cicy3f_nn_transfer-learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning for Complete Intersection Calabi-Yau Manifold\n",
    "\n",
    "We use neural networks previously trained on the configuration matrix to perform **transfer learning** to the feature engineered dataset. We want to explore the possibility to improve the results even further by feeding the network additional information coming from engineered features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infrastructure\n",
    "\n",
    "We print information about the current OS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current OS:                  Linux (kernel release: 5.6.12-arch1-1, architecture: x86_64)\n",
      "Number of available threads: 8\n",
      "Current CPU frequency:       2300 MHz (max: 2800 MHz)\n",
      "Available RAM memory:        7523 MB (tot: 15758 MB)\n",
      "Sun May 17 09:47:00 2020       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 440.82       Driver Version: 440.82       CUDA Version: 10.2     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  GeForce 940MX       Off  | 00000000:02:00.0 Off |                  N/A |\n",
      "| N/A   63C    P8    N/A /  N/A |      5MiB /  2004MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                       GPU Memory |\n",
      "|  GPU       PID   Type   Process name                             Usage      |\n",
      "|=============================================================================|\n",
      "|    0   1400850      G   /usr/lib/Xorg                                  4MiB |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "from mltools.libos import InfoOS\n",
    "\n",
    "print('Current OS:                  {} (kernel release: {}, architecture: {})'.format(InfoOS().os, InfoOS().kernel, InfoOS().arch))\n",
    "print('Number of available threads: {:d}'.format(InfoOS().threads))\n",
    "print('Current CPU frequency:       {:.0f} MHz (max: {:.0f} MHz)'.format(InfoOS().freq, InfoOS().freqm))\n",
    "print('Available RAM memory:        {:d} MB (tot: {:d} MB)'.format(InfoOS().vmav, InfoOS().vmtot))\n",
    "\n",
    "# print info on GPU\n",
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "We import the Python modules we use and print their versions to keep track of changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.7\n",
      "Matplot version: 3.2.1\n",
      "Numpy version: 1.18.4\n",
      "Pandas version: 1.0.3\n",
      "Scikit-learn version: 0.22.2.post1\n",
      "Tensorflow version: 2.0.0\n",
      "Keras version: 2.2.4-tf (backend: tensorflow)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "import matplotlib as mpl\n",
    "import random     as rnd\n",
    "import sklearn    as skl\n",
    "import numpy      as np\n",
    "import pandas     as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow       import keras\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=UserWarning) # ignore user warnings: nothing that I can really do anything about it...\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "mpl.rc('axes', labelsize=12)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# print the version of the modules\n",
    "print('Python version: {:d}.{:d}'      .format(sys.version_info.major, sys.version_info.minor))\n",
    "print('Matplot version: {}'            .format(mpl.__version__))\n",
    "print('Numpy version: {}'              .format(np.__version__))\n",
    "print('Pandas version: {}'             .format(pd.__version__))\n",
    "print('Scikit-learn version: {}'       .format(skl.__version__))\n",
    "print('Tensorflow version: {}'         .format(tf.__version__))\n",
    "print('Keras version: {} (backend: {})'.format(keras.__version__, K.backend()))\n",
    "\n",
    "# fix random_seed\n",
    "RAND = 42\n",
    "rnd.seed(RAND)\n",
    "np.random.seed(RAND)\n",
    "tf.random.set_seed(RAND)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Session Preparation\n",
    "\n",
    "in order to save the results of the analysis, we define where to store images, log files and models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import path, makedirs\n",
    "\n",
    "ROOT_DIR = '.' #-------------------------------------------------- root directory\n",
    "IMG_DIR  = 'img' #------------------------------------------------ directory of images\n",
    "MOD_DIR  = 'models' #--------------------------------------------- directory of saved models\n",
    "LOG_DIR  = 'log' #------------------------------------------------ directory of logs\n",
    "OUT_DIR  = 'output' #--------------------------------------------- directory for predictions, various outputs, etc.\n",
    "\n",
    "DB_NAME = 'cicy3f' #---------------------------------------------- name of the dataset\n",
    "DB_FILE = DB_NAME + '_analysis.h5' #------------------------------ full name with extension\n",
    "DB_PATH = path.join(ROOT_DIR, DB_FILE) #-------------------------- full path of the dataset\n",
    "DB_DIR  = 'original' if DB_NAME == 'cicy3o' else 'favourable' #--- subdir where to store images, models, logs\n",
    "\n",
    "# define full paths\n",
    "IMG_PATH = path.join(ROOT_DIR, IMG_DIR, DB_DIR)\n",
    "MOD_PATH = path.join(ROOT_DIR, MOD_DIR, DB_DIR)\n",
    "LOG_PATH = path.join(ROOT_DIR, LOG_DIR, DB_DIR)\n",
    "OUT_PATH = path.join(ROOT_DIR, OUT_DIR, DB_DIR)\n",
    "\n",
    "# create directories if non existent\n",
    "if not path.isdir(IMG_PATH):\n",
    "    makedirs(IMG_PATH, exist_ok=True)\n",
    "if not path.isdir(MOD_PATH):\n",
    "    makedirs(MOD_PATH, exist_ok=True)\n",
    "if not path.isdir(LOG_PATH):\n",
    "    makedirs(LOG_PATH, exist_ok=True)\n",
    "if not path.isdir(OUT_PATH):\n",
    "    makedirs(OUT_PATH, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also create a log file to store debug and related information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-17 09:47:02,893: INFO ==> New logging session started. Log is at ./log/favourable/cicy3f_nn_transfer_learning.log.\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "\n",
    "from mltools.liblog import create_logfile\n",
    "\n",
    "path_to_log = path.join(LOG_PATH,\n",
    "                        DB_NAME + '_nn_transfer_learning.log'\n",
    "                       )\n",
    "log = create_logfile(path_to_log,\n",
    "                     name=DB_NAME + '_nn_transfer_learning',\n",
    "                     level=logging.DEBUG\n",
    "                    )\n",
    "\n",
    "# these lines provide the same setup also for the Jupyter logging\n",
    "logger = logging.getLogger() #------------------------------------------------- get the current logging session\n",
    "\n",
    "fmt = logging.Formatter('%(asctime)s: %(levelname)s ==> %(message)s') #-------- customise the formatting options\n",
    "\n",
    "handler = logging.StreamHandler() #-------------------------------------------- handle the stream to the default (stderr)\n",
    "handler.setLevel(logging.DEBUG) #---------------------------------------------- print everything\n",
    "handler.setFormatter(fmt) #---------------------------------------------------- set the formatting options\n",
    "\n",
    "logger.handlers = [handler] #-------------------------------------------------- override the default stream\n",
    "\n",
    "# we are ready to go!\n",
    "log.info('New logging session started. Log is at {}.'.format(path_to_log))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We finally set the _memory growth_ property of the GPU in order to avoid overflowing its RAM memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU setup: 1 physical GPUs, 1 logical GPUs.\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.experimental.list_physical_devices('GPU') #--------------------------------------- list of physical GPUs\n",
    "\n",
    "if gpus: #----------------------------------------------------------------------------------------- set memory growth only if GPU is active\n",
    "    try:\n",
    "        for gpu in gpus:\n",
    "            tf.config.experimental.set_memory_growth(gpu, True) #---------------------------------- set memory growth\n",
    "            \n",
    "        logical_gpus = tf.config.experimental.list_logical_devices('GPU') #------------------------ list of logical devices\n",
    "        print('GPU setup: {:d} physical GPUs, {:d} logical GPUs.'.format(len(gpus),\n",
    "                                                                         len(logical_gpus)\n",
    "                                                                        )\n",
    "             )\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "else:\n",
    "    print('No GPUs in the setup!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Dataset\n",
    "\n",
    "We first load the dataset we built during the preanalysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-17 09:47:03,423: DEBUG ==> Database loaded.\n",
      "2020-05-17 09:47:03,424: INFO ==> Shape is 7852 rows x 7 columns.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# load the dataset\n",
    "if path.isfile(DB_PATH):\n",
    "    df = pd.read_hdf(DB_PATH)\n",
    "    log.debug('Database loaded.')\n",
    "    log.info('Shape is {:d} rows x {:d} columns.'.format(df.shape[0], df.shape[1]))\n",
    "else:\n",
    "    log.error('Cannot load database from {}!'.format(DB_PATH))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dense Format Extraction\n",
    "\n",
    "We now extract the matrix from the available features and the labels. Notice that in this case we do not flatten the output as it will be processed by _Conv2D_ layers in _Keras_: we reshape it as if it was a black and white image, that is we take each matrix of shape (width, height) and output a matrix of shape (width, height, 1):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-17 09:47:05,622: DEBUG ==> Matrix shape: (7852, 15, 18, 1)\n"
     ]
    }
   ],
   "source": [
    "from mltools.libtransformer import ExtractTensor\n",
    "\n",
    "# extract the labels\n",
    "h11    = df['h11'].values\n",
    "h21    = df['h21'].values\n",
    "\n",
    "# we extract scalar and vector features\n",
    "num_cp     = np.array(ExtractTensor(flatten=True).fit_transform(df['num_cp'])).reshape(-1,1)\n",
    "dim_cp     = np.array(ExtractTensor(flatten=True).fit_transform(df['dim_cp']))\n",
    "dim_h0_amb = np.array(ExtractTensor(flatten=True).fit_transform(df['dim_h0_amb']))\n",
    "\n",
    "# extract the tensor features\n",
    "matrix = np.array(ExtractTensor(flatten=False).fit_transform(df['matrix'])) #-------- old shape is (batch, width, height)\n",
    "matrix = np.reshape(matrix, (-1, np.shape(matrix)[1], np.shape(matrix)[2], 1)) #----- new shape is (batch, width, height, 1)\n",
    "\n",
    "log.debug('Matrix shape: {}'.format(np.shape(matrix)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Validation Strategy\n",
    "\n",
    "We now define the validation strategy and split the dataset into training, validation and test sets.\n",
    "\n",
    "We will take out 10% of the dataset to be our **test set**. For the remaining 90% of the dataset, we will use **holdout validation** with $\\frac{1}{9}$ of the training samples as validation (effectively we use 80% of the total set for training and 10% as validation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-17 09:47:05,655: DEBUG ==> Train set size: 6280\n",
      "2020-05-17 09:47:05,656: DEBUG ==> Validation set size: 786\n",
      "2020-05-17 09:47:05,658: DEBUG ==> Test set size: 786\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# divide into training and test sets\n",
    "num_cp_train, num_cp_test, \\\n",
    "dim_cp_train, dim_cp_test, \\\n",
    "dim_h0_amb_train, dim_h0_amb_test, \\\n",
    "matrix_train, matrix_test, \\\n",
    "num_cp_train, num_cp_test, \\\n",
    "h11_train, h11_test, \\\n",
    "h21_train, h21_test = train_test_split(num_cp, dim_cp, dim_h0_amb, matrix, num_cp, h11, h21, test_size=0.1, shuffle=False)\n",
    "\n",
    "# keep a holdout validation set\n",
    "num_cp_train, num_cp_val, \\\n",
    "dim_cp_train, dim_cp_val, \\\n",
    "dim_h0_amb_train, dim_h0_amb_val, \\\n",
    "matrix_train, matrix_val, \\\n",
    "num_cp_train, num_cp_val, \\\n",
    "h11_train, h11_val, \\\n",
    "h21_train, h21_val = train_test_split(num_cp_train, dim_cp_train, dim_h0_amb_train, matrix_train, num_cp_train, h11_train, h21_train, test_size=1.0/9.0, shuffle=False)\n",
    "\n",
    "log.debug('Train set size: {:d}'.format(np.shape(matrix_train)[0]))\n",
    "log.debug('Validation set size: {:d}'.format(np.shape(matrix_val)[0]))\n",
    "log.debug('Test set size: {:d}'.format(np.shape(matrix_test)[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also provide a way to visualise the predictions by comparing the predictions as function of a (scalar) base feature (e.g.: `num_cp`) with respect to the ground truth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_counts(base_feature, label):\n",
    "    '''\n",
    "    Returns unique values and counts of the label as a function of the base_feature.\n",
    "    \n",
    "    Required arguments:\n",
    "        base_feature: the feature considered as base for the comparison,\n",
    "        label:        the label we are interested in comparing.\n",
    "    \n",
    "    Yields:\n",
    "        [ unique value of base_feature, unique value of label, count ].\n",
    "    '''\n",
    "    \n",
    "    for n in np.sort(np.unique(base_feature)):\n",
    "        uniques, counts = np.unique(label[np.argwhere(base_feature == n)[:,0]], return_counts=True)\n",
    "        \n",
    "        for u, c in np.c_[uniques, counts]:\n",
    "            yield [n, u, c]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the use of `num_cp` will only be necessary to show in a simple way how well the algorithm can predict $h_{11}$ and $h_{21}$. `num_cp` will be taken as a representative of the matrices in the test set and $h_{11}$ and $h_{21}$ will be shown in a 2D plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Rescaling\n",
    "\n",
    "Before proceeding we rescale the input matrices to have entries in $\\left[ 0, 1 \\right]$: we divide each entry of each matrix by the range of variation of its entries (since they are all positive, we divide by the maximum) in the **training** set. We then apply the **same scaling** to validation and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# compute the scale factor\n",
    "scale_factor = np.max(matrix_train) - np.min(matrix_train)\n",
    "\n",
    "# rescale the sets\n",
    "matrix_train = matrix_train / scale_factor\n",
    "matrix_val   = matrix_val   / scale_factor\n",
    "matrix_test  = matrix_test  / scale_factor\n",
    "\n",
    "# define the shape of the tensors (batch axis excluded) since we will use it later one\n",
    "input_shape  = np.shape(matrix_train)[1:]\n",
    "\n",
    "# then apply the usual rescaling to the other features\n",
    "num_cp_scaler     = StandardScaler()\n",
    "dim_cp_scaler     = StandardScaler()\n",
    "dim_h0_amb_scaler = StandardScaler()\n",
    "\n",
    "num_cp_train     = num_cp_scaler.fit_transform(num_cp_train)\n",
    "num_cp_val       = num_cp_scaler.transform(num_cp_val)\n",
    "num_cp_test      = num_cp_scaler.transform(num_cp_test)\n",
    "dim_cp_train     = dim_cp_scaler.fit_transform(dim_cp_train)\n",
    "dim_cp_val       = dim_cp_scaler.transform(dim_cp_val)\n",
    "dim_cp_test      = dim_cp_scaler.transform(dim_cp_test)\n",
    "dim_h0_amb_train = dim_h0_amb_scaler.fit_transform(dim_h0_amb_train)\n",
    "dim_h0_amb_val   = dim_h0_amb_scaler.transform(dim_h0_amb_val)\n",
    "dim_h0_amb_test  = dim_h0_amb_scaler.transform(dim_h0_amb_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building\n",
    "\n",
    "We the build the architecture of the CNN. In this section we use **transfer learning** from the networks previously trained on the configuration matrix to predict $h_{11}$ and $h_{21}$. We then add other inputs to the matrices in order to improve the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras              import Input\n",
    "from tensorflow.keras.utils        import model_to_dot\n",
    "from tensorflow.keras.models       import Model, load_model\n",
    "from tensorflow.keras.layers       import Conv1D, Conv2D, Dense, Lambda, Flatten, BatchNormalization, Dropout, LeakyReLU, concatenate, Activation\n",
    "from tensorflow.keras.optimizers   import Adam\n",
    "from tensorflow.keras.regularizers import l1_l2\n",
    "from tensorflow.keras.callbacks    import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
    "\n",
    "def cnn_full_model(model_name,\n",
    "                   h11_model_path,\n",
    "                   h21_model_path,\n",
    "                   num_cp_shape,\n",
    "                   dim_cp_shape,\n",
    "                   dim_h0_amb_shape,\n",
    "                   num_cp_neurons_h11=[30],\n",
    "                   num_cp_neurons_h21=[30],\n",
    "                   dim_cp_neurons_h11=[30],\n",
    "                   dim_cp_neurons_h21=[30],\n",
    "                   dim_h0_amb_neurons_h11=[30],\n",
    "                   dim_h0_amb_neurons_h21=[30],\n",
    "                   learning_rate=0.01,\n",
    "                   batch_norm_momentum=0.99,\n",
    "                   dropout_rate=0.3,\n",
    "                   leaky_alpha=0.3,\n",
    "                   l1_reg=0.0,\n",
    "                   l2_reg=0.0,\n",
    "                   last_relu=False,\n",
    "                   last_layer_h11=1,\n",
    "                   last_layer_h21=1\n",
    "                  ):\n",
    "    '''\n",
    "    Build a convolutional neural network with given parameters.\n",
    "    \n",
    "    Required arguments:\n",
    "        model_name:             the name of the model\n",
    "        h11_model_path:         the path of the CNN model to predict h11\n",
    "        h21_model_path:         the path of the CNN model to predict h21\n",
    "        num_cp_shape:           the shape of the tensor for num_cp\n",
    "        dim_cp_shape:           the shape of the tensor for dim_cp\n",
    "        dim_h0_amb_shape:       the shape of the tensor for dim_h0_amb\n",
    "        \n",
    "    Optional arguments:\n",
    "        learning_rate:          the learning rate of SGD (Adam optimizer)\n",
    "        num_cp_neurons_h11:     the list of units in each layer of the network for num_cp (for h11)\n",
    "        num_cp_neurons_h21:     the list of units in each layer of the network for num_cp (for h21)\n",
    "        dim_cp_neurons_h11:     the list of units in each layer of the network for dim_cp (for h11)\n",
    "        dim_cp_neurons_h21:     the list of units in each layer of the network for dim_cp (for h21)\n",
    "        dim_h0_amb_neurons_h11: the list of units in each layer of the network for dim_h0_amb (for h11)\n",
    "        dim_h0_amb_neurons_h21: the list of units in each layer of the network for dim_h0_amb (for h21)\n",
    "        batch_norm_momentum:    the momentum of the batch normalization\n",
    "        dropout_rate:           the rate of the dropout in percentage\n",
    "        leaky_alpha:            the slope of the LeakyReLU activation function\n",
    "        l1_reg:                 the l1 regularization\n",
    "        l2_reg:                 the l2 regularization\n",
    "        last_relu:              whether to add ReLU activation function on the output layer\n",
    "        last_layer_h11:         the no. of layers to insert between the concatenation of layers and the output node for h11\n",
    "        last_layer_h21:         the no. of layers to insert between the concatenation of layers and the output node for h22\n",
    "        \n",
    "    Returns:\n",
    "        the compiled model\n",
    "    '''\n",
    "    # regularization\n",
    "    regularization = l1_l2(l1=l1_reg, l2=l2_reg)\n",
    "    \n",
    "    # import the pretrained models\n",
    "    if path.isfile(h11_model_path):\n",
    "        h11_model = load_model(h11_model_path)\n",
    "\n",
    "    if path.isfile(h21_model_path):\n",
    "        h21_model = load_model(h21_model_path)\n",
    "        \n",
    "    # freeze the layers in the pre-trained models\n",
    "    h11_model.trainable = False\n",
    "    h21_model.trainable = False\n",
    "    \n",
    "    for n in range(len(h11_model.layers)):\n",
    "        h11_model.layers[n].trainable = False\n",
    "    for n in range(len(h21_model.layers)):\n",
    "        h21_model.layers[n].trainable = False\n",
    "        \n",
    "    # then build the network for num_cp\n",
    "    num_cp_in  = Input(shape=num_cp_shape, name='num_cp_input')\n",
    "    num_cp_h11 = Lambda(lambda z: z, name='lambda_num_cp_h11')(num_cp_in)\n",
    "    num_cp_h21 = Lambda(lambda z: z, name='lambda_num_cp_h21')(num_cp_in)\n",
    "    \n",
    "    for n_layer in range(len(num_cp_neurons_h11)):\n",
    "        num_cp_h11 = Dense(units=num_cp_neurons_h11[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='num_cp_h11_{:d}'.format(n_layer)\n",
    "                          )(num_cp_h11)\n",
    "        num_cp_h11 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='num_cp_h11_activation_{:d}'.format(n_layer)\n",
    "                              )(num_cp_h11)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            num_cp_h11 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='num_cp_h11_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(num_cp_h11)\n",
    "    \n",
    "    for n_layer in range(len(num_cp_neurons_h21)):\n",
    "        num_cp_h21 = Dense(units=num_cp_neurons_h21[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='num_cp_h21_{:d}'.format(n_layer)\n",
    "                          )(num_cp_h21)\n",
    "        num_cp_h21 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='num_cp_h21_activation_{:d}'.format(n_layer)\n",
    "                              )(num_cp_h21)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            num_cp_h21 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='num_cp_h21_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(num_cp_h21)\n",
    "    \n",
    "    if dropout_rate > 0.0:\n",
    "        num_cp_h11 = Dropout(rate=dropout_rate, name='num_cp_h11_dropout')(num_cp_h11)\n",
    "        num_cp_h21 = Dropout(rate=dropout_rate, name='num_cp_h21_dropout')(num_cp_h21)\n",
    "        \n",
    "    # then build the network for dim_cp\n",
    "    dim_cp_in  = Input(shape=dim_cp_shape, name='dim_cp_input')\n",
    "    dim_cp_h11 = Lambda(lambda z: z, name='lambda_dim_cp_h11')(dim_cp_in)\n",
    "    dim_cp_h21 = Lambda(lambda z: z, name='lambda_dim_cp_h21')(dim_cp_in)\n",
    "    \n",
    "    for n_layer in range(len(dim_cp_neurons_h11)):\n",
    "        dim_cp_h11 = Dense(units=dim_cp_neurons_h11[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='dim_cp_h11_{:d}'.format(n_layer)\n",
    "                          )(dim_cp_h11)\n",
    "        dim_cp_h11 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='dim_cp_h11_activation_{:d}'.format(n_layer)\n",
    "                              )(dim_cp_h11)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            dim_cp_h11 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='dim_cp_h11_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(dim_cp_h11)\n",
    "    \n",
    "    for n_layer in range(len(dim_cp_neurons_h21)):\n",
    "        dim_cp_h21 = Dense(units=dim_cp_neurons_h21[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='dim_cp_h21_{:d}'.format(n_layer)\n",
    "                          )(dim_cp_h21)\n",
    "        dim_cp_h21 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='dim_cp_h21_activation_{:d}'.format(n_layer)\n",
    "                              )(dim_cp_h21)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            dim_cp_h21 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='dim_cp_h21_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(dim_cp_h21)\n",
    "    \n",
    "    if dropout_rate > 0.0:\n",
    "        dim_cp_h11 = Dropout(rate=dropout_rate, name='dim_cp_h11_dropout')(dim_cp_h11)\n",
