Artifacts_of_DR_on_Hybrid_CNN_for_HSIC.ipynb

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    "colab": {
      "name": "Artifacts of DR on Hybrid CNN for HSIC",
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      "collapsed_sections": []
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      "name": "python3",
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  "cells": [
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      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "pqziN5QJGAHP",
        "outputId": "b835dcee-d560-4e66-860c-4bcbfbbf6931"
      },
      "source": [
        "# Colab Requirements\r\n",
        "!pip install spectral\r\n",
        "import spectral\r\n",
        "from google.colab import drive, files\r\n",
        "## Mounting Google Drive\r\n",
        "drive.mount('/content/drive')\r\n",
        "## Basics\r\n",
        "import gc\r\n",
        "gc.collect()\r\n",
        "import warnings\r\n",
        "warnings.filterwarnings('ignore')\r\n",
        "import time\r\n",
        "import numpy\r\n",
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "from PIL import Image\r\n",
        "from operator import truediv\r\n",
        "import scipy.io as sio\r\n",
        "import os\r\n",
        "import seaborn as sns\r\n",
        "## Ploting\r\n",
        "from plotly.offline import iplot, init_notebook_mode\r\n",
        "import plotly.graph_objs as go\r\n",
        "from matplotlib.pyplot import cm\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "init_notebook_mode(connected=True)\r\n",
        "%matplotlib inline\r\n",
        "## Sklearn \r\n",
        "import sklearn as sk\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score\r\n",
        "## Dimensionality Reduction Methods\r\n",
        "from sklearn.decomposition import PCA, IncrementalPCA, KernelPCA, SparsePCA, TruncatedSVD\r\n",
        "## Deep Model\r\n",
        "import keras, h5py\r\n",
        "from keras.layers import Input, Conv2D, Conv3D, Flatten, Dense, Reshape, Dropout\r\n",
        "#from plotly.offline import iplot, init_notebook_mode\r\n",
        "from keras.losses import categorical_crossentropy\r\n",
        "from keras.models import Sequential, Model\r\n",
        "from keras.utils import np_utils\r\n",
        "from keras.optimizers import Adam"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting spectral\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ce/06/6a89035cde4eac3ed94e1888f850af653386e8ee827edc72ffc8e445bcb7/spectral-0.22.1-py3-none-any.whl (212kB)\n",
            "\r\u001b[K     |█▌                              | 10kB 13.9MB/s eta 0:00:01\r\u001b[K     |███                             | 20kB 11.8MB/s eta 0:00:01\r\u001b[K     |████▋                           | 30kB 7.8MB/s eta 0:00:01\r\u001b[K     |██████▏                         | 40kB 6.6MB/s eta 0:00:01\r\u001b[K     |███████▊                        | 51kB 4.3MB/s eta 0:00:01\r\u001b[K     |█████████▎                      | 61kB 4.8MB/s eta 0:00:01\r\u001b[K     |██████████▊                     | 71kB 4.9MB/s eta 0:00:01\r\u001b[K     |████████████▎                   | 81kB 5.2MB/s eta 0:00:01\r\u001b[K     |█████████████▉                  | 92kB 5.5MB/s eta 0:00:01\r\u001b[K     |███████████████▍                | 102kB 5.2MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 112kB 5.2MB/s eta 0:00:01\r\u001b[K     |██████████████████▌             | 122kB 5.2MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 133kB 5.2MB/s eta 0:00:01\r\u001b[K     |█████████████████████▌          | 143kB 5.2MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 153kB 5.2MB/s eta 0:00:01\r\u001b[K     |████████████████████████▋       | 163kB 5.2MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▏     | 174kB 5.2MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▊    | 184kB 5.2MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▎  | 194kB 5.2MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▊ | 204kB 5.2MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 215kB 5.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from spectral) (1.19.5)\n",
            "Installing collected packages: spectral\n",
            "Successfully installed spectral-0.22.1\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ieCRzWBoIAUA"
      },
      "source": [
        "# **Downloading and Loading Hyperspectral Image Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mScdIYsGGr4k"
      },
      "source": [
        "## Load HSI Dataset\r\n",
        "def LoadHSIData(method):\r\n",
        "    ## Please reload if downloading error appear.\r\n",
        "    HSI_url = ['http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat',\r\n",
        "           'http://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat']\r\n",
        "    for i in range(2):\r\n",
        "        file_name = HSI_url[i].split('/')[-1]\r\n",
        "        data_path = os.path.join(os.getcwd(),'/content/drive/My Drive/Colab'+str(file_name))\r\n",
        "        if os.path.exists(data_path) == False:\r\n",
        "            print(\"Downloading data file from %s to %s\" % (HSI_url[i], data_path))\r\n",
        "            urllib.request.urlretrieve(url=HSI_url[i], filename=data_path)\r\n",
        "            print(str(file_name)+\" is Successfully downloaded\")\r\n",
        "        else:\r\n",
        "            print(str(file_name) + \" already exist\")\r\n",
        "    data_path = os.path.join(os.getcwd(),'/content/drive/My Drive/Colab')\r\n",
        "    HSI = sio.loadmat(os.path.join(data_path, 'Indian_pines_corrected.mat'))['indian_pines_corrected']\r\n",
        "    GT = sio.loadmat(os.path.join(data_path, 'Indian_pines_gt.mat'))['indian_pines_gt']\r\n",
        "    return HSI, GT"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGvsHa96IsQK"
      },
      "source": [
        "# **Dimensional Reduction Methods**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ME0Lds_eHejK"
      },
      "source": [
        "def DLMethod(method, HSI, NC = 75):\r\n",
        "    RHSI = np.reshape(HSI, (-1, HSI.shape[2]))\r\n",
        "    if method == 'PCA': ## PCA\r\n",
        "        pca = PCA(n_components = NC, whiten = True)\r\n",
        "        RHSI = pca.fit_transform(RHSI)\r\n",
        "        RHSI = np.reshape(RHSI, (HSI.shape[0], HSI.shape[1], NC))\r\n",
        "    elif method == 'iPCA': ## Incremental PCA\r\n",
        "        n_batches = 256\r\n",
        "        inc_pca = IncrementalPCA(n_components = NC)\r\n",
        "        for X_batch in np.array_split(RHSI, n_batches):\r\n",
        "          inc_pca.partial_fit(X_batch)\r\n",
        "        X_ipca = inc_pca.transform(RHSI)\r\n",
        "        RHSI = np.reshape(X_ipca, (HSI.shape[0], HSI.shape[1], NC))\r\n",
        "    elif method == 'KPCA': ## Kernel PCA\r\n",
        "        kpca = KernelPCA(kernel = \"rbf\", n_components = NC, gamma = None, \r\n",
        "                         fit_inverse_transform = True, random_state = 2019, \r\n",
        "                         n_jobs=1)\r\n",
        "        kpca.fit(RHSI)\r\n",
        "        RHSI = kpca.transform(RHSI)\r\n",
        "        RHSI = np.reshape(RHSI, (HSI.shape[0], HSI.shape[1], NC))\r\n",
        "    elif method == 'SPCA': ## Sparse PCA\r\n",
        "        sparsepca = SparsePCA(n_components = NC, alpha=0.0001, random_state=2019, n_jobs=-1)\r\n",
        "        sparsepca.fit(RHSI)\r\n",
        "        RHSI = sparsepca.transform(RHSI)\r\n",
        "        RHSI = np.reshape(RHSI, (HSI.shape[0], HSI.shape[1], NC))\r\n",
        "    elif method == 'SVD': ## Singular Value Decomposition\r\n",
        "        SVD_ = TruncatedSVD(n_components = NC,algorithm = 'randomized', \r\n",
        "                            random_state = 2019, n_iter=5)\r\n",
        "        SVD_.fit(RHSI)\r\n",
        "        RHSI = SVD_.transform(RHSI)\r\n",
        "        RHSI = np.reshape(RHSI, (HSI.shape[0], HSI.shape[1], NC))\r\n",
        "    return RHSI"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CsQ5W00WIywa"
      },
      "source": [
        "# **Train Test Split**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6elRPtHbIntC"
      },
      "source": [
        "def TrTeSplit(HSI, GT, testRatio, randomState=345):\r\n",
        "    Tr, Te, TrC, TeC = train_test_split(HSI, GT, test_size = testRatio, \r\n",
        "                                        random_state = randomState,\r\n",
        "                                        stratify = GT)\r\n",
        "    return Tr, Te, TrC, TeC"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xHAVNF2iIx6s"
      },
      "source": [
        "# **Padding and Pacthes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xdnkdbl8IqW_"
      },
      "source": [
        "def ZeroPad(HSI, margin = 2):\r\n",
        "    NHSI = np.zeros((HSI.shape[0] + 2 * margin, HSI.shape[1] + 2* margin, HSI.shape[2]))\r\n",
        "    x_offset = margin\r\n",
        "    y_offset = margin\r\n",
        "    NHSI[x_offset:HSI.shape[0] + x_offset, y_offset:HSI.shape[1] + y_offset, :] = HSI\r\n",
        "    return NHSI\r\n",
        "\r\n",
        "def ImageCubes(HSI, GT, WS = 5, removeZeroLabels = True):\r\n",
        "    margin = int((WS - 1) / 2)\r\n",
        "    zeroPaddedX = ZeroPad(HSI, margin = margin)\r\n",
        "    # split patches\r\n",
        "    patchesData = np.zeros((HSI.shape[0] * HSI.shape[1], WS, WS, HSI.shape[2]))\r\n",
        "    patchesLabels = np.zeros((HSI.shape[0] * HSI.shape[1]))\r\n",
        "    patchIndex = 0\r\n",
        "    for r in range(margin, zeroPaddedX.shape[0] - margin):\r\n",
        "        for c in range(margin, zeroPaddedX.shape[1] - margin):\r\n",
        "            patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1]   \r\n",
        "            patchesData[patchIndex, :, :, :] = patch\r\n",
        "            patchesLabels[patchIndex] = GT[r-margin, c-margin]\r\n",
        "            patchIndex = patchIndex + 1\r\n",
        "    if removeZeroLabels:\r\n",
        "        patchesData = patchesData[patchesLabels>0,:,:,:]\r\n",
        "        patchesLabels = patchesLabels[patchesLabels>0]\r\n",
        "        patchesLabels -= 1\r\n",
        "    return patchesData, patchesLabels"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sg6cFp5kJQQS"
      },
      "source": [
        "# **Computing Accuracies and Assigning Class Labels**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Uuj7WisJOv9"
      },
      "source": [
        "def ClassificationReports(TeC, Te_Pred):\r\n",
        "    Te_Pred = np.argmax(Te_Pred, axis=1)\r\n",
        "    target_names = ['Alfalfa', 'Corn-notill', 'Corn-mintill', 'Corn','Grass-pasture',\r\n",
        "                    'Grass-trees', 'Grass-mowed', 'Hay-windrowed', 'Oats', \r\n",
        "                    'Soybean-notill', 'Soybean-mintill', 'Soybean-clean', \r\n",
        "                    'Wheat', 'Woods', 'Buildings', 'Stone-Steel']\r\n",
        "    classification = classification_report(np.argmax(TeC, axis=1), Te_Pred, target_names = target_names)\r\n",
        "    oa = accuracy_score(np.argmax(TeC, axis=1), Te_Pred)\r\n",
        "    confusion = confusion_matrix(np.argmax(TeC, axis=1), Te_Pred)\r\n",
        "    counter = confusion.shape[0]\r\n",
        "    list_diag = np.diag(confusion)\r\n",
        "    list_raw_sum = np.sum(confusion, axis=1)\r\n",
        "    each_acc = np.nan_to_num(truediv(list_diag, list_raw_sum))\r\n",
        "    aa = np.mean(each_acc)\r\n",
        "    kappa = cohen_kappa_score(np.argmax(TeC, axis=1), Te_Pred)\r\n",
        "    return classification, confusion, oa*100, each_acc*100, aa*100, kappa*100, target_names"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DeiCTMJeJvN_"
      },
      "source": [
        "# **Writing Accuracy in CSV file**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4-G2Y7UJp3R"
      },
      "source": [
        "def CSVResults(file_name, classification, confusion, Tr_Time, Te_Time, DL_Time, kappa, oa, aa):\r\n",
        "    classification = str(classification)\r\n",
        "    confusion = str(confusion)\r\n",
        "    with open(file_name, 'w') as CSV_file:\r\n",
        "      CSV_file.write('{} Tr_Time'.format(Tr_Time))\r\n",
        "      CSV_file.write('\\n')\r\n",
        "      CSV_file.write('{} Te_Time'.format(Te_Time))\r\n",
        "      CSV_file.write('\\n')\r\n",
        "      CSV_file.write('{} DL_Time'.format(DL_Time))\r\n",
        "      CSV_file.write('\\n')\r\n",
        "      CSV_file.write('{} Kappa accuracy (%)'.format(kappa))\r\n",
        "      CSV_file.write('\\n')\r\n",
        "      CSV_file.write('{} Overall accuracy (%)'.format(oa))\r\n",
        "      CSV_file.write('\\n')\r\n",
        "      CSV_file.write('{} Average accuracy (%)'.format(aa))\r\n",
        "      CSV_file.write('\\n')\r\n",
        "      CSV_file.write('\\n')\r\n",
        "      CSV_file.write('{}'.format(classification))\r\n",
        "      CSV_file.write('\\n')\r\n",
        "      CSV_file.write('{}'.format(confusion))\r\n",
        "    return CSV_file"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uu118b1EKBOk"
      },
      "source": [
        "# **Plot and Save Confusion Matrix**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1WC-_5EiKAgV"
      },
      "source": [
        "def Conf_Mat(Te_Pred, TeC, target_names):\r\n",
        "    plt.rcParams.update({'font.size': 12})\r\n",
        "    Te_Pred = np.argmax(Te_Pred, axis=1)\r\n",
        "    confusion = confusion_matrix(np.argmax(TeC, axis=1), Te_Pred, labels=np.unique(np.argmax(TeC, axis=1)))\r\n",
        "    cm_sum = np.sum(confusion, axis=1, keepdims=True)\r\n",
        "    cm_perc = confusion / cm_sum.astype(float) * 100\r\n",
        "    annot = np.empty_like(confusion).astype(str)\r\n",
        "    nrows, ncols = confusion.shape\r\n",
        "    for l in range(nrows):\r\n",
        "      for m in range(ncols):\r\n",
        "        c = confusion[l, m]\r\n",
        "        p = cm_perc[l, m]\r\n",
        "        if l == m:\r\n",
        "          s = cm_sum[l]\r\n",
        "          annot[l, m] = '%.1f%%\\n%d/%d' % (p, c, s)\r\n",
        "        elif c == 0:\r\n",
        "          annot[l, m] = ''\r\n",
        "        else:\r\n",
        "          annot[l, m] = '%.1f%%\\n%d' % (p, c)\r\n",
        "    cm = pd.DataFrame(confusion, index=np.unique(target_names), columns=np.unique(target_names))\r\n",
        "    return cm, annot"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E5-AUvTaKWTC"
      },
      "source": [
        "#**Global Parameters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IpWngqcoKbW0"
      },
      "source": [
        "HSID = \"IP\"\r\n",
        "Num_Classes = 16\r\n",
        "DLM = \"PCA\"     ## \"PCA\", \"iPCA\", \"SPCA\", \"KPCA\", \"SVD\"\r\n",
        "WS = 11         ## 9, 11, 13, 15, 17, 19, 21, 23, 25\r\n",
        "TeRatio = 0.50  ## Percentage of Test Samples\r\n",
        "VeRatio = 0.50  ## Percentage of Validation Samples\r\n",
        "k = 15          ## 15, 17, 19, 21, 23, 25, 27, 30 -- # of Dimensions\r\n",
        "adam = Adam(lr = 0.001, decay = 1e-06)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wK4JS5jJKqXi"
      },
      "source": [
        "# **Main Function for Dimensional Reduction and Creating Patchs for CNN**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "978YR2RAKoom",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00aa7ef9-2a4f-4529-f049-516ad9679ad2"
      },
      "source": [
        "HSI, GT = LoadHSIData(HSID)\r\n",
        "## Reduce the Dimensionality\r\n",
        "start = time.time()\r\n",
        "RDHSI = DLMethod(DLM, HSI, NC = k)\r\n",
        "end = time.time()\r\n",
        "DL_Time = end - start\r\n",
        "## Create Image Cubes for Model Building\r\n",
        "CRDHSI, CGT = ImageCubes(RDHSI, GT, WS = WS)\r\n",
        "## Split Train and Test sets (for 2D and 3D Models)\r\n",
        "Tr, Te, TrC, TeC = TrTeSplit(CRDHSI, CGT, TeRatio)\r\n",
        "## Split Train and Validation (for 2D and 3D Models)\r\n",
        "Tr, Va, TrC, VaC = TrTeSplit(Tr, TrC, VeRatio)\r\n",
        "## Reshape Train, Validation, and Test sets\r\n",
        "Tr = Tr.reshape(-1, WS, WS, k, 1)\r\n",
        "TrC = np_utils.to_categorical(TrC)\r\n",
        "Va = Va.reshape(-1, WS, WS, k, 1)\r\n",
        "VaC = np_utils.to_categorical(VaC)\r\n",
        "Te = Te.reshape(-1, WS, WS, k, 1)\r\n",
        "TeC = np_utils.to_categorical(TeC)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Indian_pines_corrected.mat already exist\n",
            "Indian_pines_gt.mat already exist\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4x_crC4Sil6R"
      },
      "source": [
        "# 3-D CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TVRwWB2hiu24",
        "outputId": "4b8353ce-0cc9-49b0-b335-367bbdfecfd5"
      },
      "source": [
        "input_layer = Input((WS, WS, k, 1))\r\n",
        "conv_layer1 = Conv3D(filters=8, kernel_size=(3, 3, 7), activation='relu')(input_layer)\r\n",
        "conv_layer2 = Conv3D(filters=16, kernel_size=(3, 3, 5), activation='relu')(conv_layer1)\r\n",
        "conv_layer3 = Conv3D(filters=32, kernel_size=(3, 3, 3), activation='relu')(conv_layer2)\r\n",
        "conv_layer4 = Conv3D(filters=64, kernel_size=(3, 3, 3), activation='relu')(conv_layer3)\r\n",
        "flatten_layer = Flatten()(conv_layer4)\r\n",
        "dense_layer1 = Dense(units=256, activation='relu')(flatten_layer)\r\n",
        "dense_layer2 = Dense(units=128, activation='relu')(dense_layer1)\r\n",
        "output_layer = Dense(units = Num_Classes, activation='softmax')(dense_layer2)\r\n",
        "model = Model(inputs=input_layer, outputs=output_layer)\r\n",
        "model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\r\n",
        "## Training Time and Fit the Model \r\n",
        "start = time.time()\r\n",
        "history1 = model.fit(x=Tr, y=TrC, batch_size=256, epochs=50, validation_data=(Va, VaC))\r\n",
        "end = time.time()\r\n",
        "Tr_Time = end - start\r\n",
        "## Prediction Model\r\n",
        "start = time.time()\r\n",
        "Te_Pred = model.predict(Te)\r\n",
        "end = time.time()\r\n",
        "Te_Time = end - start\r\n",
        "## Prediction and Computing the Accuacy\r\n",
        "classification,confusion,oa,each_acc,aa,kappa,target_names = ClassificationReports(TeC, Te_Pred)\r\n",
        "print(classification)\r\n",
        "## Writing Results in CSV File\r\n",
        "file_name = str(WS)+str(DLM)+str(k)+\"_Classification_Report_3-D_CNN.csv\"\r\n",
        "CSV_file = CSVResults(file_name, classification, confusion, Tr_Time, Te_Time, DL_Time, kappa, oa, aa)\r\n",
        "files.download(file_name)\r\n",
        "## Writing Confusion Matrix\r\n",
        "file_name = str(WS)+str(DLM)+str(k)+\"_Confusion_Matrix_3-D_CNN.png\"\r\n",
        "cm, annot = Conf_Mat(Te_Pred, TeC, target_names)\r\n",
        "cm.index.name = 'Actual'\r\n",
        "cm.columns.name = 'Predicted'\r\n",
        "fig, ax = plt.subplots(figsize=(15,15))\r\n",
        "sns.heatmap(cm, cmap= \"Spectral\", annot=annot, fmt='', ax=ax, linewidths=0.5)\r\n",
        "plt.savefig(file_name, dpi=500)\r\n",
        "files.download(file_name)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "11/11 [==============================] - 8s 636ms/step - loss: 2.5581 - accuracy: 0.2015 - val_loss: 1.9172 - val_accuracy: 0.4145\n",
            "Epoch 2/50\n",
            "11/11 [==============================] - 6s 584ms/step - loss: 1.7945 - accuracy: 0.4449 - val_loss: 1.5259 - val_accuracy: 0.5113\n",
