diff --git "a/docs/notebooks/ex11h - next frame prediction convolutional lstm.ipynb" "b/docs/notebooks/ex11h - next frame prediction convolutional lstm.ipynb"
new file mode 100644--- /dev/null
+++ "b/docs/notebooks/ex11h - next frame prediction convolutional lstm.ipynb"
@@ -0,0 +1,1863 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LEO272XuOmKC"
+ },
+ "source": [
+ "# Next-Frame Video Prediction with Convolutional LSTMs\n",
+ "\n",
+ "**Author:** [Amogh Joshi](https://github.com/amogh7joshi)
\n",
+ "**Date created:** 2021/06/02
\n",
+ "**Last modified:** 2023/11/10
\n",
+ "**Description:** How to build and train a convolutional LSTM model for next-frame video prediction."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "crkzpIjXOmKD"
+ },
+ "source": [
+ "## Introduction\n",
+ "\n",
+ "The\n",
+ "[Convolutional LSTM](https://papers.nips.cc/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf)\n",
+ "architectures bring together time series processing and computer vision by\n",
+ "introducing a convolutional recurrent cell in a LSTM layer. In this example, we will explore the\n",
+ "Convolutional LSTM model in an application to next-frame prediction, the process\n",
+ "of predicting what video frames come next given a series of past frames."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SLdtFHzmOmKD"
+ },
+ "source": [
+ "## Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "UOm_0NRkOmKE"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import keras\n",
+ "from keras import layers\n",
+ "\n",
+ "import io\n",
+ "import imageio\n",
+ "from IPython.display import Image, display\n",
+ "from ipywidgets import widgets, Layout, HBox"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VyMI6jyPOmKE"
+ },
+ "source": [
+ "## Dataset Construction\n",
+ "\n",
+ "For this example, we will be using the\n",
+ "[Moving MNIST](http://www.cs.toronto.edu/~nitish/unsupervised_video/)\n",
+ "dataset.\n",
+ "\n",
+ "We will download the dataset and then construct and\n",
+ "preprocess training and validation sets.\n",
+ "\n",
+ "For next-frame prediction, our model will be using a previous frame,\n",
+ "which we'll call `f_n`, to predict a new frame, called `f_(n + 1)`.\n",
+ "To allow the model to create these predictions, we'll need to process\n",
+ "the data such that we have \"shifted\" inputs and outputs, where the\n",
+ "input data is frame `x_n`, being used to predict frame `y_(n + 1)`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "KFDXdhSCOmKE",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "1a94cb96-d696-4e18-e5ed-724c3938c931"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Downloading data from http://www.cs.toronto.edu/~nitish/unsupervised_video/mnist_test_seq.npy\n",
+ "819200096/819200096 [==============================] - 27s 0us/step\n",
+ "Training Dataset Shapes: (900, 19, 64, 64, 1), (900, 19, 64, 64, 1)\n",
+ "Validation Dataset Shapes: (100, 19, 64, 64, 1), (100, 19, 64, 64, 1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Download and load the dataset.\n",
+ "fpath = keras.utils.get_file(\n",
+ " \"moving_mnist.npy\",\n",
+ " \"http://www.cs.toronto.edu/~nitish/unsupervised_video/mnist_test_seq.npy\",\n",
+ ")\n",
+ "dataset = np.load(fpath)\n",
+ "\n",
+ "# Swap the axes representing the number of frames and number of data samples.\n",
+ "dataset = np.swapaxes(dataset, 0, 1)\n",
+ "# We'll pick out 1000 of the 10000 total examples and use those.\n",
+ "dataset = dataset[:1000, ...]\n",
+ "# Add a channel dimension since the images are grayscale.\n",
+ "dataset = np.expand_dims(dataset, axis=-1)\n",
+ "\n",
+ "# Split into train and validation sets using indexing to optimize memory.\n",
+ "indexes = np.arange(dataset.shape[0])\n",
+ "np.random.shuffle(indexes)\n",
+ "train_index = indexes[: int(0.9 * dataset.shape[0])]\n",
+ "val_index = indexes[int(0.9 * dataset.shape[0]) :]\n",
+ "train_dataset = dataset[train_index]\n",
+ "val_dataset = dataset[val_index]\n",
+ "\n",
+ "# Normalize the data to the 0-1 range.\n",
+ "train_dataset = train_dataset / 255\n",
+ "val_dataset = val_dataset / 255\n",
+ "\n",
+ "\n",
+ "# We'll define a helper function to shift the frames, where\n",
+ "# `x` is frames 0 to n - 1, and `y` is frames 1 to n.\n",
+ "def create_shifted_frames(data):\n",
+ " x = data[:, 0 : data.shape[1] - 1, :, :]\n",
+ " y = data[:, 1 : data.shape[1], :, :]\n",
+ " return x, y\n",
+ "\n",
+ "\n",
+ "# Apply the processing function to the datasets.\n",
+ "x_train, y_train = create_shifted_frames(train_dataset)\n",
+ "x_val, y_val = create_shifted_frames(val_dataset)\n",
+ "\n",
+ "# Inspect the dataset.\n",
+ "print(\"Training Dataset Shapes: \" + str(x_train.shape) + \", \" + str(y_train.shape))\n",
+ "print(\"Validation Dataset Shapes: \" + str(x_val.shape) + \", \" + str(y_val.shape))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KsDeIsFNOmKF"
+ },
+ "source": [
+ "## Data Visualization\n",
+ "\n",
+ "Our data consists of sequences of frames, each of which\n",
+ "are used to predict the upcoming frame. Let's take a look\n",
+ "at some of these sequential frames."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "-bUSUZI-OmKF",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 692
+ },
+ "outputId": "35cdc91e-42ee-4eaa-8dc3-a2fbb42e09cb"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Displaying frames for example 544.\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxsAAAKSCAYAAAC6FsZtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeXxcd3no/8/su2aTRhrtiyXLtuzETrxmLyFkJ0DC0gUI5QXc29Ly4nJ7Ay2lhEJL4d62wM0tbSmhLA0kJEBISIEsJc7qOE5iS161b6PZNPu+/P7w7xws77Ely5Ke9+vlV6zRzOgc5esz5/l+n+/zaKrVahUhhBBCCCGEmGfaxT4AIYQQQgghxPIkwYYQQgghhBBiQUiwIYQQQgghhFgQEmwIIYQQQgghFoQEG0IIIYQQQogFIcGGEEIIIYQQYkFIsCGEEEIIIYRYEBJsCCGEEEIIIRaEBBtCCCGEEEKIBSHBhhBCCCGEEGJBLLlg4/7770ej0Zz0zz333LPYhzcvUqkUn/vc57jxxhvxeDxoNBruv//+xT4swcoYf7t27eKP//iPWbduHTabjdbWVt797ndz6NChxT40wcoYg/39/dx11110dnZitVqpra3l6quv5tFHH13sQ1vxVsL4O94Xv/hFNBoNfX19i30oK95KGH/PPPPMKc/xxRdfXOzDOyf6xT6Ac3XvvffS0dEx57HlciEIh8Pce++9tLa2cskll/DMM88s9iGJ4yzn8fflL3+Z5557jrvuuosNGzYQCAT4xje+waZNm3jxxReXzXkudct5DI6OjpJMJvnABz5AY2MjmUyGH//4x9x+++1885vf5CMf+chiH+KKt5zH37EmJib40pe+hM1mW+xDEcdYCePvT/7kT9i8efOcx1atWrVIR3N+lmywcdNNN3H55Zef1XNzuRxGoxGtdmks5Pj9fqanp2loaOCVV145YbCJxbecx98nP/lJfvCDH2A0GtXH3vOe97B+/Xr+9m//lu9973uLeHRCsZzH4M0338zNN98857E//uM/5rLLLuP//J//I8HGRWA5j79jfepTn2Lbtm2Uy2XC4fBiH474/62E8XfVVVdx5513LvZhzIul9Zs/C8ry0wMPPMBf/MVf0NTUhNVqJZFIEI1G+dSnPsX69eux2+3U1NRw00038frrr5/0PX70ox/x+c9/nqamJhwOB3feeSfxeJx8Ps8nPvEJfD4fdrudu+++m3w+f8KxfO973+Oyyy7DYrHg8Xh473vfy/j4+BnPwWQy0dDQMG+/E3HhLIfxt2PHjjmBBkB3dzfr1q1j//795/cLEgtuOYzBk9HpdLS0tBCLxc7p9eLCWE7j7ze/+Q0PPfQQ//AP/3C+vxZxgSyn8QeQTCYplUrn9Tu5GCzZlY14PH7CLENtba369y984QsYjUY+9alPkc/nMRqNDAwM8JOf/IS77rqLjo4OZmZm+OY3v8k111zDwMAAjY2Nc97vb/7mb7BYLNxzzz0cOXKEr3/96xgMBrRaLbOzs/zVX/0VL774Ivfffz8dHR385V/+pfraL37xi3z2s5/l3e9+Nx/+8IcJhUJ8/etf5+qrr2bPnj24XK4F/f2IhbXSxl+1WmVmZoZ169a9+V+WWBArYQym02my2SzxeJyf/exn/OIXv+A973nP+f3ixLxY7uOvXC7z8Y9/nA9/+MOsX7/+/H9hYl4t9/EHcPfdd5NKpdDpdFx11VV85StfOevVnItOdYn59re/XQVO+qdarVaffvrpKlDt7OysZjKZOa/N5XLVcrk857Hh4eGqyWSq3nvvvepjynv09fVVC4WC+vj73ve+qkajqd50001z3mP79u3VtrY29euRkZGqTqerfvGLX5zzvL1791b1ev0Jj5/Orl27qkD129/+9lm/RiyclTb+FN/97nerQPVb3/rWm36tmF8raQx+9KMfVc9Nq9VW77zzzmo0Gj2r14qFsVLG3ze+8Y2q0+msBoPBarVarV5zzTXVdevWnfF1YmGthPH33HPPVd/1rndVv/Wtb1V/+tOfVv/mb/6m6vV6q2azufrqq6+e+Zd0EVqyKxv/9//+X3p6ek75/Q984ANYLJY5j5lMJvXv5XKZWCyG3W5n9erVvPrqqye8x/vf/34MBoP69datW/mP//gPPvShD8153tatW/na175GqVRCr9fz8MMPU6lUePe73z0n8m5oaKC7u5unn36az3zmM2/6nMXFYyWNvwMHDvBHf/RHbN++nQ984ANn/TqxsFbCGPzEJz7BnXfeydTUFD/60Y8ol8sUCoUzvk4svOU8/iKRCH/5l3/JZz/7Werq6k79SxCLZjmPvx07drBjxw7169tvv50777yTDRs28OlPf5onnnjilK+9WC3ZYGPLli2nXU46vkoBQKVS4R//8R+57777GB4eplwuq9/zer0nPL+1tXXO106nE4CWlpYTHq9UKsTjcbxeL4cPH6ZardLd3X3SYzt28IqlaaWMv0AgwC233ILT6eShhx5Cp9Od9WvFwloJY7C3t5fe3l7g6Af/DTfcwG233cZLL72ERqM5q/cQC2M5j7+/+Iu/wOPx8PGPf/y0zxOLZzmPv5NZtWoVb3/723n44Ycpl8tL7rN4yQYbZ3J8RAvwpS99ic9+9rN86EMf4gtf+AIejwetVssnPvEJKpXKCc8/1f/MUz1erVaBowNao9Hwi1/84qTPtdvtb+ZUxBK0HMZfPB7npptuIhaL8eyzz56QzyoubsthDB7vzjvv5KMf/SiHDh1i9erV5/Qe4sJYquPv8OHD/PM//zP/8A//wNTUlPp4LpejWCwyMjJCTU0NHo/nlO8hFt9SHX+n09LSQqFQIJ1OU1NTc07vsViWbbBxMg899BDXXXcd3/rWt+Y8HovF5mwsOl9dXV1Uq1U6OjpOu8wnVpalNP5yuRy33XYbhw4d4te//jVr166dt+MTi2cpjcGTyWazwNFAWCw9S2H8TU5OUqlU+JM/+RP+5E/+5ITvd3R08Kd/+qdSoWoJWgrj73SGhoYwm81LcsJ62ZW+PR2dTqdGnooHH3yQycnJef0573znO9HpdHz+858/4edVq1Uikci8/jyxNCyV8Vcul3nPe97DCy+8wIMPPsj27dvn9fjE4lkqYzAYDJ7wWLFY5N///d+xWCwS/C5RS2H89fX18cgjj5zwZ926dbS2tvLII4/wh3/4h/N6vOLCWArjDyAUCp3w2Ouvv87PfvYzbrjhhiXXLwRW2MrGrbfeyr333svdd9/Njh072Lt3L9///vfp7Oyc15/T1dXFX//1X/PpT3+akZER7rjjDhwOB8PDwzzyyCN85CMf4VOf+tRp3+Mb3/gGsVhMXcZ99NFHmZiYAODjH/+4mjsolo6lMv7+x//4H/zsZz/jtttuIxqNntDE7/d///fn9XjFhbNUxuBHP/pREokEV199NU1NTQQCAb7//e9z4MAB/vf//t9LcmZPLI3xV1tbyx133HHC48pKxsm+J5aGpTD+4GgTXYvFwo4dO/D5fAwMDPDP//zPWK1W/vZv/3Zej/VCWVHBxmc+8xnS6TQ/+MEP+OEPf8imTZt47LHHuOeee+b9Z91zzz309PTw93//93z+858Hjubb3XDDDdx+++1nfP1Xv/pVRkdH1a8ffvhhHn74YeDozZ4EG0vPUhl/r732GnA0wH300UdP+L4EG0vXUhmD73nPe/jWt77F//t//49IJILD4eCyyy7jy1/+8lldP8XFaamMP7E8LZXxd8cdd/D973+f//N//g+JRIK6ujre+c538rnPfY5Vq1bN+7FeCJrq8Ws8QgghhBBCCDEPll7ilxBCCCGEEGJJkGBDCCGEEEIIsSAk2BBCCCGEEEIsCAk2hBBCCCGEEAtCgg0hhBBCCCHEgpBgQwghhBBCCLEgJNgQQgghhBBCLIizbuqn0WgW8jjEEnWh2rTI+BMncyHbBMkYFCcj10CxmGT8icV0tuNPVjaEEEIIIYQQC0KCDSGEEEIIIcSCkGBDCCGEEEIIsSAk2BBCCCGEEEIsCAk2hBBCCCGEEAtCgg0hhBBCCCHEgpBgQwghhBBCCLEgJNgQQgghhBBCLAgJNoQQQgghhBALQoINIYQQQgghxIKQYEMIIYQQQgixICTYEEIIIYQQQiwICTaEEEIIIYQQC0KCDSGEEEIIIcSCkGBDCCGEEEIIsSAk2BBCCCGEEEIsCAk2hBBCCCGEEAtCgg0hhBBCCCHEgpBgQwghhBBCCLEgJNgQQgghhBBCLAgJNoQQQgghhBALQoINIYQQQgghxIKQYEMIIYQQQgixICTYEEIIIYQQQiwICTaEEEIIIYQQC0KCDSGEEEIIIcSCkGBDCCGEEEIIsSAk2BBCCCGEEEIsCAk2hBBCCCGEEAtCgg0hhBBCCCHEgpBgQwghhBBCCLEgJNgQQgghhBBCLAj9Yh/AxcpqtVJbW0tzczPXXHMNiUSCJ598kkQiQTAYpFQqLfYhCiGEEEIIcVGTYOMUbDYbLS0tbNu2jXvuuYexsTGmpqYYGxtjdnZWgg0hhBBCCCHOQIKNU7DZbLS2tmI0GnnllVeYnJxkZmaGWCyGTqfDaDRSLpepVqtUKpXFPlwhhBBCCCEuOhJsnILdbqenpwedTsdTTz1FIBBgdHSUVCqFXq9Hp9ORz+fVgKNarS72IQshhBBCCHFRkWDjFFKpFEeOHKFSqZBOp8nlctTX19Pc3MyqVasA2Lt3L7FYjGAwSC6XW+QjFkIIIYQQ4uIiwcYphEIhdu7cST6fJxKJ4PV6ue6662hvb+cP/uAP0Ol0fOMb3+DgwYNkMhkJNoQQQgghhDjOsgg2NBoNOp2OarVKuVyel/c0mUzU1taSSqWIxWLY7XbWrl1Le3s7TqeTUqmERqOR9CmxoAwGA3a7nZqaGlavXk25XObAgQOk02mSyeS8jXchhBBCiIWwLIINnU6H1WqlXC6TyWTmJQBwu91s2LCBSCRCNBqlpaWF3/3d36WxsRGtVks4HKZYLJLNZmWDuFgwNpuN7u5u1q1bx5//+Z+TzWb5whe+wODgIAcPHiSVSi32IQohhBBCnNKSDja0Wi0Gg4GamhpaWlooFAoEg0EKhQKJROK8goBjAxat9mjvw0wmQyaTASCZTBKLxUgkEhSLxfM7ESFOwWAw4PV6cTgcJJNJUqkU2WyWfD4/Z0VPihQIIYQQ4mK0pIMNs9mM2+2mp6eHu+66i1QqxUsvvcTMzAyvvvqqGhici2KxSCwWI5PJoNPpyGaz9Pf3MzMzg9VqJRwOMzAwwP79+6XnhlgwDoeD9evXY7Vaefzxx4lGo+zfv59oNIpOp8Nms1EoFCiXy5RKJQk4hBBCCHFRWdLBhsVioaGhgebmZlpaWkin08zMzGAwGHjjjTfO670rlQrFYpFCoUCpVFK/zmazxGIxQqEQ6XRaVjXEgiqXyySTSfL5POl0Wi297HK58Hg86HQ6pqam1O8VCoXFPmQhhBBCCNWSDjba2tq444476O7uZseOHRSLRTo7Ozl48CDPPvssiUTinN9bWdlIJpPE43EymQxms5lischjjz3G1NQUkUhkHs9GiBNFIhGefvppKpUKkUgEi8XCpk2baGxs5B3veAdOp5PvfOc7HDhwgIGBAWZmZhb7kIUQQgghVEs62DAajXi9XlwuF3a7nVKphNvtpqamRt1nca5KpRK5XI58Po9Op0Ov12O1WqlUKsRiMSKRiKxqiBNoNBqAeUtnqlarlEolisUiyWQSrVaL3++nra2N9vZ2HA4HLpcLs9l83mNeCCGEEGK+LelgQ9kgrtfrqVarVCoVCoUCxWLxvG/2MpkM4+PjGI1GWltb6enpobe3V+2pkUgkpOyomEOn02E2m6lWq2Sz2XkJOGpqati4cSOpVIpMJoPf7+d973sfXV1duFwucrmcGhhLVTQhhBBCXGyWdLChVONRZpOVfRXns+KgrGIoAYzRaMTv9+Pz+bBarZRKJcrlMoVCQW7uhEqr1WI0GnG5XOq4UFYkzifo0Ol02O124GhlKmU1r66ujkqlQrlcJp/Pk8/nJfgVC0q53ur1emw2GxqNhlQqpV4TpTiBEEKIk1nSwYZer8disWA0GtFoNORyOUZGRhgfHz/ngKO+vp7u7m6MRiNWq5W2tjZuuukmampqSKfTTE1NEQ6HicViUoVKAKhBRmNjI7fccguVSoU9e/YQjUYZGBg4r14YpVKJRCJBJpNBq9VSKpXYv38/yWQSs9lMOp1mYGCA/v7+86q+JsSZWCwWWltbaWlp4cMf/jBGo5FvfetbDA0NMTY2Jj1fhBBCnNSSDjaUmTadTgf8tnJPOp0+q1UHjUaj/oGj+fF2u53GxkZMJhN2u53Ozk76+vrQ6/XMzs6SSqXI5XIUCgWZyRPA0RUHh8OBz+ejt7dX3cxtNBo5fPjweb13pVIhn8+rgW21WiUej2OxWDCZTKRSKaLRKPF4fD5ORYhT0uv1eDwe/H4/mzZtwmQy8fOf/5xgMIhev6Q/SoQQQiygFfsJ4XA4MJvNeDweHA4HqVSKZDLJ2rVrue666/B6vbS1tVFTU4PH4yGTyTA7O0s0GiWTyZDP5yWNSgBHV8NuvvlmOjo62Lp1KxqNBp/Px+joKG+88Qazs7Pn/N6lUolkMkk2myUej2M2mzEajQA8/fTTTE1NSQUqcUFYrVbWrFlDbW0tTz/9NPl8nv7+fqampoCj3e6LxaKa3ieTMUIIIWAZBhtarRatVquueCirFsfSaDTYbDbsdjt1dXXU1dUxOzuLXq+noaGBjo4OGhoa6O3tVSv85PN5stksmUxGzVEWAo7ehK1atYr29naamprQ6XRq8QKz2Xxe712pVNSqaErPF4PBgFarZWRkhOHhYdLp9DydiRCnptfrcbvdWCwWhoaGiMfjRKNR0um0up8ok8mo+5TkGimEEAKWWbBhNBqpq6ujWCxy3XXXkcvlaGtrw2KxnPBcv9+P0+mkpqYGu92upkb5fD7a29uxWq1zSokqJUilS7M43rFFBeBogKBsDj9fuVyO6elpteRte3s7XV1dWK1WyuWyukFXiIWWTCZ58cUXMRgM6mpdY2Oj2ufI6/Xy1FNPceTIEcbGxgiHw4t8xEIIIS4GSz7YOPbGX6fT4XA4qKurY+3atQBcdtllOByOOa/RarU0Njbidrsxm82YTCb1fU4VSCildavVqgQbYg5l75ASnM5nYFoqlYjFYlitVhobG/H5fPh8Pkwmk7rqIel84kLIZrMMDw9TqVQIhUKYTCZuuOEG2tvbedvb3kZbWxuRSIRMJkMkEpFgQyy4+e5rJIRYGEs62BgbG+PRRx+lr6+PlpYWjEYjzc3N1NfX4/V6AfD5fBgMhjmv02q12Gw2zGazOhut0WioVqsnTbuCo4GMzWY7YcVDCK1Wi8lkUvdSFItFAoEAgUDgnFc3XC4Xzc3NmEwmLBYLdXV1XHPNNdTX11MulwkGg8RiMRKJhKxsiBNoNBp1PM5XMQuLxUJ3d7datMBms3H99dezdu1aGhsb0ev1allwSaESC8VgMNDQ0IDb7ebGG2/E6XTyy1/+ktHRUUKhkKSVCnERWtLBxszMDL/5zW8oFovcdNNNeDweGhoa0Ov1dHR0qM873QftqYKL4+l0OqxWKxaLRYINMYfSY8NgMKDRaCiVSoTDYcLh8DkHGw6Hg+7ubsxmMzabjdbWVm677TZsNpuaK59IJEin03JjJ06gBBvKeJyPMWI2m2ltbaVcLjMyMoLD4WDr1q1s2rSJSqVCNptVex3JaptYKHq9nvr6etrb2/ngBz9IY2Ojeq1NpVISbAhxEVrSwUahUCAej3P48GEefPBBmpqa2LZtG3q9nnQ6jVarxefzAdDf308ikaC2thar1aq+hxJsKHn3tbW1NDc3z1meLZfLhEIhnnjiCYaGhqSevJg3SnUpm82GzWYjn8+TyWRoaWlh48aNOJ1Ompqa8Hg8OJ1OtZFaIpFQ9xnJjZ1QKJMiTqeTyy+/HL1ez9DQEIlEgsnJSbLZ7Dm/d7lcJpPJqCvA1WqViYkJampq0Ov1FAoFRkdHGR4eJplMzuNZCfFbRqORlpYW6uvr2bdvH4ODgwwNDREMBqlUKpjNZsrlsloRTVKshFh8SzrYKBaLxONxDhw4QCwWo6urS+0/MD09jU6nY8OGDWg0Gn72s58xNjbG2rVrqa2tVd9DCSrMZjNms5m1a9fS1NSkfl/Z7BsMBnn00UcZHx8nkUhc8HMVy5PFYqGmpoa6ujoaGhqIxWKEw2Ha2tq47LLLaGhoUPu8AKTTaZLJJLFYjFwuNy+b0MXyodfrcTqdNDc3c+ONN2I2m9m5cyeBQIBYLHZewUalUlFnjTUaDZVKhdHRUbXLvbLiMTIyMk9nI8SJjEYjbW1teL1e3njjDdLpNEeOHCEUCqHRaDCbzRQKBQApwSzERWJJBxuKUqlEPB5nfHycp556Cp1ORywWU2f1NBoN/f39RKNRCoUCdrv9hPfo7OxU85GPlclkGBsbY2RkhFgsdtYNA8XKpRQqqK2tpa+vj/r6eurq6k5aBre2thaXy4XL5cLtdpPNZkmlUrS0tNDa2kpNTc0JVdGUWTsZh+J4drud9evX09bWpqbhZbNZpqenGRgYOK9N20pBgmq1qnarV8qMHzx4kGg0SjQana9TEeKk8vk8R44cIRAIkEwmKRQKatppd3c3Xq+X/v5+xsbGiEaj59XnSAgxP5ZFsFEoFIhEIkSjUQ4dOnTS5yizG/v37z/pPo0bbriBzs7OE2ZBEokEr732GgMDA4RCIVnVEGek1+vxer1oNBquvPJKcrkcGzduxOPxzHmeRqOhvr6e2tpadSO4sux/fHf7YynBhhDHc7vdXHvttbS3t7Np0yYsFgt+v5/JyUkef/xxhoaGzvm9y+WyukdISZMyGo1otVp27drFwYMH1QZ/QiyUbDbLq6++ilarJRwOo9Fo1DH/nve8h3Xr1vHd736Xp556igMHDkiwIcRFYFkEG4qzzc889jkOhwObzUZzczNdXV1qipWy0TEajTIwMMDIyIikrIiTisVivPHGG6RSKbq7uzEajbjdbkwmE+vWraNYLNLa2nrSFTWXy4XFYsFgMKDT6c44fpWNv8pNnhDHU3q+aLVadW/FfCiVSupki8PhwOPxUFtbi9frVVc9JAgWx9Pr9WqhgvkYizqdjrq6OnQ6nbp/sre3l/Xr1+Pz+dQqbJJCJcTFY1kFG+eivr6etrY2tmzZwrXXXqteGAuFAslkkrGxMX75y18SCoXI5XKLfbjiIjQ1NcWDDz7Ihg0b2LRpE/X19bS2tqLX61m3bh1w6qpnx69gnOnmUEnRcjgcEmyIEyid65U9PtVqlWKxqHb1Ph+FQoGpqSnMZjM+n4+Wlha6u7vVPW6JREImZMQcWq0Ws9mMVqudt8p5ZrOZ3t5ejEYjsViMSqXCTTfdxDXXXINGo1FTTHO5nJQFF+IiseKDDaXaj8vlUkuXwtGl2qmpKQKBAPF4XPZqiFMqlUokk0kCgQB79uzB7/fT19eHyWRSexwowUEgECCdTmOz2TCZTHPeR6PRoNVq0Wq1WK1W3G73nCBFKS964MABhoaGzmuzr1ielAaTx3azV6qXneuNl9FoVK+PFosFu93Ohg0baGxsRKfTkclkyGazsrIhVMoKrNlspqurC6PRSCAQIJPJEI/Hyefz5/zeyr61UqmkXjPT6TSxWEwtXDA7O0s0GpVrpJh3Wq0Wu92u9h0ymUwcOXJEvU+UCZeTW9HBhkajobW1lW3bttHa2jpnpjgUCvH888+zb98+9QZRlmTFyRQKBcLhMLlcjq997Ws0NTXxu7/7uzgcDmZmZqhUKqxduxaLxcJPf/pTDh06RHd3N/X19ep7KEGF0WjEYrHQ3t7OFVdcMeemsVgsMjMzw/e+9z0OHz7MzMzMopyvuHhpNBq1Ip9Wq6VYLDI5Ocno6Og5r8w6nU4uu+wyrFYrVqsVn8/He9/7XrxeL6VSiampKcLhMLOzs2oVILGy6fV6PB4PdXV1vPvd78bj8fDCCy+oEzKBQOCc37tSqZBMJtV0KYDBwUEsFgtWqxWNRsPAwAD9/f0yQSjmnclkorOzk9bWVj796U/j9/v58pe/zO7duxkcHCQSiSz2IV6UVnSwAUcvikajEZ1OBxy9ccxkMgQCAY4cOcLk5OS8pCCI5a1arZLP54lEIuh0Og4cOIDVaiUSiagzcWazmaGhISYmJtDpdMTj8RPep7a2Fr/fr86OKONO2T8UDoeJRqPEYjFJERAndXxhgWN7DpyOTqdTV0WMRqMa4NbU1NDU1DRnn0Ztba0aTGcyGQqFwrzl5Iulz2AwUF9fj9/vp6mpidraWmZmZjCbzacs4nK2qtWqGtQq10lljM/MzJDL5c5rJU+I09HpdLjdblwuF8VikXQ6rfa8Ur6v7B+W6+Fvrfhg41gajYZgMMiBAwfYuXMn3/ve90in0+e15CtWjkKhQDAYJBKJMDo6qi7pw283SWazWUqlEvv27VMD3GNt3bqV2267jUqlMudClUwmeemllxgcHGRycpJoNCqzdmJeWa1WampqcLvdNDQ0kEqlCIfDdHd385a3vIX6+nrWrFmDxWLBYrGoTVVDoRCpVEotiyuE0+nkpptuoqOjg6uvvhq3201rayuhUIiBgQEGBwfP+b2VlQ29Xk88Hkej0aDX6zGZTDz11FMMDAxw+PDheTwbIX5L2TPk8/l49tlnKRQKvPHGG4yPj1Mul7Hb7erki5So/y0JNv5/pVKJbDar5n7G43Hi8bikBYg3RZlhO9O4OT6ANRgMGI1G9Ho9LpcLm82mbhZX9mpMTk4yNTVFPp+XC5g4KxqNBoPBgMlkwu12U1tbi9lsVtPzjqX0enG5XDQ0NJDJZKitraWjo0Mt0exyudT0FeXDtFQqnRAci5VNyWt3OBxYrVZ1r082mz3p2HszlM3fer0enU6HwWBQU/xyuRyzs7MyQSgWjDL+EonEnL1qNpuNmpoazGYz4XCYeDyufl9IsKFSenRMTEwwNjZGKBSSD09xwShV0Xbs2MENN9yAxWJBp9NRLBZJpVKMjY3x2GOPMTExofY4EOJMlDKhANdffz2bNm2it7dXLfF9rNraWnw+H2azGbvdTqVSoVQqYTQacTqd6o3dsUqlkqSZihMoQa5SdEWpilYoFM57oqRYLDI1NYXRaMTn8+H1eunt7WX16tXo9XpisZhMEooFk06nee655zAYDOr+jPXr17N+/Xpuvvlmuru7eeSRR3j++ecZGhpidHR0kY/44rBigw2l6o/RaMRkMlEqlYjFYkQiEYLBIMlkUj5AxQVjtVrVngUul0ud/SsWiySTSWKxGKFQiEgkIrnI4pRKpRLxeJxEIkGhUMBoNGK1WnG5XLS0tOB2u1m1apUagBzL6/VSW1urXhPPhlINSIhjHVtZD367z+J8VmWVz2ulj4zRaKS+vp6GhgZsNht6vZ5yuUyxWJSqaEKl7F+br/s5ZSwr11qNRoPH46GtrY3Ozk61X5vNZjthcmYlW7HBhtLMr6uri0suuYTR0VH27dvHa6+9xi9/+UtSqZRcsMQF09TUxBVXXEFXVxc6nU69QMZiMXbu3KmuukWjURmX4pSCwSD/8R//QWdnJy0tLdTX19PS0kJbWxurVq2iVCqpN2bHU/pzKM0A4fQf0FqtFofDQU1NzXmnxojlRaPRYDKZsFgsaLVayuUyU1NTjI6Okk6nz+k9lZLLVqsVm82G0+nktttuo7GxEYfDweTkJMFgkGg0KmlUAvhtdT6NRjNvpbnNZjN9fX0YDAZ1Vfftb387O3bsUEuES5+XE624TwidTodWq1U3Qir58aVSiVAoxNTUFBMTE7KqIS4oq9WKx+NRSzdWq1VKpRKpVIrJyUmmp6fJZrNSw1ucVi6XY3x8HIPBwMzMDCaTCYfDgclkUvdaKMGEMsus1WrVCirK+Dq2mpXSKPB4x85WSwAsjqVs2j52ZSObzZLJZM7qBkwZo1qtVq3qYzabqa+vV5uaut1uuru78fv9zM7OEo/HyeVyMh4F8NsxaLfb51QbPd+qecp+JKPRiNFopFwuq1UkldTTYrEofYeOs6KCDa1WS2NjI263m1tuuYVLL72UmpoapqamePnll3n00UfVUqVCLBaNRkMkEmFwcJDXX3+dBx54gEgkQiaTWexDExe5YrGoptrdd999NDQ0cMstt+D1eolGo5RKJdra2nA4HDz33HMcPnyYxsZGfD6f+h5KoKFU+Kmrq2Pjxo1qwKEEwpFIhAceeICBgQEmJiYW5XzF8mOxWHA4HNjtdurq6sjn88zOzuL3+7n66qupr69n1apV2O12/H4/Wq2WZDJJKBQimUxKAQ2BXq/H6XTidru56aabcDqdvPHGGwSDQQ4fPnxevTCO7/Oi1WoZHBxUJ3U0Gg379++nv79fmkoeY0UFGxqNBpvNhtvtpqenh02bNhEIBJidnSUQCDA0NCQzx2LRKdWnZmZmmJqa4vDhw+eceiBWFqXfSywW44033iAQCLB27VrS6TSBQIBisYher8ftdrN//35effVVVq1aRUtLywnvZTabcTgcAHOqTSkrGtlslkOHDtHf3y9FC8QZKXs4lD0Xx6brHUv5jHY6nTQ2NpLL5dDpdGpKYGNjI6tXr8ZqtQJHA+xisaiu/MpsslBWHzweD729vXi9XhKJBEajkfHx8fN672P7vCjVIuPxOOFwWN3rFolEmJ2dPe/zWE5WVLABRz9ArVarmkf60ksv8cwzz3DkyBGZERGLLpFIMDg4SCAQYHx8nFAoJGNSvGnKykMqleK73/0uZrOZXC5HpVKhpqYGg8HA1NQUsViMsbExLBbLCe+xatUqrrvuuhOaU2WzWV577TXGxsaYmppidnZWJmnEaWm1WjweD8VikSuvvFLdR+TxeE54rtvtpr6+HovFgsvlolwuk81msVqtdHR0YLFYTihgoGwMl6wEAVBTU8Pv/M7v0NrayrZt2/B4PHg8HmZmZhgbG2NycvKc37tcLqt9XhKJxJw+L6+88gpjY2MMDQ3N49ksDysu2FCqWCjR6cjICC+//DLpdFpmRMSiUbo+K13IlS7hqVRKPkDFm1apVMhkMmQymTPOsM3MzJzwmEajwWw2nzTQLRQKTE5OMj4+rtaSF+JYyoyvsiKm0WiwWq04nU46OztxOBz09fXR2Nh4wmu9Xi9+vx+j0XjSIPhklJ8j10oBYDKZ1MpQra2tuFwudDodXq+Xmpqa83pvZW9bqVQin8+re970ej2Tk5Ps27ePaDR6zu+v7C85dtXv2FXlpTrOV1SwUalUGB0dJRwOk0gkePzxxxkYGCCZTMrMnFgUVqsVs9lMc3MzPT09JJNJjhw5wqFDh3jyySeJRqMyNsUFpcwsX3LJJVxxxRV4vV70er2a3hcMBnn22WcZGhoiHo8v9uGKi1A8HucXv/gFbW1t+Hw+mpub8fl8eDwevF4vuVwOj8eDzWY74bVKFatjq/Kd7uZKCWTsdruUGhXAb/u8KFXylH1m85FmVyqVmJmZQa/Xq8FLZ2cnHR0d6v6hN/uZrayOeL1e3vGOd+D3+9US+LFYjFwuRzAYJB6PMzw8zNTUlNp4eqlYUcFGtVolFAoRCoUYGRlZ7MMRApPJRE1NDbW1tbS0tHDkyBGmp6cZHBxk9+7dEmiIC85ms9HS0kJ7eztr1qzBbDaj1WoplUpks1lisRj9/f0MDQ1J0QJxUplMhldffZVQKMQtt9wyJzWqublZfd58zNBqNBp1FeTN9nw5fs/IUpwxFien0+nmBKzlcnleslfK5TLxeByj0Yjf78fn81FfX099fT1arZZsNvumS94qwZHL5eKmm26it7eXpqYmTCYTk5OTJBIJDh8+zMzMDEajUd2nJMGGEOK0vF4vDoeDLVu2sH79erq6ugiFQuzfv5+nnnqKqakpSesTi8Lr9dLX10dbWxt6vV79sE6n0/T396t7imKxmNSRFydVLpdJJBJMTk7yyCOP0NzczJVXXkldXR2pVIpisUhdXR0Oh4ODBw8yMjKC2+0+YQ+HUv7WaDRis9no7u5WqwApqVrpdJqnn36a/v7+M27+VVJeurq6uP7667HZbNTU1KibfJUZ5HQ6zZEjR0gkEtK3YwlSxozRaESj0VAulwmFQkxOTp5z2qfFYqGtrQ2TyYTNZsPhcHDllVfi9/uxWq0Eg0FisZjaUPXNqKur44477qC9vZ1Vq1bh8XjUa6/T6cRsNmMwGGhra6Ouro7LL7+cZ555ht/85jdks1lSqdQ5ndOFJMGGEBeYRqPB7XbT0NDAVVddxY033sjs7CyRSIShoSGef/55CoWCbAwXi8LlcrFmzRqam5vnzAym02kOHjzI4OAgkUiERCKxyEcqLlaVSoVUKkUul+OJJ55Qewi1t7cTCATIZrP09vbS0NDAc889x86dO2lvb6ezs3PO+yjpJTabjfr6etra2uakSpVKJTKZDM8//zw7d+48Y6680jOmq6uLD37wg9TW1tLU1ESxWGRiYoJEIsH+/fvVwhzT09Ok02kJNpYYZbVLCTYqlQqRSISZmRlyudw5vafFYqGrqwu73Y7VasXr9XLrrbfS1NREPB4nEokQj8fPKY3K6/Vy55130t7erq5oKJS+MrW1tcDRwh1KgNHf38/s7KwEG0KIk9Pr9ZjNZnQ6HZVKhSNHjvDSSy+xb98+Kd8oLiqZTIaZmRmGh4f5zW9+w9TU1CnTp5TUhS1bttDd3Y3VasVisagN3dLpNLFYjFgsxvj4OLlcjlgsJukry1SlUiGRSFAqlXjmmWfweDxqL4yBgQFqamo4cOAAw8PDxOPxk/Zr8fl8bNq06YTJl2KxyMjICNPT02qPjTPd5K1atYqrrrqKdevWUVdXh91uVwMal8uF2WwGoKWlBZPJRDAY5LHHHmNoaIhUKvWmZ6zF0mMwGNSKZw6Hg1KpRDqdpqGhgb6+PrxeL01NTWr6s9FoJJvNqsUyisXiGScK9Xo9FosFr9fLxo0b6erqoqmpSd3IDpywX0n5WgmgNm3aRKFQ4NVXX+WZZ54hn8+fcyB1IUiwIcQiMJlMWK1WNdjo7+/nhz/8IZlMRmbRxEVDo9GQyWQ4ePAg/f39/PKXvzxtQyylBOTb3vY27rjjDurr6/F6veqs4szMDIODgwwNDfHMM88QiURIJpOSjrVMKelJ8Xicxx577ITvazSaMwaavb29rF279oQbuEKhoKZgTU9Pn1X++rp16/ijP/oj3G43fr9fvbHT6XRqCldTUxOVSoW+vj5isRijo6PqGJVgY/kzmUy4XC5cLhctLS3qZEtzczOXX345jY2NrF+/Xi1uUCwW1ap/6XT6rMaIsj+jt7eXD33oQ/j9ftrb29Vg91jH7ysymUwYjUZ27NjBpZdeygMPPMDrr79OIpGQYGMp0Wg06HQ6mpqa2Lhxo3pTWK1W1YGkVAeYnJwkk8lINSvxpinL/Tt37iQYDPLqq6+SyWTkw0xcNHK5HKFQSC3FnEgkTjtjp9PpuPzyy+no6GDt2rV4PB7MZrNaRtfj8aglIl0uF0ajkdHRUVKpFKlUikwmI6mDK8zpAg2LxYLb7aatrY3e3l78fj96vV4tW59MJjl48CCHDx8+ZVNJJZWmubmZrq4uLr/8crUKlrKZ/PibOWWfiDKzvX37dtxuN08//TSHDx+mUCjI5/0So9FosNvtuFwuenp6gKNV905WDc3pdKqrXnV1depGbI/HQ3t7O263+4SqZ5VK5U1lI+h0OnV81dfXU1tbqwa+x65kVCoVQqEQ6XSaaDRKOp1WV0GU1RGn00lDQwNarZbZ2dmLdpVYgo3j6HQ6jEYjl1xyCZ/+9Kfxer00NjaqteWVnM5wOMyTTz7J9PQ0w8PDcvERZ61arTIxMcHExAT79u1TLypyoyUuJul0mpGREWZnZ5mamiIUCp32A9VgMPD2t7+dG2+8kYaGBjwej3ojZ7PZsFqt1NfX093dTS6X46qrrmL37t0cPHiQmZkZJicn5d+AUNXU1LB69Wq1BLPD4UCv11Mul0mn00QiEZ577jn27dtHOBw+6XsoN2PXXHMNd911F42NjTQ2NqLVatX+H4rj/66kAN55550kk0mSyaTaGVo+75cWrVaL2+2mUqmwbds2Ojs7Wbdu3ZzKaAqPx6P2eVG61Ctj5VQBarlcplQqnfX1S6/XY7fbcbvddHR04Ha7T1jlq1arlMtltULlnj17mJ6e5n3vex9NTU1qIO3z+ejq6gJgZGREgo2loqmpibVr17Jx40a1hrLRaESr1aqzcR0dHWru6czMDPl8nsnJSXK5nKQDiLOiXBDOZW+GVqvFbDbT19eH0+lUa9IrJfeUjZmzs7NqhZWLeXlVXBz0ej0Gg0Gd2dPpdITDYQKBAAMDA0xPT8+5yTp2E2ZnZyd1dXXqB6eyonEs5WulUozVasXv97Njxw7Gx8dJp9NqPv/F+oEpLhyLxUJLSws+nw+DwaDO/BaLRaanpxkfHycajZJMJk95HVWa+NbU1FBfX4/T6TyhWZpGo6FYLBKNRtXrpkajobOzE5vNplbCcrvdao+QpbAhdyXL5XIcOHCAfD7PqlWr0Gq12O12tFot3d3d1NfX09raitfrPeG1drsdi8Wi9uk4m1Q/g8GAyWQ6Y+llrVarruz29PTQ0tKCwWBQq6qlUin27dtHOp0GUIONaDTK0NAQ0WiUvXv34nA4aG5uprm5GavVSl1dHaFQ6IRr7sVEgo3jXH755fzZn/0ZXq+XlpYWtFqtWgPZ5/NRrVZpbW2lXC6zefNmNee4XC4zMzNzyuVcIeaDcoPn8/n4+Mc/Tl9fH36/H4vFQiAQIJFIMDQ0RCAQYPfu3bzxxhsEg0GmpqYW+9DFRU7p8Nza2sq6deuYnp7mlVde4cCBAzzyyCNkMpk5kynKBIzb7eaDH/wgfX19rFu3jvr6evU5yoef8oGtfK18kK9du5ZPfvKTHDhwgJGRESYmJgiHwzJpI6itrVXT8gwGgzp2stksr776KkNDQ4yPjxMOh095M6j0MWpoaKCnp0e9GTx+BjmdTqvXyt27d6PRaPjYxz7GqlWrsFqt6PV6WlpaWL16NalUilAotPC/AHHOZmdn+elPf0pzczNr165Fp9OpfV56e3upVqvqvd3xNBrNnOuW8t9TjTGtVquWUD5TU0mDwYDdbqerq4tbbrlFLUSQz+eZmJhgaGiIP//zP2d4eFh9TblcVss8w9EV5927d3PnnXfS3NyM2+2mp6eHaDQqwcbFTK/Xo9frqa2txefz0dPTQ21tLQ6HQy37eOyMHPx2X4fdbqdSqbBmzRoqlQqvvPIKhUKBUqkk1YTEgrBYLKxdu5aWlhYaGxvV1TeTyYTb7cZoNFIoFNQKQDqdjoMHDxKPxykWi7InRJzAbDZjMploa2ujo6ODxsZG8vk8s7OzDA0NMTExQT6fPyEAUCZhzGYzXq9X/TA/WdfndDpNOp0ml8uRTqex2+00Njai1+txOBzU1NSoq8Wzs7MSbAjgt5/PGo2GUqlEIpEgEAhw+PBhRkZGyGQyJ70J1Ol0aLVaamtraWtrw+v1otPp1FK50WiU4eFhyuUyGo2GVCrFwMAAsViMoaEhdDodAwMDFAoFWlpasFgsOBwOdR+SuLhVKhWSySTRaJT+/n6y2Sxr1qzB6XSqZeWtVitGo5FIJEIsFsNisWCxWOa8j3L/p9Fo1M9YZYUNjl7jlKpoIyMjZywHbrFYqKurw+fz0djYiNvtVlfKDhw4wNDQkLrR/FSi0Sjj4+Pqz1LSu95sQ8sLbcUHG0pzlhtvvJHbbruNtra2OSsap8rpVKJZs9nM3XffTSwW48tf/jJPP/00sVjstINFiHNVV1fHJz/5Sbq7u9Wa38pFxu12q5vFKpUKmzdvJplM8uMf/5hwOEw8HmdmZmaRz0BcbOrq6vD7/dx4443cfvvt5PN5ZmZmeOONN3jkkUdOWfJTo9Fgs9lwuVy0t7fT09ODXn/0I0W5AVRWMwKBAPv372d8fJx9+/ZxySWX8Ad/8AeYTCbsdjsej4fu7m50Oh3T09NSkU3MoVRF27t3L0eOHOGHP/whk5OTpxwnZrMZs9nMxo0buf7661mzZg0ajYZkMsnk5CTPPvssf/d3f6c2eFNuGiuVCqVSCbPZTKFQoLW1lY9+9KOsW7eOxsZGVq9eTX9//4U8dXEOSqWSetP+zW9+k9raWt7znvfQ1tZGMBgkm82q6VRPPvkkL774Ii0tLbS3t895H6UsssViwefzcfXVV6ubypUxk0gkePjhh3nhhRfOmEFQW1vL5s2bueyyy9i8eTPVapVQKMTQ0BD/9m//pqYGns7o6CgTExO87W1vo1qtLpmU0xUfbCilGt1ut7okdfwHJhxdylI6n2YyGTQajZpL6nQ60el0uFwuHA6HWk9eiPOlXOzMZjM+n4+Ojg6amprw+Xxqnw7lecfObhxbk1tJixkbGyMWi6mb2YQA1NUJu91OTU0Nk5OTDA4OMjExQTKZJJfLnfCBplRTUQIVm82GXq+nUChQLpeZnp5Wc98BxsbGGBwcZGpqipGREWpqahgaGsLlctHQ0IDBYKCmpka9lgqhUPocpNNp4vE4iUSCdDp92k7QShCszCDbbDZyuRyRSIQjR44wOjpKKBQ65V42paGfVqtVf45Op8NgMFz0M8jiqEqlQrFYZHZ2Vu1llclkiEQi5HI5isUiMzMzHD58mPHxcbW08fGpSHa7Xe2rcezNvVLWORwOq4UDzpQ5oFw3lb1uhUKBRCLB7OwsoVDorFZ1bTYbNpvthFWYi92KDzaU8mONjY309vaqpfUUysxcMplkz549RCIR9uzZg9Fo5IMf/CCtra1YrVY0Gg3t7e2sWbNG3WQmxPkyGo14PB46Ojr44Ac/SHNzM319fWqaH5w6L95sNmM0Grn++uvZsGEDTzzxBF//+tdJJpPSSE2ojEYjDocDg8FAuVzmjTfe4F/+5V/UFdrjK6womy29Xi9vfetbWbVqFT6fD4BgMEg0GuWf/umfeOKJJ9TXKB/k5XKZQqHA8PAwwWCQ9evX8+EPfxiLxUJ3d7da/EAIRTabZXBwkFQqxejoKNPT06e9IdNoNHR0dNDT08PWrVvZsmUL6XSaiYkJdu7cyXe/+11mZmZOe2NYKBTo7+9Xg+alNIMsfqtcLhMOh4lGo3znO99Rq5lVq1W16EA2myWfz9Pf369ONB9r1apVvOc97znhOpjL5XjttdcYHR1ldHSUcDh81tWolKyZTCbDoUOHOHTo0FntV9NoNFx66aVs2LCBtWvXnpB9czFbscGGMhPscDioq6tT894rlQr5fF6NgJULTDweZ3BwkEgkwvDwMCaTiYmJCfVmUClldmwXUiHOl1K5x26309LSQlNTExaL5aSrb8psjbJvSJkBUUpG1tXVqR1RY7HYIp2RuNgoeezBYFDdpB0MBk/Z90LZr6asSij9D/L5vFqMYGxsjLGxsVP+zGg0ysjICHV1dWrevMFgULvjipVL2RNpNpuxWq1otVq1MWAgECAUCp1x9lfpAm2xWLDZbKRSKbV07eTkJMlk8rTBg1KIw2QyyUrbElculymXy2/6M09pgwDgcDiw2WzqtalSqVAoFAgEAkxOTpJOp89pn64SxCqrMKcb10qPIqXqn8vlAo5WZ1MqUF7MVmywYbFYMJvNXH755Vx11VX09fWh0WhIJBLMzMzw3HPP8U//9E/k83l1c5pSWjSbzWI2mymVSrS2tvKBD3yAzs5OmpqaWLNmDfv371/s0xPLhLKB1uv10tPTg8/nUy+Ax+bFVyoVDh06RCAQ4NChQ0xNTfHWt76Va6+9FqPRiF6vp6GhgTVr1jA2NsbMzIwUMRAAjI+PMzMzw/79+3nwwQfVjZWnmqWzWCxs2LCB9vZ2tmzZQltbG8lkkpGREX784x+rFaxOJxqN8sorr2Cz2SgWizJrLFRKb4yWlhbWr19POp2mv79fHV+zs7Nn3IirUG4Ow+EwAwMDHDlyhKmpKQqFwhkbCl533XV0dHTg9/uX1AyymB8ul4tVq1axadMm3vrWt6oTycrm82AwyK9//WsGBgYIBoNv6r2VIMNkMtHY2Eg0Gj3pqopCo9Hg8XhwOp1cccUVvOMd78DpdAIwNTXFyy+/zNDQ0EXdp2jFBhtKjrLP56OlpUWd8U2lUmqjvv7+/tNuQBseHlZnBeFoStaxs85CnA9lVcPpdOJ0OtU8TSUVJR6PqzMhlUqF8fFxNd9+bGyMtWvXEo1G1RlCs9mMy+UiEonIB6dQ5fN58vn8WZftVlaEa2pqqKmpwW63E4lESCQS6p6Ms3kvSU0Rx1J6ECg9MbxeLxaLhVQqRTAYZGZm5pzLyysz0cpYP91Ei5JX39TURHt7u9rYrVAoSC+tFcRsNlNXV0ddXR0ej0ctxlIsFtX9Q8q4fDNVHpU0LvjtKrHdbj/lZ7Jer0en0+H1eqmtrVU7jgOkUikSiQSRSIRUKnVRX09X5F2x0rCno6ODLVu2sGXLFrVD+HPPPcdDDz3E+Pj4abuEFgoF9u3bRzAYlJQUMe8MBgNWq5X29nZuv/122tvbsVgsFItFpqamCAQCfOMb35hTj1upGpTJZMjn8+RyOfbv389VV13F2972NtxuN729veTzednkKOaFRqOhUqmoaVPDw8NMTk6escNyY2MjV1xxBZdccolaZ16sbEr5+a1bt3LLLbdgNpsJhUIMDAzwyCOPEI1GT7sp/HSUTb5ut/u0z9Pr9fh8Pnw+H9dffz3r16+nvr6eSqXC4OAgL7/8MoFA4JyOQSwttbW1bN26le7ubrWxM0Amk2HXrl2MjIyohQbOtqN8Pp8nlUqp49hms9HT00M6nVYzFo6l1WppbW3F4/Fw++23s2HDBtatW4fZbGZgYICBgQFeeOEFBgcHz5gauNhWbLBhs9nweDx4PB68Xi/BYJBkMkkgEODgwYNn3EBbqVTUmvHHRqpCzAedTofVasXtdtPW1obf71dnVSKRCFNTU+zZs+e06SrDw8Po9Xq6u7uBowGMw+FQCxoIMR+q1apaKz6bzZ4xd1hZGenq6qKpqQmtVku1WlWrpMm1dGUymUy4XC4aGxtZt24dyWRS3T+kVEZ7M6mfx66cKfveTtbZXqHk6LvdbrWKld/vR6fTkcvliMViBIPBiz43XswPg8GA1+vF6XTOuUblcjkmJyfVvT9vZqKkUqmok4Hlclkdlw6HQy3ocmxaqVarVe8DOjo6WLNmjRowK13FA4GA7NlYCpQLTyQSUZuqTE1Nkc/nz5jTecUVV9DW1obP55ObNzGvPB4PGzduZP369Vx++eVYLBai0SgzMzN897vfZWRk5Ix5ohMTE8RiMS6//HK5gRMLRqPR4PV6yeVyZyzHaLPZcLvdrFu3jltvvZXa2lpMJhOhUIjXX3+d4eHhc569FkubwWDAZrNhNBopl8scOnSI73//+wQCAZLJ5Jva25PL5Ugmk2p6i5KS9corr5z0s9psNtPa2orP5+Nd73oXLS0ttLW1odPp2LNnD5OTk7z++uuMjo6e9X4RsXwcWzlqdHSUH//4x4yPjxOJRN7U+4RCIV555RUcDgeTk5O4XC41oLnhhhsYHBzk+eefJx6Pqz+3trZWDXz9fj+ZTIbJyUl27tzJ97//fRKJBIlE4qLfg7nigw2FMnMRi8VIpVKn3WijVKpob29n1apVapOXSqUiechiXihNhBoaGqivrweOBg+RSIR9+/YxOjqq7hU6lUwmo66+CTGfjm/aZ7Va1fK5p6KUtfV4PDQ0NNDZ2YnNZlNXRoLBIMFg8KxTEsTyolSIVLIGgsEgb7zxhho0vJnP1WKxqFbnUzbiKmXulfKnx37G6/V63G43fr+fDRs20NraisPhoFqtMjU1xZEjRwgEAsTj8TeVny+WB6VKaTAYZHJyksOHDzM5Ofmm3yebzZLNZtWeGsq4M5lMdHV1odFoeO2110in02i1WkwmEzU1NbhcLux2OxaLhdnZWSKRCGNjY2csxHExWfHBhnIBs9vt+P1+tZzYqej1evXD8pprrmHt2rXU1tZSqVQYHh5mz549hMPhC3DkYiXJ5/MMDw8zODjI5OTkWd2U9fT0sHr1arV7rhDzQUkFUHpw6HQ6mpub1XSAk/F4PNTV1bF+/Xp+53d+Rw00kskke/fuZWhoiCNHjjAzMyPBxgqlpCgp1XVmZmaIRCJvulpZtVplbGyMaDTK5s2bmZqaUhtWtrS0cPPNNzM+Ps7u3bvV2WCDwYDP58Pv99PQ0IDX6yWZTJJIJPjVr37Fyy+/zNTUFKlUSjaIrzCZTIaRkRHS6TSTk5Pzco0KBAL853/+Jz09PdTX1+NyuXjb295GIBBQS9N3d3fjcrno7e3F4/FgMBjYt28fP//5z3nxxRc5ePDgPJ3hhbHigw1AnflwOp1n7JGh0+moqalRS5H29PRgMpkoFouEw2HGx8dlJlnMu1KpRCQSIRwOE4/HSaVSZ3yN3+9n/fr1aulGkApA4vwps3y5XI5KpYJWq8XlcqmdyJXmkseyWq3U19fT3d3NVVddhcvlwmg0ksvlOHToEMPDw4TDYbXDvVh5kskkyWRSTVk6H7Ozs8zOzqoFXHQ6HQ6HA4/HQ19fH1qtltdff51KpaL2eFFmkGtqarBarUSjUSKRCAMDA7z22mvzc5JiydBqtWg0GnWfZCqVIhqNkkgkzrvEbCKRYGBgQG2k6nA46O3tpb6+niNHjpBMJtmyZYua3WCxWOjv72diYoKXX36Zxx57bJ7O8sJZscFGPp8nnU6r7endbjc6nY66urqTzgKbTCYaGhqoq6vjxhtvpKWlRd20q1SlOnDgABMTE2d1IyjEm2EwGKitrSUWi52xtLLyYXnJJZdw/fXX09zcDDAnBetirsctLm75fJ6DBw+SSqWYmJhQb9B0Oh3r169ndnaW/fv3MzExob7GarXi8/mor6+nvr4ejUZDKBTi0KFD/PznP2dmZoZwOKwGMELMhyNHjvDkk0+yadMmtcz9TTfdRHt7O6VSSU2HdrvdrF+/HpvNRjQaZWpqiocffpihoSGGhoYW+zTEBWQymbBarTQ0NNDd3U2lUmFkZIRAIMAvf/lLwuHwed/jxeNx9u3bh8lkYnp6mmKxiMvlwmKxsG3bNgqFAvX19ZjNZnVF7YknnqC/v3/JrWgoVmSwUa1WKRaLZLNZ9YPN4XCoXXFPFmwYjUYaGhpoa2vjLW95C83NzXi9XgCGhoYYHBxUy6Bd7FUBxNKj0+lwuVxqZYzTsVqteDweurq6uOyyyzAYDFSrVZLJJIODgwQCAbmhE+esWCyqpcGV3GOLxYLBYGDVqlVkMhlCodCcYMNiseDxeHC73bhcLvU54+PjvPjii8zOzi7iGYnlanJykldeeQWfzweglrV1Op0Eg0FcLhdbt26lpqaGxsZGisUir732GmNjYzz66KMMDAws8hmIC02p2uj1emlpaWF2dlZN9dy1a9e8FAhIp9MMDw9TW1tLOBxWf6bJZGLdunXq84rFIgcOHGB8fJznn3+el19+eclmzqzYYGNqaopsNsvo6CjhcBiLxYLVasXv93PVVVcxPT3NoUOH1JsyZa+G0ljF6XSSSqVIpVI899xzvPHGG4yNjZHJZCTnWJw3pVO9EriaTCaam5tJp9Mn3YSrVASy2+1s376d3t5e+vr6MBqNBAIBJiYm2Lt3L9PT08zOzkoqlThv+Xye1157jWw2y1ve8hYaGhro6+ujtraWYrGI2+1WG1C1t7fT09ODy+VS9x498cQTUn1KLKhgMMjAwABr164lFAqphQy8Xi87duzAbDZTX19PtVpVU/kee+wxRkdH33SlIbG01dTU4HQ66e3tZdu2bTQ3NxOLxRgZGWHnzp1MT0/PWz8gpbS9wWBQG00Cagqq0qU8mUzyzDPPsG/fPgYHB8lkMkt2z9CKDDYApqenCQQCjI+PEwqF1NJ4fr+fK6+8kn379nHkyBE12DAYDOomR6VU2cTEBIFAgBdeeIEXX3xxkc9ILCflcplsNqtehJSOtolE4pTBRm1tLQ0NDVx77bVcddVV1NXVodfrCQaDvPzyy/T396tBtqxsiPOlBBuxWIzLLruM5uZm1q1bx5o1aygUCjidTvr6+li9erV67ZycnGT//v288MIL/Mu//IsEGmJBhUIh9YYxHA6rVX28Xi/bt29XsxhisRjDw8MMDw/z+OOPMzExISVuVxilgMDWrVv5/d//fXX1dWRkhOeee25eK5EpwYbRaFQnFZUJQCXgiMfjhEIhfvOb37Bz504KhcKS3s+2YoMNxfj4OLt27WL9+vXU1dVRX1/P9u3bcTgcRCIRDAaDWp1i06ZNuN1uYrEYkUiEX/7yl4yOjkpHUTHvlJQnj8dDOBymXC5js9kwm81s2LABi8XC4cOH1SVVjUaD3W7H7XbjdrvxeDxUq1Wi0SgHDhzgv/7rv5iYmJCVNzFvSqUSExMTlMtlZmZmaGxsxG63YzAYaG1tRafT4ff78Xg85PN5jhw5wv79+/mv//ovDh8+vGRn6MTSYTAYsFgsaDQacrmcOuaUIKNUKpFOp5mYmODpp59mbGxM9g6tUHq9HpPJhF6vp1KpMD09zdNPP83w8DCZTGZemjcr+0H8fj+XXHIJ3d3dtLa2qnuG4bflxGtqaiiXy7S1tTE9Pa32zVqqVnSwUa1WOXz4MAaDAavVyqWXXkprayutra20tLRQKpXweDxqTmdTUxP5fF5t7vMv//IvHDx4UFJSxLyLx+PE43GcTieBQIBqtYrZbMZms7F9+3bq6+uZmZmZE2w4nU7q6+vx+XzU1dURCoWIRCK8/vrr/OxnP5MPTzGvisUiR44cIRKJMD4+TlNTE3q9HoPBwJo1a+jt7UWj0aDRaDh8+DD79u3jueee4z/+4z+WdDqAWDqMRiM2mw2tVks6naampkb9nkajoVAoEA6HGRoa4pFHHmFycpJ8Pi/XyhVIr9djs9kwGAzqpvAf/ehHJBIJ0un0vNznmc1mfD4fq1ev5tZbb6W5uZlVq1ZhMpnmVPFTKvwZjUZ6enpIJpOkUikJNpay2dlZRkdHmZmZIR6PYzKZsFgsuFwutTqFx+NBq9USCASYnZ3lpZdeYmxsjEQiIYGGWFDJZJKBgQFSqRR1dXWYzWbWrl2L0+lkcHAQv99PbW0tNpuNTZs20dzcjMlkYmpqitdee429e/fS398vH55i3inNTZWOz/l8nnK5rM4aazQaNR95cHCQ5557jgMHDpDJZN50kzYh3gyLxYLRaKSzs5OOjg56enrw+Xxz+sBUq1X0ej0OhwOXy0VTUxOVSoWJiQlp3LcCJZNJJiYm2LNnD1qtlv3795NKpcjn8+d9rXI6nbjdbtrb29mwYQMtLS20trZitVrVAHdsbIxyuYzFYsFsNtPX14fZbKa9vV29hk5PT1MqlZZkOtWKDzYmJiYIBoNccsklTE9P43a7MZvN+P1+fD6f2tV0dnaWXbt2MTIywve//32mpqakgopYcOFwmF//+tesWbOGjRs34na7ectb3kIikaBUKhEOh9m8eTMNDQ34/X4cDgeHDx9mYGCABx54gAcffHBJXpjExU/pHG6z2SgWi6RSKXWsKbN0qVSKSCTCCy+8wLe//W3y+bzs0xALrqamBrfbzfbt27n66qvp7u5m1apVaiU/5ebRYDBQV1dHOp1mw4YN2O12IpGIBBsrUDAYJBQKsXfvXjUTYL5WX/1+Pxs3bmTLli28853vVNOpwuEwu3fvZnx8nJ/85CdkMhmam5upr6/nE5/4BN3d3WzevJnOzk727dvHyMjInOvsUrLigw2dToder6darZLP5+fkdOr1esrlMplMRk1HUTqTKjl8QiykXC7H5OQkLpeLRCKhrrwZjUY6Ojrwer00NDTgcrnI5XLkcjkOHjzIwYMHZYZOLAiNRoPFYsFisbBq1SoaGhpoaGjA4/FgNBrnPNdgMGCz2dRGqIlEQoINsSCUflk2m401a9bQ0tLC2rVraWxsRKfTMT09TSKRIBQKodPp1Ea+XV1dWCwWurq60Gg0vPbaa2qan6y+rRzHNryd73s7l8tFe3s7tbW1AOr9ZDAYZO/evQSDQWZmZtSCMPl8nqmpKbV/kcvlorW1lVWrVqk9P5aaFR9smEwmHA4HGo2GVCql/h1QA5CZmRkGBgb4zne+QyAQIJlMUqlU5EIkFlwikWDPnj2USiXGx8epVqs0NTVhtVq56qqr1FSASqXC66+/zsTEBA899BDPPffcks7vFBcvg8GAz+ejtraW22+/nY6ODjZv3ozP55uzyRF+27+op6eHyy+/nMHBQcLhsFw7xbzTarX09PTQ0dHB29/+drZu3aqmpAwPD/PCCy/w+uuv89RTT6lN29avX88f/dEf4XK5eMtb3kJXVxfPP/88mUyGZDIp+4rEvGhpaeG6667DbreTSqXYvXs33/zmN4lEIkxMTFAsFtXgdmJiArfbzSuvvEIul+PSSy+lqamJK664Arfbzc9+9jMJNpYSo9GIXq9X008aGhpwOByYzeY5z9NqtZhMJnXZy2w2k0ql5MNSXDAajYZKpUKhUFBz3ZV8+Wq1qj4+OjrKwYMHmZqaIpFIyKqGmFd6vR6n04nD4aCvr4+6ujpaW1vx+Xzk83kikQiRSIRcLofBYECn06l9NtxuN52dnaRSKYxG45LNOxYXL41GQ11dHS0tLTidTvR6/ZzStwcPHmRoaIiZmRnMZjPFYlGt9me323E4HHg8HpqamsjlcgwPD0uwIeaF1Wqlrq4Ok8mE2WzGaDSqqaaFQoFisajeU1YqFTUtNZlMUi6X0el0OJ1O6urqsNlsi3w252bFBhtutxun08m1117Ltm3bWLt2Lb29veh0ujmBhMlkoqGhgVQqxWWXXUZtbS3PP//8eberF+JsKBsYLRYLmUyGTCajbvZWgpBIJMLs7CyPPPIITz/9NMlkkkwmIwGxmFc1NTVs2bKFtrY2fu/3fo/6+nqcTicA+/btY2Zmhl/96lcMDg5SV1eHy+Xijjvu4JZbbqG7u1tNvXrllVdIp9NyDRXzSqfT0dfXxzXXXIPH4yGZTPKzn/2Mxx9/nHA4TCAQoFgsUigU0Gg0DA8Pk8vleOmll2hubmbDhg2YTCZuvPFGhoeHeeCBB5Zst2ZxcfF4PKxZs0bdMxQIBOjq6sJkMhEIBNTKaMemcYVCIbWRoFarpbGxEb1ej9vtXsxTOWcrLtiw2WyYTCZaWlrw+/20t7fT2NiI2WwmnU6TzWZJpVJotVr0ej0Wi4W6ujosFgvNzc0Ui0XMZjPZbFZm5sSC0Wq1GAwGHA6HuvJms9kwGo3qBUuh7Ds6dvOjBBpivhmNRnUl2OVyYbFYCAaDZDIZjhw5wvT0NGNjY2o/l1gsRjAYJB6Pqx3u6+rqaGhoUPe9SZU0MV+U3gTK57XBYKBcLquBbTKZPOHamMlkiMfjuFwuNYvB6/WSTCZP2jxViHOh0+nU/WzVahWHw0FHRwcmk4l4PE42myWdTlOpVNTP/fr6etxutzoO9Xr9ST//l4oVFWzo9XrWrVtHU1MTt912Gxs3blTTAsbHx3n22WfVWvAWi4X6+nrWrl3L3Xffjdfr5eabb2ZwcJBXXnmFUqmkLnEJMd+sVis+n4/Ozk5uu+02deZNaZoGv23+4/V6sdlsrF+/nmQyyRtvvMHIyMjinoBYdpxOJ9dccw1+v1/NLf7Xf/1XBgcHGRsbU1fUCoUC09PT6HQ6Ojs7aWpqoqWlhc7OTjZu3Mg73vEO+vv7+elPfyqpfmLeaLVampubWbt2rfpYe3s73d3daLVaotEopVJpTlPTfD7P9PQ0FouFcrmMyWSiq6sLo9GIxWJZjNMQy1SlUlH3A3d1dfGxj32MZDLJ6OgoqVSKsbExAOrr67Hb7axZswaXy4XVal0Wk4crKtjQaDRqTqYyW5zP55mdnWV6epqhoSGGhoY4cuQIdruddDqN0+kknU5jNBrxeDwkEgnq6upIpVKyuiHmndLF1OPxqM0lW1paqKurA45Wp4pEIlSrVXQ6nZrLaTKZqK2tpampiaGhIbRaraxwiHml1+vxer14vV4MBgPVapVYLEYoFCIUCs3Zy6bc0CUSCWZnZ/H5fOqMXUNDA1NTU+oHrxDzxWg0Yjab1Wuf2+2mubmZfD6v7mPLZrNqtcm6ujrsdrvaZVzZC2cymdRiB0Kcr3Q6TTAYxGKxYLfbMRqN+Hw+teFkKpVSx5vf71f3eNhsNqrVKpVKhUqlMi9dzBfLigo2dDodl1xyCdu2bcPtdhOLxXj88cf5zW9+w8zMDNPT02oVCq1Wy/DwMPl8nhdffJGGhgZWr16NXq/npptuYnh4mJ/85CdMT08v9mmJZaSxsZF169axbt06br31VjUtIJ/Ps2fPHoLBIE899RSpVEpdZv393/99urq62Lp1K21tbWqFC6UUrhDzwWKx0NPTQ2NjI1qtFrvdTmdnJ/l8nng8rm50PDY1KpFIMDk5id/vR6PR4HK5WLNmDcFgcMmmA4iLlxJkKDdkO3bsYN26dQSDQbU3ViAQUNOjPR4PfX19WK1WrFbrnFUPIebLrl27+MpXvsLmzZu59dZb1WaoFosFv99PuVympaUFOFrtT0npA9RJmVwuRzKZXLJjdEUFG1qtFrfbTUNDg7pEGg6HGRwcJBQKEQwGT4gaZ2dniUQi2Gw2tZ293+8nn8+fUFNeiPNltVppbGyktbWVrq4udDqdmnc8Pj7O1NQU/f39JBIJotEotbW1xGIxCoWCmnfs9XpxOp1Uq1UJNsS80el0WK1W7HY71WoVq9VKbW0tDQ0N1NbWUq1WyWazlEoltFotWq0Wq9WKTqdTPzCVvhvHV/0T4nxVq1V1D4bJZMJoNOJyuXC73dTU1OByuYhGozidTmw2G/X19dTU1OD3+9VZZaUqkKwKi/kUjUY5cOAAPp+PWCyGxWLBZrOp/V6OX+WtVquUy2XK5TKFQoFSqcTs7CzRaHTJfqavuGCjqalpTlWA9vZ2urq6AIjFYpTL5Tnl7pTmKhaLhUqlouZ06vV6+cAU8665uZm3vOUteL1e0uk0w8PDPPTQQ0QiEY4cOUI2myUUClEulwkGg9TU1PDSSy9RKBRoa2ujqamJK6+8EqPRyHPPPccrr7yy2KcklpFjb8RsNht33XUXqVSKoaEhYrGY2ofI4/Fgt9vp7u6mo6ODmpoa9fVCLIRSqcSjjz7K6OgoN9xwAxs3bkSv16sV/UwmE/X19XR2dqobdrVarRoMK+kqyoZySZEW80VpKJlIJIjFYvj9ftavX4/X66W3txeDwTCnv1uxWGRsbIxEIsH+/fuJRCLs2rWLkZERhoaGFvlszs2KCjYA7Ha7OutbqVRwuVzU1dURiURwOBwUi0W1i6NSEUjJlVMeUxpV6fUr7tcnFpjdbleb9lUqFWKxmNphdHJycs4SajqdplAoEAwGCQaDtLa2qjN2nZ2dDAwMLOKZiOWmXC6rqXkGgwG9Xk9bWxuVSgWn00kqlWJ8fJxYLKaWxfX7/dTX188JMmTWWCyESqWibrJdv349PT096oSgsip37AyyMgaPnUXO5/OkUikJNsS8ymazZLNZrFYr+/btIx6P43Q6KRQKNDY2zum7AVAoFJiZmWF2dpbBwUGmp6fp7+9nbGyMTCazyGdzblbc3fLxS6Tbt2+nq6uLqakpJiYmiMfjBINBzGYzHo8Hn8/Hpk2b1IZ/0uRHLCSXy6XOdOh0OiKRCB0dHRiNRqLRKIDaaRRQ+2wEAgF142N9fT2lUonnn39+MU9FLDMzMzP827/9Gx0dHdx66614PB51dtjj8eB0OvF4PJRKJYxGIwaDQb3ZO7aBVSqVWrKpAOLiValUGBwcJBAIYDKZ2L9/P11dXbS3t+P3+9Wc+GNv6tLpNJOTk8TjcQ4dOkQkEuH5558nGAwuyS7N4uIWCoXYvXs3+/fvZ9euXZjNZtxu9wn71yqVCslkknw+TywWI5fLEYvFyGQyS/YedMUFG+VymWKxiFarRaPR0NTURHNzM62trYRCIXVzrVI1xel00t7eruZ0LtX/0WJpMJvNeL1etbmk0+mktrZWnRVR9mEoH5Z6vZ5isUg2m1VL69ntdrxer5RuFPMqlUqxZ88e4vE4V199NXa7Xa2IpswaK+lSMHfmWFlJLhaLZDKZOWNYiPmgVEeLx+Ps37+fbDZLoVBAq9ViNBqpr68HmJOukk6nmZmZIRwOq40pX331VaLRqDSdFPNOWeFYiVZUsFEsFvnlL39JMBjkyiuvpKenB71ej06nU9OiPB4PjY2NGAwGLBYLRqPxpDmdmUxGllnFgqhUKmrp2sbGRt773vcSi8W49tprSaVSzMzMUC6XcbvdWK1Wtm7dSn19PQ0NDXIDJxZMOp1Wb8iUjeFr1qzB7XbT0dGBw+GYcyMHRwtwzM7OMjMzw8TEBENDQ7z22mtMTEws2aoq4uJWrVYZHx9ndnaW0dFRdu7cqW4Qh7nBRqFQUGeOI5EImUyGYDBIPp+Xz3ch5tGKCjbK5TJ79+4lFovR3t5OR0eHukHMbDaftCrA8SqVCvl8nlwuJ91vxbw7tqY2gNvtZseOHeRyOXp6etTNuOVymaamJux2u3qjp7z+2P8KMV8KhYKacvLCCy9QX1+PTqfD7/erNePht2kq1WqVRCLB9PQ0hw8fpr+/nyNHjvDyyy+Ty+XkZk4smGg0SjQaVfdwCCEW14oLNkZHR5mdneWJJ55gcnKS1tZW/H4/dXV16kbGY2c+MpmM2rBqdHSUaDTKiy++SCAQIBKJLPIZieXm4MGDfO9736Ojo4MtW7aoue8Gg4Ha2lqcTqdaetRms6lNrI7fXJZOp2XmWCyIfD7PoUOHmJiYYGZmBpvNxmOPPYbVaj3hufF4nFQqpTb/i8VisvlWCCFWmBUVbFSrVaanpwkEAthsNiYmJti0aZNaCtfn8825aatWq+TzeSYnJwmHw7z00ksEAgF+85vfMDs7SzKZXOQzEsvNyMgIjz32GNu2bePSSy9Fp9OplX/cbjcAPp/vpK89dhOu0u9AiPmmlGUEOHDgwCIfjRBCiIvdigo2FNVqlUAgoHZkHBgYoLa29qQ3cZlMhkgkQjKZZHJyklQqxezsrKRRiQURj8c5ePAgOp2O+vp6vF4v7e3t2O12Ghsb0ev1c1belGpU2WyW6elp4vE4b7zxBsPDwxw8eHCRz0YIIYQQK92KDDYApqammJqakl4E4qIyOzvL7OwsiUSCQqFAU1MTV111ldqlWa/Xz9mPUS6X1Woqr776KuPj47z88sscOXKEdDq9iGcihBBCCLGCgw0hLmbHdmXO5XLU1NTw2muvYTAY5jyvUqkQCARIp9NqBRal54akUQkhhBBisWmqZ1m25kxVmsTKdKGqHq3E8aec8/H/PZnjexqsFBfyXFfiGBRnJtdAsZhk/InFdLbjT1Y2hLhISRlbIYQQQix12jM/RQghhBBCCCHePAk2hBBCCCGEEAtCgg0hhBBCCCHEgpBgQwghhBBCCLEgJNgQQgghhBBCLAgJNoQQQgghhBAL4qz7bAghhBBCCCHEmyErG0IIIYQQQogFIcGGEEIIIYQQYkFIsCGEEEIIIYRYEBJsCCGEEEIIIRaEBBtCCCGEEEKIBbHkgo37778fjUZz0j/33HPPYh/evHr11Ve5/fbb8Xg8WK1W+vr6+NrXvrbYh7WirYTx98EPfvCU56jRaJicnFzsQ1zRVsIYBDh8+DDvfe97aW5uxmq10tvby7333ksmk1nsQ1vRVsr42717NzfeeCM1NTU4HA5uuOEGXnvttcU+rBVlJYy1VCrF5z73OW688UY8Hg8ajYb777//lM/fv38/N954I3a7HY/Hwx/8wR8QCoUu3AGfI/1iH8C5uvfee+no6JjzWF9f3yIdzfz75S9/yW233cbGjRv57Gc/i91uZ3BwkImJicU+NMHyHn8f/ehHuf766+c8Vq1W+djHPkZ7eztNTU2LdGTiWMt5DI6Pj7NlyxacTid//Md/jMfj4YUXXuBzn/scu3fv5qc//eliH+KKt5zH36uvvsqVV15JS0sLn/vc56hUKtx3331cc801vPzyy6xevXqxD3FFWc5jLRwOc++999La2soll1zCM888c8rnTkxMcPXVV+N0OvnSl75EKpXiq1/9Knv37uXll1/GaDReuAN/k5ZssHHTTTdx+eWXn9Vzc7kcRqMRrXZpLOQkEgne//73c8stt/DQQw8tmeNeSZbz+Nu+fTvbt2+f89jOnTvJZDL83u/93iIdlTjech6D3/3ud4nFYuzcuZN169YB8JGPfIRKpcK///u/Mzs7i9vtXuSjXNmW8/j77Gc/i8Vi4YUXXsDr9QLw+7//+/T09PCZz3yGH//4x4t8hCvLch5rfr+f6elpGhoaeOWVV9i8efMpn/ulL32JdDrN7t27aW1tBWDLli289a1v5f777+cjH/nIhTrsN21p/N94E5555hk0Gg0PPPAAf/EXf0FTUxNWq5VEIkE0GuVTn/oU69evx263U1NTw0033cTrr79+0vf40Y9+xOc//3mamppwOBzceeedxONx8vk8n/jEJ/D5fNjtdu6++27y+fwJx/K9732Pyy67DIvFgsfj4b3vfS/j4+NnPIcf/OAHzMzM8MUvfhGtVks6naZSqczb70gsnOUw/k7mBz/4ARqNht/93d89p9eLC2c5jMFEIgFAfX39nMf9fj9arfainsFb6ZbD+Hv22We5/vrr1UADjo69a665hp///OekUqnz/0WJ87YcxprJZKKhoeGszvfHP/4xt956qxpoAFx//fX09PTwox/96KzeY7Es2ZWNeDxOOBye81htba369y984QsYjUY+9alPkc/nMRqNDAwM8JOf/IS77rqLjo4OZmZm+OY3v8k111zDwMAAjY2Nc97vb/7mb7BYLNxzzz0cOXKEr3/96xgMBrRaLbOzs/zVX/0VL774Ivfffz8dHR385V/+pfraL37xi3z2s5/l3e9+Nx/+8IcJhUJ8/etf5+qrr2bPnj24XK5Tntuvf/1rampqmJyc5I477uDQoUPYbDb+4A/+gL//+7/HbDbPzy9RnLPlPP6OVywW+dGPfsSOHTtob28/p9+XmH/LeQxee+21fPnLX+YP//AP+fznP4/X6+X555/n//2//8ef/MmfYLPZ5ueXKM7Zch5/+Xwei8VywuNWq5VCocC+ffvYtm3bOf7mxJu1nMfa2ZqcnCQYDJ50hWfLli08/vjj5/0zFlR1ifn2t79dBU76p1qtVp9++ukqUO3s7KxmMpk5r83lctVyuTznseHh4arJZKree++96mPKe/T19VULhYL6+Pve976qRqOp3nTTTXPeY/v27dW2tjb165GRkapOp6t+8YtfnPO8vXv3VvV6/QmPH2/Dhg1Vq9VatVqt1Y9//OPVH//4x9WPf/zjVaD63ve+98y/JLFgVsL4O96jjz5aBar33Xffm3qdWBgrZQx+4QtfqFosljnn9+d//udnfJ1YWCth/K1fv77a09NTLZVK6mP5fL7a2tpaBaoPPfTQaV8v5sdKGGvH2rVrVxWofvvb3z7l9/793//9hO/9z//5P6tANZfLnfXPutCWbBrV//2//5df/epXc/4c6wMf+MAJMxMmk0nN4yuXy0QiEex2O6tXr+bVV1894We8//3vx2AwqF9v3bqVarXKhz70oTnP27p1K+Pj45RKJQAefvhhKpUK7373uwmHw+qfhoYGuru7efrpp097bqlUikwmw/vf/36+9rWv8c53vpOvfe1rfPSjH+WBBx7g8OHDZ/+LEgtiOY+/4/3gBz/AYDDw7ne/+029Tiys5T4G29vbufrqq/nnf/5nfvzjH/OhD32IL33pS3zjG984u1+QWFDLefz99//+3zl06BB/+Id/yMDAAPv27eP9738/09PTAGSz2bP8LYn5sJzH2tlSxpzJZDrhe0q2y8U8LpdsGtWWLVtOu2Ho+MoFAJVKhX/8x3/kvvvuY3h4mHK5rH7v2NxMxbF5cQBOpxOAlpaWEx6vVCrE43G8Xi+HDx+mWq3S3d190mM7dkCfjPKP5n3ve9+cx3/3d3+Xb37zm7zwwgunfG9xYSzn8XesVCrFT3/6U972tred9BjF4lnOY/CBBx7gIx/5CIcOHaK5uRmAd77znVQqFf7X//pfvO9975PxuMiW8/j72Mc+xvj4OF/5ylf4zne+A8Dll1/On/3Zn/HFL34Ru91+2teL+bWcx9rZUu4LT7ZfJJfLzXnOxWjJBhtncrJf+pe+9CU++9nP8qEPfYgvfOELeDwetFotn/jEJ066AVun0530vU/1eLVaBY4Oco1Gwy9+8YuTPvdMF6rGxkb6+/tP2Bzp8/kAmJ2dPe3rxeJbyuPvWD/5yU+kCtUStZTH4H333cfGjRvVQENx++23c//997Nnz54TyjOLi8tSHn9wNA//U5/6FP39/TidTtavX89nPvMZAHp6es74enHhLPWxdjb8fj+Aurp2rOnpaTwez0lXPS4WyzbYOJmHHnqI6667jm9961tzHo/FYnM2G52vrq4uqtUqHR0d53RRuuyyy/jVr37F5OTknHreU1NTANTV1c3bsYoLZ6mMv2N9//vfx263c/vtt8/T0YnFtFTG4MzMzElL2xaLRQA1hUEsLUtl/CncbjdXXnml+vWvf/1rmpub6e3tnY/DFAtoqY21M2lqaqKuro5XXnnlhO+9/PLLXHrppQv2s+fDkt2zcS50Op0ajSoefPDBee+I/M53vhOdTsfnP//5E35etVolEomc9vVKbvzx/0j+9V//Fb1ez7XXXjuvxysujKUy/hShUIhf//rXvOMd78Bqtc7rMYrFsVTGYE9PD3v27OHQoUNzHv+P//gPtFotGzZsmNfjFRfGUhl/J/PDH/6QXbt28YlPfGLJ9HBYyZbyWDuVd73rXfz85z+fU1L3ySef5NChQ9x1113z9nMWwopa2bj11lu59957ufvuu9mxYwd79+7l+9//Pp2dnfP6c7q6uvjrv/5rPv3pTzMyMsIdd9yBw+FgeHiYRx55hI985CN86lOfOuXrN27cyIc+9CH+7d/+jVKpxDXXXMMzzzzDgw8+yKc//ekTSraJpWGpjD/FD3/4Q0qlkqRQLSNLZQz+z//5P/nFL37BVVddxR//8R/j9Xr5+c9/zi9+8Qs+/OEPyzVwiVoq4+83v/kN9957LzfccANer5cXX3yRb3/729x444386Z/+6bweq1gYS2WsAXzjG98gFoup2SuPPvooExMTAHz84x9X94985jOf4cEHH+S6667jT//0T0mlUnzlK19h/fr13H333fN6XvNtRQUbn/nMZ0in0/zgBz/ghz/8IZs2beKxxx7jnnvumfefdc8999DT08Pf//3f8/nPfx44utHohhtuOKuUlH/6p3+itbWVb3/72zzyyCO0tbXx93//93ziE5+Y92MVF8ZSGn9wNIXK5/NJbvwyslTG4NVXX83zzz/PX/3VX3HfffcRiUTo6Ojgi1/8In/2Z38278cqLoylMv6amprQ6XR85StfIZlM0tHRwV//9V/zyU9+Er1+Rd02LVlLZawBfPWrX2V0dFT9+uGHH+bhhx8GjnauP3az+n/913/xyU9+knvuuQej0cgtt9zC//7f//ui3q8BoKkev+4jhBBCCCGEEPNAEg+FEEIIIYQQC0KCDSGEEEIIIcSCkGBDCCGEEEIIsSAk2BBCCCGEEEIsCAk2hBBCCCGEEAtCgg0hhBBCCCHEgpBgQwghhBBCCLEgzro7jUajWcjjEEvUhWrTIuNPnMyFbBMkY1CcjFwDxWKS8ScW09mOP1nZEEIIIYQQQiwICTaEEEIIIYQQC0KCDSGEEEIIIcSCkGBDCCGEEEIIsSAk2BBCCCGEEEIsCAk2hBBCCCGEEAtCgg0hhBBCCCHEgpBgQwghhBBCCLEgJNgQQgghhBBCLAgJNoQQQgghhBALQoINIYQQQgghxIKQYEMIIYQQQgixICTYEEIIIYQQQiwICTaEEEIIIYQQC0KCDSGEEEIIIcSCkGBDCCGEEEIIsSAk2BBCCCGEEEIsCAk2hBBCCCGEEAtCgg0hhBBCCCHEgpBgQwghhBBCCLEgJNgQQgghhBBCLAgJNoQQQgghhBALQoINIYQQQgghxIKQYEMIIYQQQgixICTYEEIIIYQQQiwICTaEEEIIIYQQC0KCDSGEEEIIIcSCkGBDCCGEEEIIsSAk2BBCCCGEEEIsCAk2hBBCCCGEEAtCgg0hhBBCCCHEgpBgQwghhBBCCLEgJNgQQgghhBBCLAgJNoQQQgghhBALQoINIYQQQgghxIKQYEMIIYQQQgixICTYEEIIIYQQQiwICTaEEEIIIYQQC0KCDSGEEEIIIcSCkGBDCCGEEEIIsSAk2BBCCCGEEEIsCAk2hBBCCCGEEAtCgg0hhBBCCCHEgpBgQwghhBBCCLEgJNgQQgghhBBCLAgJNoQQQgghhBALQoINIYQQQgghxIKQYEMIIYQQQgixICTYEEIIIYQQQiwICTaEEEIIIYQQC0KCDSGEEEIIIcSCkGBDCCGEEEIIsSAk2BBCCCGEEEIsCAk2hBBCCCGEEAtCgg0hhBBCCCHEgpBgQwghhBBCCLEgJNgQQgghhBBCLAgJNoQQQgghhBALQoINIYQQQgghxIKQYEMIIYQQQgixICTYEEIIIYQQQiwI/WIfgBBCCCGEEMuRRqNBq9Xi8/no6OjAYDBgNpspl8vkcjlKpRKpVIpCoUAkEiGfz5PNZimXy4t96PNGgg0hhBBCCCEWgE6nw2g0csUVV/Cnf/qnuFwuGhoayOVyTE9Pk0gkOHjwIKFQiGeeeYZQKMTIyAjpdHqxD33eSLAhhBBCiBVNq9ViNBppbW3FYrFgMBjQarUUCgUqlQqZTEadgc5msxQKBYrF4mIftlgC6urqaGlpYdWqVdTX11NTU4PH4yGfz1Mul7FYLGSzWRwOBzMzM7jdbmKxGOVyWR1/S52mWq1Wz+qJGs1CH4tYgs5y+Jw3GX/iZC7U+AMZg+Lk5Bq49Gk0GkwmE83Nzfz5n/85XV1d+P1+jEYjgUCAVCrF0NAQ4XCY3bt3c+DAAYLBIMFgcLEPXcbfEvCe97yHj33sY9TX19PR0YFOp0On01GtVimXy1QqFUqlEsVikXA4zMzMDH/3d39Hf38/gUCATCaz2KdwSmc7/mRlQwghxEVLo9Gg0Wioq6vDbrej0+nQarVUKhXK5TLFYpFisUg+nyeTyaiPCXG2TCYTra2ttLe309raSktLC/X19RiNRgwGA6lUikqlgsPhYHZ2llKpRLVaVWefl1NuvTh/yjWqpqYGu91OW1sbzc3N1NTUYDAY1GsagF5/9DbcZDJRLpfV/R0dHR1ks1kymQyFQoFyuXxBJ9fmm6xsiPMisypiMcnKxvJnNBoxm838t//237j66qvxer04nU6i0SjRaJRQKMT4+DgjIyPs2rWLRCLBxMTEBUs9kGvg0tfV1cVf/MVf0N7eTl9fH3a7Xb0pLJVKagBbKpWIRqPE43F+9KMf8dBDDxGPx4lEIot27DL+Lj5OpxObzcbb3vY2fud3fofVq1ezYcMGtFotOp1uzu+yWq3O+VoZa6OjowSDQb761a+ya9cu4vE42Wx2MU7ntGRl4wIwGAzodDqcTicGg4FEIqFWFlgOOXZCnA2NRoPZbKampka9mCrLw9VqlWKxSLlcVvNTlVlBIc5Eq9Xi8XhwOp10dHTQ3d1NXV3dnGBDuf5qNBoCgYCa3qKMOyGOp8weGwwG7HY7fr+fzs5OWltbsdvtGI1G9Xl6vR69Xo/JZAKOBr8ul4uWlhZaWlrQarXE43EqlYp87gvg6GqF2WymoaGB7u5udZUM5t6cVyoV8vk8lUqFQqGARqNRx5/f78diseDxeLDZbEt+s7gEG+fIYDDQ0dGBz+fjwx/+MF1dXXznO99h165dTExMLOpMhxAXik6nw2AwsGPHDj74wQ/icrmoq6ujUCgQDAZJpVIMDw8zOzvLyy+/TDgcZmpq6qLOQRUXD7PZzN13383mzZvp6+vD7/erN30OhwOTyURdXR2rV68mkUhw3XXXsWfPHu677z5isRihUEgCDnECJWDo6urirrvuorm5mTVr1uBwODAYDMBvZ/I1Gs2c2Wez2YzRaOTmm2/mkksu4fHHH+c73/kOmUyGeDy+aOckLh4WiwWHw0FjYyO9vb0nBBrKeIrH4/T39xONRunv78dqtfLe976Xuro6rFYr1WqVjo4OIpEIuVyOZDK5mKd1XiTYOEdKPl5tbS3d3d309vZSW1urVrFQLlBCLGfKikZLSwuXXnopLpcLv99PPp9XS/qZTCbC4TCTk5PodDqi0Si5XE5mAcVJKbPOZrMZp9PJqlWr6Ovrw+fzYbVa1ecpM87KaxwOBy6Xi0Qigd/vR6vVMjs7S6VSkWuxmEOn02E2m3G73axZswa/368Gr8crFovqBt5qtYrJZEKv19PQ0IDT6WTv3r3YbDZKpZJ87q9wyl4Ms9mMw+GgpqYGh8Ohjh+lf4by3HA4zPj4OKFQiAMHDlBTU0M0GsVqtWKxWNDr9dTU1OByuU46NpcSCTbOkcFgoLu7m+bmZl555RX27t3Lnj17GB8fp1AoqBcfJaVKbqzEcrR9+3be97730d7eTnNzM0ajEY1Gg9FopL6+Hq/Xq650rF+/nmAwyD//8z9z4MABIpHIRZmDKhaPkpbqcrl4+9vfTmdnJ9u3b5+zonGqGWej0YhWq2XDhg18+tOfZmBggG984xtqjr2scAiFXq/Hbrerk4Ver1dd0VCCBWW/xqFDh4hEIhw5coRwOMxb3vIWNm3ahNFoRKfT0dDQQG9vL+Pj40QiEQk2VjCLxYLJZGLz5s1cfvnl9PT0AJBIJAiHwzz//PM88MAD6rWoUCiQSCQoFArE43G1AEZrayt33XUXPp+P5uZm0uk0+/btW8xTO28SbJwjrVaL3W7HZrMxPT1NLpcjHA6TzWbV7pDZbJZsNjsveziU/SFK5FytVuf8UQIa5WshFopGo1FL97W0tLBt2zZcLhd2ux2tVgsc/fehzELX1NRQqVRwuVxEIhEaGxvVFKtcLifjVaiO3f/T19fH2rVrqa+vx2azASfmOx97zVNy8Ovq6rBYLGg0GpxOJ5lMhkQisVinJC5Cer0ei8WC3W5Xr12AOvusBLHFYpFAIMD09DT79+9namqKtWvXks/n0ev1GI1GbDYbXq+X2dnZRT4rsdiMRiNWq5XGxkZWrVqF2+2mWq2SyWQIh8McPHiQJ598klKpdNLX22w29u7dSyKR4Oabb0aj0WCz2XA6neoq7lK1tI9+EeXzeXbv3s3BgweJxWIUCgV8Ph/bt29ny5YtdHd38/zzz/Pyyy8zPT3N+Pj4Of0cg8GA0WjkXe96Fxs3bsTpdGK320kkEiQSCWZnZ5mZmVEvhqlUipmZGbmBEwvCZrNhsVjYunUrW7Zs4dJLL6WpqUltgAW/nXk+doZQq9Vis9nQ6XR84AMf4K1vfSv3338/r732GqlUinw+v2jnJC4eSpDqdDppb2+nq6vrhEBDuRGcmppicnKSQCDA+Pg4q1ev5rrrrkOn02Gz2fB4PKxatQqTyaSWKxUrmzIR2NnZyS233MKqVaswGo0Ui0VmZmYIBAJ897vfJRAIoNFoKJfLzM7Oksvl1P/qdDoOHjzIjh072L59O263m1WrVpFKpaRi0wqm0+nU0smXXXYZmzZtolqtMjU1xc6dO/nP//xPDh48eNqJ51wuR39/P7OzsySTyWV1HyfBxjkqlUpMTU1hMBgIhUKUSiVaWlpob29n+/btbN26lUwmw9jYGKlU6px/jsFgwGKxsHHjRm688UZ8Ph9ut5twOKxuth0eHubQoUOEQiG0Wq1sihQLxmQyYbPZ6Onp4brrrqOxsZGampqTPvfYm0M4Opb1ej2bNm2iq6uLX//61xw+fJhcLifBhgBQ92pYrVbcbjderxf47SqG8kGt0WiIRqOMjo4yNDTE3r170Wg0XHnllRiNRnWGsba2llQqhU6nW8zTEhcJnU6HxWLB6/XS29tLc3MzOp2OYrHI7Ows4+Pj/OIXv2BoaOiU7+F0Okmn07S0tLB9+3YsFgt1dXU4HA4JNlYwjUaD1+ulqamJxsZGdQU/HA4zODjISy+9RCwWO22wUS6XCYVCGAwGCoXCBTz6hbdigg2TyYRWq1Wbo5wvg8GgzrpVKhUymQxXX30111xzDW1tbRgMBrWs2bnOqJnNZt71rnexdu1atm3bNmeDpDJLrJTt6+npoa+vj4GBAX74wx+SSCTOOLCFeLOMRiN2u52GhgY6OjpwOBzq945dyVCqUCUSCUZGRjAajVx33XV4PB4sFguVSoXm5ma6urooFotLvqyfOD9arRaLxYLb7ebKK6+ko6MDt9tNpVIhEomQSqV47LHH2Ldvn3pDNzs7SywWI5FIEIlE1DHU2dnJW97yFiwWC+3t7ZTLZTUfX6xsXq+XNWvWcMkll3DJJZdgsViIRCIEg0EeeughRkZGiMVip32PiYkJEokE1157LXBhe/2IpUFJd49GowwODjIxMcHMzAy5XO60r7NarWzatIm2tjZcLteyCl5XRLCh0WjUPQ9Kg57zpVSjcLlcjI2NUalUWLduHVdeeeWcXOLzCW6MRiM7duzg2muvpaGhQZ1BrlarmM1mTCYTLpcLOJrW1dvbi9Pp5MknnwSObkqSYGNlWehqKMpKm1J16tifV6lU1K+z2azalOill17CarVy+eWX4/F41FQGr9dLQ0MDo6OjC3a8YmnQarWYTCacTifr1q2js7MTu91OtVolHo8TDAb5xS9+wRNPPHHK91Dq1G/ZsoVrr70Wo9GIz+cjHo/LyoYAwG63097erv4pl8tMTEwwOTnJs88+y9TU1BkzEZT+LrOzsxJoiNNKpVJq759YLHbG8WI2m1m7di2dnZ1zJvKWg2UdbCh54larla1bt+LxeDhy5Aizs7NMTk6ecQbjdJTVDIPBoG5OnJyc5MCBA+j1erRaLSMjIwwNDZ117W3leO12O9u2baO1tZW+vj7q6uowm83Aybt4Ko2HrFYrPT093H333Rw5coQHH3yQeDxOOp2WoGOZ0mq1+Hw+7HY71157LR0dHezcuZP9+/czOzs7b3XfTSYTBoOBtWvXsn79etrb29FoNKTTaeLxOAcOHOCpp56iXC6j0WjIZrNMTU2RTqeZnp7GarXy8MMP09bWxlVXXaX242hra6O/v39ejlEsXWazmZ6eHlpbW1m7di3Nzc3kcjkymQxPPvkkhw8fZmRk5LTvEYvF6O/vp76+XtJIxWkpM8+5XI7x8XFGR0cJhUJEo9Ezjp1Vq1bR0tJCR0fHspp5FvOvubkZg8HA1NQUe/bsIZlMnrQHm16vx+l00tTUxLZt21i1ahU1NTWUy2XGxsbYv3//ki9yseyDDbvdjsfj4YorrqCtrY0XXnhB3UdxPsFGtVoll8upy/PKXonBwUG1/NnExMSbmrVVjtfn83HzzTezdu1aent7cbvdp3yNcrFTas53dnZSV1fHnj17ePrppymVSmSzWQk2limtVkttbS2NjY28733v4+qrr8ZoNJJMJimXy/MWbBiNRiwWC93d3Wzfvp2WlhYAMpkMMzMzPP/883z1q1+lWCye9PVWqxWTyURbWxtr1qzB4/FQV1dHU1PTnN4JYmUym810dHTQ1dVFT08PPp+PyclJwuEwzz77LLt37yYQCJz2PZSiGatXr1a71wtxOrlcjsnJSaampgiHw2e8J9BoNHR2drJ582b1GigBhzieUiHP7/fj9/s5cOAAra2tTE1NEY1GT7g26fV6XC4XDQ0N6p5GpaLpxMQEhw8flmDjYmYymVi/fj1NTU2sXbuWpqYmyuUyLS0tTE5Onlf6hpIqotPpSKfTZLNZtb/A8PAwoVDoTVeg0mg06gbc+vp6/H7/CZ0nlQtbJBIhEomoXXKbm5vp6+tTN1g6HA6ampqoVqvEYjGpxLJM6XQ6/H4/TU1NjI+Ps3PnTg4fPkw4HKZQKGAymahUKurN17ncgGk0Gurr62loaKC7u5vu7m7MZrPa9fS5557jtddeO21AWywWGR8fp1wuq7015GZQnEqlUiEUCqkBx+zs7BmLCNTW1tLZ2Ulvb++SLxMpLgyDwYDH48HpdKrV9E7FbrdjNpvp7e1l69atNDU1AUf3Dh0+fJhAICDXtBWsWq2qTfuUFX5FJpMhGAySzWbVz+RSqYTJZMLtdlNbW8vVV19NW1sbHo8HgEOHDhEOhxkdHSUQCJxxv8fFbllfkc1mM5s3b6anp4dNmzZRX19PXV0dsViM55577rzeu1KpqP/zU6kU2WxWzTs+cOAAu3btYnh4+E29p1L20W6309TURGtr6wk5+MrfA4EABw8eZHBwkH379rFjxw7WrFmDwWDAarXicrlobW2lUqkwODh4XucqLl5arZbm5mY6OzsZHBzkwIED7Nu3j+npaTQaDRaLhWKxSLFYpFwun1N6iVarpaWlhZ6eHtatW8e6desIh8MEg0F2797NAw88oHZqPpVisciRI0fUtD75UBano1T7GxkZYXp6mlAodMbXNDQ0cN1117Fu3Tr0er3MOItTUiZejEYjtbW1eDye0+7r0Wg01NTU4Ha72bBhA9ddd52aQh2JROjv72diYkKuaytcoVAgnU7PmdytVqvq3g1lv63yOWyz2WhtbaWrq4t3v/vd+P1+amtrKZfL7N27l+HhYY4cOcL09PSSb4C7rIMNpfmYVqudU/t/PpbYK5UKiUSCXC6nBgg+n4/6+nr0ej2ZTOasVxOUFZGamhrWrFlDa2ur2mRI+Rl79uxhYmJCfc309LT6ITw5OYnVauWpp56ioaGBvr4+tYNzOp2WzZHLWLlcZnx8nGKxqPar0Gg0dHR00NraSmNjIyMjIxw6dEjtYnqulDxnpdqUMv5isRiZTOa0/6aMRiPt7e00NTVht9vlRlCcllJp72yaWSkpfs3NzVxyySW0tLSg1WrJ5XKMjY0xOTkpK7sCOHq9zOVyarqnwWDA5/MRCARO+jmp0WhwuVxYLBYuv/xydWO5Xq8nFAoxOzvL8PAw4XCYVColwcYKVq1WCQaDVCoVZmZmiMViaiEfr9fL2rVrcbvdNDc3EwwG2bt3L2azGbfbjcvlwu12Y7fbyWQyJJNJXn/9dQ4cOEAwGCSXyy35fWjLOtgA1C6fygpBqVSiWCye9x6GcrlMMBjEaDTS0NCAx+Ohs7OTzs5ONWf+bHsHKE2o6urquOqqq+jq6lLLPobDYUKhEPfddx//+Z//qb7m+A7igUCAcDjM5s2bWb16tVr2sVgsStnHZaxUKrF3714OHz5MJBIhl8uxbds2Nm/ezNve9jauu+46Hn/8cX70ox8xOjpKJBKZlw9EpaTf2NgYgUDgjP+eLBYLV1xxBZ2dneoysQQc4lSUmvW5XA6TyXTa59psNnw+H2vWrOGtb30rVqsVvV5PKpViYGCAsbEx6eMigKPXy1QqpWYl2Gw2Ojo6mJ2dPWlQq9VqaWxsxOfz8fa3v50tW7bQ0NCA0WhkfHycXbt28eqrrzI2NkY2m5VgYwWrVCqMjo4yOTnJ8PAwgUCA2tpaTCYTLS0tXHPNNaxfv57rr7+ePXv28C//8i9Uq1V1r0Z9fT0Oh4PJyUkmJib41a9+xauvvnrO6c8Xm2UdbChVmpQldWU5a3Z29pwbpuj1erxer9o0ymw2s2HDBurr67Hb7aRSKTKZDNls9qxn05TZlaamJpqbm2loaFA39w4MDKg5e6fafAtHU7nGx8dpb2+f0/hKbuguLspKW6lUmpcLiFarxeVy4XA4yGQyFItF2tvb2bRpE83NzRiNRnQ63ZyGaPNBGf9Go/G053Fs/4RVq1axatUqrFYr1WqVaDTK1NTUkl8eFr/lcDgwm810dXXh9/sZHh5mfHycbDZLJpM55esqlQqFQoFisUi1WkWn0+Fyucjn86ecLLFarVgsFjo6Oli3bh09PT2YTCby+TxTU1OMjo4SDoeJx+NSIEMAkP7/2Lvz4Liu607833697/uK7sYOAiDBTVy0kaIly5JsRZblPYnlZeLJxHGmMlNOxcnEk5+VxKlUXJlKnHjicWLLSeTYiiV5kWR5JJqSLFHiIm4gsS+NBtD7vu/9+4NzrwACFCkSIAjgfKpYEsEG+oG4fO+ee889J59HIBBAJBJBJpNBvV6HUqmEUqlER0cH70zP5gcsFVWn08FgMPBS89lsFjMzMzhz5gxmZmYW7ZaQ9UUQBIjFYtTr9eu+TzSbTTQaDUQiEUxOTvL5otVqxcDAADweD5RKJSwWC7Zu3QpBEGAymWA2m5HNZnmK/+zsLGKx2Ia6b234YEMul0OhUEAQBL7N5fP5kM1mr+lrKpVKDAwMQK/XQ61WQ6vV4qMf/Sg6OztRKpUQCAT49urVrqapVCps374dHR0d2LNnD9/WTSQSeOaZZ/jKyTuJx+NIJpNwOBwrNpElK08ul0MikaBYLK7Iw0ksFqOrqwsOhwOlUgmNRgMHDx7Eo48+uijYLJVKK5ZK0mw2eQECvV7/jr09JBIJrFYr2tvbcc8992DLli1Qq9Uol8uYmJjA6dOnly0FSNYftgpst9vxu7/7u3jggQfw+OOP4wc/+MEVC3I0Gg3k83l+nkcikcDr9UKr1V623rzFYoHH48GhQ4fwyCOPwGw2Q6VSwe/345e//CUmJiYwOTlJBTIIF4vFkEgkYDQaMT8/D4vFAqfTCb1ej3vvvRdTU1P48Y9/zNNNBUGA2WyGy+WC0+mEw+FANBpFNBrF0aNH8cQTT/DDvmR9kslkkMvlKJVKK7ID2mw2MTQ0BIlEArlczivsdXV1Abg4L21ra8OHP/xhKBQKOJ1OlEolDA8PY2pqCn/1V38Fn89HHcTXo4Wr+wsr87wTQRAglUohkUigUCj4yptWq4Xb7YbZbIbZbIZWq4XVauW5doVCAeVy+V2larGzJRKJhK9853I53jwokUhcceCpVCpYLBaYzeYrVtUgN55MJuMTKL1ej2g0imw2y4sLXI+FuxaCIKBcLvNgWiQSIZVKIZlMXleX7nq9zsc0W+0zmUyXLVvLUgN1Oh22bdsGj8fD8+9jsRg/P/JugnJycxOJRPx+WKlUeN5yoVBAvV6HRCLhK3/A4mpktVoNsVgMer0emUwGxWKR/5ux2+3wer2LuoQDF/9NabVa6PV6mEwmyOVyFAoFxGIxjIyMYHZ2Fvl8HpVKhRZfNhlWfp4Fv5FIBPPz87xaUDabhc/nQ7Vahc1m47txUqkUnZ2dMBgM0Ol0UKlUGBgYgNvthkQiQTKZxOjoKKampjbkhHAzYY2eWZpcMplEIpFAsVi8YmPHd9JsNlGtVvniCevHxrIAGo0GZDIZ3yXL5/NIJBIYGhriHezXe+Wp5WyKYONayOVymEwmHlxUq1WEw2GYzWYcOHAAHo8HfX190Ov1PKc4n8/z7dnrGSz1eh0zMzPw+XyYmZlBMBi8YuDi8Xjwvve9DwMDA5BKpZQ+dRMRBAF6vR46nQ4PPfQQent7cerUKfh8PgwNDWFqauqav3az2UQ+n+fdSSUSCebm5vDWW29BoVBAoVBgdHQU58+fv+bCCKynTDab5Q9XVsHFbrcv+zkqlQrd3d3wer343Oc+B7fbDbfbDQA4fvw4ZmZmcO7cOfh8vuu6sZObhyAIaGtrw9atWzE9PQ2fz4ejR4/C7/ej2WxCrVajVquhUqnwRR8mn8/j7NmzSKVS8Pl8UCqVvNjG7bffDq1WixdffBEjIyP8c3Q6HVpaWuB0OuFyuXgDybNnz+L73/8+MpkMyuXyhsl5JldHJBLB6XTCZrPh0UcfxUc+8hE8/fTTePzxxxGJRDA9PY1AIIBnn30W27Zt431/HnzwQcTjcYjFYmQyGdxyyy28F5BWq8X09DTOnj2L73znO3jhhRco/XOd02g00Gg0uPvuu3HHHXdgZGQEFy5cgM/nw4ULF67rnlEoFJBOp3mQy8rbAm+n5snlciQSCZw5cwaTk5P4zne+g3A4vGF3+jddsCGVSqFUKmE2m+FwOHi1gEtpNBq+Y+FyuVCtVmG322EwGOB0OmG1WnkqFQCe77dSzaRYHiGrnnU5YrEYUqkUZrMZ7e3tcDgc/ExAOp1GNpvdUHl/65EgCLBarTztw+PxIJFIQCQSLaowdi2azSbPda9Wq6jVarzELds1iMViPBf+WuVyOcTjcX64kp2F0mq1cDqdyOVyixoIsmpCBoMBNpsNJpMJjUYD5XIZfr8f4+PjSCQSG6LKBrmIBb7pdBrFYpGvIut0Op7znkqlEAwGUS6XFzWpYquBxWIR4XAYBoMBJpNpUZrB6OgoMpkMbxDZ09ODtrY2GAwG5PN5hEIhXLhwARMTE3x3hGxOKpUKVqsVMpmMZxrUajU0Gg2IxWJUKhUEg0HYbDYUCgXIZDIoFArodDq0trYin8/D7XbDYDBAJpOhUqkgEAhgamoKwWAQyWRyrb9Fch1EIhGMRiNsNhs8Hg+8Xi9KpRKKxeJ1ZQAw9XodlUoFlUplSQqzSCRCvV5HrVZDJpPB+Pg4pqam+Pmyjfo83FTBhkgkgslkQq1Ww5133snrG7NOoAvp9Xo4nU7eIA+42CtALBZDq9VCLBbzhnvMwlST6yEIAmw2G6rVKhQKxTu+VqPRwGKxYPv27Xj44Yeh1WohkUiQTqdx6tQpzMzMUJrKGpPL5Th48CB6e3tx6NAhtLW1wev1IpFIIBaL4cyZM9f8tZvNJgqFAlKpFNLpNJ/wq9VqnDp1CidOnMDExMR1BRqNRgMTExPw+/04ePAgdu3aBZPJxA99f/jDH8bQ0BCOHDnCb5RyuZw3G3S5XDCZTIhEIgiHw/jJT36Ct956i59boWB4Y2C14WdmZpBMJlEoFNDb24s77rgDt956K+6++268+eab+I//+A8Eg0G+27ZQLpfDSy+9hMnJSTgcDrS3t+PQoUO49dZbodVqceHCBfT19aG1tZU3mozH4zh37hx+9atf4dvf/vaiakNk8xGJRPB4PNixYweCwSC+//3v48SJE5iZmUG9XodarUahUMCJEyfQbDZ5tTKn0wm1Wo177rkHzWYTcrkczWYT58+fRygUwg9+8AOcOnUKkUhkrb9Fcp0EQcCePXuwd+9e3H777fzM7K5du/D000/j1Vdfva5Jf7FYRDqdRiaTQTabhU6nW9R+oVQqIRqN4syZM/jWt76FWCyGeDy+IofUb1YbOthoNBpIJpOIxWIoFou8A6hOp4PL5YJEIkF7e/uywYZWq4Xdbuc7IVciEokgCMKinh5Xi+1esMEtEomgUql44LAciUQCqVQKo9HI+ykYjUa+Bczy9FnFDbJ2BEHgPQM0Gg3/2dZqtSsGk1ejXC7zyZVUKoVCoeApK7FYbEVWaiqVCmq1GpLJJMLhMGQyGYxGI/R6Pdrb25HNZmE2mwFcbKbJqqtZrVYAF7eVWc8DlstP1hYrILCSaUYsJ5mdG1KpVGhvb0drays8Hg8mJyehUCguW2GqVqshHA5DoVDw5lhKpRIKhQItLS0oFou8h4xarYZMJkOhUIDP54Pf70cgEKDDuoSvLLNUunQ6zYvFaDQalEolxONxPlbZuR5BEKDVavkzuVwuIxQKYWpqCoFAAOFwmHbMNgjW/Fij0UCtVqNUKqFUKl3VfO9y2NlblUrFz/ywojALLbz3VioVlMvlDR1oABs82Mhms/jxj38Ml8sFq9WKrVu3wmq1wuVyoaurC9VqddmBALw9mV9Y0eedHsgsQGDbru9GvV7nh4kajQakUina29uh0+kuW4nFZrPB6XTi9ttvx/vf/364XC6oVCoEg0G8/vrrmJiYwOjoKJLJJJXkuwksLMEMgOeuX28gWK/X+eRdq9XC4XBgy5Yt6O3txZEjR5BOp1dslbfZbOLs2bMQiUS47777eFfxlpYWbN26FXq9HkajEdu2bYPBYEB7ezsAYH5+HsFgEH//93+PyclJhMPhFbkecn3kcjmkUumKle0UBIEHFmfPnkWtVsOBAwfw+c9/Hmq1mqerlkqlyx6sLZVKGBwcRCQSwdTUFHQ6HaxWK1QqFfbu3Yvt27fz1Fd27uf111/HT3/6UyQSCVpYIWg0GhgaGkI4HEY6nUY+n4fX68Xtt9+OgYEB3HvvvRgeHsaTTz6JlpYWlEol3pSUTQBZj6t4PI4nn3wSx44dQzQaRS6X29ATws2EpaCzgjosQL3WxQrWG0in0+G2227Drl27sH37dvT29i6ZE7L00I6ODuzdu5f3bNnIgeyGDjZqtRrv6BgMBmGxWHhupkwm49VOBEHg0aUgCJBIJDwH/tJdCrFYDLlcvuTjzWaTV7l4tw/uhWUfWVqJXC6HWq3mh5guzftjHcdtNhs6Ojqg1WrRaDSQy+Xg8/kwOzvLKx3R4ci1x3q+sIdZtVrlqxnXivXQYBXMzGYzLBYL9Ho9v7mVy+UVXelNJpOYnZ1FMpnkOzNqtRputxudnZ0wm83o6+uDWq2GyWRCLpdDOByG3+/H5OQkfD7fil0LuTZs91Wr1UKlUiGXy/FdhOsZKyKRiI8HuVzOK5JZLBbeULVSqaBYLF62QlSj0UChUODXVCwWeQU0nU7HX9NoNJBOp+H3+zEzM4PZ2VmUSiW6161T71Q++1pUKhXk83lkMhkkk0keBHd2dqK3txflchk2mw0Gg4HPARZeCwCeV59OpxGPx1EsFimY3UDYuVgAi3ayruUeKJPJIJVK+dlMl8uFlpYWGI1GvlNSq9V4FoJMJuNzO3YmjS38bNR72IYONtgh2WKxiO985zuw2+144IEH0NnZyUuBejwe2O12nDp1CsePH4fFYoHX610UTLDStCwFa9++fdBoNADAH6LFYhHPPvssXnnlFYyNjb2r68zn8zhz5gxSqRSmp6chk8lgsVgglUpx++23Q6lU4rXXXls0UdPr9XC73TwnnjWyOnPmDJ544gleMpcdiiNrh03ClEol7/cSjUYxNTV1zelEcrmc7yCw5mbve9/70NPTw2vIh8NhXspvJTSbTfh8PsTjcfT392N+fh46nQ5GoxEejwcf/OAHIZVKodFo+JienZ3FE088gfn5eUSj0RW5DnLt2KRdpVLhve99L/r7+zE6OsqrR11PMMjOD7FDjlKpFKFQCCdPnuS17IeHh3H+/HkeRFxKEAS+yFKtVpHL5RallzabTV744vDhw/jBD36ARCJB6aLrGFv4Ywdqr5dIJILX60VXVxdGRkZQqVSwd+9efP7zn+dNJx0OBw4cOACTyYT+/n6oVCqe2sfSqSwWCxQKBXbs2IFKpYLBwUEEg8Hrvj6y9kQi0aLGtACQSCQwOTmJWCz2rib8UqkU27dvh8PhwPvf/3709vbCbrdDp9NBLpcjl8vxYhkjIyN47bXX0Nvbi49+9KOwWq34wAc+gNHRUZw9e5YX2diIc7YNHWwAFw911+t1Poh6enogk8l4nwOWrzw5OYlz587B4XAs2dEQiUSQSqUwGAx8m40NRpZzVywWMTs7i7GxsXc9gazX60ilUkgkEkgmk0ilUjAYDLwGdCaTwfDwMEKhEF/FZtVatFotpFIpcrkcotEogsEgfD7fiuTpk5XDVlHYClqpVEIul7uqXTCWysc+l6XaWa1WXjFNpVKhp6cH/f39PHWA9XxZyUkY61KeyWSQz+d5aoxKpeKdwZvNJrLZLILBIK88FYlE6NDuTYLlE7e2tqKvr49P8laiwg5bvWOrg8ViEalUiqcRJhIJpNPpZVcPWW8jFmywwPxy7xGNRnkBDEoVXX/YfU2pVEKlUvFUpkvLIl8LltKsUqkgkUhgMBjg9Xr5mFIqlXA6ndDpdFAqlZBIJPyZya6LLRBZrVY4nU