lecture_2_fs2021.ipynb

lecture_2_fs2021.ipynb

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      "language": "python",
      "name": "python3"
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    "language_info": {
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      "file_extension": ".py",
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      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    },
    "colab": {
      "name": "lecture_2_fs2021.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/jteichma/ea5e54b772a0a3ec5633cbd90e1a3f5f/lecture_2_fs2021.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7uyVL0tBtESW"
      },
      "source": [
        "# Universal Approximation - a dynamic point of view"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWOyRmkvtf8W"
      },
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')\n",
        "#%cd /content/drive/My Drive/Colab Notebooks/\n",
        "#%ls"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ImKN5kt4xSUZ"
      },
      "source": [
        "#%tensorflow_version 1.x"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0Tbcqxit7o8"
      },
      "source": [
        "# Controlled Ordinary Differential Equations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yBrFRs18tESc"
      },
      "source": [
        "We shall in the sequel take an unusual (but familiar to many researchers) point of view towards deep neural networks. Consider a controlled ordinary differential equation (ODE), i.e.\n",
        "$$\n",
        "(\\ast) \\qquad dX_t = \\sum_{i=1}^d V_i(X_{t-}) du^i(t), \\; X_0 = x\n",
        "$$\n",
        "where $ V_i $ are some smooth vector fields on $ \\mathbb{R}^m $ with all proper derivatives bounded. $ u $ is a finite variation curve with values in $ \\mathbb{R}^d $. For simplicity we shall always consider finitely many jumps of $ u $ in this lecture, and $ u $ being $ C^\\infty$ between two jumps.\n",
        "\n",
        "A solution of this equation is given by a fixed point of\n",
        "$$\n",
        "X_t = x + \\sum_{i=1}^d \\int_0^t V_i(X_{s-}) du^i(s)\n",
        "$$\n",
        "for $ t \\geq 0 $. Notice that the integral is well defined due to the finite variation property.\n",
        "\n",
        "We shall analyze two aspects in the sequel:\n",
        "1. Connection to deep neural networks.\n",
        "2. How expressive are those networks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5lyCwdIktESd"
      },
      "source": [
        "For the first aspect consider vector fields of the form\n",
        "$$\n",
        "V(x) = \\sum_{j=1}^N \\alpha_j \\phi(\\langle \\mu_j , x \\rangle + \\beta_j)\n",
        "$$\n",
        "for some vectors $ \\alpha_j, \\mu_j \\in \\mathbb{R}^m $ and numbers $ \\beta_j $. Notice that we could also take vector fields of the form\n",
        "$$\n",
        "V(x) = W {\\phi}(x) + a - x\n",
        "$$\n",
        "for matrices $ W $ and vectors $ a $ to achive the same result.\n",
        "\n",
        "In this case the Euler scheme, which approximates the solution of the ODE, is an iterative procedure where maps of the type\n",
        "$$\n",
        "x \\mapsto x + \\sum_{i=1}^d V_i(x) \\Delta u^i(t)\n",
        "$$\n",
        "at point $ x \\in \\mathbb{R}^N $ and $ t \\geq 0 $ are concatenated. Obviously one can understand such finite concatenations as deep neural networks with at most $ m + N $ neurons in each layers and depths being equal to the time discretization with respect to the ODE's initial value.\n",
        "\n",
        "><font color=red>We therefore call the map $ x \\mapsto X_t $ a deep neural network of infinite depths. Notice that for piecewise constant $ u $ we just obtain classical neural networks of finite depths.</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-9_oBZJPtESd"
      },
      "source": [
        "Notice that one could also consider the Picard-Lindelof iteration scheme to obtain neural networks, then depth would be related to the iteration step.\n",
        "\n",
        "This obvious point of view has been taken in several papers, most recently in [Neural ordinary differential equations](https://arxiv.org/pdf/1806.07366.pdf). There are three new aspects in our approach: first the control approach, i.e. the use of additional control variables $u^i$, second, that actually every feedforward neural network is this very form, and, third, the obvious question how the choice of the vector fields influences the expressibility of the networks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SYdCS6FetESe"
      },
      "source": [
        "For the third aspect consider a training data set $ {(x_l,y_l)}_{l \\in L} $, i.e. a subset of a graph of an $ \\mathbb{R}^m$-valued continuous function on some compact set. The cardinality of $L$ should be seen as large but finite.\n",
        "\n",
        "Consider the following ODE on $ \\mathbb{R}^{mL} $\n",
        "$$\n",
        "dZ_t = \\sum_{i=1}^d W_i(Z_{t-}) du^i(t) \\, ,\n",
        "$$\n",
        "where\n",
        "$$\n",
        "(W_i((x_l)))_l:=V_i(x_l)\n",
        "$$\n",
        "for $ l \\in L $ and $ i = 1,\\ldots,d $. We actually make a _particle system_ of $ L $ particles moving precisely according to the dynamics of $ (\\ast) $. This system also has a unique solution for all times just as equation $(\\ast)$.\n",
        "\n",
        "Representability of the non-linear function on the training set amounts precisely to saying that, for given $ t > 0 $, the particle system $ Z $ can possibly reach at $ t $ any point $ (y_l) $ if starting at $ (x_l) $ for some choice of control $u$.\n",
        "\n",
        "For such questions the notion of controllability has been introduced, in particular [Chow's theorem](https://en.wikipedia.org/wiki/Chow%E2%80%93Rashevskii_theorem):\n",
        "\n",
        "<b>\n",
        "If the Lie brackets of $ (W_i) $ generate at the point $ (x_l) $ the whole space $ \\mathbb{R}^{mL} $, then any point $ (y_l) $ can be reached locally. Such systems are called exactly controllable.\n",
        "</b>\n",
        "\n",
        "There are many different notions of controllability but all are related to Lie brackets on the one hand ('geometric condition' or H&ouml;rmander condition) and certain analytic assumptions on the vector fields on the other hand."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FvArmGXOtESe"
      },
      "source": [
        "Can we reach with our particular vector fields the geometric condition? We consider some illustrative examples of course for $ d \\geq 2 $."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxtMQGx8tESe"
      },
      "source": [
        "1. Take a one point training set and choose $ V_i(x) = A_i x $ to be linear vector fields, e.g. with respect to a standard normal distribution on the matrix elements. Then the geometric condition (H&ouml;rmander condition) is satisfied at any point $ x \\neq 0 $ with probability $1$.\n",
        "2. Take a one point traning set and choose $V_i $ one layer neural networks with randomly choosen directions $ \\mu_j $ and activation functions with non-vanishing derivative at all points, then the geometric condition is satisfied.\n",
        "3. Take a general $ \\operatorname{card}(L) $ point training set and consider $ d = m \\times \\operatorname{card} $ control directions and vector fields. Assume that for every point $ x_l $ in the training set there are $m$ linearly independent vector fields. Then the geometric condition is satisfied.\n",
        "4. We consider this question also in an infinite dimensional situation, for instance on the torus: for every $ m \\geq 1 $ for \\emph{every} $ d \\geq 2 $ there are vector fields $ V_i $ on the torus such that the transport operators\n",
        "$$\n",
        "V_i f(x) = d f(x) \\cdot V(x)\n",
        "$$\n",
        "on $L^2(\\mathbb{T}^m)$ generate with their Lie brackets a dense subspace at any non-constant Fourier polynomial $ f \\in L^2(\\mathbb{T}^m) $."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fiyMZmFNtESf"
      },
      "source": [
        "Having this theoretical result at hand we know that there are actually 'generic' choices of vector fields (often realized by random choices) which at least locally allow for controllability.\n",
        "\n",
        "The actual interpretation (which is another universal approximation theorem) is the following: a generic choice of vector fields $ V_i $ of the above controlled system can be realized by random neural networks and a certain number of controls $ u^1,\\ldots,u^d$ leads to an infinite depth neural network with $ m + N $ nodes on each layer which can approximate any continuous function on a compact set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pWiUBni9tESf"
      },
      "source": [
        "This might help to explain the following curious phenomenon (we remember the standard MNIST classification from last week's lecture 1):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "azYBpFkKtESg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03d98596-a1a2-4f50-e14f-3f806dccad2b"
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPVhJSzMtESh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "b2b40bd1-9368-4840-80d0-2244778e3c51"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "#matplotlib inline # Only use this if using iPython\n",
        "image_index = np.random.randint(60000) #7777 # You may select anything up to 60,000\n",
        "print(y_train[image_index]) # The label is printed\n",
        "plt.imshow(x_train[image_index], cmap='Greys')\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XHHdqTvtESi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7b6ab8c-b7b1-4e99-e8ce-c3a3ed5948d2"
      },
      "source": [
        "# Reshaping the array to 4-dims so that it can work with the Keras API\n",
        "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
        "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
        "input_shape = (28, 28, 1)\n",
        "# Making sure that the values are float so that we can get decimal points after division\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "# Normalizing the RGB codes by dividing it to the max RGB value.\n",
        "x_train /= 255\n",
        "x_test /= 255\n",
        "print('x_train shape:', x_train.shape)\n",
        "print('Number of images in x_train', x_train.shape[0])\n",
        "print('Number of images in x_test', x_test.shape[0])\n",
        "y_train.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape: (60000, 28, 28, 1)\n",
            "Number of images in x_train 60000\n",
            "Number of images in x_test 10000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000,)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jnkvs3ettESi"
      },
      "source": [
        "# Importing the required Keras modules containing model and layers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D\n",
        "# Creating a Sequential Model and adding the layers\n",
        "model = Sequential()\n",
        "layer1=Conv2D(28, kernel_size=(3,3), input_shape=input_shape)\n",
        "layer1.trainable=True\n",
        "model.add(layer1) # (3,3) for mnist\n",
        "model.add(MaxPooling2D(pool_size=(2, 2))) # (2,2) for mnist\n",
        "model.add(Flatten()) # Flattening the 2D arrays for fully connected layers\n",
        "layer2 = Dense(128, activation=tf.nn.relu,kernel_initializer='random_uniform')#glorot_uniform is by default\n",
        "layer2.trainable=True#False\n",
        "model.add(layer2)\n",
        "#layer3 = Dense(2*128, activation=tf.nn.relu,kernel_initializer='random_uniform')\n",
        "#layer3.trainable=False\n",
        "#model.add(layer3)\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(10,activation=tf.nn.softmax))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEluVwOStESi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "283e4aa8-eb53-44a8-c04b-c57112670858"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_1 (Conv2D)           (None, 26, 26, 28)        280       \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 13, 13, 28)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 4732)              0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               605824    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 607394 (2.32 MB)\n",
            "Trainable params: 607394 (2.32 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KehzvKW4tESj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b68c813-0480-4fc8-ecf6-be7c5d153f64"
      },
      "source": [
        "4732*128+128"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "605824"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4c1ZlFBtESj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12ca7b8e-d102-407a-93ab-6d9d20d88409"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "for i in range(1):\n",
        "    model.fit(x=x_train,y=y_train, epochs=1)\n",
        "    x = model.evaluate(x_test, y_test)\n",
        "    print('\\n',x)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1875/1875 [==============================] - 48s 24ms/step - loss: 0.2090 - accuracy: 0.9366\n",
            "313/313 [==============================] - 2s 6ms/step - loss: 0.0791 - accuracy: 0.9740\n",
            "\n",
            " [0.07912828773260117, 0.9739999771118164]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cpv8R9Z7tESj"
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Input, Dense, Conv2D, Concatenate, Dropout, \\\n",
        "                        Flatten, MaxPooling2D, Multiply, Lambda, Add\n",
        "from keras.backend import constant\n",
        "from keras import optimizers\n",
        "\n",
        "#from keras.engine.topology import Layer\n",
        "from keras.models import Model\n",
        "from keras.layers import Input\n",
        "from keras import initializers\n",
        "from keras.constraints import max_norm\n",
        "import keras.backend as K\n",
        "\n",
        "m = 20*20 # dimension of state space (number of hidden nodes in each layer)\n",
        "d = 2 # number of controls\n",
        "N = 40 # length of the sequence of increments\n",
        "state = Input(shape=input_shape)\n",
        "inputs = [state]\n",
        "state = Conv2D(28, kernel_size=(3,3),trainable=False)(state)\n",
        "state = Flatten()(state)\n",
        "state = Dense(m, activation='tanh',trainable=False,\n",
        "              kernel_initializer=initializers.RandomNormal(0,1),#kernel_initializer='random_normal',\n",
        "              bias_initializer='random_normal')(state)\n",
        "output_state = []\n",
        "layers = []\n",
        "for j in range(N):\n",
        "    for i in range(d):\n",
        "        layer = Dense(m, activation='tanh',trainable=False,\n",
        "                      kernel_initializer=initializers.RandomNormal(0,1),#kernel_initializer='random_normal',\n",
        "                      bias_initializer='random_normal',\n",
        "                      name=str(i)+str(j))\n",
        "        layers = layers + [layer]\n",
        "\n",
        "for j in range(N):\n",
        "    for i in range(d):\n",
        "        #layer=Dense(m, activation='tanh',trainable=False,\n",
        "        #            kernel_initializer=initializers.RandomNormal(0,1),#kernel_initializer='random_normal',\n",
        "        #            bias_initializer='random_normal',\n",
        "        #            name=str(i)+str(j))\n",
        "        Vx = layers[i+(j)*d](state)\n",
        "        #Vx  = Dense(m, activation='tanh',trainable=False,\n",
        "        #            kernel_initializer=initializers.RandomNormal(0,1),#kernel_initializer='random_normal',\n",
        "        #            bias_initializer='random_normal')(state) # generation of the vector field\n",
        "        #Vx  = Dense(m, activation='tanh',trainable=False,\n",
        "        #            kernel_initializer='random_normal',\n",
        "        #            bias_initializer='random_normal')(Vxhelper)\n",
        "        input_control_incr = Input(shape=(1,))\n",
        "        helper = Dense(1, activation='linear',trainable=True,\n",
        "                       kernel_initializer=initializers.RandomNormal(1,0.),\n",
        "                       bias_constraint=max_norm(0.),name='u'+str(i)+str(j))(input_control_incr)\n",
        "        helperlist = [helper for i in range(m)]\n",
        "        delta_u = Concatenate()(helperlist) # increments vectorized\n",
        "        mult = Multiply()([Vx, delta_u])\n",
        "        state = Add()([mult,state])\n",
        "        inputs = inputs + [input_control_incr]\n",
        "    output_state=output_state+[state]\n",
        "state = output_state[-1]\n",
        "state = Dense(10, activation=tf.nn.softmax,trainable=True,\n",
        "              kernel_initializer='random_uniform',\n",
        "              bias_initializer='zeros')(state)\n",
        "model = Model(inputs, outputs=state)\n",
        "\n",
        "#model = Sequential()\n",
        "#layer1=Dense(128,activation=tf.nn.relu, input_shape=input_shape)#128\n",
        "#layer1.trainable=False\n",
        "#model.add(layer1)\n",
        "#layer2=Dense(128,activation=tf.nn.relu)#32\n",
        "#layer2.trainable=False\n",
        "#model.add(layer2)\n",
        "#for i in range(3):\n",
        "#    layer3=Dense(128,activation=tf.nn.relu,kernel_initializer=initializers.random_normal(0,0.1))\n",
        "#    layer3.trainable=False\n",
        "#    model.add(layer3)\n",
        "#model.add(Flatten()) # Flattening the 2D arrays for fully connected layers\n",
        "#model.add(Dense(10,activation=tf.nn.softmax))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBfwzCZXtESl"
      },
      "source": [
        "#model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_wxwI2utESn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8030bda4-b15f-414d-f727-6188a8866e58"
      },
      "source": [
        "x_train_new = [x_train] + [1/N*np.ones((60000,1)) for i in range(N*d)]\n",
        "x_test_new = [x_test] + [1/N*np.ones((10000,1)) for i in range(N*d)]\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "for i in range(1):\n",
        "    model.fit(x=x_train_new,y=y_train, epochs=1)\n",
        "    x = model.evaluate(x_test_new, y_test)\n",
        "    print('\\n',x)\n",
        "\n",
        "#model.compile(optimizer='adam',\n",
        "#              loss='sparse_categorical_crossentropy',\n",
        "#              metrics=['accuracy'])\n",
        "#for i in range(1):\n",
        "#    model.fit(x=x_train,y=y_train, epochs=1)\n",
        "#    x = model.evaluate(x_test_new, y_test)\n",
        "#    print('\\n',x)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1875/1875 [==============================] - 407s 176ms/step - loss: 0.4489 - accuracy: 0.8704\n",
            "313/313 [==============================] - 31s 83ms/step - loss: 0.3222 - accuracy: 0.9046\n",
            "\n",
            " [0.322244256734848, 0.9046000242233276]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dB70tLlntESn"
      },
      "source": [
        "We realize: if we replace the well specified architecture by a random one also obtain reasonable training results (even though speed deteriorates). Of course this is an extremely generic implementation, however, it shows the effect."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jTQieLOWtESn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "74524102-3803-4e32-aff0-44b242e0efa3"
      },
      "source": [
        "img_rows=28\n",
        "img_cols=28\n",
        "count = 0\n",
        "for i in range(10):\n",
        "    image_index = np.random.randint(10000)\n",
        "    #pred = model.predict([x_test[image_index]]+[1/N*np.ones((60000,1)) for i in range(N*d)])\n",
        "    pred = model.predict([x_test[image_index].reshape(1, img_rows, img_cols, 1)]+[1/N*np.ones((1,1)) for i in range(N*d)])\n",
        "    if pred.argmax() != y_test[image_index]:\n",
        "        plt.imshow(x_test[image_index].reshape(28, 28),cmap='Greys')\n",
        "        plt.show()\n",
        "        print('Prediction',pred.argmax())\n",
        "        print('Ground Truth',y_test[image_index])\n",
        "        count = count + 1\n",
        "        #break\n",
        "print(count/100)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 5s 5s/step\n",
            "1/1 [==============================] - 0s 316ms/step\n",
            "1/1 [==============================] - 0s 305ms/step\n",
            "1/1 [==============================] - 0s 287ms/step\n",
            "1/1 [==============================] - 0s 237ms/step\n",
            "1/1 [==============================] - 0s 318ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction 5\n",
            "Ground Truth 4\n",
            "1/1 [==============================] - 1s 511ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction 8\n",
            "Ground Truth 5\n",
            "1/1 [==============================] - 0s 193ms/step\n",
            "1/1 [==============================] - 0s 128ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "0.02\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "f03d6NuHtESo"
      },
      "source": [
        "In order to understand actual training we shall derive the first variation with respect to parameters $ \\theta $, i.e. we take the derivative with respect to the parameter $ \\theta $, denoted by $ \\partial X_T $ of\n",
        "$$\n",
        "dX^\\theta_t = \\sum_{i=1}^d V^\\theta_i(t-,X^\\theta_{t-}) du^i(t), \\; X_0 = x \\; .\n",
        "$$\n",
        "Notice that we write for convenience an additional time dependence in the vector fields since this is actually an important case. This leads to\n",
        "$$\n",
        "d \\partial X^\\theta_t = \\sum_{i=1}^d \\big( \\partial V^\\theta_i(t-, X^\\theta_{t-}) + dV^\\theta_i(t-, X^\\theta_{t-}) \\partial X^\\theta_{t-} \\big) du^i(t), \\; \\partial X^\\theta_0 = 0 \\, ,\n",
        "$$\n",
        "which can be solved by first solving the equation for $ X^\\theta $ in a forward manner, i.e.\n",
        "$$\n",
        "X^\\theta_t = x + \\sum_{i=1}^d \\int_0^t V^\\theta_i(s-,X^\\theta_{s-}) du^i(s) \\,\n",
        "$$\n",
        "for $ t \\geq 0 $, and observing that the solution of the first variation equation is actually\n",
        "given by an evolution operator $ J_{s,t} $ which satisfies with respect to $t$\n",
        "$$\n",
        "d J_{s,t} =  \\sum_{i=1}^d dV^\\theta_i(t-,X^\\theta_{t-}) J_{s,t-} du^i(t), \\; J_{s+,s}=J_{s,s-}  = \\operatorname{id} \\, ,\n",
        "$$\n",
        "and the variation of constants formula\n",
        "$$\n",
        "\\partial X^\\theta_t = \\sum_{i=1}^d \\int_0^t J_{s+,t} \\partial V^\\theta_i(s-,X^\\theta_{s-}) du^i(s) \\, .\n",
        "$$\n",
        "The proof is actually an application of Fubini's theorem, since\n",
        "$$\n",
        "J_{s+,t} = \\operatorname{id}+ \\sum_{j=1}^d \\int_{s+}^t dV^\\theta_j(r-,X^\\theta_{r-}) J_{s+,r-} du^j(r) \\, ,\n",
        "$$\n",
        "and therefore\n",
        "$$\n",
        "\\partial X^\\theta_t = \\sum_{i,j=1}^d \\int_0^t  \\int_{s+}^t dV^\\theta_j(r-,X^\\theta_{r-}) J_{s+,r-} du^j(r)\\partial V^\\theta_i(s-,X^\\theta_{s-}) du^i(s) + \\sum_{i=1}^d \\int_0^t \\partial V^\\theta_i(s-,X^\\theta_{s-}) du^i(s)\\, .\n",
        "$$\n",
        "By Fubini's theorem this leads to\n",
        "$$\n",
        "\\partial X^\\theta_t = \\sum_{i,j=1}^d \\int_0^t  dV^\\theta_j(r-,X^\\theta_{r-}) \\int_{0}^{r-}  J_{s+,r-} \\partial V^\\theta_i(s-,X^\\theta_{s-}) d u^j(s) du^i(r) + \\sum_{i=1}^d \\int_0^t \\partial V^\\theta_i(s-,X^\\theta_{s-}) du^i(s) \\, ,\n",
        "$$\n",
        "which is the desired equation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvkignhmtESo"
      },
      "source": [
        "These remarkable formulas can be interpreted in the following way: calculate first $ J_{s,T} $ which is calculated backwards in time along the solution trajectory of the forward process $ X^\\theta $ on $[0,t]$. Then integrate the derivatives of $ \\partial V^\\theta $ transformed by $ J_{s+,T} $ to obtain the derivative of $ \\partial X_T^\\theta $\n",
        "$$\n",
        "\\partial X^\\theta_T = \\sum_{i=1}^d \\int_0^T J_{s+,T} \\partial V^\\theta_i(s-,X^\\theta_{s-}) du^i(s) \\, .\n",
        "$$\n",
        "If instead one is interested in the gradient of a loss function $ L(X^\\theta_T) $, by $ \\partial L(X^\\theta_T) = \\nabla L(X_T^\\theta) \\partial X^\\theta_T $ the problem is solved, too."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CngL7w3htESo"
      },
      "source": [
        "In our approach we do also consider the controls $ u^i $ as variables which can be optimized, hence also the gradients with respect to $ u^i $ are of importance: we calculate the derivative of $ u^i + \\epsilon a^i $ for $ \\epsilon = 0 $ and obtain\n",
        "$$\n",
        "d \\partial_\\epsilon X_T^\\epsilon = \\sum_{i=1}^d V_i(t-, X^\\epsilon_{t-}) da^i(t)  + dV_i(t-, X^\\epsilon_{t-}) \\partial_\\epsilon X^\\epsilon_{t-} du^i(t), \\; \\partial_\\epsilon X^\\epsilon_0 = 0 \\, ,\n",
        "$$\n",
        "hence a completely analogue situation appears, i.e.\n",
        "$$\n",
        "\\partial_\\epsilon X_T^\\epsilon = \\sum_{i=1}^d \\int_0^t J_{s+,T} V_i(s-, X^\\epsilon_{s-}) da^i(s)\n",
        "$$\n",
        "by variation of constants."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKn10pKvtESo"
      },
      "source": [
        ">Notice that the surjectivity of the map $ a \\mapsto \\partial_\\epsilon X^\\theta_T $ leads precisely to a H&ouml;rmander condition for the vector fields $ V_i $."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MdPefjl2tESo"
      },
      "source": [
        "Let us sum up the previous considerations:\n",
        "1. We can understand deep neural networks as flows of controlled ordinary differential equations.\n",
        "2. Backpropagation appears as the classical forward-backward calculation of first variations.\n",
        "3. Variations with respect to the control can also be calculated in a forward-backward manner.\n",
        "4. Since gradients can be efficiently calculated we can set up search algorithms stochastic gradient descent, simulated annealing, etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMudAzIHtESp"
      },
      "source": [
        "Let us consider this for one layer of a neural network:\n",
        "$$\n",
        "L^{(i)}: x \\mapsto W^{(i)}x + a^{(i)} \\mapsto \\phi (W^{(i)}x + a^{(i)})\n",
        "$$\n",
        "Translating this to a controlled ODE needs the following ingredients:\n",
        "1. a cadlag control $u(t)=1_{[1,2[}(t)+2 \\, 1_{[2,\\infty[}(t)$, i.e. we have two jumps by $1$ at $ t = 1 $ and $ t =2 $ of size $1$, and\n",
        "2. a time-dependent vector field\n",
        "$$\n",
        "V(t,x) = 1_{[0,1]}(t)(L^{(0)}(x)-x)+ 1_{]1,\\infty]}(t)(L^{(1)}(x) -x ) \\, .\n",