    "        dim_cp_h21 = Dropout(rate=dropout_rate, name='dim_cp_h21_dropout')(dim_cp_h21)\n",
    "        \n",
    "    # then build the network for dim_h0_amb\n",
    "    dim_h0_amb_in  = Input(shape=dim_h0_amb_shape, name='dim_h0_amb_input')\n",
    "    dim_h0_amb_h11 = Lambda(lambda z: z, name='lambda_dim_h0_amb_h11')(dim_h0_amb_in)\n",
    "    dim_h0_amb_h21 = Lambda(lambda z: z, name='lambda_dim_h0_amb_h21')(dim_h0_amb_in)\n",
    "    \n",
    "    for n_layer in range(len(dim_h0_amb_neurons_h11)):\n",
    "        dim_h0_amb_h11 = Dense(units=dim_h0_amb_neurons_h11[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='dim_h0_amb_h11_{:d}'.format(n_layer)\n",
    "                          )(dim_h0_amb_h11)\n",
    "        dim_h0_amb_h11 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='dim_h0_amb_h11_activation_{:d}'.format(n_layer)\n",
    "                              )(dim_h0_amb_h11)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            dim_h0_amb_h11 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='dim_h0_amb_h11_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(dim_h0_amb_h11)\n",
    "    \n",
    "    for n_layer in range(len(dim_h0_amb_neurons_h21)):\n",
    "        dim_h0_amb_h21 = Dense(units=dim_h0_amb_neurons_h21[n_layer],\n",
    "                           kernel_regularizer=regularization,\n",
    "                           name='dim_h0_amb_h21_{:d}'.format(n_layer)\n",
    "                          )(dim_h0_amb_h21)\n",
    "        dim_h0_amb_h21 = LeakyReLU(alpha=leaky_alpha,\n",
    "                               name='dim_h0_amb_h21_activation_{:d}'.format(n_layer)\n",
    "                              )(dim_h0_amb_h21)\n",
    "        \n",
    "        if batch_norm_momentum > 0.0:\n",
    "            dim_h0_amb_h21 = BatchNormalization(momentum=batch_norm_momentum,\n",
    "                                            name='dim_h0_amb_h21_batch_norm_{:d}'.format(n_layer)\n",
    "                                           )(dim_h0_amb_h21)\n",
    "    \n",
    "    if dropout_rate > 0.0:\n",
    "        dim_h0_amb_h11 = Dropout(rate=dropout_rate, name='dim_h0_amb_h11_dropout')(dim_h0_amb_h11)\n",
    "        dim_h0_amb_h21 = Dropout(rate=dropout_rate, name='dim_h0_amb_h21_dropout')(dim_h0_amb_h21)\n",
    "      \n",
    "    # concatenate the outputs\n",
    "    h11_transfer_learning = concatenate([h11_model.output,\n",
    "                                         num_cp_h11,\n",
    "                                         dim_cp_h11,\n",
    "                                         dim_h0_amb_h11],\n",
    "                                        name='h11_concatenate'\n",
    "                                       )\n",
    "    for n in range(last_layer_h11):\n",
    "        h11_transfer_learning = Dense(units=h11_transfer_learning.shape[1],\n",
    "                                      name='h11_concatenate_dense_{:d}'.format(n)\n",
    "                                     )(h11_transfer_learning)\n",
    "        h11_transfer_learning = LeakyReLU(alpha=leaky_alpha,\n",
    "                                          name='h11_concatenate_dense_activation_{:d}'.format(n)\n",
    "                                         )(h11_transfer_learning)\n",
    "    \n",
    "    # improve the result of the matrix with the processed output\n",
    "    h11_transfer_learning = Dense(units=1, name='h11_improvements')(h11_transfer_learning)\n",
    "    h11_transfer_learning = LeakyReLU(alpha=leaky_alpha, name='h11_improvements_activation')(h11_transfer_learning)\n",
    "    h11_transfer_learning = concatenate([h11_transfer_learning, h11_model.output])\n",
    "    h11_transfer_learning = Dense(units=1,\n",
    "                                  activation='relu',\n",
    "                                  name='h11'\n",
    "                                 )(h11_transfer_learning) if last_relu \\\n",
    "                                                          else Dense(units=1,\n",
    "                                                                     name='h11'\n",
    "                                                                    )(h11_transfer_learning)\n",
    "    \n",
    "    h21_transfer_learning = concatenate([h21_model.output,\n",
    "                                         h11_model.output,\n",
    "                                         num_cp_h21,\n",
    "                                         dim_cp_h21,\n",
    "                                         dim_h0_amb_h21],\n",
    "                                        name='h21_concatenate'\n",
    "                                       )\n",
    "    for n in range(last_layer_h21):\n",
    "        h21_transfer_learning = Dense(units=h21_transfer_learning.shape[1],\n",
    "                                      name='h21_concatenate_dense_{:d}'.format(n)\n",
    "                                     )(h21_transfer_learning)\n",
    "        h21_transfer_learning = LeakyReLU(alpha=leaky_alpha,\n",
    "                                          name='h21_concatenate_dense_activation_{:d}'.format(n)\n",
    "                                         )(h21_transfer_learning)\n",
    "        \n",
    "    # improve the result of the matrix with the processed output\n",
    "    h21_transfer_learning = Dense(units=1, activation='relu', name='h21_improvements')(h21_transfer_learning)\n",
    "    h21_transfer_learning = LeakyReLU(alpha=leaky_alpha, name='h21_improvements_activation')(h21_transfer_learning)\n",
    "    h21_transfer_learning = concatenate([h21_transfer_learning, h21_model.output])\n",
    "    h21_transfer_learning = Dense(units=1,\n",
    "                                  activation='relu',\n",
    "                                  name='h21'\n",
    "                                 )(h21_transfer_learning) if last_relu \\\n",
    "                                                          else Dense(units=1,\n",
    "                                                                     name='h21'\n",
    "                                                                    )(h21_transfer_learning)\n",
    "    # compile the model\n",
    "    model = Model(inputs=[h11_model.input, h21_model.input, num_cp_in, dim_cp_in, dim_h0_amb_in],\n",
    "                  outputs=[h11_transfer_learning, h21_transfer_learning],\n",
    "                  name=model_name\n",
    "                 )\n",
    "    model.compile(optimizers=Adam(learning_rate=learning_rate), loss='mean_squared_error', metrics=['mean_squared_error'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then visualise the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-17 09:47:05,908: DEBUG ==> Building the full convolutional model...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"full_model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "h11_inception_same_conv_no_fc_i [(None, 15, 18, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_i [(None, 15, 18, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   512         h11_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   608         h11_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  2048        h21_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  2432        h21_inception_same_conv_no_fc_inp\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "                                                                 h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 256)  0           h21_inception_same_conv_no_fc_con\n",
      "                                                                 h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   256         h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 256)  1024        h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   61504       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   73792       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  491648      h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  589952      h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 128)  0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 128)  0           h11_inception_same_conv_no_fc_con\n",
      "                                                                 h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 256)  0           h21_inception_same_conv_no_fc_con\n",
      "                                                                 h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 128)  512         h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 256)  1024        h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   61472       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   73760       h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 32)   122912      h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 32)   147488      h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "num_cp_input (InputLayer)       [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_input (InputLayer)       [(None, 15)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_input (InputLayer)   [(None, 18)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 32)   0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "                                                                 h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 64)   0           h21_inception_same_conv_no_fc_con\n",
      "                                                                 h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "lambda_num_cp_h21 (Lambda)      (None, 1)            0           num_cp_input[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "lambda_dim_cp_h21 (Lambda)      (None, 15)           0           dim_cp_input[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "lambda_dim_h0_amb_h21 (Lambda)  (None, 18)           0           dim_h0_amb_input[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_c (None, 15, 18, 64)   256         h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_c (None, 15, 18, 64)   256         h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "num_cp_h21_0 (Dense)            (None, 10)           20          lambda_num_cp_h21[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_h21_0 (Dense)            (None, 10)           160         lambda_dim_cp_h21[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_h21_0 (Dense)        (None, 10)           190         lambda_dim_h0_amb_h21[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_d (None, 15, 18, 64)   0           h11_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_d (None, 15, 18, 64)   0           h21_inception_same_conv_no_fc_con\n",
      "__________________________________________________________________________________________________\n",
      "num_cp_h21_activation_0 (LeakyR (None, 10)           0           num_cp_h21_0[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_h21_activation_0 (LeakyR (None, 10)           0           dim_cp_h21_0[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_h21_activation_0 (Le (None, 10)           0           dim_h0_amb_h21_0[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_f (None, 17280)        0           h11_inception_same_conv_no_fc_dro\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_f (None, 17280)        0           h21_inception_same_conv_no_fc_dro\n",
      "__________________________________________________________________________________________________\n",
      "num_cp_h21_batch_norm_0 (BatchN (None, 10)           40          num_cp_h21_activation_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_h21_batch_norm_0 (BatchN (None, 10)           40          dim_cp_h21_activation_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_h21_batch_norm_0 (Ba (None, 10)           40          dim_h0_amb_h21_activation_0[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "h11_inception_same_conv_no_fc_o (None, 1)            17281       h11_inception_same_conv_no_fc_fla\n",
      "__________________________________________________________________________________________________\n",
      "h21_inception_same_conv_no_fc_o (None, 1)            17281       h21_inception_same_conv_no_fc_fla\n",
      "__________________________________________________________________________________________________\n",
      "num_cp_h21_dropout (Dropout)    (None, 10)           0           num_cp_h21_batch_norm_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_h21_dropout (Dropout)    (None, 10)           0           dim_cp_h21_batch_norm_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_h21_dropout (Dropout (None, 10)           0           dim_h0_amb_h21_batch_norm_0[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "lambda_num_cp_h11 (Lambda)      (None, 1)            0           num_cp_input[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "lambda_dim_cp_h11 (Lambda)      (None, 15)           0           dim_cp_input[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "lambda_dim_h0_amb_h11 (Lambda)  (None, 18)           0           dim_h0_amb_input[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "h21_concatenate (Concatenate)   (None, 32)           0           h21_inception_same_conv_no_fc_out\n",
      "                                                                 h11_inception_same_conv_no_fc_out\n",
      "                                                                 num_cp_h21_dropout[0][0]         \n",
      "                                                                 dim_cp_h21_dropout[0][0]         \n",
      "                                                                 dim_h0_amb_h21_dropout[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "num_cp_h11_dropout (Dropout)    (None, 1)            0           lambda_num_cp_h11[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dim_cp_h11_dropout (Dropout)    (None, 15)           0           lambda_dim_cp_h11[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dim_h0_amb_h11_dropout (Dropout (None, 18)           0           lambda_dim_h0_amb_h11[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "h21_concatenate_dense_0 (Dense) (None, 32)           1056        h21_concatenate[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "h11_concatenate (Concatenate)   (None, 35)           0           h11_inception_same_conv_no_fc_out\n",
      "                                                                 num_cp_h11_dropout[0][0]         \n",
      "                                                                 dim_cp_h11_dropout[0][0]         \n",
      "                                                                 dim_h0_amb_h11_dropout[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "h21_concatenate_dense_activatio (None, 32)           0           h21_concatenate_dense_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "h11_concatenate_dense_0 (Dense) (None, 35)           1260        h11_concatenate[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "h21_concatenate_dense_1 (Dense) (None, 32)           1056        h21_concatenate_dense_activation_\n",
      "__________________________________________________________________________________________________\n",
      "h11_concatenate_dense_activatio (None, 35)           0           h11_concatenate_dense_0[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "h21_concatenate_dense_activatio (None, 32)           0           h21_concatenate_dense_1[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "h11_improvements (Dense)        (None, 1)            36          h11_concatenate_dense_activation_\n",
      "__________________________________________________________________________________________________\n",
      "h21_improvements (Dense)        (None, 1)            33          h21_concatenate_dense_activation_\n",
      "__________________________________________________________________________________________________\n",
      "h11_improvements_activation (Le (None, 1)            0           h11_improvements[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "h21_improvements_activation (Le (None, 1)            0           h21_improvements[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 2)            0           h11_improvements_activation[0][0]\n",
      "                                                                 h11_inception_same_conv_no_fc_out\n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 2)            0           h21_improvements_activation[0][0]\n",
      "                                                                 h21_inception_same_conv_no_fc_out\n",
      "__________________________________________________________________________________________________\n",
      "h11 (Dense)                     (None, 1)            3           concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "h21 (Dense)                     (None, 1)            3           concatenate_1[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 1,669,955\n",
      "Trainable params: 3,877\n",
      "Non-trainable params: 1,666,078\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
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qfeGFFz755JPLL798zZo1t99++5gxY0THBcAnt7K9mpqampqa1tZWSZJiYmImTpzoVrA3bdq01NRU0VFrrbe39/3339+1a9e2bdtOnz49adKkm266aeHChTfddBNVygAA6BO1fAAAAACM6tChQ7NmzdqyZcuKFStExwIAgHg5OTnLli17+umnRQcCAACMqru7+/z58+fOnZML/OTbF75y/vz5CxcudHZ2Ol8fExMzevToUaNGyZV+mZmZ8m1ZZmamfCMhISGsYe/bt2/p0qUFBQXbt29PS0sL67oAABK1fACguQMHDpSUlLS1tVVUVCxZskR0OIDx9PX1vfnmm1VVVa+++mpKSsqKFSuKi4vnzJkjOi4A/8dut586dcptqr1jx47JI1Hx8fHjxo1zrdnLy8u77LLLYmJiRAcuUktLy759+3bt2rVjx46Ojo68vLxFixYtXLjw2muvjYqKEh0dAADwh1o+AAAAAAZ211137d69++jRo+np6aJjAQBAsLKyMqvVWldXx/U5AAAQPr29vRcuXGhpaZHn9GtpaZEr/Zqbm+UKQNnAwIBzkZSUlFGjRslT/DmL/eRKv1GjRo0cOVL+G9zPhG/btm316tXLli3buHFjbGysehsKAPCJWj4A0F5HR8cjjzxSUVFRVFRUXl5usVhERwQYQ01NTVVV1YYNGy5evHjDDTcUFxcvX748KSlJdFxAROvt7W1qanLOs1dbW1tfX9/Q0DA4OChJksVimeJh8uTJ0dHRogPXi2PHju3cufPPf/7zwYMHY2Nj586de/PNNy9cuHDcuHGiQwMAAEpRywcAAADAwFpaWnJzc9esWfPss8+KjgUAAMGqq6sLCgoOHTp09dVXi44FAABEup6eHpvNZrPZzpw509TUZPuK691z584NDQ05F0lISLD4kJ2dnZWVJd++5JJLnDV769evf/jhh++9997nnnuOb3QBgGao5QMAUfbs2bN27dq4uLhNmzZdf/31osMB9Ovs2bMvv/zypk2bPv7449zc3JUrV95xxx2TJk0SHRcQcWw2W72HEydOyGcTrmV7eXl506dP/9rXvpaRkSE6ap2qr6/ftWuX1Wr94IMPRo4cOW/evIULFy5ZsiQtLU10aAAAIGDU8gEAAAAwtvLy8nvvvbe6uvrKK68UHQsAAILl5OQsW7bs6aefFh0IAADA8Pr7+y9evNjS0nLx4kXnjZavuD7Y09PjumBGRsaoUaN6e3tPnz49Y8aMOXPmOCf3c94YNWoUc5UAQJhQywcAAjU3N5eWlu7cufO+++575pln4uPjRUcE6Eh/f//evXurqqp27NiRlJS0ePHikpKSefPmRUVFiQ4NMD+bzeacZE/2xRdftLe3S5IUFxc3fvx4t6n2cnNzk5OTRUdtADU1NVar1Wq11tbWZmVl3XLLLUVFRXPmzBkxYoTo0AAAQPCo5QMAAABgbENDQ9dee21MTMzf/vY3LsMAACJcWVmZ1Wqtq6vjMxEAAJiJc5Y/2YULF8rLy6urq2+88cbMzEzndH8XL17s6+tzXdB1rj/Xyf3cjB07lmn9AEA5avkAQDir1VpaWjpu3LiqqqoZM2aIDgcQr6ampqqq6oUXXmhpaZk9e3ZJScltt91GmRAQDv39/Y2NjW5T7dXW1sq/xJSQkJCdnS1Psucs25s0aRKFZ4FyLeEbPXr0ggULioqKFixYEBMTIzo0AACgAmr5AAAAABje4cOHv/nNb27cuLG4uFh0LAAAiFRdXV1QUHDo0KGrr75adCwAAABh0dXVVVRU9N5771mt1sLCQrdnOzo6fE3u53ZjaGjIuVRcXJzrnH6uk/vJ5AczMjJSUlK03VwA0CNq+QBADxoaGtasWbN///7HHnvs8ccfp0YCkclms1mt1vLy8iNHjkybNu3WW2+9/fbbJ0+eLDouwCRsNlu9hy+//FIeVLFYLFM8XHrppfzcZCjkEr5XXnnl888/p4QPAAATo5YPAAAAgBncc88927dvP3r0aEZGhuhYAAAQKScnZ9myZU8//bToQAAAANTX0tKyaNGi+vr63bt35+fnh9KU21x/zsn93B48f/784OCg64Kuc/35kpiYKL8sKyuLb7ABMB9q+QBAJxwOx/PPP19WVjZz5szKysqcnBzREQEaGRwcfOeddyoqKnbs2JGYmHjzzTeXlJTMmzeP8y8gaJ5lezU1NWfOnJEkKSYmZuLEiW41e9OmTUtNTRUdtXm4lvCNHz/+lltuKSoq+ta3vhUdHS06NAAAEBbU8gEAAAAwA5vNlpubu3Llyueff150LAAAiFRWVma1Wuvq6vjWAgAAMJkTJ04UFhba7fa9e/dOnTpVm5UODg5e/EqrB5vN5vaI3W53XTwuLi7Dg8Vi8XxQlpCQoM12AUAoqOUDAF2pqakpLi4+fvz4s88+W1paKjocILxqa2srKys3bdrU3Nw8e/bskpKSVatWMYM6oJzdbj916pRb2d6xY8c6OzslSYqPjx83bpxb2d706dMZrwgTuYTv5ZdfPnr0KCV8AABEFGr5AAAAAJjEpk2b7rrrrkOHDs2YMUN0LAAACFNdXV1QUFBdXR3iTDUAAAC68umnnxYWFmZlZb3++uuZmZmiw/FJnu6vt7fXc94/Ty0tLf39/W4tKJn3TzZq1Kj4+HghmwkgwlHLBwB6Y7fbn3rqqSeffHL+/PkbNmzIzs4WHRGgstbW1ldeeaWysvKDDz6YMGHCqlWrSktLp0yZIjouQNf6+vpOnz7tnGSvtra2vr7+5MmTAwMDkiRZLJYpHiZPnkwVmQZcS/gmTJiwdOnSoqKia6+9lp/pBAAgclDLBwAAAMAkHA7H3Llzu7u7Dx48yPgyACCS5eTkLF++fN26daIDAQAAUMe+ffuWLl1aUFCwffv2tLQ00eGoqaOjw3O6P6/kOQDdru0mJSU55/RLS0tLTU21WCypqampqakpKSmud2Xyy0aMGCFqewGYA7V8AKBPBw4cKCkpaWtrq6ioWLJkiehwABUMDQ3t27evsrJy69atkiQtXLiwtLR03rx5lLsAbmw2W72HEydOyJ1217K9vLy86dOnf+1rX8vIyBAddcRpaGj405/+tHHjxmPHjlHCBwBAhKOWDwAAAIB5fPbZZzNnzvztb3+7du1a0bEAACBMWVmZ1Wqtq6vj4h8AADCBbdu2rV69etmyZRs3boyNjRUdjmBtbW2+iv3a29s7OjpsNltnZ2fHV2w2m2cjiYmJrqV9zkq/9PT09PR0uQjQedf5rMmqKAGEglo+ANCtjo6ORx55pKKioqioqLy83GKxiI4ICNLRo0c3bdr0xz/+8ezZs/n5+aWlpbfeemtqaqrouADxbDabc5I92RdffNHe3i5JUmxs7IQJE9ym2svNzU1OThYddUQ7e/bsyy+//NJLL3300Udjx45duXLlrbfees0113AVDwCASEYtHwAAAABTefDBB6uqqo4dOzZ69GjRsQAAIEZ1dXVBQUF1dXV+fr7oWAAAAEKyfv36hx9++N57733uueeio6NFh2NIzrq+zs5Om83W4cJZASiX/znvdnR0dHV1eTblVtqXkZHhNumf6115SsDk5OSkpCTttxpAWFHLBwA6t2fPnrVr18bFxW3atOn6668XHQ4QgLa2tpdffrmysvKDDz4YP378bbfddtddd+Xk5IiOCxCgv7+/sbHRbaq9zz//vLu7W5KkhISE7OxseZI9Z9nepEmTRowYITpw/K/u7u7du3dXVlbu3bs3KSlp8eLFRUVFCxYsiImJER0aAAAQj1o+AAAAAKbS0dGRm5u7cOHC8vJy0bEAACBMTk7O8uXL161bJzoQAACAIDkcjieeeOInP/nJunXrHn30UdHhRKKenh6bzdbb2yvfcHJ7xPOuZ1MJCQmJiYkJCQkWi0XJDbcH09LS+DIioCvU8gGA/jU3N5eWlu7cufO+++575pln4uPjRUcE+DM0NLRv377Kyspt27YNDQ0tWrSouLj429/+NicCiBA2m63ew5dffjk0NCRJksVimeLh0ksvZVY3ferr69uzZ8/mzZtfe+01h8Px7W9/e9WqVQsXLkxISBAdGgAA0BFq+QAAAACYzUsvvVRcXPzBBx/MmjVLdCwAAIhRVlZmtVrr6uq4lAsAAIxoYGDg7rvvfvHFFzdu3Lhq1SrR4SAAvb29nZ2d7e3tra2t8vx+XV1dbW1t3d3dcslfT09PT09Pa2ur/EhbW1tXV1dPT097e3tnZ6fdbvfabHp6emJiYlJSUkZGRmJiYmJiolzml5iYmJGRkZSUlJiYmJ6enpycLNf+paSkJCYmyjMEJiYmpqSkaLwfAHOjlg8AjMJqtZaWlo4bN66qqmrGjBmiwwG8OHbs2JYtW/74xz9++eWX+fn5xcXFxcXFI0eOFB0XEC6eZXs1NTVnzpyRJCkmJmbixIluNXvTpk1LTU0VHTUUqampqaqqeuGFF1paWmbPnl1UVHTbbbeNHj1adFwAAECPqOUDAAAAYELz5s1ra2v78MMP+bFGAEBkqq6uLigoqK6uzs/PFx0LAABAYLq6uoqKit577z2r1VpYWCg6HGhqYGCgo6Ojo6Oju7tbLgLs6enp7u5ubW2ViwB9VQN2d3fLCw4MDHhtOS0tLSkpKSkpKTk5OS4uLiMjIzY2NjU1VZ4zMDU1NTY2Vn4wJSUlKSkpPj4+PT09NjY2LS0tPj4+KSkpJSUlNjbWYrFovE8AHaKWDwAMpKGhYc2aNfv373/ssccef/xxLpxBJ9rb23fs2FFVVfX2229nZWUVFxevXbt26tSpouMCVGO320+dOuVWtnfs2LHOzk5JkuLj48eNG+dWtpeXl5eYmCg6cATs1KlTVVVVVVVVR48ezcvLW7NmzW233TZu3DjRcQEAAF2jlg8AAACACdXW1l511VXr16///ve/LzoWAADEyMnJWb58+bp160QHAgAAEICWlpZFixbV19fv3r2bXyVAEOx2uzwxYE9Pj9dqwI6ODrvd3tra2t/fLxcB9vX1tbW12e329vb23t7enp4ePzMESpIUExMjT/eXkJCQlpYWExOTkZERFxeXnJwsFwo6iwCdhYIxMTEWi8W1UFB+MC0tje/Tw4io5QMAY3E4HM8//3xZWdnMmTMrKytzcnJER4TINTQ0tH///qqqqs2bNw8MDCxevLi4uHjBggUxMTGiQwOC19fXd/r0add59mpra0+ePCn/1ozFYpniYfLkydHR0aIDR0h6enpee+21ysrKN954IzU1taioqLi4eM6cOaLjAgAAxkAtHwAAAABzKisr+/3vf3/06NHMzEzRsQAAIEBZWZnVaq2rq4uKihIdCwAAgCInTpwoLCy02+179+5lQgYI19PTI5f2uf612Wx+7vp60Gaz+V+XXPWXkJBgsVjkGyH+pUoQYUUtHwAYUU1NTXFx8fHjx5999tnS0lLR4SDinDx5csuWLeXl5SdOnMjPzy8uLl69evWoUaNExwUExmaz1Xs4ceKE3Dd2K9vLy8u74oorMjIyREcNNQ0NDb3zzjuVlZXbt2/v7+//zne+s2bNmgULFsTFxYkODQAAGAm1fAAAAADMqbu7Oy8vb+7cuRs3bhQdCwAAAlRXVxcUFFRXVzOhDQAAMIRPP/20sLAwKyvr9ddf53d5YDJDQ0NtbW19fX3d3d3ypH9tbW1DQ0Otra0Oh8NmszkcjtbWVvllg4OD7e3tAwMD8hSCnZ2d8hSC8uLyX7lK0P9KU1JSYmNjXScATE9Pj46OzsjIiIqKkv9aLBZJkuTb8rPy6+Vl5WkG5fpAuUQwPj4+KSlJk30GXaOWDwAMym63P/XUU08++eT8+fM3bNiQnZ0tOiKYnzxvVUVFxdtvvz127NiioqI777zzyiuvFB0XMDy5bE+eZE+u2Tt+/HhbW5skSbGxsRMmTHCbai83Nzc5OVl01Aij06dPv/jiixUVFfX19Xl5eSUlJXfccQdDWAAAIDjU8gEAAAAwra1bt65YseKdd975t3/7N9GxAAAgQE5OzvLly9etWyc6EAAAgGHs27dv6dKlBQUF27dvT0tLEx0OYAzypH9ygV9XV1d/f79cKNjR0TEwMNDe3j44OOinaFD+Kz8iSZI8eaD8uP/1xsbGpqSkxMTEpKamjhgxIi0tTS4OlCQpuOJA1wbDv9ugAmr5AMDQDhw4UFJS0tbWVlFRsWTJEtHhwJyGhob2799fVVX10ksv2e32+fPnl5SULF26NCYmRnRogLv+/v7Gxka3qfY+//zz7u5uSZISEhKys7PlSfamT58ul+1NmjSJidAjR29v744dO1544YW333579OjRxcXFd955Z15enui4AACAsVHLBwAAAMDMvvOd75w8efLIkSOxsbGiYwEAQGtlZWVWq7Wuri4qKkp0LAAAAD5t27Zt9erVy5Yt27hxI+fvgE7IhYKh/PXaiJJaQbnMT/7rdjeUv/INLfZdBKCWDwCMrqOj45FHHqmoqCgqKiovL5er8QFVNDY2bt68+fe//31dXZ08b9Wdd955ySWXiI4LkKSvptpz09DQMDg4KEmSxWKZ4uHSSy/lCkvEqq2trays3LBhw8WLF2+44YbS0tIlS5YwcgUAAFRBLR8AAAAAMzt+/PjXv/71n/3sZw8++KDoWAAA0Fp1dXVBQUF1dXV+fr7oWAAAALxbv379ww8/fO+99z733HPR0dGiwwEQdvLkgfJ0gnKBn1zp19/f39XVZbfbOzs7BwYGOjo6vM4cKM8xKM86KM9A6NrgsGtXMnOg/Ij8rMViiY6OTk9Pl2cglKcQlKcTlIsDk5KS4uPjw73T9IZaPgAwhz179qxduzYuLm7Tpk3XX3+96HBgbL29vbt27aqoqHj77bctFsvy5cu///3vX3XVVaLjQuTyWrZXX18vP5uVleWcZE82derUtLQ0sTFDJ9ra2l5++eXKysoPPvhg2rRpt9566x133DFp0iTRcQEAAFOhlg8AAACAyT3++OPr168/evRodna26FgAANBaTk