examples/sequential/movielens_sequential.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MovieLens example\n",
    "This example is based on the TFRS movie retrieval example you can find here: https://www.tensorflow.org/recommenders/examples/basic_retrieval.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, Text\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow_recommenders as tfrs\n",
    "\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras import Model\n",
    "\n",
    "from tf_tabular.builder import InputBuilder\n",
    "from tf_tabular.sequence_processor import SequenceProcessor\n",
    "\n",
    "from examples.sequential.preprocessing import preprocess_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ratings data.\n",
    "ratings, ds_info = tfds.load(\"movielens/100k-ratings\", split=\"train\", with_info=True)\n",
    "# Features of all the available movies.\n",
    "movies, movie_info = tfds.load(\"movielens/100k-movies\", split=\"train\", with_info=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_df = tfds.as_dataframe(ratings, ds_info)\n",
    "movies_df = tfds.as_dataframe(movies, movie_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique users: 943\n",
      "Train set size: (3672, 5)\n",
      "Validation set size: (922, 5)\n"
     ]
    }
   ],
   "source": [
    "train_ds, val_ds, vocabs = preprocess_dataset(ratings_df, movies_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shuffle and split dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n",
    "train_ds = train_ds.shuffle(100_000, seed=42, reshuffle_each_iteration=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class MovielensModel(tfrs.Model):\n",
    "    def __init__(self, user_model, movie_model):\n",
    "        super().__init__()\n",
    "        self.movie_model: tf.keras.Model = movie_model\n",
    "        self.user_model: tf.keras.Model = user_model\n",
    "\n",
    "    def prepare_task(self, movies):\n",
    "        id_candidates = (movies.ragged_batch(1024)\n",
    "                         .prefetch(tf.data.AUTOTUNE)\n",
    "                         .cache()\n",
    "                         .map(lambda movie: (movie[\"movie_id\"], self.movie_model({'target_id': movie['movie_id'],\n",
    "                                                                                  'movie_genres': movie['movie_genres']}))))\n",
    "\n",
    "        metrics = tfrs.metrics.FactorizedTopK(\n",
    "            candidates=tfrs.layers.factorized_top_k.Streaming(k=100).index_from_dataset(id_candidates),\n",
    "            ks=[1,5,100],\n",
    "            name='factk'\n",
    "        )\n",
    "        loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True,\n",
    "                                                       reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE)\n",
    "        task = tfrs.tasks.Retrieval(\n",
    "            metrics=metrics,\n",
    "            # num_hard_negatives=2,\n",
    "            remove_accidental_hits=True,\n",
    "            loss=loss\n",
    "        )\n",
    "        self.task: tf.keras.layers.Layer = task\n",
    "\n",
    "    def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:\n",
    "        # We pick out the user features and pass them into the user model.\n",
    "        user_embeddings = self.user_model({\"user_history\": features[\"user_history\"],\n",
    "                                           \"history_ratings\": features[\"history_ratings\"]})\n",
    "        # And pick out the movie features and pass them into the movie model,\n",
    "        # getting embeddings back.\n",
    "        positive_movie_embeddings = self.movie_model({\"target_id\": features[\"target_id\"],\n",
    "                                                      \"movie_genres\": features[\"movie_genres\"]\n",
    "                                                      })\n",
    "\n",
    "        # The task computes the loss and the metrics.\n",
    "        return self.task(user_embeddings, positive_movie_embeddings,\n",
    "                         candidate_ids=features[\"target_id\"],\n",
    "                         candidate_sampling_probability=features[\"sampling_prob\"],\n",
    "                        )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "embedding_dims = {\"target_id\": 64,\n",
    "                  \"movie_genres\": 32}\n",
    "\n",
    "def build_movie_model():\n",
    "    input_builder = InputBuilder()\n",
    "    input_builder.add_inputs_list(categoricals=[\"target_id\", \"movie_genres\"],\n",
    "                                  vocabs=vocabs,\n",
    "                                  multi_hots=[\"movie_genres\"],\n",
    "                                  embedding_dims=embedding_dims)\n",
    "    inputs, output = input_builder.build_input_layers()\n",
    "    x = Dense(32, activation=None)(output)\n",
    "    return Model(inputs=inputs, outputs=x)\n",
    "\n",
    "movie_model = build_movie_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization parameters found for history_ratings. Not normalizing this column\n"
     ]
    }
   ],
   "source": [
    "embedding_dims = {\"user_history\": 64}\n",
    "\n",
    "def build_user_model():\n",
    "    input_builder = InputBuilder(sequence_processor=SequenceProcessor(key_dim=64))\n",
    "    input_builder.add_inputs_list(categoricals=[\"user_history\"],\n",
    "                                  numericals=[\"history_ratings\"],\n",
    "                                  vocabs=vocabs,\n",
    "                                  sequentials=[\"user_history\", \"history_ratings\"],\n",
    "                                  embedding_dims=embedding_dims)\n",
    "    inputs, output = input_builder.build_input_layers()\n",
    "    x = Dense(32, activation=None)(output)\n",
    "    return Model(inputs=inputs, outputs=x)\n",
    "\n",
    "user_model = build_user_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
     ]
    }
   ],
   "source": [
    "model = MovielensModel(user_model, movie_model)\n",
    "model.prepare_task(movies)\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.003))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cache the training and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cached_train = train_ds.shuffle(100_000).ragged_batch(128).cache()\n",
    "cached_val = val_ds.ragged_batch(128).cache()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "29/29 [==============================] - 31s 976ms/step - factk/top_1_categorical_accuracy: 0.0035 - factk/top_5_categorical_accuracy: 0.0193 - factk/top_100_categorical_accuracy: 0.2399 - loss: 5.0386 - regularization_loss: 0.0000e+00 - total_loss: 5.0386\n",
      "Epoch 2/10\n",
      "29/29 [==============================] - 29s 997ms/step - factk/top_1_categorical_accuracy: 0.0065 - factk/top_5_categorical_accuracy: 0.0275 - factk/top_100_categorical_accuracy: 0.3096 - loss: 4.8373 - regularization_loss: 0.0000e+00 - total_loss: 4.8373\n",
      "Epoch 3/10\n",
      "29/29 [==============================] - 29s 994ms/step - factk/top_1_categorical_accuracy: 0.0074 - factk/top_5_categorical_accuracy: 0.0308 - factk/top_100_categorical_accuracy: 0.3314 - loss: 4.7736 - regularization_loss: 0.0000e+00 - total_loss: 4.7736\n",
      "Epoch 4/10\n",
      "29/29 [==============================] - 30s 1s/step - factk/top_1_categorical_accuracy: 0.0068 - factk/top_5_categorical_accuracy: 0.0338 - factk/top_100_categorical_accuracy: 0.3608 - loss: 4.6815 - regularization_loss: 0.0000e+00 - total_loss: 4.6815\n",
      "Epoch 5/10\n",
      "29/29 [==============================] - 29s 1s/step - factk/top_1_categorical_accuracy: 0.0074 - factk/top_5_categorical_accuracy: 0.0392 - factk/top_100_categorical_accuracy: 0.3932 - loss: 4.5638 - regularization_loss: 0.0000e+00 - total_loss: 4.5638\n",
      "Epoch 6/10\n",
      "29/29 [==============================] - 29s 986ms/step - factk/top_1_categorical_accuracy: 0.0098 - factk/top_5_categorical_accuracy: 0.0422 - factk/top_100_categorical_accuracy: 0.4137 - loss: 4.5066 - regularization_loss: 0.0000e+00 - total_loss: 4.5066\n",
      "Epoch 7/10\n",
      "29/29 [==============================] - 29s 994ms/step - factk/top_1_categorical_accuracy: 0.0098 - factk/top_5_categorical_accuracy: 0.0384 - factk/top_100_categorical_accuracy: 0.4180 - loss: 4.4597 - regularization_loss: 0.0000e+00 - total_loss: 4.4597\n",
      "Epoch 8/10\n",
      "29/29 [==============================] - 29s 988ms/step - factk/top_1_categorical_accuracy: 0.0079 - factk/top_5_categorical_accuracy: 0.0338 - factk/top_100_categorical_accuracy: 0.4243 - loss: 4.4658 - regularization_loss: 0.0000e+00 - total_loss: 4.4658\n",
      "Epoch 9/10\n",
      "29/29 [==============================] - 29s 988ms/step - factk/top_1_categorical_accuracy: 0.0095 - factk/top_5_categorical_accuracy: 0.0417 - factk/top_100_categorical_accuracy: 0.4352 - loss: 4.4108 - regularization_loss: 0.0000e+00 - total_loss: 4.4108\n",
