diff --git a/.gitignore b/.gitignore
index cea0449..d7866cb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,6 @@
extra
.ipynb_checkpoints
-.idea/
\ No newline at end of file
+.idea/
+.vscode
+codes
+.vector_cache
\ No newline at end of file
diff --git a/LM-template.ipynb b/LM-template.ipynb
new file mode 100644
index 0000000..ffac706
--- /dev/null
+++ b/LM-template.ipynb
@@ -0,0 +1,2421 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-uXkcYhkIxS-"
+ },
+ "source": [
+ "# Language Modeling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "w_a3OXnSeV0z"
+ },
+ "source": [
+ "# 🔴 **Import Libs**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# install packages\n",
+ "!pip install -r \"/content/drive/MyDrive/Colab Notebooks/Torch-Linguist/requirements.txt\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 349
+ },
+ "executionInfo": {
+ "elapsed": 5248,
+ "status": "error",
+ "timestamp": 1713164859497,
+ "user": {
+ "displayName": "Ebi mousavi",
+ "userId": "11904011802302855819"
+ },
+ "user_tz": -210
+ },
+ "id": "vhlVJEkJeTsV",
+ "outputId": "ffa1cc70-baa3-44ab-fcb9-78441a1f1d17"
+ },
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'torchtext'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorchtext\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchtext\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datasets\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torchtext'"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import torchtext\n",
+ "from torchtext import datasets\n",
+ "\n",
+ "import torch\n",
+ "from torch import nn\n",
+ "from torch.utils.data import DataLoader, Dataset, random_split\n",
+ "\n",
+ "from torch import optim\n",
+ "from torch.nn import functional as F\n",
+ "\n",
+ "import tqdm\n",
+ "import torchmetrics as tm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 300,
+ "metadata": {
+ "id": "DEzYlyeqTZqQ"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "96129.21s - pydevd: Sending message related to process being replaced timed-out after 5 seconds\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Python 3.10.12\n",
+ "2.2.2+cu121\n",
+ "0.17.2+cpu\n"
+ ]
+ }
+ ],
+ "source": [
+ "!python --version\n",
+ "print(torch.__version__)\n",
+ "print(torchtext.__version__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 301,
+ "metadata": {
+ "id": "6DWjGTq6T8Jg"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "numpy --> 1.26.4\n",
+ "torch --> 2.2.2+cu121\n",
+ "torchtext --> 0.17.2+cpu\n",
+ "tqdm --> 4.66.2\n"
+ ]
+ }
+ ],
+ "source": [
+ "for lib in [np, torch, torchtext, tqdm]:\n",
+ " print(lib.__name__, '-->', lib.__version__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RwaY_YcgRayy"
+ },
+ "source": [
+ "# 🔴 **Utils**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 302,
+ "metadata": {
+ "id": "8yMS7bbmRayz"
+ },
+ "outputs": [],
+ "source": [
+ "class AverageMeter(object):\n",
+ " \"\"\"Computes and stores the average and current value\"\"\"\n",
+ " def __init__(self):\n",
+ " self.reset()\n",
+ "\n",
+ " def reset(self):\n",
+ " self.val = 0\n",
+ " self.avg = 0\n",
+ " self.sum = 0\n",
+ " self.count = 0\n",
+ "\n",
+ " def update(self, val, n=1):\n",
+ " self.val = val\n",
+ " self.sum += val * n\n",
+ " self.count += n\n",
+ " self.avg = self.sum / self.count"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 303,
+ "metadata": {
+ "id": "PpKbTUEIRayz"
+ },
+ "outputs": [],
+ "source": [
+ "def num_trainable_params(model):\n",
+ " nums = sum(p.numel() for p in model.parameters() if p.requires_grad)/1e6\n",
+ " return nums"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RTql4Ftiunfr"
+ },
+ "source": [
+ "# 🔴 **Dataset**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ujIVtjsYvxOI"
+ },
+ "source": [
+ "## 🟠 **Load the Dataset**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ek9DpCNCChzF"
+ },
+ "source": [
+ "🔰 In this session you should load WikiText2 dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 304,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from typing import Union, Tuple\n",
+ "from torchdata.datapipes.iter import FileOpener, IterableWrapper\n",
+ "from torchtext.data.datasets_utils import _wrap_split_argument, _create_dataset_directory\n",
+ "\n",
+ "DATA_DIR = \"data\"\n",
+ "\n",
+ "NUM_LINES = {\n",
+ " \"train\": 36718,\n",
+ " \"valid\": 3760,\n",
+ " \"test\": 4358,\n",
+ "}\n",
+ "\n",
+ "DATASET_NAME = \"WikiText2\"\n",
+ "\n",
+ "_EXTRACTED_FILES = {\n",
+ " \"train\": \"wiki.train.tokens\",\n",
+ " \"test\": \"wiki.test.tokens\",\n",
+ " \"valid\": \"wiki.valid.tokens\",\n",
+ "}\n",
+ "\n",
+ "\n",
+ "def _filepath_fn(root, split):\n",
+ " return os.path.join(root, _EXTRACTED_FILES[split])\n",
+ "\n",
+ "\n",
+ "@_create_dataset_directory(dataset_name=DATASET_NAME)\n",
+ "@_wrap_split_argument((\"train\", \"valid\", \"test\"))\n",
+ "\n",
+ "def WikiText2(root: str, split: Union[Tuple[str], str]):\n",
+ " url_dp = IterableWrapper([_filepath_fn(DATA_DIR, split)])\n",
+ " data_dp = FileOpener(url_dp, encoding=\"utf-8\").readlines(strip_newline=False, return_path=False).shuffle().set_shuffle(False).sharding_filter()\n",
+ " return data_dp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 305,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_iter = WikiText2(root=DATA_DIR, split=\"train\")\n",
+ "valid_iter = WikiText2(root=DATA_DIR, split=\"valid\")\n",
+ "test_iter = WikiText2(root=DATA_DIR, split=\"test\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 306,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 306,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_iter_ = iter(train_iter)\n",
+ "train_iter_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 307,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "' \\n'"
+ ]
+ },
+ "execution_count": 307,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "next(train_iter_)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wCi-ofSLCzop"
+ },
+ "source": [
+ "## 🟠 **Build vocabulary and save it**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "L02PHFuyNRb3"
+ },
+ "source": [
+ "🔰 In this section we need to:\n",
+ "\n",
+ "* Define a tokenizer using `basic_english`\n",
+ "* Tokenize the dataset and collect tokens\n",
+ "* Build the vocabulary using `build_vocab_from_iterator`\n",
+ "* Manually insert special tokens and set the default index\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 308,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from torchtext.data.utils import get_tokenizer\n",
+ "from torchtext.vocab import build_vocab_from_iterator\n",
+ "\n",
+ "def build_and_save_vocabulary(train_iter, vocab_path='vocab.pt', min_freq=4):\n",
+ " \"\"\"\n",
+ " Build a vocabulary from the training data iterator and save it to a file.\n",
+ " \n",
+ " Args:\n",
+ " train_iter (iterator): An iterator over the training data.\n",
+ " vocab_path (str, optional): The path to save the vocabulary file. Defaults to 'vocab.pt'.\n",
+ " min_freq (int, optional): The minimum frequency of a word to be included in the vocabulary. Defaults to 4.\n",
+ " \n",
+ " Returns:\n",
+ " torchtext.vocab.Vocab: The built vocabulary.\n",
+ " \"\"\"\n",
+ "\n",
+ " # Get the tokenizer\n",
+ " tokenizer = get_tokenizer(\"basic_english\")\n",
+ " \n",
+ " # Build the vocabulary\n",
+ " vocab = build_vocab_from_iterator(map(tokenizer, train_iter), \n",
+ " specials=['', '', ''], \n",
+ " min_freq=min_freq)\n",
+ " \n",
+ " # Set the default index to the unknown token, and save vocab\n",
+ " vocab.set_default_index(vocab[''])\n",
+ " torch.save(vocab, vocab_path)\n",
+ " \n",
+ " return vocab"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 309,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "23654"
+ ]
+ },
+ "execution_count": 309,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vocab = build_and_save_vocabulary(train_iter, vocab_path='my_vocab.pt')\n",
+ "len(vocab)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Print the first 10 tokens in the vocabulary\n",
+ "for token, index in list(vocab.get_stoi().items())[:10]:\n",
+ " print(f\"Token: {token}, Index: {index}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 311,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['__class__',\n",
+ " '__contains__',\n",
+ " '__delattr__',\n",
+ " '__dir__',\n",
+ " '__doc__',\n",
+ " '__eq__',\n",
+ " '__format__',\n",
+ " '__ge__',\n",
+ " '__getattribute__',\n",
+ " '__getitem__',\n",
+ " '__getstate__',\n",
+ " '__gt__',\n",
+ " '__hash__',\n",
+ " '__init__',\n",
+ " '__init_subclass__',\n",
+ " '__le__',\n",
+ " '__len__',\n",
+ " '__lt__',\n",
+ " '__module__',\n",
+ " '__ne__',\n",
+ " '__new__',\n",
+ " '__reduce__',\n",
+ " '__reduce_ex__',\n",
+ " '__repr__',\n",
+ " '__setattr__',\n",
+ " '__setstate__',\n",
+ " '__sizeof__',\n",
+ " '__str__',\n",
+ " '__subclasshook__',\n",
+ " 'append_token',\n",
+ " 'default_index_',\n",
+ " 'get_default_index',\n",
+ " 'get_itos',\n",
+ " 'get_stoi',\n",
+ " 'insert_token',\n",
+ " 'itos_',\n",
+ " 'lookup_indices',\n",
+ " 'lookup_token',\n",
+ " 'lookup_tokens',\n",
+ " 'set_default_index']"
+ ]
+ },
+ "execution_count": 311,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dir(vocab.vocab)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from torchtext.vocab import build_vocab_from_iterator, GloVe\n",
+ "\n",
+ "# Assuming `dataset` is your list of text samples\n",
+ "tokenizer = get_tokenizer(\"basic_english\")\n",
+ "\n",
+ "# Step 2: Load the GloVe embeddings\n",
+ "glove_vectors = GloVe(name='6B', dim=100)\n",
+ "\n",
+ "# Step 3: Create a new vocabulary with GloVe embeddings for tokens in the dataset\n",
+ "vocab_with_embeddings = {}\n",
+ "for idx, token in enumerate(vocab.vocab.itos_):\n",
+ " if vocab.vocab.__contains__(token):\n",
+ " vocab_with_embeddings[token] = glove_vectors[token]\n",
+ " \n",
+ "# Example usage\n",
+ "print(vocab_with_embeddings.keys())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vocab.vocab.itos_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "23654\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[2, 1975, 2, 0, 1, 2]"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# You can now use the vocabulary\n",
+ "print(len(vocab))\n",
+ "vocab(['ebi', 'AI'.lower(), 'qwerty', '', '', ''])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "B29jrEvwRqXA"
+ },
+ "source": [
+ "## 🟠 EDA"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pHtoYxEPd3bL"
+ },
+ "source": [
+ "### 🟡 Let's explore the WikiText2 dataset!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A3rnR739GbYb"
+ },
+ "source": [
+ "### 🟡 Calculate basic statistics such as the number of documents, total words, average document length, etc."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean sentence length in Wikitext-2:\n",
+ "Train: 21.69\n",
+ "Valid: 21.54\n",
+ "Test: 21.13\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "def compute_mean_sentence_length(data_iter):\n",
+ " \"\"\"\n",
+ " Computes the mean sentence length for the given data iterator.\n",
+ " \n",
+ " Args:\n",
+ " data_iter (iterable): An iterable of text data, where each element is a string representing a line of text.\n",
+ " \n",
+ " Returns:\n",
+ " float: The mean sentence length.\n",
+ " \"\"\"\n",
+ " total_sentence_count = 0\n",
+ " total_sentence_length = 0\n",
+ "\n",
+ " for line in data_iter:\n",
+ " sentences = line.split('.') # Split the line into individual sentences\n",
+ "\n",
+ " for sentence in sentences:\n",
+ " tokens = sentence.strip().split() # Tokenize the sentence\n",
+ " sentence_length = len(tokens)\n",
+ "\n",
+ " if sentence_length > 0:\n",
+ " total_sentence_count += 1\n",
+ " total_sentence_length += sentence_length\n",
+ "\n",
+ " mean_sentence_length = total_sentence_length / total_sentence_count\n",
+ " return mean_sentence_length\n",
+ "\n",
+ "# Compute mean sentence length for each dataset\n",
+ "train_mean = compute_mean_sentence_length(train_iter)\n",
+ "valid_mean = compute_mean_sentence_length(valid_iter)\n",
+ "test_mean = compute_mean_sentence_length(test_iter)\n",
+ "\n",
+ "# Print the results\n",
+ "print(f'Mean sentence length in Wikitext-2:')\n",
+ "print(f'Train: {train_mean:.2f}')\n",
+ "print(f'Valid: {valid_mean:.2f}')\n",
+ "print(f'Test: {test_mean:.2f}')\n",
+ "\n",
+ "# Plot the results\n",
+ "datasets = ['Train', 'Valid', 'Test']\n",
+ "means = [train_mean, valid_mean, test_mean]\n",
+ "\n",
+ "plt.figure(figsize=(6, 4))\n",
+ "plt.bar(datasets, means)\n",
+ "plt.xlabel('Dataset')\n",
+ "plt.ylabel('Mean Sentence Length')\n",
+ "plt.title('Mean Sentence Length in Wikitext-2')\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "a4HyLPqcsF43"
+ },
+ "source": [
+ "### 🟡 Analyze the most common and least common words in the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Least Common Words:\n",
+ "gallinae: 3\n",
+ "intergrades: 3\n",
+ "northeasterly: 3\n",
+ "tuscola: 3\n",
+ "roundabouts: 3\n",
+ "zoromski: 3\n",
+ "forrester: 3\n",
+ "kreutzer: 3\n",
+ "prefaced: 3\n",
+ "philipp: 3\n",
+ "\n",
+ "Most Common Words:\n",
+ "the: 130768\n",
+ ",: 102615\n",
+ ".: 83397\n",
+ "of: 57030\n",
+ ": 54625\n",
+ "and: 50735\n",
+ "in: 45015\n",
+ "to: 39521\n",
+ "a: 36523\n",
+ "=: 29570\n"
+ ]
+ }
+ ],
+ "source": [
+ "from collections import Counter\n",
+ "\n",
+ "# Compute word frequencies in the training dataset\n",
+ "freqs = Counter()\n",
+ "for tokens in map(tokenizer, train_iter):\n",
+ " freqs.update(tokens)\n",
+ "\n",
+ "# Find the 10 least common words\n",
+ "least_common_words = freqs.most_common()[:-11:-1]\n",
+ "print(\"Least Common Words:\")\n",
+ "for word, count in least_common_words:\n",
+ " print(f\"{word}: {count}\")\n",
+ "\n",
+ "# Find the 10 most common words\n",
+ "most_common_words = freqs.most_common(10)\n",
+ "print(\"\\nMost Common Words:\")\n",
+ "for word, count in most_common_words:\n",
+ " print(f\"{word}: {count}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 🟡 Count the number of words that repeat 3 times, 4 times, and 5 times in the Wikitext-2 training dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of words that appear 3 times: 5130\n",
+ "Number of words that appear 4 times: 3243\n",
+ "Number of words that appear 5 times: 2261\n"
+ ]
+ }
+ ],
+ "source": [
+ "from collections import Counter\n",
+ "\n",
+ "# Compute word frequencies in the training dataset\n",
+ "freqs = Counter()\n",
+ "for tokens in map(tokenizer, train_iter):\n",
+ " freqs.update(tokens)\n",
+ "\n",
+ "# Count the number of words that repeat 3, 4, and 5 times\n",
+ "count_3 = count_4 = count_5 = 0\n",
+ "for word, freq in freqs.items():\n",
+ " if freq == 3:\n",
+ " count_3 += 1\n",
+ " elif freq == 4:\n",
+ " count_4 += 1\n",
+ " elif freq == 5:\n",
+ " count_5 += 1\n",
+ "\n",
+ "print(f\"Number of words that appear 3 times: {count_3}\")\n",
+ "print(f\"Number of words that appear 4 times: {count_4}\")\n",
+ "print(f\"Number of words that appear 5 times: {count_5}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cfBasjQCE_aI"
+ },
+ "source": [
+ "### 🟡 Word Length Distribution\n",
+ "\n",
+ "- Compute the distribution of word lengths (i.e., the number of characters per word) in the dataset.\n",
+ "- This can reveal insights about the writing style or genre of the corpus."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "id": "yR8uQsv4E_aJ"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from collections import Counter\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Compute the word lengths in the training dataset\n",
+ "word_lengths = []\n",
+ "for tokens in map(tokenizer, train_iter):\n",
+ " word_lengths.extend(len(word) for word in tokens)\n",
+ "\n",
+ "# Create a frequency distribution of word lengths\n",
+ "word_length_counts = Counter(word_lengths)\n",
+ "\n",
+ "# Plot the word length distribution\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.bar(word_length_counts.keys(), word_length_counts.values())\n",
+ "plt.xlabel(\"Word Length\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.title(\"Word Length Distribution in Wikitext-2 Dataset\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 🟡 Explore Part-of-Speech (POS) Tagging\n",
+ "\n",
+ "- Perform part-of-speech tagging on the dataset to categorize words into grammatical classes (e.g., nouns, verbs, adjectives).\n",
+ "- Analyze the distribution of different POS tags and identify any interesting patterns or deviations from standard language models."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[('This', 'PRON'), ('is', 'AUX'), ('a', 'DET'), ('sentence', 'NOUN'), ('.', 'PUNCT')]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import spacy\n",
+ "import en_core_web_sm\n",
+ "\n",
+ "nlp = spacy.load(\"en_core_web_sm\")\n",
+ "nlp = en_core_web_sm.load()\n",
+ "doc = nlp(\"This is a sentence.\")\n",
+ "print([(w.text, w.pos_) for w in doc])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import spacy\n",
+ "\n",
+ "# Load the English language model\n",
+ "nlp = spacy.load(\"en_core_web_sm\")\n",
+ "\n",
+ "# Perform POS tagging on the training dataset\n",
+ "pos_tags = []\n",
+ "for tokens in map(tokenizer, train_iter):\n",
+ " doc = nlp(\" \".join(tokens))\n",
+ " pos_tags.extend([(token.text, token.pos_) for token in doc])\n",
+ "\n",
+ "# Count the frequency of each POS tag\n",
+ "pos_tag_counts = Counter(tag for _, tag in pos_tags)\n",
+ "\n",
+ "# Print the most common POS tags\n",
+ "print(\"Most Common Part-of-Speech Tags:\")\n",
+ "for tag, count in pos_tag_counts.most_common(10):\n",
+ " print(f\"{tag}: {count}\")\n",
+ "\n",
+ "# Visualize the POS tag distribution\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "plt.bar(pos_tag_counts.keys(), pos_tag_counts.values())\n",
+ "plt.xticks(rotation=90)\n",
+ "plt.xlabel(\"Part-of-Speech Tag\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.title(\"Part-of-Speech Tag Distribution in Wikitext-2 Dataset\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's a brief explanation of the most common POS tags in the provided output:\n",
+ "\n",
+ "1. **NOUN**: Nouns are the most common part of speech, accounting for a significant portion of the text. Nouns represent people, places, things, or ideas.\n",
+ "\n",
+ "2. **ADP**: Adpositions, such as prepositions and postpositions, are used to express relationships between words or phrases.\n",
+ "\n",
+ "3. **PUNCT**: Punctuation marks, which are essential for separating and structuring sentences and text.\n",
+ "\n",
+ "4. **VERB**: Verbs describe actions, states, or occurrences in the text.\n",
+ "\n",
+ "5. **DET**: Determiners, such as articles (e.g., \"the,\" \"a,\" \"an\"), provide additional information about nouns.\n",
+ "\n",
+ "6. **X**: This tag is often used for foreign words, abbreviations, or other language-specific tokens that don't fit into the standard POS categories.\n",
+ "\n",
+ "7. **PROPN**: Proper nouns, which represent specific names of people, places, organizations, or other entities.\n",
+ "\n",
+ "8. **ADJ**: Adjectives modify or describe nouns and pronouns.\n",
+ "\n",
+ "9. **PRON**: Pronouns substitute for nouns, making the text more concise and less repetitive.\n",
+ "\n",
+ "10. **NUM**: Numerals, which represent quantities, dates, or other numerical information.\n",
+ "\n",
+ "This distribution of POS tags can provide insights into the linguistic characteristics of the text, such as the predominance of nouns, the prevalence of adpositions, or the usage of proper nouns, which can be helpful in tasks like text classification, information extraction, or stylometric analysis."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 🟡 Investigate Named Entity Recognition:\n",
+ "\n",
+ "- Apply named entity recognition (NER) to the dataset to identify and classify named entities (e.g., people, organizations, locations).\n",
+ "- Analyze the types and frequencies of named entities present in the corpus, which can provide insights into the content and focus of the Wikitext-2 dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Most Common Named Entity Types:\n",
+ "DATE: 34280\n",
+ "CARDINAL: 28761\n",
+ "PERSON: 19093\n",
+ "GPE: 16455\n",
+ "NORP: 9392\n",
+ "ORDINAL: 8287\n",
+ "ORG: 8246\n",
+ "QUANTITY: 4115\n",
+ "LOC: 2015\n",
+ "MONEY: 1498\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import spacy\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Load the English language model\n",
+ "nlp = spacy.load(\"en_core_web_sm\")\n",
+ "\n",
+ "# Perform NER on the training dataset\n",
+ "named_entities = []\n",
+ "for tokens in map(tokenizer, train_iter):\n",
+ " doc = nlp(\" \".join(tokens))\n",
+ " named_entities.extend([(ent.text, ent.label_) for ent in doc.ents])\n",
+ "\n",
+ "# Count the frequency of each named entity type\n",
+ "ner_counts = Counter(label for _, label in named_entities)\n",
+ "\n",
+ "# Print the most common named entity types\n",
+ "print(\"Most Common Named Entity Types:\")\n",
+ "for label, count in ner_counts.most_common(10):\n",
+ " print(f\"{label}: {count}\")\n",
+ "\n",
+ "# Visualize the named entity distribution\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "plt.bar(ner_counts.keys(), ner_counts.values())\n",
+ "plt.xticks(rotation=90)\n",
+ "plt.xlabel(\"Named Entity Type\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.title(\"Named Entity Distribution in Wikitext-2 Dataset\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's a brief explanation of the most common named entity types in the output:\n",
+ "\n",
+ "1. **DATE**: This entity type represents specific dates, time periods, or temporal expressions, such as \"June 15, 2024\" or \"last year\".\n",
+ "\n",
+ "2. **CARDINAL**: This entity type includes numerical values, such as quantities, ages, or measurements.\n",
+ "\n",
+ "3. **PERSON**: This entity type identifies the names of individual people.\n",
+ "\n",
+ "4. **GPE** (Geopolitical Entity): This entity type represents named geographical locations, such as countries, cities, or states.\n",
+ "\n",
+ "5. **NORP** (Nationalities, Religious, or Political Groups): This entity type includes named groups or affiliations based on nationality, religion, or political ideology.\n",
+ "\n",
+ "6. **ORDINAL**: This entity type represents ordinal numbers, such as \"first,\" \"second,\" or \"3rd\".\n",
+ "\n",
+ "7. **ORG** (Organization): This entity type identifies the names of companies, institutions, or other organized groups.\n",
+ "\n",
+ "8. **QUANTITY**: This entity type includes non-numeric quantities, such as \"a few\" or \"several\".\n",
+ "\n",
+ "9. **LOC** (Location): This entity type represents named geographical locations, such as continents, regions, or landforms.\n",
+ "\n",
+ "10. **MONEY**: This entity type identifies monetary values, such as dollar amounts or currency names.\n",
+ "\n",
+ "This distribution of named entity types can provide valuable insights into the content and focus of the text. For example, the prominence of DATE and CARDINAL entities may suggest a text that deals with numerical or temporal information, while the prevalence of PERSON, ORG, and GPE entities could indicate a text that discusses people, organizations, and geographical locations.\n",
+ "\n",
+ "Understanding the named entity distribution can be useful in a variety of applications, such as information extraction, question answering, and text summarization, where identifying and categorizing key named entities is crucial for understanding the context and content of the text."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 🟡 Perform Topic Modeling (To-do):\n",
+ "\n",
+ "- Apply topic modeling techniques, such as Latent Dirichlet Allocation (LDA), to uncover the underlying thematic structure of the corpus.\n",
+ "- Analyze the identified topics and their distributions, which can reveal the main themes and subject areas covered in the Wikitext-2 dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 🟡 Generating a Word Cloud for the Wikitext-2 Training Dataset\n",
+ "This code generates a single word cloud visualization that highlights the most frequent words in the entire Wikitext-2 training dataset, providing a high-level overview of the prominent themes and topics present in the corpus."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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2yallxONxhoeHufLKK0mlUhw9ehRg/Jl94oknSKVSU5bz61//GsMweOtb3zphbJaVlTF//vwpI8ubmJiYvJYwBVsTE5PXBJIksXHjxnFf2u3bt1NSUjIeCOdkwfbUaLgdHR3IsjwpaE5ZWRmBQICOjo4J2+vq6ibV39HRQWNj47i/5hgLFy6cte0+nw9gwoT3dOno6JiyrjFzxVOvYS7lAcyfP3/CdqvVSn19/Rm2Ml9uRUXFJCF+unZO1dfTsWnTJiRJmnCfx3yoA4EAS5YsmbBv/fr1c4q8+7WvfY3f/OY3PPTQQ3PK3zsTTU1NHDp0iOLi4gl/CxYsAPIBt07mm9/8JsFgkL179/Jv//ZvlJSUzKmeaDRKf3//+N/IyMiUx33jG9/ghz/8IV/+8pe56aabzuraTmaq+5ZOp/nCF74w7ltdVFREcXExkUhkkp/nVFRVVU16vgoKCgiHw7Oe63A4KC4unvHcjo4OysvLJ5lETxdM60xIJBITxv7Z9gnAoUOHuO222/D7/fh8PoqLi8cXAMbKqKur45Of/CT/9V//RVFRETfeeCPf+973JtTR1NSEEIL58+dPGp9HjhyZNDZNTExMXmuYPrYmJiavGTZv3szvfvc7Dhw4MO5fO8bGjRvH/Ti3bdtGRUXFJAHt1EnzdJxtBORT8fl8VFRUcPDgwXNa7lRMd42z5TC9WJxOXxcWFrJo0SK2bdtGIpFg//79fPGLXxzfv3HjRrZt20Z3dzednZ38xV/8xZzKvfHGG3n88cf5+te/ztVXX31WUXcNw2D58uV8+9vfnnL/qb7Le/bsGRcoDhw4wNvf/vY51fPXf/3XE1L3XHXVVZOCSv34xz/mc5/7HB/4wAf4+7//+9O4itmZ6r599KMf5Uc/+hEf//jHueKKK/D7/UiSxF133TWnNEunaoDHEKcEHTudcy8kuVyO48ePs2zZsvFtZ9snkUiEq666Cp/Pxz/+4z/S0NCAw+Fg9+7dfO5zn5tQxre+9S3uueceHnnkEZ588kk+9rGP8bWvfY0XX3yRqqoqDMNAkiQee+yxKfvL9KM1MTF5rWMKtiYmJq8ZTk75sn379gk5FteuXYvdbue5557jpZdemqCdqqmpwTAMmpqaJgTeGRgYIBKJUFNTM2vdNTU1HDx4ECHEBOHx2LFjc2r7G9/4Ru677z527tw5IaXKXKmpqZmyrjFTxLFrKCgoAPIT4pM5VVM6dnxTU9OEvJu5XI62tjZWrlx52m0cK/fpp58mHo9P0Fyd2s4zZfPmzfzP//wPTz75JLquT1rc+PnPfz4u4M01cNTll1/OBz7wAd74xjdy55138vDDD89qsjvdAkJDQwP79u3juuuum3UhJZlM8ld/9VcsWbKEjRs38vWvf53bbruN9evXz1rPZz/72Qlmu2P3fYxHHnmE9773vdx+++1873vfm7Ed54qHHnqIu+++m29961vj2zKZzKSxeLGoqanh2WefJZVKTdDaNjc3n5PyH3roIdLp9AQ3hrn2yXT3+bnnniMUCvHrX/+aLVu2jG9va2ub8vjly5ezfPly/v7v/54dO3awadMmfvCDH/CVr3yFhoYGhBDU1dWNWxBMx1wXAU1MTEwuJUxTZBMTk9cM69atw+Fw8NOf/pSenp4JQo3dbmfNmjV873vfI5lMThBqxoTc73znOxPKG9OqTRf99mRuuukment7eeihh8a3pVIp7rvvvjm1/bOf/Sxut5v3vve9DAwMTNrf0tLCv/7rv85Y/65du9i5c+f4tmQyyX333Udtbe24n2pDQwMAzz///Phxuq5Paue6desoLi7mBz/4Aaqqjm//8Y9/fFaCyE033YSu6xNSngD8y7/8C5IkzRrddjY2b96Mrut885vfHDepHGPjxo0kEgm+//3vI8vyaUWgvv766/nFL37B448/zjvf+c5ZtWlut3tKU9K3vvWt9PT08MMf/nDSvnQ6PR5BGOBzn/scnZ2d3H///Xz729+mtraWu+++e0JaoLH8sKfekyVLlnD99deP/61du3Z83/PPP89dd93Fli1b+OlPf4osX5hPvaIok7Sr//7v/37JWAvceOON5HK5CffGMIxzIvjv27ePj3/84xQUFEzw2Z1rn0x3n8c0qyeXoaoq3//+9yccF4vF0DRtwrbly5cjy/L4eLr99ttRFIUvfelLk9okhCAUCk1oz1xNpU1MTEwuFUyNrYmJyWsGm83G+vXreeGFF7Db7RMm85AXbMY0IycLtitXruTuu+/mvvvuGzft27VrF/fffz+33nor11xzzax1v+997+O73/0u73rXu3j11VcpLy/ngQcemDWFyRgNDQ387Gc/421vexuLFy/mXe96F8uWLUNVVXbs2MEvf/lL7rnnnmnP/5u/+Rt+/vOf84Y3vIGPfexjBINB7r//ftra2vjVr341LrwsXbqUyy+/nM9//vOMjIwQDAb5xS9+MWnSa7Va+cpXvsK9997Ltddey9ve9jba2tr40Y9+dFY+tm9605u45ppr+Lu/+zva29tZuXIlTz75JI888ggf//jHxwXvM2Xsvu7cuXNSfy1YsICioiJ27tzJ8uXLCQQCp1X2rbfeyo9+9CPe9a534fP5ZgzktHbtWv7v//6PT37yk6xfvx6Px8Ob3vQm3vnOd/Lggw/ygQ98gGeffZZNmzah6zpHjx7lwQcfHM+PvHXrVr7//e/zxS9+kTVr1gDwox/9iKuvvpp/+Id/GM/fumrVKhRF4Z//+Z+JRqPY7XauvfbaaX1xOzo6uOWWW5AkiTvuuINf/vKXE/avWLFiPCXWueaNb3wjDzzwAH6/nyVLlrBz506efvrp8ZRbF5tbb72VDRs28KlPfYrm5mYWLVrEb3/723H/5LlqKV944QUymQy6rhMKhdi+fTu//e1v8fv9PPzww5SVlY0fO9c+me4+b9y4kYKCAu6++24+9rGPIUkSDzzwwCTBdOvWrXzkIx/hzjvvZMGCBWiaxgMPPICiKLzlLW8B8u+gr3zlK3z+85+nvb2dW2+9Fa/XS1tbGw8//DDvf//7+fSnPw1MP75NTExMLmkuUjRmExMTkzPi85//vADExo0bJ+379a9/LQDh9XonpSLJ5XLiS1/6kqirqxNWq1VUV1eLz3/+8+PpMsaoqakRN99885R1d3R0iFtuuUW4XC5RVFQk/vqv/1o8/vjjp5WO5fjx4+J973ufqK2tFTabTXi9XrFp0ybx7//+7xPacmq6HyGEaGlpEXfccYcIBALC4XCIDRs2iN///veT6mhpaRHXX3+9sNvtorS0VPzt3/6teOqpp6Zs5/e//31RV1cn7Ha7WLdunXj++efFVVdddcbpfoTIpyH6xCc+ISoqKoTVahXz588X3/jGN4RhGBOOA8SHP/zhWes5lYqKCgGI++67b9K+W265RQDigx/84KR9M6X7OZnvf//7AhCf/vSnhRBTp6dJJBLiHe94hwgEAgKYUK6qquKf//mfxdKlS4XdbhcFBQVi7dq14ktf+pKIRqMiFouJmpoasWbNmknpdz7xiU8IWZbFzp07x7f98Ic/FPX19UJRlFnH2lhbp/v74he/OO25pzJTup+XX3550vHhcFj81V/9lSgqKhIej0fceOON4ujRo5PG8nTpfqYaS9Pds1PT/bjd7knnfvGLXxSnTnOGhobEO97xDuH1eoXf7xf33HOP2L59uwDEL37xixn749S+tVqtori4WGzZskV89atfFYODg2fcJ0JMf5+3b98uLr/8cuF0OkVFRYX47Gc/O54qauyY1tZW8e53v1s0NDQIh8MhgsGguOaaa8TTTz89qU2/+tWvxObNm4Xb7RZut1ssWrRIfPjDHxbHjh0bP2am8W1iYmJyqSIJMYeoDCYmJiYmJiYmf4L85je/4bbbbmPbtm3jUbZNTExMTF57mIKtiYmJiYmJyZ8F6XR6QkRnXde54YYbeOWVV+jv7z/nEdFNTExMTC4cpo+tiYmJiYmJyZ8FH/3oR0mn01xxxRVks1l+/etfs2PHDv7pn/7JFGpNTExMXuOYGlsTExMTExOTPwt+9rOf8a1vfYvm5mYymQyNjY188IMf5CMf+cjFbpqJiYmJyVliCrYmJiYmJiYmJiYmJiYmr2nMPLYmJiYmJiYmJiYmJiYmr2lMwdbExMTExMTExMTExMTkNY0p2JqYmJiYmJiYmJiYmJi8pjEFWxMTExMTExMTExMTE5PXNGa6HxOT1xi6bpCIpnn1heM0HephoGeEeDSNrhlYrDIut4NgiZfa+WVsuHoRZdXBi93kP1maD/Xw2IO7aDrYA8BdH7iG5Rvq2N/Wz65DnVOeI0mwYn4F165fcCGbOiW/3HuQZ463MJRMTrl/WXkpNy1eyGU1VXMuc2d7J787dIxjg0NU+H28cclCblw0/1w12cTExMTE5E+Cffs6OXykh6uvXkxZqR9Jki52k17zXHKC7XAsyUvHOjncOcCdm1dQWejHalEudrNMLmGSGZVtB9to6hnmxnULmF9ZfM7rSGVVHnxuH5cvqaGmtACnzQpAIp3lSOcgu452MhhJcMO6haysL8fjtJ/zNgBkMznaj/fz25/soOP4AOFQnFQiSy6rYRgCWZGwWBUcThuxcIqFK6tNwfY8kk5m6W4boulgNwDRcBJdM6go8bPWqCaSSHOwuY8F84op8LnIZHP0DEXJacZFbnmeoUSSY0PD9ERjU+5322zEMpnTKjOaydIaGuFA3wCJrEqotvpcNNVklL5kjO0D7Tzf2zLnc26uWcLmslrc1vPzXjI5fdqiIzzf087LA91YZYVrqurZUlVLwG7m0jUxOR80R4f5Y18L+4Z753zOPQvXs6SgFIfFel7aVFFZgMNpxeed/Nwnk1lCIwlyOZ2G+pJJ+zOZHMOhBIlEhob6EqxWU1aCS1CwtSoKTpuVY91DxFNZ9AIDK+bNMpmevpEYhzsGONI5QFmB57wItookU17ow2W3IZ+0oqbIMj6XnapiP1v3NrO0tozF8ya/gM4VA90jPPPIbrY/eZBsOofNbqG4PEBBsRebzYKhG2QzOaKRJAVFHuyO8/MyNpmZqpIApUEv3QMRjncMctnyGvweJ2pOZ+f+Ni6VLGuX1VThslkZTqbIahpJVaV5eISjA0NkNO1iN89kCmK5LAdH+vl955E5n7MgUMyG4mrc5uvgkiGczbBnsJfftR7FriiUub1sKKsCc+3BxOS8MJJN8epQN493HZvzOTdUL2RB4NzPKccoLvJSXOSdcl9oJMHRo33Y7ZYpBdtYLM2RIz3oukFtTZEp2I5yyQm2freDZTVlFPs8yHJegBAChqIJekdiaJqOAOZXFpHTdIaiSVLZHIYwkCWZ2tICCjxOmntDJDMqyYyKIQTlQS8LzoPAY3LxaesbQQjBvJIAh9oHuH2zQJI4pyYddpuFG9ctnLTdabeysLqEhdUlPLbrGIpy/tzWc6pGZ8sgO58+RDadQ1ZkVm+az7K1tZRWBbE7rOi6TialEh5OULewjGDx1C9Mk/OL1aJgtSjYrAo5TWc4nCST1chpOtFEGp/bcbGbCMDa6kpWVpST0TTSuRyRdJrHjhynKxw1BdvzQDqRIZvKousGjK5tyIpEoNiPJJsmaCYmZ0M0nqZ3IMpAKIYkSaxeUo3HZR+fS5qYnC2xWJp9+zvZsL4eSZY4dKgHiyJTW1uMEIJjx/pYurSSkZEkmWwOVdVIJLKkUllsNgtr19Ris1mIxdL09kYYGophd1hZtLAcn8+JJEmoqkZPT5hXdrdx9Ggffp9zfAwvW1qF221ncDDG3n2d7N7TjtNhxWpVsNkszJ9fRmmJD103iEbTtLYNkk7nkICiIi8VFQG8XgeRSIrDh3upqAgwNBwnnc7h9TqoqiygtNR/cTv5LLnkBNupSKs5DncO8mpzNw6rhYymkcrmkCXY3dJDMqPistvI5jRWN1RyxeIant7bRDanMRhJEEtluXJp3Z+1YJvXEOVnUhJS3tHvTwBNN+gcimC3WqgrL+SpV48RS2XwuRwYhkF/OI6q6VgUmXgqiwQUBzwEPA4sikIyo5JI5yeaWU0nlVERQEWhj4DbiWYYxJJphmMpsqpGdXEAn9uORbnwK2PJeIaB7jDD/TEkCQJBN7e+axNL1tRgs5uqmEsRh91KeZGPFw924HPbyWRzCAENVZ6L3bRxLIqMR7Hhsef/SjweLOdxgebPEUM3GO4doXV/J7FQHC2nM/Y+tjvtXHXn5Vjk18Tn2MTkkiUSS3O4uY9tr7YQCif5wkffgMtRiCybmiyTc8PQcJyf/HQH8xtLEcDvH92D1WrhzW9ag2KR+dkvdvL5z72RAwe7aGsfRtcNcjmdkZEkHredZUursFoVItEUhw53s2NnEyBx7/uvwefLmyNnVY2W1kH27++io2MYr9dBLJEGoLKyAJvNQmd3iL37OmhqGsDlspFKqygWGa/XQWmJj0gkxZ69Hezb30UqlUUYgoKgm8s2NLBsaRWdnSH+4z+f4Q2vX0F/f4xwNInVorBubR03vG4ZNttr93v0mmh5x2CYoWiCpTWlXLeykb5wjP949EXWza9CkiTWNFayeUkdBzsGeHTXERZWFZPMZNm4uBaA9sEwC6uKLu5FXHSyGHoISbKDXIA0B/NuYaQRIg2SFVm+NDV/sVSGcDxFgcfJ/Ioi9rX0sr+tj8sWzSOn6fx86x76w3HKgl4OtvWjC4Mb1i7gxnWLKPa7ae4dZtfRLqLJNIm0SnPPMKqu8/6bLuPK5fXEkhl2Hung9y8e5nDHAH/ztmvYvLyegOfC+0ElYmnCw3EAZEVm4YoqissDplB7CVMa9PK2G9ZwoLmPUDRJoc/FvLICCgPui900kwuIms3x+/98mpef2IvVZsF1kj+VJ+hh820bOE8uXCYmfzbUVAapqQxSWRbgf3+z62I354xRdZ20lkPVdSRJwmmx4LJYZ7VCS+VUMpqGLgSKLOO12bBI8oznCSHIGQaqrqMZOroQGGJMCZK3fFMkCYssY5UVrIoywR1rNgwhyOk62dHyDSEQJ5VrlRVsSr7csTovZew2C1WVQXp7oySSGQoK3KTTOXp6w5SV+dF0A5/PhSLLHG/qZ+WKat5082oCARfpdA6Px44kSdTMK6JmXhEFQQ/btk00jfZ6HFx/3VL8fhc7X2xifmMpb3j9ygnHbLx8PoVBD4/+YR818wq56Q0rcTptABiGQXPLAM9sPczb3noZ86oLyaoaP/nZDnbvaaekxIemG4QjKSRJ4j3v3oLVpvCzn7/I/gNdrFtbR1nZa1dr+5oQbCOJNAJBsc+NRZEp8XsIx5PkNJ1Cn4uA24miyNitFrKaRoHHhc1i4andx9GFoL6skIbyP2/BVs3uIhr+MDb7Frz+L6Ios/uBptO/IpX8KXb7Jrz+v78ArTx9jnUNYbdaqCzyE/A4qS8PsutoJ6saKpAliUgyQzansXlZHe+/6XJa+0J88X+fpKYkiHdhPqhNbyhG52CYT995NfNKAqSyKh6HHZtFocjv5qYNi7hs0Tw++R+/vSia2jE0VSebyZuHSpKEP+jBYgZWu6TJqBrNXUP89o8HKAl4WLBlKeFYiuFIkmWN5Re7eSYXCE3V2PHILt73z3/JkssXYBudgEDeeMZqLk6ZmJiMsnuglwcO7mVrZwtFTjdvX7yCe5avxmW1zXjez4/s55dHD9IRi7AwWMxXt7yORcEilBkEUV0I9gz0sa27nb2DfXTFo0QzWXKGht1ipdDhpMrrZ0GwiDWl5awrqyLonPvCfjiTZu9AH890trJ/sJ+hdJKspuG2Wqn2BVhTWsGW6louq3htBBm0O6zU1BTS0ztCX3+U2ppi+voitLQM4nTamFddOO5WUl0VpLGhlMLCvIWWx3NhHOhTKZX+/ij79nfS1T2CLEsIAZqms3pVDdFoCgCbzcK11yzBN2rqXFTkJZHIEIunTcH2fON3O+gOSYTiKXTDYDiWIuB2YbXICE0gSVJ+lWf02ZWlfEqUKxbXsKy2DJfdhsc58wvhTx8DIdIIkWXcuWtWJIQRQ9OazrhWIQRCQH9vmIH+KPFYhpyawzDy22dj01ULcXum90c80jnAzsMdPL27CYfNQiqbw2m3cs8N67FbLYCgqsjPkppSPE47deWFVBb56A/HiKXy0V59LjsLq4upLw9isyg4bJb8mBr9GFgUBZvVkl+lvEjLiUIIcjmNnDom2ILFIv+pWJT/yTI0kuCpnce49arlPL+7BTWn0zMURc3pfzaC7UyTqj8nbE47heUFuAMuZNk09TYxudB09I6wY3cruw92kRt1UbrqsvlctrKWkkIvPQMR9hzuJp1RCUVStHUNk87meN2mRbxu0yKEgJbOIR55+gCarjM0kkDXDebXFnPVhvmsWlzFcDjJw0/to7N3BDWnU1sZZPPaBlYtmXvKNEMIsrpGWtNIazlyhjGnWVvOMMiMnpfVtHHN61TohsFgKsn39rzEK33dDKWSpDSNnKGjj9aXzOWIZTN0J2LsHezj9y1HWRgs4lvXvAGf3TGj5tYQguc623ik6Qi7B3qJqVnSWg7NMBBCEFezjGTSHB8Z5pmOFtaVVfCh1ZdT6vZguYTfjw67ldqaIo4d76OjI8SypZVkMjmamvsZGUlQW1s03i92uwXb6HzyQiJE/q+qMsjf/s2bJsRvcDltOJ1Wjh3vR5LA7cn7oEuSxNhhl0pwyzPlkhJsVU2nZzjK8wdb6RwK88y+ZgYiCWpLCij0ujjY0U/vSIx0NsfVKxpw2Cz0hSemqRBCkEhn0QyDw52D9IcTOGwWakoKuGJxzUW6stNHFzrdqT4MZk8LUmANELD5znkbJMkKkoRhjJzR+aqq0dcb5rGHd9PVGSIRz5BTtfxLc47PzYrVNVMKtkIIVE2ntW+EtQuqWFlfjiLLjMRTPPjHffQMR6ksyq84WS0K9lHB1GbNC6mabqAb+b61WRVcDtuoIHzxJ+KJWJr24/0c29dFIp4mGcuQjGcYGYrT2zEMgJYzeGVbE/3dYeynLNrIskSw2Mtb3nsVxbOsugkhiIQStB7po/VoHwM9I6QSWQTgdNooKvNTt6iMBcurKSyZfYwd3dfJi88cprttmKJSH+u2LGTdlslBt07lyV+/wpHdHcSjaRqXVnDdm9dQXB6YcExX6xCvPH+MQ6+24w+6ef/n34jNbiEaTtJyuJfmQz0M9oRJJVWEELi9DkorC5i/vIr6hWX4g3P3a82pGq1H+zi2r4uejmESsTSGIfD6nFTVF7N0TS2VtUVIsjRjYBLNMMioORbUFLN9Xz4asprTyebmFpgpvzAk6IpEaRoO0T4SoT+eIJrOkNV0LIqEy2qjyO2iNhhgSVkJDYVBlEtkYiBLEookj1/Dnp5emoZGGE4kyRk6LquNUq+H5eWlrKwsx++wzzgJaB8J83xLO7s6u7EpClc21HJtYz1+58zBuHa2d/JsUyu9sTh+h4N3X7aWeQX+cfO3tlCY55rbODwwyOrKcm5ZtojhZIodbZ0cGRhCFwY1BQGuW9BAbUEAi6LQE42yq7OHvT19JNUcQZeDy+dVs25eJT7HxPZYrBZWX7uM5r3tOL1OSmuKsFgvqc+vyUXm0nhi/7Rx2q0sqC2hvDifRjIcS7FjdyulRV5KCr2oOZ2uvjAHj/fypmuXs2xBOWpOZ155ARaLQt9glCdeOEJtZZC66kJau0IcaurD7bQzryJIPJnhf3+zi8aaIpY0liFJ0NQ+yB93NVFa5KW85NLQggkhCGfT/OurO3mus5WhVBKbYqHC42WeL4DHZsMiSSRyOYZSCbrjMcKZNKqhY5FlnFbrrGv8v285xsPHD7NnVKj12+ysKa2gyJk3002oWdqjEbrjMaLhECOZNOmcxofXXE6NP3DJCrcOh4V5NUU8+8ejZLM57HYrhYUejhw16OgY5rLLGsbnBCcrSM4Ei0VGQiIemzrlniLLWBSZSCSFYZyYVDscVoJBNy6Xjb7+COvX1WG1WohEU/l58En+s2NC7Z8Sl9SXVZbA5bDSUF7IbRuXEfS6KPF7KPS5sNss2K0KqqaDgOW15aiaTmnAg9/jxKIoVAR93LBmAYe7BqkqCuB3OXA5rPSEouxp6TkHgq1AiFxe6ymyCMYCgEhIkgNJciNJ56ZLNUNne+gVVD0H+RrIGippPYPL4kRCIqmlCNh8XBZcdV4EWyEMhMiByJ3R+ZFwkid/t5etTx4klczi8zvx+p1YbZY5+2hIM7zbOgfCaLrO0ppSrl3ViCzLDITjvHK8m6Odg+ORZ4djSbqHotSUFjASSzEcTeJzOcZz0UpIKJdQ1MRELMOhV9t56uFXyaZUMmmVbDpHLqePHyOEoL9rhP6uyYsOsiJRWVPETXddBkz/Ic2kVI7u62TP9iaaD/fS3xMmMpwgm82BENjsVnwBF6VVBdQtLGf1xkZWXFaPw2mb9kUYGoyx/6VWjuztpLq+mMq6uQVsazvax64/HmVkMI6azXHFdUsnHROPJDm2v4vtTx6ksNTPne+7ilg4xSvPH+Pw7g56O0NEQ/n2CwEOpxV/0MOBl9tYvr6OdVsWUr9oZi2pEIJYOMmOpw6z98VmOpoGGBmKkx4NvuB02Sks9XFkdwdrr1yA3WHF7pjeGsSiyDgdNl492s3ASJxXj3Tlo7QXzf685nSd7miMPza3cWxwmK5IlMFEgkg6Q0rNkdN1FFnGZlHw2e2UeD00FgXZMK+KGxfOx2mznpYv1PlAliR0YbCttYOnjjdzZGCIvlicWCa/+Gi3KAScTl7p6mFtdSXXL2igsSg4LnCeSjSd4WDfAE8ea8ZhsVDi9bCxdt4MozxPdyTGjvZOjg+FKHa7uX3FEir9PsayI0QyGfb19vNscyuJbJYVFWU8dbyZZ5ta6YpEMQSUeNx0hCO874r1KJLE40ebeOpYM62hETKajsdu48jAMDnDYMO8KgpcJ8z1hGEw0h/h2Mst7PvjYQLFPuTRAF0ur5O3fvpNf7LmyDE1w/6hfrb1dgBwe+NSGgOFZHSN3kSMoyND9CRjJNV8BgO7RaHA4aLGG2BhQRElrukXpHTD4GBogB19nUSyaao9ftaWVrI4OLOrTUbL8Yf247RHw2QNjTUllVxWWkXAceKevTLQzasDvTgsFtaVVlLnK6AlOsJL/V3E1Sweq52lhaUsDhZT4HCi6jrtsTAv93czlE6iyDI13gDLikqp98+eS1yR876QI5kULZERWqIhhtIpMloOm6JQYHdS6ytgQUERpS7PaU9G42qWrniU5miIvmScZE5FNwzsioWgw0m1N0CDP0il1z9nw6QdvR3sHerDEIIlhSVcW92Q719doz0apikyPFpXDl0YOC1WCuxOKjxeFhYUU+b2XnAjqExWY2gkjqLIJJJZjrcPEo6mJ2iprBaFFYsqKSn0IgyBJEtYFJlUWqWtO8Rlq2pZtbgKt8vOSCRJgd9Fgc9Je88IL7zcTFbNUVrkRZYkWruGSWdy9A1FLxnBNq3lODYyzFPtzYQzaYqdbq6eV8flFdWUuNw4LPk5WkbTiKrZceE2lcuxvLgUuzL9PNcQBgeHBnmk6Qiv9HcjIXF5RTXXzKunIRDEZ7ePl92XiPPqQC/PdbbSl4jzVEcLDQVB3jx/MVXeS6OvTsViUSgIuIhGU+PCo9WqYLdb6O2PUjOvaE7P5oGD3XR1hdh/oIvu7jBbnz3M0aN9rFo1j7LSAHa7haJCDyWlPg4f6eVnP9+JzWZhy5ULKRlVMvj8Tmpqi9i1q5WHfvUyLpeN9evrqZlXSF1tMevX1bPr5VZaW4cAEAiWLqlk8aKKs+oDIQSGIYilMmRUDV03zkjLW+Bz4XGee/Pscy7YDmcHGcj0YpEsLPQtO73GKAqlAS+lgcmBivxuJ5WFkwd6ReGJCWJpwMPVyxt4em8TiizhclhxO2y47TZ0/fQ6XQgNYcTQjUEMPYRhhBAiiRCZfEAlkUVwQutisS7FZrsMRSk8rXqmQ5LAo7jJyTlAYig7wkg2gsvixGNxjwu2oWyYuJY8J3WeQKDrQ+h6F0KkkKTKMyolFknx/NYjRCMp1l/RyILFFRQWebDZ526a4ZkiafUY+1r7KPA4KfS5x037HDYrK+vLOdDex6J5JUiSRDiWYndzN8e6BhmKJij2u5lXUoDbMfsDNRxN0tIXGo2uneFQez+6IWisKKSqOICuGxzuHCCVzRGOpzjaOYDNolBfHqS2NIh7BqFnOhRFwuVxUFpRMGF7OpVlqC/KcH8USZIoKvNRWlkwKXiUrEgUlfpxuKa/vmwmx94XW3jmkd3sf7GFeDSN3WmlsMSH050/L5NWiY4kOfByG8cPdNPZPICazbHysgZ8BRc3+JGazbH/pVaO7u3klReOkYim8QZclM8rRLHIqFmN4YEoAz0j9HWG6GkfJp1SCRR6pk2BZBiCTErl+ccO8Nj/vUR36xC6buD2OqiYV4jDaUPXDGKRFC89d5SBnjDzGktQs9Mv/PjcDlbMr2AgFMfjtJFIZakuC1BXMft7QheCcCrN/+05QEc4giEELpuVgNNBmdeLTZFRdZ14VmUklaI/nuDY4DBNQyGcVivXzq/Hbrm4a5cZTaNpKMQzx1vZ1dmNx27D73RQ4vGgGTqRdIa+WJyeaIymoRCJbJZbly9hUcncJgfnGk3X6QhH2NrUyovtXWiGQanXQyiZoiMcYTCRYElpCUlVZWtTK0OJJBU+H7Fslr5YnJc6ugg6HZT7vBMEW0mRKa7Oj01DM4gOnbA0UtPqnC1YXoskVJVXBnr4z/27EAgWBYtxKBaOhYfY3tfB/qF+OuIR4qqKYRg4LBaKnG4aAkHWlVRyVVU98wOFUy52GEJwdGSInx7dS1c8yvrSKgJ256yCbVbXebKjiRd62knkVN61eDWLC4onCLYHhgf4+bF92BVL3jRU0/jF8f08191GNJsZ1UBVckv9Yi4vr6Y/meAXx/bxfE87/akEVlmm3h/k+nmN3Na4hCrPTBN1CZui0BodoTkc4sX+Lo6GhxhIxUlrGnZZodDpojFQxPrSKjZVzGNxsATbHGI+GELQEh1hz2Avuwd7OTIySHciSlxV0YWBQ7FQ5HRR5wuyoqiMDeXVrC2pwKHM/p3eM9jLz47tI6vr3FS3kKur6gln0uzo6+Cl/m4ODg/QlYiSULPowsBltVHocLGooJi7Fq6gzH3hglJqmk5L5zC7D3XhdFhx2K3ouoGW00cn5vnjbFYLlaUBgn5XPjr8SV3sdNiYVxHkwPFeVFVjaCSBw26hrDgfjCeRzBKNp5EliWw2Pz8sL/ZTGHDjuURSvEHexPj4yDCRTBpDCBYXFvPGxkVsqpw35WKobhiEMilG0ml89unnFUIIVN3gkeYj7BvsI53TuKyiincsWcm1NfU4T4mQpxkGiwuLscgyPz20l7ia5fctx1hSWEKpyzPtAufFRJIkHA4r69fVU1Lio6jIC0KwZnUtw6EEwaAbSYLKqgKsNsu0OWpVVSOVViks9OB227HbrKRSKppmMOYuWFzsZcXyatJplUQig310zI4R8LtYubyaSCSFmtUQQqBp+YBjZWV+rtqyiJdfaSU0ajLvdtmxWBQURaaoyMvrrl+G9aQ4LTU1Rbjddvx+15Rtzqoaw9EEbX0jDIUTjMRSpLM5tDMUbK9fv5DlDefeJeucz3o6U63sGN6KS3GftmB7LlBkmRW15Tx3oIWWvhBCQMDtmLO2VggVwwiha91o2nFyuUPouWY0vRlDDwFTT2IdzjuwWubDbIKtEAiRxRATTaglFCTJhSTnP6w22cYtla8b379t6GX6M0NsKd5AiSMfCGsoE+LZwZ1TDihDD6HrfQiRdxLXtGMIoSOMMDl1L7pcMOmcE+jkcodQszuQAMXaMPM1TVeKLkgmMkgS3HLHOlasqcXhOHdaCc0wWFxTSlnwxIvDYbOwor6C1r4RZElCliScdisZNce+ll5kSeK2TcuoKyvAZlXwOOxUFvmxWqZWDUcSafY29zAQSVBdUsBQLEWmpRdFkij0uVFzGnuaexiJpSgNeoklM+xr7UXXDUoD3jMSbH0FbtZftZDGJRNX1Xo7Q+x46hDD/VEURWLB8iquvWU1Bae+OCUJu8OKPzi98NlyuJff/2wnB3a1AlA+L0jD4goWLK/KlydBdCRJR9MATQd76GkfYs+OZlKJLA6njWXr6sYF4ItBJqXy9G9203ywG4/fxfLLGmhYXE5ZVRCbzUIykeH4gW4O7+lgsCdMX2eIF585TO2CMq66acWUZarZHE0Hu3n4x9sY7AkjKzJVdcUsXFlN49JKfAUu1IxGb2eIpgPdtB/vp7t9GNcMASE8LjsrF1Sy73gPhhC4nTZKC334Z/AbH8Miy1T4vARdTpAg4HBQFfBRUxCg1OvBZbORzKr0xxMc7Bvg8MAgg4kkx4dC3L9rNysryin1ui+qWfJIKsW2tg56o3EWlRazsqKM2mABntH0bO3hCK929XB0cJjhZJLfHzqGx26n1OvJX/cFRjMMemNxnj7ewqLSIhaXliCE4JWuHv7Y0k4mp/HokWOEk2mCbic3L1lIVcBPe2iE3xw8SiSd5qXObl63sJElZSXj5nR2p423f+5W1IxKMpZGU7V8dGSfE5vThtV+SRlPnTcE0BodYTCVYGtnC68M9uC0WPFa7bhcNnRhkFBVuuJR2ka1n52JKB9cfhnlHi/Wi5C2ZSSb5sBwP4OpBE91NuOx2nBYLIQyaZ7saB6/rsOhQR5sOkCB3YnPZieazbB3qI+EmiVgd/DOxatnqCXvc/hIy2F29HYynE7isdkJ2J0E7HkNW18yTmc8yt6hXlqjIe5esoZlhaXAzDnb26JhHmo6yOPtx+iMRbEpCj6bnRJXfoE8Z+gMp1N0xqPsGujipYEu7l2+gcvKqucUiRcgq2uEMyniapZnupr54cFX6IxHsEgyDosVn92BZuhkNI226AiKJBHJpi+otjajahw41kvPQIRPvftaggE3bd0hntl5fMJxsgRW69TjzO91sGJhBb949FVi8Qx2m4XlCypYtqAcWZaw2RRcTis3XrmYpfPLcditaFpecLZeQq4HujBI5U7MZYNOJz7b9JZYiixT4vLMaD0B+fdnfzLOMx2thDNpilxurqqu4/qahikXWS2yzPxgIW9sWMiTbU0MJBMcDQ3RFA6xvLiUItelmT3Abrfy9rsun7Dt+lOszJYuqWLpkunLWLumlrVramesx+GwsXhRxbQaVpvNQm1tMbW1ky3jbDYL1dVBqqunthaprSniQx+4bsK21auml5OSGZXWnhA7D7Txwr5WWrqHyemzu0rORF1F4WtDsL3YyLJEZZGfv7hmzWmdlze7TaPr7WTTT5HJPIaWO8p0guyZY6DrPWSzL0woW5LcWK0rsFiXIE1hfxvORUloSQK2E6u+fpuPcC5KQW7ySrCa20M6eT9a7tio+XQGyKKqu9Ein2Ymj558kKk0IGO1rcBu33JGV+pwWqlrLOX40V4kWcYwDAzDOCeBUyRJ4q6rV03abrdaWFJTypKa0nx+WsOgtrSAd16/bspyGioKaZhBe9ZYWURj5cwRtT/4po2n1fbZsDuslFUFKaua+EKyO20c2dMJgCRLFBR5aVxSSUnlTIsUExFCkFM1fvvTHRzb30UupzOvoYTX3b6WN779cmyOiROZVCLDvhdb+dn3n6H5UA/H9nfxx0f34Q+6mb+s6qL5ZuRUjQO7WvH4nNzwlnVce8tqKmom3scb79T57QM7eOKXL9PZMshQf5QdTx9iyxuWAxMng2O+xr/9yQ5CA1F03aCyrog3vuMKrnvzmknCa3tTP7/7yU6e/d1eBrrD07Yzkcqw+0gne472kMnlV1T3HO1m3eJ5vHHLZHPrk7HIMqVeD3esXIrTZmVJaQkVft+UvkftI2F+8speHtx7kIymcXhgiJZQCL/Tjtt28QLnJdUcPZEYi0qL+cIN1zC/uHDCBCerabSNRPjiY09zZGCIoWSSFzu6WFhcyOsWzb/gZooCSKk5LLLMO9asZEVFGQjBguJCjg4O0R2J8WpXL36HnXetX82bly3GbbfRE40xmEjy9PEWRlJpBhNJkqqK/yRfW1mRGewM0bKvnVgojr/IR/2KecxbPPegMn8K/LG7jZFMisFUggq3j6WFpSwvKsVrs5PIqRwODbJ/uJ+eRJSomuXBYwdYEizhDbULKXJOrUk4n0QyaV7s66TS42dLZR1rSirYN9TPtt52BlMJDoUGiWYz9CbjLCwo5rrqBlJajhd62jkaHqInEeOpjmbesXAl8jQ+d5phsK2ng45YBJuisKakkmVFJZS6vAigMxZhZ18nnfEI4UyapzqbMYTgS1dcj2eaSLmGEKS1HPcd3MVTHc1EsmkCDgf1/iBrSiqpcHtRZJlQOsWB4X6OjAwylE7yykAPfcln+Ler38SigmIcc7D6yOoafck4e4d6+ear28hoGpVuH/X+IFVeP3bFQjSbpjsRoz0aZlVx+Swa7DMjlVHJZjViiQy5nE4klmbEk8LncaBpOrIiYbUqZFSNwZE4O/e0kUqrc3bZUHMa4ViKhXUlvPn6FQT9rrxAa7WgKDLFhV7m15Tw4t427DYLpUVeEsksmm5QW3np5NO1KxZK3R5kSUYXOkdCQxwfCVHjL8BlsZ5x6p2UluPF3i5G0il0IVhSWMyiwqIZLYesskKR08WyolKG0yk0w6AlMkJfMkGRy42a04mM5nA9XRQlr4C4VMkHCMshEARs+YXcU98PWV0jqqbRRF6IlJDw2xy4LFM/9zE1Q0pXxwOHKZJMscONPJNf3yzohsHxzkEe2rqPp18+PiEoWf6ddmblni9XqT85wfaMERmymcdIxP8VXesENOYePfh0kNH13rzQqTWf2CwpuD334rbUIUmTH0SLZCGWS3A83soyfz4Yz5FYMwktOeWAtVgasdquQAgVLXcYIaKje7IYRnYO7VSwWJfgdL0Nu+P1Z3CdUFzq4+13b+I7X3uUh//vRdLJ1axcW4s/cOEnJyZ5cjmdI3u78oGpoml8BW4uv24Jb/rLjVOuUjvddlZd0YDVbuFLH7wfLaez45nDLF5TS93CcqwXOYn3Nbes5qqbV1A+b/KqpKLIXHfrGjqaBuhqHSIVz9B5fIBcVpukIdNyOsN9UV5+7ii5nI4kS1zzplVsvnEZTvfkD0hNYylXvn454aE4O585PG37BkcSPPnicd795svweRzjgclcp6HJv2XZYiRm/njUFATYXF/DsaFhXurozptoDg6xqKT4ogq2AEG3i09ctYnGosJJppM2RaGxMMj7r1jPN559gdZQmOODw7zc1cPrFs2/SC2GTfXzKHK7R6PtSxR53KyqKKc7kre0WVFRRmNxIa5RP32X1crqqgqea2knq+uMpNLEMtlxwVbP6RzcfpSffPkh7E47gRIf4f4IT9yv8/bP38aaa5eh/Jmk7to31IcsSWyqqOHe5Ru4vLwa6aQptCYMnu5o5j8P7GLfcD73+G+aD7G2pPKiCLaqoZPWNeYXFPHFy6/DIkm8bYHOZ7c9ztMdzXTGI/QlY8wPFPK9a99MidONLElUenz875E9HA8P05OMMZROUux0TxmcUBeCQ6EBSlwePrrqCm6qXYDf7hzvFQG0x8J889UXeLa7lUg2w6uDvTzWfpzbG5dimaLMVE7lkZbDPNPZQiiTYp43wNsWrOAdi1bis9knCC0ZXeOJ9ib+6+DLHBoZpDse5b8OvswnVm+iITC724Sq6xwZGeL/e/mPJHIq7166llsbljDPG5gweRUIuuMxJCDoOPcWGU/vOMZzLx6nZyDK8EiCb//oWQJeJ3fdvIZVS6pZsbCS9u4Qn/ynX+N129i8rpHCAvdpWZJlsxp/3NXMS/vakSQJn9vBNVcs4C03rqLA6+Qz772en/72Zb7xw6eJJ7O4XTauWF3Hu++8AiuXxjPutdlZWVxGgcPJcDrJ0dAQ39vzEk3hYW5pXMzS4tIzKjet5dg/2E/OyMcDqfT6KXHPHrTRpiiUuT3j74HBVIJoNi/MHu8a5PP3/eGM2lNR5Oc/P33HGZ17ITgY7uPhjv2ous5X1t40pUXKgZFevrDnMdpjIQCcFit/v+pGbqud2vLs/uZd/LJtL8PpBLIkU+r08Kvr3kPAfubPWzSR5qldx3l2d/MEoVaRJfxuJ06H9Yzi1LjOoQXnyZynWemlE4hnNoQw0LV2Uskfkck8hqEPce61tCchSShKGTb75WjaSUmZhY6WO0Yutw+7fbIGcJl/IQktwU/aHx6PlCwhsaFwFYt8k02FFaUCl+sunM5bMIww2ewfScT+BYt1IU7XHcjyDKulkhVZLkRRSpHl4jMOiOVw2Fi0tIr3ffR6fvnTHfzwu0/hcNpwu+04nDaUacx/T+bjf/NGiucQjddkbqiZHDufPkgylv9oLFxexbJ1tVitypSaBEmSsDttVNcXs2bTfPa92EImqdJ8sJvFK6upX3x2QQjOBovNwsbrl1BSHpi27R6fk9LKAvyF7vHAWCNDMYrLAxMEiehIclyDDVDTWELN/DJ8Be5py65dUMailfNmFGytFoWigJua8iB2qzL+apyrpnssif1cjit0u6jw558VgSCazqAZZ2cqdLbYFIVKv4/VVeXYLJPHmCRJKDJcVltNpd9HdyRGPJulMxKlNxqj0n/hn30JmBcI4DopTYPTYqXEe2KCVlMQoMDpOJESTJYpdLvGJ/FZTcsHOhxFzeT47fef4Ob3Xs/8dfW4fS6SkSRHdzXzhx8+zYorF//ZCLYCWFdaxR3zl7OmpBLLKZM5WUhcVl7NQDrBgdAAhhAcGRkinE2jCwPlLDQPZ0qVx8/akkpsY6blioVlhaUcHB6gJTpCwO7ksrJ5lLk8KKNa2Xp/kHkeP8fDw+R0nd5kjKDDNa1oI4C/WLiSKytrCdidk1wI5nkDvHXBCpI5led72hlKJ/hD+zFuaViMgjxh1mUIQSSb4X+P7iWSTeOyWLmtYQl3zF+K326f1IdOycpVVXXEc1m+uus5srrGH7vbeHPDEio8vkm+kVO1PZVT6U3E+dSazbxuXiNlbi/W0YBYE/rS6wPBeQnYeM2G+WxYXkNOy6essSgysizj9zpw2K0sX1BBbWWQrKohyxIup43Xb1mC3+vI+0WW+rnthpXohsB2iulwJJbmwLFemjqG+M7f34HTkY8M/OqhLvqHYhw41ssVq+soK/Hx3rdtJJvV0A2Rr8dhnVTexUSRJMo8Xj61fhP/tnsnfYk43fEoDx49yNaOVhoKgmyqrGFzVQ2VHt+cYzXkA6hF0Ecj9P6u+QjPd7VhmyHYFORjG4Sz6XGBOJXLkdXz/9/lsLG4dqKgbZFlhqJJWntDWC0KdWVBXA4rmZzGwEicaDJDRaGP69Y2nm7XXFAMIdANgS4MdCGY6ilbHCjlWxtuJZRJ8lD7XvaEujHE9N/1t9atZktZAwdH+ni06zDdqQjiLJV0e4730NQ1RE7TURSZ0gIvt1+9glULKvG7Hfln+Qy0rwUzxNA5G874SUvrKbpT7eyPvkpYHcEu25nnrierZya9NHNGjo5kC+2pZoYy/aT1FEjgs/hZ6FtOvXsBbosH1VBpSxxn18g2VgU2sNC3FJtsn1BnR7KFbUNPc2XJ66hxNeBQzqZjBJp2mHTyp2TSj2EYg2dR1tyRlVKs9isg+b+crBXWtBa03IEpBdtSeyGbizZQ755HNBcHwGf1UeUqI2gLTDpekmxIShAIIosyDGMExVKOohRjt29GVmaKVisjSXbANqVZ9FxJp7Ic2t/FH36zm56uEVLJvKZYUWQURZkx4vEY2cyZLzI4bBb+8rq1eYHCBMhrJg/v6SCTzvdrZW0R8xpKZxS0ZFnC5bGz8vJ6juztIJvJ0dsxTH/3yEUTbC0Wher6YorLA5OCZ52Mosi4vQ5cbjuR4QSGYZCMZygsFRMmmMl4hs7WE8//vMZSgsVeFGX6Qer1OymuCODxOUnEJppKNXUO0doTYiSWIhxL8eCTeygu8OR9uSWJ6tIAS+rLzvj6p8JhseId084KyOT0GfMYXgjcNhu1wQBO6wwTY0nCbbNRFyzg6MAQQ8kU0XSGznD0ogi2AAUu54TAJRZFxm07cQ2FbteEa5IkCYfVMv5tz+n6hEUFwzAYaB+makE5VfPLUSwKBSV+YuEEQ10hhPEnHD3qFBRJYl1JJZeVVeGaYlxIkkSB3Um9L0iZy0NvMk5KyxHOpMloGu5pTG/PJwUOJ/N8ExfQyt1efLa8Rt5ns7MoWDTBTSBgc+Czj2rshUE0m5k2wIoEBOxO1pVVUT5qInwqeRPlCl7sK2FXfzdpTaM5HKI7HmWeNzDBGiKuZtk71Ed7NEzOMFhVXM6K4rJRjfHksmVJIuBwsKCgiPmBQg6GBoipWY6ODLE4WDwns2G7xcLCYDHXz2uc0R/6fPpJez0OvDPEL3A6ZJynaIkKTnrF2KyWaQXQrJojEkuj6wY1FQU47FZymo4QgnRGRVHyQrzVolBUMPfUchcDSZJwWaxcV9OA22blibZmdvV2M5BKEFOz9CcTHB8J8WjLMRYXlnBFZTWrSsopnUX7qo8uqIxl3Y1ms0Szc7EQnIhmGOPCcVnQy3tu2jBhf3NviJcOdxJwO3jzlcsIeJxYFBldN0hmVHYf7+Z49/B4CsdLlfm+Yt41fz2GENimeS5cFhvzfcXM8xSwc7CNQ+H+GcssdngosLvQDYMXh9rpTkXOup0t3cP0j+StlaqK/bzrDeu5bGkNQV8+wNqlli7ojO76mKC6bfgZIuoIpY4KbLKN7lQ78VyUiBqm1HHCIVgCOlIt9KY6kSUFp8VFzlBpTTbRn+nFUqJQ71mIIlnQMejPdPNKWKPO3ThBsI3nohyJ7acleZzrpJtRpLN7QWq5VrLpx8mkn5iDUGvJB3eS7KPRkVNnXK8kubEotchy6Wi9+QmQofeh5VoQIjsqWJ7AptgI2HwYQsepOJjnqsSm2JCRZrWdlyQLshxAUWoAK5LsR54xeNS5IRJO8sTv9nBgbydev5MFiysoKfXjcFpnFBhOxuM980iCFkVh0byZo2P+OWEYgmwmx0B3GC2XX3krKPISKJz9I2y1WaiZXzaeezM0GCc8nDjfTZ4WxSJTVVuE3WGdkHx8umPHtGFC5HMAn0omrTLcfyKgW0lFAPcsY0+xKLi9DvxBzyTBVtN1MmoOq0VmYW0JiiKhahq6yI/73EnavLmQ03UGE8lRX858yp90Lkc2p5Mz8tqJkVSaIwND4+dcbKEWwGG1UOadOfLp2N0r93nx2O0MJVOk1ByD8Ys3vtw22wQhRYIJwobLap0gSEgSEwQGYzT/8In9Eg63jWgoTjal4vI5SacyxIbjONyO15KR01lT4vJQ5y+g2Dm975siy7hHI+j2JuMIIJFTUXUd90XIiuSyWAmeYsrnttqwj44Bp8U6KcKvzWIZn6waQpDRtGk1J7IkUesLUOx0z5hKxW93MM8boMTloTMeIZ7L0hwJUebyTBiPMTXL/uF+sno+Mu/8QNG4T+10KJKMz2an2uvnYGgAyPv2RrOZOQm2Hqudy8vz6WIuRpCv843bZaei1I/NauHnv38ViyJjGIJoIsO8iiB1VecmI8bZkM97PrdjFVmmyOXi6up6yt1e1pVVcnBogMPDg7RERmiLhmmLhmmJjHA4NMjqknI2V9VwWUX1tNG4hRDkdH28DfX+Aqq8/jn5aZ9MQyBI8WjgKJfDxsJT5nFHOwcBwcbltVyxtGaC77phCOw2C4m0yoHWft68+cIHsZ0r+eBwMyvnJEnCIklIkgWrrMyqGJUlCZukYFMs58y6ZSiSJJbM4HbYWFBdzJWr6gl4nJecQDvGGQm2YTXE0dhBulMdXFa4hcW+5ciSTG+6i73hXcS1KKWcEGxlSaHYXorX4idgC+JSXGSNLMdiB3l+6EnaUy2UOCootBdTaCum1j2ffZGXCalD2BUnVtmKIQwiuRE6Ui1Uu2opspdikc78C2cYCdTsDjLpxzGMqVZAHChKCYqlGlkuRZYLkCQPkmQnm91KTn3ljOuWJAVJ9mO1LiabHQFUAIRIoet96HofFkvthHMiaoyWRAdN8TZ0dOZ760jrWTqS3QRtASpdM2t/JDmI3b4JIdJI0oVZ8U4mshzc14lhGFx5zWLWbKintNyPc45myHB2gu1c0Q2DjK4RzqSJZjPEVZVkTiWjaWS1UcFBGHlBQeQ1mBZZxiLJWGQZq6xgt1jwWG14bLbxf91W20VPt3IyhmGQSWVJp1SEEFhtVhxuG7Y5RGRVFDmvwRwVIpPxDMnE1EnDLwSyIhEo9IznAp0JaUJwAzGl1iSnauPm2QAev3NGTfAYVpsFj2/yGC0JerFZLeQ0nUQqS8DrRNMNBkL5/InBacLpn4wQ+Wnw8aFhjg4McXwoRFc4wkAiSTidJqXmyGoamm6MrnDnzZkuJayKjNc+t/eN3+EYf17yaYxOf6X/XGG3KJMCW5z8y6pM3n8ygokRGixWhbWvW8H+548w0D6EO+AmPqqtXXP98jkv9P0pUOH2EXS6Zo3Wrcj5iLpj5IyLZ4FgVyyTtMsWSR4fA1Y5LxSejDIalX8MzTCmNQiUJYl5vsCMQi3kx2Ch00XpqGCrG4KOeIQNxsQAZMmcSkskNP67OxHliY4mXh7ombH8wVSSvmR8/PdINjUheu5MuCxWlhaWXBRT8QuB22ljfk0JV6xO0TcYRdMNJAkaa4pZ3FBKSeGFS100HZphoM9gpjoVHpuNtWWVLCosZv2ocHskNDQu1A6kEuzq66Y5HKIzFiVnGFxTUw9MXo+TJAmrMip8CVhWXMp1NQ3jQupc8dnsVHim78++UJxEWqWqJDDpPSLLEgUeJ26njf0tvadV7+nydM8xfDYHjb4ignY3I9kUOwbaiOUy3FC5kKDdTVbXaIoN0puKcWVpAy6LlRcHO+hORcYXnspdPq6rWHBe23o2pDIqak6nKOCmurSAAu+lHSfnjGbdA5keetIdFNpL2FL8OlxK3g+t0llDLBehL9M94XhZklkRmBiV1hAGJfZyXg5vJ6yGSOkJCinGZ/WzwLuUXaEXaEkcI2AL4pcLyOhphjL9RNQRri97EzbZflarBbrWTDb7App25JQ9ErJcgsW6CJttHVbbOiyW+chK6ahZroQRCZ2VYAsgSU4s1pWo6ksIoY5vF0YYLdc0SbDtSHWzJ3KIwcwwYTXKW6puImfkeGlkL/M9tbMKtrJchN35eoSRQZIuzKDMm4E6ScQzXHnNYpYsr8ZquzRWclVdJ5JJMzL6N5hM0BmL0puIMZhKEkqniGazJHMqqVxuXCsmyE9i7IoFu8WCXVGwKxa8NjvFLhdFLjfFztF/XW6KnC68Njs+ux2fzYHTYrlo6VeEIVCz2viM22JV5jyhlkZTCI09c1pOQ8udntZx5sZxWrHaJCRsDivyOfLTMox8tOgxrKNRLmdDViQsU4zpQr+bQr+bkWiK/uEYqxdW0dozTDyVRZIgnZ1dS67qOseHQjy8/xBbm1sZiCWwKgo+hx2fw06Z15NfYJFlZFnK+xfFE/RfRE3nqcijE525YFXk8YUTQxio+jkcX6fpZSRL0oxKVGma6LbTYbVbuf4vtvCb7z/BnmcPIUTe9668vpRr79qM8mfkLlFgd+CexWdzjAl9LE73Lp47FEnCfqoW8qQxIEvSlKaEJwd/mqntEhIFdteUEc9PxWO1jQvRhjAIpVPjZptjqLrGQCoxXuPzPe0839M+a9mnkta08Wiss2FVFEqcnvMW6fRiI0kShQVu3nDVzNHsz12FJ/+Y27hPaTkymjb7gVPgttpYUlTC4sJi0prG4dAg27s7eKW/hyOhIULpFM93t5PRNVaVlk/pq22RZIIO57jHd8DuYHFhMQuCM2eWOBNSGZWhSAJV07GeZA6r6QbRZIbhaPKM/D5Ph1+276PY4eYttSsJ2Fx0JcN8++CzDGUS1HqCrClyMJJN8mTPMXYPd7OhaB4ui5XDkX5eGe6kIxFmIB1nRbDikhZsx/rRbrXgcV28NI9z5YwE24QWRzWylDoqcFtOTNAUScFvLcBvnWjmKhDkDBXVyKIZGrrQR1/yAhmZnKGiifwkxqm4qHLWUOao5EhsP43exfgsAULqIJ2pdhyKkyW+lVjlM9fWCqGTzT6HltvPqS8MSS7E4XozTtedWK2LmSktztkgSQ4s1oWcegsMEUPXOycd353qwyIp3FL5Ou5vewiAQnsBOSNHWp9dcybLTmS5/py0fa74Ai62XLuYJ363l76eMCVlfnx+Z94XRZaYeeqYx2qbOqjRmWAIkdcEqVl64zFe6e/lpb4uDg4N0JuIz17AKFldzwc2UGfWKNlkhVK3hyVFJSwd/Zvn8xNwOHFarBdeyJWYoOEUYmrt5XQY+gmNw+lO7GdDP90E36dcy9kicWaT6Nn6IRxP8cKeFi5fUcuTLx6jwOtiOJIglcmxYAYzed0w6I3G+fZz23ilqxdV0/A5HMwvDrK6soIlZSVU+nz4HHbc9rxlQMdIhF/vP8TP9+w/nUs/rwgxd5No/aTxKCGd0wmyIQTG6fixnuP5kKzIlDeU8u6v3MVAxxDJaApvgYfiqkJk5dw+S5c6NsUyJwHuUkKSZh6P0hxcgmauAOyzWAGMYZHl8cUiQT4S7am6YF0IUtoJTavTYsUmz638k3FbrXMKXgcgI+G0WOb06AhhkDViIAQOy9m5RQkhyOhhLLIDi2RHksbcTgw0kSFnJDFEDqvswSq7kc/She102gVzDxR4MrIkjQuHgrwmdrbPkWYYjKTTJFR15gNnQZIkXFYr68oqWVdWyfGRYf5r3ys81tZEXM1yJDTI7v5eNlXNw3mK5Z1VUajxBXi1vxcMnf5kguF0inMtspUFvWi6wXN7WphXUkCR340iSwgBsVSGl450cqxzcJIJ87mmxl1AKJscD3wVyaYZyaYosLnoSkaY7y8mlE0RUzMU2l0EHXnN9Tsb13N77Qqe62vmwbY957WN5wKP04bDZkU3BOq5VGicJ85IsFWNLJrQpgzcZJWsWKcwdW1NHOdAdDftyWYiuREyehpNaBhCp9pVO+FYp+JkbfBynuj/LaHsIKX2CgYyfQxm+6hy1lFgPTtfBmFEyKn70PWuSfs87vfjcN2GrJzfoDiSZENRquGUGIlCpDD0yabRBgJZUnDIE1dLZOnSc9weo6jYy113b8bnd/HIQy+zf28HS5ZVUVTixeV2YLHM7i9QP78U+xxMQudCRtM4MDTAAwf3sK27g0j2/JrSqoZOVzxKVzzKE21NAJS43Kwtq+SGukY2V9WctonO2aAoCm6PY7zPs5nceBTg2TAMg2RSHf9Y2x3WOZkwzxU1k0M/y2TfZ4NikbGfFFQkm82ha7O3R9cM1BkCnI1pLOOJDIlUli1rGugeiMz6cQglU/yxpY3tbflFLpfVyl+sXclbVy2j3De1iZbDOvvzdKHRR30L50Imp40HXLLIMs5zGPgjp59rDfCZYXNYqV544tuSTmToPNpPw8oaJOUSu3nnCUWa26Lm+USQD+g016WOsbRP5xMxR7MVIZjovz1FXwrEhAWlKytqWFZUetqBtyo8Pqq8p5Nvdm59pBlpDoT+G02oXFH6+dNq06kIDF4Z/g7V7qsoc63FoQQAyBkpelLbaYo8QlhtYnHB22n03YLLcu61h1O3S5zxOLfJyvjihW7kA4/NNloHU0mGUkky+plpbKdjfkEhNzcsZCid5NnOtvHUThvKq3Ce8op2WiysLi3nd81HSQPHR4Zpj4bZWDnvnLZpw+Jquoci/OzpPWw/0EZNWRCfy05G1egLxYglMyyvL+MtVy0/p/WeSo23gO5UhIiaIqKm6U1HqfYEqHIF6E1FiecyDGeSqIZOtefEAo5VlgnYnATtLpyK9SLZocydYr8br8tOIp1lOHLpWIRNxxnNHGyyHYtkITOFpjAncuREFjgxYd85/Bwvhp7HbfGwPriJUkcFTsWFRbLyw5ZvTyrDrjhZ4V/P80NP05I4hs8aYDDTR85QWexbftaCnJp7FUMfYOJHxI7Nvgm784ZRs+Pz/eG1olgqkSTLBGd/YaTQjYFJR7sVFzEpzkBmOH+cEOwcfhVN6Hgt5zoC37he7qxK6Wwb5t++/ihdnSGS8Qy93SO8vKP5JI3t7HzzP+6mompyjtLTIa3lODA0wINHDvBCdwdxNXvG5jpnSyid4o+dbbzc103Q6WJlSRk3NyxkTWkFPvv5NfGQJLDZLRSV+RnoyQeQikdSJGJpPL6ZAxhomkF/Zwh9NOiRL+jGVzBZKJckaXzYCJizFjYcipNNn8c0W7Ngs1sJFJ14jiLDCTKp2Ve+1WyOWGT6QHKKImMAX/mvJ3ndFYsoL/LT1jMya/CoUCrNS50nXDquaqxjc30NJR7PtO8mVdNJ5y7OuJ6OrKYxnJxboL3h0aBRkDd5Cromu0ycPL5g7uMrlskQTV88n/AxTr13Q90hHv73x/jr778Xu/Pi5hv+c0IIQUrTLokAa0A+ivkc25Mz9PFFGkmScFutkzSxiiTjOSl/9YJgMa+vXUC56/T8QJVRt5vzgTjpv2eDhMy6or/GIjtRTgq6aZVdVLmvpNS5mhcH/j8Upn6+4rleUtogLqUIr61qymPOhP9qeYqFvkquKjkRvMgQgq8f+TW3V13OAl/ltOcGHI5xc/O0lmPvYD+5UVeo6eZNz3W20h2PnrP2jzGmwT055ZNVkqdc53FbbWysrKHE5SaVy9GdiPFyfw+rSytYXDhTJo7To6TAy53XrGRJbSkv7G+ldzhOLJnBYbOwsrGClQ3lXL6khuqSwDmrcyrmuYM8rR1nJJsinE3Rl4qxwFdCtTtATypKTM0SyiZRDW2CYHupKqOmY3FdKbuPd/PqsW5ae4fpGohQVeK/ZK/jjN5YbosXm2xnRB0ipSVwjvrY6kInmosQzUUIWE8II82Jo2gixwLvElYFNuBU8hOWsBqa8imVkfFZ/TR6FtOf6eFwdB8juWF81gANnoVndqUnoeWOYhjhCdskyYnLdReyUn7GeVtPD3k0yrKLvLlzXlMhyCKM2KSjF/rqyRgZnhvayWB2mG8f/yFCCFYXLKPeM/tqmBApNK2dnPoqNtumUaH6xEfAMFLk1FdH0x71IUkebPbLsTuuRlGmfwHPhKbpRMJJbHYLNtuZCd9n++D0JeI83d7M75uPcTwcIpxJz37SeWTMRGwsfUUonRo3U4bzLdhKWGwWGpdWEgklSCWyDPZE6O8aoXHpzPdYzeQ4sqcj76MLlJQXUFQ6eSXfajvhm6rndNKJmc21DUMQCSUIDyfHy74YON12yqpPvLO624aIRZIznqOpGrFIivDw9GbsxQUe3nbDauLJLFWlAfweB6sXVWHMkl82k9Poj50ot6EwmPepncH8OpzOMBCfu0n9hSCZVWkLhWdcKhsLktU+EiYyKnx6bDaqApPH10QzTEE8q85qYhxJZxhKpkiepYne6ZIIJwhjo6DUj67p9DT1Y5xildB1rIf+toHTM8M3mYB0kj+0GP3fbBhCMJBMoBkXX4sPeYusoVSS3BysVuJqloiaf05kSaLY5cFyihm0XbFQ6vRwkAEEEM9mEYLx9EN/SkiShNMy2YpPkmSskgur7MoLvNPMJUYyx8joIRSHjXMZ/imsJkhpk79/Xalh0vrM76ISl4dytxe7ouRzw0bD/L75KDc3LKTQOXHBL6fr7Orv5rfNR+mOT547TsVwKsmuvm5KXB4WBIvw2GzTmqknVJX9Q/0cH8krVWyKQmNBcEp3AkWSKHQ4efP8Jfz08F76kwl2dHdilWXetmgFq0rLkZk8r9MNg2Qux9GRIcKZNCuKyyifIXiURZEp8rtZv2gedeWFpLIqmmYgyxIOm4WAx0nA48R6nnOD13gKsMoykWya7mSE/lSMRYESqt0F7BrqJJ7L5AVbXWeeO3Be23I+WVpXzvKGClp6QrT3hfnZk6/y/ls34nPbL1rMmJk4IwmuzFFBpXMe+yIv8/zQUyzyLUeRLPSnu2lPNqMZEzUviqSgiRwZPU1aT5E1MoTVEIdje9GMHNIpfqySJKGgsDywhif7HuFo/AAei5d5rnp81tMxi5kaXevEECdPABVkpQirfd0FC6wkSRJCyKP1KYwJtggNISYLX8X2Qpb7F+FQHCzw1CNJeR/bBd4GCm2z+6gYRgQ180fS6V8hYUWWr0MazWVrGAly6j6Sie+RUw8iRBRJcqBpTRjGCC73O5HlwGlfY1GJj79495bTPu9k/IEzvx/t0TC/az7KY61NNI0Mk5tFmLjQGEKQ1nLU+gomrIaeT2x2C5dds5jDr3aQSmRpPdrH8QPd1C0qnzZYUk7VGO6PsmdHM2omh2KRqZlfQlXtZJMur9+JbdSkN5nI0NsZmtHXSNd09u5oIhZOXtTJvS/gomFxxehzKWg/3k9f5wjzl1bhcE290j/YF6GzeWBGTbPTbqW+8kQ/5aNFzp6SQSAmBIRR5Jn9MJOqSmtohObhkZkLvsBkNI2eaJTmoRC1wcCUgaQEcHxwmK5IlFQuh8NiocznpWqKHLYumxXvqGWDbgiah0OjaVSm12Qc7h+kYyRywSNGt+7vIC0PsOWOy1EzOR785m/zAdtOChQVHYoTHYqbgu2ZIuUn2mPPhqrrZGaxhsgZOqFMiuF0ckKu4YuJIQStsRHS+sxjWQjBUDpJ/2jkYoskU+crmJR+xWO1Mb+giGe6WgBoigwzmEqwKHjutGZniyTJpHPDdMSfIaq2YwidUtcaCu0LscgOBtJ7iakdLAzcMX5OR/wZLLKLoH0hFtlOJNvGYGYPGW2ESveVBO0LsSmzu/cYQierR+lObqMnuZ2ckWIke5ze1Iu4LMU0+G4mb+R9egvrOUOjPTlIPJdmKBujLTnAyyPNo3UaJHJpcoY264K9y2plfrCQxYUl7B3sI5FT+cWR/fQm4iwqLKbA7kAA0WyG9miYV/p7OT4yTIHTiSNnYWSWBfxINsNT7c2Esxkq3F4qvT5KXB4CdgcOiwVZksjqGqF0muZIiBd7uuhOxHBarDQWFLKsuGzqYGmjrjc3NyygMxbh2c5WBlMJnu1sI5ROs6iwiEqPD7fVhiyBqhskciqhdIqBZIL+ZJwSl4cKj3dGwRby1gRelx2P04ZuGKg5HYuiYFHkcxZUcjaKHR78NidpPUd7YoShTJJbapZT5QqQ0rL0pqIMpOIokky1+/yn2Dxf+D0OrlxZTzSR5vm9rTy/N/9eWbe4msaqYkoKPDhslktGg3tGgm2BrYiFvmUMZQc4GN3DUHYAu2xHFzpWyUqhfaLD9iLvMuK5KB3JFhJaDItkHfU/kAnairHJU00cJercjfhsAdoSTfitBdS5558Tx3/DGICThEdJcqAoVaMpfS5sdEpJcjIxQJWOEJNX+RRJptxRQrmjZDxYlMvizPvYzuHlaxgRVPVFtNxhBPpJdQoMvZ9M+hHU7AtYLIuwWDdhGGG0XAvZzFNYrcuxO64+7WvzB1xce+P59XGYCiEEQ6kUv2s+ym+OH6E1MnJJ+jDYFIVFhcXMDxZOSiVxvrDaLCzfUM+8+SUk4mn6OkPs3t7EvMYSFq2ch2KZ6LOdUzV62od54fEDdLcPY+gGdQvLaFxSSUHx5A9PUZkfX8CNrMikElnaj/VzbH8XjUsqsZwS+TWbydF+vJ9nf79vRnPeC4HTY2deYymVtUX0dYYID8fZu7OZinmFLFxZjdU28VWZiKU5+Eo7B15um7K8rKohhMCiyCTSE1fnW7pDyLJEbcX0JvY2RSHoOmEe3jESYSSVpnIKYS+lquzq6GZneydDczT7vVAYQjCUSPGrfYf4i7UrKfN5Jgi3umEQSqV55OAReqMxDCGo9PtYUlqM1zHZgiHgdFDq9WCVZTTDoDU0wv6+fgJOBwWuyeb0HeEIW5taaB4OTdp3vnG4Hbht+YU5XdM58lIT629cSaDkxOKsrMh0HZ9bwB2TyeSjCTuxjmosI9k0g+nEjMJhXM2ye7CHlKZeMt8FQwi6EzG64hHmef3T+sIOpJK0x8KE0ikUSaLA4aQxUDjJXNhvd7C2pBKX1UYqp3I8MsyRkSGWFpZO0vhdTFQjTlLrB0kipydojz+JRbLjt9cxkj1GX+rlCYLtQHovDiWAx1qJZTwzhkRX8gU81ir8thpOdoObDQkZXWQR6Pk0jMiTFC2ngyEE0VyS5ng/YTVOW+KED7SBIKurrCqoo2AWCzZZklhaVMoNdY0MppL0JmIcDg0xmEpS5fUTcDhAQFTN0B2PEc9mubyymkqPj4PDA7MKtqqu0x2P8Up/D7IkUeR0U+Ry4bc7cCgWZFlC1XRGMim6YjHiahaX1cqy4lJunb+YUvf07ZclifpAkNsXLMGmKLzQ3U5PPMazna283NdNuceLy2pFRkI1dJKqSjiTJpLNIIDNVTWoc7BcSGZUeoaiNPcME4olUXM6axdWM7+ykLSqMRxNEvS6KCk41656J3BarJQ4vETUFG3xEKqhMd9XTMDmxGN10BYPMZCOU+hwUey4cPFU5ko2p/Hcq83kZok/ISGhGwZ2mwW300Z73wi/236I1t4Q9ZWFlBR48brs2CzKaS0qrGisYF7puRf4z0iwtcpWalwNKCUKB6K7CashBAY1rgYKbEVE1BBZ44Q/0xL/KgSCtmQT8VwMi2yl1FHOmsDlBG3FWGUrLmXiy1aSJDwWH0W2EnrlTgpshVS5as7uakcxjNiEFDtIdhSlgnMeCnNWJJAseTXO+BdWgJg8yHrTg2T0LPXuarzW039QhZFE01oBGxbrUiQ5P0EWIoOmNZHNPoMkeXG634rdfg263k0q+VNyuX2o2efPSLC9GIjRyMfPdLTw62OHaY+Gz2jyMpbSx6Yo+XQqo1ExDSHQDGP8T9V11DM0Z/PYbLy+bj4+u/2CpUhQFJniMj+bb1xOLJyi/Xg/h15tR7HIJOMZisr842l91GyO0GCcAy+18NTDr2LoBt6Ai02vW0b94vJJgipAQaGHqroiCgo9hAZj9HYO87uf7uTaW1ZTWOLDarPko1imVAZ7I7zy/DEOvdqOxapgtVkmpNy5kFgsCkWlPq66eQW//clOEtEUe7Y3YbNbyaRVisr82OwWDEOQTmZpbxpg25MH6G4bwuNzkohNnEgMhuPkcjp+j5MXD7bjcZ4Q0g4091ISnHk12u9wsKSshB3t+eBRe3v7mN9emM+X6bBjkWVyhkE8k6U1NMJvDx3lyMAQfod93Jx3NgT5oGC6EOij4zmZVUmq6rivX07PTzyi6QyKnE/HY5FlFFmeHEl6CsZS+Dx84DABp4OVlWUUul3YFAXdEITTaQ71DfL7w8cIpdI4rRZWVJSxft7Uvm5eu52aAj/VBX5aQ2FimSyPHDiCphssKi3GZbMikfc3jmQybG/r4IXWDpKqittmJaleOD/uBesaWFya15ApFoVlmxZyy4dfT0V96fgxbQc6Ge4OIV2C5lyvBWQJqjw+XFYbEjCUTnEsPERPIkqVZ7J1Vyqnciw8zKNtxy4Zbe0YyZzKH7vbKHN5WBQsmaSFTeVy7Ojr4MDwAKqh47c5WFFUSqnLM8ks1G21saSwhBVFZewZ7GU4neKF3nbK3V42VdQQdDinfHbHMgdEsmmyuk6pyz0hl/C5RAgDq+yi2LGSQsdi0towL/T/A2G1Gadlds2yVXZT5FhK0L6QvuQu5NNwIZMlBaclSKP/jaT0QQxDpdK9kWLnirO5pLwCwllIMpfFb3Xjt7oJjgqxkiThkK2sKmig1D675WG118cNtY0kVJUXutsZTCaJq1kODPUjyLtluCxWgk4nq4rLeMfSVUjk43jsY3IA0pPx2GysKCkjmcsRyaZJ5nK0hEcm5IqWJQm7YsFjs1HmKWR+QSHX1tRzU/2COc2Ur6ich89up9Tl5sW+LnoTcaKZvIY5Z+SzICiSjE1RcFqt1PgLKHA4WFNaQYFjZpP5VEblSPsAz+xuYm9zD4lUloFwkg/eKlNe6GVgJM5LhzupKgnwhssWzaG1Z065y0dvKspQJkHA5qTY4UEib6bclhghrmZZFCjBabGNW+bowkATBlldy2cDQJDSVBRJHs+PPWY9JsjPOXOGPn5/soZOWsvlv8Un5dMWQuS/56NlayLfz2k9h0vXxo8dOz6TzfFfv9tJMj2zabwkSVgteW14ZnSOllU1dh/rZvex7vxYsVlw2Cwzukudyl+/9apLR7AFcFncLPAuZYF39pxeTsXFuuAm1gU3Tdp3lWPq/Kv5G6SjCY2ALUiFowqfNXCmzT2l7Bzjpr+AhIIknb9VnRlaktfOTsgTJ8EUWuPd4QMMZkJU1ZThnMIEZPaaVAwjiiwXIstBJCn/sdL1AXLqPgwjjNW2GofzNhSlGMVSh5Y7Tk59CS137EwvcHI7zsDk7nTMG3KGQWcsyn/vf5WueHROQq1EPky922rDabHgtFgIOJwUOV0EHE7cVis22YJVkcnqGumcRjKnkhhdaQylU6ijAT3ypnAa6kkfiKmQJYmgw8XrahtwWC6ET/dErnnTKkIDMTJplf6uEbY/cZADL7exeNU8ikr9SDKEhxN0HB+gu30IwxB4fE7Wbl7A5huXUT5v6sjkikVh+fp6OpoHeWnrYRKxDM/+di9NB3toXFKBv8CNpukM9kZoP97PyGCcsnlBquqKaT3Sy2Bv5MJ2xEl4/S5uvHMDh3d3cGx/N9Fwkq2/3cOhV9uYv7yKQKGXnKrR2z5M27E+DENQXV+C021nz/amCWUNh5OkszlUTeeR5w6yorF8fCWzayAyay64QreLK+tq+O3BI4SSKdpHIvzk1X3s7xtgcUlegEuoKscHh3mlq4eMprGivAyvw8ZTx1rmdL090RjxTIZ0TiOVy5FSc8QzGY4ODpMdDa4WTqc50DcwGqXYetKfhSK3i4DTOeOHrMjtZnVlOTvaO/n3F3ayoKSIhsIgAZeTTC5H20iYPd19aIaBVVFYWFLM5vqacYHwVBRZpi4Y5PoFjfzk1b2k1Bzb2jpoCY2wqKSIqkA+oEU4leZQ/wA90ThBl5Nl5aXEsyoH+yYH5rsQWO1WXveuqwgUTdS4O71O5q9puGCmc39qSEjM8wao8vppiY6QzKnsG+rn102HuL1xKQ5LPl2NIQSqodESGeH3bUfZ2deJXbGQ1rSLlhv3VJwWK4+2HcM/6gdb6vZiHRVYVV2nOTrCw82HOBjqR5EkKjxe3lC7cMp0PLKU12TfvXg1g6kEHbEIO3o7yGg5UprKhrJqnJYTqXyM0clwWtMYSifZP9RPRtd4S+PS04yKfJrXrBRRYM9b4rmtpXisFah6HHVCnJGTPfTnHsn6YmCRFSqdQSqdQfozYeo9ZawvnH9GZUmSRGNBIR9afRmXV1TxfFcHzeEQoUwKwxC4bTYqPV7WlFXwutpGipxu+pNxGgJBGgJBqn1+7MrU6RIrPT4+suZy1pdXcnh4iM5YhMFUkng2O7pQL7ApFoqdLmr8BawurWBNaflpj4WlRaU0Bgq5oW4+O3o6OTQ8QE8iTjybRRcCu0WhwO6kwuNlfrCIVSVlLAoW45zFgq19IMwTLx9j56EOltSW0FBRxP9t3Tu+XwgYiCRo7w9fEME2rauEsklWBCvGhcZ6bxGHIwNYZYUy56giCUjksnQlIyRyGZpiQ0TV/KL4y8OduBQbbqudee4AHqudrKERyqQYSMdIaio9qSgZXaM9HmJPqBu3xUaB3cW80cBU8VyWgXSciJriWHSIUCbv37sn1E2R3YPXaqfM6R1PO2QIwVAkOatgOxuGEKSzOdLZ01s4Tp+nheYLP6OegZOFHoEgpA7RlWylxFFBtavunNUzZr5yLiLynSn5axUIEQNO1vhZRs2TJyIhYZUtOJQzDDAkNBBpZEsFEmN53gS61kZOfQVZ8uF03j7q8yshSVYkOYAkuTGMMzfjG7unhpHPUWkY4rRyljoctjlnWRBCEMmk+d+Du+mNx+a0Ii+R92epCwS5obaRtWUVLCkqwWebm1O8bhjEcyrtkTBtkTDHRobYM9hH00iI6KhpzVhwnJPx2mwsLiymLnB2EZ/PFIfTxlvecyWBQjd/+MUuOpr6iQzH2fHUoYkHSvlJktfnZPMbVvAXH7qWQJF3xsWGZetrScbTjAzFOLK7AyEEXS2DdLUMnihWygvBRWV+3vOZN5BOZgkPxS+qYKtYZApLfHzgb9/ED//5UQ7t7iCTytLdNkx32/BJbZdwuGxc/caVbLhqES1HeicJtqsX5TWOgyNxbtq0hDduWTouAD6/u2XWdD8um5Wl5aV8aPNlfH/bSwwnU/TF4vTF4jxx9ERdEvmV+2vm1/MXa1eSzuV4qaObZHb2D9Vnf/sYe7r7ZvQ9bQ2FaQ2FeeCVvZP2veeytdyzYQ2l3qkXBSVgYXERn732Sh7ce5CfvbqPIwNDHOofnHScLEnUFxbwVxvWcE1j3YwWDDXBALevWEp7OMIfm1tRNX28byaUK0n4HHb+Yu1KagoCPH285aIJtopFZunGyYEPy2qLecff3nYRWvSnwZhP3/XVDXTGIhwMDdAeC/P9/S/xXHcr68qq8FntJDWV4+FhDoUGGEylKHA4WV5YyssD3SRyFzao2FRYJJl1pZUcGO7nB/tf4smOJlYVl49rnVtjI2zr7WAknUIARU4X60ureF3N/GkNZ11WK2+oW0hzJMSDTQfoikd5eaCHfUP9lLo9LC0spWBUiE7mVAZTSbriEXqTcUDiyspaXl97ZkLZXMnro06eDwiQpFGT4BNaqLH5mmZkscoXL3r+6XBj+WpssmXK7/9crF3G8NhsbKmuY0v17HPgKq+fz1x2JZ+57MoZj1NkmQKHk9fXLeD1dec6y+xE7BYLiwqLWXQOoyLvbeqhazDCbVcu5903bQDgiV0nFDABj5Ogx8muruHpijhnVLh8uC127IqFeu+JBf8GXxEWScZvdVDqzFtoZfQcz/c3840DzzKYnvi9+sC2BwHwWu382xVv4fKSWlrjIX7Rsptftu2dcOwDza/wQPMrOC1WNhTP4web3gbAjsE2/uf4SxwY6Z1w/KdfegSAQoebDyzayF82rh/fp8gXzif5VM6XT+4lJdjqQiehxRjM9pHVs+wcfpaMyNLoXUSZ89yFYZdwcnLAJoE2KmBeSAyEEUcYCU4WbCXJgSxPFnaCtgCqkWMoO0KJ4/Tz+EqSNa+VFhlOCPQqmtZKLncIRSnFbr9uglCd9zWREVOYRs8VwxAMDcR48tG9HNjbyfBgDPU0zE2/8f27qaicm6mCJgz6kwn+0NI0p3Q+iiRzWUUVb1m4lI2V8/KaWcWCTZmb3zLkJ+Q+m50lRSUsCBZybW09qq4zkknTEhlh32Afu/v7ODTUT+qkNpW6PFxbUz+nOs4XDoeNa29Zw8IV1ezf1crenc20HRsgEc37abq8DirmFbJkTQ1rNs2nfnEFbq9j1oUGSZJYs2k+pVUF7N7WxKsvHKerdYh4NJVPT+FzUFlTxIrLGrji+iVU1hTR1TqEc5ogTRcSSYKK2iI+8v9u49Vtx3n1heO0HO0lOpJACAgEPSxYXsXl1y1l6doaLFaF6Mj00ZML/W5uuGIhykkfjpULKpglkC8AHruNW5YupqYgwFPHWtjd3UNPNEYmp2G3WihwOlhUUszV8+tZW1VBpd/Hgb4B6gsLONA7uwBnGGenqzo1n+ap+J0OKv0+Srwe/mrDGlZXlbO1qZW93b30RONkdR2fw868gJ9NdTVcu6Ce2oIC7LNYMEhAdcDH37/uaq5prOP5lnaODAwxlEii6TpOm5VSr4c1lRXcuGg+C0uKiGWz7O7unbHc840wBJKpmT0v3FCzgIFUgpSm0hoNk9FyHBge4Fh4GHnUpG8seOCSYDHvXb6eaDbDkZHBS0KwdVmtfG7dFv7Y3cavmw/RHgvTk4iNmxjnjLxpoSCfC/1NdYt5z7J1c/IGvXvpGkrdXh48foC9Q32ohk5vIsZQOoky7v+ZN4/UR1PKyIDbakWRzq+JfELrYzC9j3LXOpK5PqJqB2WudbgsxVhkJ4bIEVFbCNgaCatNxHJdWORz6yNskewkRQTVmDkK/unis7qI5lL0pcMk9ewEy4B6dxle68wp9kymJxxPY7MqLK+f2uLTalWwWS0kM+f/2W7wFfN3q25AM3Tc1hOKp8uLa1iysQxZkvBY8nMbh2Ll2ooFrCuahy6mVrzIkkTQnteoNvqK+cSyq7l30cYpj5UkCbt84nt5VVkjawurUY2p57+yJOM9qY1uh43Pv+v6WdMPni+WNZSfl3IvKcE2J1Q6Ui08M/AoutCxyXY2F13LfM8SrNK58/OQlUIkyTFqkgxCZNH1Hi6kBleINLncoYm+voAku1GUyQ+rx+JiMBvifzt+Ra27CqtkHRcwFnvnM99bO3OFkh1ZKUTXOtCNYWRRgpY7TE7diyTZsNo2ICtFnBzISpBBiDTSFIL2XNA0naGBKD/4zlO0NPUTDSdRVW3WiLCSlNcolpUHTmslKZLJsHewj3A2PavBkiLJ3L5gCW9esJilRSUE7I4zWj0aSzVhU5QJPlEFDiflbi8rikt5c+NieuIx9g/182p/Ly2REJVeH5dXVJ9WXeXVQW69exObX78s//Ir8U3IvXrabZclXB47NfPLKCz1sXbzAlKJLNroS85ikbE7bXh8TvwF7mmjA08qV5KwO21U15fgD3pYu3kB6ZR6UrkKDqcVb8BFoNCDxapQVVfEe//mZuLRFL6Am9IpFjNq5pfy9g9ey+vfuh5FkSks9eOeJf8uwGXXLqZhSQWZtIrNbqW6oQRlmjQAkiRhsSoUl/vZ+LqlLF1Xm+8TNT+ptNosuL0OCgo9uDwOBIL1Vy/ka/e/F4DqupIJOYEVRcalTOy3kWgK3TAo8M7cdlmScNusrKospzrg5y0rl5DJ5XNdyqOaKo/NRtDtwmu35U15i4v4hxuuJZHNUuLxUOSefhL49zdcQyKrnrEpZoXPN2XApvXV/z97/x1lR5pd94K/L/z1Nr1FOnhTQAGF8q6rq6q9ZVP0TVKOQ1GiSD3KzUhP0sy8pSeNvKgnPlGkWmRTJLvZ3pbt8oWC90jvfV7vws4fN5FAIjOBTCAThW7WXgsLwL3hb8QXZ3/nnL2baI6EKds2yYAfZbEv+FBzE9viMTL791BZPA9ZlvArCjG/j7jfjypLt3wOr2bpaoMBnurq4EBjA/lKBdNxcL2qgrSuyEQMg2TAj6GqBHWNnzm0nye6tqHJMt01yWXPa1cyzv/jkSN84b49CASdiTjKdd8nAn4+vnsHB5qqL+Jt8Rjh68StDEVhf2M9v/u5T2C7LnWh4LJMdrlQ4fd+53/y+d/6OA3X9diO903xrf/6Q774z356SU38A2wcQVXl0127aQ/HeGNimFOzk4zlM+RNs3r/aTq7IjEO1zXzcGMbPbEkV1LzW+bRuhFokkxbKEpTMMznuvfQFU3wxsQwJ2YmGM1nKJgmhqLQEYmzL1nHI43t3F/XTL1/bU/r6xHWDJ5u7aQrmuD07CTvTY/Rm55nupinYFl4eOiyQtwI0OAPsS0SZ3eilsP1zTQGV4rVbRZ0OYwiDGbLZxjMfRfbLVLvP0RC34kqBYnp20mbQ5yc+11UOURAqUOTQ/iUajySM8cYzP+AgjVNxhxgIFthtnyGWt8BGnwPIAmFK5mvUnbmWahcomBPk670kzB20hJ4nIBaFTmt9R1kIPttLqf/jKHc94loHeyJ/8Idn9+xhT5emznPVDmNT9a4Pr78xW1Pf0Bs7wBCXHUW8VadaDctm7JpEfBt/ZiqSTJxfeV71lDUFf3pkhD4FQ2/sr5YSpNkNN1PbJXtrwafom7IYUNRZI7surVd6FbB0LZm/N20rZadApezJ1gwp5CFQkJvYHfk6Ia2oQqVeqOJh5NP4XkeQTVMk6+VsLq5RsCS3IgQAbyrlj9eBccZw3VTSFLNXVFG9rwClvkWsLysRhJhZKV9xfKapBFSAoBH2SljiWvrWd6tS3MkKYaqHcK2LlLM/3cUdTu2dRnTfAdJbkD3PbvMv9fzTFw3h+dVkKTb67HJ58q880YvZ04OUSqZtLYnaW2vIRYPkkkXeO3lizS1xNm9twUhCcZHFxgdmkM3VD70kX109dQTiax/djZVLnFmZmpdJvdHG5t5rqObg3UN+NdQoLwTKJJEWNcJ6zqEoCMapyee5MHGVqYKOcK6QdK/XCVvtPAejmdR59uFT155zX0BneZtNTRv21hJT96aZbZyhdnyZWzPYk/0EwSUGuTF31vTFeI1YeI16w9ihgvvYDoF6n27Cal1qy6jagqJ2jCJ2ltv1xfQ6dzZeNNlAiEfgZCPtu7V97cWknWRVT13bwZJlogmgkQTt544WO3alcrmksjCjegfm0OSJLpabv07CiEIaBoBbX33aMjQ2V1fe+sFgT0NG7uO60Ui4CcRWEUMUNcI6pv3rElCEPP7ViXXN0JXFNpiUdpi0VW/D+k6oRqd7jV+El1RaI5GVvXWhcXSPr9vTdErx3a4cnyAcmG54n0hU+T8G5dX+Nv+JCGiV4lVnT+Ih0dbOEZrOHrL9VpDEf763sN8unMnAPfXNRNUNeZzRUbm0ziuy/0d1esthKAhECLQ2EZHJM7TrZ1kKmUqjoMkBIassJApEpQ0ukMJ4oaf7THBh5KdvJEeJmb50G947z/U2EpQ1nitb4g2OYpPUShWTF67PMSelnraQ1F+Zff9fGzbdmp8ARoCywXhkj4/n+raxZ5kHX5VY19NA+p1+hitoQg/1bOXBxtbSRh+gqqOZsg8rGpsC8d4sqWDdKWM6TiokkRI06kPhGgJRjY8EZsw/EQ0neZgmAM1DcyViuSt6mTQVSEin6IQUnXihp86f5C44btlO84TLR00hyIULJOI7ls32ZYljZbAY9QYe1EkH0V7Bg+PqNZBUG1CEjIRtY3O0PNkzREQEj45ToP/fgw5jk+O4+FQY+wlom6jxtiDLFRkSSeg1KNKfoSQqDX2YXlFksYeBDKypOOXE6jXZX0jWhvtoWco2rOAhyFvTnvQ8YU+ZCHxRO2eFSQ2rm+mY+5fPtREg1wYmub4lXF2tNYSDlwTm7Jsl+GpFCPTKbqaVloSfoBrkIRYdu1+UrBpxHa2PM5Yqdr7ldAaUMXGA5iqWnIjdcbNA9w7hSJ3IqQIuFeV4xxcJ4VZeQvdeBohtm6WEqqk0XEmqFR+tCJjK0lxFHVls3tSj3EwtmfV7dXqty5NlqQkuv4UlfKLVMovYJnHcN0MQvKj6Q+haQeXLe8407jOJEKoyPLtlYHnsiXee7ufcslix64mHn1qJ9t3NhGJ+RnoneadN3ppao7zoY/sIxAwmJpMc/ydAfp7p/A8j70H2tCN9d+iObNCf/rWPp4SgqfbO9lTU7clpHY1GIpCWyRKWyRaVbZzvRVKlrLQFp3zNrdcURISstCw3BJD+bfoDD6KX45XFblvE3PlPor2AlGteU1i+5cZFwenuTI8s6pI1IXBaZprb2+y6AOsRMm2mCsXWKgUyZplclaFvGViuvaSermHh4RAlqqqk4ai4pdVAqpGRDOI6X5iuo+QZmzq01fMlpgdmyc1naaULzN4bpTKolCHbdkMnRtl3SICP6YIqBp7k/XsTa5eNrgWkr4AH2rtWvH5VDnP8FwK03aWiO1VhHWDsG6s6tc6OLtArlTBUFQkIUj4/Hy6ezfOgkuNHEC5gdhuj9XQoIcoz1moklwVm3IsDFVBFoJaf+imBD2sGRypb+FI/eqVOUlfgKRvpQVISNPZHq9h+yZ7ziqSTK0/SK1/84Qydyfq2J3Y+PgvCZW4sbLn/HpocpCEvJOEsXPNZRr8h9f8DqAhcOSWx6JKfmp9+2+53EaRMvPsCrfw4foDaPK9V43heh4l26qKDVVKZK0KOatC0TaxHAfbc5fK9yUhUIRAlWQMRcUnqwRVjajmWxo7ryqT3w3saK2lb3yOdy+OIAQ010TIlyr0js7iOC6j02lMy+Gx/Z136Yg+wL2EOyK2nudiuhVmKqNczh0nb2do8nXQ7O8ipFwrJSzYWVLmDBWniCwU/EqYhF6PRFWxbaEyhYOD49qUnDwAQTVKXKtDFgqOZ5OzUqStOWy3GhSE1QQxrQZV0rFck5ydImPO4eIiC5mk3ohPDiGvkn1VtD3IpSQOV7haHuJ5Jcqlv0BRdyKEb0k1eLPheS6OM41ZeQ3busDy8mcDSWlCUVa+zGuNJDEtSskpYbsOMS2yKLywPpMiIUKo2iF8/s9jmcdx3SyKXIuqHcTwfRRJWl766TpTeF4JWelC1W5v0K+ULYYHZnFdl8c/tJvHnt5FIlmdqcykiqiqjKIq1NVHqa2PsK2rjngiSPHPK/zgW6fZsauJffe1YfjWRz5Lts1UIX/L5fyqyq5k7fvm56dKMqs1RzX678xqYC34lQStSgJdCjBePLUpgfRPWiieMcv0Z+eYKd36/rmKznCCjnBi1T600Zk0l0dm6V4lK1uuWDjrabLdIFzPY6qY48zC+ntJY5qf9lCMOv/KDILrlnG88qLv9TWoUmRZdcdqmChkGM6nlxQf10JQ0TlS24Iqra7euRps1yVvVZgq5pivFJku5hjKLTBRzDJbyjNfKbBQKS2pk5uOjYu3ZKWgyzJBVSekGsR0H7W+IA3+ME2BCE2BCPHFz2K6H1WW7+heL+XLjFwaZ/DsCIVMkTM/usDIxTGg6hFdypfZ/WAP0gYsEn6cUbYtLqdnmS0XsDeo3WDIKp3hRNVmzXaZzRY4NzqFaTu0JKIokqBoWiiyTE0oQMmy6J+ep6suQcG0yJdMVFlGvu5a722p58TgOIHrKgks26Fveh7bdalYNplimWQoQMm0mEhlUWWpel8IQb5cYS5XJLdosVWxHVoTUaIBH3O5Agv5EqZtU7Is4kE/XXWJZT7OPwmYKeUYyqVYqKzfQ3tfvJF6f+iu2dzdCq7n0Z+Zoz+3fqHMGiNIVyRJRFs941VnRJGERNYuEZPkdfcrD2TnGS9kKNg37w+t84XYE6vf0P1kOg4Zs8R0Kc98ucBUMcdAbp7pYo7ZcoH5coGMWabsXBs7odq6pUgShqwQUo1qZl+vZvYb/BGagxHq/SESup9aX5Co7kMS0pbFCV3NSR7Zu42FbJGXTvShKTKm5XBxeJrh6RSxkJ+ju1q5f8fmafO8n7g6CXFmYZK8VVmzP3c1CAQ+RWFXtJ6YfusqjNWQL1WwbRdNlfEb778Wyq1wR8TWxaVo5zibfpPxUj9lp0DJzlGws2wL7iasJvBwGcyfYyB/loKdRZIUavQm9kcfJa5VZ3Cv5E6St9O4nkPGmkcgaA/uIqzEkWWFjDVPb/YkQ4WL2F61J6QruJ/dkaMoQiNlztCXP814sRfHc3Bx2Rt5iG3B3QSV6IrjVtUdyEobwjqF510VDKhQKb+Kpj+OJIJIcsOmlyR7novnpjErb1Eq/C9u7OmVlUYUZdcKkglQcUymy7MMFkYpORU+VPcwlueQNbP4FIOwevPSFiEEspwgFP4tLPMsjjOLJCVRlHakVUpvhNBR1L2o2gE0/ZHbOl/bdkmnCiAE23c1Eo1dm50WkkBRZCpla0mARgho3Zbk8NFO3nr1Ej/89mk6u+vXTWxNxyFbubmHpwASfj9BVVuRMX0/4Hkellei4uSw3DKq5MMnR1EkbcnDLG/PICEv+pmVEUKgSUF8cgQhpMUJphIVN4fjVqqTHUIQVGpRhH5TwuB5LmUni+UWCWvXKiUK9hwCCV0KIoSM7ZYpOgtV7zlJw3LLy1QrHc+i5KRwvGrPpiJ0dDmEtslCH1uFkXyK/3L+TV4Y71v3Or+2+yH+1p5HlgXKV9HdUkNrXWxJHfl6vHtuGHsLSk9dz+PY7Ai/+eY31r3OoWQzf3XnA3zYvzx7YjkpStYAFWdm0ZLs2lgVDzyNcouqlndmRvjDK+9xZn7ypst1hhP84ZM/TZ0vtKptyfUwHWfJzuByepbXJgc4NTfBeDGDtQ4vaddzsHAoORZpswxkln2vCImEEWBPvJ4H69o4kGyk0R8hohkYsnJb7TBGQKemOcH08CyqXvXYvTrsG36d5u4GDj2zF0X7ySI7N+KqeNP51BT/8dwbvDszSslZv7qtKsnsitXxKzuOsE1LUDRNrkzOYtoOM9k8j+3YRsinMZnOEfX7eGJnB7PZAn/wo+P8xrMPM5cr8O2Tl1BlmZ9/9CD+xOoVE47rslAo8qdvn8GnqSiyxFyuSDIUIFeq8FbvCH9x7By//bHH2ddaz2Q6x2uXBumbmicW9DGTKfDI9naOdrfw5pVhLo7PULZs+mcWONzRzF97+shPHLE9PT/J/33pHY7NjK57nX/z0Cf4SOtOpLvQ9rUeOJ7L14fP85/Pv7nudZ5s7OJv732UfYnVBXCafAl6cxOYrs2uSAu6dC1Z0uiL41dWd7j4/thl/qz/NEO51E33/6Gmbv7fR56nxnfrDHzFscmYZSYKWc4uTPLm1BBnFyaZKK5PNNXxHEzXoWhbLFRWTlbqskKDP8T+eCNH69rYl2ikxhcgrOobmrRcL3yaytFdbbTURHntzCCXRqZJRvxoikJrXYzDO1vY39mIT7/3MuUbhet55K0Kp+bG+f+efImB3MK63ndQjXd9isr+RCO/ufcxgmr9bRHbC4NTTC/kaKuPs7ez4Y5+z6vxfqFkkitVKFcsXNdDkgSaKhP06QR8+ob8cG/EHRFbWSjE9Fo+0vhLnFh4mZQ5Q0dwL9uCuwBwPZeCneWd+e/yQOJ5esKHWKhMcSn3HscWfsiz9T+/tK3ZyhjtgV08VfcFZKHg4iIh4XkeFzPvMm9Ocl/sCXrC9+F5HrZnIgsVB4fBwjkWKlN8pPGLyEKlL3+Ki5lj+JUwwWB0xXELYaBph7Gt81jW6eu+sSjk/h0CBd33HJJUFVPajIfS81w8r0C5/EOKhf+G4wzeeFSo6h50/cFV1x8ujvHqzNucyVzCkHSeqD1K2szw3alX6A6282jNrUtuFs1BULX7uNXjrmr7ULU7yyB6noftuOi6gq6ry4SgZFlCN1TyuRLOdUG+36+TrAmjqAoXz41tSEH5qrn8zSAW+63ulZli8JgtXeFi9jtMFE/R5L+Pg/GfIaa3AWB5Jd6Y+U8YchjPc5kpX0ISCu3BBzkQ/2lUfNhemdHCMXpzL5Eyh3A9F1UyeKLut0gaXcg3+bUdz+Jy9oeMFN7mEy3/eunzEwtfxpBCdIWfwidHGCuc5OTCH2N7FnG9HdMpEtWal7aRNkc4mfoT0pVRHM8iYXTRHXqKtsARxBYra96L2LWo1uh53tJz4FWdLNi5rW7LZO43CzP5rzFT+CaOV0SVwlyfow8bh1GkzWnXqDg2g7kFEkYAeZUyhqsvQdtzGcmn+P7oZb49cpHL6ZlNl/qzPZfpUo7p8RwvjveSNAI809zDJ9t3sy/eiCbLGx43AhE/O4500bqzid7jg3zu736Mpu6tUYK8V1EltQ7D+TT/n5MvciE1TeUW4/T1kIWgI5zg7+57nPtrmplO5VFlmY7aOL/+7EP0T8/zzeMXqY+F0K4Thbv+/ri/o5lUocTYfGblDq5DsWLROzWPEPDFxw+RLpb5wZlqi1VtJMjzB7ZzcXxmmfAYwLbaOL/42CEGZ1N8+Y1T1EdDZEpldjXXsaeljtcuDdJRG1+xHlTfW5ZdbU9ZFaLaA6usQ1htNZi2Xa0QWWPzsiRQZAnpHpjo/UnCucwwP5o5T8GurPjuXx74Je5PrKzM2wjyVoXxQmZNYnvVZsh2Hfoyc3x18CwvjPcylk9v+thZcWyGcimGcim+MXyeen+Yz2zby0dad9IRjqNKGx87bwVVkWlviNPeUE3MXEuQ3Nvv1o3A8zyKtsnJuXH+wbvfYbaUv6k9343wKSp7Yg3888PP0RKMLuvx3wj+7MXTvHKyj089tocdbbVo6p11sZq2wxtnBnnhvSucH5wiV6wQMDTa6mM8eaibJw52URMNIAlxW7/nlkoCOp7NZHmIiFZDWE2gCo2YVktSa+Tt+e8sW7ZGb6JWb1kStJEWgxwPj5Q5jSYZtC8SZgBlsYc3b6WrZDl7jOnyCAKwPQtVurnfq2Y8jmmdxbIuAtdKPlx3gXzuX2NZ5/D5fwpVu4/NuEy2fYlS4Y8pl3+I66zMYkhyM6p2BEVdvZ9kMD+KKqn8XNun+fPR6rWrNRKYjknRvnm53/sFSRL4fBrFQoXK4qyMLFdvUlmRCAYNpicz2Pby7JWsSPgDOqn53DLSeysokoRPVbEqK18kV+F5HnOl4rpnvLYegnrfbpJGJycW/hhnFSGwkpNGILEz8jxHkl9kunyRt2d/j23BR4hp7Qzm32SieJp6YxeP1v46QsiU7TRhrQnpju9dj4XKEOfSX2NP7NM0+e+jZKc4sfDH1QwuUHDmeXP2v7A3+mkSyQ4Egt7sSwzkXyWiNRLT3j/VvfcbFctmeGKBb79+gdlUnqBf5/DuNg7tbCbIbXpS3wWky29QH/ocMd9jyGJ51l25TTG51WC6DoO5Be5LNq25TM6u8JX+M3xr+AK92TnKtn1X9OsXykW+NniOt6aHeKyhk1/oOURHeONWawC6T+Nn/+GnSTbd3vo/zqi4NpfTs/zjd79Hf3ZuQ6RWk2S6Ikn+2f3PsiNai7GoYmyoColQNfCpj4TJVyzKpr1EbK/6h28Upm2TyhdpjIVRZJl40E/wFqV3IZ+OpshIomrRZTo2IUPHr2mcGBrn/Ng0qizxiUO7Vs1CjM9n+Fdfe5VLY7Oreq/rqsInD+/isw/tJRle2ZN7K/zrr/+Id6+Mki2t/l58am8Xn31wDzua1yc69wHWhy92PM0X2h5d9T6sNaJ3vP28bTJWyHBgjbHT8Txmy3n+uPcE3x29zFQxu2QZtZXwgOlSjv9++RgvjF/h4227+WT7bpoCW6srYTsusiT9REkWFGyT1yYH+P+d+REzpfy6hFGvIqTqHK1t5R8cfJqmQARlExIMhbJFKluiLnH74meO6/GH33mX7799mamFLLbtVpNSpk22UOLyyAwnLo3yc8/dz97btAPaYq17D9dzkJCQRLUcoSpkI+N4yzNxitBRJW0FO3c9F8dzUBEoi32v1y/jeg6yUGkL7OTh5MeXPpeETERbWxFNkpIYxlO49jCVyovX7xHXnadc/i62dR5F3Y6qHUJVdyPLzQgpeuuz9hw8L4djD2PbfZiVt7CtSzjOCK6bAm58sSsYxnPo+iNr9vZano0kJGLatcFBFvKi0/ctD+l9gaYp1NZHGOqfYWoiTUtbktCiFYquKdTUhRkanGV8dJ5kTQh/QMdxXCpli3y+hKptrPxPkaoeXdmbEVsgUy4zmE6xLRIjrL+/inBCiKqaIyqK0HG9lRlqz3OpMbqpNXZgyGEiaiOa7KfopIjQzHxlAEXSaQ4cIqAkAYEhhZeeuTuB5VbI23NU3DztgQfR5RA+OUpEbaTs5nA8k4I1w0zpIqc9G02qBl5FZwGfHCVvzfylJrbT8zm++aPz3L+7hWjQT9m0uDIyw7HzLs89tLYoyvsPCV1uQN+ClozrYToOg9mFVQP6q9nc/+vCW5yam2C6lKPsrL+C407h4lFyLMbyGb4zcpGh3AI/1bmfJxu7NmSpACBJEtG6CIp67Vq6rott2jiWgxG8Pbuxex1Fy+TE3Bj/+cKb9GZmMTcwoRhQNO5LNvIbex9lR6wWQ1aXrpFpO2SKZTwPFvIF/JpCwNDwvGqfq+U4TGdyGwoEARS5Wgp3fnwGx3UpVMwl3+i1IAmx2NYiEKJatS9JVSuSzto4j+3YhqYoxAO+VbNWjuuRLVWYyxZWfQ6MRT/OjZ7LVRTKFvO5Ipni6m06uVIZ6ydYlfv9QkwL4VWyTJXTyEKiJ9RUbZXzPDTpzkPvqxnbtb47OTfOH1w+xoXUNPPlIvYG+jLvFO5ipnEgu8Af956gNzPLZ7bt5dGGjk3bRypX4tzgJK+fHWRiLkupYqGpMjWRAHs7GnhgVxsttdFN29/dRsYs88LYFb7Ue5yRfGpDz39c9/FEYxdf3H6Y5kAEWWxO5WmuUObcwCRvnB3k/OAUc+k8lu0S8Gk010Y50N3E4Z0ta/bhlk2LS8MzvHDsChNzmaWWLCGqsbllu1i2ybGLI9TGghiaQk/rxifctpTYSkImoddTcHIU7Sy2a5G302SsORL6cuXj6kVfeeElJIJqlJKdZ6Y8Sr2vbamnTxIKPjmILvuQbAm/Elois5ZbWcr+rgYhFFT1AIbvYzjuNLZ17rpvPTx3AcvNYDtDWOYZJLkWSQojRAjLPLlie7Z9iULh9xdthAp4bhbXTeG6Czj2KJ6XZSWhrUI3PoRuPIOstK56DQB0SUMSEhmrKm7jeR4XMr24nodPvrXdhePMYJrv3HK51SCJMLrx+IbXM3wanT31jAzO0Xt5kp6djUvE1h/Q6eqp5503ennhu2eQJYm2bTXMzeV4981ezIpNY1N81f7FteBTFOoCQcbzN+8bsT2XF4cH6IjG2Vtbf6/OCyyDT46hyyGEkJCEirxIgj3PxXJLKJKOsdhzCyBvQPzs2vl7S5+4no2Li4ezmEX28C32q0tCRpV8WG4Jz3OxPRMXm67QE+hyeKn3VpeDf6lJLYDjeJRNmz2dDYQCBpblMDGboVRef3/h+4GQvp+KM03JGsRQ25C2SEzPdG2GcgsrxDAyZokTs+P8Sf8p3p0ZIWdWbulNvVWwPZe5coFjMyPkrDIZs8yHmntIGoF1jx2VsslX/913ePynHqK5ux5VV7FNh9HLE7z1zeN84e99AvUnoB/seuStCm9PD/PlvpOcnJvYEKmNaAYP1Lbys90H2Z9oRLkhMHNcl+lMnj9/9yyTqRyddQl2NNZweXKWsyOT5MsVihUTz4WKbXOsf5T3BsaYyxXRFJldzXV01sU5MTjB+bFpdFVBlqC7Lkl7TYxXLwzy7ZOX0BSZuVyRoKEzOp/hjStDjMylef3yIOliCUNVVr0HSqaFaTtMpnO82z+GLEvUhAI83NO2giQnQ35++enDTKdyFEyLsmkxNpfh3MgUA9O3Vvm/FT5zdA9HupvJFCuUTYu5XIELI9OcGZ669cof4LYxkJ/inbnL9OYmaQvU0hNqYqGS57XZCzxSs5NG/51Vb+Qtk4lViO10Kcfrk4P8xdA5Ts6Ob6iXfbNhuQ4TxSyvTgyQM6uqy083daPJqz8368XkfJYfnR7gxRO95AoVYiEffkPDcVyGplIMT6cYmFjgk4/sZvttEKP3GxmzxHdHLvHVwbNcSs0sqVOvBzWLLTSf7djL9mgNym2WH6+GvrE5vvzDExTKJrOpPMWyieN5qIpMJGBwfmCKC4NTfO7J/cTD/hXxe7Fs8drpAaYXctiOS2dTgvt3tNDZnMSyXS4NT/PS8V4KJZO3zg2xrTHBtsYEqrKxc9haYotMRK2hydfJRGmAlDWD6ZYx3Qq7wuvpCa0S3jb/DkaKlzmfeZvR4hUQENfqaPJ1oss+GnzbKDl5Tqd/REAO4+IR1+qo97UTVNYuf5DkOJr+MK6Xo1SwsO1rKslVOHhuGttNg31x8TMFWHmT2fYAjjMBnoPnlamWN98qEJPRtAcWS573IMTa2cNWfyMFp8iJ1FkyVo5vTb7IXGWBGj1Os+/WNgqOM04x//u3XG41KErbbRHbQNBg/8E2XnvxAtMTaYrXeTgGQgZ7DrQSifo5c2IYs2JTWxchkynSd3kSRZG47/A2jA0YbIc0nc5onBPTt1aFfXtilI5ojKCmsS0Su+ezJVVCu/rD7ZOjFJ0FMuYoASWBQML2SkhCW1IeXwuSkJGFutgPP49fTlC05yjY8yhCR0ZdFIASLFSGiWiNWG6JkpPB9ipIQkEXKoYcIaDW0Ojbjy4HsdwyrmehShsvnftJwMRshumFHDMLeUzL5kzvBCG/QcWymU8X7qiU527A8YpkiyfJVU6hy43LVJAbQj+Nuklej6bjMJRLYbkunuchhCBjljk2M8aX+07y2uTAXc003Awlx+bU3ASW66IIiccbO6ldh3ALgG3avPWt4xx+7sDS8yhJgkKmyLvfPcnnfvNjqPduZfqGkbcqvDk1xFcGz/LW9DCVDWTa47qPh+rb+cy2fRyta1vRFxbxG+xpqSMR9COEQJVl7mtroC4aQgiBIsvoikxjLExtOETU78OyHXY11VK2bJKhAIZa1VkI6CqHO5oRQhAP+vHrGnWRII/uaMd0HHyqSjIUoCYUQJUlkiE/z+ztIhbwEfYZRPw60YAPRZKQhCBo6Dy5q4OFfBGfprCtJk7Eb5ArV3j5Qj/3dzStILZBn84jO9uxnKoKc8WyOTs8Sa5c2RRie7Czib3t9ZiWQ9myGZtLg8cHxHaLcXyhn7RZxMVjrFgVRVUlhTfnLrEz0nLHxLZgmUwUs7ieVy3cE4K5coFXJvr5ysBZTs6NbagfcyuRNku8PTNMybHwySr317YQuoMB78LQNG+dHwYPPvbQLhqTYXRVwXZc5jIFTvdNcGF4inDQ+LEjthmzzPdGL/PVwbOcmZ/c0IRgnS/Isy3b+WT7bvbGGzaV1ALMZQrMZQorPq+YNjNmnvlMgcm5DKoi8dkn9xMN+pbFn6WKxakr41i2gyxJHNnVxqce30trXQzbdti9rZ6KZfP2uSHGZ7P0js4ym8rTWLOxMvZNI7YxrRZNMggo10RFhBBoQmdf9BH6cqdJm3Ookkqt3kxX8JqFTEJvwHJN/MrqwV6LvwchJEYKl5g3J6vy1XIQ13ORhESLvxtZKAzmzzHvTOICQSWyrh4bWWnC8H0M8CgX/wLbvozn3axndY0XtFfE89Yrdy+q9jvqXvzBL6LpDyLdom+tNdCE6VnMV9J0BFsZKoyS0GPsiWyn0bcOHzmvsmpv77JFsPG8Ep6bA2yEiCLLjdxurbM/oLFnfytHH+2ho6uOQPAacTcMlY7uOh58bDvvvHGF997uX+qnNQyVru31PP70Lnz+9UuLxwwfe2vq+PPL5245pTBbLPCd/it4Hnx4WxfbojECd8nT9np4nsdcpY+KkyNjTWC5JSZL5yi7OWJaC6v6At2AOt8uxosnGSuexHQLgITjVWj07cenxMhbc2StSeYq/ZhukanSBcp2hpjeTkCOE1BrMOQwfdmXiWhNVJwClltELJLekFJPQuugL/cSCb0DPCjZaVTJhywUfEqctuCDTBRPYToFNCmA7VUw5AiNvn1wk94Ox3GZGJpjdiJFQ2uCZEMUVbu9Ycm2HBzHRTfe/8zXbCrP5aEZyqZF0K9zfmCKSMBHqWJiWg5B/71tii4LP4oUxHaz2O7yCgjXW7vUf6OwPZfZcp6cVSGu+zEdi1Nz43x18AyvTvbfdvnlVsEDzi1M8WcDZ/ApKo81dBBew+7jRtiWg2aoyIuzz7Iio+oqZtni1pOgPz4o2CbvTI/wpwOneXNqaEPl4zHdxyP1HXy2Yy8P1rWvqlwfC/iIBXwcaFu5/s6mWnY2rQxk6yJB9rSsnAA+3NnC4c6VPrNP71ld2Kc+evMJqbBP58P7evjRxUGEENRGAiRDfhZyRa7cRA1ZCIGmyGiKTMinkwwHllkQ3SlUWUaVZQKGRsWyCd/j489PAoYK0+wMt9DsT3AmPYwsScT1EEW7gr0JGh9lx2KuXCRvVQipOjmrUs3UDp7jxOzY+1bhshaKtsWxmVF0WSGgauyJ1+NXbu8eH5icJ1eq8OH7e/j8k/uXiD1UY6q2+hh/9vJpjl0a4W98YnVB1nsNrudRsE1eGu/ly30nuZSa2RCprTWqpPbzHfvZEa29LfXj9UCWBOGAQWt9jKBPR5YkLNupktr5LDPpPF955Qz7u5rY09mwTJnadhzGZtI4jkcooLOzvY72hni1nUOW2NFWy6ce28vA+Dz9xTnGZjMMTi68f8R2W3D3mt8l9UaSN5QeX4/u0IGbbluXfXQG99IZ3Lvq9z45eNPvbw6BLNfj9/8cslRPsfjH2NalxV7Ym/uI3R40JLkGVTuAP/AraNpBhFj5cHueR8lZ3hPTEWil1d9I1ioAHhE1hCyUdfmjSXI9Pv8X1vzew6uSX3emmn22h5HkOgzjw+jGMxs+SwBFkalvjPFrv/kcwbCBcl05gRCCQNDgCz//EJIkuHJxgmKhgiRJ1DVGeOypXezY07xMSflWiOgGe2rqiPv8pMqlWwbFval50hdPM5JL88munXTHEyR8fvS7qJrs4TFROkWqMkzJTuPhMFZ4j4w1jh7+MAElSUxrxZCvPdiK0IlprehSEEnINPr2I5AYzr/Fhcx3AA9NChDXO/ARJWWOMJR/i4I9h1+JM1E8xbzURyeg+wIk9S6KgQWG8m8yXjxFwuik1thBXN+GIukElCQ7os9zPv0N5iv9RNQWIlojQbUORRj45SgH4z/Dxcx3GMq/ieWVUCU/Tf4DNPhu/ky6jkvv2VF++Ofv8uQnD/HgM3tui9i6jkt6Pk8xV6a1ex0TPVuMZDRAd2sNFdMmGromvhQOGlXLqci9bYPUFP4irmfieiVcz1yc5PAjhLZCTOpOYToOY4U0tb4g/dk5vjZ0jhfGe2+b1AoE8pKaoofreZueuXhvdpSk4Sdh+DlS03rLIEIIQTgeZH4iRU1LEl9Ap1SosDCZJhDxb4q39PsNz/OouDan5sb5gyvHeG9mlMoGArOwqvNofQc/13OQ+5JN6/b9vBfR05BkJpvn7MgUiiyhSILnD2zHUN//SbcPcHcgCQlJSDiLfs2265Ay82iysimkwwOKiwJS3ZEkp+cn+F/9pzg+O3rbpFZCXKdEWx03N3Ny0fZcXpnopyUQJawadEeTt/Wcm5ZDyK/T3ZxcEasJIaiNBmmrjzE8fXPLpHsFjueSNSscmx3hd8+/yVAute5KJUkIYpqPZ1u280vbD9MajN0yfnWcRX0H+waPek25aUuMJASJSICH9rbz3NGdNNZE8GkquVKZc/1TvPjeFd45P8xcusA754dpqongu46UOq5HrljB9TxiIT9+Q1t2rJIkONDdREMyzOh0mrl0nvHZmyvZr4YtFo9aDs/zcHFxFi0wvMWHTxYS8uIg8H5BSD4M/8dRtIOUil+mUvoOtjMMnsWaWdoNQQGhoChd+HyfxPB/AVleW9zK8Rwu5vqWBkVg6Zpd7WEcL01VM9a+Rhp8Ny+3UJR2guHfXNeROs4spcKXKJe/g2334ZN/dl3rrQZJEkTjq5ejyrJEfWOMX/+t55maTJNJFzB8GjW1EQLBjZepaLJMUyjMh9o7+WbvJYr2rXtLZotFvnblIj8aGeKptg5+audeuqJxApqGKkmILTQZh+rLb3/s8zdd5qmG31n2/7DWwJP1f2/ZZy2BQ7QEDq26fmvgMK2Bw2tuX5dD7I5+nN3Rj6+5TLP/Ppr99635fUit40jyi2t+vxZUTeGpTx1ipHcKf6iaRbhqkWNV7EX7VA9FVZAVCddxsS1n2edCEuQzJS6fGmZmIk1dcwxZkZGVzRFMuB001UZpqo0yMD7PN187X/3QWxwDPQ/bddnfs7YS8PsNSWiYziwF8wIVexJZChHU9+BXO9lstToPGM6maA3E+PrgeV6Z6F9VRGf14xSL7w+x9B65mhEwZAUXj4JVoWhbS+XOjufieC62e2c5jVcnBmj0R+iO1JA0bl5yL6sy+x/fxat/9jaZuRzNPQ2MXprgzI8usPuhHqQ78Oy7F3CV1F5Jz/IvT73CpfTMhpTnNUnm8cZO/ubuh+iOrAxWf9xQHw3xuQduZ6L9A/ykoEaPULIrpMwCtmczXU7z/ckT1OoR/PLm9B1UHJuB7DwJI8B/v/QuZxcm1z2Jd/2YeVUAzZBVAqqGJsm4nkvOqlCyLWzPXZogdFz3jttDvjp4lq5IkoZAmMg6K16uRyRo4NdV8qUKrushxLWMret5lCoWtu1SH7+3W35g0afWrPDe7Aj/8J3vkjZvnZS5CgHEdT/Pt+zg7+x7jKh2axFC23bIzueZHp4ju5Bf4mEALd0NNHWunRjwGxqHd7by9372KVTlWptbNOSjpTZGS12U+WyBc/1TnOod5+nDPcuyrd6itRlU1d5vVIkXQqAqMm31cS4Nz5AtlFnIrix9vhXuKrEt2GVGirP05saZLKco2CUcz2V/rIP7op2bIoF+p5DlegLBX8PwfQyz8haV8g8wzffA2/jFvQYdTT+MbjyHpj+MIreCuPnAZroW3596lZJTWRQ+FuTtIhkrR0yLICGYN9M0+er4WOPTtyS2G4EkJfAF/gqul6dS/gHl4lcJhH5t07a/AgJq6yPU1IWrytkbyNLeiKhu8LO7DvD66DDlvL3umctUucQ3+y7xw8E+7qtr5NltXTzS0k5zeGsl6j/A6pgeS/GtL71OMV/GMh0e/eh+dt/fwfjgLD/65knyuRJWxebpzxwmVhPi3LsDvPfKRcrFCgvTGfYc6WD/g10Y/ve3cbExGeavf6ZaCuV5MJcu0Ds6SzR4a8G39xM58yzjmf+O7WbQ5TpsN8dU7k9oif4aUeMosti83mnX8zifmqY/N89b08NkzdXVW2+ELAQdoTj3JZvZk6inI5SgwR8mrBnI173cPaoz4nmrwlg+w/nUFOcWpnhvdoz5cuG2g7SSY/HOzDBdkQQ/3bX2pA+AZqh88tef45v/+Qd87/dfJj2bIZIMc9/Te/n433gG9Q59Ad9vlB2bcwuT/KN3v8dIPrUhUhtUNJ5t2c7f2vMIjYHIj4WY3wf4ALfCh+vv49sTx3hp+jRzZpazmRHiWpBf636eRt/maBQUbYvjs2Mcmx3h3MLUuibzAfyySlckwcGaZnbF6mkPxqjzhwgoyzNoHtVJ2IxZZii3wLmFKc4uTHJ8dpycVVlGijaCgm3y/dFLNAXCPNXUveH1D3Q1MZPK89KJPna01lITvaZ1kMmXeO/yKP0T83z0wXvZeaCKrFnm1Yl+/tXpV0hVihu6og3+MJ9o383f3PXQulvpLr7Tz/f/5+tceLePWN3y8fb5X3zspsS2tT7G0d1ta4o51caCPHmwm3P9U4zNpClV1r4fZUmsWaiUjAbw6yqz6TzZwvrigetxV96mZcfkR7PneGP2AkOFKYpOBdO1cT0XzwO/YtAdbOJ6auZ6Hq/PnmMgP4XtORyIdXJ/fOMPwEYhhIwQfoToQJZq0PVHcd05bLsP2+rDcUZx3Ck8ZwHPy+F5FTzPBiSEUBEiiCTFkORaFLkVWe1EUXciS7VIUhwhhVYtPb4RuqzzC22fXSJmF7K9TJdn6Qq20+SrKvlOlKbpyw9XbX829RpISFINitJGBQ/LPL6p21+5P7Hkb3unUGWZrliMn9m9jz86f5qJfG5d63lAxXGoOA7HpsboTc3z1d4L7EnW8VBTK4camojpP5mWHPca0nM5Bi+O4wsaPPuFo+TSRc4dG0BVFSpli0yqwCd+6VH8AZ1oIoiiKriOi2XalIsVnv7MYQIhA+0eUJnVNIWmRcsBz4O6RIiZVO6mA/69gJn8NwjrBwnpB1DlGI5XomheZjL7xwS0HcibKArmeC4vjF9BUFX6vNmLXQD1/jAP1bXxZFMX20JxQqqOfzFDq0nKdWXI1+B5HlHNR60RZEe0ludadrBQKfDm1DAvjvdyKT1zW1ZCA9kqGX+yqYs639rZASEE4USIj/zVp3n0c0exTRtFkwnFgkRqwvesZdt6kLMqHJsZ5T+ce53hDZLaWl+Q51q28ys7HqDeH1r1t7sRnudxrG+M3/3uWziey/2dzXz+4X00xMLLliuZFr/73be4MDpNybTprI/zzIEeHt21bcU2Xzjdy3eOXyJXqrC7tY6/9ZGHV1XktxyHyYUc71wZ4dL4DNPpPKWKhRAQDfhorYlyoKOJ/W0NhP3GT0KF+Qe4TdQaET7Z9ABHEj1krAKKkKk1ojT54hjy5ryb0pUS3xq+gItH1ry59oFA0BlO8GjDNh6sa6MtGCOg6vgVFV1W0CT5ujLka/A8j6juo94fYl+igby1m7lygZfG+3hhrJfRQnpDz/xVnFmY5OTcBAeSTcT1tdtbBifn+b1vXnP0EKJq9zWTyjObLtA39g0SkQCGpmDaDul8iVSuRNhvkMrdTC/n/cdCpcj3Ri/zpcvvMVXKbYjUdoYTfLJ9N5/r2E9wkdSuJz4dujgOAn7j3/4C0RtELKO14TXWqiLk16lLhNbcj09TaUxUk0G5krlk57MqbnKsIZ+OpspYtkvF2vi9taXEtqq0WuEro6/z9vwlhoszFOyV7LvsmLgrZs09LM/hvVQv0+Xqg9Pmr6XGuDsZNCE0hJxAkuJ4tCMrXWhaGtfLVe18vDJ4JuDgeS5VQSgJ0BBCR4gAkhRCSFEkKQ5szJNVFhLN/mvmxJez/WhC5UB0FwGlOgjU6Al680Pk7TvJJq+OqgqqDp6L485s+va3CpIQ+BSVj3ftYCyX5YWhfmaLG7s+BcuiYFnMFPMMZ9KcnJ6kORxmZ7yGfbX17EjUEDd8W9ac/5OEmZksX/7SGxSL5jIxt6bmOA890kN3z0pBl3y2zMJsjrqmGI1tSZwmh7d/eI7UXI6OnY1s297AydeuEAgbPPjMHnxBg1DUTyQeQNMV6ls2ZzZ8M5DJlzh5aXzp/5btMDA2v2ExhLuNij1G1HiQoL4LSWh4noMkdEbS/wHP23xSnqrcOgCJ634erGvjicZOdsbqaAlGCSorvc9XgxACRQgUScOvaiQJ0BSIUOMLsTtez8vjffxg7ArTpfVNhF1FybHpz8zx9tQwn9y2Z83lXMdlrHcSd/FFr2jVychcKk8xV6K5uwGxSZN7dxMZs8ybU4P8Ue9JLqSmNxTgtgSjPNuync9t20dLMLqh/Vq2w0wmz8RCFjx4/tCOZd97nodpObx5aZihmQUsxyVdKNFRn1hBbD0PhmZSHOsbRZFluhoSKwJMz/OYyRR45Vw/r18YZCKVZT5XpFixsB0HgUBXZc6OGBzrG2N3Sx3PHOhmd0sdhvb+T7B9gLuPhUqOvvwk46V5SraJAMaK85ykn8dqd1Pvi93xPmzPZb5ya9HSel+Ip5q6eai+je2RGhoC4XULNwkhUIWMKskEVZ0aw6MlGKXGCHAg0ci3Ry7y1vQwC+s4juuRt0zOLUxydn6Sxxs711zOsl0Wcsu3LQmBoSnURAO4rke+VLWyclwP1/UI+3WCPo3J+ZtbP76fmC8X+PbIRb46cJb+7PyGdCB2Rmv5RPtunm/ZQb1/Y+XWtmUTjPjYfaRz3RZziiIhLWZYb9omIq7xVcdxl8V8V1vM1gNNkZElqdoLbN9DxNbDo+SYfG/qOD+YOsFUeQHX84ioARJaCFmS6c2Nr7m+QNAeqMVybabLKfrzkwwXZ+4asb12IAKBUu2HvUlP7FbDdC1ydoG8XVwitlkrT9bKYbqbH2h6nonn5fG8AnDnRMF1PWam0oyPppibzVLIl7FtF02TeerDewlHq+dkVmxKJRNJEgSDxuKDsrGATwhBazjKZ3t2IwuJV0YGGMttfICzXJeZYoGZYoGzs1Ocnp7i2NQ4ndEYnbEEXbEE2yIxavx/OS1t1gPXcclkSqRSeSpli7nZHJlMiZ7tDfTsaFiV2EqyQFYk7MWZOsdxEZJAkiTqWxIcenwHfefHGB+Y5cqZUXYdakdIAs9jSVn7XoHjuGTy10ib53m0NcTpaLozq4ethhAajlfEdUtIsobrWdhOGknysR6l7s1GZzjBU01dPN3Uza5YHcFN8MZRJInmQIQ6X5BaX4CwpvPt4YsM5TcmODJZzPH61CAfbdu1ZsbRMm1e/dO3qJSqgoSu41IuVvAcj9adTTR01P3Y9dlmKiVemxrkKwNneG92dEOkdlsozrMt2/l42y56ojUb2q8QgqBPpzkZYXw+w/hChrJpLdlFAVXbj1yBuWwB160GUwv5EtPpPLbjLuvtKpkm6UKJQtmkIRamvTa2IpkwOpfmxdN9fOfEZXonZqv6ENEQrckohla1GckUy4zNZZhO5RiZTZMulKgc2cXhrpYN+zB+gB9/vDl3kclyCp+sIQsJDyi7i8//XVIsVoTE9mgNz7fu5InGTraF4viUO5touUp0t4UTNAYiRHUfEc3g5Yl+Josbi7OuZOY4NT/Bw/XbVlVAB0hGAnzm0dvrV/cbd9/pYj2YLxf49vBFvjZ0jgvp6Q21w+yO1fPJ9t0809xDa2jjkyM1TXEKmSJDFydo29mIqt066ebTVTRFoVSxSOfXnoQ2LYf57OIkhICKZWPZDqoiYzsuxfI1ruK53tpmAHc4x7tlxNZ0bEaKM3x97C2myin8skFHsJ7toWaa/AlM17kpsQVo9iWJaUFUSWaukmGkMHNXypHvRdToCSbLM7w2+w71RrVoe7w0jS5rJLToOrbgcStLiepkioPnFbCtXmzrLJ5nIkkbCzyWb9OjXLK4dH6cs6eGuXJxksmJFNl0EctyCIYMDj3QuURsp6fSnDk5jGO7PPTYdmLxILJye3f5ofpGVFkmrOm8MjJAf3qBinN7MvuO5zGayzCay/D62DBNoTB7knXsramjJ56kORSmKRTGp6g/9sInm4lwxMdHP34f+XyJUsnijdcuc/bMKFAd2EqFCmODs8xPZ/EFZ4klQ4SjfmobY5w/Nsj59wbIZ8tEE0GS9RFy6SKZhTzxmjATg3PkMkUs08bwaSiKxNxkmgvHh6hpjBKvCS1Zq7xf0DWFppoII1MpShULn66yrTVBR8u9TWzD+gFKVj+zXhlNTuK4BYrWABH9KLJ09/qDJQTtoRifat/DR9t20hbcfM9pVZLZEa0joOgIBH9w5RiFW5REX4+MVeZSeoaJYpYmf3hZf+9VCEDRlCWS5VgOtllgYTINgLdOsax7BVmzzOtTQ/xZ/xnemRnekC1FeyjGR9t28tHWXWzfIKm9iqCh0ZKM8vblEVL5EtliBctx0JRqSFO2bAamF6hYNolwgELFpFSxmMsWyBbLxK9TKp/PFUkXyjiuh09Taa+NL7vHFnJF3rw0zNfePc/IbJpYyM/BjiZ2tdTSEAvj19Uqkc4WuDQ+y3t9o0yl8vzo/CC6qpAMBehpuv136Af48cRIcRZZyPSEmohqgWX3VFjdelV8Q1boidTwhc79PNu6g5jm2/SxU5cVjtS24FNUPM/j68Pn193nCzBbytObmWO2XKBhjcxjPOznw0e2b9Yhv69wF9WPfzB6hT8bOM3l9Oy6Sa0Atkdr+VzHXj7U3ENT4PaSfIoqMzU8x7f+28tsP9SBZlwjtp17W2jf1bxinVjYT9CvMZcucGl4hiO72jBuIMSO6zKfLXK2f6L6gQdn+iZpSIRprYuRL1XoHZ1bWr5sWWuWKpdNG9txkWUJVd14DLdlxDZrF3lvoZex4iwIwd5oO59qepADsU50WWW8OMd/7P3GmusLIdBljVojil82SFsFpsvprTrcex49oW2UnBJvzr3HyVRVZTWg+nms5gE6gqsY+t0A181gW5dvuZyHietMUym/hFl5E0mKoqoHbuuYPc/DNG3Onx7hT/7HG1y5NIFju+i6Ws3oFyvIslgK9gAmx1N8/5unmBxPEY74OPJQN37l9rIzQgj219ZTHwiwLRrlL65cpC81z3ypeEf2H7brMpxJM5xJ88PBPppCYR5paePhpjbaI1Fq/UFCuoa6yebYP47w+3UOHb5W+jc3m6Wvdxqo3h/FQoWLJ4YASM1kGemdYvv+Vtq66xm8NMnpN/uwLZtDj++kpauW4StTHHvlIqqqYFYsOnY2Eor4UTSFRH2E3nNjnH6rl31HuwjHAu87sXVdj0LJZGo+R75YQdcUZFnCb6h0Nr9/FSC3QjLwPJPZLzNf/OHSZ4oUpSXyN1Fu4bm9WZCFIGkE+Xznfj6zbS81vuCtV7pNSELQHIzwuc79nE9N8/b0MCVnfQGa63mkKiWOz45R27J91RYF3a/zM//g08s+y87nOP/mZd759slNOYe7gaoNncW7MyP8z97jnJgbw9oAKW/0h/nMtr18rG0X7aHbrwQKGjqtiShQtZCYyeQoVqxrxNa0uDIxi+O5bG+qYSqdY3Q2Xc2qzmeWEduZTJ5MoZqF8OsqrTXRpYSB53mcHZnilXMDDM2kCPt1ntzTyRefvp+GWHhZ5tf1PMqmxR+89B7fePcCk6kc7/WN0RgPs60+jnoTH9sP8JOHJl+SlJknaxXRpeUT3itb7zYXmiTTGU7ws90H+dS2PVsai0hCYnesnk+076E/t8C7MyPrXtf2XKaKWS6mptYktrcDz/PIlSpML+Tpvkfes47rkjZLvDY5yO9depvRfHrdcai6WF30Cz2H+FBzzy1V+G+GUqHC/FSGQqbI4PmxZd997FeeXJXYttXFSEYC9I7O8ubZIfZ01LOtMYGhqciSqFas5Mu8d3GEt84NAVVxqO+9fRHLdji8s4VsocIrJ/uWtpnJlymUTZxFAns95tIFimULQ1NuK+u+dcTWKnIy1Y+HR1QN8LGmIxyKd6Ns8AGLayH8ik7KzJOxNr+X9McFUS3MYzUPcDRxkIxVLfeIqGFkIa8rQ2iZp0jN/8wG9iiB8KFr92H4nr+tY3Ycl5mpLP/13/+QiYkUqirT3JqgpTWJZdm88eqlFevU1Ibp2tHA8MAsJ94dYN/BdvyBOys7rAuE+HTPbh5obOHPLp3j670XmS8VKdn2HXu0ma7DYCbFYCbFn1w4y5GGJj7SuZ0Hm1qoDwSXPHE/EJ1aCUmWSNSG+cQvPLLq95/7a0+u+Cz6YIj9D65etbHvaBf7jnZt6jHeKWYW8nzvrYv80seP0FgTIVso88I7V3jvwsg9TWwNpZm26G9QcSaxnAVkKYChtiGQEXdJTD+q+XiudTs/231o3YqPdwJZSNT7gvz2/if4tde+wmg+ve6SwaJtcXJunA81d7Me8wrP8whGA0RqIvSdHsJ17kJp4h0OQZ7nUXZsTs1N8G/OvkbvBrINEoKgqvHLO47w0dad1N1hEBs0NJqT1yZYJlM58mWTaKBaTVAyba6Mz+G4Hs3JCEFDI1+qkC2WGZlNs6/9mn7FdDpPulhGkSXCfoO66LVjq1g2J/vHOTU4gabItNfE+M1PPEpA11ao90tC4NNUfv6JQ1wcnWEuV2RsPsOpoQnmc0Xqo/e+9cgH2DxIQnA6PcjL02dXTM7/5o5PsDvSuiX7FUBzIMJH23bxuc79W7KPG6FIErvidXxx+2HOzE9sSIhvvlzkUnrmttSR14Ljepzqm+D3vvk2X/pHG4l7V2IzIjfX80ibJV6fGuSfHf8hWbO87neLIiRagzF+Y88jPN3cve7e6LXw8McPcviZvTi2u+RjK6sysiyhGauXqe/uqOfYxTi9o7NcGprmf/9v3+epQ920NcTwGxrZfJnTfRMcvzRGvlRBliU6mhJMzmX58g+P8+UfHkcgluJtVZHJ5MuMz2ZI50vEw36EEEvaCP1jc2TyJWIhH4nwxkn8lkUoJcdkuDCDB+yJtFGjR2/LiNkn62hCwXTtpf6Ev8zQJJWkXp3pFgimy7Posk5M28wsikCSmzGM5/D5P4Os3B5ZSKcKvPi9M0xOpIjFg3zhFx7mgYe7CYV9nD89siqxjcT8NLfEsWyH/ivTWOZmeAhXsz/NoQi/dvABPt2zi69cPse3+68wnElvWreL5Tq8PTHKiekJtkXjPLetm0/17KQldG8LBX2ArYOqyNTGgnQ0JdFUmZDfoCERXvJyu5chhIahtGIoLQB4OBTMy/jVLmSxce/BjcCQFXbG6vj13Q/jv8OesI1AlWS6I0mO1rWRsyrrFkQp2ian58exN1CSm5nLMXJxbDG4uDs9d3eComNxam6Cf/Tud5goZDfUFxbT/fz2/sf5cEsPEe3OS9l9ukp9LIShKpQtu0psS9dUYUtXM7auR3M8QiLoZ3ix73V4bnkP9dWMbdin0xgPLwtkL4/PMji9QMm0qI+GeHhnOwFDu2mwGzQ0eppq6J2cYzKVI5UrcXF05gNi+5cMfblJnqrbx4fq96Pc4Fyhb5Iq8moIqBoP1rfxVzoPbNk+VkNQ0dgZq+VoXRtvTg2tuz1hoVLkcnp2U4/Fth0KpQqF0r3BGdKVEi+N9/EvT71Mxiyte7QXQE+0hl/f/TBPN3ej3AaHuhGqpmBXbCZG5riyWC3Xc6id5s46VG11StjeEOfQ9mZ6R2fpH58nnSvxjdfOISSBQODh4ThVj2NVkelsTvL//OKH+fOXTvHKiT5SudKSNZQQgiO7Wjk/OMWrJ/uojQV59oEd6ItaBS8d7+Xy6AyFsklPaw3NtRuPn7eM2NquQ9aqBgVJPYIhr0+98kYsiXFsQFHrJxVXr5+47rV6bOEM9UYNhxM3n5lT1b1EE3+4jr3ISCKAkEJIUhxJiiJu004onytz8tggluXw0U8d5NCRDuKJ4E3r5nVdJRT24TouqYX8pokBCVG9aoas0BKK8It7DvJUWyfvTo7x8vAAp2amMG+z//Z6OJ5HybbpS83zpWKBl4YHeLq9k09376QuELytcrShoVlOvDfIxfMTzM1mcRyXUNjHto4aHnq4h/aOGvyr+LV6rsfIyBwXL4zTe3mKyYk0uVy5mi0K6jQ0xThwXzu79zaTTK4edL3x2mW+8bXj1NVH+M3f/gilksU7b/Zy6uQQE+MpTNMhENRpbIzx7Ef209qaQF9j1m8jGBme4/f/6ytMz2T4+CcO8cBDXSQSq5ei2rbDN/7iOC+/dIHm5jh/5WcfoqUtcU9kyT3PI5Mv8/tff5tY2E++VGFsKo2hq1iOi99Q+fDRHXfk3bxVqF6/a8dlOxnGs7/Pttj/hiytFP3aTHSGk3yh8wBR3cfyo9haCCGQgWeaezg9P7FuYmu5DuOFLEXbIqJ5K6poCpki/69P/Z8Ul+wnqsIZ4WSY537pCeQt9rFVJPmObOEyZpm3p4f5d2df2xCpFVSFv355xxE+3LKdsKZvigaBoGot0VoTpX9qnqmFHIVyNYg1bZtUvshMJo/neTQt2k+cGpxgeDbF6Gx62bam0znShTKxoI+mRHjZuDE8m2I2W60UC/o0uhqS1ftxjXO4+nkyFMCvVzMrxYrJ+ELmjs/5A/x4ocmfoD83RcYqENOCSNeJ7h1Nbqd2i4RQj9S08mzzDkKaflddxIQQRDUfH2vbxYm5cUxzffFUwTaZKuYoOza6JK94tlzPw7YdXA80dZ0Vio5D2bRv22f3eqiSfFtJuauYKeX57sgl/seV90hV1k9qJQRHalv5uZ6DPFy/bdPKyWfHFnjru6d453tnqFvU+njjWyc4+vx+jj5/YOmz6yFLEo8e6MTxPL7y8hl6R2exHBdu+IkDhsbezgb++qcforUuyi88f5ju5hqOXRxhOpUnEjA4sruVwzta+Td/8gqXhmf4g2+/y0vHe0lGApQqFucHp5hJ5ZGEoL0hzo62tX1118KWvU2vv/VkId32A1Z2LGzXQZFktC2c5brX4HouC2aaiBpGlRSyVo7cKqXYY6VJ/MqtZ8CFFEXTHl7HnsWi1Y+8aF90+zArNhNjC0iSxI49zSRrQqt6A14PTVPwB3Q8zyOfKy3rv90MCCFQZZnaQJCo4aMhGOK+2gauLMxzfHqcYxPjzJQK2Hco5mI6DrPFAqlyiYVykfOz0zzf2cPRxhbqAuvrFXQclzdfv8IrL19goG+GYqGCtKgOPD62wGD/DBcvTPDUh3Zz5IFOam7wIBsenuObXz/OmVMjZDMlLMtBN6o9ERPjCwwMzHD54gQPPNjNs8/vo74huuIYspkSfVemSKUK5PNl/vTLb3PqxHD1t/E8KhWb0pDJYP8sT31o96YxEFVViMT8vPVWL2+8fpnOrto1ie3oyAIXL4wzNDBLMhkiFr93VKoDPo3dHfVIkkBTFRRZwt+mocgSmiJV1VLvAU7ruAUct4imVEVuytYorrd8tttyUxTMK7jezf0S7xQRzWBfooGjdW23FVC4bpGKdZls6bskQr+KIlfF9srWZUqV46hyEwHj0ZuOb/clG2kORBjMLlBxb1014lF9V00Ws8R1/wrlUc1Q+chffRrbvNq3K1A0mUgyTNuu5i3vBdckGe027ckWykVemxrgf/aeoDczh7NOUisLwZ54A5/v2M/TTd1Etc3zARdCoKsKHXUJhmZSTKaz5BeJba5kMjqXwXZcgoZGIuQnaGjEg35KpsVMJk+uWCbo07Fsh/lckULZpK02RlN8OdmYTufJFMtL//6jV0/y/ZO31qoYncswna5aR5m2Q7a4tc/MB7j3kNTD5O0yjueRs0osmyT0tqZip8YIcn9NC3sTDUibkN3bKPyKxgO1rdQaQYqWua4JMNfzKNgmk8UsrcEo8g0vxLGZNH/0w+N4CH71ow9QGwvy7bcu8KMzA2tu07ZdplMbs25bC7qs3DaxnS7m+ObwBb46eJaRfGpD5cePNnTw2W17ebCunZB25y4AV3H55BBTw3N86KcfpLFjUYi2f5rLJwbpPzOyKrEFiIX8PLa/k5pokLN9k/SOzjCfLWLZDj5dpTEZYUd7Hfu7GulprUWVJRqTEZ441MWeznqKZQtNVaiLB4mF/Dx6oJP5bJGRqRSz6QKGpuC4LvliBQ/Y1V7Hge4maqL3UCmyLCSCio+KaZG1ihs2cL6anZ0pp8nbJfyyQUS9dwLWrYbt2bw1d4JHag4T0yJczPZxOn0BQ15+gw8VxmgLrGz2XmWLeF4OISJVj94NP6jXP5DrC05c16NcNpHkqnWPssGshJCkLTW412SZxmCYOn+QnYla9tTU8WBjK5cW5ri8MEt/aoG5YvGOpPlt12Ukm2Eqn2e2VGAkm+bJ1g52Jmpu6oNbqVgM9M/w7W+eZHBgho7OWnbsbCQeDyHJgly2zLmzI5w/O4ZZsdA1hUce247hu9Z/4fNpyJJEfUOU+w62U1Mbxu/XkCSJdKrA8eOD9PdO89abvdTWhnn+YwdWPRbH8ViYy/PNr5+g9/IU3dvraWyK4ffr2LZDOlVgZjpLIhlC2aQAPRLx8eBDPbzy4gWuXJpkfHyBtvbksvO7iiuXJxgfSxGLB+juqScUvnuqvbdCOGjw4L72Nb+X5Rtf4+8PyvYoefMidcGqwNF0/ivYbgbBNYLmeCUsZxbvxmnaTUZHKMGR2lYSxu0ph3pYWM4khfKrRAM/vURsHWeOsnUWz7MI8Oia6wshiOl+2kIxTs9PMFteXzuE63lMFLJ0R2rwoZKdz5Gdzy0eE2zb23JtGBUgSRKyKi+2W3hs5QyHJsm3NeO/UC7y8kQfXxk4y+m5iQ2QWomDySY+tW0PTzd1b4nwl67KdNTH+dF5iblskVypjOO4ZItlhmYWkCRBUyJCwNCojQaJh/wIIFeqMDqfYXtTDXO5ItliBdt1ifiNpezuVWRLZUqLkxG5UoUTAzd3clgNrudh/hi0HnyAzcWOcDMJPUTeKlO5wZJRk7Ym9N6XaGBfooGItrWtImtBkSSSRoCOcJypUpa8tb5S4IpjM1HI0hKIrBgGHddbsom5yguujM1x/PIYzTVR9FXiSttxlio47hSGrKDcRkXVdDHHN4bO8/Xh8/RmZtctFKVIEk80dPKZjr08UNtGTN/ceGZ+IoVjuxx9fj/+UHXbrdsbOP92HwtT6TXXkyRBTSxIOGjQ0ZhgYjZDrljBdlw0VSYeCdCYDJOMBJfidiGgJhqkJrpy/H90fwfZQpmXjvcyOp2ivDjOKrLE9tZaPvLQTg70NKHdRjXTlhFbQ9Zo8sVZMLMMF6bJWHkcz93QzMdEaZ7R4ix5u0yzP0m9UfVs8jwXxxkCb3P6L+8lCCmELDcAYpnQ1mR5lrHSFLvCyxvsFSGznny440xQLn0LTX8UVd0JrG8GyHVzOPYgtj2A5xURQkOSalCUDiS5YTG7uzokSeDzaRTyFYqFCrbt3FK6u1QySacKSJJELBG8K96OsiQRMQzuq2/kvvpGxrIZLszPcG52ht6FOUZzGSbyOVLltf27bgXTdTg+NcFMocB8qcgnuneyv6Z+VXLreR65bJkXvn+Wc2dG2bOvhY987D4O3NdGKOzD8zxs26Vnez3ZbInB/hlOHB+ko6uOjs7ape3U1oV57ImdWLZDc3OcZE1oKWNSKVskakJ8o3yc0ZF5zp0dXZPYgkehUOFHL1/k8Sd38fBj22loiKKqMq7rUSlbTEykSCSDt8zIrxeGT6Oru47WtiT9fdP0Xpmmu6eBltbls4mmaXPl0iTT0xm6u+vZvXc9kzx3D4am0tpw5z7QWw9pmShUtnICVa7BUK5dT8/1FtsSto6AqZLEjlgt9yUb72ArVWuzOy1Daw3GiOl+ZsvrEy10PY/ZcgFrsaVh+OIYZ350cel7IQT5dAFFlVFUBdtycF2X+vYanvulJ5G0rRnrFEmqltNtMGObqhR5daKfrwyc5djs6LpJrSIkDiQb+VzHPp5s7CLp25oJaV1V6KiPI8sSuVKFVKFM0bQWiW0KWUh01icwVIWAXs3chn0GJdOif2qensYk4/MZChUTSQgifoP62PKWDMt2cRard3yaSnMigrFGH9paiAf91K0S2H2An2xIQpCq5JkuXxWhuzYemeuoAtkodEnmUE0zXZH310pOQtAdqeHU/MS6ia3lOsyV8qxWoJeI+Pn4w7vB8whd13LVXhfjYw/tJhxYSeLLpsXp/gnevbh+hebVcHVCcCPZb9dzmS8X+fbIRb46eJbe7Ny6RUpVSebh+nZ+uusA99e0EN6CCQohCTzXxazYXNXwq5QtwENaxztCVxWaa6M010bv6Dha62M8c2Q7sZCPi8Mz5Ivlajl70Md9Pc3cv7OF2tjt6RJsGbENKj72RNs5lx1mMD/Nxewojb4ENXrkljeJ47lkzQIvz5xmrDSP47k0GHE6g1eVDG1Kxa/geZtTanAvQVX34vN/Hk1Seb7hmipsjR7nocQhnmt4YsU6AeXWmQ3HHqaQ+11AQpKigAyYgIokhRYzuct/F88rYVsXKBX+iErlJVw3hRB+FHUnuvEchu9ZFKWDtQJdXVdoak5w8fwYvZcmaGiOLSNXN8JzPWZnsvRfmUJRZTq66m5rtuZ2cfWoWsIRWsIRnmztYDyX5fjUOO9NjXNpfo65UoGFcomSdXv9G6O5DN/ovUTeNAnfp9MVW/kSchyX+bkcP3r1Erbt8MRTu9h/oG0pEymEQFVlDt6/jXff7mN6KsPw0By9V6aWEVshBHv2tax6HLqhcuBgO6dPDnP54gTzc3lc10OI1fvHJFkiHg/yzLN7SCSv/YaSJPD5NTq7Nt4HcTNIkiAQ0HnokR6mptJcODfGnr3NNLcs95icmc4wPDxHpWzR1BLf9OPYKBzrAkKqQUiJFc+TY12qfi7Fbjoh9H4goPUQ0Hqu+/9OkoHnCOv3LX1mOnMUrItIbF1LSNII0B1J0hyIbtk+1ouWQIToBmbLPSBVLmItlhkWMkWmh+cQgOu6TA/PUsiUiNdHCEQCFLJFcvN5MjNZPvzzj2/NSXA147D+wMzzPPJWhdenhvijvhOcnp9Yf7ZBSOyO1/Hz3Yd4pKFj07MN10NTFNpr46iLk2nzuQKZQplsqcLIbBpZEvQ0JjE0FSEEyXCAumiQuWyB/ql5XM9jfCFLsWxiaAqxoI+If3kgWdVmWOybDQf4yKEdy9SY1wNDVaiPhW+94Af4icKbs5c4lRpACLHCt7ayTiuxjaDBH2Z7pIak8f5Ooggh6Aon8G2gddB2XRYqpVVjqrDf4MjO5QrShqrQ0ZjgqYNdqxLbYtnE9bw7JrY+Rd0Qqa2eR5Efjl3hf1x5j7F8Zl0Vf4JqGfeeeD1/fdeD7I3V498iJ4BkQ4ypoVne+d5p2nc1ATB4fgzN0Eis0o62lehqTrKtIU6+VCGVKyGJalZYV5U70h3ZsugqqgU4ktjOtybeIWeV+c7EMTRJ5dGaPQQVY9mMleu5WJ5D2TGxvaro1LH5K3xl9E0WzBx+Wac71MiO0GL2wHMoFf4Y153eqsN/32D4PoXP//kVn3cG27DclYNhnZEkqq33pWlj2/1Uyi/gOKO4zhySFEPVDqLpjyFJVZJ1lTjY9gjl0rcplb6KEGEkqQZwsa0LOM4knpshGP5NxBoKqYGgwf5D7fRdmeTF75+lpj7C/oPtGIa6TBTKdaqzR8VihQtnxjj2Vj8+n8b9RzsxfO9fX7Umy2yLxtgWjfHpnl2M57O8NDzAS8MD9KbmyVTKVGx7w564C+USr4wMYigK//DBxzGU5R53pZLJ+FiK1EIBn08jFPZRMW3mZldO5ATDPgxDI7VQYHYmu+J7160KL1iLhteu41ZnRRc9hqn+E9txcBwXRVl9EPf5VB58pAefX79rokyapvDwoz289MI5BvqnGRme49DhbRjX+ZqdOjHM/FyeuoYI27bVEAy+PyVYV1HK/HM0/+dQjedB+G/47l+g+T+LajwD4t7O4CT9zy/L1gJIqAS13UjS5vX73IjuSA3bQvFNERi6U9T5w4TVjZyrR8YsL2X4jn70EEc/egiAUr7M//XbX+LBTxxi59EewvEguYU8F9/p5d3vntrSPuuNEFvX8yjZFm/PjPCfzr1B3wayDZok0xKM8Xf2PcahZDPBDV27jUOVJRpiIYKGTqZYZiFXIlUokS9VmE7nUGSZ7obkUoY1GQpQHwszOpdhcHoB1/WYSuUomhaJoJ/aSHDFfefXVbTFKqOgobG3rZ77u5rvCWG6D3BzVMtWbTwcBLfTfnVnuJwd40BsGx9vOrLC7mcrcCDZRK0v9L6PnUJASzCGoayfXtieuyG14Lb6GLWxIH599fhQUSQChkbQuP3rLqgSW3md19PxqqT25Yk+/s/Tr5Azy+s6HwEEFI29iQb+6f3P0haMoW2h5/WuB7pIzWb5xu+9hKrKeAIcy+Xjv/okO+7v2LL9rgVZlogEfUSCmzcJumXEVpdUOoMNPFN3kB9MnWC0OMt/G/g+r82e4/54DyHlWgCatYoM5CeZLM3Tl5/gRKqfK9nxpQb7++Pd3B/vxpC33svwXkWdnlz1IXms5oFlKsk3g+flKRe/QpmvANeIpVT6Bpr+GJHYv+L6EmXbuoRZeQ1JricU/sdo+iO47hzFwu9TKX0P03wH0zyBrj+06v6i8QBPP7+XF79/hqH+GX7vP7zAwSMdHDrSQS5XXjwmmJ/LMz+X581XL3HsrT7SqQLd2xt47Kld+FZR+30/IC3aBf3c7gN8fscezs/O8L3BXl4Y6mMku3HFy7lSkR8O9bM7WcsnunYS0K7d26WixfR0dZulksm/+CdfXTOQchwX13UJR/yUV+kpKZdMLl2c4OSJIQb6ZpiZyVAomJgVC9O0qVQWJ5g8bqo6rsgyjY1RlLtQGn4VkixobknQ2VXH3FyOgf4ZBgdm2bk4ywhw4vggCwt5jjzQSWf3+5utBRByDZ5XxvPyCJYTW8/LISQfiHunB3gthPR93Mi2ZClMe+y3t9THtjuSpDUU27LtbwR+RUXbQF+q51V7xVYjgo7tcOlYHx//m88QilVLc4OxAJFkmMvv9W+pj60uq+v2jy/aJu/OjPC/v/cDpku5DZXQbQvH+T8e+Cg9kRoM+e5UJMiSRFttjLlcgYV8kfH5DHO5IhXbwdBVuhqSGItVP4mwn4ZYiLJlMTidwnGrisilikVbbYzayMrJpqrwlM40eUqmxdh8hvu77q12hw+wOjyvgmn3YTkT+LT7UeS72xJiyCph1Y/vLsWt+xIN1GxR2f9GIBY9qzfSduguemSzzvHm2cPb8QB5jayeIsvsaK3ll56/f93HsBIC/w1Jh5thoVzkB6OXq6TWWr9YnKGoHKlr5e8feIqOcGLLNTeiNSGe+ZmHOfzMXsb7pqq+xz0NRJKhNe1+ftywdarIQhBUfPxM25MUnQpvzV0kYxW4kBlhMD+9bBbkvYVeLmRHEAhM16K0mLkFuC/WybMNh9gRbvlLPUt61a7mRmzcwsFCkurRtIMIuQbXmcK2LmCa71Iq/imG7zOIxWyS56Zx3SyK2oluPI0QfiQpjN//s3huCct8D6tybE1iK8sSNXUR/sbfeZYv/d6rjI8u8NqLF3jvrT5c18XzoFio8G//j28B1X87jsv2XU38/K8+js+vbal41EZw9fpLQiALlb21dWyLxvhU907enRjlL3ov0pua35Bl0EKpyO+fOcHRxhZalehSD5zjOJQWSaqmKdTWh9HXmJm8imDQIB5fHphduTTJN752nLNnRiiVLPx+jfqGKO3bjGrWU8ClC+MMDtzaQ06I6rHczWdQCIEQcPiBLgb6Z+jvm+byxQl27mrCth1mprMMD81h2y7bOmppbUvetWNb85ilGlgktlAtC/c8F7wiQuiAftczB7cFsVLMqPoMbF0FhUDQHIhSd8dCQ1cNglYPktZ7D/uU9RPCqzA9Z9XSMyEEmqFy+Vg/0doIiYYY8xMprhzvR9W2VhnbkJV1+R+mKkVemxzk3579ETOl/LorUQxZ4b5kE39n72Nsj9SgyXc2TqTKJWZLBfK2uewn7I4mlqmDCiGQhGBbbZwLo9Ok80VG5tLkShV0RaY1GcVvXAtMk+EA9dEQjuuRL1eYzuQYX8hSMi0SYT+1q/TBttfGqQkH6J+aJ10ocXJwgk89sPu2z+0D3D24XoFC+Q0QMoa2767vf190Gykzz8nUALsirRhb6OohIegKJ9830agbEdggsfU8b0MCs7fS8hBATTTA0V1t697matvwydq6SpEnClm+NXKBL11+j/wGSG1Q1fhwcw+/tP0wrcHYTa3ENgtCCDRdIdkQJbIYM6q6wtxECt2nEa358W+b2FJ6LiFI6mG+0PoYTb4kb8ydpz8/SdrKL1uu6FQoOtduBoEgpPh4uGYXT9UeYGek5S91thagPz9M1srTFWxHlRRemnmT4cIYO8Nd7I3sIKGvJ8shIUkRguHfRlX3IIQfzytgmm9TLPwh5fIP0I3ngUVi65XwqCCkOJJ0tYlbRlF7UNQeTPNdbLt/zb0JIdA1hf0H25H/hsQ7r/dy+sQQUxMpbLuaMXZdj5mpDIoi0dSS4MDhbRx9pIeenY1IkrgnJzOEEPgUFZ+iEtUNav0B9tU28PbEKN8b6KU/PU9lHQTXcl1GsmleHh7kY13bqV20ARKSWMqMRiJ+fuqnj9K8hgT7VSiKRCx29XfzKBVNvvG147zzdh+hoMGTT+/i0P3bCIV8aJqCrEhUKhaVsrUuYnv1vN8P7DvQyo9eucipk0P098+QThXQdIX3jg2QzZZobU3Q0prAt4pi8t2GJNXgeRU8N41Vfhm78hqa/6dA+BFSBCH0qhhb5W0c6yxgAwqq8RSydj+SFMOxh3Gs8+CV8Nx5HHsQMFGNj6HoDy0S5K3FaPq/Evc9TlDftfSZ5WQYzfxnmiO/iibXbPo+a30BkkZgEwLAqhe36+aoGu1VSbrrVXC9MpJY34vbp6gb9p22HWfVpIOqq3zkV57m5EtnOfa9U8iKjG056D6VZ3/xCZRbiOrdCQxZQb1FKfJsKc8rE/18qfc4I7n0upXgg6rGg3Xt/Fz3QfbE69HvkNS+MTHMi6N9TBZyGIq6jO//2r6jK2wvJCFor4thqAqZYoWxuQymbWNo1Wytcp0vZkDXSIb9hAwN03YYmJpnOp3DtB2SocCqGduexiTttTFODk6QL5mcG57izNAkO5prV1Vj/QCbA9uZp1g5RrHyFq5XwMPBp+4l5HsOVWnEdYuUrUvkyy9gO/MIJHz6IQL6g8hynIp5iUzxmxQqbyAJnbJ5Dkn4CfoeJ+R7Btj6ycWhwgzHFq7w0vQZolpwWTLnlzufoSd0JwJ51yAhaAyEieu+DVWYbBWultaut4QXqiO07d6J/8QNxyAEiiyj+O7geohq1c6tzmMsn+brw+f52uA5JorZdZ9DVPPxkdYdfLZjH92Rmi0tP74RQghkRV5mM3fi5QskG2McfmbviuUn5jLkS5ujMn096mLBTS1BvootHZmvvlA6gg0YskZboJbe3DjDhRlmKmkyVgHTsXFxUYSMXzGIaUEafQk6gw3sjrTS5q/bwv4EBSGMxR5RCded2aL9rAUJSW5GlmuRpFpkuRZVe2DVJYcLY8yZKbYFWpgvpziXuUytnqQ3N0RYDa2T2Gooyi50/VEkuWkxc1RV66uUX8O2LgLX+ng9HPBcBMsJgxA+JKkGSfhuec2EJAiGDO47vI1kTYi997UyNZkmnSpQKVt4HhiGSjQWoKEpRltHDc0tCTT9xyNoUGWZ+mCI+mCIhmCI1nCE7w308s7k2LpUlE3H4YWhfo42tSwRW11TiC2WK1YqFi2tSXbvaVqXYh1Uy5OHh+e4eGGcfK7MQw/38PxHD9Dalli2jdRCAcO4972hE4mqjc9A3zTjowv0Xpmiq6eet9/spVQ02bmriabm+B2JDWwWJLkG1x6p/nFnsK0LSOYJZHU7QkRhcawRUhRZ3Q5IeG4Gs/R1dKkOSYuBm8MxT+E6Ayj6k8jqbvDKCCnO3QjIAHLmaUL6nmWfuV6ZVOlVGkI/U9We22Q0BSJEdOOOe8QkoS1a/HiUzbPIUrUE0bQHcd00mrqN9aRIVUlG2eCxrKUbrGgyR54/gBHQmRycoVI00f0aDR217Ht0J/IWBjXVjO3a209Virw62c+f9p/mQmp63aQ2rOo8tmhLcX9Nywrv3tvBaxNDmI7DkfqWFdmniL4yGyVJgm11cQxNZS5bYGw+DYChKfQ0JpeVKiqyRDTgoz4eZmwuw+mhSbLFCpIQJEJ+4sGVIoyxoI992xo4PzrN2eEpJhay/OHLx/nsg3vZ21ZPyLe65oBpO8xm8sznivgXS6I/wPpRsS5Rts4ghIZP7cLzLGQpvii651G2LlAov44iJVDlJjzPpFh5ByE0AvpDyHIMXd1GyTqBItWgqzuQpSiKXM/dMg7fE20lpBqrZiID8ubFtJIQbAvH8SnaPZMI0GVlQ+O4h7futgeAwckF5rMF6mIhWu5QnXctVAWdbl6KPFPK883hC3xr6AID2YV1k9qY7ufjbTv5ZPsedsXq0LewdcN1PdKzWUKxAKqmkE8XyGeKK5brOzOyZoXki8d6OT84tenH9tkn9nF4V+utF9wg7hp7aPQlqDdi7I20MVqcY7qcImXlF3uSXBRJJqAYJLQQTf4krf5aFCGt/qAKGcP3MVx3472N1fWr1hZC6Ahh4HkWlnUO15xjZWgiloickMJIIoiQ/FRLCmWqEd7Vy+iAZ+FhLfXZuW4W113AczNUszPXQ0ZT96MZjy/a59QtCjStRM4uUrCLuJ7L5dwAcS3K4zUP8N2pV5ivpNZ32kJDVtpAGNeVQwqECCDL9Vjm27Bk6XDtEV3tXpeEr3rt3JUPyGrQdZWu7Q109tRTLltk00XKZQvP8zB8GpGoH11X7wlycrtoCUeoDQSI+/yosswrI4PkzJuXpXjAublppot5upwEmizj8+s0Nsbw+VQKhTJjI/O0tiWIRNbn6+m6HpMTacolC01XaGtP0taeXPEsTU9nyKwywN1rkCSJPXubuXB+jOHBWU6eGCIaC3Dl8iSyLNGzvYHaunujfEZISTz6cew+hDCQ1R5s6xRCiiOkaHXMQUNIYarZRAWQsc330Hyf4dpzZ+F5JqrxJEKqpTp2yAixdRMRrlehYk/heEUcN0fJGkWVqnY1Hg6mPYW3JnW7c9T7w4Q2RXBIRZHrCBpPUqwcw3Kmq20uzhia0o2mtK9rKxLXFHHXi7UCG0mSiDfEOPL8fUz0T5FLFQjFgzRsqyWwzuf6dmHI6priUQXL5I2pIb42eJ5T8+PrDi4jmsFjDR18tmMfR2pbNq3McqqQ44H6Fj7duXtd4jNCiGrJsa5SrFR7YGVJwqepK4gtQDhg0FoTZXBqgRP94xQrJmG/QTzox6etPAdZktjf3sjEfJa5bIHJVI5Xzw3geh79U/M0JyIEDQ1Flpb8agtlk4V8ifH5DCXTYk9b/ZrE1vOqQb3juNiOi+U42I5LKl9a8s/18ChUTBZyRRRZQpVlFFmq/ln8XVeLk67+lI7rLG67uo+FfJH8dXoMZdMmnS8xnyuiXt3u4j7uRmnkavA8c7G6IoCu7kCVGxFCR5LCuF6FsnmWfPkVQr5nUKQIHh4Vux/FTGCou9HVToShUTJPoCkdRPyfRFUabr3jTcTBWCcHYx2rlrJupo+tJAStwRj6Xcz43QqS2LyxczWcHZjk8ugsD+9t30JiK9bM2F5Vjv/+6GW+NXyB3szculwyJARR3eAjrTv4XMd+eu5CptaxbI69cJYjz+wlVhvhyskhzr55Bd1YnrAaujBGS3f9qts40zfBKyf7Nv3Yju5p4/Cmb/UuElsASUjE9TBx/U6DUJVg+LevI2EbhJAXy/kUXDeFWXkLyzq/fBHhQ5KSSHI9styIomxHkhuQ5RokKYEQQYTQQGhLGc1rhLaI52Zx3WkcZwrbHsCxB3GcCVxnBs/LLjsWWW5EUXcgSRHWmk2UhYTruUyVZ7mc6+fxmqPU+2qQEOvzFxQKQvgWLZKc6ltv8YH1sHG9PJ7n4LoLSN5iIO2VWZxLW22Di39v7DcQouptey+UjW4FdFnhwcYWPM8jXS7z2tjQLdfJmhVGsxlyNRUSPj+6rtDQFGP7jkbOnxvj3bf7SNaG2L27ebHv+No9UqlY5LIlFEUmGDJQFktLFEUCURVXsO2q6rRuqEsCUelUkdMnhxgbXdiS67DZ6Oyup31bDZcuTnDuzCi1dRGymRJd3fU0t8bfdzXkq5DkGsDBtQeR1V0o6kEqxT/BVScQcgRQcOw+bPMtPC+LQMPzbHBzVMnr4stRBJCULiT5arna1g/VjluiaPVStAYxnTlyleNYTrVM3cPGdrMEtb1IW1QKXWME8Ct3Pi4IIZClMPHQXyVT/Apl81R1MlTdQ9D3FLK0MbuWzYDreuRTeS6+3ctY7ySlfBlf0GB+fIEdD3QTTgS3jEAYyspSZM/zsDyXk3Pj/PnAGd6bHV1XT60AgqrOw/Xt/FzPIfbHG9A2MdtQ5w+iSBJF20SVpFt67wqqPrGxgA8hYC5bRJYE3Q1J2uviK6pcIn6D1mQU23W5MjGLZTt01CeIBX1rZiuaExEe39NBtlThxdO9TKZyvHy2n3eujNAQC1MTDqCrCrbrUqyYzGULzGYKlC2btpooTYm177eFfJErE7OULZuKaVM2bcqWxfBsmvGF6sS943oMTM/z8rl+4kE/hqpgaAq6qhA0NHY01eLXtRWTwqZtc3pokpJpVbdt2ZRNi9lsgb7JuaXlxhcyvHl5iLGFTHXb122/qyFJPOhDucukSVd7sJxJiuZx8qWX0JQ2dG0nmjDwPBPbncNyxiibZ4FzAKhSHYpcu2FCtVUYKswQVn00+OIb6jfdKCQhaPSHN5Us3+sYm00zPJ3iod2330O7HviUlT22V5Xj35ga4st9J+jLzK+rykUWgoQR4MnGTn55xxGaA5ENazjcDlzXY2Z0HmtRKLTv9AjvfP8M3fuXX7tCtoTrrh7Pq6q8bv9uj6p1p+26uDcYE0uSIOw38BsquqasatW0GfixfBKqojJ3Hpy4bg6z8iMK+f+MbV24bvthVHU3uu8ZdOND13m13v6A6TozVCpvUC5/B7PyDp67AFiUS1/Ddabwh/4Guv4IQqw+ex9SAoy445xMn1/qtZUQiwIatx40BVWibpmncO0xhGqApwI2jj2ydP6V8kuAhOcVse0RwMZbJL1QzaB7novrFfG8MkL6oMTqRqiyzMH6Rj5TLnF8epyidWvPupFshlS5RMLnRwhBJOrnk5+5n+npDO++24+qKTi2S0dHLZIs4XkerusxPZXh3NlRGhqi3H+kg1DYhyRJNDXHCQR05mZzDPRN03tlioamarm6bTm8/VYvr//oMjPTmS3Lkju2S8W0cBwXz60G0+WyVR30bIdioUImXURIVZEoVZVRVWVVYYhQyKCzq476+iijI/O89spFPM/j4P3bSCTuHeucqniUjetMIqs7kJUehBTCtS4jqzsBF7vyAq6bxQj/TrUn1xnDrry4fDtIcBd6aa+HJHQ0uYGyPYFAxvVMHK+a0RdCxlBaiPufRt0iddGE4d+UclYAIRR0tZPayP+2Kdu7UziWzYkXz/KDP3yVUCxAtDbC8IUxTrxwhmK+xKOfeQBli3o2b1RF9rxq2d9QboHfu/g2782OYq5DuEUAAVXngdo2fmPPo3SE45semG2PJbmSrnrMHqxpXJa1rfEFV2RxrwrMNSUihHz6YtZRIRkOkAytVIgN+6rEFqrlwgD10RDRwM37vHoaa/i5xw1qIgG++tY50vnSIgFNMTA9X50n5mpvXzXrGQv4qIuGiN+kh+zEwBj/8EvfWzqW1WA7LicGJjgxMLHiu3jIx7//1U/R05hEv4HYzOeK/O3/++uUKtZNw+4rE3NcmZhb9bt/+tPP8NTeLiKBu0tsFbmOSOBzBI2nKVbeJlv6OtnS96mJ/F1UuQUhNAxtLw3xf4ksVRMlnmdT/RWuf39I1ZaqTeveXD/+fPQNdoab+XjTEeQt1IiRhKDOF1zyc/7LAYGhKgS30DVDrNJj63keZcfiQnqaf3X6FUbzaex1JJYkIagxgjzZ2MXv3PckQXV9olSbAd2n8fN//5NL/4/XRXjiM0f4qb/z/LLl/uBf/AWh2OqxVFt9jHTn+ioePM/DtKqVK6WKVZ2sW/xbVxV2d9ZzoLuJltoou7etniG+U/xYEtvNgll5lWLhD5eRWtDxB7+Iz/95ZLmdzerHEFINhu+TaPojVErfJp/717jufPU4zLchX80gG8ZTq66/O7Idx3MYLIzyyaZnMGSNidIMdUaSWuPmwkIAkhxF1e+jVPifZLP/BN34MLLchG0PY5ZfwbGHkaR68rl/Q6n4v/CwcN0F8EwcZxzPSyFEjGrptYnrTuG6KTR156Zcn580BFWN7liCA7X1vDk+esvlc5UKFftaqbrfr3H0oW4W5vP82Z++w8svnueN168QjQaIRHyUSiaphQKlkonrunzqs4fZf7AdAFkWdHTWsmtPMwsLeV568TynTw3T1BJHAOPjKRbmCzz4cDfRWIDLF1cGTJuBubkcx98bZGY6Q6lkUiyaXLk0SS5XxrRsvvvt05w9PbKUwe/ormP7jgZq1lDl6+yqY8euRq5cnuTM6arx+qH724nfU8Q2jueVAada1SFHUZRdmKWvoGgHAYHnOQhhIIkIeEXM0tfx3PytNr3lkIRBUNtFUNtJyeqjJvBxwvqhG5baumxISNXR7wHxk62AWbb4+n/6Pn/ldz7F7oe34wsZFLMlzrx6ga/+++/w4Mfv3zJie6OPrbfot/svjr/AibkxSs6NLTKrI6DqPFTXzr84/Bwxw4e0BffC6xPD/GCkl4Jlrshg//FzX+Bo/er9WE2J8BKxDfsN2mpX15wI+3WaExFkSeAsZhMa4iFi6xAwqY0E+ZlH7+Ojh3bywule3usbo29yjtlsgbJpIUsSQZ9OYzxEd0OS+zqaOLCt8aYZWzw21Fu4YnWPazXHq36/ngLJm2//zrZwe7CdGTwcVLmBkP85dLWHsflfw3HTGOpuVLmRovsO2eI3iQV/FgDLGUcSfmQpBijVljPhx3Fm8bzNF765FQQCQ9bRpa3VsRAIorpvS7PC9xqSET9TC1nmM4Ut3ItYkbF1PJfe9Bz/5Nj3Gc6n1q0cX2MEeL51B7+17/FNm7y9XWy/v4NKceXzkKiPEI6tbhf1Vz/x4G2NA6lsiYvD07x8vI83zw6QK1bI5ss8tLedtvo4mrI17/u7RmxzVokFM0vaLFBxTGzPXbdIxVUk9DA7wy2bcjyWeYFy+QUs88x1nwqC4d/C8H0EWW7ZVFuO6ktaIElxdN9zIDSy6X8MVP1cTfNd5FIrityConavWD+mhTmaOMjB2F58soGERL1Rw9O1j6CuowRFlhvx+T+PWX4d27qCbQ8hUPE8C3BQ1V2EIv+ccvlblMvfx7XnUJQuZKUFx5kgk/4H+HyfRkgRrMoxyuXvIKQQirq2jL7reji2s6jAtka/9E8ohBCENJ0didp1Edu8VaFyXdZECIGiSDz9zB7a2pMce6efs2dGmZrMMDY2jyJXS4+7eurYvqORow91EwoZS+sC/MzPP0z7threebOXocFZ+nunq/ZBtWE+8rEDHH2omyuXp1iY3xpSNT2V4fvfOc3IyDye61Vn8kwb23awLJuL58fovTyJkKq2HQ8+3E0sFliT2DY2xejqricUNiiXLLp66qmti6DdQ+qk1TFDIMlNSEobQoSRtQO4hf+CEBEkuR5FP4JV+jaFhV9cFJHaj5DrFoWl3s9jv/p8ChpCP48m195Va6KAqqP+hBJbz/MoZIpE6yL4QgaSJOEP+YjWRijlyluaULre7sf1PCYKWf71mVc5Mz9Byb51NQlUJx0eqmvjHx36EFHdt1QttNn42wce5ou77l81iOqIrF0p8Ikju3lqbxeW4yBLEgFj9QyZJAR72xv46t//Ra5e9LDPIOi7deanWuIO0YDBRw7t4Mm9nVi2g+O6uIsZW2lR0V6TZfTFct6bic88tKOdr/zOL3C7N4AkJOqiQdRVAsTaSID/9fd+7qb+5LdCIhTAfwurua1A2bpAtvgNTHsEIWQECtHAp9GUdoQQBPRHAEGu9H0yha/i4aBIMaLBnyNoPAqALMKEfR9lLvvvmVj4u0iSn4j/M0QCn7kr59DiTwIeC2aehB665fK3CyEEYc34S0Vsj+xsJVso8/rZQXa111EXC236eCQAv3xNPMr1PE7NTfJfLrxJf3Z+3aQ2ofv5SOtO/tquoxiLpPb9jIXr25KrjglPfu4osrr6PVSt6tv4McdCPu7f0cL21hqO7Gzhf3zvPa6MzvIf/+x1/tbnH6WreWsqPrc0Iqw4Fucyw7w9f5HR4ix5q4zpVcWibmeW8mC8a5OIrUul8hJW5V3gqriPjqY/hK4/iSw3b5lIixAykpRE0x7A8H2Ecunb1WPwSpjmWyjlThS1ixtvIlnIyELCdE3SVmbZazCmRTBuqbKnoyg7CEf+OeXKD7Gti3huHiFFUNU96MZTqNoeJDmBrj+J5+WRpBhVxeSXKRb+CMcaBKFWxbC8Apr2APoaGWaAsZE5/ut/eIGa2jB/5Zceobbu1uXjpmnTe3GSb3zlXTq663n+EwcJRzZfDvxuQJNlEsb6jt3xvBWDjRBVRekdOxupq4/wyGM7KJVMbNtFkkS178GnEg77iET81b7a65BMhnj08e3s3tNEPlfBth0kqdrjnKgJEYsFiMUCtLUnkRaJ9I04dLiDf/xPP4MsS7S1J1HWGPhWQ1t7kl/+a09QWqdMfDwepL4huub3qirj86moqoLrehx9qIv/P3v/HSfXmd53ot/35MqpqzpnAI0ciMScw5Azw8kjaWxFa3W9smTp2rtee/W5u7Y/9np977Xla1mSJcuWrTQaTdIEzpCcGZLDHEHk2Dnn7sp18v2jGk2AaADVQDeA4eD3IUii6oS3qs55z/s8z+/5/cIRA3FLCY4J9NDP4/sOktwAIoCs7SaU+M/I6nYQYRTtbiS5E9/PV4Wk5DSKfgBJqd73ktKOGvw0N4M+dx4BtR3xIeljz3cwnRF0pRlJrD21LqhoH9nAVghBJBFieniGxq4MkUSY/GKR6eFZwonQuvvYqpKMDwzmF/hK72F+PN5P3jZrvsIqrs1MpUjWqtAYWPtF5Hm0hKNkrQrD+UUminl0WWFjPEUmGL7itRELGsSCV08MCSEIaCodl6no1rK/LASRgE6khmD4aggZ2mWD8OuFIsu0p6/tc95sGNoOFCmF6xeWemZlNKUVWU4DAkVOEzYeQFe6l+yAfCShoyrtnF/WCmFgaNtIx/7xEotGQlVabthnSOkR3p/v58TiCK2huouu3/vT22gIrM1vU7XX0X+qAttCyaRQsRieWuRf/dkPaatPEAnqlySRYmGDn314zzWf5zwV2Qfenx3jK32HeWdmZFWeuwXHZK5SpGBb1Adu/lpFvUyvbDi+9iKGsiwRkCUMTWHv5lZyJZN/91cvcrR3nHdOjRALG2QSa5/0WbfAtuCUObY4yNdHXqO3ME7WLtYmcnQFNAXWprfLcUawrfdx3Q8omEIECAQ/i6xU+zfWE0IoSHI9RvBTmJUf4fvV4Np1RrDtQ7jOGPKHJuDx8iTHsmcYLI6iCuWihdC+xE7i8SsLclUrL2E04z4kpQHXnQTfrIpkyY0oSgdCGChKJ7LcRlUUSgLfBBHE8/M41lE8L4ckxVHV+9CNx1GuoDBayFd4981e2trrqNQY3EiSoFCo8MarZ5kaz/LQY9t/YgNbz/dr8rOFqrXI5R5MRkCjMaDR2LS6B6EkCVKpCKnU5SeOVF2EVN3l38/UR69ZcTgWD7Jr99qJOxQKFeZmC5TLFtFogAMHN9ySImRVG58PIEQCyXjog7/L6SWRqQvR8cH7UgRZWr8Mfy1YKXD1vBJThW/SFP0FNHntM62aJF2iYvtRgaorPPCFu3jvB0c5/XZflYqcLVMulLn/83cui76tB86rIg/m5/ne8Cm+N3KKRevqVmQXwvY8BvLz/NmZd/mN7ffQEIysSxJiopjnxbF++rJzKELCA348NsCnurawOZEmqN569/vV4DsD+JXvw1LrEWiIwFMI9VK/yIv2c2fBPorv9CGCX6rqb/yUsJ5UuR5Vrr/s+0IoKHIa5Qp+2kJIyCJC8JJ2ihuDoKwTUg0sz2HRLl70fLf92gOjq0EAuiz/tFwaAAxNLdA/NkfJtKqU5FwJQ7vUQ7s+EbnmwFYgCCgqkpA4MT/Bd4ZO8MpENSG4Gpiuy+G5cf524Di/1LOPpB68qRXbodPj5BeKdGxtRtNVXvvOIUbOTbBhVzub93WRrKEAtVoIIUhEg+za0ERjXZTJuRzvnxlle1fDT1ZgO2/m+eHk+7wzfxYfn5QWIa3HiKpBNOnK3lCXw9bY2vgd2dYxXHcUOB9syUhyCk2767LiTWsNIQKo6k4kOY3rFKkqopq4zjC2feySwLavMMypXB8JNUpcuzjQCMi1URirN5OGqm5DVbddYbvzNkaACKKqWxGhX8HRjuF7WYSIoKibUZSNS+rSawdZllA1Gc/1mZxYwLmCqMatjorjMFnM17RtRNNuKbn+WxGjI/OcOzeJADq7M7S2pVDV29/ZWsDzTTzfRFkSYrHdOTz/4t5L251nofwK9eHPr4uPrSykdenbvBWgqAp3P70Pq2IzNTxLMVdCVRVaNzdz58f3Iq9nYKsozJSLHJ0f55mhk4wVr80mb9Es8/zoGZpCET7ftYuGQOSqqsWrxRuTwwxmF0gZQZpCUSquw5n5GZ4bOkddIETbT2BgCz74dlX53DkB7iyoW+AqgS1+Dt8+BOarEPgsiAA3yoP1Nq4fbaEMMW3lnsWourbrTFWSbhk16BuBTCLCHT1Xr75Ha2BxXBaiyiIaKSzw7MgZXhzrZbpybW1bo8Usz46cJhMI8bmunRjytcVAa4HeI0NMDc/R3J1hcTrHa8+8jx5QKSyWCIT0dQlsoeojHg0btDUkmFks0D8+x3xufewm1y2wzdkl3l/ow8cnoYa5q24rO+Md1BsJgrJ+RcP4yyGkrE0PmmOfqAojLUEIHVluQZJTSwbg6w8hZCQRQVba8dyJJUU/8Lw5bPs4RuBixbJ5K0tYDvKF1o8TVG5sBVMIHVXtQf1QJWq9IIlq/1ahYF4iF/6TAtf3WDQrnJ2fu/rGQDoYIqT9JC7abgzyuTLHj41w9vQEyVSYu+/ZhKbJP1V92+sJy5mm4gwTD9wDwEL5ZRyvcBEd2fHyWO4sVf/dtYcs1qdv81aAkATxTIynf/1x5iYWKRcqhKIBko2Jy1LD1golx+a1qQFeHu/nTHbmmo/jAzmrwl+cO0Q6EOaR5o1kjLW1KXp3apSddY083bWFiKbjeC6TpQK//ePv8mTHJtoi8TU7142CkNsR4V+retkX/hjf+3GNOwYQSie+X4YbtC65jbXDxkgjpmtTdk0EgqgaxF/qHl/bBJ5AFtJPVcV2/+ZW9m9eG72dy8L3KToW3x85zQ9GzzFyjQlBqLL3hgsL/Pm5QzSHYhzItBFStJvyvFuYzrEwncX34dS7/QSCOk/98v089+evMt4/zZ4Ht67buWVJIhkJIgnBfK5IyaxN42G1WLfZ0vU9im5VGGlHvIPPtNzNxkjTVfa6MXDdQXzvgkqa0JHlRm54NlRIyFIGW2jgV9XdPC+H6wxdsmlQNggp1Syf53uIVYp3+L518Wde1TiVJY/d9YfreJgVe1l0qlZUHBvTddFlBX3JEuJmzvN50+TcwizHZ6euuq0kBE3hKFHt1vBivRVg2w7ZxRKu5+N7PqdOjvHWG73MzubZu6+Tu+/d+JENgm4GTHeSnHl4ObCdyH8Z33dR5A/o755v4nr5y/haXz+E+OjWHDzXY+jUKI1d9TS0p29oX/jRuQnGS1kG8wvXfSwfmKuU+G+n3yGs6jzUtIGIunasHdvzUGUJY8kbVxISEVXH9b0rif/e2hAyEABJASkENfZCCrkRAp9DBD63vuO7jXVB2bEYLE4xWJwmqOjcl95KxbVYsIqk9AjGWlkAiZsrRvRRhev7vDE1yDszI8xVrr+yaHseQ/l5/v3Rl/nn+55ge6J+WUzqRqIq5ArzU4scffUMd398N22bGpFk6QYwJP1lfaWy6azb+dYtsNVklYweZ6g0TVKLoEsKvu/fEjeg585Us6BLECgIcW19hNcLIUUQyMsiHr5fwvMuzaq3BhspuiXemDvEnak9BGRjWWyoliDXdUcoFb98TWOU5Qyh8K9ddbtLlNb8D/7j+yu8vwLm5vL09U7heT7JunDNHqvHZqY4PD3JrkwDexuaL1og38hr7vxnPDw9wdfPnMC5jOH1hYhoOu3RODH9xvqW3soYG13gj37/R+RyZfL5MnNzBcyKzY5drTz58V3E4ytTvG7j2hAz9hMz9i//XVdaaAh/gXjgruXXLGeGk9N/H8FtZsFqUc5X+Ndf+o/81h/8Klvu3Ih2A5VmX58aXPNj9ufm+Muz7xGUVR5pXrskU0IPMF0qMpxfpDOWpGhbvDM9Skwz0H5KWjWqz5APPysvfcZfvJ1YcZ/lf5/fd9n+5zLb3gLrs48KDi308cz4u5zOjrI70cVddZsZK83zpwM/4uc7HmRLbJ0rjh9hXLiWXOmavdr7tcDxPZ4dOXNN+14OtudxcmGKPzr5Or+14z62JRpu+D2nBzSycwVe+/YhBk+O8qv/8gtoAQ3XdfHXkSHp+z624zE+k8P1fDRFXvNWlvNYt8A2o8f5ZPOd/OfeZ3hn/iz7khtJG3EC62hUXSs8qj6TH8DHZ31K4leD75sfqoA4+N6lwh6DxVF+OPkqJbfM10a/Vw1ml977VPPjPN5w/xXP4zoTlIt/fk1jVNSemgLbk8dGGR+dp1KpfpcTYwv4XpVG+tIPT1zZb9Svik31np3g+OFhVE1hy44W9BoXgJPFAn979iR/evQ92qJx7m/t4N6WdrrjyRtK8fV8n1dGBvmLk0c4Mj151e0FcGdTK8nAT6ZA1npBiOpdOT2VpVyxiMeD7Lmjgwce3sKOnWvTa/9Rg4+/ZlWtTOgTGOrFwl9CKATUTqR1Uoz/SENUr+lgNLDu1OMbhSNzE3xj4DghReOuho41Oeanu7fy5bNH+Mevfp+QouL4HiXb4n/ecSct4RvDGrrpKH0Zv/Jd8JaeHyKGSP45iA89P90hKP13IADaHnzrTbCPVPt55VaEdi8EPgcX3q9+BZxT+JW/BesIYIJUD/rDCP0hUH4651bfX7u58zzenT/HzngHPZFmhoozqEKmPZRm3sxjejdnvflRwfGBSUamF2nJxNnZ1XjJ+71jsxzrn6Q+EeGeHR03foBXwWuTgzQFY0gbJLYmLi+Sth7Ydd9mXNdj5OwEP/PbTxGMGEwNz9HQlqah/fJibNeLUsWmb3SW00NT2I5LXV2IgL4+z8J1e8LG1CD3pbdhew4vTh/hK8Ov8P5iHxvCTWT0OBE1gCYpSEg1c0aDsk7GiF/32D58Ot+38b3rp2mtHn71vB8SaVnp+9gV30paT+GsoKbXGrw6xVuSoqjavits4eH7Fp43i+uOgV9CVrrRtIOo2h1XPT7AiaMjvPnK2WVfVMty8H2fXLbMD545chXlTx/H8SgVTSpli0xDnCef3kMoXFsV0/E8CpbJRCHPfKXMSC7Ls/3naI3G2JysY2tdhk3JOpoiUSTWJzN9dn6WHw728ePhAU7NzWDVoIgsSxKPd3aTDoZuZ8svQKY+xt/7tYcoFU1cz0NTFWLxIMlUGMO4HVitBM/3r1t5/jyi+l4k6eJkiyyFaYn9P1AvoCffRm2QZImOba0UF4pUChUCkVsrkRVe8hBeMGun3Fmey5tTQ4RVjYQRYHP8+hdo9cEwn9+wg/FijkWzjCrJNIejbE1mCP9ECkddA/S7EXIzOCfwzR+DM8jK1l82vjsF7nA1oFW6QH8AvBI4ffjlv0ZIQXz9362INAABAABJREFUEYQIg2/iW6/il74KfgH0+6re2c4gVJ7D96Yh8AXEFZwOPqpwfR9vjebO8yg5FiHFICTrDBVnqt70ksJK9fjbWB3OjMxwtHecuyWxYmBrOx4j0wucHJxc98A2oQdxPHdVaskV1+G5kdPENIOQotIeWRvHl1qQbkly91N7KN23mXg6iqLIpBrjPPzFgxihtW+Hcz2Pqfk8b58Y5ruvn8C0q/FOZ0OSeGR9xHrXLbBVJJk6PcodyQ30FSY4tNDLeHmWo4sDRJQAuqQii9WJv2yLtvGljoeuvuFVIIRBVdazOpH5fhnXGcD3S4CyZI2zvvB9D98r4Tj9Sx5r56EgxKWLnoxRR1SNsGhlmTargkT1Rh1xNYp+VQ9bkJVWQpF/cKUBAQ6+X8R1RzErP8ZzJxEigKbfdfn9LsD2XW0oiszQwAzjo/NMjFaTBZ7rUS5byPKVv1dFkUlnorR21HHH/i62bGteNWXPp6pGPFbIMVbI0bswx7GZSRpGI9SHwmSCYVojURrD1b+ngyESRoCAqq6qv8/xPPKmyXgxx1A2S9/iHKdmZzg1N8NEIU/Fda56DENW2FXfwO5ME+HbwlEXIRDQ2Lip4WYP4ycKaxnYKvKl1TFJqIS0jWty/J82SJJEU3c9L3z5VU6/3UuiIb6shKwHVO56ej/KTVD41iSZXakm7m7oQJVknh85w9H5iZr3X7TKvDY5SFQz+NXNQeoD1ycm9eOxAQZyC0gIIppGSzhOUyhKUFF/ahJ/Qm4DuRFfCoNzbimwvRxccMfA2IMwHgO5vVqxtd/FL/4pvvkSQrsLCOM7ffjmK+DNIoI/C9q+ajXXncYvfRmsw/hyy09pYOvhrXG4GVIMHM+l4lqAT8kxObo4SEStrn9v49oxly2SLVYIGyuvfQO6gixJ9I/XJt55LTBkhbvqO7i7vp2pcoGXxnvpzdV+vulKkedGzxDWdD7XuYOUcWPaq4QQRJIhUo3x5dcCIZ1A6PJxxPNvnWZwYv6y718Oru9TNm3mskWGJ6sWTVAt6OzpaaGpbn1aQNctsLU8h4nyHK/NnGDaXMT0bIpOhTnrGgWMWDsxIElKIYSB75+ng1i47ji2dRxVu+PGWP745ao6szvOB7ZD1aBbki7N3pTdMv2FYc7k+rCWKrzn8gNsjW2kI9RKWLnymCUpga7fXdPQPC+PLLdQLv0NjnMa1x1FUTqvut+W7S20tqeYmcoxOb7IyWMjfPMrbxGOGDz8xA5iicvfuIJqU3s4YtDUnKR7UwPB0PWrxpUcm+FcluFctur3pig0hZaC3FCYVCBIwjCIagZBVcVQFAxZQVn21BTVgMHzcH0P03Ep2hYF22KhUmayWGAkl2Ugu8B8uYRbI59JFoKGcISf2bKThnB4XTwhbyYGZxc4MzGDpsg8tKX7Zg/npwKu72HX0NNdC/LmUVQ5iSansdw58uYRHC9HTN+Lobat6HN7G5eHkATRVITxvilGz02wOJNbTvQFIgHu/MRe1sVD6XLjAdrCcfalW3mgqZu9dS0IIQgpGvNmifFiruaF/mQpzw9Hz5I2Qvzshj1EVA3pGpPDYVUjrGrLehyTpTzDhUUkBJ/s3ExT+OKFkO/7lFyrJv2Ga4UmK2jSDaSPCwnQq/Y+tdxnvoswHgV1P0KqrgN8LJAbwOljeX3hnAW3D5RWhPEEnBeElFvAPonv9IN9FPwvLgle/fTA8bya9DBWgx3xdkZLs5zOjTFZWeDrI68zWJxmV7yTpHaFtqzbuCpsxwUBocv42KuKjKrIFCtrT/nWJZmOSJI769t4sGkDO1NNzFWKaLJMvv8oU+XaLYH6cnM8O3yauBbgE+1bCMjrn8A7d3iQ/HyROx7ailpj4ej144O8fnRg1edyPR/TcrAcZ5nqL0uCfVtauaOnhWT0J6xim7WKvDx9nL8ZfpmKZ6MKmYQWJqwYqJKCzOoffI2BtSnXy3IbQorgux8E2Z5foFz+BrLShBBt62r74/sOrjtJufy3eN7F1C8hRZDlS0UFRksTHF08Tc7JL1OPh4pjmAsWmqSyKdK1ZuOTpAiafj+O00u5+GXM8vPo+n1X3U8IiEQDRKIBOjfUU5eJ8MJzx4jEAjz5qT20dawff78WnK/m9mcX6M9+QD0XVL0e47pBWNMJqxqaIqMsecO5voftetieS8m2WaiUyZkmlndtim6Cqr3Pg20dPNG5gcBVlPGKpsVkNs9svkTFtqsZN0OnOREjETJQr1NUxfd9TMclV66QjqwNJfrs5Cxfefso0YB+O7C9QbA9D7MGpkAtmCk+Q9y4G0kPUDCPM134W4SQsJxJmqI/jyTfOOrURwGSLNG2uZmm7kvpuqquIl2FzbJm4xCCiKqzIVbHA41dPNW2hdZQHFWW8X2fJ1p7mC4X+Gr/EebN0rKC5ZXg4zNWzPHlc+/TFIxxf2MnUc24pnnk7sZ2tiQzjBay9C7OM5xfoD+3QM6scE9j2yWBreN7vDR1CtNdv57FHYlWNkZuVfaIqPbeyi3LQW0VKogY+KOcX1H67jS48yBC+NY7Fx3FX2o/wpsD34Qbkdy/hWC6zpolBc9jb7Ib07UZlKeJqgGOZ4dpMBLcn9lKSo+s6bl+2qAqMp7rkS1eqkcDULEcimULfQ1ZMIqQSBpBtibqeaCxm0+0byGuBZAlibhm8GTrZuYrJb47dJKCY139gFRZVicXpvh6/xHqA2HuaehEZn1F3E6+1cf4wDS77ttMrYL2xZLFQn7l77pW6KpMIhqkvT7Bzzy6h86mFJr6E9ZjO2vleH7yEBXPRpdU2kMZtkTb6AhliKkhNHl11E+AlLY2ZWtF3YpkvrRULV2CX6ZS+iaathtdfxhJrkesg0iK7zt47hSW+Rrl0