        "$$\n",
        "\n",
        "The corresponding neural network at 'time' $ 3 $ is just\n",
        "$$\n",
        "x \\mapsto L^{(0)}(x) \\mapsto \\phi(W^{(1)}L^{(0)}(x) + a^{(1)}) \\,\n",
        "$$\n",
        "which is a one hidden layer neural network with $\\phi$ readout.\n",
        "\n",
        "The parameters $ \\theta $ are the matrix weights $ W^{(i)} $ and shifts $ a^{(i)} $.\n",
        "\n",
        "The backwards first variation can be constructed by looking at the derivatives of $ V(t,.) $ with respect to $ x $ and solving the corresponding equation backwards: at 'time' $T=3$ we have\n",
        "$$\n",
        "J_{s,3} v =v + 1_{[0,1[}(s)(dL^{(1)}(X_{s-})d L^{(0)}(x)v-v) +1_{[1,2[}(s)(d L^{(1)}(X_{s-})v-v)\n",
        "$$\n",
        "which is nothing than encoding the outer part of the chain rule.\n",
        "\n",
        "Calculating now the derivative of the network with respect to, e.g., $ a^{(1)} $ amounts then just to calculting the variation of constants integral which gives the correct partial derivatives."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q2FY6anitESp"
      },
      "source": [
        "Let us look at this phenomenon in an example following [Deep Learning with Keras and Tensorflow](https://github.com/leriomaggio). We consider a one hidden layer neural network as above with loss function $ 0.5*(y-\\hat{y})^2 $ whose gradient is easily calculated in terms of the scalar product of the error with the respective gradient of $ X^\\theta $ with respect to the network parameters.\n",
        "\n",
        "What is implemented in the sequel is a simple gradient descent (with momentum): let us denote the loss function by $ L(\\theta) $, then\n",
        "$$\n",
        "\\theta^{(n+1)} = \\theta^{(n)} -  N \\, \\nabla L(\\theta^{(n)}) - M \\, \\nabla L(\\theta^{(n-1)}) \\, .\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_PpCDotVtESp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b3b69eb-d99e-40b8-b95a-d0f583f6e1a4"
      },
      "source": [
        "# Import the required packages\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy\n",
        "\n",
        "print(pd.__version__)\n",
        "\n",
        "# Display plots in notebook\n",
        "%matplotlib inline\n",
        "# Define plot's default figure size\n",
        "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n",
        "\n",
        "import random\n",
        "random.seed(123)\n",
        "\n",
        "# calculate a random number where:  a <= rand < b\n",
        "def rand(a, b):\n",
        "    return (b-a)*random.random() + a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.5.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fherwMnQtESp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52a91c61-8f41-41f7-aa48-4ce0c205d39d"
      },
      "source": [
        "#read the datasets\n",
        "train = pd.read_csv(\"intro_to_ann.csv\")\n",
        "\n",
        "X, y = np.array(train.iloc[:,0:2]), np.array(train.iloc[:,2])\n",
        "\n",
        "print(X.shape)\n",
        "print(y.shape)\n",
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(500, 2)\n",
            "(500,)\n",
            "[1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 1 1 0 0 0 0 1\n",
            " 1 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 1 1 0 0 0 0 1 1 1\n",
            " 1 0 0 1 0 0 0 1 1 0 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 1 0 1 0 0 0 1 1 1\n",
            " 1 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 1 0 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 1 0\n",
            " 0 1 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 0 1 1 1 0 0 0 0 1 1 0 1 1 1 0 1 0 0 1 0\n",
            " 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 1 1 0 1 1 0 1 0 0 1 1 0 1 1 0 0 0 1 1 0 1\n",
            " 1 1 0 1 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 0 0\n",
            " 1 0 1 1 1 1 1 1 0 1 0 0 1 1 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0\n",
            " 0 1 0 0 1 0 0 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 1 0 1 1 1 0 0 0 0 1 1 1 1 1 0\n",
            " 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 1 0 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 0 0 0\n",
            " 0 0 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 0 1\n",
            " 1 1 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 1 0 1\n",
            " 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 1 0\n",
            " 0 1 1 1 0 1 1 0 1 1 1 1 1 0 0 1 1 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rH3UykVltESp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 693
        },
        "outputId": "cd4c896e-a9be-4ffd-c592-8aff453cd291"
      },
      "source": [
        "#Let's plot the dataset and see how it is\n",
        "plt.scatter(X[:,0], X[:,1], s=40, c=1-y[:], cmap=plt.cm.Wistia)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7944f35f9c60>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-a0uYKDEtESp"
      },
      "source": [
        "# Make a matrix\n",
        "def makeMatrix(I, J, fill=0.0):\n",
        "    return np.zeros([I,J])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXX8OFDctESq"
      },
      "source": [
        "# our sigmoid function\n",
        "def sigmoid(x):\n",
        "    #return math.tanh(x)\n",
        "    return 1/(1+np.exp(-x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wKQ8v62OtESq"
      },
      "source": [
        "# derivative of our sigmoid function, in terms of the output (i.e. y)\n",
        "def dsigmoid(y):\n",
        "    return y - y**2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVKEMOKitESq"
      },
      "source": [
        "# the multilayerperceptron class (with one hidden layer)\n",
        "\n",
        "class MLP:\n",
        "    def __init__(self, ni, nh, no):\n",
        "        # number of input, hidden, and output nodes\n",
        "        self.ni = ni + 1 # +1 for bias node\n",
        "        self.nh = nh\n",
        "        self.no = no\n",
        "\n",
        "        # activations for nodes -- these are list of respective length\n",
        "        self.ai = [1.0]*self.ni\n",
        "        self.ah = [1.0]*self.nh\n",
        "        self.ao = [1.0]*self.no\n",
        "\n",
        "        # create weights\n",
        "        self.wi = makeMatrix(self.ni, self.nh)\n",
        "        self.wo = makeMatrix(self.nh, self.no)\n",
        "\n",
        "        # set them to random vaules\n",
        "        for i in range(self.ni):\n",
        "            for j in range(self.nh):\n",
        "                self.wi[i][j] = rand(-0.2, 0.2)\n",
        "        for j in range(self.nh):\n",
        "            for k in range(self.no):\n",
        "                self.wo[j][k] = rand(-2.0, 2.0)\n",
        "\n",
        "        # last change in weights for momentum\n",
        "        self.ci = makeMatrix(self.ni, self.nh)\n",
        "        self.co = makeMatrix(self.nh, self.no)\n",
        "\n",
        "\n",
        "    def backPropagate(self, targets, N, M):\n",
        "\n",
        "        if len(targets) != self.no:\n",
        "            print(targets)\n",
        "            raise ValueError('wrong number of target values')\n",
        "\n",
        "        # calculate error terms for output\n",
        "        output_deltas = np.zeros(self.no)\n",
        "        for k in range(self.no):\n",
        "            error = targets[k]-self.ao[k]\n",
        "            output_deltas[k] = dsigmoid(self.ao[k]) * error\n",
        "\n",
        "        # calculate error terms for hidden\n",
        "        hidden_deltas = np.zeros(self.nh)\n",
        "        for j in range(self.nh):\n",
        "            error = 0.0\n",
        "            for k in range(self.no):\n",
        "                error += output_deltas[k]*self.wo[j][k]\n",
        "            hidden_deltas[j] = dsigmoid(self.ah[j]) * error\n",
        "\n",
        "        # update output weights\n",
        "        for j in range(self.nh):\n",
        "            for k in range(self.no):\n",
        "                change = output_deltas[k] * self.ah[j]\n",
        "                self.wo[j][k] += N*change + M*self.co[j][k] # N is the learning rate, M the momentum\n",
        "                self.co[j][k] = change\n",
        "\n",
        "        # update input weights\n",
        "        for i in range(self.ni):\n",
        "            for j in range(self.nh):\n",
        "                change = hidden_deltas[j]*self.ai[i]\n",
        "                self.wi[i][j] += N*change + M*self.ci[i][j]\n",
        "                self.ci[i][j] = change\n",
        "\n",
        "        # calculate error\n",
        "        error = 0.0\n",
        "        for k in range(len(targets)):\n",
        "            error += 0.5*(targets[k]-self.ao[k])**2\n",
        "        return error\n",
        "\n",
        "\n",
        "    def test(self, patterns):\n",
        "        self.predict = np.empty([len(patterns), self.no])\n",
        "        for i, p in enumerate(patterns):\n",
        "            self.predict[i] = self.activate(p)\n",
        "            #self.predict[i] = self.activate(p[0])\n",
        "\n",
        "    def activate(self, inputs): # here the network is constructed as a function\n",
        "\n",
        "        if len(inputs) != self.ni-1:\n",
        "            print(inputs)\n",
        "            raise ValueError('wrong number of inputs')\n",
        "\n",
        "        # input activations\n",
        "        for i in range(self.ni-1):\n",
        "            self.ai[i] = inputs[i]\n",
        "\n",
        "        # hidden activations\n",
        "        for j in range(self.nh):\n",
        "            sum_h = 0.0\n",
        "            for i in range(self.ni):\n",
        "                sum_h += self.ai[i] * self.wi[i][j]\n",
        "            self.ah[j] = sigmoid(sum_h)\n",
        "\n",
        "        # output activations\n",
        "        for k in range(self.no):\n",
        "            sum_o = 0.0\n",
        "            for j in range(self.nh):\n",
        "                sum_o += self.ah[j] * self.wo[j][k]\n",
        "            self.ao[k] = sigmoid(sum_o)\n",
        "\n",
        "        return self.ao[:]\n",
        "\n",
        "\n",
        "    def train(self, patterns, iterations=1000, N=0.5, M=0.1):\n",
        "        # N: learning rate\n",
        "        # M: momentum factor\n",
        "        patterns = list(patterns)\n",
        "        for i in range(iterations):\n",
        "            error = 0.0\n",
        "            for p in patterns:\n",
        "                inputs = p[0]\n",
        "                targets = p[1]\n",
        "                self.activate(inputs)\n",
        "                error += self.backPropagate([targets], N, M)\n",
        "            if i % 5 == 0:\n",
        "                print('error in iteration %d : %-.5f' % (i,error))\n",
        "            print('Final training error: %-.5f' % error)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6lnw39jbtESq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6775181d-a8c7-4fcf-d4e6-a3dc6c493e2a"
      },
      "source": [
        "# create a network with two inputs, ten hidden, and one output nodes\n",
        "ann = MLP(2, 10, 1)\n",
        "\n",
        "%timeit -n 1 -r 1 ann.train(zip(X,y), iterations=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "error in iteration 0 : 31.50196\n",
            "Final training error: 31.50196\n",
            "134 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2wmL6kEytESr"
      },
      "source": [
        "# Helper function to plot a decision boundary.\n",
        "# This generates the contour plot to show the decision boundary visually\n",
        "def plot_decision_boundary(nn_model):\n",
        "    # Set min and max values and give it some padding\n",
        "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
        "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
        "    h = 0.01\n",
        "    # Generate a grid of points with distance h between them\n",
        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
        "                         np.arange(y_min, y_max, h))\n",
        "    # Predict the function value for the whole gid\n",
        "    nn_model.test(np.c_[xx.ravel(), yy.ravel()])\n",
        "    Z = nn_model.predict\n",
        "    Z[Z>=0.5] = 1\n",
        "    Z[Z<0.5] = 0\n",
        "    Z = Z.reshape(xx.shape)\n",
        "    # Plot the contour and training examples\n",
        "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n",
        "    plt.scatter(X[:, 0], X[:, 1], s=40,  c=y, cmap=plt.cm.BuGn)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQqVABnLtESr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "outputId": "dd6ddb58-bf45-42aa-b907-57798d7aeb09"
      },
      "source": [
        "plot_decision_boundary(ann)\n",
        "plt.title(\"Model with 1 iteration\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Model with 1 iteration')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlBLkkUbtESr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcf53b69-8aa2-409c-bc58-429245bb84b5"
      },
      "source": [
        "# create a network with two inputs, ten hidden, and one output nodes\n",
        "ann = MLP(2, 10, 1)\n",
        "\n",
        "%timeit -n 1 -r 1 ann.train(zip(X,y), iterations=300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "error in iteration 0 : 29.51828\n",
            "Final training error: 29.51828\n",
            "Final training error: 25.23173\n",
            "Final training error: 25.07840\n",
            "Final training error: 25.04339\n",
            "Final training error: 25.01602\n",
            "error in iteration 5 : 24.98375\n",
            "Final training error: 24.98375\n",
            "Final training error: 24.94682\n",
            "Final training error: 24.90732\n",
            "Final training error: 24.86717\n",
            "Final training error: 24.82774\n",
            "error in iteration 10 : 24.78989\n",
            "Final training error: 24.78989\n",
            "Final training error: 24.75409\n",
            "Final training error: 24.72051\n",
            "Final training error: 24.68916\n",
            "Final training error: 24.65995\n",
            "error in iteration 15 : 24.63276\n",
            "Final training error: 24.63276\n",
            "Final training error: 24.60744\n",
            "Final training error: 24.58386\n",
            "Final training error: 24.56190\n",
            "Final training error: 24.54144\n",
            "error in iteration 20 : 24.52238\n",
            "Final training error: 24.52238\n",
            "Final training error: 24.50463\n",
            "Final training error: 24.48809\n",
            "Final training error: 24.47269\n",
            "Final training error: 24.45833\n",
            "error in iteration 25 : 24.44495\n",
            "Final training error: 24.44495\n",
            "Final training error: 24.43247\n",
            "Final training error: 24.42081\n",
            "Final training error: 24.40992\n",
            "Final training error: 24.39972\n",
            "error in iteration 30 : 24.39017\n",
            "Final training error: 24.39017\n",
            "Final training error: 24.38121\n",
            "Final training error: 24.37280\n",
            "Final training error: 24.36489\n",
            "Final training error: 24.35744\n",
            "error in iteration 35 : 24.35041\n",
            "Final training error: 24.35041\n",
            "Final training error: 24.34378\n",
            "Final training error: 24.33752\n",
            "Final training error: 24.33158\n",
            "Final training error: 24.32596\n",
            "error in iteration 40 : 24.32063\n",
            "Final training error: 24.32063\n",
            "Final training error: 24.31556\n",
            "Final training error: 24.31073\n",
            "Final training error: 24.30614\n",
            "Final training error: 24.30175\n",
            "error in iteration 45 : 24.29755\n",
            "Final training error: 24.29755\n",
            "Final training error: 24.29354\n",
            "Final training error: 24.28968\n",
            "Final training error: 24.28598\n",
            "Final training error: 24.28242\n",
            "error in iteration 50 : 24.27899\n",
            "Final training error: 24.27899\n",
            "Final training error: 24.27567\n",
            "Final training error: 24.27246\n",
            "Final training error: 24.26935\n",
            "Final training error: 24.26633\n",
            "error in iteration 55 : 24.26339\n",
            "Final training error: 24.26339\n",
            "Final training error: 24.26053\n",
            "Final training error: 24.25774\n",
            "Final training error: 24.25501\n",
            "Final training error: 24.25233\n",
            "error in iteration 60 : 24.24970\n",
            "Final training error: 24.24970\n",
            "Final training error: 24.24712\n",
            "Final training error: 24.24458\n",
            "Final training error: 24.24207\n",
            "Final training error: 24.23960\n",
            "error in iteration 65 : 24.23715\n",
            "Final training error: 24.23715\n",
            "Final training error: 24.23472\n",
            "Final training error: 24.23232\n",
            "Final training error: 24.22993\n",
            "Final training error: 24.22755\n",
            "error in iteration 70 : 24.22518\n",
            "Final training error: 24.22518\n",
            "Final training error: 24.22282\n",
            "Final training error: 24.22046\n",
            "Final training error: 24.21809\n",
            "Final training error: 24.21573\n",
            "error in iteration 75 : 24.21335\n",
            "Final training error: 24.21335\n",
            "Final training error: 24.21095\n",
            "Final training error: 24.20855\n",
            "Final training error: 24.20611\n",
            "Final training error: 24.20366\n",
            "error in iteration 80 : 24.20116\n",
            "Final training error: 24.20116\n",
            "Final training error: 24.19863\n",
            "Final training error: 24.19606\n",
            "Final training error: 24.19344\n",
            "Final training error: 24.19075\n",
            "error in iteration 85 : 24.18800\n",
            "Final training error: 24.18800\n",
            "Final training error: 24.18517\n",
            "Final training error: 24.18225\n",
            "Final training error: 24.17922\n",
            "Final training error: 24.17609\n",
            "error in iteration 90 : 24.17282\n",
            "Final training error: 24.17282\n",
            "Final training error: 24.16940\n",
            "Final training error: 24.16581\n",
            "Final training error: 24.16202\n",
            "Final training error: 24.15801\n",
            "error in iteration 95 : 24.15375\n",
            "Final training error: 24.15375\n",
            "Final training error: 24.14920\n",
            "Final training error: 24.14431\n",
            "Final training error: 24.13903\n",
            "Final training error: 24.13330\n",
            "error in iteration 100 : 24.12706\n",
            "Final training error: 24.12706\n",
            "Final training error: 24.12020\n",
            "Final training error: 24.11261\n",
            "Final training error: 24.10417\n",
            "Final training error: 24.09469\n",
            "error in iteration 105 : 24.08395\n",
            "Final training error: 24.08395\n",
            "Final training error: 24.07164\n",
            "Final training error: 24.05737\n",
            "Final training error: 24.04060\n",
            "Final training error: 24.02056\n",
            "error in iteration 110 : 23.99621\n",
            "Final training error: 23.99621\n",
            "Final training error: 23.96606\n",
            "Final training error: 23.92802\n",
            "Final training error: 23.87910\n",
            "Final training error: 23.81522\n",
            "error in iteration 115 : 23.73084\n",
            "Final training error: 23.73084\n",
            "Final training error: 23.61900\n",
            "Final training error: 23.47160\n",
            "Final training error: 23.28031\n",
            "Final training error: 23.03798\n",
            "error in iteration 120 : 22.73989\n",
            "Final training error: 22.73989\n",
            "Final training error: 22.38413\n",
            "Final training error: 21.97101\n",
            "Final training error: 21.50168\n",
            "Final training error: 20.97679\n",
            "error in iteration 125 : 20.39568\n",
            "Final training error: 20.39568\n",
            "Final training error: 19.75640\n",
            "Final training error: 19.05682\n",
            "Final training error: 18.29702\n",
            "Final training error: 17.48252\n",
            "error in iteration 130 : 16.62625\n",
            "Final training error: 16.62625\n",
            "Final training error: 15.74637\n",
            "Final training error: 14.85984\n",
            "Final training error: 13.97520\n",
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            "error in iteration 135 : 12.15267\n",
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            "error in iteration 275 : 6.57468\n",
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            "error in iteration 280 : 6.55973\n",
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            "error in iteration 285 : 6.54431\n",
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            "error in iteration 290 : 6.52841\n",
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            "error in iteration 295 : 6.51207\n",
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            "Final training error: 6.50543\n",
            "Final training error: 6.50209\n",
            "Final training error: 6.49873\n",
            "21.5 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Th3z2fkVtESr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "outputId": "1c73cb87-f712-46f5-e8f4-91c67e5f658b"
      },
      "source": [
        "plot_decision_boundary(ann)\n",
        "plt.title(\"Model with 300 iterations\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Model with 300 iterations')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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j6tSpGD16NDZt2oSPP/4Y//vf/xAaGmpve9ttt+Gdd97BuHHjcO+998JoNOKll15CVFQU5syZU+U44+PjoSgKnnvuOeTk5MBsNmP48OGIjIzEokWLcPXVV6NXr164/PLLERERgePHj+P777/HhRdeWClxPlunTp3Qtm1b3HvvvUhMTERgYCBWrFjhMGE8//zzAdgKeYwePRqKojgsJQ8ATz/9NNauXYuBAwfitttug8FgwNtvv42SkhI8//zzVf4efPDBB3jzzTcxadIktG3bFnl5eXjnnXcQGBhYYQSPiMjrvFUOj4iosTuzXLcrZ5frFsJWrvmee+4RsbGxwmg0ivbt24sXXnihQtljIYQoKioSd911lwgLCxN+fn5iwoQJ4sSJEw5LYKekpIjbb79dtGjRQhiNRhEdHS1GjBghFi9ebG/jabnucpGRkQKASElJsR/79ddfBQAxaNCgSu3PLtcthBC//fabOP/884XJZKoQ97XXXiv8/Pwq9fH44487LS1drqSkRMyaNUt0795dBAYGCqPRKFq2bCmuv/56ceTIEYfnLF68WHTs2FGYTCbRtm1b8fLLL1f6fgshxIkTJ8Sll14qAgMDhb+/vxg/frw4cOCAy3jKnV2uWwgh3nnnHdGmTRuhKEql0t3r168Xo0ePFkFBQcJisYi2bduKGTNmiC1bttjbOPs+CSHE7t27xUUXXST8/f1FeHi4uPHGG8X27dsr/YxVVRV33nmniIiIEJIkVfj+Ovosbd26VYwePVr4+/sLX19fMWzYMPHbb79VaOPs879+/foK73Pr1q1i+vTpIi4uTpjNZhEZGSnGjx9f4T0SETUEkhBnjW8TERERERE1MVxjRERERERETR4TIyIiIiIiavKYGBERERERUZPHxIiIiIiIiJo8JkZERERERNTkMTEiIiIiIqIm75zb4FXXdZw6dQoBAQGQJMnb4RARERERkZcIIZCXl4fY2FjIsusxoXMuMTp16hRatGjh7TCIiIiIiKiBOHHiBJo3b+6yzTmXGAUEBAAAvuowHH7KOff2iIiIiKgRMX9/MR56NcrbYTRZakkh/lx0rT1HcOWcyxzKp8/5KQb4KUYvR0NERERETZklwAcGs6+3w2jyPFliw+ILRERERER1ID5BxQf7Ld4OgzzExIiIiIiIqA7MnXgNtn8b7O0wyENMjIiIiIiIaln/Jd2ZFDUyTIyIiIiIiKjJY2JERERERFSL4hNU3G3t6u0wqIqYGBERERER1ZL4BJVrixopJkZERERERLVk7/3TmBQ1UufcPkZERERERN4wYMccDH2wyNthUDVxxIiIiIiIiJo8JkZERERERDUUn6Bi1m9J3g6DaoCJERERERFRDbHgQuPHxIiIiIiIqAbiE1Rvh0C1gIkREREREVENcbSo8WNiRERERERUTeX7FlHjx8SIiIiIiKiauG/RuYOJERERERERNXnc4JWIiIiIqBr6L+mOYQuivR0G1RKOGBERERERVVF8gophKwZ6OwyqRUyMiIiIiIiqyGdqL2+HQLWMiRERERERETV5TIyIiIiIiKqg/5LunEZ3DmJiRERERERUBf+2buftEKgOMDEiIiIiIqImj+W6iYiIiIg8NGDHHAx9sMjbYVAd4IgREREREZEH+i/pzqToHMbEiIiIiIjIDRZcOPcxMSIiIiIiciE+QcXd1q7eDoPqWJ0mRvPnz0efPn0QEBCAyMhITJw4Efv27XN73hdffIFOnTrBYrGgW7duWLVqVV2GSURERERETVydJkYbNmzA7bffjj/++ANr166F1WrFqFGjUFBQ4PSc3377DdOnT8f111+Pf//9FxMnTsTEiROxc+fOugyViIiIiMghn6m9sP3bYG+HQXVMEkKI+rpYWloaIiMjsWHDBgwePNhhm8suuwwFBQVYuXKl/Vi/fv0QHx+Pt956y+01cnNzERQUhDWdR8FPMdZa7ERERETU9HBtUeOmlhRi88KpyMnJQWBgoMu29brGKCcnBwAQGhrqtM3vv/+Oiy66qMKx0aNH4/fff3fYvqSkBLm5uRX+EBERERHVBqnPSG+HQPWk3hIjXddx991348ILL0TXrs4XryUnJyMqKqrCsaioKCQnJztsP3/+fAQFBdn/tGjRolbjJiIiIiKic1+9JUa33347du7cic8++6xW+507dy5ycnLsf06cOFGr/RMRERFR08TNXJsWQ31c5I477sDKlSuxceNGNG/e3GXb6OhopKSkVDiWkpKC6Ohoh+3NZjPMZnOtxUpEREREFJ+gYtZvSQCCvR0K1ZM6HTESQuCOO+7AV199hZ9//hmtW7d2e07//v3x008/VTi2du1a9O/fv67CJCIiIiKiJq5OR4xuv/12LFu2DN988w0CAgLs64SCgoLg4+MDALjmmmvQrFkzzJ8/HwAwa9YsDBkyBC+++CLGjRuHzz77DFu2bMHixYvrMlQiIiIiIjvf5x/Adk6ja1LqdMRo0aJFyMnJwdChQxETE2P/s3z5cnub48ePIykpyf71gAEDsGzZMixevBg9evTAl19+ia+//tplwQYiIiIiotrCtUVNU52OGHmyRdIvv/xS6djUqVMxderUOoiIiIiIiMi1relHADhe307nrnrdx4iIiIiIiKghYmJERERERFTGsn4yZi/gaFFTVC/luomIiIiIGjquLWraOGJERERERE0ekyJiYkRERERETZ6t4AI1ZUyMiIiIiIioyWNiRERERERNGgsuEMDiC0RERETUhPVf0h3DmBQROGJERERERETExIiIiIiImq5/W7fzdgjUQDAxIiIiIqImiWuL6ExMjIiIiIioyYlPUPHBfou3w6AGhIkRERERERE1eUyMiIiIiKjJ2Xv/NGz/NtjbYVADwsSIiIiIiJoUri0iR5gYEREREVGTwaSInGFiRERERERNAgsukCtMjIiIiIiIqMljYkRERERETQILLpArTIyIiIiI6JzHtUXkDhMjIiIiIiJq8pgYEREREdE5jUUXyBNMjIiIiIjonDZ34jVcW0RuMTEiIiIionOWZf1kJkXkESZGRERERHROYsEFqgomRkRERERE1OQxMSIiIiKicw4LLlBVMTEiIiIionMON3OlqjJ4OwAiIiIiotrUf0l3DOPaIqoijhgREREREVGTx8SIiIiIiM4Z8Qkq7rZ29XYY1AgxMSIiIiKicwY3c6XqYmJEREREROcEbuZKNcHiC0RERETU6A3YMQdDHyzydhjUiHHEiIiIiIiImjwmRkRERETUqMUnqJj1W5K3w6BGjokRERERETVqLLhAtYGJERERERE1WvEJqrdDoHMEEyMiIiIiatQ4WkS1gYkRERERETVK8Qkq5k68xtth0DmC5bqJiM4B5pbN4N+7KyRZRsHO/Sjac8jbIRER1anypIijRVRbmBgRETVixohQtHxyFgL794QQAgAgSRIKdh3AsUdeRvGRk16OkIiobuy9fxq2Lwj2dhh0DuFUOiKiRkoJ8EOHJfMR0KcbAFtCJEkSAMC3Yxt0+OB5mJpFeTNEIqI60X9Jd8xeEO3tMOgcw8SIiKiRipg+HqbYSEiGyoP/kkGB4mtBzE2XeSEyIiKixoeJERFRIxV+6RhIiuL0dclgQEjCEMg+lnqMioiobsUnqLjb2tXbYdA5iIkREVFjJEkwRYa5bSabjDCEBdd9PERE9WTv/dNYcIHqBBMjIqLGSAjoRSUeNdULCus4GCIiosaPiRERUSOV+eMmCNX5ju9C05D3z06oWbn1GBURUd1h0QWqS0yMiIgaqdSPvoYQAkLXK70mhABkGcnvfuGFyIiIah/XFlFdY2JERNRIFR86jsN3/w96SSmErkPotn2MhKYBuo7j815H3u//ejlKIqKa42auVB+4wSsRUSOWu3krdo65HmEXj7DtZ6TIKNy5H+kr1sCamuHt8IiIagWTIqoPTIyIiBo5LScPqR99jdSPvvZ2KEREtS4+QcVybwdBTQKn0hERERFRg8bRIqoPTIyIiIiIqEEqX1tEVB+YGBERERFRg+T7/AMcLaJ6w8SIiIiIiIiaPCZGRERERNTg9F/SHUMfLPJ2GNSEMDEiIiIiogYlPkHFsBUDvR0GNTFMjIiIiIiowWDBBfIWJkZERERE1GDsvX8aCy6QVzAxIiIiIiKiJs/g7QCIiIiIiABbwYVhC6K9HQY1URwxIiIiIiKvi09Qcbe1q7fDoCaMiREREREReVV5wQWuLSJvYmJERERERF7FggvUEDAxIiIiIiKiJo/FF4iIiIjIa+ITVIxlwQVqADhiRERERERewc1cqSFhYkREREREXuEztRfXFlGDwcSIiIiIiLxC6jPS2yEQ2TExIiIiIqJ6139Jdwx9sMjbYRDZMTEiIiIionoVn6Bi2IqB3g6DqAImRkRERERUb1hwgRoqJkZEREREVG98n3+ABReoQWJiRERERERETV6dJkYbN27EhAkTEBsbC0mS8PXXX7ts/8svv0CSpEp/kpOT6zJMIiIiIqoHLLhADVmdJkYFBQXo0aMH3njjjSqdt2/fPiQlJdn/REZG1lGERERERFQf4hNU3G3t6u0wiJwy1GXnCQkJSEhIqPJ5kZGRCA4Orv2AiIiIiMhruLaIGrIGucYoPj4eMTExGDlyJDZv3uztcIiIiIiohnym9vJ2CEQu1emIUVXFxMTgrbfeQu/evVFSUoJ3330XQ4cOxZ9//olevRz/MpWUlKCkpMT+dW5ubn2FS0REREQe6L+kO/ctogavQSVGHTt2RMeOHe1fDxgwAIcOHcLLL7+Mjz76yOE58+fPx7x58+orRCIiIiKqAm7mSo1Fg5xKd6a+ffvi4MGDTl+fO3cucnJy7H9OnDhRj9ERERERkTPczJUakwY1YuTItm3bEBMT4/R1s9kMs9lcjxERERERkSd8pvbC9hXB3g6DyCN1mhjl5+dXGO05cuQItm3bhtDQUMTFxWHu3LlITEzEhx9+CABYuHAhWrdujS5duqC4uBjvvvsufv75Z6xZs6YuwyQiIiIioiauThOjLVu2YNiwYfavZ8+eDQC49tprsXTpUiQlJeH48eP210tLSzFnzhwkJibC19cX3bt3x7p16yr0QUREREQNHwsuUGMjCSGEt4OoTbm5uQgKCsKazqPgpxi9HQ4RERFRk2RZPxmzF0R7Owxq4tSSQmxeOBU5OTkIDAx02bbBF18gIiIiIiKqaw2++AIRERERNS79l3THMI4WUSPDESMiIiIiqjVcW0SNFRMjIiIiIqoVTIqoMWNiREREREQ1Fp+g4m5rV2+HQVRtTIyIiIiIiKjJY2JERERERDXmM7UXtn8b7O0wiKqNiRERERER1QjXFtG5gIkREREREVVbfILKpIjOCUyMiIiIiKha4hNUzJ14jbfDIKoVTIyIiIiIqNq4rojOFUyMiIiIiKhafKb28nYIRLWGiRERERERVRkLLtC5hokREREREVXZv63beTsEolrFxIiIiIiIiJo8g7cDICIiIqLGpf+S7hi2INrbYRDVKiZGREREjYgS6I+wi4fDt0t7CE1H3t//IevHTRDFpd4OjZoIri2icxUTIyIiokYiZPQgtHxyFiSjARACEEDouKFoPnsmDs16GgXb9ng7RDrHMSmicxkTIyKiM/h0aIWIy8cjaEhfSAYDivYfRtryVcj+6XfbjSiRl/j37opW8+cAACS54hJhxd8X7d58Anum3oXSxBRvhEdNBAsu0LmMxReIiMqEjh+GTp8uRNjFw2EMC4YhyB/+PbugzYIH0fq5+wCFf2WS98TcfDkgRKWkCAAkRYFsMiLyyou9EBkR0bmB/8oTNSayDNnflzfodcCnfSu0nDcLkCVIhtOD6ZJBAQAEX3Qhoq+b4q3wqIkzhAQhoE93SIritI1kMCB0/LB6jIqamv5LumM2Cy7QOYxT6YgaAVNsFKJmTELYhBGQfczQS63IWr0BKUv/D8VHTno7vHNCxOXjAKFDkhz/tSjJEiKuvBjJS/8PULV6jo6aOiXQ37N2/n51HAk1VVxbRE0BHzsTNXA+HVqh0/KFCJ88CrKPGQAgm4wIHTcUnT59Gf69ung5wnND0OA+FUaKHDGGBMG3Q+t6iojoNDUzG0Jzn5BbM7LqIRpqiqQ+I70dAlGdY2JE1JBJEtq8OBeKj6XSTbtkMEAyGtDmpbmQTEYvBeiaZDIi/NIx6PzFq4j/60t037QMcY/fCZ/2rbwdWiWS0bMBdE/bEdUmLa8A2T//AaGqTtsITUPGih/rMSoionMLEyOiBiygXzzMLWLs61zOJikKDMGBCLnownqOzD3Zx4L27/wPLR6+FZa2cZBNJhgC/BE2YRg6ffoygi8a4O0QKyjcexjCzRQ5YVVRfDSxniIiqijprWXQrarDkSOharCmZyH1s5VeiIzOdQN2zMHQB4u8HQZRnWNiRNSA+fc8D7rV+RNiANCtKvx6nVdPEXmu+b3Xw69re0iSVKGKlmQwALKEVvPvhSkmwosRVpS2/HunCShgu/HM/HETtJy8eoyK6LTiQydw4MaHUZqUBsD2mSxPkgr3HML+6x6Els3PJ9Wu/ku6MymiJoNzQogaMsnDZpKHDeuJEhSA0AnDnVbQkmQZEALhlybg1Gsf1nN0juX88hcyV29AyJjBlb6ftqfxmUhcuNQ7wVHjJEm1vvdV4c4D2DXhZgRc0AO+XdoDmoa8v/9D4a6DtXodIoAFF6jpYWJE1IAVbN8L2c2aFtloQH497XavBPgh7JIR8O3aARBA/j87kblqA/TCik8T/Xt0guxm3ZOkKAi8sFeDSYwgBI4+/DKK9h9F5FWXwBgWDMA2Ipf1w0YkvvIB1HQubCfX/Ht3Q+TVlyBwQC9IioLiw8eR9tn3yPh6ncv1QVUiBPL+2Ia8P7bVTn9ETkh9RgIrOFpETQcTI6IGLPe3f1FyKgWmqHCHoy9C06HlFyBrza91HkvwiP5o9cwcW/EBAQACIWMGodnd1+Lw7PnI++s/e1t31d1Ot3M+dc0rdB0p769Aykdfw6dtS0hGA0qOJULLK/B2ZA4FDuiFiMvHwfe8dhCqhpxNfyNt+SoUHzzm7dCapMirLkHze6+HUDX7Z9vSpgVaPHwrgkdeiEN3PglRavVylERE5AzXGBE1ZLqOI/c+B724tFJhAKGqEJqKI/c9B1FSWqdh+MV3RuvnH4BkNECSZUiKDElRIEkSZB8ftH3tMVjatLC3L9x7GEJ3PYVIqCoKduyv07irTdVQtO8wCnfub5hJkSQh7rE70O7NJxA4oBeM4SEwRYcjfNJIdF7+CkIvHu7tCJsc364d0Pze6wFUTPglWYYkSQjo3Q0xN1/urfCIqoxri6gpYmJE1MAV7j6IvdPvRsZ3P0Mve9osVA1Za3/DvqvurTBSU1eib5gKCFGhiEI5W5IkI/LqifZjpadSkPvbVpdV3iSDAWmfr6qLcM95EdPHI2ySbU+RCjfhZYUtWj5xF3w6tfFWeE1S5PTxLqfKSYqM8GkJDba0PtGZ4hNUri2iJomJEVEjUHI8CcfnvYbtF16G/4Zfg20DpuHo3AUo2n+0zq8t+/sicMD5Lqe9SQYDQscOqXDsxDOLoObkVR7p0nUAQNLi5Sjac6j2Az7XyTKirpnodFG/JEmAriPyign1G1cTF9A/3u0UUkOAP3zat6yniIiIqKqYGBE1IsKqQs3Mrtd1Coq/LyTZfdU72Wyq8DS89FQq9l4xG5nfr7ePdAFA8dFEHH3kZSS9+UmdxHuus7RqBlN0hMPRu3KSwYCgIX3rMSpy9fOoQG5g6+qIHPB9/gFvh0DkFSy+QEQuqdm50EutbqvMqTl5lRI2a0o6jj3+Kk48/w5MMZHQi4pRmphSl+Ge8ySjZ1OxPG1HtaPgv30IHNDT5aiRXlyC4sPH6zEqoqrjZq7UlHHEiIhcEsWlyFq9weV6IaFpSF/xo9PX9YIiFB88xqSoFpScTIJe6rrYhtA0FB88Wj8BEQAg7bPvXSZFQtWQ8e3P0At4w0kN29b0I94OgchrmBgRkVvJ734BvbjYYXIkVA1qZg5SP/nWC5HVAYOC4IsGIPbOaxBz6xXw79XF2xFVoBcUIXPlL64LWygK0j5jYYv6lPvbVqR+uhLA6XV05YSqofhYYsPZs4uIiBziVDoicqvkRBL2XzcXrZ67Dz5tWkBotptySVFQuO8wjjzwAtSMbO8GWQsC+nZHq/n3whgWDN2qQpKAmJsvR9GBozg8ez5KTiR5O0QAwKk3PkZg/54wRoRWKoohdB25v/6DzB83eim6puvkc4tRtO8Ioq6dBEvr5gAALa8A6St+RPJ7XzTM0u9EZ+A0OmrqJCGclDZqpHJzcxEUFIQ1nUfBT+Ece6La5t+rC/y6d4QQAvn/7EThzgPeDqlW+HZtjw7vP2ffp+lMQlWhZuViz7S7oGbleinCigzhIWh29wyEjhlkn8Kl5uYj7bPvkbx4ucvS0VT3jJFhkIwGWFMzIKz8WVDDx6SIzlVqSSE2L5yKnJwcBAYGumzLESMiqpL8rbuQv3WXt8OwkSQE9ItHQO9ukBQZBTv2IXvDX4CLaWbOxNx6JSRJqpQUAbYqb4bQIIRPG4vktz+rjchrTE3PwrFHXsbJBe/Cp00chKraNtatx4qF5Jw1NcPbIRB5jEkRkQ0TIyJqlCxtW6DNyw/DEhcLYVUhAEQZDbCmZ+HIA88j/x/PkzdDaDAC+/d0WZZcUhSETxrZYBKjclp2ntcS1YB+8YiYNha+XdpBqBpyNm1B+vLvUXzkpFfiIaLqsRVciPZ2GERex+ILRNToGCPD0OG9+TDHRgEAJKMBstH2nMcQEoh2b86DT4dWnvcXFuzRXk2GsODqhHtOavHQrWj/1pMIGtwbpqhwmJtFIeLSMej85esIHTvU2+ERERFVGRMjImp0Iq+8GIq/X6XCA4BtZEdSFETfdJnH/anZnq0b0nLyPe7zXBZx2ThETEsAgAolqiWDAsgSWj51N3w6tvZWeERUBQN2zMHsBRwtIgKYGBFRIxQ28SKHSVE5yaAgeFh/yP6+HvVnTctE3pad9mp7jghNQ8Y3P1U51sZC9vVB+NQEtHtzHjosfQ5xj94O3/PaVW4oSYiaMblSSerTL0uA0BExfUIdR0xENdV/SXeuLSI6AxMjImpcZBmGoAC3zSRFhiHYdfWZMyUt+gSA5PCGX6gatPxCpC1fWZVIGw2fjq3RZeVitHjoFgT06wH/+M4Iu2QEOi17CS0eugWQTk8ztLRuDlNMBCTZ+T8fksGA4GEX1EfoRFQDUp+R3g6BqEFhYkREdcanY2tEXD4OEdPHOx59qA5dh+rBfjBC16Hl5Hncbf4/u3DkvuegF5dACAHdqkIvK7NsTc/EgRsehjU1s9phN1RKgB/av/UUDIH+tqp8ZQlP+RS58KkJiJp5qb29ZPRsGwRP2xGR99iKLhBROValI2rCZD8fhI4fhqDBfSCbTCg6cATpK35E8aETNerXFBuFVs/OgX/3TvYRGEmWUbD7II7OfRElxxJr1H/mdz8hYto4p9PphKoh59ctVd5QM/vn35E74l+EjB0C385tAU1D7h/bkbPxL0BzPHWssQu7ZASUIH+nI0CSJCHq2klI/ehriFIrShKToZdaIZucJz5CCGgFhYAin7PfN6LGjiW6iSrjiBFRE+XbrSO6rnoXLR64GYH9eyKgTzdETBuH81a8gdg7rqp2v4awYHT84Dn4ndcegC0hKr/p9u3QGh2XPgdjdHiNYk/9+FvoxcUO1wQJTQeEQPI7y6vVt15UjIwVP+LE02/ixPy3kbP+j3P65j5k1EAArivyGQL90ezuawGDAj2/EJmrNkC42CtKkiQYw4LR8sm7azdYIqoV8QkqZv2W5O0wiBocJkZETZAhPATtFz0Bxd8Xknzm9CnbCEz0DdMQfumYavUddc1EGEKCHFeMMyhQAvwQfcbUrOooPZWK/Tc+AmtGNgBUmPamFRTi4F1PoXDXwRpdo6mQ/f08KlUeMX0C2i58BJLBgFOvfwRrRhaEEE7bS7KMsHFD4duto+06vj4IGT0I4dMSEDSkb4VqdkRERA0B/2UiaoIiLh0D2ccCSXEyFU0IRN8wDen/twZwUn3MIUlC2KRRbivGhU0YgZPPvwuhqlUN3a5ozyHsHHsDgof0hX/vbpAUGQU79iNrza8QJaXV7repKTmaCEtcrMufGWAbBQoc0BNRM6cgefFynHzxPbR5/gGX5whVRfjkUbBe2AtRMyZDtpghdAFJlqBm5eLky0uQ+e3Ptfl2iMgDe++fhu0Lgr0dBlGDw8SIqAkKGTPIaVIE2G6CTdHh8O3cpkojL7KPBYZAfw/amaEE+UMtG/GpNlVD9k+/I/un32vWTxOW/n8/Inh4P4/aSrKMiMvHIXnJl1AC3P+cJYMBgQN6whgZZivjDdhHp5TgALR68m5IioKMr9ZW/w0QUZVY1k/mvkVETnAqHVETpHi4v4/i51m7cnpJiX1KmytC16EXcNFvQ5C7eSuyN/zldF+isxlDg2GJi4WeX+i2rdB0mKLC7UnRmSRJghACzedcD8liqnLcRFR13MyVyDUmRkRNUPGxJJebmZYrSUypWseajux1v7mcIidUDTmbtkAvLqla31Q3hMCRe59F2vJVLtcMVSBJyP1tK3Q3UxYlRbYVw3DajQTZzwfBw/tXJWIiqob4BJVV6IjcYGJE1ASlr/jB5VQ6oWnI+/s/lFY1MQKQ8sH/AQKON0rVdUCSkLLkyyr3S3VHWFWcfG4x8rfucpnIALb1Zx2WzEezOdcj68dNTkeahKpBKyoG4CbZ0jSYY6OqGTkREVHtYWJE1ARlrfm17CbYUblrDULVcPKl96vVd9Hewzg0+xmIUiuErlf8o2o48uALKNi+t6ZvgepA6sffQlKc/7MghIAkSTAEBSBs/FCEjhuK/LKfZfkoYXliVZqajpwNf0PobhIjWbbteUREdWrv/dO8HQJRg8fiC0RNkarh4O3z0GLuzQgdN7TC6FHx0UQce/xVFO05VO3uczdtwY5R1yHs4hHw73UeIEko2L4X6V+vhZadVxvvgOpAzvo/kPblD4i4dAyErlfa9PXMtUKSwQCh6/Dv3hGH7n0WQQN6wRwXCy2/ANlrNiNr3Wb49+qC0DGD3F43++c/av29NGWSwYCg4RfAt3M7QNeR9+d25P31n7fDIi9iwQUizzAxImqi9KJiHHvsFSS+8gECL4iHZDai6OBxFO7YVyv9a7n5SP34G6R+/E2t9Ef148TTb6Jw1wFEXTMJltbNXbaVZBlC1RDQ8zwcf/L1Sq/n/bkdhbsPwqdDK4f7FglNR+b362FNSa+1+Ju6gL7d0erZe2EMDbYVQpGA6OunoujwCRy+5xmUHEv0dohERA0Wp9IRNXFqRjYyV/2CjK/W1lpSRI1bxldrsXvSbTg0Z77btpJBQeDA8x2/KAQO3vkkig6dsH2pamX/tU27y/l1C47/b1HtBE3wPa8d2r7xOAxBgQAA2WiAXJaQWuJi0OG9Z2AIDfZihOQtH+y3eDsEokaBI0ZEROSQXuhZBStHo0Hl1Ixs7L3iHgQN6oOQMYNgCAlCaVIaMr5ei4Jte2orVAIQc+sVkCTZ4ToxyWCAISQQEZePQ9Kbn3ghOvIWy/rJ3MyVyENMjIiIyKGiA8cgNN11QQZVReGuA6470nTk/PIncn75s5YjrB++57VDxOXj4N+7GyAB+f/sQtpnK1G40837rkeGkEAEXni+fQNdRyRFQdikkUyMmpD+S7pjGNcWEXmMiRER0Rn8e3dFxGXj4Ne9I4SmI3fzP0hbvgrFB495O7R6p6ZnIXv9HwgeegEkg+Py7pLBgLTPV9VzZPUnasZkNLt7BoSq2kfGTGNCETZ+GBJf+wgp733h5QhtDKFBLpOicsbQoHqIhoioceIaIyKiMs3uvR4d3n0GQcMugCkqHObYSIRPGonOn7+CsEkjvR2eV5x8/h1YM3Ps64PKle9flPrpSuRv2emN0Opc4MDz0ezuGQAqThcs//9md16NoKEXeCO0StQsz6o9qqwK2WTEJ6i429rV22EQNSpMjIiocZIkmFs1g0+HVpD9fGrcXdikkYi66hIAsC9YB2w3wZIsI+7RO+DbrWONr9PYWFMzsO+q2cj8fj30Uqv9eGlSGo7/bxFOPrfYi9HVrahrJznc66uc0DREXTOx/gJyQc3MRu6f213Hq2rI+PaneoyKvGnv/dOw/dtgb4dB1KhwKh0RNTrh0xIQNWMyzLFRAAC9tBSZK3/BqTc/gZqeVa0+o2ZMdrh3j52uIfLKCTj6YC1U7lNk+HVpD9nPB6UnU1ByIqnmfdYha2omjj3+Kk4ueA+m2CiI0lIUH00EhJvNWxsxyWREQJ/urtsoCvx7dYHsY4FeVFxPkTmXtGgZAt57xuHnWKgatIJCpH260kvRERE1fEyMiKhRaTH3ZkRcNs4+lQsAZJMJYRePQOCAXth39b2wpmVWqU9TbBQsLZu5bCMZDAga0rdaMZ8pYvp4RF8/FcbwEPuxvC07cXLBuyjae9jpebKPBWEXj0DY5JEwRYZDzclFxnfrkb7ih0qb5sq+PjCGBUPNzYeWU3tTp7S8AhTtcx7juUQyev7Po2QyAg0gMSrYtgeHZ89Hq//NhuznY5/+KBsNsKZl4tCsp2BNzfBylFQf4hNUjGXRBaIqY2JERFWiBPojYtpYhF86GsaIMGj5BchctQFpy76r85GPgL7dEXHZOACo9ERcMigwhoeg2ZyZOPrggir1K5k8+6tQrsLNsiPN7rnONj3rrJEW/56d0fGD57H/+rkOK50ZwkPQ4d3/wRzXDICAJMtQggMQe9uViLxiAg7c+DCKD5+AuVUzxNx0OUJGDbQXS8j9czuSF3+G/H922aqrXTYW/ufb1h3k/fUf0pavajLJTlUYw0OhZudCCQqAJDkvamDNzIaWm1+PkbmWs+Ev7LjoWoSMGQzf89pBaBry/tqOnI1/A5ruvgNq9OITVMydeA3wrbcjIWp8JHH2v9CNXG5uLoKCgrCm8yj4KUZvh0N0TjFGhqHD0mdhigoHZNl+wyhUDbrVioO3PYGCf3fX2fVbv/gggof0dblvjlA17Bg5A2pWjsf9ShYTevzyCWSL2Xm/uo7ig8exZ9pdVYq5nE/H1ui8/BXn/Wsaig4ex97LZlV6rf2S+fDv3slhZTihaihNzcDh+55Fh8X/g2w2Vvj+2NacSMj6cRNCxw6pUF2t/P9PLngPqR9/U633VRMhowYi4ooJ8O3SHoBA/j+7kPrJt8jdtKXafcoWM0LGDkFowmAYggJRkpiMjK/WIufXfwDdfWIQOKAXYu+8Gr6d27ptKzQNye9+jqRFn1Y7XqLaNmDHHAx90LM9yIiaArWkEJsXTkVOTg4CAwNdtuWIERF5rPWz98IUGQZJqXiDLhkUyBLQ9pVHsHPUddCLS+rk+n5dO7hMispjsbRrify///O4X1Fcioxv1iF8yhinZakhSUhb/n1Vwq0gfMqYCklJpe4VBb4dW8O3SzsU7jpoP+7TqQ0CenVx2q9kUGCOjUTbhY9Atpgq/2wUBULXETp2SFn7ytXVmt97PYoOHUfe7/8CAIzR4Qga2BuyxYTiwyeR+8c2j5IKj0kSWs67C2EXj4DQNHvMAb27IbBfPJLfX4FTr3xQ5W5NzaPRfvHTMMVEAMI2smZp0wLBw/oh96/tOHzX09BLrTA3jwJkGaWJKRBW1X5+yOhBaDX/3kprp4QQlUaNhKqh+MgJpHz4ddXfPxERNUhMjIjIIz4dW8Pf1Q26okAJ8EPI2CHI+L81dRLDmTexLtupldv5tG+FoCF9IfuYUXzkJLLWbYYoLrW/nvTWpwgc2BumqPBKyZHQdORv3YmMb6pf0cunY2u3SR0AWNrGVUiMAgf0glA15wkbAF3TYIoIdfq6JMsOb+7LCVVD1LWTULB9L+Ieux0howYCkAChQ1IUlCal4diTryHv921u4/dE+ORRCJ0w3BbbGYlc+XuMvm4KCv7bh5z1f3jeqUFB+0XzbIm7JAFl77W8z4Dzu6L90mdhDA2GKTIMAKDm5iP9yx+Q/M7nAIC4x+8AICptaCtJUoXvn15aiszv1iNx4VLoBXwyTw1H/yXdOVpEVANMjIjIIwF9ukNoeqWbxgp0HQF9utdZYpTz6z+IuNTFqA4AraAQhXtOJxZKUABaP3sfAvvHQ6ia7QbXoKDFgzfj+DOLkLVqAwBAzcrFvqvvQ7PZ1yF0zCB7EqMVFCH9yx9w6o2PHSZcntKLS1xXvSsjSqwVvpZNRlvMLs6RAAhduNzg09U6GcmgILBfPNq9+QT8unU4I0bb99kYFYZ2rz2OA7c8Wit7FkVeM9E2KuMqUbv6kiolRsFD+sLcIsbp67YRuTYVjhkC/RF17SQE9ItHxjc/QfaxOP0+lSdHKUv/DylLvoSWV+BxbET1gfsWEdUcEyMi8owiA3CzJFGSXCYtNZW+/HtETE1wOvohdB1pn6+2jwRJBgPav/UkfNq3KvtasScYsq8FrZ+ZA72oxH4DrmZm49gjL+Pkgnfh066Vbd3PnkO1MjUwZ8NfCOjTzWUb3aoi989tFY4VHTjqvuiDJMH2s3GVPrnnH9/ZcfeyDCF0NJ9zPfZOv6dG1zCEBXtQAVCBX/x5gCx7PIUvaFg/tyNrjj4z5VMY5WkJtmspzs8XqgrJaGBSRER0juIGr0TkkcJdByutX3HcrnJVtdpSfOQkjj3+CqCLCqM3oqzaVt4f25H05if248EjB8C3c1uHN8uSLEPoOprNvq7Sa1p2HvK37EDBv7trbb1U5nc/Q8svdLoBp9B15Kz/o1Lp7ewNf8GamV2hPHmF84SwrdNxMxLlihDC/scZSZHh27ktLO1aVvs65f141E6WABcjYGdTfCy2RKpaMSnwaRvn9nxJkiBKrS7bEHmLz9Re3NCVqIaYGBGRR/K37EDxsUSXN/ZC05DxzTrXHUkSlEB/yD6WasWRuXI99l45G5mrNkDNzYdeXILCvYdw9LGFOHjXkxXWIYVdcpHTeAFbcmRpEYOwKaMRNfNSRN8wFf5uNvWsLmFVkfblD4BAhQSk/P8lWUbIqIHo9OnLCOgff/pEVcOxRxfaksGz3ovQdUiShBPz30bBjn32fWsqXVvXXSY9gO2m39V0u3Lm2Ei3bVyxpmfD6mYTXqHrKDp4DHDyfhwpPpZY4wIR7t6/ZDAgd/M/NboGUV3ov6Q7hq0Y6O0wiBo9TqUjokqUAD/IPhaoWTkVEo2jc19E+/eegWxEhVEYoWmAJOP4vNehZuU67FO2mBF51SWIuGwsjGWFAvK370HK0q+qtsgeQNHewzj2mPPS1+VM0eEejXK1fPR2e1IhGRQUH0vEkfufR9G+I1WKyxlzq2Zov/hp2/sumwbobDqgT4fWaPfGEzhy33PI/ul3AEDu5q04cNMjiL3rmgrT3YoPn8CpNz5Bzvo/kPfHNnR4/1kYw0MqvGehqhCqBmtmNkyRlQtLAO4TgjOpNZ1GputIW74KMbdMdz56JElI/XRllbrN+GotomdeWrPYXBCqhuLDJ5D/z646uwZRdUl9RgIrWHSBqKa4jxFRA+fbpR0CLoiHZFBQuOcQcjdvrd3SyWcIHNALUTMvRUBv2wJeragYGV+vQ/J7X0Ate8pvadMCMbdMR/CI/vYb8Pytu5C0eDny/tjmsF/Z1wft3/2fbfG7fHpkorxUc9JbnyLprdrfC6b9e8/Av+d5VZ5mJlQNekkJ9k6/ByXHa7ZprWQ2ocu3b8EYFuLx+iuh69DyC7Fj5AyIktIKr5maR8MUGQY1OxfFh09UeM0QEoiIy8cjfMpoGMNDoBUUIfP7X5D68TdQs3PRfM71CBk7pFob1QohYE3Pws4xM2u8UahkNqH9W0/Cr3unCsmREAIQAjkb/8bhOfOrfJ3YO69B9PWVkyP7qFwVEsCzYypNTseBGx5C6anUKvdBVNe4dxGRc1XZx4iJEVEDZYwOR5sXHoBft45loxkCksGA0uR0HH3oReRvrd0n1+FTExD38K0V9pUBbEmCNSsH+6+5D6VJafbjSoAfjOEhUPMK7EmTM83vvxERl411OXqz77oHa31z2LBJIxH32B3Oy1S7KWGdsXI9jj/xao1iCJ0wHK2eurta5x596CVkrvoFgO37HXjh+VD8fKDlF6Jg135YkzOcV8qTpEr78QC2Kn2RV12MmBsvq3I8x59ZhPTPV1f5PEckswnRM6cgYto4GEJs/1CVpmYgbdl3SPno62onXzG3XYnoG6dVKwly5tSby5D26XcsukANEqfREbnGDV6JGjkl0B8d338WxgjbfitnjjQYI0LQ7q152HftAyjac6jKfQf074nIKy+2VUiTZRTuPoisHzeh+b3X267lYPNWY0gQ4h67Awdvfdx+XMsr8OhGUfaxIGzSSJdJkVBVRE4fjyO1nBhlrt6AqOsuhTk2sspTyCSDgtCxQ3Bi/luVRm2qInh4P/dlzh3QrVZY2rcEFBnN7rwGEdPHQzabKiRzWmER0pavQvJ7X0DPL6zYgZNnXlpOHkqOJHoUgxAC0HRIBgVJb39Wa0kRAIiSUiQt+hRJ73wOc0wkhBC20ZgajobKvhZbUuVkdO7M75+rxNjeXteR8uH/VdjziqihiE9QmRQR1aI6Lb6wceNGTJgwAbGxsZAkCV9//bXbc3755Rf06tULZrMZ7dq1w9KlS+syRKIGKXxqAozO1oMoCiSjEc3umVHlfmNuvQLtF81DYL94yGYTZKMBfl3bo8X9N7o8TzIoCOzfE+Y45/vEOOPTvqWtYpjL/g3wP9/1/ht+PTqh1TNz0GXVu+iycjHiHr0dPh1auTxHFJfiwI0Po/iIbcqZsKrQVdVphbezySYjDEEBHrV1RvH3rXJSBJyugNZy3ixEXjMRstlkP27v29cHUddMRMelz0EJ8PO478K97hNqoesoPZWKlI++xq4JNyNp0bIqvwdPyGYTJINiW8tWC1NEwy4e4bZktxACutUKNTvXZXEOoWrI2fg3kyIioiaiTkeMCgoK0KNHD8ycOROTJ0922/7IkSMYN24cbrnlFnzyySf46aefcMMNNyAmJgajR4+uy1CJGpTwySNdliqWJAkBfbrDFBuF0lMpHvUZOPB8xNx8ue38M24cy0dyPFmH49e1Y5XX3NTGZN2Y269EzI2XQVdVyGUbr5qiwxE2aRROPPuWy5EMa0o69kybhYC+3RE09ALIPhaUHD2JmFuvsCcbTmPX9RpPnyo+mmhb52So2l+3ksGAklOp9p+Z03aKAkur5oi982qceOYtz2I6fAL5/+6GX7eOTpMISZZx6K4nUXzohMPXPWFp3RzhUxPg39u2f1P+lv+Q9vlqlBxNhCkmAjG3XoGQhMGQjbZpz4V7DyP53c+Rve63al/TEOjvto0kSdg95U7IvhZ0+niBw413ha4DEpDy/opqx0JU13yffwDg2iKiWlOniVFCQgISEhI8bv/WW2+hdevWePHFFwEAnTt3xq+//oqXX36ZiRE1KcbwUPelgyUJYZMuQtIbn7hsVy7yyovdboDpjhBVf6JffPAYtKJil6NGQlWRt2WHw9dCxgy2r4eRz0guyhONuIduRfGh466rhQmBvD+3I+/P7fZD5lbNETZ+qNOERagacjZtgV5U7LxfD6T/3xpETPX878HyaxfuOQT/Hh1tm4q6Saokg4Kwi0cgceEH0As9u0k6Nu81dPzwBSi+PhUrDJYlCade/7hGSVHEZePQ/IGbAF239+/TpgUiLh+PpEWfImL6eBgC/Sq8N5/2rdBmwYNIXLgUKUv/r1rXtWblwBgS5LKN0HSoWTnQjp/C4dnz0fr5+wFTxTWpQtVw9KEXUbB9b7XiIKprLLhAVPsa1D5Gv//+Oy666KIKx0aPHo3ff//d6TklJSXIzc2t8IeosfP0ZjxwYG+P+wzo061mSZGuI78aa4D0omJkfLXG9X5CBgPSPvve4WtRMybbN3B1GJeqIvKqiVWOK/XDryA03WHf5VPtkt/7osr9nq1ozyGkffmDw32Ezt5YVeg6hC5QcioFh+c8A3OLWI9HmmSLGZY2LTyOq+RoIvZeMRtZazdX2P+o+MhJHJm7AMnvfu5xX2cL6B+PFnNvhiRLFUcnDQokWUbs7VfCEORf6b2VTzlsdvcMWFo3r9a1M75a63Z6XPaGP+0jgTkb/8aOUdch8aX3kfPLn8jZ8BdOvfohdoyaUaORK6K61H9JdyZFRHWgQRVfSE5ORlRUVIVjUVFRyM3NRVFREXx8fCqdM3/+fMybN6++QiSqFyVJaTAEu66cAng2bciuiiWrz1S+1sKanF6t80+99jH8uneCb+d2Dst1n1q0zGFFOkNoMHw7tXHZt2QwIGjQ+VWOqfjwCRy8fR7avDTXtj6nPEGSZeglpTj64AIU7txf5X4dOfHMW1DTsxB59UQofqf/His5kYS8v/6Df49OMIQGw5qWiYyv1iLju5+hFxZByyuoVCXQpSqu0Sk9mYyjcxfgxPy3YIqJgF5YjJITNStPDgBR101xOzrpuhiHhvCpCTj5/DtVvnbapysRNnEkDIH+la4vNA1C15D89vIKx7XcfKR+/A1SP/6mytcjqm+sQkdUdxpUYlQdc+fOxezZs+1f5+bmokULz5+aEjU0ktkES6tmbtsJXYc1NcPjfov2HYFPh9YuCwHYqpBpFZ7kC01DSWIyjj/1hsfXOpteVIz9NzyEyCsuRsTl42CKtFXbK9i+Fykffo2cX/50eJ5s9qzkvmQwIHTiCBT+t7/S3j6u5G/ZgZ2jrkPImMHwP78LIEko+G8vMr//BXpBLT6N1XUkvfUpUpb+HwIu6AHFzxclp1JQsG2Py9Oy1m1G8PB+Hl1Czc1H0cFj1QpPy81HUW5+tc49m2wxI7Bvjxr1IRkU+HXvWK1zrWmZ2D/zQbR9+WFYWjeHrqqAAGSjAWpWLo488DyK9h2uUXxERHRualCJUXR0NFJSKi4kT0lJQWBgoMPRIgAwm80wm831ER5RvQge0tdtFTcAgCQh4+t1Hveb+ul3aPXk3U5fF5qGjO9+huxjQfDw/pCNBljTM5G2fDXSPltZ4yIEorgUKUu+RMqSL6EE+EG3Wt1W+7KmZUHLL4Ti7+u837KSy62emAUAyPtnF44/8arHIx96cQkyvl6LjK/Xev5mqkqWYYwMBYRt6panFSmy125Gye1XwRQd7nqERdORvuJHhE4YjsjLxsHStgWEVUXOr1uQ+vG39gRMMhlhio2EUDWUJqbUTmWMs0gW1wUtPHXm9L6qKjmaiN2TbkPABT1Ol6XfeQDZG/8CatAvUUMg9RkJrOA0OqK60KASo/79+2PVqlUVjq1duxb9+/f3UkRE9c8YFe52+pQQAnphMTJ/2Ohxv5krf0HQ4L62EQhJqlDcQWi2xf4nn10MvbjE9np5CeU64GmSJVQV6St+ROSVFzuvnnZWkQr/Hp3Q4YPnsfeKe6o09c8QHgJLy1joJaUo3Hu4Vm6gJaMBkddMQuTl42CMCAUAlCalIvWT75D66XduNzEVVhUHbnoE7Rc/DXOzqMqvlyU2xSdPwa9HJ0TNmAwIAUmWISkKgodegODhA3DypSUwRYYhfMooKH62JLPkVApSP/rGtrarFhMkLbcAak5ejcqcC01D3h/bHL5mjAgFFBnWtEy337+zC24QNXYsuEBUt+o0McrPz8fBgwftXx85cgTbtm1DaGgo4uLiMHfuXCQmJuLDDz8EANxyyy14/fXXcf/992PmzJn4+eef8fnnn+P77x0vyiY6F6k5uR6tB8r5dUvVNh7VdRx54HlEXnkxIq+8GKao8LLr5SH9yx+Q/M7ntqQIAISos6SoqpLf+wJBwy6AOTbKo+IRkkGBIdAPMTddhuNPup/+Z2oejeazZyJoSF/7NENrZjZSP/waKR98Ve2kQTIa0PaNxxHQuxtwRvJmjApHs9nXwb/XeTh873Nu1wWVJqZg98RbEXzRAERcNhaWNnFQAvxOJ4S6DktcMyCume3YGdcqnxLZfM5MW3W4M5JtU3QEmt9/I3w7t8OxxxZW6z06pOtI//JHRM2Y5DS5F7oAIByWiBe6bh8BO1P4lNGIvHqifZqpNSsH6Z+vRsr7K05/bonOcVvTjwCI9nYYROesOq1Kt2XLFvTs2RM9e/YEAMyePRs9e/bEY489BgBISkrC8ePH7e1bt26N77//HmvXrkWPHj3w4osv4t1332WpbmpSctb/CWG1umwjSVL1pn5pOlI//Bo7E27AzvE3YdfFt2DHiGtx6rWPGuzNpZabj33X3IfMVb9APyNZc1TlrZxkMCB0/DDIFtfTbM0tYtDp4wUIGtynwtorY2gwYmddi7jH76h23JFXXoyA3t1sozdnJiuyDEmWETSsH8ImjfSoL2FVkbV6I47c97wtUSsbKZEkybbh71kjgI6cnaSUxxV28XAED6/dUfmUD/4PJYmpDqfDCVVDyckkFB06UVaFTz/jNRXQbAm8NS3TfjzusTsQ9+jtFTYYNoYEIfqGqWj/3jOQPZl66oAxIhSWtnFQariJLxERnRsk4eruohHKzc1FUFAQ1nQeBT/Fs4XbRA1N7F3XIOq6KQ5vdoWmQ+gaZKMRWkERsn7YiNSPv0HxkZNeiLR+KcEB8OnQGh0WP+1R+53jb0LpyWSnr7d97VEE9u/lciTqwM2PVn06liSh6w/vwRgZ5jRhEbqO4iMnsWeK58lX7B1X2T4Xnlap84BQNeT/uwsHbnyk1voEAENIEFrMvRnBIwbYk06hachauxknnn0borgUYVNGI2JaAszNo6EXlyBrzWakfvItis8oIhE8vD/avDTXefyahpQPv8apVz7wOLaAfvGIufly+Pc8r6wPHTkb/kLSomUoOnC0em+YqB5Y1k/G7AUcMSKqCrWkEJsXTkVOTg4CA11X/G1Qa4yImiJzy2aIuHwcgkf0h2w2oejgcaR/sRrpX/6AiKkJtqfokmRbO2IwALIEuSzpV/x8EHbJCISOH4ZDdz11zq+n0LLzkP/Xf9CLS9yOBgFwWVnOGB2OwAt7Q5Kdj7QIVUX4tIQqf18NoUH2qYrOSLIMn7ZxkExGiFLXI4TlQieMqNWkCLBNPfTt0r5W+wQANSsHR+5/HsaIUPh2bQ8IoGDnfqjpWfY2aZ98i7RPvnXZT8T08S5Lf0uKgvBLxyBp0TKPvo+hY4ei5dP3AGdsViwpMoKGXYCgYRcAAPTiUuT9tR2JL76HkuM1L19OVFs+2F+90VEi8gwTIyIvChp2Ado8/4C92AEA+Md3QsD5XZCzaQt2T7kToeOGwBQbCf9eXWAMC648JcpgAGQdbV56CDtGXwc9v9Abb6VeZa37DaFjBjnd/FRomu0mPCvHaR8+beJcJkWA7Xvr29H1PkoOVWU/IV3A0rYFfDu3g9A05G/ZCaHpCB03FKaYcGi5Bcha8yuKD5+AIagK+1ZVgWSsu38KrGmZyFnvuBy7J/y6dXC7tswQ4Adzi2gUH3Jdql0JCkDcE3cCEJV/j84Y2VN8zAge0hdBg3rj6MMvIWu150VOiOqKZf1kbF8Q7O0wiM5pTIyIvMQUG4XWz98PKHKFRejlN2yBF/ZC8aHjSFy4FJa2LXDeCueFBCRZhuxjRtiE4Uj7dGWdx+5tqR99jdDRgyB03eECfklRkPzuFy770D0cpfG03ZnUrFwUH02EOS7GYXyALXkrOngc7d/9H/zjO58+XlaYoLyNJMmIuWU6sn76HdbMbJhiIt2uJ6oq2WiEOS6m6qMjiozgIRfAL74zIATyt+1Bzsa/3FaLqwrb98ODdh5cM+ziEZAMitOfydkkWUar/81G8eETKNp3xKNziOoCq9ER1Y86Lb5ARM5FTEuAJMlOb9IkWUb41ATIFjP8e3ersEjdmYA+3Wo7zAapaN8RHL7/OQiraltzVbZUUqgahK7j+Py3kLtpi8s+Cnbug1bgenRNaDqEqqHdW0+i1fx7ETS4j0cVAwEg9eNvKlSIO5ukKDC3iIFf1w4Vj8uSvUCDbDTaR0uCh/a1VQp0kSicvWS0/DPjbimpUDWEjh3qss3ZfLt1RNfV76HNS3MRMX08Iq6YgLYvP4Suq9+D71nvqSbyt+ywTSd1wZqRjRIXa8nK+Z7XrupVBiUJEdMnVO0cIiJqlJgYEXlJ4OA+bqcIKX4+8Ove0bMn3JLk8U37uSBn/Z/YOfYGJC1ahvwtO5G/dRdSP/4Guy6+BenLV7k9XxSXIu2z750mnEIISIoMnzbNEdgvHiEjL0TbVx9Fp09ehCEkyG3/6St+ROb3v9j6OmM0Q2i2Sm3FRxMhmwwelSAHbImUpWUzqNm5jqu9aTqgiwrVBUuOJyH3j23ukwFZQsiYwQi75CKPNmg1t2yG9oufgjEs2Ha60QC5bDqeMSwY7Rc/BXNcrEfvy53UZd85nTIJ2JK/tE9XerTvlNC08sE4j0mSZNv7i1ySLCaEXXIRWr/wANosfBgxt18JU0yEt8M6J8QnqJj1G9e6EdWHpnMXRdTAyCbPqiZKRgMKdx1wnxzpAoU7D9RCZI2HmpGN5Hc/x4EbH8b+mXORuHCpyyp0Zzu1aBlyNvwN4HTCAqBCslR+U16ewPi0b4W2rz7qvnMhcOzRhTj60Eso3HMQQhcQuo6C//bh6KMvw9w82uUNv8MuVRV5f25H0cGj9q/LS5ir2bk4eOtj2D7kSuy6+BbsTLgeuyfeatso1V1iJEkwt4hB3BN3otuP78OvrFqbM9Ezp0A2GhwWgpAUBbLJaNtsthbk/bENSW9/CgAVEkKh20YKczdvRfLSFZ719ed2jxPRM3n6u9pU+Z7XDl1XvYe4J+5E8PD+CBrSF9EzL0WX799B5DUTvR1eozd34jXY/m2wt8MgahK4xojISwp3H4QpOtzN03CBooPHYU1JR/GxRJhbxDosGCCEAISO9K/W1GXI5x5Vw+E58xE89AKET0uAT7uWEJrmssy2ZFDg160D/Ht3Rf6Wna77FwKZq35B5qpfTk+rEwKWdi2rdYMORYHsY8bey++BX3xnBI/oD98u7aH4+0LNyIZv1w4oOnAMJcdP2U/JXL0RsXdd47JbSZIAxRaf4u+Ldm8+gb2XzXK45kgyGBCSMMTl51YyGBA6biiOP7PIo5GccuaWzRA+eSTMcbHQC4uR/fPvyN7wF5IWfYqCnQcQedUlCOhj2xuq+PAJpH660rafl4drmrLW/Irms6+DEujvcXU/IUSTKIVfXYbwELR760kofj4VPkco+/42nz0T1vQsZK3a4MUoG6/4BBXLvR0EURPCxIjIS9I+X42QUQOdvi5UDbm/bYU1JR2+3TrCFBUBOLhXL18/cuzJ16FmZNdRtOcwXbfdgP/8OwAg8qqL0eyemadv8BwQVhUhowa6T4wqnHR61EYvLK5erJoGa9nP2NK6OSKvsK19kRQFQggE9O2BmJsvx5EHX7BXgrOmpCPts5WIuHy8R1MyJUWBbBSIvPJinJj/dqXXZX8fj0ZQZLMJiq8PtNx8j95as3uuQ9S1k2yjQooMlFXmKz6RhIO3PIbcTVts68YkCZAlt8mQISQQYRNHwr93N0iShIIde5G+Yg0O3vU02r/1JGSzyePkNO1z91Mzm6qIqWNsSZGTRFPoOmJuvpyJUQ1wtIio/nAqHZGX5G/ZgbQvVgNwsGheVaHlFeDEc4sBWUab5++DZFScjmLopVZk//R7ncfcFCj+fu4LXUgSFH/fal+j9FQKivYf9aiSWoXLGgzIXLkeAf3j0fLxOwFZtt+QSpIESZEhGQ1o88KD8DmjzPjJBUuQuuw7WzEJzf0Ijm3EZ5jD1/T8IuhW95X69FIrtELPqmhFzZiMqGsnlV3b9jkvT1rMMRFo/87Tp9c+CeE2KQoaegG6/rAEsXdcjcABPRE4oCeib5iGrqvfhaVNc+yZeidSP/0OalaubYqjk6mGQgjk/7MTmd+t9+h9NEWhY4e6HH2TZBmWls1gadeyHqM6N8QnqJg70fVoLxHVLiZGRF504pm3cHLBu7Ceseml0DRk//wn9l45B6WJKQjsH28r0ezk5kOSJMhGI0LHO76RbWj8e3dDyydnod3bT6HVs/ciaEjfBlU0oiQxxaORhJLElBpdJ/ndzyEpnr9voWnI/e1fFGzbg+jrp5WV8q6cKNtGhQQir77k9EFdR+KC97BzzEykfPB/Hl3PWeInVBVZqze6rBQnVBWZqzZ4NI1OMpsQdf1U568bDDDFRCJk9CD3QQPw6dgGbRY8CMlosCWKZd8jSVEAWUbLx++CuXk0El9cgv+GXYVtF0xB6kdfVyrLrltVpC3/Hgdvn+e2Kl5TpgT41Wo7Os33+Qc4WkRUzziVjsibhEDqx98i9dOV8GnXCrLFhJLjSRU2JvXt2gFCVd0s1Bfw69Ie6XUfcbVJFhPaLHgQQQN729+PUDWEjhmMwt0HcfD2eS43ZK0v2Ws3o8Xcm6H4+jhvJMvI+Hpdja6TteZXmGIj0ezuGRCqBsmglK0Vg23KpBD25EcyGJCzaQuOzn0RSlAAAnp3ddm3ZDAgZNRAHHvk5QrHrWmZSH7vS0RdM9lt8ndmsn62lPdXIGTkQNvGxGcl7ELToFtVpHhYECGgb3cY3N006wKhCUOQ+c1PbvuzjTwJSLKDwhCSBKFriLp+KvL++s8Wr1VF4kvvI/HVD+HfvRPMrZqhNDkdBdv2QPdwxKspKzmValuz5ebhhjU5rZ4iIiKqPiZGRA2BpqNo32GnrzlcXHQGSZZhio2s/bhqUcvH7kRg/54AHFR669AabV99BPuuvs9r8QGAITQYljbNkf75aqdV1YQQSP34G5TWcMQIAFKW/h+yf/kT4ZeOgV/XDrZ1Zb//i5xf/kTQoN4wRkdAy81H1o+bUHzoOADAFBLoUd+yyQgYlEqjNnpBEbLWbUbIRRc6TY6EpiF9xY9O+y4+chIHbnkUbV6aC2NYiL0ynmw0QM3KxeHZz6DkaKJHcRoC/N22kRQZhuAA951JEoJHDnBdGEJREHhBDygBftDyCk6/oGrI37oL+Vt3eRI2lUlf8SPiHrnN6etC05D3z06UJjExqor+S7pzQ1ciL2BiROQlvt06ImhgL0hGI4oOHkP2ut8gSiuv3cjbsgOxhqvc9mduEVMXYdYKU2wUQsYMdlhRDyiv9NbRs0pvdcAYHY7mc65H8PB+9hEQrbAYkixBtpghdB2SLEMrKkbK0v9D8uLaqxNVcjQRiQveq3S8PBE6mzUjG3pJKWSz6/2GrOmZTqeyJb/9GYIG94EsmSqP+KgqrJk5SPtspcv+C7bvxY7RMxE8rB/8e3YGBJD/725k//JnlSrRlSSlum0jVM2jDVwlowGy0bPS2rKvT8XEiKolc+V6REy1VXQ8O9EWmgahaTj1ygdeiq5xik9QcbfV9agwEdUNJkZE9cwYGYY2L86FXzfbFDkhBGSjEfrjdyJ38z/IWrMZ2T/9BlH2FL5g2x77jbkrpqhwGKPDYU1ueBPqgkf0B4QOwPn0Ld2qImTkhfWeGBmjwtHp4xdhCA6skCQovhYIXUf+9r3IWr0BanYecjb+7fXpVaKkFJkr19s2Y3Ux4pP2xQ9O+yg+chIHbngYrZ67D5YWMbZKcJJtNKVw72Ecuf8FqFm58O/dFQF9ukNSZBTsOoCcjX9XLHygasheuxnZazdX+/0UbNuDkpPJMMVGOv2MSwYFGf+31m1fotQKa2Y2jKHBLtvpxSUNYtrmuUCUlOLATY+g5ZOzEDS4LwDbdFBJkVF6KhVHH12Iwl0HvR1mo8O1RUTewcSIqA5IJqNtBEfXUXwiyf4EXfb1Qft3n4G5bNqbZDDYJ8lJJiOChvVD8PD+ULNzcfShl5D721bbi+426Cwjm821/VZqheLvC6ELSC6WtUiSBNmv+pXeqqvZPTNsSZGDJEOSZfj36ITUD7+qk6p/xuhwBF4QD8loQNH+Iyj4b59H5yUtXo6gYf1gCPSv/JRe1VCalIq0T12P+BTuPojdE25GQN/u8O3aAdB15P39Hwp3HYQ5LhadV7wOn7ZxZcm7bZpcaVomjj7wQu1ONxMCJ59/B20WPuLwAYDQNOT9uR25v//rUXfpX/yA6BumOi8frWrI+O5nh6OzVD1abj4O3/0/mJpHI7B/T0gmI4r2H0X+3/95O7RGyWdqL8CzJXpEVMuYGBHVItnHgpibL0fYlNH2BeXWzGykLVuJ5KUrEHbxcJibRzl8Mn5mhTElwB9tX3kE+6+fi4L/9qH42ClYWjVzOWqkF5fAmtLwRosAoCQx2X2lNwm1sm6nKgwhgS7X2gC2G/PwaWNrNTFSAvwQ9+gdCL6oPyRZhhACkiSh6NBxHHt0IQp3u37Cbk1Jx/5r70fLJ2fBv+d5FV7L/XMbjj32isf7B+X99Z+9EAEAGMKC0eH9Z2EIsq39OTN5N4YGod2iedh3zf3O18RVQ87Gv3F4znzEPXIbjGHBtpLikgQIIPP7X2wbxXr4cCB12XcInTAMpsiwSmuNhKpBzc1H8ntf1FrsXmFQEDysH4KH94PsY0HJsVNI/2qNx+u66krpyWSkl21BQNXTf0l3DFvhfH87AlStCKXWXBgUH5iMnq25JPKUJJxt4NBI5ebmIigoCGs6j4Kf4tlcc6LaIFvMaP/eM/Dt1Kbyug1dR+7mrTBGhsGnfUuPNtoUmoa8v/7DwVsfR/hlY9HigZudrtERqob0r9bgxP8W1cp7qW2yjwXdfv4Qio/FaRuhC+wafxNKT9VfcuQX3xkdlz7ntp2alYv/hrlf5+UJyWxCh/efhW+H1o7XZJSq2HftfSjaf9Sj/iztWtqmZeo6CrbuRsmJpBrFF3vHVYi6borLEZecjX/j8OxnanQdhwwKgi48H+a4WCiB/jBGhMIQHAAtrwBZP25C7uatHiVIxohQtHxylr3YR3nimb9tD449urDG3yNvMjWPRvtF82BuEWNLIGXbZriSQUHKh18j8aUl3g6RasCyfjJmL4j2dhgNUmFxCo6d+g5pWVsgYJvSG+TfDnEx4xEaeJ6bs6kpU0sKsXnhVOTk5CAw0HUyzREjoloSdd0Uh0kRYJuSFTjwfOiFRR4lRUBZ9az+PWEID0HGV2sROnYI/Lp2cLBYXoM1IwtJb39WK++jLuhFxUh86X3EPXyr0zapH35Vr0kRAI+nU3myoamnQscPg2/nto73IFIUwAjE3nUtDt0xz6P+ig8eQ/HBY7UWX9ikka437DQoCBraF0qgP7TcfEgGA4KH90PI+KEwhoWgNDkdmd/+hJxNWwB3G+WeTdWQs/kftBx5IcLGD7PtH6QogKYjbMJwFO45ZCvrnpntshtrWiYO3vo4zHGx8D+/CyBJKNyxH0UHjlYtngZGspjQfvFTMEWG274u/zmVJdhR10yEmpWDlPc5D4vOLfmFJ7Bt3wvQ9FIAp/9eyck/hB0HFqJjqxmIDhvgvQDpnMHEiKi6FBnBQy9A2CUXwRgZBkvbOJc3lBDCtnePB4UUzmQMC4aanoWDtzyOZvfMQNglF9krkglNR/bGv3Dy2behuth3pj4oAX7w7doBkiKjcO/hSvGkf7EaQlXRbNa1MAQH2tYcyRK0wmKkLPkSye9+Xu8xF+4/4naxvlBV5Kz/o9auGT5lNKALQHFeoS9wQC8YI0NhTc2stet6yhAS7LaNJMswhAZBMhjQ/u0n4dO+lW3PJUWBb8c2CBnRH/nb9uDgHfOg5xdW6frN51yP0LFDbNcpnwpXXta9fSu0e+Nx7L1itkcjRyXHT6Hk+KkqXb8hCx092LbZs4OkulzUzEuRuuw7iJLSeoyMasOAHXNYotsBIQT2HHm3UlJU9ioAYP+xjxAa2AUmY1C9x0fnFiZGRNWgBPih3aJ5tr1nym4I3ZFkGZLZBFHFp+jl1bP0omKceOYtnHrtI1sCYlBQtO+wV26ezyT7+qDZ7OsQdvFwyKbyhE1D9k+/48Rzi6FmZNvbZny1FpnfrUfgwPNhjAyFmp2L3E3/QC8q9k7wqobUj75B7F3XOLzZtP2sJKR99n2tXdLcLBqS4joxlmQJpphIr/xstdx8j/YM0nLy0PbVx2Bp3QLA6dGL8umBft06oPUzc3Dorqc8vrYhJAgRU8e4rE7n27ktAvv3PF2YpAkJSRjsMqkGAEOAHwL6dkfupi31GBnVFPctci634BAKi11PfxVCR1L6r2gZM66eoqJzFRMjompo9ey98O3UFgA8SorKCU2HNSMLxtBgt8UIhKYhf+vuSjfHWl4B8jys0FXXJIsJ7d95utIUQklREDy8P3y7tse+K++tUBpZqCpyfvnTG+E6lPLBV7C0jSubuqXZfy5CVQFIOPLQAhQfOVlr19MKCu2FDVy2q+JIS23J+O4nRE6f4LwUuKoh76/tsLRuAb9uHZz2IykKggb3gaVNCxQfPuHRtYOGXQDIrn8vdFVFyOiBTTIxMgQFuE2qAc82zSVqLPIKjsK2ybmrUWJR1o6oZjyfz0NEAGyL3YMuPN99lbWzCFVD3pYdOHDDQyhJtG1WKYSAo/on5SMVSW8tq42Q3TKEBCHquilo/+4z6Pjh82j+wE2wtI1ze17EpQm29TKO1lUZFJgiwxF947S6CLn26DqOPfIyDt75JHJ//xfWtEyUnEpB2uersfvSO5C9pvp79DiS9cNG26J5J4Suo/j4KacbvNa1tGXfQSsqdhij0HVAspULDx7Rvyx5dE6oGoKH9/f42oZAf7frkiRZgRLYdG78DSGBiJp5KTp9+jLMcbEQugdTCD3YNJcaFqnPSG+H0GBJkie3qpKH7Yhc44gRURUFD+tXYWTBU5JBQerH36DkeBJ2T7wNgQN6ImTsUAQP7QvFzxe6qto2RjQo0ItKcPSRl5H/Ty3uF+NEQP94tH3pIUgmEyBLkCQJvue1Q+T08Ti1aBmSXRR1CL9srMu+JYOCsIkXIXHh0ga/b0zupi31Mv0o7fNViLhsHGSLyWmhDlff87pWmpSGAzc9gravPApTZCh0q2or113+uXz4JRRs24Owi0dAAHA+qcs2vUX2dV6JsNK1T6W6/73Sbfs0NQW+3Tqi3ZtPQPH1sf9uuiJ0HaWJKSjYtqeeIqTawLVFrgUHdITr0SIAEGXtiGqGiRFRFck+ZgihQ4KLvW/KygMDsCdRSW99evrGWwjkbt6K3M1bcUyWETSoNwIu6A7JYEDhnkPIWr0RenFJnb8Xc4sYtF34iG2vmjOm6JQveo+99QqUnkpF5nc/Vz5ZlmFpEeP2GoqvD4zhoR5VnFMC/BCSMBjmFjHQC4uR/fPvKNp3xPM31AhYk9Nx8PYn0PbVR6H4+wEQtr2MVFvp5VOvf4TM73/xaoxFew5h59jrETy8HwJ6d4dkkFGw6yCyVm2wrwcrOZHk9kZdMhiqVBo7e8NfUPMKoPj7Ou1bMhiQ8dU6z99MI6UE+qPdG49D8bV4NH2ufJT55Avvui9MIcvw7dwGsq8vShOTUXqqaSSaDdXW9CMAWKLbGT+fZgjy74Cc/IOoXHwBACQoshlRYZ6PThM5w8SIqIpKjidV2jjSEd2qQlhV5P39H9KWfYe8P7c7aagjZ8NfyNnwVy1H6l7E9HGQFMXpjZfQdUTfMNVxYqTr0K1WyEb3+4XpJe6TvIjLx6HZPddBMhogNB2SJCHm5suR+/s2HHngeY83LG0MCrbtwc6E6xE6fhiCBp4PyWhE0b7DSP/yx4azx46qIXvNZmSv2QzZ1wchIy9E1HVToBUUIvun35Hx3c+IvcP53k5CCOjFJcj68VePLylKSpH40hK0fPzOCg8X7K/rOjK+/bnRl932RNglF9kSRBcVLIUQgK5DUmyjeamffY8cN2uvwqclIPr6qTBFhduP5f61HYkvLjnnHkLQuaNz6+vx777nUVKaiTNHjyTIgCSjS9vbYFB8vBcgnTO4wStRFZVvVipbzE6fagtVw46RMyoUHWiIuq37AMbwELftdk+6zWEBgtYLHkDw0AucJopC01G0/wj2Tr/HZf9hE0ei5RN3Ou5D1VC45xD2zbgf0Kq4L04TIfv7IrBvD8gWM4qPnUThroO11nf4pWPQbM5MyBazbfRTliApCrLW/IriwycQc8v0SueUJzXHn3oD6St+rPI1wyaNRLN7roMh0N++ialQNaR9thKJC5c2ms+BZDbBGBoEraCoyol9h/efhV98Z7ejckDZaJGuQzIYYM3IxrEnXnU4LTT2rmsQPfPSSkmn0DQIq4r9M+eicHftfXbIPU6j85xVLcCptF9wKm0DSq3ZUGQLIkMvQPOoEfC1cMSNnOMGr0R1SC8qxsnn33H6VBsATr3+UYNPigDbtECP2jlZJ5L60TcIHj7A6fdBUmT3m00aFMTeebXTlyWDAr9uHRA0qE+DqmbXEEgGA2LvugYR0xIgW07/LIv2H8XxZxbVeK1J2MSRiHvkttPXM57+JyN4RH/kBfjhxPy3EXPrdBiCT/9jY03PwqmFS6s9JTDjq7XI/P4XBA3pC1NMBLTcAmSv/wNaTl6130t9MkaGIfrGaQibMNz+c8n7eweSl3yBvN+3edSH7OP8wcvZJFkGykaWDCGBaLvwERy87fEKo9Q+7VsheualtvZn9Vu+1i3u8Tux97JZHl2Tao5JUdUYDX5oGTMOLWPGOf03h6immBgRVUPGV2uhF5eg2awZMEWfnpJizchG0pufVOspuTeUnEiGT4dWrqfraLrTNQgF2/fi+LzXEPf4Hbbpb2eUupYMBpxatAxZa1xPpQro0x3GsGCXbYSmIXTCcCZGZ2n13L0IHtav0s/P0rYF2i9+GgdueAgF/+2rVt+SwYBms651/rqiILB/TyQvWYEdF81AQL8eMIQEwZqWiby//6vxqI4otSJ7be1WBKwPpubR6Pjh8zAEBlQoJOHf6zy07/Mkjj35OjL+b43bfooOHoelXUvIHkzbPZMkyxC6jmZzZmLvtNNJTvilo+2/lw7PUxT4dmwN3/PacdSIGjwmRVRXmBgRVVPW6o3I+vFX+Pc6D8aIUKhZucjbsgNQHZdi9u/d1bYhrK4jf8vOBnHzkfbF6gojAmcTqobsX/6AmpXrtE3GN+uQv30PIqaNRUD/eEiygvx/dyP981UevUdPpvJJigJTVJjbdk1JQP94hIwY4PC18hGA5g/chH1XznHfmUFB8JC+8IvvDAjbGiihazCEuJ5yAABtXnwQx598HdnrfqtS/PXJ0qYFwiaNhLl5DPSiImSv+x3ZG/6skyl5rZ6cVSkpAmw/EyEE4h6+DXm//4vSpDSX/aR/+QPCxg9z+rqrJ+aSLMO3Q2v4dGiFov1HAQA+HVp7tDbS0i6uzv9uMoQGI3zySAT07QHIMgr+24v0L3/0qEALEVFdYmJEVBNlSY4rPh3boPVz98HSqpl9bxhJUVCwYz+OPPCCV28GMr/7GeETR8L3vMp7EQlVg1ZUjFOvfuS2n5KjiTj5/DvVikHNzHbbRmgarGmZbts1JeFTxrgsGy8pCvy6tIdP+1YuixX4de+INi/OhTEiFLrVCkBC1DUToebmezRdRQnwQ5sFD+LEC+8i7ZNva/CO6oAkofl9NyDyigm2PZcUBdB0hI4diuJjiTh46+O1WpHN0jYO/r26uAhHgoCO8Cmjcer1j132VbBtD9K/WouwiRdVLkLh4TQiY1S4PTHSS0ohdAFJdlPyu9T13lQ1FTTsArR+9j5bklZWgtw/vjOiZkzGiflvI/2L1XV6fSIiV7gbFtU5Q0igbbQkvjMkU9MqiGGOi0WHJc/A3Ny2MNRWAc52I+vbuS06LH0WhpAgr8UnSq04cMujyFy1wVYu+gwFu/Zj/4z7UXL8VJ3GkPfnf7C6WY8lKQoyv1tfp3E0NpbWzT3aS8sc57ykurlVM7R7+ykYQm2fQdlohFy2jkjxc14y+0zlbZrPmQlTbKQnodeb6BunIWL6eAC2qYGSJNm/Z+bm0ej8+atot2geWj51NwL6xQM1nJ7j17WD2zaSoiDiyothbtnM4es+HdvAv093mFvE4PhTbyDpjU+gnlW4QS8qdrgx9NnOLPiQs2kL3O0FI1TVefXMWuDTsTXavPAgJKNte4Dyz45kUCDJMuIevhWBF/aqs+s3JPEJKmb91kAqUBKRHUeMqM4YI0LRbPZMhIy80H4zoubmI23590h+e7ntCe45LvqmyyCbzQ5vYCWDAmNYMCKuGI+kNz6p81gMIYEIvPB8yL4WlJxIQt6f/9lKbhcU4dijC5H48lIE9OkGyaCgcN8RFB88VucxAbabsaQ3PnE6pU+oGor2H7FNfWqEdAhsM+UjTbYiQDegd6k/TLXwTEovKPRo5EAvcl4qPXrmpZCNBscbzXqwd04FQiB88ii3IyH1RbaYETVjsvPpZooCxd8Xgf17QqgqwiYMR/6/u3Horqeg5RVU65pCeDY1T7aY0eG9Z7Bn6l32Ii0hY4cg5tYrKuwNlr9tDxJffh8pH34F//jOkH19UHIsEQEDeqH5nJlOEzmh67CmZqJgx377scxvf0LMLdPL9kWq/PMWmoaM79bXadGYyKsnwrZvl+OEXmgaoq+fitzNrkuOnwvmTrwG278N9nYYRHQWJkZUJ4wRoej48QIYw0IqJAWGQH9EXz8VPh1a4/A9zwB63Zfd9et5Hnzat4QoVZH7+7+wpqTX+TUBW1nv0NGDXD7VlxQF4VNG12liJBkNaH7vDQifPMq2R1DZdJrSpDQcf2aRvayvmpmNrB831VkcrqR/+QNkPx/E3nE1JEWG0HRAAmSDAQX/7cXhOfMbTYnmM603Z+O1wESkKFb7sQBdwYz8KFxWGAEJ1R+hyFr7G3y7dnA5yqHlFSDvH8dTPSWDASFjBnu07sQTkqLAp1PbWumrNgQO6AXF17N9Tcq/B37dOqL18w/g4K2PVeua+Vt3e3Y9SYIhJBARl41F0lufIvLqiWg+ZyaEXnFEx69bB3R49xkcuO0J5P31n/24NT0LUddOhjE0yPFDF1lG0qJlFf5+1fIKcOiOeWj35jzIFpM9ORKaBklRkP/vHpx8bnF13rbHbA/JnH/eJEWBf68uUIIDoGU3jgqE1RGfoGK5t4MgIoeYGFGdiL3jqkpJUTlJlhE8pC9CRg9E1uqNdRaDX/eOaDlvFiytm0Pour1aU9bazTj+1BvQ8wvr7NoAYAgNqlDe2BljaDCgyHV249/6ufsRNLSvvXJZ+RoDY1QY2r7yCA7d+WSDeEKb+sFXyPz2J4SOGwZzXAz0wmJkrfsNhTv3uz+5AVpjycK84GOVZi/lyRpeCzyFXFnDTfnOp7m5k/HNOkTfMNW2CaijEQAhkPLR1xAlpQ7Pl/19INfi1Fah6xDWhjMKrAT4VfkcyaAgsH98tSuzlZ5MRs6vWxDYr6fbaY6SoiBs4kVI/2otmt09w3ZMrlxGW0BHqydnYee4G+2JjpZXgAM3Pox2i56AOTbKNvouyQAEIEk49dpHyPhmHaDICB5yAYJHXggl0A+liak4cv/z8OveESFjBkHx80HxsSSkf7kaWWs3Oy0cUytkGbLZ5FFTxdf3nE6MfKb2wvYVwd4Og4gcYGJEtU4J8ENIwhCXNwZC0xBx2bg6S4x8OrdF+3f+Z4/hdFIgI2TEAJhjo7B/5oN1eiPn6XQcrai4zpKigL7dETy8n8PXyhPF5g/ejN0Tbq6T61eVmpWL1I+/8XYYNVYCHS8GnrQlRWfc60qShGHxA9GlVQeUlpYie+1JBB/PqtY1tNx8HLjlMbRfNA9KoL+tf1m2l2TO+GYdkt/9wun5en4RdKsVsrG2kiMJeX9sq6W+XFMC/BA6Ybht6qcso2DXAWR8tbZCgY7SpOoVVdBVFcEjL6x2ZbZjj7+KTp+8CFN0hNu25dXZXK39kRQZppgIBPaLR+5vpx9glBxLxK6Lb0HwkL4IGtIXstmE4iMnkf7VWlhT0mGMDEO7t56ET5sW9lEhoaqImJaAjJXrsXvKHXWbCJ1N11FaFper6Z96SSmsmdX7nWgM4hNU3G3t6u0wiMgJJkZU60zNo90+iZYUBT7tW9ZZDM1nX2db0Oto7UTZhqEhYwYj87uf6ywGLTcfub//i4C+3R3GAdjW11R3E0xPhE0e5XrvElmGpUUM/OI713gz0HNdCXRssOTgqKEYFiFjYEkg2qgVp2opQQEwBPpjY+Ex5MsVbzoHde+Hd+a8hJZRzaFpmq0QwB0Sctb/gWOPv1qtdS1Few5h5/ibEDZhOIKH94fs54PiQ8eRvuJHtz9PoarI+mETQhNqPp1OaBr0omJkrKy736dyARf0QJuXHzq9oa0kIXDg+Yi56TIcf+pN20gJgLwtO1GaXHYj7qYSWwUCUPx9qx2fmpGNA7c8hi5fL3LfNisXlrYt3RZ9EJoGS/uWFRIjWwcasn/6Hdk//V7xuCKj3aJ5sJQV3ij/+6f85xw6dgjUrBwkvrjEw3dVO9K/WI2YW68EFCdro1QNGd/9DFF8xiinQYFPmxaQDAYUH02EXti4N0T1ff4BbOemrkQNFhMjqnWi1Oq+EVBnozWm2CgE9Onu+tqajvBLx9RpYgQAye98joC+PexT+c6OQahanY6QmONiPbrpNTeLqtPEyL9Pd/j36ARAIP/f3cj/Z1edXasu/GTJwvOBJ5Av6zAIQAfwdkAS+pT4Y152K8TGd0fMzZcjsF88AKCzpqHFr6vw9CcLsff4AVzQuRdW/u9jKGWLzpUzEuWgwX3R/u2nsG/GAx7/7tg6kRF4QTyM0eGwpmbg0J1PQi92XmjBkZQlXyLkogsBSXKavJ+tvBpa+VN/oWrQrVYcuutp6AV1e8NnbtUMbV991FbV7Mzfp/I9gp64E6WpGcj7/V9A13Hy+XfQ5qW5HpWpLifJUvVLeBsUyCYTSo4mIvev7Qg4v6vzhyKahoxv1sHcIhrQhesasZJUpc9G8JAL4NM2znl3soyIy8Yh+d0voOXU35S1tOWrEDphOMzNoivNKBCqBjU3H8nvfm47oMiIvm4KIq6YYJtuDEAvLkHGdz/j1Osf12vcRNR0MDGiWld85KTbKRNCVZG9vm6qjHlSMlhSZHsJ7bqUv3UXjsx9Aa2eugcwGspmzAhAliE0DSmffOt2o8ea0HLzHSZlldrV0Xorn/at0PqFB2x7OJVN25EMCooOHceR+59D8aETdXLd2rTZnIPHgo7ZZ8SpZ3yk/zHlY3bLZPy+aAXMhtPrJ2RFwSUDEzDmghEYc/9leOaGhyHLSoWEqJxkUODTuS1CxgxC5reeJeohY4eg+eyZFTbH1QqKkPL+CiS/9wXgQSlnwPa7euCWR237GIWHQLeqtpFWFyMYkiShNDUDSoAf9IIiZK7agLTPvq+X/bgir7rEVvLewedZkiQITUPMjdNsiRGA7J9/x+H7nkOLB2+GMSzYto+YLLut5Je5smql4f16dELUtZMRNKQvJEWGmpWL7I1/A4DjhyKqBjXPVqEz8IJ4hCYMcdm/JMvI3fyPx/EEj7zQPn3OGdlkRNDgPnX+cOhMWl4B9s94EHGP34GgwX0rJKv52/fi+BOvwpqcDsgy2rzwAIKGXlDheydbzAifNAoBfbtj3zX3N7rkqP+S7hjK0SKiBo2JEdU+XUfKh1+j+b3XO3xZCAFIMtI+W1knl9fyPVzb42G7mspesxk7/tiOqOumIOKysVB8fWw3aJKEmOunImJqAo4+/JK9OpynTM2iEDSwNySLCcWHT9gKKJxV5S/rx00IuKCHy360giLk/rmtqm/Lo/jav/eMvTLYmU+ILS2bocOSZ7H38rvrNDGsKQGB1wNOQQIgHNxL6xKwtzgdX21ejekjJld4zaDY/nr99NG30Twi1vWFdIHwKWM8SoxCJwxHq6fuhjjrZ634+SD2jqugBPoj8SXPp0gVbN+LHWNmInhYP/jHd4ZffGf4dm7rMPkQqorSlAzsnnirVwot2Kb9ua7y6N+rCwyhwfaNg7PXbkb2+j8QdOH5MLeIgV98J9somRPJ731Zpc2EQ8cORcun7wZ0YS9xbggJRNj4oVCzciH7mCH7+pRtTyBBNhpQmpyGQ7OegpqRjax1m9HsnhkwhDiuMCdUDbm/bUXJcc/3vFEC/dyOAApdr9GUwepSs3Jw+O7/wRQbBf9e5wGKjMKd+ys8JAkZMwjBw/s7PF8yKDA3i0bMrVfg5LNv11fYNRafoGLYioHeDoOI3GBiRHUibdl38GnXEuGTRlZY41JePenYYwtRtO9InVy7aN8RlJxKgSk6wulIidC0Oq2IdzbJaEDYxIsgm2yjCraNXm2vKf6+aPvyw9h/w0MeTWeT/X3R8om7ym4chO2GzKCgNDUDx594rcI6hKzVGxFzy3SnFQKFEEj58KuKc/rPYmkbB5/2rSBKrcj7Z6fHT2mjrpti2zPFyR5Oip8vIq+ZVOclgmtir6EIxw2up6fJkoylPy6vlBgBtuTIbVKEshHMZlHu25lNaH7/jbb9i5x8tqOumYj0L1aj5EQVNo9UNVsCsXYzoMho+cRdCJsw3P67Wz76UJqSgQM3PeK16nOyj2fltxV/X3tiBABQNeRs+Mv2/x9/g/wrL0bsrVdA8fe1j+hohcVIfvdzpCz50uN4jNHhaPnkLNtUREPFn4ekKDAEByLn93+Rs+43+HRuC2gacv/YVuEhhii14uBtT6D9O09DCfAHZKls9EsHZAlFh47j6KMLPY4JAEoTU12uLQRso1DVnjJYC0pPpSDTyShjxGXjXI54SQYFYZeMwKmFS6s8fZSIyBUmRlQ3hMDxea8he+1mhF82Fn7ntYOuqsjdtAVpn32P4sN1OIVKCCS/vRwt593l+GVNg1ZYjLQvf6i7GM4Scfk4GAL9HReDkGUIoSHmluk4eIvr/VMkgwHt3pwHvy7tyqahSPa1CcbwELR97VEcuOVx5P9t2/NELy7BgRsfQfu3n4IpJgJC0237BJXdNKV/8QOSFzveUcPSNg5xj91RtjbIRreqyPh6LU6++J7LZAoGBWHjh7m+MTPYyhWffOHdetnPqjrSFffrOnSh40RaotPXVU21jx45I4SAmpvv9lrBw/rB4KYMtVA1hE0ciVOvfei2P4c0HcceXYi0z1YifPJomFvEQMsrQNa6zche95tXS3Jb0zLcVjUTVhXWDNdVzdI++RbpX/6AoMF9YAwPgZqVi5wNf0EvKq5SPOFTRgMSnG8ia1AQdOH5ODn/LXtRCEeKDhzF7km3IWzyaISOGwpDUABKT6UgfcUaZK7e4LTkujMZX69FxLQEp68LXYeanYucKkzPq0++HVu7HfFSfCwwNY+ut42oiahpYGJEdSr3t62VKynVg4xv1sEYGYbY26+EUDVIBsU29UiSoOUX4uDtT0BNr7+SsGGXXOTyH3pJURDYLx6GsGCoGdlO2wWPHAD/7h0d9yHbNkZtPvs67J1+j/14yfFT2HXJLQi56EIEj7BVLis5loj0/1vjdNTO0ro5On74PGSzucJx2WhA+ORRsLRqjgO3Pua03K8hwO901TAXFB8LFD+falVkqw9BuvuCBJIkITI43OnrMiTk5ebALyAAsuRkrZcukLXqF7fXMjePdjsSAEmCubn70Sd3CncdxPFd1StZXVfSv/gBMbde4aKqmYrMHzd5VARClJTaRshqIKB3N7c38JIswS++s9vRGTUrFynvfYGU95yXWPdU4e6DyPjuZ4SOG1p5fVPZCNnJF96t33LdVSA8jMs2PZGIqPYwMaJzVvI7y5H140aETxkNS9lUsNxNW5C5akOlJ8OmZlG2TUWLSlCwc3+t3zAYQgI9bBfkMjEKnzzK9RQTRYZv57awtGtZ4UmqKLUic9UvyCy7+bZV7usG364dULjrAIr2Hq7QT7N7roNsNjueBqcoCOjTDaGjBtn7O5tWUOR28TdgG4HSqviUvj51sfohQjMiTbZW2I/oTEIIXHnRFKd9yIqC/E9XI+Dmyx2fr2rQ8guQuWqjbT8aXwtKjp9CoYOkRCsoBNwU0oDQodVxdThvSVv+PcImXgRTdHil5FCoGvTiEiQv/qxG1/CL74zwKaPh074V9OISZK//Axlfr3M8hdRNEQd7M2cfnjp0bN5rULNzEXHZOMgmoz0hUrPzcPKFd+p1KnFV5Wz+ByEj+jt9ACCEgDUlo0rrrrzN9/kHABZeIGrwmBjROa3keBISX17q9HWfDq3Q7N7rEdj3dIECa2Y2Ut7/P6R+9HWtxaFm5cIUGea+XWaOy9dNsVEelVU2xUQ4nGJiCAlE3GN32qpnyZJtrYokoWDXARx7dCGKD5+AMTIUgQN7uyxvLDQN4VPHOE2MRKkVWet+c31zo6rI+nFTg31qDQAKJNyYH41nghxP/VQEEBsRi8uHXOLwdaGqKDpwDEmLlkEvLkHsndcAQgckGRA6JEWBmpOLnE1b0HnFa1B8LPZziw4cxfFn3kLBv7vtx7LX/4Hm993gMmbJYED2ut+q8W4bPi2vAPuvexAtn74HgRf0sBegkGTZthbn4Zeqf7MsSYh75DaETxkNXVUhGwwQQsCvRyc0u3sGcn/dglOvfYSi/Uftp+T/uxt+3Tq4/Z0s2LGvejHVhKoh8cUlSH73CwQN7gPF3xelp1Jt0+ca8O8cYJvqGDp6kMs2qR9/3WCn4J5twI45rEZH1EgwMaImy6djG3RY+ixkY8XNaI2hwWg+ZyZMzaJqrepRxtfrEH39pS42etWQ9/d/FRaMG8KCIckyrBnZ9hsANScPpphIt3uyOJqaJvv6oP27z8DSspn9/PK1Eb4d26DDB89j3xWzYQgPcdu/pCiwtGrusk3KkhW2AhFl65oqvF9Nh9B1pCz9P5d9NATjisKQJ2l4M+AUBE4PHGkS0Fwz402cD1/FZJ+yWU5oGqyZuTh877MAgJT3VyDrx022qYhtW0JYrcjZtAWBF56PsItHVJryZGkThw6Ln8aBmx9F/lbbvk/W5HRkrtpgq87m4LMkVBVFh04gt6xc9bnImpaJgzc/Ckvr5vDv0w2SLKNg5wEU7txfo36jZl6KsMmjAAByWTJ/5tqhwIG9Edi/F448+II98Uz/4gdEXTPJ/oDhbEJVkffXjqoVwqhlWk5evZbkrg0F/+3Diflvo8Xcm88q3mP7Hcv8/hekfvKdl6P03Nb0IwDqfnsIIqo5JkbUZMU9chtkk9FpshJ5+Thkrlxf4xsuwDYFKPzS0TAEBlTe2LAs6UlatAyQJIRPGY3Iqy+BpWUzAEBpWibSPvseqR99jaxVG+DbqQ2czesSQsCamoGC/yo/oQ6/dDQsrZs73gPGoEDxMSPm1iuQ8uFXHr0nrcj1E9CifYdxeNbTaP38/ZD9fABNs8WtyNCLinB4zrONZuH05YWRGFUcglU+mThqKIZZyBhYHIQLSgMgp5/A3mmzEHHlxQgbPwyKvy+sGVlIX7EGaZ+uhJp1ehSw9FQqTr3+sf1r//O7IHSM4yfjkiJDQKDFQ7dgz6V32o8ff/oNGIIDEDSw9+mRjbJpi8XHTuHQHfM83seoMSs+chLFR07WSl+S0YCoaya63b9JKDJazb8Xu3fdjNKkNJSeSsHx/72JuEdvr5wYqxqsGdk4Nu+1WokRsD3Mibz6EoSMGADJbELpqRSkfbEa6Z+vrnLhiIYubfn3KNi1H5HTJyBw4PmQFBkFuw8i/bNVyP75d2+HR0TnKCZG1CT5tG8Fv24dXLYRqorwS8fgeC0kRmpGNvbPnIu2rzwKS8tY6FYVgIBsNELLK8DRh15EwY79aPnU3QgdN7TCja0xPASxt1+JwP49ceT+5xA1YzIMwYGO1/9IEpIWL3c4xSR8qvMqVYBtClbIqIE48exilCanwxjlaoNeDdlr3C9cz/1tK3aMnIHQsUPgF98ZgG36UdaqDY2uzG6obsRVBY6LGmj5Bcj4Zh3Sln2LkhPJHicm4VNGuyymICkKfNq1hG+XdvY1R6K4FIfueBL+vbshbOJFMMdGQs3OReaqDcj+5c8GP02qIfLr0RmGoAC37SRJAmQJ4ZeOwanXPoIS4AdDcCCs6Vkwhgbb22mFRUj/8kekLP2/imXDq8DSNg6RV12CkNEDIVssUHNybTGWlecHbJtZN5t1LcLGD8OB255A0KDetjVYEaGwpmcj47ufkPnd+honTeaWzWCKDoeak2cr2FJPiXfhzgM4+vBL9XKtumJZPxmzF3C0iKixYGJETZKlbZzbNpLBAN+OrWvtmiVHE7F74q0I6NcDgf3iAUVB4e6DthLIpVaEjB2CsPHDyi5+OiGRJAmQJPj3PA/hU8bgwI0Po92ieTBFR9iqN8mS7UZFkpH05ifIWPGjw+ubY5zv62S/lkGBMSIYyUu+QNxDtzpsI3QdQlWR9vkqj963XlSM9BU/It1JXI2ZpXVzxNx5NYKHXGCfLlh08BiSFy9H1ppfPTi/hesKc2XMcbGVijHkb9mB/C07qhc4VSD7uK+gWE5SFAQO6Im0L1aj4/vPwhgZbt97CLBNoRSqhszv11c7KQoc1BttXnoIkgT758MQbCvgcuZ+SeW/z5bWLdDlqzdtI7NlG80ao8Lh27U9oq6ZhP03PARrcnqV4/Dv1QWxd8+oUAmz5EQykhYtc7q+kE7rv6Q7hjEpImpUmBhRk6R7sC+IEMKjdlUiBPJ+34a837dVeily+gS3FeciLh+H5CVfYNf4mxE8vJ+tiILZhOIjJ5Dx1VqXJYG1omIYAvzdhqjlFyH989UwN4tC1LWTT2/yKWybyeqlpTg862mvbg7ZEPh0aIUO7z8H2WyssIbK0qYFWj9/P4wxEUj9wPW0RK2gyF4tzBW9qHGNrjU2VS3YICkGtFnwIIwRoZXWz0mKAsXXB21fewy7xt5Y5ZLShpBAtHnhAUiKXOFz4XKan0GB7Odja1NWyrz8XFN0BNq+8ij2XjarSnEE9I9Hu9cerzRr19QsCq2emQ1DaBBSP/6mSn0SETV0TIyoScrfsgN6aSlkk8l5IyGQvf6PeovJ97x2lW6yzmYMC4YpKhylp1KRteZXj0YlymWt3oTwySOdV4nTNBTuPQxriu3JcuLLS5H5wyZETB0D387toJeUImfj38j4am2FdTNNVcsnZ0G2mColsuU3pM1mzUDOz3+4XHifvW4z/Hud5/I6WlEx8v7cXvOAqRLJYkLI6EEI7N8TanYulEB/t0mqUFWUnExG8PB+zvs1KDBFhiF4eL8q/Y4CQNjEkZBMRrdxVLqmi01mfTu2hn/vbp6PMCoyWj55t20k7Kw4yguzNLvnOmSt2QRramaV4mxK/m3dzivXLSnNQnr2v1C1IlhMYQgP6QlF9nxUlKgpY2JETZKWV4D0L39ExGXjHCYjQtOgF5e63K2+9nk4b7+a8/tTl32LsIkjHFaJA2xPus8uUV605xCOP/lGta53LvM9rx18O7V13UjXET5lNBIXLnXaJGPlekTfdDkMgf4O14wJXSBt2cpzbmG9NyiB/oiYloDwKWNgjAyFXlgMGA2QzSbbmjxJBiQ4rTBXTjIYUJqaUanYwtmEqiKgX48qJ0YBfbt7vD+Sp4SqImziRQjo0w1KgC9KElOR+f16aNkO9mYCEDigF0wRoe56RdjEkUhevLxWYz1XeGNtka5bceD4p0jOsK3/lCBBQIdy3II2zacgNmKIx32paiFSMv9EQVEiZNmA0MCuCAk8D5KzTaqJzhFMjKjxUGSEjBiA8KljYGndHFphMbLXbkba56vtoxxVkbhwKcwtmyHowl4VprAJTYNeUoqDd8xzeuNQF/K37oZ/z/Oc3mwJXYc1JQOl1VgrANjWOB2a9TTavvIIINtKlNvXRQgBCIHoGy9D7u/bHG9m2VDIstf3L/Hp1MaDG2gFPp1dJ096fiEO3voY2i16smwNiYAky/bpi1k/bMCpNz922Qe5Z4wMQ4elz8IUFWFfD6QE+J3+GXqwN1j5lMdTi5ad3gPM5RmS2/VjhvAQmCLDoObkoTQxxXaWorj8XFWLLCNs/DAIVbXFrShodve1SHpzGVLeX1GpuU+7lm4TP0CCT7uWtRsnVZsQAnuOvIv07G0of8gmyv6r6cU4cPwTSJKMmHDX+0MBQHLGbzhw7P/ZO+/4Kqr0Dz8zc2vqzU1vtBB6F1EQFVRErNjr2n6uuq5t0VVx1+6qq+haV10VXetasGAHFRsWsNAhQICQ3vutU35/3CQQcluSmwbzfD67MTNnznmH3Dtz3nPe9/u+hqp5EZAAjeKKr7CaUxmfew1Wc0rP3YiOTh+jO0Y6AwLBZCTnsb8TN31ymxNjBFIvPo3k804i/5p7Op2Irnm85F9zN7bZh5B0xnFYhmajOJzULf+eqnc+x1vZuyEiFa8vJfbg8SHafNgtRShn3i7fO1PTOuYvCAKWwZkMuu3P7GypvdNfsORkk3LBfOzHHYFoNeOtqqHqnc+peOOjPnHiNFkOOXnVVC2s/BJn3k42nnwFiSfMxjbnMKRoK64dhVQt+bytfpFO9xj6wI2YUhI75gMFUl1UVVS3B9FoaHNuXDuLKHvhbWo/+Yb4WYcgGkO8PkUBx5Ydfk9ZRw0j45oLiZs+uS00zbE5n9KnX6d5/VZiDhobViHnNntDOOmtO1CCwdDmzAmiSOZ1F6G6PVS+3r4mkOrx+kRdgg8a+RxMnS7T2LyTqrrg9ct2FC0h1X4oomgM2Kaqbg15u15q+11jj8ql013JmrxFHDzmTgyGqG7b3FXcnloqan9BlpswGRNIsU/FaAidP6ujEw66Y6QzIMj8yyXETpsI0G7CIEgSokkg5/G/s/GEyzuf+6Kq1H35I3Vf9n1djPoVP1P+yvuk/mH+PjtYKogC9V//TMXrS7s1RmuOUaD8BcEgYTvqUIypSV3ahfOHGGXFfsIs7POOQIqPxV1URvW7y6j/7pewdn7iZkxh2KN/QxD3rMAbk+yk/fEs7CcfxdaLb+71PIfGVevCEE3QaAyz0Kra5KDyzY+pfPPjyBio04Z1xBBipozt1DWCKCJZLWw+9y9oHg+q091ObKT+u9V4KmowJtr8h+KqKppX9ltYNXrSaHKfvce3M7SX82EdMZScx2+j+LH/Bg2l29cJ0mQFJBFN1QIWZg7mNKX/6TyqlnyOtpeT0/D9rwh/vSzgNeB7VjR890vQNjq9R1n1SgRENAI/U2XFQXX9epITpvg9r2kaO4vfx6e44W8BTsXjrae0eiXZqXMiYXanUFWZ7YX/o7TqOwAEQUTTVPKL3mRQ2gkMTj8h8rutOgccerCoTr9Hio0m6fRjAwoTCJKIaDaTeGrvP6i7g2VYNjFTx2MZlt12rPjhxey44f52BVpdO3az++4n2XHjP0HpXghZ7CGTQq4EC6JIzEGdm0gGwjwogzHvPUX2rVcSPWk01pxBxB92EDmP/Z3hz9yFaAmeECzFRjP04VsQDFKHsCRBkjClJDHk3gURsTUQk+bJHf43dnIZbPwBTfFfM0hTVfC6yGhe1qO26YQmdtpE3+JCFxCNBlz5hR0VGBWVXbc+3CbNvTetn4mCu55AaWxuf50gMOS+BS2f531EOyQRTdNIv+p8ip94uV1f7fre615Ul5uq95dT9M//+M7vs9CghbG7bIiNJv6wg9odcxcUU//dLx3ura1fWcFTXkXdV70nTjOQmL54Qq/nF7k8NUGdIh8Cbk9twLMOVykOVwnB8101yqpC17DrCbYWvNziFGmAhqYpbT8LSpeyu0xfWNLpPvqOkU6/J2bquODqcQCiQPyR0yhf/E7vGNUN4mYeRMbVfyBq1LC2Y468nZQ88TIN3/+6ZwdLEn0rxxEs2ClIYlgrap0J4wnYh8HA8GfuwpiY0Baq5zvu6zv2oHFk33YVBX/7V8A+Ek85GtFsCrrDFTttApZh2bh2FHbb5n2ZNE9m4fwL/Z6zusu53NVAepTNZ0vL/SmaiorKizt+Iv+407jf9TJrPtUftX2GJBK2sMk+BMvna/plPVsvuZn0qy5oFxLXvGErpc+84VeSP/bQSZgz/BcJhtYCsiKCILL9mrtJu+xMYib6CiOrHi81n3xD2X/eRHW7kaKseCqr0Vy+nR5PaSUZ112Eda+FFtXtQQqx+KCpGoYkW4fju/7+L3L/cy/WEUNAVREkqc3xkhsa2X7VHZ2WIj8QmDRPZvaSmb0+ri+UTISgzpGGMUgInEduCGssr9z74cvNzhLKa4I74gWln5CRPBujIbqXrNLZH9Hf1jr9HtEYOB66FUEQfOpS/ZyEuYcz5P4bO+QJWYcPJufx29l168PUfvat72A3d4f84cjbQfSkMQFDbtrabdzW7bFsR08PPgmUJOzHHUnJYy/jraj22ybmoOA5V+BbFY85aFxEHaPpiycgHDyH634oZd2HtoBpXb9/tJpjp2RxwiHZpCZYcbkVVqwr5cOfCiit8QA2Fs6/kMceTO9w7Q/jH46YvZFAMBmxDM5E0zTcBcVo3v1j0uvYuL3Tjr4mKzSuXhfwc7l33/l/vhNDUgLGpATkuoaghVSjRg0LQ9QAokYOpXzxOzR89wuGRBtStBVvZW07dUK5uq7dNfXfrKL+m1VYRw7DmJyAXFNPyvknkzD38KDjCaLQoS8Apb6RvD/cSMK8I0g6bS6m9GSU+iZqPl5B1XvL+7dASx8S9eDNcIuz18dNsR9MRc3PQdsIgoFE28SA583G+LDGMhltnTEtIpRV/xAyVFDTZCprfyUj+YhetExnf0N3jHT6Pc78gpBtNFnGmec/0bm3MNht2I6ZjiE+Dm9FNbVfrERt3vOCFK0WBt1xNaD5KQopoqkqg+64mvpvVvWYPHP0xNEdCjbujaaqNK/ZHBEnI37WIUEL1gIgCMQfPpWqJZ/7Py0KQfMtgA5CEt1l+uIJnPPVHE5zDeH2KTOIPsRIfbOHz38t4v0fCqhp3FNs1e1V+fDn3Xz48+6A/a1damPW0o4TpUdWnIZr9rsRs7urCBYT6VecS/IZxyHF+lZa5fpGKt/6hLLn3kLzePvYwu7R9Mt6XAXFmLPSwnKQNEVBUxRKnngl7DHkqlrkqsAhSm19y0pYn+e9Q9jk6jq/jksgnHk7cLZE4lZ/tAL7CbOCtleaHNR//6t/Uzxeaj74kpoPvgx7fJ2+wR43jhjrIJqcRQTaNcpKnYNBCrxjFGVJb+mjkGC7rOlJh3XT2s7j8da3qewFQkDC463rHYN09lv0HCOdfo8rv5CmtVsCxruDL2yr8u3PetGqvZBEsv56GeOXvUj2LVeQdvnZDLrzGiZ8+TKpF5/W1ixh7uGIVkvgsDBRRLSYSZjXM6tdMQdPIHrM8JDqVYUPPR+R8USr2SetHQxNRbRaOhw2ZaWRddMfiTkk8OpmK4Io0rwhL2S7UFhWnIZlxWlc8fMJPH3NYZxwSDbRFt9uZXy0idNnDuHf1xxGVlJkwjQWLEprG9Oy4jRmrL8hIv12BsFsIvc/95J64fw2pwjAEB9L2qVnMvypOxFCqa8NAHYtfBjV4+0Q+tWag6Opals+j6e0km1X3IZj0/aI29Hw4+8hizgLBomGn9ZEZLzGn9bQ9PumoM/O0mfeaCe8oNN1pi+ewKw+2C0CnxDB+NxriYnyhVIKiPhWwXyft/SkIxmacUrIfoZmnRbkrIjFnExq4ozuG9xJjIYYhBAC+Rqqrk6n020G/htP54Cg8N5/M+KlfyJiahcW0qrQVPH6UhwbtvaJbdkLryTptGPbHJ62nxYzmddfDED5S+9izR3sk3kOEhqoyXKP1QaxzzuirT5OMKwjhuDcnN/t8dwFxb5wwGBhPJKEq6C43bHoyWMY/u87EY3Gtr91IDliTdPwVtbg3B54xyYcWosxGiSBl/86BbNRRNrHqZNEkRiLgdvPn8zlj3WuaGcg2idoO3lkxWmMevCttiM9nZuUcsEpRI8dEaDgr0+EI/ns46l4tXtqiH2NY9N28i64gfQ/nYdt9vS2+21es5mqD75AsloQjAaceTtpXLWuW5L4wXBtL6Dxl/XETBrt93uoKQpyXSO1n38XmQE1jfxr72Hogzf5Sh3IMprW8ozSNEqfeYOKVz/we6lkiwUNPWQuTCbNk7neO65PbTAZ45gy6lbqGrdQWfsLsuLEYkokLWkGUZaO4bz+sMeNYcywK8gr+C+K4kQQJN8uJioxUdmMy7kKg9RxMaunSbEfQnFF8J1LAYHkhIOCttHRCYXuGOkMCJzbdpF34V/JuuFSYg/dk+gsV9VS9tK7VL7WNxM385BMks84LmibVjlc1eMNueIlSBLxR07DW1FN1fvLI1pg1pAQF7qQpaK2FBoNTMzU8dhPnI0x0Ya3uo6aj76i6ZcNHdpVLVlG6kWBVx81VcVbXUfDyt/ajokWMzmP/R3RZGwvyx5kl8uYlEDus/ew7crb2hLR98aDygpLHZ9Ya6gUvSSqRo532jnKZcOM2K5C/WFjUrHHBk5WN0gig1JimDjMztodkZcIX7AojYl7iT1cdJOr58LtBIHkc04IqVKYfM6JA94xAt/O884b/4kUF4MxKQGlsbnXa5UB7LxlESNeuB9ztu8z17qQoikKisNF/jV3dzp8UTAasB01nagxw9FUlcZVa2n8aS1oGkpjM9v/dAfWkcNImDMDMSYaT3E5NR+t6FjeQBJJPut4Us47CXO2byLt2l1CxasfUPXO531eWFknNIIgkBA3moS40V3uIzlhCvb4cVTV/kazsxhBNJAYN57Y6KF9JocdFz2EhLix1DZswn+Yn0B68ixMYeZJ6egEQneMdAYMrvzdbL/qToxpSZizM1CdLhybt/eISEG4JJ50dMhdGMFoxDbnMBpW/kraJacH7U8QRUwZKWRceyHpfzqPHTc+ELFaId6KalAUCGarQQqYcC5GW8n519+InTah7Z41WSZp/jE0/LyWHX+5D9WxJ4zEvbuEssXvkHbpGR360lQVNNh995PtJlsJJ8xCio0O++XbqnYXPX4E6ZedRcmTr7Y7Xyt6uS4hn3yjC1EDVYBCzc1v5iZelcv5cckiznlwjyM0YZgdWVExBAl3khWVCUN7xjECX05SKwuAr9ffgLZ6OQA/XrouYuMYbLGYku1B2wiiiDkrDdFiRnW5g7YdKCgNTSgNTX02vlxVy5bz/kLSqceSdMZcjClJKI1N1Hy0gso3Pwkp+LAvsYdOYuj9N2JIiEP1egGBtEtOx1VQzI7r/4FrZxHQmnsUJA9TEhn28ELijzi43WFzVhrZC68kZuo4dt3ysO4cHSBIoonUxEP72ox2jBl2BZt2/Ifahg0tQgytFZdU0hIPY3j2mX1tos5+gJ5jpDPg8JZV0bR6nS90rg+dIgBTWhJB1QzwrQSbUpNo+mUDjrydISVuBUFAEEUEo4Fhj9yKJUKhddVLvwwZRqc0O6lf4V/ZaOj9N7bVN2rtp/Vn7NRxDLmvYz2hksdfpmjRC8h17WVg3QUlbL/6rg5OX9y0CV2aeAmSRNKZ8zrc3622Xewy+IQs1JY/U+vPIrOXEZc80q7WiximQyaG2GWJJLNucTJ7yUyu945j0rzIKcUFyzvp0DZAvSadrqE2O6l49QM2zb+KtTPOYsPcSyl54pVOO0VRY4eT88TtSHG+vArRaERsyQkzZ6aR+8L9GJISwuor+cx5xB9xsO/Zs1cYqSD6JP4T5swkcf4xnbJPRyeSGCQLE3KvZcqoW8lMOZpU+yFkp83l4LH3MHLIhb6wPx2dbqI7Rjo63UCubyRUjRRBElvawY7r78VTUe1L9m5xAAIVYfRNSHx5IJHAsXE7tctXdigCuTclT73qd2fAOmKIb9IUIBRPkCRssw7BkjOow7mKVz9g/TEXs+3K29lx0z/ZcsENbDr1Khp//L1jR6IYWrUrAIb42LbwJIDNBgfrTM0oAbqTFZVGx04amvesoucV1SOFcHoMkkheYV2XbOwOa5f6pL9nrL8hIkINSmMzji35QYufaopC05rN+4109/5G+pXn+hZS/OWIGSQM8TGknHNCWH2lnH9y8AaaRsr5J3XFzAOCLTed1W63V6fniI0eQk72mYwaeglDM08lyhK4LISOTmfRHSMdnW5Q+9m3IXdh0KDuC1+lcE9pJZvPuo6iRS/g3OmTxA4WNiYYDNjnHh4xe3fd+jDVS79qUeFSUb0ymqqhutwULXqBytc/9Hudbc5hIXe6NFkh4Vj/hQ01WabxpzXULVuJY0PgGkmOzfldrcXpG2cvJ/NbSz1SiL4ERKrq9jhoK9aW4nQrqKr/CxVVo6rBxaq8yoDju711vir0WuR3WdYutTHrFiezbnFGxDkqf/n9oCppgiRR8fL73R5HJ/JItljiDpsavEaRJJE4f07ovuJjMWenB5W9F0QRa85gxBAFYw9EZqy/YR8hFR0dnYGKnmOkc8AiRlmxzToEQ5INuaaeuhU/tas7FA7N6/Jo+GkNsVPH+52gaKpK1bufY8pIJeumPxI34yAEg4Rz605qPviSqAWXhrbTavbtokRAKUvzyuy+83FKn3mdhGMOQ4qLwVNaQe2y74PeuxQb41OGC9a3piLFBq6REQ7V739B+p/OA1HodJKvt6YOd1FZ2+8uQQ0R5AgIAqq6R7DB7VV44K213HH+ZBRVa6dMJysqiqpx3xtr2Ndv0jSNsqrvKSxfhtNdDvjkZdOTj2RQ6nFIUuQnk7Nu8anYtTIlaWini8bWfvINUWOGk3rBKe0Kj7b+d9nzb1H31Y8RtVsnMhgT4kMWagYwJISRjN6JZ0uoWjI6Ojo6AxndMdI5IEm9+DTSrjgHyWrx5VpIIoPcHspeeIey597sVF87bnyAYQ8vJO6Qiaiy7JvQaxqCwUDNJ9/g3lXMyJf+2U6kIWr0cKLHjUBT1aCrtJqm4S2vjrh8sLesKqBMrz88JeUIYvD4bUGU8JT430kJF7mmjt33PMngO69DU0MUh90LTVWpfONj2CtvZpBsJtSejaapWPcJw1iVV8kNz/3MubNymDYyGUEQUFSNHzaV8/qKfHaVN+3Th8bWglcoq24v4e2Vm9hd+gm19RuYOOIGpB6QuN1X7vvr9TfguOmfQPhS38WLXqDxx99JPvckYiaPBg0af91A5Rsf0Rihejo6kUeua0BTtZDOkdIYWmhCaWjCtasY86DAu0aaouDcvtuv8uOBzm9VOwF9x0hHZ39A0AIlOAxQGhoaiI+PZ9noY4mWAteL0TlwSfvj2WT8+fyA58teeLtTVe9biZ44CvvxRyLFx+KtqKF66ZeIVgujXnko4DWapvmcqICTEZXSp1+j7Pm3O21PJDEk2hj/+YtBw3Y0r8z6Yy/pKAHcBWKnTyb98rOJmTzG17ei4N5dimVoFpqyx2FqzZdq/Gkt+dfe0y7cr0lQOCllAx60gPoYgiAxfcIijAb/RVtjLAZio4zUN3txuP2HElbV/s7GHU8HuRuB7NRjGZYVXJEwUkw8uQ6As694HckWiyk9BdXpwr2rOPiFOgOOnKfuJO7QiSEXEJo3bKVo0Qs0r9kcsE3SmfPIvvXKoDu1u+54jJoPgteSOdDYW+5/oKJpCtV16yit+h6XpxqTMZYU+yGk2A9GEve/0ElVlalrykOWHZhNduKih/WZDLlO7yC7Hax89Ezq6+uJiwtekkTfMdI5oDAkxJF++dlB26RefDqV//u40zVOmtduoXntlnbHhtx3A6osIwbIQxIEAU3T2oUxtaLJMu7iCirf/KRTdvQEcnUdZc+/RfqV5wZsU/rcmxFxigAaf/ydxh9/x5hsR4qLxltZi9LQRPzsQ0m9cH6bw+TeXUrFGx9SteTzdrtFADGaxPUNmTwYX0Sbrus+5GSdFdApAmhyyTS5gudWFVV8iS9dM5CIgUZJ5bcMyTgZUez5xZq1S22k263krHqMeONgEHxOt6ugmLIX3qZm6Vc9boM/VDR2GFw0CQrpiolU1dQnduxPlD37BnGHTAy58xw1NpcRi++n9Jk3KHvhbb9qnlVLPidm6jhfnuBeizWtCxHVS7/qs89Of2XSPJnjB7hTJCsO1m97gobmfFqfYw6XQF1jHrtLP2HiiBuwmBP72syIoGkaxRVfUlD6MbLS3Hbcak4hJ/ssEuMn9KF1Ov0F3THSOaBImHckBEk296FhP3E25S8u6fZ4cYdNCegUtSKIIo7tO4kaMdSXxyMIaKpGw49rKLjjcZTG5qDX9xalz7yB6nKTdvk5SFGWtgmT4nBS+uz/qPjvexEf01tZ085BrV/xE/UrfvKFJEoimjt4WM8pziSsmsSLg6vYXb/n39FkTGBY5nxSE6d328bG5p0Edop8KKoTp7uSaGtGt8cLRVZSNP+68lCiJKnNKQIwZ6cz5O7rMWemUfr06z1ux958bK3mpehySgx7/l7T3LFc1ZhBrmztkTENiTaMiTbk2oY+KeTaGzSvyyP/+nsZcv+NGGIDO/iCIKABGVedj/34I9l25e14y6raN1JVdi18mMaf1pBy3klYc4cA4NpRSO3yldR+9l3EQ3p1+p4tO1+ioXlny2+tzzHf39nlqWH99seZOuYOBGHga3XtKnmf3WWfdjjudFeyYftTjM25kiTb5D6wTKc/oTtGOgcUpvRk34Q+WF6PqmFKT4nIeMEUv/am4I7HQVGIGjcCFJXGXzfg2UtIoL9Q/tK7VL75CfGzpmFMtOGtqqP+6597vfinJssQpoL0sa4EbvvrERz0chpeuR6jIY74mOGRe9ELQphKer0TqnHd/LFEmSSkfT57rZ/59CvOoe7LH3Bu3dUr9rwYXcbzsWUd/o1+MTVyReJWnqrOZbTcPdGOvYmeMJL0q84n7tBJbccaV6+n9OnXafptY8TG6S80fP8r64+5iGEP30LcjCkBn22toULm7HRyn72Hzadf01FpUlWpfm851e8txzwsm/TLzyHhmBlkXHU+GVedj3NHIeXPv03NJ1/38F3p9AYOVznV9WuCtFBxuEqpadhIYvz43jKrR3C6Kvw6RT58D6etBa9ijx+PKOhT4wMZ/a+vc0ChNDaHnBALQngJy+HQvGFbQMW6VlSXG3dBCarD2WOTVUvOIGKmjkMQBJrX5+HYuL3LfalOF7WffhtB63oe6ZBjsb3XOcXBcLHFjKSmYSPBdo2MhlisluQeGX9vspKiGT/UHrSNpqkknTGPwvuC5UVFhgLJ5XOKoINfqArg1TTui9/Ny9UjESLgOMZOn8zwx2/rUAsrZsoYcp+7lx03PhCwgPFAxrdzKgRd8GlFkCQsgzOJP+oQ6pat9NvGPCiDkS8+gBQd1e7ZZRmSyZD7FmDKTqPs2f9FyvwBi/XMKdD9wIJeweEqp6TyGxqbdyGKBhLixqCqXnxfzGArO76SBgPdMSqt+p7gIc/glRupqd9Akm1Sb5ml0w/RHSOdA4raZd+TcVVg4QXw1Q6q/fy7iIxX+b+P261c74smK1Qv/RLV0TOTdmNaEkP+cQOxB41tEyoQRBHH5nx23fowrp1FPTJuf2LG+huYdUvP/PsCZKYeTU3D+iAtBDJTZvfKKuTwjOBJpQCCIJJy1sxecYyWRlUjaQQssqsKsMPoYqPRwThv4FCwcBCMBobefwOIYoedWkGS0FSVIff8hfXHXNTrO5y9gepyoylqWLvUmqKQMPfwgI7RoNv/3MEpgj27jhl/Oo/6r37CuW1Xt+0eqExfPIHZS/zXbetvFJZ9zo7iJeztGNQ15iEgEtoxUpGVnnt+9ha+MgrBQ55BxOHqf5EaOr3LwA8a1dHpBO5dxdR+8QOa4l/IWVMU6r/7JWI7N/Vf/0z10i99Agtq+4eyJiu4i0opefLViIy1L4aEOEa+9CAxE0cBvklN68TGmjuEES/9E1NGZEIG+yuT5slc90Npj45hjxvD4PQTW37b+5EqtJwfS3bqcT1qQyuyn6R6f2zcLTNj/Q1MmiczaV6YMYldYJvBGdApakODfEP3J162o6ZjsMUFdAwEUUSMtpJw3BHdHqs/Uv/dagijrhH4HEVDXIzfc+YhmSF3uTVZJunM3vlM91d+Hzq8r00Ii4qa1S1OEbR3DDQ0FEI7C+BwhvcMdbjKqW3cQpOziP4meCyKJkJPebX9UoVPp3PojpHOAUfB3/9Fw4+/A74XvKZpqC2x9o2/bGDnLYHltbs03h2PU/Tgc3jL9yQ7Kw4nlW99TN6FN6E0RCZsb1+SzzsJY3KC3wmOYJCQoq2k/d+ZPTL2gcaQjJMZN/xqbLG5tDpEUZY0cgedz9jhVyGKvbM5v3prPt4ATn8riqrxy9YqZt3iZOH8C1k4/8Iec45MWhj5VwIYte6/iqLG5KB6g9+HJitEjR7W7bH6I7Wff4dcU4cWhnOsyTLuonK/56JG5YS8XjAYfPmQOv0aTdMoKP2Y7uY3OlwluD21Ac/XNm7ht83/YPXG21i39RF+3XQ3v2y6k8raX7s1biTxhceF/m4k2nRlugOdXnlbP/XUUzz00EOUlZUxceJEnnjiCaZNm+a37UsvvcQll1zS7pjZbMblcvWGqToHAKrLTf7VdxM9aTT2E2djTEpArqmj5qOveyY5W9OofOMjKv/3MbHTxpNw3JGYMlMxZ6djP/5Iqj9agdrkiPiwSWccF7S+iWAwYD/5KAofeh5tPwwtAoh68GbW9mAY3d4kxk8gMX4CmqYCGoIQXnHaSKCqMtsK36Cs6nve+FLlvKNPx+Dnb6+qGrKi8tkvvhDKtUttACycfyGPnrkBgB8vXRcxuw71xPGTuTF4BoMGB3v87150Bk1Ww5r+BdotHuhoLg/b/3QHuf+5Fyk+FiBgbRbBYKD6vWX++9lXkCHQeCGc0P2Z6YsnMHsAyHS7PFU4XCUR6au2YRNpSYd1OF5Vt5aN+U+z7wqIw1XKph3PkjvoAjKS+36XNtE2EYspCZenBv8OkkBywlQspv1Dmlyn6/S4Y/Tmm2+yYMECnnnmGQ455BAeffRR5s6dS15eHikp/sN44uLiyMvLa/tdL7yl0xM0r9kctOBhpEm96FQyrr0IVBXB4Mt5iDvsIDKuvoDt19xD8++bOtWfYDaRMHcmtlmHIFotuHYWUrVkGa783QhGA8aE+JB9iEYjwx5ZyI5r70WKjcJgi8NbW49S19jV2+w39HRuUSD6QtZ2a8HLlNf8DGjc8PQdjMzO4eBRU9A0FUn0OUiyoqJpcO/rv1PT2N4RXrvUxmx8+RIrFkfOOTrOaec/MaU4UVH9PMZFDY5y2UiOQE2jhp/WkPZ/ZwRtIxoNNP60tttj9VecW3ex8ZQ/kXXTZSSeMLtN/n9vNFWl7osfaF6X57ePpl83+q2r1q4PRaHhh98iavtAYSDlFilK5BaUVa2jI6yqMnm7XiLYTsz2wjdITpiM0RAbMVu6gigYmDDietbmPYzbW8ue3Cpf3lV8TC4jB/+hT23U6R/0uGP0yCOP8Mc//rFtF+iZZ57h448/ZvHixdxyyy1+rxEEgbS0/r8ao6MTLgnzjiDz+ot9v7RMVFvzfUSrheFP3cHm06/GU1oZVn+WnGyGP30PphR7W8J17METSDnvZMpffp/iRxajuj2I5tATzrhDJzHmg6cxpacgiC01lFb+Qukzb3RLvU6nd2h2llJe81Pb703OZo696WwuOOYMLj/xDwzPGEKz28kPm+r46OdSiqqC18WavWRmxJyjGE1iUe0wFiTswL2XcyRovinJSK+Vmxqyuz0OQNPqdTjzd2MZnOGrc7UPmqzgKa+i/vv+E97jF0HAmGwHQfDVX1LDyxtrRalvpOBv/6Lhm9Vk3Xw5xkSbb5dMFEFWqFzyGcWLFge8Xq6tp+aTb7CfcKTfHWdNVX2iMe/633HS6T+YTXZCKbGFS7Q1q8Oxqro17Qql+kPTVMqqfyQ79dhu29BdrOYUDh57NxU1qyiv+Rmv3ITVnERa0kwS4yfsF7WadLpPjzpGHo+HX3/9lYULF7YdE0WRY445hh9//DHgdU1NTQwePBhVVZkyZQr33XcfY8eO7UlTdXR6lPQrzglYnV6QJESTieSzT6D40ZdC9iXGRJH7n39gaA2XaUk2b13hTb1wPt7KGmqXfY993hF+J4ntxhdFTBkpbSvLgigQN30KsYdOJv+au2n8aU0n7lSntymv+REBEW2vyY/H62Hxp6+z+NPWQq4Cw7PPJTNlVlh9tjpHzrd/Y82n3XtNTPDG8HrVKN6Lqma5pRaHqJAhm5jvTGKOMwFzBFNdd/zlH4x44X4M9vh2k3pNUVAam8m/9u5OOxq9hiiSfO6JpJx/EuaMVMBX4LjyzY8p/+97nQ5dq132PbVf/Uj8YQdhykxFaXbQ8O1q5NqGkNcW/vNZLMOyiBqTC2htz63WnMwdNz6w3xbN3Z8wGqJJTphCZe1vdN05EoiypBEX3TE3r9lVjIDUIuIQ+PpmZ3EXx448kmQmPflw0pMP72tTdPopPeoeV1VVoSgKqamp7Y6npqZSVuZfEnHkyJEsXryYDz74gFdffRVVVZkxYwZFRf5lhd1uNw0NDe3+p6PTn7AMzcIyJCtojRHBIJEwL7w47MSTj8aQEBc01CXt0jOoeP1DNI2w1IH2DbcRDBKCKDL0gRsRjLqqf3/G46kPrW8giHi8dZ3qd/aSmWy56SwsK07rsm2tpKgmrmhK552qMXxSMZ7na0ZyojMxok4RgHt3KZvPupbSZ/+Hp7QS1ePBU15N2QvvsOnMa3DlF0Z0vIghigx98CaybrgUU9qeeleGpATSrzqf4f++s2vfQ1mh/ptVVL7+ITUffBmWUwSgNjvZ+n+3Unj/M7jyd6O63Mh1DVS9u5wtZ11Hw3e/dN4WnT5hSMYpSJIZ/9M9kWhrFoPSTghwtYgoGBg55GK/KQ2iYEAL8fQRWtrp6AwU+t2ndfr06UyfPr3t9xkzZjB69GieffZZ7rnnng7t77//fu66667eNFFHJyyk2GjMg9IxD8oMr320Nax29uMOJ5TKkCEhDkNsNDuuv5ecJ27vUPAyHARJxGCLw3b0DGo/C1zQVYqNJun0uSSedizGJDtKfSPVS7+k8u1PkasCKxl1hxLJzbfmehyCSqZiYpbL1m6SvUem29Yj4/cnjMaYkJVINE3tUoz/gpYE86/X34C2enlEdpB6Grm2gbL/vEnZf97sa1PCJvGUo7EdPb3jAoUggCAQc9A4Uv4wn/LF7/SaTZrbQ9Xbn1L19qe9NuZAYKDIdLcSZUll8sibydv1Eo2OXXudEUiyTWLE4D9gNERjNSdTUPoRLs8e9dSEuFEMyzydmKiO4a6aprWE6gXfidJQsQ/w4rA6BxY9+oZLSkpCkiTKy9vLgpaXl4edQ2Q0Gpk8eTLbt/vPdVi4cCELFixo+72hoYHs7MjErOsc2Ei2WAxxsci19SiNweOo98aYYifj2otImHs4Yssqr78k6L3RVBV3sX/53A52xcYghFGvRIqJpu6rH6ldtpKEOYeFVfixg12KSvSkUQEdI2NaEiMWP4ApLQkEAUEQkKIspF12JslnH8+2P/49okUgnYLCA3GFfGGpaytNqAiwSC3i+sZMTnD6FIUWzr+wTXFtfyfFPo2i8uUhWgkk2w/q8hg+EYuZTJw/jvt5ud87RwONlHNPAlUDKcD3WhBIPvdEyl96t/+GAh4A9JWgS3eJtmYwZfStNDkKaXLsRhBEbLGjMJsS2tqkJc0gNXE6zc5iFNWJ2ZSIxWT3219twxa2F76BwxWqvpGIxWQnMV6XwNYZOPTo281kMnHQQQfx5ZdfMn/+fABUVeXLL7/k6quvDqsPRVFYv349xx9/vN/zZrMZs1kvyLVfIwiIURY0tzdsKdnuED1pNOlXnEPc9MmAzzmoW/Ejpc/8D9f2gqDXGlMSGfnqwxjt8R1C3YI6R4JA1dufhWWfu7AUc3Z60FA6oM3RqvzfRy27TF1AFEg4diYlT77qV1J82MMLMaUkdggTFCQJKSaanCduY8OJl4PcfYlkFY2Ftp38amoCof06pUNQuS++EIMmcPPsOAbOXkH3iY0aTGL8RKrr1+F/30ggI3kWZqOt22OtXWpj4fwLI+YcSbHR2I6ZgTHRhre6jrovfujUIsR+gUHCOmJI0CaCIGBKtvv+nSKU22MelE70uJFoaDSv2YynpKLdeckWS+LJRxM1chiqV6bxx9+p+/LHXnkG91d+q9oJDFxhqJiobL+7P60IgkBMVEeRhb2padjE+m2PE06BMqMhhvG51+qiBjoDih5f9luwYAEXXXQRU6dOZdq0aTz66KM0Nze3qdRdeOGFZGZmcv/99wNw9913c+ihhzJ8+HDq6up46KGHKCgo4LLLLutpU3UijMFuI/HUOUSNzgFFoeHntdR+8g1qmDVzpPhYUv9wCklnHIfBFoemqtR/u5ryl97tMZnt+NmHMmzRze2e+YIkYpt1KPEzp7LtittoXrsl4PWZN1zq1ykKulskKzi3F1D90Vdh2Vj17jLijzg4cH+KijO/AGfeDsAnS1713nIS5x/Tael7QRAwxMeSee2FFN73TLtzUeNHEj02N/C1BglTWjK2WYdQ98UPnRrXH6tNjaw2ByiG2xJL9lRsCTdofSsL2xeMHnoZm3e+QHX9GoSWkELfR1glPWkmw7MjV8i31Tk6u03YoQsIAulXnkvqJaf7xEEUBSSJ7IVXUP7iu5Q+8waEkRu3X9CJ+wyneGsojGlJDL7jmraFHwBN1aj/dhW7734KuaYO+ylHM+jvV/kELFQN0Eiafwyeimryr74L59Zd3bZjIPLfrZaI9CMrTlzuakTRiNWc0unncl+haSpbC17B93QJ/Lm1mJLISD6StKSZGA3RvWafjk4k6HHH6Oyzz6ayspLbb7+dsrIyJk2axGeffdYmyLB7927EvVaba2tr+eMf/0hZWRkJCQkcdNBB/PDDD4wZM6anTdWJIL4X6599OwkCoGnYjp1J5nUXkX/dvSEdG0NSAiNf+iem9OQ2dSlBFImfOZX4Iw6m4PbHqPloRURtFqOtDLlvgS8kbJ+wM8EggQBDH/grG074o99wFoPdRsIxM4IWVW3dNWr7qarUrfiJ3Xc/iebyhGVn/berafhpDbEHj+8wlqaooGkUPfh8u+O7734ST3E5KRfOxxC3p5im6vYgmIxBX8yCJJF48tEUP/Zf1OY9YSRx0yeFrHeiemViD50UEcfoY2sNkuYLnfNvKFRLMl83BnCe+hhN06hv2uqTifU2YjLGk5o4nbjoYd2eGEmSmXHDr6LJUUhFzSq8chMmk400+3SsFv/14rrD2qU2LmoRZZi8c3unpb0zrv4DqZeevue+Rd+rSDCZSLv8bASDgZInXo6ozX2OKBI1ehhSTDTu4nI8RS0CRIpK09rNRI8bEfDZoakq7qJy5Jq6bplgSLQx8uWHMNpt7Y4LokD8zKmMeOmflDz1CkPuum7PDvdej0Kj3Ubuc/9g0+lX91j+YH/FsuI01i6ydasPt6eWnSUfUFHzM5rm20W3mJMZlDaPtMTD+r2DVNe4FbenOmgbARF7/Diy0+b2klU6OpFF0MKRrBpANDQ0EB8fz7LRxxItGfvanAOSuMOmMPypO/0XF1QUVLeHzWdeiydITk3O47cRN2OK30m35pNao2ntFiyDM9A8MnUrfqLyfx/jLui6LGjSWfPIvuXKkPk726+5268qU8zU8Yx4/h8hx1Fcbqre/hTX9t00/LwGb1lVyGv2RbCYyL7lShJPmo0gSWiqhiAKuEvK2X33UwEltgWjgZjJYxCtFty7S0CSGP3WY0EV81rJu+imdrtl6VedT+olp7flUflDlWVqPvqa3Xc+3ul73JfL7VvZaOoYzrcvz502nVcLLun2eJFEVpxszP83dY15bdLarT/tceMYk3MFkjhwQ4JXnP592M6RMdnOuM8WB8150xSFDSdcjiCKaLKMtyL4ZKy/k3T6XNL+eLYvF6+Fxl82UPyvxTg2bsc25zCGPXRzwOs1TaPwgWepevOTbtmRddNlJJ91QsDFDE1RkGvqMdhtAf8+mqJQ9vzblD7djR3DAUYkcotcnhp+33I/Hm8j/gQLslKPJScreIHivqak8hu27X4tZDtb7CgmjlgQsp2OTm8hux2sfPRM6uvriYuLC9pWD/zUiThpV5yDpih+V7/aavace2LA600ZqcTNnBrw5S0IAoIoEjNxFEa7DVNaEslnzmPMO09gO3q632vCIWpMLpoaPBdGk2Wix/kPHws39l40m0j9w3ySzzsxdJh2IDtcHnbf+Tjrj72EXbc/SuE/n2Xb5X9n4wmXB607pHllGleto/6bVbh2FuHaXoBcF6bE/T5rKM4tO4I6ReDb5WsN6esuCaoBMYx/r0fXTorIeJFC0zTWbXuMusY83+8tk6LWnzUNG9my86W+Mi8izF4yk+mLw0uwtp8wi5AffEFg7Pv/ZtwnzzF+2YuM+eBpkk4fmCvQ6Vedz6Db/owxNbHd8ZjJoxmx+AGiJ46ibvlKKl5fCvgcj1ZaQ+dqP/2GqrcCq8NZcrKJnjjKVxg2AILBQOL8OUF3eAVJwphsD+q0CpKE/aSjAp7X8c/23W8EdIoAisqXUd+U37tGdRJRDF0wHIQBvcijo6M7RjoRxZiSSMyEUUHDyQSDROIJswKej544MizVtb13OQSDBJLI0Af+ijk7vVM2t6GqCGFMvAPF+Ts25yOHkTje6jBacwaR+9w/ECzhvGz8I1fXUbP0K6re/ITGVeu6lJfR+NNa1BBOndLs7JBXUPftKrzVdQH/PTRVRfPKEQt5nONKQA3xsYi2mkmI6z/FoDVNZUP+UzQ2B3MONarqfqWwfDmbdzzPhu1PsaP4XZzuiiDX9D+u945j0rzQiwPG1CS0UMpqgoBo2TO5MmenM+i2PzPotj9318xexTI0i/TLzwb81AqTJASDxKA7rgGg6MHn2XHD/TSv3eL77qgaji357PrbI+z627/8frftJ8xizNJnGLPkKUb+90HGff4iOU/cjmX44A5tDQlxSFHhlQQIhSE+JnQjnTbcntoWcZRgn3uRksqve8mirmGPG9eWwxgYjSTb5BBtdHT6L7pjpBNRpLjwXphiTJCEzC7GWfvymQSSzpzXpesbf1kfsoiiYDDQuHq933Oa20Pl/z4KPenbqy9zdhr2eUd22tZIUvm/jxANge9bUxSq3v28o2iGrLDr1od9kzhF6XANCBTc9UTEVMaOdMUzRDYjBfH9Lp1/BFJYq5q9Q0Hpx9TUhxditqPobSpqV1Ndv5bCsmWs2vB3dpUsDatAb3+gVZQhlHOkNDSGVKnq4ES0LIIknT6X+NmHdM9Qf0giUlyMTwgigiSdcVzQnWRBkrAOyyZ60mgA6r78ka2XLuT3g07l96mnknf+DdR8/LVfpyj10jMY8o8FmLP2qKQJokDc9MmMfOUhrKNzMGWmEj/7EOJmHoQWxmJTOGiq2qehjeZBGcTNPIjoyWMghDJnf6HJWUjo8ACVhqALKH2PyRhLWtJhBK6jJ2IyxpNsn9qbZunoRBS9GIVORPFW1qApavD8AU3DWx44r6Z5XV6XxxcMEvGzD6H4kcWdvrZu+Uq8N/wfBlus3x0vTVZw7Sik+fdNAfsoe/ZNrCOGYjtymi+cMMjOGQCqRuJJR1H9Xqg6ND1H87o8Sv79GhlXnd/O5lYHr3nDVkqeetXvtY0/r2XrpQvJ+PP5xB06aU+fG7dR+vQbNP74e8TsNCLyaM1wbkzIZ7vRhaTtmWqowAXNKVx00kxueDhiQ3YLRXWHUV9oX/a+Iygo/QijIY7MlFmRNK3HaHWOHnvQt2v7w/iOf4zaz74j/Ypzu9S/Jiskn3Mi9St+7padrZgyUkm9+DQSTz4K0WJG9crULfuesheXhJTm35uYgyeQcsHJxE2fjCCJOLftovKNj7HkDgnpbGmahjVnUHtBmpY8ykCYB2WQee2FAB2l8g0SogAjnr+vXdFo1e3BU16FMSkhsMiDLCM3NGGI9/8MbKVqybKg99QTRI0ZTtZfLyNm8h4hJm9NHeUvvkvFK+/32LjTF0/odn6RIITnwIlC/5+SDc8+B7enlpqGDfjW1vcsBBoNMUzI/UuvLU7JipOa+o3IigOLOZGE2NG6NLhOt+n/30KdAYVS30jdNz9jO2Ja4Fh2VaNqyecB+/AUlVH//a/EHTopZK0ef4imroluaF6Z/OvuJffZexDNpnZja4qCXNfAjhvvD96HLLPjL/eRcOxM0i4/G+uw4MWGBUnEkJQQtE24SLHR2I+fhSUnG9Xlof7bVTT9siHoNZacQaRfcc6e3CxRbBPN8JRWUvnGR1S+9QmaxxuwD8f6PLZfeTvGFDvGJDtyXUOHmiiRIlk18mL1SH4xNfG1pQ6HoJChmDnRaSdDMfcrVae6xq0oqqvb/RSUfkRG8uHtJldeuRlNUzAaYvrdRGDtUhuzlvomkisWT+ggyuDaWUTtspXYjj409MLBPggGiejxIyNipzV3CLkv3IcUZW37rotGAwlzZ2KbM4Ptf76bptWhd/tSLz6NzOsvbqfQaM0dwqA7r0GuqUdT1aDiJoIgoAb5fvkj6fS5QRUhBUlq5xSBL7fRmGwPaIumqiCIFD/+MoP+dhXQcYFLkxU8pRVUvd+7CzlR40Yw4oX7Otyv0W4j64ZLMaUnU/TgcxEfd9I8meu947rdT1z0UETBiKoF+zuL2OO6P1ZPI4pGxg2/mpqGjZRWfYfTVYFBiiLFfjCpiYdikCITrhkMTVPZVfIBReVftPs3NRnjGZ59DskJXS9mraOjO0Y6Eaf0qdeImz4ZUaCjnLQs4ymrouqd4MVMd9/9JCP/+6DvRd4J50iTZRybu57A6tiwlc1nXkvKBSdhP/EoDHExeKvrqHr3cyrf+Dg8uVxVpfazb3EXlTHq1UXB7VUiE5aSeOocsm+5AsFo8O3YCZB64XwcW3aQf929fnfoosaPZMR/7kUwSnsk0QUBTVbQBCha9HynVua9FTV4KyJTfDIYIgLTPLFM87SvV2RZcRoLFvWf4ouqGl69rlB45Qbqm/KJj8mlonYVRWXLaXLuBsBoiCUjeTbZqXOQpP6X8Dx7yUxWLKaDc7Tr9n8x1PRXbLMO8YWaCSJoanihbGGGqgZFEBi66OZ2TlHbKYMBFJVhD9/C+mMvDiqjHz15DJnXX9xy3Z5+Wr9PhoS4kKHBmqIEFUzxh3XUsC4tGrU6RZrqk/Vv2x1WFNA0dv39UWo/+xZPcQVD/rEAU4q95e8jIEgSzevz2Hnzg36LPfckg27/M4JBCuhIp5x3EtUffoWzG8/+nsQgRZGWNLMlh8j/TqAAZCT3bVh1uAiCSGL8eBLjx/fJ+Ft3v0ZZ1Xcdjnu89Wza8Syjh15Oih7Op9NFdMdIJ+K48nez7bJbGXL/jVgGZfheui1Kck1rNrNr4cMh8068FdVsOW8BqRefSvK5J4W9CyQYDEHVm8LBU1JO0YPPd6gF1FkcG7biKizFnJkacJVWkESq3/+iW+PY5hzG4Duuadvp2Xss6/DBjHj+HxQ+9DyG+Fjk2noaf16LpqgMe/AmBJOhw2RDMEhoqsqQ+25g/TEXtatd1F+ZvngCs/uRUwRgMUeufpBXbmZH8TstoXnCXscbKSj9kOr6NUwccSMGKTIFKCOJP+dIc3nYcf0/iBo7HPuJR2FMtOGtrsN+wqx2dbb2RZN9qordJXbaBCyDMwOeFyQRKTaahLmHU/PBlwHbpZx3EposB3ToBFFsEVJQ/YfnKgo1n36Lt7JzCwqaxxtyJyrgtYqCc1sBDT/+7gtL0zQaV6+nasnnbQsoTavXsWHepcTPnIp1xFA0Wabhx99xbun9HJioscOJGjE0aBtNlkk+4zh23/NURMe2njmFtUtsEelrWNZpNDuLqG/aRltFaqA11XvU0Et7pObY/kaTo9CvU7Q323a/TlLCpAERmqjT/9A/NTo9gmPjdjadfCUxU8cTNSYHTVFp/Hltp+L25dp6iv/1EomnHBOWY6RpGjUfraDhh9+6Y3pEKfnXSwx7ZKHfSYwmK7h2FlK77PtujZFx7YUBJ0mCQcKUlcbwx29rOybXN1K77HtM6ckB+xREEdFsxn7CrG47mgcqMdZsoq1ZNDuL6bIuewteb8Ne+Ur79qXR5ChkV8kHDM8+u1vj9BSzl8zkk3m/sebT9q8cx8btODZub/tdrq0n/cpzAy8kGAxtstbdIXrS6KAODfgciJhJY4I6RrGHTgq5yyWIIkqzE9FiardDI0gSzevyKLzvGcyDM5FirHhKKpFr60Pa37DyV+IOmxKynV97JAlzViolj/23/XGDAVNGKprX63PUFJX6b1ZR/82qLo0TKSxDg4cjg892a+6QiI47ffEEZi+ZGbH+JNHMhNzrKav+kZLKr3G4yhBFI0nxk8hKPYaYqND3qQOlVd+31YALhKw0kV/4NrmDupbLqHNgoztGOj1K0y/rafrFv4pbuIjR4cUsV7z6AcX/eqlbY0Wauq9+ZNffHiH71j8hRll8kzFBQDAYaFq7mZ03/jNo/k4oosYOx9JJeXJDfCzJZ85DVRTEYDkeqkL0uBG6Y9RFBEEgd9B5rN36MJqm4s85irYOorklLC5AL0RbM1ukftsnOrdHo7Tqe4ZmzO+XIXUAC+dfyP283ME52pvyxUuInjCSuMMO8oV6tYZ+teTTlDz5Ssi8uXAIOxctRLtwygoA5P/lH8RMHE3CvCMwxEbj2l1K1TufgkFi5KuL2nIRtRZnpOSJl3HtLGq7XoyJwn78kUSNHIbqlWn+fROqw4lotXQ6TwtA3Ss8UIqNJu2yM0k8bS6GWJ9aqDN/N+UvvxfUKewtVE/gUMZWNFXrqJrZTYSD58CSyO6Wi6KRjOQjyEg+IqL9Hkg43RVBnaJWSipXYLWkkJVydC9YpbM/oTtGOv0eT1lV0HA0aKnG/txbkck/kETiDp2EKSsNtdlJ/Xe/oNQ3drm7mo+/pu7LH7HNnYllSBaa20Pd1z9HJCzFYLeFbBNoEhhOGE6gGkU64REfM5yJIxawteA1HK6StuOiYCQjeRaDM09m/dZHW2R693WcBEAgJ+tMNu14luA1UHw5TQ5XKbHRQyJ8F5GhVbEumHOkyT4BlMRTjiHl3BOx5AwGTaVx1VrKX/kgYiqHTWs2h97pkaSgCpQAzRu2ETt1XFDnRHG4cKzLo2nVOsqee7PteOolp5N53UXt5P0FSST+iIOJPWQCWy++Bee2XSQcdwSD77gGwWxqk8VPOecEPBXVvnvQ9uQ3+QQUfN/3QN97TVao/eIHwOcUjfjvg1gGZ7S7B8vQLIbcdR3W3CEUL3oh6L9BT9P48zpUrxyimLRG3deRUSrcn5AVFzX1G3yqbaZEEuIGvmqbT9wh2CLRHnYUvUOq/VCMhiDlQXR09kF3jHT6PVXvfEbmdRcFPK/JCnUrfkJpaOr2WLajppO18ApMyfa28DTVK1P11icU/etFkJXQnfhBdbl7ZPXVW1Xb5WtDrZoLBgNNAWo26YRPfEwuU8fcQaNjF05XGaJoJiFuFAYpCoDxudeRt+u/VNX92nKF76VvNMQQGzWEvIL/IivhJrv3vSqf011JaeV3NDp2IQgSCXFjSEucgdEQzdqlNrbcdBZ8+m7gDhSV6neXUf3uMt8kvwfqODX+vNaX/5ee4lfEQFNUFIeTms+/DdpP5f8+Iu6QiQHPa7JC9fvLOuxmmAdltD3T/MptY2bQXddS8sQrDLnvBkBDEAUEcc8r22i3ITc1UfvJt9hmH4oYbcVTXI5zZyGJx8/yb4+mgSRS8+k3AGRcdxGWQRkd8wxbbEq94BQavvuFxp/XBv136EmU+kaq31tG0unH+S0DoSkKSrOTmg+/6gPr+ic+1bYPKSpf5ke17WySEwauMEFywkFU1v4SVltNUymv+UnfNdLpFLpjpNPvqXrnM5JOnYM5K73DJEZTFFS3h9J/vwaAaLUQP2uaL5m7pp76FT+jOsOTTI6ffShDF91C68p96+RANBpIPvdEDIk2dt3SXmVOssVim3UoUlw0nrJK6r9e1a3QuM7i3JyPa1cx5kHpnU7E1lpqpfi7TlMU5Pomapd3L/9Jx4cgCMRFDyUuumMSuUGyMDbnClzuKqrrN6BqHjyeOooqvqCmYSPhrIwCSKKVKGvnwiojTVH5l+QXvYXPQfPZXduwiV0lSxmX8ycS4sawYFEaj6w4DdfsIM5RKz1V3FbT2HnD/eS+cD+S1dxu90iTZTRVY+eNDwRVpAOoX/EzVe9/QeIpR3f4LmmKgmtXESX/fr3Ddb7Cr0Hktg0S0WOGk3XD/7Wox/nPHzTExiDXNrBh3v/tOW4xkXD0DERzx1oygiCgqSrDHroFzevFlJESdIFEk2WSzzmxTx0jgKJFL2DOSiNuxpS2BStN00DVUBwutl91Z8QKSbfyW9VOoH8JuoTLtt1vUFr1TYfjPtW2/zB6qEqKfVofWNZ9Em0TsZpTcLpDl4QQBBGnq7wXrNLZnxjYe6o6BwRqs5Otl95Kw89rAN+EvjX8xLWziK2XLsS1s4jUi09j/JcvM/T+G8n8yyUMve8Gxn/1MqmXnhF6EFEk+5bL8a3M+pmEiCL2444gekJLDRVJJPOGSxm//L8MuuNqMq+7iGEP3sz4L/9L4ulzI3Tn4VH82Ett6ledQlFR3Z628JxWNFlBdXnIv+5eNK8cQUt1gmExJ5GZMouE2NEUVbTuLob7NxXISD4iIoUVNU1DVb2+iWcnqKz9jfyiN/EtLOxtt4aqetiw/UkcLZOUBYvSsKw4rdu2dgfn1l1sOed6qt5b3rajo8kytctWknfBDWE7A7vveoLCB55tV7tLaWym4tUPyLv4Zl8R51OOIeOaP5D6f2diGZpF1JjhYcltW4cPClosG1H0OWV7YT/uSIQgYjWCKGJKsfvCk8PYNY6eOCqknT2NOTsN89Cstt/bPpuaRulTr+HYsDWi4/U36f/O0OQs8usU7c32wv+hav332S4rTsqqVlJQ+hHFFSvwePcIkoiCwVdEtmXHPSiahiT2z5xLnf6LvmOkMyCQa+rI//NdmAdlEDttAoIk4di8neZ1eQCk/fFsMv58flv71tAQyWoh89oLEc1GSp9+I2D/sYdMwJSaFNQGTZZJPPVYmtflMei2P5N48tF7nKjWuiWxMQy+7c8AVAcpYhtJ6lf87BN4+NufEK3tBR6CIRgkdv31AaLH5pJ42rEY7TaUxmaqP/yKiteW4ikeGCtt1tE57Bg5lyPHx7Imv5p6R+/t2PUERRVfIiCgha1kJxAfk8PgjJO6Na7LXU1R+XLKqleiqG5E0Uxa4nSyUudgNQdWMATfRLWg9CPayxC3a4GqqRRXfNWmFLVgURpfr78BbfVynG93VKzrDTzF5RT+42kK//kfpJgo1GZn5xcDNI2qNz+h6q1PMaUnIxgkPKWVaF6ZxNOOJeuvlyFazL4dIlEg85o/4K2pb5PX7w6CIGCwxbU7Fjd9ki/XsguiDH6JRN5mNzAkxJP7/H0YYn0y7u125STfgpa7uIyG78ILrwrFpHkyxw9QpwigrGoloXJwvHITNfXrSbJN7jW7wkHTNIoqvmBX8fuomrdFfU4jv/BN0pOPJCf7TETBgMWcyPjh17Am75/B+0MlKaF/3aNO/0d3jHQGFO7dJbh3l7Q7ZkiIJ/2Kc4Jel3bZWVS+9SlydZ3f8+aM1JATFcFgwJydhjV3CEnz5wQdL+v6i6n5aAWaO7SiUiSo+fhr6r76iYS5h2MZlo3m8RA9ZSwxE0b5z6GQFRxb8qlf8TP1K36m5MlXeyyfo6ewjs5h8B1XEzUqB4CF54CsqHzxezFPf7QFt7dr+WB9TXXd2rBUlwAspkQyU44iI3kWohherS9/NDkKWbN1EaribhtbVd2UVn5LefVPTByxIKiog9tTTbOzKOB5HyoVNavaSejOusXJxJPHwfxxIRXrehRZQakLX2BFjLaSdNpckk6fizElEaWxiZqPVlD55sdtO0f2E2Yx+Par99QX20s8wBAfG6jrNjRFDb5bhE9sYd88Q8FoCKmmFy6aLNPQyeKzkSbprHkY4mL9hxMKApqiknHV+RFzjAY6TlcFoXeaBZzujkW/+5qiiuXsKHqn7ffWZ5GGRknlChTVzaghFwMQH5NDfMwI6pu24/9+ReKihxIbFbwGlo7OvuihdDoDHvsJR4YxERCwnzg74FmlyRFy9VZTFJSGZuynHO2rBh8EKTYa26xDQtgUWVSni+r3l1P8yGJKnnyV/GvvoXm9b0dNaxGNaA2bc+0qIv+6e9t3MJCcopFDGbn4gQ61SwySyJwpWdxz0UFIYUop9zfCDXGZNPImpo27j6zUOd1yijRNZUP+v1H2corazqGiqG425P87qF2yEp6ssaJ2lFReu9TWplg3aV7/De9pxZhsZ9Qbj5L5l4sxD0pHirJgSk0i9eLTGL3kKayjc3yhttdfHHCxRZDEtnwff/iU41bS+OuGDqGu+1L13rJ2vzu27IjYd1kwGKj830cR6aurJJ50VFAHUZBEokbnYB7Ut/l1/QWDoVW1LRgaBrF/FYOWFRe7ij8I2qa8+geanXsWRsfmXEG0tbVQs9DuZ7Q1g7E5f+r2rqzOgYfuGOkMeEwZKWhq8MmDpiqY0gNXFa9f+StqiN0dQZKoXf495vQUCCF0oMlK0AKqvYHa5GDr/91K/rX3UPfNKpo3bqN+5a/svOlBtpzzl4C7ZwOBzAWXIhgNfmWSJVFgwlA7h48LLxxG01Sq6tayYftT/LLpHtZt/Rdl1T+gqn0TkhdtzSCUupwgSERbM7r90tc0lYraX3B7qglWI8njraO6LnDOjdlkJ5zXicVkD3iuTbGunzPkgb9izkhGEMV2YV2CJCFFWRj+5B3ETZ+MMdkeXNhAUUDT2hYtwLcDpGkazm27KLz3376dXA2/DpQmy3jKqnzqfXtR/f7yDm07S6tNRYuex7FhW7f76w4GW+jdNV+7uNCNDgCSbQcRasdIQCTRFlhNsS+oqv2tnYKePwREyqpXtv1uNMQyZfRCRg+9HHvcWKKtWSTEjWH00MuYMupWTEb9M6HTefRQOp0Bj9LYHLI2gyBJxB02hWGPLKT+u1+o/fTbdhK6apODiteWknrx6X6LNmqygqe0grovfiB22sQ2ZaSAiCJyhFWSuoSqUv/tauq/Xd3XlkQMU0ZKUIlkAEVVOWFaNl+vKw3aLt1u4IixzRx38MVYTFewqWAr//noFZZ8+woFpZ8wccRfsJgSI2l+SDKTZ7Gl+cUgLURS7Ie0yX13Ba/cTFH5Mkoqv0VWQn9OBSTqG7eRnHCQ3/NGQzTJtslU1v1O4EmZQEbyrKDjdEqxrg+wjhhC7EFjA54XJAljoo34MHaLBUnCkbeTxtXrsB93BGKUBU9ROZVvf0r10i/R3B6af99E/vX3MuSev2BIiEP1ygiCbyfHua2AHQvu66DG5q2oofCf/2HQrX8K/Zzai9bdLU2WqV/5GxWvLqVp9bqwru1JvJU1iFHWkPfhrazp9liT5sksnH8hLO12V31Gom0CVnMqLndlgJBcgbSkmf3OaXB7a1pyigI7dRoabk/70FFRMJBin0qKfeBKkINvx6y2YSOy4sRiTsIWM2LA15waqOiOkc6Ap3bZ96RfcW7QNoIoYslOx5yRSvzsQ8m45kK2X3VHuyKrJU+9isFuI2n+MT4BA4MBTVEQJAl3aQXbr7gdzStTu+w7kk4NnmOEqlD/1U8dDpsyU0k+5wQS5sxEtJpx7Sqm6u1Pqfns2y7XSDrQMGWG3gmSRJHMpOBF/Q4bk8otZ48HwNgiVHHI6CkcNm4a5x59KmffdQXrtz3O1DF39OoLKsU+jYqa1S1S3fuGRImYjHEMzZjf5f493kbW5P0Tp7vST/8BEAgpBjEk8xRqGjaiqB46OkciUZZU0pNmhhyqPztHsdMntz0TAqHJCubs0GFdmqLgKaukeNELQYuoNnz/K+vnXEziqXOIm3kQaBp1X/5AzYcrAl5T9daneCtrSf/TuUSN8OVY+OT58bvw09KAokdepOK1pT4Zb0lCMBr6XJmy6t3lZP7l4oDnNUWh6ffNeEoruz3WlpvOYu0iW7f76UsEQWLCiOtZt/VfLZLWPkGUVqcj0TaR4dln97WZHTBI0SGfMQJCRIu1Kqqbmvr1eLwNGA2xJMZPQJJ6V8WureZUxXJUdU/UitlkZ3j2uST1s529AwHdMdIZ8LjyC6lb8RPxRxwcdMICtMWqG+JjyH32HjbNvwq5tkUKVFHZfefjVL31CYnz52DOTkNpcuDcugvrqGGMee8pBKMB164i3CUVmFIS/QsbqCqVb3+2p98WYqdPIufRv/smHC3XRY/LJWbiKBJPOYbt19wVsm6KDqiO8IqdOtyBJ3RpCVYWnjMRUQBxr5Vog+R7JM45aBa3X7iAvy++n5qGjSTGj++e0X7weBuprl+DLDuxmO0kxk9EFI0IgsTYnKvYVfohJRUrUFRfHS4BkaSEKeRknYnZZOvyuNsLX29JvA4/D0XTFOKihwVtE2VJY9Kom8jb+RJNzt3tziXGj2fkkIuQpPDyGloV634Y/3DYNvYGgsEQOn9HALmuAbmuIWh4lyBJ1HwU2LlpRYqNJvu2q0g4+rC255dt1iEkn30CBXc8jit/t9/r6lf8RP2Kn3x5ULHRKA43OY//3W9RW01RcGzOB1Fk3MfPtYUBy43NVC/5nNL/vInqCC+PLNJUvbeM5LOPx5SW1EFpU1NVUDVKnnylT2zrr1hMiUwdcydVdb9RUbMaWWnGYk4mLXEm8THD+2XeTXLCFLYXtsr9+0dDJTmh+/WXNE2juOJLdpUsbXm++pxHUTQxOP1EslPn9tq/0daCVymr7lgv0O2pYWP+vxmbc2W/Uw/c3xG0zhar6Oc0NDQQHx/PstHHEi11PSFZp+sY05JIOe9kEk8+CikuFrm2nur3l1PxxkfI+ygoRQrRamHYIwuJmz55z25PCJU5TVEp+fdrlL/wdsA2SafPJftvV4GqtL2UffH+AqrThRRt9QkxSBIovjbVH39NwR2PtdsBMiTaGPfxcwgmY4CCqipVSz6n8L6nu/6PcKAgioz77IWgORyKqvG/r/N55cvtfs9fdtxI5s8YhCGII93kbGbIOVOJjZ3MyMEXdjivaRoNzduprt+ApspEWzNItk8NWTdD1WR2FL5DSeXXLWEjPmldSbKSk3Vmu10VRfXQ5ChA1RSiLRndDn9xe+v4ad0thF8fCUDAIFmZPuGhsEUeGpt30ejYjSCI2GJHYTUHl8IPxNcPWPuVcxR32BSGP3Vn0DaaqlLy+Muobg/ZN1/uv42s4NpZyOZz/xJ0p1iwmBj50oNYhw/u6MzICqrLxZbzb8RdUByW/YaEeDJv/D/sc2e2Pc9Ut4fqD77AkBCP7ZgZfgvVOrcVsPXShX3mHBlTEhm26GaiJ4xCkxU0TUM0GvBW17Hr7/+i8cffuz3G9MUTmL0k9I6mTs+xbffrlFR+HeCsSHxMDhNH3Nhtp2V32WfsLA68Iz0o/QSGZpzSrTHCodFRwG+b/xG0jclo49DxD+hhdd1EdjtY+eiZ1NfXExcX/D2q7xjpRBTrqGHkPvcPJKul7UVuTLSRevFpJJ46h62XLsS9K7yXeGdQnS62/+kOYqaMxX7ibCxDs4iZPCb4RaKAfd4RAR0ja+4Qsv92lS/0RNzzVWmdNIgWE3Vf/ojS7ECKjcFTXkX1+8vbhee1knTqsQhG/04R+HayEucfQ8mTr6A0NIV51wcoqkrZC28zaOGVfk8rqobbq/DJ6sKAXRwyKjmoUwQQY41m6qhJbNrdUUnN1bKa1+TYjYAIgoCmKWwvfJPcQX8gNnoQqurBYk7skAuUt+tlKmr2DrP0OSmK4mRrwcsAbc6RJJqIj8kNamdnaGzeReecIp962phhV3RK+S42ekhQee9wmXWLs1/tHDX8uAZPaSXGFLvf3WlN09AUleoPvkSurUeKjiL9T+cBWlsom2g04NiS71OFDBE+m3jKMVhHDPUb/iYYJESLhYyrL2DnX4PXc2lFrq2n4G+PULzoeawjh4Gq0rxpO/EzpzL0gRtbOm4/liBJWHMHk37F2RT/66Wwxok03opq8i68iaixw4mbPgXBaMCZt5O6b1dFJAR50jyZ673jImBp/8crN9Pk2A0IxERlRzQ0rbvkZJ+Fonoor/6hrYaRr6abSnxMDmNzruq2U+SVm9hVElz9bnfpp2QkH4nZaOvWWKEoq/o+ZF6Vx1tHbcMm7PEHxuezP6A7RjqRwyCR89jfkaIsHSYNgiRhiItl2CO3svm0P/eYCU2/baTpt43ETp9E7tN3B20rCAJSbOCXQtLZx/uKG4r+J9CCJBE7fTLrj74Q1ekKOlb8EVMhhHy0aDISc9A46ld0zE0KF8Fswnb0dCxDs1BdHuq/WYVre0GX++uvVL35Caa0ZNIuOR1ZUTFIom9SCrg8Mrf991eqGzo6NK0YQ9SHacVkMGG12NodkxUXa/MW4fL4kr011LboD0V1sWXXc21tBUEiJeFghmTOx2Ky0+go2Mcp6kh+4ZuYDPHExQzr40mLSJJtEoPSjyc2alCfWTHrFicrFk/gx0v7XggAVWXnrQ+T+8zdvnydvXZxWusO7f7H021htGXPv0X1B19gP/lozNnpqA4ndV/+QNOvG8MaLvnMefg+XP6fHYJBwnbUoUi22E7VYZJrG2jcqz5R8rknBs2dEiSJpNPmUvLUa2ieviug7Ni4HcdG/7vA3cF65hTWLrFFvN/+hFduZkfRO5TX/ISm+ZxJQTCQaj+UnOwzuiXmEilEwcCoIReTnTqHsuofcHtqfcIuCdMiFgJYUbMKTQu1OKRRXv0Tg9KO6/Z4wXC4KsKqWefLB9XpLXTHSCdi2I6chik1cMiMYJCwDssmdtoEGlf17CTHU1wRso2mKLiLygOej5sxxW8O0d5IURaixuSEnOgIJlNYD3XR2PWvZMLcw8n++1UYYqN96lWiQOY1f6Dhx9/ZecsilPrwJ04DgcETf+fSJ9I4flo2OemxeLwqP2+pYPnvJTQ5g0/etpU0kBRvwRDEQVJVlU0Fm8lKu67d8fLqH3B5wiuOqGkKFTWrqGnYxJRRCymr+oFQVel9dYOe8DlV9kPJyTojIg6Spqlh1hsSSLVPJ3fQOWHnBPU0s5fMZMVi+oVz1Pz7JvIuvpmMP1/ge0a0LHg4tuRT+swbHQqNeitrgobrBsOUmRpSjU2QJExpyTg74RjtS9TonJD5mVJsNObMVFw7QxXy1elvyIqTNXkP4XCVsfezR9Nkyqp/oNGxk0kjb8bQT77v0dZMcrLO7JG+ne4qBEFscw79ISDiclf3yPh7Y5CstOY3BaO/PIcPFHTHSCdixE6bgOqVg07uVa9MzMHje9wxcu8uoWnNZqLHjwi6Clq15LOAfYQrc5sw70jiZhyEp6yS2s+/8xsK59i0HevwQR2Sh/fFuW1XWGPuS/yR0xhy/420PmD3/hvEHjyB3GfuJu/Cv/a5wlSk2VnWyFNLN3X6uo9+3h20zpGsyHy2agWS4SAs5vZy3aVVKwNc5R8NFa/cxPbCN1qOhBfKpmkK5dU/0ti8g8mjbu7Wiq6qetmY/zQ1DRvCsjg7bW6/exnPXjKTT+b9xppP+/615dycT/7Vd2FISsCUkohc14inJPAiS1dRnS4ka+i/Q7dzfwIUmt2XUMVmdfonReXLcbhK8T8BV2l2llBc8QWD00/sbdMiiqb5nrWiaAro5BmkKEKn1mstTkvPkpQwmaq634K2EQSJRD2MrlfRs7l0IkeYjkSolcmwhoqJIu7wqdiOno55cKbfNkUPL0ZTVDTFT3FERaFpzWbqlgWe5Dav2+ITVgiCpmkknTqHlAvnk73wCsZ/8V9S/6/jSlfVO58FdYo0WaHx1w1hr8bGTpvA4HuuZ/iz9zDk/hvI/tufAM2vMycYJKJG52Cbc1hYfR8IrN1Rw8erdvvC7/Z5SXplmbqmBp74YBVDMk7ucK3HW9eFEVWq69cjYKBzj10Vh6uMwrJloZsGYXvhmy3y38Hw7XwMzz6PaGtouem+YOH8C5k0r/8493JVLY5N23vEKQKo/ey7dgVg90VTVZz5u3HvDl6vKxQNP68NPo6m4SmvCrrDPpARDg5RfmEAo2lqi6BBMGdAo7ji6zAchv6JV25mR/F7/LB2AT+uu5GVa65lTd6DVNd3XIBNTphCqMUpDZVku/+abZEk2TalpU5eoHeCQHrS4RgN4RU51okMumOkEzEcG7eHDD0TjQYcG7teSV0wGcm66Y9M+PJlhj9xO8MeXsjYD54m9/n7sAzLbm/P+jy2X3k77pZJS2tVeU1VqV2+ku1X3RnU8al88+PgzkyL6p0gSYhGA4IoIpqMZF7zB1IvPq29LZu2U/7Su212tOtHVlCcLgr/EVqRTrRaGP7M3eT+517s844g7pCJJMyZ6ZMOD+KYaopC0vz99+XfFZ5cuolnP9lCdeOeXCRZUVixdht/eeY3BOkgv+GPXVeG04iOyqBz4ge+60oqvwkjLt4/XrmppVp88ElPlDmVCbnXk5kyK0A/jdQ1bqOheQeq2jd5JmuX2vqdc9STVP7vIzRF6fDMaEUQRarf/6L747y2NPizW9OoeG1p2DtLkUSKjSbh+CNJOnMesdMnh70AFy4z1t/ArFv6Rm2vN5AVB145tKCPV25AUQfev4NXbuT3LfdTWPY5srKnlEN9Uz4btj9JYfnydu2jrRkkxk8kUN4eiNhiRxMbNbjnjG4dSTQyYcRf9iq/ILT8v+8znmSbRE7WWT1uh057+j4mQWe/ofazb8m68f8QoywBJKkV5NoG6r5Z1bUBJJGcx/5O7LSJbfU8WomZNJqRLz/IlgtubKd61/TbRjaddAUxU8dhyRmE5vbS8MNveCtCxw83/bqR8v++R+pFp/qtIB8sZyj9ynOpfOvTdiEuxY++hLuojLT/OwNTegrgc5Lqv/+F4n+9FJbk7uC7ryP2YF9NnVanLZQzCr5dOmOQ/K8DEU2D938oYOmPBQxJi8VskCipbqbeEXzSn5Z4GPlFb9OZOkCtxEYPI9qajcNZHFbSbSuy0oxXbuqSU1bbsCloPH0rZpOdhLiOSo5ubx07ipZQWbO6zWaDFEVmylEMSj8eUejd18japTasZ06BT/s+36ince8uJf/aexj26N8QLT4Z+L2fO5qikHHNH3Dl76bhh+AhOcFoXLWO4ideIfOaP6DJStszpVWQoe6LH6h4dWn3bqazGCQyr7mQ5HNPQDSZ2haiPOVVFD7wn26J1OzNb1U7gdBFowcqnfl+Cr38XY4E23b/r6Uu277PU9/zeUfR2yTEjiYmKqvtzKih/8fG/H9T17iFPTmfvp9x0cMYO+yK3jEesJpTmDb2Hipqf6GyZjWy4sBiTiE9aSbxMbn9subU/s7A+xbo9FtUl5udCxeR86+/oalKe8UmWUFTFXbe8lCX5VUTjjmMuOn+C521StdmLbiU/Gvv6XC+6ZcNNP0STn5Fe4r/9SLO/ALSLj8HS1b4L89WdbiaD79qd7zqnc+oWvI51hFDEK0W3EVlYdd2Mg/OJKGL4XCaqiLX1HXp2v0dVYMdpeEnrqclzaCofDlubz2d2f0RBSPxMTlMyL2Oddseo9lZSCghhnbXd0Iue28UNbyiwYraUcXP7a3j9833d7hXWXFQUPoxTY5Cxub8Sa+x0YM0/ryWpl83Ejd9cocFIUGSQBAY9shC1h93aaeU6fal/IW3cWzYSsr5JxN7yEQQRZxbdlD5xkfUfPZtr+8WDb7rOuzzjmhbkGqdIBqT7Qx7eCE7brg/Ys7R/owkWYiPGU59Uz6BF3NEbLG5SKKpN03rNh5vA1W1vxLsGSogUlL5NSMGX9B2zCBZmJD7F+qa8iiv/gmPtw6TIY6UxENJiB3V688zUTSSljidtMTpvTqujn90x0gnojR89wtbL72FtMvPaVNs0lSV+m9XU/rs/3DmdazxEy5JZ80LLilrkIibeRDGFDveipqw+42ZOh7bUYciRllxFxRT/eFX7ZyVmqVfETVqGOazjg8pntCGomBMtvs/p2k483aGbV8rtmNmBL3/oAgC1R+t6Px1Oh0wSFFMHPlXNmx/AoertKUOBQR3cHyx4gbJCpKVg0b/jdqGTVTUrqa6fj1y0FAXgbjoYV1OBo6yhM4XEhCJtnbM1dtZ9F4QB1Cjun4tlbW/kGLvfjX6znC9dxz39xMhhp7GlJHaTv1uXwRRBJORpPlz2sJ1u0rjz2tp/Hltt/qIBFHjR5J4wiy/5wRRRFNVsm++nPpvVoGqYh05tEUO3UXjrxvQ3OEtBlhWnMaCRfvvblEr2anHUd/0ZJAWKlmpx/aaPZGi0bEr5M67hkpdY16H44IgkBA7ioTYUT1lns4AZf9/q+j0Os3r8si/+i6k+FgMtjjkmjqUxuZu92sZmhXSKRBEEXN2eliOkSEpgZzHbyN6zHDUllwjQRDIuPoCSp56jfLF7+xpa4sjcEyyH0SprZZJpJBionwhfZ10jDRZxlNeTe3HX0fUnr5GOHgOLOmbmHirOYmpY+6krnEL1fXr0TQZVZUpq17ZVpCwxUpAIz4ml6FZe/LOBEHEHj8Oe/w4auo3sn77Y0FG86nEdZW46GFYLWk4XeUEWjHWUElPPrzdMVl2UFG7ilAOX3HF173uGLXmGt3Py/u9cxRz8LiATlEbgkDMtAnddoz6C0mnzkGT5YALUYIoYkpLIvncE0g88SiiRue0nVOaHJS/8j5lz70VdJdrf88t2ptE2wSGZp7GzuJ32XuXurW46LDM00mMH9+rNmmahttTg6J6MJsSwpYKd7qraHYWIggSihKeA7xv0WIdnWDs328UnT5FqW+MaO0c1RW4YGe7ds7Q7QSjgdxn78EyOAMAcZ8XcOa1F6I0NVP11qcALY5W+DklmixT9+WPYbcPB09xeUinSNM0X/KMqqLhuy+5tgHV5WbsJ88h19RTvfRLqt9bHhFnta/oD5MaQRBIiBtNQtzotmOZKUdRXPEVVXW/o2peoixpZCTPItV+KKLo/3Frjx/LsKwz2VH0drsq6K3/PSRjPkm2Sd2yc0j6SWze+XzANhnJszokGzvdFWHkJmk0O/umrk2rc3T2p6/3yfi9RThlAwRB6PAMG8iYs9ND7s5rmkbWgks7HJdioki/8lxM6cnsvvOJnjIxImiaQrOrFE1TsJpTe7SO0KC040iIHUVxxQrqmrYCYIsdSWbybGKje15oYG/Kq39md9mnOFwlwJ5Cs0MyTsJsSvB7jdNdwbbdr1PbsHd5BonQdYBEfVdIp1PsP09Snf2euuUrSTn/lIBiA5qm4a2swbEldLie7ZgZWHMGBW2T/qfzqHp3GcgK1R9+2UFpLhCaplHxygd+6xl1h9rPvyPrr5chmIPEgasaZS++gynZDqJI7CETMSYlYEi0IYgiBlscmddfTMoFp7Dtslu7LfPbF0yaJ3PdD6WAra9N6UBMVBYjh1zISC7s1HXZqXOwxY6guOLrloRgjfiYEWSmzCYuemi3bHK4ytm2O7DzkBg/keHZ53Q4Xlr1fVj992XC9tqlNi5acRqu2fvHTok/HJu2h2yjKQrNG7b2gjW9g9LYHDJsWBAENEHwX6JAEEiaP4fq95bTvHZLT5raJTRNpbB8GUXlX+CVGwDf9ygtcTpDM+e3k2f2qVFqCEL3y1zERg9h1NBLut1Pd9hVspSC0o/YOwKjtdBsdf06poxa2KF2nMtdxW+bH2inOucjnHxllYzkWd01W+cAQs+Y1RkwVL75aXDpWkHwhZKEkSRsP2F2yGKFxoR4Yg+eAIArv5DqpV8GHBvw1UxSNSrf+JCSp14NaUNnURqbKXni5aBtyha/TemTr1Jwx+NYhmRhtPscorYE5pb/Ntpt5Dxxux5i0I+IjRrMqCEXcej4+zl0/AOMHnppt50igLxdLyErTgKtqlbXr8PlqWp3rL5pO6VV34bsW0Ak0Tax2zZ2hwWL0rCsCG/RYiDizNtJ8/qtQesMIQhUvfN57xnVw9R+sTKskOGgJQpkmaTTux6C2lNomsrmnc+zs/jdNqfId1ymtGolv22+H7engYqa1fy+5QG+/e1Kvv3tT/y66V7Kqn/ssmx/d1BVL7Liaqn7pna53lGjo6DFKYKOzyNfcdZtu1/rcN2O4vdanKLO3LvvszE8+1yirRldMVfnAEXfMdIZMHhKysm//l5y/vU3MBraXpytsegVr39I5esfhtWXMckW1ovXkLBHHrng7idRnW6SzjgOBJ8jJEgSaCqOvJ00fLea6qVf4SnuuSKIFa8uRfV4ybj6DxjiYtpkxJVmJ2XPv0X5i0sAX/Jy9PgRAfsRDBKWwZnEzZhMw8quy/zq9G+ancU0NOeHaCVQUvktOVlntB0prljRLqwvEBoqWSlHRcDS7rFgURqP7Mc7RwW3P8qI/z6IFGVtr/bZsqtS9M/neqzIbF9Qt3wl7qvOx5SW5Dekzl/5hH0RDAYsAaIC+nLXuarudyprfwlwVsXlqWbd1odxuEvZe1elyVlI3q4XqW3YyKghl/aKclpN/QYKy5e17GKz1zNBwBY7kqyUY0i0TQi7v5KKb0I8V1RqGjbgcldhMfvKS3jl5pDKc/5IiBtFduqxfksQ6OgEQ3eMdAYUjT+uYcOJl5N02rHEHzkN0WzCsWUHVW99QvO6jsozgfCUVWEdPiRkDaB2Ig6yQuH9z1D2wlvYjjkMgy0Ob3kVtcu+79V8naq3PqX6/S+IO8ynwCfX1tPw7S/tcrDiZ04JmrwMoHpl4mZO1R2j/ZjG5l1htFJpbG4fflrftC2sOkvZaccRE5Udsl1vsGBRGl+vv4Efxj/c16ZEHNfOIract4D0K8/FPvdwBKPve+3YlE/Z82/51Nn2IzSvzLYrbiP32XswZ6W1OYCtz7Sm3zYRO3Vc8D5UtV0duVYmzZNZOP9C1i619ZD1wSmuWEHwvBitxSlinza+/66oWUV8TC4ZyUf2nJFAYdkydhS/Q7uQt7ZngkZdYx51jVsYlHYCQzNPCavPhuYdYT1XmhyFbY6Ry13VqZpvgiBxyLj79yqaqqPTOXTHSGfAIVfVUvafNyn7z5td7qP6gy+xzTok4HlN0/BWVNP028YO57wVNWHvTPUUmscbtIaHYDT6CiKG6Ec0da02js4AIexV5a7lL9jjelfJKhSzbnHut86Rp6iMgr//i8L7n8GYbEd1ODtVlmCg4SkuZ9OpV2E7egYJcw5Dio3GXVhK1XvLcO0qZsKXLyMGy7dEoO6LyArgRIImx266Uhx6b4rKvyA96YgeK/7Z2LyrxSmCYA4cwO6yj4mPycEeH9xRBcLf5dqrndjJ2kqaplDXuIXUxEM7dZ2OTiu6Y6RzQFL/7Sqa1m4hemxuh12j1grrxY++1OtFDSOFc3sBojG40yNIIs5tu3rHIJ2I09i8i0ZHAYIgYosdhdWc3KGNLSZwOOUehHbKegC22FFU1qwKulIrCsZ+s1u0N7NucbJi8QR+vHRdX5vSI6jNTtzNxX1tRq+geWVqP/uW2s865rtVvvkJKRec7DekTlMUlMZmqj/uf7XbIhEC53SXIytN7UQaIolvVyvc4tMiRRVfhOUY2ePG0uwsCdqvIEjEx+yRX4+ypGIxJXXIgwyGv2LVOjrhojtGOgcmikr+1Xcx5B8LiD/i4DZRB8FgQHW6KPrnc9R+Gjr5PBTmQemknH8yCfOORIqOwlNeRdWSz6l665MeDb+r++IH5FuuQIqJ8j9x0DQ0r0zNflbbaH9E01Qqa3+lpPJrmp3FPnUqTcOrtFc9tMePZ+TgizEZ90yWLOZEEuMnUl2/nkCTEUGQSE+a2e5YZspRVNQE3pEEkbSkmZ2WF9Y0jfqmrVTV/Y6iuLGYk0lLnBHxsJfZS2ayYjH7rXOkAyWPv4wpM4WEo2e0hdhpqgZoKI3NbLvydtSmfVXM+h57/HgqalbR3V2jrgoghENdUx7h5/So1DWGp4iYnnwkReXLg9y5SKr9UIyGmLYjgiCSnXYc23aHL2hkNaeE3VZHZ190x0jngEVpbCb/2nuwDMsmfvahSFEWXLtLqPv8+7BrJgUjZup4hj95O4LB0LYrZUpPJuOq80mafwx5l9yCXFXb7XH8oXm8FNzxGMMWLWwRidjjHLUmLhf8498DupbRgYCqymzMf5qahvWEqtdRU7+RNVsfYsqoW9s5LCMGX8iarQ91KPAqIIIgMGbYFZiMce36iosewrDM09lRvISOK8cCsVHZDMs8tVP34vE2sGH7kzQ6drW7l10l75OVeizDMk+PaGiQ7hwFJmpcLklnzCNq1FBUt4f6b1ZT/d7ydkWpjWlJJBw7E0N8HJ6KKmo//w6lLnJ16bqLJsvsvOEBqg6dRNLpc7EMzUJpdlK3/HuqP/gy4LMt6sGbWdtHNdDqGvNwBCm0HC5mY0I756HvCe9+rOYkRg39PzbvfKHlCdDe+YqNyiYn++wO16UnHY7TXUFR+bIQIwiYjQnYYkeGabeOTkcErSeXHfqAhoYG4uPjWTb6WKIlPX9Cp28Qo62M//xFRKulnVPSiibLNP66ke1X3NajdsROm0DGtRcSPW5PSJVz2y5Knnx1wCZs93XydG+yo+hdCss/J/yJlMCwzNPJTju23VFZcVJS+Q0lld/g9lQjiiaSEw4mK/VoYqxZAXurqd/Yokq1GfBNyDJSZpOZMhtJNId9H6om89vmf7SE0fi/l5TE6YweEtkaKxNPruP+919mzaf6GmArWX+9jJTzT0aVZUSDwbfzoGqobjf5195L85rNZN96JYnzjwFNQ1M1BElEU1TKnn+Lsmf/19e30GWmL57A7CUzQzfsAcqrf2bLrsX4FgUC78bERA1qyUMKhMCwzNPITus5KfLNO1+gsmZ1mKIHAnHRw5g86uaw+2907Ka4/AtfIWzVi9WSQkbyLNKSZiIFySlqaNrB5p0v4PJU+rUDYNzwq0mM71+5jzp9j+x2sPLRM6mvrycuLi5oW/1toaPTA9hPnI0YZUUQ/a+ACwYDcYdMxDIsG9eOwh6zo3HVOvIuuBHz4EyMyQnItfW48ntuPJ3IoShuSipX0LnVZY2Sqm86OEYGycqgtOMYlHZcWw5dONjjx2KPH4uqyWiagiiYurSrU123hmZn8LyYiuofSU+cEdHV3rVLbVjPnAKf6rtGAMnnnkjK+ScDILYoVgqCAJKAaDYz/Inbqf/hN2yzD9lT+6wlBVMQRTL+dJ6viPRzXRe+6UuEg+fAkt7fLfJ4G8greAnfd9n/91kUTYwcfBFJtsls2vkc1XW/+2klYIsdRWbK0T1oLWSmzKai5ucwW2tkdlKyPzZqEKOGXtppu+JihjFt3D0UlH5EYfly1L1yiSzmJHKzz8MeP7bT/ero7I3uGOno9ABx0ybiewEGnkRqqkrMweN71DFqxV1QjLvgwEjY3l9ocOzsUhKx2xNcqawrjo0oGEDo+uuivDpYvtIedhS9y5TRC7s8jk4QJJHUS88I6BgLkggmIwlHTw/aTdofz6Tyfx/pYbidoKxqZcjCrKrqQVYcbC98AzSF2OihuNzVbUVgTcZ4MlOOIitlDqLYs1O3uOhhDMk4hV0lH4Rsm2I/lOSEg3rUnr0RBJEhGSeTnTqX2sbNKIoTizmJuOjhPabSp3NgoTtGOjo9gSgSzClqJZwiszrtOVDC6DRV6dJ1nQlx6y3cnrqw2jU6dqGqXkQxcmHQeq6Rj6jROZiS7UHbCJIYckdRMBpJPu+kARdSN2P9Dczqo9yihuYdhLPzu233a20FUFt/xsUMZ+SgC7FaUnqlqGsrKQkHU1j2OYrq8nveIEUzJOMUMpKP6FW7WpEkM0m2Sb0+rs7+T+9/mnV0DgAcm7ZDiBVCQRR97XTCZsb6Gw4Ipwgg2ppBOM713giIpNgP7hmDuoHJaAuzpYaqeiM+/uwlM5m+eELE+x1IiJbOKQgGI+X8k1sWfwYGk+bJfeYUQatEd3jf5da8ntafDU072FH8Tq87H3kF/0VRPQHPy0ozCXGj+8Qp0tHpSfRPtM5+gyEhHkNSAvSD7fSq95aBFlhSVZMVnPm7aV6zuZct0xkomE0J2OPHE/5jWgBB7PH8g66wrxx4ICTJitRJCfBwud47jknz5B7pu78TPWk0CfOOCCnxrKkqhGgjCAKGuBjiZ/Ze+NRAJz5mBF1XolOprl9Hk7MokiYFpdlZSn3TNoJLdouUVH7TWybp6PQaumOkM7ARBJJOn8uY959mwopXmPDFfxn3+WJSLz0DwdizkaKm9GQy/3Ix45a9yMQf3mT0O0+QfM4JiBYzclUtu+/9N4IgoCntQ6I0WUF1e9h168M9ap/OwGd49jkYDVGEflQLiKKJccOvJsqS1humdYpE2wRMhvgQrQTSkw7vsRXotUttLJx/4QHlHBkS4hjx0j8Z+dI/STz5aJ/KXBDHRxBFv3XP9kWTZWJnTImkqT3KlpvO6tPx0xKnI4omOrsD3IqASGXNL5E1KghNjl1htFJpbN7R06bo6PQ6umOkM3ARBAbffR3Zf78K86D0tsPGZDsZV1/A8H/f1WPOUfSk0Yxe8iQp55+CKSURKcqKZVg2WTf9kZGvLkKyxVL9/nK2//lOmtfvKX6nyQq1X6xky/kLcObt7BHbdPoHTlcFNfUbqG/ajqp1bTJuNScxZdStJNsms/fj2miIxRY7ClvMaOxx4xiWdQaHjn8Ae9yYCFkfWQRBZOLIGxECCjgImIzxZKceG+B8ZGh1jg4IJJHh/76rTapfNBp8jk+AHXVNUWn4aQ2uwrLQxUM12mqz9XdmrL+BBYv6drHAYIhi7LA/IQiir35YpxGQlV4sVhv24oQ+hdTZ/9DFF3QGLAnzjiDxpBaZ0L1e9oIggCAQM2UsqRedRtnzb0V0XDHKSs4TtyOaTe3EE1pXWi1Dshh813XsuO5eGlb+RsPK3zAm25FiovBW1epqTn2MrLioqPmZZmcxgiBhjxsX0Vj5RkcB+YVvtYSi+DAaYslOO46slGM6rZxkMScxJucKPN4GnO5KRNFAjDULQRgYE9NWoiypTBt7Dxt3PNthRdoWM4KRQy/pUGi2J1i71MZFK07DNfvdHh+rL4k/YhpRo3PCaqt6PFS9u5zif71IwpzDGHLvX4JfIEkDZmHnt6qdQN/votrjx3LQ6L9TWL6cyprVqJoXoyGGaGs2dY1bCBZqp6FiNgUXzogk8TG5hCooDQIJcaP9nlFVmaq636lp2IimyURbs0hLPAyTMbYnzNXRiSi6Y6QzYEk59yQ0RQmo7CZIIsnnnEDZi++AEk6huvCwnzALKToqSI0iifjDD8aUlYanqAwAb2UN3srgMso6PU9FzSryCl5GVT0ISIBGccWXWC1pjM+5GqslpVv9NzTvYm3eQ6ha+/BJr9zIjqK3cbmryR10Tpf6NhnjesVx6Eks5kQOGn0rzc4SGprzAYH4mByiLOkhr40kCxal8fX6G/hh/P4bzmo/cXbQ52MrZS+8TflL77Yt2NR+/h1ZN/4fUlyM37A6TVXR3B5qPvm6J8zer4m2ZjJqyMWMHHyRry6YaMDjbeCndTehhXBCUhMP7TU7LSY7SbbJVNWtIVCekSBIpCcdDkBjcwGlVd/jdJehadDkLERRHPh2lDRgNbtKPiA3+zzSkw8POramqVTXraWk8hscrtIW9bmDyEg+ArMpIZK3qaPjF90x0hmwRI0dHjIe3piUgCk1CU9JRcTGjZs+iVA1ihAg7pCJVLU4Rjp9T039BjbvfL7td409zovTVcGarYuYOuYOjIboLo+xteCVlrA5/5OcksqvSEs8lNjoIV0eY38g2prRorqn01MYE21hlQNwF5W128XWvDK7/vYIOY/dhqa1d6w0RQFBoODOJ1Cb+07lbaAjCEJbWKnJGMeg9OMpKP0oYPtBaXMxh63sCB5vI6VV31FRswpZcWA1p5CefDjJCQf5apKFwYjBF+BwleJwldH+eSYiAGOG/RGTMY68XS9TVv19m7x4e/b8rmkKW3e/gtEYG1BmW1W9bMx/hpqG9ficKhW8sLvsE4oqljN++DURLQCto+MPPUBUZ+ASrshPqHj5MDBlppJ0+lySzzkBQ2JC6ARlTUMw6OsO/YmdJe8T2JlV8XjrKav6vsv9NzoKaHYWEuyDKSBSUvltl8fQiRzX/VC6XwsxeMur0eTQtbD87WQ3rPyNrZfdStNvm9odb964je1/vpPaz7+LmJ09yaR5Mv/d2jMqh5FkcPpJDM04FVFord/le7+IgpEhGacwJGN+2H01OgpYvfE2dpV8gMNVgsdbR33TNrbsfIG1eQ8jK/7rEu2L0RDD5FELGZZ5GhZTos8e0USq/RCmjP4bSbbJ7Cr5kLJq3zOzo1PkD4FdJR8EzGHbWfw+NQ0bWn7buz+fjP/67U/i8TaEZb+OTlfRZ246A5amNZuImTg6YBKwpqp4y6vxlFV1eQwpPpbBd11L/BHTWkKuNQQxjCKIoogjT1fs6S84XOU0OXaHaKVRWrWS7LS5XRvDWRKyjYZKcy/K7uoEZu1Sm0+t7NP+kWskGA3Y5hxG9IRRoGk0/b6R+q9+RpO75rxVf/glhjQ7eQAAszlJREFUCXODy6R7a+tp+GmN33PNazaz7Y9/w5iahDE5AbmmAU9JeZds6QsmzZMHTDFoQRAYlD6PjJTZVNf9jsfbgNEYR5JtEgbJGnY/iuJm/bbHWpyfvZ0P3383NO9ga8ErjBn2x7D6M0gWstPmkp02t8M7T1FcFFUsD9u2VjuancU43eUd1DNlxUlJ5dcEXljSUFUPZVUrGZQ+r5Pj6uiEj+4Y6QxYKl/7kNiDxgVtU/H6h13eMRIsJnKfuxfrsEF78onCSJzXFAXXrmK9RlE/wiuHt8oYbjt/+OR4I9dOp+dZsCiNR/qBEEPM1HEMW3QLBlscqtfnCKWceyLeqlry/3IfjvV5ne6zYeVvNP66gZhJowOG1JU89jKE2FXyllfhLe/64lJfYT1zCmuX2PrajE5hkCykJk7v8vUVtavwyk1BWmhU1v6C23NGp/N19l0IrG3YjBqkAGwwvHJHAaK6xq2oWqjizhpVdb/rjpFOj6KH0un0GYLBQMK8I8h9/h+M++wFRr/9OKmXnI5kC0+5pu6rH6l4bSlAu1pBmqqiaRr1X6+i4vWlXbYv8eSjseYO8bsj1fqS2DckQJMVNI9MwW2PdnlcncgTuoZOSztjeO38YYsdFYZSnECSbWKXxwiFpqk0NO+kum4tVXVraHaWoqqhJhsHNgsWpWFZcVqfjW/NHcLwp+5Eio0BfLLaYkuZAUNCHLnP3t2uHEHYaBr5195D/fe/+n6VFVSvF03VUF1uCu9/lur3O7vir9Ofqa5bR+haSRo1DRvD7lPTVOoa8yiv+Zmahk1tpQdkpes5ZmaTzc844T2n9OeZTk+j7xjp9AlilJXh/76TmEmj25STtNQkMq7+A6kXncq2K24LSw626KHnaVqzmZTzTyZmkk861LWjkIo3PqL6veWgdl2NLumM43y7TWHKK2uqSv33v1Dy5Ku4thd0eVydyGO1pBAbNYRGRwGBQzUE0pKChx4Fw2iIJi1xJqVV3wYYQ0CSLKQmzujyGMEor/mZncXv4/ZUtzsuiiYyk2czKP0EDFL/z7foC/pSpS71/85AkCQEqeM6pSBJiCYTqRedxu57nup032qzkx3X3YtlWDa2o6YjRltxF5VR+9m3+7V4wqR5MrOXdP27PFDx1ToKHSGhquGFZ1bUrCK/aAkeb23bMaMhliEZJ3dRSVIgPia3LWdpb6IsocVYBESio7K6MK6OTvjojpFOnzDotquIHu8rPNga5iEIAkgCUkw0w/99FxtOuAzNFXqrvm75SuqWrwRJRBDELsfk74s5IyWkyIIgCOz46wN4iivwlFUh19RFZGydyJOZcjRbdr0Q4KyI2WQjPfGwbo0xPPssXO5Kahs30aaqBICAJJoZP/xav6p3suygpmEjiurGak4hPia3U/WOiitWsL3wDb/nVNVDYfkyaho2MmnkTbpzFIBZtzh73TkSzCYSjjksaLFUwSBhP3E2u+/9d5fDgl07CinbUdhVMwccW246Cxb1tRW9R7OzhK0Fr9LQvD2s9tHW0E5NWdVK8gr+2+G4V25k2+7XGJJxKhZTEi5PNeEpIQkICAzNPDWATRnERQ+joXkXgSTCNVQyko8MYywdna6jO0Y6vY4xNYmEuYcHdDoEg4TBHo997hFUf/BF+B0rapjKOGF21+hAigkt3aw6XDg2hfdC6g4Gu42EYw/DYLfhra6lbtn3yLW6Qk8oZNlBXsHLVNX97uesr4hhtDWDcTlXYTBEdWssUTQyPvdaqurWUFL5DU5XOZJkJsV+MOlJh3cI1VM1mR1F71JS+TWatseht5iTyR10Pva4MSHH9HgbyS8KVcRYo9lZREHpR+RkndGVWzsgmHWLkxWLJ/Djpet6ZTwpNjqoU9SKaDYhWi2ojv13l0ena9Q37WBN3oMEcibaI2AxJxEfMyJoK0Vxs73wf0HbFJR8wMghF7Nl14shxwQNkzGOUUMuIT4mcNHh3EHn83vegy3hch3vJy3pcOKiwytarKPTVXTHSKfXiZsxOXR4mqoRf8TBnXOMIkzN59+SetFpQVfuNU0jbsYUGlb+1nOGiCKZ111EyvkngyigKSqCJJJ942WUv/w+JU++EhFJ8v7OpHky1/1QCtjCvkZVvazd9ghNjiIChbflDrqA9KSZndqhCYYgiCQnTCE5YUrQdpqmsWXnC1TW/tbBNpe7ivXbHmd87rUhnaPy6h/RtPAWBEorv2NoximIojF04zDxeBtpbN6JhkZs1GC/+QMDidlLZrJiMb3iHCkNTaheuS2nKBCq04XqDE9mWefAQdM01m97lHCdIkEQGTn4opDPusraX1BUd/Cx0XB76xiX82e27X4N917hdoIgkWSbQrQ1E1CJtmaRGD8+ZA5mTFQ2k0fdTH7hW9Q1bmk7bjTEkJV6LNmpx0bsOa2jEwjdMdLpdUSzKWTujiCJCOa+Ve9qXrMF4eLgD2FBEIiffQhFDz0ftF13yLrhUpLPO6nthdC20yaKpF56OoIkUvzoSz02fn9hy01nsXaRrVPXlNf8HEKm26fSlBGiGntPUN+0lcraXwOc9TlK23e/wcFj7w46GXC4ShAQ0MIIZ1FUJ053VVihNKHwys1sL3yTyppVe+3UCiTZJpM76NxuCVn0Nb3lHGkeL7Wff4f9uCMClx2QFao//OqAWPzYH2h2FlNc8bWvHo+mEhc9jIyUWcTHjIj4pL606nsUNTyHOS56KMOyzgy6Y9OKw12BIEhoWmDFQgEBp6ucQWnHYY8fR13jFpzuSiTRjD1+HEZDTNj3sTcx1iwmjliA012J01WBJJmIjRqKKOrTVZ3eQf+k6fQ6zvzCkLk7mqzgyg9Vd6ZnUZo6Sor6QzRFbvV9X0zpySSfe2LAF6ogCKT84RQqXlvqt1Dj/sKkeTILu1CosaTyG1pDOfyjUde4GZen2m9CcE9SWvkd7fOQ9kXD6S6noXlH0MlMZ+W/BaH7YqSy4mJt3kM0u8rYtxBjVd0aGh0FHDT6VoyG8BQm+yPCwXOAnt81Kl/8DglHzwCBDrLamqygulyU//e9Hrdjf2HG+huYdUvfhByWVn3H1oJXWxYqfN+Lyrrfqaz7lcyUY8jJOjOizlF59Y9htTMZE5g86paw+5VEU8AirO3bmQHfMyUhbgydEwAPjtWcjNWcHMEedXTCQ5fr1ul1mn5Zj7uoDE0JvP0vGCSqlnzei1Z1xLWzKKiN4Ju4OMJQz+sq9hNmhbVSnHD8/p+Q2pVCjS53FeEkBrvcve9UOtzlhBMC43JXBD2fGD8h7Nw6kyE+IpON4oqvaHaV4t9+Fbenlt1ln3V7nAMB145Ctl3xd7zV9QCoXrmtlpGnopqt//c3PMU9U1hVMJvAjxreQKUvnaKGJl/xVF+Q2d7fC99/F1d8QVn1yoiOGa5kthiyjEB7kmyTCfVs0lBJSpjcqX51dAYC+o6RTu+jaRTc+TjDn74L0NqtkrZW1y599g3cu0v6zkZArq6jbsVP2GYdEjDMRTBIVL31aY/ZYExJRFM1gr3XNFXFlNK7ux0DBYNkRVZC7/x1prp8pPCNGWw3y4cUQkUuIW4MZmNCuxj/QMREDfK7Y6SqXipqV1Ne9SMeuR6T0UZa4mEkJ0zxm48UvEI9gNqSz3TqgA2B6U0hhuZ1eWyYdynxRxxMzMTRaJpG028bfbmL3Sg54A/RaiH53BNJPut4TGlJaKpKw8rfKH/5fZpW947oxP5IUcUXCIhBFykKyz4nLfGwiO0aRVlScLiKQ7aLjR7WqX6jrRkkxI2ltmEz/hwkAZFoa1ZIEQcdnYHIwHxj6USU+FmHkHzuiT75bFWjcfU6Kl7/qEdfkk2/bGDbZX8jc8ElxEwc3XbcW1FN2X/e7PPdolaKH1lMzJQxGOJiOzhHmqpRu+w76r9d3WPjy7UNIV+igijq6nQBSLFPY3fZpwSbxBsNcWwv/B8NzTsQEIiLGU5W6tHY48b3aKJvcsJB7RKM/SGKJhJi93w/PN4Gqup+xys3YzElkGSbjCRZSEqYSnFF6GKdYkvoy964vXWs3foITlcZrY6aw1VOXeMWCss/Y0LuAkzGPSFxqurF460LOZaiuvDKjZhNkQyw6V16U4gBRaV+xc/Ur/i5x4YQY6IY8fx9WHOHgLgnZzFu+mTiD5/K7vufoerNT3ps/J7mt6qdQFqfjF1dtzbkzq3TXY7bU43FnBSRMdOSZgZQ22zP0Mz5ne579NDLWL/tMRodu9izgOP7abGkMG741fu1EILLXUV5zc94vHUYDbGk2KcRZembz5ZO76I7Rgc42X+/iuQzjkOTlbaJf/zhB2ObfSglT71G2XNv9tjYzWu3sPWimzEPzsSUkYLS5MCxcVvEV0i7g6ekgrwLbiRzwSXYZk9vK8Io1zVQ8dqHlC1+u0eToms/+5b0K84J2kaQJGo++7bLY4hWC/bjjyT+iIMRLGac23ZRveRzXDuLutxnfyEj+UiKK75qUVjy/3fyyg3UNzUBKhpQ15hHXeNmslLmMCzrjB57+afaD6Gg5EM8sm9sf2SlHIMkWdA0hfzCt32y3qhtK9Oi+BpDM07FFFYuj4Ao7uPcaxobtj+F09Uarqe1++lwlrJpxzNMGvnXPb0IEuHsdEHn85/6I73qHPUwWQsuwTp8cIdisq3P/uybr6Dplw19nt/ZFfoyjA4IKlSwN6rmjdiY9rixxEQNpskRuKB4WtJMrF1wxIyGaCaPupmqurWUVa3E7a3BZIgnNXF6wJ3k/QFNU9he+GbLrrjYJmxTUPoRKfZpjBx80X577zo+dMfoACbx1Dkkn3EcQLvdkNb/zvjz+Tg2betZKWrAXVCMuyB0OEBf4SmpYOeN/8SQaMMyNAvN48WxKT9ihWSD4dpZRM3n3/mKQPrJBdAUlZpPvsZTVNal/q2jc8j9951ItjjQNARRJPagcaRecAqlz71J6VOvdfcW+hSzKYEJudezfvvjLVXhW9lX9KDjfxdVLCc2eigp9qk9YpskWZgwYgHrtv0Lj7eeVmej1elJtU9nSMbJAOQVvNKSaO1zRlpXplXVQ37Rm2SlzgljRK3d7hNAfdP2oJMqDZX6pm00NhcQGz0Y8CVaJ8aPp7p+A4HzEATioof5LWY7ELneO4775/3Gmk/bvzJN6clEjc0FTaNp7RbkqtDhjJFGMBmRYqNRGpvRPIEn3VJcDPYTjwpeN0lVSD77BArvezpi9llyskk86WiMyXbkugZqPvkax8bI1n3ripR/pLFa0nC4Sgm2YCAKRswme8TGFASRCbnXsyH/3zQ0betwPiN5NsOzgy+sBe9fCqv0wP5E6wKUD7XdX7OiZjWapjFm2B/7wDKd3kJ3jA5gUi88FU1VAyrEabJCyh/m97hjNFCQq+toqq7r9XELbn8MQRRJmHOYzxkTJYTWMBhJJHrcCBKOnUntsu871a8hIZ7cZ+9Birb6dkVa5cBbJk7pfzwbb0UNVW/3XA5VbxAXM4xDxj9ARc3P1NSvR9Vkoi1ZVNevw+kuJ/BERqCoYnmPOUbgi+WfNu5eKmpWU1n7K4riIsqSRnry4cRGDUUQBJqdxZRX/xC0n5LKb4iNGkKjYzf+nRUBgyGa5H3upbp+Tci8CAGRqro1bY4RQFbqXKrrg+2gaGSnzQ1q80Bi7VIbC+dfyP28zJpPDRhT7GT/7SriDz+47buoKQq1y1dSeP+zKPWNPW6TJWcQaf93JgnHzkQwSKhemdrPv6P8hbf97vZGjR0eUkFTMBiIPWRih+PGFDuJ8+dgHTEUzSvT8MNv1C77Hs3tCdrXoNuvJvHko9Bk326KhkbK+SdT//0v7Lzpof2qYG1mymy27Q62kCSSlnRYm5JbpDAaopk04kYaHTuprP0VWXFiMSWRljh9QIex9gVuTx3FlSuCtNCorF1Ns/MEoq0ZvWaXTu+iO0YHKIakBCxDs4K2EQwSsdMm+CbMeg2NPkNze9j5139Sf9JRDL7zmg7nzYMzGPrgTRhT7FS8ujTsfpNOn4sUHeV3Jwp8YVZpl53py/fqR+GNXcEgWchIPpKMZJ96n1dupqhiWYirNBqbd6Kq3h4NnZBEM+lJM0lPmun3fFnVDyGdF1X1kGw/GI/cgNtTS3tnT0QSTYwffjXSPqFtqurBt1MVBEFoabcHW2wuuYMuYNvuV/1ekplyDEm2ScH7HWC0Okfn//QxI//7EMZke5tTBL6Q1oRjDsOaO4S8C/+K2txzk/6YKWMZ/vRdCJLUtpAhGg3YjzuChKNnsO3K22he2z5/LWyZ9n0+Dslnn0DWTZe1dgKahn3eEWT+5WK2//kunJvz/XaTdfMfsZ84y3dZi42tXcdNn8zQB24k/9p7wrMpBF2pcRZp0hIPo6JmNfVN2+i42CJiNiUwOP2kHhlbEHw7tHGdFFmIFKrqpbZxC7LswGyyEx+TE5GyAL1NRW3ofGEBkfKanxiWeVovWKTTFwy8T65ORAgaTrF3O1FsS9LV6UMEgfTLz/b95z5/j9Ydv8wFl2JMDT+WPOG4w4P+bQVBwJSa5AsV2s/QtPAdvc607Qnc3pqQxVsFRFTVw0Gjb2NoxnzMpkQEQcJoiCUr9WimjrnD76TJak4LmTCuaQpWS2qH402OwoDWlFZ927J7tX+xdqmNzE8X+pwiP89QwSBhGZJJyrkn9pgNgsHA0EU3IxikDjYIBgnBZGDYoltgn3OOzdvbdm4CockyTb9ubPvddswMshde4XPAJAlBFNtURA3xseQ+ew/G5I6hYcaURJJOmxswGkGQJOKPOBjr6NDFRkMxY/0NLFjU90nxomhgfO61ZKYc3S63TkAkJWEqU0bd0k7EZG+c7irKqn+krGolzc7IqbEqigu3pxZFDbyz1x00TaOw7HN+XPdXNmx/gi27XmDt1odYteHvVNWt6ZExexKvtzEsh87r1cWO9mf0HaMDFG9lDXJtA4aEuIBtNFX1hWSEqOWj0/PETB2POTs9eCNNI+m0Yyl9+vWw+pRio8MSFpBiel/KuqcxGqIxGW0h1dUspqQ+FxAwSNFtCcCB0NAwSNEYDdEMSp/HoPR5YfWdmngoO4rfCZo4LgpGUuzT2h2rb8qntOqbgNaoqsy23a8xZdTCsOwYKBgkAZs5t0WAIgCiSNJZx1P2/Ns9YoPt6OkY7baA5wVJwphsZ/Q7T2BKsqN5vdSt+InKNz6m5vPvsM89PEj5AQOVb+1RpUv/03kBw60FSUKKtpJ0xnEdnjkJx/rf/dwbTZaxzzuS4gA7TgMRSTQxPPsshmacTKOjAE3T+H/2zju+kbPa38+MqiUX2Zbcu3ft7bvZlE3PppNKGu2GhN75UUIL7XK5XG4oAQKhw6UECIFACAnpJJu+aZvtzeuy7lWyLVldmvn9Idlrr1VG7t68z+eT4tE775xRfc97zvkea1Z5UocoFHbT1H7XtLTUXOsKVtW8myxz0Yzs8HiP0t73MM6R3YCKJOkoyj+VqtIrsCTY5Jgpbd330dk/XUE2EBpif8tPWVP3IRz5J8/Z9WaKqipElRA62ZjS8TEacjVthBkMeXNpnmCJISJGb1SiCoP3PoIaTbGDKEkM3v3gwtkkSIqlsSZts1kkmayGWs1zBjt7U7/+cearueRiIkky5UUXkDqNTKK86MJFl6QtKjg1bVRHQsKRYbPFcGSM0bEmigq2pBy3ouod6I/rpdQ7+AxSyp8PBY+3Da9/6YqqzASb1YhOSlOnI0kYiwqRDPOz72jd2DjRADYV5qoydNkW9Pl5FF59Iavu+QFjO/YT7O6bFjka/x7o/tFdE6lx5toKsuqrkkZ9IOYcFVx5/rTjelsOqob0W71Ni5piamKiC0sLnc6MLaeR/NxVSZ2iSMTHrsPfiYuYTMXtbWXn4W8RCDkzvrZzdC87D30L58geJsRa1Cj9rlfYcfB/GHYfIhoNZjzv8fgC/Qmdoskc6bgbRZ1/kaJkBIJOjnTczfO7PsELuz7Bczs/zqGjv4+LZEynqOBU0qUWqyiUFJ4+D9YKlgoiYvQGpv+3fyfv7JPJaqyd0mQVYtEi90u7GPrnvxfJOsFklHAkbSkIqpKRUt7Q3x8j55T1yaeLRvHuOUywY+ktPOaCiqILcY3uYXSshek1ARK2nMaJmqTFJC+7gVxrPW5vG8mEFUod52DUuIsZjQZp7vor/c4Xp0SKJEk35W+ToYC6iuumRYsAxvxdaZ01AK+/B2tWuSa7lgOBsLbouRqNpk1bmzEayz0nOzSSXo+qKFR9+cMcvvnz2C46E/v1l6LPzQbAu6+J/t/dN6WHki7+WDoSjQsNOJPWLk4mPJD5wn8y5m3XLXpt0UzpHtyGPzhI4hdUIRzx0dH7MA3VN2meMxoNcLD1l/Ho8vHzKihKkD1Hvg/EolKVJZdMqQX0+nvwBfrQySbyclakFIroG3o+be1jOOLBNbpvUeoNx3xd7Gr6btwJjNmoqhEGnNsZdL3C+pWfwpYzNU3caMijouhCupL2hJPi/YzSZG8IljXCMXoDo/gDNL3/y5R+6O3Yb4gV4kOsqejAPf+i///+BvP14y7ICM/2nSl3bgGQZdzP79A85/ATL+B46+VYNzROd4yjUdSoQtf3fjMTc5cFsmxg/cpP0dH3CD0DTxOJegEw6LMpc1xAVcmlyPLif0VKksS6FR9nX/OPcXtb4osRNZ5ep1BUsIX6yrdpmktRIuw5cgdubyvHL5xUVUUnm6gtv5ZsSyW51uQF1MeLOCTjROv3MeYPs//oMKuqbOiS1OcpkQijT78yb4I1YzsPUHTj1RmfJ8kyaiRKwRXn0fWdX9Pzkz+it+WiBkNEPd5p40N9Q2nnVBWFUO/AtOPDjz1Pxec+kDrapNfjfDCVAlhqNl0W4fIlUFs0U3oGnyG1l6vQ59xOfeVbNSvZ9btejvdsS4/b28L+lp9SW3YtttzVNHf+GY+3beJxnWyivPgiakqvSvg94A8OaNgckfEHFj7jQFUV9rf+bIpTNPEYCqqqsr/lZ5yx4dvTvqPqKq5HRaF74Ckgll2gqiqgUFx4Bg1VNy7QXQgWi8X/1RcsKorPT/cPfkvPT/+EuaoMVVEItvcsSI8egXaCHb2MPvsquWduTlgfoEajRNxjuB7LoNFrJErzR/+Lyi9/hILLzkXS6VBVFUmSCHb20f5fP8K3r2kO72LpoZON1Ja9meqSKwiEBgEwmxzI0sJ8NY75u/D5e5BlI7acBvQ6S8JxBr2VTY2fZ3SsiQHXK4QjXkzGfEoKzyLbklpdcjIDrpdxe5PVdChElTAjnqZ4mmFyCm0b4xGs5As7SdJjy2nQbNty4d87u1lbk1gGWY33Ahv4w/3zdv2Rp18mPOhCX5A3bUMjHZJeR945p9L1nV9DJJqy71K4fwj3y7vJOWVdiutIMdXK44iOeuj79V8p+8h/JDxLVRSc/3hiSfevm09UVUlb3xgbFyEU9pBl0uYYub1taaM4k2YHoK3nH0i9D0yrrYkqQTp6HyIQHGJVzXunpRTHai+P7wc3/Ro63dzKk2th2HOIQHAwxQiVSHSMweEdFB+XFidJMisq30Zl8SX0u14mFB7FoM+mqOA0skyO+TVcsCQQjpEAiElC+48cXWwzBCk4+tU7aPj1NzGvqJ5oxgoxpyjq9dPy8a+jBjJTH1L8Adq/8gN6fvh7cs7YhGw04m/pwLvzwHzcwpJFlvULmh7h8XVwpP2PeHxHJ45Jkp4yx3nUlV+XMNIiSbH0PltO44yv2z34NOONZBOjMDSyi1DYk7Q2AqCk8Gw6eh+Oq10lmkuizHFuUkdvOZJt1vPZGzZw+uoiFEUl1vrr2GJxvE6n/Ws/miaVPadEorR8+n9Z+ctvIBsNSPrMfsYzqX3q+eHvafjdt2LnHR9VjkQJtHfj+ueTCc/t+8U9oKqUvP+tSHodalSJpdepKoN/eZiu7/1fRnZPZtNlEb54zc2gvTvBEkNClvSa6m8y6Xs002rIVOIrA66XKbGfRX7OqinH7baTGHC9nOSsYxTmTe+LNd+Mjh3R1J9tdOzINMdoHJMxn6qSN82XiYIljHCMBIJlQnTUw6GbPkvhFedjv+FNGMuKiXrGcD30NEN/e5TwoGvGc4cHXbgeeGoOrRUkY8zXya7D30FRwlOOq2qE7oGn8Af6Wbfi4/PSBySW1pIuxUvhtQNfJxzxoJPNFBWcSkXxhVMcR6Mhh3Ur/h97m++M9zganzO2g1yQu5a68uvn3P7FQq+T+N/3nEp9WcxZlCel0cWirDD85HZ6fvj7BREr8e1r4tDbP0Xxu66j4MrzkU1GlGAIyWCYJuc/GTUSwXugWft1DjRz5EP/Sc3/fBpTeXHM+ZMkJFlm9OVd7P3Kd1CCXrLRISVYlvf98i8M3vMQ+RefhaGokMiIm+HHnycyy0bZS6Fv0WyQJAl7/skMul5NsXiP9SZKtUFxPHnZDfSlaQadOTK9g88mcIw2YjYVEQwOJbmHWD3OojSZPUHbLnr93fQNvUgg5MSgt+LIPxVbTuOiCwSdaCyIY/STn/yE7373u/T19bFx40buvPNOTjttekHvOPfeey9f/epXOXr0KCtXruTb3/42l19++UKYKpgluhwr9hveROG1F2OwFxAZceN68EkG//rIrH8MFx1ZJnvTavS2XEKDLnx7Dy+4CWogxNDfH0uYviJYHjR3/iXuFCX69VZxuffhHNmNPUOVOS3oZCNRJZB2XDgS69MRVfz0DT1Pn/NF1q/4OPm5aybG2HIaOG3tN+gdepbB4deIRoNYzKWUOs7Fbtu0LBs8JuOcdSU0VCQWt5AkCUVRKbxkE0c//50FsynY0UvHN35Cxzd/hpxlQvEHqf3O57BtPT2lHPfQXx5O+FgyvDsPsP/KD5Jz+kayGmoJBoP86vn7uCfYQr8pDMVQFzbzdp+Dy/0F0xykqHtMfF8loLL4EgZdqRqKqlSVZrbucRScQkvXX4lE/cydd6Aw4mkiEvGh1x+LAEuSjg0rP8Xupu8RDDk5FomObY7YchppqHrnHNmQGbnZden7s6GQa519H62FQFWjHG7/A/3OF6fUmPYOPUeOtZb1K/4fBr02sRRBeiRVnacK0Th/+ctfuPnmm/n5z3/Oli1buOOOO7j33ns5fPgwRUXTNfpffPFFzj33XG677TauvPJK7r77br797W/z+uuvs27durTXc7vd5OXl8fjqS7DqTqzC36WOsdRBw2++haG4MLarGN/FUKNRom4vTe//EoGW5dn0sfDNF1H6sRsxFhVOHAt09ND9g99OUXISzA+bLotwufyJxTZj1viDA7yy7ytpRsnk565iw8pPzfn1mzvuiafTZdqbTEKWjZyx/ttTFkdvFL79vlNZV5OPLo0AilF+jZc2/PcCWTUdQ7Gdxj/cjqEgd0qK3XgvosF7H6Hzmz+b8fwhFD6b38rrxrHYsjvuA0kqqBJc7Svg8+7KhNGjueJE+S4AGBrZyYHWX8VT2SZHXVVWVr2DMsfWjOcc8TSx98gPUdWoxlojbehkM+tWfGxaKm9UCTLgejVe+zhGlslBif1sCnLXLtrmiKoqvLzvSwRDwyRL9dXpzJyx4buahWQWk+bOv8TFIBLdi0yOtZqTGm8VkaMURII+XrjjLYyOjpKbm7x/JyxAH6Pvf//7fOADH+A973kPa9as4ec//zkWi4Xf/Cax2tUPf/hD3vSmN/G5z32O1atX841vfIPNmzfz4x//eL5NFcyS2u99EYMjP9YdfdIHVNLp0OVYqb/zP0GDhOtSo+ima6j++iemdXg3VZRQ/4Mvk3/ZuYtkmWC54Q9MV/CajoIv0Dcv1y8rOn+GP54qihKkz7l9zm1aDhTbsiacomAoSGvPUboGezl+X/HW30mc8ZsNi2EiEBNMOPzOW3A98uyUXkfhARed3/7lrJwigLutAzGnSAKdTofJEKt/UeNvqQcsLraZRmd1jVRM1BadINhtJ3H6+tuoKbsaW3YjedkrqSy+hNPWfXNGThHEIrknr/kKxYVnIqfpuZUJUSXI3iM/wh+c+h2mk02U2s9mY8MtnLLmP1lb/xEK89YvasRYkmTW1H047vQcb4ccf/xDy8IpCoXd9AxsI1VdqMfbxohnHusa32DM6zs3FAqxY8cOLrroomMXlGUuuugitm9P/AO7ffv2KeMBLr300qTjg8Egbrd7yj+Chce6oRHrmhVJC4ElvQ5TWRF55566wJbNDn2BjfJPvgtg2oJSkmVURaHySx9BMi39L1jB4iNrLKTOpOA6EyzmYtbWfzS+YJr8ftbmLL1Rf3xHfWGc7hG+8MtvUPn2k1j73nNZedMWNn3gfH736D0TDpLHH+b8v5+9uM7RgIv2r97B3gtu4tB/3MKB6z/Ovsvfz+Cf/zWreSOo/N0yxKVbLuCRb/0Z979aGHnwCPt+8ywfv+Z9GA1GZBXutaZSA5sdWW/ZzO4HbPM2/2JgNORRXXoFGxs/w6bGz1FXcR1ZJvus5rSYS2msuZmzT7qT0zd8B4u5nOmf8djfedlalSNVFDVK98DMJdYzJRL1MzrWgtvbFhd60U6utYbNq79CccEWJGk8tVTGkX8Sm1d9iYJJacFLmaGRXRoafMsMpEzLFGTCvNYYDQ0NEY1GKS4unnK8uLiYQ4cS/8D29fUlHN/Xl3gH9bbbbuPrX//63BgsmDG5Z25GjURSKiQp4Qi5Z2xeVqlnhVdfACl22CVZRp9jJf/CM3E9/PTCGSZYluRaa9HrrBM9kxIj4cg/ed5sKMxbz2nrv0nv4PO4RvegqFFk2YDH25r23OMlfd8oPPjSIe569Au09rQTVY4peB3pbuMjd3ye3a0H+OJ/3MrhzhEAzv/72Tx82evsemTx9I2iHi8+rUILskz+JWfjeNvlZK2qQ41EcT+/g4G7H5yopezXhfjgTR/lazd/lkg0ghyPoNWWVPHtD36Vq8+8lKu+fBP78E7UQAgWF0mSMRlsbF71BboG/k33wNMT9YN52SupLLmEgty1HGz7NYPDWnrgKQy4XmGFxr5pMyUc8dLW/Q/6nC+ixpX7dLKZMsdWasqu0twfzWIuZlXte2iofifhqBe9zrIsokSTif1WpJZFV1HS/KYIMmHZq9J98Ytf5JZbbpn42+12U1lZuYgWvTGR9DpUNc2+s0TSouClirm2AlSFVMFVJRzBVFu+cEYJli2yrKey5FLauu9LMiJWy1PqmN/0TJPBRk3ZldSUXQmAe6yVnYe/leYsiRxrzcRfkagPRYli0FuXpNBCOOKhd+gFBlyvEIn4MJvslDnOxZ6/OeM+Vb944E56h6Y6RcBEpOjnD/yOYKQeRT22qffFa27mNu5aVOdIEzqZ2m99jvyLz0KNRidkufMvPpOCy86l47afM/SXhyk8aR1fu/mzAOh1x+5p3EE6c+2pfPmdn+K/fvPteTHzjN9s4Py/nz0vc5/o6HRmqkuvpKrkcqLRAJKsn+IgrK79IHnZz9DceXfauaLR9OItsyES8bHr8HfwBfqZ7AxElQCd/Y/h8bWxfsUnM2q+LcsGTLJt7o1dAEyGAtLVhErIi6P+d4Iyr9/YdrsdnU5Hf/9U+dL+/n5KShJ3rC4pKclovMlkwqSx+Zlg/vAdakVO0x9DknX4DqfflV5KKIFgWodPkiSUDPsHCd64VBZfQiDopHfomeN6bUjoZBPrVvw/TAbbgtqUY63FmlWO199L8h9hiZLCsxhwvUZX/2N4fO0AGPS5lBdtpaLo4kVp5piIMV8nu5u+TyTqYzw3PxgeZnSsidyBetav/CR6nVnTXJGoj+7BF1P2epElmQdeuJd1Kz4+cWz3A7Zl4RwVv+s6bBeeCUztVTQe/a/8wofw7T9CzduvIxKJoE+SFaDT6fjgFTfxr9/8cl6iRTtrV8z5nG80JElOKJ4iSRJFBSfT3Pln0qnZmYyFKR+fLe19D09zio6hMuI5TFP7Xaysfueyi/7MBLttEzrZRFQJJh2jolBSeNYCWnViM6/bfEajkZNPPpknnzzWAE5RFJ588knOOOOMhOecccYZU8YDPPHEE0nHC5YGo0+/Qtg1gqokXlSpioIaCuF66OmFNWyWjDz9cnqHT69j9OmXFsgiwXJHkmQaqm9kU+MXKCo4DWtWBbnWemrLr2XL+m9iy1m5CDZJrKp9X5Ji5dgit6HqRnqGnuZg2y/x+I6pS4Yjbo72PMiupu/O+26yFqJKkD1HfphAsjj2/25vG03tf9A8n9ffO5HOkwxFVXAnSEUcd46WLHodRTdenbL3EUqUondcRe6pG5I6RePkZefywZL5qSP9fZM2R1YwMwz6HAptG0m3LCybg2i2qkYZ8TQxNLwTt/foRORVUSL0Dj5HughJv+slXjvwdQIh56xtWerodCZqy69LMUKiqGAL2RaRKTVXzPs21i233MK73vUuTjnlFE477TTuuOMOvF4v73nPewC4+eabKS8v57bbbgPgk5/8JOeddx7f+973uOKKK7jnnnt47bXX+OUvfznfpgpmgRqJcPTL32fFj/4z1vBw0s7jeFPA9v+6E2XMt4hWZo7npd34m9sx11QkTANUI1Hcr+wm0NK5CNa9cbB85wtwq3+xzZhT8rLrycteOn00srMq2Lz6y7T3PMjg8GsTkaxcax1VpZcjSwaaOsYdiuN3lVXGfJ209fxz3usP0jHoem2ijiIxCoPDrxEM3aAp/US7il/iBeXuB2zc8ZsNbH/vHo3zLBxZtZUYCm0px0h6PblnbUarQMfmcA5+tKgvaueM32zgS3+3zemcC0kk6iMYGkGnM2Ge54jLbKgtu4YR98G40MHxn3EZi7mYUvvs0hl7Bp+lvfdBQuFj6oVZpmLqK9+KxVxKVNH2PR8IOtl75EecsuZrSzKVdy4pLzofUGnr/gdRJTill1Gp4xzqF/k790Rj3h2jt73tbQwODvKf//mf9PX1sWnTJh599NEJgYWOjo6JHGWAM888k7vvvpuvfOUrfOlLX2LlypXcf//9mnoYCRYXz/ZdNL3/S5R+7EZyT9s4cdy7/wi9P7sbz/Zdi2fcTFFVmj/+dRp+9U2MFSWgqEg6eSIX39/UxtFbb19sK09oztz7GbaeYE7RUsVsLCA/bw3+4AD+4AA6XRa23FVkZ1VypONPpC4CVukdeo7asmsWNaXOObqHY80mk6HiGt1HqeOctPNZsyrQyeaUjXElZPJzV2ds66KjseZT0usYe/0AuWeelFJgJ+rzE2ztmivrgOVdW+QPDnG055+xjYZ4KqY1q5Lq0svnVWBlplizytjU+HkOtf0Gb6B7ymOFeetorHkXOo0pqIno6H2Etp5/TDvuDw6wr/nHGTaEVfAFenG591OYt37GNi0XyosuoKTwLIZGdhIIuTDoLdhtJ2E0JG48LZg5897gdaERDV6XBoaiQgyOfCLDbkI9c7t7uBhIZiMFbzqPgqsuwFCYR6hvCOc/nmDkye2okdRpNoLZYd52HbfcnrjGUDB3RKJ+9hy5A4+3jamOhTQhH57KORjnpFVfJNdaO292pmPPkR8y7N6fZpTEisq3UV50gaY5W7vuo7P/MVI5Wyet+hK5k8QpJrPx6hFuu3/p1RrJliw2PHUXsjm5I6tGoozt3E/fb/7Oyp8lV4BVo1EG73mIru/+es7sW87NXH2BfnYeuo1oNHCc3HLss1VXfgOVJZcslnkpUVUVj6+NMV8HkqTDlrOKLJNjVnMGQ8O8tPdWUn2GJGR0OiuRqEfTnBIyxfYzaaxewumqgiVBJg1el9a3tOCEITzgJDxw4uT/qoEQzvufwHn/E4ttikCQMaqq4HLvxxfoRZaMFOatw3xcr5Sm9j/i8baPnzH57Hjhr9Y9NIlQeJTB4R2Ewh6MhjyKCk7GoM+ZgztJjzWrnGH3QVLXKahYzKWa56wpuwqP72i8j9NkpzEWQauveFtSpwjmTojBWF6MqaKEqNcfk+JOUtOpFcXnx/ngU9ivvSSpYqik1zH454fwbN9J/x/up/ima1CjCtKkZt1qNIq/6Sg9P/lTRtcPoOCTo+QqevTHpepNNHN9IPP7WgocPvo7ItEA09+HsfdOa/ffKLRtwGJeeps+kiSRa60j11o3ccwfHMDr70WWDeRl12fca6136Pm0Y2Ky09qcoth4FSUqhI8Ec4twjAQnBLocK3lbt6AvyCMyOMzI0y+j+ET6lUDgcu/n8NG7CIWHGV/UN3eC3baZxpp3oddlEQwNMzj8Gsmdn2PRo1QOkk420ed8kd7BZwEVSZJRVYWWrr9QWXwpNWVXz3s9QKn9HLr6H08xQsJsLMCW06h5Tlk2sH7FJ+hzvkD3wDZ8gV4kSaYgdx0VxRdpmmv3AzYOff6t8EgyqfbkZK2up+KW95Jz6rGUodCAk77/u5ehvzyc8XyT6fnxH8nZshFTWfEU52g8mcT18DOMbIuJy3R/7zf4D7VSdPO1WBpjUcHw8ChDf32Y/t/9A8WvTXxjj2GMP1j72W7yoEpgVmQu9xdwk7eIImX5K415/T24vS1pRsn0DD7Lisq3LohNM8Xr76W5889TmjvrZBNlRRfEegpplL73BRL3opwtWeaieZlX8MZFOEaC5Y0kUfrht1P87uuRjAaIKqCTqQwE6fvFPfT/LvNFiEBwojDiOcy+I3eiTjgzx5yaoZFdBI+MsKnhs/EIi5aIUKoxEmaTg97Bp4+NjtdVqGqUjr6HUVGpK78209vICIu5mKrSK+jofSihjRISDTXvythBk2U9ZY7zKHOcFxOY0SzKcIxbbi/h+9uuY9V3/gqgKXpkWbeShv+7bVpEx+AooOqLH8ZYbKfnR3dlbMs40VEPh2/6HGUfu5HCqy+cSKsLD7oY+MM/GfjTAzAp49710NO4HnoanS0H2WAg7BqJfe9q5N/mYb6eF4tMqvGnMCAr/NMyxFPmEX7hWklF1ETWWzaze5kKLoxL2adG0dRUeTHxBXpj6YDK1KhMVAnS2fcovkAfa+s+pOmzpJONSEiTvou0kK5WEErsQqZaMLcIx0iwrCn/5Lsoete1xxYp8cWDLstM+afejaTX0ffrexfRQoFg8WjpvDe+EEm0uIgtzIZGXkdRw5rmMxkLCYacTF+wSGRnVTDmT63O2NX3GBVFF2I0pM7xni01pVdjMuTR3vvQFPWrHEs19ZVvIS97dpLoM3GKxrnl9hI2xiW873jLvrRqddX/9QkkvW6K0udkG0reewPDjzyL/8jRGdsUHfXQ+b8/p/sHv8NUWYIaiRI42p0yVS864iF5Z6fEDMthvpHXEXtHHvcURiXwyBG+ntfOvttvWLaCCxCrfdE0TlraDc+bO/8ad4oS9xRyjuzEOboHu21T2rkKbRvpc76QwdVjDaVjNY+JqS69Ykmr/AmWJ8IxEixbDMV2im6+JuUipeRD72Dw3keJjmrPWxYITgS8/m7G/B1pRkl0DzxNbnZdmnEAMnbbSdhyGujqf5zRsWYg1pm9vOh8wlE/Y/5uUtX2qKgMuF6movhizfcxEyRJosyxlVL7ubi9bUSiPsxGO9Ys7XVF88nuB2wAnM/ZPHzZ60kjR5Z1DWStqE45lxqJYL/hUjpv+8Ws7VL8AfxNR2c9TzIezHKhoE5zisaJSnDA6OPUP1STPb0P6bLBltNA+miHRH7umgWyKHMCIacGEROZ3sFn0zpGqqrgDwyQWtVy2lmoSgSLuQxfoGfKI3qdleqyKyl3aBNPEQgyQThGgmVL4VXnT0nxSISkkym47FwG70mUViMQnLhoa36o4vY24/Y2axirUOY4F4u5BLttE4oSRlGj6GQTkiTR1P6H9EtBSSY4KYIz30iSvKR6RSUilShDVn1V2vMlvZ6slTXzYFlmmGrKsZ1/OnKWmWBHD8P/fgE1MDUFa6/Bq2lZPDrWsiQaVvoCvQy7DwMK2ZZqcq11mqKFJmM+dttJDI3sIpkjIEt6Su3p5eIXC1+gX8MoBa+/O+UIVVU40PoLhkZ2ZmxDLAI9OfoW+4YpdZxLRdGFGc8nEGhBOEaCZYuxtAhVUUmVjaBGoxhLRXGm4I2HXmed0/mqS6+aoqAlywZkjrVE0Ouz086hqgoG/dzatdxJpVinhNOnOKqKghJYPGUuOdtCzTdvwXbeaajRaOw7Wa+j8osfovO2X+D617aJsTNJQFRVBZBmlb6YKcHwCIfafsuI52D8SGxBbjGXsbr2fZqctobqm/AH+vEGepi6XSAjSRJr6j407ymls0JjJxdZTi2W0ed8cUZO0TEmO5Yxmzr7HiHXWofdtjHxKQLBLBCOkWDZEnGPke63UpJlop6xhTFIIFhC5FprMRryptTYzASTsYDq0isnOt6rqoJzdDc9g8/g8/ciyyYc+Sdjy2mks++RNLOpFOWfOit7TkTGnaO3PXL3lOOel3ajRqJJpbTHGX32lfk0Lzk6mRU//hrW9Q0ASDrdxEaVbMmi5n8+jRIKMfJ4rLZkY9jKiyZ32vJ7gx5e2/91vIFexhfGOZZaqkuvoNC2YZ5uJkYk4mPX4e8SCE6OuMYs9gX62HX4u2xe/aW0MtsGvZVNjZ+na+AJ+pzbCYZc6GQzRQWnUF50Idassnm8i9nh9XdzsO03GkbK2PM3pxzRPfAUWkQUMkOmq/8J4RgJ5oX51U0VCOaR4ceeS9mFHWI/1MOPp++fIBCcaEiSTHXplbOe55TV/znhFClKmH0tP2F/y88Ydh8iGB7GH+yjo+9h9rf8FFlKvXtclH/atP5JbzRijuUeOvoepav/3xMyxrsfsGHedt2UsRHXCM5/bUONJpY5UKNRomM+nJOiMgtJ3rmnkr1p9TRhCIjVeamKSsWn38P4DtYVvkIMkoyUJHakl3XYcws42HY33sDUejWPr419LT+mrfsf83Iv4/QMPkMgOETiFDgFRQlxtOfBlHOoqkrv0PO8fvg22nv/RTDkRCebKXWcQ1359UvaKYpE/exu+j6RqDfNSAlZkilznJt0hKpG8fq7yMQp0slmDaMURsea4tFEgWBuEY6RYNniP9TK6PM7Ui4aXI8+S7Cjd4EtEwiWBqX2c6kuvTr+1/jXfWYpSd0DTxOJxnqCtXXfj2t0X/yRqSkuihJCUVOndBkMC9Pkdanich/gpb1fYF/zj2nrvp+Wrnt5df9/sufIjwhHxrjl9pJpzlHXt37B2M5YStfk7zo1qqD4AzR/7OsoY74FvY9xCt98EWokuTadJEsYS4vI3hwTGbCpen76po8hyzL645wpnawjy5zFkNuV8podfY/gHE2t5DcbeoaeIdVCXkVhaHgHkUji51xVVZo7/0JT+134J/XuiSp+uvr/zc7D3yYcSed0LB79zpcIRzykE0mQJT1rV3wsjSqc9u+a+sq3s27Fxzl59Vc0nzPuGIUjYwRCThRFm7qmQJAK4RgJljVtX/gOYztiyjlKJIKqqiiRCACjz++g/b9+tJjmCQSLiiRJ1JRdyWnrvkllyaUU5m3EkX8yq2rehyxp2ZmFo733s33P5xkY3kHP4NPMJiWmb+hFFCUy4/PnAlVVUZTwRAPThWJ07Aj7jvyIUNgdP6Iw/lwOuw+wu+n7KEqYW24v4cy9n5k4TwkEOfLhr9L2he8ytvMAof4h/K2d9P7yHvZf81F8ew8v6H1MxljiSJvmB2AoOrZ4vuGGm3jmjvt581mXTThHedZc/t+178NkMGm6blf/kzMzWAPB0HDaMSpKUhGRYc8BegafSnKmgi/QP+9Rr9kwOLxD07gVVf9BQe7alGMkSSbXuoJ0DpLZaKfccT6FeRswmwoxaKhXzDKXMOw5wM5D3+bF3bfw8t4v8sLuT3Ok4+5Zpw8L3tiIGiPBskbx+jnywa+QvXktBVdsRV+QR3hwGOeDTy3qgkEgWEpkmRzTGquO+TroGngSLfK5ihLiYOuvNI1NRVTxs+fID1BVlWxLBaX2cxZMfczr76Wr/3EGXK+gqGH0Ogul9nOoKL4IoyFv3q/f0vW3lD2lvP4u+l0vU2o/m623+nl672d4cf33Yg9Hogw/9hzDjz0373ZmQsQ1ghqNJkylmzJuxD3l75MbNvLHL/2USDSCPxjAaraw/+hh7vj7LzVdN5ZGNbMmu+nQ6cxE4xHSVOh1WQmP9wxsI7UstUK/czt1FdcnnWMxiWi4d4g1PNZCRfFFHGhNrXpZUXzRxGspSTrKHFtp732IVJsw2Vnl7Gv+MZOdLkUJ0TP4LEMjuzhp1a2YjQWabBQIJiMiRoITgrHX99PxjZ/Q+un/pfN/f7aknCLrptUUvPlCbBefhS5HKHIJlgYVxRfHFeK0/AwkW9BnzujYEdzeZnoHn2XHwW/EmtDOc/Rm2H2IHQe/Qb9z+0Qz20jUR2f/E7x24Bv4gwPzen1foD/eqDJ1X5vewWOOz9Zb/VMiR0sR10NPp3WKwsOjeF7dO/G355XdqPGovl6nJ8eSjSzLBMPalfXm8/1SXHAaqT8TErnWOkxGW8JHR8daSLeBoKhhvP6elGPmknDEi3N0D0Mju9NGxCzmYk0NarNMDk3XtttOorzoovhfkx3Z2P878k+lzLF1yjmVxZeSY6kicaRJItdaPymydfx7QSEUdtPU/gdN9gkExyMiRgLBPJGzZSOVX/4I5qpjhbZKMMTgPQ/RfeddkCI3X/DGIhLx0efcjmt0L4oaIdtSGe8ZNH8NSU1GG5saP8+B1l/i9XdqOGNuF6NqfPHYNfAEJmM+FcUXpTljZkSjAfa3/BRVjZJoERWOjHGg9ZdsXvXleZOEDoZS183EUKf1nhqPHAHHokdLiOEnXqDkA2/FVFGaNKWu9+d/nvJdN/iXh7Ffd+m0cSvKa9Dr9ESi6VMtcyxV8/ZalRddRN/QCyhqss0AlarSK5KeL0na9pu1jpsNkWiAls6/0u/aHn//A0gU5m1gZdV/YDLmTzun1H4Og8OvpZhVwmIuIcdSq8kGSZKor3gLtpyVdPX/O94YWiXbUkVF0QUUFWyZ9lzodCY2NnyW9t5/0TP4LFElFsUy6LMpc1xAODKG29tGqqjcsHs//uCgZgdOIBhHOEYCwTyQc9oGVvz0vzh+x0s2GSm66c0YSx20ff47i2LbiUY0GmRg+FXGfJ3Ikp78vDXk56xekIXHXDDiOcK+5juJKoGJY6NjzXQPPElN2dVzoiyXDIu5mJNXf4Vh9wH2Nv9Q41lzLb0bK6gvK9qKLM39T1K/6+Upz+10FMZ8HXh8R8m1alvsZYpeY++mRD2ett4aWxR+f9t1BM6/b07tmi1qKMyRD36F+ju/hqWxFiUciQnQxf5F78/uZugvD085x3+4Db3UTERdQTSqoNPFPqd51lzetvXN3P3kffGUw+SUz2NzT4u5mMqSK2jvfSDBoxINVe+kMG990vPzc1YzMPwaqaJGOjkLa1b57I1NQVQJsafpe3h8nRwvlOIa3cvrh25j8+ovYTLYppxny1k1qTnt8a+DhITEisp3ZOSYSpKE3XYSdttJ8Wifmvb7WaczUVdxPTVlV8cjuhJZ5iJkSc/rB/8XLWm9Hm+bcIwEGSMcI4FgHqj80kcACUk3/ctfkmXyLzmbwb8+zNhr+6afLNDM4PAODh/9HVEliCTpQFXpGniCLFMx61Z8HIu5eLFNTEkg5GRv849QlOPTiGI/+kd7HsBkKKDEfqbmORU1wqBrBz2Dz+AP9qPTmSnKP5Uyx3kJd4glSSI/dxWybExgRyLmPo0pHPHg8baRl71yzuce9RwhvTMnMeppmjfHKDurErPJQSA4mNKG4sIzkj56y+0lS9I5Cg+4OPT2T5Fz2gZsF5yObMki2N6N84EnCQ9Mj5SdufczbL21mZPqh7n+7Bo2r7QjSxJOd5B1dW/F8OyThMIjSa/nyD+VooL564XV1vNPOnofIlEaV661juLC01OeX158AQPDqfpKSZQ5zkOXpjHqbOkdfA6Pr4NE73sVhXDYTXvPQzRU3zjVOklide0HaO3+Oz2Dz6CqxyJ4ZpODhqobyc9dNWO7pLjTrBVZNszCiVwem2OCpYVwjASCOca6oRFzTeovcjUSwX7dpcIxmgXD7oMcaP0l4z/8x1JFwB8cZHfT7Zyy5muaFI4Wi56BZ+ISs8kX7e29/6K48AxNO7TRaJC9zT+Mp6vEnIFwxBPrmTPwJOtXfAJbznTnQ5J0lBSeSc/gs8xWYGEyBkMe0WgARQmmHRuJporqzJx00YcY0rRxqhqNKchJEkZ93qxStyRJoqb0Kg4dTdY0U8agt1BqPyvlPLfcXsLTez+D+uoT+O99nV2PLJGfcFXF8/JuPC/vTvjwpssiZL1lM9KpF09EwHa2ONnZ4kQnS+h1MsFw7PO7edWXaOn8C4MjU9XRdLosqkuvpKLownmLBg+7D8adIkj0mXR7W2nvfZja8jcnnSPXWkddxVto7bqXRCIMtpwGasqumjujk9AzuI10suP9zhepr7wBnTxVDVCW9ayofBs1pVfhch8gqgSwmIvJta6YtxTGTLDlro47fam+qyTysusXyiTBCcQS+VYVCE4cTBXp60IkvR5T9dJt8rccaOu5P8WjsQLc3sHnqCq9bKFMypjYznJqRyQQGsIb6CY7qyLtfM2d98SLv2HqoijWmHJf851sWX9bwpStypJLGXC9EndQ5sI5ksm11BBVQox4DpEu0jRfKS+51to0NRMACh19jzA0vJOSwrMIhl30Dj0X7+cCZlMRlcUXUWo/d8aL8uLC0wlFPLR2/W3SUQlQMBpy2bDykxj06fs8bb3Vz8ar18E167iNu5aOc5SCL15zM4Rh963TFc+iikpUObapYTLaWFP/IcIRD2O+biJRH9ascrJMjnlPj+0eeJLUinIqPYPbqC69HFk2JJ2nsvhisrMq6ep/gmH3flQUskzFWMyleP3dvLD7Fgw6C8WFZ1BWtHVaOttsUVUVf8roZAxFDRMMjSSNrOv1FooKTplT2+aCMvu5dPU9luIbRcZu25QwQi4QpGPpf6MKBMuMqD+93KmqKEQXqSnjiYA/OBhX+UqFSq/z+SXtGEU1Rkm0jAuF3fQ7t5PcAVGJKkH6nC9SWXzxtEfNxsIJMQZfoIfZ1xIp+AK91JZfy4jnYIpxMZUvi7lkFtdKTnHhGbR1/2NCjS4Z0agfj68Nj2/6+yoQHOBIx924x1pprHnPjHfNK4svpij/FHqHnsfr70aWDRTmrcdu26xZ/hhg9wM2IOZw/PA7sY2YpSjOAGDedh27b7dlfJ5BnzOrlK2ZMOJpIt2mQCTqwxfoJ9uSeqMiP3cV+bmrUFWVYMjF7qbv4RzdzfhnKqgE6eh7lJ7Bp9nY8Jk5la2XJAlZ0qd9zwPzntI3TjA8gtfXBZJMrrV2VlLlZlMhjbXv4VDbbxjfXDhGrBbp+BRBgUArwjESCOYYz0u7ifoD6LJSNNCUJEaeeGHhjDrBCIc9czpusTCbHIwlqQOYMi5ld/kYI55DE0pvyVFxjuxJ6BgBWLPKOGXN13B7m+kbepE+5+zeo5Kkx27bhC1nFSOewyQs5pZ01Fe+dVbXSYVBb2VV7Xs50Pqr+JGZR8P6XS9RkLd+VjUuJmM+NWVXYc81ccaaYrLNevqG/bx4oJ9gODPbdj9gY+sDsY2YKX2Plgjmbddxy+3z4/DOD1o3ArRvGEiSxMG2XxEIuRKcpxCJ+tnbfCdb1v1vRs5xOgptmxga3pHiO0HCmlWOcY6jVccTDA3T3PkXhkZ2Mn7/smSg1H4OtRXXTkvj00pxwRayTA66+p9gaGQXqhrFaMijzLGV8qLz0essc3gXgjcSwjESCOYYxR9g4I//pOR9b0WSp+8sq5EokeFRXA8/swjWnRgYDLlzOm6xKHOcR1P7XSlGyBTkrtGUEqKo6WWOAdQku8jusVa6B55iZKwJYE6kwoMhJ6Gwm3UrPk5zxz30O1+cslCzmEtoqL5Zk+iBqqqMjjXR59xOMOTCoM+mqOA0CvM2pE2xcuSfzKZGGx29D+Fyz6auT6J74Km0jpGqqgTDLhQlgsmYP2VX3qCX+fhVq7l4cyzioKgqOlnCH4zyy0cO8ehrXTOybOutfr6/7ToAVn3nr4uWYnfGbzaws3YFgGanSCdLrKrMw2zU0+vy0eNcnGh6bnY9w+5DpFaUM5OVgaiLx3sUt7c1xQiVUHiEoZGdcyoqUVF8cZoUUpWqkjfNa81QKDzKzkPfIhgeZbJTqKhhuge34fF1sLHh0ynTElORa61jTd2HUFUVVY3OqWMpeOMi3kUCwTzQ+7M/YyyyU/jmC1EjESS9HlVRQJKIDI9y5MNfRfFp6zAumE6WyU6utS7eyyLZ7q2Utph9sSku2ELv4LNJCollZFlPbfl1muaymtMrN0nICVN22nsf5mjP/UyurwiH3Zqum4qoEqK99180VN9EY83N1JZfy7DnAIoSxmouI8daq01UQglxoOXncadm3EaJweHXsGZVsGHlpzCmcYLzsutZv/ITRKI+9jb/GPdY8wzuSMXjO5r8UVWl3/kiHf2P4Q/0ASDLRkoLz6a67AoM+hxufetGzlhdhBzfNJHjCl1ZJh2funYdqqry2I7uGdh2zBHZeM3N3MZUhzuRoyQZDeRfdBZ5529BzjITONqF877HCbRq6Ws1lU2XxRzz8/9+dkbnXXtmNW89r4787GORg31HXfzi4UMc6Z79ezATyosuYNh9IMUIiVLHuRmln7ncB0hdtxT7XA67D8ypY5RrrWFVzXs4dPR38SPKxLVUFKpLr6So4DTN8435Oukdeg5/cAC9Lgu7bTN220kpnZGjPQ/EnaJE967GItPOFylznKfZjkRIkoQ0D1L/gjcm4p0kEMwHikL7137I4L2PYL/+Usy1FSheP8P/foHhR55FCaRX6RKkpqb8GvY0/SDJozJGQy6l9nMX1KbJBEMjjHgOoagRrFkV5FiqpzkBsmxgQ8OnaWr/Y3x395iTZ80qpbH63WlrGcbJsVZjzarE6+8imbOookx7Tpwje+JOEUxewKRPy9OCSp9zO/UVb0GnM2M05FBcsCXjWZra78Ll3n+cjbF79Pp72Nf8Y05a9UVNTpZeZ0HKQC5YK6qq0tr1N7oGnphyXFFCdA8+jXN0D++89LuctTZxtEGSJFRV5X1vauSpXT2EozOv79r9gC0meDCJ44UaTDXlrPz5f2MscaBGo0g6Hbmnb6L4nW+m/6776f5+MgW96Zy59zN88sXe2B+J2v8k4YOXr+K6s2qmHV9dZeN7H9jC5379CrtbWglFPBgNuZiNBdonnwEFuespc5wfV3Q7vsZOIsdSRU1pZopyqhpNW62nMlVVc64oLjydXGsdPYPP4HLvR1UVcrPrKXdsJcdarWkOVVVo6vgTfUPPMXVTYgdmo50NDZ9OKJwSjQboc24nnXJc98C2WTtGAsFcIhwjgWAe8e1romNf02KbcUKSn7OKtfUf5lDbb4kqgYk+RioKFnMx61Z8LKH62nwTifo50vEnBlxTmzxasyporHkXOZapCxK9Los1dR8gGLqBYc8h1AlHqibjNJfGmpvZdfi7KEqERAuSyuI3TYsYdfY/hjahhcm73nJ8IZ9+MaeqEYLhYSy6maXmBYJDDLhS9YVR8PiOMjJ2mPwcbcX62ZaquHpfps6fTF52Q8JHRseapjlFk20MhFycuRoiUQV9gv5mEHOOci1GTm108OKBgQxtm8q4QMM4hz7/Vs54SyxKpsomQms/BfpYHYak08X+q4/9t/jmawg7hxn4/T+mzHHGbzZMt3lCgts27bFU1JfmJHSKAHRy7L32ocuKOPnDb5s4npe9kpqyaxJKzs8FkiSxovLt5Fhr6Op/HK8/Frkz6HMoc5xPZfHF6HSZ1cRkWyo11f5Z51B8YTJZ5iLqK99CPW+Z0flHex6MO0Vw/KZETFTi+5y29r+npcMFQq4pPZASo+IP9s/ILoFgvhCOkUAgWLbYbSdxxsa1DA6/xpivE1nSk5+3Flt246L021CUMHuO3IHHe5TjHQ2vv5tdh7/LSY23JowCmYz5lKRo8KmFHEs1JzV+gdauvzPsOZYSZDTkU116GaX2qTuzUSXI6NgRTXPbchrx+XtiSmq2TZQ7tvLawf/W1BRWnlRgraoKwfAwqqpiNubHHNoUxIq2UztuEjJDwzs0O0al9nPj0syZolBRfGHCR7oHtpE6ZUrBkWdI6hRNjFJU7LkphFtmSCzNLpZqd9WWKj66MTvlZ6TkvTcwePeDqOHY4nbTZZHEaXJ/n1lK8OWnVaZ0EnWyzJqaek5u2MCOpj0AjI41s7vpe6xb8VEK86Y7aXOBJEmUFJ5BccHpRKJeVDWKQZ8zY6nwwrz1GA15sZ5YSd7DsT5is/vszweRaCCFsx+LKgdDTgaHX5vWnFjWmG6Y7vMvECw0wjESCATLGp1spKTwTEgv3Dbv9LteTiEjrqIoEVq7/86GlZ+cNxuyLZVsaPgUgZCTQHAInWwm21KZcGGnKtrTd9bUfXBaBM5uO4lB16upla/MZZgM+aiqQvfAk3T1P0kw7ALAoM+mzLGVypI3Ja3biET9SJKcMjqlohKJal+gW7NKqS69kvbef2k8I+bwVJa8KemCPFZgnzoy4HS7iEYVdCmcI1mW8PiTyyyHwm6GRnYSjoxhMuZjt21Gr8vMkTpvQwkqpEwo1OflcNqO/0InDQPEUuUySJNLR21JTlonEWBVVcOEYwQqEnD46G85ff13Zly0rwVJkuakObQk6VhV8z72Nv8QVVU5XloaVBqq37kkG1EPu/dr2PiQGHBNd4zMxkKyTMUpI0ISMnbbSXNgqUAwdwjHSCAQCOaI3sFnSR3dUBh27ycYGp735oNmY2FamW+dLiu+mz2acpxeZ03Yd6Si+CIGXK+mOFON95FSOdD6S4ZGXp/yaDgyRnvvQwy7D7Kh4dMJnSOzya4hZU/CbLSnGTOV6tKrMBkLaO99iGDIOXHclr2avOx6hkZ24g10AxJ52fVUFF+M3bYpuQUaIgp/ffoB3nb+NSnHBMNRXj40vTmnqkZp6fobPQPbUFEmiuiPyHdTU3Y1FUUXa46S5mQZkTWM/a8/RXlu37jDadM0t1aCoSiKqqa1IxiaWo+pohKOePF4d5OXs/SajyYiP3cVmxo/R1v3P6f09MqxVFFddjWFeesX0brkaNtsUIlEp6sISpJEVcmbONz++xRnqlQUJY7ACgSLhXCMBAKBYI7wBwfQ0uMkEHIuia7skiRR5tjK0Z4HSGW3ooYJhUen2ZxjqWZ17Xs51Pab+NnHK19dRVHBafQNvTDNKTqGitvbSmff49SUXTntUYftZI7If0ZRUgmWKJTYz0x1q9OQJIlS+9mUFJ7JmL+LaDSA2XTMmawpvzrukEmanJ78nDX0OV8kVdTo8R3P09wzSk1x4miJqqrc9/xRfMHptRlN7X+K95WKPdPjUTpFCdHa9TdQobLkkvQ3DvS4vFTYLSkjVwD9w/OnnPnSoUE21qd23EPhEE/tfG7acYNeT5XDw6i2/shLglxrHRsbPk0wNEwwPIpBb00oWrCU0LLZICFjTnIfxYVn4g300tX/+MR3QgwZUGmseTc51po5s1cgmAtmljQrEAgEgmnoNHZz18lzX0MyUyqKLkrbhV5RwnT1/zvhY0UFp3Hquv+hsvhirFkVWMxlFBeeyebVX6GmLKbg1TXwFKkTt1R6BrcljAzpdCbqK25Icw8Xk2UqSjkmGZIkk2OpwpbTMC3CJkk6zbUl5UXnky6VrqTgHL7yux209cUaD0eiClFFJRKNnffgyx3c9eT0mi+vv5c+5/Okcl6P9vxTczrho691pXSKFEWlfWCMpnmUy/73zm58gQhRJfE9RZUov3/8L7g8I9MeU1VYUZaPxbT86lNMxnxyrTVL3ikCsOU0YDKk3sCJKV0mlmiXJIn6ihvY1Ph5HPmnYDbayTIVUV60lVPXfj2juqpQeJTOvsc50nE3bd33M+afWb8vgSAdImIkEAgEc0RR/ql09j9OqgWy2WjHmlW2cEalQZJkItF0W+8qvUPPUVdxQ8J0rSyTnbqK66nj+ulnqgpef/q+OOGIh2BoBLNpehShzHEekqSjtevvRKLeieOybKSy+E1Ul16edv75JttSSV35DbR2/y3pGEtWKSPeEJ/42XY21RVyzvoSrCY9/SN+Ht/RTdeQN+F5/c4XSdcLR1HDDA7vSLpIncyrhwd5/cgQG+sL0R3XhFqJOyo//9fBRKfOGd5AhC//7jX+9z2nkGXUI0mxhfS4IMPTu17k87/474TnRqIR3nTqVrxhlUOdqdNABTNHkmRWVL2D/S0/TTaCQtsm8rJTqwTmZa8gL3vFjGxQVZX23gdp730YUGMbFapKR9/DFOStZ3XtBzKusRMIUiEcI4FAIJgjyorOo3twW7xgOfFOeFXp5TNWuJoPwpExtMhWR5UAihpCJ2mXK1ZVNS6LrY1Uz0up/WyKC07H5d5HMDSMQW+lIG/DkloUeXztpKoxa2r/I7acVWSZ7OxscbKzxZlw3PEEQ8NJ5xxHknTxcelRVPj6n17no1et4aKTypGlWBRGliWG3AF+eP9+zbbNhsNdo7zv+89x2akVnLe+FItZT9fgGL979A5+/9hfCEWmF/7rZB2rqlZw9vot3Pv89nm38Y2O3baJtXUf4Ujn3fFaxNj7W5J0lNrPob7irfOqANrZ/+gUkZTJUWXX6H72t/yUDSs/vSgqpIITE+EYCQQCwRxhNhayfsUn2Nd8J1FlchQmtttfXXqVph39hSSWRpe+j5Ek6ZEl7SpgwdAI+1p+wpivXdN4s9GO0ZCXcows61MKIMwWRYngC/RN9MLSydqdwGB4hMHhHaR7HnsHn6GuYnpkLRV6fawprZpiblVVMurbFQwr/OC+ffzu8SZObXRgNujoGvKys8WJOvPeshkz4g3x56db+fPTrRPH3nHeFdz/wsM43cMoyjGnXZZkivMd/PU/f4U3EKGtf2zhDH0DY88/iULbRlzu/QSCg+h0Zgrz1mPQ58zrdSPRAO29D6UYoTDiOcToWBO2nMZ5tUXwxkE4RgKBQDCH2HJWsmX9bfQ7X8I1uhdFDZNtqaLUfi7WrJk1OZ1PYoucDThH95IsciQhU1SwRXOkK6oE2d30PQLB6epqyagovmjRImmKEqGj72G6B7ZNpOrJsolS+9nUlF09pQZLVRVc7n30DDyDN9CNTjZit52EyVhI+sibwrAn8xS1ooLT6Bl8Os0oCbttc8ZzD4+FeHxHd8bnJbUiHn2aDa+3SLz28yf49UN/5PeP/ZUht5PifAfvufTtvPfyG8nPtvHXZ1sJRzJt0CuYKZIkL7h6nnNkd1q5cAmZfudLwjESzBnCMRIIBII5xqC3UlF8YdJmoEuNqtLLcY3uTdZ+EknSUVl8seb5BpyvaOxoH4tUOfJPocyxVfP8c4miRtjX/OO4w3LsGVCUIN0D2xjxHGZT4+fR68yoapSDrb9mcGQHk2t+OvoeQ0opLnEMdQZeQ661nrzsBkbHmknsfMUU9kxGW8ZzzwWb6gq47uwaNq+wI8sS7f1jPPBSO4/v6E4qrpCKw12j3PfCIF+68VN8/u2fmFDwUxQVSYKdLU7+9FTzXN/GCY2qKoyMNREIDqHXZZGfu3ZJpaEmIhRxky6araLEm+cKBHODcIwEAsGS5qS2ZqBksc04ocm11rK2/qMcaPtVXBZ7PHKjoNOZWVf/0YwEI2Ky0unT8yzmYqpKLssoGjXX9A09z7DnQJJHFbz+bjr7HqW2/BqO9vyLwQnZcWXKOG3Lf3lGReiSJLGu/qPsa/kpo2NNceljNZ5ep1BUcBorKt+e8bxzwQ1n1/D+y1ZNiCYAVBdl84k3r+XstSV87Q87iEQzd47uf7Gd1l4P155ZzSkNDnSyRPvA7ByuNypDI7tp7vwzwZBr4pgsG6koupiasquWVM3jZIz6XNKm+CKnTcEVCDJBOEYCgWBJs/29e3h678VsvXX+eqosRxQlwuDwq/QMPoM/OIBONuMoOJUyx3mYjQUZz1do28AZG77LgOsl3GOtgERezkqKCk7NqNYGIBgeRUs/p7ryGyi0bcjY1smEwh76nM8z4HqVSNSPxVRMqeMc7LZNSFJ6OefugW1pRqj0DD5DRckldA88Sfr7St3gt8xxXlqbEqHXW9jY8BlGx5oZcL1CJDqG0ZBPSeGZZFsqZjTnbFldZeP9l62K2TdJ/luOK92dVF/IO7bW84cnZxbd2dPmYk+bK/3AJYKqqihKCEnSIctLY3k1NLKL/S0/4/j3pKKE6Oh7iFB4lMaamxfHuDTYbRuRZWPKdDoVheLC0xfQKsGJztL45AoEAkEKfJ//NsifWGwzlgzRaIA9R36E29vM+EI8zBidfY/S1f8EK6tupNR+Vsbz6nVmyhxbZ53WZjLkxXenUzsRBkPurK7j8baz58gP4v17YtcKhoYZ9hzAlrOKdSs+ltKpU9UovkBv2