7O8uXL161bJzoQAAAAdw6H44knnvjJT36ybt26Rx99VHQ4AMwgxOJA+a88eaDNZpNfLy/rf71yKWBaWtqIESPkQkFnEWBMTExqampcXFxycrJrEaBr3aBzwejo6IyMDLluUIv9FSxq+QDANJqbm0tLS3fu3Hnfffc988wzEVigjtAdPny4oqJiy5YtfX19//7v/15SUsK8VdCS3W4/deqUW8HesWPHOjs7JUmKi4sbP36821R7eXl58hzggJv333+/vLx869atI0aMKCoquvPOO+fMmcPEjAAAIByo5QMAAABgcj09PVdcccWsWbM2b94sOhYAALRWVlZmtVrr6uq41ggAAHRlYGDg7rvvfvHFFzdu3Lhq1SrR4QDA8ORCwWH/2mw2JS/r7e2Viwb9rDEhIUEu/LNYLPKNIO6OHj06Li5O3V1BLR8AmIzVai0tLR03blxVVdWMGTNEhwNjOH369IsvvviHP/zh+PHjeXl5JSUld9xxR2Zmpui4YGZ9fX2nT592FuzV1NTU1taePHlyYGBAkiSLxTLFw+TJk6Ojo0UHDr2z2WxVVVXl5eW1tbX5+fl33333d7/73dTUVNFxAQAAM6OWDwAAAID5vfHGGwsWLHj77bdvuOEG0bEAAKCp6urqgoKC6urq/Px80bEAAAD8r66urqKiovfee89qtRYWFooOBwCEcc4BKM8WKM8NKM8l2NfX193d3dHRYbfbW1tbnXf7+/vb2trkgkD5xc67vtYizwcoTw+Ynp4ul/mlpaXFxcWlpaW53ZUrANPT0+VpA93uyg1SywcA5tPQ0LBmzZr9+/c/9thjjz/++IgRI0RHBJ3q7e3dtWtXZWXlnj170tLSioqK7r777pkzZ4qOC2Zjs9nqPZw4cULugrqV7eXl5V1xxRUZGRmio4bxyDOLbt68eXBwcNGiRQ888MC1114rOigAABARqOUDAAAAEBEWL1587NixTz75JD4+3vngyZMnJ06cKDAqAAA0kJOTs3z58nXr1sl3GxoaPv3004ULF4qNCgAARKyWlpZFixbV19fv3r2bnxsAABW1t7f39/e3t7c75/3r7+/v6Ohwu9vd3d3X19fa2trf39/Z2el2t6urq7+/39cqkpKSEhMTY2Jizp07981vfjM1NTUtLS0pKSkpKSkjI0O+kZ6enpycLFcPysWBycnJ8rMJCQla7hAAQEAcDsfzzz9fVlY2c+bMysrKnJwc0RFBXw4fPlxZWbl58+bW1ta5c+eWlpbefPPNqk//iwgkl+3Jk+zJNXvHjx9va2uTJCk2NnbChAluU+3l5uYmJyeLjhrG1t7e/qc//em3v/3txx9/LM8sWlpaarFYRMcFAAAiCLV8AAAAACLCyZMn8/LyfvzjHz/66KOSJDU3Nz/66KNvvPHGmTNnRIcGAEB4lZWVWa3Wffv2bdu27aWXXvr73/8+f/78vXv3io4LAABEohMnThQWFtrt9r17906dOlV0OAAA72w2W39/f1dXl1zad+LEiddff72/v7+vr89utzc0NHz88cdz5szp7+/v7e212+0OhyM5Obm7u7u7u7u9vb2jo2NgYMCz2aioqIyMDGelX2pqalJSkrPSLzEx0deziYmJcrmg9rsCACJNTU1NcXHx8ePHn3322dLSUtHhQLympiar1frCCy988sknl19++Zo1a26//fYxY8aIjgvG09/f39jY6DbV3ueff97d3S1JUkJCQnZ2tjzJ3vTp0+WyvUmTJjFNKNR1+PDh3/zmN3/605+ioqK++93vlpaWXnPNNaKDAgAAkYhaPgAAAACR4n/+53/WrVv36aefvvnmm2VlZZ2dnUNDQydPnpwwYYLo0AAACJeGhobnnnuuvLy8r68vJiZmcHBwaGjo29/+9u7du0WHBgAAzOzQoUOvvvrqT3/6U9cHP/3008LCwqysrNdffz0zM1NUbACAQA0MDIwdO9Zms8XGxno+29fXd/fdd//ud79zfdBut3d2dra1tfX09HR3d9tstu7u7p6enra2NnkywM7Ozvb2drn8r7W1Vb7R1tbW1dXV3d3d0dHhNZLk5OTExMS0tDSvlX7JycnJycmpqakZGRlyNaD8yvT09PT09Ojo6LDsHQAwHbvd/tRTTz355JPz58/fsGFDdna26IggQF9f35tvvllVVfXqq6+mpKSsWLGiuLh4zpw5ouOCMfT29tbV1Tnn2ZM1NDQMDg5KkmSxWKZ4uPTSS6OiokQHDtPq6+uzWq2//vWvP/zww69//evf+973brvttvT0dNFxAQCAyEUtHwAAAIBI0dfXN23aNLvdfu7cOYfD4XA4oqKitm7desstt4gODQAAlTU0NGzdunXLli1HjhxxlvA5n128ePHOnTsFhgcAAExvzpw5H3zwwfr16++//375kX379i1durSgoGD79u1paWliwwMABOq+++4rLy+32+1en3333Xevu+46ddfY0dHR3d3d1dXV2toqFwS2trZ2dXX19PTIU//19PTI5YJylaBcLtjV1dXR0WGz2TwblMv85AI/Z7GfzGKxuN7NyMiQiwBTU1OTk5PV3S4AMIoDBw6UlJS0tbVVVFQsWbLE7dm//OUv+/fv/+///m8hsSGsDh8+XFlZ+dJLL128ePGGG24oLi4uKipKTEwUHRd0ymaz1XsjP5uVleWcZE82depUxgSgpaampqqqql/96ldnz55dsGDBAw88MG/ePApHAQCAcDGiAwAAAAAALbS2tv74xz9ubGwcMWKEs5ghNjb20KFD1PIBAMynvLz8Zz/7WVRUlMPh8PyqpdeJFAAAANSya9euDz74QJKkBx98cOzYsStWrNi2bdvq1auXLVu2ceNGuiIAYES33nrrr3/9a69PjR07Nhyz9MildEEv3tPT09vbK9f4ydzuyo+43W1tbfX8ReyEhASLxWKxWBITE523/d8dNWpUfHx8aDsAAASbPXv2kSNHHnnkkaVLlxYVFVVUVGRkZMhPXbx48bbbbmtpaZk7d67qtdwQ5cyZM6+88srGjRv/8Y9/5Obm/uAHP7jjjjsmTZokOi7ohd1uP3XqlFvB3j//+U95OuW4uLjx48fL1Xo33nijfCMvL48qUAj0/vvvP//886+++uqoUaNuv/32e+65Z+LEiaKDAgAA+F/MywcAAADA5BwOR1VV1UMPPdTR0eFZzHDddde9++67QgIDACB8hoaG5s+f/95773l+9kVFRa1cuXLLli1CAgMAAKY3ODiYl5dXV1c3ODgoSVJMTMy99967fv36//iP/3jmmWf44XMAMCiHwzFx4sTGxka3x+Pi4h544IFnnnlGSFSqGxgYkKf163DR2toqzwQoa2tra2trc95tb2/3WgGYlJQklyOmp6enp6c7J/1znRVQfiojIyMjIyM9Pd1isQjZagDwb8+ePWvXro2Li9u0adP1118vSdKKFSt27NgxODh4ySWX1NbWjhw5UnSM8OKLL75IS0sbM2aM/5f19/fv3bu3qqpqx44dSUlJixcvLikpYdKqCNfX13f69GnXmr2ampp//vOfAwMDkiRZLJYpHiZPnhwdHS06cECSJKmjo2PLli2/+tWvPvvss/z8/Pvvv//WW2/lh6UAAIDeUMsHAAAAwOQee+yxn/3sZ76eTUpK6ujo4NICAMB8zp8//41vfOPChQvy1+idoqOjV61aVTIWE9EAACAASURBVFVVJSowAABgbhUVFd///veHhobku9HR0fHx8ffff/+6devEBgYACNF//ud//vznP/f8yZgjR47MmDFDSEj6oXAaQLcXeBYBuk7053X2P1ejR4+Oi4sTtckAIkpzc3NpaenOnTvvu+++GTNm3HHHHfLjsbGx8+bNe/3116n70pt9+/bdcsstP/rRj374wx/6ek1NTU1VVdULL7zQ0tJyww03FBcXL1++PCkpScs4IZzNZqv3cOLECbmL4lm2N3369KysLNFRA97V19dXVFSUl5f39vYWFRU9/PDDV111leigAAAAvKOWDwAAAIDJDQwMPPDAA7/5zW98vaC2tvbyyy/XMiQAALRx8ODB//f//p/8W7lOI0aMWL169aZNmwQFBQAAzKyrq2vy5MktLS2ulyBjYmLS0tIOHjw4depUgbEBAEL0ySefXHnllW4PTpkypa6uTkg85uBW7+fJtQLw/Pnzbr/X4yzz81Py5zRq1Kj4+HhRWwrABDZs2PDggw8ODAz09fU5O/xRUVG//vWv77nnHrGxwVV5efkPfvCDoaGhr33ta1988YXbsxcvXty6devvfve7v//979OmTbv11ltvv/32yZMni4gUmnKW7dXU1NTW1tbX1x8/frytrU2SpNjY2AkTJriV7V122WUpKSmiowYU+etf//rLX/5y165dEydOvPfee9euXZuRkSE6KAAAAH+o5QMAAAAQESoqKuTriG7fdRgxYsSGDRvWrFkjKC4AAMJr/fr1Dz30kNuX6desWfOHP/xBYFQAAMCsnnjiiSeffNLtpwQkSYqJicnOzv7oo4/GjBkjJDAAgCpyc3OPHTvmvBsXF/ejH/3oxz/+scCQIo2v2j/PaQBtNltzc7Pbh3JCQoKSqj9nfaDFYhG1pQB0yOFwXHfddR9++KHbHK2xsbGHDh3yrPeG9gYHB3/0ox89/fTTzkc++uijgoIC+al33nmnoqJix44diYmJN998c0lJybx585hTUXtnzpz56U9/+swzzyQmJoaj/f7+/sbGRrep9j7//PPu7m5JkuLj48eNGydX6+Xl5U2fPn3KlCmTJk0aMWJEOIIBwqq/v3/nzp0///nPP/zww/z8/Pvvv3/VqlUxMTGi4wIAAP+fvXuPl7qu8wc+czgHOIhwUDNFkaOYm6uZeEtQUxMLFe+CN0Q2SdPcSLfS3F/lJXY1H5nsam1aXlcTyMpV0ryViUIJa67axaJASQ1BbgZyzuHM74/ZZqe5nTkz35nvd77zfP7BgzOXz/f1/c6cz/sz5zOf75e+WcsHAAA0iwULFpxwwgkbNmzI/vpCW1vb+eeff9NNN4UYDABqaurUqXPnzs18w6atre3jH//4f/zHf4SbCgCInz//+c+77rrrpk2bCt47YMCAD37wgz/72c+22mqrOgcDICizZs266qqrspdw/Pa3v91jjz1CjERpa9asWbdu3dq1a9euXVviPxm9vb3ZTx80aFBHR8fw4cM7OjrS/xkxYkTHX23zt4YPHx7WbgL1kX/WsLTW1tZdd931l7/85ZAhQ0IJRtqGDRvOOOOMRx55JNOZt7W1zZgx4+KLL77rrrtuv/32VatWjRs3btq0aWeffbbPZaFYuXLlddddd9NNN3V1db300kt77bVXlQ2+++67S5cuTV9kL2P58uXpk9uOGDFitzy77rqrBZzEwFtvvXXbbbfddNNNb7zxxjHHHPOFL3xh/PjxYYcCAOgHa/kAAIAm8oc//OGYY475wx/+kL2c74Mf/OAvf/nLEFMBQE298847++233x//+Md0+bOOHQCokQsvvPA73/lOzjU60tra2rq7uw866KCbb775gAMOqH82AAKxfPnyXXfdNf09k2Qyuc8++/jLasysX7++9Nq/NWvWZBb+vf3229lr/wYMGDBixIjs1X0lfnTxH2g4v/nNbz74wQ92dXUVvNfpw0KXngP94x//mPOJbODAgV1dXWPGjDn33HPPPffcXXbZJayETe7tt9++/vrrZ8+e3dPTk36N/uu//uv4448vv4U1a9b8oZD0vSNGjMhcZC/tfe9737Bhw2qyMxCqF198cfbs2ffcc89WW211/vnnf+pTn9ppp53CDgUA0G8uJQwAADSR3XbbbcmSJWedddaPfvSj9PkIE4nEyy+/vHnz5kGDBoWbDQBqZOjQoQ899NDYsWO3bNmSSqWSyWRrq78KAgAB++1vf3vrrbdmPmtntLa2JpPJ008//dJLLx07dmwo2QAIyujRo/fbb7///u//TqVSAwYMOPfcc8NORMCGDRs2bNiwUaNGlfn4TZs2rSnuj3/847PPPvv666+vWbPm3XffzX7i4MGDR5Rhp5126ujoqMGOAv3T3d199tlnF1vIl37At771raOPPvrUU0+tZzDSnn322UmTJm3YsCH7ZKZp3d3dV1555Ze+9CWXYgvLO++8c/PNN3/lK1/ZvHlzZqXlwIEDly5dWvDxPT09r776as6CvVdeeWXDhg3pJ+68887p1XoTJkxI/+fv//7v29vb67dLEJIFCxZcd9118+fPHzNmzLXXXjtjxgyXGAUAGpdv7QAAAM1l6NChDzzwwJVXXnn11Vcnk8lUKtXT0/PCCy8cdNBBYUcDgFrZY489br/99tNPPz39o7V8AEDgPvvZz7a0tGTW8rW2tvb09IwaNepTn/rUjBkztt1223DjARCUadOmvfDCCz09PVu2bJkyZUrYcQhZe3t7e3v7yJEj+3xk6VV/6WsNpf/z5z//Oftyf2Wu+hsxYsQOO+zQ0tJSy92t1sSJEz/+8Y+fcsop/jJDw9m8efPFF1/8yCOPPProo2vXrh00aFBXV1f6Mq0ZyWTyH/7hHw488EBXfquz22677YILLkilUvmnVkkkEi0tLQsWLLCQLxR/+ctfbrrpplmzZm3cuDH/1Vm6dOnmzZv/9Kc/Za/Ze/nll1955ZX0mswRI0Zk1uydf/756f93dnZGvNhB4LZs2XL//fdff/31ixcvPvzwwx988MFjjz1WtwYANLpkzodqAACAJnH33Xefd955vb29qVRq9uzZF198cdiJAKC2Zs6cefPNNycSiUsvvfSrX/1q2HEAgPhYuHDhIYcckp52bGtr6+npOeKIIy688MJTTjllwIABYacDIEhvvvnmTjvtlEqlDjnkkKeffjrsOMRTeuHfG2+8kb6mXzFvvfVWzhWoshf+jRw5cscddyy46u8973lPW1tbnXeqq6tr8ODBqVRq5MiRl1566YwZM4YPH17nDFC93t7e559//vHHH3/44YefeeaZnp6etra2zKXG2tra9t9//6efftqC1fpIpVLps5eWflgymVy+fHn511ylehs3brz11luvueaadevW5V8sMW3s2LHPP/98IpFIJpM77bTTmDwjRoyob2qInM2bN8+ZM+df/uVffve73x177LFXXHHFuHHjwg4FABAMa/kAAIDmtWjRouOPP37VqlXnnHPOXXfdFXYcAKit7u7uww8/fOHChVdcccWsWbPCjgMAxEQqlTrooIMWL17c0tKy9dZbX3DBBZ/85Cd33XXXsHMBUCsf+chHfvKTn9x6660zZswIOwtNLZVKrVmz5u0sJX7MrDVK6+jo2HbbbUeMGLHNNttsm2W77bbbdttt3/Oe96T/M2TIkKDSvv766zvttFP6/62tra2trdOmTbvkkkve//73B7UJqLMNGzY8+eSTjz766Pz585cvX97a2trb29vb2/ulL33pqquuCjtd/G3YsOHMM8/80Y9+1Of3P5PJ5KxZs77whS/UJ1iT6+rquuOOO/7f//t/a9asKbaKL23nnXf+xje+MWbMmN12223w4MF1SwgNYdWqVd/5zndmz5799ttvT5ky5YorrjBkAgBixlo+AIAGk0wmw44AAJET2b9vTJ48+Xvf+17YKQBiLrJVgIjz+RoglubMmTNlypSwU1Ro7ty5p59+etgpAOIm/Zlxw4YNOSv9sq1atWr1X2UvvWhvb0+v8cus7kv/m3PjVltt1WeMF154Yd99982+JX0144kTJ1566aUTJkzI3O4vigCE6LTTTps3b17YKWheS5cuveGGG+64444hQ4ZcdNFFF1988Xve856wQwEABM/l7AEAGs9nPvOZcePGhZ0C4qO7u/uWW26ZPn16OdPtBOLrX/96IpG45JJLwg5SK6effrq+um4WLlx44403hp2ilIMPPjjG73Ya0csvv/zKK6+cfPLJYQeJIhWq4US/ChBxMfuNgFCkUqkf/OAHH/zgB8eMGRN2loClq8ycOXPCDlIrsRz5xGMhXIzfdcTGpk2bbrnllpkzZ4YdpN5i2XNmi/dnxq233nrrrbfeZZdd+nzWpk2b1vzVG2+88frrr2d+XL58+aJFi15//fXVq1d3dXVlnjJ48OAReUaOHLnjjjtmfvztb3+bs6H0pQKfeOKJhx9+eO+99/7c5z535plntrW1JfxFkUa2ZcuW3/3udytWrDjqqKNKn0PHeLsav/rVr9avXz9kyJBkMplMJtMXER0wYED68m5tbW2DBg1KJBIDBw5M9yrUx7vvvrts2bKlS5f+4Q9/+O1vf/vWW28lEonMJSvzH//v//7v22+/fd1jlpJ+30IoXn755euuu+673/3uqFGj/uVf/mXGjBm+vwEAxJjr8gEANJhkMtnQ55aGaEqlUl1dXelpLepg8uTJiUQixud01FfXU/qqBZH9+0bs3+00qNWrV2+77bZhp4ii2P/Oxq9CRbwKEHHx+40AghX7KhPLkU+j9+2xf9cRJ6+//vrIkSPDTlFvsew5szV6L5qvpv1qiSV/mVtylvwlk0W/ptXS0pJKpbbZZpuLL774+eefHzhwYIzfaZAW+5FP7KsGfVq/fv2SJUuWLFny3HPPLVy48LXXXkskEoMGDerp6dmyZUsikXjssceyr8saBd63hOKFF1742te+du+9977//e///Oc/f9ZZZ7W2ulANABBzhjsAAACJZDJpIR8AzcNCPgAAAKrRhAv5IEd7e3t7e3ufvwvr169fuXLl6tWr77zzzm9/+9vpa/HlS1+vafXq1VddddWAAQM6OzuXLVvW2dkZeGwA6mbYsGFHHnnkkUcemf5xzZo1ixcvXrJkyS9+8YtFixa98cYbS5cujdpaPqizZ5555tprr50/f/4+++xz2223nX322QMGDAg7FABAPVjLBwAAAAAAAAAAELBhw4YNGzZs9913f+SRR1paWoo9rK2trbu7e+DAgePHj1+9evX2228/atSoeuYEoNZGjBhx9NFHH3300ekfV61atWnTpnAjQYgWLFhw3XXXPfTQQ4cccsgDDzwwadKkZDIZdigAgPqxlg8AAAAAAAAAAKBWVq5cmb74XkZbW1tPT08ymdxrr72OPfbYCRMmHHrooYMHD548eXIikXBRGoB422677cKOACFIpVIPPfTQrFmzfv7znx9yyCGPPfaYq1MCAM3JWj4AAAAAAAAAAIBaWbVqVXd3d1tb25YtW3p7e8eMGTNp0qSjjjrq8MMPHzZsWNjpAABqK5VK/eAHP7jqqqteeumlE0888bnnnjvggAPCDgUAEBpr+QAAAAAAAAAAAGrlrbfe2nHHHSdOnDhhwoSPfOQjO+ywQ9iJAADqIZVK/fCHP7zqqqtefPHF00477Z577tl7773DDgUAEDJr+QAAAGgAyWQykUikUqmwgwQvZ9fSP6aVv78Fj092U/1qsM/W+vtCZJ6b/8SCux/LFxqIqxh3XPWpUNl3xfhgAtA8YlzOjA0AKhDjrkxd6K/vfve7733ve8NOAQ2v0buCgsrs94LqaSs7hs0QJqep7AajnLP08axmdg8C8fjjj19++eX//d//fdxxx91+++1jx44NOxEAQCS0hB0AAAAAmlf+NF7qrxKFpgxLNNKnfi3kK5azX8HSj8x5bult9bd9AGqkdhWqRE1RBQAgsowNAMimLlTAQj6gtJxplNr1tBV0p00Spli8qOXsV60sOD0H9fH4448feOCBH/3oR3fcccfFixc/+OCDFvIBAGRYywcAAEADqM9UU52/CFL6VJrVLL1L/O1MXvlHr5wjUMG5Qvv12jX6l3KAZqNClW6koJwvweTfpQoA0LiMDUo3UpCxARBj6kLpRgpSF4ASYtmvFgtQ6562X8vJmiFM6Zm16OTMT1VxTqiRxx9//KCDDjr66KM7Ojqee+65Bx98cL/99gs7FABAtFjLBwAAAIlE9Ka1yj8naIDz1gG2VnCeMucBTgUKUI44VagSZ5IGAMoUtdJpbAAQrqh1m+oC0Oii3+2EMn8kTIAaJSdU4JFHHklfi2/kyJHPP//8Y489tv/++4cdCgAgilrDDgAAAAB9yDlRZcEfM7NZ2fcW+3+JNrNb63M1WvUKntQzQMHuQnr1XSBr8Eo3kv16AUSZClWjTasCADQoY4MabdrYAGhQ6kKNNq0uQNOKcb+ar9Y9bb+606YKU+LljlrOYnN2aiWhePbZZ6+44oqnnnpq0qRJixcvdiE+AIDSXJcPAACAqCsx3ZUzq5r422nagv8v3WbdTnJZehat+onh7B1JJpNVTtrlT36Xlv265G+9v40ARJYKFVYMAIgmY4OwYgBEk7oQVgwgrmLZr+arZ0/bZ3faVGHKnFmLQs7yNwR18Ktf/WrKlCmHHnpoV1fXT3/60wcffNBCPgCAPlnLBwAAQKNKz7kWm+5K3x7UpF2DCncXUn+VyJsybPQDC1CaCgUAZDM2ACCbugAQLP0qAfJyQ/leffXVCy64YJ999vn1r389Z86cZ5999vDDDw87FABAY7CWDwAAgEYV17m0Gp2+upoTcOZE6m9T2cv58s96CxA/ce3o6naBBQCImbhWT2MDgMrEtedUF4CwNE/PE6methnCBH5pu0ByVjlnB4F46623Lr/88j322OPRRx/9xje+8cILL0yePDnsUAAAjaQ17AAAAABArmCnG1OpVFAL+TJNVbMkLz+MBX4AjUJ3DQBkMzYAIJu6AFBrkeppYxymypm10i1X8/TA5+ygvzZs2PCNb3xj1qxZw4YN+7d/+7ePf/zjra2+iA4A0G+GUAAAAEA/1G7+EgAAAAAAACiHOTvqafPmzTfddNOsWbNaWlq+/OUvX3TRRe3t7WGHAgBoVNbyAQAAQLQEfu7M7JN01lPB84Dm3BJWNgAqUH13XfDLJaoAADQoYwMAsqkLALUWqS4x3mFqNHsVqYMG5UulUnPmzLniiiv+/Oc/f+Yzn7nsssuGDRsWdigAgMbWEnYAAAAAqIn0NFv+ZFv27aWflUwmQzmZZf52SyfJ3JX9mMxT8p/Y39bSRy/n9swhLdha9r35TwFock1eoUrcDgDNydjA2AAgm7qgLgDBatx+NV9QPW05LTdhmMxTytx6KDlLz9lBLSxatOiwww47++yzDzjggJdffnnWrFkW8gEAVM9aPgAAAKIuZ0aqzy95pGXO4pyZwcpeYJaz2KzYvbUT1CZKH4TMEShzc8Vay0wNlj8pmHOEK9hfU49A9KlQJVRZU1QBABqRsUEJxgZAE1IXSlAXgArEsl/NV5/5ozK31YRh+pxZCz2nWkndvPLKK1OmTBk/fvzgwYMXL148d+7czs7OsEMBAMREa9gBAAAAoA85s035k0/lT1Nl39JnO3WY5So4E1zwlmJn5axgiq6C1kpvqILWSj+mv+cxBQiLChVsTclsuryMABA5xgbGBgDZ1AV1AQhWjPvVfDXtaRPFVzw2c5g+X+iI5OwzqlpJ9VavXn399dffeOONu+2225w5cyZPnhx2IgCAuLGWDwAAAKIu2Fm3KLcGQGNRBQCAbMYGAGRTFwBqLaieNpB2Yhmm1ptQK4maTZs23Xjjjddee+3QoUNvvvnm6dOnDxgwIOxQAAAxZC0fAEDcZP7aW/7J2PIfmb63zFMM9uvBNRWdJASizDdz3doJRTWXOyP60ufaLHhizmzBdm5Rbq12bVJP9R+KVK9x38mlD2P1jy+ztX41VU1p6zN/sQc09GAgFKFUqNq1CYHI70lq9ytQutlgO/MyVdnnGxUE3maxraQZFZR4gFFBZYwNIqs+5SlYqkPt2iy2lTTVocQDVIf+UheioPz3bYmD5njWTn6H0+evDGT/Sta6p815Yul2mipMXHNCmR588MGZM2euXLny4osv/ud//uett9467EQAALHVEnYAAAAC1t8p7T5/hFAEOHXR0JOjmfCpv0r/6Pe0hMzBaYijlJnFL/2YAN/GUW4t4esjsWAoUjeZ35dyepK07LJSfYDKGqmmtPWZv9gDMt+AqSBwgFSoPqkCRFl93pnlfw23/M4/EFX2+SUYFQTCqKD8B0RkVJBmbNAnY4M+NeLBUR3qRnUo/wERqQ7qQp/UhWxlHocSr1FDvNMaUWb1Tkb6lrBzNaMG6lez3zDZNyZq09Pmd6d9ttMkYUpo9JyBb4j4eeGFF4444oiTTjrpiCOO+P3vf3/ttddayAcAUFPW8gEANJ2cKfyC9/ZLiH/5zUnrb9AxE5GvUNRIxROH/fr+TT1FJ0/DTUo1UNQ6cDSaQeBDkYozZATyxgtlWNLn9xQbQkOH7xcVKoJbhGqE9Y6NR+efZlQQoHi8MRo6fAWMDSK4xRiIwUFTHQKkOjQWdSGCW2x08VvIV+vY1bdfcMVpWO/b6B+uWmu4fjVfjcJX1mwzhAl8i42Skyb39ttvz5w5c//99//LX/6yYMGC2267bYcddgg7FABA/LWGHQAAgBBkT+Hnf4cg0QizL4kGCUllYv/ixm8H47dHQE2FOxSpReP17wYLbjF9zu86JwGgbmLZ+RsV1GiLjf7GAJqc6lCjLaoO0GxK9JkNNBuY0dAr0/IrWq019OECoD56enpuu+22f/7nf25ra/vGN74xY8aMlhaXhwEAqBNr+QAAYi7/BJDBztZnpmpypv2yf8zZYvbsTvZd+bdnt1ZsQ5lN5DygxLbKCVl6f9Oby2+w/K3068j0mSf/Kf2NVPrglxOggle8zzYLHrTy20kUmUrMf1+V/1KW+TYr5/ciUeglKP/XMx0m/T6M5h4B5Sunfy7/x9K1ppwYBdsvsy4Ua7Z2Q5FEyQJRYqcKDiHK7wAThfrVOg9LSqimndI5SzRbzjfDqnlDAoSlz48VxX6suCjnbCWoupCdMFFFZqMCo4L8G0s8MefBRgUQlPqXJ58ZVYdycpZoVnWAmqrPH+eL/epV3IOV36WUP3PU53arbL/YvpS4vV87GL/DBUA0Pfnkk5/5zGd+85vfXHjhhddcc82wYcPCTgQA0FycRAEAIM5yplJqIWcmpuDsTvbWM5M6xb4bUeybJcVmyDJPKTFnVuILAf06RPlftqhsK/06MuXkKedglo6UfTyzN9HnTFvFr3g5bWaeVVk7OU8pmLnE4/OPW/77sNizSm+x4A5WI357BM2mnP45/5Gln9jfMUB/C3GZ6jAUKVEgSu9UwSFE6YLYZ79az2FJjRTMWWYVzux7/mEsdlhyNlo6BkAoyvxYEWBRLtZOxYL60Jq5xaigxLaMCtKMCqDW6l+efGbM3Jjzn+wfVQfVAcJSn/4hwDmpEk8svy8t9uDS262+/So5XACE7rXXXps2bdpRRx01evToX//617Nnz7aQDwCg/qzlAwCIrfRXKIrNf2cekyg+Rx741ottLv97Eom/neMpPZdfLEDpNouFLEd/k+RMZfV5ZMqZ5ar4YBbLkK+a3Sw/TJmNV5Cnz6dkZy4nav77MKgdTBT69lUFIrVHQAUqLkzl1JrST0+U7DMrKMRlxqjpUCSolgPpnxM1G5YUVPEbKVFFFcj+/m52g8UaSf5VQjECoqqyTyLVFOVi7dRUvzIbFQQbxqggc6NRAZSv/uXJZ8bSVIfyn646QC3U57ND4HNS1ffDfW66yiNTov2Km23OwwVAdLz77rtf/vKX99hjjyVLlvz4xz9+8MEHx4wZE3YoAIAm1Rp2AAAAaqU+3/mrZuvFZu7rpuKZtuzZrOwvwZSzlRIbreYI9GtfCj44lUqlvxtR2WGp5/stwO+sBNJOdDTWHq1YsWLu3Llhp6ihhQsXhh2hWcTpUFf8W1x+ralpjBq1U+ZWSpfR8sOUKIhlbqsy0enD8/eunB3POWKpv72OcaLQDpb+Tk+I3/VRoRpLzHaHGAiqKAdYFwL80Fp9hpoyKgicUUFajAcGK1asSMR6B8nwmTGhOgRHdfCZsbHEbHcKCqV/qH5Oqthj+tuXlnhYsbmwoNqvkSY8XDHuVI23aUQrVqzYeeedw05BTTz88MP/+I//uHLlylmzZv3jP/5jW1tb2IkAAJqatXwAAM2rdmc1rkx0khST+YZBzjcPMkeyyim96ByB0L8nRzNYtGjR6aefHnaKGrrxxhtvvPHGsFNApAU1FAmqEBdruT7bio6Cr0jpHS99NPr8ymzBL8KGOC5SoYD4iX79MiqIJqOCtHgPDBJNsIM0LtUhmlQHnxmh1vrVl1bQG9S6/TqLweGKd6eaaIIdJH5OO+20sCMQsNdff/3yyy+/++67J02a9OSTT+6yyy5hJwIAwFo+AIBmFbWFfI0ifzlfnI5k9lRcPPYoxmLwxjvttNPmzZsXdopaSSaTc+bMmTJlSthBmsLcuXPNhTeiwL+UGUCmvypWEGPQ91amzx0v+N3KzI2ln1vs6SFSoRqLKgB9in79MipoIM02KkiLVJhgTZ48OZFIxGzkE+MFVM1GdWggzVYdfGZsLD4zNpxa96U1bb/+/VVDH66MiHTvtRDL8Taxl37fEhs9PT0333zzF7/4xe233/5HP/rRMcccE3YiAAD+V0vYAQAACEHO1EtEvuOSEyMiqbLVZ0qs2I91E8EjD0DMRHMokiOaqTIKDkiqGaVk9reyAUn2OQ4C0d8YpR8f8VcToEyBd/5RYFRQPaOCfj0+NWU0pgAAIABJREFU4q8mkKY6VE916NfjI/5qAiXU+tc5qPYzq5fD1SiHC4BAPP3002PHjv385z9/0UUXvfTSSxbyAQBEirV8AABNJ3vSveDUUbFZ+erlT9gX/FpG/kkrM7cn8r5wkH1XiW0FeyLngrdXc7j6PALFnlLsYFa84+lHBvVl0KBehWI726+nVB81/33Y57OKvT/z7y3/LZR5ZL/eIfXfIyBYNf396rObLVGIK1CLoUh+O/3aqXwlCmKJHrvOw5L8NssJWaKd0gOSPtvMP+b9HYGUOTIslj9no7U44ECzCfxL54Go7DN7lR9ac7ZuVFBsW322aVTQJ6MC6FP9y5PPjPlUh0Ret6w6QDSV6DP71Z0GPidVuh8upy+toDcIqv0STy/4gMxhac7DBUD9vf322zNnzjziiCO23377F1544dprrx08eHDYoQAA+BvW8gEAxE2J6fl+Pb3MZ+Vsrs+tZ09iZc/i5Mzx5MzupFKp/Nuzn5J5QLFt5W+igp0tmCT7P9lzaaWPTLG0xY5AsTCJ4gcz2B0vpl+veKK8/SrYZrGdLSF/xrT0u7TPqCXeh/nPynl/5myx4L0FZafN3pf89XWNskdAvtJ9aYnfvv7WmhJKd7MFu4vKdqf8p5f5rGKFONFXoUwUH0JUvK0SbdZoWFKwzRLyy0p2cckPlvP91GKjnRJbyQ9W+oAXPD4FD1fB/Pl7Uf7BASiozI8VQRXlPutCBZ1/iB9ajQqMCowKoEbqXJ4SxTvSzL2qQznbKtGm6lDs3oLPVR0gUV7/UNldxZTuNivowQo2WH5fmr+tnMcX3G717ZdzoPJ3zeECoD5SqdS3v/3t3Xff/fvf//6cOXOeeOKJ97///WGHAgCggNawAwAAELD8qZH8r1b06+n92lyfWy+xiQqCZd/Y5wMqiFTwYaWfnv+dj3J+7FeMcp5SzY73K0Y1r3j5bVbWTrGnlGinz030q8HS788yfzHL2eto7hFQvn51ESV+E/tV8cuJUVlrdR6KJPpzTMp5Sunby9xWHYYlpR8ceFkpZ7RTTfv5t1c5SlGVgACV0yMFVZRr8QkrUV43XosPrUYF5Ty3xI1GBWXGqGwr0OjqWZ7KebzPjP3alurQ3/bzb1cdoMrPDpX9FlTcJZZ/S6LsvrTiW6psv9iN5T+m2Q4XAHXz+9///hOf+MTTTz/96U9/+uqrrx46dGjYiQAAKMp1+QAAAACAmvANHgAgzagAgHyqAwAAVK+np2f27Nn77rvv6tWrn3322RtuuMFCPgCAiHNdPgAAAAAgMMlkMuwIAEAkGBUAkE91AACAAP3P//zPjBkzfvnLX1566aVXX331wIEDw04EAEDfrOUDAKBvJSbXa3re3Mx2k8lk3U7QG9bOFhSRMLWIEUib9Tk+obwPayp+e9To0q9I5rXIfmP399ch5/H5vyNlNthna/1952Sem//EgrvvnVkLFfSZJbqLOleoiBTE+mv0HW+IkJRQnwqVfZcqQJOI7Ceshth0oxfHijX6jjdESPpkbBBvPjM2okbf8YYISQnqQvw0eq8CNJYq60hOl5VfL/LvVUeIn3fffffaa6/913/91wMOOOCFF17Yc889w04EAEC5rOUDAKBvYf1FO5TtRurP9xEJU4sYgbRZn+MTkVchQPHbo4aWP1WZ/f8y11uWeTrzfi3kK5Gz/GDZrRV8fMH51H61T/kqOKQlnlLnF6hp3w8NuuMNGpsctatQJWqKKkCTiOwnrIbYdNP2Dw264w0am4KMDWLPZ8ZG1KA73qCxyaEuxJIDC9RNlXUkp1jktJBDHSGuFixY8IlPfOK11167+uqrP/e5z7W0tISdCACAfjB6AwAAIG7KXNtWt3ZKt1/stNPVLL3LbjyjmtayVXBe7X5Ni2ZPvgLEjAqV00J+U6oAAM2jUQYGCWMDgHpplNKgLgANoVE61SYUSB0pVixKz46pI8TDunXrzj///A9/+MO77777b37zm8suu8xCPgCAhuO6fAAAAMRKbGZn+3W1vaDSBthawbnYnAekz4Fa/bYAGoIKVey5ToYNQBOKzcCg/AzGBgClxaY0qAtAFMSmU21CwZ50EuLn0UcfnTFjxubNm++4445p06aFHQcAgAo5GQMAAADRlcySc0uxH7Nvz3lAsf+X007O/4OSf0LQYCcgg82cOXTVhyzdiFlYIPpUqNpRBQBoOLEfGCSMDQD6KfalQV0A6in2nWoTCrCOJItc5a/Yy6SO0Lg2bNhwwQUXTJw4cfz48S+//LKFfAAADc1aPgAAACIqM/2WfZLm/Om9gj9mnpW5JZm1eCyZt5Csz3YCV3qut+DO9kt28uqnlrObKufxxWa+K2sEIGpUqDp85UUVAKBRxHtgkDA2AOi/eJcGdQGos3h3qk0owDqSmX7KfnyZs2PqCA3nmWee2W+//ebOnXvnnXfed9992223XdiJAACoirV8AAAARFHO9Fv2HG1lcuZrK2uh4SZrw82c+qtE3mvXcEcSIEOFAgAyDAwAyKE0AARIp0o58t8SXibiZNOmTZdffvmHP/zhPfbY4+WXXz7nnHPCTgQAQABaww4AAAAA/I0anb46/xSz/XpuIpFIpVLp//S3qfQT08+qJgYA4arbBRYAgIZgbABANnUBgGr0t45kr+0sMfdkWoqGtmjRounTp7/55pvf/OY3zz///LDjAAAQGNflAwAAgMgJdlqxytayZ08zTVVz4ttklupbA6CefPEFAMhmbABANnUBgGpUUEdKPEVVoqG9++67l19++aGHHtrZ2fnSSy9ZyAcAEDOuywcAAAD0Q+bqfAAAAAAAAAAEaMmSJdOmTVuxYsV//Md/nHfeeWZmAQDix3X5AAAAIFoCP1Fo9oX16qngxFLqb2VurG80ACpRfXddsAVVAAAalLEBANnUBQCqEZvZMahGb2/vddddN27cuB122OF//ud/ZsyYYSEfAEAsWcsHAABAFKWn1jKTE/nzbelbis1e5N+bfUtl7SSTyfpMluRvqPSmc45SzlPyn9jf1kq/FgVby743/ykADU2FKn1Lzr05/yl9OwA0nGYeGBTclrEBQDOXBnUBCFwzd6pNqPw6kn17sUkrLxON6M033zz22GO/+MUvXnHFFY899tjo0aPDTgQAQK1YywcAAEBEZeZoc+bhsifkctaMFbs3fXsg7QQlqDaLfdMl+8fsK+BV1lqx16KEnENawf5a+wdElgpVjipriioAQKOI98AgYWwA0H/xLg3qAlBn8e5Um1CAh7FEsehzdsyrSQR9//vf33vvvZcvX75o0aIrr7yypcW3uwEA4qw17AAAAABQVDlzbPmPKX9mrl/t1GJ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2dnb2nj17fvvtt+bm5rCwsJSUlJEjR6akpAwZMoS7AgGAbMSIETfddNOHH34oOgigbL17937llVeeeOIJ0UEAAAAA5aGWDwAAAAAAeBCDwfD8888vWrTo73//+yOPPCI6DtBurU7iV1hYKP+25QDlfv36BQcHi80MwExzc/PevXszMzO/+eab2tra2267LS0tbdq0aZTjArZpaGh444033n333alTp37++echISGiEwFwEvnc2LRyLy8vT6/Xq1SqqKgosxPjvn37is4LAPZkMBhOnjy5f//+X3/99ddffz106FBDQ0NQUNCNN944bNiwYcOG3XLLLb179xYdEwBc0Zw5c6qrqzdt2iQ6CKBs3bt3/8tf/sK1NgAAAMAG1PIBAAAAAACP884777z88ssvvPDCwoULRWcB7KC+vr6goEAewWxUUlKi1+slkwI/U9HR0T4+PqKDA56uvr5+06ZNGRkZ27ZtCwgIuPfee9PT02+//XYvLy/R0QDFOHHixKxZs/Lz8997770FCxaIjgPAUZqamsrKykzL9o4dO1ZdXS1JUlBQUFxcnGnZHhNWA3BXZWVlBw8elOv39u/fX1VV5evrO3jw4JtuumnYsGE33XTTgAEDmHwPAK7pjTfeWLNmTW5urugggLIFBgZ++OGHDzzwgOggAAAAgPJQywcAAAAAADzRsmXLHnzwwYceeujvf/87g5zgluThzmbT9508efLy5cuSJPn6+kZERJgV+A0YMKBLly6igwOeqLKycvXq1ZmZmdnZ2ZGRkbNmzZo/f37//v1F5wJcXUZGxmOPPZaYmLhixYq4uDjRcQDYTVsT7kmSFBYWJhfsGSv3YmNjKYMH4K4qKiqyr9q/f//Zs2clSQoLC0tJSRk5cmRycnJycnKnTp1ExwQAhcnIyHj44Ydra2u5NAB0hJ+f35dffjlnzhzRQQAAAADloZYPAAAAAAB4qI0bN95333133nnnypUr/f39RccBnEQeGG2mqKhI/p6w1Un8GB4NOE1OTk5mZuayZcvOnDmTnJyclpY2e/bsHj16iM4FuJwLFy48+OCDmzZt+sMf/vDee++p1WrRiQDYSKfTlZaWmlXuVVZWSpLk5+fXr18/08q9+Pj4zp07i44MAI6i1+tPnDiRm5ubk5OTnZ29b9++ixcv+vj4DBgwICkpKTExMTk5ecSIEd27dxedFACUbffu3aNGjSorK4uIiBCdBVAwb2/vlStXpqamig4CAAAAKA+1fAAAAAAAwHP9+OOPkyZNGjJkyIYNG7p27So6DiBMQ0PD6dOnzQr8cnNztVqtJEn+/v7h4eGm1X2JiYkDBgxQqVSigwPuqbm5eefOnRkZGevXr9fpdGPHjk1PT588eTLVSoBs586d6enp3t7emZmZt956q+g4ANqhqqqqoKDAtHLPeM4ZEhJinGpPXoiJiWGmFADurbq6+ujRo0ePHj18+PChQ4eOHDmi1Wr9/PwGDhw4dOjQIUOGDBky5IYbbmDmPQCwr8rKyvDw8H/96198ogRs1tTU5Ovru379+ilTpojOAgAAACgPtXwAAAAAAMCjHT58ePz48WFhYVu3bu3Vq5foOIBrkSfxM460LiwsLCgoqKqqkiRJpVJFRUWZzeAXFxcXFBQkOjXgPmpqar777rvMzMwdO3YEBwdPnz49LS0tJSVFdC5AGJ1O9/bbb7/11luTJ09evHhxt27dRCcCYElFRYVxqj15QZ4O2tfXNyIiwrRyb9CgQYGBgaLzAoBj6fX6goKCw4cPHzly5MiRI0ePHi0qKpIkKSgo6IYbbhg8ePCQIUOGDh2amJjIXTwAwKEMBkNgYODHH398//33i84CKFVtbW2XLl02b948YcIE0VkAAAAA5aGWDwAAAAAAeLrCwsI777zTx8cnKysrIiJCdBzA1ckFfmZKSkr0er0kSSEhIX1bYE4VoIPKy8uXL1++ZMmSvLy8hISEGTNmzJ07NzY2VnQuwKny8vJmzZp1/Pjxv/71r0899ZToOAD+S0NDQ35+vmnl3okTJ2pra6Wr54emlXvx8fE+Pj6iIwOAw8nT7uXm5ubk5GRnZx86dKi2ttbHxyc6OjoxMTE5OTkpKSkxMTExMdHLy0t0WADwLAMHDpw0adLbb78tOgigVFVVVSEhIdu2bRs3bpzoLAAAAIDyUMsHAAAAAAAgnT17duzYsTU1NVlZWXFxcaLjAMrT2NhYXl5uVuCXl5d35coVSZLkqVfMCvzi4+M7d+4sOjigMNnZ2RkZGd98882FCxeGDx+enp4+c+ZMJjKCJ8jIyHj88cfj4+OXL19+3XXXiY4DeDqNRmOcak9eKC4ubm5uVqvVkZGRppV7AwcODA0NFZ0XAJyhuro6Pz9f7hXlf+XJSIODg5OSkuSyveTk5CFDhvBZGACEmzx5cqdOnVauXCk6CKBU586dCw0N3bVr1+jRo0VnAQAAAJSHWj4AAAAAAABJkiSNRnPXXXeVlpZmZWUlJSWJjgO4iVYn8ZOHM0ptTOLXt29f0akBV9fY2Lht27bMzMwNGzZ4e3tPnDgxLS1t/PjxKpVKdDTA/qqrqx999NFvvvnmiSeeePfdd319fUUnAjyLfMsG08q9I0eOXL58WTI5lzNW7iUlJfn7+4uODADOUFNTk5ube+zYsePHjx87diw3N7e8vFySpMDAwISEhOuvvz4hIWHgwIEDBw4MDw8XHRYAYO6Pf/zjnj17fvnlF9FBAKUqLy+PjIzcs2fPiBEjRGcBAAAAlIdaPgAAAAAAgP+vurr67rvvzs/P/+c//zlo0CDRcQC3VV9fX1FRYSztk4eGl5aW6nQ6qY0Cv+joaB8fH9HBAZdTVVW1cePGzMzMHTt2hIWFTZs2bd68eUOGDBGdC7Cbffv2zZ49u66ubunSpXfddZfoOID7k2/EYFq5l5eXp9frVSpVVFSUWeUet2AA4DkuXrx44qqjR48eP368tLRUkqQuXbrEx8dff/31iYmJcv1edHS0l5eX6LwAgGv45JNP3njjjQsXLogOAihVUVFR37599+/ff+ONN4rOAgAAACgPtXwAAAAAAAD/UVtbO3ny5Ozs7C1bttxyyy2i4wAepKmpqayszGwGv1OnTtXU1EiSpFarIyMjzQr8rrvuusDAQNHBAZdQUlLyzTffLF68uKCgIDExMT09fd68eaGhoaJzAbbT6XRvv/3222+/PXHixH/84x/du3cXnQhwN/LZl2nZ3rFjx6qrqyVJCgoKiouLMy3bS0hICAgIEB0ZAJyhubm5uLg4Ly8vNzc3Ly9Prt87f/68JEldunQZMGBA0lWJiYkxMTFU7gGAEv3www/jx4/XaDTBwcGiswCKlJeXFx8ff+jQIe6MCQAAANiAWj4AAAAAAID/0tDQMGPGjJ07d27cuPG2224THQfwdPLkMGaKi4ubm5ulNibxi42NZTAlPFZ2dnZGRsby5curqqpuu+22tLS0qVOndu7cWXQuoH2Ki4vnzJlz8ODBv/71r0899ZToOIA7aGvCPUmSwsLC5II9Y+UeZ1MAPEdjY+OpU6eMfWNubu6JEydqa2slSQoJCTF2jPJCTEyMt7e36MgAADs4derUddddl52dPXToUNFZAEU6evToDTfckJubm5CQIDoLAAAAoDzU8gEAAAAAAJhrbGycNWvW1q1bv/3223HjxomOA8BcQ0PD6dOnzQr8jh8/XldXJ0mSn59fnz59zAr8EhMTO3XqJDo44CT19fXbt2/PzMz87rvvAgIC7r333vT09Ntvv53CDCjCmjVrFixYEBERsWLFioEDB4qOAyiPTqcrLS01q9yrrKyUJMnPz69fv36mdSkDBgzo0qWL6MgA4AyNjY3l5eXy50djDynfKUalUkVFRZlWNd9www29evUSHRkA4ChNTU2dOnVauXLl9OnTRWcBFOngwYPJycn5+fn9+vUTnQUAAABQHmr5AAAAAAAAWqHX6+fPn79y5cpVq1ZNnjxZdBwAVmk5iZ9x5Lp0dRI/49DMvn379uvXLzg4WGxmwKEuXry4bt26jIyMPXv2REZGzpo1a/78+f379xedC2hdTU3NY489tmLFiieeeOJ///d//fz8RCcCFKCqqqqgoMC0LiU3N1er1UrMKAXAg2m12vz8/Pz8/FOnTuVfVV5ebjAYvLy8IiIi4q7q379/QkJCv3791Gq16NQAAKeKiYl55JFHXnrpJdFBAEX6+eefhw8fXlJSEhUVJToLAAAAoDzU8gEAAAAAALROr9c//PDDGRkZy5cv5+68gHK1LPArLCwsKSnR6/XS1QI/M9HR0T4+PqKDA/aUm5u7evXqpUuXlpSUJCcnp6WlzZ49u0ePHqJzAf/xyy+/zJ49+/Lly0uWLLn77rtFxwFcVEVFhXGqPXmhqKjIYDD4+vpGRESYVu4NGjQoMDBQdF4AcDjjbHvGjtE4257U2i1dmI8UACC7/fbb+/Xr98UXX4gOAijSTz/9dOutt1ZWVvbu3Vt0FgAAAEB5qOUDAAAAAABok8FgeOqppz799NMVK1ZQzge4k6amprKyMrMCv5MnT16+fFmSJHk0vFmBHyM+4Qaam5v37t2bmZm5YsWKpqamsWPHpqenT5o0ydfXV3Q0eDSdTvf++++/9tprY8aMWbp0KYPAAFlDQ0N+fr5p5d6JEydqa2ulFqUpiYmJ8fHx3IkAgNujbA8AYEcLFiwoKCjYsWOH6CCAImVlZY0dO/bixYvdunUTnQUAAABQHmr5AAAAAAAAruHFF198//33v/766/vuu090FgCO1eokfvJ0N1Ibk/jFxsZ6eXmJDg60j1ar3bx5c0ZGxtatW7t27Tp9+vS0tLSUlBTRueCJSkpK5syZc+DAgYULFz755JP0qPBYGo3GWJoiL8jVKWq1OjIy0rQ6ZeDAgaGhoaLzAoBjXb58uaioKD8//9SpU/lXlZeXS5Lk5eUVGRkZFxcXFxfXv3//uKv8/f1FpwYAKMnChQs/++yz4uJi0UEARdq6devdd99dU1PDhPAAAACADajlAwAAAAAAuAaDwfDkk09+/vnnq1evnjx5sug4AJytoaHh9OnTZgV+ubm5Wq1WkiR/f//w8HC5rs84yD4qKkqlUokODlxbeXn5unXrlixZcvjw4fj4+NTU1Llz58bGxorOBU+xZs2ahx9+OCwsbOXKlTfccIPoOICTyPNKmVbuHTlyRJ4c2HjjAONJRVJSEtUpANxYy09bFRUVlZWVZndUMZ1tLz4+vnPnzqKDAwAUb82aNffdd19dXZ2fn5/oLIDybNiwYfLkyfX19fwFAQAAADaglg8AAAAAAODaDAbDH/7wh8WLF69du/bee+8VHQeASzCdP0eWn59fXV0tSZJxCh1T/fv379q1q+jUQOtycnIyMzOXLFly4cKF4cOHp6enz5w5k/tqw3EuX7783HPPLV68+KGHHvrggw8CAgJEJwIcRZ7117RyLy8vT6/Xq1SqqKgos8q9vn37is4LAA7R2NhYWlpaVFRUXFxcXFwsLxQVFZ05c0Z+Qvfu3WNiYmJjY2NiYuSF2NjYvn37MjocAOAgBw8eTE5OPnnyZP/+/UVnAZRnzZo1M2bM0Ov13t7eorMAAAAAykMtHwAAAAAAgFUMBsPjjz/+5Zdfrl27duLEiaLjAHBR8nh9M8XFxc3NzZLJZDumYmJiGPEAF6HX63ft2pWRkbF27VqDwTBx4sS0tLTx48czzyTs69dff509e3Z1dfVXX311zz33iI4D2E1TU1NZWZlp2d6xY8fkOv+goKC4uDjTsr2EhASqWAG4pVY/E5WUlOj1eum/JzY36tevX3BwsOjgAADPUl1dHRwcvHXr1rvuukt0FkB5VqxYMXfu3KamJtFBAAAAAEWilg8AAAAAAMBaBoPhscce++qrr9atW8e4cwDWa2xsLC8vNxvMeuLEidraWkmS/Pz8+vTpYzaYlfH9EKuqqmrjxo2ZmZk7duwICwubNm3a3Llzhw4dKjoXFK+5ufnjjz9+/vnnb7311mXLloWHh4tOBNiu5YR7J0+e1Ol0kiSFhYXJBXvGyr3Y2FgvLy/RkQHAnlrW7FVUVBQVFWm1Wqm1jzlhYWHh4eH0hwAA19GjR48333zz8ccfFx0EUJ6lS5c+/vjj8vfbAAAAANqLWj4AAAAAAIB2MBgMjzzyyNKlS9evXz9hwgTRcQAoW6sTVhQWFsq/bTmJX1JSUlhYmNjM8DQlJSXffPPN4sWLCwoKEhMT09PT586d27t3b9G5oEhlZWVz5sz55Zdf3nzzzeeff54pSaEgOp2utLTUrHKvsrJSkiQ/P79+/fqZVu4NGDCgS5cuoiMDgH00NL5X0e4AACAASURBVDSUlZWVl5eXlpaWlpaWl5cXX9XQ0CBJkq+vb3R0dExMTGxsrPHfmJgYzhgBAK5v2LBho0aNev/990UHAZTniy++ePHFFzUajeggAAAAgCJRywcAAAAAANA+zc3N8+bNW7t27aZNm26//XbRcQC4m/r6+oqKCtNqgcLCwtLSUnmen5YFfn379o2Ojvbx8REdHG4uOzs7IyNj+fLlVVVVt912W1pa2tSpUzt37iw6FxRj3bp1CxYsCA0NXbFixeDBg0XHASypqqoqKCgwPRbn5ubK00yFhIQYp9qTF2JiYihMBaB0er3+zJkzJSUl5eXlZWVlpaWlZWVlcgnfmTNn5Of4+/tHRkZGRkYaK/fksr3w8HC6QQCAQs2cOVOr1X733XeigwDK88knn7z55pvnzp0THQQAAABQJGr5AAAAAAAA2k2v18+bN2/9+vWbNm0aM2aM6DgA3F9TU1NZWZnZ9H2nTp2qqamRJEmtVkdGRpoV+F133XWBgYGig8Pd1NfXb9++PTMz87vvvgsICLj33nvT09Nvv/12Ly8v0dHgurRa7UsvvfTRRx+lpaV9+umnlIDC1VRUVBin2pMXioqKDAaDr69vRESEaeXeoEGDOLYCUDSNRlNRUVFZWVlYWGhcKCwsLCsra2pqkp8j3z0kLCwsPDxc/mQhL1O6DABwP6+++uqGDRuOHj0qOgigPB988MH7779fXl4uOggAAACgSCrRAQAAAAAAAJTHx8dn6dKlTU1N99577w8//JCSkiI6EQA3p1ar5XG0Zo9rNBqzAr+srKzi4uLm5mapjUn8YmNjKbuCzfz9/SdOnDhx4sRLly6tXbs2IyNj7NixkZGRs2bNeuCBB6677jrRAeFyDhw4MGvWLI1Gs2HDhnvvvVd0HHi6hoaG/Px808q9EydO1NbWSlcPmomJiXfccYe8EB8fz7S3AJTIONG3Wdme8VYgkiSFhIQYq/VGjhxpLNuLiopSqRhGAgDwFP369SssLDQYDMbvyrRabadOncSmAhShsbHR19dXdAoAAABAqZiXDwAAAAAAwEY6nS41NXXHjh27du0aMmSI6DgA8P81NDScPn3arMbv+PHjdXV1kiT5+fn16dPHrMAvMTGRgUqwTW5u7urVq5ctW1ZcXJycnJyWljZr1qyePXuKzgXxDAbDRx999MILL4waNWrZsmV9+vQRnQgeR6PRGKfakxfkcnfjfLbGOfcGDhwYGhoqOi8AtENdXV1ZWVllZWVZWVlZWVl5eXlZWVlJSUl5eblGo5Gf07lz5+jo6MjIyMjIyIiICHk5IiIiKiqKk38AAM6fP79u3bpHH330hRdeOHv27IkTJwoKCgICAkpKSkRHAxTgL3/5S0ZGRl5enuggAAAAgCJRywcAAAAAAGC7xsbGSZMmZWdn//TTT/Hx8aLjAIAlZpP45eTk5OTkVFVVyb8NCwuT6xmM4uLigoKCxGaGUjQ3N+/duzczM3PFihVNTU1jx45NT0+fNGkS9+d2e23dhf3MmTPz5s3buXPn//zP/7z++uve3t7OzwaP0tjYWF5eblq5d+TIkcuXL0sms9QaK/eSkpL8/f1FRwaAa9NoNGZz6xn/PXPmjDzYQ61W9+jRQ55YzzjVnrwcFhbGjNwAAJjS6/Vz5849ePBgcXGxVquVJMnb21utVut0Or1e7+XlNXny5PXr14uOCSjAn/70p7Vr1x47dkx0EAAAAECRVKIDAAAAAAAAKJivr++6devuvPPOsWPH7t69Ozo6WnQiAGhTSEhIcnJycnKy6YNmBX6FhYVZWVklJSV6vV4yqX8wFRMTQ1kOzHh7e6ekpKSkpCxatGjz5s0ZGRkzZ87s2rXr9OnT09LSRo4cyThyt7Rz58733nvv+++/N/v//fbbbx966KEePXr8/PPPQ4cOFRUPbkw+eJlW7uXl5clHLrk0PSkpafr06cYaddF5AaBNWq3WWKFnVrBXWlqq0+nkp/n7+xur9ZKSkuSaPfnf6OhoHx8fsa0AAEApfHx8evXqdfz4ceMjzc3NDQ0N8rJarb755psFRQMURqfTqVQMPwYAAABsxLx8AAAAAAAAHVVdXT169Oja2tp///vfoaGhouMAQEfJsxuZ1fjl5eVduXJFkiRfX9+IiAizAr/4+PjOnTuLDg4Xcvr06bVr1y5ZsuTw4cPx8fGpqanp6elU1LgTjUaTmJh45syZDz744Omnn5Yf1Gq1L7300kcffZSWlvZ///d/Xbp0ERsSbqCpqamsrMxYtldYWHjkyJFz585JkhQUFBQXF2c64V5CQkJAQIDoyADwXxobGy9cuGA6pZ6xbO/06dPV1dXy0/z8/Lp162as0DNOshcWFhYVFRUYGCi2FQAAuI0LFy5ERkbW19e3+tsdO3aMGTPGyZEAJXrppZeysrIOHDggOggAAACgSNTyAQAAAAAA2EFFRUVKSkpQUNCuXbuCg4NFxwEAh2g5iV9hYWFRUZH8PXOrk/hRu4WcnJzMzMwlS5ZcuHBh+PDh6enpM2fOtGY8ekFBQXR0NHf4dlmpqanffvttU1OTWq3ev3//oEGDjh07NmvWrNLS0s8+++y+++4THRCK1HLCvZMnT8rTUslTUZlW7sXGxjLnJwBXoNfrz507d+bMmYqKCrlC7/Tp05WVlWVlZWfOnJHLjyVJ8vb2Dg0NDQ8PDw8Pj4iICAsLi4yM7N27d0RERHh4eLdu3cS2AgAAz/Hiiy/+7W9/M85/a+Tl5XXp0iW+3ges8dxzz+3evfvnn38WHQQAAABQJGr5AAAAAAAA7KOgoGDUqFHx8fFbtmzx9/cXHQcAnKS+vr6iosKswC8nJ0e+wbm/v394eLix7kIWFRVFgZan0ev1u3btysjIWLduXXNz88SJE9PS0saPH2/hnTBlypRLly6tX7++e/fuzowKa2RmZqanp8vLKpUqJibmoYceeu2110aOHJmRkRERESE2HhRBp9OVlpaaVe5VVlZKkuTr6xsXF2dauTdgwACmeQQgkFar1Wg0ZnPrVVRUyA+WlpYaiwHks1/jlHqm8+xFRkaq1WqxDQEAAJIkXbx4MSoqqq6uzuzx6Ojo4uJiEYkA5Xn66acPHDiwe/du0UEAAAAARaKWDwAAAAAAwG6OHj06evTolJSUdevWUaYCwJMZKzRM5efnV1dXS5KkVqsjIyPNpu/r379/165dRQeHw1VVVW3cuDEzM3PHjh29e/eePn363Llzhw4davY0jUYTGhqq0+kiIyO3bNmSlJQkJC1aVV5enpiYeOXKFeM1JpVKNXTo0ClTprzwwgve3t5i48E2BoPBoXPcVVVVFRQUmFbu5ebmarVaSZJCQkKMJd/yQkxMDG8kAM6k0+nOnTtXWVlZWVl57ty506dPnzt3rqKi4uzZs2fOnKmsrDSO9ff29u7Vq1doaGifPn169eoVFhbWu3fv3r17h4WF9erVKyIionPnzmLbAgAArPHaa68tXLjQdGo+Hx+fGTNmrFixQmAqQEEef/zxnJycf/3rX6KDAAAAAIpELR8AAAAAAIA9/fLLL3fcccfkyZOXLVvGEGQAMKPRaApbKC4ubm5uliQpJCSkbwsOreh49tlnn3766cjISAetHxaUlpauXLnyH//4R35+fmJ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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "h11_model_path = path.join(MOD_PATH, 'h11_inception_same_conv_no_fc.h5')\n",
    "h21_model_path = path.join(MOD_PATH, 'h21_inception_same_conv_no_fc.h5')\n",
    "\n",
    "log.debug('Building the full convolutional model...')\n",
    "K.clear_session() #--------------------------------------------------- clear the Tensorflow session\n",
    "model = cnn_full_model(model_name='full_model',\n",
    "                       h11_model_path=h11_model_path,\n",
    "                       h21_model_path=h21_model_path,\n",
    "                       num_cp_shape=num_cp_train.shape[1:],\n",
    "                       dim_cp_shape=dim_cp_train.shape[1:],\n",
    "                       dim_h0_amb_shape=dim_h0_amb_train.shape[1:],\n",
    "                       num_cp_neurons_h11=[],\n",
    "                       num_cp_neurons_h21=[10],\n",
    "                       dim_cp_neurons_h11=[],\n",
    "                       dim_cp_neurons_h21=[10],\n",
    "                       dim_h0_amb_neurons_h11=[],\n",
    "                       dim_h0_amb_neurons_h21=[10],\n",
    "                       learning_rate=0.03,\n",
    "                       batch_norm_momentum=0.99,\n",
    "                       dropout_rate=0.3,\n",
    "                       leaky_alpha=0.0,\n",
    "                       l1_reg=1.0e-5,\n",
    "                       l2_reg=1.0e-4,\n",
    "                       last_relu=True,\n",
    "                       last_layer_h11=1,\n",
    "                       last_layer_h21=2\n",
    "                      )\n",
    "\n",
    "model.summary()\n",
    "model_dot = model_to_dot(model=model, show_shapes=True, show_layer_names=True)\n",
    "model_dot.write_pdf(path.join(IMG_PATH, model.name + '.pdf'))\n",
    "\n",
    "Image(model_dot.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally we fit the model. We have two options: the first is to monitor the **validation loss** to measure the stability of the algorithm and choose the best training epoch and the second is to separately monitor the **MSE** on $h_{11}$ and $h_{21}$ and separately produce predictions for the Hodge numbers. After many trials with the first option, we decide to use the second method (even though it takes longer) because we want to get the best possible predictions in a consistent way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-17 09:47:14,338: INFO ==> Training the full model with the h11 objectives...\n"
     ]
    }
   ],
   "source": [
    "# define the callbacks (NB: with restore_best_weights we do not need to reload the model for evaluation)\n",
    "callbacks_h11=[EarlyStopping(monitor='val_h11_mean_squared_error', patience=400, verbose=0, restore_best_weights=True),\n",
    "               ReduceLROnPlateau(monitor='val_h11_mean_squared_error', factor=0.33, patience=150, verbose=0),\n",
    "               ModelCheckpoint(monitor='val_h11_mean_squared_error', filepath=path.join(MOD_PATH, 'h11_transfer_learning.h5'), verbose=0, save_best_only=True)\n",
    "              ]\n",
    "\n",
    "# fit the model\n",
    "log.info('Training the full model with the h11 objectives...')\n",
    "model_hst_h11 = model.fit(x=[matrix_train, matrix_train, num_cp_train, dim_cp_train, dim_h0_amb_train],\n",
    "                          y=[h11_train, h21_train],\n",
    "                          batch_size=128,\n",
    "                          epochs=5000,\n",
    "                          verbose=0,\n",
    "                          callbacks=callbacks_h11,\n",
    "                          validation_data=([matrix_val, matrix_val, num_cp_val, dim_cp_val, dim_h0_amb_val], [h11_val, h21_val])\n",
    "                         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then evaluate the model with the $h_{11}$ objectives:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of full_model on the validation set: 98.728%\n",
      "Accuracy of full_model on the test set: 98.855%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-17 13:27:01,524: DEBUG ==> Plot saved to ./img/favourable/full_model_h11.pdf.\n"
     ]
    }
   ],
   "source": [
    "from mltools.libplot  import Plot\n",
    "from mltools.libscore import Score\n",
    "\n",
    "# compute the predictions\n",
    "model_val_predictions  = model.predict([matrix_val, matrix_val, num_cp_val, dim_cp_val, dim_h0_amb_val])[0].reshape(-1,) #------- take only h11 predictions\n",
    "model_test_predictions = model.predict([matrix_test, matrix_test, num_cp_test, dim_cp_test, dim_h0_amb_test])[0].reshape(-1,)\n",
    "\n",
    "# compute the accuracy and score of the predictions\n",
    "rounding = np.rint\n",
    "model_score_val  = Score(y_true=h11_val, y_pred=model_val_predictions, rounding=rounding)\n",
    "model_score_test = Score(y_true=h11_test, y_pred=model_test_predictions, rounding=rounding)\n",
    "print('Accuracy of {} on the validation set: {:.3f}%'.format(model.name, model_score_val.accuracy()*100))\n",
    "print('Accuracy of {} on the test set: {:.3f}%'.format(model.name, model_score_test.accuracy()*100))\n",
    "\n",
    "# plot the distribution of the predictions and the error difference\n",
    "plot = Plot(rows=1, columns=5)\n",
    "\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_val, h11_val))).T,\n",
    "               axis=0,\n",
    "               title='Predictions for the Validation Set for $h_{11}$',\n",
    "               legend='real values',\n",
    "               xlabel='num_cp (validation set)',\n",
    "               ylabel='$h_{11}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_val, rounding(model_val_predictions)))).T,\n",
    "               axis=0,\n",
    "               title='Predictions for the Validation Set for $h_{11}$',\n",
    "               legend='predictions',\n",
    "               xlabel='num_cp (validation set)',\n",
    "               ylabel='$h_{11}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_test, h11_test))).T,\n",
    "               axis=1,\n",
    "               title='Predictions for the Test Set for $h_{11}$',\n",
    "               legend='real values',\n",
    "               xlabel='num_cp (test set)',\n",
    "               ylabel='$h_{11}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_test, rounding(model_test_predictions)))).T,\n",
    "               axis=1,\n",
    "               title='Predictions for the Test Set for $h_{11}$',\n",
    "               legend='predictions',\n",
    "               xlabel='num_cp (test set)',\n",
    "               ylabel='$h_{11}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "\n",
    "plot.hist2D(model_score_val.error(),\n",
    "            axis=2,\n",
    "            title='Distance from the Real Value',\n",
    "            legend='validation set',\n",
    "            xlabel='error difference',\n",
    "            ylabel='#',\n",
    "            binstep=1\n",
    "           )\n",
    "plot.hist2D(model_score_test.error(),\n",
    "            axis=2,\n",
    "            title='Distance from the Real Value',\n",
    "            legend='test set',\n",
    "            xlabel='error difference',\n",
    "            ylabel='#',\n",
    "            binstep=1\n",
    "           )\n",
    "\n",
    "plot.series2D(data=model_hst_h11.history['h11_loss'],\n",
    "              axis=3,\n",
    "              title='Loss Function',\n",
    "              xlabel='epoch',\n",
    "              legend='training loss',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h11.history['h11_loss'])[0] / 10)\n",
    "             )\n",
    "plot.series2D(data=model_hst_h11.history['val_h11_loss'],\n",
    "              axis=3,\n",
    "              title='Loss Function',\n",
    "              xlabel='epoch',\n",
    "              legend='validation loss',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h11.history['val_h11_loss'])[0] / 10)\n",
    "             )\n",
    "\n",
    "plot.series2D(data=model_hst_h11.history['h11_mean_squared_error'],\n",
    "              axis=4,\n",
    "              title='Metric Function',\n",
    "              xlabel='epoch',\n",
    "              legend='training MSE',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h11.history['h11_mean_squared_error'])[0] / 10)\n",
    "             )\n",
    "plot.series2D(data=model_hst_h11.history['val_h11_mean_squared_error'],\n",
    "              axis=4,\n",
    "              title='Metric Function',\n",
    "              xlabel='epoch',\n",
    "              legend='validation MSE',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h11.history['val_h11_mean_squared_error'])[0] / 10)\n",
    "             )\n",
    "\n",
    "plot.save_and_close(path.join(IMG_PATH, model.name + '_h11'))\n",
    "log.debug('Plot saved to {}.'.format(path.join(IMG_PATH, model.name + '_h11.pdf')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then keep training to find the best $h_{21}$ result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-17 13:27:01,543: DEBUG ==> Training the full model with the h21 objectives...\n"
     ]
    }
   ],
   "source": [
    "# define the callbacks (NB: with restore_best_weights we do not need to reload the model for evaluation)\n",
    "callbacks_h21=[EarlyStopping(monitor='val_h21_mean_squared_error', patience=400, verbose=0, restore_best_weights=True),\n",
    "               ReduceLROnPlateau(monitor='val_h21_mean_squared_error', factor=0.33, patience=150, verbose=0),\n",
    "               ModelCheckpoint(monitor='val_h21_mean_squared_error', filepath=path.join(MOD_PATH, 'h21_transfer_learning.h5'), verbose=0, save_best_only=True)\n",
    "              ]\n",
    "\n",
    "# fit the model\n",
    "log.debug('Training the full model with the h21 objectives...')\n",
    "model_hst_h21 = model.fit(x=[matrix_train, matrix_train, num_cp_train, dim_cp_train, dim_h0_amb_train],\n",
    "                          y=[h11_train, h21_train],\n",
    "                          batch_size=256,\n",
    "                          epochs=5000,\n",
    "                          verbose=0,\n",
    "                          callbacks=callbacks_h21,\n",
    "                          validation_data=([matrix_val, matrix_val, num_cp_val, dim_cp_val, dim_h0_amb_val], [h11_val, h21_val])\n",
    "                         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then evaluate the model with the $h_{21}$ objectives:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of full_model on the validation set: 46.947%\n",
      "Accuracy of full_model on the test set: 48.473%\n"
     ]
    },
    {
     "data": {
      "image/png": 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T/ZQ1ylqbgPRqTgROq0hZxpizjTGzjDGZToLHnVQwsd766QxgjOlojPncGLPbOe//qmi5HqpyLJRSqrYqE4ucx65kca+x11Y+wXy823eRylyXqxKr/WlC6S1BXXx2MrDWHnEeVjbWKqWUqpo3geuREZPe8FjWFIgBFrt9Rv/ame+Pv45clY0no9ziWY9KvC7Q8awF4EpScSmv01x0JTsWKOWXJmioBcBhY8xoY0x954e4ZGNML+Tes4XAKGNMhDFmCL5vd7EAadAaZ4yJNcZEG2P6uC3fgwx95818ZIjWB43c/uF84DKOH5r2OMaYTsaYC4wx9ZD7I+YhP/CVp8rbdONvnzz5O86+vAFciGQ1vu6xLBZpvMkEMMbcSmlDkj/+zmlFyqyR8+jOGNPZGPMXY8xpzvNWyJC485xVyjuWlTkvgah3CvCb0wPCpTuw3GO/2iLDhH1eiboppWqOxr/QjX+VLquc4+G3vnUx7mjMUerk5VybWiK3+irD6en6F2ttO+S683+mdAhXz8Sw65FReC5EhjFPcm2iAtXYBrT3s2yQR9JbtLW2Mknd7nXdiSQDSOXk1hqNqWaSuJftVMQ24EePfYuz1o4sd0PW/gY8DrzkJIiWV9Y7yDC7rZwEj0lU7Ny4+OoMMBHpaXe6tbYBMmKUr3JzkR94XdyTL6p8LJRSqhYqE4uQ5Oud4D/2VjHh3p2/63B5/MVqf/FvHzIqiOf+BiLuKqWUqiZn9IjNwCXARx6L9yG/l3V1+4yeYK11JTv4uv77iwv+4klFBSOe7QQaGbk9p4vGM3VCaYLGSc4ZnucyZEigzchF+lUgwVpbgNwHdzhyb+ChHH9R9yynA3Jvp+3O+i5PAv9wMvP+6vHaAuR+ToOc7b8M3GwrMLQeMhzgOOd1u5H72/6tAvtdnW26+NwnL9vzeZz9vCYD+AVJnPjUY9lqZAjBX5GGoW7Az+VV2N85rWCZNXUe3WUj96ucb4zJRRrIViL37azIsazweQlQvbtRtlGsMfIhYqXbvAZIks1NzvaUUkGm8S90418Vy/J3PMqrb52KOxpzlDo5GWMaGGMuRZK93nIa/D3XudQY08FJADiMJLL5SmaLB44io1HEICMpVNTnwKnGmPuMMfWMMfHGmLOdZZOAJ4yM3ocxpqkx5nKfJZXvHeBWY0yak6T3L2C+812quvYApxljoiq4/udAR2PMTU4CXqSTRNilgq9/HYlfgytQVjzS4yvfGHMWklBTGb46A8Qj740cY0xnwF9CxTJgiDEmxhjTARku36W6x0IppUJVpJOU75oikNtM/cOJaU2AR4C3wHfsrUbCvbtlwCXGmEbGmFORETkqagoSPwcYY8KMMS2d6z74SUB3vhtNQ2J5vBPP/8+1v0oppULCn5Db3+a6z7Ryq91XgOeMMacAONf/gc4qe4DGpuxtLcvzNnChMeZaI53cGhtj0sp9VVn+vleU51XkNmBnGtHB9V0T//FsG9L29qQTz1Oc7b5dyborVXXWWp100kknnWpoAiKAL5APRZ7LpgLJwa6jTjrppJNOdWPyF3Oc5Rp3dNKpDk3IbUfykESzLCTR+m4g3G2dqcDjzuP7ndfkIgmFD7utdzmSaHgI+CsyZOwnTtlbkNEWLNDBs1zn+fnAdrfnycD3SKLjbmCMMz8MachZ65S9EfiXj/1LcrYZ4TavpA5u8+50yjmAJAac5mt9ZGSR4W7PHwde9bH9KOeaegDYV8H97uS8JhNJbvkBSPNR/mzgNo95o4FF5ZUFXO2cl2xnn19EEnO8Hjc/2z8I1HOb1w8ZQSMHmAM8itx7+bjjiQxzP9Opw8/IMP1zq3IsdNJJJ51qw4TEUOsxPQ5EAxOQkRV3OY+jndd4jb3IqHgLnGuoK3618LHdMrHHbX408D6S+LHC2dZ2j/pe6PZ8rCtWOM+vdF6XjdyGZaAzvzewzokRE5x57tf/RCQhIxPpufwIEOYsG+4eCzxfq5NOOumkU81Mntd8t/kRznU4yXkejSS1b3LixxrktiOu9V9zPrsfQm4DUiZ2OOt4xpNzkdFiDztx4RYfdZyNx/cfZ3553ys8v9OViYvI98G1yHeYlUB3Z77nd9wk3L4nIbeX/NyJwxuBO/3sY5nX6qRTICZjbWVH7VRKKVVRxpibgOcoHVFjorX2fWPMl0hv7C3Af621U4NURaWUUnWEr5jjLNO4o5RSSimllFJKKaWUUkGmCRpKKaWUUkoppZRSSimllFJKKaWUUjUsLNgVUEoppZRSSilVdcaY040x+caYt9zmDTDGpBtjjhhjZrndhxXn3qxPGWP2O9O/nXujK6WUUkoppZRSSimlapAmaCillFJKKaVU7fYSsND1xBjTBPgIeBhoBCxC7lHuMgK4AkhF7oF+KXDHiaqsUkoppZRSSimllFInK03QUEoppZRSSqlayhgzDDgEfO82ewiwylo73VqbD4wFUo0xnZ3ltwDPWGu3W2t3AM8Aw09crZVSSimllFJKKaWUOjlFBLsCgdKkSROblJQU7GpUSnFxMWFhdTdHRvev9qvr+6j7F7oWL168z1rbNNj1qEm1MW5B7X5fVYTuX+1W1/cP6v4+1tb9C1bcMsY0AB4FBgB/clvUFVjuemKtzTXGbHTmp3sudx539bGNEciIG8TGxp7ZsWPHQO7CSaOoqIjw8PCTZru6bd12Xd9uXdj20qVL6/x3LgjM965Q+HwSCnXQeoRmPUKhDlqP0KxHKNQh0PU4GX4vhOrHrrp47rUedacOWo/QrEco1KGu1sNX7KozCRpJSUksWrQo2NWolOzsbOLj44NdjRqj+1f71fV91P0LXcaYLcGuQ02rjXELavf7qiJ0/2q3ur5/UPf3sbbuXxDj1mPAFGvtNmOM+/w4INNj3Swg3m15lseyOGOMsdZa9xdZaycDkwF69uxpa2PsCgXBem8H839Kt63brsvbrQvbPhm+c0FgvneFwueTUKiD1iM06xEKddB6hGY9QqEOga6Hxq6KqYvnXutRd+qg9QjNeoRCHepqPXzFrjqToKGUUkoppZRSJwtjTBpwIdDdy+IcoIHHvAZAto/lDYAcz+QMpZRSSimllFJKKaVUYGmChlJKKaWUUkrVPucDScBWZ/SMOCDcGHMGMAm4xbWiMSYWaA+scmatAlKBBc7zVLdlSimllFJKKaWUUkqpGhL8G7kopZRSSimllKqsyUjSRZozTQK+AAYCM4BkY8xVxpho4BFghbU23XntG8D/GWNaGmNaAH8Bpp7g+iullFJKKaWUUkopddLRETSUUsrDsWPH2L59O/n5+cGuSrUUFxcTFhbaeXjR0dGcdtppREZGBrsqSilVq2nsOjFCKW5Za48AR1zPjTE5QL61NtN5fhXwIvAWMB8Y5vby/wLtgN+c568685RSqkoKCwvZvHnzCY9DwYwbtWXboRS7lFJKKaWUUkppgoZSSh1n+/btxMfHk5SUhDNkeK1UVFREeHh4sKvhk7WW/fv3s337dtq2bRvs6iilVK2msavmhXrcstaO9Xj+HdDZx7oWeNCZlFKq2vbt20diYuIJj0PBjBu1YduhHruUUkoppZRS6mQUut3TlFIqSPLz82ncuHGtbuCqDYwxNG7cuNb39lZKqVCgsavmadxSSinfCgoKNA6FII1dSimllFJKKRV6NEFDKaW80B8WTww9zkopFTh6Ta15eoyVUso3vUaGJj0vSimllFJKKRVaNEFDKaWUUkoppZRSSimllFJKKaWUUqqGRQS7AkoppQIvKSmJ+fPn06xZs2qXs2jRIpo0aRKgmimllFLeaexSSqm6o8+4H9hxKC9g5bVsWJ+fx1wQsPIA4uLiyMnJYefOnYwaNYoPPvjguHXOP/98xo8fT8+ePX2W8/zzzzNixAhiYmIAuOSSS3jnnXdo2LBhQOvrT0ZGBr/88gvXX3/9CdumUkoppZRSSqmq0QQNpZQKYdZarLWEhemAR0oppWoHjV1KKaV2HMojY9wfAlZe0pgvAlaWpxYtWnhNzqio559/nhtvvLEkQePLL78MVNUqLCMjg3feeUcTNJRSSimllFKqFtBfTYHdWflM+H4dOwPYu0MpdXKx1pJXUIS1ttplZWRk0KVLF+666y569OjBtm3bePrpp+nVqxcpKSn885//LFn3iiuu4Mwzz6Rr165MnjzZb7kTJ07kwQcfLHk+depU7r333gqVk5GRQXJycsnz8ePHM3bsWAA2btzIxRdfzJlnnsm5555Leno6ANOnTyc5OZnU1FT69etX5eOhvEtf+D2/vvkI+Xm5wa6KUqoWKygsDkg5GruUUkoF0+jRo3n55ZdLno8dO5ZnnnmGnJwcBgwYQI8ePejWrRuffPLJca91jxd5eXkMGzaMlJQUhg4dSl5e6e9EI0eOpGfPnnTt2rUkrk2YMIGdO3fSv39/+vfvD8hITvv27QPg2WefJTk5meTkZJ5//vmS7XXp0oXbb7+drl27ctFFF5XZjou3mFRUVMQDDzzAOeecQ0pKCv/9738BGDNmDHPmzCEtLY3nnnuu2sdTlWPt13A0J9i1UKFmxxLYszrYtVBKqeMdy4f0mkt2VbVY+heQuz/YtVDqpKQJGsC6PdnMXL2HdXuyg10VpVQttO3AEe55Zym3/G8+97yzlG0HjlS7zLVr13LzzTezdOlS1q5dy/r161mwYAHLli1j8eLF/PTTTwC89tprLF68mEWLFjFhwgT27/f9gerqq6/mo48+Knn+/vvvM3To0EqX42nEiBG88MILLF68mPHjx3PXXXcB8Oijj/LNN9+wfPlyPv3006ocBuXHwd++puHWbzm4d3uwq6KUqoV2HsrjvveWcv2r87jvvaUBSVTW2KWUUipYhg0bxvvvv1/yfNq0aVxzzTVER0czY8YMlixZwqxZs/jLX/7iN6l+4sSJxMTEsGLFCv7+97+zePHikmVPPPEEixYtYsWKFfz444+sWLGCUaNG0aJFC2bNmsWsWbPKlLV48WL+97//MX/+fObNm8crr7zC0qVLAVi/fj133303q1atomHDhnz44YfH1cVbTJoyZQoJCQnMmzePhQsX8sorr7B582bGjRvHueeey7Jly7j//vurdSxVOfaugXeHwlejA1NeADp5qBDxSn+Y2PvEbCv/MBRoZw2lVAV9909473rYtqD6ZRXkwvL3fccva+HwrupvR9W8venyvpg/6cRsb9m7kLP3xGxLqVpAb3EC9OnQhOcbdqdtk9hgV0UpVctYaxn3VTqH845xaoP6HMwtYNxX6bx4fXeMMVUut02bNpxzzjkAzJw5k5kzZ9K9e3cAcnJyWL9+Pf369WPChAnMmDEDgG3btrF+/XoaN27stcymTZvSrl075s2bx+mnn87atWvp06cPQKXKcZeTk8Mvv/zCNddcUzLv6NGjAPTp04fhw4dz7bXXMmTIkCoeCeVL6rX/4ODenTRv0ynYVVFK1UL//jqdzJyjNG8QTWbOUZ7+Zi3PDU2rVpkau5RSqpqe6wZZW6v++not4OjOiq+f0Bru/63q2wsh3bt3Z+/evezcuZPMzEwSExNp3bo1x44d429/+xs//fQTYWFh7Nixgz179nDqqad6Leenn35i1KhRAKSkpJCSklKybNq0aUyePJnCwkJ27drFmjVrSuKcN3PnzuXKK68kNlZ+axoyZAhz5sxh8ODBtG3blrQ0ibtnnnkmGRkZx73eW0yaOXMmK1asKLklS1ZWFuvXrycqKqryB01Vzb518jdzTfXLyt0PT7eDwS9Cj5uOX15wBN4aAgOfgAYdq789dzuWQNNOEFXHfwstKoQ5z8BZt0NMI2kY2rEYOg0K7HaydgS2vPKMa1WnruFKqRrmSszYtRxaneV9nYNboOgYNOkAxcXgfsvS3P1yDTUGZj4Mi6ZA/KnQ7jxZXlwEh7ZCYhL8/B9JCLlpBjTt6b9ee1ZBw9ZQL/74Zbn74NAWaHmmPM/aDt8/CoOegvqJ/sv1rH9Fl5VXpjEyBUrufjiaBY3alZ2/4Tv4dizc+gWER0kCRXgU9L67NDHGFkNYOGTMhaadIbaJW12LIHu3zIuo53v7v02Xv7sDGEsKj3rfZuY6+PhOaHc+3OyMaLdtAV4Yey0AACAASURBVJgwOK2c94lSdZQmaADhYYYOp8QFuxpKqVoo/1gxmTn5nNqgPgCJsVHsPpxH/rFi6keFV7lc1494IEkgDz30EHfccUeZdWbPns13333Hr7/+SkxMDOeffz75+fl+yx06dCjTpk2jc+fOXHnllRhjKlROREQExcWlw+C7lhcXF9OwYUOWLVt23LYmTZrE/Pnz+eKLL0hLS2PZsmUVajhTFRMTl0BMXEKwq6GUqoUKCovZfiiP5g2iMcbQKCaKbQePUFBYTFRE1QfY09illFLVlLUVxmZV/fXZ2RDv5QduX8bWrc+SV199NR988AG7d+9m2LBhALz99ttkZmayePFiIiMjSUpKKjfueEu037x5M+PHj2fhwoUkJiYyfPjwcsvxN1JHvXqlP1yHh4d7vcWJt5hkreWFF17gwgsvJDy89Pvm7Nmz/dZFBdBeJzHD+Pm+n7UDMuZAt2uk8cTdvg0yr1Fb2CUjqrDotdIEjeIiWDgFUodJA83WX+H1y+H2X/3/f8+bKI1ZAx4+ftmu5TDjTrju3dJGrVf6Q+dLYdjb/vd33wZprPNmxxKIaQyJbfyX4enwTmmQifeeKFUl+zfC6o+h970Q4ZawtPEHmP0v2LsaTv+9DOW+9ku47zdIaAW5mbBnJbQ9D356WhqWLvynnIewcEnwWDIVWp0tDWi+Elq2/lr62PVafyrS0HcsHyKjj5/vura4J/St+RwW/w+uew/CI/1vWylV55kjB+DASrnGRcaUJheu/FASNGwxhNeDuFPgaDbkHYTpt0iSRdt+sHsl9LxVlhUehSWvQ6/b4bzRkhAAsPBVeVwvHn4aL4kGYZFQfEyWv3kl8QBXTJLr79ov5VqddC5sXyjJF8vfhVbnwA3T4ZuHZESHLpdBwmnw4Z+knLhm0KK7JBzsWgZNOsK5f5HrZ3ERZKZLeRHR0KIHLHxFrvUX/APyDkH7C4jc8BPUqwcNWsInd0Hf+yH1Okl6yM+Sa3a9eInxLbpL2fs3Sh0LjkCDFvDOUOgzCs65W/Z1z2pokSZxobAACnLg4Gao1wCanC6xbtot0P1GOf6ndCE8vAE0Pg1a9oCdy2DyeXJ+7lkIK96HzpdB047w+f/Jtse1Lj2pYRESt394TM6jLZa67lwqx+S0syTWXTIeFr8GS9+S1511B2z5WZJnOlxIeL1TwOZC+wGwUhJ+2fAdfPBHWP2pxPSuQ6DjxaUJHgc2y3th5j/gjMuhMB+i4qTM5e/Ksdzzm+zzyg+lPmk3wPL34Hf3QMpQ2PyjbGvvGigsIHz7Anjf6RRzxUQ57ys/lHg2ZLK8dsvPcGAT1G8k5UTFyXHOzZT3TVg4HDkAWEhsC0vflHOStV3O25nD5f2YvVs+I8U0lglg23wwhqjty6H7UPkfiG8O+9ZCdIK8747lOefuDIisL5+h8g5KsmdENOxfL++prb/K54joBDlvxUVSh+ap8hmjXjy07CnnLDMdDmZIouieVXBkH3T6A+bgZlj2HbTvD82SIWsbRMbKNo/lyufOfeud12+Gfg/I4+zdpf9TRw9DTBM5/sbIeco7BKd0gf0b5PNBVJzsV26mPI5rCvUSIHsX4VsWQd5uOUd9RsmxjWks9TZhzueWMMCUfZyzR/Y5ton8PxzNlvdrZH057sbI/0VUDGTvgfAISTIFOZ/Zu+Vxo3ayrYIcyCuUz2AmDOrFwZH9cn3JOyBlNmor24lvLvuRvQuiG0BEfecfxsprIurJ56ncvVIHkP97WyR1DI8q+zf/kByzRu3kOB3YB/Ublp8UVkWaoKGUUtUQHRlG07hoDuYWkBgbxcHcAk6JiyY6MnB3kBo4cCAPP/wwN9xwA3FxcezYsYPIyEiysrJITEwkJiaG9PR05s2bV25ZQ4YM4YknnqBNmzY89dRTABUqp1mzZuzdu5f9+/cTFxfH559/zsUXX0yDBg1o27Yt06dP55prrsFay4oVK0hNTWXjxo2cffbZnH322Xz22Wds27ZNG7mUUioEREWEcVrD+mTmHKVRTBQHjhTQKjGmWskZnjR2KaWUOtGGDRvG7bffzr59+/jxR/kROCsri1NOOYXIyEhmzZrFli1b/JbRr18/3n77bfr378/KlStZsWIFAIcPHyY2NpaEhAT27NnDV199Rb9+/QCIj48nOzubJk2aHFfW8OHDGTNmDNZaZsyYwZtvvlnh/fEWkwYOHMjEiRM577zzCA8PZ926dbRs2bKkDuoE2LNS/h7MkIZybw3ss/4Fy96SH/9XfgiDnoZmXeHwDnjnWllnbJY0gIH8oL9zqTQu/PICzBkPXz1QWl5BNnH/7QW3fAatesk817azd0vDwNdjZP75D0H65/JDfcse0Lo3fPmgNNr8J1XWufYN+bvum+PrfmibNE60HwCbZsGbV8LAJ+WH7HPuwhzYBIcL5Qf7V/qX7ktlPNul7OsKC8omVbj2z9U7GGD6rXBaL2jYCrK2Y9peAnnbpdGhuAhe6CHrNUsubUQ5tZs0lIAkb6z+uLT8dd/ID/of3ynPO/0B1n4hjxu2gm//KQ2G2bvhi7/I/OSroKgATk2RhpjWZ5eW5977eMdiOe8Zc6WR7qrXyvbWtham/F62k3q9NMYlJsltc5p2lkbRnUthcn/AwlVT4NtH4LwHpbEpe1fZskDOf9Y2aTA9Z6S8P9+9Dq5+Td5fW36BZW9L4+tlEyBJRqSjqFAaSjwdzJBjWFQo74Pkq8rtNR62fwNEd5bj7163ivQ2LyqU91jeIWlMqd/w+HWKiyhtFHLKdE9iKcgtTaDZvxEatvG+by7H8uX/NO0GqXPRsbLJLVXtZe8wh7fDz2/KefO2P0rVlPXfEvvuMCguLJ0XFimN+2u/gv/28//6zXJrUuY8U3b+wldkclnzqUzuXMkZ7lzXWZefny/7fNs8GRXIZceisstz9sC6r0uf//AYzH0Ojh2ROOHLxyNLHh6X6vb1mNK46cmES+JKtpfbtMz8B/z4b2m4de1rg9PgsMftp6MbAlaSP7aX3lYmxvUgMkbqD/L3ua7y+PvHZNuetwHp8HtJVnixp8ShsAjZ951Ooue+daVJOK9eUPa1C/4rf/eshPTPS+vg0jxNEl9WOrfbKy6Cn/4tkzfe5m8qe5s/9q2TUVQAPrkbPr239Fzl7IEnWxJTVFC6/scjy5wv/t229HGbvhLf3h3mvT7+fF3+7ejqAcx5svJlB1jJ8AHf/z95D9oi/y/46emKF+5KLClHmffG4v9VvPyqck/oclOJ7gY1qqQevka6CwBN0FBKqWowxjBmUGfGfZXO7sN5nBIXzehBnat1exNPF110EWvWrKF3b7mXaVxcHG+99RYXX3wxkyZNIiUlhU6dOpUMK+9PYmIiZ5xxBqtXr+ass2RIu4qUExkZySOPPMLZZ59N27Zt6dy5c8myt99+m5EjR/L4449z7Ngxhg0bRmpqKg888ADr16/HWsuAAQNITU0N0BFRSilVXQ9e3Jl/f53O9kN5tEqM4YGBgb1dksYupZQ6ubVsWJ+kMV8EtLzydO3alezsbFq2bEnz5s0BuOGGG7jsssvo2bMnaWlpZWKBNyNHjuTWW28lJSWFtLS0kriTmppK9+7d6dq1K+3atSu53RbAiBEjGDRoEM2bN2fWrNIfqHv06MHw4cNLyrjtttvo3r2719uZeOMtJqWkpJCRkUGvXr2w1tK0aVM+/vhjUlJSiIiIIDU1leHDh3P//fdXaBuqAo4cIHzTT5ByuTQ4Z/ws83P3SiNTX7djXVQoDU27l8vzRa/J3xkjji93xxIZ3QGkYWPyrOPXcWMK82DKhTDsHVjzGaz8CNr8ThpccjNLV1z7hfSCLn0l4DGay7Sb5W/xMdmnrG3y4/1PT8vQ9CBDzjdxbqvyzUPyd286sSveK9voBzJ6xxmXw8ZZ0kO6+42SnNDzj9C4PWyeA1/8nzSgX+32g//ar6Vu02+VxINef5JGuHb9YdUM6ZV77xLpSbnqI5kccTgNa6MzpBezS8YcSXIBeGiH9Kb11LC11DPObQSPtW7Xq8+dc/rRCFnXxdV4teYzmPUENGxDXG4mXPj/IHNt6XpTfl/6eM9K6fkb0xg6DpSGPWOkAXLHItlPkOVH9svjnrfKeq7z5upF/tmfoU0fSb5x2b+BqMVvyzkEaXCMqFe6Dz//B1ZMK23oCYuA1y+ThIGkvnJuEpOkd3BYuNThrBHwxmBpOLFF0qgW00i2velHWP8N5B+W3syHd8nQ+wW5xG5fIGXFt4A2veX9fWAznHmL9ESd8wx0uFDeax0ulDLmT5L3b9Y2wEjP1bhTYfALsGWu9JzOXCsJTulfQtFRSL4a0q6D+ZOlLoOehr2rYPFU6Pt/RBEFc8dJj+CbZ0iyydFs6WG88QdJwti3Hha8IuUdzZHt/vICDHhEGrC3/ir1PGuEjB4QFi7navCL0ni9f7309s47JI2mW+dDt6ukx3nOXvhqNHHZzi3HDm6W0WLCIiVpKipOejUfy5PzkdBSegBjYM0nsG6m/A+d2k16Q+9Nl9et/Up6bDfpIMdzzypJ+up8mfQk3rtaej3vXSN1C48kLDsL4hNg1wrZRlJf2L1CklH2robfPoDTL5L/sTa/k/+99d9AbFPp8V1wRHqAb/xe9qUgV25JEFFPpoIj0OwMZ4SG9XJsT+sl9dm/UZKV2p6HKY6A9cvl/dG2nOQAVX2n9eRYj9uIatPLudWEgcYd4JTOkJMJ676ScxYWLkl5kfXlfzzvIKTdKP97sU0lialxB2lsT2gp78FjebIsMUn+F864XK4T2xfK+/9otsSVMGl2zD24m9jibHm/tekj19Sdy6QnfYse8t5b+rbEo/YXSPLB4Z3yf9eorayzZ6UkxuUdlP+9TbPl+lG/ofSub9Re3ncbv5f/yVOT5Vp2cIvUOzKG3Li2xEaFybW/SUdJisg7KPEs75DUp7iwtJd+0TFJ+LJFkmxhi+VxvpNYGJ0gCYH71svIBA2ukDrn7JZrT2R92bfYprJu95tgxXsUHNhBVGwCFObJ/3PqdXINObRVRunYtwEKsmUUhC6XyjW0w4WliZy/TZfRn3rdJv/LrlEQ9qyS/8WmnWUdWwzdrpVr66ZZsg8NW0P8qRzJ3EJMwX6pd1Qs/O5eOQcJLWVEjrhTJf7aIjnOhUclES7hNEnmWPuVXJMy02VEggYtJJa0PVeuTXGnyDla9w2cfYesf3inXB+adpZRvcLCyYtPon50tIyusXCKHNvoBDmv+zfKaAjhUfJZwhbLNdgWy/6HR8rnjKICOf8RUXILlVZnyXv00BYpK2ublGHCnXN9sLTcmCZQL54jBYXEHFov/wPFRXJM68XLe7J+olw7s3fLe6YgV8oNC5fb0xzLlXXCIiWe1GvgbNcZPWPfeon5BzbJ+XaNzJHQErbOk32JaQS7lpNvI4g+tbNs/+BmWVaY7xz/GNlWYlsZpSJ3n5z/Jh3lOpyzR7ZXfKx01BtXomvOHql/q7PkeBUVyOcSWyQjvhXkloxwkRffmvoNmso2ti2Qc5l/SEalsEVO4qVTrvvjsAgpN7yeHH9X0m3eITmfYZFy/I7lSTw8dkRG0ijIlrrEN5c6HNoGYWHkHysmOiauNBHpaLYcO2vl3DdoKefYNULHsTz5HHXsiPzvgtStXgNntKBIOf5FBbK8qEDqZYud58684mPyvxJRD7K2c7TIUK9xK0l2riHG35CLtUnPnj3tokWLyl8xhGRnZxNfmeFHaxndv9qvru+jr/1bs2YNXbp0qVRZ1loJHpFhAU3OqI6ioqIyw96GKm/H2xiz2Fpbp29AVxvjFpy814W6Qvev9gtk7AKqfVuTQKsNsetkjVtQe2NXKAjW9SmY10XddhWMTajWLU4qve1qbs/dqlWr6Nq1a0DKqoxgxo3atG2NXVWMXTmZMKmP/Ljd808yFHh+lgzFvWKaNE7cObf0vu9znoHvH5XHHS6UIcNdGraWBhh/mnSS0StAGkhydlesnqemSKPnry/6Xif1OmlA6XABLHmjdH7XK0uTBEB6WKd/7n97kbHSUFCeZsnwu1HeE1QqKipO9m/rL/K88enSQOeSep0c1x1LpJHj8I7SZZ49UNueJw3wqz+GeZOkkaBZ17I9s126XQu/TSt97p5AAdIQsmu5x/52kyHeXe78Wd4/lTXo3zKCR9p10pjkfn78OfNW/z1eU4bCRU9I8sXe1RXuUVvCX4/e8Chp3KhN3Huwl+ElqakKbHgUpn6jiv8fB0xg6l8j5XW7Bq56tXq10dhVIaHy24jWI/TqEQp10HqEZj1CoQ51tR6+YpeOoKGUUgFgjKF+VGg3KCmllFKeQik5QymllFIqpNRvKL3KV82ARVOkJ2ybvtJLOCpWRqJ4ur30GA2LLE2uaJYM/f8uPRh3LoML/i73gD+0Rcr49mEZXSPpXGh3vgzXfunzMnLCkjell2DnS+FR537XN82g+PO/EDZksowSsMy5r3yf+6QXYdp10tszoZX0nO17v/RE/nik9Lgd+qbUyXW7EPcEDffG//PGQP+HYMpFcnsWd9e+UTryxt3zpMdi5lq5/YcrWaNBS9nu4Z2SKLFnpSRnRERLL1B3jdrDgY1yLHcuK23EPn2g9OAH6bFYmC/JGeH14IzBcNHj8PVDFO1eSXiTDtLLFwOXvyS9gec+K8fu9N/DiunSozftenl9dIL0AjYGfn1Jetb2/COcfackpcSdChu+lX2/9Fm5hcmWXyF5iCRnfHqv1KtJR7h9FhQXUfDV34ha7Az5nzoUMlpKwsfD+6TH5uUvyygITTrKbVBcQ77f8jm8fqmMXhAeJe+nFj1kOPavHpR12l8AsafIyCSDJ0hv1v0bJKmgXX85jwc3c6zT5URGhMl+NGwttzM5f4wcl9cvk/PTpi9c8A+51/yfZkpv3lPOkF6j8yfKyDCJbeR4vHWV3Nu+xy0yNH6TjrDof9Lj9+CW0h6qxkjCzN7VcN175OTmErdkovRKbnYGvH+j7EebPtD7bug4SBJCjh2RnsNxp0gdfn5ekovO/Yv0dt39m/TOb38B/DJB3uf7N0CHAXIMlr4lvV673yBJTa9fKgkzw96FTbM4YqOJSTpTjsPKD+VWMk07y3u2TW8Z9aO4UHr+H9gktyRq1Fbei+mfS0/3Ru1km4X5UsecPdJD/bP75Dy3PFP+1/o9ILdSeeNyuUVAWIRcC277lhwTR3yT0yR5KL6ZjOQR21R6K0fGyCg8BbmyL9m75LWN2kuii6vXdN4BOXerP5He643by7L9G6QO0QnyHl34ivRC7nUbtEiTnuv14jlSFE5MpJFEpKxtckyiE6TOsU1l1IGDGXJODmySHtn1G0nSWViEnOPti+T4xTeX92lBjizLz5L3QVxTSaQ6pYuMuLBrmZSZmCSjL6z+hPwiQ3SXi6TnuFJKKaUAHUEjqEIlE6im6P7VfnV9HwPdCznU1IZeyHDy9uaqjXELTt7rQl2h+1f7aewKvpM1bkHtjV2hQEfQ0G1XiI6gUWm1aRSLYG5bY1f1YlfOtpXELX8N+oySRkeA4mJpzM9cKw36R7OlEbPHzTK8OsCRA9LI2qD58YUWF0NYmPw9dgTqxR2/zpEDMpxzbOOy/9/bFkLmGtlWVXx8t4z+cf5D0jm+aWdYMFmeR9aXRuOiY9IQHtNYbovQsBUsf59jKz4g8sZp0nALMnx3zh5Zx521kgQR1wySrwKsNIq/f5M0kve9X9ZxNQAveQMuekwaj5e9I0kdyUOkrMO7pB6uIbNxu97tWS1D0ycmSXnbFkDzFNkPf3L3SyNys66StOFSWCCN5Yltyq5fWADrZ8qxatKhtB57txL/yzhpGD81RfbzaLaM5uHN/o0ydHmHC+X2AK3OKbNf7FwK790gjfZ3ObfZcB0nb3IyyS4MJ76hj+0VOA3vrvdkRfjbnh9eY1DeITmn5ZV3YLMkSVRFfhZExcv/k6961LTiYvkbFiZD5IeFh8R301CoQ6DrobGrYuriudd61J06aD1Csx6hUIe6Wo+QGkHDGJMEvAz0Bo4CHwD3WWsLjTEDgJeA1sB8YLi1dksw6qmUUkoppZRSSimllDp52YZtZEQFd2Fh0HGgTL74aqR3vd7111tyhr/Xt+olU1Vd8dLx837/aOnjqFj5+7t7yq6TOpT8dpcQ6d7Y3uR0mTwZc/zrw8Lhhmll1wE4radMLmnXl32dtwQXl2ZnlC2v9dm+13UX21gmTxFRxydnuOZ3ufT4+fUT4YqXy87zd94bt5cJ5LY0nlp0h/tXlU1o8JfcENcUsrN9L4+KqVxyRnnbq6z6DSu2XlWTM0ASQIItzG1UwrDQTjZXSimlVGgI1i1OXgb2As2BhsC3wF3GmHeAj4DbgM+Ax4D3gXOCVE+llFJKKaWUUkqpE6rPuB/YcSivUq9pVt+yJ6/iDWsZ0ZA05gu/67RsWJ+fx1xQqXoopZSqhkAmSCillFJKqZAUrASNtsCL1tp8YLcx5mugKzAEWGWtnQ5gjBkL7DPGdLbWptdYbYqOyX0RT+ladlg5pZRSSimllFJKqRNsx6E8Msb9oVKvqfwtTih3G+UlcCillFJKKaWUUqpywspfpUb8BxhmjIkxxrQEBgGuJI3lrpWstbnARmd+zdk0Gz65FzbNqtHNKKWUUkoppZRSSimllFJKKaWUUurkFKwRNH4EbgcOA+HA68DHwKVApse6WYDXLiDGmBHACIBWrVqR7e+ee/406AgpwyGhk//79gVYbm7uCdtWMOj+1X51fR997V9xcTFFRUUnuDaBV1xcDMDs2bN59tln+fTTT/nss89YvXo1o0eP9vqaQ4cO8e677zJy5EgAdu7cyX333ce0adO8rh+oelb5+q2UUqpOmj17NuPHj+fzzz/n008/ZfXq1YwZM8bruocOHeKdd97hrrvuAiR2jRo1ig8++OBEVlkppZS757pB1tbAlZfQGu7/zediz1hQWc8//zwjRowgJiamqjUEJH5FRUXxu9/9rlrlKKWUUkoppZSqu054goYxJgz4Bvgv8DsgDngNeArIARp4vKQB4LXlzlo7GZgM0LNnT1upoTzdxcdDs9uq9tpqqnKdawndv9qvru+jt/0LCwsjPDw8CLWpnKKionLrGR4eTnh4OMYYwsPDueKKK7jiiit8rp+dnc2kSZO45557AEl++/DDDwNab09hYWF1/n2mlFJKVCR2eRo8eDCDBw/2ufzQoUO8/PLLJY1yLVq00OQMpZQKtqytMDYrcOWNTfC72DMWVNbzzz/PjTfeGJAEjbi4OE3QUEoppRQAxpgngH7AHuBma+2RIFdJKaVUCAjGLU4aAa2AF621R621+4H/AZcAq4BU14rGmFigvTNfKaVOGhkZGXTu3JlbbrmFlJQUrr76ao4cOUJSUhKPPvooffv2Zfr06cycOZPevXvTo0cPrrnmGnJycgD4+uuv6dq1K3379uWjjz4qKXfq1KklyRd79uzhyiuvJDU1ldTUVH755RfGjBnDxo0bSUtL44EHHiAjI4Pk5GQA8vPzufXWW+nWrRvdu3dn1qxZJWUOGTKEiy++mNNPP50HH3wQkEa44cOHk5ycTLdu3XjuuedO5CFUSil1gmnsUkopFSyesQDg6aefplevXqSkpPDPf/4TkFEU//CHP5CamkpycjLvv/8+EyZMYOfOnfTv35/+/ft7LfuMM84gJSWFv/71rwBkZmZyzTXX0KtXL3r16sXPP/9MRkYGkyZN4rnnniMtLY05c+acuAOglFJKqZBjjEkG2ltrzwW+A/4Y5CoppZQKESd8BA1r7T5jzGZgpDFmPDKCxi3AcmAG8LQx5irgC+ARYIW1Nv1E11MppSqs4AgsfAV2rYDmKdDrdoiqXs8rgLVr1zJlyhT69OnDH//4R15++WUAoqOjmTt3Lvv27WPIkCF89913xMbG8tRTT/Hss8/y4IMPcvvtt/Ptt9/SqVMnhg4d6rX8UaNGcd555zFjxgyKiorIyclh3LhxrFy5kmXLlgHS2Oby0ksvAfDbb7+Rnp7ORRddxLp16wBYtmwZS5cupV69enTq1Il7772XvXv3smPHDlauXAlIrzallFIhoqgQVk6HXcuheSokXwPh1f9qoLFLKaVUMHjGgpkzZ7J+/XoWLFiAtZbBgwfz008/kZmZSYsWLfjiiy8AyMrKIiEhgWeffZZZs2bRpEmTMuUeOHCAGTNmkJ6ejjGmJC78+c9/5s9//jPnnXceW7duZeDAgaxZs4Y777yTuLi4kkQOpZRSSp3UzgW+ch5/hYwi/2LwqqOUUipUBGMEDYAhwMVAJrABKATut9ZmAlcBTwAHgbOBYUGqo1JKVczCVyD9Szh2RP4ufCUgxbZq1Yo+ffoAcOONNzJ37lyAkkarefPmsXr1avr06UNaWhqvv/46W7ZsIT09nbZt23L66adjjOHGG2/0Wv4PP/zAyJEjAbkVSkKC/2GD586dy0033QRA586dadOmTUkj14ABA0hISCA6OpozzjiDLVu20K5dOzZt2sS9997L119/TYMGnnewUkopFTQrp8Oi/8G+9fJ35fSAFKuxSymlVCiYOXMmM2fOpHv37vTo0YP09HTWr19Pt27d+O677xg9ejRz5swpN440aNCA6OhobrvtNj766KOSW6B89913/PnPfyYtLY3Bgwdz+PBhsrO93p1XKaWUUrWcMeYeY8wiY8xRY8xUj2WNjDEzjDG5xpgtxpjr3RYnAq57vmUho8srpZRSJ34EDQBr7TLgfB/LvgM6n9AKKaVUdexaATGNIDIGYpznAWCM8fo8NjYWAGstv//973n33XfLrLds2bLjXhsI1lqfy+rVq1fyODw8nMLCQhITE1m+fDnffPMNL730EtOmTeO1114LeL2UUkpVwa7lUC8eohuWPk+9rtrFauxSSikVCqy1PPTQQ9xxj7s6eQAAIABJREFUxx3HLVu8eDFffvklDz30EBdddBGPPPKIz3IiIiJYsGAB33//Pe+99x4vvvgiP/zwA8XFxcydO5e4uLia3A2llFJKhYadwOPAQKC+x7KXgAKgGZAGfGGMWW6tXYV0QnZlgyYAB05MdZVSSoW6oCRoKKVUndI8RUbOiAGOHIDO5wSk2K1bt/Lrr7/Su3dv3n33Xfr27cvSpUtLlp9zzjncfffdbNiwgQ4dOnDkyBG2b99O586d2bx5Mxs3bqRjx47HNYK5DBgwgIkTJ3LfffdRVFREbm4u8fHxPnt+9evXj7fffpsLLriAdevWsXXrVjp16sSSJUu8rr9v3z6ioqK46qqraN++PcOHD6/2MVFKKRUgzVNhh3P9PpotzwNAY5dSSqlg8IwFAwcO5OGHH+aGG24gLi6OHTt2EBkZSWFhIY0aNeLGG28kLi6OqVOnlnm95y1OcnJyOHLkCJdccgnnnHMOHTp0AOCiiy7ipZdeYvTo0YAkGqalpREfH8/hw4dPzE4rpZRS6oSw1n4EYIzpCZzmmm+MiUVGhE+21uYAc40xnwI3AWOAucBDwOtIcsfPvrZhjBkBjAAZmbI6I3Pl5uZW+bWBpPUoKxTqEQp1AK2Hp1CoRyjUAU6uemiChlJKVVev2+XvrhWSnOF6Xk1dunTh9ddf54477uD0009n5MiRvPDCCyXLmzZtytSpU7nuuus4evQoAI8//jgdO3Zk8uTJDB48mCZNmtC3b19Wrlx5XPn/+c9/GDFiBFOmTCE8PJyJEyfSu3dv+vTpQ3JyMoMGDeLuu+8uWf+uu+7izjvvpFu3bkRERDB16tQyvY897dixg1tvvZXi4mIAnnzyyYAcl2AxxiQBLwO9gaPAB8B91tpCY8wAJGO+NTAfGG6t3RKkqiqlVPmSr5G/u5ZLcobreTVp7FJKKQVAQmsY6//2IZUuz4/GjRuXiQVPP/00a9asoXfv3gDExcXx1ltvsWHDBh544AHCwsKIjIxk4sSJAIwYMYJBgwbRvHlzZs2aVVJudnY2l19+Ofn5+Vhree655wCYMGECd911FykpKRQWFtKvXz8mTZrEZZddxtVXX80nn3zCCy+8wLnnnhu4Y6CUUkqpUNMRKLLWrnObtxw4D8Ba+5tz25M5wF7gZl8FWWsnA5MBevbsaePj46tVseq+PlC0HmWFQj1CoQ6g9fAUCvUIhTrAyVMP42/Y39qkZ8+edtGiRcGuRqVkZ2eHzButJuj+1X51fR997d+aNWvo0qVLEGpUKiMjg0svvdRr41RFFRUVER4eHsBa1Qxvx9sYs9ha2zNIVfLKGPMl8mXqTqAh8C3wCvAOsBG4DfgMeAw411rrdyiV2hi34OS9LtQVun+1n8au4AuluGWMeQsYAMQCu4F/W2tfdZIKNwPuKf9PWWsfc15ngHFI7AKYAoy25Xw5rK2xKxQE6/oUzOuibrsKxibAWLlNedKYL8gY94ea3bbb9nypaD1WrVpF165dK77tAAlm3KhN2w6l2HWiBSJ2hcJnzFCog9YjNOsRCnXQeoRmPUKhDoGuR7BjlzHmceA0a+1w5/m5wHRr7alu69wO3GCtPb+q26lu7KqL517rUXfqoPUIzXqEQh3qaj18xS4dQUMppZSqmLbAi9bafGC3MeZroCswBFhlrZ0OYIwZC+wzxnS21qYHrbZKKaVOBk8Cf7LWHjXGdAZmG2OWAvud5Q2ttYVeXjcCuAJIBSySdLgJmHQC6qyUUkoppZRSdUEO0MBjXgOg6vcnUUopdVLQBA2llApBSUlJ1eqBrGrEf4BhxpjZQCIwCHgYOB8ZvhAAa22uMWYjkrxRJkEjkPeTDJZQuQ9cTdH9q93q+v6B730sLi6mqKjoBNemrFatWrF8+fJq1cN1a5FQVlxcHDLXb2vtKvenztSe0gQNX24BnrHWbgcwxjwD3I4maCj1/9m78zgvy3r/46/PDMgAM4zsAiKguCCrOC7lgktq6iEX/CkeTakMj53ylEdTT6VY2bGOqcfKijppmWvkRqmZhWvuigiKOyqissk4M+xw/f6YL8TODMw998zwej4e3wfzvbfP+5ov4yVzX/d1SZIkSXX1GtAqInZNKb1e2DYUmLaJcyRJcoCGJG1ISona2b+VpWa2zNbD1N68+gQoBn4L3AX8CzBnnWMrgfXmwGro9STz0lxz15Xta95aevtgw20sKiqiqKioRfRdTXmJk5QSRUVFTervWURcB4wB2gIvAPcCXQq734mIVTNkXJBSmlvYPpA1BhcWvt7g2gQtYXBhU5DXALI8B65Zewu06QmFn7HubVO9f97qXXuNehtT1xwpJZYvX97o/VCeA/uaS+2UUpMaXChJUnMTEa2ovZdWDBRHRAmwvPCQ1h3AdyPiLGAYcBzw6fzSSpKaAwdoSNI6SkpKmDdvHp07d24RN7qaqpQS8+bNo6SkJO8omxURRcBfgF9S+4+sUuA3wA9xOkNJTYB9V/aaar+VUvpKRHwN+BS1szotAeYC+wCTgc7Az4CbgKMKp5VSO5hwlUqgNCIirTN6sqUMLmwK8vre5fmZWbuelsyCwrkfLYotuk69zlmj3sbUNUebNm1YsGBBLv1QngP7mnrtVX1Xu3btmtTgQkmSmplvA5eu8f504DJgHPAVan9HOJvamQzPWWemQ0mS1uMADUlax4477sjMmTOZM2fdSRGal5UrV1JUVJR3jE0qKSlhxx13zDtGXXQCegM/TSktAZZExPXA94FrqZ0qHoCIaE/t9PL+Y0xSo7HvahxNtd9KKa0AHouI06n9heC1wLOF3R9FxFeBDyKiQ0rpE9YfXNgBqF53cIYk1VWXLl1YsGBBo/dDefYbzaV2U+27JElqLlJK46gdjLGhffOB4xszjySp+XOAhiSto3Xr1vTr1y/vGFutqqrKp6QaSEppbkS8DZwTEVdS++TxmdROCX8n8D8RMQr4M3AJMCWlND23wJK2OfZdKmhF7SDBda0aeLHqsfZp1K6N/HThveskS9oqrVq1yqUfyrPf2FZrS5IkSZK2TtN9PE2SpKblROCzwBzgDWA58I2U0hxgFHA58DGwHzA6r5CSpG1DRHSLiNERURoRxRFxFHAq8PeI2C8ido+IoojoTO1sTw+llFYta/I74LyI6BURPYH/BG7IpSGSJNVDRFweEY9GxISIaJd3HkmSJEmqL2fQkCSpDlJKk4FDNrLvQWCPRg0kSdrWJeAc4BfUDrx/B/h6SunuiDgV+AHQDfgE+Cu1gzdW+SWwM/BS4f2vC9skSWqyImIQsEtK6aCI+Dfgi8BPc44lSZIkSfXiAA1JkiRJamYKMziN2Mi+W4BbNnFuAr5ZeEmS1FwcBNxX+Po+4Ic4QEOSJElSM+MSJ5IkSZIkSZIaRUR8NSKejYglEXHDOvs6RcSdEVETEe9ExL+usbsjsGq5rkqgUyNFliQpcxExMiLGV1ZWbv5gSVKz5gANSZIkSZIkSY1lFvB94Dcb2PczYCnQHTgN+HlEDCzs+xgoL3xdDszPOKckSY0mpTQxpTS2vLx88wdLkpo1B2hIkiRJkiRJahQppTtSSncB89bcHhHtgVHAd1JK1Smlx4B7gM8XDnkMOKrw9VHA440UWZIkSZIaTKu8A0iSJEmSJEna5u0GrEgpvbbGtheBEQAppZcKy548CswGztjQRSJiLDAWoHfv3lRVVW1VqJqamq06vyE0hQxgjnU1hRxNIQOYY11NIUdTyABNJ4ckSU2JAzQkSZIkSZIk5a0UqFxnWyVQtupNSunizV0kpTQeGA9QUVGRysrKNnPG5jXENVpCBjDHuppCjqaQAcyxrqaQoylkgKaTQ5KkpsIlTiRJkiRJkiTlrRrosM62DsDWTYEhSZIkSU2IAzQkSZIkSZIk5e01oFVE7LrGtqHAtJzySJIkSVKDc4CGJEmSJEmSpEYREa0iogQoBoojoiQiWqWUaoA7gO9GRPuIOAA4Drgxz7ySJEmS1JAcoCFJkiRJkiSpsXwbWARcBJxe+PrbhX1fAdoCs4FbgHNSSs6gIUmSJKnFaJV3AEmSJEmSJEnbhpTSOGDcRvbNB45vzDySJEmS1JicQUOSJEmSJEmSJEmSJCljDtCQJEmSJEmSJEmSJEnKmAM0JEmSJEmSJEmSpJxExMiIGF9ZWZl3FElSxhygIUmSJEmSJKnF8CaXJKm5SSlNTCmNLS8vzzuKJCljDtCQJEmSJEmS1GJ4k0uSJElSU+UADUmSJEmSJEmSJEmSpIw1+gCNiKhe57UiIn6yxv7DI2J6RCyMiEkR0aexM0qSJEmSJEmSJEmSJDWkRh+gkVIqXfUCugOLgD8AREQX4A7gO0An4FngtsbOKEmSJEmSJEmSJEmS1JDyXuLkJGA28Gjh/YnAtJTSH1JKi4FxwNCI2COnfJIkSZIkSZIkSZIkSVst7wEaZwK/SymlwvuBwIurdqaUaoA3C9slSZIkSZIkSZIkSZKapVZ5FY6InYARwJfW2FwKzFnn0EqgbCPXGAuMBejduzdVVVUZJM1OTU1N3hEyZfuav5beRtsnSZIkSZIkSZIkqbHkNkADOAN4LKX09hrbqoEO6xzXAdjgyIuU0nhgPEBFRUUqK9vgOI4mrTlmrg/b1/y19DbaPkmSJEmSJEmSJEmNIc8lTs4AfrvOtmnA0FVvIqI9sEthu+pq6UJ48jqY/UreSSRJkiRJkiRJkiRJEjkN0IiITwO9gD+ss+tOYFBEjIqIEuASYEpKaXpjZ2zWFrwLL02AN/6WdxJJkiRJkiRJkiRJkkR+S5ycCdyRUlpr6ZKU0pyIGAX8FPg98BQwOod8zVvX3eFzP4Xtd8o7iSRJkiRJkiRJkiRJIqcZNFJKZ6eUPr+RfQ+mlPZIKbVNKR2SUprRyPGavwjovie0Kc07iSS1CBFRvc5rRUT8ZI39h0fE9IhYGBGTIqJPnnklSduGiPh9RHwQEZ9ExGsRcdYa+zbaN0WtH0bEvMLrRxER+bRCkqSGFxEjI2J8ZWVl3lEkSZIkaS25DNBQtuZVL+GbE17kkdfm5B1FklqElFLpqhfQHVhEYZmuiOgC3AF8B+gEPAvclldWSdI25b+BvimlDsDngO9HxN516JvGAscDQ4EhwL8AZzdmcEmSspRSmphSGlteXp53FEmS6sTBhZK07XCARgu0cOkK3p2/kA8qF+UdRZJaopOA2cCjhfcnAtNSSn9IKS0GxgFDI2KPnPJJkrYRKaVpKaUlq94WXruw+b7pTODHKaWZKaX3gR8DYxo1vCRJkiRpNQcXStK2o1XeAdTwendqx2/G7EPb1sV5R5GkluhM4HcppVR4PxB4cdXOlFJNRLxZ2D59zRMjYiy1Ty3Tu3dvqqqqGidxA6qpqck7QqZsX/PW0tsHLb+NLb19WYiI66gdXNEWeAG4F7icTfdNa/Vdha8HbuT6zb7vagry+rud58+UtbdAm55Q+Bnr3jbV++et3rXXqLcxdc3h3/Ftp7YkSZIkaes4QKOFaredH60kNbSI2AkYAXxpjc2lwLprSlUCZeuen1IaD4wHqKioSGVl6x3SLDTX3HVl+5q3lt4+aPltbOnta2gppa9ExNeATwGHAEvYfN9UWni/5r7SiIg1BiCuun6L6Luagry+d3l+ZtaupyWzoHDuR4tii65Tr3PWqLcx9cnh3/Ftp7YkSZIkacu5xIkkSXV3BvBYSuntNbZVAx3WOa4D4CPGkqRGkVJakVJ6DNgROIfN903r7u8AVK87OEOSJEmSJElSw3KAhiRJdXcG8Nt1tk0Dhq56ExHtgV0K2yVJakyt+GcftKm+aa39ha/ttyRJkiRJkqSMOUBDkqQ6iIhPA72AP6yz605gUESMiogS4BJgSkppemNnlCRtOyKiW0SMjojSiCiOiKOAU4G/s/m+6XfAeRHRKyJ6Av8J3JBDMyRJkiRJkqRtigM0gBXLl/P2y8+wfNnSvKNIkpquM4E7UkprLV2SUpoDjAIuBz4G9gNGN348SdI2JlG7nMlMavufK4Gvp5TurkPf9EtgIvASMBX4c2GbJEmSJEmSpAy1yjtAUzDtsbtp9+jlTDvgIoYednLecSRJTVBK6exN7HsQ2KMR4zCvegkzP17EkB3LiYjGLC1JagIKgzBGbGL/RvumlFICvll4SZIkSZIkSWokzqAB9Nytgjl9jqXH7vvkHUWSpDq54R8zuPSeqbw3f1HeUSRJkiRJkiRJklQHzqABdOnZhy5nfC/vGJIk1dlxw3qyc9f29Ni+JO8okiRJkiRJkiRJqgMHaLREK5bBG3+DHQZDea+800iSMtC/Wxn9u5XlHUOSJEmSJEmSJEl15BInLdGcV2HS5TB1Qt5JJElZefsxmPQDWLow7ySSJEmS1KRExMiIGF9ZWZl3FEmSJElaiwM0WqJuA+Az42DI6LyTSJKyMvMpePsRWDQ/7ySSJEmS1KSklCamlMaWl5fnHUWSJEmS1uISJy1RUTH0PzzvFJKkLH3qqzDsNCjbIe8kkiRJkiRJkiRJqgNn0ABmLVjE1X99jfcXLMo7SoOoXrKcn/z9daa+vyDvKJKkrLRq4+AMSZIkSZIkSZKkZsQBGsAbs6v5+/TZvP5RVd5RGsSHlYuZNH02T7/9cd5RJEmSJEmSJEmSJEkSLnECwIH9u7Bjx7b06dw+7ygNon+3Uq4+ZRjdO5TkHUWSJEmSJEmSJEmbEBEjgZH9+/fPO4okKWPOoAEUFQU7dy2luCjyjtJg+nRuT0nr4rxjSJIkSZIkSZIkaRNSShNTSmPLy8vzjiJJypgDNAA+nAq3fR4+mJJpmXfnLeSc3z/HU2/Ny7QO1bNhwhdh+r3Z1pEkSZIkSZIkSZIkSXXiAA2A5Yth4bzaPzO0ZPkKPl64lOolyzOtw4qlUDMXFn+SbR1JkiRJkiRJkiRJklQnrfIO0CTsWAFn3A3FrTMts2v3Mm780n60Ls54XEz5jnD6H6HIj1eSJEmSJEmSJEmSpKbAGTSAlBILliRSSpnXar1kATRCncqlsCL7MpIkSZIkSZIkSZIkqQ4coAE8/fZ8vnjDMzzx1rxsC30wBW45FV6+J9My78yr4cu/fZbbnnk30zqSJEmSJEmSJEmSJKluHKABdC1rwx47dKB7h5JsC7XvAt0GQMedMi3ToaQ1e+xQRr8upZnWkSRJkiRJkiRJkiRJddMq7wBNwc5dS/nBiYOzL9ShJ4y8JvMyHdtvx3ePH5R5HUmSJEmSJEmSJEmSVDfOoAGwfCnMfA6WL8k7iSRJTcqSxQuZ9fZ00sqVeUeRJEmSJEmSJElq1hygAfD2wzDxXHhzUt5JJElqUibf8zOqbjqTGdOfyzuKJEmSJEmSJElSs5bbAI2IGB0Rr0RETUS8GREHFbYfHhHTI2JhREyKiD6Zh+m5F1R8EXoNz7yUJEnNSZfdP838HgfRqUe/vKNIkiRJUp1ExMiIGF9ZWZl3FEmSJElaSy4DNCLiCOCHwBeAMuBg4K2I6ALcAXwH6AQ8C9yWeaD2XWCfL0Fpt8xLSZLUnOwy9AA+9aUrKe/YJe8okiRJklQnKaWJKaWx5eXleUeRJEmSpLXkNYPGZcB3U0pPppRWppTeTym9D5wITEsp/SGltBgYBwyNiD1yytksLVm+gnsmv8978xfmHUWSJEmSJEmSJEmSJAGtGrtgRBQDFcA9EfEGUALcBVwADAReXHVsSqkmIt4sbJ++gWuNBcYC9O7dm6qqquwb0IBqamoyue5bc6u5/YnXmfNxV06u2CmTGnWRVfuaipbePmj5bbR9kiRJkiRJkiRJkhpLow/QALoDrYGTgIOAZcDdwLeBUmDOOsdXUrsMynpSSuOB8QAVFRWprGyDhzVpWWQe3L6U/zi6Df27llLWfrsGv359NMfPpD5aevug5bfR9kmSJEmSJEmSJElqDHkscbKo8OdPUkofpJTmAlcBxwDVQId1ju8ANK+pMXJWVBTs07cTHXMenCFJkiRJkiRJkiRJkmo1+gCNlNLHwEwgbWD3NGDoqjcR0R7YpbBdkiRpq0ybVcnpv36Kye8tyDuKJG2ViGgTEf8XEe9ERFVEvBARRxf29Y2IFBHVa7y+s8a5ERE/jIh5hdePIiLya40kSZIkSZK0bchjBg2A64GvRUS3iOgIfB34E3AnMCgiRkVECXAJMCWlND2nnJIkrRYRoyPilYioiYg3I+KgwvbDI2J6RCyMiEkR0SfvrNo470BKaiFaAe8BI4By4DvA7RHRd41jtk8plRZe31tj+1jgeGoHxw8B/gU4uzFCS5IkSZIkSduyvAZofA94BngNeAV4Abg8pTQHGAVcDnwM7AeMzimjJEmrRcQRwA+BLwBlwMHAWxHRBbiD2htjnYBngdvyyqlNG9iznBvP2o9hvbfPO4okbZWUUk1KaVxKaUZKaWVK6U/A28DedTj9TODHKaWZKaX3gR8DYzKMK0mSJEnahIgYGRHjKysr844iScpYLgM0UkrLUkpfSSltn1LaIaV0bkppcWHfgymlPVJKbVNKh6SUZuSRsdn75ANYvjTvFJLUklwGfDel9GThRtj7hZtaJwLTUkp/KPRl44ChEbFHnmG1EcuXwsxnYfmSvJNIUoOKiO7Abqy9POQ7ETEzIq4vDChcZSDw4hrvXyxskyRJkiTlIKU0MaU0try8PO8okqSMtco7gBrevGkPMf/ui4k+n6L/aVflHUeSmr2IKAYqgHsi4g2gBLgLuIB1bnKllGoi4s3CdpfoamrefgQevBQO+zbsfnTeaSSpQUREa+Am4LcppekRUQrsA0wGOgM/K+w/qnBKKbDmY1mVQGlEREoprXPtsdQuiULv3r2pqqrKtC0tVU1NzTZV19pbqE1PKPyMdW+b6v3zVu/aa9TbmLrm8O/4tlNbkiRJkrR1HKAhSdLmdQdaAycBBwHLgLuBb1N7k2vOOsdXUrsMylpawk2uZv/L4PLdYeiXYfsBG7wh0ezbtxm2r/lr6W1s6e3LQkQUATcCS4GvAqSUqqldcgvgo4j4KvBBRHRIKX0CVAMd1rhMB6B63cEZhWuNB8YDVFRUpLKy9bo31VFe37s8PzNr19OSWVA496NFsUXXqdc5a9TbmPrk8O/4tlNbkiRJkrTlHKDRAnUeeAide/8R2nXOO4oktRSLCn/+JKX0AUBEXEXtAI1HWPsmF4X36939byk3uZprbqD2JkS3MZs5pBm3rw5sX/PX0tvY0tvXkCIigP+jdiDhMSmlZRs5dNXAiyj8OQ0YCjxdeD+UtZdGkSRJkiRJkpSBorwDKCMdekKrNpmXmffRTJYsXph5HUnKU0rpY2Am/7zBtaZVN7kAiIj2wC54o0uSlL2fAwOAkSmlVYMJiYj9ImL3iCiKiM7AtcBDKaVVy5r8DjgvInpFRE/gP4EbGjm7JEmSJEmStM1xgIa22NwP3+WjX4/m+ZsvzTuKJDWG64GvRUS3iOgIfB34E3AnMCgiRkVECXAJMCWlND3HrJKkFi4i+gBnA8OADyOiuvA6DdgZuJ/a2ZymAkuAU9c4/ZfAROClwv4/F7ZJkiRJkiRJypBLnGiLlXboxOvd96H9Lp/KO4okNYbvAV2A14DFwO3A5SmlxRExCvgp8HvgKWB0biklSduElNI7/HPJkg25ZRPnJuCbhZckSZIkSZKkRuIADW2xknalfOqsq/OOIUmNIqW0DPhK4bXuvgeBPRozz8oVK1iyeCFt25c1ZllJkiRJkiRJkiRtIZc4aUwrlsGMx2HxJ5mW+WTREh64705efeudTOtIkvLz9K0/4M3/PZa5H76bdxRJkiRJkiRJkiTVgQM0GtP7z8F934RX7sm0zJTnn6TPM9/j5fuuy7SOJCk/23XpQ01pH9q0Lc07iiRJkiQ1KRExMiLGV1ZW5h1FkiRJktbiAI3G1H0QfPpc2OXwTMvsOmAYL/c5gx77n5xpHUlSfoYfdQb7nXsjZeWd8o4iSZIkSU1KSmliSmlseXl53lEkSZIkaS2t8g6wTWlTCkNPybxM907lnHDm1zOvI0nKUeVMmPs69BsBRY63lCRJkiRJkiRJauq8o9OI3pz5AXeO/y7PTX4x7yiSpObuuRvgb5fBghl5J5EkSZIkSZIkSVIdOINGI3pr2jMM/PBOpr3Qlr2HDc07jiSpOdvr89B7f9i+T95JJEmSJEmSJEmSVAcO0GhE+xxwBC+0as0+g/bJO4okqbnr2Kf2JUmSJEmSJEmSpGbBJU4a0falbTn08KPZsXuXvKNIkpq5v778IZfcPZWqxcvyjqIN+WQW3HMuzHwu7ySSJEmSJEmSJKmJcIBGS7RwPvz5fHj70byTSJIyMmNuDa99VMWipSvyjqINWfQxzH0NFrybdxJJkiRJkiRJktREuMRJS7R4AcyeBt0HQr+D8k4jScrAFw/cmdH77kRZSeu8o2hDug+EU2+FkvK8k0iSJEmSJEmSpCbCGTRaok47w+ibYfgZeSeRJGWkuCgaZXDGnAXVPDtlCkuXr8y0zorly/ngnVdJK7Ot01jSypV8OGcOy5e7BI0kSZIkSZIkSarlAI2Wqm1HKCrOO4UkqZl77A9X0/buL/PU049nWufFB27kkxs/z6vP/T3TOo3ljSmPU/m703jhvv/LO4okSZIkSZIkSWoiXOJEkiRtVPu+FbxV9T579OqbaZ3Ou+zFrPf2Y9deu2Zap7F07rkzr3bdjx36V+QdRZIkSZKkbd6yZcuYOXMmixcvzrzWypUrKSrK99nYppBhS3OUlJSw44470rq1y/pK2jY1Zp8FzbvPaCo56tt3OUBDkiRt1JFHHA1HHJ15nT67D6PP7j/JvE5j6dStF586u+W0R5IkSZKk5mzmzJmUlZXRt29fIiLTWitWrKC4ON/ZrZtChi3JkVJi3rx5zJw5k379+mWYTJKarsbss6D59hlNJceW9F35D0ORJEk28QP1AAAgAElEQVRN1nvzFzLhufdYvGxFpnXmznqHp++4lqrK+ZnWaSzvzKvhkrun8uSb8/KOIkmSJEnSNm/x4sV07ty5UW50actFBJ07d260p8YlqSmyz2petqTvcoBGI0pLqvnouT+xrPLDzGu9O29h5jfTJEkt34OvfMQNj8/gjdnVmdaZ8ez9dJ72G96d9mSmdRrLE2/O49HX5/KnKbPyjiJJkiRJksAbXc3Etvo5RcTIiBhfWVmZdxRJTcC2+t/C5qq+n5cDNBrRu4/exOJ7L2bG3T/ItM4bs6v5+m0vcPNT72ZaR5LU8p04fEcuO24Qe/bokGmdPQ8bzbIjf8RuFYdnWqexHDVwB84+eGe+dNDOeUeRJEmSJEk5W7BgAdddd90WnXvMMcewYMGCTR5zySWX8OCDD27R9dfVt29f5s6d2yDXUt2llCamlMaWl5fnHUXSNq659VkHHXTQWtuGDRvGoEGDAFi4cCGnnXYagwcPZtCgQRx44IFUV9c+jFpcXMywYcNWv6644ooGyVQXrRqtkti+397MebkfpbsemGmdHcpLOHT3buzbr2OmdSRJLV851ey93buQysjyfxvalZazx35HZnb9xtax/XaM3nenvGNIkiRJkqQmYNXNrq985Svr7dvcWvf33nvvZq//3e9+d6vySZK0SnPrs6qqqnjvvffo3bs3r7zyylr7/vd//5fu3bvz0ksvAfDqq6/SunVrANq2bcvkyZMbNEtdOYNGIyrfpYL+595Dj0+dnGmd0jat+NrhuzKo1/aZ1pEktXAp8cHN/84bN/wbC564Pu80zcv8t+Dm0fDWw3knkSRJkiRJObvooot48803GTZsGBdccAEPPfQQhx56KP/6r//K4MGDATj++OPZe++9GThwIOPHj1997qoZLWbMmMGAAQP48pe/zMCBAznyyCNZtGgRAGPGjGHChAmrjx83bhzDhw9n8ODBTJ8+HYA5c+ZwxBFHMHz4cM4++2z69Omz2ZkyrrrqKgYNGsSgQYO45pprAKipqeHYY49l6NChDBo0iNtuu211G/fcc0+GDBnC+eef37DfQElSo2nsPuvSSy9l+PDhDBs2bIv6rJNPPnl1X3TLLbdw6qmnrt73wQcf0KtXr9Xvd999d9q0adOA360t4wwajeivU97lhUfuoceeB3D6ocOyK7RyBbz9MHQbCGXdMyuTUuLJt+azU+d29Nq+bWZ1JEk5WrmMlSsTafnSvJM0LytXwtIa8PsmSZIkSVKTctnEabw865MGveaePTtw6ciBG91/xRVXMHXq1NVP6j700EM8/fTTTJ06lX79+gHwm9/8hk6dOrFo0SL22WcfRo0aRefOnde6zuuvv84tt9zCr371K04++WT++Mc/cvrpp69Xr0uXLjz//PNcd911XHnllfz617/msssu47DDDuPiiy/m/vvvX+uG2oY899xzXH/99Tz11FOklNhvv/0YMWIEb731Fj179uTPf/4zAJWVlcyfP58777yT6dOnExGbnd5eklQ321Kf9dOf/nSL+qyTTjqJMWPGcP755zNx4kRuuukmbrzxRgC++MUvcuSRRzJhwgQOP/xwzjzzTHbddVcAFi1axLBh/7xff/HFF3PKKafU8bu4dZxBoxG9+NTfGV35ayon383CpSuyKzT7FfjrpTDl1uxqAO8vWMQV973C7/4xI9M6kqScRNDj1OvY9fSr6XjgWXmnaV669Icz74Hdjsg7iSRJkiRJaoL23Xff1Te6AK699lqGDh3K/vvvz3vvvcfrr7++3jn9+vVbfTNp7733ZsaMGRu89gknnLDeMY899hijR48G4LOf/SwdO256ifTHHnuME044gfbt21NaWsqJJ57Io48+yuDBg3nwwQe58MILefTRRykvL6dDhw6UlJRw1llncccdd9CuXbv6fjskSU1Yln3WiSeeCMDw4cO3qM/q1KkTHTt25NZbb2XAgAFr9UHDhg3jrbfe4oILLmD+/Pnss88+q5dBWbXEyapXYw3OgJxm0IiIh4D9geWFTe+nlHYv7Dsc+BmwE/AUMCal9E4eORvannsfzK0Pz6PzngfTbrtiqrN6qLbr7nDIxdBjaEYFavUsb8s3jtiNfl3aZ1pHkpSj0q5EadfMyyxetoI3ZlczoEcHiosiszrLli7hnVeeYac9KtiuTUlmdVi5Ej56CbrsBq2dZUqSJEmSpKZiU08NN6b27f/5e/WHHnqIBx98kCeeeIJ27dpxyCGHsHjx4vXOWXNa9uLi4tXTxW/suOLiYpYvr70Nk1KqV76NHb/bbrvx3HPPce+993LxxRdz5JFHcskll/D000/zt7/9jVtvvZWf/vSn/P3vf69XPUnS+uyz6uaUU07h3//937nhhhvW27dqkOGJJ55IUVER9957LwMGDKjX9Rtag82gERFn1POUr6aUSguvVYMzugB3AN8BOgHPArc1VMa8HTN8Zy74+nl86ah9iMju5hPFrWGPY6C81+aP3QpFRcEhu3ejT2cHaEhqnrag71JGHpj2IRf+cQrPzpifaZ3pT/yZuOdrvPzonZnWYdbzcM+5MPWP2daRJEmSJElNXllZGVVVVRvdX1lZSceOHWnXrh3Tp0/nySefbPAMBx54ILfffjsADzzwAB9//PEmjz/44IO56667WLhwITU1Ndx5550cdNBBzJo1i3bt2nH66adz/vnn8/zzz1NdXU1lZSXHHHMM11xzzepp8SVJzU9z7LNOOOEEvvnNb3LUUUettf3xxx9ffe7SpUt5+eWX6dOnT4Pnra96z6AREXtuaDNwNvC7rcxzIjAtpfSHQq1xwNyI2COlNH0rr52/j6YRj/4YBp8Cux+1+eMFwMqViVufeZedOrXjwF2zf4pbUsuTcd+lBrB3306cVLOU3Xcoy7ROzz325fVZJ9F/z09nWocuu8KwU6HPAdnWkSRJkrSeiBgJjOzfv3/eUSQJgM6dO3PAAQcwaNAgjj76aI499ti19n/2s5/lF7/4BUOGDGH33Xdn//33b/AMl156Kaeeeiq33XYbI0aMoEePHpSVbfz3MMOHD2fMmDHsu+++AJx11lnstdde/OUvf+GCCy6gqKiI1q1b8/Of/5yqqiqOO+44Fi9eTEqJq6++usHzS5IaR3Pss8rKyrjwwgvX2/7mm29yzjnnkFJi5cqVHHvssYwaNQqARYsWrV6CZVW7rrjiigZvy4ZsyRInTwITqL2xtab6Djf574i4AngV+FZK6SFgIPDiqgNSSjUR8WZh+3oDNCJiLDAWoHfv3psczdMkzHgB5s6CN5+Anp+mpqYm70SZaqj21SxZzqSX3mWnzu0YukOG09HXU0v//KDlt9H2bVMaqu9SRnpt35YvHNBv8wdupc7dd6Tzqd/KvA4l5bDfv2VfR5IkSdJ6UkoTgYkVFRVfzjuLJK1y8803r/X+kEMOWf11mzZtuO+++zZ43owZMwDo0qULU6dOXb39/PPPX/31mlO6z5gxgxUrVgBQUVHBQw89BEB5eTl/+ctfaNWqFU888QSTJk1aa/r5desBnHfeeZx33nlr7T/qqKPWe0IZ4Omnn95gfklS89OYfdYqW9tnrdK3b9/Vtc844wzOOGPDE6mv6ivzsCUDNF4BLkgpzVtzY0T8uR7XuBB4GVgKjAYmRsQwoBSYs86xlcAGh8SklMYD4wEqKirSpkbONAlDPwcdtodew6GQtcln3koN0b6yMrjkxL0pbdOKsvbbNUCqhtPSPz9o+W20fduMhui7tkl3v/A+D7z8Id84Ynf6dyvNrtD8t2DqHbD3GGjfJbs6jWTBwqX8/sl3OXrwDuzSNcPvm6RmLyL6pJTe2YLz2gDXAZ+hdnnIN4D/SindV9h/OPAzYCfgKWDMqjpRu97iFcBZhcv9H3Bhqu8Cn5IkSZKajXfffZeTTz6ZlStXst122/GrX/0q70iSJG1QS++ztmSAxhHAeo9lp5SO3cCxG5RSemqNt7+NiFOBY4BqoMM6h3cAmvjUGHVU0oGanT9Lu+2K13uEu9lauhBatYGi4kzL9O7ULtPrS2rxtrrv2lb94615vPZRNa99WJXtAI1Zk2H6n2qXBGkBAzTembeQB17+kB3K2zhAQ9LmvAB0iohzU0rX1uO8VsB7wAjgXWr/PXV7RAym9t9Vd1A7AGMi8D3gNmDVnJNjgeOBoUAC/gq8Bfxiq1sjSZIkqUnaddddeeGFF/KOIUnSZrX0PqtoC86pAY6LiOMjYvUAj4j4f1uRI1E77fw0an9JuOqa7YFdCtubvadfmMwzV/0/7r33nmwL1cyFO8+B1x/MtsysV3jjf4/lzZu+nmmdRpMSPHgZPOr6eFILtNV9V0Q8FBGLI6K68Hp1jX2HR8T0iFgYEZMiosUsnfIfh+/Kfx0zgMMGdMu20ICRcMJ42Knh16vLw5Ady7n6lGF8bmivvKNIaoIi4rmIGB8R5wCrRjqPq881Uko1KaVxKaUZKaWVKaU/AW8DewMnAtNSSn9IKS0uXHtoROxROP1M4McppZkppfeBHwNjtrphkiRJkiRJkjZpS2bQ+B21v/hbDlwUEaenlN4AzgH+sLmTI2J7YD/g4cI1TgEOBr4OzAf+JyJGAX8GLgGmpJSmb0HOJqemegFdV8zmk6qPsi20tAYq34PqDzIts3zhJ6xYtpiiqtmZ1mk0aSXMewPadc47iaSGt1V91xq+mlL69ZobIqILm35KORNp5UqWLl1Mm5JsZxjq2W4lPTt/DK26ZlqH4tbQdbdsa6yybDG0Lsm0RETQa/u2tC5uMXNmSWpYJ1E7kKICaBcRzwNtIuJQ4PmUUmV9LxgR3YHdqB3cfg7w4qp9KaWaiHgTGAhML/z54hqnv1jYtqHrjqV2xg169+5NVVXLmNywsdXUrDeRV4uua+0t1KYnFH7GurdN9f55q3ftNeptTF1z+Hd826ktSZIkSdo6WzJAo2dK6TSAiPgtcH1EjKvH+a2B7wN7ACuo/QXh8SmlVwvXHAX8FPg9tWslj96CjE3SwQeM4I2+d3Bk94wHAHTsA6f9AVple/OpfJd9Kfni9bQu655pnUZTVAyj/g/Cm2lSC7S1fdemrH5KuXD9ccDciNgjywGGT93237Sf8QA9vvB7uuzQO6sy8PfL4b0nYcRFsNuR2dVpLDOfhQe+A4d/B/p8OrMyr39UxbfvmsoXD+zHUQN3yKyOpGarKKU0AZhQGABxHPAq8DVgr4hYnlLata4Xi4jWwE3Ab1NK0yOiFJizzmGVQFnh69LC+zX3lUZEpJTSmiellMYD4wEqKipSWVkZ2jJ5fe/y/MysXU9LZkHh3I8WxRZdp17nrFFvY+qTw7/j205tSZIkSdKW25IBGm0iok1KaUlK6e2IGAncAgyqy8kppTnAPpvY/yC1gzdanGJWsnvr2dT+XnRLvvV1k1LitQ8r6dmlmLK222VWhwja7NBIH9WyxbVPVxcVb/7YrbCEYooIWmdaRVIOtqrvWsN/R8QV1N5E+1ZK6SHWeQp5A08pr9agTyF32JFPynan84r6P11ZLyU7wHbdgPZQVdX8n9Zb3ho67AorSzb4xGiDtW/5IvqVF1NatKxJPW3e7D+/zWjp7YOW38aW3r413BwROwEvAyVAR2BxSulEgIjoVNcLRUQRcCOwFPhqYXM10GGdQzsAVRvZ3wGoXndwhiRJkiRJkqSGtSWjBM6j9heIHwKklKoi4jjg1IYM1iJNuxOe+Cn0PwIO+1ZmZZ5+4QXa3Xsuf93hXzjxrIsyq9Noli6E28+A7oPgiHGZlVmxMvEft06mY7vW/PeJQzKrIykXDdF3XUjtjbSl1M7uNDEihlH7FPKmnlJerSGfQt7/2DOBM7f4/Dob8TVY+gVo88+szfppvbKh0PfaTR/SAO0rKyvjitHdtvo6WWjWn18dtPT2QctvY0tvH0BKab+IaAUMBh6jdgbBsoj4OfB84TV/c9eJiAD+D+gOHJNSWlbYNY01OomIaA/sUti+av9Q4OnC+6Fr7JMkSZLUBJSWllJdXc2sWbM499xzmTBhwnrHHHLIIVx55ZVUVFRs9DrXXHMNY8eOpV272mVyjznmGG6++Wa23377rco3btw4SktLOf/887fqOpKk5q859FmXXXYZr7/+Ov379wfg6quv5rzzzuOZZ56hoqKC3/zmN1x99dVEBCtXruTyyy/nuOOOY8yYMTz88MOUl5cD0K5dO/7xj39sVZ6iuh4YEYdHxAPAd4GLImJMROwVEa1TSitSSr/fqiTbgvZdobgNlGa7JEhZ2fbMbt2Dou17ZVqn0RS3hs79oWPfTMsUBezStZS+ndtnWkdS42nIviul9FRKqaowC8dvgceBY9j8U8rNW8RagzOy8smCebz0yN0sWbww0zqLlq7g0dfnULNkeaZ1WL4U3pwEiz7Oto6kZiultDyl9AKwNKV0MFADPATsCvywjpf5OTAAGJlSWrTG9juBQRExKiJKgEuAKWssvfU74LyI6BURPYH/BG7Y2jZJkiRJang9e/bc4I2uurrmmmtYuPCfv2+59957t/pGlyRJG9KU+6zBgwdz6623rn4/YcIE9txzTwBmzpzJ5ZdfzmOPPcYLL7zAk08+yZAh/3yY/3/+53+YPHkykydP3urBGVCPARrAb6l9ums8sAA4Abib2htTqoudR8Dn74R9vpRpmT133ZkDzruV40adnmmdRlPcGo6+AirGZFomIrjgqN05e8QumdaR1Kiy7LsSEPzzKWRgg08pqw5ee+R22j90CW88NynTOk++PY/v/ellHn9jbqZ1mPUCPPBteGVitnUktQTfKPyZUkq3pZS+mVL6zOZOiog+wNnAMODDiKguvE4rLCs5Crgc+BjYj9rZn1b5JTAReAmYCvy5sE2SJElSBi688EKuu+661e/HjRvHj3/8Y6qrqzn88MMZPnw4gwcP5u67717v3BkzZjBoUO0qvYsWLWL06NEMGTKEU045hUWL/jlO+5xzzqGiooKBAwcybtw4AK699lpmzZrFoYceyqGHHgpA3759mTu39vciV111FYMGDWLQoEFcc801q+sNGDCAL3/5ywwcOJAjjzxyrTobMnnyZPbff3+GDBnCCSecwMcf1z6w8pOf/IQ999yTIUOGMHp07T9JHn74YYYNG8awYcPYa6+9mtTSsJKkxu+zLr30UqC2z8iyzzr++ONXZ37rrbcoLy+na9euAMyePZuysjJKS0uB2hlB+vXrt+XfxM2ozxIn26WUvlv4evXQl/qsj7ytW7liBTNee4meuwyipG12szSklSv58I0X6Lpjf9qXZTcSdub77zP1zz+jqNvufOZzZ1BUFJnVev+tabQr60THrj0yqyGpRWqQvisitqf25tbDwHLgFOBg4OvUTkH/PxExitobXOs+paw62Hn/z/F2qzYMGHpgpnUq+nTkPw7flf36dc60Dj2GwMHfhJ32z7aOpGYvpXRD4cud63neO9QOFNzY/geBPTayLwHfLLwkSZKkbct9F8GHLzXsNXcYXPuQ4UaMHj2ar3/963zlK18B4Pbbb+f++++npKSEO++8kw4dOjB37lz2339/Pve5z1G7muH6fv7zn9OuXTumTJnClClTGD58+Op9l19+OZ06dWLFihUcdthhTJkyhXPPPZerrrqKSZMm0aVLl7Wu9dxzz3H99dfz1FNPkVJiv/32Y8SIEXTs2JHXX3+dW265hV/96lecfPLJ/PGPf+T00zf+QOgZZ5zBT37yE0aMGMEll1zCZZddxjXXXMOPfvQj3n77bdq0acOCBQsAuPLKK/nZz37GAQccQHV1NSUlJXX+NkvSNmcb6LMOP/xwpkyZwte+9jWuueaazPqsDh060Lt3b6ZOncrdd9/NKaecwvXXXw/A0KFD6d69O/369eOwww5j1KhRjBw5cvW5F1xwAd///vcBGDhwIDfddFMdv9kbtskZNCJiWGFKXICbI2K9J7lSSptdG1m1Xnt+Eivu/Aov/eWGTOu8++oLLP3jOUy5+9pM60z9+830n/0AO077Je/MmpVZnarK+VTecjav3XphZjUktRwZ9V2tge8Dc4C5wNeA41NKr9bhKWXVQaduvdj7mC/SrrQ80zplJa05dkhPytu1zrQOrdvCwOOgLNtlzSS1HCkl10SSJEmSWqi99tqL2bNnM2vWLF588UU6duzITjvtREqJ//qv/2LIkCF85jOf4f333+ejjz7a6HUeeeSR1TedhgwZstb067fffjvDhw9nr7324uWXX+bll1/eZKbHHnuME044gfbt21NaWsqJJ57Io48+CkC/fv0YNmwYAHvvvTczZszY6HUqKytZsGABI0aMAODMM8/kkUceAWqnkz/ttNP4/e9/T6tWtc8LH3DAAZx33nlce+21LFiwYPV2SVLT0Nh91rRp0xqtzxo9ejS33nord911FyeccMLq7cXFxdx///1MmDCBXXfdlW984xurZ6OCtZc42drBGbD5GTTuAXpExNvAW8BpEfF94E8ppTe3uvo2pscuQ3ml7+foPeSQTOt022k3Xtz5OHYYdlSmdTrusg8L3/sT89r2o6JTdk8ity8tp2qPUbTv3j+zGpJalAbvuwqDMPbZxP6NPqWsunlr6lPMfuiX9D3uv9iht/+9lyRJkiRJGdnEU8NZOumkk5gwYQIffvjh6uU+brrpJubMmcNzzz1H69at6du3L4sXL97kdTb0pPLbb7/NlVdeyTPPPEPHjh0588wzN3ud2on1NqxNmzarvy4uLt7sEicbM3HiRB5//HHuuecevve97zFt2jQuuugijj32WO699172339/HnzwQfbYw1+rSdIGbQN91pgxYxqtzxo5ciQXXHABFRUVdOjQYb2s++67L3vvvTdHHXUUX/jCF9YapNGQNjmDRkppJ6Ar8CVq1yi+g9pp3V+IiMqIeCwifpZJshaovFNX9v/8pfTaeWCmddq2L2P/f/02/fbc6L3EBrHfpw+h11fv41P//mvK2mU3DVlRcTH7jfoGgw4cufmDJW3z7Luap8pZr7P9gpf5ZM77eUeRJEmSJElqcKue2p0wYQInnXQSUDv7RLdu3WjdujWTJk3inXfe2eQ1Dj744NVP7k6dOpUpU6YA8Mknn9C+fXvKy8v56KOPuP/++1efU1ZWRlVV1Qavddddd7Fw4UJqamq48847Oeigg+rdrvLycjp27Lj6SeYbb7yRESNGsHLlSt577z0OPfRQfvSjH7FgwQKqq6t58803GTx4MBdeeCEVFRVMn+4qwZLU1DRmn3XfffetPifrPqtt27b88Ic/5Fvf+tZa22fNmsXzzz+/+v3kyZPp06dPva9fV5udOyqltAB4tPBaLSL6AUOAwdlEU5O3ciWdP3wUOveHzrvknUaSVrPvan6GfeZf+XjYoXTq1ivvKJIkSZIkSQ1u4MCBVFVV0atXL3r06AHAaaedxsiRI6moqGDYsGGbnUninHPO4Qtf+AJDhgxh2LBh7LvvvgAMHTqUvfbai4EDB7Lzzjvz6U9/evU5Y8eO5eijj6ZHjx5MmjRp9fbhw4czZsyY1dc466yz2GuvvTY5NfzG/Pa3v+Xf/u3fWLhwITvvvDPXX389K1as4IwzzuCTTz4hpcQ3vvENtt9+e77zne8wadIkiouL2XPPPTn66KPrXU+SlK3G7LMOOOCA1ec0Rp+1akaQNS1btozzzz+fWbNmUVJSQteuXfnFL36xev8FF1zA97///dXvn376abbbbrt6114lNjUlSHNSUVGRnn322bxj1EtVVRVlZWV5x9hyC96FP4yBvgfCEd9db3ezb99mtPT2Qctvo+1ruiLiuZRSRd45stRc+q35VYt4Z+a7DNptV1oXF2X292rlihXM/2gmnXfoTRRtcoKvTDXnn5u6sH3NX0tvY3Nt37bQb0Hz6buaorz+buf5M2XtLTCuHMZVAtD3oj8z44pjs629Rr2NqWsO/443v9r2XXXXFP7/pClkMEfTzNEUMjSXHK+88goDBgxolBwrVqyguLi4UWo15Qxbk2NDn5d9V900h59Hc2y7GcxRtxyN2WdB8+8zmkqO+vRd+d0BUfNX3huO+B7sOzbvJJKkjDx821VsN+FMnnrqiUzrTH7g98z9zcm89vxDmdaRJEmSJEmSJEnKiwM0Wqol1bByZaYlEvBeh72oatM90zqSpPyU7DiUGW33pMsO2S490qnvQBZ0GkrHHv0yrSNJkiRJkiRJkpSXVnkH2JbMq17CDf+YwXHDetK/W3bT5ix85a/Mv+/7tOk9nK4nXQURmdT5+9PPUzLpUp5sO4Ajx/6A8ratM6kzv2Yp3504jR07tuM/j9yNyKg9kqT1Hf3ZfyEddWzm/+3tO6CCvgNa/CyVkiRJkiRJkiRpG+YMGo3ovY8X8chrc5j6/ieZ1ln43ossWljDslkvwcoVmdWZP/N1dlj+AX0Xvsi86iWZ1fmwcjHvzFvIC+9+zLIVKbM6ktScvPf6izx/7/UsX7Y020JvPUI88B2ofD/TMpPf/Zjv/Wkab8+tybQOixbAi7fDwvnZ1pEkSZIkSU1KSv5uuTnwc5Ik/1vY3NT383IGjUY0dMdyfnLqcHpsX5JpnS4HfpGiNqWU9quA4uw+4oMOG8lDK5az/Q47U9GlfWZ1BvQo4+JjBtCpfWu2a+WYIkkCmPnwDXT+8DE+2mNfeu08MLtCL94MM5+BXntB+ajMykyc8gF/e2U2vTu1p1+GfQozn4XHr4Y27eH/s3ff8XVXhf/HX+fu3ORmjzZN0qR70B3aUgq0bBCU4UBFQQVxouj3+9Xv9+fAjYriQL+I4kC27C2U1RZaSuke6U5H2mbvecf5/ZH0a6mIUO5JmvT9fDzyeHBzb+7rfG5Cbno/554z4T3uOiIiIiIiIiJyzAiFQtTX15OTk6MVmo9h1lrq6+sJhdyeQxEROZbpOWtwOcsSkc4AACAASURBVJrnLk3Q6EfGGEpywk4b1loe2NTKmopM0huiXJMXdbb1yLCsVC774OVO7vtwxhhml2U774iIDCZjL/gKdXvOp7B0otvQvC/C7mUwaqHTzMfmjqQkO8x5Jwxz2qF0Ppz3Exgxy21HRERERERERI4ZRUVF7Nu3j9raWuetRCKBxzOwbzQ8FsZwtOMIhUIUFRU5GtGxyxhzIXDhmDFjBnooIjLA+vM5Cwb3c8axMo53+tylCRr9qWEnPPNNmPNZKJvvJFFZ38HBJX/hyu6nqW7I5uH8X3LFyaOdtOhqhse/CqMXwoyPummIiMibyh1WTO6wYuedeN4kWiNjyQwHnHZGpsPFwxuJhAuddqwvSGP+HLL8fjT3WEREREREROT44Pf7KSsr65dWa2srkUikX1rH8hiOpXEMBtbax4DHysvLrx7osYjIwOrP5yw4dn5XH0/jGPhpKMeTeKx3UkO0010ikcBno3iw+IkSiyectUjEoasJulvdNUREZEDd/Pw2rvrLSlbsqnfaqfjrV9h/52fY9uiPnXZeWPISW371PhY9/6zTjoiIiIiIiIiIiIiIyJG0gkZ/yhsHH38EPF5nidF5aSwp/xh/XlOMyRnNF2eWOGsRzoaP3AtG83xERIaq9p44sYSlsyfutGOjnXhsgnh3h9NOtKuDvHgLDV1tTjsiIiIiIiIiIiIiIiJH0gSNftbQGScr7MEYNwurG2P4xJzhxHOG4ckvwqQFnXQAGppbeP25vxEpGMWceQucHRPxKB2b/44vkkdg5Bw3jT7t3TG8HkPI724SjYjIYPKVs8ZxsLmLkTlhp50xH/kZdTtWUTBxntPOmWeex+6pJ3F2XobTjoiIiIiIiIiIiIiIyJE0QaMfbahq5tuPbuSTJ5fynqmF7kKr7sC77l4IZ8FH73e2YsfKJ/9E2Y6/0l0RZu+o+ygZPsxJp2vL8+x/5Lv4gymM/PQ9kD7cSSeesHzpntVkhgPc+IFpThoiIoNNyO+lNDfVacNay5Lnn8C74xl2NSc4ecF57mIdDQR2LYLwQkjPd9fpL9ZC3VbIKIKA2++TiIjI8eLkG56nqunotiYtSLFUdx7dmxcqQ1D69ScAGJGZclT3ISIiIiIiIiLHNk3Q6Ee5aUEmDU93/i5kMovBH4SMYqfbjwSyiugxQVp9WRSmpjnreDOGEUhJwxfOcHryyWNgRkkmkZDfWUNERP5ZbWMLwzf+HrwBWpb9ivip5+L1uFmVafeiW/Cvu5M9VRsY/f7vOWn0q7pt8PDnYNL74ORrB3o0IiIiQ0JVUyeVN7znqL62tbWVSCRydOHrOequiIiIiIiIiAwOmqDRj4ZlhPjeRSe4D014D4yYBeFscLXtCHDq2Zewbcx0Rmdnk53uboKGf8Q0Sq65D3whCLrrGGP4/MKxzu5fRETeXHpaKptSx1HQXkFL3hwczc0AIHPiAhr2ryFz4unuIv0powgmXwyjFgz0SEREREREjhnGmAuBC8eMGTPQQxEREREREXkDTdAYgrZs3czuv/8GM3IuZ773coyjSRrRXS+T+fcf4skdDRff4Gx1i6qGVl54+E68kWFcfNH7CfndbNnSn15dtZpYtId5s2c7+/6IiCSDTSSIx2P4/AFnjVDAx/QPfZMDm5ZyyszznP5ezB5/MuljTsLndbfCVL8KhGHeFwZ6FCIiIiIixxRr7WPAY+Xl5VcP9FhEREREREQON0TOTsjhdr7wF0a1rmDEhv+ltrndWafplT+yu7GT+h2r4MA6Z53XX13C3P23M23rL6nYV+us01/q27qxT/83kUX/wZ661oEejogMUttWL2bZXd+nu6vDaWf5n/6LrT89nfVLHnXWsB0N1Nz9Wezy37Hvrs9BrMdZ64VFT7D8Jxex6OkHnTUAmhtqWf7X77Bv+wannb0NHVx37xoeXl3ltCMiIiIiIiIiIiIiIu+eJmgMQZEJC2kzEXZnn0xWJOysEx5zCvmhBOmZOZA9ylmndNwUNodmsDb9dEryspx1+ktmOED1hI+xa/THGZblZtURERn66lY9StbOx6k/uNdZwyYSRKpX4Et00br9ZWedaFMVsc42mr1Z2NYD0O1u8lrHtiWURrfTs32xswbAwZ3ryal8lP3rXnDa2XKwlfVVzbywpcZpR0RERERERERERERE3j1tcTIEzV9wLq1zz2BKwIfX426Z+MjcKwmMWYAvNQvCmc46U0cXM+baW/B5PAR8g39OkddjeN8lHx3oYYjIIHfC+/+Hxuo9FJaOd9YwHg/eM75BfcVixp51lbNOoGACKaPnkbJnGb4TLoJwtrPWpAs+x7rloxh/4tnOGgDjZi6gMnwLU8smOe3MH5vL15jAuII0px0REREREREREREREXn3NEFjCOpu2MuBx39CxuSzKZh1obPOa5t3sP+JG2jKnMQHr/gSKQGvm5C1hHc8BelFMGKGm0afdS/cjzeUyuSTznMXSSRg+W8h1gXzrgVfwF1LRIasSEY2kQx3ExkOmTjnbChfCF6/u4jXT9llP4VoF/hD7jpAWXEJRYVX4fe6nfBX19bDjoYe4lldjElxt1pSyO/lrEkFzu5fRERERERERERERESSZ/AvRyD/pG7LMlIqn6f11b867ezcsJzp3SuZWvs4VU2d7kLdLbDsZlj5R3cNIB6L4V32S7qX3uy0Q2cDbHoEtjwFLfvctkRE3q31D8JfL4HGSred7c/DHZdC9UanmbV7m7j8D6+ysrLBaefvD/yBkhe/zLq7v01XNO60JSIiIiIiIiIiIiIig8OArqBhjBkLrAfut9Ze3ve5M4DfACXAq8CV1trdAzfKwWf49HPY01pH/riTnHZmnXw2i+v34C+YwKU5YXehUAac91OnS94DeH0+0i7+BZlBt+/eJpwDp3y1dwWNrDK3LRGRdysU6f3963W82k8wrbfjc/s7OOT3khn2E/I7WvWpjwmESeAh6g3jMe62GxMRERERERERERERkcFjoLc4+Q3w2qELxphc4EHgKuAx4HvAvcDcARndINVtUmjKno4/NIx0h50xOUFGnzYFExkGHscnn4ZPdXv/fUZOnOU+YgztpWeSsJaITtqJyLFu7Fm9H64Vz4YPuV35CWBsfho/vHgKmWG3E07ee+nH2Lj9VE4rKiTg04JlIpJ8xpgvAFcCU4C7rbVX9n2+FNgFtB928x9ba7/Xd70BbqD331wAtwFfs9ba/hi3yKBz0xRo3vP2bx8shO79R9fKKDm6rxMRERERERGRQWPAJmgYYy4DmoBXgDF9n74E2Git/Vvfba4H6owxE6y1FQMy0CSqaenilpd28IHyYiYOdzd1YvGdP6T44DPs92YS+PSd5GVnOem0Pnk9ByuWEQ4FGfGhn8MIR5Mb4jFY8jPIHQsnXOKm0Y9qW7t5/M8/JBDr4NSPX09xTupAD0lEZOB1t8L+NVA8B3xuJk/EE5YH7vgtpVWPsnvYuVz08S/h97qZPBGJNzO34wWIngaMc9IA2L+rgj1P/Rx/6Vxmnf9JZx0ROSbtB74PnAOkvMn1mdba2Jt8/tPARcA0wALPAjuBWxyNU2Rwa94D1ze//du3tkIk4m48IiIiIiIiIjKoDcgEDWNMOvBd4AzgU4ddNRlYe+iCtbbdGLOj7/P/NEHDGPNpel9gpLi4mNbWVpfDftf2Vrey60Ad2/aFKEoztLe3//svOgqeznpiwWx8eKitqSbkd/Ntbmlppi5QRLrtIr2hBtLf+Pgn7fh6OmDvBujohJH98C7ut+loj+9gfTul0UqCiU6qaurJDCSSPLLkcfUzeqzQ8YkcQ7Y8DS/fBOf+GMpOcZKobuli/L6/4fP5mXzgAaoarqY0L81Ji4onYfn/Qs1muODnbhrA7iV3klO/injjBnrO+AgB19t0icgxw1r7IIAxphwoegdfegXwM2vtvr6v/xlwNZqgISIiIiIiIiIi4txAraDxPeA2a+1e88YtHtKA2iNu2wy86dtPrLW3ArcClJeX28gx/i6VWZEIPy7IJictiLdvSxAXYx551meofP5PBIpnctr48RhH22ikLryG8As34c+dStr4UyHwzytBJOf4InDpL8Gf0vtxDDma4zshLY2lZ19PdyzOqeOL/+9n4Vh1rP9/9W7p+OSdMsaMBdYD91trL+/73Bn0bttVArwKXGmt3T1woxyERp0G1kLhdGeJnLQAy7JOZnLD82zOPIVzMxw+p4w6FeoqYMJ73TWA/BnvofnganpGzNbkDBE50m5jzKEVMv7TWlvX9/k3TIrv++/J/T04ERERERERERGR41G/T9AwxkwHzgRmvMnVbcCRe3+kA8f20hjvQF4k6GzCxCFl6ZBe4CdcYHBZerm7lCXRCwh1FfHJmJ9MNyvSA1Df2kEo7CHV9QSNjgYwHkjJdJYwxnDKCWXO7l9EnPsN8NqhC8aYXOBB4CrgMXonId4LzB2Q0Q1S8XAejaPfR04g4Oy5K+jzcv6Hv0zt1rmcPaaclIDXUQnIKoVzfuju/vuMnjIXRv0FQu62ThORQacOOBFYA+TQ+7x1J71boUDvpPjD92toBtKMMcZaa4+8s8G2auGxaqBWNhvIFdUGc7sgxf7jZz1Y2LttST+134133H4bxzYlP8Cc7zz+b+8qJ2ip73b3CkR+JMhdV//zn9eD6vEeIm0RERERERF5dwZiBY0FQCmwp2+iQhrgNcZMondZ3SsO3dAYkwqMBjb2+ygd2Fnbxjcf2chnThvFKWPz3ESspfbBr1Hb1ELK1sWkDRuHp3hW0jPt3TFefeoOPthxPy21YR7J+zVXnDYp6R2Aqp2babznGnr8GUy77iG8Pkc/tm218NA14PXDpbdB0NGy9yIyaBljLgOagFeAMX2fvgTYaK39W99trgfqjDETrLX/tD3XYLNmTyOPrz/A5xeMISvV3Uy8p+65mZJdf2PNrOs489yL3UQScRJPXIfdvxsqiuAjfwZf0Elqy8rniT7zHTonfYATL/qCkwYAW/8OS38OeRPhgpvA0STQA7u3UH3PF+nMncJJn/qZk4aIJIe1tg1Y2Xex2hjzBeCAMSbdWtvCP0+KTwfa3mxyRt/9DapVC49lA/XYDeT3bLC2qzvNP76+ez+8w/saNMcd8sHP3nqHpMfe5l212kIidv9b3yijBK5b/zbv8Y1Kv/7Evzy2QfN4D6G2iIiIiIiIHL2BmKBxK3DPYZf/g94JG5/tu/xTY8ylwBPAt4B1Q+EEF4DHGAJeg8fxChoYgwd634FsPE47FkMCDy536eidyGOwxuE7nXtDfR8eZye4RGTwMsakA98FzgA+ddhVb1gq3lrbbozZ0ff5iiPuY9C9C7libw0799exvzYbXyLV2bv1PE178foDdNbscve4xLpo7YhywOTj64yRaKqH4Btf2E7W8dVX7SDD66erttLt97lmD9hUaG6A5ibwvvWfdkd7fDVVu/F4vHhb9x/TP7fHw7tJh/oxDvXjGyCHJl4c+gN3IzANWNF3eRpDZEK8iLxDRzlZ4k21tv77iSzXZySvJyIiIiIiIjJI9fsEDWttB9Bx6LIxpg3ostbW9l2+FLgZuAN4Fbisv8foSmluKn/6xGy3EWMoeP+NeJfdRbh4Cp4R051kUoM+5p33MR5YPIyUrBFceeJYJx2AwrIJhD99L8GUVHerZwCk5vaunOHxQiDVXUdEBqvvAbdZa/cesVVVGlB7xG2bgX96hXowvgv5/XPHcvrUkeRHQv/3ORfjPuGiL7Nr4wrmli90+G7ACKmnXUVkzUOkTrkAT27hm98qCf1Z53yUHQXFTB07w+27G8s/BOkZkD8JMrPe1pcczXimzj2TLQEv+QUjj/l3ax7r40uGoX6MQ/34ksUY46P333NeelckDAExYBa9qz1tA7KAXwEvWmsPbWtyO/AVY8yT9E7e+Crw634evoiIiIiIiIiIyHFpIFbQeANr7fVHXF4ETBiY0TgW7YJ9K6DgBAhnO8u0+bLZHc8lx1dIxOFKEPOir1Bu/4onNhxffCKQ76TT1NLKq4vuJ5BZwKlnvx+vw+U6tm/diC8QpHRiubOGiAw+xpjpwJnAjDe5+sil4um7fOwuM/AOmK4mgge3QPjEf7s6w7tRMnw4JRnzIdXdOyu7o1GWvPQ8BTUbqG4IM3/UmYSCbrZt6YxamroSRLotLt8r2m7CLI+eQIktxN1USTAeDxPKz3BYEJGj8A3g24ddvhz4DrAF+CG9f5y3AM8CHz7sdr8DRgGH3jr/h77PiYiIiIiIiIiIiGMO97+Qf7LhAXjq67D0JqeZTU/+lsyNf6Hjif8mFu1xE0nE6XnlFl6v9VC9dztsfcZNB1j10iOUbv8LBa//gq3btjjrHNi9hZ7H/5P2h66jueHIN8OLyHFuAb3bce0xxhykd3uuS40xq/jHUvEAGGNSgdEMkeXid973P9Td+wUOvP6I21DFk3DPR6DqdWeJ7ZtWMbxmMdFgJsPrl7Fl3avOWq88dhv5S77Bxnu/6awB8NSS5RQ8dRUv3/sTovGE05aIHFustddba80RH9dba++21pZZa1OttcOttR+31h487Oustfa/rLXZfR//Za21b9USERERERERERGR5NAEjf6UNwEiw2C4m21HDkkpnEDME6A9Mhqvq3c7Gw+e9OGUBFpJC3p7j8uR9IIyejwptPqyyc7Oc9aJZOXTHi6iLTKalFQtrS0ib3ArvZMupvd93AI8AZwDPAScYIy5tG95+W8B66y1FQM12GQKFIzDE4oQzh3pNpQ1EoZNhbQCZ4mUtCwSxkso2oTFQ0ok01krmFtKtydMNGuMswZATm4euwNj6Mwch8/hClMiIiIiIiIiIiIiIvLuDfgWJ8eVolnwsQedZ6af/iGap5/O+PQsjMfRHBxjqJn3baqWP4g3cwRTyxbid1OifPbJ7C2+n8KUEDmZR+4ikDxp6VmceN19zu5fRAYva20H0HHosjGmDeiy1tb2Xb4UuBm4A3gVuGwgxunCyPO/Aud+CTxep514/mQaT/sBOakBXE0zGDV6HCtP+ybNFS8RGXsyk8ef4KgEp591IdWzFjAuM81ZA2DBtHHUjrqVM8N+jMNtzURERERERERERERE5N3TChpDUCIep2rLSloa3W3TUdfWzR2PPEl4y4O0rLyXOxZvcNYiHqX4wN/JqVvprgHU1tXz95uv5elbvkZza7vTlogMbn1LyF9+2OVF1toJ1toUa+0Ca23lAA4vuZqroOIJiHU7S0TjCX784BL+fOsv+M0z65x1ACaUjmBkSicTR45wOqFh14t/pfX357P9vv8HDncOiEV72LfsbzRUbXfWEBERERERERERERGR5NAEjSFod8VK/Iv+HxVP3eKsUVnXzuy2ReBPoYhqara97qxF9QZ47Q+w+CdOTxBuWb2Y0qZllNa9yPZNbieDiIgMGpsfhcU/hVp3O7bUt/WQuncxl3XdQ23FMuIJdxMaqlfcT2j7E9Qsv9tZA6Br3cO0xPx4dr0EPW3OOlU71pO+4iYqX7zdWUNERERERERERERERJJDW5wMQYWjp7Bh5ucZOXm+u0ZmCg+Eyjmz/XEaiBApmuSsRe54GH8+ZBSBL+gsUzKhnF1rRxHzhJgyZoqzjojIoDLlg5A/CfInO0vkRYIkxpzLbTuyKJuxEK/H3coWKdM+QHVrB3kz3e5Ckzbn43he/l8YfR4E3G1zUjx2OptP/w5jR0931hARERERERERERERkeTQBI0hKBgKM3rhx0gNuvv2FmamcPr7P8uqV8YQzB3JFfNmOmvFfSlsGvMZ0kI+ypxVoKS4mMLr7sYY4/TkIPCP5e4dLq8vIpIUqTkw6jSnCa/H8OULZtEdm0HI73XW2bRtJ40P/jeZ8Xr2PLiJhvf+hKkTJzhp5UxeQGvcT8HoqU5/13t9PsrKzyPF4eMmIiIiIiIiIiIiIiLJoS1O+lNnE7x6KzTudpp55pUVvPbrj3HXn35NTyzhrJO95R5mbfsF09d8i1BXrbPO3Us2sPuua1n8l+tZVdngrEO0C9/z38W75EZIxJ1l4h1NbP7th6n49SV0Nx1w1hGRIW7jw/Dw53qfW4YAY4zTyRkA2579PTmJOjqDeWQnGtn17K3OWuvu/B88L36PvbdfQ093l7POpm07ee2mD/HIfX901hARERERERERERERkeTQBI3+VLsFVv8Vdr/sNFOzdSUToxsprXuBpo4eZ53OnctoTwSIdjTTWb3dWad612YmxzZR3vUKOw82OuvQuh92L4Htzzo94dl2cAc0VkLLfpr2bHbWEZEhrvUgNO+DuLvf80NOrAtrev/0SRgv3nins5SnpwVjE/jiHSQcTvrr6GgjO14D7e4mSoqIiIiIiIiIiIiISHJoi5P+VFQO7/0V5I53mpm18BJefLaT9LJy8iJBZ52cUz+NefqHmOzJpJXOctaZO+80HnhqH9FQLldMKHTWIasMTv06BFJ6l/R3JGPkNOpmfZREtIv8ifOcdURkiJtzDZR/Anzufs8PNXlzP0znonUEu+roNl4isy931so455vsfPk+MiecSigl1Vln1tQp7Mx7iLOyM5w1REREREREREREREQkOTRBoz95vFA4w3lm4shhTPzEteDxOd33/kBoDKtyLyUlawTDTJCQo87JJSnMmj8cX2o2vnR3JyKbOqM8XFOG3+vhouExUoOO/vfw+hh93hfd3LeIHD+MGVqTM+JRaNgFuWOdPXfNmz2bdXyDjlX3EJr6fk6bd7KTzr6GNjY9cTMTO1dxoHYdr+WUceL4Eict01HP6D1/g9hMKJnjpNHvmvdBIBVSsgZ6JCIiIiIiIiIiIiIiSaUtTvrRgeZOvv7AOtbtc7d9BkDzztfZ+fOzqHzkB84aXdE4G+7/PuW7bmXsqu/x3FJ327a0P38je//+K6oe/S6Jbc8669y2aA0TlnyRohe/wt3LtjnrAPDKzfDij8Fatx0RkcFi82Pw0Kdhz3KnGf/qv5BZs5LAmj86a2yuqGB85yrag/kUxvdT8fqLzlqsvRvW3QfPfbd3kstg190GD10Di74z0CMREREREREREREREUk6TdDoR43tUbbXtFHV6G7Pe4DupoMkutuI1lc6a/TEE0RiDeD14yVBd2uDs1astZYu66cnFifR4a7T2dZMOu1k2SZaWtucdQCo3ggH12qChojIIYXTYcIFvStoOBQsm4vXHyQw0t1qE+nZ+XSYMJnRamLWQ0qOm9UzABg+Hfyh3sfPMwQWRvOHYdJFMP78gR6JiIiIiIiIiIiIiEjSDYFX8gePSYXp3HbliaSH3D7s+dPPI5xdQDhvtLNGesiPmfMZ9i3/NU3pIznplDOdtTLO+Aol9ka8kXx8E9/jrPOB0+dy55NfxeMP8Ml5k511ALjgJsCCR3OkREQAyB6Fnf8VjOPfi3bUQmp6QuSNme2sMXt8MUumf4LEpgfoKJjJ+QvcbKUCQNkpNGbfS1pqKn6H25r1G48HZl890KMQEREREREREREREXFCZ4f7WUaKH+P4BEp3TxeVVQeobXS32gQ9Hcw5cAdl3mpOir9O7v6XnKX22RwWRSfzXPckOk3YWWd8QRrfPquIb54xgpIcdx2ATdWdrN3fgdUKGiJylDZWNXPH8t30xBIDPZSkePXeH7PphlNZ/cwdzhpVVXtp+dvnyFp7Kx0PfoHdeyqddEzTbqZU/oVwdy0T654hXOVuG7DHX9/B/X+4gZvufozuWNxZJxpPcPeK3c63abOJBK898ls2vvyE046IiIiIiIiIiIiIyEDQBI0haM2jv8H/wvc4cOfnicdibiL7VlBbuZH98UwqOwK0Lvmtmw6w/NHfM2XPHYzd/GuWL1/irBOvWsOuO7/E7juvhfodzjqVde3svevztN79CTZWuT3RJSJD1zObDvLAqn1Ut3QN9FCSwr/7JfzxLrq3veCs0XBwD4FEN+3BAnyJHhoP7HITajlAS2c3DSaTxq44NOx00wF2btvMgq5nGVa9mMb2qLNOQ3sP979exZPrDzhrALS3NZO64S7aV97ltCMiIiIiIiIiIiIiMhC0xckQ5AmmkTBeYr4Ud6t1ePx4DCQSFj9x8LlbccLjTyFhPcTx4As57ARCeH1+PD4/+ILOOgGfh1ZvNlETYpjf66wjIkPbJ+eP4vwphRRlpQz0UJIi7cz/on714xSd+jFnjdLx01izuIyczl00pBRzwoSZbkL5E8nJL8TWHCAzLQNKT3HTAc457VQej/03haXjKUh399xVkB7ihkumkBdx1wBIS88i8qH/JT8t02lHRERERERERERERGQgaILGEDTrgk+zd9xsigrL8HgdTQAonk3u5NPwbXmZQCBI5tlfd9MBFl58NcsXFxGMZHNK+YnOOiZ/IsOv/BPG44WMImedwswU5l91I7FEgqIst1upiMjQlZHiJyPFP9DDSJpxMxdgp5+K8bhb3CuSlkb5uVfQuOoByqa/j5SMDCedVpPKQ90nMrnnCVZ2jWe2HUaJkxKMHxZh/AUnQmoeON5CLRLyE/C5X3yteMwU5w0RERERERERERERkYGgLU76kU0k2LttLT3dbpej93g8jMzPJBJ2d/I/bnz8Lv4+FtsZ/Nmezxo71lmruuIVRqz/DZGVN9NcW+Wss2rdBl7549d46bb/oWKno2Xv+wzztVPka3XaEBEZTDYsfYyNNyygcvNKd5H2OloX/Yjqys00L/oJtLjZruO1DRXMrH6AgM/H5LblLHnpKScdAPYsg/s/CYuud9cANu1v4dp7VvPNhzc47YiIiIiIiIiIiIiIDGWaoNGPtq56kc57PsXav//JbahuGzx0Daz8o7PE/qZOwjuf5pToy1zUdh/PrtrirNW69HdYDGmdVex85WFnnT1L7mBEfB+l0W1sXvyQsw4AT36193uUiLvtiIgMEl6fn5g3BY/X4eJerj/nBwAAIABJREFUxoPBg4dE7xZgxtGfQZ7e1as8No6B3pWZXPH4eo/DF3DXAHxegzHg9+pPRxERERERERGRZDPGXGiMubW5uXmghyIiIo5pi5N+lD9yIhUjzqRo4jy3ofRCGH8+lJ7sLJER9lMdLKOtM8Q+zwiG5eU4a8Wyx5G27znAkDpstLOOp2A88abFJPCRMmyMsw4AE98LPe3uTg6KiAwyE+eeC3PPddqwKVlUl/8nPRsfp2biueSn5eNiU5CTThjPk6svZMzBJ9mdOp0FC9wdV/ewGVSVf4vI8NHkOavAuIIIt3x0FuGgw8kmIiIiIiIiIiLHKWvtY8Bj5eXlVw/0WERExC1N0OhHWXnDOemTN7gPBdPglK84TaSH/HzskvexZMVw0rIK+NCcUc5aM8+9gt1P1eNLy6Vs5nxnnbMv+AhrAlGML8gZZ77HWQegoewC4glLnnFxalBEJHmaO6Nsr2ljRnEmHo/D31mNlbDzpd4Jhmluphos2VJNw5J7mRDbzPb6JqozpnDmCcVJ76Q07+CUjkXU2R6mxjZQUP0iZCf/eSUai7P09//B8KbXafKEafvArykbPT7pnd5YF1nbHoLccVA0y02jT8XBFtJDfgozU5x2RERERERERERERET6m96+PwQ1tHbw2AO3s25zhdNO8a57OHfHDzhz8/8j1L7fTaSrmer7riNatZ6erYuofvR6Nx1g/9M/JWP9n0lf8zsOvPh7Z531W7az8eYPsf3mS3l1zVpnHQBqt8LuV9w2RGRIe3DVPr796AY2HWhxG1rxe3j5l7DxQWeJim1bmRDbQluggDHxnWzbuslJxx5YS0NrO22hYdT2+Gjf8qKTTnVtDcMbV9IRyCUcb2XP+qVOOgDsew2W3QwvfN9dA2jrjvHNhzfwi0VbnXZERERERERERERERAaCJmgMQZvWrmD8xpvY8dyfnHY61j/OzvYAdXW1cHCdm0hzFa1trXQE82jw5BLftwqsdZKK71xMqy+bdhOha8sLThoAO9e9TH7sALm2lj1rX3TWAeDlX8Ci66HL8YlVERmyzpxYwJXzShmTn+Y2NPG9MPp0GLXQWWLY8CJqTA6RaC31JpO8wlInHZNVSiTow9/TTLqnm1DhZCednKwcmoKFpHTXEsVHdslEJx0ACib3fn+mfdhdA0gNePnU/DI+MrvEaUdEREREhjZjzIXGmFubm5sHeigiIiIiIiJvoC1OhqCJU8pZeuBayibOcdoJTzqXUe1/IxjOhWFT3ETSC0lLS8O21JJGHM+IOeBoWxBP6clENv8dMATHXuSkAVA25STqdj6ALxGleMqpzjoAzLsWOuoglO62IyJDVnF2mOLssPtQyZzeD4fOm1HKs3WfomPbMyRGnc57y8c66dgRs9gz9gp8Wx7lQMGpjJh6mZNOSijA+Eu/Rd3S2wiOmErpjJOcdAAIZ9M65zpCKan43VUwxnDuCcMdFkRERETkeGCtfQx4rLy8/OqBHouIiIiIiMjhNEFjCMpJT+V955wDKdlOO93TPsH+zlyC+aPJzijGybSJlEyKL/4ezc/diCecQ8Z7vuWiAkDRBV/n9dAYjD/E2IXuJmhMnTCOuhH3kUhY8jNSnHUAErnjidtxTk+miYgkw0svv0zL8tspOe86pk2a4KTh76hh+tabaWxtJ3N7BYH5syGjKOmdtVUttGx+jrJEPc37XuGp1bu4+CQHq1vEoyQWXU+85gAcfB07ZjLG0SSXLSufJ/bMt+hIKWTa524nEAw56cRjMVbc9R0CeaOZdd6VThoiIiIiIiIiIiIiIgNFW5wMQV2v3c6B2z5C872fgVi3k4a1lhfu/CHhFb8g/vh/sGr9eicdrKV9yc1UV+2mfufr9Ky9300HeOjpZ0lbfQuhFTfz5OJlzjrEozQ/9v9oeeir0NPhLNPZHeOp336VpT//KHuq9jvrALxW2cBtS3fRE0s47YjI0NW4ey1jOlZRvXeru0hHA93dXdSRTVd3D7TXOck0tnczPF5F1BsmlQ5aGt10iHYSa2+gniy6o1HibbVuOkDL/q34412EO/fT2dHmrNPd1U6kajHR7S85a4iIiIiIiIiIiIiIDBRN0BiCunavpL49SkddJXQ2OWkkLESat4AvRJBuGvfvctIhESfWsJsasmiOh+iu3uKmA3Qd3EKq6SHVdNJycLuzDt2t2D3LSexfTby12lmmrrmVkc2vUdi9nYN7HJ7wBF6oqOGp9Qdo6uhx2hGRoWvBez9Bx4V/4JQF57uL5I0ne/LpTExrJW/ifCiY7CQzoySLRXkfp9amszT1bObNnOakQyid9tEXUGoO4E0fTqzI3RYnk0//MM1TPoHnjG+RkZXrrBNOy6DwU3cx5cqbnDVEhgpjzBeMMSuNMd3GmD8fcd0ZxpgKY0yHMeYFY8zIw64zxpgfG2Pq+z5+YoyjPQRFRERERERERETkDQZkgoYx5g5jzAFjTIsxZqsx5qrDrvuXLybK25Ox4AsUTZpN9qnXQFq+k4bXY4jP+iRNNszO1GlMKj/VSQevj4bSCykwTaT5LB1j3W09Mmn+xWzxTWBzcBozTj7PWYdwNrmX/pTci36EN7vUWaYoL5Pm077L3un/wZSZ8511AD67YDQ/++A08tPdLHkvIgPHJhK0NNU772SmpTBr+jRSgg53X/N4WR+cyUY7ijWBmViPm1ZmOMBFC0+iJ28yJ86Zz9hh6U46r23YQv2GRbTYMN1NNTz4yAMkEtZJq7KxmzUNXlYejNEVjTtpHBIKpxEIut0CTGSI2A98H/jj4Z80xuQCDwLfBLKBlcC9h93k08BFwDRgKnABcE0/jFdEREREREREROS45/AsyFv6EfApa223MWYC8KIxZjWwm94XE68CHgO+R++LiXMHaJyDkskbj+89P8Xv94LDN8OdvfAMOkrT8GcMx5+b5aSxeEsN6yoaOTsRpct6uGNxJdeMKKcwM8knbqzlxPqHaY3sx2MMabVPwshPJLdxmHA4DRtzu9qEMYaT5y902jgkEvITCfn7pSUi/WvlI78hddM9RD74W4rHOloJop/UNDaR8eqNGI8X8/ov2D/tFEYMK3DSqnv8u5S0VRI7+CKdU04kJTWS9Mb2lc8yyzbTEhqGP9HJ6KqHqWv7uJPJcq88cTunN9xH+4EQy8vGs2Dq6KQ3ADrbW9l+8yV0pI9h7ud+56QhMlRYax8EMMaUA0WHXXUJsNFa+7e+668H6owxE6y1FcAVwM+stfv6rv8ZcDVwSz8OX0RERERERERE5Lg0IBM0rLUbD7/Y9zEamMVbv5gob8PGrdupe/A/aRz1Xi76oLtJBl0r/kzN4j8TTouQf8XtTlbreG75a3y453nagsPw224ubLqd5zefwuUnlSY3VLOZlvVPsr45BQ+WaSvvJnXs2ZAxIrkdoGv36+y9+8tg4wy/+AekTeifSRQi8u4YY+4AzgBSgYPAT6y1f+i77gzgN0AJ8CpwpbV290CNNZnCBaNp3TuagvScgR7KuxYKBunypJEWa6TDl05KSthZKx4uwLbtodufjs8fcNLwhVKx1oK1+BM9NJgsAj5Hi6OlFtBV76PNEyEt7O5x8weCtGdNxJM7xllD5DgwGVh76IK1tt0Ys6Pv8xVHXt/33/9yzydjzKfpXXWD4uJiWltbXYx5yGtvbz+uuoO9XZBi//GzHiyEd/BzP5iP23n7HT6Wh3vD9+Sddh05XtsiIiIiIiLy7gzUChoYY34LXAmkAKuBJ4Ef8NYvJh55H4P6xUJX/6Bubmok1ZegvrPZ6WPS2d5JuzeDOCFSmpvAvnFVi2QcX8j20BPMptubRYw4XtNGvLsj+cfV3k6rJ4uov/ddxy3eHhKtLeD518vSH+3xdbY00+XLwEOC5pYW7DH8czvUX/TR8ck7dFyu/jR53vkw7/yBHkZSpIdTyH3/z6hb+zQ5U84kOyP5q1oAdPVEaQ4X4/FU0BQeQ2NbG/nZwaR35px+MbsrH2F8+1paTYSek79OZtjNZJBLL/kQL6+aQE5uHrNGD3PSAPD5A5x0zc3O7l/kOJEG1B7xuWYgctj1zUdcl2aMMdbaf9onyVp7K3ArQHl5uY1E3PzuPB4M1GM3kN+zwdqu7jT/+Pru/fAO72uwHrfz9lE8loe84XvyTrsOHa9tEREREREROXoDNkHDWvs5Y8wXgZOABUA3//7FxCPvY9C/WOhizCedWM7OotuYmJ1BqsNtJyLzryQrOwdfdilmxLg3v827PL6xJ8xk+/NPUN75OnE8/C31w7xvQnHSHzebMpX6QCqjO1disPQUnkVa4TiMx/uWX3c044hMPo1AtIVErJvsWeeD99jeGmQw/n/1Tuj45O3S6k9uNdUdpHLNC4w/6QIn24EAEI+Su+pX+PduJr1nI5TdDP7kbwey5PHbGbn7AaL+dDKal7P6nu9zzuduSnonrWU7uYla9pkigqabkpoXgbOS3gGo3fIKI5d+l/bISLpLbyaUkuqkYxMJNi1/mrSsAkZOnOWkcUjFyufw+UOMmXay047IAGgDjpxlnA60/ovr04G2N5ucISIiIiIiIiIiIsk1YBM0AKy1cWCpMeZy4LP8+xcT5W2wiQTUbyceHgshd0vSVzW2sXHDXoJ5Pk4psXg8JumNiydnsevVFtrqPBjj5bKRbZQUpCW989yaHaQf3M8wjx8L7KncSfXW/cyeUJz0FkB9Sys22k1WIo5xOEGjpqWL1u4Yo/OS/5i9QSIOsS4IuDlhJ3KseLerPw32lZ/A3cosm19+jMyKe6jwRxgz8zQnDVoPUlNTzY7YCErrGjAHd0BmyRtukozji9XtoDNlON2+DLptLqmdB2lqbsGb5OfJA5VbiAay6UrNoSsRJbW+8t/+TB3t8VWveZY0f4RQVy2VW9YxYvQJR3U//057azPRV35LVWg42UU/f+df/zaPLx6L0fnir4h5U2gdNfUddwbSUF8daagfXz/ZCFxx6IIxJpXeCYUbD7t+GrCi7/K0w64TERERERERERERhwZ0gsZhfPzjRcO3ejFR3oYNix8m9PINbMqYzNwv/tlZZ+Ojv6C09kWiewNsGTmRiZOSf4LDe3ANnvaDNIaKiMcT5O5bAh0NkJrciSe71y9hvmmnJlgGQG5PLa+vX+5kgkbV2ueIvvwbPDZBZUoGZfM/kPQGQDSe4GsPrKO9J86PLpnidpLGy7+CnS/A+/+U9O+NyLHk3a7+NBRWfgI3K7NMOuUiKiMZTJy90NnqDIRDMKyQ0J71RPInEBk+Bnz/vPXIuz0+X95YUmpewBdoIxBtoyH7JDIz/vWWWUfLZBWR2badfLsRDOzMP5u0tDSMeeuJIEdzfMNnnkv3U0vpiJQxccI0gqHw0Q77LUUiEZpOuZbsrPyj/j683a9LO+OreP3BQbnS0GAc8zsx1I8vWYwxPnr/HeUFvMaYEBADHgJ+aoy5FHgC+Baw7rBVnW4HvmKMeZLe1aC+Cvy6v8cvIiIiIiIiIiJyPPL0d9AYk2+MucwYk2aM8RpjzgE+DDxP74uJJxhjLu17gfHIFxPlbQhnFxD1BLGRQsehHGJ4iRo/KanJP/EEgC+EzwvxeAIfMbweL/gCSc94/SGstdD3YW0CbzD5y94DhCLZWOMn7vETTHc3mcFjDHmRIOGAl0jQ8Vys7DLIG+dkqwCRY421Nm6tXQoUodWfkiYjO49pp3/Q3eQMIGF8PJl3FU+mXsQTeVcT9yT/+QTglPM+jM0uZXjXTnyBAOUXfznpjdbOHrYsvh88fvD6SXhCpDRuZdnazUlvEetm1MGnKMkKMiVUS3D/a8lv9GnpirK9J4vtbUHiCbe7LZROnU/xxBOdNkQc+wbQCXwduLzvv79hra0FLqV3hadGYA5w2WFf9zvgMWA9sIHeSRy/679hi4iIiIiIiIiIHL8GYgUNS+8JrVvonSCyG/iytfYRgL53et0M3AG8yhtfTJS3Ycy0+XSN+zvBYIrTzikfuI6Nr88ikjeC0pGlbiIjZpEzaSHB9U/g8frJWPA/EEz+uypPPOVcOu/8C+PaX8NiWB+axUknLUh6x1rLMzUZ1AdPx2ejBOry+Xg8gc+b/LlSXo/hRxefQDweJxBwcxLy/0y+qPdD5Pii1Z8GmZW7G8ladgMz7R6qXl3M8uJbOXlsXtI7oepVRDr2sc0/nrx4Exm7n4ZhVye1sWHbTsZ1rKYuPApM73NIes9BNr/2FPOmT0pqi8ql1G95hYqOdEImyrQXbyQw8mTwJv/PyIefeIIZFTfSbUIs9vyGhdPHJ70B0NzRw5JbryPqC3PeNTcQ8nuddERcstZeD1z/L65bBEz4F9dZ4L/6PkRERERERERERKQf9fsEjb53dP3LzeXf6sVEeXustexqilOSnSA16G6RlJRQgLycbDJzc501YvE4G7btJLPHi8Wys2I7cyYnvzM5VsGBYJQt0VIMMC7YRH5sJ71bcifP85urSVn6I85NbAFg54qdPJ75Ey6aWZLUDgDN+/A+9XW8Xc1w2tegbH7yG4d0NkJbDeS5OZEmMtCMMfnA6cDj9L5D+Ux6V3/6CPAKb72UvBwjOqNx0m0zHgNpiVaaeuJuQtFOYglLzHqIWg+2uy35iWgUiwH+sZ2JxYONdSe9RaybaNySsNCNl0SsG2wcF39GRtsbCRLFWKhuT/7jdkh3NEZeVyWdnjS6YwlN0BARERERERERERGRftHvW5yIe9tr2vj6A+u4a8Uep53qfTvovP9zbLr/e84atft3kda6i9bQcDqCOQS3P+UmdGA19T1ebGoesXAuLd1xqN6U9MzWPfsZH9tKky+fJl8+xbaKnTt3JL0DwPbnoKMWfCFYc4ebxiFLfwmPfhFa9rvtiAycQ6s/7aN3ufgb6Vv96W0sJS/HiNml2bxSdi3PBxbw4sgvMXeMowmGJXPJKJ5Mib+Z3Nxc/FMvTXpi/OhR1PoKGNa9k8KOLQzv2k4iESdvwrykt2qzptMYCzAyuoMR0d1syFiA9bpZmenM8y5lafFnWTXpa5w6a6qTBkB+RpjCT97BuE/dSkaK31lHRERERERERERERORwA7HFiThWlBXm4hkjONnViac+2flFVI67lNyxc5w10rMLqPGGCPU04rFRmrOmuAml5ZPmjVPb2YPFkBIGUpO/7H1WZgYtJo2MeBPWeOiyASJZye8AvatZGA/0tMLo0900Dpn0XsgscfKYiRwLtPrT0JDi9/DlkZV0tK4lpaQAb8DNqgn72w2vHAgxrbuLDY1+smo9zMhMbqMgM5XaKWcSWLGSAD3YuKElbTgL5iV3goa1lt89t5H3xbrx+/0kMOys3EPHtjpOHZf83/kjMwN8cmoAUkMQdPtnajgcxusx//6G71JzR5RYIkFOWtB5S0RERERERERERESObVpBYwhKCXi58uQyxhZEnHb8gSBzPvifjJ1xqrNGaiSTjIt+SnNqKXV5c5j04R846ewtOIuOGJTHV3NifBXVsQi1eXOT3nnP9BIWjfg89TaN5niAJ4d9hkvmjE16B4CSuew5+Udsm/514ide7aZxyIiZcOInwat3IYvIMaz1AK2rH2BNraVl3WPQWOkks2HzZiZ2rqQlVEhhvIoNr73gpDOibQN7A2PYnHIi6wMziXijhLrrktpo74kTrF1HijfBgeAoqgOlTImvZ9OemqR2/k/lEljxB1jyM2je66YBxBOWr9y3lm88vMFZ45BvPbqBL9+7hnjCOm+JiIiIiIiIiIiIyLFNEzSGolgPbHoUmtxucdLcGeWmZ7fyzMaDTjvbV79AVtt2supeZ+f2CieNJ597Bq+NsSc8iT3hSaTEmnjypcVJ70RCfj5bvJuceB0ZiSY+nV/h7B21S7fV8cgTj7Hi+Qf5w0tuHjcRkUElmE4gNYvSYCuhcARSspxk0rNy6DIhItFa4tYQyi500vGnDyNou4kmEngSUQI+LwTSktoI+jx0eiJgLcYmCNoOOkkhLTWc1M4hNrOEHl8aPeECSMl20gDwGDhncgFnTChw1jjk9PH5nDO5gH5YrENEREREREREREREjnGaoDEU1W+DJTfCxoecZjZWNfP0xoPcsXy3007K3qVE/Wn4iVG/dbmTRqJxL16vh05vOp3edDxeL131+5y0oruW0WwiNJpMEntXOGkArNl1gLM6n2J+zzL2b1vrrCMiMmgE04if9zNi5dcQPe/nkJLkfUf6zB4/krppn2dfcAyVIz/A+Qv/5e4478r+0ovIiDcwo3slJ8Q2sDlrITaY3NWz/F4PM089jy2MorRjHZldVTyT/VHOm5L8SSdd0Tg/WtbJja1n8Z2a07h7TT3Wull1whjDR+aM5OKZI5zc/+HeN2MEl88txRjN0BARERERERERERE53rnd3FsGRt4EOPv7kD/JaWZ6SSYfnVPCeMdbqfSMfQ++TX+iyxOicOpCJw1f3lhirRZfvBMPCWJxS+qwMU5aqZPPIa/u9yQspEx4v5MGwLzxRTxccRmZsTrGTZntrCMiMlh0ReMsuu/XjG9+mZfWrOCMz/6ccCD5fwp5OhuYXfUnajrryKjfS2rDQhg+NemdFYvuZ7YvnSZfET7bQ9qeF9hU9WkmF+UmtXPBSEtt2kF2NBST7o3yxREVpDpY/enxdfsZU