doc/source/examples/similarity_explanations_20ng.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7147902f",
   "metadata": {},
   "source": [
    "# Similarity explanations for 20 newsgroups dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e3ea6e4",
   "metadata": {},
   "source": [
    "In this notebook, we apply the similarity explanation method to a feed forward neural network (FFNN) trained on the 20 newsgroups dataset. \n",
    "\n",
    "The 20 newsgroups dataset is a corpus of 18846 text documents (emails) divided into 20 sections. The FFNN is trained to classify each document in the correct section. The model uses pre-trained sentence embeddings as input features, which are obtained from raw text using a [pretrained transformer](https://www.sbert.net/docs/pretrained_models.html). \n",
    "\n",
    "Given an input document of interest, the similarity explanation method used here aims to find text documents in the training set that are similar to the document of interest according to \"how the model sees them\", meaning that the similarity metric makes use of the gradients of the model's loss function with respect to the model's parameters.\n",
    "\n",
    "The similarity explanation tool supports both `pytorch` and `tensorflow` backends. In this example, we will use the `pytorch` backend.  Running this notebook on CPU can be very slow, so GPU is recommended.\n",
    "\n",
    "A more detailed description of the method can be found [here](../methods/Similarity.ipynb). The implementation follows  [Charpiat et al., 2019](https://papers.nips.cc/paper/2019/hash/c61f571dbd2fb949d3fe5ae1608dd48b-Abstract.html) and  [Hanawa et al. 2021](https://arxiv.org/abs/2006.04528)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a42616ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Installing required sentence transformer\n",
    "!pip install sentence_transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a62345f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import string\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.nn as nn\n",
    "from termcolor import colored\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm import tqdm\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from sklearn.model_selection import train_test_split\n",
    "from alibi.explainers import GradientSimilarity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "192fb009",
   "metadata": {},
   "source": [
    "## Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8eba6b40",
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_categorical(y, num_classes):\n",
    "    \"\"\" 1-hot encodes a tensor \"\"\"\n",
    "    return np.eye(num_classes, dtype='uint8')[y].astype('float32')\n",
    "\n",
    "class TorchDataset(torch.utils.data.Dataset):\n",
    "    \"\"\"Utility class to create a torch dataloader from numpy arrays.\n",
    "    \"\"\"\n",
    "    def __init__(self, *indexables):\n",
    "        self.indexables = indexables\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        output = tuple(indexable[idx] for indexable in self.indexables)\n",
    "        return output if len(output) > 1 else output[0]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.indexables[0])\n",
    "    \n",
    "def append_int(num):\n",
    "    \"\"\"Converts an integer into an ordinal (ex. 1 -> 1st, 2 -> 2nd, etc.)\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    num\n",
    "        Integer number\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Ordinal suffixes\n",
    "    \"\"\"\n",
    "    if num > 9:\n",
    "        secondToLastDigit = str(num)[-2]\n",
    "        if secondToLastDigit == '1':\n",
    "            return 'th'\n",
    "    lastDigit = num % 10\n",
    "    if (lastDigit == 1):\n",
    "        return 'st'\n",
    "    elif (lastDigit == 2):\n",
    "        return 'nd'\n",
    "    elif (lastDigit == 3):\n",
    "        return 'rd'\n",
    "    else:\n",
    "        return 'th'\n",
    "    \n",
    "def break_doc_in_lines(text, nb_words_per_line=18):\n",
    "    \"\"\"Breaks document in lines of a fixed number of words for visualization purposes.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    text\n",
    "        String to break in line\n",
    "    nb_words_per_line\n",
    "        number of words for each line\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    String with line breakers\n",
    "    \"\"\"\n",
    "    text_l = text.split(' ')\n",
    "    text_conc = []\n",
    "    nb_lines = np.floor(len(text_l) / nb_words_per_line).astype(int) + 1\n",
    "    for i in range(nb_lines):\n",
    "        tl = text_l[i * nb_words_per_line: (i + 1) * nb_words_per_line]\n",
    "        text_conc.append(' '.join(tl))\n",
    "    text = '\\n'.join(text_conc)\n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a1c2fe1",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8831fd4f",
   "metadata": {},
   "source": [
    "Loading and preparing the 20 newsgroups dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "437b8c1e",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Warning: \n",
    "    \n",
    "    The cell below will load the 20 news groups dataset. This might require a considerable amount of memory.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "467c838c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading 20 news groups dataset\n",
      "Cleaning text\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The default value of regex will change from True to False in a future version.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[31mSample document before cleaning\u001b[0m\n",
      "\n",
      "So its an automatic? Don't know if US spec=CDN spec. for Maximas.\n",
      "\n",
      "\n",
      "If it is the first set of brake pads on front, then this is fine. My car\n",
      "eats a set every 15k miles or so. The fact that he is replacing the\n",
      "muffler too is also ok.\n",
      "\n",
      "\n",
      "The mileage is fairly low - but typical fwd stuff is CV joints. Check\n",
      "the maintenance records with the manufacturers requirements for valve\n",
      "adjustments, timing belt changes and so on.\n",
      "\n",
      "The 60k mile service is often expensive, so make sure he has done everything.\n",
      "\n",
      "\n",
      "Well, this is one of the commonly cited methods for identifying a\n",
      "car with highway miles. \n",
      "Might check the gas pedal wear too. Ask him how many sets of tires he\n",
      "has been through. A highway car might have squeezed by on 2 sets,\n",
      "a hard driven car 6-10 sets.\n",
      "\n",
      "\n",
      "\n",
      "Well, the Maxima should be pretty reliable - but if its out of warranty\n",
      "you should get it checked out by someone knowledgeable first. Stuff\n",
      "for Japanese cars can be expensive.\n",
      "\n",
      "\n",
      "1995 model year, I believe. \n",
      "\n",
      "\u001b[31mSample document after cleaning\u001b[0m\n",
      "So its an automatic? Don't know if US spec=CDN spec. for Maximas. If it is the first set\n",
      "of brake pads on front, then this is fine. My car eats a set every 15k miles or\n",
      "so. The fact that he is replacing the muffler too is also ok. The mileage is fairly low\n",
      "- but typical fwd stuff is CV joints. Check the maintenance records with the manufacturers requirements for valve\n",
      "adjustments, timing belt changes and so on. The 60k mile service is often expensive, so make sure he\n",
      "has done everything. Well, this is one of the commonly cited methods for identifying a car with highway\n",
      "miles. Might check the gas pedal wear too. Ask him how many sets of tires he has been\n",
      "through. A highway car might have squeezed by on 2 sets, a hard driven car 6-10 sets. Well,\n",
      "the Maxima should be pretty reliable - but if its out of warranty you should get it checked\n",
      "out by someone knowledgeable first. Stuff for Japanese cars can be expensive. 1995 model year, I believe.\n",
      "\n",
      "Splitting train - test\n",
      "X_train shape: (14604,) - y_train shape: (14604, 20)\n",
      "X_test shape: (3652,) - y_test shape: (3652, 20)\n"
     ]
    }
   ],
   "source": [
    "example_idx = 4\n",
    "\n",
    "print(\"Loading 20 news groups dataset\")\n",
    "data = fetch_20newsgroups(shuffle=True, random_state=1, subset='train',\n",
    "                          remove=('headers', 'footers', 'quotes'),\n",
    "                          return_X_y=False)\n",
    "X, y = np.asarray(data.data), data.target\n",
    "target_names = data.target_names\n",
    "df = pd.DataFrame({'text': X, 'labels': y})\n",
    "\n",
    "print(\"Cleaning text\")\n",
    "df['text_cleaned'] = df['text'].str.replace('\\s+',' ')\n",
    "df['text_cleaned'] = df['text_cleaned'].str.strip()\n",
    "df['text_cleaned'] = df['text_cleaned'].str.slice(0,131072)\n",
    "df = df.replace('', np.NaN).dropna()\n",
    "df = df.drop_duplicates(subset='text_cleaned')\n",
    "print('')\n",
    "\n",
    "print(colored(\"Sample document before cleaning\", 'red'))\n",
    "print(f\"{df['text'][example_idx]}\")\n",
    "print('')\n",
    "print(colored(\"Sample document after cleaning\", 'red'))\n",
    "print(break_doc_in_lines(f\"{df['text_cleaned'][example_idx]}\"))\n",
    "print('')\n",
    "\n",
    "print(\"Splitting train - test\")\n",
    "df_train, df_test = train_test_split(df, test_size=0.2)\n",
    "X_train, y_train = df_train['text_cleaned'].values, df_train['labels'].values\n",
    "X_test, y_test = df_test['text_cleaned'].values, df_test['labels'].values\n",
    "y_train, y_test = to_categorical(y_train, num_classes=20), to_categorical(y_test, num_classes=20)\n",
    "print(f\"X_train shape: {X_train.shape} - y_train shape: {y_train.shape}\")\n",
    "print(f\"X_test shape: {X_test.shape} - y_test shape: {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a9d6dc",
   "metadata": {},
   "source": [
    "## Define and train model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6efe2a5f",
   "metadata": {},
   "source": [
    "We define and train a `pytorch` classifier using sentence embeddings as inputs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abdd5d35",
   "metadata": {},
   "source": [
    "### Define model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d91fae48",
   "metadata": {},