      "Epoch 10/10\n",
      "29/29 [==============================] - 29s 989ms/step - factk/top_1_categorical_accuracy: 0.0114 - factk/top_5_categorical_accuracy: 0.0471 - factk/top_100_categorical_accuracy: 0.4662 - loss: 4.3404 - regularization_loss: 0.0000e+00 - total_loss: 4.3404\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x2cbe63d30>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(cached_train, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 3s 300ms/step - factk/top_1_categorical_accuracy: 0.0076 - factk/top_5_categorical_accuracy: 0.0325 - factk/top_100_categorical_accuracy: 0.3200 - loss: 5.0689 - regularization_loss: 0.0000e+00 - total_loss: 5.0689\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'factk/top_1_categorical_accuracy': 0.0075921909883618355,\n",
       " 'factk/top_5_categorical_accuracy': 0.03253795951604843,\n",
       " 'factk/top_100_categorical_accuracy': 0.31995663046836853,\n",
       " 'loss': 3.975198984146118,\n",
       " 'regularization_loss': 0,\n",
       " 'total_loss': 3.975198984146118}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(cached_val, return_dict=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the user model part:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RxAkYHycCRMDTCJBoPKgeEo0HlUKRiAARMI0AicY0dM5VJNE4hy1bJgJEIPEIkGgSj3nUHkk0USHiA0SACPgIARKNB5VFovGgUigSESACphEg0ZiGzrmKJBrnsGXLRIAIJB4BEk3iMY/aI4kmKkR8gAgQAR8hQKLxoLJINB5UCkUiAkTANAIkGtPQOVeRROMctmyZCBCBxCNAokk85lF7JNFEhYgPEAEi4CMESDQeVBaJxoNKoUhEgAiYRoBEYxo65yqSaJzDli0TASKQeARINInHPGqPJJqoEPEBIkAEfIQAicaDyiLReFApFIkIEAHTCJBoTEPnXEUSjXPYsmUiQAQSjwCJJvGYR+2RRBMVIj5ABIiAjxAg0XhQWSQaDyqFIhEBImAaARKNaeicq0iicQ5btkwEiEDiESDRJB7zqD2SaKJCxAeIABHwEQIkGg8qi0TjQaVQJCJABEwjQKIxDZ1zFUk0zmHLlokAEUg8AiSaxGMetUcSTVSI+AARIAI+QoBE40FlkWg8qBSKRASIgGkESDSmoXOuIonGOWzZMhEgAolHgESTeMyj9kiiiQoRHyACRMBHCJBoPKgsEo0HlUKRiAARMI0AicY0dM5VJNE4hy1bJgJEIPEIkGgSj3nUHkk0USHiA0SACPgIARKNB5VFovGgUigSESACphEg0ZiGzrmKJBrnsGXLRIAIJB4BEk3iMY/aI4kmKkR8gAgQAR8hQKLxoLJINB5UCkUiAkTANAIkGtPQOVeRROMctmyZCBCBxCNAokk85lF7JNFEhYgPEAEi4CMESDQeVBaJxoNKoUhEgAiYRoBEYxo65yqSaJzDli0TASKQeARINInHPGqPJJqoEPEBIkAEfIQAicaDyiLReFApJkRat26diVqsQgScR6BKlSrOd2LogUSTULhj64xEExtOXnwK5DJr1iwhyXhRO5QpFIEOHTqoP+nfTiFEonEKWQvtkmgsgOdS1XAEU7FiRZekYbdEIHUEvv3226AHQDSwcpyydEg0HpyRJBoPKiUVkUAyHTt2DDzRokULadmypb8GQWnTJALz58+XBQsWBMYOwnHCuiHReHB6kWg8qJQIIhlJhgTjH71R0mAEjIQzc+ZM2y0bEo0HZxyJxoNKiSBS1apV1SckGf/ojJKGR8BINmvXrrUVJhKNrXDa0xiJxh4cnW4FQX/8kGScRprtJwqBHj16COI3drvQSDSJ0mAc/ZBo4gDLxUdpzbgIPrt2BAGQDMgGxU6rhkTjiLqsNUqisYZfImrTmkkEyuzDDQS0VWNnrIZE44Ymo/RJovGgUkJEItF4X0eU0BwC2qpBqjPIxo5CorEDRZvbINHYDKgDzSGdGRln48aNE56XcQBgNukaAiQa16BPbMckmsTibaY3HZ957733zFRnHSLgaQTq1Kmj5LMrTkOLxoPqJtF4UCkhIpFovK8jSmgeARKNeex8U5NE431VkWi8ryNKaB4BEo157HxTk0TjfVWRaLyvI0poHgESjXnsfFOTRON9VVkhmm3btsk///wjZcqUiXmgGzZskPTp00vZsmVjrsMHnUEgLeiCROPM3PFUq1aJ5vTp04KXYcuWLXLFFVdIpUqVJGvWrLaOcf/+/fLVV1+pdmvVqmVr235ozArRlC5dWg4fPix79+6Naag//PCDXHvttXLBBReoU9uFChWKqR4fsh+BtKILEo39c8dzLVohmjfffFPuuuuuFGN64YUXpEmTJraM9d1335X7779fjh49KkWKFJFffvnFlnb91EgiieaPP/6Qa665Rlk0SKm+6KKLTEEFS+q5555T6djNmjUz1UZaqJQaTnbpwus4kmi8riEb5DNLNJ9//rlcf/31SoInnnhCLShffPGFPPPMM+pvSMWtXbu2aQlPnjwp/fr1kwkTJgTaINHEn94cr0UDsI8fP64smsyZM5vW3wcffCCNGjWShg0byv/+9z/T7SR7xWg42aELr2NIovG6hmyQzyzRgFwmTpwoU6ZMkS5dugQkeeqpp2TYsGHyyCOPBEjHjJh33nmnvPPOO1K8eHGZPHmy1K9fnxaNiXM0ZojGjL5C60RbQO3oIxnaIE4iJJpkmMlRxmCWaCZNmiQ//fST9OnTR/LmzRvoRbvTMHlWrFih/n7u3DkZNWqUcqddfvnlKSTCjrdChQoqNqALLKSrr75akQysm3z58pFoLBLNZ599JsuXL5eNGzcKCOjmm2+W22+/PYU+YEnCmsGmwVigx9dee01gzcLlAwuzcuXK8uCDD0rGjBnVo0eOHFEbjd27dwvcnojb6bja448/HhTzOXv2rKxfv14++ugjgWyIB1WvXl39XHLJJSnkmj59uvz444/K0v3uu+8Ecw1xpIEDB8pll12mbk4oUKCAsrDDlbFjx8rvv/+u6ufMmTPutzu1/uFu1GXz5s3y+uuvKxkxRsztW265JWh+x4pTOF3gvVq5cqU0bdpU6eCVV16RTz/9VL1n6KtTp06SPXv2sOMDfu+//76sXr1aMmTIoORq3ry50sFbb70l9957b8BToRvYtWuXLF26VOkKMVkkiTRo0MC2WypINHFPRf9VMEs04UaKiY5ve1y4cKE8+eSTilxQ8FJgMuGlwCJVuHDhQPXZs2dLu3btlOWyfft25bJBgRvuuuuuU//G4kCiOe+OjLdoi2bevHlqQcHigiy0v//+WzU1ZMiQIIsUfytYsKBaiL///vtAd8eOHZOHH35YLUbZsmWTokWLyo4dO1Q72BQgLodFft++fYp8MBewKEGf6FPPg6uuuirQJghh7ty56v9YGNEH6uXKlUtZs6VKlQoabr169RQhIfYDWfQYMN/q1q2rvkDr559/Fiz0+fPnD6r766+/qkUYiyQWVTMltf5hgaOAjAYNGqRky5IliyKaU6dOyYUXXqiIsFWrVuq5WHEKp4uRI0cKSHPMmDEyZ84clYgD/P766y/VNt4zEEnu3LmDhon5g3gZ9A95sJkA5jVq1FA/mAtoF++jLiA1XOOPGCl0Dp0isQQ6xfPGb3s1gynqkGjMIuejenYRDTKbHnroIXn55ZclR44caseLhUcX7VIrWbKketFBHIsXL1a7Mjz/8ccfq4UgXCHRnP/CM7NEA/yw6EydOlVZF1g0sAuG5YGFBhl9sDx0Cbe4YUOAm3bvu+8+1U6mTJnkxIkTMnToUJkxY4ZylQ4fPjzQRjSXEBZJbESQ6IDfyFY8cOCALFmyRFnJsG6wyEEWXfRCjwWyW7duKv6DxQ/kBItq9OjRgR/MRWMBAcA6wJix4zdTovUPCx/jAUk///zziviwMGPRb9++vSIcvBd58uSJGafUiAY4wJrs2bOn0u+qVasUmX355ZeKAEBIusC6uuOOO5TOEPdEEg/ICQkfIHwkHmCeGIkGJAliBoHBotFWG4j87rvvVn+HdWz0aJjBlURjBjWf1bGDaP7880/lFti5c6eamHAblChRIgUSOq4DQunatWtg54QXw+h6CK1IorFGNEgPnzZtmiJ1Y+nfv7+Ko+HW3MaNG6dKNLCGYGVg8ddZcKhw5swZ5SLDolWzZs2YFlAsUMWKFVML7po1a4I2JGgAhDV+/Hh59NFHBfMzlGiwsIKMQgtcPFjckaQCt5qxwEX49ddfK2sHmxwzRRNNpP7hltq0aZOyxLChMhYQ8YsvviiwLI2ZmtEIOTWi0eNMly5doCt4BeAJKF++vMJWl8cee0x5GrAx6Ny5c5BsyOSE7g4ePBhENDi2gL/DvYaNibHAioJFC7ytpsCTaMzMRp/VsYNoevfurXaSeBFfeumliOdosEOCy+PZZ58NoATr5qabbkoVNRKNdaLBjtZoYQJw6Ar6wCIEd48u4RY3WAOwCrBIYtccbReb2gKKeAKSO0BeSCYJLXBzYaFErAabllCiQduwgMIVzEG4XY3usz179qj2br31VjVms0UTTWr9R2obLq7u3bsrnIG3LlaIBu9dr169UnQJ/cHaAfHpAiyBCcgh1KWGZ2BxwZo0WjTwUsCljfNr2IzAItKubbMYhqtHorETTY+2ZQfR3HbbbcqtA/95tWrVUh0pzHvskHSB6ybaAU8SjTWiiXRgExYKFvtQt1c4ooFfHgFgHCKEfx9WzY033qgII9yin9oCCisK1hQsF/QdriC4j506Fku9Y9cLPayGSLvoBQsWKGsZGx/tPtNuMySdwN1mtsTSP2IzsCTgkgK5Hzp0SB2YhXWAYifRwGWJM2ahBdYU0qKRkIEClx3iNohbAbtwBYSPpIrQGA2ScfQmBPVBWLByMBciJRzEiy+JJl7EfPi8HUSDybl161YZMWJEqm4JxAKMmWWAC0Fc+H/h849USDTuEw10o331cKPA3YkFDAXBf7hljPGU1IgG7hukxiPWE+kwJ9yrIDcslnpuxLLQI/4E1xUyFrX7DDtxuHowR62cDYrWP+Ypgv1IeEFB3AtuOp3c4BbRwJMAVyUIG+5Fo6tNv3NaJ6FEg8+/+eYblewBtymSLVCQ6IDNQqSNQjxLIYkmHrR8+qwdRBPL0LGjg4sMCwEm7T333KN+kG6LlEq4NHAaPVwh0XiDaIy6QZAb1il2wnCFwVdvTFZIjWiWLVsmrVu3VtluyFwKLbByEeRHxtwnn3wS+DjaQq8fhCWDzQssCsSQ4DaD5YbduZUSrX8kKOjsvr59+wYRL6wpuLncsGgwZlggSLgJTfzQeOAdhCUWjmiMmCGeA6sRrjS8y0j+gUvSSiHRWEHPJ3XtIBoEmpGbj5fLGCjWECBAiYUIExOpqW3btlUfwbzHbhNxGmTPzJ8/P+xui0TjPaLRukUWE1KWcS4EixB2uiiaaLA4Y2EyFuyK4W5DAgg2GqFF14W1A6tHl2gLvX4OcxHJDchsA9FgXiItW99kYfbVjNY/5jhcfbCeQtOrkQWGRTkS0YTDCXKmlgwQq+sM7egEC2QdYlMH96cur776asDNGI1odB1YpLCC4KaEy81KIdFYQc8nda0SDVJSdbomUh5xoC+0wFyHbxdna4y3COA5LFC4qgaZMthxhjPrSTTuEg3OXWCRRgosFiW4ynT57bff1DkaxFQQhNcF7hbsdJE0gPM4oXrFgofDlgMGDBAc5NQFfSAGALeZPh8TL9FA3nLlysmll16qzoxgjiLjLNzciuc1jUY0mP/YNGEzBQtBF1gKsKiwsQolmmg42UU0OPQM+dEfdInYGnQD2aBTkCSsRyPRIEUb7ywy9jAmY0EiwqxZs1KMNR489bMkGjOo+ayOVaKB/xcLD1xjSEmNdCob7hAc9AtX8AJiJxxpIcDig5eCd52ZP7AZ7vbmeJIBdEAdmwocToRFgmwuWKHQD85m6MOI0DH0DZcVguGIVWCjgawrfVgXKddIIoEVBKLCLQX4N86cIM0Wu2Tslo0l2kJvfBb1dUYbXFZYGK2WaP3rRAScC8PiCdcfyBQWGhJgYFWFEk00nOwiGowdmMObgA0BCBgFssIygWWKlHEj0eC9hIcCh0tvuOEGdQYLCQAg07ffflvpFUQVLZknGu4kmmgIJcHnVokGEIBscJYmWsprEsDlyhCcur05HqLBwJFqjEUJ7iEckkRMDYF3xFnCfX0DYjhwW8F1ijmCRQnkowsy2LA5wcKFlGYcusTOGvGEcCfOoy30RuXAfYWsOBRkgIW7+iheZcbSP2I0OIAKSw8FVhX+j8U6XHoznkkNJzuJRo8XWXCI1yAxAkkTF198sUpdRwo7Ute1axvP470GUSP+pm8ewGYDSTw4EBpp8xgPtiSaeNDy6bN2EI1Ph+4bsa0QjRODxE4XxICAfWrZgrpvpPzCdYPdc6QCawYbFStZYca2QWKIASHdHrvvRBfgg4I07FhddrHgZHYcIAlYLfjah3A6wyFUuMew+dBXPxn7wsYCGYC4eib0PJZZmXQ9Eo1VBH1Qn0TjfSV5jWi8j5hImzZtlAW2aNEilXCiC+KAuBA2ngIXnL7LLJ56XnoWWXi4wwxuShCvTtrENRxkAAAgAElEQVSApQmLEokXsChxoNOYKJCIMZBoEoGyy32QaFxWQAzdw40E9w/cGlgoWMIjgF07iAQxCCyeoSnXqIUT+viJp+Ad8fs3uyImA7JEejOsGsRVcUPAhx9+qNxjIB5ksYX7IsN4sIr3WcSwcIcedIWUaTtKunOwv1g8hQCJxlPqCCsMiSY2HSEuUqZMGRVzwG3ETz/9tOlvCI2tR389BbJBujgsPVzuCXcmrpjRdw8a42eJGhmSSZBEgRui8WNHIdHYgaLNbZBobAbUgeaQRoofWDOwalgiI4A4hHYLEafICOivcYglxuYkjiQaJ9H1UNskGg8pI4IocJvpLCy6z7yvL0oYOwJ2x2fQMy2a2PFP2JMkmoRBbakj7T6jVWMJRlb2EAJOWDMkGg8p2CgKicajigkRi1aNP/REKWNDQCcB4Om1a9fGVinGp2jRxAhUIh8j0SQSbWt96VgNWmnRooX62mwWIuA3BIwkY2cSgMaBROPBGUGi8aBSUhGJZOMvfVHa/xAAwSDDDL9RnCAZus48OuNINB5VTIxko60b/TjP2fhPn8kuMYgFdyFqgnGSZEg0Hp1NJBqPKiYGsYzWTQyP8xEi4DoCOJgJSwa/nSp0nTmFrIV2STQWwPNIVRCOLkgaYLGGAG6QRsHXFbBYRwCkon+stxa9BRJNdIwS/gSJJuGQs0OPI6AvweRFJh5XVATxSDQe1BuJxoNKoUiuIkCicRV+y52TaCxDaH8DJBr7MWWL/kaARONz/fFSTe8pkETjPZ1QIncRING4i7/V3mnRWEXQgfokGgdAZZO+RoBE42v18a4zL6qPRONFrVAmNxEg0biJvvW+adFYx9D2Fkg0tkPKBn2OAInG3wok0XhQfyQaDyqFIrmKAInGVfgtd06isQyh/Q2QaOzHlC36GwESjc/1x6wz7ymQROM9nVAidxEg0biLv9XeadFYRdCB+iQaB0Blk75GgETja/Ux68yL6iPReFErlMlNBEg0bqJvvW9aNNYxtL0FEo3tkLJBnyNAovG3Akk0HtQficaDSqFIriJAonEVfsudk2gsQ2h/AyQa+zFli/5GgETjc/0x68x7CiTReE8nlMhdBEg07uJvtXdaNFYRdKA+icYBUNmkrxEg0fhafcw686L6SDRe1AplchMBEo2b6FvvmxaNdQxtb4FEYzukbNDnCJBo/K1AEo0H9Uei8aBSKJKrCJBoXIXfcuckGssQ2t8AicZ+TNmivxEg0fhcf8w6854CSTTe0wklchcBEo27+FvtnRaNVQQdqE+icQBUNulrBEg0vlYfs868qD4SjRe1QpncRIBE4yb61vumRWMdQ9tbINHYDikb9DkCJBp/K5BE40H9kWg8qBSK5CoCJBpX4bfcOYnGMoT2N0CisR9TtuhvBEg0Ptcfs868p0ASjfd0QoncRYBE4y7+VnunRWMVQQfqk2gcAJVN+hoBEo2v1cesMy+qj0TjRa1QJjcRING4ib71vmnRWMfQUgu7du2SuXPnyqBBgwLtRCIaPGN8zlLHrEwEfIQAicZHygojKonGA/qrVauW1KhRI0Ai4YhGEwyJxgMKowgJR4BEk3DIbe2QRGMrnOYaW716tYBsBg4cqMgmlGjwGZ45d+6cuQ5Yiwj4HAESjb8VSKLxiP40mUCc1q