5MTk6u+A4MWTvsmczmemxx42oK6ix8JstkMlitVrjdbnR3d6O/v5+fS2O7xeVyGblcjlfMUygUKJfLkEqlaGlpQS6Xg8Fg4NkoFGysU41GA4lEAtlsFk899RQ0Gg1/IKrVaiiVSsTjccRiMYyPj+PUqVNLvgaLWg0Gw5JmVENDQ5ifn+ervtf64Mvlcjhy5Aj8fj8+8pGPoKWlBXfeeSe2bdsGpVKJ/v5+tLe3w+VywePxwO12o9Fo4Pz58zh58iR++MMf0qRug2HpckqlEjabDc1mE4lEAmq1GrfffjtaW1vR0dEBg8EAh8MBtVqNcDiMSCSCbDbLu4qvFJYmIxKJkMvloNFoFp1pKpfLSCQSGB4exne/+10Eg0EEAgGUy+UNeQNdbyQSCfbt24fe3l7cdddd6O/vh8PhwI4dOyASiXD27Nlr/toLdzay2SzS6TQajQbUajWGh4dx5swZDA8PLzsOpFIpb0p67733wuPx4JZbboHT6eSNI9l9V6/XQ6VSoa+vD3v27OGLPGR90Wg0UCqVuO2227Bjxw74fD5MTk4iGAxeV+8h4O0+B5VKBVKpFLFYDKdPn4bBYIDdbodCocDWrVtRLBZx6tQpxONxvP7666hUKrBYLDAajXjkkUdgt9tx6623wu12IxwOIxqNvuOZI7LxicVi3l/NZrNBr9fjwIEDaGtrg1arRTabXZSmz3ZjWTYDq4J24sQJXg6/q6sL9913H3w+H37+858jkUis9be54jZFsAFczF0vl8vX9FASBIHvkFxabYod0J2dnb3uzo/VapWnMRQKBYhEIrS0tMDhcPCb78DAADo7O2E0GmEymeDz+TAyMsK352iFb31YuDLCznIsV8VMqVTyKlYtLS18i1+tVsPj8aC9vR19fX0wGo0A3s49XY1dDZFIxM8SicXiZVP06vU6isUi4vE4hoeHEYlE6NzQTUQQBDidTnR1dcHtdsPhcPCHJ2s6dT3YWSTW+4XtVqTTaYyOjiIUCi0ZC2x10Gg0wmq1ore3F16vF1arlZ9FK5VK/N+HRCKBTCaD2WyG2+1GNpulFed1iKUlt7e3Y9euXZDL5bwvwfX+PGu1Gl9oEYlEKBaLiEajvL8CqzpVLpcxNzeH2dlZvP766ygUCvzfxT333AO73Q6n0wmlUgmLxQKVSsXPHpGNZWFFUYlEgkajsegsDyOVSqHT6aBWq3lZd1ZtT6FQ8J5W7GsBb5/LTafTCIVCCAaDCAaDkEgk6OnpgcFgQGdnJwAs2/dtI9g0wca10mg08Hq92LJlCw4cOMBvPAtX8d544w2cOXPmuvM5S6UShoeHkUwmMTMzw6v7yGQy7Ny5E+3t7TAajdBqtYjFYpiensaJEyfw0ksv8Rri5OYnEomg1+vhcDiwZ88eyGQy/lC7FLuhKRQKmEwmABcDUalUis7OTmi1Wt5YkmEHG1dyJ8FoNEKlUmHfvn3o7+/H/v370dXVtWTVma32eL1e7NixA3Nzc7hw4QLttt0k2Pmz5aqwXO/9o9lsIh6Po1QqQS6Xo729HR0dHejp6cGZM2eQyWSWjAO9Xo8tW7bA5XLh/vvvh9VqRXd3N+RyOe/ky3obGAwGqNVqHDx4EDt37kRPTw8+8IEPQC6XY3R0dFEJaHJzEwQBO3bsQH9/Pw4cOIDdu3fDbrejq6sLL774Ik6fPn1dwUapVEI2m0Umk0Emk0G1WoVSqYRIJEI2m8XU1BReeuklxGIxTE5OIpPJYG5uDrVaDYlEAoFAAKdOnUK5XOb35ttuuw1SqRTHjh3D0NDQCv5tkJuBWq2GzWbDtm3b8MADD0Cv1/PUu4WkUincbjeUSiVMJhOUSiVaWlqg0Wh4qvSl5XNZaiC7x5ZKJYRCIajVatTrdSgUCrS3t6Ner7/raqbrBQUbVyCXy2G32/lWl8VigSAIvD53Pp/H5OQkhoeHF3VQvha1Wg2hUIinfWUyGWg0Gsjlcrjdbrjdbv7a2dlZzMzM4MKFCzh69OiGzfPbKFguMvsZLexW22w20dnZie7u7iWfp9Vq4fF4+GG2K/VwYWcmFr7X9WI7KQaDAd3d3bjlllvQ2dnJV8LZDXRhnXqTyQSPx4N6vY6xsTGaBN5EFvYDWtjjZyXGS7FYRK1Wg91u5zsVVqsVCoWCl9hdOIlUKpVoa2tDZ2cnDh06BJPJxA8Lj42NIRAI4OWXX8bk5CScTidMJhM6OzuxY8cO2Gw2iMVijI6O8gUgGmfrA9u137ZtG7q6uuDxeHgFu+Hh4Xfdq+pSbGdjYQDKqkiWSiXMz8/jxRdfRCKRwPz8/KJAO5VK8b5ABoMBTqeT9xMqlUoYHx+/rmsjNwf2nFy4UKbX69HS0oKBgQHY7XZs3759SbChUCjg9XqhUCj4maCFX/Od3gu4mIbFKoey4i0SiQRmsxmJROKyvdXWu435Xa0grVaL7du3o6ura1HJ20qlguHhYczNzWFubg7xePy6O3ULgsArC1WrVRQKBb4NzAZxNptFsVjEG2+8geeeew4zMzPI5XLUyOomVi6X8atf/QozMzM8eGTbsCaTifeoMBgMSz5XKpVCpVLxg2xXSi9gBxu1Wu11b8eKxWK0t7fDYrHgzjvvRFdXFzo7O9HS0oJyuYxTp07B7/djZGQESqUSOp0OXq8Xhw4dgtFoxMGDB+FyuXin6JU+P0KuDSv/zco+plIp+P3+a14sEYvFPF+ZNdrbv38/enp60NXVhXA4jHg8jlQqtaQymk6nw549e+B0OlGtVhEIBHDkyBFEIhGMjIzwCn2ZTAa5XA4KhQKDg4Nwu93Q6/Ww2Wzo7+/He9/7XkxMTOD48eM0xtYJlq6ycIeNpStfj2aziVQqhUajAbFYDK/Xi7a2NrS3t0MqlUIikcDpdMLr9UIikSAWiy3pr8AW/KLRKCqVCgRBgM1mQ7lchl6vv67rI2uv0Wjg5MmTvFgFyyBpb2/n7QRUKhVMJtOSYEMsFkOj0UAikfB76JXIZDJotVrs2bOHZyvs27ePv+9mSMujYOMK1Go1Ojo64PV6eZM/4O3zFVNTU/ww7vViOfEKhWJRzin7M9bLI5lM4sKFC3jxxRd5PxBy86pWqzh37hxmZ2dx4MABtLa28nz0hbtVV0obuNrVPplMBqVSedkuzVdLEAR4PB60trbinnvuwS233MJ7ewwPD2N0dBQnTpzA4cOHodPp4HA4sHfvXhw4cABarRY7duzgZ04ymQwqlQpNBG8C7MwDe4jmcjlEIhHkcrlr+npisZifLVOpVFAoFDhw4AD27duHfD7PK1Dl8/klD1WNRoPe3l6YzWZeEvrnP/85pqamMD8/vyg4SSQSEAQB4+Pj6OnpQV9fH59I3nLLLajX6zh58iSNsXWCdVsG3l75vd5eLwzrWWWxWGCxWOB0OtHS0sKfo2azmQcPcrmcp7ksrDKZy+WQSqV4dUqj0Yh6vb4kbZWsP41GA+Pj4wiFQujr68OuXbtgNBpht9sXPWffzTP5nV7Luor39vbykvVtbW38Hny5vkMbCQUb71K1WsXc3BzC4TBeeeUVTExMXHflAIlEAp1OB51Oh1tvvRUtLS3YsmUL3G43FAoFgLcHMjuc6/V60dfXh0gkgtnZ2ev+vsjqYQ+uRqOBF198ET6fD7fccgs6OjpQKBRQKpVgMBhgMBgQCAQwOjoKlUoFq9W6pAQzAN6zoLOzc9GDj53VOHHiBN58800MDw9f13WLxWJ0dXVhYGAAGo0GuVwO586dw+TkJGZmZuD3+xEIBBCJRJBOp5FKpaDRaDA0NAS9Xg+dToeWlhbceuutcDgcePPNN6871ZCsHdakj5UBZxXJpFIptm7divb2djidTp5up1AokEqlkEqlkMvllu3Oq1Ao4PF4YDabIZPJ0Gw24XQ6USgUkEwm+Ur3wklgoVBALBbjRTS0Wi1aW1sxNTV13ek35MaRSCSQy+U84MjlcggEAshkMtc08WKpWXq9HkqlEnK5HAMDA3wyCbydZup0OvFrv/ZriMfj2L59O3K5HOLxOJrNJnQ6HbRaLe68806eurfRJ4KbUaFQQL1ex9GjR1EqldDf34+BgQGeHq/RaGCxWJBMJjE4OAixWAybzbYozWnhM1kikaCtrY0Xa1lIp9PB4/HwQ+gKhWJRKisrglEqlTbs2VsKNt6lSqUCn8+H6elpHmxcL4lEAqPRCKfTiYMHD6K1tRVbtmyByWRakjajVquhVqvh9XrR29sLABRs3ORYo55CoYDDhw/j5MmTvGJPLBbjHW47Ojpw/PhxPPvss7Barejr61vS2ZZN+LRaLV9JZlgawqlTp/DMM89cc8NAhgUbO3fuhFqtRi6XwyuvvIKf//zniEajiMfjSz5HrVZjZGQEbrcb+/btgyAI2LdvHywWC86fP0/BxjomFot5qlRbWxvEYjECgQAkEgn6+/uxdetW9PX1wWaz8c9hB25ZsHHppI2dR2NdxhuNBpxOJ/L5PGZmZngq6cLPy+fziEajfNeDpe+tRDUtcmOw3lWs0zxwMUU4EAhc8z1CEAS43W54PB4ebNxzzz04dOgQFArFojHkcrngcrlQKBQwNzeHbDaL6elp/mdarRYdHR18MYd2yzaeYrHIU9JHRkZw991388p50WiUp2hOTk7ihz/8IaRSKQYGBhalJ7PUZpb+zprcLtRsNqHVaqHVavnvL11EZFkrG7lyIwUbV6larSIajaJWqyEajSKZTF73dq9KpYLL5YLFYsEdd9wBm82Gvr4+6HQ6hMNhhEIhXimDpSewXhtsMletVjE8PEzpVOsE6w564sQJhEIhHoQMDQ3BbDbD7/djZmYGsVgM8Xh8SU6oRqPB7t27eQd7ptlsIhAIIBaLIRQK8bSl6yEIAsxmM1paWniFDKvVCpvNhlKpxPsoLHwQV6tVJJNJ6PV63nzQ4XDwClrk5qNSqWA0GtHZ2Yk9e/ZAq9UuO3FXKBS8Gp/dbocgCMhkMryy0KXBLwA+Pt7pAcpWm5vNJlQqFW6//XZ+aJg1Oy2Xy4vOg/T19cHlcq343wW5+bFqPywjQCwWo1AoAAC6urrQ09MDu90OvV6Prq4uKBQKFAoFRCIRXuiCnReRSqW80p5UKkWz2YRer4dMJuNp02zssua9G3XlebMqlUpIpVI4d+4cqtUqisUicrkctFotzp49i3g8jqmpKQiCwHdyF5JKpbx089UqFotIJBIoFosIhUKIx+M4fvw4AoHANaez3uwo2LhKrB53pVJBMBhEJBK57sm9VqvFzp070dnZiU9/+tMwm81QKpWoVCp84P30pz/F1NQUnE4njEYjPvOZz/BVcI1Gg1QqhZdffhnFYpGCjZsc66ydzWZx+PDhd3wdsPwZDVYe0mq1LprANRoNTE9PY2JiAn6//7p3NYCLD3W73Y6Ojg5etYg1lGR5/tVqdVGwwTo76/V6NJtNSKVSeDweVKvVDVs/fL1Tq9WwWq3YunUrpFIpvF4v+vv7l4w/lUoFr9cLuVzOixYsHIPLjVeWh/9OK8Ms0AAuBtPvf//7UavV+IrzzMwMstksHA4H9Ho9PB4PrFbrks8lm4MgCLyxI+ttEAwGUavVMDAwgN27d/NyymxMhsNhjI6O8oCYHRRn3Z8BwOPxAFh6/2X/Zek19JzdWPL5PPL5PGKxGI4dO7bsa9iYGBwcXPJnarUaLS0t/Jl86f1ouftTLpeDz+dDOBzGiRMnEAwG8eqrryKTyWzY3X8KNi5DLBbzUmhsNS+RSCCVSmFwcBChUIivplwrpVKJzs5Ofkg4lUrh+PHj/D2i0Sj8fj8SiQQajQbS6TTPlRcEAQaDAW63GwMDA5ifn8fo6Cg9eNeJq/k5LXwNW4FraWlBV1cX2tvb+XmearWKUqmE6elpnD9/ftn0ppUgEonQ2dmJYrEIl8uFjo4O5PN5ZLNZSKVSKBQKtLW1Yfv27XA4HJBIJHwVkHLpbw6NRgMTExOQSqUwmUwwGo28vLdIJIJGo4HZbIbD4VjyM1tYeIDVk7/SOJZKpVAqlZet2pJMJnH06FG0tLSgt7eXV8kSi8XQ6XT8HEexWITBYIBSqYRSqVz03rVaDeVymSaB6xyr0udyubBt2zbIZDKeSryQTCbjz2SHw8FTX2q1Gvr7++FwOKBWq5cc3q3X6wgEAohGo9Bqtbw7vdlsXlJxqNFoIJPJoFwuIxaLIZ/P48yZM/ycGtmYrnQ/W/jngiDAZDLBYDCgo6MD3d3dPOOAFToYGxvD5OQkb3bKZLNZhEIhpNNpnr2SzWZRKpU27ByOgo3LkMvlMJvNcDqd2LJlC6rVKo4ePQq/389z1q93O9VgMODWW2+FxWJBrVZDIBDAP/7jP2J6ehpzc3OL8pWDwSDEYjFPV+jp6UFPTw+2b9+OZDKJkydPYmJigkrgblBKpZKXEr3zzjvhdrshkUj4gdlMJsMrQ0Wj0RV974WrNfv378e+ffsQiUQQjUYRi8UwPz8PnU4Hm80Gk8mEjo4O3hX90lKnZG1Vq1W89tprGBwchNPphMvlgtFohMvlQldXF3/d5YLDS1d7r1SKmZVhZoHxpQKBAL73ve+hp6cHX/jCF2C1WnmwwSpUXZoudel712o15HI56rGxzqnVatjtdmzbtg0ikQh2ux0DAwNLAlW5XM77HLCCKewexYLgS8cv22GbnJzEyZMn4XK5cNttt8HpdEKn00Euly8ax7VaDbOzs4jH43jrrbcQCATw5ptvwufzIZPJ3JC/D3Jzk0qlaGtrg8fjwW233YZdu3bxsVoqlVAoFPDss8/iiSeeQDqdXlRIaOEzlc3xNvq5IAo2LsFSBJxOJ7Zu3Yquri6ehz4xMYHZ2dkVy9uUy+W8NB/LK2VnM0Qi0ZJ8+Hq9jmKxiHQ6zWt/q1Qq2Gw26HS6674ecvNiq3lOp5NXcBGJRKjX64hGo4hEIojH48hmsytWs7ter2NiYgJvvfUWvF4vjEYjbwin0WjQaDR43jPL+2fVihauPLPzRBt1xWa9YVWcRkZGoNVq0d3dDa/Xi2q1ikqlAoVCAY1Gg0wmg2AwCIlEAq1Wu+RQIytYIBaL+T2MYQ/P2dlZnD59GoFAYNmff6lUwtzcHCQSCc6ePQubzQaHwwGlUgmDwbCo3Dj7uoVCAeVymackTk9P48KFC/D5fBv+gb1RNJtNzM3NYXBwkBdHWRhgAoDRaITD4Vi2g7NGo+FV+a5mh41lKrDxVigUeGGWUCi0JA+fpfFlMhlMTEwgHo/zHQ7aQSPAxTFlt9vhcrl4Wikbq6xHSyQSQTKZRD6fv+4+bOsdBRuXYJ1C9+7di09/+tMALqY3jY+P47nnnkM0Gl2xAzwqlQrd3d28EgsrnVar1RCJRHjFooUP0HQ6jUAggM7OTgAXb8g9PT2YmJigVJUNjB0Mb29vX9RJvFqt4vz585icnMTExAQikciKTerL5TJ++tOf4uzZs3j00Uexd+9e/nDX6XTQaDRwOp28ugb7xW64LGDO5/PI5XJ0sPImwM4N5XI5PPPMM3jllVdw33334c4770QymUQikeC7HENDQ/jJT34CjUaDvr6+RRMy9rNWq9VQqVQ4ePAgWlpa+HuwNIKjR4/iO9/5zmXLg2ezWZw5cwYzMzOoVqtwOBy8WMYtt9zCy46yXYxms4lwOIxgMIixsTGMjY1hdHQUp06dojG2jtTrdbzxxhsYHh6GQqHgTRodDgcPVBfeSxZaeK+52h02lqKVz+fx1ltvodFo4KWXXuIB86XPzoXdpVnp5VqtRmeECCeXy7Ft2zb09vbyhTjg4k7F5OQkBgcHMTIygkgkssZXenOgYOMSbAVEoVBAoVAgm81ifHwcU1NTq5JTJwgCb1+vUCjQ2dnJO0rG43HkcjlUq1V+oK2rqwstLS28jBqrqnG1nSzJ+sR+zqzjLps0ZjIZTE9PY3x8HNlsdkXHZqPRQDQahSAImJiYgF6vh9Fo5GkxC4Me4O1JJhuz2WwWqVQKo6OjmJ2dpTSXmwSbMOVyOTSbTUxPT0Or1fLxxJrwTU1NYW5ujp+5WFhfHri429bW1sbT+RaOPbbrEI/HkUwmL5tOx8ZMsVhEMBhEuVyGwWBAMBhEpVKBTqdbMsZY1bWZmRnMzMzw3gybfeVwvWFnHn0+H+8K39raykt4sx3TUqmEaDTKS4xeWg4cAN9x1ev1S3bYms0mEokExsbGEAwGV6RLOSEA+DOZjUOWzjkzM4OxsTHeu4VQsLGEVCqFWq3mOZwzMzP49re/zfshrPQW6sJcPb1ej09+8pMol8vw+/3IZDKYnZ1FLpfjqVJtbW1wOp18lZEG8uZUqVQwPDyMubk5PP300zh37tyKT+br9TpGR0cxNTWFRqOBN954Azt27EBnZyc/P7Jw1Rm4OMmcnJxELBbDqVOnEIlE8OqrryKZTK74WRJyfdLpNDKZDJ577jm8+OKLPG2TBRbsUKNIJMKJEyeWfL7BYMBv/MZvQKfTLboPNZtNTExMYHx8HKOjo4jFYle8T7EDuIIg4OjRoxCLxTyFarmDvgu7TddqNUptWYey2Szy+Tx+9rOf4dixY7jrrrtw6NAh5HI5xGIxWK1W9PT0YGZmBj/+8Y8hkUjQ19fHy3ADbwcbarUaCoUCt912G1pbW/mfs0anx44dwz/8wz8gFotRoEFWRaPR4As0zz77LF566SVaAFmAgo1LVKtV3jRqbGwMU1NTiEajSKfTK36TKhaLmJ2dRaVSgcVigUQigUajgVKpRLlchlarhSAIyOfzsFgsvFKMRqNZ1FG3VqvRDXSTYJ1Ga7Ua0uk0nzCuVm1utgoYDof5WQ228pjP55dMBDOZDHw+H69NHovFeOohjdGbC1vkKBQK76qy3sIOuKyJFVv8YEFANBqFz+dDKpW6qnMUzWaTnzWiHbDNgQWOrGO4z+fD2NgYcrkckskkX9ybn5/HzMwMPx92abAhCALvHM5SnRjWMyEejyMSiVx3BUlCllMul3mhllQqxatLkbdRsHEJFlgMDg7i5z//OcrlMi89u9KHD2dnZ/EP//AP6OrqwqOPPgqz2cwP/9psNjQaDbhcrkVVNlgqA1tNrlQqPL2LbHzlcpl3up2ZmUEwGFz1nz3LQZ2ZmcHp06f5A59NMBfubDQaDVSrVV6OlBU1WI1/P2RtyOVy9PX1wePx4O6778aWLVt4p2UWuBw7dgzPP/88lQklV8QWS1588UW89tpraDQaqNfrPLioVqvI5XIQiUR46623lu3/8tGPfnTRIhwzPT2NkZERDA0N0a4GWRX1eh3z8/NIJpOYnp5GMBjcsI35rgcFG5dg2/KFQgHJZHJV36tYLMLn80EikSAUCqHZbEKn0/HJnEQiWdQIje1isOo+lUoF0WgUoVAIqVSKUqo2ILZyx/pYyGQyfiYiEokgEomsWPWpd1Iul3lTK7K5sSo+FosFBoOB78CySlHpdBrxeBzRaJRWkskVsYWITCZzxbKyC+8/IpEIMpkMgiBALpdDrVbzxTiWnpxKpTA7O4tEIkGpdmTFsDRPhUIBuVyOQqGASqWCWCyGcDhMJd+XQcHGGspkMnjrrbcwMzODZrMJu92OPXv2wGw2Y9u2bbwL88JKLKFQCNFoFNPT0/xg8OnTp2nVZoNizfxYoyuVSoXz588jFArhJz/5Cebm5lY9KCZkIaVSid27d6OjowM6nW5RFZahoSFMTExgZGQE4XCY7klk1UilUnR3d8PpdOLQoUPYuXMnLwFfLBZRKpVw6tQpPPXUUwgGg2t8tWSjYIstDocDfX196OjowIULFxCNRvHcc89heHiYerEsg4KNNVSr1fignJiYQCaTgcViQS6Xg9lsRqlUWrYSSygU4oHGxMQEpqenN3Tnyc2IlWRUqVSwWq2wWCy8gVUikeDlP8Ph8FpfKtlkxGIxtFot9Ho9X0lmaXORSASzs7O8FxAhq0UQBBgMBt6ramEn8FKphGw2i2g0imAwiHQ6vcZXS9Y79kxmDZ+tVis0Gg2vWhoOhxEOhxEKhdb6Um9KFGzcBPL5PM6ePQuZTIYzZ85AJpNBp9MtOZ8BXLyJsnSWYrGIQqGAXC5H+fAbjE6nQ0tLC7q7u/GRj3wEOp0OhUIBkUgEzz//PE8NIGSt1et1jI2NIRwO4yc/+QnefPNNxOPxtb4sssHJ5XIMDAygp6dnUZ+DZrOJ8fFxjI+P8x22Wq22xldL1juFQgGv1wuXy4VPfOITsNlskMlkmJmZwYsvvoihoSE6o/YOKNi4CdTrdZ4KQyvVBLiYIqDT6eB0OjEwMABBEDA7O4toNAq/34/5+XlaOSZrjlWfSiaTiEQi8Pv98Pl8a31ZZBMQBAEmkwkWi4VXqGLjMR6Pw+/3Ix6PU/EUsiIkEgm0Wi0sFgv6+vpgsVgwOzuLWCyG+fl5+P1+eia/Awo2CLkJsTLIrN9LMBjEv/7rv2Jubo4fCqfdLLKWarUa5ubmEI1GMTMzg1AoRAfCyZphDSqj0Sief/55vPLKK9S9mawYQRCgVquhVCoBXDxz+/TTT/NU9nK5TKns74CCDUJuQiKRiHcmrVQqSKVSGBwcRDAYRKFQoECDrClWfSqdTkMikSCZTCKVSlHFH7JmGo0GkskkAoEAJicnMTQ0tNaXRDYQ9kwWBIFXBB0eHsbQ0BAymQw9k6+Agg1CbkLpdBrnz5/H7OwspqamkM1mMT8/z3tWEHKjsTQCu92Onp4e2O12zM/PI5VK4fDhw/D5fJifn1/ryySbTKPR4Isws7OzCAQCVKKbrLhCoYDh4WHMzMzwdHfWhJLOBF0ZBRuE3IRYc7S5uTlcuHBhrS+HEIjFYmg0Guj1etjtdlgsFgwPD2N+fh4XLlzAxMTEWl8i2URYpUa2w1YulxGPx5FMJlEul9f46shGU6lU+AFwute9exRsEEIIuSylUgmr1Qqn04l7770XNpsNjUYD8/PzeP311zE1NUXVp8gNIwgCNBoNTCYT2tvb0dbWhmg0ilwuh9deew3j4+OYnZ1d68skhCxAwQYhhJDLksvlcDqd2LJlCx566CFoNBqEQiGEQiGcPXsWY2NjtJJMbhi2w2YwGNDS0gKXy4Xp6Wn4/X6cPn0a58+fX+tLJIRcgoINQgghlyUIAhQKBWQyGZrNJjKZDH75y1/yUszVapWqsJBVJ5PJYDabYTKZcOjQITgcDkgkEoTDYZw+fRpjY2O0w0bITYqCDUIIIZclFouhUCh4GeZ0Oo0XXngBPp8P6XSaDkeSG0Imk6GlpQVtbW34+Mc/DqvVyjs2Hz9+HOfOnaMdNkJuUhRsEEIIuaxisYjZ2VkUi0U89dRTKBaLiEajKBaLqNfra315ZJMQBAFyuRxSqRTAxXF54sQJ+P1+xGIxVKtVqtRHyE2Kgg1CCCGXlcvlMDw8DAB4/fXX0Ww2aVJHbjhBEKBUKqFSqQBcHJfPP/88hoaGkEwmqccLITcxCjYIIYS8I3Ymg3YyyFqpVqsIhUKo1+s4fPgwACAcDlPvIULWAVHzKk/2sZrWhCx0ow6G0vgjy7mRB5NpDJLl0D3wxhEEASKRCGKxGABQq9U2faBB44+spasdf7SzQQghhJCbHgssaIeNkPVFWOsLIIQQQgghhGxMFGwQQgghhBBCVgUFG4QQQgghhJBVQcEGIYQQQgghZFVcdTUqQgghhBBCCHk3aGeDEEIIIYQQsioo2CCEEEIIIYSsCgo2CCGEEEIIIauCgg1CCCGEEELIqripg43HH38cIpFo2V9f/vKX1/ryVkQul8Of/umf4v7774fJZIJIJMLjjz++7GuPHz+OL3zhC7jlllsglUohEolu7MVuMjT+3tZoNPD444/joYcegsfjgVqtxrZt2/Dnf/7nKJVKN/7CNwEaf4t9+9vfxl133QW73Q65XI729nZ89rOfhc/nu6HXvJnQGLy8arWK/v5+iEQifP3rX1/9C92EaPwt9pnPfGbZv4ve3t4be9HXQLLWF3A1HnvsMbS3ty/62LZt29boalZWLBbDY489Bq/Xix07duDll1++7Guff/55/NM//RO2b9+Ojo4OjI2N3bgL3cRo/AGFQgGf/exnceutt+K//Jf/ApvNhjfeeAN/+qd/isOHD+OXv/wlBb+rhMbfRadPn0Z7ezseeughGI1GTE9P49vf/jaeffZZnD17Fi6X68Zd+CZDY3Cpb3zjG/D7/at7cQQAjb+F5HI5/umf/mnRx/R6/Spe4cpYF8HGAw88gD179lzVa0ulEmQyGQThpt604ZxOJ4LBIBwOB06ePIm9e/de9rW/8zu/gz/8wz+EUqnEF7/4RQo2bhAaf4BMJsPrr7+O22+/nX/s85//PNra2njA8d73vvdGXfamQuPvom9+85tLPvbwww9jz549+Jd/+ZcNs9J5M6IxuFgkEsFjjz2GP/zDP8T//J//8wZc5eZG4+9tEokEv/mbv3mDrm7lrI+fxmW8/PLLEIlE+MEPfoA/+ZM/QUtLC1QqFTKZDBKJBL70pS9hYGAAGo0GOp0ODzzwAM6ePbvs13jyySfx1a9+FS0tLdBqtfjIRz6CdDqNcrmM3//934fNZoNGo8FnP/tZlMvlJdfyb//2b7jlllugVCphMpnwiU98ArOzs1f8HuRyORwOx1V9v3a7HUql8ur+csiq20zjTyaTLQo0mA996EMAgOHh4St+DbKyNtP4u5y2tjYAQCqVuuavQa7dZh2DX/7yl7Fly5Z1OenbSDbr+KvX68hkMu/qc9bautjZSKfTiMViiz5msVj4///Zn/0ZZDIZvvSlL6FcLkMmk2FoaAg//vGP8dGPfhTt7e0Ih8P41re+hbvuugtDQ0NLttz/8i//EkqlEl/+8pcxMTGBb3zjG5BKpRAEAclkEv/f//f/4c0338Tjjz+O9vb2RasZf/EXf4GvfOUr+NjHPobf+q3fQjQaxTe+8Q0cPHgQp0+fhsFgWNW/H7K6aPxdXigUWvL3QVYWjb/F4vE46vU6/H4/HnvsMQDAPffcs6LvQRajMfi248eP43vf+x5ee+01Sh29QWj8va1QKECn06FQKMBoNOKTn/wk/uqv/goajWbF3mNVNG9i3/3ud5sAlv3VbDabR44caQJodnR0NAuFwqLPLZVKzXq9vuhj09PTTblc3nzsscf4x9jX2LZtW7NSqfCPf/KTn2yKRKLmAw88sOhr3Hbbbc3W1lb+e5/P1xSLxc2/+Iu/WPS6wcHBpkQiWfLxd3LixIkmgOZ3v/vdK772d3/3d5s3+Y9v3aPxd2Xvfe97mzqdrplMJq/6c8jVofG3PLlczv8ezGZz8+/+7u+u+j3Iu0NjcLFGo9Hct29f85Of/CT/fgA0//qv//qq34NcPRp/i335y19u/uEf/mHzhz/8YfPf//3fm5/+9KebAJp33HFHs1qtXvX7rIV1sbPxD//wD+jp6bnsn3/6059ekl4kl8v5/9frdaRSKWg0GmzZsgWnTp1a8jUeffRRSKVS/vv9+/fj3//93/G5z31u0ev279+Pv/u7v0OtVoNEIsHTTz+NRqOBj33sY4sib4fDge7ubhw5cgR//Md//K6/Z3LzoPG3vK997Wt46aWX8M1vfpN271YRjb/Ffv7zn6NUKmF4eBj/9m//hnw+v6JfnyxFY/Cixx9/HIODg/jRj360Il+PXB0afxf95V/+5aLff+ITn0BPTw/+x//4H/jRj36ET3ziEyvyPqthXQQb+/bte8fDQZdWKQAulur827/9W3zzm9/E9PQ06vU6/zOz2bzk9V6vd9Hv2el+j8ez5OONRgPpdBpmsxnj4+NoNpvo7u5e9toWDl6yPtH4W+qHP/wh/uRP/gT/6T/9J/zO7/zOqrwHuYjG32Lvec97AFw8NPrBD34Q27Ztg0ajwRe/+MUVfy9yEY1BIJPJ4I/+6I/wB3/wB0uuiawuGn+X99/+23/DV77yFbz00ksUbKy25Q5Nf+1rX8NXvvIVfO5zn8Of/dmfwWQyQRAE/P7v/z4ajcaS14vF4mW/9uU+3mw2AVwc0CKRCD//+c+Xfe1Nn0dHrttmG38vvvgiHn30UXzgAx/AP/7jP6741yfvzmYbfwt1dnZi165deOKJJyjYWEObYQx+/etfR6VSwcc//nHe22Vubg4AkEwm4fP54HK5IJPJVuT9yNXbDOPvcpRKJcxmMxKJxKq+z/XaEMHGcn70ox/hPe95D/75n/950cdTqdSKHmbt7OxEs9lEe3v7O27zkc1lo46/Y8eO4UMf+hD27NmDJ598EhLJhr2FrGsbdfwtp1gsLlsdhqytjTYG/X4/kskktm7duuTPvva1r+FrX/saTp8+jZ07d67aNZCrt9HG3+Vks1nEYjFYrdYb/t7vxroufftOxGIxjzyZ//iP/8D8/PyKvs8jjzwCsViMr371q0ver9lsIh6Pr+j7kfVhI46/4eFhfOADH0BbWxueffZZKsN8E9to469WqyGZTC75+PHjxzE4OHjVNfjJjbPRxuB//a//Fc8888yiX9/61rcAXOzs/MwzzyybzkPWxkYbf6VSCdlsdsnH/+zP/gzNZhP333//irzPatmwy5IPPvggHnvsMXz2s5/F7bffjsHBQTzxxBPo6OhY0ffp7OzEn//5n+OP/uiP4PP58PDDD0Or1WJ6ehrPPPMM/vN//s/40pe+9I5f4+///u+RSqUQCAQAAD/72c/49uzv/d7v8dzBmZkZ/Ou//isA4OTJkwCAP//zPwcAtLa24lOf+tSKfm/k2m208ZfNZnHfffchmUziD/7gD/Dcc88tuY7bbrttRb83cu022vjL5XLweDz4+Mc/jq1bt0KtVmNwcBDf/e53odfr8ZWvfGVFvy9y/TbaGNy9ezd279696PNYOtXWrVvx8MMPr+j3Ra7PRht/oVAIu3btwic/+Un09vYCAH7xi1/g+eefx/33348PfvCDK/p9rbQNG2z88R//MfL5PL7//e/jhz/8IXbv3o3nnntuVbrMfvnLX0ZPTw/+1//6X/jqV78K4OKhove973146KGHrvj5X//61zEzM8N///TTT+Ppp58GAPzmb/4mDzamp6eXPFTZ7++66y4KNm4iG238xeNx3qBoue/h05/+NAUbN5GNNv5UKhV+67d+C0eOHMGPfvQjFItFuFwufPKTn8Sf/Mmf8OZ+5Oax0cYgWV822vgzGAx48MEH8eKLL+J73/se6vU6urq68LWvfQ1f+tKXbvqO6aLmpfs+hBBCCCGEELICbu5QiBBCCCGEELJuUbBBCCGEEEIIWRUUbBBCCCGEEEJWBQUbhBBCCCGEkFVBwQYhhBBCCCFkVVCwQQghhBBCCFkVFGwQQgghhBBCVgUFG4QQQgghhJBVcdUdxEUi0WpeB1mnblRPSBp/ZDk3sicpjUGyHLoHkrVE44+spasdf7SzQQghhBBCCFkVFGwQQgghhBBCVgUFG4QQQgghhJBVQcEGIYQQQgghZFVQsEEIIYQQQghZFRRsEEIIIYQQQlYFBRuEEEIIIYSQVUHBBiGEEEIIIWRVULBBCCGEEEIIWRUUbBBCCCGEEEJWBQUbhBBCCCGEkFVBwQYhhBBCCCFkVVCwQQghhBBCCFkVFGwQQgghhBBCVgUFG4QQQgghhJBVQcEGIYQQQgghZFVQsEEIIYQQQghZFRRsEEIIIYQQQlYFBRuEEEIIIYSQVUHBBiGEEEIIIWRVULBBCCGEEEIIWRUUbBBCCCGEEEJWBQUbhBBCCCGEkFVBwQYhhBBCCCFkVVCwQQghhBBCCFkVFGwQQgghhBBCVgUFG4QQQgghhJBVQcEGIYQQQgghZFVQsEEIIYQQQghZFRRsEEIIIYQQQlYFBRuEEEIIIYSQVUHBBiGEEEIIIWRVULBBCCGEEEIIWRUUbBBCCCGEEEJWBQUbhBBCCCGEkFVBwQYhhBBCCCFkVVCwQQghhBBCCFkVkrW+AEIIIYQQQjYDmUwGtVoNvV6P3t5elMtljIyMoFAoIJfLoV6vr/Ulrjja2SCEEEIIIeQG0Gg06OnpwQMPPIBvfetb+Ou//mvs378f7e3tkMvla315q4KCDUIIIYQQQm4AuVwOi8UCtVqNTCaDdDqNYrGIarUKQRAgFoshCAJEItFaX+qKoTQqQgghhBBCbgC9Xo+BgQEoFAr89Kc/RSQSwejoKDKZDCQSCdRqNcrlMhqNBmq1GprN5lpf8nWjYIMQQgghhJAboFarIZ/Po1AooFAoIJ1OQy6Xw2g0wmg0QhAEBAIBFAoFZLNZVKvVtb7k6yZqXmXItJG2c8jKuVERN40/spwbueJDY5Ash+6BZC3R+Ft/dDod3G43KpUKEokEtFotdu/ejZaWFjzyyCOQSqX4l3/5F4yPj2NwcBDxeHytL/myrnb80c4GIYQQQgghy2CB1koFdiw9qlKpIJvNQi6Xw+VyobW1FW1tbZBIJNDpdJDL5RCLxSvynmuNgg1CCCGEEEIuIRaLoVQq0Wg0UCwWVyTgMBqN2LVrF1KpFPL5PDweD37jN34Dra2tMBgMyGQyqFar/NzGRkDBBiGEEEI2DZFIBIlEAolEAo1Gg0ajwfsb1Gq1tb48cpMQBAFyuRx6vR71eh3NZhP1eh3VavW6gg6xWAyNRoNKpQKJRAKFQgGr1QqTyYRms4larYZyuYxSqbRhem5QsEEIIYSQTUOr1cLr9aK3txef/vSnkUwm8Z3vfAfBYBA+nw/lcnmtL5GsMYVCAZ1OB6/Xi/e///0olUo4c+YMEokELly4gGKxeM1fu1arIZ1Oo1AoQCwWo1Kp4Pz584jFYlAoFEgmk7hw4QKGh4eRz+dX8LtaOxRsEEIIIWTTkMvlMJlM8Hg82LdvH8LhMIxGIzKZzIbJkScXd7DYuYeF5y6azSYajQbfqWD/v3C3QiKRQKvVwm63o6+vD8ViEfF4HFKpFKOjo9d1XY1GA5VKBZVKBcDF4COVSvFdjmQyiWQyiXQ6fV3vczOhYIMQQgghm4ZOp8PWrVuhVqvx3HPPIRKJYHJyErFYjPc5qFQqaDQafCJK1g+RSASpVAqz2YxPf/rT8Hq9MJvNkEqlSCQSKBQKCAaDSCaTGB8fh8/nQzKZRCKR4F+jpaUF999/P7q6urBv3z7UajXY7XZMTU3hzJkzyGaz13x91WoVmUwGuVwOmUwGRqMRcrkcjUYDL774IoLB4E1dgepaULBBCCGEkDXBuiUDWLL6zCb57JDsSk362URULBZjdHQUkUgEyWQSpVIJcrmcv65Wq113fj658diOhl6vx6233or+/n60tLRALpcjGAwik8lgYmIC4XAYtVoNuVwOlUoFyWSS/6y1Wi26urrQ0dGBlpYWNBoNvkMilUqv6/oajQZKpRJKpRIqlQpqtRqkUimazSamp6fh9/tRKBRW4q/ipkHBBiGEEEJuKIlEArFYjHvvvRf79++HTqeDVqtFPp9HJpNBJpNBOBxGPB7H8PAwcrkcotHoihyYTSQSeP3119FoNJBKpSAWi9HV1QWDwYA777wTUqkUv/jFLzA3N4fp6WlkMpkV+I7JjWKz2fDoo4+ira0N/f39sFqtkEqlEIlEMBgMUKlUUKvVKBaLaG9vx1133YVf/vKX+MUvfoF8Po90Og1BECCVSiEWi3ngW61WV6TBXrFYRCAQgCAIcLlcaG9vR1dXF0+vYsUKNhIKNgghhCzrSo28rmfFd6Vr15P1RSwWQy6XY8eOHfjwhz8Ms9kMq9WKVCqFUCiEaDSKsbEx+P1+JBIJSCQSJBKJFZmEZbNZTExMoFwuIxaLwWQy4d5770VHRwcefPBBKBQK+P1+NBoNvhJO1geRSAS9Xo+7774bnZ2dcLlcUCgU/M9VKhVUKhUMBgOAi+lShUIBkUgEx48fR7PZRDqdhkgkglgs5md4WG+MlRh/1WoVqVQKarUaLpcLNpsNNpsNlUoF9XodpVJpw5S8ZSjYIIQQwgmCAIlEgp6eHtx7771Qq9W89GM6nUapVEIoFEI+n8fY2BgymQzi8fhVVfDRarUwm83o6urCe9/7XoTDYfziF79AOp3mKQ1k4xMEAffeey927tyJAwcOwGq18gkhKwOq0WhgMBjQ09MDr9eLmZkZ/OAHP0AymUQqlbquSZ9Op8OWLVuQy+VQKpXgcDjw/ve/H21tbTCbzSiXy7zp2kab9G1EUqkUarUaNpsNBw4cQGtrKzo6Ovg5DWDp4gb7vVwuh0gkwm233QaFQoFjx47h2WefhU6ng0Kh4DsilUoFoVAIoVDomnc3TCYT3G43ZDIZVCoVHA4HDhw4AJvNhnK5jGg0ilQqhUwms+HuhRRsEEII4Viw0dXVhU996lMwm81oaWlBpVLB3NwcMpkMhoaGEI1GUalUEAwGkc1mryrYUKvVcLvd2L9/P37v934Pw8PDGB8fx9zcHGKx2IZ7wJLlicVi7N+/Hx/+8IdhtVphNBohEonQbDYhl8v5uYmWlhZUq1X09fXhwoULePXVV9FoNJDNZq8r2FCr