      else:\n",
        "        yval_bin[i][j][k] = [0, 1]\n",
        "              "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKoCoK6ufS6q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ratio = 15\n",
        "pl.figure(1, figsize=(1*ratio, 4*ratio))\n",
        "\n",
        "pl.subplot(1, 4, 1)\n",
        "pl.imshow(ytrain_bin_plot[0])\n",
        "pl.axis('off')\n",
        "#pl.title('Sheep 1')\n",
        "\n",
        "pl.subplot(1, 4, 2)\n",
        "pl.imshow(ytrain_bin_plot[1])\n",
        "pl.axis('off')\n",
        "#pl.title('Sheep 2')\n",
        "\n",
        "pl.subplot(1, 4, 3)\n",
        "pl.imshow(ytrain_bin_plot[2])\n",
        "pl.axis('off')\n",
        "#pl.title('Cow 1')\n",
        "\n",
        "pl.subplot(1, 4, 4)\n",
        "pl.imshow(ytrain_bin_plot[3])\n",
        "pl.axis('off')\n",
        "#pl.title('Cow 2')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qx5WbaOMfXsK",
        "colab_type": "text"
      },
      "source": [
        "## FCN model\n",
        "\n",
        "Fully convolutional networks for semantic segmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFf1oVgxfXOW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Import usual libraries\n",
        "import tensorflow as tf\n",
        "from keras.backend.tensorflow_backend import set_session\n",
        "import keras, sys, time, warnings\n",
        "from keras.models import *\n",
        "from keras.layers import *\n",
        "import pandas as pd \n",
        "warnings.filterwarnings(\"ignore\")  \n",
        "\n",
        "print(\"python {}\".format(sys.version))\n",
        "print(\"keras version {}\".format(keras.__version__)); del keras\n",
        "print(\"tensorflow version {}\".format(tf.__version__))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6hRQ62Kpfo4_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## location of VGG weights\n",
        "VGG_Weights_path = path + \"vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EvI350kfrLw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def FCN8( nClasses ,  input_height=224, input_width=224):\n",
        "    ## input_height and width must be devisible by 32 because maxpooling with filter size = (2,2) is operated 5 times,\n",
        "    ## which makes the input_height and width 2^5 = 32 times smaller\n",
        "    assert input_height%32 == 0\n",
        "    assert input_width%32 == 0\n",
        "    IMAGE_ORDERING =  \"channels_last\" \n",
        "\n",
        "    img_input = Input(shape=(input_height,input_width, 3)) ## Assume 224,224,3\n",
        "    \n",
        "    ## Block 1\n",
        "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', data_format=IMAGE_ORDERING )(img_input)\n",
        "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', data_format=IMAGE_ORDERING )(x)\n",
        "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool', data_format=IMAGE_ORDERING )(x)\n",
        "    f1 = x\n",
        "    \n",
        "    # Block 2\n",
        "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', data_format=IMAGE_ORDERING )(x)\n",
        "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', data_format=IMAGE_ORDERING )(x)\n",
        "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool', data_format=IMAGE_ORDERING )(x)\n",
        "    f2 = x\n",
        "\n",
        "    # Block 3\n",
        "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', data_format=IMAGE_ORDERING )(x)\n",
        "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', data_format=IMAGE_ORDERING )(x)\n",
        "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', data_format=IMAGE_ORDERING )(x)\n",
        "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool', data_format=IMAGE_ORDERING )(x)\n",
        "    pool3 = x\n",
        "\n",
        "    # Block 4\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', data_format=IMAGE_ORDERING )(x)\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2', data_format=IMAGE_ORDERING )(x)\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3', data_format=IMAGE_ORDERING )(x)\n",
        "    pool4 = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool', data_format=IMAGE_ORDERING )(x)## (None, 14, 14, 512) \n",
        "\n",