1bK3cafsO1BpLRJOQRcSkGEUgoAiSahMJte2ckGtshNdegtmoi1aY1Yw5X1koOBEg0/tYjicZD+jNaNUaxaM14SEkUxRUESDSuwG5bpyQa26C03lAkq4bWjHVs2YK/ESDR+Fx/vBnAWwoMtWpozXhLP5TGHQRINO7gblevtGjsQtKmdkKtGlozNgHLZnyNAInG1+pL7M0A69at8zdaCZK+TZs2KtusWLFiMmfOnAT16u9uqlSp4u8BUPpUESDR+HuCOG7RzJo1S/DDQgQSgUCHDh0EPyzJhQCJxt/6dIxowhHM0aNH/Y0WpfcsAjly5AiSjYTjWVWZEoxEYwo2z1RyhGg6duwo2k0Gctm7d6+QZDyj86QVpFChQmpsBQsWVL9JNsmjahKNv3VpO9EYLZlt27aRYPw9P3wpPQhHkw1iNzNnzvTlOCj0fwiQaPw9G2wlGpKMvydDMkkPV1rJkiVp2SSJUkk0/lakrURTtWpVhQZcZXv27PE3MpTe9wgYyWbt2rW+H09aHgCJxt/at41otDVDkvH3hEg26WHVgHAYr/G3Zkk0PtefXTcD0Jrx90RIVulp1SSHZkk0/tajbRaNJhoeyvT3hEhG6fVhTrrP/KtdEo1/dQfJbSEakAtSmpHCjEwzFiLgJQS0+wzZZ7xBwEuaiV0WEk3sWHnxSVuIhvEZL6qWMmkESDT+nwskGn/rkETjb/1R+hgQ0OdqmBAQA1gefYRE41HFxCgWiSZGoPiYfxEg0fhXd1pyEo2/dUii8bf+KH0MCJBoYgDJ44+QaDyuoCjipWmiKVy4sJQqVUpWrVol/N6X/2ZK7ty5Bdjs27dPfv/9d3/PcBEh0fhehUKi8bcOPUM0OXPmlKZNm8rVV18tlStXlkOHDgnSUT/44AN57733bEf5ggsukB9//FGKFi0qzZo1kxdeeMH2PqI1eOutt0r16tXlzTffVGP1SkEG4YwZM6R3794yevRor4hlWg4SjWnoPFPRKtEcPnxYvv76a3VjSYUKFaRMmTKSPn16W8f38ccfC/rB+qUveLW1Ax835gmiKV26tLz22mvKukA5efKkZMqUKbCLeeaZZ+Txxx+Xv//+Owjq7t27qy8HGzp0qOzfvz9uNWBiXHvttXLbbbcJvtky0WXEiBHSp08f6dSpk0yfPj3R3Ufsj0TjGVVQkH8RsEI0Tz31lAwbNiwIy3z58qkNLEjHajly5Ih06dJFFixYoJrC7wcffNBqs0lV33WiyZo1q/z666+SK1cudcvu2LFjZefOnYK/X3/99TJt2jR1OeKUKVPkscceCwL/s88+k2rVqgmIauvWrXErBlYN+j1w4EDcde2oQKKxA8XobdCiiY6R158wSzQTJkwQbEhxQ0Tfvn0lb9688uqrr8o777yjhvzHH39Injx5TA//iy++UB4RrFm6kGhSwuk60dxzzz2ydOlSeeWVV6Rx48YpJMyfP7/6WuPjx48rc/TUqVOBZ6wSjenZZVNFEo1NQEZphkSTGJyd7MUs0Vx11VXy3XffKbcZXFoo//zzj9SoUUM++eQTWbx4sTzwwAOmRP/000/lxhtvVHWffPJJ5TaDy5lE40GimThxonKLPfroozJ16tSwCr/77rulSJEi8vLLLysXGWI5UHCjRo0ERLRo0SI5ePCgbNiwIfC10bCCYAFhMiAG0rp1a6lVq5bawTz00EOqH7isypYtK0OGDAm43oz10B9MYExKfL/Jli1blIw7duwIKyd2RnDD3XHHHXLJJZfIl19+KRgfrKZevXqpyT579uxAXbNEc+WVV8ott9yixoOCZAbEsrZv3x7xhcFOTtcBlh999JFyF3711Vdy9uzZoHqpuc5atWol11xzjcIVuKPA/diyZUt5++231U9oeeKJJ6R48eJqRwk3A4oRZ7TToEEDtdEAbhjPW2+9JZs3bza1AIRWItHYAqOrjZghGrjaQQAXXXSRDBo0KEh+vI/wnowcOVLFIlFwswn+D1dblixZgp4/c+aMWiewTuFdQlm2bJlaS55//nmpXbu2YJ7jfSfReJBooCjcLPDGG28ICCWW7C+409q2bRuI48DKQT20cf/996tRYlF9//331cIOMqpXr576+/r16wM7G5jPIAWj603Xe+655yRDhgxqAcXiiMmKcuLECVXnww8/DELzsssuE5jR6AsFuxskOMAt2LlzZxWDAnFp+fCMGaKBmb5w4UIVv9LWHeJZGD9IUS/+RuFApohHXXzxxeq5v/76S7kSUIA9iMVYIhENXrLJkycrIrjrrrvk2LFjqhowmjdvngwePDjFC43PsXO84YYbFFkjk82oH+CMcYD0EZuDOzNjxozqmfbt2ws+t1pINFYRdL++GaKJJDXmf8WKFZW7C5tQvTYMGDBAxXsxt+Fh0fMQG7F27drJ3LlzFbHgnUH56aefFCEh3oNCook8T1x3nWGBhiWChRy72EmTJqndOczbaCU115kmDCxe2Ol369ZNWRhoF244lNSIBoTyzTffSJs2bdT9bTC7YXlhUYW8mKi6YNHGDr9cuXIqlgS/MNx9WOAxeSELdkFWiQa7JlgMID7IgQUfBZbN/PnzFYZ33nmnrFy5MiAb0pSBE6yYrl27KlfBn3/+KbgEFVgjxjVw4EC1W9MlHNGACEDwy5cvl3vvvVcRri5WiAaygFxgKUHuCy+8UI1hzpw5KiuoZs2aSn4rhURjBT1v1LWLaBDLbdiwoXKnIeMT7xDmHArmdP369dX6gw0hLBV8hkA/EpKQEYvNKzwU4QqJxsNEA9Guu+46tYDBAkCBNfD5558rInjxxRcDu+DQYcRCNFiUQWZIlw4tqRENzGikPhvrYeEDgWDxxmSDnCiwJGAuh7PKsCuCiwqJDVaJBkQJgoB7zkgmkKFOnToKQ6RJw5Wly5gxY6Rnz54q6wYuAWPBAgwyhbsK2GOnhxJKNA8//LB60TA+uLdOnz4d1I4VooGFhR2lDs6GkhcygzBeK4VEYwU9b9S1g2iwpuA9RME8hrWsLXs9SrwD8FjACoe7He7w8ePHq03jmjVr1LsSqZBoPE40EA8LHYJyWLSwy9a7DFggWOCw49X+fT2cWIgGpjFM4XAlNaLBTgeWSGh599135fbbb5dKlSrJt99+qz6GZYB4EGJGyGgJLXrhtkI0cOOB/OCKu+KKK8KO54cfflAkiJdHp4JjdwaLB6T5yy+/pKiHlw1uSMShtDvQSDTIyEM2IGRv3ry5wFcdWqwQDdwX4caDhQXjxS5T+8TNLnkkGrPIeaeeHUSDm7sRJ4X1HrrpMo4Um0ucccOzKIgvwvUM129qhUTjA6Ixipg9e3ZFNiAILIL4P4LWN998s/Ljx0M0WEjh64+XaCLVe+mll9RuCAFxfcgSkxDJCSVKlBAs9qEFz8IasUI0MNvxdQwIQCKWFa68/vrrKqiuXyi8nHhpYDVEMvfhUtQpoPiNookGlov2U+NlwwHXcMUK0UBmZB6GK4h5wTq79NJLZffu3aZXPRKNaeg8U9Eq0WCDhM0aCjZh0Q5rIokA3gCU++67T8VsohUSjc+Ixiju5ZdfrrK9EPBGjAI+Uq8RjQ4owq0V7ovf4PqBW8sK0eg2EHSHSR+u4DMs+ngWLicEKuEKgCWDg63hCmJQSJhAYkK/fv2CiAb/AUnhJcfu7qabbgqKzej2rBANAqyQIVwJZz1Ge9nDfU6iMYOat+pYJRp4Rh555BEVxxw3blyqg4MFD3exsSDRBfHW1AqJxsNEA5cZFrNQt5hRZASGsbiGXokSi+ssERYNgulIn4QloDNSjPL3799fZbNYIRr4hnHvGKwoWEjhCqw+kB1cTUjjRtm0aZPyL+MF0zEYY12kePbo0SPI7actGgRMy5cvL0uWLFFWFLLdWrRokaJr/A3JCGgLaaOhBXEguBrDZZ1BZmNMyVgXBIksPli0oXGheJZBEk08aHnzWatEE+uoEGvFxgnuZ8R0smXLpjZYsKgRqwGZRCokGg8TDc5KIL0Y5yrCuZ0gOhY4xAeQEYLMNF000RjjJfozY5qy064zWFqwIBA/wUKvU3ghC+5UQkYaXFdWiAZtIREBJIIMMpwbMhakLuNlwDkjWIG6wGJAfAsBTlhVxoKXF0FPBEhh8SBdEyU0GQAkBTcW9ARX29NPPx3UDmJAiAUZD7DpB2CJgvRAFuGIBjEYuMaQfWYsGAPiN8Z09FgXi9DnSDRmkfNOPatEA5c73GHY0OL+Pp2SbBwhDo4jIw0kg8C/PuCJc3NwjePdevbZZyO64kk0HiYaXA8BUxZpu9hJhC44CLwjwA6fKm4V1qnJGBJIB6mw4e4KSyTRQBakNSMNEpMSF3QiUQALOM69IKUSabpWiUbvqJDEANLVBy2RHgz3Xd26dQPxFq1yxGxwhgckhNiN8TZm3LMGlxncfSBIXcKlN4NkQDa4GgiuOZ1ajTogOSQNwA+ODD9cXKgLLDlYdCjhiAZ/X7FihZJdjwdxIRwohaUT6WxOPEsgiSYetLz5rFWiwVqBdwYFZ8FwJiy0YB4ioQe/ESM2FmyI8T7DY9GkSZOwIJFoPEw0WKQQ4IZ5ip0v/PJYmLGgYUcBooF/FWdAkGJrLIgpIG0XB/5QDwsw/KsoiSYaZMlht4P8e5jbKFg49ddcI8YRiWgwXlhD4QqIQU9s9AHSwAuDwDzGjAJrBRYAyAaB9dAzSNjJjRo1SpEBLC9YLsiqQYIB/o0XyGiFRTqwCfcZdn36HI62gCCDTpJAoBVyIK6GcwrYOQIHWHbhiAYbDFwTAvcp/o2ALTYPyJ6DBYbU51jOVKW2PJJovEke8UhllWgwV+EGRiajPkAcrn8cQtbvb+jnqX2GZzXRaA9MPONL9mddP7AJgGGtYGcNFxeIRxcsPliEcbIerpnQgmA3LAksxJgcxgymRBONlg27cZjZGAfIBe4uuPYQp8BibLxXSd8MkNokgyUC15IuIGCQK9x1OCCKgjgMztXAcjBafMZ2kX6N7Bns1CAj4h+wSoYPH64OpBpLalfQ6NRQjAfj1Ac34SJDLA0koc9D4YXGiWokG6R2MwBcGTgMirNAKD///LMiHVxZE+oiNPNCkmjMoOatOlaJBqPBu4FNS+jZGW+NNDml8QTRGKHFeQ9c3Y0FBu6ncAHscKrA4oYdR7hzHk6rDoss+ocfOFxSA9KhQTIgAizsdhV9eEwH/mNpV1+ZYbRGYqkX6zNw4yHNGzKldit2uI0AYkGIQUWK1cUqQ+hzJBqzyHmnnh1E453RpD1JbCEa+PixC4ZZGro7TguQIjiPwDWIDhaD8SsLkOQAdxdcR5HSn9MCRqFjjMXitAsXEo1dSLrXDonGPezt6JlEYweKIiqTBam9SMNFphTIBrEP7O5RECOBKyiWS0NtEsnTzSSSaED2cJcgfoeECBb/IUCi8Z/OjBKTaGzUH4LlCAji3ArcWgjwg3RwxgSuM5b/EAAJI2gKXJD95mTR5OKlr8t2crzJ2DaJxt9atYVoAIFOj4XrDC60tF4QCzHecJzW8XBz/CQaN9G3p28SjT04utWKbUSDGA1iNWk1TuOWAtlv6ggwPpMcM4RE42892kY0OiEAcNCq8fekSCbpac0khzZJNP7Wo21EAxho1fh7MiSb9LRmkkejJBp/69JWojHGavbu3Rt0FYm/YaL0fkNAZ5pBbiYB+E17KeUl0fhbh7YTjdGFBmhIOP6eIH6THmnMuOpGn/5mSrPfNBheXhKNv/VoO9FoOPQdX/r/IBwUZqT5e8J4UXqQCm6HRtEEg9hMhw4deG7GiwozIROJxgRoHqriGNFgjPq7WcJ9R4uHMKAoSYQACSaJlGkYConG33p1lGg0NHCn6W+eDPcNlP6G0H7pV69erRrFVwuwREcA5KKzy3jyPzpefnyCRONHrf0nc0KIxt8QJV56/R0s+NZOfHsnCxFI6wiQaPw9A0g0HtQficaDSqFIriJAonEVfsudk2gsQ2h/AyQa+zFli/5GgETjc/2d43XCntMgicZzKqFALiNAonFZARa7p0VjEUAnqpNonECVbfoZARKNn7UnQqLxoP5INB5UCkVyFQESjavwW+6cRGMZQvsbINHYjylb9DcCJBqf648xGu8pkETjPZ1QIncRING4i7/V3mnRWEXQgfokGgdAZZO+RoBE42v1MUbjRfWRaLyoFcrkJgIkGjfRt943LRrrGNreAonGdkjZoM8RINH4W4EkGg/qj0TjQaVQJFcRING4Cr/lzkk0liG0vwESjf2YskV/I0Ci8bn+mHXmPQWSaLynE0rkLgIkGnfxt9o7LRqrCDpQn0TjAKhs0tcIkGh8rT5mnXlRfSQaL2qFMrmJAInGTfSt902LxjqGtrdAorEdUjbocwRINP5WIInGg/oj0XhQKRTJVQRINK7Cb7lzEo1lCO1vgERjP6Zs0d8IkGh8rj9mnXlPgSQa7+mEErmLAInGXfyt9k6LxiqCDtQn0TgAKpv0NQIkGl+rj1lnXlQficaLWqFMbiJAonETfet906KxjqHtLZBobIeUDfocARKNvxVIovGg/kg0HlQKRXIVARKNq/Bb7pxEYxlC+xsg0diPKVv0NwIkGp/rj1ln3lMgicZ7OqFE7iJAonEXf6u906KxiqAD9Uk0DoDKJn2NAInG1+pj1pkX1Uei8aJWKJObCJBo3ETfet+0aKxjaHsLJBrbIWWDPkeARONvBZJoXNbfrl27ZO7cuTJo0KCAJJGIZvXq1XLu3DmpVauWy1KzeyLgHAJr1qyRGjVqBHVAonEO70S0TKJJBMpR+gBx4MXSZBOOaPRnRkLygOgUgQjYjgCIBvN84MCBUrNmTdV+KNFg04X3xPje2C4IG7QNARKNbVCabwgvDcgGLxZesFCiwWd4Rn9uvifWJAL+QCB0zhuJJvR98ceI0raUJBqP6F+/WBCndevWyp2G33CtkWQ8oiSKkTAENJmgQ2ywsPkK929a+AlTiaWOSDSW4LOvsvHFCtcqrRn7sGZL/kDAuPkKlZjvgz90qKUk0XhIX5FeLL5UHlISRUkYAqltvvhOJEwNtnREorEFRnsaifRi8aWyB1+24j8Ewm2++D74T48kGo/pLPTF4kvlMQVRnIQiEG7zxXcioSqwpTMSjS0w2tdI6IvFl8o+bNmSPxEwbr74PvhTh54lmnXr1vkTURukbtOmjco2K1asmMyZM8eGFv3ZRJUqVfwpeBxSp+V5HitM69evl27duqnHW7VqpbIxWSIj4MX3xlNEM2vWLMGLx5ePr5ERgQ4dOgh+kqVgfuu5nixj4ji8hwAIBz9eeHc8QTR46fATVLKk957mKFFiEThxJqg/vxNOOII5evRoYjFlb0mPQI4cOVKM0e13x3Wi6dix438WDMjl4iwimUkySf82xDrAgyfPP3nwhPqFHdrMmTNjre2Z54ybKZDL3r17hSTjGfUkpSCFChWSggULBsbmJtm4SjQBkiHBJOVEt3VQJ8+cJ5sTZ3xHNkaS2bZtGwnG1onBxqIhYCQct8jGNaIJvHwgmYIpTb1o4PHzNIrA3qO+IhuSTBqdpx4bNtxpJUuWVFK5QTauEU3VqlXPq6L4xR5TCcXxPAI7DyoR4ULzYoaNET89z+Eq27Nnj+ehpYDJi4CRbNauXZvQgbpCNIFdHuIxF2dO6IDZWRIggLjNwROed6HpeU6SSYI5lyRDgFUDwkm0VeMK0dCaSZJZ6+YwfGDV0Jpxc4Kw73AIuGXVJJxokOKJJABhbIZvghUE/o3VeNl9pomG58KsKJp17UZAu5sT6T5LONHQbWb3tEmj7f3rPku0CyBWtPWGCinMyDRjIQJeQUC7zxK5SSPReEX7lCM+BDxONIzPxKdOPp04BEg0icOaPfkdARKN3zVI+V1CQJ+rSaQ3gBaNS8pmtxYRINFYBJDV0yoCJJq0qnmOO34ESDTxY8YaREBESDQenwYZ0qeXywpdKsdPHJc9v//mqrTFLy0m586elR9//TlmOTJlzCSXFigkfx0/Jvv+2B+oly/3JXJZoSKydvO3cu7cuZjbc/VBEo2r8LvVOb46Aym6SLA4deqUY2JcddVVcvbsWdmyZUugjwsuuEBuueUW2bRpk+zbt8+xvp1umEQTBeFry1eWSqXLR9XD/GUvykkHJiFI5tWnZ8un67+Sx0b0jSqHkw98tOANNcY67RvF3E3ZK0rK/JHTZOVnH0rviUNVvQvSpZNl0xZKgUvySb9JI2T5J6tibs/VB0k0rsLvVudvvvmm1KtXT0AE3333nWNi/PXXX3L8+HHJly9foI/27dvLs88+q0iuVKlSjvXtdMMkmigId27aVtrc2zSqHmq3aySHjh6O+ly8DyQj0QCD54Y+LVeVKCWdh/WWdZu/jRcWd54n0biDu8u9ukk0devWlWXLlsmqVavktttucxkJ892TaGIkmufffEVWf/VpxKc3bPtO/vnnH/OaiFAzWYkGVk32bNnlyF8++m4UEo3t89sPDbpJNMAnT548cuDAAf+4mMMolUQTI9GM+t9keWXFGwl/L5KVaBIOpB0dkmjsQNF3bbhNNL4DjEQT/4Wa2nUWD9HcdPV1ckPla+XN1Svkz0MHpN7NtaVSmfJy8PAhef2Dd+TrLRuVKkpffqU0rFNPiuQvJLv2/CKffvOlfPz1F0FqCiWaKldVVO3lz5NX1n+/ST755kvZsnN7xN1OoXwF5PqKVaVaxapy+OgR+XLj1/LFxq/Vv8MVJB+gjxsrXyfFixSVnbt/lpfefU1+2bdHUovRXJQtu1SrdI3cVPlayZEtu2za8b28+M5rUrRg4RQxGvTb+PYGcnmRy+R/ryyQA4cPKVHSpUsnPdt2kUNHDsmslxcoDCH7FZcWkz3798mKT1fJlxu/CSt3zhwXSbUKVeSGStdIrotyyuYd38vzb76qZGl59wPy/c7tsmzVu9be2TRANLlz55batWvLHXfcIRdddJGsXLlSVqxYITt37gxglyVLFhk+fLhkzJhRhg0bliJI3