1Whvb0cikcD09DTMZjMOHjwIj8eDer2OeDy+Iv0UyI3BKke1trbigx/8IFwuF9xu96IdDYaNM0YqlUIikWBgYABdXV0QBAGvvvoq1Go15HI5D1aq1Sqi0Shisdg1jz2dTofu7m7IZDKo1Wp0dXXhoYcegkwmQzqdRjKZRDabRT6fpzQqQsjaYo2o3vOe98But0Or1UIikSCXy6FcLiOZTCKXyyEQCCAcDiOfz19X5QyyufT29uKuu+7Ctm3bYLVaoVareUqB0WiEUqmEIAjI5XJQq9UIh8P46U9/Cp/Ph2w2y8s5Lkej0aCjowNisRivv/46JicnEQwGkUql+EO9Vqvx0pRkYxCJRFCpVJDL5di9ezfcbjd27twJs9kMhULBV5mXW31mHZzdbjc+8pGPYGZmBk899RRisRiKxeI1TcrYoeBSqQRBEFCtVuHz+XiTtUQigZmZGfj9/uvqp0BuDDZGdDodWlpaYLPZIJFcnN4uHEuNRgOBQADZbBbhcBiZTAb9/f3o7Ozk9x+j0cirV10pjfRyFAoF5HI51Go1VCoVyuUyCoUC2trasHPnThgMBjidTlgsFmi1WtTrdWSzWb5zvBGDXAo2CFlHWCUMg8GAT37yk9i+fTtaWlqgVCr5pI1N4E6ePIkzZ84gFApRsEGu2tatW/Hbv/3bMBgMcDgcvFKQRCKB0WgEALhcLjQaDfT39yORSGB8fJxXdHmnYMNgMKCrqwvNZhNHjhzB/Pw85ubmUCqV+IFhlq98ad17sn6JRCKeFvX+978fe/bsQXd3N6xWK4CLE8KFE7uF/y8Wi6FSqeD1evHrv/7rmJycxLFjx/hYu9Zgo1Ao8GCjUqlgamqKB9CpVApTU1Pw+XzX/b2T1cfGiMFggNvthsViAbD4PBjrqTE7O4v5+XmcPn0ac3NzEIvF6OjogFQq5VXKWltbYbVarznYUKlU0Gq1cDgcsNlsSCaTiMViaG9vx969e+FwONDf38/Pg7CCCAuDjY2Ggg1C1hGdTof3vOc98Hq9aG1thV6vh0QigUgkglqthiAIqNfrMJlMkMvlaGlpwalTp1AoFPjqCiELsVSBtrY2XlPeaDTyHQ1g6UFxkUjEy0vqdDocOHAATqcTL774Ij94u9wDM5lMYmhoiK8sl8tleDweqFQq9Pb2ol6v46233uKHhEul0g35OyCrSyQSQSaTQalUwmKxwOl0QqlULvvaTCbDU1VqtRrUajVaWlogCALkcjnPdc/lckin0+8Y3F5OvV5HsVjkv6rVKiQSCer1OgYHBxGNRpHL5a732yarTCwWQyqVwmq1YufOnejp6YFMJkOtVkMmk0E2m8Xrr7+OVCoF4GLAMTMzg1Qqhbm5OSSTSRw7dowHHD09PVCpVHA6nTAYDIvueyxVy2azYWBgAG63e9FZo4WsViv0ej1MJhP0ej0KhQJyuRw6Ojrg8Xig0+n4Ig67rkajgXq9vmEXWCjYIGQdMRqN+NznPofu7m643W4olUp+Q9TpdNDpdLDb7QCA7du3o1Ao4Ic//CEmJiaQSqUo2CBLsPSDu+66Cx/+8Ifhcrngcrl4bvPlVpwFQYBKpYJSqcRHPvIRPkmMx+NIJBLLBhvhcBhHjx5FqVRCPB6Hw+HgVWM+9alPoVwu4xvf+AYmJib4Kh9Z/0QiEZRKJTQaDS/1eWnuPBtrsVgM586dQ6lUQj6fR0tLCxwOBz9UazAY0NrailqthtnZWeTz+Xd9PSzYZRPSYrEIuVyOer2OX/3qV7wCFrm5sZRij8eDe+65B263G3K5HLVaDaFQCNPT0/jLv/zLRR2/2Zhj/y2XyxgZGcEjjzyCnp4e6HQ6dHR08IPjC9/LYrFAEAQcPHgQtVoNu3fvhl6vX3RNIpEILpeLN+pTKpV8l5Yt0iy3Y1Kr1Tb0mTUKNlaQIAgQi8XYsmULPB4PP+hWqVR4A5dsNsvrhbN6yhs1kiXXj63mabVa9PT0oK2tDQ6HY9GOxnI3LraSCIBX/hkdHUUul0O1Wr2qw7xkc5BIJFAoFDxQ1el0i8YUe0hWKhV+30okEhAEAd3d3VCpVHwiaDKZYLVaUSwWl50EKpVKOJ1OpNNppNNpGI1G7Nq1C16vFxqNhq/wbeSH7mYjlUqhVCrR3t4Or9fLJ2dst3VsbGxRulIkEsHMzAzPv18YlIhEIkgkEpjNZqRSKZ6X/25Vq1Wk02lUq1W+Am2xWCASiVCtVjdk6dGNSKVSoaWlBR6PBx6PByaTCYVCAdlsFm+99RZ8Ph8ymcw7ptolEgn4/X6++wFcHGfJZBI+nw+tra3o7u6GIAgwmUxQKBQol8toNptwu91Qq9VLvqZOp4NSqVzUp+OdsF3iheeXNhoKNlaQVCqFXC7Hhz/8YXzwgx+E1WqFyWRCIpFANBpFOBzGxMQEfD4ffvWrXyGRSKBQKGy4qgNk5chkMphMJnR3d+N3fud34Ha70dfXB5VKBQBLVp0XrkSzQ2oHDx7Ezp078bOf/Qzj4+P8EC8FuQS4mEal0WjgdDr5Q/XSsdFsNpHL5XD+/HlEo1GcOHECEokEX/jCF9DR0QGVSgWxWAyPx4Pu7m6k02nEYrEl72U2m7F9+3YEAgFEo1G0tbXhE5/4BD+MWSgUUKlUUK1WabK3AQiCALVazZvl9ff3w+FwAADfBfv2t7+NH/zgB/xz2HmdvXv34jOf+QzPa2fkcjlaW1tRr9eXTWG5GsViEXNzc1CpVHC73ejs7ER3dzcfexuxqdpGZDabsWvXLuzcuRO7d+8GAASDQUxPT+OJJ57A7Ozssvehhebm5hAIBHDvvfcuuu/5/X688MIL2LFjB3bv3g2z2Yy2tjaIxWJs27YNwOX7EF26g3HpLt6lWIqWRqOhYIO8M7bK53K50NHRAZPJBK1Wy1MUGo0GH3AKhQKFQgHz8/NIJBIoFosolUo0+SNLsJ0NpVIJm80Gi8XCdzQWbs2yRkRs96xUKsFut8NqtUImk0Gr1aKtrQ2HDh3C3Nwczpw5w9MUaNxtTqxhldVqRWdnJ08RYDtfrM9AuVxGKpVCPB7HyZMnkUgkMDExAYVCgfPnzyOfz6O9vZ3vwLHVv+U0m03UajU0Gg1+viiTyUAsFkMQBKRSKSSTSSQSCdrd2ADEYjFv1tfS0gKn0wkAyOVymJqawvT0NAKBwLIVn+r1Oi8acCmWRfBuJ2bseSyVSvlO3NatW9HW1oZms8kPjdPOxvrB7mNisZg3y2OpnOl0+opBIztXwYpfMNVqFdlsFsFgEKdOnYLD4cDWrVshlUpRLpd50QORSIRQKIRCoQCNRsMzChiRSARBEHjgfen7NBoN5PN5DA8PY2pq6prOIK0HFGysEJlMho9//OO499574fV6+YMbuFjTW6lUwuFwoLe3F8ViEXfffTfOnDmDQCCASCSCQCBAD1eyhFgshkajgcViQVdXFy/pt1yVjcnJSczNzeH8+fOYm5vDQw89hPvvv59X2XjPe96D2267DUePHsXXv/51hMNhjI2N0bjbpJRKJRQKBW655Rbcfffd6O/vh0gkQjabxfz8PAqFAhKJBEKhEI4dO4ZQKIQ333yT78YqlUpUKhV4vV584QtfwJYtW+ByubBlyxacO3du2fcsl8vIZDIoFouQSCQoFos4d+4cDAYD1Go15ufnMTQ0hKmpKVpZ3gDkcjm2bt2K1tZW7NmzB11dXYhGo5idncWzzz6LV155BfPz88t+LitnKpPJlqT1XSubzYbt27fzg+YdHR346Ec/CqVSiWKxiGAwiFgshlQqRffFdUYkEqFYLGJiYoL37olGo1cMGrdt24Y9e/bw+x/Dustns1n8zd/8Ddra2vDxj38cKpUK4XAYIpEI/f39kEgkePrppzEzM4Pu7m7YbLZF1wSAp0h1dXXhtttu43PDRqOBarWKQCCA733ve/D5fIjH46vwt7P2KNi4DqwZjN1uh8FggNfrhd1u5ykFDKsVzv6fDUAWKRsMBmQyGeTzeV5jnmxubMyo1Wq4XC7YbDbIZDIIgsBTTRbWgK/X6xgdHUU4HMbk5CRCoRCmpqYwNjbG85EbjQZfqa5UKqjVajyflN2QaextHhqNBnq9HjabDS6XC0qlEoVCAfl8HplMBs1mE2q1GjqdDkajke+YLSwyEAwGebla4O1VxsutODcaDV6pqlqt8l2OcrmMRCKBQCCAQqFAE70Ngt3HZDIZZDIZpFIpisUiUqkUEokE4vH4ZftYsDG0sGrP1ZJIJBAEATKZjFeZqlarvDQqW2F2u90wmUwQBAHhcJhXpqLn8PrEzvRc+lxbzsL5G8tGAcDPpGWz2UX3JrlcjtHRUcjlcsRiMYhEItRqNUgkEkxNTSEYDEIikSw6+8HYbDY4HA4+rtjYqlQqiMfjPKXwanZi1isKNq6RWCyGwWCA0WjEZz7zGfT392Pnzp1wuVz85nhpvh77vVQqhVarxcDAAP74j/8YIyMj+OpXv4q5uTlKHyAALo4RjUaDtrY2fPCDH4Tb7YZKpUK1WuW9Cf7iL/5iUZWNarXKH6r1eh2pVApnz57Ffffdh0ceeQTz8/M4duwYzp49i+npaZRKJSgUCn5DpYO5m4cgCOjo6EBXVxduvfVW7Nu3D5lMBoFAgAeqHo8H+/btQ6lUQldXF86ePYsjR47wkqDlchnnz59HKBRCKpW6qslZpVLh9eTT6TSKxSJfVf7Zz36GQCCAdDq92t8+WQOsqdrc3BxPoZqfn3/Xk6urSZ3SaDRQq9WwWq2wWq1IJpNIpVLo7+/He9/7XthsNmzZsoWnqOZyOSQSCcRiMeTzeX4AmKwfzWYTMpmMF6BYLv1uIaPRCIPBgNtuuw2PPPIIP+gdDodx/Phx+P1+/kwMh8OIxWKYnJzk4xgAbwRYLBZRq9UwODi47PseOHAA999//5LgJ5VK4dixYxgdHUUwGEQikdiw6XsUbFwHmUwGhUIBu90Oj8fDOzkDi1eIWTUqVuVCqVTCYDBALpfDZrMhkUhAp9NBo9EsGxWTzUcqlUKn0/Ga9BaLBfV6HaVSCbOzs5iZmcHs7CwCgcBlv0YsFoPP5+MlHFkjq1qtBqVSCaVSCaPRiEqlglAohHK5TAcjNxFWJUihUECpVCKVSiGdTvMxwsqVikQiaLVaqFSqJaVvlUolVCoVP0d0JWwMVqtVSKVSKBQKqNVqNBoNpNNpJJNJGn8b1KWTd9ZH41q+DlshZofNL8U6M1utVpjNZmSzWSSTSXR0dMBms8FqtcJoNC5aGGSlR6mZ5PpSr9f5BF0sFvOD1pfbEZPL5ZBIJHA6nbDb7bwCX7VaRTweRzweRzKZ5OcZWZpyvV6/4nmKS6s8st08mUwGg8HA76Fs16VQKGBubg7BYBCVSmXDBhoABRvXjD1oDQYDbwYjl8sBLK7jLBKJEAgEMDY2hkAggJGREfT39+NjH/sYZDIZNBoNTCYTtmzZAqlUilgstmEPCJGrZzabsW/fPgwMDGD//v2QSqWIx+OYn5/Hd77zHczMzCAcDr/j1/D7/QiFQti/f/+ih6fZbMatt94Kt9uNhx56CIlEAt/73vcwNzeHwcHBa6pbT9YvFiTEYjEMDw+j2WwuKtm4MPVp4ThSKpW4++670dHRwXu7XEkul8PMzAwUCgXa29vR3d2Nvr4+3sCPgt2NTSQSwWw2o1KpLFsy9GrUajUelH7+859fUoxAJBLB4XDAaDRCpVJBpVLxwIYdEGcFCRgWwFD61PrCShiz1E61Wo2enh6Uy2W+67CQIAjweDwwm8340Ic+hD179qCzsxNyuRxjY2M4e/Ysjh07hsnJyRUpnuJ0OuF2u3H77bfjfe97H1+8YRXPfD4ffvaznyEYDG745y4FG9dAEARIJBJewYAdAK/X64sOQLLulqzOcyaTwezsLAwGA2KxGDQaDXQ6HWQyGW/IdqWtP7LxsR4ZarWar9IAF+vSp1IpzM/PY35+/opBKauCcSmVSgWv1wuv14vOzk5otVpeOe1a8qPJxsAOK4rFYp7zzj7OSoIyYrEYCoUCbrcbXq+Xd4Mul8solUpLVqwlEglf4WNnkdxuN+x2O+RyOcRiMSqVCpVk3mDYyjArZ8t2y7Ra7bKTQeDt5ysrbHHpM1EQBN4XprW1FTqdbtGfi0Qi2Gw2nj0gl8uvakwJgrBhy45uVNVqFfl8nu+WsmwTtVoNvV4PjUaDQqHAdwxEIhEUCgX0ej2cTidaW1uh0WhQr9eRTCYxNTXFK0utRCqdUqnku2sLd9Kq1SoymQySySTC4TASicSGX2ShYONdEgQBGo0GRqMR9957L7q6uviqXiQSQSqVwr/8y7/gyJEjcLlcsNvtOHjwIB544AFe3eWtt95CPB5HT08Pfv3Xf51XKRCJRHj99dfX+Dska4n1amG7ZEypVML4+DgmJyf5jsWVgo1t27Zh+/bt2L59+6KHaHt7Ox566CF+Q47H4/zg+EbexiXLYw9UrVYLt9vNS9+yEo6FQgHT09OYm5tDrVaDVCqFzWaD0+nE+973PvT39/M0v4mJCRw/fhzRaHTRe7hcLnR3d/Pmf93d3bj//vt56uj8/PyGPyC5GTWbTeTzeX7YlvViMZvNMBgMy36OyWSC2+1GT08PWlpaYDabFy2C6PV6PPTQQ6jX60uKsTAsqF2YJnU1fQ60Wi0tuKwjsVgMx48fh0KhgN/v52WWdTod7r//fkxNTeHw4cM8lVgkEsFisaClpQUtLS1wuVzIZrOYm5vDq6++iscffxz5fB7pdHpFnoVtbW24/fbb0d7eviiYjcVieO2113D+/HkEg8ErNh7cCCjYeJfYqopWq0VLSwvcbjdkMhlqtRpSqRTC4TBGRkZw8uRJdHZ2Ip/P81J7APjhs3K5DIVCgXq9DrFYDJVKBbVaTTe6TYpVKZPL5dDr9Ut2GRqNBorFIq8D/04dwNmOhtlsRkdHB2+YxnJOJRIJHA4HpFIpz0dlFapoVXnzYKvO7GfO+rEUi0XU63X+cKzX6ygUCny3Vi6Xw2w2w263w+l0wmazQRAEXmUoFovxMxlsVVun08HpdEKhUPDCB1u2bEGz2UQymeQriRs9b3mzYfctVuWp0WhAoVDwZ55SqeTVnxiW367T6aBWqyGXy/lBXTZeWRdyFmiwhRLWf6PRaCxazWbYrt1y10k7a+sP+5klEgkkk0nIZDKYzWbIZDJ4vV5+7ymVSpBIJJDJZDAajTCbzVAqlRCLxcjlcggGgwgEAvD7/St6fUqlEiaTiZ/VaDQaPMtlbm5uUQW0jY6CjXdJoVBgYGAAXq8Xe/fuRWtrK6rVKvx+P3784x/j1KlTGBwcBHCxtO327duRzWbx5JNP4uTJk/D7/TwnWq/XU/UfAuDiTUmj0WDr1q246667UCqVkEwm+Z9LpVI4HA5ks9llH5YLGQwGaLVa7N+/Hw8//DAsFguazSai0ShOnjyJaDQKg8HAO4yPj49jaGgI8/Pz7xjEkI2lVCohk8nwB53FYoFarUYgEOAVpxZSqVTo7OyEUqnEI488Aq/XC4/HA0EQcPLkSQQCAQwNDSEYDMJiscDlciGTySCbzWJgYAD33HMPLBYL2traoNVqeclvdihzpVIXyM2jXC7jwoULiEajmJqagslkgtFohEwmw+7du5HL5XD69GmMjIzwz1GpVHA6nXC5XHC5XBCJRAiHwyiVSojH4yiXy4jH4xAEAe3t7RCLxThy5AhCoRC8Xu+iHZOFFSDZBHTr1q2LUgRrtRoCgQC++93vLiqoQdaPUCiE5557Dt3d3XA6nTAYDPjABz6AaDQKQRCQSCTQ29sLk8mErq4uGAwGVKtVnD17Fj/+8Y9x5MiRy/Z7WSkikQjxeBxTU1M4ceIEvv/97yOZTG6aZy4FG++SRCKB2WzmFS3MZjPm5uaQTqcxOTmJwcFBPklUKBQwGAy8OyRrlMUqX7BVFHq4EtZ92e12Y2BgANFoFPl8nq/cseZ+77T7xXY0WNEBl8uFtrY2iMVi1Go15HI5hMNhKBQKxGIxHmwsrPFNNodms7moSl6z2eTpe5dWxGO7bmxVUK/XY2BggJdjbjQaiMVi/P4GXEx1MZvNiMfjkMlkcDgc8Hq9PJ1qYVlw1rGZ9dwgG0ej0eDPw2QyiXQ6Da1WC7lcDofDgZ6eHszOzkIqlS65f+l0OigUCt7ngK1AFwoFhEIhCILAKwudP38ePp8P2Wx2UVM1APyciEajgVarXfTMZeeUMpkMhoeHMTMzw3vGkPWjUChgamqK75Sp1Wp4vV7o9Xr09vYilUphz549sNvtsFqtUCqVOH/+PF8gefPNN1f1+tgObz6fRzgcxvz8PCYmJjbVWKNg4zosrBvOypGyFRgAmJycRL1eRy6XQy6Xg0ajwZ49e9Da2oo77rgDLS0t0Gg0yGaza/ydkLXW3d2N22+/HTt27MCePXuQTqfR0tICi8XCO+h2dHSgUqnwXPqFRCIRXC4XDAYD7rnnHuzYsQO7du2CXC6Hz+fD6Ogojh07hunpaWi1WigUCqTTaRw/fhxzc3OLGrWRja/ZbMLn8yEWi+G2225DIBDgBSsuJZPJ+Lm07u5u6HQ6nkufyWRQLpdhMpkgkUjQ3t7OUxdUKhVPc1jYOPDSbtC1Wm3D5ytvdpVKBUePHkU8Hsev/dqvobu7m1cCMhgMPH/e6/XC6XSio6MDgiBgdHQU4+Pj+P73v49cLseDUvaM1ev1EAQBfr8f+Xwe09PTS6pTAcDOnTuxf//+JcFsJpPB4OAghoeHEQ6HqXP4OpVOp3H69GnU63XMzc2hWq3yVKkDBw6gWq3CZDJBKpVibm4OqVQKzz//PM6cOYPh4eFVv75UKoXJyUnMzc1hbm4O8Xh80y0yU7BxnZrNJtLpNOLxOE8ZYFKpFKanp3nVgd7eXuzevRvbtm3DAw88AJVKtWjiuNkGH3mb2WxGX18furq64Ha7eZoTO7shFothNpv5pO7SA4+sF4LNZsO2bdtw++23w2KxQCwWI5lMYnR0FH6/H6lUCsViEVKpFJVKBcPDw1d12JxsPKyvRjQaRSqV4odk2QozG2MSiQQajQblchlWqxVarZYHE6wevUqlglwuR0tLCz9zJJPJruqexurk0/1v46rVapiZmQEAHDp0CCKRiB/SjUQiKBQK6O3tRX9/P/R6PaxWK0KhEM6fP4+xsTEcPnx42dS+Sy1XDlwkEsHpdC77+mKxCL/fj/n5eWSz2U210ryRlEolBAIB2O12pNNp3iFeKpWivb2dv65arSKVSsHv92NwcBDHjx9ftZKzC++jpVKJZxAkk0nkcrlNd7+jYOP/YaX2arXauzqsIxKJYDKZUCgUlqyo2O12dHV1YWZmBuVyGX19ffj4xz8Oq9UKhULBc+8LhQKGhoYwNTWFYrG4ot8XWR8WlhtlW67sQGUkEoFMJoNer4darcaBAwfgdDpx6tQpnvokEolgMBh4kyK73Y5qtYpQKISzZ8/i5z//OfL5PIxGIxwOBzo6OlCr1fih88124yNvGx0dxf/9v/8X+/btg81mg9lsxsDAAA9Um80m2tra4HK54HQ6+UFyFgCzs2eNRoMf6GXpf1dbBYiKY2xstVoNExMTSKVS8Pl8aGlp4QfAe3p6YDAYYDAYYDKZkMlkcO7cOQwNDeH555+/rrNkNpsNJpMJu3btwoEDB2AymSAIAt8dmZubwy9/+Uv4/X569q5jLM1YLpfzggSs1DJ7nqbTaWQyGbzwwgs4deoUhoeHkc/nV+VwtlqthkKhgMfjQW9vLxKJBCYmJnDhwgW88soriMVim24HjYKN/0cikUAul/OGK1eDDWaNRgODwbAkvcVgMMDr9fIukW63G3feeSckEsmi7dxyucy31zZDVQKylEgkWtTbYGGTqWq1CpVKxdOftm7dCqVSidHRUR5sLCzJbDAYoNfrEYlEkEgkMD09jbfeegs6nY4/fB0OB8LhMK+MRrnym1Oz2cT8/DxOnz4Nt9sNALwEaC6XQzQahUKhgN1uh1gsRmtrK/889trlsFSpKwWxrLofu/eSjaleryMYDPIxlUqloFQq0Ww2eQoV+/nH43FMT0/j/PnzePnll5HP5695YqbT6Xg/ob6+Ph4E1+t1FItFxGIxnD17FtFolHZ31zGxWAylUsl37BdWFWMBRy6XQyKRwKlTp/DKK6+gXC6vWvomq1hqtVrR0tKCTCaDQCCA8fFxnDhxYlM+bzd9sKFUKiGXy9Hb24u+vj5Eo1H4fD6k02nMzc0teVguLOXH6oY7HA7I5XKoVKpFry2Xy8jn87zUqEwmQywWg0qlgkajQSaTwdjYGKampvh5Dwo2NicW7LLdrnw+j/n5eeTzecRiMVgsFlitVqjVatx+++3o7OxEOp1GJBKBx+OBTqfD1q1b4XA4oFKpMD4+jvHxcUxNTSGVSmHbtm3o7OzEnj174PF4kEqlEI/HkUqlNuWWLnlbKBRCo9HAwMAAIpEID2zZgXCWtlcoFOD3+9FoNKDRaFCpVHDmzBnkcjnY7Xbe2A94O9hgjU2dTic8Hg//c1ZyORAI4JlnnsH09PSG76C7mbFmaiqVCrVaDYVCAbVajU8E2fgqFAo4d+4cfvSjH2F2dhbZbPa6ytGyipBOp3NRn4N0Oo1z585hZGSEF8fYjBPA9Y5N6l0uF/bv34+2tjZ0dnby+xbw9qKwwWCAIAjo7u5GJBLBzMwMYrHYil2LSCSC2WyGRqPBHXfcgd7eXnR1dSEajWJ4eBi//OUvMT8/v2mftZs+2GCdSLdu3YoHHngAExMTvEFMIBBYEvk2m02USiW+TScIAqxWK1Qq1ZJgg3W3lEgksNlskMlkSCQSaDQafOXwzJkzmJ6exvz8PBKJxKbbWiMXsf4F7AZZLpcRDocRjUYxNDSE1tZWvOc974HJZMKePXuQyWQQiUQQi8Wwf/9+OBwOOBwOaDQaDA8PY3p6GmfOnMG5c+cgl8vR19eHPXv24OGHH0alUkE6neYVXhZ2WCWbD1tp9vv9iMViMJlM0Gg0vDw3UywWMTExgXq9DpvNhkKhgOeffx6hUAjbtm2DyWTir2WTOoVCAYVCgUajwXdOgLdLjobDYbzwwgsIh8NUpGADY8GGUqlEvV5HqVTiz1YWcLDStkNDQ3juued435XrYbVa0dvbC7vdvihNj1WfGh8fRyKRoEB3nZLL5TCZTGhvb8ehQ4d4BUaW0s4m9oIg8K71bW1tiEajSCaTqxJs2O12HDp0CIcOHUIikUA8HsfY2BiOHj3KK/9tRps+2PB6vejt7cWOHTvQ3d0NtVoNpVKJt956i1c3WKhSqWB6epo/KM1mM8837urqwv79+zE9PY1IJIJqtYpCoQCxWAyTycR/SSQSJJNJzMzM4NVXX0UwGEQqlUKpVKJJH1kkn8/zCR6rGc5Wnbdt24Z8Pg+32w2NRoN4PI5AIIDjx49jdHQUcrkc27dvh8lkgtVqhcfjgVKpRK1WQyaTQSaTQaVS4c2yyOYklUp5lShWChd4exLIFk38fj8OHz6MbDYLpVKJSqWCwcFBHrBeutgCAL29veju7l4yvrLZLGZmZjA5OYlUKoV8Pk9jcANi9yuVSoU9e/bA6XRiy5YtcLlcfLywn7tCoYDJZILT6URnZyeSySTvS7VSstksIpEIBgcH8corryAQCFA2wTpkMplgs9nQ3t6OvXv3wuVyobOzEzKZDFNTUygUChgbG0OtVoNGo4FKpcLevXuh1+uxZcsWCIKA2dlZBIPBJU0lr4dUKoVCoYAgCGg0GhgZGcFbb72FoaEhVKvVTV11b1MHGyKRCF1dXXjPe96DXbt2YWBgAB6PB+3t7ajVanjyySeX3IjK5TImJiaQy+UwPz8Pq9UKqVQKqVSKgYEBNJtNlMtlRCIRVCoVZLNZngLDenMUCgUEg0FMTEzghRdeWNS8jZCFcrkcRkZGUKlUEAwGIZFIeOrfLbfcAgC8y/P09DT8fj9efvllnDhxAr/2a7+GgwcP8pxlttqcy+WQSqV49RV62G5ucrkcGo2Gp7JUKpVFfTCq1SpisRgmJibw4x//mJ/1WdivYGFTNoadQ+ro6FiyiJJKpXD27FmexnI1lYbI+iMWi2E0GmGxWHDXXXehq6uLpzUtLB7QbDZ5dkBraysGBgYwNTWFubm5FZugiUQipFIpDA8P48SJE3jhhRfoUPg6ZbVasWvXLuzbtw8f+chH+DMxkUjg2LFjmJ2dxVNPPcUX42w2G7xeL0wmEwYGBtDS0oK33noLIyMj13UmaCGRSAS5XM47kzcaDZw7dw4//OEP+X11M9vUwQawuAoQ8HYu8ZVWUyqVCsbHx3k1FavVyiPreDwOsViMrq4udHR08Ci8q6sLgiAgGo3iyJEjuHDhwqYfgGR5UqkUOp0Odrsdvb29cLlcvBEby0FlWJ8CVur2rrvugtfrxY4dO+ByuaDX65f0NqjX65t6lYW8fV6tu7sbbW1tPN1kYa8NVvpWp9PBbDbD6/VCLBZjdnb2He9dBoMBGo0GHR0d/OsCFw/mVqtVRKNRnD17lu8Sk41FJpPB6XRCr9dj3759sNvt2LJlC+x2O9/JmpubQzKZ5AUC2tvb0dnZCYvFgh07dkAkEuHcuXMrsiBSKBQQiUR4+kwul6MsgnXMaDSir6+PV12MxWIYHR1FPB7H+fPnEYvF+KJIvV5HPp+Hz+eDRqOBRCKB0WhEV1cXdu7cibGxMfj9/uu+JtbctF6v45VXXsHs7CzOnTuHYrFI8zxQsAFBECCVSiEWixeVHL1SakmpVMLRo0cRCASwZcsWOBwO3HLLLdi5cydkMhlaWlqwbds2bNmyhaexCIIAQRDg8/nwne98B9FolOp6k2UpFArYbDY0m00cOnQIarUajUYDpVJpUZWNRqOBSqWCer0Oj8eDjo4O7Ny5k58nYt2fF2L58hRsbG6satltt92GO+64A1u2bOE7YAvvfTKZDDabDZlMBgMDA9BqtXzndjkikQgOhwMejwd79uzBgQMHeBWgarWKbDYLn8+Hw4cPIx6P04N4A1KpVNi+fTtaW1vx6KOP8m7zzWYTp06dwtzcHF544QVcuHABVqsVZrMZH/rQh9DZ2Qmv14v77rsPUqkUhw8fhkgkuu5Uz3Q6jenpaQQCAX4+ktL21i+n04k77rgDKpUK+Xwe58+fxze+8Q3EYjHMz8/zUtwAMD8/D71ejzNnzqDZbGLXrl1wOBzYu3cv7zi+EsFGs9mE3+/nPTzY85mC2os2fbDBqqWwnY1SqcRXXt4Jy6Fnh75zuRyUSiUkEglcLhdKpRJvziaXyxcFMdlsFrlcjh8yJyQSieDcuXMAgI6ODjSbTZjNZkgkElSrVR58aDQaPnEDFpfMFYvFiyquXA47jL5cJ3KysbFDjGq1Gtu2bYPb7cbWrVt507O5uTle5YxVSDMYDOju7oZKpUJ3dzfEYjFOnTrFuzkvdw8zmUxwu93Q6/UQi8WLUvhYrnQ6naYeLxuUTCaD1+uF1+uFVCpFuVzG6OgoUqkUzp8/j3A4DL/fj0QiwVee5+bmMD8/D+BiZ3CHw4Genh5EIhGMjo6+qx0wltpsMBhgs9kAXCyE4Pf7MTQ0hNnZWZoErmNqtRo2mw1yuZyngarVahQKBb5YsvC+wsZYOp1GrVbjZ4lsNtuyZ82uFXtPWshbatMHG+xwpFQqBXDxABk74P1OD8FKpYKhoSGEQiFMT0/DbrfD4XBAq9Vi+/bt2Lp1K58AlstlHlzk83mEQiEkk0lks1l60BIAwPnz5xEIBPDggw9i69at0Gq16O7uBgB+NoMFxAurqrA8Ufb/7L/vNK5YipZarabeBpuMWCxGb28vWltb8cgjj2DPnj08nWpiYgJvvPEGzp49i8OHD0Oj0cDhcGDHjh344he/CIPBgHvvvReTk5N49dVXUSqVkM1ml0wCRSIR2tvbceutt6KlpWXReA2Hwzh69CjOnj276OwH2VhUKhVuvfVWeDweCIKAUCiE//N//g+GhoZ4t262+hwIBCASieD1etHV1YXW1lZs27YN/f39ePDBB3nD23cTbGg0Gmi1WrS3t2Pr1q0YGxvD4OAgTp48iZ/+9Ke8hxFZn4xGIz/oDVxcrOvo6IBUKuXlZReWTGYpTuyeIxKJ4HK5IJFIYDAY1vA72Tw2fbBxKZZKdWlkvBy2ilyr1XhjNJFIBLFYvGj1WRAESCQSNJtNFAoFnoN6NWdDyOZQLpeRyWQQDocxNTUFh8MBg8HAdysajQY/ZBYMBlGv16HRaCCTyWCxWKBQKFAul1GtVlEqlVAqlaDX66HRaPh7sDGdTqcxMjICn89HKzCbDCvV7fV6odVqIQgCAoEAkskkpqamMD4+jomJCUSjUeRyOVSrVVgsFt5/gzWObG1tRaPRwOTk5LKTtkt32vL5PG9uOjIygrm5OaqCtoGxSZzZbOb9V2QyGW9oW61W+c4CGwOlUgmZTIZPBpVKJaxWK3Q63VUviqhUKshkMnR2dsLtdsNisaBcLiMWi2FychKhUAiVSoV2NdY5kUjE09KbzSb0ej36+vpgMpnQbDaRz+eRyWTQbDb5zkdXVxfcbjcfj2yutnAxhKweCjaukSAIUKvVUKvVqFQqyOVy/KG7XM6zVCrl6QnJZJIODZFFCoUCisUizp07h6eeegrbt2+HzWbjhydLpRICgQDC4TD+4z/+A7lcDr29vbDZbLj//vvhdrsRj8cX7czt3LkTAwMD/D3YQ350dBTf/OY3EYvFqBrLJiMWi7F9+3bccccdMBqNyGQyeOaZZ/CLX/wC0WgUkUgEtVqNV6SamppCtVrF8ePH0dLSws+k3XfffZiensa///u/v2PKKZskslSYw4cP44knnkC5XKaV5Q1MoVCgu7sbHR0dAC72tWhvb0c+n1/0/Fu42JHNZhEIBGC32yESiaDX69HT04NEIrFo8e5yBEGAy+WCxWLBJz7xCdx5550oFouYn5/HyZMn8fTTT/NmvGR9YwtnbJ7V2tqKz3/+87xEdy6Xg8/nQ7PZ5BknPT090Gq1kMvltMixBijYuIRYLIZMJoNWq4XD4eAl+S5dWWGN+kwmEywWC9RqNW/ItpyFNetpRY9cit04xWIxVCoVpFIp36VIJpO8o3gkEoHP50M+n4dUKuXN/TQaDRqNBiQSyaIOvQvHGTuPFI/HEYvFqGvuJiQSiaDT6WCxWPgZM+BiWmipVOL9LhaOm0KhgEwmA4PBAJFIxHfTstnsO97z2NfN5/PIZrNIp9PIZDLI5/O0o7YJSCQSSKVSNJtNyGQyuN1ulMtl5PN5GAwG5PN5lMtlSCQSSCQStLa2wmq1Qq1WA7g4VlnxlnfznkqlEhqNBjqdDtFoFDMzMwgGg8um/JH1KZ1OY3Jykt/LxGIxb9pXqVT4WbBms8mbLmu1WqjVap69Uq/XFx0kJ6uLgo1LqNVqtLS0oF6vo1KpQK/XY+vWrUseqlKpFB6PBxqNBlarddGD+3LYAKcHLbmclpYW3HPPPdDr9cjn8xgZGcE///M/Ix6PY35+np//aTabOHfuHL/ZJhIJ9PX1weVywefzLXsTDYVCvFllNBqlAgWbkCAI8Hg86O/vB3AxyG1ra0NnZycajQYSiQRqtdqiUqOlUgnBYBBKpRKNRoOnqUgkEt6p93JisRjGxsYwMzPDDwTTmNscFq4+K5VKfPjDH0a5XIbf70c6ncb8/DxSqRTMZjMMBgM8Hg9aW1uv+By9HNalnBXRqNfrOHr0KJ566inE43E6H7SBnDp1Cn/zN3+DvXv34qMf/Sg/KC6Xy+F0OtFsNuHxeABcDEBZ4AqAV2hkaXuUYXJjbPpgI5fLIRqNwul0olqt8gjZarWio6MDer0e7e3tS26ArOqUUqmEQqHgZzKY5XJMWY4h5QiSy1EoFIuC13q9jlQqxQ+3VatVPs5YSkAqleK7FFKpFGq1mldBA94+h5TNZjE3N4dYLLYoZ5psLlKplKcSNBoN3j+jUqmgWCyiXC6jVCrxSmculws6nY7v8AqCwFNDL111Zn2LFAoFVCoVarUaH7+sAhXZ+Gq1Gt9xNRgMvFcL6/Gj1+t5arHJZILBYIDFYoFer1/U6K9er1/1fYplDhSLRYTDYfh8PszNzSEcDtPCygaTyWR4YZ5gMAi1Wg29Xs8r6LF7FMMOjLPy8ZVKBaFQCIFA4IqVR8nK2NTBRrPZxPHjxzE3N4cPfehDcDqdUCgU6O3tRXd3N/bs2QNBEJZdvWP9ORYGD1eqAiSTyWAwGKgKELkss9mM/v5+vhqTTCbR2dkJhUKBWCyGQqGwKOBgVTYCgQCKxSIEQUB/fz86Ojp4Sb9yuYxCoYDh4WE888wzCIfDlE6wiV2a73znnXdix44dCIfDCAaDSCaT/AHOeiD09fVBpVJBoVBctsEaS9FihzEHBgYwPT2NCxcu4I033sAvf/lLFAoFCnI3gWg0im9/+9tob2/Hr//6r6OlpQUymQyCIMBiscBoNMLpdKJer/OCKmxBb2HKcTabveryyKxggd/vx/DwMJRKJZLJJKWLbkChUAipVArRaBSxWAwulwu33HILLBYLtm3bBrlczudYbCz5fD6k02lcuHAB4XAYJ06cwPT0NC+3TFbXpg42APAbUSAQQDAYhNls5lVaWKUCdhNknZvZzZBty14aOMjl8mW3ghdWuiJkORKJBCqVildtYeVH6/U67HY7r+rDmvap1Wp+xoMFvexjbFxWKhVks1kkEgkEg0FepYNsPs1mE7lcDolEglfu0ev1fBFEr9cjkUhAq9VCo9HAZrNBp9PB4XDw8cUq7y0MWCQSCcRiMUwmE4xGI/R6PZRKJYrFIoLBIEKhEMLh8Fp+6+QGqlQqmJ6eRr1eRyQSgUKhgE6n4/0vLk3BY8/GSqXCzzWyHbF3c79ilfiy2exqfWvkJlCpVFCpVBCJRDA5OYlSqQSz2YxCoQCTycR39ZlqtYrZ2VledS8QCGB6ehozMzNUJOUG2fTBRi6XQ6lUwi9+8QtMTU1hx44duPvuu1EqlRCPx6HT6dDR0QFBEFCtViEIAlQqFarVKkZGRpDNZvmkTqFQQCaTYWBgAG1tbfw92I2SHWoKBoO00kKWdemqs9frxec//3lks1lMTU3xpmiNRgNWqxUajQYDAwOwWCy8XvilXcNnZ2dx6tQp3ttgYf1xsrlUq1X85Cc/wdjYGN7//vdj586d/IAuq9Rit9vR2dnJG54uLGHLxk0+n0cul+Mr0263G2azGQ8//DC2b98OlUqF+fl5vPHGG3j++eeRSCTW+DsnN1KhUMDZs2fh8/kgk8ngdDqxa9cuWK1WbNmyBRaLhS/UsTEVjUYRDocRCAQwPj6OmZkZnDhxArFYDKVSaY2/I3IzisViOHHiBM6dO4fXX38dUqkUGo1m2cyRYrGIarXKCxOwuR/NxW6MTR9ssAPboVAIpVIJSqUSXV1dKBQKiEajMBgMUCqVkMlk/OErEolQrVYRDAYRj8f5YNXr9VCpVOjq6lp2Mlev13mvBEKWU6vVkM/neYdvpVKJjo4OlEolaLVaXsmlXq/D6XRCo9GgtbUVWq12yZkhlu+cSqXg9/v5oXC6uW5e9XodMzMzqNfr2L17N7LZLG/qxxZSFj6oF6brNRoNXuksk8kgm82iXq9DJBLx6j8dHR3o6+tDIBBAPB5HKBTC7Owspe1tMuys2cLO4QaDga9AX5oyBVycOM7NzcHv92N0dBR+vx9jY2N0zyKXxXY4gIupVeTmtemDDSafz6NSqeC1117DxMQErzXPDrZt2bIFf/RHf8TLrKXTaaRSKczMzGBsbAz5fB4HDx5Ed3f3ZW+MBoMBvb29iEajdGaDLGtwcBDf+MY30NfXh/vuuw8ymYz/cjgcqNVqsNlsaDabUCgUEIvFi5oULUzrCwQCmJ+fx0svvYQnn3wSqVSKHtqbXKPRwMTEBAKBAJRKJQYHB9Hd3Q2PxwO32w2v17tkxTmXy2Fubg7pdBqjo6OIxWI4evQo78txaRWgWq2GV155Ba+//jomJiaoCtAmVqlUMDw8jMnJSYyMjPB0quXOQZZKJRSLRR7MFotFfs+i8UPI+kbBxv9TrVZRrVZRKBQQCASW/Hm9XodSqYTRaFzUL6NYLPLDSqVS6R0rTcnlchiNxstu8xESiURw8uRJSCQSHDhwAMDFCj8ikYivOuv1ev76hSvPLP2KjcFMJoNQKASfz4fR0VF6YBM0m01evWx4eHjRgW2lUgmLxQIAfAw1Gg3e2T4ajfLDladPn0Y6nUahUOAVqsRiMa8G5PP5cObMGeRyOQpwN7F6vY54PA4ACAaDa3w1hJC1QsHGu7Awn16lUuGhhx5CKpXCoUOHkMvlsH37drhcLtjt9st+PiHvJBqN4tSpU/yAo81mQ19fHwwGAzo6OiCTyRZV2ajX6wgGg8jlcshkMiiVSvywr9/vx/z8PNLpNI09skiz2cTMzAzi8Timp6eh0+