{
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   "source": [
    "## Assignment: Image recognition\n",
    "- Alumno 1:\n",
    "- Alumno 2:\n",
    "- Alumno 3:\n",
    "\n",
    "The goals of the assignment are:\n",
    "* Develop proficiency in using Tensorflow/Keras for training Neural Nets (NNs).\n",
    "* Put into practice the acquired knowledge to optimize the parameters and architecture of a feedforward Neural Net (ffNN), in the context of an image recognition problem.\n",
    "* Put into practice NNs specially conceived for analysing images. Design and optimize the parameters of a Convolutional Neural Net (CNN) to deal with previous task.\n",
    "* Train popular architectures from scratch (e.g., GoogLeNet, VGG, ResNet, ...), and compare the results with the ones provided by their pre-trained versions using transfer learning.\n",
    "\n",
    "Follow the link below to download the classification data set  \"xview_recognition\": [https://drive.upm.es/s/2DDPE2zHw5dbM3G](https://drive.upm.es/s/2DDPE2zHw5dbM3G)\n",
    "\n",
    "---\n",
    "\n",
    "## Custom CNN Architectures\n",
    "\n",
    "This notebook implements **3 custom manual CNN architectures** for the xView satellite image classification task:\n",
    "1. **ResNet-style** - Residual blocks with skip connections\n",
    "2. **VGG-style** - Sequential convolutions with increasing depth\n",
    "3. **Inception-style** - Parallel multi-scale convolutions\n",
    "\n",
    "All models are trained with the same protocol (85/15 split, same augmentation, same callbacks) for fair comparison."
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUCCÈS : Fichier trouvé à : ./xview_recognition/xview_ann_train.json\n",
      "Base de données chargée avec succès !\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "import requests\n",
    "import zipfile\n",
    "\n",
    "url = 'https://drive.upm.es/s/2DDPE2zHw5dbM3G/download'\n",
    "zip_name = 'dataset.zip'\n",
    "\n",
    "r = requests.get(url, stream=True)\n",
    "with open(zip_name, 'wb') as f:\n",
    "    for chunk in r.iter_content(chunk_size=1024):\n",
    "        f.write(chunk)\n",
    "\n",
    "if os.path.getsize(zip_name) < 10000:\n",
    "    print(f\"ERREUR : Le fichier {zip_name} est trop petit. Le lien est invalide ou nécessite une connexion.\")\n",
    "else:\n",
    "    with zipfile.ZipFile(zip_name, 'r') as z:\n",
    "        z.extractall(\".\")\n",
    "\n",
    "    target_file = 'xview_ann_train.json'\n",
    "    found_path = None\n",
    "\n",
    "    for root, dirs, files in os.walk(\".\"):\n",
    "        if target_file in files:\n",
    "            found_path = os.path.join(root, target_file)\n",
    "            break\n",
    "\n",
    "    if found_path:\n",
    "        print(f\"SUCCÈS : Fichier trouvé à : {found_path}\")\n",
    "\n",
    "        import json\n",
    "        json_file = found_path\n",
    "\n",
    "        with open(json_file) as ifs:\n",
    "            json_data = json.load(ifs)\n",
    "        print(\"Base de données chargée avec succès !\")\n",
    "\n",
    "    else:\n",
    "        print(f\"ERREUR : {target_file} reste introuvable après extraction.\")"
   ]
  },
  {
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-04-13 11:34:53.754884: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1776080094.021035      22 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1776080094.097513      22 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "W0000 00:00:1776080094.678359      22 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1776080094.678401      22 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1776080094.678404      22 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1776080094.678406      22 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU activé : 2 processeur(s) détecté(s)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "\n",
    "if gpus:\n",
    "    try:\n",
    "        for gpu in gpus:\n",
    "            tf.config.experimental.set_memory_growth(gpu, True)\n",
    "        print(f\"GPU activé : {len(gpus)} processeur(s) détecté(s)\")\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "else:\n",
    "    print(\"GPU non détecté. Activez l'accélérateur dans les réglages du notebook.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48bad878",
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   "source": [
    "import uuid\n",
    "import numpy as np\n",
    "\n",
    "class GenericObject:\n",
    "    \"\"\"\n",
    "    Generic object data.\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self.id = uuid.uuid4()\n",
    "        self.bb = (-1, -1, -1, -1)\n",
    "        self.category= -1\n",
    "        self.score = -1\n",
    "\n",
    "class GenericImage:\n",
    "    \"\"\"\n",
    "    Generic image data.\n",
    "    \"\"\"\n",
    "    def __init__(self, filename):\n",
    "        self.filename = filename\n",
    "        self.tile = np.array([-1, -1, -1, -1])  # (pt_x, pt_y, pt_x+width, pt_y+height)\n",
    "        self.objects = list([])\n",
    "\n",
    "    def add_object(self, obj: GenericObject):\n",
    "        self.objects.append(obj)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "15c8d3f2",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "categories = {0: 'Cargo plane', 1: 'Small car', 2: 'Bus', 3: 'Truck', 4: 'Motorboat', 5: 'Fishing vessel', 6: 'Dump truck', 7: 'Excavator', 8: 'Building', 9: 'Helipad', 10: 'Storage tank', 11: 'Shipping container', 12: 'Pylon'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4024ddcf",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: rasterio in /usr/local/lib/python3.12/dist-packages (1.5.0)\r\n",
      "Requirement already satisfied: affine in /usr/local/lib/python3.12/dist-packages (from rasterio) (2.4.0)\r\n",
      "Requirement already satisfied: attrs in /usr/local/lib/python3.12/dist-packages (from rasterio) (25.4.0)\r\n",
      "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from rasterio) (2026.1.4)\r\n",
      "Requirement already satisfied: click!=8.2.*,>=4.0 in /usr/local/lib/python3.12/dist-packages (from rasterio) (8.3.1)\r\n",
      "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.12/dist-packages (from rasterio) (0.7.2)\r\n",
      "Requirement already satisfied: numpy>=2 in /usr/local/lib/python3.12/dist-packages (from rasterio) (2.0.2)\r\n",
      "Requirement already satisfied: pyparsing in /usr/local/lib/python3.12/dist-packages (from rasterio) (3.3.2)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install rasterio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "28a23689",
   "metadata": {
    "execution": {
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   "source": [
    "import warnings\n",
    "import rasterio\n",
    "import numpy as np\n",
    "\n",
    "def load_geoimage(filename):\n",
    "    warnings.filterwarnings('ignore', category=rasterio.errors.NotGeoreferencedWarning)\n",
    "    src_raster = rasterio.open('./xview_recognition/'+filename, 'r')\n",
    "    # RasterIO to OpenCV (see inconsistencies between libjpeg and libjpeg-turbo)\n",
    "    input_type = src_raster.profile['dtype']\n",
    "    input_channels = src_raster.count\n",
    "    img = np.zeros((src_raster.height, src_raster.width, src_raster.count), dtype=input_type)\n",
    "    for band in range(input_channels):\n",
    "        img[:, :, band] = src_raster.read(band+1)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97883932",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "#### Training\n",
    "Design and train a CNN to deal with the \"xview_recognition\" classification task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b71ed071",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# Load database\n",
    "json_file = './xview_recognition/xview_ann_train.json'\n",
    "with open(json_file) as ifs:\n",
    "    json_data = json.load(ifs)\n",
    "ifs.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80916412",
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Cargo plane': 635, 'Small car': 3324, 'Bus': 1768, 'Truck': 2210, 'Motorboat': 1069, 'Fishing vessel': 706, 'Dump truck': 1236, 'Excavator': 789, 'Building': 3594, 'Helipad': 111, 'Storage tank': 1469, 'Shipping container': 1523, 'Pylon': 312}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "counts = dict.fromkeys(categories.values(), 0)\n",
    "anns = []\n",
    "for json_img, json_ann in zip(json_data['images'].values(), json_data['annotations'].values()):\n",
    "    image = GenericImage(json_img['filename'])\n",
    "    image.tile = np.array([0, 0, json_img['width'], json_img['height']])\n",
    "    obj = GenericObject()\n",
    "    obj.bb = (int(json_ann['bbox'][0]), int(json_ann['bbox'][1]), int(json_ann['bbox'][2]), int(json_ann['bbox'][3]))\n",
    "    obj.category = json_ann['category_id']\n",
    "    # Resampling strategy to reduce training time\n",
    "    counts[obj.category] += 1\n",
    "    image.add_object(obj)\n",
    "    anns.append(image)\n",
    "print(counts)\n",
    "labels = [img.objects[0].category for img in anns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "268889bd",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "IMG_SIZE = 128\n",
    "BATCH_SIZE = 64\n",
    "EPOCHS = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "062f6672",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training images: 15934\n",
      "Number of validation images: 2812\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "anns_train, anns_valid = train_test_split(anns, test_size=0.15, random_state=42, shuffle=True, stratify=labels)\n",
    "print('Number of training images: ' + str(len(anns_train)))\n",
    "print('Number of validation images: ' + str(len(anns_valid)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0125489",
   "metadata": {
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    "tags": []
   },
   "source": [
    "---\n",
    "## CNN Architectures Definition\n",
    "\n",
    "### Architecture 1: ResNet-style CNN\n",
    "This architecture uses **residual blocks with skip connections** to enable training of deeper networks by mitigating the vanishing gradient problem.\n",
    "\n",
    "### Architecture 2: VGG-style CNN\n",
    "This architecture follows the VGG design philosophy of using **small 3x3 convolutional filters** stacked in increasing depth.\n",
    "\n",
    "### Architecture 3: Inception-style CNN\n",
    "This architecture uses **parallel branches with different kernel sizes** to capture multi-scale features."
   ]
  },
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating models...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1776080129.910622      22 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13757 MB memory:  -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\n",
      "I0000 00:00:1776080129.916889      22 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13757 MB memory:  -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet-style parameters: 11,320,205\n",
      "VGG-style parameters: 15,128,909\n",
      "Inception-style parameters: 4,529,325\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import (\n",
    "    Input, Conv2D, BatchNormalization, Activation, MaxPooling2D,\n",
    "    GlobalAveragePooling2D, Dense, Dropout, Add, Concatenate\n",
    ")\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam, Nadam\n",
    "from tensorflow.keras.losses import CategoricalFocalCrossentropy, CategoricalCrossentropy\n",
    "\n",
    "# =============================================================================\n",
    "# Architecture 1: ResNet-style\n",
    "# =============================================================================\n",
    "\n",
    "def residual_block(x, filters, stride=1):\n",
    "    \"\"\"Residual block with pre-activation (BN-ReLU-Conv).\"\"\"\n",
    "    shortcut = x\n",
    "    \n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('swish')(x)\n",
    "    x = Conv2D(filters, (3, 3), strides=stride, padding='same', kernel_initializer='he_normal')(x)\n",
    "    \n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('swish')(x)\n",
    "    x = Conv2D(filters, (3, 3), padding='same', kernel_initializer='he_normal')(x)\n",
    "    \n",
    "    if stride != 1 or shortcut.shape[-1] != filters:\n",
    "        shortcut = Conv2D(filters, (1, 1), strides=stride, padding='same')(shortcut)\n",
    "    \n",
    "    return Add()([x, shortcut])\n",
    "\n",
    "def create_resnet_style(input_shape=(IMG_SIZE, IMG_SIZE, 3), num_classes=13):\n",
    "    \"\"\"ResNet-style architecture with 4 residual stages.\"\"\"\n",
    "    inputs = Input(shape=input_shape)\n",
    "    \n",
    "    # Stem\n",
    "    x = Conv2D(64, (7, 7), strides=2, padding='same', kernel_initializer='he_normal')(inputs)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('swish')(x)\n",
    "    x = MaxPooling2D((3, 3), strides=2, padding='same')(x)\n",
    "    \n",
    "    # Residual stages\n",
    "    x = residual_block(x, 64)\n",
    "    x = residual_block(x, 64)\n",
    "    \n",
    "    x = residual_block(x, 128, stride=2)\n",
    "    x = residual_block(x, 128)\n",
    "    \n",
    "    x = residual_block(x, 256, stride=2)\n",
    "    x = residual_block(x, 256)\n",
    "    \n",
    "    x = residual_block(x, 512, stride=2)\n",
    "    x = residual_block(x, 512)\n",
    "    \n",
    "    # Head\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Dense(256, activation='swish', kernel_initializer='he_normal')(x)\n",
    "    x = Dropout(0.5)(x)\n",
    "    outputs = Dense(num_classes, activation='softmax')(x)\n",
    "    \n",
    "    model = Model(inputs, outputs, name='ResNet_Style')\n",
    "    model.compile(\n",
    "        optimizer=Nadam(learning_rate=1e-3),\n",
    "        loss=CategoricalFocalCrossentropy(gamma=2.0),\n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# =============================================================================\n",
    "# Architecture 2: VGG-style\n",
    "# =============================================================================\n",
    "\n",
    "def conv_block_vgg(x, filters, num_convs):\n",
    "    \"\"\"VGG-style block: multiple 3x3 convolutions followed by max pooling.\"\"\"\n",
    "    for _ in range(num_convs):\n",
    "        x = Conv2D(filters, (3, 3), padding='same', kernel_initializer='he_normal')(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = Activation('relu')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=2)(x)\n",
    "    return x\n",
    "\n",
    "def create_vgg_style(input_shape=(IMG_SIZE, IMG_SIZE, 3), num_classes=13):\n",
    "    \"\"\"VGG-style architecture with 5 convolutional blocks.\"\"\"\n",
    "    inputs = Input(shape=input_shape)\n",
    "    \n",
    "    # Block 1: 64 filters, 2 convs\n",
    "    x = conv_block_vgg(inputs, 64, 2)\n",
    "    \n",
    "    # Block 2: 128 filters, 2 convs\n",
    "    x = conv_block_vgg(x, 128, 2)\n",
    "    \n",
    "    # Block 3: 256 filters, 3 convs\n",
    "    x = conv_block_vgg(x, 256, 3)\n",
    "    \n",
    "    # Block 4: 512 filters, 3 convs\n",
    "    x = conv_block_vgg(x, 512, 3)\n",
    "    \n",
    "    # Block 5: 512 filters, 3 convs (no pooling, use GAP)\n",
    "    for _ in range(3):\n",
    "        x = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal')(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = Activation('relu')(x)\n",
    "    \n",
    "    # Replace FC layers with GAP (reduces parameters significantly)\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Dense(512, activation='relu', kernel_initializer='he_normal')(x)\n",
    "    x = Dropout(0.5)(x)\n",
    "    x = Dense(256, activation='relu', kernel_initializer='he_normal')(x)\n",
    "    x = Dropout(0.5)(x)\n",
    "    outputs = Dense(num_classes, activation='softmax')(x)\n",
    "    \n",
    "    model = Model(inputs, outputs, name='VGG_Style')\n",
    "    model.compile(\n",
    "        optimizer=Adam(learning_rate=1e-3),\n",
    "        loss=CategoricalCrossentropy(label_smoothing=0.1),\n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# =============================================================================\n",
    "# Architecture 3: Inception-style\n",
    "# =============================================================================\n",
    "\n",
    "def inception_module(x, f1, f3_reduce, f3, f5_reduce, f5, pool_proj):\n",
    "    \"\"\"\n",
    "    Inception module with parallel branches:\n",
    "    - 1x1 conv\n",
    "    - 1x1 conv -> 3x3 conv\n",
    "    - 1x1 conv -> 5x5 conv (using 2x 3x3 for efficiency)\n",
    "    - 3x3 max pool -> 1x1 conv\n",
    "    \"\"\"\n",
    "    # Branch 1: 1x1 conv\n",
    "    branch1 = Conv2D(f1, (1, 1), padding='same', activation='relu')(x)\n",
    "    \n",
    "    # Branch 2: 1x1 -> 3x3\n",
    "    branch2 = Conv2D(f3_reduce, (1, 1), padding='same', activation='relu')(x)\n",
    "    branch2 = Conv2D(f3, (3, 3), padding='same', activation='relu')(branch2)\n",
    "    \n",
    "    # Branch 3: 1x1 -> 3x3 -> 3x3 (approximates 5x5 receptive field)\n",
    "    branch3 = Conv2D(f5_reduce, (1, 1), padding='same', activation='relu')(x)\n",
    "    branch3 = Conv2D(f5, (3, 3), padding='same', activation='relu')(branch3)\n",
    "    branch3 = Conv2D(f5, (3, 3), padding='same', activation='relu')(branch3)\n",
    "    \n",
    "    # Branch 4: MaxPool -> 1x1\n",
    "    branch4 = MaxPooling2D((3, 3), strides=1, padding='same')(x)\n",
    "    branch4 = Conv2D(pool_proj, (1, 1), padding='same', activation='relu')(branch4)\n",
    "    \n",
    "    return Concatenate()([branch1, branch2, branch3, branch4])\n",
    "\n",
    "def create_inception_style(input_shape=(IMG_SIZE, IMG_SIZE, 3), num_classes=13):\n",
    "    \"\"\"Inception-style architecture with multi-scale feature extraction.\"\"\"\n",
    "    inputs = Input(shape=input_shape)\n",
    "    \n",
    "    # Stem\n",
    "    x = Conv2D(64, (7, 7), strides=2, padding='same', activation='relu')(inputs)\n",
    "    x = MaxPooling2D((3, 3), strides=2, padding='same')(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    \n",
    "    x = Conv2D(64, (1, 1), padding='same', activation='relu')(x)\n",
    "    x = Conv2D(192, (3, 3), padding='same', activation='relu')(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = MaxPooling2D((3, 3), strides=2, padding='same')(x)\n",
    "    \n",
    "    # Inception modules\n",
    "    x = inception_module(x, 64, 96, 128, 16, 32, 32)   # 3a: 256 filters out\n",
    "    x = inception_module(x, 128, 128, 192, 32, 96, 64) # 3b: 480 filters out\n",
    "    x = MaxPooling2D((3, 3), strides=2, padding='same')(x)\n",
    "    \n",
    "    x = inception_module(x, 192, 96, 208, 16, 48, 64)  # 4a: 512 filters out\n",
    "    x = inception_module(x, 160, 112, 224, 24, 64, 64) # 4b: 512 filters out\n",
    "    x = inception_module(x, 128, 128, 256, 24, 64, 64) # 4c: 512 filters out\n",
    "    x = MaxPooling2D((3, 3), strides=2, padding='same')(x)\n",
    "    \n",
    "    x = inception_module(x, 256, 160, 320, 32, 128, 128) # 5a: 832 filters out\n",
    "    x = inception_module(x, 384, 192, 384, 48, 128, 128) # 5b: 1024 filters out\n",
    "    \n",
    "    # Head\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Dropout(0.4)(x)\n",
    "    outputs = Dense(num_classes, activation='softmax')(x)\n",
    "    \n",
    "    model = Model(inputs, outputs, name='Inception_Style')\n",
    "    model.compile(\n",
    "        optimizer=Adam(learning_rate=1e-3),\n",
    "        loss=CategoricalCrossentropy(label_smoothing=0.1),\n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "    return model\n",
    "\n",
    "# Print model summaries\n",
    "print(\"Creating models...\")\n",
    "model_resnet = create_resnet_style()\n",
    "print(f\"ResNet-style parameters: {model_resnet.count_params():,}\")\n",
    "\n",
    "model_vgg = create_vgg_style()\n",
    "print(f\"VGG-style parameters: {model_vgg.count_params():,}\")\n",
    "\n",
    "model_inception = create_inception_style()\n",
    "print(f\"Inception-style parameters: {model_inception.count_params():,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb8b3462",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T11:35:32.232045Z",
     "iopub.status.busy": "2026-04-13T11:35:32.231146Z",
     "iopub.status.idle": "2026-04-13T11:35:32.238310Z",
     "shell.execute_reply": "2026-04-13T11:35:32.237761Z"
    },
    "papermill": {
     "duration": 0.01358,
     "end_time": "2026-04-13T11:35:32.239643+00:00",
     "exception": false,
     "start_time": "2026-04-13T11:35:32.226063+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import TerminateOnNaN, EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
    "\n",
    "def get_callbacks(model_name):\n",
    "    \"\"\"Create callbacks for each model.\"\"\"\n",
    "    return [\n",
    "        ModelCheckpoint(f'{model_name}_best.keras', monitor='val_accuracy', verbose=1, save_best_only=True),\n",
    "        EarlyStopping('val_accuracy', patience=15, verbose=1, restore_best_weights=True),\n",
    "        ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-6, verbose=1),\n",
    "        TerminateOnNaN()\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96c2bf50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T11:35:32.249609Z",
     "iopub.status.busy": "2026-04-13T11:35:32.249189Z",
     "iopub.status.idle": "2026-04-13T11:35:32.257393Z",
     "shell.execute_reply": "2026-04-13T11:35:32.256833Z"
    },
    "papermill": {
     "duration": 0.014904,
     "end_time": "2026-04-13T11:35:32.258717+00:00",
     "exception": false,
     "start_time": "2026-04-13T11:35:32.243813+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "def generator_images(objs, batch_size, do_shuffle=False, augment=False, class_weights=None):\n",
    "    \"\"\"\n",
    "    Generator that yields (images, labels) or (images, labels, sample_weights).\n",
    "    \n",
    "    Args:\n",
    "        objs: list of (filename, object) tuples\n",
    "        batch_size: number of samples per batch\n",
    "        do_shuffle: whether to shuffle data each epoch\n",
    "        augment: whether to apply data augmentation\n",
    "        class_weights: dict mapping class index to weight (optional)\n",
    "    \"\"\"\n",
    "    num_classes = len(categories)\n",
    "    cat_list = list(categories.values())\n",
    "    \n",
    "    while True:\n",
    "        if do_shuffle:\n",
    "            np.random.shuffle(objs)\n",
    "        \n",
    "        for start in range(0, len(objs), batch_size):\n",
    "            group = objs[start:start + batch_size]\n",
    "            images, labels_batch, weights_batch = [], [], []\n",
    "            \n",
    "            for filename, obj in group:\n",
    "                img = load_geoimage(filename)\n",
    "                t = tf.image.convert_image_dtype(tf.convert_to_tensor(img), tf.float32)\n",
    "                t = tf.image.resize(t, [IMG_SIZE, IMG_SIZE], method='bilinear')\n",
    "                \n",
    "                if augment:\n",
    "                    t = tf.image.random_flip_left_right(t)\n",
    "                    t = tf.image.random_flip_up_down(t)\n",
    "                    t = tf.image.random_brightness(t, 0.1)\n",
    "                    t = tf.image.random_contrast(t, 0.9, 1.1)\n",
    "                    t = tf.clip_by_value(t, 0.0, 1.0)\n",
    "                \n",
    "                images.append(t.numpy())\n",
    "                idx = cat_list.index(obj.category)\n",
    "                labels_batch.append(tf.keras.utils.to_categorical(idx, num_classes))\n",
    "                \n",
    "                # Add sample weight based on class\n",
    "                if class_weights is not None:\n",
    "                    weights_batch.append(class_weights[idx])\n",
    "            \n",
    "            images = np.array(images, dtype=np.float32)\n",
    "            labels = np.array(labels_batch, dtype=np.float32)\n",
    "            \n",
    "            if class_weights is not None:\n",
    "                sample_weights = np.array(weights_batch, dtype=np.float32)\n",
    "                yield images, labels, sample_weights\n",
    "            else:\n",
    "                yield images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c752cd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T11:35:32.268013Z",
     "iopub.status.busy": "2026-04-13T11:35:32.267491Z",
     "iopub.status.idle": "2026-04-13T11:35:32.307097Z",
     "shell.execute_reply": "2026-04-13T11:35:32.306172Z"
    },
    "papermill": {
     "duration": 0.04579,
     "end_time": "2026-04-13T11:35:32.308566+00:00",
     "exception": false,
     "start_time": "2026-04-13T11:35:32.262776+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calcul des poids de classes...\n",
      "Class weights computed for 13 classes\n",
      "Training steps per epoch: 249\n",
      "Validation steps per epoch: 44\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "objs_train = [(ann.filename, obj) for ann in anns_train for obj in ann.objects]\n",
    "objs_valid = [(ann.filename, obj) for ann in anns_valid for obj in ann.objects]\n",
    "\n",
    "print('Calcul des poids de classes...')\n",
    "y_train_indices = [list(categories.values()).index(obj.category) for _, obj in objs_train]\n",
    "weights = compute_class_weight('balanced', classes=np.unique(y_train_indices), y=y_train_indices)\n",
    "class_weights = dict(enumerate(weights))\n",
    "\n",
    "print(f\"Class weights computed for {len(class_weights)} classes\")\n",
    "\n",
    "train_steps = math.ceil(len(objs_train) / BATCH_SIZE)\n",
    "valid_steps = math.ceil(len(objs_valid) / BATCH_SIZE)\n",
    "\n",
    "print(f\"Training steps per epoch: {train_steps}\")\n",
    "print(f\"Validation steps per epoch: {valid_steps}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "af13cc0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T11:35:32.318266Z",
     "iopub.status.busy": "2026-04-13T11:35:32.317997Z",
     "iopub.status.idle": "2026-04-13T11:35:33.106938Z",
     "shell.execute_reply": "2026-04-13T11:35:33.106055Z"
    },
    "papermill": {
     "duration": 0.795593,
     "end_time": "2026-04-13T11:35:33.108471+00:00",
     "exception": false,
     "start_time": "2026-04-13T11:35:32.312878+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models créés: ['ResNet_Style', 'VGG_Style', 'Inception_Style']\n"
     ]
    }
   ],
   "source": [
    "# Create model dictionary\n",
    "models_dict = {\n",
    "    'ResNet_Style': create_resnet_style(),\n",
    "    'VGG_Style': create_vgg_style(),\n",
    "    'Inception_Style': create_inception_style()\n",
    "}\n",
    "\n",
    "print(f\"Models créés: {list(models_dict.keys())}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1c448148",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T11:35:33.118773Z",
     "iopub.status.busy": "2026-04-13T11:35:33.118530Z",
     "iopub.status.idle": "2026-04-13T17:06:29.561607Z",
     "shell.execute_reply": "2026-04-13T17:06:29.560709Z"
    },
    "papermill": {
     "duration": 19857.087661,
     "end_time": "2026-04-13T17:06:30.200820+00:00",
     "exception": false,
     "start_time": "2026-04-13T11:35:33.113159+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entraînement des modèles\n",
      "\n",
      "============================================================\n",
      "Training ResNet_Style\n",
      "============================================================\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1776080148.671156      76 service.cc:152] XLA service 0x7816500053d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1776080148.671214      76 service.cc:160]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n",
      "I0000 00:00:1776080148.671221      76 service.cc:160]   StreamExecutor device (1): Tesla T4, Compute Capability 7.5\n",
      "I0000 00:00:1776080150.649242      76 cuda_dnn.cc:529] Loaded cuDNN version 91002\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m  1/249\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:00:04\u001b[0m 29s/step - accuracy: 0.0469 - loss: 0.7041"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1776080163.918399      76 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 526ms/step - accuracy: 0.1249 - loss: 0.7195\n",
      "Epoch 1: val_accuracy improved from -inf to 0.13940, saving model to ResNet_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m177s\u001b[0m 597ms/step - accuracy: 0.1249 - loss: 0.7192 - val_accuracy: 0.1394 - val_loss: 0.5642 - learning_rate: 0.0010\n",
      "Epoch 2/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 466ms/step - accuracy: 0.1015 - loss: 0.6151\n",
      "Epoch 2: val_accuracy improved from 0.13940 to 0.18670, saving model to ResNet_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 517ms/step - accuracy: 0.1017 - loss: 0.6150 - val_accuracy: 0.1867 - val_loss: 0.5545 - learning_rate: 0.0010\n",
      "Epoch 3/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 459ms/step - accuracy: 0.1776 - loss: 0.6171\n",
      "Epoch 3: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m127s\u001b[0m 511ms/step - accuracy: 0.1775 - loss: 0.6170 - val_accuracy: 0.1775 - val_loss: 0.5431 - learning_rate: 0.0010\n",
      "Epoch 4/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 454ms/step - accuracy: 0.1521 - loss: 0.5485\n",
      "Epoch 4: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 514ms/step - accuracy: 0.1520 - loss: 0.5485 - val_accuracy: 0.0814 - val_loss: 0.5445 - learning_rate: 0.0010\n",
      "Epoch 5/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 440ms/step - accuracy: 0.1232 - loss: 0.5424\n",
      "Epoch 5: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 503ms/step - accuracy: 0.1230 - loss: 0.5424 - val_accuracy: 0.0060 - val_loss: 0.5455 - learning_rate: 0.0010\n",
      "Epoch 6/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 435ms/step - accuracy: 0.0488 - loss: 0.5484\n",
      "Epoch 6: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 499ms/step - accuracy: 0.0487 - loss: 0.5484 - val_accuracy: 0.0167 - val_loss: 0.5456 - learning_rate: 0.0010\n",
      "Epoch 7/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 434ms/step - accuracy: 0.0407 - loss: 0.5496\n",
      "Epoch 7: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 498ms/step - accuracy: 0.0406 - loss: 0.5496 - val_accuracy: 0.0167 - val_loss: 0.5461 - learning_rate: 0.0010\n",
      "Epoch 8/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 445ms/step - accuracy: 0.0343 - loss: 0.5420\n",
      "Epoch 8: val_accuracy did not improve from 0.18670\n",
      "\n",
      "Epoch 8: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m127s\u001b[0m 510ms/step - accuracy: 0.0344 - loss: 0.5420 - val_accuracy: 0.0060 - val_loss: 0.5463 - learning_rate: 0.0010\n",
      "Epoch 9/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 465ms/step - accuracy: 0.0459 - loss: 0.5407\n",
      "Epoch 9: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m132s\u001b[0m 533ms/step - accuracy: 0.0458 - loss: 0.5407 - val_accuracy: 0.0167 - val_loss: 0.5463 - learning_rate: 5.0000e-04\n",
      "Epoch 10/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 464ms/step - accuracy: 0.0356 - loss: 0.5420\n",
      "Epoch 10: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m132s\u001b[0m 532ms/step - accuracy: 0.0357 - loss: 0.5420 - val_accuracy: 0.0167 - val_loss: 0.5464 - learning_rate: 5.0000e-04\n",
      "Epoch 11/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 463ms/step - accuracy: 0.0407 - loss: 0.5434\n",
      "Epoch 11: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m131s\u001b[0m 530ms/step - accuracy: 0.0407 - loss: 0.5435 - val_accuracy: 0.0060 - val_loss: 0.5463 - learning_rate: 5.0000e-04\n",
      "Epoch 12/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 457ms/step - accuracy: 0.0071 - loss: 0.5565\n",
      "Epoch 12: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m130s\u001b[0m 524ms/step - accuracy: 0.0071 - loss: 0.5565 - val_accuracy: 0.0060 - val_loss: 0.5464 - learning_rate: 5.0000e-04\n",
      "Epoch 13/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 457ms/step - accuracy: 0.0235 - loss: 0.5495\n",
      "Epoch 13: val_accuracy did not improve from 0.18670\n",
      "\n",
      "Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m130s\u001b[0m 523ms/step - accuracy: 0.0235 - loss: 0.5495 - val_accuracy: 0.0060 - val_loss: 0.5463 - learning_rate: 5.0000e-04\n",
      "Epoch 14/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 457ms/step - accuracy: 0.0129 - loss: 0.5479\n",
      "Epoch 14: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m130s\u001b[0m 525ms/step - accuracy: 0.0129 - loss: 0.5479 - val_accuracy: 0.0060 - val_loss: 0.5463 - learning_rate: 2.5000e-04\n",
      "Epoch 15/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 463ms/step - accuracy: 0.0330 - loss: 0.5353\n",
      "Epoch 15: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m133s\u001b[0m 537ms/step - accuracy: 0.0330 - loss: 0.5353 - val_accuracy: 0.0060 - val_loss: 0.5463 - learning_rate: 2.5000e-04\n",
      "Epoch 16/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 471ms/step - accuracy: 0.0219 - loss: 0.5378\n",
      "Epoch 16: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m133s\u001b[0m 537ms/step - accuracy: 0.0220 - loss: 0.5378 - val_accuracy: 0.0060 - val_loss: 0.5464 - learning_rate: 2.5000e-04\n",
      "Epoch 17/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 445ms/step - accuracy: 0.0068 - loss: 0.5472\n",
      "Epoch 17: val_accuracy did not improve from 0.18670\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m127s\u001b[0m 510ms/step - accuracy: 0.0068 - loss: 0.5472 - val_accuracy: 0.0060 - val_loss: 0.5464 - learning_rate: 2.5000e-04\n",
      "Epoch 17: early stopping\n",
      "Restoring model weights from the end of the best epoch: 2.\n",
      "\n",
      "============================================================\n",
      "Training VGG_Style\n",
      "============================================================\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-04-13 12:13:25.015294: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n",
      "2026-04-13 12:13:25.251964: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n",
      "2026-04-13 12:13:27.044194: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n",
      "2026-04-13 12:13:27.243541: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n",
      "2026-04-13 12:13:30.253288: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n",
      "2026-04-13 12:13:30.632538: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n",
      "2026-04-13 12:13:30.661472: E external/local_xla/xla/service/slow_operation_alarm.cc:73] Trying algorithm eng4{k11=1} for conv %cudnn-conv-bw-input.23 = (f32[64,64,128,128]{3,2,1,0}, u8[0]{0}) custom-call(f32[64,64,128,128]{3,2,1,0} %bitcast.19607, f32[64,64,3,3]{3,2,1,0} %bitcast.16639), window={size=3x3 pad=1_1x1_1}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", metadata={op_type=\"Conv2DBackpropInput\" op_name=\"gradient_tape/VGG_Style_1/conv2d_106_1/convolution/Conv2DBackpropInput\" source_file=\"/usr/local/lib/python3.12/dist-packages/tensorflow/python/framework/ops.py\" source_line=1200}, backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0},\"force_earliest_schedule\":false} is taking a while...\n",
      "2026-04-13 12:13:30.666116: E external/local_xla/xla/service/slow_operation_alarm.cc:140] The operation took 1.004743474s\n",
      "Trying algorithm eng4{k11=1} for conv %cudnn-conv-bw-input.23 = (f32[64,64,128,128]{3,2,1,0}, u8[0]{0}) custom-call(f32[64,64,128,128]{3,2,1,0} %bitcast.19607, f32[64,64,3,3]{3,2,1,0} %bitcast.16639), window={size=3x3 pad=1_1x1_1}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBackwardInput\", metadata={op_type=\"Conv2DBackpropInput\" op_name=\"gradient_tape/VGG_Style_1/conv2d_106_1/convolution/Conv2DBackpropInput\" source_file=\"/usr/local/lib/python3.12/dist-packages/tensorflow/python/framework/ops.py\" source_line=1200}, backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0},\"force_earliest_schedule\":false} is taking a while...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m248/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 743ms/step - accuracy: 0.1066 - loss: 2.6909"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-04-13 12:17:07.660720: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n",
      "2026-04-13 12:17:07.891844: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n",
      "2026-04-13 12:17:09.545049: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n",
      "2026-04-13 12:17:09.741061: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n",
      "2026-04-13 12:17:12.531474: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n",
      "2026-04-13 12:17:12.898752: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 894ms/step - accuracy: 0.1067 - loss: 2.6902"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-04-13 12:17:44.336659: E external/local_xla/xla/stream_executor/cuda/cuda_timer.cc:86] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1: val_accuracy improved from -inf to 0.08499, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m301s\u001b[0m 1s/step - accuracy: 0.1068 - loss: 2.6895 - val_accuracy: 0.0850 - val_loss: 2.6448 - learning_rate: 0.0010\n",
      "Epoch 2/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 744ms/step - accuracy: 0.1397 - loss: 2.3583\n",
      "Epoch 2: val_accuracy improved from 0.08499 to 0.15007, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 812ms/step - accuracy: 0.1397 - loss: 2.3582 - val_accuracy: 0.1501 - val_loss: 2.4124 - learning_rate: 0.0010\n",
      "Epoch 3/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 745ms/step - accuracy: 0.1330 - loss: 2.3680\n",
      "Epoch 3: val_accuracy improved from 0.15007 to 0.17817, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m203s\u001b[0m 817ms/step - accuracy: 0.1330 - loss: 2.3679 - val_accuracy: 0.1782 - val_loss: 2.3726 - learning_rate: 0.0010\n",
      "Epoch 4/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 735ms/step - accuracy: 0.1582 - loss: 2.3433\n",
      "Epoch 4: val_accuracy improved from 0.17817 to 0.21302, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 814ms/step - accuracy: 0.1583 - loss: 2.3431 - val_accuracy: 0.2130 - val_loss: 2.2745 - learning_rate: 0.0010\n",
      "Epoch 5/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 722ms/step - accuracy: 0.1797 - loss: 2.2294\n",
      "Epoch 5: val_accuracy did not improve from 0.21302\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 801ms/step - accuracy: 0.1797 - loss: 2.2294 - val_accuracy: 0.1643 - val_loss: 2.3061 - learning_rate: 0.0010\n",
      "Epoch 6/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 718ms/step - accuracy: 0.1979 - loss: 2.1752\n",
      "Epoch 6: val_accuracy did not improve from 0.21302\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 797ms/step - accuracy: 0.1979 - loss: 2.1752 - val_accuracy: 0.2052 - val_loss: 2.2189 - learning_rate: 0.0010\n",
      "Epoch 7/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 717ms/step - accuracy: 0.2087 - loss: 2.1646\n",
      "Epoch 7: val_accuracy improved from 0.21302 to 0.23293, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 801ms/step - accuracy: 0.2087 - loss: 2.1645 - val_accuracy: 0.2329 - val_loss: 2.2155 - learning_rate: 0.0010\n",
      "Epoch 8/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 715ms/step - accuracy: 0.2316 - loss: 2.0997\n",
      "Epoch 8: val_accuracy did not improve from 0.23293\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m197s\u001b[0m 794ms/step - accuracy: 0.2316 - loss: 2.0998 - val_accuracy: 0.1863 - val_loss: 2.2729 - learning_rate: 0.0010\n",
      "Epoch 9/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 717ms/step - accuracy: 0.2505 - loss: 2.0630\n",
      "Epoch 9: val_accuracy improved from 0.23293 to 0.30548, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 804ms/step - accuracy: 0.2505 - loss: 2.0631 - val_accuracy: 0.3055 - val_loss: 2.1092 - learning_rate: 0.0010\n",
      "Epoch 10/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 713ms/step - accuracy: 0.2816 - loss: 2.0503\n",
      "Epoch 10: val_accuracy improved from 0.30548 to 0.36878, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 800ms/step - accuracy: 0.2816 - loss: 2.0503 - val_accuracy: 0.3688 - val_loss: 2.0019 - learning_rate: 0.0010\n",
      "Epoch 11/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 721ms/step - accuracy: 0.2909 - loss: 2.0144\n",
      "Epoch 11: val_accuracy did not improve from 0.36878\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 800ms/step - accuracy: 0.2910 - loss: 2.0143 - val_accuracy: 0.3538 - val_loss: 2.0079 - learning_rate: 0.0010\n",
      "Epoch 12/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 716ms/step - accuracy: 0.3251 - loss: 1.9511\n",
      "Epoch 12: val_accuracy did not improve from 0.36878\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m197s\u001b[0m 794ms/step - accuracy: 0.3252 - loss: 1.9512 - val_accuracy: 0.3382 - val_loss: 2.0437 - learning_rate: 0.0010\n",
      "Epoch 13/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 717ms/step - accuracy: 0.3611 - loss: 1.9519\n",
      "Epoch 13: val_accuracy improved from 0.36878 to 0.37198, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 803ms/step - accuracy: 0.3611 - loss: 1.9518 - val_accuracy: 0.3720 - val_loss: 1.9869 - learning_rate: 0.0010\n",
      "Epoch 14/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 714ms/step - accuracy: 0.3506 - loss: 1.9326\n",
      "Epoch 14: val_accuracy did not improve from 0.37198\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m197s\u001b[0m 795ms/step - accuracy: 0.3506 - loss: 1.9325 - val_accuracy: 0.3460 - val_loss: 2.0524 - learning_rate: 0.0010\n",
      "Epoch 15/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 730ms/step - accuracy: 0.3483 - loss: 1.9204\n",
      "Epoch 15: val_accuracy did not improve from 0.37198\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 811ms/step - accuracy: 0.3484 - loss: 1.9203 - val_accuracy: 0.3069 - val_loss: 2.1209 - learning_rate: 0.0010\n",
      "Epoch 16/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 722ms/step - accuracy: 0.4006 - loss: 1.8437\n",
      "Epoch 16: val_accuracy improved from 0.37198 to 0.42248, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 807ms/step - accuracy: 0.4006 - loss: 1.8437 - val_accuracy: 0.4225 - val_loss: 1.8249 - learning_rate: 0.0010\n",
      "Epoch 17/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 716ms/step - accuracy: 0.3878 - loss: 1.8611\n",
      "Epoch 17: val_accuracy did not improve from 0.42248\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 797ms/step - accuracy: 0.3878 - loss: 1.8612 - val_accuracy: 0.2984 - val_loss: 2.2130 - learning_rate: 0.0010\n",
      "Epoch 18/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 721ms/step - accuracy: 0.4008 - loss: 1.8424\n",
      "Epoch 18: val_accuracy improved from 0.42248 to 0.43563, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 807ms/step - accuracy: 0.4009 - loss: 1.8421 - val_accuracy: 0.4356 - val_loss: 1.8319 - learning_rate: 0.0010\n",
      "Epoch 19/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 720ms/step - accuracy: 0.4382 - loss: 1.7661\n",
      "Epoch 19: val_accuracy improved from 0.43563 to 0.45484, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 808ms/step - accuracy: 0.4382 - loss: 1.7660 - val_accuracy: 0.4548 - val_loss: 1.7620 - learning_rate: 0.0010\n",
      "Epoch 20/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 720ms/step - accuracy: 0.4530 - loss: 1.6940\n",
      "Epoch 20: val_accuracy did not improve from 0.45484\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 799ms/step - accuracy: 0.4530 - loss: 1.6942 - val_accuracy: 0.4431 - val_loss: 1.8735 - learning_rate: 0.0010\n",
      "Epoch 21/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 719ms/step - accuracy: 0.4630 - loss: 1.6766\n",
      "Epoch 21: val_accuracy did not improve from 0.45484\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 798ms/step - accuracy: 0.4630 - loss: 1.6767 - val_accuracy: 0.4271 - val_loss: 1.8457 - learning_rate: 0.0010\n",
      "Epoch 22/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 722ms/step - accuracy: 0.4588 - loss: 1.6890\n",
      "Epoch 22: val_accuracy improved from 0.45484 to 0.46906, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 809ms/step - accuracy: 0.4588 - loss: 1.6889 - val_accuracy: 0.4691 - val_loss: 1.7549 - learning_rate: 0.0010\n",
      "Epoch 23/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 712ms/step - accuracy: 0.4686 - loss: 1.6770\n",
      "Epoch 23: val_accuracy improved from 0.46906 to 0.47262, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 799ms/step - accuracy: 0.4687 - loss: 1.6769 - val_accuracy: 0.4726 - val_loss: 1.7410 - learning_rate: 0.0010\n",
      "Epoch 24/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 713ms/step - accuracy: 0.4706 - loss: 1.6187\n",
      "Epoch 24: val_accuracy did not improve from 0.47262\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m197s\u001b[0m 792ms/step - accuracy: 0.4706 - loss: 1.6188 - val_accuracy: 0.4154 - val_loss: 1.8703 - learning_rate: 0.0010\n",
      "Epoch 25/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 719ms/step - accuracy: 0.4799 - loss: 1.5938\n",
      "Epoch 25: val_accuracy improved from 0.47262 to 0.50676, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 805ms/step - accuracy: 0.4799 - loss: 1.5938 - val_accuracy: 0.5068 - val_loss: 1.6740 - learning_rate: 0.0010\n",
      "Epoch 26/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 727ms/step - accuracy: 0.5059 - loss: 1.5530\n",
      "Epoch 26: val_accuracy improved from 0.50676 to 0.52418, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m203s\u001b[0m 815ms/step - accuracy: 0.5059 - loss: 1.5530 - val_accuracy: 0.5242 - val_loss: 1.6245 - learning_rate: 0.0010\n",
      "Epoch 27/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 719ms/step - accuracy: 0.5124 - loss: 1.5362\n",
      "Epoch 27: val_accuracy did not improve from 0.52418\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 799ms/step - accuracy: 0.5125 - loss: 1.5362 - val_accuracy: 0.5199 - val_loss: 1.6806 - learning_rate: 0.0010\n",
      "Epoch 28/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 720ms/step - accuracy: 0.5228 - loss: 1.5349\n",
      "Epoch 28: val_accuracy improved from 0.52418 to 0.58677, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 806ms/step - accuracy: 0.5228 - loss: 1.5348 - val_accuracy: 0.5868 - val_loss: 1.5338 - learning_rate: 0.0010\n",
      "Epoch 29/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 715ms/step - accuracy: 0.5480 - loss: 1.5093\n",
      "Epoch 29: val_accuracy did not improve from 0.58677\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 796ms/step - accuracy: 0.5480 - loss: 1.5092 - val_accuracy: 0.4858 - val_loss: 1.7060 - learning_rate: 0.0010\n",
      "Epoch 30/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 721ms/step - accuracy: 0.5510 - loss: 1.4660\n",
      "Epoch 30: val_accuracy did not improve from 0.58677\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 800ms/step - accuracy: 0.5510 - loss: 1.4660 - val_accuracy: 0.5733 - val_loss: 1.5340 - learning_rate: 0.0010\n",
      "Epoch 31/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 720ms/step - accuracy: 0.5581 - loss: 1.4570\n",
      "Epoch 31: val_accuracy improved from 0.58677 to 0.61344, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 807ms/step - accuracy: 0.5581 - loss: 1.4570 - val_accuracy: 0.6134 - val_loss: 1.4730 - learning_rate: 0.0010\n",
      "Epoch 32/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 719ms/step - accuracy: 0.5777 - loss: 1.4060\n",
      "Epoch 32: val_accuracy did not improve from 0.61344\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 801ms/step - accuracy: 0.5777 - loss: 1.4061 - val_accuracy: 0.5359 - val_loss: 1.6556 - learning_rate: 0.0010\n",
      "Epoch 33/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 724ms/step - accuracy: 0.5884 - loss: 1.3988\n",
      "Epoch 33: val_accuracy did not improve from 0.61344\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 804ms/step - accuracy: 0.5883 - loss: 1.3989 - val_accuracy: 0.5768 - val_loss: 1.5249 - learning_rate: 0.0010\n",
      "Epoch 34/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 720ms/step - accuracy: 0.5974 - loss: 1.3921\n",
      "Epoch 34: val_accuracy did not improve from 0.61344\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 798ms/step - accuracy: 0.5974 - loss: 1.3921 - val_accuracy: 0.5743 - val_loss: 1.5421 - learning_rate: 0.0010\n",
      "Epoch 35/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 744ms/step - accuracy: 0.5983 - loss: 1.3569\n",
      "Epoch 35: val_accuracy did not improve from 0.61344\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m206s\u001b[0m 831ms/step - accuracy: 0.5983 - loss: 1.3570 - val_accuracy: 0.5999 - val_loss: 1.4744 - learning_rate: 0.0010\n",
      "Epoch 36/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 738ms/step - accuracy: 0.6041 - loss: 1.3602\n",
      "Epoch 36: val_accuracy did not improve from 0.61344\n",
      "\n",
      "Epoch 36: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 819ms/step - accuracy: 0.6041 - loss: 1.3602 - val_accuracy: 0.5779 - val_loss: 1.5227 - learning_rate: 0.0010\n",
      "Epoch 37/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 740ms/step - accuracy: 0.6318 - loss: 1.2932\n",
      "Epoch 37: val_accuracy improved from 0.61344 to 0.63371, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m206s\u001b[0m 829ms/step - accuracy: 0.6318 - loss: 1.2932 - val_accuracy: 0.6337 - val_loss: 1.4060 - learning_rate: 5.0000e-04\n",
      "Epoch 38/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 733ms/step - accuracy: 0.6504 - loss: 1.2730\n",
      "Epoch 38: val_accuracy improved from 0.63371 to 0.65541, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 823ms/step - accuracy: 0.6504 - loss: 1.2730 - val_accuracy: 0.6554 - val_loss: 1.3455 - learning_rate: 5.0000e-04\n",
      "Epoch 39/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 748ms/step - accuracy: 0.6531 - loss: 1.2465\n",
      "Epoch 39: val_accuracy improved from 0.65541 to 0.66821, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m208s\u001b[0m 836ms/step - accuracy: 0.6531 - loss: 1.2465 - val_accuracy: 0.6682 - val_loss: 1.3145 - learning_rate: 5.0000e-04\n",
      "Epoch 40/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 730ms/step - accuracy: 0.6674 - loss: 1.2192\n",
      "Epoch 40: val_accuracy did not improve from 0.66821\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 815ms/step - accuracy: 0.6674 - loss: 1.2192 - val_accuracy: 0.6572 - val_loss: 1.3547 - learning_rate: 5.0000e-04\n",
      "Epoch 41/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 740ms/step - accuracy: 0.6546 - loss: 1.2424\n",
      "Epoch 41: val_accuracy did not improve from 0.66821\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 824ms/step - accuracy: 0.6547 - loss: 1.2423 - val_accuracy: 0.6636 - val_loss: 1.3343 - learning_rate: 5.0000e-04\n",
      "Epoch 42/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 739ms/step - accuracy: 0.6636 - loss: 1.2159\n",
      "Epoch 42: val_accuracy did not improve from 0.66821\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 821ms/step - accuracy: 0.6637 - loss: 1.2159 - val_accuracy: 0.6447 - val_loss: 1.3320 - learning_rate: 5.0000e-04\n",
      "Epoch 43/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 739ms/step - accuracy: 0.6665 - loss: 1.2058\n",
      "Epoch 43: val_accuracy improved from 0.66821 to 0.68457, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m207s\u001b[0m 832ms/step - accuracy: 0.6665 - loss: 1.2058 - val_accuracy: 0.6846 - val_loss: 1.2777 - learning_rate: 5.0000e-04\n",
      "Epoch 44/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 736ms/step - accuracy: 0.6753 - loss: 1.1828\n",
      "Epoch 44: val_accuracy did not improve from 0.68457\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 819ms/step - accuracy: 0.6753 - loss: 1.1829 - val_accuracy: 0.6088 - val_loss: 1.4547 - learning_rate: 5.0000e-04\n",
      "Epoch 45/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 743ms/step - accuracy: 0.6757 - loss: 1.1852\n",
      "Epoch 45: val_accuracy did not improve from 0.68457\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m206s\u001b[0m 827ms/step - accuracy: 0.6757 - loss: 1.1852 - val_accuracy: 0.6117 - val_loss: 1.4678 - learning_rate: 5.0000e-04\n",
      "Epoch 46/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 741ms/step - accuracy: 0.6803 - loss: 1.1764\n",
      "Epoch 46: val_accuracy did not improve from 0.68457\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m206s\u001b[0m 827ms/step - accuracy: 0.6804 - loss: 1.1763 - val_accuracy: 0.6447 - val_loss: 1.3527 - learning_rate: 5.0000e-04\n",
      "Epoch 47/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 795ms/step - accuracy: 0.6957 - loss: 1.1534\n",
      "Epoch 47: val_accuracy did not improve from 0.68457\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m221s\u001b[0m 889ms/step - accuracy: 0.6957 - loss: 1.1535 - val_accuracy: 0.6789 - val_loss: 1.2779 - learning_rate: 5.0000e-04\n",
      "Epoch 48/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 782ms/step - accuracy: 0.6922 - loss: 1.1361\n",
      "Epoch 48: val_accuracy did not improve from 0.68457\n",
      "\n",
      "Epoch 48: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m217s\u001b[0m 873ms/step - accuracy: 0.6922 - loss: 1.1362 - val_accuracy: 0.5896 - val_loss: 1.5238 - learning_rate: 5.0000e-04\n",
      "Epoch 49/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 800ms/step - accuracy: 0.6953 - loss: 1.1513\n",
      "Epoch 49: val_accuracy improved from 0.68457 to 0.68528, saving model to VGG_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m223s\u001b[0m 898ms/step - accuracy: 0.6953 - loss: 1.1512 - val_accuracy: 0.6853 - val_loss: 1.2649 - learning_rate: 2.5000e-04\n",
      "Epoch 50/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 800ms/step - accuracy: 0.7151 - loss: 1.0961\n",
      "Epoch 50: val_accuracy did not improve from 0.68528\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m222s\u001b[0m 893ms/step - accuracy: 0.7151 - loss: 1.0961 - val_accuracy: 0.6842 - val_loss: 1.2590 - learning_rate: 2.5000e-04\n",
      "Restoring model weights from the end of the best epoch: 49.\n",
      "\n",
      "============================================================\n",
      "Training Inception_Style\n",
      "============================================================\n",
      "Epoch 1/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 649ms/step - accuracy: 0.1185 - loss: 2.4101\n",
      "Epoch 1: val_accuracy improved from -inf to 0.12980, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m224s\u001b[0m 746ms/step - accuracy: 0.1186 - loss: 2.4097 - val_accuracy: 0.1298 - val_loss: 2.5034 - learning_rate: 0.0010\n",
      "Epoch 2/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 555ms/step - accuracy: 0.2230 - loss: 2.1120\n",
      "Epoch 2: val_accuracy improved from 0.12980 to 0.16110, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 614ms/step - accuracy: 0.2230 - loss: 2.1119 - val_accuracy: 0.1611 - val_loss: 2.4492 - learning_rate: 0.0010\n",
      "Epoch 3/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 543ms/step - accuracy: 0.2679 - loss: 2.0307\n",
      "Epoch 3: val_accuracy improved from 0.16110 to 0.33819, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m152s\u001b[0m 613ms/step - accuracy: 0.2679 - loss: 2.0307 - val_accuracy: 0.3382 - val_loss: 2.0560 - learning_rate: 0.0010\n",
      "Epoch 4/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 551ms/step - accuracy: 0.3361 - loss: 1.8917\n",
      "Epoch 4: val_accuracy did not improve from 0.33819\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m155s\u001b[0m 625ms/step - accuracy: 0.3361 - loss: 1.8918 - val_accuracy: 0.2582 - val_loss: 2.2336 - learning_rate: 0.0010\n",
      "Epoch 5/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 519ms/step - accuracy: 0.3857 - loss: 1.8387\n",
      "Epoch 5: val_accuracy improved from 0.33819 to 0.39011, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m149s\u001b[0m 600ms/step - accuracy: 0.3857 - loss: 1.8387 - val_accuracy: 0.3901 - val_loss: 1.9740 - learning_rate: 0.0010\n",
      "Epoch 6/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 507ms/step - accuracy: 0.4248 - loss: 1.7683\n",
      "Epoch 6: val_accuracy did not improve from 0.39011\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 584ms/step - accuracy: 0.4248 - loss: 1.7683 - val_accuracy: 0.3478 - val_loss: 1.9933 - learning_rate: 0.0010\n",
      "Epoch 7/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 515ms/step - accuracy: 0.4472 - loss: 1.6840\n",
      "Epoch 7: val_accuracy improved from 0.39011 to 0.46053, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m148s\u001b[0m 598ms/step - accuracy: 0.4472 - loss: 1.6840 - val_accuracy: 0.4605 - val_loss: 1.7682 - learning_rate: 0.0010\n",
      "Epoch 8/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 506ms/step - accuracy: 0.4757 - loss: 1.6158\n",
      "Epoch 8: val_accuracy improved from 0.46053 to 0.47048, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 590ms/step - accuracy: 0.4757 - loss: 1.6159 - val_accuracy: 0.4705 - val_loss: 1.7710 - learning_rate: 0.0010\n",
      "Epoch 9/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 511ms/step - accuracy: 0.5005 - loss: 1.5950\n",
      "Epoch 9: val_accuracy did not improve from 0.47048\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 588ms/step - accuracy: 0.5006 - loss: 1.5950 - val_accuracy: 0.4157 - val_loss: 1.9129 - learning_rate: 0.0010\n",
      "Epoch 10/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 515ms/step - accuracy: 0.5130 - loss: 1.5512\n",
      "Epoch 10: val_accuracy improved from 0.47048 to 0.55085, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m148s\u001b[0m 596ms/step - accuracy: 0.5129 - loss: 1.5513 - val_accuracy: 0.5509 - val_loss: 1.5861 - learning_rate: 0.0010\n",
      "Epoch 11/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 513ms/step - accuracy: 0.5223 - loss: 1.5159\n",
      "Epoch 11: val_accuracy did not improve from 0.55085\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 589ms/step - accuracy: 0.5223 - loss: 1.5160 - val_accuracy: 0.4758 - val_loss: 1.7940 - learning_rate: 0.0010\n",
      "Epoch 12/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 507ms/step - accuracy: 0.5379 - loss: 1.4977\n",
      "Epoch 12: val_accuracy did not improve from 0.55085\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 581ms/step - accuracy: 0.5379 - loss: 1.4976 - val_accuracy: 0.5494 - val_loss: 1.5489 - learning_rate: 0.0010\n",
      "Epoch 13/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 512ms/step - accuracy: 0.5458 - loss: 1.4297\n",
      "Epoch 13: val_accuracy did not improve from 0.55085\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 587ms/step - accuracy: 0.5458 - loss: 1.4298 - val_accuracy: 0.5384 - val_loss: 1.6052 - learning_rate: 0.0010\n",
      "Epoch 14/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 509ms/step - accuracy: 0.5628 - loss: 1.4317\n",
      "Epoch 14: val_accuracy did not improve from 0.55085\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 584ms/step - accuracy: 0.5628 - loss: 1.4316 - val_accuracy: 0.3503 - val_loss: 2.1312 - learning_rate: 0.0010\n",
      "Epoch 15/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 513ms/step - accuracy: 0.5666 - loss: 1.4085\n",
      "Epoch 15: val_accuracy improved from 0.55085 to 0.61095, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 594ms/step - accuracy: 0.5666 - loss: 1.4085 - val_accuracy: 0.6110 - val_loss: 1.4514 - learning_rate: 0.0010\n",
      "Epoch 16/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 502ms/step - accuracy: 0.5808 - loss: 1.3690\n",
      "Epoch 16: val_accuracy did not improve from 0.61095\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 576ms/step - accuracy: 0.5808 - loss: 1.3690 - val_accuracy: 0.3471 - val_loss: 2.1823 - learning_rate: 0.0010\n",
      "Epoch 17/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 505ms/step - accuracy: 0.5817 - loss: 1.3705\n",
      "Epoch 17: val_accuracy did not improve from 0.61095\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 579ms/step - accuracy: 0.5817 - loss: 1.3705 - val_accuracy: 0.5974 - val_loss: 1.4839 - learning_rate: 0.0010\n",
      "Epoch 18/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 501ms/step - accuracy: 0.6012 - loss: 1.3446\n",
      "Epoch 18: val_accuracy did not improve from 0.61095\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 574ms/step - accuracy: 0.6011 - loss: 1.3446 - val_accuracy: 0.5302 - val_loss: 1.6520 - learning_rate: 0.0010\n",
      "Epoch 19/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 504ms/step - accuracy: 0.6052 - loss: 1.3147\n",
      "Epoch 19: val_accuracy did not improve from 0.61095\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 580ms/step - accuracy: 0.6052 - loss: 1.3147 - val_accuracy: 0.6060 - val_loss: 1.4541 - learning_rate: 0.0010\n",
      "Epoch 20/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 503ms/step - accuracy: 0.6149 - loss: 1.2946\n",
      "Epoch 20: val_accuracy did not improve from 0.61095\n",
      "\n",
      "Epoch 20: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 576ms/step - accuracy: 0.6149 - loss: 1.2946 - val_accuracy: 0.5071 - val_loss: 1.6877 - learning_rate: 0.0010\n",
      "Epoch 21/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 508ms/step - accuracy: 0.6270 - loss: 1.2768\n",
      "Epoch 21: val_accuracy improved from 0.61095 to 0.65825, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 590ms/step - accuracy: 0.6271 - loss: 1.2766 - val_accuracy: 0.6583 - val_loss: 1.3240 - learning_rate: 5.0000e-04\n",
      "Epoch 22/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 510ms/step - accuracy: 0.6638 - loss: 1.1688\n",
      "Epoch 22: val_accuracy improved from 0.65825 to 0.65967, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 593ms/step - accuracy: 0.6638 - loss: 1.1688 - val_accuracy: 0.6597 - val_loss: 1.3384 - learning_rate: 5.0000e-04\n",
      "Epoch 23/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 506ms/step - accuracy: 0.6776 - loss: 1.1594\n",
      "Epoch 23: val_accuracy did not improve from 0.65967\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 581ms/step - accuracy: 0.6776 - loss: 1.1594 - val_accuracy: 0.6419 - val_loss: 1.3695 - learning_rate: 5.0000e-04\n",
      "Epoch 24/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 516ms/step - accuracy: 0.6990 - loss: 1.1287\n",
      "Epoch 24: val_accuracy improved from 0.65967 to 0.67176, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m149s\u001b[0m 600ms/step - accuracy: 0.6989 - loss: 1.1288 - val_accuracy: 0.6718 - val_loss: 1.3056 - learning_rate: 5.0000e-04\n",
      "Epoch 25/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 512ms/step - accuracy: 0.6904 - loss: 1.1133\n",
      "Epoch 25: val_accuracy did not improve from 0.67176\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 587ms/step - accuracy: 0.6904 - loss: 1.1133 - val_accuracy: 0.6661 - val_loss: 1.3262 - learning_rate: 5.0000e-04\n",
      "Epoch 26/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 515ms/step - accuracy: 0.7049 - loss: 1.1035\n",
      "Epoch 26: val_accuracy did not improve from 0.67176\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 589ms/step - accuracy: 0.7049 - loss: 1.1036 - val_accuracy: 0.6312 - val_loss: 1.3954 - learning_rate: 5.0000e-04\n",
      "Epoch 27/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 516ms/step - accuracy: 0.6912 - loss: 1.0994\n",
      "Epoch 27: val_accuracy did not improve from 0.67176\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 592ms/step - accuracy: 0.6912 - loss: 1.0995 - val_accuracy: 0.6597 - val_loss: 1.3450 - learning_rate: 5.0000e-04\n",
      "Epoch 28/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 522ms/step - accuracy: 0.7053 - loss: 1.1005\n",
      "Epoch 28: val_accuracy improved from 0.67176 to 0.68990, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m151s\u001b[0m 607ms/step - accuracy: 0.7053 - loss: 1.1005 - val_accuracy: 0.6899 - val_loss: 1.2716 - learning_rate: 5.0000e-04\n",
      "Epoch 29/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 515ms/step - accuracy: 0.7064 - loss: 1.0910\n",
      "Epoch 29: val_accuracy did not improve from 0.68990\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 593ms/step - accuracy: 0.7064 - loss: 1.0909 - val_accuracy: 0.6810 - val_loss: 1.2809 - learning_rate: 5.0000e-04\n",
      "Epoch 30/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 517ms/step - accuracy: 0.7155 - loss: 1.0690\n",
      "Epoch 30: val_accuracy did not improve from 0.68990\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 593ms/step - accuracy: 0.7155 - loss: 1.0690 - val_accuracy: 0.6721 - val_loss: 1.2849 - learning_rate: 5.0000e-04\n",
      "Epoch 31/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 520ms/step - accuracy: 0.7238 - loss: 1.0465\n",
      "Epoch 31: val_accuracy did not improve from 0.68990\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m148s\u001b[0m 597ms/step - accuracy: 0.7237 - loss: 1.0466 - val_accuracy: 0.6799 - val_loss: 1.2816 - learning_rate: 5.0000e-04\n",