            "Epoch 3/50\n",
            "11/11 [==============================] - 6s 584ms/step - loss: 1.3898 - accuracy: 0.5224 - val_loss: 1.2218 - val_accuracy: 0.5496\n",
            "Epoch 4/50\n",
            "11/11 [==============================] - 6s 579ms/step - loss: 1.1082 - accuracy: 0.5964 - val_loss: 1.1024 - val_accuracy: 0.5687\n",
            "Epoch 5/50\n",
            "11/11 [==============================] - 6s 580ms/step - loss: 0.9106 - accuracy: 0.6550 - val_loss: 0.7838 - val_accuracy: 0.7166\n",
            "Epoch 6/50\n",
            "11/11 [==============================] - 6s 581ms/step - loss: 0.7091 - accuracy: 0.7508 - val_loss: 0.9366 - val_accuracy: 0.6721\n",
            "Epoch 7/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.8592 - accuracy: 0.7085 - val_loss: 0.7095 - val_accuracy: 0.7393\n",
            "Epoch 8/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.5727 - accuracy: 0.8003 - val_loss: 0.5030 - val_accuracy: 0.8154\n",
            "Epoch 9/50\n",
            "11/11 [==============================] - 6s 589ms/step - loss: 0.4448 - accuracy: 0.8407 - val_loss: 0.4165 - val_accuracy: 0.8493\n",
            "Epoch 10/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.3073 - accuracy: 0.8925 - val_loss: 0.8966 - val_accuracy: 0.7631\n",
            "Epoch 11/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.6768 - accuracy: 0.8070 - val_loss: 0.7979 - val_accuracy: 0.7077\n",
            "Epoch 12/50\n",
            "11/11 [==============================] - 6s 575ms/step - loss: 0.7217 - accuracy: 0.7550 - val_loss: 0.7261 - val_accuracy: 0.7627\n",
            "Epoch 13/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.6016 - accuracy: 0.8087 - val_loss: 0.4680 - val_accuracy: 0.8447\n",
            "Epoch 14/50\n",
            "11/11 [==============================] - 6s 583ms/step - loss: 0.4273 - accuracy: 0.8497 - val_loss: 0.4594 - val_accuracy: 0.8482\n",
            "Epoch 15/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.3053 - accuracy: 0.8897 - val_loss: 0.3576 - val_accuracy: 0.8774\n",
            "Epoch 16/50\n",
            "11/11 [==============================] - 6s 579ms/step - loss: 0.2474 - accuracy: 0.9206 - val_loss: 0.2746 - val_accuracy: 0.9106\n",
            "Epoch 17/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.1442 - accuracy: 0.9569 - val_loss: 0.2147 - val_accuracy: 0.9247\n",
            "Epoch 18/50\n",
            "11/11 [==============================] - 6s 579ms/step - loss: 0.1152 - accuracy: 0.9590 - val_loss: 0.1792 - val_accuracy: 0.9454\n",
            "Epoch 19/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.0740 - accuracy: 0.9798 - val_loss: 0.2300 - val_accuracy: 0.9290\n",
            "Epoch 20/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.0826 - accuracy: 0.9735 - val_loss: 1.7799 - val_accuracy: 0.7057\n",
            "Epoch 21/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 1.6901 - accuracy: 0.7238 - val_loss: 0.9722 - val_accuracy: 0.6206\n",
            "Epoch 22/50\n",
            "11/11 [==============================] - 6s 575ms/step - loss: 0.7325 - accuracy: 0.7382 - val_loss: 0.5546 - val_accuracy: 0.8119\n",
            "Epoch 23/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.4459 - accuracy: 0.8371 - val_loss: 0.3630 - val_accuracy: 0.8774\n",
            "Epoch 24/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.2824 - accuracy: 0.9048 - val_loss: 0.3209 - val_accuracy: 0.8923\n",
            "Epoch 25/50\n",
            "11/11 [==============================] - 6s 575ms/step - loss: 0.2736 - accuracy: 0.9139 - val_loss: 0.3037 - val_accuracy: 0.9106\n",
            "Epoch 26/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.1841 - accuracy: 0.9405 - val_loss: 0.2681 - val_accuracy: 0.9114\n",
            "Epoch 27/50\n",
            "11/11 [==============================] - 6s 575ms/step - loss: 0.1525 - accuracy: 0.9516 - val_loss: 0.2831 - val_accuracy: 0.9266\n",
            "Epoch 28/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.1092 - accuracy: 0.9652 - val_loss: 0.1855 - val_accuracy: 0.9469\n",
            "Epoch 29/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.0633 - accuracy: 0.9848 - val_loss: 0.1672 - val_accuracy: 0.9551\n",
            "Epoch 30/50\n",
            "11/11 [==============================] - 6s 574ms/step - loss: 0.0400 - accuracy: 0.9923 - val_loss: 0.1410 - val_accuracy: 0.9614\n",
            "Epoch 31/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.0279 - accuracy: 0.9950 - val_loss: 0.1485 - val_accuracy: 0.9594\n",
            "Epoch 32/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.0188 - accuracy: 0.9967 - val_loss: 0.1856 - val_accuracy: 0.9489\n",
            "Epoch 33/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.0505 - accuracy: 0.9853 - val_loss: 0.4326 - val_accuracy: 0.9192\n",
            "Epoch 34/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.3432 - accuracy: 0.9416 - val_loss: 0.2714 - val_accuracy: 0.9176\n",
            "Epoch 35/50\n",
            "11/11 [==============================] - 6s 579ms/step - loss: 0.1386 - accuracy: 0.9609 - val_loss: 0.1969 - val_accuracy: 0.9301\n",
            "Epoch 36/50\n",
            "11/11 [==============================] - 6s 579ms/step - loss: 0.0717 - accuracy: 0.9799 - val_loss: 0.1561 - val_accuracy: 0.9590\n",
            "Epoch 37/50\n",
            "11/11 [==============================] - 6s 593ms/step - loss: 0.0388 - accuracy: 0.9938 - val_loss: 0.2000 - val_accuracy: 0.9387\n",
            "Epoch 38/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.2218 - accuracy: 0.9248 - val_loss: 0.2400 - val_accuracy: 0.9274\n",
            "Epoch 39/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.1023 - accuracy: 0.9637 - val_loss: 0.2394 - val_accuracy: 0.9333\n",
            "Epoch 40/50\n",
            "11/11 [==============================] - 6s 579ms/step - loss: 0.0635 - accuracy: 0.9801 - val_loss: 0.1700 - val_accuracy: 0.9543\n",
            "Epoch 41/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.0403 - accuracy: 0.9881 - val_loss: 0.1388 - val_accuracy: 0.9641\n",
            "Epoch 42/50\n",
            "11/11 [==============================] - 6s 579ms/step - loss: 0.0171 - accuracy: 0.9955 - val_loss: 0.1318 - val_accuracy: 0.9684\n",
            "Epoch 43/50\n",
            "11/11 [==============================] - 6s 580ms/step - loss: 0.0104 - accuracy: 0.9979 - val_loss: 0.1385 - val_accuracy: 0.9672\n",
            "Epoch 44/50\n",
            "11/11 [==============================] - 6s 584ms/step - loss: 0.0062 - accuracy: 0.9995 - val_loss: 0.1446 - val_accuracy: 0.9649\n",
            "Epoch 45/50\n",
            "11/11 [==============================] - 6s 584ms/step - loss: 0.0305 - accuracy: 0.9964 - val_loss: 0.1526 - val_accuracy: 0.9692\n",
            "Epoch 46/50\n",
            "11/11 [==============================] - 6s 583ms/step - loss: 0.0159 - accuracy: 0.9968 - val_loss: 0.1350 - val_accuracy: 0.9668\n",
            "Epoch 47/50\n",
            "11/11 [==============================] - 6s 580ms/step - loss: 0.0068 - accuracy: 0.9995 - val_loss: 0.1404 - val_accuracy: 0.9684\n",
            "Epoch 48/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.1362 - val_accuracy: 0.9699\n",
            "Epoch 49/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.1388 - val_accuracy: 0.9719\n",
            "Epoch 50/50\n",
            "11/11 [==============================] - 6s 580ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1397 - val_accuracy: 0.9738\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "        Alfalfa       1.00      0.96      0.98        23\n",
            "    Corn-notill       0.99      0.96      0.98       714\n",
            "   Corn-mintill       0.94      0.99      0.96       415\n",
            "           Corn       0.98      0.93      0.96       118\n",
            "  Grass-pasture       0.99      1.00      0.99       242\n",
            "    Grass-trees       1.00      0.99      0.99       365\n",
            "    Grass-mowed       1.00      1.00      1.00        14\n",
            "  Hay-windrowed       1.00      1.00      1.00       239\n",
            "           Oats       1.00      0.70      0.82        10\n",
            " Soybean-notill       0.99      0.95      0.97       486\n",
            "Soybean-mintill       0.98      0.99      0.99      1228\n",
            "  Soybean-clean       0.99      0.97      0.98       297\n",
            "          Wheat       0.99      1.00      1.00       102\n",
            "          Woods       0.99      0.99      0.99       633\n",
            "      Buildings       0.93      0.99      0.96       193\n",
            "    Stone-Steel       0.96      1.00      0.98        46\n",
            "\n",
            "       accuracy                           0.98      5125\n",
            "      macro avg       0.98      0.96      0.97      5125\n",
            "   weighted avg       0.98      0.98      0.98      5125\n",
            "\n"
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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TOIps0jPLD49"
      },
      "source": [
        "# **Hybrid Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqQgzpfoLCIl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8ec351d0-52e5-4c06-bc0c-ce5d4c78cf58"
      },
      "source": [
        "input_layer = Input((WS, WS, k, 1))\r\n",
        "conv_layer1 = Conv3D(filters=8, kernel_size=(3, 3, 7), activation='relu')(input_layer)\r\n",
        "conv_layer2 = Conv3D(filters=16, kernel_size=(3, 3, 5), activation='relu')(conv_layer1)\r\n",
        "conv_layer3 = Conv3D(filters=32, kernel_size=(3, 3, 3), activation='relu')(conv_layer2)\r\n",
        "##conv_layer4 = Conv3D(filters=64, kernel_size=(3, 3, 1), activation='relu')(conv_layer3)\r\n",
        "## Conv3D with kernel_size=(3, 3, 1) can be used as well instead to reshape and Con2D.\r\n",
        "conv3d_shape = conv_layer3.shape\r\n",
        "conv_layer3 = Reshape((conv3d_shape[1], conv3d_shape[2], conv3d_shape[3]*conv3d_shape[4]))(conv_layer3)\r\n",
        "conv_layer4 = Conv2D(filters=64, kernel_size=(3,3), activation='relu')(conv_layer3)\r\n",
        "flatten_layer = Flatten()(conv_layer4)\r\n",
        "dense_layer1 = Dense(units=256, activation = 'relu')(flatten_layer)\r\n",
        "dense_layer1 = Dropout(0.4)(dense_layer1)\r\n",
        "dense_layer2 = Dense(units = 128, activation = 'relu')(dense_layer1)\r\n",
        "dense_layer2 = Dropout(0.4)(dense_layer2)\r\n",
        "output_layer = Dense(units = Num_Classes, activation = 'softmax')(dense_layer2)\r\n",
        "# Define the Model with Input and Output Layers\r\n",
        "model = Model(inputs = input_layer, outputs = output_layer)\r\n",
        "## Compiling Hybrid CNN\r\n",
        "model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])\r\n",
        "## Fit the Model\r\n",
        "start = time.time()\r\n",
        "history2 = model.fit(x = Tr, y = TrC, batch_size = 256, epochs = 50, validation_data = (Va, VaC))\r\n",
        "end = time.time()\r\n",
        "Tr_Time = end - start\r\n",
        "## Prediction Model\r\n",
        "start = time.time()\r\n",
        "Te_Pred = model.predict(Te)\r\n",
        "end = time.time()\r\n",
        "Te_Time = end - start\r\n",
        "## Prediction and Computing the Accuacy\r\n",
        "classification,confusion,oa,each_acc,aa,kappa,target_names = ClassificationReports(TeC, Te_Pred)\r\n",
        "print(classification)\r\n",
        "## Writing Results in CSV File\r\n",
        "file_name = str(WS)+str(DLM)+str(k)+\"_Classification_Report.csv\"\r\n",
        "CSV_file = CSVResults(file_name, classification, confusion, Tr_Time, Te_Time, DL_Time, kappa, oa, aa)\r\n",
        "files.download(file_name)\r\n",
        "## Writing Confusion Matrix\r\n",
        "file_name = str(WS)+str(DLM)+str(k)+\"_Confusion_Matrix.png\"\r\n",
        "cm, annot = Conf_Mat(Te_Pred, TeC, target_names)\r\n",
        "cm.index.name = 'Actual'\r\n",
        "cm.columns.name = 'Predicted'\r\n",
        "fig, ax = plt.subplots(figsize=(15,15))\r\n",
        "sns.heatmap(cm, cmap= \"Spectral\", annot=annot, fmt='', ax=ax, linewidths=0.5)\r\n",
        "plt.savefig(file_name, dpi=500)\r\n",
        "files.download(file_name)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "11/11 [==============================] - 7s 597ms/step - loss: 2.5834 - accuracy: 0.1757 - val_loss: 1.7910 - val_accuracy: 0.4684\n",
            "Epoch 2/50\n",
            "11/11 [==============================] - 6s 585ms/step - loss: 1.6801 - accuracy: 0.4613 - val_loss: 1.4192 - val_accuracy: 0.4879\n",
            "Epoch 3/50\n",
            "11/11 [==============================] - 6s 579ms/step - loss: 1.4516 - accuracy: 0.4790 - val_loss: 1.2127 - val_accuracy: 0.5601\n",
            "Epoch 4/50\n",
            "11/11 [==============================] - 6s 579ms/step - loss: 1.2266 - accuracy: 0.5637 - val_loss: 1.0020 - val_accuracy: 0.6327\n",
            "Epoch 5/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.9823 - accuracy: 0.6301 - val_loss: 0.8499 - val_accuracy: 0.6823\n",
            "Epoch 6/50\n",
            "11/11 [==============================] - 6s 581ms/step - loss: 0.9548 - accuracy: 0.6833 - val_loss: 0.7400 - val_accuracy: 0.7252\n",
            "Epoch 7/50\n",
            "11/11 [==============================] - 6s 581ms/step - loss: 0.7288 - accuracy: 0.7507 - val_loss: 0.6339 - val_accuracy: 0.7654\n",
            "Epoch 8/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.6158 - accuracy: 0.7789 - val_loss: 0.4564 - val_accuracy: 0.8263\n",
            "Epoch 9/50\n",
            "11/11 [==============================] - 6s 582ms/step - loss: 0.4528 - accuracy: 0.8389 - val_loss: 0.4536 - val_accuracy: 0.8368\n",
            "Epoch 10/50\n",
            "11/11 [==============================] - 7s 607ms/step - loss: 0.3890 - accuracy: 0.8652 - val_loss: 0.3723 - val_accuracy: 0.8825\n",
            "Epoch 11/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.3082 - accuracy: 0.8919 - val_loss: 0.3326 - val_accuracy: 0.8891\n",
            "Epoch 12/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.2319 - accuracy: 0.9206 - val_loss: 0.3054 - val_accuracy: 0.9118\n",
            "Epoch 13/50\n",
            "11/11 [==============================] - 6s 573ms/step - loss: 0.2341 - accuracy: 0.9222 - val_loss: 0.1900 - val_accuracy: 0.9372\n",
            "Epoch 14/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.1228 - accuracy: 0.9586 - val_loss: 0.1862 - val_accuracy: 0.9461\n",
            "Epoch 15/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.1100 - accuracy: 0.9645 - val_loss: 0.2240 - val_accuracy: 0.9305\n",
            "Epoch 16/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.2267 - accuracy: 0.9396 - val_loss: 0.2444 - val_accuracy: 0.9294\n",
            "Epoch 17/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.1367 - accuracy: 0.9600 - val_loss: 0.1651 - val_accuracy: 0.9461\n",
            "Epoch 18/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.0941 - accuracy: 0.9708 - val_loss: 0.2618 - val_accuracy: 0.9336\n",
            "Epoch 19/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.2020 - accuracy: 0.9482 - val_loss: 0.3030 - val_accuracy: 0.9063\n",
            "Epoch 20/50\n",
            "11/11 [==============================] - 6s 574ms/step - loss: 0.1722 - accuracy: 0.9489 - val_loss: 0.2059 - val_accuracy: 0.9309\n",
            "Epoch 21/50\n",
            "11/11 [==============================] - 6s 579ms/step - loss: 0.1093 - accuracy: 0.9706 - val_loss: 0.1610 - val_accuracy: 0.9493\n",
            "Epoch 22/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.0670 - accuracy: 0.9773 - val_loss: 0.1819 - val_accuracy: 0.9563\n",
            "Epoch 23/50\n",
            "11/11 [==============================] - 6s 575ms/step - loss: 0.0514 - accuracy: 0.9872 - val_loss: 0.1209 - val_accuracy: 0.9692\n",
            "Epoch 24/50\n",
            "11/11 [==============================] - 6s 575ms/step - loss: 0.0300 - accuracy: 0.9916 - val_loss: 0.1456 - val_accuracy: 0.9657\n",
            "Epoch 25/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.0227 - accuracy: 0.9942 - val_loss: 0.1176 - val_accuracy: 0.9692\n",
            "Epoch 26/50\n",
            "11/11 [==============================] - 6s 580ms/step - loss: 0.0275 - accuracy: 0.9912 - val_loss: 0.1480 - val_accuracy: 0.9668\n",
            "Epoch 27/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.0177 - accuracy: 0.9942 - val_loss: 0.1356 - val_accuracy: 0.9660\n",
            "Epoch 28/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.0164 - accuracy: 0.9966 - val_loss: 0.1306 - val_accuracy: 0.9680\n",
            "Epoch 29/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.0148 - accuracy: 0.9966 - val_loss: 0.1378 - val_accuracy: 0.9696\n",
            "Epoch 30/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.0121 - accuracy: 0.9988 - val_loss: 0.1287 - val_accuracy: 0.9711\n",
            "Epoch 31/50\n",
            "11/11 [==============================] - 6s 575ms/step - loss: 0.0166 - accuracy: 0.9944 - val_loss: 0.1319 - val_accuracy: 0.9731\n",
            "Epoch 32/50\n",
            "11/11 [==============================] - 6s 575ms/step - loss: 0.0111 - accuracy: 0.9961 - val_loss: 0.1120 - val_accuracy: 0.9746\n",
            "Epoch 33/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.0166 - accuracy: 0.9947 - val_loss: 0.1334 - val_accuracy: 0.9703\n",
            "Epoch 34/50\n",
            "11/11 [==============================] - 6s 581ms/step - loss: 0.0116 - accuracy: 0.9968 - val_loss: 0.1650 - val_accuracy: 0.9676\n",
            "Epoch 35/50\n",
            "11/11 [==============================] - 6s 575ms/step - loss: 0.0229 - accuracy: 0.9957 - val_loss: 0.1682 - val_accuracy: 0.9676\n",
            "Epoch 36/50\n",
            "11/11 [==============================] - 6s 580ms/step - loss: 0.0200 - accuracy: 0.9955 - val_loss: 0.1561 - val_accuracy: 0.9692\n",
            "Epoch 37/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.0171 - accuracy: 0.9956 - val_loss: 0.1307 - val_accuracy: 0.9715\n",
            "Epoch 38/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.0102 - accuracy: 0.9988 - val_loss: 0.1309 - val_accuracy: 0.9719\n",
            "Epoch 39/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.0109 - accuracy: 0.9983 - val_loss: 0.1406 - val_accuracy: 0.9703\n",
            "Epoch 40/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.0152 - accuracy: 0.9969 - val_loss: 0.1746 - val_accuracy: 0.9629\n",
            "Epoch 41/50\n",
            "11/11 [==============================] - 6s 579ms/step - loss: 0.0383 - accuracy: 0.9923 - val_loss: 0.2139 - val_accuracy: 0.9547\n",
            "Epoch 42/50\n",
            "11/11 [==============================] - 6s 578ms/step - loss: 0.0749 - accuracy: 0.9854 - val_loss: 0.1297 - val_accuracy: 0.9719\n",
            "Epoch 43/50\n",
            "11/11 [==============================] - 6s 585ms/step - loss: 0.0196 - accuracy: 0.9937 - val_loss: 0.1054 - val_accuracy: 0.9778\n",
            "Epoch 44/50\n",
            "11/11 [==============================] - 6s 577ms/step - loss: 0.0112 - accuracy: 0.9992 - val_loss: 0.1165 - val_accuracy: 0.9762\n",
            "Epoch 45/50\n",
            "11/11 [==============================] - 6s 575ms/step - loss: 0.0043 - accuracy: 0.9995 - val_loss: 0.1106 - val_accuracy: 0.9785\n",