   "outputs": [],
   "source": [
    "class EmbeddingModel:\n",
    "    \"\"\"Pre-trained sentence transformer wrapper.\n",
    "    \"\"\"\n",
    "    def __init__(\n",
    "        self,\n",
    "        model_name: str = 'paraphrase-MiniLM-L6-v2',  # https://www.sbert.net/docs/pretrained_models.html\n",
    "        max_seq_length: int = 200,\n",
    "        batch_size: int = 32,\n",
    "        device: torch.device = None\n",
    "    ) -> None:\n",
    "        if not isinstance(device, torch.device):\n",
    "            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "        self.encode_text = SentenceTransformer(model_name).to(device)\n",
    "        self.encode_text.max_seq_length = max_seq_length\n",
    "        self.batch_size = batch_size\n",
    "    \n",
    "    def __call__(self, x: np.ndarray) -> np.ndarray:\n",
    "        return self.encode_text.encode(x, \n",
    "                                       convert_to_numpy=True, \n",
    "                                       batch_size=self.batch_size,\n",
    "                                       show_progress_bar=False)\n",
    "\n",
    "class Classifier(nn.Module):\n",
    "    \"\"\"FFNN classifier with pretrained sentence embeddings inputs.\n",
    "    \"\"\"\n",
    "    def __init__(\n",
    "        self, \n",
    "        n_classes= 20\n",
    "    ) -> None:\n",
    "        \"\"\" Text classification model from sentence embeddings. \"\"\"\n",
    "        super().__init__()\n",
    "        self.head = nn.Sequential(nn.Linear(384, 256), \n",
    "                                  nn.LeakyReLU(.1), \n",
    "                                  nn.Dropout(.5), \n",
    "                                  nn.Linear(256, n_classes))\n",
    "        \n",
    "    def forward(self, sentence_embeddings) -> torch.Tensor:\n",
    "        return self.head(sentence_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7dbe859b",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9136a551",
   "metadata": {},
   "source": [
    "### Get sentence embeddings and define dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "04ae1215",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting train embeddings\n",
      "Getting test embeddings\n"
     ]
    }
   ],
   "source": [
    "embedding_model = EmbeddingModel(device=device)\n",
    "\n",
    "print('Getting train embeddings')    \n",
    "embeddings_train = embedding_model(X_train)\n",
    "train_loader = DataLoader(TorchDataset(torch.Tensor(embeddings_train).to(device), \n",
    "                                       torch.Tensor(y_train).to(device)), \n",
    "                          batch_size=32, \n",
    "                          shuffle=True)\n",
    "\n",
    "print('Getting test embeddings')  \n",
    "embeddings_test = embedding_model(X_test)\n",
    "test_loader = DataLoader(TorchDataset(torch.Tensor(embeddings_test).to(device), \n",
    "                                      torch.Tensor(y_test).to(device)), \n",
    "                         batch_size=32, \n",
    "                         shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921d8122",
   "metadata": {},
   "source": [
    "### Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47f431a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training classifier\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████| 457/457 [00:00<00:00, 972.33it/s]\n",
      "100%|███████████████| 457/457 [00:00<00:00, 1189.45it/s]\n",
      "100%|███████████████| 457/457 [00:00<00:00, 1195.13it/s]\n"
     ]
    }
   ],
   "source": [
    "epochs = 3\n",
    "\n",
    "# initialize classifier\n",
    "model = Classifier().to(device)\n",
    "print('Training classifier')\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
    "for epoch in range(epochs):\n",
    "    for x, y in tqdm(train_loader):\n",
    "        y_hat = model(x)\n",
    "        optimizer.zero_grad()\n",
    "        loss = loss_fn(y_hat, y)\n",
    "        loss.backward()\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dc01171",
   "metadata": {},
   "source": [
    "### Evaluate model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788f76d7",
   "metadata": {},
   "source": [
    "Evaluating the model on train and test set. Since the dataset is well balanced, we only consider accuracy as evaluation metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "981e503e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set evaluation\n",
      "Accuracy: 0.720\n",
      "Test set evaluation\n",
      "Accuracy: 0.664\n"
     ]
    }
   ],
   "source": [
    "def eval_model(model, loader):\n",
    "    model.eval()\n",
    "    logits, labels = [], []\n",
    "    with torch.no_grad():\n",
    "        for x, y in loader:\n",
    "            y_hat = model(x)\n",
    "            logits += [y_hat.cpu().numpy()]\n",
    "            labels += [y.cpu().numpy()]\n",
    "    logits = np.concatenate(logits, 0)\n",
    "    preds = np.argmax(logits, 1)\n",
    "    labels = np.concatenate(labels, 0)\n",
    "    accuracy = (preds == labels.argmax(axis=1)).mean()\n",
    "    print(f'Accuracy: {accuracy:.3f}')\n",
    "    \n",
    "print('Train set evaluation')\n",
    "eval_model(model, train_loader)\n",
    "print('Test set evaluation')\n",
    "eval_model(model, test_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b937b6",
   "metadata": {},
   "source": [
    "## Find similar instances"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f1d727b",
   "metadata": {},
   "source": [
    "Selecting a reference set of 1000 random samples from the training set. The `GradientSimilarity` explainer will find the most similar instances among those. This downsampling step is performed in order to speed up the `fit` step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "901d4b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "idxs_ref = np.random.choice(len(X_train), 1000, replace=False)\n",
    "X_ref = X_train[idxs_ref]\n",
    "embeddings_ref = embeddings_train[idxs_ref]\n",
    "y_ref = y_train[idxs_ref]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2221b1",
   "metadata": {},
   "source": [
    "Initializing a `GradientSimilarity` explainer instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "32ed6fb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "gsm = GradientSimilarity(model, \n",
    "                         loss_fn, \n",
    "                         precompute_grads=True, \n",
    "                         sim_fn='grad_cos', \n",
    "                         backend='pytorch', \n",
    "                         device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8477b40a",
   "metadata": {},
   "source": [
    "Fitting the explainer on the reference data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "199d13db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientSimilarity(meta={\n",
       "  'name': 'GradientSimilarity',\n",
       "  'type': ['whitebox'],\n",
       "  'explanations': ['local'],\n",
       "  'params': {\n",
       "              'sim_fn_name': 'grad_cos',\n",
       "              'store_grads': True,\n",
       "              'backend_name': 'pytorch',\n",
       "              'task_name': 'classification'}\n",
       "            ,\n",
       "  'version': '0.6.6dev'}\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsm.fit(embeddings_ref, y_ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "809d356c",
   "metadata": {},
   "source": [
    "Selecting 3 random instances from the test set. We only select documents with less than 1000 characters for visualization purposes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "81e73d21",
   "metadata": {},
   "outputs": [],
   "source": [
    "idxs_samples = np.where(np.array([len(x) for x in X_test]) <= 1000)[0]\n",
    "idxs_samples = np.random.choice(idxs_samples, 3, replace=False)\n",
    "\n",
    "X_sample, embeddings_sample, y_sample = X_test[idxs_samples], embeddings_test[idxs_samples], y_test[idxs_samples]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a53067",
   "metadata": {},
   "source": [
    "Getting predictions and explanations for each of the 5 test samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fb65a810",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model(torch.Tensor(embeddings_sample).to(device)).detach().cpu().numpy().argmax(axis=1)\n",
    "expls = gsm.explain(embeddings_sample, y_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56f940ef",
   "metadata": {},
   "source": [
    "## Visualizations "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aca9cc0",
   "metadata": {},
   "source": [
    "Building a dictionary for each sample for visualization purposes. \n",
    "Each dictionary contains:\n",
    "\n",
    "* The original text document `x` (not the embedding representation).\n",
    "* The corresponding label `y`.\n",
    "* The corresponding model's prediction `pred`.\n",
    "* The reference instances ordered by similarity `X_sim`.\n",
    "* The corresponding reference labels ordered by similarity `y_sim`.\n",
    "* The corresponding model's predictions for the reference set `preds_sim`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9636082a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = []\n",
    "for j in range(len(embeddings_sample)):\n",
    "    y_sim = y_ref[expls.data['ordered_indices'][j]].argmax(axis=1)\n",
    "    X_sim = X_ref[expls.data['ordered_indices'][j]]\n",
    "    sim_embedding = embeddings_ref[expls.data['ordered_indices'][j]]\n",
    "    preds_sim = model(torch.Tensor(sim_embedding).to(device)).detach().cpu().numpy().argmax(axis=1)\n",
    "\n",
    "    d = {'x': X_sample[j], \n",
    "         'y': y_sample[j].argmax(), \n",
    "         'pred':preds[j],\n",
    "         'X_sim': X_sim,\n",
    "         'y_sim': y_sim, \n",
    "         'preds_sim': preds_sim}\n",
    "    ds.append(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e9dc99",
   "metadata": {},
   "source": [
    "### Most similar instances"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177d2506",
   "metadata": {},
   "source": [
    "Showing the 3 most similar instances for each of the test instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4c8c368e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mSample nb 0\u001b[0m\n",
      "\u001b[34m===========\u001b[0m\n",
      "\n",
