Attacco_BoundaryAttack.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/umbertogagl97/Attacks-ART/blob/main/Attacco_BoundaryAttack.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGmEBN4oxqUO"
      },
      "source": [
        "# **Import iniziali**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHENSlCXxzcr"
      },
      "source": [
        "Import ART"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hIKN5Oqa-i6u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7ef7293-06bb-4b6b-bb0e-525cc4ce1058"
      },
      "source": [
        "#importa ART\n",
        "!pip install adversarial-robustness-toolbox"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: adversarial-robustness-toolbox in /usr/local/lib/python3.7/dist-packages (1.7.2)\n",
            "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox) (1.19.5)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox) (4.62.2)\n",
            "Requirement already satisfied: scikit-learn<0.24.3,>=0.22.2 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox) (1.4.1)\n",
            "Requirement already satisfied: numba~=0.53.1 in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox) (0.53.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox) (1.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from adversarial-robustness-toolbox) (57.4.0)\n",
            "Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in /usr/local/lib/python3.7/dist-packages (from numba~=0.53.1->adversarial-robustness-toolbox) (0.36.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn<0.24.3,>=0.22.2->adversarial-robustness-toolbox) (1.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AHA120xNx0-M"
      },
      "source": [
        "Librerie"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01RXI-DDIb3C"
      },
      "source": [
        "#Librerie\n",
        "import tensorflow as tf\n",
        "\n",
        "tf.compat.v1.disable_eager_execution()\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation, Dropout\n",
        "from tensorflow.keras.losses import categorical_crossentropy\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import numpy as np\n",
        "\n",
        "from art.estimators.classification import KerasClassifier\n",
        "from art.utils import load_dataset\n",
        "\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "#import torch"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ycpjmyb4Cxe"
      },
      "source": [
        "#**Def variabili**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4hMuySVI4GgY"
      },
      "source": [
        "#salvataggio modello\n",
        "model_save_name = 'model_DenseNet201'\n",
        "path_model_save = F\"/content/gdrive/My Drive/ModelliCNN/{model_save_name}\" \n",
        "\n",
        "#immagini google\n",
        "path_img_google=\"/content/gdrive/MyDrive/immagini_google/\""
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dherpFOH0Uau"
      },
      "source": [
        "# **Collegamento google driv**e"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vyvTOQw-aHRP",
        "outputId": "6d7b77c0-9976-4bff-dfec-75bdae550946"
      },
      "source": [
        "#collegamento google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5X10jetEyAax"
      },
      "source": [
        "# **Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAtct4Tjyb0t"
      },
      "source": [
        "Load dataset stl10"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6hypnJdQu8lY"
      },
      "source": [
        "#10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck.\n",
        "#96x96 pixels, colored.\n",
        "(x_train, y_train), (x_test_tot, y_test_tot), min_, max_ = load_dataset(str(\"stl10\")) #carica train e test set"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ez_iiYZtu_-E",
        "outputId": "7b8883de-b0f7-4672-8005-3c08cae05e10"
      },
      "source": [
        "print(x_train.shape,x_test_tot.shape)\n",
        "#5000 immagini di train e 8000 di test: 500 train e 800 test per ogni classe"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5000, 96, 96, 3) (8000, 96, 96, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbwKfK6vwaKF"
      },
      "source": [
        "Riduzione training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1kN7edZRAIwf"
      },
      "source": [
        "#x_train, y_train = x_train[:500], y_train[:500] #prende solo le prime 500 immagini di training\n",
        "\n",
        "#test set ridotto\n",
        "x_test, y_test = x_test_tot[:10], y_test_tot[:10]\n",
        "\n",
        "#test set totale\n",
        "#x_test, y_test = x_test_tot, y_test_tot"
      ],
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOY_tKeRyrTL"
      },
      "source": [
        "Stampa dimensioni"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QtNBe7ocyqyw",
        "outputId": "0b9f8810-5ca0-456d-b1d8-f575f0af3954"
      },
      "source": [
        "im_shape = x_train[0].shape\n",
        "print(\"dimensioni immagine: \",im_shape)\n",
        "print(\"dimensioni train set: \",x_train.shape)\n",
        "print(\"dimensioni vettore classi reali: \",y_train.shape)\n",
        "print(\"dimensioni test set: \",x_test.shape)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dimensioni immagine:  (96, 96, 3)\n",
            "dimensioni train set:  (5000, 96, 96, 3)\n",
            "dimensioni vettore classi reali:  (5000, 10)\n",
            "dimensioni test set:  (8000, 96, 96, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8z4jggt3ZUG"
      },
      "source": [
        "#Caricamento modello da drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOb77pSE3cyB"
      },
      "source": [
        "#load model\n",
        "model= tf.keras.models.load_model(path_model_save)"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VjnPQ4NtzzQH"
      },
      "source": [
        "# **Creazione classificatore ART**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWZ_zpbiZjyd"
      },
      "source": [
        "#creazione classificatore di tipo Keras usando il modello addestrato, poiché ART supporta solo determinati classificatori\n",
        "classifier = KerasClassifier(model=model, clip_values=(min_, max_)) #è un wrapper messo a disposizione da ART per creare un classificatore a partire da un modello addestrato"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4X4ZaKM8z9hk"
      },
      "source": [
        "# **Testing su immagini originali**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvPxa40rAgpa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cd95e07-6ce0-4420-f879-42576ecd434f"
      },
      "source": [
        "# Evaluate the classifier on the test set \n",
        "value_preds=classifier.predict(x_test) #contiene i valori tra 0 e 1 predetti per ognuna delle 10 classi e per ogni immagine\n",
        "preds = np.argmax(value_preds, axis=1) #(le predizioni vanno da 0 a 9 e indicano la classe predetta)\n",
        "acc = np.sum(preds == np.argmax(y_test, axis=1)) / y_test.shape[0]\n",
        "print(\"Accuracy on test set:\", (acc * 100))\n",
        "#print(\"classi predette: \",preds)"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test set: 70.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Suibji7V0I7j"
      },
      "source": [
        "# **Attacco Boundary**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fq6B6unj2HcX"
      },
      "source": [
        "Definizione attacco"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZ-ghSjs2Ma0"
      },
      "source": [
        "from art.attacks.evasion import BoundaryAttack\n",
        "#Boundary attack\n",
        "attack = BoundaryAttack(classifier,max_iter=500)"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYPmQSaL2Oxa"
      },
      "source": [
        "Generazione adversarial samples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gl_FoOil2SrO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "05d5ce1e1ad34f5eadd85da63a1cc97c",
            "8a79d4a1596847a1b503e194d5602c77",
            "05bfa5f0fe384c46990c659ada11c82a",
            "08316a6fdeb34e93b59c1e9443d33016",
            "67333dcd33144320814dfb5abe4c96cd",
            "470746263b54424185b0acedb9ae1bca",
            "134cdc49ffea45619d5ff3ac03fcb545",
            "29e419d452af4bb19159949bfa6b8c4a",
            "a1a703f0120d49b59ee85e5f6a36807c",
            "e4586cafb70c46088f7f34b9b77728c6",
            "bcf231198b0d488d80a1604bbfe10c1b",
            "b8d47900f7d84fc994ff90b39fb61bc5",
            "88b62e27e545422c918598e3181dc634",
            "cf58a720755e44bd8bcaf5e8143f32ee",
            "159e3d3dc3584cc6b554174bdf54adcb",
            "e204ac7ed6d54163a65cec0ab77155b8",
            "4360f975e23f4f17a605895f1cd19723",
            "06e835db54024c63be6d8e7668dc99a8",
            "5e978a7762f54ef49b4437eb96c1a184",
            "46df6da9c6df4ece9b24385dc5711c22",
            "7b73643cd6374692a832b914e2146344",
            "c3015aa889ca419181911f8621ecdf3d",
            "fde00144a6ec495ba5c8da7b8ee05f0c",
            "5dcd65016bfc4b0fb0e2e82e2c7819e1",
            "16d7d7a64e69425e985a18aec1331f4f",
            "b418bb5d40fc40c9a3f9bd3cdd6f0ed7",
            "97d8f7e2dc60455780252dd09fdfd1c4",
            "7209a6d9e13e49adb5d661edf2263108",
            "fc24e266ebff47e19334d6692c580ade",
            "148b1a38b15c4f7486b429857844af3f",
            "028ad18f500b43ca9712594a500dee8e",
            "f8562760b8ee4a9a88284635da035119",
            "7afc22bea3bb45968c84f62acc363731"
          ]
        },
        "outputId": "00daf3e7-764f-4a71-91a5-358b6b3aed32"
      },
      "source": [
        "x_test_adv = attack.generate(x=x_test,y=y_test) #aggiunge una perturbazione alle immagini del test set\n",
        "#N.B. si possono passare le classi reali del test set (con y=y_test) e in questo caso FGM calcolerà le perturbazioni in modo che il classificatore\n",
        "#non predica queste classi. Mentre, se non passo y_test, le calcolerà in modo da non fargli predire la classe predetta normalmente (la quale può essere diversa da quella reale\n",
        "#se il classificatore non è corretto)"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "05d5ce1e1ad34f5eadd85da63a1cc97c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Boundary attack:   0%|          | 0/10 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b8d47900f7d84fc994ff90b39fb61bc5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Boundary attack - iterations:   0%|          | 0/500 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fde00144a6ec495ba5c8da7b8ee05f0c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Boundary attack - iterations:   0%|          | 0/500 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3kr1yAn2UBl"
      },
      "source": [
        "#Calcolo perturbazioni"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgPraX51Z15a"
      },
      "source": [
        "perturb=x_test_adv-x_test #calcolo della perturbazione\n",
        "\n",
        "#N.B. problema nel calcolo della perturbazione, con la sottrazione si ha un'immagine [-1,1], di seguito è normalizzata in [0,1]\n",
        "min_p, max_p = np.amin(perturb), np.amax(perturb)\n",
        "perturb = (perturb - min_p) / (max_p - min_p)\n",
        "#se non normalizzo, la funzione che uso per stampare ritaglia automaticamente nell'intervallo [0,1] quindi perdo informazioni"
      ],
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mibex7yCQGAm"
      },
      "source": [
        "#Calcolo perturbazione media"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7I_gFq_5QIfR",
        "outputId": "c28b6bc8-47ee-424a-d4bc-a59c3d8b84f5"
      },
      "source": [
        "perturbation_mean = np.mean(np.abs((x_test_adv - x_test)))\n",
        "print('Average perturbation: {:4.2f}'.format(perturbation_mean))"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average perturbation: 0.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nH0_wJWt2XuF"
      },
      "source": [
        "#**Testing su immagini perturbate**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V00SgiXfaAFl",
        "outputId": "be0831b8-c228-4a0c-af94-9e77f3ebf3f6"
      },
      "source": [
        "# Evaluate the classifier on the adversarial samples FGM\n",
        "value_preds_after=classifier.predict(x_test_adv)\n",
        "preds_after = np.argmax(value_preds_after, axis=1)\n",
        "acc_after = np.sum(preds_after == np.argmax(y_test, axis=1)) / y_test.shape[0]\n",
        "print(\"Accuracy on adversarial samples:\", (acc_after * 100))\n",
        "#print(\"classi predette su test set perturbato: \",preds_after)"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on adversarial samples: 90.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLcnge6A0Npi"
      },
      "source": [
        "# **Stampa subplot prime 10 immagini**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cB1Q6Id6_CNR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2abd7ea8-3d05-4c9b-da71-9d3042c33fb3"
      },
      "source": [
        "#stampa subplot\n",
        "\n",
        "#cv2_imshow(cv2.hconcat([x_test[0,:,:,:]*255,x_test[1,:,:,:]*255,x_test[2,:,:,:]*255]))\n",
        "#N.B. plt.imshow usa range 0,1 per immagini rgb, mentre cv2 usa 0,255\n",
        "import matplotlib.pyplot as plt    \n",
        "#nel seguente ciclo for si crea un vettore delle classi predette ordinato per probabilità decrescente\n",
        "for i in range(10):#(x_test.shape[0]):\n",