            "Epoch 46/50\n",
            "11/11 [==============================] - 6s 580ms/step - loss: 0.0074 - accuracy: 0.9990 - val_loss: 0.0989 - val_accuracy: 0.9797\n",
            "Epoch 47/50\n",
            "11/11 [==============================] - 6s 592ms/step - loss: 0.0046 - accuracy: 0.9995 - val_loss: 0.1036 - val_accuracy: 0.9817\n",
            "Epoch 48/50\n",
            "11/11 [==============================] - 6s 576ms/step - loss: 0.0025 - accuracy: 0.9998 - val_loss: 0.1144 - val_accuracy: 0.9797\n",
            "Epoch 49/50\n",
            "11/11 [==============================] - 6s 575ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9805\n",
            "Epoch 50/50\n",
            "11/11 [==============================] - 6s 573ms/step - loss: 0.0019 - accuracy: 0.9998 - val_loss: 0.1142 - val_accuracy: 0.9801\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "        Alfalfa       0.96      1.00      0.98        23\n",
            "    Corn-notill       0.97      0.97      0.97       714\n",
            "   Corn-mintill       0.94      0.99      0.97       415\n",
            "           Corn       0.96      0.97      0.97       118\n",
            "  Grass-pasture       1.00      0.98      0.99       242\n",
            "    Grass-trees       0.99      1.00      1.00       365\n",
            "    Grass-mowed       1.00      1.00      1.00        14\n",
            "  Hay-windrowed       0.99      1.00      0.99       239\n",
            "           Oats       1.00      0.70      0.82        10\n",
            " Soybean-notill       0.99      0.95      0.97       486\n",
            "Soybean-mintill       0.99      0.99      0.99      1228\n",
            "  Soybean-clean       0.98      0.98      0.98       297\n",
            "          Wheat       1.00      0.99      1.00       102\n",
            "          Woods       1.00      0.99      1.00       633\n",
            "      Buildings       0.97      0.99      0.98       193\n",
            "    Stone-Steel       0.96      1.00      0.98        46\n",
            "\n",
            "       accuracy                           0.98      5125\n",
            "      macro avg       0.98      0.97      0.97      5125\n",
            "   weighted avg       0.98      0.98      0.98      5125\n",
            "\n"
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IeIyx1hZ1DHLh6S9dRERERC4Er5gi7PPgzGL98/H8t6/yiu/xBC2XLaEmOXoXdQindKtrAQCxqdOKOJJTC/UfAsBU/0uLOJJTG5b6a7H/Owf337vi9BxviPPEta44PcOb4vzMp3j/vwkwIkP/d3qSN8TpTdcQeE+c3kCPMPEsLZcVERERERERj1GSKSIiIiIiIh6j5bIiIiIiIlKiWS2X9SjNZIqIiIiIiIjHKMkUERERERERj9FyWRERERERKdG0u6xnaSZTREREREREPEZJpoiIiIiIiHiMlsuKiIiIiEiJpuWynqWZTBEREREREfEYJZkiIiIiIiLiMUoyRURERERExGN0T6Z4jT27o3jzpTls3XKQ0NBA7n2wDz16NwYg5Xg6496az4J5m8jIcNGgYWX+N/mWfMeJPBDHGy/NZuO6/fj6OenVpykPPNoPHx8HicdSePKRb9m04QBdujXg2ZcG4nS6fxfzynM/0+nievS8tMkZx+zw8+WicaOo0qst/uWCObY7krVPf0LkL6vybd9k1LU0e+hGnIH+RMxYxIr73sWVlo5xOug6eTRV+3YgauUWFt38AunHkgFo/uhNZKamseXd7wrxbebmFxpM148fpmrfdqRGJ/DXEx+z+8vf8rSr0qM1rZ8eRvm29UmNTeTbukOy64zTQfepT1Ctfweilm9m4Y3/xNhy9M1kpqSxaey3Zx2jiJy7M73Wmz5wLU3vHYh/hTJkJB5nz/SFrHrkQ2ym64Jc636hwVz80UOE9XHHuebJT9jzVd44HX6+dBx7NzUHdsXh6+TI0k0sv/sdkiNjAOjw1l3UG96X+G37+OPG50k+EA1AncG9qHhRE1b+d8I5xSki/x4up+7J9CTNZIpXyMhw8ej9X3PxJQ2Yt+gRHn/mSsaM/p6IcPcPEq88/zMJCcf56oe7mbfoER54tG+BY73x0mxCy5Xm598eZOo3d7B29V6++9qd9H3/zV80bFyF2b8/xMHIOP5YsBWADev2ER11rFAJJoDDx0nS/ijm9XmQryoO4O8xk+g+7WlK16qcp21Yn/Y0e3gw8/s/wvcNhhBUJ4xWz4wAoObAblhrmV51EOnxSTT4vysACKpdhepXdmHr+O8LFdfJOo8fhSstna+qXMcfQ1+m8/v3U7ZprTztMpKOs2PSHFY9OjFPXa1B3cBavqx4DenxSTQa+U+MNa7qzOZxM84pRhE5d2d6re/7aSk/tbuTaWWv5vsW/0e5lvVoOmoQcGGu9U7v3UdmWgbTq17PouGv0GlC/nE2GXUNFTs15ac2tzO9xo2kxSbS8d17AajQoRHl2zVkerUbOLJkIy0eHQyAb0hpmj10A2ufmXTOcYqISP6UZIpX2Lsnmugjx7hpWCecTgftL6pDyzY1mPPzesL3RLNo4TZGP3MloeVK43Q6aNy0aoFjRR6I49J+TfH396F8hSA6XVyPPbuisuvadaiNn58PrdvW5MD+WDIzXbzz+jwefLx/oePOSE5h/YtTSNp7GKzlwOwVJIYfonybhnna1hval52T5xC/ZS9pcYlseOVz6g1zJ8tBtatw+M912EwXh/74m6A6YQB0ePse/nrsf9hMV6FjO8EnMIBa13ZjzTOTyUhK4ciSjUT8tIx6w/rkaRu9ahu7Pv+VY7sP5qkLrhPGoT/cMR5c+DdBdd0xXvTuvax6+NxiFJFzV5hr/djug6TFJwFgjMG6XATXd/+/er6vdZ/AAGoO6sbfz07KjnPfzKXUHZo3zuDaYUTOX03KkThcqens+WYhZZvWBiCodhhHlmzElZbOwd/WZsfZ9oVb2fTW9OzZVxER8TwlmeK1rLXs3nmEzRsOEBZWlo/eX0i/7m8wZND/+G3+lgL7DR56EfPnbCLleDpHDiewbPFOOl1cH4B6DSqxavkeUlLS+XtNBHXrV2T6Fyvp3LU+1aqHnnPMAZXKEtKgOnFbwvPUlWlai9j1u7OPY9fvolSVcviVCyFuczhVerTB4edLlUtaE7c5nBpXX0xKdDxRyzadU0whDatjMzJJ2LE/12ef+EHtTMVu3ENYz9Y4/HwJ69GauE17qTnwYlKj4zmy9NxiFJFzV9hrve5NvRgS9xM3R39PuVb12Pbhz+4+5/la/yfOAzni3J3vTOaOSXOo1KUZpcLK4yzlT92benNg7koA4jaHU6lrC5wBfoT1akPc5r2Ub9eQkEY18l16KyIlm3WYYv3yNkoyxSvUql2e0HKl+XzSUjLSM1mxdBdrV+8lJcWdKO7aeYTSQf78vOBBHnqiPy889QN7dkflO1brdjXZvSuK3l1e5eo+79C4WVUu6dUIgKuuaUNiYir/GfIJrdvWpEHDKsyZuZ7BQy/itRdmcectk/nfe2f3w4nxcdL1syfY9fk8Erbty1PvG1SK9ISk7OMTswi+waU4MMc9A3r50gmkJyQRPn0hLZ8axponPqL1c7fS99e36ThuFA7fwt9m7RtUirSE3L/RT4tPwje4VKHG2T97BcfCD3HVyvdJS0hiz1e/0/qZ4ax6bCJtX7yNyxaOpdOEs4tRRM5dYa/13V/+xrSyV/Ndw+Fs/XAmKYdjgfN/rfsEBZCeb5yBedom7DhA0r4obtj3NTfH/kSZxjVZ9+LnAMRtCidixiIuX/IepWtUYuMbX9PxnXtY+cAEGt87kP6/v023KaPxLVP6rOIUEZGCKckUr+Dj6+S1d29k6aIdXN7rbb6Ysoze/ZpRqXII/gG++Pg4uHVkd3x9nbRtX5u2HWqzcunuPOO4XJb/3vUFPXo35vcVo/nlz4c5lnCc8WN/BcDf34fRz17JtO/u5J4HLuWdN37hrlG9mDtrAy6X5YNJI9i04QDLFu8s3AkYQ9dJj+NKS2fl/e/l2yQ98XiuH6J8Q9w/+KQfOw7A2qc+5uf2I1l+91iaPzKY7R/9TPn2jSjfrhHzLn0Qh68P9W4p/JLe9MTj+IXk/uHNN6R09ucWxl+jP+bH1rez9I6xtHh8MNs+nEnFDo2o0K4hc3r8F6efLw1uK3yMInLuzvZaT9h5gLhNe+k84f7ssvN5rWckpuCbJ87AfJe3XvTefTj9ffmy4jVMC7mSiB8Wcemsl7PrN7/7HTPb3cGfN79I7esv4fCiDRiHoeH/XcEvfR4hfmsELR676aziFBGRginJFK/RoGFlPph0C/MWPcK7/xtK5P5YmjavRv0GlfK0NSb/ZQUJ8cc5dDCe62/qgJ+fD2XKBnLlwNYsW5Q3aVy2eCfWQueu9dm14whNmoVhjKFJs6rs3H64ULF3/vAhAiqF8seNz2EzMvNtE795L6Et62Yfl2tZl+OHjpJ2NCFXu7LN6lCxU1N2fDyL0GZ1iFmzHYCYv7YR2qIuhZWwfT/Gx0lI/Wq5Pjtuc3ihxzohtHkdKnVuxraJswhtUZfoNTsA9z2dOc9RRC6cc7nWjY+T4Hp573U/H9f6iTiDc8VZj7jNe/O0LdeqHjs/m0da7DFcaelsGf8DFTs2wb98SK52AZXK0vD/rmDdC1Mp26wOsRv2YDMyiV69jdAWdc4qThH5d3E5TLF+eRslmeI1dmw/TGpqBinH05k2eSnRUYlcMaAVbdrVokpYGaZ8spiMDBfr1kawZlU4F11cL88YZUMDqVqtLDOmryYjw8WxhBRm/7SO+g1z7/aamprB++8u4L+P9gOgarWyrFm1l/T0TNav3Veo+zMvGn8/ZRrX4vdBT5GZklZgu93T5lP/lsso07gmvmVK0+LxIeyaOi9Pu47v3MuqByeAtSSGH6JSl+Y4fH2o3K0ViXvybshzOhnJKeydsZg2z92CT2AAlbo0o+aALuyaOj9vY2Nw+vvi8PXB5Hh/sk7v3ceK+8eDtRzbc5DKF2fF2L0liflsGiQi519hrvUG/7mcgIplASjTpBYtH7+JyN/W5ml3Pq71jOQUIr5fTJsxI/AJDKBil2bUuLoLuz/PG2fM6m3UG9YH35DSGB8nje+6muQD0aTG5P7lXIc372Ld81PIPJ5KYvhBKrRviE/pAKpc0kr/J4mInAdKMs+BMSbRGFM36/1kY8yLp2hrjTH1s97/zxjz9IWK899i7sz1XNnrbS7r8SarVuxh3MSh+Pn54OPr5PWspbSXdnmNV577mWdeGkjtOhUAmPzRIh64a1r2OK+OvYFlS3Zx2SVvct2V7+Hj4+T+kx558tnHi+h3eQsqVXH/Nvya69sRF5dM/0vepFLlEC7Jej7n6ZSuWYmGt19FuVb1uC7iGwbHzGRwzEzqDO5FYI1KDI6ZSWAN90xs5LxVbHr7a/rMe4tBO78gMeIw657/LNd49Ub0J25zOEfXumcLIn5YxPGDMVx/4Dv8y4ew4+NZZ/XdLrvnXZyl/Bl8+Fsu+eJJlt39LnGb91K5awuGJvyc3a5K95YMPz6XvrNfIahWZff7X17LNVaDW/sTuymcmKwZjb0zFpEcGcNNR74joHwI2yaeXYwicu7O9Fqv3KUZA9d/zNBjP9Nn1svsn7OCNU98nGus83mtL793HM5S/txw8Bu6f/4ky+9xx1mpa3NujpuZ3W71oxPJTEnjmq2TGXzoO6pddhG/X/dsrrGq9GyNX9kgIn5cArhnWffPXsF14V9SpUdrNrz+1VnHKSIi+TPW2qKOoUgZY8KBykAmkA4sBe601ubdmeXU40wG9ltrnyqg3gINrLWFvJnvvLCTHL2LOoZTutW1AIDY1GmnaVm0Qv2HADDV/9IijuTUhqX+SnH/Owf337vi9BxviPPEta44PcOb4vzMp3j/vwkwIkP/d3qSN8TpTdcQeE2cXrHW86IX5hfrpGjF03284ns8QTOZbldZa4OAMOAwkP/OLCIiIiIiInJKSjJzsNamAN8CTQGMMQuNMf93ot4Yc4sxZnGO4+wlsCczxjxijDlojIk0xtx2Ul320lpjTA9jzH5jzEPGmCNZfW7N0ba8MWamMSbBGLPKGPPiiRiM29isfgnGmA3GmOae/E5EREREREQKQw+sy8EYEwjcCCw/x3H6Aw8DvYE9wEen6VIFKANUA/oA3xpjfrDWxgITgKSsNrWBX4ATW+z1BboDDYF4oDEQV0BMI4GRAB9++CG+Z3luIiIiIiL/NtbpVatRiz0lmW4/GGMygNJAFNDvHMe7AZhkrd0IYIwZA5zqQVzpwPPW2gxgtjEmEWhkjFkFXAs0t9YmA5uNMZ8BPXL0C8adXK601m4p6AOstROBiScOJ9359dmem4iIiIiISIG0XNZtoLW2LBAA3Av8YYypcg7jVQVybhyU9+FeucVkJZgnJANBQEXcvwjIOVb2e2vtb8B43LOdR4wxE40xuR8OJiIiIiIicgEpyczBWptprZ2Be6fZrriXqQbmaHKmiedBoEaO45pnGVIUkAFUz1GWc1ysteOste1w30faEHjkLD+rWJk1axaXXXYZPTq+wrWXv8fff7nz9B+/W8N1V7xHz4te4YE7pxF15Fh2n79W7uHu/3xG7y6vMbD/u/mOu2HdPm4f9imHDsbT86JXcr06tXyeaZ8tAyA66hgP3/cVV/Z+m04tnyfyQL6rkImLi6NTp05MrnL4rM6z0V0DuHzpBG5OmE2Xj/L/q2vxxFCGpf5KlV5tz+ozPK3JPQO4auX7DD8+h66fPlrU4YjIeeLw8+Xijx/m+j1fMDR+Jlev+ZBq/TsWdVgFqn1DDwZs+ISb42cyaNsUKnXVFgUiIkVFy2VzMMYY4GogFNgC/A0MMsZ8jHt28j+4d589nenAJGPMFCAcePbUzfNnrc00xswAxmRtQFQTGA5EZMXbAfcvCtbgTohTANfZfFZxsicwhSlvvsnYsWOp0Wgj0VHuRPKvVeH8b9xvTPhkODVqleftV+fyzGPf8cGkWwAIKOXHVQPb0PeydCZ/vDjfsZf8uYPO3epTJawMv68YnV0euT+W664cT89LmwBgHIZOF9djxP9dzO3DJhUY65tvvkm9evU4dHD9WZ1rcmQMG16dRlifDvgE+OWpD6obRq1rLyE5Mvqsxj8fkiNjWPfSNKr1a48zwL+owxGR88Th4yRp3xHm9PgviRFHqH75RfT8+ml+aPl/JO49u1+snS9hl7al3Su388fNLxK9ciuBYeWKOiQR8TIuh+7J9NnYNdsAACAASURBVCTNZLrNzLoPMgF4CRhhrd0EjAXScCeWnwFn9NBGa+0c4B3gN2Bn1p9n617cmwIdAqYCXwKpWXUhuDcVisW9JDcGeOMcPqtYWFwugbvvvpvWrVvjcBgqVQ6hUuUQlvyxnV59m1K3fiV8fZ3cdkd31v4Vwf59RwFo1qIal13VkqrVQwsce+minXTp2iBP+eyZ62ndriZVq5UFoHz5IK4b3IEmzaoVONb6v/exY8cOBg0adNbnuu/Hxez7aSlpMQn51nd8dxRrnvwIV1pGvvVFYe/3i4n4cQmpBcQsIv8OGckp/P3cFHdCaS37Zy3n2J5DlG/XsKhDy6P1syNY9+JUoldsAWtJjowhOTKmqMMSESmxSvxMprW29inqonHv4JrTmBz1Jsf7W07q+yrwao6iT/Nra61dSO7lsLlistZGAVecODbGvAbsz6pbALQsKH5v5MJyMCCN2NhY+vTpw/GUWC7p1Zh7H3Q/tNvmeEyuzTrYvTOK6jVO/1vr6KhjxB5NolGT3KuerbXMmbmeW+/odsZxZma6ePPlObzy0gS2b99+xv0Ko+ag7rhS04mcu/K8jC8iUhgBlUIJaViduE3hRR1KLsbhoHy7huybuYxrtn6GM8CPfT8uYfVjE8lMSSvq8ERESiTNZBZzxpjGxpiWWc/E7Ih7ye73RR3X+ZLkdOEyMHfuXKZNm8bUb+5g29aDTJq4iE5d67Ng3iZ2bD9MSko6n374J8ZAyvH0Mxp76aKddLq4Hu5V0f9YtyaCozGJ9OrT9IzjnP7FSpq1qEbz5ufnnh+foFK0eeE2Vj004byMLyJSGMbHySWfj2bXlHnEb9t3+g4XUEDlUJx+vtQa1I25Pf7LzHZ3UK5NfVo+OaSoQxMRb+IwxfvlZZRkFn/BwAzc91x+DbwF/FikEZ1HvlmTw8OGDaNSpUqUDQ3kpmGdWbZ4Jx071eX2u3sw+sFvuKb/OMKqliWwtD+VKgef0dhLF+2gcz5LZWf9tJ6elzYhMDDvPZH5iTpyjOnTVnLnqF5nfmKF1Orp4eye9itJxey+JxEpgYyh+5TRuNIyWHbvuKKOJo/M4+47SLZO+IHjh46SGpPAprHfUa3/RUUcmYhIyVXil8sWd9baVUD9oo7jQglwOQhOd+aabcw58Xjd4A5cN7gDABHhMUyauIi6DSqddtyM9EzW/rWXp18YkKs8JSWd3+Zv5rWxN5xxjJs3HiAm+hg3DXwfh5lESkoKyf5pjK1xgPv3VcXBuf+2qUrPNgRWq0ijO64GwL9iGbpPe4pNb37Nprf0jFMRuXC6fvwwpSqHMv+K0diMzKIOJ4+0uESS9h3JdTtF7gMREbnQlGRKsdMiIZCpU6fSrVs3El3H+Wrqci7u3oDU1Az2Rxylbv2KHD6UwCvP/8yNQzoSElIKAJfLkp6eSUZ6JlhITc3A4TD4+jpZtzaC+g0qUzoo926ofyzYSnBIAO061s4TR2pqBq5M92a96WkZpKZm4O/vQ+eu9fl+7v0AlPEbxOzZs5n87JvccKRCoRNM43RgfJzuP50OHP6+2IxM5vd/FIevM7vd5UsmsPrR/xH5S9Hfn2mcDhw5Ynb6++LKyMRmev3GxiJyks4fPECZJjX5pc8jxfr+xp2f/UKTewYS+csqXOkZNH3gWvbPXl7UYYmIF3E49MspT1KSKcXOxUdD2N+iBf369cPXz0Xvfs245fZupKVm8MzjMziwL5bA0n5cObA1I+/tmd1v7V97uec/U7KPL+nwMm3a1+KDT0ew5M8ddOmWd0J49sx1XHZlyzz3aZ7of8KNA94HYPn6Z/Dz86F8hSAAQv0rEhwcjMNCUKYzzxin02L0UFo9PTz7uO6QPqx7YQrrX5ySq53NdJEWl0hGUkqhP8PTWj01lDbPjsg+rj+sD2uf+4y/n5tyil4i4m1K16xE4zuuIiMljcEHv80uX3rnWHZ/saAII8tr3Yuf41++DNdsmUxmShrh3/zB+pfPaEN4ERE5D5RkSrHjxDBmzBjGjBlDbOo/PyT4+/sw7bs7C+zXrkNtlq9/Jt+6pYt38spb1+cpf/d/Qwscr6CxTjZo0CCSbnr/jNqebP2LeRPK/HzfqOA4L7S/n5uihFKkBEiKOMIkR++iDuOM2IxMVtw3jhX3Fb97RkVESiIlmfKvl56eyeVXtaROvYpFHYqIiIiIFEMOp5bLepJ2l5V/PV9fJ8P/07WowxARERERKRGUZIqIiIiIiIjHaLmsiIiIiIiUaNpd1rM0kykiIiIiIiIeoyRTREREREREPEbLZUVEREREpETTclnP0kymiIiIiIiIeIySTBEREREREfEYJZkiIiIiIiLiMbonU0RERERESjSHU/dkepJmMkVERERERMRjjLXK2ksg/aWLiIiIyIVgijqAM9H9/V+K9c/Hf97dzyu+xxO0XLaEmuToXdQhnNKtrgWA98TpWvxQEUdyao6ubxX77xLc36fi9BxviNPbrnXF6Rne8G8TFKeneUOc3nQNgffE6Q30CBPP0nJZERERERER8RglmSIiIiIiIuIxWi4rIiIiIiIlmpbLepZmMkVERERERMRjlGSKiIiIiIiIx2i5rIiIiIiIlGhaLutZmskUERERERERj1GSKSIiIiIiIh6j5bIiIiIiIlKiOZxaLutJmskUERERERERj1GSKSIiIiIiIh6jJFNEREREREQ8RvdkinjYrshjvDBtI5v2xlMu2I+Hr29Cn7ZhAHzzZwQfz9lJdHwqbRuU46VbWlEpNCDfcR79aC3LtkRzPDWTCmX8+U//elzfvSYAB48e54EP/mLvoSSu6VqDx25smt1v5NgVjLqmEc1rlz3jmP1Cg+n68cNU7duO1OgE/nriY3Z/+Vueds0fvoH6w/sSVKsyKdHxbP3gJza+OR0A43TQfeoTVOvfgajlm1l44wukH0sGoOXom8lMSWPT2G/POCYR8Txvuda9JU4R+ffQI0w8SzOZIh6UkeninvGr6dGyMsvH9eO54S157KO/2XMokZVbo3lnxlbG39uBZeP6Ua1CIA9NXFPgWLdfXp8Fr/Vi9YT+vH9fB8Z9v41N4XEATJy1k4FdqjP/tV4sWHuIjVnls1dGUq1CYKESTIDO40fhSkvnqyrX8cfQl+n8/v2UbVorb0NjWDTiNaaVG8C8yx6nyT0DqXNjTwBqDeoG1vJlxWtIj0+i0cgrAAiqXYUaV3Vm87gZhYpJRDzPW651b4lTRETypyRTxIN2H0wkKi6FEX3r4HQYOjWpQJv6ofy07AAL1x2hX/swGlQLxs/Hwd1XNmD19qNEHEnKd6wG1YLx83UCYAxgICLK/Vv4A9HJdGpSgeBAX5rXLsu+qGQSj6fz8eyd/PfaxoWK2ScwgFrXdmPNM5PJSErhyJKNRPy0jHrD+uRpu/GNr4lZuwOb6SJh+34iflpCpYubARBcJ4xDf6zDZro4uPBvguq6Z28vevdeVj38P2ymq1BxiYhnecu17i1xiohIwZRkipxnFthxIMH93uYsdx/sOHCswL7PTd1Am7tmc/mTC6lYxp/uLSoB7gR06aYoEpLT2bQ3jvpVgxn3/TaG96lDSKBvoeILaVgdm5FJwo792WWx63dRtmnt0/at3LUFcZvC3X027iGsZ2scfr6E9WhN3Ka91Bx4ManR8RxZuqlQMYmI53nLte4tcYrIv4vDYYv1y9soyRTxoDpVgigX7M8nc3eRnuFiycYoVm+LISXNRdcWFZm7OpJt+xJIScvk/Zk7MAZS0jILHO/ZYS1YPeEyPn+8C33ahuHn475kb7+8Pqt3HGX4a0u5qWdt0jNcbNt/jB6tKvPwxDUMfXUp0xbsOaOYfYNKkZaQnKssLT4J3+BSp+zXeswIjMPBjkm/ALB/9gqOhR/iqpXvk5aQxJ6vfqf1M8NZ9dhE2r54G5ctHEunCaNw+OpWcJGi4C3XurfEKSIiBVOSKeJBvj4Oxt/bnj/WH6Hbg/OZNG83/TtUpXJoAF2aVuTeAY0Y9f5qLn1sAdXKB1I6wIfKBWz8c4LTYWjXoByHY4/z1cK9AJQN8mPsne344blLGH5pHV76YiNP3tyMj+bspEG1YD59uBNfLYxgV2TBs6QnpCcexy8kMPd5hJQm/djxAvs0uWcA9Yf1Yf6VT+BKS88u/2v0x/zY+naW3jGWFo8PZtuHM6nYoREV2jVkTo//4vTzpcFt/U8bk4h4nrdc694Sp4iIFExJpoiHNaoRwtTHurB8XD8+fvAi9kUl07KOeyOeIb1q88srvVg8ti992lUhM9PSoFrwGY2b4bLsy+f+zel/RtCqbigNq4ewY/8xmtcqi5+Pg4bVg9l+iqW4JyRs34/xcRJSv1p2WbmWdYnbHJ5v+wa39qfFYzcx99JHSD4QnW+b0OZ1qNS5GdsmziK0RV2i1+wAIHrVNkJb1j2DsxURT/OWa91b4hSRf5eiXg6r5bIickrb9iWQmp7J8dRMPp27i6j4FK65uDqp6Zls35+AtZbImOM8O2U9wy6tQ5nSfnnGiElIZdaKAySlZJDpsizeeITZKyLp1LRCnnZf/BbOPQMaAlCtQiArtkWTlJLBxvA4alQMzDP2yTKSU9g7YzFtnrsFn8AAKnVpRs0BXdg1dX6etnVv7k3bl/7DL30fJXHPwQLH7PTefay4fzxYy7E9B6l8cXMcvj5U7t6SxN0F9xOR88dbrnVviVNERAqmJFPEw35atp/uD/5K1//OY/mWaD55sBN+vk5S01088tFa2t09lxtfXEzreqGMuqZRdr8PZ+1g5NgVgHs32a8W7qXnw79y0X2/8Pr0LTw+uCm9WlfJ9VmvT9/M3Vc1oHSA+56ikVfUZ8WWGHo9soCerSqf8aNMlt3zLs5S/gw+/C2XfPEky+5+l7jNe6nctQVDE37Obtf2hVsJKB/CVSvfZ2jCzwxN+JnOHzyQa6wGt/YndlM4MVkzBXtnLCI5MoabjnxHQPkQtk2cVfgvVUQ8wluudW+JU0RE8mes9b7pV29njLkZeBBoDBwD/gZestYuvkAh2EmO3hfoo87Ora4FAHhLnK7FDxVxJKfm6PpWsf8uwf19Kk7P8YY4ve1aV5ye4Q3/NkFxepo3xOlN1xB4TZymqOM4EwO++rFYJ0U/Dh7gFd/jCZrJvMCMMQ8C7wAvA5WBmsD7wIBCjqPt8EREREREpNhRknkBGWPKAM8D91hrZ1hrk6y16dbamdbaR4wx/saYd4wxkVmvd4wx/ll9exhj9htjHjPGHAImGWPGGGOmG2OmGGOOGWM2GWPaF+lJioiIiIhIiaYk88LqDAQA3xdQ/yTQCWgNtAI6Ak/lqK8ClANqASOzyq4GvgLKAj8B4/Mb2Bgz0hiz2hizeuLEied4GiIiIiIi/x5FvXusdpeVc1EeiLbWZhRQPwR43lp7xFobBTwHDMtR7wKetdamWmtPPDBssbV2trU2E5iKOznNw1o70Vrb3lrbfuTIkfk1EREREREROWdKMi+sGKDCKe6nrArszXG8N6vshChrbcpJfQ7leJ8MBOh+TRERERERKSpKRi6sZUAqMBD4Np/6SNxLYTdlHdfMKjvB++bKS4Dw8HCuumM2/dqH8frtbTgSl8KYKRvYGB5HVHwqv77Wi2oV/nle5etfb2bB34eIjk+lcmgAI69owMAu1XONOWvFAX5fd5g3R7bNLvth6X5Gf/I3z49oyfXdawKwYms07/+0g80R8YQE+rLg9dy7zPV+dAExCak4fX8jvd5xqh33Y3BkxXM63yb3DKD+iH6EtqjD7i9/Z/Ftr5/TeCJSPHnLte4tcYpI8eaNS1KLMyWZF5C1Nt4Y8wwwwRiTAcwD0oFLgZ7Al8BTxphVuBPKZ4DPiypeOTPPP/88Ler88zxKhzF0bV6R2y+vz82vLMnTvpS/kw9GdaR25dJsCI9j5NiV1KoUSJv65bLb/LH+CN1bVMo+jk9KY+KsHdSvGpx7LD8ng7rW4Ir0qnw4a2e+8b0/qgNdR07x2DbnyZExrHtpGtX6tccZ4O+RMUWk+PGWa91b4hQRKUmUZF5g1tq3snaHfQqYhvs5mX8BLwFrgBBgfVbzb4AXiyJOOTOzZs0iODiY1uXLE3EkGYAKZfy5uVdtMjJd+fa5b2Cj7Pet6obSrkE5/t4Vm51kulyWpZujGD24WXa7sd9tZWjvOsxdHZlrrJZ1Q2lZN5Slm6M8fWoF2vu9+3GuFdo3JLDauc2Kikjx5S3XurfEKSJSkuiezCJgrZ2WtQlPaWttFWvtFdbapdbaFGvtKGttWNZr1Il7MK21C6211U8aZ4y1dmiO43BrrTnFxkLiQakOF+PGjWP06NFnPUZKWiYbwuNyzVCu3xNH9QqBhAb7uY93x7IxPJ7BPWqd1Wc8+tFaOnXqxFdVozjsl3bWsYqIiIiInAnNZIqcpT/LJXDttXdQpUqVsx5jzNQNNK4RQtfm//z2/Y/1R+je0r1UNtNlef7zjTw1pDkOhyn0+G/c3oamtcpgOr/C/dW7M71aNLfvrUKAS79fEhERETnB4dQ9mZ6knzRFzsJhvzT2BqZwyy23nPUYb0zfzI4Dxxh7Z1uM+SeB/HPDES7Juh/zy9/DaVQ9hNb1Qs/qM9o2KEeAn5NSpUrROTYEf5eDfaVSzzpmEREREZHT0UymyFmICEwl3jeTnj17ApB8LJZMl2Vn5DFmPNv9tP3f+2Ebf26IYspjnQkq5ZtdHhWfQlRcCk1rlQFg+eZoVm0/yp//PQK4NwDaEpHA1n3xPD2kRaHjLvxcqIiIiIhI4SjJFDkLreNL0+RYIIOXfgPAJ88P50BMMs8Ocyd+qemZZLrcyy7SMlykpmfi7+sEYOKsnfy84gCfP96F0CC/XOMu2hBFt+aVsmc2X/5Pa1LTM7PrR034i37twri2Ww3AvUlQeqaLjEyLte7PNcbg5+MgMuY4h44ep3mdspjUVFaUPUayw0X147k/s7CM04HDx4lxOjBOB05/X1wZmdgCNjoSEe/kLde6t8QpIsWbQ+s7PUpJpshZ8LUOfDOhYkX3vZSBAU78fZ2UC3Zvn9/6zjnZbS9/ciEAWz65EoCxM7bi6+Og/+jfs9uMvKI+d1zRgD/WH+byjlWzy0MCfYF/Zjp9fQylS/kQHOguW709hhFvLM+ub33nHDo0KseUR7uQlJLBc59vYN+RZPwDl1KmdAo3RFaglMt5Tufe6qmhtHl2RPZx/WF9WPvcZ/z93JRzGldEihdvuda9JU4RkZJESaaIB9w7oFGu4xMJZX4KqsvIdLFyWwwv3dqqwL5THu2S67hj4woFjtegWjA/PncJAI6ub3nsOZl/PzdFP7yJlADecq17S5wiIuebMeZe4BagBfCltfaWHHW9gQlATWAFcIu1dm9WnT/wAXAdkAy8bq19+0z6FkQTwyLFRHxSOqMGNsp1j6aIiIiInH8Ohy3WrzMUCbwIfJqz0BhTAZgBPA2UA1YDX+doMgZoANQCegKPGmP6n2Hf/L/PM41YRM6v8iH+3NSzdlGHISIiIiJeyFo7w1r7AxBzUtUgYJO19htrbQrupLKVMaZxVv0I4AVrbay1dgvwEe4Z0TPpmy8lmSIiIiIiIsWYMWakMWZ1jtfIQnRvBqw7cWCtTQJ2Ac2MMaFAWM76rPfNTtf3VB+oezJFRERERKREK8SS1CJhrZ0ITDzL7kFA1Ell8UBwVt2J45PrTte3QJrJFBERERER+fdKBEJOKgsBjmXVcVL9ibrT9S2QkkwREREREZF/r01A9uMLjDGlgXq477WMBQ7mrM96v+l0fU/1gUoyRURERESkRHM4bbF+nQljjI8xJgBwAk5jTIAxxgf4HmhujLk2q/4ZYL21dmtW1ynAU8aY0KwNfW4HJmfVna5v/t9n4b5+ERERERERKYaeAo4DjwNDs94/Za2NAq4FXgJigYuAwTn6PYt7M5+9wB/AG9bauQBn0Ddf2vhHRERERETEy1lrx+B+xEh+db8C+T52xFqbCtyW9SpU34JoJlNEREREREQ8RjOZIiIiIiJSohX3R5h4G81kioiIiIiIiMcYa5W1l0D6SxcRERGRC8EUdQBnYsS86cX65+PP+t7gFd/jCVouW0JNcvQu6hBO6VbXAkBxesqtrgVsuHhAUYdxWi2W/Fjsv0twf5+K0zO86RoCxekp3vBvExSnp3lDnN50DYH3xOkNtFzWs7RcVkRERERERDxGSaaIiIiIiIh4jJbLioiIiIhIiablsp6lmUwRERERERHxGCWZIiIiIiIi4jFaLisiIiIiIiWaw6nlsp6kmUwRERERERHxGCWZIiIiIiIi4jFaLisiIiIiIiWadpf1LM1kioiIiIiIiMcoyRQRERERERGPUZIpIiIiIiIiHqN7MsUrNblnAPVH9CO0RR12f/k7i297Pd92nT94gHpDLs0+dvg6caVl8HmZqwDoOPZu6g/vS/zWffx+w3MkH4gGoO5NvajYqQkr7p9wTnH6hQbT9eOHqdq3HanRCfz1xMfs/vK3PO0cfr5c9O491BrYFYevk8NLNrHsrndIjow+L3H616pO1YfuoFSjemTEJXBowmQS/lxO2b6XUPWRu7LbGYcDR4A/O257kJRtu/KMU+e9Fwls1gibmQlARvRRtt90NwAB9WtTY8xD+ISWJWrKN0R//ZO7k9NJvQ9eJeKp10g/El2ouEVERETOB6cp6gj+XZRkildKjoxh3UvTqNavPc4A/wLbLbvrHZbd9U72cddPH8VaFwAVOjSifNuGfBV2PW1fvJWWj9/E8vvewzekNM0fvoE5PR485zg7jx+FKy2dr6pcR7nW9enz80scXbeLuM17c7Vrev8gKnVqyg+tbic9PpEuHz5Ip3H38tt1Yzwfp9NBrVef4OgPc9nzwLOUbt2M2q8/xY5b/0vcvD+Im/dHdtOyl/ei0i035JtgnhA5diKxM+fnKa9y5zAOjp9Eys5wGkx5l7j5f5JxNI4KgwcQv3CpEkwRERGRfyktlxWvtPf7xUT8uITUmIQz7uMTGEDta7ux87N5AATXCePIkg240tI5uGAtwXXCAGj30m1sfHM66ceSzylGn8AAal3bjTXPTCYjKYUjSzYS8dMy6g3rk6dtcJ0qHJi3mpQjsWSmprNn+kLKNqt9XuL0r1kdnwrl3DOLLhdJazaQtGELof175Gkbelkv4ub8XuhzB/ANq0zSX+vJiD5K2v6D+FauiG/lipTp0fmfWU0RERER+ddRkiklRq1ru5ESFcfhP9cDELspnMpdW+AM8COsdxtiN4dTvl1DQhrWyHdJa2GFNKyOzcgkYcf+7LLY9bso27R2nrbbP5lDpS7NKBVWHmcpf+re3Jv9c1dekDgBMAb/OrVyFflWrkjpVk2JnXvqJLPKHcNoMmsqdT94ldJtmmeXp+6OIKhjG3wqlse3SiXSDhwi7IH/49CEyZC1vFZERESkOHCY4v3yNlouKyVG/eF92Tn1n2WdcZvCCZ+xiCuXjSd+awTL7nuPS394gcX/9yZN7ruG2td2J2nfEZbfO460+KRCf55vUCnSEnLPMqbFJ+EbXCpP24QdB0jaF8XgA9NxZWQSu2E3c+9777zEmRpxgMzYeCrcfA3RX/9EULsWlG7djKQ1G3O1C72sJ0nrNpN+8EiBYx36YAqp4fuw6emUubQbtV5/ip23PEDagUMcHD+Jao/ciU+5UA6O+4TAlk1wJR8n7eBhar36BI6g0sR8N4uE35eeydcpIiIiIl5CM5lSIpSuUYkqPVqxc0ruewc3v/MdP7YZycKbXqTODT04tGg9xmFodPsVzL30YeK3RtDi8ZvO6jPTE4/jFxKYq8w3pDTpx47nadt5/Cic/r5MKz+QqUFXsPf7xfSd/cr5iTMzk72jXya4y/+zd9/hUVX5H8ff35lMElKA0EOvAtIRELEX7IJdFBTLimtde0UFXcT1tyqu2FBXRbBgQ1QUC+KKBVBEEATphA4hvU5mzu+PGUICCaAMJCGf1/PMw9xzzj3ne4cJD9+cc8/tRcePXqXeoLPJmP7dLvdI1j71+D0ulc1b9AfB3Dycv4j0T78md/7vJB5xWOj6N21h1e0Ps+zKW8n8dhYN/3YJG8e+QqPrryD9q5msvmsUyTdehTcxYe/iFhEREZEqQUmmVAttLj2Jzd8tJHvlhjLrYxsk0f7qM5j30OskdW7FtvkrcEUBtsxZQp0urf/SmJl/rMWivNRs26S4rE7X1qQvWrVL2zrd27DstWkUpmURLPTz+9MfUP/wjsTUrblf4sxfvpqVN9zH76dfyqpbRxDduBF5vy8tro/r0gFfvTpkzPiTs4zOAbuu6WhwxSDSPvqcorQMYtu0IG/xMoI5uRRt2Up00+Q/N4aIiIhIhHmtcr+qGiWZUiWZ14M3xod5PaXel6ftpSez7LVp5db3efzv/DLyNQJ5BWSt3ED93u2Jio8l+bhuZJWTmO5JUW4+q9+fSY+RlxMVF0uDfp1oPrAfy1/fdSfWrXOW0ObS/vhqxmNRXjpcN5CcdVt32dgoUnHGtmmBRfuwmGjqXXw2vrpJpE39qrg+6bQTyJjxA8HcXWddt/MkxJPQpwcW7QOvh9onH0t8905kzZpbql1My2Yk9OxM6gefAeDfsImEw7oSlVSL6KaN8W/astdxi4iIiEjlpySzApjZp2Y2dB/Ozzaz1uH3r5rZP8PvjzOztbs/++DQbfgQLsv7jK53X0LbS/tzWd5ndBs+hPhmDRiS+THxzRoUt63f91DimtZj5TvflNlX8vHdia6dwJrJ3wGhhC9l6iwuXPMWjY7rzvxH3/zLcf5w/VN4a8QwaNO7HPvGffxw3VOkL1pNw6O6MCTz4+J2c+54gUB+Ief98RoXb36fpqf1Yfq5D+y3OGufejwdP3yVjh+PJ+Gwrqy8+QGcvwgAi/ZR64QjSft0102F6l92Pi3/GuWjcAAAIABJREFUHYrLorw0HDaYjp+8zqGfvE7d889g9T2PUJiyvtQ5jW+7hvVjXoJg6NExG59/nbrnn0m7CWPZMv4diral73XcIiIiIlL5HVQb/5jZJcCtQAcgC5gHjHLOzazQwHbinDttb9ua2QxggnPupRLnV/ub2OaNHM+8kePLrJtQ88xSx1t+XMSExDPLbAuw4et5bPh6Xqmy2bc8y+xbnt3nOAvTsnZJFgE2zVxQKs6CbZn879LRu7TbX3FufObV0C6vZXCFfhadOrjMui3j32X7vGMgPZPlf7t9j2OtvHF4qeP8ZatYOuSGPxOuiIiIyH5VFZekVmYHzUymmd0KjAEeARoCzYFngYF/sp+DKvEWERERERE5kA6KJNPMagEPAdc75953zuU45/zOuY+cc3eYWYyZjTGz9eHXGDOLCZ97nJmtNbO7zGwj8IqZjTCzSWY23syyzGyhmfXazfgjzOwdM5sQbr/AzA4xs3vMbLOZpZjZySXazzCzv4XfX25mM83s32aWZmYrzey0cN0o4GhgbHiJ7NhwuTOztvvtAxUREREREfmLDookEzgCiAU+KKf+PqAv0B3oBvQBSq7hawTUAVoAw8JlA4C3gNrAFGDsHmI4C3gdSAJ+AaYR+nybEEqAX9jNuYcDS4B6wGPAy2Zmzrn7gG+BG5xzCc65v7zG0MyGmdlPZvbTuHHj/mo3IiIiIiIHnYrePVa7y1ZOdYGtzrmicuoHAw855zY757YAI4FLS9QHgQedcwXOue3bac50zk11zgUIJY/d9hDDt865aeEY3gHqA4865/yEktWWZla7nHNXO+deDI/1GpBMaMlvxDjnxjnnejnneg0bNmzPJ4iIiIiIiPwFB0uSmQrU2839lI2B1SWOV4fLttvinMvf6ZyNJd7nArFmFmVmg8NLV7PN7NMSbTaVeJ9HKOkNlDgGKG/DnuKxnHO5e2grIiIiIiJSaR0sm9z8ABQAZwPvllG/ntBS2IXh4+bhsu3c3g7knJsITPxrYf4lex2blM0T7eOIZ/9B4xN7ElMnkczl6/n53pdZ99nsig6tlI7XD6Tt0FNI6tKKFW9+zcwrH4tYnwBN77uJtaP+A4BFRdFsxG3U6NCG6OSGrLjhPnJ++a34vAZXDqLB0AsIFvqLy5YO/Qf+9Tt+l1LrpKOpeWRvUkY+QZM7ryO+R2eimyazdvTTpE/d8fiTmFbNSb7xSmq0b0NU7ZosOLL0Xly+Rg1ocvs1xHXqAEDfp29k1s3P4ALBfb5+ERERkb3hqYJLUiuzg2Im0zmXATwAPGNmZ5tZnJn5zOw0M3sMeBMYbmb1zaxeuO2Eioz5T9gEtK7oIKoyT5SXnJTNfHrcLUyoPYC597/C8W/fT0KLiK5I3me561P5ddRElr7yWcT7LEvO/EWkPPQk/q3byqxP/2omi/oPKn6VTDABEvv1IuuHnwHIW7aKdf9+nrw/VuzSjwsEyJg+k7Wjny5znCa3X0NRWga/D7wcgEbHdKXDdX9qU2gRERERqUQOlplMnHOPh3eHHU5opjEL+BkYBcwFagLzw83fAf5ZEXH+BU8Br5nZtcDrzrmbKjqgqqYoN7/UMzXXfvIjWSs3UvewQ8hevWk3Zx5Yqz8IPc61Xq9DiGtSP6J97swVFZE66aPQQfAvzBiakdC7OxueCj2+ddv7U0P9FhTu0rRwzToK16wjukmjMrvyJTck9b2puPCs6dppc0jq1PLPxyQiIiIilcJBk2TCHpey3hR+7XzODKDpTmUjdjpeBZQ7iV5G+y+BliWOi0qe75w7rsT7V4FXdzq/ZNsfgEN2U3/57q5FdhXbIImahzQlfeGqig6lUqt5ZG86fjqBotQ0Ut/9hG2Td8yw1ji0Hf71GwlkZO3zOKmTPqLWiUeTPXcBAE1P7cPcB17Z535FREREpGIcVEmmyJ5YlJdjJ9zD8vGfk7EkpaLDqbQypn/HtimfU7QtnbhDD6H5qLsIZOeQ8eW3ANQssVR2X+XMW0idASfT6fO3ANj60x+smfxdRPoWERER2RtV8TEhldlBcU+myF4x45jx9xAsLOKHG/5T0dFUagWrUijaug2CQXJ/W0zqOx9T6/h+xfWJfQ8jMxJJphktn3iQjG9+ZOFJFwIQk5RAr3/pMTsiIiIiVZWSTKk2jnrpdmo0TGL6+SNwRYE9nyDFnHNgoV/xRdWpTVS9OuQvWb7P/XprJhDdqAGp732C84cec7v01Wk0Pa3PPvctIiIiIhVDSaZUC0c8dzO1OjbnywH3EcjfdXOaysC8HrwxPszrKfU+En0C4PFg0T4I92m+qNDxTu8BEo/qgycxHoAaHdtR74Izyfx2Vqiu72Fk/Ti39DhR4fPNMK+3+H1xfbQP8/lKvA+t1A9kZFG4biN1zzmtOK62l53Mtvm77lIrIiIisr94rXK/qhrdkykHvfjmDehwzVkU5RcyaMOOx6h+//cnWfHGVxUYWWndhg+hx4NDi4/bXtqfX0a+Vmpn3H3pM+nU40k69Xg2vfwmm//7Foe8+SzRyaHHuLR6ciQAi8+7Gv/GzdQ+6Wia3nsj5vPh35LKlgnvk/7p10Do0SXp4Xszt2v55AgSenYBIL5rR5refUPxszd9jRrQ4b0Xi9t2/vpdCjdsYsn5oSWxq+97lOSbrqL+4HMBCPqLmH3rc3/5mkVERESkYinJlINezprNvOI5saLD2KN5I8fvU0K5uz6vCH7FgiNLP3tye5JXlpQRj5dd4fUQ36Mzax8pfU/ryhuHl9uXf+PmXcYuKX/pyuLzu3z3ITMGPVxuWxERERGp/JRkishe8yYmsunFiQRz8yo6FBEREZGI8egmwojSxykiey2QnlHqeZkiIiIiIjtTkikiIiIiIiIRo+WyIiIiIiJSrVXFHVwrM81kioiIiIiISMQoyRQREREREZGI0XJZERERERGp1rRcNrI0kykiIiIiIiIRoyRTREREREREIkZJpoiIiIiIiESM7skUEREREZFqzaN7MiNKM5kiIiIiIiISMeacq+gY5MDTX7qIiIiIHAhVYo5w9Lw3KvX/j+/pfkmV+By303LZauoVz4kVHcJuXRH8ClCckXJF8KtKHyOE4sy+4aSKDmOPEsZ+WWU+z8oeZ1X6GQLFGSlV4bsJijPSqkKcVelnCKpOnFWB1yp1jlnlaLmsiIiIiIiIRIySTBEREREREYkYLZcVEREREZFqzVul7nis/DSTKSIiIiIiIhGjJFNEREREREQiRstlRURERESkWtNy2cjSTKaIiIiIiIhEjJJMERERERERiRgtlxURERERkWrNo+WyEaWZTBEREREREYkYJZkiIiIiIiISMVouKyIiIiIi1Zp2l40sJZmyi+ikRI566XYan3wYBVsz+fnel1jx5vRy23t8UQycNw5fYhyTmg8CwFcznuPfvp96h3dk7Sc/8u3Qf+GCQQD6vXAL6z6bw+oPZh6Q65Gqyxo2J+bCG/E2PwSXnU7BB+MIzP8OgKgexxJ9xmVY7fq4tC0UfPQygfnfl9lP9MCriep1PBYbj8vLxj/zY/yfvxmqjI0n9qr78bboQNHCWRSM/xe40Hc15uJbKFo0h8Cv+q5WlI7XD6Tt0FNI6tKKFW9+zcwrHyuzXdvLTqbjjedQs10T/Jm5rHhzOj/f+xIuEPq77PPkdbS97GQyFqfw9YUjyV23FYDWF59A/b4dmfWPZw7YNYmIiBzstFxWdnHE2JsIFvp5q9H5fDPkEY549h/UPrRFue0733Eh+VsySpW1v+ZMUuct461G55PQshHNzzkKgPp9DyUuuZ4STNkzj4ca1zxEYOGP5Nx5DgVvPkns0LuxBk2wWnWJGXo3Be8/T87tAyiYPI7Yy+/FEmqX2ZX/h0/JffhKcu4YSN7jNxHV+0S83ULfSd9RZxBMWUbOvRfgqdMQb7cjQ8O36ojVqqsEs4Llrk/l11ETWfrKZ7tt542LYfYtz/Jm/XP5uO8NJJ/Qg863XwhAvd7tqdvzEN5KvoBN3y2g690XA6FfhnW+/ULmDn9lv1+HiIhIdaIkU0qJioulxXlHM/eBVynKyWfzd7+xZsoPtLm0f5ntE1o2os3gk5j/6JulyhNbNWLD1/MIFvrZNHMBia2TMY+HPk9cy4//GHsgLkWqOE/D5lituvinvwcuSOCPeQRWLMTXu39o9jIvm8CiOQAEFs6CgnysfnKZfbnNa6Ewv0SBw1O/cWicuskEls6DIj+B5b/hqZcM5iHm3OsoeEezWxVt9QczWfPhdxSkZu623ZLnP2LTzAUE/UXkrt/Kije+okG/zgAktkpm83cLCBb62fDVLyS2Cn1PDht1Jb/9exL+rNz9fh0iIiLViZJMKaXmIU1xRQEyl64tLkubv5zah7Yss33f/9zIz/e9TCCvoFR52m+raHxST7yx0TQ8qgvpC1fR8aZzWPfZbLJXbtiflyAHMzM8jVsSXPMHbuMavF2OAPPg7doPV+QnuG5luaf6+g8i/vGPiB/1NhYdS9FPoSXgwQ0r8bbvCb5ovG06E9ywGt9x51C0aDYuVd/Vqqrh0V1JX7QKgLSFq2h4VBe8sdEkn9iDtEWrqHvYIdQ8pNlubwUQEZHqw2uV+1XVKMmUUnwJNSjMLP1b/cKMHHyJNXZp2/zsIzGvhzWTv9ulbunLU4muFc+ZPz7DppkL2PbrctoO6c/CMe9zxHM3c9qMJ+n58BX77Tqk6gtuSsFlpeM76ULwePF2OAxv264QHQsuiH/2F8Refi/xYz4l9vJ7KXhrTOnZyp34v3iLnNvOIvfRv1M0+wtcXk6o/PtPsRrx1Lh9LIHlvxFct5yoPifh//p9Ygb9gxo3P0H0mfquViXtrjiVer0O4bd/TwIgfeEqVr3/LWf+MJaEZg1Y8Njb9H3qBmb9YywdbzyH02Y8yTGv30N0rfgKjlxEROTgoCRTSvFn5xFdM65Uma9mPP6svFJlUXGx9P7XMGaVs/Q1UODn+2ue5MPuV/PzPS/R54nr+Pm+l2kz+ETMY3x63C3U69ORJqf03m/XIlVcMED+uAeI6nQ48aPfwXfiBRTN/QaXtgVv+57EnD2MvKduI+fmU8kbcxsxl9yKp0mbPXe7dhnOX0j0GUNDBUV+Ct58krzRwyic8hIx515L4ZT/EtX7RDAPeWNuxdOyA96O+q5WBc0HHslhj/yNL06/p9QS20Vj3uPDHsOYcfE/aXXhcWz8dj7mMdpffQafnXQ7GYvX0CV8r6aIiIjsGyWZUkrmH2uxKC812zYpLqvTtXXxsrPtarZrQkLLRpz2vzFctP4dTnhvBDWS63DR+ndIaNGwVNsmp/TGzFg3bQ5JXVqx9ac/AEj9eQlJXVvv92uSqiu4fmUokbzrXPKfuRtPvWQCqxfjadqGwLL5BNf8Ac4RXLOE4KrFeDv03LuOPV489RrvUuzt2BvMCPw+B2/jVgTWhL6rwTV/4GnSKpKXJvtBk1N6c+S4W/lywH2k/Vb20unYBkm0v/oM5j30OkmdW7Ft/gpcUYAtc5ZQp4v+PRIRqa48VrlfVY2STCmlKDef1e/PpMfIy4mKi6VBv040H9iP5a9/Uapd2m8rmdR8EFN6DGNKj2F8d/Xj5G9KY0qPYeSkbClu543xcdjovzHrltAGKlkrN9Lo2G54fFE06NeZrBW6503K52ncCqJ84IvBd+IFWM06FM36nMDqJXjbdCmeufQ0bYu3bReC61bs2okZUUeeATUSQm1btMd3zAACS+aWbhflI3rgVRS89ywAwdQNeNt1A28U3tadCG7Vd7UimNeDN8aHeT2l3u8s+fjuHDPhXqafP4Ktc5aU21+fx//OLyNfI5BXQNbKDdTv3Z6o+FiSj+tGlu4XFxERiQg9J7MKMrOFwPXOuRlmNgJo65wbYmYtgZWAzzlX9Ff7/+H6pzjq5TsYtOldClIz+eG6p0hftJqGR3Wh/9TRTKh5Ji4QJG9TWvE5BduycEFXqgyg672DWfHGV8XPpFvywsccP+kBLt78HimfzGKNHmUiuxHVpz++fqeBN4rAsgXkjb0LivwEl82ncOp4Yv/2AJaYhMvOoHDaGwQW/xw6r9cJ+E65hLxRfwsddzuKmAFXQZQPl5GK/5vJ+L+ZXGqs6FMuoWjOdFx66Lvqn/kJsVfdT/yj71G0cBaBX3e991j2v27Dh9DjwaHFx20v7c8vI19j6X8/45yF/+WDTleSk7KZbsMvJbpWPP0/GV3cdtO3C/jijHuKj5OP70507YTi+8i3zllCytRZXLjmLTKWpPD1BSMP3IWJiIgcxKpVkmlmlwC3Ah2ALGAeMMo5V2kzHTN7FVjrnBu+vcw512l/jlmYlsX0cx/YpXzTzAVMqHlmmeds/OZXJjUftEv5Lw++WurYn5nD56feFZE45eBXOHkchZPHlVnn/9+H+P/3YZl1RT9NL949FufIf/aeMtuVGuuT10oX5OeQ/8zdfypeibx5I8czb+T4MutK/nv02Ym37bGvDV/PY8PX80qVzb7lWWbf8uy+BSkiIlVeVdzBtTKrNstlzexWYAzwCNAQaA48Cwz8k/1Uq8RcRERERETkz6gWSaaZ1QIeIrTE9H3nXI5zzu+c+8g5d4eZxZjZGDNbH36NMbOY8LnHmdlaM7vLzDYCr5jZCDObZGbjzSzLzBaaWa/djL/b9mbW0cxmmFl6uG5AuHwYMBi408yyzeyjcPkqMztpP35kIiIiIiIif0m1SDKBI4BY4INy6u8D+gLdgW5AH2B4ifpGQB2gBTAsXDYAeAuoDUwByn6Wxw5ltjczH/AR8DnQALgRmGhm7Z1z44CJwGPOuQTn3Fl7eb27MLNhZvaTmf00blzZyw9FRERERKojr1XuV1VTXZLMusDW3WyGMxh4yDm32Tm3BRgJXFqiPgg86JwrcM5tf2DkTOfcVOdcAHidUHK6O+W17wskAI865wqdc9OBj4GIPrDNOTfOOdfLOddr2LBhez5BRERERETkL6guSWYqUG8391M2BlaXOF4dLttui3Muf6dzNpZ4nwvEmlmUmQ0OL23NNrNP99Q+PE6Kcy640/hNEBERERERqWKqyyY2PwAFwNnAu2XUrye0FHZh+Lh5uGw7t7cDOecmElriurfWA83MzFMi0WwO/PFnx64IHa8fSNuhp5DUpRUr3vyamVc+VtEhSRXkifZxxLP/ACD+31MIbl1P4ZSXCSyagzVqTuxld+GpF/q9T2DNUgreHYvbuCZ0cpSPmPOvw9v1KMzrJbBiIQVvjcFlpBb3b7XqUuP2seSOvIyYC2/C26EnFpdYapyd+U4dQsyZl5P39J3Fz9SMPmcYUV36AXDOoleYP/qNXZ4hKxXrmPH3kHxiD6LiY8nbmMaC/3ubpS9PreiwRESkkquKS1Irs2oxk+mcywAeAJ4xs7PNLM7MfGZ2mpk9BrwJDDez+mZWL9x2wgEKbxahmc07wzEdB5xF6P5NgE1A6wMUy5+Wuz6VX0dNZOkrn1V0KFKFeaK85KRsBiDnjoEUfvwKsVfej9VpiMtIJf+lh8i58xxy7jqPwILvib1ixy3TvuPOwdPyUPJGX03OfRfhcrOJueCGUv17D+1D4Pc54PHi0reQN+bWXcYpyeolE9XzWIIZW0sHWpBP3gv3A/Dt5f/i8DHX0+CIQ/fDJyJ/1fxH3+CdVoOZWHsAXw0cTs+Hr6Buz3YVHZaIiEi1Ui2STADn3OOEnpE5HNgCpAA3AJOBfwI/AfOBBcDccNmBiKuQUFJ5GrCV0GNVLnPOLQ43eRk4NLzz7ORyuqkwqz+YyZoPv6MgNbOiQ5EqrCg3f8ezEJ0j8Nssgqkb8TQ/BPJycNs2heoMcEE89XesZvfUTSbw+0+4rHQo8lM0dwae5Jal+o/q1IeihbOhMJ/CqeND/e08TgkxF95E4eQXoaj0bdyFU8fjNqUAsHX2YjZ9u4D6R+zXx9bKn5S+aDXBQj8AzjlwjsQ2jfdwloiIiERSdVkuC+xxKetN4dfO58wAmu5UNmKn41WE/vtb3ri7be+cWwgcW865SwnteluyrGVZfe8pDpGqwhJr42nQlOCGVcVl8Y9NhpgaYEbhJ68Vl/t/+JSY867DatXF5WYT1ftEihbN3tGZx4u3bVfyX991KXdZ43h7HANFfgIl+yiDNzaaer3bs/i5KX/xKmV/6fvMTbQbegpRcbGkzl3K2qmzKjokERGRaqVaJZkiUgV4vMQMvZeiWZ8XzxoC5Nx5NkTHEnX4yTtmNoHg5nUE07cQP+ptXCBAcP1K8iY9XVzvbduVwLrlUJBXapgyx4mpQcxZV5I39q49htnvuVvY9uty1k3b9X5OqVg/Xv8fZt04lvpHHErycd0IFPgrOiQREankPJqmiahqs1xWRKqGmKF3Q8BPQYlEsVhhPkUzPyL2sruwhNqh9hfeiEX5yL7zHHJuO5OiX7+lxnWPFJ/i7dSHwMKdZiXNyhwn+vTL8M/+slQSW57anVsy46KH/9pFyn7ngkE2f/cbcU3r0+HaARUdjoiISLWiJFNEKhVLTCL/pZEQDJTTwMAXg9WuB4CnaRv8P34OuVlQ5Mf/zWS8LTtCfE2gxP2YJcQMvq3McbztexB93DnEPTKJuEcmYUn1ib1yOL6TLipuE336ZQB8fspd+LNyI3npsh94orzU1D2ZIiIiB5SWy8o+Ma8HT5QX83owrwdvjI9gUQAXCO75ZJESjnjuZgDynx8O/sLicm+HnrjsTILrVkBMLNFnXoHLyyK4MfRo2+CaJfgO709g6a9QmI/v6AEE07dCTiZWtxFEReM2rSnuL2bQP/A0bEHe03eUGgcg7+k7MY+3+LjGnc9Q8P7zxTOhvpMvJqrXCQAUbNNmV5VNbP3aJJ/Qg5SPfyCQV0jyST1pNeh4vrlkVEWHJiIilZweYRJZSjJln3QbPoQeDw4tPm57aX9+Gfnajp1CRfZCfPMGdLjmrND70e8Ulxe8+SQuUETs+TdgSfWhsIDA6iXkP3MPFIXusyv4YBwx519P3IOvYl4fwQ2ryH/xQQCiOh1O0cIdm75YUgN8R52F8xfuMk7RT9MhJ7P0g2mDQcjNhsJ8AGIGXIULJ6ZDMj8GYP7oN5g/+o1IfyTyFzjn6PD3szjiuZsxj5GzehOzb3mWlI9+qOjQREREqhUlmbJP5o0cr4RS9lnOms284jmRK4JfkXPbWbvU5/7yv92cnEnBa6PLrPJ26oP/mw+Lj13aZrJvOGmv48p9cEip4+3nJoz9kgk1z9zrfuTAKNiawafH31rRYYiIiFR7SjJF5KAVWPorgaXzKjoMERERqeS0XDaylGSKyEHL/+Wkig5BREREpNrR7rIiIiIiIiISMZrJFBERERGRas2j5bIRpZlMERERERERiRglmSIiIiIiIhIxWi4rIiIiIiLVmsfcnhvJXtNMpoiIiIiIiESMkkwRERERERGJGCWZIiIiIiIiEjG6J1NERERERKo1rx5hElHmnG5yrYb0ly4iIiIiB0KVSN+mrJpQqf9/PKDlkCrxOW6n5bIiIiIiIiISMVouW0294jmxokPYrSuCXwGKM1KuCH5V6WOEqhVncPJVFR3GHnnOfrnSf55V6WcIFGekVKWfdcUZOVUhzqr0MwRVJ86qQI8wiSzNZIqIiIiIiEjEKMkUERERERGRiNFyWRERERERqdY8VWpbncpPM5kiIiIiIiISMUoyRUREREREJGK0XFZERERERKo1r3aXjSjNZIqIiIiIiEjEKMkUERERERGRiNFyWRERERERqda0u2xkaSZTREREREREIkZJpoiIiIiIiESMlsuKiPxJEyZM4P3/zuOPjTmc0b0+oy88pLjuh2XpPDx5ORvSC+jaLJFHLmxHk6RYAAqLgoz8YBnTFqRSw+fhqmObcvkxTcod59Vv1/HyjLXk+YOc0qUuD57TlugoD0UBx51vLWHmH2l0a57Ik4M7kBAb+uf8hekpxER5dtuviIiIlObR7rIRpZlMEZE/qUGDBvz9xGac27thqfK0HD83jf+dm05uwY8P9qVz0wRunbikuH7sF2tYvTWfr+7uzavDuvDyN2v5dklamWPMXJLGSzPW8t+rO/PV3b1J2ZbP01+sAeCL37ZiBt8/0JeE2CgmzdoIwNpt+UxftI0hRzbeT1cuIiIismdKMkVE/qSTTz6ZkzrVpXacr1T5F7+l0rZhHKd2rUeMz8P1/ZuzZEMOKzbnAjD5581ce2IzasVF0aZhHOf3acQHP20qc4zJczdzXu+GtGsUT624KK49sTmTw23XphXQp3UtorzG4W1qkbItH4BRU5Zz15mtiPJq9wIRERGpOEoyRUQiZNmmHNo3ji8+jov20qxuLMs25ZKRW8SWrELaJ++o75Acz7JwAlpmXzu13ZrtJy3HT7uGcfy4PIPCoiCzlmfQtmEcX/y2laQ4Hz1b1tx/FygiIiKyF3RPpohIhOQUBKmTUHp2MzHWS05BgNzCQPh4xz+7CeG6suQWBHdpGyoPcGyHJH5elckFT8+jW/NETu9Wnytf/I2X/9aZMZ+t4qdVmbRrGMd9pxdG+hJFREQOSloEFFmayRQRiZD4GA/Z+UWlyrILAsTHeImL9oaPd9TnhOvKErdTX9n5gXC5FzPjttNa8uEtPXnovHa8NGMtF/VtxIK1Wfy2NpvXr+mCP+B47733In2JIiIiInukJFNEJELaNoxnyYac4uPcwgApqfm0bRhHrbgo6idGs3j9jvrFG3Jo2yBur/pasiGHegk+kuJLz5T+sSGHX1ZncmGfRvyxMZdOTRMwM7o0TWDJkiU7dysiIiIHKTNraWZTzSzNzDaa2VgziwrXdTezn80sN/xn9xLnmZn9y8xSw69/mdk+ze0qyRQR+ZOKiooo8AcJBh2BoKPAH6Qo4DipU12Wbszl8wVbKfAHefbLNRzSKJ7W4URy4GENeH56Chm5RaxzJHQCAAAgAElEQVTYnMs7szdyTq+GZY4xsGcD3puziWWbcsnMK+L56SmcvVNb5xwPf7ic+wa0weMxmibF8POqTAqLgsxZmUmzZs32+2chIiJyMPBY5X7tpWeBzUAy0B04FrjOzKKBD4EJQBLwGvBhuBxgGHA20A3oCpwFXLNPn+e+nCyVi5mNMLMJFR2HyMHuueeeo/vw73lxxlo++mUL3Yd/z/PT11AnwcdTl3ZkzLTVHD7iR+anZPHEJe2Lz7uxf3Oa143lxEfncNkLC7jqmKYc3T4JgPVp+Rx2//esTwvtFHt0+6TQczTHLeCE0XNoXDuGG/s3LxXH+z9tol2jeDo1TQCgf+d6NEiM5siHZpGe4+eiiy46QJ+IiIiIVAKtgEnOuXzn3EbgM6ATcByhvXjGOOcKnHP/AQw4IXzeUOBx59xa59w64HHg8n0JpNJu/GNmg4BbgM5ADrCSUNb9nHNOT0sVkQpz4403cn2zeWXW9WtXm6m3H1ZmXXSUh1EXHMKoC3ata5wUy88P9ytVdvkxTbj8mCblxnFe70ac13vHcZTXeGJwh+JjT0LCbq5CREREDjJjgEFmNoPQjOVpwP2EEs35O+VQ88Pl2xPRX0vU/Rou+8sq5Uymmd0GPAX8H9AIaAj8HTgSiC6jfdk7Z4iIiIiIiOyBx1ylfpnZMDP7qcRrWBmX8T9CyWEmsBb4CZgMJAAZO7XNABLD73euzwAS9uW+zEqXZJpZLeAh4Drn3LvOuSwX8otzbrBzrsDMXjWz58I3tuYAx5vZGWb2i5llmlmKmY0o0WesmU0I38iabmZzzKxhuO5yM1thZllmttLMBu8mNmdm15nZ0nD7h82sjZl9Hx53Uom1zZjZ1Wa2zMy2mdkUM2scLh9pZk+H3/vMLMfM/i98XMPM8s2sTvi4b7j/dDP71cyOK9F/KzP7JhzLF0C9yP1NiIiIiIhIZeCcG+ec61XiNa5kvZl5CM1Kvg/EE8oLkoB/AdnAzg/Srglkhd/vXF8TyN6X1aOVLskEjgBiCN2cujuXAKMIZeAzCS2pvQyoDZwBXGtmZ4fbDgVqAc2AuoRmRfPMLB74D3Cacy4R6AeUvQZuh1OAw4C+wJ3AOGBIuO/OwMUAZnYCMBq4kNDNt6uBt8J9fENobTRAb2AjcEyJ61/inNtmZk2AT4B/AnWA24H3zKx+uO0bwM+EvkQPh69TRERERESqlzpAc2Bs+L7LVOAV4HRgIdB1p5nJruFywn92K1HXrUTdX1IZk8x6wFbnXPED4krM5OWZ2fZk7EPn3HfOuWD45tYZzrkF4eP5wJuEdlQC8BNKLts65wLOuZ+dc5nhuiDQ2cxqOOc2OOf29IE+5pzLDLf7DfjcObfCOZcBfAr0CLcbDPzXOTfXOVcA3AMcYWYtgR+AdmZWl1By+TLQxMwSwjF/E+5jCDDVOTc1fF1fEJr2Pt3MmhNKUO8Pf5H+B3xUXtAlp9jHjRtXXjMRERERkWrHa5X7tSfOua2E9rC51syizKw2oQmo+cAMIADcZGYxZnZD+LTp4T/HA7eaWZPwysvbgFf35fOsjElmKlBv+zNdAJxz/ZxztcN122NOKXmSmR1uZl+b2RYzyyA0W7l9+ejrwDTgLTNbb2aPmZnPOZcDXBRuu8HMPjGzDuH+FppZdvh1dImhNpV4n1fG8fadNhoTmr3cfg3Z4fibOOfyCCWLxxJKMr8Bvid0z2nJJLMFcEE4wU43s3TgKEIzo42BtPA1bLeacpScYh82rKwl3CIiIiIiUoWdC5wKbAGWEZpou8U5V0joESWXAenAlcDZ4XKAFwhNVi0gNIn2SbjsL6uMu8v+ABQAA4H3dtNu5zXCbwBjCS19zTezMYSTTOecHxgJjAzPJE4FlgAvO+emAdPMrAahZakvAkc75/ZpRyVgPaEkEYDw0ty6wLpw0TeEtg3uAcwJH58C9CF00y6EEunXnXNX79y5mbUAkswsvkSi2ZxdPxcRERERETnIOefmseOWvJ3rfiF0y19ZdY7QbYB3RiqWSpdkOufSzWwk8Gx43fA0QvdbdiV0E2t5EoFt4QSzD6F7Nj8HMLPjga3AIkK7LfmBYHjzn77Al4RmIbMJLZ+NhDeBN83sDeB34BFglnNuVbj+G+BdYI5zrjC81fBoYKVzbku4zQRgjpmdEo7RF453mXNutZn9RChxvpdQcnoWMCVC8YvITj5quI1Vcfk83bMn9WoEuOrYplzQpxEAeYUBHvtkJZ/N30pRwNG+cTwT/t4VgLFfrOaF6WuJjtqx3mXyzT1pVje2+PiTeVv4+vdt3DegNde/togVW/IIBh2tG8Rx5xmt6Nlyx/34Kan5jJqynDkrMomOMs7t3ZA7Tm8FwGUvzOfXNVlEeQxG9iC2RSHDVjc6EB+PiIhIleUxzdNEUqVLMgGcc4+Z2TpC2fR4QknmCuAuQstKLy/jtOuAx81sLKEEbhKhTYAg9BiU54GmhBLJtwktoa0P3BoewxHa9OfaCF3Dl2Z2P6HZ2KRw3INKNPkeqMGOWctFQH6JY5xzKWY2EHiMUNIaAGaXiPESQs8O3UZoBnh8iWsWkQjrm5bIaZuTuDownWXjLmboCws4tHECnZom8OD7yygKOD657TBqxUWxeH1OqXNP61aPxwa1L7fvbxZv45j2ScRFexl1QTta1K2BGXy1aBvXvbqImfcfTpTXKCwKctVLv3HJEck8MbgDXjNWbc0r1dfwgW24oE8jPGe/zCueE/fLZyEiIiJSnkqZZAI45yYCE8upvryM9u8Smhksq683CSVpO9vAjs2B9iYm2+n4qJ2Oh+90/Dyh5LasvrIJzUxuP3ZAgzLazSovRufcCuDosupEJPLqFxb/yGIGGKxJzaNGtIfpi7Yx497eJMSG/lnt1DShnF52FQw6vl+azj1ntSbG56FV/bjicq9BRl4RGXl+6iZEM/nnzTSoGc3lxzQpPr998u4WeYiIiIgcWJU2yRQRqYym1U9jTLdu5Ofn07FxPMd0qMMXv22lcVIMT3+xhilzN1O/ZjQ3nNSck7vseHTt14u20XfEj9RLjGZwv2QuPiK5uG5+ShZN68SSFL8jiR345FxWbsnDH3Cc37shdRNCj+D9dU0mjZNiGPbyQhaszaJdoziGD2jDISUSzSc/W8UTn66i1VuD6FAjnxZ5O5blioiIiOxvSjJFRP6EU7YkMXH958z9z0XMXpFBdJSxKaOQpRtzOblzXb65rw/z1mRx7SsLadMgjjYN4zi1a30uPLwRdROimb8mi5sm/E7NGlGc0T30yNtvFqdxTPukUuN8eEtPCvxBvlyYij+w41bxjRmFzF6ewTNDO9K3bUde/249149fxCe3HUZ0lIfbTmtF24Y18Hk9fOodwAM/38MVaxqS5Nc/9yIiIuXx7MVjQmTvVcZHmIiIVGper5fDWtViU0Yhb/24kRifB5/X+PsJzYmO8tCndS36tKnFd0vTAGjbMI4GNWPweoweLWty2ZGNmbZga3F//1uyjWM71NllnBifhzO61+fFr9eyeH02ALE+Dz1b1uSYDnWIjvJw5TFNSM8pYsXmXAC6NU8kPiaK6CgP55xzDk3yolkel38APhURERGRECWZIiJ/UVHQkZKaV+Y9kcZufiVqhgtvYrclq5AtmX4ObVL+fZVFQUfKtlCieEij+ND9oHsp1FQ75omIiMiBoyRTRGQv5HgDLErIpdCCBAIBZi5JY+q8LfRtW5terWqSXDuGcV+nUBRwzF2VyazlGRx1SGgJ7FcLU8nILcI5x/yULCZ8t54TO4VmLr9dnMbR7Wtj4cxx3upMfl6ZQWFRkHx/gBdnrGVrlp9uzRMBGNCzPr+uyeL7pekEgo7XZq4nKd5H6wZxZOYVMXNJGgX+IEUBx5QpU0ipUUjrXN2TKSIisjtec5X6VdXoJh0Rkb1gwC+1spnWII0XevemcaLj7rNac8KhdQEYe1lH7n9vGS/NWEvjpBgevagdrRuEdomd+usW7nt3Kf6iIA1rxfC345py9mENgdCjS07vVr94nMKA45Epy0lJLcDnNdo1iuP5Kw6lQc0YAFrVj+Nfgw5h5AfLSM0OzYA+M7Qj0VEesvMDPPX5alZszsPrgdbtszl3Q13q+H2IiIiIHChKMkVE9kJcwMvgdaGnDF0R/Irg5KtK1bdrFM9b13cr89zHL+lQZnlRwDF7RQajLmhXXNandS0m39xzt7Gc3LkeJ3eut0t5nQQf79zYvfhYz8kUERGRiqAkU0SkgmTk+bnp5BbFz9YUERGRiqHdZSNL92SKiFSQugnRpZ6XKSIiInIwUJIpIiIiIiIiEaM1WiIiIiIiUq15quAOrpWZZjJFREREREQkYpRkioiIiIiISMRouayIiIiIiFRrmnmLLH2eIiIiIiIiEjFKMkVERERERCRitFxWRERERESqNa92l40ozWSKiIiIiIhIxJhzytqrIf2li4iIiMiBYBUdwN74Pe2lSv3/445Jf6sSn+N2mskUERERERGRiNE9mdXUK54TKzqE3boi+BWgOCPliuBXlT5GUJyRdkXwK16POamiw9itSwu+BKrGzxAozkipSj9DijNyqkKcVelnCKpOnFWBp0rNE1Z+mskUERERERGRiFGSKSIiIiIiIhGj5bIiIiIiIlKtefQIk4jSTKaIiIiIiIhEjJJMERERERERiRgtlxURERERkWrNq91lI0ozmSIiIiIiIhIxSjJFREREREQkYrRcVkREREREqjXtLhtZmskUERERERGRiFGSKSIiIiIiIhGjJFNE5CB2+vfPcEnmVPq9eEep8kbH92DA/P9ycdrH9J/2b+KbNyiu80T7OOKF27loy4ecv3oSHf9x3m7H6HjTeZy/ehIXbfmQI164HU+0DwDzejj69fu4aNNkTvhoNL7EuOJzOt958R77FREROVA8VrlfVY2STBGRg9iCRyey7LVppcpi6tbk2LdHMG/Eq7zd6BxS5/7B0RPuL67vdv9lJLZtwgftBvP5KbfT6daLaHxy7zL7T+7fi063D+KLU+/gg3aDSWiVTLcHhgLQ/Oyjcc4xqfG5+DNyaPe3MwBIaNmIpmf2Y/HYD/bTVYuIiEhFUpIpInIQS5nyPYWpmaXKmp99NOmLVrHm/f8RLPAz/+HxJHVtTc32zQBoPaQ/C0ZPoDA9m8zFa1j636m0ufTkMvtvM+Rklr36KRm/r6YwPZsFoycUt01o2YhN//sVFwiy8Zt5JLRKBqD3E9fz813P4wLB/XjlIiIiUlGUZIqIVDO1Dm1B2oIVxcdFuflkr1hP7Y4tia6dQFzjeqTNX15cnzZ/ObUObVl+X/NXlGpbo1EdouvUJH3RKhod1wNPtI9Gx3YnfdEqmg04kvytGWz5YeF+uz4RERGpWHqEiYhINeOLr0H+1oxSZYUZOUQl1iAqoUbx8Xb+zBx84fJd+kqogT9zR9vt5/kSa7Du01k0OLILp3//DFtn/86qSTPo/9ljfHn6XXQfeQUNjuwS6UsTERH5S7x6hElEaSZTRKSa8efk4asZV6rMVzOeoqw8irLzAIiuGb+jLjEef7h8l76y80pt6OMLn+fPCrX/ZfhLfNxrGD9e9ySd7xjEHy9+TN1e7al7WHs+P+nWiF6XiIiIVA5KMkVEqpmMRatJ6tK6+DgqLpbE1smk/76KwvRsctdvJanrjvqkrq3JWLSq/L5KtK3TtTV5G7dRuK30faC1O7Wift9DWfrSJyR1akXq3D8ie1EiIiJSaSjJFBE5iHlifJjXg3k9xe/XfDiT2p1a0fzso/HE+Ohy3xDSFqwkc0kKACsmfkmXuwcTXTuBmu2b0fbK01n++udl9r9i4he0vfw0anVojq9WPF3uHlxm2z5jbmDOrc+Ac2Sv2kiDfp3x+HTHhoiIVA4V/YgSPcJERESqjMGZn9L5zotpPbg/gzM/pcs9QyjYmsE3g0bS/aEruGjTZOr17si3l/6z+JxfH3qNrBUbOGfpRE7+4gkWPTmJ9Z/PASCuWQMGpX5EXLPQczXXfz6HhU+8Tf/PH+fcZW+QvWYTvz70WqkY2gw9lfRFq9j2y1IA1kz+lrwNqVyw7r0D9CmIiIjIgVTpfo1sZoOAW4DOQA6wEngNeM45d9DckWtmI4C2zrkhFR2LiBy8Xo85qczyjdPnMqXrlWXWBQv9/HDNv/nhmn/vUpebspm36p5Vquz3p97j96fKTxiXv/YZy1/7rPjYBYJ8e+koAC4t+HKP1yAiIiJVS6WayTSz24CngP8DGgENgb8DRwLRZbT3HtAAKxEzq3S/IBARERERqYoMT6V+VTWVJmIzqwU8BFznnHvXOZflQn5xzg12zhWY2atm9pyZTTWzHOB4MzvDzH4xs0wzSwnPEG7vM9bMJphZqpmlm9kcM2sYrrvczFaYWZaZrTSzwbuJzZnZTeH2W83s/8zME65rY2bTw2NsNbOJZla7xLl3mdm68DhLzOxEMzsVuBe4yMyyzezXcNtVZnZSiXNHmNmE8PuW4TiuMrM1wPRw+ZVm9ruZpZnZNDNrEbG/FBERERERkT+p0iSZwBFADPDhHtpdAowCEoGZhJbUXgbUBs4ArjWzs8NthwK1gGZAXUKzonlmFg/8BzjNOZcI9APm7WHcc4BeQE9gILB9nZkBo4HGQMfwWCMAzKw9cAPQOzzOKcAq59xnwCPA2865BOdctz2MXdKx4XFOMbOBhJLVc4H6wLfAm3+iLxERERERkYiqTElmPWCrc65oe4GZfR+egcwzs2PCxR86575zzgWdc/nOuRnOuQXh4/mEkqxjw239hJLLts65gHPuZ+fc9n31g0BnM6vhnNvgnFu4h/j+5Zzb5pxbA4wBLgZwzi1zzn3hnCtwzm0BnigxfoBQ4nyomfmcc6ucc8v36VOCEc65HOdcHqGkebRz7vfw5/YI0L2s2UwzG2ZmP5nZT+PGjdvHEEREREREDh5mVqlfVU1lSjJTgXol7zV0zvVzztUO122PNaXkSWZ2uJl9bWZbzCyDUOJVL1z9OjANeMvM1pvZY+FkLwe4KNx2g5l9YmYdwv0tDC9hzTazo0sMVXLc1YRmLjGzhmb2VnhJbCYwYfv4zrllwM2EZjY3h9s13sfPqWQcLYCnwol4OrCN0Mxqk51Pcs6Nc871cs71GjZs2D6GICIiIiIiUrbKlGT+ABQQWoq6OzvvMPsGMAVo5pyrBTxPKNHCOed3zo10zh1KaEnsmYSW1uKcm+ac6w8kA4uBF8PlncJLWBOcc9+WGKdZiffNgfXh94+EY+rinKsJDNk+fri/N5xzRxFKCB3wr3KuA0JLf+NKHDfaw/WnANc452qXeNVwzn1fxnkiIiIiIiL7XaVJMp1z6cBI4FkzO9/MEs3MY2bdgfjdnJoIbHPO5ZtZH0L3bAJgZsebWRcL7UKbSWj5bDA8+zgwfG9mAZBNaPns7txhZklm1gz4B/B2ifGzgQwzawLcUWL89mZ2gpnFAPlAXolxNgEtt28gFDYPGGRmPjPrBZy/h5ieB+4xs07h8WqZ2QV7OEdEqrH21w7k9O+f4ZLMqfR78Y4y23S5dwiXFnxJoxN6lir3+KK4YN17RMXH0uK8YzllxlNcnPYx/T9/vNzxWg/uz6UFX9L2itMieh0iIiKRVNG7x2p32f3IOfcYcCtwJ6EkbBPwAnAXUN7s3HXAQ2aWBTwATCpR1wh4l1CC+TvwDaEltJ7wOOsJLTE9Frh2D+F9CPxMKBH8BHg5XD6S0GZAGeHy90ucEwM8CmwFNgINgHvCde+E/0w1s7nh9/cDbYC0cL9v7C4g59wHhGZG3wov1f0N0P/kRKRcuetTWfDoRJa9Nq3M+oTWybQ471hy12/dpa7B0V1Jm7+copx8CtKyWPz0+/z2f2+VO1Z07QQ633Ux6QtXRix+ERERqfwq3bMWnXMTgYnlVF9eRvt3CSWSZfX1JmXvtrqBHZvz7K2pzrn/lDHGQuCwnYofD9fNB/qUE1sqcNROZSuAw8tpv4oSy3BLlL9OKHEWEdmjlA9nAlC3Z3uimtTbpb7PUzcx974XOfypm3apa3JqH9Z9NguAjdNDvxvb3Qxlj39exeJnJtPivD/7z62IiIhUZZVqJlNERCpO83OPIVjgZ/1ns8usb3JqH9Z9Omuv+qrbqz11ex7CH+M+imSIIiIiUgVUuplMERE58KISatDj4Sv58vS7yqxPaJ2MRXnJ/GPtHvsyj4fD/3MTs28eC66sPc5EREQql6r4mJDKTEnmXnDO6VsnIge1bvdfxoqJX5KzelOZ9U1OPZz10+bsVV+H/H0AaQtWsnX275EMUURERKoIJZkiIkKj43sQ16Q+7a8ZAEBM/VocM3E4C//9Ngsff5smp/Zh8TMf7FVfycf3oMHRXWlyauiW9Og6idTp3pakbm2Yc/PY/XYNIiIiUjkoyRQRqUbM68GivKE/vR48MT5cUYAvTr0Tj89b3O70757hpzufZ/202XhrxFCvVwc2zpi3o5//Z+++w6Oo3jaOf88mmwqhd6UjSG8KiCAoINhFpShF/Ckq2CvYUVFfe8OCBQHFBjaaIggIKkWqgBSBhN5TSEjf8/6xy5KQEAJsstnk/njt5c6ZMzPPbDKzPDllHA6MM8i9L4dx7yfThc3I5I9bXyYoLMRb96Kvn2Hb97/z37ifC/VcRURE8isQHxNSlCnJFBEpQZqNHECLJwd5l+ve1J1Vz01g9fMTstWzmS7S4hLJSEqhxmXt2L94Ha7UdO/6Ojd1o+PHj3iXb0qYyeYJv/Dnba+QHp9EenySd50rLYP0hCOkJyQhIiIixZ+STBGREmT18zkTytx833CA932Nnu3YedyMs1smzmLLxFn5OuavPR48tSBFREQkoCnJFBGRPMWu2syO6X/5OwwREZECY3I+jl7OgJJMERHJ06ZPpvs7BBEREQkgGuEqIiIiIiIiPqOWTBERERERKdGMUdubL+nTFBEREREREZ9RkikiIiIiIiI+o+6yIiIiIiJSoml2Wd9SS6aIiIiIiIj4jJJMERERERER8Rl1lxURERERkRJNs8v6lj5NERERERER8RljrfV3DFL49EMXERERkcIQEDPqxKV9WaT/fVw2pH9AfI5HqSVTREREREREfEZjMkuocY5L/B1Cnoa45gCK01eGuOYU+RhBcfpaIMR59BpKvKubnyPJW6l3ZwOBca1DYMRZ1GMExelrgRBnIF1DEDhxBgI9wsS31JIpIiIiIiIiPqMkU0RERERERHxG3WVFRERERKREM2p78yl9miIiIiIiIuIzSjJFRERERETEZ9RdVkRERERESjRjNLusL6klU0RERERERHxGSaaIiIiIiIj4jLrLioiIiIhIiabZZX1Ln6aIiIiIiIj4jJJMERERERER8Rl1lxUREb8Lf2QMjmp1yFg2l9TPX/GWB53TitA+d2PKVyYzej2pE1/Gxu5zrwx2Etr3XoJbdsKmp5I++2vSf5tywmM4u16Hs3tfjDOUjJULSP36LchIB4eD0MEjCT73PDKj/yXl0+cg5Yh7mx79ISOtQM9dRET8z6DZZX1JLZkiIuJ3aT9/QcaiX7IXRkYRdtszpE7/jKRHrsW1bSNhtzzpXR1y2SAclWqQ9NRNJL/1EM5ufQk697xc9x90bluc3fuR8vbDJD11E46K1Qi5bDAAwS06gbUkjeiNTU7C2fFyAEyFqgQ3u4D0ed8XzEmLiIgUU0oyRUTE7zJX/4lNSshWFtyyE67d0WSu+B0y0kmbMQFHjbqYKme717frTtrPn0NyInbvNjL+mEFw+x657j+4XQ8y/pqJa08MJCeSNvNzb11ToSqZm1aBy0XmppU4KlQDIPT64aR+/wG4XAV34iIiIsWQkkwRESmSHFVr4dq55VhBWgquA7twVKsN4aVwlKmIa+dm7+rMnZvd6/Kxr8ydm3FElYfIKFy7owk6pxUEOwlq0BLXnmiCmnfEJsXj2rK2YE5ORESkGNOYTBERKZJMaDg2MT57YXISJjQcExoOgE1OOrYuJclbnuu+stb1vDeh4WSuXUxQvWaEPzwGV/S/pC+bR/jdL5P87qOEXDmEoHrNfHpeIiJS9BijtjdfUpIpIiJFkk1NxoRFZC8Mj8SmJmNTkwEwYZHYxDj3urBIb/lJ9xUe6S0HSPvpY/jpYwBCrhlK+sJpBNVsiKNmQ5LffIBS78724ZmJiIgUb0rZRUSkSHLticFRo+6xgpAwHBWr4dodDcmJuOIP4Djr2PqgGnXd6060r+PrJhyC48aBOqrXIahOYzL+mI6jeh1c2zb67oRERERKCCWZIiLif8FOcDjcL8/7jFUL3Ulfy04Q7CSk1wBcO7di924HIGPxbEIuvQnCS2GqnE3wBZeRsWhWrrvPWPwrwR16YarWhPBInJfelGvd0BvuInXyGLAWe3APQfWaQpA6/YiIFHemiP8XaPTNKSIiflfqzZne987zu5M2YwJpMyaQ8vEoQm+4CzNoBK6Y9aSMe95bL23GeEL73kvks19g09NIn/0Vmf8uBcCUq0zEE59w5Pn/YWP3kfnvUtJnf034Pa9hnCFkrFpA2ozx2WIIbt+TzN3RuLZvAiBj1QKCWl5I5EsnfvamiIiI5FSskkxjTD/gfqApkARsBcYD71trrZ9iskADa+1//ji+iEggSLyrW67lmRuWc+T5W3LfKCOd1C9eJfWLV3OssrH7SHrwymxl6b9NIf23EyeMGYt+hkU/HytwuUgdN5pU0JhMERGRU1BskkxjzIPAI8Bw4BcgEWgJPAR8AqQeVz/IWptZ2HEezxgTbK3N8HccIiIiIiIllWaX9a1i8WkaY8oAzwLDrLWTrbWHrdsKa+1N1tpUY8xnxpj3jTEzjDFJQFdjzOXGmBXGmARjzHZjzDNZ9hlmjPncGHPQGBNnjFlqjKniWXezMWaLMeawMWarMeamE8T1u06FbywAACAASURBVOftKmNMojGmrzGmizFmhzHmUWPMHmCcMcZhjBlhjNnsOd43xpjyWfbT3hjzpyeOVcaYLlnW5SsWERERERGRwlBcWjI7AKHAjyepdyNwGXAFEAK0BwYBa3F3sf3VGLPSWvsDMBgoA5yNuxW0JZBsjIkE3gbOs9ZuMMZUA8rnOBJgre3s6S7b4mh3WU+CWNWzTS3cif7dwDXARcB+z/7HAP2NMTWA6cBA4GfgEmCKMaYRcCS/sYiIiIiIiBSGYtGSCVQEDmTtdpql5S/ZGNPZU/yjtfYPa63LWptirZ1nrf3Hs7wa+BJ3ogeQDlQA6ltrM621y6y1R+e6dwFNjTHh1trd1tq1pxivC3jaWptqrU0G7gAet9busNamAs8A1xtjgoEBwAxr7QxPnL8Cf+NOlvMdizFmqDHmb2PM32PHjj3FcEVEREREii+Do0i/Ak3gRZy7g0BFT1IGgLX2AmttWc+6o+e5PetGxph2xpi5xpj9xph43MleRc/qibjHdn5ljNlljHnZGOO01iYBfT11dxtjpntaFTHGrPV0i000xnTKI9791tqULMu1gO89SXEc8C+QCVTxrLvh6DrP+guBannFcjxr7VhrbVtrbduhQ4fm+WGKiIiIiIicruKSZP6Fu0vr1Sepd/wMs5OAn4CzrbVlgA/A/SAaa226tXaUtbYxcAHuLraDPOt+sdZ2B6oB64GPPOVNrLWlPK8FpxDHdqCXtbZslleYtXanZ93E49ZFWmtfyisWERERERERfygWYzKttXHGmFHAe8YYg7sFMgloDkTmsWlp4JC1NsUYcz7uMZuzAIwxXYEDwDogAXf3WZdn8p/2wGwgGfcstq48jrEXqAvk9QiTD4DRxpjB1toYY0wl4AJr7Y/A58BSY8ylnmM6Pcf/zxPTqcQiIlIkOEKcdHjvXu9y+IgPSPvpEzLXLcVUrUnYoEdxVKwOQOa2TaROfhe7Z5unciSh1w8nqPF5AGQsmErajAnZ91/nXEKvvYOUsU8Tcv0wgho0x4SE49q1ldTvPsAVsx6AoAYtCLnhLhxlK4F1kfnfalK/eQcbfxAAU6YCoX2Pxdnw9ivY8OG0AvtcRETEP4y7nUl8pLi0ZGKtfRl4APdjTPZ6Xh8CjwJ/nmCzYcCzxpjDwFPAN1nWVQUm404w/wXm4+5C6/AcZxdwCPcYzjvzCO0ZYLynq2ufE9R5C3eL6ixPLIuAdp7z2o67hfYx3JMCbQce9sRxqrGIiBQJjuAgkrbv8y6nTRtH2C1PYspXwcYfJOXjZ0l65FqSHr2OzH/+JGzIE966odfdiXGGcuSpASS/chfB53UjuP2l2fYf3KQ9GWuXQGg4rm0bSP6/YSQ9ci3pS34l/M7REBIGgGtPDCljRpD0yDUkPd4X176dhPY7llSGDh6J6+Bu73Kb0f+japeWBfWxiIiIFAvFoiXzKGvtF8AXJ1h9cy71J+NOJHPb15e4JwI63m6OTQ6Un5g+wN1SmdVZx9VxAa97XrntY3Eex8x3LCIiRUXGkRRWjppAq6cHA5C5ZjGug3tw1DyHzJULsMlJ7ooGsC4clap7tw1u2oHk9x6D9FTsob2k/zUTZ/ueZCz6xVsnqMn5pH7xGvbgbtJ/m3LsuH9MJ/SaoTiqnI1r+ybs4bjsgVkXjoo13O9Dwgg+pyWJnz5HSNfrAIie8jsNhvRkz7yVvv9QREREiolilWSKiEhgMqXL4qh8Fq7d0d6yyJd/gNBwMIa06eOP2yDre4Ojeu1ji1HlMaXL4dqRc5SCo0Y9CHbi2r/rWP1ylYkYORbCIsC6SJ30une/7kNlOZgxlGtaGxERKV6MKTYdPIsEJZkiIuJ3oYMfI2PxLOzeY5OAJz1yjbs1sV0P7KG93vKMdUsJ6d6PlIkvY0qXw9m+JzhDveuDmpxP5rqlOQ8SFkHo4BGkzZwIKUneYhu7z32siNI4L7gM19EYUpPJ3LwGZ68B3rq1e3ciZX+8D89cRESk+FHKLiIi/peZTuo37+QsT0shY+FUwgY9iilVFoDUyWOw6WlEPD2esNufJWPZXGzcAe8mwU3akbFuSfb9OEMIv/05XFv/JX1WbiMhgCOHyVg8i7Chz4LD/fWY8tkLOCpU9VbZ/MVsknbsP7NzFRERKeaUZIqIiN+lfDwKXJm5rzQGnKGYsp7HGB85TOr4FznyWB+SR98KxpDpmS0WRxBB9ZuTuX7Zse2DnYQNfRZX3AFSv3oj70CCgnBElYMw98TkNnYfKR9kmXSoQhkOLF1/uqcpIiJSIpywu6wxZiI5n+eYg7V2kE8jEhGREqHD+/cdW0hP874NatQam5iAa+cWCA0j5Ioh2OTDuPbEAGAqVnNPDHQkkaBz2+DseDnJbz7g3rZeUzJ3boGUI+6dOYII+99TkJZK6sT/A5v9ay2oxYW4dkdj9++EyChCet9J5vZNcOSw+1hVamLjjrVc1ujRlu8aDymAT0NERPxJjzDxrbzGZOb1XEcREZHTFlmzMo1uv/LY8mtTAUj98g1sZgZh19+FKVcJ0lLJjNlAypiRkJEOQNDZ5xBy/Z2Y8FK49u0gZfyL3gQ0qGk7MrN0lXXUbUJwsw7YtBQiX/nRW5783khcm9fgKFuR0Gtvx5Qui01NJnPTKlLGPu2tF3xuW0IuvdG7PKvXCFIPaEymiIhIXk6YZFprRxVmICIiUnIkbdvHOMclDHHNcS8/eGW29UdW/H7CbTNWzCdjxfxc1wU3bkfyJ8e+vlz/rSbxrm4n3Ff6/B9In//DidfP+470ed9R6t3ZABxctvGEdUVERMQt37PLGmNCgIZARbJMHm+t/a0A4hIRETk1QcGkL/kVu2ebvyMREZEAo0eY+Fa+kkxjzIXAt0AoEAUkAKWB7UDdAotOREQkvzIzSP/1K39HISIiUuLlN2V/A3jZWlseOOz5/3PAewUWmYiIiIiIiASc/HaXPQd467iyl4CtwKs+jUhERERERKQQaXZZ38pvS2Y87m6yALuNMY2BckCpAolKREREREREAlJ+k8zvgMs87z8F5gLLgMkFEZSIiIiIiIgEpnx1l7XW3pfl/avGmMW4WzF/KajARERERERECoPJd9ub5Ee+H2GSlbV2ga8DERERERERkcCX30eYLABsbuustZ19GpGIiIiIiIgErPy2ZH583HJV4H/A574NR0REREREpHAZo9llfSm/YzLHH19mjJkCjAOe9XVQIiIiIiIiEpiMtbn2gj35hsaEA3ustWV8G5IUgtP7oYuIiIiInJoAaSKcW8T/fdw1QD5Ht/yOybzluKIIoDewyOcRiYiIiIiISMDK75jMgcctJwF/Am/4NhwpLOMcl/g7hDwNcc0BFKevDHHNKfIxguL0tUCIM5CuIYDxwd38HEneBmfMBgLj8yzqMYLi9LVAiDPQ7kmBEmdAKOLtmPltDzbG9AOeBmoCe4CbrbULjDGXAGM85Ys95TGebUKB94HrgSPAy9ba188k3PyOyex6JgcRERERERGRgmOM6Q78H9AXWAJU85RXBL4DbgWmAs8BXwPtPZs+AzQAauGe4HWuMWadtfbn040lX08dNcYcOkH5vtM9sIiIiIiIiPjMKOBZa+0ia63LWrvTWrsT9zDHtdbab621KbiTyhbGmEae7QYDz1lrY621/wIfATefSSD5SjIB5/EFxhgnEHQmBxcREREREfE76yrSL2PMUGPM31leQ7OGb4wJAtoClYwx/xljdhhj3vVM1toEWOU9VWuTgM1AE2NMOdwtnquy7G6VZ5vTlmd3WWPMAtw9lMOMMb8ft/os3OMyRUREREREpIBYa8cCY/OoUgV3w+D1QCcgHfgReAIoBew/rn48UNqz7ujy8etO28nGZH6Me5jpecAnWcotsBf47UwOLiIiIiIiImcs2fP/d6y1uwGMMa/jTjJ/B6KOqx8FHAYSsyynHLfutOWZZFprx3sCXGStXX8mBxIRERERESmSrMvfEZwRa22sMWYH2efJPfp+Le5xlwAYYyKBerjHacYaY3YDLYBfPVVaeLY5bfkdkznMGHNB1gJjzAXGmDfP5OAiIiIiIiLiE+OAu40xlT1jLe8HpgHfA02NMdcZY8KAp4DVWRoRJwBPGGPKeSYDug347EwCyW+S2R/4+7iyZcCNZ3JwERERERER8YnngKXARuBfYAUw2lq7H7gOGA3EAu2Aflm2exr3REAxwHzglTN5fAnk8zmZuJtaj09Ig3IpExERERERCSwB3l0WwFqbDgzzvI5fNxtolGMj97pU4BbPyyfymyQuAJ43xjgAPP8f5SkXERERERERAfLfknkv7v68u40xMUAtYBdwZUEFJiIiIiIiIoEnXy2Z1todQGvgauAV4AZgLrCk4EITkYLUecJI+u78hpvifqL3+vE0+N9l3nXVLm7FtevGMTBxOj3nvEZkzcon3E+pWlXoOec1BiZO59p146h2Sets+7l+8+f03fUtdfp29ZaHlInkqr8/ILhUeMGcnEgBuzFuarbXwNRfOP/Nu7zrq17cimvWfMpNCdPoMfvVPK+hyFpV6DH7VW5KmMY1az7Ndg1VvbgVvTdNpM+Ob6jdp4u33FkmkiuWvK9rSETEV6yraL8CzKmMqayAe5DoY7gTzNa4WzhFJACtfmkS39a5iS/KXsWcq5+g9XNDqNC6AaEVorh4yjOseGockypcw4FlG+jy1ZMn3M9Fk57g4Mr/mFTxWpY/8Sldv32a0IplAGj3xjBmX/UEs3o+Socx92Ac7ltOmxdvZfX/fUlGYvIJ9ytSlE0qe6X39U2NG8hMTiNmynwAQitE0fXbZ1jx9Gd8WelaDi7byEVfnvga6vzF4xxa+R9fVe7N8ifH0eXrp7zX0PmvDeO3a57k18tG0P7dLNfQ6Fv55+WvdA2JiEiRlGeSaYxxeqa6nQrsBG4HvgPigD7W2m8LIUYRKQBx62JwpaUDYK0Fayldrzq1encibm0M0ZN/JzM1nZXPTKB8i3qUaXh2jn1ENTiLCq3rs+Lpz8hMSSPmuwXE/rOV2td1AiA4Moy4tdHErt5CZloGoRWiqHheQ0rVrkr0t/ML9XxFCkqt3p1I2RfH3gX/AFDz2k7ErYsmZsrvuFLTWTVqAuWa1yUq12uoBhVa1WflM+PJTElj2/cLiF2zlVq9c15DrqzXUJ2qxEzWNSQiIkXTyVoy9wIfAhuA9tbaxtba54C0Ao9MRApc+zH3MDBxOtetH0/y7kPsmLGYck1qc2j1Zm+djCMpHN68i7JNaufYvmyTWhzesjtba0rs6s3eusn74ijXvC7lmtcFl4vU2MO0e/MuFt87pqBPTaTQ1BvUg82f/+pdLtu4FrGrtniXvddQ49o5ti3buHbOa2jVZm/dlP3HriHruYbOe2M4S+57t8DOR0RE5EydbOKf1cCFuLvJbjLGbLXWxhZ8WCJSGBYNf5vFd79LpQ6NqdalBZmp6QSXCidlf1y2emnxSThLR+TY3lkqnLT4pBx1I6pXBOCvO9+k3ZvDCQ4P5fdBL9HozqvYNWcZQWEh9Jj5Eo4QJytGjWfv76sL7iRFClBkzcpU6dycP297zVvmLBVOyoH4bPXSE5Jwls45fjK4VDjpCUeylaUlZLmGhr3J+W8MJyg8hAU3/x8N77iS3XOWExQWQrcZL+EICWbVsxN0DYmInClX4I17LMryTDKttV2MMbWAQcBDwNvGmFlAJOAshPhEpIBZl4t9f6yh3oBuNLrzKjISk3FGZU8oQ6IiSD98JMe26YnJhERFZitzRkWQnuiue2jVZn6++EEAwquW57xX72D6BXfTa94bLLl/DEd2HaTX/Df4tvaNBXR2IgWr3oDu7PtjDYnRe7xl6YnJOf4o4ywdSfrhnOMnM05SN3bVZn65JMs19PLtzLjwHnrOfZ0lD7zHkV0H6Tn3DabU1TUkIiJFx0kn/rHWxlhrn7PWNgAuAXYDLmCVMeblgg5QRAqHIziIqHrViV0bTfnm9bzlwRFhlK5Xnbi10Tm2iVsbQ6m61bLNcFm+eb1c657/+jCWPzmOzJQ0yjWrw4G/N5IYsxeHM5iwSmUL4pREClzdAd3ZPOHXbGVx62Io36Kud9l9DVUjbl10ju3j1kVT+vhrqEXdXOue99qdrHjafQ2VbVqHg39vJClmLw5nkK4hEREpUk5ldlmstQuttUOBqsDdQLMCiUpEClRYpbLU6duV4MgwjMNB9R5tqdOvK7vmLCfm+4WUa1qbWr07ERTqpOVTAzm0egvxG7bn2E/Cph0cWvkfrZ4eRFCok5rXdKRc87pET1mQrV71bm0ICgthx/RFACRu3UO1i1tRtnEtgkKdpB5MKJTzFvGlSh0aE1GjAtHHTcCz7YeFlG1Sh5rXdsIR6qT5kwOI/WcrCbleQzs5tGozLZ8aiCPUSc2rO1KuWV1ivst+DVXr1tpzDS0G3NdQVV1DIiK+4+9HlBSzR5icbExmrqy1KcCXnldAMsZ0Aj621jY8ze0t0MBa+59vIzs9xpjawFbAaa3N8G80UtRZa2l0x5V0eP8+jMOQFLOXJfe/x/apfwHw2/WjaP/O3XSeOJL9i/9lfv/nvdt2eP8+wD3eEmB+/+e5cNwj3HjoR5K27WPuDaNIzTIezRHipO3LQ5lzzbFHOCy65x06fvwQQaFO/hr+NlbjICQA1RvYg23fL8zxGJHUA/HM6zOKdm/dRacJIziwZD3zbzx2DbUf437616LhbwEw/8bnufDTR+h/4AeStu1jXt9nc15DLw3lt95PecsW3/cuHcc+iCPUyaK7dA2JiEjRclpJ5ukwxkQDt1prZ2cpu9lTdmFhxXGUtXYBcFoJpkigSz0Qz8yuD5xw/e45y/m+8ZBc1x1NLo9KjNnrHXeZG1daOj+1vj37/n9bweS6N51CxCJFz6Jhb55w3e45y/mh6S25b+dJLo9KitnrHXeZG1daOlPb3pGtbM9vK5hSf8ApRCsiIlJ4Ci3JLEmMMUHW2kx/xyEiIiIiIvkQgF1Si7JTGpNZkIwxI4wxm40xh40x64wx13rKQ4wxh4wxzbLUrWyMOWKMqZTLfsYbYx70vK9hjLHGmOGe5XqefTmMMV2MMTuybBdtjHnIGLPaGBNvjPnaGBOWZf3DxpjdxphdxphbjjvmZ8aY940xM4wxSUBXY8y5xph5xpg4Y8xaY8xVnrp1PGUOz/JHxph9WfY10Rhzn+d9GWPMJ57j7jTGPG+MCfKsCzLGvGqMOWCM2QJcfuY/BRERERERkTNTZJJMYDPQCSgDjAI+N8ZUs9amAV8BWfsF9QfmWGv357Kf+UAXz/uLgC1A5yzLC6w94Z8q+gA9gTpAc+BmAGNMT9yPcOkONAC65bLtjcBooDSwGJgKzAIq454k6QtjTENr7VYgAWjl2a4zkGiMOTdLjEdnkfgMyADqe+r3AG71rLsNuMJT3ha4/gTnJCIiIiIiUmgKO8n8wdOKF2eMiQPeO7rCWvuttXaXtdZlrf0a2ASc71k9HuhvjDGe5YHAxBMcYz5woaelsDPwMtDRsy5rApebtz0xHMKdJLb0lPcBxllr11hrk4Bnctn2R2vtH54EtiVQCnjJWptmrf0NmIY7OT4a40XGmKqe5cme5TpAFO7Hw1QBLgPus9YmWWv3AW8A/bLE9Ka1drsn3hfzOC+MMUONMX8bY/4eO3ZsXlVFREREREoWf88eW8xmly3sJPMaa23Zoy9g2NEVxphBxpiVWRLQpkBFAGvtYuAI0MUY0wh3y95Pnu0Ss7xqWms3A0m4E71OuJO7XcaYhpw8ydyT5f0R3IkiQHUg69zzMblsm3V9dWD7cS2mMUANz/ujra2dgd+BeZ7Ysra01gKcwO4sn8mHuFtG8xuTl7V2rLW2rbW27dChQ/OqKiIiIiIictqKxMQ/xphawEfAJcBf1tpMY8xKwGSpNh53l9k9wGTPY1Sw1pY6fn+4k7jrgRBr7U5jzHxgMFAOWHkaIe4Gzs6yXDOXOjbL+13A2cYYR5ZEsyawMUt8rwA7PO8XAh8AKRxLgrcDqUDFEzySJD8xiRSIzhNGUu2SVgRHhpG8J5Z/XvmaTZ/M8HdYIiIiIlIEFIkkE4jEnaTtBzDGDMHdkpnV58Aq4DDu7rJ5mQ+8CnzrWZ6H+5meC05z1tdvgHHGmAlANPD0SeofbXl9xBjzGu7uulcC5wFYazcZY5JxJ80vWmsTjDF7gevwdCG21u42xswCXjPGPAkk4h4repa1dr4npnuMMdNwt9yOOI3zEjktq1+axMJbX8WVlk6ZhmfTc+7rHFqxiYPLN/k7NBG/ujFuarbloPAQNrw/lSX3vesta/pof5ylw1k1agKdPn+Mim3OoVTtqvx8yYPsnb8q2/atX7yVBrdcBsCmT2ewfOTHBX8SIiIlkZ437FNFYuIfa+064DXgL2Av0Az447g624HluJPRBSfZ5XzcE/D87lleCERkWT7V+GYCbwK/Af95/p9X/TTcSWUv4ADuxHGQtXb9cTEe9JzX0WWD+xyPGgSEAOuAWNxjN6t51n0E/II78V4OfHc65yZyOuLWxeBKSwfAWgvWUrpedT9HJeJ/k8pe6X19U+MGMpPTiJmSfZTGWZe1Y+fMJQDs+2MNCwa/xJHdB3Ps65zbLqfmVR2Z2nooU1vdxtmXd+CcoVcUynmIiIiciUJrybTW1s6l7DPcM6hirX0cePwku9kGLLLW2rwqWWs3kKWrrbU2nuPO1Vo7DzjrRPFZa585bvkl4KUsRZ9mWXdzLjGsxT3G8kQx9j9u+SHcM9hmLYsH7vS8jt8+A7jf8zpqzImOJ+Jr7cfcQ4PBlxIcEcbB5ZvYMWOxv0MSKVJq9e5Eyr449i74x1sWUrYUUQ1qsP+vdViXi3/fdv990Gbm/At6vUE9WPvGZI7sPADA2je+5ZxbL2fj2GmFcwIiIiKnqah0lz0pY0xtoDfHHv0hIn60aPjbLL77XSp1aEy1Li3ITE33d0giRUq9QT3Y/Pmv2cqq92jLnt9WYPPRLats49rErt7sXY5dvYWyjWv5PE4RESEgZ3AtyopEd9mTMcY8B6wBXvE8Z1JEigDrcrHvjzVEnFWJRnde5e9wRIqMyJqVqdK5OZsnzMpWftZl7djh6Sp7MsGlwkiLT/Iup8Un4Swd4dM4RURECkJAJJnW2iettaWstaP9HYuI5OQIDiJKYzJFvOoN6M6+P9aQGJ3lyVjGUL1bG3b+sjRf+8hITCEk6lhS6YyKIP3wEV+HKiIi4nMBkWSKSNERVqksdfp2JTgyDONwUL1HW+r068quOctPvrFICVF3QHc2T8jeVbbieQ1J3LaX1APx+dpH3LpoyjWv510u37wecevyfCSyiIhIkRAwYzJFpGiw1tLojivp8P59GIchKWYvS+5/j+1T//J3aCJFQqUOjYmoUYHoydlnla3R6/wcE2Q5QpzeaeqCQoJxhDpxecY3b574K43vu46dMxdjLTS5/3r+HfNDoZyDiEiJozGZPqUkU0ROSeqBeGZ2fcDfYYgUWfUG9mDb9wvJSEzOVn5Wr3YsGv5WtrJr142jVO2qAHSf+X8ATK53E0kxe9k4dhql61bjqpUfAbDp05maWVZERAKCkkwREREfWjTszRxlYZXLElG9AgeXbcxWPqX+gDz3tWzERywb8ZFP4xMRESloSjJFREQKWEiZSP5++EN/hyEiIiei7rI+pSRTRESkgCVs2knCpp3+DkNERKRQaHZZERERERER8Rm1ZIqIiIiISIlmbaa/Q8iT8XcAp0gtmSIiIiIiIuIzSjJFRERERETEZ9RdVkRERERESjaXZpf1JbVkioiIiIiIiM8oyRQRERERERGfUXdZEREREREp2ay6y/qSWjJFRERERETEZ4y11t8xSOHTD11ERERECkNAPOLRHvysSP/72FS4OSA+x6PUkikiIiIiIiI+ozGZJdQ4xyX+DiFPQ1xzABgf3M3PkeRtcMZsIDA+z6IeIyhOXwuEOI9e64rTN47Gaf993s+R5M2c+0SRv7+D+x4fKHEW9d9N0D3JlwItzoCgMZk+pZZMERERERER8RklmSIiIiIiIuIz6i4rIiIiIiIlm7rL+pRaMkVERERERMRnlGSKiIiIiIiIz6i7rIiIiIiIlGzqLutTaskUERERERERn1GSKSIiIiIiIj6j7rIiIiIiIlKyudRd1pfUkikiIiIiIiI+oyRTREREREREfEZJpgSkRsOu5vJFYxiQNIOOnzycr216zHqZwRmzMUHuX3sT5KDzF4/T/8APdJv+Is7SEd66zUb0p/F9151xnCHlSnPxlFEMODyNG7ZOom7/i09Yt0KrBvSa9wYDEqbRb/dkGt/T2xvnRZOe4MZDP9J9RvY4m4+8kSb3X3/GcYpI8bV5ezyDn5xF2xu/oscdP/Drom0A7NibSKNrJtK635fe13tfrz7hfgY9MYsOg76hTf+vuPq+acxZvN27bv3WQ1xx90+0H/gN435c5y1Pz3DR5+EZ7N6fdEoxB8o9/lTibHzvdfTZ8Q39D/3IBR89hCPEWWhxim/l97u96UN9uGb1xwyIn8r1mz+n6UN9vOv03V4EWVfRfgUYJZkSkI7sOsjqF77gv3G/5Kt+nf4X43BmH4Jc89pOYC1fVelNWnwS59x2OQClalfl7Csv4N93vj/jODu8ew+utHS+qno98we8QIf37qVs41o56oVWiKL7zBfZMHYakypey+QGA9k5628AavV2x/llpWtJj0+i4dCscXZg3dvfnXGcIlI8ZWS6GPbiXLq0PYvFE/vw7LB2PPLGQrbuTPDWWfJFX5Z/1Z/lX/VnWN/mJ9zX47eex4Jx17Psy348O6wdD7+xkH2HjgDw+sQVPHJzG3588wo+mPwP+2OTAfjsp3X06FCLapUiTynuQLnH5zfO6j3a0vSRfszq8TBT6t5E6brVaPnM4EKL3/RGEgAAIABJREFUU3wrv9/tGMOCwf/HF+WvZlavEZw7/Brq9O0K6Ltdij8lmRKQtv2wkO0//UnqoYST1nVGRdLiyUH8PeKjbOWl61Rlz/xV2EwXe+atpFSdagCc/+Zwlj78ATbzzP5qFBwRRq3rOrH8qc/ISEph3x9r2PbTX9Qb2D1H3SYP3MDOX/5my6Q5uNLSyUhMJn79Nk+c1bxx7p63klJ13XG2e+sulj505nGKSPG1ZUc8+w8lc/NV5xIU5KB982q0alSZn+ZtOeV9NaxdjuCjrYQYMjJd7DngTjJ37EukffOqVKkQQe1qUezen8TOfYnM+msbg68695SPFQj3+FOJs97AHmwaN5O4dTGkxSWyavTn1B/Uo9DiFN85le/2Na98zcEVm7CZLhI27mDbT39QuWMTQN/tUvwpyZRir/Xzt7Dxw6kk7zmUrTx2bTRVu7bCEeKkapeWxK2LpubVHUk9EM/+P9ee8XGjzjkLm5FJwqYdx465ejNlG9fOUbdyu3NJiz3M5Qvfpt+eyVzy4/NEnl3Zvc2arVTr2hJHiJNqXVoStzaGmte449zngzhFpKSxbNoW5126+LbvuOh/Uxj59p/EJqTkueXtz/9G8xu+oM8jMzm/aVWa1q8AQIOaZVm4Yhd7DriTy7OrluaFj5fy8OA2OIML9p8a/rrHn4qyjWsRu+pYYh+7ajPhVcsTWj6qSMUpJ3cq3+3Hq3JhM+LWRru30Xd70ePv7rDqLisSOCq0OYfKFzTh33dzdjfaOWMxidF7uHzRGNLjk4j+eh4tnhzI3yM+otVzQ+g593XavXNPji5Y+eUsFU5awpFsZWnxSThLh+eoG3FWJeoN6sHi+8bwba3+JEbv5qJJjwOwY8ZiDkfv4col75GWkMTWr+bS8qlBLH10LK2fv4Ve896g/ZjTj1NEiq86NcpQvkwYn3y/jvQMFwtX7GLp2n0kp2ZQLiqUb1+9jN8+6s2U1y4jKTmdh15fmOf+PnziYpZ92Z+xT15Mx5bVcDgMAI/c3Iavft7IsBfmMeKWtqxYv4/IcCdnVSnFsBfmMuDxX/j5jxifn58/7/Gnwv19cGxcalq8+31w6fAiFaec3Kl8t2fV8pnBGIeDTZ6u1fpul+JOSaYUX8bQ/t17WPLAeyfsdrL8sY+Z2noof935Bk0f7ceGsdOoeF5DKrRpyM9dH8AREkz9IT1P6/DpicmEREVkK3NGRZJ+ODlH3czkVLZ9v5ADf28gMzWdlaMmUqVjU5xR7nFMy0Z+zI8tb+PP29+g2Yh+bPhwKpXOa0jFNucws8v9BIU4aXDL6cUpIsWXM9jBmJFdmLdsBxcO+ZZxP66jZ8daVK0QQWS4k2b1KxAc5KBi2XCeHHo+f6zcTWJy+kn32blNDf5YuZvflrgn/6lRuRRjn7qE716/nEvanc1bk1bxyJA2vDxuGb061ub9x7ry0qd/E3c41Xcn5+d7/KlIT0zONrFLiOfenuH5PigqccrJncp3+1HnDr+a+gO78+sVj+FKO3Z96btdijMlmVJsOaMiqNDmHC6a9AR9dnzDFYvGAHBDzFdUvrBptrplm9ahcofGbPxoOuWa1uHg8o0AHPx7A+Wa1T2t4yds3IEJDiKqfg1vWfnmdYlbF52j7qHVW7BY77K1NkcdgHJN61C5QxM2jJ1OuWZ1ObB8EwAHlm6gXPPTi1NEireGtcvx+ehLWTyxL588043texJpdk7FHPWMu1ES68r9/nO8zEwX2/YczlH+3teruaF7fSqWDWfjtlia1q9A6cgQqlSMYNvunPVPl7/v8acibl0M5VscO065FnVJ3nMox1hOf8cpJ3cq3+0ADYb0pNmj/fm528Mc2Xkg1zr6bpfiSEmmBCQT5MAR6sQEObK9zyo9Polvzu7LT21u56c2tzP7Snf302nn38mBxeuz1W339l0svm8MWMvhrXuo0rEpDmcwVTq3IHHr7tOKMeNICjHfLaTVqJsJjgij8gVNqHn1BWye+GuOuv999jO1rrmQ8i3qYYKDaPnkAPYs+If0hOzT/rd/524W3/uuJ87dWeJsTuKW04tTRIq3DdGxpKZlkpyawSc/rGV/7BF6X1yPVRv3s2VnPC6XJTYhldEfLeX8plUoHRmSYx9bdsTz+7KdpKRmkJ7h4qd5W/h73T7Oa1IlW73/tsexZM1e+vc8B4CzKpdi8T97OBCXTMyuw/meZTYQ7vH5jRNg8+e/0mBIL8qcWxNnmUiaP3YT/02YlaNeQcUpvnMq3+11b7yE1qP/xy89Hsnz56fv9iLC32MuNSZTxP+aPz6AgUkzafZof+oN6M7ApJk0f3wAkWdX5sa4qd5Jc1L2xh577XdPdJG8NxZXeoZ3X/Vv7kncmmgOef5yuO37BRzZdZC+e6YQWiGKjR9NP+04/xr+FkHhofTbO5mLJj3OX8PeIm5dDFUubMaAhGneervnrmTZ45/QbdoL9N87hdL1avD7TaOz7avBkJ7Ero3moCfOmO/ccfbfN4WwClFsGHv6cYpI8fXjvC10GjKZjoO/ZdHqPXw6qhshziC270nktlFzaNP/S666dyohziBee7CTd7un31/E0+8vAsBaePerVVww+FsuGPQNE6at5/WHOtGkXoVsx3r2wyU8dmtbgjyJ1gMDWzFx2nquuHsqt1/flErl8h63dlSg3OPzG+euX5ay5tWvuXT2a1y/dRJJMXtZ+cz4bPsqyDjFt/L73d76uSGEVYjiyiXvMSBhGgMSptHh/fuy7Uvf7VJcmRN1y5OCY4y5GXgQqAckAN8DI621cXlt59nWAg2stf+dQQh2nOOSM9i84A1xzQFgfHA3P0eSt8EZswEIhM+zqMcIitPXAiHOo9e64vSNo3Haf5/3cyR5M+c+UeTv7+C+xwdKnEX9dxN0T/KlAIvT+DuO/LA73y7SSZGpcU9AfI5HacqqQmaMeRB4BBgMzAFqAO8BvxpjOlpr0/wZn4iIiIhIieMKvC6pRZm6yxYiY0wUMAq421r7s7U23VobDfQBagMDjDHnG2P+MsbEGWN2G2PeNcaEeLb/3bOrVcaYRGNMX2NMRWPMNE/9Q8aYBcYY/VxFRERERMQvlIwUrguAMOC7rIXW2kRgBtAdyATuByoCHYBLgGGeep09m7Sw1pay1n6Nu9vtDqASUAV4DCjSzf0iIiIiIlJ8qbts4aoIHLDWZuSybjfQxlq7LEtZtDHmQ+Ai4M0T7DMdqAbU8ozTXJBbJWPMUGAowIcffojzNE9ARERERKTYCcAZXIsytWQWrgNARWNMbsl9NeCAMeYcT/fXPcaYBOAF3MnpibwC/AfMMsZsMcaMyK2StXastbattbbt0KFDz/Q8REREREREcqWWzML1F5AK9Aa+OVpojCkF9MLd1fV9YAXQ31p72BhzH3D9iXZorT2Mu8vsg8aYpsBvxpil1to5BXcageXGuKnZloPCQ9jw/lSW3PeunyI6sc4TRlLtklYER4aRvCeWf175mk2fzPB3WCIiRVYg3eMvnfMaldqdiysjE4AjOw/wQ5Mhfo5KCtK5w6+m/uBLKdesDlu+nMvCW172d0gihUJJZiGy1sYbY0YB73haKbPOLrsDmIi7S2sCkGiMaQTcCezPspu9QF3crZcYY64A1gObgXjcYzrV3p/FpLJXet8HR4bRZ+e3xEyZ78eITmz1S5NYeOuruNLSKdPwbHrOfZ1DKzZ5n58lInIy0dHRXHnDF1x6QS1euf9CAA7FpzD646XMX7YThzF0blOdVx9wPxPzlc+WMX1BNIePpFOmVAh9ejTgjhuaZdvntN+3MnfpjmzP0fxh7mZGvPUnzw1vzw3dGwCQlp7J6I+XMnvRdjIyXbRqVIlRd7anSoUI73bTF2xlzAO92F5nB5GZDq7YV4GaKaGnfb6BdI8HWHzPO2z6dKa/w5BCcmTXQVaN/oIal7YlKOz0f8+lEKi7rE8pySxk1tqXjTEHgVc59pzMH4CbrLWpxpiHgLG4H3OyAvgauDjLLp4BxhtjwnEnpDWAd3FP/BMLvGetnVtIpxNwavXuRMq+OPYu+MffoeQqbl2M9721FqyldL3qSjJFJN+effZZmtXPPsri7pfm07RBBeZ+1Juw0GA2bTv2WObrutdneL/mRIQ52XvwCP97ZjZ1zypDjw41vXXmL9vJRW1qeJfjE1P5cPIaGtQsk+04E6auZ+WGA/z41hWUjgjhqfcW8fxHS3hnRBcA/li5i9cmrOCNdz9lZdsHSQzK9Om5F/V7vJQ8Md8vBKBi23OIqFHJz9GIFB6NyfQDa+0n1tqm1tpwa20Va+3t1tpYz7rfrbWNPLPHdrLWPmWtvTDLth9Ya6tZa8taa7+x1r5hra1trY201p5lrX3Of2dW9NUb1IPNn//q7zDy1H7MPQxMnM5168eTvPsQO2Ys9ndIIhIgpk+fTunSpWnfvKq3bOGKXew+kMQjg1tTOjIEZ7CDxnXLe9fXrVGGiLBj08EZY9i2+7B32eWy/LlyN51aV/eWvT5xBQMvb0TZ0mHZjr9jXyIXtqxGxbLhhIYE0evCWmzaFu9d/86XqxnWpxktW7bEYCidGUzpTN/9vTsQ7vGtR/+Pvnum0Ov3N6lyUQt/hyMiUiCUZEqJEVmzMlU6N2fzhFn+DiVPi4a/zedRVzK9073EfL+AzNR0f4ckIgEg1eHi7bffZuTIkdnKV208QJ0aUYx4+0/aDfya6x+awZI1e7PVGTtlDa37fclF/5tCcmoGV3Su7V23etMBzqpainJR7oRy9cYDrPnvIP16npMjhuu61Wf5+v3sPXSE5NQMps7fSmdPcpqZ6WLt5oMcSkile/fuvFNrF79UjCXd+KaLWiDc45eN/IgpDQbybc1+bPxoOpf88Byl61bzd1giAuByFe1XgFGSKSVGvQHd2ffHGhKj9/g7lJOyLhf7/lhDxFmVaHTnVf4OR0QCwO/lE7juuuuoWrVqtvI9B5L4Y+Vu2jWtwsJxNzDk6nMZ/sJcYhNSvHWGXteUZV/247vXL+eqLnUpFRniXTf/751c1NrdVTYz08WoDxfz5NDzcThMjhhqVytNtYoRXHTLFNr2/4otO+IZ1rc5AAfiU0jPcPHLnzF88cUX/G97FfaGpPFHuQSfnH8g3OMPLFlPRmIyrrR0Nk/8lX1/rqVGr3b+DktExOeUZEqJUXdAdzZPKNrdqI7nCA4iql71k1cUkRJtb0gaMREp3HzzzTnWhYUGU6NyJNd3b4Az2MHlnepQtWIky//dn62eMYbGdcsTFhLEO1+u8pbPX7aTzp7xmJNmbqRh7XK0bJj72LJnxy4hLd3Fool9WPF1f7q3r8nQZ92TnYeFBAEw4PJGVK5cmQhXEOfHl2ZzREqu+zpVgXiPt9ZCzlxdRCTgaeIfKREqdWhMRI0KRE8uujMOhlUqS7WLW7F92l9kJqdRrVtr6vTryvwbR/s7NBEp4rZFpBLvzKRr164AHEmMI9Nl2bx9Ojf2Ooe5S3dkq2/ySGwyM11s3+Mek7k/Npn9sck0qecew7lo9W6Wrt3H78u+BSA+MY1/tx7i362xPDX0fNZvPcR9N7WibGn3LJoDLm/E21+uIjYhhXJRYVStEJHnsU9XINzjnWUiqdTuXPbMX4XNyKR2ny5U6dSMpfeP8XdoUoBMkANHcBAmyIEJchAU6sSVkYnNDLzujyKnQkmmlAj1BvZg2/cLyUhM9ncoJ2StpdEdV9Lh/fswDkNSzF6W3P8e26f+5e/QRKSIaxkfybmHI+j3pzv5++SVoezcl8gzd7TD4TC8/Nlyvv9tM1ddVIdfF29n78EjtD63Ei6X5ZtfN9GrYy2iIkP4Z9NBJs3cyG3XNQHg9+U76dS6OsaTGb54T0dS04/NCHv3S/O59IKaXN+tPgBN61fkh7lbOL9pFcJCg5k0cwOVy4d7x3P2vqQen0/fQOe+B0l2uFhS5jD1k8LP+PwD4R7vcAbTatQQyjQ6G5vpIn7DduZe9zQJm3b6OzQpQC2eGECrpwd7l+sP7M6KUeNZOWqCH6OSXLmsvyMoVpRkSomwaNib/g7hpFIPxDOz6wP+DkNEApDTOnBmQqVK7m6sEWHBhIYEUb6MO7l777EujPpwCc+NXUKdGlGMGdmFclFhuFyW2Yu288bEFaRnuKhUPpybLmvIwMsbAe7xmJd1qu09TlSpkOzHDXZQKsJJac8YzkdubsPoj5dw6bAfSE930aBWWd71PL4E4M4+zYlNSOXSSy/FdXYS5yZG0DEu6ozPP1Du8dM7DPd3GFLIVo6aoIRSSiQlmSIiIsXM3f2zPxqjbZMqTH37yhz1HA7Dx09fkus+MjJdLFmzlxfu7nDC40wc3SPbcrmoUF59oNMJ6zuDHTx9RzueeesJxgd3y+sUREQkgCnJFBERkRziD6dy740tKBURcvLKIiKBLgAfE1KUaXZZERERyaFC2XD692ro7zBERCQAKckUERERERERn1F3WRERERERKdnUXdan1JIpIiIiIiIiPqMkU0RERERERHxG3WVFRERERKRkc1l/R1CsqCVTREREREREfEZJpoiIiIiIiPiMusuKiIiIiEjJptllfUotmSIiIiIiIuIzxloNci2B9EMXERERkcJg/B1Afti1zxbpfx+bJk8FxOd4lFoyRURERERExGc0JrOEGue4xN8h5GmIaw4A44O7+TmSvA3OmA0ExudZ1GMExelrgRDn0WtdcfpGIMU5+JqJ/g7jpMb/MLDIf5YQGNc6BEacgXQNQeDEGRA0JtOn1JIpIiIiIiIiPqMkU0RERERERHxG3WVFRERERKRkcxXpeX8CjloyRURERERExGeUZIqIiIiIiIjPqLusiIiIiIiUbJpd1qfUkikiIiIiIiI+oyRTREREREREfEbdZUVEREREpGTT7LI+pZZMERERERER8RklmSIiIiIiIuIz6i4rASukXGk6fvQg1bq3IfVAAssf/4StX/2Wa93yrepz/uvDKN+qARlJKfzz0iT+fed7TJCDThNGUuPS89i/+F/m93uO9MNHAGg2oj+ZKWmse3PKGcV44ccPUb2HO8Zlj33Mli9zxtj4vutofNc1hFYsQ0ZiMlu/mcfShz/EZrowQQ46T3yMGj3PY/+idczreyzG5iNvJDMljbVvTD7tGMX38vtzr9qlJS2fHEiF1vVJjU1kct2bvOv0c5eCkN/fzaYP9aH+oB6UqlWFlAPxrH//J9a8+g1QML+b1c6KYtDQdtSuV57DCSl8/dlyli3eDsD5HWtxbb8WlKsYwaEDSUz+fCXLPeuO98LbV1KhUqR32RkSxOrlu3hz9FzCI5wMf7gzdc+pyKq/dzL2rT+wnu5xQ4a1Z/XynSxblPt+T0TXesmkn3sxpdllfUotmRKw2r9zN5lpGXxT/QYWDHqR9mPupWzjWjnqhVaIotv0F9kwdhpfVe7Ndw0HsevXZQDUvLYTWMtXVXqTFp/EObddDkCp2lU5+//Zu+/4qKr0j+OfZyYNAoHQey/Sq6sIKqjYVsW6IiKIv13svaLsqmtbdV1X17VgQRAExIooRRFWQUQQASnSCb0FQkgjycz5/TGTMCEJdWAS8n2/XvNy5p5yn5k7g/fJOffcS89g2X8+O6YYu712F/7sHMbWupr/9X+Wbq8XHeOGCT8yocstjK58GZ+1+zNV2jel9V1XAtDwykCMY6pfQc6edFoODo2xG0tf/fSYYpTwO9zjnpueycrhk5j70LBCZTrucjwc7ncTM34Y+Dyjq/Rh6kWP0Or2y2l8bS8g/N9Nj8e4Z0gvFszbyG03fMTw1+dw8709qFmnIolVynHzPd0ZM3wet1w3lnHvz+eW+3pQsVJckX09eteX3Hzd2PzHrp0ZzJ2VBECvC5qTtGYXdw0cT/Ua8XQ5rT4ATVtWo3JiuSNOMEG/9bJKx13k0JRkSqkUVT6OBleeyYLHh5ObnsX2WYvZ8OWPNOnfu1Dd1vdezeap81g75jv82TnkpmWy5/f1AFRsXIut/1uI8/nZOmMBFRrXBuAP/76duQ++ifMd/V+1osrH0fCqM5n/t/fzY1w/YTZNbygc4941W8jekw6AmeH8fio2qxOMsXZ+jFtmLKBCk0CMp71yB3MfOLYYJfyO5LjvnLuc1aO+Ze+aLYXKdNwl3I7ku7n4xXEk/7oS5/OTumIj6yfMokb3NkD4v5u161WicpVyTJmwDOd3LPttKyt/3073nk1IrBpPRnoOi+ZvBmDhL5vYl5VLjVoVDtlvyzY1qJAQy9zZgX/vq9eowLLftpKb62f50u3UqFUB8xj9burKqHfmHlHMoN96WaXjLnJ4lGRKqZTQoh4u10fqyk3523YvWlPkXxKrn9aKfbv2ctEPr/CnzeM55/OniK9fI9BmyTpq9eqEJyaaWj07krJ0HQ36dGffzj3s+HFJmGLcGBLjaiq3blRk/SbXncP1KRPot/MzqnRoyvK3JgbaLF5L7V4d8cREU7tnR1KWJNHg8kCM248xRgm/Iz3uxdFxl3A7lu9mzR7tSFmyLtDmhHw3jXoNKrN2dTKbN+6h06n1MI/R+bT65Ob42LAu5ZA99OjVlHmz15O9LxeAjetTaNOhNtExXlq2rsGm9Xs4/4+n8Nv8zezYlnbEEeq3XjbpuJ/E/P6S/ShllGRKqRRVIY6c1IwC27L3pBNdsXyhuuXrVqfZgPP5+d7X+bhxP9LWbuWs0Y8CsOnrOaSt28off/ovOXvSWTduBh3+egPzHnmbTk8N4sLp/+K0/9yFJ/rIL1+OrlCO7CJjLFdk/TVjvmN05cv4pMUAfn/rS7K27QZg49dz2LtuK5f+/DrZqemsHTudjn8bwNyHh9H56Zu4aMbLnP7fo4tRwu9Ij3txdNwl3I72u9nxiYGYx8PK4VOA8H83t27aQ+qeLC6+ojVer9G2Y21OaVODmNgonN8xa8YabrmvB++O78ct9/Xg/Tfm5CeOxYmJ8XLqGQ2Y+d3q/G3ff7uK8vExPP7CRSxfup3163ZzRs/GTPlyGQNvOY1Hnzmfq/p1PKyYQb/1skrHXeTwKMmUUik3LYvohIIJZXRC+fyL5kP5Mvex/vOZJM9bjn9fDguf+oAaZ7QlOiGwOMT8R9/hy86DmX3ry7R9uC/Lh02k2qktqdqlJZN73YcnJopmgy484hhz0jKJKRRjPDl7Mw/aLnXVJlKWJNHtv3fnb/tlyDt80fEv/Hjzy7R7pC/L3/qS6qe2pFqXFkzqeS/emGia33TkMUr4He1xL4qOu4TT0Xw3W93eh2Y39OabSx7Fn52Tvz2c302fz/HqczPo0KUerw6/hgv7tObnWUnsSs6gdftaXDugM88Nncr/XT2a5x6byk23n06DxokH7bNrtwakpWXz++Jt+99/jp/hr//E0HsmMv6DX+l3U1c+HrWAbmc3xuMxnn1sKk1aVKNdpzqHFbd+62WTjrvI4VGSKaVS6oqNWJSXis3q5m+r0r4pKUuTCtXd/dsaXMj9dZ0r+ma7lds2pka31qx4+ysS2zYmef4KAJLnLSexXZOjjjGhQIxNSFm67pBtLcpLxaaFT3QS2zamRrc2LB/2FYntmrBz/kogcN1HYvsjj1HC71iOe3F03CUcjvS72XzQhbR7+Domn/cgGZt2FlknXN/NDUkpPDd0KrcP+Ih/PjmN6rUqsGbFTho2rsLypdtYt3oXzsHaVcmsXplMm/a1D9pf915NmTV9TbHl7TrVwQx++3Uz9RtUZu2qZADWrUqmfqPKhxWzfutlk467yOFRkimlUm5GFus/m0mnJwYSVT6O6me0of5lZ7Bm1DeF6q4aMYUGl3cnsUNTLMpLh6H92TbzN3JS0wvUO+3VO5hzz3/BOfau3UrN7m3xREdR86wOpK0tfNH+4cSY9OlMOj15I1Hl46hxRhsa9DmD1R8UjrH5/11MXPXAiU2lVg1p/8h1bP7u10L1Tv/Pncy5+7VgjFtCYmxPWhELC8iJdyTHHTO8sdF4oqOwkOcH0nGXcDiS72aTfufS+Zn/Y8r5Dx30379wfTfrN6xMdLSHmBgvF/VpTeXE8sz8bjVrVu2kResa+SOXDRon0rJVDTYk7S62r8Sq5WnVriazpq8usjw62sM1Azox+t15AOzYnsYpbWvijfLQvFV1tm89vOsz9Vsvm3TcT17OuRL9KG1OuiTTzJyZNYt0HAdTGmIsDX6641W85WL505bxnDXqMX66/RVSliZRo0db+qV8mV9v6/QF/Dr0Pc6b8AzXbvmYik3r8H3/Zwv01ezGC0lZvI5dwb8erv/sBzI2J3Pt1k+IrZrAire/OqoYZ9/+Ct5ysfTd9jFnf/gYs28LxFizRzv6p07Mr1fzjDZcvugd+u+dSO+vnmXjpDnMf/SdAn01H3Qhu5esIzkYY9KngRiv2/4JcVUTWD7s6GKU8Dvc417rrPYMyJzM+V8/R4WGNQPPpzxfoC8ddwmnw/1udn5qEHFVE7j059fpnzqR/qkT6fbGPQX6Cud384yeTXhl+NX8Z8Q1tG5fixce/zawCuyS7Xw+dhF3PHQWb47py50Pn82Xn/zG4gWBE+9uZzXm2VcvLdBX955NWLV8R7HJ4iVXt2P2/9ayOzlwecX0KSupmBDLayOuYVdyRv79OQ+Hfutlk467yKFZJDNjM+sBvAC0AXzAMuAe59yRryW+v08HNHfOrQpPlOFXAmJ0wz3nRmjXh2eQfxoAI6LOi3AkBzcw91sASsPnWdJjBMUZbqUhzrzfuuIMj9IU58DLP4h0GIc04vMbSvxnCaXjtw6lI87S9BuCUhOnRTqOw+H/6eESPVzoOf35UvE55onYklVmlgBMBG4FPgJigDOBfZGKSUREREREyqBSeJuQkiyS02VbADjnxjjnfM65TOfcVOfcIjPzmNlQM0sys+1mNtLMKgGY2VdmdmdoR2a2yMyuCNl0sZmtMbOdZvaimXkpvQsBAAAgAElEQVRC6t5kZsvMbLeZTTGzhiFlr5jZBjNLNbNfzOzMkLInzOyjYCx7zWyJmXUt7s2ZmdfMHjWz1cH6v5hZ/SLqxZrZP81svZltM7M3zaxcsCzRzCaa2Y5gvBPNrF5I2xlm9pSZzQruY6qZVTuioyAiIiIiIhJGkUwyVwA+MxthZheZWeh65DcGH72AJkAF4LVg2Qigf15FM+sA1AVCJ61fAXQFOgN9gJuCdfsAjwJXAtWBH4AxIe3mAh2BKsCHwHgziwspvwwYC1QGJoTEVJT7gOuAi4GEYAyF768B/yCQcHcEmgXfy9+CZR5gONAQaABkFrHPfsAgoAaB0eAHDhKTiIiIiIjIcRWxJNM5lwr0ABzwNrDDzCaYWU3geuBfzrk1zrk0YAjQ18yiCCR3LcysebCrG4BxzrnskO6fd87tcs6tB/5NINkDuAV4zjm3zDmXCzwLdMwbzXTOjXLOJTvncp1zLwGxQMuQfmc65752zvmAD4AOB3mLfwaGOueWu4CFzrnk0ApmZsBg4N5gvHuDMfUNxpPsnPvEOZcRLHsGOPuA/Qx3zq1wzmUSmHZc5J2kzWywmc0zs3nDhg07SNgiIiIiImWM31+yH6VMRFeXDSZ7Nzrn6gFtgToEksI6QOgND5MIXD9a0zmXBYwD+genwV5HIOELteGAtnk3HGwIvGJmKWaWAuwicDFyXQAzeyA4lXZPsLwSEDr9dGvI8wwgzsyizOx6M0sLPiYFy+sDRa+fvl91oDzwS0hMk4PbMbPyZvZWcNpwKvA9UNnMvAeJqUJRO3LODXPOdXXOdR08ePAhwhIRERERETk6EVv450DOud/N7H3gZmAzgYQwTwMgF9gWfD2CQGI5E8hwzs0+oLv6wJKQtpuDzzcAzzjnRh+4/+D1lw8B5wJLnHN+M9vNYayIFezvwD43AE2BxQdpupPAFNg2zrlNRZTfT2Ak9TTn3FYz6wj8ejgxyX6htzMB8JaLYfkbX/LzPQeb7RxZCc3q0mfROyR9/D3fD3gu0uHIcdTq9j40G3gBie0as2bMdGbe9EKkQxIBwBMTTbfX76bOuZ2JrVKR1NWb+eXRd9k0+edIh1Yq6bdeNum4S1kVydVlTwH+SGCq68bgojjXAT8Bc4CHg6OCOwhMIR0XnOKKc262mfmBlyg8ignwoJnNITCqdzfwr+D2N4GnzGyBc25JcDGh851z44GKBBLZHUCUmT1C4FrKo/VOcF9LgVVAO2BT6JTZYCL7NvCymd3hnNtuZnWBts65KcGYMoEUM6sCPH4M8ZRZH1befw+1qPg4/rRpPEmf/C+CER3a6a/dxc65yyMdhpwAGZuTWfjMaOpe0BVvXGykwxHJ54nykr5hO5N63kva+u3Uu/g0eo37K5+3/zNpSduKbFPcCXX101rR6e+DqNalOc4XmPZVKbEce3ZnAnBK25pcfm17GjapQnp6Ng8M/iy/z4qV4uj/f11p2bYmsbFRbFqfwofv/cKalTsL7PvGW09j3epd/Dp3I4NuPY1GzaqSWKU89w/+lJ3b0/Pr9b2xM53+UJ9KieXYnZzBxI8XM2vGGgBq1qlI34FdaHZKdTyewN9zE1rUI3XFxvz2nZ8aRLMbLyS6QhzJv67ipzteJWVpEoei33rZpONeivhL9B1MSp1ITpfdC5wGzDGzdALJ5WICo3fvEUgevwfWAlnAnQe0H0kgcRtVRN9fAL8ACwgsCPQugHPuM+B5YGxw+uli4KJgmykEpqquIDDFNouC026P1L8IXCM5FUgNxlCuiHoPE0hCfwrG9C37rwP9d7DNTgKfz+RjiEeAhleeSdb2FLb98FukQylW42t7kZ2Sxpbv5kc6FDkBkj6byfovZrEvOTXSoYgUkJuRxYInRwYSSufY+NVP7F27lapdWhTbJu+EeuXwgv+7ikmsyIq3JzK+8fV81KgfAH++s1t+efa+XL6ftopxIwr/uxcXF8WaVck8fv/X3HbDR8ycvob7/tqL2LiCfydv37kuC3/ZhPM7Fv26mf88X/QfE/dl5fLvZ6Zza7+xvP3Kj1z/5640a1kdgPj4GH6du5FHbv+Cu24cD8C5nz+V37bRNWfTfNCFfH3WPXxY9Qp2/LSUs0YOOdjHmE+/9bJJx13KqoiNZAanh/7pIFX+HnwUZz0wyzm35oB+86aSvlrMfj+giNHP4GI+NwUfeV4IKX/igPrrOMi01WB/TwcfB5ZZyPMsAivePlpEvc1AzwM2vxVS3vOA+u8D7xcXk0DTAeezetQ3kQ6jWNEVy9PpyRuZfO79tPjzxZEOR0QkX1yNRBJa1CNlybpi6yR9NhOAal1bUL5u9fztRU2xbd6qRv7zNSuTWbMymdbtaxWqt2NbGlMmLMt/PWPqSvre2JnadRNYt3oXAPUbViYjPZvdyYFF3L+btCJ/JPJAn41dFLLfnaxYup1mp1Rj1fId+XGEqnxKA2KrJLBvVyoVG9dm28zFpK3dAsDqUd/S+p6ri/08RETKqogu/HO0zKw8cBugZVLlsMU3qEHNs9qzeuTUSIdSrM5PDWLle5PI2LTz0JVFRE4Qi/Jy9qghrB45lT3Lj2WSz36b1qccVbsGjRPxRnnZtmVv/rb2XeqycF5RSxscXHSMl8bNqrJp/Z5i62RsSWbfrsAo1Jqx31GxaR0SmtfDorw0G3gBmybPPfI3ISIlT6RXj9XqspFlZhcQuG5yG4F7WYoclqb9e7N91mLS1m09dOUIqNKhKbXP7cySlz+OdCgiIvuZcdbIIfizc5l9R5GThI5IYrsmAEVOjT2UuHLRDL6nO1+MW0RmRk7+9g5d67Jw/pEnmTfechrr1+3mt183FypLrFoegJ/vfyN/W+aWXWyftZirlo9gQMYkGl19Fj/f9/oR71dE5Hgys+ZmlmVmo0K29QvesSLdzD4PrveSV1bFzD4LliWZWb9jjaHErC57uIIL4sRHOg4pfZr0783iF8ZGOoxi1erZgQqNanJN0hgAoiuUw7weLmvdkAldb4lwdCJSVvV45wHK1Uzkmz8OweX6jqmvik3r0PvrwIrZK5ZuP6K20TFe7n2sF6uX72TiJ/sXbi8fH03tupVY+fuOI+rv2oGdqdewMs8NLXwJRcWEWB584lwA1o6dnr+9499uoFrXloyrfy2ZW3fRtH9vLpz2Tz5r+3/4Mvcd0f5FRI6j/wL50yzMrA2BS+7+CMwnMBv0daBvSP1soCbQEfjKzBY655ZwlErdSKbI0ajerTXl61Zl3ccld1XZ5cO+4pNmNzCh02AmdBrM8re+ZONXc5hy4cORDk2OI/N68MZGY15PgeciJUG3N+6hUqsGfHvZY/iyso+pr/gGNbjgmxdZ+HRRi8IfXFSUh7uH9GRXcgbvv/FTgbK2Heuw7LetuCNYGfKKvu1p37kuLzzxLVmZOQXKysfH8OAT5/HrzxsLtavSoRlrP5pBxqadOJ+fVSOmEJNYkcqtGxaqeyD91ssmHXc50cysL5ACTAvZfD3wpXPue+dcGvBX4Eozq2hm8cBVwF+dc2nOuZnABOCGY4lD33IpE5recD7rP5tJblpmpEMpli9zH5nbduc/ctIy8WVls29n8dcKSenXYWh/BmROpv0j/Wh2Q28GZE6mw9D+kQ5LhPgGNTjl5kup0rEZfbd8TP/UifRPnUiTfucW26a4E+rydapx4bSX+P2/n7P8rYmF2xlER3uIivJgBJ57owKnKF6vccfDZ5Od7ePtV2bhDsglOxRxPWZ0tIeoaC8AUVFeoqP3n+5cclVbTj+rMS88/g3pewsmznHlonnw8XNZuWw74z/4tVCcO+f9TqOrzyKuRiKY0bT/eXiivexddeipuvqtl0067qVIpK+5DMM1mWaWQGDh1PsOKGoDLMx74ZxbTWDkskXwkeucWxFSf2GwzVErddNlRY7GT7f9O9IhHLEFT46MdAhyAix4cqSOtZRI6eu3M9xTfEJZlA5D+9Pp8YH5r5vd0JtfnxwBzpHQtA4dHx9Ix2D5W2P6cvN1gUsYWrapyZCnz89v987461m2eCv/GPoNzU6pTqdT67FvXy5vjL42v85LT33HiqXbadupDmMPuL7znfHX5z9//vU+AAy8PDCCes0NncjJ8fHCG5fn1/nyk8VM/HgxXU6vT5MW1ajboDI9zmkKQP/UiXzW5ibSN2znt+fHElcjkT6/vkVUfBx7V21m+tVPkr0nnUPRb71s0nGXcDGzwcDgkE3DnHMHLoL6FPCuc26jWYEVtisAB45a7AEqAj4Ct1ssquyoKckUERGRsDjYCfWCv++fJjvIPy0/wQT4ffG2/CTwQMuXbC+2rEnzquzakc7ePVkFthdX/1Bls6avYdb0/XdGG/H5DYxKuCT/tW9fDj/d8So/hWEBJBGRIxFMKIu9s4aZdQTOAzoVUZwGJBywLQHYC/gPUnbUlGSKiIhIqfXZ2IWHriQicihHcF13CdUTaASsD45iVgC8ZtYamAx0yKtoZk2AWGAFgSQzysyaO+dWBqt0AI560R9QkikiIiKl1JqVyZEOQUSkpBgGhN5G4QECSeetQA1gtpmdSWB12b8Dnzrn9gKY2afA383szwRWl+0DnHEswSjJFBERERERKcWccxlARt5rM0sDspxzO4AdZnYLMBqoCnwLDAppfhvwHrAdSAZuPZbbl4CSTBERERERKesOcwXX0sI598QBrz8EPiym7i7g8qLKjpZuYSIiIiIiIiJhoyRTREREREREwkbTZUVEREREpGw7yabLRppGMkVERERERCRslGSKiIiIiIhI2Gi6rIiIiIiIlG1+F+kITioayRQREREREZGwMeeUtZdBOugiIiIiciJYpAM4HP6vbynR58eei98sFZ9jHk2XFRERERGRsk2ry4aVkswyarjn3EiHcFCD/NMAxRkug/zTSnyMoDjDrTTEWZp+Q6A4w6U0fDchEOe+5y6PdBiHFDvk81LzeZb0OEvTbwhKT5xS9uiaTBEREREREQkbJZkiIiIiIiISNpouKyIiIiIiZZuuyQwrjWSKiIiIiIhI2CjJFBERERERkbDRdFkRERERESnb/CX6NpmljkYyRUREREREJGyUZIqIiIiIiEjYaLqsiIiIiIiUbVpdNqw0kikiIiIiIiJhoyRTREREREREwkbTZaVUanV7H5oNvIDEdo1ZM2Y6M296odi6re+5inYP9SWqfCzrPvme2be+gj87B/N6OOuDR6l74ans+GkpM659ipy9GQC0H9IPX1Y2S17++JjijEmsSI93HqDO+V3YtzOVXx59hzVjvitUr/dXz1HzzHb5rz0xUaQu38DnHf5yQuIUEZFDs6r1iDp/MFarKWSmkvvd+/hXzMHT5iyiLrw1pKIHi44le/j9uK2ri+zL06oH3h7XYgnVIT2FnImv4jYuhYrViL7iQaxKHXyLvsP33fD8NtF/+iu5339YbJ8ioXQOcmScT6vLhpNGMqVUyticzMJnRrNy+OSD1qtzflfaP3wdU857gPGN+lGxcW06PTkQgIZXngnOMab6FeTsSafl4D8CUKFRLepf2o2lr356zHF2e+0u/Nk5jK11Nf/r/yzdXr+byq0bFqr3zR+HMCrhkvzH9h+XsPbj709YnCIicgjmIerqIfhXzSP73zeQM+l1oi69F6tSB/+S78l+6br8R+6Ut3C7txabDFqjDkT1GkDuV/8JtBn1KC5lKwBRZ1yF77fpZL9xM94WpwUSWsDTqjtuz3YlmHLYdA4ikaQkU0qlpM9msv6LWexLTj1ovWYDzmfFe5NIWZpEdkoaC58eRbOBFwBQsXFttv5vIc7nZ8uMBVRoUhuA0165g7kPvInzHdsF4FHl42h41ZnM/9v75KZnsX3WYtZPmE3TG3oftF2FhjWpeWY7Vo+cekLiFBGRQ7Oq9bAKVfDNnQDOj0v6Dbfpdzxtexaq623XC9/i6cX2FXXmdeTO/Ai3eQXgIG1X4AFYpRq4pEWwLwP/lpVY5VoQUw7v6VeRO2PUcXp3crLROYhEmpJMOakltmnE7oX7/+q7a+FqyteqQmyVBHYvXkvtXh3xxERTu2dHUpYk0eDy7uzbuYftPy455n0ntKiHy/WRunJj/rbdi1ZTuXWjg7ZrOuB8tv3wG2lJ2wJtjnOcIiJy9Kxag4IbEqpj9Vvj+62YJNM8WO2mWPkEYm55g5jb3yHq/L9AVAwA/h3rscYdITYeq9UUt3M93rP64Zv3JexLP87vRk4WOgc5Cn5Xsh+ljJJMOalFVShH9p79/1POex5dsRwbv57D3nVbufTn18lOTWft2Ol0/NsA5j48jM5P38RFM17m9P/ehSf66C5djq5QjuzUjALbsvekE12x3EHbNbuhN6tGTM1/fbzjFBGRQ3O7NkH6HrynXQEeL9a4I9agDRYdW6Cet10v3IZlsGd70R3FV8K80XhOOYPsDx4l+717sZpN8J5xDQC+2Z/gqdea6Oufxj9/Enij8NRohH/lXKIuu4/o65/B0+Xi4/12pZTTOYhEmpJMOanlpmUSnVA+/3VMQjwAOXszAfhlyDt80fEv/Hjzy7R7pC/L3/qS6qe2pFqXFkzqeS/emGia33ThUe07Jy2TmJB9A0QnxOfvuyg1urelXK0qrPv4fwW2H884RUTkMPh95HzyHJ5mXYi5azhRf+iDf9ks3N7kAtU8bXvi+63w4ir5crMB8P3yFaTvhsy9+H6egKdpl0B5Vhq5X/yTnPfuxTd3IlG9/0Lu1LfxdrsKt2M9OWMex9vpAqxqveP1TuUkoHMQiTQlmXJS271kHVU6NM1/XaVDEzK27mLfroLXcia2bUyNbm1YPuwrEts1Yef8lQDsnLucxPZNjmrfqSs2YlFeEprV3b//9k1IWbqu2DbNBp5P0qc/kJueVWT58YhTREQOj9uRRM7ooWT/ewA5457EKtfCv3lFfrnVPQWrUAX/8h+L7yQrHZe6E1zI9DdX9FQ4T6fz8W9egdu5Hk/1Bvi3rgJ/Lm5HEla98AIuInl0DiKRpiRTSiXzevDGRmNeT4HnB1r9wTc0v+kiKrVqSEyleDo81p9VI6YUqnf6f+5kzt2vgXPsXbuFmt3b4omOouZZ7Ulbs+WoYszNyCLp05l0evJGosrHUeOMNjTocwarP/imyPreuBgaX3N2kfEdzzhFROTwWPWG4I2GqBi8f+iDVUjEHzJq6W13Dv7lsyG76JP0PL5F0/B2/SOUrwRx8Xj/cBn+1fMKVipfCW/ni/H9MBYAt2c7ngZtIToOq9UsfzVakaLoHOQo+FzJfpQypTrJNDNnZs0iHceRMrNJZjbwGNqnmVmT4PP3zezp4POeZrbx4K1PDh2G9mdA5mTaP9KPZjf0ZkDmZDoM7U98/Rr0T51IfP0aAGyaMpfFL47jou9e4pqkMaQlbePXx0cU6Kv5oAvZvWQdycG/yCV9+gMZm5O5bvsnxFVNYPmwr446ztm3v4K3XCx9t33M2R8+xuzbXiFlaRI1e7Sjf+rEAnUbXN6d7JR0tkxfUGRfxzNOERE5NE/bnsTcOZyYu0fgadSe7LGPgy83UOiNxtOqe5EL/ni7XU30n/6a/9o36yPcllXE3Pw6MX95Df+2NfhmjS/QJuqcG/HNGgc5gYQ198dP8DRqT8zt7+BfNVe3MpFD0jmIRJK5YqZohH1HZj2AF4A2gA9YBtzjnJt7DH06oLlzblV4oix5zGwGMMo5904x5e8DG51zQ82sZ7DuoS7UcMM954Y1znAb5J8GgOIMj0H+aSU+RlCc4VYa4ixNvyFQnOFSGr6bEIhz33OXRzqMQ4od8nmp+TxLepyl6TcEpSZOi3Qch8P34YASPVzo7TeyVHyOeU7IUlBmlgBMBG4FPgJigDOBfSdi/yIiIiIiIsVxpfA2ISXZiZou2wLAOTfGOedzzmU656Y65xaZmcfMhppZkpltN7ORZlYJwMy+MrM7Qzsys0VmdkXIpovNbI2Z7TSzF83ME1L3JjNbZma7zWyKmTUMKXvFzDaYWaqZ/WJmZ4aUPWFmHwVj2WtmS8ysa3FvLlh/vJmNCtb/zcxamNmQ4HvaYGbnh9SfYWZ/Dj6/0cxmmtk/g3GuNbOLgmXPEEjGXwtOkX0tuL1UThMWEREREZGT34lKMlcAPjMbYWYXmVliSNmNwUcvoAlQAXgtWDYC6J9X0cw6AHWB0InfVwBdgc5AH+CmYN0+wKPAlUB14AdgTEi7uUBHoArwITDezOJCyi8DxgKVgQkhMRXnUuADIBH4FZhC4POtC/wdeOsgbU8DlgPVCEwpftfMzDn3WDDuO5xzFZxzdxwihmKZ2WAzm2dm84YNG3a03YiIiIiIiBzUCUkynXOpQA/AAW8DO8xsgpnVBK4H/uWcW+OcSwOGAH3NLIpActfCzJoHu7oBGOecyw7p/nnn3C7n3Hrg38B1we23AM8555Y553KBZ4GOeaOZzrlRzrlk51yuc+4lIBZoGdLvTOfc1845H4HkscMh3uYPzrkpwX2NJ5DY/sM5l0MgWW1kZpWLaZvknHs7uK8RQG2g5iH2d0Scc8Occ12dc10HDx4czq5FREREREq3SK8eq9Vlj04w2bsxuChNW6AOgaSwDpAUUjWJwLWiNZ1zWcA4oH9wGux1BBK+UBsOaFsn+Lwh8IqZpZhZCrCLwIXHdQHM7IHgVNo9wfJKBEYS84SuDZ4BxJlZlJldH5y6mmZmk0LqbAt5ngnsDCaNea8hMEpblPx9OecyDlFXRERERESkxDohC/8cyDn3e3BV1JuBzQQSwjwNgFz2J20jCCSWM4EM59zsA7qrDywJabs5+HwD8IxzbvSB+w9ef/kQcC6wxDnnN7PdHMbqV8H+CvV5HJW+P12UQGeNHELtczsRFR9H5tbd/PbiOFa++3WkwyrAExNNt9fvps65nYmtUpHU1Zv55dF32TT550iHJiIiIicpnX/I8XCiVpc9BfgjgamuG82sPoFRyZ+AOcDDwVHBHQSmtY4LTjvFOTfbzPzASxQexQR40MzmEBj5uxv4V3D7m8BTZrbAObckuJjQ+c658UBFAonsDiDKzB4BEo7Lmz922whcqyrHYNE/PmTmn/+JPzuHSi3rc+H0f7Hr15X593sqCTxRXtI3bGdSz3tJW7+dehefRq9xf+Xz9n8mLWnboTsQEZGwyDvpBoi5bwwuZSu+GR/gXzM/UN7hPKK6XQXxlXEbl5Hz1X8gbXd+e2/PAXg7ngeAb8G3+GaMLNh/6zPxNDsV38yxeHsNxFPvFDAPbssqcr95G7cr+Pdyb1Sgr1Y9ICoG/7IfyP3mHfAHJkrF3L9/qYn+qRPxlovh9zcmMOeuQy0jIbKfzj+CfP5IR3BSOVHTZfcSWNxmjpmlE0guFwP3A+8RSB6/B9YCWcCdB7QfCbQDRhXR9xfAL8ACAgsCvQvgnPsMeB4Ya2apwf1dFGwzBZhMYEGipOA+N1AyvQJcHVx59tVIB1NapSxNwp+dA4BzDpyjYtM6h2h1YuVmZLHgyZGBf9CdY+NXP7F37VaqdmkR6dBERMqUvJNugOx/9cP3/WiiLn8QKtXAGrQl6uz+5Hz8LNkv34BL2UZ0n/v3t+14Pp4Wp5H97r1kv3MPnuan4ul0QcH+m3bBv/oXiI3Hv3Iu2W/dTvarN+LfspKoqx/Nr+ftdhWe2s3Ifucust+6DavZBG/3a/LLs1+6juyXAktRjK19Nb7MbNaN/9/x/GjkJKTzDzkeTshIpnNuE/Cng1T5e/BRnPXALOfcmgP6zZveWmTy5Zz7gCJGP4PXSt4UfOR5IaT8iQPqr+MgU2mLqP8t0CjkdW5oe+dcz5Dn7wPvH9A+tO5sgreAKab8xpDnM4B6xcVZ1p3+37toPvACosrHkTx/JRu/nhPpkA4qrkYiCS3qkbJkXaRDEREpU/JOujs9PhBw+FfNw+3ZhqdWU6xuS/y//4jbGfjbdO6sj4i9czhUrgUpW/G2Owffz1/A3mQAfHO+wNuxN/5fpwR7NzyNO5L77buQuRe3Zf+MGt/PE4jq/icoVxEy9wZGO3/6FLLSAuXzviKq1wB8P4wtFHOjq84ia/tutv3w23H9bOTkp/MPCYeIXJN5JMysPHAb8HqkY5HS7afbX2XOna9RvVtravfsgG9fTqRDKpZFeTl71BBWj5zKnuUldZBdRKSMKF8Jq1IHt2M9VrclWOjfnQPPPdUb4E/ZilWrj9u+Nr/UbV+LVWuwv3ad5riUrZC5t9BuPPXb4NJ2HVBmBZ5aQjWILQ/7Mgq0bTbgfFZ98M0xvU2Rsnz+4fxaBiWcTtjqskfDzC4gcN3kNgL3shQ5Js7vZ/usxZSvV51Tbr0s0uEUzYyzRg7Bn53L7Ds0Q1pEJKI8XqIvuw//b9NxuzbhXzMfzyndseoNISqGqB7X4pwfomMD9WPiICskAdyXgcWW299ds674V88vvJ+KVYm6YDC504bnb/KvmY/31EugXALEV8bb9ZJAQVRsoeY1z27PqhFTw/KWpYzS+YeEUYkeyXTOTQHiIx2HnHw8UV4SStg1mXl6vPMA5Wom8s0fh+ByfYduICIix03Upffg/LnkTh0GgFu3iNwfxhB15cNYbHl8c7+EfZm41MD0WLKzAiONeWLL4/Zl5r/0NOlC7uQDJmeVSyC67xP45k/Cv/SH/M2+Hz/G4uKJ+b+XITcH38JvsJqNIT2lUJzbZy4mbd3WQttFDpfOPyScSnSSKRIOcdUrU/ucTmyYOBtfZja1z+tM4769+F+/ZyIdWiHd3riHSq0aMKX3g/iysiMdjohImWfxlcn56Kn8FV0B/PMn4Z8fuFW2VamD94xrcDsDt/x2OzdgNRrlX2tpNRrhdq4PNIyvjFVIxG1dvX8HcfFE930C/8qf8f34ccGd52aTO/VtmPo2EFhUKNC28LS+VSM1iilHT+cfEm5KMuWk55zjlFsupdsb92AeI13Gk5MAACAASURBVD1pGz/f+zobvjzwlquRFd+gBqfcfCm5Wdn03bL/ROPHW15mzYfTIhiZiEjZ0+2NewDIGf8M5IacdHujscTagcQxoRpRF96Gb95EyEoHwLd4Ot4/XBZYPRaH9w998P3yFQCeJp3zb4MCQEw5oq99HLdpGb4ZRdylrUIVwEHabqxOC6K6/4mcrwvensTqtgRgrVaVlaOk848gn67JDCclmXLS27dzD5N63RfpMA4pff12hnvOjXQYIiJlXt5JN0DMXfuvkcyd/Ab+Vb8Q1ec+rHItyM7Et2gavu/3Lxvh/3UKVrkWMX9+BQDfwm/zV5b1NOtaYDqsp+XpeOq0wFVrQEy7c/K3Z799J6TuxBJrEX3JPRBfCZe6k9wZI3FrFxSI1Rtsl5uWicjR0PmHHA9KMkVERERC5J10D/JPy78PZaicd+85aHvf9BH4po8ouNE8eBq0Jfer/+Rv8v82nX2/TS+2H7dhKdlvDD7ovnInv4H3gPtwiohEmpJMERERkeOtXEVyv/8QsjXiKFIi6RYmYVWib2EiIiIiclLI2IP/18mRjkJE5IRQkikiIiIiIiJho+myIiIiIiJSpjmtLhtWGskUERERERGRsFGSKSIiIiIiImGj6bIiIiIiIlK2+f2RjuCkopFMERERERERCRslmSIiIiIiIhI2mi4rIiIiIiJlm1aXDSuNZIqIiIiIiEjYmHPK2ssgHXQREREROREs0gEcjuyXryrR58cx935SKj7HPJouKyIiIiIiZZrzl+gcs9RRkllGDfecG+kQDmqQfxqgOMNlkH9aiY8RFGe4lYY4S9NvCBRnuJSG7yaUrjjndb0s0mEcUtd5E0r851mafkNQeuKUskfXZIqIiIiIiEjYKMkUERERERGRsNF0WRERERERKdt0C5Ow0kimiIiIiIiIhI2STBEREREREQkbTZcVEREREZGyTdNlw0ojmSIiIiIiIhI2SjJFREREREQkbDRdVkREREREyjTn13TZcNJIpoiIiIiIiISNkkwREREREREJGyWZIiIiIieRuEb1aPHG03ScMYa2n71F5Z6n55dV69Obtp+9Rafvx9H81SeIrlal2H68CRVo+uIQOv3wEe2+fIcqF5yVX1aueSPajHuNDt+Ooub1ffK3m9fLKe+/SHTNasfnzUVYq9v7cOnPrzMgcxI93nvooHVb33MV124ez/UpE+j+7gN4YqIBMK+Hsz8cSr9dX9D76+eIrlg+v037If1oc+/Vx/U9SDF8/pL9KGWUZIqIiIicLLwemr30GHtmzmXBOdeT9Mx/afzUfcQ2qEPFLm2pe/sNrLr/aRaccz37Nm+jyTMPFNtVg4dvweXksvD8Aawd+hINhtxKXJP6ANS7YwAbXnmPpdfdRe1B1xBVtTIANfv3IeW72eRs23lC3u6JlrE5mYXPjGbl8MkHrVfn/K60f/g6ppz3AOMb9aNi49p0enIgAA2vPBOcY0z1K8jZk07LwX8EoEKjWtS/tBtLX/30uL8PkeNNSaaIiIjISSKuUT2iq1dh2+gvwO9n77xFpC1cRtWLe1Gpx6ns/nYWWWs24HJz2fLOOCp2aUts3VqF+vHExZJ4Tjc2vTkaf2YWaQuXsef7n6l6cS8AYurUZO/cReTs2EXWhi3E1qpOTK3qJJ5zRmDfJ6mkz2ay/otZ7EtOPWi9ZgPOZ8V7k0hZmkR2ShoLnx5Fs4EXAFCxcW22/m8hzudny4wFVGhSG4DTXrmDuQ+8iSuFo1YiB1KSKSIiInISMzPKNW2Q9yKkIPCfcs0aFmoT27Auzudn3/rN+dsyVqylXJNAP5mr15Nweieia1QltnYNsjZupf4Df2HDK8NxPt9xey+lRWKbRuxeuDr/9a6FqylfqwqxVRLYvXgttXt1xBMTTe2eHUlZkkSDy7uzb+cetv+4JIJRl23O70r0o7RRkikiIiJykti3bhO5u/ZQa8CVmNdLwmkdqdC5DZ64WPbMnk9i7x6Ua9YIi42hzl/64vx+PHGxhfrxlovDn5ZRYJsvLQNvfDkANv77PWpcfRHN/jWUDS+/S4UOrfBnZJK9aRtNX3qMlm89S+K53U/Iey6JoiqUI3tPev7rvOfRFcux8es57F23lUt/fp3s1HTWjp1Ox78NYO7Dw+j89E1cNONlTv/vXXiidadBKb307RURERE5STifj1UPPEuDBwdTa8CVpC9bxe5vZuHPyWHvzwvZ/NaHNH3hEbzx5dk2ZgK+jEyyi7h+0peZhadC+QLbvPHl8aVnApC9dQcr7/47AJ7YGE4Z/iIr7nicBg8OZvfUH9gzcx5txv2H1LkL8aWmHf83XsLkpmUSnbD/84tJiAcgZ2/g8/tlyDv8MuQdALq+MJjlb31J9VNbUq1LCyb1vJfub99P85suZPlbE0988CJhoJFMERERkZNI5qp1LL/5URac15+Vdz5BbN2apC9ZAcCO8V+z+MpbWHjBAHZ/9yPm9ZK5OqlQH/uSNmFeD7H1a+dvK9eiEZlr1heqW/svfdnx+VRyd6VQrllD0petwpeeQfb2ZOJC2pclu5eso0qHpvmvq3RoQsbWXezbVfBazsS2janRrQ3Lh31FYrsm7Jy/EoCdc5eT2L7JCY1ZJJyUZIqIiIicRMo1a4TFROOJjaFm/8uJrlaF5C+nYTHRxAWvzYypWY1Gj93O9jFf4tubXqgPf9Y+UqbPps4t1+OJi6VCh1ZUPvs0kr+eXqBeXOP6VOzSjh0fTwIge/M2Ek5tT1SVysQ2qMO+rTuO/xs+gczrwRsbjXk9BZ4faPUH39D8pouo1KohMZXi6fBYf1aNmFKo3un/uZM5d78GzrF37RZqdm+LJzqKmme1J23NlhPxliSPz5XsRylzUieZZubMrFmk4wg3M1tiZj2Dz58ws1HB542C71nToEVERMqoqhf3pMPkEXT45gMSTu3Aitv/hsvJxRMTQ5OnH6DTDx/RasRLpC1azqY3R+e3qzXoGpq/8nj+66R/vIknNoYO33xA42ceYP1zb5C1ZkOBfTV4+GY2/PNt8AdWRN342khqXHsJbT56ja3Dx5ObnHJi3vQJ0mFofwZkTqb9I/1odkNvBmROpsPQ/sTXr0H/1InE168BwKYpc1n84jgu+u4lrkkaQ1rSNn59fESBvpoPupDdS9aRHBy9TPr0BzI2J3Pd9k+Iq5rA8mFfnfD3JxIuJSYZMbMewAtAG8AHLAPucc7NjWhgEWZm7wMbnXND87Y559pELiIREREpyTa++j4bX32/0HZfWjpLr7ur2HZbh49na2j91DRWP/DsQfe14pahBV5nrlzHkj/dcSThlioLnhzJgidHFlk2KuGSAq+XvPwxS17+uNi+Vg6fXOB+m87n53/9ng5PoCIRViKSTDNLACYCtwIfATHAmcC+SMYlIiIiIiJlQCm8TUhJVlKmy7YAcM6Ncc75nHOZzrmpzrlFZuYxs6FmlmRm281spJlVAjCzr8zsztCOzGyRmV0RsuliM1tjZjvN7EUz84TUvcnMlpnZbjObYmYNQ8peMbMNZpZqZr+Y2ZkhZU+Y2UfBWPYGp692Le7NHaq+mbUysxlmlhIsuyy4fTBwPfCQmaWZ2ZfB7evM7Lyj/KxFRERERESOm5KSZK4AfGY2wswuMrPEkLIbg49eQBOgAvBasGwE0D+vopl1AOoCoZPYrwC6Ap2BPsBNwbp9gEeBK4HqwA/AmJB2c4GOQBXgQ2C8mcWFlF8GjAUqAxNCYipOkfXNLBr4EpgK1ADuBEabWUvn3DBgNPCCc66Cc+7SQ+yjWGY22Mzmmdm8YcOGHW03IiIiIiIiB1UikkznXCrQA3DA28AOM5tgZjUJjOT9yzm3xjmXBgwB+gYXt5kAtDCz5sGubgDGOeeyQ7p/3jm3yzm3Hvg3cF1w+y3Ac865Zc65XOBZoGPeaKZzbpRzLtk5l+ucewmIBVqG9DvTOfe1c84HfAB0OMTbLK7+6QQS538457Kdc98RmDp8XTH9HBXn3DDnXFfnXNfBgweHs2sRERERkVLN+VyJfpQ2JSLJBAgmezc65+oBbYE6BJLCOkDoDZySCFxLWtM5lwWMA/oHp8FeRyCBC7XhgLZ1gs8bAq8Ep6imALsAIzASipk9EJxKuydYXgmoFtJX6LXxGUCcmUWZ2fXBqa1pZjbpUPWD8WxwzvkPiLNucZ+ViIiIiIhISVViksxQzrnfgfcJJJubCSSEeRoAucC24OsRBEY7zwUynHOzD+iu/gFtNwefbwBuds5VDnmUc879GLz+8iHgT0Cic64ysIdAEnqo2EcHp7ZWcM5ddBhvdzNQP/Ra0WCcm/K6PIw+RERERKSEaXV7Hy79+XUGZE6ix3sPRTockROmRCSZZnaKmd1vZvWCr+sTGJX8icB1kveaWWMzq0BgWuu44BRXgkmlH3iJwqOYAA+aWWKwz7sJjHwCvAkMMbM2wX1WMrNrgmUVCSSyO4AoM/sbkBD2Nx4wh8DI5kNmFh28/+WlBK7fhEAy3eQ47VtERERKqbwEBqDR43cXKKt4anvafPw6nWaOp8WbTxNTq3p+WeJ53Tnl3efpNHM8Ld96psi+q1xwFo2fvh+Aho/eTttPXqfLz59T9ZJzCtWt2e8yOkweQacZY2n0t7uw6MDNC6ISK9H4mQdoP2k4HWcElr2o9odTjv2NlyIZm5NZ+MzoArcqkRLK70r2o5QpEUkmsBc4DZhjZukEksvFwP3AewSSx++BtUAWgcVxQo0E2gGjiuj7C+AXYAGBBYHeBXDOfQY8D4w1s9Tg/vJGHqcAkwksSJQU3GfBuw+HSfD60UuD+94JvA4MCI7mEoy3dXBa7+fHIwYREREpffISmANFVapI0xeHsPmN0Sw4px8ZS1fR5Ln9o2i5qWlsGzOBre8Xfw/HSt27smfWL4H9rFxL0j/eJOP31YXqJZzeiVoDr2b5bUNZdOn/EVu3JnVu7geAp3wcGUtXsqz/fSw453oAek98lqj4uEL9nKySPpvJ+i9msS85NdKhiJxQJeI+mc65TQSmphbn78FHcdYDs5xzaw7oN29666vF7PcDihj9DC7Oc1PwkeeFkPInDqi/joNMpT1UfefcEuDsYtquJLDKbei2RkX1fag4RERE5OSR9NnMIrdXPucMslavZ/e0WQBsHjaGjt+OIq5hXbKSNrH354UAVOvTu+iOzUg4rSMbXnobgB3jvwbAn51TqGrVS85h5xffkLUm8Lf4ze+Mo8lT97PptZFkb9rGttFfFKjviYmiUsv6JM9feeRvWERKjRKRZB4LMysP3EZgBFBERESkTCvXpD4ZK9flv/Zn7WPfpq3ENW1AVtKm4hsGxbdpwb5NW8nds/cw9tWAlP/NyX+duWIt0dUS8VaqiK+I9p6YaFJXbS60XSTifP5D15HDVlKmyx4VM7uAwHWT2wjcy1JERESkTPOUL4cvLb3ANl9aBt7y5Q6rfaUe+6fKHnpfcfjSMgrsByi0L0984PWCv48kJ7VgbCJy8inVSaZzbopzLt451ydvISARERGRssyfkYk3vnyBbZ74cvgyMg+rfaXuXdgza95h7isLb/z+hNJTIbDf0H1ZbAzN//VXAH77x5jD6ldESrdSnWSKiIiISEGZazZQvkXj/NeeuFhi69Uma/X6Q7aNqlqZ6GqJRS7yU/S+1hfYV/nmjcnZuTt/qqxFR9Hsn4+SvX3nEb6Lk4N5PXhjozGvp8BzKXmc35XoR2mjb7mIiIhIKZSXtOQ9t5ho8HpImT6buKYNqHxONywmmtp/6UvmynX7r8f0BOpalBcs+NzrBaDSGV1Inf1rwf1ERQXqmOU/xwLrDCZ/NZ1ql/UmrnF9vBXiqf1/f2LnxGnBmLw0ff4R/PuyWfvEv0/Qp1KydBjanwGZk2n/SD+a3dCbAZmT6TC0f6TDEjnuSv3CPyIiIiJlUYeh/en0+EAAql7ci6oX92LzsDFsHjaG1Q/9gwYP3UyTv99H+pIVrHn0xfx2VS/uSeMn7sl/3eXHT9j55TTWPfkKlXp0ZffUHwrsp8V/n6Ril3YAVOjQikZD72D5zY+y95fFpM6ez9YPPqXlm8/giY1h93c/svmtwDIZ8R1OofJZf8CXtY9O0wPTZPunTuSbi4ewbeZvx/WzKSkWPDmSBU+OjHQYIieckkwRERGRUigvgRnkn8a8rpcVKNv780KWXH1bke2SJ35H8sTvChd4PVTs0o51fy9457flNz920Di2jf6i0K1KANLmLykQV9d5ExiVcMlB+xKRk4OSTBEREREhKqEim98YjT/98BYIEjmp+ErfdY8lma7JFBERERFyd+9hxyeTIh2GiJwElGSKiIiIiIhI2Gi6rIiIiIiIlGnOH+kITi4ayRQREREREZGwUZIpIiIiIiIiYaPpsiIiIiIiUqY5v0U6hJOKRjJFREREREQkbJRkioiIiIiISNhouqyIiIiIiJRpfq0uG1YayRQREREREZGwMedcpGOQE08HXUREREROhFKxos6eW3qX6PPjSm9+Uyo+xzyaLisiIiIiImWac6UqhyvxlGSWUcM950Y6hIMa5J8GKM5wGeSfVuJjBMUZbqUhztL0GwLFGS6l4bsJijPcBvmnkeufEukwDirKcwFQOn5DUHrilLJH12SKiIiIiIhI2CjJFBERERERkbDRdFkRERERESnTnG5hElYayRQREREREZGwUZIpIiIiIiIiYaPpsiIiIiIiUqY5v25hEk4ayRQREREREZGwUZIpIiIi8v/s3Xd0VOXWx/HvTiMkEIpUkSoIKF2xURQVgatee0cRX8Xuvd6LvWDv/dq7otjFggUVlWoBFVRQuvQaSiAJpMx+/ziTMAkBAgxMBn6ftWZx5jltn4E5nD1PExGRqFFzWRERERER2a2FNLpsVKkmU0RERERERKJGSaaIiIiIiIhEjZJMEYkbKTWqcsT7t9F3zTBOnT2EZmceUeZ2bQaexgm/vUDf1Z9wyszXaTPwtOJ1lpjAYUNu4qwVH9Hzs3tIrppWvK7d9Wex31Wn7DZxiojE0syZi+l/3v84qPM19O51O19/Nal43XvvjqN3r9s5YP+BDLjwKZYuXV3mMfLy8rn5xiEcdcQgOu9/NSedeB+jR00pXr9o0UrOPP0hDjn4Ou6/b2iJfS8a8DR//DF3q+Mu7z2+w6Bz6bd+OH2zhhW/qjStD0ByRjpHf34vZ634iO6Dr8cSNjySH/rsVTQ+setWxyXbx0NWoV/xRkmmiMSNQ564klBePm/VO4WRfe/mkKf+RfV9G2+8oRmj+93HGzWP58s+19H6shNoenoPABqf1A3cebP2ieSvzqblgGMAqNKkHg2PO4Qpj3+w28QpIhIrBQWFXHHZ8xx2+H6M++Febr3tDK67djB/z17KTz9N57FHh/HEExcy7vt72avBHlz931c2cZwQ9epX59XBV/Lj+Pu48spj+M9VL7NgQSYAzz/3FcefcCBffjWIb0b8VpxUfv7ZLzRosAdt2jTa6tjLfY8HZr/9Ha9nHFv8Wjt7EQAtLzqWzIkzeKveKVRpUo9G4aSy9sH7kla/FnOGjtnquEQqEiWZIhIXktJSaXxyN3655RUKstexdOwfzP34e/Y+p+dG2/7xwNtk/jodLwyRNW0+cz8eS50u+wFQtWl9Fo+chBeGWPTdRKo0C35VPuixyxk/8Bm8cPt6/sdLnCIisTR71hKWLltNv349SExM4OCD96Fjx6Z8/PF4Rn43maN7daB5i/qkpCRx8aW9mTBhJnPnLtvoOGlplbjs8n/QoMEeJCQkcHiPNuy11x5MnjwPgAULMjno4H2oWrUybdo0Zv685axdm8sLL3zFv686dqvj3pp7/OZUbVqPRd9OJJSXz5Ixv1O1WX0sIYEDH76EH/71xFbHJWJmlczsRTObY2ZrzGyimfWJWH+kmf1lZjlm9q2ZNS6170tmlmVmi83sP9sbj5JMEYkLGfvshRcUkjV9fnHZyt9mUn3fJlvct27Xtqya/Hewzx+zqd+jAwkpydQ/vAOrJs+h0QldWL98NUvHTd5t4hQRqWjcYcb0RcXLG8qDN0XrNmf58iz+/nspzZsHP8y1aFGfceOmkpWVw+TJ82jevD7/e/wzzjn3cDIy0rZwtI1t7T2+4XEHc9byoZzw+4u0vPi4Dfv88Td7HtWJxNSU4nt/6ytPZMEXPxXXdsrO5aGK/SqHJGAecBhQDbgJeMfMmphZLeAD4GagJjABeDti31uBFkBjoAdwjZn13p7PU0mmiMSF5CqVycvKKVGWtzqb5KqVN7tfh1v7YQkJTH95OADzP/uRNX8v5rifniIvK5vZb31Lh1vOZfy1z9HpzvPp890jHPzklSQkb9sMT/ESp4hILDVpWpc9alblpRdHkJ9fyNixfzJ+wgxy1+XRtWtrhn/xK1OnLmDdujyefuoLzIzcdfmbPWZ+fiHXXv0ax59wIM2a1QXgggt78suEmfQ793HOOLMr+fkFTJ26gMMPb8PVA1/l3L6P8cYbo8od99bc42e/M5IP9j2fN+uczLgBD9Hh5nNoekbQJWL6i5+RUi2dY394kiVjfmfFpJk079uTyY9+wCFP/5s+3z1Cpzv6lzsuEXfPdvdb3f1vdw+5+zBgNrA/cBIw2d3fdfd1BEllezNrFd69H3CHu6909z+B54HzticeJZkiEhfy1+aSUupX5+SMdPLX5G5yn9aXHU/zc3ry1bE3EMrb8HDy8/Uv8FGHCxl30SO0ve4Mpj77CbU7t6TW/vvw+eFXkZiSTIvzt+0HvHiJU0QklpKTE3n8iQsYNXIyh3W7kVde/pbevTtSr251Djm0JZdd3od/X/kSRx91Gw0a1CQ9vRL16lbf5PFCoRDXXzuY5OQkbrzp1OLy6tXTeeiR/gz98DrOOfcw7rrrfW688RReeP5rWrSozwsvXcY7b41l5szF5Yp7a+7xq/+cQ+6iTDwUYun3U5jy+Ac0Obk7AIXr8xl30SN81OFCfr7+BQ58+FJ+vvFF9j77SCzB+Pzwq6h1YGsa9Opcrrhk12dmA8xsQsRrwBa2rwvsA0wG9gOKR9Zy92xgJrCfmdUA6keuDy/vtz3xKskUkbiQNW0+lpRIRvMGxWU12zVj1ZS/y9y+Rf/etL32TL446mpyFiwvc5sabZpS55D9mPrcp9Ro24zlv0wHYPn4qdRo12yXjlNEJNZatmzAq4P/xbgf7uX5Fy5l/rzltG0XdBM76+zufD78ZkaNuYuePTtQWBiieYv6ZR7H3bn5pjdZnpnFo4+fT3JyYpnbvfvOONq3b0KLffZk+vSF7NemISkpSbTYpz7Tpy0sV8xbe48vGSiYbTxKaINenTEzFgwfT422TVk+YRoAmT/rHi8buPtz7n5AxOu5TW1rZsnAG8Cr7v4XUAUoPUTzaqBqeB2l1het22ZKMkUkLhTkrGPOB2PoeNt5JKWlUufQ/Wh0/KHMHPzVRts2O+tIOt31fww/+prN9m05+H9X8OO/ngB31sxeRN0ubUhITqJu93asnbVtfWLiJU4RkVibOnUB69fnk5ubx8svjWDZsixOOPFA1q/PZ/q0hbg7Cxeu4NZBb9H3nMOoVq3sPpS33/YOs2Yu5smnLiI1NaXMbTIz1/DmkNFcelkwDkqDBnvw04/Tyc5ez+Q/5rFXw1rlinlr7vGN/nkoKdWD5/danVvS+ooTmfvxuBLbJFZKZv97LuDHq54EYM3sxdQ7rD0JyUnUObQNa3SP32ncrUK/ysvMEoDBQB5webh4LZBRatMMYE14HaXWF63bZkoydzFm5mbWPNZxiOwI31/2GImVK3HGkvc4bMiNfH/pY6yaMoe6XdvSN2tY8Xad7uhP6h4ZHPfTU8Vzkx3y9L9LHKtF/96snPw3meFawTkfjCZnYSZnLn2f1D0ymPrcp7t8nCIisfTJx+M5vPtNdOt6Az/8MI3nX7yMlJRk1q/P55qrX6Pz/ldzxukP0b5DE6648pji/Z579ksuGvA0AAsXrOCdt8fy118LOKz7jRyw/0AO2H8gwz4ZX+JcD97/IZdc2pv09EoAXDigJz/+OJ2jjriFw3vst1VTmZT3Ht/09B6cPH0wfbOG0e3V6/j9/reY8dqXJY7V7oazmTVkRHFLlqnPDiO1VjXOXPo+2fOXMVdTmchWsKCq/EWgLnCyuxf1wZkMtI/YLh3Ym6Cf5kpgUeT68PJ2jTJoHjl8V5wzs67A/QRtiAuBP4F/h99f4O4xmdnWzI4HbgOaEfyq8Bvwf+4+28xuBZq7e98oncuBFu4+YzOb+csJR0bjdDtM/9AIABRndPQPjajwMYLijLZ4iDOevkOgOKMlHv5tguKMtv6hERSEhsc6jM1KSugFxMd3COImzvJXw8XQkr59KnRSVPf1z7f4OZrZM0AH4Ch3XxtRXhuYAZwPfEqQlxzm7geH198LHAKcQJCgfgv0d/cvtjXeXWZYQjPLAIYBlwDvAClAN2B9jONqDrxGMKrTNwTtno8mSIJFRERERCTGyjlNSIUVnvfyIoLcZ3FE/9+L3P0NMzsZeAJ4HfgROCNi90HA08AcIBe4b3sSTNi1msvuA+Dub7p7obvnuvuXQD7wDHCIma01s1UAZlbNzF4zs2XhSUtvCrdhxszOM7MxZvagma00s9mlJjOtFp7sdJGZLTCzO82s7F7mwa8Js919hAfWuPv77j43PP/MDcDp4dgmlef4Zna+mf0Zjm145GSqIiIiIiKye3H3Oe5u7p7q7lUiXm+E13/t7q3cvbK7H+7uf0fsu97dz3f3DHev6+4Pb288u1KSOQ0oNLNXzaxPeDhewnO9XAx8H/6gi8a//h/BRKXNCCYtPReInJDoIGAqUIugCe6LtuEngVeAAqA50JGgZvKCTcT1C9DKzB4xsx5mVjSCE+FfCO4G3g7HVtQWepPHDze9vYGgZrQ2MBp4c0sflw7onwAAIABJREFUTuSwx889t8nBqERERERERLbLLpNkunsW0BVwgglEl5nZx+E5YkoI1wqeAVwfrln8G3gIOCdisznu/ry7FwKvEswfUzd8vH8A/w5PeroUeISSVc6Rcc0CDgcaEDTjXW5mr0Qmm6Vi29LxLwbucfc/3b2AIEntsKXazMhhjwcM2Oy0OiIiIiIiu5VQyCr0K97sMkkmBLWW7n6eu+8FtAH2BB4tY9NaQDJBu+MicwgSwSLFs/K6e054sQrQOLzvIjNbFW5++yxQB8DMJoebvq41s27h/X9w99PcvTZBP9HuwI2buIzNHj+8/rGIdSsIOlQ3KPNoIiIiIiIiO9EuM/BPae7+l5m9QtABtnTH1eUEfTUbA1PCZY2ABeU49DyCDrW1wjWJpc+73xbiGm9mHxAkwRDUvJb7+OH1dxW1rxaRsrW+7Hia9+tFjbZNmfXmt4w5//5Yh1SmeIlTRES2XvfXrqf+kR1JSk8ld/FKfn/gbaa/+FmswxLZ4XaZJNPMWgHHEPRvnG9mDYEzgR+AJcBeZpbi7nnuXmhm7wB3mdm5QE3gP8CDWzqPuy8ysy+Bh8zsZoIJTJsCe7n7yDLi6gq0Bj5y96XhOP9J0ASXcGw9zSzB3UPlOP4zwB1mNtHdJ5tZNeBod393Gz86kV1SzsJMJt31Bg16HUBiaqVYh7NJ8RKniMj2mFIlhz59+rBo0Xxq1arKXXf3JSkpgf89/hmTp8wjMcHofGALbrjhZGrXqQZAXl4+99z9ASO+/o38gkI6dmzKoFtPp27d6sXHXbp0NWec9iDffHcHAJ99+jNPP/UFixatLD7P/gfszYwZi7jhuteZNy+Yj3LffRty/Y0n07x5fQBefeVbhrwxipUrryctLY3GtXI5Ynk1ErZz9o3f7h3CmAseJJSXT7WWDen97cOs+HV68dzHUnHE++iyFc2u1Fx2DcFgPT+aWTZBcvkH8F+CqUMmEwznuzy8/RVANjALGAMMAV4q57nOJZgiZQqwEniPoM9mWVYRJJW/m9laglrVoQSDCQEUJYeZZvbLlo7v7kOB+4C3zCwrfI3FI9+KSGDO0DHM/Wgs6zOzYh3KZsVLnCIi22p22jq+q7Wae+65h58m3M+rg//FXg33ICsrl1NPO5Svvh7EVyNuIz29EjfeuKGh1uDXRjJx4mw++PBavht5B9Uy0rj7zvdKHHvUyMl07doagHFj/+Lhhz7mzrvOKnEegDp1qvHIY+cz7od7GTPuHnoc0Zar//tq8XF6HNGGd9+/ml9++YVhw4axtFI+E6qvZXutmjKHUF4+AO4O7lTde8/tPq5IRbfL1GS6+wLgtM1sckyp7VcCfTdxrFcIRniNLLOI5dUE83FeUo64/gCO28z6TIIBiyLLNnt8dx8MDN7EuvjrGSwiIiK7rDE1s+iyIoMOHTpQEFpSXBMZWSMJcNZZ3el37uPF7xfMz6RLl9bUqpUBQO8+nbj/vqEl9hk9agrHHtcZgCef+IxLLu1N+w5NNzp+RkYaGRlpALiHSEgw5s5dVry+UaPaxcvujgErk8vqtbT1Dn7ySlr060VSWiqZv0xn/mc/RuW4IhXZLpNkioiIiEjFEsJZlJpH8+xUevbsyfr1qzniyHYMvPp4UlNTSmw7YcIMmjevV/z+pFMO4Z6732fp0tVUrVqZYcMm0LVb6+L1+fmFTJgwk7vv7UthYYg/Js/j8CPa0rvX7eStzy/zPAcfeC05OesJhZzLr/hHifMPGzaB22+9nuzsbCqnJHDEsmpR+Qx+uOxxfrziCWofsi/1D29P4fr8qBxXosvjcATXimxXai4rIiIiIhVIdmKIkMHUKrm88cYbvD/0Wv78cz7PPvNlie2mTl3A008PZ+DVJxSXNW5cm/r1atDjsJs5qPM1zJq1mEsu7V28/ucJM2jZqgHp6alkZq6hIL+Qr4ZPZPDgf23yPD/8dB8//HQfN950Cq1b71Vi3bHHHsAvv/zC8OHD6bg6nfTCxKh9Dh4KsXTsH6TtVZtWl/wzascVqaiUZIqIiIjIDpEc7sWz/6oq1KlThxo1qtDvvB6MGjW5eJs5c5Zx8YBnuP76k9j/gL2Ly++8413y8goY+/09TPjlAY46qj0XX/RM8fpRo6bQvfu+AFSqlAzAWX27U7tOtTLPUyQtrRKnn9GF668bTGbmmo3WN2nShFp5yXxZZ2V0PoQICUmJZKhPpuwGlGSKyC7JEhNIrJSMJSaUWK5o4iVOEZFtkRpKoGp+yRpBixixdeGCFVxw/pNcfEkv/nn8gSW2++vPBZxw4oFUr55OSkoyZ/ftzu+/zWHlymBAntGjptAtnGRWq5ZGvXrVSxzbNjMybCjkrFuXz9Ilq8peb87K5MKtu9hSUmtXp+npPUhKT8USEtjz6ANoekYPFo74Zcs7y07noYr9ijfqkykiu6T2N/Wl46B+xe+bn9OTX297lYm3vRbDqDYWL3GKiGyrtllp/Fx9LZmZmZCQw2uvfcthh7VhyZJVnN//f5x1djdOP6PrRvu1aduIjz4aT+cDW5CamsJbb46hTriWcv78TPLyCth77w19OE848SDeeGMUXbu1Jikpsfg8EIw8W6NGOvu0bEBu7noef+xTMjIq0yy8/3vvjqPHEW2pWxtmzJjBDzXW0DQndbuu291pdfFxHPL0v7EEI3vOEn666inmffL9dh1XJB4oyRSRXdLE216Li0QtXuIUEdlWXVZkkJsYolevXqRUgt69O3LRxUfz4gtfM29eJk8++TlPPvl58fYTfg6mLb/66hO4++73+EfvO8jPL6R5i/o89r8LABg5cnJxLWaRiy/pzcqV2RzT505SKiUVnwdgzZpc7r7rPRYvWUVqpWTatm3Ms89fUtzM9tdfZ/P4Y5+Sk3MnNWvWpFlOKt0zt2/gn/XLV/N5j/9s1zFE4pWSTBERERHZYRIxei2rwVtLRlAQGl5cfullfbj0sk1P9V29Rjr3P9CvzHWjR07hrLO7lShLTk7klkGnccugjWe069W7I716d9zkue66+2wAkhJ6AfBywpGbviAR2SIlmSIiIiISVzof2JwDD2oR6zBkF6Kp5qNLSaaIiIiIxJX/u+CoWIcgIpuhIQxFREREREQkalSTKSIiIiIiu7VQHE4TUpGpJlNERERERESiRkmmiIiIiIiIRI2ay4qIiIiIyG7N1Vw2qlSTKSIiIiIiIlGjJFNERERERESiRs1lRURERERkt+Yhi3UIuxTVZIqIiIiIiEjUmLvHOgbZ+fSXLiIiIiI7Q1xUEc465rgK/Xzc7NNP4uJzLKLmsiIiIiIislvT6LLRpSRzN/VywpGxDmGz+odGAIozWvqHRlT4GEFxRls8xBlP3yFQnNESD/82QXFGWzzEWfQdWnxWnxhHsnn1hnwOxMd3XXZP6pMpIiIiIiIiUaMkU0RERERERKJGzWVFRERERGS3FtIUJlGlmkwRERERERGJGiWZIiIiIiIiEjVqLisiIiIiIrs1TWESXarJFBERERERkahRkikiIiIiIiJRo+ayIiIiIiKyW3PX6LLRpJpMERERERERiRolmSIiIiIiIhI1ai4rIiIiIjGXUqMqXV8YyJ5H78/65Vn8fMMLzHrzm4226zDoXNrfcDaF6/OLyz5sfyFrZy8iOSOdHm/fTK2DWjP/0x8Y3e8+PBQMG3ros1ex4IvxzBk6ZqviStyzIRn9LyW5aQtCWatZM+RF1k8YB0Dlw3uR/s/TSKheg/ypk1n97COEVq0o8zg1b7qP5Oat8FAhAKEVmSwfeCEASY2aUu3ya0msVp21H71NzmdDwydPpOagB1n16F2EVizfqrhl62h02ehSkikiIiIiMXfIE1cSysvnrXqnULNDc3oOu4sVk2ayasqcjbad/fZ3jDr3no3KW150LJkTZ/D18TfTe8SDNDqxK3PeH0Xtg/clrX6trU4wSUigxn9vIWfEZ6y8+0ZSWrel+sBbybzhchJr1KTK6eex8s5rKVi8kIx+F1P9iutYccc1mzxc1itPkfvd8I3Kq57RnzVvvEDB3NnUuvcp1o39jtDqlaT/4yTW/zRWCabEHTWXFREREZGYSkpLpfHJ3fjlllcoyF7H0rF/MPfj79n7nJ5bdZyqTeux6NuJhPLyWTLmd6o2q48lJHDgw5fww7+e2Pq49mxIQo09gppFD5E3ZRL506ZQudsRVOp4EOt+HE3BgrlQWMDaD4aQ0rotiXXqb/V5EmvXJW/yJEIrMylYvJDEWrVJqFWHSgd2IfvzoVt9PJFYU5IpIiIiIjGVsc9eeEEhWdPnF5et/G0m1fdtUub2DY87mLOWD+WE31+k5cXHbdjnj7/Z86hOJKamULdrW1ZN/pvWV57Igi9+Yu3sRdEJ1iBpryAus4gRScPLSQ0bb3LXqmf0p86zb1Fz0IOktG5bXF4wfw6V2nUioWYtEmvXoWDJIjLOvYg1b7wIhYXRiVs2y0NWoV/xRkmmiIiIiMRUcpXK5GXllCjLW51NctXKG207+52RfLDv+bxZ52TGDXiIDjefQ9MzegAw/cXPSKmWzrE/PMmSMb+zYtJMmvftyeRHP+CQp/9Nn+8eodMd/csdV8Gi+YSyVpF+7CmQmEhK206ktG6LVarE+t8mkHpwN5IaNoHkFKqcdBYeCmEplco81po3X2LZv/uz9LJzyPnmC6oPvLW41jPrjRdIO+oYavx3EGsGP0fKPvviubkULltM9f/cQs2b76fSQV3LHbdIrKlPpoiIiIjEVP7aXFIy0kqUJWekk78md6NtV/+5oY/m0u+nMOXxD2hycndmv/UthevzGXfRI8XrD3/7Fn6+8UX2PvtILMH4/PCrOHr4/TTo1ZkFw8dvObDCQlY9dDsZ511K+nGnkj97Out+GI3n55P3x0TWvvc61a+6CaucRs7nH+LrcincRP/J/JlTi5fXjf6ayoceRqUOncn58mNCy5ey8v5bgpUpldjjtodZec+NVD3vEtb9MIr1v/5ErfufYfkfE/HstVuOWyTGVJMpIiIiIjGVNW0+lpRIRvMGxWU12zVj1ZS/t7yzl2q2GtagV2fMjAXDx1OjbVOWT5gGQObPU6nRrlm5YyuY9zcr7riGpRedzsp7byKxTr3ihDHnq2Es/88FLLvkLNb9NBYSEimYv/FARWXH7VBGK8gqJ51F7rdfEMpaRXLDJuTPmo7n5lC4YjlJ9fYsd9yydUKhiv2KN0oyRURERCSmCnLWMeeDMXS87TyS0lKpc+h+NDr+UGYO/mqjbRv981BSqlcBoFbnlrS+4kTmfjyuxDaJlZLZ/54L+PGqJwFYM3sx9Q5rT0JyEnUObcOaWeXvnxk0h02GlEqkHXMyCdVrkjvqa0hOJmmvoP9lwh61qXbBleQM/7DMmkZLSyelXafgOAkJpHbpQXKrtqyf9HPJuBs0IqV1O3K++hSAwmVLSNmvPQkZ1UmqtyeFy5eVO26RWFJz2Z3IzBxo4e4zYh2LiIiISEXy/WWP0fXFqzljyXusz8zi+0sfY9WUOdTt2paen93D6xnHAtD09B50efFqEislkz1/Gb/f/xYzXvuyxLHa3XA2s4aMIGdB0HR16rPD6PHOLZy59H3mffojc7diKpPK3Y6kco9ekJhE/l9/sPKeG6AgH0tLD+a2rFMfX5dD7sivWPvO4OL90o8/nZSW+wXNYBOTqHpqPxL33AtCIQoWzmfVw7dTuHhBiXNlnHcpa157pnjSxjVvvUz1y6+j6mnnsvajtwmtXrlNn63IzqYkczuY2fVAd3fvE1E2HZhRRtnNOzCOW4Hm7t53R51DREREZEfKW7mGb066ZaPyJWN+L04wAUaefdcWj/XroFdKvM/PyubL3tduU1xrhrzImiEvblTuOdlkXnfpJvfL/uhtsou2XbOazJv/tcVzrbzruhLvC+bOZvk1F21VvCIVgZLM7TMKuM7MEt290MzqA8lAx1JlzcPbioiIiIhIBRMq9FiHsEtRn8ztM54gqewQft8N+BaYWqpsprsvDL8/ysymm9kqM3vSInqqm9n5Zvanma00s+Fm1jhi3WNmNs/MsszsZzPrFi7vDdwAnG5ma81s0g69YhERERERkc1Qkrkd3D0P+BHoHi7qDowGxpQqi6zFPBboDLQDTgN6AZjZ8QTJ4klA7fBx3ozYbzxB4loTGAK8a2ap7v4FcDfwtrtXcff2ZcVqZgPMbIKZTXjuuee267pFREREREQ2RUnm9hvJhoSyG0FyOLpU2ciI7e9191XuPpeg1rOoxvNi4B53/9PdCwgSxw5FtZnu/rq7Z7p7gbs/BFQCWpY3SHd/zt0PcPcDBgwYsG1XKiIiIiKyC4r1FCWawkRKGwV0NbOaQG13nw6MAw4Nl7WhZE3m4ojlHKBKeLkx8Fi4Ge0qYAXB7EkNAMxsYLgp7erw+mpArR15YSIiIiIiIltLSeb2+54g4bsQGAvg7lnAwnDZQnefXY7jzAMucvfqEa/K7j4u3P/yGoLmtTXcvTqwmg1T+KqnsoiIiOw2Wl92PMf99BTn5n5O15euiXU4IlKKRpfdTu6ea2YTgP8AkWNqjwmXfV3OQz0D3GFmE919splVA45293eBqkABsAxIMrPrgIyIfZcAPc0swd3jsEJdREREpPxyFmYy6a43aNDrABJTK2338VpfdjzN+/Uqc13lw3uR/s/TSKheg/ypk1n97COEVq0AIO3Yk6nc7SgSa9UhtCaLnK+HkTPs/RL7J7doRdWzL2TFrf+lyqnnUGn/Q0hq0IjsD99k7ftvlNg2/fgzSDuyD5ZWhfWTxpP1wuN4bg4AVc48n8qHHo5VTt/u65WNFYZUZxNNqsmMjpFAHYLEssjocFm5pi5x96HAfcBbZpYF/AEUzbU5HPgCmAbMAdYR1HwWeTf8Z6aZ/bKN1yAiIiISF+YMHcPcj8ayPjMrKscrSlpLS2ndliqnn8eqh25j6YWnUbhsCdWv2DCXpWGsfvpBll54Kivvu4n0nseReshhJY5RqcOBrJ84HoCCxYtY8+ZLrP/1p43OldrtKCp3O4LMW//LssvOxpJTyOh3SfH63O+Gs3zghSy94OTissYndt3uaxfZEZRkRoG7X+/u5u6/RJS9Ey57NqLM3H1GxPvz3P2miPeD3b2tu2e4e0N3Pz9cXuju54fL67v7/e7exN2/Dq/PdPeu7l7D3TvtnKsWERER2TUUJa2lVep4EOt+HE3BgrlQWMDaD4aQ0rotiXXqA5A97D0K/p4JoRCFixaw7ucfSN5n35LH6NC5OMlcN/pr8iZNwNflbnSu1E4HkfPdcEIrluPr15H9ybukHtwdUoKa2sJFC/D160vsU7V5g6hcv0i0qbmsiIiIiMgmRExpDuHlpIaNKVy6aKNtU1q1IWfEZ8XvE6rXIKFa9SARLc+5KHkuS0khqd6eFMwNhvdIP+5U0k88s3iTWUNGbM2lyGaECmMdwa5FNZkiIiIiImVY/9sEUg/uRlLDJpCcQpWTzsJDISxl436gVU7uC2bkjvyquKxSh86sn/Rz+c41aQKVe/QisVYdrHIa6cedCoBV2nCu7E/eZen5JxW/z1udvY1XJrJjKckUERERESlD3h8TWfve61S/6iZqP/4KhcuW4OtyKVyxvMR2aUcfR2q3I1n5wCAoyC8uj2wquyW5I79k3biR1Lz5fmrd/yx5UyYBUJi5fJP7dLztvK2/KJGdQM1lRURERCSuWGICCUmJWGIClphAYqVkQgWFeGH0B9nP+WoYOV8NAyCxXgPSTziTgvlzitdXPuxo0o87jRV3XE0oMvlMTCSldVtWP/tI+U7kztr3X2ft+68DkNK2E4WZywmtzNzkLhnN6m/9BUmZQhpdNqpUkykiIiIicaX9TX05N/cL2l13Fs3P6cm5uV/Q/qa+23y8okS1WHIyJCRAcjJJezUGIGGP2lS74Epyhn+IZ68FILVLD6qc3o8V99xA4dLFJY6Z0nI/8ufOLp6CBIDExODYZpBQtBw8jlt6leIBhRIbNKJq3wtZO3QIuIMZlY/og6VXKXGOhd/8us3XLLIjqSZTREREROLKxNteY+Jtr0XteO1v6kvHQf2K39d79WPWvv862Z9/SLXLryWxTn18XQ65I79i7TuDi7ercuq5JFTJYI87HysuWzfmG7JeeiI8dcmEEuepdsG/qHxYzw37n3gmq595iNxRX5NQtRo1Bt5K4h61CGWtJnv4R+R+83nxtqmdD6XqGf0hacPj+5//Gxq1z0AkmpRkioiIiMhurShp7R8KRmtdfFaf4nWZ1126yf2W/7v/JtdV6tCZlY/dVaJs9bMPs/rZh8vcvnDxApYPvLDsg7mz8r6bi9/WG/J52duJVBBKMkVEREREoikxidzRX1O4YG6sI5Fy0hQm0aU+mSIiIiIi0VRYQPYn78Y6CpGYUZIpIiIiIiIiUaPmsiIiIiIislvTFCbRpZpMERERERERiRolmSIiIiIiIhI1ai4rIiIiIiK7tVAo1hHsWlSTKSIiIiIiIlGjJFNERERERESiRs1lRURERERktxYq1Oiy0aSaTBEREREREYkac1fWvhvSX7qIiIiI7AwW6wDKY1SrYyr083H3vz6Ni8+xiJrLioiIiIjIbq1Qo8tGlZLM3dTLCUfGOoTN6h8aASjOaOkfGlHhYwTFGW3xEGc8fYdAcUZLPPzbBMUZbfEQZzx9hwA+rtc7xpFs3j8XfxHrECRG1CdTREREREREokZJpoiIiIiIiESNmsuKiIiIiMhuTVOYRJdqMkVERERERCRqlGSKiIiIiIhI1Ki5rIiIiIiI7NZCmsIkqlSTKSIiIiIiIlGjJFNERERERESiRs1lRURERERktxYKaXTZaFJNpoiIiIiIiESNkkwRERERERGJGjWXFRERERGJQ92HP07VVk1Y8OFIJv7roeLyWl070Paey6jcoDarfp3Kr/96iNz5SwFISEmm3X2XU//YrhTmrmfGk+8x69kPNnmOZgNOpPnlp5JYuRKLho3ht2ufIJSXjyUm0OnJa6jT4wBW/vwXEwbcTcHaHABaXHk6hevzd+zFR1moMNYR7FpUkykiIiIiEoemPfIm8976skRZSs0MOr90M3/d/xpftD6VVZOms/+z1xevbzmwL+lNG/D1Af0Yd/K1NL/sFGr32L/M49c+fH9aXHEa3596PV8f0I+0xvVpeXVfAOof0wV3+GLf08hfk03jc/oAkNaoLnV7HczsFz7cQVct8UBJpoiIiIhIHFr8xffkrVhToqz+P7qwZuocFn0ymtD6fKY+OJhq+zajSvO9AGh42lFMe2QI+avXsnb6POa8/gWNTu9Z5vEbnnYUc4YMZ83UOeSvXsu0h4fQMLxtWqN6ZI77DS8MsXzsJNIa1wOgzZ2XMOXW5/FCTTy5O1OSKSIiIiKyi6jasjFZU2YVvy/MWU/2nEVUbdmY5GpVSK23B1mTN6zPmjKLqi0bl+tYWVNmkVqnJsk1qpL11xxqdW1PQkoytbq0Z83UudTrcyh5K7JYMX7KjrvAHSQU8gr9ijdKMkVEREREdhGJ6ZXJz8ouUVaQlU1SlTQS01MByF+TvdG6siSlp5Y4VtFyUpU0ln79EzlzF9P9i8fJz8pmwYff0fK/ZzPljhdpdV0/ugx9oGi3lOhdncQLJZkiIiIiIruIwuxckqqWTBqTqqZRsDaHwux1xe9LrytLQfY6kkttCxRv/+ddL/PdEZfw29WP0+KK0/n7tU+p3mEfqrffh7EnXl202/lRujSJI0oyRURERER2EWumzqHavs2K3yemVSKtcf3ifpXrFmeWWJ+xXzPWTJ2zyWNlRGxbbd9mrFu6gvyVJfuBVm3VhJoHtGbO4M/JaN2EVb9Nj1zdLioXtoMVhir2K94oyRQRERERiUMJlZKxxAQsIaF4edHn46jaqgn1j+lCQqVk9vnP2WT9OZu1M+YDMO/dr2lx1ZkkV6tCleZ70fjsPsx9+6syjz/v3a9pdFYvquzTiKSMdFpcdSbzyti27T2X8vtNT4M7OXMXs8eB+2HJxTMlztpoB9nlKcmMQ2bmZtY81nGIiIiISOwcO+cTWlx5Og1PPZJj53zCPledRV7masb/3520uu48+vz1HjU6teLni+8t3mfqA6+T8/cijprwKl2GPsCMp95j2bc/A1C5QW3+MXMolRvUBmDZtz8z48n36PL+ffT8+TVy5y9l6gOvl4ih4ZlHs+avOaz+bQYAiz4dy7olmfSe/HbRJs/t8A9CKpykLW8im2Jm1wPd3b1PRNl0YEYZZTe7+1sxCFNEREREdkEf1+tdZvny0b/ybbcLy1wXystn4lWPMPGqRzZal7tgGZ/tfWKJslnPfsCsZz/YZAzz3vySeW9umKvTC0PFSe0/F38BkLWFy5BdkJLM7TMKuM7MEt290MzqA8lAx1JlzcPbioiIiIhIBRMqjL9pQioyNZfdPuMJksoO4ffdgG+BqaXKZgKY2cdmtsLMZphZ8c9LZlbJzB41s4Xh16NmVili/dVmtii8rsQIXWb2DzObYmZrzGyBmQ3ccZcrIiIiIiKyeUoyt4O75wE/At3DRd2B0cCYUmWjgLeA+cCewCnA3WZ2RHibG4GDCRLT9sCBwE0AZtYbGAj0BFoAR5UK40XgInevCrQBvikrVjMbYGYTzGzCc8+pabyIiIiIiOwYSjK330g2JJTdCJLM0aXKRgJdgGvdfZ27TwReAM4Nb3M2cLu7L3X3ZcBtwDnhdacBL7v7H+6eDdxa6vz5wL5mluHuK939l7KCdPfn3P0Adz9gwIAB23nJIiIiIiK7jlCoYr/ijZLM7TcK6GpmNYHa7j4dGAccGi5rA/wFrHD3yEmF5gANwst7ht9HrtszYt28UusinQz8A5hjZiPN7JAoXJOIiIiIiMg2UZK5/b4HqgEXAmMB3D0LWBguWxh+1TSzqhH7NQKy0kcvAAAgAElEQVQWhJcXAo1LrVsYXl4ENCy1rpi7j3f344E6wIfAO9t/SSIiIiIiIttGSeZ2cvdcYALwH4JmskXGhMtGufs8gtrNe8ws1czaAf8HFE009CZwk5nVNrNawC0R694BzjOzfc0sDRhUdAIzSzGzs82smrvnEwwRHYcV6iIiIiISLelN9+SYvz+m0xPXFJel7FGNTk9dS59p79P7r3fp9OQ1Jfax5CR6TX6bxLTU4rLk6lXoNfktunz0UIltEytXou29l9Fr8tv0mfY+XYY+sGMvaCcIFXqFfsUbTWESHSOBQwgSyyKjgcvZMHXJmcAzBDWUK4FB7v51eN2dQAbwW/j9u+Ey3P1zM3uUYECfEMGAQGdHnOcc4AkzSyQY1TZynYiIiIjsZtrecxmrJk4rUdb5xZtZNXEaX+1/DoW566naqkmJ9Xsc3JasybMozFlXXLbvTf/H2mnzIKFkvVT7B/6FJSXwbfcB5K1cQ7U2zXbYtUh8UpIZBe5+PXB9qbJ3iGi66u7zgWM3sf864Mrwq6z19wL3RhS9FLFc9iy8IiIiIrLb2fP4w8jPymblhD9JbxIM8VH7sE5UblCbsSddUzyKTNYfM0vsV/fIziwZMb74fY0DWlO1VRPmvP4Zjc7c8LhZpfle1O11EF91PIeCtTkArP5txo6+LIkzai4rIiIiIrILSKqSRqtrzmHyoJLT1dXYvxVrZ8yn0+MD6T3lHbp98Th7HNK2xDZ1juzMkq9/Ct4kJND27sv4/YYnoVRLzeodW5I7fyktr+5Lr8lvc/i3T1P/mC478rJ2iliPHqvRZUVEREREpMJpde25zH1zOOsWLS9Rnlq/FnV67M/ysZMY3u5MZj79Pge+MoiUmhkApDWuT0JSItkz5wPQ7ILjWfXrX2XWUFauX4uM1k0pWJPDlx3O5vcbnqLj4wOp0qLhRtvK7ktJpoiIiIhInMvYrxm1undk5rNDN1oXWpdH9tzFzH1zOF5QyMKPRpK7cBk1O+8LQN2jOrPkm6CpbKW6NWl6wfH8ec8rZZ6ncF0eobx8pj0yBM8vIPP731k+dhK1D+u0w65N4o/6ZIqIiIiIxLlah7YjrWFdev78GgBJ6ZWxhAS67/MEf7/8CXWPPqjkDhHNYOsc2ZnZL3wEQI2OLUmtU5Meo4Imt4mplUhMTeHo34bwZYe+ZE2ZvfHJ42/w042EQrvARVQgSjJFREREROLcnNc/Z8GHI4vf733pyaQ1rMtv1z6BFxay76ALaHjaUcx77xvq/+NQUuvXYsX4KSRWrkSNDi0ZP3YSAEu/mcDXnfsVH2fP47uz10k9+KnfbRAKkfnD7+QuWEaLK89g+uNvUaNTK2p1aceUO17Y6dcsFZeSTBERERGROFeYu57C3PUb3mevI7Qun7zM1QD81O9W2t17OW3vuYy1M+bx03m3krcii7o9D2TFz38SWp8PQCgvn/XLVhYfp2BNDqH8wuIyLyjkp/Nuo/1D/6b5FaeRO38Jv1zxIGtnzN+JVysVnZJMEREREZFdzNQHXy/xfsWPk/muxyUbbVfnyM4sjZi6pLR5b3/FvLe/KlG2Zuocxhx7VXQClV2SkkwRERERkd1U1h+zWPzlj7EOI+YKC2Mdwa5FSaaIiIiIyG5qzuufxzoE2QVpChMRERERERGJGtVkioiIiIjIbk1TmESXajJFREREREQkapRkioiIiIiISNSouayIiIiIiOzWQhpdNqpUkykiIiIiIiJRoyRTREREREREokbNZUVEREREZLe2K4wua2Y1gReBo4HlwPXuPiQWsSjJFBERERERiX9PAnlAXaAD8KmZTXL3yTs7EHOP/6xdtpr+0kVERERkZ7BYB1AeLyccWaGfj/uHRmz2czSzdGAl0Mbdp4XLBgML3P26nRBiCarJ3D1F/ctuZgPc/bloHzfaFGd0Kc7oiYcYQXFGm+KMrniIMx5iBMUZbYqz4ttSEhdrZjYAGBBR9Fypv6t9gIKiBDNsEnDYzoivNA38I9EyYMubVAiKM7oUZ/TEQ4ygOKNNcUZXPMQZDzGC4ow2xSnbxd2fc/cDIl6lfwyoAmSVKlsNVN05EZakJFNERERERCS+rQUySpVlAGtiEIuSTBERERERkTg3DUgysxYRZe2BnT7oDyjJlOiJl/b7ijO6FGf0xEOMoDijTXFGVzzEGQ8xguKMNsUpO5S7ZwMfALebWbqZdQGOBwbHIh6NLisiIiIiIhLnwvNkvgT0BDKB62I1T6aSTBEREREREYkaNZcVERERERGRqFGSKSIiIiIiIlGjJFNERLbIzBqa2cGxjkNEREQqvqRYByDxy8zqAgcCtQArKnf3l2IW1C7AzEr8+OPuoVjFEk9Kf26bEuvP08wGA1vsDO/u5+6EcLbIzBoBbwIdCOKuYmanAL3d/YKYBhfBzM4EJrr7n2bWEngeKAQucfe/Yhtd/DOzHkDI3UfGOpZI4UEuBhL8+6wSuc7du8ckqDgTD/dOM2tWnu3cfdaOjqW8zOxXd+9YRvkEdz8gFjGVxcz+A3zj7hPDPyS+Q3DvPMvdv49tdBLPNPCPbBMzOwF4HZgO7EcwB08bYIy794hlbKWZ2b5AprsvMbMqwNVACHjA3XNiG13AzDoBTwLtgNSiYsDdPTFmgQFmNo/yJUWNdkI4m2RmITYfZ0X5PAdFvK0F9AM+AeYAjYDjgFfd/coYhLcRM/scGA3cS/A9qmFm1YDf3L1xbKPbwMxmAoeGv+efAFMJJqbu7u5HxDa6+HiQj2RmI4Eb3H2smV0L/AcoAJ5097tjG90GZvYFUIngwbjE/dzdX41JUGG6d0ZPRIy2mc1ifn+PZGZr3L1qqTIjuI/WjFFYGwn/O23j7qvN7FvgI2ANMMDdD4ptdBLPlGTKNjGzP4Db3P1dM1sZfvDsD+zn7gNjHV8kM5sEnObuU83sGaAlsA5Y7u7nxDa6gJn9TpBoDGbjB6U5MQkqzMwOK892sa7hMLNyJTyx/jwjmdlw4E53Hx1R1hW42d17xS6yDcwsE6jt7iEzW1H0cGRmq9y9eozDK2ZmWe6eYWapwCKgHpBP8D2P+QNdOR7kAagoD8nhv/c67l5oZjOAfxI8eI6NdVIUycyyCP59ro91LKXp3rl7MrPXwounA2+XWt2E4Nm7204NajMi7p1VCX7srB3+3leoe7zEHzWXlW3VyN3fLVX2KrCYoOlSRdIknGAacBKwL5ALzI5tWCU0Bm70CvirT6wfgMorTh+ADgZ+KFX2I3BIDGLZlCVAc2BaUUG4dcDcmEVUtmVm1hxoC4x39/Vmlsbmaz52pqYRy8cApwD3EDzUNQauBd6PQVybkgC4me1N8FA8BcDMasQ2rI38BuwFzIx1IKXp3rljmVlDoIG7l76HxtrMTSw7MBYo/ewUa/PM7FCCVmmjwglmBkGTWZFtpiRTttVSM6vr7kuAv83sEGA5UCF+hS9lXfgXun2Bue6+3MyS2NAstSIYChwNDI91IKWZ2e3l2c7db9nRsWxOvPV1DPsVuNvMbnH3XDOrDNwGTIxxXJEeBIaZ2T1AUrjv4w0EzWcrkjuAnwkejE4Plx0FTIpZRBEiH+TDfaAOcPdV4aJpZjYBmAA8HYv4yjAGeAKoT3B/IpxwLo9lUGX4BvjCzF4m+JGzWKzHB9C9c8eo6P3E3f02ADP7wd0r3P/pZbgaeA/IA04Olx0L/BSziGSXoCRTttXzQFeCX94fAb4l6Of4UCyD2oQhBA8iVQkemgA6UbFqMlOBoWY2ho0flGL9H3vDGJ+/vGbEOoBtcB7Bv8/VZrYSqEGQaJwdy6AiuftL4aaTFwHzgHMJmvN+GNvISnL3V8zsnfByUZPzH4AzYhfVJlUD0oBVEWVp4fKK4jzgv8Ay4P5wWSvgsVgFtAndgPlAz1LlDsR6EDrdO3eMZ4FPCf7uM8NlX1HBnj/cfbiZpRB00Sk9QOI3MQusFHf/DNizVPG7VLwaV4kz6pMp5WZm7d29zFqB8C+L6e7+504Oq1zM7Ggg392/Db8/AMioKDf6UoPBlFD0q6jsusLNvvYEFrl7hWmGamaJwAigV0Xs81aame0B/AOo7+73m9meQIK7z49xaCWY2YMEcT5KkLg3BK4Ehrv7f2MZWxEzO7WMLhGY2Snu/l4sYhKBuOon3pUgUasEZABZBD92z3P3co2WuwNji7vReiX+KMmUcivqHB5enu7uLWId064g/CD/EsFIbhXuQd7Mmrj73+HlTf7HFOv/jMysu7uPCi9vcjTRivLDQpGKnhiZ2RyglbvnxjqWzQkPsvI+QU1wF3evGi4b6O7HxTa6ksIjzQ4ATiX84wLB6KjPu3uF6AcVeb8vVV78UF/RhPvdR9YWxXq6It07dwAzmwKc4O7Tiv49hvuJv+Xu7WIdXxEzGw8McfdHIgZIvAXIcfcHYxxb5Gi9RYlA0XenODGoKAORSXxSkinlZmZzgUuBKQSDLbSljEE1Yv0fZmlmNpqy+5usJ2hm9YG7f7JzoyrJzBYRDKaUH8s4yhI5DPtmhpGP+dDxZvaHu7cJL2+qKbTH+hfkSPGQGJnZ+UB3YBDB9yXyAaRCTLcBwZx0BJ/biIgHulRgjrvXjXV88SIiGSrrHt8MeM3dSzetixkza0DQDaI7UKIWqwLck3Tv3AHC96TrCAbOeoygKf8NwL3u/kYsY4tkZquBGuEa16J7Ugow290bxDq+IuGZAY4CbmXDQGS3ACPc/ZXYRSbxTkmmlJuZnQg8QHADSqDsURtj/h9maWZ2B8FchK+yoWnauQR94Qz4P4I5M+/f5EF2fIzXEDwgDaqIiabsGPGQGIUfjqHkDzUxnzevtKLPL7xcVLuRACxz9z1iHF4J4Rq3Cwj6i9Z293Zm1h2o5+7vxDi2zc1HuBi41d2f27lRbZoFc6LmECQcIwmSzVuBz9z9+RiGJjuQmR1PkFw2Jhjp+tmK1k88/MN8O3dfFa59PYWgD+k0d68w/a/NbD7QIrK1Snhk7mnuvlfsIpN4V67JoUUA3H2ouzd392SC5h4JZbwqzENnhKMJ+pTd7O7PufvNQC/gCHe/jmA6gYtiGiFcQTDC2xozm2dmc4teMY6rBDN7fBPlj+7sWDbHzD7aRPkHOzuWLWji7iPCy0VJXB4Va1C2puFXs4hX0fuKZIqZlZ5b9Cjg91gEswW3E/y49TxQNOfkfIJpTGIq4j4+uoz7+54VKcEMOxQ4390nEvzwMYngs60QfVuL6N4ZXe7+kbv/w933c/c+FS3BDPuAoCsEBF1iviUYAbui9WlOIJi/M1JjKuZsARJHVJMp28TMUtw9L9ZxlEe4yUqdyP6O4akiFhUNEmBma929Sgxj3OSk3V6B5lrbTD+tzIpUWxQv/cnMbCxwuwejEBbVvh0N3ODuh8c4vLhiZgcDwwhGnTwNeA04Djje3cfHMrbSzGwe0NGD6ZSKarANWFFUGyvlY2ZLgYYezIv6N9CZYICV5UVNVSsC3TujpyK3BNic8EBAVQkG+KpIXQ2uBv4DvMyG1l7nAY/GsoWXxL+K9Gu5VHBWai6t4D6/MY/9lBuljQJeDne4n08wcfetBPPAYWZtCQbeiJmKlEiWJdwHBoJ5Es8vtboZFWTuPNswL12KbTxHXTOC/iYVyX8J5qD8FKhsZs8SToxiG9YGpb/3kSrSd93dfzCz9gTTv7xE8LB0oFeQAZRKSQTWhpeLPtsqEWUxZ8FcwpcCh7Hx9AvdYxVXGX4kqC0aSjDP8NtALkE/55jTvXOHuJ1gyppHgWfCZfMJplOrcEmmBaOHN3D3MbGOpSzu/oCZ/U4wEFlHgueh8939i9hGJvFOSaZsjXibS6tIP+ApggGLEoECgmYs54XX5wFnxiSyMDNLBm4CziEYbXIhMBi4q4LUGJ8T/jMlYhmCB+QlBJ9xRVA0L10CJeeoc4Kk49adHdDmxEliVPp7X4+gb1GFGWCjiLsvsGB6kLruHtMfjrbgM+BhM7sKimtm7gBiOgBZKY8ARwDPAXcBNwKXAG/FMqgynMOGrj//JvjhpipBAlIR6N4ZfeexoSXA0+Gy2VSwJvwWTO32JtCB4HOsYmanAL3d/YKYBldKOKFUUilRpeaystsIDwJSm2AgkP9v777DLKuq9I9/35ZMA02O3WRQQRBmBoYgYgLUHxhGRBhBwIiOOEQdRTISDOCIiEoQUbISHQyMkkGR0EqQNNjk1EDT0AgNvL8/9r5dp26Frmqqa+9TrM/z9NO3zrlFLW5XnbrrrL3XqmapCoCkY4GNgEPo6e72deDPtvcqGVuTpMNtH1A6jtmR9Ok2NP2QNMH2M6XjGC6lObMH1dIBF9JrSbqZ9BHSTNyFJW1HStqr+p6VtCipEdl7gXmBfwC/BXaxPb1kbB2SHgI2sX2/8vxBSW8kNVgZcHl/6F9cO0eOpIeB1Wz/o7HNYBHgdtsTZ/f5o0XSpcBVwFHA1LwsfjHgL7ZXLhtdjxbc5A4tFUlmmGO5Fffa9F1KVcUsraZ8YV+btCRtllpizd3d1rc9tXFsKWByTa3OOyQtQ9/XsrbRNWuSKtQrAg8BZ9q+u2xUvUl6AfgbqSvmFcCVze+BWuWllE/1t3erFElnAU+TltLdnt/QLQ1c60pn+kpaltT45wHbj5aOp0nS08AStq00Yml12zMG2rNXiqT5SeMWdgSWtL1Y3te8lu3jy0bXV1w7XztJJ5FWIO1FWtq5JKnyPp/tz5eMrUnSVNKe0Vebe1o7N20KhzdLW25yh/aJ5bJhjuQN7OcC8wOLkhotLEJaVlPbkpVdge+T9jvNaJwy9cTa/wbXgY8Xkbt3ngIs33XKVNSJTtK2pOWcl5B+aa4N/FnSzrYvKhpcb4uTumNuAewJnK40p+4K2/9RNLJMfYezL0RquHF7gXAG8y5gBdszJRnA9hP5TX1VJO0JXG77L6QlkzW6g9RE50+k/Y0HS3qWlHTU5FhSMvTvwKX52G35eDVJZlw7R9TepJUA00grAZ4jrQSoZelxx2PAGsBdnQOS3kwauVKT7el9k/tOSTcBk0mJfAhzJCqZYY5IugE4w/axje6IB5JGm3yrdHxNednXp2xfOtsnF5Lb2HfuJN5PupN4AHCj7S+VjK1J0r2kWamnuTFTqza5icGetv/QOLYlcLzz0PGaKM0k25w0WudTwAu2lysbVaK+w9mfB24Bvm57oMHto07SPcDbbD/SWEI3Cfit7TeWjq9J0k9JDXUWIS2n61Syb3Ilv5Ql/Qvwsu2bc2XrB6R497V9VdnoeuQq6xq2n6+8WhTXzhGWbyCtTIUrAWBW06evkGa4fpc0Ku2rwFG2q9nTnt8jrdfPSqq/2F6hXGSh7SLJDHNEaSzI4nkZSCfJnA+4r7blnZIeI1U4Xikdy0Dya3cAsBNpT8RDpIYBh9e0J0LSU6QlaVVfOPJSv6Vtv9w4Ng9prEFNbzyPJlUxVwSuJXVCvtx2bVXC6kn6CrAdqUHN+aT9jt8ALrRdSxOYXiStQko23w58GKCG78/8s/JxUgfPpUgdUP8XON32zJKxdZM0hfQGeVrj5sLSwPW2Vy8dX0dcO0eOpJttb9DP8T/b/ucSMQ1E0gdIyeXKpJVeJ7qymZ6D3OT+s+3/LBlbaLdxs39KCP2aRlomC/BIXgKyOF17TSpxNHBAbvxTFUmbSTra9ku2D7S9hu2F8h6y+YENS8fY5WRgt9JBDMEt9B3Gvnc+XpMvkJpR/YB0p/tHtSWYkm4e4HgVIyIajiaNr/g+aQndKcCFpNe1OpLWBrYiVa/fRVpSV7zhSt6/fi1wDDATuCn/fSRwbT5fk3OB0yStCiBpedIy2dq64Ma1c+Ss0X0gd2iuZfvLLLYvtP0+2+vY3qa2BDPbH7iMdO28kfTz8wfgyyWDCu0XlcwwR/Kdrz/ZPkPSvsB+pDciv7H9ybLR9aY0+Hw5UqOAXk1VbE8qElSmNB/xBNu/6ufcNsAXKuvgeRXpjucUoNfyJFc0Oy93wbwYWJie4dIzgG1t31EytqZcIfgXUjVzC1Kr+9tJezIPLxlbh6Tp7hpqn9/QTXU9w9nfQEoqP2P7xdLxzE5eXTEdOA+4HLimoq6yJ5AqGR+1/Xzj+HhSEj+lsuYq85FuMHyatF94BilZ/3Jlq0Di2vnaY/tpfrgD6XuxaRXSe9q3jWpQs5GbUL2Vvs2eDiwTUQijJ5LMMGSSFrf99ADnNift1/mN6xsPMmC7fdtXjGYs3fJeiEn9LeXNCcj9Ne2JkDRgYwXbp41mLLOTX79NSI02Hgb+WNtSvw5JiwNbkuYS7gIsYHv+wjG16g1d3ps3qdZ/4yZJPwY6r11nP+YVtos31VEaD/Gvtvs0J8nLe6+z3d28pgp5meyTNS5JjWvnayfpoPzwq6Sl8B2dmaPn2n5q1AMbgKTjgY+SqoK9mg7a3r1MVP3L+253oaej8OnNfbkhzIlIMsOQNVvXS7rM9rtLx9R2kqYDy/TXCELSgsDj3VWkMDzdy6Rrugki6b9J+/HWJHXwvJKUcFzbrCIViq1tb+j2ByYAB9dUwRqM0giTLUjfAx8nJUh9lgKOckzPA4sOcuNrmu2FRz+yXnEMaVmkKxsN0ja1XjslbW37N6XjmJ28D3d92w+UjmUwkj5FusafRKq0TwI+SWruVnwJf2ivGGEShmOGpHVJre03ykvm+ozYqOEXkaSv2T4iPz50oOdVsGTlb6R9WRf2c26rfL6o3Lr+9Px4wLuvtk8ZvagGJ2lD0v6S9YAFOoepbFwA8BTwn6Tq0D9KB9Nk+xAASde34Q0d8EXSsvi9JT1B+rcGyi+L74+kDUjJ5TtIVc3nSeNCSruXVFH/XT/n3gXUkLjdQ/r3HWzEU/Gf9bh2jixJKwOvdK5HuSv314B1geuAb1bW4O9J4JnSQQzB/sB7bE/uHJB0NvALKtgnHtorkswwHIeQ3gR1lvG93HW+il9E2UqNxxOLRTF7xwI/zHvKLsjdescBHyT9ot+7aHTJjsDp+fHOAzzHpD1xtTiNtK9od3ovU6qK7YMBJE2StCLwUH/LFAt7SdKqtu+TtBxp/9urwH9VNjbg46UDGKrcwXMaqXJ9EbCP7XvKRjXLd4CfSvoP4PzGNenDwPdIle2ibFfXxG0Ace0cWSeTfi8+mD/+PmklwC9JTZUWpfD3Z1eV/dvAzyUdSdc83Mqq7EvSd+7xnUAVe+5De8Vy2TAsebnUcqQK2zr9Pcf2lFENquUk7U1K4Ocn3flcCngROMj2d0rG1lZKQ+MXq3FvVlNO2s4m7X+aSvplfz3wMdsPl4ytQ9IdwNa275d0Rj78AmnMwXYFQ+tF0va2z+3n+Edsn1cipoFIWsX230vHMRBJ+wAH0/eadKjtbxYMrY98c2ZGs19A3uO8YC0/Q21S87VT0uPAqk4zURcGniDNxr0xNyz6H9tFO8xKepUhVNlt13AzHgBJF5FGl+xve0Z+bY8kvdbVNB4M7RNJZpgjkta0fXfpOAbStj07khYlJRpLkpKN62w/WzaqgeV4u7vlVfOGTtJpwBm1L/OUdAHpl/t/Nd44fYP0y72KBK6zFzvfYHqM1Hn0JeBh20uVja5Hc8941/GnaumC2yRpTVKlq9No4yzbd5WNqoekRYBN6ZmTWeU1SdINwO62/9o49hbgJNsbl4usf3HtnHOSnnGe1Zkb1Zxte9nG+X6vAaNN0g6kRl41rfToQ9JHST0AxpFG/mxK2sKxJHANsFMNzchCe8Vy2TBk3Xsb05bMvirY5wi99+w076R0f1zF3cT85q26X+rdJL0H+CGpu2hTLcukOxYAzpd0NX3HBexSJqR+bQ4s3+ncmBPN/UlJRy2ezQ1q1gVut/1cHhsxb+G4gF43lMYpzUpsXphWA6ra6wogaVvg58AlpEYbawM35D18FxUNLssjVaq/JgFrNRNMANt/zZWtasS1c0TcJ2lL25cD25G6tgKzOgvXsrz3MGB1SfeSlsRfDlxZ4VaIw4HVSfuwryQ1/vkb8IjtBwf7xBCGIpLMMBxD2dtYxS/L5p4dSbsB7yYt/5pCqsQcCPxvkeDa7STSL9CzSEsma3U7ffeY1Ohp4M3A5MaxtamrWcT3gBuA+UhNigA2o4KmVFnzhtK9XeceJf3c1+YbwAeaIwJyZeZ40h7NMHRPSFqjuadV0hp0zUSuQFw7X7uDgQsk/R/wRtLYp44PUEfjLGyvlbdCvI20Z3Rf4Cd5ZNmVpCrnSSVjhH7j3Id0M/EhSdXEGdorlsuGESFpPdKMpZ1qmusIIOlBYM3mmJDcle4u2ysN/Jmhm9IQ+RUq6+DXWpI+TUo4TqbnBshupNbxPyoZW5OktUhdHe9tfDx/dwWpJElX2B5wJm5NcuOfpW2/3Dg2D2mEyYRykbWPpK+SZrl+jdT5dnVSMneO7W8M9rmjKa6dI0PS6sBbgZts39c4vhnwjO3bigU3iLxP+NOkZn5L17Qns6ktcYZ2iCQzzLG8PGUn4BPA+sBVwPf7a75RktJw8XfZvqNx7E3A713pUPFaSfoKqWJ0VI2NIfoj6Ve23186joFIeifp52gF0uDzM21Hlf01kLSZ7WtKxzEQSX8Afm376Max/YH32d6yWGAtlDvf7kOa6zeRtMf5ZOA7NYzT6ohr59xR6896HvH2VlKFcAvSfseHSXsdr6rlfVJb4gztFElmGBZJ85L2QuwKbE1aqnYmsBfwRtuPl4uuf5L2I92VOxV4gPRGZFfgONvHFAytdXKzkt/Q0wxkltJd/QZSSzOItsqNSg4mzXRcisaexxrnT0L9/+Z5v+DFwML0XJNmANs2b4aFsSOunXNHjTFK+hWwAWkMyNX5z7V5n3M12hJnaK/YkxmG6zHSjLyfkEZs3AQg6fMlgxqM7W9K+iuwPemC+gipG+Gvy0bWSueRKtbnUve+oqbBWskXlZdI7kj6vuzuOPmZIkH1dTI2RMAAABspSURBVAJp7uyhwM9I8yj3Iw3qrlXN/+bjSFXrDUgVhE4F+4+dBlBhzlReeYtr59xRY4xrkUb+3EfaJ35PpYlbW+IMLRWVzDAski4ndcS8jvSG8xzbT0t6BFi/xkpmGDl5htqEmpahzY6knWyfMftnjj5JZwFvAS6l642n7a8XCapLnk33JttTOyME8mzCi21vWDq+/ki61fa6peMYiKTpthcpHcdYU2NVqyOunXNHrT/rXQ113kaqYF9DutFwte1bCoY3S1viDO0USWYYNkkrk5r87AJMAn5LWkr3plpmKnWPWxlIJeNWWkPS6cBpti8rHctQ5crRLDW9yZP0DDCx5rvHkp4ElrP9cm6itQ4wndRko8o39LXLy9QOs3196VjGkpqT97h2vr61paFOW+IM7RDLZcOw2Z5C6t53mKTNScnmq8BkSafY3r9ogMlQxq2E4ZsfuEjSVaSl07NUMENtFkkbAt8H1iPNfYOeGak1/dK8DViClLTVajLpJtL/ku5unwA8B9xVMqhuknYEbrF9R+5+exLwCrCH7VrGrXRMAS6VdCFpT+asu71x4+s1+WzpAAYR184RkpvVfIq01WAp2+tJ2oJ0M+ycstEl/TTU2RyYAPwZOKVgaL20Jc7QTlHJDCNC0gLAh4BdbL+3dDxh7pB00EDnbB8ymrEMJu/BvRg4na4B3fkmSRUkrUYa0P5b+r7x/GmRoLrkGGX7XknLAEeS9o8eYruaeXp58Pmmth+TdDGpmcVzwBa231k2ut4knTrQOdu7jWYsY0nNlbe4do4cSYcB7wGOA07MS/hXA861/U9lowNJ/wNsQpot/EfgCtJ8zOts/6NkbE1tiTO0VySZYUyStIrtv+fHA3bus/1/oxZUGDV5/9NitY8KyMu69wdupfeeTNveokxU7dTZj5dveD0CLAfMJM2eXKJsdGFuGazyFkv9hq8N105JDwAb2H5S0tO2F88VuadsL15BfF8hJWs31NzMqy1xhvaK5bJhrPor0Nmbcw9pqU93F7pqlv+0UeWdHM8HtiKNDKjZl0hvlqoeWyFpd9LStE4n1LOAUyp7I/qEpDVIjZRusP2ipIWopPvkYDe7muLG17CdRqq87U5X5a1Wce18zd5AWqUAPUvNxzeOFWX7qNIxDEVb4gztFUlmGJOazR9sjxvsuWGOva10AINYADhf0tXAo80TNe1/Ii2Rvb90EIORdAzwAdLStCnAysC+wNqkKmwtDgNuJO3D3CEfezdpT2kNmje7msl598dx42t4Vga+VtkNj9mJa+dr8z/AdyTtBbP2FR5GutkQQqhEJJkhhDlVRYVoALfnP7U7FviZpKOBXuN/Kqpo7QpsaPvBzgFJlwA3UVGSafsnks7JjzsVreuBj5WLqkfzZpek3UgJ8MH0JO4HkporheFpQ+WtW1w7X5u9SRXsacC8pArmb0lNCEMIlYg9mWHMy938+v1Gj31vc67WGWqS3kDqivcZ2y+WjmcwkgZqTFLNfrLcUGdD29MaxyYAN9pevVxkvUlaGnjB9nP5e6DT9fr0mhrAAORRMGvafqFxbCHgLtsrlYusfSSdDWwL1Fx566XWa2fb5EZkKwMP2H50ds8PIYyuqGSG14OTuj5eDvgk8LMCsYwlZzW7OdbyRt72K5K2IiUYVWvJUu7jgF9KOgp4kDQeaD/g2OY+wwoqr5cAnwNuBo4gJR4zSe359yoYV3/GAasAzb24KxNLZedEGypv3aq8dnZImo+0HH4pGlVX278vFtTApgILda5FFVyHQghZVDLD61JuEHKq7Zr3xlSnLZ0cJe1PmvV1UFu65knazPY1pePoNki1tan4v7+kp4ElbDtXCjclLaO7zfbyJWPrJmk/0pK/U0lzMieSliUfZ/uYgqGFuaRF187NgXNJcz0XBZ4lNdF7wPaQmlfNbZK2AU4Gun+uq3otQ3i9iyQzvC5JWhB41PZipWNpkzbMUINZLe6XIzWBeYLew+4nlYprMJ0RHKXjaCtJTwIrAmsBZ9leJ1eLpjUbgdUiv1HentSx9xHgHNu/LhtVO7Wh8taia+cNwBm2j22MBzkQmGH7W6Xjg1lL+L8JnNZcch5CqEskmWHMy+MXmhYCPgzMtL11gZBaqw0z1AAkvX2gc7avGM1YhkrS9BqToaZaq60Akk4nVV6WBH5j+zBJ6wLn2X5j2ejC3NKGyhu06to5DVjc9quNJHM+4D7bK5aOD0DSU8CStb+WIbzexZ7M8Hqwc9fHzwPXkjp7huFpRSfHWhPJ2aimmjGIS0lv5Gv0KeATpH2Yp+djS5E6uFYlv2nflbRfdHzzXK3Naip2LHBMo/K2RKfyVjqwLq24dpI6ti4KPAM8IunNpH2P4wf9rNF1MrAbqcFbCKFSUckMryuSlgU2A+6wfcfsnh96a0snR0nzAgeQbjCsADxMSjyOsP1SydjarA3V1jaQdCawPmn5ZPfSyUOKBNVSbai8QauunccBf7J9hqR9SU2+ZpJWB3yybHRJ7hi/EenGXPdrGR3jQ6hEVDLDmCVpReB7wJuB64BvAVeS9ulNkLSL7bMKhthGbenkeAzpTcjn6JlD+HXSHfpqOo1K2hG4xfYdktYidUJ+BdjD9t/KRtevqqutkrYD3k7fvXnVvInPtgFWtf1M6UDGgDZU3qAl107b/9l4/C1J15OWH9dUgT2Jvl3jQwiViUpmGLMkXUwacH8esANp+PkXbZ8v6QPAYbbXKxljm7Rs/uSDwPq2pzaOLQVMrqy6cS+wqe3H8vfrnaSOqFvYfmfZ6NpF0kGkmwpnAZ8FfgjsBJxte8+SsXWTNBnYyvZjpWNpuzZU3tpI0kRgRdvXl44lhNBOkWSGMUvSVGB52y/lQefPAPN3mgVImhbdZYdH0iPApNrHgkh6CFivnyTzL7ZXKBdZb52OspIWIHUYXY70BvlJ20uUjS5pS7VV0hTg/bZvlfSM7QmSNgIOsL1d6fiaJO1D6iz7XaBXollTR9Q2yo2AFiElmTF/cpgkTQLOJO0Xtu3xkj4CbGP7U2Wj6yFpN9J2iBWBh4DTbZ9aNqoQQlMkmWHM6h4JIemp5hv3GBkxfG2ZP5mrGxsBhwD3k5bLHgDcaPtLJWNrypXMrYG3kJK2rfINkYdsL142uqQt1dbmTSNJj5OqMDNrvJkk6b4BTrmmjqhtUnvlrUVdcC8FrgKOAqbmPa6LkW7QrVw2ukTS14BdgG/Tsx1iL+Bnto8oGVsIoUfsyQxj2TyS3kHPHePuj2No8/B9kVRt21tSzfMn9yclld8nNf55iHR3/vCSQfXjMOBGUmVwh3zs3cDkYhH1tXROMBcANgc+Qq62lg2rj3slrWP7NuBWYA9JTwNPF46rD9urlo5hrOiuvAFVVt5oTxfcjUgrAl6VZADb03KiWYtPAVs254tK+g2p50IkmSFUIiqZYcyS9HcaSVB/4s3e8NQ+f1LSZsB2tr/cz7mjgfNrq3TkyiW2Z+SPlwHG2X500E8cJW2otgJIeh/wnO0r8zLZM0jNXz5v+5dlowtzSxsqb9CqLri3Ax+0fVdn9U9upnRWLT0M8kqFVTrXzHxsPPB/tpcpF1kIoSkqmWHMsr1K6RjGmhoSydn4KnDCAOf+AHyNNEagCpKWBl6w/VxurLQL8Co9cx5rUH21VdI44B/A9QC2/wSsUTSoLpLusP2m/PgBBrgBVtmKgDZoQ+UN2tMF91vAJZKOJK3+2ZF0XT2qbFi9/Br4uaSv0LMd4gjq6oAbwuteVDJDCENW+/zJ3PBnku1X+jk3D3B/ZY1//gh8zvbNko4iJcAzgT/YrmnUStXVVqh/hqekzW1fnR9XvSKgTdpQeYN2dcHN3dc/S0reHgBOtH1B2ah6SFoUOJ5002se0ut4DrBnjAUKoR6RZIYQhkzSsfQ01GnOn/xzDUmRpOnAMrZf6OfcgsDjNSUiec/gEradx65sSmqqc5vt5ctGlwxWba2pe6ekX5HGElW1HDrMXZJ2B74CHEnq1vtZcuXN9s9LxjaYmrvgtkVewbAUqRt3vIYhVCaWy4YQhmN7es+fvFPSTaSlk8WTTOBvwFbAhf2c2yqfr8krwHx5NMg02/fnN041LaG7hDR/8mbSkrROtfWt1PFv3jEFuFTShaTqS7Mp1YHFoupHHq90JXBF/nOL447vHLF9Sn49P0v6d/8E8PWaKm9NjS64V5eOZSA5cd+RntUqZwGn1PI92qlY58Ty8cbxx2NPZgj1iCQzhDAcGubx0XYs8MNccbsg79MaB3yQ1Gl276LR9XUpaZnXkqQ3cgBvJnXDrcVawC358cdpVFupK8lcEOgkFiuVDGQINgK2AN4OfAmYIOlq4Arb3yoaWQvZvpD+byxVoy1dcCUdA3wAOI6e1Sr7kuZ77l8wtKZ5uw/krRzRMT6EisRy2RDCkLVh/qSkvUnxzU8as7EU8CJptud3SsbWTdL8pMrLTNLy05clbQksZ/usQT95lEh6kjTwfC3SPrd1cuI+rZalx5Lm7cxtzcsQxzVOX1f5TNe1SN8D/wEsaHu+wiG1Tu2VN2hVF9zHgQ1tP9g4NhG4yfbS5SIDSVeREvRNgOu6Tq9E2mZQTWO3EF7vIskMIQxZbrl/ALATXfMna2j805EbQ2xCqhBOJSUaz5aNqp0knU7qirkkaf/YYZLWBc6z/cay0YGkPYBNbe+cP55BurkgYCFgf9snFwyxjxzzFsBmpKToSuBy4Or4Ph2eASpvewIX266l8tZZIr10Xl3xlO0l8vFnbE8oHN4seWTRhranNY5NIN1IXL1cZCDpE/nhiaQl/B0GHgN+X/MNpRBebyLJDCHMVhvnT7aFpO1IyyaXorHs2PYuxYJqqL3aKuk6UofeyfnjpzvzOyW9FfiB7U1KxthN0qvAvcAxwCW2HykcUmvVXHlrqrkLrqTVGh++n7S94CjgQWAiqRPuhbaPLxDeLJL+ibQq5WXbf8tdro8D1iFVNve1/VzJGEMIPcbN/ikhhMBXSdWW/nTmT4ZhknQQ8EPStXh7UtV1a9IsvSrYftH2j2yfavvlfOzyGhLMbNVOgpnd3ng8GViN+qwEHAhsCPxO0p2Sfizp44XjaqPp+U/3sdoqwp35k7vRM3/ybODosmEBcA9wd/77u8A7SDMnbyPNpHxXPl7acaSbW50Gbj8C1sx/r0u6aRNCqERUMkMIs9W2+ZNtIWkKaZD8rZ1lc5I2Ag6wvV3p+DpqrrZKeg5Y1vbz/ZwbDzxqu6Zuvb1I2gD4CGlP5njb0bxkNtpSeetW+/zJ2nX2h9t+MS/hfQJYJ1eHJwLX2p5YNsoQQkckmSGE2Wrb/Mm2kDTN9mL58eOkN1Azm8dLy9XWz5GaqXyWVHndCTjb9p4lYwOQdD1wtO3z+zn3b6Q9mRuPfmQDk7QXsCWwOfA8aYzJlaTusncVDK0V8nJjM3hXa0fCPnw5Eb6kvxuKpUl6Blg8zxXeBviR7UmN89Pj91AI9YjlsiGEoejMn+xPjfMn2+JeSevkx7cCe0jaGXi6YEzddgfeY3sv4KX897bAKkWj6nEccIKkD+aut0gaJ+lDwPH5fG3WJ41b+Rfbk2zvbPvHkWAOje1xtt+Q/x7oT3UJpqTdJf1O0m35709KqmX8U8ehwKOSjpdU1c0Z0vLd7fPjjwGXdU5IWhGY1t8nhRDKiDmZIYShaNv8ybY4gNS1FeArwBnAeODzxSLqa4LtW/Pjl/K4kD9JenvRqDLbZ+U3mD8D5stL6jpjaw61fWbRAPthe9fOY0krNZvWhOGpufLW1JL5k9heX9L6pJm4v5D0PHA68DPbfy8aHHwZuFjSicArpJUAHTsA1xSJKoTQr1guG0IYkjbNn2yDnKRvSRpbUc34l26SbgJ2tn2bpN+TKnBPA4fZXqVocA2NsTVL0TO2pvrKhqRnbS9aOo62kjSZNE7pbFL34z8WDqlfbemC25SrrO8Cvk1qrHMNabn8mbZfLRTTIqSZvXfZnt44vjYw3fbDJeIKIfQVSWYIYchi/uTIasMeIknvA56zfWVuSjSr2mr7l2Wja79IMl+7RuVtR9Ie11oqb7PUPH+yP5JWJ72mHwdeBX4K3E9aZfGI7Q8XDC+E0AKRZIYQQiGSfkWqCFY5Y7Qt1dY2a8ONhraorfLWxi64kr4A7EwaDXIOcFrz+iRpIVKjt2o7NocQ6hBJZgghFCLpBFL15ULSSINZF2TbB5aKqymSoJEnaUnbU0vHMZbUWHlrYxdcSZcApwEX2X5xgOdsZfu3oxtZCKFtIskMIYRCJJ060Dnbu41mLAOpvdraRrmZymWkZZ0XRZV4zkXlbe7IqxiWBR4rtf8yhNBukWSGEEIBuUvrzPx4c3qPlLquc660NlRb20bS0qTXdGdgdeA84Ke2ry4aWAu1pfLWoi64i5A6hn+MNIFgJmlG7p5taKQVQqhHJJkhhDDKJO0BbGp75/zxDFLHXgELAfvbPrlgiLO0odraZrkr5s7Av5MS+J8BJ9ueUjSwlqm98taiLrg/ARYB/oueUStHADNsf6JgaCGElokkM4QQRpmk64DP2Z6cP37a9uL58VuBH9jepGSMOZZWVFvbLM8b3Rn4EHATaR/hh4BjbB9VMrY2aFPlrSVdcB8FVrM9o3FsPHCv7WXLRRZCaJtxs39KCCGEEbZqJ8HMbm88ngysRmG52npK49BvSVW2nwPnA7uUiGsskLSOpCMlTQF+ANwNrG/7PbY/CWwIfLVokO3xPWBhUjfZBYG3kFYD/HfJoPpje7Lt/UidZb8AbA/cK+lKSf+eq7Gl/QPontvZmYkcQghDFpXMEEIYZZKeA5a1/Xw/58YDj5ZuVNKWamsbSZoKnEnah/mnAZ5zaOx5nb22Vd5q7ILbJOkA0g2k79CzXHYv0hLfw0vGFkJol3lKBxBCCK9DtwJbkSqC3bYGbhvdcPpVfbW1xZab3VLjSDCHrFN5a+5hra7y1k8X3J27uuD+Ani8UHhNRwAPAzsBy+fHRwMD7s0OIYT+RJIZQgij7zjgBEkmdcV8NS+V+wBwPLB30eiS8ZIW7lRbbW/WOLdw/hPmgO2ZkpYFNiIlRGqcO2XATwz9OQn4naTuytuPikbV13uBbzNAF1zbMyQVq2JK+ifgRdu3Aqfkrr3HkZYhb0xKjJ8rFV8IoX1iuWwIIRQgaR/gEGA+UmfZTvXlUNvfLBkbgKTrgaNt96m2Svo3UgfcjUc/svaT9EHS/ta7gXVIlet1gattv6NkbG0jScBu9K68nQmc6grf4NTaBVfSVcAhti/LH19A6oZ7GqlR0V9sf75giCGElokkM4QQCpG0KLAJKcGcSurYWkVHTEkfA44F9qBvtfUEYG/bZ5aMsa0k3Up6Q39uZ6+rpN2AdWzvWzq+NuiqvCFpGXoqb9cB+9iupvJWexdcSU8CK9p+UdIE4AnS9+NdkiYC19qeWDbKEEKbRJIZQgihX7VXW9tK0rO2F82PO0nmOFLDp2UKh9cKbau81T5/UtIzwOK2LWkb4Ee2JzXOT7e9SLkIQwhtE0lmCCGEAdVcbW0rSfcAm9l+TNLNpM6iTwLX216ybHTt0LbKW+1dcCVdA3zX9jk5IX7V9u753IrAH22vVDLGEEK7ROOfEEIIA7L9LPCb0nGMMT8GNgd+QVqS/AfSOIvvlAyqZeYBXsqP/5U0/uMuANsP5MSzJrV3wf0ycLGkE4FXSN+fHTsA1xSJKoTQWlHJDCGEEAqSNAlY2PYdpWNpi7ZV3towfzLvG10LuMv29MbxtYHpth8uFlwIoXUiyQwhhBBGQd5HOOgvXdtbjFI4rSZpc+Bi0uv5CrC57Tvzub2BjW3vUDDEXtrWBTeEEF6rSDJDCCGEUSCp2eBFpG6jvZrT2D5tVINqsTZU3trWBTeEEEZKJJkhhBBCAZKesr1E6TjC3NO2LrghhDBSIskMIYQQCogkc+xrWxfcEEIYKeNKBxBCCCGEMEYN2gUXqK0LbgghjIgYYRJCCCGMAknv7Do0j6R3kPZnAmD796MbVZjLbgO2B84BPgZc1jmRu+DGzNkQwpgUy2VDCCGEUSDpvtk8xbZXG5VgwqhoWxfcEEIYKZFkhhBCCCHMJW3oghtCCCMtkswQQgghhBBCCCMmGv+EEEIIIYQQQhgxkWSGEEIIIYQQQhgxkWSGEEIIXST9RNLh+fHbJN05Sl/XktYYja8VQgghzC2RZIYQQmgtSX+X9IKk5yQ9lpPD8SP5NWxfZXvtIcSyq6SrR/JrhxBCCG0USWYIIYS229b2eGBD4J+BA5onJcVM6BBCCGEURZIZQghhTLD9EHApsG5edvoFSXcDdwNI+n+SbpH0jKRrJa3X+VxJG0i6SdJ0SWcDCzTObSnpwcbHEyX9UtITkqZKOl7Sm4ATgU1yVfWZ/Nz5JX1L0v250nqipAUb/639JD0i6WFJu8/t1yiEEEIYDZFkhhBCGBMkTQTeB9ycD30Q2Bh4s6QNgFOAzwJLAj8ELspJ4HzABcDpwBLAucC/DfA13gBcAkwBVgFWBM6yfQfwOeA62+NtT8ifchRpRuJbgTXy8w/M/61tgH2B9wBrAu8ekRcihBBCKCySzBBCCG13Qa4cXg1cAXwjHz/S9lO2XwA+A/zQ9h9tv2L7NOBF4F/zn3mB42zPtH0ecMMAX2sjYAVgP9vP2/6H7X73YUpS/rp75Tim59g+lp/yUeBU27fafh44+DW9CiGEEEIlYp9KCCGEtvug7cuaB1J+xwONQysDn5D0xcax+UgJo4GHbLtxbsoAX2siMMX2y0OIa2lgIeDGHA+AgDfkxysANw7ha4YQQgitEpXMEEIIY1UzaXwAOML2hMafhWyfCTwCrKhGJghMGuC/+QAwaYBmQu76+EngBWCdxtdcLDcpIn/diUP4miGEEEKrRJIZQgjh9eDHwOckbaxkYUnvl7QIcB3wMrCnpHklfZi0LLY/fyIlh0fl/8YCkjbL5x4DVsp7PLH9av66x0paBkDSipK2zs8/B9hV0pslLQQcNBf+v0MIIYRRF0lmCCGEMc/2n4FPA8cDTwP3ALvmcy8BH84fPwXsAPxygP/OK8C2pCY+9wMP5ucD/B64DXhU0pP52Jfz17pe0rPAZcDa+b91KXBc/rx78t8hhBBC66n3FpQQQgghhBBCCGHORSUzhBBCCCGEEMKIiSQzhBBCCCGEEMKIiSQzhBBCCCGEEMKIiSQzhBBCCCGEEMKIiSQzhBBCCCGEEMKIiSQzhBBCCCGEEMKIiSQzhBBCCCGEEMKIiSQzhBBCCCGEEMKI+f8TuuGcDAM6KwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uiWLiWqpMzLX"
      },
      "source": [
        "# **Plot Training and Validation Accuracy and Loss**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rDq_R0VTMyU0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "outputId": "70ac8daf-0217-40b0-fc38-bd920fe7a5e7"
      },
      "source": [
        "plt.figure(figsize=(7,7)) \r\n",
        "plt.rcParams.update({'font.size': 14})\r\n",
        "plt.grid() \r\n",
        "plt.plot(history1.history['loss'])\r\n",
        "plt.plot(history1.history['val_loss']) \r\n",
        "plt.plot(history2.history['loss'])\r\n",
        "plt.plot(history2.history['val_loss']) \r\n",
        "plt.ylabel('Loss') \r\n",
        "plt.xlabel('Epochs') \r\n",
        "#plt.title(str(WS)+str(DLM)+str(k)+'_Loss')\r\n",
        "plt.legend(['Tr-3-D','Val-3-D', 'Tr-Hybrid','Val-Hybrid'], loc='upper right')\r\n",
        "plt.savefig(str(WS)+str(DLM)+str(k)+\"_loss_curve.png\")\r\n",
        "plt.show()\r\n",
        "files.download(str(WS)+str(DLM)+str(k)+\"_loss_curve.png\")\r\n",
        "\r\n",
        "plt.figure(figsize=(7,7))\r\n",
        "plt.ylim(0,1.1)\r\n",
        "plt.grid()\r\n",
        "plt.plot(history1.history['accuracy'])\r\n",
        "plt.plot(history1.history['val_accuracy'])\r\n",
        "plt.plot(history2.history['accuracy'])\r\n",
        "plt.plot(history2.history['val_accuracy'])\r\n",
        "plt.ylabel('Accuracy')\r\n",
        "plt.xlabel('Epochs')\r\n",
        "#plt.title(str(WS)+str(DLM)+str(k)+'_Accuracy')\r\n",
        "plt.legend(['Tr-3-D','Val-3-D', 'Tr-Hybrid','Val-Hybrid'], loc='lower right')\r\n",
        "plt.savefig(str(WS)+str(DLM)+str(k)+\"_acc_curve.png\")\r\n",
        "plt.show()\r\n",
        "files.download(str(WS)+str(DLM)+str(k)+\"_acc_curve.png\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_ea8ab023-595e-4ef5-8c48-2f362d0518f5\", \"11PCA15_loss_curve.png\", 41492)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_2845b4ac-b223-4836-bfff-6f71b37d7df1\", \"11PCA15_acc_curve.png\", 35289)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}