      "Epoch 32/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 522ms/step - accuracy: 0.7352 - loss: 1.0307\n",
      "Epoch 32: val_accuracy did not improve from 0.68990\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m148s\u001b[0m 597ms/step - accuracy: 0.7352 - loss: 1.0307 - val_accuracy: 0.6821 - val_loss: 1.2916 - learning_rate: 5.0000e-04\n",
      "Epoch 33/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 518ms/step - accuracy: 0.7316 - loss: 1.0352\n",
      "Epoch 33: val_accuracy did not improve from 0.68990\n",
      "\n",
      "Epoch 33: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 594ms/step - accuracy: 0.7316 - loss: 1.0352 - val_accuracy: 0.6842 - val_loss: 1.2929 - learning_rate: 5.0000e-04\n",
      "Epoch 34/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 519ms/step - accuracy: 0.7509 - loss: 1.0023\n",
      "Epoch 34: val_accuracy improved from 0.68990 to 0.70092, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m150s\u001b[0m 604ms/step - accuracy: 0.7509 - loss: 1.0023 - val_accuracy: 0.7009 - val_loss: 1.2299 - learning_rate: 2.5000e-04\n",
      "Epoch 35/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 513ms/step - accuracy: 0.7630 - loss: 0.9748\n",
      "Epoch 35: val_accuracy did not improve from 0.70092\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 591ms/step - accuracy: 0.7630 - loss: 0.9748 - val_accuracy: 0.6974 - val_loss: 1.2254 - learning_rate: 2.5000e-04\n",
      "Epoch 36/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 507ms/step - accuracy: 0.7737 - loss: 0.9438\n",
      "Epoch 36: val_accuracy did not improve from 0.70092\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 584ms/step - accuracy: 0.7737 - loss: 0.9438 - val_accuracy: 0.6910 - val_loss: 1.2455 - learning_rate: 2.5000e-04\n",
      "Epoch 37/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 508ms/step - accuracy: 0.7732 - loss: 0.9367\n",
      "Epoch 37: val_accuracy improved from 0.70092 to 0.72155, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 591ms/step - accuracy: 0.7732 - loss: 0.9367 - val_accuracy: 0.7216 - val_loss: 1.1988 - learning_rate: 2.5000e-04\n",
      "Epoch 38/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 516ms/step - accuracy: 0.7866 - loss: 0.9227\n",
      "Epoch 38: val_accuracy did not improve from 0.72155\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 592ms/step - accuracy: 0.7866 - loss: 0.9227 - val_accuracy: 0.6938 - val_loss: 1.2455 - learning_rate: 2.5000e-04\n",
      "Epoch 39/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 510ms/step - accuracy: 0.7898 - loss: 0.9088\n",
      "Epoch 39: val_accuracy did not improve from 0.72155\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 586ms/step - accuracy: 0.7899 - loss: 0.9088 - val_accuracy: 0.7087 - val_loss: 1.2144 - learning_rate: 2.5000e-04\n",
      "Epoch 40/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 508ms/step - accuracy: 0.7956 - loss: 0.9085\n",
      "Epoch 40: val_accuracy did not improve from 0.72155\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 584ms/step - accuracy: 0.7956 - loss: 0.9085 - val_accuracy: 0.7130 - val_loss: 1.2048 - learning_rate: 2.5000e-04\n",
      "Epoch 41/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 499ms/step - accuracy: 0.7934 - loss: 0.9058\n",
      "Epoch 41: val_accuracy did not improve from 0.72155\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 573ms/step - accuracy: 0.7934 - loss: 0.9058 - val_accuracy: 0.7009 - val_loss: 1.2309 - learning_rate: 2.5000e-04\n",
      "Epoch 42/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 501ms/step - accuracy: 0.8042 - loss: 0.8856\n",
      "Epoch 42: val_accuracy did not improve from 0.72155\n",
      "\n",
      "Epoch 42: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 574ms/step - accuracy: 0.8042 - loss: 0.8856 - val_accuracy: 0.7166 - val_loss: 1.2104 - learning_rate: 2.5000e-04\n",
      "Epoch 43/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 500ms/step - accuracy: 0.8207 - loss: 0.8583\n",
      "Epoch 43: val_accuracy did not improve from 0.72155\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 573ms/step - accuracy: 0.8206 - loss: 0.8584 - val_accuracy: 0.7191 - val_loss: 1.2139 - learning_rate: 1.2500e-04\n",
      "Epoch 44/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 497ms/step - accuracy: 0.8269 - loss: 0.8474\n",
      "Epoch 44: val_accuracy did not improve from 0.72155\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 571ms/step - accuracy: 0.8269 - loss: 0.8474 - val_accuracy: 0.7208 - val_loss: 1.2083 - learning_rate: 1.2500e-04\n",
      "Epoch 45/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 501ms/step - accuracy: 0.8343 - loss: 0.8346\n",
      "Epoch 45: val_accuracy improved from 0.72155 to 0.72226, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 582ms/step - accuracy: 0.8343 - loss: 0.8346 - val_accuracy: 0.7223 - val_loss: 1.2026 - learning_rate: 1.2500e-04\n",
      "Epoch 46/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 515ms/step - accuracy: 0.8331 - loss: 0.8354\n",
      "Epoch 46: val_accuracy improved from 0.72226 to 0.72582, saving model to Inception_Style_best.keras\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m148s\u001b[0m 597ms/step - accuracy: 0.8331 - loss: 0.8354 - val_accuracy: 0.7258 - val_loss: 1.1998 - learning_rate: 1.2500e-04\n",
      "Epoch 47/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 509ms/step - accuracy: 0.8363 - loss: 0.8339\n",
      "Epoch 47: val_accuracy improved from 0.72582 to 0.72688, saving model to Inception_Style_best.keras\n",
      "\n",
      "Epoch 47: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 593ms/step - accuracy: 0.8363 - loss: 0.8339 - val_accuracy: 0.7269 - val_loss: 1.2102 - learning_rate: 1.2500e-04\n",
      "Epoch 48/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 498ms/step - accuracy: 0.8533 - loss: 0.8051\n",
      "Epoch 48: val_accuracy did not improve from 0.72688\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 572ms/step - accuracy: 0.8533 - loss: 0.8051 - val_accuracy: 0.7251 - val_loss: 1.2101 - learning_rate: 6.2500e-05\n",
      "Epoch 49/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 502ms/step - accuracy: 0.8513 - loss: 0.8072\n",
      "Epoch 49: val_accuracy did not improve from 0.72688\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 575ms/step - accuracy: 0.8513 - loss: 0.8072 - val_accuracy: 0.7219 - val_loss: 1.2097 - learning_rate: 6.2500e-05\n",
      "Epoch 50/50\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 501ms/step - accuracy: 0.8596 - loss: 0.8121\n",
      "Epoch 50: val_accuracy did not improve from 0.72688\n",
      "\u001b[1m249/249\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 574ms/step - accuracy: 0.8596 - loss: 0.8120 - val_accuracy: 0.7198 - val_loss: 1.2175 - learning_rate: 6.2500e-05\n",
      "Restoring model weights from the end of the best epoch: 47.\n",
      "\n",
      "All models trained.\n"
     ]
    }
   ],
   "source": [
    "# Store training histories\n",
    "histories = {}\n",
    "\n",
    "print(\"Entraînement des modèles\")\n",
    "\n",
    "for name, model in models_dict.items():\n",
    "    print(f\"\\n{'='*60}\")\n",
    "    print(f\"Training {name}\")\n",
    "    print(f\"{'='*60}\")\n",
    "    \n",
    "    # Fresh generators for each model\n",
    "    train_gen = generator_images(\n",
    "        objs_train.copy(), BATCH_SIZE, \n",
    "        do_shuffle=True, augment=True, \n",
    "        class_weights=class_weights\n",
    "    )\n",
    "    valid_gen = generator_images(\n",
    "        objs_valid.copy(), BATCH_SIZE, \n",
    "        do_shuffle=False, augment=False,\n",
    "        class_weights=None\n",
    "    )\n",
    "    \n",
    "    callbacks = get_callbacks(name)\n",
    "    \n",
    "    history = model.fit(\n",
    "        train_gen,\n",
    "        steps_per_epoch=train_steps,\n",
    "        validation_data=valid_gen,\n",
    "        validation_steps=valid_steps,\n",
    "        epochs=EPOCHS,\n",
    "        callbacks=callbacks,\n",
    "        verbose=1\n",
    "    )\n",
    "    \n",
    "    histories[name] = history.history\n",
    "    models_dict[name] = model  # Update with trained model\n",
    "\n",
    "print(\"\\nAll models trained.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bd62e9b",
   "metadata": {
    "papermill": {
     "duration": 1.447785,
     "end_time": "2026-04-13T17:06:33.120098+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:06:31.672313+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## Training Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d6c66672",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T17:06:35.901503Z",
     "iopub.status.busy": "2026-04-13T17:06:35.901049Z",
     "iopub.status.idle": "2026-04-13T17:06:38.871699Z",
     "shell.execute_reply": "2026-04-13T17:06:38.870658Z"
    },
    "papermill": {
     "duration": 4.434871,
     "end_time": "2026-04-13T17:06:38.876231+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:06:34.441360+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "colors = {'ResNet_Style': '#1f77b4', 'VGG_Style': '#ff7f0e', 'Inception_Style': '#2ca02c'}\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
    "\n",
    "for idx, (name, hist) in enumerate(histories.items()):\n",
    "    # Loss curves\n",
    "    axes[0, idx].plot(hist['loss'], label='Train Loss', color=colors[name], linewidth=2)\n",
    "    axes[0, idx].plot(hist['val_loss'], label='Val Loss', color=colors[name], linestyle='--', linewidth=2)\n",
    "    axes[0, idx].set_title(f'{name} - Loss', fontsize=14, fontweight='bold')\n",
    "    axes[0, idx].set_xlabel('Epoch')\n",
    "    axes[0, idx].set_ylabel('Loss')\n",
    "    axes[0, idx].legend()\n",
    "    axes[0, idx].grid(True, alpha=0.3)\n",
    "    \n",
    "    # Accuracy curves\n",
    "    axes[1, idx].plot(hist['accuracy'], label='Train Acc', color=colors[name], linewidth=2)\n",
    "    axes[1, idx].plot(hist['val_accuracy'], label='Val Acc', color=colors[name], linestyle='--', linewidth=2)\n",
    "    axes[1, idx].set_title(f'{name} - Accuracy', fontsize=14, fontweight='bold')\n",
    "    axes[1, idx].set_xlabel('Epoch')\n",
    "    axes[1, idx].set_ylabel('Accuracy')\n",
    "    axes[1, idx].legend()\n",
    "    axes[1, idx].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('cnn_training_curves.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d5d9bd64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T17:06:41.720206Z",
     "iopub.status.busy": "2026-04-13T17:06:41.719230Z",
     "iopub.status.idle": "2026-04-13T17:06:42.566700Z",
     "shell.execute_reply": "2026-04-13T17:06:42.565892Z"
    },
    "papermill": {
     "duration": 2.310235,
     "end_time": "2026-04-13T17:06:42.568576+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:06:40.258341+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5Rr+Avvvuu1rZkvR+mD17ttE2f/31l7Zuz549uX61zSkmJkZZv369smjRImXu3LnKnDlzlK5du2rb3HvvvUblC/p1vrAy8+fPN6rP+vXrtXURERGKg4ODtu6hhx7S1uX8VfaTTz7R1q1evdpo3cmTJ/OsU07F6WmbmZlp1EO0Q4cORr01p06dalSHY8eOKYqiKJMnT9aWzZ49O9d+b9++rdy+fVubzirr5OSkpKamGpXV6/XKpUuXivTeDHt05PzFuiC///670fv44osvtHVJSUlGPQOyejwoSu5ftGfNmqWtGzFihNG6OXPmaOvat2+vLR88eLC2PGfv1z59+ih6vV77HPr06WPUCyAxMdHofURERChr1qxRvvjiC63NNW7cWNtm/Pjx+R4rMDBQiY6OzvXZ5NfTdt68edryp556Ktd2CQkJyo0bN7T5Zs2aaeUDAgKUpKQkbd0XX3yRZ28FRTHulWFvb2/UQ2XgwIHaupYtW+aqgxBCiMpP7jWzVZd7TUVRlGeeeUbbztXVVbu/q1u3bp73QIqiKCdPnjQ6Xv/+/ZW0tDSjMhcvXtSmc/b2/PHHH43KZmZmFrlHdVGvj5yfUU6JiYnKX3/9pXz11VfKvHnzlDlz5igPPfSQtm2dOnW0smvXrjXa75NPPqnd9+X1fsPCwhRzc3Ot/JEjRxRFUe+lDdtK1vKC/PLLL0bHzvl0VEFMcU/n7u6uxMbGKoqiKGfPnjXaxtPTU+v1u3HjxnyvwZznZc+ePdq6nO3z9ddfN3oPmZmZyoEDB5SlS5cqn3zyiTJnzpxc11N4eHi+x8rvGjAsY9jT1snJSVt+/fr1XNsZnmdTfS954YUXtHXHjx83Wrd27do86y9EcUlPWyEq2GOPPcZrr72m5aP6/vvvmTlzJj///LNRjsxx48Zp01FRUYwZM4Y///yzwH1n/XpYUoY5mLy8vOjZs6c237FjRwIDA/PsAaHX63n11Vf59NNPSUtLK7P6Gdq3b5827eHhQb9+/bR5T09P+vXrp/1CbljWkLm5OU899ZQ236BBA6P1WT0rTOns2bNGOalGjRqFubm5Nj9mzBg++ugjbX7fvn00b96cLl26sGDBAgDeeOMN1q5dyz333EODBg1o164dXbp00fbj6upKo0aN+Oeff4iLiyMwMJA2bdoQFBREkyZN6NmzJ4GBgSZ/b4ZyfuaGv1jb2toybNgwbTTYkydPkpSUlOeAE6NGjdKmc+Z/NezhWrduXa23cUHnbdSoUeh0OkDtPTJy5EgtZ1ZaWhqnTp2iXbt2JCcn8+yzz/Ldd9+h1+vz3V9B1/TEiRNxcXHJd31OnTp1QqfToSgKX375JYcOHaJhw4Y0aNCA1q1b06NHD7y8vABISkri5MmT2rZDhw7F1tZWmx89erRRb5F9+/YxcODAXMd86KGHjHoFGLaBsrj+hRBClD251zSNynSvmZqaapRLdPDgwVrvyOHDh/P+++8DsG7dOqKionB3dwfIlaN+xowZWFpaGi2rU6eONm1YPjg4mEcffdSorJmZWa77sdJydXU16g1uaN68ecyYMYOEhIR8tzc85znf78yZM7X7viyG79fPz4+HHnqI3377DYCvv/6ahQsXsnr1aq2tNG3alJYtWxbvTRWDqe7pHnjgAZycnIDc98wDBgzQev3m7GGe3zVYp04do4Fzc7bPI0eOaOu2bNnChAkTCA8PL+itcuXKFXx9fXMtL+gayE+XLl20f68aN25Mu3btCAoKolGjRvTo0UPrBQym+15ieB7K43ujuDvJQGRCVLCaNWvSp08fbf6HH35AURSjx9VatmxJkyZNtPnHH3+80JtooNRJ0LMeeQH1ZjSnrIBRTgsWLGDOnDkF3kSbon6GDAOfedXLcFl+f0SzkupnyfnYU0HBupLKOYhAzrrnnM+q+8MPP8zLL7+MtbU1mZmZ7Nu3jyVLlvDqq6/So0cP6tatyz///KNt99NPP9GwYUMArl27xpo1a/j4448ZM2YMfn5+TJkypUj19fHx0aYjIyOLfENi+D4dHBxyPR5m+D4VRTG69gwZBhRzPrpluM7wMbGCzlvO6zrn551Vj+nTp7N06dJCr4GCrul77rmnwG1zatu2LfPmzcPBwQGAo0eP8sMPP/Dmm2/Sr18/ateurQ2aEh0dbTR4RM73YW9vr+0nq3xect7UG7aBsrj+hRBClD251zSNynSvuXr1aqNjGA44ZzigVVpaGj/++KM2n/O+s7Af7Q3LF/cHfsP7Eij6uahbt26ej/uvXr2al156qcCALWB0TRjW387OLs9rLKfJkydr08uXLycpKckojdn48eML3QcY3zMD/Pfff0XazlT3dCW5Z4b8r8HC2mdWW7527RoDBw4sNGAL+V8T+V0DBVm4cCHt27cH1B+d1q9fz6effsqTTz5JUFAQw4cP196bqb6XGN43l8f3RnF3kqCtEJWAYV7Q0NBQvv76a6NcU4brExMTWbdunTbfs2dPLl68SEZGBoqi0KZNG5PVy7BXYF75NyMiIvLcznA01Vq1anHgwAFSU1NRFIVXXnnFZPUz5ObmVmC9DJe5urrmuY+cPQ1y/hJfFgzrDbnrnnPesO5z5swhIiKC9evXM2/ePJ5++mntJiwsLMzo19+mTZvyzz//cPLkSZYuXcrrr7+u9RDR6/XMnz+fbdu2FVpfwx4wiqIYjcJc1PeZkJBgNKJzzvep0+ny7ZGa8xwZKu7NHeS+rnN+3ln1MLymmzRpwunTp0lPT0dRFIYOHVqkYxU1j5mhF154gYiICLZu3cqCBQt47rnnCAoKAuDWrVuMGTMGUK8Lw+s15/tITEw0+pJTmdqAEEKIsif3mqVXme41ly5dajTfu3dvbUR7w+B7zrI57zsLyhmcs3xhZUHteZvFMIcoYJRXtiD53S8ZnnMHBwc2b95McnIyiqLw+eef57mNYf2TkpIKHU8A0HKxAsTGxvLll19qYx5YWVkxcuTIIr2PNm3a4OjoqM3/8ssvWm7ggpTVPZ0hU9wz56xbVlv+448/jN7n3LlziYmJQVEUow4lBSnJPbOvry/79u3j/Pnz/Pjjj7z99tsMGTJEe6+//PKL9r2lLL6XyD2zKCsStBWiEnjooYeM/uAaDshgZWVl9ChSbGys9ngbqI+31KlTB3Nzc86ePWv0OE1ptW7dWpvOChxl2bt3b743b1FRUUb7aNu2LVZWVqSkpPDHH3/kezzDP3xFuakxZPi4zs2bN9mwYYM2HxkZaTRvWLaiNWjQwOjG4YcffjA6vzmDoll1DwkJISYmBmdnZ/r168eLL77IwoUL+eyzz7SyR48e1aaPHz8OqAHHMWPGMGvWLNavX0/Tpk3zLJ+f8ePHG92AvvHGG3kOQqEoCr/++qt2c5bzMzfs3ZOcnGzUg6FZs2Z5PoJUFrJ6G4FaZ8PeKFZWVtpNu+E13aNHDxo1aoSFhQU3b97Uerua2rVr14iIiMDOzo57772X5557jgULFhh9aQkPDycqKgo7OzuaNWumLV+5cqXRlyXDzxsqVxsQQghR9uReU1Ud7jWvXbvGli1bilz+2LFj2jnr3Lmz0bqZM2eSkZFhtCwsLEybNiz/77//GqVkAPXeybBHpWFw6+zZs1oPxdjY2HwDq0VleM7r1KlD7969sbGxQa/Xs2rVqjy3yfl+Z8yYkasHsOH7zfLcc89p06+99pqWGuGBBx6gRo0aRaqvpaWlUQeK69ev89hjj+UKZoN6Tr/66iuASntPd+nSJfbu3avN52yfrVq1AozPE6hpV7IGETO83ze1EydOoNfrqVevHo8++igzZsxg1apV2uCKkP1dp7J+LxEiL5LTVohKwNramhEjRmgjnRreRD7wwANaHipQH01xcXHRboJmzZpFZGQkGRkZLF682KSPgY0cOZK3335b2+egQYOYMGGCNmJofho0aKD9mr5u3TqeeuopvL29WbVqVYGPBhk+RnTkyBGef/55fH19sbKyMnpUKS9jxoxh5syZ2o3CkCFDGD9+PE5OTvz000/aL9I6nY4XXnihSO/fFK5fv270hcTQ22+/zf3338+LL77Im2++Cag5ljp37kyfPn3477//jG4aevTood3ErVixghkzZtC9e3eCgoKoWbMmiYmJLF++XCtveOPcvn17atWqRZcuXahVqxZOTk6cOHHC6ItXUfKtenh4sGjRIkaNGoWiKCQmJtKrVy969epFhw4dsLKyIiwsjC1bthAaGqr13h0wYAANGjTg7NmzgHozfOjQIXx8fFi9erXRDbPhF8mytnnzZnr27EnXrl3ZvXu30ZfFRx99VLtJa9CggTaS7Ndff42ZmRl2dnZ8//333Lx5s0zqtnPnTkaOHEnnzp0JDg6mVq1aZGZmannWQP2inVXHl156icceewxQe1G1adPGaKThLPXr12fAgAFlUmchhBCVk9xrqqrDveZ3331nFFR/4IEHcgWV9Hq9ll8XYMmSJcyfP58mTZrQv39/1q9fD6ifXbNmzejfvz82Njb8888/7Ny5k1u3bgFqqoCFCxdqQcNHH32UFStW0Lx5c6Kjo9m+fTvdu3fnk08+ATDqhR0XF0eLFi1o27Yte/bs4erVq6V63w0aNNCC1SdPnmTEiBEEBwezYcMGbRyDnPr370+TJk04deoUAIsWLeLYsWPce++9KIrC0aNHiYyM5NixY0bbjRw5kmnTphEdHU1KSoq23DDvc1G88cYbbNmyRQsW/vbbb9StW5dBgwZRu3Zt4uPjOXr0KFu3bqVTp048+eSTQOW9p+vfvz/jx4/P1T4tLCy03vo5c7sOGDCAfv36cfLkyXyD66YwfPhwYmNj6dGjBz4+Pri5uXHx4kXtWofs7zqV9XuJEHkqx0HPhBAFOHjwoNGIk1mvP/74I1fZDz74IM+yjRs3Vlq1aqXNG46yW5IRfRVFUT777LM8j1WrVi0lKCgoz2Pt2rVLsbCwyLWNg4ODMnjwYG3e39/f6FjHjh1TzMzMcm1nb29fpLru2LFDcXFxybO+gGJmZqZ8/PHHRtsYjuibsz4hISFG22/bti2/02ekW7du+dbB8JU14mlGRoYydOjQAssGBwcrV69e1Y6Rc7TlvF4LFizQyltbWxdYNjAwUImJiSnS+1MURVmxYoXi7OxcaB0MP7MzZ84otWvXLrD85MmTjY6Tc5RWQzlHYzaU33WS85wOGDAgz3oEBAQoERER2nbLly/Ps1zNmjWV3r17F+lY+V0/+V2D+R3T8DVlyhSjfeUclTevdnv69GmjbQxHyJ0xY0aR6iaEEKLqkXvN6nGvec8992jlg4KC8i3XpUsXrZynp6eSnp6uKIqi3Lp1S2nTpk2+78HZ2dloP3/++afi6OiYb/nnn39eK5ucnGx0zgxf/fv3L9H1keX8+fN51sPCwkIZOXJkvveEFy9eVOrVq5dv/Zs1a5bn8V5++eVc93wZGRn5ft75uXXrlnL//fcXek+X832b+p7OcFvDdQVdg4bnpWHDhkpAQECedfnwww+1bdLS0pQmTZrkWc5wfwUdK79rIOf7yPoupSiK0qBBgwI/Lzc3NyU0NFQrb+rvJQXVTYjSkPQIQlQSbdq0oVGjRkbLvL296du3b66y06ZN4/PPP6d+/fpYWlri7e3NE088wY4dO4wS05vCxIkTWbVqFa1atcLa2poaNWrw2GOPceDAAaMk9oY6d+7Mpk2b6NixI9bW1jg7O9O/f3/27t2bK8+WoebNm7N8+XJatmxpNEhDUXXt2pXTp0/z0ksv0ahRI+zs7LCyssLPz4+RI0eyd+9eXnrppWLvt6yZm5vzyy+/sHLlSvr374+npycWFhY4OzvTrl075syZw6FDh4w+74EDB/LWW2/Rq1cvAgICsLOzw8LCgpo1azJgwADWrl1r9GjXwoULGTduHE2bNsXDwwMLCwscHBxo2rQpU6dO5cCBA9qjS0UxbNgwQkJC+Pjjj+nVqxdeXl5YWVlhY2NDvXr1tBGnDR9LCw4O5sSJE7z99tu0bNkSBwcHrc6DBg1i06ZNfPrpp6b5UIvo5ZdfZvny5bRq1QobGxvc3d0ZM2YMe/fuNRpw4ZFHHuGXX36hWbNmWFpa4u7uzvDhw9m/f3++7aC0OnfuzHvvvceAAQOoW7cujo6OWFhY4OHhQc+ePVm6dClz58412mbu3Lls2bKFIUOGUKtWLSwtLXFwcKB58+a8+eabnDx5Mte/M0IIIe4Ocq9Z9e819+/fb9STuKCen4brIiMjtYHl3N3d2bNnD9988w29evXS7gtdXV1p1apVrl7C/fv3559//uGVV16hadOmODg4YGlpSa1atRgwYIDR4+c2NjZs3bqVYcOG4eLigo2NDe3ateP3338vda7hevXqsXPnTvr06YOdnR0ODg5069aNrVu30qtXr3y3q1OnDsePH2fevHl07twZV1dXLCwsqFGjBp06dWLChAl5bjdx4kSjHL2jR4/G3Ny82PV2d3fnjz/+YMeOHTz++OMEBwfj5OSEubk5bm5udO7cmY8++ihX2oPKdk/n4eHB/v37GT9+PJ6enlhbW9O8eXN+/PFHpk6dqpWztLTk77//ZuzYsbi7u2NtbU3jxo356quvePvtt8usfrNnz+bpp5+mVatWeHt7Y2lpiZ2dHffccw/PPvssR44cwd/fXytfGb+XCJEXnaLkSOoihBBCVFOhoaFGox9v27aN7t27V1yFhBBCCCFEpZOSkoK3tzexsbEA/Pfff7ke/a/uxo4dq6Vj6NatW5mN4yCEyJ/ktBVCCCGEEEIIIcRdb//+/cTExPDdd99pAdtevXrddQFbIUTlIEFbIYQQQgghhBBC3PUeeeQRo4GorKys+OijjyqwRkKIu5nktBVCCCGEEEIIIYS4w9HRka5du/LXX3/RokWLiq6OEOIuJTlthRBCCCGEEEIIIYQQohKRnrZCCCGEEEIIIYQQQghRiUjQVgghhBBCCCGEEEIIISqRu24gMr1ez7Vr13B0dESn01V0dYQQQgghRDEoikJ8fDy1atXCzOzu7X8g97RCCCGEEFVTUe9n77qg7bVr1/D19a3oagghhBBCiFK4fPkytWvXruhqVBi5pxVCCCGEqNoKu5+964K2jo6OgPrBODk5lcsx9Xo9N2/exMPD467uEVJdyPmsXuR8Vi9yPqsXOZ/VjynOaVxcHL6+vto93d2qvO9ppT1WL3I+qx85p9WLnM/qRc5n9VKe97N3XdA26/ExJyencg3apqSk4OTkJA20GpDzWb3I+axe5HxWL3I+qx9TntO7PSVAed/TSnusXuR8Vj9yTqsXOZ/Vi5zP6qU872flahFCCCGEEEIIIYQQQohKRIK2QgghhBBCCCGEEEIIUYlI0FYIIYQQQgghhBBCCCEqkbsup60QQgghSi4zM5P09PRyOZZeryc9PZ2UlBTJ/1VNFOWcWlpaYm5uXs41q75M1WalPVYvpjqf0l6FEEKIsiNBWyGEEEIUSlEUbty4QUxMTLkeU6/XEx8ff9cPOlVdFPWcuri44O3tLee9FEzdZqU9Vi+mPJ/SXoUQQoiyIUFbIYQQQhQqK/jj6emJnZ1duXw5VxSFjIwMLCwsJBhQTRR2ThVFISkpicjISABq1qxZ3lWsNkzdZqU9Vi+mOJ/SXoUQQoiyJUFbIYQQQhQoMzNTC/64u7uX23ElSFT9FOWc2traAhAZGYmnp6c8el0CZdFmpT1WL6Y6n9JehRBCiLIjCamEEEIIUaCsfJh2dnYVXBNxt8i61sorf3J1I21WlCdpr0IIIUTZkKCtEEIIIYpEeteJ8iLXmmnI5yjKg1xnQgghRNmQoK0QQgghhBBCCCGEEEJUIhK0FUIIIYQQmu3bt6PT6YiJianoqgghikDarBBCCFE9SdBWCCGEENXW2LFj0el06HQ6LC0tCQwMZOrUqaSkpJhk/zqdDhsbG8LCwoyWDxw4kLFjxxZ5PyUJuty8eZNnnnkGPz8/rK2t8fb25r777mPPnj1G9Vu9enWR9ylERZM2K21WCCGEECqLiq6AEEIIIURZ6tu3L0uWLCE9PZ0jR44wZswYdDodH374oUn2r9PpeOutt1i2bJlJ9ldUQ4YMIS0tjWXLllGnTh0iIiLYunUrUVFR5VoPIUxN2qwQQgghhPS0LT+KvqJrIIQQQtyVsnq0+fr6MnDgQHr16sWWLVsA0Ov1zJ49m8DAQGxtbWnWrBmrVq3Sto2OjmbkyJF4eHhga2tLUFAQS5YsMdr/pEmT+OGHHzh9+nS+dSjoOKGhofTo0QMAV1dXdDpdoT3+YmJi2LVrFx9++CE9evTA39+ftm3bMn36dB588EEAAgICABg0aBA6nY6AgABCQ0MxMzPj8OHDRvv75JNP8Pf3R6/P+35l9+7ddOnSBVtbW3x9fZk8eTKJiYkF1lGIkpI2K21WCCGEENLTtuwd+hbd2fW4R1+BifsqujZCCCHEXe306dPs3bsXf39/AGbPns0PP/zAokWLCAoKYufOnYwaNQoPDw+6devGm2++yZkzZ9iwYQM1atTgwoULJCcnG+2zU6dOnDt3jldffZV169bledyCjtO5c2d+/fVXhgwZwtmzZ3FycsLW1rbA9+Hg4ICDgwOrV6+mffv2WFtb5ypz6NAhPD09WbJkCX379sXc3BwPDw969erFkiVLaN26tVZ2yZIljB07FjOz3L/nX7x4kb59+zJr1iwWL17MzZs3mTRpEpMmTcoVDBPC1KTNSpsVQghR+SmKQkRSBFHJUSRlJJGckUxSehJJGUn5/j85Ixl7S3tcrF1wsXbB1cZV/b+1Ky422f+3tSj4b2x1JkHbsnb6V3Rhe7AE9LcvQY16FV0jIYQQwiQe+N9ubsanlukxFBR06IyWeTha88dznYu8j3Xr1uHg4EBGRgapqamYmZnx2WefkZqayvvvv89ff/1Fhw4dAKhTpw67d+/myy+/pFu3boSHh9OiRQstWJLVEy6n2bNn07RpU3bt2kWXLl2M1hXlOG5ubgB4enri4uJS6HuysLBg6dKlPPHEEyxatIiWLVvSrVs3HnnkEZo2bap+Th4eALi4uODt7a1tO2HCBJ5++mnmzZuHtbU1R48e5dSpU6xZsybf9zZy5EheeOEFAIKCgliwYAHdunVj4cKF2NjYFFpfUfFM0V7zao+FKW57BWmz0maFEEJUVqmZqYTHhRMSG6K+4tT/h8aGkpSRVCbHtDG3wcXGBTcbN2o71MbPyQ8/Rz98HX3xc/LDw9YDna549ydVhQRty1q9XhB2Z3CBC39J0FYIIUS1cTM+lRtxphkcqCz16NGDhQsXkpiYyPz587GwsGDIkCH8888/JCUl0bt3b6PyaWlptGjRAoBnnnmGIUOGcPToUfr06cPAgQPp2LFjrmM0bNiQ0aNH8+qrrxoNKgRw4cKFQo9TEkOGDGHAgAHs2rWL/fv3s2HDBj766CO++eabAh/VHjhwIBMnTuT333/nkUceYenSpfTo0SPf4NaJEyc4efIkP/74o7ZMURT0ej0hISEEBweX+D2I8lNV2itIm81J2qwQQoiyplf0JKQnEJ8Wr73i0uK4nXKb0NhQLUh7LfEa+nJO/5mSmcKNxBvcSLzBmagzudbbmNtQ27E2fo5++DllB3Nr2deihm0N7CztyrW+piRB27IW1Ae2vgOA7sIWaP90BVdICCGEMA0Px9yP95pafj1ti8Pe3p569dQfTRcvXkyzZs349ttvady4MQB//vknPj4+RttkPbrcr18/wsLCWL9+PVu2bKFnz55MnDiRjz/+ONdx3nnnHerXr59r5PeEhIRCj1NSNjY29O7dm969e/Pmm28yYcIEZsyYUWAAyMrKitGjR7NkyRIGDx7MTz/9xKeffppv+YSEBJ566ikmT56ca52fn1+p6i/Kjynaa0l72haXtFlj0maFEAIWnVjEsn+WMbrhaJ5p/kxFV6fSytRnEpsWS0xKDDGpMUSnRhOTYvz/uLQ4o+BsQloCCekJKCglOqaZzgwfBx8CnAKo5VALO0s77CzuvCzz/7+1uTVJGUlavaJTotU63/m/4XR0SjTRqdF5BoxTMlO4EHOBCzEX8qyfrYUtNWxraC93G3fjeVt3bbmluWWJPoOyIkHbsubVCMWxFrr4axC6G9KTwfLuzcchhBCi+ijuI8/FpSgKGRkZWFhYmOyRJzMzM1577TWmTJnCuXPnsLa2Jjw8nG7duuW7jYeHB2PGjGHMmDF06dKFV155Jc8AkK+vL5MmTeK1116jbt262vKGDRsWehwrKysAMjMzS/X+GjZsaBSAsrS0zHOfEyZMoHHjxnzxxRdkZGQwePDgfPfZsmVLzpw5owXRRNVU2vZaFu2xKKTNqqTNCiHuZptDN/P58c8B+OLEF3jYefBw/YcruFblKy0zjcikSCKSIohIjCAiKUKbv5l0UwvQxqXGlTj4Whg7CzsCnQMJcA4g0CmQQGf15efkh7V5yX7UdMUVHwefwgsC6ZnpXEu8RnhcOOHx4VyOv8zl+MuEx4VzJeEKGfqMPLdLzkjWyhbEXGfOkVFHMDczL/b7KCsStC1rOh3U6wnHvkeXkaIGboN6F76dEEIIIcrE0KFDeeWVV/jyyy95+eWXefHFF9Hr9XTu3JnY2Fj27NmDk5MTY8aM4a233qJVq1Y0atSI1NRU1q1bV+CjxdOnT+frr78mJCSE4cOHA+Do6Fjocfz9/dHpdKxbt47+/ftja2uLg4NDvseJiopi6NChjB8/nqZNm+Lo6Mjhw4f56KOPeOihh7RyAQEBbN26lU6dOmFtbY2rqysAwcHBtG/fnmnTpjF+/PgCB1GaNm0a7du3Z9KkSUyYMAF7e3vOnDnDli1b+Oyzz4r78QtRbNJmpc0KIe5el+MuM2PvDKNl7+1/jwCnAFp7t85nq6rrVvIt/rz0J5fjLxsFaG+n3Db5sazNrXGwdMDRyhEnKyccrRxzvZysnPBz8iPQKRBPO88KzR1raW6Jv5M//k7+udZl6jO5kXSD8LjsYG7WwGi3km9xK/kWcWlxBe7f2dq5UgVsQYK25UKp1xvdse/VmfNbJGgrhBBCVCALCwsmTZrERx99REhICB4eHsyePZtLly7h4uJCy5Ytee211wC1N9306dMJDQ3F1taWLl268PPPP+e7bzc3N6ZNm6Ztn2XmzJkFHsfHx4d33nmHV199lXHjxjF69GiWLl2a73EcHBxo164d8+fP5+LFi6Snp+Pr68sTTzxhdOy5c+cyZcoUvv76a3x8fAgNDdXWPf744+zdu5fx48cX+Hk1bdqUHTt28Prrr9OlSxcURaFu3bpagEuIsiZtViVtVghxt0nLTOPlnS+TkK6mrfG09SQyOZIMJYMp26ew/P7lRe6lWRVsDNnIzP0zCw0u5sXR0hEXGxdcrV1xsXHBxVp9udq4qv83WO5s7YyjlWOJe8dWRuZm5vg4+ODj4EMHOuRZJi0zzSiIeytF/X/WMjuLypf7VqcoStn0m66k4uLicHZ2JjY2Ficnp3I5pj45Bt2cuuj0GeBWByYfK5fjirKh1+uJjIzE09MTMzOziq6OKCU5n9WLnM+ykZKSQkhICIGBgeU66nhFPY59t5g5cyYrV67k5MmT5XbMop7Tgq65iriXq4wK+hzKos1Ke6x4pmyzpjyfFfU3QhiTe6DqRc6n6oODH/Djv+qgiv5O/vzY/0de2fEK+67vA6C+a32+7/d9uQw0lanPJCw+jLO3z/Lf7f9ITE+kb0DfIvX2Lex8xqTE8N6B99gYujHXOnOdOR52HnjZeeFl54WnnSfe9t7qvL26zMPWo9LlYq3OTNE+i3o/Kz1ty4O1E+leLbG6fhBuX4Koi+Bet/DthBBCCCFMLCEhgdDQUD777DNmzZpV0dURQhRC2qwQ4m60NWyrFrC1MrPi424f42ztzJxucxi5fiRhcWGciz7H9F3Tmd9jPmY60wW3k9KTOBd9Tg3QRv/H2dtnOR99npTMFKNyK86uoJNPJ55v8TzB7vmn4inIzis7mbF3BreSb2nL7gu4j9ENR+Nt7427jXule2RflB8J2paTVL+uatAW1BQJErQVQgghRD7Cw8Np2LBhvuvPnDlT4lHgJ02axPLlyxk4cGChj1kLIYpG2qwQQpjOlfgrvLn3TW1+apup3ON2D6DmHV1w7wJG/TmK+PR4/r78N18c/4JJLSaV+HhxaXH8fv53Tt06xdnbZwmLCyvyYF57ru5hz9U99A3oy6QWk/LMt5qXxPRE5hyaw6/nf9WWOVk58Ub7N+gX2K9E70NUPxK0LSepfl1xPHBn1NoLW6D90xVbISGEEEJUWrVq1eL48eMFri+ppUuXFph7UwhRfNJmhRDCNNIz03llxyvEp8UD0Me/D8MaDDMqU8e5Dh91+4iJWyeiV/R8efJL6rnWo29A32Ifb1v4Nmbtn0VkcmSB5fwc/Wjg1oAGrg24x+0ebqfcZuGJhVxPvA7AxtCNbAnbwqCgQTzd9Gm87L3y3deRiCO8vvt1riZc1ZZ18unEux3fxdPOs9jvQVRfErQtJxlu9VGcaqGLuwYhuyAtCawqX5JjIYQQQlQ8CwsL6tWrV9HVEMXw+eefM2fOHG7cuEGzZs343//+R9u2bfMs2717d3bs2JFref/+/fnzzz/LuqqiDEibFUII05h/dD6no04DUNuhNm93fDvP3NudfTozpdUUPj6sdo57c/eb+Dn60dA9/6ceDN1Ouc0HBz9gQ8gGo+XW5tbUc6nHPW730MBNDdDWd62PvaV9rn0MqDOAX87+wlcnvyI6NZpMJZNV51bxx8U/eDT4UR5v/DjO1s5a+dTMVD4/8jnfnflO68lra2HLK21e4eGghyVnvMhFgrblRaeDer3h6DLITIXQ3VC/T0XXSgghhBBClNKKFSuYMmUKixYtol27dnzyySfcd999nD17Fk/P3D1mfvvtN9LS0rT5qKgomjVrxtChQ8uz2kIIIUSlsi18G9+f+R4ASzNLPu7+MY5WjvmWH91wNOejz7Pm4hpSMlOY/Pdkfr7/Z2rY1sh3G0VR2Bi6kdkHZhOdGq0t7+LThedbPk9dl7pYmBUtVGZlbsWohqMYFDSI7858x7J/lpGYnkhqZipLTi9h1dlVjGs8jhENRnAu9hxz98/lUuwlbfuWni2Z1WkWvk6+RTqeuPvcvcMQVgClXq/smQtbKq4iQgghhBDCZObNm8cTTzzBuHHjaNiwIYsWLcLOzo7FixfnWd7NzQ1vb2/ttWXLFuzs7CRoK4QQolK5lXyLX87+wspzKzl84zBRyVEoStFyvRbXtYRrvLHnDW3+5dYv08i9UYHb6HQ63urwFs08mgEQkRTB89ueJy0zLc/ykUmRPL/teabunKoFbJ2tnXm/8/t83vNzGrg1KHLA1pC9pT3PNHuGDYM3MLrhaKzMrACIT49nwbEF9P+9P5MPTNYCtpZmlkxpNYXF9y2WgK0okPS0LU+BXcHMEvTpcH4zKB+pPXCFEEIIIUSVlJaWxpEjR5g+fbq2zMzMjF69erFv374i7ePbb7/lkUcewd4+96OXWVJTU0lNTdXm4+LiANDr9ej1eqOyer0eRVG0l6lk7ausvrCL8mWq85l1neV1LYryk9Xu5RxUD5XhfO6+ups39rxh1BsV1MGyApwCCHAOINApkEDnQAKdAvFx9MHSzLJEx0rXq3ls49LUv209/XoyvP7wIr1/C50F87rN49H1jxKRFMHJmyd5Z+87vNvxXS3dgKIorL64mrmH5xKfHq9t29uvN9PbTsfd1t0kfzOdrZx5qdVLPHrPoyw6sYi1l9aiV/RGn2ED1wa81+k9glyDAKTNVkGmaJ9F3VaCtuXJ2gn82kPoLogOhaiLUENyXwkhhBBCVFW3bt0iMzMTLy/jAUe8vLz477//Ct3+4MGDnD59mm+//bbAcrNnz+add97JtfzmzZukpKQYLUtPT0ev15ORkUFGRkYR3kXhFEUhMzMTQHLuVQOmPJ8ZGRno9XqioqKwtCxZwEaUnl6vJzY2FkVRMDOTB2qruoo8n+n6dL499y2/hv2a5/q4tDhO3jrJyVsnjZab68ypZVeLAIcAmrs1p02NNtS0q1mkY3599mttf9623kyqN4mbN28Wq94zms3gxQMvkqpPZe2ltXhbejM0YCgRyRHM+2ceR6OOamVdrVx5Lvg5unh3ITM+k8j4ggchKy5zzJlYbyL3e9/PsgvL2BWxCx06RgSOYFS9UVimWxIZadpjivJjivYZHx9feCEkaFv+gnqrQVtQUyRI0FYIIYQQ4q717bff0qRJk3wHLcsyffp0pkyZos3HxcXh6+uLh4cHTk5ORmVTUlKIj4/HwsICCwvT3u5LUK56McX5tLCwwMzMDHd3d2xsbExQK1ESer0enU6Hh4eHBG2rgYo6n+Fx4by6+1X+ifpHW9bVpyudfToTGhdKSGwIoXGhXE+8nmvbTCWTy4mXuZx4mV0RaszDz9GPTrU60cmnE608W2FnmXsw9p1XdvJL6C8AWJhZMLf7XOrUqFPsunt6ejLTYiZTd00F4Jtz35CgS2D1hdUkZSRp5R6o8wCvtH7FaICwsuLp6UmbOm0Ijw0nNjqWRn6NpH1WA6Zon0X9eylB2/IW1Ae2vKVOn98M7Z+p2PoIIYQQolrYvn07PXr0IDo6GhcXl4quzl2jRo0amJubExERYbQ8IiICb2/vArdNTEzk559/5t133y30ONbW1lhbW+dabmZmlusLg5mZGTqdTnuZgqIo2r6kp23pVXR7NeX5zLrO8roWRfmS81C9lPf5XHdpHTP3zdQCnJZmlrzUWn3UP+e/E0npSYTFhRESG0JIXAihsdkB3dTM7FQ+4fHhhJ8NZ/nZ5ViaWdLKqxWdfTrTqVYn6rrUJSIpgjf2ZuexndJqCk09m5b4PfSr04+LsRf58uSX6BU9P/33k7bOy86LGR1m0KV2lxLvv6T8nP2ITI2U9lmNlLZ9FnU7uVrKm8c94FRbnQ7dA2mJFVsfIYQQopp64IEH6Nu3b57rdu3ahU6n4+RJ9VG8X3/9lXvvvRdXV1dsbW1p0KAB48eP59ixY0bbpaWlMWfOHFq2bIm9vT3Ozs40a9aMN954g2vXrhWpXjdv3uSZZ57Bz88Pa2trvL29ue+++9izZ49WRqfTsXr16pK9cVGurKysaNWqFVu3btWW6fV6tm7dSocOHQrcduXKlaSmpjJq1KiyrmalJ+1VCCEqRlJ6Em/sfoPpu6ZrAVt/J39+6P8DI4NH5vnDjp2lHcHuwfSv05+JzScyp9scVj24ioMjD/LL/b/wfMvnaeXVCgtddj/BdH06+6/v5+PDHzNo7SB6r+rN45seJzY1FoAevj0YFVz6v4fPNn+Wnn49jZYNqz+M1Q+trpCArRClIUHb8qbTQVAvdTozFUJ2VWx9hBBCiGrq8ccfZ8uWLVy5ciXXuiVLltC6dWuaNm3KtGnTGD58OM2bN2ft2rWcPXuWn376iTp16hgNLpWamkrv3r15//33GTt2LDt37uTUqVMsWLCAW7du8b///a9I9RoyZAjHjh1j2bJlnDt3jrVr19K9e3eioqJM9t5F+ZoyZQpff/01y5Yt499//+WZZ54hMTGRcePGATB69GijaynLt99+y8CBA3F3dy/vKlc60l6FEBXpWsI1Ht/0OGM3jmXluZUkpCVUdJXKxX+3/2P4uuGsubhGW/Zg3QdZcf8KGro3LPb+zHRmBLsHM6HJBJb2XcquR3bxSY9PGFp/KLXsaxmVjUiKIDw+HIBa9rWY2WmmSZ7kMNOZ8X7n97m/zv20q9mOb/t8y5sd3sTByqHU+xai3Cl3mdjYWAVQYmNjy+2YmZmZyvXr15XMzEx1wb/rFGWGk/paN6Xc6iFMI9f5FFWanM/qRc5n2UhOTlbOnDmjJCcnl+tx9Xq9kpaWpuj1+hJtn56ernh5eSkzZ840Wh4fH684ODgoCxcuVPbt26cAyqeffppvHbLMnj1bMTMzU44ePVpo2fxER0crgLJ9+/Z8y/j7+yuA9vL391dCQkIUnU6nHDp0yKjs/PnzFT8/PyUzM1PZtm2bAijR0dHa+l27dimdO3dWbGxslNq1ayvPPfeckpCQUGg9y0pRz2lB11xF3MsVxf/+9z/Fz89PsbKyUtq2bavs379fW9etWzdlzJgxRuX/++8/BVA2b95couMV9DmURZstbXssjLTX8m2vpjyfFfU3QhiTe6CSS81IVYb9MUxpvLSx9mrzQxvljd1vKEcjjpbZv3sFKevzqdfrlR/O/KC0+K6F9p7b/tBWWXthbZkcL+uYl2IuKd//873y1JanlFbft1IaL22stPq+lXI88niZHbcykPZZvZjifBb1flZ62laEwK5gdifp//nNoCgVWx8hhBCiGrKwsGD06NEsXboUxeBv7cqVK8nMzGTEiBEsX74cBwcHnn322Tz3YdjjY/ny5fTu3ZsWLVoUWjY/Dg4OODg4sHr1alJTU/Msc+jQIUDtXXj9+nUOHTpEQEAAvXr1YsmSJUZllyxZwtixY/PMi3Xx4kX69u3LkCFDOHnyJCtWrGD37t1MmjSp0HqK4ps0aRJhYWGkpqZy4MAB2rVrp63bvn07S5cuNSrfoEEDFEWhd+/e5VzTyknaq7RXISrKx4c/5kzUGaNlyRnJrL6wmtEbRvPQmodY9s8yopKrRw/7mJQYJm+bzAcHPyBdnw5AsFswvzzwCw/UfaDMjqvT6Qh0DmRUw1Es6rWI3Y/sZmnfpfz+0O8082hWZset9tKTITm6omtRuej1cHgx/PU27JwD+76AI8vg1Co4u1F94v3qUbh5DmKvQnIMZKZXdK3zVKEDkc2ePZvffvuN//77D1tbWzp27MiHH35IgwYN8t1m6dKl2qNmWaytrUlJSSnr6pqOtSP4d4SQHRATDrfOg0f9iq6VEEIIUTxfdoOEyDI9hAUKkCO44uAJT+0o0vbjx49nzpw57Nixg+7duwNq4GTIkCE4Oztz7tw56tSpg4VF9i3RvHnzeOutt7T5q1evamWz9pFl0KBBbNmyBYCmTZuyd+/egt+PhQVLly7liSeeYNGiRbRs2ZJu3brxyCOP0LSpOvCGh4cHAC4uLkYDWU2YMIGnn36aefPmYW1tzdGjRzl16hRr1qzJ81izZ89m5MiRvPDCCwAEBQWxYMECunXrxsKFC2WU97uNCdprnu2xMNJepb0KUYltDN3I8v+WA2BlZsV7nd/jcMRh1l9aT3x6PAAhsSF8fPhjPjnyCT38ejCo3iA61uqIuZl5RVY9l5SMFG4l3+JW8i2ikqPU6ZTs6az/30y+qQVrAUYFj+LFVi9iZW5VrvW1sbChlVercj1mtZN0Gxb3hVtnYcQKaJB3bvi7zomfYN2Lxd/O2hmmh5u+PqVQoUHbHTt2MHHiRNq0aUNGRgavvfYaffr04cyZM9jb2+e7nZOTE2fPntXmq+QItkG91aAtwIUtErQVQghR9SREQnzRBvMpCVP8db/nnnvo2LEjixcvpnv37ly4cIFdu3bx7rvv5rvN+PHjefDBBzlw4ACjRo0y6vWX0xdffEFiYiILFixg586dRarTkCFDGDBgALt27WL//v1s2LCBjz76iG+++YaxY8fmu93AgQOZOHEiv//+O4888ghLly6lR48eBAQE5Fn+xIkTnDx5kh9//FFbpigKer2ekJAQgoODi1RfUU2Usr2Wx922tFdpr0KUp7C4MN7e+7Y2/2q7V+kb2Je+gX15qfVL/BX2F7+e/5UjEUcAyFAy2BK2hS1hW/Cy82JQ0CAG1huIj4NPmdUxQ59BbEqsFoy9lXyLqJQo4/k7wdiE9OLl4XW1dmVW51l0rd21jGovytz2D9SALcDe/0nQNsu5TSXbzrxCQ6R5qtAabdy40Wh+6dKleHp6cuTIEbp2zf8fDp1OZ/RLdmW37tI6mto2NV5YrzdsfkOdPr8FOkws/4oJIYQQpeHgWaa7V7T/6owDRsU87uOPP85zzz3H559/zpIlS6hbty7dunUD1N5su3fvJj09HUtLNXWRi4sLLi4uuQZECgoKMvrRGKBmzZoAuLm5FatONjY29O7dm969e/Pmm28yYcIEZsyYUWAQyMrKitGjR7NkyRIGDx7MTz/9xKeffppv+YSEBJ566ikmT56ca52fn1+x6iuqgVK213zbo4mPK+1V2qsQ5SElI4WXtr9EYnoiAAPqDODhoIe19bYWtjxQ9wEeqPsAobGh/H7hd9ZcWENUipoiISIpgkUnFvHliS9pX7M9g4MGc6/fvaXurRoeF87G0I38Hf43V+OvEpsWi4Lp0im62bjhbutOsFswk1tMxsvey2T7FuXs5lk49E32fPg+teetXfH+xlUYRYEbp8DVH2ycTbvf8H3qtLUzDP4S0hJzvBLynraufIPVVaowcmxsLFD4jVRCQgL+/v7o9XpatmzJ+++/T6NGjcqjisW2+sJq3tzzJvUc6/FZ78/wcbzzK5xHA3D2g9hwCNsDqQmV8gIRQggh8lXER55LTFHIyMhQH4UuxVM1w4YN4/nnn+enn37iu+++45lnntGe0hkxYgT/+9//+OKLL3j++ecL3M+IESN44403OHbsWL55MkuqYcOGrF69Wpu3tLQkMzMzV7kJEybQuHFjvvjiCzIyMhg8eHC++2zZsiVnzpyhXr16Jq2rqKJK215N1B4LI+1V2qsQ5eHDQx9yNlr9YSfQOZC32r+V7xO8Ac4BvNjqRSa1mMSuK7v47fxv7Lq6C72iR0Fh3/V97Lu+DxdrF+6vcz+DgwYT5BpU5LpcT7jOptBNbAjdkCu3blE4WDrgbutODdsauNuo/896ZS2vYVsDVxtXLLPG1hFV36bXQDH426NkwoW/oOmwiqtTcWx6HfZ/Dl6N4ckdpuvlGnUREm+q037toEE/0+y3glSaoK1er+eFF16gU6dONG7cON9yDRo0YPHixTRt2pTY2Fg+/vhjOnbsyD///EPt2rVzlU9NTTUaOCAuLk47nl6vN/0bMZCUnsS8w/MAuBB/gRF/jmBet3m09GoJgK5eT3RHlkBmGvpLO6r8xXS30Ov12uNqouqT81m9yPksG1mfa9arPGUdrzTHtbe3Z9iwYUyfPp24uDjGjBmj7a99+/ZMmTKFl156idDQUAYPHoyvry/Xr1/n22+/RafTodPpUBSFF154gT///JOePXvy1ltv0aVLF1xdXTl37hwbNmzA3Ny80HpGRUUxbNgwxo0bR9OmTXF0dOTw4cN89NFHPPjgg9r2AQEB/PXXX3Ts2BFra2tcXV0B9fHx9u3bM23aNMaNG4eNjU2uzyjrPE2dOpUOHTowceJEJkyYgL29PWfOnGHLli189tlnJf48S6so5zTrPeR1vybtu3pzcHBg+PDhWns17M3aoUMHXnrpJV566SXCwsLybK9Zg3y9+OKLWnudMWNGnu21MFFRUQwdOpTx48fnaq8PPfSQVi4gIICtW7fSqVMno/YaHBystdfx48dja2ub77GmTZtG+/btmTRpUqVqr0JUR+surWPVuVUA2JjbMLfbXOws7QrdztLMknv97uVev3uJSIxg7cW1/Hb+N64kqD39Y1Jj+OHfH/jh3x9oWqMpg4MG0zewL/aWuVM/3kq+xabQTWwK3cSxyGN5Hs/DxgMvey9q2GUHYWvYGAdj3W3dsbXI/98WUU2d26wGaAEsbCEjWZ0+u6FqBG0v/q0GbAEiTsO1Y+DbxjT7DjfIV+/XwTT7rECVJmg7ceJETp8+ze7duwss16FDBzp0yP7gO3bsSHBwMF9++SUzZ87MVX727Nm88847uZbfvHmzXAYv+6j1R7x19C2uJ18nOjWaJ7Y8waTgSQzwHYC1RxtcUUeVTTn1B3GukoS7KtDr9cTGxqIoSp6j/4qqRc5n9SLns2ykp6ej1+vJyMggIyOj3I6rKIrWe620+evHjh3L4sWL6devH56enkbv44MPPqBVq1Z89dVXLFmyhKSkJLy8vOjcuTO7du3Czs5O62G4ceNGFixYwJIlS3jttdfQ6/UEBATQt29fJk+eXOjnY2NjQ+vWrZk/fz6XLl0iPT2d2rVrM378eF599VVt+w8//JCpU6fyzTff4OPjw/nz543ey969exkzZozR8bI+q6zz1LBhQ7Zu3cpbb71F165dURSFOnXqMHTo0HI9j4aKek4zMjLQ6/VERUVpj8FniY+PL9M6ior3+OOP8+2339K/f39q1apltO7jjz+mbdu2LFy4kMWLF2vttWvXruzbtw8nJydAbWtbt27lk08+YcmSJUyfPh29Xk9gYCD9+vXjxRcLH6DEwcGBdu3aMX/+fC5evEh6ejq+vr488cQTvPbaa1q5uXPnMmXKFL7++mt8fHwIDQ01ei979+5l/PjxBR6radOm7Nixg9dff50uXbqgKAp169Zl+PDhxfjkhBCFuRRziXf3ZefJfr3968XqFZvFy96LJ5o+weNNHufwjcP8ev5X/gr7izR9GgAnb53k5K2TfHjoQ/oG9GVw0GD8nfz5K/wvNoVs4lDEIfRK7h8hg92C6RfYj95+vbFIssDT01PuaYWxzHS1l22WBz6FDa9ASixc2KquN6/EPapT4mDNc8bLQneaLmgbZhC09e9omn1WIJ1S3l1m8jBp0iTWrFnDzp07CQwMLPb2Q4cOxcLCguXLl+dal1dPW19fX6Kjo7WburIWnRzNi3+/yLHb2b+gDa8/nFeaPYvV3AboMtNQnH1RJp8o08fNhGno9Xpu3ryJh4eH/AGtBuR8Vi9yPstGSkoKoaGhBAYGlvsI5oa5K4Vq5syZrFq1ihMnTlR0VUqkKOc0JSWFkJAQAgICcl1zcXFxuLq6EhsbW273cpVRXFwczs7OeX4OWZ+fKdusYpAeoUoOAlxBZs6cycqVKzl58mRFV8WIKc9nWVxvovj0ej2RkZEVHuQ7fOMway6uIcApgDbebQh2D640j+QnpScxcv1ILsRcAGBgvYHM7JS741dJxabG8uelP/n1/K+ciz5X5O3qudSjb4A6AJq/kz9Qec6nMA2Tns99X8Cm6eq0b3sYvxF+nQCn1d7jjF4LdbqV7hhlae1kOLrMeFmd7jB6jWn2/0lTiAkDc2uYfhksrE2zXwOmOJ8F3ccZqtCetoqi8Nxzz/H777+zffv2EgVsMzMzOXXqFP37989zvbW1NdbWuU+SmZlZuf3j52rryuxWs/nh8g/88N8PAKw4t4KLsReZ69cOt5Bd6GIvo7t9Qc11Kyq9rMfv5A9o9SDns3qR82l6ZmZmWpqA8gzWKIqiHU+CRGpO/9DQUD7//HNmzZpVJT+Top7TrGstr7YsbVtUBVnt9bPPPmPWrFkVXR0hysX56PM8u/VZkrMe1QbsLOxo4dWCNl5taO3dmobuDSssiPv+gfe1gG09l3q81u61QrYoHmdrZx4NfpQR94zgTNQZfjv/G+tD1pOQnpCrrJ+jH30D+9I3oG+JevqKu1RiFOz4IHu+72y141+DftlB23MbK2/Q9sJf2QFbKwewsIGkWxB+ADJSSx9gjbumBmwBarcuk4BteavQoO3EiRP56aefWLNmDY6Ojty4cQMAZ2dnLefT6NGj8fHxYfbs2QC8++67tG/fnnr16hETE8OcOXMICwtjwoQJFfY+isLczJxX2rxCfbf6zNw/k3R9OocjDjPCwoEFVpY0SEuH81skaCuEEEJUYeHh4TRs2DDf9WfOnCnxSPCTJk1i+fLlDBw4sNBHrYUQhZP2KoTpJKYnMmX7FKOALUBSRhJ7ru5hz9U9ANha2NLSsyWtvVvT2qs1jWo0Kpcg7u/nf2fNxTVaHeZ2n1tmuWB1Oh2NajSiUY1GvNT6JbaEbeGPi38QnRpNp1qduC/wPhq6NaySP75WS/pMOP4THP4W3IOg0/Pgnf84SxVq+/tqGgSA5iPBRx0viXo9QWeuDkZ2dgPc937le4o7JVbtZZulz0y4fBBOLFdz8l45DAGdSneMsOqVzxYqOGi7cOFCALp37260fMmSJdqgA+Hh4UY9KqKjo3niiSe4ceMGrq6utGrVir179xZ4w1WZDAoaRB2XOryw7QVuJd/iWkYCj9X0YtbNKPqc3wwdJ1V0FYUQQghRQrVq1eL48eMFri+ppUuXsnTp0hJvL4QwJu1VCNNQFIUZe2cQGhcKQAPXBgxrMIzDEYc5fOMwN5NvamWTM5LZc20Pe65lB3F7+PbgtXav4WztXCb1Ox99nvcPvK/Nz+gwgzrOdcrkWDnZWdrxUL2HeKjeQ4UXFuXv4jbY/IY6GBaoA2Kd+gUaDICuL4FPJRp3KOIMHF6sTlvaQ8+3stfZuqr5W0N3QXQI3DpX+ToEbnod4q6q03W6Q6txak/bE3fSnIbuKn3QNnxf9rS/BG1LrSjpdLdv3240P3/+fObPn19GNSofzTya8fOAn3lh2wucjjpNspkZL3l58FTsKZ5NicPM5u7NzyaEEEJUZRYWFtSrV6+iqyGEKAJpr0KYxvL/lrMpdBMADpYOzOs+Dz8nP4Y1GIaiKITFhXEo4hCHb6hB3MjkSG3b5Ixk1oesJ12fzrzu80xet6T0JKZsn0JKpjoI+dD6QxlQZ4DJjyOqmJtnYfObcH5T3uvP/qm+6t4LXV4ueTBRUdQA6sVt2KSbg0cJn7xQFDWPbdbgdV2mgKO3cZkG/dTAJ8DZ9ZUraHv+Lzj2vTpt5QgP/k/tCRzQJbtMyE7o/mrpjhN2J2irMwPfdqXbVyVRoUHbu5mXvRdL+y3lnb3v8MelPwD40tmBc5ufYHa/xdhb2ldwDYUQQgghhBBCiPydunmKOYfnaPOzOs3Czyk7rYhOpyPAOYAA5wCG1h+KoiiEx4dz+MZhDkUcYvvl7SSmJ7IlbAubQzfTJ6CPyeqmKArv7HtH6wF8j9s9TGs7zWT7F1VQ4i3YPhsOL1FTCWSp2Rx6zVCDuXsWQPw1dfnFv9WXX0e1523dnoWnHUiJhUs71PytF/+G2MuYAS6Acn03DPwCrIoZ7zm7AS5tV6dd/KBDHk9o1+8Lm+7kaT67ETq/WLxjlJXkGFj7XPb8fbPU9wDg4guugWrv4CuHIC0JrOxKeJxoiDyjTns3BWvHUlW7spCgbQWyNrfmvc7v0SBDYV7YH+h1OrZFn2HU+lEs7LUQb3vvwncihBBCCCGEEELcoSgKSRlJxKfFE5cWR3xafK6Xs7Uz99e5HwcrhxIfJyYlhpd2vESGPgOAMQ3H0NO/Z4Hb6HQ6/J388XfyZ0j9IWwI2cDUnVMBeO/Ae7T1bouLjUuJ62Ro1flVrA9ZD4C9pT1zu83F2rzqD0wkSiA9BQ4sgl1zITUue7ljLTVY22QYmJmpPWtbj1dz3O6enz2oVfhe+GEv1Gqh9rxt0F8tD6DXw40TapD2wt9w+YBxQNiA7sxquH0JRiwH59pFq3tGKmx+PXu+90ywtMldzr0u1Kiv9uy9clAdtMzevWjHKEubXs8Ogte9F1qOMV4f2EUN2mamqZ9d3R4lO074AeDO0/z+HUtc3cpGgrYVTKfTMabTG9Q7/B2vuLsQb27GhZgLzDs8j4+6fVTR1RNCCCGEEEIIUYkdjzzOJ0c+4Vr8NZIyk4hPj0ef9Rh1AX749wfmd59PkGtQsY+pV/RM3z2d64nXAWjh2YLnWz1f7P30DejLhpANbLu8jdspt/nw0IfM7jK72PvJ6dTNU8w+kL2fdzu+a9QDWNwlFAX++Q3+ehtiwrOXW9qrPVE7TMzds9PCGlqPgxaPwelVaqD31jl13bVjsGIkeDaE5o/C9ZNqb9qkW3kf39wa/Dui1GqJcmARZumJcOMkfNUDHvkRfNsW/h4OfqUGegH8O0HDAvIj1++r1lXRw/nN0HxE4fsvS+c2w/Ef1GkrR3hgQe6eyoHd4Oh36nTorlIEbavfIGQAZoUXEWXOyp5O3m1Zfu0G9nr1j+vRyKMVXCkhhBBCCCGEEJXd/CPzORJ5hOvJ14lNiy1SwBYgLC6MketH8uelP4t9zG9OfcPuq7sBcLNxY07XOViaWRZ7Pzqdjjfav4Gjlfoo87pL69h5ZWex92PoVvItXtj+Aun6dAAevedRk6ZdEFXElcPwbW9YNd4gYKuDlqNh8lHo9krBj+KbW0CzR+DZ/TB0GXg1yV4XeUYdwOzUL7kDtu71oN3TMHIVTAuF0atR7n2D24NWoLgGqGUSI2HpALVHb0ESbsKOrM58Oug7u+D0DA36Z0+f21DwvstacjT8MTl7/r731HQIOQV0zp4OKUXbDzMYhKwaBW2lp21lUa83/hf/pkFaGkdtbIhIiiAuLQ4nKxmUTAghhBBCCCFEboqicD76PADmOnNqOdTC0coRRytHnKyc1GlLR22Zo5Ujtha2fHXyK/69/S/JGcm8uutVTt48ycutX8bSvPDA64HrB/j8+OcA6NDxQZcP8LL3KvF78LTzZGqbqby5500A3tn3DqsfWq0FcosjPTOdKdunEJmkDnbW0rMlL7d+ucR1E1XUiRWw+hnjNAV1ukOf98C7cfH2ZWYOjQaqPVzPb4adc9T8q1msHNTeovV6qq+swGwOGW5BKI9vRbdqrNqjNDNNrWPkGej1jnqcnLbNyk7n0GIU1GxWcF1924KtGyTfVlM1ZKSqPYcrwsbXIF7tiU/dnmqwPC+O3lCjAdw6C1ePQmp88fPRpiXBtTsdH92DwMGj5PWuZCRoW1kE9YFN06mbls5RGzU/yaWYSzT3bF6x9RJCCCFEmXv77bdZvXo1x48fr+iqlFpAQAAvvPACL7zwQkVXRYgyI21WVBZRKVHEp8cD0NytOYv7L8bMrPAHajv7dOa9A++x+sJqAH767yf+ifqHud3mFhiAjUyKZOrOqVpv3mebP0uHWqXv1fZQ3YfYGLKRPdf2EJkUydzDc3m749vF3s+Hhz7kWOQxQA0Gz+0+t0iBaFGNHF4M66ag5Tet0QD6zIKg3oUPIlYQnQ7q36fGbsL2wI1T4N0EarcFC6ui7cPODR77HTZMg8Pfqsv2/k8dAG3IN2DjnF32xqnstAFWjtDzrcL3b2au1u/kz5AWD6G71UByeTu3CU7c6UVs7QQP/q/gzz6wixq0VTLVHrP1i9kz/uphuJNbG//q08sWJD1C5eFeF1wDqJueri26EHOhAiskhBBCVH1jx45l4MCBFV0NIzqdjtWrVxste/nll9m6dWu51uPrr7+mWbNmODg44OLiQosWLZg9Ozv/X2X87ET1VxmvO2mzojILjQ3Vpn3t83j0OB82Fja82/FdZnSYoaU1OHHzBMPWDePQjUN5bpOhz+CVHa9wO+U2AJ1qdeLJpk+WvPIGdDodb3V4CzsL9XH1X8//yr5r+wrZytiv535lxdkVAFiZWfFpj0+pYVvDJPUTVcSeBbDuRbSAbevH4Zm9ahCwNAFbQzqd+kh/+2fU/xc1YJvF3BLunwcD5oLuTu/a85vhm94QdVGdVxTYOF3NTQvQ9WVw8Cza/hv0zZ4+t7F4dTOF5GhYa5gW4X1w9il4m8Cu2dOhJUiRYJQaofoMQgYStK08dDqo15ugNAnaCiGEEHcbBwcH3N3Lb4TfxYsX88ILLzB58mSOHz/Onj17mDp1KgkJCeVWByGqMmmzorIIiwvTpmvbF3E0+jt0Oh0P13+Y7/p9R037mgDcTrnNE5ufYMnpJSiKYlR+wbEF2tgrXnZezO4yGzOd6UIKtRxqMaXVFG3+nX3vkJSeVKRtT9w8wXsH3tPm3+zwJo1rFPMxeFF1KQpsmw1b3sxe1ul5NTBqXkkfMG8zAUavBltXdf7WWfj6Xri0A/79Q02hAOAaqAaIi6puT8jKL312o/rZlKcNr0LCDXW6Xm81rUNh/EuZ19ZwEDJ/CdqKshLUh7qGQdvIUxVYGSGEEKJ66d69O5MnT2bq1Km4ubnh7e3N22+/bVQmJiaGp556Ci8vL2xsbGjcuDHr1q3T1u/evZsuXbpga2uLr68vkydPJjExUVsfEBDAzJkzGTFiBPb29vj4+PD5558brQcYNGgQOp1Om3/77bdp3ry5Vk6v1/Puu+9Su3ZtrK2tad68ORs3ZveWCA0NRafT8dtvv9GjRw/s7Oxo1qwZ+/YVrVfS2rVrGTZsGI8//jj16tWjUaNGjBgxgvfee0+rz7Jly1izZg06nQ6dTsf27du59957mTRpktG+bt68iZWVVb69DmNiYpgwYQIeHh44OzvTp08fTpw4UaR6irubtNlspm6z1tbW/P3333key7DNOjk5ce+990qbrcRC40K16dp2xQvaZmlcozEr7l9Bx1pqsCNTyWTekXlM2T6FhDT1h4G/w/9myeklAFjoLJjbfS6uNq6lq3wehjYYSmuv1gBcTbjKgmMLCt3mZtJNXtz2otHAYwPrDTR53UQlpSjqoGA7Pshe1uMNNU+sqXrXlpXArvDE32oKB4CUGPh+EKx7IbtMn1nFy0tr4wQBndTp2HA1Z255ObtBTc0AYO0MD3xatHNg75490Nv1k2pv3aLKzIDLd54OcPIBF7/i1bmSk6BtZRLQGXdrZ1wz1WTZFyOOwV9vQ1piwdsJIYQQokiWLVuGvb09Bw4c4KOPPuLdd99ly5YtgBp06devH3v27OGHH37gzJkzfPDBB5ibq4+uXbx4kb59+zJkyBBOnjzJihUr2L17d66AyJw5c2jWrBnHjh3j1Vdf5fnnn9eOceiQelO5ZMkSrl+/rs3n9OmnnzJ37lw+/vhjTp48yX333ceDDz7I+fPnjcq9/vrrvPzyyxw/fpz69eszYsQIMjIyCv0cvL292b9/P2FhYXmuf/nllxk2bBh9+/bl+vXrXL9+nY4dOzJhwgR++uknUlNTtbI//PADPj4+3HvvvXnua+jQoURGRrJhwwYOHz5MixYt6NWrF7dv3y60nkJIm1WVRZvt0aNHnvsybLNHjhyhZcuW9OzZU9psJVXS9Ag5udq48kXPL4zSHfwV/hcj/hzBjss7eGP3G9ryl9u8TDOPQgZEKiEznRnvdHwHG3N1nJef/v2JoxFH8y2flpnGi9tf5GbyTQBae7Xm5TYy8NhdQ5+pBjj3fZa97L7Z0O2Vyh+wzeJWByb8BUH3qfNKJiRFqdOBXeGeAcXfZ4P+2dNnN5S+jkWRdBv+eD57vu/swtMiGArscmdCgdA9Rd/uxglIvxMz8+tQdc57EVXSfuJ3KSs7GP4D9f56mkPmcMvCnJi9n+Jy8he47z1oOLDaXYBCCCGqruHrhnMr+VbZHkQBcvzpq2FbgxX3ryjR7po2bcqMGTMACAoK4rPPPmPr1q307t2bv/76i4MHD/Lvv/9Sv359AOrUqaNtO3v2bEaOHKkN1hMUFMSCBQvo1q0bCxcuxObOQKKdOnXi1VdfBaB+/frs2bOH+fPn07t3bzw81NFsXVxc8Pb2zreeH3/8MdOmTeORRx4B4MMPP2Tbtm188sknRr0AX375ZQYMUG/m33nnHRo1asSFCxe45557CvwcZsyYweDBgwkICKB+/fp06NCB/v378/DDD2NmZoaDgwO2trakpqYa1XPw4MFMmjSJNWvWMGzYMACWLl3K2LFj0eVxj7J7924OHjxIZGQk1tbWKIrChx9+yNq1a1m1ahVPPmmaXIiicCZpr3m0x8KUpr2CtNkspm6zY8aMKVKbzXpvq1evljZbSWX1tLUxt6GGTenyt5qbmfNci+doWqMp03dPJz4tntC4UCb9nf1DRx//Pjx6z6OlOk5h/Jz8mNRiEh8f/hgFhRl7Z7DygZXYWNjkKjv74GxO3FR7gnvbe/Nxt4+1HL2imstMh9XPwKmVdxbo1J6drcZUaLVKxMYJRiyHre/Ank/VZTozNQBdkhhQ/b6wYao6fW6jmhO3rP35EiREqNNB90HzYv47EdgV9n+hTofuguD7i7adYT7bajYIGUjQtvIJ6EzdhkM5dH4VABesLGkddxVWjoXAbtB/Dng0qNg6CiGEEMCt5FtEJkVWdDWKpWnTpkbzNWvWJDJSfQ/Hjx+ndu3aWvAnpxMnTnDy5El+/PFHbZmiKOj1ekJCQggODgagQwfjG8YOHTrwySefFLmOcXFxXLt2jU6dOhkt79SpU65HlA3fT82aaj7CyMjIQgNANWvWZN++fZw+fZqdO3eyd+9exowZwzfffMPGjRvzHXncxsaGxx57jMWLFzNs2DCOHj3K6dOnWbt2bZ7lT5w4QUJCQq7cn8nJyVy8eLHAOgrTqortFaTNGpY1ZZtds2ZNnuWlzVYt6fp0rsRfAdRAp6nyy3bz7caKASt4cfuLnI0+qy0PcArgnY7v5BnwN7VRwaPYHLqZk7dOEhoXyhcnvjDKdwvwy9lfWHVO/d5sbW7NJz0+wd22/HJNiwqUkQorx8HZP9V5MwsY9CU0ebhi61UaZubQ+13wbgoHvoRmw8G7hHmZXf3Bs6GaGuHKYUiILPpAZiVxahX885s6beMCD3xS/GCzf0c1UK3oi5fXNrz6DkIGErStlOq5Zd+0XajVhNaXDqszITtgYUdo/yx0mwrWjhVUQyGEEILyGZE5n562JWVpadz7RqfToderI/Pa2toWuG1CQgJPPfUUkydPzrXOz69i8mcZvp+sL9FZ76coGjduTOPGjXn22Wd5+umn6dKlCzt27Mj3sWmACRMm0Lx5c65cucKSJUu499578ff3z7NsQkICNWvWZPv27YAaMMvIyMDCwgJXV9PnQhT5M0l7LWFP29KQNmvMlG02r7QMOdusIRcXlyLXU5SPq/FXyVDU8xjgFGDSffs6+fJ9/++ZtX8Way+uxdHKkbnd5+Jg5WDS4+TH3Mycdzu9y9A/hpKuT2fZP8vo499HG1zsWOQxZh+crZWf0WEGjdwblUvdRAVLS4SfR8Klbeq8uRUMXQb39C94u6qiycOmCT7X73snn60C5zZBy8dKv8+8xF2DPw1+UBkwF5xqFX8/Ns5QszlcO6rWO+EmOHgUvI2iQNidQchsXMCj4B9AqyIJ2lZC9VzradMX6veAtlNg46sQEw76DNi7QH0EoM8saDxEUiYIIYSoEKV55LkoDAN85dGrp2nTply5coVz587l2XOvZcuWnDlzhnr16uWxdbb9+/fnms/q0Qdq0CbzTv76vDg5OVGrVi327NlDt27dtOV79uyhbdu2RX07xdawYUMAbZAmKyurPOvZpEkTWrduzddff81PP/3EZ599lqtMlpYtW3Ljxg0sLCwICAgo93MqspW2vVbGcydttuzbrKjcwuKycxz7O+X941lp2FrYMqvTLJ5q+hSOVo5lMvBYQeq61OXpZk/zv2P/Q6/oeXPPm/xy/y/cTrnNlO1TyNCrAetRwaN4oO4D5Vo3UUFSYuGn4dm9Ky3t4JGfoG7+P1zdtRr0g93z1OlzG8smaKsosGaiel5AjU+VJuAc2EUN2oKaIqHx4ILL3zwLyXfyrft1gHyeOqnKqt87qgbquWTfWF6Muagmnp54ELpNA/M7owbGX4dfH4dlD0DkvxVUUyGEEKL66NatG127dmXIkCFs2bKFkJAQNmzYoI0AP23aNPbu3cukSZM4fvw458+fZ82aNbkGNdqzZw8fffQR586d4/PPP2flypU8/3z2wAwBAQFs3bqVGzduEB2d9+i4r7zyCh9++CErVqzg7NmzvPrqqxw/ftxoP6XxzDPPMHPmTPbs2UNYWBj79+9n9OjReHh4aI+KBwQEcPLkSc6ePcutW7dIT0/Xtp8wYQIffPABiqIwaNCgfI/Tq1cvOnTowMCBA9m8eTOhoaHs27eP119/ncOHD5vkvYi7l7TZ8mmze/fulTZbSWXls4WyCdqC2iPcz8mv3AO2WcY1Hsc9d55EvRBzgc+Pf86U7VO0HN3tvNvxUuuXKqRuopwl3YZlD2YHbK2d4LHfJWCbH59WYH+np+rFvyE9xfTHOPSNum8Ax5rQ/+PS7S+wa/Z06K7Cy4fvzZ6uhvlsQYK2lZKztbP2KNmFmAsoigKWttDjNZi4X+3mniV0F3zVHS7nPZKtEEIIIYru119/pU2bNowYMYKGDRsydepUreda06ZN2bFjB+fOnaNLly60aNGCt956i1q1jB8Be+mllzh8+DAtWrRg1qxZzJs3j/vuu09bP3fuXLZs2YKvry8tWrTIsx6TJ09mypQpvPTSSzRp0oSNGzeydu1agoKCTPI+e/Xqxf79+xk6dCj169dnyJAh2NjYsHXrVi2X5RNPPEGDBg1o3bo1Hh4e7NmTPZLviBEjsLCwYMSIEdpgTnnR6XSsX7+erl27Mm7cOBo0aMCoUaMIDw/Hy8vLJO9F3N2kzZZtm61fvz6PPPIIYWFh0mYroZDYEG3a1OkRKgtLM0tmdpqJhU59SPjb099y8tZJAGrZ12JOtzlYmMkDxHeFVePg+nF12tYNxvwBfu0rtEqVmpm5OiAYQHpS0YKgxXHrPGx+M3v+oc/Bzq10+/Rtr+YnhqLltTUahKxT/uWqMJ2iKEpFV6I8xcXF4ezsTGxsLE5OTuVyTL1eT2RkJJ6envkOEpDTE5ufYP919VGtbcO25c4HdnYjbJwG0aHqvFtdeHo3WNmZsOYiLyU5n6LykvNZvcj5LBspKSmEhIQQGBhY4Jd9U6uMj2MXJiAggBdeeEEbrb66Cg0NpW7duhw6dIiWLVsWebuintOCrrmKuJerjAr6HMqizVbF9lgUd2ubNeX5rKi/EQLGbRzH4Qi1B/Su4btIiUmptvdAC44u4OtTX2vzNuY2fNfvO4LdgwvYquqSe9ocbpyGRXeCcg5eMHoteFad/KUVdj7//QNWjFKnWz8O988zzX4zM2BxH7h6RJ1v8wQMKGUv2yzf3geX76QtmvJvwflx5zeG2MtqmoxXw8HcMv+yJmSK81nU+1lp/ZVUrhQJOTXoC88eAJ/W6vzti7D1nXKqnRBCCCHuRunp6dy4cYM33niD9u3bFytgK4Qof9Jmq7es9AhuNm44WVXvH7GebvY0dZzraPPvdHyn2gZsRR4Of5s93fWVKhWwrVB1eqgDtYGa19ZUfTZ3z8sO2LrXg97vmma/oOa1zRJSQO/gmMtqwBagdutyC9iWNwnaVlKGQdsLMRfyLmRpA4MWgcWdX7QPLCpaF3IhhBBCVGv9+vXDwcEhz9f7779f4v3u2bOHmjVrcujQIRYtWmTCGgtxd5M2K4orIS1By+taXVMjGLIyt+Kzez/jgToP8G7Hd+lfp39FV0mUl9R4OPmLOm1pD02HV2x9qhJrh+w8sXFX4cbJ0u/z2jHY8aE6rTOHQV+a9olvo7y2BcS3wg1SI/h1NN3xKxlJ/lJJ1XWpq03nG7QFqBEEvd6Gja+q86snwjN7wKZ6/9IqhBBCVEahoaEVXQUAvvnmG5KTk/Nc5+ZW8nxj3bt35y7LrCWqOWmzoqoKiwvTpgOdAyuwJuXH18mX97uU/EcMUUWd/AXSEtTppkMl1lFcDfrBhb/U6bMboWazku8rPRl+exL0Gep8l5fUXq6mVLstmFtDZmrBnRLDqv8gZCBB20rLMGibZ3oEQ22fgn/XQdhuiA2HzW/AgwvKuIZCCCGEqKx8fHwqugpCiGKQNiuKKyQuexAyfyf/CqyJEGVIUeDw4uz51o9XXF2qqvp94c+X1OlzG6D7tJLva+u7cOucOl2zOXSbWurq5WJpA75t1YHTYsLVcZxcA3KXy+ppa2YBtduYvh6VhKRHqKQcrRzxtvcG4EL0hYJ/ITczg4Gfg5WDOn90GZzfUg61FEIIIYQQQghR3kJjQ7XpuyE9grhLXT4IEafV6dptoGbTiq1PVeRcG7ybqNPXjkHc9ZLt59IO2P+FOm1hA4O/Krs8soYpEvLKa5sYBTf/U6drNgMr+7KpRyUgQdtKLKu3bXx6PJFJkQUXdg2APrOy59dMgqTbZVc5IYQQdx15xFaUF7nWTEM+R1Ee5DqrGFmDkAEEOAdUWD2EKFPSy9Y06vfLnj6/qfjbJ8fA6mez53u9DR4NSlur/Bnltc0jaGuUz7b6pkYACdpWavWcswcjKzRFAkCrsVC3pzqdcAM2lKLbuxBCCHGHpaX6K3pSUlIF10TcLbKutaxrTxSPtFlRnqS9VoysnLbmOnNqO9Su4NoIUQaSbsM/v6vTNi7QaGBF1qZqa9A3e/rshuJvv2EaxF1RpwO7qik6y1KtlmB5Z3CzkJ1qmgxDhkFb/+o7CBlITttKrZ5rdtD2fMx5OvoUcjHqdPDQZ/BFe0iJhVO/QPAD0PDBMq6pEEKI6szc3BwXFxciI9WnPuzs7NDpdGV+XEVRyMjIwMLColyOJ8peYedUURSSkpKIjIzExcUFc3PzCqhl1VcWbVbaY/ViivMp7bXi6BW9FrSt7VgbS3NL9Hp9BddKCBM79oM6GBVAi1FgaVux9anKarYABy9IiIBL2yEtCazsirbtmTVw8md12toJHvpCTdFZliys1B60F7dC/HWIugg1suNjRoOQVfOethK0rcTquRSzpy2AUy3oNwd+f1KdX/eiehE7eJRBDYUQQtwtvL3VPOtZQaDyoCgKer0eMzMzCRJVE0U9py4uLto1J0rG1G1W2mP1YsrzKe21/EUmRZKckQxIPttqKfwAut8m4OIWDI/+WPYBsspIr8+RGmF8xdWlOjAzUwckO7oMMlIgZAc06Ff4dvER8McL2fP954CLb5lV00hgFzVoC2p9s4K2qQlw/YQ67REMdm7lU58KIkHbSqyOcx1t+kLMhaJv2HQY/LsW/lsHSbfgzxdh2PdqT9yiSkuEE8vVRtrhWbB1LUbNhRBCVDc6nY6aNWvi6elJenp6uRxTr9cTFRWFu7s7ZnfjF5ZqqCjn1NLSUnrsmYCp26y0x+rFVOezvNtrYnoi84/Mp4ZtDZ5q+tRd+wOCYT5bfyf/iquIML2MNFj9NLqYcGxiwtGH7ISgnhVdq/IXsh2iQ9TpOt3BvW5F1qZ6aNBPDdqCmiKhQT/IzIC4qxATnvcr7ioomeo2wQ9C0+HlV9+ceW3b3MlpfOVQdp38q3cvW5CgbaVmZ2mHj4MPVxOucjHmIoqiFO3GRKeD++ereT6SouDfP+DUSjWYW5jkaDj4NexfCMl3BjIL2wOj14K5XC5CCHG3Mzc3L7cv6Hq9HktLS2xsbCRIVE3IOS1/pmqzcu6ql6p6PldfWM2KsysAaF+zPc09m1dshSpIaGyoNi2DkFUzB7+C25e0Wd35TXdn0PbQt9nT0svWNAK7gYWN2tP21Eq4uM04KFsQe0+4/5PidQQsLe9majqG1DgI2aXmtdXpcgxCVr3z2YIMRFbpZaVISMpI4nri9aJv6OAJA+Zlz69/GeIK2D7+Bmx+E+Y3hm3vZQdsQQ3abptVzJoLIYQQQgghhOlk5XEFuBx/uQJrUnyZ+kyuJxTj+1wBDHvaSnqEaiTxFuz4yHjZ+U25B2Gq7uKuZQ+W5eANDfpXbH2qCys7tdcyQHoSxIYXHLC1dYWazaDhQHh0Bdi7l0cts5lbgH8ndTrpFkT+q04b5rO9C3raStC2kqvrkv0YQLFSJIA6umKToep0SiysfS73P/i3Q9S8t580hb0LIC1BXa4zh3vuB7M7vWt3z4ezG0v2JoQQQgghqrnPP/+cgIAAbGxsaNeuHQcPHiywfExMDBMnTqRmzZpYW1tTv3591q9fX061FaJqup1yO8/pyk5RFJ7Y8gR9fu3DVye/KvX+DIO2gc6Bpd6fqCS2z4bUWKNFuuhQuHW+YupTUY5+lx1MbDUGzC0rtj7VScfJYG6tTtu6qUHZ4AegwyTo9xGM+Bme2QuvXoZpofDUThi2DHxaVkx9A7tkT4fsVNOHXDmszjv7gXPtiqlXOZLn3Ss5w8HILsRcoGvtrgWUzkO/j9Su5Ak34MIW9R/AVmMg4owaiD39q/GvK+bW6siMnSaDawDs/Qw2v66u+/1JtdG6BpT6fQkhhBBCVBcrVqxgypQpLFq0iHbt2vHJJ59w3333cfbsWTw9PXOVT0tLo3fv3nh6erJq1Sp8fHwICwvDxcWl/CsvRBViGKiNSomqwJoUz7nocxy6cQiA387/xpNNnyzV/rLSI9hb2uNuU86930TZiPw3e+AtS3uU1uPQ7ftMnT+/CTzqV1zdylNmBhy5k3dVZwYtx1RsfaqbgE7wajjo08HasaJrU7gAg6Bt6C7waQV3BmG8G3rZgvS0rfQMg7YXYy4Wfwd2bvDg/7LnN70GPw2HhR3g1C/ZAVsrB+j0PLxwCu6flx2Y7TBR7XELam/dlWMhI7VE70UIIYQQojqaN28eTzzxBOPGjaNhw4YsWrQIOzs7Fi9enGf5xYsXc/v2bVavXk2nTp0ICAigW7duNGvWrJxrLkTVEpUcled0Zbf98nZt+mrCVSKTIku8r9TMVK4lXAPU1Ah362Bs1YqiqN/TFb0632UKSovR2evPbaqYepWEoqiDmpfUuQ0Qr17f1O8Hzj6mqZfIZmlTNQK2AF6N1TQNoAZtQ3dlr/O7O4K20tO2kgt0DsRMZ4Ze0XM+uoSPRdTvAy0eg2Pfq+kPzhmkObB1g/bPQtsJ2Y3BkE4HA7+AL/9RR2+8dkz9gzJgbsnqUhKJUbBpOqQmwMDP866nEEIIIUQFSEtL48iRI0yfPl1bZmZmRq9evdi3b1+e26xdu5YOHTowceJE1qxZg4eHB48++ijTpk3Ld9Cw1NRUUlOzfziPi4sD1AGl9Hq9Cd9R3vR6PYqilMuxRNmrqufTKD1C8u0qU3/DoC3A0Yij9PHvU6J9hcWGoaCmvPN38tc+g6p6TgVwfjNmF/8GQHGujdLuGfRmVuid/LCIC0cJ34eSFA02zhVc0UIkRaFb/ghEnEbpNg06vVDsXegOfUvWzxD6VuOgmlzP0j5LTuffGd1/f0BKLMrhxdnXh1+HCrs+THE+i7qtBG0rORsLG3wdfQmLCyMkNgS9osdMV4IO0ve9D5e2Q+ydhP1OPtDxOWg5GqzsC6mEMwz7Dr7pBZmpcOgb8G0PTYcWvx7FFXURfnw4ewTNnYFw33tlf1whhBBCiCK4desWmZmZeHl5GS338vLiv//+y3ObS5cu8ffffzNy5EjWr1/PhQsXePbZZ0lPT2fGjBl5bjN79mzeeeedXMtv3rxJSkpK6d9IIfR6PbGxsSiKgpmZPKxX1VXF85mhzyAmNUabj0iIIDKy5D1Wc9Ireub/M5/IlEimNpmKu7Vp0g7cTr3N6ajTRsv2hu6luW3zEu3vRMQJbbqGWQ3tM6iK51QAmenU2DBdewQ6ts1LpETHo9frsfHqgFtcODp9BjHHVpNat1+FVrVA6cm4/TEGq0j1+tRtfYeE6AgS2rygdgQrAvPYMDwubQMgw8mXW46NwIRtvCJJ+yw5uxotcOIPAHR34lmZNm7czHSusOvDFOczPj6+SOUkaFsF1HWuS1hcGCmZKVyNv4qvk2/xd2LjBKPXwIFFarLpJsPAwqro29dsCv3nwB+T1fk/nleXeTQofl2KKmwv/PwoJEdnLzv+E9z7ptqlXwghhBCiCtLr9Xh6evLVV19hbm5Oq1atuHr1KnPmzMk3aDt9+nSmTJmizcfFxeHr64uHhwdOTk7lUmedToeHh4d84awGquL5vJl002g+PiM+z5zRJXU08igbr6pPJK66uoo3279pkv3uPr8717JziedKXPeYiBhtulGtRtp+quI5FcDBrzCLUTsoKbXb4tRhDE46nRoUqt8Hzq8AwCViP0qHSprfVZ+BbsUodJEnjBY7HF2EvZU5Su+ZRQrc6k5kp3U0a/M4nl7eJq9qRZH2WQpN+sHud40WmQV0xDPHj+XlyRTn08amaDEtCdpWAfVc6/H3ZfVxifMx50sWtAVwr6sGXkuq5WgI3w8nfoL0RFjxGDzxN1g7lHyf+Tm5EtY8C5lp6rzOTM3xk3wbzqyBZsNNf0whhBBCiGKqUaMG5ubmREREGC2PiIjA2zvvL5w1a9bE0tLSKBVCcHAwN27cIC0tDSur3D+sW1tbY21tnWu5mZlZuX0B1Ol05Xo8Ubaq2vmMTos2mr+dchudTmeynK43km5o01vDt/J6+9exMCv91+WdV3dq07YWtiRnJPPf7f9IyUzBztKu2PsLiw/Tpuu41DE6f1XtnN71kqNhxwfarK7vB+gM/i6k12qLYuWALi0B3YW/1MfCK9u5VRRY/7I6WBqAtRO0Hgd7PgVAt/9zdJmp0G9OwXVPT1E7aAGYW2HW8rHK915LSdpnCXkGg70nJGb3qtX5d0RXwZ9jac9nUbeTq6UKKPVgZKai06m5bD0bqfO3zsK6F9V/qE1FUWDHHPhtQnbAtu69MGJFdpnDeQ/qIYQQQghR3qysrGjVqhVbt27Vlun1erZu3UqHDnkPktGpUycuXLhglM/s3Llz1KxZM8+ArRBCzWFrKE2fRmJ6KQY8ysFwYLPo1GgOXj9Y6n2mZKSw//p+ANxt3OkXqD7enqlkcvrW6YI2zVdoXKg27efoV+o6igq046Psp0qbDofarYzXm1tBne7qdNItuHa0XKtXJDs+hKPL1GkzS3jkR+j9LjywALKyjx76Bv54DvSZ+e/nzGq1gxZAw4FgX6MMKy2qFJ0OArsYL7tLBiEDCdpWCXVd6mrT52NKOBiZqVjZwbBlYHWnd+2pX+DIEtPsOyMN1kyEbbOyl7UcA4/+AkG9wSNYXXZ5P0T8Y5pjCiGEEEKU0pQpU/j6669ZtmwZ//77L8888wyJiYmMGzcOgNGjRxsNVPbMM89w+/Ztnn/+ec6dO8eff/7J+++/z8SJEyvqLQhR6UWlRBVpman2vzF0Yz4li+7gjYMkZyQD0LV2V1p5ZQfljkUeK9E+s4K2XnZeJeqpKyqJW+fh4FfqtIUt9Mw7NY4SZDBg3bnSX5MmdWQZbJ+dPT9oEQR2VadbjYFBX6pPzAIc+wF+fwoyM/Lel2HHrDaPl019RdWVdV2BGovyblpxdSlnErStAgKdArHQqY/mVGhP2yw1guDB7HwzbJgG10p206FJjoEfh8DxH7OX9XoHHvgUzC3VX1cM//E+bKJAsRBCCCFEKQ0fPpyPP/6Yt956i+bNm3P8+HE2btyoDU4WHh7O9evXtfK+vr5s2rSJQ4cO0bRpUyZPnszzzz/Pq6++WlFvQYhK73bK7SItKynDnrYAf4X/RXpmeqn2uf3ydm26m283Wni00OaP3Sz+96eYlBhiU2MBCHAOKFXdRB7Ob4GwfeVzrM1vgv5OALPT8+Dsk3e5er2zp89tKvt6FdXZjepTt1nuex+aPGxcptlweHgxZKUZObUSVo1TO2sZunEaLh9Qpz0bgm+7squ3qJoMg7a+bcH87sn0KkHbKsDS3BI/J/XRl5DYEDL0+fw6VYg/L/1Jz1968sXxL0pfqcaDod3T6nRmGvwyxnjAsOKIDoVv+0DInXxP5tYwdCl0fsE4YXnTYZD1a/KJnyE1oYSVF0IIIYQwrUmTJhEWFkZqaioHDhygXbvsL53bt29n6dKlRuU7dOjA/v37SUlJ4eLFi7z22mtGOW6FEMby6lWbM2VCqfafI2gbnxbPvuslD+ApisKOKzsAsDKzokPNDtR2rI27jTsAJyJPkFnQ4+J5MEyNEOAUUOK6iTz8sxp+fBiW9IPrJ8v2WBe3wbkN6rRjLeg0Of+yjt5Qs7k6feMkxF0r27oVxZXDsHIsKHeu3w6ToEM+T4o0GgTDf1BTPQD8uxZ+eUzNYZvl8LfZ063HF2nQMnGXcasD3aZBrRbQ442Krk25kqBtFZGVIiFdn054fHixt0/XpzPn0BwikyP58uSX2i+0pdJ7Jvi0VqdjwuD3Z8AgN1uRXDkM3/RS8+MC2NWAsevUf9xzsnHO/vUuLR5O/1ryugshhBBCCCGqjJxBVSjb9AgAG0NK/jj6v7f/JTJJHTinXc122FnaodPpaOGp9rZNSE/gQsyFYu0zJDZEm5agrYll5WVFyR5UqyxkZsCm17Lne80AK/uCt6nfN3v6/OayqVdR3boAPw2DO2k/aDRYjQsUpEE/GLEcLGzU+XMbYfkjkJYEqfFw8hd1uaW9mttXiLz0eA2e3J4793M1J0HbKiLIJUibLkmKhL1X92o3InpFz5GII6WvlIWV2iPW1lWdP7cBNk5Tg6nnNkPYXvVXytuXIOGm+mua4aBlZ9bC0gGQeFOddw+CCX+p3d3z03p89rQMSCaEEEIIIcRdoazTI9xKvgWAm40bjpaOAPx9+W9SM1NLtL8dl3do0919u2vTzT2ba9PHI48Xa59GPW0lPYLpJN6CS9nni8ulH4QuX8e+g8gz6nStltBkWOHb1DfMa1uBQduESPhhMCTd+YEjoIuax9asCGGler1g5Co1MAtwaZvas/nwEki78wRt02Fg41Q2dReiirp7EkFUcYaDkV2IuUBv/94FlM5tzcU1RvOHbhziXr97S18xF18Y/I36Dy6Kmkw9K6F6Xsws1MTR1o4Qe0XdBsC/Mwz/HuzcCj5erRbq69oxuH4crh4Bn7vrl5ZKKSNVTW9Rq4WM9CmEEEIIIUwur562pgra6hU90SlqqjcvOy+CXINYe3EtiemJ7L66m55+PYu9z22Xt2nTXWtn52PM6mkLal7b4fcUvWdhWFyYNu3v5F/sOol8nFmT/ag/qEFbvb5owcjiSImFv9/Lnu87u2jHqNkC7D0hMVINdqangKWNaetWmNR49Tt/zJ1r0LMRPPIjWFgXfR+BXeCx39X9pMZB2B71lcWwg5YQApCetlVGPdd62vSF6OI9RhOTEmN00wBw4MYBk9QLgKBe0G1q0crqMyAlBmIvowVsmz6i/uNdWMA2i/S2rXw2v6n+8V06oPgpMoQQQgghhChEVoBWhy7XstKKSY0h807QroZtDfoGZD+Ovimk+I/KRyRG8O/tfwEIdgvG295bWxfsFoy1uRroKnZP29hQQM2RW8u+VrHrJfJx+jfj+ZQYiDpv+uPs/BiS1B7dNBoMfu2Ltp2ZGQTd6W2bngRhu0tXj2vH1Ry+lw+qHakyCxkzJzMdfhkN10+o8061YdQqNX1hcfm1g9FrwMbFeHntNlCzafH3J0Q1Jz1tqwg/Rz8szSxJ16cXOz3C+pD1uQYvOx99ntspt3GzKWKgtDDdp6t/dGLC1QHC0hLUX+PSEtR5bdpgmbkVtJ0AnV4oXrLxxkNg0xuQGgunfoU+74Gti2nehyiZrNxKN/9Tf311C6zY+gghhBBCiGpDURQtQOvr6KuN8ZFX79uSyEqNAOBu6077Wu1xtnYmNjWW7Ve2k5yRjK2FbZH3lzUAGUA3325G6yzNLWlSowmHIw5zNeEqkUmReNp5FrrPTH2m9r79nPwwN5OBC00i7rpxb88slw+ARwPTHef2JTiwSJ02t4be7xRv+/p94PgP6vS5TWq6gZK4uE1NcaAYdLTRmYGDFzjVuvPyAcea6v+dasHR7+Di32pZGxcY9au6vKR8Wqrj2Hw3MDuI3frxku9PiGpMgrZVhIWZBQHOAZyPPk9YXBjpmelYmlsWaVvD1AhdfLqw6+ouQE2RcF/AfaapoE4HdU2QbqEorOyh2SNw8Es1AfrJFdDuqfI5tsgtLRGiQ7PnI/+VoK0QQgghhDCZ+PR40vXpAPg4+HAz+SbJGckm62lrGPytYVsDSzNLevn14tfzv5KckczOKzuL9b3JMGjbvXb3XOtbeLbgcMRhAI5FHivSvq8lXtM+A0mNYEJn1qA9ARrQBULV78pcPggtR5vuOFvegsw0dbrjJHDxK972dXqAmSXo09Wgbb+PitfxCdQetRunGwdsQZ2Pv66+rhYw9o25NYz4GTzvKd5x8+LdBMath42vgnNtaDK09PsUohqS9AhVSD0XNUVChpJhlIS+IOeiz3EmSk103tC9ISODR2rrDl4vwwTrZa31uOzpw4uNBzgT5evmWbQbHYDIfyqsKkIIIYQQovq5nZwdnHWzddOeFjRZ0DYlO2jrbuMOYBRI3RRa9BQJyRnJHLiupqLztPUk2D04VxnDwciORR4r0n6zUiMABDgFFLk+ohCnf82e7jNTDYyCaQcji/wP/v1DnXbwgs4vFn8fNk7g31Gdjgm78x2smI59DzfVtB14NoS2T8E996sDojl4AwUFgXUw5Bvw71D84+bHo4GaJvHB/4G59CcUIi/SMqqQrKAtwMWYiwS5BhW6zdoLa7Xph+o+RAvPFljoLMhQMjh4owoHbT2Dwa8jhO9VH8kP35f9R0yYhHInEK4r7BfcyH+N5yPOlFGNhBBCCCHE3cgwqOpm44a7rTtXE64SkxpDhj4DC7PSfa017GnrbqsGbdt4t8HNxo3bKbfZeWUniemJ2GeNfF+A/df2k5qZCkBX366Y6XL3k2rm0UybLnLQ1qDTToBzQJG2EYWICYcrd74TezZUB1Wu2QyuHoZbZyHpdtHHXSnIhb+ypzs+pw7KXRL174OQO724z28qXo/X1HjYZjAI2v3zc+fUzUyH+BsQdw3irt75/zVIioLg+yH4gZLVWwhRYtLTtgqp61JXm74QU/hgZOn6dNZdWgeo6RX6B/bHztKOJh5NAPUPf0RiRNlUtjzIgGRlJjY1lsFrB/PQmocK78EQmSNImzOIK4QQQgghRCkY3o+627gbjcsRkxpT6v3nTI8A6venXn5q3tDUzFR2XN6R57Y5bb+yXZvOKzUCgLO1s9Yh5+ztsySlJxW637C4MG1aetqayD+/Z083Hqz+37dd9rIrh01znJCd2dMlzUULUD97gDzObS7etrs/gcSb6nTwg3kPgmZuCS6+6mBhjQeraRz6vg+Dv5SArRAVRIK2VUiQS3bP2qIEbfde3av9Kt3Dtwcud0ZobOvdVitTpXvbNnwQ7NRfwjmzBhJvFVxeFNnf4X9zIeYCIbEhbA4t5IYgZ5A26jxkpJVd5YQQQgghxF3FMKjqZuOmpTDIua6kjAYiM9h338DsINnG0I2F7kev6LXgro25De1qtsu3bFaKhEwlk1O3ThW6b0mPUAZO/5Y93SgraNsme9nlA6U/RmZ69kBn9p7gUYp8sO51we1OR67wfZAcXbTtYq/Avs/UaTPL4g+CJoSoMBK0rUJ8HHywNrcG1PQIhTEcgOyhug9p09UmaGthDc3v5OjNTINjP1RsfaqRyKTIPKfzLpwjaKvPgFvnyqBWQgghhBDibmTU09bWuKetYeqEkjLKaWubHbRt6dlS63m7++pu4tPiC9zPP7f+0fbVvmZ7bCxs8i3bwrOFNl2UFAkhcSEAuFi7aJ1xRClEXYTrx9Xpms3VgChA7ezvyiYJ2l49CmkJ6nRg1+IPHpZT/Tu5lpVMuPh30bbZOhMyUtTptk+CW53S1UEIUW4kaFuFmJuZU8dZ/Qc2PD5cy5WUl5iUGLZd3gaovxZ39MnO99rMsxlWZlYAHLpxqAxrXA4MByQ7sgT0+vzLiiIz7G1gOJ1LcjTEX8u9PGfKBCGEEEIIIUrIKOdsjvQIphiMLGv/FmYWOFk5acvNzczp498HUFPPZX2/yo9RagTf7gWWNQzaHo88XmDZpPQkrSOFv5N/gWVFEf1j0Ms2KzUCgLMPOPuq01ePQGZG6Y4TYpBWo0630u0LsoO2AOeKMEDetWNw8md12sYFur5c+joIIcqNBG2rmKzcR3pFT0hsSL7l1oesJ0Ov/oG5v879WGaNgglYm1trNwlXE65yJf5KGda4jLnVgbr3qtPRoXCp4BspUTSGvQ1uJt/Mv2Dkf9nTzn4GyyVoK4QQQgghTKOgnra3k0sftM3qpOBu455rEF6jFAkhBadIMMx727V21wLL1naorfXiPXHzBJn6zHzLSj7bMmCUGmGQ8TrfO71t05Mg4nTpjnPJIGgbaIKgrV9HsLozkNn5LVDAdYOiwKY3sue7TTPNwGpCiHIjQdsqpqiDkRmmRniw3oO51rfxzs7VU6VTJIAMSFYGDHszFJgnzDA4a/gLdYQEbYUQQgghhGkYBm1dbVyNUhiUtqdtpj6T6FQ1N6jhfrM082iGl50XAPuu7SM2NTbP/VxLuMbZ6LMANHZvjIedR4HH1el0WkeahPSEAr/bhcaFatMBzgEF7lcUQeS/2d9jarcFFz/j9YaDkV0uxXfltCS4cmd71wBwNUEvaQsrqNtDnU6+rfYGzs/Z9RC2W512qwNtJpT++EKIciVB2yomq6ctwIXovP+wn4s+x5ko9Y9QQ/eG1Hetn6uMYVL8Kh+0rd8XHGuq02c3QFwej+uLYjHsaVtgegTDfLZBfbJ/9ZWetkIIIYQQwkSy7k0dLR2xNrc2aXqEmNQY9IqaYi2r56shM50Z9wWoj6RnKBlsDd+a5352XMnuUdnNt2g9Kpt7NNemC8praxS0lZ62pWfYy7bxkNzrfQ3y2l4pxXfly/vVsVfANL1ssxilSMin93dmOmx5K3u+1ztqwFcIUaVI0LaKqeeaHbTNbzCytRfWatOGA5AZalSjEbYWtgAcvH4QRVFMWMtyZm4JLUer00omHP2+YutTDRj1tE2Jyv9xLcOgrWew+gKIvQwpcWVYQyGEEEIIcbfISoHgZqsGa00ZtDXsoOBuk7unLUDfgMJTJBimRigsn22Wog5GFhobqk1L0LaUFMUgn60OGubxfdmrMVjaqdOlGYzskonz2Wap1zt7+tzmvMscXgxRdzp5+XWE4AdMd3whRLmRoG0VU9O+phZszesRmnR9OusurQPURPr9A/vnuR9LM0taerUE1JylWaORVlktR4PuzuV8ZGnpE8ZXEamZqSYPuKdkpJCQnqDN6xW99siYEUXJ7lHrWFPNj+TVMHu9YUBXCCGEEEKUqdjUWG1Mi+okNTOV+PR4IDtY62Ltgg4192xpg7aGT5jllR4BoHGNxvg4+ADqU4o5j5mYnqg9veht700D1wZFOvY97vdgY24DFDwYWVZPWx06/Jz88i0niuDGqexgpn8ncKqZu4y5Jfi0UqdjwiHuesmOZTgIWUDBOY6LxdELaqnf5Yk4BbE5xqhJjoHtH2TP3zcLcuRqFkJUDRK0rWLMdGZaioQrCVdISk8yWr/36l7txqOHbw9cbFzy3Vc77+wUCYeuHzJ9ZcuTc201TQJA/DU4X4SRNKu4fdf20eXnLozZOEZ7pMsUDG9cs+SZIiEhUs2jBNk9bD0bZa+P/MdkdRJCCCGEEPnbdWUX3Vd0Z9CaQbm+H5jCgesH2Bq+lXR9usn3XZjolOzOA1k9Yc3NzHG1cQUKGX+hCAy3zys9Aqj5Z7NSJGQqmfwV9pfR+n3X/s/eeYdHVeVv/DOT3ntCIKGGHunSpEkRxYZtsa113dXfKu6iW9xiXXVde1txbVjXhmJbEUWqgCi9dwghvfcy5ffHydy5k0ySaal8P8+Tx3PvnHvuCZOYM+99z/vdqP3bTE+Z3qSYWXMEGAM4I+EMALIqs8itzG3Sx2q1ak7bnuE9CfSTLe5esXupva2vydGYFHsNGI8iEqqLIWu7aicOh/CWM47dRh+RcKiR23bdE/bPaWdcYRegBUHocoho2wXRFyM7VurokNUXIGsuGsHG+GR7Vs+POV5s++gsjLvZ3j4NCpJ9evhTqk3VbMvbxoGiA61fUFcFGT+qfKMWcLbwdSra5uuctAk20Xao/ZwUIxMEQRAEQWgXvjjyBSarieNlxx2yVX3Btrxt/GrFr/jdqt9xwScX8MH+D6g11/r0Hi2hX5vqYxFs7aKaIq92nunHby4eARpFJBx3jEhYfXK11nY1GsGGQ65tftOIhILqAqpMSoiXImReoo9GMPg5j0aw4W0xsuPrgYafS19GI9hwyLXVibbFx+HHl1XbLwhm3YsgCF0XEW27IA7FyHQRCSU1Jaw6uQpQC47JvSa3OM6QmCFENBSO+innJ5+6NTuEATPtlT8Pr4SiLh750AqnKk5p7fzq/JY7l5yEf0+E18+Bb/7aYleXRdvGebYAScOdvy4IgiAIgiC0GYdL7Z8JbJ8HfMVXR7/S2lmVWfzjx39w3tLzeHPPm23i6m1Mc/EFNtG2xlxDtana5+M3ZkjsEPpE9gHg55yfya9S62+zxczazLUAhPiHcGaPM5sdwxn6XFtnEQn6ImT9Ivu5NbbQiFNbVNwBKCE1zLmzGnB02nqSa6vPs/VlETIbPUZCeFLDvVZDfcPvwHf324ufTfo/++djQRC6JCLadkGaE23/d+x/Wo7VBf0vIMAY0OI4fkY/xiWNA1TV1EPFh9pgtu2I0Qhjb2w4sMLWNzt0Om1NVkWW1rYtGp1SlgVvXgglJ9Tx7o/VU+ZmKKhpKtA6F211TtrEhizb0FgI79Hw+p4W7yMIgiAIgiB4T72l3qFQ1frM9T6LMbBYLazKUCKwLUMWlGHgiZ+fYO7Sufxn538oq2u7ArT6/Fi901bvinUW7+UqDk7bFkRbfUSCFSsrTih3466CXVr9h8k9JxPkF+TW/UcmjtT+bbfmbm3yun5npRQh85Ldn9jbw1uIRgAIi4O4gaqdtR3qa9y7ly3P1uAHfVo2U3mE0QgDGwqSmaqVs/fkZtjzqToXGg9TFvn+voIgtCsi2nZB9PEIetFWH41wUdpFLo01Idm+7cMWnt+lGX0t2MTqrW9DbXnHzqeNqDHVOAipzTptK/LgzYugWOc6riqEoqPNju2R0zZBV2zB5rqtLobynGbvIwiCIAiCIHjPybKTDiJteX0523KbbrP3hL2Fe8mrzgNgaspU3r/gfWb3nq29XlJbwvPbnmfux3N5butzDvmzvqI5UTU2xC7gelOMTL/ObSkeARwjEr45rmpo6KMRpqe476iMDIzUPt8dLD7YxL18ouyE1u4T1cft8YUGLBa7oGkMgKEXtH6NLSLBUg/Z212/V1k2FBxU7V5jIDjSram6zCD7zyMHvoZv/mI/nvHntruvIAjthoi2XZCk0CTCA8IBu2h7sPggewuV83FY3DAGxQxyaSz99p1uIdqGJ9qziaoKYNUjHTufNiKrMsvh2KmoWlkIb10MhTYHta4gQgu5TM5E2yZOXqvVLtpG94GgcPtrDhEJkmsrCIIgCILQluhNHDZ8FZHwfcb3Wvvs1LMZHjecp89+mk8v+pTz+5+P0aA+TlbUV/DKrleYu3Quj//0eMu7wNykOaetvl1U7bloa3PpBhgDiAxsWeQaGDOQAVFKYN2Wt42cyhwtQ9iAgWkp0zyagy0iwWw1s7Ngp8Nr+ngEcdp6wclNqmA1QNosCIlp/ZpUew0YtyISjq21t9siGsFG/xl2w9K2tyGzobh4/CDdDlRBELoyItp2QQwGgxaRkFOZQ0VdBZ8f/lx7vbUCZHrSotO0Bc/POT9r8Qpdmll/B/8Q1f5xsb1qZzfiVPkph+MmC+PqYnj7YrtoGpUKF79gf72FRYez7WVNROHSk1BXodq2aAQb+mMRbQVBEARBENqUIyVHmpxbdXKVV8W59OOAEiT1BbbSYtL459R/8sX8L7hs4GX4G/0BqDZV89betzh36bm8tus1r+8PjmvTZkVbL5y2NsNCXEgcBoOhld4wt5+9ANTru1/XRPMRCSNajFdoCX2u7bY8R5e0LfoixD+EpNAkj8YXgN1L7e30y1y7xtNiZMd0ebZtUYTMRlAE9J2i2rYcW4A5D4Gff9vdVxCEdkNE2y6KPiLhQPEBvjz6JQD+Rn/m9Zvn8jhGg1Fz21bUV7C/aL9vJ9oRxPSF6X9UbasFvvwdWMwdOSOfo8+zhUaiak0pvH0p5OxSxxHJcP3nMGw+NLghtKewTtA7bYP9gtW5xkKusyJkNpJ0om2uiLaCIAiCIAhtid5p2zOsJ6AK1joTc90hoyzDQZCMD2latKl3ZG/un3w/X1/6NVcPuVrLc62z1PH8tueprK/0ag7g6KJ1VogMPM+0NVvMWh5ta9EINvQRCe/vf19r60Vtd2muGFm9uV4rPtwnso9LorLgBLMJ9jZECfoHw+DzXLsufhAER6n2yc2u1euwWu1FyPyDIWV8y/29ZdBcx+N+05qeEwShyyKibRdFX4zszT1vaguVs1PPJjo42q2xxvew/yHpFhEJAJNuh4Qhqp21DX7yzZP+zsKpykZOW1umbW05vHsFZDUUMQhLhOu/gNj+KsLAFl2QuwdqnBeMsP0sRQRE0CNMFRVr4uR1VoTMRvxgtCiGvD1ufV+CIAiCIAiCe9iE1QBjAFcPvVo7vzpztVfj6iMWZvae2WLfHmE9uGfCPSy/bDljk8YCaqu/TXD0Bn18QURAhHbeF07b4tpiLFYLgFNR2hn9ovoxOEbVc7BiF/E8ybO10Su8FwkhCQDsyN+BucFwcrLiJGaraks0ghccXweVDZ9nBp6jHKquYDTaRdfKPCg+3vo1RUehLFO1e0+EgGC3p+sWA8/RHRjgnIdBxH1B6DaIaNtFSYuxi7b6BZU70Qg2HETb7G4i2voHwgXP2I9XPqgC4bsJTeIRqvOx1lbCe1faow9CYuG6zyB+oL2jtsXHCqe2OB1bv0XMtnitMlU5FkVoyWkbGKpEYoD8A93O5SwIgiAIgtBZqDfXk1GWASgxcVbvWdpr3ubaNs6zdYX4kHiHmhnZFd6vv22CbGxwrIPTVO+69TTTtrkiZ61xbr9zHY57hfdyMNW4i8FgYFTiKAAq6ys1Id4WjQDQN6qvx+Of9uz5xN5Ov9S9ax1ybV34rHx0tb3dlnm2NuIGwODzVfusOyF5RNvfUxCEdkNE2y6Ks0VBXHAck3tNdnusPpF9SAxNBGBr3lbqzfWtXNFF6DMJRv9StevK4Zt7OnY+PqRxPILJYqLk/SvhxHp1IjhaCbZJjVywKS0vOmpMNVTUq6za2OBY7Yk/NIpgsDltDX6OorAN231NNVB0zKXvSRAEQRAEQXCP42XHMVlVTYoB0QNIiUjRPifsyt/lvFitCxTVFLE9fzugxOB+Uf1cvtYW0QBNi+e6i8VqobhGxRfonbXgGGfgqdPWQbR1MR4BYG5fx+3n01Omex1doI9I2Jqnds2dKDuhnesT2cer8U9bTHWw7wvVDgiDgW5GB7hbjKy9ipDp+cVbsGgfzHmgfe4nCEK7IaJtFyUuOI7ooGiHcxf0v4AAW/VINzAYDJrbttpUze7C3b6YYudgzoMQ2rAA2/MpHPquY+fjI5xtNcvP3KgaQZHwy0+cP2VtZdGhzwOLD4l3cBxoi36zCfIPqnZcGvgHNb1P4nB7WyISBEEQBEEQ2gR9bq1NrLW5Yq1YWZu51ul1rbHm5BotNsBVl62N5PBkre2t07a0tlSLB2jshA3xD2m+/oKL6K9zx2mbGpHK8Dj7end6qvfinLNiZMfLjmvn+kW6LpwLOo6uVkWaQWXZBoa6d32vsfa6IK05bS0Wu2gbFAU9R7l3L0/x84fInq33EwShyyGibRfFYDA4FCMDuCjtIo/H00ck/JjtwhPErkJorMr1sfHVIqirar5/F6CqvkormKCnwN9PPT2+5mO1uHBGTF+VcwuQ+bNaWOhovEUsIdTutNVyc4uPgblWtRtHI9jQn9dHKQiCIAiCIAg+41DJIa1tE231BbE8jUjQX+euaOtLp63eQdvYaWswGLRznjpt9U5kd0RbgF+P+DUBxgBGJ452iITwlMGxgwnxDwHsxcj08QjitPUQh2iEy9y/PijCXhckr/m6IADk7gZbVEffKWD0c/9+giAIOkS07cLoIxKGxQ1jUMwgj8eakDxBa3ebYmQ2Rl4JfaeqdskJWPt4x87HS/TRCPpNWPkBwXDNh9B7QtOLtAsMdrdtbSkUHHB42WHhGhznUJBBe62lImQ2knRO21xx2gqCIAiCILQFzpy26fHp2lb/TVmbqDHVuDVmtamajVlqB1dccBwjEtzLyLQVsgXvnbatxRfYRNuS2hKteJcvx2+Jmb1nsuGqDbx57pse7XZsTIAxgPT4dACyK7PJqczRnLbxIfGEB4Z7fY8uT8lJ+PY+WPckHFyhapZYrc33r6+BfV+qdlAUpM1qvm9L2OqCWC3N1gUB4Ngae7t/O0UjCILQrRHRtgszNNbuZvSkAJmenuE96RXeC4AdeTvcXtx1agwGOP8psC2mNjzXpd2f+miEAXV1Wjt/zDXqiW5rtBCm33iLWHywXbTVFrUtFSGzEdsf/BpiE/QiryAIgiAIguAzbKJtkF+QtpY3Goya27bGXOP2LrqNWRupMavPAjNSZ2A0uPeRMdAvUKuL4EunrTMnbGyIEm0tVgsltSVuj984Gsxdgv2Dvc6y1aOPSFibuVb7/vtG9vXZPbosB1fAy1Phh2dUken3roCnhsDjafDWfFjxd9j5kfqsYlY5zxz+TtU2ARh6gfNYN1dI1ZliMn9qvt9RnWjbXnm2giB0a0S07cKc3/985qfNZ8HgBVwx6Aqvx7O5bessdezI3+H1eJ2KhEEw5feqbTHBl79vEg3gc+qrYf0z8Pq5sPmVlp8Cu8Gp8gytPbLGLtoWhEa6NkALxcj0boP4kHjiQ+2LVy0ewcFp24xoa/SDhMGqXXRU/VsIgiAIgiAIPqPWXEtGw7qwf1R//HRbsb2JSPg+43utPbP3TI/mZsu1LaguoNYWq+UBelG1cTxC43OeRCR4E4/QFuhF288Of6a1+0b17YDZdBLMJrtIW900Io6qAji6ShlzPvkV/HsiPNoL/jMDvrvP3m/4pZ7PwZViZKY6OLFBtcOT7J+FBEEQvEBE2y5MsH8wD531EH+b+DcC/LzfktNtc21tTL1LOUABMjbC9nfb5j5mE2x5E54boxYKGRvhf3fDFwvBXO/d2KY6Tm19XTscVW/fBpZfle/aGD1H2V3HjRYdrsUj7Ff/9QuCmBYKItgiEqwWyD/QfD9BEARBEATBbY6VHtOKhelj00CZMWxFutZk2ouKtYbJYmJNpnILhviHOESouYM+1zanMsejMaD1+AL9OU9EW5soHGAMICIgwoMZ+paRCSMxNASg7SzYqZ0/bZ225bnw9nwVh2BjyAVw6SsweSH0PxtCnTikTTWQtQ0KD6vjkFjv4gqi+yghFuDkT87NP1lbob5StftNU7s9BUEQvEREW0FDL9r+lNPCto+uSkAwnK/7g//t36GyoPn+7mK1wr4v4KVJSqAtb7QdbOtb8M5lUF3i2fimWvjoerLKdE7b2Y9qbb3g2iIBIZDckE1WeAiq7AvcxlvQooOi8Tf428c31doXPwmDVKXS5tDn3UpEgiAIgiAIgk85XHJYazcuUBziH8LEnhMBtYbbU+BajYHtedu1mIEpvaYQ5OfZdnKb0xYc6zG4i0MhshDfO21tonB8SLxPYw48JSIwgrSYtCbnT0vR9vh6FYdwfJ06NvjBOf+ABe/AiF/AOQ/BdcvgD4dh0X5VjHnWvTD8EohLw6H6x7ibwBuTUyt1QQCJRhAEoU0Q0VbQSAhNoF+Uck7uLthNpe1JYXdiwExIv1y1q4tV9pEvOP4DvDYHPrgWCg7azw8+H+Y+Cn6B6vjYGtWv6Jh749fXwAe/hAP/45S/Ekr9MJKavoCIQOUK0OILXKGZXCYHN0NIHEaDUVsgF1QXQMEhsDa4e5srQmYjSfe6FCMTBEEQBEHwKc6KkOk5O/Vsre1qRIK+n/56d+kV1ktrZ1d6XozMod6Cs0JkIZ6LtiaLieKa4mbH7ihGJ4xucu60ikewWGDdU/DmhVCRq85FJMMNX8HkO5o6WA0GiEyGgXPUzsorlsAdW+CeTLj5W7j+S5hxj/fzSmklIkGKkAmC0AaIaCs4YHPbmqwmtuZu7eDZtBFzH1HVQwF2vAfH1nk+Vu4eePcXsGSeYyh96kS46Ru46j2Y9H9w/RcQ2rAYLDgIr86GDBcjKOqr4YNr4NA3AJpo2yM8GX+jv1booaC6AKurubnNFCOzuXUjAiI0Z4UtIqGopgizXnxtLs9We13vtO26hd8EQRAEQRA6I4eLm3faAkxLmaZttV+dubrV8axWqyba+hn8mJYyzeO5tYXTNjo4usnreqet3nzgCiW1JVhRa+fOkGdrY1TiKIdjf4M/PcN7Ou/c3agqgvevgpUPqIg1gP4z4DfroM8k98YKClefefpNbXl3oKvoTS+N6oJQV2k/F9MPont7fz9BEAQ6WLR99NFHOfPMM4mIiCAxMZH58+dz4EDr2ZcfffQRQ4YMITg4mDPOOIP//e9/7TDb04NuH5EAEJEEs3Wh9F/+Xm37dwNj+SkMy26Dl87SxFQAEobAVe/DTcuh90T7+d4T4VcrIX6QOq4qUE+Pd33c8o3qquC/V6nKp0B5YDhlfurX1lYh2CbaVpuqXXdHN/Ok2OZm0C9cbeNbrBaKc7bbr2vNaRuRDLbFtcQjCIIgCIIg+BRbPEKIf4hTUS8+JJ4zEs4A4FDxITLLM1sd72T5SQDGJY0jymZy8AB9pq1XTtsGITYqKIoAY9Pt7d5k2jYuwNtZGJM0xuE4JSLF6ffe7Ti1BV6eDgeXN5wwwPQ/wbWfQHhCh04NgOSR9t2TjZ22GRvB0lC7RFy2giD4kA4VbdesWcNvf/tbNm3axLfffkt9fT3nnHMOlZXNC08bNmzgqquu4uabb2bbtm3Mnz+f+fPns3v37nacefflzB5nau0fc7phMTIbY2+ElIbvtfAQ/PBs0z6mOijNhFNb4eA3sPVtWPckhs/vIOG/czHsfB8ans4T2Qsu/jfctgEGn+c8eD62n9qiY8s4MtfC0pthzb9UHm5j6irhvwtUNVSAwHCyLn5Ke9m2OI/Xhe+7HJEQ1QsiU1T71FYwmxxEX71o61CMrEAnvrbmtDUY7MXIyrMdsnMFQRAEQRAEz6k2VXOq4hQAA6IGYDQ4/1injziwFRhrDodohN6eRyMADiKybZ6eYBNim4svcHDa1rjntNXXg9CP09H0DOtJYkiidtztoxGsVtj8Crx+LpQ21O4IiYVrl8LZfwGjX8fOz0ZAMCSPUu3Cw1Cp+3lzyLP13KEuCILQGB/sE/Cc5cuXOxwvWbKExMREtmzZwrRpzv9n9+yzz3Luuefyhz/8AYCHHnqIb7/9lhdeeIHFixe3+Zy7OzHBMQyOGcyB4gPsK9xHaW2pV0/ZOy1GI1zwtHqaazXD2idU1EFlPlTkQWUe1JQ6vdRBjg2OVtlJ429RBb5aIyRaLUC+/D1se1udW/UwFB6Bi54D/4ZiD7UV8N4v4MQP6jgwAq5dyinsDzQaO20B8qvytVziVkk9E/ZkqiqneXsoDLcvhvULY71om198lCEAgeEQldr6PRKH2b+HvL3Qd4prcxMEQRAEQRCa5WjpUW1rv7NoBBvTU6bz7FZlTlh1chXXDL2m2b7fZ3yvtb3JswUIDQglKiiK0tpSsis8c9pW1VdRbaoGmhdVY4JjtLbbTtsax1oOnQWDwcCoxFGsOLEC6OZFyKxWWHYb7Piv/VzKeLjiDYhK6bh5NUfqeMhsiEHI3KzMOgDH1tr7SBEyQRB8SIeKto0pLVUiWWxs8086N27cyKJFixzOzZ07l2XLljntX1tbS22tfet7WVkZABaLBYvF4uWMXcNisWC1Wtvtft5yZtKZHCg+gBUrP+f87PWirdOSOBzDhNswbHpBuV73LnP5UqtfENYJt8KU30Nwg6jt6vtr8IMLnoW4NAzf3Y8BK+x8H2vJCay/eAf8AjC8dwWGhm031qBIrNcshZRxZO57RxsmOSwZi8XiILDmVeW5/nOWMh7jnk/V1DM2UdDXntMUGxyrjaMfv6BhcWtNGKryc1vL0E0Yotn5Lbl7oPdk1+bWjnS130+hZeT97F7I+9n98MV72ll/Hl588UUef/xxcnJyGDlyJM8//zzjx4932nfJkiXceOONDueCgoKoqalpj6kK3QB9nu3AmIHN9kuLTqNXeC9OVZxiS84WyuvKtSK2enIrc9lTqGoXDIkd4pMM1Z5hPSmtLSW3KheTxYS/0b2PnnoRtjlR1d/oT3RQNCW1JRRVd494BICxSWM10dZZkbluw8FvHAXbib+FOQ+AXyeNg0idABtfUO2TDaJtVRFk71DnktIhrHP9LAmC0LXpNKKtxWLhd7/7HWeddRbp6enN9svJySEpKcnhXFJSEjk5OU77P/roozzwwANNzufn57fbwthisVBaWorVasVo7Py13waFDNLaq4+uZnjQ8Gb7ZlZmsrlgM5vzN7OnZA9Doobw8NiHCTQGtsdUvcYw/CbiDnyNf7G9+q4lIBRLSDyWkDgsoQ3/DYnD3HBsDo6l0C+RiIRUjGW1UJbn2c3TriTIGEv093/AYKrBkLER839mYgmOJjBP/eG3BEZSdP7rmAJ7Q14eh/PsC/TQ+lDy8vIIrLf/Wx/LP0ZemGvz8Q9Lw7akqD28jqM6x26wOZi8PDWOf539fxOFfmp7UnVEX8ryWr9PQEAytiV2zYktlPW52KW5tSdd7fdTaBl5P7sX8n52P3zxnpaXl/t4Vt7zwQcfsGjRIhYvXsyECRN45plnmDt3LgcOHCAxMdHpNZGRkQ61HAzOopUEoRmOlNjXri05bQ0GA2enns07+97BZDXxw6kfOLffuU36rT65Wmv7yrCRHJbMvqJ9mK1m8qvyHYqTuYLeCdtSfEFscKwSbd102urjEZqLX+go5qfNZ3POZgwYmNt3bkdPp+04ao/kYN4TavdiZ8ZZMefj69Ei88RlKwiCj+k0ou1vf/tbdu/ezfr163067j333OPgzC0rKyM1NZWEhAQiIyN9eq/msFgsGAwGEhISusSHzpnRM7l/+/1YrBb2lO1x+LBRZ67j59yfWX9qPetOrSOjPMPh2u1F29lXu485fea097Q95zdrsBQeVlEHYfEQGIaR5gOfLRYL9fn5vnk/E6/FmjoMPrgaQ0Uu/mUnoOwEANaQGLj2U2KTR2rdS/aUaO30lHQSwxIZYLEv1Gv8apr9cNiEuGlY/YMxmGoIzt9JfeCF2kt94vto46QZ7E/38xtE2+A+Ywh25T6RdmdtSNkx165pZ7ra76fQMvJ+di/k/ex++OI9DQ4O9vGsvOepp57illtu0dyzixcv5quvvuL111/nz3/+s9NrDAYDPXr0aM9pCt0IWxEyaN2JOSN1Bu807NZadXKVU9H2+5P2aISZvWf6ZI56t25WZZbboq3eOduSqBobHMvR0qNUmVScQoi/C5FldN54BFDxEs+c/UxHT6PtOd7w2d9ghDOu6Ni5uEJED4juAyUnVOE0cz0c0+XZShEyQRB8TKcQbW+//Xa+/PJL1q5dS0pKy9k1PXr0IDc31+Fcbm5us4veoKAggoKCmpw3Go3t+gHQYDC0+z09JSo4imGxw9hduJtDJYfYX7yfPYV7WJu5lh+zf9SypZpj3al1zO3XhZ4IB0dAr9FuXeLT9zN1HPxqJby3APLUtjRC4zBc9xmGHmc4dD1VqQo5+Bv9SQxLxGg0khhmF0ILqgtcn5MxGHqOgYwNGEpOUFR2UnspPjReGych1O7ALfBT54xJw1QucGuExqiCZ2WZGPL3KxdRJ3QSdaXfT6F15P3sXsj72f3w9j3tbD8LdXV1bNmyhXvuuUc7ZzQamT17Nhs3bmz2uoqKCvr06YPFYmHMmDE88sgjDB/e/O6mjo78kriSzoXNaRseEE5CcEKL78uohFFEBERQXl/OulPrqDXV4oef9n6W15WzOUe5BnuG9WRg1ECfvM89Qu2fz06Vn2J0gnvrbX18QUxQTLNz0rtwC6oKtLoPrVFQpStEFhTb5X+2u9zvaFURxlxVTNzaYwTWoEjX4+Y6EEPKmRhKToCpGkv2TgxH12AArAY/rKkTffY9dLn3U2gReT+7F+0Z99Whoq3VauWOO+7g008/ZfXq1fTr13oBpUmTJrFy5Up+97vfaee+/fZbJk2a1IYzPf0Ynzye3YXqj+iCLxc47eNn8GNU4iimpUxjYvJEblh+A9WmatadWofFamm2iq3ghOhUuPkb+O5+KMmAOQ9C4lCHLlarlayKLEAtqP0aKqnqC5Hpt3m5ROqZkLEBgMLC/dppfa6Xvl3g31C9NXGY6/dIGgZlmVBbBqWZ6nsVBEEQhA7m6NGj9O/f3+txCgoKMJvNTuO79u/f7/SawYMH8/rrrzNixAhKS0t54oknmDx5Mnv27GnWwNDRkV8SV9J5qDJVkVWp1oS9w3qTn5/f6jXj4saxKmcV5XXlrDqwihExI7T3c03uGkwWEwDj48a7NJ4rhJpDtfah3EPkhbsXKXai4ITW9qv106K7GhNssbvvj2QdISDatTzU3AplBAowBlBVXEW1oWVjSmenq/2OBh37FlsZuarEMZS7EL3WGQiNHoZtv27Vz+8RXngIgPrEERSVVgO++Tnqau+n0DLyfnYv2jPuq0NF29/+9re89957fPbZZ0RERGi5tFFRUYSEqG0t1113Hb169eLRRx8F4M4772T69Ok8+eSTnH/++bz//vv8/PPP/Oc//+mw76M7MqHHBF7f/XqT87HBsUzpNYWpKVOZ3HMykYH2iIlJyZP4/uT3FNUUsbtgNyMSRrTnlLs+QRFw/pPNvlxWV0ZFfQXguN0sLCCMEP8Qqk3V5Fe7uchOtRcfKyyzR13ot6AF+wdr7owCPz8IiYWwBFwmcRgcUoUUyNsroq0gCILQKUhLS2P69OncfPPNXH755e0auzBp0iQHw8HkyZMZOnQoL7/8Mg899JDTazo68kviSjoPO/N3au0h8UNcisaamzaXVTkqP3RHxQ5mD56tvZ9bD23V+s0bNM/1qK1WGOo3FLardjnlbo9bd6JOa/fv0Z/EBOfX98rpBQ0bxqyhVpfvU1qvimAnhCQ0eejSFelqv6OGrbu0dsjQOYR0whg1pwydBesfBCBs7/va6YBBs3z2uwNd7/0UWkbez+5Fe8Z9daho+9JLLwEwY8YMh/NvvPEGN9xwAwAZGRkO/wiTJ0/mvffe429/+xt/+ctfGDhwIMuWLWuxeJngPmcmn8nYpLFszd3K8LjhTEuZxtSUqQyLG9asg3Z66nQtD2tN5hoRbX3MqYpTWlu/7ctgMJAQkkBGeYbDNi+XSLGH6RdWF0JDckFsiGOxh7jgGLtomzjMvYiDJN1Wz9w9MKgLRWcIgiAI3ZatW7fyxhtvsGjRIm6//XYWLFjAzTffzPjx41u/WEd8fDx+fn5uxXc1JiAggNGjR3P48OFm+3SGyC+JK+kcHCs7prXTYtJcej+mpk7F3+CPyWpiVeYq7h53NwaDAbPVzPpTKlc0IjCCccnjfPb+9oqwr1ezq7LdHldfWCw+JL7Z6/W7wkpqS1y6j8lioqS2BFB5tt3lZ7pL/Y6e+EH912DE2Geya9FrnYEe6RAQBvWVGOoqtNOG/jMw+Ph76FLvp9Aq8n52L9or7qtDf1qsVqvTL5tgC7B69WqWLFnicN0VV1zBgQMHqK2tZffu3cybN699J34aEGAMYMm5S9j6y63894L/ctuo20iPT28x8mBqr6lae13muvaY5mmFLRoBaJLVZVuslteXU2NyY4tkeALEqFiSQlMVoBbsQX6OHwoT/NRToCqjkaqEge5NXB/zkLfPvWsFQRAEoY0YNWoUzz77LFlZWbz++utkZ2czZcoU0tPTeeqpp1zeIh4YGMjYsWNZuXKlds5isbBy5UqX47vMZjO7du0iOdm9Qk3C6Ym+CNmA6AEt9LQTGRjJ2B5jAWUEOFKqMnF/yv1J28k1PWU6AUbXogVcITooWisKpl/HuopetG2pUJh+h5i+uFhLFNcUY8Xa5HqhnagqgoY8W3qMgJDoDp2OW/j5Q8pYx3P+IZDq3gM/QRAEVxCJX2gRf6PrZuyE0ASGxylX5b6ifeRW5rZyheAOeqetPh4BHIuFeRqRYCsy5mzhGq/LyC6IcTPeIH4QGBqycPP2unetIAiCILQx/v7+XHrppXz00Uc89thjHD58mLvvvpvU1FSuu+46srOzWx1j0aJFvPLKK7z55pvs27eP2267jcrKSm688UZAxX3pC5U9+OCDrFixgqNHj7J161auvfZaTpw4wa9+9as2+z6F7oNetB0Y7frD9LNTz9baa06qiverT652+rovMBgM9AxTa9acyhysVqtb19sKkQX7BWvirzP0O8T0Qm+LY+vE3ZYEYaGNsLlsAfpO6bh5eIouYg6A3hPBv+lOCEEQBG8R0VbwKdNSpmnttafWduBMuh/NxSOA98XIqg0Gqhrs+c4WrvF19mrV+eFu5NmCWsDEN3ygyD8A5nr3rhcEQRCENuTnn3/m//7v/0hOTuapp57i7rvv5siRI3z77bdkZWVx8cUXtzrGggULeOKJJ7j33nsZNWoU27dvZ/ny5VpOZkZGhoP4W1xczC233MLQoUOZN28eZWVlbNiwgWHD3Cj0KZy22ETbyMBIh2iA1pieMl1rr85cjdVqZVWmyrkNNAZyVq+zfDtRIDlcucdrzDUuC6o2bP3jQuIwtBDNFRvsvmirXy+LaNsBHF9vb3cH0bbfNOf9BEEQvKRDM22F7sf0lOm8tENlFa/NXMsVg67o4Bl1H1oSbfUL9vwq9522hX725zfOFv/xVSXaI56CkHD3xgeVg5u/Hyz1UHjYMTJBEARBEDqAp556ijfeeIMDBw4wb9483nrrLebNm6dljPXr148lS5bQt29fl8a7/fbbuf32252+tnr1aofjp59+mqefftqb6QunKWV1ZeRV5QGQFp3WopjZmJSIFAbGDORQ8SF2Fexic8FmbawJyRMICwjz+XxtTluA7MpslwVSfeasXpR1hv51mzu3NfT9JB6hA9BEWwP0di1GplORMs7xuP905/0EQRC8RJy2gk8ZGjdUE/1+zP7RvXxVoUVsWWCBxsAmC16v4hESh1EYZBdimyxcrVYSynK0wwJztXvjN9xDQyISBEEQhE7ASy+9xNVXX82JEydYtmwZF1xwQZOiEImJibz22msdNENBaMrRkqNa29U8Wz0zUmYAYMXKi/te1M7P7D3T67k5w+a0BUcDQmuU1JZombOtibbhAeFaFq8n8QjuuJUFH6DPs03uYnm2NkJioMcZqh0aD8mjOnQ6giB0X0S0FXyK0WDUCpJVm6r5KeenDp5R98BqtWoL3Z7hPZsUhPPKaWv0oyDRnocWZ2hkwK/IJa66XDt0O34BIEkn2uaKaCsIgiB0PIcOHeKee+5psfhXYGAg119/fTvOShBa5lDJIa2dFp3m9vUzUmdo7exqFdlhwOBw3pc4OG0rWs+HtuHghG3FnWswGLQ+Eo/QBXDIs53afL/OzoXPwYgr4fLXwOjX0bMRBKGbIqKt4HP0eVlrMtd04Ey6D8W1xVSblMO1cTQCOGbauu20BQpjemvt+KoSxxfz9hJvNmuHHom24rQVBEEQOhlvvPEGH330UZPzH330EW+++WYHzEgQWudIyRGt7Ylomx6f3mRX1RkJZ7SZ21RfPDerMsvl6/RO2Nactvo+xTXFWKyWVnpLPEKH0tXzbG30GgOXvgz9Z3T0TARB6MaIaCv4nIk9J2pblNZlrnO7Uqwv6Ih7tiW2aARoXbT1RFQtDLcvVuNKGm1dy9tHgk609UQUJroP2HLSRLQVBEEQOgGPPvoo8fFNharExEQeeeSRDpiRILSOrQgZeBaPYDQYm7hqZ6a2TTQCQHKY3cnujtNW75h1RVS1ibZmq5my2rJW+0s8QgfS1fNsBUEQ2hERbQWfExYQxpk9zgTUE3X94rI9+D7je6a8P4W///D3dr1vW5JZkam19Y4FG1FBUZpQ7pHTNjBEa8flN3q/8vYSZbHg3yCEu1rgwQGjERKHqHbxcaitcH8MQRAEQfAhGRkZ9OvXr8n5Pn36kJGR0QEzEoTWsTltY4JiPN7W31i0Pbv32d5Oq1kSQhPwN6roLbecttWeOW3BtYgE2/hBfkFtUoBNaIaqIsjdo9pdNc9WEAShHRHRVmgTpqVM09rtGZFQb67nkR8foayujGWHl3Gy/GS73bstac1pazAYNJdAQZUHTtt6u4gal3cQ6nXFxvL2YQTiGty2bmfm2tBHJOTv92wMQRAEQfARiYmJ7Ny5s8n5HTt2EBcn26WFzkdJTYm2oyotxv1oBBsTkicQ4q8e2PeL7Ef/qP4+mZ8zjAYjPUJ7AF44bV0Qp/VuXL2Ltjlsom1ccBwGg8HleZ121NcokXX//6DwSOv9W+PEBmgoMNel82wFQRDaCRFthTZhWi+7aLsuc1273ffLo1+SW5WrHe8p3NNu925LWhNtwR6RUFxbTL253q3x9YvbuPpayNqmDiwWyFMCazz+2vhmi7nJGK2SNNzezu0e74sgCILQdbnqqqtYuHAhq1atwmw2Yzab+f7777nzzju58sorO3p6Qjek1lzLoeJD1JhqPLreIRohyv1oBBsh/iE8NPkhJiRM4N5J93o8jqvYdomV15dTXlfeSm+FN07b1kTbeks9JbUlgEQjAGC1QkU+HP8BtiyBb/4K714Bz46Eh3vAS5Ph/avg5elQfMK7e3WXPFtBEIR2wr/1LoLgPqmRqfSP6s/R0qNsz99OSU0J0cHRbXpPi9XC67tfdzi3t2Av5/Y9t03v2x60Fo8AavuZjcKaQnqE9XB5fJtrI8JsIRDg5I/QZzKUZkB9JQDxARFgLcditVBcW+z+IjdxqL2dt8+9awVBEATBxzz00EMcP36cWbNm4e+vlsQWi4XrrrtOMm0Fn5Jflc8HBz7go4MfUVRTxOSek1k8e7HbDk9vi5DpmdNnDiNDRpKYmOjVOK6gz7XNqshicOzgVq/RO21dEm1DdPEI1S3HIxTXFGNtcHvqrzutOPQd7PkUCg6qr5qS1q+pK4ctb8Ds+z2/r+TZCoIguIWItkKbMS1lGkdLj2KxWliftZ4L+l/QpvdblbGK42XHHc51N6dtiH9IswtXvYiaX5XvlmirbRGzFRw7uVn9VyeuxofGQ2W5Nr77oq3OaZvXPd4XQRAEoesSGBjIBx98wEMPPcSOHTsICQnhjDPOoE+fPh09NaGbsKdwD+/ufZevj3+NyWLSzm/I2sDWvK2MTRrr1niHSg5pbW/iEdobveEguzLbLdHWaDASHRTdan99PEJrmbZ6F68rRc66Hdk74b0rwGppuV9gOMQPhLiBSuC11MO2d2DGX8A/0P37VhVB7m7VljxbQRAElxDRVmgzpqVMY8meJQCsPbm2TUVbq9XKa7tf0479DH6YrWb2Fu7FYrVgNHTdJBCr1aqJtj3DejbryrDFI4B7xciq6quoMlUBEG9tGPvkZrVVKm+v1i8+IhUqjwF2Z65bhCdAWAJU5kPu3tb7C4IgCEI7MGjQIAYNGtTR0xC6CSaLie8zvufdfe+yNW9rs/0+2P+B26KtL5227YletNVHfrWELeIgOigaP6Nfq/3dKUSmX8eelvEI393nKNhGpihxNn5Qw38b2hHJYPvcYa6DvcvUOv7g1zDsYvfvK3m2giAIbiOirdBmjEocRURgBOV15azPWo/JYtKqx/qan3J+YlfBLgAGxQyiZ3hPVp9cTUV9BRllGfSN6tsm920PCmsKqTXXAs1HI4BjPII7oqpDnm1wDJAFVQVQdFTLswVIiE2DnLVuj+9A4jA4tkaNX5GvhFxfY7VCbTkERdgXmoIgCILghMzMTD7//HMyMjKoq6tzeO2pp57qoFkJXZHS2lI+PfQp7+1/j+xKx4JbUUFRXDHoCi5Nu5Rrv76Wopoivs34loLqArdEQ5toGx8ST1RQlE/n35b0DHN02raG1WrVIg5cKUIG7om2DmtfF8fvNhz5Xn0BRPeGW9dDsAs/S2NvUKItqNxbT0RbybMVBEFwG7cVtOXLlxMeHs6UKep/tC+++CKvvPIKw4YN48UXXyQmJsbnkxS6JgHGAKb0nMLXx7+mvK6c7XnbGddjXJvcS++yvTn9Zk6Wn2T1ydUA7C7c3aVF28xye55tc0XIoFE8ghtOW4ctYuG9gIbogsyf7PEIRn/i4+2ZtF6LtqAiEsJneDZOS3y1CH5+HRKGwJm/ghELIDjS9/fxNVYrbH0TAkJhxC86ejaCIAjdnpUrV3LRRRfRv39/9u/fT3p6OsePH8dqtTJmzJiOnp7QRTheepx39r3D50c+p9pU7fDagKgBXDvsWs7vfz4h/iEAXDrwUl7d9Somi4mPD37MrSNvdek+hdWFFNcWq3GjPS9C1hEkhztm2rZGRX0FdRb1EMWVPNvG/SQeoRksFvj2PvvxzL+7JtgC9JsOMX2h+LgSfYuPq2N3kDxbQRAEt3F7z/gf/vAHysrKANi1axd33XUX8+bN49ixYyxatMjnExS6NlNT7Ftf1maubZN77C3cy4asDQCkhKdwTt9zGB5vz0/dU9C181P1i9uWRFuHeIQqN0RbndsgPla31e7ED1BwQLXj0ojTZeR6LNomDbO32yIi4dB3SrAFyN8P/7sbnhoKXy7q/JEMm/4NX9wJn9wCB7/p6NkIgiB0e+655x7uvvtudu3aRXBwMEuXLuXkyZNMnz6dK664oqOnJ3QBduXv4pLPL+GDAx84CLbTUqbx8pyX+fTiT7l80OWaYAvwi0G/0GK7Pjr4kUPWbUscLjmstQdGD/TRd9A+9AjtgQG1+8kVp61edHVVVA3wCyAiMAJwFGWdcdrGI+z+GHJ2qnaPEZB+uevXGo0w5nr78da33Lu35NkKgiB4hNui7bFjxxg2TAkvS5cu5YILLuCRRx7hxRdf5Ouvv/b5BIWuzZReU7SFaVuJtq/vfl1r35h+I/5Gf4bH2UXbvYWdXKxrhaxKnWgb0YJo62k8gt5tkHgG2PJ/93ym8qsAEoc6jO+Ok9eBtixGZqqFr//Q9HxdBfz8Grw0Cd6YB7uXgqmuab+OpLYc1j1pP97+XsfNRRAE4TRh3759XHfddQD4+/tTXV1NeHg4Dz74II899lgHz07oCnyb8a0muob4h3Dl4Cv5Yv4XvDjrRSb3nOy0DkFyeDLTU6YDkFeVx6qTq1y6l1607WpO2wC/AG0deariVKv99WtTV522YBd4JR7BCfU1sPIh+/GcB5UQ6w6jrgFb1N22d8Bc7/q1kmcrCILgEW6LtoGBgVRVqaJF3333Heeccw4AsbGxmgNXEGzEBMcwMmEkAEdKj3Cy/KRPxz9RdoJvT3wLqIXaxWkXa/e1uVL3Fe3DbDH79L7tiT4eoaVM25igGE0gz6vKc3l8B9E2ohckNQirtaX2TonDHJwIrTkYmiVBVy3YFr3gKzY8r3J4AXpPhlt/gHE3QUCYvc+JH+Djm+CZdFj1CJS5VgyjzflxMVTp/k0PfgO1FR03H0EQhNOAsLAwLcc2OTmZI0fsRZ4KCjzcUSKcVuwtsBsDPrnoE/468a8uRXJdOfhKrf3B/g9culdXLUJmw5ZrW1RTRI2ppsW+Dk5bN0RVm8BbUV+h1YNwOn61ffx2ddrWlKqIAFPzc2szfnoVSjNUe8BMGHC2+2NEJMHgeapdkQsH3DBsSZ6tIAiCR7gt2k6ZMoVFixbx0EMPsXnzZs4//3wADh48SEpKis8nKHR9pqVM09q+dtsu2bMES0P102uHXUuQX5D22rA45QivNlVzrPSYT+/bnjjEI4Q177T1M/ppDgNPC5HFh8RDyvimnRKHEuQXpG0789hpGxRuz7/K26+ytXxByUlY+4RqG/xg3uPQIx0ueBru2g/nPQ7xOsG4IhfWPAZPp2P46HoCsn/2zTw8obpECc56TNVwcHmHTEcQBOF0YeLEiaxfr4SEefPmcdddd/Hwww9z0003MXHixA6endDZsVqt2m6uxJBEUiJc/xw0sedE+kT2AeDHnB85WnK01Wv0om3/6P5uzrbj0efathaR4Ek8AjgKvMU1xc32s62Tg/2CCfUPdXl8r8jcAi+cCUvOh+fGwM9vuOdU9YbqYlj7eMOBAWY/4PlYY2+wt7cscf06ybMVBEHwCLdF2xdeeAF/f38+/vhjXnrpJXr1UiLS119/zbnnnuvzCQpdn7YSbfOq8vjs8GcAhAeEs2DwAofX9REJewq7bq6tbRtZWEBYq5WCbW6BwppCl93FeoE3LiQOUic07ZQ4zGF8jzNtwR6RUF8JJcc9H0fPir8qoRNg/C1KsLURHAkTfg2//RGu/wKGXqSEXQCrGcO+z4n77BrY9rZv5uIuG19UzgtwjI/Y82nHzEcQBOE04amnnmLCBPU374EHHmDWrFl88MEH9O3bl9dee62Vq4XTnZPlJymvLwdgWPywVno7YjQYHdat7x94v8X+VquVQyWHAEgKTSIysAsUWG2EzWkLkF3RsmjraTyCvq/elNBk/IbX4kLinEZY+JzdS2HJPGUaACjLhC9/By+MU5FYZtdyjT1m/dNQU6LaIxaoTFlP6X82RKsHDlpBstaQPFtBEASPcVu07d27N19++SU7duzg5ptv1s4//fTTPPfccz6dnNA9GBg9kOQw9XT9p5yfqKqv8sm47+x9h3qLekL9i8G/0FygNvTFyHYX7PbJPdsbi9WiZdr2DO/Z6sLSlhdmsVq0CsOt0WRhnHqmYwf/YM0daxNtq03Vnr+P+mJkvohIOLIK9irxnrAEmHGP834GA/SbBgveht/tgul/gvAk+8tf/9H3kQ2tUVmoCpCBygi78h37nA59CzUSOSMIgtAWmM1mMjMz6d27N6CiEhYvXszOnTtZunQpffr06eAZCp0dvSFAbxRwlYvTLibYLxiAz498TmV9ZbN986vzKa9TAnFXjEYAx4gvfb0GZ3iaOasXbfURCHrqLfWU1Ja4PbZHWK2w+jEVzWWLhIjU7ZorPg7LboN/T4RdH/tuB5qe0kzYtFi1/QJh5l+9G89ohLG2gmRW2OqC6UHybAVBEDzGbdF269at7Nq1Szv+7LPPmD9/Pn/5y1+0XDBB0GMwGDS3bb2lno3ZG70es7S2lA8OqAywQGMg1w69tkmfobFDtXZXLUaWX5WvFbiwZfS2REKIrlhYlWsRBraFcWRgJIF+gRDTT4mf2qCDwaicqfrcL8+LkdnfF3J2Nd/PFUx18D9d8bHZD7j29D6qF5z9F/jdbqwNlXANphr46Eao881DBZf44RlVKA1gzHUQ2x+GqVxmzLVw4H/tNxdBEITTCD8/P8455xyKi117wCkIjdlTYBdtbZFc7hAZGMn5/VXMXGV9JV8d/arZvl25CJkNm4EDHKO/nKGPR/C101Yv5roTveA29dWw9Few+hH7udHXwsLt8KuVKlfWRuEhWHozLD4L9n2hxF5fseoRtaYEGP9riO7t/ZijrnWvIJnk2QqCIHiM26Ltb37zGw4ePAjA0aNHufLKKwkNDeWjjz7ij3/8o88nKHQPfB2R8OGBD6kyKXHt4rSLNYepnqigKHpHqIXJ/qL9miu3K6GvsOuKaOuJqGqLOtDcBgaDY0RCov2DiH58jyMSeui2ZP3wHJz8ybNxQLlUC9V2QVLGw8ir3LvePxDruf+kPq4h7zZ/H3zTjFPX15TnwuZXVNsvCKberdrDL7X3kYgEQRCENiM9PZ2jR1vPEhUEZ+idtp6ItgBXDrEXJPvv/v9ibUas6+pFyMDRadtapq0v4hH0wq/D2B66eN2iPBeWXAC7P244YYA5D8JFL4B/IKSMg19+Cjd+DX3Osl+Xtxc+uBb+MwMOrvBevM3do+IXAIKjYOpd3o1nIyIJBp+n2hU5qoBuS0ierSAIgse4LdoePHiQUaNGAfDRRx8xbdo03nvvPZYsWcLSpUt9PT+hmzC+x3htC9jazLVa8TBPqDHV8M6+dwCVCXbj8Bub7WuLSKiz1DkseLsKetFWnwXWHImhiVrbFVG1qr6K6oYsWAe3QYouIiFhiL2pd/J66rSNHwhDL1Tt+kp49zLI3un+OKWnYM2/VNtghPOfUFu23MU/mJLZT2MNaChEsWVJ+4il65+y5/CeebNy/4ISzCMa3uvDK1XxCEEQBMHn/OMf/+Duu+/myy+/JDs7m7KyMocvQWgOi9Wi7eLqEdbD4aG2OwyJHcKohFGActNuyd3itJ/eadtVRVtPnLZhAWEE+we7fA9X4hH0grCn71uL5OyGV2bCqYYitwFhcOW7cNadyhihp89kuOEr+OUy6DXOfj57O7x3Bbx2Dhzzwuzy7X1osQRT74JQ1wXwVnG1IJnk2QqCIHiF2wqH1WrF0pC389133zFv3jwAUlNTKSjwojiR0K0J9g9mYrKqxFxQXcC+Is+zQ5cdXqYt5ub2mUtqZGqzfR2KkRV0vWJkDk7bCDedti7EI+jdBg4L1xELILyHikk443KnffSLXre59BWVLwuqCNfbl0D+QffGWPE3JfoCjLsJkkd6PB1zzACs5z5mP/H5na4VVvCU0kz4+XXVDgiFKb+3v2Y0wvD5qm2ph/3Nb5cUBEEQPGfevHns2LGDiy66iJSUFGJiYoiJiSE6OpqYmJiOnp7QiTledlzb8eVJnq0evdu2uYJk3SEeITQglJgg9XvVqtO2YX3qjssWHJ2zzTltHQrw+joe4cDXSmgty1THkb3gpuUw5PzmrzEYYMDZ8Kvv4OoPHXekZW6GNy+EL+6EuuYzj51ybC0c/rZhHikw/jfuXd8a/WdCVEPUwuHvoCTDeT/JsxUEQfAKt0XbcePG8Y9//IO3336bNWvWcP756o/QsWPHSEpKauVq4XRmaor9D/Xak549NTZZTCzZs0Q7vumMm1rs7yDaFnY90VbvRHA709YFJ6xeeHXYIhaZDL/fA4v2QVSK0z4exyMABITAlf9VkQYAVQXw1kVQdMy164+thT2fqHZoHMz8m+dzsTHqGkhvEKhrS+Hjm1vP6PKUtY+DuSEDfMJvIDzR8XV9RMLuT9pmDoIgCKc5q1at0r6+//577ct2LAjNoa+V4K1oO6fPHE2cXHliZZOH7larlaMlKsajZ1hPQm07g7ogyeHKbZtXlafVbGhMnblOK7rmrqjaYfEIVitseAH+e5XdUNBrLNzyvXKXuoLBAIPmwq/XwC/ehgRdDYgtS2DxFMj82cX5WODbe+3HM/8GAa47ll3CaISx19lu2HxBMsmzFQRB8Aq3RdtnnnmGrVu3cvvtt/PXv/6VtDS1Refjjz9m8uTJPp+g0H3Q59quyVzj0RjfHP9Gc5+e1esshsQOabH/0LihGFBbkbq6aKvPAmsOfbavK6Kqg2jbeGHs5w9+AY7je1DorFmCwuGaj+yOgvJseOtiFXvQEub6RsXH7ocQHziiDAa44GmI6auOT/0Mqx72ftzGFB1VRRsAgiJh8sKmfVLG2d0LR1dDpReuZkEQBMEp06dPb/FLEJpDv3vLW9E20C+QywZeBoDJauLjQx87vJ5blUtFvSpamhbTNaMRbNiivsxWM3lVeU77eFqEDFRxN3+Df5Nx9Pg8HsFUB18shBV/RXOUDr9ExR5E9HB/PKMRhl0Et/0AFzyjdmSBWj++dg6s/ieYnQveGns+haxtqp2UDiN+4f48XGHUtWBQxYrZ9rbzeUmerSAIgle4LdqOGDGCXbt2UVpayn333aedf/zxx3nzzTd9Ojmhe9EjrIcmsu4p3OO2U9NqtfLa7te045vTb271mrCAMPpF9QPgYPFB6mzuRi/IKMugoKZ9okAyK9T2qojACCIDI1vtrxdeXXLaNheP0AwOhch88W8QEq0KMcQ3FAIrOaGE24oW5v7jy5C/X7V7jVMLRl8RHAmXv26viLv+aZUr60vW/Ats7pKJ/+c8X8xgsEckWM2w73PfzkEQBEFg7dq1LX4JQnPonbaeFiHTc8WgKzAa1Meyjw987FA891DxIa3dVaMRbNicttB8rq1ebHXXCWswGDShV7/G1dOiYcFdasrgnUth61v2c9P/BJe9rnaVeYPRD8bdCLeut+fdWs2w+lF4fS4UNlOrw1yHYdU/7MezH1BjtQWRyfaCZOXZcKhRQTLJsxUEQfAaD6r2KLZs2cI777zDO++8w9atWwkODiYgIKD1C4XTmqm97BEJ6zLXuXXtulPrtIXriIQRjEsa18oVivT4dEBFKxwsdjM3tRGbszdz4WcXcsO6G8gsz/RqrNYwWUzkVuYCrkUjAAT4BWh5YQVVbjptXVgYRwVF4d8gaLoyvkuExcN1n9kdroWH4O35aqHXmPIc5TAAwADzHves+FhL9BoLs+wPpPj0Vqhw7gZxm/yDsPMD1Q6Ohkn/13zfdF1EQnsURhMEQTjNmDFjRpOvs88+W/sSBGeYLWatNkOv8F5EB0d7PWZyeDIzUmYAkFedx6qMVdpr+kK6XbUImQ19Ud3mcm31a1N3nbYAsSHqmqKaIqxWa9PxfRmPsP5pON7wecYvCC57Dc7+i2/XpnED4KZvYMY9dlfrqZ9h8VTY8qaKZtARuvd9DLa6DP2mQ9os383FGS0VJJM8W0EQBK9x+y9KXl4eZ599NmeeeSYLFy5k4cKFjBs3jlmzZpGf7+V2aaHbMz3Vvt3Q3YiE13Y5umwNjSuwNoPeAeFtMbIPD34IQK2llm9OfNNKb+/Iq8rDZFWOTFdFW4D4UOWGza/Od7pY1eNuMQajwaj18yrTtjGRyXDd56pgA6in8u9eDrXljv1W/B0acs4YewP0GuO7OeiZdDukzVbtyjz49DfQUIDRK1Y/qnLGQFURDo5qvm/yKIhRLnGOr/OdcCwIgiAAUFxc7PCVl5fH8uXLOfPMM1mxYkVHT0/opBwrPUa1qRrwjcvWxoIhC7T2Bwc+0NrdoQiZDb3TVl9sV483TluwC70mi4myurImr9vWryH+IYT6e5kPfOIHe/uGLx2K9/oUP3+Y8We4eQXE9lfn6itVLMP7V9t3qNWUEr7lRft1cx5Qu7fakgEzIaqhKPShb6HkpP01ybMVBEHwGrdF2zvuuIOKigr27NlDUVERRUVF7N69m7KyMhYudJLNKAg60uPStcXUhqwNLscVbM3dyta8rQD0j+rPjNQZLt/TV8XIas21rM20b5fcmLXR47FcQb+YdSXP1oYtd7beUk9pbWmLfT1xG9giEoprizFbzC7Pq1Vi+ijhNqyhKNepLfDeAqhT1Zk5/gPsUqI5ITEw617n4/gCoxHmL4bwhuKKR76HDc95N2bObnvxtLAEVYCsJQwGlYkGSujd+5l39xcEQRAciIqKcviKj49nzpw5PPbYY/zxj3/s6OkJnRT9WtLbPFs9E5Mn0jeyLwCbczZrDlvbfw0Y6B/V32f36whcctrWeOe01ZsQnOXa2saPDY512QDiFKvVHtcVmQKp4z0fy1VSxsFv1jm6Ww/8D16aBAe/wbDhOYw1Jep8+uXQc3Tbz8noB2N0Bcm26QqSSZ6tIAiC17gt2i5fvpx///vfDB1qr2g5bNgwXnzxRb7++mufTk7ofvgZ/ZjSSz1prTZV83Nu0yqo9WYlNuZU5nCs9Bh7C/eyeMdi7fWb0m/Scr9cYXDsYPwathN5I9puzNqoOSsAtudvp6q+yuPxWkOf9eWW01aXO9tarq0nW9BsorDFamm2yIPHxKfBdctUdAAoB8OHv4S6SsfiY7Puc54F60vCE+DS/0BDITu+f8j1qr3OWPWIvT1lEQSGtX6NRCQIgiC0O0lJSRw4cKCjpyF0UhxE23jfibZGg5EFg+1u2/f3v4/FauFIqRJtUyJSCPH3Mie1g9GbEJrNtK32vBBZ42sar1NtnzHAB9EI5TlQ02COSBjs3VjuEBQOFz4LV/4XGnbXUZkP7/1CMxhYjQEw6+/tN6fRuoJkWxsKkunzbHucIXm2giAIHuLv7gUWi8Vpdm1AQAAWX2wfFro901Km8fkRVVjpr+v/Sqh/KNWmaqpN1dSYarRIAGf0COvBvP7z3LpfiH8IA6IHcLD4IEdKjlBtqvZo0fvdie8cjust9WzJ3cLUlLbJaNI7bd0RbW2iKijRdmDMwGb72raIRQZGEugX6NL4+kVuQXUBCaEJLfT2gKTh8MtP4M2LVRTC4e/gpclgy+dKHqV7ot/G9J8BUxfBuidV8bCPb1QOB3cXnqe2wIGvVDuiJ4y7ybXrktIhbqDK+T2xAcqyVZSEIAiC4DU7d+50OLZarWRnZ/PPf/6TUaNGdcykhE6PvgjZ0NihLfR0n4vSLuK5bc9Rbarmi6NfcPmgyzXDQFePRgC13gz1D6XKVOWS09ajeISQ5kVbhwK8wa0X4G0Rm8sWING3PwcuMWSect5+drtWAMxgK3R75q/stSLag8ieMOhctdYtz4LD34LFjOTZCoIgeI/bTtuZM2dy5513kpVlfzp66tQpfv/73zNrVhsHnQvdgsk9J+NvaChmVV1ARnkG+dX5VNRXtCjYgnLZBhjdL3hn275mtpo5UOS+e6beUs/qzNVNzm/I2uD2WK7icTyCTkRtLXfWtnh1Z1GsH781J6/H9BoL13wINnHdJtgCnP9k21XBdcaMeyClYctbSQZ8+bsmRR9a5fuH7e1pd0NAsGvXGQw6t60V9i5z776CIAhCs4waNYrRo0czatQorT1v3jzq6up49dVXO3p6QifEZDGxv0iJdb0jehMV1EI2vQdEBkYyr58yJ1TWV/Ls1me117p6ETIAg8GgrWmzK7KxWJsafhwybV2ot9AYB6dtdfOirddOW71o255OWz3hiXD1B3DB0xCg8nktgZFYp97V/nNpXJBM8mwFQRB8gttO2xdeeIGLLrqIvn37kpqqQsdPnjxJeno6b7/9ditXCwJEBEbw65G/5tWd6gNRSEAIIf4hBPsFE+Kv2iEBqjiA/nz/6P7MT5vv0T3T49P59LDaXr6ncA+jEke5df2W3C3adqpJyZP4MftHLFjYlL3Jo/m4gqdOW4d4hKrmRdWq+irNvaG/ptXxdc4EfbyCz+kzGa58F/57Jdiyj0f/UrkK2hO/ALjsVXh5qtoGt+dT5QYeeZUqnNZaHtqJjXBkpWpH91bfgzsMvxTWPKbaez6Fibe5/z0IgiAITTh27JjDsdFoJCEhgeBgFx+sCacdR0qOUGuuBXybZ6vnqiFXsfTQUgDWnVqnne8Ooi1Aclgyh0sOU2epo6imqMka1Cba+hv8iQyMdHt8vWhbWFMI+75UsQETb6Mwyi7Uei3a5u2ztxM6wGlrw2BQO7j6Tcey80OK4s8kNtTL780T0mapbN+yTDi0Qu0sUxOEPpJnKwiC4Clui7apqals3bqV7777jv371RPGoUOHMnv2bJ9PTui+3DbyNm4dcat3BQDcwKEYWYH7ubb6aIRL0i6huKqY/aX7OVxymNzKXJLCknwyTz22rK/ooGjCAlzIP21AH4/QktNWL7i642RwJzPXa9JmwRVvwqe3QkQSzL6/be/XHDF94KLn4cOGWIbv/6G+whKVK7jXGPXVc4xj1q7VqvrZmP4n8HcthkIjcQgkDoO8vXDyRyjNhKgU778nQRCE05w+ffp09BSELoY+GmFY3LA2ucfg2MGMThzNtrxtDue7i2jbONe2sWhrW596WiisSSGy719UQmLRMQovfkx7zft4BN3OvY5y2uqJGwDT/4QpL69j7m8rSLb6EVVAtyxTne9xhiogLAiCIHiE26ItqK0tc+bMYc6cOdq5/fv3c9FFF3Hw4EGfTU7o3rSXYAswMGYg/kZ/TBaT28XILFYL32d8D0CgMZApvaaw49QO9peqhxabsjdxcdrFPp1vvaWe3KpcwD2XLbgeX+DpFrH4UPsit7X4BZ8wZB784bByvLbjz0wThl0ME26FH+1F8ajMg4Nfqy8bMX2VkNtzDPgHwYmG7WGxA2DElZ7de/glSrQF5badfIdn4wiCIAgaCxcuJC0tjYULFzqcf+GFFzh8+DDPPPNMx0xM6LS0VRGyxiwYvMBBtDUajPSN6ttm92tPksPs2fxZlVmMSBihHVusFoprigHPnbAO8QjlWXbxsDKPwtLj2mteOW2tVshvcNpG9oJg9x3B3ZLR18KafyrR1obk2QqCIHiF25m2zVFbW8uRI0d8NZwg+JRAv0AGxQwC4FjpMSrrK12+dmf+Tk38nNxzMmEBYYyNH6u93ha5tjmVOVrOlzt5ttCoEFkL8Qh6p61b8Qgh7SzagnKndqRga+Pcf8IvP4Wpd6kiZcFOsuyKj8PupbDir/C/u+3nz/4L+Hn0nExFJNjY/YlnYwiCIAgOLF26lLPOOqvJ+cmTJ/Pxxx93wIyEzo5+t5avi5DpmdNnjoP42DuiN0F+QW12v/ZEb0bIrnAsRlZWW6bVt9B//+6gL0RWWJ7p8Fph0WGt7c7atwkVuSoyCyBhiOfjdDeiesHAuY7nJM9WEATBK3wm2gpCZ8cWkWDFyr7Cfa30trMyY6XWntVHFdsbGjWUUH8V+L8pe5PTQgreYItGAPedtsH+wUQERAAti6r61zyNR2g30bazYDDAgJkw61647jP40wm4Yytc+ipMuA1SJ4C/kyzEhKGOwqu7xKep7WUAWVuh6FjL/QVBEIRWKSwsJCqq6cO3yMhICgpOs79vQqvUm+s5UKy2xPeN7Et4YHib3SvQL5DLBl6mHXeXaASA5HCd01a33gXHImSeirZBfkGEB6j3pqhR7YWC8pNa25MiZxoOebYi2jqgL0gmebaCIAheI6KtcNrgkGvrYkSC1WrV8mz9DH7MSJkBgL/RnzN7nAmoBebBYt/GgnhahMyGLcKgLeIRgvyCiAhsXRQ+LTAYVIbYiCvgvH/CzSvgnkz4zTq48FkYcz0Mmw+/eBOMXv7vVi/67vnUu7EEQRAE0tLSWL58eZPzX3/9Nf379++AGQmdmcMlh6m31ANtG41gY8HgBdpD+LN6NXWEd1V6htl3kGVXOjptPV2bNsYm+BaZHHfWFeoNC97EI+jzbBNFtHVg4BwVFQbQe5Lk2QqCIHiJh3t1BaHrkR6frrVdLUZ2sPggmRVqa9W4HuOIDo7GYlGu2knJk1iTuQaAjVkbGRLru0WbXrR1Nx4BVETCsdJjVJuqqayvdFrIzKEQmZsL14SQBMrryimoLsBqtbZrPnGnxy8AkkeoLwe3gZcMvwRWPqDaez6FqYt8N7YgCMJpyKJFi7j99tvJz89n5syZAKxcuZInn3xS8myFJjjk2ca1vWibFJbEhxd+SE5lDmOTxrZ+QRchLiSOAGMA9ZZ6siodnbZ60dZTp63t2ozyDMowUw8E2MavrwQ/CPEPITQg1OPxtTxbULupBDtGP7hmKez/EtIva72/IAiC0CIui7YxMTEtCjMmk8knExKEtqJ/dH+C/IKoNde67LT9LuM7rT2792yH1yYl27f7bMjawI3pN/pmojiKtinhKW5fr48wyK/KJyyqqWjraTyCbfyjpUepNlVTZapyKgoLPia2nypulrUVcnZC4RHl8hUEQRA84qabbqK2tpaHH36Yhx56CIC+ffvy0ksvcd1113Xw7ITORnuLtgApESmkRLi/DuzMGA1GksOSySjPaJJpW1Rtj0fwhdMWoMjPjySzGYACzIDRu2gEgLz99nbCIO/G6o7Ep8GU33X0LARBELoFLou24jgQujoBxgAGxw5mZ/5OMsozKK0tJSrISSEpHbZoBICZvWc6vNYnsg/JYclkV2azNXcrNaYagp3lmXqAPuNLn/3lKg7FyKrznVYcdtiC5ubiVb+QLqguENG2vRh+iRJtQRUkm/6Hjp2PIAhCF+e2227jtttuIz8/n5CQEMLDPc8pffHFF3n88cfJyclh5MiRPP/884wfP77V695//32uuuoqLr74YpYtW+bx/YW2xbZLy4DBp7urTkeSw5VoW1FfQVldGZGBkYAPnbYhetHWSJLZTB1Q5qeiqryKRrBaIb9BtI3s5bworSAIgiD4CJdF2+uvv74t5yEI7cLwuOHszN8JwL6ifUxMnths3+OlxzlcoqrMjkwYSWJoosPrBoOBST0n8cmhT6iz1LE1dyuTe032yTxtTtvY4FhC/EPcvj4hVCfaVjnPtbXFI0QFRRHgF+C0T7PjhziO3yeyj9tzFDxg+CXw7d9Ve08XFW3rquDTX0NtBVz+OoR6/qFMEATBG44dO4bJZGLgwIEkJNj/rh06dIiAgAD69u3r8lgffPABixYtYvHixUyYMIFnnnmGuXPncuDAARITE5u97vjx49x9991MnTrVm29FaGNqzbUcKjkEQP+o/t5trRcccm2zKrKIjFWirb4QmTduWP21RX5+0PMMinJ3aOf0O9LcpiIXakpUO2Gw5+MIgiAIggtIITLhtMKhGFkrubYrM1Zq7cbRCDYm9bRHJGzM3ujl7BR15jpNaPUkGgEaxSM0U4zM5mbwZFGsH7+g5jQvRtaeRKdCSoNrK2+v4/a8rsJPr8K+L+DoKlj7REfPRhCE05gbbriBDRs2NDn/448/csMNN7g11lNPPcUtt9zCjTfeyLBhw1i8eDGhoaG8/vrrzV5jNpu55ppreOCBB6TwWSfnUPEhTBYVBdceRci6O/pdZPrdZfp6C95m2too8jPC6Gso9PPTznkVj5Cvj0aQPFtBEAShbRHRVjitcBBtW8m11Yu2s3rPctpnYo+JGFBZzxuymn7w84TsymysWAHPipCBoxNWn11ro6q+impTNeDZFjEH0bZKRNt2Jf1Se3vPJx03D0+wWmHb2/bjne+Dqa7j5iMIwmnNtm3bOOuss5qcnzhxItu3b3d5nLq6OrZs2cLs2fYHvEajkdmzZ7NxY/MPdB988EESExO5+eab3Zq30P7oH/QPixvWgTPpHuidttmV9lxbvdPWZ/EIASEw7BIK/ewfe72KR9A/ME+UmAxBEAShbXE5HkEQugP9ovoR4h9Ctam6RadtTmUOuwp2ATA4ZjCpkalO+0UHRzMsbhh7CvdwsPggBdUF3m25Ak6V24uQ9Qrv5dEY8aEtO231Tob4YPfn6yDaOhGFO5Kfcn7io4MfceXgKxmTNKajp+N7hs2H5fcAVpVrO+MeaKFIZKfi5GYoOGg/riqEA/+D4fM7bEqCIJy+GAwGysvLm5wvLS3F3FC4yBUKCgowm80kJSU5nE9KSmL/fuc7ItavX89rr73mljhcW1tLbW2tdlxWVgaAxWLBYrG4PI6nWCwWrFZru9yrs6F/0D8sdli3+DfoyPezR2gPrX2q/JQ2B9v6NDIwEj+Dn8dzi7HaP+IWRiZiCYkhP9y+do0NivF4bEPeXmyrLkvcIOhEPwun8+9od0Tez+6FvJ/dC1+8n65eK6KtcFrhZ/RjaOxQtuZtJasyi6KaIqdP8h1ctn2cu2xtTO45WVvMb8zayIUDLvRqjqcq7aKtT5y2Tpyw+kgDr522nUi0tVgt/Hndn8mrymN/0X4+n/95R0/J90QmQ5/JcOIHKDwEuXugR7pv72GqhT2fQuwASD3Td+Nue8vJubdFtBUEoUOYNm0ajz76KP/973/xa9g6bTabefTRR5kyZUqb3be8vJxf/vKXvPLKK8THu/7g9NFHH+WBBx5ocj4/P5+amhpfTtEpFouF0tJSrFYrRuPptVlvR0MeqtFgJMYUQ15eXgfPyHs68v0MrAnU2seLjmv/nlq9hYAor/6NA04d0tq5geHk5eWRFxoPKCdvYEmpx+PHZu3GNvt84rB2op+F0/l3tDsi72f3Qt7P7oUv3k9nxgFniGgrnHYMjx/O1rytAOwt3MuUXk0/mLmSZ2tjUs9JvLLrFQA2ZW/yWrTVZ3t56rQNDwjXHMWtOW09EW1bi1/oKPYV7SOvSi2ej5Ueo7yunIjAiA6eVRsw/BIl2oKKSPClaFuRD+9fDZmbwRgAt/3gm0IbteWw+1PVDoyAkGgoPQmHV0JpJkR5lt8sCILgKY899hjTpk1j8ODBWiGwdevWUVZWxvfff+/yOPHx8fj5+ZGbm+twPjc3lx49ejTpf+TIEY4fP86FF9rXCza3hb+/PwcOHGDAgAFNrrvnnntYtGiRdlxWVkZqaioJCQlERka6PF9PsVgsGAwGEhISTqsPnDWmGk5UnABgQNQAeif37uAZ+YaOfD9jLDEYDUYsVgtFpiISExOpNlVTbVbRXYlhiS0W8GuNgP3FWrssJIzExERKIuOgQom2fY31no1vtWIoOaKaET1JSG36e9qRnK6/o90VeT+7F/J+di988X4GBwe71M9t0dZsNrNkyRJWrlxJXl5eE0uvO4tcQegIGhcjayzaFtUUsSV3CwB9IvuQFp3W4ngjE0ZqAunGrI1YrVYMXmxX90U8gsFgID4knpPlJ1sXbT0oxhAVFIW/0R+TxdSpRNv1mesdjg8WH2Rs0tgOmk0bMuxi+PqPYLXAz69Dv2nQf4b34+buhfcWQGmGOrbUw4bn4eIXvB97z6dQX6naZ1wGET1h9SOAFba/B9P/6P09BEEQ3GDYsGHs3LmTF154gR07dhASEsJ1113H7bffTmys63magYGBjB07lpUrVzJ//nxALeZXrlzJ7bff3qT/kCFD2LVrl8O5v/3tb5SXl/Pss8+Smuo8kikoKIigoKAm541GY7t9ADQYDO16v87AodJDmK0qLmN4/PBu9b131PsZZAwiMTSRnMocsiuzMRqNlNSVaK/HhsR6NaeY3P34Wa2YDQaKDOp3pDDA/ruTUJbj2fjluVCj5mlIHIKhE/4snI6/o90ZeT+7F/J+di+8fT9dvc7t0e+8807uvPNOzGYz6enpjBw50uFLEDo7rRUjW31yNRarehgxq/esVgXYQL9AxiWNA1R+7OGSw17NTx+PoK+u6y42N2x5XTk1Jsdtk97GI9hEYXCemdtRrD/lKNoeKDrQQTNpY8ITYdB5ql1dDG/Nh1WPgMX1DMYmHFwBr51jF2xt7PxAfUjxlq26AmSjr4NRV4MtFW7b250qE04QhNOHnj178sgjj/DVV1/x8ccfc++992I0GnnhBfceVi1atIhXXnmFN998k3379nHbbbdRWVnJjTfeCMB1113HPffcAyhnRXp6usNXdHQ0ERERpKenExgY2NKtBDcxWUysObmGUxWnWu/sBH0NBP0aUvAOWzGyopoiqk3VFFXbi5B5YijQY8zZSYxZrSuKTFUAFBqs9vELjno2cP4+ezthqMfzEwRBEARXcdtp+/777/Phhx8yb968tpiPILQ5vSN7Ex4QTkV9hdNiZN+d+E5rtxaNYGNyz8msO7UOgA1ZGxgYM9Dj+dmctokhiQT5NXXUuErj3NmUCPv2c2/jEUAVMMupzKG4phiTxYS/sWPTVkprS9lZsNPh3MHig8307gZc9Dx8Ug1HvgessOYxOP4DXPaqyr11FasVfnwZvrlHOXcBkkdBUjpsfwfMdbD5PzDr757PNf+AilsASBwGvcao4mkDZsKRlVCSAcfX+sYt7C4Wi/q+/SQtSBBOd1auXMlrr73Gp59+SmhoqFOXbHMsWLCA/Px87r33XnJychg1ahTLly/XipNlZGSIs6aDWLxjMS/vfJmYoBiWzV/mtJZBS+gf8Ito6zuSw5OhIQ42uzKbwhr72jQ2xL33yIHqEig+RmxIDwr8/SiqKcJqtVLYIN6GWCyE5jZfjLhF8nSFBX0RHSUIgiAIreD26jEwMJC0tJa3iwtCZ8ZoMGqL7rzqPC0DFZQrdVP2JgCSQpMYHu/a4nxSz0lae2PWRo/nVmOq0RatnhYhs5EQ2nzurLfxCGAXha1YKa4pbqW3I/uL9vPZ4c+ot9R7dG9nbMzaqDmkbXRr0TYsDq5ZCrPuA4MqoMOJ9bD4LDj0XcvX2jDXw1eLYPmf7ILtsIvhxq9h5l/BJsT/9CrUVXo+1626AmSjf6kEW4Axv9T1eZt2J2c3PNYH/j3BN25iQRC6HCdPnuTBBx+kX79+nHPOOQB8+umn5OTkuD3W7bffzokTJ6itreXHH39kwoQJ2murV69myZIlzV67ZMkSli1b5vY9hdaxrcuKa4t5Zecrbl+/t3AvAP4GfwbFDvLp3E5nbE5bgOyKbIpqfOS0zVZF42Ibdh/VWeqorK/UdpnFm81QcgJqytwfO18n2iaK01YQBEFoe9wWbe+66y6effZZrFZr650FoZMyLH6Y1rYtxgHWZa7ThMRZvWdhNLj2K9I/qj+JoaqgwZbcLdSaaz2aV1alvQiZt6Kt3mnbOMJA72bwWLQNbX78lsitzOWG5Tfwtx/+xvPbnvfo3s6wOZ0B7X07VHwIszeRAZ0doxGmLoIbvoLIhvzjqkJ49zL49j4lyjZHdQm8e7nKxLUx9W64fAkEhkJkTzjjCnW+pgS2vePZHE11sOP9hvkGwIgF9tcGzwObm2bfFyrqoT1Z9yTUlkHhYVj7r/a9tyAIHUZ9fT0fffQRc+fOZfDgwWzfvp3HH38co9HI3/72N84991wCAgI6epqCD7BYLRwqOaQdv3/gfTLLM12+vqq+iqOlaiv9wJiBXu2AEhzRR4BlVWb5xFAA2EVbs/1Bfk5lDuV1qkp3nO28J25bvWgbLwK+IAiC0Pa4LdquX7+ed999lwEDBnDhhRdy6aWXOnwJQleguVzb7zJ00Qh9XItGAJXxOilZuW1rzDVsy9vm0bx8UYTMhi3TFiC/ylFUtTlvo4KiCPDz7INp4/gFV/n08KdUNhSk+uzwZ5gsJo/ur8ditfDDqR8ACPEPYWovVQW8xlxDRnlGS5d2D/pMglvXw6Bz7ed+eAaWnA8lJ5v2LzwCr82Bo6vVsV8gXPKyikDQb9+dpNsavPFFMHvwXh1cDlUNPx9DzlcOYRv+QTDyStU218LOj9wf31NqSmH/V/bjLW86/7cSBKHb0atXL55//nkuu+wyTp06xSeffMLll1/e0dMS2oCsiiyqTdXasclicuuB8YHiA9ounmFxw1rpLbhDS05br+IRsrerMcz2h/Z64T7Odj53t3vjWq2Q15BpG9ETQqI9n6MgCIIguIjbom10dDSXXHIJ06dPJz4+nqioKIcvQegKOIi2Dbm2NaYarZBVTFAMoxNHuzXm5J6TtbanEQlZFXanrS9F28aiqm1h7I2ToaXxm8NsMfPpoU8d5vFz7s8ez8HG/qL9mnt4fI/xpMena68dKO6mxcgaExoLV70P5zxsjzU4+SMsngL7/2fvd3w9vDoLChqiI0Lj4Pov7OKpnh7pKncW1FbC/V+4P69tutgDfRyCjdH6iIS31Iei9mDvZ0ootmGph7WPt8+9BUHoUEwmEwaDAYPBgJ+fX0dPR2hDnBWH/d+x/7GvcJ+T3k3R1z4Q0da3tLXTNs5qLySsj8uKt4m2ObvcG7ciT+08AsmzFQRBENoNtyuvvPHGG20xD0FoV3qF9yIqKIrS2lL2FO7BarWyIWuD5sY4u/fZbhfWmpBsz67bmLWR34/9vdvz0lc27hXhnWirjy/Q5/ZW1Vdp36feLesu+gJmroq2G7M3OkRAAHxz/BsmJk/0eB6AJrYDTOk1RYuqADhYdJBz+57r7LLuh8EAk2+H3hPhoxuhNEN9wHj/Kpj4W0gYBF/drQRKgIQhcPUHENO3+TEnL2wodgb88BwMm2/PpG2Nsiw43OBej0yB/mc37ZM0DHqNhVNbIHeXcsj0dO+BiUfYIhtAOY3NdbD9XZjye4jt1/b3FwShw8jKymLp0qW89tpr3HnnnZx33nlce+21GFz9f5vQZThUbHdYjk4cre2EembrM7w85+VWr3coQuZinQPBNZLDdKJtRRaBxkDt2N1icRo1DZFHQFxYMlABOP4c2OMR3HTaSp6tIAiC0AF4XMY2Pz+f9evXs379evLzXc+zFITOgMFg0Ny2RTVF5FblsjJjpfb6rN6z3B4zLiSOobFqEbevaJ/DNi9XcRBtw9rGaatve+Nk8CQeYenBpU3OfXfiO68jEhqLtoNj7Q6I08ZpqydlHNy6FoZcYD+36UX44k67YDtgFty8omXBFqD/DEg6Q7WztkKGGy7y7e/aC5yNvgaMzTjaRrdzQbLiE3BCxWkQP0gJtQAWk7htBeE0IDg4mGuuuYbvv/+eXbt2MXToUBYuXIjJZOLhhx/m22+/xWzuxnnopxH6bfF/Hv9nbRfThqwNLu2Ksom2AcYABkYPbJtJnqaE+Ido4mxWRZa2YyrIL4iwgDDPBtW5Z2NjBmhtB9E2OEY1cveCO3UP9KJtwhDP5icIgiAIbuK2aFtZWclNN91EcnIy06ZNY9q0afTs2ZObb76ZqqqqtpijILQJ+oiE7XnbWXVyFQBhAWEeOz8n9rRftylrk9vX20RbAwZ6hPXwaA42ooOiNbewvlCYQxGykPaLRyioLmD1ydWAEnxnpqpt9yW1JWzO2ezxPEprS9mRr7bC9Y3sS0pECj3DehIREAHAgaLTULQFCImBBe/AeY8rJ6me8b+Bqz+EYBcibQwGmHyH/XiDi1mAFouueJkBRl3TfN/0yyAgVLV3fQz11c339QW7PrS3RyyAif9n/7fY8V+V+SsIwmnBgAED+Mc//sGJEyf46quvqK2t5YILLiApKamjpyb4AFs8gr/Rn4HRA7l9tD2r/Zmtz2h5tc6orK/keOlxAAbFDCKw8d9SwWtsbtv86nxtV1hscKznrveGPFuA2ET7Ol+/yysusrdqmKrd+3ufp4vUENFWEARBaCfcFm0XLVrEmjVr+OKLLygpKaGkpITPPvuMNWvWcNddd7XFHAWhTdBvc1uyZ4lWVXZayjSPF+YOubbZ7ufa2jJtk8KSPC4QZsNgMGhuWL2o6pAZ5oVo6248wrLDyzBZlaP2krRLOL//+dprK46v8HgeG7M3ah+6pvSaAqjvfWCMcsTkVuVSWlvq8fhdGoMBJvxaOWoThkJgBMx7Aub9C/zciP9Iv1QV3QA48D/IP9hyf4AT66H4uGr3nw4xfZrvGxwJwy9R7dpS2Pu563NzF6vVMRphxC9UMRGbMG21wOp/tt39BUHolBiNRs477zw+/vhjMjMz+ctf/tLRUxK8pN5Sz7HSY4B6qBvgF8C8fvMYHKN24+wt3Nvi+mNf4T6sqJx1/YN+wUUq8lotYNozXK0tLFYLZXVlgBfRCABZ27VmbM9xTrvExekE11w3cm3zdSYAybQVBEEQ2gm3RVtbBth5551HZGQkkZGRzJs3j1deeYWPP/64LeYoCG2CQzEyXWbZ7N6zPR5zdOJogvyCALX1zupGUaWq+iqKa4sBx4q63mBzwxbVFFHfsC3eV/EIQX5BRAZGApBf1XJEisVq4ZNDn2jHlwy8hKkpUwnxDwHgu4zvtPm5y/pMezTC1F5TtbY+IkFfgOK0pOdo+L+N8MejMP4W96/3C4CJt9mPN77Q+jX6mIPRTgqQNUbfZ1sbRiSc2qrl3dF3KkQ3OG4m3Aq2atW7PnL8cCYIwmlFQkICixYt6uhpCF5yovSEFr9kizYwGowONQee2/Yc9Wbn6w/Js/WCdU/CEwPh9XOgpvkH5/pcWxveGApsRcgwBhDT60ynXeKTRtoPclzMtbVaIb/BaRuRrB72CoIgCEI74LZoW1VV5XTLWGJiosQjCF2KpNCkJqJlkF+Q5tb0hCC/IMYlqSf7eVV5msPDFb469pXWTolI8XgOevQRBjaHra/iEcCea1tYU9iiQL05ZzMny08CMCl5EqkRqYT4hzA9ZTqgIg42Z7sfkWC1WvkhS2WThviHMLbHWO01m5MGTuOIBD0GA/h7sbVz7A0QpER6dryvHDTNUV0C+xrcssHRjtm6zdF7IsQ15AUeXwdFRz2fa0vs1LtsF9jbQRFw1p0NB1ZY/Wjb3F8QBEFoF2zRCIC2+wbUrqgJPVTx2JPlJ/no4EdOr3cQbbuC07a+BjYthgNfK5Gxozj5E3z/D9U+tQU++CWY6px2tTlt9XjstK2tgIKGh/RJwwgNjtbMAXriUsbbD1wtRlaZD9XKWCHRCIIgCEJ74rZoO2nSJO677z5qamq0c9XV1TzwwANMmjTJp5MThLbEYDA0cU5M7jmZUFu2podM6mn/PdiQtaHV/harhee2PseDGx/UztmEX29JCG2aO6uPR9AXE/ME2/XVpmoq6yub7acvQHbZoMu09ty+c7X28uPL3b7/geID2vd1Zo8zNZczIMXIfE1wJIy9XrXNtbD5leb77voITA1/I0YsgIDg1sc3GGD0tfZjLQ/Xh5jqVGYugH8wDLvY8fXxt0BYw+/Mnk8hdw+CIAhC10S/yyYtOk1rGwwGB7ftyztfdrqG2Vu4F4BAYyD9o/u34Ux9xNp/wfI/wX+vhPcWQOmp1q/xNXVVsOxWexFSgGNr4IuFToVkZzvLPN4FlrsbGuIsSFZu2sYCcKh/KCFxA+059q46bfV5tolDPZufIAiCIHiA26Lts88+yw8//EBKSgqzZs1i1qxZpKamsmHDBp599tm2mKMgtBnpcekOx7P7eB6NYEMv2raWa1ttquYPa/7AK7vsAtiCwQu4aMBFXs8DHEVZW4SBr+IRGo/fXK5tUU0RKzNWAmrxbCtABiqDNtRfieQrM1Y2u0WxOdafskcjNHZID4gegNGg/hcnTlsfMeFWaChux0+vqA9nztDHG4xxIRrBxsirwOCn2tvfazULz20OfwfVRao95HwlROsJDIMp9g/yrHrEt/cXBEEQ2g290zYtJs3hteHxwzm377mAWqe8uedNh9fL6so4UXYCgCGxQwgweldnoM2xmGHbu/bjQ9/AvyfCliXt67r9/iF7BFH8YPWAFFSRz1UPN+nuU6etLs+W5FFA0x1l8SHx6iFxUsP6vzwLqopaHzt/v70tebaCIAhCO+K2aJuens6hQ4d49NFHGTVqFKNGjeKf//wnhw4dYvjwLrB1SBB06J22/gZ/bbu+NwyMHqiJmT/l/NSsEFlQXcBNy29ixQlVBMNoMPLn8X/mrxP+ip/Rz+t5gGM8Qn61Em318QixIV4Ue8A10faLI19oebUXD7jYocBasH8wM1JnAFBeV+528bZ1meu0dmPRNsQ/hN4RKq/0SMkRLddO8IKoFEhvcEpXF8P2d5v2yd5pz5RLHgU9znB9/IgkGKQ+RFOeDUdWejXdJuz4r7094krnfcbdBOE9VHv/l44fAgVBEIQug020DfEPoVd4ryav3zH6DvwN6kHkkj1LHNYx+wrtzsphccPaeKY+4NgaqMhxPFdbBl/cCW9dBEWux3V5zPEfYNNLqu0XBAvehktfAQzq3NrH4ec3HC5JDvdhpm32dnu75yigqQCsjZ2kM23kuFCMzEG0FaetIAiC0H64LdoChIaGcsstt/Dkk0/y5JNP8qtf/YqQkKaZQYLQ2RkeNxy/Bmff+OTxRAVFeT2mwWBgUrJy21abqtmev71JnwNFB7jqq6vYXai2ZYX6h/L8zOe5Zug1GAwGr+dgo6V4hOigaK+dI3pR2Jloa7Va+figvUDhpQMvbdJHH5HwzfFvXL53WV0ZO/KVONg3si+pEalN+tgiEuosdRwvPe7y2EILTLrd3t74gnL36PHUZevsmq1vuX99c1QXw8GGCI6wBBgw03m/gBCYepf9WLJtBaHbYjabee2117j66quZPXs2M2fOdPgSui5V9VVklmcCKhrBtvNGT+/I3lw+6HJArdde3vGy9lqXK0K280N7+8LnYNQ19uNja+GlyUpQbfw321fUVsCy29DiCWb9XTlSh10E5+r+jn51FxxcoR1GBkYSHhDuMJTHTlutCJk/JKr3rPGOMu1Y/0DZlVzbPHHaCoIgCB2DS6Lt559/Tn19vdZu6UsQuhJxIXH8beLfmNV7Fn8a/yefjesQkZDl6B5dc3IN1319HTmVyhGRHJbMW+e9xbSUaT67vw2HeITqfKxWqybaehuNAI5uCGei7ZbcLRwvOw6onN6+UX2b9Dmr11mEBYQBsCpjFXVm58UqGrMpaxNmq1kbwxkOxcgk19Y3JI+A/jNUu/i4cqPaqK+xf3D0D4b0y90fP22O3el6cHnLBc/cYc8ysP1snXEF+Pk333fMdRDZyz6HzC2+mYMgCJ2KO++8kzvvvBOz2Ux6ejojR450+BK6LkdLj2JtEBD1ebaN+c3I32jFqj4++DEZZRmAPc8WukARsrpK2PeFagdFqSz5+f+Ga5dCVMMD7foqWP5neOM8yD/Y/Fie8u29UKLiJOg9CSb+n/21ibfZH/hazfDRDZC1TXu5sdvWI9G2rsruhk0YqmXpN+u07eGG09ZqhfwG53VEMoREuz8/QRAEQfCQFj612pk/fz45OTkkJiYyf/78ZvsZDAbM5jZ6gisIbcTlgy7XnBa+orFou3DMQqxWK+/se4cnfn4CS0OBhjPiz+C5mc95XRCsORycsFUFVJmqqDGrAlEebz/T0VgUbszSQ/YCZM39Gwf5BXF26tl8efRLyuvL2Zi1kemprcdUtJRna0NfjOxg8UHO5/xWxxVcYPJCOLpatX94DoZepDLi9n8JNSXq/LCLPftg4+cPo66C9U+DxaQiDc660/s573jf3h6xoOW+AcEw7W74siHfdtXD8MtPvJ+DIAidivfff58PP/yQefPmdfRUBB9zqPiQ1h4YM7DZfvEh8dww/AZe2vESJquJ57c9z+PTH2dPgXLahviH0C+qX5vP1yv2/w/qKlR7+MX24p9ps+H/NsJ398NPr6pzJ3+ExVNgxp9g0h2+uf+RVfDza6odEKoE48YxX3MegtJM2LsM6ivh3V/Ar76FmL70DOvp8H55tD7N3WMvfpZsf+DSrGibMFRl6FvNrRcjq8xXu3UAEoa4PzdBEARB8AKXnLYWi4XExESt3dyXCLaCoIgPiWdQzCBAbbErqC7goU0P8a+f/qUJtnP7zuX1ua+3mWALarFq2xKYV4LSS8sAAIWoSURBVJ2nuWzBN6JtS/EIpbWlrDiutsBFBUW1WOTN3YgEq9XKD6d+AJToOy5pnNN+tvcAxGnrUwbM1LYecupn9SEQHOMMRnsQjeDs2q1ve19EpegYnNyk2glDHT7QNcuoayFaZSJzZCVkbPJuDoIgdDoCAwNJS2vehSl0XRyKkLXgtAW4fvj1mri3/PhyNpzaQGaFilYYEjsEf6NLHpeOY+cH9nbjvPagCDj/SbjhK4jtr86Za2Hlgxhem41/wX68oqYUPtPFJs150H4fPUYjXPKycuECVObBO5dDVRHJYXanrQED0UHR7s/DSZ4tOBFtbbvMAoIhvkHMz98PphZ2eeXZ841FtBUEQRDaG7czbd966y1qa2ubnK+rq+Ott3yYPygIXRxbrq0VK9d8dQ0fHfxIe+3XI37Nv6b9i2BbVd02ws/opy1YC6oKHIRVX8Qj6AVnvSAM8OXRL6mzqEXwhf0vJMgvqNlxJvecrGWarTq5ilpz0//H6DlYfJC8arVt/sweZzb775gUmqTlFB8saoPtgKcrBgNM1jl0NjyvohKOrVHHMf2gr3P3s0vEDYA+DZEXhYfsorCn6LP+Ri5Q828N/0CY9kf78apHvJuDIAidjrvuuotnn30Wq7cPhoROh6tOW4CwgDB+M+I32vE96+/R2p2+CFlFHhz5XrWjUu2iaGP6ToFbf1B/uxse5huydxD3yWUYVv8Tmima2yrf/AXKlMBNv2kw7ubm+wYEw5XvQXzDA/XCQ/Dfq+gZkqh1iQmO8Uwk1xcN1T2YbWxQcDBK2IqRWeqhoIU1Yr7uoX+iiLaCIAhC++K2aHvjjTdSWlra5Hx5eTk33nijTyYlCN2ByT0na+2syiwAAowBPDLlEe4YfYfTohhtgc0NW1hT6BBh4AunbVRQlLa41o/duABZa/ETgX6BzOytir5U1Few4dSGFvuvO7VOazcXjQAqssXmts2vzqeopqjFcQU3SL9MZbsB7P8KVj5of230ta4Joy3R2G3rKVarilgAwABn/ML1a0deqQRoUIL08fUt9xcEoUuxfv163n33XQYMGMCFF17IpZde6vAldF1sTtuYoBiXHlJfMegKraCpfq3Q6fNsdy9VW/xB5bUbW1hbBobCOf+Am79Tu04Ag8WEYe1j8Opsx2JbrnDwG9j2TsPYEXDxiy3fHyA0Fq75GMIahNqTm0je84X2stdFyAxGuxjrZDyHta8+17alYmT54rQVBEEQOg63VSOr1eq0un1mZiZRUVE+mZQgdAfGJI0h0BioHUcHRfPqOa9y4YAL23UeNleB2Wp22C7oi1gGg8GgjaN38e7I36Hda1TCKAZED2h1LIeIhBMtRyTo82yn9praYl+HYmRFEpHgM/wDYcKtDQdW9cER1AemUVd7P/6wiyEoUrX3fAq15Z6Nk/kTFB9T7X7TIKqX69f6BcCMP9uPVz3ielSDxaw+ROrdP4IgdCqio6O55JJLmD59OvHx8URFRTl8CV2TkpoS7UFyWkya088tjQnwC2Dh6IVNznd60dYhGqGVvHYbKWPhN2uwTr0bq6EhezZ7O7w8DTa+CBZL62NUFcHnun+vuQ/bI4VaI6YPXPMhNBSh7XnCXrDXo11g9TV2YTVhiBKnG2g2HgEg6Qx7u6ViZHqnrYi2giAIQjvj8v6T0aNHYzAYMBgMzJo1C39/+6Vms5ljx45x7rnntskkBaErEuwfzLz+81h2eBn9o/rzwswXSI1Mbfd5JITac2f3F9ldFL6IRwCID44npzKH4ppiTBYT/kZ/lwqQNWZS8iQiAiMorytnVcYqakw1TmMPyuvK2Z63HYDeEb3pHdnyhwR9ru3B4oMOReIELxl7A6x93F4ABSBtDkT29H7swFA443L4+XVVtGT3JzD2evfH0Vy2KOesu5xxBax9Qm3jPPGDKsA24Oym/axW9cHu2Fq7K9dWlG3KIph1r/fuY0EQfMobb7zR0VMQ2oBDJfZohNbybPWc0/cc3tjzBnsL9wIQ6h9Kn8g+Pp+fz8g/CFnbVDt5pHtb9/2DsJ79V4oSJhC39q8YCg6qrNtv/qIKm83/txJXm+PrP0FFjmqnzYEx17k3956j4Yol8N8r6VtfT7DFQo3R6FnRt7w9qnApNMmsjw6KxoABK+qBq9tOW6vVnmkbkexZgVVBEARB8AKXRdv58+cDsH37dubOnUt4eLj2WmBgIH379uWyyy7z+QQFoStz36T7uHrI1aTFpBFgDOiQOegdtXqnqS/iEQDiQ+OhUGX3FtUUEeIfohUTiwiI4Jy+57g0ToBfALN6z2LZ4WVUmar44dQPzOozq0m/TdmbMDdsBWwpGsHG4Fhx2rYZIdFKuN34gv3cGC8KkDVm9C+VaAvqv6OuVu5XVzHVKrEXwD8Ehnrgcjf6Kbft0oacvlWPQP8Zql18DI6taxBq16rCKs5Y/xQY/WHmX92/vyAIbU5+fj4HDqi/D4MHDyYhIaGVK4TOjDtFyPQYDUZ+P/b33LLiFgCGxw/Hz+jn8/n5jJYKkLmIKXEE1ltWY1j1MGx6UZ08sR5emgznPqr+Djd+4LjvC9jVkBUfHAUXPefZQ8lB58AFTxH5xZ08mVfAz8HBXBfWcv6wUxzybEc5vORn9CMmOIaimiLCAsII8Q+xvxieBKHxUFUAObuVQNv4+6gsgOqGuIyEwQiCIAhCe+OyaHvfffcB0LdvXxYsWEBwcNsWUBKE7oC/0Z+hcUM7dA6JugIP2ZXZWttnTludKFxQXcCu/F1Um6oBmNd/nuMCuRXm9p3LssPLAPjm+DdORVt9NIIrou2A6AH4GfwwW80cLJZiZD5nwq2w6SWVqReWAIN8uOOi52iVTZe7W23dfP9quOJNh62PLXJohd3tOvRCVUXbE4Zfqty2+fsgc7OaR84uKD3Z/DXB0aqC9dHV6njtv5RwO+NPns1BELyhrgr8g1vPmzzNqKys5I477uCtt97C0rAl3M/Pj+uuu47nn3+e0FAX/18jdCoOF9tFW/1uG1eYmDyR3476LWtOrnEal9BpsFjswqnBqHLmPSUgBM59BAafB8v+D0oz1A6az+9QmfUXPgcRSapvZQF88Tv7tef9y7vdNWNvgNJMpq19nGnVNbDiPhh8PgSGuT6GLc8WmjhtQcVofXbkM6b1mub4gsGg3LZHVyvhtiIXIno49nHIs+3Y9bwgCIJweuL26v36668XwVYQuhDxoc6za2NDPCz20AhboTNQou3Hh+wFyK4YdIVbY01InkBkoMoxXZ25WhN/bVitVk20DfIL4sweZ7Y6ZpBfkLbd7kjpEeo9rZAsOCc6FS54SgmsF//bPSdsaxgMKifPL0gdH1oBb89XWXqusON9e3uki1l/zjAa4Wx7NXEO/K+pYBsYDgPnwjkPw2/WwR+PwXWfqQ+0NlY/osRfb6mtgC8XwbOjlONJEFri8Ep4rC/8Z5rKfhQ0Fi1axJo1a/jiiy8oKSmhpKSEzz77jDVr1nDXXXd19PQED9HHI7iSqd+YW0feyn8v+C+jEkf5cFY+5uSPUJKh2v3Ptouq3tBvKtz2gyomauPgcvj3RNizTDlRv1qkBE5Q4qqrObotcfZfVcQCQHkWrH/aveuztzc0DNDjjCYvP3jWg3x84cc8OvXRptfq++c4iUjQF2cTp60gCILQAbgt2prNZp544gnGjx9Pjx49iI2Ndfhyh7Vr13LhhRfSs2dPDAYDy5Yta7H/6tWrtVxd/VdOTo6734YgnDboRVUb0UHRPotr0Dtt15xco+XmpselO0QTuEKAMYDZfWYDUG2qdnDVgsqkzatSW9DH9RjnNPPWGTanjcli4mjpUbfmJLjA2Bvg16vVVkdf038G/PITe1Gykz/CkvOhLKvl66qKVGVrUFsg+83wbh5DLoReY+3H/sHQbzrM/LuqxP2n46qwyuTbIXmE3dE44Tcw9xH7dd8/BOuf8XweWdtVsZifX1PxDJ/d7rqILZye/LhYZVXm7FJ5y4LG0qVLee211zjvvPOIjIwkMjKSefPm8corr/Dxxx+3PoDQ6bBarZrTtkdYDyICPdxh0dnZqX8o6Vk0glOCI+HiF+Gq9yGsYadWdRF8dD28MQ/2fqbOhcTChc/4JqvdYIBz/wm2dekPz0HRMdeuNdVCrsogJn4QBIU36WI0GBkcO9h51IVDMbKdTV/P14m2ieK0FQRBENoft0XbBx54gKeeeooFCxZQWlrKokWLuPTSSzEajdx///1ujVVZWcnIkSN58cUX3bruwIEDZGdna1+JiYmtXyQIpynORFtfRSOAYzbup4c/1dquFiBrzNw+c7W2LRvXhl7EndprqstjNi5GJnQx+k6BG75U8QsAeXvhtblQcLj5a/Z+CpYGV/UZV4Cfy2lAzjEa4ZqP1YfZ67+EP52A6z+HaXdD6pktO4wn/RbmPGg//u4+2PBC8/2dYbGoa16dDUVH7OdrSmD1P90bSzh9MNWqong2jq3tuLl0QqqqqkhKaupQTExMpKqqqgNmdPry7r53mfXRLD46+JFX4+RW5VJeXw7AwGgP8lG7AqZa2NOw3goIgyHn+/4eg8+D/9sEwy62n8vYYG+f/ySE+/DzV3waTLxNtc21sOJvrl2Xt8/+t95JNEKrtFaMLF+ctoIgCELH4rZo++677/LKK69w11134e/vz1VXXcWrr77Kvffey6ZNm9wa67zzzuMf//gHl1xyiVvXJSYm0qNHD+3LKBltgtAseidsS+c8RS8K1zcsnEP9Qzmv33kejTc+eTzRQdEArM1cS1W9/YOzu3m2NqQYWTcgeSTc9A1EN1SzLs2A1+faK2c3wrBDV6DFVy6k0Fi1bbTfVAhwMyborDth1r324xV/hU2LXbu2Ig/eu0Jdo304HQUBDXmbP70K+fJzLTjh5GbQ/T9URFtHJk2axH333UdNjT02orq6mgceeIBJkyZ14MxOP17a8RJ5VXk8veVpTBaTx+McKrZHI6TFuF6ErEtxaAXUlKr20Avcy391h7A4lSN/6auq4JiN4ZdA+qW+v9+0P6idMQD7v4Qjq1q/RotGQOXIu0v8IPALVG1n8Qg20Ta8B4TEuD++IAiCIHiJ29ajnJwczjhDbSUJDw+ntFQtGi644AL+/ve/+3Z2zTBq1Chqa2tJT0/n/vvv56yzzmq2b21tLbW1tdpxWVkZABaLRSs60dZYLBasVmu73U9oW7ra++ln8CM6KJqS2hLtXGxwrM/mHxvUNBblvL7nEewX7NE9jBiZ1XsWSw8tpdpUzdrMtZzT5xwq6irYnrcdgNSIVFLDU10ef2CU3W1zoOiAw3Vd7f08rYnpBzcux/De5Rhy90BVAdYlF2Bd8C70UwVGLBYLxpJjGE79BIA1aTjWxOHKqdrRnPV7MJswrm6IS1j+JywGI5z5q+avOfI9hmW3YajM005ZJy/EevZf4YfnMK5+GKxmrN/8BevV3jnUOiPy++kdhiPfo9+8bM3ZhbWiQD2A6CB88Z766ufh2WefZe7cuaSkpDBypHLp7dixg+DgYL755ptWrhZ8RWltKaW16vNEeV05uwp2MTpxtEdjHS6x78Dotk5bfV67LzJlW8JggBFXQN+zYO3jYDHBnIfa5l7BkTD7fljW4Lhd/me4dX3LO1laKULWKn4BykGbswsKD0F9tSrMBlCRD1WFqp04xP2xBUEQBMEHuC3apqSkkJ2dTe/evRkwYAArVqxgzJgx/PTTTwQFBbXFHDWSk5NZvHgx48aNo7a2lldffZUZM2bw448/MmbMGKfXPProozzwwANNzufn5zs4K9oSi8VCaWkpVqtVXMHdgK74fkYHOIq2IdYQ8vLymr/ADZx9eD47/myvxh8fNZ6lLAXg8wOfMypkFOtz12OyKvfNmJgxbo1vtVqJDoympK6E/UX7yc3NxdCQw9YV38/TGyOGeUuIWX4bgdk/Y6irgHcvp2T2k9T2n4vFYiFgl128LO93PlU++ln3CUOuJ7y8lPAtKhbI+PUfKK2spnpYow/e5joiNj9N2I7X7adC4imd+Rh1qVOgsATSfkHCz6/jV5GN4fB3FP/8EXW9p7fjN9P2yO+nd8Qe+JZA3bEBKyU7/0dt/zbIn3YRX7yn5eXlPplLeno6hw4d4t1332X/fuWou+qqq7jmmmsICQnxyT2E1jlVccrheF3mOt+ItjHdULStKlJOW2jIa2+n/+dH9oQL3CwQ5gkjrlS7R05tUS7Xn16Dibc23z9ru73dY4Rn90w6Q4m2VouKW+jV8Jkyf5+9T4KItoIgCELH4LZoe8kll7By5UomTJjAHXfcwbXXXstrr71GRkYGv//979tijhqDBw9m8GD7NufJkydz5MgRnn76ad5++22n19xzzz0sWrRIOy4rKyM1NZWEhAQiIyPbdL42LBYLBoOBhIQE+dDZDeiK72ePiB4crziuHafGpvo0CzoyMJKyOuViHxwzmClpUzRR1BNmx88mZlcMxbXF/FTwE+Ex4ew+Yt+2NidtjtvzHxI7hE05myipK8Ev0k+LiOiK76eQCDd8jnXpTRgOLsdgqSd6xZ1Yz38Ky6hfQoZyyFkNRsIn3UB4RCfLPZ/3ENaQIAzrnwIgau29RETF2Ct2Fx7B8NmvMOi2fVrTZmO4+N9EhzXKqD7nIfhEOXVjNj+BdfT8ll1JXQz5/fSCqiIM+er/m1ajP4aGLefRxTuwJl7b0pVtii/e0+BgN+NJWiA0NJRbbrnFZ+MJ7tNYtF1/aj0Lxyz0aCxbPILRYKRfVD+v59bp2LsMzHWqnX6593ntnQ2jEc57HF6dqY5XPQJnXA5hTmK9zPWQu0e1Ywcop64n9EgHm2E3d7dOtNXFDoloKwiCIHQQbv+l/+c/7QVPFixYQO/evdm4cSMDBw7kwgsv9OnkXGH8+PGsX7++2deDgoKcOoCNRmO7fgA0GAztfk+h7ehq72diqKNoFR8a79O5J4QkaKLt5YMux8/PSYVeNwg0BjKnzxw+PPghNeYa1mWtY33Weu218cnj3Z7/4NjBbMpRuduHSg6RGGb/N+lq76cABIXBgnfhi4Ww/V0MWDF89XsMJ9ZjKM8EwNB/BoaoXh080WaYda/aZrrhOQCMXyxs+PBtgK/ugvpK1c8YAHMexDDhVgzOfj7PuBw2/wcyN2MoOIhh6xKY8Jt2+zbaA/n99JAT6wArAIZR18C2d8BqxnBsnfOfpXbE2/fUm5+Fzz//nPPOO4+AgAA+//zzFvtedNFFHt9HcJ1T5Y6i7b6ifRRUF7idv2+2mDlSogo19o7oTZBf2+4A7BB2fmhvj2zjaISOImUsjLoGtr8LtaXw/UNw4bNN++XvV0XLwLM8WxtJumJk+lzbPJ3TNnGo5+MLgiAIghd4/Xh20qRJHVqsYfv27SQnJ3fY/QWhK9D4g09ccJxPxx8QPYAjpUcIDwjn/P6+qWI8t+9cPjyoPpy8vONlcqtyATizx5mE+Lu/bdWhGFnxAc7q1XwWttBF8POHi1+E0DhN/DTsXmp/fYSPCpC1BQYDzHkQLGbY9CJgtef42YhLg8tfbzmnz2CAc//ZyJV0RYdmlgqdBH0Rn6EXQd5eyPwJCg5AeS5EJHXc3DqQ+fPnk5OTQ2JiIvPnz2+2n8FgwGw2t9/ETmMyKzKbnFt/aj3z0+a7Nc7J8pPUWZQLtVtGIxQfh4yNqp0wxPM4gK7ArPtg7+dQVw5b3oSxNzYVZvXRCMmNXnOHHmfY27k60dbBaWtfQwqCIAhCe+KSaNuaE0GPO66EiooKDh+2Z08dO3aM7du3ExsbS+/evbnnnns4deoUb731FgDPPPMM/fr1Y/jw4dTU1PDqq6/y/fffs2LFCpfvKQinIwkhjluq3XWvtMYfzvwDfSL7MC1lGhGBET4Zc2zSWGKDYymqKeJI6RHt/JReUzwab1DMIK19oOhACz2FLoXBoCICwuLh23u109aAMAxDL+jAibmAwQBzH1aO280vO742+lo49zEICm99nJSxSqDe+T7UlMCax+C8x9pkyoKLlGWrbbtRvTrGoWW12kVbv0DoM1kV68tUBfo4vk65tE9DGheiFDqexvEI4Jlo2+2LkO3UFZscsUD9DemuRCTB9D/Ct38HrPD1n+Cm5Y7fs7dFyGyExkJkLyg7pZy2Vqu6jy3TNrwHhMR4Pr4gCIIgeIFLom1jJ4LBYMBqtTY5B7jlSvj55585++yztWNb9uz111/PkiVLyM7OJiMjQ3u9rq6Ou+66i1OnThEaGsqIESP47rvvHMYQBKEp8aGNnLYhvnXa9gjr4XH+XHP4Gf2Y02cOHxz4wOG8p6Jt/6j++Bv9MVlMHCw+6IspCp2Js+6E0Disny/EYDUrQSowrKNn1ToGQ4PAalUxB0GRqtiLu4LarHth3+dQXwWbX4FxN4kzqD2wWqH0pBIP9F8VamcARn+4/kvo0847koqOQmnD+qn3RAgMVaLtuifVuWNrTlvRVs9bb73FggULmsRo1dXV8f7773Pdddd10MxOL2yibbBfMAF+AZTXlbMhawMmiwl/o+ubAm15tgBpMWk+n2eHYrXCTt166IwrOm4u7cWEW2Hrm1B4GE5ugt1LHf+/pct990q0BRWRUHZKxTGUZEBAKFQVqtfkb6kgCILQgbgUCmaxWLSvFStWMGrUKL7++mtKSkooKSnh66+/ZsyYMSxfvtytm8+YMQOr1drka8mSJQAsWbKE1atXa/3/+Mc/cvjwYaqrqyksLGTVqlUi2AqCCzR22sYEdw3HwLl9z3U4TglPoU9kH4/GCvALoH9UfwCOlR6j1paDJnQfRl+L9eZvKZtyL9Zz/tHRs3EdgwHmPQ6/3Qy/3+OZmBbVC876nWpbzbDibz6dooASTQqPwO5P4Nv74K358K/+8MwZ8MG1sPZxVdXdJtiCclF//QcVg9GeHPne3u7fsE5KnaBctwDH1rbvfDopN954I6WlpU3Ol5eXc+ONN3bAjE4/rFYrWRVZAPQM78mkZPWAo7yunF0Fu9wa61CJXbTtdk7brG1Q2PD99ZkC0akdO5/2wD9Qxf/YWPF3qGvIezeb7PmzMX0hJNq7e/XQ5drm7lZ5uTYkz1YQBEHoQNyu5PC73/2OZ599lrlz5xIZGUlkZCRz587lqaeeYuFC3zrtBEHwDYkh9qJbMUExBBi7RnX50YmjHQTnKb2maK5+Txgco9wSZqu9WInQzeg5mqr0ayDQhViBzkbCYM+rXwNMvgMiU1T70Ao49J1v5iUowfa/V8LzY+DjG+GHZ+DoKqguato3JAb6z4CYhsr1Obtg29vtOVs4utreHtCQdxwQAinjVbv4OBSfaN85dUKsVqvTvymZmZlERUV1wIxOPwqqC7SHqL3CeznsplmXuc6tsWzxCIHGQFIjupmoqXfZdtcCZM4YOAcGNTzAL8+CdU+pdsFBMFWrtjd5tjYaFyPTi7YJQ7wfXxAEQRA8xG3R9siRI0RHRzc5HxUVxfHjx30wJUEQfI0+HsHX0QhtiZ/Rj3P6nqMdT02Z6tV4+mJkEpEgdDsCQ2HOA/bjb/6i3EiC95z8EQ462U0UlggDz4Fpf4AF78DvdsEfj8F1n8H8f9v7rXwIqkvaZ65mk91JGxrnWKyo3zR7+7h7glh3YvTo0YwZMwaDwcCsWbMYM2aM9jVy5EimTp3K7NmzO3qapwX6PNvGou36U+tdHqfWXEtGmYoEGRA9AD+jn+8m2dGY62HXx6rtF6QKC55OzH0EbGaDDc9D0THHaITGBco8waEY2S4RbQVBEIROg+tBUQ2ceeaZLFq0iLfffpukJFV5ODc3lz/84Q+MHz/e5xMUBMF7QvxDOLPHmfyU8xNTe3knfLY3vx7xa7Iqspp8mPMEKUYmdHvSL4MfF6uCUwUHYMsbMP6Wjp5V12f7u/b26F/CkAtUhmJkcvPX9JkMwy+FPZ9AVYGKT5j7cNvP9dQWqC1T7X7Twah7Pt9vGqx+RLWPrVMF705DbLUatm/fzty5cwkPtzvzAwMD6du3L5dddlkHze70IrMiU2unRKSQEJrA0Nih7Cvax76ifRRUF7hUPPVY6THMVhVDkhbdzfJsj65W/w8BGHyu91EAXY24ATDpt2qHg7lWxf9Epdhf9zbPFiC2P/iHKPduzm6ILLa/Jpm2giAIQgfitmj7+uuvc8kll9C7d29SU9XWo5MnTzJw4ECWLVvm6/kJguAjFs9ezNHSo1pEQFchNjiW52Y+55OxxGkrdHsMBpUB+OosdbzqYZWRK5WvPaeuEnZ/qtqBEXDev5Sr2RXmPAgH/gemGiWmj7keEga1fp03HF1lb9uiEWz0GqsK7NRXKTeurUr6acZ9990HQN++fVmwYAHBwcEdPKPTl1Pljk5bUFFI+4r2AfDDqR+4OO3iVsfRFyEbGNPN8mx3vG9vj7iy4+bRkUy7W/07VOTA/i/VLgIbvohHMPpB0jD10Kv4mL0IWXgShMZ6P74gCIIgeIjb8QhpaWns3LmTL774goULF7Jw4UK+/PJLdu3aRVpaN3uyLQjdiEC/QIbEDvEqE7arExscq2XkHig+gNVq7eAZCUIbkDIORjRkHlYXw5p/dex8ujr7voS6ctVOv8R1wRZUsSBbgTiLSUVWtDX6ImQDGhVr9Q+E3qrQE+VZqrDaacz1118vgm0H0zgeAfAoIkFfhKxbOW1ry2H/V6odEgNpp2lsR1AEzL7ffmwTVaN6+05U1efa2nYrSDSCIAiC0MG4LdoCGAwGzjnnHE20nTNnzmktBAmC0HWwRSSU1paSW5XbSm9B6KLMuk9t9QTY/B8oONRyf6F59NEIo65x//qz7oRIJUZx+Fs4uMI383JGTSlk/qzacQMdtxDb0OfaHlvTdnPpApjNZp544gnGjx9Pjx49iI2NdfgS2h4H0TZC/Z6MSBhBRGAEAD9k/YDJ0no29+Hiw1q7Wzlt931pL7g1/FL14OV0ZcQCSDnT8VxPH0Qj2NDn2toQ0VYQBEHoYFwSbZ977jlqamq0dktfgiAInZlBsfatyRKRIHRbonrBlN+ptsWkMgAF9ynJsBf1iu0PqRPcHyMwVMUk2PjmHjDV+WZ+jTm+HhpyPZtEI9jop8s1t31vpykPPPAATz31FAsWLKC0tJRFixZx6aWXYjQauf/++90e78UXX6Rv374EBwczYcIENm/e3GzfTz75hHHjxhEdHU1YWBijRo3i7bff9uK76ZrYRNuIwAgiAyMB8Df6MylZOcLL68rZVbCr1XEOlyjRNiIggqTQpDaabQewUxeNMPI0jUawYTTCeY85nvNFnq0NvdPWRqKItoIgCELH4lKm7dNPP80111xDcHAwTz/9dLP9DAYDCxcu9NnkBEEQfI0+0/dA0QGm9PSuuJkgdFomL4Stb0HZKTi4HA6vhLRZHT2rrsWO94GGGJVRV3ue/5p+GWx+BU5ugsLDyv08+XafTVOjpWgEGz1GQlAU1JbC8XVgsTgWKzuNePfdd3nllVc4//zzuf/++7nqqqsYMGAAI0aMYNOmTW6taT/44AMWLVrE4sWLmTBhAs888wxz587lwIEDJCYmNukfGxvLX//6V4YMGUJgYCBffvklN954I4mJicydO9eX32anxWQxkVOZA0BKuKMrfEqvKaw4oVzp6zLXMTpxdLPjlNeVk12ZDUBaTFr32f1XngtHG9zwMf2aukxPR3qNVQUUt72jjm1xL74gaXjTcwlDfTe+IAiCIHiAS6v0Y8eOERcXp7Wb+zp69GibTlYQBMFbHETb4gMdOBNBaGMCQx0zAL/5C5jrO2w6XQ6rVReNYICRV3k+lsEA5/1TjQOw5jGoyPd2hk050lCEzOgPfZt5IOXnD33PUu2qQsjf5/t5dBFycnI44wy1JTo8PJzS0lIALrjgAr766iu3xnrqqae45ZZbuPHGGxk2bBiLFy8mNDSU119/3Wn/GTNmcMkllzB06FAGDBjAnXfeyYgRI1i/3rUM1+5ATmUO5gZnuC3P1oY7ubZHSuzZzN0qzzZzM9pDo2EXn5ZFA51y7mMw+Q445+Hm/z/nCcGRENPX8VxC1yreKwiCIHQ/Tk9rhSAIpy19o/oSaFSZcBKPIHR70i+HXuNUO38/fHQD1Nd06JS6DCc2QPFx1e4/w3k+rDv0HK0cYqCK3Hz/kHfjNab4BBQ1iFcp41XhnuZwyLU9fSMSUlJSyM5WDs0BAwawYoVydv70008EBQW5PE5dXR1btmxh9mx7kSij0cjs2bPZuHFjq9dbrVZWrlzJgQMHmDZtWqv9uwvOipDZSAhNYEis2pq+r2gfBdUFzY6jL0LWrfJss3f+f3v3HR5llfZx/DuT3klIhQQSCL1K70WRYtlVEbEtiF3BVXFfFdfGuisW7KLYEHfVVRR1baiIBFAQRQzSpYeWBAik93nePx4ySUwCKZNMZvh9ritXzlPPPZxEn7lz5j7l7dZ9nBdHc+MTCGP/2TifVqhYIiEwynGLnImIiNRTrcojzJw5s9Y3fPrpp+sdjIhIY/O0etK+RXu2ZmxlX9Y+CkrcP4FlM2y8veVtdmfuZmq3qSSEJDg7JGkqViuc9wS8MQ5sxbDtc3j7Erj8XfBr4ezomrfkd8vb9VmArDrnPAibP4GibLN0Rf/rHFeTcffy8nZNpRHK/DFpO+gWx8TgYi6++GKWLVvGwIEDue2227j66qt54403SElJ4c4776z1fY4ePUppaSlRUZVrqUZFRbFt27Yar8vMzKR169YUFhbi4eHBSy+9xLnnnlvj+YWFhRQWFtq3s7LMFe5tNhs2m63W8daXzWbDMAyH9bU/a7+93SqwVZX7Dm01lG0Z5r/f9we+50/t/1TtfX7PKP8DbPuQ9k3yb9EULIc3lM3NxxbVwyxl4kCOHk+3ENUd67bPATDCO2G42L+NxtS9aDzdi8bTvThiPGt7ba2Str/++mutbuY2NaRExK11CuvE1oyt2AwbO0/sJJKq9QbdhWEYPLr2Ud7f/j4AS/YsYfaQ2YxPGO/kyKTJtO4LVy2C966G4lzY9wO8eR5cvRiCY5wdXfNUmAObPzbbPsHQ5QLH3DcwEkbeDUsfAAxYci9M+9IxH3veVSFp2+40SduILuDf0iyPsPd7KC0xyyacYR577DF7e/LkybRp04Y1a9bQoUMHLrzwwkbvPygoiOTkZHJycli2bBkzZ86kXbt2jBo1qtrz58yZw+zZs6vsP3LkiH3B4MZks9nIzMzEMAysDqiDvCO9fIZsQEkA6enplY538y+vMbps9zIGBQ2q9j5b08tLfLQoblHlPq4q4lAyHoDNO4j0Ij9w8Oty9Hi6Ax/fOEJPtvMC25LtYj9LGlP3ovF0LxpP9+KI8czOzq7VebV6Ql++fPnpTxIRcREdQzva278f/53IEPdM2hqGwVPrnrInbAHySvL4v5X/x7q0ddzd/268PbydGKE0mfZnwzWfwzuXmom69M2wYCxc/TGEu1ENSEfZ+qmZ4Abofgl4+Tnu3gNvhl8WmqUMUlabyeHulzTsnrZS2HNywSLfELMUw6lYrRA/HLZ8YpZqSN1gJvfPcIMHD2bw4LovbBQeHo6HhwdpaWmV9qelpREdHV3jdVarlcRE8/evd+/ebN26lTlz5tSYtJ01a1alT79lZWURFxdHREQEwcHBdY67rmw2GxaLhYiICIe84Tyx/YS93S22W5X/F48MH0nQr0FkF2ezPmM9YeFheForv3UxDIN9efsAiPCLIDHWTf57lnsUa67582SJ6UnkH2ZxO4Kjx9MthF2CsfF1OLEfv6E34VfNIoLNmcbUvWg83YvG0704Yjx9fX1rdd6ZN61CRM54f1yMbFiIAxeyaEZe2vASb215CwALFgZED2Bt6loA3t/+PhuPbmTuyLnEBcU5M0xpKq37wLXfwNsXw4kU82vBOLjqA9VL/KPGKI1QxtMbxj0K/51sbi99EDqONxeOq6/DGyD/uNlOGFG7WbMJI8ykLcCeVWdM0vbTTz+t9bl/+lP1H8f/I29vb/r27cuyZcu46KKLAPNhftmyZcyYUfu6mzabrVL5gz/y8fGpttau1WptsjeAFovFYf0dzC2vaRsbFFvlnt5Wbwa3Gsw3+74hqyiLLRlb6B3Zu9I5R/OPcqLwBGAuQuY2b4TTNtqblpheWBrpdTlyPN2Ctz/csBwMGxarh7OjqReNqXvReLoXjad7aeh41va6eiVt161bx6JFi0hJSaGoqKjSsY8++qg+txQRaTKdwsqTtr8f/x3iG6efwtJCjuUfo1Vgq8bp4BRe3/g68zfMt28/MPgBLu1wKR/t+Ig5P82hsLSQLce2MPmzyTwy7BHOaXNOk8coThCeaCZu37kU0jZB3lF460KY/B9zNq6Yi4/tXWW2W3aA2P6O76PjOGh/DuxaBpn7YfULMOqe+t9v13fl7dOVRiiTMLK8vWclDLuj/v27kLKkahmLxYJhGFX2AZSWltb6vjNnzmTq1Kn069ePAQMG8Oyzz5Kbm8u0adMAmDJlCq1bt2bOnDmAWeqgX79+tG/fnsLCQr788kv+85//8PLLLzfg1bmWsoXIwv3C8fWsfrbJsNbD+GafuUDcqoOrqiRtdxwvL7GQGOoms2wBUissQhbd03lxnIksFrC4ZsJWRETcT51Twu+99x5Dhgxh69atfPzxxxQXF7N582a+++47QkJCGiNGERGHCvEJIcrf/KjhjuM7qrxhb6gTBSd4ecPLnPvBuYxbPI4Hf3iQEluJQ/s4lXe2vsNz65+zb9/T/x4mdZyExWJhYseJvHPeO7QNbgtAdnE2dyy/gyd/fpJiW3GTxShOFBwD13wBbYea20U58M5lsPFD58bVXCT/t7zd+0rH1Jv9I4sFxs8pTwx8/wxkHqj//XYnlbdrm3xv2R6CTv5BKWUNlBSd+nw3UbZol81m45tvvqF3794sWbKEEydOcOLECZYsWUKfPn346quv6nTfyZMnM3fuXB588EF69+5NcnIyX331lX1xspSUFA4fPmw/Pzc3l1tvvZVu3boxdOhQFi9ezNtvv83111/v0NfbXBWUFHA0/ygArQNb13jesNbln4T5/uD3VY7vPLHT3u7QooMDI3SywxWStjFK2oqIiJyp6py0ffTRR3nmmWf47LPP8Pb25rnnnmPbtm1cdtlltGnTpjFiFBFxuLLZttnF2aQXOGahidTcVB7/6XHGLh7LS8kvcbzQ/Ljyxzs/5t5V9zZJUvSD3z/gsZ/KF9e5vc/tXN316krndArrxHvnv8fYtmPt+/695d9c89U1HM45jJwB/FqYC5F1PrnAlq0YFl8HP84/5WVuz2aDDSdLI1is0OvyxusrohMMuNFsl+TD0ofqd5+iXEj50WyHxkNYQu2us1jMEgkAxXlw8Jf69e/C7rjjDp577jnGjRtHcHAwwcHBjBs3jqeffpq//vWvdb7fjBkz2LdvH4WFhaxdu5aBAwfajyUlJbFw4UL79j//+U927NhBfn4+GRkZrF69msmTJzviZbmEQzmH7O1TJW0j/CPoHNYZgC3HttgTvWUqzrTtEOpGSduymbYePhDe8dTnioiIiNuqc9J2165dnH/++YBZwys3NxeLxcKdd97Jq6++6vAARUQaQ8W6truydzXoXrtO7OLv3/+dCYsn8PbWt8kvyQfAw+KBp8WsQvP13q+ZuXwmhaU11ytsqM92fcYjax6xb9/c62au71H9rK1A70DmjpzLfQPvsy/s8tuR35j0+SRWHljZaDFKM+LlB5Pegj5Ty/d9dQ8s+wc4ePa5XXaqef+VcyF1U+P1U1/7fjBr/YJZZiC4kUubjLoH/Fua7U0fwr41db/H3h/MpDvUvjRCmYTh5e09Z97v/a5du2jRokWV/SEhIezdu7fJ4zmTHMgpn1l+qqQtVJ5t+8PBHyodqzjTtl1IOwdF52SFOXDs5HNJVDfw8HJuPCIiIuI0dU7ahoaGkp2dDUDr1q3ZtGkTACdOnCAvL8+x0YmINJKOYeUzV3Zn767XPZLTk7ntu9u46H8X8emuTykxzBIIvh6+XNH5Cr645AueP/t5fDzMhWOSDiRx27Lb7EldR/p679fc/8P9GJhJsGu6XcOtvW495TUWi4UrOl/Bfyb8x/6mObMwk+nLpvPc+ueatKSDOImHJ1z4HIy4u3zfqqfg09ug1IHjX1oCP74ML/Y37//dIzB/KDzXE5bcA7tXQGkzKM9RaQGyKxu/P79QOPv+8u3P74SCzLrdY/fy8nZd6xKXzbSFMzJp279/f2bOnElaWpp9X1paGv/3f//HgAEDnBiZ+yurZwvmImSnUlOJBJthsydtYwNj8fdqwGJ+zUnaJjj5/3KVRhARETmz1TlpO2LECJYuXQrApEmTuP3227nhhhu44oorOOccLWQjIq6hY2j9kraGYbDywEqmLpnKX5b8haT9SfZjwd7B3NTzJr6+9GvuG3gfrQNbMzx2OC+d8xJ+nn4ArDm8hpuX3kxuca6jXgor9q/g3pX3YjNsAEzuNJmZfWfaF9M5ne7h3Xn/gvcZFTfKvu/1ja9z89KbySvWH+PcnsUCZ/8dJjwJnPyZ+fU/sPA82LG04bNh962BV0fCV/dCYVblYydSYO18+Pef4Mn2sPh62LS47olLRyjMhi2fmG2fkPLSEY2tz1SI6mG2j2yF96+uW33ZXSeTthZr5SRsbbRoY5ZUADjwExQ7/g9KzdmCBQs4fPgwbdq0ITExkcTERNq0acPBgwd54403nB2eWzuYXSFpG3jqpG2viF4EeQUBsPrQavsfFA/lHLL/EdStSiMc1iJkIiIiYvKs7YmbNm2ie/fuvPjiixQUFADw97//HS8vL1avXs3EiRO5//77T3MXEZHmoW1QW3w9fCkoLThleQTDMEjNTWXD0Q38duQ3Vh9cza7MyudH+UcxpesULu14abUzfQbEDODVc1/llm9vIac4h/Xp67nhmxt4eczLhPg0bAHH1YdWc2fSnfZZvhcnXsx9A++rdcK2TIhPCM+Pfp5/b/k3z/zyDKVGKWtT1/K3FX/jubOfw8uqj2e6vYE3QkBL+Ogm8+P2+9fCO5dCVHcYejt0u8ScmVtbOelmndYN71be32eKec/tS2DvKiib0V2QCRs/ML+sXhA/DDqOxyOsL4SFgJdv4ywKVmbL/8zargA9Jpr9NQWrB0x6E94YC/kZ5ozX/90KF78K1tP8bT3rkJnoBWjVx6xVXFcJI+D4XigtMse83ai638NFJSYm8ttvv7F06VK2bdsGQJcuXRgzZkyd/xsqdVNxpm3roFOXR/C0ejKo1SCW7ltKVlEWm45uondk70r1bBNbJDZarE3u8Ibydkwv58UhIiIiTlfrd189e/akf//+XH/99Vx+ubkwh9Vq5d5772204EREGouH1YPEFolsOraJw3mHySvOI9AnkLziPDYf28yGIxvYeGQjvx39rcrCJ2XahbRjWvdpnJ9wPl6nqTnXO7I3r497nZuW3kRmYSYbj27k+m+u55VzXyHMN6xer2Fd6jpu/+52+wJnExIm8NDgh7Ba6vwhCsAslzC121R6hPdg+rLp5BTnsOrgKh5e/TD/HPpPJTHOBN0nQkCk+TH9YycTImmb4KMbYNkjMGQGnPUX8D7Fx5BtpbBugXl+YYUZszG94PynIbafuT3wJjNRu/Nb2PalOau37HxbMexejnX3ciLsN7CYdXg9fc0vL1/w9PvDd19zga8hf617ArNSaYSr6nZtQ4V3gCvfh7cuhJICM3EdFANjHzn1dbuTytt1LY1QJmEkrP+32d6z8oxK2oL5372xY8cyduzY058sDlOWtPWweBDlH3Xa84e3Hs7SfeYn/VYdXEXvyN6V6tm61Uzb1JNJW4sVIrs6NxYRERFxqlonbVesWMGbb77JXXfdxZ133snEiRO5/vrrGT58+OkvFhFphjqFdWLTsU0YGNz3w30czDnIzhM77WUGatI7ojfTuk9jVNyoOiVIu7XsxoJxC7jxmxs5VnCMbRnbmPbVNF4b+xqR/pG1vs+x/GN8t/875v48l4JS85MP57Q5h38N+xceVo9a36cmfaL68PzZz3Pz0pspshXx6a5Paenbkpn9Zjb43uICEobD9J9g+xfw/bNwcJ25PzMFltwNSY+ZCdcBN4L/H/7gsP9n+GJm+crnAL4hcPYD0O9ac1ZpRb4hZqK4+0Szpu2+H8wZuNu+NPurxDBnwp6uZMe2z2HzJ3DFexBRy1XXM3abfQOEd4LWfWt3nSPFDYBLF5jlEQwbrH7eXAht0C01X7OrYj3bOi5CVib+zFqM7Pnnn+fGG2/E19eX559//pTn/vWvf22iqM48ZQuRRQdE2xfDPJWhrYfa298f/J7bzrqNHSfKZ9p2aOEmSduSIkg3Z30T3vHUfyATERERt1frpO3w4cMZPnw4L7zwAosWLWLhwoWMHDmSxMRErrvuOqZOnUp0dHRjxioi4lAV69ou37+82nOCvILoEdGDHuE96BnRkx7hPQj1DW1Qn2+Of5Prv7me9Lx0dmfu5pqvruH1sa/TKrDmler3Zu5l+f7lfJfyHRuObLAvOAbmIi1PjHjCoSUM+kf35/ERj3PXiruwGTbe3PwmLf1aMrXbVIf1Ic2Y1QpdLjTruu77wUze7jRnuZGfAUlz4IfnzFIHg6eDlz98+7BZC7ei3lfDmIchMILT8vAyZ3m2GwXjH4O0zdi2fUnxrlV4W4qxlBSas1CL8yt/Lymoeq+MXfD6OTDxdeg47vR9J/+3QsxXNm4ZhlPpfD6c/5Q50xngq1nmjNtuF1U912YrX4TMOxBi+9evz6AoM1F9dDscXA8FWeAbXL97uYBnnnmGq666Cl9fX5555pkaz7NYLEraNpKsoiyyi8xFjU9Xz7ZMpH8kncM6sy1jG1uObeFo/lF7eQRPqydtg9s2WrxN6shW85MGoHq2IiIiUvukbZmAgACmTZvGtGnT2LlzJ2+++Sbz5s3jgQceYPz48Xz66aeNEaeIiMMNjBmIh8WDUqMUAKvFSsfQjvYEbc/wnsSHxNe73EBNEkISeGv8W1z/zfUczDnI/uz99sRtm+A2gLkq9m9HfmP5/uUs37+cPZl7qr3XsNbDeGbUM3h7eDs0RoAxbcfw94F/55EfzY9oz103lzDfMC5sf6HD+5JmymIxa8vGD4PUTWaidtNiMErNGa9r58NPr4F3QOVFxqJ6wPlzoc2g+vcb3R0iu3K88xQiIyOx1FTf1TDKk7dZh+CjG82SDoVZ8O5kOOdBGHZnzYlYmw02nEzaWqzQc3L9YnaUftear2Plk4Bhvp6ACIgfWvm89M2Qe8Rsxw83k971lTDCTNoapZCypnaJbhe1Z8+eatvSdCouQna6erYVDWs9jG0Z5izUlQdWsjdzLwDxwfGnLVHkMiouQhajpK2IiMiZrs5J24oSExO57777aNu2LbNmzeKLL75wVFwiIo2ufYv2vHzOyyQfSKZvm750D+9e7UJijSE2KJaF4xdywzc3sDdrL4dzD3PNV9dwZ987+SXtF5L2J3Gs4Fj1cYe0Z3Sb0YyOG02P8B6NWmv2sk6XcazgGC8lvwTAgz88SAufFgyPVWmcM050d5j4Gpx9P6yZZ9ZBLck3E31lCVufYPN4v+vqtmhZQ1hO1rr18gO/ULj2a/jkFtj6KWDAstmQthn+9EL1HzXeuxIy95vt9udAcEzTxH0qo/9uJm6T34HSQnjvCvN1RXYpP8cRpRHKJIyAn18z23tWunXSVpyv0iJkgXVL2r6+8XUA3t76tn0BTveqZ1shaauZtiIiIme8er+jWrlyJQsWLGDx4sVYrVYuu+wyrrvuOkfGJiLS6AbGDCTBI4HIyEisp1up3cGiA6J5c/yb3PDNDew8sZMj+Ue47/v7qpxntVjpHdGbs9uczei40fbZuE3l5p43cyz/GO9vf58So4S7VtzF62Nfp2eE3lCekULbwnlPwMh74KdX4adXIP849Lwczv2H+XF7Z/IJhElvwaq5sPxf5r5NH5oLq13+LoT84ePYFRcgO6uJFyCricUCFz4HOWnmQm0FmfD2RLhuKYScTHLt+q78/HYNTNrGDwMsgOH2dW1nzqx9be6nn366ESM5c9U3adsrohdBXkFkF2fbSyOAG9WzhcozbaN7OC8OERERaRbqlLQ9dOgQCxcuZOHChezcuZMhQ4bw/PPPc9lllxEQENBYMYqIuK1wv3DeHPcmN317E1uObbHv9/XwZXCrwYyOG83IuJGE+Yad4i6Ny2KxMGvALDIKMli6byn5JflMXzadtya8RbuQdk6LS5wsoCWMngXDZ5p1UGtTt7apWK0w8m5z5fWPb4KiHDi8AV4dBZPfLi/bUJAFW06WdfJtAR0nOCviqjy8zOTzwvPhcDJkHYR3LoVpS8DT1yxjABAcC+ENTFr5h5kJotTfIHUj5GVUXWTOTfz666+1Oq8xP8FwpjuQfcDerkvS1tPqyaBWg1i6b2ml/YktEh0Wm1PZbGZpF4CQNm77OygiIiK1V+uk7YQJE/j2228JDw9nypQpXHvttXTq1KkxYxMROSO08G3B62NfZ17yPApLCxnRegSDWg3Cz9PP2aHZeVg9eGz4Y2QWZvJT6k+cKDzBTUtv4j8T/kN0gOssQmkzbKTnpbM3ay97M/eyL2sf2UXZlBgllNhKKC4ttrcrfhXbiim2FePj4cPkTpO5uMPFzn4pzYenT/NK2FbU5QIIWwr/vRxO7DNrwC68wFzsq+9U2PyxWeIBoMel4OXr3Hj/yCcQrvoA3jgXju+F9C3w/tUw5LbyBdjaj3LMwmkJI05+NNuAvd9D1z81/J7N0PLl1S86KU2n4kzb2KDaLURWZnjr4VWStm5THiFjt/kHJlA9WxEREQHqkLT18vLiww8/5IILLsDDw6MxYxIROeMEeQdx74B7nR3GKXl7ePPc6OeY9vU0tmVsIzU3lVu+vYWF4xcS4hPSoHsXlxaTXZxNTlGO/XtZO7c4Fy+rFwFeAfh7+hPgFUCAVwB+Xn4EeJptfy//SgvGZRVlsT97v5mcrZCgTclOIb8sSVdPD65+kNTcVG7udbNm47mCqK5wYxJ8cA3sWWGuzP7ZX80ZpYeTy8/rfaWTAjyNwEi4+iMzcZt3DPauqvwR6oaWRiiTMALWvGi296x026StOF9Z0tbXw5eWvi3rdO3Q1pUX5PPz9KNVYCuHxeZUqRvK26pnKyIiItQhafvpp582ZhwiIuICAr0DeXnMy/zly79wIOcAO0/s5LbvbuOVc1+pdmZwia2Ew7mH2Z+9n/1Z+0nJTiElO4Vj+cfILsomp9hMzhaUFjQ4Nj9PP/w9/SkpLSGzOLPB96vI0+KJp9XTHudLG14itziXu/rdpcStK/APMxOf39wPa18295UtvAUQ0QVa9XFObLXRsj1cucicJVySD4VlP98WxyVt2wwGi4e5sJyb17WtaN26dSxatIiUlBSKiooqHfvoo4+cFJX7MgyDQzmHAGgV2KrO//2M9I+kU2gnth/fDpilESr+wc6lVfxjTEwv58UhIiIizUYTLe0sIiLuItwvnFfPfZWrl1xNRkEGv6b/yt0r7mZSp0nsz95PSpaZmN2fvZ+D2QftK3w3tvyS/FPOovW0eBIbFEt8cDxtg9sSH2J+b+nbEi+rF55Wz+q/LJ72xMK/N/+bJ9c9CcBbW94ipziHBwY9gIdVn0Bp9jw8YcJjEN0dPr8TSisk6Hpf6ZgSA40pth9MWgjvXQGGzdwX09OsLewIvsHQug8c+BmObofsNOcvKtfI3nvvPaZMmcK4ceP45ptvGDt2LL///jtpaWlcfLFKoDSGYwXH7H/8qks924qGtR5WKWnrNlIrJm0101ZERESUtBURkXqIC45j/pj5TPt6GrnFuSQdSCLpQFKd7hHoFUigdyCBXoEEeQcR5B1kb5cdC/IKwt/LnxJbCXkleeQW55JbnEtecV7l7ZI88orN7ZLSEtqGtKVtcFsSQhLMBG1wPK2DWuNl9WrQ657SbQoBXgHMXjMbA4PFOxaTV5LHv4b9q8H3liZy1tUQ3hHeuwpy081FvXpe5uyoaqfTeLjgGfjsdnO784WOvX/CCDNpC2YZhh6XOvb+zcyjjz7KM888w/Tp0wkKCuK5554jISGBm266iZiYGGeH55bquwhZRWPjx7Jg0wIMDPpH93dUaM5lGOUzbf3DIUg/fyIiIqKkrYiI1FOXll14bvRz3PLtLRTbiqsc9/P0Iy4ojjZBbYgLNr+3CWpDm+A2RPhFNMrsVJvNRnp6OpGRkVitjfOR2YkdJ+Lv5c99q+6jxChhyZ4l5JfkM3fkXHw8fBqlT3GwuAFw00pY/29oOxiCXGcxPfpeA8GxcGwH9LvWsfdOGAGrnjLbS+6Gta+YNXUDwiEgEgIizEXnAiJOboeDX2jzn6Vcg127dnH++ecD4O3tTW5uLhaLhTvvvJOzzz6b2bNnOzlC99OQRcjKdG3Zlflj5nOs4BjnJZznqNCcK/sw5B012zE9XfZ3SkRERBxLSVsREam3gTEDeXnMy3yx+wvC/cJpE2wmZuOC4gj3C3fbeq8TEibg5+nHXUl3UWQrIml/EtOXTef50c/j7+Xv7PCkNoJjYNQ9zo6ifjqMMb8cLW6gOfO4pMBc9Czv2OmvsXqaSdzhd8GAGxwfUyMKDQ0lOzsbgNatW7Np0yZ69OjBiRMnyMvLc3J07qli0ra+M20BhrQe4ohwmo/DWoRMREREqlLSVkREGmRgzEAGxgx0dhhNblTcKOaNmcdfv/sr+SX5rD28lpuW3sS8MfMI9g52dngideflZ5Zf+OF5c+ZfwYnTX2MrMc8tq7PrQkaMGMHSpUvp0aMHkyZN4vbbb+e7775j6dKlnHPOOc4Ozy05ojyCWzqserYiIiJSlZK2IiIi9TQoZhCvnvsqt357K9nF2SQfSea6r6/jlXNfIcw3rFH6zCjIYOWBlaRkpXBR4kW0CW7j0Pvvz9rPZ7s/o1vLbgyPHe4+K7NL7fS+0vwCKCkyP7KdewRyjpjfc9NPfj8KOWXtIy5VYmLTpk10796dF198kYICc1Gsv//973h5ebF69WomTpzI/fff7+Qo3VOlmbZBStraVVyELLqX8+IQERGRZkVJWxERkQboHdmbBeMXcNPSm8goyGBbxjau+eoaXjv3NaICohp8f8Mw2HViF0kHklixfwUbjmzAwADg7a1v83/9/49LO1za4FIUhmEurPbEz0+QX5IPmCuzT+s+jQkJE7TQ2pnI0xuCW5lfbqRnz57079+f66+/nssvvxwAq9XKvffe6+TI3F9Z0jbIO0ifSKiobKatdyCEtXNuLCIiItJsaPqMiIhIA3UO68yb498k0j8SgD2Ze5j61VT2Z++v1/2KbcX8ePhHHv/pcc776Dwu/vRinlv/HMlHku0JW4D8knz+seYf/PW7v3Isvxb1R2twLP8Yf13+V2avmW1P2ALsPLGTv3//d8776Dze3vI2ecWq8ymub8WKFXTr1o277rqLmJgYpk6dyqpVq5wdltsrsZWQmpsKQGxg/RYhc0t5GZCZYrajukMjLaIpIiIirkczbUVERBygXUg73hr/Fjd8cwMHcg5wMOcgl39+OYktEgn2CSbYO5gQnxCCvc122b6ytp+HH+vT15O0P4kfDv5AdnF2tf20D2nPyLiRZBZmsnjHYgCSDiRxyaeX8MjQRxgRO6JOca88sJIHfniAjIIM+74L213IwZyDrE9fD0BqbiqP//w483+bz5Wdr+SKzlcQ6htav3+oWigoKWDpvqV8tOMjNh/dTIfQDoxuM5qz484mISTBbRe4k6YxfPhwhg8fzgsvvMCiRYtYuHAhI0eOJDExkeuuu46pU6cSHe065R5cRWpuKqVGKaB6tpWkbixvq56tiIiIVKCkrYiIiIPEBsXy1oS3uPGbG9mVuYusoix74rO+PC2e9I3qy8i4kYyKHUVccJz92Ki4UTy0+iEyCjLIKMhg+rLpXNbxMu7qdxf+Xv6nvG9ecR5PrXuKRb8vsu8L8w1j9pDZjIobBcD6tPUs2LSAFQdWAJBZmMnLG15m4eaFXNLhEqZ0nUKrQMd9dH57xnYW71jM57s/J7uoPGn929Hf+O3obzy3/jnaBrdldNxoRseNpldELzysHg7rX84sAQEBTJs2jWnTprFz507efPNN5s2bxwMPPMD48eP59NNPnR2iW6lUz1ZJ23KV6tkqaSsiIiLllLQVERFxoEj/SN4c/yYPrX6I1YdWU1haWOd7BHkHMbz1cEbFjWJo66E11n4cFTeKxX9azMOrH7YnVhf9voifUn9izvA5dA/vXu11m45uYtaqWezN2mvfNzJ2JA8PeZhwv3D7vj5RfegT1Ycdx3fw5qY3WbJnCSVGCfkl+byz9R3e2/YeExImMKXrFDqGdqxXAjW3OJcle5aw+PfFbDq2qeq/hVdQpVnH+7L2sXDzQhZuXkioTygjYkcwus1ohrQagp+nX637tRk2CkoK8PHwUeK3lvKK8zice5jU3NRK39Ny07ip1030j+7v7BDrLTExkfvuu4+2bdsya9YsvvjiC2eH5Ha0CFkNDldI2mqmrYiIiFSgpK2IiIiDhfqG8vzZzwNQWFpIVmEWWUUnv062MwszK+3LLs4mLiiO0XGj6R3Zu9YLf4X7hfPC2S/wwe8f8OTPT1JQWsDerL385cu/cHOvm7mux3V4Ws3/3ZfYSnhj4xvM3zCfEqMEAD9PP/7W729M6jipxrIDHUI78OjwR7ntrNv495Z/s3jHYvJL8ik1Svl89+d8vvtzvKxexAXF0Sa4DW2D2prfg9vSNrgtkf6RWC3ldRoNw+C3o7+x+PfFfLX3q0p1dAF8PHwYFz+Oi9tfTGtaU+RfRNKBJJL2J7E+fT02wwbA8cLj/G/X//jfrv/h4+HD4JjBdA/vTn5JPrnFuVW+copzyCvOM7+XmPV5LVgI9Q0lzDfM/r3GL78wgryCnFaeoai0iH1Z+9iTuYdjBeU1jC1Y7N//GFvZtgULVovVfq7VYsVisdivqXi82FZMWl4aqbmplRK0WUVZNcY2Nn6syyZtV65cyYIFC1i8eDFWq5XLLruM6667ztlhuZ0D2Qfsbc20raBspq3VCyK6ODcWERERaVaUtBUREWlEPh4+RPhHEOEf0Wh9WCwWLut0Gf2j+zNr1Sw2H9tMiVHCi8kv8v3B73l0+KNgwKzvZ7HhyAb7dd1bdmfO8DnEh8TXqp+YwBjuGXAPN/W8if9u+y/vbHuHzMJMwFw8bXfmbnZn7q5yna+HL7FBsbQNbktMQAw/Hv6RnSd2Vjmvc1hnJnaYyHntziPYOxibzUZ6ejpxQXFM7TaVqd2mcqLgBCsPrmR5ynJ+OPSDPeFbWFpoJnYPJNXp387AsJeXqA1PqydhvmG09G1ZJaFb1i47FuQdhJ+nX51n8p4oOMGerD3sySz/2p25m4M5B+0J6+YmPS/d2SHUyaFDh1i4cCELFy5k586dDBkyhOeff57LLruMgIAAZ4fnlirOtHWrhch2J4GXP8QNqPu1RXlw9HezHdkZPL0dGpqIiIi4NiVtRURE3ERCSAL/Oe8/vLLhFV7b+Bo2w0bykWQu/fRSAPvsUqvFyo09b+TGnjfWekZvRS18W3BL71uY2m0qn+z8hLWH15KSnUJKVgpFtqIq5xeUFrDzxM5qE7UBXgGcl3AeEztOpGtY19POYm3h24I/tf8Tf2r/JwpLC1l7eC3L9y8naX8SR/OP1nidBQuBXoH4e/kT6BVIgFcAfp5+5JXkkVGQwbH8YxSUFpz2tZfYSkjPS69TktLL6oWfpx++nr74e/rb236efvh6+OLn5YeHxYMD2QfYk7mH44XHa33vpuBp9STKP4qYgBiiA6Lt3yu2g7yDnB1mrU2YMIFvv/2W8PBwpkyZwrXXXkunTp2cHZbbq5i0dWQtbKda9TQsmw0WK0z5HyTUbSFI0rdA2R9iYno5Pj4RERFxaUraioiIuBEvqxczzprBsNbDuHfVvRzMOWhP1oI5w23O8Dn0juzd4L78vfy5ssuVXNnlSsCsE5uWm8a+7H2kZKWwL+vk9+x97M/eT4mtxH5t74jeXNLhEsbFjzvtomk18fHwYUTsCEbEjuCBQQ+w5dgW0vPSCfAKqDZBe7qEcF6xmcA9XnDcPvv2WMEx+/ax/GMcLzxORr55rKzExOkU24opLio+ZXmB0/Hz9CMhJMH8Ck4gOiAaq8WKgQGYJSfK1LTPMAxs2DAMs21gYDNs9mNl360WK5H+kfakbEu/lpXKW7g6Ly8vPvzwQy644AI8PFTPuKmUJW3D/cLx9fR1cjQOsO1LWPYPs23YYPmjED8c6lI+5XD5Jx+IVtJWREREKlPSVkRExA31juzN4j8t5rGfHuOTnZ8AcEmHS7i7/90EeDXOx7+tFisxgTHEBMYwKGZQpWOltlIO5x7mQM4Bov2ja12SoS5917TwWm35e/nj7+VPbNDpP7ptGAZZRVn25G5GQYY9mXus4BgZBRnkFOVQUFpAfkl+la+KCeyKIv0iSQhJID4k3p6kbRfSrkpdYKm/Tz/91NkhnHEKSgrsM+Hdop5t2hb46Aag/A8jpKyBvd9DwvDa3ydVi5CJiIhIzZS0FRERcVMBXgE8MvQRruh8BQBdW3Z1WiweVg9ig2JrlRB1BRaLhRCfEEJ8QkgISajz9cW2YgpKzIRuQUkBhaWFLldmQKS2DuUcsrddPmmbewz+ezkU5ZjbLTvAsR1me8XjdUva2mfaWiCqYX90EhEREfejpK2IiIibc2ayVqrnZfXCy9tLSVo5IxzIOWBvu3TStrQYPpgKJ/aZ2zG94ZrP4ZWRkLEL9q6CfWug7eDa3Stti9lu2R58AhstbBEREXFN+pydiIiIiIg0moqLkLn0bPsl95iJWYDAKLj8XfAJguF3lZ+z8ona3evo71BaaLajVRpBREREqlLSVkREREREGs3B7PKkrcvOtP35dVj3htn28IbJ70DIydfS8zJo0dZs7/oODqw7/f0Oq56tiIiInJqStiIiIiIi0mgqzrR1yaTtnpXw5d3l2xc+D3H9y7c9vGD4zPLtFbWYbVtxETLNtBUREZFqKGkrIiIiIiKNpixp62HxIDog2snR1FHGblg0BYxSc3vIbdD7iqrn9boSgk+WftjxNRxKPvV9K8207eWQUEVERMS9KGkrIiIiIiKNpmwhsuiAaDytDlgHOfMgHN3Z8PucTkEW/PcKyD9ubncYC2NmV3+upzcMu6N8e+WTNd/XMCB1o9kOagUB4Q4JV0RERNyLkrYiIiIiItIosoqyyC7KBhxUGuFQMswbAPP6w/avGn6/mthK4aMb4cg2czu8I0x8HaweNV9z1l8gKMZsb/scUjdVf97xvVCYabZVz1ZERERqoKStiIiIiIg0CocuQlZSBP+bDkU5YNhg2T/MWauN4btH4PclZtu3BVzxHviGnPoaL18Yenv5dk2zbVXPVkRERGpBSVsREREREWkUDl2E7IdnIa3C7NX0zbBjacPuWZ3fPoDvnzHbFg+YtBBatq/dtX2mQkCk2d7yP0jfVvWcSvVslbQVERGR6ilpKyIiIiLSQPPmzSM+Ph5fX18GDhzITz/9VOO5r732GsOHDyc0NJTQ0FDGjBlzyvNdWaWkbVADkrZpW2DFE1X3lyVXHeXgL/DpjPLt8XOg/ejaX+/tby5WBoABq+ZWPSdVi5CJiIjI6SlpKyIiIiLSAO+//z4zZ87koYceYv369fTq1Ytx48aRnp5e7flJSUlcccUVLF++nDVr1hAXF8fYsWM5ePBgtee7sgPZB+zt2MDY+t2ktMQsi2ArNreH3mHWmAVIWQ0pPzYsyDJFufD+X6CkwNzuMxUG3Fj3+/S7Fvxbmu1Ni6sumlY209a3BYTE1TtcERERcW9K2oqIiIiINMDTTz/NDTfcwLRp0+jatSvz58/H39+fBQsWVHv+O++8w6233krv3r3p3Lkzr7/+OjabjWXLljVx5I3PIeURfnwJDq032+EdYdQsM3Fb5vtn6x1fJT88D1kn420zGM6bCxZL3e/jEwiDT87WNWyw6qnyYznpkJNqtmN61u/+IiIickZQ0lZEREREpJ6Kior45ZdfGDNmjH2f1WplzJgxrFmzplb3yMvLo7i4mLCwsMYK02nKkrY+Hj6E+4XX/QZHd8Lyf53csMCf55kLfvWYBMEnk8C/LzHLJzRE1mFY/bzZtnrCn14ET+/632/ADeZMWoDf3oeMPWb7sBYhExERkdrxdHYAIiIiIiKu6ujRo5SWlhIVFVVpf1RUFNu2VbMIVTXuueceWrVqVSnx+0eFhYUUFhbat7OysgCw2WzYbLZ6RF43NpsNwzDq1JdhGBzKOQRAq4BWGIaBYRi179SwYfnfdCwnyxUYA2/GaN0PbDYzsTpoOtZv7jOP/fAsxkXza3/vP7B89wiW4jzzXv2uwwhrZ/ZTX14BMOhWrEmPglGKseppjAufg0O/2mfN2KJ7NKyPBqjPeErzpjF1LxpP96LxdC+OGM/aXqukrYiIiIiIkzz22GO89957JCUl4evrW+N5c+bMYfbs2VX2HzlyhIKCgsYMETDfXGRmZmIYBlZr7T6sl1GYQUGpGVu4d3iNNX5r4r/pbYL3m/VqS4LjONb9RowK97DEjSfC5wmshSdg44cc7XETtnosduZ5dCstk98FwOYdxJEu11Tqp74sCRcTsfp5rEU5sOG/HO16DcH7fqZslI95xVLqgH7qoz7jKc2bxtS9aDzdi8bTvThiPLOzs2t1npK2IiIiIiL1FB4ejoeHB2lpaZX2p6WlER0dfcpr586dy2OPPca3335Lz56n/qj8rFmzmDlzpn07KyuLuLg4IiIiCA4Orv8LqCWbzYbFYiEiIqLWb1AOHzlsb7cLa0dkZGTtOzyRgmXt0/ZN65/nEdE6vspploE3wcrHsRilROx4D2P847XvA8AwsHx9IxZOzgAe8X9EtO1ct3vUKBLLwFtg1ZNYbMVEbH8bjv9uduvpR8uOA8Hq4aC+6qY+4ynNm8bUvWg83YvG0704YjxP9Yf6ipS0FRERERGpJ29vb/r27cuyZcu46KKLAOyLis2YMaPG65544gn+9a9/8fXXX9OvX7/T9uPj44OPj0+V/VartcneAFosljr1dyj3kL0dGxRb+zgNAz6/HYpzze1+12JtP7L6cwfeBGtegOI8LOv/g2XkPRBQh9q5v38Ne1aY7RZtsQ66GRz57zn4Vlj7MhTlYPn1bSgtAsAS1Q2Lp5fj+qmHuo6nNH8aU/ei8XQvGk/30tDxrO11+mkREREREWmAmTNn8tprr/HWW2+xdetWbrnlFnJzc5k2bRoAU6ZMYdasWfbzH3/8cR544AEWLFhAfHw8qamppKamkpOT46yX0CjKFiEDaF2XsgW//gd2J5nt4FgYU7UshF1AS+gz1WyX5MPaV2rfT2kJfPNA+faYh8GzamK8QfzDzEXJwJ6wBSBGi5CJiIjIqSlpKyIiIiLSAJMnT2bu3Lk8+OCD9O7dm+TkZL766iv74mQpKSkcPlxeKuDll1+mqKiISy+9lJiYGPvX3LlznfUSGkWlpG1gLZO2WYfg6/vLty98FnxPU/5h8HRzYTKAn16FwtrViWP9Qji63WzHDoBuF9fuuroaPAO8/Cvvi1bSVkRERE5N5RFERERERBpoxowZNZZDSEpKqrS9d+/exg/IifZn5HHXog3s99linyJSq6StYcDnd0Jhprnd60rocO7pr2sRBz0ugw3vQsEJ+OUtGFJzaQoACrJg+Zzy7XH/Aovl9H3VR0A49LsW1rxYvk8zbUVEROQ0NNNWREREREQcZvZnW/hpbwZZJenmDpsv85YdYs/R3FNfuPFD+P0rsx0QaSZSa2vo7eXtNS9CSeGpz//+Gcg7ara7XQxxA2rfV30M+St4nlx0xOIBkd0atz8RERFxeUraioiIiIiIQ2w5lMW3W9OAUixeJwAoLQrl1ZW7GT03iate/5EvNx6muNRW+cKcI7Dk7vLtC54268HWVmRn6HS+2c4+DL8tqvncE/vhx5fMtoe3Wcu2sQVFwTkPgoePuTiZV+1WjRYREZEzl8ojiIiIiIiIQ8xL2gmAxSsTi+VkYra4pf34DzuP8cPOY4QH+jC5fyyX929DXJg/LPk/yM8wT+p2MXS5sO6dD7sTtn9xsqPnoPeVYPWoet6yf0BJgdkeeBOExte9r/oYPB0G3gJaOVxERERqQU8MIiIiIiLSYLuO5PDlRnPBtdDg8sXArujTi/vO60x8y/LFuI7mFDJv+S5GPLmcF+Y9BZs/Ng/4hcGEJ+sXQFx/aDvMbB/bAdu+qHrOwV9g46Lyvob/rX591ZcStiIiIlJLemoQEREREZEGe2n5LgzDbA/rUj7DtX1oG24c0Z7v7hrFO9cP5Lwe0XhazUW/WpPOVelPl99kwhMQGFH/IIbdWd7+/hnsAYHZ/vr+8u1R94Jfi/r3JSIiItKIVB5BREREREQaZH9GHp8kHwQgxM+LtlEFcMw8FhsUC4DVamFoYjhDE8NJzy7gk7W/c/YPswgzcgD41ujHiC6X4N2QQBLPgagekLYRDq2HPSuh3Ujz2LbPIWW12W6ZCP2ubUhPIiIiIo1KM21FRERERKRB5q/YRanNnNU6bWg8RwoO24+1Dmxd5fzIAG9uPPYkicY+AHbZYriz8GY2Hc5qWCAWCwy7o3z7+2fM7yVFsPTB8v3n/gM8vBrWl4iIiEgjUtJWRERERETqLTWzgA/WHQAgwNuDa4bEczD7oP14q8BWVS9aNRe2fgpAkWcgNxTfRTb+/Lwno+EBdb2ofHGx3cvh0K+w7g3I2G3uazsMOp3X8H5EREREGpFTk7YrV67kwgsvpFWrVlgsFj755JPTXpOUlESfPn3w8fEhMTGRhQsXNnqcIiIiIiJSvddW7aao1AbAXwbH08Lfm4M5ZtK2pW9L/Dz9Kl+w7QtY/q+TGxaOjJ3HbsNM7P6893jDA/LwhCF/Ld9e9giseLx8e+wj5oxcERERkWbMqUnb3NxcevXqxbx582p1/p49ezj//PMZPXo0ycnJ3HHHHVx//fV8/fXXjRypiIiIiIj80bGcQt5Za5Y48PWycv3wBApKCjiSfwSA1kF/KI2QtgU+urF8e8xDtOr/Z0L9zVIF6/ZlYLMZNFjvqyAg0mzvWgb5J5PBPSdD6z4Nv7+IiIhII3PqQmQTJkxgwoQJtT5//vz5JCQk8NRTTwHQpUsXvv/+e5555hnGjRvXWGGKiIiIiEg13vh+DwXF5izby/u3ITzQh92Zu+3HK9WzzcuA966AInPhMbpPhKF3YLFY6BcfxtItaZzIK2bnkRw6RgU1LDAvXxh0CyybXb7P0xfOebDma0RERESaEZeqabtmzRrGjBlTad+4ceNYs2aNkyISERERETkzZeYV8+815ixbLw8LN41sB1Cpnm1sYKzZKC2BD6fB8b3mdkwv+NOL9jIFA+LD7Nf85Ii6tgD9rwOf4PLtwdMhJNYx9xYRERFpZE6daVtXqampREVFVdoXFRVFVlYW+fn5+Pn5VbmmsLCQwsJC+3ZWlrkirc1mw2azNW7AJ9lsNgzDaLL+pHFpPN2LxtO9aDzdi8bT/ThiTPXz0Hy8tWYvOYUlAFzaN5aYEPNZvKyeLVSYabv0QdidZLYDImDyO+Dtbz+vX3yovf3z3gyuHtS24QH6hsDIu+Gb+6FlIgy7s+H3FBEREWkiLpW0rY85c+Ywe/bsKvuPHDlCQUFBk8Rgs9nIzMzEMAysVpea3CzV0Hi6F42ne9F4uheNp/txxJhmZ2c7OCqpj9zCEhb8sAcAD6uFW0YmAlBQUsD729+3n9cmuA0kvws/nlzDwuoFl/0HWsRVul/31iH4eXmQX1zKz46aaQsw5DboMA6CosGngSUXRERERJqQSyVto6OjSUtLq7QvLS2N4ODgamfZAsyaNYuZM2fat7OysoiLiyMiIoLg4OBqr3E0m82GxWIhIiJCbzrdgMbTvWg83YvG071oPN2PI8bU19fXwVFJfbyzdh8n8ooB+HOvVrRpac6affLnJ9l5YicAiS0SOavIBp/dUX7heU9C28FV7uflYeWsNi1YvesYhzILOHA8j9hQ/yrn1UtER8fcR0RERKQJuVTSdvDgwXz55ZeV9i1dupTBg6s++JXx8fHBx8enyn6r1dqkbwAtFkuT9ymNR+PpXjSe7kXj6V40nu6noWOqnwXnKygu5dWV5ixbiwVuHd0egG/3fcui3xcB4Ovhy9y+9+D53hQoPVmqrN910G9ajfftHx/G6l3HALNEgsOStiIiIiIuyKlPvTk5OSQnJ5OcnAzAnj17SE5OJiUlBTBnyU6ZMsV+/s0338zu3bu5++672bZtGy+99BKLFi3izjtVn0pEREREpCksWrefozlmInZC92gSI4M4nHOYB1c/aD/nnr4zab/k75CTau5oOxTGP3bK+w5IqLgY2XHHBy4iIiLiQpyatF23bh1nnXUWZ511FgAzZ87krLPO4sEHzQe+w4cP2xO4AAkJCXzxxRcsXbqUXr168dRTT/H6668zbtw4p8QvIiIiInImKSqxMT9pl3371lGJlNhKuGfVPWQXmfWGx7Ydy8StK+DgOvOkkDiY9BZ4ep/y3me1aYGn1QLAur0OrGsrIiIi4oKcWh5h1KhRGIZR4/GFCxdWe82vv/7aiFGJiIiIiEh1Pk4+yKFMczHfsztH0r11CC/++iK/ppvP560CWvFQyFlYkqabF3j6weXvQGDEae/t7+1Jt9YhbNh/gh3pORzPLSI04NSJXhERERF3paJgIiIiIiJyWiU2g/krdtu3p49O5OfUn3n1t1cB8LB48PiIxwne8F75RX9+EWJ61bqPAfGh9vbPmm0rIiIiZzAlbUVERERE5LSW/X6cfcfyABjSviXtouDelfdiYH5ybnrv6fT2iYC935sXtOwA3SfWqY9+8eV1bZW0FRERkTOZkrYiIiIiInJKNpvBWz8ftm9PH92eB394kPT8dAAGRg/k2u7XwuaP4GQSlx6TwGKpUz/9KyRtf9qrxchERETkzKWkrYiIiIiInNLSrWnsPmbWsu3TpgV7i74h6UASAKE+oTw6/FE8rB6w8YPyi3pcWud+wgK8SYwMBGDzwUzyikoaHLuIiIiIK1LSVkREREREamQYBvOW77JvXzzQwlO/PGXf/uewfxLpHwlHfofDG8ydrc6Clu3r1V/ZbNsSm8GvKSfqHbeIiIiIK1PSVkREREREarTi9yNsOpQFQJdWPixKmUOxrRiAq7tczYjYEeaJmz4sv6jHpHr3NyChfDGyn/aorq2IiIicmZS0FRERERGRGrUM8GFYYksAohKWsDdrLwBdwrpwZ987zZMMAzaWJW0t0O2SevfXX4uRiYiIiChpKyIiIiIiNesRG8K/rx3AjPHp/JLxDQB+nn48MeIJvD28zZMO/QoZJ0soJAyH4Jh69xcb6k+rEF8Afk05QXGprUHxi4iIiLgiJW1FREREROSUDmQf4IMD8+3b9w+6n/iQ+PITNjqmNEKZ/gnmbNv84lI2nyzNICIiInImUdJWRERERERqVGwr5p5V95BXmgfA+e3O58J2F5afYCuFTYvNttULulxYzV3qplKJBNW1FRERkTOQkrYiIiIiIlKjt7e8zaZjmwCIC4rjgUEPYLFYyk/Y+z3kpJrtDmPBL7Sau9RNxaTtT6prKyIiImcgJW1FRERERKRGkztN5pLES/C0ePL48McJ8AqofMLGD8rbPS51SJ8dIgMJ8fMCYN3eDGw2wyH3FREREXEVStqKiIiIiEiN/L38eWjwQ7w29DW6texW+WBJIWz91Gx7B0LH8Q7p02q10D/enLF7PK+YXUdyHHJfEREREVehpK2IiIiIiJxWbEBs1Z07v4WCTLPd+QLw9ndYfyqRICIiImcyJW1FRERERKR+KpVGmOTQW/dP0GJkIiIicuZS0lZEREREROquMBu2LzHb/i2h3UiH3r57qxB8vcy3Kz/vPe7Qe4uIiIg0d0raioiIiIhI3W37AkoKzHa3i8HDy6G39/a0clacWdf24Il8Dp7Id+j9RURERJozJW1FRERERKTuGrE0QpmKJRLWqa6tiIiInEGUtBURERERkbrJPQq7lpvtkDYQO6BRuukfH2pv/6S6tiIiInIGUdJWRERERETqZvPHYJSa7R4Twdo4byv6tAnFw2oB4GfNtBUREZEziJK2IiIiIiJSNxs/LG83UmkEgAAfT7q1Cgbg97QcjucWNVpfIiIiIs2JkrYiIiIiIg00b9484uPj8fX1ZeDAgfz00081nrt582YmTpxIfHw8FouFZ599tukCdYTj+2D/j2Y7ogtEdWvU7vrHV6hru+94o/YlIiIi0lwoaSsiIiIi0gDvv/8+M2fO5KGHHmL9+vX06tWLcePGkZ6eXu35eXl5tGvXjscee4zo6OgmjtYBNi0ub/e4tNG7q5i0VYkEEREROVMoaSsiIiIi0gBPP/00N9xwA9OmTaNr167Mnz8ff39/FixYUO35/fv358knn+Tyyy/Hx8eniaN1gEqlEZoiaavFyEREROTMo6StiIiIiEg9FRUV8csvvzBmzBj7PqvVypgxY1izZo0TI2sk6VsgfbPZjh0AofGN3mXLQB/aRwQAsOlgJnlFJY3ep4iIiIizeTo7ABERERERV3X06FFKS0uJioqqtD8qKopt27Y5rJ/CwkIKCwvt21lZWQDYbDZsNpvD+qmJzWbDMAzYVD7L1tZ9IjRB32CWSNh1JJcSm8H6fccZ0r5lk/TrrsrGsyl+dqRpaEzdi8bTvWg83YsjxrO21yppKyIiIiLSzM2ZM4fZs2dX2X/kyBEKCgoavX+bzUbmiRNE/LYIK2BYPDgaOQxbDXV7Ha1TmIe9nbR5P4lBpU3Sr7uy2WxkZmZiGAZWqz586Q40pu5F4+leNJ7uxRHjmZ2dXavzlLQVEREREamn8PBwPDw8SEtLq7Q/LS3NoYuMzZo1i5kzZ9q3s7KyiIuLIyIiguDgYIf1UxObzYZ3WjKe2QfNHQkjCY/v2uj9ljnbM5DZX+8FYOvRIiIjI5usb3dks9mwWCxEREQogeAmNKbuRePpXjSe7sUR4+nr61ur85S0FRERERGpJ29vb/r27cuyZcu46KKLAPNhftmyZcyYMcNh/fj4+FS7aJnVam2yN4B+u76wty09J2FpwjeebVoGEB3sS2pWAb+mnMBmgKeH3vg2hMViadKfH2l8GlP3ovF0LxpP99LQ8aztdfppERERERFpgJkzZ/Laa6/x1ltvsXXrVm655RZyc3OZNm0aAFOmTGHWrFn284uKikhOTiY5OZmioiIOHjxIcnIyO3fudNZLOD1bCb67lphtDx/ofEGTdm+xWOifEAZAXlEpmw9lNWn/IiIiIk1NM21FRERERBpg8uTJHDlyhAcffJDU1FR69+7NV199ZV+cLCUlpdKMikOHDnHWWWfZt+fOncvcuXMZOXIkSUlJTR1+7exZiUf+MbPdaTz4Nn5Jhj8aEB/KZxsOAfDz3gx6xbVo8hhEREREmoqStiIiIiIiDTRjxowayyH8MREbHx+PYRhNEJXjWDZ9WL7RY5JTYiibaQvw054Mrh/ezilxiIiIiDQFlUcQEREREZGaFefD1s8BMHyCIfFcp4TRMTKIED8vANbtO+5yiW8RERGRulDSVkREREREavb711iKss12lwvBq3YrHjua1WqhX9tQADJyi9h1JMcpcYiIiIg0BSVtRURERESkZm2HYBs3h6LIXhjdL3VqKBVLJKzdk+HESEREREQal5K2IiIiIiJSs8BIGHgzGZcsgnajnBrKwApJ26TtR5wYiYiIiEjjUtJWRERERERcQq/YFoQHegOwascRCopLnRyRiIiISONQ0lZERERERFyC1WrhnM5RABQU21i966iTIxIRERFpHEraioiIiIiIyzinS6S9vXRLuhMjEREREWk8StqKiIiIiIjLGNYhHB9P823Md9vSsNkMJ0ckIiIi4nhK2oqIiIiIiMvw9/ZkaGI4AGlZhWw6lOnkiEREREQcT0lbERERERFxKWO6RNnb325ViQQRERFxP0raioiIiIiIS6lY1/bbLWlOjERERESkcShpKyIiIiIiLiUq2JeesSEAbDmcxcET+U6OSERERMSxlLQVERERERGXU7FEwndbNdtWRERE3IuStiIiIiIi4nIqlkhYqrq2IiIi4maUtBUREREREZfTNSaYViG+APy46xg5hSVOjkhERETEcZS0FRERERERl2OxWDjnZImEolIbq34/4uSIRERERBxHSVsREREREXFJY7qW17X9ViUSRERExI0oaSsiIiIiIi5pULswArw9AFi+PZ1Sm+HkiEREREQcQ0lbERERERFxST6eHozoGAFARm4Rv6Ycd3JEIiIiIo6hpK2IiIiIiLissrq2AEu3pjkxEhERERHHUdJWRERERERc1uhOEVgtZnuZ6tqKiIiIm1DSVkREREREXFbLQB/6tg0FYGd6DnuP5jo5IhEREZGGU9JWRERERERcWsUSCd+qRIKIiIi4ASVtRURERETEpY1R0lZERETcjJK2IiIiIiLi0tpHBBDf0h+An/ceJzOv2MkRiYiIiDSMkrYiIiIiIuLSLBaLfbZtqc0g6XctSCYiIiKuTUlbERERERFxeRXr2i7dohIJIiIi4tqUtBUREREREZfXLz6UED8vAFb8foSiEpuTIxIRERGpPyVtRURERETE5Xl5WBndKQKA7IISft6b4eSIREREROpPSVsREREREXELFUskfLtVJRJERETEdSlpKyIiIiIibmFkpwg8rRbATNoahuHkiERERETqR0lbERERERFxC8G+XgxsFwbA/ox8dqTnODkiERERkfpR0lZERERERNzGmAolEpZuUYkEERERcU1K2oqIiIiIiNuomLRdprq2IiIi4qI8nR2AiIiIiIiIo8SF+dMpKojtadn8uv8ER7ILiQjyqdW1Ww5l8fPeDGx1qIUbHx7AyA4RWE/W0nUEwzD4ee9xNh/KrNN13VuH0K9tKBaL42IRERER51DSVkRERERE3Mo5XSLZnpaNYcDybelc1j+uxnMNw2DN7mO8nLSLVTuO1qu/dhEB3DSiHRed1RofT4/6hk2pzWDpllReTtrFhgN1S9iW6RXXgltGtmNs12iHJpJFRESkaSlp28j+vWYvSzamEh1goUtsLu0iAokPD6BNmD/enqpOISIiIiLiaGO6RvFS0i4Avt2aVm3S1mYz+KaBCdIyu4/kcs/ijTy99HeuG5bAFQPaEOTrVevrC0tK+Xj9QV5duZvdR3MbFMuG/Se4+e31tIsI4OYR7fnzWa0alEgWERER51DStpElp5xgze5jAHy8sfwv91YLxIb6Ex8eQELLk99PfrVu4YenhxK6IiIiIiL10Tu2BeGB3hzNKWLVjqMUFJfi62UmLgtLSvnk14O8snI3u49UTpDGhfkxdXB8rcspFJbY+Gj9AX7cnQFAWlYhj365jRe/28lfBrflmiEJp7xXdkEx765N4Y3v95CeXVjpWJeYYK4a2IYg39q9ZcsqKOGdH/exLTUbMBPJdy/+jaeWbue6YQlcObAtgT56+yciIuIq9H/tRnYkp7Da/TYDUjLySMnIY+Ufjnl5WGgfEUjHqCA6RQeZ36OCiA3100ecREREREROw2q1cHbnSBatO0B+cSlrdh2jf0IY767dxxvf7yEtq2qC9JZR7Tmve3SdJ09c1i+OX1OOM3/FLr7ebC58llVQwrzlu3h91R4m9YvlxuHtadPS337NkexCFq7ew7/X7CO7oKTS/Qa1C+OWUYmM6BBe59q0Vw9sQ9LvR5iftIu1e6omkqcMjueaofGE+dd+FrCIiIg4h5K2jew/1w0kI6eA9TsOkmXzZu+xPPYcy2PP0Rz2Hs0jp7CkyjXFpQbbUrPNv5JvKN/v5+VBh6hAexK3Y7T5PTzQmxKbQVGpjZJSg+JSG8WV2ub3EpvZLrUZlNgMSm3mOWXbNsOotF1qs1FqMxdhKFuKoWxNBgOz/ldFhgEGBjbDbNsMA8MwTrYrbGO2y84zMOwdlN3XMMraJ49X6Ls6VWKp3fDUyh8flQ0D8vLz8Pc7isVSta86rFtBTc/hjZWar+u/S02vxTjFnR6+sJtmiouIiIjTjekSxaJ1BwB4bMk2Dmfmk1VNgvTmke0Z2TGiQYt3ndUmlFf+0o+d6dm8smI3nyQfpLjUoLDExts/pvDu2hTO79mKy/rF8vXmVBatO0BRic1+vcUCY7tGcfPI9pzVJrTecVgsFkZ3imR0p0jWpxxnftIuvtlSnkh+cflOXlu1m0l9Y7m4azAREUaV52gxaTE3ERFxNiVtm0ALf2+6xwQQGRmJ1VqezDIMg6M5Rew5msveo7nsOZbLniO57DqSw56juZTYKj9A5ReX8tuBTH5rYM0tkcb04AXdnB2CiIhIk5s3bx5PPvkkqamp9OrVixdeeIEBAwbUeP4HH3zAAw88wN69e+nQoQOPP/445513XhNG7P6GdQjH29NKUYmN7WnZlY6N69bwBGl1EiODeHJSL2aO7cgbq/bw7k8p5BWVYjPgsw2H+GzDoUrne3lYuPis1tw4oj2JkYEOjaVPm1BenWImkuev2M0nvx6kxHYykbw2hbfXAmxyaJ/u4tZR7bl7fGdnhyEiImc4JW2dyGKxEBHkQ0SQDwMSwiodKyqxsedoLtvTstmRls321Gx+T8tmX0ZenWZyioiIiEjjev/995k5cybz589n4MCBPPvss4wbN47t27cTGRlZ5fzVq1dzxRVXMGfOHC644ALeffddLrroItavX0/37t2d8Arck7+3JyM6hPPt1nTATJBe1Ls1N41sR2JkUKP2HRPix/0XdGXG2Yn8Z80+3ly9l4zcIvvxAG8PrhzYhmuHJRAT4teosSRGBjF3Ui9mntuRN77fw39PJpJFRESkebMYZ9jnYbKysggJCSEzM5Pg4OAm6dNms5Genl5lpm195BeVsjM9h9/TzCTu9rRscgpK8PKw4ulhwfvkd08Pq9m2WvDytOJlNfd5eljwslrxsFrwtFqwnvxetu1x8hr7cYulykf4yz4qZKH84/2Wkx/ot1jMRdYslpPXAlbrH7YtFqwW8wYWzPtbKt7XUvHe5ccr9l3Wf+W4/rDtgCID1ZUBKLXZOHH8OKGhYfYaw3/sqzafpqpP6YGGMoxTlWSo/kBdPxnWNSbYpWovO/L3U5xP4+leNJ7uxxFj6oxnudMZOHAg/fv358UXXwTM1xkXF8dtt93GvffeW+X8yZMnk5uby+eff27fN2jQIHr37s38+fNr1WdT/zu46u/jzvQcnvhqGwnhAVwzNL7RE6Q1yS8qZdG6/Szblk7/tqH8ZXBbWvh7OyWWE3lF/Hv1XpZvPYzFQ3N4qnNJn1iuHtTW2WHUiav+jkr1NJ7uRePpXpryeVb/l3Yxft4e9IgNoUdsiLNDOWPZbDbSvYuIjAzRf3BFRETOcEVFRfzyyy/MmjXLvs9qtTJmzBjWrFlT7TVr1qxh5syZlfaNGzeOTz75pMZ+CgsLKSwsXzwrKysLMJ9LbDZbTZc5jM1mwzCMJunLkdqF+zP/6j72bWfF7+Np4S+D2vCXQW2cHkuwrye3jmrHpG5BRERE6Hm2Bq72s+6qv6NSPY2ne9F4uhdHjGdtr20WSdu61ABbuHAh06ZNq7TPx8eHgoKCpghVRERERMTu6NGjlJaWEhUVVWl/VFQU27Ztq/aa1NTUas9PTU2tsZ85c+Ywe/bsKvuPHDnSJM/BNpuNzMxMDMNQks8NaDzdj8bUvWg83YvG0704Yjyzs7NPfxLNIGlb1xpgAMHBwWzfvt2+rZU9RURERMSdzZo1q9Ls3KysLOLi4oiIiGiy8ggWi0UzM92ExtP9aEzdi8bTvWg83YsjxtPX17dW5zk9afv0009zww032GfPzp8/ny+++IIFCxZUWwMMzCRtdHR0U4YpIiIiIlJFeHg4Hh4epKWlVdqflpZW4/NqdHR0nc4H85NlPj4+VfZbrdYmewNosViatD9pXBpP96MxdS8aT/ei8XQvDR3P2l7n1J+WshpgY8aMse87XQ0wgJycHNq2bUtcXBx//vOf2bx5c1OEKyIiIiJSibe3N3379mXZsmX2fTabjWXLljF48OBqrxk8eHCl8wGWLl1a4/kiIiIicuZx6kzb+tQA69SpEwsWLKBnz55kZmYyd+5chgwZwubNm4mNja1yvrMXbSjrS0Wn3YfG071oPN2LxtO9aDzdT1Mu3NCUZs6cydSpU+nXrx8DBgzg2WefJTc31/5JsilTptC6dWvmzJkDwO23387IkSN56qmnOP/883nvvfdYt24dr776qjNfhoiIiIg0I04vj1BXgwcPrjQLYciQIXTp0oVXXnmFRx55pMr5zl60AVR02t1oPN2LxtO9aDzdi8bT/TTlwg1NafLkyRw5coQHH3yQ1NRUevfuzVdffWWfmJCSklLp9Q4ZMoR3332X+++/n/vuu48OHTrwySef0L17d2e9BBERERFpZpyatK1PDbA/8vLy4qyzzmLnzp3VHnf2og2gotPuRuPpXjSe7kXj6V40nu6nKRduaGozZsxgxowZ1R5LSkqqsm/SpElMmjSpkaMSEREREVfl1KRtxRpgF110EVBeA6ymh94/Ki0tZePGjZx33nnVHm8OizaAik67G42ne9F4uheNp3vReLqfplq4QURERETElTm9PEJda4D94x//YNCgQSQmJnLixAmefPJJ9u3bx/XXX+/MlyEiIiIiIiIiIiLiEE5P2ta1Btjx48e54YYbSE1NJTQ0lL59+7J69Wq6du3qrJcgIiIiIiIiIiIi4jBOT9pC3WqAPfPMMzzzzDNNEJWIiIiIiIiIiIhI01NRMBEREREREREREZFmRElbERERERERERERkWZESVsRERERERERERGRZkRJWxEREREREREREZFmpFksRNaUDMMAICsrq8n6tNlsZGdn4+vri9WqPLmr03i6F42ne9F4uheNp/txxJiWPcOVPdOdqZr6mVa/j+5F4+l+NKbuRePpXjSe7qUpn2fPuKRtdnY2AHFxcU6ORERERETqKzs7m5CQEGeH4TR6phURERFxbad7nrUYZ9g0BZvNxqFDhwgKCsJisTRJn1lZWcTFxbF//36Cg4ObpE9pPBpP96LxdC8aT/ei8XQ/jhhTwzDIzs6mVatWZ/RslaZ+ptXvo3vReLofjal70Xi6F42ne2nK59kzbqat1WolNjbWKX0HBwfrF9SNaDzdi8bTvWg83YvG0/00dEzP5Bm2ZZz1TKvfR/ei8XQ/GlP3ovF0LxpP99IUz7Nn7vQEERERERERERERkWZISVsRERERERERERGRZkRJ2ybg4+PDQw89hI+Pj7NDEQfQeLoXjad70Xi6F42n+9GYui6NnXvReLofjal70Xi6F42ne2nK8TzjFiITERERERERERERac4001ZERERERERERESkGVHSVkRERERERERERKQZUdJWREREREREREREpBlR0raRzZs3j/j4eHx9fRk4cCA//fSTs0OSWlq5ciUXXnghrVq1wmKx8Mknn1Q6bhgGDz74IDExMfj5+TFmzBh27NjhnGDllObMmUP//v0JCgoiMjKSiy66iO3bt1c6p6CggOnTp9OyZUsCAwOZOHEiaWlpTopYTufll1+mZ8+eBAcHExwczODBg1myZIn9uMbTdT322GNYLBbuuOMO+z6Np2t5+OGHsVgslb46d+5sP67xdE16pnVNep51L3qmdS96nnVveqZ1bc3leVZJ20b0/vvvM3PmTB566CHWr19Pr169GDduHOnp6c4OTWohNzeXXr16MW/evGqPP/HEEzz//PPMnz+ftWvXEhAQwLhx4ygoKGjiSOV0VqxYwfTp0/nxxx9ZunQpxcXFjB07ltzcXPs5d955J5999hkffPABK1as4NChQ1xyySVOjFpOJTY2lscee4xffvmFdevWcfbZZ/PnP/+ZzZs3AxpPV/Xzzz/zyiuv0LNnz0r7NZ6up1u3bhw+fNj+9f3339uPaTxdj55pXZeeZ92Lnmndi55n3Zeead1Ds3ieNaTRDBgwwJg+fbp9u7S01GjVqpUxZ84cJ0Yl9QEYH3/8sX3bZrMZ0dHRxpNPPmnfd+LECcPHx8f473//64QIpS7S09MNwFixYoVhGObYeXl5GR988IH9nK1btxqAsWbNGmeFKXUUGhpqvP766xpPF5WdnW106NDBWLp0qTFy5Ejj9ttvNwxDv5+u6KGHHjJ69epV7TGNp2vSM6170POs+9EzrfvR86zr0zOte2guz7OaadtIioqK+OWXXxgzZox9n9VqZcyYMaxZs8aJkYkj7Nmzh9TU1ErjGxISwsCBAzW+LiAzMxOAsLAwAH755ReKi4srjWfnzp1p06aNxtMFlJaW8t5775Gbm8vgwYM1ni5q+vTpnH/++ZXGDfT76ap27NhBq1ataNeuHVdddRUpKSmAxtMV6ZnWfel51vXpmdZ96HnWfeiZ1n00h+dZT4feTeyOHj1KaWkpUVFRlfZHRUWxbds2J0UljpKamgpQ7fiWHZPmyWazcccddzB06FC6d+8OmOPp7e1NixYtKp2r8WzeNm7cyODBgykoKCAwMJCPP/6Yrl27kpycrPF0Me+99x7r16/n559/rnJMv5+uZ+DAgSxcuJBOnTpx+PBhZs+ezfDhw9m0aZPG0wXpmdZ96XnWtemZ1j3oeda96JnWfTSX51klbUXkjDJ9+nQ2bdpUqR6NuKZOnTqRnJxMZmYmH374IVOnTmXFihXODkvqaP/+/dx+++0sXboUX19fZ4cjDjBhwgR7u2fPngwcOJC2bduyaNEi/Pz8nBiZiIj70DOte9DzrPvQM617aS7PsyqP0EjCw8Px8PCosnpcWloa0dHRTopKHKVsDDW+rmXGjBl8/vnnLF++nNjYWPv+6OhoioqKOHHiRKXzNZ7Nm7e3N4mJifTt25c5c+bQq1cvnnvuOY2ni/nll19IT0+nT58+eHp64unpyYoVK3j++efx9PQkKipK4+niWrRoQceOHdm5c6d+P12Qnmndl55nXZeead2Hnmfdh55p3ZuznmeVtG0k3t7e9O3bl2XLltn32Ww2li1bxuDBg50YmThCQkIC0dHRlcY3KyuLtWvXanybIcMwmDFjBh9//DHfffcdCQkJlY737dsXLy+vSuO5fft2UlJSNJ4uxGazUVhYqPF0Meeccw4bN24kOTnZ/tWvXz+uuuoqe1vj6dpycnLYtWsXMTEx+v10QXqmdV96nnU9eqZ1f3qedV16pnVvznqeVXmERjRz5kymTp1Kv379GDBgAM8++yy5ublMmzbN2aFJLeTk5LBz50779p49e0hOTiYsLIw2bdpwxx138M9//pMOHTqQkJDAAw88QKtWrbjoooucF7RUa/r06bz77rv873//IygoyF5nJiQkBD8/P0JCQrjuuuuYOXMmYWFhBAcHc9tttzF48GAGDRrk5OilOrNmzWLChAm0adOG7Oxs3n33XZKSkvj66681ni4mKCjIXouvTEBAAC1btrTv13i6lr/97W9ceOGFtG3blkOHDvHQQw/h4eHBFVdcod9PF6VnWtel51n3omda96LnWfeiZ1r30myeZw1pVC+88ILRpk0bw9vb2xgwYIDx448/OjskqaXly5cbQJWvqVOnGoZhGDabzXjggQeMqKgow8fHxzjnnHOM7du3OzdoqVZ14wgYb775pv2c/Px849ZbbzVCQ0MNf39/4+KLLzYOHz7svKDllK699lqjbdu2hre3txEREWGcc845xjfffGM/rvF0bSNHjjRuv/12+7bG07VMnjzZiImJMby9vY3WrVsbkydPNnbu3Gk/rvF0TXqmdU16nnUveqZ1L3qedX96pnVdzeV51mIYhuHYNLCIiIiIiIiIiIiI1Jdq2oqIiIiIiIiIiIg0I0raioiIiIiIiIiIiDQjStqKiIiIiIiIiIiINCNK2oqIiIiIiIiIiIg0I0raioiIiIiIiIiIiDQjStqKiIiIiIiIiIiINCNK2oqIiIiIiIiIiIg0I0raioiIiIiIiIiIiDQjStqKiAgAFouFTz75xNlhiIiIiIjUi55nRcSdKGkrItIMXHPNNVgslipf48ePd3ZoIiIiIiKnpedZERHH8nR2ACIiYho/fjxvvvlmpX0+Pj5OikZEREREpG70PCsi4jiaaSsi0kz4+PgQHR1d6Ss0NBQwP+r18ssvM2HCBPz8/GjXrh0ffvhhpes3btzI2WefjZ+fHy1btuTGG28kJyen0jkLFiygW7du+Pj4EBMTw4wZMyodP3r0KBdffDH+/v506NCBTz/9tHFftIiIiIi4DT3Piog4jpK2IiIu4oEHHmDixIls2LCBq666issvv5ytW7cCkJuby7hx4wgNDeXnn3/mgw8+4Ntvv630EPvyyy8zffp0brzxRjZu3Minn35KYmJipT5mz57NZZddxm+//cZ5553HVVddRUZGRpO+ThERERFxT3qeFRGpPYthGIazgxAROdNdc801vP322/j6+lbaf99993HfffdhsVi4+eabefnll+3HBg0aRJ8+fXjppZd47bXXuOeee9i/fz8BAQEAfPnll1x44YUcOnSIqKgoWrduzbRp0/jnP/9ZbQwWi4X777+fRx55BDAfnAMDA1myZIlqkYmIiIjIKel5VkTEsVTTVkSkmRg9enSlh1iAsLAwe3vw4MGVjg0ePJjk5GQAtm7dSq9evewPuABDhw7FZrOxfft2LBYLhw4d4pxzzjllDD179rS3AwICCA4OJj09vb4vSURERETOIHqeFRFxHCVtRUSaiYCAgCof73IUPz+/Wp3n5eVVadtisWCz2RojJBERERFxM3qeFRFxHNW0FRFxET/++GOV7S5dugDQpUsXNmzYQG5urv34Dz/8gNVqpVOnTgQFBREfH8+yZcuaNGYRERERkTJ6nhURqT3NtBURaSYKCwtJTU2ttM/T05Pw8HAAPvjgA/r168ewYcN45513+Omnn3jjjTcAuOqqq3jooYeYOnUqDz/8MEeOHOG2227jL3/5C1FRUQA8/PDD3HzzzURGRjJhwgSys7P54YcfuO2225r2hYqIiIiIW9LzrIiI4yhpKyLSTHz11VfExMRU2tepUye2bdsGmCvhvvfee9x6663ExMTw3//+l65duwLg7+/P119/ze23307//v3x9/dn4sSJPP300/Z7TZ06lYKCAp555hn+9re/ER4ezqWXXtp0L1BERERE3JqeZ0VEHMdiGIbh7CBEROTULBYLH3/8MRdddJGzQxERERERqTM9z4qI1I1q2oqIiIiIiIiIiIg0I0raioiIiIiIiIiIiDQjKo8gIiIiIiIiIiIi0oxopq2IiIiIiIiIiIhIM6KkrYiIiIiIiIiIiEgzoqStiIiIiIiIiIiISDOipK2IiIiIiIiIiIhIM6KkrYiIiIiIiIiIiEgzoqStiIiIiIiIiIiISDOipK2IiIiIiIiIiIhIM6KkrYiIiIiIiIiIiEgzoqStiIiIiIiIiIiISDPy/15m7/PsFBIYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparison plot - All models on same axes\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "for name, hist in histories.items():\n",