mg1+uh0+mWvLZSqSCTyaBcLvP8+VgsxjtAs07jwWAQsVgMer0eIyMjyGQyl61cRQghZPOgYOMqsWoZbJVOLpdjYGAA9XodbW1tKBQKcDgc0Gq1i3YtrlQOl5CF8vk87+Ks0Wjgdrshl8vhcDjgdruX9DWoVquIx+NIJpOIRqMoFAqw2+0wGAyIx+NIJBJUbYMsK5lMIplMYnZ29rq+TrPZRCwWA4Dr/lqEEEI2Hgo2rlIkEsGTTz6JtrY23HXXXbwpkVgshtFohFarhVKpXJRGxYIOFnCww79UWYNcSSqVwuDgIKanpzE1NQWVSoWnnnpqSUllltInEolw5513oru7G7FYDJOTk3jjjTdw+vTpZdMCCSGEEEJuBAo2rlI6ncaRI0fQ0dGBnTt38l4IgiBAq9Uuef3lqlEVi0UqPUquiO1wAMD58+cv+zpBEGC1WmEwGHDrrbfC7XYjFAphbm4Ow8PDePPNN2/UJRNCCCGELEHBxlUqFosYHx9HJpPBs88+C6fTqQQXugAAXN5JREFUic7OTuh0OrhcrmW7gieTSWQyGcTjcUQiEX5ocmpqikpBkusiFothsVig1Wpx6NAheDwemM1mRKNRnDt3DsePH4ff71/ryySEEELIJkfBxlUqlUqYmJhAOByGUqmE3W7HwYMH4XQ6odfroVar+WvZIfJUKoXZ2VmMj4/jwoULGB8fx4kTJ1AsFinYINdFLBbDbrfD6XTiwQcfRH9/P68YNDg4iF/96lfUz4UQQggha46CjXepUqnA7/cjmUyiWq3CbDZDJBKhra0NGo0GMpkMiUQCuVwOZ86cwYULFxCNRnmllkKhQBVayHUTiUSQyWSQy+W8yeSZM2cwMTEBn8+HarVKJUcJIYQQsuYo2HiXyuUyxsbGAACnTp2CRqOBSqXCtm3b4HK5oNVqMTo6irm5ORw+fBivvPIK/1w6p0FWCmukplQqebBx9OhRHDlyBOl0mgJaQgghhNwUKNi4RiKRCHK5HGq1GhaLBTabDaVSCblcDqOjoxgdHUUwGKQAg6yKer2OSCSCarWKI0eOwGQyYWpqCoVCgVL0CCGEEHLToGDjGgmCAL1eD4vFgs7OTvT09ODEiRPw+Xx4+eWXceLECUpjIaumWq1ifHwcIpEIp06dgkgkQq1WozFHCCGEkJsKBRvvEit1q1KpsHPnTjgcDiiVSuTzefj9foyOjiKZTKJer6/1pZINjhUioACDEEIIITcrCjbeJZlMhra2NrhcLvz2b/82WltbkU6nMTs7i1deeQVvvPEGCoXCWl8mIYQQQggha46CjXdJEARIpVJ+OFcmk2F+fh7BYBDxeBzFYpF2NQghhBBCCAEFG++aIAhQq9VQqVQQiUQoFAr4yU9+glOnTiEcDqNcLq/1JRJCCCGEEHJToGDjXWo0Gsjn80in05iamoJarUYgEEAikaBAgxBCCCGEkAVEzauszSoSiVb7WtYF1t9ALBZDq9VCEAQkk0mUy2U0Go1NV+r2Rn2/NP7Icm7kvzcag2Q5dA8ka4nGH1lLVzv+KNgg14VudGQtUbBB1hrdA8laovFH1tLVjj9hla+DEEIIIYQQsklRsEEIIYQQQghZFRRsEEIIIYQQQlYFBRuEEEIIIYSQVUHBBiGEEEIIIWRVULBBCCGEEEIIWRUUbBBCCCGEEEJWBQUbhBBCCCGEkFVBwQYhhBBCCCFkVVCwQQghhBBCCFkVFGwQQgghhBBCVgUFG4QQQgghhJBVQcEGIYQQQgghZFVQsEEIIYQQQghZFRRsEEIIIYQQQlYFBRuEEEIIIYSQVUHBBiGEEEIIIWRVULBBCCGEEEIIWRUUbBBCCCGEEEJWBQUbhBBCCCGEkFVBwQYhhBBCCCFkVYiazWZzrS+CEEIIIYQQsvHQzgYhhBBCCCFkVVCwQQghhBBCCFkVFGwQQgghhBBCVgUFG4QQQgghhJBVQcEGIYQQQgghZFXc1MHG448/DpFItOyvL3/5y2t9eSsil8vhT//0T3H//ffDZDJBJBLh8ccfv+zrG40G/vf//t/YuXMnlEolzGYz7r77bpw9e/bGXfQmQeNvscv9XYhEItx777039sI3ARp/Sz355JO49dZbYTAYYDabcdddd+G55567cRe8ydAYXOrv//7v0dfXB7lcjpaWFvz3//7fkc/nb9wFbyKbYfydOHECX/ziF7F161ao1Wp4vV587GMfw9jY2LKvHx4exv333w+NRgOTyYRPfepTiEajN/iq3z3JWl/A1XjsscfQ3t6+6GPbtm1bo6tZWbFYDI899hi8Xi927NiBl19++R1f/7nPfQ5PPPEEHn30UXzxi19EPp/H6dOnEYlEbswFb0I0/i7613/91yUfO3nyJP72b/8W73vf+1bxKjc3Gn8XfeMb38B//a//f3v3HR3Xed6J/zu9V0zDDGaAGXSwg02UKFGkJFtda8my5FTLzsnZOCc/52idXdlZ71nHiePsOquN5Sh2Em/sWLItyZIiyyqR1ShKJEWZYAFBAESv03uv9/cHc18DAgtIApgB8HzO4SE5GMzcS7y8c5/3fd7n+f9w11134Vvf+hZyuRx++MMf4u6778bzzz+P+++/f+UOfJ2hMXjef/tv/w3/63/9L3z605/Gl770JZw9exZPPPEE+vr68O///u8rd9DrzFoef3/zN3+DDz74AA8++CA2b94Mn8+H7373u+ju7sbRo0fnnef09DRuuukm6HQ6fPOb30QqlcK3v/1t9Pb24tixY5BKpVU8k8vgati//Mu/cAC4jz76aNHfk81muXK5vIxHtbRyuRzn9Xo5juO4jz76iAPA/cu//MsFn/vMM89wALgXXnhhBY9w/aLxd3lf+MIXOIFAwE1NTS3TEa5fNP7ma21t5Xbu3MlVKhX2WDwe59RqNXfvvfeuxOGuOzQGf2N2dpYTi8Xc7/7u7857/IknnuAAcL/4xS9W4nDXlfUw/j744AMun8/Pe+zcuXOcTCbjfvu3f3ve43/0R3/EKRQKbmJigj32q1/9igPAff/731+R471aNZ1GdTnvvvsuBAIBfvazn+G///f/DofDAaVSiUQigUgkgi9/+cvYtGkT1Go1tFot7rjjjgXpRvxrPPvss/j6178Oh8MBjUaDT3/604jH48jn8/jTP/1TWCwWqNVqPPLII8jn8wuO5amnnsL27duhUChgNBrx8MMPY2pq6rLnIJPJYLPZFnW+/+f//B/s2rULn/rUp1CpVGjptsrW2/j7uHw+j+effx779u1DQ0PDVb0GuXrrbfwlEglYLBYIBAL2mFarhVqthkKhWNRrkKW1nsbgkSNHUCqV8PDDD897nP/7z372s8u+Bllaa2H8XX/99QtWJFpbW7Fhwwb09/fPe/z555/H3XffDZfLxR679dZb0dbWhmefffZK/ulW3KpIo4rH4wiFQvMeM5lM7M/f+MY3IJVK8eUvfxn5fB5SqRRnz57Fv/3bv+HBBx+E2+2G3+/H97//fezbtw9nz56F3W6f93p//dd/DYVCgcceewzDw8N44oknIJFIIBQKEY1G8T//5//E0aNH8cMf/hButxv/43/8D/a9f/VXf4Wvfe1r+MxnPoM/+IM/QDAYxBNPPIGbbroJJ06cgF6vv+Z/g0QigWPHjuGLX/wivvrVr+KJJ55AKpWC2+3Gt771LXzmM5+55vcgF0bj78JeffVVxGIx/PZv//ayvD45j8bfeTfffDN+/vOf44knnsA999yDXC6HJ554AvF4HF/60peW5D3IhdEYBLvB/Hhgq1QqAQDHjx+/5vcgF7bexh/HcfD7/diwYQN7bGZmBoFAADt27Fjw/F27duHVV1+9ovdYcdVeWrkUfgntQr84juPeeecdDgDn8Xi4TCYz73tzudyCpbSxsTFOJpNxf/EXf8Ee419j48aNXKFQYI9/9rOf5QQCAXfHHXfMe409e/ZwjY2N7O/j4+OcSCTi/uqv/mre83p7ezmxWLzg8Uu51BJuT08PB4Crq6vjrFYr9+STT3JPP/00t2vXLk4gEHCvvfbaot+HLA6Nv0t74IEHOJlMxkWj0UW/B1k8Gn/z+f1+7pZbbpn372AymbjDhw8v+j3IlaEx+BvHjx/nAHDf+MY35j3++uuvcwA4tVq96Pchi7Pexh/vxz/+MQeA+8EPfsAe48fmv/7rvy54/p/92Z9xALhcLnfF77VSVsXKxt///d+jra3tol///d///QWzDTKZjP25XC4jFotBrVajvb0dPT09C17j937v9yCRSNjfd+/ejZ/+9Kf4/Oc/P+95u3fvxne+8x2USiWIxWK88MILqFQq+MxnPjMv8rbZbGhtbcU777yDr371q1d8zh+XSqUAAOFwGEePHsXu3bsBAPfeey/cbjf+8i//Erfffvs1vw9ZiMbfQolEAq+88gruvPPOZVs5IefR+DtPqVSivb0dDQ0NuPvuu5FMJvH444/j/vvvx6FDh9DS0rIk70MWojEIdHd3Y/fu3fibv/kbOBwO7N+/H/39/fijP/ojSCQSZLPZa34PcmHrafwNDAzgj//4j7Fnzx78/u//PnucH19zz4snl8vZcy709VqwKoKNXbt2XXDpiPfxKgXA+RKxf/d3f4cnn3wSY2NjKJfL7Gt1dXULnj83Bw4AdDodAMDpdC54vFKpIB6Po66uDkNDQ+A4Dq2trRc8trmD91rw/5HcbjcLNABArVbjnnvuwVNPPcUGP1laNP4Wev7555HL5SiFagXQ+DvvwQcfhFgsxssvv8weu++++9Da2oo///M/xzPPPLNk70XmozF43vPPP4+HHnqI3YCKRCI8+uijOHjwIAYHB5fsfch862X8+Xw+3HXXXdDpdPj5z38OkUjEvsbfA15ov0gul5v3nFq0Ju5ML/QP/M1vfhNf+9rX8PnPfx7f+MY3YDQaIRQK8ad/+qeoVCoLnj/3h7qYxzmOA3B+QAsEArz22msXfK5arb6SU7koPr/QarUu+JrFYkGxWEQ6nWb/QcjKWQ/j7+Oefvpp6HQ63H333cvy+mTx1sP4Gx0dxeuvv45//Md/nPe40WjE3r178cEHHyzJ+5Crsx7GIAA4HA68//77GBoags/nQ2trK2w2G+x2+yVn3snyWgvjLx6P44477kAsFsOhQ4cW7Cmpr68HAHi93gXf6/V6YTQaa3ZVA1gjwcaF/PznP8f+/fvxgx/8YN7jsVhs3saia9Xc3AyO4+B2u5f1YmO322Gz2TAzM7Pga7Ozs5DL5dBoNMv2/uTKrLXxN5fX68U777yDz33uczV9cVvP1tr48/v9ADBvdpJXLBZRKpWW7b3J1VlrY3Cu1tZWNpN99uxZeL1efO5zn1uR9yaLs5rGXy6Xwz333INz587hzTffRFdX14LnOBwOmM1m/PrXv17wtWPHjmHr1q1X9d4rZVWXvr0UkUjEIk/ec889d8Gb9Wtx//33QyQS4etf//qC9+M4DuFweMne66GHHsLU1BR+9atfscdCoRBeeuklHDhwAELhmv1xrjprcfzxfvazn6FSqVAKVQ1ba+OvpaUFQqEQzzzzzLz3mZ6exqFDh7Bt27YleR+ydNbaGLyQSqWC//pf/yuUSiX+83/+z8v2PuTKrZbxVy6X8dBDD+HIkSN47rnnsGfPnos+94EHHsAvf/nLeSV133rrLZw7dw4PPvjgtZ3IMluzKxt33303/uIv/gKPPPIIrr/+evT29uLpp5+Gx+NZ0vdpbm7GX/7lX+IrX/kKxsfH8Z/+03+CRqPB2NgYXnzxRfzhH/4hvvzlL1/yNb773e8iFothdnYWAPDyyy9jenoaAPAnf/InLDXqK1/5Cp599lk88MADePTRR6HT6fC9730PxWIR3/zmN5f0vMi1WYvjj/f000/Dbrfj5ptvXtJzIUtnrY0/s9mMz3/+8/jnf/5n3HLLLbj//vuRTCbx5JNPIpvN4itf+cqSnhe5dmttDALAl770JeRyOWzduhXFYhE/+clPcOzYMfzoRz9akPNPqmu1jL//8l/+C37xi1/gnnvuQSQSwVNPPTXv67/zO7/D/vzVr34Vzz33HPbv348vfelLSKVS+N//+39j06ZNeOSRR5b0vJbamg02vvrVryKdTuMnP/kJnnnmGXR3d+OVV17BY489tuTv9dhjj6GtrQ2PP/44vv71rwM4v6noE5/4BO69997Lfv+3v/1tTExMsL+/8MILeOGFFwCcH2j8hc5qteL999/Hl7/8ZTz++OMoFovYs2cPnnrqKWzZsmXJz4tcvbU4/gBgcHAQx48fx6OPPkoraTVsLY6/f/iHf8CWLVvwgx/8gAUXO3fuxL/+67/ipptuWurTItdoLY7Bbdu24f/+3/+Lp59+GkKhELt27cJbb72F/fv3L/k5kWuzWsbfyZMnAZwPcOcWv+DNDTacTicOHjyIRx99FI899hikUinuuusu/O3f/m3NpzQLuI+v+xBCCCGEEELIEqCpSUIIIYQQQsiyoGCDEEIIIYQQsiwo2CCEEEIIIYQsCwo2CCGEEEIIIcuCgg1CCCGEEELIsqBggxBCCCGEELIsKNgghBBCCCGELItFN/UTCATLeRxklVqpNi00/siFrGSbIBqD5ELoGkiqicYfqabFjj9a2SCEEEIIIYQsCwo2CCGEEEIIIcuCgg1CCCGEEELIsqBggxBCCCGEELIsKNgghBBCCCGELAsKNgghhBBCCCHLgoINQgghhBBCyLKgYIMQQgghhBCyLCjYIIQQQgi5CGpoR6ppLYy/RXcQJ4QQQghZ60QiESwWC7RaLQ4cOAC73Y6DBw9ieHgY4XAYyWSy2odI1rC1OP4o2CCEEEII+Q/8zZ7dbsfv/u7voru7G5VKBblcDoVCYVXe7JHVYy2OPwo2CCGEEEL+g0gkQn19PRwOB8bGxpDNZjEyMoJgMIhisQiZTIZKpYJyuQyO48BxXLUPmawha3H8UbBBCCGEEPIfxGIxXC4XnE4nBgYGcOrUKZw9exY+nw8CgQAKhQLFYhHFYhHlchnlcrnah0zWkLU4/ijYIIQQQgj5D6VSCRMTE0in00ilUigUChCLxXC73XC73bBarRgZGcHIyAji8TjC4XC1D5msIWtx/Am4Ra6/rIXd8GTprdTyHY0/ciEruXxMY5BcCF0D1x6RSASz2QypVIpwOIxCoYC9e/eitbUVd999N66//nq8+OKLeOmllzA2Noa+vr6qHSuNv7VnLY4/WtkghBBCyKolEokgEAhYDvu1EgqF0Ov1UKlUSKfTqFQq8Hg86O7uht1uh0wmg0gkWrL3I6sbjb/Lo2CDEEIIIauSQCCAXC6HSCRCNptFsVi85teUSCRob2+H0WhEOp1GJBLBgQMH8MADD0AgEEAgEIDjOFYdiKxfNP4Wh4INQgghNUEoFEKlUkEul8PtdkMul2N8fBzxeByZTGZJPsjJ2iGVSiGRSNDU1AS1Wg2/3490Oo1kMolcLnfVr8txHCqVCiqVCoDz4zKfzyOZTLJ0olgshmg0imw2uyTnQlYfGn+LR8EGIYSQmiCVSuHxeGC32/HYY4/B5XLhb//2b3Hs2DGMjIysio2QZGWIRCLo9Xro9Xp86lOfQlNTE379619jamoKvb29mJycvOrX5jgOqVQKYrGYvdfExAR6enogl8shlUoxMDCAvr4+dkNI1hcaf1eGgg1CCCE1gf8ANxqN4DgOhUIBxWIRpVIJAoEAIpGI1ZVfLbnKZHnwjc/MZjOcTidcLhdCoRDEYjEmJiau6bU5jkOxWEShUECpVJr3i9+wG4lEaKVtHaPxd2Uo2CCEEFITZDIZOjo6YLVacfjwYZRKJZw4cQKTk5Mol8tQqVQs+CiXy6tmVo8sPYVCgf3796O1tRX79u2Dw+GA0+lEJBLB9PQ0zpw5c9Wvzc8sA+fTVVKpFIRCIZRKJY4dO4YTJ05gYGBgqU6FrEI0/q4MBRuEEEJqAr+akclkEA6HkclkUCgUIJfLodVqIZVKEYlEkEgkkMvlrikvmqxuQqEQarWaVe1RKpVQq9UolUqQSqXX9Nr85lu+ypBEIoFCoYBSqUQ+n0coFFo1ufJkedD4uzIUbBBCCKkJmUwGhw8fhkQiQTQaRaVSQVdXFzo7O3H77bejo6MDL7/8Mj744AOMjY1hfHy82odMqoS/CZNIJADO36CVy2UUCoVrXvEql8vw+/2QyWTQ6XRwOp1ob29HZ2cnXn/9dcTjcQp01zkaf1eGgg1C1jmBQACxWAyxWAyNRgOhUIhEIsHSVSg3nlwMXxllqcZIpVJBLpdDsVhkwYbBYEBTUxNaW1vR3t6OY8eOQaVSXfPsIVn9hEIh+8XnuefzeZTL5at6PYFAAKlUCpFIBJFIBKFQCJPJBLPZzFbWKpUK8vk8SqXSEp8NWW1o/C0eBRuErHN8mVGHw4E//MM/hEqlwj/90z9haGgIExMTSCaT1T5EUoP4+vJCoRC5XO6qP2Dnksvl6OrqglwuR6lUQrFYxF133YWbbroJBoOBfdjmcrlV92FLlpZAIIBMJoNCoWCFA3w+H8bHx5FIJK7qNeVyOTZt2gSNRsNSY+644w54PB7o9XpMT08jEAggEomsujQWsrRo/F0ZCjYIWefEYjH0ej1sNhs2btwItVoNs9mMmZkZiMVi1kCIEB5fGUqlUkEsFrO9Ftfa0VYoFEKj0UCpVLKVC6vVCqfTiUqlgnK5jFKpxKq0kPWNnwHm5fN5pNPpRVXp4ZujCYVCAOdX1aRSKSwWCwwGA9RqNTQaDdrb29HS0oJYLIZ0Oo1sNntNs9dk7aDxt3gUbBCyzikUCrS3t8NiseDgwYMoFovo6+vDzMwMOI6DUqlk1X+u9WaSrH4ikQg6nQ5arRa33347DAYDzpw5g2AwiKGhoWvqhVGpVJBMJtkHqVAoxMjICOrq6iCTySAQCFh9+Uwms1SnRNYZmUzGglqLxYJSqYRoNAq9Xo8bbrgBDocDzc3N0Gq1sNvtUCgU8Hq9CAaDrGEbVUIjV2s9jj8KNghZ5/jeBgqFAmNjY0ilUgiHw8hms5BKpZDL5awqEL8JjqxffJfvuro6dHZ2wmq1IpPJQCaTYWZm5pqCjbl5z7xEIoFAIAC5XA6BQIBQKETN/cgF8TPFIpGIrcry+4rmUigU0Ov1UKvVcDgcKJfLEIlEMBgMcLlccLlc6OrqglarBXA+CC4Wi8hkMqtyVpmsDBp/F0fBBiHrXCqVwocffgiJRIJYLIZyuQybzYbW1lZcf/31sFqteOuttzA4OIjJyUkEAoFqHzKpIrVajZtvvhkulwvXXXcdTCYT9Ho9AoEApqenMTU1ddWvXalUkEqlUCwWkUgk2IewTCZDT08PpqamMDIysoRnQ9YKgUDA0kF37doFjUaD+vp6WK3WBc9Vq9Wor6+HQqGAwWAAcL4SmlQqRUtLC1QqFRQKxbzv4VP4aGWXXAiNv0ujYIOQdS6fz2NiYgIcxyEYDEIikaClpQVNTU04cOAAmpubEQgEkEgkEAqFqn24pMpkMhlaWlrYGKmrq4NUKoXVamUfnFerUqmw0pF8qoBQKIREIsH09DROnjxJY5AwHMfNSydRKBTQ6XRoamqCWCxGW1sbPB7Pgu/T6XRoaGiARCKBUqm84Ozzhd6LGkmSuWj8LR4FG4SsInxpPIFAgHw+vySzHHK5HK2trQCAQqEAqVSKW265BZs3b4bT6YREIkG5XEY+n1+1FzqydOaWSuaLB5RKJZRKpSWpLx8IBCCVSmEwGKBSqeB2u9HU1ASRSIRUKoVCobBEZ0JWs2w2i3fffRejo6PQ6/UQCoXQ6/XQaDQwGAxIpVLQ6/XQ6XQLvlcqlbIqQvwG3UtdS/nKaxqNhkouEwA0/q4UBRuErCJzg41isbgkuZtSqRROpxMAMDY2Brlcjh07dmD37t2spjefM0rBBgHmV2HhZ9yWYom/XC4jFotBKpXCbrfDbDbDZrOhvr4eIpEImUyGqlARAOdXZE+cOIGpqSns378f9fX1sFgsUKvVaGhoYM+73E3cYvBlTpVKJcRium0iNP6u1Oo86jVCLpfDZDLBYrHgwIEDKJfLeOeddxAMBhEKheZtkiTrG19mVKvVYufOnZDL5RgaGkIymcT09DTS6fRVvzafJy8UCtlM9dTUFAwGAyQSCUqlEiYmJjA2NoZ4PL6EZ0VWIz7g5YPeSqWCUCgEr9d71RWi5HI5GhoaIJfLoVaroVKpcP3118Nut0Oj0SAYDCIWiyGZTNLKBgHwm+sWx3F4/fXXce7cOezatQtOpxPpdBr5fB5GoxF6vR7j4+MYHh6GRqNBXV3dvJs8flOvRCKBXC5Hc3PzvHz5crmMYrGIo0ePoqenB8PDw9U4XVJjaPxdGQo2qoj/gN24cSMeffRRlEolhMNhDA4OIplMUrBBGJFIBI1GA5vNhttuu43V4PZ6vYhGo9ccbGSz2XkXwOnpaSgUCqhUKnAch4mJCYyPjy/BmZDVjv9glEqlEAqFqFQqiEQi8Hq9yOVyV/WafCqfVquFUqmEXq/HfffdB5fLxfYKxWIx1tmeEI7jkEqlkMlk8MYbb0Cr1aJcLiOdTiMQCCAej6O5uRlNTU348MMP8frrr6O+vh7t7e0Lbvb4yRyDwQC73Q65XM6+zqeQHj16FL/85S8RjUarcbqkxtD4uzIUbFSRXC6H0+mERqPBqVOnkMlkWOlIftmM3xBE6Svrm1qtxtatW+F0OtHe3g61Wo1CoQCv14vBwcFrqhDFb8YFzueh8hWARCIRBgcHEQqFEAwGl+pUyDrGz97JZDKo1Wr24Ww2m7FhwwbU1dXBbrdDq9XCYrFAJpMhm80ikUggl8stWeogWTv4m75yuYyjR49icnISqVQK2WwWAwMDMBgMGB0dxcTEBCKRCEKh0IL0FZ1Oh+7u7gX59ZVKBdPT0+wamEwmKdgl89D4WxwKNqpIqVSipaUFCoUCH3zwAaLRKMbGxliwoVAokM/nWY4yBRzrl1arxd69e+F2u7Ft2zaoVCpYLBb4fD689tprOHfu3FW/Nr8cDADJZBIKhYJtAO7p6UF/fz9mZ2eX6lTIOiaTyWAwGKDVauF0OlnAbLfbsWPHDjgcDmzevBkqlQoCgQClUgnZbBaRSATpdJpSqMgCHMchkUggkUjgjTfeuOTzgAvnyTudTrS1taFSqczLsS+VShgeHsbY2BimpqYQi8WW/PjJ6kbjb3Eo2KiiTCaD0dFRiMVipFIpluNnNBrR3NwMuVyOvr4+tofjWlJlyOo3twIQ8Juye9cahJZKJSQSCQiFQmg0Guh0OpjNZphMJgBALpej2WRyUQKBAEqlElqtFh6PB7lcDnq9HiqVasFztVotTCYTNBoN65wbi8Wg1+vR1NQEo9EIiUQy7wO5UqlQ53qyKIsZI3Ofw1c9s9vtaGlpgcvlgkwmA3C+Mh//Gd3X17eqb/TWOn7fg9FoRHd3N5RKJZRKJYDz91nFYhGxWAyFQgE+nw+pVIrtq1hKNP4ujoKNKorFYjhy5AjK5TJCoRCUSiUOHDgAl8uF3/qt34LFYsH3vvc9nDx5EqdPn6ZgYx3jO5LylSj4TstLsaRaLBbh8/kgk8lgtVrhcDjQ0tICj8cDgUBAefLkkoRCIQwGA8rlMvbs2QO3242uri5W4Wwug8GA+vp6SKVSFoxUKhWWt3yhjrt8pSsKeMlSU6lUaG9vR2dnJ/bu3Qur1QqxWIxKpYJMJoNoNIqjR4/i8OHD8Pv91T5cchFCoRAymQzNzc34sz/7M9jtdlYRamZmBslkEgMDAwiHwzh48CDGx8cxOTlZ9X2x62n8UbCxSPyHIYAlK70okUhgNBpRLBYRjUYhl8vR0dGB5uZm1NXVsTrMHMfRrN46x/c24G/I+NSneDx+1YEAP6sikUigUqmgVquxadMm1NfXQyKRIJ1OI5PJIJvNUrlRwuTzeQwPD6NcLqO5uRlisZilPbW0tMBkMsHpdMJsNi/4Xr7SlFgsZisYl7u2SSQSyGSyVVvycS3hZ5Dtdjuam5vZz6ZcLrM9NXxxk2AwiEKhgHQ6XbMpwDKZDHa7HTabjY0xgUDA+r34/X5EIpFVnSu/HhiNRmzZsgUdHR2wWq3Q6/Vsk7Ver4dMJkNjYyMMBgOSySRsNhs4jkOhUEA+n6/az3Y9jT+6ei+SUCiEQqEAx3HIZDJLcvHUaDTYtGkTstksotEoLBYLHnjgAXR0dMyrKU9pLAQ4X1BALpezi9HMzAwmJiaQzWav6vW0Wi1bclapVDCbzXj44YdhNptRKBQwOzvLqgBRrjzhxeNxvPTSS3A4HGhvb4dYLIbVaoVcLkd7ezs4jmNllD+Ov1nl/8z/frGAg7/uarVaSCSS5Tspsih8kHjzzTfji1/8IgwGA6xWKzKZDLxeL2KxGAYHB+H3+/H2228jEolgbGzsqq9Ry02n02H79u1oamqCQqFgY7JQKOD06dMYGRnB2NgYAoEATfjVsPb2dvz5n/85LBYL3G43JBIJu86YTCZwHAe73Q6O47B9+3akUil85zvfQTabRTAYrFqFp/U0/ijYuAyhUMhm7hobG8FxHAKBAHK5HJLJ5DXP+PIDiP+PkcvlkMlkIBAIkE6nEY/H6WaPsJu3C+3XuFzgOzcFi5+FLBQK0Ol0aGhogFarRV1dHYxGI+rq6qBWq+H3+5HJZNisz2q/0K02/EpqQ0MDjEYjpFIpxGIxS50rFArI5XLIZrOIx+NsUmIllMtlpFIphMNhDAwMoFQqoaOjA3q9nlWLUigUkEqliMViiMViLFD++DkC58enRCKBXq9nq8fAb1IFZ2dnMTY2hkQisSLnRy7OZrPB5XKhtbUVFosFGo0GWq0WUqkUxWIRMpkM+Xye9UYJBAKIxWJsFrnWVjj4FWN+1axSqSCZTCIWi2F0dBQjIyNIJpN0/asx/GeaVquF1Wpl49FgMEAsFs+b7ODvrfhri0ajgVgsRktLCwKBAPr6+tjE7kpP6q6n8UfBxmVIJBLU1dXB5XLhoYcegkAgwNGjR+H3+9HT03NNTc74jbnFYpHVq+/v70c6nYZSqUQ2m8XZs2dx9uxZSmMhV02hUECn07F8+UwmA7/fj9bWVuzfvx82mw2dnZ1QKpVQKBQoFotIJBIIBoNsE91auNitJlKpFHK5HJ/73Odw4MABmEwm6PV6hMNhRCIR+Hw+TExMYHR0FEePHkUsFsPk5OSK3MyVy2VEIhGkUin80z/9E8xmMx544AG43W6EQiGkUim0trbCZrPh3XffxeHDh+FwOOB2u9lr8DcCYrEYSqUSRqMR+/btg1qtBvCbQCOVSuHFF1/E+++/j+np6WU/N3Jpt956Kx555BHYbDY4HA52AyeTyVBfXw+bzYampiaUSiXs2bMHMzMzSKVSGBoagtfrrdkVDn4iJ5fLoa+vD1NTU3j++ecxMDBQs8e8nvGfabt27cJv/dZvwW63z1vR+HgfC47j2GMKhQJyuRwPPvggPvnJT+L73/8+axharQmN9TD+KNi4DJlMBovFgvr6erhcLggEAraZtq+v75peu1KpoFAooFgsolQqsV+5XA6JRALJZJI65tYA/kLAz/zzsyr8xtVSqcRWAFKpFEqlEgqFwrLeoAsEAtazgO9Cz5es/Ti9Xs86mVqtVuRyOZjNZrhcLthsNnYjK5VKAWDeWPx4KT6y/IRCIRwOB4xGIxobG+FwOFBXVwetVguZTAalUska6gkEAkQiEczOzrIc+ZXI7a1UKigWiwiHwygUChgZGUGhUEA4HGa9WiKRCIaHhzE5ObmgPwb/wa9UKmG326FUKuftTeNn+Pi69MFgcMVWbshv8P129Ho9NBoNmpqa4HA4oNVqWX753MAROB8o8z/rcrmMlpYWcByHdDrNriu1dk0pl8vspi4WiyEejyORSLCS4KS2iEQiyGQy6PV6NDQ0wGQyQSKRzNvjyu9t5MddJpMBx3EwGAysep5MJoPRaIRGo6nqfdZ6GH8UbFyGxWLBfffdh6amJlx//fWQSCRobGzE5OQkPvroo2tqplYqldhmulgsBrFYzG4i3nzzTUxOTmJmZmYJz4ZcDX6Z85577sFDDz0EnU4Hk8mEZDLJ8j1HR0fh8/lw+PBhNsu8nBcvkUgEs9kMgUCAW265hW2Ou9CmXD4YkcvlrJFauVyGRCKBVqtlaSxz8TeTtZb2sB4oFAo88sgjuO6669DW1gaLxcJu5DQaDZRKJSwWCzo7O5FMJnHbbbfh17/+NWKxGKLRKHw+34qkA/BV9KLRKH784x9DIpGwJqR82hdfYKCvr++Cey48Hg8efPDBBUFtoVDAqVOnMDExwXoP0b61lafVaqFSqXDfffdh79696OzsRENDw4K0zo/PHotEIqjVakilUvzJn/wJfD4f/vqv/xqnTp1CLBarucAxn89jbGwMxWIRk5OT8Hq9Va9URC5OIpFAo9HAarWio6MDcrkcQqFw3jWE4zhks1mcPn0akUiEVfT87Gc/i40bN0KhUEAmk6GhoQGdnZ0YHBysWvPa9TD+Vn2wIRAIIJfLYTAY2P4KAGxWlp9R42fb+McXi6/YYzAYoFarWW4x35fgWlQqFWSzWRQKBQiFQkgkEvYfIJlMIhQK0apGDdDr9dDpdGhqaoLH44Fer2fBhkajYfWvlUolZmZmoFQqEQgElrQ/QKlUQjQahU6nY+OFX0rm9114PB5YLJYF32s0GmEymSCVSlkN78vhN/JeaJMvWXr8DLJWq4Ver0djYyPcbve8Fae5z5NKpRAIBJDJZFAoFPD7/WhsbIRMJkMkEkGhUFixgINfxbgSQqEQUqkUpVKJ1cTnr6f8ddvv92N2dpbNTJKVxeeT86v7Ho8HdXV1LGice13jq1FVKhXk83k2lqVSKWw2G8RiMfR6PdRqNZLJZLVOieGvb2KxmK0IJ5NJ5HI5BAIBBIPBVV/9Zy3iA1w+9VKn00Eul0MsFqNQKLDVVf4eL5vNYnx8HKFQCCMjI8hms5idnYXFYmErGyqVCkajEQqFYkXPY72Nv1UdbPAzslu2bMEf//EfszSRSqUCv9+PdDqNkZERxONxHD16FMFgkOWQLhYfBPC5qXwu8VIEAblcDtPT05BIJGhoaEBTUxM6Ojqg0+lQLpevqawpWRpCoRD33Xcf7rzzTrS0tMDhcLCAVqFQwG63w2q1oqmpCZlMBnv27MHY2Bgef/xxzMzMIBAILMlYCQQCeOaZZ+B2u+F0OuFwONDQ0ACRSISWlha2KfdCaVRzy4zO3WB+qXNWq9VUAWiFiEQiGAwGGI1GPPTQQ2htbcX1118/b6xdbAZZKpVCJBJh27Zt+NrXvoYzZ87g29/+NoLBIGKxWM2uBuj1erS0tGDLli247bbbWKlvvqRzMBjE22+/jVOnTsHn81X7cNcthUIBjUYDp9OJjo4OFvjy1w9+LEajUfT19SEcDuPMmTMwGo347Gc/y1JWdDodPB4Pa6ZW7Z5RMpkMOp0OTqcTGzZsgFgsxpkzZzA7O4sXX3wRPp+vahWKyMXxkysbNmzAnXfeic7OTla5c3Z2Fv39/fjOd77DJgD5fhXFYpEV3imXy/B4PHjggQewY8cOWCwWdHR0rGgWyXocf6s62JDJZDAYDGhoaMCGDRtgMplQX1+PcrmM2dlZpFIpSKVShMNhzMzMQCQSIRaLIZvNXlEuulAoZMEGP+t2LTn5fAAjFovBcRwrHWm1WqFSqeZVDKI0lpXHzzrwFXQaGxuxYcMGGI3GeRV1+FlmAOzmXKVSsTr0xWKRVQq61p9joVDAzMwMxGIxfD4f5HI5VCrVvHEkEokgFArZ/hH+73yeNIB5q3H8DPnHcRzHKh3RjPLy41colEolmpub0dHRAaPRyFah5l5n+J/l3FQ4uVwOrVaL1tZWpFIpaLVaVsmuVkmlUtTV1cFkMsFsNkOr1bKxmkqlkEgk4PP54Pf711w6wWrAXwOVSiXbr6FWq9nnH181kRcIBDAxMYFgMIjBwUGYzWaEw2FIJBIolUqWsqnX66s6gcF/liuVSpjNZlZ9j99/5PP54PV6q5ZOQy6NbwZqNpvR1NQEo9HIMkT8fj8mJydx6tSpi96oi0Qilq4Uj8fZtZdP+Vtu63n8repgY9u2bfjCF74Ap9OJpqYmyGQylvrB/yD5ngEbN25EJBLBD37wA/T09CAcDi9qdkUkErHUJqFQyHLqJicnrzrv1Gg0sjxDpVIJq9WKu+66C3V1deA4DjMzM2wvAK1srCyBQACdTgeVSoW77roLGzduxHXXXQe73c4uRhebZRaLxdBoNPB4PHj00UcxOTmJv/u7v8Po6Og1r1LN3Yz75JNPwmaz4e6774bZbEYkEkGxWERjYyN0Oh0OHz6M/v5+1izo4+cnEonYxvLt27fPm63k07WeffZZ9Pb2Ynx8/KqPmSyOUCiESqWCwWCA2+1Ga2srlEolgIUzyNPT05icnMTs7CxGRkawadMm3HHHHRCLxVCr1TCZTGhra4NcLkc4HK7ZYLGurg67d+9Ga2sr26cGnE976OnpwcTEBCYmJhAIBOgaWAVKpRJyuRx79uzBli1b0NzcDACIRqOIRCJ466238Nxzz7Hu7/l8nu0/TKVS0Ov1AIDGxkY8/PDD0Gq1cLlcKBQKOHHiRNXOS6fTwW63o6urC5/61KegVqsRj8cxNTWFX/7yl/B6vVRiuYbZ7XZ0dnZi586d2LlzJyqVCrxeL86cOYOnn34a09PT84Lgj6tUKhgaGsLs7Cw+85nPrHjD5PU8/lZdsDG3LrHdbsf27dvZfgp+lpnP6QPOzzhzHAeNRoNEIoGGhgaMj48jk8mw6gSXe7+5M9h8dYNUKrWoFAU+dWVu+gpfgUWhUECtVsPhcGDjxo3QaDSIRqOs3Gg+n6eVjSqQSqVQKBRoaWnB9u3b0dDQAJVKBQALNqDxlVUqlQpbsdJoNNiwYQO0Wi2MRiN8Ph9SqdQ13TTxdeoTiQTOnj2LQCCADRs2IJfLwe/3I5fLQSQSIZfLYXBwED09PazzKI8fg/wSLr+kPPeGtlAoIJPJYHR0FP39/WuqGkat4ic0VCoVtFotNBoNOI5jefD8dUYgECAYDGJqagqjo6M4e/YsNBoNMpkM5HI5K5drMBgQj8fn9ayoNTKZDGazeV5vDf58vV4vpqenkUgkaFWjCgQCAaRSKfuc8ng80Gq17LOPX7147733Lvr5lE6n0dfXh2w2i2w2yz6jq52aKZVK2Q3fpk2bUC6XMTU1hVAohMnJySVLeyXLg1/VMJvNMJlMiMfjCIfDCAaD6O/vRyQSueQEy9ymzNWYxFjP429VBRt8p+Nt27bhxhtvRFdXF1wuF8tbBrAgJ52/0edTTj772c9i3759eOqpp/DBBx8gnU4vWx1jlUrF8lX1ej1rwNXW1oZ9+/bBZDLB7XazqgqlUgmxWAyhUIgFHBRsrCyBQMA6FjudTrS2ti4INPhZZn7TWTgcxsjICGw2G+6880624VWv18PtdrNZv6WowFIqlRAOh5FMJvHjH/8YcrmcbczUaDSQyWQs33NsbGxBIzWhUAiPx4NbbrllwdjKZrM4efIkpqamMDk5yTYak+XBp6mYTCYcOHCAbcDlG4fG43E8++yz6OnpYd/Dl8POZDJIJBIIhUJIJpNob2/HPffcA4VCwWahV8N+G/56nclkMDQ0hMnJSbzwwgsYHx9fk6kEq4FIJEJrayscDgd27NiB7du3o1KpYHZ2Fm+//TbefPNNnDt37pKfTXwVoFQqtahJvZUikUigVqshk8nAcRympqbwox/9CF6vl60c18qxkksTCARIJBIYHBzEyMgIS52/1LgUi8XYtWsXGhsb4XK52OuslPU8/lZVsMHPtjQ1NeGmm26CzWaDVqu94GD5+PIYvxqyceNGuN1uHDx4ECqVCoVC4YqDDT6flV/xuND786srfJnU+vp6JBIJiEQiWK1WuN1u2Gw2dHV1sZsCvmMz/3utbu5cy+bmz+t0Onbzx1eWKpfL7OfNL396vV4cP34cHo8H+/fvh0wmY7n0cytBLQW+nB8fuF7KhW7W+FlL/s9zFYtFeL1eTE1NLVlwRC6O3xekVqvR0tICj8cDuVyOSqXCmioePnwYb7zxxkVfY+4q7l133QWJRAKDwQCdTnfN1fJWAn+dLhQKCAQCmJmZwcDAACYnJ6t9aOuWUCiE0WiE3W5njfp8Ph9isRjGxsbQ09Nz2c2r/KSIVqutqTQ4PlMBOF9uNBKJ4OTJk4hEImwvJ1k9CoUCotEoa8p3uc8skUjEih3o9XqWCr1S/aTW8/hbdcGGWq1mZfj4brPA/JWMbDaL4eFhJBIJjI2NgeM4HDhwAHa7HTKZDAKBAA6HAy0tLRgaGrqiHDmxWIy6ujqUSiXcdNNN2LBhAxobG+cdC89sNrOGMVqtFoVCAblcDgaDAS0tLVAoFPNSHfi0nFrNs17L+BUNpVKJ3bt3o62tDXa7HcD5POVkMol3330XBw8eZM9PpVIIh8PIZDIIh8MYGxuDQqGAy+XCHXfcAYlEAqfTiUKhgGPHjlXz9ACAdRDftm0b9u7dy8pY8hU7AoEADh48iJGRkTVXCaMWKZVKdHV1obGxEVu3boXD4UA+n8fU1BRefvll9Pf3Y3R09JKvEYlEcOrUKZhMpppslnY5uVwOExMTyGazmJmZgd/vr6mbU3JeMBjE6OgoJicn4fP5LpveptFosGPHDng8Huh0uhU6ysuLRCLo7e3FzMwMBgcHEY1GEQgEkM/nV93/nfWO4zgoFArYbDYYDIZLrlAIhUI2CbNnzx7s3r0bDoeD7ZE9efIkvF7vsh/zeh5/qyrY4GsS63Q6WK3Wefso5gYbfEnZYDCIDz/8EBzHobu7GzabjbWzNxqNsNlsiyp3NneVRCAQQKPRoFQqoaOjA5lMBtu2bUNdXd2C77PZbDCbzSyXeu7rXUylUlnT0W2t4mf8VSoVWlpasGnTJhgMBgDn849DoRAOHz6MH/3oRxd9jWg0Co1Gg/b2duzfvx9KpRJ1dXVIJBI1kdKiVCrhcDjQ2NiI9vZ2KBQKVgEon8+zJelz585VvTTleiCVStnPw+VywWKxYGZmBuFwGCdOnMDx48cv2zSULyMaDAZX5XWjUCggFAohk8kgEokgHo/Tim4NSiaT8Pv9CIfDi6pyplAo0NXVBY/HA4VCwWaQq43//zI9PY3Tp09X+3DIVeLHkkQiYQVdLhVs8PdtRqMRzc3N2LhxI6RSKSqVCiKRCMbHx1m53OW0nsffqgg2pFIpJBIJ2tvbsWvXLrS1tUEoFCKTybDuza+++irLL8/n8/D5fMhkMvD5fBCJRKivr4fH48GNN94Ih8MBk8mEpqYmjIyMXPK9A4EAXn31VbS2trLmaXwQodfrUSqVYDabF+TGA+f3bMjl8nmpVnOrF30cv7dk7mZ3sjLEYjE8Hg/q6+uxceNGdHV1QSAQwOfz4dChQzh+/Dh6e3sv+RrpdBr9/f2s/Cyf3lIr6urqsHnzZrjdbojF4nn58r29vRgdHYXX60U0GqXVtSqoVCqYmZnBzMwMK394ubSA+vp6dHR0YMuWLfN+prWKT4W1Wq1oaWlhFbZ8Ph/efPNN+P1+KkpQg/h9hfz+tYvhb/5cLhf27t2LxsZGaDQaFItFjI2N4ezZszVdkpnUtmKxiHQ6ze711Go13G43RkZGLpg2yrcV0Ov1uPXWW9HU1ITm5mZIJBIMDw8jEAigr68PMzMzNC6X2aoJNhQKBZqamtjmHn4FIxgM4sSJE/j7v//7i87G8h9wjY2NLGgwGAxwOBwXTH+aKxKJ4P3330csFsOdd97JOqJKpVI0Njay511q1maxNwB8SVJ+JoisHLFYjIaGBrjdbpY/z2/a6unpweuvv37ZDau5XA7j4+NQq9U1udFLq9Wy9LC5AXAul8PQ0BBGR0fZ5nOy8kqlEkKhEGZnZxEKhRaVylZXV4ddu3ahvb39gg0da41UKoVWq4XJZILL5UIikcCZM2cwOjqKDz/8cEVmF8mV4zs2X67LMt8pnE/XdDqdkMlkSCQSmJ6exvDw8CVLkxJyKXy1xFKpxCZnVSoV6urqLhls1NfXY//+/ejo6EB9fT1EIhGmpqbQ19eH0dFRBAIBGpfLrPY/nQBYLBbY7XZ0dHSgra0NSqUSoVAIg4ODePvtt9Hf33/JmdhyuYzp6WnkcrkrvpEqlUpIJpMYHx/Hiy++CLvdjuuvvx4qlQrpdBqVSoWlSg0MDCAYDLJ9GnPxG4P4i3FjYyP7z8FvUIrH43jrrbcwOjpKOfNVxN+E880gfT4fQqHQZQsJ8DfzXV1dqyJg5Ff+xsfHcfDgQUxNTdGschWJRCLodDoYjcbLFhTgmzo2Nzdjz549cDgcrJPu8PAwxsfHa2rvg0ajgU6nQ2trK3bt2oWGhgYkEglMTk7iyJEj8Hq9VOa2RnAcxwqV8AUxDAYDRCIRtFrtBb+H79tjs9mwb98+1vOnXC6jr6+PNf0LBAJUeIJctXA4jMHBQWzcuBGhUIhdBw0GA6677jrMzs5icHCQXfuEQiEr9FJXVweDwYB8Po90Oo2enh4cOnQIo6OjrMs4WT41H2wIBAJWtamzsxNdXV2IRqPw+/3o7e3Fz3/+c8RisUsOlHK5jPHxcTZreyUzzqVSCfF4HPl8Hs8++yzsdjurZe/1elEul1mPjNdffx1nzpyZt7mYPwfgfG15hUIBt9uNhoaGealVxWIR0WgUr7/+OgYGBhAKha7yX4wsBY7jEAqFMD4+Dq/Xu6ifh06nww033IDm5ubLzgBWE18OOpvNYmhoCP39/Xjrrbcuuz+ALC+BQAC9Xo90On3ZYEOhUMBsNqOlpQU33ngjKzaRyWRw7tw5TExM1FTZYo1GA5fLheuuuw6/93u/h3w+j0AggPHxcRw6dAixWIyCjRrCBxv8JF5dXR2MRiNr1vdxSqUSLpcL7e3t+PSnPw2LxQKDwcBK4I6Pj2N0dBQ+n4+CDXLVgsEggsEguru7EQgEoNfr2d7IvXv3YmhoiHUIB85P4Oj1etabw2g0YnZ2FpFIBMeOHcNrr71W5TNaP2o+2JiLv0lKp9Pwer0IBAKIxWJIpVKXDCD4fHyLxcLKnV2pUqmERCIBoVCIgwcPQqlUss2MfD+DkydPYmZmBrlc7oKlGxsaGtDR0bFgI2c+n8fExASrArTYhoFkefEzJjKZ7JLPk0gkUKlUqK+vx5YtW1jqQD6fx8zMDCYmJmrqRiqbzcLv9yMWi7ENnzTeVl6lUkEul2OVSEQiEUwmEyqVykXHnEajgVqtRltbG7Zt24YtW7ZAKpWyFY2BgQH2s62ln6lYLIZMJoNYLGZ9RN555x2MjY2xm9paSztcr8rlMnw+H0qlEhtLcrkcMpmMNdINBAKYmppi38Pv1TAajTAYDFCpVKxa3/HjxzEyMsL2INXSuCSrUyAQQE9PDzweD9uTsWPHDhgMBkxPT6NYLMJms0Gj0WDnzp0wmUwoFApsFX9kZARjY2PVPo11ZVUFG7x4PI6RkRFMTEwsqlSiVCrFzp070draCqvVelXvWSqVEIlEWOWCC+E/LAcHBy8Y0Fx33XVobW1dUNM5nU6jt7cXIyMj8Pv9tFGpyvhN/HzVs8tt9JbJZKwc8/79+2EymaBQKJBMJjE4OIiBgYGaSk/KZrOYnJxEPB7H7OwsgsEg3QBUQaVSQTabZU3PxGIxHA4HawZ6IUajEU6nE/v27cOnP/1pGAwGKBQKBAIBvP/++xgaGsL4+Dji8XhNbfLnA3KpVMpWmp955hk2WbQaK2mtVZVKBaOjo5iZmcH4+Dh8Ph8sFgukUimam5vxiU98Ah999BGmp6fZ55hMJoPVaoXFYoHVaoVMJsP09DQmJibwxhtvoL+/n4JJsmQmJyfx5ptv4oYbbsCuXbtgtVrxyU9+EpOTkygUChCJRLjuuuug1+tZiil/j/XDH/4QR44coWvOCluVwYZUKoVGo4FcLr/sxmy+OVtLSws6Ozuh0WjAcRyi0ShrXnalFjNI5x4XX2GqoaEBzc3NsNlsEAqFLH2KLzk6NjZGS8xVwv8sCoUCKpUKK5VXLpcveuMnk8mg0Whgs9mwY8cOtLe3Q6lUolKpYHx8nN3IV7u6k1gshlQqhV6vZyWjQ6EQfD4fzpw5A5/PV1MpN+tFoVCA1+uFQqFANBqFwWBg1esaGxvR0dEBr9c7b/JBJpNBp9NBp9NBr9dDIpEgmUxidnYWJ06cwPT0NFKpVM3VbU+lUpidnUVvby/kcjnOnTuHZDJZc8dJfoOvjtbf3w+xWAyDwQCbzYYtW7awJowKhYL179mxYwesViui0Siy2SwOHTqEiYkJxGIx+hmTJZVMJjE5OYnm5mbE43HWI4tf9RWLxTCZTJDJZGw89vT0YHJyEqFQiCbXqmDVBRscx0GpVKK+vv6yjVzEYjHMZjPbtNbd3Q2FQsFuBk+cOAG/37/sx1xXV4fW1lZs374dN998M6RSKUQiEYrFIrtRePPNNzE5OUmVgKqE4zhkMhmWwiYQCFBfX89KHF8IH8Ru3rwZn/vc51BXVwe9Xo94PI4PPvgAExMTGBgYqHqeskKhgF6vh9PpRFdXF/x+P44dO4Zz587hF7/4BaXtVQlfcpj/4FSpVLDZbBCJRNi1axdUKhVee+21ecGGRqNBfX096+ycSqXg8/nQ19eH5557jv0sa+3mLhAIIBgM4vTp03jppZdQqVQodarGVSoVnDhxAolEAhqNBs3Nzejq6kJHRwccDgc0Gg0aGhqwdetW6HQ6OBwOJJNJ9Pf349y5c/jWt76F2dlZ2nhLllwgEEA8HofVasXs7CyMRiPbS/uJT3wCANgeto8++ggzMzP46U9/inPnzlHFuypZFcFGuVxGoVBgN4EKhQJ1dXVQKpUXDDZEIhHUajVUKhU2bNjASt1KJBKEw2GkUikEAgFEo9EVyaXXaDSw2+0wGo2QSCTz2tV7vV42e0npBNVTLpcRDochk8kQj8eRTqchk8kgEolgNpvh8XgQjUbnVQkTi8WsO7zBYIBSqUQ2m0U0GsXAwAALHvP5fFV+rnK5HHK5HI2NjWhpaYHT6UQul0MkEsHw8DAmJyeRz+cp0Kgift/GxMQE+7BUqVRwOp2oVCoYGRlBoVCAVquFUqnEhg0b0NnZCYPBwFZnT5w4gd7eXuRyuZpKnZqLb4zKBxmk9nEch0gkAoVCgUgkwgoXSKVSmM1mtLW1wWw2w2QyQSQSIRaLIRAI4PTp0xgbG0MikaAVU7Is+OqewPn7qLnVpwQCAUtRjcfjOHv2LCYmJhAKhZBOp+n6UyWrItjgS9byFy6DwQCtVguHw3HBYEMmk6G5uRl2ux1f+MIX0NTUBLfbDZFIhJMnT2JkZAQnT57E+Pj4iqwk1NfX47rrrkNzc/O8/gaxWAxHjx7F0NAQ6xxMM33VUSwWMTAwgNnZWYyOjqKhoYE1sdq0aRPuu+8+HDlyBEePHmXfw6+w2e121NfXAwD8fj8GBwfx/PPPs/zRuR3oV5LJZILD4cBtt92G+++/H/l8Hn6/H2fOnMFLL73EAiFSXalUCu+++y7Gx8fR1NQEg8GAG2+8Ebt372YBxsaNG+F2u2GxWGA2m1kK3DvvvIN/+Id/QDabpRRMsqT4vRuzs7O44YYbsGXLFphMJtTV1cHj8cDhcEAsFkMsFiMQCODEiRMYHBzE//t//w/hcBiJRKLap0DWKL5fj0gkQjqdnpfqLBAIUCqVEAwGMTk5iZ/+9KcYHBxEMplEsVike6wqqflgg+M4pNNpdvHKZrPsAqdWq1FfX49EIoFoNMoGkVAohFKphEajYSX7+DSZqakpnDt3DuFweMUqY4hEIkilUtbhl++CGQgEMDIygqmpKcpdrgGlUol1n5+enmarYxaLBW1tbexxuVwOpVIJt9uN5uZmWCwWZDIZpNNpnD59GkNDQ4jFYlW/+ROLxexYVSoV4vE4hoeHWT+NXC5HY64G8FV/5HI5kskkcrkca2Rqt9uRy+VY8CuVSpHP5xEMBjEwMICJiYma2wxO1g5+NapYLCKfz7NxJhKJoFQqWdZBLBbD4OAgq6iYTCZplZ4sOf7er66uDk6nE3a7HWq1el5fK77Ai0QigVQqhUwmY6XE6fOuemo+2ACA8fFxTE9PY9u2bZiZmWFNWlwuFz71qU9heHgY//7v/85WPiQSCWw2GxoaGmC322GxWBAIBBAOh/HKK6/g3XffZUtv1bggxmIx9Pb24tSpU/jJT36CaDRK3StrRLFYxOHDhxEIBPA7v/M7sNls2L59OzZt2gSHw4GGhgY0NTWhq6sLBoMBDQ0NSKVSGBwcxNmzZ/H4448jFArVREUxmUwGtVoNiUSCSqWCM2fO4Hvf+x5VAKoxuVwOvb29CAQCGB0dhVarhc1mg1KpxM6dO7F161ZIJBKIxWKMjIxgZGQEhw4dwr/9279R2WKyrBQKBdRqNTiOQzKZhMFgAHB+9pjjOGSzWQSDQfz617/Gk08+iXg8jmg0WpP7hsjqp9VqYTQasXPnTtx6661wu93o7OyERCJhYxL4TedwANi6dSuUSiU+/PBD6l9WRasi2CiVSiiVSojFYvB6vRAIBKirq4NGo4HH40GxWITFYkGpVIJcLmdRL5/akslkWHM2v99fteXdcrmMbDaLdDqNeDyOWCzG9geQ2lCpVBCJROD1elnOsUQigVwuh81mg8fjgcvlYr00RCIRstksxsfHWZnIWkkf4DulBgIBtkcjGAwim81SoFFDOI5DqVRCoVBALpdDLpdjFdH4fTelUgmVSgXhcBijo6OYmppCKBSi1SmyLPgZZKPRCJPJBJ1OB7lcDrF44S3D3Dx5fpzSmCRLaW6fF37Cz+FwQKvVss85vrw8X33RZDJBLpejvr6epVqJRCIan1WyKoINXn9/P1544QXceOONcLvdcLvdePjhh3Hu3DkolUooFAps2bIFer0ebrcbQqEQPp8Pg4OD+O53v4uzZ88iGAxW7fjj8TiGhoYQDAYxMTGBQCBAN301plwuY3BwEDMzM9i/fz+amprYh21nZyeamprY0qzf78dHH32Evr4+PP3004hEIjW1QsWX3uUrFaXTaUSjURpzNYYvaKFWq1EoFOZtYuRn6+LxOBKJBH71q1/h6aefRiKRQDwep58lWXJCoRAmkwlarRa33347urq6WGnvj6ejKJVKtkdy165dmJ6exkcffUR7wciSamlpgcfjwb59+3DTTTdBo9HAYDAgEAjg8OHDGB8fx1tvvQWhUAibzQan04k/+IM/gEajwa233orOzk6cPXsWiUQC6XSaKqRVwaoKNhKJBCYnJxGJRFAsFiGRSFBXV4f6+nq43W6oVCq0trZCrVbDaDQin88jEAhgenoaY2NjmJiYWNHjFQgEEAqFkEqlkMvlqFQqiMfjiEQiCAaDiMVidLNQYziOQy6Xg1AoZJtu+Rs/pVLJ+mhUKhVkMhlMTk5icnISMzMzNVdCtlAosJvXagbZ5OLm9kAxGo2sLLZQKFzwXH5sxmIxZLPZmhprZPUTCASQyWSQSCSwWq2sgaTL5ZqXSpXP5yEUCiEUCiGXy1mgXF9fj1wuB4lEwgpjEHKt+Aa7drudpTKXSiWkUinWZHl0dBRDQ0MQCoUsRTiZTLIKf6VSCSaTCXq9HoVCgYKNKlhVwcbU1BTi8ThcLhemp6eh1WphMplgs9lw7733QiQSQaVSIZfL4fTp0/D5fHjqqacwMTGBmZmZFT9evplfU1MTNm/eDL/fj7Nnz2JgYAAvv/wya75FagffCFKr1aJcLiOZTKKuro59jS9YEI1GceTIEXz3u99FNBpFOBymPGVyRaRSKSwWC0wmE+6++244nU7s3r0bVqsVCoUCwG9mkHU6HVQqFTo7O7Fr1y5MTEzg7Nmz1Tx8ssZIpVJs3bqVfZ62trbCYrFArVZjZmYGIyMj6OnpwenTp1nufHd3Nx544AHY7Xbcd9996O3txcmTJyEQCJBOp2kyjVwzgUCAjo4O3HrrrbBarUgkEvjggw/w6quvIhQKYWpqipWcFwgEmJ6eRiAQwNGjR+FyudDe3g6VSoXbbrsNjY2NeOWVVzA8PFzt01p3VlWwwecyJxIJZDIZ1kFcJpPBZrOxEqPZbBZ+vx+Tk5MYGRnBzMwMstnsih0nP+vDV8PS6/VQqVSoVCoIBoPw+XyYnZ2lGuQ1RigUspQWrVYLiURywefx1VkSiQRmZmaQyWToZ0kWja+UolQqYTKZ2MpsQ0MD1Go1xGIxMpkM27cxd++GwWCAw+FANBqFUCikmzmyZPj0qYaGBjQ3N6O1tRWVSgXlchmRSAQTExM4d+4cTp06Bb1eD7PZDIvFgnw+D7FYDLvdjlAoBJ1Oh1wuR3vDyJLRarWw2+2sKTPfyyoWi2F2dnbeOMtkMtBqtQiHw6w8rlQqhc1mQyaTmVcml6ycVRVsyGQyaDQaiEQipFIp1tSPn3EuFAoIh8MYGxvDj370I5bikslkVjTlgF+CPnDgAG644QZoNBp4vV6cPHkSL774IiKRCJWqrDFCoRBms5nleHo8Hlx//fVobW1dMMusUqkgkUjQ0tKCHTt2wOv1ore3l9JayKLo9Xps2LABTqcT9957L8xmM1wuF0QiEUZGRpBIJHDkyBF4vV4YjUao1Wp88pOfxK5du9DZ2QmRSASFQoHBwUF2U0fItZLJZNi+fTu6urqgVCoRiUTwyiuvoLe3F1NTUwgEAojFYojFYvD7/RgfH4dSqUR3dzdMJhNcLhfa2tpw5513Ynx8HK+++mpNVOUjq5/NZkNnZycrRuByudDS0oLp6WmEw2GUSqV5qVHFYhF+vx8qlQrFYhEqlQoul4u1TCArb9UEG3w+KX+jVy6X50WzfMCRy+XYRmx+eW0lZ1f4NByDwQCPx4Pt27cjHA4jEAjA5/NhdHS0ah2lyYWJRCJIJBLodDoYjUY0Nzejvb0dVqsVarWadSMFftO5VKlUQq/Xw+FwoFAoQCQSUbBBFkUmk6GhoQEejwdbt26F0WhkVc2CwSBmZmZw/PhxjI2NwWazwWAwYOvWrSiXy9Dr9WhpacHp06ehUqnYSi4h10okEsFqtaKhoQFSqRTFYhHDw8M4duwYAoEAIpHIgu/x+/3w+/2sLwz/uVepVC5YuYqQKyUQCKBQKKDX61n2ik6ng8lkQjqdhkqlQqFQYPvcBAIBpFIpC0D4vhsajQZ6vZ4VOSAra1VcDXQ6HdRqNbq7u7Ft2zZs3rwZbW1tbMYZOD/rzG9sy2Qy2Lp1K/R6Pc6cOcNKoq0UPigSi8WoVCo4efIkXnnllaoEP+TSFAoFurq6YDabcfvtt8PpdMLtdkOr1SIYDGJ8fBy9vb0YGRmBVquFRqNBd3c39u/fD6fTibvuugs9PT04c+YMkskkstks7dsgl6TX63HDDTfAZrOhVCphcnISv/zlL+H1ejE0NIREIoHZ2Vmk02lEIhHIZDJs2rQJVquV7VHbvHkz7rrrLgwODuK9996jawq5ZmKxGE6nE21tbWzyxOVywe12o1gssgIYcydVMpkMvF4vC3wVCgWam5tRLBbppo4sKT7QAICNGzfO+4xOJpPw+/2sXLPBYMDOnTuh0+mg1WqrfOQEqPFgg0+RUqlUMBgMcLvd2Lp1KzweD4xGIwCwCx8fvapUKjbjnM/nce7cuRU/bqFQyPL9C4UCZmZmcOzYMWQyGUqfqjF8rrHL5cKePXvgdrvZjNz4+DgmJyfx4Ycf4vjx4zCZTDCZTDAYDNi3bx90Oh06OjoQiUTY7Ar1PSCXo1Qq0dTUxAoPxONxHD169IL7yxKJBIRCIaampjA9PQ2VSgWNRgO73Y7Ozk72dQo2yLUSCoXQ6/UwmUzgOA7FYhFGoxFmsxlerxcymWzeTDEAVjCDL3QilUrZzZ5IJKrm6ZA1pFwuo1QqsXtCq9XKNos3NzcjHo9jYmICUqkUdrud9WDjA15+3ALURbxaajbYEAgE8Hg8MJvN2LNnDzZs2ACXywWXy4VcLofjx49jZmYGZ86cgVQqZaXRbr31Vmg0Gtx4441wOp3o7+9HPp9HLpdbkTQXjuMwOzuLZDKJWCyGQ4cOsTxsCjRqj0KhwLZt21iQEQ6H8e6772JiYgIjIyMIBoOYnp5GKBRCLBbD9PQ0HA4HTp48Ca1WC7PZjNbWVtxyyy2YnJzEwYMHkcvlqn1apIYpFAo0NTXBbDZDLBZDKBTC6XQin88jHo+zJqZzPxxTqRS8Xi+cTicEAgH0ej2am5sxMTHBbvwIWQr8DLJQKMTevXvR2tqKsbEx+Hw+RCIRxGIxFvQ2Njaiu7ubBRdUKIMstUqlgiNHjkAul2Pbtm3YsmULRCIRxGIxFAoFrFYrDAYDTCYThEIhS7UXi8Xzuorncjmk02lKd66Smg42bDYbmpubsXfvXuzduxcSiQQSiQQjIyMYGhpCb28vXnvtNcjlcjgcDnR1dWHfvn1QKBTYsGEDNBoNjEYjgsEgCoXCig2ySCTCqneQ2iaVSuHxeNDc3AyRSIRkMon33nsPPT09rIv4x42OjmJsbAxNTU1wu92w2+3YunUrZDIZDh8+TMEGuSS+Mgo/g1woFGAymRCPx6FQKFjJUP56JRAIkM1mkUgk2AyySqVCfX099Hp9Fc+ErCV8kMHfnAkEAnR1dbFf4XAYfr+fFS6wWCyoq6uDy+WigJcsG47jMDAwgGKxCLVajQ0bNrDxJpVK2eqF1Wq95Gvk83lks1ma9K2Smg02hEIhmpubsXPnThgMBqTTaZw5cwZnz57F7OwsRkdHWRlZiUSCWCwGADhz5gzMZjP0ej3sdjt2794Nk8mEI0eOUGMzsoBYLIbD4UBTUxMkEglyuRwcDgd8Ph9SqRRrnjY3TSWXyyEYDLJUPqVSCZfLhVgsRqkDZFHm3tjx1abC4TA2b96MWCyGUCiEXC4HjUYDhUKB7u5utLW1oaGhgX0/IUspl8vhV7/6FWZnZ7F7927U19ezlTeNRsMq+fB9YJRKJRQKxbyKkHyzU9qbSJaSz+dDLpdjTfksFgsaGxvZfR4/BgGwCZxgMMga78bjcRw7dgyzs7OYnZ2t8tmsTzUdbLhcLmzevBkqlQrpdBofffQRnnvuOUQiEQQCgQXfIxaLce7cOeRyOdjtdshkMrbE29fXR8EGWUAsFsNms6GhoYHlH1utVthsNszOziISibAPUV42m0UkEkE6nQZwPi2GD1Ao2CCLMXcGWaVS4aabbkKpVEJ3dzeSySTGx8eRSCRgt9uh0+nQ0NAAs9nMvpeQpZbL5fDee+9hbGwMDocDJpOJXc9UKhXrC3MxAoEAlUoFuVyO9q6RJRUIBBAIBCAUChGLxdDe3o5cLofGxkbYbDaIRKJ5461YLMLr9SIcDuPDDz+Ez+fDoUOH4Pf7kUwmq3gm61fNBhsAUFdXB6fTyfLvzGYzbDYba+ry8RnnUqmEaDQKnU7HSu9ZrVbk83nIZLIqngmpZXNnmSUSCXbs2AGbzYaWlhb4/X7EYjEkk0moVCooFAp0dnZi586dsNls83JCCVmMYDCI119/HQ0NDeju7oZSqYRYLIZIJIJWq4VcLodYLEY+n4dGo4FcLmc9hfjNuaVSCdlslnLkyZIplUqYmppCOp3Ge++9B5/PB6fTCYPBALPZDJ1ON+/5HMchk8kgFouxPUXBYBAfffQRa3ZKyFKKRCIYGhpCLBbDzMwM9Ho93n777QVpfIVCAYFAAKlUClNTU0ilUgiHwyxTgay8mg02+G6mbreb3QjW19ejoaEBuVwO09PTEAgE8z5si8UiwuEwCzb4vRwAIJfLq3UqpMbNnWUWi8W46aabUKlU4PV6EY/HMT09jWAwyKpRWSwWNDQ0UJ4yuSp+vx/PPfccWltbWcUUPl3FaDSC4zjYbDYAmJc/z//OcRxKpdK8KkCEXKtSqYTR0VFMTU1BKpXi5MmT2L17NzweDys1+nGpVApjY2Pwer0syHj//feRSqVoBpksOX6Fg3e5z2CaCKwdNRtsXGiQNDU1Yd++ffB4PHC73chkMojH4xCLxVAqlaivr0d3dzesVivV+CaLks1m8eGHHyIWi6GzsxM6nQ4ikYhVteAvZnq9Hmq1mv3iGwjxKVb5fB6FQoEubuSystksJicnwXEc3n//fVitVjidTiiVSpjN5guuwiYSCTaLHIvFMDIygr6+PvT391NuPFlSlUoFfr+fBbIjIyPo7+9naXxzJRIJ1ll8fHycrXLk83m6FpJlR2Ns9ajZYIM3d9Z569at2LJlCyKRCCvDNzU1BaVSCbvdDq1Wi+bmZkgkkgWrHoRcSDwex/PPPw+Xy4UvfvGLUCgUkMvlrOY8vwENWDjLzCuXy0in09TQjyxKKpVCb28vpqenUalUYLPZcOONN8Jms2HHjh2QyWTzxhHHcQgGg5idncXAwAAGBgZw7tw5HD9+HJlMhtICyJIql8sYHR0FAJw8eRIA5m3AnWtueeaPV7MihBBezQYbHMdhZGQEH374IRoaGmC1WiEUCiESiaBUKmE0GiGRSCASiSCTyWA0GqFQKNhj/AWvWCwin8/T7B+5oFKpBK/Xi0qlglOnTiGZTMJqtUKtVkOr1bJceeA3KSy5XA6ZTIbNNPt8PvT09GBkZATFYrHKZ0RWA34ceb1eZLNZyOVy6PV6+P1+aDSaBc/1+XyIRqOYmZnB9PQ0y4mnCRWyHPjPTwpkCSFLoWaDjVKphDfeeANDQ0N46KGHsH//fkilUpbeolQqwXEcWltb590M8uktAoGAzThT525yMblcDn19fRgdHQXHcbDb7bjuuuvQ0NCALVu2sHE2dyN4JBLB+Pg4pqencerUKUxNTeGDDz5AKpWiTZFk0TKZDHp7eyEQCHDkyBF2/brQDHKlUmEpe/zvdCNICCFkNajZYIPjOITDYYjFYgwNDcFqtbK0FoVCAZVKxQIL/vnlchmpVAqFQgGJRALJZJL15chms1U8G1LLyuUy8vk8AoEASqUStFotgsEgUqkUy1OeG2yEw2F4vV74/X4MDw8jEAiwhmuUQkCuBB8w0GQIIYSQtUrALfLuqBqVd/hykNu2bUNbWxs2bNiArq4uNDU1obOzc14zIeD8ZrXh4WGEw2EcP34cgUAAhw4dQigUQiAQoM7Oy2Clbq5XYvxJJBIIhUI27vgKQR/HzypXKhWUSiWUy2WUSiXKV66Clfz3pupj5ELW0jWQrD40/kg1LXb81ezKBnB+tq9UKiEUCkEul7MqLXxllrmDn2/IxlfEGB4eRiQSQSgUQiwWo1x6cln8GKFyooQQQgghS6OmVzZ4fFM/qVQKiUTCfn3c3JlmflN4LpdDpVKhDeLLhGZVSDXRygapNroGkmqi8UeqabHjb1UEG6R20YWOVBMFG6Ta6BpIqonGH6mmxY6/hQnphBBCCCGEELIEKNgghBBCCCGELAsKNgghhBBCCCHLgoINQgghhBBCyLKgYIMQQgghhBCyLCjYIIQQQgghhCwLCjYIIYQQQgghy4KCDUIIIYQQQsiyoGCDEEIIIYQQsiwo2CCEEEIIIYQsCwo2CCGEEEIIIcuCgg1CCCGEEELIsqBggxBCCCGEELIsKNgghBBCCCGELAsKNgghhBBCCCHLgoINQgghhBBCyLKgYIMQQgghhBCyLCjYIIQQQgghhCwLCjYIIYQQQgghy4KCDUIIIYQQQsiyoGCDEEIIIYQQsiwo2CCEEEIIIYQsCwo2CCGEEEIIIcuCgg1CCCGEEELIsqBggxBCCCGEELIsKNgghBBCCCGELAsKNgghhBBCCCHLgoINQgghhBBCyLKgYIMQQgghhBCyLCjYIIQQQgghhCwLCjYIIYQQQgghy4KCDUIIIYQQQsiyoGCDEEIIIYQQsiwo2CCEEEIIIYQsCwo2CCGEEEIIIcuCgg1CCCGEEELIsqBggxBCCCGEELIsKNgghBBCCCGELAsKNgghhBBCCCHLgoINQgghhBBCyLKgYIMQQgghhBCyLCjYIIQQQgghhCwLCjYIIYQQQgghy4KCDUIIIYQQQsiyEHAcx1X7IAghhBBCCCFrD61sEEIIIYQQQpYFBRuEEEIIIYSQZUHBBiGEEEIIIWRZULBBCCGEEEIIWRYUbBBCCCGEEEKWBQUbhBBCCCGEkGVBwQYhhBBCCCFkWVCwQQghhBBCCFkWFGwQQgghhBBClsX/DxdsQ+nfNLMoAAAAAElFTkSuQmCC\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "# Construct a figure on which we will visualize the images.\n",
+ "fig, axes = plt.subplots(4, 5, figsize=(10, 8))\n",
+ "\n",
+ "# Plot each of the sequential images for one random data example.\n",
+ "data_choice = np.random.choice(range(len(train_dataset)), size=1)[0]\n",
+ "for idx, ax in enumerate(axes.flat):\n",
+ " ax.imshow(np.squeeze(train_dataset[data_choice][idx]), cmap=\"gray\")\n",
+ " ax.set_title(f\"Frame {idx + 1}\")\n",
+ " ax.axis(\"off\")\n",
+ "\n",
+ "# Print information and display the figure.\n",
+ "print(f\"Displaying frames for example {data_choice}.\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eBoyo7zmOmKF"
+ },
+ "source": [
+ "## Model Construction\n",
+ "\n",
+ "To build a Convolutional LSTM model, we will use the\n",
+ "`ConvLSTM2D` layer, which will accept inputs of shape\n",
+ "`(batch_size, num_frames, width, height, channels)`, and return\n",
+ "a prediction movie of the same shape."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "4_Z4z62eOmKF"
+ },
+ "outputs": [],
+ "source": [
+ "# Construct the input layer with no definite frame size.\n",
+ "inp = layers.Input(shape=(None, *x_train.shape[2:]))\n",
+ "\n",
+ "# We will construct 3 `ConvLSTM2D` layers with batch normalization,\n",
+ "# followed by a `Conv3D` layer for the spatiotemporal outputs.\n",
+ "x = layers.ConvLSTM2D(\n",
+ " filters=64,\n",
+ " kernel_size=(5, 5),\n",
+ " padding=\"same\",\n",
+ " return_sequences=True,\n",
+ " activation=\"relu\",\n",
+ ")(inp)\n",
+ "x = layers.BatchNormalization()(x)\n",
+ "x = layers.ConvLSTM2D(\n",
+ " filters=64,\n",
+ " kernel_size=(3, 3),\n",
+ " padding=\"same\",\n",
+ " return_sequences=True,\n",
+ " activation=\"relu\",\n",
+ ")(x)\n",
+ "x = layers.BatchNormalization()(x)\n",
+ "x = layers.ConvLSTM2D(\n",
+ " filters=64,\n",
+ " kernel_size=(1, 1),\n",
+ " padding=\"same\",\n",
+ " return_sequences=True,\n",
+ " activation=\"relu\",\n",
+ ")(x)\n",
+ "x = layers.Conv3D(\n",
+ " filters=1, kernel_size=(3, 3, 3), activation=\"sigmoid\", padding=\"same\"\n",
+ ")(x)\n",
+ "\n",
+ "# Next, we will build the complete model and compile it.\n",
+ "model = keras.models.Model(inp, x)\n",
+ "model.compile(\n",
+ " loss=keras.losses.binary_crossentropy,\n",
+ " optimizer=keras.optimizers.Adam(),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GZXFOnAdOmKF"
+ },
+ "source": [
+ "## Model Training\n",
+ "\n",
+ "With our model and data constructed, we can now train the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import tensorflow as tf"
+ ],
+ "metadata": {
+ "id": "psoByGwfPwB7"
+ },
+ "execution_count": 5,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "tf.test.gpu_device_name()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ },
+ "id": "eYXYYSUAPizU",
+ "outputId": "ec6d11d8-291b-45fc-f920-66ae77873d1a"
+ },
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'/device:GPU:0'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 6
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "JdvZBa_XOmKF",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "12a3500c-7728-4721-ba7c-478ffebda06c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/100\n",
+ "180/180 [==============================] - 46s 258ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 2/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 3/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 4/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 5/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 6/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 7/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 8/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 9/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 10/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 11/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 12/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 13/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 14/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 15/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 16/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 17/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 18/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 19/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 20/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 21/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-07\n",
+ "Epoch 22/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-08\n",
+ "Epoch 23/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-08\n",
+ "Epoch 24/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-08\n",