utEol5co/uOE9RIhhT/RznumEpdxQ2zkmqXJAlbzkpsOanlkReKN59RPSVSBDDm9xKKhLFZc5FlmatPr+Kep1sIzyBqtFyIxtMuewa2EQzHlAFtOY1UFF8yq7ocRQnT73qZvqHnCYRcGPTZFBeeTknhWZqENlRV4UjHn0j1OexzPk9Z0VZyLFUztnO+0OnMVJdeSVv3fUlGSNhyGtPKhQsEmbA046cCgUAgSMqRzntwe8dlsCcvelRUNUJT++95ac+tuL1ti2EexYVnks4pMpsc5FiqZ3yNSDTAniN3THGKYsQckhHPYZo77kkzy7gTk55EzWcTYTLYKHOchywbJ46NpypWFl+iaY7lwuYVhRNO0T9feJTzP30tjmtXU/6WDdT+xync9qcfIklhqoqyF9nS+SMaDbD78Pdo6/7HhFMEMOI5wr7mO+noe3RG84YjHl4/dBtN7Xfh9rYRCo/g9XfR2vV3Xtv/NU0O/bD7QFxiOzkSMn1Dz8/IxoWgsvhSqkuvQkJmvN5xfOlamLeBtfUfFVLdgjlFRIwEAoFgGREKuxlwvkQ6xyMYdrHz0LfYuPIz2HIbFsa4OMUFW+jqfxx/cIhkSm115dfNakEz4HppSrPX6aj0O7dTW34txiSRKUmSseWsYsRzOKmdAFnmEnKt9Rqsksm2VrGy6h3UV9xAKOxGlo0YDfMra6wVRY3gGt0Xl1zOojBvQ9LnRgty/PX75h9/wP/88Qfo5GN7rQMjQ/zPn37AP198lI0Nn5m17UuVtu774/2rjv88KvHH78OW00CutS6jeQ+2/h9ef0/8r6mbH6HIGHuO/IjT1n0DWUq+jPMHB9EiXuALaBFKWRwkSaKm7CrKHOfR73qZYMiJXmfBkX/KkmqULThxEI6RQCAQLCNGPIdQNTRkjaGyr+UnnLXpjgXdVdXpTGxs+Ey8j1FHbLdXklDVKLJkYEXVO3DknzyrawyN7E47RkVh2H2Q4sItScdUFl/MSBoJ7crii7GYi+JOVBPJnahjNUSybMBsKkxr40IxOPwaRzr+TDjiYaJJJzIl9rNZUfk2ZFl7j6pxDnaOEAy28T9//AEAUWXq86IoCvvaDuEa+yN1FYsjDjGfRKIBeoeeI5XjISHTPbCN3FrtjpHX35NCEARAIRhy4hzZgyM/uUS7TmdKadu4hZPl6DMhEg0w4HoJ5+g+FCVMdlYFpY5zsZiLZzRfKoyG3IyUMQWCmSIcI4FAIFhGKGoko/FRxc/QyG4c+Zvmx6AkmIz5bF71ZdzeZpwje1CUMJasEooKtsx4ITaZmHqehnFq6j4oBXnrqCl7M0d7/hlXextf3MfkuEvt51FSGGvKu6LyHew8dBtRJUQi56ioYAv5OWsyuIuFYXB4BwdafznpiBr/t0Lv0HOEIm7W1n0kY+f5wZc6aOv6PXqdjkg0caphVInSPfAcVaVvnpPXfSnh9XeiqOGUY9R4E9JMcI3uJV2kR0LGNZraMSrMW48k6dKkgaop50iG29vG3iM/JBL1TRwb8Ryma+AJqkuvpqbsyoznFAiWAsIxEggEgmWENas843MGR15bcMcIYmkwedkrNRVHq6qCJMmoqgKoaRXlrFkVuMda00bPLOb0TXWrS68gL3sFXQNPxRaxqkqutY6yovMpzNsw4TBYzMVsavwcrV1/n7Kjr9dZKC+6iOrSy5M6F6oai16N+TuQJB35uWvIzpp/NTlVVWjuTFVrpeIc2cXo2BFsOZmlXL5yeJDdh19I6hSNo6hhvP6uE65IXnNLqgx1JxQ1HJdiT5UCp6IoqTdJDPocSu3n0DP4TBIjZEzGfOy2kzKyLxgeYU/TD4hO25yIfRbbex/AZLRRaj87o3kFgqWAcIwEAoFgGZFjqSY7q4oxfydaV1yqojX1bmEJhFx09f+bfueL8Z3nY01TrVkVlBddQEnhmQn7rJTaz6Vn8OkUs0tkmYs11gbFVMRsOY0JH3ON7qez//H4zr+K2eSgquQKcq016HRZ5FprU6aijXiOcKjt/wiGXfF7VIG/kZfdwOq692My2DTZOBOGPYfSFuCDTO/Q8xk5Rg0VeVx3ZjWnNm7HZDBwuLOFXzz4e37/+F8JhRNF6U68AvlsSzmSpEdNEcWVkMnLUEXQai7XlC5ryUrv9NdXvJVgeBTnyE6Ofb5i0SiT0cbGhk9nnEbZO/hs3ClK/v3T3vtQ0s+uQLCUEe9YgUAgWGY01Nyc0WIm08LvhWDM18mOA/9N98BTk9Jxji0Gvf4umtrv4kDrL+NRpKlkWyqoKEpWcyAhSTKN1TfPuraqo+9R9jb/MC7QEFsIBoKDdPQ9TGf/Y+RYa1K+FuOS4scUy5SJeUbHmtl1+Pa4st78EAw6NYxSCASHNM958eZy7vjQ6Zy9rgSrOQu9Ts/qqpXc8fFv8vBtd5NlMk8ZL0sGrAsQHVto9DoLJYVnkmoppaJQXnR+RvMW2NZj0OeQ2pmUKNEgyS/LetbWfZiNDZ+lqOBUcq11FOSupbHm3Zy69r/JMhVlZBtAv+sV0oq/hJyM+bsynlsgWGxExEggEAiWGVZzGbVl19A18O8p3ewTI1FiP2NB7NKKqirsa/kJkWiAVGpwAEMjr9M9uI2KogunPVZXcQMmYz4dfY/ERQVi5Fprqa9866wdwtGxlkk9VI63U2V0rIWjPQ9QX3FD0jnauv8xkR44HYVAcJDeoefnrbBcr9dS1yNh0Fs0zVflsPLpa9chSaCXJzd1jf3/6atP5hvvuZXP/vy/xh+hxH42ep15+mSLRCDopHtwG4PDrxGNBsgyFVHqOJfigi0ZR0/qyq/DPdaMN9DL1Nc4FpWpKr0i4xRCWdLTWP0u9rX8NH5k+nunvuIGzZHGWP+rhoxTJZMR1ejIax0nECwlhGMkEAgEy4gB1ysc6fhzXKo6fdB/ReXbNDWDXEico3s1OHTH6Or/N+WO86el5UiSREXxRZQVbcU91kZUCZBlsmuqK9JC98BTxwkyHI9K7+Az1JRdlbCRbDA0kkZdbHyOZ+fNMcrPXYcsG1GUVCIUKo6CUzXNd+XpVSiqOsUpmoxOp+M9b3oH//X77zLm95FtqaC2/NoZWD4/jHgOs7f5znh9Tux19fja8bTfRe/Q82xY+amMnDi93sKmVV+gs/9xegefIRwZA2Ipr5Ull+DIP2VGdhbaNrBh5ado7f4bY76OieNmo52asjenVFqcb7JMjvh9pulVZlw6qowCgVaEYyQQCATLhMHh1zjY9utJR5JHWwz6XOorrqe4cGlFiyC2OJXQoaKtaWow5CQUdmMy2hI+Lkt6bBnWcWhBizR6VAni9XcnjE4FwyOarqN13EzQ68xUFF9MR+9DSUbImE2FOGzalMlOWWmfaOqaDIs5i9PXnElLbzblRRfEZaNBVVU8vqMxCXdJhy1nFVkmu6brqqqKooaRJV1aYY5khCNe9jX/GEUJc3xvIACPt40jHXezuva9Gc2r12VRW/ZmakqvIhwZQ5b06DVG4CYsUNVpaZ/5uas4OfcreP09BEPDGPRWsi1Vi163U2o/F7e3NcUIGVvOSswaX1uBYCkhHCOBQCBYBsTUxf6Sdlx16VXYclaRl12/6Auo5GQo0zXjc2aLxmsmGaY1PW2+ZaxrSq8iHB6jd+iZSRGwWCF+lsnOhpXaC/BlWVvNlgQc7X2QPud2yhznkWOt4UjHn/AeV3dSmLeRxpqb4zU104kqQboHttEz8HRcvEIiP3cNlcWXkJ+7WpMt4/Q5X4hLrSd7XVUGXa9QX3E9RkNeRnNDrGFwJg1zAyEX3f1P0ud8gUjUh15npcR+JhVFF2Ey5k+Ms2aVLalmpkUFp9Iz9AwebzvTN2ckZEmmrjx5eqlAsJQRjpFAIBAsA7Sqi/mDA9SUXbUgNs2UHGuN5mgRgNGQN6OF6mzJtdbjHN1LqsicLBmwJFm0ZpmKsGZVxp2BZItxed6jepIk01B9I+VFW+kdeh5/YAC9Lgt7/mbsto0ZRWAOdYxgzzWnjBpFolF2t+xEVSP4g320dP2FmKs03alyju5l1+Hb2bzqi+iOS2GLRAPsbrqdMd9kBUaVYfdBht37qa94GxXF02vPkhHrD5Ta2Y01BT4076lqY74udjfdPqXOLhL10tX/JH1DL7Cx8bMLIuc+E2TZwIaVn6Kp/Q8MDu9g8nOaZSqisebd5FirgVg6ac/gNvqc24lEfRgNMRnvMvu5GUfVBIKFQDhGAoFAsAwIhuZeXWyxcNg206y7J65Gly4qI1HuuGBRol/lRRfgHN2dYoRMif2slDUpNWVXs7/lJ0keldDpTJQ7ts7GTM1Ys8pZUfm2Wc3x4MsdbN2YPHoRjkS4/4VHGBg5/n2okkyAwhfoo3foeSqKL5rySGvX3xnzJXIqY45ES9dfsOU0kG2p1GR7ur4/E5Zm2EQ5U2LiIz9OIj6iEIkG2N/8U05b9z9LNuqr12Wxpu6DBEPDuNz7UdUI1qxycq0rJlICPb4O9jR9f8p9BoIDtHX/g97BZ9jU+PkpkTGBYCmwND9xAoFAIJiCXqdld1VackILiZBlA2vqPhBf9KX6GZKwZTdMWzAvFPm5q6mYEEU4PtohYzWXUFt+Tco57LaNNFTfFI/KjEdNYvds0GezceUty2pxuL99hHufjdWXKMpUhyUcjdDr7OPzv/h6hrOq8Sakx4hEffQ5XyBVtE5CpnvgKc1XybZUIWlY9lgt8xupOSY+kuzeFAKhIVzuffNqx1xgMubHIkCOreRlr5xwihQlwr4jP0ri/KkEQsPsb/35gtsrEKRDRIwEAoFgGVCQu1aTulhRwWkAeP29DLsPxHZyLRXYslcBKpKkm3Vvn7kgP3cNJzXeSnvvv3CO7uH4qIDRkEd50QVUFF2UsYTyXFJXfgPZWZV09j+G198NgF5npcxxLpUlb9JUH1RqP4fCvI30OV9gzNeJJOkoyF2DI/+URb23mfJ/jzXRMeDlrefVUunIBsAfDPCHJ+7lm3/8QYJoUXoCx0VEPb6OtJEbFYVhzyHN1yhznEvP4LYUI2SysyrIsVRrnnMmaBEfkSQdI57DFOZtmFdb5ouhkdcJRdwpRih4vG14vEfJsdYslFkCQVqEYyQQCATLAJ3OTGXxpbT3PpjwcQkZs8lOrnUFe5p+wLDnIMeiHCrjfVV0soniwjOoKL6YLJNjgaxPTI61mnUrPkYk4iMcGUOvs6ISQVVVjIbcJZFGJEkSxYWnU1SwhXDEg6pGMRhykKXMfj6NhlyqSi6bJyu1EY546R18lt6h5wiGRzDorBQXnkF50fkZR62e2NnNEzu7KbZlYdDLPPXabfQ6Zx7h0MnHpSOq2oQvQuERXj/4v9hyGil1nJdS5c6aVU5V6RVJFPpkZNlAY827MrB6pqRWOhwnUWPj5cKw+wDjAh/JkRl2HxSOkWBJIRwjgUAgWCZUl15BOOKhZ/Bpji06Yv81mxysrf8Ie5t/iC/QHz9juiRxVAnSO/gs/c6X2NBwC7lLYFGi11uWfCG2JEkZKY4tNQIhJ7sOf5dgaJjx90IoMkpn/+P0DD3DxpW3TBTMZ0L/SKyJp05fChxA66J/KjLF8UjnONmWSiRJh6qmFulQ1Sge31E8vg46+5+gseZmSgrPTDq+pvRqzMZCOnofJhA6FtmymEupr3yL5nql2ZBjqUUldQqgqkbJtdYmOK4syIbBMXl0/Yyup6R53SC2VaNlnECwkAjHSCAQCJYJkiSzsuo/KHNspW/oBfzBAXQ6M478kynMW0/P4DP4Ar1p51FRiCpB9rf8hC3rb8s4+iFYfhxo+SXB0AiJhAyi0QB7m+/k9PW3zTi1r9R+Ll39T8zoXFnSUV50wZRjBn02Rfmn0e96GW3OVmzM4aO/J8vkIC87cV8rSZIotZ+N2ejgUNv/EYqMAOALdLP3yA9x5J9MQ/VN8yqh7sjfTHPnn4lE/SQWpJDQ6yzYbSfFbeunq//fDLheIqoEMeizKbWfQ3nRhXPurEejAboHttE9uI1QeASQKMhdR2XJJdhyGjXPk22pYsD1SsoxKsqCOKICQSaIX0OBQCBYZlizyqivfMu0472Dz2Uwi0ooPIpzZDeO/JPnzrgMiUR99A1tp9+1nXDYg8mYT4n9bIoKTkMnGxfNrhMJ5+hePL62FCNUwhE3gyOvU1wwM5lqi7mYuvIbaO3+G+Npm8c4/u+p2PNPJstcNO14feVbcXtb8QcHySQS1d77MBtWfjLp4+6xVvY235EgVU1lcHgHgZCTTQ2fQ5J0eHxt+AOD6HQm8nNWTZMUnzhTVQmEnKhqGJOxAJ1sSnp9WTawuu6D7Gu+E1VVj7s3GUmSWFP3AWTZwIjnCHuP3IGqRieaDYcjY3T0PUaf80U2NX5+zlJiIxEfu5q+i9ffw2R5dJd7Py73XlZW3UiZ4zxNc5UUnklb9z9S1IlJGA15FOatnwvTBYI5QzhGAoFAcIIQCLsyGi+hY8TTtGiOkT8wwK6m2+M70zGC4RHc3la6+h9nTd2HsZhLlkSt0XIlGBrmYOuvNIyUGXEfnLFjBFBZcglmUwHtvQ9NCFVIyJiMBXFxhcTO0YDrJcoc55KXvWLKcYPeyuZVX6Sj/zF6B5+Jy7unQ2XYvZ9A0InZVJhwREvXvXGnKJE9Kh5vG0d7H2RoZCf+QN/EI7JspKLoQmrK3jzxnlRVlb6h5+nsfwx/cGBiXEnhmVSXXoXRkLhxbUHuGk5q/EJcfGS8v5JEYd56qkuvJMdaPRHVVdRIAlsVwmEPB1p/weZVX54TQZXmzr/g9fcmvBbAkY67yctuwJpVmnYug95KQ/VNHD76WxI5ypIks7r2feKzLVhyCMdIIBAIThD0OgvRqD+DM5L1lpl/VFVhT/MPCYWPV66K2eML9PHagf/CaMijzLGViuKLp0WQgqERokoQk9GWcof+jUxz51+IKkFNY+ei3sORfwp228kEQk6i0QA6nZlX9/8nqd5nMdntbdMcI4jVn9WVX0tt2dV4/T3sOPgNTXYcavsNm1Z9btpxX6Aft7clzdkSnX2PcLxEu6KE6Oh7hGBomBVVNzLgeoWjPf8kfJz6mqKE6B18FtfoXk5a9cWk6W451hrWrfg44YiXcGQMgz57itz+gOvVlM6gisKYrwOP72jCeqRMCEc8DKRNW5ToGXyalVXv0DRnSeEZGPTZtPc8iMd3dOJ4fs5qasqvmdP6xlDYTe/Q87hG96KoEXIs1ZQ5zhOpeoKMEY6RQCAQnCAUF5xOR9/DaHV2VBRyZrmgminO0b0EgoNpx4XCoxzteQDX6F42NHwanWxicPg12nsfwevvBECWDBQXnkF16ZWYjLZ5tnz5EAqPMjSyC23vB3XOZKolSZpQhxvxNGmS3R4da0ozpw5LVik62UxUCaS1YdR7BK+/B2vW1Ga0kwUXUlk09b9T6Xe9xOhYc8q5VBSCoWFauu5lde37Ul7NoLcm7D826mlCi7LbiOfwrB0jt/foRKpecpS42qV2CvPWU5i3Hn9wiEhkDKPBNuefUZf7APubf4qihhl/zby+TnqHnqWq5DJqyq5ZEi0KBMsD4RgJBALBCUKZ4zy6B58iGg2QfjEsoddlUZR/yqyv6xzdQ1f/kxOL2xxLDeVF5+PIPzXpgsQ1ugcJWcNiDEDF7W2jvfchdLKZoz33M3k3X1HD9A49j3N0NyetuhWzMXEK1RuNMX8XWmtzJElHceEZE39Hon6Ghl8nEHJh0Fux206aYSNarQvS9ONkSU+p/Ry6BrSIPEiMeA5Nc4y0NUpOjxYHS0Vh0PUqKyrfikGfOKUOYul4I55DjIw1gQq51loK8tbFPxvpndq5kfXWGDnWKKN+PFkmO6SQUp8p/uAA+5t/HI92HrNt/Hulo+8RzCY7pfZz5vzaghMT4RgJBALBCYLJaGPjylvY2/wjwhFPipGxAu/VdR+cdYPR1q776Ox/lMk7225vK+62Flyj+2mseVfCOgJFCWeYxKfSPbANZSItbHodRCjs4Uj7n1i/8hMZ38eJiIRO89jG6psx6K2oqkr3wL9p674fRQ3HG5EqNHf+hVL72ayofAeyrH3pkG2pRJYM8d38ZHbKmhXPKksupXvwadQU842TyGHIsVRjNOQTCg9rut5sUVHw+nuS3p/X38P+lp/hD/YjIU+cYzLkY8/frOEKyqyjRUA8Wpg+OpVM7W+x6B54GiVpvViMjt5HKCk8S9QzCTQh3iUCgUBwApFjrWbL+ttorHk3jvxTseWswmIu49iOvEShbQMnNd5KQe6aWV1raGRn3CmCqQuq2CKl37Wd3qHESnmWrDI071KPX0EJkvpnS8Hl3kcgqCVd6sQn11qDrEHZrzBvI8WFpwPQPfAULV33TjgyKuM78Sq9Q89z6OhvM7JBrzNTYj+bVK+bijJNrjsZRkOuxrFqwsahkiRTXXpFivPmI+Uq8b0HQ8PsOvzduOpe7HkYj3QEwyP0DD6LJKVybiXMJge2nFWzttBoyMORvzmprTEUyovOn/W15pLB4ddIFxUNhIY0tTEQCEBEjAQCgeCEQxdXxZrc6DISDRCJjKHXW+esR0tX/79JJ8Xc1f8EpfZzp6XUlRSeydHu+1EzFn9Inzbk8bVjnoe0naWEqiqEwqNIkoxBn5swZVGnM1NqP5fugSdJ/hrJ1FVcD0A0GqSt5x+prsrg8Kt4fJeSY6nSbGtd+bV4vK14fB3H2RGLUNSWX5dR1KOi+EK6+59IkYYpYzEXk2utT/hoqf0cQuFR2nsf5FiUJPY+1uutRCMBVFLXRWlFlk1Jn6vO/ifivYwS3Yc60eTV7W2dODZpZmTZwJraD8xZ/cyKyrfj8R4lEHIdZ1Psuaktv27JiRloFRaJRrWNEwiEYyQQCJYFG68eYfcDtsU2Y9mi15nRJ+nBMhNUVWF07Ejacf7gAOGIG6Mhb8pxoyGX+sq30tx5TwZXTe2ETYxahJQZX6CfUHgEgz4Hi7l03oq9o0qQzr4n6BncNpEumWUqprL4EkrsZ0+7bl35tYz5OuL1X8eev1h9l8qqmndjMZcAsQigooRSXl9Cpt/5YkaOkU5nZmPjZ+ke2EbPwDaC8TQ2W04DFcUXZ9zLxmSwsbL6nTS138X094SMTjayuvb9SV8DSZKoKbuKooLT6B16Dq+/G51spCBvPUUFp3K058F4s9rEzVe1RzolyhznotNNV0xUVZU+5wukdvQVPN421q/4BB19jzI6dnjiEYM+m8riS7FaKjTakh6jIZfNq79ER9+j9A4+R1SJKVzmWmupLLl0ouHsUsJiKsbjayf1ayKd8BslgrlDOEYCgWDJs+sRPXe8ZR/nc/ZimyKYQHukJ1lxeHnRBeh1Vo72/FNTMbskySn6z8THICeNFMwHw+5DtHXfN0WO2GIuo7b8Guy2TXN6ragSZHfT9/F4jzL5OfAH+2nq+ANubxsN1TdNcQhk2cCGhk/R73yJ7oFt+AK9yJKeQtsmKoovnKJEF3NYUteZqCgEgpn1ywLQySaqSt5EZfGlKEoISdJlVKt0PKX2szEZ8mnv/deE/LaEjD3/ZGrKrppw9lJhMRdTX3HDtOO15dcQDDkZHN7B8c+HXmcFFE09lfKyV1Jbdk3Cx1Q1oklaX0VBJ5umfT7CEQ+t3fcyOPwqG1Z+Cr1+bkQlDPps6ituoLb8GsLhMWTZkFAxb6lQ5jiPw+2/TzFCptC2IalkukBwPMIxEggEy4Lt793D03svZuutmfTpEcwXkqTDYi6L5+4nd1QM+pxp0aLJFBduoajgVDy+Djr7HmNoZEfSsemcIpApKjhtwRZBQyO72N/ys2nHfYFe9rf8lMbqd1FiP2vOrtfe+9A0p2gyfc7nKchbF68VOUZMze1sSu2pNxYMumy0pCo6R3fT1P4nqkouzXgnXpKkhBGUmVCQt5aCvLUEwyNEIn5Mhrw5cRBkSc/q2g9S5miiZ/BZ/IE+dLosHPmnUFy4hRFPE/tbfhofPf21MOrzqC67ipLCM5M6f5KkR5aNaSN0AIfbf0cwdLxYROy6Hl8HB4/+hvUrPp7JLaZFlvTLQvq+qGALvUPPx9MNj38tYtHDuvLrFsM0wTJFOEYCgUAgmBEVRRfS1PGHFCMkyovOT5vaJkkyudYa1tZ/iMNH76LP+XySkSrHUpmOj2xIWLPKWVH19kxuYfoVVAXX6D76nC8SDA1jNORSXHg6hbaNyNKxn0xFCXP46O9I7KTEjjV1/IlC26Y52XFXlDC9g88mud44Et0DT01zjJIRCLnoGXyGQderRJUAJmMh6ZXJICbE8BwDw69wUuPnsWaVa7uJecJksGEy2Cb+VlV11qmMkiRhy2lMqCZnt21kw8pP0dr9d8Z8HRPHzSYHtWVvpqjgNE3zFxecTt/Q8ylrpXIs1Xh8bSlmUnCN7sEX6NMUJTvRkGU9G1Z+kubOv9Dv3D7lucy11tJQfdMb8nkRzBzhGAkEAoFgRpTYz8Q5uhvn6F6mL9glcq11VBRfktGcY772NCNi1ynIXceI5yCKGsZsclDm2EqZPXE9h1YiUR97j9wZT806VpTvHN2NNauSDSs/hdEQ60czNLIzbTqVqkbpd26noviiGds0jj84pCF9S51UqJ+aEc8R9jb/CEUJM+4IhSNetKdIKkSjQfa3/JxT1/73ojfQ9Pg66Op/Il4nFcZsslPu2ErpLN8Tk1HVKJGoH51sIj93NSfnfgWvv4dgeBiDPpvsrKqMnoeK4ovpd72EqhxrTHoMCYmYWlz6fl8SzpE9WEremA6ATmemseZd1JZfx+jYYRQlSralYtEddsHyRDhGAoFAIJgRkqRjbf1H6Ox/gu6BJwmFR4FYHUaZYytVpZeh0yAXPZmYIlZ6KksuZv3Kj89JdGCcg62/xu0d350fX4jGFqxefzf7W37KpsbPI0kSY/7ueI+faIoZVVq6/kp7778osZ9FRdFFM2ySiuZ7lDTITYcjXvY13xlP45q8IJ++OE/tKCn4g/2MjjVp7kM0Hwy4XuNg26/j1sZet0BwkJauv9Hn3M6mhs/OKsUuGBqms+8xep0vxCXjJey2TVSWvIlca+20JrJasZiL2bDy0+xv+SnhiCfuAAHxuqLVdR9gwPVyWuVGCVmzOtuJjNGQg2MOGlYL3tgIx0ggEAgEM0aSdPGi+ksIhIZQVRWzsXDGhfUGvZVI1KthXHb8+nPjFHn93bjc+1KMUHB7W3B7W8nLro+n1WmLrkSiPrr6n6Rv6AU2Nn6W7KzMlcSyTA4M+lzCEXeKUTJ5OQ1p5+p3vhhfSKe236jPJRQZTTlGQmZ0rHnRHKNAyMmho/8HKAnuRsXr7+FI5z2srn3vjOb3BfrZdfjbhCM+JjvLQyO7cY7sZk39h2al1paXXc/p67/F4MjrjHqaUFHJtdZRlH8qOp1JQwQ11mtKpIsJBHODaPAqEAgEglkjSTJZpiIs5uJZqY0VF55BuiabZlMxIOPxdRCJpFcH08Lg8E7S/SRKyAwNvw5AQd66NOlNx6MQiQbY3/zTpCp9Ka8t6eLNNVM9NwoVRenT9hKnPk4n5hSlfi0y7UI11/QOPouqpo5qDbpeIRRO5VAm52Dbr49zio7Nq6JwsPXX8RTEmSPLBooLttBQfRON1TdTaj97Iv2vpDC9eIdeZ5lzBUSB4I2KcIwEAoFAsGQotZ8bFytI/vMUjfp47cDXeP3g//Dins9wqO03CVS7MiOqBNJHnySJqBIAIMdSQ46lFimjn1GFQGgIl3v/jGysLLmUgty1CR6J2VBVcjkFeYkeP84KJZzBVdO5Pgp52SsymG9ucbkPkE4sQkVhdKw547k93qPxiE3y+RU1Qp/zxYzn1orJmJ9U8nuclVXvRJYN82aDQPBGQjhGAoFAIFgyGA05bGz4zCTJbZlY1OKY0zLe2BRiBfEDrlfYcfCbBILOGV83y+RAVVPVC8UU68wmBxBL4Vtb/2FMpsKMriMhM+ppmpGNsqRn3YqPsbLqRizm0onjedkrWFv/UWrLr9E0T7alMkOHLqlFZJlLyMtOn743X2iPvmUepRsdayFdxAxU3DNwujKhqvQyVlbdiEE/VYbebCpibf1HKSoQdTUCwVwhaowEgkVAMhrIPWszhsJ8Iq5RRl/YgRpM389CIHgjYM0qZ8u6/2VoZBcu914UJYJeNtPrfC7heBWFcGSM5s57WLfiYzO6ZlHBqbR0/hVFTR5NkZAoKTxj4m+TMZ+TV3+VfueL9A49jz/Qn/L88VnSpeBFoj5GPE2oagRrVvkUJ0iSdJQ5zqPMcR6KGkFCTiuHfjyljnPpGXxa42iZYzLpx+yWkNHpzKyt+9CiKtLlZdfj83enfU6zLdX4Av30DD6Dx9uGJOkoyFtLSeFZs+x7tTD3XuY4j1L72YyONROOjGEy5scilousBigQnGgIx0ggWGDsb72Mso/fhD43e0JRK+rx0vuLPzPwxwcW2zyBYEkgy3qKCk6Z2A0/0PqrNLLFCs7RPQRDwzNSftPrLNRV3EBz55+Tjqkuu2pas1q9zkx50QWUF11An3M7h4/+NuV1VKLkWmsT34ESprXr7/QMPYuqRiaO51rraah+5zT54cl9lTIhO6uCqpLL6Oh7RMNolTLHViIRL4MjO1DVKBJ6cqzVABzpuJtsSxVljvMWRQCgzHFeGidPJj93FYPDO2jrvo/JfZpGx47Q3vMv1tZ/hIK8ddPOjKUIpq+iWqhUQknSLar6n0DwRmBeU+lcLhc33ngjubm52Gw23ve+9zE2NpbynK1btyJJ0pR/PvzhD8+nmQLBglF00zVUfekj6HOnKmrpcqxUfPb9lHzgbYtpnkCwZPF4j2oQO1AZ83fN+BrlRefTUH3zhOLdOHqdhRWVb6eq5PI0l0+3iJYw6HMoTFAor6pR9jX/hO7BbVOcIgC3t42dh76N19+r4S60UVN2DWWOCzSMVCnIW8fquvdz9qY7WVv/MWRZj9vbitvbwujYEXoGtvHq/v+kvfehObNPK9ascmrLro3/dXz0RMagz6Ywb0PcKYKpKXUqihphX8tP8AWmP7c51mqyLdWkWirJkp7iwjNncQcCgWApMa+O0Y033sj+/ft54okn+Ne//sWzzz7LBz/4wbTnfeADH6C3t3fin+985zvzaaZAsCDocrMp+383pRxT+qG3oy+wLYxBAsEyQpJ12sZJ2sYlo9R+Nqdv+A7rV3yChuqbWVf/cc7Y8F3Kiy5ImbY04jnC4fbfp7FNZk3dBxNGegaHX2fYc4DEEQqFqBKiteveDO8mlS0SKyrfGo9CpU7Hamr/A5Gon1B4lINtv5om9T3usB7t+ee8ChEko6r0MtbUfRDrJBl0SdJTYj+Tzau+RM/gsyS/RxVUle6BpxI+urr2/UnEQGQkZFbXfSD+uEAgOBGYt1S6gwcP8uijj/Lqq69yyimxVIg777yTyy+/nNtvv52ysuQN0SwWCyVv0A7OghOXgsvOQ9KnWbRJEgVXnc/A7/+xMEYJBMuEwtz1dAUGSFVEL0uGpGlqmSBL+oSpVano6HuYdA1RbdmrkqZC9Qw+k+Z8BZd7H4GQC7OxICPbkiFJMuvqP87L+76c4roQCo/Q79xOIORCUSIpx7b3PkxxwRkLXvviyD8FR/4pBEPDRJUQJkMeOp0Zf3AIX6An5bkqCgOu11hZdeO0xyzmYk5e/RU6+x+nb+j5uFMoY7dtoqrkTeRYa+bnhgQCwaIwb47R9u3bsdlsE04RwEUXXYQsy7z88stce+21Sc/905/+xB//+EdKSkq46qqr+OpXv4rFMvOu1QLBUsBYUYwajSLJyQO1qhLFVF68gFYJBMuDMsd5dA88mWJJLlHqOBe9LmsBrYoRiQYY1iDBPTJ2CFVVEool+AJ9aKln8QcH5swxAgiGR9Ci2NbvfIlQeDTt2EBwAF+gF2tW8s3P+eT4+rJoNKDpvJjDA2O+TroHnsI5ugclLn5R7jif+oq3UF/xFiJRPzrZpKlXV7LXOhX+4ABDI7uIRoNkmYqw55+ETjZmNIdAIJg58+YY9fX1UVRUNPViej0FBQX09fUlPe8//uM/qK6upqysjD179vCFL3yBw4cPc9999yUcHwwGCQaDE3+73TNr4iYQzDeK15/2R1KSJKJe/wJZJBAsH7LMRayqfR8H2/4vHlcZX6DH/srLXkltefINt/kkGtX2mVXVKIoaRieZpj2mk41o6S6kk6efOxvCkdR1v+OM+bun1T4lQ6szcjyqquD2thAOj2E05JFjnb3qmsmYn0a0I4bZaKffuZ1DR3+HNEk50D3WgnusmQHXK6yp/1DatDlfoI+u/icYcL1CVAli0OdS5jg33pwXAqFh9LoszEb7lHuLRAMcPvo7hkZeByQkSUZVo+g6zKyoescUNUSBQDB/ZOwY3XrrrXz7299OOebgwYMzNmhyDdL69espLS3lwgsvpKWlhfr6+mnjb7vtNr7+9a/P+HoCwUIx/MQLlH74HSnHSHo9I0+8sEAWCQTLi6KCU7GYS+keeJKhkV0oahiLuYQyx1aKC0+fsUrbbDHos5ElQ1qpbr3Ogiwl3v135J9CZ//jpIrIGPS55FiqZmPqNLQq+KlpZcjHkTCZMo9o9Tu309Z9P8HwsUa9ZqOduorrceSfnPF84xj0Vuz5mxkcfp1Uz63dtolDR38HqKhTInex/3eO7qaj92Fqyq5OOsew5xD7jvwIVVUmHKtwxE1770N09j025f1hzSqnuvRKHPkno6oK+5p/zOjYkYlrjvfUiioBDh/9LbKko6jgtBk8AwKBIBMy/hX5zGc+w7vf/e6UY+rq6igpKWFgYGDK8Ugkgsvlyqh+aMuWLQA0NzcndIy++MUvcsstt0z87Xa7qays1Dy/QLBQBFo6GH32VXLP2oykm15rpEaijL2+H9+B+W0WKBDMNYGQk1FPMyoKudaaKX135ppsSwWNNe+ikXdNeywS9eEa3U8k6ifLZMeWsyrjVKaZIMsGigtPp3foBZIvvmVK7ecmjYCMpwoqqkqylLqqkjfNWlzieLKzKrGYy+KqbOlT+VIjU5C7FpPBltFZ3QNP0dx5z7TjgdAQB1p/QWPNe2YVMaktuwaXez/RaJDpr4+MNauMUMQTjxQlfw66B56iquQyZNkw7bFINMD+5p+iqFGmP4/qNKfZ6+/hQOsvqK94C1nmYkbHUjf9ben6G478Uxbk/bxUUVUVt7eVQGgIvS4LW84qkWYomHMydowcDgcOhyPtuDPOOIORkRF27NjBySfHdnueeuopFEWZcHa0sGvXLgBKSxP/0JpMJkymuU0tEAjmi7Yv3k79j75KzsnrUCNRJL1u4r/eA820fvZbi22iQKCZUNhDU/tdOEf3MHkxmJfdQGPNu8gypf+tmAtUNUpb9z/jjsWxBajRkM/KqndgTyCPPddUlVzO4PDrRKP+BGlbMkZDLhXFFyU932wqZG39x9jX/GNUJqesxVIFyxxbKS+6cM7tliSJ+oq3sLf5R7OdCVk2UFdxXUZnhSNjtKRR22vuuBuHbTM63cx+67PMRZzUeCtN7b/H7W2d9IiEPf8kGqreyWsH/ltT490xXye52XXTHhtwvUxUySSFMPZ5aem6l/zctUzur5SIUHiEEc9h8nNXZ3CNEwfX6H6aO/+MP3hsw10nZ1FZcglVJZe9oR1GwdwiqWraxgsz5rLLLqO/v5+f//znhMNh3vOe93DKKadw9913A9Dd3c2FF17IXXfdxWmnnUZLSwt33303l19+OYWFhezZs4dPf/rTVFRU8Mwzz2i6ptvtJi8vj8dXX4JVN31XRyBYdCSJnNM3UXjV+RiKCgkPDeN66GncL7wOSvoi6Dc65m3XccvtQrVysYlEfbx+8Db8wUES7cIb9FZOXv2VGTVbzZRDbb+l37U96eNr6z+C3XbSvNvhC/RxsO3XjPk6OCYPrZJrrWd17fsxmwqTnquqUQ4d/R0Drpc5Xp3OaLBxUuPnMZvs82b70MgumtrvIhwZ01STczzWrEoaa96VNtUvGBqm3/UyofAoRn0uUSUYbzSbeinSWP0uSuxnZWRTIsb8XYz5OpDQkZe9En9ogBH3Ybr6n0BRQ2nP39jwOWw5K6cdP9D6SwaHd5B51C3WaykcSV8f3VjzbkrmoGdSOOKlz/kiA65XiER8ZJkclDrOxm47ac4jknOBc3Qv+5p/HP9r+vNb5jiflVWp09QFb2wiQR8v3PEWRkdHyc3NTTl2XhOy//SnP/Hxj3+cCy+8EFmWuf766/nRj47tSoXDYQ4fPozP5wPAaDTy73//mzvuuAOv10tlZSXXX389X/nKV+bTTIFgYVFVPNt34tm+c7EtWZas+s5f2XjNzex+wLbYpryh6R7YFt+9Tdx3JxLx0t77EA3V75xXO9zeoymdIoAjHX+mMG/jvO8qW8wlnLz6K3i8RyciE3nZDWRbKtKcCa1d9zHgeiX+19TnNBR2s/vIDzh1zdc1qaHNBLttEwV563CO7MEX6KWr/0ki0fTCDGWOrZQUnplWtlpVFZo7/0rP4DaAuLiAihZFPJAZHNmFwZCLLadhVgIU2VkVZGdV4Av0s7f5h/gCvZodQQkZiznxpoyqznRTS4nXE6WWegcw6LJTPq4Fr7+b3U3fj4tuxK4XCA0x7DlAXvZK1q/4f+h05llfZ65QVYWm9j+O/5VwTM/gNkod55Cdlf5zJhCkY14do4KCgonoUCJqamqYHLCqrKzUHBkSCAQCweIR67uTfCGnotDv3E595VvnrQ4gGB6J9w9Kn4Y07D5IQd7aebHjeHKsNRn1twlHvHQPbiNVD6NAcJChkZ0UFZw6FyYmRJb0OPI3A+DxtuEc3Uc6x6XMcV68SWxqYk7RsSaq4+IC2lBwje7GNbobnWyivOgiasqu1BTdUFWVYNiFooQxGfPRySZCYTe7Dn+HcMQbG6PRKbLnn4zRkJPw8RxrDUMjM9vsMhnzifi9KcfodZZZp9EpSpg9R+6I3/d0gYnRsWaaOv7I6tr3z+o6c8mw+yChSYIciZHpG3yeFVVvXxCbBCc2iyPhIxAIBIJli6pGCYVH0o5T1DChsJusOU4BG/N10dZ9Hy73Ps3nBEKDaebsxO1tASTyslcuaB8e1+g+DVLYEoPDO+bVMZpMqf3ceO1YcntyLFWanKJgaHgiUjRbYql3DxEIDrKq9n1JxSxUVaXfuZ2O/kfxB2ItQmTZSEnhmUiSLu4caI3yyBgMudRXvCXpiJLCszja888MHT4AieKC0+gdCidJS41RVXp5QtGHTBgYfi3eiyoZKgOuV6krv35BUmC14A/2kz6apuALJm8DIxBkgnCMBAKBQJAhMpKk19TXZs+RO9DrzNhtmym1n43RkDq/Ox1u71F2H/5uXP1LOzo5ceNXX6CfQ23/h8d3dMrxvOwGVtW+d06bqSYjoqkPkkok6ptXO8b8XbjHWpGQyLXWUZC7Pu58Hr8ojfXZqa/UtkN/LEVw7hgYfoUS+1lJoyit3X+nq//xKccUJUTP4LPE7kdbLZAk6SjKP43a8msxGW1JxxkNOTRWv5tDR39DbCGvxemS0MlGSuznUFSwhb3NP8Lr746n9qmMOwRVJZdRUXSxJntT4RzZTXonQ8U1uo9Sxzmzvt5cEEubTPdaSXPe30vwxkU4RgKBQCDICEmScNg2MzD8GukWgIG4itSYr4OjPf/EbCwkP3cNZY7zyLZk1lpBVVUOH/0tihohkyJ3SdJTkLduum0hJzsPfSuhYzI61syuQ9/m5DVfwaBPnD41V6QSZRhHQp43lT9/cJBDbb+JR8yOkZe9kuKC0xkYfmVKJMRiLqGh+ibysqe30EhEKOyeaFianvS1NjFkeoeeTegYjY4dmeYUHUN7LdDq2g+Sn7s6bVPXcYoLt2A05tHR+xAjnsNA7HWTZRNR5fj3mIws6Vhb/7H4/FZOXv1Vhj0HGRp+nagSJMtUREnhWZreH1pQlBBanIyoBhGKhaIgb72GGjAVu23zgtkkOLERjpFAIBAIMqai5GIGh1/LUINLJRAaonfoOXqHnqWm7BqqSy/XfLbb2xrvt5MJEmWO8xIubjt6H4k7RYkWXQrB8Chd/U9SW35NhtfMjILcNRgNeSnTnFQUSuxnz/m1g6HhmHMYmV7jMjrWQsAwxMmrv4bX34WihrCYS8ix1CZNYUuEwZCjQZxApiBvHdGoD39ggFBalTYFrz/xe6F74OkZKesdjy2nUbNTNE5+ziryc1YRjniIRPwYDLlISPQ6n6dn8BmCQSc6nYmigtMod1xAlrlo4lxJivWBKsidn1o4S1YpLvcBUjuH6rz2IcsUoyGXYvtZ9A09T2KnTsZkyJuojRMIZotwjAQCgUCQMTmWalbXfYCDbb+OL3ozcZFiY4/23I/FXIQj/xRNZ435OjVfYXxh7Mg/mbqK66c9rihh+p3bSb1IVOgZfIaasjdn5AhkiiTpWFH5dg60/iLZCIoKTiPHUjPn1+7oezRFvU3MOex3vphxf6LJFBWcSlv3P9KMUqivuAGLuSQuz3xn2nmTpU+5vS2zdIpk8nNWJRVa0IJBnzMl0lhRdCEV89CHKhNK7efS1f9EihESJkM++TmrFswmLaysfDuh0HA8rXNcaCUWWTQactnQ8OlZ118JBOMIx0ggEAgEM8KRfzK51np6h55nxHOQcHgMXzCTiI5ER++jmh0jWWOPFb3Oit22iRL72eRa6xI6NeHI2JRmsMmIRL2oagRJ0r7wGldbzcSZcuSfzJq6D9Hcec+UyJEk6Sl3nE9dxXVIkoSqqrhG99I98NSEWIQtp4HyogunpJWFwm56Bp+h3/USkYgXkyGfUsc5lBSeOSHHrKgR+pwvkM457B16ltrya2Ysd242FlJqP4feoedI7EDHHL9xKWxbdgOybEJRgilmjTVnTfyIVjuTp+1Vl12pcY7lg8VcTFXJ5XElx+ORkJBoqLl5yTVLlWUD61Z8nGH3QXqHnsMfHECvs1BUcBrFBactKXlxwfJHOEYCgUAgmDEmo42asiuBKxka2cn+lp9lcLbKmL+DYGgkZWH7ODaNcsU1ZW+mvGhryjFaF1OSpNPc9NI5soeugX8z4mkCVLItVZQXXUBxwRZNi01H/snYbZsYdh/EHxxCr8uiIG/dRDqXqqo0d/6ZnsGnmSxR7hzdh3N0D9WlV1JTdjVjvi52N30vLtYQW/hHoj6aO++he2Abmxo/h9GQSyTijdedpCYS9RFVQuhnsQBdUfV2FDVCv/NFQEZCigsMKBQXbKGh+iYgpjiYXsFOQqczU5qk4WtB3lp6Bp8jlcMnoUevtxCOuCccKRUFnWxiVe37yMtekflNLgNi0U+Zzv4npjie2VmV1Fe+BVtO4yJalxxJkinIW7tgkvuCNy7CMRIIBALBrPEHhxh2N83o3GjKyMAxskx2CvM24hzdS+JFb2zBXFx4etq59Los8nPWMOw5lGQuABlH/imanJrW7vvo7HuUyQ7LmK+Dw0d/i3N0D2tqP6BpHknSJRSKAOh3bo87RRxnc+z/23v/hTWrgubOe+K1U9OjIYHgIAdbf8XGxs+gk7U6OjK6WaQqjfm76Oh9mKHh1+NHVEymQvJzVlNRfDEWczEAfc4XOXz093GnKdlrEnuNN6z4ZFJRjDLH+XH1uWRIlBWdS13FDThHdjHiOYyqKuRYaigqOPWEjUAoSoSmjj/GnVOJ2Hs1ptCXY605YZ1BgSAThGMkEAgEghmjKBGOdPwpnpKVeQqOLBk0RYvGaax5N7ubbsfr7z5+JmTZwPoV/09zZKOq9DKGPQeTPi4BFcXpZZKdI3viThFMdVhijsnQ8A66rfVUFF+kya5EqKpKZ//jpFZtkzna88+UPaZUFEbGDuMc3Yss6cix1uHxHiWVc2i3bdIcNTueEU8Te4/cgaIqk66hEgw66Qu9SFHBqVjMxYz5Ojl89PeAGo8kTcdkyKes6HxK7WcldIqC4RHCYQ8GQw4N1TfR1H5XQhGGXGs9teXXxhvanqI5lXO5E3OKtsf/mipZ3jv0HJIksbLqxkWxTSBYKgjHSCAQCAQzZupiK9OCd5niwjMy6kFi0Fs5adWt8ejJMwSCTvS6LIoKtlBWdB5mo3ZpY1tOI6tq3sPh9t8fJyAhIUt6Vtd9gBxLVdp5ugb+zeRIUbIx5UUXzLh+IxwZwxfoSTNKiav2pZe81iJuMD5nqeNcjWOPO1OJcKD1F/GeU1PtUVFAVTnQ+gtOX/8dugaempRel2Q+NUpl8cXTnLTRsRaO9vyTEc+hiWO2nEZWVt4YcwJHdqOoESzmYsocF1BqP2tJF+tHo0EGhl+h3/ky4chYvEbrbAptG2bsoPoC/fFIUTJUegafpbLksgXp3SUQLFWEYyQQCASCGZF+sZUKGaMhh+rSzIvcdbKJMsdWyhxbZ3jtYxQXno4tdxV9Q8/jHmtlXMygxH6mpv5FqqrGa4pSO4XBkItgaHjGPWm09QCaGD2jayRGYnh0PwW5azI+c2hkF+GIJ8UIlXBkjKGRnbhG96RVkgtH3Hj9PVP6XzlH97K/+SfTHKoRzxFGPEdYW/8R1tR9EFVV51VZcK7wB4fY3fQ9giEn4w6uL9CHy72XXOsK1q/8xIxqvQZcL5POeY+Ne4WqkjfNxHSB4IRgaUmPCAQCgWDZ0O96iZn+jORlr+CkVbdmlEY3X5gMNqpLr2T9yk+wfuX/o7Lk0gybumpzRGYjIW005GLQZ6cdp9dnE1tQzxUqvUPPoSjpFfyOx+1tTRvhkNDh9rbGm/amZ/I4RQlzqO3/4k7R8a9BLHXv0NHfEFVCy8IpUtUoe4/cQTA0PH4k/t/Y+8btbeHw0d/OaO5Q2IOU5n0hSTKhcLr+UQLBiY1wjAQCwbJi1yN63tUQWGwzBEA47J7hElzGZLBllPa2VJEkiWxLFemcEYM+G5MxfxbXkSlznJ/2OhVFF2UgV62NqBIgmKJuKRkSkubgVXZWBenuTZJ0ZJmONUQdHN4xRXkvEdGon0HXa9qMWGSco3vxBwdIHtVRGRrZiT84mPHcRkNOyjRFAFVVMBpyM55bIDiREI6RQCBYdgTOv4/vf7Zvsc14w2PQ58wwaUthcPi1eGPR5U950QWk9gAkyhxbkaXZZa9XFl+SwgmTsOWsorL4ElZUvX3i2FwxE9vzclaikjoFUCWKLach7vQlfw4lZIryT5uQLgfw+NrTR6QkHWO+jozsXiycI7tIvyyTcI7syXjuooItaKkBLCo4LeO5BYITCeEYCQSCZclJbc2LbcIbnuJCbYutRKgoBGaw870UKS7YkkLZTCLXWkvlHNRt6HQmNjV8horii6ZIbet1VqpKL2f9iv+HLOspc5zHmroPTzRMnR0SFnMpRoMt4zML89ZjMuSTfKkhYzTYKMzbgCN/M3bbSSR25mSMhjxqK66dapmkAzWNa64CS6xhaTKiSoh0ITYJSbO8/WQs5mKKC88kubMsUeY4TwgvCN7wCPEFgUAgEMwIi7mUooItDLheYSYF/7JsnBM7okqQfudL9A49RyA4hF5noahwC2X28xakhkmSZFbXvp9caz1dA/+OF87H0ufKHFupLHkTuiT3qqoqbm8LzpE9KEoIS1YJRQWnoddZEo7X6czUV7yFmrI34w/0gyRhMZUgy1N/zscdDV+gj0h0DAkdOw9/h8wdWZXK4ktnVKMjSTrWrvgouw9/D0UJTamxkpCRZSPr6j86EfVZU/dB2nsfoXvgSSJR78Q4R8Gp1FVcj+k45yw/dzVd/Y+nsT5KvsbGwItNlqmYdIqCKspE36dMaah6JxLShLS+JEmoaqw+q9R+Lisq3zajeQWCEwnhGAkEAoFgxjRW3wwcU72KSS6nV1AzGQrmJKIRjnjZ3fQ9vP4uxheVkaiPjt6H6R54io0rP02OtWbW10mHJMlUFF9IedH5BMPDqKqKyWhLmYIWDI2wr+UnjPnaY3VBkoSqRmnpvJcVVe+g1H520nN1snGKOltimySsWaUTfxcVnBZ/ndI7seP9fyqKLqa48Iy045ORY6nm5DX/SVf/4/Q5X0RRQsiykZLCM6kovpgsk2OSvTpqyq6kquRNjPm7UNUIFnNJUtGJ/JzVZJmKCQQHkwhbyJhNhRTkrp2x/QtJqf0sOvoeSjlGr7NSmLdxRvPLsp7GmndRVXo5A65XCIc9GA15FBWcNmO1RIHgREM4RgKBQCCYMbJsYHXt+6guvZIB18txeWaJnsFnSRWdqCy5dMY9fSZz+Ohv8frH+/tMXvCrRKMB9jT/iNPX35ZRr6TZIEmyJlGJqBJkd9P3JtIJY319Yo8papim9rvQ68xz2ny0oeo/CAQHcXtbmBqZkJBlA7bsRsb8naiqQl52PWVF55Ofs2rW180y2VlZ9R+sqHwHihpClowpI1CyrCdXgzMrSTLrVnyMXYe/SzgyxtTXX8Kgt7Cu/mNz8j5bCMwmO9WlV9Le+6+kYxqq3zktOpgpWSYH1aVXzGoOgeBERThGAoFAIJg1FnMxNWVXT/xty2nkYOuv4kvVcQcp1kdlrnoQ+YMDOEdTFaKrRCJjDLheTRl9mSuCoREGXK8QDI9g1OfgKDiVLJM94dgB1yv4g/0p52vu/CuFeZuR5blZ2Ot0ZjY2fobB4R2x5riBQXS6LIoKTqXUcc60VLW5RpIkdFLMQVVVFV+gl3DEg9FgQ6/LYnD4dSJRLyZjPnbbZk39eizmEk5Z8zV6Bp+md+h5whEPBn0OJYVnUV60FaMhb17vaa6pLr0Kgz6b9t5/xZ29GGaTg/qKt2K3zSxaJBAItCEcI4FAIBDMOY78k8leV0nP4LM4R/egKlFyrNWUObaSl71yTvrKDLsPaBgl4RrdN6+OkaoqtHb/na7+f8euKMmoqkpbzz8oKTyLlVU3Ttvl7xt6kXT1JKHwCK/s/zJraj9AbnbdnNgqS3qKC7ZQXLBlTuabCUMjO2nrvh9foHfaY+MpfEfku6ktezPlRRelfa8YDbnUlF09xTFfrkiSRHnRBZQ6zmXU00Q44sNkzCfXWrcsejEJBMsd4RgJBAKBYF7IMhVRX3ED9RU3zMv8ihIhnXMBKqqavuZpNrR230dX/xPHrjjpen3OF1HVKKtq3zvlnFB4FC21PsGQk91Nt7Op8QvkWKvnzObFonfoeZra7yKZOtp4rZCihGjpuhdfoI+VVTcuWjqcqioEw8OggsmYv2B2yJKe/Nw1C3ItgUBwDOEYCQQCgWBZYs2qIL1zIWPNKp83G0Lh0YlIUWJU+l0vUVly2RQhBKMhj0DIiRbnSFEVWrv/xsaGz8ze4EUkHBnjSMfd8b+0qRj2Dj3H6FgrGxs+taBpcYoaobv/SboGniIUHgbAoM+lvOgCKosvRpYNC2aLQCBYOJZHRaJAIBAIBMdhy2nAbLSTupGpSqn9nHmzYcD1Kul7z8gMuF6acqzEfmba846hMOI5jD84NCMblwr9zu0zit75Aj3sOXLHvEf+xlHVKPtbfkZr930TThFAOOLmaM8/2dt8ZzxaKRAITjSEYyQQCASCZYkkyayqfW+8D07in7O6iuvnVYo4FHZrSq+Kpc4do6jgNLJMxTGZbo0EQsvbMfL6u5FSOrHJUPH6u3GO7p1zmxLRO/QcrtG9JHZcVUY8h+keeHJBbBEIBAuLcIwEAoFAsGzJy17BSY2fx5bTMOV4lqmYVbXvo7L4knm9vsGQg6qmb5pq0OdO+Vsnm9jY8BmsaXoRTUYvZ2Vs30yIKkEiEV+8+efcIUmzST+T4tG5+adr4Kk0I1S6B57S9LoLBILlhagxEggEAsGyJsdaw8aGWwiEnARDLvQ6CxZz2YKoeBXln0Jr199SjlFRKC48fdpxk9HG5lVf4nD77+l3vphyDpOhIG1D19kyNLKLzr7HcXubgfGamvOpKLoQnQbp7HQU5K2ld+iZGZ6txntkzS9RJYg/0Jd2XDA8TDjixWjImXebBALBwiEiRgKBQCA4ITAbC8nLXok1q3zBpI1NxnzKHOenGCHhyD8Fa1ZZ4kclidrya9DJZlLVSlWXXjGvimjtvQ+xv+Wn8eavMWI1NQ+w6/DtRKKBWV+jMG9DvCZsJvehrXHubMkktXG5NI4VCATaEZ9qgUAgEAhmwYrKt1BqPy/+l4zEsZonR/4prKp5T8rzTQYbGxo+hV53fKpcbI6q0isomcc+TKNjLRzt+Wf8r+PT51TG/F20dd836+tIksz6lZ/AoM8mtWBGIhRK7GfN2oZ0yLKBHEs1qe2TsJhL0ess826PQCBYWEQqnUAgWJZsf+8evr9tBbfcXrLYpgje4EiSjobqG6ksuZQB10sEQ6MYDNn8//buPzbKOsHj+OeZmXb6c2Zapp3+BMqPFRUFZKEXdD0wXdm73VXcDYm3lwj6hwmH5AgYj80ZjYkct5o1nIeJl1xW3L2Y9bxb1Ntd3FWOH6tWvIhd8Q5QsNBCmYEW+mtKf8zMc3+0lEXotAVmnpnneb+SSegzz/P0Awyd+fA83+83VLpYBXmV459Akq9whupv26JIx36d6zqohDmkwvxqVQbvvmya71RoO7N7dGHVq0so3P6B6qofuEp5m5yCvAotuvUZnW7/gyIdjRqK9SjHE1BuTrE6ew6NcZShKf7b5SuceV3fe6JqQt/WoeZ/TbKHqZrQ+AvPAsg+FCMAWat/2a/0wu4fUI6QEfK9QU2r/N41H+9x56u6fKmqy5dO6rj+wQ5FOvZrcOi8PO4ilZcuGvPWvavp7P0iSSkaljCH1Nt3UoHi2ZPKdjU5nkJNrfiOplZ857Ltp9vf17HWNxRPXBgpasNXryqmLNHsqT9KWxEpK1mkrt6jaju7R5cvIDz869CUJaqYMv7Vq94LJ9UTbZZkyF80WwV5oZRlBnBjUIwAAMhCppnQsZNv6NTILGoXy0RL+DcKBu7QnLqH5XZ5J3Km1AadoMrgXSovXaz2zk/VP9AhjztfwcB8eXNL0prDMAzNqv0rBYpv0snIe+qOfiVpeJKP6vJ7VF6yOGlJ6+uP6MjxV0aPu6jEd4tumr5a3pxAKuMDuA4UIwAAslDzqR2Xradj6tICqO2dn+rQVwnNnfU3457HX/QNtZ//JOlVI5eRo6KCmusLPAFuV65CpfUp/z7jMQxDZSULVVaycGTacnNCky30D3ao6chPNBTru+K5892H1XT4Od1x898rx1OYgtQArheTLwAAkGUGh7p1MvJukj1MdXQ1qafvxLjnqi6/Z5xb6VyqCN553eOLspVhGBOega7l9G9HStHV/jwT6h/sGL3CByDzUIwAAMgyZ89/MjoGZyyGXIp07B/3XP6imZpedf/oUV8/S2F+teqqH7i2oA4STwwq0tGoq5eii0y1nb3WtZwApBq30gEAkGWGYj0jY4riY+5jytRQrHtC55tW+V0V5lerNfy70bWMhhd4Xaqa8oYbssCr3Q0N9ShhxsbfL9athBmTy+AjGJBp+FcJAECWyc3xjTuTnCFDuR7/hM8ZDMxXMDBf8fiAEmZMHnc+i5hOwkTLo2F4Rta6ApBp+IkHAECWKStZOG5pMZVQaMqfTfrcbrdXOZ5CStEk5XgK5S/6hpItDmvINfJ3xxpIQCbipx4AABmoJ3pcx07+h7448QudOP0bDQyeH30ux1Os2tC9SY42FAzcoaKC2tQHxaiplX+p8aY/T/73BsBK3EoHAEAGicX69L9f/Ys6ew7JGPn/S1Omjre9rdqK5aqrekCGYWh61f0yTVOtkd/r4nTSw1NLJ1Reukg3TVtl6e/DiUp9t+gb0x7SFyf+bWTLxdsdDRmGW7fMeJSyCmQwihEAABnCNE19fmybunqHFwf9+jii1vA7crtyNa3yezIMl2bU/EDVoXt0puNjDQydV46nSOUli5SfV25FfGh4odoS3y06ffYP6o4ek2G4FCi6SRXBO5Wb47M6HoAkKEYAAGSIzp7D6uo9mnSfltM7VV3eIM/IYH9vTkC1FdyelUnycktVV33/+DsCyCiMMQIAIENEzu3XeG/NCXNIHZ1/TE8gAHAQihGArDbnuX/XvPs6rY4BXDPTNBWPD8g0Exoa6lHyBUIlyZjw+kTZLhbvU3vnp4qc26+evhar4wCwOW6lA5DVmnZ6tHXl51qmu6yOAkzK4FCPTp55T6fP7lMsHpUhl3JzSzU83XOymc1M5eYE0hPSIolETF+d+k+1nd0r808WTS3Kn6rZ0/5avsI6C9MBsCuuGAEAkGYDg+d14NCzag3/TrF4VNLwRAsDgx0ab7pnt8urKYHb05DSGqaZ0P999bJOnfnvy0qRJPVeaNUfjzyvnujxSZzPVP9Au6IX2hSL99/gtADshCtGAACk2eHjr2hgqEtX3jaXvBRJ0vSq++R2eVOSKxOc6/pcHV2fjfGsqYQZ19HW17Vgzt+Ne65Ix0dqCe9UX/9pSZJheBQqrdf0qvvkzS25gakB2AFXjAAASKO+/tPq7Dms8ccSSZJLhuHWxXVw6qofUHV5Q4oTWqutfa+Sfzwx1R09Nlp2xtLc9pYOH/+Z+vrDl440Ywp3NOrAoc3qH+y4MYEB2AZXjAAASKOunuTTcV/kK5qlUt+tisUvKC93ispLFyvHU5jidNbruxDWREpjX/8ZFeRVXvW5nuhxtZz+zchXX78Kl9BgrFdfnnhNt81ed11ZAdgLxQgAgDQyJ3C7nKSRhVy/m+I0mcc9sj7T+PuNfTvhqbN7Zch1xQK5lyR0rvug+gc7lJc75RpSArAjbqUDACCNfIUzJrCXIV/hrJRnyURlJQs1PDPf2DzuAvkLZ475fE/0WJJSdElvX+tk4wGwMYoRAABpVFRQI1/hTCV7CzZkqDLozCnoK4N3jUwuMXY5qgndK5crZ8znh8dljc8w+BgE4BJ+IgAAbMc04+rs+ULtnZ+qJ3pCpjmx29fSZU7dwyPjhb7+NuySZOim6avkzQ2kP1gGyM3x6fbZ60duqbtUjoyRP6uK4Lc0teI7Sc9R4rtV433EMQz3SEEFgGGMMQIA2IZpmjrdvk/H2/5LQ7Hu0e0FeZWaWbNSpf65Fqa7JN9broU3P6mW8E6F2z9QwhySJJX45mhqxV8oUHyTxQmt5Suaofq5/6BIx4dq7/xU8fiACvOrVVn25/IXjV9mqsqW6lTkvSSjuQyFpixxxGQWACaOYgQAsI2W8G91vO2tK7b39Yd18Og/69aZaxQMzE9/sKvw5pZo9tQfaWbNSg3FonK7vfK4862OlTFyPIWqCX1bNaFvT/rYfG9Qc+oe0aHmn8mQrhhvVFw4XbNqVt6gpADsgmIEALCF/oEOHW97e4xnh68dfHHiFyr1z5XLyJy3P5crx7G3zaVSeeli5edV6FTkPbV3fqqEGVO+t0xVZUtVGfxW0jFKAJwpc94ZAAC4DuGOD6SR6wNjGYr16FzXQQUDC9KWC9YpLpiqOXWPWB0DQJZg8gUAWa/xkc+0+4fvWx0DFuvrP61kpUgaHsDf1x9OTyAAQFahGAGwhcZHPtMLj/OB18lcrlyNt/6NKVMuIzc9gQAAWYViBACwhSn+edK4i3qamhK4LR1xAABZhmIEALCFYGCe8nKDGvutzaUpgfnK95anMxYAIEtQjAAAtmAYbt02+2+Vm+P7+jOSpOLCaZozfXXacwEAsgOz0gEAbKMgL6RFtz6jSMdHipz7SLFYVN7cUlUGv6VgyYKMmqYbAJBZeIcAANiKx52v6vJlqi5fZnUUAEAW4VY6AAAAAI5HMQIAAADgeBQjAAAAAI5HMQIAAADgeBQjAAAAAI5HMQIAAADgeBQjALZxR7DO6ggAACBLUYwA2MaHt/1Ue/4x3+oYAAAgC1GMANhK3xM/0bz7Oq2OAQAAsgzFCAAAAIDjUYwAAAAAOB7FCAAAAIDjUYwAAAAAOF7KitHmzZu1ZMkSFRQUKBAITOgY0zT11FNPqbKyUvn5+WpoaNCXX36ZqogAAAAAICmFxWhwcFArV67UmjVrJnzMc889pxdffFEvv/yy9u/fr8LCQi1fvlz9/f2pigkAAAAA8qTqxM8884wkafv27RPa3zRNbd26VU8++aTuv/9+SdLPf/5zhUIhvfnmm3rwwQdTFRUAAACAw2XMGKPm5maFw2E1NDSMbvP7/aqvr1djY+OYxw0MDKi7u/uyBwAAAABMRsYUo3A4LEkKhUKXbQ+FQqPPXc2WLVvk9/tHH7W1tSnNCQAAAMB+JlWMNm3aJMMwkj4OHz6cqqxX9eMf/1hdXV2jj9bW1rR+fwAAAADZb1JjjDZu3KjVq1cn3WfGjBnXFKSiokKSFIlEVFlZObo9Eolo/vz5Yx7n9Xrl9Xqv6XsCAAAAgDTJYlRWVqaysrKUBKmrq1NFRYV27do1WoS6u7u1f//+Sc1sB8DZmnZ6tOqJfm2wOggAAMgqKRtj1NLSoqamJrW0tCgej6upqUlNTU3q7e0d3WfOnDnasWOHJMkwDK1fv17PPvus3n77bR08eFAPPfSQqqqqtGLFilTFBGBD/ct+pRceH3tsIgAAwNelbLrup556Sq+++uro1wsWLJAk7d69W0uXLpUkHTlyRF1dXaP7PPHEE4pGo3r00UfV2dmpu+66S++8847y8vJSFROATd0RrJN0weoYAAAgSximaZpWh7iRuru75ff79fub71WhO8fqOAAssuTgRi3dRDECAMDJYgN9+mDrSnV1dcnn8yXdN2Om6wYAAAAAq1CMAAAAADgexQgAAACA41GMAAAAADgexQgAAACA41GMAAAAADgexQgAAACA41GMAAAAADgexQiALZn/867VEQAAQBahGAGwpcZHPtPuH75vdQwAAJAlKEYAbOvCGwc0775Oq2MAAIAsQDECAAAA4HgUIwAAAACORzECAAAA4HgUIwAAAACORzECAAAA4HgUIwAAAACORzECAAAA4HgUIwAAAACORzECAAAA4HgUIwAAAACORzECAAAA4HgUIwC21bTTo39aUml1DAAAkAUoRgBs7cPbfqoXHg9bHQMAAGQ4ihEA21vQfNTqCAAAIMNRjAAAAAA4HsUIAAAAgONRjAAAAAA4HsUIAAAAgONRjAAAAAA4HsUIAAAAgONRjAAAAAA4HsUIAAAAgONRjAAAAAA4HsUIgO1deOOA1REAAECGoxgBsL2mnR7t/uH7VscAAAAZjGIEwBEaH/lMLzwetjoGAADIUBQjAAAAAI5HMQIAAADgeBQjAAAAAI5HMQIAAADgeBQjAAAAAI5HMQIAAADgeBQjAAAAAI5HMQIAAADgeBQjAAAAAI5HMQIAAADgeBQjAI7Rv+xXeuHxsNUxAABABqIYAXCUBc1HrY4AAAAyEMUIAAAAgONRjAAAAAA4HsUIAAAAgONRjAAAAAA4HsUIAAAAgONRjAAAAAA4nsfqADeaaZqSpGg8ZnESAJmou3dAsYE+q2MAAIA0uPief7EjJGOYE9kri5w8eVK1tbVWxwAAAACQIVpbW1VTU5N0H9sVo0Qioba2NhUXF8swDKvjwGa6u7tVW1ur1tZW+Xw+q+PAAXjNId14zSHdeM0hlUzTVE9Pj6qqquRyJR9FZLtb6Vwu17htELhePp+PH95IK15zSDdec0g3XnNIFb/fP6H9mHwBAAAAgONRjAAAAAA4HsUImASv16unn35aXq/X6ihwCF5zSDdec0g3XnPIFLabfAEAAAAAJosrRgAAAAAcj2IEAAAAwPEoRgAAAAAcj2IEAAAAwPEoRsA12rx5s5YsWaKCggIFAgGr48CGXnrpJU2fPl15eXmqr6/Xxx9/bHUk2Ni+ffv0/e9/X1VVVTIMQ2+++abVkWBzW7Zs0aJFi1RcXKzy8nKtWLFCR44csToWHIxiBFyjwcFBrVy5UmvWrLE6Cmzo9ddf14YNG/T000/rwIEDmjdvnpYvX64zZ85YHQ02FY1GNW/ePL300ktWR4FD7N27V2vXrtVHH32kd999V0NDQ7r33nsVjUatjgaHYrpu4Dpt375d69evV2dnp9VRYCP19fVatGiRtm3bJklKJBKqra3VunXrtGnTJovTwe4Mw9COHTu0YsUKq6PAQc6ePavy8nLt3btXd999t9Vx4EBcMQKADDM4OKhPPvlEDQ0No9tcLpcaGhrU2NhoYTIASJ2uri5JUmlpqcVJ4FQUIwDIMO3t7YrH4wqFQpdtD4VCCofDFqUCgNRJJBJav3697rzzTs2dO9fqOHAoihHwJzZt2iTDMJI+Dh8+bHVMAABsZe3atfr888/1y1/+0uoocDCP1QGATLJx40atXr066T4zZsxITxg4VjAYlNvtViQSuWx7JBJRRUWFRakAIDUee+wx/frXv9a+fftUU1NjdRw4GMUI+BNlZWUqKyuzOgYcLjc3VwsXLtSuXbtGB78nEgnt2rVLjz32mLXhAOAGMU1T69at044dO7Rnzx7V1dVZHQkORzECrlFLS4vOnTunlpYWxeNxNTU1SZJmzZqloqIia8Mh623YsEGrVq3SN7/5TS1evFhbt25VNBrVww8/bHU02FRvb6+OHj06+nVzc7OamppUWlqqqVOnWpgMdrV27Vq99tpreuutt1RcXDw6htLv9ys/P9/idHAipusGrtHq1av16quvXrF99+7dWrp0afoDwXa2bdum559/XuFwWPPnz9eLL76o+vp6q2PBpvbs2aNly5ZdsX3VqlXavn17+gPB9gzDuOr2V155Zdzb2oFUoBgBAAAAcDxmpQMAAADgeBQjAAAAAI5HMQIAAADgeBQjAAAAAI5HMQIAAADgeBQjAAAAAI5HMQIAAADgeBQjAAAAAI5HMQIAAADgeBQjAAAAAI5HMQIAAADgeBQjAAAAAI73/y8hBfJl0BtXAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWKPTnqttESs"