      "\u001b[31mOriginal instance - \u001b[0m \u001b[31mLabel: misc.forsale - \u001b[0m \u001b[31mPrediction: misc.forsale\u001b[0m\n",
      "One pair of kg1's in Oak finish with black grilles. Includes original packaging. $200 + shipping Firm.\n",
      "\n",
      "\u001b[31m1st most similar instance - \u001b[0m \u001b[31mLabel: misc.forsale - \u001b[0m \u001b[31mPrediction: misc.forsale\u001b[0m\n",
      "Kirsch Pull down Window Shades - White, Light Filtering - 73.25\" Wide, 72\" High, can be cut to\n",
      "width - Brand new, unopened - \"Best Quality\", Vinyl Coated Cotton - Mounting Brackets included - $35 (Bought\n",
      "at $60 at J.C.Penney)\n",
      "\n",
      "\u001b[31m2nd most similar instance - \u001b[0m \u001b[31mLabel: misc.forsale - \u001b[0m \u001b[31mPrediction: misc.forsale\u001b[0m\n",
      "Hey, collection. I am interested in buying any in good condition. I am particularly interested in any of\n",
      "the older, exotic models (eg five ] transformers into one etc... I am looking at paying around $20-$40\n",
      "depending upon the model, size and original cost etc. I will also pay airmail postage and packing. I\n",
      "am also happy to buy any old sci-fi related toys eg robots, rocketships, micronauts etc... There is only\n",
      "one catch. I live in New Zealand so you have to be willing to post the items there.\n",
      "I hop that someone can help me out. Cheers\n",
      "\n",
      "\u001b[31m3rd most similar instance - \u001b[0m \u001b[31mLabel: misc.forsale - \u001b[0m \u001b[31mPrediction: misc.forsale\u001b[0m\n",
      "HP 9872B 4 pen plotter. $150 Fujistu M2451E 130 meg SCSI tape drive $150 Sony 40 meg SCSI\n",
      "disk drive (sticks once in a while) $50 Dead Maxtor XT4380E 338 meg ESDI drive $100 Dead Miniscribe\n",
      "20 meg SCSI drive $10 Adaptac SCSI to ST-412 interface board $20 Daughter boards from tape drives ?QIC-02\n",
      "- QIC-36? $20 Twist Terms (VT100 terms that the head twists on for 80x25 or 80x72) $150 14\"\n",
      "Analog RGB color monitor (15.7 Khz works nice with amiga's) $100 Spool with 90+ feet of 50 conductor\n",
      "ribbon cable $75 All prices are or best offer. Prices do not include UPS shipping. All items working\n",
      "except those stated as Dead.\n",
      "\n",
      "\u001b[34mSample nb 1\u001b[0m\n",
      "\u001b[34m===========\u001b[0m\n",
      "\n",
      "\u001b[31mOriginal instance - \u001b[0m \u001b[31mLabel: sci.crypt - \u001b[0m \u001b[31mPrediction: sci.crypt\u001b[0m\n",
      "I am looking for some Public Domain (and exportable) code for encryption. Nothing elaborate, just something that will\n",
      "satisfy a marketing need :-) Oh yes, UNIX platform.\n",
      "\n",
      "\u001b[31m1st most similar instance - \u001b[0m \u001b[31mLabel: sci.crypt - \u001b[0m \u001b[31mPrediction: sci.crypt\u001b[0m\n",
      "Hmmmm. I think, with really large keyspaces like this, you need to alter the strategy discussed for DES.\n",
      "Attempt decryption of several blocks, and check the disctribution of the contents. I don't think it's at all\n",
      "feasible to keep 2**80 encryptions of a known plaintext block on *any* amount of tape or CD-ROM. And\n",
      "certainly not 2**128 such encrypted blocks. (Anyone know a cheap way of converting every atom in the solar\n",
      "system into a one bit storage device?) Actually, a keysearch of this kind shouldn't be much worse than\n",
      "the simpler kind in terms of speed. It's just that you have to do it over for *every*\n",
      "encrypted message. Dumb question: Has anyone ever done any serious research on how many legitimate ASCII-encoded 8-byte blocks\n",
      "there are that could be part of an english sentence? For attacking DES in ECB mode, it seems\n",
      "like a dictionary of this kind might be pretty valuable....\n",
      "\n",
      "\u001b[31m2nd most similar instance - \u001b[0m \u001b[31mLabel: sci.crypt - \u001b[0m \u001b[31mPrediction: sci.crypt\u001b[0m\n",
      "As am I If \"high quality secure NSA classified technology\" means handing my key over to whomever, I'll\n",
      "take PGP any day. Right now they are billing it as voluntary, i.e. bend over, here it comes.\n",
      "As soon as enough Wiretap chip based units are out there, how much easier do you think it\n",
      "will be to redefine \"on your own\" to mean write it yourself and don't even THINK about distributing\n",
      "it...? Get honest, no one is going to buy this trash if they KNOW it's compromised already, and\n",
      "less will buy it if the algorithm is not disclosed. The NSA knows that making this stuff available\n",
      "to the public means handing it to whatever foreign powers are interested in the process. Since when has\n",
      "export control stopped anyone (especially software wise) Ask yourself carefully if \" high quality secure NSA classified technology\n",
      "\" is something they are going to hand out. Not unless you can drive a NSA van through\n",
      "the holes. uni (Dark)\n",
      "\n",
      "\u001b[31m3rd most similar instance - \u001b[0m \u001b[31mLabel: sci.crypt - \u001b[0m \u001b[31mPrediction: sci.crypt\u001b[0m\n",
      "You're reading far too much into this (aside from the obvious fact that you shouldn't hold anybody to\n",
      "what they wrote in a 10 year old book in a rapidly changing field like this.) Quite simply\n",
      "she says that the security should not DEPEND on the secrecy of the algorithm. A secret algorithm can\n",
      "still be secure, after all, we just don't know it. Only our level of trust is affected, not\n",
      "the security of the system. The algorithm *could* be RSA for all we know, which we believe to\n",
      "be secure. They have a much better reason to classify the algorithm than to protect its security. They\n",
      "want to protect its market share. If they publish the algorithm, then shortly manufacturers would make chips that\n",
      "implement the algorithm and standard but do not use a key stored in escrow. And of course, everybody\n",
      "would buy them. The whole push of this chip is that by establishing a standard that you can\n",
      "only use if you follow their rules, they get us to follow their rules without enacting new laws\n",
      "that we would fight tooth and nail. Quite simply, with Clipper established, it would be much harder for\n",
      "another encryption maker to define a new standard, to make phones that can't talk to the leading phone\n",
      "companies. The result is tappable cryptography without laws forbidding other kinds, for 99% of the populace. To get\n",
      "untappable crypto, you would have to build a special phone that runs on top of this system, and\n",
      "everybody you talk to would have to have an indentical one. That's the chicken and egg of crypto.\n",
      "The government is using its very special ability to solve chicken and egg problems of new technologies to\n",
      "control this one in a way they like. It's almost admirably clever. When the EFF started, I posed\n",
      "the question here \"What are the police going to do when they wake up and discover they can't\n",
      "wiretap?\" and nobody here had an answer (or even thought it was much of a question) Then came\n",
      "the backdoor and Digital Telephony bills, which we fought. Now we have their real answer, the cleverest of\n",
      "all.\n",
      "\n",
      "\u001b[34mSample nb 2\u001b[0m\n",
      "\u001b[34m===========\u001b[0m\n",
      "\n",
      "\u001b[31mOriginal instance - \u001b[0m \u001b[31mLabel: comp.graphics - \u001b[0m \u001b[31mPrediction: comp.graphics\u001b[0m\n",
      "Sorry if this is a FAQ but : \"Where can I get a 286 (16 bit) version of\n",
      "POV-Ray ? \" Any help would be greatly appreciated. I need the 286 version since Turbo Pascal won't\n",
      "let me run a 32 bit program from within my program. Any info on this would also be\n",
      "a great help. Thanks, Byron. bkidd@esk.compserv.utas.edu.au B.Kidd@cam.compserv.utas.edu.au --\n",
      "\n",
      "\u001b[31m1st most similar instance - \u001b[0m \u001b[31mLabel: comp.graphics - \u001b[0m \u001b[31mPrediction: comp.graphics\u001b[0m\n",
      "FOR IMMEDIATE RELEASE Editorial Contact: Single Source Marketing: Myra Manahan (714) 545-1338 Genoa Systems: Joseph Brunoli (408) 432-9090\n",
      "Neil Roehm (408) 432-9090/Technical Genoa Presents High Performance Video Graphics Accelerator SAN JOSE, Calif USA -- Genoa Systems\n",
      "Corporation announces WINDOWSVGA 24, a True Color 24-bit graphics accelerator card that delivers up to 16.8 million colors\n",
      "at speeds faster than the competition. Plus it offers a full range of resolutions, high refresh rates as\n",
      "well as unique proprietary performance features. The card is available in both 16-bit ISA bus and 32-bit VESA\n",
      "Local bus versions (models 8500 AND 8500VL). With 1MB DRAM on board, the WINDOWSVGA 24 card offers maximum\n",
      "resolution up to 1,280 x 1,024 and supports a refresh rate of 72Hz at 800 x 600 and\n",
      "resolution up to 1,024 x 768 non-interlaced. Both models provide performance many times greater than standard SVGA boards,\n",
      "yet conform to all current video standards. WINDOWSVGA 24 features Genoa's FlickerFree(tm) technology, which eliminates screen flash and\n",
      "flicker to make viewing much more comfortable. the cards also come with Safescan(tm), a utility developed by Genoa\n",
      "to eliminate the black border around the screen and thereby provide 100-percent screen use for overscanning monitors. WINDOWSVGA\n",
      "model 8500VL takes full advantage of the speed offered by the new VESA Local bus technology. Most VL\n",
      "bus cards will only handle data transfers up to 33MHz, but the 8500VL will transfer data at the\n",
      "full speed of the CPU, up to 50MHz. Genoa is also offering this card in the \"TurboBahn\" combination\n",
      "packaged with their TURBOEXPRESS 486VL motherboard. Built around the Cirrus Logic GD-5426 GUI accelerator, WINDOWSVGA 24 offers the\n",
      "user an exceptional price/performance value. Genoa's advanced proprietary drivers act to \"turbocharge\" the chip, thereby providing an affordable\n",
      "accelerator card with power and performance that surpass many of the more highly priced chip cards. The Genoa\n",