        "    # Block 5\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', data_format=IMAGE_ORDERING )(pool4)\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2', data_format=IMAGE_ORDERING )(x)\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', data_format=IMAGE_ORDERING )(x)\n",
        "    pool5 = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool', data_format=IMAGE_ORDERING )(x)## (None, 7, 7, 512)\n",
        "\n",
        "    #x = Flatten(name='flatten')(x)\n",
        "    #x = Dense(4096, activation='relu', name='fc1')(x)\n",
        "    # <--> o = ( Conv2D( 4096 , ( 7 , 7 ) , activation='relu' , padding='same', data_format=IMAGE_ORDERING))(o)\n",
        "    # assuming that the input_height = input_width = 224 as in VGG data\n",
        "    \n",
        "    #x = Dense(4096, activation='relu', name='fc2')(x)\n",
        "    # <--> o = ( Conv2D( 4096 , ( 1 , 1 ) , activation='relu' , padding='same', data_format=IMAGE_ORDERING))(o)   \n",
        "    # assuming that the input_height = input_width = 224 as in VGG data\n",
        "    \n",
        "    #x = Dense(1000 , activation='softmax', name='predictions')(x)\n",
        "    # <--> o = ( Conv2D( nClasses ,  ( 1 , 1 ) ,kernel_initializer='he_normal' , data_format=IMAGE_ORDERING))(o)\n",
        "    # assuming that the input_height = input_width = 224 as in VGG data\n",
        "    \n",
        "    \n",
        "    vgg  = Model(  img_input , pool5  )\n",
        "    vgg.load_weights(VGG_Weights_path) ## loading VGG weights for the encoder parts of FCN8\n",
        "    \n",
        "    n = 4096\n",
        "    o = ( Conv2D( n , ( 7 , 7 ) , activation='relu' , padding='same', name=\"conv6\", data_format=IMAGE_ORDERING))(pool5)\n",
        "    conv7 = ( Conv2D( n , ( 1 , 1 ) , activation='relu' , padding='same', name=\"conv7\", data_format=IMAGE_ORDERING))(o)\n",
        "    \n",
        "    \n",
        "    ## 4 times upsamping for pool4 layer\n",
        "    conv7_4 = Conv2DTranspose( nClasses , kernel_size=(4,4) ,  strides=(4,4) , use_bias=False, data_format=IMAGE_ORDERING )(conv7)\n",
        "    ## (None, 224, 224, 10)\n",
        "    ## 2 times upsampling for pool411\n",
        "    pool411 = ( Conv2D( nClasses , ( 1 , 1 ) , activation='relu' , padding='same', name=\"pool4_11\", data_format=IMAGE_ORDERING))(pool4)\n",
        "    pool411_2 = (Conv2DTranspose( nClasses , kernel_size=(2,2) ,  strides=(2,2) , use_bias=False, data_format=IMAGE_ORDERING ))(pool411)\n",
        "    \n",
        "    pool311 = ( Conv2D( nClasses , ( 1 , 1 ) , activation='relu' , padding='same', name=\"pool3_11\", data_format=IMAGE_ORDERING))(pool3)\n",
        "        \n",
        "    o = Add(name=\"add\")([pool411_2, pool311, conv7_4 ])\n",
        "    o = Conv2DTranspose( nClasses , kernel_size=(8,8) ,  strides=(8,8) , use_bias=False, data_format=IMAGE_ORDERING )(o)\n",
        "    o = (Activation('softmax'))(o)\n",
        "    \n",
        "    model = Model(img_input, o)\n",
        "\n",
        "    return model\n",
        "\n",
        "n_classes = 2;\n",
        "  \n",
        "model = FCN8(nClasses     = n_classes,  \n",
        "             input_height = 224, \n",
        "             input_width  = 224)\n",
        "model.summary()\n",
        "\n",
        "#plot_model(model, to_file=path+'FCN8.png', show_shapes=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EU9fq5zjfvRP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import optimizers\n",
        "\n",
        "epochs_n = 100 #50 #200\n",
        "\n",
        "sgd = optimizers.SGD(lr=1E-2, decay=5**(-4), momentum=0.9, nesterov=True)\n",
        "model.compile(loss='categorical_crossentropy', #categorical_crossentropy binary_crossentropy\n",
        "              optimizer=sgd, #sgd\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "hist1 = model.fit(xtrain,ytrain_bin,\n",
        "                  validation_data=(xval,yval_bin),\n",
        "                  batch_size=32,epochs=epochs_n,verbose=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouf4t_Xlf3Mm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for key in ['loss', 'val_loss']:\n",
        "    plt.plot(hist1.history[key],label=key)\n",
        "    plt.title(\"categorical cross entropy (cost function)\")\n",
        "    plt.xlabel('epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pw0qK53Ef41O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for key in ['acc', 'val_acc']:\n",