ahRI6lZs6a888478tZbb6l6jz/+uJQoUUL69+8vGTJkUP+/+uqrZffu3eoZ9IMYRbhyySWXSLt27aR8+fIqfoHA+GeffSYvv/xyiseN/RQtWlTuu+8+ue6665RF8MEHH8js2bNVwD1cwTUpd955p9x0003y66+/ysKFC1UdO4kGmAEb9FO2bFkV85k6dars2LFDjT80RoNEhB49egiubpk7d25AbOM4r7zySrn//vulYsWKsnXrVhk3bpz89NNP6lno4p577pG8eQhKl94AACAASURBVPOqL3tcvHixbNiwIcXw8e6hDYy9TJkyqj76fO655+T06dPW3htmnUXHzwzRPPxAK2l/34MyZvZUaVL3HimYt4Byq2XOlEkRAgLg+Hf/h7vL33+fVyKys1BGPDtJlrz3ZkAwI9Fs2r5FOjRuKWf+OaPa03WwKM96eX6Kwdx2Q00Z3rWvWsARxMckh8vqwOGD0qrvo7I3JIsNGS6T+4yQahWrqLaOnTgu2bJkVf09MWagjH7iqbDJAHly5ZYFo6YJMsmM9f44eEBGPzdZxvYYFJQMgGcm9x2hSOG+x9vKT3t+UfUg25cvrpDdv+2ROUsXy1MPPyEnTp1U48Rnqt7CZ2X+speCxlowb36ZN2KK5M55/usfkOGWPWs22X/gDxn93BQZ33Nwiv6jaz7ME0lONAg2YwG/+OKL5cyZM/L3338LSAVz7e677w6QBpDp27evIhss9ligdLn00kvV4nnkyBEpV66cHDx4/usVPvzwQ6levboisZdeeklAaHgGZIaCOtWqVUvxBW3IuHrhhRcUwUAOLMT6a4PnzJmjbgSGrLrofu666y5ZtGiRkl+PA8+A1OrXr59CuSDAoUPPJ6ucOHFC0qdPrwgRRFq5cmVbkgEuvPBCefvttwOxFj1+yAd8gWUo0YAkP//8c0UQTZv+FyvW40Q9fAYMsmbNKugD7YI0oaM2bdrI4cOHJXv27IHPoIOvvvoqgAE+W7BggSIkkN2uXbtU4gHG//XXXwuwtPp1E3SdRVlxrBANFskFy16ShW+8Iqf+Pi11b7pVLZ4nT58SLOpjnpuiMq7+OXtW6t1cR/o//IRKY67V5t6AhaKJBosnrI1hMyfKh2s/k7/PnJGq5SrJkC69JGf2i6TX+MGy6stPAqOpULKszBg4Vo4e+0v6Tx4p32zZJFkyZ5b6NepI99adZOcvu6TtU4+rRVmXx1t0lAfvaiSbtn8vY+dMky0/bJVLLs6jrK6mdzaULJmzKEvImHUGmWYOHCcVSl2lLIY5S19Q1k+JopfLIw+0lqvLVlBWhTHrLBrRgNgOHjks/Z4eLt9u+05AZLDi0B4+u7tLS/n94J9K7KxZssqcYZOU1QMLCrE0pIEXL3KZtG/0oFxTrrJcfFFOEk2UeQ6rAQsadtAPP/ywIgOk8taqVUst9Fh4QRTr169XLWFB++ijj+T666+XBg0ayBtvnHcrI3CNhQlB7Hff/c+C1Avjn3/+qSyEESNGqEwqZHINGTJEGjZsKK+//rrce+9/cx/vyPfff68sIWRfvfjii3Ly5ElFSNOnT1cWDnbsr776agqiwX1v3bp1U4swNlp4DqRRuHBh1dfSpUsDdSD/a6+9pkixZcuWyorBeEFI2NEDh1y5clnOOoOl0b17d/niiy/kscceUxYDvvYYF/527dpVkQHwMWadRSMaPA9sgGmmTJlk9OjR0rlzZ9m/f79aQzBWbB5AQuPHj1d9QVcYsy6PPPKIPPPMM0rPbdu2VRjjeegIcj399NMKSyuFRBMj0WDxOnj4/O4stCBravLz/wv8WVs0cGt1Hdkv6PFp/UfJdRWqyEvvvq4sHmOZM3yylL+yTNAuXxMNnhs2c4K89v47QXWuLX+1oE243hp3axf4DG1dVaK0tO7bRb77IfiCxccefEhaNrg/yHqC6+n9515VC3zzXg8ra8BYBnXuKfVr3KY+NxLNLddVlzHdB8g3WzbKw0N6BiVEZMmUWeaNnKoW/XiIBv12GtorhZtsxOP9BFZa97EDZc2/iRl3Vr9VhjzaWz5c97l0HzMgyIWoSHDQeAHphvZv6qVJYotm5MiR0rt3b7UQTpgwIQgeuHlgCYTuqkEAIB4s0HAD3X777WpXPmPGDMHiZSyaaD7++GNFXkYrBIsaFl9YQDfeeKN8+un5pBtYO9h9//7774rUjAULIkhgypQpatHWRfcDUnnqqaeC6oBAQVChC+fGjRsViYAcly9fHlSndevWAssJxUp6MwL6f/zxhxoLLCS45owFbrFWrVqpz+Mhml69esnYsWMDTcEKgT6yZcuWgoSBMwgYJASC0wUED+LBpgGbDV1Attg0wMp57733TL0yuhKJJgp82qKB6+n0mb/DPv3xus9lwNTRgc800UyYN10WvbUkqE7PNp3lgbr3SJ+Jw+S9z9aEXcwfGdJTvtp0fueoiebU6VNSvcVdcjbM4cYlk+ZKkQKF5OaWdynXltptzl8me/b/Jo26tU0hM8gMRLT0/bdl+MyJ6nMQ1jNPjZZlq5bLkOnjUtQpe0UpmT9yagqi6dSkjbRt2EwGThsjb61JORlBaCC2eIjm9N9/S41WDZTVZiyt72kiXZq1k/Fzn5EX3j6/I+3RppM0qXuv9Bo/RD74IngxwucN69SXvg91JdFEmeeIkdx6661SoEAB+e234IPBsCyw2CCeor+aVzeHHTKIBfEM1D927JhUqlRJ/TYWTQCwGOCmCS1YMLEb79Kli0ybNi3qola6dGl1sBFxIBChLrqfGjVqKHedscASwu4e1gssJ5TMmTOrxfeXX36R4sWLp+gX1hA+x8JthWhAmFisQVogydByzTXXyJdffhk30YC0tJWp20QMBtZe/vz5FakYC9xil112mVojdKxq4sSJKma2ZMkS6dSpUwr9R1VGDA+QaKKAZMV1NmDKKHn7o/eDetBE0230U/LRuv92D3gIbrW7b6kr4YgGAf8WvTuFlRYxkFrX3iht+j0mG7dvkZLFrpBFY2aoZ8OdUYELDAcpt/64Q5o/eX7n2eKuxtK1RQeZMG+GLHrrP1eE7hAxpQ/nv5HCdfZ072GC5Ae0g/ZCC+IwiMfEQzSI0dzzaKsUbSGB4Ml2j8qkBbNkwRvnA8E4j1Ox1FXqedQLLZogadFEnuhYTPWis3r16rAPIoCNAhcSfP7GAmsHiz1iKDfffHPAIjE+owkg3MKI52BNIH4BKwWuIGOB+whBaiQPIKgNGbAwo0QiGsSKQIzGAqtr8+bNQXEafH8P4hVw+SHeEa5glw8ZrBANAvqwPJ544gnBwh5atLURr+ss3Dg10SCWFZpggaSDK664Ioho4E7EOR0kFUCHGO+aNWsU8dj1vUYkmsjvn/rEK0SDbLFOQ58MK+2AR7pLg1p3KDcd3HXI1JrSb6R6NtJhSGSjgbwQ20GBpQCLAdYMrJrQgsXo4wVvyPGTJ4NcZ9rd16BLC5UZFlrKXVla5g6f4hjRaKIDCWM8oQWJDVP7jaJFk8o8R8AcQWiUSERz+eWXy48//qjcOz//HHwFERZP+P9BQMhYwtdIhxZNNIgB6Ywo4zPhrA24gSZNmhRwwyEgjbpwDcHyAvFYJRq4+xBLmj9/vhpbuII+kIVnhWi0axLWjHbFGfvC+wUrEMQQj+vMDqKBHCClBx54QJo1a6ZcaLD0UBBHQpJAqKsvyrKZ4mMSjU+IBhlcd3R8IKy0erG/8+GmKraSO2cuWfHsy4pkOg7uEdOcuO3GWjKia19lKcBiCC1IwX5tyrwUrjO4peCeenxU/xSp2WgDFhosNacsmg6NW6hMvBGznpYlK8+n0hpLu/uaqyQCWjSpTwNkfWGniwUnnnRW7ILhusFChH9HyurSRIOrXGC5pNBTu3byv//9TwYPHiyDBg1SHyPLCpljWOywQCOWogsWfaQ5WyUaEBaIEX1oKylUNrjVihQpYolomjRpooLtSAjo2bNnivEDe1gb8cZo7CIao0DITsUtBJATFiriZ9gIWCkkGp8QDcRs2LW1/Lw3OIiI8ytvzXghRdrxW9MXqdTk2x5qLIh5RCs6FgTXW9v+XVOcy2lQ63YZ8EiPFESjYyDzXn9RphgSInR/gzv3kno16jhGNDq2BIJt0buzOreky+WFi8rsYZPCZr1FwyPs50mcDIAdfYsWLVIEhFPDSWeewR1WoUIF6dOnj0qnxZkXnFcxFk00Y8aMkSefTGmZ62A4ds8ITqOAeNAWCAeJCMai7wCzSjRoE0STM2dORSY4b2MsiNv88MMP6k9WLBqkCyODDm6pG264IcX7BdyAmReIRo8f7jy4H4ENNiDa6jXz7pBofEQ023b9IK37PSan/z17A3N7Qq8hUr1KNZWNhqw0XZ5o9Yg0q9dQLfB9nx4WlETQ9t6m0qlpW5UKPHH+TFUF51QWj39WZYhNe2G2SlPWBbcvLxj1jDqbEpp1BkvnhXEzJWP6DCrrDNlnutS+/mYZ1e185o9TFg3a7tW2i9x/x90qrXr5Jx/Itl07VabZHTfdos7o4AAqLZrUJzoWeKT8IuUYCyFiBbogPoNDm7Ao9He/4zNkqcElhDMoOFODzCosptgRIxhtdLFpokEMAIFxo4sOgXnEA5CVBdcbfqMMGDBAWTihWWw4iInAOhIT7CAaZKIhIw0uNFhcOl4F9xGy5PSYrRANEiqAH+JEON8C3HRB9h4sKizoiSYabBYQt4K7DlYM5NAFmWlIHsBhXejFSiHRxEg0WLD2H/jv5Qut1nPcoMCZFJ11ZmcywLdbN0uG9BnUochP13+pLJgbKl+jbhX4/sft0u6pboLMNF0wgSY+OVQdioQV9NWmb+TY8WPqhgIswvhb+wGPB07lox4IZd7IaZIjazYV70AdXOUPqwHnfXAOCGeDQm9vRoozDnMiS+zr7zbIlp3bpGLpclKxZFl5afkyaXrnvY4SDV7i/h2fkDo31BCkVKMgO2/pyreUO+350dNJNDGsEvrQIha7999/X7lykHKM1FecrcBC9Mkn589qwYJBEB3PIMsMhw5RkFU2b948dRYFhKK/AkITDc5rwBpBsBn1QWoI9KPUqVMniIDQNzKxQFyQBynOcM8h8QDB68aNG9tCNCAUyAfXGeJQcO3hb0hQQAYe4iY4Q2SFaDA+EArGg2QGBNkxJlg6wAlutebNm6s4TaJjNDgjg5R2EDwy8iAbrDvoCbKAhPE14VYKiSZGookGsvH2ZieIBl8TMPiZcYKstVurVVeH0HDCH3+fvniu/Pbn7ylEzJo5izzSpI3gqw5gqaAOrCFcjTPz5QVBbiZduVLpcuqMzTXlK6tFG88jlRh9rJq7NOLXBMC1hoQEkBgWfriwJi18Vnb9+nPYK2ii3QwQa9aZcdA4N1OxVDm5KHt2deUMzj7pDDykkiOl3FJJYteZxgWLDg75IcsKwXjs7mE9YBeOU+IoWPhBErBasABr8tFtYAHFif5HH31UXa+CookGMQUcnoS1ghsIQFAgEBCQ8eClbguxAbiUkM6M+YtFH9YALKDU0ptjzTrT/SDzCjcDgFwQtwFBog+MAdf02/U1ASBVxD6QCo6UaRA4Eh5w5gdJDvHeDGBXjAakjQOaiBUBZ5xzQtwOssKatVpINFYRdKE+CCBv7jzKVRTrl4YhQSBzxkzqy8fCncUJHUbGDBmkwCX5VSYZTuPHWhAzypY1m+onVtlibTvcc5ATLr1Tp0+rK3NCi3bfPbN4jsxesshKVyJpgGg0QFgEsauF68SOL/syEg38/tiMIAMN1hPOqUQrICWkNsONYzzsGa1evJ9jkYXlgQy30LNA8baV2vM4xY/zLLCgtDVoZ/tm24LeQV6Qyw69azlINGY1wnqeQCBfnryybOp8OXHypIpf6XvTIFzRgkVkfK/BgqSASOnPcQ3C40QDlwcOUGLhRqzFSyWUaLwkW7yyGONXsdTFRZdwEablQqJJy9pPkrE/1ry9uqEZMaJtu3bIT3t2S/mSZVXMCWXua4tVgoNlC4tEY3rGJBPRhJ7EjwYKrDC4I9NyQeIGMtdmzpwZlFDiJCbpzll+4+MTT+/0JEt6kYI54qvMp32BQI1rbpDm9RuprzbIlSOnOk+ELD1cixN61Y/pAZFoTEOH9GQE23GX2b59KQ/2mm6YFX2BAInGF2qikPEggK8VMGbgxVM31Wd3nr9UNZG7snhlx5UqKHCdxRL7iLd9Pk8EzCCgU8SN6dNm2omnTsItGggH37W6t6dQDpHM6eORl88SgUAiAKBI5MsSL/R6nnsxThPvWPh8ciDgRnwGyLlCNHSfJcekdW0U/1oz+KIt/Hi1BOY5rRqvqijNyeWGNeMa0QRZNRfH/5XOaW52cMD/IfBvbMbr1owWmFYNJ69XEHDLmnGVaIy7PSHZeGUuelsOA8l43ZoxAqljNbjHy+rX8HpbQZTOqwhoknFrg+aK60wrA1cpBK5TANmgXHz+2hIWIhBA4OQZkYMnRE6cP6zqJ5KBvEGbqn8vjiThcH4nAgGkMeOeNPxGcSt5xlWiwcCDyEYRzb+Eg38jBZol7SIAYjn5d4Bg3HxR7FBC6FzX3xXDjDQ70GUbGgGQSvbs2dV/NcEgNoMNmvEi1kQi5jrR6MGmIJxEosC+PI+A36yYSIBqC97qxYieVxgF9AQCbhOMBsEzRGMkHP1vu7661BMaj0MIfW27/sreOKom1aN4SfRPUg3sX3eant9pdZ7Ho1O+E7GjZXxn3LJgQqX1HNHEDmfyPqm/2RDfbjhw4MDkHShHRgRiRAAXbKIk+CKTGKXjY9EQINFEQ8iFz0k0LoDOLj2NAInG0+qJKhyJJipEiX+ARJN4zNmjtxEg0XhbP9GkI9FEQ8iFz0k0LoDOLj2NAInG0+qJKhyJJipEiX9AEw2SAfA1uSxEIK0jQKLx9wwg0XhQf8iwqVWrlpBoPKgcipRwBPg+JBxy2zsk0dgOqfUG+WJZx5AtJA8CdCX7X5ckGo/qkK4CjyqGYiUcARJNwiG3vUMSje2Q2tMgXGewbBCjSesHN+1BlK34FQG96eK74FcNuvR9NP6FK3GS032WOKzZk3cR0NYMJORhTe/qKZpktGiiIeTi59zJuQg+u/YEAnSbeUINloUg0ViG0LkGmObsHLZs2fsI0Jrxvo5ilZBEEytSLj1Hq8Yl4Nmt6wjoOCXv/HNdFZYFINFYhtDZBnSshj5qZ3Fm695CgNaMt/RhVRoSjVUEE1CfO7sEgMwuPIOAcXNFa8YzarEkCInGEnyJqcwXLzE4sxf3EeBcd18HTkhAonECVQfa5AvoAKhs0lMIcI57Sh22CkOisRVOZxvji+gsvmzdPQQ4t93DPhE9k2gSgbKNffCFtBFMNuUJBIyBf8ZkPKES24Ug0dgOqfMNGskGvfHldB5z9uAMAjrRhfPYGXy90iqJxiuaMCEHd4ImQGMVTyCAzRLmL36TZDyhEkeFINE4Cq/zjRvJBpdv4mfgwIHOd8weiIBJBELnLOYrL441CaZPqpFofKKo1MTk7jAJlJgGhsB5mgaUHGGIJJok0r1xp0h3RBIp1udDCSUYbXXTivG5YuMQn0QTB1h+eZSE4xdNJbecoQSD0fI7ZZJb55FGR6JJUr3rQKsOttLCSVJFe3RY3Ox4VDEuiUWicQn4RHUb7oVH30wYSJQG0lY/ofONbrK0pX9aNGlc3yScND4BHB4+CcZhgH3ePC0anyswXvHDLQhMiY4XRT4PBMLFYGjBcG6EQ4BEk0bnRSjhxBvDwSJToEABKV26dBpFMO0OmwSTdnVvduQkGrPIJUk9K4SD60N42M7/EwFXGOmNRmqjIcH4X9dujYBE4xbyHusXhBO62GABqlGjRsRT2yAaFKSssvgTAegYuj937lzEAZBg/KlbL0lNovGSNjwgSyTCgWihmWr6ck/45Uk2HlBenCKkpj98tmbNGnVhqy684ihOgPl4AAESDSdDRARicaulS5dO1QcJGRclwuptBIw3gBt1R+vF23rzq3QkGr9qLoFyRyIcuNWMN/CSbBKoFItdGa/n19aoUZdonhlkFkFmdVo0nAPxIxDOrVapUiVZv359oDGSTfy4JrqGkWRC+6Z7LNHaSBv90aJJG3q2fZThrBzdCe+zsh1u2xrUwf9wBMMMQttgZkMhCJBoOCVk3bp1caMAK2bevHlB1oyxkVatWknr1q3jbpcVnEMAJIMAf7hiVl9VqlRxTmC2nDQIkGiSRpXxDWTWrFmKYMyQTHw98em0gAAIBz8dOnRIC8PlGONEgEQTJ2B+f7xjx44pyCVr+gv8PizK7xICx8+cTdEzyIaE45JCPNoticajirFbLFguIBldQC55MqWXLCQZu6FOk+39eeqM/HnyTGDsJJs0OQ0iDppEkwbmg5FkSDBpQOEuDtFIOCQbFxXhsa5JNB5TiBPiaHdZnszplRXDQgScRODEmbPyy7HTqouZM2eq2A1L2kaARJPk+kfQHz8oJXNmTvLRcnheQUBbNiAZkA1L2kaARJPk+q9ataoaIa2ZJFe0B4e3+9hpQbIArRoPKifBIpFoEgx4IrujNZNItNlXKALahUarhnODRJPEc0ATDa2ZJFayx4e27fBJJeHatWs9LinFcxIBEo2T6LrcNonGZQWwe6H7jJMACJBoknge6GyzS7Nl5HmZJNazl4dGovGydhInG4kmcVgnvCcSTcIhZ4chCOjsM56pSdtTg0STxPon0SSxcn0yNBKNTxTlsJgkGocBdrN5Eo2b6LNvIECi4TxgjCbJ5wCJJskV7IPhkWh8oKQEiEiLJgEgu9WFXURTtPgVckv9u6XIZcUkR86c8vPOH2THlu9kw1dfyN7dv7g1PPbrAwRIND5QUgJEJNEkAGS3urBKNBdccIG0frSb1G5wb2AIf//9t2TIkEH9/+/Tp+V/E0bLR+8td2uICem3yg03Sfkq18inq1bKtk0bE9KnHZ3Ua9xE8hYoKEsWzJUjhw7a0WTcbZBo4oYsKSuQaJJSrecHZZVoHu7VV26+/U7568hhWfH6Evn6s0/kx21b5ZL8BQSLb5P2D0vGTJnklbnPyZIFc5IWSYyzQdMH5bmJY+X9N1/3zTiHTJkpJcpeJd1bN5O9v/zsitwkGldg91ynJBrPqcQ+gawQTYWq10rv0RPk3LmzMurJ7rJx3VcpBLvsihIyZOosOXfunHR54F756+gR+4T3UEskGvPKINGYxy6ZapJokkmbIWOxQjQgGZDNq/Nny6vzZkdEqWXnrnJHw8ayaOY0efOlF4Key5Qps5SqUFHKVqwsxUpcKT/9sEM2r/9atm74Vk6dOn81iS7p0qWTVo92k6OHDqk+K11bTSpcc50UKVZcft+3Rz5btVI2fb0urByoe3nJUnJV5SpSpkIlOXL4kOzc+r18+eEqOXTgQIo6F6ZPLzVuv1OKlywt+QsXlt/2/Cq7dmyXj1a8I6dO/idX8VKllUV3ZdlycvmVJeW7b7+R3bt+lNOnTqnxGkv2HBdJuSpVpeI110mWrNlk09drZcPaL2X/3j0p+q973/2Sv3AReWn2LLkkX3659uaaUqJ0Wfnr6FHZ/M06Wf3OW4rgQ0usct9wS20pWa6CXHdzTcl5cW755P335NhfR+XnnTvkgzeXmZbbzKtCojGDWvLVIdEkn04DIzJLNFmzZZeZS94ULGydGjcIu1jrTjJnySL5ChaSE8ePy+/79gb6xt+fmjBVEQDK8WN/CdpF+XH7NhnarbOcPHHCQDQXyPMrP1SL/uuLFkiHHr3Vop8xU0ZJl+78V00vmvWMvPniohQaa/NYd6lz9/k40snjxyVz1qzq34f+/FMmDOyjEhd0wcL+xNCRUqxESfWnY0ePSrYcOdS/QU5j+vYMxDOur1Vb4D684MIL5cILL5R/zpyRs2fPqrE80qhBoM2ClxYVuKnQzj///KOeg0sRz45/qrd88/mnQTIPeHqalC5fUcb26yWP9hskGTJlCtTBg3genxlLPHIDj5p160n6DBkEJIy4mpw7p1yfk4Y8ZVpuM68KicYMaslXh0STfDq1TDTw62PhPHr4sHRsWC9uhEBQPYePURbRqrffkDcWPy/7ft0tBQoXUbGOmnXrq90+FlMsyiggExAN/g+LZMqwQbJ980bJmTuPVK9zuzRu85D67PEH75eDf/4RkOnuZi3lgXYdFHnNnTxBkcrFefLI9bVulWYdO8vxv/5Sbj1tQXXo0UctwiuXLZVX5s1WpFK4aDFp2KqNXF/zVlnx2qsyd8rEoDGn5jpDFh7chwi6I4bz+eoP5Mzfp6Vspavl0f6DFUkNfryT/LRje6BNTTQgxfnPTFbWGgjh2ptryf1tH5Lcl+SViQP7ylcffxioY0bu1GI0ZuSOeyLwHI0ZyJKyDokmKdV6flBmLZoqN1aX7kNGynfrv5Zh3R+LG6FK110vvUaMVa6mET26qp29Lshk6zduspSpWElZD+u/+CyIaPCfET27pnCTPfrUYEUE4wf0kXWffKTqwGp67o3lyirp27Gt/LH/tyBZkTFX6867FAF8uOId9Vnlajeoxf/rTz8Jck/lL1RYJsxfLLt37ZQn27eKmWg0CS2cPlXefmVxUD2NA4gExKmLJpqlC+fJy3OeDapT+657pO3jPeSdV1+SBc9MDnxmRu7UiMaM3HFPBBKNGciSsg6JJinVao1o4OPv0m+QfLFmVZCrJVaoGrZoI41at5Ppo4aGTX2GhfJI76eCstW0RQM3T7v6t8mZM38HdQdLCIvj/GmT5N0lL6vPQFZwz324/G2ZMWZErOJFfG76K8tUfKX1nbfGTDR9x06ScldXUa60wweD40EY05y33pMDf/wuT7RskoJohnTrIt9vWB/Ul7Ym137ykUwY0CemMUWSOzWiMSN3TMKEPETXmRnUkq8OiSb5dBoYkVmLBgsnFiIEvnu1axE3Qj2GjZarr79RWRm7dmxLUR/xkREzZ6uYwbj+T6rPNdEgRtOtxQMp6iAGg9iDMemg/v1NlXts4fQp8vYrL8YsJ6wXJA5cUbqM5M6bT7JmyyZZs2dXLjQE4ZvXvjkmooG76/mV560rWG/hChIhUNo3uEPFdlC0RdOlSUM58Pv+oGqFLysmY2cvDBuniVfuSERjVu6YATY8SKIxg1ry1SHRJJ9OLRMNgtvj5y5Sge02d9ZOYV1Eg2zwlBkqU6tr88ZBCQK6HuIZk55/WbZt3iiD62IWrAAAIABJREFUHnvENNEgnnFP81Yya9xIlakVS0F8pk3XHurQ6Ynjx2TPLz8r1xtiOdVq3qLiQC1urxkT0SDgP/ft91MlmnwFCsr+fXtlxqhhAdeeGaIxI3ckojErdyz4hj5DojGDWvLVIdEkn04tEw0amLzoFXUwc+CjD8v27zZFRAmBa5DKn3/slx3fbVbPtX7sCbnt7oYq2B+acYXPEW9AsoAx8G7Gornmppul2+ARKeIZkYS9OM8lMu2l11S227h+vWTr5o2BZATUmbnkLcmUOXNcrrOxcxYKLI229VK6+yLJES/RmJU7NdeZGbnNvCokGjOoJV8dEk3y6dQWomna4RG564Hm8utPu6R/p/ZB50uMkD0xZKRUvbF6UDwGZ0+QFrzshYWy+H8zUiCsA9EzRg8PBOnNEA3cXlMXL1Huuf6PtA9KOkCnJcqUlQpVr1NnU7Zu2iA31b5dOvV5Sp3uR4KAseBcy8T5i9UZmUgxGmN8SNdFrAkxpwGPdgwQbbQpFS/RmJVbE02fDq3VGSZjMSN3tHGF+5xEYwa15KtDokk+ndpCNDjzMm7O85IrTx6Vfjtr7KigQ5Y4V9KgWQtp3Lq9ijN0ffD+gHWQJ28+Gf2/+ZIpSxYZ3v0x+X7jtwGZcH6k3/jJcurECXmyfUv5898YhRmiQaODJk+XkleVV2dvXnxuZqAfpO+Oenae5MqdW3q0bq4u/yxdoZIMmDhVkSdiT7jRACVDxowqFRmEGY5o6j/QTJp16KRSlycPHRA0Y1AHZIv2Yf3huh5dEJ/pM2ai/PzjTun3cNvA3+MlGrNyI/MPmW+zJ41X6dzGYkZuM68KicYMaslXh0STfDq1hWjQSMly5aXnsDHqICIu0NSWQd78BaV0hYpSqOhl6lDk5GEDU2RPISOs9+jz51GQWbVz6xYpXqqMWuxRRj3ZTbZ8+1/GlVmiUedBpsxUJ+2xa9+w9gvl8itXuaq6adrongM5IoUZMSJYQUitRpYZbiD4c/9v6qYAHHIMtWiAw6BJ0xUxbd+8SbZv2SzPz5gawPneB1upcz5HDh2Szd+slX2//iqXXl5cqtxwo5w+fVpG9uoWdBlnvERjVm7ErxDHQibfhi8/l/Vffh50V1u8cpt5VUg0ZlBLvjokmuTTqW1Eg4aQGPBA2w5S9aabBWdgdMEByI1rv5L/TRgT8WZgXMeCm59xLQyyupB1BXLB7vrbr74IQt4s0aAR3EyARfOqq6uqK11ACLj6BWnQy5e+EtQPrmTp3HeAkgkHS5Fl9vF7K2Te1Kdl/LxFYdOb0cCdjZrIXU2aqStdMA5kkRnLnY0ekGuq11BXyeh2gQ+sLBwmNZZ4iQZ1zciNoD/OEuGGA8SecP4I55DMym3mVSHRmEEt+eqQaJJPp7YSjW4MCx1IB1bCnp9+Utla4e7jCgcnCApWxP69e2OuY1Yt6Edlkf2bShypHWSd5S1YSFkyxvvNovWLBRtEBhdbuILPETv6Y9/e81e/2FzMyg1X6KmTJ1QmYSLlJtHYPAF82hyJxqeKi0Vss+doYmmbzxCBWBAg0cSCUvI/Q6JJYh2TaJJYuT4Z2u5jp+X4mbMyc+ZMqVKlik+kpph2I0CisRtRD7VHovGQMtKoKNsOn//ahbVr16ZRBDhsIECiSeJ5MGvWLMFP1vQXSJFsGZN4pByaVxEg0XhVM4mVi0STWLwT2tu6devUDc4ol2bLKFnS/5c1llBB2FmaRIDxmTSp9rCDJtEk+VzQ7jNaNUmuaA8Oj9aMB5XikkgkGpeAT1S3tGoShTT7MSJAa4bzwYgAiSYNzAcdq8FQ82ROL3kypU8Do+YQ3UJAkwz6ZxKAW1rwVr8kGm/pwzFpSDaOQcuG/0XgxJmzApJBOjMKU5o5NTQCJJo0NBeMZKOtGz38rBcyUSANTQVbh3r8n7MCktEEg/MyHTp04LkZW1H2d2MkGn/rz5T0oYRjqhFWIgIhCJBgOCUiIUCiScNzA4SjC5IGWFIisHr1avXHmjWDv3WTWP2HAAhG/xAXIhAOARIN5wURiIAASKZWrVqKZFatWkWciAARMIkAicYkcKyW/AiQaJJfxxxhYhAg0SQGZ/biQwRIND5UGkX2JAIkGk+qhUJ5AQESjRe0QBmSAQESTTJokWNwBAESjSOwstE0iACJJg0qnUOODQESTWw48SkiEA0BEk00hPh5mkWARJNmVc+B24wAicZmQNlc8iBAokkeXXIk7iJAonEXf/buYQRINB5WDkXzFQIkGl+pi8ImEgESTSLRZl/JjACJJpm1y7FZQoBEYwk+ViYCAQRINJwMRCACAiQaTg0iYA8CJBp7cGQrSYgAiSYJlcohuYIAicYV2NmpHxAg0fhBS5TRDwiQaPygJcroCgIkGldgZ6dJiACJJgmVyiHZgwCJxh4c2QoRINFwDhABJgNwDhABRxEg0TgKLxv3MwK0aPysPcruJQRINF7SBmXxFAIkGk+pg8L4GAESjY+VR9GdRYBE4yy+bD3tIECiSTu65kjjRIBEEydgfJwIRECARMOpQQSYDMA5QAQcRYBE4yi8bNzPCNCi8bP2KLuXECDReEkblMVTCJBoPKUOCuNjBEg0PlYeRXcWARKNs/iy9bSDAIkm7eiaI40TARJNnIDxcSLAZADOASIQHwIkmvjw4tNEIBICtGg4N4hABARINJwaRMAeBEg09uDIVpIAgUGDBgl+dIlENPh7zpw5pXLlykkwag6BCDiPAInGeYzZg08Q0CSjf4cjGv23c+fO+WRUFJMIuI8AicZ9HVACDyGQLl06qVmzpqxatUpCiQYENHjwYBk4cGCQ5eMh8SkKEfAkAiQaT6qFQrmFgCYT9N+qVSuZN2+eIp4aNWookkGhNeOWdtivXxEg0fhVc5TbMQRg1RhLrly55NChQ+pPtGYcg50NJzECJJokVi6HZg4Bo1UT2gKtGXOYslbaRoBEk7b1z9FHQCDUqqE1w6lCBMwjQKIxjx1rJjEC4awaWjNJrHAOzVEESDSOwsvG/YyA0aphbMbPmqTsbiNAonFRA+vWrXOxd3YdDQFYNWvWrFGPId2ZxdsIVKlSxdsCpmHpSDQJVD6IZdasWUKCSSDo7CrNIdChQwfBD4t3ECDRJEAXkQjm6NGjCeidXRCBtIFAjhw5ggZKwvGO3kk0DuuiY8eOAQsGxLJ3714hwTgMOptP0wgUKlRIChYsGMCAhOP+dCDROKgDuMnwg7Jt2zYSjINYs2kiEIqAkXBmzpwpjOG4N0dINA5hT5JxCFg2SwTiQIBkEwdYDj5KonEI3KpVq9KScQhbNksE4kGgZMmSgvgNLBpYNiyJR4BE4wDm2ppBPGbPnj0O9MAmiQARiBUBkAzIBoUutFhRs/c5Eo29eKrWtDXDNGYHwGWTRMAEArRqTIBmYxUSjY1goimQCzLNkFmGBAAWIkAE3EdAWzV0n7mjCxKNzbjTbWYzoGyOCNiAAInGBhAtNEGisQBeuKokGpsBZXNEwCYEdHrz2rVrbWqRzcSKAIkmVqRifI5EEyNQfIwIJBgBEk2CATd0R6KxGXsSjc2AsjkiYBMCJBqbgDTRDInGBGipVSHR2AwomyMCNiFAorEJSBPNkGhMgJasRJM7d24pXLiw7Nu3T37//feYkcmWLZvUqFFDXaV/4sSJmOv54cFLLrlE3Zv166+/yoEDB/wgckJkxDwpVaqU0rlfvhCORJOQqRG2ExKNzdhbsWhuvfVWqV69uvz2228yffr0mCSrX7++Oreza9cumTt3bkx1Ij2EtOwZM2ZI7969ZfTo0TG3tXDhQmnevLm61w1t6NKuXTu59NJLZerUqfLHH39EbK9s2bJy//33q9TwN954I+Z+E/Fgjx49ZOzYsdK5c2d55plnInZZuXJl6dq1q2zcuFHGjx+fCNFc6+OCCy6QH3/8UYoWLSrNmjWTF154wTVZ4umYRBMPWvY+S6KxF0+12OLHzK0AI0aMkD59+qgd4pVXXik//PBDqtJdeOGFimCKFCkiH3/8sSIpKyUS0aRPn14mTpwoR44ckX79+qXoYvjw4dK3b1/p37+/4N+6fPrpp3L99dcLiGTLli0RRWvcuLG89NJL8uyzz3rue0RiJZoXX3xRkSUKLLzjx4+HHW80LFGpe/fuUqxYMRk6dKjs37/fikpN1y1Tpox06tRJkX+4DQzm27XXXiu33XabrF692nQ/iaxIokkk2sF9kWhsxt4OooFI48aNk549e6Yq3T333CNLly5Vz+CbIGvWrGlpNJGIJlOmTHLy5EnlUjNev27sLG/evCncbWmFaHLlyqU2FpkzZ1aQPPjgg/L888+H1UUsWH722WdSrVo1KV26tGzdutWSTs1Wvv322+Xdd9+VxYsXS9OmTVM0A6sG4/aTO5FEY3Y2WK9HorGOYVALdhHNwYMH1aJ+6tSpiBIuX75c7Si9QDThhEwrRIPvO8EdWgMHDpQBAwbI+++/L1iow5VkIRqbX5uENEeiSQjMYTsh0diMvR1Es337duU6a9mypSxYsCCshCVKlFBX3CBIDddZqEWDmAncVpMmTRK0ZyxwueHviAXBPaNLOIsGi2eBAgXk4YcfVu6gOXPmqMdfffVVFQhGqVOnjtx9993KV//JJ58E2rOLaJCkULt2bbnjjjvkoosukpUrV8qKFStk586dYbFBAB/xofLly0u+fPlk06ZNAivh5ZdfjqhtuKrq1auniBvWG0h83rx50q1bt6gxGu1GAk6wABBrQ2wq9ELVaFjCcrjxxhulUaNGkj9/flm0aJFgw7Fhw4bA9xrpAcSKSbp06WTKlCnK2hw8eLDUrVtXkWC5cuVUnAXyghh1yZkzp3J/Iv5y1113qbkDrFFGjRolu3fvVv+GWw0u0SFDhqRw72XNmlW5cWFhI3a1fv16NVc++uijFC7FeOWz8rqSaP7f3pmA21S9f/y95iGZIlOKBkMydDX5hUuaVISKSAndm0JIhMwhZFZyzUKSFBmKMjUpU8mUpEIqMs/z//9drNMZ7z1nn73P2fuc73qe+6h79l7rXZ+1zvrud73v2jcceuHdS6EJj5/P3WYIDbZexo8fL+vXr5cqVar4tRABasQPEN/AAuotNBMnTpRnn31WatSo4bOHnjlzZjlz5ozalsH2TFpCg+A2RE1vC2ERRsG2HoL8KJ07d1aLEMTI/TXsZggNMpsgEnnz5pVz587J2bNnJXv27HL+/HklbgsWLPDgU7NmTSV4EBhcA3HUf+IXIgnvA/W4FzDCYor4yYULF9Q9V1xxhVoclyxZIoidBUoGABssxvPnz1cLc7NmzZQYg8mgQYM82kmP5VtvvSXNmzcXeD1YgOHNIl6HBAkd/0GFoTDBFhc4IN43YMAANa+OHz+uGOIzlE6dOikxRcHfb8G1+CxLliyKB+YKyh133KFED2XRokVK+L2398ANMRu9qB8+fFggXijr1q1T2YnHjh1zcQnVvnC+rhSacOiFdy+FJjx+lggNnjrxdAuPpmLFivLjjz96tINFH0+WeNpFxtmhQ4csExo0nN52j1VCA89k1apVKjAOEUPCABZfCAPEBMKAJ2c8MaNg0dq6dasSxpYtWwoC9BBGxDuQxQcPB94CvDFdUPfq1aslf/780q5dO+VFgCc8Czw0QLAQiwgkNPASsF2ms6/gccFTxGINr8G7pMcS16cVozHCBEIDgYZXg3mFBwB4X02bNlUeLT67/vrrPTyw9GI0/oQGDzAQXHiFEDQIrfbOMUfgZULQkSmJNvWYGbHPyNeWQmOEmjn3UGjM4eiqxQyPBkIDEcEiCw8Bi6x7wQIxdepU5dGgPWSDWeXRmCE0mzZtUgt+oAJvpWTJkj5ZZ3gCR6o1srCGDh3qcXvt2rWVN+MerMYijy02LKjYpnEv8BQmTJigtpHatm3r+gj/37p1a/W0j8w593LzzTfL999/L9gK8ic08DogKBAjbHXBU0CBkNWvX189BHj/qYhwhSZUJtpjgF1g475Nht+BX8OGDQWJJXPnznV134jQ6DGBR4O2ICC6YLsWbcOjwRblwoULPYQmVPuMfG0pNEaomXMPhcYcjqYLDTJ+sEjh72hgOwN/dkAXPJFi7xuH5vBkaHehwVYJtmACFTwJYyvHO70ZsRjEO/D0DS/BvWABRb3w7PQftUprKLHFgxRrPIljQdQF8RV4L9dee63s3LnTpwqIGa73JzTVqlVTAg/vCh6NLg0aNJDZs2fLyJEj1dka9xKu0ITKRAsNPEEIsd4G0zZByCFe8OYQt9PFiNDAs4OHFyi2qB+QEKtCbAfFqH1GvrYUGiPUzLmHQmMOR0uEBlsN2IJwX+SwlYbYDc42IAaD+IPdhcbIORp4C1qcAp3T0Onc2NpCLEAXxBLuvvtuufXWWwVp1/j8tttuUx+7Cw3aADs8eeMafwWxJ2z7+BMajA3GCN6S+0FTbG3u2rVLeaXwdPQ2EeoPR2iMMHGPgWBL0bsgqI/YEGJuSKkPR2jmzZun4lTgjjnqXfBwhDgNWNWpU8dDaOAZhmKfka8thcYINXPuodCYw9ESocGWDbLKsGiVL19etYGT+8gOw2E5xBZiVWjg4ehDj4GEpkSJEipz6plnnlHeCDwjPJW3atVKsULW1x9//KEWfHhFWADdhQZtaK8IHo2/ouNP3kLjbl9aUwgJC1iAdQlHaIwwiaTQ6OQPjAsOEnsX/B6ZgrgOXiSKUfuMfG0pNEaomXMPhcYcjpYIDSodPny42n5B0BsZPxAeBLz1E3ogoUE8Ak/a7vvh2khkASHgHUzWGe5Jb3G0Khlg8+bNKkiNPnpv+fgbNgS6EczH3xtB35HlpQviLUhz9t46A0tsvWFbyT0bSt+H9HJkAXoLjW4L17lvOen7EBDH6XrEa5CAYIbQoI5QmRhdyI1snSELEZwQ7PfOBoTtmItIFsB1bdq0odCYvPbYuToKjcmjY1YyAGI0KEhlRWxBn1HRKbD6PEsgodHZUEjN9X5tDFKmcd4lVKGBZ4DzG97FKqFBwgP29XEeCIkR6RW9lQURQJDbvSALDTEgb6EB10aNGqnzJZq5vg8eEhZ2bOl4C43OusL2HeI03gXBbzwUINEB3hTYuYt2IJa4Rmed+cs4DJVJuELz8ccfS7169Xz65y/rTKd2Y7sRr1LyLjqRAdfhjBKKUfvSmwv+PqdHY4SaOfdQaMzh6KrFbKFBxQgAI+aAV8DgyRtJAPotyYGERr+eBnvi2KbQWV+IH+CpEkH2YIUGNuBpH+/w8hdvsUpodB9wMBXiuH//fhdnLPBIlYXXohcQHYzG9qLePsMNOHyI8zDwXLyFBhlXECUkFWBrzf2t1foJHXW4Cw3e2IDtTLx2BnUHensxkgHw5I44iPtLUtNiibZ0AoL3ffgsVCZGF3J4zMi4QxIG+uvdR39Cg0Oq8Lpxlgbnmdwz/+CRL126VM0jbAODH4XG5MXHxtVRaEweHCuEBqmy+uwH0nyR7qtLIKFBcBsLBd4wgG0yLKYQG2xfYAsJT6mhCA0WaqSsYrHHQogna50qa5XQoI94USfOekAA0N727dvV+RQEk9EfbFHptxHg9+gzDhriWix06D+yxnD4EodbvYUGbWhBwHu78EYAxHZwGBFvXMC75PAE7i40+kWbeEsz/jtQgSeGeAS8Mfy3LmmxxDXwQF9//XV1ZgheFmx2PwgbChOjQgOxQNwLHhnOwkAk8MYALRCBDmwifRkMUVauXKniiBAtZOihYEvO3QM0ap+Rry09GiPUzLmHQmMOR0s9GhxMRHAVac54Ksdim57Q4HMcRsQr/PFFx+KLp1LEHPBaFQhGKEKDWAkWOywY2FJCHYgfoVgpNKgfbUFskU2GtpGNhsUa517gsbkXHM7EWxGQzowsLTxBY8tmzpw5ftObcS/4It0W3gLiKijImoJXhCdx7z8TAC8KooaFy7t97+mE4DeC4NgChWeGkhZLfI6gP873YEsPXiTOt8A29xIsk3AWcog43toNlqhHv04GdgQSGnwGkQY7zBWdEQhxgVfnvT0Zjn2hfnUpNKESM+96Co15LFVN4Xg0Jpviqg7bZRAdPKEGen19sG1jYcD2HdKJI/0Hr7DowsuA6Kb1slH0BU/iSG3GQu/9ypm0+ortH3hKofzht2DZeV8XDEskbuAgaKA+hMLEqJ36nBNSwUMpiFNh3iEzMK1zVKHUGc61FJpw6IV3L4UmPH4+d9tRaEzuIqsjAUcSoNBEb9goNCazp9CYDJTVkYBJBCg0JoE0UA2FxgC0tG7Ba2NwoBKvjNF78iY3wepIgARCJICkGcQ3ITbuiRUhVsPLDRKg0BgEF+g2Co3JQFkdCZhAAIk0SNPGn4nAD0tkCVBoLOANjwaCA4/G/WWYFjTFKkmABIIgoLfNKDRBwLLgEgqNBVDp1VgAlVWSgEEC2pvB7Xg9EUvkCVBoLGKuvRqcHvf+k74WNclqSYAE/BCgNxP9aUGhsWgMtFeD6ik2FkFmtSSQBgEkACAug3+5ZRbdqUKhsZC/TnXWYoN/6d1YCJxVk8BlAu7bZcw0i/60oNBYPAbuYoOmkBygX0fPRAGL4bP6uCIAzwXvaMO/utCTsccUoNBEaBy8BSdCzbIZEohLAvBiIDI6PhOXEGzUaQpNhAcDsRv8oOh/I2wCmwuBgP7rnvrPRodwKy+NMAGIihYWCkyE4afTHIXGXuNBa2xEACJTo0YNgcjgzwywkAAJGCNAoTHGjXfFAQEKTRwMMrsYEQIUmohgZiNOJEChceKo0WY7EqDQ2HFUaJMtCFBobDEMNCIGCFBoYmAQ2QVrCFBorOHKWuOPAIUm/sacPQ6SAIUmSFC8jATSIUCh4RQhgQAEKDScGiRgDgEKjTkcWUsMEqDQxOCgsktRIUChiQp2NuoEAhQaJ4wSbXQCAQqNE0aJNkaFAIUmKtjZaAwSoNDE4KCyS+YQoNCYw5G1kACFhnOABJgMwDlAApYSoNBYipeVO5kAPRonjx5ttxMBCo2dRoO22IoAhcZWw0FjHEyAQuPgwaPp1hKg0FjLl7XHDwEKTfyMNXsaIgEKTYjAeDkJBCBAoeHUIAEmA3AOkIClBCg0luJl5U4mQI/GyaNH2+1EgEJjp9GgLbYiQKGx1XDQGAcToNA4ePBourUEKDTW8mXt8UOAQhM/Y82ehkiAQhMiMF5OAkwG4BwggdAIUGhC48WrSSAQAXo0nBskEIAAhYZTgwTMIUChMYcja4lBAhSaGBxUdikqBCg0UcHORp1AgELjhFGijU4gQKFxwijRxqgQoNBEBTsbjUECFJoYHFR2yRwCFBpzOLIWEqDQcA6QAJMBOAdIwFICFBpL8bJyJxHo1auX4EeXQB4Nfp87d26pVKmSk7pHW0kgagQoNFFDz4btRkCLjP7Xn9Do3128eNFu5tMeErAtAQqNbYeGhkWDQEJCgiQlJcmyZcvEW2ggQL1795aePXt6eD7RsJNtkoCTCFBonDRatNVyAlpM0NAzzzwjU6ZMUcJTvXp1JTIo9GYsHwY2EGMEKDQxNqDsTvgE4NW4lzx58sihQ4fUr+jNhM+XNcQfAQpN/I05e5wOAXevxvtSejOcPiQQOgEKTejMeEccEPD2aujNxMGgs4uWEaDQWIaWFTuZgD+vht6Mk0eUtkeTAIUmmvTZtq0JuHs1jM3YeqhonM0JUGi8Bmjt2rU2HzKaFykC8GpWrFihmkO6MwsJgEBiYiJBhEgg7oUGwpKamioUmBBnDi8ngTgnAMHBT3JycpyTSL/7cSs0AQUme/rQeAUJkEAcEzjp23eIDQUn8JyIS6GBB4MfV8krIvhhIQESIIFgCRy8fOHlf+HdjB07Nti74+q6uBMaD5GhwMTVZGdnScASAhCaUyJy2dNZs2aNJc04udK4EhqKjJOnKm0nAZsT+OuS2NCz8R2n+BQaejI2/8bSPBJwKIHLYoMtNGan/TeGcSM0Ht5MSYdOYppNAiRgbwLYQttDr8Z7kOJPaOjN2PuLSutIwOkE6NX4jGDcCE3lypUvdZ7ejNO/xrSfBOxNgEJDoaHQ2Ps7SutIwPEEuH0Wn0KDw5kpKSkiOIxZ2PHTmB0gARKwMwEKDYWGQmPnbyhtI4EYIEChodBQaGLgi8wukICdCVBoKDQUGjt/Q2kbCcQAAQoNhSYaQnN1/qslV85csuvvXXL6zOmwvkklipaQDBkyyK+7fg2rnrRuzpwps1xb5Fo5cfKE7Nm3x7J2WLH9CQSau6VKlJLz58/L9p3b7d+JSFtIoaHQhCM0WOCTbkuSWnfWkiIFi6gFf+tvW2Xrjq3y5dovZd/BfX6ndP+X+st9/7tPmnVrJht/2RjWtP8s9TMlWlWaVAmrnrRuhsh8OPxD+eaHb6Rt/7ZpttP4ocZyV8W7pO+YvrL3wF7LbIrVim+/5XZp1aiVHD56WDb/utnVzeMnj8uPP/8oW3ZsUQt6tIq/uVu8cHE1P/AXRx9+8WHZuz+6435d0evkvir3yblz52TiRxM9UBW7upjUrlbbB9+JUyfUfMV3d+dfO83FS6Gh0BgVGgjL8M7DpeQ1/x3EOXf+nGTKmElBxULRfVR3tTh7l1gVmpLFSsqsobNUd8e8P0YmfDjB3C9sHNT24pMvyrP1ng3YUyyIr418TVauWRkVGv7mbt4r88qcEXPk/IXz8mibR+XYiWNRsQ2N1q9VXzo800GyZc0mJ0+flKpNq3rYcmeFO2V0t9Fp2gdBHzJ5iIfQh9UhCg2FxojQFMxfUKa9MU3y5c6nnjIXrFggX63/So4cOyKJZRMFkxkTPkvmLNIvtZ98/MXHHqBjVWjaNGkjz9R9RvUVT4X1X6of1vczHm/WQjN/xXyZvXi2QpAgCZI/T36pXK6yPHbfY+p3nd7sJCvXRl5sAs3d7Fmzy4WLF8LeCjY65ldecaV0f7671Li9huz+Z7eYAjdoAAAgAElEQVQULVhUTp05FVBoVm9cLakf/PenQa4pdI1UKlNJfX/xEHnm7Bnp9VYvWfzNYqMm/XcfhYZCY0Rohr86XO6+9W61Tdaiewu/X67H739cOrfoLP8e/FceefEROXvurAt2LApNhoQMMn/MfNVHPG1jQWz+WnPZsG1D+F/UOKpBC80b499wCY1797GQDu44WLb9vk0ad2occTJmzl2zjM+RPYfMHjpb8AAIgR40YZAsnbRUfecCeTRLvl0iXYZ18V0AExKkd+veUrtqbeURNX6lsYqlhlUoNBSaUIXmxmtvlPcGv6e2xpp0biJ///t3wDmoBanPmD4yb9m8oIQGLn+l0pUk8eZEQYAVC8qajWtk/db1cuo0ZqxncY/RFL26qDR8oKFcf8318tufv8nX676WtZvXqqczf+V/lf4nd5S/Q7DldfjYYfn5t5/l81Wfy569ngH/YGI0iC283f1tmT5/unzx3Rcyse9E+XDJhzJg3ACPpls3bi05suWQ4e8O92sX9s/L3VhO5i6dq+zRBd4hnjjvqnCXlChWQn7Y+oN8t+E7FbNAbMC9IE5UrFAxGTNzjNxQ/Ab1lFu6ZGkZPX20h/BhLBFfwzUJGRJUe9/++K1s+Nm/OMKGyjdXVh4rGP/+5+8yc9FMtRC1f7q9YOt01PRRPqhDsT09oUHl2KZC/2o1ryVHjh9xtQexL3N9GWUjWCFWsmbTGsHT+8Ej+q9y+c4ExFjgLeE+FNyDOecvVhFIaLBdhcSWt957y9UA6m1Uu5Hi+fm3n6vYyK1lb5Wr8lyl5uesT2cFXMQRS6lSsYqanxCMVRtWybyl85SdSbcnyZJvlsj6LetVW9hZ+GjkR9I/tb989vVn6ner3ltlSGhwL7a/h3Yeqtpf+OVC6TGqR1g6o/42DV+s6cEwLt51Fs6bAVo1bCUtGrRQC2Hfd/qmOQEhGnly5VFPRhAmXQJ9WbEAj+01VsqULKMuxV73FTmuUP8N7ym5Z7Jgj969aKHpMKiDDOs8TJAhhsBxzuw51WXf//S9tO7XWi5cuOC6LWPGjNK3dV+VkKDbwfUJCQly6OghafdGO48khWCEpk+bPuopsOmrTZWtc0fPVbY/kPyAh6D0aNVD6tSoI6+8+Yos+36Z5+RLSJAFby+QvLnzyv3J96utSBRwTO2VKmWvL6v+H9lveIpFmTJ3is/iPq73OLXQdh3eVWCXjpu9PPhlWbF6hboPYtT2qbbqMwg4EjkgCOAEb2LO53M8bAPXt157Sy2UKJoxRBzs4WWgnntb3utxX6i2ByM0iDFA7Fr2aKkEV5euz3WV+vde2q4Eo+zZsqsxhRi17N5Sduze4TNfH7j7Aenbpq+6Tj+QgAPEGzHGT7/61OOeQHP3m+nfyNHjR9W46XJbudtkTI8x6rsCzg9Vf8hjbkKY2vRrI+u2rPNoAw9AmMsYE4wHtsDw3cBcVvO5cWsZOGGgfPDZB+o+bNthzrg/IIUjNKgTsddZQ2YpjrVa1gr4sOYD1N8vKDQ+VCg06cycEV1GCL4IgyYOUk9kRoq/Lyu+iPCAsIAgpjN13lT1RImnQsQ96tasK6t+XKVEAE/OukBo8CU7eeqkvP/p+2q7Zd+BfUqsOjbrKOVLlZf3Fr6ngpu64MmyT+s+SkywmOCJHDEACAAWOqRKN3y5oev69IQGi8DicYtV1o6Oy+gFs/PQzvLFqi9cdWGhhmjgybPbiG4e+MrfVF4mvj5Rln63VDoN6aQ+w1P6oI6DVHYfeE+eO1n178biN0rPF3oqrw+Cj8VMFy00WMgmfTRJ7bNjEcJiiqdj7MF/PPJjtTBCJDZu3yhZMmWR28vfLhgbLLQQDIiuLr1e7CUPV39YPe0Pnzpcfv7950vMkurIU488pUQVXqG70BixPRihwdM7YhA1nq2hFm6U5x57TlKeSJGffvlJ3pz0pmz5dYvkzpVbPUxgHmBsnu32rEcmILzQkV1HyvETx6XH6B6qbyjwbDA/cubIqbIMsbjrYkRoMA7wFnu93UvNaYxZ49qNlfD88scv8uQrT7rqh3cJbxgC3X9cf1m6aqkcP3Vcyt1QTromdxUkHsCDcRcaf9/BcIUGIrdyykplB3Yu3L3rkL/zFBoKTahbZ9MHTldflOd6Pudy3UOdeP6+rBAviNjaTWulVd9WHh4IJv07Pd5RT9MvDXhJvl7/tYfQYMFb9OUiJRru5aq8V8mMQTPUF/O+5+6TA4cPqI+xNVUwX0FZt3mdx2KKhRGeSKGrCqmnOO2FpSc0jyQ9ohZ9BFd1gBULxsw3Z6o07/YD27vMwmKPNvJckcfnSRHbL/A0Ogzs4Ap0Y6tsVLdRyhPp+GZHj22yAnkLqCw3PIm7P0lroRn/4Xh55/13fIYH2zI3XXeT/Lb7N7WF416wwEKIIXQQPBTs/S8cs1BtkyIuoj0tfV+7pu2U2GB7yl1ojNientBAODB/kPr8dJenlQnw7pZPXi6HjhySJp2a+KTVv9DoBWlev7l6eBk5baSru1MHTFUPJC++/qKHmOACbFnBc8LWpG4HvzciNPAKHnrhISXsusCr/uStT9Q8TGqW5MpUQ0AfD1XYWp32yTSPsSlcoLDg+4fAv9VCg4Yn9J0gFUpVkLYD2so3632zR4P+3lNoKDShCg3cabjV9drW89lfxpYass28C7Y33J/e/X1Z9RMpniwXrlzoU4f2QsbOGivjZo/zEZpAgfduKd2k3j311BYF4g/plf7t+qszCM90fUY2bd+kLk9PaN7p+Y56CoY3476v//6Q9+W6ItfJg88/6BI51Pd8w+elZYOW0nFwR1m+erlqAwI0/+35ypt4IOUB11kRMMV2JbbB/GUAQYSwoD+Y8qBrgdVCg208LJShlAb3NpAuz3WRkdNHytS5U9WtOgAPjwqerHfRfLyFxojtWmgQrNZZZ2gPcQ3EJ+rWqKu8Moi39jRuLXOrpPZOVXFAxAO9Cw5ZLhizQHksz/d+Xn0MD3rl1JXKO6zbpq5fRHNHzZUC+QpItaerubxoI0Lj3q57Q3rsEHDf9sc29RHin4id3dPiHo/tZn2fbj8SQjPo5UFS846a4u2VhzKf1LUUGgpNqEIztNNQqVa5mrzQ9wWfp8AmDzeRh6o95IIKtxtbX1gQcL33l8X9wCaCj9USqwV00+FF4WkOKa144tcFW2fwWKo3q672k71LowcbScdnO8roGaNl8seTXR/jqfDO8ncq7wapndj6wcFPeCIowQoNvB8IBJ72n+r8lEfzeIqGh4JtO2zf6YL2sP2D/X+cCUG55cZbZFK/ST7bfENeGSLVb6uutnx2/eWb/YOkCRRsgemzJVpoareqHfDwIBZabJVhSwZ9Bo9cOXIpbxXFXWi0R+C9Raf7A5FcMWWFCoa7ezRGbE/vHA28zC7Du3jMPcw7JCMMnTJUZiyY4XcdhH2Iu2C7Df+WLlFapg2cppiBnb+i5zrGFXE3FCNCEyie+Ub7N6TWXbWUxwQPDQ8ZX737lcrUxNj5K0/XeVrF1iIhNB+P+ljg/Yaze0Gh8S/LjNH45+L67UtNX5KmjzRVGS7eAWPvW5G1gj1w9wU10JcVsQnEKOq8WMfva14QV5g3ep7K4GnevbmH0EAg7n7qbnWOwbtoT8h92+TmG25WwWtsWyDegyAxtoPwgye4UIQGYoKFOK2Cp1U8tboXbEsgzqIDrVgosWB674dDfCBCsPHgYd/MKXBB/GX6gulBCw2EGU+rFUtXVCbBC9t/aL/a2sFDhLfQYLwx7v62c3AtHiiwn+8dozFiuxaav/b95eHRIBYDzxjxM+8sO31P77d7yyfLP/E7FHgYwHjf3fRutdWoDy4iHRjnRfyV3i/2VnEUbK0hw89qocHWLbYAL8pFtZ3m3U+0r/tqtdDgO7Vs0qVklYdaPST/7P8nnZUhjY/p0fjAodCkM530wo3USjzppFWQHdPs0WYqKwrZUbr4eyrs1KKTPHH/EyrY/9W6r3yqxbkdJAt4b9/Ao0GMxnvbSlegbUD8BnEcFKTHwtPClxXbdDqgjM8Gdhgo99x5T9Aeja4LAX9/r5x5svalQC+SC9zfx4YtRgR3kQmGp2rs1yPLrlHHRh59f7Xlq+pMTnKvZBVTCqak59GgXbSPrSbEcNztxhhgLNw9mrQSGGAPBGt8n/E+MRojtqcXo/HXf4wXxu3dT96VEe+O8LkEyRrwaCDWOskD2ZCfT/jcI9bjfSNiOMj0q9WiliuWZ6VHg/bH9hyrUvsDzWek0COJwWqhuf9/90u/l/qpJAA8/IRVKDQUmlC3zpDm+sGwD5RLjbRhZIL5K/hyTxkwRfDSS+9F0t+XVQfUsb2FbS7vogUDT594CtVFC02/sf3koy8+8rlPBzSfePkJ2bFrhyuw/ceeP6RBuwY+1yPojeB3MFtn2Hab3G+ySmBI6Z3il8MTDzwhnZp38glEY6sOmWoQKGTLoR5/HgMy4ZAS7b31l9YXPz2h0VsiiAVhm8a9INX3waoPeggNgu1Lxi1RcQ083evsLNyH1NpxfcaprSjvGI0R240ITZECRWTeW/PUGSHE6ryLTkqAtwOvRxeIO7K4aj9f2+M8Dj6/MueVsvCdhXLgyAHlZetitdDobUp8rxCEd0/L14s/bLFSaDCvkZaNscU5MJwHC6tQaCg0oQoNiGFfGfvLeALHYS7vV4HgSzqi6wi15eOeqpvWlxUBW2RpQaCe7/O8R0YbzoQg6wxnaPDE7+7Ga6GBV4Lgt3swXu9n42AhhAZfWuyDY0sAWT91W9f1qAveFwQNJRih0U/sgUQO9WCb6tOxn6pkAOy7uy8cSDxAth28KgThkTSALSz3gkwjBIixyIOL+0tIkVUHgcJiibcv6Ky69IRGJy94JxjgSXlIpyFqgXH3aPSYD2g3QI35oq8WKe+qeKHiai4gOI8DnDgv5R6jMWK7EaGBfYi3QOy8BRlsENtDUF97j5qv3q5ERhU8ab31ii0seM9VKlVRB3CHTR0WMaHJmiWrYPzgSWHXAGetMK4YGwgNElTgYVohNIg3wpvq8HQHlRqOg66IrfrbwgtJeCg0FBojQgNqyJxCfALvocKTJL4AWIRuuekWFWvBAUgs+jhUpxfA9J4K8QXSL/zDl2zTr5vk5utvVocPUeBBeW8f6QObeFrFk/jqn1bL73t+FxyWQ+oqBAjBVncB0ocrIVgr1qyQY8ePqS8v4h0I6sP+9IQGnh3axtM+Uqe9U37dZ5Y+YOid+YaFbGSXS+m2WOzwBOuvoC+IdUGkcLhv8/bNauHElhE4ewfB0xOaR+95VF5LeU2xwdsTft39q2KF5AicQMeZHW+hgV1IrMALL7FVqQvG49Vhr8rs4bNVppz3gc1QbTcqNLAJMaHCVxVWgXvMg0IFCqk+IdHBe/sW9iNtHgkLVROrqjiXfgEsYouYC0hNhzh5PBwEePN4egc2/R1u9k4G0EzRlwHtB0jFUhWVjSgYK5xfghAhuSVcocF30n0rFwkqEBpd8IAI7899WzkkcXG/mEJDoTEqNCCHlF5sC11X7Dp1sFAXnBCfNn+aTJwz0e+J4rTeF4UvOWISEBcEJCFeWMyQ6urvTdBaaKo+XVUd2Hvq4aeU1wIbvt/4vUybN83n5DW8g5ebvawy5PQJe7wcFJ5Jy8daBpXejKQBBNS9s+D8fRlx0BEHHr0PaWIRWfTOIrVwB0pf1vUhRoVT70jl1W9LwFbg2A/GehwIxfXpCQ2uQYwm+fFkgVeEguD74EmDBWdzvNOb3fuEDDMkMWDMsf2oD/IhiI0gO0TXu4Riu1GhQZuIu7Wo30KlQcNDhqcFDxDbk3hVjr+CRAZsV8FjgFeGggUYmZJvz3zb57VHVm+duduI+Y+XXIIrDtXiYUads/r/tzMHehecvj+9A5veLLBbgFRvpMMjdud+SNWwwLgWBL6CxpshkwEMzCpsd+EAILbMEHDF06G/DLBQqsYijL33UOuCiGjPJNA7zrQdEEe8MwvZUu6vyAnFzkhfCy54+kRsxYynTSzIKEgICLRFAk55rsyjXoPvjxO2p5aMX+KTxu7Nxmzb02KPbTs8tYfyh/WQIIDi/kaESI8vHnyyZcmmHrD8zV+8qBYvrMXLbPFw5IhCj8ZnmCg0jpi5NDKSBODF4PApXj7adURXWfz1f6+Oh8hgKwfxg0Dpz5G01elt4bAwYnfY/sO2s36RLMQenhrO9sBTw4FO9y09W/ebQkOhCWfrzNaTm8aZSgBxGyyAiE1hywxbLNiqwtuSsQjirQt47Xw0/+iXqR2OUmXw+pAqjjghWOIgJzwsbO3B44Lw4O0Z+vVAUTIztGZx/OugSHJysvphEYkLjwYDXbnypVeiy39/IJPjTwJpEsCrh1o/2Vptk2LLDVt3+DMOSCBAWrpjnrBtPs4QGxySRUYf/iwzEgDwVgjEw/BSVTB3VKHQxKdHg16npKQI/lyAFMHRbkdNWxprAwJY/EKJf9jAZEeagG1LeJHpxRtt3TkKTfwKTWpqquBHsv//ceTCtp6mNI4ESMDJBC7/GaA1ay79GQaWONo689g+o1fDuU8CJGAFAXozfqnGTYwGvadXY8U3i3WSAAkoApdFBv9Jb8ZzTsSV0KDrrlhNXhHBDwsJkAAJhEvgckozqmG2mS/MuBMaJARAbFyFghPuV4z3k0D8EoDAwJM5eQkBRcb/VIg7odEYXJ6N/oX2bpAswEICJEAC6RFwExiKTNqw4lZogMUVs0lvQvFzEiABEghAIDExUXky+JeFHk3AOaDSni8XddaGhQQuE1i+fLn6r6SkJDIhAQ8C+tQ/BSb9iRHXHk36eHhFPBOAyNSoUUOJzLJll/7MLwsJkEDoBCg0oTPjHXFCgEITJwPNblpOgEJjOWI24FQCFBqnjhztthsBCo3dRoT22IYAhcY2Q0FDHE6AQuPwAaT51hGg0FjHljXHFwEKTXyNN3sbAgEKTQiweCkJpEGAQsPpQQIBCFBoODVIwBwCFBpzOLKWGCRAoYnBQWWXokKAQhMV7GzUCQQoNE4YJdroBAIUGieMEm2MCgEKTVSws9EYJEChicFBZZfMIUChMYcjayEBCg3nAAkwGYBzgAQsJUChsRQvK3cyAXo0Th492m4nAhQaO40GbbEVAQqNrYaDxjiYAIXGwYNH060lQKGxli9rjx8CFJr4GWv2NEQCFJoQgfFyEghAgELDqUECTAbgHCABSwlQaCzFy8qdTIAejZNHj7bbiQCFxk6jQVtsRYBCY6vhoDEOJkChcfDg0XRrCVBorOXL2uOHAIUmfsaaPQ2RAIUmRGC8nASYDMA5QAKhEaDQhMaLV5NAIAL0aDg3SCAAAQoNpwYJmEOAQmMOR9YSgwQoNDE4qOxSVAhQaKKCnY06gQCFxgmjRBudQIBC44RRoo1RIUChiQp2NhqDBCg0MTio7JI5BCg05nBkLSRAoeEcIAEmA3AOkIClBCg0luJl5U4i0KtXL8GPLml5NOvXr5dKlSo5qXu0lQSiRoBCEzX0bNhuBLTI6H/9CQ1+17t3b1m2bJndzKc9JGBbAhQa2w4NDYsGgYSEBOnZs6fybLyFBr+DyOjPo2Ef2yQBJxKg0Dhx1GizZQS0mKCBYcOGSfv27SUpKUmqV6+uRAbl4sWLlrXPikkgFglQaGJxVNmnsAjAq3EvefLkkUOHDqlf0ZsJCy1vjlMCFJo4HXh2OzABd6/G+yp6M5w5JBA6AQpN6Mx4RxwQ8PZq6M3EwaCzi5YRoNBYhpYVO5mAP6+G3oyTR5S2R5MAhSaa9Nm2rQm4ezWMzdh6qGiczQlQaGw+QJEyb+3atZFqyjHtwKtZsWKFspfnZvwPW2JiomPGk4ZGjwCFJnrso9pyamqqQFwoMFEdhphoHGKjBSc5OTkm+sROmEuAQmMuT9vXBoHBj3fJkSmD7W2ngfYicOLcBR+DtNBQcOw1VtG2hkIT7RGIUPvwXLQXgyYhLPmzZpLsFJgIjUDsNnPy3AU5cf6C7D91ztVJCA3FJnbHPNSeUWhCJebQ6ytXrqwsp8A4dAAdYvb+0+dcgkOxccigRcBMCk0EIEe7iZSUFBWLgcgUy5kl2uaw/Rgn4C42Y8eOdcVvYrzb7F4aBCg0MT493GMyN+XOFuO9ZffsQkCLDZIEIDYs8U2AQhPj46+3zPJny6RiMiwkECkCu4+fESQM0KuJFHH7tkOhse/YhG0ZvZmwEbKCMAggSWDX8TNq64xeTRggY+BWCk0MDGKgLmihoTcTw4Ns865tO3xKWbhmzRqbW0rzrCRAobGSbpTrptBEeQDYvHD7jJMABCg0MTwPdLbZNTmz8LxMDI+znbtGobHz6ETONgpN5FhHvCUKTcSRs0EvAjr7jGdq4ntqUGhiePwpNDE8uA7pGoXGIQNlsZkUGosBR7N6Ck006bNtEKDQcB4wRhPjc4BCE+MD7IDuUWgcMEgRMJEeTQQgR6sJs4SmeMnrpebDdaXYtddJrty5ZeeOX2X7ls2yYfV38tfuXdHqHtt1AAEKjQMGKQImUmgiADlaTYQrNBkyZJBmbdpLrTr1XF04e/asZM6cWf3/2TNnZPzQgfLlks+i1cWItJtY5W65JfE2+WbZ57Jt408RadOMRh56vJEUKFRY5rw7WY4cOmhGlSHXQaEJGVlM3kChiclhvdSpcIXm+U5dpdr9teXYkcOyeO4cWfft1/Lbtp/lqqsLCRbfRi2flyxZs8rsyRNkzruTYpYk+lnnyadkwrDB8sX8uY7pZ59RY+WGsjfLy80ay1+7dkbFbgpNVLDbrlEKje2GxDyDwhGa8pVvl1cHDpWLFy/IG51flp/WrvYx7Nrrb5A+o1Pl4sWL0rphPTl29Ih5xtuoJgqN8cGg0BhnF0t3UmhiaTS9+hKO0EBkIDYfTp0oH06ZGJDS0y++JA/Uf1xmjH1L5s96z+O6rFmzSanyFaRshUpy3Q03yh+/bpdNP6yTnzf8KKdPX3o1iS4JCQnyTJv2cvTQIdVmxdvvlPK33SHFrisp+/7eI98u+1w2rlvr1w7cW+KmUnJzpUQpU76iHDl8SHb8vFW+X7lMDh044HNPxkyZpPr9taXkTaXl6qJF5Z89f8rv23+RLxcvktOn/rOrZKnSyqO7sWw5KXHjTbL5x/Wy+/ff5Mzp06q/7uWKXFdKucTKUuG2OyR7jpyycd0a2bDme9n71x6f9h9s8IRcXbSYzJqYKlcVvFpur5YkN5QuK8eOHpVN69fK8kULlMB7l2DtrlKzltxUrrzcUS1JcufNJ19/sUSOHzsqO3dsl6Xz5xm228hXhUJjhFrs3UOhib0xdfXIqNDkyHmFjJ0zX7CwvfB4Hb+LtW4kW/bsUrBwETl54oTs+/svV9v4ffeho5UAoJw4fkxQL8pvv2yTvu1flFMnT7oJTQaZ/vlKtejPnfGuJHd8VS36WbJmkYSES39mekbq2zL//Rk+I/Zs25fl3rqX4kinTpyQbDlyqP8+tH+/DO3ZRSUu6IKFvUPfAXLdDTepXx0/elRy5sql/hviNKjrK654xl01agm2DzNkzCgZM2aU8+fOyYULF1RfWj1Wx1Vn4WuKC7apUM/58+fVddhSxLVDur8q61d942Fzj+FvSelbKsjgbp2kTbdekjlrVtc9uBDX4zP3Eord4JH04EOSKXNmgQgjriYXL6qtzxF9uhu228hXhUJjhFrs3UOhib0xDVtosK+PhfPo4cOSUv+hkAlBoF7pN0h5RMsWfiKfzJwuf/+5WwoVLaZiHUkPPqye9rGYYlFGgZhAaPD/8EhGvd5Lftn0k+TOl1+q3nu/PP7sc+qzdk89IQf3/+uyqW7jp6Vhi2QlXpNHDlWikjd/frmrxj3SOOVFOXHsmNrW0x5UcscuahH+fN5HMnvKRCUqRYtfJ/WfeVbuSrpHFn/8oUweNcyjz2ltnSELD9uHCLojhrNq+VI5d/aMlK14q7R5rbcSqd7tXpA/tv/iqlMLDURx6tsjlbcGQbi9Wg15ovlzku+qAjKsZ1dZ/dVK1z1G7E4rRmPE7pAnAs/RGEEWk/dQaGJyWC91yqhHk/i/qvJynwGy+Yd18vrLbUMmVPGOu6RT/8Fqq6l/x5fUk70uyGTr9uZIKVOhovIefvjuWw+hwf/0f+Uln22yNt17KyEY0qOLrP36S3UPvKYJn3ymvJKuKc3l373/eNiKjLkatR9RArBy8SL1WaU7q6jFf903X3tsT11dpKgMnTpTdv++Qzq3fCZoodEiNG3MaFk4e6bHfZoDhATCqYsWmo+mTZEPJo3zuKfWI49K83YdZdGHs+Tdt0e6PjNid1pCY8TukCcChcYIspi8h0ITk8MantBgj791t17y3YplHlstwaKq3/RZeaxZCxnzRl+/qc/wUFq92t0jW017NNjmafHwfXLu3FmP5uAJYXGc+tYI+XTOB+oziBW251Z+tlDeGdQ/WPMCXjdm9jwVX2lW+56ghabr4BFS7tZEtZV2+KBnPAh9mrRgiRz4d590eLqRj9D0ad9atm74waMt7U2u+fpLGdqjS1B9CmR3WkJjxO6gjPG6iFtnRqjF3j0UmtgbU1ePjHo0WDixECHw3alF05AJdXx9oNx61/+Ul/H79m0+9yM+0n/sRBUzePO1zupzLTSI0bRv2tDnHsRgEHtwTzp4+Ikn1fbYtDGjZOHs94O2E94LEgeuL11G8hUoKDly5pQcV1yhttAQhG9Sq1pQQoPtrumfX/Ku4L35K0iEQGlZ5wEV20HRHk3rRvXlwL69HrcVvfY6GTxxmt84Tah2BxIao3YHDdjtQgqNEWqxdw+FJvbGNGyhQXB7yOQZKrD9bO1aPt5Fesh6j3pHZWq91ORxjwQBfR/iGSOmfyDbNv0kvdq2Miw0iGc82uQZSX1zgMrUCqYgPvPsSx3VodOTJ47Lnl071dYbYjl3JtVUcaCm99OUUSgAACAASURBVCcFJTQI+E9e+EWaQlOwUGHZ+/df8s4br7u29owIjRG7AwmNUbuD4et9DYXGCLXYu4dCE3tjGrbQoIKRM2arg5k92zwvv2zeGJASAtcQlf3/7pXtmzep65q17SD31a2vgv3eGVf4HPEGJAu4B96NeDS33V1N2vfu7xPPCGRs3vxXyVuzPlbZbm926yQ/b/rJlYyAe8bOWSBZs2ULaets8KRpAk+j+UO+232B7AhVaIzandbWmRG7jXxVKDRGqMXePRSa2BtTU4TmyeRW8kjDJvLnH7/Lay+09Dhf4o6sQ58BUvl/VT3iMTh7grTgee9Nk5nj3/EhrAPR7wzs5wrSGxEabHuNnjlHbc+91qqlR9IBGr2hTFkpX/kOdTbl540b5O5a98sLXbqr0/1IEHAvONcybOpMdUYmUIzGPT6k70WsCTGnHm1SXEKb3pQKVWiM2q2FpktyM3WGyb0YsTu9fvn7nEJjhFrs3UOhib0xNUVocOblzUnTJU/+/Cr9NnXwGx6HLHGupE7jpvJ4s5YqzvDSU0+4vIP8BQrKwPFTJWv27NLv5bay9acfXTbh/Ei3ISPl9MmT0rnl07L/cozCiNCg0l4jx8hNN9+izt68P2Gsqx2k774xborkyZdPOjZrol7+Wbp8RekxbLQST8Se8EYDlMxZsqhUZAimP6F5uGFjaZz8gkpdHtm3h8eMwT0QW9QP7w+v69EF8Zkug4bJzt92SLfnm7t+H6rQGLUbmX/IfJs4YohK53YvRuw28lWh0BihFnv3UGhib0xNERpUclO5W+SV1wepg4h4gab2DApcXVhKl68gRYpfqw5Fjny9p0/2FDLCXh146TwKMqt2/LxFSpYqoxZ7lDc6t5ctP/6XcWVUaNR5kFFj1Ul7PLVvWPOd2vIrV6myetO0+/YcxBEpzIgRwQtCajWyzPAGgv17/1FvCsAhR2+PBhx6jRijhOmXTRvlly2bZPo7o12c6z31jDrnc+TQIdm0fo38/eefck2JkpJY5X9y5swZGdCpvcfLOEMVGqN2I36FOBYy+TZ8v0p++H6Vx7vaQrXbyFeFQmOEWuzdQ6GJvTE1TWhQERIDGjZPlsp3VxOcgdEFByB/WrNaxg8dFPDNwHgdC978jNfCIKsLWVcQFzxd/7j6Ow/yRoUGleDNBFg0b761snqlCwQBr35BGvRnH832aAevZHmxaw9lEw6WIsvsqyWLZcro4TJkygy/6c2ooPZjjeSRRo3VK13QD2SRuZfajzWU26pWV6+S0fWCD7wsHCZ1L6EKDe41YjeC/jhLhDccIPaE80c4h2TUbiNfFQqNEWqxdw+FJvbG1FSh0ZVhoYPowEvY88cfKlvL3/u4/OGEQMGL2PvXX0HfY3RY0I7KIrucShyoHmSdFShcRHky7u83S69dLNgQMmyx+Sv4HLGjf//+69KrX0wuRu3GVujpUydVJmEk7abQmDwBHFodhcahAxeM2UbP0QRTN68hgWAIUGiCoRT711BoYniMKTQxPLgO6dru42fkxLkLMnbsWElMTHSI1TTTbAIUGrOJ2qg+Co2NBiNOTdl2+NKfXVizZk2cEmC3QYBCE8PzIDU1VfCTI1MGKZYzSwz3lF2zKwEKjV1HJrJ2UWgiyzuira1du1a9wRnlptzZIto2GyMBxmc4BzQBCk2MzwW9fZY/WybJnzVTjPeW3bMTAe3NJCcnC35Y4pcAhSbGx97dq7kmZxbJnum/szAx3nV2L4oE6M1EEb4Nm6bQ2HBQzDZJx2pQL8XGbLqsz5uAFhn8ntlmnB8gQKGJk3ngLjbcRouTQY9wN0+euyAQGaQzo3DLLMIDYOPmKDQ2HhyzTXMXG9QNwUHJkZHbaWazjqf6Tpy/IBAZLTA4LwOR4bmZeJoFafeVQhOHc8FbcOIQAbtsAQEKjAVQY6RKCk2MDKSRbkBwUJAwwOKfwPLly9UHSUmef3WTvP4jAIHRP+RCAv4IUGg4L0ggAAGITI0aNZTILFu2jJxIgAQMEqDQGATH22KfAIUm9seYPYwMAQpNZDizFQcSoNA4cNBosi0JUGhsOSw0yg4EKDR2GAXaEAsEKDSxMIrsgyUEKDSWYGWlcUiAQhOHg84uB0eAQhMcJ15FAukRoNCkR4ifxy0BCk3cDj07bjIBCo3JQFld7BCg0MTOWLIn0SVAoYkuf7ZuYwIUGhsPDk1zFAEKjaOGi8ZGkgCFJpK02VYsE6DQxPLosm9hEaDQhIWPN5OAiwCFhpOBBAIQoNBwapCAOQQoNOZwZC0xSIBCE4ODyi5FhQCFJirY2agTCFBonDBKtNEJBCg0Thgl2hgVAhSaqGBnozFIgEITg4PKLplDgEJjDkfWQgIUGs4BEmAyAOcACVhKgEJjKV5W7mQC9GicPHq03U4EKDR2Gg3aYisCFBpbDQeNcTABCo2DB4+mW0uAQmMtX9YePwQoNPEz1uxpiAQoNCEC4+UkEIAAhYZTgwSYDMA5QAKWEqDQWIqXlTuZAD0aJ48ebbcTAQqNnUaDttiKAIXGVsNBYxxMgELj4MGj6dYSoNBYy5e1xw8BCk38jDV7GiIBCk2IwHg5CTAZgHOABEIjQKEJjRevJoFABOjRcG6QQAACFBpODRIwhwCFxhyOrCUGCPTq1Uvwo0taQrN+/XqpVKlSDPSaXSAB6wlQaKxnzBYcQkCLjP7Xn9Dgd71795Zly5Y5pFc0kwSiT4BCE/0xoAU2IpCQkCA9e/ZUno230OB3EBn9uY3MpikkYGsCFBpbDw+NizQBLSZod9iwYdK+fXtJSkqS6tWrK5FBuXjxYqTNYnsk4GgCFBpHDx+Nt4IAvBr3kidPHjl06JD6Fb0ZK4izzlgnQKGJ9RFm/0Im4O7VeN9MbyZknLyBBIRCw0lAAn4