      "user will enjoy optimal speed and reliability for such programs as Windows, AutoCAD, AutoShade, 3D Studio, OS/2, OrCAD\n",
      "and more. Driver updates and product bulletins are available on Genoa's BBS at (408) 943-1231. Genoa Systems manufactures\n",
      "and markets an extensive line of graphics adapters, motherboards, audio and multimedia cards for IBM-compatible personal computers. All\n",
      "products come with a two year limited warranty on parts and labor. Genoa products are currently distributed worldwide\n",
      "through authorized distributors, resellers, VARs and systems integrators. For more information contact Joe Brunoli, Marketing Manager, Genoa Systems\n",
      "at 75 E. Trimble Road, San Jose, Calif. 95131; Tel: (408) 432-9090 or (800) 934-3662; Fax: (408) 434-0997.\n",
      "\n",
      "\n",
      "\u001b[31m2nd most similar instance - \u001b[0m \u001b[31mLabel: comp.graphics - \u001b[0m \u001b[31mPrediction: comp.graphics\u001b[0m\n",
      "Well, the temp file thing creates an obvious problem: it is impossible to use cview for viewing CD-ROM\n",
      "based picture collections. And it is the ONLY non- windows viewer that works properly with my Cirrus-based 24\n",
      "bit VGA.\n",
      "\n",
      "\u001b[31m3rd most similar instance - \u001b[0m \u001b[31mLabel: comp.graphics - \u001b[0m \u001b[31mPrediction: comp.graphics\u001b[0m\n",
      "If you are looking for viewer try VPIC60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for sample_nb in range(3):\n",
    "    title = f\"Sample nb {sample_nb}\"\n",
    "    print(colored(title, 'blue'))\n",
    "    print(colored(f\"{len(title) * '='}\", 'blue'))\n",
    "    print('')\n",
    "    \n",
    "    print(colored(\"Original instance - \", 'red'), \n",
    "          colored(f\"Label: {target_names[ds[sample_nb]['y']]} - \", 'red'), \n",
    "          colored(f\"Prediction: {target_names[ds[sample_nb]['pred']]}\", 'red'))\n",
    "    print(break_doc_in_lines(f\"{ds[sample_nb]['x']}\"))\n",
    "    print('')\n",
    "    \n",
    "    for i in range(3):\n",
    "        print(colored(f\"{i+1}{append_int(i+1)} most similar instance - \", 'red'), \n",
    "              colored(f\"Label: {target_names[ds[sample_nb]['y_sim'][i]]} - \", 'red'), \n",
    "              colored(f\"Prediction: {target_names[ds[sample_nb]['preds_sim'][i]]}\", 'red'))\n",
    "        print(break_doc_in_lines(f\"{ds[sample_nb]['X_sim'][i]}\"))\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f4cc21",
   "metadata": {},
   "source": [
    "### Most similar labels distributions "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70521e8c",
   "metadata": {},
   "source": [
    "Showing the average similarity scores for each group of instances in the reference set belonging to the same true class and to the same predicted class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "829c9688",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_distributions(ds, expls, target_names, figsize=(20, 5)):\n",
    "\n",
    "    for i in range(len(ds)):\n",
    "        fig, axes = plt.subplots(1, 2, figsize=figsize, sharex=False)\n",
    "        d = ds[i]\n",
    "\n",
    "        y_sim = d['y_sim']\n",
    "        preds_sim = d['preds_sim']\n",
    "        y = d['y']\n",
    "        pred = d['pred']\n",
    "        df_ditribution = pd.DataFrame({'y_sim': y_sim, \n",
    "                                       'preds_sim': preds_sim, \n",
    "                                       'scores': expls.data['scores'][i]})\n",
    "        title = f\"Sample nb {i}\"\n",
    "        print(colored(title, 'blue'))\n",
    "        print(colored(f\"{len(title) * '='}\", 'blue'))\n",
    "        print('')\n",
    "\n",
    "        print(colored(\"Original instance\", 'red'))\n",
    "        print(colored(f\"Label: section {d['y']}, {target_names[d['y']]}\", 'red')) \n",
    "        print(colored(f\"Prediction: section {d['pred']}, {target_names[d['pred']]}\", 'red'))\n",
    "        print(break_doc_in_lines(f\"{d['x']}\"))\n",
    "        \n",
    "        df_y = df_ditribution.groupby('y_sim')['scores'].mean()\n",
    "        df_y.index = target_names\n",
    "        df_y.sort_values(ascending=True).plot(kind='barh', ax=axes[0])\n",
    "        axes[0].set_title(\"Averaged scores for each true class in reference set \\n\")\n",
    "        \n",
    "        df_preds = df_ditribution.groupby('preds_sim')['scores'].mean()\n",
    "        df_preds.index = target_names\n",
    "        df_preds.sort_values(ascending=True).plot(kind='barh', ax=axes[1])\n",
    "        axes[1].set_title(\"Averaged scores for each predicted class in reference set \\n\")\n",
    "        fig.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8dcd9629",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mSample nb 0\u001b[0m\n",
      "\u001b[34m===========\u001b[0m\n",
      "\n",
      "\u001b[31mOriginal instance\u001b[0m\n",
      "\u001b[31mLabel: section 6, misc.forsale\u001b[0m\n",
      "\u001b[31mPrediction: section 6, misc.forsale\u001b[0m\n",
      "One pair of kg1's in Oak finish with black grilles. Includes original packaging. $200 + shipping Firm.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mSample nb 1\u001b[0m\n",
      "\u001b[34m===========\u001b[0m\n",
      "\n",
      "\u001b[31mOriginal instance\u001b[0m\n",
      "\u001b[31mLabel: section 11, sci.crypt\u001b[0m\n",
      "\u001b[31mPrediction: section 11, sci.crypt\u001b[0m\n",
      "I am looking for some Public Domain (and exportable) code for encryption. Nothing elaborate, just something that will\n",
      "satisfy a marketing need :-) Oh yes, UNIX platform.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAAFgCAYAAAA2IxyjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACE9klEQVR4nOzdebxVZd3//9dbtFTEmfypiafUHBAhPVAOGBLaQKmVSmoalhpWava18jZz6NbCrMyhNEecMzXNxBxyAMQBDjIccOoOMcsJFVGchc/vj+vastzsfSbOYZ/h/Xw89uOsfa1rWmvtA599nWtdSxGBmZmZmZmZmZmZmVlrrVTrDpiZmZmZmZmZmZlZ1+QBZjMzMzMzMzMzMzNrEw8wm5mZmZmZmZmZmVmbeIDZzMzMzMzMzMzMzNrEA8xmZmZmZmZmZmZm1iYeYDYzMzMzMzMzMzOzNvEAs5lZgaQ6SSFp5Vr3ZXlJOlXSi5Keq3VfqpE0TtKpte5HW+TPyeYrqK3jJV20gtqSpEslLZA0ZUW0aWZmZh3Pce6K1Zni3PJrL+nvkr65Ato9WdKVbSh3r6RDO6JPFdoaKunxFdFWbu8ISc9LWiRpvRXVrllH8wCzmS23HAAskPThWvfFEkn9gP8HbBMR/1+t+9MeutOXotaKiF9ExAoJsoFdgN2Bj0bEkBXUZqfQmb4ImplZ5+A4t/PpjnHuihYRX4iIy5rLJ2mepBErok+1EhGTImLLFdGWpFWA3wJ7RMQaEfHSimi3M5A0WtJ9te6HdRwPMJvZcpFUBwwFAtizA+rvMYOJeeZoe/273A94KSJeaEM/uuw578p9X1Ek9Womy6bAvIh4vQ11+/ybmVm34Ti3/TjObV/d5Ti6mxZclw2AVYE5bay/uTjerGY8wGxmy+tg4EFgHPBNAEkflvSKpG1LmST1lfSmpI/k91+SNCPnu1/SdoW88yT9RNIs4HVJK0s6TtK/JL0m6RFJXynk7yXpN/k2uSclfb/sFrC1JF0s6VlJ/8231PUqlP11LjsXGNnUweZ+/Tf343FJny3Uc3yhj9MkbZL37SRpqqSF+edOhfrulXSapMnAG8DHJW0l6U5JL+c29ivk/2I+/tdyP46t0McRwJ3ARvnWq3E5fU9Jc/I5v1fS1k2d8wr1NtWvkZKmS3pV0tOSTi4ru0u+zq/k/aMLu9eRND4f00OSNqty+ifmn6/k49ox/yV8sqQzJb0EnKyyW/G07C2BVT8PFY656nUty1f1+CWtKulKSS/l458qaYO8b7SkubnuJyUdWKUf7x9T4Xi+Kenf+bP70yrnrDQr9zxJt0p6HdhN0kaSbpA0P7d7VM77beAiYMd8jk/J6a39ff104XrPlDSskP9eSf+br9trku6QtH5hf8XPitK/K7/Ox/y8pPMlrVblmDeXNEHpd+5FSdcW9lX8HEs6HDgQ+HE+9r9VO6dmZtZjOM6l6XhIjnPbJc7V0vjucEnP5Ot5bGH/yZKuV4opXwVGL8+1V9kSFJIOk/Ro4TO4vaQrSIP5f8vn+sc5b1Nx3seUYrDXJN0JrE8TJO2l9Lvyav58fb5Cns0k3a0US78o6SpJaxf2V/vcDpHUkOt+XtJvq/RhmKT/FN7Pk3SspFn5c32tpFWrlK30XaRizCrpE0BpKY5XJN2d62jqs9fiOD7nP1nSnyVdns/HHEn1hf2bSPpLLvuSpHML+76VPwMLJN0uadMqx9zUd5uKn0ml38fzWfod45VKdVsXFxF++eWXX21+Af8HfBfYAXgX2CCnXwKcVsj3PeC2vP1J4AXgU0AvUsA+D/hw3j8PmAFsAqyW0/YFNiL9YWwU8DqwYd43BngE+CiwDvAP0kyTlfP+G4E/Ar2BjwBTgO8Uyj6W21oXuKdYtuxYtwSeBjbK7+uAzfL2j4DGnEfAQGC9XOcC4CBgZWD//H69XO5e4N9A/7x/rdzGIfn9J4EXSbcAAjwLDM3b6wDbV7kuw4D/FN5/Ip+z3YFVgB/na/ehaue8rL7ezfRrGDAgX5/tgOeBvfO+TYHX8rGvks/LoLxvHPASMCTXexXwpyrHVFd+bYDRwHvAkbn8asDJwJXVyjX1eajQZsXrmvcFsHkLjv87wN+A1Umf9x2ANXP7rwJb5nwbAv2r9OP9Yyocz4X5eAcCbwNbVyk7DlgI7Jz7tzowDTgR+BDwcWAu8LnCOb2vUL5Vv6/AxvmafjG3t3t+37fwmf8X6TO5Wn4/tgWflTOBm0m/U33yOf1llWO+Bvhpbn9VYJcWfo7HAafW+t9Vv/zyyy+/OscLx7mOc5e2t6Li3GtyfwYA84ERef/JpM/g3rkfqy3Ptc/X5tDC5++/wOB8fTcHNi2cuxGFfjYX5z1AWgLiw8Cu+dxcWeWYh5Bi1N1zXRsDW1Xo3+Y5z4eBvqRJJ79rwef2AeCgvL0G8OkWfp7m5XO5UT53jwJjqpQdzbLfRarGrCz7vaQlsWlr4viTgbfy9ekF/BJ4MO/rBczM/evNB2PkvUi/M1vnfpwA3F/lmCt+t2nBv0ejKXzH8Kv7vWreAb/88qvrvkhrtb4LrJ/fPwYck7dHAP8q5J0MHJy3zwP+t6yux4HP5O15wLeaaXsGsFfevpvCAGFuO/J/jhuQBt9WK+zfH7inUHZMYd8eVA+8Nyd9YRgBrFKh/3tVKHMQMKUs7QFgdN6+F/h5Yd8oYFJZ/j8CJ+Xtf+f/1Nds5vwM44OB0s+APxfer0QKJIe15Jw3168K+X8HnJm3/we4sUq+ccBFhfdfBB6rkreu/NrkQOXfZflOpsoAc3OfhwptVryueV+QB5ibOf5vAfcD25Xl6Q28AnyNCl92qh1T4Xg+Wtg/Bfh6E+f48sL7T1U4Z/8DXFo4p8UB5lb9vgI/Aa4oy3878M3CZ/6Ewr7vsvRLecXPCunLzuvkLww5bUfgySrHfDlwQfEcteRzjAeY/fLLL7/8yi8c55b3f68KZRzntn+cu1Uh7VfAxXn7ZGBiYd9yXXs+OIB7O3B0lX7N44MDzFXjPNJs5/eA3oV9V1N9gPmPpfNYYd/7/auwb29gegs+txOBU8i/w634PM0DvlF2Hc6vUnY0hbiaZmJWlh1gbkls2po4/mTgH4V92wBvFvoxn8q//38Hvl32O/QG+Q8NZXmrfbdp7jM5Gg8wd+uXl8gws+XxTeCOiHgxv786p0H6K/nqkj6ltH7dINJfNCH9pf//5VtqXsm3yGxC+itxydPFhiQdrKW3Gr4CbMvSW642Kstf3N6UNJvg2ULZP5L+olqp7FPVDjYi/g/4Aek/7hck/UlSqc+bkGZlltuoQp1Pkf5CX62/nyo7NwcCpQeYfI0UnD6Vbz/bsVp/m+pHRCzJ7VbrR7km+5Wv8z35dquFpFkTpetT7dyUFJ/+/QZphkFrNNXvcs19Hso113eg2eO/ghR4/0nplsdfSVol0hrHo3LeZ5Vun9yqFcfSmvNW/hnbqOxaHk8KCitp7e/rpsC+Zfl3Ic3Qbq7v1c53X/KMjUKdt+X0Sn5MCvCn5FsDv1XoW1O/X2ZmZiWOcx3n1iLOLb9eTcVb7XLtaWG8W2i3Wpy3EbAgPvgcj+VuV9IG+fP4X6XlQa4kn/9mPrffJs1ufywv4/ClFh4jtD3Obm3M2pLYtLVxfHnfV1VaFmYT4KmIeK9KP84q1PkyKZbeuELeit9taP33LOtmvDC8mbWJ0tqn+wG9JJX+E/swsLakgRExU9KfSX+1fB64JSJey/meJt1WeFoTTUShrU1JywF8FnggIhZLmkH6Tw/S7XQfLZQtrpH7NOkvqetX+c/02bL8/ZroExFxNXC1pDVJ/2GeTpq98TSwGTC7rMgzpP9si/qRAo33qy3r74SI2L1K+1OBvfJ/4t8H/lzW/2qeId1qB6QHreRy/63Sj3JN9ov0petc4AsR8Zak37E08H6adAvc8qrWv/L010mBXUl5gNbU56FctetarurxR8S7pNkTp+QvobeSZgJdHBG3A7fn36dTSZ/zoS3oV2uVf8aejIgtWli2Vb+vOf8VEXFYK/tYKlvps/Ii8CZpCZH/Vtj/wc5EPAccBmldROAfkibS/Oe4qd8BMzPrIRznOs4tsyLi3JJNSLPlIZ3LZwr7ys9le1370vWtpPy8VY3z8md5HUm9C4PM/SrU0ZJ2i36R6xgQES9L2pt0PVIHq3xuI+KfwP5KD5f8KnC9pPWiDQ+ybkbx+FoVs9L8Z6+8/tbG8eVt9ZO0coXPTOnfrauaq6SJ7za30vRn0nF2N+cZzGbWVnsDi0m33QzKr62BSaQHokAKxkaR/gp7daHshcCYPBtAknorPTyjT5W2epP+Q5oPIOkQ0syOkj8DR0vaWOmBDz8p7YiIZ4E7gN9IWlPSSkoPivhMoexRkj4qaR3guGoHLGlLScMlfZi0ttWbwJK8+yLgfyVtkY9pO0nrkf6j/YSkA5Qe4jIqn7NbqjRzS85/kKRV8muwpK0lfUjSgZLWyv+xv1povzl/BkZK+mwO2v8fKQC4v4Xlq/Yr7+8DvJyD7iHAAYWyVwEjJO2Xz8F6kga1sN2i+aTj/Xgz+WYAu0rqJ2kt0m1jQIs+D+WqXddyVY9f0m6SBig9dOVV0u22S5RmY+wlqTfpWiyi5ddzeUwBXlN6IMpqSg/e2FbS4Cr5W/v7eiXwZUmfy3WvqvTwlI9WyV9U8bOSZyJdCJyppQ9Q2ljS5ypVImnfQnsLSP9+LKH5z/HzNP/5MjOz7m9vHOc6zl2xcW7JzyStLqk/aV3eaytlas9rT7q+x0raIV/fzbX0AW/lsVHVOC8ingIaSAOPH1L6I/+Xm2j3YuCQfN1Wyp/xSnfz9SHFyQslbUxaExxo+nMr6RuS+uY48pVcpENj7dbGrDT/2SvX2ji+vOyzwNj879KqknbO+84H/id/7koP69u3UiWq8t2mBZ/J54GPSvpQC/pqXZAHmM2srb5JWuvp3xHxXOlF+mvygUp/GX2INJt0I9K6TgBERANpduG5pMGf/yOtyVRRRDwC/Ia0ptvzpBkKkwtZLiT9ZzYLmE4Kdt8jfTGA9EXgQ6QHpCwArmfp7foXkm7xmQk8DPyliWP+MDCW9Jfp50i3+5QGL39LCuTuIP1HezFp/amXgC+RAt2XSLfuf6lwu2X5sb5GWiPt66QZC8+R/gr/4ZzlIGCe0u1hY0hfapoVEY8D3wDOyf3/MvDliHinheWb69d3gZ9Leo300Ik/F8r+m3S74/8j3W41g/RwmFaJiDeA04DJSrddfbpKvjtJwfgs0kMwyr/kNPV5KFfxulbIV/X4STOor8/lHwUmkG4tWwn4Iel8vgx8BjiiSj/aTUQsJn0mBwFPkj4PF5EevFMpf2t/X58mPSjkeNKX5adJXwSajTma+az8JLf9YP78/4P0YJdKBgMPSVpEesjK0RExtwWf44uBbfLn66bm+mtmZt2W41zHuSs0zi2YQPrM3AX8OiLuaCJvu1z7iLiOFGNfTXoo302kB9RBekjcCTk2OrYFcd4BpHWCXwZOIj0Xo1q7U0iD6GeSHmQ3gWVnxEOaLbt9zjO+7Fia+tx+HpiT48GzSM8rebNaf9pRi2PWFnz2yvO3Ko6vUPbLpHWr/w38h/RHMiLixtzun3KfZwNfqFJVte820PRn8m5gDvCcpIr/RljXpgjPUjez7kXSF0gPYqgUoJiZmZmZdUmOc7snpaUGniQ9qK4ly7iZmXUqnsFsZl1evj3oi/m2tI1Jfy2/sblyZmZmZmadmeNcMzPrCjzAbGbdgUi3Ti0g3Tr4KOn2NTMzMzOzrsxxrpmZdXpeIsPMzMzMzMzMzMzM2sQzmM3MzMzMzMzMzMysTTzAbGZmZmZmZmZmZmZt4gFmMzMzMzMzMzMzM2sTDzCbmZmZmZmZmZmZWZt4gNnMzMzMzMzMzMzM2sQDzGZmZmZmZmZmZmbWJh5gNjMzMzMzMzMzM7M28QCzmZmZmZmZmZmZmbWJB5jNzMzMzMzMzMzMrE08wGxmZmZmZmZmZmZmbeIBZjMzMzMzMzMzMzNrEw8wm5mZmZmZmZmZmVmbeIDZzMzMzMzMzMzMzNrEA8xmZmZmZmZmZmZm1iYeYDYzMzMzMzMzMzOzNvEAs5mZmZmZmZmZmZm1ycq17oB1P+uvv37U1dXVuhtmZmZmrTZt2rQXI6Jvrfth3ZPjZDMzM+vKqsXKHmC2dldXV0dDQ0Otu2FmZmbWapKeqnUfrPtynGxmZmZdWbVY2UtkmJmZmZmZmZmZmVmbeIDZzMzMzMzMzMzMzNrES2R0UZLqgYMj4qgV1N4w4J2IuH9FtGdmLVd33Phad8HMrMPNGzuy1l2wLsJxstmK5VjUzKz2ah0re4C5i4qIBqBNC7hJWjki3mtlsWHAIsCBs5mZmZl1Wo6TzczMzFYsL5HRyUjqLWm8pJmSZksaJWmwpPtz2hRJfSQNk3RLlTp+Iqkx5x+b0+6V9DtJDcBPJT0paZW8b83S+5zvLEkzcvtDJNUBY4BjcvrQFXU+zMzMzMzAcbKZmZlZZ+UZzJ3P54FnImIkgKS1gOnAqIiYKmlN4M1qhSV9AdgL+FREvCFp3cLuD0VEfc5XB4wEbgK+DvwlIt6VBLB6RAyStCtwSURsK+l8YFFE/Lqdj9fMzMzMrCUcJ5uZmZl1Qp7B3Pk0ArtLOj3PgOgHPBsRUwEi4tVmbtsbAVwaEW/k/C8X9l1b2L4IOCRvHwJcWth3TS47EVhT0trNdVrS4ZIaJDXMnz+/uexmZmZmZq3lONnMzMysE/IAcycTEU8A25MC6FOBr7Zj9a8X2pkM1OWHkvSKiNnFbpR3q7mKI+KCiKiPiPq+ffu2R1/NzMzMzN7nONnMzMysc/IAcycjaSPgjYi4EjgD+BSwoaTBeX8fSU0tbXIncIik1XP+dZvIezlwNR+clQEwKpfdBVgYEQuB14A+bTgkMzMzM7Pl5jjZzMzMrHPyGsydzwDgDElLgHeBIwAB50hajbSu3IhiAUn1wJiIODQibpM0CGiQ9A5wK3B8lbauIs3+uKYs/S1J04FVgG/ltL8B10vaCzgyIiYt53GaWTuZN3ZkrbtgZma2IjhONuuEHIuamZkimr2ry7opSfsAe0XEQYW0e4FjI6KhrfXW19dHQ0Obi5uZmZnVjKRppYe9Wc/lONnMzMxsWdViZc9g7qEknQN8AfhirftiZmZmZtZZOE42MzMzax0PMPdQEXFklfRhK7grZmZmZmadhuNkMzMzs9bxQ/7MzMzMzMzMzMzMrE08wGxmZmZmZmZmZmZmbeIBZjMzMzMzMzMzMzNrEw8wm5mZmZmZmZmZmVmb+CF/NSJpNFAfEd9vp/qGAcdGxJfaoz4z6zrqjhvf4W3MGzuyw9swMzMrcaxs1nWsiFi0pRyzmpnVhmcwd1GSenVw/f7jg5mZmZl1SY6VzczMzFacDhlglnSwpFmSZkq6QlKdpLtz2l2S+uV84ySdJ+lBSXMlDZN0iaRHJY0r1LdI0pmS5uTyfSu0+RlJM/JruqQ+ki6XtHchz1WS9pLUX9KUnHeWpC0q1DdP0i9zngZJ20u6XdK/JI3JedbI/XlYUqOkvaqdgyqnaiNJt0n6p6RfFcqel9ucI+mUsj6dLulhYF9Jn5f0WH7/1UK+RklrK3lJ0sE5/XJJu+frMSn3+2FJO+X9w3L6zcAjknpJOkPS1Hws32nu2puZmZlZ0xwrO1Y2MzMz607afYBZUn/gBGB4RAwEjgbOAS6LiO2Aq4CzC0XWAXYEjgFuBs4E+gMDJA3KeXoDDRHRH5gAnFSh6WOB70XEIGAo8CZwMTA692stYCdgPDAGOCvnrQf+U+Vw/p3zTALGAfsAnwZKgexbwFciYntgN+A3OVCtdA4qGQSMAgYAoyRtktN/GhH1wHbAZyRtVyjzUm7vJuBC4MvADsD/V8gzGdiZdB7n5vMB6TzfD7wA7J7rGcUHr8f2wNER8Qng28DCiBgMDAYOk/SxSgci6fAc6DfMnz+/yuGamZmZ9WyOlXterOw42czMzLq7jpjBPBy4LiJeBIiIl0nB2tV5/xXALoX8f4uIABqB5yOiMSKWAHOAupxnCXBt3r6yrHzJZOC3ko4C1o6I9yJiArBFnsWxP3BDRLwHPAAcL+knwKYR8WaVY7k5/2wEHoqI1yJiPvC2pLUBAb+QNAv4B7AxsEGVc1DJXRGxMCLeAh4BNs3p++WZFtNJge82hTKl87AV8GRE/DOfvysLeSYBu+bXeaQvIBsDCyLidWAV4EJJjcB1ZfVPiYgn8/YewMGSZgAPAesBy8xgycd4QUTUR0R9377LTJoxMzMzs8Sxcg+LlR0nm5mZWXfXGdZgfjv/XFLYLr2vtrZZLJMQMRY4FFgNmCxpq7zrcuAbwCHAJTnv1cCepJkbt0oa3sa+HQj0BXbIszeeB1atUldT9QMsBlbOsx6OBT6bZ7GML6vz9RbUO5E0E2MocC8wnzSjZFLef0zu60DSrJQPValfwJERMSi/PhYRd7Tw2MzMzMxs+TlWThwrm5mZmXVSHTHAfDdpzbP1ACStS7rV7Ot5/4EsDd5aaiVS0AdwAHBfeQZJm+UZHacDU0mzFiDdrvcDgIh4JOf9ODA3Is4G/kq6va4t1gJeiIh3Je3G0lkVlc5BS61JClwXStoA+EKVfI8BdZI2y+/3L+2IiKeB9YEtImIu6XwdSwqmS/1+Ns9+OQio9hCU24EjJK2Sj+MTknq34ljMzMzM7IMcKztWNjMzM+tW2v3pxxExR9JpwARJi0m3rh0JXCrpR6QZAoe0strXgSGSTiCtiTYKQPkBIhFxPvCDHLiWbhn8e973vKRHSeuwlewHHCTpXeA54Be5vluBQyPimRb26yrgb/n2uQZSIFvtHIyWtCdQHxEnVqswImZKmp7repp0O2OlfG9JOhwYL+kN0heRPoUsD7E0GJ4E/JKlXzb+ANyQH2hyG9VnelxEuvXyYUkiXbu9q/XdzGpj3tiRte6CmZm1kGNlx8pm3Y1jUTMzU1qSrHOTtCgi1mhj2dVJ68JtHxEL27dnVkl9fX00NDTUuhtmZmZmrSZpWn6AXJfhWLnrcJxsZmZmXVm1WLkzrMHcYSSNAB4FznHAbGZmZma2lGNlMzMzM2sP7b5ERkdo64yMiPgHS9d6MzMzMzPrdhwrm5mZmVktdesZzGZmZmZmZmZmZmbWcTzAbGZmZmZmZmZmZmZt4gFmMzMzMzMzMzMzM2uTLrEGc3uQdBHw24h4ZHmetN3OfZoH1EfEi+1U373AsRHhR1Ob9SB1x43vkHrnjR3ZIfWamZmVk7QnsE1EjF2OOvoCtwAfAo6KiEnt1b9CG6NJ8fv327tus66qNbGo40szs+6pxwwwR8Shte5De5K0ckS819XbMDMzMzOLiJuBm5ezms8Cja2J+yX1iojFy9mumZmZWY/WoiUyJB0saZakmZKukFQn6e6cdpekfjnfOEnnSXpQ0lxJwyRdIulRSeMK9S2SdKakObl83wptfkbSjPyaLqmPpMsl7V3Ic5WkvST1lzQl550laYsK9d0rqb7wfpn2c54zJTXkPg+W9BdJ/5R0apVzM0/SryQ15j5sntM3kHRjPmczJe1U5fQeKenhXH6rXHaIpAfycd8vacucPlrSzZLuBu6StJqkP+W+3gislvPtK+m3eftoSXPz9sclTc7bJ0qaKmm2pAskqXAOfiepATha0g6SJkiaJul2SRtWOQ4zMzMzs2Xk7w6P5e8KT+QYfoSkyTnOHpLj3HNz/n1zjDpT0sSc1kvSr3P6LElHlrUxCPgVsFf+TrCapP1zjD1b0umFvIsk/UbSTGBHSWMlPZLr/XXO82VJD+V4/B+SNqhwXH0l3ZBj6qmSdu64s2hmZmbWeTU7wCypP3ACMDwiBgJHA+cAl0XEdsBVwNmFIusAOwLHkGYhnAn0BwbkwA+gN9AQEf2BCcBJFZo+FvheRAwChgJvAhcDo3O/1gJ2AsYDY4Czct564D/NHFZT7b8TEfXA+cBfge8B2wKjJa1Xpb6FETEAOBf4XU47G5iQz9n2wJwqZV+MiO2B8/IxAzwGDI2ITwInAr8o5N8e2CciPgMcAbwREVvnY9gh55lEOmfkny9J2jhvT8zp50bE4IjYljQw/aVCGx/K5+Bs0rXeJyJ2AC4BTqtyHGZmZmZm1WwO/AbYKr8OAHYhxb/Hl+U9EfhcjqP3zGmHA3XAoMJ3kPdFxIxc7tr8nWAd4HRgODAIGFyYqNIbeCjX/yjwFaB/rrc0qeQ+4NM5Hv8T8OMKx3QWcGZEDAa+BlzU0pNhZmZm1p20ZAbzcOC60jrBEfEyaQD56rz/ClJwWPK3iAigEXg+IhojYglpgLUu51kCXJu3rywrXzIZ+K2ko4C1I+K9iJgAbJFnHO8P3JCXcHgAOF7ST4BNI+LNZo6pqfZLt+Y1AnMi4tmIeBuYC2xSpb5rCj93zNvDSYPGRMTiiFhYpexf8s9pLD0/awHXSZrN0gH6kjvzNQDYNfefiJgFzMrbzwFrSOqT+3x1zjuUNPgMsFueldGY+1pso3RutiQNrt8paQbpDw0frXQQkg7PM78b5s+fX+VQzczMzKyHerLse8Fdhe8MdWV5JwPjJB0G9MppI4A/lpZvK8TD1QwG7o2I+bnMVaR4GGAxcEPeXgi8BVws6avAGzn9o8DtOVb+ER+MlUtGAOfmOPlmYE1JyzznxXGymZmZdXctWiKjld7OP5cUtkvvq635HMskpAd8HEqaXTu5tHwEcDnwDeAQ0oxaIuJq0uyGN4FbJQ1vZZ+L7S9v/5c5lmaU2lhcqP9/gXvy7OIvA6sW8r/ewnrvJ52jx1k6o3lH0rlcFfgDaWbyAODCKm2INMg+KL8GRMQelRqLiAsioj4i6vv2XWbFEzMzMzPr2crj6mLM/YEYOyLGkCY2bAJMa+IuwrZ6q7Tuch58HgJcT7qj77ac5xzSHX8DgO/wwVi5ZCXSLOdSrLxxRCwqz+Q42czMzLq7lgww3w3sWwrsJK1LGrz8et5/IEtnxbam3X3y9gGkW9A+QNJmeZbD6cBU0q10AOOAHwBExCM578eBuRFxNmlZi+2Wt/1WGlX4+UDevou0hEVpzbi1WlHfWsB/8/boJvJNJPUfSdvyweOeRLrlcCIwHdgNeDvPpC4FyC/mWRb7UNnjQF9JO+Y2VslLppiZmZmZdYj8PeChiDgRmE8aaL4T+I6klXOedZupZgrwGUnrS+pFuvtxQoW21gDWiohbSUv8Dcy7ivH4N6u0cQfw/lrQheUAzczMzHqUajNy3xcRcySdBkyQtJg0WHkkcKmkH5GCvkNa2e7rwBBJJwAvkAdoJY3JbZ4P/EDSbqRZDXOAv+d9z0t6FLipUN9+wEGS3gWeI69ZLOlW4NCIeKYl7bdUhXrXkTSLNBNj/5x2NHCBpG+TZicfATzQRJ+KfgVclvs3vol855Guw6Ok9eOmFfZNIgXjEyNisaSnSWs7ExGvSLoQmE06X1MrVR4R70jaBzg7D5CvTFpjutp60mZWA/PGjqx1F8zMzNrTGUoP7RZp0sZMUtz6CWBWjvkvJC1P8XPSs1VuLlYQEc9KOg64J9czPiL+WqGtPsBf8x1+An6Y008mLVm3gDTh5mMVyh4F/D5/D1iZNLFjTNsP26xrcixqZmZKS5+t4EalRRGxzPpkLSy7Ommttu2bWNd4hZE0D6gvrVFtUF9fHw0NDbXuhpmZmVmrSZqWH3Zs1u4cJ5uZmVlXVi1W7og1mDuMpBGkmbrndIbBZTMzMzMzMzMzM7OerNklMjpCW2cvR8Q/gE3buTvLJSLqat0HMzMzMzMzMzMzs1roUjOYzczMzMzMzMzMzKzz8ACzmZmZmZmZmZmZmbWJB5jNzMzMzMzMzMzMrE08wGxmZmZmZmZmZmZmbVKTh/z1VJL2BLaJiLHLUUdf4BbgQ8BRETGpvfpXaGM0UB8R32/vus2s/dUdN75D6p03dmSH1GtmZtbeJNUDB0fEUbXui1lP01ws6pjSzKz78wDzChQRNwM3L2c1nwUaI+LQlhaQ1CsiFi9nu2ZmZmZmnVJENAANte6HmZmZWU/kJTLaiaQ6SY9JGifpCUlXSRohabKkf0oaImm0pHNz/n0lzZY0U9LEnNZL0q9z+ixJR5a1MQj4FbCXpBmSVpO0v6TGXOb0Qt5Fkn4jaSawo6Sxkh7J9f465/mypIckTZf0D0kbVDiuvpJukDQ1v3buuLNoZmZmZraUpN6SxueYebakUZIGS7o/p02R1EfSMEm3VCi/oaSJOXaeLWloTl8k6UxJcyTdle8SRNJhOeadmWPg1XP6BpJuzOkzJe2U07+R+zBD0h8l9VqR58fMzMysM/AAc/vaHPgNsFV+HQDsAhwLHF+W90TgcxExENgzpx0O1AGDImI74KpigYiYkctdGxGDgHWA04HhwCBgsKS9c/bewEO5/keBrwD9c72n5jz3AZ+OiE8CfwJ+XOGYzgLOjIjBwNeAiyoduKTDJTVIapg/f37ls2NmZmZm1jqfB56JiIERsS1wG3AtcHSOc0cAbzZR/gDg9hw7DwRm5PTeQENE9AcmACfl9L9ExOBCDP3tnH42MCGnbw/MkbQ1MArYOde/GDiwvAOOk83MzKy78xIZ7evJiGgEkDQHuCsiQlIjaeC4aDIwTtKfgb/ktBHA+RHxHkBEvNxMe4OBeyNifm7zKmBX4CZSgHtDzrcQeAu4OM/sKM3u+ChwraQNSWs6P1mhjRHANpJK79eUtEZELCpmiogLgAsA6uvro5l+m5mZmZm1RCPwm3yn3i3AK8CzETEVICJeBSjEquWmApdIWgW4KU/YAFhCGqgGuJKl8fi2kk4F1gbWAG7P6cOBg3Obi4GFkg4CdgCm5vZXA14o74DjZDMzM+vuPIO5fb1d2F5SeL+EssH8iBgDnABsAkyTtF479+Wt0rrLecB6CHA98CXSzA+Ac4BzI2IA8B1g1Qr1rESa5TwovzYuH1w2MzMzM+sIEfEEacZwI+kuvK+2svxE0gSM/5ImdxxcLWv+OQ74fo6PT6FyfFwi4LJCnLxlRJzcmv6ZmZmZdQceYK4RSZtFxEMRcSIwnzTQfCfwHUkr5zzrNlPNFOAzktbP673tT7rFr7ytNYC1IuJW4BjS7YEAa5GCbYBvVmnjDuD9taDzOtBmZmZmZh1O0kbAGxFxJXAG8ClgQ0mD8/4+pdi5SvlNgecj4kLSUm/b510rAfvk7QNIS8cB9AGezTOei8td3AUckevsJWmtnLaPpI/k9HVze2ZmZmY9ipfIqJ0zJG1BmvlwFzATmA18Apgl6V3gQuBcST8nrRF3c7GCiHhW0nHAPbme8RHx1wpt9QH+KmnVnO+HOf1k4DpJC4C7gY9VKHsU8HtJs0ifl4nAmLYftpm1t3ljR9a6C2ZmZh1lACluXgK8SxrkFXCOpNVI6y+PKBaQVA+MiYhDgWHAj3JsvYi8zAXwOjBE0gmkZS1G5fSfAQ+RJoA8RIqjAY4GLpD0bdJSdEdExAO5/B2SVsr9+x7wVPueArPOzbGomZkpwsuAWfuqr6+PhoaGWnfDzMzMrNUkTYuI+lr3wzqWpEURscaKbtdxspmZmXVl1WJlL5FhZmZmZmZmZmZmZm3iAWYzMzMzM+tRajF72czMzKy78gCzmZmZmZmZmZmZmbWJB5jNzMzMzMzMzMzMrE08wGxmZmZmZmZmZmZmbbJyrTtgZmbLp+648c3mmTd25AroiZmZmZn1NM3Foo5Dzcy6P89gXk6S6iWd3coyJ0s6tqP61BqS9pR0XK37YWZmZma1IekiSdvk7UW17g+ApHmS1m/H+u6VVN9e9ZmZmZnZUp7BvJwiogFoqHU/2ioibgZurnU/zMzMzKw2IuLQWvehPUlaOSLe6+ptmJmZmXUVLZ7BLOlgSbMkzZR0haQ6SXfntLsk9cv5xkk6T9KDkuZKGibpEkmPShpXqG+RpDMlzcnl+1Zoc11JN+U2HpS0XU7/jKQZ+TVdUp8KZRdJOiPX/w9JQ/LMhbmS9sx5+kuakuuZJWmLCvU0SlpbyUuSDs7pl0vaPR/fLTnt5HyspXaOKtTzU0lPSLoP2LKQPigf2yxJN0paR9JHJE3L+wdKisL5/Zek1SXtK2l2vh4TK/S7TtJj+Xo8IekqSSMkTZb0T0lDcr7Rks7N28vUKamXpF/n9FmSjmz+02JmZmbWM9QoRl4mFs6x6d6FPFdJ2quF8e4HZvdWaj/nOVNSQ+7zYEl/yXHlqVXOzTxJv8rx9BRJm+f0DXLcOzO/dqpyeo+U9HAuv1UuO0TSA/m475e0ZU4fLelmSXcDd0laTdKfcl9vBFbL+faV9Nu8fbSkuXn745Im5+0TJU3N8e8FklQ4B7+T1AAcLWkHSRMkTZN0u6QNqxyHmZmZWbfWogFmSf2BE4DhETEQOBo4B7gsIrYDrgKKy0SsA+wIHEOaHXsm0B8YIGlQztMbaIiI/sAE4KQKTZ8CTM9tHA9cntOPBb4XEYOAocCbFcr2Bu7O9b8GnArsDnwF+HnOMwY4K9dTD/ynQj2TgZ1z/+fm9sjHd3+F/FsBnwOGACdJWkXSDsDXgUHAF4HBhfyXAz/Jx9gInBQRLwCrSlozt9cADJW0KfBCRLwBnAh8Ll+PPSv0A2Bz4De5T1sBBwC7kM7f8RXyV6rzcKAOGFS41suQdHj+wtEwf/78Kt0xMzMz6z5qGCNXioUvBkbnfq0F7ASMp2XxblFT7b8TEfXA+cBfge8B2wKjJa1Xpb6FETEAOBf4XU47G5iQz9n2wJwqZV+MiO2B8/IxAzwGDI2IT5Ji118U8m8P7BMRnwGOAN6IiK3zMeyQ80xiaTw/FHhJ0sZ5uzRp49yIGBwR25IGpr9UaOND+RycTbrW+0TEDsAlwGmVDsJxspmZmXV3LZ3BPBy4LiJeBIiIl0nB8dV5/xWkgcuSv0VEkAZMn4+IxohYQgoe63KeJcC1efvKsvIlu+S6iYi7gfXyoOtk4LdKM4TXrnJ72jvAbXm7kRTEvpu3S314ADhe0k+ATSOi0kD1JGDX/DqP9AVgY2BBRLxeIf/4iHg7n6sXgA1IAeuNEfFGRLxKXpIiB/9rR8SEXPay3A6kweud8/tf5J9Dc3/I52CcpMOAXhX6AfBk2bm/q3Bd6irkr1TnCOCPpXOcr/0yIuKCiKiPiPq+fZeZaGNmZmbWHdUqRl4mFs7x5BZ5xvH+wA05fmtJvFvUVPulZdUagTkR8WxEvE2ahLFJlfquKfzcMW8PJ8XVRMTiiFhYpexf8s9pLD0/awHXSZrN0gH6kjsLsequuf9ExCxgVt5+DlhD6Q7ITUjXqjzO3k3SQ5Iac1+LbZTOzZakwfU7Jc0g/aHho5UOwnGymZmZdXcd9ZC/t/PPJYXt0vtq6z5HSyuPiLHAoaQZBZNLt8yVeTcH8B/oRw7iV87bV5Nm6r4J3CppuKTvFW453Ig0k2Foft0LzAf2YWkAWq54vItp+zrXpXY3Jc0QGUgK8Cflvo8hBbKbANOqzBopP/fF67JMv1pYp5mZmZm1TbvEyE3EwpcD3wAOIc2orRjvtrLPxfaXt/8tjvfL2ivG1P8L3JNnF38ZWLWQv9Lkj0ruJ52jx1k6o3lH0rlcFfgDaWbyAODCKm2INMg+KL8GRMQerTo6MzMzs26ipYOfdwM3SvptRLwkaV1SYPZ10syMA6k+4FrNSqSB2j+Rlm64r0KeSbnu/5U0jHSb3KuSNouIRqBR0mDS8g+PtbJ9JH0cmBsRZyutj7ddRPwO+H1ZvvVJt8PNVVpD+Vjg+61oaiJpZvAvSef8y6RZwQslLZA0NCImAQeRbkUsHftpwMSIWCLpZdLyGv+T+7RZRDwEPCTpC6RB4Zdaew7KjrNSnXcC35F0T0S8J2ndarOYzaw25o0dWesumJn1VDWJkZuIhccBU4DnIuKRnHeZeDf3u83tt9IoYGz++UBOu4u0hMXvJPUC1mhiFnO5tYD/5u3RTeSbSOr/3ZK2JR13ySTSknk/B6YDuwFv5th87ZznRUlrkM7F9RXqfxzoK2nHiHhA0irAJyKi2nIfZt2WY1EzM2vRDOYcKJ0GTJA0E/gtcCRwiKRZpIHRo1vZ9uvAkHx723DyusiSxkgak/OcDOyQ2xgLfDOn/yA/dGMW8C7w91x2Riv7sB8wO5fblqVrPJd7CHgib08CNqYVwXZEPEy6nW5m7uvUwu5vAmfkYxlEPg8RMY80M6K0Ftx9wCsRsSC/PyM/8GQ26YvMTEkbSbq1pf2qYJk6gYuAfwOz8rU/YDnqNzMzM+s2ahgjV4yFI+J54FHg0kJ9FeNdSbfmu/Va1H5LVah3ndzPo0lrT5O3d8tLUEwDtmmmT0W/An4paTpNT5Y5j7QUxqP5GKYV9k0iTaSYGBGLgafJsX1EvEKatTwbuJ0Pxu3vi4h3SIPPp+drP4O07rWZmZlZj6Olq0is4IalRRGxRk0atw5VX18fDQ0Nte6GmZmZWatJmpYf4lar9tscI0tanbQ+8vatmBHcYSTNA+pLa1Sb42QzMzPr2qrFyh21BrOZmZmZma0gkkaQZi+f0xkGl83MzMys52jrA+iWm2cvm5mZmZl9UFtj5Ij4B+nh0J1GRNTVug9mZmZm1vE8g9nMzMzMzMzMzMzM2sQDzGZmZmZmZmZmZmbWJh5gNjMzMzMzMzMzM7M2qdkazLaUpNHAHRHxTA3abvOTys2sc6g7bnyT++eNHbmCemJmZtb+HCubdW7VYlHHoGZmPYdnMGdKanU+RgMbtaaApF4d0xUzMzMzsw9yrGxmZmZm1fToAWZJdZIel3Q5MBv4maSpkmZJOqWQ7+CcNlPSFRXqGS3pJkl3Spon6fuSfihpuqQHJa2b8w3K72dJulHSOpL2AeqBqyTNkLSapM/mso2SLpH04Vx+nqTTJT0M7Cvp85Iezv26S9JKkv4pqW/Ov5Kk/5PUV9IGuc2Z+bVTheP4UfnxS+otaXwuM1vSqA64FGZmZmbWyThWXuY4HCubmZmZVdCjB5izLYA/AMcAGwNDgEHADpJ2ldQfOAEYHhEDgaOr1LMt8FVgMHAa8EZEfBJ4ADg457kc+ElEbAc0AidFxPVAA3BgRAwCAhgHjIqIAaRlTI4otPNSRGwP3AVcCHwt92vfiFgCXAkcmPOOAGZGxHzgbGBCzrs9MKfYeUl75HPxgeMHPg88ExEDI2Jb4LZKBy/pcEkNkhrmz59f5RSZmZmZWRfjWJnli5UdJ5uZmVl35wFmeCoiHgT2yK/pwMPAVqQgcjhwXUS8CBARL1ep556IeC0HqAuBv+X0RqBO0lrA2hExIadfBuxaoZ4tgScj4okq+a7NPz8NTIyIJ8v6dQlLg/RvAZfm7eHAeTnv4ohYWNZuteNvBHbPs0GGVihHrvOCiKiPiPq+fftWymJmZmZmXY9j5aTNsbLjZDMzM+vu/JA/eD3/FPDLiPhjcaekI1tYz9uF7SWF90to3/P8elM7I+JpSc9LGk6aYXFgU/kLKh4/gKTtgS8Cp0q6KyJ+3tpOm5mZmVmX5Fg5caxsZmZmVoVnMC91O/AtSWsASNpY0keAu0lruK2X09dtS+V5NsMCSUNz0kFAaYbGa0CfvP04aRbH5hXyFT0I7CrpYxX6dRHp9r/rImJxTruLfPugpF55lkhRxeOXtBHpFsYrgTNItwyamZmZWc/iWNmxspmZmVlFnsGcRcQdkrYGHpAEsAj4RkTMkXQaMEHSYtJtcaMl7QnUR8SJrWjmm8D5klYH5gKH5PRxOf1NYMecfp2klYGpwPkV+jtf0uHAX5Se6P0CsHvefTPpdr9LC0WOBi6Q9G1gMSmAfqC54wc2B86QtAR4lw+ucWdmncC8sSNr3QUzM+vmHCs7VjarxrGomZkpImrdB2tnkuqBMyNiaLOZO0B9fX00NDTUomkzMzOz5SJpWkTU17of1nFqGSs7TjYzM7OurFqs7BnM3Yyk40gzJ1q6npyZmZmZWY/gWNnMzMys/XkN5m4mIsZGxKYRcV+t+2JmZmZm1pk4VjYzMzNrfx5gNjMzMzMzMzMzM7M28QCzmZmZmZmZmZmZmbWJB5jNzMzMzMzMzMzMrE38kD8zsy6u7rjxTe6fN3bkCuqJmZmZmfU0dceNd7xpZtbDeQZzFyGpXtLZte6HmZmZmXUfktaW9N0W5FuUfw6TdEsH9GOepPXz9v35Z52kAwp5HA+bmZmZdUIeYO4iIqIhIo6qdT/MzMzMrFtZG2h2gHlFioid8mYdcEAh3fGwmZmZWSfkAeYak9Rb0nhJMyXNljRK0mBJ9+e0KZL6VJstImlDSRMlzcjlh+b0RZLOlDRH0l2S+ub0wyRNzXXfIGn1nL6BpBtz+kxJO+X0b+Q+zJD0R0m9VuT5MTMzM7MONRbYLMd6Z+a48WFJjZL2aqpgjlmnS9qsLH1Yjk/HS3pc0vmSVsr79s91z5Z0epV6FxX6NjT37ZhiPCxpDUmX5rpmSfqapF6SxuW6GyUdU6HuvpLuzDHyRZKekrR+ni09u5DvWEkn5+17JZ2eY+InCvF2/0KcPEvSFi096WZmZmbdiQeYa+/zwDMRMTAitgVuA64Fjo6IgcAI4M0myh8A3B4Rg4CBwIyc3htoiIj+wATgpJz+l4gYnOt+FPh2Tj8bmJDTtwfmSNoaGAXsnOtfDBxYqROSDpfUIKlh/vz5rT0HZmZmZlYbxwH/yrHej4CvRMT2wG7AbySpUqE8GeF8YK+I+FeFLEOAI4FtgM2Ar0raCDgdGA4MAgZL2ruZvk2KiEERcWbZvp8BCyNiQERsB9yd69w4IraNiAHApRXqPAm4O8fI1wP9mmi/aOWIGAL8gKVx9RjgrHzu6oH/VCroONnMzMy6Ow8w114jsHueFTGUFOQ+GxFTASLi1Yh4r4nyU4FD8gyLARHxWk5fQhqoBrgS2CVvbytpkqRG0mBx/5w+HDgvt7k4IhYCnwV2AKZKmpHff7xSJyLigoioj4j6vn37tu4MmJmZmVlnIOAXkmYB/wA2BjaokG9r4ALgyxHx7yp1TYmIuRGxGLiGFIsOBu6NiPk5vr0K2LWNfR0B/L70JiIWAHOBj0s6R9LngVcrlNsF+FMucxuwoIXt/SX/nEZaugPgAeB4ST8BNo2IipNCHCebmZlZd+cB5hqLiCdIM4YbgVOBr7ay/ERSYP5fYJykg6tlzT/HAd/PszpOAVZtonoBl+VZI4MiYsuIOLk1/TMzMzOzLuNAoC+wQ56V+zyVY8VngbeATzZRVzTzvt3lQeaBwL2k2cUXtaL4e3zwu1H5cb+dfy4GVs7tXQ3sSbrb8FZJw1vfazMzM7Oub+Vad6Cny7cKvhwRV0p6hfSQlQ0lDY6IqZL60MQSGZI2Bf4TERdK+jBpsPpyUoC8D2mGxgHAfblIH+BZSauQvkT8N6ffBRwB/C6vs7xGTvurpDMj4gVJ6wJ9IuKp9jwHZrZ85o0dWesumJlZ1/UaKT4EWAt4ISLelbQbsGmVMq+Qllm7U9LrEXFvhTxDJH0MeIq05NoFwBTgbEnrk2YO7w+c08K+lbsT+B5pyQokrQP0At6JiBskPU66i6/cZGA/4HRJewDr5PTngY9IWg9YBHyJtHRdVZI+DsyNiLMl9QNKS3WY9SiORc3MzAPMtTcAOEPSEuBd0iCvgHMkrUYaXB5RLCCpHhgTEYcCw4AfSXqXFAyXZjC/TgrsTwBeIAX2kNarewiYn3+WgvajgQskfZs0M+OIiHggl78jP5jlXVIg7wFmMzMzs24gIl6SNDk/4G4qsFVeSq0BeKyJcs9L+hLwd0nfIsWPpfiUXNe5wObAPcCNEbFE0nH5vYDxEfHXJro3C1gsaSbpLrzphX2nAr/P/V5MujPvX8ClpQcKAv8DIGlM7vP5Od81kg4iLXHxHPBaHlT/OWkQ/L9NHXvBfsBBOQ5/DvhFC8qYmZmZdTuK6PC71awGJC2KiDVq0XZ9fX00NDTUomkzMzOz5SJpWkTU17ofXZmkYcCxEfGlGndlGfmOv8UR8Z6kHYHz8nIgK4TjZDMzM+vKqsXKnsFsZmZmZmY9RT/gz3mW8zvAYTXuj5mZmVmX5wHmbqpWs5fNzMzMrGfLazLfW+NuVBQR/6TphxOamZmZWSut1HwWMzMzMzMzMzMzM7NleYDZzMzMzMzMzMzMzNrEA8xmZmZmZmZmZmZm1iZeg7kHk/QD4IKIeKPWfTGztqs7bvwyafPGjqxBT8zMzLoPx8pmZmZmLeMZzJ2IkhV5TX4ArL4C2zMzMzMzaxPHymZmZmadkweYa0xSnaTHJV0OzAZ+JmmqpFmSTinkOzinzZR0RZV6Jkl6OL92yunDJN1SyHeupNGSjgI2Au6RdE/et7+kRkmzJZ2e03pJGpfTGiUd07FnxMzMzMwscaxsZmZm1vl5iYzOYQvgm8CawD7AEEDAzZJ2BV4CTgB2iogXJa1boY4XgN0j4i1JWwDXAPXVGoyIsyX9ENgt17kRcDqwA7AAuEPS3sDTwMYRsS2ApLUr1SfpcOBwgH79+rXy8M3MzMzMqurSsbLjZDMzM+vuPIO5c3gqIh4E9siv6cDDwFakgHo4cF1EvAgQES9XqGMV4EJJjcB1wDat7MNg4N6ImB8R7wFXAbsCc4GPSzpH0ueBVysVjogLIqI+Iur79u3byqbNzMzMzKrq0rGy42QzMzPr7jzA3Dm8nn8K+GVEDMqvzSPi4hbWcQzwPDCQNBvjQzn9PT54nVdtTcciYkGu815gDHBRa8qbmZmZmS0nx8pmZmZmnZgHmDuX24FvSVoDQNLGkj4C3A3sK2m9nF7ptr+1gGcjYglwENArpz8FbCPpw/mWvc8WyrwG9MnbU4DPSFpfUi9gf2CCpPWBlSLiBtKth9u33+GamZmZmbWYY2UzMzOzTshrMHciEXGHpK2BByQBLAK+ERFzJJ1GCmIXk24LHC1pT6A+Ik4E/gDcIOlg4DbyTI+IeFrSn0kPRXkyly25ALhN0jMRsZuk44B7SLNDxkfEXyUNBC4tPLH7fzr2LJhZa80bO7LWXTAzM+twjpXNzMzMOidFRK37YN1MfX19NDQ01LobZmZmZq0maVpEVH34m9nycJxsZmZmXVm1WNlLZJiZmZmZmZmZmZlZm3iA2czMzMzMzMzMzMzaxAPMZmZmZmZmZmZmZtYmHmA2MzMzMzMzMzMzszbxALOZmZmZmZmZmZmZtcnKte6AmZktn7rjxi+TNm/syBr0xMzMzMzMzMx6Gs9g7iEkjZO0T4X0jSRdX4s+mZmZmZl1NEn1ks5eAe3Mk7R+R7djZmZm1tl4BnMXI0mAImJJe9QXEc8Ayww8m5mZmZl1BxHRADTUuh9mZmZm3ZVnMFch6WBJsyTNlHSFpDpJd+e0uyT1y/nGSTpP0oOS5koaJukSSY9KGleob5GkMyXNyeX7Vmizr6Q7c56LJD0laf3c9uOSLgdmA5vkNhty3lMKdcyT9CtJjZKmSNq80MSuku7P/dwn56+TNDtv95L0a0mz83EemdPHSnokp/26I863mZmZmVlrSOotaXyO12dLGiVpcI53Z+ZYuE+Oz2+pUH6YpAmS/prj47GSDszlGiVtlvP1lXSDpKn5tXNOX0/SHaXYHdAKPgVmZmZmnYIHmCuQ1B84ARgeEQOBo4FzgMsiYjvgKqB4m906wI7AMcDNwJlAf2CApEE5T2+gISL6AxOAkyo0fRJwd85zPdCvsG8L4A8R0T8ingJ+GhH1wHbAZyRtV8i7MCIGAOcCvyukbwjsAnwJGFuh/cOBOmBQ6TglrQd8Beif006tUA5Jh+cB74b58+dXymJmZmZm1p4+DzwTEQMjYlvgNuBa4Ogcw48A3mymjoHAGGBr4CDgExExBLgIODLnOQs4MyIGA1/L+yDF7vfl2P1GPhi7v89xspmZmXV3HmCubDhwXUS8CBARL5MGkK/O+68gDdSW/C0iAmgEno+IxryExRzSgC3AElLAC3BlWfmSXYA/5TZvAxYU9j0VEQ8W3u8n6WFgOmkwe5vCvmsKP3cspN8UEUsi4hFggwrtjwD+GBHvFY57IfAWcLGkrwJvVChHRFwQEfURUd+37zKTs83MzMzM2lsjsLuk0yUNJQ3wPhsRUwEi4tVSXNuEqRHxbES8DfwLuKNQd13eHgGcK2kGaTLJmpLWAHYlxfVExHg+GLu/z3GymZmZdXdeg7l9vJ1/Lilsl95XO8fRyjZeL21I+hhwLDA4IhbkpThWrVJ3cbvYtxbdwhcR70kaAnyWtFbz90kD8GZmZmZmNRMRT0jaHvgi6S67u9tQTXnsXozrS3H8SsCnI+KtYsH0aBQzMzMz8wBzZXcDN0r6bUS8JGld4H7g66TZywcCk1pZ50qkAdo/AQcA91XIMxnYDzhd0h6kpTcqWZM04LxQ0gbAF4B7C/tHkZbAGAU80Io+3gl8R9I9eWB5XeAdYPWIuFXSZGBuK+ozsxVg3tiRte6CmZnZCidpI+DliLhS0ivAd4ENJQ2OiKmS+tD8EhktcQdpuYwzcruDImIGMJEU158q6QtUj93NzMzMujUPMFcQEXMknQZMkLSYtAzFkcClkn4EzAcOaWW1rwNDJJ0AvEAa/EXSmNzm+cApwDWSDiINDD8HvAasUda/mZKmA48BT5MGpovWkTSLNANj/1b08SLgE8AsSe8CFwI3AH+VtCpp1vMPW1GfmZmZmVlHGQCcIWkJ8C5wBClePUfSaqTB5RHFApLqgTERcWgr2jkK+H2Or1cmDSyPYWnsPoc0GeXfy3k8ZmZmZl2S0tLB1tEkLYqINZrJ82FgcZ49vCNwXkQMamU784D60vrRtVBfXx8NDQ21at7MzMyszSRNyw9SNmt3jpPNzMysK6sWK3sGc+fSD/izpJVIS1McVuP+mJmZmZmZmZmZmVXlAeYVpLnZyznPP4FPLmc7dctT3szMzMzMzMzMzKylVqp1B8zMzMzMzMzMzMysa/IAs5mZmZmZmZmZmZm1iQeYzczMzMzMzMzMzKxNPMDcDUiql3T2CmhnnqT1O7odM2uduuPGU3fc+Fp3w8zMrENJGi1poyr7xknapx3auFfSMk9GN7PKHIOamRn4IX/dQkQ0AA217oeZmZmZdQ+SBCgiltS6LwCSegGjgdnAM7XtjZmZmZkVeQZzJyapt6TxkmZKmi1plKTBku7PaVMk9ZE0TNItFcoPkzRB0l8lzZU0VtKBuVyjpM1yvr6SbpA0Nb92zunrSbpD0hxJFwFawafAzMzMzFYQSXWSHpd0OWkg92c5Npwl6ZRCvoNz2kxJV1Sop3+ON2fkfFvkuh+TdJWkRyVdL2n1nP+zkqbn+PQSSR/O6fMknS7pYWB/oB64Kte7WoVD2DXHyXNLs5mVnJFj6UZJowr9/ElOmylpbNkxrJRnRZ8qqVeuo3QuvpPzXC5p70KZqyTt1eYLYGZmZtZFeQZz5/Z54JmIGAkgaS1gOjAqIqZKWhN4s5k6BgJbAy8Dc4GLImKIpKOBI4EfAGcBZ0bEfZL6AbfnMicB90XEzyWNBL5drRFJhwOHA/Tr16+tx2tmZmZmtbUF8E1gTWAfYAhpksHNknYFXgJOAHaKiBclrVuhjjHAWRFxlaQPAb2ADYAtgW9HxGRJlwDflXQuMA74bEQ8kQe3jwB+l+t6KSK2B5B0KHBsvnuvkg2BXYCtgJuB64GvAoNIMfH6wFRJE3PaXsCnIuKNsuNYGbgKmB0Rp+U4d2FEDM6D35Ml3QFcDBwD3JTj9J3yufsAx8lmZmbW3XkGc+fWCOyeZ24MBfoBz0bEVICIeDUi3mumjqkR8WxEvA38C7ijUHdd3h4BnCtpBikYX1PSGsCuwJW5rfHAgmqNRMQFEVEfEfV9+/Ztw6GamZmZWSfwVEQ8COyRX9OBh0mDtlsAw4HrIuJFgIh4uUIdDwDHS/oJsGlElCZEPB0Rk/P2laTB4C2BJyPiiZx+GSkGLbm2FX2/KSKWRMQjpAFtchvXRMTiiHgemAAMJsW/l0bEGxWO44/kweX8fg/g4BwrPwSsB2wREROALST1Jc2wvqFSbO442czMzLo7z2DuxPIsju2BLwKnAne3oZq3C9tLCu+XsPT6rwR8OiLeKhZMS++ZmZmZWQ/yev4p4JcR8cfiTklHNldBRFwt6SFgJHBrXlJiLhDlWVvRn5Yoxr3LE8jeD+wm6Tc5PhZwZETcXiHv5cA3gK8DhyxHm2ZmZmZdlmcwd2JKT8l+IyKuBM4APgVsKGlw3t9HUnv8keAO0nIZpXYH5c2JwAE57QvAOu3QlpmZmZl1frcD38p3tSFpY0kfIU142FfSejl9mSUyJH0cmBsRZwN/BbbLu/pJ2jFvHwDcBzwO1EnaPKcfRJplXMlrQJ9WHsckYFReR7kvaXb0FOBO4JDCOtDF47gYuBX4c461bweOkLRKzvsJSb1z3nGkJefIM6fNzMzMehzPYO7cBgBnSFoCvEtaj07AOfnBJm+Sbu97n6R6YExEHNqKdo4Cfi9pFukzMZG0dt4pwDWS5pBmcvx7OY/HzDrAvLEja90FMzPrZiLiDklbAw/ku9oWAd+IiDmSTgMmSFpMWkJjtKQ9gfqIOBHYDzhI0rvAc8AvSGs6Pw58L6+//AhwXkS8JekQ4Lo8mDsVOL9Kt8YB50t6E9gR+B+gISJubuJQbsx5Z5JmTP84Ip4DbsuTKhokvUMaUD6+cPy/zesqXwEcSFpa7mGlkzEf2Dvne17So8BNzZ1Ts+7IcaiZmQEooiV3ppm1XH19fTQ0VHv2ipmZmVnnJWlaRNTXuh/djaQ64JaI2LbWfWlPeQZ0I7B9RCxsLr/jZDMzM+vKqsXKXiLDzMzMzMyslSSNAB4FzmnJ4LKZmZlZd+UlMszMzMzMrENFxDygW81ejoh/AJvWuh9mZmZmteYZzGZmZmZmZmZmZmbWJh5gNjMzMzMzMzMzM7M28QCzmZmZmZmZmZmZmbWJB5jNzMzMzMzMrNXqjhtf6y6YmVkn4AHmGpA0WtJGVfaNk7RPB7Y9T9L6rcg/WtK5eftkScd2VN/MzMzMzBwrm5mZmXUt3X6AWUmnOU5JvYDRQMWg2czMzMxsRXGsbGZmZmbLq9MEk+1JUp2kxyVdDswGfiZpqqRZkk4p5Ds4p82UdEWFevpLmiJpRs63Ra77MUlXSXpU0vWSVs/5PytpuqRGSZdI+nBOnyfpdEkPA/sD9cBVud7VKhzCCEkNkp6Q9KXCMU2S9HB+7ZTTN5Q0Mdc1W9LQnL6HpAdy3uskrVGo/8e5j1MkbZ7zf1nSQ7n//5C0QTtcCjMzMzPrZBwrO1Y2MzMza0/dcoA52wL4A3AMsDEwBBgE7CBpV0n9gROA4RExEDi6Qh1jgLMiYhAp0P1PTt8S+ENEbA28CnxX0qrAOGBURAwAVgaOKNT1UkRsHxFXAg3AgRExKCLerNBuXe7vSOD8XPcLwO4RsT0wCjg75z0AuD33cSAwQ+m2vhOAETl/A/DDQv0Lcx/PBX6X0+4DPh0RnwT+BPy4Qr+qknR4DvQb5s+f35qiZmZmZrbiOVZeQbGy42QzMzPr7laudQc60FMR8aCkXwN7ANNz+hqkgHogcF1EvAgQES9XqOMB4KeSPgr8JSL+KQng6YiYnPNcCRwF3Ak8GRFP5PTLgO+xNCi9thV9/3NELAH+KWkusBXwJHCupEHAYuATOe9U4BJJqwA3RcQMSZ8BtgEm5/5+KB9LyTWFn2fm7Y8C10raMOd/shX9JSIuAC4AqK+vj9aUNTMzM7MVzrHyCoqVHSebmZlZd9edZzC/nn8K+GWeATEoIjaPiItbUkFEXA3sCbwJ3CppeGlXedZW9KdFTVd4fwzwPCnYrycFtkTERGBX4L/AOEkHk475zsIxbxMR365Sf2n7HODcPFvjO8CqreivmZmZmXUtjpUdK5uZmZm1i+48wFxyO/Ct0rpqkjaW9BHgbmBfSevl9HXLC0r6ODA3Is4G/gpsl3f1k7Rj3j6AdMvc40BdaZ024CBgQpU+vQb0aaLP+0paSdJmwMdz3WsBz+bZGgcBvXIfNwWej4gLgYuA7YEHgZ0La8b1lvSJQv2jCj9LszXWIgXeAN9som9mZmZm1n04VnasbNZm88aOrHUXzMysE+j2A8wRcQdwNfCApEbgeqBPRMwBTgMmSJoJ/BZA0p6Sfp6L7wfMljQD2Ba4PKc/DnxP0qPAOsB5EfEWcAhwXW5nCXB+lW6NI60XN0PSapJ+LmnPwv5/A1OAvwNjct1/AL6Z+7oVS2d5DANmSppOCoLPioj5pKdvXyNpFikw3qpQ/zo5/WjSbA+Ak3PfpwEvNnVOzczMzKx7cKzsWNnMzMxseSnCy4C1hqQ64JaI2LbWfems6uvro6GhodbdMDMzM2s1SdMior7W/eiqHCs3zXGymZmZdWXVYuVuP4PZzMzMzMzMzMzMzDrGyrXuQFcTEfNItwCamZmZmVmBY2UzMzOznsczmM3MzMzMzMzMzMysTTzAbGZmZmZmZmZmZmZt4gFmMzMzMzMzMzMzM2sTDzB3ApLqJZ3dyjInSzq2o/pkZmZmZrY8unqMK2lPScfVuh9mnVndceNr3QUzM+sE/JC/TiAiGoCGWvfDzMzMzKy9dPUYNyJuBm6udT/MzMzMOrseN4NZ0sGSZkmaKekKSXWS7s5pd0nql/ONk3SepAclzZU0TNIlkh6VNK5Q3yJJZ0qak8v3rdBmo6S1lbwk6eCcfrmk3XPdt+S0k3M79+Z2jyrU81NJT0i6D9iykD4o93OWpBslrSPpI5Km5f0DJUXh2P4laXVJ+0qanc/FxAr9HpzrXFVS73yMfiq4mZmZWUGN4st1Jd2U23hQ0nY5/TOSZuTXdEl9KpRdJOmMXP8/JA0pxJ575jz9JU3J9cyStEWFerpqjFsn6bF8PZ6QdJWkEZImS/qnpCE532hJ5+btZeqU1EvSr3P6LElHNv9pMTMzM+t+etQAs6T+wAnA8IgYCBwNnANcFhHbAVcBxdv41gF2BI4hzV44E+gPDJA0KOfpDTRERH9gAnBShaYnAzvnsnOBoTl9R+D+Cvm3Aj4HDAFOkrSKpB2ArwODgC8Cgwv5Lwd+ko+hETgpIl4AVpW0Zm6vARgqaVPghYh4AzgR+Fw+F3uWdyIipubjPhX4FXBlRMyu0F8kHS6pQVLD/PnzK2UxMzMz63ZqGF+eAkzPbRxPigcBjgW+FxGDSDHgmxXK9gbuzvW/Ror1dge+Avw85xkDnJXrqQf+U6GeLhnjZpsDv8l92go4ANiFdP6Or5C/Up2HA3XAoMK1XobjZDMzM+vuetQAMzAcuC4iXgSIiJdJAfDVef8VpMCy5G8REaSA9vmIaIyIJcAcUjAJsAS4Nm9fWVa+ZBKwa36dR/oCsTGwICJer5B/fES8nfv5ArABKYC+MSLeiIhXybfrSVoLWDsiJuSyl+V2IAX2O+f3v8g/h+b+QPpSME7SYUCvyqeMn5O+cNSTBpkriogLIqI+Iur79l1mko2ZmZlZd1Wr+HKXXDcRcTewXh50nQz8Ns8QXjsi3qtQ9h3gtrzdCEyIiHfzdqkPDwDHS/oJsGlEVBqo7sox7pNl5/6uwnWpq5C/Up0jgD+WznG+9stwnGxmZmbdXU8bYG6tt/PPJYXt0vtq61dHhbSJpKB3KHAvMB/Yh6VBcLV2ARY30VZzSu1uCvwVGEj6MjIJICLGkGbcbAJMk7RehTrWA9YA+gCrtrEfZmZmZpa0V3xZOWPEWOBQYDVgsqStKmR7Nw+mfqAfebB15bx9NWmm7pvArZKGS/peYfmNjejaMW75uS9el2X61cI6zczMzHqknjbAfDewbykglLQuaQbE1/P+A6keEFezEimQhnRr3X3lGSLiaWB9YIuImJvzHEsKjltqIrC3pNXyWnpfznUvBBZIKt2SeBDpVkrysXwD+Gf+wvAy6dbD+wAkbRYRD0XEiaQvBJtUaPePwM9It/yd3or+mpmZmfUENYkvc50H5jaHAS9GxKs5vmuMiNOBqaTlH1pN0seBuRFxNmkQd7uI+H1EDMqvZ7p4jNsqVeq8E/iOpJVznnWXtx0zMzOzrqitswa6pIiYI+k0YIKkxcB04EjgUkk/IgWLh7Sy2teBIZJOIN3qNwpA0pjc5vk530MsvZ1uEvBLKn9ZqNb3hyVdC8zM7Uwt7P4mcL6k1Unr3x2Sy8yTJJYG+fcBH42IBfn9GfmBLQLuAmbm2SgXRcQX84Na3o2IqyX1Au6XNDzfhmlmZmbW49UwvjwZuETSLOANUjwI8ANJu5Fm4s4B/p7LzsjrKbfUfsBBkt4FniMtRVFJl4txW9q3MsvUCcwGPgHMyufpQuDcNtZv1iXNGzuy1l0wM7NOQEvvjrO2kLQoItaodT86k/r6+mhoaKh1N8zMzMxaTdK0iKivcR8cX3ZTjpPNzMysK6sWK/e0JTLMzMzMzMzMzMzMrJ14gHk5eXaJmZmZmbUnx5dmZmZm1pV4gNnMzMzMzMzMzMzM2sQDzGZmZmZmZmZmZmbWJh5gNjMzMzMzMzMzM7M28QCzmVkXVnfc+Fp3wczMzMx6oLrjxjsWNTMzwAPMnY6k0ZI2qrJvnKR92qGNeyXVL289ZmZmZmYdTVK9pLNbWeZkScd2VJ/MzMzMbKmVa92BWpIkQBGxpNZ9AZDUCxgNzAaeqW1vzMzMzMxqLyIagIZa98PMzMzMKutxM5gl1Ul6XNLlpIHcn0maKmmWpFMK+Q7OaTMlXVGhnv6SpkiakfNtket+TNJVkh6VdL2k1XP+z0qaLqlR0iWSPpzT50k6XdLDwP5APXBVrne1Coewq6T7Jc0tzWZWcoak2bn+UYV+/iSnzZQ0tuwYVsqzok+V1CvXUToX38l5Lpe0d6HMVZL2avMFMDMzM7NOqzwGzvHt3TntLkn9cr5xks6T9GCOS4flGPdRSeMK9S2SdKakObl83wptNkpaO8e0L0k6OKdfLmn3XPctOe3k3M69ud2jCvX8VNITku4DtiykD8r9nCXpRknrSPqIpGl5/0BJUTi2f0laXdK+Ob6eKWlihX4PznWuKql3PsZt2+tamJmZmXUVPW6AOdsC+ANwDLAxMAQYBOwgaVdJ/YETgOERMRA4ukIdY4CzImIQaVD4Pzl9S+APEbE18CrwXUmrAuOAURExgDRz/IhCXS9FxPYRcSVpdsaBETEoIt6s0O6GwC7Al4DSgPFXc/8HAiOAMyRtKOkLwF7Ap/Jx/KpQz8rAVcA/I+IE4NvAwogYDAwGDpP0MeBi0qxqJK0F7AQss9CWpMMlNUhqmD9/foVum5mZmVlnViUGPge4LCK2I8WOxaUq1gF2JMXUNwNnAv2BAZIG5Ty9gYaI6A9MAE6q0PRkYOdcdi4wNKfvCNxfIf9WwOdIMfxJklaRtAPwdVJM/EVSPFtyOfCTfAyNwEkR8QKwqqQ1c3sNwFBJmwIvRMQbwInA5/K52LO8ExExNR/3qaQ4+8qImF2ez3GymZmZdXc9dYD5qYh4ENgjv6YDD5OC1S2A4cB1EfEiQES8XKGOB4DjJf0E2LQwGPx0REzO21eSBoO3BJ6MiCdy+mXAroW6rm1F32+KiCUR8QiwQU7bBbgmIhZHxPOk4H0wabD50hwglx/HH4HZEXFafr8HcLCkGcBDwHrAFhExAdgizzbZH7ghIt4r71REXBAR9RFR37fvMhNTzMzMzKzzqxQD7whcnfdfQYo7S/4WEUEatH0+Ihrz0nNzgLqcZwlLY90ry8qXTCLFxrsC55EGqDcGFkTE6xXyj4+It3M/XyDFxEOBGyPijYh4lTTwW5ogsXaOaeGDcfj9pIHtXYFf5J9Dc38gDXyPk3QY0KvyKePnwO6kCSe/qpTBcbKZmZl1dz11gLkUqAr4ZZ4tPCgiNo+Ii1tSQURcTZrJ8CZwq6ThpV3lWVvRn5Z4u7CtVpQrdz+wW55dXarryMK5+FhE3JH3XQ58AzgEuGQ52jQzMzOz7qMUly7hgzHqEqo/66VSbDyRNLA7FLgXmA/sw9KB3mrtAixuoq3mlNrdFPgr6W7AXUrtRsQY0ozuTYBpktarUMd6wBpAH2DVCvvNzMzMur2eOsBccjvwLUlrAEjaWNJHgLuBfUtBpKR1ywtK+jgwNyLOJgWk2+Vd/STtmLcPAO4DHgfqJG2e0w8izTKu5DVSgNoak4BReR3lvqTZF1OAO4FDtHQd6OJxXAzcCvxZ0sqkc3GEpFVy3k9I6p3zjgN+AJBnTpuZmZlZ91MpBr6ftPQEwIFUH/StZiXSYDEsjY0/ICKeBtYn3T03N+c5ljQA3FITgb0lrSapD/DlXPdCYIGk0rIbxTh8EmkSxT/zzOuXSctr3AcgabOIeCgiTiQNem9Sod0/Aj8jLR9yeiv6a2ZmZtZttPWv/d1CRNwhaWvgAUkAi4BvRMQcSacBEyQtJi2hMVrSnkB9DjL3Aw6S9C7wHOm2ujVJg8nfk3QJ8AhwXkS8JekQ4Lo8mDsVOL9Kt8YB50t6k3RL4v+Q1q27uYlDuTHnnUmaFfLjiHgOuC2vf9cg6R3SgPLxheP/bb5t8ArSF4Y64GGlkzEf2Dvne17So8BNzZ1TM1ux5o0dWesumJlZN1ElBj4SuFTSj0jx4SGtrPZ1YIikE0jLWYwCkDQmt1mKiR9i6TIUk4BfUmEwuom+PyzpWlI8/AIp3i75Jim+Xp20xvMhucy8HPeWBrLvAz4aEQvy+zMkbUG60+8uYKakjYCLIuKL+WGE70bE1ZJ6AfdLGh4Rd7e032ZdmeNQMzMrUVo2zdqDpDrglojoVk+PzsF4I7B9ngXSpPr6+mhoaOj4jpmZmZm1M0nTIqK+1v3oLiQtiog1at2PzsJxspmZmXVl1WLlnr5EhjVD0gjgUeCclgwum5mZmZmZmZmZWc/Ro5fIaG8RMQ/oVrOXI+IfpAefmJmZmZm1imcvm5mZmXV/nsFsZmZmZmZmZmZmZm3iAWYzMzMzMzMzMzMzaxMPMJuZmZmZmZmZmZlZm3iAuYykRfnnRpKub0H+WyWt3dH9aUG+PSUd18T+QZK+2NL8ZtY11B03vtZdMDOzHsSxspmV1B033rGomZkBPfAhf5IEKCKWNJUvIp4B9mmuvoj4YnN5OpqklSPiZuDmJrINAuqBWwFakN/MzMzMehjHyo6VzczMzFqr085gltRb0nhJMyXNljRK0mclTZfUKOkSSR/OeQdLuj/nnSKpT1lddZIel3Q5MBvYRNKPJE2VNEvSKRXar5M0O2+vLunPkh6RdKOkhyTV533zJK2ft3+Y+zpb0g8K9Twq6UJJcyTdIWm1Cu2tIenSfGyzJH2tsO+0fGwPStogp42TdL6kh4BfSRot6dy8b9/ch5mSJkr6EPBzYJSkGflcFvN/OR/TdEn/KLRxcj7P90qaK+mo5buqZmZmZtYeHCs7VjYzMzPrLDrtADPweeCZiBgYEdsCtwHjgFERMYA0+/qIHBBeCxwdEQOBEcCbFerbAvhDRPQHtszvh5BmK+wgadcm+vJdYEFEbAP8DNihPIOkHYBDgE8BnwYOk/TJQtu/z22/AnytvHyud2FEDIiI7YC7c3pv4MF8bBOBwwplPgrsFBE/LKvrROBzucyeEfFOTrs2IgZFxLVl+e8DPh0RnwT+BPy4sG8r4HOkc3WSpFUq9B1Jh0tqkNQwf/78SlnMzMzMrP04Vk46fazsONnMzMy6u848wNwI7C7pdElDgTrgyYh4Iu+/DNiVFAA/GxFTASLi1Yh4r0J9T0XEg3l7j/yaDjxMCgy3aKIvu5CCSSJiNjCrSp4bI+L1iFgE/AUYmvc9GREz8va0fCzlRgC/L72JiAV58x3gliplr4uIxRXqmgyMk3QY0KuJ4yr5KHC7pEbgR0D/wr7xEfF2RLwIvABsUKmCiLggIuojor5v374taNLMzMzMloNj5aTTx8qOk83MzKy767QDzDk43p4UPJ8K7L2cVb5e2BbwyzxDYVBEbB4RFy9n/U15u7C9mNatff1uRESVsq9XyE9EjAFOADYBpklar5k2zgHOzbNdvgOs2k59NzMzM7MO4Fj5fY6VzczMzGqs0w4wS9oIeCMirgTOAHYE6iRtnrMcBEwAHgc2lDQ4l+sjqbnA7nbgW5LWyGU2lvSRJvJPBvbLebcBBlTIMwnYO69B1xv4Sk5rqTuB75XeSFqnFWU/QNJmEfFQRJwIzCcFz68BfaoUWQv4b97+ZlvbNTMzM7MVw7GyY2UzMzOzzqIz/4V9AHCGpCXAu8ARpODuuhwUTwXOj4h3JI0CzskPBHkTGCFpTeCiSk+ujog7JG0NPCAJYBHwDdJtbZX8AbhM0iPAY8AcYGFZnQ9LGgdMyUkXRcR0SXXVDlDSmFz2fNLMk9/nh6UsBk4h3TrYFmdI2oI0++QuYCbwb+A4STOAX5blP5l0XheQ1rP7WBvbNbMVbN7YkbXugpmZ1YZjZcfKZjXlONTMzEq09I4yq0ZSL2CViHhL0mbAP4At8wNBrEx9fX00NDTUuhtmZmZmrSZpWkTU17ofXYlj5ZZznGxmZmZdWbVYuTPPYO5MVgfuyU+FFvBdB8xmZmZmZoBjZTMzM7MezQPMLRARrwGeyWJmZmZmVsaxspmZmVnP1mkf8mdmZmZmZmZmZmZmnZsHmM3MzMzMzMzMzMysTTzAbGZmZmZmZmZmZmZt4jWYzcy6qLrjxgMwb+zIGvfEzMzMzHqSUhwKjkXNzKwHzGCWtLak77Yg36L8c5ikW9qp7TpJs/N2vaSzW1Dm/vZou6Uk3Spp7RXZppmZmZlZOUnzJK3f0vi9QvkfSFq98H5RK8vvKem41rZrZmZm1tN1+wFmYG2g1QFqS0lq0SzwiGiIiKNakG+n5e9Vy0XEFyPilRXZppmZmZlZE9ambfH7D4DVm8tUTUTcHBFj21rezMzMrKfqCQPMY4HNJM2QdKakuyQ9LKlR0l5NFZQ0WNJ0SZuVpQ+TNEnSzcAjknpJOkPSVEmzJH2nQl3vz4yW1FfSnZLmSLpI0lOS1s/7SjOpleucnfs6qlDPvZKul/SYpKskqUJ74ySdJ+lBSXNzuUskPSppXCFfaaZIb0njJc3MbZbaGyzp/pw+RVKfVp5/MzMzM7MPkHSTpGk5Hj68bHcxfj+jQtnzJDXksqfktKOAjYB7JN1TyHtajmMflLRBTusr6YYcu0+VtHNOHy3p3Ly9b46JZ0qaWNh/U47j50n6vqQf5u8LD0patyPOlZmZmVln1xPWYD4O2DYiBuXZxqtHxKt5QPdBSTdHRJQXkrQTcA6wV0T8u0K92+d6n8xB8cKIGCzpw8BkSXcAy9SbnQTcHRG/lPR54NsV8nwVGAQMBNYHppaCW+CTQH/gGWAysDNwX4U61gF2BPYEbs75Ds11DYqIGYW8nweeiYiR+fjXkvQh4FpgVERMlbQm8GalA8rn4HCAfv36VTlsMzMzMzMAvhURL0tajRSb3lDY9378XqXsT3PZXsBdkraLiLMl/RDYLSJezPl6Aw9GxE8l/Qo4DDgVOAs4MyLuk9QPuB3YuqyNE4HPRcR/9cHl5LYlxeKrAv8H/CQiPinpTOBg4HflnXWcbGZmZt1dT5jBXCTgF5JmAf8ANgY2qJBva+AC4MtVBpcBpkTEk3l7D+BgSTOAh4D1gC2a6McuwJ8AIuI2YEGVPNdExOKIeB6YAAwutP2fiFgCzADqqrTztzx43gg8HxGNucycCmUagd0lnS5paEQsBLYEno2Iqbmvr0bEe5UaiogLIqI+Iur79u3bxKGbmZmZmXGUpJnAg8AmNB07l9tP0sPAdNKki22q5HsHKD1bZRpL498RwLk5dr8ZWFPSGmVlJwPjJB0G9Cqk3xMRr0XEfGAh8Lec3kiVmNxxspmZmXV3PWEGc9GBQF9gh4h4V9I80uyDcs/m9E+SZglX8nphW8CREXF7MYOkuuXtcBVvF7YXU/06lvItKSuzpLxMRDwhaXvgi8Cpku4Cbmyf7pqZmZmZJZKGkQZ5d4yINyTdS+WYvFLZjwHHAoMjYkFe+q1a2XcLdyoWY+aVgE9HxFtldb+/HRFjJH0KGAlMk7RD3lUeUxfj7Z723crMzMwM6BkzmF8DSusGrwW8kAeXdwM2rVLmFVIw+cscADfnduAISasASPqEpN5N5J8M7Jfz7kFayqLcJGCU0vrOfYFdgSkt6EubSNoIeCMirgTOIC0B8jiwoaTBOU8ftfChhmZmZmZmVawFLMiDy1sBny7bX4zfy61JmuixMK+p/IUWliu6Aziy9EbSoPIMkjaLiIci4kRgPmmWtZmZmZlV0O0HCyPiJUmTJc0GpgJbSWoEGoDHmij3vKQvAX+X9C3SrIcxEXFohewXkW6Je1hp6sN8YO8munUKcI2kg4AHgOdIAXHRjaT1k2eS1nL+cUQ8l4PwiiT9HGiIiJubaLuaAcAZkpYA7wJHRMQ7+WF/5+T18d4kzTZZ1Ib6zaydzRs7stZdMDMza4vbgDGSHiVNaHiwuLMsfv97RPxI0oyIGBQRMyVNJ8XxT5MmbpRcANwm6ZmI2K2J9o8Cfp+XzVsZmAiMKctzhqQtSHcq3kWKyQe19YDNuhvHoWZmVqQKz7ezDpYfBLg4It6TtCNwXhMPMely6uvro6GhodbdMDMzM2s1SdMior7W/bDuyXGymZmZdWXVYuVuP4O5k+oH/FnSSqSHjxxW4/6YmZmZmZmZmZmZtZoHmGsgIv5JeoCgmZmZmZmZmZmZWZfVEx7yZ2ZmZmZmZmZmZmYdwAPMZmZmZmZmZmZmZtYmHmA2MzMzMzMzMzMzszbxGswrmKR5QD3wHnBARPyhleV/AFwQEW/k94siYo1WlN8T2CYixramXTPrPOqOG/+B9/PGjqxRT8zMzLo+SeOAWyLi+rL0jYCzI2KfmnTMrJMojz3LORY1MzPPYK6dtYHvtqHcD4DV29poRNzswWUzMzMz626UtNv3m4h4xoPLZmZmZs3zAHMHknSTpGmS5kg6vGz3WGAzSTMknVGh7HmSGnLZU3LaUcBGwD2S7inkPU3STEkPStogp/WVdIOkqfm1c04fLencvL2vpNm57MTC/psk3SlpnqTvS/qhpOm5/nU74lyZmZmZWdcg6WBJs3IMeYWkOkl357S7JPXL+cblmPZBSXMlDZN0iaRH86zhUn2LJJ2Z4967JPWt0GbfHJ/OkXSRpKckrZ/bflzS5cBsYJNKcXSuY56kX0lqlDRF0uaFJnaVdH/u5z45f52k2Xm7l6Rf59h5lqQjc/pYSY/ktF93xPk2MzMz6+w8wNyxvhURO5CWxDhK0nqFfccB/4qIQRHxowplfxoR9cB2wGckbRcRZwPPALtFxG45X2/gwYgYCEwEDsvpZwFnRsRg4GvARRXaOBH4XC67ZyF9W+CrwGDgNOCNiPgk8ABwcKUDlXR4DuQb5s+f3+RJMTMzM7OuSVJ/4ARgeI4hjwbOAS6LiO2Aq4CzC0XWAXYEjgFuBs4E+gMDJA3KeXoDDRHRH5gAnFSh6ZOAu3Oe64F+hX1bAH+IiP4R8RQV4uhC3oURMQA4F/hdIX1DYBfgS6SJIOUOB+qAQaXjzLH9V4D+Oe3UCuUcJ5uZmVm35wHmjnWUpJnAg8AmpOC3pfaT9DAwnRSEb1Ml3zvALXl7GinwBRgBnCtpBimYX1NS+VrNk4Fxkg4DehXS74mI1yJiPrAQ+FtObyzU/wERcUFE1EdEfd++y0w6MTMzM7PuYThwXUS8CBARL5MGkK/O+68gDdSW/C0ighRHPh8RjRGxBJjD0rhyCXBt3r6yrHzJLsCfcpu3AQsK+56KiAcL75uKo68p/NyxkH5TRCyJiEeADSq0PwL4Y0S8VzjuhcBbwMWSvgq8UaGc42QzMzPr9vyQvw4iaRgpEN0xIt6QdC+wagvLfgw4FhgcEQvyLYTVyr6bg3aAxSy9pisBn46It8rqfn87IsZI+hQwEpgmaYe86+1CkSWF90vwZ8bMzMzMWq4YR5bHmNXiyqiSXs3rpY0WxNFRZbvYN9ECEfGepCHAZ4F9gO+TBuDNzMzMehTPYO44awEL8uDyVsCny/a/BvSpUnZNUqC8MK+p/IUWliu6Aziy9KZwCyKFtM0i4qGIOBGYT5plbWZmZmZWzd3AvqWl3/LzOe4Hvp73HwhMamWdK5EGaAEOAO6rkGcysF9ucw/S0huVNBVHA4wq/HygFX28E/iOpJVzH9bNdweuFRG3kpYAGdiK+szMzMy6Dc9G7Ti3AWMkPQo8Tlom430R8ZKkyfnBIX+PiB9JmpHXZJ4paTrwGPA0KaAuuQC4TdIzhXWYKzkK+L2kWaTrPBEYU5bnDElbkGZp3AXMBAa19YDNbMWYN3ZkrbtgZmY9VETMkXQaMEHSYtIyFEcCl0r6EWnSwiGtrPZ1YIikE4AXyIPAksbkNs8HTgGukXQQaWD4OdLEiw8sAddMHA2wTo6P3wb2b0UfLwI+AcyS9C5wIXAD8FdJq5Li6R+2oj6zLsOxp5mZNUdLV1cwax/19fXR0NBQ626YmZmZtZqkafkBcbaCSFoUEeXPCinP82FgcV6WYkfgvIgY1Mp25gH1pfWja8FxspmZmXVl1WJlz2A2MzMzM7POrh/wZ0krkR5yfViN+2NmZmZmmQeYzczMzMysZpqbvZzz/BP45HK2U7c85c3MzMysMj/kz8zMzMzMzMzMzMzaxAPMZmZmZmZmZmZmZtYmHmA2MzMzMzMzMzMzszbxGsxmZp1U3XHjW5Rv3tiRHdwTMzMzM+upmotJHYuamVmXmMEsaW1J321BvkX55zBJt3RAP+ZJWj9v359/1kk6oJCnXtLZ7d12lf5cJGmbCumjJZ3bju206PybmZmZ2YrnWLlqfxwrm5mZma0AXWKAGVgb6FRBW0TslDfrgAMK6Q0RcdQK6sOhEfHICmhqbTrZ+TczMzOz961NJ4vVHCubmZmZ9RxdZYB5LLCZpBmSzpR0l6SHJTVK2qupgpIGS5ouabOy9GGSJkoaL+lxSedLWinv2z/XPVvS6VXqXVTo29Dct2OKM0IkrSHp0lzXLElfk9RL0rhcd6OkYyrUfbKkyyRNkvSUpK9K+lXOf5ukVXK+eyXV5+1DJD0haQqwc6GuvpJukDQ1v3bO6UMkPZDPzf2Stszp/SVNycczS9IWZef/jOYvl5mZmZmtQI6VHSubmZmZ1UxXWYP5OGDbiBgkaWVg9Yh4VekWvAcl3RwRUV5I0k7AOcBeEfHvCvUOAbYBngJuA76qdDvf6cAOwALgDkl7R8RNTfTt2Ij4Um5zWGHfz4CFETEg71sHGARsHBHb5rS1q9S7GbBb7t8DwNci4seSbgRGAu/3R9KGwCm5zwuBe4DpefdZwJkRcZ+kfsDtwNbAY8DQiHhP0gjgF8DXgDHAWRFxlaQPAb0onP8qfUXS4cDhAP369auWzczMzMzan2PlThwrO042MzOz7q6rDDAXCfiFpF2BJcDGwAbAc2X5tgYuAPaIiGeq1DUlIuYCSLoG2AV4F7g3Iubn9KuAXSkEqa0wAvh66U1ELJA0F/i4pHOA8cAdVcr+PSLeldRIClxvy+mNpFsNiz5V1udrgU8U+rCNpFLeNSWtAawFXJZnXQSwSt7/APBTSR8F/hIR/yyUrSoiLiCdb+rr65f5AmNmZmZmK4Rj5U4WKztONjMzs+6uqyyRUXQg0BfYIc8SeB5YtUK+Z4G3gE82UVd5gNfhAV9ELAAGAveSZkBcVCXr2zn/EuDdwqyTJbTuDwMrAZ+OiEH5tXFELAL+F7gnzw75MvkcRsTVwJ7Am8Ctkoa35vjMzMzMrKYcKztWNjMzM1uhusoA82tAn7y9FvBCnrGwG7BplTKvkG6P+2XZrXhFQyR9LK8n9/+3d+9RlpXlnce/PzBiFASEjjEk2ApMEG8tFsySUbyhw6QNMnhhMEEZJQ5qgsss4+CEJGocbWSt6IiMyJCISbxrOvTQAUG0UfFCF9B0g1FQbBwUEW9cgmDT/cwf5208FqeqTxVVdc6p8/2sVavO3vvd737qOX15zlvvfvexwJeAy4FnJtk7yc7AccClfcY21cXA67ZvJNmz3aq4U1V9GjgVOHiGvvv1tRbzXm3NuZd0HbsI+JOuGFa0l7sD32uvT+g6/ljghqp6L3Ae8CRm/hklSZI0WNbKM7NWliRJWkAjsURGVf04yWVJrgHWAwe22+Em6ayPNt15tyR5AXBBklcCW4GTqurE1mQ98D5gfzprsa2uqm1JTmnbAdZW1XkzhLcR2JrkauBcfrmeG8DbgTNb3FvprP32beCDrVAHeDNAkpNazGf1lZRf/TlvTvIWOrfs/QzY0HX45BbDRjrv9xfozAZ5F53b/k6lc/vhdi8Fjk+yhc6tlO+oqp905f+Cqvqz2cYoafY2r1o56BAkSSPAWnlm1srSA2NNKknakfR43sdYaDM17nvgiObPxMRETU5ODjoMSZKkWUtyRVVNDDqOQbNWXhjWyZIkaZRNVyuPyhIZkiRJkiRJkqQhMxJLZCyEqlpH5+EhkiRJkrpYK0uSJKlfzmCWJEmSJEmSJM2JA8ySJEmSJEmSpDlxgFmSJEmSJEmSNCdjsQZzkj2Al1XV/95BuzurateFemp2ks3ARFX9KMmXq+qwJMuBw6rqI63NBPDyqjp5Pq8taXQsP2XtrNpvXrVygSKRJI0L6+Vp4zkH+Juq+vpCX0saJrOpR61FJUnjMoN5D+C1gw6iW1Ud1l4uB17WtX/SwWVJkiQtsj2wXu4Vw4kOLkuSJM1sXAaYVwH7JdmQ5N1JLklyZZJNSV4404lJDklyVZL9pux/VpIvJFmb5JtJzkqyUzt2XOv7miSnTdPvnV2xPaPF9obW7/mtza5JPtj62pjkRUl2TnJu63tTkjf06HtZkouTXJvknCQ3Jtk7yfIk13S1e2OSt7TX65KcluTyJNcleUbb//i2b0OL4YB+ky5JkqSRMW718luSfCjJF1utfEySd7X2Fyb5tdZuXZKJ6fpMsn+Szya5uuVrv6nXkiRJWurGYokM4BTgCVW1IsmDgIdW1e1J9ga+mmRNVdXUk5IcBpwBvLCqvtuj30OBg4AbgQuBY5J8GTgNeCrwU+CiJEdX1T/PENt9txe22w23+wvgtqp6Yju2J7AC2KeqntD27dGjz78CPldV70xyJPCqaa491YOq6tAkv9f6OAI4CfhfVfXhJA8Gdu6zL0mSJI2OcauXAfYDnt3i+wrwoqp6U5LVwEqgO57p+vwwsKqqVid5COMzgUeSJOk+41gABXhHko3AZ4F9gEf2aPc44Gzg96cplgEur6obqmor8FHg6cAhwLqqurWq7qVTdB4+x1iPAM7cvlFVPwVuAB6b5Iw2eHx7j/OeDnysnXMhncK9H//Uvl9B51ZE6BTb/yPJfwceXVU/73ViklcnmUwyeeutt/Z5OUmSJA2hcaiXAS6oqi3AJjqTKC5s+zfxy1p4u/v1mWQ3OoPOq9u1766qu6ZexDpZkiQtdeM4wPwHwDLgqVW1ArgFeEiPdjcDdwNPmaGvqbM47jerY761ovnJwDo6s4vPmcXp9/Kr7/nUn/ue9n0rbXZ7e5jKUcDPgX9J8pxp4jq7qiaqamLZsmWzCEmSJElDZlzq5Xta+23Alq4Z2tuYcqfnA6nBrZMlSdJSNy4DzHcAu7XXuwM/rKotSZ4NPHqac35G59a4d065Da/boUke09aSOxb4EnA58My25vHOwHHApX3GNtXFwOu2byTZs92muFNVfRo4FTi4x3mXAS9t5zwf2LPtvwX4jSR7JdkF2OFTv5M8Frihqt4LnAc8aUfnSJIkaeSMW708K736rKo7gJuSHN3a7JLkoQ/0WpIkSaNmLNZgrqofJ7ksnQfcrQcOTLIJmAS+McN5tyR5AXBBklfSmdl7UlWd2JqsB94H7A98HlhdVduSnNK2A6ytqvNmCG8jsDXJ1cC5wFVdx94OnNni3gq8Ffg28MHtD0gB3gyQ5KQW81mt3UeTHE9niYsfAHe0Dwlvo1PUf2+mn73LS4Hjk2xp/byjj3MkPQCbV60cdAiSpDEzhvXybO3Tq0/geOADrcbeAryEznIa0kizHpUkzUZ6PKtDfWizNO572MgwabOTt1bVvUmeBry/3d64KCYmJmpycnKxLidJkjRvklxRVRODjmMpGOZ6eVCskyVJ0iibrlYeixnMY2hf4BNthsUvgD8acDySJEmSJEmSliAHmOeoqtbRecjH0Kmq65n5YSuSJEnSghrmelmSJEnzZ1we8idJkiRJkiRJmmcOMEuSJEmSJEmS5sQBZkmSJEmSJEnSnLgGsyQN0PJT1j7gPjavWjkPkUiSJGkczEf92c1aVJI0NjOYk+yR5LV9tLuzfX9WkvMXII7NSfZur7/cvi9P8rKuNhNJ3jvf154mnnOSHLQY15IkSdJwGWSN3Grga9rrvurf7fXzYknyL0n2WMxrSpIkjZqxGWAG9gB2WDwvpqo6rL1cDrysa/9kVZ28SDGcWFVfX4xrSZIkaejswQLWyEn6umOy3/q3q35eFFX1e1X1s8W8piRJ0qgZpwHmVcB+STYkeXeSS5JcmWRTkhfOdGKSQ5JclWS/KfufleQLSdYm+WaSs5Ls1I4d1/q+Jslp0/R7Z1dsz2ixvaF7ZkiSXZN8sPW1McmLkuyc5NzW96Ykb+jR91uSfCjJF5PcmOSYJO9q7S9M8mut3bo2Y6Rnn0n2T/LZJFe3fO039VqSJEkaWQtVI38xyRrg663OPD3J+lbP/rcefXXXv8uSXJzk2na33Y1ddwBun0md1uf22vXYrn7WJflUkm8k+XCS9LjeuUnen+SrSW5o5/1dkn9Ncm5Xu81J9k7ysFbzX92uuf16hyT5ctt/eZLdZpl/SZKkkTdOazCfAjyhqla0mRQPrarbW7H61SRrqqqmnpTkMOAM4IVV9d0e/R4KHATcCFwIHJPOrXunAU8FfgpclOToqvrnGWJ7Y1W9oF3zWV3H/gK4raqe2I7tCawA9qmqJ7R9e0zT737As1t8XwFeVFVvSrIaWAl0xzNdnx8GVlXV6iQPYZpfSiR5NfBqgH333XeacCRJkjRkFqpGPrj1+51WJ95WVYck2QW4LMlFwP36bf4K+FxVvTPJkcCrerQ5hk79+mRgb2B9ki+0Y08BHg98H7gM+A/Al3r0sSfwNOAoYE1rd2Lra0VVbehqeyTw/apa2X7+3ZM8GPg4cGxVrU/ycODnUy9inSxJkpa6cZrB3C3AO5JsBD4L7AM8ske7xwFnA78/TeEMcHlV3VBVW4GPAk8HDgHWVdWtVXUvnUHaw+cY6xHAmds3quqnwA3AY5Oc0Yru26c594Kq2gJsAnamMwBO214+pe39+mwzMPapqtXt2ndX1V29LlRVZ1fVRFVNLFu2bE4/qCRJkgZqvmvk77TXzwdenmQD8DVgL+CAGeJ4OvAxgKq6kM6EjV5tPlpVW6vqFuBSOjX49mvfVFXbgA3cv+7d7v+2wfNNwC1Vtamdc22PczYBz0tyWpJnVNVtwO8CN1fV+hbr7a32/xXWyZIkaakb1wHmPwCWAU+tqhXALcBDerS7GbibziyI6UydeTHdTIx50waZnwysA04Czpmm6T2t/TZgS9fsk21Mmb0+iz4lSZK0NM1njfxvXa8D/ElVrWhfj6mqi+Yp5l7u6Xq9lenv2tzebtuUc3rVytfRmZW9CXh7kr+cn1AlSZJG3zgNMN8BbF8TbXfgh1W1JcmzgUdPc87P6Cwl8c4py1Z0OzTJY9JZe/lYOrffXQ48s63XtjNwHJ1ZFf3ENtXFwOu2byTZs92yuFNVfRo4lU6x+4D06rOq7gBuSnJ0a7NLkoc+0GtJkiRpaCxUjdztM8Br8stngPy7JA+bof1lwEtb2+fTWcpiqi8Cx7b1nZfRuVvw8j5imZMkvwXcVVX/CJxOp/7+JvCoJIe0Nrulz4caSpIkLSVjUwBV1Y+TXJbkGmA9cGCSTcAk8I0ZzrslyQuAC5K8ks4siJOq6sTWZD3wPmB/4PPA6qraluSUth1gbVWdN0N4G4GtSa4GzgWu6jr2duDMFvdW4K3At4EPtkFtgDcDJDmpxXxWX0n5Vfv06hM4HvhAkrcBW4CX0FlOQ9I82Lxq5aBDkCSNsQWskbudQ2fJiSvbA/duBY6eIay3Ah9Ncjyd54j8gM5AeLfVdNZPvprOHYRvqqofJDlwuk5bPTtZVWtmuPZ0ngicnmQbnZr4NVX1i/awvzOS/Dqd9ZePAO6coR9p4Kw/JUnzLT2e2aE+tRkb9z2cTx0TExM1OTk56DAkSZJmLckVVTUx6DjGWXsQ4NaqujfJ04D3tyU7Rp51siRJGmXT1cpjM4NZkiRJ0kjYF/hEu7PuF8AfDTgeSZIkzcAB5gegqtbReSieJEmSpHlQVdcz8wMEJUmSNETG6SF/kiRJkiRJkqR55ACzJEmSJEmSJGlOHGCWJEmSJEmSJM2JazDPgyQTwMur6uRZnHNnVe06h2sdDVxXVV+f7bnT9HcUcFBVrZqP/hbL8lPWDjoEaWhsXrVy0CFIkjQta+XFZ62sxWQtKklygHkeVNUkMLlIlzsaOB+4X9Gc5EFVde9sOquqNcCa+QlNkiRJ+lXWypIkSUubS2TMIMnDkqxNcnWSa5Icm+SQJF9u+y5PsluSZyU5f5o+/izJ+iQbk7x1Nm2SvLztuzrJPyQ5DDgKOD3JhiT7JVmX5D1JJoHXJ3lukquSbEryd0l2aX1tTvLWJFe2Ywe2/SckeV97/cgkq9v1rk5yWK8czHOaJUmSNIKsla2VJUmSwBnMO3Ik8P2qWgmQZHfgKuDYqlqf5OHAz6c7OcnzgQOAQ4EAa5IcXlVf2FEb4MfAqcBhVfWjJI+oqp8kWQOcX1WfaucDPLiqJpI8BLgeeG5VXZfk74HXAO9pl/tRVR2c5LXAG4ETp4T8XuDSqvrPSXYGdp0mB5IkSZK1srWyJEmSM5h3YBPwvCSnJXkGsC9wc1WtB6iq23dwm93z29dVwJXAgXQK5H7aPAf4ZFX9qF3rJzNc5+Pt++8C36mq69r2h4DDu9r9U/t+BbC8Rz/PAd7frre1qm5jSg7avvtJ8uokk0kmb7311hlClSRJ0hJhrdxHrWydLEmSljoHmGfQis+D6RSObweOmWUXAd5ZVSva1/5V9bdzaLMj/9Znu3va9630OXt9ag6S/OU07c6uqomqmli2bFmf4UiSJGlUWSv3VytbJ0uSpKXOAeYZJPkt4K6q+kfgdODfA49Kckg7vluSmYrPzwCvTLJra79Pkt/os83ngJck2avtf0Rrfwew2zTX+yawPMn+bft44NL+f2IuoXObIEl2TrJ7jxwcPIv+JEmStERZK1srS5IkgWsw78gT6TwkZBuwhU5BGeCMJL9OZ025I7pPSDIBnFRVJ1bVRUkeB3ylrf92J/CHwA+3t5+uTVVdm+R/Apcm2UrntsATgI8B/yfJycCLu69dVXcn+a/AJ1sxvx44axY/7+uBs5O8is7MjdcAD++Rg4HbvGrloEOQJEkad9bK1sqSJEmkqgYdg5aYiYmJmpycHHQYkiRJs5bkiqqaGHQcWpqskyVJ0iibrlZ2iQxJkiRJkiRJ0pw4wCxJkiRJkiRJmhOXyNC8S3IrcOOg4xiwvYEfDTqIEWTe5sa8zY15mxvzNnvmbG4GlbdHV9WyAVxXY8A6uSf/jRx9voejz/dw9Pkejr5ReQ971soOMEsLIMmk6zfOnnmbG/M2N+Ztbszb7JmzuTFv0njw7/ro8z0cfb6Ho8/3cPSN+nvoEhmSJEmSJEmSpDlxgFmSJEmSJEmSNCcOMEsL4+xBBzCizNvcmLe5MW9zY95mz5zNjXmTxoN/10ef7+Ho8z0cfb6Ho2+k30PXYJYkSZIkSZIkzYkzmCVJkiRJkiRJc+IAsyRJkiRJkiRpThxgluZBkkckuTjJ9e37nj3arEjylSTXJtmY5NhBxDpM+slba3dhkp8lOX+xYxwmSY5M8s0k30pySo/juyT5eDv+tSTLBxDmUOkjZ4cnuTLJvUlePIgYh1EfefvTJF9v/5ZdkuTRg4hz2PSRt5OSbEqyIcmXkhw0iDiHzY7y1tXuRUkqycRixidpfln/jS5r0dFnbTz6rNNH31L9zOAAszQ/TgEuqaoDgEva9lR3AS+vqscDRwLvSbLH4oU4lPrJG8DpwPGLFtUQSrIzcCbwn4CDgON6/EfzKuCnVbU/8G7gtMWNcrj0mbPvAicAH1nc6IZXn3m7CpioqicBnwLetbhRDp8+8/aRqnpiVa2gk7O/Wdwoh0+feSPJbsDrga8tboSSFoD13wiyFh191sajzzp99C3lzwwOMEvz44XAh9rrDwFHT21QVddV1fXt9feBHwLLFivAIbXDvAFU1SXAHYsU07A6FPhWVd1QVb8APkYnf9268/kp4LlJsogxDpsd5qyqNlfVRmDbIAIcUv3k7fNVdVfb/Crw24sc4zDqJ2+3d20+DPBJy/392wbw13QGKu5ezOAkLQjrv9FkLTr6rI1Hn3X66FuynxkcYJbmxyOr6ub2+gfAI2dqnORQ4MHAtxc6sCE3q7yNuX2A/9e1fVPb17NNVd0L3AbstSjRDad+cqb7m23eXgVcsKARjYa+8pbkdUm+TWc2wsmLFNsw22HekhwM/E5VrV3MwCQtGOu/0WQtOvqsjUefdfroW7KfGR406ACkUZHks8Bv9jj0590bVVVJpv0NU5JHAf8AvKKqlvxvhucrb5KGT5I/BCaAZw46llFRVWcCZyZ5GXAq8IoBhzTUkuxE57bAEwYciqRZsP6TpMGyTh9to/iZwQFmqU9VdcR0x5LckuRRVXVzG0D+4TTtHg6sBf68qr66QKEOlfnImwD4HvA7Xdu/3fb1anNTkgcBuwM/XpzwhlI/OdP99ZW3JEfQGSh4ZlXds0ixDbPZ/nn7GPD+BY1oNOwob7sBTwDWtbusfxNYk+SoqppctCglzYr135JkLTr6rI1Hn3X66FuynxlcIkOaH2v45W+UXgGcN7VBkgcDq4G/r6pPLWJsw2yHedN91gMHJHlM+7P0X+jkr1t3Pl8MfK6qxnlWUD850/3tMG9JngJ8ADiqqhwY6Ognbwd0ba4Erl/E+IbVjHmrqtuqau+qWl5Vy+msJejgsjTarP9Gk7Xo6LM2Hn3W6aNvyX5mcIBZmh+rgOcluR44om2TZCLJOa3NS4HDgROSbGhfKwYS7fDoJ28k+SLwSToPCrkpyX8cSLQD1Nax+2PgM8C/Ap+oqmuTvC3JUa3Z3wJ7JfkW8KdM/1T2sdBPzpIckuQm4CXAB5JcO7iIh0Off9ZOB3YFPtn+LRv7Dyd95u2Pk1ybZAOdv6NDf6vbQuszb5KWFuu/EWQtOvqsjUefdfroW8qfGeIvFCVJkiRJkiRJc+EMZkmSJEmSJEnSnDjALEmSJEmSJEmaEweYJUmSJEmSJElz4gCzJEmSJEmSJGlOHGCWJEmSJEmSJM2JA8ySJEmSJEmSpDlxgFmSJEmSJEmSNCf/H1B4GKBVBjllAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mSample nb 2\u001b[0m\n",
      "\u001b[34m===========\u001b[0m\n",
      "\n",
      "\u001b[31mOriginal instance\u001b[0m\n",
      "\u001b[31mLabel: section 1, comp.graphics\u001b[0m\n",
      "\u001b[31mPrediction: section 1, comp.graphics\u001b[0m\n",
      "Sorry if this is a FAQ but : \"Where can I get a 286 (16 bit) version of\n",
      "POV-Ray ? \" Any help would be greatly appreciated. I need the 286 version since Turbo Pascal won't\n",
      "let me run a 32 bit program from within my program. Any info on this would also be\n",
      "a great help. Thanks, Byron. bkidd@esk.compserv.utas.edu.au B.Kidd@cam.compserv.utas.edu.au --\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distributions(ds, expls, target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad76da8",
   "metadata": {},
   "source": [
    "The plots show how the instances belonging to the same class (and the instances classified by the model as belonging to the same class) of the instance of interest  have on average higher similarity scores, as expected. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}