+ "Epoch 25/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-08\n",
+ "Epoch 26/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-08\n",
+ "Epoch 27/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-08\n",
+ "Epoch 28/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-08\n",
+ "Epoch 29/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-08\n",
+ "Epoch 30/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-08\n",
+ "Epoch 31/100\n",
+ "180/180 [==============================] - 46s 257ms/step - loss: 0.0235 - val_loss: 0.0236 - lr: 1.0000e-08\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Define some callbacks to improve training.\n",
+ "early_stopping = keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=30)\n",
+ "reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\", patience=20)\n",
+ "\n",
+ "# Define modifiable training hyperparameters.\n",
+ "epochs = 100\n",
+ "batch_size = 5\n",
+ "\n",
+ "# Fit the model to the training data.\n",
+ "with tf.device('/device:GPU:0'):\n",
+ " model.fit(\n",
+ " x_train,\n",
+ " y_train,\n",
+ " batch_size=batch_size,\n",
+ " epochs=epochs,\n",
+ " validation_data=(x_val, y_val),\n",
+ " callbacks=[early_stopping, reduce_lr],\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_FiQlZhpOmKG"
+ },
+ "source": [
+ "## Frame Prediction Visualizations\n",
+ "\n",
+ "With our model now constructed and trained, we can generate\n",
+ "some example frame predictions based on a new video.\n",
+ "\n",
+ "We'll pick a random example from the validation set and\n",
+ "then choose the first ten frames from them. From there, we can\n",
+ "allow the model to predict 10 new frames, which we can compare\n",
+ "to the ground truth frame predictions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "id": "Er70Wo8EOmKG",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 532
+ },
+ "outputId": "90ad19bd-8e24-4dea-f05a-5236b15e364c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1/1 [==============================] - 0s 32ms/step\n",
+ "1/1 [==============================] - 0s 38ms/step\n",
+ "1/1 [==============================] - 0s 39ms/step\n",
+ "1/1 [==============================] - 0s 38ms/step\n",
+ "1/1 [==============================] - 0s 44ms/step\n",
+ "1/1 [==============================] - 0s 46ms/step\n",
+ "1/1 [==============================] - 0s 43ms/step\n",
+ "1/1 [==============================] - 0s 40ms/step\n",
+ "1/1 [==============================] - 0s 42ms/step\n",
+ "1/1 [==============================] - 0s 52ms/step\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": "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\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "# Select a random example from the validation dataset.\n",
+ "example = val_dataset[np.random.choice(range(len(val_dataset)), size=1)[0]]\n",
+ "\n",
+ "# Pick the first/last ten frames from the example.\n",
+ "frames = example[:10, ...]\n",
+ "original_frames = example[10:, ...]\n",
+ "\n",
+ "# Predict a new set of 10 frames.\n",
+ "for _ in range(10):\n",
+ " # Extract the model's prediction and post-process it.\n",
+ " new_prediction = model.predict(np.expand_dims(frames, axis=0))\n",
+ " new_prediction = np.squeeze(new_prediction, axis=0)\n",
+ " predicted_frame = np.expand_dims(new_prediction[-1, ...], axis=0)\n",
+ "\n",
+ " # Extend the set of prediction frames.\n",
+ " frames = np.concatenate((frames, predicted_frame), axis=0)\n",
+ "\n",
+ "# Construct a figure for the original and new frames.\n",
+ "fig, axes = plt.subplots(2, 10, figsize=(20, 4))\n",
+ "\n",
+ "# Plot the original frames.\n",
+ "for idx, ax in enumerate(axes[0]):\n",
+ " ax.imshow(np.squeeze(original_frames[idx]), cmap=\"gray\")\n",
+ " ax.set_title(f\"Frame {idx + 11}\")\n",
+ " ax.axis(\"off\")\n",
+ "\n",
+ "# Plot the new frames.\n",
+ "new_frames = frames[10:, ...]\n",
+ "for idx, ax in enumerate(axes[1]):\n",
+ " ax.imshow(np.squeeze(new_frames[idx]), cmap=\"gray\")\n",
+ " ax.set_title(f\"Frame {idx + 11}\")\n",
+ " ax.axis(\"off\")\n",
+ "\n",
+ "# Display the figure.\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7aso8WxFOmKG"
+ },
+ "source": [
+ "## Predicted Videos\n",
+ "\n",
+ "Finally, we'll pick a few examples from the validation set\n",
+ "and construct some GIFs with them to see the model's\n",
+ "predicted videos.\n",
+ "\n",
+ "You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/conv-lstm)\n",
+ "and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/conv-lstm)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "K20bvhqgOmKG",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
+ "referenced_widgets": [
+ "57250892f03d4510883dde26a7948f8c",
+ "ea6ceebbde6d4e61bd55fc68714bab87",
+ "0bb358788f814696bcb6888157d08e9c",
+ "8d35228abb69421a8b94552e4179ed19",
+ "a9e020a1e9b44d3bab2074d38ca76127",
+ "3431c28442a64a0a81254ebbfb6353ef",
+ "296319668d974286bce6e97ba76b1b4a",
+ "25e5d343640d427abacc42749c169f60",
+ "39d7e8f24ad94efeb22c3672e5c37f7c",
+ "487bc4aff3404633bf7b3962dd2efa2f",
+ "53cea4bf5b6145f5a6b52a895983d135",
+ "e2a9d2bb729941f695a5f90afcef8438",
+ "15512c83756242f2b50ea69d3afedd13",
+ "0736ae15135a4af7be822237d7c0d4b4",
+ "39627437f84f4f498056d6c852972dd5",
+ "2c2f5bfe45394c63b8325ccc2b72e68e",
+ "9cb92be4f32349f7bc46ba961f50331c",
+ "d8de8c5ae99e42d1bdb17ea3c81788c1",
+ "8a21c341fab64552bdffe8ed2c850ac2",
+ "c4244b9848a844309dc393c4f622de4a",
+ "adfe174610de4f85bffdff65186411d3",
+ "4a253062f70e42a7b31e63109906af01",
+ "7b97c840f51342588ba4f89a2def5bbe",
+ "d976290b37f34e00babb1a71011bf719",
+ "e676fe105c7a4ad4a1fb33fc87950443",
+ "7572bdbfd3ac4e769c669ba8e7c6f82f",
+ "977906d959ac436b98616194d9b75905",
+ "58e5b82b80324514a6e8e6f84528bf49",
+ "d5e9d8fd77bc46e3a857a37676bf70af",
+ "27eb122008624c1ea3d7ba46e53a2ab3"
+ ]
+ },
+ "outputId": "d25ace38-d947-466d-c970-74ac525de94d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1/1 [==============================] - 0s 33ms/step\n",
+ "1/1 [==============================] - 0s 35ms/step\n",
+ "1/1 [==============================] - 0s 35ms/step\n",
+ "1/1 [==============================] - 0s 35ms/step\n",
+ "1/1 [==============================] - 0s 38ms/step\n",
+ "1/1 [==============================] - 0s 38ms/step\n",
+ "1/1 [==============================] - 0s 45ms/step\n",
+ "1/1 [==============================] - 0s 42ms/step\n",
+ "1/1 [==============================] - 0s 43ms/step\n",
+ "1/1 [==============================] - 0s 44ms/step\n",
+ "1/1 [==============================] - 0s 32ms/step\n",
+ "1/1 [==============================] - 0s 34ms/step\n",
+ "1/1 [==============================] - 0s 35ms/step\n",
+ "1/1 [==============================] - 0s 37ms/step\n",
+ "1/1 [==============================] - 0s 38ms/step\n",
+ "1/1 [==============================] - 0s 38ms/step\n",
+ "1/1 [==============================] - 0s 43ms/step\n",
+ "1/1 [==============================] - 0s 43ms/step\n",
+ "1/1 [==============================] - 0s 44ms/step\n",
+ "1/1 [==============================] - 0s 43ms/step\n",
+ "1/1 [==============================] - 0s 32ms/step\n",
+ "1/1 [==============================] - 0s 33ms/step\n",
+ "1/1 [==============================] - 0s 36ms/step\n",
+ "1/1 [==============================] - 0s 37ms/step\n",
+ "1/1 [==============================] - 0s 39ms/step\n",
+ "1/1 [==============================] - 0s 39ms/step\n",
+ "1/1 [==============================] - 0s 40ms/step\n",
+ "1/1 [==============================] - 0s 42ms/step\n",
+ "1/1 [==============================] - 0s 44ms/step\n",
+ "1/1 [==============================] - 0s 55ms/step\n",
+ "1/1 [==============================] - 0s 32ms/step\n",
+ "1/1 [==============================] - 0s 33ms/step\n",
+ "1/1 [==============================] - 0s 35ms/step\n",
+ "1/1 [==============================] - 0s 36ms/step\n",
+ "1/1 [==============================] - 0s 39ms/step\n",
+ "1/1 [==============================] - 0s 40ms/step\n",
+ "1/1 [==============================] - 0s 39ms/step\n",
+ "1/1 [==============================] - 0s 41ms/step\n",
+ "1/1 [==============================] - 0s 46ms/step\n",
+ "1/1 [==============================] - 0s 44ms/step\n",
+ "1/1 [==============================] - 0s 33ms/step\n",
+ "1/1 [==============================] - 0s 34ms/step\n",
+ "1/1 [==============================] - 0s 35ms/step\n",
+ "1/1 [==============================] - 0s 36ms/step\n",
+ "1/1 [==============================] - 0s 41ms/step\n",
+ "1/1 [==============================] - 0s 38ms/step\n",
+ "1/1 [==============================] - 0s 39ms/step\n",
+ "1/1 [==============================] - 0s 41ms/step\n",
+ "1/1 [==============================] - 0s 43ms/step\n",
+ "1/1 [==============================] - 0s 46ms/step\n",
+ " Truth\tPrediction\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "HBox(children=(Image(value=b'GIF89a@\\x00@\\x00\\x86\\x00\\x00\\xff\\xff\\xff\\xfe\\xfe\\xfe\\xfd\\xfd\\xfd\\xfc\\xfc\\xfc\\xfb\\…"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "57250892f03d4510883dde26a7948f8c"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "HBox(children=(Image(value=b'GIF89a@\\x00@\\x00\\x87\\x00\\x00\\xfe\\xfe\\xfe\\xfd\\xfd\\xfd\\xfb\\xfb\\xfb\\xfa\\xfa\\xfa\\xf9\\…"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "296319668d974286bce6e97ba76b1b4a"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "HBox(children=(Image(value=b'GIF89a@\\x00@\\x00\\x86\\x00\\x00\\xff\\xff\\xff\\xfe\\xfe\\xfe\\xfd\\xfd\\xfd\\xfc\\xfc\\xfc\\xfa\\…"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "15512c83756242f2b50ea69d3afedd13"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "HBox(children=(Image(value=b'GIF89a@\\x00@\\x00\\x86\\x00\\x00\\xff\\xff\\xff\\xfe\\xfe\\xfe\\xfd\\xfd\\xfd\\xfc\\xfc\\xfc\\xfb\\…"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "8a21c341fab64552bdffe8ed2c850ac2"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "HBox(children=(Image(value=b'GIF89a@\\x00@\\x00\\x86\\x00\\x00\\xfe\\xfe\\xfe\\xfd\\xfd\\xfd\\xfa\\xfa\\xfa\\xf8\\xf8\\xf8\\xf7\\…"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e676fe105c7a4ad4a1fb33fc87950443"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "# Select a few random examples from the dataset.\n",
+ "examples = val_dataset[np.random.choice(range(len(val_dataset)), size=5)]\n",
+ "\n",
+ "# Iterate over the examples and predict the frames.\n",
+ "predicted_videos = []\n",
+ "for example in examples:\n",
+ " # Pick the first/last ten frames from the example.\n",
+ " frames = example[:10, ...]\n",
+ " original_frames = example[10:, ...]\n",
+ " new_predictions = np.zeros(shape=(10, *frames[0].shape))\n",
+ "\n",
+ " # Predict a new set of 10 frames.\n",
+ " for i in range(10):\n",
+ " # Extract the model's prediction and post-process it.\n",
+ " frames = example[: 10 + i + 1, ...]\n",
+ " new_prediction = model.predict(np.expand_dims(frames, axis=0))\n",
+ " new_prediction = np.squeeze(new_prediction, axis=0)\n",
+ " predicted_frame = np.expand_dims(new_prediction[-1, ...], axis=0)\n",
+ "\n",
+ " # Extend the set of prediction frames.\n",
+ " new_predictions[i] = predicted_frame\n",
+ "\n",
+ " # Create and save GIFs for each of the ground truth/prediction images.\n",
+ " for frame_set in [original_frames, new_predictions]:\n",
+ " # Construct a GIF from the selected video frames.\n",
+ " current_frames = np.squeeze(frame_set)\n",
+ " current_frames = current_frames[..., np.newaxis] * np.ones(3)\n",
+ " current_frames = (current_frames * 255).astype(np.uint8)\n",
+ " current_frames = list(current_frames)\n",
+ "\n",
+ " # Construct a GIF from the frames.\n",
+ " with io.BytesIO() as gif:\n",
+ " imageio.mimsave(gif, current_frames, \"GIF\", duration=200)\n",
+ " predicted_videos.append(gif.getvalue())\n",
+ "\n",
+ "# Display the videos.\n",
+ "print(\" Truth\\tPrediction\")\n",
+ "for i in range(0, len(predicted_videos), 2):\n",
+ " # Construct and display an `HBox` with the ground truth and prediction.\n",
+ " box = HBox(\n",
+ " [\n",
+ " widgets.Image(value=predicted_videos[i]),\n",
+ " widgets.Image(value=predicted_videos[i + 1]),\n",
+ " ]\n",
+ " )\n",
+ " display(box)"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "provenance": [],
+ "machine_shape": "hm",
+ "gpuType": "V100",
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.0"
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "57250892f03d4510883dde26a7948f8c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_ea6ceebbde6d4e61bd55fc68714bab87",
+ "IPY_MODEL_0bb358788f814696bcb6888157d08e9c"
+ ],
+ "layout": "IPY_MODEL_8d35228abb69421a8b94552e4179ed19"
+ }
+ },
+ "ea6ceebbde6d4e61bd55fc68714bab87": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ImageModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ImageModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ImageView",
+ "format": "png",
+ "height": "",
+ "layout": "IPY_MODEL_a9e020a1e9b44d3bab2074d38ca76127",
+ "width": ""
+ }
+ },
+ "0bb358788f814696bcb6888157d08e9c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ImageModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ImageModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ImageView",
+ "format": "png",
+ "height": "",
+ "layout": "IPY_MODEL_3431c28442a64a0a81254ebbfb6353ef",
+ "width": ""
+ }
+ },
+ "8d35228abb69421a8b94552e4179ed19": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "a9e020a1e9b44d3bab2074d38ca76127": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "3431c28442a64a0a81254ebbfb6353ef": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "296319668d974286bce6e97ba76b1b4a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_25e5d343640d427abacc42749c169f60",
+ "IPY_MODEL_39d7e8f24ad94efeb22c3672e5c37f7c"
+ ],
+ "layout": "IPY_MODEL_487bc4aff3404633bf7b3962dd2efa2f"
+ }
+ },
+ "25e5d343640d427abacc42749c169f60": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ImageModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ImageModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ImageView",
+ "format": "png",
+ "height": "",
+ "layout": "IPY_MODEL_53cea4bf5b6145f5a6b52a895983d135",
+ "width": ""
+ }
+ },
+ "39d7e8f24ad94efeb22c3672e5c37f7c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ImageModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ImageModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ImageView",
+ "format": "png",
+ "height": "",
+ "layout": "IPY_MODEL_e2a9d2bb729941f695a5f90afcef8438",
+ "width": ""
+ }
+ },
+ "487bc4aff3404633bf7b3962dd2efa2f": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "53cea4bf5b6145f5a6b52a895983d135": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "e2a9d2bb729941f695a5f90afcef8438": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "15512c83756242f2b50ea69d3afedd13": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_0736ae15135a4af7be822237d7c0d4b4",
+ "IPY_MODEL_39627437f84f4f498056d6c852972dd5"
+ ],
+ "layout": "IPY_MODEL_2c2f5bfe45394c63b8325ccc2b72e68e"
+ }
+ },
+ "0736ae15135a4af7be822237d7c0d4b4": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ImageModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ImageModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ImageView",
+ "format": "png",
+ "height": "",
+ "layout": "IPY_MODEL_9cb92be4f32349f7bc46ba961f50331c",
+ "width": ""
+ }
+ },
+ "39627437f84f4f498056d6c852972dd5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ImageModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ImageModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ImageView",
+ "format": "png",
+ "height": "",
+ "layout": "IPY_MODEL_d8de8c5ae99e42d1bdb17ea3c81788c1",
+ "width": ""
+ }
+ },
+ "2c2f5bfe45394c63b8325ccc2b72e68e": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "9cb92be4f32349f7bc46ba961f50331c": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "d8de8c5ae99e42d1bdb17ea3c81788c1": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "8a21c341fab64552bdffe8ed2c850ac2": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_c4244b9848a844309dc393c4f622de4a",
+ "IPY_MODEL_adfe174610de4f85bffdff65186411d3"
+ ],
+ "layout": "IPY_MODEL_4a253062f70e42a7b31e63109906af01"
+ }
+ },
+ "c4244b9848a844309dc393c4f622de4a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ImageModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ImageModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ImageView",
+ "format": "png",
+ "height": "",
+ "layout": "IPY_MODEL_7b97c840f51342588ba4f89a2def5bbe",
+ "width": ""
+ }
+ },
+ "adfe174610de4f85bffdff65186411d3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ImageModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ImageModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ImageView",
+ "format": "png",
+ "height": "",
+ "layout": "IPY_MODEL_d976290b37f34e00babb1a71011bf719",
+ "width": ""
+ }
+ },
+ "4a253062f70e42a7b31e63109906af01": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "7b97c840f51342588ba4f89a2def5bbe": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "d976290b37f34e00babb1a71011bf719": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "e676fe105c7a4ad4a1fb33fc87950443": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_7572bdbfd3ac4e769c669ba8e7c6f82f",
+ "IPY_MODEL_977906d959ac436b98616194d9b75905"
+ ],
+ "layout": "IPY_MODEL_58e5b82b80324514a6e8e6f84528bf49"
+ }
+ },
+ "7572bdbfd3ac4e769c669ba8e7c6f82f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ImageModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ImageModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ImageView",
+ "format": "png",
+ "height": "",
+ "layout": "IPY_MODEL_d5e9d8fd77bc46e3a857a37676bf70af",
+ "width": ""
+ }
+ },
+ "977906d959ac436b98616194d9b75905": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ImageModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ImageModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ImageView",
+ "format": "png",
+ "height": "",
+ "layout": "IPY_MODEL_27eb122008624c1ea3d7ba46e53a2ab3",
+ "width": ""
+ }
+ },
+ "58e5b82b80324514a6e8e6f84528bf49": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "d5e9d8fd77bc46e3a857a37676bf70af": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "27eb122008624c1ea3d7ba46e53a2ab3": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ }
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file