    "    axes[0].plot(hist['val_loss'], label=name, color=colors[name], linewidth=2)\n",
    "    axes[1].plot(hist['val_accuracy'], label=name, color=colors[name], linewidth=2)\n",
    "\n",
    "axes[0].set_title('Validation Loss Comparison', fontsize=14, fontweight='bold')\n",
    "axes[0].set_xlabel('Epoch')\n",
    "axes[0].set_ylabel('Validation Loss')\n",
    "axes[0].legend()\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "axes[1].set_title('Validation Accuracy Comparison', fontsize=14, fontweight='bold')\n",
    "axes[1].set_xlabel('Epoch')\n",
    "axes[1].set_ylabel('Validation Accuracy')\n",
    "axes[1].legend()\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('cnn_comparison_curves.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed1394c",
   "metadata": {
    "papermill": {
     "duration": 1.463681,
     "end_time": "2026-04-13T17:06:45.504090+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:06:44.040409+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "## Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0f798a78",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T17:06:48.356421Z",
     "iopub.status.busy": "2026-04-13T17:06:48.356102Z",
     "iopub.status.idle": "2026-04-13T17:06:48.971317Z",
     "shell.execute_reply": "2026-04-13T17:06:48.970362Z"
    },
    "papermill": {
     "duration": 2.130882,
     "end_time": "2026-04-13T17:06:48.973439+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:06:46.842557+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "\n",
    "def evaluate_model(model, model_name):\n",
    "    \"\"\"Evaluate model and return predictions.\"\"\"\n",
    "    y_true, y_pred = [], []\n",
    "    all_images = []\n",
    "    \n",
    "    for ann in anns_valid:\n",
    "        img = load_geoimage(ann.filename)\n",
    "        t = tf.image.convert_image_dtype(tf.convert_to_tensor(img), tf.float32)\n",
    "        t = tf.image.resize(t, [IMG_SIZE, IMG_SIZE], method='bilinear')\n",
    "        all_images.append(t.numpy())\n",
    "        y_true.append(ann.objects[0].category)\n",
    "    \n",
    "    X_valid = np.array(all_images)\n",
    "    predictions = model.predict(X_valid, batch_size=128, verbose=0)\n",
    "    \n",
    "    cat_names = list(categories.values())\n",
    "    y_pred = [cat_names[np.argmax(p)] for p in predictions]\n",
    "    \n",
    "    return y_true, y_pred, predictions\n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, title):\n",
    "    \"\"\"Plot normalized confusion matrix.\"\"\"\n",
    "    cat_names = list(categories.values())\n",
    "    cm = confusion_matrix(y_true, y_pred, labels=cat_names)\n",
    "    cm_norm = cm.astype('float') / np.maximum(cm.sum(axis=1)[:, np.newaxis], 1e-10)\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(12, 10))\n",
    "    sns.heatmap(cm_norm, annot=True, fmt='.2f', cmap='Blues',\n",
    "                xticklabels=cat_names, yticklabels=cat_names, ax=ax)\n",
    "    ax.set_xlabel('Predicted', fontsize=12)\n",
    "    ax.set_ylabel('Ground Truth', fontsize=12)\n",
    "    ax.set_title(title, fontsize=14, fontweight='bold')\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "    plt.yticks(rotation=0)\n",
    "    plt.tight_layout()\n",
    "    return cm\n",
    "\n",
    "def compute_per_class_metrics(cm):\n",
    "    \"\"\"Compute per-class recall, precision, specificity, and F1-score.\"\"\"\n",
    "    metrics = []\n",
    "    n_classes = cm.shape[0]\n",
    "    \n",
    "    for idx in range(n_classes):\n",
    "        tp = cm[idx, idx]\n",
    "        fp = np.sum(cm[:, idx]) - tp\n",
    "        fn = np.sum(cm[idx, :]) - tp\n",
    "        tn = np.sum(cm) - tp - fp - fn\n",
    "        \n",
    "        recall = tp / max(tp + fn, 1e-10)\n",
    "        precision = tp / max(tp + fp, 1e-10)\n",
    "        specificity = tn / max(tn + fp, 1e-10)\n",
    "        f1 = 2 * precision * recall / max(precision + recall, 1e-10)\n",
    "        \n",
    "        metrics.append({\n",
    "            'class': list(categories.values())[idx],\n",
    "            'recall': recall * 100,\n",
    "            'precision': precision * 100,\n",
    "            'specificity': specificity * 100,\n",
    "            'f1': f1 * 100\n",
    "        })\n",
    "    \n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f1e7ad1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T17:06:51.891859Z",
     "iopub.status.busy": "2026-04-13T17:06:51.891038Z",
     "iopub.status.idle": "2026-04-13T17:08:45.733435Z",
     "shell.execute_reply": "2026-04-13T17:08:45.732624Z"
    },
    "papermill": {
     "duration": 115.210663,
     "end_time": "2026-04-13T17:08:45.738218+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:06:50.527555+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "Evaluating ResNet_Style\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 18.67%\n",
      "Mean Recall: 9.80%\n",
      "Mean Precision: 5.93%\n",
      "\n",
      "============================================================\n",
      "Evaluating VGG_Style\n",
      "============================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-04-13 17:07:36.137672: E external/local_xla/xla/service/slow_operation_alarm.cc:73] Trying algorithm eng12{k11=2} for conv %cudnn-conv-bias-activation.40 = (f32[128,64,128,128]{3,2,1,0}, u8[0]{0}) custom-call(f32[128,64,128,128]{3,2,1,0} %bitcast.1226, f32[64,64,3,3]{3,2,1,0} %bitcast.1233, f32[64]{0} %bitcast.1235), window={size=3x3 pad=1_1x1_1}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBiasActivationForward\", metadata={op_type=\"Conv2D\" op_name=\"VGG_Style_1/conv2d_106_1/convolution\" source_file=\"/usr/local/lib/python3.12/dist-packages/tensorflow/python/framework/ops.py\" source_line=1200}, backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0},\"force_earliest_schedule\":false} is taking a while...\n",
      "2026-04-13 17:07:36.402712: E external/local_xla/xla/service/slow_operation_alarm.cc:140] The operation took 1.265203203s\n",
      "Trying algorithm eng12{k11=2} for conv %cudnn-conv-bias-activation.40 = (f32[128,64,128,128]{3,2,1,0}, u8[0]{0}) custom-call(f32[128,64,128,128]{3,2,1,0} %bitcast.1226, f32[64,64,3,3]{3,2,1,0} %bitcast.1233, f32[64]{0} %bitcast.1235), window={size=3x3 pad=1_1x1_1}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBiasActivationForward\", metadata={op_type=\"Conv2D\" op_name=\"VGG_Style_1/conv2d_106_1/convolution\" source_file=\"/usr/local/lib/python3.12/dist-packages/tensorflow/python/framework/ops.py\" source_line=1200}, backend_config={\"operation_queue_id\":\"0\",\"wait_on_operation_queues\":[],\"cudnn_conv_backend_config\":{\"conv_result_scale\":1,\"activation_mode\":\"kNone\",\"side_input_scale\":0,\"leakyrelu_alpha\":0},\"force_earliest_schedule\":false} is taking a while...\n"
     ]
    },
    {
     "data": {
      "image/png": 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T/S3pEc3Z132ZYyEnNTU1sdey/PKe/QseANFcJUBWzwRpj5EuVaoUDh8+jGfPnuHKlSu4d+8eoqKisG/fPnz48AHnzp3DvHnz8Oeff+b5/kXt88jJw8MDlpaWoi/gxYsXR58+ffLc5++//0ZcXJzo9YgRIzB27FgYGBjgw4cPKFGihNzy973Hev36NUaPHi227tWrVxgzZgyWLFkih5wVTPny5fH777/jyJEjAICgoCCxBid1dXV07txZbJ/ffvsNVlZWonlXjh07hhcvXqBcuXIAsuZQsbe3R0REBBYuXJjrPVVUVFC/fn0cPXoUABAaGoqwsDDY29sDyGqsGzlyJN6/f48JEybILVZ1dXW4urrC1dUVw4YNQ2BgILp37y7afv/+/QL1MsmuXr16sLW1xe3bt/HmzRusWbNGNB+Uuro6unTpUqDjZD8PNTU1MX36dKlpS5cu/U15JCKioo89V4iISGEGDRok9ou7n58fwsLCcqVLS0vD+vXrRRONurm5iba9fv1a9KUSyPpym/119rQ/Ss4hJVeuXBH9PXv2bKk9ciIiIpCWloaKFSuibdu2GD9+PIKCgsQaHm7evJnv+2eP8caNG2K9PSIiIsR6MBTG55GTQCAQ6x3QtWtXqY08XyQkJIi97tKli2jS0uy9CnLK2UiUcwiJPPXq1QsvXrwAAFhYWIga2QICAkSNDvkxNjaGMOvpjWLLt06S3LNnT9Hfjx49wrJly0SvW7ZsCX19/Vz7DB06VPR3SkoK2rZtm+tzz0v2/QGgY8eO3z25c14mTpyIQ4cOIT09Pde2kiVLir3Ofi5+S10YPHiw6O/x48eLhgS1bNky12S50mQ/tz5+/AgbGxuMHDky11KnTp0fNvkvEREpDnuuEBGRwtjY2GD69OkYP348AODFixeoUaMGPD094ejoCIFAgAcPHuDYsWN4+fIlGjduDADo0aMHpk+fLvoi2KZNG/Tq1QulSpVCSEiIaP4LgUCAYcOG/fA4rKysoK2tjXfv3gEABg4ciEOHDuHFixdS5zgBgJEjR+LatWto1KgRjIyMYGhoiOfPn2PTpk2iNAWZC8Tf3x+rVq1CamoqMjMzUa9ePbGnBX0Z0qOurg5/f3/Zgv1Ovr6++O233wBkPUknPznnfOnatSs6dOiA2NhYsblEcjI0NISamproy/GECRMQFhYGNTU11K9fHzVq1JAhiq9WrFiBQ4cOAcjqiXPo0CGsXbsWCxYsgFAohK+vL27fvg1DQ0O5vF9+WrVqBT09PdHQlOwNCTmHBH3Rt29fHDx4UNQYefHiRZiamsLLywsWFhZIT0/HtWvXpL5n06ZN0a9fP9GcMXfu3IG1tTVatWqFatWqQSAQICoqSubYLly4gJkzZ0JfXx/16tWDtbU1SpQogZiYGGzfvl2UrlSpUmINHDmHPvn7+8PDwwPFihVDq1atxOZE6dKlC8aMGYOkpCR8/PhRtD57o1V+WrRoAWtra9FTvry9vdG6dWtUrVoVmZmZePjwIf7++288fvwYmzZtgoODw7d+FEREVJQpbCpdIiKiz5YuXSrU0NCQ+jQQSHjax7lz5/J8goiKiopwwYIFYu+T19Nf8nrKTEGeQDNx4kSJ+ahRo4awTJkyEp+W4uHhkWe8mpqawmvXronSS3takFAoFO7cuVOoqakp9VgaGhrCbdu2ie2T/WlB9erVE9tWkCetSJLzaUH5yetpQU2bNpUYS86nHH15gs0XPj4+EvebP3++UCgsWHnmzFv2+hIRESH2WQcEBAiFQqHw48ePQhsbG9H6Fi1aFOgzk5eBAwfmirlcuXLCtLQ0qfv8999/wt69e+d77n05p/bt2ye2f3p6unDcuHFiT6TKa1m6dOk3x5W93ueVt8DAwFz7Ojo6Sky/a9euXGlHjhwplqZ8+fLC9PT0XOnyuo7cvXtXaGxsnG9+c9ZZIiL6+XFYEBERKdyQIUMQExODqVOnwt3dHYaGhihWrBiKFy8Oa2tr+Pn54ezZs6hcubJon7p16yIiIgIjRoyAjY0NihcvDnV1dVSqVAldunTBpUuXpE7G+SNMmzYNs2bNQpUqVaCmpobKlStj3LhxOHfuHLS0tCTuM2rUKAwdOhQuLi6oUKEC1NXVoaGhARMTE/To0QPXrl0r8PCBdu3aITQ0FAMGDICZmRk0NTWhqakJU1NT9O3bF7du3ULHjh3lGfIPt2fPHgwbNgzly5eHuro6zMzMMGvWLGzYsCHP/datW4cePXqgbNmyuebDkVVqaio6d+4s6t3QsGFDDBo0CEDWJMCBgYGi4Sh//fUXli9fLtf3z4ukXhZdunRBsWLSOyoXL14c69evx61btzBo0CDY29tDR0cHqqqqKFmyJCwtLdG6dWsEBATg8ePHueZEUlVVxaxZs3D37l2MHj0atWrVgp6eHlRVVVGiRAmYmJigRYsWmDt3Lu7evSs2PKygtm7divXr16Nz586wt7dH+fLloaamBi0tLZibm8PX1xfXr19H165dc+27d+9e+Pj4QE9PL995hPz9/cXqS/fu3fOcdFkSCwsLhIeHY968eXBzc4Ouri5UVVWhra0NOzs79OnTB/v27cs1Bw4REf38BEJhAR/NQERERET0i/r48SPKlSuHN2/eAMga5pRzeBoREZE0nHOFiIiIiJTWlStXkJycjK1bt4oaVho3bsyGFSIi+ibsuUJERERESsvY2BiPHz8WvVZXV8eVK1fg6OiowFwREdHPhj1XiIiIiKhQWVlZ5ZumVq1a2Lp1ayHkJou2tjYcHR0xY8YMNqwQEdE3Y+MKERERERWqu3fv5pumXLlyhZATIDY2tlDeh4iIfm1sXCEiIiKiQsVR6URE9Kvho5iJiIiIiIiIiGTAxhUiIiIiIiIiIhlwWBD9lLQaTFd0FhQi6cQkRWeB6IdLz1DO4QLFVAWKzgLRD5X8IU3RWVCIUlrK+e+2ioDXNGWSqaRD/Yqr/Rr1XMtxkKKzkK+UW8sVnYV8secKEREREREREZEM2LhCRERERERERCQDNq4QEREREREREclAOQeBEhEREREREREgYJ8LeeCnSEREREREREQkAzauEBERERERERHJgMOCiIiIiIiIiJQVH50uF+y5QkREREREREQkAzauEBERERERERHJgMOCiIiIiIiIiJQVnxYkF/wUiYiIiIiIiIhkwMYVIiIiIiIiIiIZcFgQERERERERkbLi04Lkgj1XiIiIiIiIiIhkwMYVIiIiIiIiIiIZcFgQERERERERkbLi04Lkgp8iEREREREREZEM2LhCRERERERERCQDDgsiIiIiIiIiUlZ8WpBcsOcKEREREREREZEM2LhCRERERERERCQDNq4QEREREREREcmAc64QERERERERKSs+ilku+CkSEREREREREcmAjSsk1dmzZyEQCJCcnKzorBAREREREREVWWxckZMXL15g8ODBMDExgYaGBoyMjNCyZUucOnVK0VmjfPT3roE72wYj6dg4/L2yF2pY/ZZn+kFtaiFsy0AkHh2L+zuGYN7AJtBQUxVtV1ERYHLP+ogOGYTEo2MRGeSPsd3q/OAovt32kGA0a9IQNR1t0aVjO9wOD88z/fFjR+Dl2RQ1HW3Rxrslzv99Tmy7UCjEimVL0aieO2pVt0O/3r54/Dj2B0bw7ZQxZkB54965PRieTRvCtYYdunduj4jbecd94vhRtG7VDK417NC+dUtcOP817rS0NAQsXoD2rVuidi1HeDSqg8njx+D1q5c/OoxvpqzlzbiVK+59O7ehQ6vf0aR2dQzw7YToyNt5pj9z8hi6tW2JJrWrw7ejD65c/Ftse72a1SQu2wI3/sgwvtmObcFo/ntDOFe3Q7dOBbiuHTsKn5bN4FzdDu18cpf3qRPH4de3F+rXdoZjNSvcvRP9I7P/3ZS1nitj3MpaxxVOICj6y0+AjStyEBsbCycnJ5w+fRrz58/H7du3cfToUTRo0AD+/v7ffdyMjAxkZmbKMaeUU9sGVTHXrwlmbvkbrv3WIfzhSxyc1xmGOsUlpu/QqBqm92uEWVv/hkOPVRgw/xDaNrDBtL4NRWlGdHJDXy8nDA84CoceqzBx7Wn80dEVA1vXLKyw8nX0yGEsmDcb/Qf6Y/uufbC0tIJf/95ISEiQmD701k2MHTUCPq3bYsfu/WjQsBGGDfbH/fv3RGk2bViHbcGBmDhlKoK27YSWlhb8+vVGampqYYWVJ2WMGVDeuI8fPYxF8+eg3wB/BO/YCwtLSwwa0AeJUuIOC72JCWNGwNunLUJ27kP9ho0xYuggPPgc98ePH3EnOgp9+g9E8I49WLBoGWJjYzB8yMDCDCtfylrejFu54j59/AhWLJmHHn38sC5wF0zNLTFycH8kJUqOOyLsFqZPHI3mXj5YF7QLdeo1xISRQ/DowX1Rmr1HzootYyZNh0AgQL0GTQorrHwdO3IYC+fNQX8/f4TsyrquDewv/boWeusmxo3Ouq5t25V1XftjyNfrGgCkpKTAoboThgwfWVhhfDNlrefKGLey1nH6dbBxRQ4GDhwIgUCAa9euoU2bNrCwsICNjQ3++OMPXLlyRZRu0aJFsLW1RYkSJWBkZISBAwfi/fv3ou2bN2+Gjo4ODh48iKpVq0JDQwNPnjxBXFwcWrRoAS0tLVSpUgUhISEwNjbGkiVLRPs+efIEXl5eKFmyJEqVKoX27dvj5Uvpv6jGxsZCIBBg+/btcHNzg6amJqpVq4Zz585J3SchIQGdOnVChQoVULx4cdja2mLbtm1iaerXr48hQ4Zg9OjR0NPTQ7ly5TB16lSxNMnJyejTpw8MDQ1RqlQpNGzYEGFhYQX8tOVrSDsXbPrrFgKPhuHO43gMXvQXUj6moUczB4npXWwq4nLEU+w4FYEnL9/g1D+PsPN0hFhvFxebijh08S6OXnmAJy/fYN/f0Tj1zyPUsKpQSFHlL3DLJrRu2x7ePm1gamaGiVP+hKamJvbv3SMxfXDQVri514Fvrz4wMTXFoCHDYF21KraHBAHI+iUkOHAr+vb3Q4OGjWFhaYUZs+fh9atXOH3qZGGGJpUyxgwob9xBWzfDp007tPJuAxNTM4yf9Cc0tTRxYL/kuLcFB8K1tju69+yNKiamGDhoKKysq2Ln9mAAgLa2Nlau3YjfPZrBuIoJbO0dMGb8JERHRSIu7nlhhpYnZS1vxq1cce8M2QpP77Zo3soHxiamGDFuMjQ1NXH44D6J6XdvD0It19ro1K0XjKuYorffYFhYVcW+XSGiNPoGBmLLxb/PwNGpFn6raFRYYeUraOtmtG7bDl4+bWBqaoYJkz+X9z4p17WgQLjVdkePXr1hYmoK/8FDP5d3sCiNZysv9Pfzh4ura2GF8c2UtZ4rY9zKWsfp18HGFRklJibi6NGj8Pf3R4kSJXJt19HREf2toqKCgIAAREZGYsuWLTh9+jRGjx4tlv7Dhw+YO3cu1q9fj8jISJQpUwbdu3fH8+fPcfbsWezZswdr167Fq1evRPtkZmbCy8sLiYmJOHfuHE6cOIFHjx6hQ4cO+eZ/1KhRGDFiBG7dugVXV1e0bNlSaov4x48f4eTkhL/++gsRERHo168funXrhmvXroml27JlC0qUKIGrV69i3rx5mDZtGk6cOCHa3q5dO7x69QpHjhzBjRs3UL16dTRq1AiJiYn55lee1IqpwNGiPE7fiBGtEwqB0zdjUMumosR9rkQ+g6NFeVFjinF5HXg4m+Po1QdiaRpUrwKzinoAAFvTsnCtZoTj1x5IPGZhS/v0CdFRkXBxdROtU1FRgYuLG8LDbkncJzw0FC4u4jclt9ruCA8NBQD8++wZ4uNfw9nl6zG1tbVha2cv9ZiFSRljBpQ47rRPuBMdiVou4nHXcnbF7bBQifuEh4XC2dlNbJ2rW22ES0kPAO/fv4NAIIC2dil5ZFtmSlvejFu0TiniTkvDvTtRcKrlIlqnoqICp1ouiLwt+YeayNthcKopHndNFzep6RMT4nH5wt9o7tVafhmXUVpaVnk757iuObu4Sr1OhYeFwtn1265rRY3S1nMljFtZ63iRIVAp+stPgI9iltGDBw8gFAphZWWVb9phw4aJ/jY2NsaMGTMwYMAArFy5UrQ+LS0NK1euhL29PQDgzp07OHnyJK5fv44aNWoAANavXw9zc3PRPqdOncLt27cRExMDI6OsX1i2bt0KGxsbXL9+HTVrSh+OMmjQILRp0wYAsGrVKhw9ehQbNmzI1egDABUqVMDIkV+71A0ePBjHjh3Dzp07UatWLdF6Ozs7TJkyBQBgbm6O5cuX49SpU2jSpAkuXLiAa9eu4dWrV9DQ0AAALFiwAPv378fu3bvRr1+/fD9HeTEoXRzFVFXwKum92PpXSf/BspKBxH12nIqAfmktnArwhUAAqBVTxdoD/2B+8EVRmgUhF1GquAbCtgxERmYmVFVUMGXDGWw/GfFD4ymopOQkZGRkQF9fX2y9vr4+YmIeSdwnPj4e+voGudLHJ8R/3v46a51B7mPGx8fLK+vfTRljBpQ37uQkaXEbIDYmRuI+CfHx0MuRXk/fAAlSYkpNTUXA4gXwaNYCJUuWlE/GZaSs5c24lSvuN5/j1tUTz6Ounj6exEo+vxMT4qGrnzO9ARITJMd09K+DKF6iOOo2aCyfTMtB0ufrWs7rVF7XtXgJ1zV9A+nXtaJIWeu5MsatrHWcfi1sXJGRUCgscNqTJ09i9uzZuHPnDt6+fYv09HR8/PgRHz58QPHiWXN8qKurw87OTrTP3bt3UaxYMVSvXl20zszMDLq6uqLX0dHRMDIyEjWsAEDVqlWho6OD6OjoPBtXXLN1kStWrBhq1KiB6GjJEz1lZGRg1qxZ2LlzJ/799198+vQJqamporx/kT3/AFC+fHlRT5uwsDC8f/8+180iJSUFDx8+lPi+qampucaCCjPTIVAp/Opbx74yRnVxx9Alh3E9+jlMK+hiwSAPxHV7jzmB5wEAbevboGPjavCdsQ9Rsa9hZ1YW8/1/R1zCOwQfy3tSLiIq+tLS0jB25DAIhcC4iVMVnR0ikrMjB/ehcVNP0Y9AREREBfFz9K8pwszNzSEQCHDnzp0808XGxsLT0xN2dnbYs2cPbty4gRUrVgAAPn36JEqnpaUFQRGdDXn+/PlYunQpxowZgzNnziA0NBQeHh5i+QcANTU1sdcCgUA0Me/79+9Rvnx5hIaGii13797FqFGjJL7v7NmzUbp0abEl/fHfEtN+i/g3H5CekYkyuuK/OpfRLYEXie8l7jOlV31sOx6OzYdDERnzCgcv3MXk9WcwqnNt0STWswY0woJtl7DrTCQiY15h24nbWLb7KkZ1ri1znuVBV0cXqqqquYZ/JSQkwMBAco8dAwMDJOT4hS8hIQEGn38hMTAwzFoXX/BjFiZljBlQ3rh1dKXFHS81j/oGBrkmzEtMiId+jvRpaWkYO2o44uKeY+XaDUWm1wqgvOXNuJUr7tKf4845eW1SYgL09CXnUU/fAEkJOdPHS0wfdusGnjyOgWcRGhIEALqfr2s5r1MJEq5TXxhIuK4lxEtPXxQpaz1XxriVtY4XGYp+EhCfFkQAoKenBw8PD6xYsQL//fdfru3JyckAgBs3biAzMxMLFy6Ei4sLLCws8Px5/pMgWlpaIj09HbdufR0L+eDBAyQlJYleW1tb4+nTp3j69KloXVRUFJKTk1G1atU8j599wt309HTcuHED1tbWEtNevHgRXl5e6Nq1K+zt7WFiYoJ79+5JTCtN9erV8eLFCxQrVgxmZmZii7QL+7hx4/DmzRuxpVjlut/0vpKkpWfi1r04NKhuLFonEAANqlfBtchnEvfR0lRDZo7OSl8ajr40imlpqCEzR6KMTCFUishFQU1dHdZVbXD1ymXRuszMTFy9ehl29o4S97FzcMDVbHUFAK5cvgQ7BwcAQIWKFWFgYIirV78e8/3797gdHib1mIVJGWMGlDhuNXVYWdvg+lXxuK9fvQJbeweJ+9jZO+BatvQAcPXKJdhlS/+lYeXp48dYtXYTdHR0UZQobXkzbtE6pYhbTQ0WVlVx4/pV0brMzEzcvH4VNrb2EvexsbXHjevicf9z9bLE9IcP7IWldVWYWeQ/3Lswqal9Lu8c17VrV6+IXaeys7N3wLUr4te1K5cvSU1fFCltPVfCuJW1jtOvhY0rcrBixQpkZGSgVq1a2LNnD+7fv4/o6GgEBASIht2YmZkhLS0Ny5Ytw6NHjxAYGIjVq1fne2wrKys0btwY/fr1w7Vr13Dr1i3069dPrIdL48aNYWtriy5duuDmzZu4du0aunfvjnr16onmackr7/v27cOdO3fg7++PpKQk9OrVS2Jac3NznDhxApcuXUJ0dDT69++f5xOJJGncuDFcXV3h7e2N48ePIzY2FpcuXcKECRPwzz//SNxHQ0MDpUqVElvkNSQoYNcV9PSsji4edrCsZICA4c1RXFMNW49mTXK3fpwXpvX5+pjlw5fuoW8rJ7RrYIPK5XTQ0KkKJveqj8OX74kaVA5fvo8xXd3R1MUMlcqWRit3Swxp54yDF+7KJc/y0K1HT+zdvRMH9+/Do4cPMWPaVKSkpMDbJ+uXugnjRmPp4oWi9F26dseli+exZfNGxDx6iFUrliEyIgIdO3cFkNWw1KVbd6xbswpnT5/C/Xt3MXHcaBiWKYOGjYrGmHVljBlQ3ri7dvfFvj278L8D+xDz6CFmz8iKu5V3VtyTx4/BsqVf4+7UpRsuXbqAwC0bERPzCGtWLkNUZCTad+wCIKthZcyIoYiOjMCMOfORkZmB+PjXiI9/jbS0TxLzoAjKWt6MW7nibt+5O/7avxtHDx1AbMxDLJozHSkpKWjW0hsAMHPKOKxdvliUvm3Hrrh2+SJ2BG3G49hH2LR2Be5GR8KnXWex4/73/j3OnjqOFl5tCjOcAuva3Rf7du/CwQNZ5T1relZ5e32+rk0cNwYB2cq7U9duuHTxArZu3oiYR4+wekXWda1j5y6iNG/eJOPunWjR0OzYmBjcvRMtmp+jKFDWeq6McStrHadfB+dckQMTExPcvHkTM2fOxIgRIxAXFwdDQ0M4OTlh1apVAAB7e3ssWrQIc+fOxbhx41C3bl3Mnj0b3bt3z/f4W7duRe/evVG3bl2UK1cOs2fPRmRkJDQ1NQFkXSwPHDiAwYMHo27dulBRUUHTpk2xbNmyfI89Z84czJkzB6GhoTAzM8PBgwel9iCZOHEiHj16BA8PDxQvXhz9+vWDt7c33rx5U+DPSiAQ4PDhw5gwYQJ69uyJ169fo1y5cqhbty7Kli1b4OPIy+4zUTAoXRyTfeuhrF5JhD98Ca8xIXiVlNULyahMKbFeKHMCz0MoBKb0ro/fDLQRn/wBf12+h6nrz4jS/BFwFFN61cfSoc1gqFsCcfHvsOF/NzFrq+xDmeSlabPmSEpMxMrlAYiPfw1LK2usXLNe1I3yRVwcVLLNyu3gWB2z5y3A8oAlWLZkESpVNsaSZStgbm4hStOzd1+kpKRg2tTJePfuLRyrO2HlmvVFZsy6MsYMKG/cvzdtjqSkRKxeuQwJ8a9hYWmNZavWiSb7e/HiOQQqX3uT2TtUx8w5C7Bq2RKsCFiMSpWMsXDpcph9jvv1q5c4d/Y0AKBTO2+x91qzYQtq1HQunMDyoazlzbiVK+6GvzdDcnISNq5ZjsSEeJhZWGF+wGrRMJ9XL8TjrmbviEkz5mLDqmVYt3IpKhpVxswFATAxMxc77qnjRyAUCtHIo3mhxlNQHs2yrmurlmdd1yytrLFi9bps5f0cKtmuaw6O1TFr7gKsWLYEy5cuRqXKxlgU8PW6BgDnzpzGlInjRa/HjvoDANDfzx8D/AcXUmR5U9Z6roxxK2sdLxJ+kqfxFHUC4bfMyEpFwrNnz2BkZISTJ0+iUaNG33WM2NhYVKlSBbdu3YLD5+6CPxOtBtMVnQWFSDoxSdFZIPrh0jOU87ZUTLVoDB0k+lGSP6QpOgsKUUpLOX/LLCrDoalwZCrpV8riar9GPddyL/rfMVIuFP3vf8p5tf/JnD59Gu/fv4etrS3i4uIwevRoGBsbo25d2ecdISIiIiIiIiLZsHHlJ5CWlobx48fj0aNH0NbWhpubG4KDg3M9lYeIiIiIiIjom7CnmVywceUn4OHhAQ8PD7ke09jYGBwRRkRERERERCQ7zlxDRERERERERCQDNq4QEREREREREcmAw4KIiIiIiIiIlBUfxSwX/BSJiIiIiIiIiGTAxhUiIiIiIiIiIhlwWBARERERERGRsuKwILngp0hEREREREREJAM2rhARERERERERyYDDgoiIiIiIiIiUlYpA0Tn4JbDnChERERERERGRDNi4QkREREREREQkAw4LIiIiIiIiIlJWfFqQXPBTJCIiIiIiIiKSARtXiIiIiIiIiIhkwGFBRERERERERMpKwKcFyQN7rhARERERERERyYCNK0REREREREREMuCwICIiIiIiIiJlxacFyQU/RSIiIiIiIiIiGbBxhYiIiIiIiIhIBmxcISIiIiIiIiKSAedcISIiIiIiIlJWfBSzXLDnChERERERERGRDNhzhX5KSScmKToLClFl4B5FZ0Ehbi1opegsKISWmqqis6AQ6ZmZis6CQqgIlLO8oYQ/lmVkCBWdBYXQKa6m6CxQIcrMVM56rqRho5iqEl7MiXJg4woRERERERGRsuKjmOWCnyIRERERERERkQzYuEJEREREREREJAMOCyIiIiIiIiJSVnxakFyw5woRERERERERkQzYuEJEREREREREJAMOCyIiIiIiIiJSVnxakFzwUyQiIiIiIiIikgEbV4iIiIiIiIiIZMBhQURERERERETKik8Lkgv2XCEiIiIiIiIikgEbV4iIiIiIiIiIZMBhQURERERERETKik8Lkgt+ikREREREREREMmDjChERERERERGRDNi4QkREREREREQkA865QkRERERERKSs+ChmuWDPFSIiIiIiIiIiGbBxhYiIiIiIiIhIBhwWRERERERERKSs+ChmueCnSEREREREREQkAzauEBERERERERHJgMOCiIiIiIiIiJQVhwXJBT9FEjE2NsaSJUtErwUCAfbv36+w/BARERERERH9DNi4UsS8fv0afn5+qFSpEjQ0NFCuXDl4eHjg4sWLis7aL2t7SDCaNWmImo626NKxHW6Hh+eZ/vixI/DybIqajrZo490S5/8+J7ZdKBRixbKlaFTPHbWq26Ffb188fhz7AyP4Pr71TXBtVlPErPDGX+MawMFYV2raPSPqIm5tm1xL4GA3UZolvk65tocMqV0YoRTYvp3b0KHV72hSuzoG+HZCdOTtPNOfOXkM3dq2RJPa1eHb0QdXLv4ttr1ezWoSl22BG39kGN9s1/ZgeDVrBPda9ujZtQMib+ddx08eP4p23s3hXssendq2wsXz56SmnT1jKmo5WGNb0BZ5Z1tme3aEoHWLJqjv4og+3TsiKiLvuE+fOIaOrT1R38URXdt749IF8fKeMWU83KrbiC3D/fv9yBC+y45twWju0RDOTnbo1rk9IvIp7xPHjsKnZTM4O9mhnU/ua9qpk8fh168X6rs7w9HWCnfvRP/I7H+3HduC0fz3hnCubodunb4h7upS4j5xHH59e6F+bWc4Viu6ce/cHoyWzRrBraY9enTpkG/cJ48fRRuv5nCraY8ObVrhQo7ze82q5Wjj1RzuztXRwN0ZA/v1RER42I8M4bso671bWeNW1uvazu3B8GzaEK417NC9IHEfP4rWrZrBtYYd2rduKXZ+p6WlIWDxArRv3RK1aznCo1EdTB4/Bq9fvfzRYXwTZa3j9Gtg40oR06ZNG9y6dQtbtmzBvXv3cPDgQdSvXx8JCQmKztoP9+nTp0J/z6NHDmPBvNnoP9Af23ftg6WlFfz695b6eYfeuomxo0bAp3Vb7Ni9Hw0aNsKwwf64f/+eKM2mDeuwLTgQE6dMRdC2ndDS0oJfv95ITU0trLDy1apGRUxtZ4eFh6LhMeMUop6+wbah7tDX1pCYvveqy7AbeUi01JtyHOkZmfjfP/+KpTsd8UIsnd/6a4URToGcPn4EK5bMQ48+flgXuAum5pYYObg/khIll3VE2C1Mnzgazb18sC5oF+rUa4gJI4fg0YP7ojR7j5wVW8ZMmg6BQIB6DZoUVlj5OnHsMJYsnIs+/f2xddsemFtYYsjAvkiUEnd46C1MGjcSrbzbIHD7XtRr0Aijhg/Gwwf3cqU9c/oEIsLDYGhY5keH8c1OHjuCgEXz0KvfQGwK2QUzc0sM9+8vNe7bYbcwZfwotPRqjc0hu1G3fkOM/WMwHmYrbwBwcXPH/46fFS1/zp5fGOEU2LGjh7Fw/hz0H+CPkJ17YWFhiYH9+yBR2jUt9CbGjRkB79ZtsW3XPtRv2Bh/DB2EB9muaSkpKXBwdMKQ4SMLK4xvduzIYSycNwf9/fwRsmsvLCzzifvWTYwbPQLePtniHiIh7upFO+7jRw9j8YK56NvfH0Hb98DC0hKD/fpKjTss9BYmjB0JL582CN6xF/UbNMLIYYPF4q5c2Rijx03E9j0HsH5zEMr/VgH+fn2QlJhYWGHlS1nv3coat7Je144fPYxF8+eg3wB/BO/Iuq4NGiA97rDQm5gwJuu6FrIzK+4R2eL++PEj7kRHoU//gQjesQcLFi1DbGwMhg8ZWJhh5UlZ63iRIBAU/eUnwMaVIiQ5ORnnz5/H3Llz0aBBA1SuXBm1atXCuHHj0KpVK1E6gUCANWvWwNPTE8WLF4e1tTUuX76MBw8eoH79+ihRogTc3Nzw8OFD0T4PHz6El5cXypYti5IlS6JmzZo4efKkTPnNzMzEvHnzYGZmBg0NDVSqVAkzZ84UbR8zZgwsLCxQvHhxmJiYYNKkSUhLSxNtnzp1KhwcHLB+/XpUqVIFmpqaMuXnewRu2YTWbdvD26cNTM3MMHHKn9DU1MT+vXskpg8O2go39zrw7dUHJqamGDRkGKyrVsX2kCAAWa3jwYFb0be/Hxo0bAwLSyvMmD0Pr1+9wulTsn3e8tS/iTmCL8Rix6XHuBf3DqODbyLlUwY61a4sMX3yhzS8fpsqWupVLYuUTxn4341nYuk+pWeKpXvzIU3i8RRhZ8hWeHq3RfNWPjA2McWIcZOhqamJwwf3SUy/e3sQarnWRqduvWBcxRS9/QbDwqoq9u0KEaXRNzAQWy7+fQaOTrXwW0WjwgorXyGBW+Dduh1aereGiakZxk6cCk1NTfxv/16J6beHbIWLmzu6+fZGFRNTDPAfCitra+zcHiKW7tXLl1g4ZyamzZqHYsWK3vRd24O3oJVPW3h6+aCKiRlGT5gCDU1NHDogOe6dIUFwdnVHlx69YGxiin4Dh8DSqir27BCPW01dHfoGhqKlVKnShRFOgQVt3YzWbdrBy6cNTE3NMGHyn9DU0sT+fZKvaduCAuFW2x09evaGiYkp/AcPzbqmbQsWpfFs6YX+fv5wcXEtrDC+WdDWzWjdNkfcmgWIu1dvmJhmizskW9ytPsftWnTjDv58frf6fH6P+3x+H5R2fgdvhaubO7p/Pr/9BuU+v5s294SzixsqVjSCqZk5ho8ci//ev8f9+3cLK6x8Keu9W1njVubrmk+bdmjl3QYmpmYYPykr7gP7pcQdHAjX2u7o3jPr/B44aCisrKti5/asuLW1tbFy7Ub87tEMxlVMYGvvgDHjJyE6KhJxcc8LMzSplLWO06+DjStFSMmSJVGyZEns378/39bU6dOno3v37ggNDYWVlRU6d+6M/v37Y9y4cfjnn38gFAoxaNAgUfr379+jefPmOHXqFG7duoWmTZuiZcuWePLkyXfnd9y4cZgzZw4mTZqEqKgohISEoGzZsqLt2tra2Lx5M6KiorB06VKsW7cOixcvFjvGgwcPsGfPHuzduxehoaHfnZfvkfbpE6KjIuHi+nVoi4qKClxc3BAedkviPuGhobluxG613RH+Oe//PnuG+PjXcHb5ekxtbW3Y2tlLPWZhU1MVwK6SDs5HvxKtEwqB89Gv4GSiX6BjdHI3xoHrz5DyKUNsvauFAW4vaIHz037HnM6O0C2hLte8f6+0tDTcuxMFp1ouonUqKipwquWCyNuSu7tH3g6DU03xsq7p4iY1fWJCPC5f+BvNvVrLL+MySkv7hDvRkajp/DUOFRUV1HR2xe3wUIn73A4PQy1n8bhdXN3F0mdmZmLKxDHo2qMXTM3Mf0TWZZKW9gl3o6NQI1fcLlKHN0TcDkVNZxexdc6utRGR43O69c91NG9UBx19WmD+rGl4k5ws7+x/t7S0rGta9uuPiooKnF1cER4WKnGf8LBQsfQA4OpWW2r6oui743b9+eO+Ex0JZxfxel7LxRXhUs7v8PAw1MpxD3N1c5d6PUhL+4R9e3aipLY2LCys5JV1mSjrvVtp41bi69qd6EjUyhF3LWdX3M4rbudvi/v9+3cQCATQ1i4lj2zLRFnrOP1ait7PjUqsWLFi2Lx5M/r27YvVq1ejevXqqFevHjp27Ag7OzuxtD179kT79u0BZPUQcXV1xaRJk+Dh4QEAGDp0KHr27ClKb29vD3t7e9Hr6dOnY9++fTh48KBYI0xBvXv3DkuXLsXy5cvRo0cPAICpqSnc3d1FaSZOnCj629jYGCNHjsT27dsxevRo0fpPnz5h69atMDQ0/OY8yCopOQkZGRnQ1xdvUNDX10dMzCOJ+8THx0Nf3yBX+viE+M/bX2etM8h9zPj4eHllXSZ6JTVQTFUFr99+FFv/+t1HmJXXznd/B2NdWFcojT+23BBbfybyJQ7feo4n8f/B2LAkxnnbIHhIbXjOOYNMoVxD+GZvPpe1rp54uejq6eNJbIzEfRIT4qGrnzO9ARITJJfj0b8OoniJ4qjboLF8Mi0HyUnJyMjIgF6OOPT09fFYStwJ8fHQy1HH9fT1kZit/m7dtB7FVFXRoXM3+WdaDpKTP8edo7z19PKOO1d56+uLdUV2dnNHvYaN8dtvFfHs2VOsWb4Efwzuj7WbQ6Cqqir/QL5RUlKSxPLW1zdAbIzkuOPj4yWmTygi16uCkFvcBj9X3Hmd39LilnZ+54z7/LkzGD9mJD5+TIGBgSFWrN4AHV3p83IVJmW9dytt3Ep6XUtOklbe0uNOkBC3Xh5xp6amImDxAng0a4GSJUvKJ+MyUNY6XmTwaUFywcaVIqZNmzZo0aIFzp8/jytXruDIkSOYN28e1q9fD19fX1G67I0tX3qL2Nraiq37+PEj3r59i1KlSuH9+/eYOnUq/vrrL8TFxSE9PR0pKSnf3XMlOjoaqampaNSokdQ0O3bsQEBAAB4+fIj3798jPT0dpUqJt4xXrlw534aV1NTUXD15hKoa0NCQPD8I/Vid3Y0R9ewNQmOTxNYfuP51iNCdf98i6tkbXJ3VFG6Whrhw53VhZ7PQHTm4D42bev7y9TI6KhLbQwIRuG0PBD/J+Fd5aeLRXPS3qbkFzMwt0K5VU9z65zpq5Oj1QvQzq1HTGSE79yI5OQn79uzCuFHDsTloR64vbkT0c0pLS8PYkcMgFALjJk5VdHaIfhlsoiqCNDU10aRJE0yaNAmXLl2Cr68vpkyZIpZGTU1N9PeXLziS1mVmZgIARo4ciX379mHWrFk4f/48QkNDYWtr+92TyGppaeW5/fLly+jSpQuaN2+OQ4cO4datW5gwYUKu9ytRokS+7zV79myULl1abJk/d/Z35Ts7XR1dqKqq5pokKyEhAQYGBhL3MTAwQEKOngsJCQkw+NxqbmCQ1VCUEF/wYxa2xPepSM/IhGEp8TluDLU18erNRyl7ZdFSV4VXTSNsuxCb7/s8if8PCe9SUaWM4n8NKf25rHNOXpuUmJDrV9wv9PQNkJSQM33uX30BIOzWDTx5HAPPIjQkCAB0dHWgqqqaa/K7xIQE6Eupj/oGuXvnJCYkQO9z+tCb/yApMQGtmjWEq1M1uDpVQ1zccyxdNA9ezaQ3thYmHZ3Pceco78Q8ylvfQEJ5JyTk+gUtuwoVjaCjo4tnT79/eKU86erqSizvhITcv+x9YWBgIDl9EbleFUSecedxLc+VPv7nilue53fO9FrFi8OoUmXY2jlg8p8zoVpMVeo8D4VNWe/dShu3kl7XdHSllXe81LLRlxB3ooS409LSMHbUcMTFPcfKtRuKRK8VQHnrOP1a2LjyE6hatSr+++8/mY5x8eJF+Pr6wsfHB7a2tihXrhxiY2O/+3jm5ubQ0tLCqVOnJG6/dOkSKleujAkTJqBGjRowNzfH48ePv+u9xo0bhzdv3ogto8aM++68f6Gmrg7rqja4euWyaF1mZiauXr0MO3tHifvYOTjg6pUrYuuuXL4EOwcHAECFihVhYGCIq1e/HvP9+/e4HR4m9ZiFLS1DiPAnyXC3+tpjSCAA3K0NceNR3k+laulUEerFVLDnav5fJsvraEG3hDpe5tNgUxjU1NRgYVUVN65fFa3LzMzEzetXYWNrL3EfG1t73LguXtb/XL0sMf3hA3thaV0VZkVkToIv1NTUYWVtg+vXvsaRmZmJf65dga2dg8R9bO3sxdIDwNUrl0Tpm3m2Qsiu/QjasVe0GBqWQdcevRCwav2PCuWbqKmpw9K6Km7kivsqqtlJLu9qtg74J0fc165eRjUpnxMAvHr5Am/eJEPfsGj8g6am9vmadlX8mnbtyhXY2TtI3MfO3gHXsqUHPl/TpKQviqTGfTWfuK/8/HFbWdvg2lXxen796hXYSam3dnb2uH5V+vktTWamUCFP9JNEWe/dShu3El/XrKxtcD1H3NevXoHtN8R99Yp43F8aVp4+foxVazdBR6doDPcDlLeOFxmKfhIQnxZE8paQkICGDRsiKCgI4eHhiImJwa5duzBv3jx4eXnJdGxzc3PRpLFhYWHo3LmzqFfL99DU1MSYMWMwevRobN26FQ8fPsSVK1ewYcMG0fs9efIE27dvx8OHDxEQEIB9+yQ/lSU/GhoaKFWqlNgir6EX3Xr0xN7dO3Fw/z48evgQM6ZNRUpKCrx9snogTBg3GksXLxSl79K1Oy5dPI8tmzci5tFDrFqxDJEREejYuSuArB5DXbp1x7o1q3D29Cncv3cXE8eNhmGZMmjYqOjMxbHmxH10qVMF7VwrwbycNuZ2cURx9WLYfjGrASygZw2M97HJtV9nd2McDX2OpP/E/8kurqGKSW1sUb2KHirqF4e7lSE2+7si5vV7nI18WSgx5ad95+74a/9uHD10ALExD7FoznSkpKSgWUtvAMDMKeOwdvnXCZfbduyKa5cvYkfQZjyOfYRNa1fgbnQkfNp1Fjvuf+/f4+yp42jh1aYwwymwzt164MDeXTh0cD9iHj3E3Jl/IiUlBZ5ePgCAKRPHYEXAIlH6jp274/KlCwjeugmxMY+wdtVyREdFon3HrLh1dHRhamYhthQrVgz6+gaobFxFITFK0rFLDxzctxuH/7cfsY8eYv6safiYkgLPVllxT5s0DquWfS3v9p274srliwgJ3IzYmEdYv3oF7kRFoE2HrLg/fPgPyxcvQER4GOKe/4t/rl7BmOGDUdGoEpxd3SXmQRG6dvfFvj27cPDAPjx69BCzpmdd07y8s65pE8ePQcCSr9e0Tl274dLFC9i6ZSNiHj3C6pXLEBUZiY6duojSvHmTjLt3okVPoIuNjcHdO9GisexFQdfuvti3+3PcDyXEPW4MAhZLiHvz57hXfI67cx5xxxS9uLt064H92c7v2TOyzu+W3ln1fPKEMVi+NNv53aU7Ll26gKAtWef3mlXLERX59fxO+fABKwIW43Z4KOKe/4voqEj8OXkCXr96icZNPBQSoyTKeu9W1riV+rq2Zxf+d2Df5/M7K+5Wn+OePH4Mli3NFneXbrh06QICt2xETMwjrPkcd/uOWXGnpaVhzIihiI6MwIw585GRmYH4+NeIj3+NtLSi0XiqrHWcfh2cc6UIKVmyJJydnbF48WI8fPgQaWlpMDIyQt++fTF+/HiZjr1o0SL06tULbm5uMDAwwJgxY/D27VuZjjlp0iQUK1YMkydPxvPnz1G+fHkMGDAAANCqVSsMHz4cgwYNQmpqKlq0aIFJkyZh6tSpMr2nvDVt1hxJiYlYuTwA8fGvYWlljZVr1ou6UL6Ii4NKtgmeHByrY/a8BVgesATLlixCpcrGWLJsBczNLURpevbui5SUFEybOhnv3r2FY3UnrFyzvkjNxXHwn2fQ19bA6FZVYVhKE5HP3qBzwAXEv8ua26aCXnFkCsVnoTUtWxLO5gbosPh8ruNlZgpRtWJptHethFLF1fEyOQXnol5h7oFIfEr//kY8eWr4ezMkJydh45rlSEyIh5mFFeYHrBYNE3n1Qrysq9k7YtKMudiwahnWrVyKikaVMXNBAExyPB3n1PEjEAqFaJRtPo6ipIlHcyQlJWHtqgAkxMfDwtIaS1euFXWnfpmjjts5OGL6rPlYvWIpVi5bDKNKlTF/8TKYmllIe4siqbFHMyQnJWLdqqzyNre0wqLla0Tl/fJFHFRUvv4KYmvviD9nzsPalQFYs3wJKlaqjDmLlomehqSqoooH9+/i8KEDeP/uLQwMy6CWixv6DRwMdfWi8VQsAPBomnVNW7ViGRI+X9NWrF6X7Zr2HCrZfv1xcKiOWXMWYMXyJVi+dDEqVTbGoqXLYZbtmnbuzGlMmfT1HjR21B8AgP5+/hgwcHAhRZY3j2bNkZSUiFXL84g7W3k7OFbHrLkLsGJZtrgDJMQ9UUrc/kUj7t+bZp3fq1d+Pb+XZTu/X7yIg4rK1/Pb3sERM2fPx8rlS7Hi8/m9YMkyUdwqqqqIjXmEQwf3Izk5CaV1dFDVxhbrNgUVqSeDKeu9W1njVtbrWtb5nYjVK7PitrC0xrJV67Kd388hyHZds3eojplzFmDVsiVYEbAYlSoZY2G2uF+/eolzZ08DADq18xZ7rzUbtqBGTefCCSwPylrH6dchEAqFCn6OB9G3+5iu6BwoRpWBRWPMe2G7taCVorOgEFpqin8CjSKky9Cr7memrOWNn6Onr1xlZCjnv15qxdhhWplkKvpRgQqipGGjmKoSXswBaP4iXRW0vNcqOgv5StnfT9FZyNcvUh2IiIiIiIiI6JvxUcxywU+RiIiIiIiIiEgGbFwhIiIiIiIiIpIBhwURERERERERKauf5FHHRR17rhARERERERERyYCNK0REREREREREMuCwICIiIiIiIiIlJeCwILlgzxUiIiIiIiIiIhmwcYWIiIiIiIiISAYcFkRERERERESkpDgsSD7Yc4WIiIiIiIiISAZsXCEiIiIiIiIikgGHBREREREREREpK44Kkgv2XCEiIiIiIiIikgEbV4iIiIiIiIiIZMBhQURERERERERKik8Lkg/2XCEiIiIiIiIikgEbV4iIiIiIiIiIZMDGFSIiIiIiIiL6ZaxYsQLGxsbQ1NSEs7Mzrl27lmf6JUuWwNLSElpaWjAyMsLw4cPx8ePHb3pPzrlCREREREREpKR+tTlXduzYgT/++AOrV6+Gs7MzlixZAg8PD9y9exdlypTJlT4kJARjx47Fxo0b4ebmhnv37sHX1xcCgQCLFi0q8Puy5woRERERERER/RIWLVqEvn37omfPnqhatSpWr16N4sWLY+PGjRLTX7p0CbVr10bnzp1hbGyM33//HZ06dcq3t0tObFwhIiIiIiIiop/ep0+fcOPGDTRu3Fi0TkVFBY0bN8bly5cl7uPm5oYbN26IGlMePXqEw4cPo3nz5t/03hwWRERERERERKSkfoZhQampqUhNTRVbp6GhAQ0NDbF18fHxyMjIQNmyZcXWly1bFnfu3JF47M6dOyM+Ph7u7u4QCoVIT0/HgAEDMH78+G/KI3uuEBEREREREVGRNXv2bJQuXVpsmT17tlyOffbsWcyaNQsrV67EzZs3sXfvXvz111+YPn36Nx2HPVeIiIiIiIiIqMgaN24c/vjjD7F1OXutAICBgQFUVVXx8uVLsfUvX75EuXLlJB570qRJ6NatG/r06QMAsLW1xX///Yd+/fphwoQJUFEpWJ8U9lwhIiIiIiIiUlICgaDILxoaGihVqpTYIqlxRV1dHU5OTjh16pRoXWZmJk6dOgVXV1eJ8X/48CFXA4qqqioAQCgUFvhzZM8Vop9I+EIvRWdBIdquv6roLChESM+ais6CQmgUY7u/MknPKPg/Lb8KddZxUgIqKkV/Docf4Wn8B0VnQSEqGxRXdBaIRP744w/06NEDNWrUQK1atbBkyRL8999/6NmzJwCge/fuqFChgmhYUcuWLbFo0SI4OjrC2dkZDx48wKRJk9CyZUtRI0tBsHGFiIiIiIiIiH4JHTp0wOvXrzF58mS8ePECDg4OOHr0qGiS2ydPnoj1VJk4cSIEAgEmTpyIf//9F4aGhmjZsiVmzpz5Te8rEH5LPxeiIuJjuqJzoBjvUpQzcPZcUS7K2nOluHrBfxn5laRnKt+/Iey5QvTresyeK0pF8xfpqlC6c6Cis5CvNyHdFJ2FfPHuTkREREREREQkAzauEBERERERERHJ4BfpyERERERERERE30ogUM4JqOWNPVeIiIiIiIiIiGTAxhUiIiIiIiIiIhlwWBARERERERGRkuKwIPlgzxUiIiIiIiIiIhmwcYWIiIiIiIiISAZsXCEiIiIiIiIikgHnXCEiIiIiIiJSUpxzRT7Yc4WIiIiIiIiISAZsXCEiIiIiIiIikgGHBREREREREREpKQ4Lkg/2XCEiIiIiIiIikgEbV4iIiIiIiIiIZMBhQURERERERETKiqOC5II9V4iIiIiIiIiIZMDGFSIiIiIiIiIiGXBYEBEREREREZGS4tOC5IM9V4iIiIiIiIiIZMDGFSIiIiIiIiIiGXBYEBEREREREZGS4rAg+WDPFfomvr6+EAgEokVfXx9NmzZFeHi4orP23baHBKNZk4ao6WiLLh3b4XY+sRw/dgRenk1R09EWbbxb4vzf58S2C4VCrFi2FI3quaNWdTv06+2Lx49jf2AE32fPzhC0bdkEDd0c0bdHR0RF5B336ZPH0LmNJxq6OaJ7B29cvvB3rjSxMQ8xZrg/POo5o7F7DfTp3h4vXjz/USF8M2/7ctjeywnHB7tiVUc7WJUtmWf6khqqGNbABHv71cSJwa4I8q0OZ2NdmY6pCPt3bUMnbw941HHCwF6dER15O8/0Z08dQ4/2LeFRxwm9O/vgykXxsk5MiMfcaRPQrkVDNKtbE2OGDsCzJ49/ZAjfZc/OELTxbIIGro7o270AdfzEMXRq7YkGro7o1t4bl6TU8dHD/fF7XWc0ql0Dvbu1x4u4olPHAWDHtmA092gIZyc7dOvcHhG38477xLGj8GnZDM5Odmjnk/uadurkcfj164X67s5wtLXC3TvRPzL7323n9mC0atYItWvaw7dLB0TmE/fJ40fR1qs5ate0R8c2rXDxvHjca1ctR1uv5qjjXB0N3Z0xsF9PRISH/cgQvouy3sMYN+POy68Q91/7dqBPh+Zo08QZIwd0w73oCKlpn8Q8xOxJI9CnQ3O0queIA7uCc6UJ2bQareo5ii1+3Xx+ZAjfRRnLmn4dbFyhb9a0aVPExcUhLi4Op06dQrFixeDp6anobH2Xo0cOY8G82eg/0B/bd+2DpaUV/Pr3RkJCgsT0obduYuyoEfBp3RY7du9Hg4aNMGywP+7fvydKs2nDOmwLDsTEKVMRtG0ntLS04NevN1JTUwsrrHydOn4EyxfPQ8++A7EhaBfMLCzxx+D+SEqUHPftsFv4c8IoeHq1xsbg3ahTvyHGjRyMRw/ui9L8++wJBvbphsrGVbBszWZs2b4Xvr0HQENdo7DCylMDCwP4162CLVeeom9wKB7G/4cFrW2go6UmMX0xFQEWtrZBuVIamHzoDrptuYn5Jx4g/n3qdx9TEc6cOIpVS+eje+8BWLNlJ0zNLDBmqPSyjggPxYxJY9CsZWus3boLtes2xOTRQxHzMKushUIhJo8eiuf/PsP0+QFYE7gTZcuVx8jBfZGS8qEwQ8vTyeNHsGzRPPTqNxAbgz/X8UF51/GpE0bB07s1NoV8ruMjxOv4s6dP4Nc7q44vX/u5jvcZAA2NolHHAeDY0cNYOH8O+g/wR8jOvbCwsMTA/n2QKO2aFnoT48aMgHfrtti2ax/qN2yMP4YOwoNs17SUlBQ4ODphyPCRhRXGNzt+9DCWLJiLPv39Ebh9D8wtLTHYr6/UuMNCb2Hi2JHw8mmDoB17Ua9BI4wcNlgs7kqVjTFq3ERs23MA6zYH4bffKmCQXx8kJSYWVlj5UtZ7GONm3L963OdPH8OGFQvRsUd/LF4XAmNTC0wZORDJSZKvP6kfP6LcbxXRvd8Q6OoZSD1upSqm2LL3hGiZu2zjjwrhuyhjWdOvhY0r9M00NDRQrlw5lCtXDg4ODhg7diyePn2K169f4+zZsxAIBEhOThalDw0NhUAgQGxsLADg8ePHaNmyJXR1dVGiRAnY2Njg8OHDCoklcMsmtG7bHt4+bWBqZoaJU/6EpqYm9u/dIzF9cNBWuLnXgW+vPjAxNcWgIcNgXbUqtocEAcj64hkcuBV9+/uhQcPGsLC0wozZ8/D61SucPnWyMEPL0/bgLWjp3RYtWvmgiokZRo2bAk1NTRw6uFdi+l3bg+Ds6o7O3XvBuIop+voNgYVVVezZGSJKs3ZFAFzd6mLg0JGwsLJGhYqV4F6vIXT19AsrrDy1r/4bDkW8xJGoV3icmIKFJx/iY3oGmlcrIzF982ploa1ZDBP+dwcRz9/hxdtUhP37Fg/jP3z3MRVh17ataO7VBs1a+sDYxBTDx06GhqYWjvxvn8T0e3cEoZZLbXTs1hOVq5ig14DBMLesiv27tgEAnj19jKiIcAwbMwlWVauhUuUqGDZmEj6lpuL08SOFGVqedgRtQUufbHV8/BRoaGri0AHJdXzntqw63uVzHe83MKuO785ex1cGwLV2Xfh/ruMVjSqhThGq4wAQtHUzWrdpBy+fNjA1NcOEyX9CU0sT+/dJvqZtCwqEW2139OjZGyYmpvAfPDTrmrbt66+eni290N/PHy4uroUVxjcLCdwC79bt0Mq7NUxMzTBu4lRoamri4H7J5b09eCtc3dzRzbc3qpiYwm/QUFhZW2PX9q/l3bS5J5xd3FCxohFMzcwxbORY/Pf+Pe7fv1tYYeVLWe9hjJtx/+pxH9gZhN89W6Nxcy9UMjbFwBEToKGpiZOH90tMb25tg55+w1G3UVOoqUv/gUdVVRW6+gaipZSOrtS0iqCMZV1UZB+ZUFSXnwEbV0gm79+/R1BQEMzMzKCvX7AvGP7+/khNTcXff/+N27dvY+7cuShZsvCHUqR9+oToqEi4uLqJ1qmoqMDFxQ3hYbck7hMeGprrC4ZbbXeEh4YCAP599gzx8a/h7PL1mNra2rC1s5d6zMKWlvYJ9+5EoYbz1zhUVFRQo5YLIqV0eY8ID0WNWi5i65xdayPidigAIDMzE5cunoNR5cr4Y1BfeDapg749OuLvs6d+WBzfopiKABZlS+LGk2TROiGAG0/ewKa8tsR9apvoIjLuHYY3NMG+fjWxqZsDutasCBXB9x+zsKWlpeHenSg4ZSs7FRUVONV0QdRtyWUddTsM1WuKl3VNFzdEfk6f9ukTAEA9W48kFRUVqKmpISLsprxD+C5paZ9w904UatbKXccjpMQdGR6KGs6563hkeCiAz3X8wjkYVaqM4f590aJxHfTt3hF/nykadRzIijs6KlLs+qOiogJnF1eEh4VK3Cc8LFQsPQC4utWWmr4oSkv7hDvRkajlIl7etVxccftz+eV0OzwMNXNcy13c3KWmT0v7hH17dqKktjYsLKzklXWZKO09jHGL1jHuXzPutLQ0PLgXDQcnZ9E6FRUV2Ds5406kbMPwnz97At/WTdC3oycWTh+P1y/jZM2u3ChjWdOvh40r9M0OHTqEkiVLomTJktDW1sbBgwexY8cOqKgUrDo9efIEtWvXhq2tLUxMTODp6Ym6dev+4FznlpSchIyMjFyNQvr6+oiPj5e4T3x8PPT1DXKnT4j/vP111jqDgh+zsL1JTkZGRgb0cvzarqenj4QEyXlMTIjP9eu8rp6+qMt9UmICUj58QNDmDXB2dcfi5WtRt0EjTBg1FLduXP8xgXyD0lpqKKYiQNKHNLH1SR8+Qa+4usR9ypfWRD1zA6gIBBizPwpbrz5De6ff0M3Z6LuPWdjeJCchMyNDctlJGR4jrayTPteNSsZVUKZceaxfuQTv3r5BWloatm3dgNevXiKhiNTx5C91PMe5raevj0QpeUxIiJdyTkio427uWLwiq46PLyJ1HACSkpIkxq2vbyD13I6Pj5ecvoiUZUEkJ0kvb2lxJEi4lktKf/7cGdR1cULtmg7YFrgFy1dvgI5u0filV1nvYYybcQO/dtxv32Tdu3V09cTW6+jqI1nKvbsgLK2rYejYaZgyfwX8/hiPl3H/YuzgXvjw4T9ZsywXyljW9Ovh04LomzVo0ACrVq0CkPXP/MqVK9GsWTNcu3atQPsPGTIEfn5+OH78OBo3bow2bdrAzs5OavrU1NRc4yKFqhpFap4DZScUCgEA7vUaoEOXHgAAc0trRISFYv+eHXB0qqnI7H0XFYEAyR/SsODkA2QKgXuv/oNhSXV0rFEBW648VXT2FKZYMTVMm7MY82dOgVcTd6ioqsKppgtqubojq+/Orynzcx2vU68BOn6u4xaW1rgd/vPWccpfjZrOCN65F8nJSdi/ZxfGjxqOTUE7cjXkEBEVdU4u7qK/q5hawMLaFn06NMeFM8fxe4uiN7Et0c+IPVfom5UoUQJmZmYwMzNDzZo1sX79evz3339Yt26dqPfKly/bQFb3xuz69OmDR48eoVu3brh9+zZq1KiBZcuWSX2/2bNno3Tp0mLL/LmzZY5DV0cXqqqquSbJSkhIgIGB5MnADAxy/wKckJAAg8+t5gYGhlnr4gt+zMJWWkcHqqqquXouJCYm5Gr9/0JP3yDXRKBJiQmiLxhZxywG4yqmYmkqVzHBqxeK73L6JiUN6ZlC6BYXH4esW1wdiR8+Sdwn4b9PeJqcgsxs7QWPEz9Av4Q6iqkIvuuYha20ji5UVFUll52UeUKklbVutrphYW2DdUG7cfDUJez+6zTmLl2Nt2/foPxvFeUfxHfQ+VLHc5zbiQkJ0JNyHurrG0g5J/SzHbMYjE3E67hxFRO8LAJ1HAB0dXUlxp2QkPuXvS8MDAwkpy8i16uC0NGVXt7S4tCXcC2XlF6reHEYVaoMWzsHTPpzJlSLqeLAfslj/wubst7DGDfjBn7tuEuVzrp355y8NjkpATpynOOrpLY2fqtYCXH/Fo0fjJSxrIsUwU+w/ATYuEIyEwgEUFFRQUpKCgwNsy5icXFfv2yEfh73mJ2RkREGDBiAvXv3YsSIEVi3bp3U448bNw5v3rwRW0aNGSdzvtXU1WFd1QZXr1wWrcvMzMTVq5dhZ+8ocR87BwdcvXJFbN2Vy5dg5+AAAKhQsSIMDAxx9erXY75//x63w8OkHrOwqampw8KqKm5c+xpHZmYmbly/Chs7e4n7VLNzwD/XxeO+fvUyqtk6iI5pbVMNT3M82u7pk8coW/43ueb/e6RnCnHv5Xs4GZUWrRMAqG5UGpFx7yTuE/H8LSqU1hS7llfU1UL8+09IzxR+1zELm5qaGiysquLm9auidZmZmbh5/Qqq2kou66q29rj5z1Wxdf9cuwwbCelLltSGjq4enj15jHvRkXCr21C+AXwnNTV1WFpVFauzX+p4NSlx29g5iJ0TQFYdt7FzEB3T2qYanuSs448fo1w5xddx4HMeq9qIXX8yMzNx7coV2Nk7SNzHzt4B17KlBz5f06SkL4rU1NRhZW2D61fFy/v61Suw/Vx+Odna2YulB4CrVy5JTf/1uELRvEOKprT3MMYtWse4f8241dTUYGZhjbAb4vfu8JvXYGUjvaf3t0r58AEvnj+DXh5PFypMyljW9Oth4wp9s9TUVLx48QIvXrxAdHQ0Bg8ejPfv36Nly5YwMzODkZERpk6divv37+Ovv/7CwoULxfYfNmwYjh07hpiYGNy8eRNnzpyBtbW11PfT0NBAqVKlxBZ5DQnq1qMn9u7eiYP79+HRw4eYMW0qUlJS4O3TGgAwYdxoLF38Nf9dunbHpYvnsWXzRsQ8eohVK5YhMiICHTt3BZDV0NSlW3esW7MKZ0+fwv17dzFx3GgYlimDho0ayyXP8tCxSw/8b/9uHDm0H7ExD7Fg9jSkpKSgRcusbqHTJ4/D6uWLRenbdeyKq5cuYlvQZjyOfYQNa1bgTlQE2rTvLErTqVtPnDpxBAf37cKzp4+xZ0cwLp0/C592HQs7PIl23nyOFrbl4FHVEJX1tPBHI1NoqaniSOQrAMB4D3P0rV1ZlH5/2AuU0iyGIfWroKKOJlyq6KJrzYrYFxZX4GMWBe06dcdfB/bg2F8H8DjmEZbMnY6PH1PQ1NMbADB76nisW7FElL51h664fvkidgZvwZPYR9i8biXuRUfCu10nUZqzp44h9MZ1PP/3KS6eO41RQ/qhdt2GqJljYlRF6tC1B/63bzcO/+9rHf+YkoIWrb7W8VXLvtbx9p264sqli9gWuBmPY77W8bbZ6njnbj1x6vgRHNybVcd37wjGxfNFp44DQNfuvti3ZxcOHtiHR48eYtb0rGual3fWNW3i+DEIWPL1mtapazdcungBW7dsRMyjR1i9chmiIiPRsVMXUZo3b5Jx9040Hj58CACIjY3B3TvRorHsRUHnbj2wf+8uHDq4HzGPHmLOjD+RkpKClt5Z5T1lwhgsX7pIlL5jl+64fOkCgrZsQmzMI6xdtRzRkZFo1zGrvFM+fMCKgMW4HR6KuOf/IjoqEtMmT8DrVy/RqImHQmKURFnvYYybcf/qcXu174rjf+3DqaMH8TT2EVYtmoWPKSlo1MwLALB45kRsWRsgSp+WloZH9+/i0f27SE9LQ2L8Kzy6fxfPnz0Rpdm4chEiQv/By7jniI4IxayJf0BFRQV1Gzct9PikUcaypl8L51yhb3b06FGUL18eQNaM21ZWVti1axfq168PANi2bRv8/PxgZ2eHmjVrYsaMGWjXrp1o/4yMDPj7++PZs2coVaoUmjZtisWLF0t6qx+uabPmSEpMxMrlAYiPfw1LK2usXLNe1DX8RVwcVARf2yAdHKtj9rwFWB6wBMuWLEKlysZYsmwFzM0tRGl69u6LlJQUTJs6Ge/evYVjdSesXLO+SM0R0+j3ZkhOSsT61cuRmBAPMwsrLFy2Bnqfu1G+fBEHFZWvfTZs7R0xZeY8rFsZgLUrlqCiUWXMXrAMJmbmojT1GjTGyHFTELR5HZYsmI1KlY0xY+4S2Ds4FXp8kpy5Fw8drWLo5VoJesXV8eD1fxi1L1I0IW0ZbQ3RvBoA8Pr9J4zaFwX/elWwsVs5xL9PxZ5bcQj551mBj1kUNGjSFMnJidi0dgWSEuJhamGFuUtWi8r61Uvxsq5m54AJ0+dg4+rl2LBqKSoYVca0eUtRxfRrWSfGx2PVkvlZw4sMDPF7s5bo1ntAoceWl8Y56ri5hDqe/bF+tvaOmDpzHtauCsCaFUtQsVJlzF6Yo443bIxR46cgcNM6LP5cx2fOWwJ7x6JRxwHAo2nWNW3VimVI+HxNW7F6XbZr2nOoZIvbwaE6Zs1ZgBXLl2D50sWoVNkYi5Yuh1m2a9q5M6cxZdJ40euxo/4AAPT388eAgYMLKbK8/d60OZKTkrBmZQAS4uNhYWmNgJVrRcOhXryIgyDbpOv2Do6YMXs+Vi1fipXLFsOoUmUsWLJMFLeKqipiYx7hr4P7kZychNI6OqhqY4u1m4Jgmq1OKJqy3sMYN+P+1eOu09ADb5KTELJxFZISE2BiZomp81eIJpx//eqF2DUtMf41hvX52tC/b/tW7Nu+FdUcnDBr6XoAQMLrl1gwbRzevn2D0jq6qGrrgPmrtqK0jvjEuYqkjGVdVPwsjzou6gTC7JNjEP0kPqYrOgeK8S5FOQNvu/5q/ol+QSE9lXOSVI1iytmpsri6qqKzoBDpmcr3b4i6ktZxImXwOP6DorOgEJUNiis6Cwqh+Yt0VSjbZ5eis5Cvl+vb5Z9IwXh3JyIiIiIiIiKSwS/S1kZERERERERE34rDguSDPVeIiIiIiIiIiGTAxhUiIiIiIiIiIhlwWBARERERERGRkuKwIPlgzxUiIiIiIiIiIhmwcYWIiIiIiIiISAYcFkRERERERESkpDgsSD7Yc4WIiIiIiIiISAZsXCEiIiIiIiIikgGHBREREREREREpK44Kkgv2XCEiIiIiIiIikgEbV4iIiIiIiIiIZMDGFSIiIiIiIiIiGXDOFSIiIiIiIiIlxUcxywd7rhARERERERERyYCNK0REREREREREMuCwICIiIiIiIiIlxWFB8sGeK0REREREREREMmDjChERERERERGRDDgsiIiIiIiIiEhJcViQfLDnChERERERERGRDNi4QkREREREREQkAw4LIiIiIiIiIlJWHBUkF+y5QkREREREREQkAzauEBERERERERHJgMOCiH4iyR/SFJ0FhZjQ1ELRWVCIlE8Zis6CQmipqSo6CwqRliFUdBYU4t6Ld4rOQqEzKVNC0VlQiOLqyvlvJx/CoVxKaChnPaefG58WJB/suUJEREREREREJAM2rhARERERERERyYD91oiIiIiIiIiUFIcFyQd7rhARERERERERyYCNK0REREREREREMmDjChERERERERGRDDjnChEREREREZGS4pwr8sGeK0REREREREREMmDjChERERERERGRDDgsiIiIiIiIiEhJcViQfLDnChERERERERGRDNi4QkREREREREQkAw4LIiIiIiIiIlJWHBUkF+y5QkREREREREQkAzauEBERERERERHJgMOCiIiIiIiIiJQUnxYkH+y5QkREREREREQkAzauEBERERERERHJgMOCiIiIiIiIiJQUhwXJB3uuEBERERERERHJgI0rREREREREREQy4LAgIiIiIiIiIiXFUUHywZ4rREREREREREQyYOMKEREREREREZEM2LhChS42NhYCgQChoaGKzgoRERERERGRzNi4ouQEAkGey9SpUxWdxR9ue0gwmjVpiJqOtujSsR1uh4fnmf74sSPw8myKmo62aOPdEuf/Pie2XSgUYsWypWhUzx21qtuhX29fPH4c+wMj+D6H9m5Hr/bN4NO4Fv7o3xV3o25LTfs45gFmTRyBXu2bwbOuAw7sDMrz2LuCNsKzrgPWBsyTd7Zl8vfhPZjSty2Gt2uIBaP6IvZelNS0F48fxOJxAzG6S1OM7tIUyyYPzZV+sLe7xOXkvpAfHco3ObR3O3q2awbvRrUwvF/+ZT1z4gj0bNcMLeo4YH8+Zb0zaCNa1Cl6ZQ0Au3eEwKdFY9RzcUDv7h0QGZH3uX3qxFF0aN0C9Vwc0KW9Fy5dED+3p08ZD9fqVcWWYf79fmQI32XX9mB4NWsE91r26Nm1AyJv5x33yeNH0c67Odxr2aNT21a4eF487rWrlqOdd3PUdamORnWc4d+/JyJuh/3IEL7LiYO7MKy7F3q2dMeUoT3x8G6k1LRnjuzHtBF90a9tI/Rr2wizx/rnSn/9whnMGT8YA9o1RtemtfD44b0fHcJ32bMjBK1bNEF9F0f06d4RUfnU89MnjqFja0/Ud3FE1/beuHThb7HtM6aMh1t1G7FleBGs59u3BaPZ7w1Rq7otunZqh9v51PPjx47Au2VT1Kpui7Y+ue/dp04cx4C+vVCvtjMcqlnizp3oH5n976as/7MoY9z7d29DZ28PNK3rBP9enXEnUvq9GwDOnToG3w4t0bSuE/p08cHVS+LndmJCPOZOm4D2ng3RvF5NjB02AM+ePP6RIXwXZSzroiC/74RFYfkZsHFFycXFxYmWJUuWoFSpUmLrRo4cKUorFAqRnp6uwNzK39Ejh7Fg3mz0H+iP7bv2wdLSCn79eyMhIUFi+tBbNzF21Aj4tG6LHbv3o0HDRhg22B/373/9p3vThnXYFhyIiVOmImjbTmhpacGvX2+kpqYWVlj5+vvUMaxfsRCdfPtj6fptqGJmgckjByI5KVFi+tSPH1Hutwro0X8odPUM8jz2vegIHD24G8amFj8i69/txoVT2LdxOZp17InRizaggrEZVv75B94lJ0lM/yDiFpzqNMaQ6cvwx9w10DUoi5VT/0BywmtRmpmbDogtXQaPg0AggINrvcIKK19/nzqGdcsXorNvfwR8LutJI/Ip6/IV4PsNZV2liJU1AJw8dgQBi+aid7+B2ByyG+bmVhju3w+JiZLP7fCwW5gyfhRaerXGlpA9qFu/Ecb8MRgPH9wXS+fi5o5Dx8+Jlmmz5xdGOAV24thhLFk4F336+2Prtj0wt7DEkIF9pccdeguTxo1EK+82CNy+F/UaNMKo4YPx8MHXa1qlysYYNXYitu0+gLWbglD+twoY7NcHSYmS65AiXDl3AsHrlsCnax/MWL4VlUzMMXfCELxJlpzH6PAbcK3vgQlzV2Hq4g3QMyyLueMHIzH+lShN6scUWNrYo0OvQYUVxjfLqufz0KvfQGwK2QUzc0sM9+8vtbxvZ6vnm0N2o279hhgrpZ7/7/hZ0fJnEavnx44cxsJ5s9Hfzx/bdu2DhaUVBvbvjcQ87t3jRo+At09bbN+Vde8ePsQfD7Ldu1NSPsCxenUMHT5S4jGKAmX9n0UZ4z5z4ihWL52P7n0GYPWWnTA1t8CYYf2RJOXcjgwPxYzJY9CsZWus2bILtes2xOTRQxHzMOvcFgqFmDxmKOKeP8O0eQFYs3UnypQrj1FD+iIl5UNhhpYnZSxr+rWwcUXJlStXTrSULl0aAoFA9PrOnTvQ1tbGkSNH4OTkBA0NDVy4cAG+vr7w9vYWO86wYcNQv3590evMzEzMmzcPZmZm0NDQQKVKlTBz5kyJecjIyECvXr1gZWWFJ0+e/MBocwvcsgmt27aHt08bmJqZYeKUP6GpqYn9e/dITB8ctBVu7nXg26sPTExNMWjIMFhXrYrtIVm/7guFQgQHbkXf/n5o0LAxLCytMGP2PLx+9QqnT50szNDytH9nIDw8W6NJc29UMjaF/4iJ0NDUxIm/9ktMb2FdDb0G/oF6jZpCTV1N6nFTPnzAgunjMXj0ZJTU1v5Buf8+Zw5sh+vvLeHSqAXKG1VBB79RUNfQxOVThySm7/HHFNRt3hoVTcxRrmJldPYfA6EwE3fD/xGlKaWrL7aEX70A82rVYVCuQmGFla99OwLRtGVrNGnhjUpVTDFo5ERoamrieB5l3dv/D9RrnH9Zz59WNMsaALYFb0Yrn3bw9GqNKiZmGD1hCjQ0NXHowF6J6XeGBMLZ1R1de/SGsYkp+g8cAkurqti9I1gsnbq6OvQNDEVLqVKlCyOcAgsJ3ALv1u3Q0rs1TEzNMHbiVGhqauJ/+yXHvT1kK1zc3NHNtzeqmJhigP9QWFlbY+f2r72vmjb3RC0XN1SoaARTM3MMGzEW/71/j/v37xZWWPk6sjcEDZp6o97vLVGhsgl6Dh4LDQ1NnDv2P4npB46ZjiYt26KyqQV+MzJG32ETkCkUIjL0uiiNe+Pm8OnSB9UcaxVWGN9se/AWtPJpC08vnwLW8yA4u7qjS49eMDYxRb/P9XzPDvHedmpFvJ4Hbs127zY1w8TJn+/d+yTfu0OCtsKt9td7t/9g8Xs3AHi28kZ/v0FwdnUtrDC+mbL+z6KMce/ethXNvdqgqacPjKuYYtiYydDQ1MLRQ/skpt+7Iwg1XWqjQ9eeqFzFBD37D4a5ZVXs370NAPDs6WNER4Rj2OhJsKpaDUaVq2DY6En4lJqK08ePFGZoeVLGsqZfCxtXKF9jx47FnDlzEB0dDTs7uwLtM27cOMyZMweTJk1CVFQUQkJCULZs2VzpUlNT0a5dO4SGhuL8+fOoVKmSvLMvVdqnT4iOioSLq5tonYqKClxc3BAedkviPuGhoXBxEf/Hy622O8I/zx/z77NniI9/DWeXr8fU1taGrZ291GMWtrS0NDy4Fw2HGs6idSoqKnBwcsadyLy7XuZn1eJZqOlaBw41XGTNplylp6Xh6cN7sLSrIVqnoqICS/saiM1j6EB2nz6lIiMjHSVKlpK4/W1yIiJvXIJr4xZyybM8iMraKUdZ15BfWTsWsbIGgLS0T7gbHYWazl/zpqKigprOrogID5W4T8TtUNR0Fj+3nV1rIyJcfPjLzX+uo3kjd3TwaY55s/7Em+RkeWf/u6WlfcKd6EixOL7EfVtK3LfDw1ArR9wuru5S06elfcL+PTtRsqQ2LCys5JV1maSnpSHm/h3YONYUrVNRUYGNY008iM67G/0XqakfkZGejpLaks/vouhLPa+Rq7xdctXbL7Lqufg5m1XPQ8XW3frnOpo3qoOOPi0wf9a0IlfPo6Mixe6zKioqcM7r3h0WmqvRxNXNHeFhoT8yq3KltP+zKGHcaWlpuHc3CtVrit/Dqtd0QZSUIZlREWFwqil+btdwcROlT/v0CQCgrq4hdkw1NTVEhN2UdwjfRRnLuigRCIr+8jMopugMUNE3bdo0NGnSpMDp3717h6VLl2L58uXo0aMHAMDU1BTu7u5i6d6/f48WLVogNTUVZ86cQenShfvLWFJyEjIyMqCvry+2Xl9fHzExjyTuEx8fD319g1zp4xPiP2/PGjKib5D7mPHx8fLKukzevklCZkYGdHTF86ijp49nT2K/+7jnTh3Fw3t3sHhtcP6JC9l/794gMzMDpXT0xNZrl9bDy2cFG298YMtKlNY1gKV9DYnbr50+Ak2t4rAvQkOCRGWtl6OsdfXxVIbxxudOHsWDe3ewpAiWNQAkJycjIyMDejmGNenp6eNxrORzOyE+Hno5rgV6+gZISPh63rq4uaN+w8Yo/1tF/PvsCVYvX4Lhg/tj3eYQqKqqyj+Qb5Sc9DnuXHHo43FsjMR9suI2yJU+Mcf16vzfZzBxzEh8/JgCAwNDLF+9ATq6uvIN4Du9e5uMzMwMlM5xfpfW0UPc04Kd39s3LoeuvgFsinAvlZy+1vMc5a2Xd3nr5qgfuvr6Yl3und3cUa9hY/z2W0U8e/YUa5YvwR+D+2NtEannSUnS792x33LvNig69+WCUNb/WZQx7jfJWfdu3Rzntq6uPp5KObcTE+Ilpk/8HHMl4yooU6481q9aguFjJkNTqzh2b9uK169eitIomjKWNf162LhC+apRQ/KXSWmio6ORmpqKRo0a5ZmuU6dOqFixIk6fPg0tLS2p6VJTU3ONixSqakBDQ0PKHlTYXr98gXUB8zB90Wqo/4LlcnxPIG5eOIUhM5ZBTV1yfJdP/YUadX+Xuv1X8frlC6wNmIcZv2hZ56WJR3PR32bmFjAzt0TbVh64+c+1XL1efjU1ajojaMdeJCcnYf/eXRg3ejg2Be3I9cX+Z3RwxxZcOXsCE+atEvtVV1llr+em5hYwM7dAu1ZNceuf66jhXPR6qhFR/ooVU8OfcxZjwcwp8P7dHSqqqnCq6YJaru4QCoWKzh7RL4PDgihfJUqUEHutoqKS60KclpYm+juvhpLsmjdvjvDwcFy+fDnPdLNnz0bp0qXFlvlzZxcw99Lp6uhCVVU11yRZCQkJMDCQPJGngYH4L9mi9J9bzQ0MDLPWxRf8mIWtVGldqKiqIjlJPI/JiQn5TmAqzYN7UUhOSsTQPp3QqoETWjVwQkToDfxvzza0auCEjIwMeWT9u5XQLg0VFVW8zTG55bs3iSilm/eXw1P7Q3ByTzD8py5GBWMziWkeRIbh1b9P4NrEU255lgdRWeeYAC85KQG6+t9Z1nezynpIn05oWd8JLes74XboDRzcvQ0t6yu+rAFAR0cHqqqqSEwUP1cTExNy/cL1hb6BQa7JMBMTcv8ill2FikbQ0dHFs6eFO1eUNDq6n+POFUcC9KVcf7Lijs+VXi9Hei2t4jCqVBm2dg6YNHUmiqmq4qCU+S0Km3YpHaioqOaavPZNciJK53N+/7U7CId2bsGYWQGoZGL+I7Mpd1/reY7yTkzI1RvpC30DAyTlqB9JCQm5finOrqjVc11dOd2744vOfbkglPV/FmWMu7RO1r075+S1SUkJuXomfqGnbyAl/dd4LKxssDZwNw6cvIRdh05jzpLVePvmDcpXqCj/IL6DMpZ1UaLoJwHxaUGktAwNDREXFye2LvTz2EYAMDc3h5aWFk6dOpXncfz8/DBnzhy0atUK586dk5pu3LhxePPmjdgyasw4mWIAsibss65qg6tXvjbuZGZm4urVy7Czd5S4j52DA65euSK27srlS7BzcAAAVKhYEQYGhrh69esx379/j9vhYVKPWdjU1NRgZmGNsBvXROsyMzMRdvMarGwKNqdOTvZOzli+eTcCNuwQLeZWVVG/SXMEbNih8K7kxdTUYGRqgXvhN0TrMjMzcS/8BowtbaTud3JvMI7u3AK/KQtQyUz6/BKXTx6CkaklKlYpWl/OvpR1aI6yDr0hQ1nXcMaKLbuxbOMO0fKlrJdtVHxZA4Camjosravin2tfz9XMzEz8c+0Kqtk5SNynmq2DWHoAuHb1MqrZ2Ut9n1cvX+DNm2QYGBrKJd+yUlNTh5W1Da5LiNtWSty2dvZi6QHg6pVLUtOLjisU4tPnMfyKVkxNDVXMrcQmo83MzERk6D8ws7aVut+hXVuxP2QDRs9YChOLqoWRVbn6Us9v5Crvq1LrrfR67iD1fb7Uc33DovFFRE0t69597ar4vftaXvduewdck3Tvtnf4kVmVK6X9n0UJ41ZTU4OFZVXcun5VtC4zMxO3rl9BVVvJ53bVava4mS09ANy4dlli+pIltaGjq4dnTx7j3p1I1K7bUL4BfCdlLGv69XBYEH2zhg0bYv78+di6dStcXV0RFBSEiIgIODpmXaQ0NTUxZswYjB49Gurq6qhduzZev36NyMhI9O7dW+xYgwcPRkZGBjw9PXHkyJFc87IAgIZG7iFAH+X0ROhuPXpi0vgxsLGphmq2dggK3IKUlBR4+7QGAEwYNxplypTF0OEjAABdunZHb99u2LJ5I+rWrYejRw4jMiICk6ZOA5DV6tulW3esW7MKlStVRoWKFbFi2VIYlimDho0ayyfTcuDdvhsWz54Ec8uqsLCuhgO7gvExJQWNm3sBABbOnAh9gzLw7T8EQFbPpKexDwEA6WnpSIh/hUf370BTqzh+q1gJxYuXgLGJeK8ODU0taJcqnWu9ojTw6oigpTNRycwKlc2tcfZ/O5H6MQUujbImoN26ZDp09A3RqtsAAMCJvUE4HLIBPf6YAv0y5fH2c08fDU0taGgVFx035cN/CL10Bj49i+bjWn06dMOiWZNgbiVe1k2+lPWMz2U94GtZP8le1q9f4eH9O9DKo6w1NbVQqnTRKWsA6NTFF9OnjINV1WqwsbHF9pCt+JiSAs9WPgCAPyeNhWGZMhg4+A8AQPvO3TCwbw+EBG6Cm3s9nDx2GHeiIjB24p8AgA8f/sOGNSvRoNHv0DcwwLOnT7Bi6UJUNKoEZ9fc1y1F6dytB/6cNA7WVavBppottgdvRUpKCjy9suKeMnEMypQpC/8hWXF37Nwd/ft0R/DWTahdpx6OHz2M6KhIjJ+cFXdKygdsWrcGdeo3gIGBIZKTk7F7Rwhev3qJRk08FBZnTs1ad8aaBX+iirk1TC1tcHTfdqR+TEG937N6k62ePwW6+mXQoZc/AOB/O7dgT+BaDBwzHQZlyyP5cy8nTa3i0Px8fr9/9wYJr14i6fPj1+M+z89UWlcPOt/Zy0/eOnbpgRlTxsOqqg2q2thiR0igWD2fNmkcDMuUgd/g4QCA9p27YmBfX4QEboabe12cPHYEd6IiMGbiVABZ9XzjmlWo36gJ9A0M8O/Tp0Wynnfr3hOTJoxBVZtqqFbNDsFBWfduL++se/fEz/fuIZ/v3Z27dkefnt2wdfNG1Pl8746KjMDkz/duAHjzJhlxcXF4/SrrcdyPY7LmtjAwMBD9+q1oyvo/izLG3bZTd8ydPgEW1jawqmqLPTsC8fFjCjxaeAMA5vw5HgaGZdBn4DAAQOsOXTHcryd2Bm+BS+06OHPiKO5FR+KPsVNExzx36hhK6+ihTLlyiHl4HysWzUXtug1Rw9lNQg4UQxnLmn4tbFyhb+bh4YFJkyZh9OjR+PjxI3r16oXu3bvj9u2vT2WYNGkSihUrhsmTJ+P58+coX748BgwYIPF4w4YNQ2ZmJpo3b46jR4/Cza3wLvJNmzVHUmIiVi4PQHz8a1haWWPlmvWiLvQv4uKgIvjawcvBsTpmz1uA5QFLsGzJIlSqbIwly1bA3NxClKZn775ISUnBtKmT8e7dWzhWd8LKNeuL1BwxdRt54E1yEoI2rkJSYjxMzCwxbcFK0WRor1/GQSVb97vE+FcY0ruj6PXe7Vuxd/tWVHNwwpyADYWe/+/h5N4I798k469t6/EuKREVqphh4JSFokluk16/hCBbWV84sh/p6WnYMG+i2HGadeiJ5p2+NhLePH8SQqEQTnWK5k1aVNYbpJe1IGdZ98pd1rYOTpiz7OcoawBo7NEMSUmJWL9qGRIS4mFuaYXFy9eIuki/fBEHFZWv5W1n74g/Z87D2pUBWL18CYwqVcbcRctgapbVG0lFRRUP79/DkUMH8O7dWxgYloGzS230GzgY6urqColRkiYezZGUlIS1qwKQEB8PC0trLF25VjS86WWOa5qdgyOmz5qP1SuWYuWyxTCqVBnzFy+DqVnWNU1FRRWxsY/w14j9SE5OQmkdHVS1scXajUGiz6YocKnXBG/fJGFP4Fq8SUpAZRMLjJ6xVDQsKP6V+Pl96tBepKelIWDGWLHj+HTpgzbd+gEAbl4+j7WLvn75Xj57Qq40itbYoxmSkxKxbtVyJH6u54ty1fOv57dttnq+ZvkSVKxUGXOy1XNVFVU8uH8Xhw8dwPvP9byWi1uRq+cezZojKSkRq7Lfu1d/vXfHxcVBoCJ+7541dwFWLFuCZUuz7t2LA1bALNu9++yZ05gy8Wuv2DGjshqk+vsNgp//4EKKLG/K+j+LMsbdoElTvElOxOZ1K5CUEA9TcyvMWbxadG6/eiF+77axc8CEaXOwcc1ybFy9FBWMKmPavKWoYvr1Op0QH49VS+cjKTEBegaG+L1ZS3TtJfl/c0VRxrIuKn6SUTdFnkDIWYzoJySvnis/m6cJKYrOgkLEJL5XdBYUwkS/pKKzoBB6JYrOl7jCpKqinP/Z3HvxTtFZKHQmZUrkn+gXVFxdOX/T45cW5RL/rmgMmSxsBtrKee/W/EUua1Zjjyk6C/m6M6fo9JiVhnOuEBERERERERHJ4BdpayMiIiIiIiKib6WipL1n5Y09V4iIiIiIiIiIZMDGFSIiIiIiIiIiGXBYEBEREREREZGS4sTb8sGeK0REREREREREMmDjChERERERERGRDNi4QkREREREREQkA865QkRERERERKSkBJx0RS7Yc4WIiIiIiIiISAZsXCEiIiIiIiIikgGHBREREREREREpKY4Kkg/2XCEiIiIiIiIikgEbV4iIiIiIiIiIZMBhQURERERERERKik8Lkg/2XCEiIiIiIiIikgEbV4iIiIiIiIiIZMBhQURERERERERKisOC5IM9V4iIiIiIiIiIZMDGFSIiIiIiIiIiGXBYEBEREREREZGS4qgg+WDPFSIiIiIiIiIiGbBxhYiIiIiIiIhIBhwWRERERERERKSk+LQg+WDPFSIiIiIiIiIiGbBxhYiIiIiIiIhIBhwWRPQTKa+jqegsKIR+SXVFZ0EhNNWUs/1b3ztA0VlQiH93DVJ0FhSiWsXSis4CFRL2OlcuQqGic6AYBtrK+T8LEbFxhYiIiIiIiEhpsfFbPpTzZ1EiIiIiIiIiIjlh4woRERERERERkQw4LIiIiIiIiIhISfFRzPLBnitERERERERERDJg4woRERERERERkQw4LIiIiIiIiIhISXFUkHyw5woRERERERERkQzYuEJEREREREREJAMOCyIiIiIiIiJSUnxakHyw5woRERERERERkQzYuEJEREREREREJAMOCyIiIiIiIiJSUhwVJB/suUJEREREREREJAM2rhARERERERERyYDDgoiIiIiIiIiUFJ8WJB/suUJEREREREREJAM2rhARERERERERyYCNK0REREREREREMuCcK0RERERERERKilOuyAd7rhARERERERERyYCNK0REREREREREMuCwICIiIiIiIiIlxUcxywd7rlAuU6dOhYODg6KzQURERERERPRTYOOKgvj6+kIgEGDAgAG5tvn7+0MgEMDX17dAxzp79iwEAgGSk5Plm8kfrKg04mwPCUazJg1R09EWXTq2w+3w8DzTHz92BF6eTVHT0RZtvFvi/N/nxLYLhUKsWLYUjeq5o1Z1O/Tr7YvHj2N/YATfZ+f2YHg2bQjXGnbo3rk9Im7nHfeJ40fRulUzuNawQ/vWLXHh/Ne409LSELB4Adq3bonatRzh0agOJo8fg9evXv7oML7J7h0h8GnRGPVcHNC7ewdERuQd86kTR9GhdQvUc3FAl/ZeuHRBvKynTxkP1+pVxZZh/v1+ZAjfZce2YDT3aAhnJzt0K0hZHzsKn5bN4Oxkh3Y+uev4qZPH4devF+q7O8PR1gp370T/yOx/t/6edrizqSeS9vvj78UdUMOibJ7pB3k5IGxtdyTu88f9Lb0wr29daKipirZP6OKMlMNDxZbQNd1+dBjfTFnr+fZtwWj2e0PUqm6Lrp3a4XY+9fz4sSPwbtkUtarboq2ken7iOAb07YV6tZ3hUM0Sd4poPVfauJX03q20cbOeK015K2PM9Otg44oCGRkZYfv27UhJSRGt+/jxI0JCQlCpUqVCz49QKER6enqhv68iHT1yGAvmzUb/gf7YvmsfLC2t4Ne/NxISEiSmD711E2NHjYBP67bYsXs/GjRshGGD/XH//j1Rmk0b1mFbcCAmTpmKoG07oaWlBb9+vZGamlpYYeXr+NHDWDR/DvoN8Efwjr2wsLTEoAF9kCgl7rDQm5gwZgS8fdoiZOc+1G/YGCOGDsKDz3F//PgRd6Kj0Kf/QATv2IMFi5YhNjYGw4cMLMyw8nTy2BEELJqL3v0GYnPIbpibW2G4fz8kJkqOOTzsFqaMH4WWXq2xJWQP6tZvhDF/DMbDB/fF0rm4uePQ8XOiZdrs+YURToEdO3oYC+fPQf8B/gjZuRcWFpYY2F96WYeG3sS4MSPg3bottu3KKus/spU1AKSkpMDB0QlDho8srDC+Wdu65pjbtw5mhlyF6+BtCH/0Ggene8OwtJbE9B3qW2J6z9qYFXIVDv23YsCSk2hb1xzTfN3E0kXGxsO4yzrR0mjUrsIIp8CUtp4fOYyF82ajv58/tu3aBwtLKwzs31t6Pb91E+NGZ13Ttu/KupYPH+Kfo55/gGP16hhahOu5ssatrPduZY2b9Vx5ylsZYy4qBIKiv/wM2LiiQNWrV4eRkRH27t0rWrd3715UqlQJjo6OonWpqakYMmQIypQpA01NTbi7u+P69esAgNjYWDRo0AAAoKurK9bjJa/9gK89Xo4cOQInJydoaGjgwoULou1r1qyBkZERihcvjvbt2+PNmzeibZmZmZg2bRoqVqwIDQ0NODg44OjRo2LxjRkzBhYWFihevDhMTEwwadIkpKWlAQA2b96MP//8E2FhYRAIBBAIBNi8ebN8PthvELhlE1q3bQ9vnzYwNTPDxCl/QlNTE/v37pGYPjhoK9zc68C3Vx+YmJpi0JBhsK5aFdtDggBkNVAFB25F3/5+aNCwMSwsrTBj9jy8fvUKp0+dLMzQ8hS0dTN82rRDK+82MDE1w/hJf0JTSxMH9kuOe1twIFxru6N7z96oYmKKgYOGwsq6KnZuDwYAaGtrY+XajfjdoxmMq5jA1t4BY8ZPQnRUJOLinhdmaFJtC96MVj7t4OnVGlVMzDB6whRoaGri0IG9EtPvDAmEs6s7uvboDWMTU/QfOASWVlWxe0ewWDp1dXXoGxiKllKlShdGOAUWtHUzWrdpBy+fNjA1NcOEyVllvX+flLIOCoRbbXf06NkbJiam8B88NKuOb/sat2dLL/T384eLi2thhfHNhvhUx6ajkQg8EYU7TxMxePlppKSmo8fvNhLTu1iXx+WoOOw4exdPXr3DqVtPsPPcPdSwKCeWLj1DiJdJH0RLwtuPhRFOgSlrPQ/cmu1abmqGiZM/X8ul1POQoK1wq/31Wu4/WPxaDgCerbzR328QnF2Lbj1X2riV9N6ttHGznitNeStjzPRrYeOKgvXq1QubNm0Svd64cSN69uwplmb06NHYs2cPtmzZgps3b8LMzAweHh5ITEyEkZER9uzJuuDcvXsXcXFxWLp0ab77ZTd27FjMmTMH0dHRsLOzAwA8ePAAO3fuxP/+9z8cPXoUt27dwsCBX3shLF26FAsXLsSCBQsQHh4ODw8PtGrVCvfvf/21U1tbG5s3b0ZUVBSWLl2KdevWYfHixQCADh06YMSIEbCxsUFcXBzi4uLQoUMHOX6y+Uv79AnRUZFwcf36y7SKigpcXNwQHnZL4j7hoaG5vlC61XZHeGgoAODfZ88QH/8azi5fj6mtrQ1bO3upxyxsaWmfcCc6ErVcxOOu5eyK22GhEvcJDwuFs7P4L/iubrURLiU9ALx//w4CgQDa2qXkkW2ZpKV9wt3oKNR0dhGtU1FRQU1nV0SEh0rcJ+J2KGo6i5e1s2ttRISHia27+c91NG/kjg4+zTFv1p94U4SG56WlZdVx5xxl7eziKrXswsNCxdID+Zd1UaNWTAWOZmVwOvSJaJ1Q+H/27jssiqMBA/h7R1dRmi0WUKkqzYZgxwbGAnbFXmPvIsZu7A3F3nsXjZ89scSORgUs2BAwGowezYaIwPcHeHByIHAHd7jvL88+T9ybXeZlluWYm5kFzgU+Rx3rMnKPuR4SCUfzUtKpQ2ZliqNlLTOcuhkuU868nAGe7eiPB5v6YMuElqhQUj/fcuQWr/Nvr/Ns7uVBgZn+qHJ2qV+ornPB5hbq726h5uZ1Lt33o7e3EDPTj4edKyrWo0cPXL58GREREYiIiMCVK1fQo0cP6esfPnzAmjVrsGjRIri7u6Nq1arYsGED9PT0sGnTJmhoaMDIyAgAUKpUKZQpUwYlSpT47nEZzZo1C82bN0eVKlWk5/r06RO2b98OBwcHNGzYEH5+fti7dy9evXoFAFi8eDG8vb3RtWtXWFlZYcGCBXBwcICvr6/0vFOmTIGLiwvMzMzQpk0bjB8/Hvv37wcA6OnpoVixYtDU1ESZMmVQpkwZ6OnJH66fX2JiY5CUlARjY2OZ/cbGxpBIJHKPkUgkMDY2yVw+SpL2+pvUfSY5P2dBi43JKrdJlnWMkkhg9E15I2MTRGVRPiEhASuWLUZL959RrFgx5VRcAbGxsUhKSoKRkWzbGRkZIyoql5kzlK/rUh/TZs/DirWbMXTkWNy5dRNjRgxGUlKS8kPkQUxaW3+bw/ibHBlJ5OQ2zqat1ZFJcT1oaojxOuajzP7XsR9Rxqio3GP2XXiE2Tuv4eyiTnh7dDhCNvfFxbsvsGh/+mi/m49eYdDSM2g79XeMXHUOZqWL489FHVFMTytf8+SU0K9zhe/lJupzn84JweYW6O9uwebmdS6z/0dubyFmVidfZxKo81YY8FHMKlayZEn8/PPP2Lp1K1JSUvDzzz/DxCT9JhEaGorExETUq1dPuk9LSwt16tRBSEjWi2/l5rhatWplOr5ixYooV66c9N/Ozs5ITk7Go0ePUKRIEfz7778y5waAevXqISgo/dPOffv2YcWKFQgNDcX79+/x5csXFC+e+1EMCQkJmeZFpmjoQEdHJ9fnovyXmJiISeNHIyUF8JkyQ9XVyVfNW7aS/r+5hSXMLazQsW1L3P77RqbRAKTeGtiWw4TOtTFq9XncfPQKVcoaYPHgRojs9gHz99wAAJz5O0Ja/l54amfLo6390KGBJbadua+imuc/XudERERE38eRK2qgX79+2Lp1K7Zt24Z+/foV+NcvWlT+J7mKuHbtGry8vNCqVSscO3YMd+7cwa+//orPnz/n+lzz5s1DiRIlZLZFC+YpXEdDA0NoaGhkWiQrKipKpoMrIxOTzJ/4R0VFwSSt19zEpGTqPknOz1nQDAyzyi3Jso7GJiaZFo6LjpLA+JvyiYmJmDRhDCIj/8Xq9ZvUYtQKABgYGEBDQwPR0bJtFx0dlekTj6+yzJxFeQAoV74CDAwM8eKf51mWKUiGaW39bY6obHKYyMkdJaet1ZnkbTy+JCWjlGERmf2lDIrgVfQHucdM7+mMPeceYuvp+7gfHoWj10IxbdsVTOhUK8tF1OI+fMbTl7Go8pN6rD8i9Otc4Xu5RH3u0zkh2NwC/d0t2Ny8zmX2/8jtLcTM9ONh54oacHNzw+fPn5GYmIiWLVvKvFalShVoa2vjypUr0n2JiYm4efMmqlatCiB1oUEAMkO0c3Jcdp4/f45//01fiPT69esQi8WwsrJC8eLF8dNPP8mcGwCuXLkiPffVq1dhamqKX3/9FbVq1YKFhQUiIiJkymtra+doWLmPjw/i4uJktgnePt897nu0tLVhU7UaAq5fk+5LTk5GQMA12Nk7yj3GzsEBAdevy+y7fu0q7NIeKV2ufHmYmJREQED6Od+/f4+7wUFZnrOgaWlpw9qmGm4GyOa+GXAdtvYOco+xs3fAjQzlASDg+lXYZSj/tWPln4gIrFm/BQYGhvlR/TzR0tKGlU1V/H0jve2Sk5Px943rqG7nIPeY6rYOMuUB4EbANVS3s8/y67z+7xXi4mJhUrKkUuqtKC2ttGv8m7a+cf26TNtlJK+tr1+7mmV5dZT4JRl3nr5GE/sK0n0iEdDEoQJuPHwl9xg9HU0kp6TI7EtOTkk7Vn7vSlFdLVQqWyLLDpuCJvTr/Ma313l293J7B9yQdy8vRNe5YHML9Xe3UHPzOpfu+9HbW4iZ1YmqnwTEpwWR0mhoaCAkJAQPHjyAhoaGzGtFixbFkCFDMGHCBJw6dQoPHjzAwIED8fHjR/Tv3x8AYGpqCpFIhGPHjuHNmzd4//59jo7Ljq6uLnr37o2goCBcunQJI0eOROfOnVGmTOpikBMmTMCCBQuwb98+PHr0CJMmTUJgYCBGjRoFALCwsMDz58+xd+9ehIaGYsWKFTh8+LDM1zAzM0NYWBgCAwMhkUiyfCSajo4OihcvLrMpa0pQz9594X9wP44eOYxnoaH4bdYMxMfHw8OzPQDgV5+JWL5sibS8V49euHrlErZt3YywZ6FYs8oP9+/dQ9fuqevkiEQiePXshQ3r1uDCubN48vgRpvhMRMlSpeDatJlS6qwMPXr1weFDB/C/3w8j7Fko5v2WmrutR2ruaZO94bc8PXc3r564evUydmzbjLCwZ1i32g8P7t9H565eAFI7VrzHjULI/Xv4bf4iJCUnQSJ5A4nkDRITcz9aKT908+qDo4cP4vj/jiD8WSgWzp2JT/HxaN3WEwAwc+okrPZbKi3fuXtPXL92Gbt3bEF42DNsXLsSDx/cQ8cuqZk/fvwAv2WLcC84CJH/vsTNgGuYOGY4yleoCCfn+irJKM/Xtj76+2E8exaKubNT27pdWltPmeyNFb4Z2rpHT1y9chnbt21G2LNnWJvW1l27eUnLxMXF4tHDEISGhgIAwsPD8OhhiHRuszpYcfg2+rpVh1dTG1hVMMSKYa4ooqOF7X88AABsHNdC5jHLJ26EYeDPtujU0BKmpYvD1bEipvV0xokbYdJOlnn966N+9XKoWEofdW3KYt/U1khKTsb+C4/l1kEVhHqd9+yVdi//PfVePufb69xnIlZkuJd3T7uXb89wL39wP/1eDqRe5w8fhuBZ2nUeERaGh2p2nQs2t0B/dws2N69zwbS3EDPTj4VrrqiJ7NYimT9/PpKTk9GzZ0+8e/cOtWrVwunTp2FomDoyoFy5cpg5cyYmTZqEvn37olevXti6det3j8uOubk52rdvj1atWiE6OhqtW7fG6tWrpa+PHDkScXFxGDduHF6/fo2qVavi6NGjsLCwAAC0bdsWY8aMwfDhw5GQkICff/4ZU6dOxYwZM6Tn6NChA/z9/dGkSRPExsZiy5Yt0sdIFxQ391aIiY7G6pUrIJG8gZW1DVav2yidAvEqMhJiUXofpINjDcxbuBgrV/jCz3cpKpqawddvFSwsLKVl+vYfiPj4eMyaMQ3v3r2FY42aWL1uo1qtEdPCrRViYqKxdrUfoiRvYGllA781G6RTAV69+hcicXoXsb1DDcyZvxhr/HyxasUyVKxohiXLV8I8Lfeb1//hrwvnAADdOnnIfK11m7ahVm2nggmWjWYt3RETE42Na/wQFSWBhZU1lq1cB6O0zP+9ioRYnN7WdvaOmDlnIdavXoG1K31RoaIpFiz1QxXz1GtcLNZA6JPHOHnsd7x79xYmJUvBqW49DBo6QjqaTB20dEu9xtesSm1rK2sbrFq7IcM1/i/EGT4OcHCogbnzF2PVSl+sXL4MFU3NsDRDWwPAX+fPYfrUydJ/T5owFgAweMgw/DJ0RAEly97Bi09gUlwP03rWRWnDIgh+JkG7aUfwOjZ1kdsKJfWlnSYAMH/PDaSkANN7OeMn42KQxMXj+I1nmLHtqrRMOZNi2O7tBqPiupDExePq/X/RaMx+SN7GF3i+rAj2OndPvaetyXgvX5t+L4+MjIRILHsvn7tgMVb5+cJveeq9fNmKVTLX+YXz5zB9SvooSe8JYwAAg4cMx5Bh6nGdCzW3UH93CzU3r3PhtLcQM9OPRZSS8s04aKJC4NMXVddANb4kCfPH9fOXZFVXQSV0tYQ5uNDYY4Wqq6ASLw8MV3UVVEJPW+P7heiHUFiGdZNyCPUvDF7nwqL7gwxVaLDksqqr8F2XxqnPiNmsCPOdOxERERERERGRkrBzhYiIiIiIiIhIAexcISIiIiIiIiJSwA8yS4yIiIiIiIiIckvExYKUgiNXiIiIiIiIiOiHsWrVKpiZmUFXVxdOTk64ceNGtuVjY2MxbNgwlC1bFjo6OrC0tMSJEydy9TU5coWIiIiIiIiIfgj79u3D2LFjsXbtWjg5OcHX1xctW7bEo0ePUKpUqUzlP3/+jObNm6NUqVI4ePAgypUrh4iICBgYGOTq67JzhYiIiIiIiEigfrRZQUuXLsXAgQPRt29fAMDatWtx/PhxbN68GZMmTcpUfvPmzYiOjsbVq1ehpaUFADAzM8v11+W0ICIiIiIiIiJSWwkJCXj79q3MlpCQkKnc58+fcevWLTRr1ky6TywWo1mzZrh27Zrccx89ehTOzs4YNmwYSpcujerVq2Pu3LlISkrKVR3ZuUJEREREREREamvevHkoUaKEzDZv3rxM5SQSCZKSklC6dGmZ/aVLl8arV6/knvvZs2c4ePAgkpKScOLECUydOhVLlizBb7/9lqs6cloQERERERERkUAVhqcF+fj4YOzYsTL7dHR0lHLu5ORklCpVCuvXr4eGhgZq1qyJly9fYtGiRZg+fXqOz8POFSIiIiIiIiJSWzo6OjnqTDExMYGGhgb+++8/mf3//fcfypQpI/eYsmXLQktLCxoaGtJ9NjY2ePXqFT5//gxtbe0c1ZHTgoiIiIiIiIio0NPW1kbNmjVx9uxZ6b7k5GScPXsWzs7Oco+pV68enj59iuTkZOm+x48fo2zZsjnuWAHYuUJEREREREQkWCKR+m+5MXbsWGzYsAHbtm1DSEgIhgwZgg8fPkifHtSrVy/4+PhIyw8ZMgTR0dEYNWoUHj9+jOPHj2Pu3LkYNmxYrr4upwURERERERER0Q+hS5cuePPmDaZNm4ZXr17BwcEBp06dki5y+/z5c4jF6eNMKlSogNOnT2PMmDGws7NDuXLlMGrUKHh7e+fq64pSUlJSlJqEqAB8+qLqGqjGlyRh/rh+/pL8/UI/IF0tYQ4uNPZYoeoqqMTLA8NVXQWV0NPW+H4h+iEUgvUSSYmE+hcGr3Nh0f1Bhio0WX5V1VX4rvOjXFRdhe/6QS4HIiIiIiIiIsqtwvC0oMJAmB+LEhEREREREREpCTtXiIiIiIiIiIgUwGlBRERERERERALFWUHKwZErREREREREREQKYOcKEREREREREZEC2LlCRERERERERKQArrlCREREREREJFBiLrqiFBy5QkRERERERESkAHauEBEREREREREpgNOCiIiIiIiIiASKs4KUg50rRIWIpoYw73xiMQfZCUno7iGqroJKlHObqeoqqETM+VmqrgIR5QP+sUZEQsO/WIiIiIiIiIiIFMCRK0REREREREQCJeJQM6XgyBUiIiIiIiIiIgWwc4WIiIiIiIiISAGcFkREREREREQkUGLOClIKjlwhIiIiIiIiIlIAO1eIiIiIiIiIiBTAaUFEREREREREAsWnBSkHR64QERERERERESmAnStERERERERERArgtCAiIiIiIiIigeKsIOXgyBUiIiIiIiIiIgWwc4WIiIiIiIiISAHsXCEiIiIiIiIiUgDXXCEiIiIiIiISKBG46IoycOQKEREREREREZEC2LlCRERERERERKQATgsiIiIiIiIiEigxZwUpBUeuEBEREREREREpgJ0rREREREREREQK4LQgIiIiIiIiIoESiTgvSBk4coWIiIiIiIiISAHsXCEiIiIiIiIiUgA7V3KpcePGGD16tNLKikQiHDlyROF6FWZ9+vSBh4eHqqtBREREREQkOCKR+m+FATtX5OjTpw9EIlGm7enTp/D398fs2bOV9rUiIyPh7u6utPNR7u3dvQvuzV1R29EWXl074W5wcLblz5w+iXat3VDb0RYdPNrg0sW/ZF5PSUnBKr/laNqoPurUsMOg/n0QERGejwnyRoi59+3ZhVYtXOFUww49u3XGvbvZZ/7j9Cl4tnGHUw07dPLMnPnsH2cwZGA/NK7nBMfq1nj0MCQ/q59nQs19+MAedPVoiRYNamJIv+4IuX832/IXzp5Gr85t0KJBTfTr7onrVy7KvB7/8SOWL5qDTq2bomXDWujTpR2O+u/Pzwh5MtizDh7uH4OYP6fi4rpBqGVTLsuymhpi+PRpjPt7RyPmz6kI2DIUzeuYK3ROVRHiPQ1gbuZmbnmYu/DmFmJm+nGwcyULbm5uiIyMlNkqVaoEIyMj6OvrK+3rlClTBjo6Oko7H+XOqZMnsHjhPAweOgx7DxyGlZU1hgzuj6ioKLnlA+/cxqQJ4+DZviP2HTyCJq5NMXrEMDx58lhaZsumDdizawemTJ+BnXv2Q09PD0MG9UdCQkJBxfouIeY+ffIEliycj8FDhmH3AX9YWllh6OABiM4ms8/EcfDw7Ig9Bw6jsWszjB05HE8zZI6Pj4dDjZoYOWZ8QcXINaHmPvfHKaxZvgi9+/+C9dv2o4q5JSaOGoyYaPm57wUHYvZUb7Rq0x4bth9A/YaumDpxFMJCn0jLrPJdiBvXr+DXmfOxbe/v6NC1B5YvnosrF88XVKzv6uhaHQuGu2HO1gtwHrAWwU9f4eiSXihpUFRu+RkDm2JA21oY63scjj1XYuPvN7FvbjfYW5TJ8zlVQYj3NIC5mZu55WHuwptbiJnpx8LOlSzo6OigTJkyMpuGhkamqT6rV6+GhYUFdHV1Ubp0aXTs2FHmPMnJyZg4cSKMjIxQpkwZzJgxQ+b1jNOCwsPDIRKJ4O/vjyZNmqBIkSKwt7fHtWvXZI7ZsGEDKlSogCJFisDT0xNLly6FgYFBlllcXFzg7e0ts+/NmzfQ0tLCxYupn8wmJCRg/PjxKFeuHIoWLQonJydcuHBBWj4iIgJt2rSBoaEhihYtimrVquHEiRMAgJiYGHh5eaFkyZLQ09ODhYUFtmzZIj32n3/+QefOnWFgYAAjIyO0a9cO4eHh2Xz3C86ObVvQvmNneHh2QBVzc0yZPhO6uro44n9IbvldO7fDpX4D9Ok3AJWrVMHwkaNhU7Uq9u7eCSC1d3zXju0YOHgImrg2g6WVNX6btxBvXr/GubN/FmS0bAkx987tW9G+Yye08+yAKlXM8eu0tMyH5Wfes3MHXOrVR+9+/VG5ShUMGzEqLfMuaZnWbdth8JBhqOvsXFAxck2ouQ/s2Y6f23WAextPmFWugrGTpkFXVw8n/3dYbvlD+3aiTt166NqzL0wrVUa/X0bAwqoqDh/YIy1z/24QWrZqC4eatVHmp3Jo49kJVcwt8fBB9iNiCtLILi7Y8r9b2HHiDh6Gv8GIxf9D/KdE9P65htzy3VvaY+GOizh9/QnCI2Ow4chNnL72GKO61svzOVVBiPc0gLmZm7nlYe7Cm1uImdWFWCRS+60wYOeKAv7++2+MHDkSs2bNwqNHj3Dq1Ck0bNhQpsy2bdtQtGhRBAQEYOHChZg1axb++OOPbM/766+/Yvz48QgMDISlpSW6deuGL1++AACuXLmCX375BaNGjUJgYCCaN2+OOXPmZHs+Ly8v7N27FykpKdJ9+/btw08//YQGDRoAAIYPH45r165h7969CA4ORqdOneDm5oYnT1I/tR02bBgSEhJw8eJF3L17FwsWLECxYsUAAFOnTsWDBw9w8uRJhISEYM2aNTAxMQEAJCYmomXLltDX18elS5dw5coVFCtWDG5ubvj8+XMuvtvKl/j5M0Ie3EddZxfpPrFYjLp1XRAcdEfuMcGBgahbV/YPSpd69REcGAgAePniBSSSN3Cqm35OfX192NrZZ3nOgibE3ImJqZkz1k8sFsOprjOCgwLlHhMcFAinDN8jAHB2qZdleXUk3NyJePzwAWrWqSvdJxaLUaN2Xdy/GyT3mAd3g1Czdl2ZfbXrusiUr2Zrj6uXLuDN6/+QkpKCO3/fwIt/IlDLyQXqQEtTA46WZXHuVqh0X0pKCs79HYo61crLPUZbSxOfPn+R2Rf/+QtcbCvm+ZwFTYj3NIC5mZu5mVtWYc8txMz049FUdQXU1bFjx6SdBwDg7u6OAwcOyJR5/vw5ihYtitatW0NfXx+mpqZwdHSUKWNnZ4fp06cDACwsLLBy5UqcPXsWzZs3z/Jrjx8/Hj///DMAYObMmahWrRqePn0Ka2tr+Pn5wd3dHePHpw7Ht7S0xNWrV3Hs2LEsz9e5c2eMHj0aly9flnam7N69G926dYNIJMLz58+xZcsWPH/+HD/99JO0DqdOncKWLVswd+5cPH/+HB06dICtrS0AoHLlyjLfB0dHR9SqVQsAYGZmJn1t3759SE5OxsaNG6XPT9+yZQsMDAxw4cIFtGjRIst657eY2BgkJSXB2NhYZr+xsTHCwp7JPUYikcDY2CRTeUmUJO31N6n7TDKfUyKRKKvqChFi7piY1MxGmTKbIDwsTO4xEokkc3kTE0SpQZ6cEmruuNgYJCclwdBINoehkTGeR8jPHR0lkVs+Jio998jxk7Fk3kx0btMMGhqaEItFGDd5Buwdayk/RB6YlCgCTU0NvI7+ILP/dcwHWJmWlHvMnzeeYmQXF1wOCsezlzFoUrMy2jW0gYZYnOdzFjQh3tMA5mbuVMydGXPLli8suYWYmX487FzJQpMmTbBmzRrpv4sWzTy3vHnz5jA1NUXlypXh5uYGNzc3eHp6okiRItIydnZ2MseULVsWr1+/zvZrZzymbNmyAIDXr1/D2toajx49gqenp0z5OnXqZNu5UrJkSbRo0QK7du1CgwYNEBYWhmvXrmHdunUAgLt37yIpKQmWlpYyxyUkJEhvcCNHjsSQIUNw5swZNGvWDB06dJDWc8iQIejQoQNu376NFi1awMPDAy4uqT3EQUFBePr0aaZ1aj59+oTQ0FDkREJCQqZ5kSkaOlyrhogK1OH9uxFyLxhzFvuhdJmyCA68heWL5sDEpCRq1lHfKVLZGb/iBFZPbIegnSORkpKCZ//GYPuJO2o15YeIiIjyVyGZdaP2OC0oC0WLFoW5ubl0+9rJkZG+vj5u376NPXv2oGzZspg2bRrs7e0RGxsrLaOlpSVzjEgkQnJycrZfO+MxX0d7fO+Y7/Hy8sLBgweRmJiI3bt3w9bWVjoK5f3799DQ0MCtW7cQGBgo3UJCQrB8+XIAwIABA/Ds2TP07NkTd+/eRa1ateDn5wcgdVRPREQExowZg3///RdNmzaVjqx5//49atasKXPewMBAPH78GN27d89R3efNm4cSJUrIbIsWzFPo+wEAhgaG0NDQyLRIVlRUlHRa07dMTEwQFSXJXD6t19zEJPWT3ChJzs9Z0ISY29AwNfO3i7hGRUlgnE3mTOUlWZdXR0LNXcLAEGINjUyL18ZER8Hom9EpXxkZm8gtb5h2jSd8+oSNa5ZjyKgJcGnQGFUsrODZqTuaNHPDvl3b8idILkniPuLLlySUMpL9MKCUYVG8inon/5jYj+g8eQ+MW/wGq05LYe+1Ah/iPyPs35g8n7OgCfGeBjA3c6di7syY+5vyhSS3EDPTj4edKwrS1NREs2bNsHDhQgQHByM8PBznzp3Lt69nZWWFmzdvyuz79t/ytGvXDp8+fcKpU6ewe/dueHl5SV9zdHREUlISXr9+LdOhZG5ujjJl0p8YUaFCBfzyyy/w9/fHuHHjsGHDBulrJUuWRO/evbFz5074+vpi/fr1AIAaNWrgyZMnKFWqVKZzlyhRIkeZfXx8EBcXJ7NN8PbJ0bHZ0dLWhk3Vagi4nr5gcHJyMgICrsHO3lHuMXYODgi4fl1m3/VrV2Hn4AAAKFe+PExMSiIgIP2c79+/x93goCzPWdCEmFtLKy1zgGzmGwHXYWfvIPcYO3sH3Lguu5j09WtXsyyvjoSbWwuW1lVx+2aAdF9ycjJu37yOarb2co+pamuP238HyOy7deOatPyXL1/w5csXiMWyH+2IxWKkKNj5rSyJX5Jw53EkmtRMn7YpEonQpGZl3Lj/IttjEz5/wb+Sd9DUEMOjUVUcu/xQ4XMWFCHe0wDmZm7mZm5ZhT23EDPTj4edKwo4duwYVqxYgcDAQERERGD79u1ITk6GlZVVvn3NESNG4MSJE1i6dCmePHmCdevW4eTJk9IRLlkpWrQoPDw8MHXqVISEhKBbt27S1ywtLeHl5YVevXrB398fYWFhuHHjBubNm4fjx48DAEaPHo3Tp08jLCwMt2/fxvnz52FjYwMAmDZtGn7//Xc8ffoU9+/fx7Fjx6SveXl5wcTEBO3atcOlS5cQFhaGCxcuYOTIkXjxImdvzHV0dFC8eHGZTVlTgnr27gv/g/tx9MhhPAsNxW+zZiA+Ph4enu0BAL/6TMTyZUuk5b169MLVK5ewbetmhD0LxZpVfrh/7x66du8BIPWPDq+evbBh3RpcOHcWTx4/whSfiShZqhRcmzZTSp2VQYi5e/Tqg8MHD+Do76mZ585OzdzOIzXzFB9vrMiQuVuPnrh65TK2b92MsGfPsHaVHx7cv4+u3dM7JuPiYvHoYYh0ilt4WBgePQyRzvFVB0LN3albLxz7/RBOHf8dEWHPsGzBbHz6FA+31h4AgLkzJmPDKl9p+Q5deuDGtSvYv2sbnoc/w9YNq/Eo5D48O6XeK4sWKwb7GrWw1m8pAm/dROS/L3Dq2BGcOfk/1G/cVAUJ5Vux7yr6tq4JLzcHWJmaYMW41iiip43tJ24DADb+2h6zBqf/TNauWh7tGtrArKwh6tmZ4uiSXhCLRVi6+3KOz6kOhHhPA5ibuZkbYO6vfoTcQsxMPxauuaIAAwMD+Pv7Y8aMGfj06RMsLCywZ88eVKtWLd++Zr169bB27VrMnDkTU6ZMQcuWLTFmzBisXLnyu8d6eXmhVatWaNiwISpWrCjz2pYtW/Dbb79h3LhxePnyJUxMTFC3bl20bt0aAJCUlIRhw4bhxYsXKF68ONzc3LBs2TIAgLa2Nnx8fBAeHg49PT00aNAAe/fuBQAUKVIEFy9ehLe3N9q3b493796hXLlyaNq0KYoXL67k707uubm3Qkx0NFavXAGJ5A2srG2wet1G6RSIV5GREIvS+yAdHGtg3sLFWLnCF36+S1HR1Ay+fqtgYZG+Xk3f/gMRHx+PWTOm4d27t3CsUROr121UqzVihJi7pXsrxMREY81KP0SlZV61dkOGzP/KjEpwcKyBuQsWY5WfL1YuX4aKpmZYumIlzDNk/uv8OUyfMln670kTxgIABg8Zhl+GjSigZNkTam7X5m6Ii43G1vWrEB0lQRVLayzwXQujtKHCr/+LlMld3c4BU2bPx+a1K7FxzXKUq2CK2QuXo1IVC2mZab8twoZVvpgzfRLevo1D6TJl0f+XEWjbvnOB58vKwXP3YGJQBNP6u6K0UTEEP32FduN34HVM6oK0FUqXQHKGJ8fpaGti+sCmqFTWEO/jP+P09SfoP/sQ4t5/yvE51YEQ72kAczM3cwPM/dWPkFuImdXF9z6op5wRpWR8Pi8VSgMHDsTDhw9x6dIlVVelwHz68v0y9ONI5m1KUGI/JKq6CipRpc1vqq6CSsScn6XqKhAREeWJ7g8yVKHjFvUZjZqVg33Vf7H9H+RyEJbFixejefPmKFq0KE6ePIlt27Zh9erVqq4WERERERERkSCxc6UQunHjBhYuXIh3796hcuXKWLFiBQYMGKDqahEREREREVEhw1lBysHOlUJo//79qq4CEREREREREaXh04KIiIiIiIiIiBTAkStEREREREREAiXmvCCl4MgVIiIiIiIiIiIF5KlzJSUlBevWrUOdOnVgYmICDQ2NTJumJgfFEBEREREREdGPL089IBMnTsTSpUvh4OCAHj16wNDQUNn1IiIiIiIiIqJ8xklBypGnzpVt27ahQ4cOfGoNEREREREREQlenqYFxcfHo1mzZsquCxERERERERFRoZOnzpWmTZvi5s2byq4LERERERERERUgkUik9lthkKfOldWrV+P69euYO3cuoqKilF0nIiIiIiIiIqJCI0drrujr62fqLfry5QumTp2KqVOnQldXFxoaGjKvi0QixMXFKa+mRERERERERERqKEedKx06dCg0Q3GIiIiIiIiIKGfE/FNfKXLUubJ169Z8rgYRERERERERUeGUpzVXZs2ahXv37mX5+v379zFr1qw8V4qIiIiIiIiIqLDIU+fKjBkzEBwcnOXr9+7dw8yZM/NcKSIiIiIiIiKiwiJH04JyKzo6Gtra2vlxaiIiIiIiIiJSEq6vqhw57ly5ePEiLly4IP23v78/nj59mqlcbGws9u3bB1tbW6VUkIiIiIiIiIhIneW4c+X8+fPSqT4ikQj+/v7w9/eXW7Zq1arw8/NTTg2JiIiIiIiIiNRYjjtXJk6ciOHDhyMlJQWlSpXC2rVr0aFDB5kyIpEIRYoUga6urtIrSkRERERERETKxVlBypHjzhU9PT3o6ekBAMLCwlCyZEkUKVIk3ypGRERERERERFQY5GlBW1NTU2XXg4iIiIiIiIioUMpT50qlSpW+u6KwSCRCaGhonipFRERERERERPmPTwtSjjx1rjRq1ChTAyQlJSEiIgJXrlxB9erV4ejoqJQKEhERERERERGpszx1rmzdujXL14KCgtCyZUt4eXnltU5ERERERERERIWGWNkntLe3x+DBg+Ht7a3sUxMRERERERGREolF6r8VBnkaufI9pUuXxoMHD/Lj1ESC9vlLsqqroBJaGkrvBy4U4j8nqboKKmFQVEvVVVCJ6HOzVF0FlbCZcFzVVShwZyc3VXUVVKKMgY6qq6ASYoGuZZCckqLqKqjElyRh5tbUEOZ1Dgg1N8mj9L9YoqKisGnTJpQvX17ZpyYiIiIiIiIiUjt5Grni6uoqd39sbCwePnyIz58/Y8eOHQpVjIiIiIiIiIjyF58WpBx56lxJTk7O1AAikQiVKlVCs2bN0K9fP1hbWyulgkRERERERERE6ixPnSsXLlxQcjWIiIiIiIiIiAqnXK+58vHjR9SsWRNr167Nj/oQERERERERUQERFYKtMMh150qRIkUQFhbGeVlERERERERERMjj04Lc3Nxw+vRpZdeFiIiIiIiIiKjQyVPnytSpU/H48WP07NkTly9fxsuXLxEdHZ1pIyIiIiIiIiL60eV4QdtZs2ahffv2qF69OqpVqwYAePDgAXbv3p3lMUlJSYrXkIiIiIiIiIjyhZhLfihFjjtXZsyYAXNzc1SvXh3Tpk3jmitERERERERERMjjo5hnzJih5GoQERERERERERVOeepcISIiIiIiIqLCj5NSlCNXnSsPHz7ExYsXc1y+YcOGua4QEREREREREVFhkqvOlTlz5mDOnDnfLZeSkgKRSMQFbYmIiIiIiIjoh5erzpWRI0eifv36+VUXIiIiIiIiIipAfFiNcuSqc6V27dro0KFDftWFiIiIiIiIiKjQEau6AkREREREREREhRmfFkREREREREQkUJwVpBw5HrnSqFEjlC5dOj/rQkRERERERERU6OR45Mr58+fzsx5ERERERERERIUSpwURERERERERCZSY84KUggvaEhEREREREREpgJ0rREREREREREQKYOcKFTgzMzP4+vqquhpERERERESCJxKp/1YYsHNFifr06QORSASRSAQtLS2ULl0azZs3x+bNm5GcnKzq6mVpxowZcHBwUHU1VGbv7l1wb+6K2o628OraCXeDg7Mtf+b0SbRr7Ybajrbo4NEGly7+JfN6SkoKVvktR9NG9VGnhh0G9e+DiIjwfEyQN/v37kJb96aoV9sefby64P7d7HP/eeYUOrZrhXq17dG1Q1tcuSSbe/2alejYrhUaONWAa30nDB3UF/eCg/IzQq7t3bML7i1cUaeGLXp064S738l85vRJeLRxQ50atujombmtz/5xBr8M7IdG9ZzgUN0KDx+G5Gf18+zgvt3w/LkZGtV1QP9eXXD/Xva5z/5xCl3a/4xGdR3g1bkdrl6WzT17+mQ416gqs40eNig/I+TJvj270KqFK5xq2KFnt8649532/uP0KXi2cYdTDTt0yqK9hwzsh8b1nOBY3RqP1LS9hXqd96xniktTm+DhQjccHu0C+4olsiy7Z1hdhC37OdO2aWBtaZmWtmWw/Zc6uP1bc4Qt+xk2PxUviBi59r9De9G7ozvautbG6IFeePTgbpZlI549xW+/jkXvju5wr2+Pw/t3Ziqzb8cmjBzQHe2bO6Nr68aY5TMaL56H52OCvBHsz7dA37MItb2F+F5NqG1NP4Ycda7MmjUr19vs2bPzu+5qyc3NDZGRkQgPD8fJkyfRpEkTjBo1Cq1bt8aXL19UXT2FJCYmqroKSnfq5AksXjgPg4cOw94Dh2FlZY0hg/sjKipKbvnAO7cxacI4eLbviH0Hj6CJa1OMHjEMT548lpbZsmkD9uzagSnTZ2Dnnv3Q09PDkEH9kZCQUFCxvuvMqRPwXbwAAwYPw469h2BhZYURQwYiOovcQYF3MGXSeLTz7ICd+/zRqElTjB89Ak8z5K5oaoYJPlOw59Dv2LB1J376qRyGDxmAmOjogoqVrdMnT2DJwnkYPGQY9hw4DEsrawwd3D/LzIF3bsNn4jh4eHbE3gOpbT1m5DCZzPHxH+FYowZGjRlfUDFy7c/TJ7Fi6QL0HzQUW3cfhIWFNcYMG4ToaPm5g4PuYPrkCWjTrj227T6Eho2bwnvsCIQ+fSJTrq5LfRw785d0mzVvUUHEybHU9p6PwUOGYfcBf1haWWHo4AE5au89Bw6jsWszjB05/Jv2jodDjZoYqcbtLdTr/GeHsvjVwwbLTz9B6yWXEfLvO2wb7ATjYtpyy/+y5RZqT/tTurVY8Be+JCXjRGCktEwRHQ3cfBaNBf97WFAxcu2vs6ewfuViePUdDL9Ne1HJ3ApTxg5BbIz89v6U8AllfiqPvr+MhKGxidwyd+/8jTbtu2DZuh2Yu2wdvnz5gl/H/IJP8R/zM0quCPXnW6jvWYTa3sJ9rya8tqYfhyglJSXle4XE4sx9MKK0sTnfHi4SiZCSkgKRSISkpCQlVbNw6NOnD2JjY3HkyBGZ/efOnUPTpk2xYcMGDBgwAOHh4ahUqRLu3LkjHTESGxsLQ0NDnD9/Ho0bN8aFCxfQpEkTnDp1CpMmTcLDhw/h7OyMvXv34tatWxg7dixevnyJ1q1bY+PGjShSpAgAoHHjxqhevToAYMeOHdDS0sKQIUMwa9YsaZtltHXrVvTt21dm35YtW6SjcFavXo2TJ0/i7NmzmDBhAszMzDB69GjExsZKyx85cgSenp4y18L//vc/zJo1C3fv3kWxYsXQoEEDHD58GACk5xg9ejQAYOPGjRg/fjwOHTqEpk2b5uh7/UlJ/VReXTuhWnVbTJ4yDQCQnJyMFk0boVv3nug/MPMn8RPGjUZ8fDxWrl4n3dejW2dYWVtj6vRZSElJQbPGDdCrT1/07tsfAPDu3Tu4NnTBrDnz4d7qZ4Xq+/mLckZA9fHqgqrVqmPi5KkAUnO3btEEnbv1QJ/+AzOV95kwBp/i47Fs5Vrpvr49usDSygY+U2fI/Rrv379Hk3q1sWr9ZtRxclaovloaig+y69Etta19fk1v65bNUtu634DMbT0xra39MrR1z+6dYWVljSnTZ8mUffnyBX5u2RR7Dx6BtbWNwnX9Kv6z4vfQ/r26wKaqLcZPmgIgNXc7d1d06uqFXn0zt/UU77GIj4/HkhVrpPsG9OoKCytreP86A0DqyJX3795iwdKVCtdPHl1txdu7Z7fOqFa9OiZlaG+3Zo3RtXsPue3tPW4M4uM/YkWG9u7VvQssrawxZfpMmbL/vnyBn1s2w96Dh2GlxPYWQfExr4XxOq868bjC5zg82gXBz+Mw3f8+gNThw1enNcW2y+FYezb0u8f3bWiGMe6WcJp+NtPPXTlDPVye5opWiy4h5N+3CtcVAM5Oztnvuu8ZPdALljbVMHTsZACp7d2rfQu07dANnXv2z/bY3h3d4dHZC56de2RbLjYmGt3aNMHClZth61BTofqWMdBR6PivCtvPt7KewlHY3rMkf/9PjBwpbO39JUk5uQvbezVNDcWv88LW1gBQRKuQzFf5jqH+D1Rdhe9a3b6qqqvwXTl6B5ucnCyz/fPPP7C1tUW3bt1w48YNxMXFIS4uDgEBAejatSvs7e3xzz//5HfdCw1XV1fY29vD398/18fOmDEDK1euxNWrV/HPP/+gc+fO8PX1xe7du3H8+HGcOXMGfn5+Msds27YNmpqauHHjBpYvX46lS5di48aNcs/fpUsXjBs3DtWqVUNkZCQiIyPRpUsXma/v6emJu3fvol+/fjmq8/Hjx+Hp6YlWrVrhzp07OHv2LOrUqSO37MKFCzFp0iScOXMmxx0rypL4+TNCHtxHXWcX6T6xWIy6dV0QHHRH7jHBgYGoW1f2l49LvfoIDgwEALx88QISyRs41U0/p76+Pmzt7LM8Z0FLTPyMhyH3USdDDrFYjDp1nXE3OFDuMXeDg1D7m9x1XepnWT4x8TMOH9qPYvr6sLS0VlbV8ywxMbWtM7aLWCyGU3ZtHRQIJ2fZzM4u9REcFJifVVWqxMTPeBTyALWd6kr3icVi1HZyxr0s2u7e3UDU/uYNlpNzvUzDhm//fROtmtZHF89WWDh3JuIydLiqWtbt7Zxl+6W2t4vMPmeXeoWuvYV4nWtpiFC9fAlcfiyR7ktJAa48kaCGqUGOztHZqQKO3YlUSodmQUlMTMSTxyFwqCX78+1Qqy5C7mc/jD43Pn54DwDQL64e06IE+/Mt4PcsgmxvvlcDIIy2Vhdfl7ZQ560w0MzLQcOGDYOFhQV27pSdq1u7dm3s2rULHTt2xLBhw6QjFQiwtrZG8Hfmxcrz22+/oV69egCA/v37w8fHB6GhoahcuTIAoGPHjjh//jy8vb2lx1SoUAHLli2DSCSClZUV7t69i2XLlmHgwMy93Hp6eihWrBg0NTVRpkyZTK93794908iW75kzZw66du2KmTPTe4zt7e0zlfP29saOHTvw119/oVq1arn6GsoQExuDpKQkGBsby+w3NjZGWNgzucdIJBIYfzOU2tjYGJIoSdrrb1L3mWQ+p0QigTqIjYlFUlISjL7JbWRsjPCwMLnHRMnJbWRsjKhvMl366zx+9R6PT5/iYWJSEivXboKBoaFyA+RBTEzWbR2em7Y2UZ92zInY2LS2Nvqm7YyMEREuP3eURCLn2jBBVFR67rou9dHYtRnK/lQeL188x9qVvhgzYjA2bN0NDQ0N5QfJpa/t/W0OY2OTLK9xiZzcxiYmma5xdSbU69ywqDY0NcSQvJOdxiB5l4AqpYp+93j7iiVg/VNxTNqnvA6JgvA2LgbJSUkwNJJtb0MjY7yIkH+d51ZycjLWrViIqrYOMKtsoZRzKkqwP98Cfc8i1PYW8ns1obU1/VjyNPb63LlzcHV1zfL1pk2b4uzZs3mu1I/o61Sp3LKzs5P+f+nSpVGkSBFpx8rXfa9fv5Y5pm7dujJfy9nZGU+ePMnTNK1atWrl+pjAwMDvjkJZsmQJNmzYgMuXL3+3YyUhIQFv376V2dRpLjClq1XbCbv2+2PT9t1wrlcfkyeMyXKeLBVezVu2QoNGrjC3sESjJs2wePkahNy/i9t/31B11YhyrbNTBTz89y2CnsepuipqZ9XSuQh/FopJMxequipEpCR8r0aUf/LUuaKrq4tr165l+frVq1ehq6ub50r9iEJCQlCpUiUA6WvYZFyjJKvFYrW0tKT///UpRBmJRKJ8fRJR0aKyn/qJxeJM6+x8W3c9Pb3vnrdBgwZISkrC/v37v1t23rx5KFGihMy2aMG8HNQ+e4YGhtDQ0Mi0EFxUVBRMTOQv9GdiIvsJvrR82icFJiYlU/dJcn7OgmZgaAANDY1Mv0ijo6JgnEUdjeXkllder0gRVKhoCls7B0ydOQcamhr4/cgh5QbIA0NDJbW1RH3aMScMDNLaOvqbtouOyvTp1lfGJiZyro3Mn4ZlVK58BRgYGOLFP88Vr7QSfG3vb3NERUmyvMZN5OSOkmRdXh0J9TqP+fAZX5KSYaIvu56Hib4O3rzNviNeT1sDrR1/wr6AwjeVuXgJQ4g1NBDzzeLUMdFRWS5Wmxurl87FjasXsWDFBpQsVVrh8ymLYH++BfqeRajtLeT3akJra3UhLgRbYZCnenp5eWHXrl0YOXIknjx5Il2L5cmTJxgxYgR2794NLy8vZde10Dp37hzu3r2LDh06AABKlkz9ZRYZmf5UgsC0+a/KEBAQIPPv69evw8LCIsvh+tra2jke1VKyZEm8e/cOHz58kO77tu52dnbfHblUp04dnDx5EnPnzsXixYuzLevj4yNd1+frNsHbJ0f1zY6WtjZsqlZDwPX0jsLk5GQEBFyDnb2j3GPsHBwQcP26zL7r167CLm1h4nLly8PEpCQCAtLP+f79e9wNDsrynAVNS0sb1jbVcDMgPUdycjJuBlyHrZ2D3GNs7exlygNAwPWrWZZPP28KEj9/VrTKCtPSSm3rGwGybX0ju7a2d8ANeW1t75CfVVUqLS1tWNlUxd83ZNv67xvXUT2Ltqtu6yBTHgBuBFxDdbvMU/u+ev3fK8TFxcIk7d6mal/bOyBTe1/Psv1S21v2Q4PC2N5CvM4Tk1Jw70Uc6lmmv5kWiQAXC2PcjojN9thW9mWhoynGkb9f5nMtlU9LSwsWljYIvJX+Oz85ORmBtwJgU80umyOzl5KSgtVL5+LqxXOYv3wDyvxUXhnVVRrB/nwL+D2LINtbwO/VhNbW9GPJ05orCxYsgEQiwcqVK7Fq1SrpSIzk5GSkpKSgW7duWLBggVIrWlgkJCTg1atXSEpKwn///YdTp05h3rx5aN26NXr16gUgdWRH3bp1MX/+fFSqVAmvX7/GlClTlFaH58+fY+zYsRg8eDBu374NPz8/LFmyJMvyZmZmCAsLQ2BgIMqXLw99fX3o6Mhf0d/JyQlFihTB5MmTMXLkSAQEBGDr1q0yZaZPn46mTZuiSpUq6Nq1K758+YITJ07IrAsDAC4uLjhx4gTc3d2hqakpfXrQt3R0dDLVR1lPC+rZuy+mTvZGtWrVUd3WDjt3bEN8fDw8PNsDAH71mYhSpUpj1JhxAACvHr3Qv09PbNu6GQ0bNsKpkydw/949TJ2R+lQNkUgEr569sGHdGphWNEW58uWxym85SpYqBdemzZRTaSXo3rM3Zk71gU216qhW3RZ7dm5HfHw82nh4AgCm/+qNkqVKY/iosQCArl69MLh/L+zctgX1GzbCmVMnEHL/PiZPTV1XJ/7jR2zeuA4NGzeBiUlJxMbG4sDe3Xjz+j80bd5SZTkz6tmrL6b+6o2q1aqjenU77NqZ2tbtPFLbekpaW49Ma+vuPXphQN+e2L51MxqktfWD+/cwbUb6E1Ti4mIRGRmJN2lT8yLS5gSbmJhIPxFUtW5efTB7ug+sq1ZHtWq22Lt7Oz7Fx6N129S2njl1EkqWKoWhI1LbunP3nhg6sDd279gCl/qN8OfpE3j44B4mTUlt648fP2DTutVo0rQFjE1M8OKf51i1fAnKV6gIJ+f6Ksv5rR69+mDar5Ok7b07U3t7o1SpUtL27tajJwb27ZXW3o1x+uRxPLh/X/qzDaS296vISOlUzK9zwI3VqL2Fep1vvBCGJd3tEfxPLIIi4tCvkRmKaGviYNqIlCXd7fEq7hMWHX8kc1yXuhVw5u5/iP2YefRoiSJa+MlAD6VLpP7+qZy2fsubdwmZ1ndRFc+uPbFkzlRYWFeDlU11HNm/Ewnx8Wj+swcAYPHsX2FcshT6/jIKQOpI0+fhqU9P+pKYiKg3rxH65CH09Irgp/IVAQCrlszFhT9PYto8X+gVKYrotE/CixYrBh0d9RiVLNifb4G+ZxFqewvxvZpQ25p+HHnqXNHW1saOHTswYcIEnDhxAhEREQAAU1NTuLu7y128VChOnTqFsmXLQlNTE4aGhrC3t8eKFSvQu3dvmUdab968Gf3790fNmjVhZWWFhQsXokWLFkqpQ69evRAfH486depAQ0MDo0aNwqBBmR9f9lWHDh3g7++PJk2aIDY2VvooZnmMjIywc+dOTJgwARs2bEDTpk0xY8YMmfM3btwYBw4cwOzZszF//nwUL14cDRs2lHu++vXr4/jx42jVqhU0NDQwYsQIhbLnlpt7K8RER2P1yhWQSN7AytoGq9dtlA4nfBUZCbEovd0cHGtg3sLFWLnCF36+S1HR1Ay+fqtgYWEpLdO3/0DEx8dj1oxpePfuLRxr1MTqdRuz7LBShRZurRAbE4N1q1cgSiKBpZUNVqxeL5368epVJEQZrld7B0f8Nm8R1qxcjtV+y1ChoikW+/rBPC23WEMD4WHPcPzoEcTGxqCEgQGqVrPF+i07UcVcPRZBbOneCjEx0ViTsa3Xprd1ZKRsZgfHGpi7YDFW+fnCb3lqWy9bsUqaGQAunD+H6VPSR1F5TxgDABg8ZDiGDCvYazkrzVq6IyYmGhvX+CEqSgILK2ssW7kORmlt/d+rSJl7k529I2bOWYj1q1dg7UpfVKhoigVL/aTtKBZrIPTJY5w89jvevXsLk5Kl4FS3HgYNHQFtbW2VZJQnvb39EJXW3qvWbsjws/0vxOL0takytvfK5ctQ0dQMS1eslGnvv86fw/Qpk6X/njQh9Q3t4CHD8IuatLdQr/PjgZEwLqaNsW6WMCmug5CXb9Fn3Q1I3qd+GvuToV6mx8JWLlkUtSsboeeaAHmnRLNqpbG4e/r7mZW9awAAfE89xvLTT/IpSe40auqGuNgY7Ny4GtHRElQxt8LsJauli9y+/u+VTHtHS15jeN/0JwIe2rMNh/Zsg61DLSxcuQkAcPxI6nRd7xGyj3IeO3kWmrdql9+RckSoP99Cfc8i1PYW9ns1YbW1OigsT+NRd6KUbxfQoEKtcePGcHBwgK+vr6qrkq+UNXKlsPn8Jf/W11FnWhqFZaalchWmx8Iqk662MNtbBGG+sak68biqq1Dgzk7OftH3H1UZA/X5g70giQX6R8u3HZpC8SVJmLk1NYR5nRfR+jFyjzzyUNVV+K4VHqp/ZPj35GnkSkbv379HTExMpkVOAaBixYqKnp6IiIiIiIiISK3lqXPl06dPmDlzJjZt2pRp1fKM8vLoXyIiIiIiIiIqGOIfYwCOyuWpc2Xo0KHYtm0bPDw80KBBAxgaGiq7XpRHFy5cUHUViIiIiIiIiAQlT50r/v7+GDBgANatW6fs+hARERERERERFSp56lwRiUSoUaOGsutCRERERERERAWI04KUI0+PZGjXrh3+/PNPZdeFiIiIiIiIiKjQyVPnytSpU/Hs2TMMGjQIt27dwps3bxAdHZ1pIyIiIiIiIiL60eVpWpCFhQUA4M6dO9i0aVOW5fi0ICIiIiIiIiL1JRJxXpAy5KlzZdq0aWwAIiIiIiIiIiLksXNlxowZSq4GEREREREREVHhlKc1V4iIiIiIiIiIKFWeRq7MmjXru2VEIhGmTp2al9MTERERERERUQHgo5iVQ+nTgkQiEVJSUti5QkRERERERESCkKdpQcnJyZm2L1++IDQ0FGPGjEGtWrXw+vVrZdeViIiIiIiIiEjtKG3NFbFYjEqVKmHx4sWwsLDAiBEjlHVqIiIiIiIiIsoHIpH6b4VBvixo27BhQ5w4cSI/Tk1EREREREREpFbypXPl77//hljMBxERERERERER0Y8vTwvabt++Xe7+2NhYXLx4Ef7+/hgwYIBCFSMiIiIiIiKi/CUuLPNu1FyeOlf69OmT5WsmJiaYNGkSpk2bltc6EREREREREREVGnnqXAkLC8u0TyQSwdDQEPr6+gpXioiIiIiIiIiosMhT54qpqamy60FEREREREREBYyrpSpHnjpXvgoLC8PJkycREREBILXTxd3dHZUqVVJK5YiIiIiIiIiI1F2eO1fGjRuH5cuXIzk5WWa/WCzG6NGjsXjxYoUrR0RERERERESk7vI0AmjJkiVYtmwZ2rdvj2vXriE2NhaxsbG4du0aOnbsiGXLlmHZsmXKrisRERERERERKZFIpP5bYZCnkSsbNmxA27ZtsX//fpn9Tk5O2Lt3Lz59+oR169ZhzJgxSqkkEREREREREZG6ytPIlfDwcLRs2TLL11u2bInw8PC81omIiIiIiIiIqNDI08iVUqVKISgoKMvXg4KCULJkyTxXiojk09QoJGPilKywDAVUNh0tYa7dLhZog7+NT1R1FVQicK67qqtQ4Mq0WajqKqhEzKlJqq4CFSCh3su1NYWZmwo3of68Klue3rl36tQJGzduxPz58/Hhwwfp/g8fPmDBggXYuHEjunTporRKEhERERERERGpqzyNXJk9ezYCAwMxefJkTJs2DT/99BMA4N9//8WXL1/QpEkTzJo1S6kVJSIiIiIiIiJSR3nqXClSpAjOnj2L33//HSdPnkRERAQAwM3NDa1atUKbNm0g4tAiIiIiIiIiIhKAXHeufPz4ET169ECHDh3g5eWFdu3a5Ue9iIiIiIiIiCifcVyEcuR6zZUiRYrgzz//xMePH/OjPkREREREREREhUqeFrStX78+rl27puy6EBEREREREREVOnnqXFm5ciUuXbqEKVOm4MWLF8quExEREREREREVALFI/bfCIE+dK/b29njx4gXmzZsHU1NT6OjooHjx4jJbiRIllF1XIiIiIiIiIiK1k6enBXXo0IFPAyIiIiIiIiIiQh47V7Zu3arkahARERERERFRQRNz4IRS5GlaEBERERERERERpcrVyJV//vkHYrEY5cqVAwB8+vQJq1evzlSufPny6Ny5s3JqSERERERERESkxnLcuXL37l04OjrC19cXw4cPBwB8+PAB48ePh0gkQkpKirSshoYGbGxsYGtrq/waExEREREREZFScFaQcuR4WtC6detgamqKoUOHZnpt586dCAsLQ1hYGEJDQ/HTTz9h3bp1Sq0oEREREREREZE6yvHIlfPnz6N9+/YQizP3x5QuXRqmpqbSf3fv3h1Hjx5VTg2JiIiIiIiIiNRYjjtXwsPDYW1tLXuwpibs7e2hr68vs79SpUqIiIhQTg2JiIiIiIiIKF+IOS1IKXK1oG1ycrLMv0uUKIE7d+5kKvftGixERERERERERD+qHK+5Ur58eQQFBeWobFBQEMqXL5/nShERERERERERFRY57lxp3rw5du3ahdevX2db7vXr19i1axeaN2+ucOWIiIiIiIiIKP+ICsF/hUGOO1fGjx+PxMRENG3aFH///bfcMn///TeaNWuGxMREjBs3TmmVJCIiIiIiIiJSVzlec8XMzAx79+5Ft27d4OTkBHNzc1SvXh3FihXD+/fvce/ePTx9+hR6enrYvXs3KlWqlJ/1JiIiIiIiIiJSC7la0LZ169YICgrCggULcPz4cRw+fFj6WtmyZdG/f39MnDgR5ubmSq8oEREREREREZE6ylXnCgBUrlwZ69atAwC8e/cOb9++hb6+PooXL670yhERERERERFR/uGjmJUj150rGenr60NfX19ZdSEiIiIiIiIiKnRyvKAt5Y8+ffpAJBJl2tzc3FRdtRwzMzODr6+vqquRZ3t374J7c1fUdrSFV9dOuBscnG35M6dPol1rN9R2tEUHjza4dPEvmddTUlKwym85mjaqjzo17DCofx9ERITnY4K82bdnF1q1cIVTDTv07NYZ9+5mn/uP06fg2cYdTjXs0Mkzc+6zf5zBkIH90LieExyrW+PRw5D8rH6eCLmtf27piro17dCre87aun0bd9StaYfOnm1wOUPuxMRELF+6GJ0928CljiNauDbA1MneePP6v/yOkWtCbW///XvQqU0LNHWpgUG9u+HBvbvZlj//52l4dWiDpi410LuLJ65dvijzeoNa1eVuu7dvzs8YuXZg7y60c2+K+nXs0bdHF9z/znX+55lT6OTRCvXr2KNbx7a4ckm2vdevWYlOHq3QsG4NNG3ghGGD++Le3aD8jJAng9vWwMOdQxBzYjwu+vVCLauy2ZYf3r4WgrYMRPTxcXiyeygWDmkKHS0N6esPdw5B/J+TMm3LRqjXUyCF+vPN3MydnR8htxAzU/5YtWoVzMzMoKurCycnJ9y4cSNHx+3duxcikQgeHh65/prsXFEDbm5uiIyMlNn27Nmj6moVuM+fPxf41zx18gQWL5yHwUOHYe+Bw7CyssaQwf0RFRUlt3zgnduYNGEcPNt3xL6DR9DEtSlGjxiGJ08eS8ts2bQBe3btwJTpM7Bzz37o6elhyKD+SEhIKKhY33X65AksWTgfg4cMw+4D/rC0ssLQwQMQnU1un4nj4OHZEXsOHEZj12YYO3I4nmbIHR8fD4caNTFyzPiCipErgm3rUyewdNF8DPplGHbv94eFpRWGZdPWQYG3Mdl7HNq174jdX9t6VHpbf/r0CQ9DHmDA4KHYve8QFi/zQ0R4GEaPGFqQsb5LqO199sxJrFy2EH0GDsHGnQdgbmmFcSMGIyZafu67QXcw89eJ+LmdJzbtOoAGjV0xefxIPHv6RFrmyKkLMtukabMhEonQ2FV9/tj+4/QJ+C5ZgAGDh2H7nkOwsLTCyKEDEZ1F7uDAO5jqMx5tPTpgx15/NGrSFBPGjEDo0/T2rmhqhgmTpmDPwd+xfstOlP2pHEYMGYCY6OiCivVdHRtbY8Evrpiz4zKcf9mC4GevcXR+F5Q0KCK3fBfXqpg9oDHm7rgCh34b8cuSE+jYyBqz+jeSlqk/bCvMOvlJt1YTU9+P+F98VCCZckKoP9/Mzdw/em4hZlYXYpH6b7mxb98+jB07FtOnT8ft27dhb2+Pli1b4vXr19keFx4ejvHjx6NBgwZ5+z7m6ShSKh0dHZQpU0ZmMzQ0xIULF6CtrY1Lly5Jyy5cuBClSpXCf/+lfkocGxuLwYMHo3Tp0tDV1UX16tVx7NgxAEBUVBS6deuGcuXKoUiRIrC1tZXptFm/fj1++uknJCcny9SnXbt26NevHwAgNDQU7dq1Q+nSpVGsWDHUrl0bf/75p7Rs48aNERERgTFjxkhH3Xx16NAhVKtWDTo6OjAzM8OSJUtkvo6ZmRlmz56NXr16oXjx4hg0aJCSvqM5t2PbFrTv2Bkenh1QxdwcU6bPhK6uLo74H5JbftfO7XCp3wB9+g1A5SpVMHzkaNhUrYq9u3cCSO0d37VjOwYOHoImrs1gaWWN3+YtxJvXr3Hu7J9yz6kKO7dvRfuOndDOswOqVDHHr9PSch+Wn3vPzh1wqVcfvfv1R+UqVTBsxKi03LukZVq3bYfBQ4ahrrNzQcXIFaG29a7tW+HZIbWtK39taz1d/J5FW+/euQPO9eqjd9/+qFy5CoaOGAXrqlWxb09qW+vr62PNhs1o4eYOs0qVYWfvAO/JUxHy4D4iI/8tyGjZEmp779u1HW08OuLntp6oVLkKxvtMg66uLo4fPSy3/MG9O1HHuR669+oHs0pVMGDICFhaV4X//t3SMsYmJjLb5b/Ow7FWHfxUvkJBxfqu3Tu2waN9J7TxaI/KVcwxacoM6Orq4n9H/OWW37t7O+q61EfPPv1RqXIV/DJsFKxtbLB/b3put1atUaeuC8qVr4Aq5hYYPW4SPrx/jydP1KeTYWSHOthyIgg7Tt/Fw+dRGOF7CvEJiejtZie3fN2q5XDt3gvsO/cAz/+Lw9lb4dh/PgS1rNNHu0ji4vFfzAfp1srJHKEvY3Ap6HlBxfouof58Mzdz/+i5hZiZ8sfSpUsxcOBA9O3bF1WrVsXatWtRpEgRbN6c9ajbpKQkeHl5YebMmahcuXKevi47V9RY48aNMXr0aPTs2RNxcXG4c+cOpk6dio0bN6J06dJITk6Gu7s7rly5gp07d+LBgweYP38+NDRSh/d++vQJNWvWxPHjx3Hv3j0MGjQIPXv2lA6J6tSpE6KionD+/Hnp14yOjsapU6fg5eUFAHj//j1atWqFs2fP4s6dO3Bzc0ObNm3w/Hnqmyx/f3+UL18es2bNko66AYBbt26hc+fO6Nq1K+7evYsZM2Zg6tSp2Lp1q0zGxYsXw97eXpqtICV+/oyQB/dR19lFuk8sFqNuXRcEB92Re0xwYCDq1pXtPHCpVx/BgYEAgJcvXkAieQOnuunn1NfXh62dfZbnLGiJiam5M9ZRLBbDqa4zgoMC5R4THBQIpwzfJwBwdqmXZXl1w7bOeVvfDQqUKQ98v63fv3sHkUgEfX31WNhcuO2diMcPH6CmU13pPrFYjFp16uJ+sPzpLPeCg1CrjmzuOs4uWU5/iY6S4Nrli2jdrr3yKq6gxMTPeBhyH7Wd0nOIxWLUdnLG3eBAucfcDQ5CHSfZ3HWd62dZPjHxM44c2o9ixfRhaWmtrKorREtTDEfLMjh3O1y6LyUFOHc7HHWqlpN7zPUHL+FoWUY6dcisbAm0rFMZpwKeZfk1ujarhm2nsh+WX5AE+/PN3NJ9zP1j5hZiZsofnz9/xq1bt9CsWTPpPrFYjGbNmuHatWtZHjdr1iyUKlUK/fv3z/PXVmhBW1KOY8eOoVixYjL7Jk+ejMmTJ+O3337DH3/8gUGDBuHevXvo3bs32rZtCwD4888/cePGDYSEhMDS0hIAZHrZypUrh/Hj06dojBgxAqdPn8b+/ftRp04dGBoawt3dHbt370bTpk0BAAcPHoSJiQmaNGkCALC3t4e9vb30HLNnz8bhw4dx9OhRDB8+HEZGRtDQ0IC+vj7KlCkjLbd06VI0bdpU2mFiaWmJBw8eYNGiRejTp4+0nKurK8aNG6eMb2OuxcTGICkpCcbGxjL7jY2NERYm/42mRCKBsbFJpvKSKEna629S95lkPqdEIlFW1RUSE5Oa2yhTbhOEh4XJPUYikWQub2KCKDXJ9D1CbevYLNra6Dttnfn7lHVbJyQkYPmyxXBz/znTfUxVhNrecWm5jYxk62hoZIyIcPntHR0lyVTeyMgE0VHyM508dhRFihZBwybN5L6uCrExsVlc51nnjpJIYPRNexsZGyP6m7a8dPE8pniPx6dP8TAxKYmVazfBwNBQuQHyyKREEWhqiPE65oPM/tcxH2BVwVjuMfvOPYBxcT2c9e0BkQjQ0tTA+v/dxqI98t9stq1nCYNiuth5Jvt1ewqSUH++mZu5gR87txAzq5OMsw/UVUJCQqbpXDo6OtDR0ZHZJ5FIkJSUhNKlS8vsL126NB4+fCj33JcvX8amTZsQmNYxl1ccuaIGmjRpgsDAQJntl19+AQBoa2tj165dOHToED59+oRly5ZJjwsMDET58uWlHSvfSkpKwuzZs2FrawsjIyMUK1YMp0+flo46AQAvLy8cOnRIeqHu2rULXbt2hVicemm8f/8e48ePh42NDQwMDFCsWDGEhITInEOekJAQ1KtXT2ZfvXr18OTJEyQlJUn31apV67vfn4SEBLx9+1Zm4zxJItVLTEyE9/jRAACfqTNUWhcqGCeOHkZzt9aZ3sj8qGrVdsLOff7YuG036tarD5+JY7Jcx6UwaGBfERO6O2PUitNwHrIVXab7w92pCiZ5ucgt39vdDqdvPENk1PsCrikREZGsefPmoUSJEjLbvHnzFD7vu3fv0LNnT2zYsAEmJibfPyAb7FxRA0WLFoW5ubnMZmRkJH396tWrAFKn7ERnWEhPT08v2/MuWrQIy5cvh7e3N86fP4/AwEC0bNlSZuHYNm3aICUlBcePH8c///yDS5cuSacEAcD48eNx+PBhzJ07F5cuXUJgYCBsbW2Vtvhs0aJFv1tG3g/SogWK/yAZGhhCQ0Mj0yJZUVFRWf5gmZiYIOqbT3SjoqJgktZrbmJSMnWfJOfnLGiGhqm5v13QNCpKAuNscmcqL8m6vLoRalsbZNHW0VGZP+n5KjX396+NxMRETBo/BpH//ovV6zepzagVQLjtXSIt97d//MdER2XZ3kbGJpnKR0dnHtUBAEF3buF5RBjaeKjPlCAAMDA0yOI6j8ryHmVsknl0TnRUFIy+Ka+nVwQVKprC1s4BU2fMgaaGBo5msV5RQZPEfcSXpGSUMpT9PVrKsChefTOa5avpfRpgz5/3sfVkMO6HvcHRK48xbdNFTOjmjG8/tKxYqjhcHc2w9aR6PSFJqD/fzM3cwI+dW4iZKXd8fHwQFxcns/n4+GQqZ2JiAg0NDekapV/9999/MjMtvgoNDUV4eDjatGkDTU1NaGpqYvv27Th69Cg0NTURGhqa4zqyc0XNhYaGYsyYMdiwYQOcnJzQu3dv6QK0dnZ2ePHiBR4/fiz32CtXrqBdu3bo0aMH7O3tUbly5UxldXV10b59e+zatQt79uyBlZUVatSoIXOOPn36wNPTE7a2tihTpgzCw8NlzqGtrS0zGgUAbGxscOXKlUz1sbS0lK4Jk1PyfpAmeGf+QcotLW1t2FSthoDr6cOhk5OTERBwDXb2jnKPsXNwQMD16zL7rl+7CjsHBwBAufLlYWJSEgEB6ed8//497gYHZXnOgqallZY7QDb3jYDrsLN3kHuMnb0DblyXHTZ+/drVLMurG6G39Y1v2/p61m1ta+8gUx4AAr5p668dK8+fR2Dthi0wMFCPaRJfCbe9tWBpXRW3bgRI9yUnJ+PWzQBUs7OXe0x1O3vcuimb+++Aa6hum7n8sd/9YWVTFeZqsubIV1pa2rC2qYabN9JzJCcn4+8b12Fr5yD3GFs7e5nyABBw/WqW5aXnTUlRyZPt5En8kow7j1+hSQ0z6T6RCGjiaIobD17KPUZPRwvJySky+76+p/h2SHhPNzu8jv2Ik9efKrfiChLszzdzS/cx94+ZW4iZ1YmqnwSUk01HRwfFixeX2eSNpNXW1kbNmjVx9uxZ6b7k5GScPXsWznIevGFtbY27d+/KzCJp27atdHZJhQo5X8CfnStqICEhAa9evZLZvs4V69GjB1q2bIm+fftiy5YtCA4Olj51p1GjRmjYsCE6dOiAP/74A2FhYTh58iROnToFALCwsMAff/yBq1evIiQkBIMHD87UgwekTg06fvw4Nm/eLDNq5es5/P39ERgYiKCgIHTv3j3T04XMzMxw8eJFvHz5Ujp/cdy4cTh79ixmz56Nx48fY9u2bVi5cqXMGjA5ldMfpLzo2bsv/A/ux9Ejh/EsNBS/zZqB+Ph4eHimfjL7q89ELF+W/pQjrx69cPXKJWzbuhlhz0KxZpUf7t+7h67dewBIfXPq1bMXNqxbgwvnzuLJ40eY4jMRJUuVgmtT9VmjoEevPjh88ACO/p6ae+7s1Nzt0j6RnuLjjRUZcnfr0RNXr1zG9q2bEfbsGdau8sOD+/fRtXv69RIXF4tHD0OkvbvhYWF49DBEOt9V1YTa1l69+uDwoQP43++H8exZelu3TWvrqZO94eebnrt7j564duUydmxLa+vVqW3dpVtqWycmJmLi2FF4cP8e5sxfhKTkJEgkbyCRvEFionr80QkIt727ePXCsSMHcfLY7wgPC8WSebMRHx+PVm08AAC/TfPB2pXp00s7du2BgKtXsHfnVkSEP8Pmdavw8MF9tO/cXea8H96/x4U/z6B1uw4FGSfHuvfsjd/9D+DY0SMIexaKBXNmIj4+Hq3beQIApk/xxqoVS6Xlu3bvhWtXL2PX9i0ID3uG9WtWIuTBfXTumpo7Pv4jVq9YhrvBgYj89yVCHtzH7Om/4s3r/9C0eUuVZJRnxaEb6NvKHl7Nq8OqojFWjGqJIrra2J62AO1G79Yyj1k+cf0pBrZxRKfGNjAtUwKuNcwwrU9DnLj+VKbTRSQCerW0xa4/7iLpm84YdSDUn2/mZu4fPbcQM1P+GDt2LDZs2IBt27YhJCQEQ4YMwYcPH9C3b18AQK9evaSjXr4+cTfjZmBgAH19fVSvXh3a2to5/rpc0FYNnDp1CmXLlpXZZ2Vlhe7duyMiIkL6aOWyZcti/fr16NatG1q0aAF7e3scOnQI48ePR7du3fDhwweYm5tj/vz5AIApU6bg2bNnaNmyJYoUKYJBgwbBw8MDcXFxMl/L1dUVRkZGePToEbp3l31DvXTpUvTr1w8uLi4wMTGBt7c33r59K1Nm1qxZGDx4MKpUqYKEhASkpKSgRo0a2L9/P6ZNm4bZs2ejbNmymDVrlsxiturAzb0VYqKjsXrlCkgkb2BlbYPV6zZKh5K/ioyEWJTeB+ngWAPzFi7GyhW+8PNdioqmZvD1WwULi/R1b/r2H4j4+HjMmjEN7969hWONmli9bqNarVHQ0r0VYmKisWalH6LScq9auyFD7n8hzvBAeQfHGpi7YDFW+fli5fJlqGhqhqUrVsI8Q+6/zp/D9CmTpf+eNGEsAGDwkGH4ZdiIAkqWNcG2tVtq7jWr0tt65bdtneETa3uHGpgzfzFWr8zQ1svT2/rN6//w14VzAICuHT1kvtb6zdtQq7ZTwQT7DqG2d9MW7oiNicGmtSsRHSWBuaU1FvutlU7z+e9VJETi9Ny29o6YPmcBNqz2w/pVy1G+ginmLl6ByuYWMuc9e+YkUlJS0MytVYHmyanmLVshJiYG69esQJREAksrGyxfvV46Heq/b9rbzsERs+cuwtpVy7HabxkqVDTFomV+qGKe2t5isQbCw5/h+LgjiI2NQQkDA1StZov1m3eiyjffG1U6eOEhTEoUwbQ+DVDasCiCQ1+jnc8+vI79CACoUKq4TKfJ/J1XkJKSgul9G+Ink2KQxH3E8WtPMWPzRZnzutYwQ8XSJbDtpPo8JSgjof58Mzdz/+i5hZiZ8keXLl3w5s0bTJs2Da9evYKDgwNOnTolXeT2+fPn0jVGlUmUkpKifh9JEH3Hpy+qroFqJAv0x1VcCFYwzw/q+IlxQdAQC7O938YnqroKKqGjmbupoj+CMm0WqroKKhFzapKqq0BEpFS6P8hQhaUX5T+RSZ2MbVj5+4VUjNOCiIiIiIiIiIgUwM4VIiIiIiIiIiIF/CADmYiIiIiIiIgot4Q6BV/ZOHKFiIiIiIiIiEgB7FwhIiIiIiIiIlIAO1eIiIiIiIiIiBTANVeIiIiIiIiIBErMJVeUgiNXiIiIiIiIiIgUwM4VIiIiIiIiIiIFcFoQERERERERkUDxSczKwZErREREREREREQKYOcKEREREREREZECOC2IiIiIiIiISKDE4LwgZeDIFSIiIiIiIiIiBbBzhYiIiIiIiIhIAZwWRERERERERCRQfFqQcnDkChERERERERGRAti5QkRERERERESkAE4LIiIiIiIiIhIoMacFKQVHrhARERERERERKYCdK0RERERERERECmDnChERERERERGRArjmChEREREREZFAifksZqXgyBUiIiIiIiIiIgWwc4WIiIiIiIiISAGcFkREREREREQkUJwVpBzsXKFCKTk5RdVVUA2B3viSBNreAm1uwf5862hqqLoKKqGtKbxBtFEnvVVdBZWwGPW7qqugEo9926m6CirxPuGLqqugEgmJyaqugkoYF9NWdRWIVE5472iIiIiIiIiIiJSII1eIiIiIiIiIBIpPC1IOjlwhIiIiIiIiIlIAO1eIiIiIiIiIiBTAaUFEREREREREAsVZQcrBkStERERERERERApg5woRERERERERkQI4LYiIiIiIiIhIoDjiQjn4fSQiIiIiIiIiUgA7V4iIiIiIiIiIFMBpQUREREREREQCJeLjgpSCI1eIiIiIiIiIiBTAzhUiIiIiIiIiIgWwc4WIiIiIiIiISAFcc4WIiIiIiIhIoLjiinJw5AoRERERERERkQLYuUJEREREREREpABOCyIiIiIiIiISKDEfxawUHLlCRERERERERKQAdq4QERERERERESmA04KIiIiIiIiIBIqTgpSDI1eIiIiIiIiIiBTAzhUiIiIiIiIiIgVwWhARERERERGRQPFhQcrBkSsCtXXrVhgYGEj/PWPGDDg4OGR7TJ8+feDh4SH9d+PGjTF69Oh8qR8RERERERFRYcHOlUKoT58+EIlE0s3Y2Bhubm4IDg7O8Tm6dOmCx48fK1QPf39/zJ49W6FzqIN9e3ahVUtXONW0Q8/unXHvbvbfxz9On4JnG3c41bRDJ882uHTxL5nXz/55BkMG9UPj+k5wtLXGo4ch+Vn9PNu3ZxdatXCFUw079OyWi9w1ssj9xxkMGdgPjes5wbG6eubet2cXfm7piro17dArh23dvo076ta0Q2fPNricIXNiYiKWL12Mzp5t4FLHES1cG2DqZG+8ef1ffsfINUFf4wLMfWDvLrRzb4r6dezRt0cX3P9O7j/PnEInj1aoX8ce3Tq2xZVLf2VZdt5vM1DHwQZ7dm5TdrUVtnfPLri3cEWdGrbo0a0T7n4n95nTJ+HRxg11atiio5z2TklJweqVy9GscX041bTD4AF9EBERno8J8kaI93IA6N2wEq7Oao4nvq1xdEJDOJgaZFl2/6h6+GdVu0zb1iFO0jJjWlnh/FRXPFr6M+4ucsfuES5wMDMsgCS5I9Tr/ND+3ejYujlcnR0xsFdXPLiXfe5zf5xG9/at4ersiF6dPXDt8sVMZcLDQuE9ZhhaNnRCs3q1MKBnZ7yK/De/IuTakYN70N2jJdwa1sSwft3x8P7dbMv/dfY0+nRpA7eGNTHAyxMBV2UzR0dJsGDWr+jc2hWtGtXGpNG/4MXziPyMkCfKvsbP/nEGvwzsh0b1nOBQ3QoP1fSeRj8Gdq4UUm5uboiMjERkZCTOnj0LTU1NtG7dOsfH6+npoVSpUgrVwcjICPr6+gqdQ9VOnzqBJYvmY/Avw7B7vz8sLa0wdPAAREdFyS0fGHgbPt7j4NG+I/YcOIzGrs0wdtRwPH2S3lEVHx8PB8eaGDlmfEHFyLXTJ09gycL5GDxkGHYf8Iel1Xdy37kNn4nj4OGZIfdIOblrqG/u06dOYOmi+RiU1tYWllYYlk3moMDbmOw9Du3ad8RuOW396dMnPAx5gAGDh2L3vkNYvMwPEeFhGD1iaEHG+i7BXuMCzf3H6RPwXbIAAwYPw/Y9h2BhaYWRQwciOlp+7uDAO5jqMx5tPTpgx15/NGrSFBPGjEDo08yd7+fP/YF7wUEoWVKx3x35IfWeNg+DhwzDngOHYWlljaGD++fonrb3wBE0cW2KMSOHybT31s0bsHvXDvw6bQZ27N4PPT09DB3cHwkJCQUV67uEeC8HgDY1fsLU9tXge+IRWs3/Cw9exGHHcGcYF9OWW37Qhhuo4XNKujX97Ry+JCXj+J30P6TDXr/H1P130XzOeXRYehkvoj5i13BnGGVxTlUQ6nV+9sxJrFy6EH0HDcWmXQdgbmmFscMHIyaL+9rdoDuY+esEtPZoj827D6JBY1f4jBuBZ0+fSMu8/Oc5hvbvCVOzSvBbvxXb9vqjz4BfoKOjU1CxsnX+j1NYu3wReg34BWu37UcVC0t4j8468/3gQPw2zRvubdpj3bYDqNfQFdMmjkJYaGrmlJQUTPMehch/X2DWwhVYt30/SpUpiwkjByI+/mNBRstWflzj8fEf4VijBkap8T1NHWT84F5dt8KAnSuFlI6ODsqUKYMyZcrAwcEBkyZNwj///IM3b97gwoULEIlEiI2NlZYPDAyESCRCeHg4gMzTgr6VlJSEsWPHwsDAAMbGxpg4cSJSUlJkynw7LcjMzAxz585Fv379oK+vj4oVK2L9+vUyx1y9ehUODg7Q1dVFrVq1cOTIEYhEIgQGBir4Hcmbndu3on2HTmjn2QFVqpjj12kzoauniyOHD8ktv2fnDrjUq4/effujcuUqGDZiFGyqVsXePbukZVq3aYfBQ4ahbl3ngoqRazu3b0X7jt/k1s1B7n79UblKhty7M+Rum5bbWT1z79q+FZ5pbV05Q1v/nkXm3Tt3wDlDWw8dMQrWVatiX1pb6+vrY82GzWjh5g6zSpVhZ+8A78lTEfLgPiLV6JMvQV/jAsy9e8c2eLTvhDYe7VG5ijkmTZkBXV1d/O+Iv9zye3dvR12X+ujZpz8qVa6CX4aNgrWNDfbv3S1T7vV//2HJ/DmYNXchNDXVb7m2Hdu3oH3HzvBIa+8p37mn7d65HS71GqBPvwFp97TRafe0nQBS/xDZtWM7Bg4agiauzWBpZY3ZcxfizevXOH/2z4KMli0h3ssBYGBTc+y5GoH915/jyat38NkbhE+fk9DF2VRu+diPiXjzNkG6NbAuifjPSTh2O/1efeTvl7j86A2eR33E48h3mOV/D8X1tGBTrnhBxfouoV7ne3duQxvPjvi5rScqVTbHhMnToauri2O/y7+vHdizE07O9dG9Vz+YVaqCgUNHwtK6Kg7tT7+vrV+9As71GmLoqPGwtLZBuQoVUb+RKwyNjAsqVrYO7tmOVu06wK21J8wqVcFo72nQ0dXDqWOH5Zb337cTtevWQ5cefWFaqTL6Dh4BC6uqOHJwDwDgxT8RCLkXjNETp8K6anVUMK2E0ROn4nNCAs6dOVmQ0bKl7GscAFq39cDgIcPhpMb3NPpxsHPlB/D+/Xvs3LkT5ubmMDZWzi+FJUuWYOvWrdi8eTMuX76M6OhoHD4s/4b+7XG1atXCnTt3MHToUAwZMgSPHj0CALx9+xZt2rSBra0tbt++jdmzZ8Pb21sp9c2LxMTPCHlwH051XaT7xGIxnOo6IzgoUO4xwUGBMuUBwNmlXpbl1VGeczsX3tx5yXw3D239/t07iEQi6Ourx5txXuPCy/0w5D5qO6W/gRSLxajt5Iy7wYFyj7kbHIQ6TrJvOOs615cpn5ycjOlTvNGjdz9UMbfIj6orJOv2dkFw0B25x6Te02RzO7vUl7b3yxcvIJG8kbnv6evrw9bOHkFZnLOgCfFeDgBaGiLYViiByw/fSPelpACXHr5Bzco5m8bT1dkUR2+9RPznpCy/hlc9U8R9TMSDF2+VUm9FCfk6f/zwAWrVkb2v1apTF/fvBsk95l5wIGo51ZXZ5+RcD/fS7mvJycm4evkvVKhoirHDBqJ1swYY2KsrLp4/m285ciMxMRGPHz1AjdrpGcRiMWrUrosHWWR+cC8INWvLZq5V10VaPvHzZwCAtnb6yByxWAwtLS3cC7qt7Ah5kh/XOFFBY+dKIXXs2DEUK1YMxYoVg76+Po4ePYp9+/ZBLFZOk/r6+sLHxwft27eHjY0N1q5dixIlSnz3uFatWmHo0KEwNzeHt7c3TExMcP78eQDA7t27IRKJsGHDBlStWhXu7u6YMGGCUuqbFzExMUhKSoLRNx1SxsYmiIqSyD1GIpHILy+RX14dZZs7ixxyc5sUntyxWWQ2+k5bf9tZmd33KCEhAcuXLYab+88oVqyYciquIF7jwsodGxObxXVunGWOKIkERsYmmcpHZyi/fctGaGpooEv3nsqvtBJ8be/MP6/GkGRzTzP+JrexSXp5ieSN9BwZZfe9LGhCvJcDgFExHWhqiPHmney0Fcm7BJQsrvvd4x1MDWBdrjj2Xs281kTT6qXxcOnPeOrbBgNcq8DL7ypiPnxWWt0VIdTrPC429/e16ChJphEohkbG0qklMdFRiP/4ETu3boKTS30sW7UeDZs0xa8TRuHOrZv5EyQX4mJjkJyUlDmDoXGW02PkZjY0RnTa77yKZpVQqkxZbFzji3dv45CYmIg92zfhzev/pGVULT+ucco5cSHYCgP1G9tLOdKkSROsWbMGQOrNaPXq1XB3d8eNGzcUPndcXBwiIyPh5JS+0JumpiZq1aqVaWrQt+zs7KT/LxKJUKZMGbx+/RoA8OjRI9jZ2UFXN/3NT506db5bn4SEhExzf5NE2mozL5YoMTER3uNHAwB8ps5QaV2IlCnkwX3s3b0DO/YcKjTznYmy08XFFCEv4xAYEZvptauPJXCbdwGGRbXRvZ4pVvevhbaLLiLqvXp0sJByfH0vW79RE3Tx6g0AsLCywb3gQBw5tA+ONWursnr5QlNTCzPnL8PiOdPh0aI+xBoaqFm7Luo41//ue3siyrnC0glE3yhatCjMzc1hbm6O2rVrY+PGjfjw4QM2bNggHb2S8WaZmJhYIPXS0tKS+bdIJEJycrJC55w3bx5KlCghsy1eOE+hcwKAoaEhNDQ0Mn0KEBWVuRf8KxMTE/nlTeSXV0fZ5s4ih9zcksKT2yCLzNHfaeuoHHyPEhMTMWn8GET++y9Wr9+kNqNWAF7jQsttYGiQxXUelWUOYxOTTJ9aRkdFwSitfODtvxETHYW27q5wrlkdzjWrIzLyXyxfuhDt3JvmT5Bc+tremX9eo2CSzT3t21FMUZL08iYmJaXnyCi772VBE+K9HACi3yfgS1IySurLfsBioq+DN28/ZXusnrYG2tYsh71Xn8t9Pf5zEsLffMCd8BhM2BWIpOQUdHWRv45LQRPqdV7CIPf3NSNjk0wLv8ZER0lHv6SeUxNmlavIlDGtVBmvX0UqsfZ5U8LAEGINjcwZYqIyjeD5Sm7mmCiZkYmW1tWwfsdB/P7nVRw4dg7zfdfibVwcypYrr/wQeZAf1zhRQWPnyg9CJBJBLBYjPj4eJUum/rKMjEz/BZGbBWNLlCiBsmXLIiAgQLrvy5cvuHXrlkJ1tLKywt27d2VGody8+f3hlz4+PoiLi5PZxk/0UaguAKClpQ2bqtUQEHBNui85ORk3rl+Hnb2D3GPs7B1wI0N5ALh+7WqW5dVRlrkDvpP7euHN/TXzjVy0ta2ctg74JvPXjpXnzyOwdsMWGBio12M7eY0LL7e1TTXcvHFdui85ORl/37gOWzsHucfY2tnLlAeAgOtXpeXdW7fF7gNHsHOfv3QrWbIUevTuhxVrNuZXlFzJ8uc74Brs7B3lHpN6T5PNnbG9y5UvDxOTkjL3vffv3+NucBDsszhnQRPivRwAEpNScPefONSzKindJxIB9a1K4tazmGyPbV3jJ2hriuF/858cfS2xSARtTfV4qyzk69zSuipu3ZS9r926GYBqtvZyj6lu54C/v7mv3Qy4hupp9zUtLW3YVKuOf7555PQ/EREoXeYnpdY/L7S0tGBpVRV3bqa/D09OTsadm9dRNYvMVavb43aG8gBw68Y1ueWLFdOHgaERXjyPwOOH91GvoatyA+RRflzjRAVNPX5jUK4lJCTg1atXePXqFUJCQjBixAi8f/8ebdq0gbm5OSpUqIAZM2bgyZMnOH78OJYsWZKr848aNQrz58/HkSNH8PDhQwwdOlTm6UN50b17dyQnJ2PQoEEICQnB6dOnsXjxYgDIdri5jo4OihcvLrMpa0pQj159cPjQARz9/TCePQvF3NkzEB8fj3Ye7QEAUyZ7Y4Vv+veuW4+euHrlMrZv24ywZ8+wdrUfHty/j67dvKRl4uJi8ehhCEJDQwEA4eFhePQwRDq3WR306NUHhw+m5Q6Vk9vHGyuWycm9NS33qrTc3bPJHaZeub3S2vp/37R127TMUyd7wy9DW3fv0RPXrlzGjm/auktaWycmJmLi2FF4cP8e5sxfhKTkJEgkbyCRvEFiovoMIRf0NS7A3N179sbv/gdw7OgRhD0LxYI5MxEfH4/W7TwBANOneGPViqXS8l2798K1q5exa/sWhIc9w/o1KxHy4D46d+0OADAwMEQVc0uZTVNTE8bGJjA1q6SSjPL07NUX/gf3S+9pczLd0ybK3NO69+iFq1cupd3TQrFmlR8e3L+Hrt17AEj9neTVsxc2rF+DC+fP4snjR5gyeSJKliqFJk2bqSSjPEK8lwPAhrNP0a2eKTo6VYB56WKY29Ueejoa2H89dUTKsl414N3WJtNxXZ1NcSYoErEfZEfz6mlrwLutDRzNDFHOSA+2FUpgcQ8HlDbQlXlcs6oJ9Trv2qM3/nf4IE7+7wjCw0KxeN4sxMfH4+e2qfe12dN8sNZvmbR8p249EHD1Cvbs2IqIsGfYtG4VHj64hw6du0vLdOvZF2fPnMRR/wN48U8EDu3bhauXLsCzU9eCjidXx269cPzoIZw+/jsiwp7Bd+FsfPoUj5Y/ewAA5s+cjI2rfaXl23fpgZvXr2D/rm14Hv4M2zasxuOQ+/Do2E1a5q+zpxF46yb+ffkPrlw8h4kjB6FeQ1fUcnKBulD2NQ6k3tMePgzBs7R7WkRYGB6q2T1NHaj6Mcs/yqOYueZKIXXq1CmULVsWQOrK7tbW1jhw4AAaN24MANizZw+GDBkCOzs71K5dG7/99hs6deqU4/OPGzcOkZGR6N27N8RiMfr16wdPT0/ExcXluc7FixfH//73PwwZMgQODg6wtbXFtGnT0L17d5l1WApSS7dWiImOxppVfoiSvIGVtQ1Wrd0gHWr6KvJfiDP8MDs41MDc+YuxaqUvVi5fhoqmZli6fCXMLSylZf46fw7Tp06W/nvShLEAgMFDhuGXoSMKKFn2Wrq3QkxMNNaszCa3OENuxxqYu2AxVvllyL1CTu4pWeQepvrc8tp6ZTZtbe9QA3PmL8bqLNr6zev/8NeFcwCArh09ZL7W+s3bUKu2E9SBYK9xgeZu3rIVYmJisH7NCkRJJLC0ssHy1eul06H+i4yEWJT+uYqdgyNmz12EtauWY7XfMlSoaIpFy/xQxdwyqy+hltLvaSsgSWvv1Ws3Sts7MjISogwLvme8p/ktX4qKpmZYtmKVTHv36TcQ8fHxmD1jGt69ewvHGjWxeu1GtVrvS4j3cgD43+1/YaSvg3GtrVFSXwcPXr5Fz1XXIUlb5LacoV6mdSQqlyqGOubG6O53NdP5kpNTUKV0MawfWBuGRbUR+yERQc9j0HHpZTyOfFcgmXJCqNd50xbuiI2Jxsa1KxEdJYG5pTWW+K2TTnn571WkzP3c1t4R0+csxIY1K7B+lS/KVzTFvCV+qJzhaWeNXJth/OTp2LllA3wXz0NFUzP8ttAX9o41CzyfPE2auyEuNhpbN6xCTJQEVSysMX/ZWmnm168iZf7YrGbngF9nzcfmdSuxee1ylKtgilkLl6NSlfTMURIJ1ixflDpFyqQkWri3QY9+vxR4tuzkxzV+4fw5TJ+SPuLde8IYAMDgIcMxRE3uafTjEKVwFSNSoV27dqFv376Ii4uDnp5ejo/7+Fmgl23h6LRVOqHepQTa3IKVmCTMC11dpl0UpBQIs62tRh9VdRVU4rFvO1VXQSXeJ3xRdRVUIiFRsbUGCyvjYtqqroJK6Gl9v0xhsD9QfUbpZaWzg+qn7X0PR65Qgdq+fTsqV66McuXKISgoCN7e3ujcuXOuOlaIiIiIiIhIOfiBnnKwc4UK1KtXrzBt2jS8evUKZcuWRadOnTBnzhxVV4uIiIiIiIgoz9i5QgVq4sSJmDhxoqqrQURERERERKQ07FwhIiIiIiIiEqjC8jQedSe8VeSIiIiIiIiIiJSInStERERERERERArgtCAiIiIiIiIigeKIC+Xg95GIiIiIiIiISAHsXCEiIiIiIiIiUgCnBREREREREREJFJ8WpBwcuUJEREREREREpAB2rhARERERERERKYDTgoiIiIiIiIgEipOClIMjV4iIiIiIiIiIFMDOFSIiIiIiIiIiBbBzhYiIiIiIiIhIAVxzhYiIiIiIiEig+CRm5eDIFSIiIiIiIiIiBbBzhYiIiIiIiIhIAZwWRERERERERCRQYj6MWSk4coWIiIiIiIiISAHsXCEiIiIiIiIiUgCnBREREREREREJFJ8WpBwcuUJEREREREREpAB2rhARERERERERKYDTgoiIiIiIiIgESsSnBSkFO1eoUBKLeQMQFDY3CYAO72uCIdQ3sU+Wt1N1FVTCsPZwVVdBJZ5f8lV1FVTCRF9b1VUgIhXhtCAiIiIiIiIiIgVw5AoRERERERGRQPFpQcrBkStERERERERERApg5woRERERERERkQI4LYiIiIiIiIhIoMQCXWhd2ThyhYiIiIiIiIhIAexcISIiIiIiIiJSADtXiIiIiIiIiIgUwDVXiIiIiIiIiASKj2JWDo5cISIiIiIiIiJSADtXiIiIiIiIiIgUwGlBRERERERERALFaUHKwZErREREREREREQKYOcKEREREREREZECOC2IiIiIiIiISKBE4LwgZeDIFSIiIiIiIiIiBbBzhYiIiIiIiIhIAZwWRERERERERCRQYs4KUgqOXCEiIiIiIiIiUgA7V4iIiIiIiIiIFMBpQUREREREREQCxacFKQdHrhARERERERERKYCdK0RERERERERECmDnCkmZmZnB19dX+m+RSIQjR47k+9ft06cPPDw88v3rEBERERERkSyRSP23woCdKz+ArDonLly4AJFIhNjY2DydNzIyEu7u7opVrhDYu3sX3Ju7orajLby6dsLd4OBsy585fRLtWruhtqMtOni0waWLf8m8npKSglV+y9G0UX3UqWGHQf37ICIiPB8T5I0QcwsxM8DczM3c8jA3c2dUWHIP7twQD4/PRMz1Zbi4fTxqVTPNsqymphg+g9xw/+h0xFxfhoB9k9DcxSbL8uP7Nkf8nZVYNL5DflRdIYf270bH1s3h6uyIgb264sG97Nv73B+n0b19a7g6O6JXZw9cu3wxU5nwsFB4jxmGlg2d0KxeLQzo2RmvIv/Nrwh5IsTrXIiZ6cfBzhXKUpkyZaCjo6PqauSrUydPYPHCeRg8dBj2HjgMKytrDBncH1FRUXLLB965jUkTxsGzfUfsO3gETVybYvSIYXjy5LG0zJZNG7Bn1w5MmT4DO/fsh56eHoYM6o+EhISCivVdQswtxMwAczM3c8vD3MxdGHN3bFEDC8Z5Ys66k3DuvgDBj1/i6OphKGlYTG75GUPbYECH+hi78AAcO/yGjQcvY9+SgbC3Kp+pbM2qFdG/Qz0EP36R3zFy7eyZk1i5dCH6DhqKTbsOwNzSCmOHD0ZMtPz2vht0BzN/nYDWHu2xefdBNGjsCp9xI/Ds6RNpmZf/PMfQ/j1halYJfuu3Yttef/QZ8Itave8V4nUuxMz0Y2HnioBcvnwZDRo0gJ6eHipUqICRI0fiw4cPWZbPOC0oPDwcIpEIe/fuhYuLC3R1dVG9enX89Vd673BSUhL69++PSpUqQU9PD1ZWVli+fLnMOZOSkjB27FgYGBjA2NgYEydOREpKSr7kzYkd27agfcfO8PDsgCrm5pgyfSZ0dXVxxP+Q3PK7dm6HS/0G6NNvACpXqYLhI0fDpmpV7N29E0Bq7/iuHdsxcPAQNHFtBksra/w2byHevH6Nc2f/LMho2RJibiFmBpibuZlbHuZm7sKYe2QPV2zxv4odR6/j4bNXGDFnL+I/fUZvD2e55bu3roOFm87g9OUHCH8ZhQ0HLuP0lQcY1dNVplxRPW1smdsHQ2fvQezb+IKIkit7d25DG8+O+LmtJypVNseEydOhq6uLY7/7yy1/YM9OODnXR/de/WBWqQoGDh0JS+uqOLR/t7TM+tUr4FyvIYaOGg9LaxuUq1AR9Ru5wtDIuKBifZcQr3MhZqYfCztXBCI0NBRubv9RwCoAAMZOSURBVG7o0KEDgoODsW/fPly+fBnDhw/P1XkmTJiAcePG4c6dO3B2dkabNm2kvcnJyckoX748Dhw4gAcPHmDatGmYPHky9u/fLz1+yZIl2Lp1KzZv3ozLly8jOjoahw8fVmrWnEr8/BkhD+6jrrOLdJ9YLEbdui4IDroj95jgwEDUrSv7JsalXn0EBwYCAF6+eAGJ5A2c6qafU19fH7Z29lmes6AJMbcQMwPMzdzMzdyymDtdYcutpakBR5sKOBfwSLovJSUF5wIeoY5dJbnHaGtp4tPnRJl98Z8+w8Wxisw+X58uOHXpHs5nOLe6SEz8jMcPH6BWnfT2E4vFqFWnLu7fDZJ7zL3gQNRyqiuzz8m5Hu4FBwJIfb969fJfqFDRFGOHDUTrZg0wsFdXXDx/Nt9y5JYQr3MhZlYnokLwX2HAzpUfxLFjx1CsWDGZLeN6KfPmzYOXlxdGjx4NCwsLuLi4YMWKFdi+fTs+ffqU468zfPhwdOjQATY2NlizZg1KlCiBTZs2AQC0tLQwc+ZM1KpVC5UqVYKXlxf69u0r07ni6+sLHx8ftG/fHjY2Nli7di1KlCihvG9ELsTExiApKQnGxrKfUhgbG0Mikcg9RiKRwNjYJHP5KEna629S95nk/JwFTYi5hZgZYG7mTsXcmTH3N+WZO3WfGuc2MSwGTU0NvI5+J7P/ddRblDEuLveYP6+FYGQPV1SpWBIikQiuTtZo5+qAMibp5Tu1rAkH6wqY6nc0X+ufV3GxsUhKSoLRN+1tZGyMqCzaJjpKkmkEiqGRMaLTPgyMiY5C/MeP2Ll1E5xc6mPZqvVo2KQpfp0wCndu3cyfILkkxOtciJnpx6Op6gqQcjRp0gRr1qyR2RcQEIAePXoAAIKCghAcHIxdu3ZJX09JSUFycjLCwsJgY5P1AmcZOTun9w5ramqiVq1aCAkJke5btWoVNm/ejOfPnyM+Ph6fP3+Gg4MDACAuLg6RkZFwcnLKdI7spgYlJCRkmheZoqGjVvNiiYiIiNTJ+EUHsXpqNwT5T0VKSgqevZBg+9Hr6N0udVRH+dIGWDShA1oPWYmEz19UXNuC8/U9Z/1GTdDFqzcAwMLKBveCA3Hk0D441qytyuoRUSHGkSs/iKJFi8Lc3FxmK1eunPT19+/fY/DgwQgMDJRuQUFBePLkCapUqZLNmXNu7969GD9+PPr3748zZ84gMDAQffv2xefPnxU677x581CiRAmZbdGCeQrX19DAEBoaGpkWyYqKioKJiYncY0xMTBAVJclcPq3X3MSkZOo+Sc7PWdCEmFuImQHmZu5UzJ0Zc39TnrlT96lxbknMe3z5koRSRvoy+0sZF8erqLdZHtN57AYYu4yFVatpsPecjQ8fExD2MjWno01FlDYujmu7vfHu5nK8u7kcDWtZYGi3Rnh3cznEYtUPwy9hYAANDQ3pqJOvoqOiYJxF2xgZm2Ra7DYmOko6+iX1nJowqyz7/te0UmW8fhWpxNrnnRCvcyFmVidikfpvhQE7VwSiRo0aePDgQaYOGHNzc2hra+f4PNevX5f+/5cvX3Dr1i3pqJcrV67AxcUFQ4cOhaOjI8zNzREaGiotX6JECZQtWxYBAQGZzpEdHx8fxMXFyWwTvH1yXOesaGlrw6ZqNQRcvybdl5ycjICAa7Czd5R7jJ2DAwIyfA8A4Pq1q7BLG51Trnx5mJiUREBA+jnfv3+Pu8FBWZ6zoAkxtxAzA8zN3MzN3LKYO11hy534JQl3Qv5BEycr6T6RSIQmdSxxIzgs22MTPn/Bv2/ioKkphkdTBxy7kPpo2/M3HqFmxzlw6jpfut26H4G9J/6GU9f5SE5W3QMHvtLS0oaldVXcupnefsnJybh1MwDVbO3lHlPdzgF/35Bt75sB11DdzkF6Tptq1fHPN4/j/SciAqXL/KTU+ueVEK9zIWamHw87VwTC29sbV69exfDhwxEYGIgnT57g999/z/WCtqtWrcLhw4fx8OFDDBs2DDExMejXrx8AwMLCAn///TdOnz6Nx48fY+rUqbh5U3bu6qhRozB//nwcOXIEDx8+xNChQxEbG5vt19TR0UHx4sVlNmVNCerZuy/8D+7H0SOH8Sw0FL/NmoH4+Hh4eLYHAPzqMxHLly2Rlvfq0QtXr1zCtq2bEfYsFGtW+eH+vXvo2j11+pVIJIJXz17YsG4NLpw7iyePH2GKz0SULFUKrk2bKaXOyiDE3ELMDDA3czM3wNxfMXfhzr1i5zn09XSBVxsnWFUqjRWTu6CIng62/576x+XG2T0xa0Rbafna1U3RztUeZuWMUc+xCo6uHAaxWISlW1OfkvL+YwIehEbKbB/iPyM67gMehKrHCA4A6NqjN/53+CBO/u8IwsNCsXjeLMTHx+Pntp4AgNnTfLDWb5m0fKduPRBw9Qr27NiKiLBn2LRuFR4+uIcOnbtLy3Tr2Rdnz5zEUf8DePFPBA7t24Wrly7As1PXgo6XJSFe50LMTD8WrrkiEHZ2dvjrr7/w66+/okGDBkhJSUGVKlXQpUuXXJ1n/vz5mD9/PgIDA2Fubo6jR49Kh9UNHjwYd+7cQZcuXSASidCtWzcMHToUJ0+elB4/btw4REZGonfv3hCLxejXrx88PT0RFxen1Lw55ebeCjHR0Vi9cgUkkjewsrbB6nUbpUNNX0VGQixK74N0cKyBeQsXY+UKX/j5LkVFUzP4+q2ChYWltEzf/gMRHx+PWTOm4d27t3CsUROr121UqzVihJhbiJkB5mZu5gaY+yvmLty5D565DRPDYpg25GeUNtZH8KOXaDdslXSR2wpljGRGm+joaGH6sNaoVM4E7z8m4PSV++g/dTvi3qvf45az07SFO2JjorFx7UpER0lgbmmNJX7rYJQ29eO/V5EQi9LnDNjaO2L6nIXYsGYF1q/yRfmKppi3xA+VzS2kZRq5NsP4ydOxc8sG+C6eh4qmZvhtoS/sHWsWeL6sCPE6F2JmdVFYnsaj7kQp2a0kSpQmPDwclSpVwp07d6QL1KrSJ+Gsu0ZERESFmGHt3I0S/lE8v+Sr6iqohL4uP7sWkh+luS89jlF1Fb6rgaWhqqvwXZwWRERERERERESkgB+kr42IiIiIiIiIckvEWUFKwc4VyhEzMzNwBhkRERERERFRZpwWRERERERERESkAI5cISIiIiIiIhIozgpSDo5cISIiIiIiIiJSADtXiIiIiIiIiIgUwGlBRERERERERAIl5uOClIIjV4iIiIiIiIiIFMDOFSIiIiIiIiIiBbBzhYiIiIiIiIhIAVxzhYiIiIiIiEiguOKKcnDkChERERERERGRAti5QkRERERERESkAE4LIiIiIiIiIhIqzgtSCo5cISIiIiIiIiJSADtXiIiIiIiIiIgUwGlBRERERERERAIl4rwgpeDIFSIiIiIiIiIiBbBzhYiIiIiIiIhIAZwWRERERERERCRQIs4KUgqOXCEiIiIiIiIiUgA7V4iIiIiIiIiIFMBpQUREREREREQCxVlBysGRK0RERERERERECmDnChERERERERGRAjgtiKgQSU5OUXUVVEKYqYW7cntSkjBbXEMszAYXYmsL9Wf7zdvPqq6CSkQF+Km6CiphPe5/qq6CStxd2FrVVVAJLU2B3th+lAk1P0gMVePIFSIiIiIiIiIiBbBzhYiIiIiIiIhIAexcISIiIiIiIiJSANdcISIiIiIiIhIoERddUQqOXCEiIiIiIiIiUgA7V4iIiIiIiIiIFMBpQUREREREREQCJeKsIKXgyBUiIiIiIiIiIgWwc4WIiIiIiIiISAHsXCEiIiIiIiISKFEh2HJr1apVMDMzg66uLpycnHDjxo0sy27YsAENGjSAoaEhDA0N0axZs2zLZ4WdK0RERERERET0Q9i3bx/Gjh2L6dOn4/bt27C3t0fLli3x+vVrueUvXLiAbv9n767Dotq6OAD/BpRQQCRMsAhBpUFAsUAEE0zsVmzFQlTEuBZiYzd2o1e9omJj3mtgY2MrJQZSs74/kCMjYHwgwzDrvQ/Pcz2zz7AX++Q6e+/TqRNOnDiB8+fPQ19fH02aNMGLFy9+6/eKiIjyIwDGCtKXNGnXQDrEYvncXeUzavmdXCw9XT5bXFFBPhtcHltbXvftd4kp0q6CVOiqK0m7ClJhMupvaVdBKm4EtpB2FaSieDH5PLCVKF404r7yJFHaVfgp6yoav1zW3t4ednZ2CA4OBgCIxWLo6+tj6NChGDdu3E/XT09PR+nSpREcHIzu3bv/8u/lniuMMcYYY4wxxpi8kvaYn3wcF5SSkoL//vsPjRs3FpYpKCigcePGOH/+/C99x+fPn5GamgotLa1f/8XgVzEzxhhjjDHGGGOsEEtOTkZycrLEMmVlZSgrK0ssi4mJQXp6OsqWLSuxvGzZsrh79+4v/S5fX19UqFBBIkHzK7jnCmOMMcYYY4wxxgqtmTNnolSpUhI/M2fOzPffM2vWLGzbtg179+6FiorKb63LPVcYY4wxxhhjjDE5Jfq/3sdTsPz8/DBy5EiJZd/3WgEAHR0dKCoq4s2bNxLL37x5g3Llyv3wdwQFBWHWrFk4duwYzM3Nf7uO3HOFMcYYY4wxxhhjhZaysjI0NDQkfnJKrigpKcHGxgbh4eHCMrFYjPDwcDg6Oub6/YGBgZg2bRoOHz4MW1vb/6uO3HOFMcYYY4wxxhhjRcLIkSPRo0cP2Nraonbt2liwYAE+ffqEXr16AQC6d++OihUrCsOKZs+ejUmTJmHLli2oUqUKXr9+DQBQU1ODmpraL/9eTq4wxhhjjDHGGGNySlT4RwX9Fi8vL7x79w6TJk3C69evYWlpicOHDwuT3EZHR0NB4dsgnmXLliElJQXt2rWT+J6AgABMnjz5l3+viIgoXyJgrAB9SZN2DaRDLJbP3VU+oy56J7pflZ4uny2uqCCfDS6PrS2v+/a7xBRpV0EqdNWVpF0FqTAZ9be0qyAVNwJbSLsKUlG8mHwe2EoULxpxX4v+IO0q/JRlJXVpV+GneM4VxhhjjDHGGGOMsTzg5ApjjDHGGGOMMcZYHvCcK4wxxhhjjDHGmJwqGoObpI97rjCpEIlECA0NlXY1GGOMMcYYY4yxPOPkym969+4dBg4ciEqVKkFZWRnlypWDm5sbIiIihDJFJXFQpUoVLFiwQNrV+OO2bdmMpq7OsLMyQ5eO7XEjMvKH5Y+E/QOPFu6wszJDW8+WOHP6lMTnRIQlixfCpYETalubo3+fnnj69MkfjOD/s33rZjRzc4a9jTm6de6Amzd+HPfRsMNo3bIp7G3M0b519rjDjx3BwP690dDJHlZmJrh3986frP7/ZfvWzWju5gwHG3N0/8WY27RsCgcbc3Ro3RJns8ScmpqKhfOC0KF1S9SpbYUmzvXgP94X796++dNh/LbtWzejWRNn2Fubo1un32hr61za+ugRDOzXGw3r2sOqVuFsawDYsW0zWjZ1QR07C/To4vXTuI8dOYy2Hs1Qx84CXm1b4ewZybhXLAtGW49mcLK3RiMnewzq3ws3I6//yRD+L/K4bwO8f8vb/r1/9zZ0a+OO5g1tMbRvZ9y9fSPXsk8ePcDU8T7o1sYdTeqYY8/2jdnKRF79F/5jhqBjKxc0qWOOiFPH/2T1/2/yun93r1cFEZMbI2pec+wbVQ8WlTVzLbt9WB1EL26V7WfdAHsAQDEFEfxameKIX0PcDWqGy381wfxuViiroVxA0fy6nds2w6OpC5xqW6BXVy/c+oXzWHvPZnCqbYFO7Voh4rvzWFYz/5qM2pam2LppQ35XO0/k9ZjGigZOrvymtm3b4urVq9iwYQOioqKwf/9+NGzYELGxsfn+u1JTU/P9O5mkw/8cQlDgTHgPGoxtO/eienUTDPTuk2t7Xrt6BePGjELrNu2wfVcoGjm7YMTQwbh/P0oos27NKmzdvBETAyZj09YdUFVVxcD+fZCcnFxQYf1U2OFDmDtnFrwHDMaWHXtgbFwdg7z7Ii63uK9dgZ/vKHi2aYetO/eioXNjjBw+BA+yxJ2UlARLKxsM8xldUGH8lrDDhzBvziz0/xqzkXF1DP5BzNevXcF431HwaNMOW3KI+cuXL7h75zb6eg/Clu27ETR/MZ4+eYwRQwcVZFg/FfbPIcwNnAXvgYOxZeceGFf/SVtfvQK/saPg2TpLWw/Loa2tC29bA8CRw4cwP2g2+nkPxqZtu2FcvTqGDuz3g/a+ignjRsOjdVts3r4HDRu5YPSIoRJxV65cBWP9JmLb7n1YvX4TyleoiMED+yI+Lq6gwvopedy3Ad6/5W3/PnnsMFYsmoOuvQdg6brtqGZYHeN9BiA+Lue4k798QbkKeug9cDi0tHVyLPPlSxKqGVbHkFHj/2TV80Re9++W1hXg37omFvxzD80DT+HOi/fYNMgB2mo5v4Wp/+rLsBkfJvw0nn4CaeliHLz6EgCgqqSIWvqaWHQ4Cs0CT6H/6suoVkYNa7ztCzKsnzoadggL5s5GX+/BCNm6G0bG1TFsUD/E5bKdR167Cn+/0Wjl2RYbt+1Bg0YuGOMzFA8fRGUre+L4UdyMvA5d3TJ/OozfIq/HtEJBJAM/MoCTK78hISEBZ86cwezZs9GoUSNUrlwZtWvXhp+fH1q1agUgo7cHALRu3RoikUj4N5Dx/mwDAwMoKSmhevXq2LhR8smJSCTCsmXL0KpVK5QsWRLTp09Heno6+vTpg6pVq0JVVRXVq1fHwoULJdZLS0vDsGHDoKmpCW1tbfj6+qJHjx7w9PQUyojFYsycOVP4HgsLC+zatSvXWBs2bIinT5/Cx8cHIpEIoq/vjYyNjUWnTp1QsWJFlChRAmZmZti6dWu2dYcNG4axY8dCS0sL5cqV++n7wQMCAlC+fHlE/qTXSH7buGEd2rTrAM/WbWFgaIiJAVOgoqKC0D27cyy/eVMI6jjVQ8/efVHNwABDho2AaY0a2LZlE4CMXiubN4agn/dANHJuDOPqJvhrZiDevX2L4+HHCjK0H9oUsh5t2raHR+u2MDAwxIRJU6CiqoLQvTnHvXXTRtSp64QevfqgWjUDDB46PCPurZuFMi1aesB74GA4ODgWVBi/ZXPIerT+GnO1LDHvyyXmLZs2wjFLzIOGDodJjRrY/jVmdXV1LFu1Fk3cm6JK1Wowt7CE73h/3Ll9C69evSzI0H5oU8h6tGn3XVur/EJb9+6DagZZ2npLlrZu9bWtHQtnWwPA5o0b4NmmPVp5tkE1A0P4TZwMFRUV7A/dk2P5bZtD4FjHCd179kHVagYYOGQ4TExNsWPbFqGMe7MWsHeoAz09fRgYGsFn9Dh8+vgR9+/fK6iwfkoe922A92952793bwtB01Zt4dbCE5WrGmD4WH8oK6si7EBojuWr16iF/kNGoZFrUxQvnvMNeW3HeujlPRRODVz+YM3zRl73776NDLD1fDR2XnyG+68/wm97JJJS0uHlWCnH8u8/p+Ldh2Thp56JLpJS0oXkyocvaeiy5DwOXH2JR28/4eqTePjvvAHzSpqoUFq1IEP7oS1fz2Mtv57Hxn09j/2d23lsSwgc6jih29fz2IDB2c9jAPD2zRvMnTUdU2cEolixwjX9prwe01jRwcmV36CmpgY1NTWEhobm2gvh8uXLAIB169bh1atXwr/37t2L4cOHY9SoUbh58ya8vb3Rq1cvnDhxQmL9yZMno3Xr1rhx4wZ69+4NsVgMPT097Ny5E7dv38akSZMwfvx47NixQ1hn9uzZ2Lx5M9atW4eIiAgkJiZmG5Y0c+ZMhISEYPny5bh16xZ8fHzQtWtXnDqVc3fBPXv2QE9PD1OnTsWrV6/w6tUrABlP82xsbHDw4EHcvHkT/fv3R7du3XDp0iWJ9Tds2ICSJUvi4sWLCAwMxNSpU3H06NFsv4eIMHToUISEhODMmTMwNzf/QQvkr9SUFNy5fQsOjnWEZQoKCnBwqIPI61dzXCfy2rVsFyB16joh8to1AMCL588RE/MO9g7fvlNdXR1m5ha5fmdBS03NiDtrHRUUFGDv4IjI69dyXCfy+jWJ8gDgWKduruULm/8n5hv/R8wfP3yASCSCurpGflQ7z/7vtnaU3bYGMuK+e+cW7LPsqwoKCqjt4IjIyGs5rhMZeR21v9u3Hes44UYu5VNTU7B39w6oqavD2Ngkv6qeJ/K4bwO8f8vf/p2K+/fuwMrWQVimoKAAKzt73LlZ+Ibp5Rd53b+LK4pgpl8KZ++9E5YRAWfvxcC6Sulf+g4vx0r4+8oLJKWk51pGQ7UYxGJCYlLh6DWeeR6zs5c8j9nZO+Z6XroReR217SXPYw6OkucxsViMgIm+6NqjNwwMjf5E1f9v8npMY0VL4UpXFnLFihXD+vXr0a9fPyxfvhzW1tZo0KABOnbsKCQFdHV1AQCampooV66csG5QUBB69uyJQYMyuhSPHDkSFy5cQFBQEBo1aiSU69y5M3r16iXxe6dMmSL8f9WqVXH+/Hns2LEDHTp0AAAsXrwYfn5+aN26NQAgODgYhw4dEtZJTk7GjBkzcOzYMTh+zdpWq1YNZ8+exYoVK9CgQYNssWppaUFRURHq6uoScVSsWBGjR3/rVjd06FCEhYVhx44dqF27trDc3NwcAQEBAAAjIyMEBwcjPDwcrq6uQpm0tDR07doVV69exdmzZ1GxYsUf/PXzX3xCPNLT06GtrS2xXFtbG48fP8pxnZiYGGh/16VYW1sbMbExXz/POPlr62T/zpiYmPyqep7Ex2fErZUtbh08efw4x3ViYmJyLB9bSGL6mYRcYtb6SczZt43cY05OTsbC+UFwb9ocampq+VPxPMq3ttaRnbYGgIT4hFzaWzvXuGNjYrINF9DS1s4W95lTJzDedzS+fEmCjo4ulixfA83Sv3aB/6fJ474N8P4tb/t3YkI8xOnpKK0lGUdpLW08e5pz3EWBvO7fWiWVUExRATGJkg81Yz4kw6Dsz/dFi8qaMKmggTFbruVaRrmYAvxa1cC+/17g45e0vFY5X/zoPPb0ye+dx+KytHfIutUopqgIr87d8r/SeSSvx7TCQiQr424KOe658pvatm2Lly9fYv/+/XB3d8fJkydhbW2N9evX/3C9O3fuoG7duhLL6tatizt3JCdVsrW1zbbukiVLYGNjA11dXaipqWHlypWIjo4GALx//x5v3ryRSGwoKirCxsZG+PeDBw/w+fNnuLq6Cr1v1NTUEBISgocPH/5W/Onp6Zg2bRrMzMygpaUFNTU1hIWFCfXJ9H0PlPLly+Pt27cSy3x8fHDx4kWcPn36h4mV5ORkJCYmSvwUpvlLGEtNTYXv6BEAAD//yVKtC/uzbO3ssWXHHqwN2QLHuk7wG+OT61hwVjTw/s2Y7OroUAl3XiTi+tOEHD8vpiDC0t62gAiYsKNgh6YXtDu3b2Hblo2YNHWmMNyfMZa/OLnyf1BRUYGrqyv8/f1x7tw59OzZU+ilkVclS5aU+Pe2bdswevRo9OnTB0eOHMG1a9fQq1cvpKSk/PJ3fvz4EQBw8OBBXLt2Tfi5ffv2D+ddycmcOXOwcOFC+Pr64sSJE7h27Rrc3Nyy1ad48eIS/xaJRBCLxRLLXF1d8eLFC4SFhf3wd86cOROlSpWS+Jkze+Zv1TsnpTVLQ1FRMdvktbGxsdDRyXnCOx0dHcTGxmQv//VJgY5ORs+l2Jhf/86CVrp0Rtzf3xDGxmbvlZNJR0cn5/KFJKaf0cwl5rifxJx928gec2pqKsaN9sGrly+xdOWaQvNUG/hJW/9gG89WPkZ22hoANEtr5tLesbnGoa2jg7jv9u2cyquWKAH9SpVhZm6JSVOmQ7GYIvaF5jwWvKDJ474N8P4tb/u3hmZpKCgqZpu8Nj4uFlpashPH75LX/TvuUwrS0sXQ+e5NPjrqyniX+OWH66oqKaKlTUVsP/80x88zEysVtVTRJfh8oem1AuTveUzra/lrV/5FfFwsWjV1hqNNLTja1MKrVy+xcF4gPJpKf64heT2msaKFkyv5oEaNGvj06ZPw7+LFiyM9XXJcp6mpqcTrmgEgIiICNWrU+OF3R0REoE6dOhg0aBCsrKxgaGgo0dukVKlSKFu2rDC3C5DRu+TKlSsS9VNWVkZ0dDQMDQ0lfvT19XP93UpKStniiIiIgIeHB7p27QoLCwtUq1YNUVHZZyH/Fa1atcKWLVvQt29fbNu2Lddyfn5+eP/+vcTPGF+//+t3ZlVcSQmmNWri4oXzwjKxWIyLF8/D3MIqx3XMLS1x8cIFiWUXzp+DuaUlAKCinh50dHRx8eK37/z48SNuRF7P9TsLWvHiX+O+KBn3pQsXYG5hmeM65haWuJSlPPA17lzKFzaZMV/6jZjNcoj54ncxZ954RUc/xfJV66CpWTiGh2TKta0v/qStL8huWwMZcZuY1sSli9/2VbFYjMsXL8Dc3DLHdczNLXD5ouS+ffHCOZjlUv7b99JvJbv/JHnctwHev+Vv/y4Oo+qmuPbfRWGZWCzGtX8vwrSWhRRr9mfJ6/6dmk648ew96hp/u1kWiYC6xjq48iT+h+s2t6oApWIK2HP5ebbPMhMrVXVLonPweSR8LhxzrWTKPI9dviR5Hvv30oVcz0tm5hYS5QHJ81jTFq2wZWcoNm3fI/zo6pZB1x69sWjZ6j8Vyi+T12NaYSESFf4fWcBzrvyG2NhYtG/fHr1794a5uTnU1dXx77//IjAwEB4eHkK5KlWqIDw8HHXr1oWysjJKly6NMWPGoEOHDrCyskLjxo3x999/Y8+ePTh27MdvkDEyMkJISAjCwsJQtWpVbNy4EZcvX0bVqlWFMkOHDsXMmTNhaGgIExMTLF68GPHx8UKXP3V1dYwePRo+Pj4Qi8VwcnLC+/fvERERAQ0NDfTo0SPH312lShWcPn0aHTt2hLKyMnR0dGBkZIRdu3bh3LlzKF26NObNm4c3b978NEmUm9atW2Pjxo3o1q0bihUrhnbt2mUro6ysDGVlyScW+fVwoVuPXvAf74uaNWuhlpk5Nm3cgKSkJHi2bgMAmOA3FmXKlMVwn1EAgC5du6NPz27YsH4t6tdvgMP/HMKtmzfhP3kqgIweOl26dceqFctQuVJlVNTTw5LFC6FbpgycXRrnT6XzQdfuPTFpwjjU+Br3lq9xe3hmxD1xvC/KlCmDYSMy4u7UtRv69eqOkA1rUa9eQ4QdPojbt27BP2Cq8J3v3yfg9atXwvCvJ1/HBGvr6Ag9eqSpS/eeCPgac80sMbf6GrP/15iHfo2589eYN25YC6csMU/8GnNqairGjhyOu3duY+GS5UgXpwtz7pQqVSrXN1IUNIm2rmWOLZu+a2u/r23t811br1+LevUbIuyfr209+Qdt/bhwtTUAdOnWA5P9/TLau5YZtmwKQVJSElp6ZsxNNWmCL8qUKYshw0cCADp26Y7+fbpj04Z1cKrfAGGHD+H2rVsY758x51XS589Yu3oF6jdsBB0dXSQkJGDHti149/YNGru6SS3O78njvg3w/i1v+3fbjt0x56+JMDKpAZMaZtizfRO+fEmCWwtPAEDg1PHQ1i2LPgOHA8hoz+jHGQ+mUtNSEfPuLR5G3YVKiRKoqJfxxpmkz5/x8vm3Ic6vX73Aw6i7UNcohTLlyhdsgLmQ1/179YmHmNvVCjei3+Pa03j0aVgNJZQVsePCMwDA/G5WeJ3wBbP/lhxq39GxEo5Evs6WOCmmIMLyPraopa+JXisuQlEkgq56xnVmwucUpKZTwQT2E5279cAUfz+Y1sg4j23bnHEea+GRcR4LmJhxHhs87Ot5rHN3ePftjs0h61C3XgMcOXwId27fwvhJGecxTc3S2ZLExYoVg7a2DipXqYrCQF6Paazo4OTKb1BTU4O9vT3mz5+Phw8fIjU1Ffr6+ujXrx/Gjx8vlJs7dy5GjhyJVatWoWLFinjy5Ak8PT2xcOFCBAUFYfjw4ahatSrWrVuHhg0b/vB3ent74+rVq/Dy8oJIJEKnTp0waNAg/PPPP0IZX19fvH79Gt27d4eioiL69+8PNzc3KCoqCmWmTZsGXV1dzJw5E48ePYKmpiasra0l6v29qVOnwtvbGwYGBkhOTgYRYeLEiXj06BHc3NxQokQJ9O/fH56ennj//v3//Xdt164dxGIxunXrBgUFBbRp0+b//q7f5d60GeLj4rA0eBFiYt6huokplq5YLXQnfP3qFRRE3zp4WVpZY2ZgEIIXLcDiBfNQqXIVLFi8BEZGxkKZXn36ISkpCVMnT8KHD4mwsrbB0hWrsyWIpMnNPSPuZUsWI/Zr3EuWr8oS90soZEkRW1paY8asICwJXoDghfNRqXIVzFsYDMMscZ86cRwB/t+2p3FjMk723gMHY8CgoQUUWe5yijn4BzFbWFpj+qwgLM0l5ndv3+DUyeMAgI7tPCV+18q1G2BrZ18wgf2EW9NmiI+Pw7LgH7S1Qpa2trLGjNlBWLI4S9yLcmjribm09WDptzUANHFvhvj4eCxfugixMTEwrm6KxUtXCt3nX79+BQWFb/u2haUVps+cg6XBC7Fk8XzoV6qMoAWLhbgVFBXx5PEjHNgfioSEeJTS1ESNmmZYtW5ToXrjgjzu2wDv3/K2fzds7I73CfEIWbUU8XExqGZUHdPnLRMmuX375jVEWfbv2Ji3GNizg/DvXVs2YNeWDTC3skXQkrUAgKi7tzBmSB+hzIpFcwAArs1aYczEvwoirJ+S1/377ysvoaWmhJHNq0NXXRm3XySi29ILiPmQMf9ehdKqEJNkQqRamZKobaCNLsHns31fOU0VNDHPSJiFjWso8VmHhRG48KBwzKPl6pZxHlu57Nt5bGGW89ib765RzS2tMG3GHCxfshBLv57H5sxfDAND49x+RaEjr8c0VnSIiKhwpGdZvhGLxTA1NUWHDh0wbdo0aVfnjyhEw2ILlFgsn7urfEYtO10g81t6IXlqWNAUFeSzweWxteV1336XWDiG0BU0XfXC0dOpoJmM+lvaVZCKG4EtpF0FqSheTD4PbCWKF424bz7/KO0q/FQtvcIz71luuOdKEfD06VMcOXIEDRo0QHJyMoKDg/H48WN07txZ2lVjjDHGGGOMMcaKPJ7QtghQUFDA+vXrYWdnh7p16+LGjRs4duwYTE1NpV01xhhjjDHGGGOsyOOeK0WAvr5+tjcRMcYYY4wxxhhjP1U0RjdJHfdcYYwxxhhjjDHGGMsDTq4wxhhjjDHGGGOM5QEnVxhjjDHGGGOMMcbygOdcYYwxxhhjjDHG5JSIJ13JF9xzhTHGGGOMMcYYYywPOLnCGGOMMcYYY4wxlgc8LIgxxhhjjDHGGJNTIh4VlC+45wpjjDHGGGOMMcZYHnByhTHGGGOMMcYYYywPeFgQY4wxxhhjjDEmp3hUUP7gniuMMcYYY4wxxhhjecDJFcYYY4wxxhhjjLE84GFBjDHGGGOMMcaYvOJxQfmCe64wxhhjjDHGGGOM5QEnVxhjjDHGGGOMMcbygIcFMcYYY4wxxhhjckrE44LyBfdcYYwxxhhjjDHGGMsDTq4wxhhjjDHGGGOM5QEPC2KMMcYYY4wxxuSUiEcF5QvuucIYY4wxxhhjjDGWB5xcYYwxxhhjjDHGGMsDTq4wxhhjjDHGGGOM5QHPucIYY4wxxhhjjMkpnnIlf3DPFcYYY4wxxhhjjLE8EBERSbsSjP2uL2nSrgErSAmfUqVdBanQUJXTzoX8+ESupKbJ32VI8WJyupHLX1MDABQU5LO9xXJ6i6HdYY20qyAV8Tv7SrsKUqFSRC7Vol5/lnYVfsq4XAlpV+GnisjmwBhjjDHGGGOMsd8mnzngfMfDghhjjDHGGGOMMcbygJMrjDHGGGOMMcYYY3nAw4IYY4wxxhhjjDE5JeJxQfmCe64wxhhjjDHGGGOM5QEnVxhjjDHGGGOMMcbygIcFMcYYY4wxxhhjckrEo4LyBfdcYYwxxhhjjDHGGMsDTq4wxhhjjDHGGGOM5QEPC2KMMcYYY4wxxuQUjwrKH9xzhTHGGGOMMcYYYywPOLnCGGOMMcYYY4wxlgc8LIgxxhhjjDHGGJNXPC4oX3DPFcYYY4wxxhhjjLE84OQKY4wxxhhjjDHGWB5wcoUxxhhjjDHGGGMsD3jOFcYYY4wxxhhjTE6JeNKVfME9VxhjjDHGGGOMMcbygJMrjDHGGGOMMcYYY3nAw4IYY4wxxhhjjDE5JeJRQfmCe64wxhhjjDHGGGOM5QEnVxhjjDHGGGOMMcbygIcFMcYYY4wxxhhjcopHBeUPmeu5IhKJEBoamuvnJ0+ehEgkQkJCQr7+3p49e8LT0zNfv1PWNGzYECNGjJB2NRhjjDHGGGOMsUKlUCVX3r17h4EDB6JSpUpQVlZGuXLl4ObmhoiIiF/+jjp16uDVq1coVapUvtZt4cKFWL9+fb5+p7RUqVIFCxYs+O319uzZg2nTpuV/haRs25bNaOrqDDsrM3Tp2B43IiN/WP5I2D/waOEOOysztPVsiTOnT0l8TkRYsnghXBo4oba1Ofr36YmnT5/8wQj+P/IY996dW+Hl0QSuTtYY0KsT7ty68cPyJ46FoVv7lnB1skbPTq1xIeK0xOefP3/GgjnT0a6FC1zr2aC7Vyvs2739T4bwf9m+dTOauTnD3sYc3Tp3wM0bP27ro2GH0bplU9jbmKN96+xtHX7sCAb2742GTvawMjPBvbt3/mT1/2/bt25GsybOsLc2R7dOvxG3dS5xHz2Cgf16o2Fde1jV4rgLm53bNsOjqQucalugV1cv3PpJ3MeOHEZ7z2Zwqm2BTu1aIeKMZNwrlwWjvWcz1Hewhks9ewz27oWbN67/yRD+L/La3vJ6XJPHczcgn9u5d1NT3F3hhfjtPXF6divYGun+sPyQFjVxPbgd4rb1xP1VHRHYyx7KxRWFz/u5meLS/DZ4s7k73mzujpOzWqKJtd6fDuO3yes2zoqGQpVcadu2La5evYoNGzYgKioK+/fvR8OGDREbG/vL36GkpIRy5cpBlM9THpcqVQqampr5+p2yRktLC+rq6n/0d6SkpPzR7//e4X8OIShwJrwHDca2nXtRvboJBnr3yXWbu3b1CsaNGYXWbdph+65QNHJ2wYihg3H/fpRQZt2aVdi6eSMmBkzGpq07oKqqioH9+yA5ObmgwvopeYz7+NF/sGRBIHr0HYhVITthYFQdo4d5Iz4u55hvRl7FNP+xaNaqNVZt3Il6DZwxYcwwPHp4XyizZEEgLp0/iwlTZiJk+36069gNC4NmIOL0iYIK66fCDh/C3Dmz4D1gMLbs2ANj4+oY5N0Xcbm19bUr8PMdBc827bB15140dG6MkcOH4EGWtk5KSoKllQ2G+YwuqDB+W9g/hzA3cBa8Bw7Glp17YFz9J3FfvQK/saPg2TpL3MNyiNua4y6MjoYdwoK5s9HXezBCtu6GkXF1DBvUD3G57N+R167C3280Wnm2xcZte9CgkQvG+AzFwwff4q5UuQrGjJuIrbv2YeW6TShfoSKGDuyL+Li4ggrrp+S1veX1uCaP525APrfzdnWrYXYvB0zffgWOo0IR+SQO+ye5Q7eUSo7lveoZYFo3O8zYfhWWQ3dhQPAZtHOqhqldbYUyL2I/wX/jJdQZHYq6Y0Jx8sYr7BznClN9zQKK6ufkdRsvFEQy8CMDCk1yJSEhAWfOnMHs2bPRqFEjVK5cGbVr14afnx9atWolUTYmJgatW7dGiRIlYGRkhP379wuffT8saP369dDU1ERoaCiMjIygoqICNzc3PHv2TFhn8uTJsLS0xIoVK6Cvr48SJUqgQ4cOeP/+vVDm+2FBDRs2xLBhwzB27FhoaWmhXLlymDx5skQ97969CycnJ6ioqKBGjRo4duzYT4c1icViBAYGwtDQEMrKyqhUqRKmT58ufH7jxg04OztDVVUV2tra6N+/Pz5+/JitnkFBQShfvjy0tbUxePBgpKamCvV++vQpfHx8IBKJhCRUbGwsOnXqhIoVK6JEiRIwMzPD1q1bJer2/bCgKlWqYMaMGejduzfU1dVRqVIlrFy5UmKdZ8+eoUOHDtDU1ISWlhY8PDzw5MmTbPWdPn06KlSogOrVq+f6t/kTNm5YhzbtOsCzdVsYGBpiYsAUqKioIHTP7hzLb94UgjpO9dCzd19UMzDAkGEjYFqjBrZt2QQgIzu+eWMI+nkPRCPnxjCuboK/Zgbi3du3OB5+rCBD+yF5jHvHlhC08GyHZi1bo0o1A4waNwkqKio49PfeHMvv2rYJtR3qolO33qhS1QB9BgyFsUkN7N2xRShzK/Ia3Jp7wMqmNspXqIhWrdvDwKj6T3vEFKRNIevRpm17eLRuCwMDQ0yYNAUqqioI3ZtzW2/dtBF16jqhR68+qFbNAIOHDs9o662bhTItWnrAe+BgODg4FlQYv21TyHq0afdd3Cq/EHfvPqhmkCXuLVnibvU1bkeOu7DZsnEDPNu0R0vPNqhmYIhxEydDRUUFf4fuybH8ti0hcKjjhG49+6BqNQMMGDwcJqam2LHt2/7t3qwFajvUQUU9fRgYGmHEqHH49PEj7t+/V1Bh/ZS8tre8Htfk8dwNyOd2PqxVLaw7ehcbj9/H3ecJGLr8LJKS09DDxTjH8g4mZXD+7htsP/MQ0e8+Ivz6C+w480iit8uhf6MRduU5Hr5KxIOXiZi8+V98/JKK2sZlCiqsn5LXbZwVHYUmuaKmpgY1NTWEhob+NJM4ZcoUdOjQAZGRkWjWrBm6dOmCuB88Sfr8+TOmT5+OkJAQREREICEhAR07dpQo8+DBA+zYsQN///03Dh8+jKtXr2LQoEE/rMeGDRtQsmRJXLx4EYGBgZg6dSqOHj0KAEhPT4enpydKlCiBixcvYuXKlZgwYcJP/w5+fn6YNWsW/P39cfv2bWzZsgVly5YFAHz69Alubm4oXbo0Ll++jJ07d+LYsWMYMmSIxHecOHECDx8+xIkTJ7BhwwasX79eGNK0Z88e6OnpYerUqXj16hVevXoFAPjy5QtsbGxw8OBB3Lx5E/3790e3bt1w6dKlH9Z37ty5sLW1Ff5eAwcOxL17GReeqampcHNzg7q6Os6cOYOIiAioqanB3d1doodKeHg47t27h6NHj+LAgQM//Rvll9SUFNy5fQsOjnWEZQoKCnBwqIPI61dzXCfy2rVsF1516joh8to1AMCL588RE/MO9g7fvlNdXR1m5ha5fmdBk8e4U1NTEXX3NmzsHIRlCgoKsLFzwK1cuvnfunEdNrUlY7ZzqCNRvqa5JSJOn8C7t29ARLjy7yU8i34CO/s633+dVKSmZrR11nZRUFCAvYMjIq9fy3GdyOvXJMoDgGOdurmWL4z+77gdOW5ANuO+e+cW7Oy/7a8KCgqws3fEjchrOa5zI/I6attL7t8Ojk65lk9NTUHo7h1QU1OHsbFJflU9T+S5veXyuCaH525APrfz4sUUYGWgg+PXXwrLiIDjkS9Qu3rZHNe5cPctrAx0hGRKlbLqcLPRx+H/nuVYXkFBhPZO1VBSpTgu3nub/0H8H+R1G2dFS6F5W1CxYsWwfv169OvXD8uXL4e1tTUaNGiAjh07wtzcXKJsz5490alTJwDAjBkzsGjRIly6dAnu7u45fndqaiqCg4Nhb28PICMpYmpqikuXLqF27doAMpILISEhqFixIgBg8eLFaN68OebOnYty5crl+L3m5uYICAgAABgZGSE4OBjh4eFwdXXF0aNH8fDhQ5w8eVJYf/r06XB1dc31b/DhwwcsXLgQwcHB6NGjBwDAwMAATk5OAIAtW7YI9SxZsiQAIDg4GC1btsTs2bOFJEzp0qURHBwMRUVFmJiYoHnz5ggPD0e/fv2gpaUFRUVFqKurS8RVsWJFjB79rWvk0KFDERYWhh07dgh/o5w0a9ZMSEL5+vpi/vz5OHHiBKpXr47t27dDLBZj9erVQg+ZdevWQVNTEydPnkSTJk0AACVLlsTq1auhpKSU6+/5E+IT4pGeng5tbW2J5dra2nj8+FGO68TExEBbWydb+ZjYmK+fv8tYppP9O2NiYvKr6nkij3G//xpzaS3J+pXW0kb008c5rhMXG5NDeR3ExX2LZ/jo8QiaMRntWrhAUbEYFBREGD1+Miysbb//OqmIj8+IWytbW+vgyeOc446JicmxfGwhaMdflW9x63DcsiAhPiHHuLW0tfH0Sc5xx8bEQOu7Y5qWtjbivov7zOkTmOg7Gl++JEFHRxfBy9dAs3Tp/A3g/ySv7S23xzU5PHcD8rmd66iroJiiAt6+T5JY/jbhC6pX1Mxxne1nHkJbQwXh01tAJBKheDEFrDx8B3N2Sz5AqlmpNE7OagUVJUV8/JIKr1lHcfd5wh+K5PfI6zZeWIhkZdxNIVdoeq4AGXOuvHz5Evv374e7uztOnjwJa2vrbBPJZk22lCxZEhoaGnj7Nvesa7FixWBnZyf828TEBJqamrhz59vkVZUqVRISKwDg6OgIsVgs9MLIyfdJn/Llywv1uHfvHvT19SUSGD9KUgDAnTt3kJycDBcXl1w/t7CwEBIrAFC3bt1s9axZsyYUFb9NYJW1XrlJT0/HtGnTYGZmBi0tLaipqSEsLAzR0dE/XC/r30AkEqFcuXLC77p+/ToePHgAdXV1oWeSlpYWvnz5gocPHwrrmZmZ/TCxkpycjMTERIkfHifJCoM9Ozbj9s1IzJgbjFUh2zFo+BgsmDMd/146L+2qMcbyga2dPTZt34PVG7bAoa4T/Mb65DqPC2OMSUu9muUxpq0Fhq88B8dRe+E16yia2uhjXHtLiXJRL9/DfuRe1B+7D6sO38GqYQ1goqcplTozVhQVquQKAKioqMDV1RX+/v44d+4cevbsKfQOyVS8eHGJf4tEIojF4oKs5h+ph6qqal6rBOD/q9ecOXOwcOFC+Pr64sSJE7h27Rrc3Nx+OsHsj37Xx48fYWNjg2vXrkn8REVFoXPnzsI6WZNFOZk5cyZKlSol8TNn9swfrvMrSmuWhqKiYrZJsmJjY6Gjo5PjOjo6OoiNjcle/mvWXEcnoztmbMyvf2dBk8e4S32N+fvJa+PjYrM9vc6kpa2TQ/kYaGlllE/+8gWrli7E4BFjULdeQxgYVUebDp3h3Ngd2zet/yNx/K7SpTPi/n7Sv9jY7E96Muno6ORcvhC046/6Ydw/2MazlY/huGWBZmnNHOOOi43NNQ5tHR3EfXdMi4uNhdZ35VVVS0C/UmWYmVvCf/J0FFNUxP5c5nkoaPLa3nJ7XJPDczcgn9t5zIcvSEsXo0wpyfuCMpoqeJ2QlOM6AZ1tsPXUA6w/dg+3ouOx/+JTTNr8L8a0tUTWd3ykponx6HUirj6KxaRN/+LGkzgMblHzT4bzy+R1G2dFS6FLrnyvRo0a+PTpU56+Iy0tDf/++6/w73v37iEhIQGmpqbCsujoaLx8+W1s44ULF6CgoPB/T7BavXp1PHv2DG/evBGWXb58+YfrGBkZQVVVFeHh4Tl+bmpqiuvXr0v8PSIiIn67nkpKSkhPT5dYFhERAQ8PD3Tt2hUWFhaoVq0aoqKicvmGX2NtbY379++jTJkyMDQ0lPj5nVdl+/n54f379xI/Y3z98lQ3ACiupATTGjVx8cK3XgZisRgXL56HuYVVjuuYW1ri4oULEssunD8Hc0tLAEBFPT3o6Oji4sVv3/nx40fciLye63cWNHmMu3jx4jA2qYH/Ll8UlonFYlz59yJqmlnkuE5NMwv8d1ky5n8vnhfKp6WlIS0tDSIFycOogqIixFTwyd6cFC/+ta0vSrb1pQsXYG5hmeM65haWuHRRsufNhfPnci1fGOUa98WfxH2B4wZkM24T05q4fOnb/ioWi/HvpQswM7fMcR0zcwuJ8gBw8cK5XMsL30tU4G+1y408t7dcHtfk8NwNyOd2npomxtWHMWhkXkFYJhIBjcwq4tK9Nzmuo6pcDGIxSSwTp4u/rpv7cA8FBZHE65qlSV638cJCJCr8P7Kg0CRXYmNj4ezsjE2bNiEyMhKPHz/Gzp07ERgYCA8Pjzx9d/HixTF06FBcvHgR//33H3r27AkHBweJYToqKiro0aMHrl+/jjNnzmDYsGHo0KFDrvOt/IyrqysMDAzQo0cPREZGIiIiAhMnTgSQ+0FORUUFvr6+GDt2LEJCQvDw4UNcuHABa9asAQB06dJFqOfNmzdx4sQJDB06FN26dRPmW/kVVapUwenTp/HixQthvKGRkRGOHj2Kc+fO4c6dO/D29pZIDP0/unTpAh0dHXh4eODMmTN4/PgxTp48iWHDhuH58+e//D3KysrQ0NCQ+FFWVs5T3TJ169ELe3btwP7QvXj08CH+mjoZSUlJ8GzdBgAwwW8sFs6f+y2mrt1xLuIMNqxfi8ePHmLZksW4dfMmOnbuCiCjbbt0645VK5bh5PFw3I+6h4l+Y6FbpgycXRrnS53zgzzG3aFzdxzctwuHD+zDk8cPMW/2NCQlJaFpC08AwPQAP6xcMl8o365jV1w6H4Htm9fj6ZNHWLdyCe7duYXWHTJ6XZVUU4OltS2WL5qLq/9dwqsXz/HPgVCEHdqPeg1yHtonDV2798Te3Tuxf99ePHr0EDOmZbS1h2dGW08c74tFC761daeu3XAu4ixCNqzF40ePsHzpYty+dQsdO3URyrx/n4B7d+8Iw/uePHmMe3fvCGObC4Ou3Xti766vcT/MIW4/Xyyan0Pc67/GveRr3J1/EPdjjruw6NytB/bt2YkD+0Px+NFDzJ4+BUlJSWjh0RoAEDDRF0sWzRPKd+zcHefPncXmkHV48vgRVi4Lxp3bt9ChY8b+nZT0GUsXzceNyGt49fIF7ty+hWkBE/Du7Ru4uLpJJcacyGt7y+txTR7P3YB8bueL9t9EL9fq6NLICNX1NLHIuy5KqBRDSPh9AMDqYQ0kXrN86HI0+rmbor1TNVQuowZni4qY1NkGhy5HC0mXqV1tUbdGOVTSVUPNSqUxtast6tcsj22nH+ZYB2mQ122cFR2FZkJbNTU12NvbY/78+Xj48CFSU1Ohr6+Pfv36Yfz48Xn67hIlSsDX1xedO3fGixcvUK9ePSFhkcnQ0BBt2rRBs2bNEBcXhxYtWmDp0qX/9+9UVFREaGgo+vbtCzs7O1SrVg1z5sxBy5YtoaKS8zvqAcDf3x/FihXDpEmT8PLlS5QvXx4DBgwQ4ggLC8Pw4cNhZ2eHEiVKoG3btpg3b16u35eTqVOnwtvbGwYGBkhOTgYRYeLEiXj06BHc3NxQokQJ9O/fH56enhKvo/5dJUqUwOnTp+Hr64s2bdrgw4cPqFixIlxcXKChofF/f29+cm/aDPFxcVgavAgxMe9Q3cQUS1esFrqOvn71CgqibzlISytrzAwMQvCiBVi8YB4qVa6CBYuXwMjo26vxevXph6SkJEydPAkfPiTCytoGS1eszreEUH6Qx7idXZsiIT4ea1cGIy42BobGJpizcLkwLOjtm1dQyNILpZa5Ffynzcaa5YuxaulC6OlXxvQ5i1DNwEgoM+mvIKxcugB/TRqHxMT3KFeuAvoOGAaPtl4FHl9u3Nwz2nrZksWI/drWS5avytLWL6GQJeFraWmNGbOCsCR4AYIXzkelylUwb2EwDLO09akTxxHg/+24PG7MSACA98DBGDBoaAFF9mNuTZshPj4Oy4J/ELdClritrDFjdhCWLM4S96Ic4p6YS9yDOW5pcnVrhvj4eKxctgixMTEwrm6KhUtXCsNE3nx3TDO3tMK0GXOwfMlCLF08H/qVKmPO/MUwMMyIW0FBEU+ePMLBUaFISIhHKU1N1KhphpVrN8HA0CjHOkiDvLa3vB7X5PHcDcjndr4r4hF0NFQwqaM1ypYugcjHsfCYeliY5FZfVw1i+tZTZdbOqyAiBHS2QQWtkohJ/IKD/0Zj8qZvPfd1S6lizfAGKFe6BN5/TsHNJ3FoOfUwjl9/UeDx5UZet3FWdIiIiH5eTHatX78eI0aMQEJCQq5lJk+ejNDQUFz7+tquPyUiIgJOTk548OABDAwM/ujvKuq+pEm7BqwgJXxKlXYVpEJDtdDkvwuWjHT9ZPkjNa1IX4bkqHgxOd3I5a+pAUDixl+eiIv2LUautDus+XmhIih+Z19pV0EqVIrIpVp0XOF/WUglrcKfECsim0PhtHfvXqipqcHIyAgPHjzA8OHDUbduXU6sMMYYY4wxxhgrFOQzBZz/OLnyB3348AG+vr6Ijo6Gjo4OGjdujLlz5/58RcYYY4wxxhhjjMmMIj8siBVNPCxIvvCwIDnDj0/kCg8LkiPy19QAeFiQvOFhQfKlqAwLeiYDw4L0eVgQY4wxxhhjjDHGCitZedVxYVdoXsXMGGOMMcYYY4wxJos4ucIYY4wxxhhjjDGWBzwsiDHGGGOMMcYYk1s8Lig/cM8VxhhjjDHGGGOMsTzg5ApjjDHGGGOMMcZYHvCwIMYYY4wxxhhjTE7x24LyB/dcYYwxxhhjjDHGGMsDTq4wxhhjjDHGGGOM5QEPC2KMMcYYY4wxxuQUjwrKH9xzhTHGGGOMMcYYYywPOLnCGGOMMcYYY4wxlgc8LIgxxhhjjDHGGJNT/Lag/ME9VxhjjDHGGGOMMcbygJMrjDHGGGOMMcYYY3nAyRXGGGOMMcYYY4yxPOA5VxhjjDHGGGOMMTkl4pcx5wvuucIYY4wxxhhjjDGWB5xcYYwxxhhjjDHGGMsDHhbEGGOMMcYYY4zJKx4VlC+45wpjjDHGGGOMMcZYHnByhTHGGGOMMcYYYywPeFgQY4wxxhhjjDEmp3hUUP4QERFJuxKM/a6PyfK52RZT5EMfY4zJuqSUdGlXgRUgVSVFaVeBsT+udKNJ0q6CVCSdmSrtKuSLN4mp0q7CT5XVKC7tKvwUDwtijDHGGGOMMcYYywMeFsQYY4wxxhhjjMkpEXeOzxfcc4UxxhhjjDHGGGMsDzi5whhjjDHGGGOMMZYHPCyIMcYYY4wxxhiTUyJ+X1C+4J4rjDHGGGOMMcYYY3nAyRXGGGOMMcYYY4yxPOBhQYwxxhhjjDHGmLziUUH5gnuuMMYYY4wxxhhjjOUBJ1cYY4wxxhhjjDHG8oCTK4wxxhhjjDHGGGN5wHOuMMYYY4wxxhhjcoqnXMkf3HOFMcYYY4wxxhhjLA84ucIYY4wxxhhjjDGWBzwsiDHGGGOMMcYYk1MiHheUL7jnCmOMMcYYY4wxxlgecHKFMcYYY4wxxhhjLA94WBBjjDHGGGOMMSanRPy+oHzBPVcYY4wxxhhjjDHG8oCTK4wxxhhjjDHGGGN5wMOCGGOMMcYYY4wxOcVvC8of3HOFMcYYY4wxxhhjLA84ucIYY4wxxhhjjDGWB5xcYf+3J0+eQCQS4dq1a9KuCmOMMcYYY4wxJjWcXJFzPXv2hEgkgkgkgpKSEgwNDTF16lSkpaVJu2oFZse2zWjh7gxHW3N079wBN29E/rD80SOH0aZVUzjamqNDm5Y4e+aU8FlqaioWzQ9ChzYtUbe2Fdxc6mHSeF+8e/vmT4fx27Zt2Yymrs6wszJDl47tcSPyx3EfCfsHHi3cYWdlhraeLXHm9CmJz4kISxYvhEsDJ9S2Nkf/Pj3x9OmTPxjB75PHmAGOm+PmuHNSVOLetX0LPJs1Rn17S/Tu5oVbN38cd/jRw/Bq3Rz17S3Rpb0Hzp05lWvZ2X9NhoNVDWzbHJLf1c4zeY1bXrdzjlt+4vZuXRt3d/gg/pg/Tq/oD1vTij8sP6S9I65vHoa4Y/64v2sUAoe6Q1np27SiaqpKmDO0Ke7tHIm4Y/44sbQvbEwq/OkwmJzi5AqDu7s7Xr16hfv372PUqFGYPHky5syZI+1qFYgjhw9h3pxZ6D9gMDZv3wPj6tUxZEBfxMXG5lj++rUrmOA7Cp6t22HLjr1o6NwYo4YPwYP7UQCAL1++4O6d2+jrPQibt+9G0LzFePLkMXyGDSrIsH7q8D+HEBQ4E96DBmPbzr2oXt0EA737IDaXuK9dvYJxY0ahdZt22L4rFI2cXTBi6GDc/xo3AKxbswpbN2/ExIDJ2LR1B1RVVTGwfx8kJycXVFg/JI8xAxw3x81x56SoxH007B8snDsbfb0HYcOWXTAyNsGIQf0RF5dz3JHXrmKS3xi09GyDDVt3o35DF4wdORQPH9zPVvbk8WO4eeM6dHXL/Okwfpu8xi2v2znHLT9xt3OuhdlD3DF9/Uk49l2OyAevsX9ud+hqlsyxvFdjM0zzbowZ607AsutiDJgdinbOtTC1f2OhzDJfDzjbGaD3X7th22MJjl1+iIPze6KCjnpBhcXkCCdXGJSVlVGuXDlUrlwZAwcOROPGjbFjxw5oaGhg165dEmVDQ0NRsmRJfPjwIcfvOnXqFGrXrg1lZWWUL18e48aNk+gF07BhQwwbNgxjx46FlpYWypUrh8mTJ//J8H5oU8h6tG7bHq0826KagSHG+0+BiqoK9oXuzrH81s0b4VjXCd179UHVagYYNGQ4TExrYMe2zQAAdXV1LF25Fk3cmqJK1Wows7CE73h/3Ll9C69evSzI0H5o44Z1aNOuAzxbt4WBoSEmBkyBiooKQvfkHPfmTSGo41QPPXv3RTUDAwwZNgKmNWpg25ZNADKehGzeGIJ+3gPRyLkxjKub4K+ZgXj39i2Ohx8ryNByJY8xAxw3x81x56SoxL1103p4tGmPFh5tUNXAEL4TAqCiooIDoXtyLL9960Y41HFC1x4Z5zDvwcNQ3bQGdn09h2V6+/YN5s6ejikzAqFYrPC9WFJe45bX7Zzjlp+4h3nVwbq//8PGQ1dx98k7DA36G0lfUtGjuXWO5R1qVcL5m8+w/dgNRL9OQPjlh9hx7IbQ20VFqRg8G9TAhGVHEHH9KR69iMP0dSfw8EUc+nnWLsjQCj2RqPD/yAJOrrBsVFVVoaCggI4dO2LdunUSn61btw7t2rWDunr2bO+LFy/QrFkz2NnZ4fr161i2bBnWrFmDv/76S6Lchg0bULJkSVy8eBGBgYGYOnUqjh49+kdjyklqagru3rmF2g51hGUKCgqobe+IG9ev5bhO5PVrsLevI7HMsU5dROZSHgA+fvwAkUgEdXWN/Kh2nqWmpODO7VtwcJSM28GhDiKvX81xnchr1+Dg4CixrE5dJ0R+nW/nxfPniIl5B/ssf0t1dXWYmVvk+p0FSR5jBjhujpvjLtJxp6bg3p3bsLN3EJYpKCjAzt4RNyKv5bjOzchrsLOXjNvBsS5uRF4X/i0WizFl4jh07dEb1QyM/kjd80Ju45bX7ZzjFpYV9biLF1OElXF5HP/vobCMiHD834eoXVMvx3Uu3IyGlXF5IZlSpXxpuDkY4/CFjF5pxRQVUKyYIr6kSE538CU5FXXMK/2hSJg84+QKExARjh07hrCwMDg7O6Nv374ICwvDq1evAABv377FoUOH0Lt37xzXX7p0KfT19REcHAwTExN4enpiypQpmDt3LsRisVDO3NwcAQEBMDIyQvfu3WFra4vw8PACiTGrhPh4pKenQ1tbW2K5trYOYmJiclwnNiYGWt+V19LWQWwu5ZOTk7FofhDcmjaHmppa/lQ8j+ITcotbO9e4Y2JioK2tk718bMzXz99lLNP59e8sSPIYM8Bxc9wZOO7sikLcCfEJSE9Ph5aWZByltbURG/uDc5iW9nfldSTKb1y3GoqKiujQqWv+VzofyGvc8rqdc9zyE7dOqRIoVkwRb+M+SSx/G/8J5bRzHsKz/dgNTFtzHOFL+iDxRADu7PDB6WuPMWfjaQDAx6QUXLgRDb8eDVBeWx0KCiJ0bGIO+5r6uX4nY3lR+Po8sgJ34MABqKmpITU1FWKxGJ07d8bkyZNRsmRJ1KxZExs2bMC4ceOwadMmVK5cGfXr18/xe+7cuQNHR0eIsvTbqlu3Lj5+/Ijnz5+jUqWMDLG5ubnEeuXLl8fbt29zrV9ycnK2saCpUIKysvL/G3KBSE1NxbjRI0AE+E2cLO3qMMYYYz909/YtbN+6ERu27JY4lxd18ho3Y7KunmUVjOlWH8PnHcDl289hUFEbQcOb4lWPBpi1IWMy395/7cYKv9Z4FDoGaWnpuBb1CjvCb8DKmCe1ZfmPe64wNGrUCNeuXcP9+/eRlJQkDNsBgL59+2L9+vUAMoYE9erVK88XHsWLF5f4t0gkkujZ8r2ZM2eiVKlSEj9zA2fmqQ4AoFm6NBQVFbNNDBYbGwMdHZ0c19HW0ck22W1cbAy0vyufmpqKcWN88OrVSyxduabQ9FoBgNKaucUdm2vcOjo62Z4IxsbGQufrExIdHd2MZTG//p0FSR5jBjhujjsDx51dUYhbs7QmFBUVERcnGUd8bGy2p9eZtHV0sk36Gh/77Wn3tav/IT4uDp7NXFDX1gx1bc3w+tVLLJoXCM9mjXP6ygInr3HL63bOcctP3DHvPyMtLR1ltCQnry1TuiRex+Y812NAXxdsPXId6w9cwa1Hb7H/zB1MWnkMY7rWE+5XHr+MR5Oha6HtOg1G7eainvdKFFdUwONX8X88JlkikoH/ZAEnVxhKliwJQ0NDVKpUCcW+m8Cta9euePr0KRYtWoTbt2+jR48euX6Pqakpzp8/DyISlkVEREBdXR16ejmPlfwVfn5+eP/+vcTPqLF+//f3ZSpeXAkmpjVx+eJ5YZlYLMblixdgZmGZ4zrmFpa4lKU8AFy8cA7mWcpnJlaePX2KZSvXQVOzdJ7rmp+KKynBtEZNXLwgGffFi+dhbmGV4zrmlpa4eOGCxLIL58/B3NISAFBRTw86Orq4mOVv8/HjR9yIvJ7rdxYkeYwZ4Lg5bo67SMddXAnVTWvg8sVvcYjFYly+dAFm5pY5rlPL3BKXL0nGfenCeZiZWwAAmjZvhU07QhGybY/wo6tbBl2698bCpav+WCy/Q27jltftnOMWlhX1uFPT0nE16hUa2VQTlolEIjSyqYZLt57nuI6qSnGIxSSxTJxOX9eVLPv5Sypex36EppoKGtc2xIEzd/I3AMbAyRX2E6VLl0abNm0wZswYNGnS5IdJkkGDBuHZs2cYOnQo7t69i3379iEgIAAjR46EgsL/v6kpKytDQ0ND4ie/hgR17d4Te3fvxN/79uLxo4eY+ddkJCUloZVnGwDApPG+WLxwrlC+U5duOHfuLDZuWIvHjx9hxdLFuH3rFjp07AIgI7HiO2o47ty6ib9mzUG6OB0xMe8QE/MOqakp+VLn/NCtRy/s2bUD+0P34tHDh/hrakbcnq0z4p7gNxYL53+Lu0vX7jgXcQYb1q/F40cPsWzJYty6eRMdO2eMTReJROjSrTtWrViGk8fDcT/qHib6jYVumTJwdikcT/3kMWaA4+a4OW6g6MbdqWtP7N+7Cwf3h+Lxo4cInDEFX5KS0NyjNQBgysRxWLponlDeq1M3XDh3FptD1uHJ40dYtTwYd27fRLuv57BSmpowMDSS+FEsVgzaOjqoXKWqVGLMibzGLa/bOcctP3Ev2n4OvVrYoIu7JapX1sGiUS1QQlUJIYeuAABWT2iDqd7f6noo4h76edqhvUstVC6vCWdbA0zq64xDEfeEpEvj2oZwrW0ofH54US9ERccg5JD0J/FlRQ/PucJ+qk+fPtiyZUuuE9lmqlixIg4dOoQxY8bAwsICWlpa6NOnDyZOnFhANf19TdybIT4+DsuXLkZszDsYVzfF4mWrhK7Cr1+/hEjhW+rbwtIa02cFYdniBViyaD4qVaqCuQuDYWhkDAB49/YNTp08DgDo1N5T4netWLMBtnb2BRPYT7g3bYb4uDgsDV6EmJh3qG5iiqUrVgvDm16/egUF0beEmKWVNWYGBiF40QIsXjAPlSpXwYLFS2D0NW4A6NWnH5KSkjB18iR8+JAIK2sbLF2xutDMjSOPMQMcN8fNcQNFN25Xt6ZIiI/DqmWLERsbA6PqJpi/ZEWWc9griLI83DC3tMLUGYFYsWQRlgcvgH6lygictxgGhoXv7Tg/Iq9xy+t2znHLT9y7jt+EjmYJTOrjjLJaaoh88BoeozfibXzGJLf6ZUtBnKWH/KyQUyAiBPR1QQVdDcQkfMLBiHuYvOrbizJKlVTGVG9XVNTVQNyHJOw7eRsBq44hLT33KQnkEU83lT9ElHUMB2M52LhxI3x8fPDy5UsoKSlJuzoAgI/J8rnZFlPkIx9jjMm6pJR0aVeBFSBVJUVpV4GxP650o0nSroJUJJ2ZKu0q5IvEL4U/2aShUvgH3XDPFZarz58/49WrV5g1axa8vb0LTWKFMcYYY4wxxhgrTAp/+odJTWBgIExMTFCuXDn4+eV9AlnGGGOMMcYYY4WLSAZ+ZAEPC2IyiYcFMcYYk1U8LEi+8LAgJg94WJBs+yADw4LUZWBYUOGvIWOMMcYYY4wxxlghxnOuMMYYY4wxxhhj8oo7x+cL7rnCGGOMMcYYY4wxlgecXGGMMcYYY4wxxhjLAx4WxBhjjDHGGGOMySkRjwvKF9xzhTHGGGOMMcYYYywPOLnCGGOMMcYYY4wxlgc8LIgxxhhjjDHGGJNTIh4VlC+45wpjjDHGGGOMMcZYHnByhTHGGGOMMcYYYywPOLnCGGOMMcYYY4wxlgc85wpjjDHGGGOMMSaneMqV/ME9VxhjjDHGGGOMMcbygJMrjDHGGGOMMcYYY3nAw4IYY4wxxhhjjDF5xeOC8gX3XGGMMcYYY4wxxhjLA06uMMYYY4wxxhhjjOUBDwtijDHGGGOMMcbklIjHBeUL7rnCGGOMMcYYY4wxlgecXGGMMcYYY4wxxliRsWTJElSpUgUqKiqwt7fHpUuXflh+586dMDExgYqKCszMzHDo0KHf/p2cXGGMMcYYY4wxxuSUSFT4f37H9u3bMXLkSAQEBODKlSuwsLCAm5sb3r59m2P5c+fOoVOnTujTpw+uXr0KT09PeHp64ubNm7/3dyQi+r2qMiZ9H5Plc7MtpsjjIRljTNYlpaRLuwqsAKkqKUq7Coz9caUbTZJ2FaQi6cxUaVchX3xJk3YNfk7lN2aLtbe3h52dHYKDgwEAYrEY+vr6GDp0KMaNG5etvJeXFz59+oQDBw4IyxwcHGBpaYnly5f/8u/lniuMMcYYY4wxxhgrtJKTk5GYmCjxk5ycnK1cSkoK/vvvPzRu3FhYpqCggMaNG+P8+fM5fvf58+clygOAm5tbruVzRYyxX/blyxcKCAigL1++SLsqBYrj5rjlAcctP3HLY8xEHDfHLR84bo6bFU0BAQEEQOInICAgW7kXL14QADp37pzE8jFjxlDt2rVz/O7ixYvTli1bJJYtWbKEypQp81t15GFBjP2GxMRElCpVCu/fv4eGhoa0q1NgOG6OWx5w3PITtzzGDHDcHLd84Lg5blY0JScnZ+upoqysDGVlZYllL1++RMWKFXHu3Dk4OjoKy8eOHYtTp07h4sWL2b5bSUkJGzZsQKdOnYRlS5cuxZQpU/DmzZtfruNvjFxijDHGGGOMMcYYK1g5JVJyoqOjA0VFxWxJkTdv3qBcuXI5rlOuXLnfKp8bnnOFMcYYY4wxxhhjMk9JSQk2NjYIDw8XlonFYoSHh0v0ZMnK0dFRojwAHD16NNfyueGeK4wxxhhjjDHGGCsSRo4ciR49esDW1ha1a9fGggUL8OnTJ/Tq1QsA0L17d1SsWBEzZ84EAAwfPhwNGjTA3Llz0bx5c2zbtg3//vsvVq5c+Vu/l5MrjP0GZWVlBAQE/FKXtKKE4+a45QHHLT9xy2PMAMfNccsHjpvjZszLywvv3r3DpEmT8Pr1a1haWuLw4cMoW7YsACA6OhoKCt8G8dSpUwdbtmzBxIkTMX78eBgZGSE0NBS1atX6rd/LE9oyxhhjjDHGGGOM5QHPucIYY4wxxhhjjDGWB5xcYYwxxhhjjDHGGMsDTq4wxhhjjDHGGGOM5QEnVxhjjDHGGGOMMcbygJMrjDFkzmvN81szVjRs3rwZ6enp0q4GY+wPkcfztVgslnYVCpw8xsyYLOPkCpN7OZ245OWiJTPO+/fvAwBEIpE0q8P+MHnZruXdpk2b4O/vj0mTJsldgiXrNp6SkiLFmkgX7+tF1+zZs3Ht2jWIRCK5amciEl6b+s8//+Dz589SrtGflzXmYcOGYfDgwVKuEWPsZzi5wuSaWCwWTlx37tzB5cuX8e7dO7m5IRGJRNi7dy9MTExw5coVaVeH/WEikQiHDh3CggUL5OKinIiEH3nSokULdOzYESdOnIC/v7/cHM+AbwnigIAAbN++Xcq1KTiZ2/iTJ08AyE+iXN727bNnz2Lz5s2YNm0abt26JRcJltjYWADftumjR4+if//+wrVbUUVEQsxnz57FiRMn0LFjRynXijH2M0X7yMTYD2R9IjBx4kS0bt0aXl5ecHR0xIQJE/Do0SMp1/DPe/PmDf777z8sXrwY1tbW0q5Ogci8EE1MTERaWpqUa/NnHT16FMnJyQAyEompqalYvHgxVFVVi/TNV2ZvtKSkJIhEIohEIty8eRPR0dFSrtmfJxaLoampCT8/PzRs2BDHjx8v8gmWzZs3C8drIkJycjKOHDkCQ0NDKdes4IhEIoSGhqJdu3aIjIyUdnUKTOZxTF56KTk5OWH8+PGIj4+Hv78/bty4UaQTLF5eXujevTueP38uLPv06RO0tLSgoqJSpIfMZG7bu3btwvLly9G4cWPUq1evSB/Lsyqq2zQr+ji5wuRW5okrKCgIq1evxtKlS/Ho0SPY29tj3bp1iImJkXIN/6zr16/Dzc0N+/fvh42NjbSrUyAynwQdPHgQI0aMwLlz54rshcr9+/fh5uaG0aNHIyUlBQoKCihevDg+ffpUpBMrAKCgoICXL1/C3d0dN27cwMGDB+Hg4CA8AS2qMhPG6enpUFdXx7hx49CwYUOEh4cX2QTLP//8g+7du2PNmjWIjo6GSCRCeno6Xr9+jaSkJGlX74/LvAF59uwZFi9eDG9vb5ibm0u5Vn9e1pvqBQsWoEOHDoiPj5dijf68zARSx44d0bFjR3z48AEBAQGIiooqsgmWQYMG4cSJExg/fjyePn0KAEhOToaWlhaAjGN9Ue6d+Pr1a6xbtw6HDh3Cq1evAACKiopFOqkEZOzfmdcp79+/l/isqLY1Kzo4ucLkFhEhJSUFp06dwsSJE+Hs7IwDBw7gwIEDmDZtGmrXro3U1FR8+fJF2lX9I96+fYvy5cvj/v37+PjxI4CiP3GaSCTCnj174OXlhcqVK6NixYpQVFQUPi9KJ20jIyPs3r0ba9euxdixY4XtOD09HSVLlgRQtOL9XnR0NMqWLYvWrVujbdu2WLduHaysrIpszFkvRhUVFfHlyxdoaGhgwoQJaNy4MY4dO1YkEyxNmzbFokWLsHHjRixfvhzPnj1DiRIlULx4cWE7zxpzUWt/kUiE06dPY968eShZsiRatmwp7Sr9cVmH80ZERCAxMRH79+/HhAkTkJiYKOXa/RlEBCUlJQDAnDlzcPbsWTx58gShoaEYN24cbt++XeQSLOnp6WjQoAHCw8OxY8cOjB8/HnFxcYiNjRX+FgCE3olFwfftV65cOUydOhWurq44fvw4QkJCAHxLKhVFWXuVz5o1C23atIG7uzu2bduGjx8/QiQSFflrVSbbikm7AoxJU3p6OmJiYuDi4oLTp0+jU6dOCAoKgre3N5KTk7F69WrUrl0bdnZ20q5qvnN1dUXx4sWRnJyMgQMHYuvWrbC1tZUY51vU3L59GyNGjMDixYvRq1cvYfn9+/dRrlw5qKurS1y4y7rWrVtj8+bN8PLyQkpKCubMmYO0tDSUKVMGgOS8DMnJyVBWVpZWVfOdg4MDXFxcsHv3bujp6aFatWrCZ0VtG8+6zS5evBj//fcfoqKi0KlTJ3Tt2hUTJ04EAISHhwMApk2bJpFUlFVpaWkoVqwYBg8eDLFYjFmzZiE9PR0dOnRAxYoVUbZsWQCQiDUhIQGlS5eWVpX/iFu3bmHRokUoWbIknj59inLlykm7Sn9U5rbu6+uLHTt2wMvLC82aNcP69evx/v17LF++HOrq6lKuZf7KPF7NmzcP06ZNw86dOzFu3DgcOHAAu3btwsSJEzF9+nSYmpoWmeOboqIi0tPT4ejoiBMnTqBRo0bQ1dWFhoYGXr58iU2bNuHDhw/Q1dWFkpIS7ty5gw4dOqBq1arSrvr/JetxPDo6GsnJydDX14eNjQ2mTp0Kf39/rFq1CsrKyvDy8hKSDEXlegWQPDcvWbIEs2fPxvjx4/H3339j3rx5iIyMhK+vL0qVKlXkYmdFCDEmJ9LT03Nc3qZNGzI2NiY1NTVav369sPz169fUoEEDWrVqVUFV8Y8Ri8VERPTq1St6+fIlPXv2TPjs2LFj1KpVK7KxsaHLly8TUe5/K1l38eJFsrGxoYcPH9Lnz59pyZIl1KBBAzIwMCBnZ2d6+fKltKuYLzLbOzU1lYiI9uzZQ8WLF6chQ4aQhYUFVahQgbp27UotW7akpk2bUrt27Wjo0KFCeVmXGcehQ4do/vz51LVrVzI3N6eTJ08SUcb2nfk3Kkp8fX2pfPnyNGnSJFq0aBGJRCLq378/paWl0fv378nPz48cHR1p8ODBMr+P51T/hQsXUqVKlahXr15UrFgxqly5Mjk4OFCDBg3I1taWatWqRdOnT5dCbf+8TZs2kba2NvXv358ePHgg7er8cWfOnCFNTU1hn05NTaXDhw+ThoYGdenShd6/fy/lGuYvsVhMKSkp1LJlSxo5cqTEZ+vWrSMDAwNq06YN3bt3T0o1zD+5HZtOnz5NysrKVKxYMTI1NSVnZ2cyMTGh2rVrk4ODAzk6OlJaWloB1zZ/ZD0fBQQEkJmZGVWrVo2qVq1KK1eupNTUVLp27Rp5eXlRvXr1aPv27VKs7Z93+fJlGjx4MB08eFBY5ufnR/b29jRu3Dhh/5b18xgrmji5wuRC1gPwjRs36P79+5SYmEhEGQdxa2trsrKyEsokJCRQ06ZNycnJSWZP1pkyT9r79u0jR0dHqlSpEjVu3JgCAwOFMmFhYeTh4UH29vZ0/vx5aVU132XGnpKSQkREERERpKenR7179yZDQ0Py8PCgcePG0aZNm8jY2Jg2b94szermi8yYT58+TfPnz6e3b98SEVFoaChpaGiQtrY2jRs3jmbPnk3jx4+nMWPG0JQpUygyMlKa1c4XuSVMTp48Se3btydzc3M6deqUsPzEiRMUHR1dUNX7o86dO0cGBgZ04cIFIiL677//SEFBgUJCQoQyiYmJNGjQIOrXr59MJ5eyHs8XLVpEU6ZMEf4dHBxMpUuXpvr165Ofnx/t2LGDtm/fTsHBwbRs2TKZTyBmttuLFy/owYMH9Pz5c+GzpUuXUoUKFWjMmDH06NEjaVWxQBw6dIj09fUpNjaWiL79XXbv3k0ikYiGDBkinOOLkk6dOlHnzp2z7b8DBgygkiVLUv369WU6uZZ13z58+DBt3LiRbt68KbTlxYsXSVVVldq1a0fR0dHCuT3r30OWb7j/+usvKlOmDB08eJDS0tLIxcWF9PX16datW0REdOXKFerUqROZmJjQsWPHpFzbP2Pfvn1kampKlStXpnPnzgnL09LSaPz48eTg4EB+fn4UHx8vvUoy9gOcXGFyZezYsWRoaEiqqqrUvXt3Onr0KBFlPPkxNDSkKlWqUKNGjcje3p6srKyEE7esJ1gOHDhAJUqUoPnz59Pp06fJz8+PFBQUaNKkSUKZo0ePUsOGDalhw4b05csXmb75yurs2bNkaWlJ7969IyKitWvX0sCBA2nixIkSF6EODg60c+dOaVUzX+3atYs0NDRo4sSJ9N9//wnLDx06RCoqKuTr6yvF2v0Zmdvr2bNnyd/fn/z9/SWSZSdPniQvLy+qVasW7dixgyZPnkyamppFprdSeHg4OTk5ERHR9u3bSU1NjZYtW0ZERO/fv6ezZ88SEdHHjx+Fv5Ws7+OjR48mPT09mjJlCj1+/FhYvnz5ctLT06OJEyfSq1evsq0nqwmWzPbas2cPmZubU9myZcna2po6dOgglAkODqYKFSrQuHHjZPomO6ucttO7d++SkpJStif4jx49oooVK5JIJKLevXsXVBXz3fcxZ/578uTJpK+vT//++6/E50FBQVSvXj2aOHGizCYXssY8atQoKlu2LOnq6lL16tXJ399f2JfPnDlDysrK1L17d3ry5Emu3yFLxGIxJSYmkrOzs5AQP3jwIJUqVUo4jmcet86fP0/+/v4yf12am48fP1Lfvn1JU1OTxo4dS1++fBE+S09Pp4kTJ1K1atWEvwtjhQ0nV1iRlvUiY//+/VStWjUKCwuj9evXU6NGjahx48b0zz//EBHR48ePacKECTRlyhRauXKlcOKS1QvxTNHR0dSoUSNavHgxERG9e/eO9PT0qE6dOqSurk4TJ04Uyh4/flxiyFBRcP/+fapcuTLZ2toKTzmznqyJiCZOnEiVKlWSuEGTVVeuXKEyZcrQ8uXLc/x87969VLJkSerRowclJSURkexekH5v9+7dVLp0aWrdujW1bNmSqlevTuPHjxc+P3v2LPXo0YPKli1LpqamwjA4WZPTzdPRo0epWrVqtGrVKipVqhQtXbpU+OzgwYPUsmVLevjwobBM1tt8x44dVLZsWbp06ZKwLOvfZfHixVSpUiUaOHBgkUmgEWUk0VRUVGjRokV0+PBhWrp0KRkZGZG9vb1QZvny5aSiokKTJk2S+fNX1jb98OGD8P9paWnUt29fcnBwoAMHDgjLY2JiqF+/frR7925SVlamTZs2FWh980PWmJ8+fUrPnj0Tzl1ERE5OTmRgYEAnT56k169fU1JSEnl6etKCBQuE/VrWEixZj0fnzp2jBg0a0IULFyguLo7Gjx9P9vb2NGLECCHBcvbsWRKJRDR16lRpVTlficVievv2LRkaGtLr16/p+PHjEgnyz58/07x587L1tJT1BEtu2+nHjx+pd+/eZGtrSwsXLqTk5GSJdVasWCHzsbOii5MrTC4cOXKEhgwZQgsWLBCWXb58mVq0aEEuLi4UGhqa43pF4eD98eNH8vf3p0ePHtHLly/J1NSUBgwYQG/fvqXOnTuTSCSiUaNGSbuaf0TmBduDBw+oVq1aZGVlJfRgISJas2YN9enTh8qUKUNXrlyRVjXz1Zo1a6hOnTr06dMnYdn3FzBbt26lMmXK0OvXrwu6en/MhQsXSF9fX0gqXb9+nbS0tKhYsWI0aNAgoVxCQgI9evRIZmPP2pa7d++mQ4cOUVpaGn38+JHatGlDCgoKFBAQIJRJSkqili1bkpeXl8zdcP3ItGnTqHXr1kT07Tj9fXzTp08nDw8PmU8kZUpPT6dx48ZRt27dhGVisZguXbpERkZG1L17d2H5hg0bKCoqShrVzDdZ223mzJnC+TosLIxSUlIoMjKSOnToQCYmJvTXX3/R1q1bqXHjxtSgQQOKi4sjc3Nzmbv5zroNBwQEkL29PWlpaVHHjh2FHg1isZhcXFyocuXKVLlyZapRowYZGxsLiTRZ3t63bdtG3bp1owEDBkgsnzp1KtWuXZt8fHyEBMv169dlNnmYWxs1bdqUGjZsSGpqarRmzRph+bNnz8jJyYm2bt1aUFX847Ju61u3bqVJkybR7NmzKTw8nIgyrl179uxJ9vb22RIsmYrCNToreji5woq8Bw8ekImJCZUoUSLbcIjMBIubmxtt3LhRSjXMX5kn7YcPHwq9UDJ7akybNo1atmwpPAWbOnUqmZiYkLGxMb1+/VqmL8qyytplOjOm+/fvU61atcjOzk6Yh2T37t3Up08funPnjlTq+SfMnTuXatasKfGUN9OpU6eE2HP6XJYtXbqUBg4cSEQZT3urVq1KPXv2pLlz51Lx4sXJz89PyjXMu6z759ixY6lSpUq0bt06evPmDRFlDBepU6eOMMRt9erV5ObmRrVq1RJuQmQxwZJZ56zx+/j4kJWVVbYbyuTkZNq/f79QrqgMgcrUsWNHiV4qmebMmUMODg4UExMjhVrlv6zb6fz586lUqVI0efJkcnR0pGrVqlFQUBAlJydTVFQUBQQEkI6ODllaWpKLi4swnLdOnTq0cOFCIpK99p80aRLp6OjQvn376MSJE9SiRQuqVKkSrVixQiizZ88eWrVqFS1dulTYD2T5ZlMsFlOXLl1IXV2dHBwcsh2rpk6dSo6OjtSrVy+JnjyylmDJGld0dLTE0MVNmzZRtWrVqEmTJsKyDx8+ULNmzahRo0Yy3b65GT16NJUtW1YYki8SiWjWrFlElJFg6dGjB9WpU4f++usvYd9mrDDj5AqTC+Hh4WRnZ0e1a9emEydOSHz277//kqOjIw0fPlwqdctPWcfk29ra0owZMyQm/Wrfvj01a9ZM+LePjw8FBQXJ/MR/YrFYiD0+Pp50dXWpQYMGEp8TZUxmXLZsWXJ3dxeSDJlDY2TZ3bt3hf/fs2cPKSgoSMyyT5RxQTdkyBBauXKlzN1o/Irk5GQ6f/48JScnk7OzM/Xs2ZOIiJ48eUIVKlQgkUhEI0aMkHIt80dgYCCVLVtWYrK/TMePHycvLy8qVaoU1a9fn7p06VJk5o6KiIgQ/n/VqlVUuXJl2rdvn8Q+HB8fT3Xr1qUtW7YQkeSxQZa8fPmSrl69SkREmzdvFiYg37RpE1lbWwvDWTPt2rWLqlatWqSGQBER3bp1i/r37y/Mj0aUcd4yNTWlwMBA4dwVHx8vkTAeM2YM6evry+TEvidPniQzMzNhew8PDydVVVVydnYWhv7lRNb275z2y9TUVBo5ciRVq1aNpk+fnu0hwJgxY6hfv34ymST+np+fH1lYWFDp0qVp/PjxFBUVRcnJyTRx4kQyNjYmW1tb8