/E7ZiXWYTE8sKyJipKrmDg8PektACqeBF8Ixpzu5v5FRERERERERERERI6gFTSGIo8Xyk6F1OSeoDlSOODj4zNzmTMyw2nHm1VMrSefg4ES/KluWu8973w2MpaRHesY0bGJ1f6ZXLjgFCetjYlSGuIptMX9rEm4mQQCMLMkk/fNnUT51KlcVl7krAOwt6GD1XsaicUTTjsiIu9GQ3sPZS0rsf4URrWtoraly02o9QAtrW0cNHk0t3dB466kJ+IJS2rHPmK+MN3eVNp9WYTopLW5OektWg9iraXTl0GjycTfuCP5DeBAUxdjE9tp9WYRM37KYjtpaO9x0iIRh+X/Cyv/4Ob+RURERERERERERETehCZoDEHba9q46i8ruetVt1uPsPc1uPtD8Ni1EI86SVTuqyLjlR+S42lhZM8O9jx4PfFE8pc7b67aygS7na2hGewIncD02GraGg4kvVPb1Ebgxe8S8iTw+nxkvPpT9hw4mPQOwO6VT2Ke/SZpr97Ipgd/5KQBvZMzXv7T/9B15+Xc99wyZx0RkXerID3EtrLLqU+ksWnkRxmRFXYTyhlN5vBRFPtbyMnJgxHlSU94PYa2/8/efcdJUd9/HH/N9uuNK3AHHL13RBClKTY0sceoiSlGo4kmJsb4MzExsUYTYxJ77DX2SpMuvXOUu+O443rvt73N/P7Y2707DrAwK4Kf5+PBQ9gy75nd2Zl1v5/5fDMmY/F3kOaroY+3ggZjFtlZmbpneZMH49Ss9AtUkKE20JBxmu4ZAONykvjMMpuYYDsBDXbEzGBgWpTeI4MRLnwUzone+VEIIYQQQgghhBBCCCGEOJQUaJyEypocVLW62FnRFt2g5mLwOqG1DPyuqEQ4OloxEcBrTMBrSiTJ14AvoH+XBo+9GVAwmi0YTDYUwGVv1T3H4bRjVd14jPF4DXGYCeDsiMLVzoCrsRxN0/AoMdASnaudAeo7PAzx5pOhNmCv1/8qcSGE0IvRoHDplT9h8q3vctnVP8dkjM7XoBavwuqWZIJeF3kdsRR3RCfn/DmzMao+4ry1mH2tZA2dSP+0eN1znt9YRbXLQIxBxUSAdwq8FNXbdc+ZPTydqaOGoqEQMMbxw1mjyIlWEQ3QHjOAjph+UVu+EEIIIYQQQgghhBBCCHEoKdA4Cc0ekcGfLhjNbeeMiG7QqAth2nUw7y6wRWfqkUGDh1MTN4YYbwNmXxsNgy8mxmLUPWfAiIk4zakkucpIclfQZsshJ1f/1y87I4OGhNH0dRXSz1VAq7U/A3MH6Z4D0H/6xTjiB6IYzSTMuikqGQDjc5IpmHgnn+b8knlnXRC1HCGE0INSt4e4ty5HqdkRtYztG1cxrH0Tdltfcvxl7Fr2WlRyDPnv4TAkURo3iXLrSJIrV4LXoWuGxx+krWA1WbRQFzOUFusA5rsXsaagWtccAMXvZkbdK3hNCaSavYwue1n3jLAWp49b/reTW/+3C6c3ELUcIYQQQgghhBBCCCGEEKI70/FeAaE/s9HAaUP7RD1Hs8RRmPUd+sRbSY9SRpzNwqnX3kvTyichLoNzzro+KjkJ8YkE4rMxuUrQUNBSB2OzWnTPMTtrGKRW4NRCXTr6UY+tvRQy9C0GUVWNj1asYayrDpMWYPNnS0gZcgpJsfpvk8Wo8MNRBuhnhmT9p58RQgh9aaCp8DUcrkIRStQTNEBFIZobFd6Kr+Mor2nRz9E0OV8JIYQQQgghhBBCCCGE+PpJB42Tkb0O/ncNFHwc1ZjVu0tofvFqPn7xb3j8wajlxOx+FcOBxcTkv46xalNUMsoLt5Pcuofm+GG0xg0hpWYN9VX6TwvStnsRLqeDjrhBtMcPwuEJ0LT1Hd1zSpscjD7wDJo5Fre1D2PbVrJjV5SuFq/fS8NHf+bgkv/gXfNIdDKEEEIvfSfADz+EnClRi5gyYy77k04nxttEmWUYE+dfHZUc37AFpASbGOHcykjvHpozZoBV3ylObGYjySNn4w5oDHFsY4BzL5usM5k1MlvXHAAssSgzf82IRC+ZKUlw2s36Z3RKi7fyrysn8s8rJxJnlXplIYQQQgghhBBCCCGEEF8PKdA4GalB8Dkg4IlqTDDgx6a5MaleonohajBAUAVN1ULbFgWaGiRyJbKioACqqkYhRw2ldF6KrCkKaPpvk6ZpKKhoGCJhmqb/9gCgBtA0jaCqoAWlTbwQ4put0e7l3R1V1LVH7xyZGmdl/qhMUmJNTBuQwNCsFN0zNE1j7bIPcBtjabbl0mHti7t0G8VV9bpnfT+zCkUNUKxlU6kD/Ox8AAAgAElEQVT14QzDbkbov0kAbNqygdImNyXVDewtyI9OCIDfQ3LeMyTmvQBBf/RygMY9y6jf+gHR/bIkhBBCCCGEEEIIIYQQ4kQglwyejJKy4Zr3wBDd+pu5k0aS1+dNzkqMIcZijF7QtOuw+W0Y4tPQBsyISrP4gaOmsmdNLjmt29BQqO53FqMGDNM9J3ncOXRsfJ4+9lA3C7cpheRJF+ueMyg9gTezL+O0sicxaiq7EmYyc0KUrhbvO5GMc39HelslhnGXRSdDCCF08s72Kt7aVklFs4tb5w+PTojXjmvvxxQ44hlUvpuUxkLoN1HXCLc/SEbHHtyWDHwGGwA2TxMNNaUMzcnUNct1cBMBcxyKJQW/phHwtkB7NWSM1DUnGAiQVL4UtyUdU9CJe9f7cPqFumZE1OyAfR8ACgyYDn3HRyfH1ULHwj+jBoOk5Y7FlD40OjlCCCGEEEIIIYQQQgghTgjSQeMk1eIOEFSje6Wm0aAweWAa/VJio5bhD6r8e9FOtu/aybKN23h/e3lUckzOOsYn2DHFpWJJSGGKrR7F3ap7zs7iSvxBDb8xDr8xjoBmYGvhQd1z/F4XQ5vX4FJisRsSGOApxttcpnsOhPqOLPON4xXnNJq1hKhkfN1UVaPZ4T3eqyGEiIIzhqUxLTeVuSPSoxdiicOWNZxcq53EpFRI6q97hM1kpM2chSXgAMCkegGN2MQ++melD8KqevH6/AR9bmLMRohN1T3HaDJhTxhCrLceS8CBIWuc7hkRfYZD8gBIGwIpudHLsSVhGHEehiFzMCXnRC9HCCGEEEIIIYQQQgghxAlBCjROQmVNTm54ZRtvbKk43qtyzMqanOSWvclotYjT/BvZv2kxWjRahFdtw+VyUuBLp8CbgcfZDnW7dY9pyV+N3xhLbexIamNH4jYn4dq/Uvec2tJ84tx1uOL644rLwYKX6rzVuucAlDe7aF36EJM33cKH63dFJePrtnBPLde/sp19Ne3He1WEEDobm53M3y4bz8QBUZqjA8BgpGPOfZRPuYP6uY9AXJr+EQaFgeffil+Dwc6dZHpKKB78A8YNH6J7Vsyky1EMRsZ7tzLKt5eOwQsgPkP3HIARF/wKQ2JflIxRTD7nB1HJACCuD1z2PFz8FNgSo5djMDLo4rsYcuXfwGyLXo4QQgghhBBCCCGEEEKIE4IUaJyEkmPNjMtOYlhm/PFelWMWZzXRYuwDmopfNRKMTUVRojDJiSUegwJBVUNVVRQ0MMfon2ONR1GDkX8a1ACaRf+uExZbPIqigRoATQXAGBOd/SExxkxNzFCKLSNJS9V/EPJ4GNQnlnHZiaTHW4/3qgghTkAOb4BnPviUuM3/YsnCtylucEQlZ0K6kSFpVppsuZgT0/nuAB9Gg/7nyK0bV6IG/ZTETaYyZiTBwsW0OT265xD007rwL3jtzaiN+6ld+g/9M8I0Dba/CLvfil5G2J53YOvzoUwhhBBCCCGEEEIIIYQQ32qm470CQn/JsRb+dOGY470auuiXHEPu/Bt5a+0giEniygXnRSWnKHYSlY5MctVCUGCFcyrjY8egd1P60XOupKX8EwY5d6JoGvXmbIbMu1bnFOiXO4LS1CmMqPsAAyrl1hGMPu07uucApMZZuOri79LaNJ0RYwdGJePrNjY7mbHZycd7NYQQJ6gOt59MVzE51DHct48Gu4ehGVEoknM1oygG6oIJxCg+DB1V+mcA3o5QTtBgIaBYiAs04nC5SI7TuSOE34XmbsdlTMKoeoltic60ZkCoeDH/o1D3jPFXRC8HYP9icDXD5B+CUb56CyGEEEIIIYQQQgghxLeZ/EosvvHmj8th/rjrorZ8TdN4fG0Vs40pmE1GNA0aSOX5jRX8WedCl+wEI8lpVuobU9E06J9sIz7ZqGsGQG1NJYkteTRY+qNiwBZ0sG/7Z0yZtUD3rJaiDbS+938Eg0GK957KsKv+DtHocgK0u/yUNjuZkJMUnU4qYV4HNORD9hQw6P/+CCFObn2TbJjGXczz+1JQssZxa//oFHxpmaNpM6XT31iOQTXjGHIh0eiVlDtsLEn7GunvKEUDSuJPITMlSfccT9CA32hltGsbGgYqbN9HU1UUQxQavhmMcMkzoHwNzeTOfxjUoBRnCCGEEEIIIYQQQgghhJApToRweAPUtzsZ78ujwZxNiyWLaYEd7Kvp0D+s5SC+gEqVIZtqYzbeINBUrHtMU+V+FIK4rRl4rX3QDGYcZTt0zwFo2bUIj6rhMPdBrdgMnrao5AC8sbWCuz7cy4EoTRcQkf8BLPwNVG+Pbo4Q4qSkKAo3nDWO2266iTuvmE2sJToD8x/ua+OzpgTwuah3Kfx9swOPP/j5T/wyNI1h5f+D+AzK6E+VcSAj45yYK9brmwPsfPt+gm475TFjKY8ZjalyPXs++0D3HACto4a6T+6jbvGD4GyOSgaEpk5bcsDBR/sdeAM6vzeHUoPgj8LUM0IIIYQQQgghhBBCCCF0IwUa4lsv1mIizmalxtyflEAjiYEWSk2DyE6O0T8sPhOLQcGk+VHUABajAvEZusfEpfXFpnrI9Bykr+cgccEOTKkDdM8BiB0wCRt+Ev31kJQN1sSo5ACcMyaLa2cMZGBabNQyABg8B6bdABmjopsjhDip2czGqHb72bVrKxPVvTTb+pNgDDCsYQllzU59Q1wtaI1FFPvSsMf2o9GUSWvAAmWf6RqjqSqJNetxWTPwmhLwmRLwGWNxFXyqa05Yw8onaCneTFvhWhrXvxCVDIDt5a0oi28nfelNrC6oi1oOACvvhTevAZ/O+4AQQgghhBBCCCGEEEII3UivZfGtZzQo/HDGQJ5f+RNO8W4koBnZETOTX88YqHtWjbEfK3yncG7gDQyaxhLvhUwxDWGwzjmDY/2024KYnA0ABMwJ9OsT0DklxDVoPuuSioh1VmMe9QOGYYha5degPnEM6hMXpaV3k5QDk38Q/RwhhDgGismGpoFF82LQAriUGMxGnY/ARhOaphJUVey+IAoKwaAfjFZ9c4CgYkRBRev8t0ENoJlsuucAmGKTMRFEAcxxqVHJAIi3migy98MRTKRfjCVqOQAkDwS/Cwzm6OYIIYQQQgghhBBCCCGE+MqkQEMIYPaIDPolTKJj+QpUUyzfnT+F3PQE3XNeX1/IXOcWmqwD0DAwwruHV1fn8acrTtc1R9n+HLHpuexScwgEVUb3S8BS+D5MvAxsSbrl+AIqb77/Hpe2L8Ro0Cja0s6GnEc4fVi6bhnd7Vz2Bt6ilQy+6E4ysgdFJUMIIU4UF8yazkcfXMxM9yr2m8bhHHkpg9L0LWILWhJZHRzLGP9HWDUXKgZa7RnsTjyD8TrmKAYD3jHfIyvvMeJVB5oCLcZ04k/7vo4pXSzTr8PR7ACjhZxJV0YlA2B0v0TM1/wFX1BldN/odZgCYOqPort8IYQQQgghhBBCCCGEEMdMpjgRotMwaztTvFs5xbWW3FhfVDIcrU3E4cFpSsFlSsKCiretXv+g9iocqhV3EHyagWaXGrrd1axrjN3jJ91VjNGo0GFKZ3CghJpWh64Z3QV3v0W/5vVU71sftQwhhDhRTMlN5fJrbsD5nf/S/9L7uPncyRgM+k6pkl/TwV5HMorRRNAUi2aKQTOYeHmPF03TPn8BX8LUiZOxmYw4NAsu1UJaQiyDBg3TNQNAVTU+eetZrAeXYTmwiA/ffUX3jIiAl+Qtfyd9w70oXnv0coQQQgghhBBCCCGEEEKcEKSDhhBhqYNh5q/AHANxaVGJ6NuvP/VVaeT4G9AUhSYSSemn9wQn4E4dQ0vBJvAbMWgqre0+HGYL8Qn9dM1JjrXQ1GcawcoVpAUq2GCZyfjs6LWKTzvvTuoKNzJ6+oKoZQghxIkk2lM/Vbe5yQ5W0mLpi9MY6sCUEGhBba/BG1CxmY26ZSmtB2k3JNIQO4hAUGU4buiohlh9zysOX4DspvUELQkYtSBJNWsJqtdj1Lm4BYDG/XTs/RQ1GCRt0oUYh8zSP0OIo1AUZTUwHQjPdVetadqIzvvOBB4HBgCbgR9pmlZ+PNZTCCGEEEIIIYQQQohvC+mgIUSYosDo78Cw+VGLuOq0IawefBufWs9ipWUui/r/lp/MHaNrhqZp/Ns+G7O3mVO0fUwln8G+Ip7wzKfNr+9H3qjAr7P3E2sxEjRYOSepirHx0btCeFCyiWkjBhBvDEYtI0LnK8OFEOJE1DfJRrVpABbVg6IFsaouApqCkpiF1aTvOaXB1BdjwEmqs4QsbylurweXVf8ps+ItJmpST8Xk60DxO2nPnBGd4gyAPsNJHDGbxGHTMfbTc1IYIb6UX2qaFt/5J1yc0Qd4D7gLSAW2AW8ex3UUQgghhBBCCCGEEOJbQQo0hAjTNFj3KGx9LmoRCTYzd146g8smZHDx6AT+cMUsUuMsumZUtripbHHhN8VRZR1CtXUoHeY0tICfbWUtumbRdAClZDlVwRRqlAzcznbY+aq+GWENhXR8eDuVix7Gt/D30S2g2PkavHQhVG6JXgZwsNHBnz/cS0WzK6o5QgjxVY3NTqJ10AI2mKYR52/BG4RXYq/l0pnjUBT9ihp8AZWX1h3EonrJNraSZWjH7Q3y2oYS3TLCDAaFC6cMIisGsuIMXHTKIN0zwlwBjXzTCPJNo2nzRakIJEzTIBj4/McJEXIJsE/TtLc1TfMAdwMTFEUZeXxXSwghhBBCCCGEEEKIk5tMcSJEmKZCxQaISYVTfhq1GLPRQHpbHnjaQi0odOb2B4nBi6aY6TCFpmpJ8Tdg01w4fTp3nvA78avgVxVAwxk0gadd34wwdwtur596fxx9OmpDA1E6Dg720FgY2o6WUug/LToZQE2bh91V7dS0uxmQFhu1HCGE+KqMBoU7Foxj3ZYrMGzYjZo4hGsuuJix2Um65uyraefUto9oteZQaxwBQJK/EW3/YuxnTiPBZtYvrKOG2N0vUWPOwmjQSN3yOAw+DWKS9cvotOmj/9LvwOugaGxrq+Ksn96rewYAqkrJSzegtVUx8MfPYU7Wd0ozccJ7QFGUB4H9wB80TVsNjAHywg/QNM2pKEpJ5+2F3Z+sKMr1wPUA/fv3x26PXre0k5nT6fxW5Z7o2ZkxWte+bu0HX2K/P5G3O+rZX/K17K7He/Jlc6Pk25othBBCCCGEEOLYfO0FGoqiWIEngLMItdMtBu7UNG1x5/0yF7I4PgxGuOTZ0H+j7YJHQA1GJWtgWiwttmya3H3I8NehKgZ8BitF5jEs6KfvgFp7XC5Ndi9jfQWARiAQQ4HtZ4zSNaVT9lT6jDub5No8rDNuBEMUGwCd8VsYuQD6TY5eBjBzaBojsqbSJ17fLipCCKEnm9nIWQPNUGiE+A7QuTgDwONXsWg+goaur6aqYsCmefAFVH3D/G7c/iDVHX6MBoV+5iC2gEffjE6qsxkUA5rBgMHVFJUMALQgWlsVAXcHfmeLFGiI7n4P5AM+4ErgY0VRJgLxQOMhj20HEg5dgKZpzwDPAEydOlVLSOj1EPEFHa/X7ni+Zydqdr1b6Xq+twa+5LJO1O2OevZXeC3DerwnXzY3ir6t2UIIIYQQQgghvrrjMcWJCagEZgNJhOY9fktRlFyZC1kcd7ZEsMRFP8cSF8qKApvZyM/mjOa/8Tex0HIOq0yzeSz2JqZMnMiQdH237ePVG/B73fjNiQRMifgNMeRvXUmTw6trDgD2WmjYB65WtPINoQKXaIlJhv6ngim6hROKopCeYNV1mgARHYqiWBVFeU5RlHJFUeyKouxUFOW8bvefqShKoaIoLkVRVimKMvB4rq8QusueAgv+EfoTBaP7JrLbOoXYQBsm1YdVdaEFA7SkT9N9KrAPK8xsa4lhbHAfo/z7+Kw1lRWVukZEjJz/E+y2LFzGJAbMvyk6IQBGMwN/9F8G/PBpYrPHRi9HnHA0TdusaZpd0zSvpmkvAeuB8wEHcOiX0URA2mMIIYQQQgghhBBCCBFFX3uBhqZpTk3T7tY0rUzTNFXTtE+AUmAKMheyELqYPiSN+75/Gmf19XBubCE3f/cMfjwzV9dCAF9ARS1eiWKyUWUbTmXMCOpsgxnl282e0lrdciJ2vER5dQ2bm6zYC1dC7S79M8KKlsHL34Gm4uhliBONFBeKbzdFgaxxEJ8RlcUnxZoZdf6NrLKdiS8QoCNo4eOUH3HxggW6nruaHF7WrN9AptZMhzkDuyWd/mo1H63ZhMMb0C0nLKfsXcbEOxiR6Gdoxdug6twNpJutzTaWNaXgDUSxgFGcDDRAAfYBE8I3KooSBwzpvF0IIYQQQgghhBBCCBElX/sUJ4dSFCUTGE7ox8Ab+YJzIXc+94SeD/lknzNUtu/4ijdAfN8csPlIiTficDi+9DKOto2BoIrBEodXScFvCrW7VzQVHwE0v0/3z6Pba6FDScJtiKVZMxNw+jEfY8YRty9gBEsWeHxfeY7kb4Jv+j56ItE0zUmoaDDsE0VRwsWFaXQWFwIoinI30KQoykhN03qdu4QQh3fGiL6MzfkjBbUdWE0Gbs5OwmbWdyqwA/UOhvvzMRigxRSaBiQlUM8A3wFKGhxM6J+sX5jXQaDoU3a1WlE1hVPKt2K110BSjn4ZnWrb3Ty6/ADeQJAEm4l5IzN1zxAnHkVRkoFTgTVAAPgeMAv4NdACPKwoyqXAQuBPwG45bwkhhBBCCCGEEEIIEV3HtUBDURQz8BrwkqZphYqifOG5kOHkmA/5RFznL0O27zibc+zt1I+2jYbBZ2DYtYoks4OAYsEWaGOn5RTmj8glQceW9EX1dl4t7cv1rgPkqnYK1DG8tNfMHYNij3nw7rDbN3pe6M9J4Bu/j56gvmpx4YleWAgnf+GPbN/xZQLGZVoB8Htc+D1f7vmft31m1YPPkopPS8ZvDM3u4FH8qOZETEGvvp/JYACPKQuT2YFfM9BuiCfGpxxz4d/httEYCJCbaKDdE6SPVTshjy1h3/R99ARjBu4FRgJBQuekizRN2w/QWZzxGPAqsBm48jitpxBCCCGEEEIIIYQQ3xrHrUBDURQD8ArgA37ZebPMhSzECeS7Z83lDZeT4SUvkqS2szFuHuMvvJlUHYszNE3jhVV7udTxJk5zH1oMOWSo7eSUvs364hzOHKXzVcLBAKx7BCo2wYTvwfjv6bv87jztUL0DsqeA7dBDn/imOpbiwpOhsBBO/sIf2b4T29G2b0JsHK8lTaO2ZjO5vmI0ReGAMpiWPlMYPTBD1+lU9tfZ2dDSl+863sCAynJtAQPtVk5JP/bX/9BtTADu+c4wAl4XcWlZx7z84+1k30e/LpqmNQKnHOX+5YSKN4QQQgghhBBCCCGEEF+T41KgoYR+/X4OyATO1zTN33nXPuDabo+TuZCF+AaLs5q47vKL8Lz8OjiCjD73PMyD9S2YsHsD+JsOkoALuzkNAFUxM9Gfx4qyVv0LNBryoWQVxKbBthdhxPlgjdJA0dZnYc87MOEqmHlzdDKErqS4UIgTm9lo4PcXTuatxVeRXXw/QcVIycgf8Ltzx+tanAGwcPV6zvOspSJ2LKpiYJx/D++tWsbUQVfqnkVrGdZPfoM14IbTboER5+m7fCGE+JrMfHAl1W3uoz5mnfUWcpSmoy/I2g+8NV95PcpsdE1ulzTgKy9HCCGOl0BQpbTJybBMKXz9pilucJCVZCPeetxnHj8iTdPYV9PB2Oyk470qQoiTnKZpkd9IXtlUTnG9nb98d+xxXitxKLcvSIxF32mIv4ptZS1kJtronxrb677n1pXy2MoD7Lhrvv6/uwkRBcfrm+CTwCjgLE3Tuv/68j4yF7IQJxzboFOhZhekDdR92VaTgYAxBkULomgqmmLArHlpVZJIjTPrnkdcOpgsaM4GlIQsMNn0zwjLmgDlG6DvhOhlCN1IcaEQJ4esJBu3jAsSaLeBYmDseEDHzk9hztYaDAYjAUNo2QZFAXsdvqCK1aTz/9Q27gevHcyxULZOCjSEECes6jY3ZQ8uOPqD7r4K7m4/+mPsdpBuPEKIbwBV1bB7AiTFRuH3i0PsrW5neGYCFpOBhz/dz9NrDvLZ7+YyIC00iOHxBylucMig+3GkaRpnPbKGabmpvPXzGVHN8viDR50WePGeWg40OLjlzGG97vtgVzW3vpnHf384lfmjdb4w6htC0zR2V7Uzsm+C/v9/JsQJwuULYDYaMBsNPW6/f1EBdo+fBy4ZH9X8NUWNXPv8Fm49azg/Pj2Xuz7YC9CjQOOBRQUcaHDwyBUTSI79Yr/d+IMqRkXBYPj2DdKvL27C7vFz7ti+ui1zR0UrlzyxgZd/Mo1Zw9MJqhr+oHrMU88f6oOd1dy3qICNd8zDdMg+GXbZUxsxGRSK7z+/1333fJIPQIcnQFJM9L93HQ+f7K6hb1IM/VNjyEiIzrhVdZublFgzsZZvbiHpyeLwe3kUKYoyELgBmAjUKYri6PxzdWcb3kuB+4BW4FRkLmQhvvnO+C187xVIytF90VaTkfFjJ7LOPIPEQDOJgWY0NcgncZfq3z0DKPcn8gA/5rGOM3jE9DPavbpHdBl2FlzzLgyeFcUQoaNwceGFhykuHKsoyqWKotiQ4kIhvvkGzsTUbwKmnCmQMzUqEWk5I/AGFWKDHcQE7fhVMGYMj86Pf9lTIXUQGAww7jL9ly+EEEIIIY6qtt3NtrKWXrf/bWkhE/76KU5vQNc8VdV6/LvZ4eWC/6xj+B8Xs7Gkmc0HQ+vSvSvRg4sLueA/63hrW+UXzml2eClucBzz+mqa9vkP+oZ5d3sVQ+9chMun33vX4Qkta0vnvuIPqnyUV6P767O7qo2Rdy1h9f6GIz7mxtd28MiyosPed6A+9J7n13R86ewD9fYT4v1+YHEh3318PVf/d3PUMh5aUsgf3t8TteULcSzcviCj/7SU29/Z3eu+Zz47yBtbKmlx+nh9cwWBoPqVMrwBNXK+anH6uOm17T3OSysK6gH45/Ii/r50f+T28HG3w+Pn6c8OsrKwgbe2VRJUNXwBlfsXFfDqpnIgVIx25/t7qOm23GF/WMxNr+34Suvc3Qc7q2l1+o55OXpYvb+h1/lo3YEmXtlY1uO2q5/dzM9fPfZt725HeSsAKwtD55Tb3s5j5F1LjmmZ7W4/wUO+y/zh/T002r00OQ7/mnsDQQAC6tHPMTWf05HxUK9uKif3joV0ePyf/+DjqKrVxS9f38mlT27gpy9uo7zZycNLC3t9JzwW3kCQmQ+u5Ldv5em2THFkX3uBhqZp5ZqmKZqm2TRNi+/257XO+5drmjZS07QYTdPmaJpW9nWvoxDim+XqGQMJTLmOA4Zc1ICPlYkXc/l5ZzM4PV7XHH9Q5R+f7OCMupc517eUASWv8uTqA7pm9NBSChseg7aK6GUIXUhxoRAnmfh0uPBROP8hiEmOSsQls6bwYeYvqNbSadCSeD/1p1w2f3ZUspq1eJ72ncsTvgvYG4xyK/6AF1y9Bx+EEEIIIU5Wqqbx4vpS2t2H/+HeGwgy44GVXPbUxl4D0898dhCADSXN/N97u8mrbDtqlscf5O6P9tHk6Hm1SG27h/11oVk0A0GVMx5axX9WdP1eUdfhifz9+//dhMUU+sl3X00728paUFWNT3bXAvDi+jKW7K1j+B8Xc9vbeQz/4+LIc19YX8rmg82Rf5/z6FrOemTNUdf5SNvRfYDkB89t4d7OK1uP1Y9f2MI9i7/6bzV/+Xgfz3xW0uO2VYUNXPHURuzdBmf+8vE+AqoWKVKo7/DQYPdwLNpcPQednl5Twi1v7GTx3rqvtDxvIMhv3s3n7o96NvDc3jmYtiy/PnLblxlgDV9NHx4M+6J2VbYx/5+f8eKGsi/1vKMprOtg8j3LvvSA2+d5f2c1AAW1HVS3ucm9YyFrDzTqmvHhrho2dvs8hX2UV8Pv3v5ig1/eQLDX8eBwToSiGPH1Wl/cxNy/rz5igcGbW0O/R7+/s5oGuwdfIHSM8Pi7PvezH1rFne/v4aWN5fzu7bwex8juluyt45T7lvd4rtsXZOrf1nH/ogI8/iDPrj3Ioj11/KJb4URKt44Ybl/Xc19YX8amg82sLeqa1nBbWSvXPLuZ0X9awjOfHeSPnd02lhfU8/rmCh5YHLpOLry9S/Z1HVc9/iDNh/kcFTfYaXf7+evH+TQ7vDy4uJD7FobOVYV1Hfz6zV3c9eHew27zoT7cVU1D57l4V2Vbj9fd6Q2Qe8dCPt5Tf6SnH1VFs4sfvbCVix5fH7mtocPDNc9t5q4Pu47/3Y/zzQ4vmqbx9JqSXoWW5c3OLzWobzX1PCeEj59ftfh0Z0Urpz+ykac7z8WqqqFpWqTjSVmzEwh9J/nflgoeWVbE9vJWVhYcuegQwGwMPb+2/cudL55aE1qPQ88zVa2ur1y08fiqYqbeu+yI92uaRu4dC3l46Re/vrO+o2sfLm92cvMbO3l8VQkHm5xfaR0PJ68y1KlyS2nv3/1mPriSG17Z9oWW0+L00e4KvXaqqh3xO8hv38pjTZG+595j4Q0EeXxV8RG/8+tNepQIIb7xrCYj10+OJ3ignoAhjV+mF2MYkqZ7TpvLT6y9lEytkXZjOhMDe1lRUweM1j0LgLw3YM/bEPSGupCIbyxN08qBI/bF0zRtOTDy61sjIcQ3XVaSjduvOhfXG2+hBH1Mv+w8YlL0LSwM+3jVZ5xe8ggWJcDyDxoY/Ys7otfKc8kdoSlVzn8YMsdEJ0MIIcTJKWkA3P3VplYoswF3H+YOaz/w1hw571a5clkcuz01du7+OJ+7P85n7e1z2VXZxim5qWQmWlEUhU0Hu37E3lnZRqLNzNCMeIKqRnjM9OevbieoangDKvnvdjBtUCp3Xzim13e2Zfn1vLihjE921/D+TTPpnxqLpmmc/dgWAEofOJ9Hlx+gus3Nf1YVc9GkbPqnxtJo7znwFEAdLWUAACAASURBVB6kuHdhAQBrb59Lk8NL/9QY8ms7uOvDvfgCKu9srwKgqN5OSqyFv3wcGpgqe3ABmqZFBobb3X4MClS2urnnjX3MGp7OI8v2k//Xc3nms4PYzEbGZSfx6PIi5o3MoMnh46k1JSz+1RnEWUys62y5fihN03rNE7/uQBMmo8L0wWm8uL4Ulz/ITXOGRh6/an/oh/yHrgg9XlU1tpS1MCIzgZQ4C5qmsaKggWmDU0m0mXvlvbC+DIDThvRhbHYSqqrx4xe3AlBYZ+eU3FSg6wrd3VXtTM1N5dT7V0Rem7C3dtQwrG8qs4an99o2CA2gxVpNxFtDP8G3HDJQWtUaGgw6dADeH1QxKArGQ/aPvdXtNNg9zBsZ6ii7LL+eZYVNQBN3nj+KovrQYGP4ea9truCHM3JZtb+BBxcXcv64LMb0SyK/tqszRiCoRlrJO7wBgp3T8gC0ur7YleP+zoGXovpQEdHOijZ+PLPr/tX7G6hqdXPN9M+fGtnjD2L3BEhPsALw5tbQVfwf7KqO7AeH+vELW/AGVF7/2fTIbXmVbcTbTAw5zMVdgaBKk8OL2ajg9AVZ11mY8crGcmYO6YOi0Gu//LKaHD6q29yR7ejuljd2AvDQZeMPm5Nf08GovgkoisKtb+5i0Z46Dt5/Pu1uP7uq2pg7IqPXc77z2Hqc3gArb5tzTOv9RWiaxrs7qpk/OvOI0wjUtLl5eWM5Z4/JZFiKDEEdD69tLqe0ycmke5bxrysnsqO8lUun5ODxqzi9Ae7uPN73ibcw7b4VXDE1h4cum8C+bp1z7J0D8K9vLqek0YnDG+CJqydT1eomqGrk9okDQlNLNNq9nHLfchbdcgb9U2N5Z0fo/PLsulL8QZWXNoY6XuyqbKO+w0Nmoq3HIGj3rggPd3bT+MP5owA4e3Qm28tbaT7kGPrXj/MxdZ7v6sPFEVVdhZANdg9L99WzZHc16w+2smB8XxyeAE9dM4XHVh3g8VUl2MwGPH6V8mYnKzo7RFx16sBIcV74uN3u8hNvM6FqGi5vsMfUZe1uP7/63y4sJgN77j6bix5fz8T+yXzwi9CBsLCzwPLRVaVcdVroOPbpvjoK6+z87IzBxFiMeANBbnx1B5MHJPPLeT2nnqpqdQFQVO/A5QsQazGRV9U15aPTG2BvdTsJ3c55+2o6GJudxAOLC3lpQxkb/u/M0LLa3Jz3+FZuOXMYv5k/nEOFP9/njs2KnLvcnYU3Xn/PQfYGu5dB1q7P9/byFoZnJvRYD4DrXtrG9MGpXHfGYABe6ex+8uSqEs4dk8W8f6zhe1P7R85dVz6ziTevnx75TgKwqaQ50n3qUH/5eB9nj87CZjLiDwaobgvtC8UNDlqcPk7JTelxrHV6AxgUBZvZwO7qDgLB0L7XaPcyMqvrdTj9b6sY0y+Rhbeccdjc7n7x+g4m9U/m0sk5JMaYI/twXbuHrKTeU5G0dhYvPL6qhN+dExpSCARVfv/uHn5yei5j+vX+f6bGbkWi2SmxdHR+fpocXvZWtzOhfzKD+sTx0JJCxvRLYsH4ntPceANBHlhUyE1zhpCRePjpUTZ1FhXmpMbS4fH3+B5V3ebu0QFH00Lfcw0Ghc0Hm3luXSlPXjMFo0Fh8j3LiLMY2ffXc/nB85upaHGx9vZ5PbLaXX7e3VHFuzuqPn+aU0L7V05KLJnd1r3d7WfJ3lounpQTKU4+ksV7arnxtR1sufPMI27/a5sqeHjpfrz+ID+b0e9z1+lYydlRCHFiiMvAmDMZY91uGHVBVCISY0x0xOTQ7kokKdhMsWEQWel9opIFwOiLwO+GkdHZHiGEEMeX1aBgtRkgaARz9OY+1QIBFEXDiIaiBojqtVMBH2gqqPq26RZCCPEtcAzFErl3LDz8D3d2OyQkHP5JX7EYRIhDNXQrfrjsqQ2RKxjjrSaykmw9rky95IkNQGgQ/4FFBZHbw2281+xvpNnpo7DOTt+kGNITrPz+3d0EVY2lv56Ft/MK5iaHj+89vZEN/3cmOyq6BpsW7qnlsVXFABgUeGBxAU9cPaXHOgKsL+551X54cOjiSTn8e8WByFWNYWf/8zNumdc1+P30mhIm9O/qNPdRXg13fbCXlFgzrS5/pCtAfk1HZBAibENJM+G6ghfWl0YGyCtaQgNMjy4vQlU15o7M4McvbuWx709m2qBUDAqYjAaueS405UT+X8+JDCCGB+Z3detA8uL6Un40cxBPrinh4aX7OX9cFk9cPYVt5a1c93LoCs/lv5nF0IwEqlpd5Nd0MLpfYuT5+bUdjMxK4OFPu9b/w13VPL6qmFNyU3F1Xs29p7prEAxCBSGKAh6/yj2LQ+9F+Ph0sNHBoD5xKIqCpmlMu38FkwYkM3t4OhdO6Edbt9c9POAGPa8cB5jxwAoGpMby3k0zUdWuq4ov+M+6Hnm1bV2DNd07oXQfdLv2+S2RQa9Fe+pYuq++R1t5hzdAcqyF4gY7lz65kXa3n+9M6Ne5jqGBmJJGB795K4+b5gzhnDFZkdfhTx/t5dN99TTYvWTEWxjfPwWg1yDJj14IFcBcfeqAwxYkaJrG29uqmDMinYuf2EB1m5uzOqc1njQgtB9Wd65LebOTgWlxPZ4fLtqB0MVXlfYOvtt5pfnhzh1NDh+aBqP7JZFX2UZlS2jZW8pa+M7j64izmHjzhhk8taaE1fsbeO266ZHX8N8rDuANBFlV2MiC8X35xdzDF43sqw195hrtXs7/11o+vvn0XgU3q/c3MndkBm0uHw8v3c+d549iZ0Ub1zy3mQcvGceV0wawaE+oC0BNu5s/f7iPFYUN3Hb2cG6YPSTS6QR676dHs3B3LS0uHz+YPpCCzs9Bi9PHpU9u4PKp/Y+4TWFrihq57e08Lp+Sw8OXT+hxX227mxfWl+HxB3l5Yzn7atp54oooXfgmjqp78cyv/rcLIFIk0V14OolP8+t5CPi/93pPeVLSGLpCf/HeOr73zKbI1fWXTcnh4cvGR4ok7J4AP31pK69dN53CzkIwRYH1JT3PSa9sLOe2c0bQ5PCSYDORFGOOdGXortHhxWoyMHFAMp/m9+4+8fz6UsZlh77vbSlt4ZIn1vc4zl/02Hpq2ruOkws7O0k9taaEx1eFuiZ4OosOwsUZABtKmijqPG/GmI34gyoT/vopV506gJRYM4+vKiE9wcrIrAQunZxDRmchli+gRjoXdT9frSwM3dZg97Fkbx1nj87k9nd30+byo2oavz5rOG9srmBlYQMrCxtYML4fQVXl4sc3MCU3hRhz11S5++vsZKfEsLRbh5Dz/rWWihYXg9O7jo0FtR2RDiUNdi/ri5s4dVAqu6pC78ubWyv4zfzh1La7WbSnjmtnDMTlD/LEqhKeWlPCjopWspNjuH7W4EgxQaPDy8Zu7+X+ug7SE6zEWYw0Orxc+uRGFozry+NXTwZCx/aP8mpYXlDP8oL6SIFGeJ+zewPM+0eoS9eb2ypJi+vqqNL9uA4ctjhjd1Ubt765i5JGJy+sLyPRFhruDnfCuOLpjbQ4fTzzgynMG5mByWigsK6Dcx9dy4zBacwZkR7pvAKhYooX1pcybVAqaXGh93TfUab6anf7eXF9GRdM6MvC3bXsr7Nz78ICfjF3CIk2Ex2eAO/trGLO8AxW7W/ocWwNd/kIv7dFDU4+ya/g3R1VbC1r4bPb5/bKCxfjzhuZwd7qduI6i2OufGYTALlpsaz+3VyeWB3at88YfjaJNjOqqjH6z0sYkZlAXlU7Dm+Avx9y7A4Ld3rLq2xj/N2fRs6h3bvjXPT4ej74xUzuXVjAc+tKKX3gfH7+6nZaXX5q291kJ8cA4Oz8bhP+TvpxXg3njs0iqGrYzMZIp5Q4y9Gngm6we5h2X6hIdkh6HLecOYx/LT/Ast/M5tVN5Ty8dD95Ve3cf/E4VFXj/Z3VzBuZwe7qdmYN6xP53hGe1m1NUSOXT+3fI6Oi2UWjw8uSzo5m4e+s0SYFGkKIE4PRBOc+AGow9PcosJqM3Hz+VP6z+HasrjpM6YP53bzelaR6aYkfSsmwXzMsLoHoNNgXQghxXFnj4aInQ+eu+MNfVaeH8+bN5b2OZlRXC9PnX97rhz89aef9jYCzBXNqlKdSEUIIIbrJTo4h946FvW7PjNGodx/+vFdm47DPOdb1WH/HvM9/oDip1HUWZEzonxz54fqMYX1Ye6CpV9vwsJc2lLGuuIkRmQkcaLATHg8PX/3bJ97K35b0bGv99rZKUroNTtS0e3hwcSF13Vp1v7cjNIA1ISeJuSMzeHT5Aa59fgvTBoW6Pvxy7tBIAUd34fWekBMayPIdptX0f9eWRv7efcAC4KHOdW09pLDj7o97Tq0RFt7eqlY3++sdked2ePw8urxzehJFoc3ljxRkJNhMzO7WieLXnQOKEBr0cnoDXNxZABPKzufsMVmRQapl+fXUtXv4rFur7KfWHOT2c0Zw3r/WRrpChBXV2XlkWRFPrzkYue3VTaGW/6s7B4ZizEYK6+w9BibOfGQN43OSuGhSduS2NpeP/JoOrnp2M1edOoCKZhfrikOt+XdWtEX+hAsfAE7/26rI3x9YXMhHeTWcOyaLEVkJNDl8NDl8vL+zij+8v5dPb53FW9uqIo8PX0F9pKkvdla0Rv7e6vIxLDOe6jY354zJZOm+noOcdk+A4gYHlz21seu16eyEsb/OTiCo8tjKYvIq27jptR08eMk4Lp/an/319sjrBdDg8LG8oL7z9Wvgrg/2sr28lWGZXR0sqtvc5KTEUtzgICvJRrzVhD+o8sSqEv65vIiBabGRq3PDy6poCQ3gLNlbx6A+cdy7sIBXf3oqpw8LXVDVfWqPv36cT0O7k0/2dg20egNBLEYDjXYv6QmhrjfhK+3H9Eskr7KN9zqv9G9z+SNFNHXtHh5aUoiqwUd51Vw8KQfoGuCBUJHP4YoZmh1eXt9W0+NxVa2uXoUlP35xK2/dMINV+xt4bXMFQ9LjIwPd93yS3+MK6tImZ6TQ6u+fFqFqoGlw87yhvQr0vYFg5IrwsK1lLWhaqO1+uJ18VqKNn728jbkj0pk7MoOyZhcPL93PjbOH8MbWCu5bWMBlU3IobXJy3RmDmT08PTSw11nUFC7UWl/cxLL8eoZlxvPPZUU0OXyR4oDNpS24fEHq6kODyrEWGY6KNpcvwPt5dVS3eRjTL5GhGfF8uCu0P/7+3JEs3VdHs9NLZYs7MogMof3/gcUFFNU7uHxKDm9vr+qx3HHZSeypbu8x9cE726u4cc6QHr9BFNU7OOW+5ZF/a1rXYOeZIzOwWYw8tqqYeJuJFqeP4ZkJmAxKpCCsu/DnNiPh8Fe7Q6g4aWx2InurO9hR0dajqLF7cUZ3/1px9GmyihscfNxZzFHX4YlMV/L65q5jXqPdS6Pdy9oDTT2e+8vXd0b+XtniYll+faQYBEIdtT74xczIsea1zRVcNDGbDd0KH1YVNnCgwYHdG2BDSXNk+hmgx3kwLPz6Hmx0kmA1YTEZKGl0MCA1Fgh1J7n62c3cdvZwKptCx5Fmhw+XL8Af3t/LysIG/raksEdOeFvHZidFtn/tgaYe2/vzV0PT1WQmWvn+tNBvREX1du5fVEBhnZ2b5w2NFAd11+L8/Gmb9tWEis6uPnUAgaDGm9sqI/eFixp+/MLWHl1Vwvvy7qo2vIFgpPvJ9a9sJ8Fq4l/fnxiZwmPjweZeU1A9vHR/pOC1+/kaQp+rRz4t4uZ5w0iKNfPW1kpuf3d357JCr0n4O+Hjq0oYkBpLhyfAQ0v289CS0DHzJzMHEWMxUtXqYsG/QwWXbn+Qez7J57l1Xd/BKlpc3PLGTu66YDQmgxL5btho92JQYFhmPCsLG0g4pLizrNnVYyq1HeWtzBmRQb3dg8evRrquHGlGrHd31vYoVAo9NtQl48/dptLZVdlGo90bWecmhw+b2Qj4+d7Tm5g/OjPy2O4FwTe/sZN+STaaHD6K7jsvUqCR3FlItLOiNfTdoFuN5/bylh7HhpJGZ2SfarR7qWgO7fsFtR3sqmyLTANkMRnwBVQeumw803JTKazr4EDn+7PxYDNnDEvnr5/sIzs5hsun9ufsf34GgM1siGyjpmmsPdDI+OzkHh1z9CRnRCHEiUNRolacETaqbyL/Od1HcPcKzLPGoxzlC+CxcHoDfPjiw5zatoh3Ur7DNT+7rfNEFgWuFtj9JoxcAMkyoCaEEF+r2NSoR2Ql2bjp2muintNid7Pq5QdIcBzENvtXzJo+/fOfJIQQQujgSEURdrudhCN20Dj8ldPHQu+CD/HN1uL08es3d1FU14HVZODxqyZFBtWfvXYqO8rbqGxxUdfhIdZijEwnAvDnj0I/ZN8wezA3zB7MSxvKAMirasdmNjB3RHqvwa9GhzfSRjwsPC962MrOH87fvGEGAVXj0eUHWFPUSFaijQSrieyUmMhjh2XE8/1pA/jrJ/mRK3nH5fTuLHPakDQ2lDTj9ge5dHIOCTYTL3aub9ihxQ1hO7sNhIU9eMk4nll7ELcvGBlwmtg/mV2Vodcr7N8rDpAaZ2FgWiw7K9qwewJ80jkgZjIoPa6W/s/KAz1+oDcZFAKqxssby9lT3c6C8X1ZtKeW6Q+siDzmrFGZvLejipJGB16/yh3njeTBzsITo0Hh2W6DIYdjNChcOiWbt7ZV9cgubXJS2uSkttvA38S/ds0z330Ar7t4m+moU4bsq+nodaXurW/mAfDk6hJe67bccXd/yo675tPo8PYYYA3rfuVxrMVIeZOLH84YyIJxfXsVaNzXbb8NK6yzY1BCV10v2VdHQeeV8EFV43fv7OZ37+zmgUvGHXFbmhy+SAv77tOpPLx0P+eN7cvPX93OlIEpvHvjaTzz2UH+uTxU9FDe3POKVYvRQFFngU+z0xf5jFW3hR7X6vRFBsggdDX9sIyeRRAj/riEjAQrDXYvT10zmdOHpbO/s9BhTOeV9ocbxP00v47hmQkU1tn5zVt52ExGzu7sHnI4voCKP6gSZzVx3cvben02DjY6GZAayw2vbO9x+we7qiP7jNsfxBQMDXQ7fUEufGxd5HHriptweLve53ChSFGDnZvmDIncPuuhVeT2iaOmzc27102K3H55twKcyDI7p3ZZtb+xxz6zpayFP7y/F4CXOzsu7K1uJzHGHHmPBqbFUt7soqzJyTXPbe414Nfu9pNgNWH3Brj9/QI2lrUxa1g6z1479YivodDHxpJm/vRJaP+YNzKD88Zm8eGuGmYPT+fGOUO4sXN/KajtoKLF1WOfDBesXTChH1lJNs4b25efvLiVug4PQ9LjDtupZUtpS4+B/e76Jlqp7fDiC6jMHp7OUz+YQqvLx8LdtSzdV4fLG2RAWiy7u01L8q8rJ7J4Tx1L9tVRVG8nPcHaY6qg04f2YdKAZGraPLzbWVz16zOHRzonQdf++WW8ef10HltVTEFtR2QqrL5JNmra3JGOD2E/PX0Qpw/rw3UvbevRkWjOiPRIgR/AGQ91FeKNz0mitNGB3RvkH51FTs9dO5WfvbyNOX9fDcDIrAScvgD3LsxHURSuPnUAN84Z0qOg7/NMGpiCLxCkuMHBsIye35Gf/uwgKTGhsZWAqjH6T0sjxTVHeg8b7d7Pne6qvsMbKb480OCIDIR3L5gE+P4zm3jj+uk0O3xcOC4Dv2pgSbdOIN2LLcKFIJdOyWF3Zc/jqdsf5Ok1Jb2mvIFQ8cbW0laK6noW0Nq9AX7y4jZOyU054nZ070b2UV5Xkd3iPbVUtrp4dl0pqgZ3nj+yx7mn+1R3itKzKKm7xXtruXhSNr/pPLeHPXeY7yMf5dVE1mH7H8+irsPD/no7afFWMjvHq+zdzgnfnzaAN7ZUsLygq8Ditc0VTBmYEukSFfbujiq+d0p/pg1KZWNJM0kxZgb1iePuRb0Ll1qcPraXt/YokAF6FGFVtDgj41rVbe4e3yHDUx3NHJrG+uLmyPm2qN4e+YxWt7m57e28yLR7235/OgmExs8ufbLnuSt8XgGY/sCKSPeahg4vG0q6iofC+/Pt7/TsCDQ8M57CWjur9jdEOlR1L1D2+FUGpMZS1uxi/P1rAbjnorH84AtM0fZVHH1SFiGE+BYyFn+KpXY7StW2z3/wV9Tk8DLEvo04g59B9m295iHVVWMh7HodKrZEL0MIIcRJr3DfLka1riJXraR1w8vHe3WEEEIIIaIqKcZMVauLug4vKbEWclJiI/dZTUZmDEnjilP6c8uZw/jp6YMiLZ27G5aRwCWTc/jwl6czZWCocHdgahzXzxrc67F5lW2RttyHM7hPaOA5I8GKzWwk3mrin98LtajOq2qjT4I18kP18Mx4nvnhVM4bFxpQXlfchMVkID3eSkrnVYCXTMpmze/m8Np1p0YyTh+Wxt3fGcM7P58Rue3Vn57KuWOyGJjWtf1HctHEflw5bQArfzuHCzuvPh2cHse9F40FYHdVz8G9y6fk8P5NM7liak6P2+88fxQ/Oi038u//rCzm/Z3VZCZayfvT2ez8vzMYnB7HU2tK0DT40Wm5nD+251zr10wfgKrBnqp2Hr58PD+fPYS7LhhNdnIMpw1JO+I2XNzZGWNU3wQm5CTjC6g9fvQP21Lawuis+B63TeifzIjMwxeNdbj9nzvIBaHBydhD2n2/dkjRR1DVeHtbJc0OH/1TbCz/zawjLq/V5cfuDTAgNZYx2V0FOlMHhgapluyr6zFAFnbHeSNJiTWzLL/+sJ1i/rakkORYM9MHp/KzMwYxe+jnF6Z/uKuGn78aGgzeXt7Ks2sPRoqXwro3A7ymc0BkeGY8I7O6XtcHFheyp6qdfy4vikwnEFbc4OyVGx50217eyoS/fBoZWBvbr3fBUuE955KTEsO6A02RDiWaBje+toP8w7S6/+5j6yiqt/PA4gLG/Hkplz254bCFSyWNDhbtqes1TUP3gp7/rDxw2OdCaOC83e3nzxeOZsH4rn194e7ayGsKoUHBz4oaKW5wsKUstKzuA8gA/VNDx6otZa0cztoDjb1ua3X5ewx4h6fR+TS/Dk3jsMeHOSMzAFhTHBrAn3GUz53Qz5wRGWQnhwZw0+P/v737Do+jvPY4/j3qXbIsybZsyU1ucscNjCvF2DTTW0IJvcfUQEKIaYHk3hAgpBACCRBqKCHEgUDCBdMSWigxphobYxuDwUXutvzeP2Z2vbvSqli72tXq93kePbZmp5yZnZmz2vfM+2YzdWAFJXmZjOkd3jA9pEcRUwaUs3u/htfukO6FXDRjELWVRcH7UXVpXvBeHupfi74O9koTaWiPwmABQE1FAZnpaVQU5nDYbj35ct0Wvt6wla75Wdxw2Agm9u/K778zjoNHVnLIaC9/LFi+jrKCbMoLdhZoHDiiBxfNGMScfQYEp02s6cr8S6Zz8QzvvOzVJZeHz9yDnx05kkZGVQqTk5nGqZP6MqFfV+45ZUJw6KCjxvbixIl9WL1xG598FX4PPHZ8NdMHVRC56mtmD+OnR4xosI2u+Vn85dxJPHWul29f+GgVfcvy2XtIt7D3paLIK4rZ4aCmvICLZwwK++wROmxNNJNryuhfXsAnX21g6erwIoH1W7bz2erNnLDHzsbm+h2OsoLsYG8b4PWIEbBs9aYGvWdFGlVVwrRBzfca+8qir/li7Wa+Xr+V8oJspgxsuMykmjJqe+wcpqZfWT6TQ+Y7zx+OLbKnr4Bz96pha/0OHnit8WLJ1xav5qCRleRnpXPzMaOajDfQU8dZ977Jj//mbe/B1z4L9kIUKvD569DRPRno9xx16qS+YfNc+NDb3PHip7yzrPH7fDTXzVvIAbe8yN8XrKSsIJvszPBm/cKcDL43cxDgDSMX8Mx7Kxk+92luf2ERkY667RW+qtvCsbf/i/1veSFsufF9dt4TFq3awH2vNn4sAw7/9SthbVuVxTsfeL7mr95QdVfPHkZNxc7PTDN+Pj9YYAUEizMAXvrEK3j5wO/NK9Tm7eGFzIH8/lXdFhav2kBZQTYlUXq7mFRTxsT+ZSz9ZiOLvlpPdkZag89bALOGhRdjDu8Zv2EzVaAhIhJp4vkw/ftQe3DcNtGrSx6LBp7CKxnjWDbkFLoXxaenDgCqJsDsW2HIgfHbhoiIpLyKXn1Zm9GVbTsc2yv15JOIiIiktvQ044J9vMaeQX7j8LzzJ/HY2RMbzGtmPH/JNC6bNThs+oiQHiv8Nh9qK4sY0K2QZy4Ib1Rf/PVG3vxsDbv3K2VSTVmDbfTxCzR6hfSS0b3I+//7X9TRv7yAvQZX8PCZe/D3OVPoW5Yf1qi1dfsOzCzYVXx5YTa9u+YHx+YGmDnUa/gNHVph0oAyfnP8mAaNe5HuOHEsNx2z84n9Ur9L7t2quzC0sojS/Cwuf/Td4OuH7daTi2Z4DQo9isOLW7oWZDH34KG8+cN9g9PO36uG+07bPdjNdOAL87ysdEb2KmHG0G5h65g2qILnLp7G85dOZ/Yor+jilEl9eemyvditeue+HDu+ijn7DOCuk8fzuxPGBofkGNGrJHgcQodBgZ1dn/fpmkufkIbpo8dW8dScycHfA42F4A2ZEdmF/9jeXZh7UC33nTqBm48Zxf2n7c7sUT3DGvjmHlRLY5787xesWr+F0rwsKkOKg4pyvKejjxzTi4fO8AptMtKMPWvKKMjO4PffGcdho3tyxYHh6w0UQDxy1h7ce+oETpvcj7F9Snn8reVs39GwP/Q1G7dx2OhePHD6HvzggFq2+MPmXDxjIL87YSyPnLUHj5w1kQ+unclH182irCCrwTqunbeQL+u2cPMxo4KNxIft5hXr9OqSywR/2oq1mxkW0kCyZuM2Drr1xWCPMqEcMHVgeVgDZMDtL3waLFbISk8LXtcA1x82nKfmTCYnM51JNWU8/d7KBk/NB3q0GNO7S7AB8+3P1zLj5/ODT9y/vmQ1PUtyufuEkfxPJv7QDwAAIABJREFUyPv44cq6qA2GAZu37Qgrlnn+kmkcO7467Hw4cmwV18wexoyQLuQjn44OOOP+d7nwwbeYdfP8sOnnTPMaOBeuCC84CZw7L/pPrgcKyYb1LCJSvzLvOgkUyOw/vEeDeaZFNL7uWaMCjfaQnmacOrEKAIcjNyud