      },
      "source": [
        "Several remarks are due here:\n",
        "1. There are different ways to convert time to depth leading to slightly different ways how to implement backpropagation. It is very efficient to have the 'continuous-time' formulas in mind.\n",
        "2. It might be also useful to think about controls being themselves trajectories of a stochastic process, for intance a generic semi-martingale.\n",
        "3. Take controls being $ d $ Brownian motion (with respect to Stratonovich integration) and assume a H&ouml;rmander condition valid for the particle system starting on the training data set, that is the ODE on $ \\mathbb{R}^{mL} $ given by\n",
        "$$\n",
        "dZ_t = \\sum_{i=1}^d W_i(Z_t) \\circ dB^i(t) \\, ,\n",
        "$$\n",
        "where\n",
        "$$\n",
        "(W_i((x_l)))_l:=V_i(x_l)\n",
        "$$\n",
        "for $ l \\in L $ and $ i = 1,\\ldots,d $. Then with probability one a (possibly only small) neighborhood of $ (x_l) $ is hit by the particle system. A random search can exhibit such trajectories and whence solve the supervised learning problem by simulation.\n",
        "4. One can also view this as a *deterministic mean field control* problem, where the controls *only* depend on the behavior of all particles."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzQctJxVtESs"
      },
      "source": [
        "In the sequel the same implementation done in Keras."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZXj8ybjtESs"
      },
      "source": [
        "modelann = Sequential()\n",
        "layer1=Dense(10,activation='sigmoid', input_shape=(2,), kernel_initializer=initializers.RandomNormal(0,0.4))\n",
        "layer1.trainable=True\n",
        "modelann.add(layer1)\n",
        "layer2=Dense(1,activation='sigmoid',kernel_initializer=initializers.RandomNormal(0,4.0))\n",
        "layer2.trainable=True\n",
        "modelann.add(layer2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-D9wm2idtESs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a586abc6-8730-433f-a483-5b63a9afa9eb"
      },
      "source": [
        "modelann.compile(optimizer='adam',\n",
        "              loss='mse',\n",
        "              metrics=['accuracy'])\n",
        "modelann.fit(x=X,y=y, epochs=1000,batch_size=50)\n",
        "#x = modelann.evaluate(X, y)\n",
        "#print('\\n',x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "10/10 [==============================] - 1s 3ms/step - loss: 0.5301 - accuracy: 0.2540\n",
            "Epoch 2/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.5182 - accuracy: 0.2960\n",
            "Epoch 3/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.5077 - accuracy: 0.3420\n",
            "Epoch 4/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.4980 - accuracy: 0.3800\n",
            "Epoch 5/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.4893 - accuracy: 0.4180\n",
            "Epoch 6/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.4815 - accuracy: 0.4420\n",
            "Epoch 7/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.4741 - accuracy: 0.4660\n",
            "Epoch 8/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.4674 - accuracy: 0.4820\n",
            "Epoch 9/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.4604 - accuracy: 0.4960\n",
            "Epoch 10/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.4528 - accuracy: 0.4980\n",
            "Epoch 11/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.4443 - accuracy: 0.4980\n",
            "Epoch 12/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.4335 - accuracy: 0.5000\n",
            "Epoch 13/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.4209 - accuracy: 0.5000\n",
            "Epoch 14/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.4050 - accuracy: 0.5000\n",
            "Epoch 15/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.3853 - accuracy: 0.5000\n",
            "Epoch 16/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.3619 - accuracy: 0.5000\n",
            "Epoch 17/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.3351 - accuracy: 0.5000\n",
            "Epoch 18/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.3075 - accuracy: 0.4980\n",
            "Epoch 19/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.2799 - accuracy: 0.4980\n",
            "Epoch 20/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.2570 - accuracy: 0.5340\n",
            "Epoch 21/1000\n",
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            "Epoch 726/1000\n",
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            "Epoch 750/1000\n",
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            "Epoch 751/1000\n",
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            "Epoch 755/1000\n",
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            "Epoch 762/1000\n",
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            "Epoch 768/1000\n",
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            "Epoch 786/1000\n",
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            "Epoch 787/1000\n",
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            "Epoch 791/1000\n",
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            "Epoch 792/1000\n",
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            "Epoch 793/1000\n",
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            "Epoch 794/1000\n",
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            "Epoch 800/1000\n",
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            "Epoch 805/1000\n",
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            "Epoch 809/1000\n",
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            "Epoch 811/1000\n",
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            "Epoch 813/1000\n",
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            "Epoch 814/1000\n",
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            "Epoch 815/1000\n",
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            "Epoch 816/1000\n",
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            "Epoch 820/1000\n",
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            "Epoch 834/1000\n",
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            "Epoch 835/1000\n",
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            "Epoch 836/1000\n",
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            "Epoch 838/1000\n",
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            "Epoch 840/1000\n",
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            "Epoch 841/1000\n",
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            "Epoch 847/1000\n",
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            "Epoch 848/1000\n",
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            "Epoch 849/1000\n",
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            "Epoch 850/1000\n",
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            "Epoch 851/1000\n",
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            "Epoch 852/1000\n",
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            "Epoch 853/1000\n",
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            "Epoch 854/1000\n",
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            "Epoch 855/1000\n",
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            "Epoch 856/1000\n",
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            "Epoch 857/1000\n",
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            "Epoch 858/1000\n",
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            "Epoch 859/1000\n",
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            "Epoch 860/1000\n",
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            "Epoch 861/1000\n",
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            "Epoch 862/1000\n",
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            "Epoch 863/1000\n",
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            "Epoch 864/1000\n",
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            "Epoch 865/1000\n",
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            "Epoch 866/1000\n",
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            "Epoch 867/1000\n",
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            "Epoch 868/1000\n",
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            "Epoch 869/1000\n",
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            "Epoch 870/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0943 - accuracy: 0.8740\n",
            "Epoch 871/1000\n",
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            "Epoch 872/1000\n",
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            "Epoch 873/1000\n",
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            "Epoch 874/1000\n",
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            "Epoch 875/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0942 - accuracy: 0.8720\n",
            "Epoch 876/1000\n",
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            "Epoch 877/1000\n",
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            "Epoch 878/1000\n",
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            "Epoch 879/1000\n",
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            "Epoch 880/1000\n",
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            "Epoch 881/1000\n",
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            "Epoch 882/1000\n",
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            "Epoch 883/1000\n",
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            "Epoch 884/1000\n",
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            "Epoch 885/1000\n",
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            "Epoch 886/1000\n",
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            "Epoch 887/1000\n",
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            "Epoch 888/1000\n",
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            "Epoch 889/1000\n",
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            "Epoch 890/1000\n",
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            "Epoch 891/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0939 - accuracy: 0.8740\n",
            "Epoch 892/1000\n",
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            "Epoch 893/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0939 - accuracy: 0.8760\n",
            "Epoch 894/1000\n",
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            "Epoch 895/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0938 - accuracy: 0.8760\n",
            "Epoch 896/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0938 - accuracy: 0.8760\n",
            "Epoch 897/1000\n",
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            "Epoch 898/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0939 - accuracy: 0.8760\n",
            "Epoch 899/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0937 - accuracy: 0.8760\n",
            "Epoch 900/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0937 - accuracy: 0.8760\n",
            "Epoch 901/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0937 - accuracy: 0.8760\n",
            "Epoch 902/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0937 - accuracy: 0.8760\n",
            "Epoch 903/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0937 - accuracy: 0.8760\n",
            "Epoch 904/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0937 - accuracy: 0.8760\n",
            "Epoch 905/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0936 - accuracy: 0.8760\n",
            "Epoch 906/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0936 - accuracy: 0.8760\n",
            "Epoch 907/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0936 - accuracy: 0.8760\n",
            "Epoch 908/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0935 - accuracy: 0.8760\n",
            "Epoch 909/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0935 - accuracy: 0.8760\n",
            "Epoch 910/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0935 - accuracy: 0.8760\n",
            "Epoch 911/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0935 - accuracy: 0.8760\n",
            "Epoch 912/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0934 - accuracy: 0.8760\n",
            "Epoch 913/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0934 - accuracy: 0.8760\n",
            "Epoch 914/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0934 - accuracy: 0.8760\n",
            "Epoch 915/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0933 - accuracy: 0.8760\n",
            "Epoch 916/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0934 - accuracy: 0.8760\n",
            "Epoch 917/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0933 - accuracy: 0.8740\n",
            "Epoch 918/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0934 - accuracy: 0.8740\n",
            "Epoch 919/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0932 - accuracy: 0.8740\n",
            "Epoch 920/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0932 - accuracy: 0.8760\n",
            "Epoch 921/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0932 - accuracy: 0.8760\n",
            "Epoch 922/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0932 - accuracy: 0.8760\n",
            "Epoch 923/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0931 - accuracy: 0.8760\n",
            "Epoch 924/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0931 - accuracy: 0.8760\n",
            "Epoch 925/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0931 - accuracy: 0.8760\n",
            "Epoch 926/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0930 - accuracy: 0.8740\n",
            "Epoch 927/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0930 - accuracy: 0.8760\n",
            "Epoch 928/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0930 - accuracy: 0.8760\n",
            "Epoch 929/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0930 - accuracy: 0.8760\n",
            "Epoch 930/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0929 - accuracy: 0.8760\n",
            "Epoch 931/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0929 - accuracy: 0.8740\n",
            "Epoch 932/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0928 - accuracy: 0.8740\n",
            "Epoch 933/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0928 - accuracy: 0.8740\n",
            "Epoch 934/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0928 - accuracy: 0.8740\n",
            "Epoch 935/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0928 - accuracy: 0.8740\n",
            "Epoch 936/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0928 - accuracy: 0.8740\n",
            "Epoch 937/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0927 - accuracy: 0.8760\n",
            "Epoch 938/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0928 - accuracy: 0.8740\n",
            "Epoch 939/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0926 - accuracy: 0.8740\n",
            "Epoch 940/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0926 - accuracy: 0.8740\n",
            "Epoch 941/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.0926 - accuracy: 0.8740\n",
            "Epoch 942/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0925 - accuracy: 0.8740\n",
            "Epoch 943/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0925 - accuracy: 0.8760\n",
            "Epoch 944/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.0924 - accuracy: 0.8760\n",
            "Epoch 945/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0924 - accuracy: 0.8740\n",
            "Epoch 946/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0924 - accuracy: 0.8740\n",
            "Epoch 947/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0923 - accuracy: 0.8740\n",
            "Epoch 948/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0923 - accuracy: 0.8740\n",
            "Epoch 949/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.0923 - accuracy: 0.8740\n",
            "Epoch 950/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0923 - accuracy: 0.8740\n",
            "Epoch 951/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0922 - accuracy: 0.8740\n",
            "Epoch 952/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0922 - accuracy: 0.8740\n",
            "Epoch 953/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0922 - accuracy: 0.8740\n",
            "Epoch 954/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0923 - accuracy: 0.8760\n",
            "Epoch 955/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0921 - accuracy: 0.8760\n",
            "Epoch 956/1000\n",
            "10/10 [==============================] - 0s 2ms/step - loss: 0.0920 - accuracy: 0.8740\n",
            "Epoch 957/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0920 - accuracy: 0.8760\n",
            "Epoch 958/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0919 - accuracy: 0.8740\n",
            "Epoch 959/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0920 - accuracy: 0.8760\n",
            "Epoch 960/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0918 - accuracy: 0.8760\n",
            "Epoch 961/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0918 - accuracy: 0.8740\n",
            "Epoch 962/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0918 - accuracy: 0.8760\n",
            "Epoch 963/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0918 - accuracy: 0.8760\n",
            "Epoch 964/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0917 - accuracy: 0.8760\n",
            "Epoch 965/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0918 - accuracy: 0.8740\n",
            "Epoch 966/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0916 - accuracy: 0.8760\n",
            "Epoch 967/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0916 - accuracy: 0.8740\n",
            "Epoch 968/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0916 - accuracy: 0.8720\n",
            "Epoch 969/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0915 - accuracy: 0.8740\n",
            "Epoch 970/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0915 - accuracy: 0.8720\n",
            "Epoch 971/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0914 - accuracy: 0.8760\n",
            "Epoch 972/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0914 - accuracy: 0.8760\n",
            "Epoch 973/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0913 - accuracy: 0.8760\n",
            "Epoch 974/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0913 - accuracy: 0.8760\n",
            "Epoch 975/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0913 - accuracy: 0.8740\n",
            "Epoch 976/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0912 - accuracy: 0.8740\n",
            "Epoch 977/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0911 - accuracy: 0.8760\n",
            "Epoch 978/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0911 - accuracy: 0.8760\n",
            "Epoch 979/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0911 - accuracy: 0.8780\n",
            "Epoch 980/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0910 - accuracy: 0.8760\n",
            "Epoch 981/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0910 - accuracy: 0.8760\n",
            "Epoch 982/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0909 - accuracy: 0.8760\n",
            "Epoch 983/1000\n",
            "10/10 [==============================] - 0s 4ms/step - loss: 0.0909 - accuracy: 0.8740\n",
            "Epoch 984/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0909 - accuracy: 0.8760\n",
            "Epoch 985/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0908 - accuracy: 0.8740\n",
            "Epoch 986/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0907 - accuracy: 0.8760\n",
            "Epoch 987/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0907 - accuracy: 0.8740\n",
            "Epoch 988/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0906 - accuracy: 0.8760\n",
            "Epoch 989/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0906 - accuracy: 0.8740\n",
            "Epoch 990/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0905 - accuracy: 0.8740\n",
            "Epoch 991/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0905 - accuracy: 0.8740\n",
            "Epoch 992/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0904 - accuracy: 0.8760\n",
            "Epoch 993/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0904 - accuracy: 0.8760\n",
            "Epoch 994/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0904 - accuracy: 0.8760\n",
            "Epoch 995/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0903 - accuracy: 0.8740\n",
            "Epoch 996/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0902 - accuracy: 0.8760\n",
            "Epoch 997/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0901 - accuracy: 0.8760\n",
            "Epoch 998/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0901 - accuracy: 0.8740\n",
            "Epoch 999/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0901 - accuracy: 0.8740\n",
            "Epoch 1000/1000\n",
            "10/10 [==============================] - 0s 3ms/step - loss: 0.0900 - accuracy: 0.8760\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7944f3ec0a00>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uc9gRLoQtESs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "2f01450c-da50-4473-8103-e9851df2c528"
      },
      "source": [
        "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
        "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
        "h = 0.01\n",
        "# Generate a grid of points with distance h between them\n",
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
        "                    np.arange(y_min, y_max, h))\n",
        "# Predict the function value for the whole gid\n",
        "Z= modelann.predict(np.c_[xx.ravel(),yy.ravel()])\n",
        "Z[Z>=0.5]=1\n",
        "Z[Z<0.5]=0\n",
        "\n",
        "Z = Z.reshape(xx.shape)\n",
        "# Plot the contour and training examples\n",
        "plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n",
        "plt.scatter(X[:, 0], X[:, 1], s=40,  c=y, cmap=plt.cm.BuGn)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5512/5512 [==============================] - 9s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7944f3ec16f0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "6pAiuQEJtESt"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}