        "  value=value_preds_after[i,:]\n",
        "  value_sorted=sorted(value,reverse=True)\n",
        "  classes=[]\n",
        "  for j in range(value.size) :\n",
        "    ind=np.where(value==value_sorted[j]) #restituisce l'indice in value del valore uguale a value_sorted[i], quindi è la classe\n",
        "    classes.append(ind[0][0]) #classes è il vettore finale\n",
        "  \n",
        "  #in seguito per ogni immagine del test set si stampa un subplot\n",
        "  fig = plt.figure()\n",
        "  print(\"Immagine \",i)\n",
        "  #originale\n",
        "  ax1 = fig.add_subplot(321) #subplot con 3 righe e due colonne\n",
        "  ax1.axis('off')\n",
        "  ax1.imshow(cv2.rotate(x_test[i,:,:,:],cv2.cv2.ROTATE_90_CLOCKWISE))\n",
        "  ax1.title.set_text(\"originale\\nclasse reale: \"+str(np.argmax(y_test[i,:]))+\"\\nclasse predetta: \"+str(preds[i]))\n",
        "  #perturbazione\n",
        "  ax2 = fig.add_subplot(322)\n",
        "  ax2.imshow(cv2.rotate(perturb[i,:,:,:],cv2.cv2.ROTATE_90_CLOCKWISE))\n",
        "  ax2.axis('off')\n",
        "  ax2.title.set_text(\"perturbazione\")\n",
        "  #perturbata\n",
        "  ax3 = fig.add_subplot(325)\n",
        "  ax3.imshow(cv2.rotate(x_test_adv[i,:,:,:],cv2.cv2.ROTATE_90_CLOCKWISE))\n",
        "  ax3.axis('off')\n",
        "  ax3.title.set_text(\"perturbata\\ntop5 classi predette: \"+str(classes[:5])+\"\\ncon i valori: \"+str(value_sorted[:5]))\n",
        "\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Immagine  0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Immagine  1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+luiFS0TOUw0Ofdovw2j/iy0DURRFUXSLi8FAFEVRFN3iYjdBFEVRFN3iYstAFEVRFN3i4jwDUbQP/ND3fpN67yjzISZJ6I0ceZ5TlgHE4gvHyYPT/OCP/DCXHvkYm2c/SD5Tx5l5zl+8yPrlJXYGO4zyQG+YoZoBAZsIFkutZhERvA+40mMzixUIQSmdg7JETIZSzUbmCocCKiXOK64MiIAYqKUZGhQjQmINZVHgg2LE8L/+wA/wkrtexMrjH+Tc6cf46ONnWVlbZdaOuNQ39FxGI0mwxkBiUa90+0N+7b/88TOdUyB6HvjRH/3RW7rp+W1ve9vzpl7HYCCK9gErSuk9ibEEF2gklpSMXBxBFUnrbO1s88QjD9HA42sWXCAf9ej2+uQmkNoEL9VPYwRVAWvBK0YBr/iywJqUen2C9uQE8weO0O30uHD6Y9WUpCI4DYCSWiF3AV8CCCEErCpBHKAEDC4oxhhqqfDaL/wi7r3vXvorl3j4Y4/S664z7G2Rac7GyBKkRqueYNMEVFDnycuCkSv29NxHURSDgSjaFyyOemJxXhFxGAxGBTVCEIMPSuGVC48/zvxEg3SihZSwcmkZHY4QD8452pNzFOsFgUARAjWbIQSwQmospQvcfve9fPbnvpoDCwconKHfG/CxAydYufAES4tnEPFYaxjlI9LEYjODDw4nKTiHMZbUVhc8PoBBeODBB/iir/gyfHeVC4/9DZ2dLa4sr+KLHi54BqFBPTVIkuHUEigofImqYGP22Sjac/FTGEX7QIKCMTinoAHV6ko8BIdHKF0gSw1+2KHnusxMztEdDsFBzVpGxmKTGllSp54FAgFjlXotwahi6pZRr+S+Bx7ktW/8Iggl6yvLvP8DHyFLDDPTbe797JfjKVi79CSlQqNew7mAD4FEFJNUX+ZGhXpiUGNw3nPy1Am+6s1fgx30OH3mY7z/gx9iaWmDQXeDdiOjM7IktRSxBmvBWkteWIJYXHAk9nnTkhpFL1gxGIiifSBNa4h6jCaUpVJ4geAxGLz3tDJDs5agviSzKe7MFcpmnXSixvpWh0azTr3epByBakGSJAQ1pDZFAaOG2blp7nzJnfz5u/476xubLC5tMuh1+PIvfDWnjh1ieXOTJLWkSUrA4QslELAGrAgSHGmWYUTI0gQRmFw4wNd/87eSjzqcPfsEH/noQ5xb3sLkXZqJ0C8NwaQkiSDGEDTBOEfwiqLVmAb7TPMRRVH0mRaDgSjaB2ytjcu3sYkhSEYiBh+GBIUAtDJDVjNMLkxCUVJ0hjQabRZXLjNUx+rKCq2sSSqTJKnFJJbMGLI0RY1gPMwfXGBzY5v3ve9DLK+tMRiOODg/xU6/w4cf3mJjY5OX3nsfH+p2GK4sIsaQYVAUsGTWYhNLzRissdSyhDe+6U2cOHmCh97731k8e5aNtXV6O9scqDu8QK41bGIwJBgUFxwqhmGZk4ghyTKS7JYeYxZF+0IMBqJoH/jSN7+Jv3jve7l07iwesAZatZTCKnhDKjA9NcH8gaMsXjjDVr2kXlZjBDZXRwwGfXwemKo3wStZLUEtmKT6Kk+TjOnpGXZ2drjn3hdTO2/o9TvMzx7k3e/7EOcvXGRuboaXPfggd91/H/0/Wyc4R9BAGbQajGiE1JgqIMhSXv7AK3ngsx9k9fxpHj/9GCvLS2yuLjNtCqxk7JAiaYLFYDNLECWUOeIFYw1GDdYmNOJ/oSjac/FjGEX7QNlZ4RX338Nko83jj50GcpwVGiYFHN7D4eO34QqHJAafj8jEkjYshIAJAsZSOI9XJS9zRARrU0QUU6u+xIejDSamMg4sTDA1nVKWgbTZ4PYX3cPJY0fY3tlkdm6GRnOKzvYa1hpq1oJACI4sq+4GOHH77Xzea18L+RpXznyUmnp63U2s62Mk0ClqmEadRiMjFIoLJWJTlASMIQ0OY6CWCcH5vT79UXTLi8FAFO0Dq0sXabUmuP1Fd9CcnuOhD72PUOZktTqhLJg/dIgD87P0h5tIq4710xSqFP0+7bSGCRkSUvK8uhVRUVqNCVQV7xyzBya5dOUym9tbWGvZ2tgiBE+gxl13388rXv4Ax48c4KMf/gtGoz6HThxj2N1ANWCMQQSSWo1GPaNRr/Ga176epox45P3vZXNljY889CRNKZlpWrZck5waeEeqTXJfEjRgyhLBEIxiDaRJiij4OAtqFO25GAxE0T6gWYNLS5dobPc5cvJOJl73xTz6139DZ22VgwdO8dLPfZBub5nhcEhmM+bmjtHdzhkWyubOFmki1DKLd0CS0G7XsSIYW80UNBwVLK6u0Ov1qWUp6+trOO9otiY5dugAC/PTpJnQ7/fZWLvCwYNHWWpPMOp30BDI0gSbCvUk5djBBWrFDounz/CHf/oR+qOSctBlfhI2yAhpE+M9NZOieUDLnCyzFKVHEESquweMgDGCSBxAGEV7LQYDUbQPXLq8zMGjC/RW1rn4xCMU3vDAKx+kXp/m8KEZHvnIhyizLiU5O1tbHD12N0Vasp2UOBPIapBZQ7AZNk2ppwlGUvz4joThqGBtfZ3trR6JtThXkqaWU8eP85IX344Ex7DvOHv2Ilcun2VmZoGDR49y+ck+GIu1BivKi198gmMLh1i6fJ5HT5/j4kaXpgQOt4Rh7hhoRuEVDYrTklJ71R0JXhACQcBgSawlS1OM0Y8HB1EU7Z0YDETRPtDd6OJGBdMLk3gcjXSKYX8bustceHSNK4MOZtaTDxyK4PyIvBgSpGB+fp7eOkgwZEmKiEEQSu9J05REhK4LlKMS56rph8UIL7v3Xl7/+tdw8OAsjeYE29tdnjhzlp3OBpdXljl57ARnn3gcE0ZoSDk03eL+l91DZ32bJ5/c4NJKl6ZVbptJwTvWyhqFVlMeF85TGku9brAmwSYp4h0ANhGSRLBGScQjGlOkRNFei8FAFO0DIVj6nYLhaJ2Dx+eZnm5w8dJ5VpfOk+50KJqWtFejMdmkljW4dPkKZQHBKavLq/jBkCMHjnLg+IvYXl3D+RITlDStWgEGw5LBoERUqTVqJImwMDdFmsD68kWarTbv/IP3cP78RZLMst3pcHtWY/bAIfqbq0y0GnzB57yKcliwtblFp9dD1fNZJ2cpB9usj4SdEeM7Bhw2EWpZDcGQWEuaJgRTtQ4Yk2BMNX9C0DLOMxBF+0AMBqJoH/AekjTDlwVXFjeYmJhjdn6aC4uevre4jU1atJicbtNuTtHdHjHyA0ZFoDcckKoiNrAwnbGxNKR0Ae8cXpUkbSGpoVTFZpbMJoSy4NGHH8M7T1nkFD7w6CMXMKaG9w7vYH1jjUOHjrA87PKS209wYG6Oy6vLPHLmLGtbQ+6aT5hvJqzmQr9MIbNY9QRV0npGmmTgqWYpSGtAiagjsQYVQRGC2thNEEX7QAwGomgfSNMUJeA9eOc589iTHDg8gSSKaySYdgPbTnFlQaffZ2ltlcZEnaBgjMWHnAuXluj1As6DSEqWJRgVgjc0Gw3ajQZlWeJCABLOX9rg0SevgBqSxJCYlCRNSdMMVcPm5hYHJpocOTDHS++6k25nm3NnL9HvOl40kXByoYW6kjSp0y87mDQhTTLSLBnfgWBIkwxNEkwqSBCEq9kULYlN0eBBY8tAFO21GAxE0T5QlAUiUDqPGKG/02fZj2g1JxA7YtAf4VTxlIjmZA1L6SEflRSjnFa9hmiNtDmBDYHhsGBYFiQmI5GSiZkDHD12jEuLSxSuxLmAkGJNoCgdEgTnHd4rWb2FqqXfGzJIHHceP8z2dodzV5ZYX9/iaC1wpGHIXEFarzO7kJGujGg2WwwLxRqPhJLUCt56EpuSWIvPUoSEdiulLEqGeY54D67c69MfRbe8GAxE0T4gElAgSRK894Cls5WTZZ4gOVMzTbLUoQI2yZidP0BvUDLKtxCbkOeBY4cO0khrGGtITYISKFSxarjrxffQHQSuLK+hpcN7hyjUsgyosiL64JiammJh4SDHj9/J9uZlDk0mtOoZpdQZjTKONaY4Ug/U0mqiIGPbaN7l8Nw0nVHOvS+/l5k0ZWNzk24eKINg0oz25ASTzQYT7Qn6wy6nH3mY3CulV7y6vTz1URQRg4Eo2heMFTQIYizWWPJiiIhQDKHf7dCanceEhLI0lL0CkYSJWgsaSq/VIcXQbk3QaE0w2W6xsb7K5uY6HiWpNWnUGmAzFIu1KVkNQuFJ0gQFRnlBYoXZuRla023m5qa49/ZZasU2Uquh3nBqfooTacJwZ52dnR5r2wOcrlGfPsgrjt/GysqTrJ1/lGTmMCePHKM1PU8wGf3hgLX1y2yuXeDRxzYZDkpSYzFGcKXHJulen/4ouuXFYCCK9gFjDIhBVXGuxBglTQXEMkxgNMjRJKWVWY4duZ2N7QGd7hal71GUPWYmZjlx/Bhnzp8lH3YYjoYkWY1EBLWW4XBIvdYgBKXRyrCmjvWBXrdPPzhUod5sUMtqtNM2M2bA4eYktYN3sLm5Q+/iGY43hVHRZaPf4+KVS6goxxYO0Olso4cXuPu24yxsdBl45b3v/yuaRw+jQOkLRkWBy5Vi6LFiEWtwRclkq0mtVtvr0x9Ft7wYDETRPlCl9PV47ynLAgGMCF4D9abFJkAqDN2ApbULFL7GyvYGK1eWkBI6G9ucPf1Rlje6LCwcx9oGqVGMVYyxDIZ9picmQJVRUTIzNUGWNLiwtI73HpFAPU2ZbDWZrA+5/65jJLbG2saQ/tImC2lKPtjCtAJrgz7pxBQkCWd7gWRumtrMPKubK2hrmnw4ZJhv0Sjr+CTD+4J2bYJeMSIkgjFSjSNIUsQKLsTcBFG012IwEEX7QFarowp5WWDU44oSxBIQTJbgKQlFgRpLGbYoyzplPmI0HGB8ytqwZKl3iS999YPMzx2jWypLKxcIKGnSQNIGvWGXyel5riwvYuyAY4emERFUA0lmsanF+D6f/4pXU+SGs+cvMFWrkeYjLmycZ24qwXQd7dYEZr7NY4vL9E2NGZOyubaEWMN0K+We++5lRMKVS5erv0UL8iSQpilpVidNMxINuBCwNsG5Yq9PfxTd8mIwEEX7QMBQlkU1ja+vWgi883hXkGRgDBjxGE2omTrT0/PUshorV1YI3nHvfS/jxSfv4PJHP8CV04/wkvsf4OV33cO5xfNIbYIkTSnzARPtGvWsxqCfMxoUZGlKUE+rXuPwwjRv/rIv5tDsAqPRkKPHDhGG26xcWWaQ7zClMxTOsTbMeeLJZWqz8yRe2VxaZu2yI0lTrjQsZ544iy+VepbiyxJfJuNZBzOsJqSNOomAOId6xWjY69MfRbe8GAxE0T5Q5HkVBJQFrnQYY1HvUa8EnyLqUZTU16gXdXy5w0SzQWrreD+kXpb85bvfw+rWJu1Gg+Fff4hTRxc5ceIkhTW88w9+l83OAKWal8AY2Nxcw/kca4Q7Th3m67/kDcw3J8nLkiQrGPU2uXLhcTrDDbRdY7k/YGury8PL28zMHuSuUy/GIoiC80pZOnZ6WxR5jnc5QUpUPLVWnenpObwanEJWzyBALavhXSDxcQBhFO21GAxE0T7gXIl3gcFwgCqkqZBYS9KoEcThiiFJQ8mlZGewyeqVbbwIeTkiMcqFy+eZmpzjFbe/giSxoI7L29vsPHqaEwcXeM1LTvChM0uc28pR6WPFMsoLxAgLc22+8FUP0E5buFDi7ICd7Ut0N7c5d+UKSdZAmhlbW2sMS6FWm6bMCx574jTteh0j1R0KE5PTHD54GIPS29lmu7tFf1TQyDLUBySxZFkNDYpYSG2KMR4fYm6CKNprMRiIon1ARDACIgkYEDFYaxAVbOIJ0gfTYDAa0M+HtKdaDEeeYd7DazVQcH52klYjQSRBA2TzBzAY1oYjDmU5d5+c41L3MpnNGOY5pVeOHzzA13zF32F2ZpJsskU3X+PiuccYjrYo+iW9wRCcY3uzy2Aw4rZj95NODxnl25RhwAjodHoUI0e45JlIM4y1KHDy8AIHF46Sl47eMJCXI2oYVBSrlsKXeO8oyjhmIIr2WgwGomgfSIzBNjOSLKHIyyowQFEAMYhNmZt5CWfOnWYw7FDkOQZDLQ0gUI4KhkXBxIRlZnKS2akJUhOYmwqVXRoAACAASURBVJpEFM5eOM/a4kV8f5t8JAQjHJyf5Rv+7huZm6mzvr5Kb7DFxSsfo98fEELK1som1kF/0GGzu0Var/PkhdOkNShxYC2bvR6+VIKzjAYDOmlJ4ZRBf0S3GDE3scOh+VlaaR1KYdQbYDLIveJcIATQOOlQFO25GAxE0T4gGIJXnBsRVFGvpNZgjcWrRQl8+CMfZlT2SVNLWToatUnmDhyindVo1FocmD3MTKtNPUsZDfps7+xw+YkzhMRy4coGZy6vUUsajPIed95+hK944+uZn2rRG6yysXOelYvrrGytcfjAEdq1NrcduIMnz5znyvJpWhMLTEzOENSQj7qEfIRzEEKJMYZaW5icaaPBMho5cpezuLrB8nqfR568wpHpJnccPcjM7CwjL/SHHh8cwRhMEnMTRNFei8FAFO0DVwMAFKy1jIohrgyk1kJSoqFkahZsP+Powu2cPHQ7DUlpmkBmlMQaOoOCctBjuF2w0elxeX2bxZV18iKnCI7NQclguMVdd9zBF7/2QRo1x+rqeba2F1ndWaeXe/p9T384wDuHLRJecu/d7PgOh2eOUK83sGLxPmdrZwebpXQ62/R6IxqNJqMyZ+SGaHDMTjfJiwIJMBgoZzY2Wetuc+fRwxyYnyO1DQrncAomjhmIoj0Xg4Eo2gf6ox6KoN4B1Qx93jlQmGq0OH7oMNMTMzTSKeZnDzEcjVhfW6Hb6ePKnLzIGamysr5J7j21eoOTd51k4fhBPvbIk2zsbHH08DSHFg7zinvvJktytjubXF5+mF43pzd0bHWUtbUh5XCdUVEyWVvnf/7sByjye+n1HDZRCNW4hnajjQ+eVC3NpMZsaxK1KTmKLwq6wx5b3Q0IBXnZZ9gvGOTC6OIS02ubHD40wcTkAZyvE+KthVG052IwEEX7QJbV8UGRJENEWJidY7qZMdusMTExSWNqns5gQGdjmc3NR8myGqV3DFXZ7vXZ6eyQtZuUqXDkxElcUbB0eYVut0vpBtx52wlOHl/g6LFTGBEuLj7MRneJfj6iN1C21gu2dwpKr3R6Ba4ocZ2cpSsXWJg/hEn69Lcvc+rOuym1xurKGhsbq/TzFbwvWN1cY3b2II2sgdQy2s1JZibm2djZIC9XqA+ETn/Adm9I8AG35DlUKAcOHsGFbK9PfxTd8mIwEEX7QLvRptme4PDhQ5y6/Q6sqWFCQFXZ2F7j3X/5VxS9Fe684wTdXp9evw8mYb3bZWOry/TMNBoUTWFtY5WLl9ZYW+6QWc99Lz7Ii150kJkjt2OzhEc++mHWthfpj7boDpTNTk6/F7Aho5YFmrWEkydeQpGXPHp2kaNH4T3v/nOOH1tgZvYgL7rrXu6/714ka/LY42d59JGHuXT2DL3OgPZUSghK3dZotxpMT53g1ImjnL+4yNmLF9ja3KEzyvFWcevb+CKwMD2116c/im55MRiIon3g/s96JVPtJq3JFs2JNtur61xaPMd7P/iXbPVHnDpyFIfhY4+fZafbp5eXTE5NstntUmvUyE2JKx1bnW0WL67TGyjTrZQj8wlZwzHyhrxwXDj3YRZXztMddOj0h3Q7gVGuWGNo1AMz7WluP3UnE41pMGBwbG1tc8dtt5E1EoIIvc4Ok60mSdrj1FzCi97weWyPXs3FpTXWVpZZXVnEUCVfShJLYjJuO3aCdrPN42fPsbqxxjAvUaeIbuIk3k0QRXstBgNRtA9Mz05z6dyTjM5uoa6DCcLjFxZZWV3j8MIszm3RzUs2eyMGRY5Xz+qVK0y0UowLbG+PyEcOVyTkhWWibamnI0I94dLlDqtL5ymTj1CaTQof6PQKBkMlz0EMTE1McNuR2zg+f4jEQpCAMQlChqJMTU7iXMn66grD3g7b28tMTbQZbWwj3tOaaPPiQ0d52W0vZRhezspGh5XVNfqdDfLRgMJ65mfmaN03wSNPnmZl9QqFD2wOoXTDvT79UXTLi8FAFO0Dg51VWpOTLJ0+z/b2KsO84OLKKoMQOH3lCq1GnYmJCXr5EIdndipjasqiKM5bdvpDajalPVXHNAO+GJEIjIohRVljee1J2jOBWj1QFIHSCaqGWmqYata48+TtHJheIEEIQTCJJRVLCIFSA4QC1SpAGPRHPLFzARVBgqORCo0NpbV4lumZKRqtCaZnD3P4toOk7bvp5CWXV5dZW15ma2ub++5KaGYJ5y8uEhTWBuVen/4ouuXFYCCK9oF3v+uPeHxxhdLlJKllZ1BSaGB6JsWI4iXgTMnBQ03aGaiO6OewtVPQ6/eQmmF2YZITB+dRMSwtrdDtObZ7PbzfpjajeJT+UChzCAjeBw5OTfDS2+5hZmIaKwCCr36gBHzwQMC7EmMN1gqoxaA4hc3NbbY7W1hradZS5jtdptstsnPnaKWGuYVDNNvz3LlwkLvuvwefNul2eize8wo+/PBDLF1ZZGVjeU/PfRRFMRiIon3hoQvLrHV7oCVpkhBUmJ6d5cD8PNbnEAYcXJhjUAzo9Xfo7AzxpCSNhMlUaLaaTE23qTfn2dlewwfHMB9i1GJMlYcgiOCd4n3AA0WhtNNpmrUG3gfEmGq+AwHwgMeVJZiEgFBPEiyWQpUkSTBAmtUxSZOggZ1eQFKh1p6irznL3T6r5Qp+eB4tS6bbdebnDzF34Ah3zSzwkje+htLU6eZ+T899FEUxGIiifWFtp4tNMubmD3LH7S/iwVe8kkYt4fLKYzz2+AdpNxqY1JOFjMInSL3BdLtGvZahKjQbDeqp5fLyZba3rjAc9XHOYSShKDyowY+n/w0hpd+Dnc0hrVNtCAEfCtIkhQAqYMQQgkM0oMFjbIIaQUVQQFSQALVGk3SYk5cFqCfJMrJ6g+ASNMvolQ6yBp28w+JKF118iIyHmKhnzE+1OHzoMHMHjwJfuNdvQRTd0mIwEEX7wGtf9yUcOnInzdYEtx07gA0drlw+z9r6GdqTDVrZFAbF93awAdrNFkcPLOBRyrLAa8nltVW213ewBowBY4W8cOQesrRGEXISW0NlmqXOOl4FawLqHGliEVF8cCCK02o2RBGLGAGjBAUxBmMU75QQPM16xvzcDP3hEPWOdrtOWRYUZYFzVRkwNNvTtCamKYsCFzz94Nje6fHE8iM009O86mu/b6/fgii6pcVgIIr2gYMTM9x2cIqFheOsr53l3OJf0uv0qVlDo9VksjXF2soG/Z0+RxcOEGopIVwdeFfQHe7gXM7EVBsNgWLUpRh5ghrUGEZ5SWoy7jlyO0s7CefZxBhAIDUWq57SDdEQMFRTIqsoiGAMBAWL4F2J14BIghFBBKaaDVqNJqUrSBKLcw4QjBEIivceH8BYg5GEWppgbJ10chpRGBX9vTvxURQBMRiIon2ht3aF9VRYOfMxzi09RNqqU5QFaepI67AxzLlyYROCp1GWrF9cotayZLNtvCsJZclEu02r2aSzvU0+VPLCgxFK56lndR48dTeTtRrLWx2CBgSoZykiivqAKKSJRUNAVLHWEqxBjJAiaAggBh2Xhao7QYNWy4AQFGOUPC8wphqvoKofX897T2ITRKimIQ6Q2vqenfcoiioxGIiifeADjz5E/fHHSWolpemCOBoNy8xMg5pP8UWHAo81yoWVZXRUktRqjPIRxkCzUWdyYpK6qVOmgUv5MkUoCUFJbJMH77ibA9OzDHp9nHcEVYwxtBJIrSLWIGqrAYTWYoypvsgDSAC1igsGRJBdAUHhShTB6/jK3xiKYoRQNTsE9YgoYgQRASwi422iqCrVUMQoivZSDAaiaB9QHdDLOxhVmu2UeiMlsUKRQ3CBJPPUWoHSWXrrAyYmJgktiwkBSQytdhORQDF0FIUyGgVKElJS7j9xJwvNSdSXGLH4EBAEYwwiggZFANUAVlBViqIkSRQNkGYQfIJIwAclqCGoQ9Xggscai2JQVZxzqOr4iz5ACFXLAQHVgAJgcD5UAUO4evdCFEV7KQYDUbQP+GRE1lCMhSxNaTam8G5IYlOazTqF62CM4HJPPUmQAH7kSNoJqTWEUNId5KS+RX/YIS8KEsl45R33cmxqnlKVMBgR1KPjLgAr1dW9wvgL3RMQrLWAVAFDYggoGMWrAooPJWWpVQsB4EPAo6gVQgg45xBJ0RDI0gSMoSSAASsGQVH1SDCICp6YtTCK9loMBqJoH7A2UEsTGu0mOGEwHDDRzmg0G2RZQu4gSVO8KWm2Wnhf0pxs4nGoCXj1jIoSVcNOt0sIwqkDR7nt8GF0OCQvSmxiEU0wiUWBxFjStOq/x4IGsCZFESRxVZN/UFJSShRUKMuSEAQfAirVWAARwYVqzAEaUBQrptqmVq0O1kjVBRECxjAOYBQRqe5WiKJoT8VgIIr2gcJ5rBV0OCBLLfVaA5tAJinGtbAyYlgMSWoW9Z7Zdptmu8awyEksFMMeVlpcWr7MZmeTZtbi7mPHqBlBE4NIHa8Bj6HX7+PKApOkJEmKCDh/te/fYcVgJSUUDvUO00jHXQNK6QN5GcBaQvBYA0EdXhVrqi//xCakaRUwQNV6QPBogOADJjWYNAWqcQbEloEo2nMxGIiifaDIPRo8ZUgIUmcqq5EkCeUgZ7vbpT7TAhw1UdJmgq3XKFzAlQHBk9DE0KA/6uE9nJifZ7reIASHD0AQvC/Jg6Eoq7kEgneogqVquvfGYGzVPSAGghWQBKeBFChFSNIahS+BaqyBihKu3kngHIlNMAjOjWcVlGq8gIiSpCmSGUQDXpUkEawF52IwEEV7LQYDUbQPHJ0+yWZviVajDlbIiwFG6qgRsnYANbRbswyG21WOACM479AgjEY5fmQYDrdRVWpJjdsXDqHeUfhAKB3BG0ZlTkkKRkD4+B0FSZKCJtUdBaYaQChGSKyppjD2DpEqgREY0izDlx5f5hCqGY4Sm4JWAxOtVHclINWYAMy4DwIhSSw2VOMQzHhf1RiFKIr2UgwGomgfODQzD5qjZoBNBa+G0iklHusCiTqMFVoTM2goSRMohgVBBVdAszHNdu8Cznmmay3qNqMcjkhs1Vw/Kh0mSTAYEiOoF1QUVShCSZpkGDGUzmGsxfsAKFaV3AfEJiiCMZZEFZ8IJkvGsxRWXQXVPMYGLwoCEgKlKim2Ws+Aes/VCYmMWBQHGoOBKNpr8QbfKNoHWvU6h+YO4X2BTZUUQ2ItnkCwATFC3WZktoFgUXUkiSAeGskUrfoUgRqGwPG5GZLU4KS6nc8VJVbAKlj1tNMUVAgo3rvqzgJjUWswJkHUYiVBEIKASVIwCaULFKMcHxwheFKbkFqLNYbMWgwGCYooFHk1jkCDUniHp7p1UYMf300QxnMVpKjqXp/+KLrlxZaBKNoPVKmlKe3mJNgCJCcET6aGUBhMsAyHfZKGUp+sUfpBNYBPUoJXev0tjEBqaky3ZzBGSUx1NW8Txauh8A6SFGsExWGsodGsY2xCGF/JG5QQHGWo7lAgrYGCBsVYgxJApAowrKVqBBBUIE0tgWpSosRaMBajATUQUCxgTNVFoCqgiuLHrRBRFO2l2DIQRfuBCIlJWJg6gkVJrJKYOpYawUEY9ZmcyMArwRmUDHUJKsqo7LPV3SC4gnqakiAULpDValW/vUmrfv20TiPLkKROmtaomRRrFDHVhEE+eDwBpx6sYKyQiFJPE4wErAFrDd4HjJhqAiIEF6pBhEYY313gMUYI3ldjA7TqHgjqUYHCl+MJiSB4XwUYURTtqdgyEEX