        "    plt.plot(hist1.history[key],label=key)\n",
        "    plt.title(\"Accuracy\")\n",
        "    plt.xlabel('epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MW0vXgP-f85t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model.predict(xval)\n",
        "y_predi = np.argmax(y_pred, axis=3)\n",
        "y_vali = np.argmax(yval_bin, axis=3)\n",
        "\n",
        "print(y_vali.shape,y_predi.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9kJbdU_gpJT",
        "colab_type": "text"
      },
      "source": [
        "## Metrics and results\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UTpbe66gAwu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def IoU(Yi,y_predi):\n",
        "    ## mean Intersection over Union\n",
        "    ## Mean IoU = TP/(FN + TP + FP)\n",
        "\n",
        "    IoUs = []\n",
        "    Nclass = int(np.max(Yi)) + 1\n",
        "    for c in range(Nclass):\n",
        "        TP = np.sum( (Yi == c)&(y_predi==c) )\n",
        "        FP = np.sum( (Yi != c)&(y_predi==c) )\n",
        "        FN = np.sum( (Yi == c)&(y_predi != c)) \n",
        "        IoU = TP/float(TP + FP + FN)\n",
        "        print(\"class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}\".format(c,TP,FP,FN,IoU))\n",
        "        IoUs.append(IoU)\n",
        "    mIoU = np.mean(IoUs)\n",
        "    print(\"_________________\")\n",
        "    print(\"Mean IoU: {:4.3f}\".format(mIoU))\n",
        "    \n",
        "IoU(y_vali,y_predi)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVLAnY4pgDAW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Dice_score(Yi,y_predi):\n",
        "    ## mean Intersection over Union\n",
        "    ## Mean IoU = 2TP/(FN + 2TP + FP)\n",
        "\n",
        "    Dices = []\n",
        "    Nclass = int(np.max(Yi)) + 1\n",
        "    for c in range(Nclass):\n",
        "        TP = np.sum( (Yi == c)&(y_predi==c) )\n",
        "        FP = np.sum( (Yi != c)&(y_predi==c) )\n",
        "        FN = np.sum( (Yi == c)&(y_predi != c)) \n",
        "        Dice = 2*TP/float(2*TP + FP + FN)\n",
        "        print(\"class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, Dice score={:4.3f}\".format(c,TP,FP,FN,Dice))\n",
        "        Dices.append(Dice)\n",
        "    mDice = np.mean(Dices)\n",
        "    print(\"_________________\")\n",
        "    print(\"Mean Dice score: {:4.3f}\".format(mDice))\n",
        "    \n",
        "Dice_score(y_vali,y_predi)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NU6Rw3jHgEq-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trJCdiXGgGP1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def give_color_to_seg_img(seg,n_classes):\n",
        "    '''\n",
        "    seg : (input_width,input_height,3)\n",
        "    '''\n",
        "    \n",
        "    if len(seg.shape)==3:\n",
        "        seg = seg[:,:,0]\n",
        "    seg_img = np.zeros( (seg.shape[0],seg.shape[1],3) ).astype('float')\n",
        "    colors = sns.color_palette(\"hls\", n_classes)\n",
        "    \n",
        "    for c in range(n_classes):\n",
        "        segc = (seg == c)\n",
        "        seg_img[:,:,0] += (segc*( colors[c][0] ))\n",
        "        seg_img[:,:,1] += (segc*( colors[c][1] ))\n",
        "        seg_img[:,:,2] += (segc*( colors[c][2] ))\n",
        "\n",
        "    return(seg_img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nc0xTi23gIV-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "shape = (224,224)\n",
        "n_classes= 2 #10\n",
        "\n",
        "for i in range(10):\n",
        "    img_is  = (xval[i] + 1)*(255.0/2)\n",
        "    seg = y_predi[i]\n",
        "    segtest = y_vali[i]\n",
        "\n",
        "    fig = plt.figure(figsize=(10,30))    \n",
        "    ax = fig.add_subplot(1,3,1)\n",
        "    ax.imshow(img_is/255.0)\n",
        "    ax.set_title(\"original\")\n",
        "    \n",
        "    ax = fig.add_subplot(1,3,2)\n",
        "    ax.imshow(give_color_to_seg_img(seg,n_classes))\n",
        "    ax.set_title(\"predicted class\")\n",
        "    \n",
        "    ax = fig.add_subplot(1,3,3)\n",
        "    ax.imshow(give_color_to_seg_img(segtest,n_classes))\n",
        "    ax.set_title(\"true class\")\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}