5y6eFuw5I1RuVjoPnL5H2Pk0tneXsB4rFq3yip4m1pTx7d17c99pE8LW8fhby9lW77h81uAGDaO5WWnBa64qJHd1K8ph2ZpNrFq/hT5l+UwfXMF9p+3uFT2YMSCk0G3lus1h8XT181plSS6Z6cbJe/YlLyuD6q55jPbv7es2bWdsn1IOH9Mr2HAeEJnL3v7RjLD7YaB3pNMm92P3ft45e979O4ctmXf+pGBDc31E1zGl+VkcNbYq7HPCTw8fwT2neMesKCeDriG5EcLz7Y4d3nF8Z+4MnpozOTi0xR9PmcDdJ48PFlFcNmsw886fxAuXTueIMb2455TxjK72BlA/aGQlQyuLWbtpGw+8tjT4ntT2KOKgEV7u2mdIt7CCip8eMZxff3u34O+hBRnL1mzki4hehq44YAhPXzCFt380gztPGst9p03gD98Zz0y/l6FL9hvU6D0Y4PG3lrG1fgel+ZlhvX4FXHlQLT87yis+nTygjJK8LPqXF3Db8WO47fgx5Gfv7F29ND+LS2cOYkZtN645ZBhXHTyUUyb1JTczvUFhY6irDx7KgqtnBj8bADx29kSevmAKz140lVe/vzf/+eG+PNrI570NW+s55a7XMfM+9wSM9Xvm6F9ewIhe3nvRJT+LfYaEfza5dt5CNm/bQWFOw17iT5nUlzNCCngDBYuP/mdZcFrPktxgceHpU/rxz4um8tJle1GSl0VZQXajQwUF7tNXHTw0bHpoLxi3zV/E2OpiPvnx/mEFd28uWc3Ln3wdzA/RrN3knTNm8LfvTubmY0Zx5lTvnpORZvQvL+Anh4cXL0Xr5ea7D7/Hob96iVtChiLKy0rn+sOGs62+4eeR7kU5bK3fwTufr6VvWR4bt3hFHKH3NfB6xKkqzaNuy3beWLI6WCQFBItqAKb7xYUBoQWisaYhTkREIpVUeT9xlJ5mnHjoAWytn0V2RpyGNglIS4ceI+O7DRERSXk1vbqTdfLdfLNmDQf1i0/3fiIiIiLJ5KCRlfQsSKNHmfdl+NBGnrgPyEhPo6bc+4I3LyudeedPpm/ZzuEW9hnSjSfeXhF88nxAt0JuPW40kweUs2D5Wo67/d+s3bSNvQd34+jxVVz+6LvM84f8AIKNErWVOxtLe4Q8pTi6ugQzY2zIk48Z6WmcPa0/v3ruE44Z533PEeh2O7QB7K6Tx7O9fge5/pOEuVnpDK0sYlRVSXCegf78uZnpbNpWz3WHDgsOgwA7G82C8frrGlnlxbXX4IrgE5JnTu3PxTMGkuFXraSnhT+HXOavK1DkAXDAiEr6l+/8Ar22RxGPv7WcSTVlZGWksWcTRS2R9hnSjZv/+RG1PYq4/rDwBoPayiJe+eRr5uwzgPKCbHarLuHNz9YwoKKAqw4eyosfr+KMqf1Zu2kbx47tyZCqMmbeNJ9V67cyqHthWMFLbtbOr94D3b4fMKJH8H196Iw9SEtr+Hh3YP/H9enCSXv2ZZ/absz4+Xw2bq1nZFUJfbvm8ee3vK7PDx5eQZ6/nbysdCYPLGfeOys4cqzXffm88yeRkbazGGH6oAqmD6pgUcgT4WdO7c9pk/tiZmHHfELf0mDDzpAeRQ0a9M+ZvrPBd1BFAf/6dA371nYPK3wICDyVHhAYdgS8Ypo/+kOijK4uYZ8hFYysKgmeF3Wbt/OD/Ydw4IgenPT714Lr+Hz1Js7bq4a6zdvDulSfVFPGaVP6BYfnKCvIZu2mrWENO0W5GeRkpvPUnMn0Ls0PnvveftXwwGvh3bmHuvW40Tz2n2X89KkPwrpbD7hw34GMripmwVc7Czweet0798/bq4ajxlaFDX3QmOMmVNO7a35wKJnuRTms27yNguwMyIbfnjA2bNitw3bryaNvLiM9zTh2fBXrN2/nz28tD2vYC9grovEp4KSJfbjl2Y95+/O19CzJDTbsju/Tlf8uC3/vA71wvLZ4NRWF2Rwxphe/fu4THjlrIof/+mWAsAbg4yZUR+1dRmLvsFHd2UY6+/kN5iV5DQukQm31C6xuOnoUh4zuGfZaYGiCwFP1E/uXsfiGA9i8rZ4HXv2MuU+8589XxqGjezLr5heCQ09s3raDyuIclq/dHLbebhFDlkTqX17AC5dO5/R73uCUSX3D7kuBvJeeZiy8emZY/hhdXcLo6hKuOGBIcFp1aR4r121hfJ9SPl+9kZ8fNYop/7Pz+ov8Xv66Q4fx6+c+oaaigMjatFuOHR32OaA4N5M1IcUMgafxR/bamTuPGhfexjCxpown3l7OkX7PURfvN4gVazfx9wUrWb9lO2ZGUU54ocukAd4xeuLt5by7bC3dirKDcfzvkSODx+yDL+roXpzDPrUVfP8xrzDz/L1qmDmsBzmZaaSnGcXZxoR+pUwZWB68h4zpXUqGfxwLsjP4xbGjefytZfxn6ZrgvWvf2m48895KcjPTOXXyziKCvQZ3C4vzqQVfcPDISqpK85g5tDvH/e7fAPz1vEmccc8bwZ4vSvMygwWO5+9VwyNvLmPZmk0MqCjAzHjojD3CckngXA4MnzV7VCU3HT0qLOcG7NG/K8++/yXdirJZuW5L2GvDexYHC18Ajt6tB68sXhss7gnVWBFFQG2PomDjPsDls4YwuqoLJ0zsHSzqXL1hKzcfM4rPvtnIrJtfCFv+sbMn8vN/fBT2Oe+AET2oLs3jtvne8h9eO4uaHzwJELyOqkvzGFlVwgfXzmxw7v7627sFh7M6dnwV97+6M49VFudw4sQ+fLpqA394eTGXzhzET5/6IGz5s6f0Jj3N2Fa/c7ibwPt154nj+Hz1Ri4LKbSNNHNod66aPZSSvCxmj+rJ7FFert/NL4oa07sLC6+eyZArnwrb53nvrODUSX3Zo39X/vfpD1m4Yl2D3qTqd7ioPVkcN6GaG5/5kPe/qOOIMb34+Mv1bN24g1nDu4f1WlWSlxkscnrzszXsP7w7Pzl8OKdP7kdZYRZ7XP8sAH26hn92DAzfEg8q0BARSRAzi39xBrD0m4384tmPOGGP3gzrWdL8AiIiIlFUV3ShuqLppydj4Z/PPkPde39n1GGX0qey8S8QY+LrT2DBYzDmO5CvJ7pERESkoQEV+RQWNnzKszEF/pf5g7oXhhVnAEzo15V/fX/vsGkH+k+zjghpzBnXt5SinExuOWY0/csLuOWfH7Fnvy4cOrqSt5au5vy9dnbr3j2kQCO0t45Ql84czKUzd/bscfF+g/j3oq+DQ3mA1+NApHnnTw77fbRfrFHvHO/MnUFhdgarN2zlf5/2usXuEvHk9DHjq8nNyghu58qDaoMFGjOGdgsWZwDsP7w7Nz6zs3vt0G7crzywll/+38f0Lw8/nofu1pNN2+o5zW8oKivI5p25M/jLW8sbHW4m1LCeRZw5tX+wJ4xQlSW5waedAW44fATffeAtTp/Sl4k1ZUz0GxPvOnk8dXV1FBZk8/CZE7nrlcWM9N+De0+dEBzSYccOx6oNW4INNoeN7hlskGmsOAO8Rsbjd+/N2X4BRK8ueRwzrpo7X/qUA4f3oKIoO1igMdgfZuX/Lp5GaV4WW+rr2XtwBeP6NHw6O1RhSANgZM8vAQeOqOTaeQsB6F6UzcIV4a93CWn0PX9aHw7erbrR4gzwnphfsXYzZ0ztx1frtnDOXjXs/bPnOWhkJcW5mcHu0Qd2K2RcSJHRtEHlHDyyki75WUwb1PDvAq8IwMIKNCqKwouFXrl8L658fAH3v/oZNx09ijkPvkVGmnf+De7esHeIqtI89h5cwT/f/5JrZg/lvRV13B/SxXu3whyOHVfN8jWb+N7MwSz5eiNXPbGA0dVd+O38RX7PH/WNNu4dOrpnsAAnoGdJLr87cSzfbNjKw298zokT+4QVRwGNDgXwh++MY3u9o3fXPLZs38Gjby6jd2ke1x4ynHWbt8GOev78jtc4dcSYXkwdWM7Ln3xNRVEOBdkZrN+ynTOm9OPzNZsoysnkrGk13PLsx4BXpHLAiB4s/WYj391nAHe+9GnYtotzM8lIM7bvcBwzvpr+5QUsvuGAsHlCi7Z+fOjwBvFL/KSZcfqUhj1mRLNlm9cgG9pwHXD7CWPZvG1Hg/tVTmY6ew/pFizQGNS9kMz0NB4+ayL73/wCm7bVs3X7Dh44fQ82bN0eViTSLaQn6dDhLEJVlebx5Hd35qGSvExK87IYGlKkmBFR+JWXlcFjZ+8ZNq1P13xeW7yafWorOH1Kf5xr+AR+qG9N6M23JngPpKQbPH7Onsz+5UsAHBjRU8y9p07g6QUrudl/0j9QLJCWZtx63GjSGikeuPaQYVy63yCq/IbiguwMfnL4CP6+4JlgPovmslmDycpIY0Zt9wavVZbkBntTqijM4Yyp/Zj3zgr2H96DfiHFjZfu2z/YHnHCHr15Y8nqYM698sBa9ujflSE9ipg+uIIbn/6AW579mPLCbE6b3I9n3lvZYMik8GNXzexRlcH8MjGk+KZLfhZj+3Rh2VteDw9VXXLo3TWf+ZdMp6o0l1Mm9WPbjh3BYzi+b+PDZm3d7p2rJbmZjRZngPeZ5tn3v6RLXlaDAo2C7PD78hWzBlBY2HjeCj2/7jp5PF3zs7ht/iKeeHt58PNbQK8uuZzm934xe1QlNz7zIfuP6EF+dkaDBn/weiEKXHcB5QXZwR5WIrc/uncXlr+zIlgc11ibUmju/OGBtTz3wVfBHmEC59uVB9Zy2azB5GSms2f/Mv65cGXwvj/C7y0pUMwYKEAcUFHAxP5duf+18CHEjh5bxdWHDOWeV5Zw7byFzBrePezaBjg5YpiX3Kx0/nvVfmzcup3czHS+7xf6DvELXob1LGb+wuXMX7SWrPQ0pgwsIz8rg/zsjLBr/5L9BvG3d1eQn50RHLINvGLFkyb24dn3v2RUVfh3l9vqHX3LdvYOMqO2O3lZGQzvVUz9DseQHkWcNrlvsNedwpwM/j4n+hBysaACDRGRFLd20zYWfbWBL9ZtYVjP5ucXERFJtLoPnmPgN8+zbMkh8S3Q+Px1eP+v0HeqCjRERESkzQJfgB8yqnV/fBdkZ3DfqRN48eNVwScE09OMC/cdyJlT+7F54wZKS4rDnlQFr4EsPc3ISDMm9G3ZZ5lTJvXllIgvzFsi2GV3Xmbw6d5z9xrASXv25dVPv6Z3RANEZnoaR4zpFfy9KCeTKw4YwrXzFtIrooCipqKQxTccwN2vLObKxxeEDdlx8qS+Db7gB68Bas4+A8OmFeVk8u3dm+/pzcyiFiVEGtitMKyRsDF9yvL50UE7uw4P7c3jtCn9+GbDVmp7FDGhb9ewopposjPSueaQYWHTMtO9hqit9TuYMqCc3fuVcsUBtVQXetN3FgRlBocJaUqgeKCprru7F+dw3l419CjO5cn/rmjwemiDbVZGGmN6R+9h5hfHjuZPb3zOnL0HBJd7/Jw9gz3CzBjanV8/9wkDQsaoB/jDd8Y3ur7aHkVs37GDMb1Lw3oDAYLDIVx36DBe/GgVmelpzD24lgv2HcCqOq8QJLLXlkjn7T2Af77/JZMHlDOmd2lYgUZamtElP4trD/GKDob1LOZPZ07EOcc502sozs2krq4u2GtIv7J89h5SwbpN2+lXXtCggXhAtwKG+I3UjfUEE01owcpXfm8kR/tP6xflZHLNQYO4aGYtvbrkBhsyD/KLku46eRx3vbyES/YbFNYIeMUBQxjes5gJ/tAOgR5mfnbkSPKy0jnr3jcpL8zGzPjFsaNZtWFrsHeegBe/Nz3YMH37CWP54pvw3jck+QSGzuraSIFGXlYG0TrgqCrNY84+A1i5bkvwfO9bls/rV+zDOfe9yYXTe1MdMlxCQKAQZESv4qiFapHmXzqdvMz0qI3y0QR6Ugr06mFm/OTw4eRkpjOgovleXUaGFEtFxjq0spihlcUM7FbI0tXhwzVENuAHFOdmhhUhgtfDSWSBU2O6FmRzXQuLnS6fNYTLZw1pcp6rZ4fnmchce+GMQVw4YxA7djjqNns9BaWF18SEMbOw4r9QXfIyuWzWYMb1KWXmsO5kO+/9CJwfxXmNLxdpzcat/vzRe4UJFLSNru7C+1/Uhb2Wkd668+eB03fnmw1bg4WsZ0/rz+Zt9Xx792oApg8qZ8nXG8POy95d88Pez9AemgZ3L+Sc6TWkpRlbtteHbStwb71kv0HBYUb+dOYeZKWncdv8TwAaDCMU6frDhvPga0uDPWsFBD57pKUZOWk7ezgbWVXC6OoufLpqA9kZ3psb6KVm7sFDWbp6I/sM6UZamjUoODpibC+yM7weVY4Y06tBzy/RFGRnBAtlSvxrIRBft6IcZtaWc+SExouVnjh3Epu31zOuTynnTPeG69qwZTsHj6zkohkDg59Fh/UsZscOx0kT+zC8ZzEX/eltZtR2o395Ab/61m7kZKaFfaZOT7Owz3pPnDuJ4sz6sM+j8WDNVYx1FGPHjnWvv/56osNolbq6uqjVWalA+9fxpfo+dqb9q/O7QWzth9hEMbM3nHNjm5+z4+qIeQs613WTirR/HV+q72Ng/z7/6ms+//RjRo0aQ05WHGvKt2+F1YuhbIA3WOYu6gx5Czpu7koGibp2E3nP2JVt97lsXvNfTs4thrlrY77tWNG2k2i7LThXWiv0HO3ox1u5q+Vae7zXbtpGUU5s//5uKoZlazZRmpcV1gAQL39+7VNG9CkPexq3tdZu2tagcSrAOcf2Ha7BcBiRkuUz8a7G8fhb3rATs1tRyLNwxTpm3fwC/7hwCjUhjYptORb/9/6XjOhV3GB4msace9+b/PWdFdx8zCi++8BbAGE5u63vyfb6HazeuC1YXBHNU//9go9W1nH61H4YRpbfoPSfz1Zz6K+8oTUWXLVfcEigSFu213P8Ha9y0b4Dg0UILfHRyjr2/fl8gBY1pNbV1ZGbl88lD7/DGVP7Neip47/L1pKeZsy6+YVGh5XYFes2b6Mw5Lu/eFwnTy/4giE9ioLFaC0RyziUu1qmtcd8w5btPP/hV+wf0UNEW0WLY/O2eq6d9x7nTPcKwOJp3eZtXHD/G1xz6Mhdbmz916KvWfrNRo4c27Yh0ZMhd+1qDM45rnriPQ4aWcmY3i3vVfWh15dy+/xFPH3BlLDPRbsax5frNjPnwbe45djRDXojCvXGkm+o7VEcHE7jF8eO5rz7/8P0QeX8PqTwr73ek4seepvR1SVhRaQff1nHMb/9N6vWewV2TeWWh9/4nIv/9DZPzZncaM9PjXlr6RoO8Xt/OXpsFT85YkST8weOxeZt9fxz4ZfsP7x72Hu2eVs9Nzz5PkN6FPK9R97lzR/uGzb80K5Yv2U7895ZzlFjq+Kau+p3uGYLMyO1R+5SDxoiIp1AtOrVWNuyeSNZWTlYU+W0IiIizehVVkqvLqMgI85/rmRksaVLf7I7SAGjiIiIJL9oxQfx0txwHrG09+AyCgt3vTgDmj4+ZhbsKSKVtaYwI2BIj6IWFQa0xvTBLe+p7urZwxjSo4j9hjbsWj8WMtLTmi3OAJg5rDszhzWMYXR1F8oKsqntnh+1OAO83kkeOmOPVsfX3y9KCgwd0xIZ6Wn8/OhRjb42zO8p5+PrZrW60Sialj693BYz4vT+S2LlZ2fEvDijKTmZ6cEeaOKtKCeTm44Y2uKhyhqze7+u7N6Kgq5UZGbMPXho8zNGOGpsFUe1sbAlVEVRDvedtnuz843p7Q33sWdNV176+OvgZ4v0BLVZ/OyokQ2m1VQU8uL3pnPBg29x4vimr78jxvRi+qDyFhVUBoyqKgn2XJad2fL9zslM54ARDePJyUwPngNHj6tu8fqaUpCdEbN1NSVWeTbW1IImIiIxsWzRQj6+cT9e/+vtiQ5FREQ6urfuhz8eDms+a37eNvjneys54Y5X+SCi20sRERFJPmZWbGavmtl6MxvW/BIiEiul+VmcM72GnMz499Syq/79/b35xVGtb0BsibQ04x8XTuHOk8bFdL0Z6Wkdprdb2TXKXSKJc9d3xvP+NTODw3MU5SRXnwU5men8+ttjGNqj+Z4aWlOcEXD4br2YOrCcs6fV7Ep4EmfJdTaKiEiHlZ2Tx6acCrKLyxMdioiIdHQF5VDYAzKaH6O7LUryMqkoyiavHboEFxERkTbbCBwA/E+iAxHpzK45ZBi1PVrWxXp7Sk+zYCNcPIQOLSPSCspdIgmSkZ5GRrrXY9QZU/txxpT+iQ6pXXXJz+Kuk8c3P6MkhAo0REQkJsoqe1N24SOJDkNERFLBgH29nzgb06eUMX1K474dERERaTvn3DbgKz1tLpJYx+/eO9EhiHQYyl0iiZeZnsbls4YkOgyRMBriRERERERERERERGLGzM41s9fNbIuZ/SHitVIze8zMNpjZEjM7LkFhioiIBCl3iYhIe1EPGiIiIiIiIiIiIhJLy4Frgf2A3IjXfglsBboBo4B5Zva2c26BmXUHHm5kfUc4576IZ8AiItLpKXeJiEi7UIGGiIiIiIiIiIikluJqmFsc01UuzgHm+r9kV8KW5QB87sqYtOWWmG6rKcMrsnjiwvgPBdYWzrlHAcxsLNArMN3M8oHDgWHOufXAi2b2F+B44DK/IWtSW7ZtZqcDpwNUVVVRV1fXltWxYcOGNi0fC8kQAyiOSMkQRzLEAIojUjLEkQwxQPLE0RKJzF0iItK5qEBDRERERERERERSywXvxnf9dXVQWAhAr7nFLL7hgPhuL8SEq/7abtuKg4FAvXPuw5BpbwNTW7Kwmf0N78nlQWZ2m3PuD5HzOOd+C/wWYOzYsa7Qf5/aIhbrSIUYQHFESoY4kiEGUByRkiGOZIgBkieONoh77oplcWGyFMUojnDJEEcyxACKI1IyxJEMMUDnikMFGiIiIiIiIiKd2J43PMuyNZt2adkXs8+nl61q+EJI7wItFdY7QTTF1a1ap4gknQJgbcS0tUCLWu+cc/vHPCIREZGmxT13xbq4MFmKYhRHuGSIIxliAMURKRniSIYYoPPEoQINERERERERkU5s2ZpNu/70/9zjYG7k99WE9S4gIhJiPVAUMa0IaNs4JCIiIvGj3CUiIjGVlugAREREREREREREpFP4EMgwswEh00YCCxIUj4iISHOUu0REJKZUoCEiIiIiIiIiIiIxY2YZZpYDpAPpZpZjZhnOuQ3Ao8DVZpZvZnsCs4F7EhmviIiIcpeIiLQXFWiIiIiIiIh0MmZWamaPmdkGM1tiZsclOiYREUkpVwCbgMuAb/v/v8J/7WwgF/gSuB84yzmnp5BFRCTRlLtERKRdZCQ6ABEREREREWl3vwS2At2AUcA8M3s73l8y7nnDsyxbsymem0hK3XIdKzdZg+k9S3J56bK9Yruxnw+HtZ95/8+uhC3Lm11kcQ4wdxe3V1y9iwuKpJDiaphb3G6b+2N2LXBgu21vVzjn5hLlzuKc+wY4pD3jERERaY5yl4iItBcVaIiIiIiIiHQiZpYPHA4Mc86tB140s78Ax+M9LRY3y9ZsYvENB8RzE0mp7ta9KFz1RsMXNrPrhRFRfO7KmLTlPgC6mWPl5oaFIZHiUigi0plc8G67bq779UPadXsiIiIiIiISO+acS3QMMWFmXwFLEh1HK5UBqxIdRBxp/zq+VN9H7V/y6u2cK090EPHUQfMWdOzzqiW0fx1bqu8fpP4+dtT961B5y8xGAy8753JDpl0MTHXOHRQx7+nA6f6vg4AP2i3Q1JKoczuR15S2rW2n8nZTYdsdKnftqhj93ZUMn0+SIQZQHJGSIY5kiAEUR6RkiCMZYoDYxqHc1TKp+N63heJIrhhAcURKhjiSIQZIzTgazV0p04NGR0zMZva6c25souOIF+1fx5fq+6j9k0TqiHkLUv+80v51bKm+f5D6+5jq+5dECoC1EdPWAoWRMzrnfgv8tj2CSmWJOrcTeU1p29p2Km+3M2+7o4nF313JcLyTIQbFkZxxJEMMiiM540iGGJIpjo6krbkrWY654ki+OJIhBsWRnHEkQwydLY60eK5cREREREREks56oChiWhFQl4BYREREREREREREOg0VaIiIiIiIiHQuHwIZZjYgZNpIYEGC4hEREREREREREekUVKCRWKneVbD2r+NL9X3U/om0XqqfV9q/ji3V9w9Sfx9Tff+SgnNuA/AocLWZ5ZvZnsBs4J7ERpbSEnVuJ/Ka0ra17VTebmfedmeUDMc7GWIAxREpGeJIhhhAcURKhjiSIQZInjg6k2Q55oojXDLEkQwxgOKIlAxxJEMM0IniMOdcvLchIiIiIiIiScTMSoE7gX2Br4HLnHP3JTYqERERERERERGR1KYCDREREREREREREREREREREZE40xAnIiIiIiIiIiIiIiIiIiIiInGmAg0RERERERERERERERERERGROFOBRgKZWbaZ3WFmS8yszsz+Y2azEh1XrJnZuWb2upltMbM/JDqetjKzUjN7zMw2+O/dcYmOKZZS7f2K1BmuOzP7o5mtMLN1ZvahmZ2a6JgkdXSSayjl7oPKXR1bJ7nulLsk5ZnZc2a22czW+z8fJCCGAX4Mf2zHbSbk+k7kvbO981Ki8nwi82+C39+E56xEXMudTYLP76j5wszyzOxXZrbKzNaa2fwYbbPR/TWzLDN72MwWm5kzs2kRy003s//zY1kcrzj815rcdzPbzczm+8dspZl9dxdjiHp/MbPdzewZM/vGzL4ysz+ZWY+QZWN2PJqJ41sh58d6M9vovz9jYn08/HU1et8zs1r//Vrt//zDzGpDlovl8Yh67zWzo8xsoX+c3jOzQyKWjdkFwkkNAAAYnUlEQVSxCFln1Puwmf3Ifz/2CZkW02slWgxmdqqZfezv61NmVhmxTCzPi0bvVWbWx9//0HP0hyHLxfxYiKepe2ict9vuectft3LXzvUod4XHkfC81VQc/mvtmrssCfJWtDisk+YuFWgkVgawFJgKFAM/BB4ysz4JjCkelgPXAncmOpAY+SWwFegGfAv4tZkNTWxIMZVq71ekznDdXQ/0cc4VAQcD14Z+0BFpo85wDaXifVC5q2PrDNedcpd0Fuc65wr8n0EJ2P4vgdfaeZuJur4Tee9s77yUqDyfyPybyPc3GXJWIq7lzibRny+j5YvfAqXAEP/fC2K0vab290Xg28AXjby2wV/mknaII+q+m1kZ8BRwG9AVqAGe3sUYmrq/dPHj6AP0BuqA34csG8vjETUO59y9IedHAXA2sAh4E2J+PCD6fW85cATe+1EG/AV4IGS5WB6PRmMws57AH4ELgSJ/W/eZWQXE5VgENHofNrP+eMdkRcRLsb5WGsRgZlOBHwOz8d6TT4H7Q16Px7Fo6rNtSchr14RMj8exEE8ic1d75y1Q7gql3BUuGfJW1DgSlLuSIW81iKMz566MWK1IWs85twGYGzLpr2b2KTAGWJyImOLBOfcogJmNBXolOJw2MbN84HBgmHNuPfCimf0FOB64LKHBxUgqvV+N6QzXnXNuQeiv/k9/4I3ERCSppJNcQyl1H1Tu6vg6yXWn3CUSZ2Z2DLAGeBnvS412kajrO5H3zvbMS4nM84nMvwl+fxOasxJ1LXc2yfj50swG4X2538s5t86fHJPzLtr+Oue2Ajf5r9U3styrwKuhT1zGI44W7PuFwN+dc/f6v28BFu5iDFHvL865R0LnNbNbgedDlo3Z8Wjlfe5E4G7nnPN/j9nx8GNp9L7nnHsD736EmRlQT8h9KcbHI9q9NwNY45x70n9tnplt8F/7khgfC2j2Pnwr8D3gVxHxx/RaiRLDQcCfAsfKzK4BlplZf+fcJ8ThWOyKWB8L2SnZclc88xYod0XEoNwVHkfC81ZTcdDOuSsZ8lYTcXTa3KUeNJKImXUDBgILmptXEmYgUO+c+zBk2ttAKj2F3Kmk6nVnXtdpG4H38aof/5bgkCRFpeo1lGKUu1JMql53yl3SSVxvXve2L1lEV7vxZGZFwNXARe21zYjtJ/z6TtV7J8rzQPu/v4k6pxN9LUu7aixfTACWAFf5r71rZocnLsR21dy+7w58Y2Yvm9mXZvaEmVXHYsPN3F+mRJkec9HiMLPefhx3h0yO+fFo6r5nZmuAzcAv8J6CjYsoMbwOLDSzg80s3bwu4rcA7/iLxfRYNHUfNrMjga3OubjmhCZiMP8n9HeAYf6/8bhOmvpsu8TMPjez35v3BLSkNuWthpS7Epi7kiFvNRFHu+WuZMhbzcTRaXOXCjSShJllAvcCdznn3k90PBJVAbA2YtpaoDABsUgbpfJ155w7G++8nAw8ipfgRWIqla+hFKPclUJS+bpT7pJO4HtAP6AnXhezT5jXnWh7uAa4wzm3tJ22FybR13cq3ztRnk/I+5vAczqh17K0m2j5ohfel9VrgUrgXOAuMxuSqEDbUXP73gvvSdzvAtVEdI+9q5q6v5jZCOBK2mGYhmbucycALzjnPg2ZFvPj0dR9zzlXgteV/bnAf9qyndbG4Jyrx2vgu8+P6T7gDP8pboj9sWj0PmxmBXiNfHPasO42xYDX6HeUmY0ws1y889MBef7rsT4W0e5Vq4BxeEMpjMF7z+6NthJJCcpbjVPuSmDuSoa8FS2Ods5dyZC3osZBJ85dKtCIIzN7zsxclJ8XQ+ZLA+7BGzP23IQFvAtauo8pZD3emFChivDG7ZIOpCNfdy3lnKt3zr2Il8TOSnQ80jGkeu7qhHkLlLtSRke97lpDuUs6qpbkF+fcv51zdc65Lc65u4CXgP3jvW0zGwXsA/y8rdtq7bZD54319Z2ozyxJ+FmiU+f5RObG9s5Z8byWJbk0kS82AduAa51zW51zzwP/B8xIYLjtpbl93wQ85px7zTm3GbgKmGhmxbu6wabuL2ZWAzwJfNc598KubqOtcfhOAO6KmBbz4wFN3/f8RqXfAHebWUVbttOaGMzravynwDQgC5gK/M6/Z0IMj0Uz9+GrgHsiGhtjrqkYnHP/BH4EPIL31P5ivM8Dn/uzxPS8iHavcs6td8697pzb7pxbiXfezjDv6WlJQcpbUSl3JTh3JUPeaiyO9spdyZC3moujM+eujHitWMA5N625eczMgDuAbngnwbZ4xxVLLdnHFPMhkGFmA5xzH/nTRpJ63eSmtI5+3e2CDLzxy0Saleq5qxPmLVDuSgkd+brbRcpd0qHsYn5xhHflGZdtm9kcoA/wmXcroQBIN7Na59xu8dx2FDG5vhP1mSUJP0t02jyfRLmxvXLWNOJ0LUvSC+SLd5qbMYU1t+/v4B2ngMD/dynPNnV/Ma9b9n8A1zjn7tmV9cciDv/1PfGeyn44YtGYHo9GRLvvpeE98doT+DJG22ouhixgvnPudX/6a2b2b7yGoLeI7bGYRpT7MN6+9zKzs/15y4GHzOwnzrmf7MK2Wh2Dc24359wvgV8CmNlA4Argv/6y8T4von22jfV2JPkpb3mUu5IndyVD3gqNo71y1zQSn7eajKMz5y71oJF4vwaGAAc55zYlOph4MLMMM8sB0vEuuhwz65DFQX5V3aPA1WaW7yeT2XjVgCkhld6vJqTsdWdmFWZ2jJkVmDd+2X7AscCziY5NUkrKXkOQevdB5a6UkbLXnXKXdAZmVmJm+wXuT2b2Lbwxd//eDpv/Ld6XQKP8n98A84D94r3hJLi+E3LvbM+8lMg8nwT5t93f3wSf0wm7ljujRJ3fzeSL+cBnwOX+a3vifeHd5lzS1P6aWbb/GkCW/5r5r6X5r2V6v1qOmWXFIY7m9v33wKFmNsq8LtV/CLzonFuzi6E0en8xs5541/svnXO/aST+mB6PaHGEOBF4xDkX2WtSzI5HU/c9M9vXzEb704uAG4HVwEJ/2Zgcj2buva8Bk81/6tjMRuN1Ix9oGI3ludHUfXhvvKEMAq8tB85gZ4NTrM6NqDH46xxmnmp/3pudc6v9ZWN5XkS9V5nZBDMb5O9zV+AW4Dnn3NoYHwuJkIjclai85W9buSucchfJkbeai4P2y13JkLeajKNT5y7nnH4S9IM3lo0DNuN1Sxr4+VaiY4vxfs719zP0Z26i42rD/pQCfwY24CXY4xIdk96vVu1fSl93eJWOzwNrgHXAu8BpiY5LP6nzk+rXkL+PKXcfVO7q2D+pft0pd+mnM/z45/lreF11rgH+BeyboFjmAn9sx/1OyPWdyHtne+elROX5RObfRL2/yZSz2vNa7ow/iTq/m8sXwFDgFf96fw84NN77i9fVdORrffzXpjXy2nNxiqPJfcfrunwZXkPLE0DVLsYQ9f6C1wW3i5i+PmTZmB2P5u5zQI5/juwdZflYHY+o9z3gSOB9P66v8MaRHxHr49FUDP7r5wIf+9fNIuCieByLKOdro/dh/7rZJx7nRrQYgBK8xr0NwBfA9UB6HM+LRu9VeA2Qn/pxrADuBrrH+1joJzG5q6lzwX89Lnmruf1FuavT5i6SIG81F4f/ervnLpIgb0XGQSfOXeavWERERERERERERERERERERETiREOciIiIiIiIiIiIiIiIiIiIiMSZCjRERERERERERERERERERERE4kwFGiIiIiIiIiIiIiIiIiIiIiJxpgINERERERERERERERERERERkThTgYaIiIiIiIiIiIiIiIiIiIhInKlAQ0RERERERERERERERERERCTOVKAh0g7M7HozmxOD9fzBzK71/z/ZzD5oyby7uK31ZtZvV5ePNzM72MweSHQcIiLStFjlwEQysxvN7MxExyEiIm3X1N9UZjbIzP5jZnVmdr6Z5ZrZE2a21sz+lLioRUREYsPM+piZM7OMRMciIiLSEspdkopUoCESZ2ZWDpwA3BbL9TrnXnDODYrFuszsOTM7NWL9Bc65RbFYf1s1loCdc38BhpnZiASGJiIiTYjMgWY2zcw+j9G6G+SuGK33JDN7MWLy/wA/MLOsWG9PREQSp5G/qS4FnnPOFTrnbgGOALoBXZ1zRyYkSBERERERERFJKSrQEIm/k4C/Oec2JTqQFHQ/cHqigxARkahOIgVyoHNuBfA+cHCiYxERSVWRT0OZp8XfWbR2/ih6Awsifv/QObe9tSvS010iIiIiIiIi0hgVaEhSMrPFZnaxmb3jdyf7oJnl+K81eLLV712hxv//H8zsV2b2pD9Mx0tm1t3MbjKz1Wb2vpmNbkEMVWb2qJl9ZWZfm9mtIdt/ycx+4cf2vpnt3cSqZgHPh6x3oZkdGPJ7hpmtMrPd/N//ZGZf+Oueb2ZDo8QX9hSymY02szf97ngfBHJCXutiZn/192W1//9e/mvXAZOBW/3jFdjP0GNabGZ3+8svMbMrAl9+Bt4PM/tff92fmtmsJo7r98xsmR/nB4FjZ2ZpZnaZmX3iH++HzKzUX2y+/+8aP8Y9/N+fAw5o4tiLiHQ4qZoDzSwfeBKo9GNbb2aVTd3/zSzHzP7oT19jZq+ZWbdouStiHxpd1n+t2MzuMLMVfk661szSzWwI8BtgD3+9a0JW+RzKOSIireLf5x/x88mnZnZ+yGtzzexh/169DjjJvN6RrjOzl4CNQD8zm+jfw9f6/04MWUeD+RuJoam/k4J/U5nZs8B0duaW+4ErgaP930/x5zvZvL/pVpvZ382sd8j6nJmdY2YfAR/50w40s7f8XPSyhfQA2FTO91+f7S+7zs+TM/3pjeaxNr1ZIiKSMNHyZUiufNDPY2+a2ciQ5Yb4uXCNmS0ws4NDXss1s5+Z9z3eWvO+u8sN2ey3zOwz876T/EE77q6IiKQA5S6RtlGBhiSzo4CZQF9gBN5TuK1Z9gqgDNgCvAK86f/+MHBjUwv7X279FVgC9AF6Ag+EzDIBWOSv70fAo7azmCDScOCDkN/vB44N+X0/YJVz7k3/9yeBAUCFH/O9TcXqx5sF/Bm4BygF/gQcHjJLGvB7vCfAqoFNwK0AzrkfAC8A5/rDmpzbyCZ+ARTjfeE5Fa+7+u+EvD7B38cy4KfAHWZmjcQ5CDgXGOecK/T3fbH/8vnAIf76K4HVwC/916b4/5b4Mb7i/74Q6GNmRU0cHhGRjijlcqBzbgNewcZy/15e4JxbTtP3/xPx8k8V0BU4E9jUwtzV6LL+a3cB24EaYDQwAzjVObfQn+8Vf70lIetbCIxERERaxLyC7ieAt/Fyyd7AHDPbL2S22Xi5qYSdf/ccj9dLXiFQB8wDbsG7l98IzDOzriHrCJ1/SUQMzf2dFOSc24vw3HIs8GPgQf/3O8zsEOD7wGFAuT///RGrOgQvV9aaV4R/J3CGH/9twF/MLDtk/kZzvpmNB+4GLvGPzxR2/u3UaB5rbL9ERCS5tSBfzsbLX6XAfcCfzSzTzDL95Z7G+w7xPOBe/7s3gP8FxgAT/WUvBXaEbHoSMMjf3pXmFauLiIg0S7lLpO1UoCHJ7Bbn3HLn3Dd4N+1RrVj2MefcG865zcBjwGbn3N3OuXrgQbwvsZoyHq+R6BLn3Abn3GbnXOgTy18CNznntjnnHsRrfIr2VG0J3heLAfcBB5tZnv/7cf40AJxzdzrn6pxzW4C5wEgzK24m3t2BzJCYHgZeC1nn1865R5xzG51zdcB1eA1hzfIb6o4GLvfjWgz8DO+L0IAlzrnb/eN7F9ADb6zmSPVANt6XlZnOucXOuU/8184AfuCc+zxk34+wprsGDhzXkibmERHpiFI1Bzamqfv/NrwGrRrnXL2/X+uaWV9Ao8ua14vGLGCOv39fAj8HjmlmfXUo34iItMY4oNw5d7VzbqtzbhFwO+H321ecc392zu0IGQ7rD865Bf6wIjOAj5xz9zjntjvn7scbcuqgkHUE53fObYuIocm/k3bBGcD1zrmFfnw/BkaF9qLhv/6Nvz+nAbc55/7t56K78Iondw+ZP1rOPwW40zn3jH98ljnn3m9DHhMRkeTUXL58wzn3sJ/jbsTrCWp3/6cAuMFf7lm8Qvtj/Yazk4Hv+vmj3jn3sv/3VsBVzrlNzrm38RrYVIwuIiItpdwl0kYaE1WS2Rch/9+I11jUUitD/r+pkd8Lmlm+Cq/oINpYw8uccy7k9yVNxLca72kuAJxzH5vZQuAgM3sCbzz70RAshrgOOBLviaxAdWAZsLaJeCujxIS/3jy8L+1mAl38yYVmlu432DWlDMgi/Gm0JXiVkQHB98o5t9HvPKPBMfb3fQ5e49tQM/s7cKH/BHVv4DEzC62IrKfxQo+AwHFd08Q8IiIdUUrmwCiauv/f48fzgJmVAH/EK+aIbIBrTKPL+tvLBFaEdPaUBixtZn2FKN+IiLRGb7xhrULvnel4vU4ENHbvDZ1WSUSvGDT8W6Sp+3eTfyftgt7AzWb2s5Bp5scTWO/SiPlPNLPzQqZlEZ43o+X8KuBvUWLYlTwmIiLJqal8uYSQ+7tzbod5Q3MFcsVS51zo31GBHFmG1xj2CdFF5p/m/k4UEREJUO4SaSP1oCEd0QYg0PsEZtY9DttYClQ30XtDz4ghPKqB5VHmfQcYGDEtMMzJbOA959zH/vTj/Gn74HXL3sef3mC4kAgrosQUcBFe108TnHNF7BwyJDB/6BeWkVbhPYUc+lRYNbCsmZga5Zy7zzk3yV+fA37iv7QUmOWcKwn5yXHOLWsiviHA4lY8TS0i0tF19BzY2P086v3ff9r5KudcLV73hgfiDbMVbV07NxR92aV4Ty+XhWyvyDk3tJn1DsGrzhcRkZZZCnwacX8vdM7tHzJPY/fc0GmBQu5QkX+LNJUPmvs7qbWWAmdE7FOuc+7lKPEsBa6LmD/P7wmkJdvqH2V6U3lMREQ6lubyZVVgRv/p4l54+XE5UOVPCwjkyFXAZhrPIyIiIm2l3CXSRirQkI7obbzeF0aZWQ5ebwyx9irel3k3mFm+meWY2Z4hr1cA5/vjZh2J12jT2NNN+NMjhxN5AK+73rMIGd4E7+ncLcDXeA1wP25hvK/gjUF8vpllmNlheF3Uh653E7DGzEqBH0UsvxLo19iK/R42HgKuM7NCv/veC/GeRG4VMxtkZnv5Yy5v9mMK9ODxG38bvf15y81stv/aV3i9iUTGOBV4srVxiIh0YB09B64EukYM3RX1/m9m081suN/D1Dq8gsH6kHU1mruaWtY5twJvrMufmVmRmaWZWX8zC8S5EuhlZlkRq1TOERFpnVeBdWb2PTPLNbN0MxtmZuNasY6/AQPN7Dj/75yjgVq8bnBborm/k1rrN8DlZjYUwMyK/VwYze3AmWY2wTz5ZnaAmTXXuxTAHcB3zGxvP1f1NLPBLchjIiLSsTSXL8eY2WF+Af0cvO8N/wX8G6+A/1L/b7NpeEOAPeA/mXwncKOZVfrr3MP/Pk5ERKStlLtE2kgFGtLhOOc+BK4G/gF8BLwYh23U4yWGGuAz4HPg6JBZ/g0MwKvquw44wjn3dZTV3Q3sb2a5Ietfgfdl4UTgwYh5l+BVDL6Hl7RaEu9W4DDgJLzu5I8GHg2Z5SYg14/3X8BTEau4GTjCzFab2S2NbOI8vMS5CO9434eXLFsrG7jBj+MLvEa+74fE8BfgaTOr8+Oc4O/fRrzj/JKZrTGzwJjNxwK37UIcIiIdUkfPgc659/F6kVrk388raeL+D3QHHsYrsFgIPM/OAsHmcldTy56A18X8e3h582Ggh//as8AC4AszWwVgZj3wGgT/HGU/RUQkQkg+GQV8ipc3fofXU2BL1/E1Xg9IF+EVsV8KHOicW9XC5Zv7O6lVnHOP4fUA+ICZrQP+C8xqYv7XgdOAW/3tf+zH0pJtvQp8B2+oyrV4eSzQm0hTeUxERDqQFuTLx/Hy12rgeOAwv7fArXjDJs/yl/kVcIL/NxfAxcC7wGvAN3j5S20BIiLSZspdIm1n4UOxikhzzOwk4FR/mI6WLvNj4Evn3E1xC6yTMbODgOOdc0clOhYRkc6is+ZAM/sZ8Ilz7leJjkVERERERDoHM5sL1Djnvp3oWERERFpCuUukZaKNLS4iMeSc+37zc0lrOOeeAJ5IdBwiItK0VMiBzrmLEh2DiIiIiIiIiIiIiHR8KtCQTsvMqvG6hG1MrXPus/aMR0REpL0oB4qIiIiIiIiIiIiItD8NcSIiIiIiIiIiIiIiIiIiIiISZ2mJDkBEREREREREREREREREREQk1alAQ0RERERERERERERERERERCTOVKAhIiIiIiIiIiIiIiIiIiIiEmcq0BARERERERERERERERERERGJMxVoiIiIiIiIiIiIiIiIiIiIiMTZ/wN52z/OwqCNRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-17 15:20:31,972: DEBUG ==> Plot saved to ./img/favourable/full_model_h21.pdf.\n"
     ]
    }
   ],
   "source": [
    "from mltools.libplot  import Plot\n",
    "from mltools.libscore import Score\n",
    "\n",
    "# compute the predictions\n",
    "model_val_predictions  = model.predict([matrix_val, matrix_val, num_cp_val, dim_cp_val, dim_h0_amb_val])[1].reshape(-1,) #---------- take only h21 predictions\n",
    "model_test_predictions = model.predict([matrix_test, matrix_test, num_cp_test, dim_cp_test, dim_h0_amb_test])[1].reshape(-1,)\n",
    "\n",
    "# compute the accuracy and score of the predictions\n",
    "rounding = np.rint\n",
    "model_score_val  = Score(y_true=h21_val, y_pred=model_val_predictions, rounding=rounding)\n",
    "model_score_test = Score(y_true=h21_test, y_pred=model_test_predictions, rounding=rounding)\n",
    "print('Accuracy of {} on the validation set: {:.3f}%'.format(model.name, model_score_val.accuracy()*100))\n",
    "print('Accuracy of {} on the test set: {:.3f}%'.format(model.name, model_score_test.accuracy()*100))\n",
    "\n",
    "# plot the distribution of the predictions and the error difference\n",
    "plot = Plot(rows=1, columns=5)\n",
    "\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_val, h21_val))).T,\n",
    "               axis=0,\n",
    "               title='Predictions for the Validation Set for $h_{21}$',\n",
    "               legend='real values',\n",
    "               xlabel='num_cp (validation set)',\n",
    "               ylabel='$h_{21}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_val, rounding(model_val_predictions)))).T,\n",
    "               axis=0,\n",
    "               title='Predictions for the Validation Set for $h_{21}$',\n",
    "               legend='predictions',\n",
    "               xlabel='num_cp (validation set)',\n",
    "               ylabel='$h_{21}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_test, h21_test))).T,\n",
    "               axis=1,\n",
    "               title='Predictions for the Test Set for $h_{21}$',\n",
    "               legend='real values',\n",
    "               xlabel='num_cp (test set)',\n",
    "               ylabel='$h_{21}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "plot.scatter2D(np.array(list(get_counts(num_cp_test, rounding(model_test_predictions)))).T,\n",
    "               axis=1,\n",
    "               title='Predictions for the Test Set for $h_{21}$',\n",
    "               legend='predictions',\n",
    "               xlabel='num_cp (test set)',\n",
    "               ylabel='$h_{21}$',\n",
    "               colour=False,\n",
    "               alpha=0.65\n",
    "              )\n",
    "\n",
    "plot.hist2D(model_score_val.error(),\n",
    "            axis=2,\n",
    "            title='Distance from the Real Value',\n",
    "            legend='validation set',\n",
    "            xlabel='error difference',\n",
    "            ylabel='#',\n",
    "            binstep=1\n",
    "           )\n",
    "plot.hist2D(model_score_test.error(),\n",
    "            axis=2,\n",
    "            title='Distance from the Real Value',\n",
    "            legend='test set',\n",
    "            xlabel='error difference',\n",
    "            ylabel='#',\n",
    "            binstep=1\n",
    "           )\n",
    "\n",
    "plot.series2D(data=model_hst_h21.history['h21_loss'],\n",
    "              axis=3,\n",
    "              title='Loss Function',\n",
    "              xlabel='epoch',\n",
    "              legend='training loss',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h21.history['h21_loss'])[0] / 10)\n",
    "             )\n",
    "plot.series2D(data=model_hst_h21.history['val_h21_loss'],\n",
    "              axis=3,\n",
    "              title='Loss Function',\n",
    "              xlabel='epoch',\n",
    "              legend='validation loss',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h21.history['val_h21_loss'])[0] / 10)\n",
    "             )\n",
    "\n",
    "plot.series2D(data=model_hst_h21.history['h21_mean_squared_error'],\n",
    "              axis=4,\n",
    "              title='Metric Function',\n",
    "              xlabel='epoch',\n",
    "              legend='training MSE',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h21.history['h21_mean_squared_error'])[0] / 10)\n",
    "             )\n",
    "plot.series2D(data=model_hst_h21.history['val_h21_mean_squared_error'],\n",
    "              axis=4,\n",
    "              title='Metric Function',\n",
    "              xlabel='epoch',\n",
    "              legend='validation MSE',\n",
    "              ylog=True,\n",
    "              binstep=int(np.shape(model_hst_h21.history['val_h21_mean_squared_error'])[0] / 10)\n",
    "             )\n",
    "\n",
    "plot.save_and_close(path.join(IMG_PATH, model.name + '_h21'))\n",
    "log.debug('Plot saved to {}.'.format(path.join(IMG_PATH, model.name + '_h21.pdf')))"
   ]
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
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