7QJpZ8AExDTImKPvbuJCjaULazNCRpXBVP34xGqAWVC0uDMF4vDiMK2lkTbI0xdgGpXcUpcc5SDIhTRNGo4LcCSKWxIIVQb1HxrMRAp+YmZBqAGDwnhAUqAb8GSPYJMHlOdhqLICYcasAgrUJwSkaSiSxuBCqVgVVvKu2VYYSjAdMlSExiqI9FYOBKNoHnCtRHKmp0TRTrMkGKSVJAB0puYe8B4lAORySNDLSNMG7gKihnTTZkoLU1kjSlFpmcV5JsoRazZKm4L0nT+qUfh2RML7Kt4hRRMPHgwIj1UBBEwyhymQM/P/svXecbldZ6P991i7v+047M3NmTm/pwIEQCEVpCdJERFEQJJRwrahcvV7Aiw2DinqVKz+56g8LUgxFxQYogoUiVQiQQHpycvo5c6bPvHW35/6x9iTvmUwNITM6z/fz2Z+Zd6+y11p77bWe1Z6nVGPsQHIQvCVDtCAOA6I4IggcqqDqcAF+iaDwiomcgPM7DQld6A0dqfq41WYGDGOjMWHAMDYBDkeeh2jhiKIQooRU1NsDyCPyJCPJGlTiCEJozkwxODRMQEyW57Q6LZLcWyeMoiouiAhwEGUICoXiFuwOdBJAqFQi4jjCOfzRQclKWwWUFgohzwVc5vcmBH4JAEpNgoGfBQiCAOfKMDgC50jy3J8gWDhtkOcEUegFAC28hKGOQu6VNgzD2EBMGDCMTUAYOTTIEE1BHZWgSkoHcSGiSupyeocrbN+2ndlmgyIpaKUNyAOisAdN2sRhSC2KqMRVCALEBTgJKLKCIFByIM0S0iShKAqG+nrLnf2OLO9Qmh64V9eAImSa+42F4jv3e2f0xWscDAMlcF61cFEUFEWGcwV5rvf5Vb+8kWf5vUsQ94bRwk4TGMYmwDYQGsYmIN4Voig46D3UQ1gL0EIpxOsaGNk5wvbhATKXQ+ioVSvEUS9aRHTaTYos8waDVEmzDkWW4ScE8vLya/6tJKfRSdCiYHigBwIhiELCsEIYVO7VByCBQ0WQALICssKvDTgXsqBiOHSOAIiCkEC85UIXRBSFN1fslQr5I4RJJ/FHCWVB0FBvx0D13r0KhmFsHDYzYBibgHh/Qt/2EG2HuCEIzjny2ZwizQkrFcLIIXlBGEQ4aVKQ0E6hk+dk7YQkSYmiKlkOeaE48Tv5tfBT+rk4ApQ0S2kmTUSUvv4qmUKS+I46UL+uX+QFEtxn6jiKonIvAGS578CjMCQQKLTwGg1xOCAMAjqFPy5YFBkqBSLeCiJw76kDVbyVxOI+dcaGYWwcNjNgGJuAyXml0wvuUMhUs0magGiGOkVVaSYtJuoTzDWmSTrzpKmQ5w5XFOX6fUReeEFAkXITX0iBo1CB0lixiJClOWhGrValwHfUgfNr+0rhdQGoltP3ASJCrgWFCmEQEjhHKAEqjgxHoQVOvPpiUCLnUJSsPD4oIlRrVZDANzhaoAheQbE/HWEYxsZiMwOGsQnoVOdozXSYOBsz12lRaAdxrtxc560HIhlF0SEIfEdckYg0L9CKI88hSQtUIAx9Bx7GIWknQVEKgcCF4BwiUIsCeuIISo2ARV4QEODlhhAXBITk5GHgZwoKEHGgXvlQXtoeWGhAtMgRcWS5V0rknCNP/XRCFJVmigvxMwKFersKkhE5hzMTxoax4djMgGFsAmbm62SuQ9Tj6NCmkymiNTQPcCo4yQkCoEgpMkclrNHTW8OFAYrSW+sldDFapN4aoQhJklAUuV+/D0Mk8LoK8iKjv7dGf0+/7+hFKAjIVEjSgqxQ0izHBQHVwBEDTvyoH4UwdGjg9w1IueuwPGVIkSt5uQ+gEkdUKxUEf8QwzdqkWac0elRQFAV5XpB0so0sesMwsJkBw9gUSE+FoLeHZL5FX88As3kTVyRQOMIwJNcEJwGhi1CtEWYxgiMKIqgIaZLTSRrkxYKyILwmQSdEEnuVweRAQS2ucsGBvfT1D5BlbVQLnAtR5wjEUeA7/KJQ8k5GnmfgQgIXkKnvwMMgBOftDmheoOIIRLwg4MBJQRw5iswvYwRh5GcUkhTNc4IgQjWEABg0Q0WGsdGYMGAYm4D+/kF2jexjvDKPq8/T3jZHMdsiUyUoCiQvcAhOIyhCtIC8gF1Du4grVY6cOMr27b0EoqhzIEoIFEGA5go5FE7Zv2OAqx97KQd3DRO5zB8jTBUNcpyDnIxcHXnqR/nOOSqVKlnuZwxcoDgNKNTbLoiqFdI0I+10cBEUWUIgYWkl2SHOeTXHWYYWBaFz5FlOkjYJghDNQ2KzWmgYG44JA4axCegLocg7DA32MBs2qI7GJGNK1izQPCUOQypRRJZlKLkf6WcZWeIoJCNwQp5AjjdNnOZ+L4DkkBMQlCoDqwE86qJdhC4AvL2BIg7QPMcVOYoQhSEQkotXPJRmKUiISIEU4pcHxJsgljAgDEKqsZLnObGEpGmKBA7Jc1wQQxiR536pwKs0hjTNyPKMuFYl2rVtQ8veMAwTBgxjU1Bvp2jUoqBAspigNkQQT0AjIQwcYeg32rWLNoojz4WeoEZf7xCpy0nzhEIzkIhOpoSFEEYhkJbT+gE4b5OgKAoURZ1QkPsd/2Un7ZxDxS8RUGS4WFAN7lVOpKVxoSjy1g+zoiDPUkQEKWcRgiCk0WjgXEAYFOSFEoYxaZKRFgmqOeRCEEQUlYCenf0bXfyGseUR0/5lGIZhGFsbO01gGIZhGFscEwYMwzAMY4tjwoBhGIZhbHFMGDAMwzCMLY4JA4ZhGIaxxTFhwDAMwzC2OCYMGIZhGMYWx4QBwzAMw9jimDBgGIZhGFscEwYMwzAMY4tjwoBhGIZhbHFMGDAMwzCMLY4JA4ZhGIaxxTFhwDAMwzC2OCYMGIZhGMYWx4QBwzAMw9jimDBgGIZhGFscEwYMwzAMY4tjwoBhGIZhbHFMGDAMwzCMLY4JA4ZhGIaxxTFhwDAMwzC2OCYMGIZhGMYWx4QBwzAMw9jimDBgGIZhGFscEwYMwzAMY4tjwoBhGIZhbHFMGDAMwzCMLY4JA4ZhGIaxxTFhwDAMwzC2OCYMGIZhGMYWx4QBwzAMw9jimDBgGIZhGFscEwYMwzAMY4tjwoBhGIZhbHFMGDAMwzCMLY4JA4ZhGIaxxTFhwDAMwzC2OCYMGIZhGMYWx4QBwzAMw9jimDBgGIZhGFscEwYMwzAMY4tjwoBhGIZhbHFMGDAMwzCMLY4JA4Zh3A8RuU5Ert/odBiG8dBgwoBh/BdDRI6KyDM38PmvEpHPbNTzDcNYPyYMGMZ/EUQk3EzxGIbxnwcTBgxjE1CO5n9eRG4RkWkReaeIVEu37xaRr4nIjIh8TkQuXxTuf4nITUBDRN4PHAA+LCJ1Efk5EblaRE4u8bxnlv9fJyIfFJHrRWQOeFXprSoifyEi8yLyFRF5dFf4N4jI3aXbLSLyfeX9hwNvB769fP5Mef95IvJVEZkTkRMict23qCgNw3gAmDBgGJuHlwHPAS4CLgV+SUQeA/wZ8OPAduCPgA+JSKUr3EuB5wGDqvpS4DjwfFXtU9XfXuOzvxf4IDAIvLfr3l8Bw8D7gL8Tkah0uxt4KrANeBNwvYjsVtVbgVcDny+fP1j6bwCvLON/HvATIvKCNabNMIxvMSYMGMbm4fdV9YSqTgFvxnfyPwb8kap+UVVzVX030AG+rSvc28pwrW/i2Z9X1b9T1aIrnhtU9YOqmgK/C1QXnquqf6Wqp0v/fwHcCTxhuchV9ZOq+vXS/03A+4Grvon0GobxIGLCgGFsHk50/X8M2AMcBF5bLhHMlNPu+0u3pcI9GM++3z1VLYCTC88VkVd2LV3MAI8ERpaLXESeKCKfEJFxEZnFzx4s698wjIcWEwYMY/Owv+v/A8BpfIf8ZlUd7Lp6VPX9XX51UTyLfzeAnoUfIhIAo6uEOS89IuKAfcBpETkI/AnwGmB7uRTwDUBWiOt9wIeA/aq6Db+vQJbwZxjGBmDCgGFsHn5KRPaJyDDwi8Bf4DvdV5cjaxGR3nIzXv8K8YwBF3b9vgO/GfB55Zr/LwGVJUOez5Ui8v3l6YL/gV+e+ALQi+/wxwFE5L/hZwa6n79PROKue/3AlKq2ReQJwDVreL5hGA8RJgwYxubhfcDHgSP4DXq/rqpfBn4U+H1gGriL+3b7L8dv4jcfzojI61R1FvhJ4E+BU/iZgpMrRVDy98BLyue+Avh+VU1V9Rbg/wCfx3f8jwI+2xXu34CbgbMiMlHe+0ngV0VkHngj8JdreL5hGA8RorrUjJ5hGA8lInIU+BFV/ZeNTothGFsPmxkwDMMwjC2OCQOGYRiGscWxZQLDMAzD2OLYzIBhGIZhbHFMGDC2PN9qK3si8lERufZbFf8qz940pohF5JCIaGmz4Mc2Oj2bFRF5l4i0FtuTMIxvJSYMGA8pD6Z53UWdy8L1yw9G3A8mqvrcUo3wpkZEPikiP7LonorIxQ/yowZV9Y/L+F+26P01y2deucY0/5qIfF1Esgdq/EhEhkvNiGsWCEXkZ0XkSGl46bSIvHU91h5FZFRE3icis6VhqgV7EKjqq4Dnri8XhvHNYcKA8V+BwdIoTp+q/tpGJ+ZbxXo6m/8sqOp7u95dH14fwRHgK2uM4i7g54B/+CaS8b+BW9cZ5kPAY1V1AK9w6dHAT68j/N8AZ/GaJncAb1nn8w3jQcWEAeMhQ0T+nEXmdcv73yMiN5dKcj5ZmsFdCLOsad8H8Pz9IvI35ShwUkR+fxl/v1ea2Z0TkRtE5Kldbk8QkS+XbmMi8rvl/ap4E8CTZT6+JCI7S7f7jbi74lswH7ycqeDFJopDEfk28aaMZ0TkRhG5usv/BSLyqTKuf2aR/v/lworIm/FWCH+/fDe/LyKfLoPdWN57Sel3WZPKDwLXAu/RNe5sVtV3q+pHgfkH8jAReRK+M3/nesKp6t2qOrMQDVAAa5pBEZFn41U9v15VZ0tFTl9dz/MN40FHVe2y6yG7gKPAM7t+X4rXiPcsIMKP8u4C4i7/38A3nsN4TXe/XrodwqvFPYXXqPdOYGSZ5wbAjcBb8ep0q8BTSrdXAZ/p8vtyvLngEHgtfgRXLd0+D7yi/L8P+Lby/x8HPoy3ARAAVwIDpdsn8QqFlkrXdUAKvKjM/+uAe4CoK/9fK/NfA/YCk8B34YX5Z5W/R7vS97t4dcNPw3eS15duq4W9XzrL8r246/djgHPAE8t8XlumsVK6thFx5AAAIABJREFU/yHwh8vkdeF9hcu4HwRy4IIHUK+uB65bZ5gAPwNx5eI6sMbw1wBz3Kea+dFrDPdG4GNlmieBLwFXLfJzNXByo79Xu7bOZTMDxkbzEuAfVPWf1ZvKfQu+03tSl5+lTPsCTACPx3ciV+L137+XpXkC3uLe61W1oaptVV1yjVhVr1fVSVXNVPX/4DvWy0rnFLhYREZUta6qX+i6vx3fceaqeoOqzq2xDJY1FVzSbaL45cA/quo/qjcH/M/Al4HvEpEDZXn8sqp2VPXTeAFlgWXDrjGdsIpJZVX9SVX9yXXE180rgX9X1XseYPj18tPAF1X1hgcSWFXfp36Z4FK84aWxNQbdBzwb+ASwC6/a+e9FxKw4GhuGCQPGRrMHb64XuNdU7gn8KHaBpUz7UnbGXy477TG8Fb1ny9JGfPYDx1Q1Wy1BIvI6Ebm13Nw1A2zjvun2H8Y3/reVSwHfXd7/c/xo7wPlhrLfFm8UaC0sayp4sTte8PkBOd+k8VOA3WWYaVVtdPk/tsawa2UtJpUfKK8EHpKNliKyBy8M/OI3G5eq3om3xfCHawzSAo6q6jvULxF8AP+On/zNpsUwHij/5TYkGZuexWvBp/GGbgAQEcF3Lqe6/Cxl2neluJcSck8AB0QkXEkgKPcH/BzwDOBmVS1EZJrS3G7Z8L9UvEnf7wc+KCLbyw74TcCbROQQ8I/A7cA7lnvWUvmTLlPBS+RrIR9/rqo/ukTaDwJDItLbJRAc6Aq/bNglnrMcCyaV37wGv2tGRJ6MFyg++GDGuwJPwAtBt/gqRw2oichZYK+q5uuMLwQuWqPfm4DnL7pn2t+MDcVmBoyHmsXmdf8SeJ6IPKMcSb8WP+38uS4/S5n2RbxZ38tExInIduBtwCfVW+lbzH8AZ4DfEm8GuFp2QIvpBzL8GnAoIm8EBhYcReTlIjJajuAXNpAVIvJ0EXmUiAT4deQUv6lsLSxnKngprgeeLyLPEZGgzMfVIrJPVY/hp/3fJCKxiDyF8zudZcOW7ovfzVL3HohJ5bVwLfDXqnreRkDxOiCOLhdIRCLxG0od/n1Vy3fQffT00BJBP4rfw3BFeb0R+CpwxYIgUIa9epnn/oiI7Cj/fwTw88C/drl/UpY/6vi3eKHt2vI9vAgvAH52Gf+G8S3HhAHjoWaxed3b8WvZ/xe/B+D5wPNVNekKcz/TvuX9C4F/wm+S+wa+E30pS1A28M/H7/g+jp+Kf8kSXj9WxnkHfoq9zfnT9N8J3CwideD3gB8s1/J34Ue1c/hjap/CLx2shSVNBS+TjxPA9wK/gBdYTgCv575v+Rr85r4p4FeA96wj7O8BLxJ/auNt5b3rgHeX7+vFuopJZRF5u4i8fY35XghTBV7M0ksE+1m5k/wT/LT7S/GCYgtfhgthj3H+LBMA5Z6KswsXMAuk5f+IyH58vfr6Ms99MvB1EWngZ4H+EV+uq6a73PvyPfjNorPAG4DvVdWJpfwbxkOB2SYwNjXyX9y0bzl6vFhVX77RaflWUy5j3I4XsF6vqn+yhjAfB35GVderBwAR+SVgXFX/6AGEfTlwWFV//gGE3Qf8pao+aVXPS4d/B/ADwDlVfbAVPhnGkpgwYGxqTBgwDMP41mPLBIZhGIaxxbGZAcMwDMPY4tjMgGEYhmFscTaFMFDuQH7Qrc3JCjrh1xD2gHh97MEa/auINMTreDcMwzCMDUXWYw57o/UhfysvVtAJ/y141nk63Mt7VwA3AM3y7xUrhK8vunLg/3a5/wj+GFcdf/Rtz6LwjwU+XbqP4Xdgg1c6szhuBV5buv/CIrcW/nz8SOleAf4Mf2TuLPA/u575CPy59uny+hfgEesos0N4laxN4Da6bBYs4ffFeN0DTbwugW63Efwxrkn82f/PA0/ucn/7ojx2gPlF9aTd5X57l9tuvIW602W5HVr07LcAd+KPod0GvHKtdQD4Wfxxybky/reyjN7+ZcrkGvzRuQbwd8DwCn5XrIvL1Z/S7WhZLxbK5+Ndbj+IPyEwi7dZ8G5KmwyL4r+kLOPru+6tVvduXuSeAR/uCv/H5bML4FXr/F6H8ef9G2UZXrOCX8FbNpwsr/9NucS6hne8bNg11Ntry/jm8Edhf7u7fqyWB+C/4+1czOG/06es9Z13+bkKX+9/fa3pWqXc19xm4Nued5R5m8fb6HjuEu3CraX7LcALFoV/K/7bmsZriIy63Jdtc4EYf1T4aJn/qxc99/X448zzZRm/fpH7UZb5ZtZ7sXIbfKhMX3c+fnlR+KtZg52LB5S4/ywXD1AYWGvFXhRmsUGXuKzEP1u+zJ8uf8driKuvfKlP63qZ54DDZbz/P/CpLv8jpfvLymf1Aw9fJu4Lykp/aBn364B/6/r9m8C/A0PAw8vK+J2l22BZGQVv9OWngZvWUWYLRnVqwAvxDeLoMn6fWX74b+T+wkAVbzvAlWl5Af6c/XIGcd4F/Nla6gmwE29W99tZWhh4E/Cw8tlPxDc6T1pLHcBrrBss/x8G/q37Q1+l7A7jG6KnlfXlfcAHlvG7WjpWrD8sMi61KO793Nd59+FtQ7xtCX8fL+vR9Svk6by6t8hN8I3uK7vu/RReU+SXWb8w8H688qo+vErmWfxRwqX8/jhe6NiHV5N9C/DqNZbtSmFXrLfAT+AtScZl2BuAN6wlD2VdbOBtdkgZ1zgQrLXNwBvO+hpeAVa3MLBiulYp9zW3GXiDYteV/h3w3fg6f6h03wskwHPL+J6HF8h2lO6/Uta5YWC0zMeblnnW4jY3xisAewpeWdnVi/z/HF6YCst3eAyvc2TVb2a9Fyu3wYdYwfhX6edqHgxhAP+x/01ZkSbxRmMoX84vlYVwDq/cZNuiBF6LV/AyAfziCs94V3dl67pfwXcQj+y6N4qXuHaUhfORMm3T5f/7uvx+krKRX2N6f7hM76fXUsiL0rpYGHg2XtlJ9wji+MJLXCWua/EjxoURxFuAP+hy31M+76Ly92/g1cyuJZ2/AnxiGTcpn3tt173TwLO7fv8aS3Q65UfxU0Bzjem4FD9C7++69++UDeUK4X6ERcLAIneHVy6klI3CIvdefINy1VL1ZIV4Q5YQBpbw9yHum3VZcx3AGzn6F5ax+LeE/98A3tf1+yJ8w9i/hN8V07Fa/WGNDRu+QX0P3hhS9/0fxGuavI5lhIGl6t4i96vK99a7hNtnWIcwUNaBBLi0696fA7+1jP/PAT/W9fuHgS+ssWyXDbueelv6+Z+UMyOr5QGvxOo/FuVZgd1reeelnzfgR/3vYon2eal0redinW1GGeYm4IXl/0/E62Lodh8Hvr38/8vAD3S5XQOcWCbe89rcRW4nWSQMLOHnbZw/k7vsN1O+6zfgFahNlt/GSrN6y7bBPIjCwIp7Bsr18o/gO9BDeEnsA6Xzq8rr6XhNcH14rWTdPAUvNT0DeKN02alfC6rawQsi3VrlXowfFZ/DF+o78cZTDuCFhCVt1K8xvVfhJa/nLA4sIm8QkY+sI/mH8RKvdt27qby/Gtdyf5vussT/jyz/fhswJd62/DkR+bB4C3bnUer9X8kYzFPxQtZfl/6H8NPkN3b5uXFxHkpjNW28FsHfWD17UMZxRM9XP3u/uNeDiNxUpuNDwJ+WdWQxL8Q3GJ9edP83RWRCRD67nAraNTy/hrcaeHN5a9U6ICLXiMgcXmB+NLBWBTmH6Xovqno3ZeewjN+V0rGW+vNeERkXkY+LyKO7HUTkKSIyi++sXwj8f11uA8Cv4juMlTiv7i3BtXh1xY1l3NfDpUCmqnd03Vup7p1X1ov8rla2K4UF1lxvwc8CLdSt1fLwUSAo1UYHwA/hR/lnS/cV33mpIOqH8O9uNbrTtSYeSJshIjvx+V541peBW0Xke0q1zi/ADzBu6g626P99IrJtieiXanPXRNmuPpX7l8Fy38x/x88CXUVpXAz4g2XiXlMbDBwTkZMi8k55gNYvV9tAuJLZ15cBv6uqR1S1jtfN/YPi9asv8CZVbanqjWUGzmtE1sj78COLBa4p76HezOxfq2qz7FTejC/gpVhLeq8r89laHFhVf0tVv3vx/RXow0/bdTOLn45blvIjvIrzO+x/Al4sIpeXHc4b8dJgT+m+D1+ZfwYvFN2Dn0JczFPw097LGYO5FvhgWT4LeVhI97J5UNVBvGW/1+D1u6+FB1Q+K6Gql+PtCFyDHy0uxVIf/f/CC4h78evQHxaRtRqd6ebt+Hr+sfL3qnnUB24Gdz3lt5rf1erPy/CDgYP4PR4fE5HBrjx8RlW3lfH8Dn5UtMCvAe9Q1dU2MC2ue/ciIj3Ai/Aj1AeDPvz6azcr1b3F5TcL9JWdwGplu1JYYG31VkR+CHgcfpZwLXmYxwtWn8F3kL+Cn6FYqPervfO34dee7/c+VknXmlhvmyHebsl7gXer6m1lHDl+Jup9+Dy+D/jxLoHxn4CfEZFREdmFX5KA+9rNhbiXanPXw3XcNzBdYKVv5tX4mfKT5YD3Orwa8KUMB67WBq/HjPuKrCYMrGT29TzTs+X/Ib6zWeBs1/9N7svYevgE0FNKuIfwm3X+FnwjISJ/JCLHytHVp4HBZU4ArCW9J3jwqNNl4KZkAP+RrsQrgM9ol0139dr3fgX/cR8tr3n89BX4GZG/VdUvqWobv479pCUk4IXR1XIN7g9w/gex4K87H0vmofwA3w68Z8GAyyo80PJZkVJgfT/whiVGsAfwU2bvWRTmi6o6r15f/bvxm7q+az3PFZHfwc/UvLirwV1zHnX9ZnDXU36r+V2x/qjqZ0uhvqmqv4lfunvqEnk4hW+APwAgIlfg93q8daWMLFP3uvl+/Fr6p1aKZx2st+4t9j8A1Mv3vFpcK4W9l1Xq7Qvw68bP1fvsF6z23B8G/hv37TN6OfAR8aabYYV3LiLPxy83/cUy5bFSutbMWtsM8ZY8/xw/8/WarvvPxC9jXF3m8SrgT8t6B35w+FX8jMjn8JtsU+4vcN+vzV0rIvIa/Gzr88qOfSFvK30zB4G/lftMgN+K38e1szxZVy+vhQ22sEwbrOsz474iqwkD95p9XcLtdJmpBQ7gd/uudWSzJkrp7y/xSwUvBT7SNbX8WvwyxBPL0dXTyvtyv4jWlt51TxGtwM3A5d0jAOByVp9OW3IaX1X/QFUvUdWdeKEgxO9mBT8t1p32++WjnFFYqcH9PnyD+8muZ07jN890N06PXiEPDi91713GvZubgQsXVdqV4l4vEfe3wPcK4LOqemSVsMrSdWhJRORN+E1Mz1bV7tHaeuvAeszg3kzXexGRC/F7bO5Yxu9K6Vi1/ixipfLpzsPV+NHRcfGmgV8HvFBEvrIozP3q3iIe8BTuMtyBt3B4Sde9lereeWW9yO9qZbtS2KU4r96KyHfijTE9X1W7jSatlocr8G3lHapaqOo/4b/lBXsJK73zZwCPE5Gz5Xt7CfA/ROTv15Cu9bJim1GW6zvwg7YX6vkGvK4APl12hoWqfgn4Il4ApeyMX6Oqe1X1Qvz6/A3qLY52s9LS6bKUsyJvAJ6xhpmv7m/mBF6AGuy6qqp6SlVfrap95fUbD6ANXniP61cboCtvigjw055vwW9AqVIefcFv5LoTvzu9Dz/1fL0us6mBlXdsv4uVN6g8sSyQb+Ctey3c/2382liV+47Z3Ptczt9AuN703u/eKmWlLH2a4GfwjfRrWOU0Af5DbbBoE1iZv0fiK9OBMl+/0eX+Hfh1pyvwjclbgX9fFMc1+BmF+22QKd0/DvzqEvd/Cz8iG8Lvmj/DfZujngU8pqwnA/ipxdNAtXR/FXB0hfx+oaxbVXyHsNJpgqD092r8DFCV8pgQfv3zKWWZ1/DT/vPc//jl7cAPLbo3iN8jUsV3ZC8r30H3xqwq923Aumwhf6Xbz5f1atcSaV6xDpR1cmHn8yPwH/jvLvpmrlumPA7jp4mfWqbtelY/TbBcOpatP2V9e3IZRxV/pGoc2F66vww4UP5/sKwrf1P+7sFbc1y43oL/7kYXpW/Jule67cML7Rctk68qfibnR8v/Xel2NaAr1L0P4KfFe8v8rXSa4NX40dte/Azjzdz/NMFyZbtS2BXrbfleJil3uK8nD3gB6g68YCH4b7UJPGwN77x/0Xv7i9J9eI3pehfwrmXcVmwzlvD/dnw70beE21X4afIryt+PKdP17PL3QplLWdYn6NqIt1KbW7pVyjp1Er9RtMp9m7pfhp/5vt+pLVb/Zn4W/20fLH+P0tWvrbMNfiL3nUjZXr6rTywKfzUP0mmCA/jplcmy4N9W3nf4tesTZUavB4ZKt0M8iMJA6ecu/Ogh7rq3p4y3jq/4P87ywsB603toUVy/AHx0hfSdJwx0Vc4b8FNyXwEe0+V2v/jwm8fut8MX32HdVFbas/ipuWCRn5/A72qeBj4M7F/k/jHg15ZJ+158g3vxEm7dZ1zHOP+M6w/gz9bXyzL9B+DyLvdfBt67QpkdKt9RC99RP7PL7WXAzV2/X1WWcff1rq5G4UZ8Q7ownfy0Rc/6dpYWtEaBL5VhZ/ANz7OWeLfnXYvcOpx/zvcX1lgH3lmWaQMvqP0O5wsady9Oy6J0XYPfud7Am0Ee7nL76FrTsVL9odwgVz5jEvhX4HFd4d6Mbywb5d8/pmz0lkjvdSw6TbBS3Svdf55Fgu2iNmXxu7m6dFuYBVqu7Ibx7VqjLMNrutyeip/KX/gt+IHHVHn9NuefHljpHS8bllXqLX6JNFtUtz66xjwIfvPf8TL+W4FXrKfN6PL3Ls4/Wrhauv4V+NFl4lqtzbi3XcQLl8r5OkDqwMu6/L8G3zfM408DvLbL7Wn476qJb19etkR6lmxzS7ejS9SvQ6XbPfglh+50vX2N34zDb6i9vUz33XQN7pZIx0pt8EvLtDTwQsJ7WDQwYY3CgNkmeJAQkTa+U3ibqj7o2hT/MyLfhPnZrY58k2Zwtzoi8qfAX6nqx1b1bDxoiEiMF3Au1/On9I0NQNZhDtuEAcMwDMPY4mwK2wSGYRiGYWwcJgwYhmEYxhbHhAHDMAzD2OIspT/AMIyHmJ/7iZdrnmeknRYuDKm3MzqdDmlagATkScbBnYO8/o1v4OQt32DqyJfpDFXJ3AhHjx9n4tQZZpuztDsF9VaMagwUBKEQEFCpBIgIeV6QpTlBHBAIFIWSZhmkKeJiFL+zOEsyv31aUrJcydICERAHlShGC8WJEAaONEnIC8WJ42de9zoedtkljN3+Ze654zZuuv0IY+PnGA7anGw46llMLQwJnIMwQHNlvtHi/f/w8TXrdTAM48HHhAHD2AQEoqR5TugCiqygFgZExHQko1BFoirTszPcecvXqZGTVwLICjrtOvP1Bh1XEAUhufi/zgmqAkEAueIUyJU8TQhcRLXaT99APyM79jA/V+fYHd9A1XfwmRaAEgVCJyvIUwChKAoCVQrJAKXAkRWKc45KJFz1Hc/m8KMO0xg7yc3fuJX6/ASt+jSxdphsBxRSobcaEkQhqKBZTidNaGfJhpa9YRgmDBjGpiAgoxoGZLkikuFwOBXUCYU48kJJcuXY7bcz0l8j6u9FUhg7eRZttZEcsiyjb2A7yURCQUFSFFSCGKGAQIhcQJoVXPjwwzzu25/EjtEdJJmjUW/yjR0HGDt2J2dO3IVIThA42p02URgQxI68yMgkgizDuYAo8AP5vACHcOXjr+TZz/8u8vlzHLvtq8zNTnP67DnypE5W5DSLGtXIIWFMpgEFCUmeoioESyo4NQzjocS+QsPYBIQoOEeWKWiBqh+JF0VGjpBmBXHkyFtz1LN5hga2M99qQQaVIKDtAoKwQhxWqcYFBQUuUKqVEKeKqwa06ymPuvLxXPWsZ0ORMjF2li986Ubi0DE02Mfhx11BTsL4ybtJFWrVCllWkBcFoSgu9J25U6EaOtQ5sjzn4KEDvOD7XkTQrHPHXd/gC1++gTNnJmnOT9JXi5lrB4SVCAkcQQBBENBJAgoJyIqMMLAVAsPYaEwYMIxNQBRVEM1xGpKmSpILFDkOR57n9MaOnkqI5ilxEJHddZq0p0rUX2Fieo5aT5VqtYe0DaoJYRhSqCMKIhRw6hjePsjFD7uYz3zyX5mYnOLEmSma9Tm++zuexKF9uzg7NUUYBURhREFGnigFBYGDQAQpMqI4xokQRyEiMDC6g5e84lV02nMcOXInN970de45O43rzNMTCo3UUbiIMBTEOQoNcVlGkSuK+j0NwVJ2xQzDeCgxYcAwNgFBpY+sM0MQOgqJCcWRFy0KhQLojR1xxTEwOgBJSjLXolbr48TYKVqacW5sjN64h0gGCKMAFwbEzhFHEeoEl8PIzlGmJmf43Odu4Oz4OM1Wm50j25htzPG1m6eZnJzikYcfxQ3zc7TGTiDOEeNQFAiIg4AgDKg4R+ACKnHIs573PA4cPMDXP/uvnDhyhMnxCeqzM+yoZuQCHa0QhA5HiEPJigwVRyvtEIojjGPC2BSfGcZGY8KAYWwCnvt9z+Pzn/0sJ+85Qg4EDnorEUmgkDsigcFt/Yzs2MuJY3cxXU2ppn6PwNS5Ns1mg7xTsK3aA7kSV0I0ABf6rjwKYwYHh5idneURhy+lctRRb8wxMryTT33uBo4eO8727UM8+vGP57LLH0Xj3ycosoxCC9JC/WZEJ0TOeYEgjrjiyidw5eMez7mjd3D7HbcxdvYMU+fOMugSAomZJUKikABHEAcUohRpB8kFFzicOoIgpGatkGFsOPYZGsYmIJ0b47GXP4KBWh+333YH0CELhJqLgIw8h937LyBLMiR05J02sQREtQCKAlcIuIAky8lV6aQdRIQgiBBRXMV34q32JP3bYnaM9rNtMCJNC6KeGhde8ggO7tvDzOwUw9uHqPVsY25mnCBwVIIABIoiI479aYADF17Ik6+6CjrjnL7rJiqaU5+fIsgaOCmYSyq4WpVaLaZIlKxIkSBCCcE5oiLDOajEQpHlG138hrHlMWHAMDYB584cp7e3nwsvuYiewe18/YbPUaQd4kqVIk0Y2bWLHSPDNFpTSG+VIB8kUSVpNOiLKrgiRoqITscfRVSU3lo/qkqeZQzvGODk6VNMzUwTBAHTk9MURU5BhcsefjmPveJK9u/ZwU1f+zztdoNdB/bRmp9EtcA5hwiElQq1akytWuFpVz2dHmlzyxc+y9TYODd+/W56JGWoJ2A666FDBfKMSHvo5CmFFrg0RXAUTgkcRGGEKORmH8UwNhwTBgxjE6BxjZNnTlKbabDn4MX0X/0cbv3KV5kbP8fOHYd45Lc/nvn6WVqtFnEQs337PuZnOrQSZWp2migUKnFAngFhSF9flUAEF3hNQa12wolzY9TrDSpxxMTEOFme0dM7wL5dOxgdGSSKhUajweT4aXbu3MuZvn7ajTm0KIijkCASqmHEvp2jVJJZTtxxF//0iRtptFPS5jwjAzBJTBH14PKciovQToGmHeI4IElzBEHEnx5wAs4JIraB0DA2GhMGDGMTcPLUWXbuHaU+NsHxO28hyR1XPuHxVKuD7N41xC033kAaz5PSYXZ6mr37Hk4SpcyEKZkriCsQB44iiAmiiGoU4iQiL08ktNoJ4xMTzEzXCYOALEuJooBD+/fzsEsvRIqMViPjyJHjnD51hKGhUXbu3cupuxvgAoLAEYhy6aUH2De6izOnjnLrHfdwfHKeHinY3Su0OhlNjUlyRQsl05RU6/5EQi4IBYWAIyAMAuIowjm9VzgwDGPjMGHAMDYB85PzZO2EwdEBcjJq0TZajRmYP8uxW8c53ZzDDed0mhmKkOVtOkmLQhJGRkaoT4AUjjiMEHEIQprnRFFEKMJ8VpC2U7LMqx8WJzz68GGe/vSnsXPnMLWefmZm5rnzriPMzk1yauwsB/cd4Midt+OKNlpE7Brs5fJHP4K5iRnuvnuSk2Pz9ATKBUMR5BnjaYVEvcrjJMtJXUC16ghcSBBGSJ4BEIRCGAqBU0LJETUTKYax0ZgwYBibgKIIaMwltNoT7Nw/wuBgjeMnj3LuzFGi2TmSnoCoXqE20EMlrnHy1GnSBIpMOXf2HHmzxZ4de9mx/xJmzo2T5SmuUKLIzwI0WynNZoqoUqlVCENhdPs2ohAmzh6np7ePj3z00xw9epwwDpiZm+PCuMLwjl00ps7R31vjqd/2RNJWwvTUNHP1Oqo5jzk4TNqcYaItzLYpTwxkBKFQiSsIjjAIiKKQwvnZAedCnPP6EwpNTc+AYWwCTBgwjE1AnkMYxeRpwukTk/T3b2d4ZJBjJ3IaeUA2OUUvvQwM9tHXs435mTbtvEk7Kai3mkSqSFAwOhgzeaZFmhXkWUauShj1IpEjVSWIA+IgpEgTbr35NvIsJ006JHnBrbccw7kKeZ6RZzAxOc6uXXs425rnYRceYMf27Zw6d5Zb7jrC+HSLy0ZCRnpCznWERhpBHBBoTqFKVI2JwhhyvJaCqAKkiGaEgUNFUIRCA1smMIxNgAkDhrEJiKIIpSDPIc9y7rrtbnbs7kdCJauFuL4aQV9ElibMNRqcGT9Hrb9KoeBcQF50OHbyDPV6QZaDSEQchzgVitzRU6vRV6uRpilZUQAhR09Ocuvdp0EdYegIXUQYRURRjKpjamqaHf097NmxnUdedjHzczPcc+QkjfmMS/pDDo72ollKFFZppHO4KCQKY6I4LE8gOKIwRsMQFwlSCMKCNcWAMIjQIge1mQHD2GhMGDCMTUCSJohAmuWIExqzDc7mbXp7+pGgTbPRJlMlJ0W0Q1wLSHPotFOSdofeagXRClFPP0FR0GoltNKE0MWEktI/tIO9+/Zx8sQZkiwlywqEiMAVJGmGFEKWZ+S5Eld7UQ1o1Fs0w4yL9+9mZmaOe06fYWJimr2Vgj01R5wlRNUqw6Mx0Vibnp5eWokSuBwpUqJAyIOcMIgIg4A8jhBC+noj0iSl1en7jqfqAAAfCUlEQVQgeQ5ZutHFbxhbHhMGDGMTIFKgQBiG5HkOBMxNd4jjnEI6bBvqIY4yVCAIY4ZHdlBvprQ700gQ0ukU7Nu1k1pUwQWOyIUoBYkqgTouu/QRzDcLTp8dR9OMPM8QhUocA94qYl5kbNu2jdHRnezffzEzU6fYNRDSW41JpUq7HbOvto091YJK5BUFuaAP7cyze/sgc+0Oh684zFAUMTk1xXynIC0EF8X0DfQz0FOjv6+fRmueO265mU6upLmSa7aRRW8YBiYMGMamwAWCFoK4gMAFdJIWIkLSgsb8HL3DI7giJE0daT1BJKS/0gs1pd47R4Sjr7efWm8/A329TE6cY2pqghwlrPRQq9QgiFECgiAirkCR5IRRiALtTkIYCMPbh+gd7GP79m0cvnCYSjKDVCpo7jg0so0DUUhrdoLZ2TrjM00yHac6uJPH7r+AsbG7GT96K+HQbg7u2Ufv4AiFi2m0moxPnGJq/Bi33jZFq5kSuQDnhCzNCcJoo4vfMLY8JgwYxibAOQfiUFWyLMU5JYoEJKAVQrvZQcOI3jhg354LmZxpMjc/TZrXSdI6Q/3DHNi/j7uOHqHTmqPVbhHGFUIRNAhotVpUKzWKQqn1xgSuSpAX1OcbNIoMVaj21KjEFfqiPoZck909A1R2XsTU1Cz143exv0doJ/NMNuocP30SFWXf6A7m5mbQ3aM8/IL9jE7O08yVz37hP+jZuxsF0jyhnSRkHSVp5QQSIIEjS1IGenuoVCobXfyGseUxYcAwNgHepG9OnuekaYIAToRcC6o9AUEIREIra3Jm/BhJXmFsZpKx02eQFOYmZzhyx02cnZxndHQ/QVAjcooLFOcCmq0Gg/39oEo7SRna1k8c1jh2ZoI8zxEpqEYRA709DFRbXH7ZPsKgwvhki8aZKUajiE5zGtdbMN5sEPVvgzDkSL0g3D5IZWiEc1NjaO8gnVaLVmeaWlolD2PyPKGv0k89aVOEgnPi9xGEERIIWWG2CQxjozFhwDA2AXGliip00gSnOVmSggQUCC4OyUkpkgR1AWkxTZpWSTtt2q0mLo8Yb6WcqZ/kuU96PCPb9zGfKmfGjlGgRGENiWrUW/MMDI5w+uwJXNBk365BRATVgjAOCKIAlzd4ymOfRNJxHDl6jG2VClGnzbHJo2zfFuLmM/p6+3Ejfdx24iwNV2HIRUyNn0ECx2BvxCMedZg2IadPnvJ50YROWBBFEVFcJYpiQi3IioIgCMmyZKOL3zC2PCYMGMYmoMCRpolX45v7GYI8y8mzhDAG58BJjtOQiqsyODhCJa4wdnqMIs84/KhHc+nBizh105c4fcctPOzyK7niskdwz4mjSKWfMIpIO036+ypU4wrNRod2MyGOIgrN6a1W2D06yPd913PYNTxKu91i775dFK0Zxk6fpdmZZZsOkWQZ460Od959lsrwCGGuTJ05y/ipjDCKOF0LuOvOI+SpUo0j8jQlT8NS62BMoCFRrUooIFmG5orTYqOL3zC2PCYMGMYmIOl0vBCQJmRphnMBmudorhR5hGiOokR5hWpSJU9n6e+pEQVV8rxFNU354qc+zbnpKfpqNVpfuYFDe09w4MBBksDxkY9+iKm5JorXS+AcTE2Nk+UdAidcdGg3L/nOZzLSM0AnTQnjhHZ9itPHbmeuNYn2VTjbaDI9Pc/NZ2cYGt7JZYcuJUAQhSxX0jRjtj5N0umQZx0KSVHJqfRWGRzcTq6OTCGuxlBAJa6QZwVhbhsIDWOjMWHAMDYBWZaSZwXNVhNViCIhDALCWoVCMrKkRVhTOpIy25zi3OkZchE6aZvQKcdOHWXbwHYee+FjCcMANOPUzAyzt97BgZ2jPO1hB7jhrjPcM91BpUEgAe1OgjhhdHsf3/HEK+mLesmKlCxoMjtzkvmpGe45fZowriE9MdPT47RSoVIZJO0k3HbnHfRVqzjxJxT6BwbZvXM3DqU+O8PM/DSNdkItjtG8QMKAOK6ghSIBREGEczl5YbYJDGOjMWHAMDYBIoITEAnBgYgjCByiQhDmFNIAV6PZbtLotOjb1kurndPq1MnVbxQcGR6gtxYiEqIFxCM7cDjGW212xR0efnA7J+dPEQcxrU6HNFf279zBi57/DIaHBogHepnvjHP8nttotadJGin1ZguyjJmpeZrNNhfsu5xosEW7M0NaNGkDc3N1knZGcTKnP4pxQYACB3ePsnN0L500o94q6KRtKjhUlEADkjwlzzOS1PYMGMZGY8KAYWwCQucIemLCOCTppF4wQFEAcUgQsX3oYdx1zx00W3MknQ4ORyUqQCBtJ7SShP7+gKGBAYa39RO5gu3bBhCFI8eOMn7iOHljhk5bKJywc2SYH/yeZ7F9qMrExDnqzWmOn/4GjUaTooiYHpsiyKDRnGNqfpqoWuXuY3cQVSAlgyBgql4nT5UiC2g3m8xFKUmmNBtt5pM22/tn2TUyTG9UhVRo15u4GDq5kmUFRQFqSocMY8MxYcAwNgGCo8iVLGtTqKK5EgWOwAXkGqAUfO3Gr9FOG0RRQJpm1CoDbN+xi764Qq3Sy47h3Qz19lGNI9rNBjOzs5y68y6KMODY6UnuOjVOJazR7tS5+MI9PP9ZT2dkWy/15jkmZ48ydnyCselxdu/YQ1+ljwt2XMTddx3l9Nk76O0fpX9giEIdnfY8RadNlkFRpDjnqPQJA0N9aBHQbmd0sg4nzk1ydqLBLXefZs9gDxft3cnQ8DDtXGi0cvIio3AOF5ptAsPYaEwYMIxNwIIAgEIQBLSTFllaEAUBhClapGwbhqARs3f0Qg7uupCaRPS4gtgpYeCYayakzTqtmYTJuTqnJmY4MTZBJ+mQFBlTzZRma5rLLrqI51z1eGqVjHPnjjI9c4JzsxPUOzmNRk6j1STPMoIk5GGHH85sPsfuoT1UqzUCCcjzDtOzswRxxNzcDPV6m1qth3baoZ210CJjeLCHTpIgBTSbyl2TU4zPz3Dx3t3sGNlOFNRIsoxMwdmeAcPYcEwYMIxNQKNdRxE0zwCvoS/PMlDYVutl/67dDPYPUYu2MTK8i1a7zcT4GPNzDbK0Qyfp0FZlbGKKTp5TqdY4eNlBRvfv5Bu33M3k7DR7dw+ya3Q3jz38cOKww8zcFKfO3kx9vkO9lTE9p4yPt0hbE7STlIHKBK953JUkncPU6xlBqFD4fQ19tT7yIifSgJ6wwnDvABpEdFDyJGG+VWd6fhKKhE7aoNVIaHaE9vEzDI5PsXtXP/0DO8jyKoUdLTSMDceEAcPYBMRxlbxQJIwREUaHtzPYEzPcU6G/f4DathHmmk3mJs8yNXUrcVwhzTNaqszUG8zOzRL39ZBGwp4DB8mShDOnxpifnyfNmlx8wQEO7h9l775DOBGOn7iZyfkzNDpt6k1leiJhZjYhzZW5ekKWpGRzHc6cPsboyC5c2KAxc4pDFz+cVCucGxtncvIcjc4YeZ5wbmqc4eGd1OIaUonp6xlgqH+EydlJOukY1aYw12gyU29R5AXZmZxdibJj5x6yIt7o4jeMLY8JA4axCeir9dHT18/u3bs4dOFFBK6CKwpUlcmZcT71xf8gqY9x8UUHmK83qDca4EIm5ueZnJ5ncGgQLRSNYHzyHMdPjjN+do44yHnUpTu55JKdDO25kCAOueWmrzE+c4JGe5r5pjI116FRLwiKmEpc0FMJOXjgYSSdlFuPnGDvXvj0pz7D/n2jDA3v5JLLDnP5ow4jcQ+33X6EW2+5mZNH7qI+16RvW0RRKNWgQl9vjcFtBzh0YC9Hj5/gyPFjTE/NMtfukAdKNjFDnhSMDm7b6OI3jC2PCQOGsQm4/DFPYFtfD70DvfT09zFzboKTJ+7hs1/+ItONNof27CXD8Y3bjzA736DeSRnYNsDU/DyVWoWOS8nSjOm5GU4cn6DeVAZ7I/aMhMS1jHbu6CQZx+75GifGjjLfnGOu0WJ+rqDdUQLnqFULhvoGufDQxfTXBsGBI2N6eoaLLriAuBZSiFCfm2Wgt4cwqnNoe8glz3wyM+0ncfzMOONjZzk3dgKHN74UhgGhi7lg3wH6evq4/cg9nJscp9VJ0UwRnSITO01gGBuNqOr/a+9ediRLjsQM/2bufk7c8laVdekb2U1SokhpZkGAo500ECBAL6CX0NNpo8VshNFOgmYhoKXRNGfI7maxu6u6bnmLjDjH3c208Kii3iATKP+AQi0qI7JwEki3MDM3u+v/Q9d98P7uv/4X/9PXv2c/vcXLFWrCV98+45vvXvLRowcsNwuur2be3Oy4nW+pXtnvJo7WiXEcUBWmfaHMkRdvLlksAuu05/xxxHcDgz4lx0uyvmGuxtXNzO3OmfYgCg+OTvji4y/47PwpMYCJohoRwHGCtG2KiLBeLTg/f8DJ0Yb96wukVtZHGx48/YTlg8fsLPLi9RUvfnzJ9uo10/6W7X6Hl8Junvn73/+OFz9+T61GkMBRSvznv/nvctc/g677kPXMQNfdA7eXP7I+PuaH333DxcWP7KaZP774kVszfvf996yXC46OjriZdhQqD04GTk4CjlNq4HK7YwyJzckCXRl13hMF9vOOOY88f/l7NmfGuDDm2chFcFfGpJysRn7x05/x+PQREcFM0BhIEjAzshvYjLuhGrnd7vnHy29xEcQKyyQsXzvrZ3/g9OyE5fqI0wcf8dEXT0ibX3E1Zb778Tkvnz/n7dsL/uKXkdUQ+eaPzzCHl7f5rh9/133wejDQdffAf/vbv+GrZy/IZSKmwOVtZnbj9Cyh4lQximaePF2xGcB9z3aCt5czN9sbZFQePDrmJ0/OcVF++OEF1zeFi5sbar1gPHMqznYn5AkMoVbjyckR/+qLX3N2dEoQAKG2v3CMahUwasloUEIQ8IDiFIc3by64uHpLCIHVmDi/uuZ0s2b4+mvWSXn46CmrzTm/ePSEX/7lr6lpxfXVDc9+/Rv+1//5kh++f8aL18/v9Nl3XdeDga67F7789jkvr2/AMylGzIXTBw94fH5OqBPYLU8ePeR2vuVme8nV5Y5KIi4jx0lYrVecnG5YrM65vHhJtcJu2qEeUG17CEyEWpxajQrMs7NJp6zGJbUaotrmHQhABSolZ9CIISxiJBCY3YkxokAaFmhcYW5c3hiShHFzwtYnnl9v+TG/oO6+wXPmdLPg/PwpDx9/zC/PHvEv/v2/IeuC66ne6bPvuq4HA113L7y8vCbEgYfnT/j5z/4Zv/3NX7EcI9+9+Af+4au/Y7Ncoqky2MBcI7JYcroZWYwD7sJquWSRAt89/46Lt9+z228ppaASmecKrtTD+F+zxPYGLt/sWH++ATOqzaSYwMAFVBSzgrjhVtEQcRVcBAfEBTEYlyvSbmLKM3glDgPDYomViA8DN7nAsORquuLZi2v82ZcMfMnRYuD8ZM1HTz/i4ZNPgH931z+Crvug9WCg6+6Bf/vX/4GnH/+C1fqILz59TLArvv/uG16++ic2x0vWwwmKU28uCQab1ZpPHj+i4uQ8Uz3z3csfuXh1SVBQBQ3CNBemCkMamW0ihhGXU364ekV1IajhpZBiQMSpVkCc4m0aokhAVEAdcxBVVJ1aHLPKajFw/vCM7W6H18JmsyDnmTnPlNJeA8pqc8r66JQ8zxSrbK1wcXnDPz7/e1bpd/zr//if7vpH0HUftB4MdN098OTojC+enPDo0We8evkHvn72P7i52jIGZblecbw+4eWL12wvt3zy6DE2JszeNd7NXO8uKWXi6GSDmzHvr5n3FXPFVdlPmaQDv/74Z/xwGfmGN6gCAkkDwSu57HAzlDYS2cVBBFUwh4BQS6a6IRJREUTgZLVkvVyRy0yMgVIKIKgKmFNrpRpoUFQiY4poWJCOTxGH/by9uwffdR3Qg4GuuxduXn7PqyS8+Kf/zdc/fElaL5jzTEqFtIDXu4nvv30DVlnmzKs//sC4DgwPNtSSsZw52mxYr1ZcXVww7ZxprqBCLpXFsOC3n/+K43Hk+dsrzA0BFkNCxPFqiEOKATdD3AkhYEERFRKCm4EofngttHKCm7d/A8wcVWeaZlRbv4K7v/+6WisxRERoY4gNUljc2XPvuq7pwUDX3QP/8/9+yeKrr4hjJus1SGG5DJydLRlros5XzFSCOt++eI7vM3Ec2U97VGG1XHB8dMxCF+Rk/Gl6zmwZMyeGFb/9+a94fPqA25stpRbMHVVlHSEFR4IiHloDYQioajvIDcTAg1NMQQT5/wKCuWQcofrhk78q87xHaGkH84qIIyqICBAQObwnjrvTWhG7rrtLPRjounvA/Zab6Qp1Z7VJLJaJGIR5AitGHCrj2sglcPPqlqOjY2wdUDMkKuvNChFj3hXm2dnvjUwkkfjLn/yCR6tjvGZUAtUMQVBVRAQ3b8OF3CAI7s48Z2J03CANYDUiYlRzzBXzgrtSrBI04CjuTikFdz8c9AZmLXOA4W60EWdKqdYCBnt3e6HrurvUg4Guuwdq3DMsHQ0wpMRqeUItO2JIrFYL5nKFqlCmyiJGxKDuC3ETSUExy1zfTqS6Zru7Yppnogz81c//JZ+enJPdsds95hU/lACC2GHCIIcDvWIIIQRAWsAQFcNBneoOONUyOXvLEADVjIrjQTAzSimIJNyMIUVQJWOgEEQRHPeKmCIuVPrWwq67az0Y6Lp7IARjTJHlZgVFuN3dcrQZWK6WDENkKhBTompmtV5Ta2Z1vKJScLU2nnjOuCuX19eYCZ8//oQvPvoI3+2Y5kyIAfGIxoADUQMptfo9AdwgaMIRJJaW8jcnkcg4uJBzxkyoZri0XgARoVjrOcDtML5Y23t6yzoElVaCMEOVQwDjiEi7rdB13Z3qwUDX3QNzqYQg+O6WIQUW45IQYZCEljVB9uzmHXEMeK082GxYbUZ280QMMO9uCLLmT8+/483VG1bDml99+imjCh4VkQXVjYpys91S8ozGRIwJESj1Xe2/EEQJkrC54LWgy3QoDTi5GlM2CAGzSlAwL1R3grbDP4ZISi1ggJY9wCpuYNXQpGhKQOszoGcGuu7O9WCg6+6Beaq4VbJFTBacDCMxRvLtxMX1NYuzNVAYxUmrSFiMzMUo2RAqkRXKku3+hlrhJ+fnnC6WmBWqASbUmplMmXObJWC14A6Blrqvqmho5QFRsCAgkeJGArIIMY3MNQOt18DFsXc3CUohhogilHKYKiitX0DEiSkhgyJuVHdiFEKAUnow0HV3rQcDXXcPfHL6U97c/MB6uYAgTPMtKgtchWFj4Mpm/YDb3UXbEaBCqQU3Yb+fqHtlt7vA3RnjyM8ePcVrYa6G5YJVZZ8nMglUQHh/oyDGBB7bjQJtDYSiQgzaRhjXgkhbYARKGgZqrtQ8gbUJRzEk8NaYGKTdSkBaTwB6qEEgxBgI1voQ9PC9Wo9C13V3qQcDXXcPPD07B59wvSUkobqSi5OphGJEL2gQ1kdnuGVShHk3Yy6UGVbLUy5uvqWUyum4ZhEG8m5PDC1dv88FjRFFiSp4FVwcd5gtk+KAipJLQUOgVgOc4M5UDQkRR1ANRHdqFHSIhymFrVTQ5hgrVRwExIzsTiK0r1PwWnk3kEgl4BTwHgx03V3rF3y77h5YLxY8ffiUWmdCchJKDIGKYcEQFRZhYAhLhIB7IUZBKizjCevFCcaIYnz28IyYlCLtOl+ZM0EgOASvbFICFwyn1tJuFmjAg6IaEQ8EiQiCCWhMoJFcjHk/Ua1gVkkhkkIgqDKEgKKIOeIwT62PwM2Za6HSri661cNtAjvMKki4+10//q774PXMQNfdB+6MKbFZHUOYQSbMKoMrNitqgd1uS1w6i+ORXG9bA58krDo327eoQNKR080Zqk7U9mk+RKe6MtcCMRFUcAoalOVqgYaIHT7JK45ZIVu7oUAawcHN0aA4BiItwAiBlgQQXCClgNGGEsUQQAPqhisYTgBUW4nAXcAdpx6yEF3X3aWeGei6+0CEqJFHJx8TcGJwoi4IjFgB2285PhqgOlYUZ8BLxMXZ5y1vr19jZWaREhFhLsYwjq1ur6nV9dOC5TAgcUFKI6MmgjqibWBQtUrFKF4hCBqEKM4iRVSMoBCCUquhom0AEUKx1kSowuF2QUVVsFpbb4C38oB5xQXmmg8DicBqbQFG13V3qmcGuu4eSEOAaoguGTgiby8oNuEpklYDvg/MpdXx5/0tHsA9UGwHWqlS0JJZDiuGlNCwJNfCnCulQByElCL7/cxUBJFADBBE8FqRwzRC4M+TCWkNgFYrZg60hj9VIcRImSYIrRdA9JAVQAghYsVxy0gMFLOWVXCnlvZe2TJoBbRtSOy67k71YKDr7oFSMk4h6chKT3gpr0lkooHvnanCdANRIO92xOVASpFaDHFlE1e8lZkURmJKjEOgVCcOkXEMpAS1Vqa4INdXiNjhU35A1BG390GBSmsUVFOsbTIGDmOMFaSC0DYZ4sYQA2lIhKC4g7uigVYisDaYSAW0dRoSNbZFR+7tvb1nBrrurvVgoOvuAUWpNeKmpBQhzWTxtg+gJupcmMuWcUgQ4fbiDadnDwgMlFrZTTvm2rYTprRAQyKgkAqCgzn6bu/ANAPCOCaGIaFKuzoo5bCrgMOGQqhVQEvrTQitBACHSYKhZQFCCKgeXoMSVJlrbTcI3t02qJWQYgsA3FqE4YrJ+2ij67o71IOBrrsHYlI8FMQzuDKGBZkJ0Yi4k7WyfjDy8OQhl7dbbDZ2eQs1kOIKn/cMMbJMiXFYQAiIBlQCVowQnArkMpPnGTPjbLM+dPYrpU4cVg+8nzXgCMVrayyUdri/z+hLmzgYgxO0jRY2M8wKqkat/uev9VbeqKW+L0G8f41bv03QdfdAbyDsuntgeBpxHBTWn6+Iy4CbY9JmDZw/Oefhg2OKVojKcjEypDVuiWl/i5XSFga5k8uElUJLCNTDn1bz382V7TTjZjw4XkEQQorEOBLD+H4egATFRZAAxaBYqw2oRt6NGI6qBCCFSJC2uVBDwqytK25DhdoVwnma21VCeRdoeNtj4P6+V6HrurvTMwNddw8Mn81sHkZ8H9EzCD8q9bJiuRLHkZgUqUYMCZVbjJl9hqlWyn5mnjMpLSgVqjkqrZPfraX0qygBJ5fM7XyLiLM5WlAc5rkd1MFbXd+qIeHPq45TSodeACi1HeApRoKAubWJhigKxBCYrF0XNCu4GCJtCyLw/taBO21Lov15nHHXdXenZwa67h54fe1Ma9DPI29ub8kziBdcHXfndt7x6uYVV9u3zNM1OQu1Kmp2qN8nqrVAwJFDE1/EUMwFDsuKRYSSK3hhuVxgtIM6aKvtO9ZmAbgf0vcBEaG6YS7EEAmqRAm4KAXF3FBp44vBSao4TjlcHxQRFssFSGi/cNxwhDaguN2O6LrubvXMQNfdA9Piit3FxKvnA1fTDvMJUT0017XtgUjBbCKEdhCPksjV8FGpFeZsuECM7QCPQyRPM45jAkEjqCICyxRYDQkOEwGtGoFAixsiGgKRSo2hZQoMRBS8DR+qh90D736BuFVElFLbUCJVpeaWTkjpsKbYpGUEzNteBSkkVbSvMO66O9czA113D1xc31B0Iq2UiT1TccSXeA2oCyqVEADLWFHGuGS1XqIx4Djr5ZqoA265bSMUYZ5nzGqr38eIhDaroFrhaL3kaHXUDnoRjEBxYc5GMSeXiobAIigDoNI+9eMQo+Kh9Q3IoevwcMsQq0499AGMQ2IxjgjtimEue3KZDkuPDDOjVmOeyl0++q7r6JmBrrsXZDUS1ivm6x2b1TGX9Ra1GUyJMVJ9RiUQNeG+JJYBQUkhwSjkuTLNW6q9GxZEmySoQpKhjQymAsZyWPDFTz5hc3RMKXvcDdWIqxJEMdqBb+bUqVBrAY0EDRRvB3gMEbTtHfBquChBpAUCCirGkBQrrYwRYmoZhTnjtRJCwj1CAE77oqKuu2s9GOi6e+Do6JSn55/ycrxGb67Zn1xhlzuKO8EMqYYiqCewiBtUg6dnTxnGBX949g0PH64J4rgqiBMBCwGvDhVMnc8eH/PXv/nn/PTpA5KWdo0wOx4qqlApVFdqbp/yVZVxXFBqyxhocNQD5m13QVqM5FzI04QmsDITJB62JCui2sYcl4KbEVWppTLnW0KIeI0MfWth1925Hgx03T2wiWB14ux0xWXcsng0ML9wyq3hNTPEyJgSpRSc2j7pl0KZFZNCUKHOUGmriXNtvQBSoRIIh5GBiwB/8fOnRA1A2zdgQ8BrRa3iCClGIFKlDR7KJYNERAwxaeUBaSuIJQZiiCwGp9bKIJGcMxIUqRUNA8REra1U0EYaQ86FUgvDckF6enKnz77ruh4MdN29cLPPeNphGFIGwvKMMLyC7UwMSoyt0W5vexylVmEVlmzWZ2St5DpjXkASU3GiCTFFIB/S+gG07SQwMxzHVTBq6/g/HNKqiksrEWAFHQT38H44kR+WC6XUth8WM2rJiAhyyCKEENlut6gGYjCqOTEO5LmQbca9QhVCSNgYWD05uuvH33UfPOnTv7qu67ruw9ZvE3Rd13XdB64HA13XdV33gevBQNd1Xdd94How0HVd13UfuB4MdF3Xdd0HrgcDXdd1XfeB+3+Kw6ane4zw8QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Immagine  2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Immagine  3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Immagine  4\n"
          ]
        }
      ]
    }
  ]
}