vLyIkdHRzI3Ny8yx/GsDh06RLq6usJE7ImJiRQcHEzFihUThrJ//PiRPD09ZX4idiY/OLnCipQfnXAPHz5M9vb21L59ezp9+rTEZ3fu3CkSJ2uib5OWLlmyJNv8KatWrSJ9fX0aMmQI9evXj7S0tGS+6/i9e/doyJAh1Lp1awoKCiKijItTfX19atq0qUTZpKQkcnV1JZFIRPXr1y8Sbf7w4UOytLQUem0QEfXo0YNKlSpF+/btozdv3lBMTAz5+vpSmTJlisQklz+6wLp79y6Zm5vTxYsXiSjj9eOdOnWiNWvWyHzsYrGYPnz4QE2aNKE5c+YQUcZcUQcPHqQOHTrQoEGDhETD8+fPKS0tLdtruWXVpUuXSCQS0eTJk4VljRo1ImNjY1qzZg1FRUXRrVu3qFmzZmRvby/TNyCfPn0iKysr8vLyojlz5pBIJKKVK1cSUcaErXXr1qU2bdoICVSxWEyjRo2iOnXqUEJCgjSrnidTpkwR3opCRLRz504qU6YMValShc6cOSNR1sfHh2rWrEmBgYEST/7Pnz9PgwYNonLlysnsUM+3b9+Sv78/JScn0+HDh0lXV5dWr15NDx8+pJo1a1K5cuVo/vz50q5mnnw/l87nz58l3u7n7e1NtWvXptmzZ0sMcSX6dvyXpfO3WCyWqG9ISAjp6enRxo0baebMmVS5cmXq1KkT3bp1i1JSUujEiRPUu3dvGjp0KAUGBgrHb1k+jk+bNk3oZZlp7dq1ZGNjQ+np6dmGAerq6gq9ir98+ZJjD0bGCiNOrrAiI+uJa+3ateTj40PDhg2jsLAw4bODBw+Sg4MDtW/fPtvF2vffIWvEYrGQ4c86SS3Rt7iePHlC06ZNI2tra2rcuDFdu3ZNGlXNN9euXSNdXV3y9PSkjh07UrFixWjevHlElPFGgfLly5O7u7vEOsOHD6djx44ViclriTKe2GZO+Ofj40NEGRdgAwYMICUlJapatSpZWVlRxYoVZfZmI6vMC6tjx47R6NGjydPTk0JCQujGjRtElDEMUENDg6ZPn04pKSnk5+dHTk5OMjtcIusxKbMXhoeHB3Xt2pVWr15NLVq0IGdnZ2revDnVqFGDWrZsSenp6RLJhaJwMZqYmEjz5s0jJSUliQRLu3btyNzcnBQUFMjS0pIcHR2LxBPeq1evCm+9mTZtmsRnkZGRVK9ePbKwsKAaNWpQs2bNqFSpUkJPF1l07Ngx6tSpU7abx+7du5OSkhLNmDGDPn78KPHZ6NGjSVdXV2LS2oSEBAoJCZGYvLkwy+2a4/Pnz5Senk5t27al8ePHC9ty+/btycrKirp16yaz+3XWmGfNmkXNmzcnAwMDmjFjhjDJeEpKCvXv359q165Nc+bMydaDRVZjJ8qYsHf06NESQ9HDwsKoVq1a5OXllet5WpaPZ9HR0aSgoEBNmzaVOBfv3r2bSpQoITzky9w2Lly4kG3C8qyfM1aYcXKFFTljx44lXV1dGjBgADVs2JAcHR3pr7/+kkiw1K1bl1xcXGQ+ufC9lJQUMjMzo+nTp+f4eebFaXJysszPuXH9+nVSVVUV3gaTOexl+PDhwsRnp0+fJkNDQ3J0dKRly5bR4MGDSU9Pj168eCHNqudJTheV8fHxNHXqVLK2tpaYnPjo0aO0detW2rFjR7a3DMiyPXv2kIqKCnXr1o2cnZ3JzMyMGjRoIAz5mzx5MmloaJCBgQFpa2vLbFIp64Xkvn37hNcpL126lNzc3EhdXZ0CAgLo/PnzREQ0YcIEiclOZVVuN04fPnygBQsWZJu09+7du3T48GG6dOmS8DeT5Se8aWlpFBMTQ3p6eqSrq0s9e/bM9urdJ0+e0N9//02DBw+mOXPmSAwNlFWZSbHQ0FCJ3qUdO3YkExMTCgkJydaLYdGiRcJNp6zdcGfdv2/evEmXLl2iL1++CH+HDx8+kImJifCwJDExkby8vGjr1q1FYi4hPz8/0tHRoeXLl1NgYCDZ2NhQs2bNhLZPSUmhAQMGUJUqVWjz5s1Sru3/Z8CAAbR3714iymjva9eukYqKCikpKWXrfRQWFkZmZmbUtWtXOnnyZMFX9g+7desW6enpkbu7u/BigUePHlG9evWoT58+Er2oHz9+TCYmJjk+BGWssOPkCpN5WS8uVq5cSVWrVhUuRHfs2EGKiopkZmZGEydOFC5mdu/eTf379y9SWXCxWExxcXFka2tLY8eOJSLJG4yoqCiaMmVKkRiTHx0dTTo6OtS+fXuJ5V5eXmRhYUHVq1en5s2b0/r16+nu3bvUoEEDMjc3J0tLS5l+upvpwoULNHv2bIll8fHxNG3aNKpZs6bQ/kXRy5cvydLSUpiokijjbWCdOnWiRo0a0dOnTykhIYEuXrxIW7ZsoSdPnkixtv+/rMc1X19f0tPTow0bNghzTLx584aePn0qsY6rq2u2t2zImqxxBwUFCePuM3348IHmz59PIpEo2z6Qqagc11+8eEEXL16kKlWqUOfOnbMlWIoCX19fiYnmb9y4QQYGBtSpUydhHgaijB4bpqamOSZYiGT7qf7YsWOpXLlypKmpSZUrV6YJEyYIPW8GDBhAdnZ2NHbsWGrUqBHZ2trm+nYsWbJ3714yNjYWeiacOnWKihUrRubm5tSkSRMhYZycnEyzZ8+WyfaNi4ujUaNGZZuEdfv27VSmTBlq06ZNtsn0jxw5QmXKlJHonSfrsrbdzZs3qVy5cuTm5kZxcXFElPHq+Dp16pCHhwcdPHiQzpw5Q+7u7jlOasyYLODkCpNZHTp0oAMHDhBRxgV5WloaBQYGCl2o9+zZQ5qamjRnzhzq06cPlS1blqZOnZrtJC1rB++f1Xfp0qWkoKBAu3fvllg+btw4ql+/vswOj8jq8ePHZGdnR61atRKe5s+cOZNKlChB06ZNo9WrV5OJiQkZGRnR/fv3iYjo3bt3Mjtxb9bx2u/evSNvb28yMzMThkBl+vDhA7Vu3Zq0tLRo8ODB0qjqHxcdHU3ly5enPXv2SCwPCwuj6tWrC08Ji4qZM2dSuXLlKCIiQuIiPXN7iIuLo3PnzpGbmxuZmZnJ9OtYsx7bbt26RWPGjCFVVVVat26dRLmEhARq1aoViUQi8vf3L+Ba/hlZJ+TOnCsr81wVHh5OVapUoa5duwrDJvz8/Gju3LnSqWw+SUhIoO7du5ODg4PwdhAioi1btpCDgwN17dpVuMkmyjjnm5mZ0bJly4Q34MmirNcge/bsIX19fTpw4ABFRUWRv78/1alTh3r37k2xsbF09epVGjhwIDk4OFC7du2EY4CsXbd879SpU0Lvs/3791Pp0qVpzZo1tG/fPipVqhS5ubnRkSNHJNaRpQTL98ff9evXSzwQ2LRpE1WoUIGGDRuWbd67S5cuyVSsvyozkXbjxg0qX748ubq6CtdkISEh1Lp1axKJRGRpaUkNGzYsEsM7mXzi5AqTSR8/fqT27duTqqoqHTt2TFgeExNDL168oKdPn1LNmjWFi8+bN2+Sjo4O6evr05IlS4hINm8+goKCaMuWLcKwl5ykpKTQ6NGjSSQS0ZAhQ2jUqFHUu3dvUldXLxK9NjJFRUWRu7s7tWrVivr27UtlypShsLAw4fOnT5+SSCQS2lvWZF48Z72h3rJlC40YMYL+++8/GjJkCNnb2wsTm2YKCgoiExMTat68Ob1+/bpA6/ynZN1Xnz17RpaWlrR06VIikrzJsLOzoz59+hR4/f6UxMREcnFxEbqPR0dH09GjR6lHjx7k6+tL7969o4sXL5Knpyd5enoWmYvRcePG0cCBA+nq1as0YcIEUldXp9WrV0uU8fX1pXr16lHDhg1l8lieVWb99+3bR46OjlSpUiVydXWlzZs3C0M5jx8/TsbGxlS/fn1q3rw5KSsrS/TskFVv3ryhYcOGUf369Wnq1KnC8q1bt5KtrW22BIuLiwt16dJFJtv8+x43ISEhNHfu3GzDeJctW0Y1a9aktWvXElHGZJ7JyckyOzn1vXv3hJvogIAAev78ORERxcbGUmJiIjVo0IBmzJghlLe2tqaqVasKQ1xlsa0zicVi+vTpE7m6upKDg4PEm542bNhAFStWpGHDhgkPgbKS9eN4VufPnyeRSCS84e7mzZtUoUIFaty4Mb1//14od+/ePXr69GmRGN7J5BcnV5jMiouLEybtzLypzjwgHzp0iIyNjYUhAWfPnqW2bdvSkiVLZPqJT8uWLUlVVZX27NnzwwQLUcaJu1mzZlSvXj3q0qWLMOFnUXLv3j1ydXUlVVVV4U1BYrGYUlJS6Pnz52RhYUE7d+6Uci1/X+Y2euPGDZo8eTKlp6fTu3fvqGrVqrRgwQIiyngbTOYTzawJlnHjxtGsWbMoNjZWKnXPL6mpqbnOK5CZTMt6cykWi6lZs2YSF+myLiEhgRo2bEgjRoyg9evXU5s2bahRo0ZUv359srW1pX79+hFRxsTOsnwxmrV9IyIiyMTERHjK+fLlS/Lz8yMNDQ3hZvPTp0/k5eVF27dvz/E7ZNGBAwdITU2Npk6dSlevXqUWLVpQrVq1KCgoSJgfK3MizH79+tHNmzelXOO8yXrjeOzYMerQoQMZGhpK9MbJTLB069ZNYl+XxbeG1KlThzZs2CD8+9OnT1SlShUSiUTUo0ePbOU7dOhAdnZ2RCQZpyzFTET077//UoUKFWjdunU0ZMgQEolEEm+EevbsGVWqVIm2bdtGRBlvd+vcuTNt3rxZZq/VcmqjV69eUYcOHcjJyUl48xdRxnVapUqVqHv37tne7liUPH36lBo0aCDxsCszweLm5pZjj2pZbX/GOLnCZFpcXBx5e3uTkpKSRBfSzCECS5cupWfPnlGLFi1o0KBBwklP1p4IZO2B0K1bN9LQ0KBdu3b9NMHy+fNnIiKZ7kL9Mw8ePKAmTZpQ06ZNJSZB9Pf3p6pVq8rcRK6ZFxTXrl0Tet4cP36cpk2bRgMGDJB4+vns2TMaNmwYmZmZkZ2dHXXs2JHU1NRyfAomK76ffDYsLIy6dOlCPXv2pClTpgjLW7VqRbq6uhQYGEjr16+nUaNGkYaGRrYx7LIitwvJhQsXkrW1NampqdHEiROFCf4GDx5MPXv2/KXvkBVz584lHx8fGjJkiMTyFy9ekL+/P4lEIrKzs6MaNWqQhYWFTA+Byurp06fk4OAgDPNLTEwkfX19MjIyIlNTU5o3b57QgyUlJUXmzl8/MnLkSHJxcSFXV1fS1dWlypUrS/Tk2LZtG9nb21Pz5s0lEkqytq2vXr1aOA9nnrdfvnxJjRo1In19fbp+/bpE+QULFlD9+vWFV6vLsmHDhpG2tjaVKFFCGMab+erdJ0+eUIMGDah79+60efNmatq0Kbm4uMjk65aJJOub9fXBRBnt3bZt22wJlmXLlpGHh4fMxZqb3I5Pvr6+VL58eYlrmJs3b5K+vj7Z2dnJ9GvkGcuKkytMpuR08omJiaF+/fpJJFhev35NnTp1osqVK1OFChXIxsZG6DIvaxfiixcvpjZt2khMZti5c+dfSrAUhTcK/IrMIUJubm505coVmj17NqmoqMjcW2Iyt+9bt26RqqqqMCY988bS2NhYeIqdeQHz9u1b2rlzJ3Xo0IF69eol0z2Ujh49Srq6usLTraNHj5JIJCIvLy9q3749aWlpUZ06dejRo0dERDRo0CBydHQkAwMDql+/vswOe8t6XFu+fDmNGjWKPD096eDBg5ScnExxcXFCzJlcXV1p6NChBV3VP6p3794kEonI0dEx24X2ly9fKDw8nHx8fOivv/4SEiuymmjIekz+8OEDLV68mF68eEGvXr0iQ0NDGjhwIKWnp1PdunXJyMiIpkyZkuNErrIma3tt27aNNDU16dKlS5SUlERv3ryhHj16kK2trcQcLGvXrqU+ffrI5M3n90mTadOm0dSpU4Xt+9WrV2RhYUFmZmZ09uxZYW6wevXqkaenpzSqnG8y2zokJIQ0NDRIT0+P1qxZk61X5cqVK6levXpkYGBALi4uMjuvTNb6zps3j7y8vMjR0ZFWrFghTFCcmWCpV6+exBAhWU0m/cjjx48pPj5e+Hd8fDzZ2NjQ3LlzJeaRu3btWpFKLjHGyRUmM7IeeF+/fk2PHz8W/p2UlER9+vQhJSUl+ueff4goYyz3hQsX6NChQ8JJXha7zO/fv58qVKhAvXv3pv/++09Y/qsJFnkRFRVFLVq0oDJlylDx4sVl7s0aWYcC6ejokKmpqfDZ27dvKTAwkBQUFGjZsmVEJDnJbabv30oga+7cuUNDhw4lU1NTWrRoEQUFBQnDoIgyhkIZGxtT3bp1hWUxMTH07t07iXHbsmrs2LFUpkwZ8vf3py5dulDVqlVp0KBBwhPv9+/f0/nz56lp06ZUq1YtmTyeZbp48aLw/3PmzKHjx48TUcbfQCQS0apVq4Sed0Q5J4hlOX6ijLd+bdq0iYgyzldEGfG3bdtWuPn28fGhsmXLUvPmzWV6MvKAgADh7VaZ5+PZs2eTpaWlRDtm9jQtX758tgm7iWTr5tPHx4dsbGwkXqubmSifN2+ecMx6/fo1WVlZUYkSJcjMzIw6depEdnZ2wnld1h+OvHz5kt6+fUsjRoygatWqUXBwsPCmmEwpKSn05MkTmR7emGncuHGko6NDc+bMoeHDh5O1tTV17dpVeF36y5cvqX379mRiYkL79u0joow2lvV2PnPmDAUFBVFQUBD5+PhQ1apVqV27drR3715hnx80aBC5ubkJ63y/P8vS/s1Ybji5wmTO+PHjydTUlDQ0NMjd3Z3Wrl1LaWlplJKSQn369CFlZWU6fPhwtvVk8Qln5sk2LCyMKlWqRD179uQEyw/cvXuXWrVqJXPzEWR9glOiRAlq2LAhVahQQaJnQnx8vHBhHhISQkRFq2dSZgz3798nHx8fMjc3J319fdq4cSMRfUscPX36lEqXLi3MM1MUYifKeAVntWrVhKTgkSNHqFixYrRlyxahzMmTJ6lhw4bUsmVLmZ689t69e2RiYkJ9+/alESNGkKKiokSPK29vbypRogRt2bKlSA5pFIvFlJSURB4eHtShQweJz7p160YdOnQQbi5HjBhBISEh9OrVK2lUNV9ERERQzZo1qXHjxvTixQth+bp168jc3FyYGy3zOHju3DkqVaoUValShdasWUNEsrmf37t3jywtLcnd3Z3Cw8OF5YGBgSQSiWjOnDlCEu3169fk4uJCampqFBERIZSV9YT59zfLgwcPpqpVq9KyZcuEXg09e/aU2C5k+QZ727ZtZGhoKBzHw8PDSVFRkWrWrEnt27cXhuw+e/aMxo8fL5PH75ysWrWKdHV1ydramjQ1NcnCwoKaNWtGkydPJiUlJWrTpg2tWrWK7t69S2pqarR161ZpV5mxP4aTK6zQ+77LfNmyZWn9+vW0f/9+atGiBTk4OND06dMpPT2dPn78SAMGDCCRSCRMiCjrMi8qDx8+/MMEy549e4rkjcjvktWL0cuXL1Px4sVp8uTJlJaWRitWrCAdHR2JBEtCQgJNnDiRRCKR8MS7qMi8yHzy5AlFRETQyJEjSVVVlcaNGyeUSU1NpbS0NGrSpAmNGDFCWlXNF9/fLO7YsYOcnJyIKOMCXV1dXXgj0sePH4W3LFy9elXmn+5+/PiR1qxZQ9ra2qSmpiYMncjaU6V///5UsmRJ2rp1a5GYdyInYWFhpKSkJPTaSUtLo6FDh5KjoyP5+fnRgAEDSF1dXUg+yLLdu3eTs7MzOTs7C2+LuXnzJmloaJCPj4/EkKdTp05RixYtaOHChTJ7o515PHv48CGZm5tTkyZN6OjRo8Lns2bNyjHBYmZmRhYWFkWizbPKmkQYMmQIGRoaUo8ePahRo0akra0ts8ey7+3Zs0d4PXxoaCiVLl2aVqxYQUuXLiV1dXXq1KkTRUZGSqwj6wmWVatWkZKSEm3fvp0+ffpEx44dI2dnZ2rQoAG9f/+eLly4QOPGjaNq1aqRubk5aWtrU9euXSk1NVVm92/GfoSTK0xmHD9+nAIDA4UnWUQZ3eRHjhxJNjY2dOLECSL6NoSiqJyss/rnn39yTLB0796dRCIR7d+/X4q1Y3lx6tQpGjZsmPDvhISEXBMsAQEBJBKJhDcsFBV79uyhYsWKUWRkJN29e5eGDBlCZcqUoUWLFkmUa9y4sTDpqSw+0c4qc2jA2rVrydXVlcLDw0ldXV3irQq7du2iIUOG0Lt374RlsnpRmtleBw4cIF1dXTIyMiJvb29hedZESmaiPOtk5bIq63aalpZGYrGY0tLSqGvXrtSrVy/hVbXx8fHk5eVF9evXJwcHB7p27Zq0qpxnjRs3lnij086dO6lhw4bk7OwsDBHavXs3KSoqkre3Nx08eJBu3bpF7u7uNHDgQJmdgD5TZr0fPHggJFiOHTsmfD5r1ixSUFCgoKAgoRfH69evydbWlipVqiRTCZbcjkffb/eZpk+fTj179qQuXbrIbC+8x48fC9eZ8+fPF5L/b9++pZiYGLK3t6fZs2cTUcacUcbGxlSpUiWaMGECEcn+uYuI6MSJEyQSiYTJ5jNjmjVrFpUtW1bocScWi+nLly80depUatasGamoqGSbj4ixooKTK0wmPHz4kEQiEYlEIvrrr7+I6NtBPDk5mUxNTbO9YYJIdp/sZsaWkJAgjMXPXHbw4MEcEyz9+vUTxvQy2ZbZ1u/fv88xwRIXF0fTp0+n27dvS6uK+S4xMZFmzJghvFKbKGO/Hz58OGlra9OIESNo4cKF5OvrS0pKShKv85RVc+bMoe7duxNRRptWqFAhW6+kpKQkatasGXXv3r1IXIxnevnyJT169IhWrVpFFhYW1KtXL+GzrDdqmTctRcGZM2eEtz1lWrp0Kenp6QkTXhJl9L778uWLMHm1LIqJiaHFixdnG666Y8cOatiwITVs2FBIsBw6dIhq1KhBenp6VLlyZZmegD63OSTu3btH5ubm5OrqKpFgyRwitHnzZmHZy5cvqV69ehLbRGGWNeaHDx/Sf//9R+/evROWZ02aZP3/rL1MZW0fP3PmDBkbG9Pff/9Nw4YNI5FIRFFRUcLnt2/fJj09PWE42MOHD6lTp060YcMGmU2M5yQqKorq1atHHh4edOrUKWH57NmzqVq1avTy5UsiktxG4uPjqXXr1tSnTx9KSUmRuX2csZ/h5AqTGcePH6cyZcqQu7s7vXv3TuKAPHDgQGrXrp3MPfnISWZc+/fvJycnJ6pUqRK1aNGCduzYITzVzUyw9OnTR2JiSFb0ZE2wZB0KU5QuSK5cuUIlS5Ykc3Nz+vvvvyU+e/jwIQ0ZMoRKly5NlStXpmXLlhWJxApRRk+d0qVLC68n3bdvH5UvX546dOhAZ86codDQUHJzcyMzM7Mi89rh7yUmJlJwcDBZWFhQ3759heXDhg2TSETI2s3X9+Lj46lTp04kEonI29ubNmzYIHzm5uYm82+G+ZHZs2fT3LlzhX9nJlgaNWokJFhevHhB9+7dowsXLsjssLesN5Dbtm2jadOm0fjx44VzdNYhQlkTLJs2bcoWq6xcy2Q9Ho0fP56sra2pVKlS5O7uTv37989xmO73xzBZPaZ5eHhQ2bJlSV1dXRiGntlut27dIltbWxo+fDgdPXqUmjVrRh4eHkXyrUCZb2ts0qQJRUVFUXh4OCkrK9Pu3bslymVt56lTp1Ljxo0LuqqMFQhOrjCZcuTIESpRogT17NmToqOjKS0tjZKSksjGxoYGDBgg7erlm7///pvU1NRo8uTJdOHCBWrSpAlZWFjQggULhATLP//8Q2pqahJvE2FF0/v372nVqlUkEonI19dX2tXJd8+fP6eOHTuSSCQSJrDNerPx8OFD6t+/Pzk5OcnsG1O+v4FIT0+nZ8+eUZMmTWj69OlElHEDfujQIapZsybp6emRjY0NtW3bVma7zf9M5t/kw4cPFBwcTObm5uTg4EDu7u5UoUIFmbu5/hX//PMPderUifT09MjZ2Zn27dtHixYtIg8PD5keApRV1u30w4cPNHz4cFJRURHedEYkmWCJjo7+4XfImtGjR1PlypXJ09OTunTpItEz5eHDh2RpaUlNmzalAwcOSKwny9v7rFmzSEdHh44fP06JiYnUtWtX0tDQEOaKKkoy22nRokVUokQJMjIyor1792Z7Vfq0adPIysqK9PT0qF69ejLbG+tXREVFUdOmTcna2pqKFy8u9L78fj/OjN3Pz49MTEwoMTGxSP49mHzj5AqTOWFhYVSyZEkyMTEhT09P8vT0JAsLiyLztpynT59S7dq1af78+USUMfmjnp4eGRgYUK1atWjx4sVCguXIkSMSXVFZ0ZWQkEDr16+ne/fuSbsqf8SLFy+oXbt2VKpUKbpy5QoRSV6Y3b9/n16/fi2t6uWbzLk1Ms2cOZM0NDSE7tNEGRfvDx48kOihJ4s3Xr8yD0Pm/3/69In27dtHvXv3pn79+gnxyupNdmZcb968ofv371NSUpIQU0xMDN25c4fc3d3JxcVFGA4WGBgozSrnu8wJip8/f07+/v4SkzQTZczB4uLiQubm5vT27VtpVTNfZG7ru3fvpgoVKgg9GQ4cOJBt2M/9+/epXLly5OPjI5W65iexWEwJCQnUtGlTITGeeY22atUqIsqYb6Qo3EB/fzyLjIyk+/fvU5s2bcjMzIy2bNkiMSk3UcZwz8jISJntjfU7oqKiyNnZmWrVqkUXLlwQln/f9k+ePCEvLy/hPM9YUcPJFVYo/exEfOzYMdLV1SVTU1M6deqUcAEuq2+Kyerdu3e0aNEievXqFb169YoMDAyE3im2trZkampK06dPL7Jv0GC5KwoXqJkx3Lx5k8LDw+nQoUNCwiEuLo5atWpFpUuXFp7iF6WL0WXLllHHjh3pn3/+kVher149GjFiRK5vT5DFLuRZ63znzh169OiRRHIs6+e5bdey2vaZ8YSGhpKNjQ2VLVuWnJ2dKTAwUOLmKz09nc6ePUu+vr6kpaWV7S0ismzlypVUtWpVYRLmFy9e0IQJE7IlWDZs2EBDhgyRyW2cKOMtfnFxccK/Fy5cSD179iSijOSRmpoarVixgogyEuSPHz8mooxX8cp64jDTly9fqG7dunT58mXav38/qampCb2UkpOTafXq1XTy5ElpVDXfZN0+IyMj6cmTJxQbGyt81rJlSzIzM6Pt27cL16FjxoyRuCaV1W38d9y/f5/c3d3J3d1dGO6ak48fPxZgrRgrWJxcYYXC/3PSOXbsGKmqqpK3t7cwS3tRkJaWJsywPmrUKGrfvr3wRpHMt6e0bNlSOLEzJmt27txJOjo6ZGZmRiKRiOrXry885YyLiyMPDw8qU6YM/fvvv1Kuaf5auHAhde3alYoXL049e/YU5t2YMWMGNWrUSNjPZT2JlrX+Y8aMIUNDQ9LU1CRHR0dhCBRR9uN+Tj1aZNXBgwdJTU2NAgMD6d69e9SvXz+qXLkyjRo1KtvTbSKS6clrc3L37l2qXr062dnZZUuwaGhoSAwRyiRr5/DExESqUaMGValSRXjbz8yZM6lVq1a0c+fOHBNJ3t7eQlki2Ys5636ZmVT6+PEj1atXjxo3bkxaWloSMT969IiaNGlCW7ZsKfC6/gm+vr5UrVo10tHRoS5dutDhw4eJKOPv0qpVKzI3NycfHx9yc3MjTU1NmU0Q50VUVBQ1b96cbG1ts70RSNaP64z9Ck6uMKnLeoG9Zs0aGj16NPXq1YuOHj2abQzr9w4fPkylSpWizp07y2RPjswTzYMHD+jBgwfC5H6Zyzt16kSdO3cW/kbDhw+nkJAQIfnCmKz5999/SUtLi1atWkVv376lR48eUceOHal+/fq0du1aIiJ69eoVubi4UNWqVYvkfEKnTp2idu3akbGxMbm7u9PGjRtJJBLRjBkzpF21PMt6PN+5cydVrFiRDh48SDt37qQpU6aQqqoqjR49Woo1/PNevHhB9evXFyZxTUhIEObQqVWrFo0ZM0Y4XxWFeRhyS5I9ePCAatWqRdbW1hIJFn9/fxKJRLRnz54Cr2t+u3XrFtnZ2ZGJiQnFxcXRpUuXyNLSklRUVCQm8f3w4QM1b96chg4dKrNtnbWd9+zZQ02bNhXeaBQREUHa2trk4uJCRBnbdUJCAjVr1owaNGggc0mkTFnbKiwsjCpVqkRHjx6lxYsXk6enJzk4OFBoaKhQduDAgdS6dWtq3bp1kZ0r61fcvn2bRo4cKRe9dRj7HidXWKExatQo0tXVJU9PT2rcuDEpKirS+PHj6dmzZz9c7++//6by5cvLbMJh9+7dpKOjQ4aGhlS1alVhyEBycjJ5e3uTk5MTTZo0iQYPHkwaGhpCAoYxWZJ5kbp69WoyNzenT58+Ccuio6OpXbt25OLiIlyIvn79+qf7vizJjDXra9bv3r1LzZs3p4YNG5JIJKJ27dpJs4r5Kjw8nPr27UszZ84Uln369Ik2bNhAGhoatGbNGinW7s9KTU2lZcuWUVRUFL1+/ZqMjY1p4MCBlJaWRq1atSJdXV3q379/jj1YZNm2bduE/8/czu/fv081a9YkW1tbIcESHR1NK1askOmn+lnn0Hjy5AnVrl2bnJycKCEhgQICAqh8+fI0ffp0unnzJp07d47c3d3J0tJSZt/6lfUm+eTJk9S5c2fS0tKirl27CgmW9evXk4KCAtWrV0/4sbCwKBJJhtDQUBoyZIhEwuzs2bPk5eVF9vb2tG/fPmH5x48fZXqurPzGCRYmbzi5wgqF8PBwKleunMQwgNWrV5OWlpbwNPdHB2hZHb/58uVLqlmzJq1evZr2799PQ4YMIQUFBaEL7evXr6l169ZUp04dsrOzKzJvk2BFW9Z9NfOC+s2bN0SU0TXe2NhYuNHKvPi8desWiUQiOn36dAHXNn99f9P0KxfXERERtGTJkiJzIX7r1i0yMjIiNTW1bG+3SkxMpI4dO1KfPn2ISPZuMnOSGcPjx4/pyZMnRPRtMtcpU6aQp6enMIRi+vTpZGRkRJ6enjL7QCBTcnKy0Lv02bNnVLx4cWrSpInweebf5erVq6SpqUnu7u7ZJqWWtW0+69vKsk6i7+7uTiKRiBwcHCghIYF8fX3JxsZGWObq6lokkgw+Pj5kbm5OAwcOpCZNmlCZMmWoY8eOwlwy169fJz8/Pxo3bhwtX75caF9Za+es7t69S3Xq1CFNTU2aPHmyxGcRERHk5eVFderUoZ07d0p8VhSObYyx38fJFVYo7N+/n4yNjent27cSFx7BwcGkoqJC9+/fl2Lt8lfWJ9hv374lHx8f4cLj/fv35OvrSwoKCsLM+58/f6bPnz9ne8sIY4XZvXv3hG7/O3bsIHd3d3r//j1FRkaSoqIizZo1S6L8gwcPqGbNmjL9BoGsF9NLly4V3gCU281UTgljWbwJyekmYu/evVSzZk2qWbNmtokNR4wYQY0aNSoSTzQzY9+zZw+ZmZnRkiVLhEQiEVH//v2pUaNGwjbg4+NDc+fOldlXimfatWsXtWnThqysrOivv/4iIqLTp0+Tnp4eNW3aVKJsbGws1a5dm0QiEfXo0UMKtc0fp0+fpoYNG9KpU6cklrdr147MzMzo2LFjZGFhQfb29hQfH09JSUl0+vRpevr0aZF4W0x4eDiVKVNG4k0wixYtIgcHB+rYsaPQg+X7/VrWkkk5Hc9CQ0PJycmJTExMsr1e+ty5c+Tq6kr9+vUrqCoyxgoxTq6wApfTBfXevXtJSUmJHj16REQkzLPw7t070tPTo/379xdoHf+UzJP2gQMHqEuXLsKkX1kvxhMTE8nX15eUlZWFOSgYkyXp6ekUEBBAIpGIRo0aRSKRiNavXy98vnz5cipWrBhNnz6dnjx5QnFxcTRhwgSqXLmyxCuJZUnW49q9e/fIyMiIzM3NhR46snaD8auyxh0fHy8R5+7du8na2pq8vLzozJkzQhknJyeZvsn+3j///EOqqqq0cOFCid4oYrGYAgMDydHRkXr27El9+/YlNTU1evDggRRrm3fLly8nDQ0N8vHxoREjRpCCgoIwIfWZM2eofPny5ObmJpRPSkqi3r170/Xr12V6P7h79y41aNCAmjVrJvSybdu2LdWsWZOio6OJKGOuCUtLS7KwsMiWQJP1ZOKhQ4dIV1eXoqKiJJbPmjWLVFRUqHPnzkKChUg2e25kbaPk5GSJ3kkHDhygxo0bU/PmzSUSTEQk8bplxph84+QKK1BZT7Y7duygI0eOCMtdXFyodu3aEjdXz58/J0NDQ6GcrMoa9/Hjx0lNTY08PT2pRYsWJBKJaNGiRRLlExMTaciQIaSlpcU9VpjMatasGSkoKNDgwYOJ6FuCIS0tjVavXk2qqqpUuXJlMjExoQoVKtB///0nzer+37Lu31OmTKFWrVqRlZUViUQiqlWrljAUQpZvLH9m2rRpZGtrS61ataL58+cLy7du3UoWFhakpaVFjRs3pjZt2pCdnZ2QQJfFG7BM6enplJSURB4eHjRmzBiJzzLbOj4+nsaMGUOurq7k7Oyc7e0ZsmbVqlVUvHhx2rt3r7CsY8eOtHDhQnr37h2lpaXR6dOnydjYmCwsLGjSpElUv359cnR0FG4+ZXk/iIqKInd3d2revDk5OTmRlZWVMCQm0507d0hPT4+6dOkinUrms8x99PTp01StWjU6evQoEX1LRCQlJZGRkRHZ2tpS3759hYSyrMmaHJk7dy41bdqUnJ2dqX///sJ12N9//02urq7UvHlzunjx4g+/gzEmnzi5wgpM1pPO/fv3SVNTkzw8PCgiIoKIiE6cOEENGjQgExMT2rdvH+3evVvo2SHLF2NE3y5OXrx4QbNnz6aFCxcSUcaTkRkzZpCioiItX75cYp0PHz5I9GhhTJakpaVR+/btydnZmRQUFGjr1q1ElLEvZO4Pd+/epb///pt2794tPPmVZQsWLCA1NTU6ceIE3b9/nzZu3Eh2dnZkampapBMsq1atonLlylFQUBB5enqSra0teXt7C5/v3buXTE1NqU6dOhKvac36VFhWpaWlkaWlJQUFBRGR5HlOLBYLr9f+X3v3Hpfj/f8B/HV3IiQtKcnSwWFzXBnmtIZVWiLHnLOFIdYsh28Uo5hJamM5ljnHiM1hzGlkzWFyiCKbDdvoIHRQqfv9+8Oj69ctO1hUd17Px8Nj87k/9+19033d1/W6PgcR+cfd7yq7w4cPi0qlko8//lijvXXr1tKyZUsxMjKSrl27yurVq+WXX36Rd955R3r06KGxc0pVuPi8cuWK9OjRQ4yNjWXLli1Ke8n39uuvv2rtZ/3v/o26dOkiLVq0kOTkZKXtt99+k4EDB8rMmTPF3t6+1DRAbRMQECB169aVoKAgmTZtmlhbW0uLFi0kMTFRRB5NAXR1dZX27dvLxYsXK7haIqpsGK5QuZs+fbqMHz9emjRpItWqVZMuXbrIyZMnReTRNq0DBw4UExMTadWqlbi6umrlInDFJyclT1J++eUXUalUYmpqqoQrxUJCQkRHR0dWrFhRrnUSPW9qtVr8/f01Fmou/lxo6xSgJykoKJBhw4bJpEmTlDa1Wi2HDx+W5s2bS5s2barMFKHHL74iIiJkw4YNIvJoF6SwsDBp2bKljBkzRumzefNm6dChgwwfPlzrR2+IiDKFVUSkTZs2GtOciv9+rl27JosXL64yu15duXJFunTpIh4eHnLq1CkREenbt6/Y29tLTEyM7N27V5o3b64RJhZvOS2i3euNPO7q1avi4uIiPXv2VKa8iWj/eiMl69+2bZsEBwfLihUrlHVGcnJypFWrVtK0aVMJDQ2VzZs3S48ePaRPnz4iImJpaSkzZsyokNqfhStXroidnZ3s2rVLacvKyhJHR0d57bXXlLZNmzaJn59flQgLiejZYrhC5erzzz+XOnXqyMmTJyUlJUVOnjwpDRs2lO7duysBi8ijE9eMjAyt3M6u5In18uXLlZNQEZGlS5eKSqWS9957TzIyMjSe98knn4hKpZLo6OjyLJfomSj+rJ4+fVpiYmLkiy++kOvXryvhqL+/v+jq6ioBS0hIiLi7u0tWVpZWTw0paciQIdK5c+dS7TNmzBCVSiVt2rRRRqNp63suWffGjRtl/fr14u7uroxMEvn/gKVVq1by/vvvK+1bt26VDh06SO/evSUhIaE8y36mLl++LC+99JJyJ3vDhg1ibm6u7GxXbMqUKdKuXbtSx3ptVnJaTKdOncTBwUFjWsxPP/0kKpVKY9qQiPb+vP+d4r8LV1dXrR+tIaL5bzRlyhRp0KCBdO/eXbp27SodOnSQr776SkQeBUaDBw+Wtm3bip2dnTg7Oyujsjp06KCxvlZl9/jPZUJCgpiZmcmlS5dE5P9H1/35559Sr149Wbp0aanXYMBCRCUxXKHn6vEvnXfffVcGDhwoIv//pXbp0iUxMzMTJycnOXLkyD++RmVWXOv58+elSZMm4unpKbt379b4Ai8OWObPny+ZmZkazw8LC1O+1Im0zdatW8XY2Fg6dOggNWvWlObNm8ucOXOUE++AgABRqVTSsWNHMTQ01No1Vv7qmBQdHS0ODg6yceNGjSkvGzdulKFDh4qrq6sMGDBA426+Nil5HPP39xcjIyOxtbUVIyMjeeeddzT63r17V8LDw8Xc3FwWLFigtK9fv166desmv//+e7nV/awlJCSIqampsnjnH3/8IbNmzRJzc3MZMGCATJs2TYYNGybGxsZaHSL9lSdNiykqKhK1Wi0//fSTvPrqq1UibPg3rly5okxfrgojskQe7QBkbW2tjFaJiIgQAwMDsbe3l7Vr1yr9MjIyJDU1Vfl9YGCgWFpaaixqW5mVPI7fvXtXRESys7PF3Nxc5s6dqzxWWFgoWVlZ4uDgoEz/IyL6KwxXqFzs3LlTHj58KEOHDhV3d3cRefTFVryo4cqVK0VPT0/69++v1VuxijxazM7ExESmT5/+lxcQixYtEpVKJfPmzVO+1Im0xZPChQsXLkj9+vVl9erVkp2dLYWFhfLhhx9K586dJTg4WBl99u2330pERITW7phS8r3v2rVLNm3aJEePHhWRR8PHPTw8pHPnzrJixQrJzMyU9PR08fDwkKCgIAkPDxdbW9tSC2Bqm/T0dHF3d5dz587JzZs35csvvxQLCwvx8vLS6Hfnzh2JiYkpNTVC2xbpftKoi9atW8uXX36p/D4tLU2++eYbeeutt6RHjx4ydOhQZWRLVVRyWkzxz7+IiLu7uzg5OWnVTZGyunTpkkyePFlr33PJugsKCsTHx0dZlPrrr78WY2NjmTFjhvTt21esra1l27ZtGs9PTk6WgQMHioWFhdacv5V8z+Hh4fLhhx8q66fMnj1bHB0dZfny5Uqfhw8fioODQ6nNB4iIHsdwhZ6Lkl9cQUFBolKp5M6dO7Jz505RqVSyefNmjf5ffvml9O/fX6ysrLR6i84HDx7IgAEDlN1RihUUFMiNGzc0Fj8LDQ0VfX19CQwM1Fj0kKgyKzntbefOnUr7119/Lba2tnLz5k2lLScnRyZNmiStWrWqUlMjRESmTZsmtWrVkldeeUVUKpUEBgaKiMi9e/fEy8tLWrVqJbVr15ZXXnlFmjZtKiIi8fHxYmNjozV3dp8kLCxMmjVrJr169VK2ms3JyZFNmzZJw4YNZfDgwU98XmFhodZNDbly5YocPHhQRB4FaTNmzJCIiAj5/vvvxdbWVhYtWvSXzy2eDleVFU+LcXNzk2PHjknfvn2lSZMmVWrx2qelbe+55Gdy7dq1cvXqVbl586b8/PPPkpSUJLa2thIeHi4ij6a/6evrS+3atWXv3r3K83JycmTLli2ltmjWBlOnThUzMzNZt26dso5SSkqKjBs3Tuzt7WXYsGEyb948cXJykubNm2vVFHUiqhgMV+i5Sk5OluDgYGUr5cLCQpk6daoYGBhIVFSUpKamSlpamri7u8uaNWvk22+/FZVKJefPn6/gyv+bhw8fSpcuXeTzzz9X2r799lvx8/OT2rVri42NjTg5OSknNPPmzRMTExPlIoVIG/z+++9St25deeWVV2TdunUiInLgwAFp0KCBpKSkiMj/X1xmZ2eLgYGBxpoc2qjkRVNSUpK0bdtWTp8+LX/++ads2LBBdHR0xM/PT0RE8vLyJDExUZYvXy7btm1TRm74+vpKx44dS00HrMwev1jcv3+/NG7cWCwtLeXOnTtKe05OjsTExEijRo3E2dm5vMt85hISEqRWrVqydOlSKSoqko8//li6desmtra20r59e1GpVKJSqWTs2LHi6+sru3btku3bt1eJLaafRvG0GH19fWnatKnyuedFaOVX8rO9YMECqV+/vpw9e1ZpW716tXTq1EljG2JPT0+JjIzUuoV6n2Tv3r1ibW2t7FhZ0rVr1yQ6OlocHR3F1dVVRowYoZWbKxBR+WO4Qs/Nrl27RKVSSf369eXEiRNK+61bt2Tu3LliYGAgNjY20rBhQ2nevLnk5+dLfHy82Nraau3uCvfu3ZNmzZrJ6NGjJTk5WebNmydNmzaVfv36SUREhKxevVrs7e3lgw8+UJ5T8gKFSBscPnxYdHR05PXXX5fevXvLpk2b5MGDB2JlZSVDhw7V6Hv79m1p06aNfPfddxVUbdk8Pn1p3rx58t5778no0aM1LqC3bt0qOjo6Mnny5FJb7sbFxcnEiROlTp06Ghcv2uTixYvK+z1y5IhYWFhIr169NPrk5uZKdHS0eHp6at0d/JLOnj0rNWrUkOnTpz/x8dTUVAkICJCGDRvK0KFDpVu3bvLKK6+Iqamp/Pnnn+VcbcVLSkqSiRMnKoEKgxXtcuXKFfH19ZUdO3ZotK9Zs0YsLCzk4MGDkpeXJ7169ZJp06YpxwFtDxmWLl0qDg4Okp2drbQ96bhVso0/20T0Txiu0HOTmJgo77//vhgYGCiL3pW8GDl37pxs2bJF486uv7+/ODo6avVIjoMHD4qenp5YW1uLkZGRLFu2TONuvrOzs8bUpxflDidVLe+++660adNG+vXrJ127dpXdu3dLfHy81K1bV7y8vCQhIUFSUlJk5syZUr9+ffntt98quuSn1q9fv1JhUUhIiKhUKnn99deVO7rFn+GvvvpKDAwMZPTo0Ron7Fu2bBFnZ2etHZG3f/9+UalUynbLIo8Ctnr16knv3r01+haP3BDRvikSIo++l2rUqCEBAQEa7d9++63GtM41a9ZI+/btlYute/fucf0s4cWntvn6669FpVJJvXr1NKb6iDz6LPTp00dMTEzE3t5eY1qMNp+3FNceGhoqrVq1Uo7Vxe1FRUWydevWUsdrbX7PRFR+GK7QM/FXJ9GXLl2S4cOHS/Xq1eXAgQNK38fveFy+fFl8fHzExMREa+/slnT9+nU5ffq0pKWlabQXFRXJgAEDZObMmaJWq/llTZXe45/t4ovn3bt3i7e3t+zbt0/69u0rXbt2lfXr18upU6fEzs5OLC0txcbGRmxsbLR2V6D79+8ru/7cvn1b+bxGRkaKSqV64poba9eulc6dO5f6bGdlZT3/gp+jCRMmSK1atTSmdx0+fFjMzc3F09OzAit7dq5fvy5169ZVdrQrNnfuXGnYsKEkJSUpbTdu3JAGDRpUmR1i6MU1adIkUalUMnfuXI1QWOTRzoexsbGycuVK5bytqgRoJ0+eFJVKpSzeWywrK0t69+4tK1asqJjCiEirMVyhMit58fXDDz/IsWPHNHYPSE5OlpEjR8pLL72kEbAUy83NlV27dsnIkSO19s7uv5Gfny8zZ84US0tLrVz4jV48xZ/T69evy/bt2zUeS01NlWbNmsmSJUvk9u3b0rdvX3nzzTdlz549UlBQIKdPn5bvv/9e/vjjj4oovcxKBsCff/65NG7cWCP4DQ0NFR0dHWWxR5HSdza1MUD9u3onTZok1atXl02bNmlMEVKpVDJt2rTyKvG5uXbtmrz++uvi4eGhbCU8f/58qVu3bqm7+hkZGVKrVi3ZvXt3RZRK9NT+biSZj4+PVK9eXTZv3qwx+uxx2j4VqFjx8evTTz8VPT09CQwMlO+//17i4+PF2dlZWrduXWVCJCIqXwxXqExKnogHBARI48aNpVGjRmJvby+TJk1SHktKSpJRo0ZJ3bp1n3gymp+fL7m5ueVSc0VYt26dTJo0SczNzbVmq0IikUfBiqmpqahUKnFzc5OYmBi5fPmyiDwaUt6lSxdJTU2VS5cuSd++feWtt97S2KK2KsjIyJAGDRpIx44dNUYqhIaGiq6ubpXcnnPx4sVy6NChUu0TJ04UQ0NDjS2Wz5w5U2Uuuop3wPHw8JDRo0eLmZmZ7Nu3r1S/EydOyPjx4xmUk1YoGaxs3rxZPv74YwkLC9MIDb29vaVmzZr/GLBoi38zLbGwsFDWrFkj5ubmYmFhIc2bN5fu3btz8Voi+s8YrtAzERISIvXq1ZO4uDi5f/++BAQEiEqlEh8fH6VPcnKy9O7dW1xdXSuw0vKXnJwsTk5O4unpKZcuXarocoieyq+//ipt27aVN954QxwcHMTHx0esra1l+fLlEhMTI+7u7rJnzx4RebToaY8ePaRXr15au/7EX51M37lzR6ytraVdu3YaAcuiRYtEpVIp60ppo5IXIcWjbbp37y7GxsbKCI6SnJycxMrKStasWaPRXlUuRC5fvixvv/22GBoaSmhoqIhojkIKDAwUOzs7uX79ekWWSfTU/P39pW7duuLm5iaNGzeWpk2baiyw/+6770rt2rUlKiqqymwnfvbs2X+clnnz5k25dOmSJCYmKsdDjlwhov+C4QqVWVJSksYF1q5du8TY2Fjef/99qVmzpowZM0bp+9tvv2nlIodldfv2ba292CS6cuWK9O3bV/r06SPbt2+X2NhYcXJykj59+ohKpZL27dsra5MkJydr5W5fjy+ivXXrVvnkk0/k4MGDcvPmTRH564Bl48aNWn0iXlBQILm5uXLz5k2N99GvXz+pW7euHDt2TGkrKiqSd999Vxo1aiROTk4VUW65uHr1qjg7O0vPnj01prkGBgaKgYGBnD59ugKrI3p6e/fuFQsLC2Xr4du3b0tERITY2tpq7IzVr18/6d69e0WVWWYlzzEPHTokKpVKVq9e/ZcBy5OmQr6I56lE9GwwXKGndvbsWdmxY4dyRzMvL08iIyMlMzNTjh07JlZWVhIZGSkiImPHjhWVSiX9+vXTeA1+cRFpl+TkZOnZs6c4OzvL5cuXJTs7W+Lj48Xd3V3WrVsnItq7m8JHH30k7733nhIKTZkyRV566SVp3ry5WFhYyNixYyUhIUFEHgUsjRo1kjfeeKPUBbY2Biz79u2TcePGScOGDaVOnTri5uYmq1atUh7v06ePmJmZydGjR5XFLr28vCQxMVFr/73/reIpQi4uLnLmzBlZsGCBVK9encEKaYXi86zi/y5ZskRatGihMSIlPT1dZs+eLR07dtQYiaWt52glj0mff/65rF69WtkNaeXKlZKTk1OB1RHRi0AHRE9hw4YN8Pb2RlRUFHbv3g0AqFatGkaPHo06depg165dcHJywogRIwAAVlZW8PDwQF5eHtRqtfI6Ojr80SPSJk2bNkVERAQAYOLEiTh79iw6dOiAb775BsOGDQMAqFSqiizxPzM0NERCQgIWL16MAwcO4NKlS9i7dy8SExMRHByMCxcuIDQ0FAkJCTAxMcGZM2eQkJCA5cuXa7yOnp5eBb2D/yYqKgrvvvsuDA0N4evriwULFuD69euYOXMm/ve//wEAYmNj4eTkBBcXF3h6esLBwQGJiYlo1qwZVCqVxnG9qmncuDE+++wz6Ovrw9XVFTNnzkRcXBwcHR0rujSiv5WXl6ecZ12+fBnAo/OxvLw8JCUlKf1MTU3h4uKCEydO4ObNm0q7jo6OVn62i7+DZs2ahaCgIBgbGyMqKgo9e/bEhAkTsGHDBuTm5lZwlURUpVV0ukPa48svvxRDQ0PZtGmTZGZmlnq8qKhIevbsKT179hSRR7sAeXp6SlRUlEYfItJeJe/ml5wuoo1K3uVcuHChtG/fXkaMGCEDBgzQGIXy5ZdfSseOHWXYsGHKgtRZWVlavcbIsmXLxMDAQDZt2qRxJ/vKlSsycuRIqVevnsZW04sWLRI/Pz+ZPHmy8nejze//aSQnJ4uHh4ckJiZWdClE/ygmJkaCg4NFROSDDz4Qe3t7yc3NlQsXLoiNjY18+OGHylRHkUdT4Fq1aiWnTp2qqJKfqYyMDGnZsqUsWbJEo93Pz0+qVasmq1atkvv371dQdURU1alERCo64KHK7+LFixg0aBD8/Pzg4+OjtIuIxt3q2NhYeHl5oUOHDrh//z6Kiopw5swZ6OnplepLRNopJSUFkydPRnp6OhYvXowOHTpUdEn/iVqthkqlUo5L8+bNQ2RkJPT09HD06FE0bNhQ6btu3TqsWrUKtWrVwuLFi9GkSRMAQFFREXR1dSuk/v9qx44d6Nu3L3bu3IlevXqhsLAQenp6ynv5+eef4ePjg+zsbHz11VewtrYu9RrFz3lRPHz4EPr6+hVdBtE/CgsLg7+/P7p06YILFy7g6NGjaNGiBQBgy5Yt8PHxgZeXF5ycnNCkSRPMmDEDd+/eRXx8vNaPKhYRZGRkwNHRESEhIRg2bBgKCgpgYGAAAHjrrbeQlJSEBQsWYNiwYVp37Caiyk+7j6JUbn7//Xfk5uaia9euKJnHFV+UFLe5ubkhJiYGtra2cHFxUYKVoqIiBitEVUTjxo2xcOFCWFlZwdLSsqLL+U/UajV0dHSgUqmQmJgIAAgICMDUqVOhr6+PhQsX4rffflP6Dx8+HIMHD8bLL78Me3t7pV3bTs7z8/Oxb98+2NraKu+vZLAiIrCzs8P//vc/nDlzBjdu3Hji67xIwQoABitU6eXl5QEAJk+ejC5duiAuLg7Dhw9H8+bNlT4DBw7E2rVr8fPPP2PixIkYNWoU8vPzERcXp5VTgR6vV6VSoW7dumjdujXCw8ORn58PAwMDFBYWQq1Ww87ODubm5nj//fdx8uRJAADvMRPRs8SRK/SvzJ8/H2FhYUhLSwNQesQKACQlJeHOnTvo1KmTRvuLdoeT6EVR8o6gNikOVgBgzpw52LlzJ4KDg9GzZ08AwMKFC7Fp0yY4OTnBz88PL7/88t++hrb5888/sWDBAvz444/w9PTEtGnTAGiO5Lly5Qpat26NHTt2wMXFpYIrJqK/s3//fpw7dw4dO3ZEp06d4Ovrixo1aiA0NBTBwcGYMGECjI2NleNWZmYm7t27hwcPHqBp06bQ0dHRunO1ksfgc+fOQU9PDw0aNECdOnVw4sQJjBs3DlZWVti+fTv09PSgVqsxaNAgzJw5E7NmzUJqaiqOHTumdQE5EVVu2nMUpQplb2+PnJwc7N+/H87Ozk8chbJ27VpkZGTgjTfe0Ljo0KYvayL697QxWBER5fg0ffp0REdHIzo6Gq+++qrSZ8qUKQCAjRs3QldXF+PHj4eNjc0TX0Mb1a9fH9OnT0dISAhiY2MBANOmTdO4wLpw4QIcHR01/l6IqPKJjo5GYGAgPDw80LVrVwDAkiVLAACWlpaYPHkyAMDX1xe1a9cGAPzyyy8aCzOr1WqtO1crPgZPnToV27Ztw++//47evXtj5MiRcHNzQ2BgID7++GPY2NigY8eOuHz5MvLy8tCyZUu0bNkSR44cYbBCRM+cdh1JqcI4OjrCwMAAK1asQLNmzZQ7ucUjWO7fv4+UlBS8+eabWn3RQURVU1xcHDp37qwEw6dOnUJsbCxiY2PRsWNHPHjwAH/88QeOHj0KNzc3TJkyBTo6Oli8eDFefvllTJw4UXmtqjDF0cLCAjNmzCgVsOjp6SErKwtRUVFo1qwZrKysKrhSIvormzdvhq+vL6Kjo+Hq6qqEJ8X8/PygVqsxZcoUPHz4EB4eHpg1axbS0tIQHx+vnMNpy3mbiGiE23v27MGOHTuwYsUKZGZmYuXKlVi4cCHy8/Ph6emJ119/HZ999hnu3buHt99+GyEhIdDR0cGNGzdgaWmJgoIC6OvrV4ljOhFVDpwWRP/a5s2b4e3tjX79+sHf3x+vvfYaAOCPP/6Aj48P7t+/jyNHjmjd3Q8iqtomTZqEwsJCLFmyRDkp379/P4YMGYJr167h119/xbp167Bjxw7cuHEDjRs3xtGjR1GnTh2sX78egwcPrrJ3OG/duoWQkBCcOnUK/fv3h7+/P/r06YNff/0Vp0+f5mLkRJVUWloaBg4ciP79+2PChAlKe3Z2Ni5duoSHDx8q07TDw8Mxd+5cWFhYwNDQEPHx8Vq3jtDji0rv27cP33zzDWxsbPDRRx8BABITExEUFIQ7d+5g3LhxGDRokMZrZGZmYs6cOVi7di2OHTvGkXlE9MwxXKF/raioCNHR0Rg/fjzMzc3RokULqNVq3Lt3D2q1GsePH4e+vr5W7p5BRFVXUlIS7O3toa+vjytXrqBJkya4f/8+3nzzTdy5cwfZ2dkYMGAAnJyc4O7uDjMzM0RGRsLb21t5jap8XLt16xbmzZuHn376CVevXkWdOnWQmJjI4zlRJZaWlgYnJyeEhISgT58+AIDIyEgcOnQI27Ztg6WlJaytrREXFweVSoWzZ8/iwYMHaNeuHXR1dbVqjZXRo0fjlVdeweTJk6FWq3H9+nW4u7vjl19+wZgxYxAeHq70LQ5YsrKyMHDgQIwePRoAcP36daxfvx7bt2/HqlWr0KZNm4p5M0RUpWnHOECqFHR1deHj44OTJ0/C09MTarUaDRs2xPDhw5W7IIWFhTwRJ6JK47PPPsOWLVugr6+P9evXw9vbGzt37kTt2rURGxsLPz8/bNy4EYsWLYKXlxd0dXXh4OAAMzMzjdepysc1CwsLBAQEwN7eHo6OjkqwwuM5UeV2//597N69G4cOHUL//v0RGRkJMzMz7Nu3D+Hh4bh16xbmzp0LAGjTpg3eeOMN6OrqoqioSGuClfz8fLz22mvK1EwRQaNGjbBs2TI4Ojri2LFj2Ldvn9K/RYsWmDt3LgoKCnDhwgWl/eWXX4aXlxf27t3LYIWInhuOXKFnhnc4iagyWblyJcaOHYstW7agf//+OH36ND766CPUqFEDvr6+eOedd5S++fn5SE9Px/vvv49bt27hxx9/fOGOZ5mZmTA2NtbKnUOIXkQHDx5Ev379YGpqCiMjI4SFhaF169YwNTVFZmYmunXrht69e2P27NkVXep/8viUxOjoaMTHxyMiIgKGhoY4evQoAgICYGZmhgkTJqBHjx5K32vXrsHa2lrZYlpb1pUhIu3GIw39J0/K5F60CxEiqryWL1+O8ePHY9u2bejfvz8AoG3btli9ejUKCgqwZMkS7Ny5U+m/ZcsWDBkyBBkZGfjhhx+Uu7svEhMTE+VChMEKUeXXvXt3pKSk4MCBAzh79iy6desGU1NT5XEjIyNYWlpWYIVlUzJYKSwsRGJiIs6cOYOgoCA8ePAAXbt2xdy5c5GWloalS5fi4MGDSn8bGxsGK0RU7jhyhYiIqpQdO3agb9++2LlzJ3r16qW0T58+HWPHjkV2djb8/PxgYGCA8ePHo1evXjh16hQSEhLw3nvvad16BEREJaWlpWHUqFFIT0/H8ePHtfLm15NCkZycHCxcuBB79+5Fp06dEBISAkNDQxw5cgSzZs2CiCAsLAxt27atoKqJ6EXHKJeIiKqM/Px87Nu3D7a2trh27ZrS3qdPH+zduxfVqlVDy5YtERYWhocPHyIyMhKxsbF4/fXXMWbMGK1bj4CIqFh6ejo++eQTjBo1CqmpqTh27JhWjsIrGaycO3cOKSkpSElJQc2aNTF16lT07NkTx48fx4wZM/DgwQM4OTnhf//7H5o3bw4HB4cKrp6IXmQcuUJERFXKn3/+iQULFuDEiRPw8vJCXFwcrl69iq+++gp2dnbKPP5z585hxIgR6N69O8LCwiq6bCKiMjl79iwCAwNhZ2eH0NBQ6OnpafUovKlTp2L9+vXKOlB+fn6YPn068vLyMH/+fOzfvx+dOnXCnDlzUKNGDeV5nApERBWF4QoREVU5t27dQkhICHbv3o179+7h/PnzaNCggbLwdnHAcvXqVdja2vJEnIiqhLt378LY2BgqlUrrNhoouYDtoUOHMHz4cKxduxY6Ojq4ePEi/Pz8MGHCBERERCAnJweffvop1q9fDz8/P0ycOLHUArhEROWN4QoREVVJt2/fxrx583D8+HF4eXnB398fwJPvamrbRQgR0d/R5qBh7dq1OHnyJExMTJStpAHg66+/Rp8+fRAVFQVvb29kZ2cjJiYG3t7ePH4TUaXAcIWIiKqs4hEsp06dgqenJ6ZNmwZAuy88iIiqqmvXrmHMmDGIj4/He++9h4iICBQVFUFEoKenhwkTJiA5ORk7duyAkZGR8jwG5ERUGXAcNBERVVkWFhaYMWMG2rVrh6+//hozZ84EAAYrRESVwOP3eG1sbPDRRx+hU6dOWLt2LU6dOgVdXV1ltKGJiQnUarVGsAKAwQoRVQoMV4iIqEqzsLBAQEAA7OzskJqaWupknoiIyp9arVaC7tzcXGRlZQEAXF1dERgYiHbt2mHUqFE4deqU0uf48eMwMzOrsJqJiP4OpwUREdEL4c6dO6hTpw50dHQ4LYiIqJIIDg7Gnj17kJubi7Zt22LGjBmwsbFBXFwcgoKCEBcXh1dffRVt2rTB+fPn8eOPP8LAwIDHcSKqdDhyhYiIXggvvfQSdHR0NO6WEhFR+VKr1cr/L1q0CKGhoXB3d8eQIUPw3XffYdCgQTh27Bg6d+6M2bNnw8XFBVlZWXB2dsaZM2dgYGCAhw8f8jhORJUOR64QEREREdFzV3K3tuPHj+PkyZOws7ODh4cHAOD+/fvo0aMHdHV1ceDAAdSsWRP79u3D8uXLcePGDURFRaFly5YctUJElRJHrhARERER0XPj7e2N3NxcJVj54Ycf0KVLF0yfPh0PHz4EAOTn56N27dr49ttvkZycjGXLlgEAXFxcMGHCBFhZWcHT0xPnz59nsEJElRLDFSIiIiIiei5++eUXZGdnQ19fX2l7+eWXMX/+fBgaGiI+Ph4AUK1aNRQWFsLExASvvfYaMjMzlf7du3fHmDFj0K5du1I7BRERVRZ6FV0AERERERFVTba2tvjqq68AACtWrICXlxesrKwwcuRIFBUVYdasWXjppZcQEBAAPT09qNVqpKamQk/v0WVK8VSinj174s0330SNGjUq8u0QEf0lrrlCRERERETPVVpaGhwcHGBkZIQTJ07AyMgIt2/fxurVqxEUFAR3d3dYWlri9u3buHDhAi5duqQELFxjhYi0AcMVIiIiIiJ6ph4PREQEiYmJGDFiBNRqNeLi4pSAJSoqCmFhYWjUqBGWLl2Ktm3bQkdHB4WFhUrAQkRU2XHNFSIiIiIiemZKbnmfmZmJzMxMqFQqtGzZEhs2bIBarUbnzp2RlZUFc3NzjBw5ElOmTMHVq1dx5MgR6OjoQESUBXCJiLQBj1hERERERFRm27dvx507d5RQJCgoCL169YKDgwOWLFmCO3fu4NVXX0VMTAzUajW6dOmCrKwsWFpaYsSIEZg6dSo+/fRTzJ49GyqViuEKEWkVHrGIiIiIiKhMdu/ejf79+2PFihXIz8/HsmXLsHLlSvTv3x99+vTB5MmTMWfOHNy8eVMJWACgSZMmyM3NhYWFBUaPHo1x48YhOjoaGRkZ4OoFRKRNuOYKERERERGVWUREBCZPnozFixcjIyMDDg4O6N27NwAgJiYGY8aMgbe3N6ZOnYoGDRrg/PnzCA0NRVRUlLK2SkZGBgDA1NS0wt4HEdF/wRWiiIiIiIjoP8vJyUHNmjXxwQcfQK1Ww8/PDzVq1MDKlSuVPoMGDQIAjB07Fjo6OvDz80OrVq2wdu1aAFAWr2WoQkTaitOCiIiIiIjoP9m/fz+++OILxMfHAwA+/PBDrF69Grm5ufjxxx+RmZmp9B00aBBWrlyJiIgIxMbGarwOdwUiIm3HoxgRERERET216OhoBAYGwsPDA05OTkr7qFGjkJOTg0mTJqF+/foYN24cjI2NAQADBgyAqakpunbtWkFVExE9HwxXiIiIiIjoqWzevBm+vr6Ijo6Gq6srateurfG4r68vCgoK4O/vDwAYP3680qdbt24A/n8qEBFRVcCjGRERERER/WtpaWlYvnw5Pv30UwwcOFBpz87OxqVLl/Dw4UN06tQJkydPBgBMmzYNWVlZCAgIQM2aNZX+DFaIqCrhEY2IiIiIiJ5KamoqGjRooPw+MjIShw4dwrZt22BpaQlra2vExcVh8uTJePDgAfbs2YPg4OAKrJiI6PniVsxERERERPSvpaWlwcHBAa6urhg8eDC++OILXLlyBZ07d4anpyfu3buHadOmYeTIkQgKCgIAiAhUKpXyXyKiqoYjV4iIiIiI6F8zMzPDmjVr0K9fPxw6dAhGRkYIDw9H69atYWpqiszMTNSuXRtqtVp5DoMVIqrqGK4QEREREdFT6d69O1JSUpCdnQ0bG5tSjxsZGcHS0lKjjcEKEVVlnBZERERERETPRFpaGkaNGoX09HQcP34curq6FV0SEVG54MgVIiIiIiIqk/T0dKxatQpxcXFITU1VgpWioiIGLET0QtCp6AKIiIiIiEi73bx5E8ePH4e9vT1++OEH6Ovro7CwkMEKEb0wOC2IiIiIiIjK7O7duzA2NoZKpeKIFSJ64TBcISIiIiKiZ4a7AhHRi4jTgoiIiIiI6JlhsEJELyKGK0REREREREREZcBwhYiIiIiIiIioDBiuEBERERERERGVAcMVIiIiIiIiIqIyYLhCRERERERERFQGDFeIiIiIiIiIiMqA4QoRERFVaY0aNYK3t7fy+yNHjkClUuHIkSMVVtPjHq+RiIiItAvDFSIiInqu1qxZA5VKpfyqXr06mjRpAl9fX9y+fbuiy/vX9uzZg9mzZ1d0GURERFQJ6VV0AURERPRimDNnDmxsbJCXl4e4uDhERkZiz549SExMRI0aNcqtjq5du+LBgwcwMDB4quft2bMHS5cuZcBCREREpTBcISIionLRs2dPtG3bFgDg4+MDU1NThIWFYefOnRg8eHCp/jk5OahZs+Yzr0NHRwfVq1d/5q9LRERELy5OCyIiIqIK0a1bNwDAtWvX4O3tjVq1auHnn3+Gm5sbjIyMMHToUACAWq1GeHg4mjdvjurVq8Pc3Bxjx45FZmamxuuJCIKDg2FlZYUaNWrgrbfewsWLF0v9uX+15sqJEyfg5uYGExMT1KxZE61atUJERAQAwNvbG0uXLgUAjSlOxZ51jURERKRdOHKFiIiIKsTPP/8MADA1NQUAFBYWwsXFBZ07d0ZoaKgyVWjs2LFYs2YNRo0ahUmTJuHatWtYsmQJEhIScPz4cejr6wMAgoKCEBwcDDc3N7i5ueHMmTNwdnZGQUHBP9by3Xffwd3dHfXr18cHH3wACwsLJCUlYdeuXfjggw8wduxY/PHHH/juu++wbt26Us8vjxqJiIio8mK4QkREROXi3r17SE9PR15eHo4fP445c+bA0NAQ7u7uiI+PR35+PgYMGID58+crz4mLi8OqVauwYcMGDBkyRGl/66234Orqiq1bt2LIkCFIS0vDp59+infeeQfffPONMqpkxowZmDdv3t/WVVRUhLFjx6J+/fo4e/Ys6tSpozwmIgCAN954A02aNMF3332HYcOGaTy/PGokIiKiyo3TgoiIiKhc9OjRA2ZmZmjYsCG8vLxQq1YtxMbGokGDBkqfcePGaTxn69atMDY2xttvv4309HTll6OjI2rVqoXDhw8DAA4cOICCggJMnDhRY7qOn5/fP9aVkJCAa9euwc/PTyNYAaDxWn+lPGokIiKiyo0jV4iIiKhcLF26FE2aNIGenh7Mzc3RtGlT6Oj8/30ePT09WFlZaTwnJSUF9+7dQ7169Z74mqmpqQCA3377DQDQuHFjjcfNzMxgYmLyt3UVT09q0aLF072hcqyRiIiIKjeGK0RERFQu2rVrp+wW9CTVqlXTCFuARwvF1qtXDxs2bHjic8zMzJ5pjf+FNtRIREREzxfDFSIiIqq07OzscODAAXTq1AmGhoZ/2c/a2hrAo1Ektra2SntaWlqpHXue9GcAQGJiInr06PGX/f5qilB51EhERESVG9dcISIiokpr4MCBKCoqwty5c0s9VlhYiLt37wJ4tJ6Lvr4+Pv/8c2URWgAIDw//xz/DwcEBNjY2CA8PV16vWMnXqlmzJgCU6lMeNRIREVHlxpErREREVGm9+eabGDt2LObPn4+zZ8/C2dkZ+vr6SElJwdatWxEREYH+/fvDzMwM/v7+mD9/Ptzd3eHm5oaEhATs3bsXdevW/ds/Q0dHB5GRkejVqxfatGmDUaNGoX79+khOTsbFixexb98+AICjoyMAYNKkSXBxcYGuri68vLzKpUYiIiKq3BiuEBERUaW2bNkyODo6Yvny5QgICICenh4aNWqEYcOGoVOnTkq/4OBgVK9eHcuWLcPhw4fRvn177N+/H++8884//hkuLi44fPgwPv74YyxatAhqtRp2dnYYPXq00qdv376YOHEiNm/ejPXr10NE4OXlVW41EhERUeWlkpLjUomIiIiIiIiI6KlwzRUiIiIiIiIiojJguEJEREREREREVAYMV4iIiIiIiIiIyoDhChERERERERFRGTBcISIiIiIiIiIqA4YrRERERERERERlwHCFiIiIiIiIiKgMGK4QEREREREREZUBwxUiIiIiIiIiojJguEJEREREREREVAYMV4iIiIiIiIiIyoDhChERERERERFRGTBcISIiIiIiIiIqg/8Dvtg/cxsXi+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 68.53%\n",
      "Mean Recall: 75.46%\n",
      "Mean Precision: 67.98%\n",
      "\n",
      "============================================================\n",
      "Evaluating Inception_Style\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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5tRM7OzscOXIEnz9/Fi+hO3nyJM6dOydOs337dixduhT6+voFOvaIESPQt29fAMDmzZvh5+cnMbiS3yV/8ihrIiKSHw6uEBGRWOPGjcWDK3Fxcfjjjz/ydd2VzF8upN2iOfM2PT09qcfI+gtxfgZ1cpJ1dsnX608A6TMTcrqNdIkSJXDs2DG8fPkSV69excOHDxEaGorDhw/j8+fPOH/+PBYtWoRZs2bl+v7Kdj6y8vDwgLm5OR48eAAAKFKkCAYOHJjrPv/++6/EwMrYsWMxceJEGBoa4vPnz3kOOBXE9x4rKioKEyZMkNj27t07+Pn5Yfny5XLImfzkt3yzfnEPDw9H6dKlczxu5vQ1atQQf4mXJvOMqsyyXuMmNTUVMTEx4uffM6tImsx5TUhIwKdPnyTKPnM7EQgEOb5v5nMpz3YCpLeNhg0bomHDhpg4cSLmzJmD6dOni19/9OgRHB0dC3TMrl27Yvz48YiKisKjR4+watUq8fWcypQpg1atWuXrOJnPX/ny5TFmzJgc0+Z0fSMiIpIfLgsiIiKxYcOGScxe8PHxQXBwcLZ0QqEQmzZtEn8Jc3FxEb8WFRWF48ePi5+/e/dO4nnmtD9K1i9hV69eFf89f/78bDNZvrp79y6EQiEqVaqEjh07YvLkydi1a5fEwMOtW7fyfP/MMd68eRP37t2TeI/MyyoK43xkJRAIMGLECPHznj175jjI81XmL9cA0KNHDxgaGgIA9u3bl+N+WQcRclraIQ/9+/cXz64wMzMTD7KtWLECJ06cyNcxjIyMIEq/m6LE43svkiwrNzc3iedz5sxBSkqKxLZnz56J/85cnyIjI9GtWzeMGzdO4jFq1CgYGxvnOChw4cIFiXj37t0LoVAofp55WVvm8i1o2Wat+zt27BD/nZiYKFGvbG1tpc5a+RGGDx+Oc+fOSe0nihUrJvE8c1+T37qura0tMQsq80ypXr16QUMjf799Zu13mzVrlq2sx44dCzs7O9StWzdfxyQiou/HmStERCRWo0YNzJkzR3zHjzdv3qB27dpo3bo17O3tIRAI8PjxY5w8eRJv375FkyZNAAB9+vTBnDlzxF/AO3TogP79+6NEiRIIDAwUX/9CIBBg1KhRPzwOCwsLFC9eHB8/fgSQfk2GP//8E2/evMnxGicAMG7cOFy/fh2NGzdG5cqVUbp0abx+/Rpbt24Vp8nPr/a+vr5Yu3at+A4rDRo0kLhb0NelClpaWvD19ZUt2O/Ut29f8V1U8vPLe9ZrWfTs2RNdunRBREQEdu7cmeN+pUuXhqampvjL+ZQpUxAcHAxNTU00bNgQtWvXliGKb1avXo0///wTQPpsgz///BMbNmzAkiVLIBKJ0LdvX9y5cyfXWR/KyNraGi1btsSxY8cAAH/++SdsbW3RsmVL6Ojo4N69e/j333/Fd4wZPnw41q1bh6SkJMTGxsLOzg6dOnVC5cqVkZCQgNDQUJw7dw7x8fEIDw+XOqgmFArh6uqKXr16ie8W9FXJkiXRqVMn8fOKFSuK/46KikK/fv1gZWUFgUAAX19f6Orq5hhbq1atJGZQDR8+HDdu3EDFihVx5MgRiUGj0aNHf+cZLLj//e9/WLVqFSpUqIAGDRrA1NQUWlpaePDggcTdgKpVqwYzMzPx88znAgC6d+8OFxcXqKmpoVevXhLLnHx8fLBo0SKkpKQgKSlJvD0/dwn6qm/fvpg7dy6io6ORkpICV1dXdOrUCSYmJkhOTsaDBw9w7tw5vH37Fv/880+eS8qIiEhGCrqQLhERKTF/f3+RtrZ2jnfL+PrIfEeR8+fPi0qVKpVjWjU1NdGSJUsk3ie3O43kdpeZ/NyBZurUqVLzUbt2bVGZMmXEzzPfLcjDwyPXeHV0dETXr18Xp8/pbkEikUi0b98+kY6OTo7H0tbWFu3evVtin5zuCiIS5X0HpJxkvVtQXnK7W1Dz5s2lxpL1Ljhbt26V2M/Ly0vqfosXLxaJRPkrz6x5y1xf7t69K3GuV6xYIRKJRKKkpCRRjRo1xNtbtWqVr3Mmi/zeLagg5RsdHS2qU6dOjnWpZMmSEsc6fPiwqGjRogVqv5nrspOTk0hfX19qG85aZyMjIyXuMpb5ERUVJRKJcm/noaGhokqVKuWazxEjRkjsk9ddeXKri/mRuc3k1hecOXNGYr+kpCRR+fLlpaa/ceNGtvfp2LGjRJo6depIzU9u9ebSpUsiQ0PDPPObU5siIiL54bIgIiLKZsSIEQgPD8fMmTPh5uaG0qVLQ0NDA0WKFIGlpSV8fHxw7tw5VK1aVbxP/fr1cffuXYwdOxY1atRAkSJFoKWlhSpVqqBHjx64fPkyxo4dW2gxzJ49G/PmzUO1atWgqamJqlWrYtKkSTh//nyOv6aPHz8eI0eOhJOTEypWrAgtLS1oa2ujevXq6NOnD65fv446derk6/07deqEoKAgDBkyBCYmJtDR0YGOjg6MjY0xaNAg3L59G127dpVnyD/cwYMHMWrUKJQvXx5aWlowMTHBvHnzJGY2SLNx40b06dMHZcuWzdfdlgoiOTkZ3bt3F//67+7ujmHDhgFIX36xc+dO8XKNv/76S3x3pJ+JgYEBLl26hE2bNqFJkybi9qinpwcHB4dss8E8PT1x9+5djBkzBtbW1ihWrJj4orTOzs4YP348Ll26lO2iul+Zm5vj+vXr6NixI/T09KCrqwsXFxccO3YsW50tV64c/ve//8HV1fW7rpVjaWmJ4OBgzJw5E7Vq1UKxYsWgoaGB8uXLw8vLCydPnoS/v3+BjyuLkydPYtWqVWjfvj1q1qyJMmXKQENDA0WLFoWVlRV8fX1x584duLu7S+ynra2NY8eOoVmzZihRokSe75N5aR6Q/wvZZubi4oJ79+5h2rRpcHBwQIkSJaCuro5SpUrBwcEBw4YNw+nTp1G/fv0CH5uIiApGIBLlsPCciIiIiFRCw4YNcf78eQDpy/y+3iKYfpzIyEhUrFgRIpEIurq6eP36tdwuFkxERIWP11whIiIiIiok586dw6dPn+Dv7y++aG6PHj04sEJE9JPj4AoRERERUSFp1KiRxHM9PT3MmDFDQbkhIiJ54eAKEREREf2yXr16hcaNG+eZzsvLC/Pnzy+EHKXT09ODs7MzFixYgEqVKhXa+xIR0Y/BwRUiIiIiFXfu3DlFZ+GHEQqF4ts95yYyMrIQcgPwcodERL8mXtCWiIiIiIiIiEgGvBUzEREREREREZEMOLhCRERERERERCQDXnOFfkq69sMUnQWFiLuxStFZIPrh0lR0taqaQKDoLBD9UJ+SUxSdBYUoqq2aH7fZl5Mq0PlFmvfP8N0q8bbyfw/izBUiIiIiIiIiIhlwcIWIiIiIiIiISAYcXCEiIiIiIiIiksEvskqMiIiIiIiIiApMwDkX8sCzSEREREREREQkAw6uEBERERERERHJgMuCiIiIiIiIiFQVbyEuF5y5QkREREREREQkAw6uEBERERERERHJgMuCiIiIiIiIiFQV7xYkFzyLREREREREREQy4OAKEREREREREZEMuCyIiIiIiIiISFXxbkFywZkrREREREREREQy4OAKEREREREREZEMuCyIiIiIiIiISFXxbkFywbNIRERERERERCQDDq4QEREREREREcmAy4KIiIiIiIiIVBXvFiQXnLlCRERERERERCQDDq4QEREREREREcmAgytERERERERERDLgNVeIiIiIiIiIVBVvxSwXPItERERERERERDLg4Arl6Ny5cxAIBIiPj1d0VoiIiIiIiIiUFgdX5OTNmzcYPnw4qlevDm1tbVSuXBlt2rTBmTNnFJ01yoN35/q4/9csxF1dhn93jEPtGlVzTKuhoYZJg5vj3tEZiLu6DNf2TkRTF0uJNIM6ueH63kl4e2Ex3l5YjHPbx6KZq9WPDqPA9gQGoEVTd9Sxt0aPrp1wJyQk1/SnTh5Hu9bNUcfeGh082+DCv+clXheJRFi90h+NG7ihbi0bDB7QF8+eRfzACApOFWMGVDfuvbsD0LKZOxxr2aBXt864eyf3uE+fPAGvNi3gWMsGnbyyx33m9Cn4DOqPhq6OsK9pgQf3w35k9r+bqpY341atuA/uDUT7Vk3R0MkeA3t3Rejd3OM+e/okurZvjYZO9ujZ2ROXL/4r8frcGZPhUquGxGO07+AfGcJ3UdXyZn+uOuWtijErBYFA+R8/AQ6uyEFERAQcHBxw9uxZLF68GHfu3MGJEyfQqFEj+Pr6fvdxU1NTkZaWJsecUlYdm9XCwrFe+G39cTh3X4iQh69wdI0vSusVk5p+5tA2GNjBDWMW7Yd9h7nYdOAi9v4+CLbmlcRpXr2Nx7SVf8ClxyK49liMc9cfYv+ywbCsXq6wwsrTiePHsGTRfHgP9cWe/Ydhbm4BH+8BiImJkZo+6PYtTBw/Fl7tO2LvgSNo5N4Yo4b74tGjh+I0WzdvxO6AnZg6YyZ27d4HXV1d+AwegOTk5MIKK1eqGDOgunGfPH4Mvy9aAG8fXwTuPwQzc3MM9R6I2FzinjRhLDy9OmL3/sNo6N4EY0YMw+NMcScmJsKulgNGjB5XWGEUmKqWN+NWrbj/PnkcK5YuQv/BQ7E1cD9MTM0x2tcbsbHS474TfBszJo9Hm3btsS3wAOo3dMfEMcPx5PEjiXROLm7436lz4ses+YsLI5x8U9XyZn+uOuWtijHTr4WDK3IwdOhQCAQCXL9+HR06dICZmRlq1KiBMWPG4OrVq+J0S5cuhbW1NYoWLYrKlStj6NChSEhIEL++bds2lCpVCkePHoWVlRW0tbXx/PlzREZGolWrVtDV1UW1atUQGBgIIyMjLF++XLzv8+fP0a5dOxQrVgwlSpRA586d8fbt2xzzHBERAYFAgD179sDFxQU6OjqoWbMmzp8/n+M+MTEx6NatGypWrIgiRYrA2toau3fvlkjTsGFDjBgxAhMmTIC+vj7KlSuHmTNnSqSJj4/HwIEDUbp0aZQoUQLu7u4IDg7O59mWrxE93bH10GXsPHoV95++wfDf9iAx6Qv6eDpLTd+9dV0s2nwKJy+GIuJVDDbuv4iTl0Ixspe7OM2xf+/i5MVQPHkehcfP32Hm6v8h4XMy6tpUK6yw8rRz+1a079gZnl4dYGxigqkzZkFHRwdHDh2Umj5g1w64uNVD3/4DUd3YGMNGjIKllRX2BO4CkP6rQMDOHRjk7YNG7k1gZm6BufMXIerdO5w983dhhpYjVYwZUN24d+3YhvYdO6GdVwcYG5tgyvSMuA9Lj3v3rp1wcXVDn/4DUN3YGL7DR2bEHSBO07ptO3j7+MLJWXr/oAxUtbwZt2rFvSdgO9p6dUTrdl6oVt0EE6bMgLaODv7845DU9PsCd8HR2Q09+vSHUXVjDB46AuYWVji4N1AinaaWFgwMS4sfJUqULIxw8k1Vy5v9ueqUtyrGTL8WDq7IKDY2FidOnICvry+KFi2a7fVSpUqJ/1ZTU8OKFStw7949bN++HWfPnsWECRMk0n/+/BkLFy7Epk2bcO/ePZQpUwa9e/fG69evce7cORw8eBAbNmzAu3fvxPukpaWhXbt2iI2Nxfnz53H69Gk8ffoUXbp0yTP/48ePx9ixY3H79m04OzujTZs2OY4OJyUlwcHBAX/99Rfu3r2LwYMHo1evXrh+/bpEuu3bt6No0aK4du0aFi1ahNmzZ+P06dPi1zt16oR3797h+PHjuHnzJmrVqoXGjRsjNjY2z/zKk6aGOuwtK+PstQfibSKRCGevPchxIERLUwNJX4QS2xKTvsDF3lhqejU1ATp5OKCorhauhYTLL/MyEH75grDQe3BydhFvU1NTg5OTC0KCb0vdJyQoCE5Okh9AXFzdEBIUBAB49fIloqOj4Oj07ZjFixeHtY1tjscsTKoYM6DCcQvT486cRzU1NTg6OSMkOEjqPiHBQXDMdJ4AwNnFNcf0ykhly5txi7epRNzCL3gQForajt/iUFNTQx1HJ9wNkf5Dzd07Qajj6CSxzdHZFXdDgiS23f7vBlo2roeuXq2weN5svFeia86pcnmzP0/3q5e3KsasVARqyv/4CfBWzDJ6/PgxRCIRLCws8kw7atQo8d9GRkaYO3cuhgwZgjVr1oi3C4VCrFmzBra2tgCA+/fv4++//8aNGzdQu3ZtAMCmTZtgamoq3ufMmTO4c+cOwsPDUblyZQDAjh07UKNGDdy4cQN16tTJMU/Dhg1Dhw4dAABr167FiRMnsHnz5myDPgBQsWJFjBv3bfrk8OHDcfLkSezbtw9169YVb7exscGMGTMAAKampli1ahXOnDmDpk2b4uLFi7h+/TrevXsHbW1tAMCSJUtw5MgRHDhwAIMHF976ZkO9YtDQUMe72I8S29/FfIC5UVmp+/x9JQwjerrj4q3HePoiGo3qmqOdux3U1SXXAdYwqYBz28dCR0sDCYnJ6DJ2I+4/ffPDYimIuPg4pKamwsDAQGK7gYEBwsOfSt0nOjoaBgaG2dJHx0RnvB6Vvs0w+zGjo6PllfXvpooxAyocd1x63PrZ4jZERLj0Qc7o6Ojs6Q0NEaMkMeWHypY345bY/qvHHR8fn96+9SXzqK9vgGcR0tt3THQ09LKcJz0DA4kfkxxd3NDAvQkqVKiEly9fYP2q5Rgz3BsbtgVCXV1d/oEUkKqWN/tz1SlvVYyZfj0cXJGRSCTKd9q///4b8+fPx/379/HhwwekpKQgKSkJnz9/RpEiRQAAWlpasLGxEe/z4MEDaGhooFatWuJtJiYm0NPTEz8PCwtD5cqVxQMrAGBlZYVSpUohLCws18EV50zTITU0NFC7dm2EhUm/qFdqairmzZuHffv24dWrV/jy5QuSk5PFef8qc/4BoHz58uKZNsHBwUhISMjWcSYmJuLJkydS3zc5OTnbukhRWioEaoX/YWfc4gNYM60bgg9Ng0gkwtOX0dhx9Cr6tJP8RexhxFs4dp2PksV04dXEHhtn90Kzgf5KM8BCRERE3zT1aCn+29jUDCamZujUtjlu/3cDtbPMeiEiIpLm55hfo8RMTU0hEAhw//79XNNFRESgdevWsLGxwcGDB3Hz5k2sXr0aAPDlyxdxOl1dXQiU9GrIixcvhr+/P/z8/PDPP/8gKCgIHh4eEvkHAE1NTYnnAoFAfGHehIQElC9fHkFBQRKPBw8eYPz48VLfd/78+ShZsqTEI+XtTZnjiY5LQEpKKsroF5fYXsagBN7EfMhxn85jNsLAZQzMW06HrdccfPqcjPBXkkuphCmpePoiGrfDXmD6yqO48/AVfLs1lDnP8qBXSg/q6urZln/FxMTA0NBQ6j6GhoaIiYnOnj7j1wJDw9Lp26Lzf8zCpIoxAyoct1563FkvdhgTEw2DXOLOlj465/TKSGXLm3FLbP/V4y5VqlR6+85y8drY2BjoG0jPo4GhIeKynKe4mJhsP/RkVrFSZZQqpYeXL57Lnmk5UNXyZn+uOuWtijErFUXfCYh3CyIA0NfXh4eHB1avXo1Pnz5lez0+Y73uzZs3kZaWht9//x1OTk4wMzPD69ev8zy+ubk5UlJScPv2t3WBjx8/RlxcnPi5paUlXrx4gRcvXoi3hYaGIj4+HlZWud8COPMFd1NSUnDz5k1YWlpKTXvp0iW0a9cOPXv2hK2tLapXr46HDx9KTZuTWrVq4c2bN9DQ0ICJiYnEI6dObtKkSXj//r3EQ6OsQ4HeVxphSipuh71AI0dz8TaBQIBGdc1wPY/royR/ScHrqPfQ0FCDZ2M7/Hku99vEqQkE0NZSjolimlpasLSqgWtXr4i3paWl4dq1K7CxtZe6j42dHa5lqisAcPXKZdjY2QEAKlaqBEPD0rh27dsxExIScCckOMdjFiZVjBlQ4bg1M+K+Jhn39WtXYWNrJ3UfG1s7XM90noCMuHNIr4xUtrwZt3ibSsStqQVzSyvcvP4tjrS0NPx3/Rpq2thK3aemtR3+uy4Z9/VrV1DTxi7H93n39g3ev4+HQWnl+AKmyuXN/jzdr17eqhgz/Xo4uCIHq1evRmpqKurWrYuDBw/i0aNHCAsLw4oVK8TLbkxMTCAUCrFy5Uo8ffoUO3fuxLp16/I8toWFBZo0aYLBgwfj+vXruH37NgYPHiwxw6VJkyawtrZGjx49cOvWLVy/fh29e/dGgwYNxNdpyS3vhw8fxv379+Hr64u4uDj0799falpTU1OcPn0aly9fRlhYGLy9vXO9I5E0TZo0gbOzMzw9PXHq1ClERETg8uXLmDJlCv777z+p+2hra6NEiRISD3ktCVqx6yz6ebmgRxtHmFcrixWTu6CIrjZ2/JHeUW+a0wuzh7cVp69TsyraudvCqKIBXO2NcXSVL9TUBFi67dsVx2cPbwvXWsaoUl4fNUwqYPbwtqhf2xR7jkmPTxF69emHQwf24eiRw3j65Anmzp6JxMREeHq1BwBMmTQB/st+F6fv0bM3Ll+6gO3btiD86ROsXb0S9+7eRdfuPQGkD0r16NUbG9evxbmzZ/Do4QNMnTQBpcuUgXvjJooIMRtVjBlQ3bh79u6Lwwf24+gf6XHPm5MedzvP9LinTvLDikxxd+vZC5cvXcSObVsQ/vQp1q1eidB799C1ew9xmvfv4/Hgfph4CWNEeDge3A8Tr+lWBqpa3oxbteLu2qMPjh4+gGP/O4KIp0+weN5sJCUmonVbLwDA7GmTsHblMnH6zt174uqVSwjcuQ0R4U+xad1q3A+9iw5dugMAPn/+hFXLluBuSDAiX7/Cf9euwm/0cFSqXAWOzm4KiVEaVS1v9ueqU96qGDP9WpTjp/SfXPXq1XHr1i389ttvGDt2LCIjI1G6dGk4ODhg7dq1AABbW1ssXboUCxcuxKRJk1C/fn3Mnz8fvXv3zvP4O3bswIABA1C/fn2UK1cO8+fPx71796CjowMgveP4448/MHz4cNSvXx9qampo3rw5Vq5cmeexFyxYgAULFiAoKAgmJiY4evRojjNIpk6diqdPn8LDwwNFihTB4MGD4enpiffv3+f7XAkEAhw7dgxTpkxBv379EBUVhXLlyqF+/fooW1b6RWR/pAOnbsFQrxim+7RCWYPiCHnwCu18V4svclu5nD7S0r5dV0dbWxMzfFujWkVDJHxOxslL9zBg2g68T0gUpymtXwyb5/RGOcMSeJ+QhLuPXqHN0DU4ey33pWOFqXmLloiLjcWaVSsQHR0FcwtLrFm/STxl9k1kJNQyXZXbzr4W5i9aglUrlmPl8qWoUtUIy1euhqmpmThNvwGDkJiYiNkzp+Pjxw+wr+WANes3iS9crGiqGDOgunF7tGiJuLhYrF21EjEZca9etzFT3K+hpvZtiqmdfS3MW7gEq1cuxyr/ZahS1QhLV6yCSaa4z/9zFjOmThY/nzh+DADA28cXQ3yHF1JkuVPV8mbcqhV3E48WiI+Lxca1qxAbEw1TcwssXbVevCzo7ZtIifZtbWuPWb8twoY1K7B+1XJUqlIVC5auhLFJ+s0B1NXU8fjRAxz78w8kfPwAw9JlUNfJBYOHDoeWlpZCYpRGVcub/bnqlLcqxqw0fpK78Sg7gaggV2QlpfDy5UtUrlwZf//9Nxo3bvxdx4iIiEC1atVw+/Zt2GVMnfuZ6NoPU3QWFCLuxipFZ4Hoh0tT0X9Laj/JemKi7/UpOUXRWVCIotqq+Vsm+3JSBTq/SPPWdZum6CzkKfHiHEVnIU+/SHX4tZ09exYJCQmwtrZGZGQkJkyYACMjI9SvX1/RWSMiIiIiIiJSeRxc+QkIhUJMnjwZT58+RfHixeHi4oKAgIBsd+UhIiIiIiIiKhDOuJILDq78BDw8PODh4SHXYxoZGYErwoiIiIiIiIhkxyvXEBERERERERHJgIMrREREREREREQy4LIgIiIiIiIiIlXFWzHLBc8iEREREREREZEMOLhCRERERERERCQDLgsiIiIiIiIiUlVcFiQXPItERERERERERDLg4AoRERERERERkQy4LIiIiIiIiIhIVakJFJ2DXwJnrhARERERERERyYCDK0REREREREREMuCyICIiIiIiIiJVxbsFyQXPIhERERERERGRDDi4QkREREREREQkAy4LIiIiIiIiIlJVAt4tSB44c4WIiIiIiIiISAYcXCEiIiIiIiIikgGXBRERERERERGpKt4tSC54FomIiIiIiIiIZMDBFSIiIiIiIiIiGXBwhYiIiIiIiIhIBrzmChEREREREZGq4q2Y5YIzV4iIiIiIiIiIZMCZK/RTiruxStFZUAi9disUnQWFeL5nqKKzoBAa6qr5K4Kqxi1QU824RSKRorNQ+FSzqFFUmx876denil0awIkPRAAHV4iIiIiIiIhUF2/FLBc8i0REREREREREMuDgChERERERERGRDLgsiIiIiIiIiEhV8aI5csGZK0REREREREREMuDgChERERERERGRDLgsiIiIiIiIiEhV8W5BcsGzSEREREREREQkAw6uEBERERERERHJgMuCiIiIiIiIiFQV7xYkF5y5QkREREREREQkAw6uEBERERERERHJgMuCiIiIiIiIiFQV7xYkFzyLREREREREREQy4OAKEREREREREZEMOLhCRERERERERCQDXnOFiIiIiIiISFXxVsxywZkrREREREREREQy4OAKEREREREREZEMuCyIiIiIiIiISFXxVsxywbNIRERERERERCQDDq4QEREREREREcmAy4KIiIiIiIiIVBWXBckFzyKJGRkZYfny5eLnAoEAR44cUVh+iIiIiIiIiH4GHFxRMlFRUfDx8UGVKlWgra2NcuXKwcPDA5cuXVJ01n5ZewID0KKpO+rYW6NH1064ExKSa/pTJ4+jXevmqGNvjQ6ebXDh3/MSr4tEIqxe6Y/GDdxQt5YNBg/oi2fPIn5gBN/Hu5UN7m/pi7jDQ/Hv0s6obVY21/TD2tkheH0vxB4aikfb+mHRoHrQ1lQXvz6opTWur+qOt/uH4O3+ITi3pBOaOVT90WEUyMF9gejYpincXewxqE9XhN7NvazP/n0S3Tu0hruLPXp38cSVi/9mSxMR/gR+o33h0cARTdxqY2Dvznjz5vWPCuG7HNgbCM+WTVDf0Q79e3XBvTziPnP6BLp4tUJ9Rzv06NQOly+czzHtwrkz4WRvhT0BO+SdbZnt2xOANs0bw6W2Lfp074K7d3KP++9TJ9ChbUu41LZFl/ZtcTFT3ClCIVYsW4Iu7dvCrW4tNG9cH9Mn+yHq3bsfHUaB7dkdgBbN3FG3ljV6duuEO3nEferkcXi2aY66tazR0Ut6n7ZmlT+aNHSDo4MNvAcqZ5+2d3cAWnq4w9HBBr26d86zvE+fPAGvNi3g6GCDTlLiPvP3KfgM7o+Gbo6wt7bAg/thPzL7323v7gC0bOYOx1o26NWtAHHXyiHu06fgM6g/Gro6wr6m8satqv+7VTVuVazn8u7Lz5w+hSGD+qOBqyPsaprjvhLGDKhuHadfAwdXlEyHDh1w+/ZtbN++HQ8fPsTRo0fRsGFDxMTEKDprP9yXL18K/T1PHD+GJYvmw3uoL/bsPwxzcwv4eA/I8XwH3b6FiePHwqt9R+w9cASN3Btj1HBfPHr0UJxm6+aN2B2wE1NnzMSu3fugq6sLn8EDkJycXFhh5aljPVMsHFQPvwVeg/OIPQgJj8bROe1QuqSu1PRdGphhTl8XzAu8BrshOzHE/ww61jPF7D4u4jSvohMwbdsluIzcDdeRe3Au5CX2T2sNyyr6hRVWrs6cOo5Vyxah36Ch2LxrP0zMzDFmuDfiYqWX9Z3g25g1ZTxat2uPLQEHUK+hOyaNG46njx+J07x6+RxDB/ZCVaNqWLl+G7bvOYS+A4ZAW0u7sMLK0+mTx+H/+0IM9B6K7YEHYGpmgVFDByM2h7hDgm5j+qTxaOPZHtt3H0T9ho0xYcxwPMkU91fnzv6Nu3eCUbp0mR8dRoGdOnEMyxYvxKAhvti19yDMzM0xfMggxObQtoODbmOK3zi08+qAgH2H0NC9McaNHI7HGW07KSkJ98NCMdDbB7v2HsTipSvwLCICY0YMLcyw8nTy+DH8vmg+vH18sXv/YZiZW2Co94Ac4w66fQuTJoyFp1dH7Nmf3qeNHuErjhsAtm3ZiMCAnZgyfSZ2Bqb3aUO9latPO3niGH5fvADeQ3wRuO8QzMzMMdR7YM5xB93CJL+x8GzfEbv3H0ZD9yYYM3KYRNyJiYmws3fAiNHjCiuMAksv7wXw9vFF4P5DMDPPI+5M5S2Oe4SUuGspd9yq+r9bVeNWxXr+I/ryxMTPsK9VCyOVNGZAdeu4UhAIlP/xE+DgihKJj4/HhQsXsHDhQjRq1AhVq1ZF3bp1MWnSJLRt21acTiAQYP369WjdujWKFCkCS0tLXLlyBY8fP0bDhg1RtGhRuLi44MmTJ+J9njx5gnbt2qFs2bIoVqwY6tSpg7///lum/KalpWHRokUwMTGBtrY2qlSpgt9++038up+fH8zMzFCkSBFUr14d06ZNg1AoFL8+c+ZM2NnZYdOmTahWrRp0dHRkys/32Ll9K9p37AxPrw4wNjHB1BmzoKOjgyOHDkpNH7BrB1zc6qFv/4GobmyMYSNGwdLKCnsCdwFIHx0P2LkDg7x90Mi9CczMLTB3/iJEvXuHs2dkO9/yNMLLHltP3MXOv8Nw/0Ushq86i8SkFPRpZiU1vZNleVwJjcTe8w/x/N1HnLn9HPvOP5SY7XLsejhO/vcMT16/x+PX8Zi54woSkoSoa1GusMLK1Z6A7Wjj2RGt2nqhWnUTjJ80Azo6Ovjz6CGp6ffv2QVHZzd0790fRtWMMchnBMwsrHBwX6A4zYbVK+DsUh9DR46DmYUlKlaqArcG7tDTNyissPK0e9c2tGvfCa3btUc1YxP4TcmI+4j0uPfu3gknFzf07DMA1aobw9t3BMwtrXBgT4BEunfv3uL3hb9h1rxFUNdQvst3BezYDs8OndDWsz2qG5tg0rSZ0NHVwdEc4t4TsAPOrm7o3S89bp9hI2FhaYl9e9LLu1jx4lizYQuaerSAUbVqsLa1w4TJUxEWeg9vIpVnptLOHZn6NGMTTJ2e0acdlt6nBe7aARfXb32a7/Ac+rTB3/q0OfPS+7R/lKhP27VjG9p36IR2GXFPmT4LOro5x7171064uLqhT78BqF7dGL7DR6bHvftbPW/dph28fXzh5ORcWGEU2K4d29C+Y5a4cylvcdz9B2SUd0bcgZnibpsRt7Pyxq2q/7tVNW5VrOfy7ssBoHVbT3j7DIOjksYMqG4dp18HB1eUSLFixVCsWDEcOXIkz9HUOXPmoHfv3ggKCoKFhQW6d+8Ob29vTJo0Cf/99x9EIhGGDRsmTp+QkICWLVvizJkzuH37Npo3b442bdrg+fPn353fSZMmYcGCBZg2bRpCQ0MRGBiIsmW/fdkuXrw4tm3bhtDQUPj7+2Pjxo1YtmyZxDEeP36MgwcP4tChQwgKCvruvHwP4ZcvCAu9Byfnb7Mv1NTU4OTkgpDg21L3CQkKyvZB28XVDSEZeX/18iWio6Pg6PTtmMWLF4e1jW2OxyxsmhpqsDcpg7NBL8TbRCLgbNAL1LUoL3Wfq2GRsDcpIx5MMSpXAh51jHDivwip6dXUBOhU3xRFdTRxLeyN3GMoKKHwCx7eD0Vtx29lp6amhtp1nXAvJFjqPndDglC7rpPENkdnV9y9EwQgfXDx8qXzqFy1KsYMG4TWTethUJ+u+PfcmR8WR0EJhV/wICwUdRy/xaGmpoY6js64ExIkdZ+7IUGo4yhZx52cXXEn03lKS0vDrKkT0bNPf1Q3Nv0heZeFUPgF98PuwdFJsrzrOjojJDhI6j4hwcGomyVuZxc33MkhPQAkJHyEQCBAseIl5JFtmQmF6X1a5v5HTU0Njrn1acFB2T5oO7u4ic+TuE9zzt6nBStJn5Zz3LmVd5BEegBwdnHNMb0y+u64nX/yuFX0f7fKxq2C9fxH9OU/A1Wt4/RrUb6fG1WYhoYGtm3bhkGDBmHdunWoVasWGjRogK5du8LGxkYibb9+/dC5c2cA6TNEnJ2dMW3aNHh4eAAARo4ciX79+onT29rawtbWVvx8zpw5OHz4MI4ePSoxCJNfHz9+hL+/P1atWoU+ffoAAIyNjeHm5iZOM3XqVPHfRkZGGDduHPbs2YMJEyaIt3/58gU7duxA6dKlC5wHWcXFxyE1NRUGBpKzDAwMDBAe/lTqPtHR0TAwMMyWPjomOuP1qPRthtmPGR0dLa+sy8SwhC401NXwLv6zxPZ38Z9hXllP6j57zz+EQQldnFnUEQIBoKmhjg1/hWDxvv8k0tWoaoBzv3eCjpYGEhKF6DL3T9x/EfvDYsmv9/HxSE1NhX6WGSX6+gZ4FhEudZ/YmOhsM1D09A3EU3LjYmOQ+Pkzdm3bjEE+w+EzfAyuXrmIKeNHYsW6rbB3qPNjgimA+LivcUvWWT0DA0RESK/jMdHR2c6TnoEhYmK+1d+dWzdBXV0dnbv1lH+m5UAcd5a2rW9ggIhw6eUdEx0N/SxtW9/AADE5tNvk5GSsXPY7PFq0QrFixeSTcRnFxeXcp0UUpE8z/NZfifs0Kecyp3NT2L7GnbW8DQwMcyzv6OhoqemVJab8kFvchj9Z3Cr6v1tl41bBev4j+vKfgarWcaXBuwXJBQdXlEyHDh3QqlUrXLhwAVevXsXx48exaNEibNq0CX379hWnyzzY8nW2iLW1tcS2pKQkfPjwASVKlEBCQgJmzpyJv/76C5GRkUhJSUFiYuJ3z1wJCwtDcnIyGjdunGOavXv3YsWKFXjy5AkSEhKQkpKCEiUkf+GtWrVqngMrycnJ2WbyiNS1oa2tPNe1+NXVs66I8V1qY+Sac7jx4A2MK5TEksENENn1ExbsuSFO9/BVHByH70bJolrwcjXFxjHN0MzvoFIMsMibSCQCALg1aIQuPdIHGE3NLXE3OAhHDu5VisGVH+F+6D3s3b0T2wMPQvCTrH+VtxShEBPHjYZIJMLEqTMUnR0iIiIiUgIcolJCOjo6aNq0KaZNm4bLly+jb9++mDFD8gO8pqam+O+vX3CkbUtLSwMAjBs3DocPH8a8efNw4cIFBAUFwdra+rsvIqurK/3Cp19duXIFPXr0QMuWLfHnn3/i9u3bmDJlSrb3K1q0aJ7vNX/+fJQsWVLisXjh/O/Kd2Z6pfSgrq6e7SJZMTExMDQ0lLqPoaHkL/ji9Bmj5oaG6QNFMdH5P2Zhi/6QiJTUNJQpVURie5lSRfAm7rPUfWb0dMLus/ex7dQ93HsWg6NXnmL6jssY36m2xPWlhClpeBr5HrcfR2H69su4Ex4F33a2Uo9ZmEqWKgV1dfVsF3GNjY3J9ovHV/oGhtkudhsXGyP+NSz9mBowqmYskaZqtep49yZSjrn/fqX0vsYtWWfjYnKO28DQMNt5iov59stQ0O2biIuNhWfLxnCtbQ3X2tZ4E/kaK5YugmfLJj8mkAISx52lbcfGxMAgh3ZoYGiI2CxtW1r6FKEQE8ePxpvI11i9YbPSzFoBAD09OfVp0d/Si/u0ApzLwvY17qzlHROT/RfNrwwNDaWnV5KY8iPXuHMp72zpo3+yuFX0f7fKxq2C9fxH9OU/A1Wt4/Rr4eDKT8DKygqfPn2S6RiXLl1C37594eXlBWtra5QrVw4RERHffTxTU1Po6urizBnp15e4fPkyqlatiilTpqB27dowNTXFs2fPvuu9Jk2ahPfv30s8xvtN+u68f6WppQVLqxq4dvWKeFtaWhquXbsCG1t7qfvY2Nnh2tWrEtuuXrkMGzs7AEDFSpVgaFga1659O2ZCQgLuhATneMzCJkxJw+3H79DIrrJ4m0AANLKrjOv3pQ8K6OpoIi1jpsZXaWmijH1znr2gJhBI3K5ZUTQ1tWBmYYWb17+VXVpaGm7euIYaNtIHf2ra2OG/G5JlfePaFdS0thMf07JGTbzIcju/F8+foWz5CnLN//fS1NSCuaUVblyTjPvG9auwtrGTuk9NGzvcuC4Z9/WrV2CdcZ5atGqLXfuOYMeeQ+JH6dJl0KN3f/iv2fjDYikITU0tWFjWwPWscV+7ChtbO6n72NjaSpwnALh29TKsM6X/OrDy/NkzrNmwBaVKSV9Gpyiamul92vVrkn3a9dz6NFs7XJfWp2XE/bVPu341e59mqyR92te4r2WN+2pu5W0ncZ4Aybh/BjnGnWs9t5MoS+AnjFtF/3erbNwqWM9/RF/+M1DVOq40FH0nIN4tiOQtJiYG7u7u2LVrF0JCQhAeHo79+/dj0aJFaNeunUzHNjU1FV80Njg4GN27dxfPavkeOjo68PPzw4QJE7Bjxw48efIEV69exebNm8Xv9/z5c+zZswdPnjzBihUrcPjw4e96L21tbZQoUULiIa8lQb369MOhA/tw9MhhPH3yBHNnz0RiYiI8vdoDAKZMmgD/Zb+L0/fo2RuXL13A9m1bEP70CdauXol7d++ia/f0a08IBAL06NUbG9evxbmzZ/Do4QNMnTQBpcuUgXtj5fhVHwBWHL6Nfh410KOxBcwr62GFbyMU0dHAjtOhAIBNY5pK3Gb52LVwDGppg071TVG1bAm421XG9J5OOHY9XDzIMruPC1xrVECVMsVRo6oBZvdxQX3rStjzzwOFxJhV1x598L8jB3D8zyOICH+CJfNnIzExEa3aeAEA5kyfhHWrvl1wuVPXnrh2+RJ279qGZxFPsXn9atwPvYsOnbuL03Tr1Q9nTh/H0cP78fLFMxzcG4DLF87Bq1PXwg4vR9169sXRwwfw19EjCH/6BIvmzUJSYiJatUuPe9bUiVizYqk4fZduvXD18kUE7NiKiPCn2LhuFcJC76Jj1x4A0mfsGJuYSjzUNTRgYGiIqkbVFBKjND1698GRg/vx5x/pcc+fOwuJiYlo45ke9/TJfljl/y3urj164/Lli9i1PT3u9WtWIfTePXTuml7eKUIhJowdhbB79zB3wWKkpqUiOjoK0dFREAoL/zbyOenVO6NP+yO9T/ttTnqf1s4zvU+bOmkCVmTq07pn9Gk7MvVpofek9Gkb1uLcPxl92uT0Pq2REvVpPXv3xeGD+9PjfvoE87LGPdkPK5Z/i7tbz164fOkidmzfgvCnT7FuzUqE3ruHrt16iNO8fx+PB/fDxHfei4gIx4P7YeI1/MqgZ+++OHxgv7i8s8U9yU+ivMVxb8uIe3VG3N1ziTtc+eJW1f/dqhq3KtZzefflQHrM9++H4WlGzM/Cw3FfiWIGVLeO06+D11xRIsWKFYOjoyOWLVuGJ0+eQCgUonLlyhg0aBAmT54s07GXLl2K/v37w8XFBYaGhvDz88OHDx9kOua0adOgoaGB6dOn4/Xr1yhfvjyGDBkCAGjbti1Gjx6NYcOGITk5Ga1atcK0adMwc+ZMmd5T3pq3aIm42FisWbUC0dFRMLewxJr1m8RTR99ERkIt0wWe7OxrYf6iJVi1YjlWLl+KKlWNsHzlapiamonT9BswCImJiZg9czo+fvwA+1oOWLN+k1JdI+bAhUcwLKmL6T2dUFavKEKeRqHd9D/wLj4RAFC5dHGJmSoL9lyHSCTCjF7OqGBQDNHvE/HX9XDM3HFZnKZ0KV1sHtsM5fSL4v2nZNyNiEabaUck7kqkSI2btUB8XCw2rVuF2JhomJhZ4PeV68UXMX37JhJqat9Gxa1t7THjt0XYuGYFNqxejkqVq2L+kpWobvLt7jgNGjXBuEkzsGvbRixfMh9Vqhph7sLlsLVzKPT4ctLUIz3ujWtXIiYmGqbmFli2er14ucSbN5EQqH2r4zZ29pg9bxHWr16BdauWo3KVqli0dCWMTZTvrkC5ada8JeLi4rBuzQrEREfDzNwSK9dukIhbLVPctnb2+G3BYqxZ6Y/VK5ahcpWqWOK/EiYZbfvdu3f499xZAED3Tl4S77Vu83bUrlO3kCLLnUeLloiLi8XazH3aum99WmSkZHnb2dfCvIVLsHrlcqz0T+/Tlq1YLY4bAPr2T+/T5mTu09YpV5/m0Ty9L1+7eiViMuJevW5jpr78NdQy/eplZ1cL8xYswepVy7HKfxmqVDXCUv9VEnGf/+csZkz79r934vgxAABvH18MGTq8kCLL3bfyziXuTP1a5vIWx71CStxTc4jbVzniVtX/3aoatyrW8x/Rl5/75yxmTP0289tv/GgAgLfPMPgoQcyA6tZx+nUIRKIsc/2JfgJJKYrOgWLotVuh6CwoxPM9QxWdBYXQUP85pkDKm8rGraaak0lV8mOIalZxiQEu+vVlXU6sKgQq2sBVtXnr/CJTFXQ9Nyg6C3lKPDJY0VnI0y9SHYiIiIiIiIiowHgrZrngWSQiIiIiIiIikgEHV4iIiIiIiIiIZMBlQURERERERESqSlUvmiNnnLlCRERERERERCQDDq4QEREREREREcmAy4KIiIiIiIiIVJSAy4LkgjNXiIiIiIiIiOiXsXr1ahgZGUFHRweOjo64fv16rumXL18Oc3Nz6OrqonLlyhg9ejSSkpIK9J4cXCEiIiIiIiKiX8LevXsxZswYzJgxA7du3YKtrS08PDzw7t07qekDAwMxceJEzJgxA2FhYdi8eTP27t2LyZMnF+h9ObhCREREREREpKIEAoHSPwpi6dKlGDRoEPr16wcrKyusW7cORYoUwZYtW6Smv3z5MlxdXdG9e3cYGRmhWbNm6NatW56zXbLi4AoRERERERER/fS+fPmCmzdvokmTJuJtampqaNKkCa5cuSJ1HxcXF9y8eVM8mPL06VMcO3YMLVu2LNB784K2RERERERERKS0kpOTkZycLLFNW1sb2traEtuio6ORmpqKsmXLSmwvW7Ys7t+/L/XY3bt3R3R0NNzc3CASiZCSkoIhQ4ZwWRARERERERER5ZNA+R/z589HyZIlJR7z58+XS/jnzp3DvHnzsGbNGty6dQuHDh3CX3/9hTlz5hToOJy5QkRERERERERKa9KkSRgzZozEtqyzVgDA0NAQ6urqePv2rcT2t2/foly5clKPPW3aNPTq1QsDBw4EAFhbW+PTp08YPHgwpkyZAjW1/M1J4cwVIiIiIiIiIlJa2traKFGihMRD2uCKlpYWHBwccObMGfG2tLQ0nDlzBs7OzlKP/fnz52wDKOrq6gAAkUiU7zxy5goRERERERGRiiro3XiU3ZgxY9CnTx/Url0bdevWxfLly/Hp0yf069cPANC7d29UrFhRvKyoTZs2WLp0Kezt7eHo6IjHjx9j2rRpaNOmjXiQJT84uEJEREREREREv4QuXbogKioK06dPx5s3b2BnZ4cTJ06IL3L7/PlziZkqU6dOhUAgwNSpU/Hq1SuULl0abdq0wW+//Vag9xWICjLPhUhJJKUoOgeKodduhaKzoBDP9wxVdBYUQkP91/oVIb9UNu58ruf91ajkxxDVrOJQ+8V+GaXcpali2wYgUNEGrqrNW+cXmapQrPM2RWchTwn7+io6C3lSzU9yRERERERERERy8ouMtRERERERERFRQf1q11xRFM5cISIiIiIiIiKSAQdXiIiIiIiIiIhkwGVBRERERERERCqKy4LkgzNXiIiIiIiIiIhkwMEVIiIiIiIiIiIZcFkQERERERERkYrisiD54OAK0U/k0S5vRWdBIVr4X1B0FhRi72AnRWdBIcqX0lF0FqgQiRSdAQVQV9EPsSJVLGwAKlrcSEtTdA4UI/7zF0VnQSEMimkpOgtECsdlQUREREREREREMuDMFSIiIiIiIiJVpaIz7OSNM1eIiIiIiIiIiGTAwRUiIiIiIiIiIhlwWRARERERERGRiuLdguSDM1eIiIiIiIiIiGTAwRUiIiIiIiIiIhlwWRARERERERGRiuKyIPngzBUiIiIiIiIiIhlwcIWIiIiIiIiISAYcXCEiIiIiIiIikgGvuUJERERERESkonjNFfngzBUiIiIiIiIiIhlwcIWIiIiIiIiISAZcFkRERERERESkorgsSD44c4WIiIiIiIiISAYcXCEiIiIiIiIikgGXBRERERERERGpKq4KkgvOXCEiIiIiIiIikgEHV4iIiIiIiIiIZMBlQUREREREREQqincLkg/OXCEiIiIiIiIikgEHV4iIiIiIiIiIZMBlQUREREREREQqisuC5IMzV6hA+vbtC4FAIH4YGBigefPmCAkJUXTWvtuewAC0aOqOOvbW6NG1E+7kEcupk8fRrnVz1LG3RgfPNrjw73mJ10UiEVav9EfjBm6oW8sGgwf0xbNnET8wgu9z5MAedPdsjub1a8O3f3fcv3cn1/Tnz5xC3y5t0bx+bQzs0R7XLl+QeD02JgYLZ09F59aN0bJBXUwcNQQvnz/7kSEUWCeHijg6zAmXJtbHtn4OqFGheK7pi2lrYEJzU5wY6YLLExvgoI8jXI31xa93qFUBuwfVwbnx9XBufD1s6VsLLpleVxb/O7QHfTu1QLvGdTFqcE88CM25rJ+FP8bcqWPRt1MLtKxnhyP7dmVL89fhfRjapxM6eLiig4crxgzpjRtXL/7IEL7L3t0BaOnhDkcHG/Tq3hl37+Tetk+fPAGvNi3g6GCDTl7Z2/aZv0/BZ3B/NHRzhL21BR7cD/uR2f9ue3YHoEUzd9StZY2e3TrhTh5xnzp5HJ5tmqNuLWt0lBK3SCTCmlX+aNLQDY4ONvAeqJx92t7dAWjl4Q4nBxv0zmd5t2/TAk4ONujs1QYXM8UtFArhv3QJOnu1gUtdezRzr4dpk/0Q9e7tjw6jwFT1f5iq1nNVLe99ewLQurk7nGvns32fOoH2bVvAubYNOrdvg4sXJNv3imVL0Ll9G7jWtYdH43qYroTt+8iB3eju6YHm9R3y+TntJPp2aYPm9R0wsIcXrl3+V+L12JhoLJw9BZ1bu6NlgzpK+TkNUN22Tb8GDq5QgTVv3hyRkZGIjIzEmTNnoKGhgdatWys6W9/lxPFjWLJoPryH+mLP/sMwN7eAj/cAxMTESE0fdPsWJo4fC6/2HbH3wBE0cm+MUcN98ejRQ3GarZs3YnfATkydMRO7du+Drq4ufAYPQHJycmGFlad/Tp/AOv/F6D1wCNZt3wtjU3P4jRqCuFjpcd8LCcLc6X5o0cYL67fvg2t9d0yfMBLhTx4BSP/HNd1vJCJfv8TsRf5Yv2MvypSrgPEjBiMx8XNhhpajplZlMLqpCTZeiEDPTf/h4dsErOxmC70imlLTa6gJsLqHLSqU1IXfwXvosPYafvvrAd59/FaO7z4mY9XZJ+i16T/03vwf/ouIw++drVHdsEhhhZWn82dOYuOq39G9rzdWbtqN6iZmmDZ2KOLjYqWmT05KQvnyFdHPeyT09A2lpjEsUxb9hozAik2B8N8YCNtadTBn0ig8C3/8I0MpkJMnjuH3xQvgPcQXgfsOwczMHEO9ByI2p7YddAuT/MbCs31H7N5/GA3dm2DMyGF4nKltJyYmws7eASNGjyusMArs5PFj+H3RfHj7+GL3/sMwM7fAUO8BOcd9+xYmTRgLT6+O2LM/vU8bPcJXIu5tWzYiMGAnpkyfiZ2B6X3aUG/l6tNOnjiGpYsXYHBGeZuamcM3l/IODrqFyX5j0a59RwRKKe+kpCTcDwvFQO+hCNx7EEuWrcSziHCMGj60MMPKk6r+D1PVeq6q5X0qU/sO2HsIZubmGDYk9/Y9xS+9vAP3pbfvsTm074C9B7Fk6UpERIRj9Ajlad+Sn9P2wdjUDH6jvPPxOa091m/fn8fntBVYv2MfypQrj/EjBinN5zRAdds2/To4uEIFpq2tjXLlyqFcuXKws7PDxIkT8eLFC0RFReHcuXMQCASIj48Xpw8KCoJAIEBERAQA4NmzZ2jTpg309PRQtGhR1KhRA8eOHVNILDu3b0X7jp3h6dUBxiYmmDpjFnR0dHDk0EGp6QN27YCLWz307T8Q1Y2NMWzEKFhaWWFPYPqv+yKRCAE7d2CQtw8auTeBmbkF5s5fhKh373D2zN+FGVquDuzegZbtOqB5a08YVTPGKL9p0NbRxYk/j0hNf2hvAOo4uaJLz36oWq06+nkPg6m5JY4c2AMAePniGcLuhmDUhKmwsKqJylWrYdSEqfiSnISzp44XYmQ56+FYGUduv8b/gt8gPPoz5h97gCRhGtralZeavp1deZTU1cTY/XcQ/PI9It8n4dbzeDx690mc5sKjGFx6EosXcYl4HpuINefC8flLKqwrlSyssPJ0eO9ONG/THs1aeaJKNWMMGzcV2jo6OPXXEanpzSxrYoDvGDRo0hyaWtIHnhxdG6COcz1UrFwVlapURZ/Bw6GjWyTPX9UK064d29C+Qye08+oAY2MTTJk+Czq6OjhyWHrb3r1rJ1xc3dCn3wBUr24M3+Ej09v27gBxmtZt2sHbxxdOTs6FFUaB7dyRqU8zNsHU6Rl9Wg5xB+7aARfXb32a7/Ac+rTB3/q0OfPS+7R/lKhPC9ixDV4Z5V09U3n/kWPcO+GcqbyHDh8JCysr7M0o7+LFi2Ptxi1o1rwFjKpVh42tHfwmT0NY6D1ERr4uzNBypar/w1S1nqtqee/KaN9tPdPb9+RpGe37SA79eUB6++7dbwCqVTfG0GEjYWFphX17vrXvNRu2oJlHevu2VsL2/e1zmlfG57TpGZ/TDktNf2jvriyf04bD1NwKRw7sBpD5c9q0TJ/TpuFLcrLSfE4DVLdtK4PMKxOU9fEz4OAKySQhIQG7du2CiYkJDAwM8rWPr68vkpOT8e+//+LOnTtYuHAhihUr9oNzmp3wyxeEhd6Dk7OLeJuamhqcnFwQEnxb6j4hQUHZvli5uLohJCgIAPDq5UtER0fB0enbMYsXLw5rG9scj1nYhEIhHj4IQ606TuJtampqqFXHEaF3gqXuE3o3GA51HCW21XZyEacXfvkCANDS0pY4pqamFu4qQdwaagJYlC+Ga+Fx4m0iANcjYmFTsYTUfeqbGSLk5Xv4NTfDyVGu2Du4Dvq5VoVaDn27mgBoZlUGuprqCHn5/gdEUXBCoRCPH4bBzuFb2ampqcGutiPu35PPUr7U1FSc//sEkpISYVnDRi7HlJVQmN62M7dDNTU1ODo5IyQ4SOo+IcFBEukBwNnFNcf0yijnuHPp04KD4Ogs2ac5u7iJ4xb3ac7Z+7RgJWjbwPeV953vKO+Ejx8hEAhQvLj0PqOwqe7/MBWt5ypc3vfD7qFulvKu6+iMO7n1544FbN8JytO+0z+nhUr5nOaUx+c0J4lt+fucpom7wbfkHcJ3UdW2Tb8WXtCWCuzPP/8UD4Z8+vQJ5cuXx59//gk1tfyN1T1//hwdOnSAtbU1AKB69eo/LK+5iYuPQ2pqarZBIQMDA4SHP5W6T3R0NAwMDLOlj46Jzng9Kn2bYfZjRkdHyyvrMnkfH4e01FTo6UvmUU/PAC8iwqXuExsTLTV9bEbcVYyqoUy58ti01h+j/aZDR1cXB3bvRNS7t+I0ilSqiCY01NQQ++mLxPbYBCGMDIpK3adiKR3UNiqFE3ffYuSeYFTWLwK/5mbQUBNg44UIcTrj0kWxtV8taGmoIfFLKsbvv4PwaOWYYvvhvfSyLqVngBcyrjcOf/IIY31648uXL9DV1cW035aiSjVjmY4pL3Fx6W1bP1vbNkREuPQ6Hh0dLTV9jJK02/z4Gre0Pi2iIH2a4bf+StynZTmmvoGB0pyb+BzKWz+P8s5+nnIu7+TkZPgvW4LmLVop5McAaVT1f5iq1nNVLe/4HMs75/YdI6U/18+jfa9YtgQeStK+f+zntOUZn9OK4MDuHUrzOQ1Q3bZNvxbOXKECa9SoEYKCghAUFITr16/Dw8MDLVq0wLNn+bso1ogRIzB37ly4urpixowZeV4MNzk5GR8+fJB4cJ2kctHQ0MSsBcvw8vkzeDZzQ8uGdRF86zrqOrv9NNP4shIIBIj7JMRvfz3A/TcJOB36DlsvPUOHWhUk0j2L+YzuG/9D3y03ceDma8xsa4lqSnTNlR+lUhUjrNqyF8vW70TLdp3x+2/T8Tz8iaKzRSR3QqEQfuNGAQAmTZup0LwQkXwJhUJMHDcKIhEwaepMRWfnh8n+Oa0Ogm/d+Kk/pxEpIw6uUIEVLVoUJiYmMDExQZ06dbBp0yZ8+vQJGzduFM9eEYlE4vRCoVBi/4EDB+Lp06fo1asX7ty5g9q1a2PlypU5vt/8+fNRsmRJicfihfNljkOvlB7U1dWzXQguJiYGhoY5XMjT0BAxWUb4Y2JiYJgxam5oWDp9W3T+j1nYSpbSg5q6eraLosXFxUDfQHoe9Q0M80xvZmGFDTv344+/L2H/n2ewYPk6fHgfj/IVK8k/iAKK/yxESloa9ItqSWzXL6aJmATpA3XRCV/wLPYz0r5VZYRHf4JhcW1oZFoblJImwsu4RNx/k4DV/zzFw3cJ6FZX8TEDQImS0ss6Ppeyzi9NTU1UqFQFpuZW6DdkBKqbmOGPA4EyHVNe9PTS23bWC+DFxGT/hesrQ0ND6emVpN3mx9e4Ze7Tor+lF/dpWY4ZGxOjNOemVA7lHZtHeWc/T9nLO/2L12hEvn6NNRs2K8Wv2l+p6v8wVa3nqlrepXIs7+gc82ggpT+Pzal9jx+NyEjlat+5f06TvgQ/f5/TamDDzgP44+/L2P/n2YzPae+V4nMaoLptW2kIfoLHT4CDKyQzgUAANTU1JCYmonTp9E4sMjJS/HpQxtrezCpXrowhQ4bg0KFDGDt2LDZu3Jjj8SdNmoT3799LPMb7TZI535paWrC0qoFrV6+It6WlpeHatSuwsbWXuo+NnR2uXb0qse3qlcuwsbMDAFSsVAmGhqVx7dq3YyYkJOBOSHCOxyxsmpqaMDO3xO0b18Tb0tLScPvGNVhZ20rdx6qmLW5lSg8AN69flZq+WLHiKKWnj5fPn+Hh/VC41m8k3wC+Q0qaCPcjE1C3mp54mwBAHSM9hLz6IHWf4JfvUVlPV6Ivr6JfBFEfk5GSecQlCzWBAJrqytG1ampqwsTMEsE3r4u3paWlIejmdVjI+fooaaI08ZpuRdPUzGjb1yTb9vWrV2Fjayd1HxtbO1zPlB7IaNs5pFdGX+O+njXu3Po0Wztcl9anZcT9tU+7fjV7n2arNH1aDnHnUt7WUsr7Wpby/jqw8vz5M6zbuBWlSulBmaju/zAVrecqXN4WljVwI0t537h2FdYF6M+vXZXSvsePxotnz7B2g3K17/TPaVZSPqdJ/9wF5PQ57Uo+Pqfdg2t9d/kG8J1UtW3Tr0U5vgHQTyU5ORlv3rzBmzdvEBYWhuHDhyMhIQFt2rSBiYkJKleujJkzZ+LRo0f466+/8Pvvv0vsP2rUKJw8eRLh4eG4desW/vnnH1haWub4ftra2ihRooTEQ1tbO8f0BdGrTz8cOrAPR48cxtMnTzB39kwkJibC06s9AGDKpAnwX/Yt/z169sblSxewfdsWhD99grWrV+Le3bvo2r0ngPSBph69emPj+rU4d/YMHj18gKmTJqB0mTJwb9xELnmWh47deuOvowdx8q8/8Cz8KZYvmoukpER4tPIEACyYNRmb1viL07fv0gM3rl7GvoDteB4Rju0b1+Bh2D14duwqTnP+zCkE3byB169e4tK//2DCCG+41m+E2lkuKqcoAddewNO+PFrZlIORQRFMamkGXU11/C84fSBwVltL+Db6dv2fgzdfoYSuJsZ5mKKKvi5cTQzQz7Uq9v/3SpzGt1F12FcpifIldWBcuih8G1WHQ9X067QoC68uvXDiz0P4+/hRPI94itW//4bkxEQ0bdkOALBk7lRsXbdCnF4oFOLJo/t48ug+UoQpiIl6hyeP7uP1y+fiNFvXrcCdoJt4G/kK4U8epT+//R8aNmtZ6PHlpGfvvjh8cD+O/nEYT58+wbw56W27nWd625462Q8rln9r29169sLlSxexY/sWhD99inVrViL03j107dZDnOb9+3g8uB+GJ0/Slz9FRITjwf0w8ZpuZdCrd0af9kd6n/Zb1rgnTcCKTH1a94w+bUemPi30npQ+bcNanPsno0+bnN6nNVKiPq1HRnn/L0t5t82Ie9pkP6xcnjnuXrhy6SJ2ZinvLhnlLRQKMWHMSITeu4vfFixGaloqoqOjEB0dBaFQOQYRAdX9H6aq9VxVy7tnpvYd/vQJ5s+VbN/TJ/thpX+m/rxHL1y+nNG+w59ifUb77tz1W/v2GzsSYffuYq6Stu/sn9PmSPmctlycvn2Xnrhx9VLG57SnmT6ndROnOX/mZMbntBe49O9ZTBgxGK713ZXmcxqgum2bfh28oC0V2IkTJ1C+fPrta4sXLw4LCwvs378fDRs2BADs3r0bPj4+sLGxQZ06dTB37lx06tRJvH9qaip8fX3x8uVLlChRAs2bN8eyZcsUEQqat2iJuNhYrFm1AtHRUTC3sMSa9ZvEUwXfREZCTfBtDNLOvhbmL1qCVSuWY+XypahS1QjLV66GqamZOE2/AYOQmJiI2TOn4+PHD7Cv5YA16zfJbUBIHho1bY738XHYtnEN4mKiYWxqjgXL1oqnm7578waCTHHXsLHDlNkLsGX9SmxZtwIVK1fB7EX+qGZsKk4TEx2Ftf6LERcbA33D0mjWog169vcu9Nhycjr0HfSKaGJIg2owKKqFh28TMHx3CGI/pS9bK1dSG2mZlrO9/ZCM4YHBGNPUBLsH10HUxy/Yc+Mltl/+dm0h/aKamNXWEobFtJGQnIJH7xIwPDBY4q5EitagsQc+xMdh5+a1iIuNRnUTc8xeskZ84buot5FQy7TeOjb6HYb3/zZodnDPDhzcswPWdg5YuHIzAOB9fCx+/20qYmOiUbRoMVQzNsOc39egVh3luUWxR/P0tr129UrEZLTt1es2ZmrbryXitrOrhXkLlmD1quVY5b8MVaoaYan/Kphkatvn/zmLGdMmi59PHD8GAODt44shQ4cXUmS582jREnFxsVibuU9b961Pi4yMhEBNsk+bt3AJVq9cjpX+6X3ashWrJeLu2z+9T5uTuU9bp1x9mrTyXpVLedva1cJvC5ZgTQ7lHfXuLc6fOwsA6NrRU+K9NmzZjtpZ7p6mKKr6P0xV67mqlnez5unlvW5Nevs2M7fEyrUbxcv+3rx5DYFa9va9duVyrF6xDFWqGOH3HNp3t06eEu+1frNytO/0z2mx2LZxdcbnNAssWLZOvMzn3ZtIiWulfPuctgpb1vmjYuWqUj6nRUv5nDak0GPLjaq2bWXAa+/Ih0CU+eIYRD+JpBRF50Axoj+q5oV8PVdfVnQWFGLvYKe8E/2CypfSUXQWFEJVP9ikqeDHEPWc7un+i1PBogYAqGjTRkqqahZ4/Gdh3ol+QQbFtPJO9AvS1VR0DuSj7MD9is5Cnt5u6pR3IgXjsiAiIiIiIiIiIhlwWRARERERERGRilLV2bPyxpkrREREREREREQy4OAKEREREREREZEMuCyIiIiIiIiISEVxWZB8cOYKEREREREREZEMOLhCRERERERERCQDLgsiIiIiIiIiUlFcFiQfnLlCRERERERERCQDDq4QEREREREREcmAy4KIiIiIiIiIVBVXBckFZ64QEREREREREcmAgytERERERERERDLg4AoRERERERERkQx4zRUiIiIiIiIiFcVbMcsHZ64QEREREREREcmAgytERERERERERDLgsiAiIiIiIiIiFcVlQfLBmStERERERERERDLg4AoRERERERERkQy4LIiIiIiIiIhIRXFZkHxw5goRERERERERkQw4uEJEREREREREJAMuCyIiIiIiIiJSVVwVJBecuUJEREREREREJAMOrhARERERERERyYDLgoh+Iu8/pyg6CwqxroeDorOgEP+9ilV0FhSiefHyis6CQmhrquac3OfRnxWdhUJX1aCIorOgEMJUkaKzoBDamqr5W6aq3nxEV4vlTT8f3i1IPlSz9RMRERERERERyQkHV4iIiIiIiIiIZMBlQUREREREREQqisuC5IMzV4iIiIiIiIiIZMDBFSIiIiIiIiIiGXBwhYiIiIiIiIhIBrzmChEREREREZGK4jVX5IMzV4iIiIiIiIiIZMDBFSIiIiIiIiIiGXBZEBEREREREZGK4rIg+eDMFSIiIiIiIiIiGXBwhYiIiIiIiIhIBlwWRERERERERKSquCpILjhzhYiIiIiIiIhIBhxcISIiIiIiIiKSAZcFEREREREREako3i1IPjhzhYiIiIiIiIhIBhxcISIiIiIiIiKSAZcFEREREREREakoLguSD85cISIiIiIiIiKSAQdXiIiIiIiIiIhkwGVBRERERERERCqKq4LkgzNXiIiIiIiIiIhkwMEVIiIiIiIiIiIZcHCFCl1ERAQEAgGCgoIUnRUiIiIiIiIimXFwRcUJBIJcHzNnzlR0Fn+4PYEBaNHUHXXsrdGjayfcCQnJNf2pk8fRrnVz1LG3RgfPNrjw73mJ10UiEVav9EfjBm6oW8sGgwf0xbNnET8wgu9z/MheeHdrhS4eTvAb2huPwu7mmPZ5+BMsmjEO3t1aob17LfzvQEC2NF9fy/rY4D//R4ZRIKeO7sPI3m3Rt40rpo/siycP7uWY9uzxw5g9dhAGdXTHoI7umDdxaLb0Ny6exfzJw+DdqQl6NK+DiCcPfnQI3+XaySNYOqwbZvfywPopQ/HycViOaUOv/4t1k4dgXv82mNOnJdb4DULQv6fEr6empOBUwAasGj8Ac/q0xGKfTji4ej4+xEYXRigFsn9PANq1aAy3urbo17ML7t3JvW3/feoEOnm2hFtdW3Tr2BaXLki27Q1rV6GTZ0vUd6qFxvUc4evdD3fvBP/IEL6LqvZpxw7vxaAurdCpqRPG+/TGwzz6tAXTx2FQl1bwbFgLR/dn79N2b10Hz4a1JB6+vdr/yBC+y97dAWjp4Q5HBxv06t4Zd/Oo56dPnoBXmxZwdLBBJ6/s5X3m71PwGdwfDd0cYW9tgQf3c+4vFEne7Tuz+XNnoq6dJXbv2i7vbMtMVdv33t0BaOXhDicHG/TOZz1v36YFnBxs0NmrDS5milsoFMJ/6RJ09moDl7r2aOZeD9Mm+yHq3dsfHUaBHdwbiPatmqKhkz0G9u6K0Lu5x3329El0bd8aDZ3s0bOzJy5f/Ffi9bkzJsOlVg2Jx2jfwT8yhAJT1TquaHl9J1SGx8+AgysqLjIyUvxYvnw5SpQoIbFt3Lhx4rQikQgpKSkKzK38nTh+DEsWzYf3UF/s2X8Y5uYW8PEegJiYGKnpg27fwsTxY+HVviP2HjiCRu6NMWq4Lx49eihOs3XzRuwO2ImpM2Zi1+590NXVhc/gAUhOTi6ssPJ08Z+T2Lp2KTr3Howl6wNhZGyK2X6+iI+LlZo+OTkJZctXRK9BI1BK31BqmkVrd2HzgVPix4zFawEALg2a/rA4CuLK+VMI2Lgc7XsOxNxVO1GluikWTBmO9/HSYw4LuQnnhs0wZeFazFq2BQaly2LB5GGIjX4nTpOUlATzGrbo2n9YYYVRYHcu/4MTO9eiYcfeGDJ/PcpVNcaO+X5IeB8nNb1u0RKo79kDg+asgu/CjbBv0BxH1i3Co+AbAADhlyS8jniEhu17wWf+OnQdMwvRr18gcMnUwgwrT6dPHsPy3xdioLcvduw+CFMzc4wYOgixsdLbdkjQbUybNA5tPTtg555DaNCoMcaPHo4nj7+17SpVjTB+4lTsPvAHNmzdhfIVKmK4z0DExUqvQ4qgsn3a2ZPYsmYpuvYdjKUb0/u0WeNz79PKla+I3oNHQC+HPg0AqhgZY+vBU+LH/JWbf1QI3+XkiWP4ffECeA/xReC+QzAzM8dQ74GIzam8g25hkt9YeLbviN37D6OhexOMGTkMjzOVd2JiIuzsHTBi9Dipx1AGP6J9f/XP2dO4GxKM0qXL/OgwCkxV2/fJE8ewdPECDM6o56Zm5vDNpZ4HB93CZL+xaNe+IwKl1POkpCTcDwvFQO+hCNx7EEuWrcSziHCMGj60MMPK098nj2PF0kXoP3gotgbuh4mpOUb7eudYz+8E38aMyePRpl17bAs8gPoN3TFxzHA8efxIIp2Tixv+d+qc+DFr/uLCCCdfVLWO06+Dgysqrly5cuJHyZIlIRAIxM/v37+P4sWL4/jx43BwcIC2tjYuXryIvn37wtPTU+I4o0aNQsOGDcXP09LSsGjRIpiYmEBbWxtVqlTBb7/9JjUPqamp6N+/PywsLPD8+fMfGG12O7dvRfuOneHp1QHGJiaYOmMWdHR0cOTQQanpA3btgItbPfTtPxDVjY0xbMQoWFpZYU/gLgDpA1ABO3dgkLcPGrk3gZm5BebOX4Sod+9w9szfhRlarv63PwBNW3qhcYt2qGxUHd6jp0BbWwdnj/8hNb2pRQ30GTIabu4e0NTUlJqmZCk96Okbih//XfkX5SpUQg1bhx8ZSr4dPxSIRs090aBZW1SqWh39h0+CtrYOzp88KjW9r99cNG3TCUbG5qhQ2QiDRk1FmkiEe0E3xGnqNWmJ9j0GoaZ93cIKo8Au/7UfDu4tUathC5SpZIQ2A0dDU0sbt84dl5q+Wg07WNWth9IVq0K/XEU4t+yAslWq4/n9OwAAnSLF0HfKYtR0bgjDClVQ2dQKrfuPwOunDxEfrTy/+gXu3A7P9p3QxrM9qhubYOLUmdDR0cH/jhySmn5P4A44ubihV98BqFbdGEN8R8LC0hL79gSK0zRv2Rp1nVxQsVJlGJuYYtTYifiUkIBHj5RnxpKq9ml/7A9As1bf+jSfMVOgraODM8dy7tP6+oxGvcYe0MihTwMANXV16BkYih8lSun9qBC+y64d29C+Qye08+oAY2MTTJk+Czq6OjhyWHp57961Ey6ubujTbwCqVzeG7/CR6eW9+9vMndZt2sHbxxdOTs6FFUaB/Yj2DQDv3r7F7wt+w+x5i6ChoXw31FTV9h2wYxu8Mup59Uz1/I8c6nngrp1wzlTPhw4fCQsrK+zNqOfFixfH2o1b0Kx5CxhVqw4bWzv4TZ6GsNB7iIx8XZih5WpPwHa09eqI1u28UK26CSZMmQFtHR38+Yf0er4vcBccnd3Qo09/GFU3xuChI2BuYYWDeyXruaaWFgwMS4sfJUqULIxw8kVV6zj9Oji4QnmaOHEiFixYgLCwMNjY2ORrn0mTJmHBggWYNm0aQkNDERgYiLJly2ZLl5ycjE6dOiEoKAgXLlxAlSpV5J39HAm/fEFY6D04ObuIt6mpqcHJyQUhwbel7hMSFJTtA6eLqxtCMq4f8+rlS0RHR8HR6dsxixcvDmsb2xyPWdiEQiGePAyDjYOjeJuamhpsHBzxIDT3qZcFeY9//z4O9xbtlGIaX4pQiPBH9yUGQdTU1FDTvi4ehd3J1zGSk5OQmpKCosVL/Khsyl1KihCR4Q9hbP1tgEtNTQ3G1g54+TA0z/1FIhGe3LmF6MiXqGqZc9tP+vwJAoEAOkWKySXfshIKv+B+2D3UcfzWVtXU1FDH0Rl3QoKk7nMnJBh1HSXbtpOzW47phcIvOHJwH4oVKw4zMwt5ZV0mKt2nPcjep9nKoU+LfPUc/To0g3e3Nlg6dwqi3kbKml25EQrTyztz2aipqcHRyRkhwUFS9wkJDpJIDwDOLq45pldGP6p9p6WlYcZUP/Ts0x/GJqY/IusyUd32XfB6fuc76nnCx48QCAQoriT/44XCL3gQFora2eq5E+6GSF+OevdOEOo4Oklsc3R2xd0s7eL2fzfQsnE9dPVqhcXzZuN9fLy8s/9dVLWOKwuBQPkfPwPlG5YnpTN79mw0bZr/pR0fP36Ev78/Vq1ahT59+gAAjI2N4ebmJpEuISEBrVq1QnJyMv755x+ULFm4I+dx8XFITU2FgYGBxHYDAwOEhz+Vuk90dDQMDAyzpY+Oic54PSp9m2H2Y0ZHK8c1KT6+j0daWipK6elLbC+lp49XzyPk8h7XL/2DTwkf4e7RVi7Hk9XHD+kxlywlGXOJUvp4/SIiX8fYs2Ul9AwMlXqWSlafP7xHWloaipaU/LW9aEk9RL3KeZZY0ucELPHpjJQUIdTU1NC6/yiY2NSWmlb45QtOBW6AtYs7dIoUlWv+v1d8XDxSU1Ohn6Vt6xsY4FlEuNR9YqKjoZ+lbesbGCA2S7u98O8/mOo3DklJiTA0LI1V6zajlJ5yzGZQ+T5NX7J9l9TTx0sZ+jQzK2uMmDgLFStXRVxMNPZs34DJIwZgxdb90FWCuh4XFye1nhsYGCIiXHo9j46Olpo+RknKMj9+VPvesXUTNNTV0aV7L/lnWg5UtX3H51DP9fOo59nPU871PDk5Gf7LlqB5i1YoVkw5fiSIj8+o5/pZ4tbPvZ7rZYlbz8BAYkmNo4sbGrg3QYUKlfDy5QusX7UcY4Z7Y8O2QKirq8s/kAJQ1TpOvxYOrlCeateW/qUqJ2FhYUhOTkbjxo1zTdetWzdUqlQJZ8+eha6ubo7pkpOTs62LFKlrQ1tbu0D5osJz5tgR1KrrAn3D0orOilwc3bsNV86dxtRF66Cl9evXOy2dIvBZuBFfkhLx9O4tnNi5BnplyqNaDTuJdKkpKdjnPwsQidB6wCiF5LWw1a7jiF17DyE+Pg5HDu3HpAmjsXXX3mwfgOnn5+DoKv7byNgMppbWGNy1FS7+cxpNW3kqLmMkd2Gh97AncCd27j6oFLMtqfAIhUL4jRsFAJg0baZC81IYmnq0FP9tbGoGE1MzdGrbHLf/u4HaWWa9EFHBcVkQ5aloUclf6NTU1CASiSS2CYVC8d+5DZRk1rJlS4SEhODKlSu5pps/fz5Kliwp8Vi8UPY70OiV0oO6unq2i2TFxMTA0FD6BQ4NDQ0RExOdPX3GqLlhxmBCTHT+j1nYipcsBTU19WwXeoyPi0UpOXxBfPfmNUJuXUeTVl4yH0teipdIjznrxWs/xMeipF7uMf91YCf+t287Js5biSrVlW+qeG6KlCgJNTU1fMpy8dpP7+NQPMssnszU1NRgUK4iyhuZwLV1Z1g5NsC/f0iu2f46sBIf9RZ9pixWmlkrAFBKrxTU1dWzXewwNiYGBjm0QwNDQ8RmaduxMTHQz5JeV7cIKlepCmsbO0yb+Rs01NVxNId1/4VN5fu0LBcWfh8XCz05DnoVK14cFSpVwZtXL+R2TFno6elJrecxMdl/yf3K0NBQenolKcv8+BHtO+jWf4iLjUHbFu5wdqgJZ4eaiIx8Df+li9CuRe4/FBUWVW3fpXKo57F51PPs5yl7PRcKhZg4bjQiX7/Gmg2blWbWCgCUKpVRz7NcvDY2NibbLKyvDAwNEZcl7riYmGwzQTKrWKkySpXSw8sXhXvNQ2lUtY4rC0XfCYh3CyKVVbp0aURGSq47D8pY2wgApqam0NXVxZkzZ3I9jo+PDxYsWIC2bdvi/Pmcb4k4adIkvH//XuIx3m+STDEA6Rf0srSqgWtXvw3upKWl4dq1K7CxtZe6j42dHa5dvSqx7eqVy7CxswMAVKxUCYaGpXHt2rdjJiQk4E5IcI7HLGyampowNrNEyK3r4m1paWkIuXUd5lb5u6ZObs6eOIoSpfTh4OSWd+JCoqGpiWqmFhIXo01LS8PdoBswtbTOcb//7d+Bw4GbMWHuClQ3syqMrMqVhoYmylczw9O7t8Tb0tLS8PTuLVQqQDwiURpSMw2gfh1YiYl8hb5Tl6BIceW5GB4AaGpqwcKyBm5c/9ZW09LS8N/1q7C2sZO6j7WNrUR6ALh29XKO6cXHFYnw5csXWbMsFyrdp5lL6dNuyqdP+yrx82e8ef0Sejl8sSlsmpoZ5X1NsryvX70KG1s7qfvY2Nrh+jXJHzSuXrmcY3pl9CPad4vWbRG4/wh27T0kfpQuXQY9+/THirWbflQoBaK67Ts97usFqOfWUur5tSz1/OvAyvPnz7Bu41aUUrKLVWtqasHc0go3s9Xza6hpYyt1n5rWdvgvSz2/fu0Kaubyf+zd2zd4/z4eBqUV36+pah2nXwuXBVGBubu7Y/HixdixYwecnZ2xa9cu3L17F/b26Z2Ujo4O/Pz8MGHCBGhpacHV1RVRUVG4d+8eBgwYIHGs4cOHIzU1Fa1bt8bx48ezXZcFALS1sy8BSpLTHaF79emHaZP9UKNGTdS0tsGunduRmJgIT6/2AIApkyagTJmyGDl6LACgR8/eGNC3F7Zv24L69RvgxPFjuHf3LqbNnA0gfdS3R6/e2Lh+LapWqYqKlSph9Up/lC5TBu6Nm8gn03LQplMPrFwwAybmVjC1qIH/HQxEclIi3JunXyPFf/40GBiWQc9BwwGkfwh5+Sx9vWtKihCx0e8Q/vgBdHR1Ub7it4sQp6Wl4eyJo2jUrDXU1ZWre2nRvjvWL5mFaqaWMDavgROHdyM5KRENmrUBAKxdPAN6BqXFt1X+377tOLBzPXz95qJ02fKIj03/ZURHtwh0dIsAABI+vkf0uzeIz/jVJPLlMwBAKT2DHG9ZXdhcWnXC4bULUKG6OSqZWODKsYP4kpyEWg2aAwAOrp6PEvqGaNptEADg3yOBqFDdDPplKyA1RYiHt68h+MJptMlY9pOakoK9y2bidfgj9PSbh7S0NHzMmBGkW6w4NDRyvvNKYereqw9mTZsES6uaqFHTGnsCdiAxMRGt26XPqJox1Q9lypSF74gxAICu3XvDe2BvBOzYCtd6DXDqxDGEhd7D5OmzAACJiZ+xdeN61GvYCIaGpREfH48DewMR9e4tGjf1UFicWalqn9auUw/4z8/o0yxr4H8HApGUlIjGLdL7tOXz0vu0XoO/9WkvIiT7tKePHkBXVxflK6X3aVvXLEMdl/ooXbY84mKisHvrOqipqaFe4+aKCVKKnr37YvqUibDKKO/AjPJu55le3lMn+6FMmTIYMSq9vLv17IVB/Xpjx/YtqFevIU6e+Auh9+5h2ozZ4mO+fx+PN5GRePcu/bbzERnXdzAwNBT/Cqxo8m7fpUrpZftyraGhAQMDQ1Q1qla4weVCVdt3j959MSOjntfIVM/bZtTzaRn1fHhGPe+eUc93bt8Ct0z1fGpGPRcKhZgwZiTuh4XCf/U6pKaliq/NUbJkSWhqaikm0Cy69uiDuTMmw8KqBqxqWGNv4E4kJSaiddv0ej572iSULlMGPsNHAwA6d++JoYP6InDnNri41cffJ4/jfuhd+E2dCQD4/PkTtqxfi4aNm8LA0BCvXrzAav/fUalyFTg6K8cPYqpax+nXoVzffuin4OHhgWnTpmHChAlISkpC//790bt3b9y58+2uK9OmTYOGhgamT5+O169fo3z58hgyZIjU440aNQppaWlo2bIlTpw4ARcXF6npfoTmLVoiLjYWa1atQHR0FMwtLLFm/Sbx1NE3kZFQE3yb4GVnXwvzFy3BqhXLsXL5UlSpaoTlK1fD1NRMnKbfgEFITEzE7JnT8fHjB9jXcsCa9ZuU6hoxbo088CE+Dru3rkV8XAyqGZtj2sJV4mVB0e/eQE3tW9xxMVEYO7ib+Pkf+3bij307UcPWAXOWbRRvD7l5DdHv3qBxi3aFF0w+OTdoho/v43Fg53q8j4tB1epm8Ju7QrwsKObdG4kph3//eRApQiH85/pJHKd9j0Ho0GswAODmlX+xYem3LyWr5k/JlkbRrF0a4fOHeJzdvxUJ8XEoV9UYvSYuRLGMZUHvo99BkKmOf0lOxJ9b/PEhJgqaWtowrFAZHXwnw9qlEQDgQ2w07t+8DABY4zdI4r36TVua7bositLUoyXi4uKwYe0KxERHw8zcEv5rNoinkb/N0rZt7OwxZ95irFvtjzUrl6FylapYvGwljE3S27aamjoiIp7ir7FHEB8fh5KlSsGqhjU2bNmlVHcWUdk+zd0D7zP6tLjYGFQzMceMRd/6tKi3byTqeWx0FMYM+tanHdm7E0f2pvdpv/mn92kxUW/x+5xJ+PjhPUqW1IOltR0WrtmOkkr0C7dH8/TyXrt6JWIyynv1uo2Zyvs11DL1a3Z2tTBvwRKsXrUcq/yXoUpVIyz1XwWTTOV9/p+zmDFtsvj5xPHpAxTePr4YMnR4IUWWO3m375+FqrZvafV8VS713NauFn5bsARrcqjnUe/e4vy5swCArh09Jd5rw5btqF3HEcqgiUcLxMfFYuPaVYiNiYapuQWWrlovXhb09k0k1NS+xW1ta49Zvy3ChjUrsH7VclSqUhULlq4U/49SV1PH40cPcOzPP5Dw8QMMS5dBXScXDB46HFpayjGgpKp1XBn8JKtulJ5AlPXiGUQ/AXnNXPnZPHn7SdFZUIjEL6mKzoJCPIn/qOgsKERzi/KKzoJCaGuq5krd8CjV69eqGhRRdBYUQpiqmh85VbVtp6apZnknCVXzM0tRbdX8zV7nFwnbYuJJRWchT/cXKM9M4ZyoZm9PRERERERERCQnv8hYGxEREREREREVVOYlZvT9OHOFiIiIiIiIiEgGHFwhIiIiIiIiIpIBlwURERERERERqSjeLUg+OHOFiIiIiIiIiEgGHFwhIiIiIiIiIpIBB1eIiIiIiIiIiGTAa64QERERERERqSgBL7oiF5y5QkREREREREQkAw6uEBERERERERHJgMuCiIiIiIiIiFQUVwXJB2euEBERERERERHJgIMrREREREREREQy4LIgIiIiIiIiIhXFuwXJB2euEBERERERERHJgIMrREREREREREQy4LIgIiIiIiIiIhXFZUHywZkrREREREREREQy4OAKEREREREREZEMuCyIiIiIiIiISEVxVZB8cOYKEREREREREZEMOLhCRERERERERCQDLgsiIiIiIiIiUlG8W5B8cOYKEREREREREZEMOLhCRERERERERCQDLgsi+olUNSyi6CwoRJIwVdFZUAiLCsUVnQWFKN1htaKzoBCv9/koOgsKUVlf9fo1NTXVnH6traJxqyo1FV1mUESLX6+IVBVbPxEREREREZGKUtGxULnjsiAiIiIiIiIiIhlwcIWIiIiIiIiISAZcFkRERERERESkongrZvngzBUiIiIiIiIiIhlwcIWIiIiIiIiISAZcFkRERERERESkorgqSD44c4WIiIiIiIiISAYcXCEiIiIiIiIikgGXBRERERERERGpKN4tSD44c4WIiIiIiIiISAYcXCEiIiIiIiIikgGXBRERERERERGpKK4Kkg/OXCEiIiIiIiIikgEHV4iIiIiIiIiIZMBlQUREREREREQqincLkg/OXCEiIiIiIiIikgEHV4iIiIiIiIiIZMDBFSIiIiIiIiIiGfCaK0REREREREQqipdckQ/OXCEiIiIiIiIikgEHV4iIiIiIiIiIZMBlQUREREREREQqirdilg/OXKFsZs6cCTs7O0Vng4iIiIiIiOinwMEVBenbty8EAgGGDBmS7TVfX18IBAL07ds3X8c6d+4cBAIB4uPj5ZvJH0xZBnH2BAagRVN31LG3Ro+unXAnJCTX9KdOHke71s1Rx94aHTzb4MK/5yVeF4lEWL3SH40buKFuLRsMHtAXz55F/MAIvs++PQFo3dwdzrVt0Lt7Z9y9k3vcp0+dQPu2LeBc2wad27fBxQvf4hYKhVixbAk6t28D17r28GhcD9Mn+yHq3dsfHUaBHNwXiA6tm6KRsz0G9e6K0Lu5x3z29El0a98ajZzt0auzJy5f/DdbmojwJ5gw2hfN6juisWttDOjVGW8iX/+oEL6LKpY1AHi3ssb9zb0Rd2gI/v29I2qblck1/bC2tghe1wOxB4fg0dY+WDTQDdqa6uLXB7Woiesru+LtvsF4u28wzi3piGYOVX50GAV2YG8gPFs2QX1HO/Tv1QX38qjnZ06fQBevVqjvaIcendrh8oXzOaZdOHcmnOytsCdgh7yzLTNVreeq+j+McatY3LsD0KKZO+rWskbPbp1wJ4/2ferkcXi2aY66tazR0St73GdOn8KQQf3RwNURdjXNcf9+2I/M/ndRxZgB1a3j9Gvg4IoCVa5cGXv27EFiYqJ4W1JSEgIDA1GlSuF/YBeJREhJSSn091WkE8ePYcmi+fAe6os9+w/D3NwCPt4DEBMTIzV90O1bmDh+LLzad8TeA0fQyL0xRg33xaNHD8Vptm7eiN0BOzF1xkzs2r0Purq68Bk8AMnJyYUVVp5OnTiGpYsXYPAQXwTsPQQzc3MMGzIQsTnEHRx0C1P8xsLTqyMC9x1GQ/cmGDtyGB5nxJ2UlIT7YaEY6D0UAXsPYsnSlYiICMfoEUMLM6xc/X3qOFYuXYT+g4diS8B+mJiZY8wwb8TFSo/5TvBtzJwyHq0922Nr4AHUa+iOSWOH4+njR+I0L188h8+AXqhqVA2rNmzD9j2H0HfgEGhraxdWWHlSxbIGgI71TLBwoBt+230DziP3IiQ8Bkdnt0XpkrpS03dpYIY5fZ0xb/cN2PkEYMiKs+hYzxSz+ziL07yKScC07VfgMmovXEftw7ngl9g/tRUsq+gXVlh5On3yOPx/X4iB3kOxPfAATM0sMGroYMTmUM9Dgm5j+qTxaOPZHtt3H0T9ho0xYcxwPMlUz786d/Zv3L0TjNKlcx+kUgRVreeq+j+McatW3CePH8Pvi+bD28cXu/cfhpm5BYZ6D8ixfQfdvoVJE9Lb95796XGPHuErbt8AkJj4Gfa1amHk6HGFFUaBqGLMgOrWcWUgECj/42fAwRUFqlWrFipXroxDhw6Jtx06dAhVqlSBvb29eFtycjJGjBiBMmXKQEdHB25ubrhx4wYAICIiAo0aNQIA6OnpScx4yW0/4NuMl+PHj8PBwQHa2tq4ePGi+PX169ejcuXKKFKkCDp37oz379+LX0tLS8Ps2bNRqVIlaGtrw87ODidOnJCIz8/PD2ZmZihSpAiqV6+OadOmQSgUAgC2bduGWbNmITg4GAKBAAKBANu2bZPPiS2Andu3on3HzvD06gBjExNMnTELOjo6OHLooNT0Abt2wMWtHvr2H4jqxsYYNmIULK2ssCdwF4D0AaqAnTswyNsHjdybwMzcAnPnL0LUu3c4e+bvwgwtV7t2bINXh05o69kB1Y1NMHnaLOjo6uCPI9Lj3h2wE86ubujdbwCqVTfG0GEjYWFphX17AgAAxYsXx5oNW9DMowWMqlWHta0d/CZPQ1joPUQqySyOvbu2o41XR7Rq64Vq1U0wfvIMaOvo4M8/DklNv2/3Ljg6u6FH7/4wqmaMwUNHwMzCCgf2BYrTbFizAs6u9eE7chzMLCxRqXIV1GvgDj19g8IKK0+qWNYAMMLTDltP3sPOv8Nw/0Uchq/+B4nJKejT1FJqeifLcrgSFom95x/i+buPOHP7Bfb9+xC1Tb8NJBy7HoGT/z3Dk9fv8fh1PGbuvIqEJCHqmpctrLDytHvXNrRr3wmt27VHNWMT+E2ZAR0dHfx5RHo937t7J5xc3NCzT3p5e/uOgLmlFQ5klPdX7969xe8Lf8OseYugrqF8l2tT1Xquqv/DGLeKxb0jU9zGJpg6PSPuw9LjDty1Ay6u3+L2HS4ZNwC0busJb59hcHR2lnoMRVPFmAHVreP06+DgioL1798fW7duFT/fsmUL+vXrJ5FmwoQJOHjwILZv345bt27BxMQEHh4eiI2NReXKlXHwYHqH8+DBA0RGRsLf3z/P/TKbOHEiFixYgLCwMNjY2AAAHj9+jH379uF///sfTpw4gdu3b2Po0G+/2Pn7++P333/HkiVLEBISAg8PD7Rt2xaPHn37tbN48eLYtm0bQkND4e/vj40bN2LZsmUAgC5dumDs2LGoUaMGIiMjERkZiS5dusjxzOZN+OULwkLvwcnZRbxNTU0NTk4uCAm+LXWfkKAgODlJ/lNycXVDSFAQAODVy5eIjo6Co9O3YxYvXhzWNrY5HrOwCYVfcD/sHuo6ScZd19EZd4KDpO4TEhwER0cXiW3OLq4IySE9ACQkfIRAIEDx4iXkkW2ZCIVf8OB+KOrU/VZ2ampqqF3XCXfvBEvd515IEGo7Oklsc3R2xb2QIADpA4yXL55H5SpVMdp3EFo1qYdBvbvi33/O/LA4CkoVyxoANDXUYG9SBmeDXoi3iUTA2aCXqGtRTuo+V8PewN64jHjpkFHZEvCoXRUn/nsmNb2amgCd6puiqI4mrt1/I/8gvoNQ+AUPwkJRJ1O9VVNTQx1HZ9zJqLdZ3Q0JQh1HyT7NydkVd0K+tYu0tDTMmjoRPfv0R3Vj0x+Sd1moaj1X2f9hjFu8TSXiFqbH7ZilfTvmFndwULYBBGcXt1zbtzJRxZgB1a3j9Gvh4IqC9ezZExcvXsSzZ8/w7NkzXLp0CT179hS//unTJ6xduxaLFy9GixYtYGVlhY0bN0JXVxebN2+Guro69PXTp6SXKVMG5cqVQ8mSJfPcL7PZs2ejadOmMDY2Fh8rKSkJO3bsgJ2dHerXr4+VK1diz549ePMm/UvEkiVL4Ofnh65du8Lc3BwLFy6EnZ0dli9fLj7u1KlT4eLiAiMjI7Rp0wbjxo3Dvn37AAC6urooVqwYNDQ0UK5cOZQrVw66utKn6/8ocfFxSE1NhYGB5CwDAwMDREdHS90nOjoaBgaG2dPHRGe8HpW+zTD/xyxs8XE5xW2YYx5joqOhnyW9voEhYnJIn5ycjBXLlsCjRSsUK1ZMPhmXQXx8PFJTU6XEYIDYnGKOiYZ+lhko+voG4qmpcbExSPz8Gbu2bYajixuWrd6A+o0aY/L4kbh984a0QxY6VSxrADAsoQsNdTW8i0+U2P4u/jPK6RWRus/e8w8xJ+AazizsgA9HfBC2uTf+vfMKi/fflEhXo6oBovYPxvvDPlgxtCG6/HYM91/E/bBYCiI+LqOe60v2UXoGBoiJyaW8s9RzPQNDifQ7t26Curo6OnfrmXV3paCq9VxV/4cxbhWLO8f2XcC4DZUnpryoYsyA6tZxZfF1JYEyP34Gyje3V8WULl0arVq1wrZt2yASidCqVSsYGn7rJJ48eQKhUAhXV1fxNk1NTdStWxdhYTlfiKog+9WuXTvb/lWqVEHFihXFz52dnZGWloYHDx6gSJEieP36tcSxAcDV1RXBwd9+7dy7dy9WrFiBJ0+eICEhASkpKShRouC/+CUnJ2dbFylS11aq61rQN0KhEBPHjYJIBEyaOlPR2flh0kQiAEC9Bo3QtUcfAICZuSXuhAThyMG9sHeoo8jsFYpfqazrWVfE+M4OGLn2PG48eAvjCiWxZFA9RHb9hAV7/hOne/gqDo4j9qJkES14uZlg4+gmaDbxkNIMsMjb/dB72Lt7J7YHHvxpPtjI269Uz4mIiOjH4cwVJdC/f39s27YN27dvR//+/Qv9/YsWLSr3Y165cgU9evRAy5Yt8eeff+L27duYMmUKvnz5UuBjzZ8/HyVLlpR4LF44X+Y86pXSg7q6eraLZMXExEgMcGVmaGiY7RfgmJgYGGaMmhsalk7fFp3/Yxa2Uno5xR2dYx4NDA2zXUQtNub/7N13WFNXHwfwbwIIoijTraAyVZYLnHVWsA5wK+5FcW/EgXvUuhEcuBXEhdbX3TrqxlEZKo4quIpiGNaBioT3D2wgEhBIWN7vp899nnpz7sn55ZybhJMzJDD4Kn1ycjKmTh6PmJh/4LdhU5H5hVdXVxdqamoKYoiDflYxGxhmWgQ0Pj5O9otKWp7qMKlRUy6NSfUaePkiRoWlzzsh1jUASP5NwucUKcrpyo+GK6erjRcJ7xVeM6uvA3advoetJ+/g9uM4HLr8CN7bL2Nyt3pyi6glf5biUcxr3Hz4Ct7bLiMiSoKRnWzzM5wc09X70s7j5d+jEuLiMv2y9x8Dw8ztPCEu/ZfA0Js3kBAfD5f2rdGkvjWa1LfGi5h/sHr5Eri0b5M/geSSUNu5UD/DGLfA4s7y/s5l3JKiE9O3CDFmQLhtnL4v7FwpApycnPDp0yckJyejXbt2co/VrFkTJUqUwMWLF2XnkpOTce3aNdSqVQsAUKJECQBASkpKrq7LzpMnT/DPP+mL9l25cgVisRgWFhYoU6YMKlWqJJc3AFy8eFGW96VLl2BsbIzp06ejfv36MDMzw+PH8msXlChRQq7MWfHy8sLr16/ljsmeXt+87ls0SpSAVa3aCLlyWXZOKpUiJOQybGztFV5jY2eHkCtX5M5duXwJNl+2lK5cpQoMDY0QEpKe59u3bxERHpZlngVNQ6MELK1q41qIfNzXQq7A2tZO4TU2tna4miE9AIRcuQSbDOn/+yPk6ePHWLthC3R19fKj+HmioVECFpa1cP1aet1JpVLcuBaCOtaK/zCubWOHG1fl6/payGXUtrGT5WlVuw6efLWd39PHj1GhQiWVlj+vhFjXQFoHyM2/Y9HStqrsnEgEtLStgqtZrI9SUlNdNhrpP1Jp6pdrsx6xIRaJ5LZrLkwaGiVgYVUL10Lk2/m1q1dg/aXdfq2OjR2ufdXOr165DGubtPvC+adO2LnnILYHBcsOI6NycOs/GKv8/PMtltwQajsX7GcY45adE0TcGmlxX/3q/r6aXdy2driqKO4s3g+KGiHGDAi3jRcVhb0TUH7sFuTr6wsTExNoaWnBwcEBV69ezTZ9YmIiRo4ciYoVK0JTUxPm5uY4evRorp6TnStFgJqaGiIjI3Hnzh2oqcl/SS9VqhQ8PDwwefJkHD9+HHfu3MGwYcPw/v17DBkyBABgbGwMkUiEw4cP49WrV3j79m2OrsuOlpYWBgwYgLCwMJw/fx5jxoxBjx49UKFC2mKQkydPxi+//ILdu3fj3r17mDp1KkJDQzF27FgAgJmZGZ48eYKgoCA8fPgQq1evxoEDB+Sew8TEBFFRUQgNDYVEIslySzRNTU2UKVNG7lDVlKB+AwYheN8eHDp4AI8ePsT8ubORlJQEF9cuAIDpXlOwasUyWXq3vv1x6eJ5bNu6GVGPHmKtrw9u37qFXn3S1iIQiURw69cf/uvX4uzpU3hw/x5meE2BUblyaNW6aPzKCwB9+w/Egf178b/fDiDq0UMsmp8WdyeXtLi9p3nCZ1V63L3d+uHSpQvYsW0zoqIeYb2fD+7cvo0evdwApP0R4jlxLCJv38L8xb8iRZoCieQVJJJXSE7O/Wil/NCz7wD878A+HP3fQURHPcTSRXPxISkJP3VyBQDM8/bCWp8VsvQ9evfFlUsXsWvHVjyOeoRN631x984tdOvRR5amT79BOHXyGA4F78Wzp4+xb3cALp4/C9fuvQo6vCwJsa4BYPXBUAxqVwturSxhUUUPq0e0gLaWOrb/kTYtcuOENnLbLB+9Go1h7a3RvbkZjMvroJVdVXj3dcDRq9GyTpa5AxqhSe1KqFZOB7WNDTB3QCM0t66MoLP3FZahMPTuOxCHDuzDkUMHEfXoIZYsnJPWzjuntfM5M6bCb/VyWfqevfvhyqULCNi+BdFRj+C/bg0i79xCty/1XVZXFzVNzeQONXV1GBgawtikeqHEqIhQ27lQP8MYt8Di7v8l7t/S4l4wLy3uzl/u7xleU7A6Q9x9vsS9PUPcd26nxw0Ar18n4u7dSDx6+BAA8DgqCnfvRsrW6ChsQowZEG4bJ9XbvXs3JkyYgFmzZuGvv/6Cra0t2rVrh9jYWIXpP336hLZt2yI6Ohr79u3DvXv34O/vL7dMRk5wzZUiIru1SBYvXgypVIp+/frhzZs3qF+/Pk6cOAE9vbRf0SpXrow5c+Zg6tSpGDRoEPr374+tW7d+87rsmJqaokuXLmjfvj3i4+PRoUMH+Pn5yR4fM2YMXr9+jYkTJyI2Nha1atXCoUOHYGaWtpNEp06dMH78eIwaNQofP37ETz/9hJkzZ2L27NmyPLp27Yrg4GC0bNkSiYmJ2LJli2wb6YLi5NweCfHx8FuzGhLJK1hYWsFv/UbZ0PAXMTEQi9L7IO3s62LRkqVYs3olfFYuRzVjE6z08YWZmbkszaAhw5CUlIS5s73x5s2/sK9bD37rNxapNWJ+dGqPhIR4rPPzQZzkFcwtrOCz1l82FeDFi38gEqd3Edva1cWCxUux1mclfFevQLVqJli2ag1Mv8T9KvYl/jx7GgDQu7uL3HOt37QN9Rs4FExg2WjzozMSE+Kxcd0axMdJYGZuiWU+66H/JeaXL2LkRihY29pj9oIl2LB2Ndb7rkSVasZYtMwHNUzTd0v5oVUbTJ42Czu2+GPF0kWoZmyCBUtWwta+XoHHlxUh1jUA7Dv/NwzLloR334Yor1cK4Y9eobP3/2SL3FY10pF1mgDA4qBrSE1Nxay+DqhkUBqS10k4cjUKs3ek/yJmVLYkNk1ogwr6pfD63Ufcio5DR+9DcrsSFba27dLauf9aH8TFSWBmYYkVvusz1HcMROL09zQbO3vMXbgE631XY92alahazRhLlvugpmnR2xUoO0Jt50L9DGPcwoq7nXPa/b02Y9zr0uOOiZF/X7Ozr4uFvyyFr89K+KxKi3vFal/Z/Q0AZ8+cxqwZ6aOgPSePBwC4e4yCx8jRBRRZ1oQYMyDcNk6qt3z5cgwbNky2C++6detw5MgRbN68GVOnTs2UfvPmzYiPj8elS5egoaEBIG0gQG6JUlO/GgdNVAx8+FzYJSgcn1OEebt+SP729LHvkVYRmW5S0Iy6+hZ2EQrFP3s8CrsIhUJDTXiDaNXVhLk4MAkL/8IQFoGueQ6t72SoQrNlFwq7CN/0x6gGmWY6aGpm3uTk06dP0NbWxr59++Di4iI7P2DAACQmJuK3337LlHf79u2hr68PbW1t/PbbbzAyMkKfPn3g6emZaWZJdoT3jYaIiIiIiIiIig1Fm5wsWpR5kxOJRIKUlBSUL19e7nz58uXx4oXitfcePXqEffv2ISUlBUePHsXMmTOxbNkyzJ8/P1dl/E762oiIiIiIiIjoe+Tl5YUJEybInVPV9C6pVIpy5cphw4YNUFNTQ7169fD8+XP8+uuvmDVrVo7zYecKERERERERERVZiqYAKWJoaAg1NTW8fPlS7vzLly9lm7N8rWLFitDQ0JCbAmRlZYUXL17g06dPst15v4XTgoiIiIiIiIgESiQSFfkjp0qUKIF69erh1KlTsnNSqRSnTp1Co0aNFF7TpEkT/P3335BKpbJz9+/fR8WKFXPcsQKwc4WIiIiIiIiIvhMTJkyAv78/tm3bhsjISHh4eODdu3ey3YP69+8PL6/03bM8PDwQHx+PsWPH4v79+zhy5AgWLlyIkSNH5up5OS2IiIiIiIiIiL4LPXv2xKtXr+Dt7Y0XL17Azs4Ox48fly1y++TJE4gzbGdetWpVnDhxAuPHj4eNjQ0qV66MsWPHwtPTM1fPy62YqVjiVszCwq2YhYVbMQsLt2Im+j7xLwxh4VbMxdsPKy4WdhG+6c/xTQq7CN8kvG80REREREREREQqxM4VIiIiIiIiIiIlfCcDmYiIiIiIiIgot3KzGw9ljSNXiIiIiIiIiIiUwM4VIiIiIiIiIiIlcFoQERERERERkUBxVpBqcOQKEREREREREZES2LlCRERERERERKQETgsiIiIiIiIiEijuFqQaHLlCRERERERERKQEdq4QERERERERESmB04KIiIiIiIiIBIqzglSDI1eIiIiIiIiIiJTAzhUiIiIiIiIiIiWwc4WIiIiIiIiISAlcc4WIiIiIiIhIoMRcdEUlOHKFiIiIiIiIiEgJ7FwhIiIiIiIiIlICpwURERERERERCRRnBakGO1eIihGxQMeaaWuqFXYRCkVKSmphF6FQ3N4ytLCLUCjMR+4r7CIUiqh1PQq7CFRAkj9LC7sIhUJDXZgf3ilSYX6GqasJ869Uaaow6xsQZn2TYsJ8tyciIiIiIiIiUhGOXCEiIiIiIiISKBHnBakER64QERERERERESmBnStERERERERERErgtCAiIiIiIiIigRJzVpBKcOQKEREREREREZES2LlCRERERERERKQETgsiIiIiIiIiEijuFqQaHLlCRERERERERKQEdq4QERERERERESmB04KIiIiIiIiIBIqzglSDI1eIiIiIiIiIiJTAzhUiIiIiIiIiIiWwc4WIiIiIiIiISAlcc4WIiIiIiIhIoETgoiuqwJErRERERERERERKYOcKEREREREREZESOC2IiIiIiIiISKDEnBWkEhy5QkRERERERESkBHauEBEREREREREpgdOCiIiIiIiIiARKJOK8IFXgyBUiIiIiIiIiIiWwc4WIiIiIiIiISAnsXMmlFi1aYNy4cSpLKxKJcPDgQaXLVZwNHDgQLi4uhV0MIiIiIiIiwRGJiv5RHLBzRYGBAwdCJBJlOv7++28EBwdj3rx5KnuumJgYODs7qyw/yr2gwAA4t22FBvbWcOvVHRHh4dmmP3niGDp3cEIDe2t0demI8+f+lHs8NTUVvj6r0PqHpmhY1wbDhwzE48fR+RhB3uzeFYD2P7aCQ10b9OvdA7ciso/79xPH4drRGQ51bdDdNXPcp34/CY9hg9GiiQPs61ji3t3I/Cx+nggxZgDYExSAjs6t0biBLQa49fxm3H+cPI6undujcQNb9OzaCRfOy8e9fu0adO3cHk0d6qJlUweMGD4It8LD8jOEPPnf/iAM6OaMTq0aYNwwN9y7E5Fl2seP/sb86RMwoJsznJva4sCenZnS7N6xCWOG9kGXto3Qq0MLzPUah2dPovMxgrwZ3MoUN37tgKcbuuH4jDawr66fZdqDni3xakvPTEfguGayND5DGmZ6fPeE5gURSq7sCQpAB6dWaFTfBv375OD+PnkcXTo5o1F9G/To0lGunScnJ2P1iqXo0aUjmjS0R7vWzeA9zROvYl/mdxi5JtTPMKG+rwm5vnl/C6O+hfpdjb4P7FzJgpOTE2JiYuSO6tWrQ19fHzo6Oip7ngoVKkBTU1Nl+VHuHD92FEuXLIL7iJEI2nsAFhaW8HAfgri4OIXpQ2/+hamTJ8K1Szfs3ncQLVu1xrjRI/HgwX1Zmi2b/LErYAdmzJqNnbv2oGTJkvAYPgQfP34sqLC+6cSxo1i2ZDHcPUYicG8wzC0sMMJ9KOKzidtrykS4uHbDrr0H0KJVG0wYMwp/Z4g7KSkJdnXrYcz4SQUVRq4IMWYAOHn8KFYs/QXD3EdiZ9B+mFtYYLTHsCzjDgu9ielTJ6Gza1cE7A5Gi5atMWncaLm4jY1NMMVrBoL2/4aNW3eiYqXKGOkxFAnx8QUV1jf9eeo4NqxZCrdB7vDZFITqphaYMcEDiQmK4/7w8QMqVKqCQT+PgZ6BocI0ETevo2OXnlixfgcWrliPz58/Y/r4n/Eh6X1+hpIrLg2rYm4vOyz97TZazz6J208TsWfiDzDUUfw5M3DNRdQe+5vsaDr9GD6nSHHo2lO5dKfCY+TSDV93uSDCybGTx49i+a+LMfznkQjYnXZ/j/o56/s7LPQvTPdMu78D96Td3xPHpt/fHz58wN3IOxjqPgIBu/dj6XIfREdHYfyYEQUZ1jcJ9TNMqO9rQq5v3t/CqG+hflej7wc7V7KgqamJChUqyB1qamqZpvr4+fnBzMwMWlpaKF++PLp16yaXj1QqxZQpU6Cvr48KFSpg9uzZco9nnBYUHR0NkUiE4OBgtGzZEtra2rC1tcXly/JfYv39/VG1alVoa2vD1dUVy5cvh66ubpaxNG7cGJ6ennLnXr16BQ0NDZw7dw4A8PHjR0yaNAmVK1dGqVKl4ODggLNnz8rSP378GB07doSenh5KlSqF2rVr4+jRowCAhIQEuLm5wcjICCVLloSZmRm2bNkiu/bp06fo0aMHdHV1oa+vj86dOyM6OjqbV7/g7Ni2BV269YCLa1fUNDXFjFlzoKWlhYPB+xWmD9i5HY2bNsPAwUNRo2ZNjBozDla1aiEoMO1X7tTUVATs2I5h7h5o2aoNzC0sMX/REryKjcXpU38UZGjZ2rl9K7p0647Orl1Rs6Yppnt/ifuA4rh37dyBxk2aYsDgIahRsyZGjh77Je4AWZoOnTrD3WMkHBs1KqgwckWIMQNAwI5tcOnSHZ1cuqBGTVN4zZgNLS0tHDoYrDB9UMB2NGrcFP0HDkH1GjXhMWosLK2ssCcoUJbGqX0HODg2RpUqVVHT1AzjJ03Fu7dv8eDBvYIK65sOBO2Ac8cu+PEnFxhXr4nRk2dAU0sLJw8fVJjewqoOho6cgBZtnKGhUUJhmvnL16Jt+84wrmGKGmYWmDBtLmJfxuDBvaLzK9jPP1pg57lH2HUhCvf/+ReTtl9H0qfP6NOsusL0ie8+IfbfD7KjRe0KSPqUkqlz5ePnFLl0r98nF0Q4ObZz+1a4du2OTi5dUaOmKabNnAOtklr47WAW93fADjRq0hT9B6W18xGjxsLSqhb2BKXd3zo6OvDbsBk/tnOGSfUasLa1g+e0mYi8cxsxMf8UZGjZEupnmFDf14Ra37y/hVPfQv2uVhSIRaIifxQH7FxRwvXr1zFmzBjMnTsX9+7dw/Hjx9G8ufxQ6W3btqFUqVIICQnBkiVLMHfuXPz+++/Z5jt9+nRMmjQJoaGhMDc3R+/evfH582cAwMWLF/Hzzz9j7NixCA0NRdu2bbFgwYJs83Nzc0NQUBBSU1Nl53bv3o1KlSqhWbO0od+jRo3C5cuXERQUhPDwcHTv3h1OTk548OABAGDkyJH4+PEjzp07h4iICPzyyy8oXbo0AGDmzJm4c+cOjh07hsjISKxduxaGhmm//CYnJ6Ndu3bQ0dHB+fPncfHiRZQuXRpOTk749OlTLl5t1Uv+9AmRd27DsVFj2TmxWAxHx8YID7up8Jrw0FA4Osq/OTdu0hThoaEAgOfPnkEieQUHx/Q8dXR0YG1jm2WeBS05OS3ujGUUi8VwcGyE8LBQhdeEh4XCIcPrBACNGjfJMn1RI8SYgbS470behkOGNisWi9HQsRHCw0MVXhMeHoaGX7XxRo2bIiKL9MnJn3Bg/x6U1tGBubmlqoqulOTkZDy4Hwm7+o6yc2KxGHb1HRF5O/vhxbnx/t1bAIBOmTIqy1MZGmpi2Jro4c/b6UPbU1OBc3deor6p4tE4X+vTvDoOhDzB+08pcuebWJbDnVWdcXmhM5b0qwe9Uoo7oArDf+284Vf3d0OHRojI7v52yN39/fbtG4hEIujoFI36FvJnmCDf1wRe37y/v//6Fup3Nfq+qBd2AYqqw4cPyzoPAMDZ2Rl79+6VS/PkyROUKlUKHTp0gI6ODoyNjWFvby+XxsbGBrNmzQIAmJmZYc2aNTh16hTatm2b5XNPmjQJP/30EwBgzpw5qF27Nv7++29YWlrCx8cHzs7OmDQpbWibubk5Ll26hMOHD2eZX48ePTBu3DhcuHBB1pkSGBiI3r17QyQS4cmTJ9iyZQuePHmCSpUqycpw/PhxbNmyBQsXLsSTJ0/QtWtXWFtbAwBq1Kgh9zrY29ujfv36AAATExPZY7t374ZUKsXGjRtl+6dv2bIFurq6OHv2LH788ccsy53fEhITkJKSAgMDA7nzBgYGiIp6pPAaiUQCg6+mDBgYGEASJ/ny+Ku0c4aZ85RIJKoqulISEtLi1s8UtyGio6IUXiORSDKnNzREXBGJ6VuEGDMAJCYkKoxb38Agy7jjJBLof9XG9Q0MMsV9/s8zmOY5CR8+JMHQ0Ai+6zZBV09PtQHk0b+vEyBNSYGevnzcevoGePZYcdy5JZVKsX71EtSytoNJDTOV5KksfZ0SUFcT49W/H+TOx77+ANMK3/6Dwb66PmpV0cW4zdfkzp+KiMHhG8/wRPIOJkalMb2rNYImNIfz/FOQZui0LyyJCVm9l2d9f8cpuL/1DbK+vz9+/IjVK5ainfNPct8NCpNQP8OE+r4m3Prm/Z3R91zfQv2uRt8Xdq5koWXLlli7dq3s36VKlcqUpm3btjA2NkaNGjXg5OQEJycnuLq6QltbW5bGxsZG7pqKFSsiNjY22+fOeE3FihUBALGxsbC0tMS9e/fg6uoql75hw4bZdq4YGRnhxx9/REBAAJo1a4aoqChcvnwZ69evBwBEREQgJSUF5ubmctd9/PhR9qY+ZswYeHh44OTJk2jTpg26du0qK6eHhwe6du2Kv/76Cz/++CNcXFzQuHFaL3JYWBj+/vvvTOvUfPjwAQ8fPsz2dchYjq/ngqaqaXKtGqJCVL+BAwL3BCMxMQEH9u+F1+Tx2Lpzd6YvOd8r3+ULEf3oIZb6bS3soqiMW/MauP00ETej5NeYOHg1fYpQ5LPXuPMsEdeXdEATSyOcj8z+8+x7kJycjKmTxiE1FfCaMbuwi0P5SOjva0LE+5soTTGZdVPkcVpQFkqVKgVTU1PZ8V8nR0Y6Ojr466+/sGvXLlSsWBHe3t6wtbVFYmKiLI2GhobcNSKRCFKpNNvnznjNf6M9vnXNt7i5uWHfvn1ITk5GYGAgrK2tZaNQ3r59CzU1Ndy4cQOhoaGyIzIyEqtWrQIADB06FI8ePUK/fv0QERGB+vXrw8fHB0DaqJ7Hjx9j/Pjx+Oeff9C6dWvZyJq3b9+iXr16cvmGhobi/v376NOnT47KvmjRIpQtW1bu+PWXRUq9HgCgp6sHNTW1TAuDxcXFyaY1fc3Q0BBxcZLM6b/8UmBoaJR2TpLzPAuanl5a3F8vDhYXJ4FBNnFnSi/JOn1RI8SYAUBXT1dh3PFxcVnGYWBoiPiv2rii9CW1tVG1mjGsbezgPWcB1NTVspz/XtDKlNWDWE0NCfHycSfEx2W5WG1u+C1fiKuXzuGX1f4wKlde6fxUJf7NJ3xOkcKojJbc+XJltRD71WiWr2mXUINrw6oIPK/4F9GMHr96B8mbD6heXnWLuytDVy+r93JJlu+7Bgru73gF7wfJycmYOnk8YmL+gd+GTUXmV21AuJ9hQn1fE2598/7O6Huub6F+V6PvCztXlKSuro42bdpgyZIlCA8PR3R0NE6fPp1vz2dhYYFr1+SHbH/9b0U6d+6MDx8+4Pjx4wgMDISbm5vsMXt7e6SkpCA2NlauQ8nU1BQVKlSQpatatSp+/vlnBAcHY+LEifD395c9ZmRkhAEDBmDnzp1YuXIlNmzYAACoW7cuHjx4gHLlymXKu2zZsjmK2cvLC69fv5Y7Jnt65eja7GiUKAGrWrURciV9wWCpVIqQkMuwsbVXeI2NnR1CrlyRO3fl8iXY2NkBACpXqQJDQyOEhKTn+fbtW0SEh2WZZ0HT0PgSd4h83FdDrsDG1k7hNTa2drh6RX5h5SuXL2WZvqgRYsxAWtyWVrVxNSS9zUqlUlwLuQIbGzuF19jY2OJaiHwbD7lyCdZZpE/PN7XQ11H6j4aGBszMrRB6I0R2TiqVIvRGCKxq22RzZfZSU1Pht3whLp07jcWr/FGhUhVVFFdlklOkCItOQPNa6R0+IhHQzKo8rv+d/RDpTg2qooSGGvZeevzN56moVxL6pTTxMjFJ6TKrwn/t/NpX9/e1kCuwzu7+DpG/v0OuyN/f//3h9fTxY6zdsAW6ukVjesh/hPwZJsj3NYHXN+/v77++hfpdjb4v7FxRwuHDh7F69WqEhobi8ePH2L59O6RSKSwsLPLtOUePHo2jR49i+fLlePDgAdavX49jx47JRrhkpVSpUnBxccHMmTMRGRmJ3r17yx4zNzeHm5sb+vfvj+DgYERFReHq1atYtGgRjhw5AgAYN24cTpw4gaioKPz11184c+YMrKysAADe3t747bff8Pfff+P27ds4fPiw7DE3NzcYGhqic+fOOH/+PKKionD27FmMGTMGz549y1HMmpqaKFOmjNyhqilB/QYMQvC+PTh08AAePXyI+XNnIykpCS6uXQAA072mYNWKZbL0bn3749LF89i2dTOiHj3EWl8f3L51C7369AWQNtLIrV9/+K9fi7OnT+HB/XuY4TUFRuXKoVXrNiopsyr07T8QB/btxaHf0uJeOC8t7s4uaXHP8PLE6gxx9+7bD5cuXsD2rZsR9egR1vn64M7t2+jVJ72T7vXrRNy7Gymb7hUdFYV7dyNlc3wLmxBjBgC3fgNwMHgvDh86iKhHD7Fo/hwkJSWho0va9ELv6Z5Ys2q5LH0vt/64dOkCdm7bguioR1i/dg3u3L6NHr3SRpolvX8P39UrEBEeiph/niPyzm3M8Z6OV7Ev0aZtu0KJURHXXv1w/H/B+P3YITyJfoQ1S+fjY1IS2v7kAgBYOm86tqxbJUufnJyMhw/u4uGDu/icnIy4V7F4+OAu/nn2RJbGd9lCnD55FFNmLUZJ7VKIj5MgPk6Cjx+zHxVSkNadvIe+P9RAzyYmMKuog1/714e2pjp2XUibr75mqANmdLPOdJ1b8xo49tdzJLyT/0OylKY6ZvWwRb0aBqhqoI1mVuWwY0xTRMW+xZlbLwokppzo238gDuzfi//9duBLO0+7vzt9ub+9p3nCZ1WG+9utHy5duoAd2zYjKuoR1vv5fGnnafd3cnIyPCeOReTtW5i/+FekSFMgkbyCRPIKyclF449tQLifYUJ9XxNqffP+Fk59C/W7Gn0/uOaKEnR1dREcHIzZs2fjw4cPMDMzw65du1C7du18e84mTZpg3bp1mDNnDmbMmIF27dph/PjxWLNmzTevdXNzQ/v27dG8eXNUq1ZN7rEtW7Zg/vz5mDhxIp4/fw5DQ0M4OjqiQ4cOAICUlBSMHDkSz549Q5kyZeDk5IQVK1YAAEqUKAEvLy9ER0ejZMmSaNasGYKCggAA2traOHfuHDw9PdGlSxe8efMGlStXRuvWrVGmCOyw4eTcHgnx8fBbsxoSyStYWFrBb/1G2XDCFzExEIvS+yDt7Oti0ZKlWLN6JXxWLkc1YxOs9PGFmVn6ejWDhgxDUlIS5s72xps3/8K+bj34rd9YpNaIaefcHgkJ8Vi7xgdxX+L2XeefIe5/IBand9jZ2dfFwl+WwtdnJdasWoFqxiZYvnoNTDPE/eeZ05g1Y5rs31MnTwAAuHuMxM8jRxdQZFkTYswA8KNTeyQkJGCd32rESSQwt7CCj98G2aJ3L17EQCxOb+O2dvZYsOhX+K1ZBV+fFahazRhLV/rI4harqSE66hEOHzqIxMQElNXVRa3a1vDfshM1TYvGwq4A8ENrJ7xOTMDOjX6Ij5egpqkF5i3zky1yG/vyBUQZ4o6XxGLUoJ6yf+/ftQ37d22DtV19LFmzCQBw5OAeAIDn6CFyzzVh2ly0bd85v0PKkYNXn8JARxOeLnVQrqwWbj1JRM/lf+LVv2nrVlUx0JbbOQ4AalbQgaO5Ebr9ejZTfinSVNSuWhY9m5igrLYGXiR+wNlbL7D4QAQ+fVZuuqoqpbXzeKzzS7u/zS2s4LPWP0M7/weiDPe3rV1dLFi8FGt9VsJ39QpUq2aCZavS7+9XsS/x59m0Uai9u7vIPdf6TdtQv4FDwQT2DUL9DBPq+5qw65v3txDqW6jf1YqCb/1QTzkjSv36WxYVO8OGDcPdu3dx/vz5wi5KgfnwubBLUDiKws4cVHBSUoRZ36/eFJ1fDguSw5TfCrsIhSJqXY/CLkKBU1cT5pfY5CLUIVeQNNSFOVD8s0A/w4R6fwv1O6q2xvdR3922/FXYRfimfYPqFnYRvokjV4qhpUuXom3btihVqhSOHTuGbdu2wc/Pr7CLRURERERERCRI7Fwphq5evYolS5bgzZs3qFGjBlavXo2hQ4cWdrGIiIiIiIiomOGsINVg50oxtGfPnsIuAhERERERERF9IcxJoEREREREREREKsKRK0REREREREQCJea8IJXgyBUiIiIiIiIiIiXkqXMlNTUV69evR8OGDWFoaAg1NbVMh7o6B8UQERERERER0fcvTz0gU6ZMwfLly2FnZ4e+fftCT09P1eUiIiIiIiIionzGSUGqkafOlW3btqFr167ctYaIiIiIiIiIBC9P04KSkpLQpk0bVZeFiIiIiIiIiKjYyVPnSuvWrXHt2jVVl4WIiIiIiIiICpBIJCryR3GQp84VPz8/XLlyBQsXLkRcXJyqy0REREREREREVGzkaM0VHR2dTL1Fnz9/xsyZMzFz5kxoaWlBTU1N7nGRSITXr1+rrqREREREREREREVQjjpXunbtWmyG4hARERERERFRzoj5p75K5KhzZevWrflcDCIiIiIiIiKi4ilPa67MnTsXt27dyvLx27dvY+7cuXkuFBERERERERFRcZGnzpXZs2cjPDw8y8dv3bqFOXPm5LlQRERERERERETFRY6mBeVWfHw8SpQokR9ZExEREREREZGKcH1V1chx58q5c+dw9uxZ2b+Dg4Px999/Z0qXmJiI3bt3w9raWiUFJCIiIiIiIiIqynLcuXLmzBnZVB+RSITg4GAEBwcrTFurVi34+PiopoREREREREREREVYjjtXpkyZglGjRiE1NRXlypXDunXr0LVrV7k0IpEI2tra0NLSUnlBiYiIiIiIiEi1OCtINXLcuVKyZEmULFkSABAVFQUjIyNoa2vnW8GIiIiIiIiIiIqDPC1oa2xsrOpyEBEREREREREVS3nqXKlevfo3VxQWiUR4+PBhngpFRERERERERPmPuwWpRp46V3744YdMFZCSkoLHjx/j4sWLqFOnDuzt7VVSQCIiIiIiIiKioixPnStbt27N8rGwsDC0a9cObm5ueS0TEREREREREVGxIVZ1hra2tnB3d4enp6eqsyYiIiIiIiIiFRKLiv5RHORp5Mq3lC9fHnfu3MmPrIkE7d2HlMIuQqEorZUvb1VF3vOEpMIuQqGoalCysItQKJ5s6FnYRSgU9jNPFHYRCtxl7zaFXYRCoaWhVthFoAKkVlz+GlKxT5+lhV2EQlFCXeW/2RMVOyq/C+Li4rBp0yZUqVJF1VkTERERERERERU5efo5uFWrVgrPJyYm4u7du/j06RN27NihVMGIiIiIiIiIKH9xtyDVyFPnilQqzVQBIpEI1atXR5s2bTB48GBYWlqqpIBEREREREREREVZnjpXzp49q+JiEBEREREREREVT7lec+X9+/eoV68e1q1blx/lISIiIiIiIqICIioGR3GQ684VbW1tREVFcV4WERERERERERHyuFuQk5MTTpwQ3taJRERERERERERfy1PnysyZM3H//n3069cPFy5cwPPnzxEfH5/pICIiIiIiIiL63uV4Qdu5c+eiS5cuqFOnDmrXrg0AuHPnDgIDA7O8JiUlRfkSEhEREREREVG+EHPJD5XIcefK7NmzYWpqijp16sDb25trrhARERERERERIY9bMc+ePVvFxSAiIiIiIiIiKp7y1LlCRERERERERMUfJ6WoRq46V+7evYtz587lOH3z5s1zXSAiIiIiIiIiouIkV50rCxYswIIFC76ZLjU1FSKRiAvaEhEREREREdF3L1edK2PGjEHTpk3zqyxEREREREREVIC4WY1q5KpzpUGDBujatWt+lYWIiIiIiIiIqNgRF3YBiIiIiIiIiIiKM+4WRERERERERCRQnBWkGjkeufLDDz+gfPny+VkWIiIiIiIiIqJiJ8cjV86cOZOf5SAiIiIiIiIiKpY4LYiIiIiIiIhIoMScF6QSXNCWiIiIiIiIiEgJ7FwhIiIiIiIiIlICO1eowJmYmGDlypWFXQwiIiIiIiLBE4mK/lEcsHNFhQYOHAiRSASRSAQNDQ2UL18ebdu2xebNmyGVSgu7eFmaPXs27OzsCrsYhSYoMADObVuhgb013Hp1R0R4eLbpT544hs4dnNDA3hpdXTri/Lk/5R5PTU2Fr88qtP6hKRrWtcHwIQPx+HF0PkaQN/v3BKJbx7Zo1dgewwb0wp1b2cd9+o8T6NO1A1o1tkf/ni64fOFcpjTRUQ/hOX4k2v3ggDZN62No/x548eKf/Aoh14J2BcD5x1ZoWNcafXt3R0TEt+vapaMTGta1RjfXzHV96veT+HnYYPzQxAF2dSxw925kfhY/z44c2I1hPdujW1sHTPLoh/uRt7JM+yTqIRZ7T8Swnu3RuYU9Du0NyJRm15Z16NzCXu4Y0c81P0PIk927AvBTu1ZwrGeD/n164NY36vv3E8fRpaMzHOvZoIdrR1zIUN/JyclYtXwperh2ROOG9vixVTPMnOaJV7Ev8zuMXBNqO+/jWBWnPJsjbF4b7B7hAOsqZbNNr6OljpmdrXBuWguEz2+L4xObormFoezxU57NcXdxu0zHzM5W+R1KruzbHQiX9m3Q3MEOg/v1xO1vvJef+v04err+hOYOdnDr3hmXzv+ZZdpf5s+Go30tBAVsV3WxlSbUdi7U7yxCre89QQHo5NwaTRrYYqBbT9z+Rtx/nDyObp3bo0kDW/Tq2gkXv7q/N6xdg26d26OZQ120auqAEcMH4VZ4WH6GkGtCbeP0fchR58rcuXNzfcybNy+/y14kOTk5ISYmBtHR0Th27BhatmyJsWPHokOHDvj8+XNhF08pycnJhV0ElTt+7CiWLlkE9xEjEbT3ACwsLOHhPgRxcXEK04fe/AtTJ0+Ea5du2L3vIFq2ao1xo0fiwYP7sjRbNvljV8AOzJg1Gzt37UHJkiXhMXwIPn78WFBhfdOpk8ewZsUSDBo2Apt27oWpuQUmjHZHQrziuCPCbmLO9Mno0LkLNgfsQ7MWreA1aTQe/f1Alub5sycYMbQfjE2qw2f9VmwLCsbAIT9Ds4RmQYWVrRPHjmLZkkVw9xiJXXsPwNzCEiPchyA+m7r2mjIRLq7dELQ3ra7HjxmJvzPUdVLSe9jXrYux4ycVVBi5dv70CWz2W4aeA92x3D8Q1WuaY/bkEUhMiFeY/uPHDyhfsQr6DR8DPX1DhWkAoJpJTWzd/7vsWOyzOb9CyJMTx49i+a+LMfznkQjcEwwzcwuMdB+aZX2Hhf6FaZ4T0blLNwTuPYAWrdpgwthRsvr+8OED7kbewVD3EQjcvR9LV/jgcXQUxo0eUZBhfZNQ27mzTQVM7WAJ3z/+Rhefy7gX8wYbh9SDfqkSCtNrqImweUh9VNYribEBoXBedh4zg2/j5b/p79Pd1lxG0/lnZMegjdcAACciXhRITDnx+4ljWLXsFwx1H4FtgftgZm6JcSOGIz6L9/Lw0Jvw9pqMji5dsG3XfjRv0RpTJozGwwzv5f85e/oP3IoIg5FRufwOI9eE2s6F+p1FqPV98vhRrFz6C4a6j8SOoP0ws7DAaI9h2XyO3cSMqZPQ2bUrdu4Oxg8tW2PSuNFycVczNsFkrxnYtf83+G/diUqVKmOUx1AkxCv+TlDQhNrG6fshSk1NTf1WIrE4cx+M6MvYnK8vF4lESE1NhUgkQkpKioqKWTwMHDgQiYmJOHjwoNz506dPo3Xr1vD398fQoUMRHR2N6tWr4+bNm7IRI4mJidDT08OZM2fQokULnD17Fi1btsTx48cxdepU3L17F40aNUJQUBBu3LiBCRMm4Pnz5+jQoQM2btwIbW1tAECLFi1Qp04dAMCOHTugoaEBDw8PzJ07V1ZnGW3duhWDBg2SO7dlyxbZKBw/Pz8cO3YMp06dwuTJk2FiYoJx48YhMTFRlv7gwYNwdXWVawv/+9//MHfuXERERKB06dJo1qwZDhw4AACyPMaNGwcA2LhxIyZNmoT9+/ejdevWOXqtP6ion8qtV3fUrmONaTO8AQBSqRQ/tv4Bvfv0w5BhwzOlnzxxHJKSkrDGb73sXN/ePWBhaYmZs+YiNTUVbVo0Q/+BgzBg0BAAwJs3b9CqeWPMXbAYzu1/Uqq8b5JUE/iwAb1gVasOJnjOAJAWd5efWqNrzz7oN3BYpvTeXhPxISkJS1b6yc4NH9gbZuaWmDxtFgBgltckqKurY+a8xSopY0altZTf2Kxv77S69pqeXtft2qTV9eChmet6ype69slQ1/369ICFhSVmzJorl/b582f4qV1rBO07CEtL1f2q/VjyXuk8Jnn0g5lFbbiPmwogLe4hPZzwk2svdHMbnO21w3q2R8dubujU3U3u/K4t6xBy4QxWbtqtdPkUqWpQUuk8+vfpgVq162Bqhvp2btsCvXr3xSAF9e05aTySkt5jtW96ffd36wkLC0tM956j8Dlu34pAv97dceTkaVSsWEnpMqtipf7i2M7tZ55QOo/dIxxw69m/mHco7ddnkQg4O/UH7Lz0BP5/RmVK39OhCoY0r472yy7gs/SbX4MAAF4dLNHC0gjtlp5XuryXvdsonQcADO7XE7VqW2PS1PT38s5OrdC9lxv6D878Xj7dcwI+JCVh2eq1snND+veCubklPGfMlp2LjX2JIf16YZXfBkwY7YFebv3Ry62/0uXV0lBTOg+g+LVzVQ1nL27fWb79F0bOFLf6Tk5RzWj1gW49Uat2HUyZNhNAWtwdfmyJHr37YuCQzPe31+Tx+JCUhBVr1snODerbE+YWVvCaOVvhc7x9+xYtmzSA74bNaOjQSKnyllBXfkJEcWvjAKCCr6hFwojgO4VdhG/y61KrsIvwTTm6C6RSqdzx9OlTWFtbo3fv3rh69Spev36N169fIyQkBL169YKtrS2ePn2a32UvNlq1agVbW1sEBwfn+trZs2djzZo1uHTpEp4+fYoePXpg5cqVCAwMxJEjR3Dy5En4+PjIXbNt2zaoq6vj6tWrWLVqFZYvX46NGzcqzL9nz56YOHEiateujZiYGMTExKBnz55yz+/q6oqIiAgMHpz9H2L/OXLkCFxdXdG+fXvcvHkTp06dQsOGDRWmXbJkCaZOnYqTJ0/muGNFVZI/fULkndtwbNRYdk4sFsPRsTHCw24qvCY8NBSOjvIfPo2bNEV4aCgA4PmzZ5BIXsHBMT1PHR0dWNvYZplnQUtO/oT7d++gfoYPUbFYjPoNHXE7i6Ght8JDUb+ho9w5h0ZNcCsiFEDae8Sli3+iqrExJowahg5tm2HYgF44d/ZUvsWRG8nJaXWdsV7EYjEcsqvrsFA4NJKv60aNmyI8LDQ/i6pSycnJeHgvErb1HGTnxGIxbOs54N6d7IfZfss/z59gYNe2GN67A5bNn4ZXL2OULa7KZF3fjbKsv4iwULn0ANCocZNs6/vtmzcQiUTQ0SmjimIrTajtXENNhNqVy+DS3+m/bKamApf/joOdsa7Ca1pZlUPok0R4d7bChektcGhcY7i3qA5xFn8Ea6iJ0Mm+IoKvP8uHCPImOfkT7kXeQQOH9PdmsViMBg6NEBEeqvCaW+GhaPDVH1COjZogIsN7v1QqxZwZU9F3wGDUqGmWL2VXhlDbuZC/swiyvpM/4W7kbTR0lP+u1tAx6/s7IjwMDb6qb8fGTbNMn5z8CQf270FpHR2Ym1uqquh5JtQ2XlT8t7RFUT6Kgzz1tY0cORJmZmbYuXOn3PkGDRogICAA3bp1w8iRI2UjFQiwtLRE+DfmDCoyf/58NGnSBAAwZMgQeHl54eHDh6hRowYAoFu3bjhz5gw8PT1l11StWhUrVqyASCSChYUFIiIisGLFCgwblrmXu2TJkihdujTU1dVRoUKFTI/36dMn08iWb1mwYAF69eqFOXPSf+21tbXNlM7T0xM7duzAn3/+idq1a+fqOVQhITEBKSkpMDAwkDtvYGCAqKhHCq+RSCQwMDDMlF4SJ/ny+Ku0c4aZ85RIJKoqulJeJyYiJSUF+vryZdTXN8Dj6My/8AJAfJwEel+l19M3kA1NTYiPQ9L799i5dROGeYyGx+gJuHL5AqZPHovV67bAvl6D/AkmhxISsq7r6NzUtWHRqcec+Pd1AqTSFOjq68ud19UzwLMn0XnO17xWHYydOheVqxojPk6CoG3r4TVmMFZv2Qdt7VJKllp5iV/qW/+r+tY3MER0lOI2nlbfX7cPQ8RlUd8fP37EqhVL4eT8E0qXLq2agitJqO1cT7sE1NXEiHsrP8Rb8vYTqhspbo9V9UvCUU8f/wuNgfvWv1DNQBuzOltBXU0M31MPM6VvXascdLTUceBG0VlDKjHhv/dy+frTMzBAdLTi+o6TSDK99+sZGCIuLr2+d2zZCDU1NfTo3Vf1hVYBobZzoX5nEWp9y+7vTJ9jBll+jsUpiFvfwCDT59j5P89guuckfPiQBENDI6xZtwm6enqqDSAPhNrG6fuSp86V06dP45dffsny8datW8v9sU+QTZXKLRsbG9n/ly9fHtra2rKOlf/OXb16Ve4aR0dHuedq1KgRli1bhpSUFKip5W5Ibv369XNd5tDQUIUdORktW7YM7969w/Xr1+XiUeTjx4+Z5kWmqmlCU7NorOVB6dMDm/7QEj3dBgAAzCyscCssFAf37y70zhVSrXoOTWX/b1LTHOZW1hjWqz0unjmJtj8VvYVtVS05ORmek8YBQJZDraloE4tEiHv3Cd7BtyFNBW4//xfly2hicPPqCjtXujWogvP3JYh9833P0b975zZ279qBbYH7i82vhESUO/UbOCBgTzASExNwcP9eTJs8Hlt27s7UkUNEuZenyXFaWlq4fPlylo9funQJWlpaeS7U9ygyMhLVq1cHkL6GTcY1SrJaLFZDQ0P2///tQpSRSCTK152ISpWS/9VPLBZnWmfn67KXLPnt9RKaNWuGlJQU7Nmz55tpFy1ahLJly8odv/6yKAelz56erh7U1NQyLZIVFxcHQ0PFC3kaGsr/widL/6XX3NDQKO2cJOd5FrSyurpQU1PLtOBhfHxcpt7//+gbGGZa7DYhPk72QZyWpzpMqteUS2NcvQZiXxT+dBE9PRXVtaTo1GNOlCmrB7FYDYlfLVSXmBCXaSSSMkrr6KBSlWqIeV40poPqfqnvrxf9i4/L/AvXf9Lq++v2IYHBV/WdnJyMqZPGI+aff+C3YVORGbUCCLedJ7z/hM8pUhiUlu9wNyxdApK3nxRe8+rNR0S/eo+My608jH2HcmU0oaEm36lQSVcLjUwNsPda0ZkSBAC6ev+9l8vXX0Jc1u/lBoaGmd77EzLcF6E3byAhPh4u7VujSX1rNKlvjRcx/2D18iVwaa+adWKUJdR2LtTvLEKtb9n9nelzLC7T59J/DBTErSh9SW1tVK1mDGsbO8ycswBq6mr47eB+1QaQB0Jt40WFuBgcxUGeyunm5oaAgACMGTMGDx48kK3F8uDBA4wePRqBgYFwc3P7dkYCcfr0aURERKBr164AACOjtBs9Jib9j87QL3MDVSEkJETu31euXIGZmVmWo1ZKlCiR48WHjYyM8ObNG7x790527uuy29jY4NSp7NfaaNiwIY4dO4aFCxdi6dKl2ab18vKSrevz3zHZ0ytH5c2ORokSsKpVGyFX0jsKpVIpQkIuw8bWXuE1NnZ2CLlyRe7clcuXYPNlYeLKVarA0NAIISHpeb59+xYR4WFZ5lnQNDRKwNyyFm5cTY9DKpXixrUQ1LbJPH0LAOrY2OH6Nfm4r4VcRh1rO1meVrXr4OlXW9s9ffIY5VWw0KeyNDTS6vpqiHxdX82urm3tcFVRXdva5WdRVUpDQwM1LawQ/lf6e4JUKkX4jauwqGWTzZW5k/T+PV788wx6WfxBV9CyrO8rV7KsP2tbO7n0ABDyVX3/17Hy5MljrPPfAl3dwh9GnZFQ23lySipuP/8XjUzTp7+JRICjqQFCHycqvOavx4kwNtSWW2jUxEgbsf9+QHKK/A8IXepXRtzbT/jzbtEaQq6hUQIWVrVwLUT+vfza1SuwtrFTeE0dGztcuypf31evXIb1l/d+5586Yeeeg9geFCw7jIzKwa3/YKzy88+3WHJDqO1cyN9ZBFnfGiVgaVU78/0dkvX9bW1jK5ceAEKuXMoyfXq+qUj+pLgjuiAJtY3T9yVP04J++eUXSCQSrFmzBr6+vrKRGFKpFKmpqejdu3e204a+Zx8/fsSLFy+QkpKCly9f4vjx41i0aBE6dOiA/v3TVtovWbIkHB0dsXjxYlSvXh2xsbGYMWOGysrw5MkTTJgwAe7u7vjrr7/g4+ODZcuWZZnexMQEUVFRCA0NRZUqVaCjo5PllBsHBwdoa2tj2rRpGDNmDEJCQrB161a5NLNmzULr1q1Rs2ZN9OrVC58/f8bRo0czTRVr3Lgxjh49CmdnZ6irq8t2D/qapmbmKUCq2i2o34BBmDnNE7Vr10Edaxvs3LENSUlJcHHtAgCY7jUF5cqVx9jxEwEAbn37Y8jAfti2dTOaN/8Bx48dxe1btzBzdtrq8yKRCG79+sN//VoYVzNG5SpV4OuzCkblyqFV66Lxqx8A9HIbgAWzp8GyVm1Y1bbGnsAdSEpKwk8d06Z0zPP2glG5cvh51HgAQPdefTFq+EDs2rkVjZs2xx8njuHunVuYMm22LM/e/QZhltdE2Nath7r1GyLk0gVcOn8Wq9dvKYQIM+vXfxBmTvdErdp1UKeODQJ2ptV1Z5e0up7xpa7HfKnrPn37Y+igfti+dTOafanrO7dvwXt2+k4Dr18nIiYmBq9iYwEAj7/MgzY0NJT9WlLYOnfvi1WLvGFqUQtmVnXwv32B+PAhCW2cOwMAViycAQPDcug/fAyAtA6Ep1/Wa0j+nIw4SSwePbiHkiVLomKVagCALX7L0aBxcxiVr4T4uFjs2rIOYrEYzVs7FU6QCrj1H4hZ06eiVu06qG1tg8Av93anL/U9c5onypUrh9Hj/qvvfhg2qD92bNuMps1a4MTxI7hz+7ZsZ4nk5GRMmTAWdyPvYJXvOqRIU2RzucuWLQsNDcVb/hY0obbzrRceY3H3Orj17F+EP32NAU2NUbKEGoJvPAcALO5RB7GvP2L5ibQth3ddeQq3RtUwvaMldl56AmMDbbi3qIEdl57I5SsSAa71KuPgX8+RksNdhQpS774DMc/bC1a16qBWHWvsDtyOD0lJ+Klz2nv5nBlTYVSuHEaMmQAA6Nm7HzyGDUDA9i1o0uwH/H7iKCLv3MLUmWlrpJXV1UVZXV2551BTV4eBoSGMTaoXaGzZEWo7F+p3FqHWd59+AzBnphesatdB7TrW2LVzO5KSktDRJe3+njXdE0blymPU2LT7u5dbf7gP6Y+d27agafMfcPL4UUTevo1pX+7vpPfvsXnjejRv0RKGhkZITEzE3qBAvIp9idZt2xVanBkJtY3T9yNPnSslSpTAjh07MHnyZBw9ehSPHz8GABgbG8PZ2Vnh4qVCcfz4cVSsWBHq6urQ09ODra0tVq9ejQEDBshtab1582YMGTIE9erVg4WFBZYsWYIff/xRJWXo378/kpKS0LBhQ6ipqWHs2LEYPjzz9mX/6dq1K4KDg9GyZUskJibKtmJWRF9fHzt37sTkyZPh7++P1q1bY/bs2XL5t2jRAnv37sW8efOwePFilClTBs2bN1eYX9OmTXHkyBG0b98eampqGD16tFKx55aTc3skxMfDb81qSCSvYGFpBb/1G2VDKF/ExEAsSq83O/u6WLRkKdasXgmflctRzdgEK318YWZmLkszaMgwJCUlYe5sb7x58y/s69aD3/qNRWqNmNY/OiMxIR4b161BfJwEpuaWWOazHvpfRh68fBEDcYZtM6xt7TFrwRL4+63GBt+VqFLVGIuW+qCGafpOEj+0bINJXrOwc6s/Vi5dhGrGJpj/y0rY2tUr8PgUaefcHgkJ8Vibsa7Xpdd1TEwMRGL5ul74y1L4+qyEz6q0ul6x2hemGer67JnTmDUjfRSV5+S0zih3j1HwGFmwbTkrzVq1w7+JCQjcshYJ8XGobmqBWUt8oftlWpDk5Qu5Nh4veYXxw3rJ/n1w93Yc3L0ddWzrYcGqtF3HJK9eYuk8L7z59zXKltWDlbUdlvhtR1ld+YVzC1M7p7R7e62vD+K+1Peadf4Z7u1/5LY+trWriwWLl8JvzUqsWbUC1YxNsHzVGll9v4p9iT/PngYA9OrmIvdcGzZvQ/0GDigKhNrOj4W/gH6pEhjd1hRGOpqI/OdfDNt8A3FfpgVV0i0pty3si9cfMHTzdUztYInfxjbGy38/YsfFx5m2bW5saoDKeiURfP15QYaTY23bpb2X+6/1QVycBGYWlljhu142zefFC/n6trGzx9yFS7DedzXWrVmJqtWMsWS5D2qaFr1dgbIj1HYu1O8sQq3vH53aIzEhAev9ViNOIoG5hRVW+23I8v62tbPH/EW/Yu2aVfDzWYGq1YyxdKWPLG6xmhqiox7hyKGDSExMQFldXdSqbY0NW3YWmfcAobbxooDrbKmGKPXrBTSoWGvRogXs7OywcuXKwi5KvlLVyJXi5k2SMAMvrZWnfuBi77HkfWEXoVBUNfj2uk3fI7FAv9jYzzxR2EUocJe9hfmLqZZG7hbV/14I9NaGUP/CSE7Jv7UQi7IS6sVlVQzV+l6+oo45eLewi/BNq10Kf8vwb1G6Obx9+xYJCQmZFjkFgGrVqimbPRERERERERFRkZanzpUPHz5gzpw52LRpU6YVnTPK6SKpRERERERERFTwxAIdYadqeepcGTFiBLZt2wYXFxc0a9YMenpFa8cEITt79mxhF4GIiIiIiIhIUPLUuRIcHIyhQ4di/fr1qi4PEREREREREVGxkqfOFZFIhLp166q6LERERERERERUgDgtSDXytKxz586d8ccff6i6LERERERERERExU6eOldmzpyJR48eYfjw4bhx4wZevXqF+Pj4TAcRERERERER0fcuT9OCzMzMAAA3b97Epk2bskzH3YKIiIiIiIiIii6RiPOCVCFPnSve3t6sACIiIiIiIiIi5LFzZfbs2SouBhERERERERFR8ZSnNVeIiIiIiIiIiChNnkauzJ0795tpRCIRZs6cmZfsiYiIiIiIiKgAcCtm1VD5tCCRSITU1FR2rhARERERERGRIORpWpBUKs10fP78GQ8fPsT48eNRv359xMbGqrqsRERERERERERFjsrWXBGLxahevTqWLl0KMzMzjB49WlVZExEREREREVE+EImK/lEc5MuCts2bN8fRo0fzI2siIiIiIiIioiIlXzpXrl+/DrGYGxERERERERER0fcvTwvabt++XeH5xMREnDt3DsHBwRg6dKhSBSMiIiIiIiKi/CUuLvNuirg8da4MHDgwy8cMDQ0xdepUeHt757VMRERERERERETFRp46V6KiojKdE4lE0NPTg46OjtKFIiIiIiIiIiIqLvLUuWJsbKzqchARERERERFRAeNqqaqRp86V/0RFReHYsWN4/PgxgLROF2dnZ1SvXl0lhSMiIiIiIiIiKury3LkyceJErFq1ClKpVO68WCzGuHHjsHTpUqULR0RERERERERU1OVpBNCyZcuwYsUKdOnSBZcvX0ZiYiISExNx+fJldOvWDStWrMCKFStUXVYiIiIiIiIiUiGRqOgfxUGeRq74+/ujU6dO2LNnj9x5BwcHBAUF4cOHD1i/fj3Gjx+vkkISERERERERERVVeRq5Eh0djXbt2mX5eLt27RAdHZ3XMhERERERERERFRt5GrlSrlw5hIWFZfl4WFgYjIyM8lwoIlKslJZaYRehUKQitbCLUCgq62kVdhEKhZq4mIz9JJU469WqsItQ4Cq1m1PYRSgUCWfmFnYRqAAVl2H8qlZCnfuuUPEjFuoNq2J5uvu7d++OjRs3YvHixXj37p3s/Lt37/DLL79g48aN6Nmzp8oKSURERERERERUVOVp5Mq8efMQGhqKadOmwdvbG5UqVQIA/PPPP/j8+TNatmyJuXP56wQRERERERERff/y1Lmira2NU6dO4bfffsOxY8fw+PFjAICTkxPat2+Pjh07QsShRUREREREREQkALnuXHn//j369u2Lrl27ws3NDZ07d86PchERERERERFRPuO4CNXI9Zor2tra+OOPP/D+/fv8KA8RERERERERUbGSpwVtmzZtisuXL6u6LERERERERERExU6eOlfWrFmD8+fPY8aMGXj27Jmqy0REREREREREBUAsKvpHcZCnzhVbW1s8e/YMixYtgrGxMTQ1NVGmTBm5o2zZsqouKxERERERERFRkZOn3YK6du3K3YCIiIiIiIiIiJDHzpWtW7equBhEREREREREVNDEHDihEnmaFkRERERERERERGlyNXLl6dOnEIvFqFy5MgDgw4cP8PPzy5SuSpUq6NGjh2pKSERERERERERUhOW4cyUiIgL29vZYuXIlRo0aBQB49+4dJk2aBJFIhNTUVFlaNTU1WFlZwdraWvUlJiIiIiIiIiKV4Kwg1cjxtKD169fD2NgYI0aMyPTYzp07ERUVhaioKDx8+BCVKlXC+vXrVVpQIiIiIiIiIqKiKMcjV86cOYMuXbpALM7cH1O+fHkYGxvL/t2nTx8cOnRINSUkIiIiIiIiIirCcty5Eh0dDUtLS/mL1dVha2sLHR0dufPVq1fH48ePVVNCIiIiIiIiIsoXYk4LUolcLWgrlUrl/l22bFncvHkzU7qv12AhIiIiIiIiIvpe5XjNlSpVqiAsLCxHacPCwlClSpU8F4qIiIiIiIiIqLjIcedK27ZtERAQgNjY2GzTxcbGIiAgAG3btlW6cERERERERESUf0TF4L/iIMedK5MmTUJycjJat26N69evK0xz/fp1tGnTBsnJyZg4caLKCklERERERERElBO+vr4wMTGBlpYWHBwccPXq1RxdFxQUBJFIBBcXl1w/Z47XXDExMUFQUBB69+4NBwcHmJqaok6dOihdujTevn2LW7du4e+//0bJkiURGBiI6tWr57owRERERERERER5tXv3bkyYMAHr1q2Dg4MDVq5ciXbt2uHevXsoV65cltdFR0dj0qRJaNasWZ6eN8cjVwCgQ4cOCAsLw9ChQ/Hu3TscOHAAO3bswIEDB/D27VsMGTIEoaGh6NSpU54KQ0RERERERESUV8uXL8ewYcMwaNAg1KpVC+vWrYO2tjY2b96c5TUpKSlwc3PDnDlzUKNGjTw9b652CwKAGjVqYP369QCAN2/e4N9//4WOjg7KlCmTpwIQERERERERUeEoDlsxf/z4ER8/fpQ7p6mpCU1NTblznz59wo0bN+Dl5SU7JxaL0aZNG1y+fDnL/OfOnYty5cphyJAhOH/+fJ7KmKuRK1/T0dFB5cqV2bFCRERERERERPli0aJFKFu2rNyxaNGiTOkkEglSUlJQvnx5ufPly5fHixcvFOZ94cIFbNq0Cf7+/kqVUanOFVLewIEDIRKJMh1OTk6FXbQcMzExwcqVKwu7GHkWFBgA57at0MDeGm69uiMiPDzb9CdPHEPnDk5oYG+Nri4dcf7cn3KPp6amwtdnFVr/0BQN69pg+JCBePw4Oh8jyJvduwLQ/sdWcKhrg369e+BWRPZx/37iOFw7OsOhrg26u2aO+9TvJ+ExbDBaNHGAfR1L3LsbmZ/FzxMhxgwAe4IC0NG5NRo3sMUAt57fjPuPk8fRtXN7NG5gi55dO+HCefm4169dg66d26OpQ120bOqAEcMH4VZ4WH6GkCdCvbeFGveBvbvQs/OPaNu0Ln4e1BuRtyOyTX/mjxPo170j2jati4G9XXHl4jm5x9+/f4+Vvy5Atw6t0bZZPfTv2Qm/7d+dnyHkibtrQ9zdMx4Jf8zEufXDUd+qcrbpR3VvhLCAMYj/YyYe7JuIJaOdoFkifSCzWCyC95BWiNw9HvF/zMTtoHGYOuCH/A4j14Tazhk3487O9xC3EGOmnPHy8sLr16/ljoyjU/LqzZs36NevH/z9/WFoaKhUXuxcKQKcnJwQExMjd+zatauwi1XgPn36VODPefzYUSxdsgjuI0YiaO8BWFhYwsN9COLi4hSmD735F6ZOngjXLt2we99BtGzVGuNGj8SDB/dlabZs8seugB2YMWs2du7ag5IlS8Jj+JBMw9gK04ljR7FsyWK4e4xE4N5gmFtYYIT7UMRnE7fXlIlwce2GXXsPoEWrNpgwZhT+zhB3UlIS7OrWw5jxkwoqjFwRYswAcPL4UaxY+guGuY/EzqD9MLewwGiPYVnGHRZ6E9OnTkJn164I2B2MFi1bY9K40XJxGxubYIrXDATt/w0bt+5ExUqVMdJjKBLi4wsqrG8S6r0t1LhP/34MviuXYMBQD/hv34uaZhaYNMYdCfGK474VfhPzZk5B+06u8N+xF81+aIXpk8fg0cMHsjS+K5fg6uULmD5nEbbvPoRuvfph1dKFuHjuTEGF9U3dWtXBL6OcsGDrWTQaug7hf7/AoWX9YaRbSmH6nm2sMc+9DRZuOQO7vj74+ZeD6NaqDuYObyNLM9GtGYa5NMD4lUdg19cHM9adxIQ+TTGiq0NBhfVNQm3njJtxf+9xCzHmokIsKvqHpqYmypQpI3d8PSUIAAwNDaGmpoaXL1/KnX/58iUqVKiQKf3Dhw8RHR2Njh07Ql1dHerq6ti+fTsOHToEdXV1PHz4MOevY+5felI1TU1NVKhQQe7Q09PD2bNnUaJECbk5X0uWLEG5cuVkjSUxMRHu7u4oX748tLS0UKdOHRw+fBgAEBcXh969e6Ny5crQ1taGtbW1XKfNhg0bUKlSJUilUrnydO7cGYMHDwaQ1tg6d+6M8uXLo3Tp0mjQoAH++OMPWdoWLVrg8ePHGD9+vGzUzX/279+P2rVrQ1NTEyYmJli2bJnc85iYmGDevHno378/ypQpg+HDh6voFc25Hdu2oEu3HnBx7YqapqaYMWsOtLS0cDB4v8L0ATu3o3HTZhg4eChq1KyJUWPGwapWLQQF7gSQ1jsesGM7hrl7oGWrNjC3sMT8RUvwKjYWp0/9oTDPwrBz+1Z06dYdnV27omZNU0z3/hL3AcVx79q5A42bNMWAwUNQo2ZNjBw99kvcAbI0HTp1hrvHSDg2alRQYeSKEGMGgIAd2+DSpTs6uXRBjZqm8JoxG1paWjh0MFhh+qCA7WjUuCn6DxyC6jVqwmPUWFhaWWFPUKAsjVP7DnBwbIwqVaqipqkZxk+aindv3+LBg3sFFdY3CfXeFmrcewK3o4NLN7Tv6AqTGjUxcao3tLS0cPR/BxSm3xe0Ew0dm6B3v8EwqV4TQ34eDXPLWjiwJ72d3w4PRbufOsO+XkNUrFQZnVy7o6aZxTdHxBSkMT0bY8v/bmDH0Zu4G/0Ko5f+D0kfkjHgp7oK0zvWqYbLt55i9x8RePIiEaeuPcSePyLkRrs41qmKwxfu4vjl+3jyIhEHzt7Bqat/o36tKgUV1jcJtZ0zbsb9vcctxJhJ9UqUKIF69erh1KlTsnNSqRSnTp1CIwXf2S0tLREREYHQ0FDZ0alTJ7Rs2RKhoaGoWrVqjp+bnStFWIsWLTBu3Dj069cPr1+/xs2bNzFz5kxs3LgR5cuXh1QqhbOzMy5evIidO3fizp07WLx4MdTU1AAAHz58QL169XDkyBHcunULw4cPR79+/WR7fHfv3h1xcXE4cyb9V7j4+HgcP34cbm5uAIC3b9+iffv2OHXqFG7evAknJyd07NgRT548AQAEBwejSpUqmDt3rmzUDQDcuHEDPXr0QK9evRAREYHZs2dj5syZ2Lp1q1yMS5cuha2trSy2gpT86RMi79yGY6PGsnNisRiOjo0RHnZT4TXhoaFwdJS/KRs3aYrw0FAAwPNnzyCRvIKDY3qeOjo6sLaxzTLPgpacnBZ3xjKKxWI4ODZCeFiowmvCw0LhkOF1AoBGjZtkmb6oEWLMQFrcdyNvwyFDmxWLxWjo2Ajh4aEKrwkPD0PDr9p4o8ZNEZFF+uTkTziwfw9K6+jA3NxSVUVXimDvbaHGnZyM+3fvoF4DR9k5sViMeg0ccTtC8XS12xFhqNdQPu4Gjo3l0te2scPFc2fwKvYlUlNT8df1q3j6JBoNHBp/nV2h0FBXg715RZy+kf6LWmpqKk5ff4iGtRV3hFy59QT25hVlnSkmFfXQztEcx688yJDmKVrWqwHTqgYAAOua5dHIxhgnM6QpTIJt54xbdo5xf59xCzFmyj8TJkyAv78/tm3bhsjISHh4eODdu3cYNGgQAKB///6yKUX/DVDIeOjq6kJHRwd16tRBiRIlcvy8ud4tiFTv8OHDKF26tNy5adOmYdq0aZg/fz5+//13DB8+HLdu3cKAAQNkW13/8ccfuHr1KiIjI2Fubg4ActtGVa5cGZMmpU9XGD16NE6cOIE9e/agYcOG0NPTg7OzMwIDA9G6dWsAwL59+2BoaIiWLVsCAGxtbWFrayvLY968eThw4AAOHTqEUaNGQV9fH2pqatDR0ZEbZrV8+XK0bt1a1mFibm6OO3fu4Ndff8XAgQNl6Vq1aoWJEyeq4mXMtYTEBKSkpMDAwEDuvIGBAaKiHim8RiKRwMDAMFN6SZzky+Ov0s4ZZs5TIpGoquhKSUhIi1s/U9yGiI6KUniNRCLJnN7QEHFFJKZvEWLMAJCYkKgwbn0DgyzjjpNIoP9VG9c3MMgU9/k/z2Ca5yR8+JAEQ0Mj+K7bBF09PdUGkEeCvbcFGvfrL3Hr6cuXUU/fAE8eK27n8XESBekNER+fHtPYSdOwdOFsdOvQGmpq6hCLRZg0bTZs69ZXfRB5YFhWG+rqaoiNfyd3PjbhHSyMjRRes/uPCBiU1cYp3yEQiUTQUFfDhoNX8euO9PVmlu48jzLamgjbORop0lSoiUWY5X8KQb9nv+5BQRFqO2fcjBv4vuMWYsxFScbZB9+Dnj174tWrV/D29saLFy9gZ2eH48ePyxa5ffLkCcRi1Y8zYedKEdCyZUusXbtW7py+vj6AtGFNAQEBsLGxgbGxMVasWCFLExoaiipVqsg6Vr6WkpKChQsXYs+ePXj+/Dk+ffqEjx8/QltbW5bGzc0Nw4YNg5+fHzQ1NREQEIBevXrJGtvbt28xe/ZsHDlyBDExMfj8+TOSkpJkI1eyEhkZic6dO8uda9KkCVauXImUlBTZ6Jr69b/9JVXRtlupapm33SKiglO/gQMC9wQjMTEBB/bvhdfk8di6c3emjhyi4ip4TwDu3ArHwmVrUKFCRYTdvIGVvy6AoVE51G9YdKcCZqeZnQkm92uOscsP49qdZ6hZ2QBLxzojZsAPWLwtbRHIbq1qo1dbGwycuw93omJhY1YRv452RozkDQKOhxZuAERERDk0atQojBo1SuFjZ8+ezfbar2db5BSnBRUBpUqVgqmpqdzxX+cKAFy6dAlA2pSd+AwLRpYsWTLbfH/99VesWrUKnp6eOHPmDEJDQ9GuXTu5hWM7duyI1NRUHDlyBE+fPsX58+dlU4IAYNKkSThw4AAWLlyI8+fPIzQ0FNbW1ipbfLZUKcWL7mWkaNutX3/JvO1Wbunp6kFNTS3TIllxcXFZrhRtaGiIuDhJ5vRfes0NDdN+LYyT5DzPgqanlxb31wuaxsVJYJBN3JnSS7JOX9QIMWYA0NXTVRh3fFxclnEYGBoi/qs2rih9SW1tVK1mDGsbO3jPWQA1dTX8dlDxnOiCJth7W6Bxl/0S99eL1ybEx2UahfUffQNDBekl0NdPS//xwwf4+63CyHGT0aRZC9Q0s0CXHn3Qqo0Tdu/cmi9x5Jbk9Xt8/pyCcvryn6Pl9ErhRdwbhdfMGtoau06GYevhv3D7USwOnY+E94Y/MLlvM9mvlgs92mFpwHnsPXULtx/FYteJMPjsuYzJfZvle0w5IdR2zrgZN/B9xy3EmOn7w86VIu7hw4cYP348/P394eDggAEDBsgWoLWxscGzZ89w//59hddevHgRnTt3Rt++fWFra4saNWpkSqulpYUuXbogICAAu3btgoWFBerWrSuXx8CBA+Hq6gpra2tUqFAB0dHRcnmUKFECKSkpcuesrKxw8eLFTOUxNzeXjVrJKUXbbk32VH7bLY0SJWBVqzZCrlyWnZNKpQgJuQwbW3uF19jY2SHkyhW5c1cuX4KNnR0AoHKVKjA0NEJISHqeb9++RUR4WJZ5FjQNjS9xh8jHfTXkCmxs7RReY2Nrh6sZXifgS9xZpC9qhBgzkBa3pVVtXA1Jb7NSqRTXQq7AxsZO4TU2Nra4FiLfxkOuXIJ1FunT800tlB2/FBHsvS3UuDU0YG5ZCzeuhcjOSaVS/HU9BLWtbRVeU9vaFjeuycd9PeSyLP3nz5/x+fNniL4aMixWU4M0VX4R+MKS/DkFN+/HoGW99OnAIpEILevVwNXbzxReU1JLA1Jpqtw5aUrql2szpEmVT5MilUIsLhpDxgXbzhm37Bzj/j7jFmLMRUlh7wSUk6M4YOdKEfDx40e8ePFC7pBIJEhJSUHfvn3Rrl07DBo0CFu2bEF4eLhs150ffvgBzZs3R9euXfH7778jKioKx44dw/HjxwEAZmZm+P3333Hp0iVERkbC3d0905ZUQNrUoCNHjmDz5s1yo1b+yyM4OBihoaEICwtDnz59Mu0uZGJignPnzuH58+ey+YsTJ07EqVOnMG/ePNy/fx/btm3DmjVr5NaAyamcbruVF/0GDELwvj04dPAAHj18iPlzZyMpKQkurl0AANO9pmDVivRdjtz69seli+exbetmRD16iLW+Prh96xZ69ekLIO2LrVu//vBfvxZnT5/Cg/v3MMNrCozKlUOr1m0UFaFQ9O0/EAf27cWh39LiXjgvLe7OLmlxz/DyxOoMcffu2w+XLl7A9q2bEfXoEdb5+uDO7dvo1Se9vbx+nYh7dyNl25VFR0Xh3t1I2XzXwibEmAHArd8AHAzei8OHDiLq0UMsmj8HSUlJ6OjiCgDwnu6JNauWy9L3cuuPS5cuYOe2LYiOeoT1a9fgzu3b6NGrDwAg6f17+K5egYjwUMT88xyRd25jjvd0vIp9iTZt2xVKjIoI9d4Watw9+vTHkd/24fjh3xAd9RDLf5mHpKQkOHdwAQAsmOWFDb7p02q79eqLq5cvYnfAVjyOfoQtG3xxL/I2XHuktfNSpUvDrm59rFu9DDdvXEXM82c4dvggThw9hGY/tC6MEBVavfsSBnWoBzcnO1gYG2L1xA7QLlkC24/+BQDYOL0L5rqn19PRi/cwzKUBureuA+OKumhVvya8h7bC0Yv3ZJ0uRy/dg2e/5nBqZI5qFXTRqZkVxvRsjEPnIgslRkWE2s4ZN+P+3uMWYsz0feGaK0XA8ePHUbFiRblzFhYW6NOnDx4/fizbWrlixYrYsGEDevfujR9//BG2trbYv38/Jk2ahN69e+Pdu3cwNTXF4sWLAQAzZszAo0eP0K5dO2hra2P48OFwcXHB69ev5Z6rVatW0NfXx71799CnTx+5x5YvX47BgwejcePGMDQ0hKenJ/7991+5NHPnzoW7uztq1qyJjx8/IjU1FXXr1sWePXvg7e2NefPmoWLFipg7d67cYrZFgZNzeyTEx8NvzWpIJK9gYWkFv/UbZVMgXsTEQCxK74O0s6+LRUuWYs3qlfBZuRzVjE2w0scXZmbp694MGjIMSUlJmDvbG2/e/Av7uvXgt35jkVojpp1zeyQkxGPtGh/EfYnbd51/hrj/kfuV0s6+Lhb+shS+PiuxZtUKVDM2wfLVa2CaIe4/z5zGrBnTZP+eOnkCAMDdYyR+Hjm6gCLLmhBjBoAfndojISEB6/xWI04igbmFFXz8NsgWgHvxIkZuQS9bO3ssWPQr/Nasgq/PClStZoylK31kcYvV1BAd9QiHDx1EYmICyurqolZta/hv2YmapmaFEqMiQr23hRp3q7bOSExIwOYNaxAfJ4GpuSV+XbVONi0o9qV8O69jY4+Z837BpnU+8PdbhSpVjbHg19WoUTO9DXvPX4oNfisx33sq/v33NSpUqIShP49B5649Czy+rOw7fQuGutrwHtIK5fVLI/zvF+g8aQdiE9IWua1avqzcKJTF2/9EamoqZg1tjUpGZSBJfIcjF+9htn/6dpUTVhzBrKGtsWpCBxjplUKM5A02/XYdC7eeLejwsiTUds64Gff3HrcQY6bviyg19auxn0TFwIfPhV2CwvH1UG36vqWkCLO+NdQ5qFJIEt8lF3YRClz1DvMKuwiFIuHM3MIuAhGRSml9J0MVlp9TvCNTUTKheY1vJypk/AZLRERERERERKQEdq4QERERERERESnhOxnIRERERERERES5JRYVk+14ijiOXCEiIiIiIiIiUgI7V4iIiIiIiIiIlMDOFSIiIiIiIiIiJXDNFSIiIiIiIiKBEnPJFZXgyBUiIiIiIiIiIiWwc4WIiIiIiIiISAmcFkREREREREQkUNyJWTU4coWIiIiIiIiISAnsXCEiIiIiIiIiUgKnBREREREREREJlBicF6QKHLlCRERERERERKQEdq4QERERERERESmB04KIiIiIiIiIBIq7BakGR64QERERERERESmBnStERERERERERErgtCAiIiIiIiIigRJzWpBKcOQKEREREREREZES2LlCRERERERERKQEdq4QERERERERESmBa64QERERERERCZSYezGrBEeuEBEREREREREpgZ0rRERERERERERK4LQgIiIiIiIiIoHirCDVYOcKFUsp0tTCLgIVIKG+34vFwoxcmirQ+1ugYZfRFt5XkfjTcwu7CIVCv+fmwi5CoYjfPbiwi1Ao3n38XNhFKBRCXbuiZAm1wi4CUaHjtCAiIiIiIiIiIiUI7+ciIiIiIiIiIgIg3BFXqsaRK0RERERERERESmDnChERERERERGREjgtiIiIiIiIiEigOCtINThyhYiIiIiIiIhICexcISIiIiIiIiJSAqcFEREREREREQkUR1yoBl9HIiIiIiIiIiIlsHOFiIiIiIiIiEgJnBZEREREREREJFAibhekEhy5QkRERERERESkBHauEBEREREREREpgZ0rRERERERERERK4JorRERERERERALFFVdUgyNXiIiIiIiIiIiUwM4VIiIiIiIiIiIlcFoQERERERERkUCJuRWzSnDkChERERERERGREti5QkRERERERESkBE4LIiIiIiIiIhIoTgpSDY5cISIiIiIiIiJSAjtXiIiIiIiIiIiUwGlBRERERERERALFzYJUgyNXBGrr1q3Q1dWV/Xv27Nmws7PL9pqBAwfCxcVF9u8WLVpg3Lhx+VI+IiIiIiIiouKCnSvF0MCBAyESiWSHgYEBnJycEB4enuM8evbsifv37ytVjuDgYMybN0+pPIqC3bsC8FO7VnCsZ4P+fXrgVkT2r+PvJ46jS0dnONazQQ/Xjrhw7k+5x0/9cRIjhg9Gy6YOqGttiXt3I/Oz+HkmxLh37wpA+3at4FDPBv1yGLNrR2c41LNBd9eOOK8gZo/hg9GiqQPsi2jMgGrrOjk5GauWL0UP145o3NAeP7ZqhpnTPPEq9mV+h5Fru3cFoP2PreBQ1wb9eueivutmUd+/n4THsMFo0cQB9nWKdn0LtZ0Lsb6DdgXA+cdWaFjXGn17d0fEN+I+eeIYXDo6oWFda3RTEHdqair81qxCmxZN4VDPBu5DB+Lx4+h8jCBv3J2sELm2O+J39cefizqivqlhtulH/lQLoau7Ii6wP+6v74FfBjaEpoaa7PEmtcpjn1cbPPTvhff7B6Njw2r5HUKeBAUGwLltKzSwt4Zbr+6I+MZ3v5MnjqFzByc0sLdGVxfF9e3rswqtf2iKhnVtMHxI0azv/bsD0eWntmjhaI+h/Xvhzq3s4z79+wn06tIBLRzt0beHCy5dOCf3+PxZ09C4bm25Y/zI4fkZQp7s2x0Il/Zt0NzBDoP79cTtb8R96vfj6On6E5o72MGte2dcOv9nlml/mT8bjva1EBSwXdXFVopQ2zh9H9i5Ukw5OTkhJiYGMTExOHXqFNTV1dGhQ4ccX1+yZEmUK1dOqTLo6+tDR0dHqTwK24njR7H818UY/vNIBO4Jhpm5BUa6D0V8XJzC9GGhf2Ga50R07tINgXsPoEWrNpgwdhT+fpDeUZWUlAQ7+3oYM35SQYWRa0KM+8Txo1j262K4f4nZ3NwCI7KJOTT0L3h5ToRLl27YVUxjBlRf1x8+fMDdyDsY6j4Cgbv3Y+kKHzyOjsK40SMKMqxvOnHsKJYtWQx3j5EI3BsMc4tv1PfNv+A1ZSJcXDPU9xgF9V236Ne3INu5UOv72FEsW7II7h4jsWvvAZhbWGKE+5AcxR209yBatmqN8WNGysW9dbM/AgN2YLr3bOwI3IOSJUtihPsQfPz4saDC+qaujatj8cCGWLgnFI0nH0LE43j8NrMdjMpoKUzfo2kNzOtbHwv33IT92GB4+F1AtyY1MMetnixNKU0NRETHY7z/5YIKI9eOHzuKpUsWwX3ESATtPQALC0t4uA9BXDb1PXXyRLh26Ybd+9Lqe9zokXiQob63bPLHroAdmDFrNnbuSqtvj+FFq77/OHEMq5cvweDhI7AlcC9MzSwwfqQ74uMVxx0RdhOzpk1Gx85dsDVwH5q3aIWpE0bj4d8P5NI5Nm6K/508KzvmLPq1IMLJsd9PHMOqZb9gqPsIbAvcBzNzS4wbMTzLuMNDb8LbazI6unTBtl370bxFa0xREDcAnD39B25FhMHISLm/BVRNqG28KMj4w31RPYoDdq4UU5qamqhQoQIqVKgAOzs7TJ06FU+fPsWrV69w9uxZiEQiJCYmytKHhoZCJBIhOjoaQOZpQV9LSUnBhAkToKurCwMDA0yZMgWpqalyab6eFmRiYoKFCxdi8ODB0NHRQbVq1bBhwwa5ay5dugQ7OztoaWmhfv36OHjwIEQiEUJDQ5V8RfImYPtWuHbtjs6uXVGjpimme8+BVkkt/HZgv8L0gTt3oFGTphgwaAhq1KiJEaPHwrJWLezeFSBL06FjZwz3GAkHx0YFFUauCTHundu3osuXmGtmiPlgFjHv2rkDjTPEPHL0WFjVqoWgr2J29xgJxyIaM6D6utbR0cFa/8340ckZJtVrwMbWDp7TZiLyzm3ExPxTkKFla+f2rejS7av61spBfQ8egho1M9R3YIb67vSlvhsV3foWajsXan3v2L4FXbr1gMuXuGd8I+7AndvRuEkzDBw89Evc477EvRNA2i+8ATu2Y9hwD7Rs1QbmFpaYt3AJXsXG4sypPwoytGyN6VgHW/64hx1nHuDus0SMXn8RSR8/o39rc4XpHS3L4fLdWOy58AhPXr3FqbB/sOfCI9Q3NZKlOXnzGebs+guHrj4uqDBybce2DPVtaooZs77Ud7Di+g7YuR2Nm6bX96gxWdS3e3p9z1+UVt+ni1B9BwVsQyfXbujQ2RXVa5hiyvRZ0NTSwuHfghWm3xO4Ew6NmsJtwGCY1KiJ4SPGwMKyFvbvDpRLp1GiBAwMjWRHmTJlCyKcHNu1cys6d+mODp27oHpNU3hOnwUtLS0cPqg47t27dsCxcVP0HTAE1WvUhPvIMbCwqoV9QQFy6WJjX2LZLwswZ+ESqKkXreU3hdrG6fvBzpXvwNu3b7Fz506YmprCwMBAJXkuW7YMW7duxebNm3HhwgXEx8fjwIEDObqufv36uHnzJkaMGAEPDw/cu3cPAPDvv/+iY8eOsLa2xl9//YV58+bB09NTJeXNi+TkT4i8cxsOjo1l58RiMRwcGyE8LFThNRFhoXLpAaBR4yZZpi+KhBh3XmIOL+YxAwVX12/fvIFIJIKOThlVFFtpea7vRsKrb6G28++7vhsjPOymwmvS4pbvLGrUuKks7ufPnkEieSX32ujo6MDaxhZhWeRZ0DTUxbCvaYAz4emduampwOnwf+BgbqTwmit3Y2Ff00A2dcikvA7a1a2CE389LZAyq0Lyp7T6dmwkX9+O2dV3aGimTtHGTZoi/MsPWrL6dsxc31nlWdCSkz/hXuQd1HdIj0MsFqOBgyNuhYcpvOZWRCgaODjKnXNo1AS3wkPlzt28fg3tWzdDL9ef8OvCuXid4UfJwvZf3BnjSIu7ESK+iuM/t8JD0cBBvr4dGzVBRIbXSSqVYs6Mqeg7YDBq1DTLl7LnlVDbOH1filZ3JeXY4cOHUbp0aQDAu3fvULFiRRw+fBhisWr6y1auXAkvLy906dIFALBu3TqcOHHim9e1b98eI0akTQ/w9PTEihUrcObMGVhYWCAwMBAikQj+/v7Q0tJCrVq18Pz5cwwbNkwlZc6txIQEpKSkQP+rDil9A0NER0UpvEYikWTqwDIwMEScRJJv5VQ1IcadkEXMBt+IWVH64hIzUDB1/fHjR6xasRROzj/J3pMKm8rq27B41bdQ27nQ6zvz/WqA6KhHCq9Ju7/l1yYxMDSA5EvcEskrWR4Z6RsYFJnXxlBHE+pqYrxMTJI7H/s6CRaVdRVes+fCIxiU0cIf83+CSCSChroY/ici8WtwzteqK2wJiVnXd1Ru6tvAAJK4r+rbMHOekiJS34mJiWn3t/5XbVLfAI+jFd/fcRIJ9L56nfQMDOSmljg0boofWrVBpUpV8OzZU6xfsxITRrtjw9ZAqKmpfZ1lgUtM+C9u+frTMzBAdLTi+o6TSDK9TnoGhoiLS6/LHVs2Qk1NDT1691V9oZUk1DZeVHDEhWqwc6WYatmyJdauXQsg7QuWn58fnJ2dcfXqVaXzfv36NWJiYuDg4CA7p66ujvr162eaGvQ1Gxsb2f+LRCJUqFABsbGxAIB79+7BxsYGWlrpc6IbNmz4zfJ8/Pgx07zIz6IS0NTUzFE8RJQ/kpOT4TlpHADAa+bsQi0LEVFWmtWugCldbDDO/zKuPXiFmhXK4NfBDpja7T0W71M8+oG+b23btZf9f00zc5iamaN7JyfcvH4N9b8a9fK9uHvnNnbv2oFtgfuLzfoVRMUNO6mKqVKlSsHU1BSmpqZo0KABNm7ciHfv3sHf3182eiVjR0hycnKBlEtDQ0Pu3yKRCFKpVKk8Fy1ahLJly8odS5csUipPANDV04Oamlqmhf/i4zL3gv/H0NAw06JacXESGBhmv0tBUSLEuPWyiDnuGzErTF9MYgbyt66Tk5MxddJ4xPzzD/w2bCoyo1aAb9R3FvWnsL4lxau+hdrOhV7fme/XOBhmE3fGX7EBIE6Snt7Q0EiWR0bxcXFF5rWRvPmIzylSlNctKXe+XNmSeJn4XuE13r3qIvDcQ2w9dR+3nyTg0NXHmBV4A5O62KK4/I2pp6ui+o6Lg6HBV/UtyXmeBU1XVzft/v5qEdf4+DjoZ/G+ZmBoiISvXqeEuLhsp85XrlIVurp6ePb0ifKFVgFdvf/ilq+/tDiyjvvr1ykhw/t/6M0bSIiPh0v71mhS3xpN6lvjRcw/WL18CVzat8mfQHJBqG2cvi/sXPlOiEQiiMViJCUlwcgo7Y0kJiZG9nhuFowtW7YsKlasiJCQENm5z58/48aNG0qV0cLCAhEREXKjUK5du/bN67y8vPD69Wu5Y9IUL6XKAgAaGiVgVas2roak7wwglUpx9coV2NjaKbzG2tZOLj0AhFy+lGX6okiIcf8Xc0guYrZREPOVYhQzkH91/V/HypMnj7HOfwt0dfXyo/h5lmV9h3yjvq98H/Ut1HYu1PrOdH+HXIaNrb3Ca9LiviJ3LmPclatUgaGhkdxr8/btW0SEh8E2izwLWvJnKW4+jEML60qycyIR0NKmEkLuv1J4jbamOqRS+ZG3KV/+XVx+wdco8aWdX5Gv75Ds6tvODiGK6tvODkB6fWe8d/6r76zyLGgaGiVgYVULN66mxyGVSnH9agjq2NgqvKaOtR2uX5WP+2rIZdSxscvyeWJfvsDr14kwMCoaf3D/F/e1EPm4r129Auss4qhjY4drX8d95TKsv7xOzj91ws49B7E9KFh2GBmVg1v/wVjl559vseSUUNs4fV/YuVJMffz4ES9evMCLFy8QGRmJ0aNH4+3bt+jYsSNMTU1RtWpVzJ49Gw8ePMCRI0ewbNmyXOU/duxYLF68GAcPHsTdu3cxYsQIud2H8qJPnz6QSqUYPnw4IiMjceLECSxduhRA9l9uNDU1UaZMGblDVVOC3PoPxIH9e/G/3w7g0aOHWDhvNpKSktDJJW2tmZnTPOGzMv2169O3Hy5fvIAd2zYj6tEjrPPzwZ3bt9Gzt5sszevXibh3NxKPHj4EAERHR+He3UjZvM+iQIhx9/0S86GvYu78JeYZ0zyxOkPMvfv2w6WLF7D9q5h7KYj5YRGNGVB9XScnJ2PKhLG4c/sWFiz+FSnSFEgkryCRvEJy8qdCiVGRvv0H4sC+L/X9UEF9e3li9QoF9b31S9y+X+q7Tzb1HVX06luo7Vyo9d2v/yAE79sji3tBprinyMXdp29/XLp4/kvcD7HW1wd3bt9Crz5p6y+IRCK49esP/w1rcfbMKTy4fw8zpk2BUblyaNm68H/Z/s/q/93CoDbmcGthCovKZbF6eGNoa6pjx+m07Vf9RzeX22b56PWnGNbOEt2aVIdxudJoZVMJ3r3q4uj1J7JOl1Ja6rAx0YeNiT4AwLicDmxM9FHFsFTBB5iFfgO+1PfBtPqePzetvl1c0+p7utcUrMpQ325f6ntbhvq+fUtBfa9fi7Onv9S3V1p9typC9d3LbQAOHdiHo/87iOhHD/Hrwrn4kJSEDp1cAQBzZ3phrc8KWfoeffriyuWLCNyxFdFRj7BxnS/u3rmFrj37AADev3+HNSuW4lZ4GGL+eY7rIVfgOX40qlStBodGTQslRkV69x2IQwf24cihg4h69BBLFs7Bh6Qk/NQ5Le45M6bCb/VyWfqevfvhyqULCNi+BdFRj+C/bg0i79xCt15p72tldXVR09RM7lBTV4eBoSGMTaoXSoxfE2obLwoKe5vl72UrZq65UkwdP34cFStWBJC26rWlpSX27t2LFi1aAAB27doFDw8P2NjYoEGDBpg/fz66d++e4/wnTpyImJgYDBgwAGKxGIMHD4arqytev36d5zKXKVMG//vf/+Dh4QE7OztYW1vD29sbffr0kVuHpSC1c2qPhPh4rPX1QZzkFSwsrbBmnb9s+POLmH8gznAz29rVxYLFS+G3ZiXWrFqBasYmWL5qDUzN0rd//PPMacyeOU32b6/JEwAAwz1G4ucRowsosuwJMW5FMftmE7OdXV0sXLwUvt+IeVaGmKd+idm9iMQMqL6uX8W+xJ9nTwMAenVzkXuuDZu3oX4DBxQF7ZzbIyEhHmvXZFPf4gz1bV8XC39ZCl+fDHGvVlDfM7Ko75FFt74F0c6FWt+yuFdD8iVuv3UbZXHHxMRAlGGh+4xx+6xajmrGJlix2lcu7oGDhyEpKQnzZnvjzZt/YV+3HvzWbSxS65ztvxQFo7JamNmrLsrrlkR4VDxc5p9E7OsPAICqhqUgzTA1evG+UKSmpmJW73qopK8Nyb8fcPT6U8wOTB+RW7emIU7MTV+HY8mgtPeyHWcewH3N+QKKLHtOzmn3t1/G+l6/MUM7j4FYJF/fi5YsxZrVK+GzMq2+V/r4wixDfQ8aklbfczPW9/qiVd9t2jkjMSEe/mvXID5OAjMLSyxfs142Lejlixi5+9va1h5zFizBBr/VWL9mJapUM8bi5T6oaZq2O46aWA1/P7iHo4d/w9s3/8LQqBwaOjbG8BGjUaJEiUKJUZG2srh9EPcl7hW+62XTfF68kL+/bezsMXfhEqz3XY11a1aiajVjLMkQd3Eg1DZO3w9R6rdWKCXKRwEBARg0aBBev36NkiVLfvuCL959YrMVkuLRV616Qm3lxeTHCdUTbIUXdgEKnkiIQQMw6LW5sItQKOJ3Dy7sIhSKdx8/F3YRCoVYoB9iJUsU/i5LhUHrOxmqsCf0n28nKmQ97Cp9O1Eh+06aAxUX27dvR40aNVC5cmWEhYXB09MTPXr0yFXHChEREREREamGMLsEVY+dK1SgXrx4AW9vb7x48QIVK1ZE9+7dsWDBgsIuFhEREREREVGesXOFCtSUKVMwZcqUwi4GERERERERkcqwc4WIiIiIiIhIoIrLbjxFHbdiJiIiIiIiIiJSAjtXiIiIiIiIiIiUwGlBRERERERERALFEReqwdeRiIiIiIiIiEgJ7FwhIiIiIiIiIlICpwURERERERERCRR3C1INjlwhIiIiIiIiIlICO1eIiIiIiIiIiJTAaUFEREREREREAsVJQarBkStEREREREREREpg5woRERERERERkRLYuUJEREREREREpASuuUJEREREREQkUNyJWTU4coWIiIiIiIiISAnsXCEiIiIiIiIiUgKnBREREREREREJlJibMasER64QERERERERESmBnStERERERERERErgtCAiIiIiIiIigeJuQarBkStEREREREREREpg5woRERERERERkRI4LYiIiIiIiIhIoETcLUgl2LlCxZKamG8ARN+rNx8+F3YRCoWOFj+S6ft2e32fwi5CodDrsrawi1AoEoI9CrsIREQFitOCiIiIiIiIiIiUwJ/JiIiIiIiIiASKuwWpBkeuEBEREREREREpgZ0rRERERERERERK4LQgIiIiIiIiIoESc7cgleDIFSIiIiIiIiIiJbBzhYiIiIiIiIhICexcISIiIiIiIiJSAtdcISIiIiIiIhIobsWsGhy5QkRERERERESkBHauEBEREREREREpgdOCiIiIiIiIiASK04JUgyNXiIiIiIiIiIiUwM4VIiIiIiIiIiIlcFoQERERERERkUCJwHlBqsCRK0RERERERERESmDnChERERERERGREjgtiIiIiIiIiEigxJwVpBIcuUJEREREREREpAR2rhARERERERERKYHTgoiIiIiIiIgEirsFqQZHrhARERERERERKYGdK+cEJz0AANHrSURBVERERERERERESmDnCsmYmJhg5cqVsn+LRCIcPHgw35934MCBcHFxyffnISIiIiIiInkiUdE/igN2rnwHsuqcOHv2LEQiERITE/OUb0xMDJydnZUrXDEQFBgA57at0MDeGm69uiMiPDzb9CdPHEPnDk5oYG+Nri4dcf7cn3KPp6amwtdnFVr/0BQN69pg+JCBePw4Oh8jyBshxi3EmAHhxr1/TyC6dWiLVo3sMax/L9y5lX3cp38/gT5dOqBVI3v07+GCyxfOZUoTHfUQnuNHol1zB7RpUh9D+/XAi5h/8iuEPBFqfTNuYcV9aH8Q+nd1RseWDTB2mBvu3YnIMm30o78xb9oE9O/qDKcmtjiwe2emNEHbN2H0kD5wbdMIPX9qgTlTx+FpEYzbvX1t3PV3Q8K+YTj3axfUNyuXbfpRnWwQ5tcb8XuH4cGmflgypDE0NdRkj0/qZo8Ly7oiNmgIHm8fiD3TnGBWWTefo8g9obZzIcYtxJjp+8HOFcpShQoVoKmpWdjFyFfHjx3F0iWL4D5iJIL2HoCFhSU83IcgLi5OYfrQm39h6uSJcO3SDbv3HUTLVq0xbvRIPHhwX5ZmyyZ/7ArYgRmzZmPnrj0oWbIkPIYPwcePHwsqrG8SYtxCjBkQbtynTh7DmuVLMGj4CGwK2AtTcwtMGOWOhHjFcUeE3cSc6ZPRwaULNgfuQ7MWreA1cTQe/f1Alub50ycYMaQfjE2qw2fDVmwLCsbAoT8XqfdJodY34xZW3H/+cRz+PkvRd7A71mwOQg1TC0yf4IHEBMVxf/z4ARUqVcFgjzHQMzBUmCYi9Do6dumJFRt2YNHK9fj8+TOmj/8ZH5Le52coudKtaU38MqQJFgRdR6Px+xAeHYdDczrAqGxJhel7NjfDvP4OWBh0HXYjg/Czzxl0a2qKuf0cZGma1amEdUdu4YfJwejg/T+oq4lxeE4HaGsWnT0vhNrOhRi3EGOm7ws7VwTkwoULaNasGUqWLImqVatizJgxePfuXZbpM04Lio6OhkgkQlBQEBo3bgwtLS3UqVMHf/6Z3juckpKCIUOGoHr16ihZsiQsLCywatUquTxTUlIwYcIE6OrqwsDAAFOmTEFqamq+xJsTO7ZtQZduPeDi2hU1TU0xY9YcaGlp4WDwfoXpA3ZuR+OmzTBw8FDUqFkTo8aMg1WtWggKTPsVLDU1FQE7tmOYuwdatmoDcwtLzF+0BK9iY3H61B8FGVq2hBi3EGMGhBt30M5t6OjaDT91ckX1GqaYPG0WtLS0cPi3YIXp9+7aCYdGTdGn/2CYVK+JYSPGwNyyFvbvCZSl2eC3Go2aNMeIsZNgbmmFylWroekPraCnb1BQYX2TUOubcQsr7uDdO+DUsQt+/MkFxtVrYvTkGdDU1MKJwwcVprewqoNhoyagRRtnaGiUUJhmwfK1+PGnzjCpYYoaZhaYOH0uYl/G4MG9yHyMJHfGdLbFlpN3sOPUPdx9moDRfn8i6WMyBrSxVJje0ao8Lke+wO5zD/Ak9g1OhT7DnvMPUN88fbRL59lHsPP0PUQ+TUBEdByGrzqNauV0YG9qVFBhfZNQ27kQ4xZizPR9YeeKQDx8+BBOTk7o2rUrwsPDsXv3bly4cAGjRo3KVT6TJ0/GxIkTcfPmTTRq1AgdO3aU9SZLpVJUqVIFe/fuxZ07d+Dt7Y1p06Zhz549suuXLVuGrVu3YvPmzbhw4QLi4+Nx4MABlcaaU8mfPiHyzm04NmosOycWi+Ho2BjhYTcVXhMeGgpHx0Zy5xo3aYrw0FAAwPNnzyCRvIKDY3qeOjo6sLaxzTLPgibEuIUYMyDguJM/4f7dO6jfMD0OsViM+g0dcTsiTOE1t8JDUd/BUe6cQ6MmuBUeCiDt/e3ShT9RtZoxJowchg5tmmFY/144d+ZUvsWRW4Ktb8YtOyeIuJOT8eBeJOwbpN+vYrEY9vUdEfmNqX+58f7dWwCATpkyKstTGRrqYtibGuF06DPZudRU4HTYczS0LK/wmiuRL2Ff00g2dcikvA7a1TPG8RtPsnyeMqXSOp8S3hSNX/UF284FGLcQYy5KRMXgv+KAnSvficOHD6N06dJyR8b1UhYtWgQ3NzeMGzcOZmZmaNy4MVavXo3t27fjw4cPOX6eUaNGoWvXrrCyssLatWtRtmxZbNq0CQCgoaGBOXPmoH79+qhevTrc3NwwaNAguc6VlStXwsvLC126dIGVlRXWrVuHsmXLqu6FyIWExASkpKTAwED+V2cDAwNIJBKF10gkEhh8NaTYwMAAkjjJl8dfpZ0zzHmeBU2IcQsxZkC4cb9OTERKSgr0v4pb38AAcVmUMT5OkmkEip6+AeK/dB4nxMch6f177Ny6CQ6Nm2KF7wY0b9ka0yePxc0b1/InkFwSan0zbmHF/W9iAqQpKdD96n7V1TdAQrxqyiiVSrFu1RLUsrGDSQ0zleSpLMMyWlBXEyM2MUnufGzie1TQ1VZ4ze5zDzAv8BpOLXbBv8HDEenfF+du/YNf9/6lML1IBPw6tAku3YnBnSfxKo8hL4TazoUYtxBjpu9P0ZlQSUpp2bIl1q5dK3cuJCQEffv2BQCEhYUhPDwcAQEBssdTU1MhlUoRFRUFKyurHD1Po0bpvcPq6uqoX78+IiPTh8z6+vpi8+bNePLkCZKSkvDp0yfY2dkBAF6/fo2YmBg4ODhkyiO7qUEfP37MNC8yVU2zSK1zQETft//eo5r+0BI93QYAAMwsrHArPBQH9++Gfb0GhVk8IlIh32ULEf3oIZat3VrYRVFKszqVMLl7XYxddx7X7r9EzYplsXRYE8T0rIfFu29kSr/y5+aoXU0fraceLPjCEhF9Bzhy5TtRqlQpmJqayh2VK1eWPf727Vu4u7sjNDRUdoSFheHBgweoWbOmSsoQFBSESZMmYciQITh58iRCQ0MxaNAgfPr0Sal8Fy1ahLJly8odv/6ySOny6unqQU1NLdMiWXFxcTA0VLzgnaGhIeLiJJnTf+k1NzRMm6McJ8l5ngVNiHELMWZAuHGX1dWFmpqabNTJf+Lj4mCQRRn1DQwzLXabEB8nG/2Slqc6TGrIv18aV6+B2BcxKix93gm1vhm3sOIuo6sHsZoaEr+6XxPj46Cnr3wZfZctRMilc1ji4w+jcoqn2xQGyb8f8DlFinK68ovXltPVxotExYvuznJriF1n7mPr75G4/Tgeh65EwXtHCCZ3s8+0rekK96ZoX98Y7WYcwvO4rNfjK2hCbedCjFuIMRclYlHRP4oDdq4IRN26dXHnzp1MHTCmpqYoUULx4m6KXLlyRfb/nz9/xo0bN2SjXi5evIjGjRtjxIgRsLe3h6mpKR4+fChLX7ZsWVSsWBEhISGZ8siOl5cXXr9+LXdM9vTKcZmzolGiBKxq1UbIlcuyc1KpFCEhl2Fja6/wGhs7O4RkeA0A4MrlS7D5MjqncpUqMDQ0QkhIep5v375FRHhYlnkWNCHGLcSYAQHHrVEC5pa1cONaehxSqRQ3roWgtrWtwmvq2Njh+lX5uK+FXEYdGztZnla162TamvXp48coX6GSSsufV4Ktb8YtOyeIuDU0YGZhhdDr6d8lpFIpQm+EwKqOTZ7zTU1Nhe+yhbh07jR+We2PCpWqqKK4KpP8WYqbf79CS9v0colEQEubyrh696XCa0pqqkP61chgqTT1y7Xpf6mscG+KTo7V4TTjEB6/fJMPpc87wbZzAcYtxJjp+8POFYHw9PTEpUuXMGrUKISGhuLBgwf47bffcr2gra+vLw4cOIC7d+9i5MiRSEhIwODBgwEAZmZmuH79Ok6cOIH79+9j5syZuHZNfi2CsWPHYvHixTh48CDu3r2LESNGIDExMdvn1NTURJkyZeQOVU0J6jdgEIL37cGhgwfw6OFDzJ87G0lJSXBx7QIAmO41BatWLJOld+vbH5cunse2rZsR9egh1vr64PatW+jVJ236lUgkglu//vBfvxZnT5/Cg/v3MMNrCozKlUOr1m1UUmZVEGLcQowZEG7cvfoOwP8O7MOx/x1EdNRDLF00F0lJSfipkysAYJ63F9b5rJCl7967L0IuXcSuHVvxOOoRNq33xd07t9C1Rx9Zmt79BuHUyWM4FLwXz54+xv7dAbh0/ixcu/cq6PCyJNT6ZtzCirtLz3449r9g/H70EJ5EP4LP0vn48CEJP/7kAgD4dd50bF6bvlthcnIyHt6/i4f37+JzcjIkr2Lx8P5d/PMsfWFX32ULcfrkUXjOXoyS2qUQHydBfJwEHz/mfF26/Lb6tzAM+tEKbq0sYFFFF6s9mkNbSwPbT90FAGwc1wpz+6dPvT56LRrDnGujezNTGJfXQSu7KvB2a4ijVx/LOllW/twMvX4wx4Clf+Bt0ieU1y2J8roloVVCrVBiVESo7VyIcQsxZvq+cM0VgbCxscGff/6J6dOno1mzZkhNTUXNmjXRs2fPXOWzePFiLF68GKGhoTA1NcWhQ4dkw+rc3d1x8+ZN9OzZEyKRCL1798aIESNw7Ngx2fUTJ05ETEwMBgwYALFYjMGDB8PV1RWvX79Wabw55eTcHgnx8fBbsxoSyStYWFrBb/1G2dSBFzExEIvS+yDt7Oti0ZKlWLN6JXxWLkc1YxOs9PGFmZm5LM2gIcOQlJSEubO98ebNv7CvWw9+6zcWqTVihBi3EGMGhBt36x+dkZgQj43r1iA+TgJTc0ss81kP/S9DhV++iIE4wy+31rb2mLVgCfzXrsb/2bvrsKi2Lgzg74CBCiqILRaooBImYDe2mNit2K2IHdfCVoxri93x2d15r93dGJSJ5Pr+QI6MhHpBhmHe333mea5n9gx7zdmzz5l19t5n0bxZyJU7DyZNn4v8Ft8Xs6xYpRoGDRuN1csXY9a0ScidJy/+8pgF22IlEj2+2Ojq/mbcuhV3xWo18T7AH6uWzIe/nw/yFyiEv6bPVxalfvvmNVRR4vb1eYueHb6f72xZtxJb1q2EdbGSmOoZsSj/rm0Ri+8P6dVJ7W8NGDYONeo0+NMh/ZLNpx7CNEMajGpZClmN0+LaIx80GLNLWeTWLLOh2kiVyRv+hQgwunVp5DBJB58Pgdh94SnGrP4+6se1dlEAwMFJzmp/q8usI1h95O6fD+oX6Go718W4dTHmpEJb7saT1KkkrpVEib558uQJ8uXLh8uXLysL1GrS11BN14CI/pSPOvoFNzLg9Q5K3rwDks4okMRUuONyTVdBI/y3dtd0FYj+uORy6D55z1/TVfip8gWNNV2Fn+K0ICIiIiIiIiKieEgmuTYiIiIiIiIi+l0/3kGM/hsmV+iX5M2bF5xBRkRERERERBQdpwUREREREREREcUDR64QERERERER6SjOCkoYHLlCRERERERERBQPTK4QEREREREREcUDpwURERERERER6Sg93i4oQXDkChERERERERFRPDC5QkREREREREQUD0yuEBERERERERHFA9dcISIiIiIiItJRXHElYXDkChERERERERFRPDC5QkREREREREQUD5wWRERERERERKSrOC8oQXDkChERERERERFRPDC5QkREREREREQUD5wWRERERERERKSjVJwXlCA4coWIiIiIiIiIKB6YXCEiIiIiIiIiigdOCyIiIiIiIiLSUSrOCkoQHLlCRERERERERBQPTK4QEREREREREcUDpwURERERERER6SjOCkoYHLlCRERERERERBQPTK4QEREREREREcUDpwURaZHwcNF0FSgRhero/k6dQjfz/jr7/dbBsch6OnpbBh0NG/5bu2u6ChqR23WjpqugEQ/nNdF0FTQihb5uHruTDR3tnxMavwVERERERERERPHA5AoRERERERERUTwwuUJEREREREREFA9cc4WIiIiIiIhIR6m46EqC4MgVIiIiIiIiIqJ4YHKFiIiIiIiIiCgeOC2IiIiIiIiISEepOCsoQXDkChERERERERElG/PmzUPevHlhYGAAe3t7XLhwIdayixcvRvny5WFsbAxjY2NUq1YtzvKxYXKFiIiIiIiIiJKFDRs2YMCAARg9ejQuXboEW1tbODk54e3btzGWP3bsGFq0aIGjR4/i7NmzMDMzQ40aNfDy5cvf+rsqEZGECIAoMX0N1XQNNCM8nF9XXRLK/a1TUujp6JhcHQxbT0fHX79+/1XTVdCIbBkMNF0FjcjtulHTVdCIh/OaaLoKGpFCXzev2adJqekaJIxLTz5ougo/VTxv+l8ua29vj1KlSsHT0xMAEB4eDjMzM/Tu3RtDhw796evDwsJgbGwMT09PtG3b9pf/rm5+C4iIiIiIiIhIKwQFBeHDhw9qj6CgoGjlgoOD8e+//6JatWrKNj09PVSrVg1nz579pb/15csXhISEwMTE5LfqyOQKERERERERESVZkyZNQoYMGdQekyZNilbOx8cHYWFhyJo1q9r2rFmz4vXr17/0t9zc3JAjRw61BM2v4N2CiIiIiIiIiHSVFsxWdXd3x4ABA9S2pU6dOsH/zuTJk7F+/XocO3YMBga/N62TyRUiIiIiIiIiSrJSp079S8kUU1NT6Ovr482bN2rb37x5g2zZssX52mnTpmHy5Mk4dOgQbGxsfruOnBZERERERERERFovVapUKFGiBA4fPqxsCw8Px+HDh+Ho6Bjr6zw8PDB+/Hjs27cPJUuW/E9/myNXiIiIiIiIiHSUShvmBf2GAQMGoF27dihZsiRKly6NWbNm4fPnz+jQoQMAoG3btsiZM6eyZsuUKVMwatQorF27Fnnz5lXWZjE0NIShoeEv/10mV4iIiIiIiIgoWXBxccG7d+8watQovH79GnZ2dti3b5+yyO2zZ8+gp/d9Es+CBQsQHByMJk3Ub6U+evRojBkz5pf/rkpEJEEiIEpEX0M1XQPNCA/n11WXhHJ/65QUesnrqtEv08Gw9VQ6GDSA1++/aroKGpEtw+8tiJhc5HbdqOkqaMTDeU1+XigZSqGvm6tNpEmp6RokjMtPP2q6Cj9VLI+RpqvwUxy5QkRERERERKSjdDTnn+B0M8VIRERERERERJRAmFwhIiIiIiIiIooHJleIiIiIiIiIiOKBa64QERERERER6SguuZIwOHKFNEKlUmH79u2argYRERERERFRvDG58pvevXuH7t27I3fu3EidOjWyZcsGJycnnD59WimTXBIHefPmxaxZszRdjT9u/do1qFW9CkoVs0ar5k1x/dq1OMsf2L8XDerWRKli1mjsXA8nTxxXe15EMG/ubFStWA6li9uga6f2ePr0yR+M4L/ZsG4NajtVgX0JG7Rp2Qw3rscd98H9+9CwXi3Yl7BB04bR4z586AC6d+2ISuXsUczaEnfv3P6T1f9PdDFmANi4fg3q16qKsqVs0b6VC27+JO5DB/ahSYPaKFvKFs0b18fpk+pxL1rgiSYNaqO8fXFUKWePHl074Ma1q38yhP9EV+PW1Xa+Yd0a1K5RBfbFbdCmxW/EXTyWuA8eQPcuHVGprD2KFU26cevqMWznlvVo26gW6lYqhT6dW+HOreuxln3y6AHGDRuAto1qwamMLbZuWB2tzPXL/2LU4N5oUb8anMrY4szxI3+y+v+Zru7vjpUt8M+UOni2sDH2Dq+KYvlMYi27bXAlvF3aLNpjTd9ySpk5HUtFe359v/KJEcpv2bh+DerVqooypWzRrpXLT/u1Qwf2oXGD2ihTyhYujevj1A/Hsb8XeKJxg9ooZ18clZPocWz9ujWoVaMKShe3RusWTXH9JzEf2L8XzvVqonRxazSJoS8XEcz3nI1qlcrBvoQNXDsnzTZOyQOTK7+pcePGuHz5MlauXIl79+5h586dqFSpEnx9fRP8b4WEhCT4e5K6fXv3YJrHJLj26In1m7ahUCFLdHftFOv+vHL5EoYOHoiGjZpgw+btqFylKvr17on79+8pZZYvXYx1a1ZhxOgxWL1uI9KkSYPuXTshKCgoscL6qf379mD61Mlw7dYTazduRcGChdDDtTP8Yov7yiW4uw2Ec6MmWLdpGypVqYYBfXvhQZS4AwMDYVesBPr0H5RYYfwWXYwZAA7s24NZ06ags2tPrFq/BQUKFULv7l1ijfvqlcsYMXQQGjRsjNUbtqJi5aoY1K+3Wty58+TFYPcRWLdlBxavWI0cOXKiV/fO8PfzS6ywfkpX49bVdr5/7x5M95gM1+49sXbTVhQs9JO4L1+C+5CBcG4YJe4+McRdPGnHravHsGOH9mHRnGlo1dEV85avR36LQhjevzsC/GKOO+jrV2TPkQsdu/eBSSbTGMt8/RqI/BaF0Gug+5+serzo6v5uUMoMY11sMW3nTVQbexA3nwdgQ/8KMDVKHWP5DvPPoGj/ncqj/Mh9CA0Lx85/XqiVO3zdW62c66JziRHOLzuwbw9mTpuCLq49sXr9FhT8hePY8G/HsTUbtqJSDMexPHnyYoj7CKzfsgNLVqxG9hw50TMJHcci+vJJcO3eE+s2bUPBQpbo4drpl/ry9Zsi2nj/Pj3VYl6xbDHWrlmF4aPGYNXaiDbewzVptfEkQaUFDy3A5MpvCAgIwMmTJzFlyhRUrlwZefLkQenSpeHu7o769esDiBjtAQANGzaESqVS/g0ACxYsgLm5OVKlSoVChQph1apVau+vUqmwYMEC1K9fH+nSpcOECRMQFhaGTp06IV++fEiTJg0KFSqE2bNnq70uNDQUffr0QcaMGZEpUya4ubmhXbt2cHZ2VsqEh4dj0qRJyvvY2tpi8+bNscZaqVIlPH36FP3794dKpYLq283PfX190aJFC+TMmRNp06aFtbU11q1bF+21ffr0wZAhQ2BiYoJs2bJhzJgxcX62o0ePRvbs2XHtJ1dgEtqqlcvRqEkzODdsDHMLC4wYPRYGBgbYvnVLjOXXrPZCmXLl0b5jZ+Q3N0evPv1gVbgw1q+NuAomIlizygtdXLujcpVqKFjIEn9N8sC7t29x5PChxAwtTqu9VqBR46Zo0LAxzM0tMHzUWBikMcD2bTHHvW71KpQpWw7tOnRC/vzm6Nm7b0Tc69YoZerWawDX7j3h4OCYWGH8Fl2MGQDWrloJ50ZNUd+5EfKbW8B9xBgYGBhg5/atMZZfv8YLjmXKoU37TsiX3xzde/WFpZUVNq1fq5SpWbsu7B3KIFcuM5hbFEC/QUPx+dMn3L9/N7HC+ildjVtX2/lqrxVo1OSHuA1+Ie6OnZDfPErca6PEXf9b3I5JN25dPYZtXb8KNes3glNdZ+TJZ44+Q0YgdWoD7N+1PcbyhQoXRZdeA1Cpei2kTJkqxjKlHMuhvWsvlK1Y9Q/WPH50dX93q1EQq088wvrTT3DP+wMGr/oXgcGhaFEuX4zlAz4H4+2Hr8qjYuGsCAwOw/8uPlcrFxwarlbu/ZekdVFzzX88jrX94Ti2MY7jWP8kdhxb5RWljZtbYMRP+vK1q71Qpuz3Nt6zdyxtvOv3Nj5+YkQbP5qE2jglH0yu/AZDQ0MYGhpi+/btsWY7L168CABYvnw5vL29lX9v27YNffv2xcCBA3Hjxg24urqiQ4cOOHr0qNrrx4wZg4YNG+L69evo2LEjwsPDkStXLmzatAm3bt3CqFGjMGzYMGzcuFF5zZQpU7BmzRosX74cp0+fxocPH6JNS5o0aRK8vLywcOFC3Lx5E/3790fr1q1x/Lj60LlIW7duRa5cuTBu3Dh4e3vD29sbAPD161eUKFECu3fvxo0bN9C1a1e0adMGFy5cUHv9ypUrkS5dOpw/fx4eHh4YN24cDh48GO3viAh69+4NLy8vnDx5EjY2NnHsgYQVEhyM27duwsGxjLJNT08PDg5lcO3q5Rhfc+3KlWg/MMqULYdrV64AAF6+eAEfn3ewd/j+nkZGRrC2sY31PRNbSEhE3FHrqKenB3sHR1y7eiXG11y7ekWtPAA4likba/mkRhdjBiLivnP7JkpHabN6enoo7eCI69euxPia69euotQPbdyhTLlYy4eEBGPblo0wNDJCwYKWCVX1eNHluHW1nf+nuB21PG6dPYaF4P7d2yhe0kHZpqenh2KlHHDrRuJeoElMurq/U+rrwTaPMU7cfqNsEwFO3HqLkuaZfuk9WpbPh20XnuFLcJja9jKFMuPmzPo4M6EmPFoXh3G6mBNvmhB5HLOP4Th2LZbj0rVrV9WOewDgqEXHsdj78jja+NUrsHeMHnNkX660ccfobfxqEmnjlLzwbkG/IUWKFFixYgW6dOmChQsXonjx4qhYsSKaN2+uJAUyZ84MAMiYMSOyZcumvHbatGlo3749evToAQAYMGAAzp07h2nTpqFy5cpKuZYtW6JDhw5qf3fs2LHK/+fLlw9nz57Fxo0b0axZMwDA3Llz4e7ujoYNGwIAPD09sWfPHuU1QUFBmDhxIg4dOgTHbx1Q/vz5cerUKfz999+oWLFitFhNTEygr68PIyMjtThy5syJQYO+D5Hu3bs39u/fj40bN6J06dLKdhsbG4wePRoAUKBAAXh6euLw4cOoXr26UiY0NBStW7fG5cuXcerUKeTMmTOOTz/h+Qf4IywsDJkyqR+cM2XKhMePH8X4Gh8fH2T6YUhxpkyZ4OPr8+35dxHbTKO/p4+PT0JVPV78/SPiNokWtymePH4c42t8fHxiLO+bRGL6GV2MGQAC/ANijNskU6ZY4/aNoY2bZMoULe6Tx49iuNsgfP0aCFPTzPBcuBQZjY0TNoD/SFfj1tV2nmBxm2pZ3Dp6DPsQ4I/wsDBkNFGvo7FJJjx/GvP+Tg50dX+bGKVCCn09vPugflHz3YevsMhu9NPXF8tngsK5MqL/in/Uth+58Rq7/32JZz6fkTdLOgxrZI11/cqj9sQjCBdJ0Bj+i/96HPtx2ltsx7FhUY5j85LIcSyyL4+pjT/5nTZu+r39Km08hs9Rm/r7xKDSlnk3SRxHrvymxo0b49WrV9i5cydq1qyJY8eOoXjx4lixYkWcr7t9+zbKli2rtq1s2bK4fVt9gbySJUtGe+28efNQokQJZM6cGYaGhli0aBGePXsGAHj//j3evHmjltjQ19dHiRIllH8/ePAAX758QfXq1ZXRN4aGhvDy8sLDhw9/K/6wsDCMHz8e1tbWMDExgaGhIfbv36/UJ9KPI1CyZ8+Ot2/fqm3r378/zp8/jxMnTsSZWAkKCsKHDx/UHpwnSaRZJUvZY83GrVjqtRaOZcth2OD+sc6JTk50NW4iIm3Uqlw+3HoegMuP1dcU2X7hOfZffYXbL99j7+VXaD37FIrnz4Sylpk1VNPEU7KUPdZu3Ipl345j7jyOESUYJlf+AwMDA1SvXh0jR47EmTNn0L59e2WURnylS5dO7d/r16/HoEGD0KlTJxw4cABXrlxBhw4dEBwc/Mvv+enTJwDA7t27ceXKFeVx69atONddicnUqVMxe/ZsuLm54ejRo7hy5QqcnJyi1SdlypRq/1apVAgPD1fbVr16dbx8+RL79++P829OmjQJGTJkUHtMnTLpt+odE+OMxtDX14+2EJyvry9MTWNe8M7U1BS+vj7Ry3/LmpuaRhyUfX1+/T0Tm7FxRNw/Hkh9faNn/yOZmprGXD6JxPQzuhgzAGQ0zhhj3H6+vrHGkSmGNh5T+TRp08Isdx5Y29hh5NgJ0E+hjx3bY54Tndh0NW5dbedxxh1HXx6tvI+Wxa2jx7D0GY2hp68fbfFafz9fGJskjTr+Cbq6v/0+BiM0LByZ06svXps5vQHevv8a52vTptKHc2kzrDn18xFNT30+w+fjV+TLYhiv+iaU/3oc8/vN49ioJHQci+zL493Gfb6XV9r4b3yORPHB5EoCKFy4MD5//qz8O2XKlAgLU5/XaWVlpXa7ZgA4ffo0ChcuHOd7nz59GmXKlEGPHj1QrFgxWFhYqI02yZAhA7Jmzaqs7QJEjC65dOmSWv1Sp06NZ8+ewcLCQu1hZmYW699OlSpVtDhOnz6NBg0aoHXr1rC1tUX+/Plx7969WN4hbvXr18fatWvRuXNnrF+/PtZy7u7ueP/+vdpjsFv8V/NPmSoVrAoXwflzZ5Vt4eHhOH/+LGxsi8X4Ghs7O5w/p76a/LmzZ2BjZwcAyJkrF0xNM+P8+e/v+enTJ1y/djXW90xsKVN+i/u8etwXzp2Dja1djK+xsbXDhSjlgW9xx1I+qdHFmIGIuC2tiuDi+e9tNjw8HBfPn4O1jV2Mr7G2sVUrDwDnz52Jtfz39xWE/EbS90/S5bh1tZ3HGPf5n8R9Tsvj1tljWEoUKGSFy/+eV7aFh4fjyj/nUbho4q3blth0dX+HhIXj6lN/lLfKqmxTqYDyVlnwz8O4R1vUK2WGVCn1sfns05/+nezGaWCSLjXeBMSdsEkskcexCzEcx2xiOS7ZxOM49jsXbf+UyL78QrS+PI42bmuHCzG18W99eWQbj9rfR7Zx2yTSxpMKlSrpP7QB11z5Db6+vmjatCk6duwIGxsbGBkZ4Z9//oGHhwcaNGiglMubNy8OHz6MsmXLInXq1DA2NsbgwYPRrFkzFCtWDNWqVcP//vc/bN26FYcOxb1SdYECBeDl5YX9+/cjX758WLVqFS5evIh8+b6vkN67d29MmjQJFhYWsLS0xNy5c+Hv76/c4cfIyAiDBg1C//79ER4ejnLlyuH9+/c4ffo00qdPj3bt2sX4t/PmzYsTJ06gefPmSJ06NUxNTVGgQAFs3rwZZ86cgbGxMWbMmIE3b978NEkUm4YNG2LVqlVo06YNUqRIgSZNmkQrkzp1aqROrX7F4mvof/pz0bRp1wEjh7mhSJGiKGptg9WrViIwMBDODRsBAIa7D0GWLFnRt/9AAECr1m3RqX0brFyxDBUqVMS+vXtw88YNjBwzDkDECJ1Wbdpi8d8LkCd3HuTMlQvz5s5G5ixZUKVqtYSpdAJo3bY9Rg0fisLf4l77Le4GzhFxjxjmhixZsqBPv4i4W7Rugy4d2sJr5TKUL18J+/ftxq2bNzFy9DjlPd+/D8Brb29l+teTJxFXijKZmipXDjRJF2MGgJZt2mHsSHdYFSmKIkWtsW61FwIDA1HPOWKNptHD3ZA5S1b06jsAANC8VVu4dmqL1SuXo1yFijiwbw9u37yJYSMj1n4K/PIFy5b8jQqVKsPUNDMCAgKwaf1avHv7BlWrO2kszh/paty62s7V4i5qg7Wrf4jb/Vvc/X+Ie8UylK9QCfv3fot7TBxxP056cevqMaxR8zaY9tdIFLQsgkKFi2LbhtX4+jUQNeo6AwA8xg2HaeYs6Ni9L4CIRXCfPY64MBUSGgLfd2/x8N4dGKRNi5y5cgOI+I6/evF9ivNr75d4eO8OjNJnQJZs2RM3wFjo6v5eeOAe5nYqjatP/HDpsR9cqxVE2tQpsP50xHfSs1NpePsHYsLW62qva1UuH/Zefgn/z+qJg3SpU2BQ/cLY9e8LvH3/FXmzGGJUExs8fvsJR2++TrS4fqZVm3YYM9Idhb8dx9b+cBwbNdwNWX44jnWNchzbv28Pbv3kOLbx23GsWhI5jrVp2wEjh7spffmaaH15RBuP7Mtbtm6Lzh3afOvLI9r4rZs3MOrHNr5oAXLnyYOcOXNhnmdEG6+chNo4JR9MrvwGQ0ND2NvbY+bMmXj48CFCQkJgZmaGLl26YNiwYUq56dOnY8CAAVi8eDFy5syJJ0+ewNnZGbNnz8a0adPQt29f5MuXD8uXL0elSpXi/Juurq64fPkyXFxcoFKp0KJFC/To0QN79+5Vyri5ueH169do27Yt9PX10bVrVzg5OUFfX18pM378eGTOnBmTJk3Co0ePkDFjRhQvXlyt3j8aN24cXF1dYW5ujqCgIIgIRowYgUePHsHJyQlp06ZF165d4ezsjPfv3//nz7VJkyYIDw9HmzZtoKenh0aNGv3n9/pdNWvVhr+fH+Z7zoGPzzsUsrTC/L+XKEMFX3t7Q0/1fYCXXbHimOQxDZ5zZmHurBnInScvZs2dhwIFCiplOnTqgsDAQIwbMwofP35AseIlMP/vJdESRJrkVDMi7gXz5sL3W9zzFi6OEvcr6EVJEdvZFcfEydMwz3MWPGfPRO48eTFjticsosR9/OgRjB75vT0NHRxxsHft3hPdevROpMhip4sxA0CNmrUR4O+Pv+fPga+PDwoWssKc+YuUaSKvX3tDpfe9jdvaFcNfk6ZigedszJ87E2a582DarLlK3Hr6+njy+BF279yOgAB/ZMiYEYWLWGPR8tUwtyigkRhjoqtx62o7d6pVG/7+fljgGUfcelHiLlYcE6dMw7y5UeKeE0PcI2KJu2fSiFtXj2GVqtXE+wB/eC2eD38/H+QvUAgTZsyH8bdFbt+9eQ29KN9vX5+36NHeRfn35rUrsXntStgUK4mp85YCAO7duYkhvTorZf6eMw0AUL12fQwaMT4xwvopXd3fOy4+Ryaj1BjiXBRZ0hvgxvMANJ95QlnkNqdJ2miL0JpnNYJDwcxoOj36XTHDwgWFc2VEszJ5kSFtSrwO+IpjN19jyvYbCA4Nj1ZeU2rUrA1/f38sjHIcm/vDcUzvh+PYhElTMd9zNubFcRzb9cNxbHESOo5978ujtPGF39u4t7f6sTtqXz53dkQbnzlnnlpf3r5jRBsfH7WNL0xabZySD5VIElgSmxJUeHg4rKys0KxZM4wfnzROCBJaQo1c0Tbh4fy66pJQ7m+dkkJPS8a8JjQdDFtPW8Y3J7DXP1kjI7nKlsFA01XQiNyuGzVdBY14OC/6KGxdkEJfN1ebSJPy52W0wY0XnzRdhZ8qmitprIkUF45cSQaePn2KAwcOoGLFiggKCoKnpyceP36Mli1barpqRERERERERMmebqYYkxk9PT2sWLECpUqVQtmyZXH9+nUcOnQIVlZWmq4aERERERERUbLHkSvJgJmZWbQ7ERERERERERH9lG7OVk1wHLlCRERERERERBQPTK4QEREREREREcUDkytERERERERERPHANVeIiIiIiIiIdJSKi64kCI5cISIiIiIiIiKKByZXiIiIiIiIiIjigdOCiIiIiIiIiHSUirOCEgRHrhARERERERERxQOTK0RERERERERE8cBpQUREREREREQ6irOCEgZHrhARERERERERxQOTK0RERERERERE8cBpQURERERERES6ivOCEgRHrhARERERERERxQOTK0RERERERERE8cBpQUREREREREQ6SsV5QQmCI1eIiIiIiIiIiOKByRUiIiIiIiIionjgtCAiIiIiIiIiHaXirKAEwZErRERERERERETxwOQKEREREREREVE8MLlCRERERERERBQPXHOFiIiIiIiISEdxyZWEwZErRERERERERETxoBIR0XQliH7X11BN10AzwnX06/rhi27u8DSp9DVdBY1Iqc/rJ7okTAf7tRR6unltKzA4TNNV0Ii0qXWzLw8L173vNgDk6bpB01XQiBdLmmu6ChphkEzmgdx7/UXTVfipgtnSaroKP5VMmgMRERERERER/TZe10oQunnphIiIiIiIiIgogTC5QkREREREREQUD5wWRERERERERKSjVJwXlCA4coWIiIiIiIiIKB6YXCEiIiIiIiIiigdOCyIiIiIiIiLSUSrOCkoQHLlCRERERERERBQPTK4QEREREREREcUDpwURERERERER6SjOCkoYHLlCRERERERERBQPTK4QEREREREREcUDpwURERERERER6SrOC0oQHLlCRERERERERBQPTK4QEREREREREcUDkytERERERERERPHANVeIiIiIiIiIdJSKi64kCI5cISIiIiIiIiKKByZXiIiIiIiIiIjigdOCiIiIiIiIiHSUirOCEgRHrhARERERERERxQOTK0RERERERERE8cBpQUREREREREQ6irOCEobWjVxRqVTYvn17rM8fO3YMKpUKAQEBCfp327dvD2dn5wR9T21TqVIl9OvXT9PVICIiIiIiIkpSklRy5d27d+jevTty586N1KlTI1u2bHBycsLp06d/+T3KlCkDb29vZMiQIUHrNnv2bKxYsSJB31NT8ubNi1mzZv3267Zu3Yrx48cnfIU0bP3aNahVvQpKFbNGq+ZNcf3atTjLH9i/Fw3q1kSpYtZo7FwPJ08cV3teRDBv7mxUrVgOpYvboGun9nj69MkfjOC/2bBuDWrXqAL74jZo06IZblyPO+6D+/ehYb1asC9ug6YNo8d9+OABdO/SEZXK2qNYUUvcvXP7T1b/P9m2aR1cGtRA9XLF0a1DC9y+eT3O8kcP7UebpvVQvVxxtG/REOdOn1B7/suXL5g1dQKa1K2K6uVLoK1LfezYsuFPhvCfbFq/Bg1qVUW50rbo0NoFN3+yrw8d2IemzrVRrrQtWjSpj9Mn1ff1ogWeaOpcGxUciqNqeXv0dO2AG9ev/skQ/pMN69agtlMV2JewQZuWv9HGS8TSxg8dQPeuHVGpnD2KWSfNNg7obtwb169BvZpVUaakLdq1dPlp3IcO7EPj+rVRpqQtXBrVx6ko7Tw0JARzZk6DS6P6KFe6OGpWrYBRw9zw7u3bPx3Gb1u/bg1q1aiC0sWt0bpFU1z/SdwH9u+Fc72aKF3cGk1i2N8igvmes1GtUjnYl7CBa+ekeQzbvGEtGtaphooOdujU1gU3b8Qd9+GD++DSqA4qOtihVbMGOHNKPe7xo4fBsXhhtUe/nl3/ZAj/iS6fs9RxqgKHEjZo+4v9WqN6teBQwgbNGtbDqShxh4SEYPaMaWjWsB7KlC6GGlXKY+QwN7x7++ZPh/FbOla1wKVp9fBicVPsH1kdxfKZxFp2x9Aq8FnRPNpjXf8KauUKZE+P1X3L49H8Rnj6dxMcHFUdOU3S/ulQfouutnFKHpJUcqVx48a4fPkyVq5ciXv37mHnzp2oVKkSfH19f/k9UqVKhWzZskGVwEseZ8iQARkzZkzQ99Q2JiYmMDIy+qN/Izg4+I++/4/27d2DaR6T4NqjJ9Zv2oZChSzR3bVTrG3uyuVLGDp4IBo2aoINm7ejcpWq6Ne7J+7fv6eUWb50MdatWYURo8dg9bqNSJMmDbp37YSgoKDECuun9u/dg+kek+HavSfWbtqKgoUKoYdrZ/jFEbf7kIFwbtgE6zZtQ6Uq1TCgTy88iBJ3YGAg7IqXQJ/+gxIrjN9y5OBezJvlgXadu2Ox1yaYFyiEQX1c4e8Xc8w3rl3G+JFDULt+QyxetQnlK1bB8MF98OjhfaXMvFkeuHD2FIaPnQSvDTvRpHkbzJ42EadPHE2ssH7q4P49mDV9Cjq79oTXui0oULAQ+vToAr9Y4r525TJGug9CfefGWLV+KypWrorB/Xvj4YPv+zp3nrwYPHQE1m3egUXLVyN7jpzo3b0z/P38Eiusn9q/bw+mT50M1249sXbjVhQs+JM2fuUS3N0GwrlRlDbeN4Y2XizptnFAd+M+sG8PZk6dgi7demL1hi0oWKgQenfrEmvcV69cxnC3QWjQsDHWbNyKSlWqYlDf3krcX79+xZ3bt9DZtTtWb9iCqTPm4OmTJxjQp0dihvVTEX35JLh274l1m7ahYCFL9HDt9Et9+fpNEcew/n16qu3vFcsWY+2aVRg+agxWrY04hvVwTVrHsEP792LOjCno1LUHVqzdjAIFLNG/Z9fY+7WrlzF62GDUa9AIK9duQYVKVeE2oDcePrivVs6hTDnsOnBceYybNDUxwvllOnvOsm8PZkydjK7f+rUCBQuhZxz92tUrlzDMbSAaNGqCtTH0a9+/3z2wdsMWTJs5F0+fPEa/3knn++1c2gzjmxfD1O03UGX0ftx8HoBNgyrB1Ch1jOXbzT2Fwn23K4+yw/YgNCwcOy4+V8rkzWyI3cOr4r73BzSYfAQVR+zD9J03ERQSllhh/ZSutvEkQaUFDy2QZJIrAQEBOHnyJKZMmYLKlSsjT548KF26NNzd3VG/fn21sj4+PmjYsCHSpk2LAgUKYOfOncpzP04LWrFiBTJmzIjt27ejQIECMDAwgJOTE54//97ZjBkzBnZ2dvj7779hZmaGtGnTolmzZnj//r1S5sdpQZUqVUKfPn0wZMgQmJiYIFu2bBgzZoxaPe/cuYNy5crBwMAAhQsXxqFDh346rSk8PBweHh6wsLBA6tSpkTt3bkyYMEF5/vr166hSpQrSpEmDTJkyoWvXrvj06VO0ek6bNg3Zs2dHpkyZ0LNnT4SEhCj1fvr0Kfr37w+VSqUkoXx9fdGiRQvkzJkTadOmhbW1NdatW6dWtx+nBeXNmxcTJ05Ex44dYWRkhNy5c2PRokVqr3n+/DmaNWuGjBkzwsTEBA0aNMCTJ0+i1XfChAnIkSMHChUqFOtn8yesWrkcjZo0g3PDxjC3sMCI0WNhYGCA7Vu3xFh+zWovlClXHu07dkZ+c3P06tMPVoULY/3a1QAisuNrVnmhi2t3VK5SDQULWeKvSR549/Ytjhw+lJihxWm11wo0atIUDRo2hrm5BYaP+hb3tpjjXrd6FcqULYd2HTshv7k5evbu+y3uNUqZuvUbwLV7Tzg4OiZWGL9l41ov1HVugtr1GiJvfnMMHDoKBgYG2PO/bTGW37x+NUo7lEWLNh2RN585OnXrjYKWhbFt41qlzM1rV+BUpwGKlSiN7Dlyon7DpjAvUOinI2IS09pVK+HcqCnqOTdCfnMLDB0xBgYGBvjf9q0xll+/1gsOZcqhTftOyJffHN169oWllRU2rv8ed83adVHaoQxy5jKDuUUB9Bs4FJ8/fcL9+3cTK6yfWu21Ao0a/9DG0/xCG+/QCfnzR2nj66K08Xrf2rhD0mzjgO7GvcZrJZwbN0X9b+3cfeQYGKQxwM7Y2vkaLziWLYe2HSLaefde6u3c0MgI8xctQ3WnWsibLx+sbe0wZNgI3L51E6+9XyVmaHFa5RXlGGZugRE/6cvXrvZCmbLfj2E9e8dyDOv6/Rg2fmLEMexoEjqGrVuzAvUbNkXdBo2QL78FhgwfjdQGBti1I+b9vXHtKtg7lkPrdp2QN785XHv0QSHLwti8YY1auVSpUiGTaWblkT59wo6Cji9dPWdZ47UCDb/1a/mj9Gs7Ym3nq+AYpV/r0bsvLAsXxoZv/ZqRkREWLF6GGjVrIW++/LCxtYPbsJG4fesmvJPI97u7kyVWHX+Idace