IeHs19GY4TUjAOAEKjXF2vDOGCfjzaujNxPCAs2uWEqDQWIqXlTuZgLtXw9iMk0eStkebAIUm2iPA9l0E1q5daysa8GpWrFihbLLbuZnExERbsaIxJJAWAQoN50fUCEBYUlNTVft2E5moQQmxYQgOfpKTk0O8k5eTQOQIUGgix5otXSagBcZHXLIXIKNgCZzc53MlxIaCEyxAXhdJAhSaSNJmW8qD0V6MwpH3ZhEITDaKjKHpcXDzpdsOblL/wLsZO3asoap4EwlYRYBCYxVZ1utDwENkIDB5y5KSWQQgOKf2ipzcR7ExiynrMY0AhcY0lKwoLQIUmQjNj7+WU2wihJrNBE+AQhM8K14ZBgGX0NCTCYNikLdeFhtsoTE7LUhmvMxSAhQaS/GychDw8GZKPk4oVhM4tU9kz3JuoVnNmfUHTYBCEzQqXmiUAL0Zo+TCuI9eTRjweKvZBCg0ZhNlfT4EKleufOl39GYiNzuQHHBwk0p3Zspz5LCzJf8EKDScGZYToNBYjti3AW6fRQE6mwxEgELDuWEpARzKTElJuXRWpnCSpW2xcjcCFBpOBxsRoNDYaDBi0RQKTZRGlUITJfBs1h8BCg3nhaUEKDSW4g1cOYUmSuDZLIWGcyDiBCg0EUd+qUEKTZTAs1kKDedAxAlQaCKOnEITJeRsNjABbp1xdlhKIByhKXZ1HqldrZyPfSdOnZG9B47K1h1/y86/Dlpqv2Mrp0fj2KGLRcMpNLE4qjbqUzhCc2eFEjK62xNp9ubHn3fLkMlLZfOvf9mo1zYwhUJjg0GgCZoAhYZzwVICZgjN6o1/SOoHX7nsvKZQXqlU5hpJLFtcihTMLWfOnpdeby2Qxd9ssbQvjqqcQuOo4Yp1Yyk0sT7CUe6fGUKz5Nut0mXYXJ+eJCSI9G79sNSuerOcPH1WGr8ySXb9za00BYpCE+WZz+bdCVBoOB8sJWCl0MDwTBkzyNDODaRKxZKy8MtN0mPUfJ/+ZMmcUXlAd1UoISWK5Zcftu6W7zb8Llt2/C0XL3pe3vihylKsUF4ZM3OlFLrqSql5Rykpd2MROXLspKzeuFPmLd0gF7xvEpEc2bLIw0nlpOz1haVgvivkt9375fuNf8iK1b/45QuRLF2ikLKpYulisn3XPmXT+i275czZc+GPCYUmfIaswTQCFBrTULIifwSsFhq0WfKaq2TWkBZy4uQZqdVylMdCnS1rZknt1VjKXl9ImYdrcmTPov57ytxVMmr6Cg+zx/VuIpXKFJP2A2dLv7Z1JGuWTHLu/AX1L8pX636Vdm/M9rineOG8MqRTAylRNL8cPHJCjhw7JcUL5xOIyWdfb5buI+f7iNOrLe+Tx+6r5GMThOaFvjPl7Lnz4U0oCk14/Hi3qQQoNKbiZGXeBCIhNBkyJMjKKe0FotKk82T5+bd/lBkZEhJkUMd6knTbjTLr03Uyee4q2XfgqNxYvKD0fKG2lCpxtfR9Z5HMXbrBZbYWGgjSkClfyOKvt0hCQoLcc2cpadWoqhTMl0teefMjWfb9Ntc9A9rXlXvvKi1vTvpcZi5aq36f98ocMuSV+lK+VFF5efAcD8+m8UO3SYdnasq6Lbtk0IQlsn3nPilc4EpJeaKqPFy9XEDPLKTZRaEJCRcvtpYAhcZavnFfeySEBpAn9G0iFUoVk7YDPpBv1u9Q3LEtNarbE2qR7/jmHI9tsgJ5r5BZQ1sq7+f+5NE+QjPhw29kzPtfeoxfg3srSZfn7pMZC9bI0ClfuD5bPrmdZM6UUao2HerhueTPk1MqlCoqu/85JNt+36uuz541s6yY0l7+2X9UmnSaJEeOn3LVkzFjBhnXq7ESpwbtxskfew4Ynz8UGuPseKfpBCg0piNlhe4EIiU0g16uJzXvuEk6D/1Yvlj1szKhRYMq0qphVek6fJ7fjDSIEMTowZS3ZN/BY+oe7dEk95yhPA73csuNRWRSv6ayHMI1eI7roxmDnpWbrisob89cKdPnr5bTZwLHWBCPGd+niXy4ZL0MGLfYZ7I0e/ROad24urw28hP59KvNxicThcY4O95pOgEKjelIWWE0hObjUSmCA57P9ZyuAuoo2LqqftuN6nDnLj8HOxNvLq6u6zDoQ1m5ZruH0NRu9bbs3X/UYzBLFrtKZg1t4ROnqXxzcRn+6mNq6w6HSddt3iVrN+2Uxd9slX/2H/GoA8kGHZ65R/0O13gXbdOMBatl6JSlxicThcY4O95pOgEKjelIWWGkhSZXzqyybFI71exDrca4Fnd4H/BCduz+Vw4ePuEzMEUK5pE9ew/J9AWrwxIaVIwMNcRXat5ZSm4oXkDFh5CdBq+k75hFruA+vBV4LYGEpkC+K+TQ0ZNq+2/8h98Yn0wUGuPseKfpBCg0piNlhZEWmvv/V1b6vfSISgJAMoAuOrMrudcM5WUEU/TWWSgejXe9EL57q5SRp+vcobws9+w2JA0geQAHUFM/+DoYk4xdQ6Exxo13WUKAQmMJVlaqCVgdo8EZlzE9GqkgO2IeiH3oUqdGeenR6kEZPWOFTP54VVCDYobQ6IbK31RUJr7+lGz8ZY806/au+nXRq/PI3FEp8u2Pv0mbfrOCssnQRRQaQ9h4kzUEKDTWcGWtlwlYJTTYqkI8o8PTNSV3ruyC19Tg/In7WUqkDL83uLk61Pl8n5lqwdflqrxXyOR+TVUa8iMvjpEDl7fWQhUanKGZNrCZ/P3vEWnRfZocPX7a1QaSE5Ck4B3418kDgyd9Lu9fTofGTThYOrBDPamaeL10GPihrFx7KW5kqFBoDGHjTdYQoNBYw5W1mig0EIFfd+1zMcW7ziA0uiz9bpv0fnuhHD/53yKvP7ut3LUysuvjcuHCRZVFtnn7X1IgXy65586bJGf2rCpNGenKuoQqNLhvaKcGUq3yDSodGeKw++9DcuO1BaRuzfIqffr53jPVWwh0QWo14kdX579SNm3fo+zKliWz/K9SSeXx4IxOpyEf+by1IKRJRaEJCRcvtpYAhcZavnFfuxkejTdEZHbtO3BMLd7zlv0k3//0e5qc7771eql/b0W5tcw1ckWOrOraHbv+lbEffOVKhQ5HaHD+pWWDKtLg3oqSL3dOVRXevfbTtj+lf+pn6hyNd8FbBJ6td5cgY61g/lzqYwgqss2mffK9ehtBWIVCExY+3mwuAQqNuTxZmxeBcITGbJh4gwC8oX8PHvfr/ZjRXp5c2SVXzmzy5z+H/L4TzV8beAP1qdPn5MDh42aYcKkOCo15LFlT2AQoNGEjZAVpEbCT0MTVSFFo4mq47d5ZCo3dR8jh9lFoojSAFJoogWez/ghQaDgvLCdQuXLlS22UfNzyttjAZQIHN4sc3CTJycnqh4UEokmAQhNN+nHStktoiiSJZCsQJ72OcjcpNFEeADbvToBCw/lgOYGUlBTBFppkLyBSOMny9tgA0uo+UBjWrPkvdZtcSCBaBCg00SIfR+264jToM70a60ee3oz1jNlCSAQoNCHh4sVGCdCrMUouxPsuJwHQmwmRGy+3lACFxlK8rFwT8PBq8t4skrcs4ZhNwE1kmARgNlzWFw4BCk049HhvSAQ8xAZ3UnBC4hfwYgjMwU0iJy+9pociYw5W1mIeAQqNeSxZUxAEfMRGCw7+RbIAS2gE3ASGIhMaOl4dOQIUmsixZktuBFJTUwU/LOYQSExMVJ4M/mUhAbsRoNDYbUTizB53sVEp0DYry5cvVxYlJdkzLRvCon9sho7mkICLAIWGk4EEAhCAyNSoUUOJzLJly8iJBEjAIAEKjUFwvC32CVBoYn+M2cPIEKDQRIYzW3EgAQqNAweNJtuSAIXGlsNCo+xAgEJjh1GgDbFAgEITC6PIPlhCgEJjCVZWGocEKDRxOOjscnAEKDTBceJVJJAeAQpNeoT4edwSoNDE7dCz4yYToNCYDJTVxQ4BCk3sjCV7El0CFJro8mfrNiZAobHx4NA0RxGg0DhquGhsJAlQaCJJm23FMgEKTSyPLvsWFgEKTVj4eDMJuAhQaDgZSCAAAQoNpwYJmEOAQmMOR9YSgwQoNDE4qOxSVAhQaKKCnY06gQCFxgmjRBudQIBC44RRoo1RIUChiQp2NhqDBCg0MTio7JI5BCg05nBkLSRAoeEcIAEmA3AOkIClBCg0luJl5U4mQI/GyaNH2+1EgEJjp9GgLbYiQKGx1XDQGAcToNA4ePBourUEKDTW8mXt8UOAQhM/Y82ehkiAQhMiMF5OAgEIUGg4NUiAyQCcAyRgKQEKjaV4WbmTCdCjcfLo0XY7EaDQ2Gk0aIutCFBobDUcNMbBBCg0Dh48mm4tAQqNtXxZe/wQoNDEz1izpyESoNCECIyXkwCTATgHSCA0AhSa0HjxahIIRIAeDecGCQQgQKHh1CABcwhQaMzhyFpigECvXr0EP7qkJTTr16+XSpUqxUCv2QUSsJ4AhcZ6xmzBIQS0yOh//QkNfte7d29ZtmyZQ3pFM0kg+gQoNNEfA1pgIwIJCQnSs2dP5dl4Cw1+B5HRn9vIbJpCArYmQKGx9fDQuEgT0GKCdocNGybt27eXpKQkqV69uhIZlIsXL0baLLZHAo4mQKFx9PDReCsIwKtxL3ny5JFDhw6pX9GbsYI464x1AhSaWB9h9i9kAu5ejffN9GZCxskbSEAoNJwEJOCHgLdXQ2+G04QEjBOg0BhnxztjmIA/r4beTAwPOLtmKQEKjaV4WbmTCbh7NYzNOHkkaXu0CVBooj0CcdL+2rVrHddTeDUrVqxQdjvx3ExiYqLjmNPg2CRAoYnNcY16ryAsqampyg4nikzUAZpoAAQHP8nJySbWyqpIIHgCFJrgWfHKIAhogfEWlwoVKgRxNy8xk8CPP/7oUx3EhoJjJmXWFQwBCk0wlHhNUATgwWgvBjc0bdpUIDAUmaDwWXbR1KlTVd3vvvuu+hfezdixYy1rjxWTgDcBCg3nhCkE3EUGAvP000+bUi8rMY8ABGfDhg0CT4diYx5X1pQ+AQpN+ox4RToEKDLOmiIdO3ak2DhryBxvLYXG8UMY/Q5ooaEnE/2xCNYCLTbYQmN2WrDUeJ1RAhQao+R4nyLg7s0sWbKEVBxCANtnEBtuoTlkwBxuJoXG4QMYbfPpzUR7BIy3f++996qb6dUYZ8g7gyNAoQmOE68KQKBy5crqE3ozzpsievuMKc/OGzunWUyhcdqI2cxeCo3NBiQEc7h9FgIsXhoWAQpNWPji+2YcykxJSVHnZN588834huHA3lNoHDhoDjWZQuPQgbOD2RQaO4yCcRsoNMbZ8c7QCFBoQuPFq90IUGicPR0oNM4ePydZT6Fx0mjZzFYKjc0GJERzKDQhAuPlhglQaAyj441WCs3+/fvln3/+kcKFC0vevHkJ2wICFBoLoLJKvwQoNJwYhglYKTSjR4+WHj16yODBg6VFixaGbYylG3/55ReZM2eOZM6cWTp06BB21yg0YSNkBUESoNAECYqX+RKg0ERuVkyZMkW6du0qJ0+elBw5csju3bvDbpxCEzZCVhAkAQpNkKB4GYUmGnPg4MGD8tJLL8n8+fOlRIkS8vvvv0v27NkpNNEYDLZpmACFxjA63kiPxto5cOzYMbnzzjtlz5498uSTT8qgQYOkZMmSauuMHo217Fm7uQQoNObyjKvazBCanTt3yuLFi2XZsmWSNWtWueeee6RRo0YyZsyYNGM0f/zxhyxdulTdh2SBpKQkqV69uuTLl89jDC5evCidO3eW/Pnzq38///xzdd+WLVukePHiUr9+fXWfv7Jq1Sr1ah38DZcrr7xSypYtq/7OToECBfxeD+9jxYoV8sUXX8iRI0eUTTVq1JDrrrvO0LzYt2+f4M0LQ4cOlQYNGqg6rr76agqNIZq8KZoEKDTRpO/wtsMVmi+//FItoOfOnZMMGTKoLaHjx49L1apV1SLdt29fv8kAH330kbRs2VIgIrjn9OnTcuHCBSUAEJJrrrnGRRa/v+qqq9S2U/v27aVt27YqxnHq1Cl1D0rv3r2lTZs2HqMB7wE/GTNmVF4EvIqjR4+quiZPnixVqlTxuH779u1y3333yaFDhyRTpkzqB23g/unTp6vPQi0nTpwQiM21117rupVCEypFXm8HAhQaO4yCQ20IR2jgycB7OXDggPTv318ee+wxyZ07t3z33XfSrl07+ffff+Xw4cM+QrN69WqpU6eOuhZvjr7rrrsEC/J7772nguWlS5eWTz/9VHkgKFposN0Er2b8+PFy++23y969e+X9999XbUMU1q9fL4UKFVL3bNu2TW1ZlSlTRmbPnq1SrM+fPy8ff/yx4AWU5cqVU56LLkjFhpCgT/A+Hn30UcmSJYt89dVXShAhpAsXLpRbbrkl7JGm0ISNkBVEgQCFJgrQY6XJcIQG21jjxo1TXkb37t09kGzdulWJEDKsvNObsaCvW7dObWlVqlTJ476ePXvKqFGj1GLfrFkzD6HB/8AT8t4mQ+o0fj9t2jSpXbu2ugfbdt26dVNbdxA99wIvDF4L7MBWH0qfPn1k+PDh8vrrr8sLL7zgcT3sbNiwodqig8iFWyg04RLk/dEgQKGJBvUYaTMcoXnwwQeV94L4R7FixXyIYHHGIu0uNPAMsC2G2Aru9S5r1qxRAoA4ChZ+FO3RQBQQ14Gn4V5wHYQCns3zzz+vPoL38dRTT8mNN94okyZNUrGZtEq9evWUh/Pzzz/7xG/QPvpXtGhRgTcWbqHQhEuQ90eDAIUmGtRjpE2jQoPYCsQC8Quk6/oriJuMGDHCQ2h++uknl0dy9913+9yG+A62wMqXLy/Lly/3EBrEaGCvd5kwYYK88sorSmxat26tPj5z5ow0b95cCQ5KqVKlBO3hD4XB04LduqAv2JJD8WcTfo8tNBT0VW/pGZ0CFBqj5HhfNAlQaKJJ3+FtGxUaBMnxlF+kSBHl0fgrEBmIjbtHg2wxxHLSWtThtVSsWFFwwBHFPRkgWKHBfYjJILFg5syZsnLlSkFGGUrBggXVFpgWFfQF/UjPJgT03377bb/eWyjTgEITCi1eaxcCFBq7jIQD7TAqNOgqAvK//vqrCqDnzJnTp/fYxpo1a5aH0CADS3sX8+bNC4qYUaFxrxxeyw8//CDwfmbMmCHZsmWTzZs3S548edRlSByAt4K+eG/NBWVkCBdRaEKAxUttQ4BCY5uhcJ4h4QgNsrHw3q4PPvhAbUe5l7Nnz6pssh07dvgkAyBzC2nGiIfoYHxa5MwQGvf6kY6Nszvudrdq1UplsOE8kP6Lo1aNJoXGKrKs10oCFBor6cZ43eEIDTK9kPGFbSfEU3A+RZdOnTq5MrS8s86QDYassLp16yoPA+dvdEG2mc78wr8oRoQGZ2pgX69evVR6si5IRqhVq5Zs3LhRNm3apA5PoixYsECaNm0q119/vXz22Wceh0YRn4E4IaEAAhVuodCES5D3R4MAhSYa1GOkzXCEBgheffVVdRYGJ/tr1qypzrHgVD0ORz788MNqm8pbaLDY43UsuA4Le7Vq1SRXrlzy7bffqqwu/A5BfH1634jQIHsN7WPLDNlx+qwOtuvwIkrEiWC3e8GfskbmGgQTNuGQJ94+sGjRIuV5wXu74447wh55Ck3YCFlBFAhQaKIAPVaaDFdoIBoDBgxQHgEOSaIgY2zIkCFKOAL9mQBkl/Xr10+lFOPMDQQBizkECOKFgL0uRoQG98Jr6dixo4rNIAsNBZly8HBefPFFSUhI8BlGeFqffPKJym7D9h+8LbzhAOeEKlSoYMqwU2hMwchKIkyAQhNh4LHUXLhC487izz//VGLhvoUWDCskCOBgJ7LY3LfRgrk3mGsghgj0Q7yCTU3GmwrgleHMTzBxpGDssOIa/pkAK6iyTn8EKDScF4YJmCk0ho1w2I3Y2gul3HDDDSruY0Wh0FhBlXVSaDgHTCVAoQkdJ+I3oRS8+Xnq1Kmh3BL0tRSaoFHxwjAJ0KMJE2C8367TefG6GBZnEYCAvfvuu+pFofhhIQGrCFBorCIbJ/WmpKSo4DeyrswKoZDAXAAAAfJJREFUeMcJuqh3k0IT9SGIGwMoNHEz1NZ0VAsNRAZiw+IcAnh3GwrSuVlIwEoCFBor6cZB3TpOg67Sq3HOgNObcc5YxYKlFJpYGMUo94FeTZQHIMTmtcjQmwkRHC83TIBCYxgdb3QnoJMC8CoW/D0YFnsS0JlmsI5JAPYco1i0ikITi6MahT65b6GheQpOFAYhjSYhMMgww78UGXuNTTxYQ6GJh1GOUB8hNngHmPvffYHgoDAjLUKD4KcZd4GhyERvHOK5ZQpNPI++RX2H2Hi/dNKiplhtCAQSExPVdhn+ZSGBSBKg0ESSdpy15S42/v66ZZzhiFp3ISz6J2pGsOG4JkChievhZ+dJgARIwHoCFBrrGbMFEiABEohrAhSauB5+dp4ESIAErCdAobGeMVsgARIggbgmQKGJ6+Fn50mABEjAegIUGusZswUSIAESiGsCFJq4Hn52ngRIgASsJ0ChsZ4xWyABEiCBuCZAoYnr4WfnSYAESMB6AhQa6xmzBRIgARKIawIUmrgefnaeBEiABKwnQKGxnjFbIAESIIG4JkChievhZ+dJgARIwHoCFBrrGbMFEiABEohrAhSauB5+dp4ESIAErCdAobGeMVsgARIggbgmQKGJ6+Fn50mABEjAegIUGusZswUSIAESiGsC/wfqqqqjraBgfAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "image/png": {
       "width": 240
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"user_model.png\", width=240)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py310",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}