496rDxi48iICg0PRskL+GMsHfA7G2/dflUelotkQGByGnReeKWWGN7HGoWveGLvxKq4/C8CTd5+w78or+HxMOkkGXW3jlHwkmeSKoaEhDA0NsX379p9mEseOHYtmzZrh2rVrqF27Nlq1agW/OK6YfvnyBRMmTICXlxdOnz6NgIAANG/eXK3MgwcPsHHjRvzvf//Dvn37cPnyZfToEXcGe+XKlUiXLh3Onz8PDw8PjBs3DgcPHgQAhIWFwdnZGWnTpsX58+exaNEiDB8+/Kefg7u7OyZPnoyRI0fi1q1bWLt2LbJmzQoA+Pz5M5ycnGBsbIyLFy9i06ZNOHToEHr16qX2HkePHsXDhw9x9OhRrFy5EitWrFCmNG3duhW5cuXCuHHj4O3tDW9vbwARWfwSJUpg9+7duHHjBrp27Yo2bdrgwoULcdZ3+vTpKFmypPJ5de/eHXfvRvzACgkJgZOTE4yMjHDy5EmcPn0ahoaGqFmzptoIlcOHD+Pu3bs4ePAgdu3a9dPPKKGEBAfj9q2bcHAso2zT09ODg0MZXLt6OcbXXLtyJdoPjDJly+HalSsAgJcvXsDH5x3sHb6/p5GREaxtbGN9z8QWEhIRd9Q66unpwd7BEdeuXonxNdeuXoF9lM8JABzLlI21fFITEhKCe3duoUQpB2Wbnp4eSpRywM1YprPcvH4VJUqr7+tSDmXUyhexscPpE0fx7u0biAgu/XMBz589QSn7Mj++nUaEhATjzu2bKGX/PQ49PT2UsnfE9WtXYnzN9WtXUdpePW4Hx3Kxlg8JCcb2LRthaGiEggUtE6rq8fKf27iD9rZxQLfjvnP7Juwd1Nt5afu44o7ezh3LlMP1OOL+9OkjVCoVDI3SJ0S14y32/R3HMezqFdg7Ro878nNSjmGO0Y9hV5PQMezu7VsoZa/en5eyd8SNWPqpG9evqPWDAGDvWBY3rqn3/5f+uYjaVcvBpWFteEwci/cJvH5ffPCc5df7tev/oV/79DHi+22UBL7fKfX1YJvXGMdvfZ+mJAIcv/kGpcwz/dJ7tCqfH9vOP8OX4IhRKSoVUN0mBx6+/oiNAyvi9hxn7B9ZHbWK5/wjMfwXutrGKXlJMsmVFClSYMWKFVi5ciUyZsyIsmXLYtiwYbgWwzy79u3bo0WLFrCwsMDEiRPx6dOnOJMAISEh8PT0hKOjI0qUKIGVK1fizJkzaq/5+vUrvLy8YGdnhwoVKmDu3LlYv349Xr9+Hev72tjYYPTo0ShQoADatm2LkiVL4vDhwwCAgwcP4uHDh/Dy8oKtrS3KlSunNgIlJh8/fsTs2bPh4eGBdu3awdzcHOXKlUPnzp0BAGvXrlXqWbRoUVSpUgWenp5YtWoV3rz53gEbGxvD09MTlpaWqFu3LurUqaPUy8TEBPr6+jAyMkK2bNmQLVs2AEDOnDkxaNAg2NnZIX/+/Ojduzdq1qyJjRs3xlnn2rVro0ePHrCwsICbmxtMTU1x9GjElIgNGzYgPDwcS5YsgbW1NaysrLB8+XI8e/YMx44dU94jXbp0WLJkCYoUKYIiRYrE+fcSkn+AP8LCwpApk/qBKlOmTPDx8YnxNT4+PsiUyTR6eV+fb8+/i9hm+uvvmdj8/SPiNokWtyl844g7WnnT2MsnNe+/7WtjE/UYjE0ywc835hj8fH1iKG8KP7/v5fsOGoa8+czRpG5VVC1TDEP6uqLf4OGwLV4y4YP4DwL8A2Lc1yaZMsW673x9fGDyQxs3yZQJfj+UP3niKCo6lkC50nZYt3olPBcuRUZj44QN4D+Ks43Hsr9jbONxfCeSIl2NOyHbeWzlg4KCMHfmdDjVqgNDQ8OEqXg8Re7veB/DTL+XV45hv/FZJraAgG/72+SH/WeSKdZ27htDOzf54XvhUKYcRo2fhDkLl6FHnwG4/O9F9O/tirCwpDFlQlfPWQJi6dd+3H9RRcT96/1aUFAQZs+chppJ5PudySgVUujr4d37r2rb3334iiwZ0vz09cXymaCwWUasOv5Q2ZY5vQEM06REnzpWOHLdG02nHcPuSy+wslc5lCmUOcFj+C90tY0nFSot+E8bJKlbMTdu3Bh16tTByZMnce7cOezduxceHh5YsmQJ2rdvr5SzsbFR/j9dunRInz493saxyFyKFClQqlQp5d+WlpbImDEjbt++jdKlSwMAcufOjZw5v2dvHR0dER4ejrt37yoJiB9FrQcAZM+eXanH3bt3YWZmpvbayL8Vm9u3byMoKAhVq1aN9XlbW1ukS5dO2Va2bFmlnpEjXIoUKQJ9fX21el2/Hvc0hbCwMEycOBEbN27Ey5cvERwcjKCgIKRNG/ciV1E/A5VKhWzZsimfwdWrV/HgwYNo67R8/foVDx9+7/Ctra2RKlWqWP9GUFBQtNFMop8aqVPHPO+UKLFs3bgGt25cw8TpnsiWLTuuXv4Xs6ZOgGnmLChZOulOoUgIJUvZY/WGrQgI8Mf2rZvgPqQ/lq/eABOTX7uqRqQtQkNCMHRQf4gIho4Yrenq0B9S3am28v8WBQrCokAhNKnvhEv/XIg26oWSj5CQELgN6gcAcB85RqN1SSitK+THzecBuPz4+6h+vW+/S/ddeomFByLWI7nxLAClLUzRvrIFztx9p4mqEiU7SWbkSiQDAwNUr14dI0eOxJkzZ9C+fXuMHq1+MpMyZUq1f6tUKoSHhydmNf9IPdKk+Xk2+lf8l3pNnToVs2fPhpubG44ePYorV67AycnppwvMxvW3Pn36hBIlSuDKlStqj3v37qFly5bKa6Imi2IyadIkZMiQQe0xdcqkOF/zK4wzGkNfXz/aIlm+vr4wNTWN8TWmptGvlPj6+sL0W9bc1DQi++/r8+vvmdiMjSPi/nEhOF9fH2SKI+5o5X1iL5/UZPi2r39cvNbfzzfa1etIJplMYyjvo1wtDfr6FYvnz0bPfoNRtnwlmBcohEbNWqJKtZrYsHrFH4njd2U0zhjjvvbz9Y1132UyNY02msfP1xcmP5RPkyYtzHLngbWNHUaOmYAU+vrYGcv898QWZxuPZX/H2Mbj+E4kRboad0K28x/Lh4aEYOjg/njt/QrzFi1NEle1I0Xu73gfw3y+l1eOYb/xWSa2jBm/7W+/H/afn2+s7TxTDO3cL47vBQDkzGWGjBmN8eL5s1jLJCZdPWfJGEu/Ftf+i4j75/1ayLfEqferV5ifhL7fvh+DERoWjswZDNS2Z05vgLfvA+N8bdpU+mhonxtrTjyK9p4hoeG4++q92vZ7rz4gZ6akcbcgXW3jlLwkueTKjwoXLozPnz/H6z1CQ0Pxzz//KP++e/cuAgICYGVlpWx79uwZXr36vojVuXPnoKen958XWC1UqBCeP3+uNl3n4sWLcb6mQIECSJMmjTKF50dWVla4evWq2udx+vTp365nqlSpog1zPX36NBo0aIDWrVvD1tYW+fPnx71792J5h19TvHhx3L9/H1myZIGFhYXa43dule3u7o7379+rPQa7ucerbgCQMlUqWBUugvPnzirbwsPDcf78WdjYFovxNTZ2djh/7pzatnNnz8DGzg4AkDNXLpiaZsb589/f89OnT7h+7Wqs75nYUqb8Fvd59bgvnD8HG1u7GF9jY2uHC1E+J+Bb3LGUT2pSpkyJgpaF8e/F88q28PBwXPrnPIpY28b4miLWtvj3ovq+/uf8WaV8aGgoQkNDodJT70b19PURLomf7I1JypSpYGlVBBcvfI8jPDwc/1w4B2sbuxhfY21jq1YeAM6fOxNreeV9RRL9bl+xibWNn/tJGz+vvW0c0O24La2K4MJ59XZ+Mc4+zRYXz8fQzqOUj0ysPHv6FPMXLUPGjElj2lukyP19IVpfHscxzNYOF2I6hn2LO/IYFrW/jzyG2SahY1ghq8L4J4Z+rWgs/VRRazu18gBw4fxZFLWJuf8HgLdvXuP9+wCYZk4aUyZ0/ZwlWjuPo1+zjqFfO/9DvxaZWHn27CkWLl6epL7fIWHhuPrEHxUKZ1W2qVRAhcJZcfFh3HdQrV86N1Kl1MemM0+iveflx36wyK6+pox5NiO88PmSYHWPD11t40mFSpX0H9ogySRXfH19UaVKFaxevRrXrl3D48ePsWnTJnh4eKBBgwbxeu+UKVOid+/eOH/+PP7991+0b98eDg4OatN0DAwM0K5dO1y9ehUnT55Enz590KxZs1inBP1M9erVYW5ujnbt2uHatWs4ffo0RowYAQCx3ibawMAAbm5uGDJkCLy8vPDw4UOcO3cOS5cuBQC0atVKqeeNGzdw9OhR9O7dG23atFGmBP2KvHnz4sSJE3j58qUy37BAgQI4ePAgzpw5g9u3b8PV1VUtMfRftGrVCqampmjQoAFOnjyJx48f49ixY+jTpw9evHjxy++TOnVqpE+fXu2RUFOC2rTrgK2bN2Ln9m149PAh/ho3BoGBgXBu2AgAMNx9CGbPnP49ptZtceb0SaxcsQyPHz3EgnlzcfPGDTRv2RpAxL5t1aYtFv+9AMeOHMb9e3cxwn0IMmfJgipVqyVInRNC67btsW3zJuzcERH3xPERcTdwjoh7hLsb5kSJu0XrNjhz+hS8VizD40ePsHDeXNy6eRPNW7ZSyrx/H4C7d24rU76ePH6Mu3duK/NdNa1Zy7bYvWMz9u3agSePH2LGlPEIDAxErbrOAIAJo92xaN5MpXyT5q1x4expbFizAk+fPMLyRfNw9/ZNNGwWMeoqnaEh7IqXxMI503H53wvwfvkCe3dtx/49O1G+YsxT+zShZZt22LF1E3bt3I7Hjx5iyoSxCAwMRN0GDQEAo0e4Yd6cGUr55i3b4uyZU1jjtRxPHj/CogWeuH3rJpo1j4g7MPAL5s+ZievXrsD71UvcvnUT40cPx7u3b1C1upNGYoxJ67btsW3Ltzb+KIY2PswNc2bF0MZXfmvj87+18RZxtPEnSauNA7obd6u27bB9yybs2hHRzif9FdHO6zlHtPNRw9zgOTtKO2/VFmfOnMLqlRHt/O/5nrh183s7Dw0JwZCB/XD75k38NXkqwsLD4OPzDj4+7xASkjSSiADQpu23Y9i3vnxCtL58iFpf3vLbMcwryjHs1s0YjmGLFuDY0W/HsGERx7DKSegY1qJVe+zcthm7/7cdTx49hMfEsfgaGIi69SP299iRQzF/7vf93axlG5w7ewprV0Xs7yULPXHn1g00cYlo51++fMbcmVNx49pVeL96iYvnz2JI/17IZZYb9o7lNBJjTHT1nKXVt37tfz/0a/W/tfORw9wwd1bUdt4GZ0+fwqof+jWXb/1aSEgIhgzoi1s3b2BCEv1+L9h/B20qmsOlbF4UyJ4e09qWRNrUKbDuZMSIlHld7DGiiU2017Uqnx97L72A/+focXjuvQ3n0mZoUzE/8mUxRKeqBeBklwPLjtyPVlZTdLWNU/KRZNZcMTQ0hL29PWbOnImHDx8iJCQEZmZm6NKlC4YNGxav906bNi3c3NzQsmVLvHz5EuXLl1cSFpEsLCzQqFEj1K5dG35+fqhbty7mz5//n/+mvr4+tm/fjs6dO6NUqVLInz8/pk6dinr16sHAwCDW140cORIpUqTAqFGj8OrVK2TPnh3dunVT4ti/fz/69u2LUqVKIW3atGjcuDFmzJgR6/vFZNy4cXB1dYW5uTmCgoIgIhgxYgQePXoEJycnpE2bFl27doWzs7Pa7ah/V9q0aXHixAm4ubmhUaNG+PjxI3LmzImqVasifXrNr8YOADVr1Ya/nx/me86Bj887FLK0wvy/lyhDR197e0NP9T0HaVesOCZ5TIPnnFmYO2sGcufJi1lz56FAgYJKmQ6duiAwMBDjxozCx48fUKx4Ccz/e0mSWiPGqVZt+Pv7YYHnXPh+i3vewsVR4n4FPb3vSUC7YsUxcco0zJs7C56zZyJ3nryYMccTFlHiPn70CEaP+P5dHTp4AADAtXtPdOvZO5Eii12V6rUQ4O+PZYs84efrA4uClpg6e6EyLejtG2/oRRmFUtSmGEaOn4KlC+di8fzZyGWWBxOmzkF+8wJKmVF/TcOi+bPw16ih+PDhPbJly4HO3fqgQWOXRI8vNtWdasPf3x+LFsyBr48PChaywuz5i5Th1G9+aOM2dsUwfuJULJw3G/PnzoRZ7jyYOnMuzC0i9rWenj6ePHmE3QO3IyDAHxkyZkThItZYtGw1zC0KxFgHTXCqGfHdXjAvjjYeJdFtZ1ccEydPwzzPKG18dgxtfGQsbbyH5ts4oLtx16gZ0c4Xzv/ezucu+N7OX79W/37b2hXDhMlTMX/ubMybE9HOp82eq8T99u1bnDh2BADQsmlDtb+1cOlKlCwV9xpqieV7Xx7lGLbw+zHM29tbbXRd1L587uyIY9jMOfPU9nf7jhHHsPFRj2ELk9YxrJpTLfj7+2HJgrnw9fVBgUKWmOn5t9Kfv/lhf9vYFsPYCR5YNH8OFnrOglnuPJgyY67SZ+np6ePh/XvYu2sHPn78ANPMWWDvUBZde/SOc024xKaz5ywx9GuecfRrtnbFMWHyNMyPpV979/YNjn/7fjdv4qz2txYtW4mSpewTJ7A4bL/wHJmMDDC0oTWyZDDAjWcBaDb9GN59iFiDMFemdAgX9ddYZDOCY6HMaDz1aIzvuefSSwxa+Q/61SmMia2K48Hrj+jgeRrn7yedhV11tY1T8qESEfl5Me21YsUK9OvXDwFx3E5vzJgx2L59O658u23Xn3L69GmUK1cODx48gLm5+R/9W8nd11BN10AzwpP31zVWH77o5g5Pk0r/54WSoZT6WjL2kxJEmA72ayn0kszA4UQVGJw07ryT2NKm1s2+POzHX/86Ik/XDZqugka8WNJc01XQCIMkM1Qhfp75Bf28kIblNkn6CbFk0hySpm3btsHQ0BAFChTAgwcP0LdvX5QtW5aJFSIiIiIiIkoSeFkrYTC58gd9/PgRbm5uePbsGUxNTVGtWjVMnz795y8kIiIiIiIiIq2R7KcFUfLEaUG6hdOCdAunBekWTgvSHZwWpFs4LUi3cFqQdnuuBdOCzDgtiIiIiIiIiIiSKm251XFSp5uXToiIiIiIiIiIEgiTK0RERERERERE8cBpQUREREREREQ6i/OCEgJHrhARERERERERxQOTK0RERERERERE8cBpQUREREREREQ6incLShgcuUJEREREREREFA9MrhARERERERERxQOnBRERERERERHpKM4KShgcuUJEREREREREFA9MrhARERERERERxQOnBRERERERERHpKN4tKGFw5AoRERERERERUTwwuUJEREREREREFA9MrhARERERERERxQPXXCEiIiIiIiLSUSrejDlBcOQKEREREREREVE8MLlCRERERERERBQPnBZEREREREREpKs4KyhBcOQKEREREREREVE8MLlCRERERERERBQPnBZEREREREREpKM4KyhhqERENF0Jot/1KUg3m20KfXZ9RMlVaBj7NV2hq/s6JCxc01XQiDSp9DVdBY0IDA7TdBU0Qlf3t3HtqZqugkYEHhis6SokiDcfQjRdhZ/Kmj6lpqvwU5wWREREREREREQUD5wWRERERERERKSjVLo3iPSP4MgVIiIiIiIiIqJ4YHKFiIiIiIiIiCgeOC2IiIiIiIiISEepeL+gBMGRK0RERERERERE8cDkChERERERERFRPHBaEBEREREREZGu4qygBMGRK0RERERERERE8cDkChERERERERFRPDC5QkREREREREQUD1xzhYiIiIiIiEhHccmVhMGRK0RERERERERE8cDkChERERERERFRPHBaEBEREREREZGOUnFeUILgyBUiIiIiIiIionhgcoWIiIiIiIiIKB44LYiIiIiIiIhIR6l4v6AEwZErRERERERERETxwOQKEREREREREVE8cFoQERERERERkY7i3YISBkeuEBERERERERHFA5MrRERERERERETxwOQK/WdPnjyBSqXClStXNF0VIiIiIiIiIo1hckXHtW/fHiqVCiqVCqlSpYKFhQXGjRuH0NBQTVct0WxcvwZ1a1aBY0kbtG3ZDDeuX4uz/MED+9Cofi04lrRBs0b1cOrkceW5kJAQzJk5Dc0a1UPZ0sXgVLU8Rg1zw7u3b/50GL9t/do1qFW9CkoVs0ar5k1x/VrccR/YvxcN6tZEqWLWaOxcDydPHFd7XkQwb+5sVK1YDqWL26Brp/Z4+vTJH4zg9+lizADj1rW42adxf8cluezvzRvWwrl2NVSwt0PHNi64eSPuuA8f3AeXhnVQwd4OrZo2wJmTx2MtO+WvMXAoVhjr13gldLXjTVfbOfe37uxv13rFcMerK/x39ceJOa1QslC2OMv3algCV5d2gt//+uH+Gld4dKuM1Cn1leeHtymDwAOD1R5Xlnb802GQjmJyhVCzZk14e3vj/v37GDhwIMaMGYOpU6dqulqJ4sC+PZgxdTK6duuJNRu2omChQujVrTP8fH1jLH/1yiUMdxsI54ZNsHbjNlSqUg0D+/bCg/v3AABfv37Fndu30Nm1B9Zs2IJpM+biyZPH6N+nR2KG9VP79u7BNI9JcO3RE+s3bUOhQpbo7toJvrHEfeXyJQwdPBANGzXBhs3bUblKVfTr3RP3v8UNAMuXLsa6NaswYvQYrF63EWnSpEH3rp0QFBSUWGHFSRdjBhi3rsXNPo37Wxf298H9ezF7+hR0du2BlWs3o0BBS/Tr0RV+fjHHfe3KZYxyH4x6zo2wct0WVKhUFUMG9MbDB/ejlT125BBuXL+KzJmz/OkwfpuutnPub93Z300qFsIU10qYsPoMHHt44dqjd9g5sSkyZ0wbY3mXylYY36kCJq4+A7vOy9Btxn40qWiJcR0rqJW7+eQd8rrMVx5V+69LjHBIBzG5QkidOjWyZcuGPHnyoHv37qhWrRo2btyI9OnTY/PmzWplt2/fjnTp0uHjx48xvtfx48dRunRppE6dGtmzZ8fQoUPVRsFUqlQJffr0wZAhQ2BiYoJs2bJhzJgxfzK8OK32WoGGjZuivnNj5De3wLCRY2GQxgA7tm+Jsfy6NavgWLYc2nbohHz5zdGjV19YWhXGxvVrAABGRkaYv2gZajjVQt58+WFtawe3YSNx+9ZNeHu/SszQ4rRq5XI0atIMzg0bw9zCAiNGj4WBgQG2b4057jWrvVCmXHm079gZ+c3N0atPP1gVLoz1a1cDiLgSsmaVF7q4dkflKtVQsJAl/prkgXdv3+LI4UOJGVqsdDFmgHHrWtzs07i/dWF/r1u9Ag0aNUXdBo2Qz9wCbsNHw8DAALu2b42x/IZ1q+BQphxat4uI27VnHxSyKozN3+KO9PbtG0yfMgFjJ3pAP0XSu6GmrrZz7m/d2d99GpfE8r3XsOrADdx55ovesw8gMCgE7ZyKxljeoXAOnL35EhuO3sazNx9w+N8n2Hj0drTRLqFhgjf+n5WH74fAxAhHq6hUSf+hDZhcoWjSpEkDPT09NG/eHMuXL1d7bvny5WjSpAmMjIyive7ly5eoXbs2SpUqhatXr2LBggVYunQp/vrrL7VyK1euRLp06XD+/Hl4eHhg3LhxOHjw4B+NKSYhIcG4c/smSjuUUbbp6emhtL0jrl+9EuNrrl29Anv7MmrbHMuUxbVYygPAp08foVKpYGSUPiGqHW8hwcG4fesmHBzV43ZwKINrVy/H+JprV67AwcFRbVuZsuVw7dt6Oy9fvICPzzvYR/ksjYyMYG1jG+t7JiZdjBlg3DoXN/s0ZRv395UYX5Ms9ndIMO7evoVS9g7KNj09PZSyd8T1a1difM2Na1dQyl59fzs4lsX1a1eVf4eHh2PsiKFo3a4j8psX+CN1jw9dbufc3xGS+/5OmUIPxQpkw5HLT5VtIsCRy09R2ipHjK85d+sVihXIqiRT8mbLAKfS+bHvwiO1chY5M+LRuu64tbILlg+tA7PM0X/HECWEpJemJY0RERw+fBj79+9H79690bRpU5QpUwbe3t7Inj073r59iz179uDQoZiz2/Pnz4eZmRk8PT2hUqlgaWmJV69ewc3NDaNGjYKeXkQuz8bGBqNHjwYAFChQAJ6enjh8+DCqV6+eaLECQIC/P8LCwpApUya17ZkymeLJ48cxvsbXxwcmP5Q3yWQKXx+fGMsHBQVhzsxpcKpVB4aGhglT8XjyD4gt7kx4/PhRjK/x8fFBpkym0cr7+Pp8e/5dxDbT6O/pE8tnk5h0MWaAceta3OzTuL+B5L+/A/wDEBYWBhMT9f1nnCkTnjyJeX/7+vjAxCTTD+VN4ev7Pe5Vy5dAX18fzVq0TvhKJwDdbefc31El5/1tmj4NUujr4a3/F7Xtb/2/oJCZSYyv2XD0NjJlSIPDM1pCpQJSptDHov9dwdT155UyF+94o+vUvbj3wh/ZTNJheOsyODSjBUp0XY5PgSF/NCbSPUyuEHbt2gVDQ0OEhIQgPDwcLVu2xJgxY5AuXToUKVIEK1euxNChQ7F69WrkyZMHFSpUiPF9bt++DUdHR6iijNsqW7YsPn36hBcvXiB37twAIpIrUUUmbmITFBQUbS5oCFIhderU/zXkRBESEoKhg/pBBHAfMUbT1SEiihf2abpFl/b3nVs3sWHdKqxcu0XtHIaSJ+7v5KO8jRkGN3dA37kHcfGON8xzGmNa9yrw9nPE5DVnAQAHLn5PNt94/A4X73jj7mpXNK5oiZX7rmuq6pRMcVoQoXLlyrhy5Qru37+PwMBAZdoOAHTu3BkrVqwAEDElqEOHDvE+EKVMmVLt3yqVCuHh4bGWnzRpEjJkyKD2mO4xKV51AICMxsbQ19ePtjCYr68PTE1NY3xNJlPTaAsF+vn6INMP5UNCQjB0cH94e7/C/EVLk8wVPwAwzhhb3L6xxm1qqn7FRyn/7QqJqWnmiG0+v/6eiUkXYwYYt67FzT6N+xtI/vs7o3FG6Ovrw89Pff/5+/pGu2ofKZOpabTFT/19v1/lv3L5X/j7+cG5dlWULWmNsiWt8dr7FebM8IBz7Wp/JpDfpLvtnPs7quS8v30+BCI0LBxZjNUXr81inBav/T7H+JrR7cph3eGbWLHvOm4+8cHO0/cxavlJDHaxj3WNjvefg/DghR/Mc2RM4Ai0m0oL/tMGTK4Q0qVLBwsLC+TOnRspfljQq3Xr1nj69CnmzJmDW7duoV27drG+j5WVFc6ePQsRUbadPn0aRkZGyJUr13+un7u7O96/f6/2GDjE/T+/X6SUKVPB0qoILp4/q2wLDw/HxfPnYG1rF+NrbGztcCFKeQA4f+4MbKKUjzwpff70KRYsWo6MGY3jXdeElDJVKlgVLoLz59TjPn/+LGxsi8X4Ghs7O5w/d05t27mzZ2BjZwcAyJkrF0xNM+N8lM/m06dPuH7taqzvmZh0MWaAcetc3OzTlG3c33YxviZZ7O+UqVDIqjAunv++/8LDw3HxwjlY29jF+JqiNna4eEF9f184dxbWNrYAgFp16mP1xu3wWr9VeWTOnAWt2nbE7PmL/1gsv0OX2zn3d4Tkvr9DQsNx+f5rVLbLo2xTqYDKdnlw4XbMC2qnMUiB8HBR2xYeFv7ttTH/GE9nkBL5smeMNWFDFB9MrlCcjI2N0ahRIwwePBg1atSIM0nSo0cPPH/+HL1798adO3ewY8cOjB49GgMGDFDWW/kvUqdOjfTp06s9EmpKUOu27bFtyyb8b8c2PH70EJP+GoPAwEDUd24EABg1zA1zZ09Xyrdo1QZnzpzCqpXL8PjxI/w9fy5u3byJZs1bAYg4KXUb2Be3b97AX5OnIiw8DD4+7+Dj8w4hIcEJUueE0KZdB2zdvBE7t2/Do4cP8de4iLidG0bEPdx9CGbP/B53q9Ztceb0SaxcsQyPHz3EgnlzcfPGDTRvGTFXWaVSoVWbtlj89wIcO3IY9+/dxQj3IcicJQuqVE0aV4F0MWaAceta3OzTuL91YX+3aN0eO7dtxu6d2/H40UN4TByLr4GBqNOgIQBg7IihmD9nhlLepUUbnDtzCmu8luPJ40dYvNATt2/dQJNvcWfImBHmFgXUHvopUiCTqSny5M2nkRhjoqvtnPtbd/b3nC3/oENtG7SqXgSFzEwwp08NpDVICa/9NwAASwbXxriO5ZXye849RJe6dmhayRJ5smVAleJ5MKpdOew591BJukzqUgnlrHMhd9b0cCicAxvGOCMsXLDx6G2NxEjJG9dcoZ/q1KkT1q5di44dO8ZZLmfOnNizZw8GDx4MW1tbmJiYoFOnThgxYkQi1fT31ahZG/7+flg4fy58fd6hYCErzF2wWBk6+vr1K6j0vme+be2KY8LkaVgwdxbmzZmJ3LnzYvpsT1gUKAgAePf2DY4fOwIAaNHUWe1v/b10JUqWsk+cwH6iZq3a8Pfzw3zPOfDxeYdCllaY//cSZWj4a29v6Km+J8TsihXHJI9p8JwzC3NnzUDuPHkxa+48FPgWNwB06NQFgYGBGDdmFD5+/IBixUtg/t9LkszaOLoYM8C4dS1u9mnc37qwv6s71UKAvx8WL5gLX18fFChkiZnz/o4StzdUUS7q2NgVw7iJHvh73hws9JwFs9x54DFjLswtkt5dYuKiq+2c+1t39vfm43dhmiEtRrUti6zG6XDt0Vs0GL4ZbwMiFrk1y2KE8Cgj5CevOQuRiOlBOUwN4fM+ELvPPcSY5SeVMjkzG8JrWD2YGBnA530gztx8gYp918DnPW/HHBWXH0oYKok6h4MoBqtWrUL//v3x6tUrpEqVStPVAQB8CtLNZptCnz0fUXIVGsZ+TVfo6r4OCYt9fbXkLE0qfU1XQSMCg8M0XQWN0NX9bVx7qqaroBGBBwZrugoJ4sPXpN8/pzdI+pNuOHKFYvXlyxd4e3tj8uTJcHV1TTKJFSIiIiIiIqKkJOmnf0hjPDw8YGlpiWzZssHdPf4LyBIREREREVHSotKChzbgtCDSSpwWRETJja5OFdHFfk1X9zWnBekWTgvSLZwWpN0+asG0ICMtmBaU9GtIRERERERERJSEcc0VIiIiIiIiIl2le4NI/wiOXCEiIiIiIiIiigcmV4iIiIiIiIiI4oHTgoiIiIiIiIh0lIrzghIER64QEREREREREcUDkytERERERERERPHAaUFEREREREREOkrFWUEJgiNXiIiIiIiIiIjigckVIiIiIiIiIqJ4YHKFiIiIiIiIiCgeuOYKERERERERkY7ikisJgyNXiIiIiIiIiIjigckVIiIiIiIiIqJ44LQgIiIiIiIiIl3FeUEJgiNXiIiIiIiIiIjigckVIiIiIiIiIqJ44LQgIiIiIiIiIh2l4rygBMGRK0RERERERERE8cDkChERERERERElG/PmzUPevHlhYGAAe3t7XLhwIc7ymzZtgqWlJQwMDGBtbY09e/b89t9kcoWIiIiIiIhIR6lUSf/xOzZs2IABAwZg9OjRuHTpEmxtbeHk5IS3b9/GWP7MmTNo0aIFOnXqhMuXL8PZ2RnOzs64cePG732OIiK/V1UizfsUpJvNNoU+50MSJVehYezXdIWu7uuQsHBNV0Ej0qTS13QVNCIwOEzTVdAIXd3fxrWnaroKGhF4YLCmq5AgvoZqugY/Z/Abq8Xa29ujVKlS8PT0BACEh4fDzMwMvXv3xtChQ6OVd3FxwefPn7Fr1y5lm4ODA+zs7LBw4cJf/rscuUJERERERERESVZQUBA+fPig9ggKCopWLjg4GP/++y+qVaumbNPT00O1atVw9uzZGN/77NmzauUBwMnJKdbysRIi+mVfv36V0aNHy9evXzVdlUTFuBm3LmDcuhO3LsYswrgZt25g3IybkqfRo0cLALXH6NGjo5V7+fKlAJAzZ86obR88eLCULl06xvdOmTKlrF27Vm3bvHnzJEuWLL9VR04LIvoNHz58QIYMGfD+/XukT59e09VJNIybcesCxq07cetizADjZty6gXEzbkqegoKCoo1USZ06NVKnTq227dWrV8iZMyfOnDkDR0dHZfuQIUNw/PhxnD9/Ptp7p0qVCitXrkSLFi2UbfPnz8fYsWPx5s2bX67jb8xcIiIiIiIiIiJKXDElUmJiamoKfX39aEmRN2/eIFu2bDG+Jlu2bL9VPjZcc4WIiIiIiIiItF6qVKlQokQJHD58WNkWHh6Ow4cPq41kicrR0VGtPAAcPHgw1vKx4cgVIiIiIiIiIkoWBgwYgHbt2qFkyZIoXbo0Zs2ahc+fP6NDhw4AgLZt2yJnzpyYNGkSAKBv376oWLEipk+fjjp16mD9+vX4559/sGjRot/6u0yuEP2G1KlTY/To0b80JC05YdyMWxcwbt2JWxdjBhg349YNjJtxE7m4uODdu3cYNWoUXr9+DTs7O+zbtw9Zs2YFADx79gx6et8n8ZQpUwZr167FiBEjMGzYMBQoUADbt29H0aJFf+vvckFbIiIiIiIiIqJ44JorRERERERERETxwOQKEREREREREVE8MLlCRERERERERBQPTK4QEREREREREcUDkytEhMh1rbm+NVHysGbNGoSFhWm6GkT0h+ji8To8PFzTVUh0uhgzkTZjcoV0XkwHLl05aYmM8/79+wAAlUqlyerQH6Yr7VrXrV69GiNHjsSoUaN0LsEStY0HBwdrsCaaxe968jVlyhRcuXIFKpVKp/aziCi3Td27dy++fPmi4Rr9eVFj7tOnD3r27KnhGhHRzzC5QjotPDxcOXDdvn0bFy9exLt373TmB4lKpcK2bdtgaWmJS5cuabo69IepVCrs2bMHs2bN0omTchFRHrqkbt26aN68OY4ePYqRI0fqTH8GfE8Qjx49Ghs2bNBwbRJPZBt/8uQJAN1JlOvad/vUqVNYs2YNxo8fj5s3b+pEgsXX1xfA9zZ98OBBdO3aVTl3S65ERIn51KlTOHr0KJo3b67hWhHRzyTvnokoDlGvCIwYMQINGzaEi4sLHB0dMXz4cDx69EjDNfzz3rx5g3///Rdz585F8eLFNV2dRBF5IvrhwweEhoZquDZ/1sGDBxEUFAQgIpEYEhKCuXPnIk2aNMn6x1fkaLTAwECoVCqoVCrcuHEDz54903DN/rzw8HBkzJgR7u7uqFSpEo4cOZLsEyxr1qxR+msRQVBQEA4cOAALCwsN1yzxqFQqbN++HU2aNMG1a9c0XZ1EE9mP6coopXLlymHYsGHw9/fHyJEjcf369WSdYHFxcUHbtm3x4sULZdvnz59hYmICAwODZD1lJrJtb968GQsXLkS1atVQvnz5ZN2XR5Vc2zQlf0yukM6KPHBNmzYNS5Yswfz58/Ho0SPY29tj+fLl8PHx0XAN/6yrV6/CyckJO3fuRIkSJTRdnUQReSVo9+7d6NevH86cOZNsT1Tu378PJycnDBo0CMHBwdDT00PKlCnx+fPnZJ1YAQA9PT28evUKNWvWxPXr17F79244ODgoV0CTq8iEcVhYGIyMjDB06FBUqlQJhw8fTrYJlr1796Jt27ZYunQpnj17BpVKhbCwMLx+/RqBgYGart4fF/kD5Pnz55g7dy5cXV1hY2Oj4Vr9eVF/VM+aNQvNmjWDv7+/Bmv050UmkJo3b47mzZvj48ePGD16NO7du5dsEyw9evTA0aNHMWzYMDx9+hQAEBQUBBMTEwARfX1yHp34+vVrLF++HHv27IG3tzcAQF9fP1knlYCI73fkecr79+/Vnkuu+5qSDyZXSGeJCIKDg3H8+HGMGDECVapUwa5du7Br1y6MHz8epUuXRkhICL5+/arpqv4Rb9++Rfbs2XH//n18+vQJQPJfOE2lUmHr1q1wcXFBnjx5kDNnTujr6yvPJ6eDdoECBbBlyxYsW7YMQ4YMUdpxWFgY0qVLByB5xfujZ8+eIWvWrGjYsCEaN26M5cuXo1ixYsk25qgno/r6+vj69SvSp0+P4cOHo1q1ajh06FCyTLDUqlULc+bMwapVq7Bw4UI8f/4cadOmRcqUKZV2HjXm5Lb/VSoVTpw4gRkzZiBdunSoV6+epqv0x0Wdznv69Gl8+PABO3fuxPDhw/HhwwcN1+7PEBGkSpUKADB16lScOnUKT548wfbt2zF06FDcunUr2SVYwsLCULFiRRw+fBgbN27EsGHD4OfnB19fX+WzAKCMTkwOftx/2bJlw7hx41C9enUcOXIEXl5eAL4nlZKjqKPKJ0+ejEaNGqFmzZpYv349Pn36BJVKlezPVUm7pdB0BYg0KSwsDD4+PqhatSpOnDiBFi1aYNq0aXB1dUVQUBCWLFmC0qVLo1SpUpquaoKrXr06UqZMiaCgIHTv3h3r1q1DyZIl1eb5Jje3bt1Cv379MHfuXHTo0EHZfv/+fWTLlg1GRkZqJ+7armHDhlizZg1cXFwQHByMqVOnIjQ0FFmyZAGgvi5DUFAQUqdOramqJjgHBwdUrVoVW7ZsQa5cuZA/f37lueTWxqO22blz5+Lff//FvXv30KJFC7Ru3RojRowAABw+fBgAMH78eLWkorYKDQ1FihQp0LNnT4SHh2Py5MkICwtDs2bNkDNnTmTNmhUA1GINCAiAsbGxpqr8R9y8eRNz5sxBunTp8PTpU2TLlk3TVfqjItu6m5sbNm7cCBcXF9SuXRsrVqzA+/fvsXDhQhgZGWm4lgkrsr+aMWMGxo8fj02bNmHo0KHYtWsXNm/ejBEjRmDChAmwsrJKNv2bvr4+wsLC4OjoiKNHj6Jy5crInDkz0qdPj1evXmH16tX4+PEjMmfOjFSpUuH27dto1qwZ8uXLp+mq/ydR+/Fnz54hKCgIZmZmKFGiBMaNG4eRI0di8eLFSJ06NVxcXJQkQ3I5XwHUj83z5s3DlClTMGzYMPzvf//DjBkzcO3aNbi5uSFDhgzJLnZKRoRIR4SFhcW4vVGjRlKwYEExNDSUFStWKNtfv34tFStWlMWLFydWFf+Y8PBwERHx9vaWV69eyfPnz5XnDh06JPXr15cSJUrIxYsXRST2z0rbnT9/XkqUKCEPHz6UL1++yLx586RixYpibm4uVapUkVevXmm6igkicn+HhISIiMjWrVslZcqU0qtXL7G1tZUcOXJI69atpV69elKrVi1p0qSJ9O7dWymv7SLj2LNnj8ycOVNat24tNjY2cuzYMRGJaN+Rn1Fy4ubmJtmzZ5dRo0bJnDlzRKVSSdeuXSU0NFTev38v7u7u4ujoKD179tT673hM9Z89e7bkzp1bOnToIClSpJA8efKIg4ODVKxYUUqWLClFixaVCRMmaKC2f97q1aslU6ZM0rVrV3nw4IGmq/PHnTx5UjJmzKh8p0NCQmTfvn2SPn16adWqlbx//17DNUxY4eHhEhwcLPXq1ZMBAwaoPbd8+XIxNzeXRo0ayd27dzVUw4QTW9904sQJSZ06taRIkUKsrKykSpUqYmlpKaVLlxYHBwdxdHSU0NDQRK5twoh6PBo9erRYW1tL/vz5JV++fLJo0SIJCQmRK1euiIuLi5QvX142bNigwdr+eRcvXpSePXvK7t27lW3u7u5ib28vQ4cOVb7f2n4co+SJyRXSCVE74OvXr8v9+/flw4cPIhLRiRcvXlyKFSumlAkICJBatWpJuXLltPZgHSnyoL1jxw5xdHSU3LlzS7Vq1cTDw0Mps3//fmnQoIHY29vL2bNnNVXVBBcZe3BwsIiInD59WnLlyiUdO3YUCwsLadCggQwdOlRWr14tBQsWlDVr1miyugkiMuYTJ07IzJkz5e3btyIisn37dkmfPr1kypRJhg4dKlOmTJFhw4bJ4MGDZezYsXLt2jVNVjtBxJYwOXbsmDRt2lRsbGzk+PHjyvajR4/Ks2fPEqt6f9SZM2fE3Nxczp07JyIi//77r+jp6YmXl5dS5sOHD9KjRw/p0qWLVieXovbnc+bMkbFjxyr/9vT0FGNjY6lQoYK4u7vLxo0bZcOGDeLp6SkLFizQ+gRi5H57+fKlPHjwQF68eKE8N3/+fMmRI4cMHjxYHj16pKkqJoo9e/aImZmZ+Pr6isj3z2XLli2iUqmkV69eyjE+OWnRooW0bNky2ve3W7duki5dOqlQoYJWJ9eifrf37dsnq1atkhs3bij78vz585ImTRpp0qSJPHv2TDm2R/08tPkH919//SVZsmSR3bt3S2hoqFStWlXMzMzk5s2bIiJy6dIladGihVhaWsqhQ4c0XNs/Y8eOHWJlZSV58uSRM2fOKNtDQ0Nl2LBh4uDgIO7u7uLv76+5ShLFgckV0ilDhgwRCwsLSZMmjbRt21YOHjwoIhFXfiwsLCRv3rxSuXJlsbe3l2LFiikHbm1PsOzatUvSpk0rM2fOlBMnToi7u7vo6enJqFGjlDIHDx6USpUqSaVKleTr169a/eMrqlOnTomdnZ28e/dORESWLVsm3bt3lxEjRqidhDo4OMimTZs0Vc0EtXnzZkmfPr2MGDFC/v33X2X7nj17xMDAQNzc3DRYuz8jsr2eOnVKRo4cKSNHjlRLlh07dkxcXFykaNGisnHjRhkzZoxkzJgx2YxWOnz4sJQrV05ERDZs2CCGhoayYMECERF5//69nDp1SkREPn36pHxW2v4dHzRokOTKlUvGjh0rjx8/VrYvXLhQcuXKJSNGjBBvb+9or9PWBEvk/tq6davY2NhI1qxZpXjx4tKsWTOljKenp+TIkUOGDh2q1T+yo4qpnd65c0dSpUoV7Qr+o0ePJGfOnKJSqaRjx46JVcUE92PMkf8eM2aMmJmZyT///KP2/LRp06R8+fIyYsQIrU0uRI154MCBkjVrVsmcObMUKlRIRo4cqXyXT548KalTp5a2bdvKkydPYn0PbRIeHi4fPnyQKlWqKAnx3bt3S4YMGZR+PLLfOnv2rIwcOVLrz0tj8+nTJ+ncubNkzJhRhgwZIl+/flWeCwsLkxEjRkj+/PmVz4UoqWFyhZK1qCcZO3fulPz588v+/ftlxYoVUrlyZalWrZrs3btXREQeP34sw4cPl7Fjx8qiRYuUA5e2nohHevbsmVSuXFnmzp0rIiLv3r2TXLlySZkyZcTIyEhGjBihlD1y5IjalKHk4P79+5InTx4pWbKkcpUz6sFaRGTEiBGSO3dutR9o2urSpUuSJUsWWbhwYYzPb9u2TdKlSyft2rWTwMBAEdHeE9IfbdmyRYyNjaVhw4ZSr149KVSokAwbNkx5/tSpU9KuXTvJmjWrWFlZKdPgtE1MP54OHjwo+fPnl8WLF0uGDBlk/vz5ynO7d++WevXqycOHD5Vt2r7PN27cKFmzZpULFy4o26J+LnPnzpXcuXNL9+7dk00CTSQiiWZgYCBz5syRffv2yfz586VAgQJib2+vlFm4cKEYGBjIqFGjtP74FXWffvz4Ufn/0NBQ6dy5szg4OMiuXbuU7T4+PtKlSxfZsmWLpE6dWlavXp2o9U0IUWN++vSpPH/+XDl2iYiUK1dOzM3N5dixY/L69WsJDAwUZ2dnmTVrlvK91rYES9T+6MyZM1KxYkU5d+6c+Pn5ybBhw8Te3l769eunJFhOnTolKpVKxo0bp6kqJ6jw8HB5+/atWFhYyOvXr+XIkSNqCfIvX77IjBkzoo201PYES2zt9NOnT9KxY0cpWbKkzJ49W4KCgtRe8/fff2t97JR8MblCOuHAgQPSq1cvmTVrlrLt4sWLUrduXalataps3749xtclh87706dPMnLkSHn06JG8evVKrKyspFu3bvL27Vtp2bKlqFQqGThwoKar+UdEnrA9ePBAihYtKsWKFVNGsIiILF26VDp16iRZsmSRS5cuaaqaCWrp0qVSpkwZ+fz5s7LtxxOYdevWSZYsWeT169eJXb0/5ty5c2JmZqYkla5evSomJiaSIkUK6dGjh1IuICBAHj16pLWxR92XW7ZskT179khoaKh8+vRJGjVqJHp6ejJ69GilTGBgoNSrV09cXFy07gdXXMaPHy8NGzYUke/99I/xTZgwQRo0aKD1iaRIYWFhMnToUGnTpo2yLTw8XC5cuCAFChSQtm3bKttXrlwp9+7d00Q1E0zU/TZp0iTleL1//34JDg6Wa9euSbNmzcTS0lL++usvWbdunVSrVk0qVqwofn5+YmNjo3U/vqO24dGjR4u9vb2YmJhI8+bNlREN4eHhUrVqVcmTJ4/kyZNHChcuLAULFlQSadrc3tevXy9t2rSRbt26qW0fN26clC5dWvr3768kWK5evaq1ycPY9lGtWrWkUqVKYmhoKEuXLlW2P3/+XMqVKyfr1q1LrCr+cVHb+rp162TUqFEyZcoUOXz4sIhEnLu2b99e7O3toyVYIiWHc3RKfphcoWTvwYMHYmlpKWnTpo02HSIyweLk5CSrVq3SUA0TVuRB++HDh8oolMiRGuPHj5d69eopV8HGjRsnlpaWUrBgQXn9+rVWn5RFFXXIdGRM9+/fl6JFi0qpUqWUdUi2bNkinTp1ktu3b2uknn/C9OnTpUiRImpXeSMdP35ciT2m57XZ/PnzpXv37iIScbU3X7580r59e5k+fbqkTJlS3N3dNVzD+Iv6/RwyZIjkzp1bli9fLm/evBGRiOkiZcqUUaa4LVmyRJycnKRo0aLKjxBtTLBE1jlq/P3795dixYpF+0EZFBQkO3fuVMollylQkZo3b642SiXS1KlTxcHBQXx8fDRQq4QXtZ3OnDlTMmTIIGPGjBFHR0fJnz+/TJs2TYKCguTevXsyevRoMTU1FTs7O6lataoynbdMmTIye/ZsEdG+/T9q1CgxNTWVHTt2yNGjR6Vu3bqSO3du+fvvv5UyW7dulcWLF8v8+fOV74E2/9gMDw+XVq1aiZGRkTg4OETrq8aNGyeOjo7SoUMHtZE82pZgiRrXs2fP1KYurl69WvLnzy81atRQtn38+FFq164tlStX1ur9G5tBgwZJ1qxZlSn5KpVKJk+eLCIRCZZ27dpJmTJl5K+//lK+20RJGZMrpBMOHz4spUqVktKlS8vRo0fVnvvnn3/E0dFR+vbtq5G6JaSoc/JLliwpEydOVFv0q2nTplK7dm3l3/3795dp06Zp/cJ/4eHhSuz+/v6SOXNmqVixotrzIhGLGWfNmlVq1qypJBkip8Zoszt37ij/v3XrVtHT01NbZV8k4oSuV69esmjRIq37ofErgoKC5OzZsxIUFCRVqlSR9u3bi4jIkydPJEeOHKJSqaRfv34armXC8PDwkKxZs6ot9hfpyJEj4uLiIhkyZJAKFSpIq1atks3aUadPn1b+f/HixZInTx7ZsWOH2nfY399fypYtK2vXrhUR9b5Bm7x69UouX74sIiJr1qxRFiBfvXq1FC9eXJnOGmnz5s2SL1++ZDUFSkTk5s2b0rVrV2V9NJGI45aVlZV4eHgoxy5/f3+1hPHgwYPFzMxMKxf2PXbsmFhbWyvt/fDhw5ImTRqpUqWKMvUvJtr2/Y7pexkSEiIDBgyQ/Pnzy4QJE6JdBBg8eLB06dJFK5PEP3J3dxdbW1sxNjaWYcOGyb179yQoKEhGjBghBQsWlJIlS4qLi4s4OjqKjY1NsunHo9qzZ49kzpxZWYj9w4cP4unpKSlSpFCmsn/69EmcnZ21fiF20h1MrlCyEtcBd9++fWJvby9NmzaVEydOqD13+/btZHGwFvm+aOm8efOirZ+yePFiMTMzk169ekmXLl3ExMRE64eO3717V3r16iUNGzaUadOmiUjEyamZmZnUqlVLrWxgYKBUr15dVCqVVKhQIVns84cPH4qdnZ0yakNEpF27dpIhQwbZsWOHvHnzRnx8fMTNzU2yZMmSLBa5jOsE686dO2JjYyPnz58XkYjbj7do0UKWLl2q9bGHh4fLx48fpUaNGjJ16lQRiVgravfu3dKsWTPp0aOHkmh48eKFhIaGRrstt7a6cOGCqFQqGTNmjLKtcuXKUrBgQVm6dKncu3dPbt68KbVr1xZ7e3ut/gHy+fNnKVasmLi4uMjUqVNFpVLJokWLRCRiwdayZctKo0aNlARqeHi4DBw4UMqUKSMBAQGarHq8jB07VrkriojIpk2bJEuWLJI3b145efKkWtn+/ftLkSJFxMPDQ+3K/9mzZ6VHjx6SLVs2rZ3q+fbtWxk5cqQEBQXJvn37JHPmzLJkyRJ5+PChFClSRLJlyyYzZ87UdDXj5ce1dL58+aJ2dz9XV1cpXbq0TJkyRW2Kq8j3/l+bjt/h4eFq9fXy8pJcuXLJqlWrZNKkSZInTx5p0aKF3Lx5U4KDg+Xo0aPSsWNH6d27t3h4eCj9tzb34+PHj1dGWUZatmyZlChRQsLCwqJNA8ycObMyqvjr168xjmAkSoqYXKFkI+qBa9myZdK/f3/p06eP7N+/X3lu9+7d4uDgIE2bNo12svbje2ib8PBwJcMfdZFake9xPXnyRMaPHy/FixeXatWqyZUrVzRR1QRz5coVyZw5szg7O0vz5s0lRYoUMmPGDBGJuKNA9uzZpWbNmmqv6du3rxw6dChZLF4rEnHFNnLBv/79+4tIxAlYt27dJFWqVJIvXz4pVqyY5MyZU2t/bEQVeWJ16NAhGTRokDg7O4uXl5dcv35dRCKmAaZPn14mTJggwcHB4u7uLuXKldPa6RJR+6TIURgNGjSQ1q1by5IlS6Ru3bpSpUoVqVOnjhQuXFjq1asnYWFhasmF5HAy+uHDB5kxY4akSpVKLcHSpEkTsbGxET09PbGzsxNHR8dkcYX38uXLyl1vxo8fr/bctWvXpHz58mJrayuFCxeW2rVrS4YMGZSRLtro0KFD0qJFi2g/Htu2bSupUqWSiRMnyqdPn9SeGzRokGTOnFlt0dqAgADx8vJSW7w5KYvtnOPLly8SFhYmjRs3lmHDhiltuWnTplKsWDFp06aN1n6vo8Y8efJkqVOnjpibm8vEiROVRcaDg4Ola9euUrp0aZk6dWq0ESzaGrtIxIK9gwYNUpuKvn//filatKi4uLjEepzW5v7s2bNnoqenJ7Vq1VI7Fm/ZskXSpk2rXOSLbBvnzp2LtmB51OeJkjImVyjZGTJkiGTOnFm6desmlSpVEkdHR/nrr7/UEixly5aVqlWran1y4UfBwcFibW0tEyZMiPH5yJPToKAgrV9z4+rVq5ImTRrlbjCR01769u2rLHx24sQJsbCwEEdHR1mwYIH07NlTcuXKJS9fvtRk1eMlppNKf39/GTdunBQvXlxtceKDBw/KunXrZOPGjdHuMqDNtm7dKgYGBtKmTRupUqWKWFtbS8WKFZUpf2PGjJH06dOLubm5ZMqUSWuTSlFPJHfs2KHcTnn+/Pni5OQkRkZGMnr0aDl79qyIiAwfPlxtsVNtFdsPp48fP8qsWbOiLdp7584d2bdvn1y4cEH5zLT5Cm9oaKj4+PhIrly5JHPmzNK+fftot9598uSJ/O9//5OePXvK1KlT1aYGaqvIpNj27dvVRpc2b95cLC0txcvLK9oohjlz5ig/OrXtB3fU7/eNGzfkwoUL8vXrV+Vz+Pjxo1haWioXSz58+CAuLi6ybt26ZLGWkLu7u5iamsrChQvFw8NDSpQoIbVr11b2fXBwsHTr1k3y5s0ra9as0XBt/5tu3brJtm3bRCRif1+5ckUMDAwkVapU0UYf7d+/X6ytraV169Zy7NixxK/sH3bz5k3JlSuX1KxZU7mxwKNHj6R8+fLSqVMntVHUjx8/FktLyxgvghIldUyukNaLenKxaNEiyZcvn3IiunHjRtHX1xdra2sZMWKEcjKzZcsW6dq1a7LKgoeHh4ufn5+ULFlShgwZIiLqPzDu3bsnY8eOTRZz8p89eyampqbStGlTte0uLi5ia2srhQoVkjp16siKFSvkzp07UrFiRbGxsRE7Ozutvrob6dy5czJlyhS1bf7+/jJ+/HgpUqSIsv+To1evXomdnZ2yUKVIxN3AWrRoIZUrV5anT59KQECAnD9/XtauXStPnjzRYG3/u6j9mpubm+TKlUtWrlyprDHx5s0befr0qdprqlevHu0uG9omatzTpk1T5t1H+vjxo8ycOVNUKlW070Ck5NKvv3z5Us6fPy958+aVli1bRkuwJAdubm5qC81fv35dzM3NpUWLFso6DCIRIzasrKxiTLCIaPdV/SFDhki2bNkkY8aMkidPHhk+fLgy8qZbt25SqlQpGTJkiFSuXFlKliwZ692xtMm2bdukYMGCysiE48ePS4oUKcTGxkZq1KihJIyDgoJkypQpWrl//fz8ZODAgdEWYd2wYYNkyZJFGjVqFG0x/QMHDkiWLFnURudpu6j77saNG5ItWzZxcnISPz8/EYm4dXyZMmWkQYMGsnv3bjl58qTUrFkzxkWNibQBkyuktZo1aya7du0SkYgT8tDQUPHw8FCGUG/dulUyZswoU6dOlU6dOknWrFll3Lhx0Q7S2tZ5/6y+8+fPFz09PdmyZYva9qFDh0qFChW0dnpEVI8fP5ZSpUpJ/fr1lav5kyZNkrRp08r48eNlyZIlYmlpKQUKFJD79++LiMi7d++0duHeqPO13717J66urmJtba1MgYr08eNHadiwoZiYmEjPnj01UdU/7tmzZ5I9e3bZunWr2vb9+/dLoUKFlKuEycWkSZMkW7Zscvr0abWT9Mj24OfnJ2fOnBEnJyextrbW6tuxRu3bbt68KYMHD5Y0adLI8uXL1coFBARI/fr1RaVSyciRIxO5ln9G1AW5I9fKijxWHT58WPLmzSutW7dWpk24u7vL9OnTNVPZBBIQECBt27YVBwcH5e4gIiJr164VBwcHad26tfIjWyTimG9tbS0LFixQ7oCnjaKeg2zdulXMzMxk165dcu/ePRk5cqSUKVNGOnbsKL6+vnL58mXp3r27ODg4SJMmTZQ+QNvOW350/PhxZfTZzp07xdjYWJYuXSo7duyQDBkyiJOTkxw4cEDtNdqUYPmx/12xYoXaBYHVq1dLjhw5pE+fPtHWvbtw4YJWxfqrIhNp169fl+zZs0v16tWVczIvLy9p2LChqFQqsbOzk0qVKiWL6Z2km5hcIa306dMnadq0qaRJk0YOHTqkbPfx8ZGXL1/K06dPpUiRIsrJ540bN8TU1FTMzMxk3rx5IqKdPz6mTZsma9euVaa9xCQ4OFgGDRokKpVKevXqJQMHDpSOHTuKkZFRshi1EenevXtSs2ZNqV+/vnTu3FmyZMki+/fvV55/+vSpqFQqZX9rm8iT56g/qNeuXSv9+vWTf//9V3r16iX29vbKwqaRpk2bJpaWllKnTh15/fp1otb5T4n6XX3+/LnY2dnJ/PnzRUT9R0apUqWkU6dOiV6/P+XDhw9StWpVZfj4s2fP5ODBg9KuXTtxc3OTd+/eyfnz58XZ2VmcnZ2Tzcno0KFDpXv37nL58mUZPny4GBkZyZIlS9TKuLm5Sfny5aVSpUpa2ZdHFVn/HTt2iKOjo+TOnVuqV68ua9asUaZyHjlyRAoWLCgVKlSQOnXqSOrUqdVGdmirN2/eSJ8+faRChQoybtw4Zfu6deukZMmS0RIsVatWlVatWmnlPv9xxI2Xl5dMnz492jTeBQsWSJEiRWTZsmUiErGYZ1BQkNYuTn337l3lR/To0aPlxYsXIiLi6+srHz58kIoVK8rEiROV8sWLF5d8+fIpU1y1cV9HCg8Pl8+fP0v16tXFwcFB7U5PK1eulJw5c0qfPn2Ui0BRaXs/HtXZs2dFpVIpd7i7ceOG5MiRQ6pVqybv379Xyt29e1eePn2aLKZ3ku5icoW0lp+fn7JoZ+SP6sgOec+ePVKwYEFlSsCpU6ekcePGMm/ePK2+4lOvXj1JkyaNbN26Nc4Ei0jEgbt27dpSvnx5adWqlbLgZ3Jy9+5dqV69uqRJk0a5U1B4eLgEBwfLixcvxNbWVjZt2qThWv6+yDZ6/fp1GTNmjISFhcm7d+8kX758MmvWLBGJuBtM5BXNqAmWoUOHyuTJk8XX11cjdU8oISEhsa4rEJlMi/rjMjw8XGrXrq12kq7tAgICpFKlStKvXz9ZsWKFNGrUSCpXriwVKlSQkiVLSpcuXUQkYmFnbT4Zjbp/T58+LZaWlspVzlevXom7u7ukT59e+bH5+fNncXFxkQ0bNsT4Htpo165dYmhoKOPGjZPLly9L3bp1pWjRojJt2jRlfazIhTC7dOkiN27c0HCN4yfqD8dDhw5Js2bNxMLCQm00TmSCpU2bNmrfdW28a0iZMmVk5cqVyr8/f/4sefPmFZVKJe3atYtWvlmzZlKqVCkRUY9Tm2IWEfnnn38kR44csnz5cunVq5eoVCq1O0I9f/5ccufOLevXrxeRiLu7tWzZUtasWaO152ox7SNvb29p1qyZlCtXTrnzl0jEeVru3Lmlbdu20e7umJw8ffpUKlasqHaxKzLB4uTkFOOIam3d/0RMrpBW8/PzE1dXV0mVKpXaENLIKQLz58+X58+fS926daVHjx7KQU/brghEHYHQpk0bSZ8+vWzevPmnCZYvX76IiGj1EOqfefDggdSoUUNq1aqltgjiyJEjJV++fFq3kGvkCcWVK1eUkTdHjhyR8ePHS7du3dSufj5//lz69Okj1tbWUqpUKWnevLkYGhrGeBVMW/y4+Oz+/fulVatW0r59exk7dqyyvX79+pI5c2bx8PCQFStWyMCBAyV9+vTR5rBri9hOJGfPni3FixcXQ0NDGTFihLLAX8+ePaV9+/a/9B7aYvr06dK/f3/p1auX2vaXL1/KyJEjRaVSSalSpaRw4cJia2ur1VOgonr69Kk4ODgo0/w+fPggZmZmUqBAAbGyspIZM2YoI1iCg4O17vgVlwEDBkjVqlWlevXqkjlzZsmTJ4/aSI7169eLvb291KlTRy2hpG1tfcmSJcpxOPK4/erVK6lcubKYmZnJ1atX1crPmjVLKlSooNxaXZv16dNHMmXKJGnTplWm8UbeevfJkydSsWJFadu2raxZs0Zq1aolVatW1crbLYuo1zfq7YNFIvZ348aNoyVYFixYIA0aNNC6WGMTW//k5uYm2bNnVzuHuXHjhpiZmUmpUqW0+jbyRFExuUJaJaaDj4+Pj3Tp0kUtwfL69Wtp0aKF5MmTR3LkyCElSpRQhsxr24n43LlzpVGjRmqLGbZs2fKXEizJ4Y4CvyJyipCTk5NcunRJpkyZIgYGBlp3l5jI9n3z5k1JkyaNMic98odlwYIFlavYkScwb9++lU2bNkmzZs2kQ4cOWj1C6eDBg5I5c2bl6tbBgwdFpVKJi4uLNG3aVExMTKRMmTLy6NEjERHp0aOHODo6irm5uVSoUEFrp71F7dcWLlwoAwcOFGdnZ9m9e7cEBQWJn5+fEnOk6tWrS+/evRO7qn9Ux44dRaVSiaOjY7QT7a9fv8rhw4elf//+8tdffymJFW1NNETtkz9+/Chz586Vly9fire3t1hYWEj37t0lLCxMypYtKwUKFJCxY8fGuJCrtom6v9avXy8ZM2aUCxcuSGBgoLx580batWsnJUuWVFuDZdmyZdKpUyet/PH5Y9Jk/PjxMm7cOKV9e3t7i62trVhbW8upU6eUtcHKly8vzs7Omqhygonc115eXpI+fXrJlSuXLF26NNqoykWLFkn58uXF3NxcqlatqrXrykSt74wZM8TFxUUcHR3l77//VhYojkywlC9fXm2KkLYmk+Ly+PFj8ff3V/7t7+8vJUqUkOnTp6utI3flypVklVwiYnKFtEbUjvf169fy+PFj5d+BgYHSqVMnSZUqlezdu1dEIuZynzt3Tvbs2aMc5LVxyPzOnTslR44c0rFjR/n333+V7b+aYNEV9+7dk7p160qWLFkkZcqUWndnjahTgUxNTcXKykp57u3bt+Lh4SF6enqyYMECEVFf5DbSj3cl0Da3b9+W3r17i5WVlcyZM0emTZumTIMSiZgKVbBgQSlbtqyyzcfHR969e6c2b1tbDRkyRLJkySIjR46UVq1aSb58+aRHjx7KFe/379/L2bNnpVatWlK0aFGt7M8inT9/Xvn/qVOnypEjR0Qk4jNQqVSyePFiZeSdSMwJYm2OXyTirl+rV68WkYjjlUhE/I0bN1Z+fPfv31+yZs0qderU0erFyEePHq3c3SryeDxlyhSxs7NT24+RI02zZ88ebcFuEe368dm/f38pUaKE2m11IxPlM2bMUPqs169fS7FixSRt2rRibW0tLVq0kFKlSinHdW2/OPLq1St5+/at9OvXT/Lnzy+enp7KnWIiBQcHy5MnT7R6emOkoUOHiqmpqUydOlX69u0rxYsXl9atWyu3S3/16pU0bdpULC0tZceOHSISsY+1fT+fPHlSpk2bJtOmTZP+/ftLvnz5pEmTJrJt2zblO9+jRw9xcnJSXvPj91mbvt9EsWFyhbTOsGHDxMrKStKnTy81a9aUZcuWSWhoqAQHB0unTp0kderUsm/fvmiv08YrnJEH2/3790vu3Lmlffv2TLDE4c6dO1K/fn2tW48g6hWctGnTSqVKlSRHjhxqIxP8/f2VE3MvLy8RSV4jkyJjuH//vvTv319sbGzEzMxMVq1aJSLfE0dPnz4VY2NjZZ2Z5BC7SMQtOPPnz68kBQ8cOCApUqSQtWvXKmWOHTsmlSpVknr16mn14rV3794VS0tL6dy5s/Tr10/09fXVRly5urpK2rRpZe3atclySmN4eLgEBgZKgwYNpFmzZmrPtWnTRpo1a6b8uOzXr594eXmJt7e3JqqaIE6fPi1FihSRatWqycuXL5Xty5cvFxsbG2VttMh+8MyZM5IhQwbJmzevLF26VES083t+9+5dsbOzk5o1a8rhw4eV7R4eHqJSqWTq1KlKEu3169dStWpVMTQ0lNOnTytltT1h/uOP5Z49e0q+fPlkwYIFyqiG9u3bq7ULbf6BvX79erGwsFD68cOHD4u+vr4UKVJEmjZtqkzZff78uQwbNkwr+++YLF68WDJnzizFixeXjBkziq2trdSuXVvGjBkjqVKlkkaNGsnixYvlzp07YmhoKOvWrdN0lYn+GCZXKMn7cch81qxZZcWKFbJz506pW7euODg4yIQJEyQsLEw+ffok3bp1E5VKpSyIqO0iTyr37dsXZ4Jl69atyfKHyO/S1pPRixcvSsqUKWXMmDESGhoqf//9t5iamqolWAICAmTEiBGiUqmUK97JReRJ5pMnT+T06dMyYMAASZMmjQwdOlQpExISIqGhoVKjRg3p16+fpqqaIH78sbhx40YpV66ciEScoBsZGSl3RPr06ZNyl4XLly9r/dXdT58+ydKlSyVTpkxiaGioTJ2IOlKla9euki5dOlm3bl2yWHciJvv375dUqVIpo3ZCQ0Old+/e4ujoKO7u7tKtWzcxMjJSkg/abMuWLVKlShWpUqWKcreYGzduSPr06aV///5qU56OHz8udevWldmzZ2vtD+3I/uzhw4diY2MjNWrUkIMHDyrPT548OcYEi7W1tdja2iaLfR5V1CRCr169xMLCQtq1ayeVK1eWTJkyaW1f9qOtW7cqt4ffvn27GBsby99//y3z588XIyMjadGihVy7dk3tNdqeYFm8eLGkSpVKNmzYIJ8/f5ZDhw5JlSpVpGLFivL+/Xs5d+6cDB06VPLnzy82NjaSKVMmad26tYSEhGjt95soLkyukNY4cuSIeHh4KFeyRCKGyQ8YMEBKlCghR48eFZHvUyiSy8E6qr1798aYYGnbtq2oVCrZuXOnBmtH8XH8+HHp06eP8u+AgIBYEyyjR48WlUql3GEhudi6daukSJFCrl27Jnfu3JFevXpJlixZZM6cOWrlqlWrpix6qo1XtKOKnBqwbNkyqV69uhw+fFiMjIzU7qqwefNm6dWrl7x7907Zpq0npZH7a9euXZI5c2YpUKCAuLq6KtujJlIiE+VRFyvXVlHbaWhoqISHh0toaKi0bt1aOnTooNyq1t/fX1xcXKRChQri4OAgV65c0VSV461atWpqd3TatGmTVKpUSapUqaJMEdqyZYvo6+uLq6ur7N69W27evCk1a9aU7t27a+0C9JEi6/3gwQMlwXLo0CHl+cmTJ4uenp5MmzZNGcXx+vVrKVmypOTOnVurEiyx9Uc/tvtIEyZMkPbt20urVq20dhTe48ePlfPMmTNnKsn/t2/fio+Pj9jb28uUKVNEJGLNqIIFC0ru3Lll+PDhIqL9xy4RkaNHj4pKpVIWm4+MafLkyZI1a1ZlxF14eLh8/fpVxo0bJ7Vr1xYDA4No6xERJRdMrpBWePjwoahUKlGpVPLXX3+JyPdOPCgoSKysrKLdYUJEe6/sRsYWEBCgzMWP3LZ79+4YEyxdunRR5vSSdovc1+/fv48xweLn5ycTJkyQW7duaaqKCe7Dhw8yceJE5ZbaIhHf+759+0qmTJmkX79+Mnv2bHFzc5NUqVKp3c5TW02dOlXatm0rIhH7NEeOHNFGJQUGBkrt2rWlbdu2yeJkPNKrV6/k0aNHsnjxYrG1tZUOHTooz0X9oRb5oyU5OHnypHK3p0jz58+XXLlyKQteikSMvvv69auyeLU28vHxkblz50abrrpx40apVKmSVKpUSUmw7NmzRwoXLiy5cuWSPHnyaPUC9LGtIXH37l2xsbGR6tWrqyVYIqcIrVmzRtn26tUrKV++vFqbSMqixvzw4UP5999/5d27d8r2qEmTqP8fdZSptn3HT548KQULFpT//e9/0qdPH1GpVHLv3j3l+Vu3bkmuXLmU6WAPHz6UFi1ayMqVK7U2MR6Te/fuSfny5aVBgwZy/PhxZfuUKVMkf/788urVKxFRbyP+/v7SsGFD6dSpkwQHB2vdd5zoZ5hcIa1x5MgRyZIli9SsWVPevXun1iF3795dmjRponVXPmISGdfOnTulXLlykjt3bqlbt65s3LhRuaobmWDp1KmT2sKQlPxETbBEnQqTnE5ILl26JOnSpRMbGxv53//+p/bcw4cPpVevXmJsbCx58uSRBQsWJIvEikjESB1jY2Pl9qQ7duyQ7NmzS7NmzeTkyZOyfft2cXJyEmtr62Rz2+EfffjwQTw9PcXW1lY6d+6sbO/Tp49aIkLbfnz9yN/fX1q0aCEqlUpcXV1l5cqVynNOTk5af2eYuEyZMkWmT5+u/DsywVK5cmUlwfLy5Uu5e/eunDt3TmunvUX9Abl+/XoZP368DBs2TDlGR50iFDXBsnr16mixasu5TNT+aNiwYVK8eHHJkCGD1KxZU7p27RrjNN0f+zBt7dMaNGggWbNmFSMjI2UaeuR+u3nzppQsWVL69u0rBw8elNq1a0uDBg2S5V2BIu/WWKNGDbl3754cPnxYUqdOLVu2bFErF3U/jxs3TqpVq5bYVSVKFEyukFY5cOCApE2bVtq3by/Pnj2T0NBQCQwMlBIlSki3bt00Xb0E87///U8MDQ1lzJgxcu7cOalRo4bY2trKrFmzlATL3r17xdDQUO1uIpQ8vX//XhYvXiwqlUrc3Nw0XZ0E9+LFC2nevLmoVCplAduoPzYePnwoXbt2lXLlymntHVN+/AERFhYmz58/lxo1asiECRNEJOIH+J49e6RIkSKSK1cuKVGihDRu3Fhrh83/TORn8vHjR/H09BQbGxtxcHCQmjVrSo4cObTux/Wv2Lt3r7Ro0UJy5colVapUkR07dsicOXOkQYMGWj0FKKqo7fTjx4/St29fMTAwUO50JqKeYHn27Fmc76FtBg0aJHny5BFnZ2dp1aqV2siUhw8fip2dndSqVUt27dql9jptbu+TJ08WU1NTOXLkiHz48EFat24t6dOnV9aKSk4i99OcOXMkbdq0UqBAAdm2bVu0W6WPHz9eihUrJrly5ZLy5ctr7WisX3Hv3j2pVauWFC9eXFKmTKmMvvzxexwZu7u7u1haWsqHDx+S5edBuo3JFdI6+/fvl3Tp0omlpaU4OzuLs7Oz2NraJpu75Tx9+lRKly4tM2fOFJGIxR9z5col5ubmUrRoUZk7d66SYDlw4IDaUFRKvgICAmTFihVy9+5dTVflj3j58qU0adJEMmTIIJcuXRIR9ROz+/fvy+vXrzVVvQQTubZGpEmTJkn69OmV4dMiESfvDx48UBuhp40/vH5lHYbI///8+bPs2LFDOnbsKF26dFHi1dYf2ZFxvXnzRu7fvy+BgYFKTD4+PnL79m2pWbOmVK1aVZkO5uHhockqJ7jIBYpfvHghI0eOVFukWSRiDZaqVauKjY2NvH37VlPVTBCRbX3Lli2SI0cOZSTDrl27ok37uX//vmTLlk369++vkbompPDwcAkICJBatWopifHIc7TFixeLSMR6I8nhB/SP/dm1a9fk/v370qhRI7G2tpa1a9eqLcotEjHd89q1a1o7Gut33Lt3T6pUqSJFixaVc+fOKdt/3PdPnjwRFxcX5ThPlNwwuUJJ0s8OxIcOHZLMmTOLlZWVHD9+XDkB19Y7xUT17t07mTNnjnh7e4u3t7eYm5sro1NKliwpVlZWMmHChGR7Bw2KXXI4QY2M4caNG3L48GHZs2ePknDw8/OT+vXri7GxsXIVPzmdjC5YsECaN28ue/fuVdtevnx56devX6x3T9DGIeRR63z79m159OiRWnIs6vOxtWtt3feR8Wzfvl1KlCghWbNmlSpVqoiHh4faj6+wsDA5deqUuLm5iYmJSbS7iGizRYsWSb58+ZRFmF++fCnDhw+PlmBZuXKl9OrVSyvbuEjEXfz8/PyUf8+ePVvat28vIhHJI0NDQ/n7779FJCJB/vjxYxGJuBWvticOI339+lXKli0rFy9elJ07d4qhoaEySikoKEiWLFkix44d00RVE0zU9nnt2jV58uSJ+Pr6Ks/Vq1dPrK2tZcOGDcp56ODBg9XOSbW1jf+O+/fvS82aNaVmzZrKdNeYfPr0KRFrRZS4mFyhJOG/HHQOHTokadKkEVdXV2WV9uQgNDRUWWF94MCB0rRpU+WOIpF3T6lXr55yYCfSNps2bRJTU1OxtrYWlUolFSpUUK5y+vn5SYMGDSRLlizyzz//aLimCWv27NnSunVrSZkypbRv315Zd2PixIlSuXJl5Xuu7Um0qPUfPHiwWFhYSMaMGcXR0VGZAiUSvd+PaUSLttq9e7cYGhqKh4eH3L17V7p06SJ58uSRgQMHRru6LSJavXhtTO7cuSOFChWSUqVKRUuwpE+fXm2KUCRtO4Z/+PBBChcuLHnz5lXu9jNp0iSpX7++bNq0KcZEkqurq1JWRPtijvq9jEwqffr0ScqXLy/VqlUTExMTtZgfPXokNWrUkLVr1yZ6Xf8ENzc3yZ8/v5iamkqrVq1k3759IhLxudSvX19sbGykf//+4uTkJBkzZtTaBHF83Lt3T+rUqSMlS5aMdkcgbe/XiX4FkyukcVFPsJcuXSqDBg2SDh06yMGDB6PNYf3Rvn37JEOGDNKyZUutHMkReaB58OCBPHjwQFncL3J7ixYtpGXLlspn1LdvX/Hy8lKSL0Ta5p9//hETExNZvHixvH37Vh49eiTNmzeXChUqyLJly0RExNvbW6pWrSr58uVLlusJHT9+XJo0aSIFCxaUmjVryqpVq0SlUsnEiRM1XbV4i9qfb9q0SXLmzCm7d++WTZs2ydixYyVNmjQyaNAgDdbwz3v58qVUqFBBWcQ1ICDg/+3de1xP9x8H8Ne3G7mFRJKlJCyXlLuQeyUp14ZNtmiIxZDlfonZcmljuTOXuY3czd1Say6TS0jZGLZREroQ9X3//vDo/PrKrqhOXs/Hw2Pz+X6+p/fx7Zzv5/M+n4uyhk69evVk7NixyvdVcViH4c+SZFevXpV69eqJo6OjToJl0qRJotFoZNu2bQUe66t28eJFadKkidSpU0fu3bsnJ0+eFAcHBylZsqTOIr5paWnStWtXGTFihGo/67yf87Zt28TNzU3Z0Sg6OlpMTU2lQ4cOIvLs9/r+/fvi7u4ubdu2VV0SKVfez2r//v3y1ltvycGDB+XLL78ULy8vad68uWzfvl2pO3ToUPH29hZvb+9iu1bWP3Hp0iUZPXr0GzFah+h5TK5QkfHxxx+LmZmZeHl5SceOHUVfX1+Cg4Pl5s2bf/m+Xbt2SdWqVVWbcNi6datUqlRJbG1txdraWpkykJWVJf7+/uLs7CyTJ0+W4cOHS7ly5ZQEDJGa5DZSly9fLg0aNJCMjAyl7MaNG9KrVy/p0KGD0hC9ffv23177apJ7rnm3WY+Pj5euXbuKi4uLaDQa6dWrV2GG+EodPnxY/Pz8ZPbs2UpZRkaGfP3111KuXDlZsWJFIUb3ej19+lTCw8MlISFBbt++LXZ2djJ06FDJzs4WT09PMTMzkyFDhrxwBIuabdy4Ufn/3N/zxMREsbe3l8aNGysJlhs3bsiSJUtU/VQ/7xoa169fl6ZNm4qzs7Pcv39fpkyZIlWrVpWQkBCJi4uTH374QVxdXcXBwUG1u37l7SQfO3ZM+vXrJxUrVpQBAwYoCZbVq1eLnp6etG7dWvnTsGHDYpFk2L59uwQEBOgkzKKioqRv377SrFkz2bFjh1Kenp6u6rWyXjUmWOhNw+QKFQmHDx8Wc3NznWkAy5cvl4oVKypPc//qBq3W+Zu///672Nvby/Lly2Xnzp0SEBAgenp6yhDa27dvi7e3t7Rs2VKaNGlSbHaToOIt77Wa26C+c+eOiDwbGm9nZ6d0tHIbnxcvXhSNRiORkZEFHO2r9Xyn6Z80rqOjo2XRokXFpiF+8eJFqVWrlpQpUybf7lYPHz4UHx8f+eCDD0REfZ3MF8k9h2vXrsn169dF5P+LuU6bNk28vLyUKRQhISFSq1Yt8fLyUu0DgVxZWVnK6NKbN2+KoaGhdO7cWXk9998lNjZWypcvL66urvkWpVbb73ze3cryLqLv6uoqGo1GmjdvLvfv35egoCBxcnJSyjp16lQskgyjRo2SBg0ayNChQ6Vz585SuXJl8fHxUdaSOXfunHzyyScyfvx4Wbx4sfL5qu1zzis+Pl5atmwp5cuXl6lTp+q8Fh0dLX379pWWLVvKli1bdF4rDvc2Ivr3mFyhImHnzp1iZ2cnSUlJOg2PhQsXSsmSJSUxMbEQo3u18j7BTkpKklGjRikNjwcPHkhQUJDo6ekpK+9nZmZKZmZmvl1GiIqyK1euKMP+N2/eLK6urvLgwQM5f/686Ovry6effqpT/+rVq2Jvb6/qHQTyNqa/+uorZQegP+tMvShhrMZOyIs6EREREWJvby/29vb5FjYMDAyUdu3aFYsnmrnnvm3bNqlfv74sWrRISSSKiAwZMkTatWun/A6MGjVK5s6dq9otxXN9++230qNHD2nUqJHMnDlTREQiIyPF0tJS3NzcdOqmpKRI06ZNRaPRyMCBAwsh2lcjMjJSXFxc5Pvvv9cp79Wrl9SvX18OHTokDRs2lGbNmklqaqo8evRIIiMj5ddffy0Wu8UcPnxYKleurLMTzBdffCHNmzcXHx8fZQTL89e12pJJL7qfbd++XZydnaVOnTr5tpf+4YcfpFOnTjJ48OCCCpGIijAmV6jAvahBHRERIUZGRvLLL7+IiCjrLCQnJ4ulpaXs3LmzQGN8XXK/tHfv3i39+/dXFv3K2xh/+PChBAUFSYkSJZQ1KIjUJCcnR6ZMmSIajUY+/vhj0Wg0snr1auX1xYsXi4GBgYSEhMj169fl3r17MmHCBLGystLZklhN8t7Xrly5IrVq1ZIGDRooI3TU1sH4p/Ked2pqqs55bt26VRwdHaVv375y/PhxpY6zs7OqO9nP27dvnxgbG0tYWJjOaBStViufffaZtGjRQnx9fcXPz0/KlCkjV69eLcRoX97ixYulXLlyMmrUKAkMDBQ9PT1lQerjx49L1apVpUuXLkr9R48eyfvvvy/nzp1T9XUQHx8vbdu2FXd3d2WUbc+ePcXe3l5u3LghIs/WmnBwcJCGDRvmS6CpPZm4d+9eMTMzk4SEBJ3yTz/9VEqWLCn9+vVTEiwi6hy5kfczysrK0hmdtHv3bunYsaN07dpVJ8EkIjrbLRPRm43JFSpQeb9sN2/eLAcOHFDKO3ToIE2bNtXpXN26dUtsbW2VemqV97yPHDkiZcqUES8vL/Hw8BCNRiNffPGFTv2HDx9KQECAVKxYkSNWSLXc3d1FT09Phg8fLiL/TzBkZ2fL8uXLxdjYWKysrKROnTpiYWEhP/30U2GG+5/lvb6nTZsmnp6e0qhRI9FoNFKvXj1lKoSaO5Z/Z8aMGdK4cWPx9PSU+fPnK+UbNmyQhg0bSsWKFaVjx47So0cPadKkiZJAV2MHLFdOTo48evRIunfvLmPHjtV5LfezTk1NlbFjx0qnTp2kffv2+XbPUJtly5aJoaGhREREKGU+Pj4SFhYmycnJkp2dLZGRkWJnZycNGzaUyZMnS5s2baRFixZK51PN10FCQoK4urpK165dxdnZWRo1aqRMicl1+fJlsbS0lP79+xdOkK9Y7jUaGRkpNjY2cvDgQRH5fyLi0aNHUqtWLWncuLH4+fkpCWW1yZscmTt3rri5uUn79u1lyJAhSjts165d0qlTJ+nataucOHHiL49BRG8mJleowOT90klMTJTy5ctL9+7dJTo6WkREjh49Km3btpU6derIjh07ZOvWrcrIDjU3xkT+3zj57bffZM6cORIWFiYiz56MzJo1S/T19WXx4sU670lLS9MZ0UKkJtnZ2dK7d29p37696OnpyYYNG0Tk2bWQez3Ex8fLrl27ZOvWrcqTXzVbsGCBlClTRo4ePSqJiYmydu1aadKkidStW7dYJ1iWLVsm5ubmEhoaKl5eXtK4cWPx9/dXXo+IiJC6detKy5YtdbZpzftUWK2ys7PFwcFBQkNDRUT3e06r1Srba4vI3+5+V9QdPXpUNBqNTJs2Tae8YcOGUr9+fSlbtqy0adNGVqxYIb/88ot07dpVOnbsqLNzSnHofCYkJEjHjh3FxMRENm/erJTnPbfr16+r9lr/q8+odevWUq9ePYmPj1fKfv31V+nTp49MnDhRbG1t800DVJvg4GCpVKmSTJ48WYKCgsTKykrq1asncXFxIvJsCqCrq6s0a9ZMLl68WMjRElFRw+QKFbjx48fLsGHDxM7OTkqUKCGtW7eWkydPisizbVr79OkjFSpUkAYNGoirq6sqF4HLbZzkbaT88ssvotFoxNTUVEmu5AoJCRE9PT1ZunRpgcZJ9LpptVoZM2aMzkLNudeFWqcAvciTJ09kwIABMnLkSKVMq9XK0aNHxd7eXhwcHIrNFKHnO19hYWGyfv16EXm2C9K8efOkfv36MmTIEKXOxo0bpXnz5vLuu++qfvSGiChTWEVEHBwcdKY55f77XLt2TebPn19sdr1KSEiQ1q1bi6enp5w6dUpERHr06CG2trayadMm2bdvn9jb2+skE3O3nBZR93ojz7t69ap06dJF3NzclClvIupfbyRv/Fu3bpWZM2fK0qVLlXVGMjIypEGDBlK7dm0JDQ2VjRs3SseOHcXLy0tERCwsLGTChAmFEvurkJCQIDVr1pTdu3crZWlpaeLk5CSNGjVSyjZs2CCBgYHFIllIRK8WkytUoL788kspX768nDx5UhITE+XkyZNSvXp16dChg5JgEXnWcE1JSVHldnZ5G9ZLlixRGqEiIosWLRKNRiMffPCBpKSk6Lzv008/FY1GI6tWrSrIcIleidxr9fTp07Jp0yb56quv5MaNG0pydMyYMaKvr68kWEJCQsTDw0PS0tJUPTUkr379+omzs3O+8gkTJohGoxEHBwdlNJpazzlv3N98842sW7dOPDw8lJFJIv9PsDRo0EA+/PBDpXzLli3SvHlz6d69u8TGxhZk2K/UlStXpGLFisqT7PXr10uVKlWUne1yjR07Vpo2bZrvXq9meafFtGrVShwdHXWmxfz000+i0Wh0pg2JqPf3/a/k/lu4urqqfrSGiO5nNHbsWKlWrZp06NBB2rRpI82bN5dvv/1WRJ4ljN555x1p3Lix1KxZUzp37qyMymrevLnO+lpF3fO/l7GxsWJmZiaXLl0Skf+Prvvjjz+kcuXKsmjRonzHYIKFiPJicoVeq+e/dN5//33p06ePiPz/S+3SpUtiZmYmLi4ucuzYsb89RlGWG+v58+fFzs5OvL29Zc+ePTpf4LkJltmzZ0tqaqrO++fNm6d8qROpzZYtW8TExESaN28upUuXFnt7e5k+fbrS8A4ODhaNRiMtW7YUY2Nj1a6x8mf3pFWrVomjo6N88803OlNevvnmG+nfv7+4urpK7969dZ7mq0ne+9iYMWOkbNmyYmNjI2XLlpWuXbvq1L1//74sWLBAqlSpInPmzFHK161bJ+3bt5fffvutwOJ+1WJjY8XU1FRZvPP333+XKVOmSJUqVaR3794SFBQkAwYMEBMTE1Unkf7Mi6bF5OTkiFarlZ9++knefvvtYpFs+CcSEhKU6cvFYUSWyLMdgKysrJTRKmFhYWJkZCS2trayZs0apV5KSookJSUpf580aZJYWFjoLGpblOW9j9+/f19ERNLT06VKlSoyY8YM5bXs7GxJS0sTR0dHZfofEdGfYXKFCsSOHTvk6dOn0r9/f/Hw8BCRZ19suYsaLlu2TAwMDKRXr16q3opV5NlidhUqVJDx48f/aQdi7ty5otFoZNasWcqXOpFavCi5cOHCBalataqsWLFC0tPTJTs7W0aNGiXOzs4yc+ZMZfTZd999J2FhYardMSXvue/evVs2bNggkZGRIvJs+Linp6c4OzvL0qVLJTU1Ve7evSuenp4yefJkWbBggdjY2ORbAFNt7t69Kx4eHnLu3Dm5deuWfP3112Jubi4+Pj469e7duyebNm3KNzVCbYt0v2jURcOGDeXrr79W/p6cnCy7du2Sdu3aSceOHaV///7KyJbiKO+0mNzffxERDw8PcXFxUdVDkZd16dIlGT16tGrPOW/cT548ET8/P2VR6p07d4qJiYlMmDBBevToIVZWVrJ161ad98fHx0ufPn3E3NxcNe23vOe8YMECGTVqlLJ+ytSpU8XJyUmWLFmi1Hn69Kk4Ojrm23yAiOh5TK7Qa5H3i2vy5Mmi0Wjk3r17smPHDtFoNLJx40ad+l9//bX06tVLLC0tVb1F56NHj6R3797K7ii5njx5Ijdv3tRZ/Cw0NFQMDQ1l0qRJOoseEhVleae97dixQynfuXOn2NjYyK1bt5SyjIwMGTlypDRo0KBYTY0QEQkKCpIyZcpI3bp1RaPRyKRJk0RE5MGDB+Lj4yMNGjSQcuXKSd26daV27doiIhITEyPW1taqebL7IvPmzZM6depIt27dlK1mMzIyZMOGDVK9enV55513Xvi+7Oxs1U0NSUhIkMOHD4vIs0TahAkTJCwsTL7//nuxsbGRuXPn/ul7c6fDFWe502Lc3d3l+PHj0qNHD7GzsytWi9f+W2o757zX5Jo1a+Tq1aty69Yt+fnnn+Xy5ctiY2MjCxYsEJFn098MDQ2lXLlysm/fPuV9GRkZsnnz5nxbNKvBuHHjxMzMTNauXauso5SYmChDhw4VW1tbGTBggMyaNUtcXFzE3t5eVVPUiahwMLlCr1V8fLzMnDlT2Uo5Oztbxo0bJ0ZGRrJy5UpJSkqS5ORk8fDwkNWrV8t3330nGo1Gzp8/X8iR/zdPnz6V1q1by5dffqmUfffddxIYGCjlypUTa2trcXFxURo0s2bNkgoVKiidFCI1+O2336RSpUpSt25dWbt2rYiIHDp0SKpVqyaJiYki8v/OZXp6uhgZGemsyaFGeTtNly9flsaNG8vp06fljz/+kPXr14uenp4EBgaKiMjjx48lLi5OlixZIlu3blVGbgQEBEjLli3zTQcsyp7vLB44cEBq1aolFhYWcu/ePaU8IyNDNm3aJDVq1JDOnTsXdJivXGxsrJQpU0YWLVokOTk5Mm3aNGnfvr3Y2NhIs2bNRKPRiEajEX9/fwkICJDdu3fLtm3bisUW0/9G7rQYQ0NDqV27tnLdsxNa9OW9tufMmSNVq1aVs2fPKmUrVqyQVq1a6WxD7O3tLeHh4apbqPdF9u3bJ1ZWVsqOlXldu3ZNVq1aJU5OTuLq6irvvfeeKjdXIKKCx+QKvTa7d+8WjUYjVatWlRMnTijlt2/flhkzZoiRkZFYW1tL9erVxd7eXrKysiQmJkZsbGxUu7vCgwcPpE6dOjJ48GCJj4+XWbNmSe3ataVnz54SFhYmK1asEFtbW/noo4+U9+TtoBCpwdGjR0VPT0+aNGki3bt3lw0bNsijR4/E0tJS+vfvr1P3zp074uDgIAcPHiykaF/O89OXZs2aJR988IEMHjxYpwO9ZcsW0dPTk9GjR+fbcjcqKkpGjBgh5cuX1+m8qMnFixeV8z127JiYm5tLt27ddOpkZmbKqlWrxNvbW3VP8PM6e/aslCpVSsaPH//C15OSkiQ4OFiqV68u/fv3l/bt20vdunXF1NRU/vjjjwKOtvBdvnxZRowYoSRUmFhRl4SEBAkICJDt27frlK9evVrMzc3l8OHD8vjxY+nWrZsEBQUp9wG1JxkWLVokjo6Okp6erpS96L6Vt4y/20T0d5hcodcmLi5OPvzwQzEyMlIWvcvbGTl37pxs3rxZ58numDFjxMnJSdUjOQ4fPiwGBgZiZWUlZcuWlcWLF+s8ze/cubPO1Kc35QknFS/vv/++ODg4SM+ePaVNmzayZ88eiYmJkUqVKomPj4/ExsZKYmKiTJw4UapWrSq//vprYYf8r/Xs2TNfsigkJEQ0Go00adJEeaKbew1/++23YmRkJIMHD9ZpsG/evFk6d+6s2hF5Bw4cEI1Go2y3LPIswVa5cmXp3r27Tt3ckRsi6psiIfLse6lUqVISHBysU/7dd9/pTOtcvXq1NGvWTOlsPXjwgOtnCTufarNz507RaDRSuXJlnak+Is+uBS8vL6lQoYLY2trqTItRc7slN/bQ0FBp0KCBcq/OLc/JyZEtW7bku1+r+ZyJqOAwuUKvxJ81oi9duiTvvvuulCxZUg4dOqTUff6Jx5UrV8TPz08qVKig2ie7ed24cUNOnz4tycnJOuU5OTnSu3dvmThxomi1Wn5ZU5H3/LWd23nes2eP+Pr6yv79+6VHjx7Spk0bWbdunZw6dUpq1qwpFhYWYm1tLdbW1qrdFejhw4fKrj937txRrtfw8HDRaDQvXHNjzZo14uzsnO/aTktLe/0Bv0bDhw+XMmXK6EzvOnr0qFSpUkW8vb0LMbJX58aNG1KpUiVlR7tcM2bMkOrVq8vly5eVsps3b0q1atWKzQ4x9OYaOXKkaDQamTFjhk5SWOTZzocRERGybNkypd1WXBJoJ0+eFI1GoyzemystLU26d+8uS5cuLZzAiEjVmFyhl5a38/XDDz/I8ePHdXYPiI+Pl4EDB0rFihV1Eiy5MjMzZffu3TJw4EDVPtn9J7KysmTixIliYWGhyoXf6M2Te53euHFDtm3bpvNaUlKS1KlTRxYuXCh37tyRHj16SNu2bWXv3r3y5MkTOX36tHz//ffy+++/F0boLy1vAvjLL7+UWrVq6SR+Q0NDRU9PT1nsUST/k001JlD/Kt6RI0dKyZIlZcOGDTpThDQajQQFBRVUiK/NtWvXpEmTJuLp6alsJTx79mypVKlSvqf6KSkpUqZMGdmzZ09hhEr0r/3VSDI/Pz8pWbKkbNy4UWf02fPUPhUoV+7967PPPhMDAwOZNGmSfP/99xITEyOdO3eWhg0bFpskEhEVLCZX6KXkbYgHBwdLrVq1pEaNGmJraysjR45UXrt8+bIMGjRIKlWq9MLGaFZWlmRmZhZIzIVh7dq1MnLkSKlSpYpqtiokEnmWWDE1NRWNRiPu7u6yadMmuXLliog8G1LeunVrSUpKkkuXLkmPHj2kXbt2OlvUFgcpKSlSrVo1admypc5IhdDQUNHX1y+W23POnz9fjhw5kq98xIgRYmxsrLPF8pkzZ4pNpyt3BxxPT08ZPHiwmJmZyf79+/PVO3HihAwbNoyJclKFvImVjRs3yrRp02TevHk6SUNfX18pXbr03yZY1OKfTEvMzs6W1atXS5UqVcTc3Fzs7e2lQ4cOXLyWiP4zJlfolQgJCZHKlStLVFSUPHz4UIKDg0Wj0Yifn59SJz4+Xrp37y6urq6FGGnBi4+PFxcXF/H29pZLly4VdjhE/8r169elcePG0qJFC3F0dBQ/Pz+xsrKSJUuWyKZNm8TDw0P27t0rIs8WPe3YsaN069ZNtetP/Flj+t69e2JlZSVNmzbVSbDMnTtXNBqNsq6UGuXthOSOtunQoYOYmJgoIzjycnFxEUtLS1m9erVOeXHpiFy5ckU6deokxsbGEhoaKiK6o5AmTZokNWvWlBs3bhRmmET/2pgxY6RSpUri7u4utWrVktq1a+sssP/+++9LuXLlZOXKlcVmO/GzZ8/+7bTMW7duyaVLlyQuLk65H3LkChH9F0yu0Eu7fPmyTgdr9+7dYmJiIh9++KGULl1ahgwZotT99ddfVbnI4cu6c+eOajubRAkJCdKjRw/x8vKSbdu2SUREhLi4uIiXl5doNBpp1qyZsjZJfHy8Knf7en4R7S1btsinn34qhw8fllu3bonInydYvvnmG1U3xJ88eSKZmZly69YtnfPo2bOnVKpUSY4fP66U5eTkyPvvvy81atQQFxeXwgi3QFy9elU6d+4sbm5uOtNcJ02aJEZGRnL69OlCjI7o39u3b5+Ym5srWw/fuXNHwsLCxMbGRmdnrJ49e0qHDh0KK8yXlreNeeTIEdFoNLJixYo/TbC8aCrkm9hOJaJXg8kV+tfOnj0r27dvV55oPn78WMLDwyU1NVWOHz8ulpaWEh4eLiIi/v7+otFopGfPnjrH4BcXkbrEx8eLm5ubdO7cWa5cuSLp6ekSExMjHh4esnbtWhFR724KH3/8sXzwwQdKUmjs2LFSsWJFsbe3F3Nzc/H395fY2FgReZZgqVGjhrRo0SJfB1uNCZb9+/fL0KFDpXr16lK+fHlxd3eX5cuXK697eXmJmZmZREZGKotd+vj4SFxcnGo/738qd4pQly5d5MyZMzJnzhwpWbIkEyukCrntrNz/Lly4UOrVq6czIuXu3bsydepUadmypc5ILLW20fLek7788ktZsWKFshvSsmXLJCMjoxCjI6I3gR6I/oX169fD19cXK1euxJ49ewAAJUqUwODBg1G+fHns3r0bLi4ueO+99wAAlpaW8PT0xOPHj6HVapXj6OnxV49ITWrXro2wsDAAwIgRI3D27Fk0b94cu3btwoABAwAAGo2mMEP8z4yNjREbG4v58+fj0KFDuHTpEvbt24e4uDjMnDkTFy5cQGhoKGJjY1GhQgWcOXMGsbGxWLJkic5xDAwMCukM/puVK1fi/fffh7GxMQICAjBnzhzcuHEDEydOxCeffAIAiIiIgIuLC7p06QJvb284OjoiLi4OderUgUaj0bmvFze1atXCF198AUNDQ7i6umLixImIioqCk5NTYYdG9JceP36stLOuXLkC4Fl77PHjx7h8+bJSz9TUFF26dMGJEydw69YtpVxPT0+V13bud9CUKVMwefJkmJiYYOXKlXBzc8Pw4cOxfv16ZGZmFnKURFSsFXZ2h9Tj66+/FmNjY9mwYYOkpqbmez0nJ0fc3NzEzc1NRJ7tAuTt7S0rV67UqUNE6pX3aX7e6SJqlPcp5+effy7NmjWT9957T3r37q0zCuXrr7+Wli1byoABA5QFqdPS0lS9xsjixYvFyMhINmzYoPMkOyEhQQYOHCiVK1fW2Wp67ty5EhgYKKNHj1b+bdR8/v9GfHy8eHp6SlxcXGGHQvS3Nm3aJDNnzhQRkY8++khsbW0lMzNTLly4INbW1jJq1ChlqqPIsylwDRo0kFOnThVWyK9USkqK1K9fXxYuXKhTHhgYKCVKlJDly5fLw4cPCyk6IiruNCIihZ3goaLv4sWL6Nu3LwIDA+Hn56eUi4jO0+qIiAj4+PigefPmePjwIXJycnDmzBkYGBjkq0tE6pSYmIjRo0fj7t27mD9/Ppo3b17YIf0nWq0WGo1GuS/NmjUL4eHhMDAwQGRkJKpXr67UXbt2LZYvX44yZcpg/vz5sLOzAwDk5ORAX1+/UOL/r7Zv344ePXpgx44d6NatG7Kzs2FgYKCcy88//ww/Pz+kp6fj22+/hZWVVb5j5L7nTfH06VMYGhoWdhhEf2vevHkYM2YMWrdujQsXLiAyMhL16tUDAGzevBl+fn7w8fGBi4sL7OzsMGHCBNy/fx8xMTGqH1UsIkhJSYGTkxNCQkIwYMAAPHnyBEZGRgCAdu3a4fLly5gzZw4GDBiguns3ERV96r6LUoH57bffkJmZiTZt2iBvPi63U5Jb5u7ujk2bNsHGxgZdunRREis5OTlMrBAVE7Vq1cLnn38OS0tLWFhYFHY4/4lWq4Wenh40Gg3i4uIAAMHBwRg3bhwMDQ3x+eef49dff1Xqv/vuu3jnnXfw1ltvwdbWVilXW+M8KysL+/fvh42NjXJ+eRMrIoKaNWvik08+wZkzZ3Dz5s0XHudNSqwAYGKFirzHjx8DAEaPHo3WrVsjKioK7777Luzt7ZU6ffr0wZo1a/Dzzz9jxIgRGDRoELKyshAVFaXKqUDPx6vRaFCpUiU0bNgQCxYsQFZWFoyMjJCdnQ2tVouaNWuiSpUq+PDDD3Hy5EkAAJ8xE9GrxJEr9I/Mnj0b8+bNQ3JyMoD8I1YA4PLly7h37x5atWqlU/6mPeEkelPkfSKoJrmJFQCYPn06duzYgZkzZ8LNzQ0A8Pnnn2PDhg1wcXFBYGAg3nrrrb88htr88ccfmDNnDn788Ud4e3sjKCgIgO5InoSEBDRs2BDbt29Hly5dCjliIvorBw4cwLlz59CyZUu0atUKAQEBKFWqFEJDQzFz5kwMHz4cJiYmyn0rNTUVDx48wKNHj1C7dm3o6emprq2W9x587tw5GBgYoFq1aihfvjxOnDiBoUOHwtLSEtu2bYOBgQG0Wi369u2LiRMnYsqUKUhKSsLx48dVlyAnoqJNPXdRKlS2trbIyMjAgQMH0Llz5xeOQlmzZg1SUlLQokULnU6Hmr6sieifU2NiRUSU+9P48eOxatUqrFq1Cm+//bZSZ+zYsQCAb775Bvr6+hg2bBisra1feAw1qlq1KsaPH4+QkBBEREQAAIKCgnQ6WBcuXICTk5POvwsRFT2rVq3CpEmT4OnpiTZt2gAAFi5cCACwsLDA6NGjAQABAQEoV64cAOCXX37RWZhZq9Wqrq2Wew8eN24ctm7dit9++w3du3fHwIED4e7ujkmTJmHatGmwtrZGy5YtceXKFTx+/Bj169dH/fr1cezYMSZWiOiVU9edlAqNk5MTjIyMsHTpUtSpU0d5kps7guXhw4dITExE27ZtVd3pIKLiKSoqCs7Ozkpi+NSpU4iIiEBERARatmyJR48e4ffff0dkZCTc3d0xduxY6OnpYf78+XjrrbcwYsQI5VjFYYqjubk5JkyYkC/BYmBggLS0NKxcuRJ16tSBpaVlIUdKRH9m48aNCAgIwKpVq+Dq6qokT3IFBgZCq9Vi7NixePr0KTw9PTFlyhQkJycjJiZGacOppd0mIjrJ7b1792L79u1YunQpUlNTsWzZMnz++efIysqCt7c3mjRpgi+++AIPHjxAp06dEBISAj09Pdy8eRMWFhZ48uQJDA0Ni8U9nYiKBk4Lon9s48aN8PX1Rc+ePTFmzBg0atQIAPD777/Dz88PDx8+xLFjx1T39IOIireRI0ciOzsbCxcuVBrlBw4cQL9+/XDt2jVcv34da9euxfbt23Hz5k3UqlULkZGRKF++PNatW4d33nmn2D7hvH37NkJCQnDq1Cn06tULY8aMgZeXF65fv47Tp09zMXKiIio5ORl9+vRBr169MHz4cKU8PT0dly5dwtOnT5Vp2gsWLMCMGTNgbm4OY2NjxMTEqG4doecXld6/fz927doFa2trfPzxxwCAuLg4TJ48Gffu3cPQoUPRt29fnWOkpqZi+vTpWLNmDY4fP86ReUT0yjG5Qv9YTk4OVq1ahWHDhqFKlSqoV68etFotHjx4AK1Wi+joaBgaGqpy9wwiKr4uX74MW1tbGBoaIiEhAXZ2dnj48CHatm2Le/fuIT09Hb1794aLiws8PDxgZmaG8PBw+Pr6Kscozve127dvY9asWfjpp59w9epVlC9fHnFxcbyfExVhycnJcHFxQUhICLy8vAAA4eHhOHLkCLZu3QoLCwtYWVkhKioKGo0GZ8+exaNHj9C0aVPo6+urao2VwYMHo27duhg9ejS0Wi1u3LgBDw8P/PLLLxgyZAgWLFig1M1NsKSlpaFPnz4YPHgwAODGjRtYt24dtm3bhuXLl8PBwaFwToaIijV1jAOkIkFfXx9+fn44efIkvL29odVqUb16dbz77rvKU5Ds7Gw2xImoyPjiiy+wefNmGBoaYt26dfD19cWOHTtQrlw5REREIDAwEN988w3mzp0LHx8f6Ovrw9HREWZmZjrHKc73NXNzcwQHB8PW1hZOTk5KYoX3c6Ki7eHDh9izZw+OHDmCXr16ITw8HGZmZti/fz8WLFiA27dvY8aMGQAABwcHtGjRAvr6+sjJyVFNYiUrKwuNGjVSpmaKCGrUqIHFixfDyckJx48fx/79+5X69erVw4wZM/DkyRNcuHBBKX/rrbfg4+ODffv2MbFCRK8NR67QK8MnnERUlCxbtgz+/v7YvHkzevXqhdOnT+Pjjz9GqVKlEBAQgK5duyp1s7KycPfuXXz44Ye4ffs2fvzxxzfufpaamgoTExNV7hxC9CY6fPgwevbsCVNTU5QtWxbz5s1Dw4YNYWpqitTUVLRv3x7du3fH1KlTCzvU/+T5KYmrVq1CTEwMwsLCYGxsjMjISAQHB8PMzAzDhw9Hx44dlbrXrl2DlZWVssW0WtaVISJ1452G/pMX5eTetI4IERVdS5YswbBhw7B161b06tULANC4cWOsWLECT548wcKFC7Fjxw6l/ubNm9GvXz+kpKTghx9+UJ7uvkkqVKigdESYWCEq+jp06IDExEQcOnQIZ8+eRfv27WFqaqq8XrZsWVhYWBRihC8nb2IlOzsbcXFxOHPmDCZPnoxHjx6hTZs2mDFjBpKTk7Fo0SIcPnxYqW9tbc3EChEVOI5cISKiYmX79u3o0aMHduzYgW7duinl48ePh7+/P9LT0xEYGAgjIyMMGzYM3bp1w6lTpxAbG4sPPvhAdesREBHllZycjEGDBuHu3buIjo5W5cOvFyVFMjIy8Pnnn2Pfvn1o1aoVQkJCYGxsjGPHjmHKlCkQEcybNw+NGzcupKiJ6E3HVC4RERUbWVlZ2L9/P2xsbHDt2jWl3MvLC/v27UOJEiVQv359zJs3D0+fPkV4eDgiIiLQpEkTDBkyRHXrERAR5bp79y4+/fRTDBo0CElJSTh+/LgqR+HlTaycO3cOiYmJSExMROnSpTFu3Di4ubkhOjoaEyZMwKNHj+Di4oJPPvkE9vb2cHR0LOToiehNxpErRERUrPzxxx+YM2cOTpw4AR8fH0RFReHq1av49ttvUbNmTWUe/7lz5/Dee++hQ4cOmDdvXmGHTUT0Us6ePYtJkyahZs2aCA0NhYGBgapH4Y0bNw7r1q1T1oEKDAzE+PHj8fjxY8yePRsHDhxAq1atMH36dJQqVUp5H6cCEVFhYXKFiIiKndu3byMkJAR79uzBgwcPcP78eVSrVk1ZeDs3wXL16lXY2NiwIU5ExcL9+/dhYmICjUajuo0G8i5ge+TIEbz77rtYs2YN9PT0cPHiRQQGBmL48OEICwtDRkYGPvvsM6xbtw6BgYEYMWJEvgVwiYgKGpMrRERULN25cwezZs1CdHQ0fHx8MGbMGAAvfqqptk4IEdFfUXOiYc2aNTh58iQqVKigbCUNADt37oSXlxdWrlwJX19fpKenY9OmTfD19eX9m4iKBCZXiIio2ModwXLq1Cl4e3sjKCgIgLo7HkRExdW1a9cwZMgQxMTE4IMPPkBYWBhycnIgIjAwMMDw4cMRHx+P7du3o2zZssr7mCAnoqKA46CJiKjYMjc3x4QJE9C0aVPs3LkTEydOBAAmVoiIioDnn/FaW1vj448/RqtWrbBmzRqcOnUK+vr6ymjDChUqQKvV6iRWADCxQkRFApMrRERUrJmbmyM4OBg1a9ZEUlJSvsY8EREVPK1WqyS6MzMzkZaWBgBwdXXFpEmT0LRpUwwaNAinTp1S6kRHR8PMzKzQYiYi+iucFkRERG+Ee/fuoXz58tDT0+O0ICKiImLmzJnYu3cvMjMz0bhxY0yYMAHW1taIiorC5MmTERUVhbfffhsODg44f/48fvzxRxgZGfE+TkRFDkeuEBHRG6FixYrQ09PTeVpKREQFS6vVKv8/d+5chIaGwsPDA/369cPBgwfRt29fHD9+HM7Ozpg6dSq6dOmCtLQ0dO7cGWfOnIGRkRGePn3K+zgRFTkcuUJERERERK9d3t3aoqOjcfLkSdSsWROenp4AgIcPH6Jjx47Q19fHoUOHULp0aezfvx9LlizBzZs3sXLlStSvX5+jVoioSOLIFSIiIiIiem18fX2RmZmpJFZ++OEHtG7dGuPHj8fTp08BAFlZWShXrhy+++47xMfHY/HixQCALl26YPjw4bC0tIS3tzfOnz/PxAoRFUlMrhARERER0Wvxyy+/ID09HYaGhkrZW2+9hdmzZ8PY2BgxMTEAgBIlSiA7OxsVKlRAo0aNkJqaqtTv0KEDhgwZgqZNm+bbKYiIqKgwKOwAiIiIiIioeLKxscG3334LAFi6dCl8fHxgaWmJgQMHIicnB1OmTEHFihURHBwMAwMDaLVaJCUlwcDgWTcldyqRm5sb2rZti1KlShXm6RAR/SmuuUJERERERK9VcnIyHB0dUbZsWZw4cQJly5bFnTt3sGLFCkyePBkeHh6wsLDAnTt3cOHCBVy6dElJsHCNFSJSAyZXiIiIiIjolXo+ISIiiIuLw3vvvQetVouoqCglwbJy5UrMmzcPNWrUwKJFi9C4cWPo6ekhOztbSbAQERV1XHOFiIiIiIhembxb3qempiI1NRUajQb169fH+vXrodVq4ezsjLS0NFSpUgUDBw7E2LFjcfXqVRw7dgx6enoQEWUBXCIiNeAdi4iIiIiIXtq2bdtw7949JSkyefJkdOvWDY6Ojli4cCHu3buHt99+G5s2bYJWq0Xr1q2RlpYGCwsLvPfeexg3bhw+++wzTJ06FRqNhskVIlIV3rGIiIiIiOil7NmzB7169cLSpUuRlZWFxYsXY9myZejVqxe8vLwwevRoTJ8+Hbdu3VISLABgZ2eHzMxMmJubY/DgwRg6dChWrVqFlJQUcPUCIlITrrlCREREREQvLSwsDKNHj8b8+fORkpICR0dHdO/eHQCwadMmDBkyBL6+vhg3bhyqVauG8+fPIzQ0FCtXrlTWVklJSQEAmJqaFtp5EBH9F1whioiIiIiI/rOMjAyULl0aH330EbRaLQIDA1GqVCksW7ZMqdO3b18AgL+/P/T09BAYGIgGDRpgzZo1AKAsXsukChGpFacFERERERHRf3LgwAF89dVXiImJAQCMGjUKK1asQGZmJn788UekpqYqdfv27Ytly5YhLCwMEREROsfhrkBEpHa8ixERERER0b+2atUqTJo0CZ6ennBxcVHKBw0ahIyMDIwcORJVq1bF0KFDYWJiAgDo3bs3TE1N0aZNm0KKmojo9WByhYiIiIiI/pWNGzciICAAq1atgqurK8qVK6fzekBAAJ48eYIxY8YAAIYNG6bUad++PYD/TwUiIioOeDcjIiIiIqJ/LDk5GUuWLMFnn32GPn36KOXp6em4dOkSnj59ilatWmH06NEAgKCgIKSlpSE4OBilS5dW6jOxQkTFCe9oRERERET0ryQlJaFatWrK38PDw3HkyBFs3boVFhYWsLKyQlRUFEaPHo1Hjx5h7969mDlzZiFGTET0enErZiIiIiIi+seSk5Ph6OgIV1dXvPPOO/jqq6+QkJAAZ2dneHt748GDBwgKCsLAgQMxefJkAICIQKPRKP8lIipuOHKFiIiIiIj+MTMzM6xevRo9e/bEkSNHULZsWSxYsAANGzaEqakpUlNTUa5cOWi1WuU9TKwQUXHH5AoREREREf0rHTp0QGJiItLT02FtbZ3v9bJly8LCwkKnjIkVIirOOC2IiIiIiIheieTkZAwaNAh3795FdHQ09PX1CzskIqICwZErRERERET0Uu7evYvly5cjKioKSUlJSmIlJyeHCRYieiPoFXYARERERESkbrdu3UJ0dDRsbW3xww8/wNDQENnZ2UysENEbg9OCiIiIiIjopd2/fx8mJibQaDQcsUJEbxwmV4iIiIiI6JXhrkBE9CbitCAiIiIiInplmFghojcRkytERERERERERC+ByRUiIiIiIiIiopfA5AoRERERERER0UtgcoWIiIiIiIiI6CUwuUJERERERERE9BKYXCEiIiIiIiIieglMrhAREVGxVqNGDfj6+ip/P3bsGDQaDY4dO1ZoMT3v+RiJiIhIXZhcISIiotdq9erV0Gg0yp+SJUvCzs4OAQEBuHPnTmGH94/t3bsXU6dOLewwiIiIqAgyKOwAiIiI6M0wffp0WFtb4/Hjx4iKikJ4eDj27t2LuLg4lCpVqsDiaNOmDR49egQjI6N/9b69e/di0aJFTLAQERFRPkyuEBERUYFwc3ND48aNAQB+fn4wNTXFvHnzsGPHDrzzzjv56mdkZKB06dKvPA49PT2ULFnylR+XiIiI3lycFkRERESFon379gCAa9euwdfXF2XKlMHPP/8Md3d3lC1bFv379wcAaLVaLFiwAPb29ihZsiSqVKkCf39/pKam6hxPRDBz5kxYWlqiVKlSaNeuHS5evJjv5/7ZmisnTpyAu7s7KlSogNKlS6NBgwYICwsDAPj6+mLRokUAoDPFKderjpGIiIjUhSNXiIiIqFD8/PPPAABTU1MAQHZ2Nrp06QJnZ2eEhoYqU4X8/f2xevVqDBo0CCNHjsS1a9ewcOFCxMbGIjo6GoaGhgCAyZMnY+bMmXB3d4e7uzvOnDmDzp0748mTJ38by8GDB+Hh4YGqVavio48+grm5OS5fvozdu3fjo48+gr+/P37//XccPHgQa9euzff+goiRiIiIii4mV4iIiKhAPHjwAHfv3sXjx48RHR2N6dOnw9jYGB4eHoiJiUFWVhZ69+6N2bNnK++JiorC8uXLsX79evTr108pb9euHVxdXbFlyxb069cPycnJ+Oyzz9C1a1fs2rVLGVUyYcIEzJo16y/jysnJgb+/P6pWrYqzZ8+ifPnyymsiAgBo0aIF7OzscPDgQQwYMEDn/QURIxERERVtnBZEREREBaJjx44wMzND9erV4ePjgzJlyiAiIgLVqlVT6gwdOlTnPVu2bIGJiQk6deqEu3fvKn+cnJxQpkwZHD16FABw6NAhPHnyBCNGjNCZrhMYGPi3ccXGxuLatWsIDAzUSawA0DnWnymIGImIiKho48gVIiIiKhCLFi2CnZ0dDAwMUKVKFdSuXRt6ev9/zmNgYABLS0ud9yQmJuLBgweoXLnyC4+ZlJQEAPj1118BALVq1dJ53czMDBUqVPjLuHKnJ9WrV+/fnVABxkhERERFG5MrREREVCCaNm2q7Bb0IiVKlNBJtgDPFoqtXLky1q9f/8L3mJmZvdIY/ws1xEhERESvF5MrREREVGTVrFkThw4dQqtWrWBsbPyn9aysrAA8G0ViY2OjlCcnJ+fbsedFPwMA4uLi0LFjxz+t92dThAoiRiIiIirauOYKERERFVl9+vRBTk4OZsyYke+17Oxs3L9/H8Cz9VwMDQ3x5ZdfKovQAsCCBQv+9mc4OjrC2toaCxYsUI6XK++xSpcuDQD56hREjERERFS0ceQKERERFVlt27aFv78/Zs+ejbNnz6Jz584wNDREYmIitmzZgrCwMPTq1QtmZmYYM2YMZs+eDQ8PD7i7uyM2Nhb79u1DpUqV/vJn6OnpITw8HN26dYODgwMGDRqEqlWrIj4+HhcvXsT+/fsBAE5OTgCAkSNHokuXLtDX14ePj0+BxEhERERFG5MrREREVKQtXrwYTk5OWLJkCYKDg2FgYIAaNWpgwIABaNWqlVJv5syZKFmyJBYvXoyjR4+iWbNmOHDgALp27fq3P6NLly44evQopk2bhrlz50Kr1aJmzZoYPHiwUqdHjx4YMWIENm7ciHXr1kFE4OPjU2AxEhERUdGlkbzjUomIiIiIiIiI6F/hmitERERERERERC+ByRUiIiIiIiIiopfA5AoRERERERER0UtgcoWIiIiIiIiI6CUwuUJERERERERE9BKYXCEiIiIiIiIieglMrhARERERERERvQQmV4iIiIiIiIiIXgKTK0REREREREREL4HJFSIiIiIiIiKil8DkChERERERERHRS2ByhYiIiIiIiIjoJTC5QkRERERERET0Ev4HJnr7+tPmCVYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 72.69%\n",
      "Mean Recall: 75.63%\n",
      "Mean Precision: 75.48%\n"
     ]
    }
   ],
   "source": [
    "# Evaluate all models\n",
    "results = {}\n",
    "\n",
    "for name, model in models_dict.items():\n",
    "    print(f\"\\n{'='*60}\")\n",
    "    print(f\"Evaluating {name}\")\n",
    "    print(f\"{'='*60}\")\n",
    "    \n",
    "    y_true, y_pred, preds = evaluate_model(model, name)\n",
    "    cm = plot_confusion_matrix(y_true, y_pred, f'Confusion Matrix - {name}')\n",
    "    plt.savefig(f'confusion_matrix_{name}.png', dpi=150, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    \n",
    "    # Compute metrics\n",
    "    correct = np.diag(cm).astype(float)\n",
    "    total_true = np.sum(cm, axis=1).astype(float)\n",
    "    total_pred = np.sum(cm, axis=0).astype(float)\n",
    "    \n",
    "    accuracy = np.sum(correct) / np.sum(total_true) * 100\n",
    "    recall = np.mean(correct / np.maximum(total_true, 1e-10)) * 100\n",
    "    precision = np.mean(correct / np.maximum(total_pred, 1e-10)) * 100\n",
    "    \n",
    "    results[name] = {\n",
    "        'accuracy': accuracy,\n",
    "        'mean_recall': recall,\n",
    "        'mean_precision': precision,\n",
    "        'best_val_acc': max(histories[name]['val_accuracy']) * 100\n",
    "    }\n",
    "    \n",
    "    print(f\"\\nAccuracy: {accuracy:.2f}%\")\n",
    "    print(f\"Mean Recall: {recall:.2f}%\")\n",
    "    print(f\"Mean Precision: {precision:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "25933f35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T17:08:48.555808Z",
     "iopub.status.busy": "2026-04-13T17:08:48.555193Z",
     "iopub.status.idle": "2026-04-13T17:08:48.969610Z",
     "shell.execute_reply": "2026-04-13T17:08:48.968743Z"
    },
    "papermill": {
     "duration": 1.882061,
     "end_time": "2026-04-13T17:08:48.971708+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:08:47.089647+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Results summary bar plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "model_names = list(results.keys())\n",
    "x = np.arange(len(model_names))\n",
    "width = 0.25\n",
    "\n",
    "accuracies = [results[m]['accuracy'] for m in model_names]\n",
    "recalls = [results[m]['mean_recall'] for m in model_names]\n",
    "precisions = [results[m]['mean_precision'] for m in model_names]\n",
    "\n",
    "ax.bar(x - width, accuracies, width, label='Accuracy', color='#2ecc71')\n",
    "ax.bar(x, recalls, width, label='Mean Recall', color='#3498db')\n",
    "ax.bar(x + width, precisions, width, label='Mean Precision', color='#e74c3c')\n",
    "\n",
    "ax.set_xlabel('Model', fontsize=12)\n",
    "ax.set_ylabel('Score (%)', fontsize=12)\n",
    "ax.set_title('Model Performance Comparison', fontsize=14, fontweight='bold')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(model_names, rotation=15, ha='right')\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3, axis='y')\n",
    "ax.set_ylim(0, 100)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('cnn_results_summary.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "44a9e95e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T17:08:52.023520Z",
     "iopub.status.busy": "2026-04-13T17:08:52.023050Z",
     "iopub.status.idle": "2026-04-13T17:09:05.913299Z",
     "shell.execute_reply": "2026-04-13T17:09:05.912272Z"
    },
    "papermill": {
     "duration": 15.385619,
     "end_time": "2026-04-13T17:09:05.915365+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:08:50.529746+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Per-class metrics for best model: Inception_Style\n",
      "--------------------------------------------------------------------------------\n",
      "Class                Recall       Precision    Specificity  F1-Score    \n",
      "--------------------------------------------------------------------------------\n",
      "Cargo plane          92.63%      90.72%      99.67%      91.67%\n",
      "Small car            83.37%      79.09%      95.24%      81.17%\n",
      "Bus                  63.02%      54.40%      94.50%      58.39%\n",
      "Truck                31.93%      47.53%      95.28%      38.20%\n",
      "Motorboat            83.12%      69.63%      97.81%      75.78%\n",
      "Fishing vessel       75.47%      75.47%      99.04%      75.47%\n",
      "Dump truck           67.03%      58.77%      96.69%      62.63%\n",
      "Excavator            88.14%      83.87%      99.26%      85.95%\n",
      "Building             80.52%      87.68%      97.32%      83.95%\n",
      "Helipad              82.35%      100.00%      100.00%      90.32%\n",
      "Storage tank         74.55%      75.23%      97.92%      74.89%\n",
      "Shipping container   75.98%      67.97%      96.83%      71.75%\n",
      "Pylon                85.11%      90.91%      99.86%      87.91%\n"
     ]
    }
   ],
   "source": [
    "# Per-class metrics for best model\n",
    "best_model_name = max(results, key=lambda x: results[x]['accuracy'])\n",
    "print(f\"\\nPer-class metrics for best model: {best_model_name}\")\n",
    "print(\"-\"*80)\n",
    "\n",
    "y_true, y_pred, _ = evaluate_model(models_dict[best_model_name], best_model_name)\n",
    "cm = confusion_matrix(y_true, y_pred, labels=list(categories.values()))\n",
    "metrics = compute_per_class_metrics(cm)\n",
    "\n",
    "print(f\"{'Class':<20} {'Recall':<12} {'Precision':<12} {'Specificity':<12} {'F1-Score':<12}\")\n",
    "print(\"-\"*80)\n",
    "for m in metrics:\n",
    "    print(f\"{m['class']:<20} {m['recall']:.2f}%{'':<5} {m['precision']:.2f}%{'':<5} {m['specificity']:.2f}%{'':<5} {m['f1']:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3cfac5c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T17:09:08.768303Z",
     "iopub.status.busy": "2026-04-13T17:09:08.767561Z",
     "iopub.status.idle": "2026-04-13T17:09:55.881286Z",
     "shell.execute_reply": "2026-04-13T17:09:55.880481Z"
    },
    "papermill": {
     "duration": 50.094689,
     "end_time": "2026-04-13T17:09:57.376310+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:09:07.281621+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot of per-class F1-scores for all models\n",
    "fig, ax = plt.subplots(figsize=(14, 6))\n",
    "\n",
    "x = np.arange(len(categories))\n",
    "width = 0.25\n",
    "\n",
    "for i, (name, model) in enumerate(models_dict.items()):\n",
    "    y_true, y_pred, _ = evaluate_model(model, name)\n",
    "    cm = confusion_matrix(y_true, y_pred, labels=list(categories.values()))\n",
    "    metrics = compute_per_class_metrics(cm)\n",
    "    f1_scores = [m['f1'] for m in metrics]\n",
    "    \n",
    "    ax.bar(x + i * width, f1_scores, width, label=name, color=list(colors.values())[i])\n",
    "\n",
    "ax.set_xlabel('Class', fontsize=12)\n",
    "ax.set_ylabel('F1-Score (%)', fontsize=12)\n",
    "ax.set_title('Per-Class F1-Score Comparison', fontsize=14, fontweight='bold')\n",
    "ax.set_xticks(x + width)\n",
    "ax.set_xticklabels(list(categories.values()), rotation=45, ha='right')\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('cnn_per_class_f1.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0649ec36",
   "metadata": {
    "papermill": {
     "duration": 1.473961,
     "end_time": "2026-04-13T17:10:00.188656+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:09:58.714695+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Testing\n",
    "Try to improve the results provided in the competition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "db2c428f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T17:10:03.065969Z",
     "iopub.status.busy": "2026-04-13T17:10:03.065179Z",
     "iopub.status.idle": "2026-04-13T17:10:03.118102Z",
     "shell.execute_reply": "2026-04-13T17:10:03.117094Z"
    },
    "papermill": {
     "duration": 1.407444,
     "end_time": "2026-04-13T17:10:03.120010+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:10:01.712566+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of testing images: 2365\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "anns_test = []\n",
    "root_dir = './xview_recognition/'\n",
    "test_dir = os.path.join(root_dir, 'xview_test')\n",
    "for (dirpath, dirnames, filenames) in os.walk(test_dir):\n",
    "    for filename in filenames:\n",
    "        rel_dir = os.path.relpath(dirpath, root_dir)\n",
    "        clean_filename = os.path.join(rel_dir, filename)\n",
    "        image = GenericImage(clean_filename)\n",
    "        image.tile = np.array([0, 0, 224, 224])\n",
    "        obj = GenericObject()\n",
    "        obj.bb = (0, 0, 224, 224)\n",
    "        obj.category = os.path.basename(dirpath)\n",
    "        image.add_object(obj)\n",
    "        anns_test.append(image)\n",
    "print('Number of testing images: ' + str(len(anns_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8f769c65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T17:10:06.086391Z",
     "iopub.status.busy": "2026-04-13T17:10:06.085628Z",
     "iopub.status.idle": "2026-04-13T17:10:49.097730Z",
     "shell.execute_reply": "2026-04-13T17:10:49.096686Z"
    },
    "papermill": {
     "duration": 44.507563,
     "end_time": "2026-04-13T17:10:49.099574+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:10:04.592011+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Préparation des images de test...\n",
      "Prédiction en cours sur 2365 détections...\n",
      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 246ms/step\n",
      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 959ms/step\n",
      "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 289ms/step\n",
      "Test terminé.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "predictions_data = {\"images\": {}, \"annotations\": {}}\n",
    "all_test_images = []\n",
    "metadata = [] \n",
    "ann_id = 0\n",
    "\n",
    "print(\"Préparation des images de test...\")\n",
    "for idx, ann in enumerate(anns_test):\n",
    "    image_data = {\n",
    "        \"image_id\": ann.filename.split('/')[-1],\n",
    "        \"filename\": ann.filename,\n",
    "        \"width\": int(ann.tile[2]),\n",
    "        \"height\": int(ann.tile[3])\n",
    "    }\n",
    "    predictions_data[\"images\"][idx] = image_data\n",
    "\n",
    "    image_raw = load_geoimage(ann.filename)\n",
    "    image_tensor = tf.convert_to_tensor(image_raw)\n",
    "    image_tensor = tf.image.convert_image_dtype(image_tensor, tf.float32)\n",
    "    image_resized = tf.image.resize(image_tensor, [IMG_SIZE, IMG_SIZE], method='bicubic')\n",
    "    img_final = image_resized.numpy()\n",
    "\n",
    "    for obj_pred in ann.objects:\n",
    "        all_test_images.append(img_final)\n",
    "        metadata.append({\n",
    "            \"image_id\": ann.filename.split('/')[-1],\n",
    "            \"bbox\": [int(x) for x in obj_pred.bb]\n",
    "        })\n",
    "\n",
    "if all_test_images:\n",
    "    X_test = np.array(all_test_images)\n",
    "    print(f\"Prédiction en cours sur {len(X_test)} détections...\")\n",
    "    \n",
    "    # Moyenne des prédictions des 3 modèles (ensemble)\n",
    "    all_preds = np.mean([\n",
    "        model.predict(X_test, batch_size=128, verbose=1) for model in models_dict.values()\n",
    "    ], axis=0)\n",
    "\n",
    "    category_names = list(categories.values())\n",
    "    for i, pred in enumerate(all_preds):\n",
    "        pred_category = category_names[np.argmax(pred)]\n",
    "        predictions_data[\"annotations\"][ann_id] = {\n",
    "            \"image_id\": metadata[i][\"image_id\"],\n",
    "            \"category_id\": pred_category,\n",
    "            \"bbox\": metadata[i][\"bbox\"]\n",
    "        }\n",
    "        ann_id += 1\n",
    "\n",
    "print(\"Test terminé.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c67e5e85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T17:10:52.085356Z",
     "iopub.status.busy": "2026-04-13T17:10:52.084691Z",
     "iopub.status.idle": "2026-04-13T17:10:54.199285Z",
     "shell.execute_reply": "2026-04-13T17:10:54.198314Z"
    },
    "papermill": {
     "duration": 3.586769,
     "end_time": "2026-04-13T17:10:54.201065+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:10:50.614296+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved ResNet_Style_final.keras\n",
      "Saved VGG_Style_final.keras\n",
      "Saved Inception_Style_final.keras\n",
      "\n",
      "All models saved.\n"
     ]
    }
   ],
   "source": [
    "# Save all trained models\n",
    "for name, model in models_dict.items():\n",
    "    model.save(f'{name}_final.keras')\n",
    "    print(f\"Saved {name}_final.keras\")\n",
    "\n",
    "print(\"\\nAll models saved.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2a0d6112",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-13T17:10:57.013358Z",
     "iopub.status.busy": "2026-04-13T17:10:57.013028Z",
     "iopub.status.idle": "2026-04-13T17:10:57.050146Z",
     "shell.execute_reply": "2026-04-13T17:10:57.049323Z"
    },
    "papermill": {
     "duration": 1.500589,
     "end_time": "2026-04-13T17:10:57.052062+00:00",
     "exception": false,
     "start_time": "2026-04-13T17:10:55.551473+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "with open('prediction.json', 'w') as f:\n",
    "    json.dump(predictions_data, f)"
   ]
  }
 ],
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  "accelerator": "GPU",
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   "isGpuEnabled": true,
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   "language": "python",
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   "start_time": "2026-04-13T11:33:56.031072+00:00",
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