jeBi3n5kpREUTdfsl9/cQTbt/lS26eJL30PC1aWrwx/h5HS+JoGttVxhJGkDAgJxzm96v2FAF1XaWpNUbjOG2K94QNlx6HsFKB4bQbctUIACSPAPS3t/NKOvYSu0Dfm+z6W63JsdJLnjp/j2Mgk+YqJLElkIiEe2baBh7Z00RANo1xHcOu4HqPzWd4bHOULB3Z+ZC1XPuqwXIeyszYL/KJ1mpixH9udpewMEVA7qAt9jHOz/4xM+NOotwPbVUHVFA5+/A5c28UybTzXQ5IlNENdpiSvNwxZIRMIs6eumb+7aS87Eo0XKQ0LIcgEwvy9LQeYKOV4eaKPBbNcU93W9T3GSjn+4/FXiOsGd9S1EFS1Vc8l706Pcmx2ivFijrxtEpBV9maa2BSvo3kF8SjLc/jdU99n3iqu8ky1QULw/9zy5K0d2EpRLqn2i6U/F3V02uBnwZrGd3ovc7hEdbufMpRde82SguchIbgvs4170lsouyaSkEhqkepPc4vS6i3HpWCa5CsWluvQGIsSVBWkdVKvvVakokF8qiJSu7qbCAd0ZFnC93zKls3g5AKjs1la0msjOBdSNJpDUe5t6OKzXTsuEXwSQtATz/B3Nu5lopTjzakhzBoLH5bncmJhij8+9SYtoThtkTiKkNdtHaZoCnpAW9VzJxEN0JhaffwllqyoNEWmLh5i54YmHj/QQ1tD4rrWrFfDugW2pmszWalWxbrDjfz9DR9nR7wD+Qb0r14NqrYfSWoATnJxG79JPvt/40XyGIGnkeUmQFqTSahKlfLw3EnK5e9QLPw+Hw5qAWSpYUWRJ8dzEEhE1Q8yfTE1gkDgXGPl8OrjXQrEvdw1HUM3FNraU/SeneSj61C5OoRUjcc6N/Bru/bTEYtfcVvfh4GZBf7D868xsZhnb0czd7Q3UbEdfnymn3//7CtYjstTu3poiEau2aB9oVTmnYFRvnv4NJ/fv4NrPtCKuP273yiYnkNpjSpXQsgIZEpWL55XIhV8FF1uxPMrsE4+th9l+H7Vj3luYoGhU6Pk5wtE6yK0bW6mrimJkFbnS75aKEJie7KBL3bv5hNtWzGUlR/9khDEtQD/dM/DZK0yb00PU6oxWeL6Hn25Of7w5Bv8o533s6euBXmVn+kvTx9mopjnodZufm7TTnqSaRQhIwlx2WMpkoxyxXVF1QrH8/1lerUkxEXOAnDelu6DbRQhEdeChJRbnHZf63csDJDrQe5CBH/mMtvEfuqqtQBlx8b01jawfX32NGHVYEu0laQWqV5zt/jjcDKX50en+vju0dOcnZ7lP/7sJ9nf0UJYv7XugY2tad7vHeMH75wloKk8csdGUrEgxYrNkd5xvvfmKSbmcnzpsdqET68ETZK4q6Gdn+vewz0NHWjyynOnIklsjNXxv+1+mN987ZsMFxZqZgGUHJvDs+P828Mv8H/ue4LGYGTdngd1jXFKuTJj/VO0bKjnwjWaJIkV2UNffGQPjx3oWfW5FFnC0FRSsRCJaOC62YU1n3e9DqxJCiktwnhlnk2RZhJaGOkWWeTKcgOatgfHOYvnjlz0nu9nKRb+AMt6CyPwCQz9IYR8/RRaz1vEMl+iUv4WtvUOvpddYVwtqNrupYD6YgRkA8efY6g0RmeoBYCB4jA+HgF57ZXMfL+M503jewsI+dqy1YlkmKc/v5/sYolU+jb1pjEc4ee27OTpjVtoiVw9++V4Hn/91lGmcwV+9uAuPrl7M4lQAN/3eXTbBv73rz3PM0dOsyGToiF67d/vTL7I2cnZa97/Srg17vifDhRti6y5NuwIWYqSMw9juROoUh0hbSuOu4jvu7cVPa8Bju3y3vNH+NrvPoNrOwQiBqVcBT2g8dnffop9j+9a18rtZ7t28LPdu9kUT6NfZXEhgJQe5De234t37BVenRxclSjZoZlR/rr3MIokszt1dcX+C/HP73yU3sU5+rLzfL33BNPlIgFFYVOijs90b6MpdPG8qUsK/8uWpy4blAhAlRSydokXJ09xaH6QtBFhb7KT9lAdEdVAlWRsz2HOLHAuP8mh+UGKjsVvbX6CA6kuGgMfDRVwIbfhi7qq5Y/cDdJKfZ4S67gsvGWRtSoU7droo7Wi4FT43vh7uHjsindwd90WtsRa1vQca42meJQv7tvBgc4WfuOvvrPqxNSNwua2DI/v62F2scSfPfcuX3nhMJIk8P1q/208HOChOzbw6N7rFzv8n7fdw1Ntm2kNJ66qhaJKEu2RBP983xP8i/eepz83V1M7B0DFtXllcoA/O/suf2fjHtrC6zPvhKIBRnsn+N3fPMLG3e1oAW05xbf/se3svn/LJfu0NyRoq49f0/mEENWA+QZeS+s2g9XpUT7WuI8/H3yB4dIMRefWoaMKoaAbj+M4/VTKE8CFD8WqBY9tvolr91FRvo6ibEJRt6EobUhyA5JUhyRClxFY8PC8Ep43i+dO4bojONYJbOcMrjuC507j+ysJaMmo2n5049EV+3t7Il0s2jn+auiby1XbrJ1ne3QT3eH2S7a/FNWK8RW38H3AxnOnMCsvYJafRwgdRbk2mrOqKTS1JqlvjKMb6/uwTBgGTeEoQ7lLEwY3G6ok8UhHN5/Y0MMd9U2kAyGUq1B7PL9KqTk0NEpTPMa25gwNsQiKLOH7Poos88jWLr7y9jH6ZubY0VpPMhSs0pcdl9d7h3m7f4TRhSwV20FXZJoTMQ52t/JATyeyJPGjk7282TfCmYkZBmcXqdg2v/xfv7Y8yW1vqeexbRvZ3VaV0/d9n4nFPC+e7ufYyCQLpTKGqrCxPsVj2zbSlUlelJGTJIHpuLx6dpAfnDjHVLaAoarsamvg3k0dbKyvu+gzF0yLQ4NjvH5uiKG5RVzfozEa4ZFtG9jV2kgs+EECZ7FU5neff43tzfVsbkxzbnKWtwdGmS+WiegaB7tbeXJnD5HLqCZ+FFG0LRattZln60OfImu+hyY3ETcOIBDY7gwR/Q5ksfaJtI867IrNM3/yI+765F427O4gFAtSWCxy7tAAz/7pi+x+aPu6Brb1gQjNoRjBGqqPYqk6ujme4Wc27Mb2PF6fGqz5XJbn8uJ4Lwk9QEhR2RirPTGcCYSxXJeyY1OwTHK2yWQxz1SpwEPNXZcEtrKQOFDXfUXxqLxd4S8HX2O6kuWRhq18qnUv9UaMgKxWKX9CVOdNz6HgVBgozvJfe1/i+YljtAZTNAevh3bvVe38/ErVYscrVP/uLYI7syQQpV/gNeuBb1W39xaqfa944E5SpQgbS9tfw7Wi7kRovfiVZ/EL/xGhPwhSvNpT602BX66qKtfogvBRQtYqU1iFXUstuC+9lc3RFsbK8wwWJvmb4VeBau/t3XWbSenrowh7PVAkCUXXSAQDSNKtGdQCaIrMno3NJCIBTgxM0js2S7FioyoyTakoPW1pNrfXEzKuv9LcFo6TCUQwLlOpvRBCCHRZYXeqiV/p2c9/P/MuZ7MzNZ3Hp9rr/a3B46SNEE+1baYptPbe3aqu0tCeJpaKEggbSPJy3wLKZXpe1RvULrNWWLdoI66FeLB+J67vcTw7yLMT7zFUnKYlmCauhgjIGqqsInGennH1m0gREoa8NpQIRenGMJ7AcyexrNcved/3C7huAdcdxbFPI5lvIklxhBRGiBBCGEs9uCpCKPi+C9hVT1y/gu8X8f0CnpfDc6fwvAUuVD/+MDRtP7rxGLKyYcX36400+xI7iatRcnYBIWBrdAMbI12kjas/eG37DKXCH111Ox8X3yvgOgO47hiy0omuP3zV/VaCJAk0TYEbwGLZksrwKzv3sru+kfenJjg1N0PeMmvOlq01ZCFIBYLsyjRyd3MrOzMNbEykiGh6TRQTx/WYyhVYKFbY0dxAOhJCWaKIVP3wJLY116PKJ5hYzDNfKJMMBZkvlnn22FmeO36WoKqSioRojKlYrovjupStD2iFyVCQDfUpTNthtlBCkQX7O1qWx9eWihFfCiYd12MmX+RPXn6H4blF4sEArckYFdvh+NgUZyZn+eV797K5KUNQqy7SLMdlKlvgW4dPkQgatKXijC/mefFUP7OFEl+6cxfNierEXbEdvn/0DK+cGcR2XZriVSrO2EKOv3j9MPk7TO7a0EYyVKXJ2a7HybFp8mWTYyOTaIpMPGgQDejYzpLC5ap/+tU9yD3fv2nX10rI2yZzldp9SK+EiH4HqlyHJFR0uREhVHSlieboL6JI8TU5x08TPM9jbmyezQc2sGlfN6qmYJs2CMHLX38L31vf60iVpKvQdS9FQFE5kGlj0axQdCyOzI1ffaclLJhlfjh6jrCq8zPdOg3B2hbxXz13jNFClpCqEtUN7o10EFY1JAQNoUtZKUIIouqVPYHfnuvnxOIYGSPKo43b2Z1ov2zlJeNHqTOizFSy/Fn/q7wyfZqMEWFT9FKvzFrgO+NgvY5vvVUNWJ2z4C/gl7+Nb78HIoRQehChX6xub5/DN38ATn81sHUHwC/hF/4dvghUtzeeQNQg5vhhCCmFrz+MQMa3j+CX/xp8F5BAqAi5E+SOa/qcqw2B3Fts7pw3y+SsytU3XAXSRoyUHqE5mKItWMdgcYpD8/08N/E+3eHGdQlsT0/OMJnNU7Qs5gtlJrJ5fHx+dv8umhNRiqbFyYlp3h8ep2haGKrC3d3t9NTXXdY650JYjstENscLZ/pZLFXwfZ/udJI9bU20JeNr/nkuByEE0ZDBlvZ6mupi7O1pwXI8ZEkQCegkIsHLKiavFqokr6pyLQlBSNV4uHkjM5UizqBH/2psgMoFvjlwjLCq8XjLJpJrbAPU3F1PKLrynJnI3HrJlmvBugW2ZddipDSDKikUnAqvzZ7kdG6EhkCSiBLAkDVUSa72udR40XSG6nms4fo58wBCCqDqBzD8PJ5fxrEPczkTdM+bwfM+nHWRAHkpuJUBF993qFZ/V9eDpqg7MYKfQdMPIkkr97cokkxLsIEGI03OqQocRZQQipCRa8jeeu4k5dJXah6TEDEUdTO68TiqfqDm/W4W6kNh7m/rYEtdmj31TZxbmGMou8hYPstkscBsuUTONPHXkUipShLpYIiWSJT2WIKNiRTb6jLsyjQSVNVVUTEcz2O2UMT1PJLhIAHtYiEzISATDaPKMtlShYJpUbFteqdm+eZ7J9AVmQd2dLG5MU3Y0KjYDiXLpi4cXL7fNtSnaE3GiAcMJhbzZMsVvnBgx/I4NUVePm/JsvjRyT5eOTvEQ5u7uHdTO/XRMEXL4ujIJH/5+mG+e/g0dZEQbak4UH0Ili2HzroEd3W3EdI1xhdzfPfwad4bHKMrneRz+6qB7emJaV442YfreTy6bQM7WhqQhKB/Zp4/+fE7vHiqn0w0TLLz4vvjzOQMu1sb2dnaQncmiabIlEwbXVXQV6G4J2DVVBnH87A8lwC3hifholVmehVWA1eCKseQRBeWO0PZGUYSOppcT0Tfc8sKn9zKkBWZzh1tlHJlyvkyRAIUFosUsyXat7bcstWRpB7kvsZOCrZJ1iozmF+4+k5LGC4s8PzIGeKawee6dhJSrm7fFtMNfHzSgRCZYJj6YJh0IIQmrc7z/kK8O9fPeHmBJxI72B5rviKdUAhBSNa4L7OZvx58k8MLQ+xPdV1zYItQquJO59uLLvGIVT+w3QEQWjUAlU2Qm0Hd/qHjqR/0wIoEwvgYvrdw8TEARBKhP4av7FwSl6qORSjd+FIEobTiO8PVKq1QECJWHVsNtn4fhnRJt/LVYbkujueh3yKFoOlygYU1YrtcCNO1WTALTJYXmKlkCcga9UYcXV6fZ8ZkNs/L5wZxXI/uTJJY0MC0HWRJ4Hk+7w+P897QGLIkEQsYWK7Ls8fPIgnB1qYM+mV676HK2JrM5vnBqV5czyNi6Liey7GxKWzXIxYwiAVuLJtHVWTqYiHqYjfGA3Y1yATCPNm6mbxlUnYsJkq1W52eXpzhO0MnCas6DzdvqIlpUysSmSjBiEF2Nk+lbNGypNTvef4t+xxaLdYtsJ2uLPI3w68wb+WZN/OUXZMZM8vp/Og1H/P+9PY1C2wBZLke3ahWI8slBcc+ie+XqK3U4wHeBV64q4VAiACKsplA6IvoxiPI8uUfnuPlaSpuhfZQC/VGlcJZcU36isPE1AhNgUttJC6EJCXRaqq8CoTQkOR6NG0fmnYnsnzlY98q0GWFlkiM5nCUR9q7GM3n6Fucp39xnpFclqligbxlUnJsSrZNxXEwXQfLdbGXfOzO+9V6PstB8HkVQ2mpUqpKMposYygKQUUlqKpENZ2EEaQrHmdTso4tqTSt0fhVKceXg+/7VOyq95euVM3GL4RAYKgKkhDL1djFUoUTY9MMzi7wj564l4/t2EQidPlqRsTQiRg68WAATZFRZZl0JHTJuaBqOfTdI6cJGxqPb9/Anvam5e2a4lGODE/w8tkBHt+xkab4B1m/RMjgZw7sXKY2baxPMVso8TdvHeXt/hE+t6+6cHuzb4TJbIFHt3UvU4h9HzY1pHm9d4ijI5P0T8+xt6P5ogA0X65Wch/a0k3oOgQuBGLVv5XluWuupHmtcDyXuUppzQJb250nZ75P2R7A9YpIQkVTGogbd6HKaaR1tEP7KEIICCdCvP7tdxk5M040FWZ+cpHhU2MgBK99651l0Y6uHW20bm6+ySP+AM2hGI80b2DRKvPl3vdZrFEp2QfOZWf5xsBx6gMRHm7eiCpdWYzx6a5L+7uuFyOlefJOhYgSIK5dfQEsC4mMEUUWMuPlRRauQ3FZyA0gfwxhfKy27ZVOUDprChOFXAeBT6+4bfW9py99TygIuQnkpjXTP7iSsNflUHEd7HUQvbwW2K7LVDnP4hrpE5xHb36CweI0k+UFps0sZdekPZThkfhOGoz4mp7rQuTKFRKhAE9t7yEVDlK2bAxVIVc2eX94nMVyhf/pvv00x6MslMr8v/72h5yamKYxHrmiVofjefTPzvP9Y2f5zJ6tNMQiuJ7H6YkZjo257Giuv2GBre24eJ6PIkvLfuAAhbJJvlSllEdDxppQka8HG2J1PNm2mbxd4fvDp8nVSHf38XlvZpSQopHUAxzItF/zWvLDyC8UGTg5ypn3BjDLFl/8rSexKjZjfZNEUxEaa7DlPN/6MZctspAvkytWKJs2juvheT6yLFAVmZChEQsHSESCREL6DeuzXbfVScW1GC5NA0sG5zXw06+GsHJlytG1QJYbMAKfRJZbKBX/FMc+ietOs5Ji8dpBR5bTKGoPwdAvL6k0X1n85+jiKaYqszQYaTSpmu1zfIdXZt6mO9x+1cBWUXuIJf6/NYxNRojgEtX6FkmnrhLne8TaY3HaY3Eebu+q9qzaNuPFPBOFPFPFAnPlEguVMlnTpGCZlB2bsm1jeu5ykOtTfXArQkKRJUKqRljViOkGqUCQhnCYxlCEjniCplAETb72ysKHIUsSQlRNri/tIfNxXA/fr45PEoJsqcLA7DyqLHPXhjbCazSpn/e5PTM5w8Obu4gHAxcFv4aqsLutidd7h5nOFSnb1WSPpsgEVINU+IMqqxCCTCREIhRgMlvA830E0D8zj+tVJ8XR+Yv7pDWl+lCeL5VxXBftgqxyQyxCQyxyXUEtVL9DdZXXu+U5VG6RwDZrVZipFCjW6J931eOZ7zBbfA6BjK40YLoLZCvv4HpF0qGnkeS17/35KMPzfGbHFpjom+T4ax/Yp0mSQFEVzr7Tt5xI+9xvf/yWCmwBOiJJPte1k/Fijh+MnqXkWDUFt47vcTY7wx+fepPWcJwNsbrrqr5eCyqujet7OL6L7bkoVxGA8fGpODY+1b5bZxXCWT+NkIV01e/0w6i49jX7wK815swis+Xims/lr86c5PjiMPWBOAdSG9kV76wpsXK9qAuH6EjFSUeq5zr/bJwtlChbDulwaJk2XBcO0Z1OsFAqM18sXzGwrSy1LA3NL/LSmYGL+OdtyXi1/ecGYXqxwEKuRDwcoCUTB6BUsTjaN86pwWmEEGztqGdndyPBmxzc7ko1YXkOM5Uir0wM1JzQsTyXt6aH0WSZhmCUzki15fB6587+4yO8+u336D8+SjFX5rO//hjFfJkXvvoWG3a1XzWwdVyPYtlkerHA+2dGOTkwRf/4HNMLBYplC9txMTSFSFCnOR2ju6WOHd2NbG6vpy4eIhxYf92TdRSPivHZ5rvX9JhtocyaHu88JCmCpt+Hph+kXPwq5dJf4zhn8X0TcFkbewsBKAihoigbMYJfIBj6WSBQW8+l7yCEIKx8MDGG5CCu72F7V68aC6Ejr0Ld2Pe9C6jVPkKsfVLhRkISgpCmsVFLsTGRutnDuSpkSSJqVPtxC6aJ5Vw8GfpAvmLi+h5BTUVXFWzXo2Q5VcuOYGDNMnznlQYt2yGoq8gfoqvIQiIa0BECSpaNvTRWWZLQVjBI1xQZRZYoF6sZPlWWKJkWE4s5/uTld/jTV99beRweWK6HdsGsFQsYayJsIAsJfZXJN9NxKK9RIHm9GMwvMFpcO+G0mcJ3SAYfIxV4CEWO4noVitZpzs39DonA/ai3A9tVwQgZ/NYf/OoVRY6Wtw3eeoJnQgjaQnH+8c77GS0ucmJ+knKNgYDpOpxamOb/ev9H/Kv9T9IWjt9QtdWwoqMKhalKjpHSPBsj9Zd95lZFpFxOZEcxXZuArKFJtw474by3OXBF2uiNhCJJaKsMbEuOdcuwXU4uTDNnro02wYXYn9rIow27aDAS+FRXgJ7vrar97lqgyNKKz0RJqg7C831cz0MSAp9qv7MQtZHJJQEtiSj/6UtPoyvVBNX5XukbSWJ9/9wYLx7qpaspyT/4zL0AHOuf4D9/+w36xuYQCLpbUvz6p+7m4NZaxFXXD5IQ7Em18KubYbSwyLnsXM0tcXnb5I2pIf7wxOv8i/0fw5CV6/6eB06OIskSv/g7n+YP/rcvA1DfmsKq2BSzV74PXM9jPlvklSMD/I/vvc30QgF3hYRGsWJRrFhMzud578wo337lOFs7G/jiI7u5b1cXqrK+yc11mxkzRpwvtt2/psdcfw9cjUDwc+iBR3DsY5iVl7HM13CcM9d9ZFnpRNPvQdcfRFV3IklJoHbahkT1szu+g7qkoOj67hJNdu2/F8+bwbaOYJmv4HuLxJK/t+bnuI3LQ5UlWpIxVFlmdD5LtnyxsIXr+QzMLlCxHTLRMIlQgHzZJKgpeL7PYqlM2NDWxDdMiA/6bQsV65LMrOt7LBTL+L5PSFfRlh6qjutRNi9dvJi2g+24BDUVdYlGFNQ0WpIxDnS1sr9zZUuEznQCY53U+TRJJqqtjkY1b5aYKhXoia9Pwm016M/NMbyK/serwfWLqFIUeckSRBI6upLBq7lV4zYuhBAQjtfmD3qr9jBLQpAJRvjn+57gd97+PicXpmquPpiew7szI/zJ6bf4lc0H6I7euOTihkg95/JTvDPXT0aPku66l4S28m/h+C6jxXn+y7kXWbCKbIw2UqevZItzc1BxHP74jXcomBa/89iDN3s4ABiysmqv38lSlfrbGo6vz6BWgRPzE8yWr51ufjn0RJrJ2SWOZYcYKc6iyypbY600GgmUm8CGa4pFiRgaI/NZzkzOsqmhjqlsnqNjk3xq11bqo1e+zkOaSmMsSkBR+Np7x/j0nm2EdY3ZfBEfSIWCKPKNmbumFwo4nsfGlg+qi3/76nECuso/+dLD6KrMD989y/PvnLnpgS1U9Ve2Jxv4Z3se5X954zssWKWaxdMWzTIvT/Tzhyde51e3HCS2ynXKh+F7PpIsYYQuTqBKkoR0lWJI39gcf/vjozzz+ikqpl27lZHlcLR3nIm5HH2jc3zpiTvWtXK7boGtLCQCyq2Xeb4clhcTIoDka6jaXSjKVgLBL+J5MzjOKJ47guuM4nnzS6rHFXy/TFWGX12i7waqVF4pgaw0o8itSHIbspxGSEkkKbakqry6ia1OTzBemeIHU6+yJ74NhOC9+aN4eCS061cy830b1xnFtt/Dtt7Dsc/ielP4XhZZvrW91z6KkIQgrGtsacrQPz1H/8w8mxvThHRtydLH4cWTfUgIOusS1IVDSELQlU5iu2d55ewgn9y95Yo9tuchSwJZlrBcZ8WQRQiBrirsbG2gd2qO+UIZN+0t05FLls07A6NEDYOGaGRZcMpyXQoVk4nFHJloGFmS8HyfyWyB+WKZnsa65ftuU0Mdg7MLRAyd3W2NKwo/aYq8Yv/vWkCTZeL66lgJs5UiE6XcuoxnNai4NqcXpxkqLK7ZMRUpjulMYrmz6EoG1y9QtM6hSkkEP5ktCjcTQqxvleZGQAiBgkR3NMXf33oXf3TyDY7OTeDVmOiwPY/nRk5THwjzqY5ttEeux0andjxQv4UTi2McXhjimfHD9Bam2J1ooy1UR0QxkIWE6drMmgX6ClMcXhjmXH4S2/e4s24D3ZFbR2PC96vzbcG8NZgiAEFVI6ytbq03VsyuS5V0NfB9n7LrcHhunJnK2mgTXIjR8hzPT7zPUHGamBbC8Vy+N/4un2+9m12JTqJqbYmutYKuKjyyeQOv9g7yx6+8jeN6SJLg3u4O9rY3ETUMjoxM8NyJc4wt5lgolfhvr73HsyfO8sCmTvZ3tLKpvo6/e9ceXjozwFsDozhelTH2UE8nj2/dCKx38amKsmkjIUjHQ9iOy+hMlnOjszy6byN3bGzGclzOjsxytK92Jff1hBCCoKKxM9XIP9n9IP+/Y68wUcrVrFUwb5b4Wv9RWkJRHm3ZROo6lJKDkQCKIjMzOg9U22Teeu4Iju0QSVz+uP3jczz75il++M5ZSpXq/BMPB+hpz9DRmKQuFiIU0KprSdshXzSZnMtzZniakekFShWb6fk83371OKlYkIf2biQZXZ974NbgstxiEEJGiAhIESS/CbBR1By+l8f3c0sBrQ04S3TdqmR+1X+2SjdGBJBEGCFFkaQooF1XZbU73E7JrXAq10tvYXC5t3JbbBOdodZrOqbnZXGdIWz7JI59CtcdxHUn8NxJPC+HkMIoSjeafs81j/s2rg1CVJvvP7l7M3/04lt8/+gZChWLjfUpLMfl2Ogkh4bGeWBzJxsb6lBlmUQwwPaWBjrSCb71/kkc12Nrc4aIoWPaDtmyiaZI3NnddlGAGAnopCMhjo5M8v0jZ+jKJJFEVZwqGQoSCxpEDJ3P7dvOf/rhG3zr/ZPMFUs0J6IUKhbvDY5xcnyaJ3duojEeWT62JATZssl/+fG7PLK1m2hAZ2h2gZfPDKApMvds6Fgewz2b2jk5Ps3RkUkiAZ3tzfWEdY2yZTOZKyCAnsY0HXXrY1puyCp1xuom2ZlKkbHSzfdNPrUwTX9ujuIa+jCmgo+RrbxFYfEEihTF8yvYXpZM+LPI0o2zBHA8l7dm+zm2MModqQ62x5vXVCHy2MIoh+eHSeph7qvfeFXrmJ92nPdpPJhpY7pcrZocX5isef95s8x3hk4S0Qw+3qaRCax/NXRDpJ6PNe3E9GxOZsd4d66fwcIMMTWIJikIIXB9l7JjsWCXmDcL+MBjDdu4O72RtH5l/YufdoQVjbi2uvtmtJhlrrL2VdLVwPE9Ds2OMlrMrgst+s3ZMwgED9XvJK1HsTyHoeI0r86coimQXJfAdnNDmnQkRFi/NNEgCVFlPakKm+rrsBwXRZboqkvSEIugyBKNsQj3b+qgZNk8sqWbgKqgKQqtiRgBTanqd3S1kQqFqq1QnoeuKrQn4+uWdF4Jkqgm4yVJwrQdXj8+iCxJ9LRmyCTCzGWLBHSFXGltLZyuB5IQRLWqyvFYMcs3Bo7V3D7k+j5T5Tx/ce59IprB3fUdq07En8emPR2UcmVe/NpbzE4s8Af/5K+oFE22HOime+fKsYRpObxzcpiX3+9jIV8maKjct6uLg9s6aMnEiIcDBHQVVZGRJIHreli2S6FsMZctcmZ4mjeOD/L+2VGm5/N865XjdDWniAT1dfHIvR3YXgXVYLQq9oRcu9H8WiOlJ9ge68GQdWbNeXygTk+yKdJJUovXdAzfd/H9RRx7ANcZxHEHcJ1+HGcA1xnC93NIciOysgld6UBWOlGUTmSle10/222sDFkIDna1Mjqf5Z2BUV463c97g6N4vk++bLKvo5knd/TQkogiRDUjuyGT4ucO7uL54+d4q3+EY2NT6EsTh6Eq7Ghp4MPskUwkzL6OZs5OzvKdw6dIhgOossyWxgx3drcRCxoYqsJdG9oYml3k5PgU3z9yhoCm4nguJdPmno3tPLVz80VCUfGgQTxgYDsuzx49g+15zOVL+Pjct6mDvR0fCOR0pZN8bMcmXu8d4t2BUU6OTSFJEr5f7evYWJ9at6AWqp6dDcEogtqJtotmmeHCItPlwg1ZoH8Yvu/j+j6vTPQzkJ9fU4Jw3DiI79uU7D48v4IkAkT0dlLBR1CkG2mtIAgrOgOFWZqDCXqitesE1IKArDJr5ik4JhW3k+it4dx0yyOuB3i4eQM5q0zRsRjIz9e8b39ujmeGThJWNT7WupmIur7MrpCic09mE5qs8PrMOc7kJpgoLzBSunjMspCIqgZbYs1sizXzSOM2NoQzNVuzVGybk1MzFEyTqXyRkK4R0lTyFZOy7XB3ZxvJQIBnTp1hW0OGLfXVFob5UpkXz/Wzp6WR5lgUWZIYml/kzPQMM8Uinl9llDyysYuwriMQLJTKvDowxODcApbr0pVKsrOpnmTwxlYBAaKaQZ0RXNXcOVMuMFxYZMEskdBv/Jh936fiODwzdIqZcnFdmisGi1NsijRzT3oLAVnD9T02Rpp4cfoYBWd9Aq7zYoqXQ0BV6Uon6UqvzJbIRMNkrkJJToaC3Nl143+zCxENGXiez8mBKSzb5YVD59jYXEdzXQxdVXCWAqsb2ctfC2QhkTJCPN2xjQWzxA9GzzFZrt0G6OTCJF/vP0ZAUTmYabumJG9TVwbHdvE8j2RjHEkIune0svuBLdS31624z9DkPEd7xxmdzhINGRzY2sYXHt5NT1sGXVOuyEby/To2tNSRioVwPY8j58Y5NzLDsb4Jmupi1CfXPnF4wwLbsmuSt8uUXQvHc2qmL12IsBKgMXBj6Eu3GmQhU2/ULVv91Arfr+B5C3juFK47geMM4NgncOwTuO7Y0lYqvp8DdIzAp9G0vSjqVmS5ASF+cujkHzUIIUiFg3z6jq20JmOcHJtmoVRGliR2tTZyz8YOujPJizxu40GDj+/qIR0JcXxsiqlsAdt1MVSFpniU7kzqEsn1eDDA/s5WLLdaCS6aNkKIZZEnqApBJYIBvnhgB2/0xjg1MUO2XCGsaOxqbeTO7jYqQ1kKko6SjtAYj3B/Tyd14RDxoMFrZ4eYLRRpq4uzpTHNvs4W6mNhPM9jbirHxOAs8nSO7UqUmUyQ6VKJsu1gqArpSIytzfXURT4IqHRF4d6N7QQ1lUTw+qtsuqxQZ4SIqDp526xpdrI8l+H8Aofnxni8pee6x7Ba+FSDhDenh5hchUdeLVDlFKngowTtTVjuDIoUJqhtQBaRG0qpVSSJXck2Xpk+i7EUYPj4lB2LvsIMjufi+T5NwTgpPYztuUyVc8yZBSQhkdSD1BsxFq0i05U87pJ4y8ZoPSFFY0O0nq5Ihpy99h6WH3U0h2I82rKJvG3xjYFjzNZYgfOBo3MTBBWt6pPb0IW2BloAV0LGiPJw/Va6whmOLAzRX5hmwSphew6eX00iBhWdtBFhU6SBfakuklpoVWq/Fcfh0Og4Q/OLyJJEzjQJqAqGojBXLKHIEvd0tvM3h4/zM7t3XBDYlvjakePEDIN0KMR0IcdrA0Ocm5lbqoJVpWbu7mglpGkgYLZY5Mj4JNlymVzF5Mj4JAFVYU+zvqxxcKMQUjWSRoigotWsyl5xHc4sznA2O8vBTNs6j3Dl859enOa1yQHy9voEmQKB53t4y6raPrbnrLt41E8DOhuTnBmZ5sXDvZwenmJiLsdn7ttBOl5dI+SKFRbyJZLRW8/fFqArmuLpju0UbIsXxnvJWrVdgz7w2uQAcd0goursSjVd0Zd7JZTyFSKJEE/+4v2YFRt8iMSDyOrlBZ1ODEwyODmP7/u0pGN89sGd7NzQVNN1LIQgnQhzz44ObMfl1MAUluNy5Nw4uzY0/eQFttWKgsdUZYGh0jRDxWmmK1U/L/caJPR7oi18vvXedRjpRw+uO4PvZXHdcRznBJb5LrZ1BM+bQogwQoogKx3IcjvgYVaeR5IihEK/gqw03ezhrztcz2N0MYeuyFeUuL8VkImGeXJnD0/uvHrwJITAUFXu7+nk/p7Omo4vBNRFgnxqz1Y+tWfrJe+7rodVsXFsh2QkwCf3bOGTey72m3Qdlz//87fhnk0EIwY7WhrY0VKtrvm+z86GDLbloAc01AtkjT3XZ3Jolrd+cIy3XzjJvge28Llfe4hM85Wrs9GAzm89vnYUeUkIgopKZzTJiYWpmq0LxopZXp0Y4L6Grqpi4Q1asHi+T8E2+ebAcfpz82tuneG4OUp2L2VnEMfLIwkD1y8R0ragSvGbagVmey4jpQWeGT1KUNaouDZb4o3sSbRRdCxenj7DollClWV6og2EFZ3Bwhzvzw9Rci1KjsUnW3ezNdaEId84+txHET3xDB9v38pcpcT3hk9RdmvzdT9PA9UkmYZAlC2JzLJf+HohoGhsiTWxJdaE5/uUHJOiU12LaLJCSNEJyNdPc3d8j5/bs5OvHzlB0bK5v6uDsWyO90bGONB2db2K1waGOD09y/62Zj6+pQdFlpgvlYkbBpbr4vs+iiyzNZPmgQ2d5MoV/v7Xvk3f7Dwb03Uk18EW8UqQhURcM2gOxTibnal5vzOL0xyeHWN3qmnVivTXA8fzGC/l+Gr/EWbKBdwaBXBWi5ZgHfNWgffme2kKpDA9m9O5UZoDKULy7YLB9WBrez0ziwW++8ZJBiYXuHdHJwe2tBEPV6/9slV1EdnWubYMn7XE3nQLedska5m8NjVQMx3e8lx+NHqOkKKT1IN0RpKrmjcHT40xdGqMzm0tpJuTRJMhxFWeg8OTC8wuFgnoKu0NCe7oWb3uTiIaZG9PC+lEmMm5HAMTcyzk1yehvG6zyfmgdqI8z3/pf5b35s+Rd679Q0gILM++HdjWiEr5G1Qqz+PYp/G9PAgVgYYkN6HpB9C0O9G0g8hKG2blx5iV56/pPL7v43k+5YpFwNAuMsteDRzHxbZdXNdDliUCgcsvMHzfx3V9LMtG19VrOmfFdvgPL75GRyrBbz24trZUHzWUCxWGzkwyN51l/0NbCYQufSjLiswv/ZOPr7i/7/mM9E4xM7FI19Zm6ls+YF0oqsyOOzew484N2I6HdoXffb0RkFV2ppo4m52tObCdrRR5d2aUM4szbE82ILP+qra+71NyLA7PjfONwaPMrIOi52LldSbyX8bx82hSCscv4rjzNEZ+gUzo4yg30e5nrlLgbHaSjBHh73TeRc4u8zdD71T7vWSFmUqef7r948uBku/7xFJBdifbWLRKvDc/xLuzA3SF08tV4Nu4dmxNZPjFnn2MFhd5b2a0Zt/XkmPz7swof3DiNf6vg08SUas02xsBSQjCqkFYvT6F0ZXQGI0Q0TTCuk5U12lNxMiaJmXLvsju6bwFDHBRe8jA/ALxgM7Btpbl6mtdqEr9PG/10xKPcrCjddnarT5SZStUbBu48X3iCT1ATzy9qsB2uLDAezNjPNi0gU2xKhNtvedOz/dZMEu8NTXENweOrVtQC/BQZgdfG3mNP+n7AYokL9vs/MOeT/7UMg/XCvFIgE/cvZVH922iWDKpi4eXBPqq77dl4nzirq1kEreOovlKuLehE9N1mK4UODE/WbMNUM42+cHoGQxZ4Td33Luqdg7P8Tj5Vi/P/cWrdG1v5eATu9i8v5NIIoSqqygrMD4WCmWKZYtULEhLfeKa+qmFEBi6Sk9bhtnFIgu5MmWztkToarGuabIFO8+/Pf01zuXHKLvXLmwSkHUaA0m6w41rOLqPNsqlr+PYpxEigKrtRdcfQNX2oKrbQBhVgSsUrlfFznZcRscW+E9/8EN+439+lK7Oa+tDPn5ijFdfP0dv3xQd7XX89m8+fvlz2i79/TN841vv8oXPHWDjhltHtfKjiNxCkWNv9aIbKp67eqaF63oce6sP1/Vovor5981EUFHZW9fCtwdPUKa2CdenukD790df4j/c82niWmDdl+YV1+H4/CT/x9vPMl9ZH2XR6eK3SQUfIxl8AEVK4PllCuYJBhb+3yQC99zUwLbkWuTsMs3BBJIQpPQwpmszWcnSEkxQb8Qu+g1M1+aFydMcXRxBIMjaZRoDsZo8ZW/j6pAQbIjV8c/2PMxvv/5tRguLNQe3ebvCG1ND/PujL/NbO+4jeRP6LdcaiiQtB2hCiOXWDx+WF94XXnme51OyrQ9e9at/rhTkq5JM4AIfW7G03826opN6iK2Jer4zdLLmfXzg0Owof3b2Hf6PvY+v2gv3WlCwTX480c/vH39tXYNagIZAnF/uepSnmvYxY2ZRJYWOUIaYGrwpdj8fNUhCENRVAprKh/Mh6XiYVCx0SdvVrQZZCO5u6MDxPf7Vez9gqly7Ovd0ucAPx84S1w1+fVvt7LVd9/fQs6+TuYlFDv/4FM/9xSt89fe+z7aDG3noCwfpueNSpl/FtLEdF01VCBrXngyWhCAU0BECyqaF7awt0+w81i2wzdkl3pk7R29+jIprIhB0hurpibaQ0eNYns1fD7+Mj88j9btoDtTh+C6LdpGh4jSncyO4vse2WDtPNe5ne6z9hsuj/yTD902qqs1FXKcPSyj4mICPqu0EAmtDJ/TBcz1KJQuvxirXSti4oZ5wWOeVVzWmp69soXJeUKhcsXGvIdC6jdpQKlQ4/lYfL37zXXqPjaLoCkff7EVRZJ74ubvYe/9m8tkSJ98d4JXvHmZ6bJ6nf+k+dt2ziWgihGXajPZN8/2/fJ3jb/Xh43Po5dOEwgZb93fxqV+5/6q+aQALM3le+e77nDk8RLloksxEOfDodvY9uLmm/WtFQNHYm24hrgXI22bNHm1lx+bY/CR/eOINfrlnPw3ByLo9UBfNMq9NDvLfzrzNeCm3bosz33fRlSZ0uREhFHw/SFDtxmd9HkSrQUDWiKgBJsvZqpCaXUKTFFJ6GAmJOavAhTFBX2GGrF1mW7yZjlAdb83210yZvY2rQwiBLsl0R+v4nT2P8H8ffoHB/HxN16YP5OwKzwydoi2c4BNtW6gPrm9riOt7mK6D7a1sb/ZhBGS1ZgGpK0KAoaoEVJXpQoGZQgFdVjg9PctkvrA837Qn45yZnuXtkVGeCm9CliQWyxWCmnpRMubS6ubNW8SnjCDbkg2EFJWiU/u9lbXKvDE1xJ+efodf2bwfVbp8n9/1Yrpc4Jmhk3y1/yjT62Dv82EcWxwioYVpC6VpDCSQEOiyStExOZHtw/ZdOoJpWkO3brL3VsYH14mP47pUTAdNlZeUeSWkm3g/1AohBCFFY3+6lX+44z7+9aEfUnbsmm2Axoo5vjN4kuZgnMdbNxFQrj5PKapCKCqjBzSiqTA77+3hnR8c490fHqd1Y8OKga2uKSiKVBXlsq59DeD5PqWKhe9XxU6vleF5NaxfYOuUeH+hj7Jr4QMPZXZyX3o7naF6AorGnJnj66OvYXkOu+Pd7El2IyFhejaLVoH+4gTfGnuLOTPHSGmGbbE2UvqNs5n4SUc48lvY1nvY9glcZxDbOobrjGGZryHLGWSlC0XpQVE34/vXT2X0fZ9XXj3LG2/2US5btLQkuetgN4klX6yp6RyH3h9ifGIBEDTUR7nnro3EYoHqzR3SSddFiMUCKwa2U9M5jh0fpbdvCsNQCRgalnV9Ev2SEAzPL/LV949zdmoW1/PY3dLI/Rs7iAcCZMsVjo9P8ebQCMWKhaEq3Lehg22NGQKqyhsDwxwbn+If3H/n8jH/9shJDFVhT2sTUUNnYHaBZ0+do2RZOK5HUzzKnR2t7GxuwPE85oslnj11jolsHsf1aE3G2N/WwpaGm/+wUzWFtk0N7L5nE/lsmbqGGHc8sBkjqNG6ob66UAtotPc0IskS/+PffodCroy7lIWTFZlUQ4y7ntjB7OQigZDOtv1dpJsTpDKxqy5gfL9agnj+K2+iagoHH92OospMj83zwjfeobEtRUNb6qKe3euBLARJPcjedAuLVnlVgg55y+SZ4ZM4nsvnunbSE0+vWtThSvB8n9OL07wwdo4Xxno5uTBVc1XsWhDRd+J6WUxnAk3J4Lg5ivZpovpeJLH29M2V4PoeOavMG7N9nM5OsmCVqXgO3ZE0XZE6nh2b5G8G36bkWjQG4+yMt1B2LQYKs/y33lfRJJm2UIqYFqTsWgwX58haZYpONdFadC1OTI9zaK7ae6tJCltjTexI3PbtXi2EEAQUlQP1bfz8pr38xdlD9OZma9rX833mzRJf6T1MXDN4sKn7unwaV0LZsTiVHefQwiAjpao1Vq0Clk+33MGD9VuuvmENkIXEIxu7ODE5ze++9DqJYADf9+lOJZGW7ADv7WoH4K2hEQ6NVH04JUnwd/fuJh2+NcVwNEmmMRhlb7qV1ycHa56bXN9nvJjjGwPHKLs2X+zeRUMgsmbWMb7v4+Pz3swYz4+e4ZWJAQbyc+terQV4a+4sObtEWo+R0iMcSG3EkDW+NfYWWatI2bXo0yfYl9rI9tjaCGjNmZOMlwfJOlXFb9dz2BG7k6iWZKBwkllzAse3CSlR2oObOJF7h72JBzmVew9NMkhpGSzPRAjBojVH0c3jeBbNgU6ag52UnAInc+8SU1MUnTwxNUlzoIuYduOp1YuFMqeGpnj71DATczkc1+PJg1vY29NCvmQyPLVAc12MjsZbm/YtLa077m/sYnrLnfzZ2XdZMGtr27Q9l8HCAv/97DukAkH2pJoJqVdv5zLLFpNDs5x+p58z7w/gOT4b97TT2rMyKzYaNAjqKrlihYm5HJ7vr1oTwfd9TNthYHwOx/NIxcIXCZ+uJdYtsC05Jv3FCXx8Mnqce9PbuLNuM2Hl/KJIoEoKtucSVgNk9DjGknCD63tsiDQhkPjGyGu8PnuSpBahTo/drtp+CAWrn0XzGKY7Q0BppC5wD6oUQzceR1G3ojmDH7L26ce2jiBJCWSlBVluWw5sfd/F87NIfnqJqlwbfB/y+Qq27VJXF8F1PUZG54lEDO69eyOe5/P6G+dwHI/6TIxS2WJqOsfrb5zjicd3IMtXvjlc1+PI0WHOnpsik45gGCqDQ7OXVGstd4FF8ygLlSOXPZYkVJrCT+KRxveripT5iklbMsZ8scy3jp6iPRlHyygcGhnn7aFRQppGXSpIxXZ4/lQvkhD01KcZmFvkrYGRiwLbU5MzhHSNTZk6HNfj1f4hFkplOlNxhBCENQ1Zqvb95coVvvb+cTRFpjEawfd9JrN5XjrXTzocpK7GRcxs+XWy5kkcr4QmJ8gEHyCotKzKN9l2c2StEyyaR5GEQZ1xgJDaSUNritKuNk4fHqKlK8Md928mHP2gh0szVBrbUtQ1xPjGH4WQLsjAybJELBlm+4Fu3vrhCaLJENsPdtO+qbaWAt+HYq7Mey+fJpoM0b6xAU1XmR6bZ/jsJGP909Q1xtcssBVCoEoyj7Vs4sjceM2BLYCHz2Qpz/eGT5O3Te5t6GRXqonWcBzlGhdpvu9jey69uTmOz0/wzswoh2ZGGSos1FxNvla4Xp7Z0rMsVt5GlRK4fpGKM4LvO4zl/utycBvRd5EKPrIuYxCAIsmk9DAPN2xBlxUyRpSEFiSk6NxZ143p2Ti+x8ZIPU3BOBXX5p6Mz0Q5iypJxLVglXacbGOqnCOoaHSG69AkhbCiE1cD7K/rxPE90nqY8Dpbz3yUIQlBRNV5vGUTs+Ui3xlyGSos1Lx/b26WbwwcI6Rq3NvQuWa/hena/Hj6NC9MnuRkdpQZM4+9CrG1O5Id+L5PNl/hRN84/SNz2I5LKKCzs6eJ7tY6NFXBUFT2tjbjeR5Rw+Du8z2wgQCbM3UElKpC8oMbOkmHQ8wWS4Q0lXggwO7mRjakk2iKTHsiDl2QDoeYK5bwAV2RCesauixzf3cH5ocofE9t6aEhGia6gn/pecwuFOgbmWVgdA7f93n8ns3EI8E1qZoIIUjqAZ5o7an2WdeojgxVMZyB/Bxf7z9KwTa5M9PO9mQDmWAYeRXPsAvh+T5F26Q3N8fRuXHemh7m8Ow40+XCNTlyXAtGSrOUHZOQYmCWbV6ZPslnWu/i3ble7qrrIaGFGShOcWRhYM0C25JbYMocxfYsGgPtLFqznM6/T0uwi+nKKABhJUbFLXMs+xbDpXNsi+5n2hxb2j+P6zkElTCj5X5aAl3YQmWyMoIQErKQ6c0fY3/qEaJqgpASQZFuvGvoQr7EWyeHef6dM0zO51FkiXNjs2zrbGR7VwPzuRKHzo0xNLV4ywe2UG1fqA+E+WznDiZKOX40eo45s7Y2I9N1ODE/yV+cfY/AFpWtiforVm7Pvj/I0VfPsDiTQ5IkkvUxGtvTtG1uovEyrYRN6RiJSJDhqUWGJufpHZlhY+vqii/5ksnpwSnG53J4rkdbQ4J4ZH30ANbtirQ8h5lKtfLWGa6nLZi+IKitLlo0oVDGxPLsi1SSZSERVYI81rCHk9lh3pw7zeHFPrbEWtkV77rsOa9M0/loYqb8OqOFb1K2R4nqm1GlOHWBu5CkMJK0GVXdjO9XcN0pHPtU9Y/Th+uO4bkTVXEpvwQIfL9MpfQtFHXLUtDbiCSlarD88bFsl56eRvbt7WBqKsdbb/dx5uwk99y1kWyuzFtv97NhQz2ZTBRJEkxPZzl8dJjHHt3O1ZweikWTgYEZVFXmqY/txPN9nvn+UcbGL140WV6WmfJrDOX+6rLHkoRBTN+GLsXw8YkaBnvbmtnemGEqX+C5U+cYy+aIBQyOjk2SLVf40r5dNMeiLJTL/PPvvcDpqZmaMue265KrmMiSYEO6jvZknKCmEtRUXM9nKl/g64dP8PSOLdQlq/0gZ6dnGZxf5M7OtpoD27nyO4zkv4HlzRFSOwmr3QSUamKoVlTcaYZyf81M+VUUEcSLlWmSwgSllQ27bwR836eYL5ObLxII6ZTyFUqFCpqhcuCRbYTjQSRpbe9zWQj2p1vpiaWZKhco2KvTBpipFPje8Cl6s7McrG9nV6qRhmCUlB4koQcIKiqykFacnyzXpeLa5G2TRbPMXKXEeCnL0fkJ3p0eZaS4eEXlxLCqk9CMahWkdGU6/9Wgykksdx7PK2N61eyxIqIIScVyppe3M5T1s+qQhERENThY1wUruJzdW7/xktcMWWV3so3dH3o9Y6zM9olrQbbfrtCuKRqCUT7evoWcbfL94dPMrIL2+c70CAk9QFjVOZBuRVsDtdze/DTPjh/hrdk+hBDUGzESWghdUrikOW8F1BsxFvNljp4Z4+V3e6t9s5JAkiRaGxN4S8sOQ1XY0/xB0u5g+wdzZzxgsCldvYjbEnHaEvErnnNDXYoNdakV37uns/2S1x7fvOGqn8NxPYolk3OD05wamGLf9jaiocBVn7+1Iqzq3FXfTlc0xdnFacxVJA/Oz1lf6TvM6cVpDmba2ZLIUB+IkDQCJLQghqIgrWCV4/s+ludSdmxyVoUFq8xsuchwYYGj8xO8PT3CTLlwxSpyQg8QUXXKjs1MjbZVV4MqybREm7i3bisjpVnemjvDZ1rvpOSYbIo0sz3eTnn8XXrzE2tyvuXzCpW4Xse26H6mKqO8NP23eL6LAFpDG2k02hkuneOFqW8QU5MsWLOElRh5e5FZcwJZKCiSStaaoyu0FVXSWSzPkrPniakpfHw6gj0Elcgyy+BG49zoLK8dH2QhX+aB3d001UX5va+/uvy+IkuUKhZDkwt86dE9N2WMq4UiybSG4/xM925ylskbU4M1J9cd3+PF8V4aghF0WbkiY2x+cpGJgRlidWF69nbSs7eravdzBZuwTa1pmtIxBifmGZla5LuvneCzD+6iOR1DkeUrTqO+X7VfOtI7xnNvnaZi2siSxI7u9bH6gXUMbF3fo7QkGFWvxy9RnxRL/QbCEZRd+5IMqhCCmBpie6yNvsI4I6VZTmaHrxjYmp7FaGmazlAT8gX9o3m7SNYu0hLMXMMn8ZcC5hvVyylW1fuatY5TsPrxsSnZo2TNo9QF7rxoGyEMFKUdRWmHwMfwvHls6yi2dWiJqjyE5y/ie1mKhd9DkptQ1e2o2j5UdRuy3Iqidl9+xEIQjQRoaowTCuqEghqBgMbMXAHP85mbK5DLVzhzdoLZ2arfpqJItDQna1lXkMuV8YF4NEA4bFCp2GzsrufEydGLtpOFRkBuJKptwfddfBx838X2cljeIitJa7QlY2yur0OWJCK6TipUrcxO5gqUbJtUKEhroiqUkwoF6UzFyVVM5kvlDxQtz38PVDPF5xMsqVCQOzta+ZtDx3ild5DRVIKNmRRddUkkBPPFMpO5AkfGJjk7/QFtrzV+44VtLHee2fLreL6J5ZtkzZMk9X0E1dblhZxtORfLd64Ckizh2i6eW/v+AlBVBSOocfcTO7j347sJRQK4rodtOWj62tvrCCFIGkEeat7ASDHLifnJVWf4K67D0fkJTi1Okw6E2ZaoZ1Osju5oHSkjSEBRUYS01Icr8KmqZRZskzmzxHgxx0B+nnPZWU4vTtdUXZKFxOZ4mr3pFmbKRb4xcOwav4EqGiJ/B/yrn1eIm6difRu3LnriGT7etoW8XeH5kbM1+5s6vsdL430EFJWUHqQnnrnufvXXZ87Sl59GETKbog3cld5IdzhDVAvW1IXXFEjQ1zfLK+/2oWsqv/CpAySiQSZnc0RCOrp64ytW14KGuigNdVFikQDDk7VX0muFKsk0BCJ8vH0Lc5UiE6XcqmujJcfmjakh3p8dozkUY2uino2xOrqjKeJ6AE1SkJfnTr+qNOz75KwKs5Uio8Us/bk5zmZnGMjN10SJViWJfelWuqJJzizO8NJ43zV9/g8jroZpCdTRGqqj6JpUXJux8jy27+DjowgJZR0CQ1koKEJFQkKTDCzPxPTKhOQoilCRhYwiFBzfIanVM1zuJaHW4Xg2M+Y4ATmE73vknAUGi6dRJQ1DCpLQ0lWrKUkjrN488UCA08PTzGWLPHFwMz/z0G4A/vR77yy/HwnqREMGx/snb9IIrx176pr5TOd2So7F29PDVGq0AbI9j28OHCeqGYRVjbZwYsW5s3NbC83d9TRvqK9Zo2RTW4bNbRlO9k8ymy3yzOsnkWWZe3Z2Up8IEzQ0VEVGliUkwPOp9j1bDoWyyZnhGV549yyvHBkAoCUTZ8+mZtLx9VGtXj+7H3zcpYWRIWsXBZqwJLevBJgzcxSdMqZnAZdWqJqDdcTUEL2FcSbK81c852Rlnv947iv86x2/TkT6gLJ8MjfAyzPv879u/vnLj9dfkiLEwvdtWAqKqgGtje9Vlj7X+kKSwshy7Sq/stCQhIrru0hCQ5auTtWWpCS68SC68SCeV8R1+jHNV7HMV5ftgczKS5iVHyJEGN14kHjyD694TE1bIWvj+yCq/UHRaIBPfXIPB/Z1Lt1MfvXtGlYWYqkq53lVa6Hq/3uX/BiGXE9r5PPUhx7G8fLYXh7byzFdeomJ4vN4/qXVN1WW0ZWLbwMfkKWqmqXrediuiyJJeL6P7XroirKkfFnd1nZdVEnC9jxKlrVs0RDSNQ52tHCgo4X3hsd49uQ5Xukb5JGebj6+vQdJEhiqwj974gE6U4nlc/i+f8PV/ISQkaUArltBICGLwDIdXdFkgmGD6dEFFmfzuK5HIKijGSqO7VIpW1gVC8dxqRRN8tkyRlBH01VkpVqdjCZCzE1mmZ/OkcxEUXWFYNioWtfkK9VgtWIjS4JCtkQoYmAENRKZKG0bGug9NkpdY4Lubc2YZZuF2RydW5rRNGVdNFMeb+3hxMIUw4WFVVGSL4TtuYwXs4wXs/xg9Ozy66okE1I0AoqCQGB5DgXbwnTdmuX+P4xMIMSjzZt4vHUTPxg9d03HuBCKdGv28t3GTw72pVuqzK1ykTemBmvuayw5Ni+O9aHLCv9o5wMklpSSr/U2P5OfYMEqsiXWzM913MXDDZd6dV8JpuXw8mgf0/N5/tEvP0J9KoIQgvamDyiOvu/juFVBQ8+rpsJURa6KrsgSruuRL5nomoLjuLiejyxJ6JqCrikf2OaZNo7rLXnVSmhq9f3z43A9D9/zcT0f1/UQkiAa0hFC4DgepuVgL1kCSUJg6Aqqqtyw54kmK3yxexdvTw2zaJUprUJI6kJUXIe+3Bx9ubmLXtdlhbBSpWT7S9sVbeu6fLybgzE+3bGd9kiCinv5NqbVIiBrjJfneXeul9HyLBOVBb4x/AZFxyRvl1mwCpQ9u6Y10GpQcav09YKbY96eIqllqNMbyVpz5O1FgnKEopMnrTfSEGjl/YXXaEi0EFUTLFozBOUQdXojab2Zg6lHCSlRJCQUSWW4eO6WkGTKFiqoiszm1vSK358iy6iyTOU6dVhuFh5p3kjOqpA1yxxbmKy59Shvm3y9/xhBReVLG+4gohmX/F71bSvQn66CSFDnwLZ2hqcXeenQOXJFk7949l1+9M4Zdm1oZlNbhvpkmHBQR5ElLNtlMV9mZHqRo33jnB2eIVesIASEAxqffXAnnU0pNHV91MHXLbCVhYQhaxSdCkW3gvOh7L8kBDE1iCQkZio5CnaF+hU0SQxZR5EUKq5N0bm2BabjuzXYDfngFzErr2Dbh7Dt47jOCJ6XBco3TEY/EPgkscR/qHn7dOB+ys4EeescMX079cFHWY2FjxBBFHUrirqZUPiXcN1JLPMNTPNlbOvdKl3Z6V39B1mCLAmamxK4rsfw8BwbutLU1UVxHI9SySQavTrHPpWsZnVm5/JkcyUkITh2fHQF8SgJRQovLcqrv5iPT8WZZqr4Ih6100obYxGihs7ZqVlOT86wtTHDeDbPsbFJPrG9h7ZEjL7ZeRzX4+zULFsbM5yZmmVgboFNS9yukmUxkc3TkUqwv72FxliE//zK2wzNL2IoCo3RCI2xCM+eOMvndm+jKR5lsVSmZNvUR8Koa8URqwGG3EBL+NMM5/8GXa4jE3yQsFZlR6TqY+y5r4f/9m++w7/8e3+CEdL5md94jHue3MXwuUm+8V9eZHxghqEzE8xOZHnp24fY/9BWHvrUXho76pAViXue3Mlf/u6z/PG//CaKInHn4zv4+X/8FJWSxX/9N99mrH+akXNTCCE4+e4A3dua+cz/9BDNXWm+9NtP8Nxfv8lf/u73yc4VUDSF7q3N/IN//QVUTVmXB21Y0Xi6fSsz5QLPDJ9a02PbnrskTlX9+/XOLaok8amO7XystYeEHqQldHOz6bdxG+exL92KvwUmSzl6PxSkXAnzZomXxvqIqAb/ZPdD1zWGBatIxbPpiTawK7H61op8sUK+WCEU0GlrSKy4jWk5nBua4c++9TaTczl8z2fvtlYev2cLPR0Zekdm+Vd/+CyP37OF90+NMD6dpb0pycfu28rDBzfh+zA9l+fL33uPk32TlE2bjqYkT9y7hfv3VWnGL71zjqnZHAu5MkPj84xMLBCPBPg3/+hpEtEgR8+O8dyrpzjRO4Hj+sSjAT7z6E7u29tNKHBjescFEFMNfrFnH4tWmUOzY2t6fNN1MF3nErbUtUKXZP7eloPsS7cgCUHGWLsK0iMNu3hu/BBfGX6VxkCCX+l6lKnKIp9tvZPnpw7z9dE3CCsGdyQuz0K8FlieyWi5n7FyP45n80DmU2SMJg4vvMax7Ju8t/BjomqCe9MfRxEqBecZFEklLCIElDBRNUVjoJ3+wkl+NPUNXN+h3mihJ7J7Tcd5PVAUCQSUrZUTJ2XLpmRaxEI/mZoJAniybQuW5zJ34nVGi9ma950s5fjW4AkCisYvbNq3ZmPataEJ03ZYzJd559Rw9VxzeaYXzvCj985W7cmq/1TvS7/KYPQ8fzkwj4YMPn3/Tp66ewux0PqJUK5bYKtKCnValKJTYaK8cElgqQiZ5mAdp3OjDBYnmbNydHOpqEzRqWB5Np7vXZZWMlmZY7A4wWBxnJxd5Mczh5aFqEpOhYHiOIZ0Ocqch+tOUSl/F7P8DK47ie8X8P0Kvm8BLjfSHa56ztqRCtxJVNuM65soUgBdXl1Dd5XKKQMyvq8gyy0YgafQ9ftxvTlc5xyeW/uCZCVomsLPfuEAb73dz+//5xcAiMUC3HmwmwP7upBlwbPPH+PY8VGGhmfJ5sr83h/8kLbWFI88tIVgUOfOA928/sY5/t3vPks8HqI+E72k3PsBLfWD16tkT+mCu63GMcsyj/Z0o8oyf/jKW1QcB1WSube7gwMdrcQCBlsbMuxpbeTfPPdjQrpKezJBIhigProUiBdKfP3wCU5OTCPLErIQdKaS3LOkepmJhPlnjz/A90+c5V98/wVMx8VQFfa3N/N39+++xm/72mAoGTqjP09z+JMIFHSlDkVUKyVGUGPL3k7+6e//It5SlSCRrvYsNnem+fl//BS25WBbDrIkISkSwbBBJLZUaZEEjW11/Mr//jRWpfogCkWrk5oeUPm533wcy3RwlhIVsiKjGSrxuqrpel1jnE/+0n08+vn9OI6HEKAHNIyQvubZ7vMQQtATz/B0xzZyVoVXJgfW/BxrMatIQvDzm/bxifatNIaiSAgagxE0Sb6uKsZt3Mb1QgiBJslsTzbwj3Y+wP/57nPMmaWaqw/T5QLPjZyhJRTl0x07alL7XHEcS56wYcW4JvHJUsXGcbxl/8ZyxeZf/MH3mJzNc2BHO4/dvRnH9fjbHx3l04/upD4VwXE9nn3lJK8d6icWNrBtl7HpRQolk1/6zJ0ossQLb53ljcP97NzYRCCo8cdffZ07trXysfu2oioSh0+P8YPXT9PamKCtMYHr+rx/apSu1jp+6dMHCRoaluMSiwSQJEFjOsZnHtvF5x7fjQ8Mjs3z599+m52bmm9cYLs0Id9R18LnOndiex7H5te2hxTWZu4MKRq/uf1eHmjsJqEH8fFJB8KoQsJeA8X5lmCKL7TfwyeaqzZGETWw3FbSFW5kxlykTo+yKdJ83ee6EFE1QZ3exMbIDnx8IkocRSjsiB2kJ7IbDw9ZKITkan/jF1p/nYhS1RxpCnShCBVDDnBX3RM4vo2Pjyo0dDkAvs8nmn5hTcd7LWiui3G8f5IXDvWyobmOutgHDCPTcjk7PMPZkRm2d9UmVHmrQQiBISs81NRN2bH5veOvrsqlYSA3zzNDJ6kzQny8bW0U3SVJYteGJkKfuZvOxiTPvHGSUsWqske8K9+RuqqwqS3N0/dt475d3USC+rrqIK1bYGtIGi3BOoZLMwwVp8jZJTzfW242VyWFDeEmXpaO0V+cpLcwzqZIM3Ht4oxZb36cOTOHKino0spKXzISru8yb+UwPZv+whjqBUptKT1GT/hSwQXfd3Ds45RLX8M0X8J1RoCfLH9DVQqjSmuTZaxeaCpCxEGKI/kNKEobvnd5MQVFkWioj/Frv/ogDfXVSlEsFuTAvk62b2tGCIEQsHlzI7FYgFy+gu/5aJpCfX10Wfxn29ZmmprimBUH1/UIhw3CYR1dV5EkwYbuDNGIwfxiCV1TiMUC7N7VRnPTyhn0q0FXFH7pzjsIaR8slgKqwj988C6aYlFiAYNYwOCJLRvY1pjBcqp05LZknHQ4hCxJtCdjfG73Ng4uqV/GAlVqbTRgkAmH8PH5+LYe7uz4oE81Ew7RGKtS2QxVYXdLI1HDIFeuYHseqiyRCYeuWUn3WiEJFV1Jo3NpYkSSJAIhnZauS3vU9YBGfcuVVQeFEKi6suJ2kiSRvspvqKgyyUwUMjfW7iugqFU6petiey5vTg/f0PNfDYas8PmunTzdvo3OSBJVkvF9n5Cq0RCMMFrMrrt68m3cxpUghCCi6ezPtPIb2+/hD0++wVQpX1Ng4vgeY8Usf372EPWBCAcybUS11Wf5Y1oQbcmBwfKcVdtwKYqEkAS2W02qaarMZx/bzfd+fALbdljMl8jmK7xzbIiyaRMOaniez+DYHO1NKeYWiwgh8DyfO7a2sLE9jarInOybZH6xyOxikaBpc/TMGPPZIocTISRJYmY+T8V0GJ/O0lofByASMmhvSrKpI4OmKriehyJX2z0qps2Z/ikmZqvCcfPZEiMTC1i2g+/7N1RQM6RqPNy8Actz8HyfEwu3Tq+jABqCET7ftYsn2zZTHwyjSFL12a3q1AVCTJTy130eTVKo06OwQk5ha7QV02tAlxRCytpWrmShEFTCJLSLn+VBJUKQS8V66vSG5f835A8SPxE1vuLxNfnG2L1dCTu7GxmZXuTHh/v4N3/5Aq2ZOAv5Em+fHKJvbJbZbJFIUOexfZtu9lCvGZIQ1BlhHmzqZtEs80en3qxZzd3yXE4tTPPVviM0BiLsSDVet/2gEBA0NDa1pYkEdfb0tHCkd4z+sTnGZrIs5suULRvP81EViaCh05AM096YZEt7PVs66uloTJKKrX+b07oFtmHFoCfayhuzp1i0i4yUZtkUaSGxFLhqksKOWDshxWCiPM9rMyeRhcz2WDsxNUTFtRgoTvLO/BnmzDxRNUjaWJliF1GDdIaasDybM/khdic2LVdoZSERU8M0Bi7llTv2Scqlb1ApP4PnTa3XV7EKCFZDI15vCKEgRAKkywcekiQRDhvcsfuDxIFhqDR9KFgJBXU2dF++d7i1JUnrFQKkaqBrcBFp5zoEexVJYlfzxdk8RZbZ336xQmprIk7rZRQsg5pGdzpFd3pl9UqAHc0Nl31PEoKQprGjqfae6tu4sUjoQe6sbwN8AorKG1NDNYs5rBcUIdEQjPBk2xaebt9KVzS1LO8vhECXFDoiCSZKebwaBKBu4zbWE7KQiGsBPta6hfFSjm8PnmSiRtVu23M5l53lL84eIqBo7E41rbpy2x2p53RugslKltHiPD2x1VVxwgGdgK5SLJvkCxUiYYODOzs42TuBaTs4jkfFtEHAHVtalgPILV0NZFIR6hLh5eC2rTGJoVWTtYamIMsSFdNGkgQl02JDe5rGdBSBYHNnPaGARnMmvpwAjoUDJKJBDL16v0tLi9WK5fDa+wPkCmWa6+PomkIooC33694M1AcjPNy8EVlIfHdI5d2Z0RtmtXM5aJLMxlgdT7Vt4am2LTQFo8ueuWLJrqoxGF2TwPZKCKsGYdY+QIypKaSgfEsEn+uJplSUB3Z3gw9H+yd478wIiiwxvVjA8Xw6GhLctbWdDS2r7ye9laBIEs2hGE+1b2GinOfZ4dM1C/EVHYvDs2P8j7Pv8hvb76U9HF8TlXlDU+loTNKSidPVlGJyPsd8rkSxbGHZLp5fTbbpqko8EiCTCNNUFyUZDa25i8XlsK6B7bZoGwFZp+yajJZmWbDyy4GtImRaQ2k2RprJ2kVO5UbI2SXO5sdIqCHKrsXZ/BjDpWkqnkVPoOWylA1D1mkKpDFkjXkrz8HkNnT5yg8/z53DNF+iUnnupga1QgSRpDokOYMkZdC0gzdtLLdxG7dxKVJGiAeauoloBgk9yLszI0yW8jeF6hvTDDbF0tzX2MlnO3dSHwgvL8zOQ5MVOiJJ3p4e+Qnjn9zGRxWyJJEOhPhc504WzQovjJ2r2VbFx+e1qQEywTABWWF7smFVC7SDqW5OZscYKszy+uxZ4lqQ+kDtfehBQyWdCCMJwdvHh7hnTxeqKuN6VQFERZEJBjSSsSA7e5rpbEmhKjLlig34yLLE3GL1s6qqfNHiTlAVVwwGVCIhg56ODHfu6iAaDmDZDpbtEjTU5WBZlqUVF4flisWxs2Ns7Mjw8MGNIARvHR286UI/reE4T7T2ENMMYnqA92dHWTDLNQuJrSXSRohtyQYead7I4y2bSAcuZbqFVZ3mUGzNe4NvFKJqgqh6bSy2nyRoqsLmtgx1sRCb2tKMTC1QNm0URSaTCNPTlqG7KfUTo1h+JeiyQmckyS9s2stcpci7M6M12xDmbJMfjZ6jKRTji127aA3HUK6zcgtLLDxFpqs5RVfzB4Ud36/OiTcqgL0c1o+KLGt0hRvYHmun5FkktPBFnldCCDSh8mj9bubMHGfyowyVphkqTV9yrKQWYUesg63RK3smRpUwj9bvR/0QZdnxXFzfvSjYte0jWObreO7IZY4mI0QQIUUQGCCqCqYArjeN752XyxeAhiw3gjj/dXqwZDeDX8Hz8lQpzh+ezBVUdS+avh9F3Yai9CArKwfvjlei4k5RcS79fpZHLAIE1RY0KVEz7cj1TEx3lpIzikAQUtvR5CQCBdcvYbmL2F4W1zfxfQeBQAgVWQqgSTFUOYF8DZYfvu/j42C5izheHscv4fnWkhK1D0JCQkUWOrIURJUiyFLoms51vfB8B9vLLtkqeUhCJai0YChXt49yfZOceRrPN/HxMZR6AnI9snRl0ayqTVEey53H8Qp4WNUJQyjIUhBNii/9TjKs4O13+eN6WN4CJXsY9wr93AGlEV1Oo1xlnB8c18fx8hSdIRyvhCYnCChNKKIq5GW581jeIq5XWvosPkLISEJDlcJoch2KCKzK6gqoKl+7i9hefvka9Wu05gqpbehyCqmGayqs6tzb0EFXNMk3+o/xxtQQA/l5FsxyzfSg60FI0agPhtmZbOKxlo080NRNUFl53Los0xlJrouVxG3cxvVgQ6yOz3buoOhY/Hi8j3yNCzTX9/ne8GlimkFY1dkQS9Xsobkl1sQdiQ6eGTvMDyZOIAuJPYkOgoqOKslXVQyOqUG6WusYHJ/jpXfOIUkSoYDK7EKhakEX1IiGDbpa6/jRm2fYt60NTVMolEzq4mHar9JqIUsSyViIO7a2cqp/spoESIapWFWRpF09zahXUQ8VVKu5puXQOzxLxbQ51TdVdStYWrfMLhSYz5YYGp+nXLHpG5nFtByaMjGiIQPlCj6W14N0IMzjrT1sjKf56973OTw3zmhhkZxl1mTHc72IqjqNoSgH0m080drDHenmS+wnzyOiVQPb27j1ocgSDckIDevkhXorQZcVtiUa+Lsb91J2bI7OjVOukTlWcm3+8twh6owQH2vtoTEYXTeV9POthzcb6xbYVmkdQX6+8xGSWoQ6Pbos6HQh7k1vY6qyiOO7DBdnsP1qT4agalhsyBoPZHbwQGbHZanI56FIMrEV+k3zTpE5K8eGcJVm6vsOlvkGjn16pZEjRAhJrkdRNlUDTrkVIYUBtarEVvwrzMpzLBnDIMv1hMK/hiTFl4/v+0U8fxHXGcexjy+JUmXx/QrnPXGFMDBCn0HXH0SS0ldc1JecUYZzf81w/m8uu01I7aIn8Q+pDz5MreYIljfPWOE7nFv8T4DE1uQ/pSH0KLLQyZonmSm/yrx5iLIzjuPlEchoUoKQ1kHS2E86cB9htR1JGDUHV57v4HhFKu4Us+XXyZonKNj9mM4sjl/E920kyUCT4hhKAxG1i5i+g4S+m5DaeUN7hQBcr8Rc+R2Ozv4Onm+iy2k2xv8BbdHPX3Vfy53n6OzvULJH8XFoj/wc7dEvEdY6V9y+GvDbVJwZ5ivvMVP6MTn7DKYzg4+HKscJqx2kjDupDz1CQK5HCHkpwL06PN9irvIup+f+P1Tcy/c9dUZ/idbI5y47zhVGTt46x4n5f03eOksm+BBdsV8mpm3DcueYKr3EbOVNCnYfljuH51soIoguZ4jpW6kPPUJM24omJ5HEyouOi78jD8fLsVB5v3oNWScoOePYXr5mAbbNyf+VptCTNSUoACQh0RKK85s77uOhpg18f+Q0r0z0M17KUXEcLM9Zs0qEoLrg1SWFoKKyPdnAJ9q3ck9DJ5kVKg0XQpMUuqLJSyq5t3EbtwL2Z1rJWmVyVoXXJwdrDm5KjrWk9qnyi5v2kdSDNT0LRkvzNARitIaSvD7Ty++fnSJjRNkUaSCmBtHkD5LWK+Hhhq3sae1AkgTf/OER/stXXwMgFQ+xoT1NMhYiHg3yxY/dwdeff5//P3v/HSZXep33or+d964cOuduNHJOk/NwhhwOyWEWKVHZkqwr20fBtiz7Hvs5wbrX51qWbcnXh7ZsyRJlBYo5c8jJEYMwwCB3A51zV3flqh2/80c1Gmh0A91IAwzJlw+IQdUO3961w3q/tdb7/rcvvY7jesSjFu+7dxMdzUk0TaExHUW5JJNhmRqxiIWmKZi6xi99/B6+9sNjfOOFd8iXbCxDZdv6VrZvbFlcPho2Fu1/LkUiFuLeXV288FY/n/+bV2isi7Kxu4lN3U01SzTg6JkxXj54jqlMHtf1+foP3yEaMfnwI9vYsamV2C0itlALzDcmGvhX+97PG1NDfGPwBIdmRpmuFrF9D8f3b1qZskTNUu2CNdCe+jY+0bOD3ekW4sbVJ2ovZGx/gjsHjuuTLVaua11FkUjHfjSs62RJ4rHWXqYrBcqew8n5afw1PjuLrs2fnjlAVNN5f/smYtqtFW+63bileXpNUtgW77oqxZKQ+Hj7/WyNd/JG5jQnckPk3RKarNIZauSB+q1siXcQ167/4hwojfN65h16ez8FQOBP47mnCYKVsp8mVugzWKFPoWobqfW8SlxKFB37NWqnzq0dgWRhmO9DVlbq3RFAgOscXhCpeo5ggVAIUaSQ/ReI2D/FtD5Sy/reZlT8car+DNPlFxgrfp2KN0HtGMQly1SoVCbIVN5ivPhtNiT/XzSEHkZibdnUijfKWPEbDBf+DtfPXpJhu7gPPyhRCUpUvHHmq0dQS9+lM/oZNiT/AayRxL03IcjbZziX+69Ml19cODeXnBevQtWbJFM5yFjpm2xO/jYIgSzdWbL2bpCn4o0DEqfn/n/knTMEwuHSY3GEgxPkKLj9jJe+R0f0U7RHP05U711l6wG2P83puT9gtvIablDk8mt0LZCRrxrQXg1bU01sTjbycxv2cnBmlB+M9XFwepjpSummZCFMRaMrmuS+pi6e7tjCuliakKYhr2G8hqLSFU2j/CRj+xPcoXikZR1O4DNdKXI6e+UqpMsxWy3xzaFThBSdX91yz5ru3t8//nVO5MZqHuEIEDBWnme8nF3T+m2hJPtS3axrr+O3fv6xxZ5ViZri+4Xsx4auBn737z255HtZrlXTbOxu5Av/18+jKhfvycfu3sCjd21YLNurT0b4pY/fuzBxt7D9BdFBgIf39fLQ3nVXDEgfuWs9D+1fvygUJQGf/sDuxX0+etd6Hl74/sL4oKaT8W7GuHc3dLC/vp3h4jxvTA3xwvg5jsyOkbHLN2X7Uc1kQ6Keh5q7eap9M22ROJqsrOm3jv6E2N5xODsyze/9l29f17otdXE+/49XT0C8l/CJnh3kXZucU2W4mF3zeuOlPF84exhT0fhQ55bb3qZwK3FLie2Fh+tqyyhIrIs002KleLplP74IkJDQZJWQYmAo2hVT53N2blV/28lKhoJ78aHpescJgpUsbDSi8d/DMB9HUVqQJI2VMp8SGpKkIkStvLiWhZWukHGtZXU1fSeK2oVu30Ol9Nc4zmu1b0WVUvG/APICuV1ZSCikttEV+xz1oYfwguLin5nKK+TsU/hibf1KV4eg5Axyzv0TCu5Zqt4UqmQR0tox1SZkVKr+LCV3EDfIInApe0Ocnf9jDKWemL4JRb66aEGmcoCRwpeYqbyKG+S5kL2WJR1dTqEpcSQkXD+H7c8R4AABEa2HqL6eO0lc61ZgrnqQocJfMVt5A0GtxFWTYoS1DnSlHoGP7U9TdM5TdgY5M/9HqHK4Vva+BsiSTp25n531v48TZBeuowIld5ip8nPY/sxNOQ7Hn2eq/Dyun1sktZbagqW2oMlRfGFT8cYpuQOAIBAVxorfQJFMOmM/haleWVCr7I3Qn/0TZsqv4IkSsqSRMLaTMHZjLthdVf1JpssvUHbHFq4hiGg9tEc/tVBGXo+ltqLK117GtBhwXqJauK++nYrnMlLMMliYY6g4z3g5z0ylSNaukHOqVHwXN/DxhUCRJHRFxVRUQopOgxWhKRSlJRSjO5amN56mwYoQUnUimoG+hpLJC1AkiaZQlL994mdXndG1FI3EKlmM68HjrevZV99O1V97l29rKH5HkXFNlvnNHQ/yi5v2r2n5WiuHTtq4tRmCrmiS39h6Hz+zfvea10kb4etSFF4LxsrzvDbTz6HMACXP5ve2fYiW0Crlt5LMQ809bEs1Ya+xpK6/MI0ha7SE4jSY0TUHZmHVIK5d/zVuKBd7XFXlynuVAPkK30uAfFlGVF6hokK52valC1taGbIsL3s7XrpHSZJu69tz0Yxv4dnZHkmQNsM82tJL0bUZKs4zUJhjpJhlopxntloia1fIuzZV38MNfAIhUCUZQ1ExFY2oplNvRWgOxWgJxeiN19EdS5E2QoQ0nahqoMrymrNTmqywv76d7z39K6suG1Z1Usa120ethp9at4un2jetucVFkWTqzfCaJj0Bjp8Y5bvffQchBL/1mx+oecLeZGiywr+77yNrfv4rkkxEMzBW6J0PmTqbu5bGA6osM5MrcX48g6YqdDelCJkaVddjaq5ArlSlJR3j8b1LJ8m3p5r5X/c+wW/teGjNx9JkxQipV68iezehSjKf6tnJk20brrkNypBVkoZ1w6RWCIHnB8xkixTLNo7nE6xi+bMS2hoSpGI3/x66YzqrDUXDuELfw9Xww+mDvJPrx7iiTy3MObklVkG+N0ggLlNllExM8ykM42EUpW2B1F4BksrFUycQosyVs0W1S0iSLBTFxDAeBXRAwnFeBQSBP0ml/EUkKYoV+iiStDwAUSRzgWA2EggPgUsgPKr+DCV3CN+/OcQ2ax9F4COh0RB6hMbQo4S0dhTJQkLCFzZld5jJ8g+YqbxKIKqUvVHGit/ETDRgyS1X3HrOPsV46TsLpDaLhIKldtAYepSYsWVJv2MgXHxRpewOkXfOEtXXE9M3/0iXT5TdUWYqr5KpHMAXZUCmPvQgzaEnCWsdyJIJCHxRpuyNMVb4GnnnLEJ4BGuWCZLQ5DgJYzsBXm1d4ZJ3TjFvv33TiG3Vm8D15xEEmEo9bdFPENM3oSsJZDQEPo6fJeecYDD/lzj+PG4wz1z1LaL6eloiT624XdfPkbWPM1N+EU8UkdDoiH6GxtAjWGrLYuY6EFXqrPsZyP0589VDeKIESKhymLR1F4pkUOujv7HrSZVlIrJBRKvttzEUYXOygZLnUPFcqr6H43u4QYAvgsWskUQtuFMkGUWWMRUVS9WwFI2IphPVjOtWMJQkCU1S6IldWa37ViOmm7eMSL1bkCSJBitKg3Vre7gCIZivVvj3h15lvFTg4+u38lT3hitOZBiKSr0VWVH85nYgqYfZnepAlWS+MPDamoItSZKIaBfvm7XgrblztFhxmkJREvrag6Ff3/A4RW9tvbwroTP83lJWFcLDdY5RKf8lscT/tapuQbn0N8hKHbq+B/kqDgg3G5qsENcV4rpJIATN4RjbU82Lz07b93ACf3EysGZddvHZqcoyqqRgqiqWomGpGlHNIKzpi9YmQSAYG5vn0KFBhoZmyeUrBIEgHDaor4+ybVsbmzY1Ew7VrkNZqk1OrY8vt727GRBCEASCc+emSSZD1Ncvt7BLGaFbQpgvoFpxmZ7OLRCRmy/idSGh1R5J3JTtNaWi/PIH71ryWf94hjdPDpMImzzz4DYSEQtVkfH9gFLV4fDZUc6Ozi4Tj7JUbdFB4L0KSZJIGhbJWzAhvRqKZZv+sVlePz7I+dFZihUH16+R2uvpwvrFp+/iod3rbvo47xhie73IOQUiqsWOxPorLjNYHGfanl/8t++NLvNmlaQwZuiTyIuZ2itDQlnI2AIES/pmrw4JWUljGPcDNp4/SOBPAAGeexbbfh5V7UE37lq+piQjIS/rP9TkyJr7K9cCJ5hHRqMp/CBt0Y8SN7ahXZbViug9yJKGFxTJVN8kEA4zlVdpj34cU2lCWiHrUlvmJTKVN3CDLIpkEtHX0xn9NAljJ5bajCybi6WhYuGB6/hzpLy9qHIUU1lbL+R7FVn7GHPVQ7hBDlkyiGrr6Yz+FElzN6ocuXhuREBM34wup+jPfp68c4pArC1wqxE5BUWyllw1tj+zJhGltcIXFXxhE1Jb6Y7/Ag2hhzCUuiXXbyBcwlontp9hovhtnGCeojtA3jlFs3hyxYCs6s+QtY/hLIi3xY2tNIQeImnuWjZ+U22h4o1j+7PknZO4QZ6C00d79OM37TgvR0jVCan6Cm7AP8GPEwqOzUylhCLJdMYSV11WCEHVc3l9YoT+bIY9DS21ctH3yCReSNVpC6WoeO6KWaOsU+bo/DDnizPISOiywo5kB+tjjeiyihN4DBVnOTI/TMV3CISgK1zHrlQHMdUi55Z5c/Y8L02fIamH6C9O02zGWR9rYkPsynZqF7AtcQO+cO9JBATBNI79MmshLqrahSRHYY2tRFeDEIJS2eHYyVGGRjLkixVAIh416WyvY/f2dgxdXTah6FZdTr98mu5t7XTUxVFX6CO+Vriuz8REli/+3QGmpnKYpoahawhJMD9fYnIiSyxq0tNdv0hs3w3MzBR4+ZUz7NzRsSKx/QmWImTqbOxYGvudHp4GBPdt7+LerZ3I0kUBzSAQGLpKseLwzvlJnnlg220Y9UVMlvOcyc3gBj7va12br+5wcZ535iY4X8iQ0C2eat9MnXl7e4Wn5wq8eXKI5w/1c3ZkhtlsEf86srSXYq5wc9oPLsd7nth2hpvZovZwX92OKy5zeP40r2feWfx3EEwjxKXN6CqK3ICu70KS1jALsixjW631Qq4xGJGVFLpxH4b5OJXy34KoAg6ecwRHfRlN34V0G9R/LyCs99AYfpSkuWchs7UUmhwlae6m5A2TqR4ABFVvvFY6LNwV1ym5Q8xXjyz2XZpqE+3Rj9Mc/iCypC970V0gcYaSxlBuX+bp3YIXVMjZJym554HaOW6JPEXK3IcqL529lSQZhRD11v3k7OPY/iwVb/R2DPuq0OU4KXMfrZGnUaTQsgkPWdIw1SbaIh8mU3kDJ5jHDXJUvUncoIiuLO91cvw5Ss7A4r+Txo7apMgK94ssKSSMHUyrLy0Q2wIFp//mH+gdjonhWUbOTZObKxEKG3RvbqGpLYW80Hs33D9FKV+ho7eRcOzmzQIX8xUmhjOMnZ+mXKiy75HNpBpiqKsovP4o4Fx2jjcmhkmZ1qrE9kcdvggo+w6e8JGRGCpkkCWZuG7RFkpR9hyenThBNXAJKQaSBK7wFzJ0tdeqLwIqvk040PGDAE/cPn/WHzXoxs2xGBRCUKm6/PDlU7z8Rj/nh2bIFWpxViJmsa6rgWKpyj17e4hGllZzuLbLsRdP0n9kgMbOelrWNdLU1UAsHUW6TuuQSsXh8OFBXnutj4ce3MjOnR0kk2ECIcjnK8xM52lvTy+Ka71bOHduilOnJuhov71xzfXqS9wJmMgUKFYc2hoSy0QSZVkiGbEIWzrHzo3fphFeRMYuc3h2lIrvrpnYCiHwRcDp7DRl3+Hexq7bSmyrjsuhMyN87eXjHOsfX8zOShLoqoqqrr3c/1Jot0iw7pbd0eKymcJbdRNtjnUvlp1cCUk9xobIRaugICguUU6VJAtF7azZ+6ypx0u9xNoHWLD1EQRrzJ5KyHIKy/oYduV7C9k2ge+P4brH8P1JVPXq1ka3EmnzbqLa+hUJ6gUYSh0htQNZMghEdaG0NIMvqigsX2+ueoSKN4HAR5UixPTNtFyB1P44wvamKHujC33HMrqSojH0viv+BrVzJlFn3Uum+tYdSWxNtZmG0EOoKyiVX4CEQlTfiKYkkFwFgY8XlHH8+RWJrS9s3CC3+G9DrUdZoXR/8XslhSrXXgiBcHCD7PUf0HsQruNx6KUznHhrACEEqfoY8VSEhtbkYr/dzHiW2cksDS3Jm0psq2WH8cEZ3nr+FG/+8ASNbUmiCevHgti+MzPJ9wf72VbXwKc33u7R3F5Yis66SAPtoRSKJPPy9FmmqjlGy/O0hVK4gc9gaZY9qS62J9posuKE1Qt95TJpM8KH2nZxcG6AzbEWnmjeSspYexm2G/jL4pFrgSLJd1TvN4AQDkEwj++PgHARCGQ5hqK0Ikm1550gwPdHCII5EA6SFEVWmlCUOoTwEcE8vj9GIAooSvOCroi1MGHg4LonkKUYQlQQoowkhZCVFpQrTDQ7rs/QaIa//spbTM7k8f2LVWwzmSJz2TIzmQIdrSnWddUvsReSFZn6tjSn3jzL8KkxGjrqaN/YQlN3A6mmBMmmBLqpX5M/pm27nD07hSzLPProFrZta12xr/lS+H5ApeIwNZWjUKji+wGarpJKhkmlIoRCFydQh4czeJ5PKKTjeQGZuRKe66PrCg0NMZLJ8CJpLpcdJiay5AsV3nzzPLOzBc4PzPDWW7WJbFWtrdPUFEdZmHD0/YBiscr0dJ5i0cYPAgxDoy4dIZkMY5ra4jJDQxkaG2PkchUqVYdkIkwyGWJqKk8+XyEatWhpSSyOX0JCIJibK5HJFKlUHGRZIhazqKuLErvsPeB5fm0yYKZAqWwTBALT1Kivj5GIhzCMi/FwEAT09U1hhXRiUZNKxSWTKWLbHooikUpFaGyML1nnelCuOsxkiziej6ZcJFaeH5ArVZnNld4zVS+XozOaojOawvY9Xpg4d7uHw9hMjlffGeSdcxMAmLpKa32CdDxEPGxi6Op1ede21N2aioVbR2yFwA5ccm4JSzawVB1VUtcsgCKEoBo4lD0HX/hYik5YNZd517WFLpYoBCLADZYLUbSYdbRalxQHigpwsQ9IknRkpZG1WuRIko7E0pLgICig4LFWxV5JslC17ShqB4GTA6rUyO0krnPkthLbmLEJ4yriPQCSpKDKFpocxfZr4l2+qFxRxKjgnMVZIBWm2kjS2LVIOH4CKHmji6RLkQxMpZGQtrKn8aWIaOvQlTvTkN1Q0sSN7VddptaPo6HJMWRJxxcVAlwCcTV5/0vvU5mr37fSZf/93nzRXS/y8yVOHhqkpauOT//9x9BNjUXJ1QXsfejWMK+6pjgPf2g367e1cfLwANKPif2QHwQM5OeZLBXYVvej3T6xGgIRMGcXeXn6DOcK05iKzkQlS9qIYC8Iy4RVnfe3bOcbo0cYLmfYm+pkU6yFBjPGdSbrlmCoNEvFW5sF2EpothLUmXeWV2YQZLCrz1OtfBkhPMBH1TZjWZ9E03cBEkLYVCvfwnUOEfgzyEoTpvVhTOtpEC6ud4pK+e9w7dcwQx/DCn1uIe7wCYJ58tl/ga7fhQhy+P4wklyHab0f03qGlfQJiqUqBw4PMjtXXEJqL8D3A84NznBucIamhjjxS8hTKGrx4V9/kqd++TH63x7g0A/e4Qd/+TKVQpWt929k/wd20dLTSKwuim5qqxJUqIlphSM1IlcqVbFtD8PQrhiACyEoFKocPz7K97//DsMjc9i2SyRismNHOw8+sJEtW1oWyerXv36YubkS7e0pbNvjyNtD5HIVQiGdhx/axKOPbqa1NYmiyMzOFvjmN49w8tQ44+NZbNvl298+ynM/PAlANGby/ie38/TTOwmFDIKgVi595MgQzz9/ipHROVzXJx632L+vm/vv30BvbwOuG3D6zCT//t9/lw89vZszZycYGppl+7Y27rq7hxeeP83pM7Xs8Gc/ew9bty7EFBLYjserr/Xx+uv9TExkAejtbeSRhzdx7729aFrtNw4CwexsgTcPnOfVV/uYmMjiuj6pZIh771vP/fetp6MjvThR4boB//VPXqCjPc2mTc1MTuZ46+AAmUwRXVe5775ePvrRvTTcQBl2UyqK5we8cOQcHQ1J6uJhFFlCCMiXq7x5apgzw9PLSphvN8SCxsZkuUDCsBa96G3fI+dUiGomhnJ1nmT7HiXPQZNkogs6FkIIck4tDg+p2nXrc1wJR/vGOT+WIQgEIUNjU1cjn31iD3s2thGxjOsitbcSt4zYVnyHd7KD/IezX+WBuq081riT7kjTil62K0EABzN9fG/yEFPVLA/Vb+Mjbfdc1fan6jucKQyt+F1YMdkQ61zYdgWWEDAFWQqzdmJrLitZFiKPEC7SNdiuSJKMpu3A984TBLWLUgRZfK9vzdu42ZDQMZUGVGktpFNCuuQSEsJHXEGFtewO4QUFAHQlSUS7+Q3j72XY/hReUARAkUJY6uqkFkCVIzVSiL6o/ntnQLmmvmgZlYuK14KAlQVoFNlCu4TIV70J/KuQYNufueS86j8WZe2XIj9fQlFkoolQjdTCjxu3f9eRqZYZL+YXA40fZ5Q8m+O5UV6YOs0f7/9ZIprJ98ePM1C8KFJnKTqPNW5mf7qb16b7+NrIEQ5Y5/ls1710RW5cuOl/O/ZlTuTGrnv93978FJ/rvv+Gx3Ez4XnncZ0jWKFPYVofpyZCOYckX4hLRC2TKxziiT8AJIr5f4tjv4xu3IUsN6HrD6DrD5DP/pPFLO+lCPxJJEkjFPuHyHKCSvmrlIr/Hd24F1lu5PJJfNv2GByexQ+urjcyMjZHsVRdQmwvQNEUNu7vpWtbB9vv38Szf/Ei3/6TH/K1P/4u2+7fyCd/58NsvW8jVmR1YbpQSGff3m6effYEX/nqIUBi164OLGvlGNRxPN566zx/+mcvEwrpfPxje4nHQ5w6Nc6bB84zOjrHL/zCg2zedFEg8/zADGf7JtmypZVPffIugiDgpZfO8Ld/+yaqpvDE+7bS0BCjvj7KM8/s5amndvKlL7/F6dMTPPG+rdx9dy0OUhSZRCKMYWiLY3n++VN85auHqKuL8DM/fS/hsMGRI0M89/wppqYLfOan7qa5OYEQgny+ypG3B7lrfw+mqfH6G/30n5tm584Otmxp5W+/+CZHjw7T1BRf3P7ISIavffUQT31wJw0NMfr7pnjjjXP89d+8QV1dlC1basdZrbp8/etv8+JLp2lvS/K5n7mPUEjn9df7+dpXD5PLVXjmI7tpv6y0emBwhhMnx2hvT/HUB3YQi1nMzBSIxiwM/cYEnO7a3M7oTJb/+YMjvPrOAJ1NKWIhg6rjMZHJky9V2d7TxCcevvrE+rsNARQ9h59/8a/43Z2PLZYmn5yf5N8ff5lf2XQ3e+varypwdXJ+kq8MHWd9rI6fXb8PAFcE/MmZN5CAp9o3syW5uvbAtWBoco7ZbC2O6m5J8TuffYT17XeuksgtI7Ylr8rJ/BDT1RzfmjhAVAuR0CM0W6k1rS8BTVaKjFPgfHGCtBFja6GTPakre1zOO3n+buSHi/92A4+cV0KXNe5Jb1sktrUHssRFYYVgoTR5beVKkmQtI7aBPwviWomFjKJ2wiWllIEo4vvX/xK+UWhKpCbwc5ODX1cUCURthl6RLHT1zswy3i74QXnx/MiStkyw60qQJAlFMpFlgyC4c4itIhm13uCbXApkKo0kjG3MVl4BYKr8Amnr3gVF5OUvg6nyixSc2kSRriRJGDtv6njuRARBQDFX4c//4DuMnJtibGCG00cGOfDcSZo70vzsbz1FLBWm7/gIL3/rKKcODdCxvomP/fLDtK+rTURUyzZ//cc/wHE8DFPj9JFBhIB9j2zmyU/dRSwZ5vzJMV77/nGOHziH5waomszjH9/Hvkc2k6xbe4brgm/nicwUb02O8c7MJMOFHHm7ChLEdZN1iRSPtvewt6mVemtpED5bKfFv33qZ8VKRZ3o383BbN88Nn+P5kfOMFfLYvkfMMNmSbuDR9m4ebOuu5fkvuzaFEOQdmxdHB3h5dJDzuXmKjo2pqnTFktzT0s7TPZuIaPqSGfWCY3NqbobvDpxlOJ9luJBjrJij7Lp8pe8kL44OLjvmn9+6h5/bsgt1hTYaWZIYKeR4YWSAl8cGGS/m8YWgzgqxq6GZxzrWsaeh5Yqz+lm7wuGpcb4/2E9/dpa8Y2MoKu3ROHc3t/NYxzraoyt7dfbPZ/gnL30XTZb5Px94gvZonIOTY3x/qJ+TmWnyTpWQqtEeS/CRnk3sb2ojaV65fN0NAtzAJ6wahFSj1jeWnyDrlNhKbfIuEIKcWyaqWTzYuBEn8BkpZxgpzy0htqas4Qrvmiyk4MZ0X01ZQ19FVfh2QFHaUdR1lItfwHX7MMyH0dQtSFKUC5P2khQmFPrEgjCUjKw2EfiTBEEWRWm+uLGrPKM1fR+K0gpoqOo6FLkezz2JbqS5nNgGgaBcdVY94RXbw/dXXmi8f5KD3z/KsZdPAbDt/k08/WtPoOsar3/rMM/++Yv4ns/dH9yzyhkCw9DYtq2Nn/+5+/ne947zR3/8LG1tKXbt6mDvnm42bGhacvh9fVO89dYAkYjJ//KPnlzIQsrs3NlBKGzw6qtnefHF00uIbblsc/99G3jmmT00NMQAwZ49Xfxv//tXOXx4kHU99TQ0xDAMjdbWJEII4rEQuqZSVxels7NuYQw1v+ILma933hnh7aPDtLam+Hu//BBtbSlkWWLbtnYkSeLU6XHePHCOjz6zd3Esd+3v4f4HNhCPhxgfm8e0ND7zU3ejaQrPPX+SbK5MsbhQXecHKIrMb/7m++nqqscwVPbs7iSVDvPNbx7l2R+cWCS2Bw+e59SpMTZvbuGzn7mH5uY4kiSxdWsbtuNx+vQ43d11y4jt0NAsTz+9i/c9vpW6uiiyLOH7AbIsY5g3Rj0aklE+9ehOtnQ18vKx84zPFsiXqpi6ys7eFnaua+aeLZ20NyRuaD+3Cpe3RohL/n81bE428dx4PxPlPOPlHM1WDNv3eHVqkF/eeDet4cTNHi7ZYpVy1SURsdjY2UhXc+23vlPbCG8psT1bGMMTPoEfUGdEiWnXJmHeaqWJqbV1JipzDJSmrkpsU0acn+m8aBMiCJi2swyVJpb0+NZIqQoLGS5BgLgGH1hJMpeQUQDfH7tMkGpNW0KWE0uznkGFwF/JY/fdgSyZNdXnm8RshRAEOIuev7V9aKhrEen6MYIvHAJxISBRrkmhWJb0hYznnYMLx3Cze+tNpZ6ksZuQ1knZHaLqTTKQ+zNK7gAJYzu6nAJJwvEzzFZeZ6r8HFV/EhmdiLaOxvDjN3U8dyIkScIKG3zgM/cweHaSF79xhNauOh54aidmSCccNZEkaO5I89hH9+J7Pvn5Ep57sYpFBIKxwRkc2+P+D2xn533rmRqd4+t/9jJb9naxbmsbkUSIHff2sml3B4qiMD+b57mvHqZtXeO1EVsEx2en+fzRAxzPTFFwbISAkFbzL58qzTKQm+fYzCQf6tnEM72babuEmPlBwFS5xOm5aaLDOv3zGZ4d6qfsupiqStF1GC8VGMzNc3Z+lvFigc9s2rHkyvSCgL75Wf7y1FHenBwhUy5jqiq6olBwbMaLBU5kpnl9fIRf33kXPfEUhlq759wgYL5aYbJUwAsCTEVFlWo9X5amLSPiAGFtZY90TZY5OjPJeCnPq2PD2L6Hqapk7SoTpQLnc3Ocy87xzLrNfLBneQn52flZvnnuNM8O9TNdLqJIMsbC+uPFPKcyM7w1OcovbNvLjromdGUpOXECn+F8lqrvMlMu8erYEN8dOMvkwrbcwGeyWGQon+W+5g5kSeJMboIfTJ5kopJlxinwFwOvsSHWxL5UF23hFG2hFCFF5w9OfgddVqkGLtFLfGXzXoW/OP8aBa+KhETFd9gQa6InsjQjsC/dzYHMef7z2efpidazL93N9kTbqtfXx9r3cX/9lUVbAhFQ9T3mnSJDpVn6C1MECD7TeS87ku1sja+teubdhKI0YlpPo2kb8b0R7Mp3cKRXMawn0bQFFVhJWWixUhdaPlRq19zavS9rIpYKkqTU3CIkGbGgCbJ8WalWprvKI9801GV+vYX5In/5+19mfiJLQ0cd935oHy29jdS1pkk2xJAVmVDc4r/80y+Qmy2saeyyLBEK6Tz00Ca6uuo5eWqcs2cneeWVsxw4cJ7Nm5p55pm91NVFUFWFiYksk1M5WpoT9PY2oC+oN5umTltrEsNQGRycwfP8xT5Yw9BobknQ2ppA01SEEFiWzvr1TRw9OszcfGlxLLKsIIRAlmsKvooiX1G4ang4QzZbZv36Jrq76xeXM02d9o40J06OMTo6h+9f/C3rG2r9rtGYWevvNVQSiVr8bFk6vhfgerVsei1DHGL9+iYsq9a7bJoa7W0p0ukw589PL263v3+aStWlvS1Fe3sKXa85gpimTldXHQMDM0xO5hbJ8gVEoxZdnXW0tCTRbrKugqrI1MXD7N/UQXdzmrLt4HlB7Th0lUTEIhGxbpk40e2EoahsSzVzYn6CI7NjJFos3s6MEddN2sMJIurNF551XA/P94mEwjQmo+h3uE7GLYuG7cBlsppFIKgz4tQbCaw1liFD7SEZUg3qjBiWYpB3y8zauauuYykGW+LdSz7r9qrISLyTu6iGWhOJUi8KSAmHIMiw9oxtBFm6VLxC4HmDC36214YaSb60X8S9JpJ9s3ErLNyXb1PckJjHewtigbCudryXqsq998+PtGrv6/VBkQ1i+ga6oj/NQP7PqXpTZO1j2AtEVpXCIIEflCi6A9jeDJKkkbR20xr5CGGtc/WdvMchSRKartK7rQ1ZkTn2eh/NnXVsu6tnyXLReIhoPERTe4pycXnZrOv41DXF2bqvh+aONJOtSf7u888zP13A6XUJ/IBcpsjUyByyIlEuVDl/coxSvoIQ4hpmcyVSpkXUMNhV30xnPElTKEJIrVWOZCplvjfYx6m5GZ4bOUdbNLaE2F5Azq5ycHKM8WKenfVN7G1sJaLruH7A6bkZXh0f4u3pcYIg4JH2bhpCEdSFXr2B3BxfP3eKZ4dq74mPrd9CbzJNRNOpej6D+XleHRviueFzmIrKr+zYR088hSLLhFSVjck6Pr2xVvY2UsjxN2feoX8+w/a6Jn52y65lY+2KJVfMuAZC8Pb0OJ2xBPe1dLC9vomwplHxPN6enuCVsUFeHx8mpGrc1dxG2gwtnufRQo4fDPXzrYEzuL7PJzdsoyuWJKRpOL5P/4JS84ujg/hC8Ht3PUxLJLZ4Di5AAFXP42v9pyi5Dl3xJE91byBphhbOc4WBfJb1yTSWqpHQQ2xPtNEbbWBnsoOkHiJphIlpFpqs0B2p5xMd+yl5VVRZIaZZ6LJKekEAypA1dqc6KXs2SKDJKu2hFA3m0h68XckOIqpB1ikT10Ok9bVpNDzYsBFnBe2NxeMVAk/4lDyHmWqek7kxvj56mJFyhkcaN1+TUNWV4PsBxZLNfK604B8KmqYQjZgkYtfuWSqEhyQZ6Po9+Go3km1SrXwLRe1ZJLa1q2K5tc61wPP6ULVaJtj3pxBBHkVpYyUtEV1XaG1KrKqj0tKYIGQtbdmSZJlwPETXlnbaN7bQ2ttELB1dVG4HaOysZ+fDW2jsXHsJpCRJpNMR4vEQLS0JtmxuZmBwllOnxnnl1T5MU+Ppp3dRVxelWLKZnS2Qy1X4/OefX7Kdicksk5M5mhrjVKsu4XBt/KapEQ7paAt+qRfOdV1dBMfxqFRcgkBccw9iLl9hbq7IqZNj/N//93NLvhsazjA9naepKY5tX7yuDV1FUWq+6LqhYpra4nhkWa5FFAtytooiEw4ZGMZF0R9JkrBCBpGIycDAzCJRnZ8vMT9f4uChAXK5pTFu/7npmvhU2cFxvCVl3vF4iGjUuumk9gIUWSYaMoi+i1ZN1wNvQbxOv6Q652KkV/vvQAicNfh/X1h3e6qZoeIcx+cn2J5q5qWJc+xNt1FvhpepRN8M6Grt2pJlGVW987Uybhmx9YRP3q0RtEYjQUg1lgk/rQUJPYyl6hS9KkXv2jKiF8hBNXCYdy7O8slSEjCA2k0qhI3vjy+IMKyu0ivLyWVG5p53ikDkWabMsuoYvcWHTQ0BgmsrtbqjIdWUb2sZSBkICISLF9w+8v5uQgiBH5RXJaqKZCxm7oXwCcTa+/MC4V6xJ/VHDxK6kqIp/CSBcBgufJGKN07JHaDkDgJi0e9Zk+PEjC1E9Q3UW/eTtu5CucU2WoEQzNqzTNnTzDs57MAmEAGqpGIqBg1GPU1mI1HtxoPldwN1zQkaF6yBzJBOOGbiOB52xeXM28OcPTZCLBlG1XWMUC0rEATBWp3PgNrTsiUS42O9W9BkhY5YgpRpIUtSTUTQ9whpGvkTRziXneNEZppnercs247t+9i+R3M4ys9v3c3WuqbFIHson0WTZYbzWfpzc5zKzJAwTFRZp+p5HJuZ5HuD/di+x09v3smnN2ynJRJDVxT8IGC6XKQpHOGPDr/Os0P97GxoImFa1FthTFWjK56kK157J5zKTPPsUD+KLNEaifJIe8+ysV4JvhAECDak6vjc5l1sSF0M4rfXNWJ7Lt8aOEvffIb+bIZ0U40UCSE4Mj3Bc8PnqbguH+3dwue27Fok74EQjBXzJAyT/3b8IC+ODPC+znU8pq9bsZQ4EIIDk6O8v2s9H+hez+ZUA6aqLtpQjBbzpM0QuqLQaMVptFYubQZI6CHub7iyz3xI1XmocXUBszozel0iTpcT5KvBC3w2xJqYtYu8MnOGN2b7qDMitIdvrDe/UnU5dHSIoydHcd3aszqZCLFzaxt37e5eZe3lqPnUvllrf5JURJBHVtLI8tqOVQRlHOcAQZDD9wYIghySFMZXN6JqC7+FpOB5Q9jVZxHCxfdHUbVNKGoXKxHbkKWzfXMr33v+BPPZ8qJd0wUoskR7a4qezjrCoaXPYcPSueeDe+jY3IpurhyDSZLEvvfvIhy/9movVZVpbIzT2Bhnw4ZmWluTjI9neenlMzzwwEbq6qKLc8++7y9mWhfHZ2hs2dJKW1tqydhq5gTLx1p7/t1AvdLCWBx3+Vii0ZqY1eZNLUvGcil5liVpVYGtFSMSsTD2S0ZeW07Ctr1lY0mlwqRSYbp7GpadBl1XlmXmf9QRCIEb+DiBT0jVqHoeg4V5ck6VjfEGJECXVTRZIedUKbu15Np0pUDWWTu/aTQjNIdiTJTzvJ0Z4+3MGP94x6MkjFtTCRmPmIRMHcfxKFbunJa3K+GWEVs/CCh5teA8qoXQ5OvbVUg10WQVJ3Cp+Fc/oUWvwvHcpdLYgrxbYqg0Qb2RWPxUUVuQ5TC+P7/wiUvgzxAEcyiKwWqnRZbrkZWls4a+d47AH0UEm5DWrPYrCPyZy4isBLcga3q7IC0o0epyEkUy8EUFLyhS9aaJG8uD0zsVS1+0grVk94Xw8UV1gcRfXVBDU6KL1j6+sHH8uTWNSwh/oT/XXtPyPxqQUeWaZZQmx3DlHKbajKU2LpTRyyiyhak0EdM3EDe2Y6rNyO9Cr9xoZYzD80c5mT/NeGWSolfCFz66rBNVw9yT3s/9dfe8Z4itospL7Hlqt4HArjicOHgeu+LyiV99lEjMZOD0BF//H69c8z4uBIB3N7ev+J2lajzY2sUPhs5xdn6WTKUWNK+UGWqLxnmicz076puXfN4VS7CzoZnmhRLdvuwse5taCAEz5RKn5mYYKWTZkKzj57fsIW2FFjOZiizTHInxvo5eXhsf5vnh87w6NsSWdMOKZcY3ik2peh5rX8fGVP2yz7fWNfLm5Cglz2EgN8/dTbVzVvZcjs5McGZuhm11jXxywzZaIhdJjixJCz22bbw4OsBbk6O8PDrEnobWK/bI1oVCPNTWxc765sVzLUkSqqTQFVs6sVvKVxjun6KpPU0sGUK5hSWAQRCQmcozOZJh+103V4RQlRXqjRgfadvNgcw5DmTOszXRdsPEtliq8oOXT3Hg8ADOArFta0kSCRvXRWwRHkEwi+eeXbAoNNCNB9H0HYCMLDegGw9y6SS7onYhyRFkKYrAxfP6CPwJZLkBJBXfG0RCQVHbajoiyMhyGt+fJvAnUJQWdOtxZHnlSQzL1Nm6qYV7963j2MlRZueKVG0XSZIIWzotTQne9/Bm2lqTy0pwfc9naniWxs56NEO7YnKhtffGRXHCYYPOzjo6O9O8/nr/4kRDOGJQVxclGjH56Z++F1WRl5FWcyETegGVikupZOM43kKJbi0umJ0toGkKVmi5RZGsyDVdgauEELG4RTodpa0tySc+vm/F+ykc0q/bMueCTVCl4hAOm8hybRKxXLYpFqvEEyGUBQudZLJGXrdta+XJJ7cvKTdeHG/UfNf9gO9E+CJg3i5zcHYUU1Frz+W5cUxFZUeqBSQJQ1HZGG+gPz/LCxP9mIrKmdwM8iX36mBhjjm7zPmFv9+ZmyDnVOmJpohqJqossz5Wx3gpxw/H+1BlhXWxNJZyY6JcV0JbQ5x0LMTkXIGJ2RylqkPoKvfp7cYt9bH1FlLrmqwu+dGuBbqkokgyfrCylc+lmLXn+ZPzX13ymSaprI928GTTPYufyWrngsjCpeN18LxTyHISSbp60CkrKWS5ntrpq41JiCqOfRBF3YQmbVg1XSGEAOHhuacvK2FWkaRrL0260xHSOsk5p/D9CrY/R9Hto0E8BEh37M2xCElG5uIMcyBcBN6q5Za+sKl6kwSsTjpNpQl1QTDKFxUq3gSBcJC4+sPDDQq4Qe7HitgGuJTcIU7P/yFFp4+ovp6u+OdoDD122yykhBA4gcP3Jn/I4fmjVIMqISVE2kghIxEg8IWPJquod4gYTW6uiF1xyc+XqZRsZidzhGMWsWR4WbblcgRBgGZouI5PLlMkO5vn0Iun8Vxv8XqtlGyKuTIzEzl8N2B+Os/0WJZUY4xQ2FgWrPkioOJ5VFwXx/dr+gxCEAgouw5eUPu3FwT4QYCsLD+PTaEIO+tXDn4jmk59KMxkuUDOri4e43Ahy1A+S1jT2ZCsozG88vM/pGk83NbNa2PDnMxMM12+NVUnPfEUG5IrqwEnDIukYTFvVyg6Fyd6J0sFxot5qr6Hrqj4QtA/v1yrIVOtENNNhIDzuQwV78rVQfsbW2mJxNZk0TcxnOHP/u13+NgvPcSOe9YRitz4NR74Aa7r43s+ZuiipYTn+rz9ah9f+7OX+eNv/vYN7+dyqLLChlgThqwyVJpltrq2ns4rIQgE5bLDmf5JvBVscK5rjNp6ItqVs+C6sQfdWCqwZFpPLfl3OPIrK64rFkizEC6GcS+6cd+axiTLErGIxd/73AN8/4WTHD0xykymgKLIdLQkuW9/Lw/c04u6wn1bnC/xn3/rf/Avv/jbrIt2Ius3Prnvuj5zc0UURUbTlEVC5nkB09N5JidzpNORxVLZlpbEQiZ3HlmWqKuLoqpKLZZ1a5Uol24HaorBo6NzDA9naGpKADVrob6+yRohTK7QXx828IOAQqFCoVBZtMm5ME5JkujsTFNXF6FcdlBVhfr6KIoiEwQCz/MJArFAaq8vdvL9gHy+wsmT4/T2NmKaGo7jMTySYX6+xK5dF1t21vc2cubMBNWqi6GrpFLhxbG4bq3M1jTWZsH0o46aAGGVrw29w3SliKlo7Eg183jrBralLr6XPr1uN18fOs5fnz9CgxWhyYrxcPM6QqqOJMHrU4MczowyXs5T8V2+OXySpG7xs+v3sSFejyrLdESSNFpRvnj+KJ/t3Y2lrF5ter3Y0tXEoaZRhibnOT+e4eTAJLvWt6Iq8h0Zv98yYqtIMiHVIOeWKXoVPHF9pZKVoBbQqJKCvkrWtz3UxH/Y/TtLPtNkFU1Wl5RWqErnglLgJRAOjv06mrYLuDqxlSQDWalHlpsIgtHFz6vV76Hpu1DVnkuEGlaCoFaSW8BxXkcE+Uu2rV/Wv/ujgZi+gUzlDWx/mqo3yVz1EJ3Rz6LIYQTiposM3UzIKKhyhAu/pxcU8YIiAm+Zn/EFCCFwgxxZ59ia9hHW2hf9aIMFQlxyRwhrnUvExS5HwenDCeav+P2PIhxvloniN8k7p4CAtujHqLceuq2+yL7w6Sue41T+DHmvwMbIeh5teJA9yZ1Yikk1sJmpzhDVIkTVd88TU5Ik5Cu8fL71l69x/MB5xgdnqVYchvomaWxN8em//xit3fWLPTWL26IWfEmyRCwZZsfd63j2iwf4P/7+n5JIR7jv/dtpaE1iLGQ0jr91nm994TWmx+bIz5f5qz/+AbFUmMc/vpd7n9hOqqGWUbygipy3q7w5McrrEyOcmZthslSg4Do4vo/tebgLE6W1eomViXdE12kIrfz8VCQJTVZAQCAudoBk7QrzdgVL1WiLXrmUU1cUOmMJFFliolhYELm6ll7itSFumKSukEXVZHkxk3zp5MNkuUjesXGDgBdHB3jp7wauug9Jkig6Dv4V7NkAGkPRBZGr24NyyWakf5q5mRz7Ht6EYd7aNoILuFAuKCFRdKtU/atPqK8G2/GYzhSYyRRvzgDvYMiyRCoR5jMf3c9nPrr/2tZVZMIxC+Um9WTOzBT4g3/3XerqIguiSBEkCSanchw9OszZs5N87mfuI5msJRLW9zaxb2+OPz85xh/8wXcWrHriOK7HxESOQqFKV2cdH/jARQsZ09R4441zFPIV7r67F0mCl14+w+DgLD/z0/fS07O8H7irqw5Zlnn9jX5AoqW1ZtnT2BCns7PmB7t9WzvDwxm+8Y0j/NEfP8tjj24mlYpSqdqMjs5j2y6bNrawb991ZPuhRtiF4I//0w946qkdNDXG6T83zZtvnsMyNd73+MVqun37uunrn+Tll8+SyRR54P4NpFIRCsUKw8MZRCDYvbuTvXuvbyzXC7H4/FuqSXKhSvB2EC5dUdmQaOC/PvhTV13u3oZO7m24st7HZ3v38NneKyt/C2ocS5YkTFXlydaNmDfZu/ZSbOluYs+GNs4MTzMymeVPvv4G/+qX309dIlIjtwvL3Skk95adCVVWSWpRcm6Z4fLMYlnytWKikiHvljEUjbB69fpxGQlLuUyUYAXCpGqbUJQ63IWeTwCxQGxD4Z9f07gUpR1N34ldvUhsA3+CSvlvkaQQpvV+rjabFgTTlAr/icCf5lKVQkmKIas/egI3ddZ9TJWfo+j2E+BQcPo4l//v9CZ+bUk29E6ELOlYahOypBOIKgKfojtI0T1PTF+5N0wQUPbGmCh+d037MJR6IloXhpzGDjI4QZaRwt+yPvkPkKUrE6GZ8ouU3ZHrOq73KtygQNY5Tu3xLmModQsTD7cPnvA5U+ijGlQJKyF2J7ezL7UbQ64pQ5uyQatV64mS38VWg7Z19fzS735oSTnxBXzk5x/kqc/ci+/5NbVORUZRZEJRE1VV+Ie//ym0S8rLYqkI//w//QKhiIFuaOy6bz0bd3XiuV7NwsHSeOyje4nELSQJdtyzjvXb2vD9AN8LkBW5ploZ0jGsSysgBIemx/nPb7/JicwUXhCQNC16Eqmaif0CuXp1bIiB3NUncWoqwNcWGDu+j+v7KJJ0VWN7CQlLrREeO/BxA/8aFRXWBl1W0FbIal0NVc/DDQJUSSZhmjSHrz55IiHRHIlctXRNleU1ZWsv3erNRG6uxPGD59ENleAK9jC3Ar4ImKhkcYWPLMlco+7PMuQLFfouUZn9CZZD01U27ltHZnyeVHOSUPTGewUtS2PbthaOHh3l9OlxqlW35ukdNWlrTfFbv/kB9u/vJrLgiavrCvvv6iEeD/HsD07w7e8co1CooqoKyWSILZtbaWtbWoKfSITo6alH0xT+7ksHKOQrhCMmn/7U3TzwwEZSqeXvpT17upiazPHaa318+ctvEQhBY2Ocjz6zh9bWJKqqYBgqjz26hYb6GC+8eJovffkgpZKNrquk0xF27uxY9KS9HiQSIXp7t9LakuSVV88yPj6PJElsWN/Eo49upre3cXFZ09R45iN7aG9L8+prZ/nbL75JqeRgmhp1dVH27+8mnX73JmsvwBc2BXeSWfsMRXccOygRCI+Uvo7m0C7i+vLWlh8lnM5OczI7xc50K82h+DU+q68NkgSP7VuPLwK++NxRTgxM8pv//qs8cdcG7trSSXdzikjIuGPSU7eM2IYUg65wI0PlaebsAn2FcTrDjdQZaxQ3EILh8jQj5VlKvk2blabJurr3ac4t8mrmGPuSm0nrMQ7MneTg3ClCqsnD9XtYH61d6JIUQVF7keUGgmByYW0Pz+vD9wZQ5AYk+erlwIrShqbtwK5+65JPfVznCCVh43n9GMbjaNoGQFkoTRYEfhbXPUq18m3s6vcQl4kEyXISVb2yNcF7FbqSos66n7I3RsE5g+1nGC9+Cy+o0Bx+kpi+YcWMmxsUKLlDVNwxTLWRpLnr3R/8Qk9nXN9C1j6KLyrMVd/CVBvQ5DiWurT00QuKzFUPM1r4MmVvbaRTkhQSxm6yxglmKi/hBUUmSz/AUttpDr8fQ61bnKQRIsALSoyXvs105RXs22gPdduwGOcGzFReRVcSRLWNqHJ4oefs3UUgAqbsGdzAI6UnSekpTMVY/M0kpOsSz7tRaJqKllz5MR+JXT14TKSXBmWKIi9mWaEm+HIpQQUIXxKQGqa+apbNDwLm7Qr//7ff5NDUGJ2xBB/oWs89ze1EdANNVlBliZLrMlrIrUpspYX/XQtq+1Bqwh/+lSuLBALb9wGBJsso0vU22KyOa92uJskoC7P3dzW18es77151B7qs0nqVDPXV4qSJ4QyvfOcYQ2cnCUVNDFOrlQovrJPPljjyylmOHxigVKhghQ32PrSJ3fevxwobDPdNcfiVs5w9NoLv+aiawn1Pbmf73T2ousKJtwZ5+Vtv03d8FE3XOH1kGEWVefKT+9m0qwNJkigXbZ7/2mGOvXmOUr5K14Ym7npsM73bVrcAuhKEEBTcKl8ZPkjBrZDQQ4TUG1NdzRUq9J2fuqFtvLtQkOUkidTnLwpJ3eo9qgpN3Q383b/7Js09jaSaEoutCuFEmKd++dFrzgZFIiZPfWAn99+3Acfx8P2gprqtKoRCBqlUmEjEXKIKHAmbbNnSSkNDjEKhgusGyHKNeEcjNRudSyGEoLurnv37u3nkkc14XoCu1UqH4/HQCv2oEqah8fDDm9i+o51K2UEIgW6oNDbE0HVlcSzxuMWuXZ20t6cpFqt4no+8oHgcj1kkEiF0XWXz5hZ+/19/kq6uOnRdYdOmFhobY0uqbX7tVx9ZJMW+H1BXF8EwNAxDY/PmFqpVB0mSiEYt0unIkn5ZWa712d59dw+96xspFat4foCy4EebiIeIxy/Gy5qm8Pd/7TEAGhrXLty2VniBzWTlKEPFl5m1z+D4RTxhEwgPgcANl0kYXcRZSmwnym8za5/BDSok9W66ow/f9LG9GzgxP8n3x85wan6KtnCcT3fvXKZsv1aUKg7/8YsvYTtXr0q54LNcKFfRFBnb8RienOdrL73DC4f7iVgGYcsgZKgrthpcCR95cCt7Nt78CYhbRmyjqsWORBcvzR7HEz4vz5ygwYxzT3rzqi+KQAQUvSrfHD/AWHmWQAQ0mkl6wlcXDih4ZV6dPcq+5GZm7Rx9xRqpkIA3545fQmwVVHULitpO4FwgtgJEBcd+HUXtQZWvnjWVlRSavhVF7cH3zi9+LkQR1z1G4M/gOodQlCYkKVZTLhRVgmAe3xvG9/oJgpnLtqrUBBr03ZedDwcvqOCLKoGwCYSz+PeFXkyAIKhQdM5hKGlkyVj4o6NIOopkosjhmrjObSgXkCWVeut+bH8GLyhS8caoeBNMlr5LyT2HpbZhKKkFuxaZQNi4fh4nmMfx5xAENIUevyKxvXCOAlFd8IS9eJ7K3gjiQim8CCg4Z5ElHU2OLZwfA1nSkCUTTY7AZedIkiQUyaQl8kFK3hC+V6HqTTNZ/D62N0NU34AmJ4AANyhQ8cbIO6cpucMYSj2qHKboDKyqdh03NlNn3UPRPU/FG6XqTzNS+BJFp4+w1o2mxIFaiXPJHSJbPYYXFAlr7bh+Hju4OsFdFLMSldr5CWrnxxcORbcPL7hYKlf1J8g7J/FEseaTKxko6MiSjipHah61t4GoAahylLixnTn7MCCYKb9CxR3FUOtq2f9LfzsUFMlEV5KEtHZi+mbCWsdNH5NAUPYqBCLAUkx0+eZ7+P4owvY9TmWmOTE7heN7PNrewzO9W2i9rLdzslSolRHfAsQNk7huMuDPXbVv1gsCJop5fCFImSFC2p0jnpGyQoRUDTeoZRk3puqXedTeDNREbwQ//MohSvkKvdvaiCVDjJ6fYXYyu6AdAW/+8CRz03l6t7WhGyrFXIVnv/QWqYYo3Rub0QyFpo5UzVNZlijMl3j+64dpbEvSuaGJpo4U67a2MjOZI90QY9d9veimRn1LgtobXeDYHjMTWXbes45y0eadA+exIgbNHWnCl0zaHMwMUFhDxZgX+GSdEgPFGV6ePkPJs9kSb6XxGlSVVzpf+XyF/oHL3/V3LmrXtIFu3PXu7VOWiCbDJBriCCEo5SvIC4q60nWmzDVNoakpfk2ZzQvetx0daxMLCwKBaWm0tqZobU2tuvyFx0U6HSGdXkXLRa5ll6NR86rLxWMWO3defKfF4xbxy5SjL83AAsQuuT9SqdVbeBRFJn4Zgb3yuKVl+7tZcIMy5ws/ZKDwErP2Gar+8olOxy8SiOWxlieqTFWOkbH7aTS3U2duJKrduBjZu406M8y+unbWx+ppDkXZGG+47m25ns+rxwYoVVbRaJEkFFlCkWVcrxZL+0HA1FyRqbkikgSqoqAtVGWtFfu33PxYDG4hsY2oJlsTXbSYKSarc5wtjPK9ycNUfIctsQ6arBSmvDQw8AKfgldhuDTN0ex5Xpp+h6xbIqwYdIcb6Yk0X2WP4AYes9UshqxxPHcOCdiT3EglcDiQOb5kWU3fhmqvw3WOcEEACsBxDmFY7weuTmwlyUBR12GaH6BU/M8sUckVVXx/AN8foCYGFUaSFIRwFjK0K8+OyEoTqr4dRV36Y+fsU2Tto4skNhDuwt8OBbcPb8H31glyTFdepOCeXSAeOjLaYiltnXUfEe3mqkheC0JaK02h9wEwVX6BotOP7c9gV2ZqhEmKoMg1NcZAOLhBEV+UAYGhNJA2r9yzU3QGmK2+TtWbrp0bLp6nkjt4kfzjMVV+nqx9HEW2Fs+PLGmocoTW8NNYWgvSZeXRsqRTb91Pzj7JVPl5bH+akjdEpTSJWT2MJseokc4iTpBBCJ+o3kt96CGq3hQVdxxvhYftpdCVJHXWvTj+HOOlb1Pxxim6fVS8UQwljbq4jwK2P4MqhWgKP4kimcxVD2E7Vye2TpBjtvIaeecsgaguuY4cf25J5jdnn8INiuhy/OK1JGko6KSsu0gY2xZ7gt9t6Eqc+tB9zFZfoegMUvUnqPoTKy5bs/4x0eQYltZKXN9CvXU/KXMvsmzeFPJZ6xEN8BZmjGXpWks4f3zhBjXrGNv3sFSN9ck0bZHYMsI4kJsnZ19fO8tqaIlEaYvGeH1iiPO5ObJ2hahuoFw2cVPxXA5OjeH4PlvTDaTNlYM8RZKRkfADgRsEt6Rc+XI0h6M0hiNISEyVC/RlM2xNX3/AcyUIAeVClWNv9HP/+7fz8Id2YVg6h185ywvfOAKiJhp29LV+SsUqG3d2EIqY5OaKnDs5zkj/NE3t6cWyd1ggUpLEqSND5LNlVE2hvacBu+Iw1DdFe089971/B6FIbULcsV2EAM1QWbe1lR13r8P3AgbPTjI7mSOfLS8htt+feIeh0uyqx+YFPnmvwlQlT9GrktIj3F3XS0d4ZSGvtaBqu8zMFZmeza++8I8xNENjy70b2HzPckEs/V3qrb5uvLct598jWBD6K77O2dx3mbVP4wsXTQ4RUurQ5BDzznl8cWXnlKjWDEgU3HF0OUzGPnvdxNb1fQ72jRIxDZIRi0TEJKQvV8C+FWi0ojRaN6f0WyAoVmxKN2jhI0SNJF8gvWvFtS6/VtwyYqsrGu1WHY837eJb4wfI2AXeypxlsjLPnuQ61kdbSeoRVKmWHQtEQNm3mazMcyo/zIHMGcq+XZt9jrWzM9mzahmzLEnoskrGyfFO7hydoWbWRzs4WxhepqisKO2o2mYUpRHfn0SSY7XP1G4kaW39HYrSiGE+hV19Hs/rhxUzch5C5K4q7Q4gSRa6vg/DeBDpMq/NnHOSkcJXKLp9V92GL8pk7WNk7eWCRRGtF1Ntuq3EFiRixkZUOYqh1DNTeZWKN4brZ/GCIm5QWBRCuuB9WyN0USJaDyH1yiULBbefkfwXKXlDq4whIGsfXfEbVQqTNHZgqPULvruXjFxSMNVG2qOfQJEM5uwj2N7UYoa2zAgyGopsYshpwnoP9dYD1IceZKr0Q2RJW9MLMKKtozXyESRJZbZSI+pukKPiTQATixnTqNZLwthBe/QTVP1piu7VxWIAXD/LZOlZpsrPs5r9UNkbpuwNr/idkAJCWtttIbZeUKTsjlH1ptHkOLKkIpBr1QnLHmcCgV9Tp/anqPqT5O0TFNx+VCVGTNuAIl99NvxKmKpOk3VzOIGDG3gUvSJZJ4cvAgpekb7iOYLLhHlCikVHqI2otvJLyQs8cm6erJuj5JVxhVszdpc0wmqYlJ4kqkZQryCiN1WdYawyjixJNBj1NJoNKCsoMAciIO8WGC6PYgc2TWYjzWbjsu1m7Dmm7RkEgkajgZSexBUes3aGnJuj6tsEBCiSgiEbxLQojUb9Fce3Ei4I9UCN4FQ9l+oCyRVCEAjBZKnICyPnGSveGnLQHI6yMVVH3DAZzM/z3PA5HmztJmVaKAsesAXH5vjsFK+O1+6JfY1tS+x0LkVY19EVBTfwma9WmSrVPHBvJVKmxeZUA+2xUcYKeb517jRJw6QhFEGRLgqpuIFPwXHIVMq0RKJYqnZNkzBCCIr5KpWSTbopTqKudi3XNcWJxkNIkkSpWCWbKVLMV5gcziymqfY8sIFoIoQQgoHTE5w8OIjvB2iGiuf42GUH31+7B7IV0tm6rxtVU2qlonELx/awq0vfw/2FSU7lxtdwdBKqLGMpOs1Wgj2pLu6rX39DGdv5bJmxiflFi5+fYGXopsb2BzdTLdkU5ou4toth6UQSYYzQjZWC/wTvfQQioOLN0Zf/Nhm7DxmFuNFBnbGBhN6FqcR5a/bzVK5ikxjVmgmpdSiSju3nyVT76Io8dF3jcVyfv3rxbWKWQXMqRlMySjoWIrZQkhsxdSKmQcjQ7ljlYABVkdm+roWKfXu8adOxW+MAc0uNp0KqwSfbH2CsnOHIfD9Zt8RAaZKB0iQSEhHVJKKayJKCE7gUvcoSr1pVUmi10jzRtJvdydUJmS5rNFp1vDV3kqnqHPuSm4lrEQIRYFwmkiFJGpq2Dd14ANc5hqptxbSeRjfuQb5cMfkKkCQTVdtAKPorlAp/jO+NsDK5XXXkqNoWDPNJNH3fCseVWFDHvf6bI6R1oK1wXBIqhpImqm0ACUylEWWNxF6Vw0T0HjSvtl1NSSBLq11SEiGtlXb1EzSFH2e28iYF5zRldwTbn18QZwpq5aNyHFNrJqKtI2HsIKzVVPdqangujjeEIseR5QSaHCWsdSNL1/8SVGRroVz7yiW2cWMzltpC2j7KbPkF8vYxal13EqocwVQaiRtbSJt3EdU3EAiHqL6OqL4ex89iqA3LSPOSsyPJRPQeetRfJm3dw2zlNQpOP46fodbbFyesdZIy91Fn3YciWciuTlTvpepNYmmtqFfoD69l7luI6Rso2lXy5SoVx8XSNeqi4TUL1hhK/YqCX4psEVY7QbBwLtaeMbLUZmy7GS+ooJhNKNJywukFZXL2CUaLX2eq9MOazYDagKk0oStpVDm05Lerkdoqjj9HxZvE9mbwRJFM5U0mtF6MWIqQfH39eIfmj3Jw/sgCCS1R8av4C+Xu45VJJivfXxacd4Q6+FzHp9m0ArGt+lUmq9O8kzvJifxpxirjFL0SgQiIqhHaQ63sSGxlS3QjjWYDprL8/BzNvcPfjX4NVVJ5ovFRnmp6H8oKv2kgAgbKQ/zF4F8zZc/woeb380zLB5cR0jPFPr4/+Rye8Hiy8TH2JfcwXBnlzcxBzhT6mLUzuMLFkA3qjDRbY5v5UPP7iV6DkJeuqKxLpIjpBpOlAm9PT9AeTdASidVKEl2H54bP89LoIDm7inILAoSQprOjrolH2nr4zsAZ/uPh1wkEbE7VE9I03CCgb36WL/edZKSQY3OqngfaOmm5gkBTnRmiIRTGUFTO5+b49sAZHm7rRpGkmn0RgpRpkbpCxvd6IEkSdze3M5if50tnj/Pl/hOEdZ37WzqJ6QZItbLJvGNzPjfHkekJfmHbHjqj8asKZi3bD6BqMooq41RdHNtFURQc28P3ahM5iipjmBrb9nfz4Z+7n3DUqtlh2R6aplAu2Rx88TSu7fFzv/0UsWSI8aFZXvzGkcv2JSFJNXuflWYFZVnCXNLjvXBtXLbohlgzhry6urMsSViKTqMZZ3uynXvqeolp1g1VX8xkCgyNrs2T/McZQRBQLdn0HR5g6OQopXyZeCpC59Z2urd3YEXMO44cpOsitLYmVy0V/gluHL5wGCu/xZx9Hk9UaTC3sin+Ybqjj6LJtXj16NwXrkpsFUknpNZhKHGcoETuBkQ3PT/gyLkxCgslvJIEUcukLR2nsyFBR0OSjvoErek48ZCJoakYmoKuqRiauqAkLK1pAu9WwjI0fv1j9+EHt6fsoLX++gXQroZbSmxlSSauhfmN9R/mL4ee4+WZ42TsWo+SQFDwKhS8yvL1kFAkhdZQHb/U8yT7kr1EVlFEBkjpMT7e+gg/mDrAIw17WBdto+xVkCWJTdHlUuSqtpVQ5O8jhI2u77i+Y5SjWNZPIfwM5fJf4XtDXKpyvDpUVG094fDfwzAfR1ohw9ISeYqWyFMrrHvjMNV6OmKfoiP2qWteN2Xu5a6mz1/XfmVJxVDqaI08DTx9jWt7uN4owzOfIx7+OInwp2kIPURD6Ppm364VuhKnIfQQIdmnoDikIj+Prvau+OJVJJM66z7qrLV5AV6AKlukzX2kzeUTHZcjovewKfXbbEpd3dMxpLWxJf27ALzeN8zLJ09xoH+EnZ3N/KP3309HXeKaxngpJEkmbmxhT+MfXtf6m9P/lO+/9Sp+ELBt92ai+tLyPyEEWfsYQ/n/yVT5OeSF89qb+FWiWu9VM69CBMxX32ao8JdMlL5HTXDqFVrCTxHSro/YlrwSJa8ECMJqCEuxyDl5XOGiSiphNYShLCX/SS2Bdhl5FAvPwjOFfr4y9k0GSoO1cuaFPm9Zkil6JU7kT3Mid5qt8c081fQ+tsU3v2tiVLbvMmvPcabQx38f/AJFr7iYVRMCKn6Vqeo0KS2JJ67NGsVUVTan67mruY0XRwb54tnjvDQ6SEskhh8IRos5snaVT6zfSnM4ytGZlUvObxRb0g18dtMOpspFXh0b4ndf+i6NoQgxw6TkOsxWygRC0BKO8U/3P8TmVMMVJ4IMVeXu5g765jMcnBrn3xx4if9x4jAx3aj59Houv7x9L7+0bfV7+1qwPpnmo71bqHguX+o7wb87+Cqf1w7QGKrZnORsm7xTxfEDQprGJ9ZvIYhcW2AhyRLJ+hjJdIyhs1N0rp8mno4w3D/F3EweIQTJdJTmjjRjg7O88+Z5dt2/Hs/1GR+cobW7AafqIoJa+amqKeTmSrz8raPYFXfJ1K2iKRimxsxkjmrZQVFlVPXaQ5Z/tvXD17zOzYAQgplMkaHRH0Nxv2uEW3V5+/nj/PX/96skGuLE66IcHZ2DL73Jx/7RU9z11O7VN/Iu47Ofufd2D+HHBr5wGC29gSvK6HKYnuhjrIu+D0W+tjJ1U45hyjGyfpayd/PuSyEgX65yslzl5MhFoThNkWlMRlnXlGZdc5reljp6m9I0JaPomoosS8jSgjiTdFHE7N2Cqihs6X7v9RmvhltKbC8gqYf5xe4nub9uC2/MnubQfD8j5RnsYGl2U0IipoVYH23hnvRm7k1vpt6Moa9hthXAkHU2RDvoDDejScpiEHlXahvBCqWXkhRBVddxM5okrPAvoKgbKZf+Asd+FSivsoaEJIUxzaexIp9DVTfBGjOlP+4IRIVi9WUMbROOO4zrTaCr766HGkDIuA9L34ss3ZpyiluJvd0tbG1r4E9fPMR8cbVr9fZD4DFXPUSm8iYSCpbSzObUP8FUG9ZgFyURNzbR4D2yQGyh6k3gi1UEE66CD7W8n/c3Pbb45Kj4Ff5k4C84VxygPdTKEw2PsCOxbck6iqRgyksrCnzhM1ad4IsjX2WkMkZci7Mzvo2703tpNBqQJInp6ixvzr3F29l3OJU/jRABlmKxPtpz3eO/FpT9Eu/kT/DW/GFUSeUDTe9jXbibuBbFFT4z9iwTlUlarVZM5dorJnRZ4ffufoTtdaf54fA5+uYznJqbIaxpdMeTPNW9gcc71vHD4XOMlwq34Ahr2brN6Qb+9/se59mhfn4wfI5z2TlGCllMRWVTqo77Wjp5pncz7dE4xipZzsc71hHVDb5x7hRvTowyVS4yXS4R1XU6ognqrFvjubwxVcc/2HUv97d08q3zZzk5N8VUuYhAEFJ11iVSrE/UcU9zBz2J9HUJTMmyxCd+9WG+/8W3+KP/95eIp8Mk62Ns3tO5qIz89M/cy6vffYdv/MWrfOE/1KoX0o1xfvVffISG1iQ771nHi998m//1F/8rsVSE7Xf1kG6KoRkXz2tdU5yd967nf/7Rs/yrv/ffscI6n/q1R9l5b+/NPGW3DL4fMDNXZHwyd7uHcsejXKjwpT/8Fp/5Zx9j/Z5urIhJcb7EsZdO8r0/ff6OJLY/wbuHQPjM2efxA4dGaxtxra3W3nWNUGUTRTYQwsMNbn3c4/oB43N5prNF3uobQZFlVEUmahm01yfobkzR05SipzFNR0OCZOQnHOBm4F0htrIkE1FNtsQ6aLXqeKJ5DyWvStYpUvFrJvG6rBJRTWJaiIhqEdfCxLRQzVJhjTMYkiShIJN1CmScHG1WA0k9iixJyCx/gddUXW886yFJNZKqG3ejqG147mlc9yiee4LAnyEQeSBAwkSSEwseuFvQtB0oajeK3ATSnVdqc6ciEGVK9ktErfdTqHwXz59CiOBdV+mVJQNuoPT5dkJXVXRVxdJUsu+B6872M9jeNJ4oo0ghwloHplIjtavdNzXvWBNVicCid/WNKcZaioWlXHwJqZJa0wtAQpUUQmqIuLZ6b17Zr/CdiWeZtKcJKyEeqX+A+9J3kdAT6LKGhERUjVBnpIhoEV6bPcBgeZjnZ16iJ9KF/C4Y0Re8En4loMVq5nMdP0WjWU9ICaHKCkII2q1W7NhmDFnHkK/vfkibIT7Us4n7Wzspuw5eIFBkCUvVqLNCJAyLD3ZvZG9jK5aqol6ikJw0Q/zeXQ/z6zvvps4KXbFceUu6kX9+98MUXYcGK0LMuDhWSZLQZJnmSIyPrNvMA61dlL3aOGSp5l8bN0zqrTCqvPo7yVJV9ja20BVL8PNbbRy/lslWZBlTUWkMXSzXliWJulCYP3zkg1Q8j7Zo7Irlr/e2dNCTSOEGPg3W8pJvTVZoCIV5oLWTTal6Sq6DE9QqiC749IZUjZhhEtX0Zc0tXbEE/+3Jj+GKgK5Ygri+tBLiwnGv29rKp9NRysUqqqagGypCQDwdxrB06lsSPPLMHvY8tBHX9mr2QoZKujGGosrsun89nRubqJYdVE0hlgyz96ENNLenF8vzrLDB1n3d/MP/8xP4fs0HubE1iaop7HtkE92bW5aM7clP34UIxDKbqtsBz/MZGJ5lcHgWZxUrjZ+gNgmQmcjS0tNIujmJrMhYEZO61hTZmVszmXU5ZjNFTvVN8M6pMUbH5ylVbDRVIR61aG1OsHlDC1s2NBNfxSYNaqXVfeenOX56jHODM8xmijieT8jSiUZM2ltSbF7fRE9XPck1KA3fTAhREzUbn8wyOJJhbHKezFyJXL5MueLguj5+INA0BdPQiEdN0skIzY1xOtvTdLaliITf3fJrQUDVzxLgYykpdCVyXe89CRkJGbGwzXcDQSBwAh/nEqGkXKnKbL7E6ZFpLF3DNDTChkYyEqK9Lk5bfaL2dzpBfSKMpV87if9xxrtCbKH2QrRUA0s1aBQJAgS27+IJHyFqSqK6rKLJynWX2JW9KifzA7yWOUbOLfJMy0MYisa54ijT1XmeaFrF2+8GIctRJGkDitKCpm0jCGYQooQQNrWssIokmTWhKrkeWWmANQTmP8FFCOHh+1ls9xx1sd+i4hzFC+bxghkkFCrO2/jBHI43gKa0IUkGnj+FJFlErfehKW34wTzF6vM43gBCOKhKIyFjP5a+E4CyfRA/mCMICrj+FJ4/jSyHSEd/BVmK4/pjVOyDVN0TyFKEePjjaEr74u8ohIvjnadkv4HjDYPwkCSDeOhjGNp6vGCGin2YivM2CA8khZC+H8vYgyInqLqnKFZ+gKnvouocwRdFdLWLkHEP5iq+gn4QkC1V+KvXj9LbmGYqVyRTLNMcj7J/XRu9TWtT+OybnOXI4DgjmVytz9M0uGd9B5tbGpjOF3l7cJy6WJh713cu7ve5E+cA2NrWSDJs0T+V4c3+EXLlKrIE29ub2NnVQn00TMl2ODeZ4YVT53F9n9ZUnLG5HM2JlfsWA+ES4FLrZq5NSknS2q2rbD9DyR3igmiWqdaj3OZJCS/wmHPmeSd3Etu32ZbczJbYRhrM+iXPQEMxaFDq2ZXYwVR1mjczhzhXHGS0PE6r1YS6al/7jcEXPpZicm96Pxui61Aldcl5NxSDG9FolKSaekB9KEx96MqZzCt9rysKG1KrX9cxwyBm1F91HOoCyay7yjjWAkmSiOoGUX31a0ySJAxFZXv96iVhaStE2lrFY12WiRkmMePag8+QprO7sWXV5ayQQVvPlc8likxdU5y6K1itxJJhYpd5gtY3J5ZuQpGJxC1648vbBZJ1UZJ1S6+6xtbVxeyEEJR9h+FSholKlqJXJRACQ1ZJ6mFaQklarSSytLYJo6rtkstXyMyXyMwXycwVycyVmMuWmMkUGBy5crnjXLbEc6+cueoy14Keznru3tNFV/v1qzhfjmMnRzl0dIjJmRsXbpMkiXBI5zMf3U99eulvJ0sS4ZjF7PgcDR11hGIWpVyZ+Zk8oTUQycsxN1/iyDvDHHh7cMnnrU0J9u7sZOvGi9e45/kcfmeY1w+e53TfJFMzeQrFKo7rIcsyplHzsD38zjDrexp54K5edm9vX+ITewG+H5DNlXn2pVMcOznK6Pg8c9kylYqN7wtUTcHQVWJRkwNHBljf08Bdu7vZva19iXfsrYDjeAyPzXHy7AT9A9NMTufI5ioUilXKVYeq7eItkFohBPJCdtEwNEKWRiRskohb1KeidHfWsWNLK13tdYSsd1e1WpKUq+qgXA3+gguEjIK6go7HWqGpMo/tXMfITI6ZXJFMoUzZXru+TiAEZdtdso4E6JrKiZBBLGQu/DFIhC3q42EaE1EaExEaE1EaEhGiloG6zCf5J4B3kdheilpmVbph4/PLkfdKvDxzhHozydnCEAWvjCLJzNo5jmTP3nJiCxcC7hiyHAPeGyVT7yUEoojjDaIqaVSlDlPbgh/M43iDaHIDVfcEjjuAoXZTrD6PqjQjoSGwqThpNKuRfPnrBKKELIVBCuH5UxTK30ZVmlHlOhxviHL1VWQ5gqa2oyjphQfpgnk6GrIcxQ9yVL13iJiPoCltXBAvsd1+SvbLON4QqtKEhITAvdg/LQJAoMgxQCYIihSq30eWw1jGfhxvgFz5K0iSUbuOAijbbwGgK63IVxHnEUJQqDp8/eBJPrh7E+lIiJCuMTAzx3y5QnMyRkhf3X/zggF7PGTgB4JC1eZvXj/GP/7QQ6iKwpmJWUbn82xtbSRqGbh+wHfePsO+njaEEJydmOXg+VFAkIpYeH7AK2eHUBWFfT2tjGRyfO+dPhzXoykRxfMDsqUq8dDKLxtVDqNIYUDGFw5lb5ycfZKYvhHlKllCIXyq3hSzldeYLr+w8KlE0tiJqtx88/hrgR3YTFQnybl5BIJ1ka5lpPZStJrNtFotSBym6BU5Xxqg0axHvcWPcQlIaHF2JbYvI7W3E0IIvEBwbjbDwNw8M8UStufViJ1p0BSLsrG+joRlXrEfVgCzxRIDc/OMzOfIVqv4foCpqcRMk5Z4lK5kgvpI+KYftxAC2/OZLZWYK1eYr1TIVaqUXRfH83F8H88PkKWaf6ChKliaRsTQSVgmdZEwzdEopqb+xF5qDcg5ZfoKUxydH+Z8cZqZap6y7xAIgS4rxDSLZitBb7SJvaku2sKpRcVuANvxmJktMDmTI5evkitUyOUr5PJlsrkK2XyZbK723/liBc9bRXm+7HCmf5Iz/ZM35fju3tPNuq76m0psxybmeeVAP33np294W5IkkU6G+NATO5YRWyNkcN8z+zjwnbc5f2yYUNQkP1cknymw78md17yvUsXhVN8k3/nhUpvHnq56olGTrRtbFlXX3zw8wPdeOMHbx0fJ5paWp/p+QKnsUCo7TE7nGRzJMJMp4PkBOza3Yl1C6lzXZ3Imz3d+eJwXXzvD5HR+mZ2J43g4jkehWGVsIsvQSIbJ6TzFUpVH7tuILN/cCpya7zScH5rhxJlxTp6d4Oz5KcYnslSqVydivh/g+wG245EvVIDa5IamKjTURzndN8GWDS3s2NJGR1vqlhJcCQldiWAHRdyghHcdbUQCQcWfp+rnUGQdS71+VwddVfn4fdvJ5MvMFcvMFyuLf7LFCtnShb+rVF13VVeU2vjAdj1mch4zuYt+6qoiE7MM0rEw6ViIdDRMOhoiHQuRioRIRUOkohapSIhExEJX1z7h/6OK20JsbxWqvs254ig/2/VBzhZqtgyWYmLIGuUVRKp+gvcePH+OinMQUClWnsf1x3C9YVSlEc2oqfDKkk4i8tOU5/4JqpzE0ndje33Yzil8416ypb8hbD6AqW0BScd35ik7Bwm7j6AatYedF2QIaT3EQ59AkeMIUUWSanYWmtqEpjYhhE2u/JVlY6w4R7Cd04TM+4iFPoKERiByyFIYSVKRJANVaUSSDCTJwvNnKBaew9L3Yhn7EMKrCZqpXUTMxwGfqdzv43gDeME8+iqqs0IIchWb5kSM923rRdcUvnPkDM+dPMf4fJ51jelV9bXDpk5bKkZDLIwAzk1l+NvXj/EbT9xLaypGWzrO+FyevqlZdrQ3MzGfZzpfpKs+SdQyee7EOd7oH+bxrb00xCNUXZeXzwzSGIvQno5zfirDO8MT/NMPP8ymlgZm8kWODI4tKde5FJocI6x1YKlNVLxxyu4ww4W/od56AEttQpHDi3Y/goBAOPhBBSeYp+CcYbbyOln7HSQUTLWJhtCjGHJqlbNwa2EHDjP2RX/NlJ4irF45UxhSLaJqFEMx8ITHZHVqUYn5VkKRFMJqmLT+7p2v8VyeI2NLhaLWpVN0JOOEdB0/CCg6DodGxnnp/CDHxiYZyeYoOw6aopAKW/SkU9zd0cbdne30pJNEjKUTIJ7v0zc7x4HhUd4aHuXU9AwzxRKeHxDWddLhEL11Kfa0NbOvvY319Wks7cZKwoIgIFu1mS2VmC2WmCwUGZrLMVkoMFUoMl0sUbBtKq6L7Xm4nl975igKlqYSNQySIYv6SJi2RJyedJK2eJyOZJz6SPi6x+f6PmO5PCcmlxMYCWiJx9jW3Ii6QpbqZkAsRH5vj00wVSjirxAJbm6spyUWw9SuPWyZs0scmjvP98bf4UDmPEWviirJaLKKDHgiwA1q57rOiDBY2spTLTtZF2nAUmsBez5f4Y1D53nr7UFmMkVm5woUizae/+6UNP4owwwbPP7TD/LN//IDBo4PIwKBosq0b2zh/o9e2b/+WpHLlZmZrZU2B4Hg3NAM3/j+MY6eGKFUXt3yJF+o8sbB89iORyxi0tNVj6Gr+EHATKbAD18+xZe/dZhK1VkTmZnPlXnryAC5fIWGuhibehvRruP6Xgl+EFAuO5zqm+CVN/s5cGSAqZk8vn9jmjKu5zM2kWVsIsuxk2OcH5rh/rvWsXlDC3WpW9MGIKMQ09ooexkK7gRlL0MgvDW4cFxE2cuQd0ep+PMYSpy4dmX7yNWgKjI7uy9m/f0goFR1mM4Wmc4VmcoWmc4WmckVmS9WKFRsilWH0sLfxaqD43lrukY8P2CuWGGuWKHvEteysKFTHw/TlIzSnIrSlIzRmIiQiFjELJOGRIS2upujOjw+m6NYcbB0lWQ0ROQ6Lbhcz2d6roAXCCKWTjRkoN+k6/1S/EgRWxbUlC/Y4ghq5cnVwFmmUPoTvPcgRIAfzFG2DwEO2dL/BCT8IIeurSPARkKrZWklE0UOoyqNqEojjj9MIEp4fgbXn6Bsv0nVPb24bU1pR1xi7q2prehqD6pSC+Ylae2lia4/USsvNvYvWvsoUmLhGHxcf5xS9RUcbwhJ0hHCx/OnEQultgCyFCFiPook6UiSjCKnEKJMEJSusNfl2NhcRyJsosgyTYkoEVNnNJOjpyF1VaNIPwjon5zl8OA4uXIFWZLJlatUXBc/qAVxOzqayJWrHBkcp7s+xet9Q6xrTC1kX31mCiX6pzIolwTCIV0jZGiUbIdsuYokwbb2WvllczJGYzyKuIKQmyypJM1dlNwBJkrfww1yjBW/xmzldaLaOky1CUUOIVErW3aDArY3Q9EbxPWzCHxkySCkttAYepy0eRfabc7YBiKg6l+ceTYUHW2VF7UmqxiKju3blLzyFc/XahBreaMuQJFUDFl/11SYoUZwfuer317SBfWLd+3hp3ZvpzuVpGA7vDk0wh+++Boj81nc4OKSbhBQzrqMZvO8PjjChzZn+OSubexoacJQ1cVjP5eZ588PHuHF/gFmS0szNblqlVy1yvnMHG8OjXJf9yQ/t283u1qb11ymegFCCFzfJ1upMlsqc3JqmqPjkxwbn+R8Zh7bW6UHUwh8z6PqecxXqgxnL4oRScCmhnoe7e3h3u521i9kqK81g+v4PgdHxvjXz75AyVmayVEkiSc29vIvnniExuit618tOS5/8sZBXjo/iH3ZBJcmy/zL9z/GExvNaya2buBzaG6Avx06wLH5YaKaSW+0gbgWIqpZKJJMxXPIuRVybpl5p8RfDb5O1Xf5qc57WBdtQJFk8sUqh47VSlZ/gpsLWZFp7KrnZ//lJ5kemaVSqBJLRUi3JNGMm9dfWChWmZ0r4bo+5YrD179zlBOnx9dEai/AcX3ePDTA+u4GYjGLlsY4hWKVt4+P8OVvHqZcuTZP0Krt0Xd+ir/+6lv8L7/yGHWpyIplztcCz6uVRB89OcoX/u4NhsfmcG+Bl/LsXJHvPHecwZEMTz2+jQfu7iWdvPnPCFnWaLJ2krHPknNHmbXP0GBtJao2raqtIoTADUqMlt4gU+3DFw6WkqTB3HLTxqfI8mL5cG/LxYqJQAgK5SqT8wVGZ3OMZfKMZnKMZ3LMFyuUbRfb9S7+8Xw8z1/TW71kO5SmHQan5y+OQ5JIx8K01cV5dGcvP/vonptyfC+9fY7Tg9O0Nya4b3s3m7sar2s7xYrNs2+dIVeyWd9Wx56NbTSlb34c9q4SWyEEAUHN7mfB5gJAkWQUSb7hwEmXVRqtNIPlCSq+TdW3OZE/z/niGK3WVfqBfoL3BAQuXjCNoEp3w1cAA0mSmM3/J/xgDtcbAmoexRcgSfJSEifJSJJGQ/yfETLuRpIuBLoBlwqJSehLtnMtWJxYEd4lBEIAEkJUKVVfomwfoDn5b9DUdjx/mlHv3GUbkZGX+NFKl2xnbbC9Wr+MJAn8IEAErCnjUqw6fPGN42xra+AXP/AgEVPnnZFJfniif3GZDU11HB+Z4ujQBBPZPC+eGuCZfVtIhS0ENeGfvd2t/OtPvx9jIRANggAkiWLV5vjIFELUMkWqLBMsPhOujISxDQkVgWCi9D0CUcXxM8z6s7BMCEKi9kvUenFVKUxE76U98jFaox+55HzeXizpFRKsSlTFwnmqqapf//PSF2t7eS7iDihtmiwUmS9XaInFODE5xf/nBy8ykS9c9Thc3+erx0+hyDJRw2BjQx0CKDkO//Gl13ljaJjCKub0uWqVF/oHyVcd/u1HPkDCMq/JT9fxfQbm5vn2ybN873Qfo7kc7k3K8gng1PQMp6ZneK7/PJ/atY2PbN1EzDSuiXyHdZ3uVJKtTY0cGB5d8p0vBKPZHAdHxnh6y9V7/G8EJyenGMsVlpFagKZYlE2N9SRD1y60M13N8e2xtzk6P0RLKMnjTVv5cOtuOsLpJTFHzilzNDvM10YO8crMWb4+epjeaCP1ZpSkfmtUrO90SJJ0SXmsWFOG6XoghKCcrxCKWrRvuCQT5vlUilXM8LVdz1eC4/rk8xWmZgtMTGV5/tXTFEpLy1ov2Jithu8+d4Jtm1ppqIty9twU333+ONn88srAC8O+2jYrVZfX3zrHBx7bSjhkEL7OjBjUMtFz2RKvvNHHn/zPVyiWrq1sdy3jvRRCwMmzE8xny8xnS3z243ejaze3HFaVDDojD3Ku8AOqfo7B4ktocojN8WfQ5QiSJF/yHhC1JIjwEMInwGesfJBT2a+QsftRJJ243kZLaO9NG9+VIEsS8bBFPGyxsa1hyXe5UpWxTI7BqTkGp+cZnJpneCbLVDaP4/o1z3MhCAJBIFgsnb8afCGYztWyxnWxMDx6c47j0KlRXjjSz47eFrpb0tdNbEsVhx8e7KN/dJbdG9toSEXf+8S25FUZLs/QVxhjojpPyavgi4CdyR52J9bRYCZuaPtpPc7HWh7hL4e/w0BxnM+f+wpxLcze5CaeaLz1/bU/wa2F50/geqPo6nrg4oNfV7uoOFkqzgnkqwgCSGhoSjOa0k6x+iKq0oChrScQBbxgDl1pAW58dlhXO3G8IYqVH5CK/iKg4QfzyJJJICoI4SBJBoqSBAIK1e/hBTM3vN/L8db5EVpTMcK6xtDsPNlKhXVN6VVfOFXXJRABIVMnbGhM54u8cHJplkKRZbobUkxkC3z/WB/DmRw7OpqIWQYCaE7EODM+w7PH+/jQ7s0AjGcLhA2dsKFTFw0RCHjj7DD3bOhgfC7PcCZLInx1oZCovp4NyX9Ia+RDTJdfJu+cpOyO4Pjz+KIKCGRJR5EsDKUeS2shpm8iaewkqm9Ak+PcKaRWlVQi2sWAueSXcQIX6yoWLE7gUPGr6LJGTI0gX4eIhkBQ8ioE4r1VQjlVKDJXrtA3O8vfvn2cyUJxTeRcAC/0D9AUi9Bbl6LsuvzpgSMcHh2nuAqpvYCK63Jqapr//uYhfu2+u4iZaw88R+Zz/PNvPcv5zBxV11s1OLle9M3M8hcH3yZfqfJr9+1HvUYrn+Z4lAd6OpcRW4CxXJ7XBoZvGbEVwKuDI8yVl1twyJLEw71dpELXZ4Xx4tRphkqztIZSfLR9L5/quBtDURcnIC8gqlncU9dLT6QBARyYPceB2XOsjzaxL/3u28ndCUjEQ7S3pCiXHYolm1LZwXFvvspzdjrP//FTf8jv/tlv0NR9kQD0Hxngy//h2/yzv/iHN21fpYrNyTPjvPxmH1X74rFIEpimTtjSqdgupVUIYWa+xPHTY4RCOsdOjnH81PiyZQxdXew9zRcq+MGV7/1ACF5+vY+u9vQNEdu5bIkfvnyKv/7KgVWP4VKoioxpalimjixLFEtVKlWHYI2viamZPN989h0kWeaTH9pLOHTzqiQlFOJ6O92RhzmbL1Nypzgx/3eMlQ/SHrqLlN6LF9QmFZygRNYdwi/ZZJ0hxitHmK2exvYLCALqjI10RR5Gk2/vZFU0ZLDBrGddc5ogqCUfLohKTc7nGcvUMrxjmTwTc3km5gvMZIvYrncTDEpvDxRFJhkLIcsSk5k8xfL1Wy5eDe8Ksa36Di/NHOfVmZMMliYp+zZO4BGIACEgpJqsj7Ry6XxGIASvzBznfHEST/jsSq5jX2r9VfejyRrdkRZ+peejTNvzuIFHRLVoMFOk9ZtTa/4T3D643jiON4ql71hCznS1m4pzDNs9jqXvu8oWJGQpRH38dyhWvs907t8ghIskqZjaZtKx30BaA7HNl79B2X6rJlTlDTGT/7eoSjPx8Ccxta2EjQcQQLH6AiOzvwIIJMkgHf0VTG0bpr4D2z3LWOY3kOUIhrYeVU7eVD9cTVUoVGz+5LkDzBZKxCyTj+zZTH00jAR84ZUjnJvKcGxkkortMFcs055O8Mm7t9MQC3N3bwdvD41zeGCMVCREPGTRGIuiyBdNxNc1pBiZzfI3bxzlvvUdRE1zUeH23vUdALxwcoDvHj2LHwiils4n9m9jT1crm1sbeGJ7L1949QhfPPAO7ek4mqrQGLtaGZOELKnocgJV30pIbcMTFYLARuDV5PsFINUytbKkLZLcmviUdc1ZzqHiIU7mnqXi5+iK7Gd74mm067S0uRymYtJmtSyIiwmmqtPk3TyWsvLkTM7JM+dkcQKHsBqiPdSKIl0kLzIKiqTgi4CiW7xi9jcQAePVCZzg2krmbjemCkUG5+aZLZV4bWBoCUFUJAkBVySN85UKxyenOTAyhqmqfPnYCbLVypIzJAGyLC+W21+OfNXmO6fO8smd2wjp2pr7TSOmwb2d7fTNzF4TqZUATZHx/JVc2JfDF4KxbI7vn+0nFbb47J5rE92pC4fZ09ZC0jLJVqpLzk2+anN2ZpbBuXnaE/ElLQY3igul2geGR5ivLM94qbLMw+u6SYWs65qSOpUfZ84p8UD9Bu6p6yWkrhxwy5KEJinUGVE+3r6fU7lxzhWnma7WRHOiEZN9OzuIRUzWUjmTK1QZHJ5lfGplH9uQpdPRmqS74+aIPa3raqAudXOD9W2bWmlvSVG1HXxf4PsBjutTqjhUKg7lsk254lCu1IjvdKbAqwf6VxXOuhwiEBSzJYLLKhnsqsvs+NzNPCQmpnI8++JJTp4Zx/N8TEPj7r3d3LdvHZ1tKVRNwXV9zg1M89XvHmV4NIOzQhmvEILT/ZPkC1WGRjNLeq337+7igbvW0dVeRzhk4PsB07MFfvDyKd48NEB1BQXdIBC8fXyEZz6wC9EsrivjWSzZPP/Kab7x/WNk85WrXqUhS2fn1jbW9zSwrrOedCqCoas1L2ok/CDAdX3Gp7KcH5rl+Kkx+s5PLZkMWDJ+IcjMl/jm94/R1BDj3r09xKI3x5e1FlcobEx8GFdUOF94npI3TaZ6lqIzjiqbVP0sALP2GQruOLKk4Qkbxy/iLUx6p431rIs9QWto/20XWJIlCVmRlikbR0yDRNikpymN4/rYnofj+biej+355EoVMvky07kiw9NZTo5McW7i5iir32rIkkTUqlVf5EtVbGftStLXgltKbAMRUPJsvjTyCm9kTjNUnqbkVZctV/WdFbIHAlf4HJzvY6qaxQ18OkMN1JtXI6i1Mr32UBNNZppACFRZQZWVZbOzV9yC8BAiTxDMEwR5EGWEcGv9j4tCLbduvkRWWtD1qwckbuBzYm6Krw2c4IHmLvY3tBNWdc7mZvjzM4e4q6GD97WtX5PVxPXCDXwOz4zxysQAv7Pr4Vu2n0uha+uISx9DVZaq2WlqB/HQR/CDeVSlgVrWziQZ+QVUpR5FThI2HiTQS4BCSN+NIkfx/NmF7KmCKtcvktqQsX+BbK5cvm5oW1DkFCHzfoSwkSUTSTLQlY5aT61iETbuR1Na8YMsECBJGpraiSQZWPouFDmG588hSSqa0oKl70VXu5DQCBn7UeTEkn3GrKcQeGjq6lYcUOuT3dPdSjxkUrZd4pZBR10STa0RoT3drfQ0pPh/2Pvv4MryPLsP/Pyuv89beJ/el/e+qn33eM6MZrjUkBEckiIZIhnUMhQrcqXdiBUZmhVDEUtSlIYccjhDcXx3T3tT1dVdLstXpc9EAglvHp631+8fD4lMJEwCSKAqq7tOBBKJ9+677t37u7/zNec8fnAQLwgwVIWQppEMm+iqwgvH93O0t4OG7RDSVRIhk3uHuklFbpDvsK5haipN2+WF4/sx1BuKuV2JKE8eHmYgk6DeaiuPmprKUDaJLEl0xCO8cHw/Q5kkXhCQCBkEQMzU6Yxv3qMjhIQsDGRp7730ml6FJesadTdPWh/YVe87TVLp0DP0h3qZac5xoXKJfZFhMnoG+RYCHhBwtT7OeP0akpCIqzH2R0ZW+bkaskZIDlG0i0w1Z3B8B0Na7Y/tBR5lt8rF6iWa3tqx+G5Gvt7gtfFJdEWhatnEDZ1nD4xwtLODpGnScBzOzS/wyvgEs+XV3pdeEDBRKPH9S6OEVJWFag0/CEiFTO7r6+FUTxcd0QhBANOlMt84f4mZW0qGXd9nsVbnzNw8yZBJwtza9ZcwDF44uI+vn7tIrlZfRW6FgJCqMpxK0huP0RWLko2EiRs6uqKgyjJ+ENByXHK1OpPFEhcWc1xdKmB7ayfbju9zrVDkOxev8Oz+EbKR8JZJqCpJdEWjPDI0wA8vX121fi8IyNXqvDI2wa/ec3xXia3lepydm2e2XF0jHqfJMgeyafalUxjKzqYruVaFpmvTYcTpC20ugCYQaJLMsUQvhqyQs6orc5Z4zOSxB/dz6tjWRGfGJ5f4/svnNyS20YjByaN9fP6549s7oA0QMjUSu+yJGgnrRMI35hHBcmmk6/o4y/2A7d8+zZbN5EyBN98d3zKxLeUqTF+eZX58kUalyfsvn2PqUjvzabVsxs9Mouyyh2ex3KB5eY5a3ULTFH7+C6d4/KH9DA9kiIYNJEng+wE9nXEiYYM//vrbXBydx18n23rl6iITUwWarXaQ0DRUnn7sIJ95+ij7BrPEogaKIuP7AYN9aToyUSzL5cyF6XX7epcKNeZzFYYHMoS2mfH0/YDX3rrKj167zNxCacNS4mQixIkjvTx83zDDAxlSiTDxmImhq8i3kCzfDxjsT3P0YA8P3jPEpdF5Tr87zvtnp9Zd93UC/xffeo/ObIwjB7rRd9HGKKRkOBj7EqacZrL+CkutSzS8PNw0bDh+A8dfXfmhihDdoXsYijxDT+g+dPlODOr2FpIkMDQVY/m6d1yPUr3JQqnGUrnGdL7MdK7MdL7MfLFKvrJ13ZWPG5IQ6JqCAJqWs2stObdiz4htW1rb5jvz7/C9+XeZbxXwg4C4GiatRZElmSvVmQ0/LxAMhTtwfJeFVpGrtTkmGoubEtuiXeXFxbf4XNejRBRzSz27QeDi+0U89zKeO4XnzeMHRQK/suJBGwQu4AL+tkRXdgJdf+K2xNYLAmbrZV6dv0ZU1dgXSyMMweXSEt+dukxI0Xiie5goe0ds/SBgolrk5dmxj4zYqnInqry2tl+WIsjasTWvh/QHb1rmxnUjRHjFs3Y9aMoAMLDh+7q6D13dt/m+LisnrwdFzqDIG0fpNWVgeR9uwNCObLq9W+H7Af3pOEd71++FONrbse7r1zGQSTCQSax67XDPDaIfBJCr1lko1+hPxznW17nKUkWVZboT0Q19aXVJoTcVpzf1s1tJIQmJqBLlqexjfGP2u0w3Z3mz8C4yMkPhAaJqBIGg5ta51pjkjfzbTDamSahxTiVOkNKSqwJ2cTVOp55lsbW4sq5jsSOk9SSSkGm6TWZbc7xfOsNCK4cb7H5J4V6i5bqcnV9AkWSykTA/f/wwLxzcz0g6SVjTsFyPE92dpEImf/jOh1St1WVOi7UaPxm7ttLTnQqZfPnoIZ49MMKhjixJsx1cydXqxAyd//zuB0wUSqvCmK7v88HsPPf29WyZ2OqKzL5Migf6e/nx1WvUbZtsOMxQOslQKkF/IkZfPE4mEiYdCpEwDcK6hiZJKyIytutRajZZqNa4mi/w7vQsr45PMlVaS5qajstorsCPRsf5yrHDhPWtTZCFECRMg88c3M8rY9fWEOdyq8XLV8f5yvHDqPLu9dA1HYeXr16jZtlrQsZhTeXJkSHihrFjMu34Hh4+miyjy7ef8ggEEUVHIOH4Hu5y0F3XFLo7tz5eOa5PJLzxNaKqMulUhP3Dm4/FdxOEEMiyQJYldH31ubQdF9/3lzN+W4PruJRzVa6dm6bVsLh4epRIop119j0fIQSP/dxmVVjbh+N4OI6HLEvcd3KA5x4/zL7h7Cp1VkkSJBNhHr5/mLHJJQqlOvOLa718y9UbFQaGrnBgpINf+MK97B/KrvKllSRBKKRx+EAXzz95mNmFEvXG2ky06/nMzpeo1lvbJrZjEzlefXOUq9dyGyof9/ckeeT+EZ54eD+H9ndhGptb/0mSIBYxiEUMejrjDPWn6emMEw5pvPPBxLrZ2yAIuDS6wCunR0nGQgz2p7d1HJtBIEjqQ6iSSUzrYal1kaJ9jYa7RMsr4/kWPj4SEopkoMtxwkoHKX2ETuM4af0AhpLYtf3ZTfhBQNNyyFcb7Z9KnUK1QaHathMq1BqUai1K9SblepNK06Jlf7Ke4X4Q0LLbpdTt3uG94VN7Rmxtz2WyscjXpl9nvlUkJBuMRLo4FO2jN5TG9r1NiS1An5khqUVQJZklq8xkfXHTcuSyU+N786d5Ons/EeX2JRCet4TrXsKx38ex38Z1RvH8GQgs9jIruxlkeesPuawZoWA1KVhNVElmoVEloRlbzk5/ik+xEyxV64wvFjkzNc9CucozR/cR28B/9lNsDl3WeTj1ANONGd4vneXD0lkqToVD0QMk1ThCCEpOhUvVK4zV2tnaE/GjPJJ6YE3grlPPciC6j9HaGBWnyg8XXmbRytGhZ5GFTN2tM9WY4VLtCiPhYUZrV2l4nywbtHLLIqbrPDjQy2/efw+d0ciKAnBIkzja1YEsSZybX+TV8clVD8667VC3bxDBx4cH+aWTxzjYkVlVVtwVjfDzx4/wwew8uVqDun0jsxIEAZdzSzTsrZdxCyEwVJUvHjlIw7aRhOBgR5bjXR0c6sjQG4/dVmlZU2Q6ohE6ohGOdGY52dNFOhTij94/Q6HRXDNBqFoW3754mecP7tuSb/V1hDWV+/q66Y3HuJovrIqoN2yH8ws5rhWKHOrI7jiDejP8IKDSsvjJ2AQtd3VZmgDipsmz+4fR72BbpqKhCJmm61B3LOLa5llNn4CCXccPfAxJRZW216t8t+GdmVk6I2Gy4fAdnce9QChq0rO/i2qxRiwVoXu4g9iyx62syKS6Ehx8YGRvtm2qfP7ZY/T3Jje0HAmHdO4/OcCVsYV1ie3NSCXCPPPYIQ7v79qU3D9wapDvvXyembkS3joZq4VchWq9RWd2a6I6QRDgOB4vv3aZC1fmNlRl7uqI8dSjB/jM00cZGdy+mKosS2RSER59cB/xeIha3eLS1QVa6/jhep7Pq2+OcnCkg45sFNPYXVeSiNpJWMnSZd5Dyb5G1Zmh4RZw/SZ+4CEJGVUKYyopYmofSX0YVRh3JLi4m2j30tpUGxblRotKvUWp0SJfqTNfrDJfrDFfrLJQqlKoNja0QdwIsiRQlbvjWK/D932alsNMrozn+WiqvGf2cXs2ylXcBm8XrjDdyIEQnEgM8Qu9j3JPch+6rDLTWOL/d+XrG35eCIEua3QYCUKyQcmps9AqbbpNSUiElK0pVvpeHtt6mWbjT7Ct14G9qfXeS/SEolScFkWrgS4rLFkNBqPJFVpbsVvkWw3qrg1Bu1cpa0ZIaO3od8Vu0XAdXN/H8lwabntAHIgmiartbG/Tdci1atQcmyAIkISgOxTDVFQEbf+/+UaVotXE9T0iqk7GCG+7DNoLHFy/heO3cAMLL3DwA5cAb5WCtkCs9FtIQkZCQZY0FKEhCx1F0pB3qGb80wAhBLqqcKK/E3OXS7iuY7Fc45VL15jKlzg50M1nT2ze+/4pNoYsJJJags93fQZFUrlQucRYfYILlUu4y60PspAxZJ2oEuFY7AhPZB6hP9S7Zl0ZPc3J+DFmGrNcro2yaOX4zvwP8INgucRSJapG6DV7+fmeL/Dvx/+ARnPz4OLdiL5EnOcP7qM7trYaQBKCrmiELx45xBsTU/jrZC4EYGoqv3jyKEOpxJqHqxCCZMjkWFcH5+cXGS/cRGyBiUKJhtNWPN8qYVQliceGBjAUhb5EnN54DE3ZGWFSZJnhVJLfeOAUi7U63754eY0IluW6nJtfpNKySIXMLas4y5JEMmTy6NAAuVqdfONG4CMAapbFj69eozcWw4jc+fSh5bhMlcpcWsyt8a41VJWBRPyO/XM7jThhRWOmUWCsluNUcmBDO6QgCGh5Dm8vjdP0bLJGjJi6O32CHxden5jknp5uYrq+bWLbWu7vU2Xpjj2c10MoajJ8vJ9MT5Kr70/wud96hlR38vYfvEOoikxHJsY9x/tvK9S0byhLX08SWRYbZkJlSdDdGeepRw9uKiAvhCCditCVjREyNaq1te0g+WKNZnPr81HfD5ieK/L622MsLlXXXcbQVR5/cB/PP3lkR6T21nXdc6yfL71wkkrtTSam8uuWac/MlTh3aZYDI5271kd+M4SQMOQYXeZJusyTu77+3YLn+9iuR9NyaNoOLduh3nKYL1aZzJWYWCwysVhgaqltAbRdSMtzPlNXMDUVU1PbtkPd2z/n190pbsX1+fd1V4aNdCjWQxAsl1PXmpwfn2d8No/n+SRTUcxdtPK6GXtHbJ0G7xWvEhCQUMN8ufch7k8dWNUTthWktCghRado1yg7m9eSG7LGcLiH8focIDDlGwOWJCSMZS/bIHBptb5Lo/77uM6H2z62uwVZM8KF0iIV28JUWhRaDYajN3qIzhYW+Ma185wtzOMFPjHN4IsDh/n54WPENIMz+Xnezk1TtpoUrSaXyjk83+efPvACD3b04/k+H+bn+LOxM1wsLeL6Prqk8NvHHubJ7uF2A7jd4s+unuEnc+MUrSYHExl+eeQET/UMb1oK3r55ArzAwQtsam6OgjVO3hqn7ExTdRZoemVsv4brW/iB2zZvEW0iqwoTU05gykkiagcxrZuE2ktc6yespFCEgSzUjyVC5wc+/vJx+YEHBCAk5GUSLqHsWhnfrZAliZ5kjN/97V/Zk/UDHO3r5GjfzuTef1ohEITkEFE1QkgOoWzDOB6gL9TDr/X/Ehcql3i/dIbx+gQVp0pAQEyNMhjq577kKQ5G9hFV1y/vloTEcHiQ3xz8Vd4ovMXZ8gUWrRyWZ6FJOp1GB8djR3g88zBRJUK32Undq6PL+rp2PqpQCSthYmqUkHz3TO574lEe7F9L7K8jZujc19eDLis43trshSrLHO/qZF86RUjbOJMwkk6RDocYLxRXvZ6r1Wk5zrKB19YghCCsazy5b2iLn7j9+pKGyd9+/CHenpqhYTursrYBbZ/DiWKJzmiEyBbLkaF9fl44tI9XxiZWEVtoWxe9eGWMzx8+SCoc2rZf7q3INxq8MTG1htQCZMMhHhnqX9XisBMci/dypjjFmfI0P168yFAkQ0jWUSQJ6SbPez/wsXyX6XqBP5p4g4rT5MHMPrrNxB1t/+PG33vskR1/drpcZqpcpjsa5XB27ywTQzGTX/8nP08s89F4i5uGytGD3ej67asZYlGTbDpKNGJQKq9PPKJRk4HlHtqtoCMbIxox1iW25UpzXXGp9RAEAbbj8f2XL5DLV9clmAAHRjp45onD7Bvane9QCHj+ycO8f26KpXxt3eMAOHNhlqMHexjqv70bw8eBIAjaol9bKNCUJLGmB/nWdbVLbH1cz8fz2+uutSxmCxVGZ/Ncmc0xOptnbKFAvWVvu7VR0M6cy5KEIksokkTY0BjoSHCgJ8OBngz7uzP0ZeI7qqKrNSyqzbVKxddLnh3Xo1RtMl9YP4CyHhzHI1+u897lGb712nmay9f2cHeKZHR3NQGuY8+IbdOzmagvEgDH44Nk9cQaQZStwJR1NKFg+y6t26h4KkImqoT4N1f/lKFQDwktsrLNTiPNL/c9B4DnXqXV/Cauc27b+3M3QRESIgAfn4rdpGQ3OZnuZrrWLrcbiaX428ceIaRoBMCH+Tn++9Pf5umefcS09kU/VslTd2z+0T1PMRBJULEtUrqJJsm8ujjNtyYukNAM/uOzv4auKBRaTRK6gSbJ7UHB91EliX/91C/ScB1+5/2XOb04ybFUJ1lzMxGgAMuvMVb9CVdrPyZvXcX2agT4BIFPwPUs7erJmh94uJ6FRY26u8RyDhchJAQCWWhE1U6OJ36B4chjmMreR39vRcMtMFV/i9HaSxSsa7iBhSHH6DaPczD2GbqM4yhid0tzPsXHC1M2+Fsjv3VTf8/2h1ZD0jmVOM7x+JG2YvzNVQpIyJKMfBt7H4EgrsZ5oeMZnss+hb9yDwkkBJKQVkj33x75G/j4KEJZl4jfmzjFyfgxfPxV6ssfJwxFIRsO07VOtvY6JCGI6hoDqQSjufyaXlFDUXhsaABzg/LD6+iMRojqa7M5XhBQbVnYrouxB1msrUKWBL3xGPf0dlNsNik1V08uA2CyWOJEd+e2iK0sBPf0dDOQTDBdrtB0bkyyPT/gwkKO8UKB7liEyDrnZzvI1xu8dm1y3fc6ohGeGB68o/UDPNV5mNNLo/xo4SJ/Of0uU/U8X+g9xdFYDwktjCLJ1F2L6Uaet/PjfHv2A8ZqOUxZ5bHMAfZFfnaDeGfnF1is1YlsEgDaDUiyRLIr/pGRH11XGBnMrKj83w6pRJiOdGxDYptKhBjaRi9pOhFesQG6FdWahbWB8vCtCAJoNC1eevUSlerG2b7PPnOUgZ7dnQspisQDpwYZn1jiwpW5dZe5NrnE+MQSzZaz4fF+nAgCOHdlHmcLFlY9HXF6uxKbrqtu2UwsFLk0k+PKbI7LM0tML5WpNi38oE1276S3NBoy2NeV4kBvloO9bSLbn0liaAqyJNpKy8u/d4I/+sF7/MF3317zurVMbC9P5viX/+Vl5D/58bbWGwTt8vSbW1sePDpIb8feaKzsGbF1fY+K01Ymy+hxDFnb0aAlX+892iBFfjPaYiwhHkufRJNUZCGvJCJiyg0ZfMv6MZ43ySoptZsgpCSy3IcsdyFJCYSIgFCWVXP3duBV1LUiSJsha4axPY+6U0eVJPSbotsFq8FPZscZq+QJgLLdotCq4wbeyrlM6SEGIkn2xdJokowpqyv9XjP1EhXb4pdHTpA0QgjACCtIQuD47SlzXDf5/MAh4ppBTDPoDLWVRSu2tSGxbbpFphvvcb78Tcr2NC2vihdY21ScvVGeHKz8Q7uM2XbamdqPIVtbd/OcK32dK5Uf0PCKeIFNgI/j1Wm5Jcr2LEfjX+Jg/AXETSTlbOk7jFVfJ6n1cSzxeWJqJwV7ktHqqyy1xml6ZQQSYSVJ1tjHwehTxLQupHUIRxAE2H6Ta7XTzDTPUbJncfwGslCJKFm6Q0fZF3mUkJJY9/O3wvabFKxJJmpvk7cmaHhF3MBGEgqGHCEsp8kaIwyE7yWiZFGk9R9gNWeJqcb7zDTOUbbncIJmOxChZOkNHWcgfB8xtWPTLPt88xLnyt+haM3wSOav0mkeoumWmGy8z0zjDFVnATew0aQQCa2H/dHH6TAOYsjrX4tle46pxgdM1z+g4i7iBx5hJUWveZzhyEPLVQK3P0ft1gmda7VRzlTeZaoxTrfRxzMdnyepbW3CI4RAXrbsuY4gCPjzmT9gKLyfA5EjxNTEbdchAGkL5fi6vDkpUSQZhbuD0F5HwjTIRsKbPriFEMiSRHc0wrVCcQ2xVWWJQ9kM2m0ygTFd37CPtO442J6P8TF2PVz/rg93Znh7amYNsQWotizcbZSNXYciSTw61M+1QpGr+dUiN34Q8Pr4FCPpFPvvgNjWbZvJYokrubVWFTFdZySdYjh955PxhBri5/rup+HZvLk0xun8VS5WZjFlDVlICCHwAx/bd6m7NhWniSokfmvkSU4lBzDkj/ZLbjoO37x4iXMLi/TH47w1M43r+3zh4EGeGBqkIxKh0mrxwfw8Xz9/kUKjiakqPLdvhMcGB+iKRim3Wrw7O8s3L1xiulzh106d4KnhIdKhEK7vM1Uu8z+/9COeHRnhwmKO2WqF7miM5/fv44mhQZbqDf7gvfd5fXKSmm3z0tgYSdOgLx7nnzzzNBLsKgltVJt863d/yEOfu4eukU7ef/EsP/7TNwiAz/+NZznxxOFd3Z6mKvR2JVfE2W6HcEgnEtk4AxaLmNsSFguFNDR1/fGnZdm4W+ypbDQtPjg3TbnSWNcjV5YlBvtSHD3YTSxq7to5vL6ek0f7ePXN0Q2Jrev5zC2WmZwpcHj/+mKaHyd83+ebL52htBwUEELQaNkUyw1S8RBCEhRKDbqyMX7uuRMrxNYPAiqNFlO5EtcWiowvFJhYLDJXqFK3bFq2Q8txsex2Kf92iawQkIqE6M8mGOpIMtSZYqgzSWciSkhXMVQFQ1PQVRVNle+4cuY6FLmdIKq31k8i+kGA5bh31LkpCcFnHjrEA4f7PnkZ25tPsyykHdPBlufg+h6KJKPd5gETUUwey6xfa69JNz7rOGfx/VsfpjKyPIBhfhlFPYokZ9q+okJfJrQfDVESYns+dD3hGEWrSdlu0RWKrZRWNV2Hv7x2HoHg+b79mIrGZLXE96YurwoQGIpCQjdW1CJv7sWyPA8v8Eka5sqNo6xMutuTJU2S6Q7fED/RJBnX9/HW2De1UbKnGa+9wpXKixTtCbxgb7w0k9ogqvjoyyfnGh8y23yfijvPzdlmHw/Lr5FrXWJW66PLPEZcu1FO2XCL5O0JfHwK1iRL1jXOlL5JyZ6h6VXwlqsVFEmn4ZXYH3183e27vk3Jnua9wtdYtEapOUs4QYtg+ftQxBgLrcvMNc9zPPF5Oo2DqBvY5gQEFK0pRquvMVF/m6qzSMur4gbt6KNALJeF68w2z5HWhwgra0mcH3jMNM5yofxDFluXqbkFHL+5spUlMcaidYWZ5lkORZ+mL3xqw31y/CZFe5acNUbFWcDHZ7T6CjONs9TcJWy/QRD4yEIh1xqj0zhE1ti/7rpmG+e5VHmJqcYH1Jw8btBEQqEgJshb18hZY6iSgbyNsuKUnuVE/D6qTpmqW8EJ7rR3P2DJWiSjdXziVIz3AlFdJ7kFNWJJEmQj4XV7MxVZZjidvG2Ja1jXNuyDbTku7jp2Ox8HumMxjA2yzzXb3jaxvT5pfWSonx+PXWMsX1hTqff6xCTPHRhhJJ3a8aRqrlLl7Nwilrv2uu5PxDnV07UrYkeKJHMi2U8AZI0Yr+dGmW2W1l3WlDWOxHv4TNdxnuw4RNaI7tqkcavwg4CJUomLuRxHOzv41RMnWKzV+P7oVWKGwSP9Gh/MzfOtS5c50dVJbyxGzbJ5a6bdL//Cgf2YisLBdAbtuMz/+pNXqVrWis9qEATUbZs3JqfZl07zyEA/iLbI1ItXxzjW2UFM13h+/wj5RoOGY3O8s5ODmQwRXbtNzcjOYDVsXvnzN7nv+RMU5kqMfTiBoimke1K8/MevceKJw7u6PVWVyWaiW1ZwNk2V8CYqxeGwRia1uU3dzQgZGuoGxNay3VWeuJuhVrd454OJdX12AVRF4p7j/STioU3LaHeKVCJERzZGJKxTq68tYYW2GNbk9ObE1vVbVJ31yfFeoR3o7+VLzx5fOX9Xri0yOVfkiftGGOxNg4Dp+RJTc8VVgYOm5fDP/+QlcuUaleXy3WrDomE72y4vVmSJeMigNx2jJx2nNxWjOx0jHQ0TDxlEQzoxUyca0jE0dU/Ho2fvP0B3Js5srsy1+QITcwWmFkvUm/YdKRhLkiAa0hnsSnHfoT6euXcf/Z3JNR6+u4U9I7aykIgoJpbtUHEaOP72JgHXL47FVoma2yQkG8TVzUmfJqn0hW5fNuR7cwSrfK4kJLmDcPRvoelPIcvdCLF3Vjm7iZ5wnAvFRXLNGvdnbyZKNpdLOe7J9PBI5yCO762UKN8MCbGhsEhcM1AlmTP5eQYi7VLypusgL9tlQDuAsVXVyJqTY7z2KpfK36Ngj2//YLcASajEtV4iSvZjEZFaskapOHNs1LThBhYVZ46SPbWK2F5Hwy1yrf42NSdHy6vSH7qXsJJECAnHb1J3C6iSSUhZbfUC4AUuJXuWdwt/wZXqK+hSmL7QCZJ6P5oUwvUtys4cE/V3GKueBgJEQqLHPLpulrRoTXO58mMuVn5I2V4grnUxFHmIqJJBkXS8wKXlVam7eRy/iSnHkG4hga5vU7SneL/4dabq72HIMfrDp0hp/aiSiRc4VJwFpuofMFF7G8dvgRAMhR/YNHPrBQ6LrVHGa6epukvE1S4GwveiSiZ+4ND0KlSdHFE1iypWE6EgCKi7eS5VXmS0+ipu4NBp7KcrdARDiuAGFiV7loI1geXX2/u0RcTUODE1zmj1IgvW7JY/txnuls4kx7cZq5+jU+8nosRRpI/+/gprGlHj9mOzhGhPwtfrHZYlstHIbe1jNHlj1UbH89btC/04EDeMdcfggLb1z0Z9d7fDQCLBwWyG8/OLLNVX+0JOlypczi1xpDNLJrK9YOyNdZR5f3btZFYAQ+kk9/R2r3mvYrWQhCByizjhWKlA0jBJGusHM2Oqyb2pQdJ6mKOxXibqS+SsKk2vPWHTJIW4atITSrA/2sWpxAAxzdxR+9RuwPF8QprGfT09DKeSlFstfnh1jMlSie5olPFCkWKzyQv799EdjVKzbS7kckyUSsxWKhzOZumNx+iNx/i9t99d64sdtDPDhzJZHhscwFRVCo0mpyenKDSaZLNh7u3p4fTUNHXb5lR3Nw/19+3Z8fqeT2GuhKqrjJ+dxHN97nn2GGbE4E//5Td2fXuyLJFKhLacwdRUeVM/VtPQSG7DQ1hV5Q2Jpuv6W7pngyCg3rA5c2FmXXVlAEWRuff4wJ6VASuKTDYdIbmskrweCqU6cxt4OV9H3c3xfuE/7cUuboiwkuWh7N/hniM3rutSpUGp2uRzTx5d+T7L1SZ//O33aNzUe+p6Pq+ev7ZuP+pGELTV7VOxEJlYmHQ0TOb6/2Nh0tFQ+ycWIhUNYajqtqyzdgND3Sl6MjFKtRaLxSqLhRpz+Qpf/8lZxmbzdCQjnNzXw1DP5n7gN0MIgarIRAyNjlSEfb0ZutKxPSO1sIfE1pA1es0UBbvCRH2BslPDC/xtPShmm3mmGjlqbou+UIYuY3d6BILA4uYyZCFMFOUwhvmLCBG+ayTBt4LuUIyzhXlsz2MomiLXrAFtFc7ecJxCq8mLM6N4QcBYJU9E3XpJ+IF4hul6mbdz0/hBgCwEtu9xItVNb3h7Ig9e4DDdeJer1Zf3jNQCKEKnyziKIhkfi1hBwy1ie5uLnNlenYZXXPe9irOAF7iYcoyTiS/TEzpKRMkgCRnLr1NzlnB9C2Mdg/F2Se57XK78GCEkDsSe5ED0STL6EJpsLpPMaQw5xvny9xmvvkVC7SWh9RBWVg9Urm8x1XiPy9UfU3PydBj7OBx/jm7zGHG1E1Uy8AKHplem6uRoeRWiauct2c0Ay69ysfIS12pvocth9kcf40DsKTL6EIqkEwQeJXuWqNrBpfKLTDc+xJSjpLUBYtrGEV7Pd5hqvN/2u448yGD4flL6IJoUwgscGm6RsjO3vJ1bH+oB040Pmay/R8ur0R++lxOJL9AXOokmhfDxKFiTXCj/gNHqq9TcJUx5d3pBbN8iZy1QsHI4gYMiFDr0LjJ6J4qk0vKaLFmLLFkLAESU2HKm9uOntwEBtt9aFkT7eGCoyqaCTysQoMvKGmIrC0FI1TDV2wu4yZLYUOHU82/fGvNRwVDkDXoFA1zfW+6z3sF6VYWTPV18MDO3htjanseHs/Oc6u3eEbGt2zYThRJXl9aWISdDJvsyKQaSa++5DxbnkSWJ+zt70BUFL/C5Wizw4sQYj/cNbEhsAcKKzpF4L/ujXZTsOvPNMg3Pwg8CdFklqYXp0KOE1bvDuiyqaStWUEnTJB0yaTkui7Uadccmomn0xNrP4aiu0xmJkG80KDa3rqx6rLODqK4jSxIRTUNTFBrOR+8QIYRAD2ssTi5x9pWLZHpTHHn4APPXcjhb7DfdDlRFJhzWN1UwvhmKLKFsYp+iawrh8NaTIbIsbZh587c4tjiOR6FUZ3a+tC4RFqItkrV/uGNTUn6nSCXCJOIhpmY3mNNUWywVqpuqyLe8Mler39+zfVwPCW2Ih7J/Z9VrjZZNudpcVY3juB6lSoP4JqXot0IIiBg6sZBOLGQQDxnEwgapcIhsIkxXMkpHIkJnIkI6Gia8y3ZIdwJNVehIRuhIRmCkffwXri0wvViiIxnlmfv28/S9+7a8PiEJFEnak4qBjbBnV3tEMTmeGOJsZYLx2gIXKlP0mGmyenxTtVwAL/Cp2HVeWvyA6WYeL/DpNlLsi6yN4O4EQpi0D91b+VtRhhFC/0SQWglBygjRF4nTF45xOJEla4QZiqYICLA8j7QR5rne/fxkbpwfTo/SGYpwIJbhud79hJX2TRTTDPojCeL6+jfsgUQGSQi+O3WJH0xfwQ98TEWlMxSlNxIja0Y4nlpNPvojCTzfx7ylbLzqLDBee4W8NXrb45OFhiHHUCUTRRhIQkbQ7pcOApYFplw8PDzfxgts3KBtFaRKJt2hkx+b5U+Ad9te4XZ38PrLNL0KESXD8cTnOBx/ftV7qmQQWafU9/o6S/YsV6uv4QU23cZRjie+QErrW7mmFUknrQ9xf+pXmGmcJdcaZb51mW7r6BpiW3WXmGmco2BNktR6OZ0WSfcAAQAASURBVJb4PCeTX1q1jIKOLkdIrJN5hnYJctVZ5GL5RbzAoj/8OIfiz9Jp3LAHEkIhpQ9wj/IVak6Oy5WXmW9eZrrxIUc3IbY+LnlrggfSv8qJxBeIazfGBhUdQ46Q0vvXnqcgWC5ffo26WyCqZhmOPMRw5OGVcUlGIWuMEPA8Da9Eubw7JVIBAXPNaS5Xz7PQmsXxbSQhkdBSPJF5gYSWYq41zdnyu8w1p9Elk5SWoeZW8bfVf77z/fMDn6K9gB+0SawkZKJKAlMOU3fLJNQshhxCEhJNr47tW2hCw5DDQMCiNUNcTeMFHk2vhhe4SEIirqZxfZeGV8UPvGVBKoWoksCQt57p0BR5S/6pgra6763TKEWWiBkagtuHCmRp48nnjQ7/jx+bPbOC1fp728bxrg4OZNO8PzOHc0tJ85n5RUaX8pzq6dq2cvFMucLoUoGqtbYV5UA2zaFsZt0y5KulAsVWk5imM5JIMV0t85ejl1ioV7m/q2dL21YlmawRI2t8NAq8O0XLdSk1m3RGIrRcl7rjoMgSEV1Hk2Usz6PSahHVdWzPo2ZbbTKjbP3ZpyvyTZUL7Rqgmy8XSQg839+WvcdOoGgK++8d4txrl1iaKXDowX0kOuNMXZ4jFNvdliJJEmiasqF37fqfkTas8Li+Pn0b9nqyJDbMxvnB1saWesNmdr60YRmyLEvEYyFSyTDKDq3FtoJIWN+0TLvRtClXmtiOtyHBloSCId0meCwEEjJuYLf9arkR8JCEiiw0JCRAoj1L9FbcKa5DEQa6HEWTIiS0oTWbSMZCCAE/evMKB4c6ABidyOF6Hsn47a9DIcDQVDriYYY7U+zrzjDUmaQ/k6AnFSMTCyPE7van7zVURSabCBMJ6UiSQFVljD2y6dkt7BmxTWhhHkof4huzp6k6Lb41+xaapPJk9jgRxcD2b1yUfuDjBB4tz8YN2qJTb+Uv82dTr1Gwq4RknQPRHg5Hd6cURlGGcZ3z+H67jKDtlXo9Ir0dE4e9w4odjl9AEmEk6cZNpckyj3QO8kjnIEFg0x8ZWc5C+wxGB2HZTuaZ3n0807s6svJrB+5Z+f+JdBcn0huTB1WSOZRIcCB+HCFMBPqqG/LZ3n08e8v6f3X/qXWOA67VXqVgXcPbpOdQQkaTIyS0fvpC95LSR4irvRhyFFUyESj4uHiBje1VaXoV6m6eqjNP2ZmhZE8hkOgyjn1sxFaVQsiSjuttXKKiSDqatH6WQ14mevujT2xru0HgU3EXmW9eQJY09sceJyTH10x6JSETUdNk9CEqzhxle4aiNc1g+L5Vyy00L1N25hFCIqn3czj27Lb2B8Dy6ixZE9TcJQCGww+QUNcPTrX96A6z0LpMxVlkpnGWI/HPABs9BASaFOJg7Cmiase29svzbXKtUSy/waDxABl9aN1gW1ofJKX179q15Ac+bxZewZAMnsp+hqgaZ8la5A8n/i0DoRE0SeNc+X2Kdp7Pdv08veYgs81J3i68iuNvveRp833wcPzmcgCmDQkZRdKRUGh5DV7OfQ1ZKMw1r6HLIe5LPMVI5BhnSq/zbullvtj9f2MgdIj51iTzzQmSWpaD0Xux/CY/XPwTHkt/kbpbZrx+ActvoQiFexJP0PQaXK6+h71s32XIIY7HH2UksnXBPFWW0bc6SVvnspGFhLGNif+G2EVWe32M9Jb9AT3fxwuCFU/BdjDv+m/aQorceK3YaGy5J2+76E3EOdiRIRsJM1tZbfEwXSozmsuTrzc2Vam+FUEQcHlxiUu5pTXvCeBIZwcHO9b3YHxuYIRvjl3iq1cu8MLQPv7i8nnKVou/e+/DHE7vnR3NRw0BLNbqvDU1w0MDfUyXyyzW6mSGwxxIp1ms1WhNTvPm1DQnurtYqjcYKxQ50pGlPxHH9TxqjoPn+zieR922KbWahDR1W0GIuK4zV6kyX6uRq9dRJZnEFnrct4tQzOSv/MOv8NIfvcbDX7qXo48exGq0Ccm9zx3f1W0psoSub2/qK66LmK4DTVXQNGXL2d/lFW783haDUfWmxfxiZcP3NVUhm45ub792AE1VUG8TJLBsl2q1hZ5evw9Zl6IMRZ7cZA0CIRRkoVK0JyhYV7C8CopkoEghwkqakJJBFSaSUAkCH9uv0/QK1JwF3KCJ47cIq530hx6iJ/wgcXVt4Pv4wR5K1SZ/8f0P+C+1dwCIhHS+8tyJVSXLG0GVZXqSMV649wAHe7MMZBPEwwaa3C49t1x3JXsp+OQQ3I5UlHj47qhk2Qr2jNjqksq+SDef6byP782/y1Qjx78b+y4/yZ3lgdRBosqNk1RxGozV5phr5hmtzfJu8SqXKzO4yyVvD6QO8EDqwIoP7Z1C05/Att/D99sP1sCv4zoXaEt93S2RiAA/KDOX/y0Skd8mGvq5dZeynDNUG1+j3voRipylI/HPUZX1xXJ2gpb9AeXafyQS+kVCxuMItn9xB/hM1t+i5i5uulxSG+RI/IscjH92mciK5Rjy2ps/LKdJLK99JQcaePi4aNsU4NpNxNRuTDmB5W38wAnLaZLa+jYWppwgrnYjb9MOqOVVqDk53MBGF2Ey2jCKtHFpVNvrV6fpVWh6pTXvl505mm6JkJwgpQ2gStuPmlt+jaI1DbRLxGNq14aEHiCudhFW0uRaY5ScOQL8VcrRN0MWCml9CF2KbLjMevADl4qzgBNYQEBESa/JVl+HhIwhxzDlxK6InFWdMrnWPJONMd4tvg4IAgK8wKXoFCjaeapOmYgSYyA0gkDQFxoirXegbqA0vV003AJnSn9ByZ4iWB5fk/ogg+FH6TCOUHNLtLwGn+36r5hpXKXp1Ujr3USVJPcln2bBmkZZJvpdej9Fe5GpxhVGIse4WjtLjzFM06tTdgoMhA5yIHKSudYEryx9k2OxRwgIOBF/hG5ziGv1C1yqvr8tYqsIadte6DdDCPa0t2en8IOA6VKZiWKZmVKFxVqNYrNJudWibju0HAfL8bA8D9trK23anoflurQcd43y827iYDbDqd7uNcQW4HIuz5m5hW0RWz8IuJzLc2UdYhs3DQ5lM/TF18+m9sXifHb4AF+7cp7//uXvcSzTyT97/Fk6QpENtSI+iWhnBwM+mJ/n35x+E8f3+OVjx3iwr5e4ofPIQD9+EPC7b71NrtHAVBS+cOggz+/bR8o0uVoo8H+8+RbjhSKj+TzjxRLfvXKFJwYH+PKRrQsxPTY0yGi+wP9x+i3+7em3ON7Zwe986Qu7fryKKjN8coDBo30ISbTVigXc98IJ7n1ue04Rt4MsS+jbyNbeDooioW5SprxXaDRtFpc29hNV1Xb/616Ln2mavKHC83U4jketbpHZgNhG1W4e7fxHt93WbP1dFlvnsfwqaeMAB2JfoD/8CKacas8Vbz7WoF0ZZ/t1rtV+wsXS16i4szS8AqacJKaurfBIxUN86eljPPfwQZZKteXX2tZMW3lu2K7H2Hyef/fdIkIIFFkiGw8z1JlkpCu9/JNisCNBSNc+MWNWRzJCLGJwl3Tf3BZ7p4osBBHF5DcGn6XhWby+dIGyU+d8eZLx2sKqL/TtwhXOVyYRCGzfobmcuQW4N7mPz3Xfz+FY/65FNzT9STT7LXxvHt9fBBxcb5JG7Q8wQ38FSd56Y/Re43omdiNoylGS0V4UuZ9G60c3eb/u1g3jE+CwkTXS7RDgkbNGqbtLm5KDpDbI8eTPsy/6DLq0lT7nW6WTQEIBdmYrtVvoDz1IrnWZsj1DsM45S2qD9IROEVfXL99VJA1N3r4sv+O3sJcF0Wy/yffn/1cUoW+4noZbwvJqyELDXScbaHmNFdscU47t6Jx6gYvl1wCBIUeRhbpp9FiTQqjCIMDH9S1sr4G+gU2PQGAqsXaZ+jb2zcen5dVWVKJVyVwjLrWyDSFQhN7uJ/bunNgGtHuMHs08y8OpJ1ftd1iJLmdlg2W7nvb1LwXitq0bW95+4NPwClyufB/br99kkWXTYRxGFgphJYYQEm8VfkjTrTEUPkxK60QIsdwScCPQpMshdMnECRzKToGLlXe4L/k0La/OQmuSMa/KZOMKPh5xNYMkJGJqClOJoEkGiqThBFsX5oJ22d9WvSfXgxACdYsWH3uJpuMwUSjx9vQMZ+YWuJrLU7VsLNfF8X1cr60sf72Xt12e2M7QXi9VDK5ndPdwPwWwP5PiVE8X37t4ZY1g1nVi+/zBfVueQF9dKjBRKNKw11bv3NfXw0AysbKuhmPz/sL8qmVc36czFOW+zh5c3+f8Uo5xpcSBZJqO8NrA2Q/mznKxMktGj/Fgeph90bvfl9bzffricX77oQf4zXtOAQFxwyCqt8f0hGHwzMgwp7q7cH1/2btZX3m/Px7nHz7xOLbr4Xge0rIFVkhTiek6QQDf/Ot/ja7ojYDEc/uGeXigj4RxYzzsikT4mw89yG/c067E2kobwE5wPSMqaavvTavRIj9TpO/Q7rShXd+Wchsith1IkrRl26DdhGW7lKuNDd+v1lq89OolPjw/vadzIstyKG/ioQvtvmF7E69YIaRNPdr9wKPpFTlX+hOK9lUGwo9yKP5zdBhHUKXQcqvaLcco2mOkIavsiz5PTO3hTPG/MNt4B1UK8WDmtzHkxC37IWhZLlMLRZYKdR44PoCqyPh+gCd8lC1UOwSwokTveB6z+Qq5cp0PxufQFBldUdA1hc5EhN50nIFsgqHOFMOdKXrSsbsykzvUneKRY4M0LZdM4uNLHG0Ve9dRTrsXNKPH+LWBp+g1M7y6dI6rtTlKTm3Vcg3PonFT6aZAEFVMHs8e5bmOezgS79+1bC2AJEUxzb8CgUur+U18f4HAr9Co/z5+UMIwPo+iHEBIe+OxtJuQJBMJE1nOwhb8Nj9q+IFHrnWxPZHeBEORx+gN3Ych77zvqT0YfLwDQlzr4XD8C2hyhNnGe1SdRbzAxpQTdJiHGY48Tl/o/g29XgXtPpLtIsDHXyZrAT5Nt7x5uRMsW/UYa5SM2+vwCAIfCWlLPq7r79ONXuIbWdXN/Eelmwb04DZ9pTs7T9zS3yyQNg2iSGLnx38rwkqEmJrA9ls4gU2/OYwXeJTsApqkoQqVkBKm6laZa07ToXdRsJcoO8Vdsfqx/Tole4qGW1h1Dtr2TR6IdpbaDzyGQoeJKHFiamrDHtjrvbNRJclY/Rw1t0xcTaNKOmElTkLLsj9yEoFAlTRK9hKyUJCQV6oxtivAdKd3+Mc9aSg0mrw9Oc0bE9Nczi2Rq9UpNptUW9Zdo7J8K6K6zr50ikMdWc4vrK66KTebXM0XGMsX2J/Zml/zh3PzTJbK6xLy+/t6GEjGV76jqm3z9dELq5YRAmzPp2pbOL7H98avIAnBrx4+sS6xfa84wRtLo4xEOugPpT4RxDagXdbYEVk/sCdL0gqRXQ+6otAd3TyLfjCzutw7ZhjEjNVBvvY+hIGPZzKbm8rz4n95ld/6n35119bZJvm7NwYIwUduCQXg2C6V6saBQd8PqNZaVGvbCx7uBfzAvyN7NC+wmai9QtEeR5didJv30mkcR5M3vy7bXt8yuhylwzhOh3Gckj3BUusiM/W32Rd7YdXyMwslTn9wjTfPTBD4Afce6aPetDn9/jgdmRgPnWxX2mmqzK89eYqx+QJTSyXmi9UNFZJd38e1fZq3BPJm8mUuTueIGBoRQyMaMkhETPrTcfoycfqyCfrScdKx0LY1DHYbPZk4n3/kCJ7vk4rd/bxoT4nt9YfTSKQbQ9YYDHdwpTrDRH2RRatE2aljey4+PoqQCSkGSS1Cj5lmX6SbY/EBBkOdhJTdtt4RKOpBzNCvIkkprNYPcZwzeN44rcZf4DqXUdRDyPIAstyBJKURUgQhQgih0T5tMuyBo5sQ8i1WQzKWc4mg/ie43nyblOsPoSmHEFuYcDvuDJbzAbZzlXbWVcbUH0JXjyJJUYLAw/XnaVlv47gTy8uohIwn0NW1JUCuN0/TehNJhDH0+5GlxKbb9/HIW2PrZgWvI6xk6DSOEFHavZK+71OsNvnGy+c4ONTBQr5CsdIkHjE4MtzFkZFOgiCg2rB47+I0M4tlHNcjHjE5MtLJocH2eq7N5jk/tsBioUoQtHslTh3sYaQvjSLLXJstkCtWaVou5WqTxWINgoDPP36ErnRsRypuiqTTaRzBlOP0mCdoemX8wEOTQsS1HpLaEKay2Tnb2QNSEspKiagidO5J/TwhJXVb8icJmYw+tOZ1WahIQsYLnB33d0rIy17CAU7QXCZTG1cTuL690oPdPp7Ng1k7OVMCablEu/1pL3A27ftuC1Bs/YF8tvweBXuJq/WLVJ0yp/M/JqN1cDh2gpga50T8fq7Vr/B24TXOld9fkSB6NP0MUTXOSPgwl6vneHXph8TVJJqkY8gh1F3o8216ZfLW+IbCZe2+pLbncd5eoOHVKLsF0l4nYSXG1dpZis4iY/VzOL5FtzlMUstSsBf5sPwqnUY/hhzCkMNk9G7KTp6cNdPuG9c62xnrOww8bdbrtuV13NGnt48AcD2P92fmePnqNd6bnuVqvkChsXX12o8TsiTRn4zz8GDfGmLrBQGTxRLvTs1uidg6nseZuQVmymtbNXpiUQ51ZEmaN9oeQqrKk/1DW9rP9UgtwGyjyFyzxEikg8hdonj8KaBebhAEAZFEGM/1yE3l1wQ7pi7OMPreLrsoCHbVRuXjCpM5rk+z+dErWO8EQcCObcegTWyn66exvCpZ4wgxre+2pPZWaHKIuNZHWMlQceaYa76/htiOTS0xNpVnqDfFS29cwfN9DF3h6tQSLdu9QWwVma88fJSlSp18pUG+WidXrrNQqrJQqrFQqpEr17Acd8Py3Zbt0rJdCjdl3RVZIh1t2/2ko2HSsbb9T0e8raDcVlKOEgsZG3qs7wVMXaWvI/GRbe9OsafE9mb0mGm6jCQn4oNMNZZYaBUpOjUsz8UPfBRJJqwYpLUovaEMA6EOlFUZnN2FECqqdhxJTiMr/Vitl7GtV/C8GTxvEtt6DVnpQZK6kOUsQkSWfzQQKgKZvRjSZPUAhrH6ZnO9GSRh4vsVHG8Gx50kFfv7SMRuS26DoIHnlfD9Cu2+3SbVxp8jRRLo0iFcb46m9Sot+x2k66p0gUcQ3DrpEsvLvo7rTqKpR9mKwkEQeJTtmU3JQ1IbIqxkVrKYfgClapP/67vv8OWnjmPqKvWmxeR8kVyxxkB3EtNQee39MWZzFWzXw/cDipUGc0sVOpIR4hGDWsOmXGtRb9p4fsBSqcbUfJG/9SuPoZgys7kyb5y5hmW7dGdiNFsOttM2R7+T3Ikmh8jI+8kYu9frfDvoUhhDibfzmEKhP3SK7tAxtB30xgKElASaFKLplam5S/iBt+3MpSqZRNR2VsD2mzS9Cq7vbJitbnhFWl4NRegYcmzTHuGdQhISITmBvHwsllfF8upE1bXCM0EQ4PjN5XLqrcELXBzfpsvoI6t3oQgFJ3BWCOyB6FF0SWeqeY2G2y7TjihR5OUy3+HwASQhMVG/ihu4JJQUD6WeoMfsR5fubFLe8srkrasbvm/5LRZak3QY/ShCwQ0clppzNL0aByKn2tZK4SOE5RjX+4NNOUKn0Ue3NcS+yHE0yUCVNHrMYWTRFqMKRLv/PaamVsqdJSGTULMMhY/c0THd7QiCgKpl8+70DF89e4HXxicpN1ubji+KJBFSVSKGTkRT0RQFTZZXfHWvi48okkAREvlGg/MLOSqt3REYWw8dkQj39/Xypx+cXaNkPF+p8s70LF85fhhD2dxGabpUYTxfWHdf7+vroSceXTVhi2o6X9p3aOXv6yV+1/2Fr5dpe8t2dOuh5rYzuyktTMddroQM7WO7t6ebqrV33+fdgInz0ziWw6lnjtGstfjW7/4Q3dQQ8o3vcXFiicomfaQ7Qbvq4+4q9dwJPM/HsnffCuluhB+4FO3x5eq3JJq0fiXD7aDLMTQpiuOPUran1ryfL9YJgoCnHzzAS29cASAaNvD9YNW5liWJwY4kgx1tC1LX86k0WswXq8wVqswVKyyUqixV6hSrTYq1JsVag1K9he1uHCh3PX+FGF+HoSpk4mG6k1G6UjG6k1Gy8QjpaIhkxCQZMUlETKKmjrQLgd+fBnxkxBbak8qUHiOl3x0PlyAAIWIo6nEIgmWl5BLgEQRlXKcMXLjNWnYXhvkLtxBbD1nKEDG/hCJ30bBeJV/+/xANfQVNPbJMsDeGJEXRtSOo6j4EKp43T678/yZsfhFNPYjtXKBhvYKuHiUR+RuAhh9Ulq2PrmeJfPygRL35HVxvhpDxPKb2MNIWSrUD/GVitBmx7V8jKuT7AeVai2hI5/OPHyEWNvj6y2d58+wEi4UqnekoX/vRWU4d7OX4/i50TeX82Dw/fvcqDx8f4Pj+bhJRk2P7upajhT4Xry3yn77xJr/1cw8RWvYNK1YahHSNLzxxlI5khEbLxtDVDaX971ZoUoiokiWspGh5VWaa50jrw6hiZ36+Sa2PsJKkZM9QsKYo2bPr2udsBl0Ok14WnnL8JrnWVdL6EDFprYqx5zsUrElqTg5Tjm+oVHynEMiElSQhJUndK1J25qm6C6QZXDPZafsGF2i5VfQtRodPJR7c9H1TVjgUO86h2PpKnxE1yrH4PRyL37Ol7W0VQeDTdIsUrI2zH45vU7BzpLUuuox+JKHg1tokJqllSWrrq852Gv10GquvjS5jgC5jYNN96jYH6TbXF1H7aUHNtnl/do7/8423eXd6Fm+DrEVIVUmFTBKmSSpkko2E6YxGyEbChDWVsKYR0lQMRUFXFDSlTXR1ReGd6Rn+zatv7imxjegaI5kkhzuzvDs1u6psutyyuJJb4lqhxKGOzKaU4c3JaRZqdfxb0hiKJPHEyCDp0ObPlLFSAUkIBmOJlfI8y/O4WMjRG4nRGV474ZVEu+xdkxR06W4Rh9wYuqLwuYMHNnzfD3wWmjWWWnVOpG7fe3qlnCOhmyQ0E/UOhNd2G9VibUX5uFlr8Z3/8CNOPnmYUPRGMLa4WMbbhAj8LCNYVlH/WUBAgO3Vbqr62lnqIQj85bYtF8df258sye2g4fXschDAUrFGEASbegErskRqOdN6dKDd6uD7PrlKnalcmclckYnFIlO5MoVqg1rLom7Z1Fs2TcvB2UTVvuW4TC+VmV4qr7wW0lW6ElEGOhIMZpMMdCTpScWIhnTChkZYb5c3h+4if9yPEh8psb2bEAQegV/Ett+l1fwqVutFgmBjJduPE5p6EFXpRwgNVelDUbqxnTFUZQQ2EL6B9k3seDM0Wi9iO1dol077eP7SsiBUgOsv4vs1wsZnVkqgZRG/ZT0tWvZ7NJo/Ip34pxjag1sjtcviJpZX2bRfMqxkN1Tdffj4IOl4uD1wxExChkax2kSRZeZyFQqVBm+dm1xZPh41qDUsPM9nYq7Am2cnWSzWkARUai2aloN30yCSjofpycbpTLX7kcLm7mcJPwoIIRHXuugP38Ol8o+4VP4RPWbb9kiTzJVs6/VeXD9wcQMLWWiokrEmG9th7Cep9TPbvEDRnuJ86bvcl/4VVMloi0AtZ+yCwMfDxfOd5feUlZ5VTTJJ6n10GgeZbZxjvHaatD6IIUdvCFsF7XLgoj3NbPM8VTdHt3mUvtCJPTpPAgmFHvMoVSfHYusq881LdJtH0aTQSi+wF9gsNC+Tt8ZX+eV9UuH4Leru0qbK5IYcoi+0j3eKLzLfmsDzXTJGDwOhgx/hnv70wPV9Li0u8Z/f+YC3JmfWXUaVJJIhk4PZDE+MDPLQQB9DqQSRDXon10NMN1YymHuJpGnymYP7OTO7gOeuvieWGg1+NDrG/kwKsY5/8PXM6hsTU+TrqyeUshCkwyb39vas6fG8FV+7cgFVkvitk/eRkE0CoOE6/LsP3uYXDh7lM+G1VTJpPYIpq1i+S8u3+bj6RXcLtu/xg5lLfHv6Av/52b922+X/dPwDHsoOcH+mn4R+4zkbBAFu4OP6Pqp0Z2rjO8HDX7xhMadoCvvvGeRv/ou/SufgjQDahdNX+JPf+fpHul+fKNymL1+SxMfS/3srZPnOqi8FAkXSsX1Bw8tjeVWCwN+CyOgNBIFP0yvS8soIpHW1RZKxEKaucmUih+f5zC9VOHN5Fj8IyKa2lyWWJInORJTORJQHDrStgjzPZ7FcY3Q2z+jcElfmlphYKLJUqbeF3lwPx2v3I28UBAVoWA5jCwXGFgorrxmqwmBHkn09GfZ3p7lnuJv79u+ORepu4bqmhuO27QYVeWN/6DvBzy6x9Qs06v+BRv0P8f0cu2pM+BFgK2OEH9SpNr6K71fIxP8ZityJ5xdpLd5sHXR7FeUgcPG8PJp2iEbzByhSB6Z+/5b20w/c5TLkjc+vJoc2LHM1DfUmoQfRVrqjHcGTZcE/+I1neOj4wMrui+V/qw2LP/z2Ozx0bIC/9uUHCZsaH16Z5Z/8b6sfkqpye6n6TwqSWttv9lrtbUrOLD9a+DcciT/PUPgB4loXQsi03CoNr0SueYXJxgf0ho5zKPY0ISW5al0hOUl/+B6WrGvMNs/yQekbVNwcR2LPkzFG0OUQrm9Rc/IU7Cnmmxc4FH+WjD603Fe7vB4lxQPpX+U7rX/BbPMC7xX+AsdvMRR+EF2J4OEy37zI6fz/xVzzArococc8Sl/41K2Ht2sQSByKP8dM8xyLrStcrryMJBROJL6AKSfw8ZhrXuT94leZanywLTuhuxU1d5HSOqVXN0MVGn3mPnrNkZXXfhpK9j4u5Gp1Xr82yYtXxtZ9X5YEI5kUf+fxh3j+wH7UZW/DuxVx0+DZ/SP829fexHLdVSN6sdHkpdFx/vrD9687qfCDgEKjyfm5Baq3ZJZNTeXpfSPETZ3btT5anoepqsS1NgEWQNowsZf9WtfD0XgPF8qz5FoV5holeszkusv9tOKfnHp+w+tqolpgulFmJJpmIPLxnZdQ1OTn/pvPE72FPBghna7hu1/s6+OAtGwnsxEUWSIRNwmHjY99XOlIRzfNeN4OAomI0rXcTnOFoj1OV+geNLF1ISPHb5C3LlO2J1EknbCy1iv71OFeag2L3//qaeZzFf7O//hHdKQi/NWfe4h7t+BjeztIkqArGaUrGeXxY0MQgOW4LJZrXJhe5NLUIpdnlrg6lydfrW+ayb0VLcfl0kyOyzM5EPDZew/ddcQWwPF8rkwv4bge/R0J0vHdDzT+TBLbILCo13+PVuPP8f08dzuptewPsNVTqEofjjOO40yhqYeQNsnWQvs4g8BBCANZiuMHTWqNrxEEN+r3FbkbSYpSbX6dlPJ3ARU/KCNQEMsERRIRwsZnMbRTlGq/S731XYSQMLR7N98+Pj43+gs3Qjt7tzG5vDXSp0iCjlSErkyM9y5N05mJMtKbptGyKVYaZFNRWraD7/uETI2wqbFYrPHGmWub7scnHYrQ6DQO8VzX3+O13H+g5uR4t/DnnCl+C3k5Onk9Y+sFNo5vkdB61v1+hBAMhu/DD1wCfOaaFxirvcFM48yqjK2Ph++7+Hj0h+/F11YPxKrQ6TGP8njHb/FO/s+Yb16iYE3xhvyHyELDDxyc5f5bXY5wJP4CxxKfW7Zu2jtk9RGOxj/bzhZb07xb+HPOl7+PJkxcbByviSHHGAzdj+XXWbLWJyefFNTcHCV7etNlrt9nn5LZ3cGZ2XleGZtY9z1ZCB4ZHOC3H32Qk92daLIEYmdn3l+2AdpryEKQCBk8PjzIS6Pjq3pAbddjplzh/elZTvV2Y6qrS35bjsuPRseo2vaa0SasaXzu8H5MVb1tVkeRJIIAHN9HWy5Fdvw2od3os892HuVsaYaL5VlOL13lWKIPQ777S5J3C5tl7N7JT9NwbbrMj7c9TNVVTj19FD20unSy72A3v/rf/dwGn/rZhiQJlE0EhBKJEF/+zAk+8/TRLdnU7CU0TSYa2blGhCw0ekL3UXYmaXllrlS+AwQciH2BkLK5aF0QBDS8Jc6V/pSp+hu4gUVM6aHDXNsOFAlpPPnAPo7s62IpXyUQkElGyCTbXrZ3ipvHKLH8j6Ep9KRiZGJhHj7Y3/YodzwKtQYzS2WuLZaYyBWZypWYyZepNa1Ns7nB8j/bdRz4KOB5PouFKv/8939Asdrk7/+VJ/ncw1v31d4qPlJiGxBQd1sstsostkpUnAaW3yY+qlAIKwYpPUqXniBjxG+/wh3Ctl7Htk7jebNs7s8qQGhIIkrbH1VlrzXwJGl1H5ssdxIENtXGn+H6OcAnFv4VFHkAUKk1vkXL+QDLPoPtjlKo/G+oSicR8xdRlQEM7SSN1ssslv4fSFIURepCklII2jeprh7HN2o0Wi+xUPzHtH00FSKhn8PUH10+DVL7s3IPsfCvU218jXrrhwhhoKubi79ct6DZDAJ5e5NpIdBUhd/4wv28fX6K//SNtwiCAEmS6MnG+c0v3k88bHDvoT4+vDzLxfFFYuF2aV8qFkbsoiLi3QQhBLocZiB8L4YcYaL+HgvNi5TsWWpuHh8fVTIwpDBJbZisMcJQ5AH0DYQYNCnMQPg+QkqC6cYZZhpnKduz1N0ifuAiCxVdjhBRs2SNEeJqN/It6r1CSGhSiH3RxzDlBJP1d5hrXqTqLOAENrJQCCkp9oUeYzB8Hz2h40TVjj0VQGj702ociD6OKUcZr73JfPMiNWcJAFOO0WkeZCTyKIYcYax2+pNPbJ1Fis7mGdtPsXtoOA6jS3ku55bWff9QZ5bPHtrPyZ5OwtqdTZhajou3hXH2TiGEwFAUPntoP+9Oz64itgFQt2xeHB1jXya9htg2XZeXroyt8a7VFZm+eJxTPV3o8u2nI33ROJcLS3zj6iUe7OrF9X1emhwjoqok9PUnzh1GnF/sv5+/FBJvF8ZQxiSe7zrOYDiNvIcClTvFH46+Q0oP8Wz3fq5UcvyfF1/nrx14iGPJLr45dR5DVmh5DiW7ydcnzvL20iQN1+FQvINnuvdzIJ6l6licLczx4txl5hoVvjJwnIezgyR0E9vzmG2U+YtrH/L64jXcwOPt3BRJ3eRwopPf2Hc/Ashbdb47c4nRco6W59JlRnmsc5gHs5v3zu8EkiQwIwaF+RLVYg3HulHqruoK8czm1kU/i1BUmXBo45YFQbsEuCsbR/2EV6Upks5I9Dmm66exvTpVZ4aL5b9ksXmetHGQhNaPKadQJBOBRICH4zdpuHnK9hRL1kUK1lWabgFZqCS0QQbCj67ZTqnaZKlYw/dB09vjkWU7zOXKpOJhMsmdiVZtBiEEqiKjKjLh5Xl5EEBnMspQR4p79rV7cRtWux+3WGuSK9eYL9WYzVcYXygwsVjc9f3aC/hBQLVpsVisUag0aFp7o+r9kRDbIAgYr89zoTLFWG2+TWrdBi3PXom2ykJCl1TCir5s+ZPhaLyfQ9E+Ioq5qw8f23odz5uAdXrnJKmjbfWj7EOWO5GkBAIDxHWLn72FLF8vHRAIYZII/3WE0AkCC8/PI0lhNPUokogghISi9GMAmjKCH1hIIrScoU0iCZOQ/jiKlMXzCwhhoipDqEo/mrofEMhSBlN/BEmK4XoLtImtiir3IVBQlUFioV9DU/cjhIauHiMwHYLAuq3VD2wtF37rNysJQTYZ4R/+5jMkb/LMOjTUQTSk05uNI4B7DvURCeks5Ku0bBdZksgkwqhKu27/+YcPMjlfpNlyiIZ04lGTI8OdRJb7aId7UkRMbdOHw0eFgfB9RJQMQgjS69jvbBWSkNGlCH2hU0TVDgZCp2h4ZRy/2S7fFiqK0DGVOFElS0RJI28gqCKEIKQk6JGOE1e76TGP0fTKOMuWMEJIqJKOJkWIqhliaudKZvjW9YSVFIOR+0lo3QxGHqDlVvACByFkdDlCTO0gofViSJFN+2YSWi/3JH+OA9EniKmd6PLOHzQRNcNg+AESWg9lex7Lb4tEqLJJXO0ipbUFkXQ5QpdxiJTev4a4fxJgew1q7iINN/9x78rPDBaqNabLFer22ge3EILjXR08MtR/x6QW2oTyoxKRUWWZ+/p76EvEydUbWDf12lqex0/GJviVk8dJhcyV3inH88jVanwwO0/rlt7cpGnyQP/te2uv46HuXizP4fWZSd6ZnyYIoOV5PN43yFB8/VLai5VZlqwaiiSTt+p8Y+Z9RqsLdBgxTFnfUE35Oh7LHuBkcvfJ3EbItWrUXYupeomr1TzXakU+KMwwGElyobRAlxlFFhJN12G+WeXedB9lp8U7S1PENIPuUAxVkugwI9yT7uX04gTzzSqW3z73khBEVJ3jqW7Ol+ZRJJmjyS56QzG6Qu3MrRv4/Nn4B8iSxOFEJwKYb1T59tR5ekNxOkPtfdgtWE2b9186y3s/PItjO0iSRLD8tcTTUf7q//DLu7atnxZoqkIsuvF943o+1Zq1KnM3M1NkeipPsdSgkK/T15/Ec30WFsr096c5dqyXcMQgv1TlzdNXqVRbBEFAb1+Kw4e76eyMU8jX+Pa3P+Tw4W5mZ4uUSk2SyRCHj/Rw6NDtxcx2AoFMTOvnUPzLXCx/nYJ9laozS8NdIm+NElJSqFK4XU0mpLb2R2Bj+3WaboGGt4QfuEhCpcM4xr7oC0TVnjXbuTqxxCvvtJ0DAtq9oNV6i1Q8xFMPHtgTYrvu8QpW1OFdz6Mp2qTQdj0atkOlYVGotm2Gahv4596N8IOAZsvZ82zynhLbtl2GyzvFK5zOX+LD0jjTjfyygMPGkBDEtTDnKwM8mDrAg6mD9ITSKNu0G9kIrnuJwC/d8qqKoh5FN55BVe9FUfYhyR1IUoSPw6msbSxtEDaf33Q5QzsB2sZCO6oyiKqsVh41tJM3bUhGkTtR5PX7WBS5C8XsWvWaqW+u/HqnkCRBImryq59dXeo80JVkoOvG5CVsapw62Lvhevb3Z9nfvzoDfvPy3dk43dm9qwzYDrrMQ3SZh26/4BZw3ZQ8qfWR1O68x0KRNOJaN3Htzh5ammSSNfaRNfbteB1RNbuuNc9OYSoxTCVGl7lxOUyfcpK+0MkN37/bUXMXqToLeMHm4+6n2D0sVGrk6+v71MYNnZF0ir747pR/LtXrWB+RcqwkBJlwmHt7u5kslpit3LBi8XyfiUKJ0aU8XbHIClmtWjZn5xbJN5pr1JCzkTBPjGxdGXskkcQPAkxFZbZWRQCD8SSP9w7QEVq/V+t7s2e4XJ2nZDeouk1KdoPpRgFZSBiyisTmFhkpPfKREtvuUIyK0+JieZFcs8ZjncNcLueYb1aQhCCsam1/TVnlaKKTRzuHaHku54vzzDbKlO0WveE4+2IZ9sUy/MW1M6vExRRJImOEeb7nIO8sTWHKKs/2HOBooj0H8AKfit3i29MXOJ7s5kA8iyYpFO0G54rzjNcKZIzwjnzeN0Kz1uK7v/cjoukImZ7Uckly+zuJJLbeR/mzBENXSSY27k90XZ9iqbEqubC0VOXs2Wkq5SaRiME3//J9Dh3uJp+vUau2iMZMOjtivPrqFWq1FgiB7/mcOztNq+Xw2GP7KRTrfPUv3uZLX74X01RxHY+LF+YolRr09aUIh3c/USCEQEZhKPoUPj6T9VfJt67Q9ArU3Dlq7tzt1kBYyZLRDzMUfZre8EPrikeFTI2O69UBQZvYWrZLsdygUtsb33HX82lYNtWGRbW5+qfSaFGqtyjXm1SW3680W+33Gxb1lr2pfdDdBt8PaLTsPW+d2TNiGwQBtu/wYWmc/zzxIy5VZ2h5NhICQ9YIywa6rCAjwXI0wvE9mp5Nw2tRtGu8tnSe0eosZafBZ7rupc/M7Erm1veLBMHNUQ4ZSc4SCv8Whvl5JKnt0/gpdoq2GrIX2LhBk7u9h/lT3DnaEbgA269j+TVsr4EbtPACGz/wCPBod29KCCGvZI4VSUeTQmhSBFloe2LxsxW05f9b2H4D12/hBE0838bHIwg8fPzlLHU7aCAJCYGMLBTk5eNQhI4qGagrKtQf/xjSVpyeoOosfNy78jOFcqtF3V4/kNARCZMJh1bsau4UE8USjQ22tVd4bHiAt6ZmmKtUV43uru/z5uQ0hzqyK8S22GjyxsTUmii9qSoMJOIc61pr/7URFEnmcDrLoVRmzVNlo7tttllirlkCIKIYRJTt9frp0kcrRdITilGvWFwuL+L6Pk937eNfX3iFq5UlYqpBSg+Ra9aIaQb3Z/qREIQVjaRmIoCWd2flfX4QULKb5Fp1puslmp7D9bN7ItWDKklbU6/cBlzb5cq7Y/wPf/QP2XdyEOUOhIZ+VhAKaXRmNy7Rtm2X+cUynucRBPKquXNnV5xnnz3K3/97v89nP3+CAwe7GB1dYGa6gGO7fP/7Z/nSl++hqzOO5/m89OJ5zp+bYd++Dnzfp1ptEY+bPPfcUcyQxte/9h4XLsxQKNT2hNhehyEnOBD7HDG1l+n6G+StK7S8ErbfwAtsgsAlIFhWPZaRhY4qhTCVBBn9CAORx8noB1A28IQ/dqCbYwdWB/CnF0p89ycXNu1rvR2uWzM1LZeG7dC0HJq2TcNyqDbb2deFYo3Fco2lcp3Fco1cuUalaeFuQ0DqZtxlHRZA+zw0rU9wxtYLfOZbRf7t1W8zXlvADbx2D60WpdtIMRTuJGNEMaR2ZM7xXSpOg9lWgYn6AktWhYrTYNEq8c3ZN9EllV/qewxT2Y2bRnDzY7BdonsUM/QLK5Y3n2I1rl+IAR5+4K/6fWPif+O3G1i0vAole4rgNv1fDa9AxZ6lJZc3XW4zCGS05QFstxAEPpZfp+WVdm2dINClyK7u553A9S0sv7aup5sidDQ5graJtVPbxsPB8utYXpW8fZVc6wole5Kqs0DDLbZJYmBBIJCFiiqFMOQ4ETVDRMmS1AZJ6cNElCyaHEGVTBShb6iUvVvwA2+ZxLZouiVKzhQle5qqM0fFnqPpFVeIrhtYeIGDJBQUSWuTcmFiyBFCSpqI0kFYyRDTuomrvRhybBXhbRP6vXnSBEFAgIcXuCsq5Nd/W16F2caHVJzZDT/v+hYNL39b1eTtwJDj6LcpK/9phu15G6r0RnRtTQ/qTnC9NO1KLk/tIya2J3u6GEwmODe/SNNZTaTenprh84cPMJxOQgCFRoP3pmfXENHuWJQTPV3bsjZquS4V26JmrxZQkSVBRyhMRFu7rhe6j3Nvamg7h7cKB2N7U165EXpDccarBa5VC8Q0g0OJLIokMVpZImOEyRgRcs0ashCYys3XkSBAbCuMLCHwl235boYiJHRJ4a8deIjHOocxZAUv8PGCAHUP7mlJlkj3JJGVtodoEAR3Xe/z3YZISKena/3yewDLdpmdL1GtWeiaiiy3z6dpaqTSEVRNJhIx6OpKQBAgCUGtZiHJNSYn8vzge2dXfQcHDnZiWy6K2n6WPfDgCPFECFmWSCZDGIZKtdra68NGlUz6wg/SbZ6i7ubItc5TdqZougVsv0GAhyQUVBEmrGSJaX10mMcw5eS2Womu3xIdqQjVeot8sb6FzwR4foDj3WTd43pYrke12WIuX2V6qcR0vsJ0vsT0UplcuY7l3JmloCJLqLKMttyrqysymdjt1YaD5WfIR5V7atku1UZrTeXObmPPiG3JqfHK0jlGq7P4BEQUg8903seXex9if7RnQ7GgIAiouS1O5y/ye+PfZ65ZIGeVea84yqFYHw+kNjYu3yokuQshTIKgXUYlhImsjHA3ZFjuRlzPxrmBTdMr0/SK7b4Ft0jTK678bvcytP+2/fqymfbt8Vruf7/jfTTlJAdjL/B4x39zx+u6DttvcrH8HV7L/etdW6ckFE4lf4VHs39r19Z5J8i1LnOm9BeMVl9a816PeQ8nk7/ESPTJdT7ZVmL1ApuSPcX58je5Wn2Zllfe9Hv3AxfHa9Lw8hTs1YJMEaWDbvM4Q5HH6As9gKnsfpn4zRO4hptnpvEe47XXmG+dpeEWNvlkG15g43k2Fm1l8fI6yRFFGCT0frqMo3SbJ+kJnSIkJ1eV3+x00rY20hngBz4NL0/ZnqZoTy7/TFC0Jml5JfxNBfJgybrC0uIVXmX3rvNHMn+TE8lfXGX99LMESWxc3qoI+bZ9nVuB7XqcmZtnvlrbli3EbsBUVU71dnFuYZGLC7lV743m8syWq1iOi+v7TJUqTJfXesQPJhPc37dxK8l6OLe0yB+ef5/vjF+h4TgI2nOyrnCE/+mJ5/nc8Nr5wRd6PlltBD2hOLKQKFgNOswIuqRyIJblbHGOz/YeJqNvbcIa3Pr3TQRWLCtwhxSVJatB3bXwgwBBm9R2hWL0ReK8tjBOlxnlcKKTmmORa9UZiCR3hdwGfrCiyK8ZKk/84sOc/uZ7BH7AwJE+VP3G9FRscj/9rCIS1untTqAqMs4G5ai24zE2kSMaMVZUfYUQN1kotgWm/JXxo30NdHTG+P/+y99Eltvn/fp143k+V67MA8tWjDeVo3/U344sacS0XmLa9saQzdC+R27+G65N5ymVG3Smo6uWW/W55d+O65GvNri2UGRiscDYfIFrCwUmcyVylTr+HWR914OgbVnZnYqyvzvL/p40B3oy7O/J0J28veCa5/tcmcrdUTZ6O6g3ba7NFfH2+Hm1Z8S27DR4O39lWbBG4tcHnua5znvoNBK3/WxY0Xksc5SsHudfXfkGl6rTTDfzXKhM7gqx1bWHcZ0LeG6b2Aa4BMHOs4U/7ai7OU4v/Tvmm+fxAmulLDNYlaW9/re/PJH+tPz4kw7br2P7G0cpi/Y1Lld+wJXKi7T8Cq7f2nIwYz3U3SXGa68RAFnjMCZ7QGzxsf0aF8vf5Wr1ZSrOLI7f2tX+UzewKFjjlO0ZrlZfRpMidJpH6A89QG/oFCElg9ihEJ0TNCnb0yy2LlK2ZynZM1TdOWyvhhc47awt7cztdaumT/HRw1RVDGX9x2vdsdeIKO0ENcviD9/5YI0v7EeF+/t6eGtyZg2x9YELizlO9nYRBHB+fm0ZfEhVGU6lONSxuVXHrXhtZoK0EeIfP/gEb87P8NsnH+B710YRAvqiH69lza0QqwvDtgxdVggI0GWZ4UgaRZI4muzi5bmrhFWNlH77ntOJWoH/NPo2U/USHxZmma6X+OHsFR7NDvL5/iMrnrXP9x7i318+zf/y4YvossIj2SH+7tEnUCWZ//vJ5/jaxDn+xYc/pO7YaLLMsWQ3/+j4M6i70F/7R7/zdb75b78PtImC1bTxHI+//N+/h6IqK6WUHQMZfufF/+cdb++nDZIkiEcNDh/o4vyl2XXJieO4nH53nP3DHVuyqzFNlY7OGOGQxje/8T7Pv3CUSMSgWKzj+xCN/nRXNH71+x/yB19/c9VrkiTx8KlBTh66QaD9IGB6qcxUrsz0UomppRJTS2XmixXK9Rau56/8OJ6H5/u7QmoTEZOBbILhjiRDnSmGOpP0ZRLETB1FkVGXM7eKIq3qq98I9abN//i736He/GgqfvwgwHJcmuuIKu4m9ozYtjybyUaOADgU7eN4fIguI4kibT6ha0cSBaascSjWx8nEEEtWmbJdZ6FV2pV9040XsJ338L15gqBK4NdwnDP4fh5JyiLWaSr/WYYXuFSdBaruPH5w5xOyT7Fz+H6AY7s0GzaxRAjppshro25Rr7bIdu0OIXT8BvY6Jcqe7zJRf50rlR8y3zpH3d0dL+g2CQvQpBBhJXXH67sVlldlrnmGc6W/pGhPUneX9khQKVghlg4NWl6ZlldmsXUJWagMhB9Ck3dmSl5zcozVXmG08iLOchl1O6DwyRGQ+FnAZuXGC9UahWaTgJ1nOQqNJj8eu8Zr1yZ3hSTvBP2JOAezadKhEPnG6nHiSi7PXKWKJAQXF9daHo2kUxzuyKBvQP43Qs22iWo6+xIpLuRzHMt00BEO86/ePU2huTfiLjuFLEsrytDrwfN8XHdt4EkIwfM9B7k33UdaD6FKMk90DtMfTjAQThJWNJ7vPcg96dWZql/fdy+SkMga7bHlV4fvpek5NF0HWZJQJYmkFiJj3Bh7hiIp/uahR6g67eBIUgu1M6NBwIFYlt/Ydx8lu4nj+8hCEFMNtNvM4baKR750H30Hb5R5X9/urU8SYw97Nj/JEEIQCRvce3yAS1cX8Oy144DteLz21lU+98wxkvHbB0SEkBgcyPALv/gA77w9ztmz0/i+j6YpPPDAMI89fueJpbsZ9x8fIHWzWJkAU9foTEfJpm4oIjcth//5j18kX23QtB2atkPLctvWa7ugUG9oCh3xCL3pOL3pGD3Lv1OREGFDI6SrhPT2b0NTUXYYaPKDgKVy/SMjth8V9ozBub5H1W0/aAbDHSS1yG1J7c0Qoi0yNRDqIK6FKdVrNNzdqd+X5B5M81cgcLGslwn8Ip47RaP27zDD/xWy3I8Qd27D8NME/9PMz10B3/exWjblUp1o3OTmqXFuvszYpTme/eKpXdmW7TdWZWzbgmAOY7Ufc7nyfRaa57H82q5s6zpCSpqE1ou6gbjDTlF380zX3+ZC+dvMt87jB3sbMbwZAQGWX0Xz23YEd2IZ5AYWdXeJsjOzi3v4KXYbXdEI6fD6E8lys8W1Qom5cpWe+Pb9OcvNFm9PTfPH75+h1Nz7nraNYKgqhzqyHO7M8Or45Kr3xgoF5ipVFEnm6tLaEv8jnVmOdGW3XV4qCYG0/Bk/CKjaFmkjRLHVpO58dPf0VqCpMtom/qG249Fsrb/P3aEY3aEbGeiMESFj3JhY94Ti9IRWBzCHo6uz34cSm4tyieUe3YPxtcu1PdHVNevcTfQf6qF3f9ftF/y0BHlDRMI6D947xNe/9wGO465Rm/X9gMWlKu98OEEqGaavN0kopGEYKpGIzl/7r5+gszMGATzyyH4MUyUWN3ngwWHicZNqtYXn+Wi6Qn9/GlWV6epK8Pf/288Sjd14Rh852kM2G6W7K/HRnoBdRm9nnHQiRLnWotG0CQhQZJmAANtxV7Lenh9wbnKB6h1a7SiSRDSkk41H6EhEyMbDdMYjpGMhEmGTRNgkHjaIhQziYQNDVfasJF+WJVRZWpUs2W0EQVsFeqPS+d3CnhFbIcSKz1lYNrZFam+GqeioQkES0q75pgkho2kPAhKSlF72tR2j2fgzgsBC1R5AUYaR5CySFEes9Il9OsD+LEEAkpCQkG/bp7jbCIIA23IZvTCD6/ikO6KksjFaDZuJq4sICXr70ziey9S1JRq1FgszReZnSrQaNmNX5nEdj0xnjFQmirGFMqRbYfsNbK/R7oMK2gRtsn6ai+Vvs9C6gOPvfoYkpnaT0AbZzXut5VWZabzHhfK3mG1+uGvr3S66QyeIaT3I0qdBs592ZCNhumMRQqpK4xbC5fg+H8zM8fq1Sb549OCWhaSCIGCuUuX05DTfuXCZ92fm92LXt4UDmTQnu7s4PTGNe1OmIl9vMJYvoskyxVsyqXHD4EA2TV98+5Ulg/EEAJoskzZD/N7Z9wirKgII74Ig127C0FS0TdR9Gw2LcqWB63kou6SQ/UmCrMjIys/ece8mdE1hqD/N4f1dfHBuat1Aie8H/Pj1K/T3JHng1BAHMjeCac89f3Tl/8nUjUx+MhnmoYfXt+VLJsN85SurrRj7+9P09+9dEGQ9BIGP7depOnM03Dy2X8UNbAh8hJBQhIEuRzGVNDG1B0UYtxUzzJfqnLsyx+hEbjmgciNScN/Rfu4/vjPbLyFAVxTiYYNkJEQyYpKItP+fjobIxMJk42HSsTDZWJhYSP/Ix4QDfRmODHWR2UJmf6ewHJfR6SXevjiF7ezdnHrPiK0mKXToccbdFnWvhevv7CAabgsncAnLBglt98yRhWSi6Y8hy93Ichet5rdwnDM06r+PYr+Jqh5DVoaR5S6EFEegI4QKyLBi5bH7RFeSUijK0K6v904ghIQhRzDlxLZLkf3Ax/LXCofcDFUKtY217+B8GnIcVdpdoRpJKMTVPoajT+L6Vru89Kb+xbYKrbfqNTdoYfvNO84I+n5ArdrkjZcuEk2EOHKyn2g8RLNhMT9TQNUUfN9nca6dpbWaDq2mjdVymLq2xIdvjSOEIJmOcPhkPwMj2/d+9QILJ2jgBw5BAAV7nLOlr7LQvIATrE9qZaGjSWFUSUcSKhIykpCXz5OH61s4fhM3aOKtOUdimdj27+CMrY8g8FlqXeFq9eXbklqBWFZsjqFJYRTJQF4+husPRD/w8ANv2cqqfSyO38T265uWNUso9IXuJ6Tc2cNfEjKaFMaUE1v+zPUe4o36bSVkZElH2cUqlbadws9uINBUVQZTCYZSCc7f0oMKcHFxiW9duEzCNDja1UE2HEaW1grkBEFAy3UpNVvMliu8NTXDDy9f5cPZ+ZUpV1jT2iqcnveRKxt0xiIc6szQGY0wc5NAlOP5nJ9fRJWlNQqYBzvSjKRThPXtX2/3d/Viex4RVeOh7j7+y4UP8YKAR3v66Y9tTpRt36XuWNTcFk6wdSXQlB4hoW1/smeaGiFDY7nCdg2aLYelQo18oUbnXeKn/ik+WRBCYJoqLzx1mOnZAjPzpXWvtUuj8/zkjVFiUZOjB7vR9b0PAl3fj71IMLa8MhV7hoJ1lZx1gbI9ScPN4/gN/KCtiqzJkbZTgdpHp3mMpDZMVO1FkyMbzjUvX1vkpdOXCQJWlR4DW84ySkIQ0lXCpk7E0AgbGlFDJxkx6UpF6UnF6U5F6UnFyMbCmLp2VxQlHB/p5pefOcn+/u3PFbeKar3FD96+zJmrc59MYhtWDA7H+pmoLzLdWKLqNvEDf8s+lUEQ4AYe080lyk6drBFnMNy5a/vneUsEQYUgqCLLA2j6Y7juJYKgieucwXXOLC8pEEJFiARCRBBSaNkSSAEkdnvyputPEIn9g11d551CFQY95ikiSgdBsPWLMSDA9puMVX+Mz8aEuMM4RETpQLmDEk1VDpPWR3b8+fWgSDqDkYcZCD+EH3g4fn25PLfBYnOJqlvBlD0k4Sy/V6fkTLPYvEDVvTPf0MAPcB0P3dTYf6SHnsEMobCOqsoM7e/kwodTBAFcPj9DLBFm8P4sMxN5fvzds5x5Z5xI1CDTGWf8ygKLc6UdEVtoW8E03TJu0OLdwn9mvnkeN7i5/LFt4aPLUVRhElU7SWgDRJQsuhxGlUIoQscLLGy/Sd3NU3XmqTrz1N2l5T7RJq7fQhEaUbWLqLJ797nt15mov85M470NlxFIqFKIsJIhpQ2RNQ6S0PqJqFkMOYYqzBUzd8dv4fhNLL9Czc1Rdeap2HOUnGnqTg4naOL47b7X69e8QCaqdtJhHMKQtl96ejN0KUJWP4AX2XoJas4apWhPbJhh1+UYaX2YuNpzR/t2MxJa/57bNd3tONKR5eHBfq7k8ji39F1ZrssbE5PMV6r8/PEjPDTYT9zQUWVpRYXUW7ZiWKjWODO3wEujY1xeXFqVAVZlifv6epirVJkpV9ZY7+w1FEliMJngwf7eVcQW2gJStz7tBXBfXw8DyZ0Ruf2Jdu+9EIK+aIwHuntpuS4pw0TbJMNRdZpM1Qtcqs4xUVui6rbwlytRbofPdp/g0ez+be9rOKQRj5kYurphyfHiUpX3z07xmadjCLFztfRP8bMLVZF54uED/OSNUYrlBvXG2gBrAPzk9BU0XUHTFEYGM5jG7lcOBUGA43o0Gjb1hk08ZhLZxR7pgADLKzNTf5vRyveYbb7bthJcB00vT9meYJZ3uFL5Nv3hR9gf+zxd5kl0KbbuvTafq6ApMv/tf/0s0cjm7VBCtMdfTVHabQeKjKEqhE2N3nScwWySwY4kA9kEvZk4qUhoT8t87xQhY/MKk92AkAQhXdvzcW7PjiKpRXkic4wXFz7gYmWK8do8g+EOYkpo04O6XvboEzDVWOJsaYKiXeeB5AFOJYZ3bf8a9f9Iq/lVfG+GINhskhgQBDZBsAgsstcVqbK8dbP6jwqmkuCe1K8B7Qys4EZmoW0rEKx67Tr8wKPizDNRfwPf35jYHol9gYHIwxjy3aVqeR3tsnoFWYpjLCv1fmOywJWyy18ZOcXJ1A1CMNv4kHf95h0TW1mRyHbG+dwv3s+PvvUBi3NlTtw/SDwVbvv8+QG+7+M5HpIkUFUFRZGAtrhUOGqgKBL3PjxCZ09ix/vh+E1K9iRL1lXGqj++5V2BKpmktEGOxL9EX+h+wmp6Sz2krm9RdeaZbZ5hqv4Wc80zRJUsMbUbWdq9aPJc8yy51pVN1Z1NOclg5GFOJH6JjLF++dV16PL6VSN+4FF1FphvnmWueYb55lnKzix+4KJKOiORpzDk2B37ukbVTg7FP8uh+Ge3/Jl38n9As1zckNjGtV6OxL/Egdhzd7Rvn2I1htMpHh3q50ejY4wXSmvedzyfK0t5fudHryALwVAqSSYcQlcUbM+j3GyxVG+Qq69/7WqyzGAqwX/33BN8/9IoXzt7kcni2u3sNfricR4Z6udrZy+s4onldfp/DVXlVHc3vfGdjfWO77cnlEJGliQSugE6beXRwF+3XckPAn60cJE/mTjN2fL0yuurCw3XQlp+ph2Idu6I2MqyRDoVprsjztjkWgEtgJm5Ei/+5BKPPrB/mQB86t/6KbYHIQQhU+OLLxxnqVjj3MX1PcubLYfvvHiWxaUKv/7zD3LqeD+yJN1RQOW67c1172Hb8ZibL/GT06OcfneMX/+FB3n6sUM7PrZb4Qcul8rf4FL5m1Sc9r0slhNMAnEjPRy0/2lLkQV4gc212o8p2RMcTfwSh2JfRl6nQknTlBVrpJutsdZAgK62BZ6Gu1Ls786wvzvNSHeavkwcRdo73/q9gqmr6OreEltJCEKGyl7z+z07ipCscywxyC/2Pca35t7ij6d+QsVt8nznKXrMzcvxWr7N2dI1/tO1Fxmrz3EqMcwT2eN0G7unlOq5Y3juNfhUFGlbGK1NkNYSpPW2XUDTsxirTXE4NozyqZr0rsFxPOam8rx/eoxCvkYkblKvtVhaqPDWK5dZmC3RN5RhYKSDs+9e49x7E8iSIJWJcv+j+/n+X75HfqFC31CGRGpnCrwATa/Itfoba0itQKLTPMaR+BcYCD2IJoeRhbb8kLk9ZKER13qJqB2MRJ9czt7WiSi7G9iZb56j4sxt+H5E6eBo4kscjX8FXd55NlUgEVU7CSkpBiOPYns1ivY1phrvULDG2R97Bk3avVaKT3H3QwCnerr5O48/zP/rey9RszYuVfeCgGvFElOl8grh8oNgQyN7U1U41tXJP/3sMwwlkxzpyPJ6ZOpjIbZRQ2dfJs3BbJrLufymZPHBgV66Y5EtWVGsh39/5h2ims5vHl0tkPfPT/+YZwdGeKJvcM1nzpWm+e7sh1yqtMcBWUjEVZO4ZjLTKLWPQTEQQlB1Wti+gyFrPNN5hMPxHh7KbB7s2gzZdJSBvvSGxNayHM5fmeNf/fuX+Jt/9UmSiRCy/MmaEH+KuwP3nRxkdDxHqdxgZq607jKu6/P+mSlmZkucOt7PF58/zsF9nRg7LE32vID5xTJXxhe5NDrPpdF5xiaXsG0Xz/NpWbur2H6p/A2uVn9A1Zlbdk9J0WXeQ4d5jKjShSZHEULGDxys5XLlhdZ55pvvYft1yvY0o5XvoQiDg/Evrln/UE+KUqXJ91+7yJMP7CdkrH9eIobG7/2DX0NX5ba9jiyt/GymhH43w9S1TcXudgNtYnsXZ2xnGkucLU+s+56g3ZcpgE4jwXC4i2v1Bb4+/Tpv5S/Ra6bpMpPE1TCapAACN/Couy2WrDLTzTzzzSJzzQJdRpKnO05wPDG4YwGq9eGy5+nXn0KM1iYREWmF2NbdBu8Uz7I/OoCyd3GSuw57PfVQFImO7gQPPXUI1/EwwzqhkIbn+WQ6Yzi2RyIdQdcVEukwjuMhSwJJkoinwnzl1x9BEgIjpBG+TUnNZshbVynbszS94o19EwYj0acw7Qd4/1yY15tj/I3nHwLgD3/8Hsf6O7lnePOy1ratl4IkFFRMNClEqV4nQAUVai2bSzOLzBQqPH10mHh4+/3TfuBSsqdW7futGAg/zHDkCUw5fkfZ1PbxtPuJVQw0KYyhxEnqQzh+k5jag/xp4OdnCkIIYobOI0P9/ONnn+Dfv/EOs5XqKpGlm+H5W5Ooy0bCPDkyyK/dc4J96RSqLHOgI0NHZOcBrDuBJATZcIin949wNV/c8PgAnhgeJBsN73hiU7Nt5HU+O1erUrPXDxy8XRhjqpFHkxUeTe3n8z0n6TaTKELin33wZyiSzJd6T3EqOUjBqnE6f5WvT79LRNG5NzlIX2jnAfX+3hQnjvTyypujuOv06AVAtdbi9bevUqo0eOT+EY4f7qWnO0F4A8E/z/OxLIdaw6JWt6jUWpQrTbqyMXq7E0TCu6sof32bjuth2S6W1bY2sSxn+W8Xy3ZpNCxmF0rrWhhdP9pWy+W1t65ybSqPrinouoKhqei6svK3rinomoqqysi74Jf7swJdU3j+ycM0GhbffvEchdL6lR624zG/WKZ22uLS6DxdHXEGelMM9KXoyESJhHRCpoahq3iBj2N72I5Ly3KpNyzyxTr5Qo18scZCrkqp3KBWt6g3LOoNm5bVLruXZWkl63mn8AOPmrPIWPVFyvY0qmTSG3qAA7HPE9P60aQIitCX218EAT5+4NFhnmAg8gQl+xoXyl9lsXmegnWVqfobdIfuI6quVuWezZV56Y1LNFoOX/3+h6uCTF946hhffvY40B7zelKf/PYBTVX44qNHsWyXgwMdmDvQPdgOhNTO2N61xPZafZE/mfrJhu8LxIqS8WKrRMO1KAd1luwKV2tzRBUTXdZQrouyEGD7Dg3Xouo2sZdLV4tOjZ/kzjLVyHE8PsRznbtjZfIptg4v8Kg4dS5WrnKuPErJqTLbWsQPfCpOjUWrgB/sTea75Tp8beLcskKxYKxSQAjByXQ3T3YNE1I0rlULvLs0w2h5CS/wUSWZx7qGOJHqZq5RYbxSYLFZo2g1GI6lsX2XiWqRk+ke7kv3ElY1pmolXpodpWg3EcCheAf3ZXrpiyRoee3lvzd9iabr0BmKcLm0tKc3pyRJGKZGZ49Ku8/7xgAavUW1zgjdGIyuL9M3mMH3gzseeG2/gc0Nj0pJyByMvcDB2GcoFOIsFqeYXFxkoVQjEwsxmSvSl95+/5xlB1yYKpKKhDjaH8bzfEr1JrOFCo63s2ur5VWw/Mo6IlVtaFKEjLGfuNp7xyXCt0ISEpoIoUk7Uxhs2DYThRIXFnJY7mobh75EjKf2715bxqfYO8iSRCYc5jMH9xPVdX54+SrvTM+Sq9U3zMZuhJihc7yrkydGBnlksJ9DHZmVkrfOSISuWHRdFeaPAsmQyVMjg/zRe2eotFprsraSECRNg3t6u0kY2yNefhBwubBEvtVgolIirGq8PDUOtAVqylaLim39/9n77yhL7/u8E/y8Odx8b4VbOXd1dU7IGQRIgllMlkbBtixZK9me9ay9Hntnz4zH4z/Gs7tnx/axx1rLsjWWLMqiSFEUIwgQgUgNdKNzqqqunOvWzeHN+8etru7qqupYDTQoPDw4BOreN9w3/H6/5xueZ8vytpHiIjm7wlC0lc+0HuCxpgFCcv0cQrJW79c1k+yLd1DzHZr0KAu1POfyM+xLdNJuJtGlO8toRcM6/T2NDA2kOX1+c4su3w/IFaocPzXJwmKBt94bJRELEQ5pqKqMKAr4foDretiOh227OI6H5bjYdp1U1iyXZx8bJBox7orYlsoWo+OLvH+6rlpqrx7DcTxc18P1/Po/ro/nedf8u4/jeFRr9pYiO0FQ9//80U/PYhjq1QyXfCXbJV3z7yKyLCHLEqoqoSr1PsbB/jQ7+9MkEx9OEOd+R3NjlGce34njerz46nmy+Y0+9FC3q8kXq+SLVaZms1wanScWNQmbGqoqoaxe+yAI6n7LXv0e245LpepQqdpUqjblioVzD0WA1s43sJmpHKVgT+MHDh2hh9kZ/wLN+t5VocLNoRHGlFKElSZEQSIIfOarJ8nZE8xXT20gtgNdjfzC8/ux7Y2/qaf9aqWpIAj3heDT3UJTZH7hqb34QUBzMoJ6j1XKJUEgYuq0pKLoqkLoHvR5w10Q24JT5lLx9r0UvcCn6FbXPG5vhrxT5r2VYc7mJ6l69rYRW1V9GLj/jL9V9fCHfQqboO4duNrFgO05VN0aAQGqKHM4sRtZvDfZKNf3eWV2BFEQ2Z9qJapqLNcq/PHI+/RFUnRFEvUMQQAhpf6SlByLb4+doUkPM18p8n5mhqrrEFY0/uvoSR5q6mCxWuJUZpaUZhJWNF6eGcYLfGKqjuv7vLc8jRf4RFSdgl3j2+OnKTk2aSOMFwRk7cpqtcG9Q33wvPnoudV37oVQQatxgIHoszTpgxSFAqaqkggbHB2e5Ln9dfP2IIDpTJ6xhRUcrz5B7OtqIR4yOD+9SL5SRZGlevY5GiIZNjk1OcdbFyZIRUJUbIewruH5AYv5Em9fmkCSJJpjYXqbk8RvMXtreUU8f+tFvi5FMaQYsnj/jQOz+SLfOXUeU1ORrptE48b2Z2TuN/Q1JPnbjz6wQeFzsKmRxlvITCqSxKG2FgSgYl99BjRZpjMRu6X3ShIEnuzrJmFsfN72taYJqbdGdmRRpCFk8snBftKRMHtamhleyjCdy7NcLpOt1rBcF9v18IMASRRRJQlDUYhoKqmQSToaoSeZYG9LM7vSTTRH1pe164rMk73dRDVtTUBKEGB3uvkDeV50WaYnlWR3uon3Z2apOutLEFVJ4nBHKy3RCKp8++Nm0bYYyWZYLJcQBIE3Z6765lquy/7GNO2RzQNqGatEzXPojTSxP9G5RmoBFFHC8h1c368rzEoqvZEmPtt2kP/3ue/x9vIIXaHUHakiQz1r1dGW5BNP7GRqZoV8sbZlFsuyXS5PLnN5chlRFOpkTpUQBQE/ANf1cFwP1/U33ceena3Yzt2VflaqNheG5/mLH53EdurHcxwPz/M2Vdu9XXiez/hU5pa+K7DqranUiZaiSDxfs0k3RT8mtltAkkR6Oht4/qldIAi88sZFljLFG94723ZZypRYymyvJ/12ok5s38XxK5hyilbzEE36nhuS2isQBAFFMGk1D7NUu0DOnqDmZVmsnmYgul6rorstRXs6sWEfxXIN9R73n34YkCWRgXuognw9RFEkHjH46jP7qdku/e0N9+Q4d3ynIopJX7hlO8/lpghJ27cA1YzPoGpPbtv+tgvCNvbhVVybiuugihJR9c4XN5IgEpXD7IsPkndKNOoJ0noDAgKqqBJTItvmMbwZyq5DkxHm0eZuBuONXMov8Ruv/ikz5TxpM4Ipq7SHYyR1E1kUma8U+fPxV/la7z6gXj3QF01xqKGdb4+d5td3PkB/rIFTK3NMl3MICPx4+hJf7N5NqxnF9j1OZGaRBZHBeBN5u8ZLM8P8z0c+xYFUK0XbYrKYZb5avGe/+X6DgIghxdkV+wwprW+NDMZCOoYqc2p8jkd3duMHUKzWuDC9xNnpBeKmTtWysR2Phwc7eefSJMvFMv0tDYgCyJKEoSnMZApkihVAYClfXl3MBSwXysyuFKk5LjOZPACHettu6Zz9wFsVj9gcYl0e5q6vzb1Avlrj/MISXz2wh5ZYBOWakrzobWa8PooYbGpksOnOJ1xNlnm4u5OHu+/MdxDq2dZPDg4w2NrAxewyzjWWdQt+EaUo0k2CmHZriytNlnmgs519rWlm8oU1cjtfLFFxHKq2Q7BKbOukViNpGrTGovQ2JOiMxzDVrSPcj/Z08mjPnf/eu8GV37ezuYFzC4sbiK2pKjw/2H/LwYB1+wbaIlHKjkOjWSc0reHo2me6LHOgqWVLux/Lc3ADn6hikNDWEyJVkim6NexrbOw0UeZIsoeQrHEuN8NMY5Y98Tu3IEvGTR450sfoxDKvvnmRYsm6aYmm7wfULGetrPODguf5lCoWyysfPskJYC1DXKV+HQrF2geSIfwoQ1Ek+nuaME0NVZF5+9hlpmZWsOzt7Xf9IOEHLllrDC+wiSo7iShtt23vqIhm3bFBaWbFurwmPnUtJEnctPz9zPAKpq4SDac3fHarCIIAzw/qlRaui+sFa+Kra8cXRRIh4+e2BF8UBcKGxuce331Pj3PHxLYn1Mxf63xqO8/lpmjW4zi+w4q9iLOaiREQiCgxokr8tvYlSc0gbZ+tyP2IqVKOqXKOJiO8Trn3TiAIAqZs0BNuRxYkxFWRoPr9yNGgJe4pSRiINTAQa0ARJeKqQaMeouTaWJ7LeHGFNxfGma8UkUQR1/co2FXcwCcgIKyopHQTTaoT/PZQjKxVgwAKtoXte1wuZnhpZnjdMaOqRtm1yVkVLM/lQLIVU1YxZZW2UIySu7UYzM8bZFGnPXSYVvPgOvVqWRRRDY2QrrKQK2G7LpPLORqiYXa1N/H0nl7ms0X+5ffeYKC1gZJlk4yYPLevf13m9eEdnXi+T386xeNDPeTLNZYLZaKGxueODCGJIj85NczofOaWia0sagg3sJyp+SVsv4IfuGt2PvcLIrpGf2OKfK1GKmQgIK9lGW+3hPVj3B3eW5zh35x+i7xVw/N9So6NG/h8rW8vv7bzEHu121vsaLJMbypJb2r7xBA/bNQXbT65ag3PX/98SoJAyjR5tLsTQ7kDYisItIajtIajZKoVQorKp3sHbnl78QZlgyFZY7aSo+bVgwqCUG+hiqsmmqgwU1mh4NxaddmWxxdFGlNhfvWrD1EoVjl7YZZcvoJ7hy0WH+Nj3AySJNLeEudXv/YwnW0JfvTTs4xPZSiU7n1gQKDu4RyPGZhb9InfLgICLD+Pj4chJ1DFO8vYa2IUTYysikuV1pScFVlEFMXVioiN1+fY6SmaGiIM9d36WB8EdRJbsRwqVj1wWaparBSrFCo1ao6L43lr87koCIQ0lU8d2kHYuP+qyD5KuOPVXJvZQJt5b9LIN8KSNc8fTfw7Fq1ZbN9GEiSea/4Cn0p/+QM/lzuB49cfZFWUCICq69RV1QQRL/DxgwBZFFcXB/VYjhf4BIAsiKirAlo+wdq+oJ5VlcV6DipYPc7p7Bwz5TwiLVRcGwEBXarf8oB6ma+3Sv4EBGRRRBZuLFP+7spppivza/sICNBFld/q+0VM+d5lklRRRpOueVwFIAiouDbfnTyHAPzD/U/ToJvMVgr8bH5s7auiIK7zT67/xqtWD5IA/dEGfvfJr6GJEsJqthCg6tq8XisjINSJchBw9c781YEmhhmMfnrTntGwrvLM3n6ODk+SL9cwVGW1xNhAFkVSEZNcuYLjeSiSSNzUNy8nvu6SiqJAWypWL0OT6mJ0t2qSDvUe2hsJNtW8HDl7irK7QlhuvK9EIARBoGzZ/B+vv0PcNNYJ5jzU3cH/8KmnP7yT+yuG3clmfnXwEMvVEjm7xivTl1mofLgZrSC4YvkVrGX/7mREkmVpW1oWvCAgW63yysgY5etEnKK6zsG2FpKmedeKoc919yHeZgA1ohiookzJscjbVRr1q+rnCSVEzbPJ21WcwEMV5LUxXhIFbN9dl6m/U8iyRHNjlH/0dz7Ff/nWO7z65jDzS3k8z9+WEl9gNbB8/4xhH+PDhSAIGLrCp5/dw+F9nfzg5bO88uZFpmayaz2z23ksURSQJRFdkzmwp5PHHxpgaMf2eaOv2e/cTaWVcPUdCai/exMzGdqa44RMjaWVEtPz2Q37n1nMEY3cfH175Rxdvy62dmlmmWMjM5wYm+HSzBKL2dKW47QkirQkIjyys/OWiG2wqpp/faBbWG0dvJ89c+817q80xS0gqTbwN3v+PhW3yDsrr3KhcOrDPqXbwrfHT3Mxt8Q/3Pc0WavC33ztj/m1gQd4vm2QNxbGGCuu8PnOXby1OEHVs1molriUW2TFqvBkSx9/d9fj6JLMaDHDf7r0LpfySwAcSLbxpe497Io3s2yV+d0Lb/Hm/DhFp8b3lfNEFY1mI8L//sgvIAkCGavMX06e49W5UfJ2lbhq8LnO3Xy6Yycheeso2/PpR7E8BwioejXmqktMVecRPyRSUPNcPN8nompEVI2CY/HdiXPUvFsruwkrGqasIAkC3xo7xVd69qFJMgvVIpIgEtfq2WFVknh9fown0z3kbYuxwgo56+4i+R8VyIJGTG2n1dyDyMaMiybLDLY28N13z1JzXVIREwGBpUIZ1/NZypdIhkMo0mr2dJNnRRZFXN9fl8UQVr96p0+WLkUwpSSyoONu4VU9XnqTiJJmKPYCwn00HOYqVRaKJf70N/4bdFledw3WruPH+EDQH0vRFYnjBwG27zFXLpK3b+R9fu9RqzrMTGa4fGmeYr6KbTl3lMl//vMHaErH7/p8spUqr46MU7SsDefREDJ5qr9nWwJHYeX2M0CtRpywrLFkFZitZtcR21YzgSYpjJeWuFxcZGesFT8IWLQKFJ0auqSg3aFw1GYImRq/+rVHOHKghzfeGead42NMzW6t2n6r0DWZkHnv7To+xkcTqWSYr33hMM88NsjpCzO88c4Ip85Nky/e/TimKhLp5hg7+9PsGWzl4L5OEjETTVNQ5O0pqa0nZaI4fpmal8P2NxfFuhksr4jtlxAFGU2K4Ps+f/rDE/ziZw/T16nxxvFR/uR7x0lE1wfeFzJFutpurcKmaju8cnqU7x49z8hshlLNqgus+f62pkNy5RrjC1lmV1u0riAWNuhtTtKaujOv8J8H3D8ruVuEiERMSRCRo8SUBLK4fZPOBwFNkhEEuFzMsFAt0qRHmCplydTK5O0aZdciJKsU7Ro/mrnIL/Yd5DPtQ0C9NFaTZDJWhf/t5Ms83NTF5zp3IQkix5an+cbo+/y93U+Q0Ax+rf8ImiiTqZU5kGpjf6oVVZSQhHpG95uXTyIKAr+582HCssZMJc8fDL9LRyjOnmQacwtym1TiBMqqKXfgo4kqF4qXP7QMZlw1ONzYzmtzl/m/vfUXxBSdzkiCBj2EKko3jbaLgsDeZAtf7zvAa3OjvDZ3Gc/36+VuHTt5trWfznCCv77jAb41dprvTpyl1YxR9Vzaw/EP5kd+yNCkCE36DiRh83dNEECRJXZ3plnIl2lLxjA1hfdGp7k0u4Ttenz28E4aYyG2oqmNsTB+EPDTM5dZyJfobb4yidx8Mbxc/H1EQSOsP4kqX+2FEwSRpNrFgnyegrO5aX3emeF8/vu4vsWO6HMYcvymx/sgENZUGsMh/vi9k6uCO9JapqolFuGRD6mX8q8iZFFc811VPa9e8fEhZcZqVZvTxyd4+funGBteoFqx8VZFp+5kCD78cP+2ENtMucKLl4Y3lCErkkhrLMKRjrZtuWJ/cuE0IUXliwND6/7+r4+/zSOtHRxJb2xTGIy1cmxlnMlyhpHiPPsTV9+doWgrP1VM3l25jCrJPNW0k4CAn8ydZcUq0aTHiCjbU4l0RUnVNFR27UjTlo7zzOM7mZxZYWIqw/RslvnFPIVSjWrNwbIcXNevZ8JkCU2TCZsakYhOImbSkAzTmIrQ0hyjuSlKa3OMePTORK6uIJUI8bnn9/HQwftTdT0eN0ndgnBUU0OEr3zuEE8/umPDZ5IkErlNC7z21gS//JUH+cwn9mzcnyySjN9eaWxvVwN/9289Q6lkbfhMEAVat+GdvBaiKGLoKi3NMaJRgz0728gXqkzOZJiczjI7n2NppW7dUyxZWLaL63oICChKXZlaUxVCpkoiZpJKhWlIhGlujNKWjpNMhggZKiFTIxzSkKQbV/7d9vkLMgmtl4q7QtYao+jM4AUHtlyTbAbPt8jZYxScaVQxTEzpRJJEfvWLD9CYrAe7NEXmwFA7X//MoXXbfuvHJ4iYN86ilmo25yYX+LM3TnF+apGlQpma7W6b5dH1cD2PC9ML/NEr76/7e1jX+Mpje/nyo3s+sp66d4uPHLEVVtV561ZC8n0r/rIVUlqImKpzKb/EbCXPUy19DOeXGCtmCAhIaibyajlsTNHZHW9mKJ5ezYjWMwaTpSwz5Tz7k63sS7UiIJCzqlwuZLiQW+DJlj66I0ma9DAC0BVOsDtxtTcgZ1U5kZklb1cZL2XRRZm8U2O2XGCilKUvmtqS2F4sXqbo1v3R/MAn5xQoe9V79vJqsszfHnqYtHFN+Zhm8Pf3PsmOaAMxVefxdA8d4TgFux5hbzEjDMWb6IkkcXyftBElrKhEFK1ermyEiag6n+0cIq7qNOgmT6R7SBsRCk4NPwjQJYXeaBJJrGdtn2sboNWM4vgeUVVHEgRMWaU1tP1RsbyTZaR4npnqJI80PENCSd2V6rTj28xWp8jYSxxKPHzb26timKTWy/UkM52IYKh1H0JREHh4RydtyRgdDXEkUSARNnBcHwToT6cIaSqf2NePvom6oKkqPL27l1y5RthQSYZNgiCgoyFO2NDq+x/s2vRtt70pRMHEDzZm0FvMfcxWT29JbL3AYcUa45z/PVbscdrNw7SZ+9Gl2Kon3ocDQ1Foi0exPY9ctYYkXi3ADN1jr7mPcX/C932OvT3Kj759nLMnJykV7izbIogCZkijKR1D3oYMX6FWY3g5w4WF5Q3Z2nQkwoG2FmLbpMw8WyoS0zYuMC9kltiRSG2yBeyPd/CKEWehlqfeeBOsrRsGYy30RZqYqqzws8WLjBYXAJgsZ6i4NgcSnXSam+/3bmDoKoau0pAM0bnqdVss1SiVLWzbXbXR8fD9AMt3+cboCR5v7WFHqpG4YaBpCqahYhoq4VDdd1QUb01F/0ZQVZnmxijNjR/tbI+myrQ0x2hpvn3ruc1g6AptLQnaWjYq5t4JQqZG6CZE6V5AliViEYNo2KCtxaezLUlhqEqxbFGp2liWg217dU9t36+vtUVhTVRJkSUMXa4/v9cQWVWR7mkrjyQotBgHWaiepeblmC4fJaq00WoevqlVX721wGei/Abz1ZNYXomE2k3a2I8gCHS2Xs3E9rQ3EA0b7OhuWrePnrYUscjWYlULuRJvnR/n++9d4NzkAqXavddfiRga8ZBBsWKRr1ydCyRR5NzkAg8NdtLZGL/n53E/4kMntmfz7+MHLhElTs2rsVCbwfYtDMmkM9RHs9aKJt3ZpOgFHjk7w3xthqyzTM2rEgQ+umSS1ttI6+1ElBhe4FFy8hzNvk6n0UeH2Y0ph9ftJ++scCr3Hm1GF21GF6Z8Z83rDXqIuGowUlhmtpzn1wcf4kJukbFSlpCs0GrWB2IRgRYzQkIzUa8pPay6Dnm7hiSIpPTQmr9eTNUxZYXlWnk1e7r1IFN1bUquRUIz6YkkERBoJcaueDP90YYbevaV3Sp5+0p/WYAkSBxJ7Llndj+KKPF4en302JRVnm3tX/vvtlCMttD6CWwwfnVgSptXSfFzbXXRkYii0aBfvYeNRphGY3NFakmo7+Pa/dxLSIKMIIhMVEbZ7z5ITLm7ydQLPFbsZaYq43dIbE2SaveGv8dMnZh59d1sSURpSVxdEDXFNl7PnW2bK92KosCO1o2fXbuOuJrFvXWktF6a9EEy1igVb2XT77iBRdYep+QskrUnWKydo0EbIKX1EVXSqNIHby2RMA0e6elkuVShULNoi0XQVYUgCD4Q+5aibfHS9AgVx2FfQ5quaIKL2SUu5ZbJWzUEQSCu6vTFUuyIN9xQGdjxPGbLBc5nF5mvlKi4NqIgEFV02sMxdiebSej6uj74K7jS73i5sMJoPsN8pUTZsREEiCoaHZE4e5LNxFR9XXTaDwIKdo2/GDuPISscbmojqupczC5xOZ+hYFv1/m/dZDDRSE8kQVi9dwtNL/CZLRe5mF1ktlyk7NQXPhFFozUcZXeymdSqqvtWmJ/NcezNEU4dG6dasVFUmc6eRuLJELqhMDa8wOzUCvFkiME9bQgI2LZLPldhcS5HqVDFDGn07Wzhwcd3kGgI09B09wRmOlfgjbEJSvbGxVx3MsHDXZ131aoSBAEThRx5q8ZcuUjRtjg2f9VqsOTYFO2Nma8raDUSvNC2n6JTZVesfd1nCTXEU81D5JwKxzPjnMpNAXUdhn2JDh5r3EG7ee9EvmRZIhY1iEW3XjRbnkvQK7E31UJrKLamk/FhwQ8CZnMFXhsdRxFFDrS3MtC0/eT/Y9w7CEJdl+Vmz979AklQaQsd4XLxZZb9Iou1M4h5iYqXIaUNEJabUURzHcn1AxfLK1Fy51muXWSs9Aor1mUU0SCp9dFsbMy+d7YlSG8yJu7f2Yaubb4uXsiVeOX0KN979zxnJ+Y3VK3cK+iqQlsqxmB7I0cvTa393fN9xhezXJxevCfE1vN8SjWLxZUSlZqN4/ob1J1vFT0tKRoT2+cEcwX3AbE9TtZZJqE0IAoSWXuZqlfBCWymq2M8nHqGVqMT9Q68JoMgYLY2yencexTdPF7g4Qc+Nb9Kg9rEocSjDEb3IQsSVa/CO5lXmDdnCMuRdcTW9W2Gi2d5Zen7PNv0OdL6ramyboaUZpJQDY4uTVJ0LFrNKDFNZ6acoyeSukrQBFBEecOCQFq17vECn0ytQlvIRQQKTo2Ka5PUzLVotCSKuIGPfV05ri4rhGWNXYlmvtS1lyYjjBfU1T5DsrpOqOZ6NGpJKl6NnF0AoEFLcCA+hHKfqcp+lBGWI3SH+jmdP7622A8IyNtZVuxlfHyCwKfV6MALPApODsuvW0iIgkijliYsR5irTVPzqniBS8658z4uRdSJyLemIG670/hBCT+w8P0Snl9EEERC2qNrWVXHm8ZxZwhwENDQlAEUqQlBUHD9LI47heMtEAQegiCiyj2oUhuiaOIHNVxvgZozAnhIYgLfLyBKm5fgaVKE9tAhcs4U46U38YKt7TOcoMJ89QwL1XMktR7S+m4a9R3E1Q4iShOmnEK8Rp34XkKRJMKaypnZBTLlCg91t9MUCTGdy2PfhoDWnaLoWHxr9CzTpTwvdA2yO9XMDyYuciYzT9lxcHyPmKqzN5XmE+19PNnWQ0IzNlybom1xbmWRn86M8u7CFHPlYr3/XRAIyyq9sSSPt3TzbHsfbeHoOoG4IKhnq44vzfLqzGWOL84yWy5g+V7d5kWS6Y+leKqtl8dauuiMxNe294OAvF3jP55/D1NWyVs1IqrGS1MjXMwtUXYcXN+j0QhxuKmd5zr6eaC5ndhd2KJtharrcDG7xMvTlzm6MMl0KU/VdQgAQ1boiiR4rKWLZ9v76I4mMOTNF1DD52bXyo8jMYM9B7t46IkdNDRHMUMa3/nGO8zNZGlujfPFX3wIRZGxag7LiwVGL85z/tQU+VyFVGOEBx8foK0rhSzfXca2ULM4M7/A2xNTGz6LGzqDTQ3sbLo7kckAmC+XuLiyxEyxgCQI/HTy8tpnZdumKxqnydx8gaRKMs8270IQhE1t6R5I9QLQpEWZr+UJgoCYYvJ40w72JzoJb1Mp8p1Ck2S+2LNxEf5hwQ8CpnN5/vDoCQxFwVTVj4ntbcJy3bpgpyR+aPokHyWIgkRc7aIr/ASWXyRvTzFZfoucPUna2Edc7UKTYkiCiiAIBIGP69eoeCvk7Qnmqu9TdpcQEGnW99IZfoywsnFNEw1t/q7v3EINuVSzePP8OH959Dynx+e29TffClIRk/09LeuILcBMJs+lmWWeOzCwbesV3w9YKVQYmVni8kyGqYUsxbKF5bh3TOa//tyBn09iC7BQm6PkFhmK7GNP4/O4gcel4hneW/kZITmKKYVp0m/fM1cURAggosTpD+8iodYH39P5Y5zOv4sumTTrbTTrrUSVBDsj+xguniNjL9KstyKLyuriyuJ0/hghKUyL3kFYvvPMXVwzCKsaS7USaSOKIkp0h5O8MjdKsxGhzbxx6YwuyXSFE7SGYpxcmcELfCRBZKSwjCJKDCWa13K1Kd1korTC5WKG5mwEXZLpiSSJqwZ7kmmWaiVemx9lKN6M63ss18ocaewkomhb5nv9wCNnF5itLeAFPhWvRlyNsjPSg/wxub2nWLYWuFg8i+1blNwCDyafwAs8LhXPYflVFFHFDzz6w0PsiOziRPYoll9DERXKbhldurPIrCgot5y1rNrHqdinCIIaQeDgeHMgyOjKLkRJo+acp1R7hZpzkSBwEQUdUz1E1PwsitSM6y1Qqr1OxTpOgEeAh6HsIhH6KqrYj+PNUqj+mFLtZ4iChiJ1YrvTyOLWi+dmYzcVN0vBmSNTu4zPjYXFAnwy1igZaxStGCGl9dEeOkSLvgdTSaGLUVQphHgPWyEWiyW+d+Yiw0sZRpczPDXQQ1TXeGdsmvlikT2tH4xV2YpV4fXZMU4uz7JYLdMRjpPUDHJ2jclSjp/OjDKSzyCLIi90DyIGrE2kNdfh3MoCv3fuXV6dGaM9HKM3liSuGTi+x3ylxNmVBd6enyRrVflrA/toC0fXyIfluVzILfEvT7zBxewSEVWjO1rvn/eDgLlygXMri5zMzLFULfMLfbvpjSY3LBQXqyV+MHERSRQp2Ra90SRJ3WShWmKikOUvxs4xU86jiCJPtPbceKF5m7fb8lxG8xl+79y7/HR6lEYjREckTkIzCIKAhWqJ0XyGo4tTLNcq/NLAfvpiyU17oyZGF1leKCArEn2DaX7tt5+hZ6B57Xr/7KVzSJJIKKIzuKed8DUqnsVClbd+eoG//NN3OfbmCJGowS/+rSdJJEOId+iZ6Ho+5xcWeWNsktn8Rk/voaZGDrW13HXpvAC0hCNrVj9+EKCI0tqH8UiUh1s76Y1vXdkii1sT+JCs8XTzEA+l+sg5FbzAp1GLoIpbB7EytTI1z6XqOgiAJim4gUfZsWkLxTBlhflKEVkUaV0NVvtBwFhxhbiqr/nK5+0amVoZN/AhqIsZNuohTEXF9jwKTv1zy3NpC8WIqfrabwmCAMf3WaqVKDpWvXRUEIirBs1m+J56y3+MO8OVlq3z80voikx7PEp4k9L6j3E96u/hjthnsPwi48VXKbnzZO3LZO16kEsUFGShbvHnBw6eb+FzNQisiCYJtZfeyHN0hh7d9CjZfIWa7dLatH4dvpgpIssiyVh9HXTlPp6dWOB7R+uZ2k3PWqhnVkOaiqHKqIpcdy8RBaaWc5Sqd1eyHA3pDLY3Il7j5AGwUqwwvZynZrsYW2SabxcrhTI/OzXGd147w9nLc3etqqPKEs8cvnXbttvBfcFEbL/GA9HHeaLxk4TlKAEBO6P7mK6OM1w8S7c5cMfEdm/8CHvjR9b9Pa23s2wtkHdWWLGXaNZbUUWVQ4lHOF84yVRljFajkwatGR+fkltguHiWB5NPEVMSN63pvxEUUUITZUxZZU8yjSyKDEQbeX1+DFWUaDLC5O0auiRjysoGawNREEhpJv9437P8/qWjfH/qAgEB+5Ot/GLfQVrMq2UUR1IdjBdW+P7kOb4zcYbeSIr/78NfRBQEfqnvEN+fOs9fTp7jP1x8BxGRznCcnfHmVeXJzSf0tzIn6Al18Fz6UWRB5nJpih/Ov0ZvqP2elSNvB3w/wHU9alUHw1CQ73FPyLYjgO5QPx1mD1WvwnDpAhOVURrUZgQBdkR2szOyl/HyCO9l30SXTNzA5XDyUUJSiLP5E2SdzUtxbwQRCVlQb6vftGafQ1d20Bj9bWSpGS8oIQlR/KBCrvJngEhz7P+OKndhO6NMZn4HRWlHEh9DFpuIGp8hbn4ZQdBx3Clmsv8IUzuMKvdSsd6nWP0pjZHfIaw/geVeYjb7P+L6+S3PRxZUOkMPIiDwbuYPyNszNyW3V2D5RWarJ5itnkAWNFrNfXSGHqbNPEBYbkYWlDrB3eZF5GKpxJm5Bf7VVz/Lb/6XPwcgFTJRZImV8genxp2zaljeMocb2/hfH/k0BxpbEQUB2/f40cQl/vPF45xcmuP3zr3LE209a0GxIAgYL+b4y/GLvDozRl8syf/jyDMcbGwlrGj4QcB4Mct/HT7F7519l987e5TBeCMJzSCiagRBwHylxO+eOcr7S7P0xpL87d0P8mx7P3FNX8vI/p/nj/PHwyf4xvBJoqpGevDgBuXcpWqZmuvwfOcAv7HrAXYl60EBy3P540sn+MbwKY4tzhBXdQ43td+R8u5mCIKAuXKR741f5PsTF+mNJvkHB5/gkXQncc3ADwIWKiW+OXKaf3vmbf7o4vv0RBM0mSHi2sYgVGaxSKlYI9kQZte+Dnp3rM8iSHK9D873fGzLgWuIbSRq8NgnhvB9n//j//VDfvSd9zn0cB/7jnQTuk0hHaiXuy2USrx4cYRXR8Y2fK7LMg90tnOk886rm65AEAS6onG6onGytSohReUXduy66/1eD0NWMW7gCHAtfjI9zHB+mdHCMoIg0BaKUXUd3l+e4b/d+wRHGtr512d+RkoP8Y8PPgvULfj+l2M/5tMdO3mmtR838HlpepjvjJ+l6tm4QcDhhnZ+eeAQe5JpslaF1+fG+Oblk5zNzvM/Hfkkz7YOkNTr1Smu7zNbzvMHw+9xOjNHybGQRYlPtu/gb+18iNA2PccfY3thuS7/9rW3aY/H+MXD+9jR/DGxvVXoUox9iV/ElJIMF35Izp7EDxx8PPzAwb6uIktARBAkJEEhbRxgV/zLtBgHkcXN343X3xvl8nSG/+5vPLPu799+8SQNiTBf+dSBtb9VbZc/ef0k56cXN1WkV2WJsKEy2NbE/t4WBloa6GiIEw1pmJrKP/mDH/DGufG7uh5hXaWzMYGpK5Sr9hrZdD2fTLHM1HKOHVu0f90OPM/ntROjfOMn7zM2W19HCtSF2ESxrnvkXeNqocgSq7JAeH6Av6oILQigKjK6ItPaECN+g77lu8F9wUR00SCuJAlJVzOhIgJdZh9n8seoetvrGRiWI+iSQckt4Pj1iIkkyHSFBmjSW5mqjtFd66dBa6bsFrlUOovlW+yM7iWmxO/6+EcaO9ibbEEWJTRR4khjO3uTaaRVH9mkZvJLfYfwgwBtE2sPSRDpizbwPx76JN6aj62Aeh2xbDTC/K3Bh/i1HQ/AapnqFSQ1k7/We4Avd+/FDwIE6oEAQ75xFkoWZaJKiNBq9i+hRlYjyPc3SSyXarz3zmV+91/+mL/zDz7NwSM96zIa9zucwOF84RSXimeRkCi6BRJqioCAqBLHlMOIooQiKVi+Rd5ZISxHUEUVQw4RUiJ3RmwFBVm8veukKp3o6h5kqQUQkIQoIGC7kwSBha4Mocl9gIgid2Cqh7CdMVx5JxBQrP6EkvUGrAq9WO44fmDhBXlcbwlBUAjrjwICmjyAJncjijeuolDFEF3hh4koaY5l/ojpynu4wdZ9eZvBDSymy8eZrZxCl2I0G7voDT9Oe+gwhhS/rX3dDFfmybCmrQvAiIKA9AH70+2IN/L1gf3sa2hZe8sVQeQTHf1kahXOrywyUy7ws5lxnm7vJaSoBMCZzDw/mRompZv8o0NPcaixDXO1zLYuahfnK317eHt+krMrC7w2N0ZfPMnuZDNV12Ekn+Hl6RHcwOc3dz/IM219RFf7YAXqugK/tedBxoorvDozxtGFKXYnm3m8tXvDb3iguYMv9uxmMH51kldFia/272W6VOC/FE8wVsjy3sI0T7f3btu1G8ln+O7YeQxJ5v+6/zEeTneuZesEoNkM8/WBvbw1P8H7y3O8szDFUKKJB5rbN+yrWrGwLYdwNEFjemNljyxLSLKI5/nUNskEmCGN7v5m9hzs5P13LvPGy+fp6mu6I2K7UCzx/3vrXV68WO/Dvh4PdLZzoL2FqL69Y+ynegbum9LNkmvz1d79zJTzvDI7yq/sOMS+ZAsnlmfojdy8L/d8doGx4gq/suMQn2gbwPF9CnaN1CpxbTTCfL57F4+ku/id1791NUu9itlKnv906V2yVpV/euRTtIdilF0bWRDX3rOPcf9heDHDSqVKS/SjLdD1YUEVIwzGvkBb6AFmK8eZKr9FpjZM1Vsh4KpdoCRohJU0jfpOesJP06DvRJfiiNx++0XNcrCdq8Fwx/V4+eQwl6aXKG8iFCWLIp99YIjPPThEX0sKXZGRxKskUBC4oZ7C7cDQFHa0NXF6bBbnGrvEQqXG2MLKthDbifkVjl+cYWKu3tImCBA2Nfb1tZJORTE0hUuTixw9N4mpqzx5sI+QrlCzXCYXskzMZymUa8RCBl9+Zh8vPLyTZDS0bdnk63FfEFtN0pFFdW0BJyAQIGBKIdzAxQ5s/MC7bZXSgIDF2iwjpfNMVi5TcLJYXg0Pj6XaPK1Gx1rDsyAISEjsiu7n6MrrzNVm6AsPUXLzDBfP0mZ00qi1oNxBr+/1cKoupXyFdGu9dEoWpHWlUvXSpq1vTb1XSMAUVZYXC6iajBnb2GMoCgK6rLDZ0kIUBDRJvuFxNsMVe5+sU0QRJBatDCHZvO+zn0EAruNRKtVwXe9Dsye6UyzV5ig6OdqNLjrMbs4VTmH7dWImIF5jWV7//7AcZclawPEdLK9GzbuzLJ8oiLc9EQgYCIJ+TRbzyrPhr5bwSAhX3uVABEEmCDz8oEyp9jNqzkUaIr+FLDXg+UWmV/7+6ncDWJ24hDWZfwkEmZsFVgRBQEYnpfXyaNP/hdnK+1wsvMhybQRnEzXlrVCPDHuU3WWmyu+yVLvI2dxf0mYeoDfyBDGlfcto8O1Ak2Xihs5YJovrewRBwPHpWSZXcjRFtr8n5UZoNsPsSTWvm4gFQcCQZLoicQbijQznlnl/eZZHWjoJKSortQoTxRwrVoX+WIojze0YsnJ1jF8dwyKqxlCyiYu5ZUZzGVZq9XuRsaqcySzg+D690SQDsRRRVVsjNXV1/LpewCPpLs6vLHK5sMLF3NKmxLY7kqA/llpX4iusKpv3x1N0RuLk7RrnsgvbRmxzVo2JYpalaomkbtYFrJSNv8GUVYaSTZzPLjFeyLJY3TyQ67o+nhegqjJmaOM8pGoymibjOj6lwiY2IoJAJGbQM9DMsbdGGD4/S6V89Xtl22a5XEGXJeKGgSJJa+fqeB75Wo3JlRzHZ+Z4bXScS0vL5KoblZmTpsELQwPsTTdvOwlN6AYC9bLe67MkoiB8oKS3QTdpNsKUHIukbjIQa0AWJc5m5+ulxTdBRzhOTNP5zvhZFipFHmruojeaWsu0XpmjQ7KKKGwMN+ftGqcyc/zO7sfoCifWlP/rmZH7ez7+q4zTcwsUatZHbAVy/0AQBGRBJaK00huJ02Y+gOvXcPwKblAXiBUFBUU0kUUdRTTQxCiyqG/JIS6OLTC3WODsyByLmSI/fO3c2mf5UpWZhRxtzXGgvgyxXI/vvXuBTHG9n64gQDJs8juffZTD/W00JyLoyr3T5RAEAVWW6EjFODcxv47YViyHhez2JAXPjy8wtZDFDwIipsbBHe386qePkIyaqIqEKIp8781zHLswTchQ+dyjQ7Q1xhFFAdvxuDyb4eX3hnn1/RFefm+Yw4PtNCUiKHep8bAV7gti6wUewSYTgeM71JfVEgK3H90YK13i/dzbLFsLNOuttOqdKKKKJEj8bPnFTQeWwcg+zubfZ746zXR1nJpXYb46zZHk44Tk8KbKnbeL5cUC505O8ekv3b10/IXT0yQbI+zad3f+dbeKB5J7ma7Ok3eKVIKAlBpnb3xwQ7b4Y9wZXN9h2VrkbOEEy9Y8p3Lv0mn2EZYjuIHHTHUKy7eoedUtM+uiINFitDNWHuZM/jiGZJJ3snf0DtWJ8u0NyquGXBv+rkhpREHF8eaw3enVntpFLOcSIfUBBEHD9RcBD10ZQhQNCtUfEwT1iKgomkhijCBwqdqn0ZXduN48rjuPIt+85LE+IWrElDbUcIi42slc9QzTleMs1S5h+7c+CQT42H4Z2y9TdpYpuvPMVU+TNnbTGXqQpNaDKt75O9kai/Bkfzd/8M5x5gol/t3PjhLSFLqSCZ7o677j/d4JIopGehNhHkEQiGkGraEo57MLTBSzOH59HF+uVVislrA9j5lSgX/y5g/r21y3j5rnMppfwfZcsnaVmlvP/pVsi8lSDlEQ6IkmiKjalp58vdEEUVXnfHaRufLGXk+oE6KUvrHsSRQEGvUQjXqI5WqZ2XLhVi/LTZG1qsxXiti+R86u8T8ffQlFvL65BNzAZySXoezY5O0aFXdzgTNZFpEkAc/zcZyNAmK6rqDpyppg1I7drRu+o2oy0bhJEEBmqYhjX81CTGbz/NcTp5nNF9BkeZ1/r+P7VB2HYs1isVRmvljE2kTETBQEfmHvEIfaW4no219iOVXI8/r0OCcW57C8q8ePazpf27mHA02337J0p9AlBUWSkAQRTZQIySqKKOGtKnmzupi94lEQAJbnrRHy9lCcz3QM0R9tYLFa4jvjZ2kxIzzXvoO+6M3FmFzfp+zaNBohFElaCxbdCZZKZS7ML3F2boG5QpGK7SCLEiFNIR2NsLe1mV3ppg33VBQEqo7Dyek53h6fYiqbp+a4hDWVnlSSB7raGGhKoVxXeVZ1HMYzWc7NLzGeybJcrmC7Hpos0RA2GWpuYl9bmrb4xqxmsWbx2sgYp2YXeHqgh4e6O3h/eo7jkzNMZ/NYnoepqnQn43xyaIDGsLk2dtiuy1yhxJnZBUaXMywWK1iuiyQKxE2D/oYkB9pb6G/c+vq7ns9ULsexyVnGMlmylSqe76PKMjFdoyMR55GeDtKr/uMr5SrnFxY5MT3HQqHEqdl5Fosl3hqbYDZfIKKvD4T+9hMPMdCYWkeGgiDAcj2OTc1wZnaBmVwBy3UxVYX2eIxHejrpbUhusNLzfJ9MucLvv3WM9kSMx/u6MBSF41OznJqeZ6VSQRJFkqbBg13t7G/fPmuuew1JUJCkGLp0pYe9HnQGH0GQEJFuuUVIkkSK5Rq5QoVsvsL50as9swFwYKidvTvq46ntukwsZhmeXabmrG9paoyF+epje3lqby+JsPGB+MgqkkRbQ3TDsaq2w2J+e4jt1GKOTKGCKAp0phP88qcOs6c3vc6vOBbS18TQIqZOUyKMuvo8NsRDhHQV3/d55f1RvvHi+/zWlwz62xuQ7lDj4Ua4L9hIzatScotYXg1N0gmCAD/wWbTm0CQdXdqotnkrmKiMMFEeoVFLczD+CAk1hSpqVL0KJ3LvrGW8rkVKa6TN6GKmOsGFwklUUSfAZ3f0IKqoszCbJbtSplq2kFWZju4GdEPl8qV5KmWLcFgn1RQhCGDi8iICAj0DTRghjZmJDIVchZXlEtMTy4yPLpJIhQiHdWansyiqhKrKjI0sQhDQ0ByloSnK/EyWbKZMOKqTbksgyyLnT02hqHWLB1X74G6jIetYns18bRnX94gqYQKg3UgjfYi+nz8vEBBQRY0mrYUHk08QlqNElChxJYkgiGtl9E16GllQiMhRbN8iIseQBImk2sChxMPElDg7I3soe2VkQaZRS6NsQybxbiCJcULao1Sds6yUv4EkGPhBFV3Zia7uQhYbUaUuHHeBlfIfIgoGflBZzf4qCIKKruzEUIbJlv8ERe5ARCfARRRunUTWM3UJDDlOVGmhQetjsXaJpdpFlq0Ryu7yupKmm8HDoejMU3TmydnTrFjjtJr7aTX3k1S778gP94rdj+v7xA0D1/dJhgz2tabZ2Xx3KrO3C0UUt7QA0ySJ8KqgT86q4a8GKMuOvWZpY3l1ZeMbodmMkLrG2sz2PQp2DQGBhGYg32CBElN1VEmi4jqUHGvzfidRQt2iOsWQFQxZWS0Fvb3y9Buh4jprNjS+73Nqee6G81iDEaJBN7e0cwlFDDRDpVq2yK2UN3wejhqEowbFfJWJ0UUefWbnhu8Eq1oDALWqg3+NmmWhVuPU7Dyn5+pergIgri6WrvRI3QiaLPNUXzcvDA3SGo3ck+zpe/MzDGczuL7PeD7L421dnM8s4fk+lntrffPbBeHaoJ+w3kNWESVUsf5MrtTKRBSd6VKOpWoJZ9WlICCgwQjRGooyXcohAC/PjrAj1nhLxFaXFZqNCMeXZ2gLxVBFCcevE2d1lXDfCs7PL/Lq8BjvTswwVygSBAGaLOMFATXHqf+7H7CzeWNJo+15vDsxw9GJaS4vrxAAFcumaNnEjRnGMiu8sHsHD3V3rNtuZCnDD88N8+7ENPlqDVEQkSURy3VxfZ/3JmZ4aqCHF3btoDMZX7et5bpcWFjm5YuXies6NcflL89cZKFQpOa4VB2XmusyGo/w3GDfum1n80VeunSZly6OkClV6sFOUcTxfVzP46ihc3Fhma8d2kNfw0YRt0KtxrHJWX58YYSLC0tUbadeYioIuL6P7XlEdZ2hdCPN0XowsOY6LBRKjK/kyFaqZCtVXK8eKFqpVKheV8p/veq95/sULYvvnr7AG5cnmc0VcH1/7ZgCU5ydW+Tze3dysL2FuHk1gBcAZcvm1eEx+htTKKJE2bZ58/IkxZqF7XmUbRvXq88z+9o2V//9KEAUpDv2nm9KRgh6A2YX8+iawpG9XWufKbJIa1OMplS93aliOZyZmKdUs9ZEpABMTWFneyMvHNlJMmwifkAtQ4okkk5ENhyvZrtkihvniTvBSqFCqWIRNjT62xrY09eyjtRC3bJRlkSCIKBmu1w7DYcNjV09aco1m7fOTnDs4jTHL02TjJo/v6rIPh6TlVGaSmnSejsBAcvWPDPVCdqNLuJKfZC/4pXkBx5e4OH4Nj4+ju9Q86pIgowkSKs17AKWV8UPPEwpRFSJISBQdPNcLl2k5ObRN7EIkQSZ3vAgC9YMF4tnSKoNNOtttBgdyILM9GSG5cUCgiAgiiLlYo2de9s5cfQysWQIqT3J8kKBYrHGynIRTZM5d2qKeCLE8mIB23Kp1RyqFZvhczPs2NWGbqiMDc8jigLxZJiJy4s0NEaJxl1mp1cYvTBPPlfGMDWW5vMkGsLMrPoV2tc8QJbrMpMvcHJ2Hj8IiOoaQ02NtMe3x6Qc4HxhhIydIyQZBKtjSMbOrXvB7wTlksXoxBSu6xGJGkyOLaNqMkN721lZKrIwn0cQoKUtSVdPfWHv+wGL83kWF/IU8lVsy0UQwDA12juSNLXE0G5Sw++6HksLBS6PLKCoMnv2d6DrdbN71/FYWSkxPZkhn63geT5mSKW1PUlTOoZ5DwzWJVEmqTWQ1DaSl7iapDvUt8lWV5FUG0iq9W37I0Pbfn43gyJ1YKjBphlUQZCZzQ+Rr5ZImJeJGRVEIUwi9HU0uQdRNAlpDxHg43hTBIKNoe4jbn4JVeoEBDRlgKjxAsXaT3G9JVS5i4j+LIrcgSTeXs+SgEBYaSKkNNJs7Ga5NsJ89QzL1ggFZ5ais4jtl26L5JbcBUqlRVbsMfL2NN3hR2nWh1Y99m59opNFkXQ0wtcO7qVQreEFAaaqoMsfjN3QRtzYG5uAdYQyCAKCoL7Abw3F+IW+3Tc9QkRVaQ/H1ra/yqRuXma+dsybHmWrnVw57zvdwUbU56q6JkKDEeIrfXu2JNdXEFLqFkibIdkYIRzRKeSrzM9k8VdVcK/8/mRDhGQqzOzkCufPTJPPVojE9DVyCvVxdmYyA7C6MLnR+dcX1TeDAER0jYNtrfzqAwcZaEyhK/emd+pyboW4pvNYWxc/mRjldw49xPH5Wd6em9qQFfwwoYgi/bEUF7JLfHf8HI1GqO4/L4prhHO2UuBCdhGoZz4VUSKhGWvtQZlameH8MovVEnm7xumVeUBgR6yR9nCMlGbyTFs/51bm0SWZiKLh+j6toSj7Uq03zYQEQcBSqcz3zl7kR+dGANjb2kx/Y5KEaWC5LoulCsulMg1hc0M2ECBTrnBqdo6GUIgHutppi0dxXI/LmRXeHpvixQsjaIrMUHMj0WsygZbr4XgejeEQ+9taaAyHCGkK+WqNEzPznJldoHLuEsmQsYHYXkHZtjk9u8D5hSUkQeCBrnYSZj0IuFSsZ6pihr5uvHQ8H9t1MRWVHb0NNIXDRDSViuNwaXGZE9Nz/PDcJWKGxm8+9sA6YluxHc7NLfGH757g3YkZ9rQ0c6i9lZZYPTNbsmwWi2WWy2Vihr5W7WAqKn0NSQxVIQjgO6fOcdyeZaCpgSf6ummJrdeHSF/332Xb5rWRcf7z0RNIosjulqb6/hSFbKXK+flFfnKh3useUhUOtLeiblLiOZsv8vrIOLIkko6GeaSnA0NRyNdqzOQKdCZjGOpfzd7saFgnGtapWg7L2TJPHNl6nVW1bM5PLuB56yeLxliYw33tdDTE7/HZrockiSQ2IdKO69216vIVlGsOluPSHInQ0hDd9PmSRBFFkQgCqFrOBk4QNlR2dDbS05LkwvgCxy9Os7sn/fNLbDXRYNGa5UTuHRq1SbzAYaw8jCgIDEUP0KjVo0hO4JCzV1i257B9i/naDDW3ypI1x/nCCTTJwJTCdJg9SEg06W1EKqPMW9Mcz76JIYUoeyXmq9P4gY+2hShOp9nHhcIpLpcuAXAk8RiSUF9QVis2oZBO74405XKNV390hh27WikVazz6zBDdfU2cOTHJ8mKBBx8fIBoz+U//5ieYIZ2BoVaG9jWyOJdj9OI8xWINy3Lx/YBioUaxUAUEOrsbOPJoXQb7pR+cwrIc0q0JajWHS+fnSDWE2bW/g3RrgqX5wlrUvWTZvD0xzT/78ct4QcBAQ4q/+/jD20psLxbG6Qt3sjc2sKaCLCCgiHc3IGaWi/zwu2dZyRTZuauNt98Yplqx+PqvPsb0RIaL52awLJf9h7r5xV97FDOk4boex45e5tT7EywtFrCqDr7vI0ki+w9386nPHaCtI7mlV6Pn+czP5njj1Qv87JUL9O9I0zfQjKYp+F7A3GyWd98a5b13Rslny3iej2Yo7NrTziNPDLJjqBVd/6s5EWwFUzuIqR3c8vM3R/IUal18cvdztMQ3Wtbo6k50dX2mKWo8v/bvkhglpD9ESH9o285ZQECXIrSHDtJm7qfoLjBbOcls5SRZe4Kyu4LlF3D8W+3FDcjZU5ScZTLWGPuTX6XNOIAiGrdcGlW0bOYKRQabGtZF4DPlChXboSOxfe/0zWD7HlXX3VRl1fY8Kq6NKAjremDrWdC6D3c6FOa39z5U7wO/RVKuShIRVSUgoGDX1kTyNkPRsXB8D30183q99QHUs8aW527qEVvz6hkeWRS3TREZ6tZsIaV+PjFV59d3PUBM1e44MNHWkSSRDLM0P8Ps1Ar5XIV44qoNV3NLjOaWOO++OcLwuVneee0iuw92EokZCAhUKxbD52c5f2oaQYBUUwRlE7JyO9BkiZRpsr+thV86tI8j7a33tPzODwJ0WSaiqqtaFBJPdHTzZ5fOslzZnuzEraDBCKGIEiFZIaWb9ESSqKJETNXpiSQxJIXH0z3YnsfRpUlCskp/rIHn2gfoCMdRJIlyxeZ8doGRQgZJENAlhefadtAfqwcml2tl3lwYZ65coNWMMF3KU7At3MAnoRs0GCE+37UL1/d5d3GSiuugihKPpLvZm7y1zNvb41O8dPEytufxhb07+fqhvevGFj8IyFaqSKKAJm98VpZKZdriUb5ycDef3T24FlzIVqr87y+/wffPXWJ4cZmJbI69xtVz2pVuIm4YiAJ0JePrnpmjE9P8+zfe5fjULMcmZ/nawb2bnnu+WuPM3AK7W5r4e88+Rlcysbbgtl2PmuNssJrqTMb4/N4hnujrZmdzA5J4Net0cWGZPz52km+dOMtPh8f4Gw8f5tpiuLlCkZ8OX+btsSn6G1P85mMPcKTzasl9EAS4vs98oUg6Gln7TXFTJ262sJ8W/CDgxPQsZ+cW6IjHeby3ix03qMDxfJ/ZfJH/fPQES6Uyv3xkP185sJvuVKKevHFchpeWmc0XeevyJPtam+lMxklHN4opXs6s4Pk+n9kzyNcP7iVuXl3/liwLWRQ3vcd/lTDQ3URXm0vVctYqW65AVWQ0VabmuIwtZNeqk64gnYhwoG9j+8e9higImJqyoWXM9f0NpdJ3Cs/z8PwARZbQtwh+yLKIpsi4rke+VNswBwuCgK4o7OxqYmR6mbHZDNnrepS3C/fFUxySI/SHhxAQOJV7l6pXIqakeLbp8wxFDxBW6pmYopPjePZNfrb843Xbj5YuMFq6UC+51Fv49Z6/jyGFGIruxw88jmff5M3MywA0aa082fgpZqrjVNzypoQsosRI6+2E5QiSILEzum/dQxME9XIu1/EQVqMkkZiBrNQHVaH+JXzXx6o56zMKq5kMSRKQZYlqxaJSquHYLp5XL/cSJZFqxUKSJPzVkhNBFGhpS9CcjjE+urhWri0I3DDqvh3wAx9rVT3akHUiSghDuhoJvf0uzM1h1RzmZnJ09zbxha8e4T/+u5/yn//9qxx6oIeHH9/BpfOznDg2xsOPD7BrbzuSJLGyXCSZCnPwgR6SyTCFQoWXf3iav/iz92hpSxCLm+sWf6yeceDXyfTLPz7D0TeHGdrTxt/4rWcxzbqIWSFf4eUfneGVn5ylq6eRr/1KnUyfPDbOG69dJJ+rohsqO3Z+cD1dH+PeQxBEokoL0VgLO6LPkbOnma4cY3JVKMr1LdzAJmBjf+H1cIMqc9VTlBYXeLL5vyNt7Ebh1toqLiws8bs/O8p/+OUvr/v766PjnJ1b4H/41DNbbLn9KDs2i9USPcr6TGIQBBRti4VKCUkUaY/E1kTwkrpJyghh+x4LlRJ5q0ZM05FucaQIKxod4ThBEDBRzFJxbfwg2LS8dbKQo2TbJDWDJmPz6G/OqpGtVTHC68f7IAhYqVVYqVXQJZl06M49yq9HfPV83MBnrlIkZ1UJKyryHQ7YPQPNNLXEuHh2mmymxKUzszz4xFUfwHRbgo7eRsyQRna5xO/+f37Ic5/bT//OVgRR4PKleY6/PcrMRAZJEtmxqw0zfLXqRBTqC1tNluoWDVfmq9XPBa6KfsmSiCJJdCfjPLejn8/uGqRjGwOoW8FcDRTYq/21Z5YXSeoGNdf9QMV4PtF29br3xRp4pq0fgJQe4lDD1WqVgdjWiqRJzeRgw9baAIPxJgbjTTc8jwY9xG8O3X6Q78pa5MfnR1gqlnh+Zz+f3jWwIWAmCgKp0NZtHrIo8kBXO88N9q/LmCdMg71taU7PLVC0bGZyBfa2XiW2pqrQ37h5ZcKRjlZ+2pDijcuTZMqVtczP9eOm6/vIkshvPfYgXcn4uiySKkubZpU0ue4d275J7+5AU4qD7S386fEzzOeLa9dIEIT6OLSS5fWRcUxV4a8/dJDDnS3reo4FQUCRJDoS8S2v1+2ibNuMLmU4PTtPVzLBp3fvWCO1AJoi09eQ4umBXr518iynZxc42NG6KbGt2A4HO1r59NDAOlILfGS8dOtaPLcSIA1W18jumpetiLRqzSewVRWQLIkUSy6XJ5cplK11JTxdrUm621M4rs9ivrSBuMXDBj3NN28h2G4IgoCuKht4gO8H65Sc7wayJCGJAq7nbyiVvwJVljB1hZWCw1K2uGm1jyQJJGMhRFFgpVChUttcT+Kuz/ee7PU24QUuab2NA/GHeD79Ra4YHkmChHiNCE1SbeC55i/wTNNntthT/YFXVhVTddHgYOIR9scfvKp+TH2/A+HdwHoLnPW7EjDlMGm9jUZtPXkZuTjH6fcniMZM9hzs2rBpe1eKYqHKN//wTXRDYWBXG+nWOGdPTHLy2DiaJhOO6HT1NPKT750kkQxhWS5De9sJhTRee/Esl87O0NXXxMDOFo69NcrFMzO0diRp70wxuKeNH3z7OKmmCKVCja6+G09+d4uCU+bFhTcAmK7MM1WZ5Xj2LHElAtRthj6dfhJNuvtsR1tHkqef202qMcLxo2Ocen+cx57eyeEHe/nBd99nZmqFxfn8KrEV+KW/8QQEAYJYv/eu4zEw2MJ///f+kMmxZXIH1mc1BEBRJErFKt/55rucOz3NkYf7+OovPYJpXj3/k8cnOHNykp6+Jn7j7z5Hc3MUBIEDh7uxLZezp6c4fvTyx8T25xgCEgm1k5jazmD00xSdeUZLr3K5+BoFZxYvuPmgHOBTdBZ5feFf8XzL/5OU3od0F8Ou7XpU7A+2l3ChUuJMZoGe6PqFqBv4TJfzXMguoUkyBxta1+zJmowQ3ZEEcc0gb9X4/sRFvtS7+5Yzog1GiP0N9XdrOJdhvJClMxwnrG5cgL05P8lcpciuRBMDsc0XFuPFLKOFDK3h9Qtax/cZLawwVszSGY6xK7l9Y2lKN+mJJkjpJiXb5seTw3y5bzcNxvWBtltDR08D7d0NmGEdx/ZYml/v3SwrEgNDLRx5pI9Xf3yWUrHGd//03bUSNd8P8FfL52RF4ulP7SaRvHou6WiYT+4coCkcZiafZ7lcpmw5VB2nnilVZGK6TnMkzO50M0c62tjT0kRzJPyBiKQAPNTaQc11SRkmQ6lGfv3730YU4KmObtKhD1Yt/KMOy/MYWcpQsR0Gmxvpa7j9RXlzJERbLEJokyxOVNcIqSoly7qtzJGwmoEyFBnP97cMaEmCQMLQ2du2PerbAqDJCmFNwwuCdQtzz/fJlCpMruRIhkye6O8mpN57zYpspcaFhWUkQWRPSxORTQioKAp0peJossR8ocRKZfPKIk2W6EzEaP8Aq322G5ZfQhF1JG5+7S2vwGLtHFnrMoIgE9c6adB2YMpbP+fnRub5zk9OcvzcFI2JyDqy+Lln9tDdnsLzfQqV2rq2FUkUMFWFiPHB65hcUUa+/g3wgwDPu/U2qhvB1FU0RaZq2awUNq+MMTSFWMhgYaXE5bkVHHfzY/tevc2oajs43s0TBHeC+4LYEgQICMg3KWcVhLrPq3yLp33FwmczUaMb9XUXnTxz1SkkQWJHZO8G8ts/2EJrZ5JYIoRpaqiazKe/dIjwqh9gOGqw91AXvTvSqz2fKooi09wSx3W91YWGgBnSaGqN1SWvBQHdqPd2tnelECUB3dBQVZn4Z8M4touiSGi6iiDCL/2tJ5Dlej37ZtYP2wlFlGnV6wu+BjUBBEjC1esqi9K2qEUD6IZCLGEiyyLRmEEyGcYwNWSlLqwlKxK27a5F7BRl/b0VRYHmdAxNV6hW7XWqn1CPmbiOy3/5jz9jaTHPE88M8cwn92CG1pcITo0vU606DO5uo6k5irQa/RVFkbaOJGdOTbI4n8N1vS1LnX+e4AcBFcvmH/zJ9/ntZx7iQGe95ObMzAJ/fvwsXzi4i65UnN995R1MVcXzfc7PLaHIIo/3d/OFA0Mb+ncqtsP52UX+yzsn+eqRPextSxPWbzwx5Cs1TkzN8hcnzlOsWbi+z850Iy/sHWRv+/YKX1yJ7EqIiKJEQu1gb/xL9EeeYaF6jvHSG8xVz2D5myvxXkGAR9GZ52z+L9gvfpWk1rPld8cyK4wurXBydp6FYolvn7xqO1CsWZyZW7gnarM3wkg+w3cun6MvmmRnsmlVtMTjlenL/HDiEpbn0hlO8Xhr95rIlCgI7E0188mOfr45cobfO/suhqTwdFsvSf1q1tr2XKZKed5ZmOJIUzud4Ti6LKNLMr2xJJ/uHOTFqWF+/9x7gMDjrd1EVQ0/CCg5Ft8cOcM7C5P4QcChpjYONm5eCvb+0ixpM0JrKErfKvl1PI8/v3yWN+cmCIKA7kiCw403V9e+VQjAQLyBL/QM8R/PHeMPLhxDl2We7xggbYbXroHn1wMER1c9bPtiqU1LpiVJ5MEndtDZ00hjOkpHz/psoCAI9OxI89znD7Awl+fC6Wk8199QWxBPhfjcVx9g59529GuCeelImC/u3sknd/TheB7uKqnwV7O2oiAgrYr8GLK82vOtIN8DVcutsK+xud63LNZL3Hc3NFHzXLqicZrvkNjm7SrfmzrLT2YvEVV0vtK9n8eae+8bv9x7AS8IyFVrWK6LoSqENXXTDOfNENY1TFXdNIN2pcy3btK2MZ8+kytwcmaOM7MLzOaLFKo1aq6H7XnMryoz3wiaLJO6RvH4VpGtVDk9M8/x6VmmcwXylRpVx8H2PFYqVYq12rp+YICa61GybAIgauiEVfUDeT4s12WlXMH1fV4bGefc3OIGi5QgqItaLZcqaLK8ZZYubuiEtA/mvLcbQeBjeUVeX/wXiIJMZ+hRWs1DhOSNgUgvsJktH+N8/i9YsUbwVl0VJEElrnXTH3me7vBTyJtYd07MZPD9gH/2336O+HUWmrHV9b0fBFjXXWNNkevlwB/Cta2LNTkb3jBRELZNcbgxHiIa0snky8wtF3A8D1lcLx4VMXUaE2FOj85xYniactUiHjbW9f46rs/scgHvSiXqttR6bsR9QWwD+EDLiK6HH/jYvsWytYAbOFwonGamOkGr0UlfaH2/XyRqoKgSjc0xItGrC7RE8uqkKkkiZkhbRzgFQVinXnylvKVZT6xFha7syzBVgoDVMuP6dteW4wRBQFM6vm5f9xKaqLIrVm+mfydzEkmQaNYbSKpxbN/hePYs/7X0A4aivfSHu4gqdx45lyRxVSK8/lJquookCWsiKYJQzzxciZaNDs9x8dwcs9MrFApV7JqD43jksmV8z+f6BnbX9fjRX55kcnwJw9QwTJV4YmPjfbFQI7Nc5K3XLzE7tbLus/nZHPOzOXr6mrAs968EsSUI8IKA0aUVytbVBUfVdpheKVCxbDzf58L8EnFD5/GBHvZ1pJlYzvGd989xuLttTQREFASKNYv3xqZ56fwoT+7orkeclZtfx6mVHMcnZulOJdjXkcbzA2RRJHmDcrntQL0EU8UUkxhSHEOKk9J6WbaGmSwfZbL87toEuhl8XCZK79BuHiaipFHEjdYzUF8MWp5HplShbNmcWVWohTpR6kklONj+QVqayCQ1g5xd45+/9zI90SQhRSVn1xjOLTNWWKE9HOOXdhwgpZnrPFp7okk+1z1Eplbhp9OX+ffnjvKT6WGajDCKKFF1HfJ2jRWrymKlRJMRpjVUz6iKgkCzGeGv7zxEplZmJJ/h355+ix9NXqJBN/EJWCiXuJBbImfVeKF7kGfb+4hrG6+rKSskdYPh3DL/y7sv0RGOE1JUFqtlzq8sMl3KszvZzOd6hohp6xe0Y4UVxgpZCnaNmutSdm0millsz+VSbpm/HL/AmZV5NEnGkBX6YslVcl5f5LSGony2eycLlRIvTY3wny8c52ez4zSbYXRZpuq6FKz6NViqlvitPQ/RGYljsJHYCoJAV28jbZ1JdF1B3UQYT9cVhvZ28Gu//Qzv/myYkQtzZFfKBH5AOGrQ0dPA3kNdHHigh0jMXCcspUgSCdMgwebP5v0A85qMvybJRDUNzw/Q5FtXAb4el/JLvLkwzrtLk4RkDUNSeKTpztTMPzIIAlzPr7dFCcKasu/tQhGldf7W12OzPfpBwE8vXeYnF0a5tLhMQD2o0hwNo0oyslS3xMptkXm8AlEUbirGdi2CIODkzDzfP3uJE9OzVB2XlmiEuGmQViIooshULs9CYaNFiu/7eEGAAMhi3S/5gyAxfhDg+D4CEFJVooa+phx/LeKGTmciTjoaJhXefC5UpBvfq1uBF/gUnTLni+NAXRiv22ylSb9768obH9dhoXaaTG0Y2y9hSklS2o5Nie189SSXCj9grnocx1/fw1nz8gSBi4BIX/S5jcfxA0KmxlBfekPS5ArqavEC3jWK8kHAXQuo3ik8P6BUszccX5LELfthbxdtjTGSUZP5TIFMvsL47Ar97et7w5NRk650As/3WcqWeem9S7zwyC6aVjPfNdtlZjHH+5emsF2PZNREV+8NBb0viO2HDT/wyDkrvLb0I2y/Rtktktbb2Bs7TERZX7bR3plClER0Y/Mo5RVs9tn1f7tC1Db73vV/v3bbDzoqJIsSCbV+HcYrs2iight42L5Dq9HEvLVEf7iL0dIkCTV2V8S2rmjN2owoiltNIAEnj0/w6k/OUqnYxOImqYYwkiQRBAHH3xvbMlgiiAIPP76DsctLnDs9TWd3A/sOdq+75oIooCgypqkRvS5yF42Z7BhqZefuNqQPSNL9owLb9UjHIhzpbqM1HqUxssSfHTvDYqG0pv5YqNY4Nj7DSrnC/o40T+7oIWps7VN6LRRZQhJF5gslBmyXvqYkyZB500zvdkIQxLpdkBQnqrYSU9qIKR0MF1+k4ma3VFGueBmWahdp0PpJaJ2bfidpmuxKN1KsWUznCjzRd7XVQRZFGsOhbRWDuxUMJpp4orWbl6ZGeGt+kprnUHVdTFlhf0MLT7f28mx734bFXkhR2ZtK82s7D9EejnFqeZ7TmXnc1RK/gPrCKK7pDCYaadBNlGueAUOS2dfQwm/sfoCfzlzm1PI8Rxem8IOgHvRDoC0c5VOdAzzfMcCOeMOmCzdBEHiouZO2UJQ35sZ5fXYcy3OpuA4xTefx1i6eax/goeaODeTo1PI8P5i4yEKliL1q5zFbzuP4PpcLK+TsKiFFRRUlDFnhs907SWom+mrG1ZAVBuON/NrOQ7SFYpxcnuVCbolTmbm1ayAiENN0eqJJms0Iqrg1obpZdY4gCERiBvsOd9PYHGN2eoVysV42Z5gqDU1RWjuSRGL3L3m9VYiCUFcQvkv+mbUqZK0KNc9FQGCmkt+WQHvOqnKpsEjWqrIj1khP5IPvv9sKgiCs9Stbrofluni+/4GUlE+s5Pjx+WHeHpuiMxnnuZ39dCXiRHUNVZaQJRHb87i4uHRLv+NWMV8o8frIOD+5MIKuyHxp/y56Ugniho4uyyiSxBuXJ3h3YmbDtrIkoa6uLaq2g+P7qKv9t/cSsihiqvX71N+Q5NO7d9CwBXEFMBSFzi16fK+4hdwNql6Ni8VJ/nz6FQBUSeGLrU/dc2LrBw4L1TM4QRXHryAJGrq0sU+64maYKb/HXPUEjl9FEUM06buRRY2cNU7JXWSpdh5dStBs7CWsrBewbEpGWM6WuDS+SH9XI6oibbhmolgXQXLcq9Zwjudhud5aQuqDhOd5LOXK64g21Hteo+b2eBL3tTXQnIxwdmyebLHCsQtT9LU1rPutyahJT2uKiKlRrFj88O0LFCs2/e0NaIrEUr7MyeFZ5jNF/CCgqzlBPHJv5qEPndj2hgeJq0katY0KqR8c6n23phRCFbV6pjY8RLvRvaHENtV0e5YiP29wfZekGqXiVZmszNGkp7A8m0dSB/nzmReperUP5kQCeOOVC5w5NcUDD/fz+DM7aW6OoWoyxUKV73/n+KabSaLI3oOdPPToAEffHOb40TFeefEssXiI7t6rpX2JZIhEMkR3XyNf/NoDm2ZlzZCKeo8iTvc7rgzgfhDg+uuLHTuScVriEWRJJKQqRHSNqn3VMzNbqZEpVag5Lp/cM0BEV295QZWORXigp503RyYYWcwwmyvQmYqzu7WJlk0EQe4lBEHAkGK0mvuJqR0IgsClwouU3Qxb1aAsWcO0O/NbEtuwphLWkohC3Q/u6YHee/gLbg7X92k2w3y+Z4i2UJTTmXmyVj2T0mCE2JNsZn9DC0l988VWTNN5ON1JbzTFsaUZxgsrZK0qbuCjiTJRVaPJDLMj1kBfLLWO1AmCgCZJPNveT3s4zumGecYLWYqOhUBdnKk/luJIU9uaSu1msD2PvliSF7oG6YkmOJddJGdVkQWJllCEAw0t7Ew0bcjWQt0jtysSJ37NZ4fZvNxZEkSSmrGBXCuCRLsW44W2HaTVCJcLGTK1Sn1xLEqEFY0mM8RALEVvJHlbWaitoKgynb2NdPZuLV50LeYqBS7mlpivFO762FfQFUmyI9ZIaotn435Box6myQhjyioxRWdnvHlbSuQmyiv8cPoCs5U8v9C1774itqIgENZVkiGDpVKZxWKJbKVKQ/jO+r9vBxcXlri4uIwqSzzR383XD+3BuMYequo4q/2125sBm8zmOD+/SM11ebyvi19+YD+mcrV81HJdzswtrM5n699BTZaIrZbyliybiZUc/Y3JO1IRrs91AsHq/26EsKbSHqvPawFwoL2Fvobkh2T5BkWnwtnCKKfydXsoXVR5omFrF4Ttgo/Lin0Zz7fRpCgRpQVd2kiml2rnWKydpeZlMaQE7aEH6Yl8AkUwWKyd43LxJTLWMFl7jMXauQ3EVhBhdiHPN3/0PkN9aTRFXkuyDPY0sbM3jSSKxEydcs1eE5ByPZ9yzaZcq3u9fpCwXI/L85kNYk2GqtAQ3Z6xt60pRmc6QSyk1wUXCxWutwDUVYWudIIDA228fvIyozMZVgoV0skImiqzUqwyu5THDwJkSWT/jjbSye0Ta7wWH/qq/Ejy8Q/7FJBFmUYtzZfaf+XDPpX7Hs16A816ippnkXOKTFfnAdYI7b2qmb8eAbC4kEdVZbp6GmjvSK6pGZ85OYXn+pueiSAKpFviNDZHeeq53dSqDieOjfPi90/ylV96mHiirtjWN9DMhbMzFPNVymWLltY4iiLheXWla98PkGUJ8QPsL/tQsaqGKonCWtN/QEChWqNoWeu+qqwqpl6z8bp2A1EQSCeiiILAD05foikSpj0Ru6UeL1NV2N+eZm9bmgvzi7x0fpRjEzP4QfCBE9srEAWZsNzAgeTXydszTFeOYfubCyzk7Rkq3sqmn12LlmiEF3bv2O5TvW34Qb3PMqEZfKKjn0909N/2PhRRoi0cpS18+/dHEOojys5EIzsTt0bSrofn11sSWkNRWkNRPtO98+YbreLp9l6ebr+74EKxZvHKxTEANGSGlGYc0cPz/bX3xKq5VHHxGv0PpQduOL/Ef7x4lNfnx7Ztn1/s2s3fHHzwvie2/dEGnkj3ISAQ0ww+17FrW+7B5UKGE5lpap5LwfmAAr63iCsKvntb08zmi5yZXeDU7DyP9HSuI5lQDwwFQb3lYzsyuoWaheN5JE2Dlmh43fE832d4McNMrrBW2bFdKNsOFcclpCq0x6PrxJ8832c6V2BkKYOzieCOJIo0R0LsaG7k3NwCPz5/iai+l5ZoZF2PuR8EWKvWYZuVd9dLihVkUaRkWZTsG/uMRnWdwVUP4EtLywwvZWgMh4joV63VriiXl2oWiiyhSfKG1qrtQsmtMFKavif7vhH8wKfkzOEHDhGlG1NObWgV8Hyb2cpx8vYkoqCQ1PrZm/glklq9jS6lDVB1Vyg6s9TcHIu1M/RG1rsLuK5PuWpRLFvML60P8omCwM7eNKoskU5GmMsW16kmFyo1ppfz7Oy4t2Ku1yIIAso1m9MT8zjXqRWHdJW21PZUd4UNjd09aZZzJSRJZFdPekNqWhCgJRXlM48OcXk2w8JKkWyxSra4vqVAkUV2djXz0K4umn9eie1muFIrvlQu43r18piwqm7wJIN6pKRk21RWBwhBEIgbOtqqh+K18IOAXLVaLxkAoppKaAvhAz8IsD0Py3GxPQ8vqPejXKmvV0QJTZbRlY3HuRFs16Vs15UmBUGgIWSuNWF7q6VuNdfFWfWNgtUm8LVjSmtRwu2I2gVBgOV6FGq1tYlEEkUimoahyBuOYcoGZ/LD+IGHIAjMVZeQBJHR0iQ139rsEPcEggA9vU3MzWQ5c3ISSRKRZZGZ6RWOHx1DUSUk+caTcEtrgqee2021YvP2zy7R1BzluRf2YZgau/a2MzWxzEs/OsMf/f5rHH6ol1g8RK1qszCXRxBh7/5OHnx04IbH+HmBgIChqjRGQowtZ2lLREGASwvLlGq3ZwLelojy3K5+TFXh37z8Ft95/xxfPrybtkTspj1A2XKVuXyRxnCIjmScT+0e4D+8/h6F6oe7cBQEEUOK0xt5koIzy7I1sun3ql4Oyy+v2Uhsvb96BtD1fSRBoGTbVG13LQt+e5mCre0N6tg6axDc5POPBm6WF7m3KNQsXru0ShgFqNkOy6UKfhDQEAkRBAHTK3l6m5IMNjd8pFVLP4qIqDpf7t7Pl7v3b9s+/SBgspxltpInqd37LOidQAA+NdTP+YUl3p+aI6rrRHWd7lVf2SAI8PyApVIZWRJpjUUJb7IGu100RkKYqkq2WmM6VyBTriCLIn4QkK/W+O7pC1xYWNr2Np+4oRMzdIYXl5nM5tcdt2TZvDJ8mZ+NTmw5B3UlEzw90MO5uUX++L1TpKMRHuzuIG7UrQ/9IMB2XaayBbqTcRIhY9N+2PZ4DFNVGMtkuTC/RFcigSQKawQ1qmtrAS9TVehrTPJEXzcvXxrlz06cRRIE9ral1xTovVUxo9Oz8/Q0JOlOJtDF7V/a+4FPwakwXprd9n3fDAE+VS+Hj4cppVDFjYSo5C6SsUaoelkiSgvNxt41UgugSiESWjcRJU3BnqFgbyTojxzo4dCuDlzPx1kViFJkCVmWUNX69dYUme6mBKfG5rg2BpIpVLgwvchgez0A+0Fk1W3XYyFf4vzUAu51AZmoqdHTvH0l4od3dnBgoA3T2FpoLhrSeWh3FyuFCn/201Ms5UqrvfwBoiigKTKNiTC/+YVHGOxsWtXT2X7cl8QW6jXrv/4n32Z8JUs6EuFvPHCQXzl8YMP3ZgoFfv/oMf70xBmgnn7/Z5/6BE/0dhHV15eWlW2bf/rjl3lrvN68/PeeeJhff/Dwpku+kmVzam6eV0bGODk3z3y+SNGyMFSFxlCInc0NPN7TxVN9PcT0W69jPzu/yB8eP8kPzl/CUBX++Fe+Tk8ygSJJ5Ko1js/M8tLwKKfnFlkul/F8n4im0RqLsqu5kUe6Onmqv4ftyhPansc7k1P8i5dfZ3wlC0BnIs5vP/ognxrsR78uevvp9OMUnXLdFFo28HyfrJ3nzcz7HEnspd3YXmXaG+ELX30Az/d5541h3njlIoapMLSnnV//7Wf5/p8fJxS6+X3p7m3kEy/sJZ+v8Ie//zrp1gR7D3RimCrPf2Y/re1Jfvz9k3zrG0cp5CuYpka6Nc6Dj/bTmP6rtQAVBfi1Rw7xjaMn+bPjZ2iLR2mOhtl3B2JGoiDQnUrwDz75BP/4mz8kauh8as/ATRf109k8f/jWCd6fnEUWRXRV5lO7d3Cw84M3Rt8MzfpOjE1KpK7AC2w83yLAQ7jB8DuxkuOH5y7xxX27aIlG+MZ7p/je2Yt0pxL86gMHONx56+q9oiAi3GDEqC+o7o3s/seA7lScf/3ffH7tv7957AyeH/BIXyedyRiu5zOyuMyPz41sUDz9GB9NlByLmXKOFaty3xJbgAe7O/jCnizfOFYfX968PEF/U4rmSJiy7TBfKDGTy/P8zn5++4mHtoXYHmxvpTMR56XFEf74vZOcnp2nLR4jV6lyYnoeU5VpCoe2nRgMNKUYbGrgZ6Pj/ODcJWbzBXobkpQtm/MLS9iuR1TX6GtIsljaWHGTjoZ5brCPseUs3z51ln/+g5/SnUrQmYyjKzK5ao3ZfJHpbJ5/9bXP8VB3+6bE9vG+Lr535iJvj0/x714/yosXRmmMhLAdl7Jt8w+fe4KdzVerU5rCYf7+M4+yWCpxfHKW0zPztMWjpKMRJFFkuVRmciVHoWbx3z//JK2xKPo9IAwVz2LRypJ1buwCcE8QBHh+DQhQxBCysLHcd6byLhU3A0BM6aTVPLzhO7qUQJPiuMEYVS+34XNZFrFdgfmFAmcu1Qn8UF+arrbk2thsaAq7Opv53rvn1207ncnz1oUJPv/Q9lR83ArmskVeOTVKxdqoIJ6MmAxtY/Y4fItWRmFD4yvP7OfQYDvvnJnk8myGmu0QjxgMdTfz2L4eoqZ+z6oK4D4mtqIg0haNMp0rsFgskSlXNvUzm8kXmC+UcFazjaLjcnFpmSMdbUSv4zWe7zO6nKVs2TRHwsR0fQOpDYDXR8f5ztnzHJ+epWhZ1FwX16uX5FVdl0LNYiqf552Jab516hy/dGgfT/R0Y96CAlmweh6O7yOvysu3x2K8OzXDt0+f453J6brvm+vi+fUejJJts1yuMJHNUbZtnunf2i7kdlBzXH46cpn/9O77jGezOL5PTzLO7zz6II/1dKFukhX67uwrzFbrSq1XPIYVUebzrc8QVcKoN7Fs2gotrQn+9t97DoS6XVLgB3z2CweQVYVEqi5G9dhTO9l/qJtwpH5jYwmTr/ziw7zwhYP10mNRQNMVojGDX//tZxFFATNcHwBDYY1HntjB4K5WUg1h9NWXVBAEunua+I2/8wl++W8+Qaoxgq7X+250Q2XPgU66+5rq5ceejyCKyIqIaaoY5kfD1Hw7IAj1qpvHBrrY3daM63vIorSmYBnWNTRZ4l989dPr7GjaEjH+t69/mrhpYCgyXzm8Bz8ICGkqkijQHAvzv37t04RU9ZYEoIZamvhHLzy55ot4pVfsfjGY16QIknDjdyDArxvN32BcX6lUOTY1y9985DCjyxmmcnk+NTSA4/m8OjJ2W8RWErQbnpMX2Lj+7WXdP8at40rZ5xVMr+RpiUfoTMbWlEp3NDfy719/j3zl1isPPM8nny0zfH6WuakshVwFy3aJJ0z2P9DLjl33R7DnryIu5BdYrJbu61qHK2X+n90zSFcyzuuj4xybnGVsOcuF+XoPbEhT2d+W5lBHK9FtshmLaCq//sghWmMR3rw8wdnZRU7PLBA1NA60p/nM7kGy1Sp/cer8zXd2GwgpKp/ZswNTU/jRuUtcXl7h4sIykVUy++xgLwnT4HtnLm5KbAVBoC0e5XeefIiDHS28MjzG6FKGkzNzBEE9kxc3dD67Z5DORGzLXvmEafDbTz5EVyrOuxMzjCxluLiwhKkqdCRiG6YFSRRoioT4Z599jp9euszb45NcXs7y/tQsgiAQ1lR6GhLsbU1zsKMF4x5lwVasPFOV+Xuy75tCEBAFBYJ6UHh9ILb+ls2U36XqrSAJGjG1g5S2sW1GElQkQSXAx9ukwnAxU+T190Z55Z1h2prrQfZX3hnm0UM9PPXgAG3NcUK6ysHeNgxNoea4a9XIFcvm0swyPz5+iecPDiBvEtTYThSrFqfH5/jJieENnzVEQ/SmU8RC2yfOdKuBJkGoh9E7m5M0xsPYbr2VQRJFNEXG0JV7rip+XxLbuipwQHcyzum5BZbLZbLVGoWaRfw6f7HZfJHF0lV5di8IuLSUoeqsj2B4vk+hZrFUKuP6Pi3RCEnT2HBxv3PmPH9x9jynZxfI12qIokhc1+hMxDEVBcv1mC8WWSqXmSsUyVarrFTq/zw30EfjbYgvBEHdU+3V0TF+cOESb09Mka9ZGIpCWzSKKkkUbYtctUbNcZFFgcZtimTWHJcfXxzmT0+d5dzCIgD9DUn+zqMP8XB3JwnT2DTqNBTtpcNM10uYfZsVK8eynSWihNGlO5/4FFUico0wVy5b4I/++bf4lX/yJWS5/vdwRF8jtQCSJBBPhognN15zrXH9Ql6SxA3bX4GqyTQ0buz/E0UBw1AxPgTT7fsRgiAQ0jZvCbiCtusyrqos0RwN8+KJYQZbG2lLRdck6B3Po1Cp0RqPrnvWJpayDM9lONDdQipirnveDVXZ4Id7P8Hxa2x0Dr0WAqIgI9zESsRfbUvQZZm3x6dpi0V5qLuDM3MLXFi4uVrotZAFDUnY+p7ZfvmmXrwfY/sQAAuFMjPZAt0NCVzf59zcIuVVn8ybwXU95qaz/Own5zh1bJxCrkKlYuHYHr7v09aZIt2a2EBspyeWOXdympWlAmZI4/kvHMQw7++xzfZcpso5Tq3MMlbMsGyVKTs2XuCjSTJhRSOq6nSHkvRFG+gKJ4hr5pYxo5HCMj+cPseplblNP48qGocbOvh678Fbtg9arpUYLiwzUcowVykyVykwUVrhcrGePZqt5Pk/h4/y4szFTbePqTpPpvv4ZNvOusrzKmbKeV6ZH+HVuXpbwyNN3TzfNkh7KH5L55W3q/zuhTeZKGVxfI+nW/p5tnWAtLF+rosZOgfaW2hPxPjk0ADlVes2URRRJZGwptEUCa0F7iVBYLC5kf/pM88iCSIdyc2rbPa1pfn7zzyK6/vrlNwlUaSvIcXXD2k8PdBDxXbwgwBVlkiaBq2xKJbr0teQJAjYsA6J6hpf2r+LB7raSZq3vnAXRYHWWJQXdu3gQFsLJcvC9QMUSSRu6KSjERRJpCUa5Rf279p0nlMkiZZohGd39LG7pZlCrYbluHWFd1FEXxWZaovHNvV3Fla1KoaaG0mYBp8eGqDquHhBgCKKmJpC23VaEYIgIIsiHYkYL+zewQNdbRRqFvZqT6UqSxiKQtI0aAib60qpJUGgORrmn372E/hBQHfyzktTV+wCk5WFm3/xHkBAQBMjOH4Fx6/gXmOt5wUuOXuMnD2B61dJan0ktX5kYbNn41q1j42jxMWxRabms/zC8/tpb4kDMDOf49TFGc6NzNPWHEcWRVJRkyP97bx5fmKtFSsIYG6lwDdeO0FTLMyuzmaMTSzZtgNV2+Gt8xN8+80zLBc2BmH60kkO9rZ+ICrnm0EQBFRFQlU+HPX9+5LYQv1B7k0lMVWFoAz5Wo2lUnkjsS0UWCiWiWgqScNkKp9neDlDZXWwufLoWq7HbKGA5db/3hKNkDKvilrYnseZ+QX+/Mw5TszMU7FtOhMxHunqZG9LMw3hEKok4Xo+uWqV4eUMRyenOTk7z/nFJb7x/mlius6j3Z0bznEreIHPiZk5RjIZRpZXaI/H+EJbK93JBDFdQxZFaq5LoVZjJl9EEgUOt996pmYrWK7Li5dG+NaZc5yarUfghpqa+NXDB3iit3udMMH16Dbb8FftTBzfZb62xMLS1iqwdwqn5nDmjYt4jveRNBT/GFchCgJtqRiRayx9XM9jKV/m/fEZnt+3Y13PRkhTaUlE6oqEHzHk7Wlsb6MP4hWoookiGgg3WTRLoogkCLw/Pcc7E1N8ce8Q3ak45+cXN6gf3gyqaGxqRn8FVS9PyV3CC5ybZps/xt3jYGcr745P8/tvHCOqa/hBwHKpzJ62ZtLRG1ul2ZbDhdMzvPyDU5x8d4zZ6ZUNQ28orFOrbSxNsyyX8ZEF3nrlAoapsnNvO939TZv64X7YsD2XyXKO1+ZHOJmZZaqcZblWpuhYWH6dCCmihCbKGLJKgx6iO5zk8517eKZ1a92Dgl3l1MocP53bmOUASGkmYUWrZ2Fucdq5XMzw7fFTXMgvkLOr5K0qNc/FX70xZdfmQn6RC/nFTbdv0EN0hBJ4wfr3+oq9zKvzI/hBwGK1yGCsiVYzdtM50fE95ioFvjt5hqVaCU2UOdTQji5tfq+vBCy7Vr3GbwRBEEiYBg91d9zwe43h0JaBfl2R6UjE6djCmiaCtqVCsyrL9DUk6WtI3vRcN2wrSTRHwjRHtn7Pomkd2FqoThQFkiGD5F1kwwxVoSeVoCd1a0TzSnb9Zue+2Xamqt70Xt0MQRCQsfNMfWjEViKitFD1Vig4s1TdFYLARxAEXL/KaOElql7dai+lDdCg7dg0AeQGNVzfQkBE3KS6cCVXxnE8HjnYg7ma0OhIJzh5YYaVXJ1ACoKAoSq8cGQnF2eWKNeuBiRrjsv56UX+00vv8elDgzywo4OGaGhby27nVgq8eX6cH78/zLmphTW3iSuImhq7utLs6vgwnWY+XNy/K0cBepIJzNUez1y1xkKpxEDjVcn8im0zXyySr9XojMc40NbCxKkc86uZVMfz1nocLNdlPJNbk+duXc3YQl3ooViz+ObJM5yeXaBs23Ql4nxqsJ8v7h6ivzFVl1+5RoVurlCkPR7D831Ozy9ybmGRH1y4RDoS5mBbyy1lVT0/4PWxCZbLZfa3tvDJwX4e6mynPb5+4nI8j8VimZJtkwrdusJk3evxKoJVs+9XR8f405NnODVXJ7W70018ee8uPjO0A0USb3jui9YKtVUFZJ+AFStHya1cKw63LRAliYbWBK7j4nn+Xx314fsYQRDw3ug0/ekGZrMFbMclnYhgOS6iKDK1nMPUVDzfR5UlmmJh4qbO5cUVZrMFkmGDgHq0cWYlz7HLMxwbmSai67SnoqTjEcqWw9jiCjXbpWNV0W8xX2JmJU/VdvC8AEWW6EunSEVMTk/MU7UdCpUaCNDVkGCwrb4oqXkFFNFAEmRueZV6F9fGx2O2eoqyl9nyeyG5EU2M3FQ9PBky2N3SzGsj4yQMg65kHFEQEAWBpHl7KrOGnEAXt1YkdvwqRXuOvD1DUusGwJQVPtHRR3c0wZHGOw+meb5Dyc2wWBum5peAAFUwSWqdpLSuDcqW2wVRgIii8fmeQd5ffp1G8/7JSB/qbMUPAt6fnGWlUqmrhMciPDfUf0Nl7yAIuHxpgZe+d5LXXjxLpWwhySLJhghmSCObKVHIVbbcPhY3icQMZqdWkBWJ86emaGqJ3XfE1vJczuXm+f7UeV6avchUOQeAJsnEFJ1GJYyIgO27VF2H5VqJ+WoBx/duqj7cqId5uqWPBj2E43tYnsuKVWGsmGGxtnVA6kYQEdBlmWYjQrNRF7VZrJaYLucoODVMWaUjFKfF3PzexhSd7khyQ4Y4oRrsiqfpCCWYKmUZLS4zWlxmVzxNXLsxqSo6FmeycyxWS/gEdEdS9EYaiKn3LoPiBT5lt8pibYWsU6TkVrE8Gy/wEBBQRBlD1okrYRq1BHEljCbdu4qBgIAggIJTIu+UKLoVSm6Vqmfh+C5u4NZLJAURSZDq5ydphGWTiGLSoMbRJXWD5eMHDT8IcHyHJStLxi5QdMr13xB4+IGPJIjIgrR67gZRJUxcjRBVQsjbML4GBBTdMvO1DCt2fht+0e1DEmQa9SEy1gglZ4G56gkMOYkqhsjaY4yXXsf1q5hSipS2g4iy+ZxleUVsv4goyKjixsCJKAoEAVQtZ43YVi2n7jd+zRpUkkQO97dzoKeVQsUiW7qq/Gs7Hj87O0axajG1nGN3V5rOxjiNsRDmHfSoB0GA5Xgs5ouML2Q5NTbHz86NMTq3uYL3nq40h/raSEVNfN+nWqzx2p+9zUOfPUSyOX7bx/8o4r4ltgLQlYgT0lQEIF+tsVBcP/HMF0tkylVc36c1FuWBjnb+7NRZbM9jJpdnqKlxjbzWXJexbHatTzcdjZAw6p/Zrsv4SpYXL41StC1USeKx7k6+uGcXOxo3es8JQr2k5bmBPjzf59JyBsv1eHNsksPtrQw0pNb1GW4FPwgYXs6wJ93E1/fv4Ynerg1iTVAvf7m+POVWIAlX5eaDIKDiOJyanef3jx5fKz/enW7mS3uG+NKeoVuyW5koz5Cxc6v79LEDl1ajaduzqrIq0b27gzNvXqJcqBJJXC3BVlSZtv4PTqTqY9QRAD98/yJfeVjlnUuTLBXK7O9uoVyzaY5H+K9vnmR3R5qQrhBeVXY0NYXxxSzffOsU6hOHiJkGELCQK3FpZonlYoVLs3UFzHjIIF+p8d7oNBNLWTpSMcK6xuRyjlfOjOIHAYaq4Lg+hUqNx4e6efHUMJosMZ3JU3Ncnt7du0Zsp8vHUUSDkNyAKSduqf/1jq5LEOAFNkvWCFPl96i4W9v5xNUOQnLDTffZEo3w2T3/f/b+O8qu/L7uBT8nn3tuTpUTKiHnRqNzN5uZYpQoi1aWJVmWLNtjv/F4bM88v/fWcpjx2DPjcZIsW7IsUiJNijl0s5udu9ERORRQqJxvzveeOH/cQjWqUVWoAgpokOLGqlWoG04+v/Pb37D3Tl66OsnnBvfQFQnRsB06o2Fab5LVey8CciuGvJGHpkfemmGy8johpR1JUIloPn5t143iG1tFxckyWnqFhdolHM/Cw0MRdTxcYlo3sD3E1nIb2F4DEQlN8iMKIjHd4Pf3H+N7c8/QZdzcYuluIR4w+MDOfg50trFYKiGJIr2xyHLf+fqT6HKpzusvXeb1F0cwGxaJlhD9w630D7cRSwZ5+dkLnHlrYt3vR6J+OrpjaD4Fy3QYOTfLfQ8PEYlt7Xq6k/A8j/FShm9MnuUbk2ep2iaqKNEXjNEbiNHjjxLX/ciCSNU2yZk1pss50o0Kx5O9DIeSG4aMugNRfsF/hM/aNlXHpGKZnM8v8OWxk7dMbAdDSX6x/ygNx1557dWlcb49dZ6iVSeuGTzZMcQH29e275JFkbgeQHnPuZdFiVZfkEdb+/ly9SR1x+ZMdo4DsQ4i2sbBpmyjysuLYyt64IfjXfQGolsO72XNIkv1LBW7OXEXBAFNVBkO9qAsK+86nkPJqjJXTzFRmWekOMlUdZH0ctDb8uymqr6kEVWDdPhaGAp0MRDoottoJa5G0NbJJN8KTNeiZFXJWyWKVoXp6gJztTSLjSzpRp6CVabmmDRcE9d1kUUZTZTRJZ2wGiCpRmjVYwz4u2j3xWnV44QU/8r+3g48zyNnlZivpak7zR5PQRDwSz52hnpv+GzNabDYyDJTXeJyaZLxyjyL9QxFq0LNbeB4LrIgoosaYTVAQg3T5kvQbbSyw99Bn9FOWAlsKtHiem4z2OM2qDkmdcek7jaoOw3mamkuFSewvdVtNg4uk5V53s5uvR86pPhp0WOElZuPP5Kg0mkcY7LyCgVzkunKq1TsFD4pQrp+haI1DQi0GQdJ6MOo0o3BX89zqdoZak4OWVDxSTdm/BOxAPpMhhffvEJ/d3MeMT6dQZZEWuPvKjGLgkDI0PnYfbtIFSu8Mzq7ovsBzbnSqbE5RmZS7OpKcnigk8GOBG2RALqqoCky1cZ7dS2aKuRLhUozmOG4NCybSs0kU6oyMrPEiZEpxhay1MwbK3IA2qNBntg/wL7e1qbTiu1SSJf4o3/8Jfr2dv+U2N4LSAb8RHx6UzG4XmfpPcR2PJMjV6thKApd4RBDyRg+RaFqWUxk8xTq9XeJrWUzkW1mbH1KU9n4msJfrlbn5fFJyg0Tz6OZde3qWJUdXgsJv8FjAzv44junmcjmKTYanFtY4khnBwc6Nke8BOCTe3ZxoKNtTVJ7O9DkprjPNVJ7fmGJf/nsi4xmMggI7G1r4ecP7uNTe3a+x3d0fbTqcSRBwnTNprWSEmI42Icmbm/kVZJEEh1Rnv4fLxHviBJtCSMpzW2MJkP8yj/93Lau76fYHFRZptww8WkKHh5jixlkUeKB4R6KtQY7O5McG+xa1Z/0wf2DnJ6YX+mtDRk6e7pbsV0Xvz7DLz12eKVvNhYwKFZ7bpCuF0WB+3Z0caS/k9MT8zx37ir7e9upNkwe3jnErs4WloplBtrevWfP5b9B0ZojoQ3S7T9Gm28vhhxDEjRkQW2SXEG4Ze/la1lay61QMOd4K/2nZBtjON7aQkwCEkl9iKBy87FBkSR6ohF++8Ekkigsi4TAI/29N/3uexGQkwSUJJKgrrttBXOOq6UXaPftJ671I6PetFz6ZvA8j1xjhouFZ3go+Wt0+w/hAUVzAUXcWNBqqyhZi+SteXxSmHbf5n1q3w+YtsNcvsilhRQzuQKyKFKsNdjdliRs6OuS29GL84ycmyWXrZBoDfHER/bx13/rMYyAhiiKzEykNyS2siIRjhi0tIWZmcgwNZ6iXlt7gvR+oe7Y/GDmIj+cvUTVNtElmZ3hFv7mroc4nuwjrK5u8/E8j4bjcLGwSFjVafPd3BdREkQMRcVQVBJ6s1Q4pG7e2eC9iGi+GzKos9UChtwcA3VJodsf5WB865UPEdXHBzuG+dbUOSzX4XR2lgeKfeyLtq/bA+y4LqlamTdSU3iAKkocjHXQaWxdxf9sYZSvzzzHSGkKaB67Ni3Ovz70d4koQRzPIWMWOZkb4enFE1wsTqy7rJJdpWRXmaouciJzllYtxvH4Pp5suY9efzuaqNyWfkjDMak5DebraS4UxzmZu8xIaYKKXd/Q8MtxmyS3aFdZamS5wtTKe4OBLh5PHuVodBftvgT6NmSYzxZG+dLkU0wtCzFJgsRwoJt/c+j/BLBsHdTMfl8qTfLM4hucyJzFdO01l2d6LqZrU7QrzVLh/OVmYsho5zf7P8Ph6DDSTQKIBbOZ0c6ZJRbqaeZraebqGebrKRbqGcp2Dde7MTtouTZ/Ofscfzn73JaPw9HoLj7b+QT3xXbf9LOioNBmHKTVt5eGU6Rip6nY72pNCIgYUpy+wGNEtbV9xy2vRtleoGZnMeQkQeVGN4fdA23kClW+8cwZJEkEr0n4P/mB/ewfvlGM7/hwDzOpPLlyjSuz6Ru8l2umxcmxOU6OzaFIIvGQn85YiGQkwFQqv+qzntesQn365GUkQaBQq7OUKzOVyrOQK61Um64HQ2uWR98/3E00cG97h99p3NPEVgA6wyFCutbM2L5HqW48lydfa5LX9nAQn6LQF4swspRmPJujcJ23Zd22mVzO2PZEwgSvU1FtWvssrlw4e9pa6A5vLkPqVxUe2dHHQukc5YbJZC7PVL6waWIb0DSOdLXTsgXRqc1ClWVksemFeW5+kX/x7AtcXGoOBvvbW/jN40d5crD/pt6h1yOqhjmVv8jp/Ai2Z9Pv7yapxWjR40jbWO4piAI+v84TP//ADe8Fo/eufcJPMgSgNRxgdD5NQNdIhvyML2WJBQxawgF8qkwy5N+UOvhWkQz5CRk6siShyhKW45AINvvev/P2RSzH5UBPGztaVkdha06BicprTFReQxODJPVh2o0DdOj7SehDqNKtX0seHiVrkbHSC1zIf5uSvbjhBCogx2nRdxGQ1+/fuobZfJHnrozx0d3DtAT9yLcx4RMFmZDSTkTtItMYW2dfHNL1UZ5f+Dc82vp3adF3ogi3Ptm/tlTba2ZSO419yIIKCMS1rZPzmyHdmCBvztKi36iEea/h6lKGL71xmvNzSwy2xDBthz9+9W3+5qP38+E9g7Ssk5G/fH6W+Zksqiazc28nv/y3PoDu29q9pvlUEskQ0+NpUotFLHPtyfL7hbO5OU5lZ0nVK0iCSH8wzv/7gc/R7gutS+Q0SeJQ7CdTAdqQFfbF2tkRjHEpv8R4KctoIUXBrK1rI5RpVLlUWCS1nIEeDrfQF4zhl2+flDmeS8YsULQqBGU/C/UM35p7iWcW36DmbM3HfrGR5btzL3OhOM4v936MY7E9txxkBFZI4Du5S2TN4i0v53qMlmcYK89yKj/Cpzoe5Xh837Ys93o4nkPZqWF5NorQnJJXnTrPLr3JN2ZfYLG+9WoTj2bGv11PbOqY/unkd3kxdfKmQYD3EwIih2O/jus5TJZfxlwRPBRQxSBHEr9Bu+8IirA2qcs2xihbi3i4aFJwTdXkSNDHRx/dw4OH+hmfTSMg0NsZIxxcO+AoCPCpB/Zgux5fev4k0+n8uttvOS4LuRILubXbYlzPo1Cp86XnT970WLwXkijw5MEhPvvgProTf7VsKNfCPUtsr0XuusJhIrrOWDZHulKhYdtoyzY0E5ks+VqN3miE9mAQXZYZTMQZTWeZyDV9vaB5wVQsk5lCM+rRG40Q1NSVddQsi7FMFm+Z2LaHgkQM302HA0EQUCWJoWQcRWxGxBaKpRtKpteDKAj0x6MYyp3pM9FlGUkUOTE1w399/W2upJsD5OHOdn73ofs51t2JLG7cU/tevJh6k6QW53cGfgFJkJirLfGduef45b5P45NudyL8LnwBnY/+6qNrvifcBaU313NxPQvLrWEtCw5YXg3brWOvvFbHuvbjNV8vWvNk1yEPzeU6TFXexHLrKIKOLPlQBA1Z1JEFH4qoI4t68z1RW/6to4g+ZFFHEpTbevjfLnqTUV4ZmeBAbzttkSAzmcKKLdS17drM9XStX7Rhbc4/9Zp4xurXmiIpHzk4zK7OFgxN2VCx2XTLzNfOkapf5oL4bTQxQFBpJaR0EFTaCSlt+OUEiuhr/gg6giA1rQGWr4WanaPiZMg3pkg3RsmZU1TtHKZbuemEYE/kU0TVnk21+9quy2g6yze/8k2GknEe7u/l/p4u2sLBLZf9C4JAUhumyzi6LrEFcLHIm1M8M//Paffto913gLg2QEBJogoGoqjgec6yPVAdy6vRcKo03BINp0jdKTYFPDyXHYFHlzPkUcJKO88v/mcORj9JXOtDEd8dJ2zXJGtOcSr7TYrWAoIg0enbx1DoEaJqN1U7x7dm/hkfav8HtOgDAExXTnOl+AK7wx8moCQ5k/8Ok+W3qTsFVNHP25mv4ZNDfKT9fwE8BEQWa5d5eem/Ml+7hIBAX+AY+yIfQ5dunuHbbjw3MkZ7OMgvHj9IMujHcT2ms3m++PpphlsT6xLbhbk8hXyVZGuI4b2daHqT1G5l/JZlccUCrVpp4KzRo/V+4s3UFFPlpp96lz/Mp3v2r5DatfbzTlpG3Atolv7KfLRrN3PVIql6mUuFJc5k53mife0gzmw1zzuZmZW/H2vrJ6lvrhx1M3DxmK4uYroW351/hdcyZ6k7t2YXdq2U9b+NfxvHczkUGcaQb20ekarnmK4ukje3t5/exeNc4SogIAnSpjKMW4XjuWTNAkktStVu8M3ZF/jR4lukG/lbWp4mqiS1KK16bFPzhbxZvrdJ7fK1a8hxjiV+m4Hgk+TMCSy3hl9OktB3EZBbUURj3eu8aqfRpTCt+j6Svj20+Q7d8BnLdnBdj3BQZ+9QO9fquUzTQZI8NHU1ZRIEAVWW+djRnQR8Kn/+/CkuTN9dga2QofHRwzv5lQ8epT0aXHP/JVnilW+8yff+6FkWJ5boHG7nyS88wv5Hm9dydj7Hs3/+MudeHqFaqhFvj/DIzx7nwU8eRZIlzLrJyR+d49VvvcX0yByu66IbGr/3b3+djsE2Cukibz11ile+9RaVfAUjaPCxv/EBDjy6m8lLs4y8Mcr81UWWptPsun+IRs1k9NQ4Rz98gEc+ez+hRJDxs1N8+z//kPRsFvDY/+huHv7MMfoPbD0Qfs8S22voCocI+3Rcz6PcMFkslemOhGnYDjOFIqWGSdTw0RYMoCsyQ4k4PxRHmSsUydWa/bd1y2Kp1CTFAL2xyKoeWMtxyNVqK7d0WNdXRKtuBkkQSfr9SMuqZ6WGScXc3CAvAFGfr+kFegce0KokcXJ2nrFMllOz81hOk0R8bOcQe1pb8KvqltfbcEzCSpB2vaWpSOc5VJ369otHiSKBiJ/58SWyC3lauuPE26PNidh2r2wZnudhumXezPx38uYMrmfjeQ4u7vJvB89zl39f97fn4NF83XEbWN5GAiYeBXOGqp1FFCQEJERBXPVbECRExNW/Bamp5CfIRNUejid+q0ly7/LErrclwvdO1hEESAT9BHV9paQfVnM223FZKpR57vxVRuZS2K5Luljm0I4O2qMhon4fi/kyf/LcWxzq62BHa4y5bJFnz45yZT6NJAjs61278sH1PArVOo7j8eboNJdmUxiawmBbnAd3rj0Qeng4XgPHa9BwS1TJUrIWWRIvNwMHgoYsasvnonnsEcTmN71r59fC8RqYbg3TLWO5NTw2JgeSoNLjP0av/wEMeXMTjfZwkF85dohUucJkJsf5+UVeG5+iJRjgaHcnBzpaiW1BSC6otNKq7yGsdFKwZtf9nItNxU4xXXmLVP0yqhhAFlVEZARBWBZk8fBwcL3lHyxcz8bxLBzPQpdCtPh2N9VT1W4Ox3+WsdIJTqS/REhO0hu4jw7fbjQpSNFa5LXUn9JlHGQo9Ciu5zBfu8ilwnMcjH4a13MomAurSqhtr07FzuF4JprkZ2foCRzXpGxnaNEHafftQhQUREFeuTdNt0pU7aLHf4SitcDJ7DfoNPaR1AaQt7mN4mbIVWt0RkIMJOOoclP5NuTTKNcb1K31S4PLpTqNmoW/WyfRErwlpU1REJoldjR9cO/QULplNK8pj9FimnS9WZnVogd5pK0fWbyzfpD3OlRRWilHztQrXCmkOJud47G2/huEjRzPZbqc53RmduW79yd7iWvbV5roei4XiuO8lD7FxeIEJauy7EAhoIkKPUYbbb4EQdlAESQszyZvlpiuLTFTXVxRi74Gy7OZq6X486mnCSl+BgPdt1TyuzPYy4nsOS6Xpm54T0AgqBgktQhxNUJECaJLKoooY7oWRavCXC3NfD1N2b5RgK3hWlwqTfDMoo/+QCdRZW0CcatwPIesWSKsBHlq/jVeSZ9mqZFdpZQtCRJJLUJI9uOTNSRBwlruJ06beap2feXYRtQAOwKdm753HM+9Z0nt9RAFGZ+UoNXnJ6oN4nkOkqChSYHludP656TNt5+I2oPjmahiAG2NoOZbZ6eYnMvyM0/sJRRotheUynWefuUiyViQx47dGEwSBYGwX+fh3X1EDB9PvTPCj06PUjOtO3pEZVFkR1uMT9y3iycPDtIRC63bxuI6Lq7j8tCn7wMBLr0xyvf/+Dl6dncSjAb4zh8+QyBi8InfehJVV5m7usAzf/YinQNtdA638/bTZzjz0kWS3XEe//wDCKJApVAl1hbBdVxe+eabTF6Y4dHPHSfRGaWYKfPMn72I5lNp1Ewy83l8QZ3HPv8AX/7X3+ITv/VBdh8fpJAqMn1plkDUz4/+/BXu//ghIi1h7IbNie++zds/PEM4ESLesTWbqnuf2EZChPVmBK9imswWinRFwkznC+RrdWzXJW4YdIRD6LLCUDKOJIpNElwuU6zXmz1NhXdLU3ojkZVSZM/zsD2P2nWN37osoWxShVdcNsi+lkVp2DYNZ/NZKF2W75idzXS+wEKpzEKptIpsn5qb52h3J1HDt6IavVmoksJ8bQlZkJAEkaVGBp+ss927YNYtLr8zxmvfPUlmLseTX3gIf9hg6tIcExdm+NivPb69KwSapZMm89WzpBpX2G4Lo2uwvQb2Fsu23oVA3SksG5TffTXTtkiQTx/bQ1c8TEDT0FUFXZGRRIG/9vBB2iLvPiwEAQxNZWd7gqjfh6EpxIN+gj4NRRLpiof5zP17UCSR1kgAQ1VIhPw8MNzDvp42woZOMuRHV2XaIkFiAR+qLNHbEuXDB4d4e2yGoY4EyZAfn6owtpjl3NTCusT2vfBwsbwallOj5uTuyPFSRIN2fR8Hoj9HWO3adF+pX1XZ2ZJgKBlnRzzK1XSWNydnGFlMc3EhRU80zO62JI8P7iBmGGt6Jl4PWdRo0XeyM/QRTua+jOWur54LNLOwt+Ft63pNgqaJfjp8e9AEg4w5ScGcY6z0GnWnSK//PgrWHHlzlgeTv0pM7cHDo2rnWKiPkDWniCgblZgKyIJKQusjpLQhIJDQ+ug09t/wyZDaRoexj7DSRsFq4c30V6jYWaJqNzJ3l9hqskzdsslXa7SEAjiux3y+hCJJK6RzTSyTP1ESkTch9LcWHMel0Vg+N7qyrTYUt4uS1SDbqFJ3bGRBJK4Z9Phv3XfzJwWiINDlj7Av0s5itUSmUeFKMcVUOUdfcLUOyFKtzNVSmlS9jCpK7Iu20+2Pbqs4k+O5vJm9QMEqU112SIirYfaG+9kXGqBFjxJSAuhiU1HY9RyqTmPZB3WB19Jnmakt0nDfDeLYnsNYeZbnlt4mrAToNrZuVdLqizEU7Ga0PM1iPYsmKnT6Wug2Wuj0tZLUIoSVAAHZhy5pKKKMJIg4nkvNaVCwyszWljhfGOPt3CVMd3WQqWLXuFKe5q3sRT7Yet9N+1a3Asd1yTQKpOo5Xs2cZa6Wano1iyrtvgT7Qv10+BJE1RC+5W0XBRHHdai7FhW7SqqRY66WZrIyT0QNMhjo2vT6P9BylKHg+pZAebPEpdIEo+WZVa9Lgsj9sb0MbGFd19CuJ+jw3VxI8b0QBAFFMFDErQVrDDmBcRPhxoV0kcm5LOp1VoOqKjM9n8ey1w9gi4JANGBwdKiLSEBnsCPBm5enuDC9RL5c29aZpCBAVyLC4f4OHtjZw6GBTjpiG7dOep5H375u9j68C82nUis3GD01yfTIHJ2D7Zx+8QI+v87SdAZVU8jM55i7usjMlQVae5NcOHEZ13U59MRedh8fQhAF6pUGul8jPZNl9vI8mq5w7GOHCMUCVItVXvr660yPzKH5VFRdIdEZZehIP7VSjb693VgNi3OvjpCey5FPl3jzqVPYpkUwFsC1XcbOTmFbDtmF/E8esW0PBon4dERBoGpZzBWb5cSj6QwVy0KVJBJ+Pwl/05h6RyyKLstUGibzxRLpShUBgdlik9jKokhnJIT/PX2Aq2xx7uL+iWuUWG4XJvN5XNfDr6r0x6JkqlUK9QYvjk3QHgqiyxJDycSWiPVwsI+FWpqZ2vxy5kngYGQXsrC9l1KtUufZv3gVf9hgbjlrK4gC2cU8r373nTtEbJtZPReHu3sVbAUervf+9MUJgoChqTy6e8fKa8nwu31eT+xdLdogiSIRv859g2s/MCN+Hx/YN7DqtZCh05OIbLgdbZEgYZ/Oj86N4nkeAgKe56HJEvo94n0rIGDIcdp9BxgKPkm778CWbSNczyNXrTFXKJEuVwhqGsMtcRq2g+t5nJ5doGbZfHT30Ka8DQNKkv7gYxStBcbKL2K6Ve70dS4IAqrgo823i1bfMJnGBCez32CpfpWo2k3dKSMJCgE5sZI59ckRZEGlYmeJqB3Lg7O3/NM81w5buweCcpKQ0oooSCiCjib5sV0Tz9tcEHI7saejhQtzS/zlyfO0hYLYrst4OstQa5yWDc6j7lNRFBmzYVOtbD0w5nketZpJaqGIIEAsHkBR7p1saMGsU3eaytm6pBBSdXzyvWVF9H6geQ9JPNy6gwv5eXL5KpPlLO9kZm4gtmOlNJfyi9ieS0jWebJjmJCqb2vw3MNjpvauJ2+Xr4VjsT08lDjAzmAvsrB25sz1XA7aQyS1KM8uvsmV8tSqvlwXlzey59kd6iOiBAkqWyMuqqiwJ7SDTKPAXC1Fj9FKn7+DLl8LHb4WgrIPaYMMpodH2eqnz9+BX9Z5KXVqFfmGJsF7LXOGx5NHELfYxrURGq7JheIYE5V5pirzmK5NqxZjT7ifg5FBdod20KbHUQR53XUWrDILtQxT1QUkQaLfv3mxsvtjezji7Vz3/anqIjWncQOxlQWJQ5FhPtByo4L+bG2BudoCYSVEr79rlcDoRGWakBIkqt79VpCN0JxPgHJd4FCWmwKs7/WLfS+agXyFvb1t9LXEGGiPc3ZinqvzGWbSBRbzZQqVWyO5kigQ9vvoiAXpbYmyu7uVw/0dDLTHV0Q5b4aW7jhGUEdWZEKxALqhkl8sEGkJU85ViCRDGEEfCNDal6R7ZweJziiSLJJbKpDsitPam0RaPjZGsJnRrhSqWKZNKBElkmwSbH/YT7wjRrVYw3VcNJ+KP2wgKxKaoRFJhqkUqwhAvdpAqJk0aiaRlnDz+lbh0Af20jnYTiCy9WqTe2MWuAGCukbcb2AoClXTYr5Yavr5ZbLULIuY4SMZMPApCp7nrZDcQr3OfLFEplJFVxTmiiUEQSBm+Igbxrt9gYKALAoYqkJpWX67Ydlr+kOthWaJdGNFeEpXZLQtZkHvFGzHpTMc4v7uTnbEY5xfWOSHl69SMS2+e/EyIV0noGl0blIoC6DL10ZA9lOwSrieS0KN0u2/UV3udmHVLc68dIn/+xf/znLNPfj8Or6Aj1KucpNv/xQ/6VBkiYN9HbxwfozZbBHPc4kFDfZ1v1u6HFTayFsz1Oz8cob77kATg4TUDjp8B+gPPkq778YM4s1QqNW5sLDEdC7PVK5ArlqnJxrmyeEBhpJxGrbNS2OTfPmtMxzuat8UsW2KSHVwMPp5XGzmq2epOJmV7Op2w/WavcmuZ6OKPgREwmoHASWB6VRXXnc8h5pTQJMC4HmYTgXHs9GlIKIgIyJhOjUc18LFpeYUMZ3VGWdREJd7odcmvKIgLfsZN9EMyr0/wav7d3RRrjd4fXyGkYWmmJ8iSfz1+w/QsYGtWywRIBDUKRaqzM3ksC0HSd785LpaabAwk2NhNocgCHT3JbcsPnUnUXcsnOXnqCxKaNI9Pz25qzia6Oap2RijxTRz1SJvpaf5RPceNLFJdmzX5UohxeViChGBiOrjsbYBjG3M1r4XESXI/fG9fLztIbqMlg0/KwoiIcXPh1qPIQoC1rzN5dLkqnLbdCPPydwIXb4Wdil9W96eHf5OVEGh7poMBbow5M379l4rVz4QHiSuhpirpRktz6zK3NacBmPlWUp2haga2jbBzJrT4M3sBRbqWRzPoU2P81DiAE+23LfpbGhYCRBWAuwM9W65rNh3k75mv+xb0+5IQECXNILKjUJmTtVipHSZVr2FgUDPqs+YXgPH2z5Nlu2CoTfJ9+hUiq5la5z5dBHHdfHpm7uPREEgaGg8uncH9w12MbGU48LUIpfn0syk85RrJjXTpGHZmLaD7bi4brPFh2XtEVkSUWUJXZUxNJWwX6c7EWF3VwsH+9tpiQQ3Zc95PfJLBRpVE0/3qJZqWHUbf9SPZqgYQR+HntjL4z//IKF4EMd2aNRMdENDEAX8IYN6tUE+VSTeEUVAwDItJFlC92vIikS93KCcr2CEfDSqDUqZEsnOGKquYpk24nVl0qL07n0jyRKqrpDsivOhX36Utr4WJFmiUTObQYZb8Fm/558coiDQGmiS1WKjwUKxjOt5TOTy1C2bgUSMlkBzUtckqSJDiQRzhRLzxTKZao2I7rFQLCELAgOJGLoirxqOFEki5jeadj9Aod6guo5P1HvhuC5LpTLOcjQnoKqreg7fT4Q0jU/v3cXPH9xHzPAxms4ymctzJZVhsVTmuxdGCGgqP7d/DwHt5r67ABeLV+k22jkU2X4BhVUQmobY0nX9x42aSaPaQPfdG8f3p3j/IC+XMv/SY4fX/cxg6ElAIFW/TM3JNYW/vMZNe2JvBSIyiuhDFQ1afXsYCj5Jh3EY7RZVl6dyef74xNt4wId3DfKL9/XSGgys9NAE0PiZvTv55pmt+QfKokpM6+OR5N/mdO5rTFVep2gtYHnVbasEEAUZARHXsyjbKXLmLGG5FUGQKNtpylaGoJIkqnVRs4voUoDZ6tlmBhWHrDmDKIgktH4UQSektJJqjKFJflzPJduYxPJWZyx1KYiLS8GaJ2/OIQoywU2oT78fSAT8fO7IXh7f2c9isYwsifTFIoiiuGELTN9AC4nWEFcuznH5/Cyz01m6euOI4s1FlMyGzeileU6+MUa10kBRJfYc6sYfvHcml+813rpX+n/vFbQbIXZHWjmXm2emkudSfpHpSp6BYAIByJs1rpbSzFbyGLJKfzDOUCi5oTfy7WJfeIAHYvtuSmqvhyiIPJI4RKZRYLGeuUHB+GxhlAPhIYaC3UjC1ibvAflGP9itQhFl2vUkH217kNTk90k13m1T8fCoOyZT1QUCsg9J2ty86WawPYfZWjPIZUg6jyeP8JG243T4bm0Mez8FJq9hb3gXc/XFVTey53lUnCo9Rhc+SUcVFSzXouGaSIKIviwsWLRLyIKMIirUnBqO5+B5Hn7ZjyoqW65+2iw6WiOMTqX49o/O8uChHXhCs+9WliS62iJbXp5PU9jd3cLu7hYc16XasJhK5ZnPFkkXKxQqdSp1k4Zt4zguoiCgyBJ+XSMa0GkJB+hMhJutX75bv9YUVebyO+PEO2KousLUpVkESaB3dxeRRIiBg31MXpjh7EsX6dvXjVmzKGbL7Dw2gKarDB3ZwYXXLnP6+fOouoooCpRyZXp2dRJri5LoijM7usC5V0boGGglu5AjPZvloc8cw/OgUly/9cnn12jpSZDoiPLcX7zKw589hqop5NNFQrEA7f2tyFv0kL7niS1ASzBAIuBnoVxmrlTC81gmthadoRCt11nliKLAUDLOG1PTLJTKZKpVFFFksVRGEkWGEnG090Q6/IrCcCLOdK6A53nMFotka1X6iW04PHieh+k4XE5nsNxmRqgtGFwh2u834n6DwUSc9lCz3GMwEeOffeRJ/uG3f8BUvsCVdIbvXBghbhh8YvfwpsqVZmuLhJVgs2RjE58XhOakX9ygH2Utv0xJlmjvSzI/kaJaquNYDlfPTHHu1ct0Dm7OSulmcD0Pd7lvrYnmbxFpw+29Hh7LwgvLi7im9nuzI+NxzQe1+de1LRBWfq+/HHGDh/01Ze+myM+1PVodv702gWyeP6EpSrXO/opIK+fnmsBL85iBeG0bBWFlve7yem/cJ2H5Wti8cvLtotd/Pz3+Y+Qb08xWTzJTfYfF+kWqdlN1b/VWbnUW/e40XBJUQko7vYEH2BF4iJi2A1W8PUuqjnCI33zoPu7v7V5e23vXDpIg8CvHDtGxhYoLaN5vuhzm/uRv0Bd8iLHSi0yUXyNvNkVXvJWy300tbeX8QpPUBuRWVDGAi0u2Mc3r6T+jaC6CIGDIEQaDjzAUehS/HEMT/TzW+ju8kf4S72T/EgGBHv8R9oY/SlBJ4HgWR+N/jVO5b3A2913Cagd+OUa7b3jVQekw9pE1pzmX/wGnc98monbwqc5/1tym5pW6aquvibG9X1AkidZQgNbrFJAvLaRoCwWIGGtnmfYc6uHt168ycn6WqyPz/NkfPMff/kc/QyCoI0rie4jgtdK55v04MbrID791ipd+eAFBFAgEdY4/NkzoFsq87hT8srpCwhqOTdWxNv2c+auCo4luTmVmmankSTcqPDd3hb7hGAIi57JzTJRyeEBcM3isbeCOtjqpgsKR6C6Gb4FI6pLKntAORsvTvJw+veq9VCPPTG2Jglkhpm1tbNsuqKLMQ4n9fGfuJdKN/KonqOO5zFZTDAW60beJ2F6PY7HdPBjff8uk9l6G7dmcyLzFy+nXeSh+jOPxo5SsMmcLFwirYe6PHsbD43vzz9Dha6PL18ErmTcomEVMt8GHW59gMLADQ771cas5V2mez/fOPfcMtCEAX3v6FP/6vz6D58HewTZ+9iOHOLBr6z7U10MSRYI+jb09rezt2XoP+a1AWE4QdQ61g+vx5f/nN5m7ukjfvm4+9TsfJrZM1r/wjz7D9//bj/jK/+tbZBfyKKrCwMFe+g/8Nqqu8MjnjiNKIj/681f41n96Gs/zCIT9/MP/9rv07OrkyS88zMtff4Mv/vOvUciUMII+fvbvfJx9D+1k7Nw0oiggiAIIQlMfYnneKIoigigyeGQHn/t7n+Cr//bbPPullzDrFrHWCB//rSdp7dt80OwafiyIbWsgQNJv0LBs0uUKxUadhWIJ03HoCAdX9SVJgsBwIo5PUUjlC6TKFRRRJF+roysyA/E4urx6t4O6xqHOdp4bHcf1PC4sLDGTL3Lf+r30K6hYFq+MT1FfFp/qiYbp2uJE825BlST2tCb5Ww/dzx+eeJOxTI4LC0t85dRZ2oNBjnbf3AswooawPJuKUyNwk8FFQCQot/LX+v4LbJAl06UwsrD6AREIG/zSP/4sX/t33+fSW1e59NZVIokgw0cH+Lnf/+im9vdmOJ2e48tXT/Py/DiaJLM72sK/euBjfKLrX2y6PPNCdon//a0fMlspAPDXBg7w6b699IXWb3Z3PI/5SpGX5yd4OzXDZDlPrtGMaAUUje5AmMOJTh7rGKArEL7BZ1gUlBuO1zVYrsNctcSbS9NcyC4yUcqSqleoWs3eNUNWSOh++kNxjrV2czDeziMtv88Dyd9aZ2tFFPHddX197DxfuXqa2UqBz/Tt42/tfYCg2nx/qpznhbkx3knNMFXOU7JMJEEgqvnoDUbZH2/jkbYddAcidzWeHFI78MsJBoKPY3l1qnaanDlNyVqgbC1RdbLU7AINt9y0cnJrOFgr/ZeiICMv2y/Joo4uhlZsgsJqJxGlk5Da0VRVFjTEbeg3j/h0DnVuXOIvCgIP9fdsWQDueiS0ASJKJ7vDHydvTpNpjJNtTFCwZmk4Bcxrx8MzV1S5ZVFHEwPoUhhDjhKQkwSUVsJKOxG1G00KoolBJEGhx3+EVn14pRRcQFy2r2pG5iVBpUUb4MPtfx93+XjLoooiNMmdiMyOwDE6jb14nruiFI4gNO2YlsmpIUU4HP0s+8IfWz42EpKgIAkyH2r/+6tEuww5wie7/ldU0UAS7p3qj6+9fY5P7N/J4Z61iW2iJcSR4wNMj6cZOTfLmy9f4f869t+57+FBBne1s7SQB8AybRbnCpw7OcnCbI4Lp6e5dHaG+Zkctu0Qifn5zBeOk0iGNharusuI6QaGpCAiUHcsso0KJatOSN18OelPOvZG2hgKJXl1cZxco8oLC6P8yuAxZEnkTG6eqUozuxjX/DzSNnBHx9n+QCdtegz1Fse7/kAnu0J9vJo+c4NS8lR1nonK3PtGbKGZOW3X48zVUlScd50OPDwqTm2l/Ww7oYkKjyQP0ePfnuD9vQZZkHk4cZx0I0tAbgZ/42qUbqOTV9JvcDRygPHqFCEliCRILNVTxNUIn27/KBkzx6vpNwjIAfoDt56VL9sL5M1JZMFHu3Fw9fbJIrsGWvn7v/4BzGUrQkWWMHzqqr7bHxeIkkiyK8Y//dLfQ1FlbNvBsRxkVcIXeHdcjbaG+dzf+Tg/81sfxHHcZvWrKuMP+ZqWYz6VBz99H4c/uB/HspuJDUEklAgiKRLR1jAf+dXHefTnjuO5zWCkEfKh+VT2PjjMzqP9SLKErMr8i+/9Y8KJEK7r0bunE0mR0HwaO48O8Pf/09/EtprZeVES8QV0FHXr48uPEbH14wGlRoPTswvULRtJFOkIBYkb7xIsSRAYbkngUxRcz2M2X2ym+T1vOWMbW/HBvYaIrvNQXw9/8uZJMpUqi+Uyb03Psrslya7W9aNm6UqVF0bHmM4XsF0Xv6Kwt7WF/vi9qeR4zXPrA4M7mC+W+Oa5i0zk8pxbWOKPXn+LZOAxOsKhG4jU9cg0CpzOX+KFpTdQJRUBUEWVv97zSXzviV42vUdlgsotRFwUmb49XfzC//JJHv/8AzSqJv6wj7beJO07tr68tdATjBBRfaRqFWzXpWKZjBXzDEeS+OWb3xpFs858Nc1Y0aXh+NAkmeHwMH3BfoLK2sRzvlLk+bkxnpoeYaKUo9CoU3NcLLc58c7VIVWrcrUwxUtzOT7SPcyn+vbQ6tvYh7BimZzOzPPdyYtcyC2Sb9QoWyZV28R0HRy3GViQBJEJKceF3CIvz4+zO9bKJ3p28cGuwRUv5o1QsRosVkvMVoos1koUrab33VNTl/nGxDlmygUKZp2abWF7LgICiigykk9xKbdEtz9CTyBy0/VsFwQEJEFGkmTAQPc8DClGWO3Gdhs4nonjmbievfzj4uG8J2u5nNVesWWSkQW16T18nefwdmKhWOb1iWkeGegl7jf43vnLvDY+RVs4yEd3DbKzNYkgCPg2aUu2FprHRkGSFBTRwJDiJLRhLLeK7dWXj0fTLufd8m2x+U+QEQV5mTyqTdIvaCjLvbTXrlVF0FYFRm7YBqG5DYa49pgpLBPY671v14IoSGhSoNmn+569NOTIDZ/1y7ENl7edaNg2qWKFjkgQURSZzxcp1W8Uf5rM5DdsgZFkkSMP9FOrNmjULcavLDI1nqKQr/DaCyMUlrUHMqkST3/rJK/86CKNukW5WKNSbmDbDrFEgIc+sJsPfeoQurF1u7c7CVWU6fRHCKk6ebNGul7hZHaWx9vW9mu9l3F9WXVTeGZ72h8MWWVXpIXhcAvncnNMlXNcKizS7gtxubBEql4mqvrYGW6hw7izpHAw0EVMDd3yNeSTNFq1GG2+BHPLZbjXsFjPslDPbMdm3hKacxeBuBZBl7Qbia1dx93mlhYBgf3hQTr15CqhpZ8kCIKAT9JRxHetCjVJIygHqTt1yk6VC4XLtOst+CQfo+VxJqvT1B0Ty20G57dann49PM9lvnqSs7m/IKR03kBsBUFAVeRVqsg/zhAEAUmWVjKz60GSJQIRP0TWqTQTljVu/Gs/hyVZwgj5MEI3BiE1n4p2XftgvOPdZ696Xd+yqiuoenjD7dwsbunspWtvYrkV2v1PbMtG3Awxv4/4supxxbQ4MTWN6Ti0BPzEDWNVabEgCLQE/MT8TSubmUKBfL3pu2koCl2R8A0ZDkWS6A6H+eze3Xz1zDlytTqvTU4T0FQ+yx52tiSuK91slrAulMo8PzrG186cx1y293mgr4f97a2b7ld9vxAzDD6xeyfFeoPvXbzMUrnMWzOz/NHrb/E7D95P23W9fO/Fkege+vwduJ6LIIjLJZES8m0MNmtBEJtRotaeBI7tUi3VCMeDJLtiq26G20FE9bEzkmQgHOdibomCWeeFuTHajSC67+bl5AvVEifTczScZrZ+T7SFvlAUv7L2Q2mqlOfbExf4/tQlRosZTMdGlSS6/GFCqo6AQN6ssVQrM10uMFspkmvUqNgmn+7bQ38ovuZyoZm9W6qVeX7uKnOVZQVwQSSmG8Q0H4aiggdFq8F0OU+mXiVdr7JUr2A6Nrok80TnwLrLXws122KsmGWuUuR/jLzN5UIaz/MIqzptRrAprGY1KFoN6g2bgVAcWdzYa+5OQxCaFjEyKtvo1rDtWCqXeWF0nCd3DjBbKHJmbgFdaaq9vzo+xc4NAm63AlGQUCUDVbp3SlPvJkYvznHihREyS8U134/EAxy8bweHjvev+f5GyFVqfOfMJX794aPooshTF65wcW4J33vULMfTOUx7Y5GzcNTP/Y8Mo6oyL//oIqffGieXKZNNl1c+YzZsFufyq76nqjKDu9s5/ugwD31gN8nW7ZlAbBeE5ZLZ/dF2TmdnyZs15qoFnpq5xJF4F35Zu2O2eHcCmiStBIhN1yFvbeRtvnmIgsCucCsHYx2czc1RtkxOLE3QF4ixWCthuQ4dRphD8a47Lr7V4UvetGprI4iCSFQN0WO03UBss2aRjLn2vXg3EZB9N3jBNv3urZX2m+2CgMDecD8hJYBtO7x6YZJMocLPPXZgW9dzr0EURAKyQbfRyZXSGIuNJYYCOwgrYXzSPBElzM5gc26iiSpx7dYTR65nU7Uz5MzxHwvf3p/i1nBLI1/JHKPhpO8asdUVhZhhEPHplBoNXp+cwXQceqMRIj591URZEAQ0WaYrHOailmKmUEQRRVRJoi0UJKhpq9S5rn3HUBU+u383U/k8Jyanmc4XeHrkKoV6gwPtbSQDBpokY7suuVqN0XSWN6dnuLDYHJCHk3E+vmuIgUT8x+IBvCMe5WO7hig2Gjw9coVivcH3L16hMxzmU3t30RYMrLkfLVoMx3MoWOXl7BaId2CAsE2b2bFF3nzqDMVsGc9tliYku2McfmIvnQO336MgiSI7o0kOJTq4mFvCch1+NDPKh7uGiOv+Dc+j53lMlwu8nXpX/v7htj7ajOCa3ytbJk9Nj/CtiQtcKaTwKyr7Yp3c19JNTyBCUNEQEChadSZLOU6l53g7NcPVYoZvjJ8nqvkIKBot6xBuVZSIaT66A5GmcEgoRpc/TKsRJKr5MGQFDyibDSbLOV6aH2esmKVo1nlzaYa47uf+lm58srJp4pmqlXlxboxT6VkmyzkeaO1hIBSn1Qjgl1VcPMqmSaZRYbpc4HCig3bj3pL3v1fRsGxS5QoBVeXF0XF8iszRnk7GMznG0tn3e/N+4rA4l+flZy8wcWVxzfc7e+KEo/5bIraKJNEWDnDNMnY6W8B0HHbFV1eenJ5ZuKmvrCAIJNvCPPjELuKtIQZ2tjE1liK9VKSYr1GrNrBtF0FoCob4AxqxRJDO3ji79nWx52A3nT3rB8jebxxNdPNGapLxUoaCWeO1pQm+On6aJ9oH6TDC65K1glmjYNbxyypx/fb627cDcc2PITcDnAWrxqX8IkWzTkC5fYLe6Q+zO9JKXDMoWQ3eTE2xWCuRbVSRBIGeQJRD8dvrB9wMknoU4yZqujdDUDZo02+8HqtOnYJVou6Y6NL7l71URBlpjV5874bi6duHKAgMBLrwSRq27XJhcpGJhewdJ7b5co1y3STk0witk5HbCq4R/+naLLPVecAjWo7Q6WsnqoYZq0yxWF/Cci10Uafb6MQvG+wMDfLC0qvokk5QCRDXonT42qg5NerLtlCGZDRbUW4R1yq0fopbg+s2dYXqpoXpvKvofKcQ9GmEja1fk9sY0vOoWnOYbgHXc5BEHUNqo+FkEQUFTYohCip1ZwnTKRJUd1C2pnC8Op7noElxdCmBtEbZmgDEDJ32UJD0fJXRdAbH8+iLRYn41u6/6YtGCOkaU7lm72NQ0xiIRZHEtcUUFEliOJng8wf2IggCb03PMlcs8o2zF3juyhg90TCGomA6DgvlMqlyhYbtoMsyvdEIP3dgLw/0dhNbR/jjXoMAHOhoo2qaZKtVXh6bJF+v8+VTZ4kbPp4Y7CfhvzEae6l0ldnaEo1lv0Fo+scdiOxiO00FapUGP/rya8xcnifeHsUI6hQyZRYmUzQqDX7u7358W9bTE4hwIN7OdycuUrQaXMgtMlbM0ukPr/SOroWS1WCilOVqIYMAGIrKsZZu4vraEeyzmXl+MDXC1WIGn6ywL9bGLw4d5sNdQ2jSam+6bL3KKwsTVCyTkXyKyVKOZ2au0O2P8ETnwJoTI0kUGQjH+cLgIbL1KkeSnQyG4hiKuurznudRd2w6/WH+7PI7XCmkyTaqXMwtMlspMBjevGH6ZDlHzqyRb9T4aPdOPtW3h4Px9lW+ia7nUbNNLuVThFV9XWL+U6yGKIpIoshcochr49M80NfN7tYk84UilnP3vVd/0hFvCXH4eD+t7WEsy8WxHXKZMunF4i15xq5adsDgs4f3rvw9kIzx0EAPH9y9usR2LJXF2KQnYSDk4/D9/Rw40sf4lUVmJtOkFouUCjUsywYEdJ9CJOansydO32ALsUTwnuqpXQt9wRgPtuxgopzlXG6e+WqBP7r8Gul6hf2xdhJ6AF2SERCwXQfTdajaJjOVAgWrxpF414bEtikY6GK7Lrbn4nguBauOuVx14y6PjzmziibKSIKILIpIgoi0LHiyGXQYYVp9QTRRpmg2OJWd5QczF9kZbiGgaAiCgOu5WMslynHNT+ImwdRruKZ4vC/azksLY4wUlsg0KuTNGhHVoD+UoCdwZ9uhZEEiLPvRxNt76huyTky9sWTa8VzKdo2iVUaX7l7bwHtxrZ//rqxLEGnT42iSQuMmlRvbiavzGdKFCkOdiW0htgCO55AzCxiyD8/zKNsVKk6VoBcg08gSVkLIgkTZrlBzasTUCD2+LvySwa7QEFElgl826PS1UbLLzNebAcemWv6tXw9NYntn7O1+UlFtmKSKFbKVGsVqnUK1TrneoGbaNGx72arozqz72GAXDwz1bPl720ZsbbfKUu1VytYkHh6KGCCuHaFiz+J6Jq3GI2hSlEztbQrmZfrDv8h06TuAi+PVCKpDJPRjBNW+NZcfNQw6QiHOzi+ueN3tiDUztmuhLxYlrOu4Xh4AnyIzkIjd9MHxxGA/AU0j6vPx+tQMuWqVimVxdn4Rd1mhURZFNFkmbhj0RiP87IE9fHh4EL/649UXIYsiR7s6MR2X+WKZkaUU0/kCXzl9joCm8lh/H8Z79ulcYZSBQDf7wsMrvmYCAuoW5bhvhnq1wQtfe51/8B/+BsNH+9ENjUqxyhtPneb7f/LCthHbgKIxEIqzL9bGq4uTmK7DqwuTDIbjBNX1yz0nSzku5ZeoOzaqKLE70kJ/KLYSpb8Gz/NwPI9vjJ9jopTF8Vx6gwk+1beHT/XtWXPZMd3gsfZ+KrbJv3rnOQpmnZOpOQ7H5zje2rNuqXOnP0znjo1LDAVBwCcr/PzAAc5lF5itFChbJiWzweV8moFwYtOCI0u1CnXb5uH2Pv7JkSeJaPoNMvyiIOBXNI4mN+fF91M0YSgKCb/BU5eusFgq0xMLE9Q1XI8bNAJ+itvHrv1dDO3poF6zqJbrVCsN3nrlCs985zTjlxe2dV3H+7vX7I3e3Z4kuoXAqCAIyIqiK1a1AAEAAElEQVTE0J4OhvbcXPjvxwGSIPJkxxAV26RkNZip5FmqlfnDkVeJa366/RESerOaqGKbFMwa89UiebNGmy9IVPXxQEvfmsvONaqk6mXKlknNNqk5FlXbZLyUZbFWApplw9OVHM/NXcEvq/hkBZ+k4JMVQqpOqx7EkG/em5z0BdgXbedMdo4rhRSzlTz//PQPOZ7spd0IIQsiDdehbDWQRZEPd+7kA22DqJssH+7yR3igpY+XF8dJ1cvkzRq263Is2cOeSOum9BJuBz5J2xbbFV1SiShrV/E0HJOiVaFFv31i63oetmdjuc0fZzmo4eI2K8+WFf+vuRVc+5c1C9ju9tigbQQBAU1U8Unacg+pgwBYtkMqX6ZSN/E8CPt1goaGIku4rkvdtMmWatjLwU5DVwkZGrqqrLyfKVWxneY+aopMyNDw+zQs2yFdqPDGpSlqDQtZFBEFAZ+m0BJ510Jzy/siCBiyj2OxQxyLHbrh/UeTD6z5vaAS4Nd2fGHVay16khZ9+9pumr7qP83Y3gye52E5DulilauLGV4fnebs1DyTqTzZcvWuFXELAu8vsc3Vz2O7NdqNJwlrO8k2zjBd+g67Y3+bS7n/TETbg+WWsL06fqWbojmK7VYYjv4mnmczW3mKbOPkusQ2bvjoiYTxXdfUvSMeW5/YRiMk/X58ioznQUjX2BG/ObEFuK+7k92tSS4sLPHc6BjvzM4zXyxRMU10WSEZ8DOcjPNwXw+PDPQRWydrvBZEQUCVJHyKjCw2TZi3q+fwGun2KQqO56ArMtJNStt8qsJ93Z38/iMP8E+/90Msx+HSUoqnR0ZJ+P3c1925TM6aA2dANoipESJKaBUB2m5TAUEQ0H3qiu+W53n4/DrRlvAtqaRthC5/mMc6+nl1cRKAVxbG+WDXAEPhxJrnxvM8LhfSXMg2o4g+WeHjvbsIrCEY5Xge6XqF1xenyTZqAOyLtfHhrqENtymoany0a5g/OH+CktWgYptcLWYYK2bZH799xURNktkZTvK2MctoIY3pOqQb63uNrYcdoRi/NHyE2DqZ6p/i1tAbi/D5I/v4xumL/PWjB9gRi5Kv1vFrKnvbt0c87adYDUkS8Qc0/IHmfTw3ncXwb3+wsj+x9kT9F48f2vZ1/Tgiqhl8rm8/rb4Af3DpVUYKS7ieR65RJdOorPrsNe0LEQFVamZY18N3p8/zB5deZWGZxK6Fim3yRmqKN1JTN7y3O9LKP9z/JI+0bq4c/SOdu8ibNf5H403yjRo12+K5+Surtx2BdiPE0Xj3lopbW3xBDse6iGsGS/Uy5rLd4M5wC7sjd1ZRV6BJbLfDS1QRZQx57coo23NouLeWXfOWLencZfJad0yW6jlma0vM1VKkzQIVu0bJrlG1qzRcE9O1Md13ya/l2cuCRXceoiBgSNqKHR40rSvnsyW++OxJTlycoGE5fPzYLj52bCfdLREqDYszY/P8xXMnWcyWAY/7dnbz8ft3sbevjWrD4vTYHH/8gzfJl2tYtsOOthifeGA3TxwYYGopxx9+53XOTszjOC4vnRlDVxUO9Lfzj77w5N1KVAPXzpezoowvCOINVoObdarYCKZbwfZurwLnJx2e59GwbCZSef7j06/y+ug01caPV5Z72xhCzVlCFgMoUghJ8KEIARpOFlWKISBhu2Vq9gKOVyeqHSDfOEe+cYGLuf/Q9DkVNALK+g+MnmiE33v4fn79/sMrr4V1fV2ri75YlH/5Mx/BdGyKjQYz5SK2t3kVO5+icLCznV2tSSznWlSvOaiLYpNAqpKEusXsyd62Fv7ph5/g//yBR5qZTlnatkxvxKfzqb3DPDHQieUW0OQwAe3mPY0BTeXxgT6+/9u/uuLuqUnyirhJ0S7z3OLrAMzUFpitLXIqf5GQHFhWvZX5UOtDaNvYC6MbKg9+6ihnXx5BECHaEmbu6hJTI3MceXLftq0HIOnzczTZRVTzkW/UmC7nuZxPsT/WRmKN0tm6YzNaSHO12CxDDigaH+ocWjOT2nBs3knPUnWaUcKEbtAbiNyUCAo0CfOOYIxUrULFNlmqlZks57aF2AKENR2/3DzHrudRt7c2eKliU/jqaOLO93P9VYNfUznW3cm+tlZ0WUaRJYK6x0d3Df5U8uLHHDXLRhQE9J8Q5c07Ab+s8Xj7IPui7bydmeHE0gQX8gss1kqUrQaeBwFFJaTqtOhBDsY6uL+ll70bkDrP47ZK5prf3/wCYpqPX9hxmD2RNp6eucTJ7CyLtSI120IRJQKKSlIPsDfSzlAosSEpfy8EIOHz84H2Ib46cQrH80jofgZDCTp8d9oiR0CX1G3REpGQUIS1y5ltz8G8RWJrew6pRp7TucucLlxhtDxN2a7heE4zU3stK3tddna1//rdlRYSlo/p9WzSdT1EUWB3Twu/8uEjzKYL/Idvvkoy4icWMjg/scD3Xr/Ib3z0GD0tUeqWxX9/6i1ePjtBIuTHsh1+dHKUR/bv4IkDAxi6Sq1h4VObz5MdbTH+b7/8If7Ld08giiIP7e1lZ1cSWZLuKqkFaDgFpiqvMFr6Ia7n0ht4mF7/w4TUZqWX6ZZ5O/Nfcb3by543nBJZ8+p2bPJPLEr1BicuT/Hvn3qNuWzhpoKG9yK27cmqSTHK1ji2W8HxGtheDVWMICAS0w9QtecoWeP45W4MuZ26vYShtNMb/FlUMYwgyKji+gOyLIoENG3TisOyJK5kc0uWyUQhh67Imx6srmVWb8cjci0okkR4m5d5DaIgNCfBfhnLMZsR7E2UCF/7nh5Y+7OyIBHXIkAzY+vhIQvyilqgLEjb8pD71h8+wwtfbRJox3EppEvgwXf+6FlkVcasmdi2w+CBW/cwWwuSINLiC/BExwDfmbyI5Tq8k57jYLxjTWJ7KbfEeDFLw3EIKRqHEh20+PzIa0xMLNfhYnZpRTk7oftJ+AI3ncQIy/1cMd1AWRY7K1sN8stZ3+3AtdIjgOsKsWCT2fewptMZWF/Q5ae4dYjL1lyrAmeCsK5a+U/x44M/f/00sYCPj+0bvi27pp9kiIKALim0GSEeVwY4FOukYjcwnSYx8WCl/1UVJQKKRkjR8UnrH8+PdO3iQKxjJbu5VRiyQpc/soV9EIlqPu5LdNMfjFOyGpiu3WxpWt5+VZIwZJW4tvbzYz00bbJEfLJKc7z22B9tZ0cwflfGCAlxW6q0REFct2z6Voit5dqMV+Y4kTnLydxl8laJkl2lZje23Z5nuyG854h6QDxocHiwk1jQwK+rtEYDZEtVplN5ZtMF3rw0Ta5Uxacu21umi+zvbydbqtIeC7G7p5Wn375MuWpyZKiTwc4EsWDTm1SWJMJ+CV2VkSSRkKETDb4/lVcVJ83Z3FcoWfOAS83JoImhFWLreCaT5ZdwbjPb6nrubS/jJxkNy+atqzP86YvvMJ3OY2+TTdndxi3NSB2vTq5xjtH8nyIgE1C68as9VK1p0rU3ydXP4uHSEfggAiJx/QjjxS/juFUMpQNVChNSB8k22sk3ziMio8sthNQhVKnZI+i4Lv/+nRN8YmAnXYEQL01PcCWXaf4dDPEnZ9/hsZ4deJ7HmdQCV3NZXK+ZaXy4q5f9yVZEAU4uLvDMxChnU4v4FIXxfA5REPn00C52xRI0HIeJQo7np8bJ1WsICOxJtHC0rYPOYIhcvcapxXlMxyHXqDFbKlK1LA61tvPhvkH0O9Dv5nkOjlem3HgH16siizE0uRfPa1C1LiAgYSj7kKQQNfMCDWceUVDRpG50pZ9i/SUEFGQxiuCpNOwJqtYVRDT86kEsdwnbzeE4BSQxhK4Mocnt626PJmrsDjXl1mdri4TkAEGlKdDRcEyWGhk2S4Y2Qv++Hlxn+UYSBERBaJqgXxcllxWJWNv2CmMIgkBU8/Gxnp38cOYylutwNrPA5UKaoy1dNzx4T2fmGS82lWljusHjHf0o0tol5bbnslAtrvjILtbKfG3sLG8tTd90u1zP42R6jupyJrXu2FSsjftDPJr3zny1yGQpx0K11LQMskzqjoXpOivCKRPFLFOl/KrvbgWGrBJW9W0rpf8pfoq/CpjNFxAENvQLXwvXsoV/le43SRAJqz7C6u2LMrb6grT67q4quyiIBBRtzTaV24FHM9B5LjeHt0yUD8W76A3cHaEldyUQenu4Vo69Fprlp5tfR8mqciJ7jhPps1wtz7DUyN10G0UENElFFzVUSUYRZGRBQhGbgft0o0DeLGHdZqbwVqHIEkGjKTamqwq6quA4HvWGhWnZBHwqn3lo36oMazISoD0WImBoPLinl5BfJ1us8vaVGS5MLnJ4sJOjw/eW5oXnOTTcMrZXBzwaTnH5/9fed6k7+R8rRWPPoym2VKlRaZjULZuGZeM4d6caIBnys7Nra/3Jl+dTPHd+jEuzSzcltaosocoSkihuw+x/bbzXEm+zuCVWFtH2IggSihhCQEQSDXQpSUTbR82ex/UsFClIVNsPCPjkdmL6IQREQuoQoqDgk1tpNR7BdPJ4OMiigSi8uzkecHJhnn2JVjzP41x6kdNLC/RForT6/TwzcZUDLe0EVBVREAlpOuBRapj8z5GztAcCtPoDGIqMT1bw8PArCjGfgbwcKfWA8WVS63kecZ+B7bq8MT8NeARVlYplcja1yKnFee5r7ySoaiii1FRmvMWz6XkelXyVd547x9JMhkgyzP6HhmnpudbT6eG4FUqN1zGUvUiCge2kadgzNJxpRMGH6aQw1N2Yzhy2k0EQFETBh06znNty0rheA9uepm5NNh8eAhTrL+JhAyKioGM5S7ieuSGxlUWJxLJ32PnCFcJKcOXvnFlgKjfPntDgut/fLHbe18/goWY21rEd0nM5ZkcXqVfqTUnxlYfc9g8LhqywP9ZGfyjGSC7FUq3M5XyKuUqR3uC7RLq8rJw8Vy0iCyJtRpDjLd2sd2t7nkfRrK/0T+UaNd5cmubNTRDb98LxXKwNsg0ls8HVYoZT6TnGihlmKgUy9SpFs07Vtmg4Npbr4HgejuuuZD5uFYog3vPZWtf1yKVLzM3kSC8UKBWq1OsWrushSSKaJmMEdcJRP8mWEK2dEXTfzcVhrsFsWGTTZaYn0qQXi9QqTbsVWRYxAhrxlhAd3THau6IrWfi1cOXCLCPnZikVa8QSQYb3drBjaPMl5+dPTTF6cY5qpUEw5OP+R4aJt4Y2VMEtFWrMTWdYmM1TyFUwG83joukK4aifts4oHd0xgmHfpo/Hq89dZH46i+HXGN7XycDO9qZFgGkxNZZifipLIV/FbNgggO5TCYV0km1hegdb8RnrH3vP8ygVaqQWCqQWCuRzFaqVBpbprNjbGIZKJB6gvStGR08MSRLvOSIYNXyoskzdslG2UL0zO5Vh8moK23Jo747R1RfHt4Vr9af4yUHVMpkoZ5v9x3h0GCGGQ0ni2t3JuFmesy3E1uVd/Y73opmRv/n94XkeVafOS+mTPLv4JqPlmTUzvYakE9fCJLQIUSVIUDYwZB1NVFFFpUlmBQlZkJDE5u+XUqc4U7hCwSqvseY7j3K9wVQqz3BngmK1TrZUpTMRJho0CBo60aCPjniI3b2tyJJIudZoCkSqCo7roqsyTxwYYD5b5NTVOV6/2NQRuZ7YyrLUtG95H0tOfVKU4dDHWaqfx/Mcolo/MW1gzc9G1R0ElXZkYevBLtOtULCmKFlzt7vJN8DzPOqmzUymwPhClnSxQqpYoViprSgIm5aDcwftca7H0cHOLRHbumVzemKet65Or6nKHTF0uhMRehIR4gGDgK6iyFKz+vAOPYIO9a7PSzbCLc1KE777SPjuu+H1qL6XKHtveF1ApsP/oVWvSaJO0nf/uusQgIFojFy9RtFsoIgSPaEIlzIpDre2o0oSAVUhrOl0BULEdB+iIDBbKvI/R87yK3sP0xuKcLi1A8fzKJh1dkYTfGHP/hXlwYZjcyG9xLMTV/nk4E46AkEats3rc9P4ZIU9iRZ0WcbxXGZLBX7vyHH2Jlua5Zsetzypt02H2asLfPnffIfpy/O0dCdQ/snniCTDaIbafGB4Nq5XJ6AdQpO7KdXfxHQWUMQEshSjap7D9Wp4noMkBJClBKrUjihoGMpOCs4reJ5Nw57G9WqE9IfxcJnN/2s0uRdD3Y2h7KZqjVC3xjbcXtdbFl9oZBgpjWN6NrbngOeRMfNcLU+t+3DaChRVXhGGKheqnPjuSaqlGo7jruKyofj2W8ZIokhE8/FYRz9zlRLpeoWRfIqzmflVxHa0kGGilKNsmSR0PzsjLXQFIusu16MpIHWNk+uSTEjVMeStR6LajCBhdW2xtEy9yjupWb4/dYkX58aWPQ1FIppOWNVp9QXQJHmVdcVSrcxspUjJurXSnGuiLfcq6jWTscsLnD85xci5WWYmUmTTJaoVE2eZfPr8GuGIQaItRFdvgoFd7Tz85B78QX1DT1HP88imS1y9tMCF09OMnJtmdjJDqVDHsmxkRSIcMejoiTG0u5P99/UxvLeTQFBfk2yml0p872tvMTayQGdvnI997ih9g62bIi2NusVrz13kqW+epFKs0TfUyv6jfWs2FHqeh+O4TI2luHh6motnphm/skh6sUCtYuJ6Hj5DJdkWZsdQK3sO9rDnUDddvYlNiba99PR53nzlCtGYn8/84gP09LeQTZU4+/Y4Z96eYPTiPJnFIrWaiSAITZ/VZJDhPZ18/tcfxmesnXEq5qtMjaW4OjLPxJVFpifSpBYKFJdJsiAKaJpMMOyjpT3KwK42DtzXx56DPQRDPsR7yOamLRxkNlfkuUtjdMfCq0pHe+MRwusIIl48M8Oz3z1DMV/hyIMDfOYLx/H5fryU+H+K7cF8rcCbqamVsftYooduf2TTqsq3B68pqrQNHh+u566bDZUE6aZ2QtdaaE7nr/CD+dcYq8zivEdLxS/pdBmt9Pnb6fW30+1rpUWLElVD+GXfhm1UE5U5RkqTW9+xbYAqS5iWw+mrcyxmi6QKFURBoCsRpjUapFw3GepM8typUZYKZSRBpNow6WuLMdSZoFwzefvybFMTRmpWwIUMHe09vf09yQinrs5xanSWUrVOPORnT2/rXd1XnxxjT+RztNT34OESVnsIyGtvQ7txiB7/I/ikrVfule1FRotPbzuxrdRN5jJFLs0scXZygbMT88ymC5Tr5paqDrYTAX1rz4alfJmR+TRzudXieoIAO5Ixjuzo5Gh/J7s7W2iPBvGpyj0bVL1n0y2CAEOxOPlGncVKmTZ/gF3xJC/PTDCez9Ebbg7iY/kMr81Ok6pWkESRhm1TMk1s171pt2DNsklVK1zNZ3l+cmylcV8QBMKavmIrpIgSO+NJukNhfLdASN6LerXB7NUlRk81B8zpkTlmrsxTLgyjGcsXoyAiC2GE5VMkCAqCIDezrZ5ASH8MTe4i1XgH00kRkh5ElVpxvQaWk8Nxyzjuuxeo7WbxPBdRDCAgIgoGouBDQOJmGVB32YfsbH6EudoSDdck3WiWfluuRVQNrajXbRfqlQbP/sWrfPzXHqd7ZweaT105mdoWb9jNQhElPtQ5xDPTo2TrVa4WM5zJLvDh7uGVIMZri5MsVpvHtTsQ5nhr94YPRhEBQ1a4xpFajSD3JbsY2oJf7DUEVY1dkRsVcU3H4e3UDH868javLEwgAFHNR28wyp5oKwPhOG2+IBHNhyEraJKMKkk8M32Fr4+f41I+teVtuR14nofruNj2ss3CFsd9URLQtPXvQ8/zwIMrF+b46n9/mbPvTFKrmqiqjO5TCUcMEARcx8WybBbm8kxPpDn9xjjReJC9h3rw+VXEDbIFuUyZEy+M8MNvnWTk7AyCKGL4NXyGgiGquE4zu3j6jXHOvDnBiRdH+MJvPsrh4wOEo/4bSPOeg920tIWZGkuRXixy5cIs9ZqJz7h5CePSXJ6ZiQylfBV/UKe7L0HPjuSag59l2sxMZvj6n73GiRcvUS7WV45LMOIDD2zLYXosxdjlBd557SoPPLGLT3z+PvqH2xDFzXl5VqsmlVKdzGKBl5+5wF/8txep1yxUVUZWJHRdwbZdSoUauUwFQRCoV611l700n+frX3yNt165QqNuIckimq6g+RSMgIbneliWQzZdZmE2z7mTE7z2/CV+4+98iMPHBwhF7h3V7pBP55XRSU5OzdEdC6NeN9H8+aP71iW2M5MZpidSZFMlQhHjnp1U/BR3FmWrwdnsPC8tXEUAdEnhsbZBWu+4aFQTHtBwrBUBptvptbU9h4azdh+tLEqoNyG2rudRsit8d/4VpmtLq0itgIBf1jkYHubDbcfZE+pbaaG61yGKAt3JCHt7WzEtm2++eh7H9fjAwQH272jHpykMdsT59EN7+e6Ji3z71QtYjks0oGNoCkOdCUzbZnwhw7nxpl2Zqkgc6O/gob19q9Z1385uZtMFzo4vcHZ8nn072u86sRUFGUOO0xN46KafDcodxLVBfPLWy+5VK4Ahx29lE9dFoVLnwvQiP3znMs+eHqVQrd/8S/cgRhczzGYLNxDxtkiQn3tgHx8+MEx75O62ctwq7lliCwJD0QQvTo8zls/SGQzRG47w1JjFSCZNfySG53l84/JFgqrGPzz+KFHdx0Qhx3NT4+9ZUhPue4tnBJBEgd3xJP/pY59FX+6TvHZiBWC2XEQQmirJ2yGQBCCIApIsIskSjuMgSRKyKq9kFZbpNaKgrfzlUwawnRzFxis4UgWfOITjFnG8Oq5Xx7QXqIqXUMQYxcZL1K0JZDGCoe7F9eqkyl9BEoOE9ccx7ZkmoRUEBEFeXs/6kEWJpBbj8eT9lO0qbXqCbqO92fcharTocWRhewWxVE1m34PD+MMGoXiAQMRAWD4+snxnxLckUeRAooOBcIyZSp5co8ZoPs1kKcdQOIHrebyxOE2qVkFEoCcQ4b6b+LOKywJQ16wRkrqfD3QO8DO9u7dtuxerJZ6fvcqrCxNAs6z6Mzv28eu77qPTH1pXqCqqzWyq1Gu74DgutuXQaFhUSnUK2Qq12tYj/4GQj+GNfDs9aDQsvvgHz3PxzDSmaRMK++gdaGF4byfhqB9ZkaiU6izO5Zm8usT8TA5JEthzuJtIzL/hNWZbDs997wzf/8u3mJ3MoKgy0USQg/f10d4dQ9MVKsUa46NLjJybJZcpM3V1if/6/3ma3/4HH+P4YzvxGauDM+Gon/7hNsYuL7A0X2BhLs/45UX2HLq5h9u5U1MszecBiCeDHDi2A2GNbLPruszP5Pjjf/dD3jlxFc/1CAR1dgy3MbS7g1iiWQmRXixw5q1J5mey5LMVnv/+GYr5Kr//Tz9JJLq5iWGt2mBpvsCbr1zhS//lBRzHJdnazALHW4J4nkcuU2FhJsvifIH9R/vwB9cmdADReGAl0+0P6kRifvp3ttHVkyAY1mk0bFLzBa6OLDB+ZQGzYbMwk+N//vHLROMB9h/pW/OYvB/wqwr39a49bmykkJ9Nl6iU6viDOh3dMRItd4fI3E3U7abHqCy+/20OjutSd5oK1qoo3XXhNtfzVvxWBYQV+5q30tP8YPYi4+UsqihxINbB4XgnEe32+5A3i5rTWK7a4rbKEBuuRcle22ZOFiQ0ceMgdt1p8E52hNHSNHVndeWRT9I4FN3J7/b/LBE1eEv2RO8KKt5d6KrCR4/t5KPHdgLwyx86uuZn9vS2srunBdO1V0Q8rwW8WqNBfueTD950XS2RAL/9M2t7y96LkEUf0k3mrOtBEjQkYfsSI5bt8NL5Mb7y0hnOTMxv23LfD0xn8qSKq+3UREHgFx48yEcODNO2Dql1XQ9BuLe0H+5ZYisAQ9E43x69RM220SSJiKaT8Bu8ODPBr+w7hCSIOJ6LT1EIqir5Ro3vXh3BdFaXtsiiiCyILFUqq7JDQUWlI9CcHHzz8gU+v2sfsiCwUCmhiBJR/c48KIygTv+BHg59YA8XXx9l9/2D7L5/kHDi2oUjo0pttAR/Y4V0ioKfkO9hgvqx5vERVJZKXySsP4pPGaZmXaJqnqc1+Bu0yb9D0+C7meU12EVYf3Tle3guCBICEgHtGAH1yKa2O6D4eThxhKASICT7lx9od6gcVRDQDJW/+DffQZIlVE1eeYC2dCf4Z3/+d7d/nct4rL2f0UKGkXyKmUqBE4tT7AjFuJxPM13OU3MsuvxhdkZaiOsbT/RVSWJXpKWprm1BqlYhXats+J2t4nxukbFiBg/wSTJ7Y2387t4Hm+X5G5yb2nLP7d3C2MgCL/7wPCdfH2NpoYBjO7dUzrbvSC//x//3l9Z9v2HanHtnkpnJNI26RWdvnI//7FE+/JnDqKqCIDZzDJ7n4boelmmTWigyNb5EPBlCv0l551uvXuHV5y4yN5UlFPFz7OEhfvF3niAcNVZ6Oj3Pw2zYjF6a54t/8BwXTk2TTZV59tunCEcMDj9wY//Q7kM9nD89xdJ8gXymzDsnrm6K2F44NblCbBMtIQ7dv7Zt2uJsnhd+cJaTr4/h2C47htv4/K8+zOHj/fj82koW2XVdKuUG3/zSCV546ixL8wUun5vhW186wS//7pNI0s3v93rV5I2XRjh/apJA2Mfnf/VhHnhs56oSb9fzMOs2S/N5fAGNSGz9eymaCPDwB3czvK+TvoEW+ne2oenKquPtuh7ppSIv//A8/+M/PYfneYxfWWTiyhJ9g633TNb2+I5u7utbm9gqG5RM1yoNzIZNOOonnvzxiJxvFX9y/m1em5/mye5+fm3v5p5LdwpXCxn+1Rsv0heO8Uu7DjIQuTvCTNeQN2ucz81zKjtLSNGp2RZXiinO5uaZqeQRgIjq4/f3PEpC3/72nI1geTZFq4LpWrdl81e3G+SttX2FDVknehO7wrpj8kb2/A1+tyIibXqcX+/7JCElcMtZ5Zq9TODvYTRcmy+Ov8rxxAD9geS6vsA/KVBEH9I6FlE3gyQot/zdtfDS+XG++spZzk8tbtsy3y8sFcqrss2SKNAZC/Porh0kQ+s/m/PFKpoq499Eddndwj1LbAH8ikLDtonpPpJGgJCm0ROM8Mr0FD2hCB2BIMfau3hlZpK/+8x3iWg+uoIhWowA6nXR1c5AiEOt7Xzp/Gl+7+lvEVBVfmP/UQ61tHGktYOaZfPi9DjPTF7FcV0CqsanB3fxeM+OO7JfgiDQ2p3g9/71L1Mt1TGCOrH2KOLyNjcjHxLSdc3xgiAioOLx7k0Z0A5Ss65St8aQxCAB7RiCICMJq0+rgIR3XZTq+siKgAibuNEFofloaPMll3s072ymz2pYnH/tCn/jf/95WnviiNeJrCjanblsrx2Vh9r6+OHMFa4U0ixUS7ydmuFz/ft4ZX6Cotm88YcjSfbH226axVdFmaPJzhUbioVakbFihmy9elMv280iU69QMJvRap+ssifaSkBpiqpttHXT5Tzp+vaS7LVg2w4vP3uB7//l20yMLlEp1bGsW58s1KobKyM6tsPCXB7LbK6jZ0eSwV0dhMJrl296horh12jrjCDJ0rrZWs/zcGyXl54+z+TVJSRJZN+RXj7/G4/Q2h5BlFaX6Wq6ws69nXzhbzzGv/onX6VarnPh9DQH7ptn574ujMDqB8Hwng46e+Kcf2eSfLbCmbcmsMxmv+6a2+15zE6kmZ3MUK00CEcMevpbaGkL3/BZ13GZGlvixafPYVsOhqHx+V99mKMPDRKK+FbGnuu3/WOfO0oxX+XZ75wil6nw+ouX+cTPHyMWDyLJG2c/XNcjmykTjvn55d95guOP7SQQ8t1QyuwzPPzBpuqntEGWXBRFjjw4iG056D4FTVfX7IFu74px5MFBzp2c4tTrV3Ecl7npDLlM+Z4htoosUSxXmc0X8TyPA11t2K7bFMjbAJIsIYoCqiZj+O+dScR2ombblMwGdef9UaG9Ho7rUTQbVC3zhr7Nu4GK3eBcbp4/v/oOsiDieB51x6JqN7enJxDlVwePsS/a/r5kt3Nmibpj3haxrTp1Mo3CDa+rokxI9hOUN75nG67JpdIElrv6egkpfnaHdtCqxZCEWxeQqzh17LsY/L0VqKLEJzsPEVB0tA3srn6cIYkqPf6HcD2boNK+Smh2S8sR1G3J2Dquy2ymwFdfOcvITGrF9eLHGVXTomG9ex8pksTR/k7Cfm3daodSpc4bZybp7Yixe3DzYpd3GvcssW36tAn8td37cD2P7mCYoKrxiYFhhqJxOgJBdFnmiZ4dDERilEwTnyzTYvjZn2ylJxxZmdSHNZ0HOroJaxqW66KIIp2BIIIgkDAMHuvupTMYpGg28DwPXVboj8SQBIGY7uPDfYNULJOguj2TCUEQUHWFrqGtK36tmhQqQ8hiBNdrIAoGirRe78B2OM41cbOel+2CJEskOmNMXZqlXm2gG9pKKaHPr9Ozc4NS1NtEqxFgb6yVC7lFFqolpst5rhYyvLIwQcU2UUWJnZEkO9fod30vZFGkKxDhUKKdolWnaDY4nZnnudmrfHbH3m0sb/Ou+9+7pfTr4VxmgZF8aoWo3ylYps3sVJbv/s+3uHx+lnq9GVn3GSqRmL+pQLzFEtGO7o17ZARBwOdTuDYWlwpVSoW1y92uff564bL14LpeU7zo6iLVcoPOnjh7D/XQ1Rtfk+gJgoDPr7FjZxv9w21cPj9LpVxnZjLN0nyevqHVfUyBkI/e/hZaOyLMTWdZms9z5cIcg7s7mhULa2zP6bcmyGXKuK5He3eM4b0da+5HJlXi6sgCC3N5ZFlk75FehvZ0EIoYaxJEURRp7YzQP9zG6dYwC7M5MqkSoxfmOHR/P75NZAX8AZ3BXR3c/+gw4XVKmJtEbXNjSjB08woaRZGIJQIM7mrj9BtjgEexULtpMORu4vJimpcuT3B2doGuaJh9na0sFSv86NJVHhroYaBl7es7ENTRfCqO7WKZ7z/x+ynuLESaqsCO51Iwa9iuiy7LdPsj7Im28UhrP4+09uOX3x9l7Pl6ipJdJazeera4YJWZqS3d8HpQ9hNRghsG0D3Pw3Jtso3iDR61Qdmgz99+y602ntdUa041ctSc99/31HYdfrRwgbxVxXJtuo04eyOdqKLM29lxzuSm+VDbPnYEkziey+XiAgu1PB9q28fF4hzpRol94S5afTcGPX8coIgGB2K/iOe5hNXuW9Z0kQQFTQrhl1vwSbdegWHZDj88eYXRuTQ1c32vZVWWiAcNWqJBon4dTVFQbxIU3i7s69sav6hb9iplbFkSGWiNo8k3OsA4jks6W+aVd8Y4eW6Ky/Egi+ki/T1JYmGDF16/Qt20sG2XXQOtOI5LpWqyd7idr37/HR4/PoxlO+SLNfLFKrW6BZ5HtW7xwKEd9HbF0Dc5L1gL9yyxvYbDrasJzHAswXDsXeGdrmCYruDqm3VnfLXEtSJJtAeCtAduLGtRRIkWf4AW/9qDsyGqq9Z3L0ESg0jiT2ZJmqRI9OzqoJitYFsOqqasEKBA1A9rqG9vFxRR4nCik5OpWeYqRTL1Kq8uTHAxt0jdsekPxhgMJ4htolRdFAR8ssLP9O5mspznQnaJq8UM3xg/hySK3N/STYsvcIOnZfOh7bBUqzBTKVC2GvQGo2uKTkU034pPYs22GMmnSNcrtBuhVcu9tswrhTR/MXqKy/kU1h2ONFYrDd56dZTRS/PU6xaxZJCdezsY2tNJLBFA0+Rmr/cWlhmJbTyRUhSJvsFWAgGdYq7KzGSGV5+7iKRIDO5qJ7mc0dzqZNCxXS6dnaGYr+G6Hi0dEXr6kxv244qigK4rdO9IMH5lgUbdIpMqkVos3kBsJUlkx3ArfYOtzE5lqZTqvPnKZbp3JG8gts3sscM7J65SzFcRRIHO3jhD6/QepxYKTI01rWI0XWHPoe6mWvAGQQVZloglg8SSQRZmc9iWzdhy369vExnDZGuInfs6b3q+thuKKhNLBFcexmbDxn4frSzei7cmZkiXK+iKzGQmh4CArsicGJtmIBlbl9h29sSJxQMUC1XSS6WfXF/bn7DduVWEVZ3jyV5UUaJmWzieiyrKxDQffcE4g6HEtvj73iomKvMUrDJd3DzAuxZs1yFrFpit3khsE1qEhBbZ8PsuLg3XWlNVWZUUosqtz41cPBbqGXLvo4ftNXh42J7DC0uX2BXqIK4FCCm+pi2RIGJIGqPlRQ6bfXS7cWRBxHJtJisZXk1d4VRukn3RrtvS0xAFYc2sXdMy8M73IEuCQlK/fV0SQRBp8e3jUOxXUG9x7uw4LtlSjefOXKVQqd24DsDQFA7s6GBnZ5LORIhY0E/Q17TH2YrF2+0gbKyvWbEWXNddJRwlCgLxoLF28kVozrMUSURVZUIBnXDQh+u6TMxmGZtOM7yjBctyGJ1MsZAqEjA0ertinBuZoy0Rpm5aWLZDOldBAHYPttEwi7x1dpJYxLj3iG2jZpJPFUnP5SjnyjSq5srEQpIlVF3BF9AJRgNEW0MEo4FNWUkAeK6H2bBYnEqTW8hTKdYwGxae66FqCr6gj1hbmNaeBJqhbThxu4ZKscb42SkWp9MomsLe40PEO6JNItCwyMznWZpON9dVs5onVVWa/peJIC3dcYzg2pYSnusxfWWe6ZE5auX1I397HxyifcetPSCgebNVSzWWptLklgrUKw2sazYYukogYhBrj9Lak0CSt1aa49gOxUyZ1EyGYrbc9Oo0bTzPQ5REFFXG59cJRP1EkiFC8QD6bdbbq5rC3geGKOXK1Ep1jLCBEdTxXA/tLtTy74m2sDOS5M2lGXKNGs/NXiXTqOJ6HgcTHQyEYuuKMq2Fh9r6uJhPUTZNJss53k7NkjdrjBUz9IdihBS92V/ngeW51G2bollnplJgtJAB4JO9u9cktjuCMboDEU6m56g7FhdzS3x97ByHEh3ENANFErFdj7LVYKFa4sTiJM/OjuKTFGKaQbaxfjbzdlEpN3j7tVFM00bVZI4c7+cjnz7Mzv1d6Pqdyf7LskRXX5wD9+2gUm6Qy5Z58+UrpBaLHDy2g4GdbSTbIrS0h/EH9U2Lkbmuy+ToEo3lrHO5WOPS2RkKuY3LuRt1m8xSCcduBhHKpTrl0o0PRICuvgR9Q6288fIV6nWTt18Z5aOfOXqD/ZBjuywtFBi7vECtahKOGHTvSNLaubYFQi5TZnG5D9d1XZbm8rz63EV038bn4OrIApVyM6vvOM0e1mv7cTNEE0G6+rY3KOi6LrWqSSFboVSsUa00ME0b23JwHRfX8SiX6oxdXlh5SLuui3eXvAM3g8lMno5IkN3tSX54YRRRFIgHDGqWheWsf2x37e/k7DuTvHNilNmpDEvzBVrafzwzMBtBRKBiWVzILDFVKlC3LXyyQl8oQmcwTEB5t5TQ8zxKVoOxfJbFaoW6Y6OKEnGfwUA4RlRfbediOg6ZWoWrhSy5etNf3K8odAXCdAZCN63Kcj0P03F4a3GGuuMwEI7RHQzdtgif7bqcX1okVamyJ5mkIxQioGgciHVwIHbnqpNuBxOVeZYaOYZc65YqubJmgenq4po9th2+BJ3Gxh6cntcU01oLIgLSbZwT27V5K3eJ8jrCVncfAlHVIKT6SGhBknoIv6whixL7It20+yIoy/vrk1V2BFpYrBf59uxJYqqfveFOYuqtK0IrgoyyRvbc9hws18LxnDvenrZdiGuDxLXBW/5+3bIZW8gwvpi9we9VEkXiQYOH9/Tx5IEB9va2EQlsbCl1r0CRJGRJXMnaCjQtotYKNEqiSCziZ9dAG7lijX3D7Rze200qU+Lc5TniET8PHe1HQODrT59iZj7HzoFWMvkK/T1J5lMFanWTtmSYSNBH0K/z+PEhpudy/Pm336Jav70Kq20ltq7rUsyUmbw4y4UTVzh/4gqzowsUUiXq1QaCAKpPJRj1k+iI0TnYxuChXvYcH6JzsBUjuHH00WpY5JaKTF6c5e1nz3L57TEWpzKUcxVc18UfNkh2xRk+0sd9HzrAwMFeoi0hlJsw/9xinm//4bO8+PU38IcN/i9/9DtE28KUcxUmLsxy6oULnHr+AkvTaYrZctN/MeQj0RFj6MgOPvSLDzNwoBd1DWLrui5vP3uWr//7p1icTK+7Df/wv/zNWya29Uqd1EyWK6cnOPmj84yemiC7UKBarCHKIqFYkI7+FnYdG+DYRw/SPdxOIGJs2NMGTVJeLdeZu7rIxdevcPbVy0yPzJGdz1Mt1XBdF0VTMEI+4m0ROgba6N/fza5jA+zY1004fusRU1mVGTzYy/kTV8gtFhg81Ef//m6KmTKl/J3vC036AuyMtNATiHC5kOJUZg7X89AkiYPxdnqCaxOI9RBSdX5uxz5sx+EHUyPMVoucyy5yLruIX1ZJ+vz4FRXPg7pjkW/UyTWqK7HQfbG2dfu8BsJxDic6OJWeY7Kco2DW+f+de4UD8XZ6/BEMRaXh2MxXilwupCiYDfpDMT7UNcSZzDwvzY+vudztgNmwmRxL4Tgu7Z1xHv3QHvYf7b2hp3M7cS2Y84nP34e5LCSVy5Q5984kF09PE4372X90Bwfu66N3sJVka4hQxEC7CdH2PI/UYmGlDHTk3Cwj52a3vH2WaWM21s4CROMBevqTJNtCzE1lGbu8yPxslkjcv0rUql4zOf3GOKV8Fdf16B1ooXegBXWdAGGlXKeYry6v3+H7f/k28PaWttvzPKqVxqZ9+XyGum19rZ7nUSrWSC8WmZlIc3VkntnJDKmFIuVSs9TYbFjYloNtu81A6r3DZVdBEsWV7IdHs18rU66iLk8s1kP/cBv7DvcwcXWRuZksLz9zgY989jD+gHZH76e7DQ+Pq4UsXx+9wKmlefKNOpIocH9bF58e2M3+eCuaLON6HhXL5OXZSZ6eHGWikKPm2KiiSFcgzOPdO/hQzwBRzYckiliOw1Qpz/PTYzw3PU62XsXFI6CoHEi08YHufo62duJX1u7Bu7a+M+kF/v2p1/ErCr+wcz9t/sBtE1vLcfjLCxd4bXqaf/DQw3SE7n3F67SZ52p5huFgD52+jUnoe+F4LlfK01wsTtxwm6qiTI/RRpdv4/mQKIhoooqIeEMpsuXaVOy1g4c3g+06pBt5XkydXFex+W5CoKnK/bPdx3hpaYSRwjy7wx08kBggqa99nfhljajqJ90ocSjae9stZJqortlL7eFRsqpU7DqhHxMrpdtFtWFxbmoB+z1BSAGIBHQ+cGCAv/WJBwgZ+l1XUr8d+HUVXZFXiK2LR7lmbqj9cE248dqUwHZcGg2LYKCZVFQVGdf1UBUZURC4MrbEkX3dvHVmikq1QUerQCTkI2A0dTY0VcayHW43Dr2txLZSqPHi117ne3/8POPnppsrUCRESURWmgO/1bBIz2ZZnExz/rXLPP0/4Imff4DP/t5H2H3/+lEU23KYn0jx3Jdf5Rv/8Wlq5fqK4Ii4rIxZzlXILxW5/PYYL3z1DT752x/kg194kI6B1puSuGuoFqqYdYtKscbrPzjN1/7d9xk/N40oiSsZWc/1yC4WyMznSc1mOfT4bvr2dgFrDB5Cc5IdjPipFmvNi8D1cByXeuX2ezcs0+bKqQme+u8v8tLX36BeNRElcUVoxHVcsgs5UjNpzrx8iaf+9EV+/X/7eY599ADRZGjNLPM11KsNzr96mS//2+9w7pURACRZRJSklePhOi6FdIncQp4rJyd44asn2P/wTj7zux/m0c/df8v7Va/U+dFXXuO1775DaiaLP2zQ3t/CuVdHePW7J/nHf/y7t7zszWJXtIXDyU5GCins5ZLdvuUy5OgtWCv0BKP85u5jdAcjfGX0NKOFDKbrUHdsJkv5Vb2xoiAgixKyIKKIIm1GgLC6dmmJJsl8qGuIumPzJ5feItuoYrkuJ1OzvJOaReBaz7qILsn0h2L8/QOPcjjRyR9eOLFiE3Qn4DgupXwVz/UY2NVOPBm6a5Pwwd0d/Obf+wivPneRF546y9jlBcy6TSZV4rnvn+G575+hqy/B8cd28tAHdtG/s22FPK4t1gS16wZ6aXlc22qPsKrJ61aSCIJAe1eMPQd7mJvKYtsOJ18fo6M7trJtnudRKTd4/cURzIaNIMDgrnZ6+9efXFqWg9l4txeouQ3ilso+NU1BljevgS7L4rpEe6uoVRu88eIIT3/zJBdPT2PbDrIsIckSkiQ0la6FZr+uonpYpkO9du/01V6P1lAA07GZztUxbYe5fInvnLlIzG8Q0tfPGOo+lcc+spdGw+Y7X3mDb33lDaKJAAfu6yMQ1JsK0Vu8Fq+pSt9LyDcaLFbnkQSBX9t7GF1SeGZqlO+NX0ZAIKLqDEbj1G2LM6kF/o8Tz9HhD/GR3iEGIzEWq2Wenxnn//Hmi0iCyId7B4hoPlK1Ct8Zu8SXLp3hULKdX9i5H0NWOJma57npMcYKOVRJ5oH27tUbtHx4qsuk9n999VlajQB/c/8xDrd0NBXv/4ri7ewluo1WWrQoirjJqjvPo2CVOVe4ykhp6ob3O30tdPla8MsbP2ObNkwyQcWgaFVWlcRWnTqztaUtl+u7nkvWLPBi6iRXy9OY7vvfy35tH1r0ED/Xc4x3chPMVLJcLi4QVf1N6yXXoe5Y1B0LTZIZL6c4X5jlYx0HOJEe5XCsl4Cs3bK4lCHrGJK+bDu1mnXM1dMs1jME5b8a3tp101pTMEpVZHZ1tfC3PvEAEb/vx+5YxAMGQZ9GsdbkJa7rkSpVVua+a0FRRGzHpVo3qdVNFEUiGQtyYXSeUrmBZVdRFJHezhiqIjMyvsgjxwY4NzKHZTvvPn+2+VhtK7H90Zdf5ft//DwTF2YA0AyN4SN9tPUm8YcNXMcjnyowe3WRmcvzNJYnHkOH+2i5iSDM1KVZvvfHz/ODP3keaznbkeyK0b+/p/ldD5ZmMlx88yrFdIlSvszX/8MPqJaqfPZ3P0LHwOYMpx3HpVKs8uyXXuEHf/I8kxfnkFWZjv5Wkl0xRFGglKuwNJ0ht1RA92vsf2QXqr52lFcUBZ78hQc58oG9FHMVKoUqpXyFifMzfPFffmOTR3Z9nH35Et/4j0/z9jNnsS0HSRZp39HC0OEdhBNBzLrF9OV5xs9NUc5XyacK/OE//hJm3eSRzx4j1rp+KdvZV0b46r/7PhdOXGnuiyTSt7ebrsE2wokgoiRSzleZn1hi4vwM1VINz/VoH2ilb2/3usvdDGrlBj/84kv8vX/3G/zlv/8BAMGIn2AsSGomc1vL3iwGwnEOxNv5xvh56ssm8o+09dFm3HomOqIZ/OyO/TzatoO3lmZ4ZXGC89lFlmplSmazNM6QVeKaQVcgwp5YC/e39LA72kJiAxXlViPILwwe5FCig29PXOCt1AyzlQJ120aVJOK6QX8ozoOtvXyydzcx3Wj2lxvBm1oW3Q4EgXd7o4P6plsOtguRmJ+PfOYwxx/byZULc7z87Hlef/EylVK9qSo8meFbf36C156/yCMf2ssXfusxfLq6LuHzHG9lojG4q5399/URDG8tKxmJ+RnYub6wQ5PYdvPMt08B8M5rV3n4yd20djSrBBzHJZ8rc+btcUzLJhD00TfUSmtnZN1leq7HtYS/oko8+YmDJNvCKwHHzUCSRTq6Y5vqr21i+x5YX/3TV3n++2dYmM3hLvvv7jnUw56D3XT0xInGAvgDOrqhUK2YvPHiCF/8w+c3XTZ9N/GRvUN889QFvnX6Egv5En/9v/wFyYDB3//II+xIbixoEk+G/v/s/XeQXPed5Yl+rk9vK7OyvIf3np6USEmU91KbaTc93TumZ3bmvZ1YvxETu/Pe7G7M7L6e2Y7d7Z7e7mmrllpSSxQl0UgUCTp4j/Lepffm2vdHFgoAUQAKQAEEJZ6IIliVmdfnvb/z+57vOTz/hX3E4n7+6j++zv/+P36P7Xu62X2wl97BVkJhL4oqrfu4t3eGbxtv9aDRME0Ot3Xyn+97jITHBwI82t5FXq9zOrXItmicwXCUTL3Gn146he04/PN9j7K/tQNFEnEc2Btv5785+hJ/eP4Yu2IJQpqbE8kFji7MsDnSwv/8xMeblVkBnujoJery8LdjF/jW6IUbiG2zZ9HircVZ/t3Jo0RdHv7bw08zEIqgfIAqMvcD09VFfpY6RVjxcyi6Pt8LC5sfLb3FO5nzNOwbJ5/2h7fQ612f/FoVFQZ9XZwrjKFfE/mT1YucyY/x5c46Lsm17sm4rF7kzfQ5/mb2lYeC1EJTKZDRy/zB8MuYTrOveE+4mx5vlOlKmm/PHudcfo68XmWynGLA30q2UcZxHD7buY9OT4RXly+giBKbA3duWAqgiQoRNUBEDZDRr3exvlCYYJOvmwFf5wbalD68aBgmc+n8DZXMrpYgT+3oJ+R9//re7wWd0SAtAS/z2SLQVJGcm1nk8we33fQzbbEghmnxw9cuMD6T4vDuXrYOtXH83Ax/9I030XWDQ3v6eHRfG5fGl0jnKsSifgI+N4IooCoS6xSA3RE2bJSZWy5w7o1h5kYXkWWJwb29fP2/+CydgwlUl9ysDDrN6A+9ZlDKlZm8MMvU+Tl2Pb6VYMvNpTfZpTxvv3CK1//2XQzdRPOofPZ3n+Xw83tpaQ+jqDIOK1Ll5QLf+79f4diPz1LOV3j7B6cIRP189V98at3um298+xjFXJlqqc7TXznCR772CPGelpVBuYBtWVSKNVJzGcr5Kv6w95a9vJpbo6UzSqQtjGVZWKZFIOzjz+/wGL8XS1MpXvrzNzh/dBjLsukYbOWTv/URDjy3E7fPhSSLOLZDvaIzcX6GH/7Ja5x85TzVYpUX/uhVglE/j31uP7Jy42VQylUYPTnJ8PFxAMLxIL/5r77C4O5evEF3sy9RaPb6GQ2TWrnO9OV5ps7PsvnAAK0999Zb5zgOes0gEPWhqM3zJohCM1LlDqsSdwuRZpXzytpkUeSRRA9x992b4YiCgCpJtHr8PNM5wMHWLmqmgWFb2I6Dg4MoNHOXVUnCLSl4FRWXJN9S1iIKAj5FY0ckQacvSMUwMGwT2wFRYLVa61U0QqprNcj9i307eKq9DxCIaO7bPpg+1buNI4kedNtCk5pGJreCJIkEwx7Sy0XqNf2Bm/gIYtPxONziY/ehPvo3J/jcLx/h8tk5Thwd5fL5eQq5CssLeX764lnKxRq/+U+fw+d33TDjKgBur4a0onKId4Q4/ORmuvru7FqXJAnVdfNbr8en0dHTQldfjNnJFDMTSRZms3T3xXB7NQq5KhdPz9KoGeDA1l1dJDrCt6yEK6q8uk5Jkti2t5sde3vweO+E1AgoqnTbvtyNhGlaXDg1w/E3Rkgu5BFFkV37u/ns1w/Tt6lZYVc1GUkSVyuW+WwFzaU+tEOsmM/DF/dt55H+brKVKqIo0hb00x4K4FFufmx/+O2TXD7f7OlOLhZILRdp1A0unJ5hcnQJ9UpF/Q4mE/7l//QlNt3EcOz9gldRaPP6SXh9KKKE4zjIssjWcIyZYp5krQw0K6ink4tsj8ZJeP24ZXl136NuD4fbOvmLy2dYKBfp9geZKxUoNuocaB0goLlWlSyIMBiOkvD6mS7mSFbLxNxXJ/ss2+H1uWmOLkzTGwjzz/Y+Qk8gjCquHcN1t7gSqfdBgoPDpeIkDUtnuZHlmfh+vNLa1apmNbTId+df463MOVKN/A3v6fO2szu0iVbX+hxrNUlld3CQ4dLUdcTWdCzma8v8P1Mv8LXu5wgr/ptGlgA0LJ3h0jSvpU7ybuYiNfv9d0K+AlEQCKtefnvomWa11AGPrOJTXNiOw6/2PcYXuw6iiBJuSUWTZCzHxrAt3JLKvkgvmwIJgsrdEy5BEGjRQvR4EzcQ21Qjx8/SJ/HIGk/E9qGus3L/QYVlO5SqjRsq17Ggjx09iVveE2y7QqH6bcr1n2E7VbzaYXzuj+NSNq2+J1n4n/Foh3Cre5HEB+ehsKktRmckyJmpRaBJbM/PLpMsVogFfKhrqF4VReJLn9iLrpurWbaKLPErnz+IZTeLAD6PhtulEAl62LGpHVkS+eQz27FsZ5XYSqKALInEW/z87i8/QTR0b8WWDbsCk7MZcssFjIZJrDPC9kc2sfuJLbi82g0n+koeZPtAK7se30JLR/SWlYPzR4c58co58ukivqCXJ794kGd/6XHaB1pRr+mLcxyHWGeUL7lV8skil94ZIz2f4+Lbo4yemGT7o5tuuo5rcendMcKtQR759D6e+9Un6BxKoLmvt9S3TIuuoTYaNf2WMmdhpflaFiVQJEDBsR08/jtzLFsLr3/nXYaPjVMp1ugYSvDRrz3K018+TLQ9fN22OrZDKB5Ac6nMjy6RXsgyP7bE+TeH6dnWQe+2zhuWnU8VSc/nqFcauH0uth4eZM9T22hpD9+wv1c09q09LWw9OIA34Fn3JMLNIEki4dYg+WQRva7jOA4LE8uMn50h1nH3Nu13goVqgfFiGt02kUWRXZEE3f4wbvneB/ayKBJQXQTeIy/WTYvTswv8dGSSf/nxJ+9omVccmO9k+1rcXlrc67uJCIJA1OUhegf5u5pLoX9TglymzPxMhlLx7vqe7hWSJOLxarg9KrFEgJZ407F39OICb756iTPHJ0kni7z7+ghPPreDzTs7cXuuJ32CKBCN+69pqzCxHWfDXX8lSSQa87PrQC+zkyn0htmM/dnSRldfjPxKv/CVmc4d+7pp7Qjf8oHq8WkEgh4WyDZ7Zct1vD7tpjE8Dwss0+bk22OkloqYpk3/5gSPPrOV3Yf68fpufg9t1PWHtcUWWZLwaxoVt065oSNLIhGvB5dyc4k6wPnT07z7+iiGbjZ/VrKg6zX9rmXXV4zQHiZosoxbVlbNcK5c1xGXG8eBimFg2jYNyyLfqBNze9Gk60mmKkq0ef2Ytk2+UadmmlQMA9txaHF5bjBzCaoaAVVjuVJeXeYVHE/Ocz6zxHK1wr54O92BIKp096R2oVjk6MwM55PLVAyDmMfLztZWHK6XzI5mMrw0PkbNMPjHh4/gkuXrXntlYoKy3uCfHXlk1Wm1ZhgMp9O8OTvDVC6PblvEvV4e6epiV2uCqOfee94FBDo9cTKNAjWrQc1qMFaeo2RWOJcfo8fbRpsril/xoAgKpmNRNMrM19KMl+cYL8+RN0qYzvWTnIog8dHWg/R629Yta3ZLGoei23k5eYya1bjOh6Js1jiaPkPZqLIjNECPJ0FYDaCKCg4ONatBwSizUEszUZ5nsjLPXDVFzmhWrCRB4mB4K5OVBZYb2Xs+bncLQRBQBIkOz9q+Hgl36Jaf98oa3nXEs90One44u4JDnM6NYF9zdzUdi4nyPN+Zf41LxWkGfZ1E1SAuSUVAwHQsdNugbuvUzDoVq07ZrNHrbWNroJeYdmd+JVfhYNo6VTNF2UzSsIqYTgMHGxEJRXSjSSF8chyP3IK4QeZWlm1TruvXVRoFQSDsc9MdD918ax0Dw1ogX/4rgt4vI0sJFLkNRbq+hcjveg5ZiiMI984R7gQdkQBDiRZiAS+pYgXHgWKtzstnR4l43fTEbjxPgiAQW2P807pGoVJVZAIrPkqRmxBXVZFpj987md8wYmsYJvaKFlsURbQV5+O1IAgCsiIRjgcJ32YnyoUqF98dY/LCHKIo0tIR5hO//hQdQ4kbZI1Xlju4q4c9T21jeTrNwsQyCxPLnH3j8rqJbblQZf9zO3n6y4cZ2NW95gNMkiU8ATeedWQrbjRs26aUrXDi5fOkF3LIikT/ji4e//xBWtYgfYIo4At52Hywn30f3cFr33yHaqnG6KkpJi/MrklsLdPCsqzVz6uupgv0WiReEAQEAQIRH4ENGuS7vBrPfOUIJ145z+xKTMr42RkcBx751L4NWcet4ADD+RSn0vNYjoNXUvho5xAR7f463NmOzXy+yNGxafj4fVvNA4PHp3Ho8SHOn5xmfjrD+OVFevpjRFren5gqYaVSHWnxE2nxk+gII0kixUKVsUuL5DMVxi4v0j0Qu4HYiqJI72ArJ98co1SokVoqsDCdYfeBvg3fzlDUy55D/fzwb09gWTaXzs6y78gA7V1RMukSIxeaplXhqI+BzW2EwrcesEaiPuJtIS6fm8OybIbPzXPkqS0PPbG1bZuZidQqAWvvjDC4rf2WpFZvGCwv5lcl4w8b5nMF3pmcY2QptTo8/MnlCZ7e3Me2tjgB99r7JksSbreCewMr5mvlI7/fsB1nTYMya+Vv0oquRBCarp3NyJHr4dB0GnYckFbed+W+vZYJn+042LaDsKJuee/SBkJR+oIRRnMZvjcxzPO9m/Cp2h1VWB3HoWoYfOfSJd6em20OCr1e8vU6r0xMMF8qUjevyl+ztRrH5ucp1uv87sHr/SqytRrH5+fJ1Kr83uEjQNPx+c3ZWV4aH2OxVKLF3Ww3GctmmS0UyNZqPNXbR8s9kltREOjxJNjk7+ZiYZLFepqGrTNTXWa+lmK4NE1YDeCWNGRBwnZsKladrF4k3cjfUOmCJkF9rGU3ByPbCCrrH0PIgkS7O8bjLbt5afmd66rA1kqF+K3MOaarS8RcYfyyG1mQcXDQbYOKWSerF0jWc5TN6iphc4kqj7bs4un4fr6/8AYZvXADEf9FQ0j1szXQx7ZgP+cL49e9VrUaTJTnWaxnuFyaIiB7UEQFgeZ5uEJuddugbunUrAZPxPbQ6Y7fMbF1cCgbS6TrI+T0cUrGInWzgOFUsRy9qXpDRBI0FNGLWwoRUDuIaINEtSHccuSeJNOO49Awr5epK5KI16Xi1W6ugHIcHcNaxrCTeLQjaMrQmnm8bm3vXW/bvcCtKuzta+fyQoofnxlpKggd+MmFcRIhP8/KEm3hh9/UDjaQ2IZa/LhXqrPlQoWJ87OMnJykd1sHiqrctXx0aTLJ/NgylUIVb8BN16Y2Bvf03tT06EqFdMvBAY79+CwLE8vkk0XGzkxjWTbiisnIrRBs8bPt8BD9u3oeygZwy7SZujTH0lSKRk0nkgjRu62Trk03758QBAGXR2P/R3fw9g9OUS3VmB9fZmFsGduybzie3qAHb9CDKAoYdYPpi/NcemecLQcH8IU8tzSd2gi4PBqPf/4gP/nGW2ze30+92sC2bTbt7WfvMzvu67oBUrUyZ9KLjOTTSIJIzO3lqfb+1bzYD7E+eDwaew71s21PN+dPTvPuG6N4fS72Hu4n1hpYt6nb/UIw7KV/Sxu9Q62MXVrEwaGQq2Cu0ZspySLbdnfxSthLJlUiuVhg7NIi2VSJSGxjibrHq9E71EpbV4TFuSyzEymW5nNkUkUWZrJkkkUEQWDLzk7ibcHbKiTibSF6B+O89RMJc4Uoz09nCIW9uDwPV4/ltXCcZlXxSj+T6pJx36In1DQssuly81w+RBE/1+L49DynZxZQZIlEoFlVnEpnefniGCGP+6bE9sBjg7R3b6xaJdLyYDOG14OaaVDUG1QNA4+iNBVBwFK1hCiAX9OQxGZrRbsvwFypSMXQsR1nlbzWTYPxfA63rBB1e3DLMiHNhSLJzJdLmLaNJFwdC6RqVTL1KgFVuyGfvC8Y4bP9W5BFiT++cII/vXiKqMvDwUQngdvEA10L23G4kEzyw7FRYl4vHx8cYlNLC+VGg3fn53hrdmbdjuNrYSqf58djY4xm0jzT18+j3d24ZJnL6RR/cfYsL49PEPN4ebK3967X0YSAW9J4IraHiBLgaOYsS7U0Ng6WY5Ns5Eg2cutckkBA8bIrNMhn2p8g4Yquu1oLK8UMJJ6O7yerF3k3e4GsXrzuPXVbZ6Iyz0Tl9g72IgIBxcfu0BCf7XiSXk8b5wpjjJZmyK0RS/SLBEWU6fa08rHWw+T1Eov19HWTRDYOFbPGeHluXcurWY0b3KxvB8sxSNeHmau+y0LlBDl9At0u3/ZzmhQkpm2hw3uQds9+wmovonCX9Gc10/fqtsuSiCKvreJwHAvDmqemn6auXwBsqo230M0xVGUITe7DwcYwF9HNESy7hEvdgSr1IIpubLuGYS2gm5N4tccQxeb9yTAXMaxFJNGPKg9iOxUaxmVMK4WDiSQGUOU+VLl73bs2lGjhme39zKRyXJxv5kvPZgq8eHoY23F4fHMvnS1BNPnhmxC9Fhu2dS0dEdoHWhk5NUUpW+bCWyN8+9//iKe+coRETwvheBBPwH3H5jEzwwvkU80blS/spW9717oGw/Hu6Go1tVapk57P0qjquH23fxB1DCZI9MTQHjJTjSswdZOR45M0qs0+kFhnhLa++G3JpqzKdG1qWz0HpWyZXLKI3jBuyJ0NRv20D8SJtIVIz+eYvjTHd/7gxzyZPEz/zi6iiRC+kPe+HSNBFPCHvXzuHz5HOVfF0A00t3pTFcBGomw0eHNpiuPJWQp6nZDq5lC8i4FgC6Vag6RepqobOIBbUTBtm1K9QWc4QHBlUJqv1kmXKxiWBQi0+DxEfR4ahkmx3qBumNQNk4Bbw7Ydyg2dkMeFb8UV1bKbldtcpYbl2IQ9blq8Hjyaiu041A2ThXyRmmHgOODTVFp8HgJuF7ZtkypXMW0bw7SoGga6aeFVFfpaIkjrmNzZKEiySDwR5LnP7KGQq3L53BzVcoNsusT2Pd0EQp7VHsn1bpOqybes+FqmRSZVQtUUPD4NRbm5bNCybBp1Y7UiKMCqw+x7IYoC/ZsT9G9OkFwqUMxXuXRulnd+NsyRp7cQDHtWK8LXwnEcbMuhUq6j6yYer4bnNgZMoijiD7g5+NgQP/ruScqlOvMzWS6fm2dyZAnbdpBlif2PDRJYRz9KJOZncGsb7d1RpseTLM3nmjm2HpX+Ta243tNqcQW2bdOom82JJcch0uJ/oJN9giDg9bmQpOY6S4UauUyZtWrkpmmRXMxz7sQUk6PL98WUYiNwYX6ZtqCfrx/aTdjrxrJtlotl/rvvvESqWGFLYm1368c/enMTj58nGJbNVDHH6dQiQ6EokiiQqlU5n04S1Nx0+ZoqL7+q8mRHLy9OjnAuvYRXUfApGrptcSmb4lRqgW3ROAmPH02SGQhF6PAFuJhJcj6zTLvXjySIlIwGp1OLFPQGhxOdhN9DbCOam05/kITXz6/ae/jX777GN0bO4ZEV9sTb1t36YTkOr01NUdJ1vrZzJ5/evBmPomA7DtviMX4yOUm2evfxMifm5xnNZtgSi/Gru3cTdjf3Y1s8znA6w9Hpac4sLfF4T889qo4cSmaVNlcLbYkYLknlaPosS/UMVau+7qWookJUDbIjOMCn2x9n0Nd1V9slCAJdnlY+ljiCiMjJ/GUyegHjDs2fXKJKixZiZ3CQz7Y/SZe3FUkQ6XK30qKFfuGJLTSrtoejOygaFV5LnWShllohqPf3Znulyp9tjHEh/03mK8do2EVAQBQUVNGDJKgISKufsLEw7QaGXaVhFZirvkOmMUbZWGJL8LOEtN67qtxKgoBXU9ENc3Wvryg+HGct3z4bw5qnUn8d3ZzGtsuU668jil4CgCp14mBimNOUa69Sqr9C1P+fIXnCiLhxnDoN4yLp4h+gtQyhCJ2AQE0/TaXxM7zaY6hyH7XGCaqNNzGsJRxHRxTduJTtBL1fQxS863pu+90aB/o7KdUaVF8/xWymgGXbnJtZolCts5gv8eSWXtojQYIeFx5VQZGlhy6nd8OIrcujcegTe0jOZjj10wsUM2Ve/cabvPF3x9n/ke0cen4PWw8O0tIRRnWpzQqDcHsb9uWZNOVcM7dUVmRUj0p64fb9DvWKjnPFptppxuKUsmVc3rUHcNeipT2CP/LwSvQsy2ZmZGE1vkNzqzhw2+NimzZ6w1yV6DmOQ6PaoFqq30BsVZfCloODHH5+L6/85VHq1QYnXj7H2dcvsXl/P4c+vptdT26lvb8Vza2iupU1B/V3vY+mxfJ0mpaO5rkQBAHLtKiW6lQKVRK9d5abtxaafVomhm2t2thbjsPZ9CJ/PXaGU+kFJEGg2xfkqwO7UQSRN8amGV5KMZPJYzkOrQEftuNwcnqe33r8AE9t6kM3LV4fneKnIxPkqnUE4JH+bj67eyvz+SLHpuaYzRVIlyr0RMMoksjFxSSH+7v42NZBREGg0tD53plLvDs5R6neYE93G5/csZndnW3UTZOLC8v8zYnzLBSKmJZNf0uEj24d4MmhPgzb4scXR1kuNgn4Qr5IslRmU2sL/83zz+C7iYP3/YBpWKSWiwRDHgY3J8imioxeWmB8eBGv30XPQIx4IoTboyKt0/imozvK575++KavVys6P/r2SSIxP0Pb2onG/CjXkGdBaFYCLcummK9y9vgkF05OgwCqptA71HqDDBlWep0Umac+vrNJoE5OMzm6zN/8yVEM3eLQU5twu9VVUuzQ7G83TYtyqc7wuTnymTI79veyddftXcPdHpUjT2/m9ZcuUC03mJ9OozcMJkeXEQQBj09j5/5efIHbT/ZIkkjPQJxnnt/JX/7hz2jUDX707ZPUawYf+eQu+je1IisygtjccMdxsCyHek1ncS7H9Ngygijw6a8eWiWZDwKiJNA7GOfC6Rkq5Qazk2kunpllcFs7qiqv5uhZpk0uU+bNVy/yg28eQ5ZFdGt91YCr98MrfgHNH9O0Vqu+V9Zh6GbzGhKb19GV6/VO7nsuVSHocePVVBzHaeYfetz4XCryAzy2Dyv8qspYPsN/vHCcx9t7ccnyShxPli8P7WB3LAE0e26/vnknx5fn+bNLpxnNZegLhknVqry7NEfF0PnP9z1CzNN8lu+OJZgtFfjjCyf4N8de47nuQdyywrn0MqdSi/QFwnyqf/NNtyugahxu6+Kf7DnCvz3xBt+fHMarqGyLxpHX4Y7sOA6T+Rwhl4tWr2/VKEwUBFyywmAkyln97iOqJnM5GoZJSHPRsEyWyleJWFBT0S2TZKWMaVmo91BxcRyomnUsx6bH28ZnO56i39vJj5ff5mJxEt02MB0Ly7ZxrqE8wooZoyxIK9W/BI+37OHJ2F4i2r1LHLcGeomqATo8MV5LnWSxlsawTUzHwnZsnGtE0AJX5OnN7VEFhV5fG0/G9vJEy178ylW5drcnQa+3bbUSrEkqXsl9SzOqW0ETFUKKj4p6tQ1PFiRCiu8D4SocULx8ofMZuj0Jfrz8NqOlWUpmtXnOnasmmNCcKBYQVozRmudfEiQkQcQtaUjr7Xt1HEynzpnsnzFfPYFhV5AEFU3045XjRFyDeOU4quhBQMTCQLfKlIwlso0xalYW3SpTszKMl17BweFgy++iiHfeSijLItGAh3yltvrsaBgmtUazh196r+mkoODVHsGlbKdSf4tk4f9LIvTfoyq9173P534an/tpjNTfQxCujsclKYRL3b5SkR1DEiOAiG5OYdslPNojOE6NdPH3CXo+S9D7JQTBTbn+GsXqd3Cpe3GrO1kzjnQNxAJePrZrCFWW+A8/eot0qYJlO8yk8/zlG6d56cwoBwY6OTzUxVAiStTnRVspHmy0AZ5LkXGpd956s6H15P3P7kTVFDS3ylsvnMRomOg1nXdePM2xH58l2hZm5+ObefzzB9n3zA7UdfQKVYs19EbzZr8wvsQf/w9/w5/+q2/d9nOOA5ZxdcbOtmyq5dq6ZvHdXg3lHs2P7icc26aULa9GWZw7OszFd8ZuaTyy+lkHDP2qYYihm6uV3/diYFc3n/+HH0N1qbzwR6+g1wyMhsmFt0e5dGwcf8jL4J5envjiIR779D68QQ/CBg3MCukS//rX/w/+yb/9Nfp3daO5VfSGwfk3R3jpz1/nv/+Lf3rP65ivFHhpdpSz2UV8sorh2MyUcgznU5T05ixkty/Es12b2BtrX33oVBoGn9y5mWK9wXdPX+S3nzjIjvYElxfTbGuL887kHMvFEv+v5x6nIxSk0mjwL/7mRdpCAdyKTMO0GGiJ8BuP7uMf/Onf8i8//iTbO1qZzuQZS2VXHgzQ4vPy7772KbKVKv/hp2/zyuVxelvCpMtV/vdX3uQfPX2YrW3NEPu/OnaWly6O0RMN0x5qVjPfGJ3iC3u38w+eOIhXUzEt64GSWoDZqTT//Nf/CMu2sCwHe4Vs2LZDqVDjwqlZLgqzcAc3xF0Hem9JbHXd5NK5WS6fm8NxHNo6I3T3x2ltC+EPuJBViVpVZ3k+x/D5eRZmszTqBi63wtbdXWza3rEmsb2CvYf7SS0VKJfqjF1aZHE2w//9737IN//0KAObE4Rb/E1i1TAp5Cssz+dYmM1imjZDW9vp7F2fi7KqyWzb3U20NUAhV2FxLkdqqcjiXBaXW2HXgT5CYe+a1eW1EE+EePLjO5mbzvDTHzbjwX7y4hmOvT5CW1eYrr4YXr8L27KplOukl5vryueq+P0uDj+5mQddBpVliUef2cYbr1wkmyqxtJDjB986zsxkin2H+wmGvVQrOtPjSc4cn2RyZBmvT+Pwk5s5+srFW4bLXwvTsGjUDaoVnVq1QbWqMzuZplJu3hv1hsnSQpaRiwu4PSoej4rL0zQjU1X5jvKAd3UmWCqUOTO3yI72Vmq6wU9GJtiaiBP3P3zS4AeNnS0JegJhKobOH184SbJaJu7x8bVNO/l0/xbafU0SpIgSA6Eo/8uTn+DPLp3mldkJ0iMV/KrK7lgb/+Ou53ikrQt1xYQqqLr4VN9m4h4v/+niaX7/9NsYtkW7N8BzPYN8qm8zWyK3njD1ygqf6d/CfLnIt0bP45EVgpqLnkDotvvlAPlaHU2Sbsi+FQCfqt7S+f66Za3Rh1wxdOaKBf7w5An++NSp616zHBvLttnf3kH9HoktOFStxur6vZKLg9Ft7AgNMFdd5ljuEqOlaeZrKXJ6Gd3SkQQRr+ym1R1hwNvFvvBmNvm7CSn+NXqa7x4xLcxn25/kidgeLhUnOZsfZ7w8R0YvUDar6LaBiIhb1ggqPhKuKP3eTnaHBunztuNXPIhcvz1D/i5+z/c17FXZ7RWCdnfbfTCynX3hLe/xAGhOlCl3K499wBCAfZEt7AoNMlGe50JxgpHyLLPVZfJ6iapVx3ZsVFHBI7nwyC4CipeEFqXT00qXJ06ft52oFlrX+ixHZ6r8Oqn6ZQy7giy46PAcZFv4i7S6diII4g2TAs2ja2M7FnOVdzif+2vSjWHqVo7l2jnmK8fo9d+ZQSeAKst0RINMLmexrauTojXdoFitE/ZtvO+OKAQIuJ+n0jiKKvdj2Vlsu4imbEYUfejmNA1zgmTxf0MsNUmxg40stWCaczjq9nU/opouxSLdLSE+unOQF05eplBtKjFsxyFZLPPD08O8dHYEQRDwuzSifg9Bjwu3qqDdRJJ9N3hu1xCf2HPzycabYUO/RZIksuXgALHOCI999gBvfPc4J14+R61cxzQs0gtZ3vz+Sc6/OULnUIKPfO1RjnxyL57AzcOMGzUdy2jeUJpk1cIy7ryJ/4occD0QJXFdJPH9guNAvdK4Wk2wHUz97jLXnBUJxVoQxWYm7pf/6Sc4/PxuXv/2MY7/+CzphRyWYVHIlDh/dJjJC7O8+Eev8sQXD/PYZ/aT6I3dcySPbTkUs2V8Ye/qJIPmVtHcCsXs7Xsq1oOqaTBaSPGT+TFEhGZklG3RsJoSkw5vkM/0bONrA7uvm52N+jy0+L2r5HMgFkEW81xYWCJdrjKTzfPT4QnOzi2hSE3zjIV8iVSpTMTrwe9SSQR8+F0aAZeL/pYoddNkNpOnYTYdmL2qyhNDvfg0FY+q0BMJU2k0GE9lqOoGlxaT/LuXj+JemfXP1+p0hoMkS+VVYtsfi7A50ULM70VcqWw96J5x23ao129ehbjiqM0dSJmsdVTimhX+psx4diLF0lzTZO1Kj729Uklt1A1MwyIQdLP3yAC/9DtP4Q/e+sEkSiKPP7sNf9DNy987zfE3RzF0i3SySKlQbRLNleNtWzaGYWGu3LNkRbyjvmJJlth3ZIBMskkyBaFJwiItfh55eguaS1n3ORVEgXgiyK/87tPE20K88v3TpJYKlIo1aiM6c1NpRFHEoXlPsAwLw7SwLQfb66C5FO6IwW0ARFGgoyfKp79ykO9/4xiTo8vks2WOvzHK+ZPTSJKIbTsYukm9ZtDWEeajn9nNvkcGuXh6hlzm9veKb/2nN3n1+2dWDBAdHLu5//WaTqXcfKDnMmV++uI5jr0+2jQtEptVW1EU2L6nh6//9pO0tofWtU/n55f58YVRdNPCpcjYjkOloSOKAn935tIqudnVmeB/+sLH7vrYfRDx97btwbKd1fvmF4e2Ydo2sigSVF34VW1V9iYITeFhfzDC7+19hN/asX+1d9YtK4Q013WRPIIgEFA1Hm3vYVskTs00cXBQRAm/ql63bGj21v7bpz6JKkmr8mRBEJAFgV/esotP9m3Cp6iEbhN5dgUCENA0ktUKhn39PcwBynoDy37vvU1Y886oWxbl91R33bJCm9/P5pYWHu/pWXMb+kLh69yV7wYOYDvW1aqcICAh4JFc9Ps6SLha0FsPY9jmirFX831XqqOaqOCSNDRJWX/Fbp1onh+JiBrgQHgbOwIDNGwDy7FXq7ZXtkUURBRBQl3ZHlWU16zCXnnvvaJYq/O945d48fQwlcb15y7sdfPVR3bx1LZ+3OuoUJ2bWeKFk5d4Z2z2htf293fwmf3b2N1zd5m168GVcy6KCv2+Tjo8cZ6y962e8+akh7NaGRdWJgIER2RkPsMf/egEydwJPn9gB185spOA59aqI9NpMFM5im6XERAZ8D/L1tDnCKq9yOKVSeg1+ltxEFHo8B7ELYU5m/sLZitvUzUzLNRO3RWx9WgK27rivHV5GvOasUimVGFkPsXhzevvaV0fBETRj9/zaZZy/zV+18dpGOdxMHBrh2jud3Ns1xr8r3Gpu7hyLARBRhIjCOus1v7w9DDfPX6R5UKZakOnXNcp1W4sfNnX8KlcpUaxVkcSxRVDP9ioccLm9rtTZm749JDLq5HoixOI+unc1MYzXznC5ePjXHxnlOmL85RyFWqlOvlkkfRCjsnzs3ziN56mfaB1TTIpydIqSfKFvfRu6yCauHN78FhXFF/Qva7c+qb/1MNLbAWax+XKJsY6I3QOtd2VI/HWw4O4b9Hvp2gykbYw/oiPaFuYRz65j7EzU1x6d5Sx09NkFvPUqw0KqSLFXIXZ4QU+8vVH2fnY5nszmBJoVn+b+sDmH6787wYVjTRRwqdoaJJMSW9g2jaaJNPrj7A90spjiV4Ot3bfEIejyRKKJCIJAook4VYVZFHEccC0LEzLYmtbnK8d2HndQKkt5GcqnadYq6MpMqIgIIsiblVeeehelUWKooBPa8rmFVFEkyXKDWgYFoZlI4kiv/Hofvyuq86cPpdGV/iqvCngcuFR1asyufehDyIQ8vD8FzbWxfp2FU+f38VXfuNxBjYlmBxLklrKU8hVqFYaGIaFYzfz13wBFx3dEXoG4mzZ2cWWXZ109rSsq9/XF3Cz60Af0XiA/Y8OcuHUDDOTSTLJEpVyA9uym87pXo1Ep49Ee4jeoVa27e5icOv6ckOb2+Cw/5FBjr0xQnp5JYZCEglHfew+2HvHngWyItHaHuJjn9vL0LZ2Lp2ZaVad57IUclUqlQaCAJqmEI75ibUG6eyJMri1ne17uhEfsFRWEARUTebI01vwBz2cfGuc4fNzJBfzFLIVRFnE69VIdITZtL2DXQd62bKzE82l0N0fo1y6fc9fNlViejx5ywkTy7IpFWqUCjfGVUXjAYw7mFj8yJYBhuJRzPeQGFEQrqvCtfge3naY+4X33mtvF0UmCFdywdf37JNEEZ+o4lNur1xxyTLdN6nERlweIncQewbNbe0OhRjNZkhVytQMA/dKj23dNJnM5a5zRVYlCbcsM9NooFvWqjmWblmkq1XmS0Xi3qvHpzsUIrC8RNDl4tHubtxrEFhNktclm74d1hJuiIKAKiiodyEd3GhIgoRHlvDIDzYy5VYQEHCpCi5VodLQqTZ0CtUGlYZOi99DoVpft5O7LIm4VAVVkqjqBtWGTrZcw7Rt2sMBavqDifESBAFNUtAkBbj9/cq0bBSnRCpbYyqVJ7Mic70dbMci15jEsnVCajcJ9y5Cah+yeGuvCoGmmawqeGlxbabVvYucPkXNzFLQZ9a7m9fB61LZN9jBf/rJSRrXqEKT+TLnphbvA7EFAQVV7kWWWqkb56jr51HlHlzKFgRkZLEFWUpgWMu4BReq3IVtV7GcIqLgYb1EcyqV48TEHHXdXPcw+wrJNdbZ+nMn0M27cyK/L7oHSRLxh734Qh56t3XSs72THY9uZvryPKMnJxk+McHydJqpC3OU81XCrSGe+MIB4l03DlhdHm118BaM+Nj/kZ3sfvrOTTRcHpXgAzY+uV8QRAFvwL1KHBM9MR79zH4Gdq89S3srBKM+vKFbP6BFUUBzq3RvbqdjoJXe7R1sPTzIzOUFxs9Oc/GdMeZGFlkYb7pXa26VQMRH347b9xHeDLIi0bulkxOvXMDQTYItflJzWUZPTZLou/f+WmgOmj7RvZmhYAs1y8CybRRRWjUo6fWHiWieG66ZK/0iq7+v/L8DeDSVkMeNblq0+Lxsa29KhTPlKh5VYalQXpHdCtctb5V0rtxN6obJpcUkuzrbqBkGqXIFSRRpDfgo1OqEPW5USWJPVxt+l0a5oWNaNn6Xir4S0ySu9AG+nwiGPHz6q4du/8Y7wK1kwtCU8G7f20M0FiCTLFLIV6lW6ugNE8tciQCRBVxuFX/QTaw1SGt76I6jb/xBN0Pb2mnvjjCwpY10ski5UKPRaLr4SqKI6lLw+jVCYS+xRJBIi3+l8rl+9A618qVfe4xMstkzJ0oC4aiPSOzu7meiKJLoCBOJ+enqa2H3oTz5THmV+AMoiozHq+IPeojE/MRaA+s6Ps98chdbd3ehN0y6+2O3rX6vF9FYgH2PDNLeFWHfIwMU81X0hokoiWiaTDDsJdEZprU9hNfnotEw+NwvH+HIU1uItwVJdN58MvTIU5uJxgN37aJ8p9fOlrYYm1qjt33fw2bI8SHuDZIg8ER3N0dnpvnZ1DQeRWUwGqFmGJxaXCRbq103sRF0uegOhXhrdobvXb7MnrY2FFFkMpfjjenp1fv8Fexvb+dCMslkLs+rExPsak3gU1Uapkm6WsVybLqDIQYiDyYH/kNcD5cis7+vg1jAS7Wh0zBMXr88xY/OjNzxstpCfp7dOciOzlYapkXDMPm/XnmH+Wzx9h/+QMKmbuWxsfAr7fiU1tuS2vdCFl0ElA68coyysUTDurtjpckSPfEImzpiXJhZor4yqZkuVjk1scB0MkdXS+iOVJ+mlaRSfwPTzqCbM1Tqr2NZGdzqLlzqbmQpiogbr+txqvW3cDBR5V4ksdmWIYkBQt6voxtj5Mp/hii4AAdZihP0fvkaU61bo2GY1O5S/fkw4b4K+q/kynYOJugcTLDj0U1MHRnixMvneOuFU0ycmyE9n+XtH5ykd1vHmsQ2HA/gWnEyFiURf8TH9iND93OzH3qIokisM4KsNE+frMrEOiMP5LhIskSsM0qsM8rWQ4MsTaXo33mR17/9LqOnpiikS5x5/RJdm9vvidi6PBpPffkw598cIZfM4/a6KOUqmKbF4U/s2ZB9CaguDsa7OBi/++18L9yKwp6uNo5NzvHy5THGUpkVF2ODR/p71jUjq0jNivCFhSSLhRKFWp2abrCzI0Fb0I9XU/jIln6OTc2RrlRxKzK6ZdEW9HOod+P2ZSOguRQGt9w/SdRaaJo8SXT1tdDVt75+1ruFJIkEgh4Cu+4tG/JmEAQBn9/FM8/v2vBlq6pMZ08LnT0bd4wOPbG+rPC7gc/vYnBr+7oq3pqmcOSpLeta7q4Dfey6D1nEN4MqSSC9vzFXH+LBQxQE9rS18UxvHycWFvjO5Ut0+APIokCx0WBzSwsT2asGkC0eD4c6Oji1uMCLoyNcTqdwyTI1w6RumgyEwzSuIbeDkQgf6e/nlYlxfjY1xXAqjSpLWLZNzTRp8/nxqRoD78fOfwgUWaI3HqY3fnWSrVCr8+Ozd05sQ143Ia+bHV2J1b99851zP8fEFsSVBGtF9CAJdxe7KIsuZMFFs6f57pQLoigS8Gg8v38zS7kiC5kiDs0e29H5NC8ev8xXn9hN0Ou6rme+WXXtIuD5DKJ4faqDg43tNLCdGj7XUwiCCwQBG52rsUIiXu0JbLuMJIaukxyDTMD9aariGzSMCWyngiCoiIIb2Lge9g8KHminujfoYfsjm0j0xfGFvfxf/9Vf4tgO4+dmSM1n1+wBbB9oJRhtXgSVYpXZkQVsy15xpfzFnNGWFGnFUKlZ+cks5kjOZrBt54H2BmtulZ6tHbQPtBJNhPiP/8M3mB9bZmkqxfTl+Xvq6XR5NB7/3AFMw+TysXHmx5OEYwF2PLaJQx/fvcF7sn7EfF5kUcTv0jAsi02tLaiyRNDjYiAWwSVK7O9qR1Nkjo5O89rIJJbtEPd72NvVTtDjoiMUIOrz4JJl9nS341EVBAG6IyEiXjd+l8bjg70EPS6OTTdlIfu623lkoBuvpqLKEr9yZC/fP3uZs3NL1AwDn6airfRuSoJAW7D5nfHdIjD8QaBe05mZTOFyq7g9Ku6VPml5Aw0GPsQHD7bjYNgWNdOgZhnolknDtjAsC2PF6MZybKwrLsUr/VqC0MyabPbyNF1NFVFEFSUUUUIVJVRJxiXJeGSl2df1c3idWZZNvaZTrTQwTRvHtu/K16ulNYD2EBsl6laD2doUXZ4+JEEkWV/CwSGitiAgslSfJ+FqR7cbFI0CDbuGAyiCQkiN4pf91KwaRSOPW/IQVJukwnIsMo0kgiASkINo0v2XrAqCgF/T+OqOHcS8Xk4tLbJULtHq9fFMXx8uWeH16SminuYkWUDT2N/ezm/u3cfPpiZJVatoksS2eJz+cJhsrc5wOr16fWuyzNO9vSR8viaxTacp6Q08ikKrz8dAJEK7f2Pztj/Eh3gQEJDwKa007BKm3cBy7k5q3fxsA1nU8Ep3P6GryBIf3TPE8bE5SrUGxRUD1kypwt+9c5G2SIDDm7uJBjwoK5OYoujGpW7Dpd6oOFWkBGHfL91ynYIgoCn9aMo/XvM1WQoT8HzmrvcJmhMmXdHg7d/4gBC8Te/1zfC+WLCF4wEOPreLP/rvvoGpm03n49raF2r31g5aOiJIskgxU2b4+AT5VJFQLLBhDrwfNMiKxOYD/fjDPjILOZam00yen6Wcr9xVn+1GbM/h5/fwrd//IQsTSRpVnVqp1pTV3uUpElbkzx/52qM89cXDyKrU7Ct+n7MpHxu8Vu4d5VBfs0Ia9rjZnoizNJ/DqVs8NtDDYwNrS8N3dlydZf3X15jDDMWv3mj393QA8Pk9a90EJTpCAX73yZtLfJ/dOriu/VkPrlSZ74YgpJaL/P7/9H06eqJ09bbQ2ROlrTNCKOJFViRkRUKRm/9eifv5eSQiv8hwnGboR8My0S0L3baomjrZepWZcp7ZSoFUrUSqXiHbqFLU65SNBhVTp2E3e9YtmrE48gp5VUQJbYXEhjU3Ec1DRHMT1jzE3D46PUF6/GH8atNASBUlNElGFaV1O88+jLBth0ZdJ5MqMTORYmosSalYQ2+YN7jkrgdf/nuP0tF9e1n0+4W8keXPp/9P/rOBf4kmaby8/H1Mx+CZ+PO4JDffnPsTfqXnd0k3ljmbP8FyfQEHG4/sY3tgD0eiT5FqLHM0/So93n6ORJ9CRKRh1Xkl+QPCSoR94SPEpQenKukOhfj1vXv5dfbe8NqzA9fXU4MuF58YGuITQ+tTY2myzO5Egt2JxO3f/CE+xAcEkqDQ6tpJQZ+jYiapW3kcx76jqqvtmFTMZapmBk0MEHXdvcJRFATCPjdfeXwX5VqDY6NzNAwTy3ZYzBX53777On//Y4d4dGsPrSE/LkVed3rB+4m9ve24lIfHmXt7Z+tdfe592QNnxcXySvSH5lKR1bWlWbGOCEN7ern87hgLE0kWJ5P83f/5Ml/555/CG9h4W+0PAkRJpGMgweYD/WQWcxTSJYaPjfPGt4/xyb//zPuyTYZhYlk2juOgqDKqS90QY7TZ4QUWJ5N0b26nfTCBpVs4joN2mz7L9wOlYp3jR8foGYwTiv48xXY4OJgI3Pkxb9QNRi4uMHJxYaXft9meEI546e6P0TsYp3cwTs9AnI6uKB7f3UmMPsTDCxuHgl7nWHKG05kFzmQWGSukyDZqK/NU1/731rAsk4Z1fQ/QdDkHXH+7udLFnvAE6AtE2BFOsK+lg13RNuLuD27Vql5r8OZPLvO3f/42E8NL3OtM30ef3/VQE1tZVGh1tZPT0xiOgVvyULMqpBpLtLu60O0GPtmHJmp0untwSR5sx2S4dIHvzv8l+8OP0OXpxSW5yOkZCnqWsBqlYde5XDzLL3X/NiHlw57TD/EhHmZIosZA4FmmK0fJNSbINSZo8+zFJa2/ulgx06TqlykaC8Td2+n2PXrP27VvoIOG0XRXP3pxGmgaq+XKNf7tt1/jZ+e7eP7AFh7d0kNr+OF/7uzsTrCj++GZFLtbCrFhxPZnf/sulUKVrs1t9G7rxBda20zDMi2mL83zt//+R6vEtntLO5FEaM1KjSAIHHhuJ3NjSyRnX6OUq/DiH/8Eva7z7K88TudgG+oaZix63WBhYpnL747jOA77n9tJvPPhfYDfCa4cp4//2pMsTCxz7o1hZkcW+e7/+RLVUpWP/vLjBCK+G2JFHNtBrxtMXpjl/JvDtA+0su3wEKHYjeHoZ9+4zNzIItG2EJv3DxCKrx2gbts2lXyVb/7/XiQ5kwYHYp1ROgZb76nyVqvUefsHp3n5L4+STxb40u89Tyge4MJbo5x94zK/86+bso1Xf3CGqdFl6jWDlniAvUf6qZQbzE9nOPzkZtxelZNvjTM7maK7P8b5k9MoqkypUOXg45uItQWZHksyMbyELIvksxU+9vl9DGxpY+TCPKffmaBe05Fliee/fIB4W5Aff+cU2VQJ1SVjmTY9g6109bbwkxfPcunsLBfPTDM9tszW3d0Mbbt5P6Dt2BT1Bv/wjW+RrG1MhBE08xr/4PEv0eL23nXWXs0YJ1t7lWL9HRrmIo7TQBK9uOReIp6PE3AdQl2HlEdzKQxsTrA4n6NW1VcntTKpEoV8lcvn51AUGUWVcHtUovEAHd1ROrujdHRH6eiJ0tIaQFHWJ12eKGb44+FjvLU8tWHF/T5/hC/17eITXUNgXgbnAZsrKFuuC2w3bYuRQorfO/qde1rsvpZOfn3TAXZENv5B1rBMJktZ3l6e5t3UDMP5JFXToL5CTHXbwt5g+YVz3f83f1uqlcg0KpzLLvLtqXMEVBc9vjB7ou080dbPQCCKR374JsnWgmXZ/OTFc3z/b44xO5let4PqeyGKAv6gh66+Frz+h8c1di0ogkK7q5uMniJvZIlpCQpGjvnqDD45QFxrR0SiYKS4UDhJupHEwaFslqhbNWzHQkBg0LeFmeokk5VRNMnNZGWUuNZGWI2iiPd+/qdKWf5q/DQvzd15v+Sd4NHWXr7Yt5O9LR33dT0f4kPcL1xxKr4TiMiE1X62hj7P5fx3GSu9hI3JUOB5guqtfEWarSyp+iUu5P+W+epxwmofA76PElJ672U3gOZYfG9/Bx5NpSMa5AfHhymvxOPYDpyZXGR8McNf/ew0/YkIWzrjdEaDtEcDzdxXTUFTZVRp41uzBJr9wHe6Pz8PerkNI7YL40sce+kcRt0gkgjR0hmhpSNMIOJH1WRsy6ZcqJKcTjN5cY6pC3PNDVAlDn9yD73bOm+67ERvjMc/e4BCusSbf3ecfLrET7/5DsPHJ2jrb/Z3Ng2mBBo1nWK6RC5ZoJgpUUiXGNrXx47H7jzk915h2w5Gw6BR1WnUGjRqBnpdp1HTmTx/ff7Y7MgiF98eRXWraC4V1d2sejazW9U1sy97t3fy3C8/jtEwufTuGPNjS3z/D3/CiVcv0N4XJxgLoLoUbMumVq5TSJfILRco5soUUiWe+9XHGdq7tmlKZiHH2y+cIr2QJRQPEuuKEOuIEoz6cHmag+xapUFmMcf0xXnGzk5TypaRZJGthwbY/dSdO1dfi3q5wct/8QZPf+UIP/nGW+h1HZfXhSiJTJybWR3UjV1aJBz1cfjJLvxBN16fi3SyRKlQxbKaGZyVUp1ctkxLa4Byuc4nv3iAXKbM0nyOXKaMaVoIosBTn9jJ7FSai6dnKOSrVEp1WlqDDG5JkEmX+PF3T/HZrx9mcS6L5lLYtb8Pt0/D69PweDU2b++gVmnQMxBn14FeAjeZ3LkCB7Acm5lyjsVq6Z6O17UIq25Mx75rKXi5cYbF0p9R0S8iCi40uQNRULCdBlVjlFpxioY5Q9TzPC7l1k7csdYA//i//BS1qk4+WyGdLJJeLpBaLpJaLpBeLpLPlnGc5k11aT7P5MgSbo+G26vh8aj4Am5irQHibSFa20N09EQZ2LQ2GdNti+VaiYlSds3X7waKKFE06uDUsUv/K46zcefq9hCQQv8epKuSHIerxPFeIAoCn2nc2/f0vUjWyrybnOHd5AwjhRTJWplMo0rJuDEL70HAcuxmP6plUgBS9QqLlSKX80lemR9jKNjC3pYO9rV00ucPI4sPr6nT1Ngy505OMzuVxjAsXG6V7Xu6iLeF8PpdnDk2yejFBVrbQxx5ajOiIFCv6SSXikyNL5NNlQmGPew9PMDHP7e3GUXVEXq/d+uWkEWFdncXS/U5luoL7AkdQgAmK2Pk9Sztri4Mx+DN9KuE1SiHok+gCApztWlmq5Ory+n1DrJYm2OhPkvC3clI6SJbA7vxSr57HlBezif59uQ5/m76Isu1+3dveKqtn8cSvfT67zzu8BcVjuNQqNY5PbXI8EKKxXyRYq2Badm4VRmfS6UtHKA/HmVrR4y28I0T+A3DZCad4+J8islklmShTLmuIwAuVSEW8DCYaGFXd4L+dbidP4wwbZvxpQxnphYZW06TKdUwLAuvptIZDbK3t42tHXHCvlubJBqmxVK+xOuXJxlbypCv1hEFgVjAy/bOVo5s6kaWxLuK0xQEiTb3Hgr6LPPVY4wWf0SqfomA2olfbkeVfMhCkwfYjonp1KiZOUrGAkVjgYIxiyyohLU+JFFlptJ0F3ZuM8Ha4TmASwpd9zdnxfshXaywmCszuZylVNOR3+Nx0zBMGoZJoVJnMVvk3OQSbk3BrTWjmiSp6RMhihtPKPcNdvBbz21sIsUHBRtGbA3dJDWbYXkmjaxIeAJufCEvbq+GJEs4ttMknbkypWwF27bxhTw8+cVDHPr4HiJtoZsuW3WpDO3r43nbxu3VePvF02QX82QWc0ycm8UTcKNozV0xdYtauU6tXMdaISwdQwkk+cHr28u5Ci/9+evMjSxRrzUwDQvLMDF0i1L2+grd2z84xcTZGSRFQlbl1f7DcDzIR7726JoOw26vi30f3dk0pQh5OffmMIuTSZamUoyGp3D7NGRZbhq11I3V4+I4TZMpvWHc1GzKMi1yyQLjZ2cQRAGP34Uv5MPtuxq/ZDRMKsUquWQRUzdx+1zsfWY7T3zhEF1D99azZJkWi5Mpthzo5+Qr5wGaEmdNQW9crZgNbmkjs5JD2d4VJbgGmbxy25IkkVDYS+9QK21dEabGlikVa8QSQaIxP/2bEwQjXv7q5DSNhkmkxUdHd5Sh7R2El4t8/xvvUq/txbJsQlEf/ZsT10W3xNuChKN+2roidGyg0+yDRqb6InVjkoDrIEHtMLIURkDGdnR0a4lM9QUK9XdQ5fbbEluXW2Xb7ua1W68ZVEp1SsUapWKNcrGZC1os1Mhny+QyZfLZKoVchWK+ysJMhlpVR5JEvH4XgaAbf8jDjj3dNyW29xcWjnEenNwDXKcAzv0hhcvVEkWjjuXYd13ZB9Ati2StxFvL05zOzHM5n2KqnCW/KjV+eGA7DmVTp2zqzFUKjJcynM8t8fbyNLsibRyKd7M13IrA3fWU30+MXlpsklrdIpYI8swndrLvyACBkAdNkynkKowPL9ISD/DcZ/agqjKmYVEs1pibSnPirXHGLi2Qy5RxHJu+Ta2od5iD/KChCAod7m4uFc9QtapoooakSNiOxWJtjgHfFkzbYLm+QKenhz7vEDWrymRl7Lrl+OQAUS3GXG2G0dJFlutzPNnyLJp0b+1MF7JL/N30BV6cvXzfSK0iSjyW6OWXBvZyINZFWPvFbMG6U1QbBudmlnj1whgjC2mW8iUK1To1oxnHpsgSmiwR9LrZ0dmKS5FvILaGZfGDU5c5OjzNXKZAqlShVGus5peqsoTPpRIP+jgz3cpzO4c4sqn7AxXVVazW+enFCd4cmWZ0MU2yWKFS17FsG02RCHvdnJqa59BAF09t62dz+9pxi6Vagwuzy3zn2AWGF1MkC2WquoGIgM+tcXZ6kXOzS+ztbcey7yzz1LArnMr+CXWrQF6fpmGV0O0SVTNDujGCSwwiiy5EoXk/cxwbyzEw7Ap1q4DpNAAHR/SSbYxTMzNYjoGzqhu6+ZMqrPbeQGzrhskf/uhdlvMlCpU6+UqdTLFy0/xg07YpVq+aTD0IeN5n49D3Exv2VNtycIBcssjk+VnyyQLlQpX0QhbLsLAsG1EUUVQZt99F16YE8e4W+nd28+QXD9E51IZ6G2dGf9jL9kc3EYj4iXVFGT8zTXI2Qz5ZoJQtY+gmOA6SIuHyaMS7ooTjAWKdUXY/tfWm0uj7iVq5zjsvnubiO6MYjVvLF6cuXK1iX4t4V5Qdj26+aXROtC3EoU/sIRgP0DHUysS5WdLz2ZXqbBFrJeBYUWXcPheRRJBIIkRrdwtbDw7i9q0tReva1Mb+Z3fi9rnILuUp5SsU0kUyC80sUASQFRmXRyXeFSXWEaFvRxeHP7GHTfv7cHnvrVdSEAU8ARf1qr6aPZpLFkjOZghGr86y7zncz9ilRabHl5kYWUKSRWRZxDRtbNuhXtNpNJo3G4f3BsuvGBU57/n7NbGya0GSRFwu5YY8UkEUse3mej/IKDZOosmdtHg+RcB1gCtl3+bMpoPjGKQq36FuTN5yOVdw5Vy5PU1n5JbWq4MHx3EwDIt8tkI+U27+m6uSz5RYXiywNJ8jtdys8s7PZHGmMyjK+1VVE0HuBPtWvTIiCEqTjNpZcK6ZwBLcIHgBFYSVfXAMcGor71uJ7hC8ILUjSO0gBEC4P1FC5RUDp6pp4Ffu/PtqOTbJWplz2UWOp+Y4ujTJeDGDbt9dqPr7gaJe56JeZ7SQ5nRmgeFCikPxbnZH2+nwBnFJDw/xm5/OkEuXcXtUNm1r55NfOkCi42oLTyDoQZJENJdCoj18XYbwlh0dtLaFePHbcOb4FN/7xnHibSES7WHk9+37dHtIgkxIjVAyS7glN5rkwiPIaJKLpfo8T8SeRZM0Yq4E6UaSk7m3sR2bnJHBdQ1pFQWRdnc3OT3LcOkCfjlIVIsjC3d3fh3gcm6Z705f4Eezw8xVChu0x9fDJ6scjHXx94YOcCDWie8uvqe/iCjXG5yfXeYv3zjNW6Mz4Di0R4Ls6W0n4NGQRBHdMCnVGywXyoiCgHSTSf7JZI6RxTSyKLK5rYWwz4NHVbAdm0K1wfhyhvHlLHOZAuV6gy0dMYIe10NvUuc4DqZt85ML43zznfOML2UIeV3s6Wkj4nOjSBLlus5EMsuF2WUWskVqukHA7aLtPf2iumkxtpThb94+y8vnx/BqKls74rSHA7gUmbphkiyUeO3CBNlylUKtfkfbajg1LuW/i+nUrt8HLBpWcd2ZtLpdQdcnuJOpad2u3fg3w+Jv3zxHrnzjax/i/cc9PbUNy6JUb85A9B0ZRG3x0XNhlvnxZTKLOUq5CnpNxzQtgl43fr+HYMxPe1+cgd09bNrfj6LK654Zd3tdDO3rpX9nFyMnJxk/O838+DL5VJFGVce2bVRNwR/x0dIepmMwQf+OLlp7Ymv24V6By6MxsLubWqWObdn0bOvEvQG9R6pbYcvBATx+N6Zxd315oVhgzR7YaxGI+jjw0Z1s3t/P6MlJxs/NsDSVopyroDcMBARcXpVA1E+sI0zX5nb6d3UTjgdXs3Dfi6F9fUQSYbYdGWJ2eIHUXJZitkSjqmMYZtN63KXiC3uJd0UZ2NnNpv39+MNexA1wf1NdCrse38LIiQkyCzmmLs5hGiZzo0tsP7IJx3GwTJtKqU64xUejbrA0nyObLtHeFaFSqjM7mUIQBAqZCgIClmmRXi4wObpMPlvG5VHRXDKNhsHyYp7x4SXmpzPEEyG6elsoFWvMz2ZwezUyqSKbtnfgct/8OvJ4NUzTZmkux9x0mlDEi8//wZtdt50GLqUbRWrhWi1zUywj4Fb6kEQ/Nndnub8WNE3GH3QjyRIer0Yg6MLrd+P2NmcdS8UatZqOY72PlTRBQXR9Fse51cNMQsDAMS7iGKcBC8Q2BDEOUhzESJO4XhlQOw2w82At49hJsBZAUBDkLYiuT4G8CcTQfdulZK1MvlG7Y2JbNhpMl3K8uTzFj+dGOJWZvytX3ocFhm0xXc4xVynwxtIkn+rexlPt/WwJxQmp7oeiApPPlqmUG4SjXga3tNHWeb0kVZJFJEnEtmwaDQM/V+89bo/Gzv29VCsNhs/Pc/zoKIefGMIf9BAM3Z+Jk42AIAgookqHp5uQEiakRpAEmR7PAKqoEVZbkASJ3aGDjJUuM1kZJaxE6XT3IiAgi1fv11E1hlvykNXTfCT+CWRx/WOPK7hyhU8WM3xr8iw/mhtmvnJ/skODqov9LZ38xqaDHGntfqhl8g8TbNthLlPgu8cv8uqFcTyqwr6BTh7b3MOOrgTxoA9VlqjUdVLFCqNLadrDAfriN5qIKZLEgYFO/G4Nn0tlS0ezRzLscWNaTdntTy9O8uLpYS7OLXN8fI6xpQy7uhNI6sNNbE3bZjKZ5RtvnWV4IU1XS5CP7hjk6W39dLWEcKsK6VKFt0dmeOHkJc7MLPHyuTHawwG+fGQncPVZnC5VeHdshp9cGEcSRB7d1MPnDmxjR1crPrdGudbg8nyKH5y6zCsXxijX9TvaVhGJgNqB5dzZ5zYC8k0yc03rzqrOH+LB4Z6IbanW4Ojo9NU/COBsi9G2NUanKODQPPkuReZAXyetwXt3ihUEAVmV2XZkiG1H7t6u+1q0dET46r/4NF/9FxuyuFWE40F+6199dWMXehMIokAg4mP/szvZ/+zOe16eKIrEOiPEOiPvS26sx+/m8//wOf7i3/wd1XKNd390hkvvjLH7ya0887VHAGg0DN5+bZjkYh6AeFuIrbu6CYTcSLLIibfG8Afc2I6NP+TBcZq5qq+/dIFatcGhJzYTjvq4eGaabKrET35wllKhysc/v5/+zYlV86iZiRSKIvP8l/YTCHkIhr1ruvdGY358fo3xyws0Gga7D/Th2/zBI7ZuuR/HMbHsMo5jIaxUF5t5oia6lUISvaji2pKkm8FxHEzDwjAsDL0pydd1k1ymzNxUmtnJNLPTaeanMyQXCzQaBpIkoqgSiiITCntRNYV44v3JWRMEDcH7m7d8j+OYOPpR0E+CUwV5O6L7iwjaUwhi9CqhfS/sCrZxAqf2dzj6WzjmJA4GotQO3L8B7XKtTK5RpcsXuu17r/S1l4wGpzPz/M3EWX62OPG+9c/eD1iOzUK1yP99+W3OZRf4ysBuHm3tJaJ5UN5nYlGvGRi6idfvIrqGmZ+8EptlWja16o0DQLdHpbO3hW27uzn66kXeeX2E7Xt6HmpiCyAJEp9t/9p1f3ss9pHrft8Z3MfO4L6bLsPBwXIsLMfCJ/vZ7N+JeIffK4emWWJWr/GnI8d5cfYyqXrljpaxXvhklcPxbn5z8yEOx7vvyzp+XlEzDC4vpHj57CiiILCtM84/+tgRtnbEkaWr57zF76UnFubAwM39XQCe2NLHE1v6blBxyZJIbzzCZz0u6obB5fkkumlxaS7J5vYYLvXhzYcGqDUM/u74JabTeWRJ5OO7N/HVR3YRuaaPtj0c4IuHd2BYFulSlflckRdPD/OZA1vR5KvPsrGlDO+MzmI7Dq1BH7/z7GH6YmGUFW+YsM/DoaEu+lojXF5IMb6cxbDWr+yRRQ87w1/Bdh48mfTKH9y2sl9U3BOxTRYr/Nkbp5q/NJuSSBbKNAyTiM+DZdss5ovs7WmnKxrcEGL7IX6+cWXwLEoisY4o//R/+w1yqSKNWgOP300w6kdYkQx5fS6+8huPr7mc3/7nH7/u93SyyLkTU2zd3c3nfukI7pW4oNmpFIGghwOPDvL8lw5c95k9h/rZc6j/hmXfbJ2yIvGFX713C/n3GzHfF1ku/Xmzj1ZqXancNtEw58lUXkCV2/Br+3FueNBcn0N75Xw6TrMPf3Euy+ToMuMjS0wMLzE5tkwuXea9xT5BaEq+ozE/fZsS9G9qpX9Tgr6hVlrbQ/dpzzcAdhqn+tc4+psI6mFE3z9DUG8+4F6F6EXUnsSR+7ArEZzqf8Iu/wcEeTdIcYT7lMy2XCuRbdxeTnXlPFqOw7cnz/OfRk8wUcrcl216WPB2cobRYobP927n14YO0OkNvq99t6ZpYds2iiKv3r+uhabJq3215eLaUj9fwE3PYIw3XnGYGF6i9gB7vt4vNCfkHGaqkyQbi/R6Bgipdxbx4zQXRFav8W/P/JQXZi9RNu5P9UgSBJ7uGOS3Nh9iT/TmrvofYm0sZItcnF2mbpiossSvPbWfvnjkOlJ7J7jdVz7i89AeDuBzadiOTb5af+jVK47jUDdMXj03RqWus7+/g51dietI7bXY0ZVge1crE8ksC9kiE8tZBhMtqLKE4zgsZIsML6bxaiqPbuomEfKtktorkESRkMfFI5t6SBUrZMrVdW+vIroYCnzynvZ5o/H+a3hugwewgc3L/E6udeG236eNwD2NlvrjEX7/1z7b/EWA7526hOPA3p52OiIBDMtmfDnDW2MzKHd5U/kQv1iwLJv0fJYzr11i8uIs9XIDRVNo64uz7fAgvqAH+SE3PPmgo9I4Q8Oap1w6w3L5L1GkMKLgwrIrGFYa26mhmK1UjdEVefLVG1tP+L/Cq25d/T2bLvPdv3yH2ckUC7NZyuU6hm6uVm5Nw8JxQNVkEh0hunpjdPfH6O6L0dnbQijsQVkxU1MUCVmW3hcjuPXCabyKY02CGGsSWmX7nS1AbENQdzervvYyTv1bCJ5fA+H+VKmXayVyjdsPMBwcUvUKv3/+DX66ML6h8VQPM3KNKt+ePEeyVuYfbnuULaH4+7YtqiYjyxKGbq5ZkXW5VVxutemEvJhn664bK1GyLOL2aDgOFIs1TPPnX043XrnM2+nXSDaW6Pdt5kjLU3e1nMlSjj8efpfvz1yiat4fUisLIl/u38VX+3ez9X281j7IyJSqzGULiIJA2Otmc3vsvhvpaErTYblYq2Pa9l1HcT0oGJZNqlQhWaxg2jadkeBNSS1A0OMi4G625+mWxXQqR18sArJE3TDJV2uU6g1a/B42tceQb9JfLIoiffEwrg/4GE4UBdoiAfz1h3diMHobB+uNQrpUYblQJl+pU2noVOo6DcPErSp4XSoeTSHkcRMP+mjxPxivo3u6ulRZIn5NFTZXqdEVCbG9M45LUbAdh7DHxXdOXKDSePDa+A/xwUK90uDkTy6sxPsYxDsj+EIe9IbJxXdGOXd0mG2HB/nU338Gzx32rgZCHnYf7ENvmKja1cs+1hrE84h2Q8XwFxmGnUUWI4hC8xgLggIIiKIbVUgAIqKgIiDc1iq/VKjx8gunqVV06nUdAYFAyENXX4REe4hER5jW9hAtrQH8AXfTYGol5sfl0ZBl8aFzp70VHOMMWGmQe0DquS5/dj0QBBnEOILUjWNO4ehHEdxfBe4PsU3WyuT1W1dsjZXc3P94+RivL46T02tYvyBfGNtxyDVqvLE4Sd00+QdbD7Mr0ob6PphKBYIeXB6VSrlOJnljX2cg5CEQ8pBeLjI2vMhTH99xw3tM06JWaQ7G9Ib50A/ANwJtrk6ein8Cw9bxyQHCyp1JCx3H4UJumb+ZOMOP5oap3CdSq4oSvzSwl8/1bmdzKI72EBiX7QoOktCiVKyr9wgBAZekkXA9nNE2VV2nWG0gSyLxgBeXIt9zj3yyUGZ0Kc34UpblQoniiruybtmYlkWy0KxAKpJ4ZwWs9wmmZZEr11bdid8YnmJ0KX3TCQDdNFnMNV2/bdu5ripd0w1quoHjOEiiSMTnuWl+qiBAyOt+6I21bge3qvDffv2j/NnoCUYLKR5P9POR9o1pjbwbOI7DdDnHHw2/zbZQgo+0D7I5tvETY5ZtkylVOTO9yOhimqlUjnSpQrVhYFgWpm1j2Q62bSOJ4sqPgCJJeDWFqN9LV0uIoUSUPb3ttPi9yBvgyfNebOidUzct0uUK2XKN9rCCZdnMZAoUaw2sDxutP8RtMHZmmotvj+INeHjyizuIJEKomoKpm+TTRYaPTzB1cY7jL53jyS/eWT6XqspEWm50s71S5fgQVxH1fILQLQ2Sbg5Vuj7myTQtMsnmA1EQBVrbg/T0x+kZuPLTQjwRwuvTEKUPFoldE9YiOBUEwdt0NL4bCC4QfICBY04Bd2c8tx6UjQbZRo2KoeNVbvwe6LbF+ewi3xg/w08Wxm5Lgn8e4QA5vcZby1NYjs0/2vYoW8JxPPKDvW+0tAYJBD3ksmUWZrOYpoV0zXemJe4nGvMzMbLEpTOzLC/kaYkHrlM4lAo1xoeXAHC5lJvGvf08wSv78cq3cjK/ORzH4Wx2ke9Mneel+RHS96GnVgB8isaX+nbyhd6dDAZbcMsb2585Opfi7MQiLUEvj+/oQ1rnYDKsBgir74nAMS1+fHyEt7JneXxnH5u67sxr4cLUEmfHF9nU2cKeoY4NJzmW7WDYFqIgoCnKXWWmXkG53uDN4WneHZtlIpklW65S05sSZ1m6OnCvNJrmpdyHQfr9gO04GJa1ysGXC2WShfJt5atC0z8S07JXJ7Ut28FaSYIQhGbB62aLEQBNlh4KM757gSyJ7OxtI5h3gQCJhJ89/e9f24Dt2Mg5EXvJIRDT2NQTo9d/Z+0Wt0LDMJlIZjk9tcDFuSRTySypYoVspUrdMNdVGBIEAZciE/K6iPm9/OzSJFs74uzuaWMwEd1QVcWGEtttHXEuzif5xrvniPo8WJbNXLbAUKLlljKH98JxHCzHpGhkqFglalaZulVBt+tYjoXtmNiOjSAIiIKEJMjIgowqunBJXjTRg1vy4ZUDqKIL8R4yGu8Fzf0wKBpZSmaOqlmiblcx7AamY64Y84hIq9vuwSMFCCgR/HIYSbhz18YHgYZVo2zmKRrZlX2qYNg6tmOB0HSwk0UFRVDRJA9+OYRPDuOVA9e5VL4X42enKecrPPWlw+x5ehtw1XXPsR1ae1p44zvHefvF03dMbB3HxnB0ykaBilWgbBZoWFVMx8ByTCzHBAQUQUURVTyyn4AcJaBEcEu+h/I83C8EXBsX6i0rEi2tAQq5CpZlY5p20216JkO10mBxLksw7CUYchOMeAlFvITCXnx+F5L8wWtfcBydZmyPAdxlZccxgGbuXjMK6P6VACzHIdeokm1UbyC2lmNzIbvEtyfP8dLcyC8kqb0WZVPnZ4sThDUPf29o3wOvqnX1thBp8bEwl2VpIc/yQp72rquDl9aOMG2dYSzTZnoixff/5hgHHxukpTWIIEAuU+bk2xOMXFxAEAQSHeHbxuz9IsNxHM5nl5qkdm6EperG59SKCMTcXp7r3MzXB/fS4wvfl2tqIVPkrQvT9LdFeGRbz02JreM4NAyL+XSBntbQmn2plu1wYniWyzNJ+tsjd0xsZ5N5Xj87gSDAroH2DeeCsiiiSBK241Bt6LdVFd0KR4en+dt3znFhLonfpbKts5X2SICQ141bkVFlGVkSOT+7zA9PD99xPuv7BVEQ0WR5lYDu6m5jqC2Kz3V7hZFXU9jWEV9tL5QlcVV67DhNEnQzJYhDUwa9EUoRyzHQrTJVM03DLmE5Oo5j45Yj+JU2XNL7YzL584ZkocyJyXneHpnhzPQiM+n8HRl/XYHjOKvV/cVciQtzSc5OL3JxLsnhoS4ODnTSEdmYc7ahd9BHBnvQTZtjE7MMLyQRBYGQ182X9uygPXz76oVhN6iYRQpGmoKeYbkxS15PUTQylMwcNauC6eiYtoHlGIiChCwoSKKCKmp4JD8+OYhPChJQo4TVBAE5glf245ED+OQQsqDcd5Ji2gZVq0ReT5HXkyw3Zkk3FigYacpmnprZ3A/LMVf2QcUte/HLIUJKnJjWSauri5AaJ6zGcUkepLvM27sZymaBTGORinlV0iYAkqjQ6R7E854ZbsdxqFhF8nqSVGOBZH2WdGOevJ6ibOap2zVMW2/m2woqqqjhEt145RBRrY2o1kZc6ySqtRFQorgkzw0zqflUEUWV2Xpo8IZzJIgCHQMJ+nd2c/zlc+vez2vPRVZfJtWYJ6cvk9OXqZgFGnZ9ZaKhGYukiR40yU1IaSHm6iKudeJXInc166uIKkGlhZjW8YEixsX6cex1VGw1uQO3cqO51rXwB9w8++ndLM/nyecqlEv1pgvydJpqpYGhW3h9LmKJAImOMG0dYRKdYWKtQQJBN16fC49fw+dz4XKrD3XmJoAgenBQcKw0grXQJKnCHZAHx8SxMzjWHM1MXN/t3UvuEdlGlWStfJ0zsgNMFrN8b/oiP5odJvcLTmqvwHRsvj9zkajLg0tSGAhEH5isrncwTqIjzMWzs5QKVSZGlq8jttGYn56BOLFEkOWFHN/7xrsszmbp6I0iCAILs1lGLsyTSRaRZYmd+3rwbUCs3c8brgy6Rwtpvjl5lpfnR1i8H6RWEEi4/XykfZDf3nKIDm8Q6T5NwkcDXrb3ttIa9t/yWWSYNgvpAj985zK//vED+Dwbf7+Nh3zsHminoyV4XwxuvC6VsNeNadmkS1WKtQYBj+umfZ9r4Yq50gsnLnF6apGQ18XT2wb43KFtDLRGkcWrSgnHcZBFkZfOjn5giK0iiUR8bjRFxmro7OpJ8OXDOxlI3Lm83K3KeDQFSRQxLYvlQvmm5lmO0+yBNu5BwWk7FlUzTV6fIa9PkdenqZppDLuKjUXCvZs+39M3ENuKmaZu5bEdE030E1A77nobrsW9KAIeZpiWzUw6z9HhKV44dZnRxTS6uXEZ9ZZtM53OM5ctcnkhSbJQ5qlt/fTHI8j3qN7bULbUGvTxhQPb+Oj2AQrVGqIgEF3R27tukpcKYDkWdatCqjHHRPk8o6VTzFfHsW4jwVuttNk1qkCe1HWvC4j4lTCtrm66PJvo9W4jrMTwykEUUdvwSq7t2NStKjl9ianKJS4W32G2MnLL/biyDw29Sl5PMcsoIKCKGj3erewIPkKXZxMhpQVVdG8YQVqqTfKz1LcZL19PEj2Sn1/q+S/o825DEMRmRItjUDJyjJfPcq7wJvO1MerWTSRZDuhODd2uUSZPWl9kunoJgIAcod+3i63Bg3R7NuGTwwjXuOjalkO1XCc1n73pdpfzFfT6+vJT61aVbGOJycoFhksnmKkOY9i3bvY3rWZFN6svMVE5v6713AwBJcq+8DM82/pL97ScjcCVwdp6rp+54v9Bw5xdYxkWttM8fpLopdX3tdsS22jMz2/844/iOA7JpQJzUxlmJlLMTKaYn86QTZfQGya5TIXlhTzHj45hWTaaSyHREaK7L0ZXXwvdfTESHWH8QTcer0Y4+pA6rIsdIHrBmsMxTuNYT4DUA9x+Qs1xTLCWwDgH5mjzM3IfcH+ratl69QYzqEKjxrdXKlXpdZhL/SKhYZl8Y/w0YdVNSHMTdz+YazHREaJ/U4KJkSVCER/WewYZsiwxuKWNw09u4kffOUmtqvP6KxdvWI4ki8QSQR77yFaC4Yc76udBo6mycliulfjj4WO8sjB6X+THIgKtbj/PdW7it7ccpsN7f6tLO/oS7OhL3PZ9pVqDsxOLvHRihK99ZA8+7swjYD3Yt6mTfZtuHbFzL2jxe+huCeE4DrlKjbMzi4Q8LoIe17rHT7bjkCpWmMnkqeoGj2zq5tmdg2xpv7FvsW6YFGp1KnUd5SE2NrwWiiyt9DsGGV/OMpnMspQv0RML37Lnca1xhEtRiPg8hL0u6obJxbll6sZW3Or1zzzHcTAti7GlNHVjfWO498J2LErGIlPlnzJefIWsPs57m5pdYhDdc6O5YbJ+gdnym1TMNK3unewK/xKyeO/Xt4CAYVuk6mVKegPLsZFFkZDqxqdoqzFxzXuLTdFoUDF0dNvCdmxEQcAlKQRUFz5ZveGYGbZNyahTNhoYdlM+LgkiLklufuYWGfTNz1ss1Uo4jkNY8+BTtNtKwXXTYj5b4M/eOMmPz4xQWId7viA0J+sEQWj6rzgOtuOsuNLfHJZtM7aU4Y9/epyRxTS/8+whelrCqLJ813P6G0psLdsmW66xVChRqjVwgNlssyK4rSO+pkuX49iUjCyn869xOvczUo25DdseB5uikaFoZBgtnUJCpsuziafiX6TbuwWXtDEP9Stf9rpV4Vz+KCdyrzBfG7+nLdftOqOlU4yWTjHg282hyMcY8u9BFdd/c74bVK0SVbOI6RjIqFiOQbI+y0vLf8FE+TyWc3c3JICimeV0/qeMlU+zLXiIj7Z+HY8UAKd5o7Rtm3d+cJpzR4dvugyjYRCI3Lxf6sq5sLEYLZ3irfQLzFZHsPlgzKTeL9i2jWXZqO/J1rMsGxyu68PzKltQxaszt82wDBPTylE1RnCw8am78Knry0sWVm52ifYwifYwBx4dBJr9t/lshfHLS4xdXmRidImZiRTpZBG9YTI7lWZmMoXjNKXoLrdCa3uIR57awm/+3rMbcFQ2HoK6F8c4BXYGp3EUGwHR+09A7sZxJK6WKK7861z9sZexa3+JU/tuU4IshBHUx5s9t/cR2UaNZL05ELjyEHph5hI/nhtmvlq4r+v+oCKv1/n+zCXibh+f693xQHrGRFHk0BOb2L6ni1giuKZnQN9QK89+ejcLM1lOvjOOYztXZX+CgCgKxBJBfunvP8HQtvYP/QWuwZVrP12v8L+c+Smvzo9Svk9GUSHNzRd6d/Brm/YTd9+6/9dxHAzTQlzp57zyrDRXql7Xxqpc+dsVcmJa9nXVM0kUbiIvtrFth0yhwsWpZRwHdMOkYTQn5cWVa+c6dYIAtrOyDtvGaf5pdTvhKgGyHQfrPdsii+Kakugr72sOkJvSZwdnJVGyuQ2CcPNJ2vZIgG2drbhVhZph8Gc/O0VnJMj2zlaUaypBVyKc7JXIkuY+XiOpNc2V10CRJJSVaJtrK7W24zCymOb8zBIN00R5wH339wKXKvOxXUP82eunODk5z46uBENtLUT9ntVjfyXNxcHBcVg9f+p72oTaw342tcV4e3SGt0ZmmM8V8WjKdVm3pm2Tr9R5c3iGYu1O3YSvjLFznM78P0xX3kC3mxNOAivn7DZjPJcYoGImma8ex7RrdHkPE3NtveVn1gNZEJgqZ/mPw+/yw9nLZBoV4i4fX+3fwye7ttKxEhNn45BtVPnW1DmOLk0yVc5SNhp4ZJXt4Va+0LOTT3RthWuuMdtxmKvk+d7MeV6eH2WhWsCyHQKqiz3RDj7Ts53nOjatfcRWSO10Jcd/+e4LmLbF7255hGfah27Zw287DvPZAn/4yjFeOHVptX/6vRAFYYXINqOcVEXCoyq4FAVVlmgYJjXdbJqsmSa2ffUaWquiX67rvHxulJpu8M8/+QT9rZG75jobSmx/emmC7528xEw2T9Dtuq5E/3sff3RNYrtcn+EnyW8yWT5P7WZVwA2Chcl8fRxhpS93I5HW5/lp8luMl85RtW50rLwXTFcukteTLNTGOdLySQLKxjWFr4WckaJqlXGLXsbKZ3k1+dek6nP3RGqvRcUscC7/Jjk9xafb/z5hNY6EzHO/8jg7H998y0Gi44B2m8GY5Ri8vPxXXCy8Q95I/cKTWoATb01w/K0x/tF/8fx1f//R351Cb5h8/uuHV//WEfzP1sindQAb0y4wV/g/EAXXPZs/SpJIOOpjz+E+duzrxjRtLNOiXKqTXMwzP5NlYTbL/EyGqbFkU8I8lWE0vniPa75/ELRnEPS3caxZcIo49Vex9DMI6m4EZTtInQhCcEWebOPYlWasj3ERxzgN1hw4VUABqQvB/QUQ7q9F/hUpMjSlthdzy/z1+GmmS7n7ut4POobzSX66MM5gsIWdkbbbf2ADkOgI4TjBW8qf+4Za+Sf/1ad49/URTh+bJLVUwLYdQlEvm7Z1cODRQQY2J9BcH/bXXgsHmCxl+YOLb/Hy/Ag1c2Oed+9F1OXh728+xGd6thPVbv/dLlUb/N7vf5vn9m3io/uHaIsGeOfSDH/0g3dpGCb//p99Eb9bI10o859eOoFp2fyDTx1BEOB//evXuDC1RENvEtTPPradf/i5G7PWXzs9zkvHRxieTZHMl9ENk9/8N3+9+ize2tPKZx/bztN7BlY/I4kiU4tZxuZSvHlhimKlQSTg4dCWbj772DbiIf9qxWVmOcd/+PZRLk4vY68MlH/rk4f4ytO7b9iWb/3sLFNLOTpiQVyKzA+PDbOcLRH0utgz2M7HD21ma3crkrT2OEGTZTa3t/DpfVv4xtvnGF1K8//5zk94els/Bwa6aA/7kUWRUl0nU6owspAmX61zaLCTRzb1rOybQEc4gM+lIgoC52eXeePyJB2RANGVyJJcpcabw9O8cPIy52aW0D5gnhAeVeHzB3dwfHyO09OLfPPtc0wms3xkxwCb22IEvW5qKw7Ty4UyI4tp5rMFemJh/sFHr/fh2NQe49HNPbw7PkuxVufffOen/PLjezg40EnI46ZYa3BmeoFvvn2eZLF8U7J0O5zK/Cnz1RPodhUBCbccJqptwiO3MFl6ZZXsroWINoBHbvaD1608yfrFDSG2Wb3K60sT7Ai38Vubm8fltcVx/mT0GALwmZ7tJDyBlSomHE/NMBho4aPtQ3hkhdFimjeXJ/n3F48yEGihzx9Zdd0/np7lT0aPcTazwJF4D1/s24VXVknWyiiiRFC5+aS3bluczy3yb868iuM4/IudT7GvpRPXbXr45zJ5Xjh5iR+fHVnzPMmSSIvfw/7+TnZ0JehpCZEI+fG7NURRWFFhNsfqjtM0FivW6izmikyn85yfWeLE5DzZa1y5r8CyHd4ZneFv3z3Pl4/spL/17rjOhrK7E1PzdEaDfO3IrtXMqyvojoau+920TTL6Ai8t/QXT1csrpPb++qSLSLS5+ggokQ0jtqats1yf4dXkN5ipjFCzSvdkVrDmOhyDnJ7kTP51qlaJJ2JfIKLGEe5TP05eT1IxC8zpo7yZfoHkBpJaaM7+1a0Ks9URXln+K55L/AphJUaiN0ZLe/i2nxdu4eRZtUocz77E+cKbFPQMNjfvCXBLPvxyCLfsRxEULMeiYdfJ6ynqVuWWn/2godEwKBVv7JMs5qvUa9efW1lcux++2UsUJOR+ilztVar6ZYL3YDQlCAKSJDSdXYFyqU46WSS1WGBxLsfifJblhQLJxTylQg3LbGbeGsb9cwm+VwhCoBnP4+g49VeAGtgLOI0ijn5qxfFYBkSaU+EW0AC7Ak6JpumUAsoORO/vIIgtK++9fygadTL1CnXLpKjX+YOLbzJVzmLeMLmxMfDICgm3nw5viJjbS1TzEHV58SsaLklGlSRUUUYSBCzHxrBtGpZF2WiQ12tk61WW62XmynlmK3lKRuOmPV33E6Zj825qlnZvkIFAC54NdrJdC/JtBs+CIKAoMrFEkCee286eQ/3oetPMRVFkvD6NQMjzgSe1df0speoL+D2fRFO2rUSS3T1sx+FCdom/HD/Fq/OjVO8TqY27ffzOliM82zlEq9u3vv5sATyaymwqT6nWIGZapPJlxubThHxuppeybOqKs5wrk8pXiPjd+Feyin/l2b0s58q8dWGKty5OU7qJpLCvLcLHDm6mvz3Ka2fGWcqW+NXn9uNzN2WO0YCb/vbr+y8rtQYvnxihvz3KkW09CAhMLmX5/lsXURWJjx3YRHtLU2IdD/n4tY/tZzlX5mdnJ3j70jSV+trV8GrdYHgmyemxBVrDPvYMtuNzqUwt5zg2PItp2YiiyLae1rUPlyDQGQnyxcM7qRkmL58bY2I5S7Zc45Vz42hKU+Jo2Ta6aVFtGMSDPobart8/l6Lw7M5BCtU6i7kS3z52gXfGZvG5NEzLpqob5MpVEiE/T23vZ7lQ5sLs0prblK/WODe9xOmpRRqGQc0wqRsmo4tpHKcpAf/bd89zamoBr6biUmRcisxQWwsf3TF4XVV+Ipnl7PQiU8kc9ZXl1A2DmXQegEvzSf7o1Xf5wanLuBRldVnP7hpkoPWqJ4AoCER8bn7n2cP8xRunOTY+x1ujMwwvpvBqKrIkrSoDdNOi0tBxqQoh741RiyGPm8ODXXzh4Ha++c45hhdS/MGP3+bPPS4UScS0bCoNnbpu8g+ePcRfHT3DQm79BSDLNkjVL7FUO0PNyuGVY3R7H6PP/xRuKYIkqCxUjt2S2CqiF68cQxMD6HaZfGNq3eu/FWqmwaFYN788sJ8efwgQOBjr5r878SJvJqcYDMZWiC2ENTf/753P4JFV3LKCLIrsq3cS1jz8yci7nM0u0O4NokoyqXqZY6lZJotZPtG1lV8e2Idf0ZBEkYbVHAP51lAIyIKIblm8nZziL8ZOooky/3T7I+yKtOOVtVtWQSt1nZMTC3z/5GXq7xlnuRWZrR1xntkxwJ7edkJeNz6XiltR0BR5VU3yXjiOQ0vAQ0ckwI6uBE9u7SNfqXNyap6fnB9neCF13boapsWPz47Q1xom6vcQ9Ny5Ym1DiW1dNxlojXKgr/O6L+J74Tg2VavA0dT3mKpcon6Li1EV3QSVq+60quhGEprLNm0D02lQsypUzKbTbcUs3LRCJ4sKW4OH8MqBDemv1e0Gy/Vpfpr8FpPlCzTsmxusCIgElAhhNYZHCqKJLmRRxbAb6Hadilkgpycpmfk1JRU2FgUjzeXicUDgqdgXCKjRDa88A2T1JYaLJ0g25pivja1Jal2il5AaI6S0oEluFNG10o+rUzGL5I0UBT2N4az9IHVwaFhVRkun6XAPsDP0GEGt5Z5cOqtmifHyWU5kX6Wgp9e8DmRBoduzhW7vFqJqArfsQxU1RERsHCzboGZXyOtJ5qqjzFXHKJo37/lVBBWvHMQj+/BIAdySD4/sxyP5CSpR2t0DN/3sg8DifJb0comJkSUy6RLH3xxbfa1abTA3k6E1EVrXspo3LRlNasO2axh26rafuRa27VAp18lnKuQyZbKZUvPfdJlCrkqlVKdSrlOrNKhWdWrVBvWaQb1m4NgOgZCHaCzA0JYHUx27KwgSgrwNwfPL2GICR/8ZmGPgFJs/t/4wiB0I6iEE13MI6mEQ7n8VwLRtlqol3lyeYqKY4d3kLNW77IFaC5ok0+cPszkYp9sXos0TIOryElBdeGUVj6zgkRVUSUYWmg6bkiCu5iRbTjMbr2Gb1E2DmmVQNnSKep28XmeukmeimGG4kGS6lL9vOaNrIV0vczI9x/HULE+23brf/EHiihrioe1Fv0dYdp6GcQmP/Rj3OiFuOw6nMvN8e/Icr8zfn0grWRBJePz85uZDPNexiYTHv24zI0kU6W+PspwtUanp5Mo18pU6IZ+b9miAsfk0vYkIy7kSAtAS9K3KZjd3x+lvi5ItVbkwtXzTdbRFA0QDXvwejYvTyxQrDR7b2UvE31TZyZKI9h6flHJNp7+9hSd39bO5O44kCsyni4zPZzg9Ns/eoY5VYuvWFLb1JhjstFjIFDk7cWvVTaFax+fWeGRHDwc3d+HWVLLFCn/4wjuML2aYWMjclNgCuFSFgUSUX3liL5vbYpyZXmAimSNZLK84JYNLkfFoKu1hP/v62q8rvggrEstndw7hVhXeHJlmbDHNyGIa07JRZIkWv5ed3Qke29JL2Ovm6OXpmxLbcl3nzPQi3ztxEXOFLJq2vSr1bpgWY0sZZtN5ZEladRp+fEsvT23tv248vZAtcnR4itOTC5i2jWE1l1fTm/fsTKlKqdbg0nwSSWwuSxFFNrfH6I1FVl2oBUFAEgS2dyX4e0/uY3tXK2enlxhPZphJ56npBpIo4tFUgm6NvniEbZ2tPLa554b9kyWR7pYwXzq8k7DPzbHxOWZSeRZyxRUC7WFze4zHt/TyxJZefnZxkmTxxj7Ym8FydOYq71AzM4iCRJf3CJuDnyai9SOujIHF24yFRUHCJYXQpABVM0PZvPn34U6gijL9/ijbIwlckozjOEQ1D9vDCY6nZliqNp/7giCgSjJbw9dft35FY3OwWUleqpVWq5hzlQJjxTRuWeH5zi30B9Zn7GXjcCw1w8nMPKIo8lubD7O/pROXdHufj+GFFEdHplYzi6+gxe/hme0DPLN9gE1tLcSDvtV9uh0EoZljq0gSPpdG1O+BGLSF/Qy1Rnn1/DivXZogXbrq5ZEslHlzeJreljAHB7vWtd/XYkNZUc9Kw/5crkBnJLhqB/5e1K0qM5VhLhXfpWHfaEwiIOKVA3R7NhN3dRFW4/jkEC7JgyJqSKwQW8fEtHUado2qVaJiFqmYhaarspFZcewtYDp6c5lSgEHfLlzivffWWo5JprHA8ezLjJRONqNu1oAsKHR6hmh1ddOitRNUWlYIuoYsKBiOgWE3qJolCkaKdGOB5foM87VxzPcQSgeHspnnQuFtvHKAg5GPEVAiG26CtVyfXY3yea/ZUovWTpurj7irk5AaJyBHUEUNRdRwaFaXa2aJgpEhoy+yUB1ntjqC4dw42HRwqFllzuWP0ubuwyeHVyct7hSOY5PVlzmbf52MfuNDU0DEL4fYEjjIkH8PHe5BfEpwzYkBB4eqWaTLs4mx8llGiidYrE+tucyg0sLe8NOE1BguyYtb8jb/Fb1okgdVen9dR+s1g8X5LAtzOfKZMqePT66+ZhoWobCXoW3rI4rNXj2LhjWP5ZSB25+rSrnO2eNTlIo1ioUaxXyVYq5CIVelkK9SyFUo5KtUyg1sy0YQBVxulWDIQyTqJxj2NOOAwh5CES/hqO86J9iHEYLoAWUPohjFkQdwzEs41gzYWbBL4DRoRgKJTUmy4AMxgiC1I8hDCMpeBGUzCDfOjN8vjBXT/NnoCZaqRYp6HfseyYImybR7AgwFW+j3RxkIRunzR2n3BAhr7ruKNPG/x8jmyhZm6pUVcptlpJDiUm6Zy/kkqftg+vNeWI7DZCnLj+eGORTrQpMezoi2nz+sBGreI2zH4fQ1pDZVX/9ge71QRYlef4Qv9e/i0z1biaieO3LSlkSR/rYIF6eWqNR1UvkKpWqD3kSERMTH6FyaJ3b1s5wto8gS8fDVQackCEha07zzVpnFLlXBpSr43BqKJCGKAgGPi5Dv1vegPYPtHN7WTXClihf2e+hNhFnMFilf00d5RZ3jlkQ0tanGuBUcB3pawzyzZ5BYqLk/iYifgfYWjg3Pkine3tDO9f9n77+jLMnv607wE/55/9J7U1neV1d7h0YDDdOwdKA3okhRZlej1Yw0K81IOpoZkaMdLY/cjiSQIAUaACQc0UB3A+1NdVWX91mV3rvnbdj942VmVVZmVqV5WQbA7YM+6Iz3IuK5iN/9fu/3XkVmZ2MNjeEAu5prGZlLkcwXKepGZaRJkXGrCjUBL63x8IrJHS2xEB6tm/aaCIMzSTKFEmXTRJVlYn4P3XUx2uLhiqGPKOJ3q+xursOlLC3Qe7VKVJC5TtfkztrIsjnk2qCPo10t645GaQgHVhzzciky+1rrFztqI3MpErkiJcNEEgXcioLPpVIT9NEcDdEQWVnR5dEUehoqM7rbG2oYT2bIl3QEQSDkddEej7C7pQ6vpvC5h3ZxqKORPS11tzWWXYDlmEyVzmM4JXxyHbXuvUS0TsR1rhdV0YsierCdCcpVGhn0yAp+VVuU+C5c/xs8AXTbImuWF2fGDduiPzPLUC5JslykaBkYtsVAJoGDg26Zi4rPRKkSw+dXNLqDa4/UupSc4mJyitF8iodr2nikthVNvPN9qWyYnB+Z5Mzg+BIFlN+l8dyebj5zeCfbG+MrzuivBwvnURfyE/V7CHndyJLIS6evki1VrhkOcG5ogv2tDexprV/Td+RmVJXYelSF00MTTKSz9NTFlgyOH2hroCZQuUBlzRSXMycoWMst9CVBJqjE2BU8ys7Aw9S6WtCktS3wFhx8E/oks+Uxpkoj8/Eu0+h2iVqtmajWgCRu/mVnjSS92dOcT723IqkVBQm/HKLFs53dwUdo8W7HJwdv+0OsOL4lGSpc4VL6GEP5K+TM9BJJbIUMVuS2UbWensBBvHJ1HRUrhYHZJX+TBZVmTzfb/Afp9h8gpjWgiKvPujpOhbQO5i/hSfnpz11cVaY9XhpgojhAjdaMX7mzFHklFK08E6V++nMXV9zuk4P0BA7zRM1nCSmx234OAgJeOUiHbw8RtQ6P5KMwmyVtJLi1O6CIGvXuNrr9B+9ZXvLtEI54aWiKMFw7y9x0hpq6G98VRZFo6YjT2rHU6TFTOoHlrLxwsOwcicIrAGjynQPJU3N5vv6Vd0nMZknM5dBLlUUFQsX0xOPV8Pnd1NaH8Pgq/z8c9RKrDVBTGyReF5w3yvGhubY+qqtqEFQEuRNBasNxMjjmVbDGwU5W5moxARFBcIEYAqmh8ngxDsLdNyAZL2QYL2z+Rq+KEo3eIN3BGAeijRypaaYnGMd7G+fGjWLhmxBzeYm5vOyLNpIsF7iYmOSdqQFOzoxyPT1LztA3TdRvh0S5wIczo1xJTbMrUodyF7rsDxIcx8Cyk+jGtXkTmjwODpIYRZFbkMUYtp1FN3ux7BwgIEsxFKkZSQrNm/Sk0Y3rWHYGUXBhmENshtguGEVdTk7x9f5zvD5+fZkzeDWgSTJdgSifat3Jl7oO4JaUdRuNSaJAR32UfFEnWyhT0g1yxTLdjTFCfhdvnO6jbJhMpbIVYhu6O116t6ZQE/YtklqofCJBr4uhqeSikdVGoCkS0YBnkdQuwDvvr6GvcRxFmI+cPNzZxOHOjTkxx/xeYn4vD3e33PZxD3e3rPqYsNfNs7s7eXb35hVc3fUxuutjm97PzRAEgXjARzzg46ENdMgWIEsSdSE/daHbG6K9eHjnuvbrYJExxrEdg6DahFeOr5vUAkiCgiQo2NhYq6gJ1wtx3gV42bEQKtNGTsX8rGgaXElN8d2hi2SMEqbjYDs2jgMzpRzWLeM0tmNjOzaKKKGsoxA2VkhT7wlQ6/YzUUhzPjHB/mgjsiDd9oo5lc5ybXKWydTS6+DB9gY+dWgHO5pqqh5tp0gSu5prsR2HiVSWty7faLxMZ/L0Tc8xNe/WvR5UldjO5QoMzSa5PD7Ne71DS7b9k08/TU3Ah+PY5MwUg/nlMQQAHinAzsBRnqv7RSSkdS1kBUFAEVRqXS3UulrYFXyEvJlhojTITHmMsBJHFuRN504Zts5w4SrnUu+s2nEOylF2BB/iqZov4JH8ayI9oiARVGPsVh6hzbuTt6e/xcXMMbJGYomsttK5TXMy+UOCSpRW7w5kcetmplTRRaO7k+frfok6dxvqGizSBUHAI/vZHjhMrauVlyf/hL7c+RVjgmzHYjB/mUZ314aJ7Zw+wUDu0oqfhyTINLg7eDj2AhF1dfnSSgipcXYFHyGlz3A88UolXmoeDjYJfZITiVdp8+7acsfqjSAU8RGK+BBEgWDYw4s/e+eZ2PHMf6Fkjq2wxVlceEY9H1uTK3KxqHPp3AiqKqNqMj6/C01T0FwKgZCH+sYwDc0R6psjNDSHqa0P4fO7bztH/UBBkBCEMIL68L0+ky2FiEBQddEVjPF80zaeaeii1R/eslzOlSAAEc3DE/UdPFTTwomZEf6y7wynZ8eYKeUwtihj0nYcpos5vjl4gY5AFFnZXAYfzDuuWhUzNdtaPykXROGOJnt3C5adJl96k1zhBwiiG8Psw7JTuNRDhHy/iKi4yZffIl/8IZadAERUuRWP6ym8ricBiUL5GLnCS1j2HJIYAxycDS5MF2I3xvIZ/ujqCd6Y6COxBZFWqijRFYjx2bZd/GLXQVwbnMGWRJHWukoMSypXpFDSKZYNDnY34VIVhqY+JFsoM53MEQl47hqx9WjKclXefCN9s2UkTZFxqcuXpwt+xnd/on79KBV1Eok8hfzy72l9YxiPR73v1gv3KxwcDLuAg4Mq+pA3mBTgzCcQVDhAdQqQRcugYOoYtoUiSosu9HPlArIo4ZFVJEFkqpDlq9dP8fr4dX6j5yEeqWmlwRPEJckcmx7in538/pL9+hQNn6KR0otMFDJ0BNZWzNgZquXF1t1kjTJfuXaC/9+V9/kn+56j2RdCFVd/zb0Ts4zNLU1BUGWJF4/spKMmsmV57ZIo0lkb5cXDO/ng2jDlm2LsRufSXJ+au7fE9lefOMQvPnpgxW3u+YuU6RjkzBQpY+X5vJjWwNHox5CrNDvqlSvy4y7f3qrsDyBRnqQ/d57JFeSpUMmC3RY4xEdqfw6XtH5H00q3N8zz9b+IicGl9AfkzNSyxw3mL9Ofv0BYrSGi3TmjbiOQBYU6VyufafxtIlrDuqXCoiAR1ep4NPYp8mZm1YLGRLGfpD5Fm7Nz3Rd7x3FI6TNMFodW3B5UYrR6d1DnWj4bshb45TCHIs9xNvUWhVtMzsp2kZHCNWbKI9S62lDuQbdtLejorqW+MVyJ97kFgsBizAGAS2lHFJd/bwUEZDGCX9uPXzuMJt/5OyfJIuGIj5b2KC3tNbR0xmlpj9PUGiUS8//4ENifYIiCQETz8OmWnfzG9oeoc/u37Ca4VmiSzON17eyJ1POV3hN8Z/Aig7nklplMZYwSPxy9xt/a8TBeReX2tfHVYVs2pmlhlAwKmSLJ6TTFXGnRSXatcHk0dhzt2tA5VBumOUo2/02Cvl/F63qckn6WXPEHKHIHbvUIZfMqM6n/lXjwn+N1PYWDQzr/VTL5r6EqXchSDYnMH+J3fxK/50UQVDL5r1PUT637XBYiWqaLeX7/7Gu8PTGwJZE+oiDQEYjypa4DfL59z4ak94v7EgUifg8Rv4dsoczoTArTstnWHEcQBIq6Qd/4HHPpPO31EeLhrXVRX8DCHOrW7HvhXw8u+vum+bOvvMMHN/laLOBf/58/z+GjnQ/6S7xrEABJUBEoYGPdMdpnJVTIcRHDLiIKMmoVRhIB8obOWD7DZCFLnafSqdZti0upSbyKStRV+T1mjRKXU1NsC8Z5sWUXTd7QfLxYrpIze8t+G70hmrwh+jJzvDXZT6M3NC/hFxbjl0QB5FvIav38CJBfcVG2TP7Pc6/zx73H+a2eh2n2hVZVjIzMppnJ3Gg8yaJIazxMT30cv7v6iqub4XdrbKuP0VEToXdybnHOeCaTZ3R2/ZGDVSW2LkXGUeZb77csIBYWzrpdpmitLPlRBI2AEiGkrl1Pfi8wkL+4quRVQKDLv49HYp9A2+QPRxZUnop/nryZ4UrmxJJu4QIuZ45T62rZMmIb0xp5JPZJolrDYl7YRtDs2Uazp4e58gRZc3mMSMZMkjPTWI6JvE6HSxubjJkgscJsLVSKJc2e7g2dN1Q6vgElQrOnh4H8RXS7tGS7YetczZwirNbeVp59L1Es6IwMzTI2PLdsWyTmp7HlhjFBc/Dvr3rjqFi5KwhrvHQ0t8X4T3/xO0iyhCRXcgtFSaxENfz0pv7AQ5Nk9kbq+Z0dj3Ckphm3rCDeRx+sX9H4jZ6HqHcH+LPrpzmbGN+S49iOQ0ov8sHUEB9p7CakbWw+eujyKO99+0NOvHKWif5pTMPC2UA0RtO2Ov7wnX+1oXOoNhxsbHREQWN+icpC2rjt5DCMQUTRh0s7gChWRiU0ZRumOU6xfBK3uh/LSqKp+5ClhvntPchizSpHvN25OPRn5/h359/mjfF+StbWuB/vDNfyt7Yf5dmG7tt2SdaD5poQM+kcE4ksbXVhGqIBMoUSrbVhzvVNoBsWEb8Hj7bxe5AoCiiyRGneSfun+CnuBwiIeKQIZStL0Uyg2xsZG3DImZPkzRlkwYVPWZ96bzW4ZZW3JvvIWzrPNnShCBI/HOvlfGKSX+g8wK5Q5ThuSaHRE+DM3DjvTA7QE6ohZ5Y5MT3MGxN9eG9xOG70BDgcb+b03Cj/7eoHTBdz7I3U45YV5koF0kaJFm+YjzSuvLaNaB6eaegiWS7wR73HqfX4+Wzrbpq8oRUfP5PNky7cWNuqssTRruZNXU/WA6+mcqiziYGZ5CKxTRdKzObW75VRVWJ7ZXyGVy5c48zQ+JJ2ctTn4W8/e5TdTbWYtkHZWtl1UBIkFFHbkHb+biGpTzNe7CdlTK+4vc7VRodvN2GlZlMyk4XnBpQIPf6DpPQZxorLK39z5QnGCtdp8fRUvSDgkfy0eHro8u3b9GciCTItnm2MFq6tSGxtxyJjJMiaScLq+hYsRTNLzkitaFAF4FfCRNSNO+kKgoAsKNS72hktXENnKbG1HJOx4nUM++65sa4XQwMzfPdrJxb/Wy8bpJIFIlEfT39s9xJiK93UrbXsAoadwLZLSKIPVYohrENNIUkiwUhlfz+VXf14wa9oPNPQxZe6DrAzXItXvv+kdaIg4JVVnmvqRhElvnr9FCdnR7fkWIZt8dr4dQ7GmzZEbI/9zSl+8MdvcPXDPrKJyjz6RpFfIdrrXkGRGvC7P85c9v+LWuzCdopoSg9u7Sg4Dg4WOCCg3BRhNx+J5RhUjNZsBEFGmL8PCcjrug5BZWbt7NwEf3rtQ96ZHKC4BaRWAA7EGvmt7Uc5WtOKR66eL0BzTYh3zw9SLOnUhv0Vt1tJorsxzuXhaTRFIux3IwjCYme6pBuUdJNUvoRhWuRLOtPJLC5NwaXKKNLScS+vS6UpHuS109f49jsX2dtZj6bIqKpMPOglFlx/N7iSZWlT0k1KukkmX8KwbLKFMjOpHC5VxqUqyNLmJfw/xVI4jkOxbPBv/vhHvPjUbg7t2Pj87L2EKCjEXNvJmpMk9QHS+gj17gPIaxiLW8B08RKJcj+GXcCvNBDVNt7suBl7I3XUuv2ULYs/6f2QjFFCQuBzbXv4WNN26jwVs616T4Bf6jqMbh/nGwNnsXEIqi52hev4XNsejk0vVRxKosjD8RY0UeYHo1c4PjPMGxPXEQUBt6SwPVRL523kyaIgEHN5+UzbbsYKaV4ZvYpf1niheQc17uXjCtliedFZGypFrvpwAEW6O+orRZKoDwWQblLxFXSDXHH96+qqEtv3rg9TMkxaYiFG59I83tPG2aEJvC5t0dpeEIRV501Nx6BsFTBtY0tnRjeDsWIfs+WxFbunAG3eHbR6qjfzKgoSHb49jBX7mCwNYN1iVGU6BhOlQSaKg1UntjGtkXbfLtxydWZ24loTQWV1y/IFZ+v1EtuyXbxt1JJb8uLbpMGWIIiE1BokUeHWeFvbsUjoU8s+m/sJTS1RPvaZG2MClmkzNjzH1HgK01x+3iVjhHTpfbL6GQxrFscxEQUVTW4g5Hocn7YPRbqz/fxKCxXLtEnO5Ri4PsXEaILkXJ5SycCxbWRZwutzEY37aWiJ0LGtDpdbva2j530Nx8bBADsDTgk2IKFCath0Tme1EdU8fKy5h8+27WZ3uO6284OWbZM2CkiCiFfWlkmnthqCIBDWPDxW10bZNpkt5RnKLS+ubRaWY/PhzAhThSxN3iDKGl+nZVrMjM7x8lfe5Oybl8inK/OemlslXBvE7XPNS/bX/huob7+PVE+CAIKKJATxuJ5AEv3IUiOK1ASCiCzVYTtldPMakhjAAQxrFNtJoypdiGIEQfShG/2ocheCoGJY41j22j9Dy7E5OTPKN/rP8dbEAFmjOsYxN8M1r1749e0PcTTeQlCtrudCc02IsmGgKjL1EX+l4CqJbGuO8+HVEXqa44suxrmizrn+cb73/mUMy2J8NsPkXJZCSeff/PnrqLKE36Pxu595lLD/hrIs4vfwyK42ekdmOHZ5iFPXRlFkie2tNTy1r3NDxDaZLXKyd5QfnbqGadmMTKdIZAu8ebaPoanE/OsJ8Hc+82hFyfNTVA0OYNkO14ZnSOdKd3z8/QpJUGn2PspY4UPy5jQj+WP4lFqavQ8j3WH8y3Eckno/vemXmCtdBcAn19DgPrjp8/p0yy4sx8anaJTm82zLtolbUmj3R2n0BhbHEDyKysFYE25ZIVEuYNgWLkmh0RskpLrYF22Yj7+78XqCqpvD8WZq3D4mChkK82MTqigTd3tp9lZmTwUEmrwh/uGep6lxeYm7Kmt2RZSodwf45e7DDGUTtPjCyzrDC9BNE+OmUTVREAh73XdtrEgSK+7ZN18zDdOibK7NKO5mVJXYjibStERD1Aa8qJLE5w/vZndTHT8414vlVN4wRVRXnTs1HZOMMcdkaYgmz/0xH3QzHBzGCtdJrtKt9UpB6lxt6yZmd0JQiVHraiGgREnqy489UxplsjRIT+BQ1Zx5BUTiWgNN7u5Nm20tIKBE8MlBBMQVpa66XUK/DUFdDbpdXrVbKgkyiqBtutAgIOCRfYgryLEdbPJmZol79f2GeG2QeO1Scp9K5Hj1b86Szy1d5OnmFLP575AuHcNyCshiAEFQsJwCmdJxSuYIMSdPyPU4irS+of7xkQRXL4xy9eIYw/2zzE5nyKYL6GUT23GQJRGXRyMY9lBTF6S1s4ade5vZtquBQMjzwFT0HTsFZh+O2YtjTc3n2Bqsn9gKiP5/CML9E3Hkk1Web9rG59v3sDtcj3oH+/+0kedPBt7AI2t8suEwjZ6NvxYHh6xRZDA3zUhxjpxRRBREvLKLOleIZm+MuLZyHEXM5eWJunZmijn+8+X3KZpGVU1oHGCmlKc/m6A7GFucrboTDN3k1A8vcPXDPvLpAsGYn679bWx/qItoQxiXR1v3LLo3UJ35sWrAcSxsO4NlZ9CN64iiF8McxlK241L2osgt+N2foFB6j5J+AQDbTqCpuytEVvTgd79A2biIZU0himEMcxhJXNu1Z4HU/tXA+S0zivIrGvujDXyp+yCP17XjWUNm5HrR1RjjU4/uQhQEdrZVRo8UWeJwTxOmZdMQDdAcDwGVRaLfrdFSG8ZxHDrqlxYhRUFAU2TkW7oxLlVhW1Ocn392P/0TCfKlMqIg0lwTWiTNsiTy5N4OdrTV0lq79LesSBJP7O2guylOa23l85EkkaDXtfjfnQ23nIsoEPa5F+s2+7oaCPpcNNeElr0Hezsb8HtdtKyw7af48YUkKNS6dxF37UQv5JkpXUZIC+SMacJaOx4pijUfj2k5OkUrQUofpGimyBhjTBXPMZo/TsFK4FfqqfPsJ6huvnu9J7JUCbg7sroyUBJEAqqLozVLvV5mSlmuZCZIlHM8Xbt9Sb61IAj4FY1d4Tp2hVcfN6wUb918pGF5F1oSRXaEatkRWr/0+q6utlY72AZu0lUltuCgydJikLcsiexvree/vHGc7HymmSJoeOUAquhaNqsIDkl9mnOpt/HJwfmM1vtHlly2isyUx8ibK8di1LqaCanxqnebRUEkqtZTozWvSGxzZoo5fYKilcMrr7yoWy88ko+IWk9QXVso9FqgSi40yTuf37u8Ym46Bqa9fnmY5RiLF7VbISJuajZ4ARXzAmlFkl/J7zUr4XsPCBzHQVFl8itUcdOl90iV3kWRIkRdH8cltyIIKradJ29cJlH4Eanim6hSDUHp6JqOBTBwbYr337jCsTevMnBtCsNYuRCQSReZnkhx7dI4x9/pZe+hdp786E4OHO2kpi543xtOOdYEjv4BTvkNHP002BNsqFMLgADe3wbx/iC2iijxeH07n2/fy+5w3R1JLUDGKPKXw+/ikTQORTqpd4cXDSxunuNbCxGYLqU5nejnvdmrXM2Ok9bziIJIUPHQ6o2zP9zO4Ugnnb66ZfsUBIEat59Pt+7iw9lRTkyPbIkc9UpqisPxprUT27LBh6+cpZApomgKux7t4eO//gz7ntqB23dvc7A3C9spYVopTGsGRW7BsqaxbAHLrvxNEqOocith/6+TLb6EaU4ClRlbt/bIYtxPwPMz5EovY1rjOLaAIreiKu3IUhxWub47joONw7m5Cb7ef5Y3x/uZLVc/2zioujgYa+RnOvbxfFMPsDULwvpogM8+vnvJ3xRZorspTnfT0g69x6Wyt7OBvZ13jmS7GYIAHpfC0Z2tHN25stmiLIk8fWDlxoMiSzy1b2mkTdDr4qEdLTy04/ZxOQs40N3Ige7GdW9bCbbtMDw4iz4/MxwIuolG/ahalZe+9zkEAWaSWc72jpHOFZElifpYgLaGCIIgUCjpjE2nmU7msCwblybT3hAhEvAym8qRzpXIFsqYpk3A58KybRLpAm31YWqiFbPAuXSe4ckkpbKJIEBdNEB9PICvCsZDgiDiliN0BT6KbueYLl1gLP8hiXI/MW0bfqV+ce42Z04ykn+f6eIl8uY0iXIfWWMCBwuXFKLRc5gW76NI65AxbyXKtsFgbobjc/0cCLfikTXuhXBBlWUUSVwcI3UcyJbKW2a4eCss2yFbLC9ZRsuiiCKvnwNW9dcd8XoW7d8FQeC9a0ME3BqO4yxKCWVRwSeHiWuNjBX7lu0jYyY4m3obj+SnJ3CIkBpHE933BcFN6JPk5w2OVkK9u53AbaS2m0FEraXG1czV7Mll22xsskaS2fJY1YhtWK0hrNYgVcmdGipdT0VQUEQVw1pObC3bwFxlTvZOe15tKWE7dsVFbz4ge6NwqJhErRQ0IACKcP/NF96MVDLP5NgN6Z7jOExPpkklCzQ0LyVNc8VXUKQotb6fJ+R+bMm2MM8gCi6SxTfI6ecJutZCbGF2Os13v3aC99+8QnI2h6LKhCJePF4NRZUQRRFBmM+iNmzKZYNCXiefLXHy/etMjScp5HWeeWEP4ejdibPYCByniFN+Hbvwl2AuGMwJILioXG7X+x0R4T7JR1ZEkZ5gnN/sOcqucC3qOp1eC1aZlJ5nupSmbBuULB3TsStFFlHCI2mEVC8eSVtR/lQ0dT6YvcbXh9/jeq5iFCciIAgCKT3HYH6aC6lhRvKz/GrHMyt2bmVRpMbt41e3HWE4m2Q4n6r6jftKcpq5Uh6Ca5MDW6bN0OUx9JJOvCnKI58+xEMf34d4l2abthK2naRsXMIwR2mI/gcEQQNsMoVvUTYuoZt9aEoXitxCxP87K+5DEARUpZWI8ttrPm4l095mIJPgj64e553JQVJ69eeO/YrG4XgzP9exn+eabj+zZ9s2w1cnKBd1Yg1hglE/pmGSmcuRzxYxdBPHqTjJu9wq/pAXX8iDtMrCrnKttMinCxSyJcolHUO3cGwbQRCQFAnNreIPefAGPMiKtOz54/3TFHIl4g1hXF6N9FyOTKJCEHxBD5G6IIqqkM8USE1nKBbKiKKIP+wlUhNAVuUV73u27VDIFsml8hRzZQxj/rVJIi6vRiDsxeN3IStbRzAd26FU1Pmbb58imcjhOA5797fy+FPbicVvn7P64wZBELgyMMXoVIqJ2Qy249DWEOW3PvswmiJzZXCKY+eHGJpIYFkOHrfC7o56nju6jfPXJ7jUP8lsKo9uWLhdCmG/m4t9kzx5qJOnD1WKHB9eGuFs79jiTGR9PMBHjmxjR3vthsjJSmjxPYpu5xEQmC1foWjOMWy+u+QxKX2IlL50XlVAwiNFafQeocP/EWKunqqcz0qwbJvRYhJVlCoqNEHEp7hQBIk5PYdhW9iOQ1j14JU1mjwRjsY6GS4kluwnYxTJGiVMx0ZCIObyo4oyc+UsRcvAdhxUUSam+ZBFielSprJvbDySSkBxIwoCc+Ubxwyobvyya8VxIJ9LxaUqi8TWcmwmklkM6+4oEQ3TYiKVwXZuNAFcqozXtX7zqqpeVR7pakEQBLyaQnMkyL/+9uuAw0d3byPmu1G99slBuv0HGS/2r0gUcmaKV6f+nL78BY5EPkKLZzteOYAkyPeU4M6URymtkJO6gLBai0femgumTwkRUlYfFM+bGWbLE7R6d1TpeGG8m5xLXQmSKK/qemw5ZqXzuU4oorZqzI6FiWnrG3JbvhkODgUri73CHK2AiEf2I1QpF20rcOpYH//hD15a8jdNU3j06e0cONK+5O8lY4iI5zk0eeXKuE/dQ7r0HqaVWHH7zXAcB71s8J2/PM77b1whlcjh9qg0tsbYf6SdHXubqG0M4fW6ECUBo2ySSuYZGZjl4pkRTn/QRzpVYHRojh997yxur8rHXjxw/y76zT7s0kvzpFYANBD9CHIbCGEQFNZHbgXg3nftKhmxXv7+7sfZHqrZcCbnlcwo17ITXEyP0JebJKXnEQQIK152Blt4rm4vB8LtRDT/sgzcvtwkx2avcj03gYiAW1LxK25kUSJvlsibZRJ6lndmLhPR/Pxy+1MoK2Shu2WFZxo6+fZgA0m9SFqv7uxZb3qGuVIB23FWjVa4GY7tkJnLYpk2bbubqWuL37/f73XCwcTBQBBkbCeLQBnHLmJaY+AYyGL1C8ELpHaykOH3z77GB9Mj5Lcg0kcVJR6tbeUXuw/xeF37HR9vlE3+4Pf+iKHLY/zK//QZnv7CQ0yNzPHGXx3n3LtXmZtIYpo2/pCH9p1NPPT8Hh55YT/R+tB8tM6N75LjOOglg8mhWU6+fpGLH1xn+OoEc1Np9KKBokoEon5auus4+vG9HHpmF/HGyBJy6zjw5X/515w/do1f+n99mq49zbz6F+/x3ktnsG2Hwx/ZxRd/72PUNEX48LULvPSVt7h+bgS3R+XhF/bxud95joaOGiRJWnJeju2QSxc49cZlTvzwPL2nB5mdSGIaFv6wj85dTTz6qQPse7yHmqbIlpFbw7SYGE/y5g8vkkrNz61rCgePtAM/WcTWth0cBD7+6E46m6McvzDMv//Lt3jh0R3EQz6+/fp5YmEfv/ezT1AT8XF1YJp/9p9eoqU+TEk30Q2LwzubCfk9/PuvvcX/80tP09USp3domvHZDOPTaU5dHuFnPrqflroI+VKZP/iT1zl1ZYSasI+6WHUaLgIi3YGPEVSauZ59meHce5SsNA4WjmMt4RMCIoIgVkitHKEr8DE6/c9VRYJ8O+TNMv/u8it0+uOULZOw6uFgpJWw5uWbwydJG0WKps5Ttds5Em0n5lr+XbQcm5NzA5xIDJIzSmiizGeaD9IdqOWViYv0ZafImWXq3EE+33yYRk+Ivxg6RkovYtoWO4INPFGzDVmU+PrQB6SNEgWzzKOxLh6Od1HjWv55hH0efC5t0RnZMG3OD09QLBs4vs01hu4Ex3HIl3XODk0umfMNeFxEfesfq6nqFWV/a0X2IgjQHA3x5PZ2irpBQziIV7uxEPLLYXYGHuLDxKvkzcwq0SIOg7mLjBZ6qXe1syNwhO2Bw8RdTdU85XUhWZ5GX6HTuICAEsFVpWysW6EIGm7JjyZ6KK9ArktWntQKMuWNwicHq9b9vRnC/D8rYaOx6z45cNtzzVsZ0sYc0U1EItmOxXRpdEXnZVEQCSjhdWf83k088lQPO/fdkIIJAmiajOZWUdWllwFBkHAcs+JWugJsjPnP6s4XOkM3Geqf4fWXzpNK5AmGvTz/mQN8/LMHicYDSLKIKM5/I4TKYqu+OULP7iae+thuJseS/Jf/6xUunxtl4NoUp4/1s+9w+7Iu8/0Cu/wmWGOV/xDrEFzPI3p+ZV5KvEGyImwsOqaaiGgenm/axuN17ZvK5Pza8HtA5cZtOzYOlc88oed4f/Yq51KDPF+/ny82P0KLd2nH81J6hIF85RrX4I7w213PczjahSyIjBRm+c7oCV6bOsecnuWViTN8uvEQUS2waqbsp1t3cj0zW3VimzXKjBcypPUiYW1t9wNBrMRfefxu1A1UqO9XyFI9btVDsXyKsZlfgnkFkCp34/d8Epe6v+rHtHHozyT4N2df59jUECVr/cXSteCF5u38YvdB9kXXJ/cFmB5L8J3/+hrHXz3PxMAMlmktZhUnpjKkZq/Qe2aIc+/28nu//yWCUf+SzFPbsrl6aoDf/90vk03mK8+37Ir6wAHTMCkVdWbHk5x7r5eLn7zOi7/5DDuOdC47F8uwuHS8j4sfXOO9753BNEwc4O3vnMQX9KC5VC5+cJ3eM4NYpk0pX+bVP3+fQMTPR3/hURo7bniKOLbD7ESSL//Lv+bcu1fJJAvYN7225FSaU7NZzr9/jSMf3cOnfv1J9j2+fd3v31pQKhlcPD+GsYI54k8iDm5voqMpiqbIRIMeokEviUyBfKGMadm01odprAkiCgKNtUH2dDfQNzqHCIT9bqJBL6oiEwv4aG+MIc53d5OZIuMzad45M8ClgamKOtOBom7QXBsiWyhRR3XXkjFXDyGtjR3BzzFRPM1cuZeMPkbZzmI7BiIKLjlIQGkk5tpBg+cAbilyR6OpasDCZqqU5vPNh9gdasQjq2SMIhfTYyiixP+w4+NMl7J8f/wcPlnjiRW6xyP5OaZKGfaGmnko2s717DRfHTjGP971Al9sOYzlOIwU5ricHudscpg6d4DL6XF+qf0x9oSacEsKBVPnYnoM23H4u9s+QsYo8crEBc4lR3iufteyYzaGA8T8HsYSldxYw7I4PzLJZDpLTdC3OGK6FSibJuPJLJdGpzBvIrY1AS9N0fV/d6p6pjcbEXhUhcZwANtxmMkUkEURj1bZLgkyEbWWZ2t/lh9N/iV5a+UAXhsL3bYYL/aR1Ke4kH6PWlcrXb59tPt24ZEDd5VMZMwE5gqzoVCJxnFJ3qpKd2+GIAhokpuAEmFmBfOLsl1cMUZno/BIftyrmHxtKTbAbV2SD78SRhPdK7ojp/RZpkrDGya2juNg2DrDhSsY9vLPXxIUmt09922GLYDLpSKEBaanMvRdncCxHZrbYjQ0R5Bu6Q65lS7yxhV8xnXccutirIbjODjoZErvg2OgyXeOUMplS7z/+hVy2RKiKPDcp/fz0U/tp7YhhHwHeZKiSDS3x/mlv/0MX/kPP+LC6SHGhue4eHroviW2mL1gp0DwIWiPI3p/A0GsBSS4j6Xqt4MqSmwP1fAr2w6jSStLD9cK3b5BMmRBmp8nEskYBUzHImMUeHPqIpqo8Gsdz+KVb8xBTZZSJMpZfLKL7cFGHo5twydXXBQ7fXW82HQEB4fvjJ0gbeQ5lejn8fgOfMrKhYEjNS10BqIMZhMUzOrN2jrAVDFLorw2YiuIAsFogFwyT7lYxtS3Jlv1bkMUBCRBRlU6iQX/Ec5CQVYQEAQ3khhed2TPnWDaNmfnxvny1eOcmB7eMlL7sx37+NnOfWwP1a7Z/fpmvP/SGURJxBf08IXf+yg7H+rCH/KQSxe4eOw6x14+x9CVca6dGeKVP3uXz/3t51BdN5oDoiTij/jo2tdKejbD9kMdtO1sJN4QRnUp5FIF+i+M8oOvvsPcRJITP7xA+84mGjtrCUSWjnI4Dpx56zK7H+nm9/7NL+ANenj5q+9w9p2rvP/9syiqzI7DHXzud57DcRy+++U3uPJhP+ff7+XAU9sXia1t2WQSOf7r//INzr3bi64bHHxqB0ee201Dew2KS2FuPMk7f3OaC+/3cur1iyiqhC/ooXPP2uZv14NyyeDyxVHMVbwcftLgdato89JxURAQRQHbdrCcSplaFITFERBREJElEcu2EUQBSapk0AtCxexLlSVEQcDBwbYrBcpdnXX83s8+seQ2F/C5Fk3HqglRkFBwE1AbccthWryPYDk69nzXVkBAFGQkQUEWPaiiB2EF9c5WQEBAEUXq3EGCqhtRECmVMgzl5jiXHOHLfW9jOhaW7eBaxaF4ppSjLztNxihxNTOBYVvENC+6bfLG1BUS5RwFU6dg6rT6YsiCxM+2HuVMcpjTiUEORtpo9IQZys9xMTXOV/rfxaHyWbullY/ZWRuhPuTn7NDE4t/KhsVfH79A0ONiW/3WOe0PTCf55vEL6LcUoZqiIbrr1n/cLaPgFSv6ygX/pbNXeXpHO911scVtmuRmR+AoWSPJmeSbJI0ZVmM1hqNjmAlyZpqEPsVEsZ/z6XeodbXR5Omi3tVGQInclIG3NcibmVXNjdySD1movhPizVBEFY+08nyhbpfJW9kqHktDvo+J2s2QBImQWkOtq4XhwtVl22fKYwzlL9Pl24sqrV/WWbLzDOYvktSnVpRKy6JCu28Xyn1iRrASspkiF88O89YPL+ELuBAQOPVBP9t3N3HksS5q60OLj416PsFk5o+ZzP4pufI5XHIzgqBi2XmKZi+Z8ikC2kP4tAOrH3AexYLO+VNDmIZJU2uUnXubqW8K35HUwvxsnSrTvaOent2NjA3PkZjN0tc7yUc382ZsJezZSqyP3IGg7EaQ6tlwp/Y+QYsvzLONXbT6QlXZX5evjiPRLrr9DXhkDRGBolXmYnqE92d7mSgmOZ8a4nxqkIdjN6rZObNEwdKJaj4a3BH8NxFWTVLo8NWyN9TK+7NXyZolzqWHOBTtwsfKCyu/orEnUs+l5BR9mbmqvLYFTBdzJMsF4M5SW1mR6NzXwuz4HFODM6RmVjYnfNAgixKKKCEKblR5ZSOiasK0LY7PjPC1vrO8NzW4JfJjv6LxqZad/GznPrqDMTwblOQnptNs29/KM188ytHn9xKpDaJoMkbZpLGzFlmVKeZLpGayfPijC7z4m88AN44lCAK1TRF+7v/xcYyySaQmQCDiw+11IcoiRtmgpaeBYNTHn/7+d0jNZBnpnWB6NLGM2AJ4Am62HWjjkU/sRxRFbNvh6qlBElNpuve1sO+JHg49uwu9ZJCYztB3foTxgRmyqRtF9ly6wLGXz3HuvV5y6QLP/szDPP35I7TvbMQb8CDJIsV8mcauWhRV5uRrF7j4wXXqW+N07GquWBFUaf3kOJX52ssXf9qxXcACob3pLwDURCqZyJNzWaYTWUJ+DzPJHH2jMzxxsINcvoxulrhZoXXzbjxulUjQgypLZPMlDu+qFCnm0nk0RUZRtqb5JAgCEgqSFASp+mNzm4GIMH/tq9z7FVEmqLqJuwI8XrOt4kouytS5Vj5vr6wSVD3UuAIciXUgIqBJCqP5BMlynqb5ZIG+7DQODpIgcjjSRr0ryJXMBBPFFIZtEVTcRDUfj8a70CQFVZSpXUGGDBWVbVtNGL9bWzT7BXj/6jD1oQCyKNFRW92GguPA9clZvnfqCsevjyzZFvN7KmQ7vP6xgU0RW8O0SBXuLOM6OzzO/tal3R1RkAgoYfaHnkQV3VzJHGeiNLiCU/IN2FgUrCwFK8tEaZCxYj/DhSvUaE3Uulqoc7dT62pGEbQtIZglK79qVqkmbb3BlSTIqxIzyzEoW0Vsx66IfTf5+iVB2dRM6t1GTG2g1budkULvMklzwUwzmL/ElewJdgUfQVxH5U63y0yWhjmZfI2SVeTW4osiaNRqzdS5WpHvgsxloxgbSXDu1BBNLVHaumoQBIHx0QTjI3NcPudaQmwD2mF07ySp4tukSm/Pu0qLOFgICPjUvUTcH8Etr2GuTDcZH6lIlhpbY0TjfhR1fZcdzaXQ2BolHPUxMjjL7FT1CjjVhuPogIUgRkCs4UEntS5JZleklifrO6uSP9vsifFc3T6ert1FgzuKIkqL7rXd/gY0UeX16fNMllKcTQ4tIbaGbWI6Fqoo45OXXwddkkq9O0yrN87Z5CADuWn023TsBGB/tIH3pgarTmxnirk1x8oomsKRj+/n4vu9TA7OcP3MENsf6iLetDVGhHcLqiihinfHfda0bY5ND/PXA+d5e7K/6vJygBqXj+eauvlS10G6AtF1m6fdDFVT2H6og0c/cYCaphuLRc2t0tBew47DHVw5OcD0SIKJwVnKJQPNoy3J83b7XGw/WLkG33o/kzwadS1RHvvUAf7mj94gk8iTTuRIz6187axpitDcXUcw6scyLXY91ImiydiWTWNnLa3bG/AG3EiyRNeeZmRZIpfKo5duFPozyTxvf+ck2WSBho44R57bzc6HunB5btwXfUEP2/a3cejZnYwPTNN/cZQrJ/tJzmQI11RPrqqXTWams0xPpbGtByet4F4g5Hfz0O5WBscTfPuN86iKTKlssLOjnu7mOFcHp0ndJgNXlSV2ddSTyZZ463QfVwansB0H24Gju1vpblndG+YnBUHFTbsvzlB+jqliCkEQiGsByrbJYG6Wk4lBxgtJPpjrY5tRR1Tz0eqNMlvOMllMoQgSDe5wRd1klpBLGTzznVfbcSiYOh/M9WM7NnN6HrekEFTd1LoC9GYnmSllkUWRmOZf1SzR61LZ2VTL9oY4J/pGF/8+lyvww3PXMEyLZ3Z10tMYx6ttfq2bLZa5NDbN6xeu89blAZL5pWrLfa0N7G6uw62un4ds6q4zmyvwnVOX0GT5tuN2I4k0+iohuzFXIwclH0ElSm/2FGPFPhL61KrxLQtwcEgbs6SNWYbylwkpcZo922jx9lDjaiGuNeKWvFUlm7pdWjWrVBbVFTNOqwkRaVXyVJEZGJs2SVqALMj39czorQiqMVo824moHzCnTy7ZZmMzUx7lxNyriIJMk7sLnxxGvs2iy3Zs8maa8dIAl9Mf0J87v+IsuF8Jsyv4CB45ULUM4a3A7HSG2akMP/9rjy/mweayJf76q+8zPDCz5LGKFCbm+SQuqZGcfo6yOYWDgSi4cCvtBF2P4pLbkMQ7S4xs2yGfL+E4DsGQG9W1sUuOz+/C5VYxDYtisfqdmGpBEFw4yDBfDHjQ0eILcyjWRLu/OpXafaE2Ho5to9V7Yy6vUnkXaPfV8kTNDsaKcxyb7WUwv9QzYKFgJSIir3Jt8iseGt0RTiX6mS6lMVcpRC6gKxCjyRNEESUMu3qdnelSnmR5bS68iiqz76md7HxkGydfOceZ1y8Qrg3w0McPUNsSQ5TF+9pxfTWoorSmOKjNwHEq8rqTs6N8vf8s70wMkKyy+7EA1HkCPNPQxZe69rMjVLPpzyNSG6Slp34Jqb0ZobifeEN43iRKp1zUcWwbbiou3ekcREkkFA8QiPqQFQmjbGLoK6/DKg7M3sXn+cNeFKWyrgvFA4tdXkkS8AbcCGLFDMu2Kq7mtmWTmcty9dQAlmXRva+Vhvb4ElJ7M9q2N1LTFKH39CDJ6QzjA9OE4oGqTWvkciUG+6cx9J/sbq0AKLLIkwc7qY/d6HwF/W4e3tNKPOxDUyQe29eO163SOzhNOlck4HXx+Wf2Uh8LUCjqeFwq8bAXURQ4uqe1Mmsb8rKrs5542EdjPIgii3x4aYTZZA7bcQj63AgCq3qq/LhCFSUer9mG76YxGres0uKJsivYyFy54jrulTQM20S3TWRBZHugHgcoWyYBxU1PoB4xI5DWi6iiRETz0eyJ0O6NUbB0FFGizRcjpHqxsEnrRSzHxiuptPlidPkrubX7Qs3M6XkcsyJDvt09cXtDDQ93t3JtYnZJ03JgJkn+zFUmU1ke7m6hozZCTdBHxOdBU+Q1mSTatk3JMJnLFZhO57g+Ocd7vUOcHZpgLru0CFwf8vNoTyvd9RsrimyK2E5ncvz3905zoLXhtov6TLGEdZtIBZ8SZG/ocRo9nVzNnuJq5kMS5UlyZgrDMbjT4KXpGMzq48zq41xIv0+Hbzc7A0dp8HQSVuK4JG9VFgaWYy3JXbwZlbiXLSa2gnhb0lq5yVeH2IqCdF9ELK0VqqhR62phV/Bh3p99aZnJU9kuMlS4QnoywcHwMzR7tuGTQyiiWnHbRsChYhJl2DpFK8d4sZ/L2RP0586v6Ibslnw0e7axK/jIfX/xdhwHx3GWuGIuzMwsGHvcDEWKEPY8S9jzLI5j42AjbnAeThJEECo5Zc4G41xty8a2bRDgvo6xlWpBcOM4WQTnwZaUioLAgVgDD8Vb1nTjWgu6/HXEVojhWUCzJ0qdK0TZNkjpq2eOrvZ7c0kKQdU7X/gsLIkOWAkhzU29N0BYdTNdyq3tRawBiXKBjF5ak8WaIApE6kM883OPkppKc/n4dQr/949ITWU48OxuAjEfqkut/F7X+OWXFZloQ3jTr2MzUKSt7dguuB9fTc3wld4TvD81VPVOrQDUuP18tHEbv9C1n55QzR2fsxZE60OE4qv/DhRNQZsnhc68GdRKSw/btilkSxQyRUpFHaNsYJn2opGUYzuYuoUgCIuOxStBcyto8zO8wvz8pTy/YHV51BuGZoKAKEksfKsd21l0aE7OZMndJE0evT5FMb+yJ8ncRGpxm142mB1PzufAV+c6k0kXuX5tqir7epAhCAIuVeF3vrg0tq8hHuSXP3lk8b9VReaZw908c3h5ZNWOjqXeJO0NFSVJe2OU9sYbqpLtbbVsb6ut5uk/kPDIGr/Z9dSyv4c174qmTQDbAsv9XwKKe8W/f6b54Ir7+JnWIyv+/SOrHHMl1If9HO1u5vLYFG9e6l/iUDydzvHy2V4+uD7CnpY69rTUsa0uRtjnxq0qyJKEJFbk7gvXG9txsGwbw7Qp6AZz2Ty9E7OcG57g4sg0meLy67VHU3h2dycH2xsJezc2n72pu45bUTjQ0sDv//wncN3GMesffvV7eO7QThYEgZjWQEStZU/wUS5njnMu9Taz5QnKVgHTMVdxT14KwylzNXuSa9kzdPn3cTD8DB2+PVXp3jqOzWokW0TacnIjIN7hNTgrErCNHWv1bNj7FWG1loPhZxnMX2a8OLAsE9dyTObK47w6+VV8cogarZmoVo9PDqKIKrZjUbKLzJUnmCwNkTHmVs0slgWFNs8OHop+DL8SuguvbnPwejVcbpVzJwfZvqcJURAYuD5NqWhQU790zsOyC4CAKCgIgjxvmb+xoo0kiwQjHkrjOnPTGYr58rozhR3HITGbI5ctoWkKvsC9dwleDYK8E0c8C9YEmAM4jo5wH0vUb4eA4mJ7qJbOwCYlsUKluGE5Nn7ZjXYbsuOSVDRRwXJs9JtUO6sVFG+FLEhoojJf+Taw1+BG1+QN0ugLVpXY5owyeVNf03fdth0S40n8ER+d+9uYGplj6PIYw1fH+dZ/fJm2Xc3UtkTxBj2r5obeinBtkJ//x5+p1svZEFRRQtuijm3F0M9iLJ/mD86+zqnZsS2ZqQ2qLj7Tuouf69xPe6B682Vuj4bmXsd14ZavseM4mLpJJpnn8vE+Lp3oY7h3gsRkmnymSLloYBomlmVRLugrFi9vhihJq0ZMiaKIJK2SZOBUzs3QTfLpG6T2ta9/wGtf/2BNL8227FUJ8EbgOA7pVIG++5nY3vJ2LlzfDMPCNCwsy8aybRyb+WsIi5FPkiQiySKyLCHfB2oOx3Gw7UqmsmlWzt2eL3gslCoWiiWiVDGlkmQJSRKXSOurBcuysUwb07QwLXtRVbBQN1mYNV54HxVFmi/y39330bLmz9Gw59+zG+e5+HnPG3fJkoiiyIjS5scM74TtDXF+/tF9DEwnGJxJYt107XCAZL7IW5cHeOvyAAABl0ZN0EfU78GjqWiKjCpL6IZJ2TTJlXRms5Uubb58+2u0psjsb23g80d3b2qed1PENub38OmDO5DF2y96awLe2xLfmyEg4pfDHI48x/7QUwzmL3ExfYy+3DlSxsyddzAPG4tr2TNMFAfYEXyIx2KfJqat35b/ZoiiDILISm0nC2NNxHszsLGWkbWlELbMlflBgIBAWK3ls01/h2+M/CHTpWHMVSTtOTNF3swwmL+0LB9w4Z/VihgCAl3+/RyNvkCLZ+uCvquJzp46ZqYz/Od/+zJen1ZZ+JcMnvroLh56fNuSx07lv4YkeAhoD+FW2jZ1XE1T6OiuY3Y6Q++lcabGU/TsblzXnG25ZHD9ygTT4ylCUR9NrffvzI6gPQXld0B/B8c4hWCcBPWRe31aG8LBWCPdgdimu7UiAj7JRcYskrNKlG2TlS3wIGuUyFvlivnGTdcyw7Zuq/pZgACL52vjrMllvc4ToN4d4DRja3g1a4PtOBRNg6Kp41VubyqXmc3ydx/7ZxTSBUzTwjYr9xHHdihkilz54BpXT1ynsipb2/FbehruObHVRPm2RYzNwHIqkT7/4tQrnJkdo1xFGfkCNEnmN3qO8mLbLpq81TWnESVhU4v6clHn0vE+/vT/+A79886/oiQQCPsIxfzUNrvRPCqKJnP5RB/pudXVD7CwkN7w6Sy/W67DCEoQxQ2lIayGYkFnYiLJ8NBs9XZaZVR8UJb+rVw2OXNqkIvnRhjsn2ZyIk06VcAwTCRJwuvT8AfcNDVF6OiuZeeuRrq316Np99YLxXFgeirDhXPDXDw3yvDgLDMzlSK2ZTvIsojP5yIa81PXEKKzq5aeHQ20tMXw+auf0Z6Yy3H18jiXLowy0D/D1GSaXKZIuWwiigJej0Y46qW+MUxXdx0HDrXR3BrFtZ5CUxUwNZnm8sUxrlwcY2hwlpnpDNlMEd0wkSUJn08jEvXT2Bxh2/Z69h9qo3GNxpubgSpL7Gmp43/+3LP886+9ymQqi2mvzm0ypTLZcpn+6cRN32mBhR91JYHMWVFxcjPcqsL+tnr+6eefoSEc2FRbbVN3nYDHxSNdLUh3uED/xlOHCbjW5hi7cDEUHQlVlGjz7qTO1cbh6EeZKPZzPXuWgfxFilaeO10NHWxyZpoLqffJGgmeqvkCje7ODXdu5XnJ6kofsWHrd5S9bRa2Y6/qygwgCCLSA2T4VG1UZvVkomodLzb+Nsdmv8e13Fny5spxUs5CP2cdN1WvFORg5Bl2BR+hRmu6r+dqb4Y/6OGRp7bT2h5nYiyJA8Rq/DQ0RQjf4pKZLLyOV92OT9276eN6vBoHjnZy+ng/+WyJ175/Dl/QzeFHu5bFDN0Kx3GwTItv/8UHXD43SrlsUNsQYuf+rQ1Y3xSkZkT3Z7CdHI5xHiv77xE9MwjaswiCp1IYe0BwON5EZyC66QqxIko0eqMUMuOcTQ6yM9BMVFvZ6bA3O8Zgfhobh6ReiezZE2plspyiYM4HxzsWhVXyxA3HomjpCIAmrs2lPqp5iLqqnz9eMA2yxp2JreM4lHIlyqvMjtu2A3fouN0K8x47wQpUpMjKFnRsDdvi9OwY//flY5ydG686qRUAj6zyD/Y8wUcbt1Hv8VdNil8tXD01wNf+8Af0XxxFVkQ+9etPsf/J7cQawiiaUumOiRXp+h/8nS+Tzw5u6fkoqozXf0NJ84W/81EOPbuLUOzOhlCKKlVyetdB9G3boVjUmZnKMDmRZHIixeR4monxJHOzWebmcivG/Bx79xpXLo2jquv/Xh59pJsv/drjuFybW2Pd3Km0bYeZ6QzvvX2Vt167xOxsjmJBRy+bGKaFZdqLqo9ctsTsdIbRoTlOfzjAD/wumpqjPPbkNp58Zicu9+ZTORJzOY6/f51vfv34kr9/6rOHeOaju/D5lhLR3isTvPX6Zc6dHmJ2JkuxqGPoJqa50IGsrMuy2RIz0xn6rk1x4lgfLpdCXUOIT37mIE8+s2NT57yASxdGefv1y1y6MMrsbI5SUUfXzfkOeKWDjACFfJlkMs/I0BxnTg7y0ndO09YR49EnezhwqJ14FU3MboVeNrl0YZTXf3iRa72TpBJ5SiWjcp7zWdQLHdtCvszcXI6hgRlOHu/j2984QWd3LU99ZCe79zYTCm9NHOeCfH1ncy3/xy++wFffPs2xa8PLzJ1uxgJ5vbGWXt/9qjka5BMHtvPi4R3zDsyb66BvitjKoohvDYS1JrBafX51LLwoTXKjSW68coCwUkOTu5sDxtOMFa8zkL/EVGmYkrV6NdLBpmBVOnNMC7xQ/6uE1PiGOpua6KmQ4hW6gKatb33H1rFWJbYiIoqoPFCGT1uBBXJb72rj8fhnafb0cCnzAYP5S6vKiu8ECQm/EqHNu5NO/16a3dsIqbH7Ot7nVkiSiNujEop4KZdNHBw0TSGTLlbGAG66mJvWHIKgIgqbf30er8bBhzt45btRBq9Nc+X8KKZpMXhtij0HW2lqi+EPuJcsavSyQWI2x8D1KU4f6+fUB33MTKaJxPzs2NNEV8+d83PvFQREkLcjqEdx7ASY57HzGYTSyyC3IYg1IPhAkFlb+02okGLx7mZK17p9dASiRKpA+DRRZX+ojaHcNGeSA4TUSh7fNn8DPsWF4zgUrDLnU8P8YOI0vZlxAGbLGb7c/0MejW1nuDDLQK5iJlW0ysyUV55fzpslpkppREEkqvqQ11BIiGgeImvIm10vKsS2TB23jyvQ3CrP/dITGOXq5ddG6u7tfO1C1E+1ZXOGZXFseoi/6DvDiZmRqufUSoJAjdvPb/Q8xPNN26jz+DeUU7uVKOZKjPROcvXUIIIAz3zhKM988ShNXbVoLnXxWupUVptY8wvmrYTmUojUBvAGPeQzBUzDIlYfpmVb9a/Vx969xokP+pidzlLIlygUdAoFnWKhTKGgUy4Zq0qvc9kSuezG5rDbOmoqPg+bhKbJSJKIYVhcPDfCa69e5MK5ESbHk5jmyvt3HAfLcrAs0HWLfL5MMllgdibL1GSK/uvTvPDiARqbwhXTrw3CMm1SyTz915ca902MJcnnyvh8rvlzsXnjh5d4+40rXLs6QTKRu/25mw6WaVMumzA/8aG5lFXNzNZ8vpZNIpHjRz84z6kPBxgenCOdLqyeXexU1DS6bqLrZuV9TORJJnKMjya5fGGMx56qENw7Fd7XA9O0GBtN8PqrFzn94SBjowly2RLWKr9Lx5mXVFs2etkkl4M5ciQTOcZGE+w/2MbjT22nZ2dDVc9zAaIg4FEVdjTW8BvPHKanIc5bl/q5Mj5DoYo561G/h4PtjTy9s4MD7Q00RSrKmM3eN6qqEzo/MokqSzRHQxTLOu/0DjGSSPNIVwvb6mP419i1XQmyqOAXQ/jkIPXuNmpdrTR7tjFZGmKs0Md4sf+2UuWilWcgd4GTyR/xSPST+JX13/g981m1OssvjMXbRAFVC6ZjULJXjpCQRAVN9NzzeYv7AYIgIAsKca2R2XJlkXzrjJ4sKEiCUpF328Z8FpiEPB9zpEkefHKIgBIhpMaJz0dKxbVGNNG15UZh1cbsdIbzpwa5dG4UWRZBuDERvmNvM098ZOfiY2UpiuAIOFX4PkuySE1DkE984TDf+rNjjAzOcunsCNOTaS6fGyFWG8Dnd6NoMoJQubGWijrpZIHpyRQDvdPkskXcHpVDj3Rx9Mke/MH7d8bWLvx3HLMPxxoCJw9OAczLOGYvGFEcMQi4QLhhwHJ7CEjqQ8DdJbbbQzXUuquzqPfIGk/U7OSdmcuMFRO8O3OFyWKKJk8Ur6zhOJC3SgzlZ7iWnSBnlmjxxGjxxvlgrpe5co6MUSBjFFFFGctxGMxP05edoM1XWzEnozJTO1ZI0JsZQxJEWr1xlDVIYX2KSkB1IQsiZhVVN2WrIkW+EzSPygu/+QxOFWNJ1E12lTYLTZIqVfcq7lO3LN6fGuSvBs7x3tQgWaN6c5kAsiDS5g/z+fa9fLp1JxHNc8cxq3uBclEnk8xTzJXwBT107WulriWGy7N0fWUaFoOXx0jP5bCMrSW2klzpuu58qJNTr1/i0gd97H9iO7XN0dvOEi+Q7/V0a6/3TvLW65dJJ/N3lDfej3C5FBzH4cypQV79/jlOnRggk16/k7fjOBQLOgN90yQTeSzL5oUXD9DeEUes8vc2ny+Tn/fHyOXKvPPmZV7+3jn6rk1SKm6M6NTVB4lE19/0WoBeNhkemuVHr1zgvbevMjWZxlqFXN8JhYLOQP8MyWSeZDJPNlPi8ad6kOXNF+dKRZ3eqxO8/upFjr9/nempjZtK5nJlrl2dJJXMk0rlyWaLHHqoY0vkyYIgoMoS2xtrCHhcdNREuDQ2zZXxaa5NzDKVyt1WorziPqnM0TZHQ2xvjLO9oYZdzbV01UUJeqonS68qsX3/+jA1fi9Rn4eBmSSvXriOV1PIl3WCHtemiO0CBEFAQCKq1RHV6mjx7GDKM8RA/iJD+SuMF/spWFlWaoWX7SJnkm/R4duLJnlQ19lx8yvhVR2HC1YWwy5hO/aWyVMNu7yqrFYVXHilrZNQPEiouGXq9OXOcylzjKnS8E0xTQJ+OUSbdwcBpWKKY9g6Dg6iIKGIKoqg4pK8FVKrxAiqcQJKZP7ZD2bhYHRolvffvIrX5yI4b9u/AO2WRXDI9Rhlc5SyOYwmNyGJG7/gCIKALEs88ZGdZFNF3nntEsP9M4wPJxgfTgAgKxKqWrmBmKaNoZtLKu6xmgD7jrTxzAt76N5x/3ZrAezit8C8sMIWC+zpyv/WBQGc6i7g14J90QaiVepiaqLM9kAjT9Xu4rXJSkbtVCmFgFDJxnUcjPkiiiSItHlreKZ2NzsCTZQtgyuZMfJWmZDioctfj+3Y9OWm+OboBzwa305Y8SEIApPFJO/OXGa0MIcqyewLt+GW7jw3JYsSHlnBLStVJUumbaOvQSYrKzJd+9qqdtz7AZoko1Rpce04DqZtc3xmmK/3n+XdqcGqux8rokRXIMqnWnfyxY69RLX7t0gsKRKqpiCKIpZlMd4/TTaVx+3TkGQJ07DIJvMMXRnjjW99SDZ157GtaiAQ9fHU544wcGGEkWuTvPs3p7Fth9btDQQiPhRVwjJs9LJBMVciNZullC8TjPnp2tuy5uMUizrFgv5AklqoFJ0G+me4dnWCDz/oX9JBFgQIBN34/G5cLgVZruR867pJNlMkky5i3NKNdBxIJvK88aNLRGI+vF6Nupty6auBfK5EIV8mmy1x5uQg3/r6CYYHZ5d0aUVRQFVlXG4FRZERRAHLtCiVDEpFY1l3sqUtRu0Gz9M0LAYHZvjhD87zwx+cJ5tdXhiQJBGvTyMU8qBqCrIsYlkOetkglyuRTheXdXZTyQKnjg+QzZTweFUOHGpHXYcfyK0wdJOrl8d55aVzvPvWVfIrmKTJioTP5yIYdKPOd/Mty6ZUMshlS2QzxWXd8JnpLO+/c41iQcflUth3sG3D57gWNIQD1If87Gur59rkLBdHphmbS5PIF0jlS+TLOmXDxLBsTMvCsm0kUUSRK+MoLkXG71YJez1E/R7aayLsaamjJRaqSiburagqsR1NpPGoFSJ7fWqOeMDLrz95iH/xzR8ym83TVVv9wHmP7KPdt4sGdwedvr2cSr7GtewZskZyWebsQvbtUP4yEbWWiLo+a/KQEkddZZGv2yXyZhbDLqNJ1e8o2Y5FycqTW4XYapJ7kXz9JMNxHGxshvO9vDv7HUYK1zDsysVERMQrB9kdfJQj0Y8SU+uRxJ+MmeRUsoBeNvn7/+Q5vH7XbRdtAe0QE/oFUqV3cHBQpRqEFaT7ihhBle/8GxIEAV/AzSe+cJhg2MOxt3oZ7p8mkypQKOgVN8WbbjCiKOD2qHh9LsJRL3uPtPORT+yltaNmSVzR/QhBqsdxqueuCyLc5bl5WRDZFa4jrFXnOiYIApqo8IXmR8CBk4k+psppCmYZwzZxqLgh+2UXta4QT9Xs4iN1ewkqHlySwksTp5krZ2j2xDga7Wa2lOF69hW+PXqCq9lxGt0RREQG89MM5qdwcKjRghyJdq+J2AK4JBm/olWV2FqOve6K9o8LXJJclW5/xf3Y5nxigq9cPcHxmZGqd2oVUaQrEOWzbbv5YsdewlsgS68mPD43tc1RapojTI8mePd7pwjFA7T21KO4FMqFMuMDM5x75yqDV8YJRvwYpepKtleCN+DmyEd2c/HYNU6+dpF3v3easf4pdj/cTUNHDR6vi3LJIJ8pkJzOMNw7QalQZt8T29dFbGtqAmzrqUdfRcZq2zaZTJGpieVrpUDQTTTqQ92A4VJDY7gqnVDLtHnr9ctMT6YXSa2iSERjfuK1flrb4tTWBwmGvLg0BdtxyOdKTIwlGRyYYXQkQXIuV5H13oR0qsA7b1ylri5ELO6vahcvny+TnMuhl02++82Ti1JlURTwB9yEwh6CIS+RiJdgyIPbU4kn08sm6VSBRCJHJlMkn60QSse2aWqOEo1trGM7NZXmnTeu8Mr3z5HPLS1yaS6FcNhLXUOIxqYIza1RfH4XmiZjGjbZbJHpqQzDgzNMjKeYnkyj6zfitIpFnauXx/n6nx2jpjZIU1NkXWaXN2N0JMGPXrnA229eoVhYqt5xe1SiUV/lPJsjNDZF8PlcqJpMuVwpZEyOpxgemmVyPMX0VGaJd0IuW+Ls6SEkWaKhKUIk6tsSWfICBEEg4vNwtKuFhzqbsWyb8WSWkbk0s5kc2ZJOUTco6gaGaaEpMm5Vwa0qBD0uakM+WqIh4vOjqVtZN6wqsV2Q7QzPpRmZS/H8nm6awgEs21mTo+VmoEluWjw91LlaeUP6BmeSb5Ixk6xUqRzKX6bbt3/dxDaq1aNKq3ev0sYsJTu/JcS2bBXJm5lVXZHdkpeQGq/6cR802NhkjSSvTf0FY8W+JXm2LsnHzsDDfKT256qWbfygQNNk/EEPoiQuyrJXe/05/TyOY5Io/ojZwvfRpAZkcfmcYNTzMWr9v7Dmc/AFXHzsswc58HAnF04NcfqDfgavT5FM5BdnwARBwO1VqWsI07OrkUee6aG5PX7PnR/XCsH9RQQnVc09gnD3lBgiAmHNQ4s/jOcOpkfrgSAI1LpC/EbncxyOdnF87hrXsxMk9DyWYxNVfewMNvNovIcuXz2e+XD7g5FODkY6513KK4qJq5kxHon38PrkeS6khrmQGl48jiSI1LlCPFmzi05f3ZrJlSbJ+Kr4eqHi3PuTSmw1UUbZdLyeQ9m2GM4m+bfn3uDs3ARFq3rzXVCZqW3xhfmFrgO82LqLgFp9l9ZqQ5JF2nc18fTnH+KlP3mLmbEk/+1//QaKJoMgYFs2oiQSqQ3yyV97kmKuzGvfWFv0zmYgiiL+sJdf/aefQXMrnHztEkNXxrl+dhjTtHBs50bsi1yJL2nsql23G+2Tz+7kwOH2VSPASiWD4+9f50/+21vLtu3a08wnP3NgQx1Nr8+1qe7dAmamM8xM35CjulwKTS1RPvrxPTz93C6CIc+qBCUxl+P1Vy/y2qsX6O+bXtZx7Ls2ydXL4+w71EZ0EzLfW5HLlrh6ZYJCocyZk4NAxfQrGvVx4HA7Dz/WTc+OBqKxlf0ESiWdyfEUly+McerkIOWSTn1DaEP39XK58vm+9cblFUlt97ZannxmJ4891UNN7cpu5o7jkM+VOfFBH9/56w/pvz5NsVBeJLflksHF86O8+v3zvPj5Q9TWBtcll6+8ZoPXf3iRDz/oX05q3Qo7dzXyked3c+ho56qS7IWow/fe7eVvvnmS0ZEE+k0FjWymxNlTQ7z6g/O8+LlDeH3aXVnXCoKALEm0xEK0xEJbfrz1oqrE1qOp9E8n6J9JkC2WOdDagD0/BL3WHMLNQhVdPFv7syT0Sa5lz1C2l0sUpkrD83Ll9aHW1YLrNiYuCX2SvJklqFQ/jiRjJm87Q+yR/ES1+1umeTdQtHKcSLzCRGloCamVBJkaVzPP138JTby/i2iMQwABAABJREFUK/JbgbqGME2tUV757hmefWEPPp8LYZVswnT5GGVrBEnwIgleHEoY9nLpn+XcPkJiNcRrAzz98d08+fyuyuK1aFDIl7FtB5dbweVWkRVpMW/uQVJ/C9ozVF/2d/fm/CRRpCcUxy1tTUyLJsocjnRxKNy5GKoFN7IOhTWI/bt8dfxS65PolsFb05cWZcwA9a4wLzQc5EttT6zJOGoBsiCiVdkkyHLsqs7sPkjQJHnT86mmY9OfmeOfnniJK8npNcm614sGT5C/t/sJnm3oxCs/OHnTjZ01fO53n6Nrbwsv/9k7jF6bIjWTQRAFappi7DraxdOfP8Luh7s58/ZVjr189q6clyBAMOrnt/6XL/L4pwc48cMLnH+vl7H+KQqZEoomE4z4iDdG6N7fxoEnt9NzqH1dxwiFPYTCq9/DCwWd672TK27z+jTqG8I0t1ZfPbgRKKrE3gOt/NbvPktLW+yOEVDhiJfPfvEwkZiPv/qLY1y9PLFku2079F2f4srFUR57cnvVznNqMs0bP7y4KPkVBNi1u4lf+vUn6dlRj6rdPl9b0xRa2+O0tMb46Cf2ouvmhk2uzp8Z5oP3rjM2kli27eFHu/nsFw+zY3fTHd9Lr0/jyWd2sGNnA//pD1/l9IeDFG9ypjcNi29/4wS7djdWutDrLMAcf/86H37Qt6SIsYBnn9/NZ794hJa22B2JaCTm42Mv7GPf/lb+4F9/l/7rU0vUCulUnm/82fscOtxGe1dtVYovDzqq+g58bE83718bJq/rPLujE1EQGJxJsLOxhoh36w1fBEHAcRxkQaXHf4jZ8gSTpcFljyuY2cW5yvXMTHrlAGG1BlfRs6KJ01jxOmljlgb3+i7Ua0GiPMFUaXjFbQICPiVMXGus+nEfJDiOTcHMcjb1NvotRCyoxNjmP4AmVr6HPwnd2n/1P36NscE5oHKRLpUMbNvmu18/Pn/Rr7wHT310F7/0208vPq89/M9x1uAgLa3QxV0LFoLmRbFSkZR8Em6PikPFjU8Qtz6EfKvwoJmK3QpZFNkeqsElKVtSTxAWTMvW+fnefJ0WBZEmb4y/t+2TfL75YcaLScq2QUj10uSOUusKoa1zxEAUxKobBVWI7b2N3blXcEnKpqTIZcvk1Owo//Hie1zdAlIrCQKtvjD/aN/THK1pwSOrW37NUTSF//eX/zaGbuLyaPhvE9dR3xrnZ//Bx3nhV55AkkRqmqNI8o3vpyAI+IMeDj6zk20H2jB1E8uyAAFJlnB5VDx+F6IksvOhTv7nL//txY7qjX3A7/xvP0epoOPxu/CHbmyTFZl/9Rd/D9OwCEZ9+EKe+b9LNHTE+f+89I+xLJtoXWhJJ2vhPZQUie59rTR11fHCrzyBOe+bIAgCkiRW5oRdCi6Ptszj4U640+d0x09xHRm7W41DRzr4mS89QlNzZE0yUkEQECWRA4fbSCRyDA7MUi4tVTFMTaYZHV5O+jaDdKqAIAjYto3brXLooXZ+8deeoKklinYHUrtw3gCCJCBS6e5v5CMoFQ3efv0KVy4tzxzff6iNT37mAN099Xd8LxdjRcWKh8cXfu4opZLBqRMDSx6n6ybH3rtGfWOYjq61KTwdx6FUNHj5e2cYGZpbsk3VZA4ebufTnztMY3PkjtL2hfNUVIn6hhC//BtP8F//82sM3ORa7TgV+fSbr10iFPFRW1fdzO0HEVUltp21FWcry7KJ+r3Ikkht0M/PPbyPmsDdcfVc+CLEXE14pJUX3jYWpqNjO9a6Yn9EQaLB08Fo8RqlFUjmXHmCRHmSslWsqhzZcixmyxNMl0ZX3B5QosS0BlTx/nWLvRvQ7TJz+gRpfXZZ9JJb8hLXGh944rEevPC5gxSylXm0hd/FSsqJhuals9ma3LD1JzePCskF7rNIjZ9UyMICsb1/q76CIKAKMjWuICHVS4evDsuxUUUZt6RWDKnWCUkQNvS828Gej5ZYL7LJHFNDs8yMzlHMljDN9c9HegMeHvvMkXU/r1rQJGnDxLZkmbw3Ocif953i9NxY1XNqXZLMtmCc39v1GEfizQRU113JqRVFgfq2tY0LKZpMpCZIpGb1RaooiXh8Ljy+28un3V4Nt3f5cQVBIN64si+HIAo0dNSs+BxVU2jqqrvtMQVBQHOr847IGyuA/rijtT3O4aMddPfUrWuGUxAEAgE3Xd217NjVwJmTQ0u2JxN5pqcyWJZdtZnLipmjg6rJdHTV8PO//BgtbTEUZWOuwXfqpq6GS+dHGeyfpnCTCZMgVDrCn/zMAbq21a2rULJgbtnVU8fe/S2Mj1YykW/G6RMDHH6ok+bWyuu9Ewzd4vSHg4wOJyjdVHQQRYFIxMfP/MLDNDVH1tWxFgQBWZHYtaeJvftaSCcLJOZueHlYls27b/Xy6BM9RGO+LXFJfpBQXSmyqtASDS35m9+tUTQM5C0cal4JsiDfwZ34RsdqPWj2bKMve3bF7qlulxktXqe5tI0Wb8+6970apkvDTJQGVpVP12hN1LvatsyN+UGB4egrmoZBpeMj/oRl/O4/3H6j0zUfoL3STejWm0zR6EORapHF28/o2I6BbRewMVCkCKxJSPpT3M+QBZHuYAztPia2CxAEAU1S0KTNz18vyt6rjPXQ2mKuxNk3L3Hh3auMXZ8km8ihl4z5+fP1EeS6tpp7TGw35ops2BbvTw7yjf6zfDA9QsGs7kytV1bZG63nS10HeKyuDZek3BVS+1P8FLdi154m9uxrWbfEFSodz2jMz7bty4ltuWyQSRfI5UoEg9Udu4rF/Tz57A66ttUh3kVl1UJ98Pix60xNpZc4YiuqzJ79LWzf0YD3DkWe1eDxaGzf2cjli2PLiO3MTJbhwVl27mkiXnN7vwvHgbJu8tbrl0inl6o6vT4Xe/Y3s217Rbq9XgiCgNfnYu+BVnqvTCwhto4DU5MphgZnaWqJErqNGuQnAXdl9fLetSEOtjUuI71bibyZWXTDvRWyoKIICuIGZtdiagN17jaGC73kzNSy7SP5q9S72qhxNeOSNn9RMW2D67mzjBf7lnUhARRBo97dTp27ddPHeuDhODirzLQVrBxTpWG6fPuRhM1nkz0IkGUJaX5xOT6SYGIsyaGHO5c8pq93Esu02bbzRpd2Jv8tIu7ncSltOI6BIChIgndJt9uwEuT08+TL53GwcCnthNxPIQv+O3bFbdvGMCz0klkxFVnn61Jk6b7Osl0Cx8ZBB3MUx54FJw1OCbCozM5qIPoQxChIDQiCfz7j9u5DoEJI6j3+qkW1PCiolDnvzTXBcRwcx+HDl8/y8p+8yaVj18glNza/Lskibp8LX+jeLmy0dboiO05l3vrM7DjfGrzAe1ND5KrsfuxXNA7EGvl8+x4+2rgNRfzJuA88yJjLFbg4MU17NExd0Ici/XgUp/0BF13bamlo2niShd/vpql5hec7FXKby1aX2CqKRHNLlEce27al7rsrwXFsMukiVy6Nkb4l89flUnjsyR58AfeGO8FQiR9qXOHzsG2H0dEEM9OZOxJby7JIzGY5f25kybwuQCjk4cjDXXecR74TunvqiNcEuHxxqRzbshwG+2fYvbf5p8R2M0+2bJuSYeJRFQRBoKDrmNZyYvH6pX6awkFaoiFsx8bBXpcEeD2o3CBtJktD5K2Vg5D9SghVcm/oy6VJbpo9PYwV+riaPblse9KYpj93gXp3B+3eXcjixl5n5XU4jBf7uZ49y1x5ZTOEmKuBBnfHYibrTzIkQcYtrdxlzBoJrmQ+pN7VTkSrwyP5UEQNSZArfcYf8wVOX+8k771xZRmxPXWsj3LJWEJsZ/MvASIuoxXLKSAJXtxKGx5lO6KgIQgief0i07m/Ilc+iyT6cTCQxRB+7SCysPJnkMuWSMxkSc7lyKQKZDNF9LI5T2zXTm9jNUGeeG7n+t+EuwzHzoM9jmP24+gncczrYE2AkwEMQALBC2INgtyOoOwDuQfkZgQxzN12zZJFkZDmxiOrW67+cBwHy7ExbBPLsddd3JAEcdE5+UGHbTskxpN877/+iIvv91Iu6Li8GoGoH7dXQ1IkRnonMEoG4doggai/ku2qmxRzJTJzOSzTwhv00NRdR/ueFrr2t93T17SRuJ+B7Bxf6z/Du1ODZIzq5tT6FI1DsSa+0L6X55u3VSWK6KfYeoynM3z9w/N8Zt8Ooj73jw2xbW6JUlcfwrXO+eKb4XIpq7rpmqZNqbhygsZGEQh6aOuIU98Yrup+1wLTtBkcmGFuNrfECVoQwO1W2bu/ddPJCeGIj2jMj6rKy6KkJsdTzM7c2XC2VDQYGpghlchjWzfuaoIgEAh52LGrcdNrzXhNgHDEi6JIyzKNh4fmSKeW+//8pGFT7DJfNrg2Ocv+1nokQeDqxCwzmdwyac9UJoduVT6AopUlZ6bxSH5U0YUialUjFgukNm3McT17lrQ+t+LjarRm3NLGKxrNnm6mfLsZzl+haC+vrA8VLqPOaQTkMDFXAyLrqwxXslgtMkaCd2e/w1jx+oryWkmQ2eY/SIO746cSUEARNQJKFEXQMJyl1X7D0RktXONbY/+RHcGjNLm7CKs1uEUfkqjc9v1bcGwFARERUZCRBRlJVJAF5b6WgOu6iWlYlEsGpmktCYO3LZt8rrzi3O1k9k8R5vNTBWTcSistof8Rr7odAY1s+RSmnaDG93lcSgdj6f/IXP57uOTmZRLmBWv98ycHefuHFzl3coi56cz83M76sfdw231ObB0cu4BjnMcpfhOn/H1wVrvZzIA1iGMcxyl+E9QjiO7PgfYUghDa2rC3W6CKMvWewJZeS3TLpGCVyZtlsmaBtF6gaOmsl9oGFQ+HIp13fuADAKNscOLlMwxfGadc0PEE3HQfaOfAs7tp6KzFG3Dzh3/vy0wNzXLwI3s4+skDOLZDNpln5Mo45966xOTQLA2dtXzs157mI196HPcGJXnVgiau3RXZcmwyeok/vvohb070kyhXd2HmkmQOxRr5xe6DPF3fuahiqQZKpklWL2M7DlG3p+oGZD/FjyeaW2Ob7qpJsrgqMTZNi3KVs4tjcT+t7fcmUtI0bfquTVEuLx1NkGWJcNhLfWN4011kWRbxB9wEwx5mppY2xaan0iQTd86nLxZ1+vuml3krqKpMJOKjti60qXOsnKdEMOTB53eRTCzlH+OjCbLZ6hYFH0RsithOpbP893dPs6upFkkU+ep7pzk5MEbAvfSmOpHKoM8HC1/LnuGtmW8RUWvZHXyUbv9+3JKvKospB4eileOHk3/OWPHaMnKzgBbvNvzyxqtOLtFLu3cXk4FBzqTeXLZdt0tcz53FcAxebPwtgkoMgbVXGheyWL8/8Udcz56nvIIDM0Cdq3U+j/f2Rg4/KZAEmaAapcXbw1D+Mqaz9CJoY5E25jg2+9I69iogIaJKLjTRjVvyEdUaqXO10OTpptHThWeVLvH9gP7eSQauT3Hh9DDjIwl+8K1Ti9tSyTxDfdPs2Nu87HmyGCLq/QR+9QC6Oc5c8WXGMv+Zjsi/RJXiGHYKWQwSdj+DJjdjWFPM5v8Gy16ukjANi1e/e5q//u/vMz2R3tLXe3/AwSn/CDv/x2CeW8fzdNDfxbYGEcwBJN/fBe5efm9FhhzY0pnD67lJXpk4w7szV5goJrBWGK9YC3YHW/gvR/9Olc/u3sAsm5x5/SKF+SiNJ7/wMF/4By/QsuOGy70/4mdmNEFzTwMPf/LgvClPBdPDs3z5n/0lJ35wllf/9G28QQ/P/Nyjd/113Iz1SJEzeokvXznOd4cuVb1TC/BQTQt/Z9djHIo1Vr1oc3Zqgq+cO026XObfPvcCdb77917wU9w/iNcE8Pk3V3wSRXFV0ynHdlZUT24GobCHhnvQrQWwTIu+a5PLOqmaplDXEKpa/VdzyXg9GrcGa+ayJQqFO3fASyWDwf4ZnFuK9l6fRrymeiZqbo+Ky6UCS4ltOl1Y5pL9k4hNEdvWWJh/+plnUOcduOJ+H7/2xCGe39O95HH/4ps/wqNWFmgOUDRzXNcnGSlc5c3pALWuFhrdXTR5uqhxNeOVb69jvxW2Y813ac9wNvU2E8WBFfNrBQS8cpA27258cmhDrxkq3btaVwv7Qk8yWRpaMVKobJcYzF/kTwb+Nw5FnmVH4CFCShzpNtJk27FIGbNczXzIycSPmNMnV5kTFnCJbh6PvUidq/XHXka7VgiCgFcO8FT88/y1/h9XdEdePxwsLEpWgbJVJGummNMn6cudRRFVAnKEDt8eDoSfJqLWIa8zZmSr4Q+6UVWZctmkkC8zepP9vKyIPPr0dvYcbFv2vLj3s8S8n0aVarGcAqpcS3/iX+DMZwNX4oAEBEFFFFTccieGlcR2ll78yyWD/t5JvvMXx5mbrkh5BKFipBCvC+L1u9Y9F9PZc3/nNdulV3GKfwXmFSrZEh5QjiAq+0BuATEMggKOBXYWrHEc4xyO8SHYM2BN4pRfxhaDiN7fuGvnrYgSNW7fqkt/07KZSmRJZYtLFk1+j0ZH4+1HIWzH5pXJM/z1yAcM5KYoWvqGSe2PGyzLYvTaBEbZoHl7A7sf20ZDV92S67qsSgiigGmaWIaF4LmxLdoQ4ef/8YtYpsXxH5zl9b94j20HO2jsvncFz7WaR00Vs3x/+Ar//dqpqs/UKqLIMw1d/M6OR9geqtmykRPHWdlt/scBw3Mpvnf+CqeGxynoBm5V4Wh7E794dD8e9famRyXD5E+PnSZdLPGPnn9i8e//+/ffpKc2xpG2Ri5NTPPyxWscbGngrd5BsuUy+5vr+dSe7exqXB6vMjCb4Ltnr6DJMi/u30F98MF0XI5GfXi9D9Yohcej3bPZTdO0GRqYXUZsVU0mGvMjCNURNymytKKxU1k3K2NTtrMk3upWlIo6I0Ozy9RobrdCKOytGgFXVRlFXV44LJcMDMPCcVY2Cr0f4DgOhmUhiVIl3nELTnNTxFaVJeL+G1/0hzubifo91IeWEtPWWAjPTfp3BxvDLmPYZfJmloyRYLR4nXNpL27Jh1cOEpSjBJQIHjkwL1lWkYWKZNR0TEyn8tysmSSpT5HQJ8noCdLGLIazcmVFFlWORj9GVK3b9IyvLCg0ejp4PP4iL0/8KTkzfQuJctDtEjPlMY7N/YDLmRPEtUZiWiMBJYImetAkDd3WKVsFskaS2fI4M/oYaX2GpDGDvUoGokvy8ET8s7R6d6BVwaDqxwmyoNLo6eLp+Bc5kXiFqdLQss7tRuDM/4NjY81nvBatiklZxkwwXLjCntDj7AgcIaBE7xtpeCweYP+RDgzDQtNkPvPzDy1uE0UBf8CNP7DUiEkS3ChSDFkMIYkeBEdCFiOYVhLHsXAcCxa/6wIgIok+HEdfVkjIZUu89/plErNZLMumoSXC0Se2sf9IO8GwF0kW1/1eub3rd5G8W3CsKZzy6zjGeRAkBHkvgudLCHIXiKEKyUUBQaRiVW2CU0TQnsCxBnBKf4NTfhfMEezS9xG0j8ybSm19wUQWRUKqa8Ub4mwqzxsnr/H2mX68LnVJDnJXc+y2xNawTUYLc3xz5Di9mXHKduX36JFUoloAr6ytu0vc7lseRfKgwrYd0rNZLNOmpjlGpD68rBOjqDKiKGCUTPSywc3LS1mRaOyuZ+fD2+g/N8xo7wQnf3junhLbtczYzhRzvDF2nT+9dpJ0lTu1AcXFUw0d/GbPQ3QH42jS5gxbflLxfv8wRcPk+Z3dNEeCWLaDKktrmnV1HIdUoUgiv7TJMJfLkw36MSybuVyBq5Mz7Gms4289eYR0scR3z17m1Mg4zZEbUUeSKHJ1cpY3e/sJuFw81N5M2HNv5fabgdujrivu5X6Aokq43femcO84DqlkHuuWLnQ2U+Tdt64yNDBTFUuKZCLP5Hhq+fFtB8Mw0Q3ztrO8lmmTShWWFbpmZ3O88v2znDsztMoz14eZqQzTU8vVb7btoJcr5FZdR4TUAhzHYTqdZyaTY1tDHEUSN33dtGyb0bk0pwfHuTw2zXiiouCVRBGvplAfCbCjIc6u5jpaYqFNHWsBVf1l7WmuW/GC98K+HhrCK3dhHWxKdp6SnidJpasqCypuyYtL8qKKLiRRRkJGmncLtamQC90uUbYKFKwsJatQIR6rwCV66fLvY3fw0Yr0eZMfliAIuCU/Xb59FGtyHJt9iaQ+s2wW1sEmqU+R1KeYLg3jlYO4JC+yoCKLMpZtYjgGZatAzkxTtHK37TL65Qh7Q4+zN/gYPiV0X8933gsIgoAquqhzt9LgbidrJskYK89aVwOWY5IxEmSNJCWrgG6V2Bk8Sky7e1mwt4PmUtBcCnsOthKrCdC+hpBxVa6naFynYPTioQfDniVTOoaDyWzhu2hyM2VzBFFwYzslwMFyigiiinCL03ixoHP+1DCGYeH1aTzxkZ08+4m9NLZE15Xd96DAMU7jGFfAyYGyD8H7Swjq0yB4V77mCAABkGpBbqqYSQFO6WWwhnHKbyO4P1Pp8G4xZEEkoK68WJxL53nnTD+HtjcTD/uWxLdFg7ev4hdMnXdmrjCQm6JsG8S1APvCbewJtRJV/aji+klHQH5AXLHXAsehVNBxbBtfyIvbu/wzcHk0JFmiXChTzi/vbKqaQnNPA/XtNVw50ceV49d58XefvxtnvyI0SUa5jbt3qlzknckBvt5/jsFsoqrHjru8PFnfyS907WdnuA5JFO96mfH1wX6uJxPsjMV5rHlpYsGbQwNcnZtlV7yGx5pbSRQLvDrQhypKtIVC9KeSDCST6LZFxOVmRyzOw43NqNINrw7dspjIZTk2NsJQKkXZMgloLnbHa9hTU0uNtzqS6KDbxdXJWUaSaWoDPtpjEcJeV9XmlC3bQVNkDrU2sLuxDt20eONqP6lCkWyp8j03bZuhRIpXLs3QXRPl0c4WOuIRVPnBvX+omowkP1hrN1mWUNW7T2wdp0LY8gV9WSfUMCwmJ1LLInq2AqZhY+jWqsTWcSp5sre6IUOlkzo6nGB0uLrXupVgGBamuTFim8gVePfqIG9c6qMtHmFnUw07m2qpDfrQNlCISeQKfHBtmPd6h+mdmGEylSVdKGHZDgKgKjJBj4uTfaN01EZ4pLuVx3e04dO0TTlcV/XKEPGt3D3c37L2Rb6Dg+GUMcwyGbM6XwKvFKTDt5sj0eeJag2LBHmzEAURrxxkf/hpTNvgQvo9pkujq872FqwcBevOA+grHguJiFbL9sARDoWfJaTW/JTU3gQHh7JVZK48zmRpmKnSMEl9GsuuroHC7Y4/WRrCSb6JLKqooouAsnEr/2qjpi64ooNiIV8xj7o5/y3kepxk6S2mc19Dk5sw7TQF/Qph97Pk9Itky2ewnBKC4CJXPgNApnwCRYojCksX5YZuMjGSwLYcGluj7DvSTktHzaYuWvc19JNgT4MYRFAPIKhPItwhD3gBguAB9SBYozj6GXBSOPo7CK6PAVsvuZNFkYDiWrGDXjZM0rkSLz65m4BXQ1zHwrZo6Ryfu0bZNnCJCkdj2/hUwyG2B5qqkkH74ENAlEQqVY6Vi7PeoBtZkcilC2RTeVbqxQaiPgJRP6V8manh2a084TtCk1Y3jyoYOu9PD/HtoYucTYyv2xH7dqhz+3mqoZMvtO/hYKypinteH85NT/Lm8CCO4ywjtuemJ/nRQD8AjzW3ktV1Xh/sJ10u0xqsdEULpkHJNLlgGpycHEeTZPbW1uFRFEzb5lpiju/39XJmagKPXEmlKCXnuJaYYyqf46MdXcQ9m5eN7m2qo2yajCYzXJqYZnAuRWc8wpG2psURtLXAcW5IRQ3bXtLR8qoqPXVxBMClyPg0DRAWfVlMy6Z/JkHfTIKeuhh+l/ZAk1oARZHvemTOZiGKwj0h445T6ZYaurneOO+qwnYcbHv1ppNt2+iGtcyp+G7Dtp1lM75rxeBMivd6h3jjUj8edZRt9TF2NNbwyYPb6WmIr5ncOo5DtljmRxf6+N6py1wanaJ4i4zcobKumE7nmE7n6J2YZWA6SbZU5pldncQC3g37fdz1q4MsyCji3ZktUASNoBqj3buTvaHH6fDtqfoxREHELXo5Gv04muThcvo446V+8maGav0KXZKXuNbI9sBh9oaeIKLeuev2k4SFrulI4RrXsqe4lj1Lzkwt63yLgoQmupGESvdf4IaccjmcxX/sefmxaRuYjn5bZcBUeZhL6WMElAg7A0fvm+KDosgrSp9GBmexTIud+1oW/xb2PIduzZItn6RgXEVAxa10UOf/FXL6aYp6Pz51H6adIlV6l4LZR658ioB2FFkMLtm/bTsUCjqO49DcFicY9v74klrAsQbByYLUjiB1IYjr8wsQBA+O1AJyB+gf4JhXqUQDbT0kQSSgaiv+IlyqTH0sQDJbwKXKqKqw5puO4ZgM5qcxbYtWb5zHYtvZE2q9b34b9xqCKOAPe8ml8uRSeYr55bLcUDyI6lJITKSYG0/SfaB9+X7m55Us06R4j50xVzOP0i2L03NjfHPgPMemhrGqPJu6M1zLp1t2cji+3BDvfobtOFydm8W0bZ7v6GJ3vAbDtjk+PspfXjzPS95eWkMhPIrCbCHPG0MDfP96Lw81NPHxzm58qkpfMsHfXLvK9673EnF7+Hhn950PfAeE3C6e39lNIl/k9Mg47/cP817fELsba+9IbEVRQBZFTNsmVy7j1VQyxRLpQnExJWPhcdoKRHXhmyEI4FEVnuxuY2AmySnvOG5VIeJ9cMewRPHHP2KwWrBtm1LJuKekdi2wLBtdv//PczU4jsPAdIKr4zM4DuTL+qJ8eEdTDZ11UdbC3CpRfg7Hrg3z9ffP0Tsxi3WbgsACSobJueEJErkCLkXmiR3tqzZL74S7Tmz9SpgmTxeWY1K2ixh2eXFmsVpQRQ1VdFOjNbE79CjbA0cIbmHOqyAIaJKbI5GPUqM1czb1Fn25s+TNLLpdvC0Rus1eUUUNl+SlxdPD/tCTdPr2okoP7lzJVsByLJL6NOdS73A6+ToJfWrJdhEJTXLNz24HiCh1eGT/osR9VTgVF2XLsTAcnaKZp2BmyBgJyna+Iju2S9gryMZHC9foy52n3btr3UZo1YRhWAgyiJKIZdkrVhIvnB7GvIXYuuRGmoK/Q9EcRDcnEUU3XmU7ihTBr+1dfFymdIKp3F+SKb6PIkeJeT+NIi2NAxCEio2+oYPLrTxw8qv1wrEz4OgIYgjEjXXsBcGHIMZxsMBOgnN3TJZkQcSvaIu1nulElnypIqvK5ErURQP89WvnePxAB9GAZ7Hj4NYU6qKrf89txyFrFLFx6PTXEXcFfkpqb4IoidS2xpkdSzAzMkd6NrPM/KO2LY7L62JiYIrR3gmM5wyUWyRx2USObDJ/X5gZ3UpsK1F8lazar/R+yHtTg+hVVtOIgoBHVvEoKqZtP1DRO47jUDB0vrBjF8+3dxJxVxZ0tV4f748Oc35mioJRKXBdmp3mw4lR4l4f/+ChR6j1VkarDtY3kjcM/urKRV4f6udjHV0AmyJQA7NJHMCrKnTGI5QNk68eP7MszmQlqJJMxOthOJnm5NAYLZEQQ3MpEvnimha6C9BkmcOtjRxpb+IvTpzj9MgEqizzRHfboinpT/HjC8epRBOutI4WRQHX/LjVVsPjUW9rHOU4zpLs2pshSSIut7IhefB6obmVDf3mddNiLJFmLLE02aI5FqIxEsCrrc3bxLIdptI5/vjNk/RNza3rt+44MDKX5i/fO0vY6+aRba0o61CGLOCuE9s2707qXG2MFfq4lPmA/twFUsY0hq2zUOq4+d9rwYJ0TkBEEVXavDvZEXiIbv9+gmrsrhn5iIJIu28nDe4Oxot9nEq+xpXMCUpWEWcxqfF2r2uhhyiiihodvt0cDD9Hq7cHj1xlKaIgzAfZrPyl2apqooCIKKx8XFFYe97vwsItZ6Z4b/ZvOJt8i9KyWCSBkBpf7HQ3ujsQNylDL1tFxop9nE29RW/m1IpyecPRmS4NM1K4yvbAkU0dbzOYGEsSi/nxB9ykk3lGhpbPGo+PJvAHl88rSqIXn7oL1F2r7j/gOoJP24tpZ1GkMLDcCEqWJcJRL6WiTiFfXhKu/uMJm8pvXISNftcEYd5cCsDibpWABYHKHN/8Z/h//fmbvPHh9cVtkihg2g7f+NHZJc87uruVf/c/fO62+xaFyl69kgv1dgWln0BIskjbriaunRpgYmCa2dEEpmEtmUFv3dGIN+Th2qkBLn1wjSMf20dzTwOCIFR0JQ4MXBxh6PIYiirjC90b99IFuG5yRV4gtXmjzL899ybHpocomNVXIdiOw2vj11AliX+87xnirsp78CB0xgRBwK0o7IjGF0ktVH6PdT4/F2emFxeIY5kss4UiO2Nxom4P5k0Lx7jHi0dWGE2n0W0LdY2RS6vh5PAYP7jYy2Q6iypJNIaD/O5TR9dEKAUBjnY0M5nJ8a9fegO3IrO/uZ7OeJSoz1uxHRQEpFuKXKIoLLqlCgiVGWlBQJEkfvnhA/zxeyd5+9oAQbeLhzserM78T7F+iIKALEsrruP9ATcHj7Sza0/Tlv/OO7pqb2scJYoi8iqF+1jcz4HDbXTfhUSHnbubNuRfMpHMMp3OYVhL12iHO5qI+dd2P3Ech0yxxJ+9c5qB6cTiOMHNqPy2Fz6ryr3r1kLZ+ZEpTvSN0lYTpiW2/oipe7LC0EQXLd4e6t1tPFXzeTLGHLP6BHPlCVL6DBljjryZoWjlKNslzPmuroODIIgogooiaiiihk8OElZriKh1xLVG6t1teKQgqqihiPfGPVUVNZo83dS4mngy/nlGi9cYK/YxUxojZcyQN9PodhnbMZEEBVXU8MpBQmqMmNZAvauDJk8XXimAJrmRtsA4ptWzg7rm1lUdpD2SH5HqV7wPhJ9iR/ChFR2fZUFBk9ZuCmNjc2z2Ja5mTq4Y79Tl28fR6Mcr7tGia5mx0UagihrNnm3EtAZqtBbenvkmWTO57HFZI8FkaeieEtuXv3OKJ5/Zyc59LZw/PcRX/tPrhCNL5z1npzMcfXLbho8hoKKIFVK7ElxuhZ7dTcxMZRi8Pk36lkDxHzcIQgAHFexMpdu6Edh5sOaokOPwxgnyOiEgoNzkFv/3f+5JfvPFh29+wIoc23OHarkkiIQVL1O2SdEqY9g/7sWN9UFRFfY+sYN3vnmCzFyWkd4JRq+O077nhoqi53AnsYYI/WeHOf2jCxQyRZ7+mUdo2dFAIVPk3FuXOfbSaaZHZglGA7TvvrcLflWUkW8iVXPlPP/mzOt8ODNK3rhzHuRGUTQN3p0c4D/IKv/T/mdxSfdfEcV2nGXdJwEIudzLuswLwzILxQGAkmUwmc/Sn0rw7v+fvf8Okyu9znvR386VY1d1zgE5Z2AwOXKYSVEUJdKSbFkyZdmW7eNj3SCH5/icc+17dB2Oo7IsURIpiXk4HHJywMwg59jonLty3vH+UY0Gerob6G40Bj0k3nnwYFBVe+9vV+397W+t9a73HZ6rsloyTPKGzvbaeoq6geq6u7njszs38dzmdVi2jSAISKKAV1VRl6CKDNAZi/Brj+zlFw/uQEBAlSVsx0GTZVRZ4lOBjTyzeS5l+lcf3jsT6Ms4Dvz2xx/DrSqzdOVf2L8Dy7bxKA+qtT8NECUBl1tdsIVJ0xRa22M88cyWe+q/DlX1eUVZ/LqXJBFNUxCEauXxVni8Gl099Tz5zOq3Q34QiiovGmDfDkOJNJPZ+euzzc1xwt6lrcsNy2IokeE7xy5QrMxNXiqyRGtNiC8e3Ma21npUWWIsnePNi328dv46I6m5leIjVwdZ1xD76AS2N4PTauDpkfxE1HoMTxnD0TFtA8sxsBwLG2tmUr9pLyIgzFT3xGrP7kygq4ouNMmDyN1LVN/d+QkoQtWeyC358coBWr0b0K3q+VmOge1UKzsC4ux5yIKKKrnQRDcuyXtPz0MR1fsS+GuSZ9Usiq7nz9JXuEDWSM5bKNRqLeyNPkO7bxMu0bNq3+ONa1cSQrR5NzBRHuB46uV5n8ubWRKV8VU55krx7Kd2UhOtVvolSaSlI8YXf/GhOZ957Yfn8PhW3vNe/V4Xn+y9fheHHt/AsXeuMTac5PypQRpbo9Q3rR1hrVWF3ArmBRxrGMxr4FRAWMb36+g41tBMb60K8nrgw1nAiYIwZ2Edj/hmH9DpXIlL/RPs2zzXN9swLCZSOV45eoWgz82mzjpcH6jmaKLMhmATyak8g4VpUvpPdnJjuZBViQ37u6lpjJAcT5OdzpGayNB+yxrIG/Sw47FNjF2fYODCCBffvcp4/xRun4Zl2uQSebKpPLZpE2uKsO/5nfftfFRJQrlFiXikkOHPe0/y6ug1snr5nvIPHGC6XOSNsevEXT5+ef1eXLJyzzhbumWhW9a8e+fW8XwQBcNAt+bT82RRXJKnoyJKhDU3naEIT3Z0LviZOq8f9yoILPk0bUbMaWVQJImgWyLoXriFSpEkfB+gOH7wsxF57noh4Ppo+b8+wN1DlkX8ARe6bs5RRrZtG0M38XjUZQka3gsIQjWo9Pld5LJzNQ4sy8ayLDxr2Lt4KlsgU7w5bgHwulTa41F8S7znJjN5Xj57lUxx7jyvSCLbWur424/vZWNTHL+7avFXF/LTHA3RUhPiT944yUjypoXRwFSK65NJcqUKfvfyvrc1kc6URQVZVIDVkahfKxCEahDukf14PgRV03sJ27YZuDTG+aPXGRuYpnNTE7sf20ggsnzK2+RIkrPv9tJ/aRRRFNj2UA/dW1rwh5YX8F7Mvk9SH59nsSQisiV0iCZPFy7Jc0+o6KIgElJjNHm6FwxsDbtM0czOBtz3w9e2ubVm1pahrjHMwUfWs37zXKXQgetT6JV7pxztcqts2NrM4Sc38tbLF3n71UsAHHx8A22d8Z84yx9B3oIjvg/mVRz9BHblVUTXU1Qr2ne4BhwL2ziBo78J9jQIAUTtMAgfTl+9wMzieubft1p6pHJFvvvmeQYnUuDAlq4GOhqj9I8lefNkL7phUShVSGWL7NrQTDhw8172yBqH4xs5mepjuJTgUnaYLl8dte7Qh3Jeax2CIBCI+nj0Z/az66kttG5ooqlnLmVNlET2PLudqeEk+XSR6ZEkpWvzE2fN6xo49Ok9dO+cLy71YcEtKbP00aF8mh8MXeK7AxdIVeazau4FLMdmtJjl2wPnafQFeayhk6C6+vZQlm0zlM0wXSzQGAjgU28GaB5FxbJtioYx2+/rOA4Vy2I4myWvz3dOWOoTIubxUuv1ocoST7Z14l6gcqmK0pKrqneLc6lR3p68zkghjSrJ/Ezrdtr8NWhrsFr+AB89CIKAJInEa4NkMqU56xVDN0lM5+dVSO8HBEFAVSVi8QD5XGWOzkG5pJNOfbBVbm0hkS/OWmzBTDGkJkTQszR7L9OyGE5kee3C9XkJvY54hKe39rCroxH3LYlvj6bSrCocXNfGeDrPH752bPa9smEylEgzlEizsWl5grk/MTOPYVqkM0UGRpIkUwUKxQr6TD+fLIloqozP6yIc8tDTEcftWnq1UjdMevunuHJ9Esdx6Omopb0luqx93MBUIsfl65MMDi/urRrwuzmwq4Nw0LOmFGQlRcLlUek9N4yhW2za27nswFavGFw/P8J7Pz5HU2ccVVNQFHm2pbBS0sllihi6SV1zdMFKq+3Y6HaZoeIVyh+wTxIQ0EQ33f7teCX/PQ0oXZJnRpRsPkfTxp5lH8iCsirm4XeDWDywoHBB1/r6eabnqwlRFPAH3Rx4dD0To2kunR3mtR+eY3I8Q2dPHdHaAF6fC0WVZq71O39RXr9GR/dChif3H4K6A0HvxLEGwbyCU/xLbKeAIG8AqR5B8MMtdF/HscEpgDWGY17BqbyMox8FZJA7ELSHPrzAVhCQFxF1KhR1zl8fozbiQxRF3j8/gCQKDE+kuTY0zbMH1zOVynPkbD+t9ZE5ga0mKuwId7A70sW7iSu8M3UZj6RxILaOJnd0DmX1pxHCTM/R/o/vRJ7pj9Xc858tDR21PPz5fWhulXPvXGZiYJpyoYwoifhCXurb42x9ZCN7n91OIHL/ksRuSUEWRCaKOV4b7eXb/ecYLmTuvOEqwrAt+nJJvnbtBEHVxa6apkU9mpeC4WyG6+kkXkXDoyjolslgJsPrA/0gCGyvrZ+j7NscCKDJMpcSUxwZHqQ5EKRiWZydnGAom0a/Czp+T6SGjbE4748Oc2R4kK21dfhUDdO2yVYqVCyTiMtN2P3heD0PF9L8ePQSZ1OjeGWVQ7F2Gr3hB4HtA6waJEmkuSVKf9/cRHy5bDA6nMK27TWhNK1pCo1NEQb6prFuEZLK5ytMjmewLRthDYxzIWSK5VmxSKgmuTviEVRZWhKTJJkvcXFkksGp9JzXParC1tZ6HlrfNieovQFhpnJ7aF0r3zp6nnSxNJuomMwUGE5kfjoD20KxwsBwghPnhjhzYYThsRTpTJHSDMdbVWQ8bpWasJfujlrqYoFlBaWlksF3f3SGt97vxbYdnnl0Ix9/cgvtLTXLHuv4VJYfvXGRV966tOhn2pqidLXFCAXc3PeIaAaiKNLSXUdLdx39l8ZWvJ9SvsJo/xSVos4XvvpkdQHnMHuamWSea+eGcSyb2qbogjeU7Vik9SnyZgbrA726oiARUCIElRrke0y1FpGQRQUBYUHFvqpVkAn3oEd6uSiVdIb6ppmayOLxavh8Gl6/i8aWyD01XS/kyhw7co10olD17pMlRoeSjA2neOfVS9Q2hghHfGguGVGaLz61ENq64ms3sJVbEdTDONYIGOdw9HdxrAEE9SCC3I0jxmYCVZHqha+DncAxr1UDWrMfMEHuQHQ9hyB/uJU3aZEnmCBUaUmHd3SiKhIvvHWBkakMumnhUmUe39NDRTf5Z//pO3MejtV9ikRVH0/WbyNRyXExO8z3Ro4xVcmyJdhCRPPhklTkGfG4pcx4mqTQ4P7JorPXtsZu+74gCqzb3UmsMcK6PR1cPztELpVHliUidSE6d7TRtqEJ/30MagFckkLe0HlrvI/vDV7gYnryvozDcmyOTQ3zzb5zeGSVrZF63PLK5rrBbIbvXr0MgFtWKJsGg5kMFctkf2Mzj7S2z7luN0RjbInVcnJ8jL84f5aOcBjTdhjMpAloLirWygPb1lCIh1tamSjkeOHaFa6mkngVBctxyOk6bllmV10DPdHlr09+IiCwpIX4A3x0IMsSXT11vP9uL4X8zaqirptMTmRITheIxnzIK1DQXU243CodXbUcefsqtxIJS0WdyYks6XSR8ApYjh8GihWdinEzaSCJIk3R0JJViQen0xy/PjzPwq2lJsS21nqaoqFFt3UpMvXhAF11UU71j2LMFFtShRIT2fyi2y2Gj3xga1o21/qn+PYPT/PK25ewLAdBAE1VcN1o5LYhX6iQL5TxejWMBZS6bodSxeDVd65QKFZwHLh6fZLB0eSKAltNVaiJ+GhqCGNbNrbtYNk2uUKl6tX1EYZpWORSBYqFCrZlI8sSvpAbX8CDg0NmOs9w7ySJ8QyCKDA9lgagpj6ErMgUsiWunR3m9DtXCUa8tHTXIYgC0boQLo86m+WyHJOEPlYNGj8ASZAJqfG7Vj9eCmxsLNuc55d7AyIiorA2brGJ0TSvvHgGQ7eoqQ1QWx8kGg9QE/NT3xShtj50T447Pprm//ytv55nP+I4DsVChb4rE/QxscjWCyObWcuUHgHR9TQ4OWynAOYgWIM4pcGZ1IcyE9gqgAlOibk+tRpIrYjaU4juz9+H8S8MVZVpiofoaYmhyBJul0quWKGsm7N5KZcqIzjMU87QbZO+/ASOY1PvDjNUnOZ6YYK+wiTflTRafTFiWhCPrN7iL317NHoifKX90Xtwpmsfkfow++rD7PvY/eujvR0EAY5PD3MxPcHRqaH7PRx+MHSRiObGr2j0BGMrsgEKaBpRt4czkxPkKhUkUaDJH+Ch5jYONrfQEpjr390aCvPpdRvxaxrHx0Z5a2iAiNvNEzPU4RNjo0RmKqqqKNEYCIIgzBO7UiSJRn+AgmGgzVCLVUliT0MTca+X7129wsmJMdLlEqooUev1sb2unvbQ8gVXflIgcJuKmMNH1mf0pxmyIrFxcxMer0YqeZN67DhQLFY4fXKAQ4+su++Brdersn5jA6oizXGAcByHTLrIhXPDHDzcsyYrthXTmg0oocq2iwW8S5ovddOibyrF2aG57TGiILC9rYFNzXV3TDa5FJl1DTHODo7PjiNbKpPILX+9tzZW3XeBTLbIm+9d45W3LmHZDqIg4PNprOuoozbmR5ZFymWTVKbIdCrPjs3NeBaged0OAtWMUfVidJBlEXmF/SvtLVG+/Ll9fPzJLeQLZXL5CrlCmR+8fI4zF0eWHXSvBdwIWqZGU/zo6+9x+p0rFLIlwrEAhz++gyc+vxfHdvjBn73D+6+cZ3osTaWk83989Y8A+PV/8wXijWHe+cFpXvmbY4wNTqNqCm+/cBoE+LV/+Vk27+uareo6OJSt4oJejQLCPeur/SAMu0zByi74noiILKozveP3H1t3tbFpWwvlks5g/zT91yY5+vZVLl8YYd9DPfy9f/LcPTmuILDq3rX3++F1R4iBalAqxrGLfwDGRarpWxswwPlgAkugWsGVQNmI6P4CovvjIHw4VMKlQJyxlTl2cQhNkRkYS9I/lsS2HTRFolDSEQVhRvF1LsZKKX75vf88j9Xg4JC3ypzPDAHLC4A2B1t+agPbtY7BfJo/uXIM60PyX74TLMfhr/rO4pJVfrFnN7Vu37IXlptjtWyOLY8OtyVey5b4wtt8Zt3G2f+v9/v57cOPLfi5Rn+A3zr0yLzXVUmiO1LDb+77Ka3K3gaSJC76jDAta0E/9wdY25Blkc6eOmLxAJMTmbl05JLByy+dZdvO1qrX7H0MGm9UbCM1fiqjKSzz5hyYSuZ56/VL7D3QhaKsPTqyYVmYt7RICAh4NXVJatNjqSzXxqdJ5ufqKAQ8LjY119Iev3OiTZEk6sMBpFvaL8u6MY8BthR85APb81fGuNo3iWU7KLJIU0OYf/prT9NUF0JRq9l/x3GwHQfbdlAVGbdreaft8ah87rkdfPfHZxBFgT3b21jftTIqpCyJBHwufF4Nx3Zmx3b24gjnr4xi3Dsdn3sLB/76v79Cc2ct/+D//CLegJve88P8+X98idZ19XRuauTTv/Io+57azNsvnGaod4Jf/99/BgCv340kiTz9xf3EGsO8+9JZWnvqeeyzuxFEAa/fPY+RLS4SuDo4VOzSgtTg1UbWSDJaur7ge5rkwS+vnax5cjrP5fPD9F6Z4NqlUYoFnXDUyxPPbmXb7ntHd41E/fzC352/MLsb3Kvq8qpC8CK4nkBSNuMYx7DLr4J5HqwJ4NaJWgIxjqBsRtAeR1B3gtQAfDh9tUtFfU2Ajx/ezLdeO0NZNzm8vYOA10W2UBWM+n//txeQJYlo0IvfM1/B8MO4Hx9gbWCtBLS3omTqfH/wAm5J5u9u2L9iSvIDrH3IioTmUha0JyuVDPL58oLbPcDahiDAnv2dTIynGR2+aaWn6ybnTg9y7swge/Z1EQje34Swy6Vw+NH1vPCdk2TSN6uN+XyZM6cGOXdmiI2bG3GtQKPnXkKserRg3RA8FUCVl6bUfnl0iksjU/Ne39neQEtNaEnBsSQKBD2uua4Llo1uLj8ouqvAtmgWGS6N0uXrQFxEdOQGylaFolVEEiT8su+On18qRsczTE5Xq2Z+n5vHD62no6UG7wKZm5Uqp3lcKs8/uZl9O9sQBIGaiI9gYGU3jzBjPC4izHFJkaS1l8FZKhwH0tM5+i6OcvF4H8ffuIgsSxTzZQqZIuODCVp76vAFPfiCHlweFVWTCUXnKkW7PBoenwvVpeD2uQhGfQtKuIuCiEcOICxwDVmOSbIygeVYVd/je1S5tRyLqcoovfmzC77vlYNEtfr7ooa8EC6dG+Yv/+gtGpoj7DnUTWt7nGDIg9ur4vXduyDKH3Tz9Kd2rOo+FeUjkI8TRARcOFIjghhAUnZURaLsAjhFqvRjBQRP9Y/oAzEMgg9hDfRkfxBel8ruDc00x0NYjk004EWWREzLJpMvcbF/gmLZYFt3A42xubTMgOLmZ1sOrep46t1rJ2l0t7Atm9REBlES8Ia8qNra+/0/6nCAiWKOHw1fJqi6+HLP7qpO+Uf0mfsAi0MQQNNk/AvYriSmcowMJdm2o/U+je4BVoIb9+nBwz2cPt7P5HgG85ZqqK5bfPPr7+MPuNm6vRXXHbzV7+U4NU3hiac3c/TdXnLZ0qw9keNAOlngT//gDX7tHz5NW3tsQUHP+wVFlpAkEWuGNeo4oJv2HeOmimFyeWyK3on5gri7OxppjoaWNM8KgoAmy3PWzKZlz6FHLxV39a0m9CTfH3uJv9P+FUzHxHYs3JIbl+SaE7gOF0d5N3GUkfIoLtFFj7+Lg9G9qOLd0wayuRL5QrWZ3O1S2NBVh6bKC+53pYeSJJFY1E8s+tG27Ll3cDAqBkbFYNOeDjbt6Zj9siVZpG1dA9oqZqdEJIJKdME+WsuxyBoJJsoDaKILTVr97J2Dw0jxWtVuaBGv2qASpd51/yw3Poim1iiPPrMZvWJSyJU5e6IfSZYIRbx0raunc929EWOSFYloLHBP9r0QHMeZJ15wPyEIMgjhatAK4FiASZWWLFLttV37aie6aTGdLiBJIhIiueJNAQ9VkTmwpQ3DtKgJ+ZCluQknn+Lm0037VnU8P0mKq8Vcid/9ra+hqDKf/OrTdO9Y+bzhOA65VJ6rx/u4dqqfTCJXbc8JeWnsrqNzWysNnWtTeO1ew3RsrueSfH/wIrVuP081dSN/CFoMD/DhQhAEPF6NhsYIl7Ojc94bH0tz+cIIBx7qWbMiPg+wOGprg+zZ38nkZJa+3rmidNd7J/n2Xx2lmK+wY3c7wWXaR94Kw7BITOW4cnmMmpif5pYo/iUWs0RJoKEpwsHDPeRzJcbHbirCG4bF5Utj/OWfvsOzz29jw6YmfP6VFxYqZYOJ8Qy91yZobIrQ3BrFvcxWyxvQFBlVltBnA1uHTLGEbd8+sLw2nqB3ouo3eyuao0G66moIeZd+fvYqrd3urmJrlTiXucBLE69QtEpYjkVQ9tPha6fH34Vbqp7QifRpLuWvIiKSp0A2lSOm1bApsP6uT6Csm1Ru2PrIIqGgG2ENWeT8NEBAwON3EQh7idYG2bC7g3hjGNOwyGeL+IIeJGlpFXpRqvofFm9DF6oqH0fxyyEKZgZrjohUlYp8OvU6XjlAXGtBFldvEWzYOsPFq5xOv0lv/gyGM9+PUBVd1GgN1LpaVu24d4uG5iger8b1qxNcuzTGUN80mXQRRZGwLfueBbYfNhyqC9g1C0FiDlXjI4JkpsCL71yc/bdp2ZQqBqoisbmjnqf2r1t0W1WUafPFP4xhfiShlw3e+c4xHMdh38d20Lm1FXFmvrxVR2ApSeDxvkmOvnSG9184yeClEfLpQnWhH3ATb6lh3e5Odj+9jR2PbZrJp/x0PSvLlsml9CR/0XuSWrePDeHae05LrlgmiUqe3tw0E6UcBbOCYVtIgohP1oi7/bR4IzR7w1X/39vsazCf4kxqhOFCigZPkOebNyMikDHK9OcTDBdSpCpFKraJgIBbUqhx++j011Dvrgq0LQWO42A6Nn25afrzSaYreUqmUe27U1Qa3EG6g3GimhdhDV5HgaCbrp46Ll+cG9jmc2XOnxvm1R+f55HHNxCOeBdkhT3A2oSsSOze18noaIp0skAqVZh9z9AtTh0foFw2GBlJsX5jAy2tNUSivkWtgG5Mr+WyTi5bIpnIMzWZY3Iiw8hQkv6+KQ4/up5ojX/Jga0gCCiKxOFH1zMynKRQqMxhDugVk/feuUalbDDQP03P+nqamqMEQ547jNOhVNTJZIqz45wYSzM8lGRkOMmzz2+nti644sA26NHwaCr5mZ5Wy7YZSWZvWzF1HIdj14e5PpGYF5Tu6WyiPuRHWaIeke04VAxjTtuSKApzem6Xirte8Vdsnden3iasBJFFmeu2yWRlGlVU2RDoAeB89hIRNcyO0BbyZoGTqbOcTJ+568DWsmxM08Ke+eIFhKrI092e1APMg23ZXDs3TCFbYnyoSjk49941kpMxWrrr8PrdbDvUQ2Iiw5EfniHeGMEyLQzDZN8Tm3F5tSUVpfwhD6EaP4NXxnjvR+dQVJmurS2EojctLERBxC35aHR3kTamKZhzPRIdHC5mj+JXoqwP6MRdTbgk74ppwVXf3BIpfYqpyjDnMkfoz1+gYC3szRjXmmnxrMOvhFZ0vHuBxFSWS2eHKZcMPF6N9u5aKhUDQ7fw38OeFGemt321POZKRZ1K2SC0SLbdcRysu/CIfIDFcetjy3YcMvkSpYpByLd2RK4+yqgUdZLjaQYvjZBLFcilCuglHcdx0NwqgaiPeEuMcDyAsgBdOZ8ucOq1C3z3v/2IgQvDQJUxI4gihUyRiYFp+s8NMd4/STgeoHl9A/Iao/WLgkCDJ0BE85AzKvTlkqt+jIKpc3RqiK9dO8kvr99DZ6DmnjAAHMdhulLgUnqck8lhTieH6c8nyOglKraJLEiEVQ/NvjCbQ/XsibWxJdxASHUjLdKqNZBP8M2BU7w9eZ2d0WYO1XaSNyq8O9XHselBLmUmmChlKVkGIuBTXDR5Q2yLNLI/1s6WcCMx1+3toCzHJm9UOJ4Y5J2J65xNjTJcTJM3KgiCQFB10emvYW9NG/tjbZQsA3WNeVGHw142b23ixz88S+UDbhMjQym+/+0TVMoGXT21BENeNK1qN4fjYNkOlmljGha6YaLrJrZlUxML0N75IEF3v9HYHOHgQ+tIJ4u8985VCoW59j9nTg4y2D9N9/p6Nm5qor4xhN/vRlHlKptIoOpGMhM/lEsG2UyJxHSO8bE0w4MJxkbSZGbcF7Ztb5mNMZaDto44Dz+6gXyuzJmTg5RKN7U1KmWD9965xvXeSdZtaGDd+npq60P4/S5kWZoV3LQtG8tyMAyTcskgkykyPZVjbCTF8FCS8bE0+VwZWZZ4+LH1CwqqLhWxgJegx8VkpmqvY1g2l0YmKeoGjuMsuH6bSOc52TfKWCo3+5pAtfp7aF0bEf/Sq+a245Av63POQRJXJtR717O5LEi0eVp4KHaAoBygr9DPlXwvJ1KnZwPbrJFlna+TTl8HuqWTrKS4Vuhb8jEM06JQ1CkWK5iWjWnZWKZNRTdIpAqzHPaKbtI3mKBSMRE/EOULgoBLk2luCN82Q5cvVJhO5ilXFrfeCfhdhINe3PeJx/9BFEs66UyRfLGCW1NoaghjWTbpbIlcvoxp2SiyhM+rEQl5Zvp8BYolnUyuRKFYwbYdFEUi5Hfj97nmqQrats2JNy4x2j9FMVcGx+HoK+epb4sRqvHj8mo8/bP7OfLDM5x7/zrHXr2IJIs0tsfY/ehNBUhFlQjHA5QXUTqLN0XYsKudgSvjvPi1I7i8KuFYYE5gewM9gR2MlK5RMnPYH7DcKdtF3k28QEqfYGNwH3WuNjTJjSJoyKKMiIQg3PBNdar/OdW/Lcec+WNg2DolK09Sn+BK7gTX8mfIG+lFLX48kp8e/w7avZuW+SveWwwPJHj9R+epbwrT1FpDZ08dja3VLOG9VBnWdZOx4SSNLTXIsnhXwW0xX6H38hiJqRyPPrtlwc/YOJh3oM7cW9z6YPnJSbE1xkP8+s88NOe1iWSOd8/2k8qWFtnqAZaLi+9fY+jyKNfPDDJ4aZRsMofjOATCPto2N7Pn2e3semoLzT0NVQ/wWzB4aZQTr5xj4MIwsiIRa64hUhdEcSkU0kWmR5JkpnOcfu0C4dogX/ntnyEQ8a0ZhpNPVmnyhXiysZvNkTquZab5g8tHSVZW396rbJl8q/8cDd4An25TaPGFV2QDdDskK0XeGL/GXw+c5GRiCEEQ8MkabknFK2tYjk3WKHN0eoCjUwO8On6Vv919gMO1XdS4fHcUXMkbFc4kRziRHOKbA6fI6RVckowmybgkBcO2yBllTidHOJ0c4XxqjJ9t38VTDetxL1K5tR2HvFHhyGQf/+Hiawzlk1iOg0uS8SkasiBi2Q6nkyOcSAxzLTdFrcu/ZrQkbsAfcLN+UyPd6+q4fGF0jhKyaVoM9k/zR7/7Gs2tUVraagiFvKiqjOM46LpFuaxTLFTI58tkMyVM0+LwoxseBLZrBJu2NmNaFoVCVZCpVJy7nkynihw90svRI73IskgsHsDnc+FyqwhilRJcqZiUijrJRA69Yq5Yg+d22Huwi4puUi4bXL4wOs/Sc2oiy9RElrdfv4SqysRqg3g8Ki63Co6DbphUyiaFQoVUIn9PFb3rQwEitySpTcvi4sgkw8kMsYAXt3oz3nGc6jrr9YvX6Z1IUL5F9VaRJNrjETY31+F3zReSXAymZZPMF7Fv+R0USURdwfr0rgNbTXLxfMPTtHla0SSVRk89Ng4Xs5dnI2/LsZAECQkRr+zBr/jJGAvbpCyEVLrIuyf6eP9UH6l0kVSm+qdY0meDWoCxyQz/6ne+t+A+VFWmuz3O7/yLz9/W7ufc5VF+/8/f5tK1hXsnAZ5+ZAOff34nG7rrl3wO9xK9A1P89Qsnefv9a2zqaeDf/fbnmE7m+c5Lp3nr/WskUwXiNQH27+zgZz6xi6Dfhe3YnL00wkuvX+D4mUGKJYO6uJ9nHtnI44fWUxvzz0kAyIrMF3/j6duOIxj18eyXDvLslw4u+plIbZCnvrB4v53H52LH4XXsOLw4tfEGevy76M2dJWMkyJvpee/rdpmzmbe5kjtBTGuixbueOlcrITWGRwqgiRqSIM8EswaGY6BbZXJmiqyRJK1PMlEeYqzcT95MzR/AByAJMhsCe1kX2E1QXVs2DLsPdLH7QNeHftzpiSy/+zsv8eu/9TzxuiCStPzg1nEcDMPizPF+vveNo+i6uWhg6zgOxn0MbB3HAvRqfCsoCMjL6p+tbm/M9OIKCIKLtdiD6zgQC/mQRZGBseVX1W4kkUzHxnLs2X/fgICAKAhIgjjjbbv26I73Ai//2Vtz/i0I1e8im8xz5o2LXHzvKleO9fKFf/IJenZ1zKETXz89QN+ZQQQBog0Rfu3/+jI7H9uE5tEY65vkpT95gxf/8FWSY2le/YsjPPdLj+Pxuxas/n6YEGf8Ww/WtfFL6/ayNVKPW1boDsRIVkr8yZVj96S9wMbhDy4fJaS6+UTrRmpc3lW7xgzb4gcj5/mL68fpzU2hiBJRzcu+WBst3ggeWSFv6FzLTXIqOcxUucD13DT//sKrCILAM40b8Mq3XxROlXP8xfVjvDXZiyxItPujbAo10OoLIwsik+U87031059PULYMjieG8CkaLd4w26PNC+6zYplczEzwr0+/QLpSwgFCqpsNoVq2hBsJqx7yZoWL6XHOpEZ4aeQiEc2Dskb82m9AEASiNX6+9JVD/Pt/+wLTU7k5a0WoVu0G+qYZ6Ju+4/48HhVd/6haVvzkQZZFtu9ow+938T//4E1OHutD1xcOTk3TZmw0vfKDVSfhFY5T4vAj6/H5NL7x5+9y7swQhj4/OHUcqFRMhgfnCzAtbYzcdXKpIx6hPhSYFRN3gFxZ58VTlwl7XHTW1czSgnXTYiKT45vvn2ckOTeW87lVPrVnI363tqz5VDdNBqfTWLes31yKgldbPrX6rmYjERG36EIRlNkTqFJoHHRbv1EHm1NaFgURWZCwnaVnHjLZEucvj/LO0etVn0THqVrl3M3gfwJR0S0mprKUywb/43++yfGzg2RyJRzHoVCcZiqRZ2I6y6//4qOcvTTC37xwknOXRzFNC8eBgeEkf/T1dynrJs88sommNW6rIiKyJ/oUeSvDxcx7mPP8Qauo2CVGS9cZLw8gChLirIH7B286B8cBB3vmurWxsZd8rW4OHmRf9Fnq3G13dV4/SSiXdM4c7+e//39f5Jd+/QlaOmII0vImYMOw+MHfHOdH3zlF39UJNm1feFEG1cn4vtqNWAPYhT8Ex0BwPYmg7gUheOftbsC8jFP+EY5xDMQYkv+fgxhlLfblTiRyjE5nsezlz8SWYzNWSnE0eY3L2RFGS0lyRgnLsVElmbDio9lTw/pAI3trugkoHn7amkwkRcIX8qAoCumpDKZhYVRMjv/4LLKq8Bv/8RfxBm9SvRJjKZLjaTwBDz272tn95BbkGdXNeEsNT/3CYcDhz/7NN6kUK5x+4wLRhjDh2mVcn6sMVZRo90f4lQ37eaiunbDmRp6h4bb4Qvx8106G8mneGL9OxVr9wKJo6vzx1WNoksRn27csWslcLo5MXufHo5foy08TUN0cjHfwjzY+SkTzIs8wharWdBbXspP8h/OvcTI5xHS5wPeGzhHVvDxS133bY6T1Eu9MVZlvf6fnIM82baTeHZylMdvY/Ip5iP/f+Zd5ZfwKyUqRK5kpXp+4tmhgO1BI8vW+46QqRRyg2Rvil7oP8ET9OvyKC3Fm3KZjczIxxO9feYfjiaFVE31ZTbhcClu2t/Lzv3iYr3/tCKPDyXtSlXuA+wNREujoquU3/slz/OgHZ/jet46TTOTnJTDuFqomL1kjZiFIssj2Xe3E4kF++MJpvv+t45TLxqpei4IAmkuZx1RdDhoiAVpjIfwejewt4pDfP3GJXKnCE1u66KmPYVoW54cm+PqRM/RNJjGsm+tjWRKpC/n52Pb1eJaZMNVNi6FEZs56wutSCbqXL651V4GtKqpE1DCvT73NY/GHCKthruWuczF7hfHKJF8f+hYBxU/BLFK2KhiOgWNXg16XtPTBBgNudm1twbuAP+LJ84P0DSawLJuAz8X+XR0EfK55mQJZFolFfMjy7S/Q9V21/L2vPMxUIkehpFMs6iTTRd549wrTyfyKFnEfFhzHIZ0r8Y3vHefC1THiNX7amiNMTucYGc+QzZc5cvw6e7e38fLblxkcSdLcECbgczM2mWZiKke5YvD6O1foaa9d04Htjd83qtZzsOZ5NNHF2fRbVOyFRadsrGqAuso/n4CITw6yJ/oUmwIHiGp1SA+UNmfhOFWxhDPH+vnzP3iDT//cftZtblySYIdt2+TSJb7xP9/mvTeuMD6cwnEcFG3xactybIrm4m0E9xqOeQnHvADmAFDBEesQ1IWrywtCCOA4FRzjFIg12Pp7VX9b4f4qeF7sm+Df/snLc16zHYfGWJBHd91+AT5vX5khXp04z/FkLwk9R8mqoNsmlm3jUK3gyYLI+cwQb0xd4K+HjnA4vpFH45tp8kR/4iu3XTvaOPTJ3Wx+aD2egBtBEKiUKpx/+wpv/PV79J4ZoO/cIO+/eJrHfvbA7HalfJlyoUygxk+8pWZOJVaSRGLNUdbt7qS+o5aJgSmuHL/O/o/tuG+BbasvzFNNPTzfsoE2fxi/os1xU5BEkQZvgN/c+jBZo8yZxBgla/Xv7bFClr/pP4dLUvh022aku6Ak3xBd+vHoZS5lJhAFke2RRn5t3UM0ekJIwlzGistx2BCs49c3PMw/PfpNEpUCZ5LDnE01szPagl9ZvGrrALIg8omWbTzXtIkWX2ROr6vjOHgklV/o2ktfPkmqMjgrYqVbFuoHetcqlslAPsk7k9er9yHwhbZdPFzbTY3LN6/vd1dNC6PFDHlT53x6bMXf2b1C1XpF5tAj6wiFvbzy0jlOnxwgfYvg0AN8dCEIApIkEa3x8dwntrNpSyPvH+nl2Hu9DA0lMVdI21U1mbr6EJu2NLFjdxsbNjURjty+L/1O45RlicbmMJ/+/B527m7jnTevcPzodSYnsljmyhLxbo9KY1OEzdua2bGrjfUbG/EuQ4H4g5BEkc3Ndexsb+S189dnX9dNi3evDnJxZBK3quA4UKzoTOeKc4JaoBrU7lhPwKMtybv2BkzLJleqMDCVmqPC7NNUgis4p7sKbINKgD3Rnbw++RaTlSlUUSFr5HFJGrtC2xkrj3Mpd4UWbzPjlUnOZS6iCAr9hSFqtaX3KoQCbnZuaWFdZ+289zLZIoPDSSwL/D4Xjx3sobEutGCPrapId2xEDvhcbOypRzfimKaFYVhMJ/OcvzJKKlNc88I0pZLOS69f4OCeTnZsaibgd3H1+iQ/fusSF66MkS9U+Kvvn6BQrPDw/m7272zH7VIZGk3yx994l+lknpHxNCPjafKFCj7v0jny9wOyqFDnamN35Cn8cpjLuWOMlpbev71SCIgElSit3g30+HfS4u0hqNQgrTFK1v2Gqsk0t9cwMpjgxLvXUVUF07TZtL35tsFtpWIw3J/gu19/nxPv9pKYzCIrMpt3tvLsp3cuup1pV4VP7hcc4zJY0+DkAAek2PJ2IEYRpFg1m2uncfT3ELRDwP0NbGtCXj75yObZfwtUbX5qI37aGiJ33P4G1fiNyQu8MnGWM+kBpsrZeb3x1Q+DDhQtnbRRYLyUIm0UGCuleLp+O1tCrYuK63zU0bqxkce/eIjDn9lLtDGCrFSfV7ZlE2+uQdFkHNtmajjJ2bcuzglsLdPCNCxkRcazgIKnospE68O0b25ivH+S0WvjVEoLax3cS4RVNwdqW3msoYvtNY20+kLIi4gPqaJEZyDKr2zYz389/w7nUuOrXrk1HZvL6Um+P3iRkObmicblJWo+iMuZSa5kJ8kaZZo9YXZGWujw1yx4joIg4JZV1odqWR+s5URiiLypM5BPMlxIsSG0uFq9APgVjU+3bqPJG5on4HQjgO7w1dDiDXM5M0HerJDVS2T0EjH33MX6dDnP1cwkGb2EALT5a9gWaaLW7V/wfvPKGntjbZxJja7JwBaq30Ew6GHbjhZqYn527+ugr3eSwYEEkxMZspkS5bKOoVsIQvUe0TQFzSXj87mI1viI1virgc7WxZlCC6GxKcLP/NwBHntyvt5GZ3ftXXeXCEL1GP/8tz81771wxEdza3TZ+/QH3ew/1EMsPt+ir7E5sqZ8V6H6HUiSSCTqq/5esQDbd7UxPJRksH+asZEU01NZstkypWJltkdVlkVkRUZzyQSDHoIhD5GIj9r6IPUNYWLxANGYn3htALdbvauK7Y1xKopMvDZAKOyhJh5gz4EuhgamGRpIMD6aJjGdI5stUS4ZGGb1epRlCUWRcLtVAkE3oZCHSNRPXWOIuvoQsViAmpifWG0Al0u566TvuoYYB7pbOTc4wXTuZgIoX9Zn1ZIXg8+lsrm5jic2dy07OZgvV7g+maSkG3NqT2Gfm9rg8pMKd3WV+mQvO0JbqVhlxkoTlO0Kje56uv2dtHqaGStPkNbT1LrinE6f43T6HGWrjCiIHKrZv+TjqKpMTcRHzQJZE/8t1VlFlqivDdLSGFnxhSiKIi5NxHVLtluWJVza3V80HwYcqtTNh/d1s6G7DpemEIv4SGdLXLhSffhc7p1g3442Du/tYseWFiRRoKEuyPGzg7xztJdyxSSRzJPNldZ8YAugihr1rja8kp8arZ7+wkXGywNMV8YoW4VFhZ6WCwERvxIiotYS05qodbXS6O6k3t2GLNy9J/NPIkJhL5/6uX187+tHGRlMcPTtq4iSgCDAxm0LB7e5TInL54d59QdnOfLaZYqFCoGQm+17O3jkmc1s2724z6dp2+TN+xfYYo2CUwDBD1I9gri8XmtBdOOIURAjYE+DeRUWodh/mIgGvTx7YAO5YoXx6SymZREJeomHfXiW4FHtACdTfbwweoJTqT7yZgmf7KbRE6HeFcavuNEkBQEBy7EpWTppPc9YKcVwMcFQMUHBrKqyaqLChmDTvT/p+4B1ezrZ+vAGatvmJkRESSTWFGX7o5sYuDDM4KVRhi/PDSZuUNsEUVhUEM7td1HfHsdxHBJjaYzKh9c3KAkCWyMNHKxr46G6djaE4gTU22fjBUFARuRgvJWJYg7DtjifGl91r+qiaXAqMYpXUYm7fWwK1y2r4nADDg7nUiMkKwVsx6HOE6AnGEe5jWqwOHNNt/ujXEiPUbR0psp5xkqZ2wa2qijT5A3TE4ij3cbSTpVkIpoXj6ySn7EaypllYsxdT01V8vTnEzPVWoFNoXpiLt9tx97gCdLkCeGSZMr3gCq+WvD6XDO2KhE2bmliYjxDKlmgkK+g6wamaSNQtZNRFBlVnQkmQtWgJxTyLNnu5QaCIQ/bdrbemxNiJmgPeXjimWUwgu4Al0uhrSNGW8cyE7JrAKom09wSpbEpwvpNjUxNZElM50inChQLFcoVE9O0EKjOp7IsoahVUVWvz0Ug6CYS9RGO+HDdI2HYKotAob0jTmtbDRs2NjI1WR1nJl2kWKxQqZhYVvV6lCQRWZFQVRmvz4XPpxEIeojW+AiGPGirrI8Q8rjY09nEwHSKbx89T+E2Irq3QpUlNjXX8cy2Hhoj85Mid0KqUOLiyOQ8QmXM76UxsnxG0V0FtrIoU6vFeKb2SSYrU5TsMkHZT0QNo0kabd6bPp6SIHE2c4GskaPF08TW4NpSjf1JgSxLdLbGaKgNzgbnNRE/XW0xJFGYpVJvWd9Ic0O4Kn8OuDWF9V11HD3ZT7liki2UyRfvY4CwTMiiQkSrI6jW0OxZz1DxMmOlPpL6BAUzS9kuULFKVOwSlmNiOxb2TA8tOAhVuRpEQUISZGRRQRFUVNGFJrlxSV68UoCIWkvc1UK9u42QEkMW14Yy9lqFP+jmyee3YegWL3/vNIN9U7z/5hVwqtnx7g0Nsz6IjuOQnM5x9vgAr790jnffuIJt2cTqguw60MnjH9vKhq3Ni2aNbcfBcCxK5n1cYDkZQAcxjiDGYCUVfME7E9hO4NhTwP1niZiWxXQqz7vnBsjkS1i2g0uT6WiMsrmjnnBgcVl/y7HJGSW+N3KM0+l+KrZBh6+OraE2NgabaPXGCKs+XJKKiIDpWBTMClOVDP2FSc6lBzmT7idRyXFk6jJhxUuztwafvHLa1VrDjaRYU3c90frwop+LNUWJNddg6AaZ6YUFGIXqDhd8T3UpBGr84EAhU8Qy7/21pYgScbePLZE6nmrs4VBdOzUu75IDR0EQcMkKzzStI6OXKZg6vdkViqzcBmm9xLuTA4Q0N7+6wUOd27doJXkxOA705qYpmtXqRs4oczY1StZY3JcdqmJTo8UMxoyeQ94sk9ZvrzbulhXafVFUSbpjUtUlybOqz7bjoNvz58isXmayXLXtEATo8Efx3MHj1yUphDUPQdVNuZS77WfvB2zH4Vo6wXA+Q8Uyibl9tMdD7GldPXHH65kkJdOgNRDGp9xM8k2XCgzlMkwU83gUhR2xBvzq6hUKTNMilywwcHmUXKqIaZjIikxzVy2NXbUoa6y66jgWpp0nXX4Lj9KNS25CEpduB7NUiKJAIOAmEHDT2T2f5blUOI5JuvweLrkBVapDEquJDdPOka+cw6uuQxYDCCtk6YmiSDjiJRzxAmtDiFYQBFpjYT61eyO5UoWj14aYyhXnCDp9EEGPiw2NcT62cz37u1uWXeCxHYepbIGzg3MTtR5VoT7sJx5YPlvtrq/8KpXGRat8e5rGttBm1vm7q558kjqnl+YBVg+yJNLcGEFRbj6QNU0m4HfhcikUZmTR62uDBG/JQEqSSCToma2glSsG5Q8xm79akASZqFZHVKtja+gwGWOK6coYKX2SrDFNxkii2yUMW8ewdSzHxMFBFEREJFRRQxVd1UBWDuBTQgSVGiJqnIhajyx8NCr3awWCIOByq3ziC3txHIdXvn+GgetTvPvGZSzL5ot/+zD1TREEqkHte29c4UffO83FM0OIokC8PsjDT23m6U/toLktelv6sulYlEzjvopHOXa+WmEVPCCssC9H0ED0ATbY2erf9xnpfIk3TvZy5OwA61piaJpC30iCofE0tuXwyK7FFbd12+RSdoSjiWvkjBJd/jo+0biHp+u2EVQXf2i1++LsjnTxWHwLf9b/Oq9NnmO8nOJ0up/9+XVsDrUsuu1HCYJAVeRJAM2j3baHXFYlFFXGth2MW1RabdtekoehKEmoMxYMRsVcdaGVWyEg4Fc02gMRDtW283Nd26ldQbB4AxGXh+dbNlAwdb7ee4qp8ur3SibKRb43cIEmT5BPtW0i5vIti1bnAOOl7Cxd+kJ6nAvpxR0WFoNuWXekXKuiTGyJVjvCzH83xrjQpVKy9NkAXECgRrt9tfYGPLJKQHExscYCW9txKJkGX796loFsCttx2BqrxysrRN2r19pxNZVgulwg6vLMCWwniwXeHR/ix0PX0CSZf73/yVUNbLPJPCdevcCP/uII5byOoRuoLoUnvrCfeHNkzQW2tqNTMC7Tl/z/EPd9mlrfZ3GLbfd7WIvCdgyGM/+DGu+zRNyP3xLYZkmWXkORIkiit+p88BMEVZborq/hq88c4Ot+D8evjzCVK1DWTQzLwnEcJFFEU2QCbo2NTbU8t2Mdu9ob8axAwbhUMRhKpLkyNledvCUWoqUmjEtdfvHorn6RGw9S3TYwHbOqJrvIc9ItuXBJa5/W+lGHKApEQt55VGxJEnG7VApFvZrR8rvn0K2rPr/KbKLfMm2sFZhS32uYlk2pYmDbNn6PdttARxREwmotYXXlWbsHuHsIgoCiSHzyZ/chSSI//NYJ+nuneOe1Sximxd/5h08hAN//q2O88uJZxodTSLJINBbgZ/7WQR59ZguB0J0zuxXTIm98+D2Dc+DogIUgKCCsTGFVQEZArVLoncLCq9APGVOpPG+dus5v/dJT1NcEUGSJTL7Ei0cu8dbp67cNbItmhVcnzlK0KqiizNN123mmfjsB5c6/qSgIxFwBfqH9UZJ6ntcmzjFeSnMyef0nKLAV8IU8FLNFcsk8pXyZQNS/4GezyQLZRA5RrPaIGRUDRVXQywbWTP+Y47BokFu1TWLmc/f2uvIrGofr2vlS904O1K4OJbPZF+LjLRvI6xX+vPck+j3QvMjqZf7Dubeo9fh5uK6DsOZeRjLToWTdTK5JgogiVpX4lwNNku9Y0RYFAbe0eowhw7ap3PJ9umVlSb3s8sw5rjWYts1YIc9LA1f5NwefYl9dC4oorrqbxjNtC/dkb4zG2RiNU+P28OPBa6t6TMdx6L8wwnd//zUqRZ3P/8YzhGp8FPMVmrpq8fiXR5v+MGDZebLlY/i1bZSMPnRrCpfc+pErFLjkRjoiv3W/h7EoDNNCN6yqsJMzo5isKqiyhGnbmKaFZVfdZWRJxHGqjCxVkVEVqarYbjsENI1ffmQPj27o4OzQBIOJNKl8iWJFRxZFuutr2NHWQGdtlIDHNZuwdBwHy7YpVUwcx8G2q/oakiiiqTKaMjfsHM/k6J9KocoyqnzzvT0dTfTUr4xZcdephqJV4q3pd7mYu0yikpyx+ZmPX2z7OTYG1t/t4R7gDhAEAZ9XnfdQFARh1oPK41ZRFlCHvlVw64at0lrD9eFp/ui77zM6meHf/qNPEo8svAB8gLUHWRZ59tM70TSFb/35u/Rfm+TIq5ewTZtkIs9Q3xTZdAlJFunoruPLf+8xtu9pR71NBetWFEydZOU+K16KHkDBccoITnFFu3AwcJwyIFb3twYe/JblUDYs6qL+2faFgNeF16VSuYP6ZNkyOJcexLBN1gea6PTV4ZeXt/CKqD42Bpq4kh0loefozS+/CrZWIcoSDZ11pCYyXHj3Khv391DbunCPW/+5QXrPDGJbNtlknqMvnWHn45uZHk2RS1evfdMwKaQXvvZMw6KYq1blNLeCuEzrreXgofp2vtKzmx3RxlXdb1ewhk+3b2aqnOeFoUurum+oVjTLlsHvnHkdt6TwcH0H7jtQcm+F5diza6CeQJwD8XZC6vIolxHNc9v+WqgmKUTxbt0r7x5VttPaY+CZts1UKU9IcxFQXSgzSfD7/X2tCpxqxTY9lePpLx3i4Me2obpVcJwlOQ7cD5hOjmzlOA3+n2c48/vo1jQzhu/3e2iLYw08e5eLC30T/PC9S5y8MoJl2njcCp99dCsP7+jkUv8EZ3vH6B9LMp0usLmznkrF5EzvCM/s28ATe7pxawrHLw3zjZdPMZ0p4FJlntq3jp8/uJ2aoJeTV0b47998m3BjI3/63WMkMwXWtcZ5/uBGDm5tRzcteoen+Z8/OEY6X2IylaesG3Q1xfjEQ5t4ck/PnPFGfR6e3dbDns65rN/2eJi60MrW93cV2KaNDG9Mv8MrE2/glT0E5ABhNcRCniqa+KBa+2Hghvra7bJgslS1HPioZcqgGnCXKyalir4WClkPsAwIgoDmUjjw6HpkWeRvvvYu1y+Pc+zINSzTxtBN3B6VnQc6+eTP7qV7QwOqdvtr+VYUTZ1EeWXB5GpBEMM4ggb2FI61wuDLTldFqBBBiLAWPGwVWcLrVrnYN8G6tjguVWFsOksyWySwgA3brbAci2k9i+U41LlCBJTlVMCqEAWBGleQoOphtJQkbdzf33k1oagyWw+v5/qZAS6+e5UXfu9lKsUKWw6vn63c5tMFzrxxkZf+55tceq9a/ckl8vzeb32NfR/bydRQgkvvXwWgXKgwOTi94LGKuSJjveMIgkCkLoSyAprXks9LENFEaba3c7UgCSI9wRhf7tnNWDHH2eQY5j1oPxgv5vjjK8eQRZFH6juXeB4CPlmbrXTWewI8UtdNl395YjySKOKS7rQ8W93ntyJWf68bKFvGkvxpTdvCWKBn937BsC3OTk/wp5dOMZxP059N878ffZ2gpvFoUwefaF+P7Ti8OdrPiwNXyOs6oiCwraaOJ1u6qPP4eWWol6lSgYvJSRp9Qfyqxngxh2nbfHnDDtaHY5xPTPDDgaucmR5nfaSGL63bTltg8f74W3EpNcUrg700+gJ8qnMjAJZt82eXT+E4cKihla7Q4qrGetmgmKvgOA6BiAdZkRcVjFsLsB0dw0pSMSfwqhtwKx0YVhLdmkCT63EcG9POMJn/JrnKaUw7hyDIhFwHqPP/LKLgxnbKTBW+Q6Z8DMNOVK0W1U00Bf8uouChbA4yVfguef0iDiYepYMaz8fwa1VxrVTpbSrmMKadpWyOUDIGEASJzui/QJPqMawpkqVXSZXeRkDCo3Rg22Vu3Gdlc4R06QjJ0mtYdprW0G/iVTciiW5sRydVeptE8UUC2i5Spbew7Bw+bTM1nmfwaZux7CIF/RLj+a/jOBYVaxzHsfGpG4h6niLk3rcq33U84uP5Qxt5/uBGRFFgZCrDn754jA1ttViWTa5Yob0hwsPbO/mD777Ll57ZRTziI50v0TeWpKybvPTeJT718BY6m6Kk8yVeePsCLkXm8d094MB0qoBuWPyDLxzGsmy+//YF3r84yPq2Wizb5k9+cJTtPU1saq9jbDrDsYtDeF0qezfOZ1n53RqdtVHa4nPnGlVe+bPjrgLbnJnnTPocje56HqrZT1SLLOrhWedaur3PA9wdJFG47TNPEG7//gM8wL2CIAgEwx52HexCEAX+5s+O0HdlAtt2UDWZA4+u5/nP76ZzXR0u9/KovNWK7X0ObKU2HPEUWJNg9eGY1xHkjiVv71jTYPaBPQmCiqD0APdfoKwm7OXw9g7+7MVj1IR8yJJItlAm4HVxaPvtz8+h6pHp4OCS1BX3WKqijCLKWI69phbSdwtFUzjwiV0c+f4Jrp3s58ybF5kYnCb2pxE8AQ84UMyVmB5JMt43SaVcoWVDI1seWs+Lf/Qab/zVu1SKFYr5MuqMNcXAxWHOv3OZ7p0dqDMKn5VShZFrE5x/9wqCKNDU04DmWRldfum4Nw8alySzMVzLVzcd5N+dfo3+XHLVacmmY3M2Oca3+s/hlhQO1rXdcRsBiLl8aJJM3qygWxaWYxN13V+7rqXALVV7ZaF6zybKhQVFpj6IkmWQN+9zC8gtkASRZn+QT3Ss51JykqlSkWdbu2kLhmn0BtAkGcO2qPX4+FjbOhRRJKfrnJ4a47Xh63y+azNTpQJX0tMcbmrnzZF+fLpKqz9EslzindFBekJRGrwBnmjpZLpcIFUuL8uGqtbtI2tUKKYTTBXz1Li96JbFjwav8fG29QQW6cV9/0dnufB+LxODCYavTZBNFnjpa0c4/dYVRFGkoT3G/me3sn53dU62LJvEWJpjL5+j/+IohWwJt0+jdX0DOx/ZQENHfE6S8cL7vZx5+wqdW5pp6q7lne+dYrh3AlM3qW2pYetDPWzY3cFo3yQv/dk7bD7Qxej1KcYGpunc3MTWQ+t476UzDF4apaGzlv3PbqW5uw7TSlM2BvAo7UiiD5+2Ed2apmwOo8n1GHaCqcL3KRiXCbsPI0shbEdHk2oRBBXbKTFZ+Db5ygX82lY0uQ7HsZBEH4Kg4Dg6o9k/QRL9RD1PIQoaZWOQsdzX0KTfRJGimHaWdPldZDFAyLWfkOsQtlNBEYMICCRLr1HQLxN07cUlN2JYKSrmOLZTFVFVxBABbTsOJqPZP8KyC9zQv3AcG92aIFs+iUfpotb3GUw7S6L4MpnKcVxyC7qdYKrwXTxyB261i4JxmXzlDJLowa20rexiXwAVw6R3JMHIZBqAbKFM/2iSUqVqpeNxKTTUBGmKhwCBrqYY6XyJ45eGGJ3KUNFNHGDfphZCfjdl3eTYxSESmSKjU5nZHtv9m1vpbo4hCgLvnx8kmS2SyZdwaQojUxke29VNc20IWRLoHUkgCgKBBTxpJVFEUlc3+XlXga1u60xVEjxb9wSbgxsJKA9oofcbNxQxb7uceBDX/kTDoWo7sVYhCALhqI89h7oRBHjxWye5cn4E07Tx+DQiNb5lB7UABeP+V2xRtoL+JlijOMYl7PIPED1fBsF/5yqlU8LR38XR3wUqIEQQlH1VMan7jJDPzaFt7ViWPauK3FIXprslzqaOO1EmBdySim6bFK2q3chKULZ0KpaBJIqod6xmfXQgySKN3fU8+aXDWLpF//khrp7o49rJPiRFAqdKIYaqHUn7lhYe+9kDbDm8AaNicOLlc5TyFcLxIOv3dqG6FI589zjf+J3vs/fZbcSaa5BkiYmBSY6+dJqJgWlEUWDzoXX4Qms/4FoIgiDgk1UO1Lby5e5d/PGVo/TnUqteuS2YOu9NDhJQXYQ0NxvDt9drEATo8sd4U+4lUSkwWc7Rl5vmYHzpya37hYDqIu6uruEcx6E3l6Bk3t7uw7At0nqRtL52GBSiIBBze9lX24QqSrw4cJVdtY1srbk5Txl2VQRnulREFARKpsH1TJKCqWMz4xGsahyoa+b4xAhB1cWOWANX0wmupKexHYi6PUTdHt4fH2Ywl17WGEMuN92hGq6lpzmbmOBgfSuX09MYtk1bIExYW7hVQ9VkfEE35Ro/qcksoijg8bsIx/yIkog/4kWZ0U4xdZPJkSTf/t1XGemdxO3V0Nwq2WSB46+cZ+jKGB//5Uepb4vNCk1NjaQ4/sp5JocTeN92Uy5UgzrLsilkixSzZWzLJjOd453vnySXLuLxaQxfG6fv/DBj/VNkkwUKuRJvf+8k/pCHUCyA6JkmUzlO2RxhKP1fKZtDmHYal9RA0LUbw0qRLL5K1PM4Yc/DKGLNTEuOgyjIGHaW6cIPCLkOEnYfxiW34GDhOAYgUDaHyevnqff/PBHPo4ioZMSjZCvHyevnCLpu+H07aHI9IfchJMGH7eiIgobl5MnrFxDRqPE8gyJFKZvDiLfoZEiiF4/ahYPFuPB1PriKdhwTQZDxa9sJaDuxHZ1M+X0MaxrTyWDZeYpGL2H3wwRde5FFP4aVRJVq0OT580rfVJLrUynAoTMepa3mzmyAimHy/vlBRqcyNMVDuDWFTL4021ro4KApMm5NRZZEVFUm6HNRMUxwqkJOhmmhKfKs04FbUwj63CSzRfIzvueyLNLZGEWVpVl9HlEU0E2LsN/D/k1tnLg0zNQMDRlgXeuHV9y869WBLEj4ZO+SRAYe4AEWQ7GsMzqVYXQqS6GkY9s2qiIR9LlprY8QCXpQZqg2N+xhptN5BsdTJDNVz0C/x0V9LEB7Q2QO1bqim0yl8gyMJcnkyziOg8+j0Vwboq4mgMelks6VeP/cAF3NNTTGQ2iqTN9IgmtD07g1hf1b25BEgWJJ58zVUYJ+N53NNfMa4ZeL5YqKLBWW8+GEtuWSTnJqmWqYgoAoCkiyyOYdrZRLBo7t0Ht5nGsXRznxbi879i4+etWlULOAgXzOqDBZyi/3FFYVgrIZQerAMa+DNYJT/gG2EERUd+BITQiCD25ltTgOUMGxxnGMczjl7+CY5wAFxDoE9QAI99/WRpEl6msCPHtgA0OTKQzDJuBz4fdod1TWlUWReneYgllmpJgkpeexHHtZzwzTthgrpUjqeTySRo22fK+8tQpBEFBUmcOf2ws4HP/xWYavjJGeylIpVrBtG2/QQzgepKmnjt1Pb+PAJ3YRigX4zD94jvr2OInxNNH6MBv3dVPMlbhw5CpHvnuc0d5xYk1RJEVicmiakavjCEBDZy1bHt6AN7j6dhsfFoQZ8aRPtG5kspznO/3nGcynVn3emy4XeHPsOgFFI6S6qfcsnqQSENgaaaRG8zJSTDNeynIuNcZkKUvcvbav2Zjmo81Xpb86OJxPjzJVztPoDS0qDjVeyjJSTM/aG60WDNNCN605ApaaKqNI0hwtkJXAsm2mSgV+OHAVTZbxyAqO46DbFoZlzwRTAhGXB1WScUkyIc1F2OVGlST0VfDrFYBtNXVMFHOcmhpjYyTO6yN9bIrUUuv1oUgLf99d21pp7qmnVKjw9vdOMnxtnB2PbuDhT+5C0WRkVcbjqz4vcpkiR398jte/eYyHP7WLHY9sIFIXJJvMc/qtyxx54RS+oIdP/srjhGpuFqUK2RK9Z4fo2dHGjkc3EmsIYxgmtmkTuuW5Wy4Z2KbF5gPdBKI+Xvmr9zn5+iU+9+tP4Y94+YN/9Tf0Xxpl4952/C1TlIzreNV1mHYWWQyiWxPo1iSmncd2KlTMMXzqFiQhgCCICGizsaPjWJTMQRqUL6NI0Zn3RRAUbKeCYU8jIKLKcWSxei6KGJ0JUIcIsGf2NU2qRxar5yHNPFttu4hlF1HkCJpcTYBoUi2KFEJcRpgkiV586hZARBLdSKIfELDtCpLoxaW0kq2cmDnfcSTBjSY3LLivcyMTvHD6MqIo8IntG5YU2JYqBud6xwj53TyxpxtVkTl5eXjOPVNdG8/8Pzf79B3ApSlIokipYjCdzhMNeilVDJLZAqIg4vdqJDImAgKKfLNNTBCY/a1URWJ9a5xvvXEWj0sl5HfT1RRmW9fC53kvcFercrfkotnTyGBxmC5fB5qoId/GKPwBHmAh2I7Dxb4J3jp5natDU5TKBqZpocgS4YCHzz6xDZ9Hmw1sHQcs2+Ht030MjacZm8qiGyYet8q61jhfeHo7ddEAkiSgGxZ9IwnePt3HiYvD5IplHAe8bpWNHXUc2t7OhvZaJpM5fvdvjvDxwxv5+CObUWSJd8/282cvHKc+FmBjZx1hv5vR6Sx/9oPjbOmup6UufFeBrYAwYyWx2sGtU5Vl/xBC28mxDC+/cHpZ24iCgKRIaKqMqiloLpm6xjBjwyl6L43jOKeYHM8sun19Y5hnP7Nrzmu245DWS4yVFvb2/LAgSHUI2gEcawCMs2Bewcn/B2zXEwjKHpAaQHBXg1vHAXSwU9jGKZzKK2AOAAaI9QjaQwjKwoqbHzYquslYIsuVgSlKFb3aezdRfS8e8XNoW/ui22qiwsZgEwOFKQaKU1zOjtATaCCuBe9YxXZmEjSDxWkuZUeYKmdp9ITp8q0N37/VRDge5Nlfeoye3Z2cf/syAxeHyUznsEyLQNRP26YmthzeQNumJtwzlK7Ora10bm2dFfoTBIHR3gke+fw+fvgnrzN4aZT+88Ozx1A0hYauOh7/4kFaNzSirYAZsZYgCAIB1cUXOraR1cu8MHjxntgADRcyvDh0mVq3n893bMUjzxdovIGeYC3rg3UMFJIkK0XOpkZ4eewyTzVsIKx6EBfQt3AcB8txyJsVKpaBR1bxKx9uQqvG5aM7ECesekjpRfpyCU4khqjzBGjwBOclooqmztHpAS5nJlf9STMyneH6WJJ8qTL72vqWOM2xahXqbmDYNkO5DN/svcAfP/15esI1TJcKjBZyFI2bFWpZuJl2FgVhyb7LS0VbIEzc7ePYxDBX09O8MzrAr2zeQ8S1uLCeL+jBF/RQKen4Q55qlTbkoaYxjPqB7yU9leWNbx0nEPby3JcfonVDw6ywVE19mIGLo/z4L97l4U/vJhD2Is6IAuqVaqX083//aaJ1IWRlbpBdKd1MYvTsbGPT/i40t8qF969jlA0e/tQuBFHgW7VBSvkKuWwCzZpEEr10Rf8VAlXLxKH0f8VyipSNQQRBQhK96NYEHqcTx9EAGwcbAQkEoVrhtJPYdglH8M2+DyAJXhwcrJkgWUDCdorYdhFZDFaDYEAQ5AXteQQUREHBdnRMO4ckeLCcArZTmT3GUiAgIomuOa/cgCKGCWg7Gc7+PoaVRBQ0Aq5d+LUdC+5rJJXl3MgEIY+LsnF75sTs0RzwezUsy6ZvNIlpWZy5NookikvStQh6XbhUhQt947xztp+2+giZQplEpsim9jrqowESmdvPr5ZtM50pEAl42NBWS13UjySK5Ms6fq+GvEjSZjVxV1GoX/azLbSFb498n4gartKR5YWzmR7JjSLe/16xB1h7MEyLb716hoHRFPu2tLJrQzMIMDGd43zvOF6XinrL5GrbNqlMkR8ducyT+9fxxN4eCqUKR8708+P3LhPwafzsMzvxuTUmEjm+/9YF3jrZy/Z1TXz+qW2ossTJS8O8daqPiWSOLz69g4DPTXNdmOHJao9BWTdIZYuUKwaKJNE/kiDY08joVAZwiAQ8+O4gmnMnCFQFO1Y/rAXDWZqv5d1ieDDBX/z+m6u6z0tnh7l0dnjR97fubpsX2JYtg2S5SKpSWtWxrASi9gxY09jWJNgT4KRwSn+FU/orELwgBGaqsBbYOXBSt24NQhBB3Yvo+Vv36xTmYTKV489fPMHxS0PEw77ZJBPA+rba2wa2bkllX7SHNyYvMF3J8urkOQKqhyfrtuGRtFk7lFufG9WFvo1um+TNEn81eISzmQFsbBrcEbaH23AcB9NZWvVEFMRF9R/WElSXwvo9nazf07ms7W797uraY3zyq09TKeu898Ipitkitu0gSiK1rTUc/uxePvMbz605n8u7QaM3yM90bKNkGnxn4DzlVaiqfRCD+RR/cPl92gMRdtU04ZXVeWsdQRBQBYmnGzcwXEzz7uR1rucS/N6Vd6hYFo/V9+BXXEhC1VfWwcGeudazRplzqVEmSzm2R5vYXbM6FklLhSbJtPrC7I+38eLwRWwcvtF/Ao+s8nh9DwHVhYiAQ5VBcTkzwYvDF7icmUAWxFWjgduOwxtnr/O1l08ymb7JwPnqJw/xiQMb7jqwvfGdu2W5mhCtlDk2MUJ/NkWdZ+ntdDm92lZRNHXKlkm6UiZVLuFVVBRRpGAa6JZJ3tCpWBbpSolEuYhPUVFmRNW6QhFG8hl+MHCVRLnIpmgtfuXuW08cx6GUrzB0ZZw9T2/GG/TMUUv2hTys29nOidcuMj2apq6lBpe3elyXWyXeXEO8KXLHYMgX9KC5VBRVxh/yYBrmbIBc7e13KFSGCFiTeJSOOdRet9JBrnKKgnGFoLaHgLaDRPHHqFItmtyI7ZQRBAlViiMKKiHXITLlY6hSHV7VxnEMHCxUqQ632oEk+ijoV9Ck2pn/v4phJ/Br2xHuYL0ni0E0qZ6KNUa2fByP2k3J6EW3pnFmnjG2XcZyShh2GscxMe0shpViqYUJ26lgWEn86lYaAr+AIkZBEBEFDcexET6QOMqXdfJlnZZIkPV1SxOfC/rd7NvYwo+PXuU/ff1NakIedq5rpqc5hltTKOsGHpeKpsrIkkjY70YSRRRZwudW8bhUtnTWAw7ffO0s05kiLk3mid3dHNzSRtDnQlNkokHPnLN2awp+t4YkiuiGRalicH04wdWhKURBRJZFtnY28AvP7SIevvctq3f1ZCtaRS7lrmA6Ft8Y/hbfHPk+XtmDS9T44I/9lbYvsjGw7m4O9wA/ocjkSmTyZZpqQzy0vYPN3dVqjOPAxw5vRBLnLnptxyFfqvAPv/QIh7a3E/RVM5zxiJ/hiTQnLw3z2ce3gRveOzfAuWtjbOtp4p9+5XFcqgwC7N3ciqbKvHnyOq8du8YXnt5BW0OES33j6IY5UwW2aK2PEIv46B2eZkt3AyMTGfwe12z/wd1CFqVVzwQDlE0D+0MhI68NTJXyTJSWSYm+VxB8CJ4vIIoh7MLvgdV/8z2nAHNsgD7wG4lhBNfHq0GtuHYE95LZIuf7xvid3/w0NSHvrOUPcMfFj1tS2VvTTU+ggUKyQl9+gj+6/ionktd5qm4bW0JtRDUf0i3PjLJtMFJMcirVxwujx+kvTFKydBrdEbaH2+n212M5FtfyvVjOnXt261y11Ggr88T7qEEQBGoaIvzK//ElPvMbzzJ6bYJivkww5qexs45gjR9pDSuorhTrQ3E+3baZrF7hxeF7YwM0Vszx/3z/Bf7Toc+yMVyLtkiv995YK5PlLBm9xJnUCKPFDP/X+Zf542vvsi5YR63bhyJKVCyTlF5ipJBmsFAVwFofrKXBG1r18S8Fbb4oX2zfzTsTfWSNMiPFNP/x4mu8NHqRbZFGIqqXkqVzOTPJyeQQOaNMo6dKVb6UmViVMZR1g7FEluk7VIZWCpck0xWK8lhTJ//4zReQBZHdtY1VurG29Cr5750/xonJEa6lE5Qtk/PTE7QFw/zdzXvZGInzZ5dO8dboAP3ZFOlKif/XOz8i6vbw97cdYGtNHV5FpTtUQ182xTd7L/Cx9nV4ldUp/ji2g6Gb6LqBL+hBkuYGTbIs4ZtpQyhki7M9/ACyIhMIL633XpJFhBmaqyiJyMIHxu9A2RhCtybxa9vnvOVROshWjlE0rhDzfoLG4N9mOPO7XEv+Syw7jyhoRD1P0hz8VSTBR1PwVxnN/iGDmf8bw0ogCApBbTftkf8VCT9t4X/MWPZrXEn8Fraj41G6aQz8Ci65hTsHnyIx38eZzH+bvtS/RRQ0fNomfOqmGToxpMrvMJn/JiWjj5I5QH/636NKUWp9n7mlh/f2sJ0SieIPSZfeBkFAEYPUeJ6jzv8lFGluq4Jl21i2jSrLhH1Lt8c7tK2D/VvacRxnhmos8rnHtiJLEg4OO9c1V9v5EPh3v/FJFFkiFvKxvjWOIFaZCQe3trNvU+vs6kQSxVnK8t5NLexc3zSH3vyph7fgOA6Fss6py9Wi0f/2ax+jNlItdB6/NMTRC4McvTDI84c2LflcVoq7Cmxtx8awDVo9zdhYVAvxM1YyH/is+qBa+6HhoyYMFQ546Gys4cjZfv7ou+/z8M4Odm5opqUuPC+LBdWFm0tV2NRZT9Dnnp20vW6VSNDD2FR2tu9veCKFbdt0tdTgcSmzi3BJhO6WGKevjDI6laFY1mmpC/Pj9y5T0S0mEjls26GzOYrPo9E7nMC2HUamMgR8LsL+uw9sBUEA52bVdrXCUNtxSFWKWLZ9z51i4nVBnvn0wlSae4XmtvkBykQpz8R97q+dhSAgEATXc0jyOhz9HWz9TTCugpODedQmGeR2BHUvgnoYQdkMYnRN2XFJoojHpRKP+NDuYCc2DwIoyPztjif5L9aLnEn3k9ELHE1c5WJmGJek4pFVXJKKiIBhm5Qsg5KlU7Z0cmYJwzaJqD6eqtvG03XbEQWRslXm5clXqFj6zGEEilaRvJknoAQQEEkbaepccZ6IP/5TFdgigKzKxJqihOMhbNtGkkRkVf6JDGqhShfdFm3g8x1VP+v3p4ZW/RiWYzNRyvM7Z17nN7c+wtZI/YKWFJIg8nj9OvyKm28NnOa18avotslkOUdGLyGL1TSOPbNPw7ZmRdVkUVq0p/VeQxVl1gdr+Vc7n+ffn3+V4UKKvFHmbGqEq9lJJEHEcRwqM2rJn2zeysZQHWdSo6sW2A5MpJhKF5ZkNXQ7uGSF7bF6/uMjn6DWczNQEwSBuMfLb2w/wC8buxAAt6xgOw6yKBJ2ufmZni04DgQ1F1/dth9FFPEqKjG3l4cb21BEkS+v387nuzahWxYOIAkCiiQRdXlQJYnPd2/mY2096LaN7djIgogkikRdnllfZFkUUYRq9fa5th5ckrIq874gVvv2VU0hnynO6VUGME2LfKaaYPX43VWRutmNWfkicoHtQq4DNAfrkcS5wZlLaaEp8Cszz0sJVaqlOfhVbKc4Q/8VkEUfouAGBBQxQEPgK9T6PoczE29IghtJqP62HqWDltDfx3ZKODhUzEky+nncSjeaHCfsfoigthtRnJ+8EASBvDGIIrfQFf3fUKUIouDCwUIWfIiil6BrL151HY5jYDvGDK1ZQhYDSKIHr7qZtH6FUxP/mO7wP8StNNAU+GUQBBzHJFs5QUG/wpbaP66ekwDp0rtUzDFylZNEPI/MGZNHU/Bq1etSN5cuuCiJItIHp6TZF+b+QOpM66gggHjLnCMJN9rkFtj/Au/dSHTfsBMSJQFNldFUGcO0yBRKZAplAt7l+devFHcV2IbVMB+vf3ZJn61z3V5N8AF+eiFLEh9/ZBN1NX7O9o7x0pHLvHL0Km0NUR7Z1cX6tvgc2q8oCAT9bjwuZU4mUhQEZEmqCkAApmVTKOlVixmva94DI+hz43Yps0pw7Y0RimWddK5E/2gS23HoaolhmBZnj17DcRxGpzJ0NdcQCqzeDarMVG2tVaIOO45DSi9hfghU5IbmCJ/7hYP3/Di3QnPPT5KNFDIM5dMf6jhuC0FCEAI4ykYEqRZJPYDjZMBOgZ0HDKq0YzeIIQQxBGIMxPiMwNTaCWoBAl4Xm9rrePPkdXZtaKomlJboMXdDmqLDV8svtD3CD8b8HE1cJannKc4EpSICklBNitqOPSN+dvP67fLV8VT9dh6ObaTGVc1sq6LK07VPYs1QIHvz10noCZrcjdS5qgIg4+UJRkuja1ol/F5BEISqv+VdCtx9lOCWFXbUNPIL3buYrhTpzyXvOkD6ICzH4VRilL/sPYUkCGyN1C8YjPgVF7ujLcRcPg7VdnB8epDe3DQTpSw5o4I5E+x4FZUmT4gmb4juQJztkSY2he9PD7koCPgUjQOxdtxbFN6e7OV0coThYpq8UUEQBEKqm83hBg7EO9gXa0UVZSbKq5dU7B9PMpW5+/2JgoBXUfEq82moiihR6/Etum3EdTNxfevnNEkmOFPVrXHfvqoZdXmIuhZPgDvAQDZNbybJhnCczmB01TyfBUHA7XPR3FNH79khCtkStmXP0oQLmRLXTg8SjPqI1oVmLcHuBSTRt6DqryhoqPJNVpKAjCbfjqUkoUo1IC2coBTQ5hxHleKoUhxFCiEgVkWlxMVpsAF1I2CjiBEkcT4dXBR8yOLi14xX7SLmeYqR3F/jYAAOqlylEJfNEQwrgYOJW+mq0o+pPvssp4iwQJtMUzhIYzhIoaIzmMjQHAkteuy1Ap9bo7slRuy8l9//7nsokojtVJcz61ribGq/vYPCauGuxaM6fYv3Vn2U4DhOtbeyYqIbJoZhYRgmumGRSBXIF8qzD8h0pkTfYAJJqnLTFaX6R5VlXC4FVZUWXPTpt+xfv2X/hmGRTM1U2KhScXoHpnAcB01VUBQRRZFRZQlVlfG41Vll4J8ECAK0N0bxezQ6mmroG0nQP5rk6sAU44ksn3lsK1u7G2aDW0EQ0BRp0fO/KaTCLF3ixnd7KyzbxradWbnyoM+FW1VIZgv0jSbwe1z0tMRJpPNMJnOMT2eZTufZu7llVSq2UM2fuSUFSRCXRKlcCmwcpksFzFX2dVwIHq9Ga+f9pcyatsVQIc1wYXHBqfsDAUHQqoJRUgMCNjgVHKcMTjXjjKCC4JlReFxb9/P33zrPtaFpoKpaPjiR4lzvGO+fH8Djuime01of5tOPbr3tvgRBQJMUtoVb8cgqXb46LmaH6ctPMFnOVG2AHGuWtqAIEkHFQ707TE+gkS2hFraG2oi7borYyKJMj79n9hgT5Ul0W2dvZC9+pboAafW08P2xFyhb5dX+eh5gjSKkutlf28pXKkX+28UjTJXyq5Y0vIGCqfP6WC8RzY1bUugJLdwDF1BdbFTqaPGGWR+sY6JUpSeXLQPLcZBm7gu/ohHRvNS7A8Rc/tmK3gfR7q/hC207ORTvwKtobAjWLamV5VBtB3G3n7xRJu72z9r6LARJEAkoLg7GO6j3BNkXaydRKVC2DARBwCur1LsDdAZihFUPhm3xaF0XfrnaO9oZiKHehUDM9bEUk+l7Q0NeK+jPpjg6Mcy56WqV+xMd6/Es8puvFKEaPw99fCff+E8/5JVvvMf2h9cTjvnJpYucefsKQ9fGOfzJnYTj/nlU5bUFB8uuMF16G8sp4TgmXqUDn9qJaRdIlN5Bk2OYVg5FCuNTOnCwyVTOUbEmqfU8iSRpODhkK+cpGgNYdhHdTuFV2qhxP0zB6CerX0CTooS0HWBD0Rwgr1/FwcZ2yrikOvzqBgRBIlF6BwcLyy4T0DbhU9qRRR9uuXFWlflWyIIfl9yMKBxnJPsHCIKM41iYdhKP0o5Hma+psKmxlq3Ndbx9dYC3rvazp70RRVp83bsWoCoSLbVhPvHQJqZvYV0EfW7a6sJEPiQV/nuaynUcBxuHE6nTdHhbiWqRe3m4u4JuWJw4O0Tf4DT5YqUa3OoWumGSL+hMJfKzdI7BkSSvvH2Z0xeHURUZ9UZgq8j0dMTZ0F1HNDw/s3P5+gSXro6TzpZmg1tdr/7dOzCFZVb3n8uXeeWty5wKz+xflVBn9h+L+jm4p4PoR9R/cDGIgkA84ice8bO1u4Gx6QzvnxvkD7/zHh2NUZrrQnOqtku5tyVRJBKofk+TyTyWbc9JOEwm8+SLFeqifsJ+N4IgUFcTYHw6y9h0lniPj9b6qsS6JImcuTpKRTeJBr34PKulJirgV13Iooi+SoGo7TiMFbMr9gv9qGGqXGCkkCGjr/XgpVqhFYQPh45ztyiWDTKF6ncqAI2xUFXoxnbIFW+qlRZKS1NsBHBJKltCrbR4YmwJtXItN8Z4OU3WKFa9JXGQBBG3pBJV/TR6ImwINhN3BWdpU4uhZJfImwVM5+Z4KrZO1sxRtiu32XLtwTRMitkSyYkMhUwRo2JgWw7BGj/x5ij+yOKVg592CIJAWHPzfMtGRotZvtN/nslSftU1ByZLeX48chW/ohFQNeo8C9v5SIJIUHWzLdIINN7VMZu81crucrEj2syOaPOSPy/M0Gp7gnF6grdPXCqixLZIE9siTcse1wdRqhgMT6VJ5++/COC9hOU4VCyLgKbRHarhQH3Lqh/DH/Kw7+ktDF0Zo/fcENNjKTz+qjdtNpln3c42nvziAbyrpBdyr2A5OkVzkHTlBC65AccxyennAQdBkBgvvEi972M4jo3sGFS5PjYVa5JE6V0irt0oUgjH1slUzlE2R5EEFxn9LA42UfdBHExy+iUqYgSf0oMsCRSNAaZKr+NXN+A4BrqVAkQC2oaqWrJjUTSHEAQRRfThERcXe5NEP151IyE7QcUcwrGr/rseZR1+bRuaPJ+h0RoNcbinnclcgRP9o/zw3FW2N9cTD/hm/WPXGgRBwONS2b+57b6O494GtoBu63x79Pt8vulTazqwregmr79zmTfeu0a+ePtF0PhUlvGphW1Fnnl0I9Gwd8HA9uS5Ib79w9NMTt9e5KZQ1Hn/VP+C73W01tDVFiMc9CCtwQt7JcgVyqSyJQQRPJqKJIn4PC62rWvEpcoUyzq6sbIgrbs1xsW+ca4MTHKpb5LaqB9RFMjmS5y9Oopl2XQ21+D3uiiWddobo1wfSVAoVQj5PYT8bhKZAnU1ft47NzBT2XWjriK9L6y5kUWJKj317mE5DoP5NBXr3ge22XKFiUyOhlAAt6rMVg9KukGiUGQ6X0Q3TdbVxfBp6mxioWKYJItFJrMFSoZBZyxK2OOeI0q0VFzJTDFSyP5U0k3vJT75yGaef2jj7L8dFm69Wiol+VYEVQ/b1Da2zagbG45F2dSxcVBFGW2GxbAchJQQE+UJjqVO0OiueuYNFoeq/nvyvVdiXA1Ylk16MsNo7wSDl0YYOD/M9EiSYr6MZVhsOtDDQ5/dOy+wTU1myCXzWIaFN+gh3vLT0U+8GCRBJKy5+cWe3STLBV4d7SVRKd55w2WiN5vgxaHLhDUPn2jdiG8VFG2XikJFZyidoTEYwKuqd+3vegOmbZMulhjP5inqOvXBALX+6mL6XmM0mWUqk6dirL6q9VpCZzBCZ3Dl62FBFKipD7NpXxc19eEFq/ayKlPbWsPP/eOP8db3TnLtzCDTIyncPo3N+7vZ+9Rm2jY0zoo/AYTjfnp2tFF3m/lDEAV8IQ9bDnQRilVtFf0hD63rG+Y4MbRvbMTl0fAF7y6Ra9p5cvpFFDFEvfc5BCRG8t8iUzlDUNuC7VQIa3vQ5BiicHNdFtJ2UjAGZv9tOwa2U0YRQ/jVbhxsQtr2GRGqzeT0K9jOrWt/AUlwEfc8hix4mCi+Qt64Rsi1naC2BQEZUXdRNieoWFN4lMUDW0EQ0OQ4db7PLfm8S4ZBPOBlZ2sD33j/LP/l5Xd5ftt61tfHCLhdVVHV22wf9rppj61+zFX9iavK4ve6200UhBXNa8tand960S4lW+DgULbKlKzSkmiWpmVTMg3KM5OaJst4VOW2fQcBv5u6eBBdN4hFfcgrnHxFQSAYdFMbD+C/Q2B7O4QCbhRl4TH4vBrxGj/SXTyAYhE/iiLNuaBVRSYc8FAfD+D3uXC75gsQqIpEvMaPKIoEA655gdkNOm60xoMpV/D6ZdQV2EFYjkXFKgMCHnlpmcD+sRQvv3cZy3ZoigfxuDXKFYMzV0cQBIHOphiRFWYVd6xrZHgizQ/fucjvffMI+7a0oigS56+Ncblvkq09DRycsSqRRJH2xgjfe+M8bk2hJuip9qpoKq11Ed44eZ11rbFVrNZWA4Ww5kFdRbEQy7EZyKeoWGZVGW+FCRDLtjEtG4TqvbgQhpJpvn3qIl/cs5WWSBBxhoI2lS/wxpU+fnThGpcnpvn3P/txtjbV4VGr93K6VObI9SFePHuF44Mj/OtPPcnhrjYC7uV5N9qOw/nkOCNrjob80YemyDDDjqsYJplcmVjYO+d6KpV1ShUD7S6sY6oWKSubb27Fev86imaRt6bfpmBWqYwe2cvj8Uc/Ei0ztmWTGEny1reO8vLX3uLayf55ll2egJvtj81XlTz/9hXef/EkyfE0G/Z184V/8nGUu7RF+ahDFATqPAF+ef1eCqbB62O9FM3VSR7eivOpCb5+/TSN3iAHa1tne8XvNcZzOb527DRf2rWN9pow2h0YDUtFxTA5MzrO985e5v2BYb64aytf2LmZuP/eswQuDU7OskQeYHGomsLep7ew9+ktt/2cJInEmiJ85teeWNJ+tx5ax9ZDt3cvUTWFjk3N/D9+/+/Ovta5tYXOrXOrzr/wzz6xpGPeCY5jYFgZNCk6I9bkAxwspwSISIILlxxfsE/1ViiSf7ZSW7EmkQQPfnU9IgvPkwICLqkeUVARBA0BAcsuUDCuM5T9SzS5loo5iSx6sVepjexWHOsb4ci1ASZzBWRRoncqwX955V0UScSjqtWq7W1C28c2dPAvPr203/12MEyLimlSMSxMy8KyHSzbpmyYq65f8EFEfR5qAstnpy5rJrQcC8uxkAQJWbjzpo7jULLKSz75sVyOV69e5+UrvYgI7Gtr5pObN9AQXDzb/otfOMDf+sJ+mGlQXukDxetR+bUvP8Kvftm5K3laQRAWpcl++tntfOqZbXe5//nn2NMRp7s9zt//pUdBYMHs3fquOv7Tv/nibNnlg59xaTK7trXwL7se47XJl9kebmBTaPkCFgl9mqPJ9xAReK5+aRNbNOhBFAXeOzfA99/MYZgWfq+L5toQf+czB9i/pZWQf2VZv5DfzSce3kRd1M+3Xj3D//jrd7Bsh5a6EM8e2sAju7torg0B1X7cjsYoFcOkqTZEZIbu7XYp1YD3zfPU1wTwulcxKy9AWHWjrJJoBFSDvcF8mpxRxnIc5BXeE9P5AoPJNJIosbOlYcHPbKyPs6E+Pm96bQ4H+dK+7extb+Yf/eX3+GAuJ+738untG3l8XSd/6w+/sWLRjJJpcDoxxnAhvaLtH2BpuDY4xX/+q7f4j//ks3OSh2+f7uPE5WH+2Vfu/gF6twgpQR6LP8LB6H4yZmbmtRCqqN52AbBWkE3m+dN/8ze8/a2j5FLL6zH0R7xMDSc4/qOz5NNF9j67je6dHfdopB8trAvG+fmunZQtk1dHr636/h0cLqQm+J0zb9B26NM0eAIrnnOXg45ohH/53BOr3prvURUe6+7g0a4O/vHfvIBrkaTmvcDlwUmyDwLbB7gFoqDhkmvJ6pexMdDtNAJCtZdVYFnaFLZjEdA20+j/DCIqd5Z/nisRXbES5PUreNU2Wvw/z1TxDQpm30pO64547/oQf/HemXnhgmHZZEp3vkeW8pmloG8qxdFrQ5zsH+XaeIKpbJ6ibsw6j9xL/OqT+/jqM0uzUroVy5qx/mTgzxktTfBI7BCHYwfoKwzwn6/97qKfd6gGw0k9dcd9F3WDE0OjDKez/KvnnkCTZVRZJqDdvjpWLVOvgjz6bEB67x5IYjUqXfX9LmXsgiDclrp8w3pGFAUE8cY+VzBWp5rQcJaxaTzs48vP7+HzT26vVghnxiHLIj6Phlu9WYHuaIzyz3/pCUzLntdn3N4U5X/5W4+jGxbhmUBYEAQCPheHtnewraeBygylWZFEvG5tTnVblkS29jTyH/6Xz6EqEv6Znt6g18Vzhzayf0s7Po+K37u8quKdEHV5Vt3ewXJsrmSnafNHiLpW1o/dN53i4vgULeHQop9ZyNrr1tcXu4ZuvH+3Hr5HJvsZLWZXXRxm1eDoONYUjnkWzOGqMrJTQZAaEdR9CMr6+z3CJcGh6o34wW9ZNyyKJf1+DGkeBEGgbFVIGWmyZrVVJGvkqr6uapSwGr7PI1wYN6qyf/nvvsOJH58lnykiySLBmgAdW1uJ1Id4+1tHKWQWp9K2bWom2lClnWWnc1x6v/dBYDsDQRDYUdPIZyqbKRiVe2IDZNgWvdlpfvvoi/zrPc/S6AmsiKK/HNwuiX63+63+feOF1T/GB3Fj+r48PEX2LhhzD/CTB0UMENS2kyqfoi/9u9hOBb+6gaj7ALqdnvd5yymTqZxlPP8iGf0ctlMmpG0n5NqGTYVc+SIFvQ9BkHDLjdR6n2K69DaTxVewHZ2KNYVfXYe4QHikSEE0qZbxwg/RrQxgISBjO2VS5ROMF14kWznPYPZrBF3biLr241pAEXop2NHagLmA6OlSsalh5U40FcNkYDrNn791ihP9I6QLJcqGiWFamLZ9zynIN7DSivCyAts6Vx2KoBJUqiIJuq0zVUmw3t+NR/YgfqAnynFsSlaFrHH7nlKA0UyWvK7TGgnRHArOWfCeHB7jwvgkuUoFj6rwZE8XtX4v3zp7gYppzXDNBaJeDxO5PC5FJlkszVbBWsIhNtbF+f75S5i2Q0HXOdDWQo3XQ38yRW8iiSgIpIplHu1qZ0NtDL/r7qtyBTPPseT7RLQoI8VhskaGgBKg27+OTl83tmMzXZniVPoEaSOFgEiju5Gtoe34ZnrCSmaRc5mzDJb6KVtl3JKbbt86NgY3IwkSk+VJLucuMFIaRhIkYlqcfdGDaKKGKIj05q8yXZlGEASGi4OUrTJt3nY2BbcQVEJkjDRXc1e4nLuIKqp4ZR9F82a1oGyVGSkNcyp9HMM2UQSFTcHNtHnbcUluKlaZgWI/J1LHqo3jkoekniSmLawSuRBkWSIc8LCUZaeqyMQj8yv4g/3TXLowQi5bZnQ4yY7dbQwOJNArJlt3trJzdzu2YXH2/X6uXBrDth1UVWbrzlZ27GpDkkSmprK8+L3TeDwqhXyZYlGntT3GoYfXEYn6Vj2ghSrdpdEbxCWtPm3wUmqSPTXNCwa2I6ksr1zq5dpUgopp4tNUtjXX8/yW9eTKFV6+eI1XL/cxlskSdLt442ofAvAbjx8k4nUzms5ydGCEE4OjAHxl/w7aoiHku1DDXA5uTHevjV5nvHTn+eVDh6PjmFewyy+BfhLHSYJTAkcHbFC2gNQ6L7B1jIs4xhkcO4kgxhA9n78/45/B2HSWoYkUF/smSOVKvH7iGuLMvFoq65zrHUP5EO1kckaJnFFCESViruCc9/oLA5xMn6K/MIBbmnuv7o/uX7OBrWlYXD1xndOvXyAxniZaH2bXU1t56FO7iTSEUV0qZ9+8eNvA1hfyUNMQxhf2ks8U6b8w/CGewdqHW1Y4UNtK3qiQ0ktczUyv+jGKps6J6WF+/9J7/OK6PbT5witKDk/lChzpG+Tk8Cglw0BAZHNDnOc2rSPicTOWyXF8aISjA8OUdJNfOrCTjpoImiyjmxbvDQzxbt8Q62tjHBscoaQbrK+L8UhXG52xKEXd4NpUgh+cv4w9czwHh554DQ91trKl4faWHLbjkCwU+eHFq/ROJ6kYFvVBHwfaW9jVsjJxLMu2GU9mmc4U0M2f7P7aB1geBEFElSI0+T+H7Rg4WKhSFE2KotghOkNfBW6xfUTBp3TQ5P8cdc5zSIIbVQyS1S+jiEHqfB9Dk+Lo5jTD+b+hzvcMEdcefEonDjay6EMR/QjIeNUuVDGMIEjUeA5jOzqKGKQz9FVE0YWAiICMIgWr63ffZ6j1PIUselHEIIoYXPzE7oB9Hc1sqF+544R3ha0ouVKF0wOj/Ombp7g4MkG6sHTW7VrBslYkO0Jb0W2dkHJT/U8VFR6OHSSqRpA+wHG3sUnrWYZLI3fcd6pUwrZt6gN+MqUyL166SkE32NPSSO90Asux6ayJkCiUePHiFX525xZOjYzTEPSzsS6OV1ExbZtr00l2NtXTn0jRHg2T13VIpdnV3EhPvAbbdrg8Nc1AKkW2XGYkk2Uil+fhzjaG0llODI9S4/OsSmBr2iYjpWGGSoM0u1uJqFHckhuf7MdxHHRb593EOwTVIK1KOwUzT1JPcDZ9mgM1DwFwKn2Syco4ATlIo7sZRVAIKEEEBCp2hTOZkxTNAs2e1pnq+DQnUsfYEdqFR/aQMTL05q/gljw0e1qxHZuIGkURVCzHor/Qx6XcBRrcTfhlPwOFPkyn+mCxHZvJygRnMqdodDchIpHQp+ktXEMUJLp83aSMFG9OvUaTp4WgEiJRmbov1hq5XJmR4RSWaROO+njt5Yts2dbMxHiGwb5ptmxrRpJEQhEv6zdVH77916cY7J8mFvPT3FZDPlfm9Zcv8NiTm2hurSExnWNiPMPZ/z97/x1l15Xfd6KffeLNuXJGIScCIAgSzKnJbnVupZYsyR5JXrYs22PrefzGfsteb0azNDPPY6/nsZ9kOUpyS2q1uludAzszNAkmEDkWCpXjzfmk/f64VUUU6lYCCiDY6u8SusR77t0nn7O/v/D9nhrjiaf33JHtFkBvKL6qvcPt4OT8BB/s2cVgJLligvXqtVGmC0W6YxHiwYYidMzfyHIbqkp/Mk5HdJ667dCXirG/s21BAKHxyAiYBn2JGCPpHD8eGqFQrXEXKlOWYLsul/NznEpPULDuMQVNWUXWX8WrfQ1pvQneFDf3H0gvg5BN7hNZRdpnkNaPkWo3wjgGagdCvDf9kq7nkS/VmJwrUK7UOX1l8gYLLUkk6GP3wN3xKJdS8kb6Kj+aPUt3IMXf3v6BZctHKiPM1ubYF9lLRF8e/GrdRKDtbsOu27z1wmnmJzIoqsKRZ/bz4d98msFD/egLfcfr9cuqmkqsJUo0GSY9nWVmdO5ubPr7CgkzwKMd2yjadf7LxdeZq5W2VG5OAiXH4oXxS7T5w3ykbw+9oc0HU85MTnNlLk084GdftA1XSmI+E23hvgsYOr3xGOPZAl8/e5FctbZUFuhJyUg6x0tXrxMP+DnS00G+WuPN0Umifh/tkTAzxRLfuXiFgKHTl4hzQcxxaWaemu0QD6zd8iOlxHZdPn/yLKamsbutBQFMF0p868IVOheEpjYj+OJ5HqVqnTcvj1Os1u9aNuin2DpUahaz+RLT6SKz+RKFco1S1aJuO40qPCSaqmLqKmG/j1jIT1s8TGcqQjISaOg5rAqBInRCxvYVSxTVIKruX/5toWKoSQw1uezzmjtLzZmk4pWw3QKuVyZs7EQRPky9hYC+UjXc5F0RLb/2bjuWoTa/r1f7/FaQDAVIhu6uYrXjulycmOVzr57mjaExLOe9c9aIBnyEb7Htb1PEtsO/fAKjCY2UmWJXeAdxI7aS2EqPvF3EUNaflC0+yxbn356UfO/yEIamMl8qY6ganZEInvS4nsnieh51x6EjEuaBnm4Chs5EvoAiBNtTSU5NTNOXiDNXLFGyrAVPz4b/qU/TmC9VEAgMVaUzEuHRbf1M5Ar8txNvU6xtbSmMJz22h3fQ4etEIJBIXOmSt3Oczr/DfbHDGKaJi0vWzlBySjwkHwEkZ/Lv0B8c4Ej8KEkzhZSN3woEOSvLdG2K3kA/xxIPUvcsLhTO8cr8i+wO71kSb/KQhLQwR+JHUVCQSASCilthtjaD5Vo8mDiOfyEDO281JkWWZzFdneRi4TwtZiu6UHCkw3RtirieoD/YT9bKMFEd56OdnyJpJrlQOEfGymzp8dsoDF0l2hJm195O3n5jmF17OykVa9TrNvW6g6YqBAIm9bqz8OKVzM0WmJnJ09OfAimpVix27u7g0P39TE/leP3Vq1y7MnPHiC1Amz9EWDdRhdjSktprxTQjxSz7Ex2Eb1LrtF2XuuMSMA12t7fQEgriM/SGr66hc6Svi4lc43460tfJc3t3LPt9POAn3uun7jicGpvasm3eCKSUVByLL18/y3g5j30b5TpbD4m0TuJVv4i0XmxkaTFAbQURBHcC5BoZZqUFRADcUZAVpP0WQnkO3iNiGwv56e9MkMlXuDQyy66+1qVJq6aqtCXC9HfeHbV7R3pcLk7ywvQp9kd7VxDbklMmpAV5ouUxTPXuqdPeLhzb5dyrl6mV6rR2Jznw2B62Hx5AW0WEcDUEon4CET/TI3MU5kt3aGvfvxBC0BEI81z3LuZrZT43dIqCXd9yNfWZaomvjpwjYpg8172LVv/mRJccT2K5Ln5DZyAVpyMSwadrBIxGW1bU7+NgVztCwI8WKmluhO25CAF721t4aKCXqm1zenKGqXyBQq1OrlrjwvQcv/bgYR7o66YtEsJ2XDqiYbpja2eYPCnJVWp88/xlDnd3MtiSQFdVMpUKl2fTXM9kSYUCKCy/duVCi1KlblGo1CiU642/lRqFco25fJmTVycoVVefe52+NoknvVue7N6IeMjPU4e3r2qbIqXEcT2+d/IK2WLzwOlTh7fTEgnekgfsfL7M2evTTKWbu2sA9LXF2dvXRiy0eX0Ry3GZnM/z6vmRFct0TaUrFeX43tVVfNfD4vEZn88zNptjdDbL+FyeqUyBuVyJQqVOqVpfIraSRvuXoWmEAybxkJ/WeJiuVJSelhj9bXH6O+IkI8Hbbk9aDUF9ANerUHdnEYCmhon77kcVW1+J937FRKbAy5eu89qVu09qdVWhPRamKxGhNRKiIx7hYO/a1SOr4bZqyCJ6hIcSRwlpQRRW3twCgV816fJ34lfXvjljPh+KIpgvV4gF/Hz6yEF+cHWYuuNQqNWpOWUMTUVTFO7vaRgVa4pC2DQJGMsnfaamoasqpqqiqSpV2+HqXIbvXR5iMJVgNJtHUxSSwQA+XcPUNMTC7xzP3dK0uxCCLl8XATW4VKotEDieTcHOU3NrTFUnKS70hJmKjzZf42R6SLJ2hkP6ERJGYmm8ReGuvJ3DUAzCWhhdMRAodPg7ma5NL2VdAcJamFZf21LgYbErsragVh3UggS1IFJK2nwdXCldBqDu1ig6RcpOidHK9aXxEkaCuBHH9myqToWAGiSkBVGFStxIENGb+/ndaWi6SiTqJxAwiEYb6tSLGY96zaFQt3n15cu4rsT0aczOFFBVBdtq3MBCCMIRH63tUfwBg0DAxOfTSd/BSaIQgpBukvIF8as6JWfr+hUrjs2Z7DQHk53sjS8PSj3Q3022UmV4PkupbtGbiLKjNUVkCyoV7jTqrsO1YppvjV2kZN8b/Z2LkF4Wr/ZNpPUaSAfUboS2F/TdCCWJV/ksOOdX/b1Q2xBqFxIfyDrSegthPgG8N77VoYDJjkALfkOnUrP44PE9tzSR2wrYnr3suXYzwlqYumtRdstoiobC3VGnvV14jsvk0Cy2ZdO1o4PWnuSmSS2AbmjohobneNTvkb7new2qUOgKRvkbO+5nslzg5ZnhO+J/fSk/x9dHLxDRfTzdtX1TNkB7O1qYL5W5NDvPj6+N0hWNsLM1hb8lsWGRvajfx+GeTgQQNAxiC2rzNdvGp2ukgkGuzGbw6TqjmTx+XSexTrYWGlUauWqVuVKFsVyesmUvEev9HW1oirI0vyhV6+TLNYqVOtW6TblmMV8oM5MtMpstMZMtMp0tMpcvL/PFXg0vnx3m5bNbI9Szs7uF43v70DV1RWBgEY7r8effP8n5kRncJuVInakI4Z09BNXNuyRcnZznz79/kjcurd7v/dShQcL+B26J2Jardd6+MsG/+twPVyyLh/w8c2THLRHbRUI7mytxfTrDG5fGefvKOMPTGcq1tZ85dc+lbrsUq3Um0wXOjcwgaARP9/a1cWxPLwcHOuhrixP2m1v+njHUOKnAo1s65k8SPCk5PTrNq5dHqVor1eMDhk48FCAe9BM0dTRVQVEUBDA0k2Yi0+AwqiJojYToTkbRVRW5MLbreliOQ6luUajUyVeq2O67SQlDUxloTfCpY/vZ39NGIhRAv0WXm9sitikjwUc6n199AiHAVE2eb3uaTt/azLszGiEwbXBxdm4pU6QKQUckjKGqRH0+HhnoRRHiXTK6inDNzVgsOY4H/Dy9YxsnRsaYyBcXNlHccXEEVWgro1BCoAqNkBbk2bbn6AsONIivZFkEWRM6jnSxpYMhjIYw00LGVVP0heyvgyc9PDzqbn2FCqgilBXZ9MbnjWili4crXRQUXOksrV8RCoZi0uHv5Oe6P40udBDvip3U3BpCqHh4uNJDStnYDvneZNDEDZ5XQlFYpmZXsxgdSfPjly/ze//6l0i1hHnpBxcYujpzwwBgGNqyMqq7VRbVFYwSNfxbSmwB3pgd5XCyi53R1IJXbgO9iRi/+ehRRjJ5Xrl6nW+cuUzUP8L/8rFnl0TGFu/pZi/19wqu9JiqFPjq9fNMVYv3XO+HtN5G2mdB5kEdQPF/AiXwqyBCIASy/hJyDWKLMEBJNjK87gzSuQRy6y1KNou2ZJiPP3lgy7wybwV118Faw5s5YSSYrk3z4/nXOBDdh6GYS0+AsB4mqL03wYH1IKWkUqziuZJQLIAveOvBJQkgWOZN+VMsh6YodAYi/KODj5N/q8rb8xN3xAbo9dkxAppByhfkgdaeDQsEtoVD/OzhfaTLFU5cH+N7l67xwoWr/PPnn6A3EdvQGIpQ8Os3BvxFI2sKtIdDPDjQze+/eIIzk22oiuCxwX4O9zRXvr8RQjQsZExN5W8+eIQH+7sxNQ3X8/CkXEa8L0/M8dr5ES6MzDI+n2c6U1gSb7zX0bD502mLh7k21Zy0TcwV2NtrEfRtntiOzuZWzQQvIl2oMJnOc2Db5t0pSlWLyUzzbHAk6KMrtfneT8+T1CybsbkcL7x5ma+fuEC6UL6t+YEEsqUqr5y7zmsXRtnT28ovPnUfhwa7aIkGV9hSbgR1t77gh25s2gv9XsXinNt2XWzXw/UWsuCKQmAdgd2NomrZXJ2eZ2gmvWJZNOBjb3crD+3o48hAJ32pGCG/2QhkCcG/+sqP+MzLJ/E8id/QeWzPAL/59DFiQR9SguW6lGsW2XKFazNZzo/PcPL6BJOZAqWahbNgLZkuVQj5TII+A+02Ahu3RWxt6ZC38ySMOIpskrFdmBjvi65fxhkyDfZ3tDGRL/D//Oq3MTSV9nCIvW2tjGZzvHTtOj+8eo2gYXCsr4fnd29vTFpuIoyL/ykWFIgFEPP7aQsH+cKpc0zlC5iaRtAwlpIgN4r/3a0Ivy40Ws1WbNnow00aKcJ6BNuzsT2LkB5GRaXd18FMbYqp6gS9gT48JLZnYyomHWYHdbdO2kpTc6tU3RpXipcYDG3HUNa/2ENaCF3o5K0sBStPxIhyvTJM1W2IlAS0IBE9TMkpMVIepj+4DRWVmltFESqmahLRw+StLFkrg6maTNemyFhpOvzrvyTvJlzXw7IcggtZWNf1uH5tjunJHNt33Fq5w1ZiMJIk6QswUdlaP9arhTRnMlMcb+ujK/juy2wknSUZDDCQjNMSChDx+fijH7+1rCjP1FQczyNbqSwRyMV7pBFgebeFQLKghn0D0Vz8bM3lCyNICZ589/urlYeVbIuT6Uk+O3TyniO1ANJ+E7w5ED6EeRwl+Ouw2VInJdwoSXYnwJ0E3nsxFV1Tb9lLeqtQ92zsNfwCRyujvDj3MiWnxOfHv7hs2S/2/Dwf6nj+Tm/iLUKgmxpCNLxs5S1MFKWU1Mp16uU6mq4SjNyaPdpfF2iKwmAkyd/b+wj/+vSPeHt+fIsLkhvPtpenh1GFoDsUpScYA9afY0wXSmiKQksoyPN7dtITj/GPPv91ygtZlKVnr1xYi2xkRG72PF4NVdshV63xxI4BfuuxB5f6agXLn9dL+yHfLSXWFIWOcJjuWIRXhq7TFg6yozVFsV4nU67SHY8uiXaeuTbFt9+4zNhcbmMH7B5ET0uMM/7ppsR2Mp2nUt9cQGTx+I7O5siW1ia2mUKFifStCSMWq/VVy5wjAZOu1Oaq6qSUVC2bNy6O8h++9iqXx7defM31PM5en+bSn8zx8Uf28alHD7Crp2XTc/ILxctU3Rp7I7uIaMu1Fu7E/P7G++5OjS9pHJ9rcxmG57NkylUs26UvGePpvYMr5lVLnGYT23N1Os3IXG5FCbKpqfzSI/fxsaN76UnGmv5WUxU0RcHy3AaRdVxMXcW/UE0bQCcW8NGViLC/p52PHd3DdK7IF06c5RsnLzI6n6PuuJwfn+F//cJ3+Zc/+wyH+jtXbRVYD7dFbMcq4/zbK/+BkBakw99Op6+dDl87nf52WswUQW1zE6H+RIxfe+AQv3Bof8OeRhGETZPOaJhDXR14UiJEo2TY1DR+56lH8d8Q0WkLh/jtRx8kaBj87eNH8Wlqw0xYehiqxr/5xIfQ1EapjBBioWymAUUIUqEAv/PkIytKm+8MBH7Vz891/QJvZF/nndzbeNIjZbZwOHaEvdGG8fbTrc/yeuYEX5v8Mo50CGoh9kUOcCz5IKbq43jyEU7l3+E/D/8hqlCI6XGea/vgkqryWtCExs7wbkpOkf92/T8R1EJ0+juXysoVFPoD23gi9RTfnv4GHhIpPQZDOzgUO0yXv4cWs41n2j7IFyc+h6EYtJptaFtkFL+VCIZ8DGxrJdUa5v/6va9hmhrhsI/WtltXrdtK7I610rLJXqyN4rWZEQYjSX5t59Glz165OsKPr42Sr9TQVIXWcJBff+TossqCfZ1tjGXzfOWdC3z99CXCPpPf/fgHaAkH+c75q/zo8jBDc2lGMjn+1bdfoiUc4JOH9nGkr5Nzk7N8/cwlxrI5pvNF/vULL9ESDvHhA7t4aFsvk7kCf/HGaaYKRcazef7wxdf58qnzPLFzG0/vHqQ1vDK75iF5a26MP7nyJnXv3oz8S2cYZKGheKztBjaffRPoCBFA4jXGeo8qIO41WJ6D7a1O8h9JPczB6H68JhQlpt8b93kzKKog1hqlkC6Snc1Tyq2ufrwapJTMjs0zN5HGFzRp7U2t/6OfgkOpLn5p+2E8KTmZXl/kcrOwPZe35sb5N6df5H85+jxh3Vy3QOzU+BTfuXiVyXwBVVEIGDq/8fBR2haeiS9evc53Ll7l2nyGK3MZ/s33X6ElFOTDB3ZxqGtj2b1S3eKb5y9zcmwKRQgifpNnd2/nI/t3UbUcfv+lE8wWSpyenObCzByvXR/lcHcnnzi4h45omH/6gSf4ypkL/O8vvEjFsjE1lQNdbfyDJ45vqSf7e42e1hhhv8FME8fKyXSBSn3zFVau6zExnydfXofYlipMpW8t0F2q1plc5be3krFNFyp8641LfP7FU0yu0Re8FbBdl2++fpG5XIlPPnqAxw9uzrbM8ixOpN/gOzPfpy/Qy5H4QbaHBvEpd6bN6odz38P2LPZFD9Ll797y8bPlKq9cHeUv3zjDRDZP3XaWgvpP7t7G03sHgUZi4LMnTjGSznF/XyeP7ezfVDZ3KlsgXVr57vnFh+/juYM76YitHgxRFQVNVbAcF4mkZjvrVjumwkF+5dHD9KVi/OVrp3l7eBIpG32+f/jdE/zdDzzEA4O3djxvi4EkjQQf6/wQWStHySkzWZ3mSmkIy7PRhUbMiNJittBqptgf3UNiHbsFXVXRVZWIb+Xny8tqGri5J0RTlCVl15h/ZdmPT1+bOKhCrKsKuBkEtCBPtTyLoZoE1OUTdSEECioDoUEiRoy62xCxMBWDqB5b+l7KbOXh5KOUYyVc6aEJlbAeRaERyegJ9BHRo5SdckO1VjFpMVuXSo+3h3bSE+ht2uMshKDVbOXh1GPsix5AFRpBLUAtViemxxb6P8Psix6g3d+BJxtTxpAWaigzC0FQC3J//Cj9wYGFnmrfUhnI3UTfQIpkKoRp6vgDBr/yPzxKe2eMxxdEn8IRP8GQya/8+uNYdRshBD6fjqarBAIGmqbQ2ZXgH/yTD9HeEQMglgjw8OM7qdfufMasJxij3R/GUFSsLSZto+UcP565zn3JTu5LNjLpz+zZzuHeTizXRRGCgKHTFgktm3SlQkE+uG8nh3s6l8rMIgvCHYd6OuhJRKlaNpbrYiz0s3dEwwRNg90dLcQDDXGpmu1gaI17uy0SImQa9Cai/PKD91F3XKq2vXDvK6SCAaJNxEGklLw6M8Lnh89wOXcPK756uYZPrRIHJXVrvtVCAaGy4AS+xRu49Zir5TmRvnLn11MvMFpe/dyHtRAhLYhkZfZKadKKca9A1TV2HB5g5vocI+fHmbg6Rb26F9O/8WfolbevM3x2jHK+SltfisGDty4M89cJPlXjiY5t5KwqZcficn7rny15u8aJ2VH+/blX+O29DxM1fGtmIR7o76Y/GW9Y/QiBrih0RMNL+gf7OlppDQep2g51x8FYmDe1hUNE/T6e272DY33LJ4S/fPTgUlblzMQME9k8/+fHn8dQG5UCb41NMlMocWF6jvu62vn0/QeWnt2KEAvzMpNkMIAiBDtakvzS/Qcp1OrYrouqKER8JsZdsnu7W+hpaZRcNsPEfJ5KbfMl7JOZAvlSdUEteHXU6g7pQoVssUI8vLkk0VoZ22jAR2di4xnbdL7MV358jq+fuMDEfAF3g2KNfkMn6DfwGxoSQblmUa7WNyRKVK5ZvHVlotGb6Xk8dWilIvJq2BXeQcpIkrYyTNdmeDX9Bj+ae4X+QB+7wzvo9HcQ0JbPh7NWloKdJ6gFSW1SQb/iVrC8Ou4a+g+3ipH5LN8+e4WvnLzARK5A3X53HQKoWO8GVhQB07kir10dpVyz6IpH2d+9cceCmVyJ3A3BFkUIEqEAT+4bpCcVW7M0WFOVJd/uxez+ehUkmqoQCZg8sqsfx/XIlWtcm83geh7nxmb4wbkh4kE/Ozo2H6S9LWIb1kMcS9xP2alQdsuNv06FnJ1ntj7HbG2OscoEqlBpNVPrEtufNOiKTrt/9QiqEAKf6l8zyqMrOi2+Vlpo7mflU3341NVLaRtCTqs/xAzVJKW2rHozq0IlrEcIryIIpQqVqBEjasRWXcfdQCjkIxR6NyKybXvjhg4EbnwpqWwbXN0XLBA02bPvXR8+09TvWkY3oOn0hGK0+kOMl7e2HLnuOpxKT/Hl62fpDERI+YL0JKL0JNbeN0NT6YxF6GwSqWuNhGiNrB4oSgYDJIOrv4wNTSXi33iJ7htzY3zp+llenx2l5r73pbnrQ+VWH69SOiBrNLz5ArdGju8ipms5Pj/66h1fT92zydRXL83LWjmulK4ytBBcXYRf9XMkfoid4R2r/va9hGFqPPDB+zj5/bPMjad56ztnaB9o4/5nD2D41q8eGrkwwXf++4tceashrNPSleS+J/be6c3+iUHSF+SZzh2UbIviUJ2pytZmpDwpma+V+eboBboDET7ct5dUE2/xRbSFQ7SFV3+2pkJBUqHVf98Vi9B10zN7W6ohPjmeyzNTLFF3XR7q70FVFGzP4+LMPNlKg2wt2visBZ+uLY25Gvpa4xzf18eO3PoTU9v1GJvNMjGfXyYos2wfOpK0J8L4tsA3uzMVQVtDOGoR3S1RQqsEmGayRcq1Op4nN6U9cG0ys6b68yI8KSlUaozP5zdFbC3bIV+qkS2tFEUL+gwSkcCqZP1mVOs233rzEi+8dZnR2eya/bThgMm2jiTbOhL0tsaJBEx8hrYkIGQ5LnXLIVuqcH06y9DkPOPzeUqrCN2VqnXeGZpsBE2CPg4Pdi55qK+FqB4hooXp8LfT4WujpTrJ6fw5TufPcaU0RIevnZ3hQfZEdi4lfCaqY2StLN3+7k0T2zuFfKXGa0NjfOXkeUbTeTpjYQ70dJAKBThxbYxLU+8G4RYDZR3xCAi4MjPPlZn5TRHbTKlC8YbrUlMV9vW00RmPYK4j4rSYsYVG20LNsjekNi+EIBb0cXSwm6lcgT/4zmtICRXL5pVL19nelmSgNbHpftvbekIoKPhVH47nUPcaN5GLiyNtPOmhKRp+1Yeu6FtantoQKSphu/NY7jyOl8H1Skhp4UkLiYMiDITwoYoQuprE1LrQ1VYE2nuulCmliyer2G4ax8vheDlcr4Ana43tlzYSB4ECaCjCQFEa+6IqUXQ1iaG2oogA4j1ojne9MnVngpozjO3O48kaAh1NCWNo7ZhaP4bailgjS+JJC9udo2pfwXZncb0KCIGCD02JYmpd+PR+FBG84/sopYcn61juDLY7s3A+iniyipQOEg+BihA6qgiiqVE0JYGhtqGpSZQtsGIRQrAjkmIgnNhyYgswVy3xw8khuoNRfm7bfYR0847J6m8VGve55K35cT5/7TSvTA+TqW++TPNuQoggEg0pqwhZvoURJMgC0p0DVFDaGn/vYZSdOpeKW1/GuVmMVEa4VLhEySkzUhlhT2Q3M7VZdEVnZ3jjEf+7DU3X2Hd8JzvvH6CUL3Ph9asIVSEznaV3dxfx1ghOvRHMses2ufkC41emKKRLzIzMcebli7z+jZPMT2Zo6U6y7+Gd9O7pWmetP8WN6AnFeK5rJ/l6lc8Pn6Zob63l36Lo3eeunSLhC/BI+wAJ8+73rAcMndZwEFVR+OKp86iKguO6TOYLdEbD9K4T7NwMdve2kYoGqVnrByIrdZsX3rzEXL6M7TYnOge3dXB8bx/xW1AJvhl+n4G5AeXxRDhAPOTH1NUVwlfFap1sqUbdtvFvouRzaHJ+Rc+usiBWValZy+hAsVLn+nSWAwMbF5AqVuukixXsJpnReNhPSyy0YcXh1y6M8J23LjM8nVmV1AZ9Bts6Ehza3sX+/nb62uJ0t0Tx6foKwi+lpFipMzaX49pUhnPXpzk1NMm1qXTTgEaxUufk1QkCPoO2WJi2eGhDSrmWZ5O388zW58lZeVJGkpAWwpMuNa/GhUKjD/dw7ABXS5d5J/c2ZafEXH2GydoEQTXEfbHDAMxbc0xUxynaRUDiU/30BHppNduWnE4WYXs2OSvD1dIVBkM7iBsJJJL5+hwT1TFqbh0hoNPXRYevE/8a7ZpDc2lOXBtlKl9kT2crHz+8h71dbSSDAdKlyjJiu4iBVJxYwMd4Js9EdnMBulLdonZDRlhXVfZ1t+I31udMmqIsCcd5UlKuW2xUKkIIQWs0xPGdfXzt5EXG5/N4UjI2n+fCxCzHd/ZtqsIAbpPYlpwSZwsXyFg5Sk6JklOi6JSXxI3azVZao41S5LYtiIJ40sJx09SdSWrOEFV7mJozguVMYLtpPFnBlTWktFCEH1UJoStxTL2PgL4bvz6IEMsVgzcCITRMrQ9T60YRt1Zi60kLx8vjuGlsd566O0ndGcVyphr/vLkFMlVbIFT2EplSRABNCaMpcXStDZ/Wg08fxNS6MdUudLUFVbk9xU/Xq1Cqv40nayxKR6hKuLEO7d2MsuXOUa6fIl97hWL9LerOCK5XRhEGutqCX99B2HyAiO9B/PqOJtslcdw8FfsShdoJCrVXqTnDOF4OgbIQiGgjaO4hbDxAyHcYU+1GUbbea8yTduN6WjgXFesyNecaljOJ7c4vnI/6ArHVUBUfmhJDV9swtR78+nb8+g5MrWvh2ri9QMPOWAs7oi28NjOCvcV9lR6SiUqeP7t6kqQvxCPt/cRN/z2rGuhJSdmxOJuZ4k+vvM2rMyNkrbV7ku4JqB0Nv1pvDulONix/xMYfs9LLIZ1RcKdAGAh9J/DeeNjeClrNKAHNvCPXleXZZK0yJae5Pct0bQYhFJ5ofYzvz7j8bPcnuVYe5lppGPMO9VdtBRRVIdEe48lffJhSrsKFE1d484VTXDs9wq6jg3Rsa6WUbwRJ5sYzvP2dMwydvM7M2DxDp0aYGprBsV3CiRD3PbmX4x+9f1NlzD9Fg1hsjyb51MABpqtFvj9xZcv7+CVwITfLF4fPENRMHmztJajf3fMU9/vZ39HGVKHEuakZFKEgBPQlYjzY17Ouj+1m0J4I055YX+sDoFipcfra1JqZmc5klIPbOmiLb2zMrYCuqbTGQkQCPubyywOVUsJ8vuHZulFiK4GhqfQKYuszdfb0tXFqaHIZIS1W6ow0a/BdA9lilblcc4vCVCRIa2x9LQ/H9ZgvlPmrl88yNJluSpIBIgEfh7d38oH7d/Lgnl6SkbXnoUIIIkEf+4Lt7Otv5/D2LrZ1Jvne25c5NTTVtEw5X67x0plrbO9K8bGH9hIP+9ckWjO1WcYrk8zU50hbGSpOlZ3hQXZHdhLTo8zV53kj8zavpl9nf3Q307UpZmpT1L1GO2DNrRI3EhzkEAJB1soyWhkha2WWHD+mqhN8sOMjGAt8QCCwPZu5+gznC+eYrI7T6msnqseYrU9zuXiJ2foMjmfjIRmvjHEscZyeQC+60vz9fnFqjkvT86RCQT50cCe/fPzQ0rLVdICSIT8BQ6dct8lVNmdjZjku9g2uA6oi6IiFN5Qt1VUFfaENwfMkhWptU+KehqbSHgtzbLCHmVyp4X/seYym8wzNpO8usZ2uzfKfrv0JuqLR4++iN9DNoegBegM9tPta8asNMnK7GVIpPVxZpmYPU6i9Srb6AuX6WSSrN+57soTnlrDdaSr2BbJ865bXryoR2sO/QVv4V1E24VnWyATWcL0CdXeSsnWWUv0tyvXT1JxRYG3yInGQ0sGTVRwvDVznxl326duJ+Z4g5n+CgLEHTYkhNjGJvhG2O8dw+v9F3Z1gsa/Pr++mLfzLtIV/FSklriyRq3yXmdKfUbHOL9t+T1apO6PUnVEKtdco1d+iLfK3CJsPLMuSO16RQv11ZoqfoVB76ab9bYxje3NU7HPM81Xawp8mFfo5/PqOWw4qLF9H42ZzvSJ1Z6xxPVW+S8k6jZSrEyeJi+PVcbw8NWeEYv11QEFXk0R8D5MMfISAsRtdbUFg3NI13xGIsCOaIuULMVXdeoEG2/O4Xsry/zn1A/4pT/Jgax9JX2DDNhR3A1JKLM8lXa9wPjvN/332Za7k5qmvIRp0L0Ho+5HW2+AOg30G6Y4h1F5AWbekWMoq0jrZUFamAqIFYTy0eVXl9xCPtuxhZ7gDv7b1E/bZWoGX5y5wKne96XKJRFc0QloIRKON40B0P6dzZ0hbKy0M7jU88rGjVAtVpJRcPTlMeiLDy2PLt3vk/Dgj58eXfaaoCpFkmMNP7+PpX3yY3cfu3ez0vQxNUdkeTfH39j7MRDnPpfwc9TvQ9vDi1DXiZoCIYXJfsvOuPn+FEHTHo/wPDx25a+t8v6M9ESEe9q8gtgCzuRKFcm1DZFtKie24XJ/OLlNT1lSFlmiQZ4/s4NLY7HJiW60zMptd0y3gZmSKVeZyzauFkpHAusRWSkmlZvHCm5c4Ozy1qj+tT9d4YFcPv/zMYQ4Ndt7SnKe7JcrHju9lW0eCf/dXr3Dxpv1fRLlq8effe5v9/W3s97XjW0Pg9VzhIm9nTxE3Ytwfv499kT1o4t05aE+gi6JTYrQyTlSP8lz7h3ClS82tsS96gJ3hXcvGixtxjsTuRxUarnQZKl/hm5Nf5dm25xsWmDQqMubqs2TtLDO1aT7S8QniC62X5wpnmaiO81DiYRJGkrpX4y9G/4wWs4W4ESduNC/pny2UmS2U2dvZwtN7Bjd0PEM+E0PVqNnOsv7bjcB2XNwbsuaLllcbqewzdQ1zoUXAk5J8td6wJJJyw9dFwNC5r6+Db71zicXbYzZfYuwWBNRui9gaqkGnv52Z2ix5u8h8PYtP9WOqJoai0WKmUIWKQKzudbsOGuWIVXLV7zNT/BNK9bdvZ5PvGhbJeLl2krnKX1GovoTtZWADdecbRc2+yrR9lWz126SCn6A19CvoaqOHdCvKrR0vi+VMLciNO+SrP2Kq8J+oOWubpHuyTK76Qxwvz7bk/4mp9QAqUnrkqy8zXfxvlOpvrrN2iaTGdPGPAIWW0Kfx69tva78WXw4edTKVbzNb/Axl6xy3LtDjYbtzpMtfJl3+Gi2hT9EW+jX8xi6Qmy95V4RgWzjJoVQnU2N3RnnQk5KpSoHfffs7/ObuB/lw7146gxGUBaXw9xKLQhFDhTRfHD7DZ4feobLFvr53GsI4jqh9D+kOIe038Mr/FSX0j0CJgFS4yUgDKV0WjY+wTiGrn4X6yzTKkFMovudBvH+sW44mB3kgsZ2QvvXbPFqeZ6Q8tyqx1YSGAOpeo4x0ujZNTI9R9+q47wNlaUVVeO5vPkHP7k5e+JMf8cqX36SYKb9rk7Vg78KC97oQAqEIYi0RnvvVx3nmlx/9aQnybcJUNbZHU/yL+z/A/3zi6wwXM1tuKyaBb4xewK/ptPhD9AZj7/mz93bheRLP81CURgb4/b4/N6I9ESYeal4yOpstkd9gZsxxPWYyRbKlyjLhKL+ps60jwc6ulhXZsUrNYnI+j2U7G/Z0zZYqzOebZ2yT0fUztp6UpAtlPvPdtyms0gssgL19bfzN54+yp7f1ts63z9A5MNDBP/3FJ/nn//UbTKaLywjW4jbN5ct86/WLJMIBBtoTq65zZ3g7O8ODdPk7m1ZnCgQdvjaebn18ze1anC9mrQxvZl9npjaFBGpujZpXx73Bei5rZZiuTmJLh98Y+DuEtBBCCPJ2nvnaHKeyJxkqLRdYLDklau7q107VsqlaNj5dX1PP5EaYmoamKHjS27DI19L+crPNl8Rx5brqxtA4h74biG2pWl9Sb1Y3eG2Yusa21sSSCBVArlJlrtD8Wl4Lt0Vsu3wd/JOd/4CqW2WqNsNEdYrJ6hQXCpfI2wV0Rac70EGPv5vjyQdo860u3LMaXC/HdPGPmS9/CcuZvJ3NvauoO6PMlv6CudLn8GQVT9bZSlK7fF1TzBY/S92Zojf+z9CUtUUdNgrHzWK504CD5Uwxmf8D6s74ur8DkNhU7StM5P89/YnfRRV+SvWTZCpfp1w/tantmCt/EUNtX+hp3VxJwnJ42F6G8dy/Jl99GdudZetUZ13S5a9Rta/RFvo14oFnUcXme6i2R5Mca+nhhfHLd3QynqvX+MPzr3ImM82nBg7waPsApvre2jRdLczz7bFLfHfiCtcKaarvM1ILgNqNMJ9EetPgXEbWvobrnEExnwBtN9JbyMDJKrijSOvEwt/XkfYpWLjfUPtQ/D8PIgKbbJ14LxHUfKh3KANlqtqa2a2BYD8JI4FP8bE/uo/fv/qHSKAv0EvrPSIIshFsP9RP52A7P/Mbz3DmlYsMvTPC9PVZStkytuWgGRqRRIj2/hZ2HNnGwcf3kOqK49+C3sOfAgxF5UC8nd/a+zD/8cKrXM5vvWen5bl8Z/wypqLxPx54jKjx/qnKaIbzZ8d56UcX+cjHDtPZnUBV3z/PrPXQHg+TCDe/txYzthuB5bhcaJKRbPSoJulvjy9lvRYhafQfD01l2NmdQtuA6nSuWG2aXRZCkAwHSEXXLhdOFyr86PQ1MsUK3iqNkqah8+sfOsZAe2JLtDoMTaW/PcHfePp+/uu3Xmd2lVLqH7wzxEN7++lORVcl+gk9znpz7ZgeJbQBslh1K3x7+htsD+3k6dYPYComQ6UrfGbkj5d9r+7VUBUVVaq8OPcDnm//ENpCNlcIwaHYYT7Z/fMLujkNmKqBsUaLjLZQ3uu4HhXL2pBoWrFWp+46mJq25CG7UZi6hq6pS8rLEijVrA0F9ny6ht94d/s8KZnMFmiPNVwyNgJFEYT95rKARaVurxpcWQu3NZPVFK2hQKaHieoR+oI95KyGIvJkdZrRyjjXy6MMFYcZDA1sitg2soQWM6U/JVP5OpYzgaR5WZAigphqB4bWjaqEUISOK6sL/bjjWO4crPLbjUCgoij+TfXmqkoEv74Nx8vc8no3Dhfby1CovcJE/t/TE/snKPhvW3RJYuN4Oar2VdLlr1F3xpBsXN7e8QoUaq9Rsc7h07eRrb5AsfbmpsYAcL08xfrrBIw9RP2PbnY3AJDSpmoPM5H/dxRqr+F4WbbaSsWTVSrWOWaKf4QrS6SCH99073NY97En3sax1h5enRnZ0u27ER6SvF3nlelhRktZfjh5lWe7dnKstQdT3Vj5yW1vg/Qo2xan0lO8OH2NM5kpRktZMrXKPetTux6E0MD3ARRZxqt+fqEk+TKeO7fQezvT+KI7glf9c6h9uaGA7OVBlgAX1B4U388gfB9qWP+8jxDS/Gh3yFrHUHT0NUQIewI9dHrO0nspYSSwPIsWM0Wbufmg6nsF3dSJGBqBsI9kV5wHP3iIetXCcVyk1yjt0gwN028QiPgJx4Ko+q0Z2f8UKyGEwFA1nuwcZLpa4K+GzzJU2PpS9my9wo+mhoiZPn5rz8Noyq1Vtd0L6BtoIRL1k2qNbEoh+P2A1nh4VVXi2VyJfHljE2/LdrkwMruijzToM+htjeE3dToSEbLF6jJbl5rlMDQ5z7aOxLrE1nE9sqUq2dLKtqpkxE8iEsBYR3xpLlfih6eGViW1IZ/B04e3s70rhd/Qt+SaFULgN3SePrydl88OU6rWl5VrLyJfrvHO1Qm2dSTY1pFsOtaJzBtYnsOTLY9gqs2JoyIUjBverbrQKXh56jdlUOtenYpbIaAGiOsJ8naOscroivGSRmqhhFnwWvrHnMi8xqHYkcbvjDhjlTGmqlPsje4DIFNP08gdr37sYgE/sYCPYq3O1Zk0x7atnygZmk2TKVeJBfy0bTDLuwi/rmHcQGw9TzJfLONsIPMb8hkEfTccawkjc1l2d7ZumNgKITD15UrlluMuuxc2itsithWnynB5hJydX/pXtEtYnoVAoCs6A8F+QlqQmL5ZUQJJpvxNspXvULNHm5JaU+0i7DtG0LgPU+tCVaINNWQUPBw8r4LjZana1yjW36BYO7EKqRIowsSnDaCpcTQlhqbE0dRYQyxISRIw9qEoG8/AqUqYgLEXv76Lmj20CikX6EoSXW3D0NrQlSSqEkYRPoQwkNJeUE+eo7bQv9ogZM3gYbnz5Ko/JOJ7iKjvEVSxuQt7JSS2myZfe4VM5Ru4soIqQgv7tQNVieDJKlX78gJhvTnD5uJ4GXK1F/E5o5Tq72AvZKxUJULQ2L8wThjXK1OxzlGxLuLKm8twJWXrPBXrPFH/I2w2gyWlS8W+wmzxM+SrP8KVJZpH9BQMrR2f1o+hdqIpURTFh0DF82q4soy1oAZtuTMLQlvL4ckaZfs8ovxlFGGSCn4CNlGGrykKA+EEH+zZzRuzYzh3uISyYNe5mJtlulLkSn6eb48nOZBoZ0e0lYFwnNgWC0wVrToz1SLXS1mu5OcYyqeZqOS5XsyQqVewN1k+cyNUIegIRLkv2cGrMyPkrOqWlxFuBEJtBd8HUZQwXu27YL/5LqFdhKyCe3P1gw/0Qyi+ZxHmUwj1/ZFl9KsGvYGWhjCIfucEyUxFQ1+DNPtV35KAtKmY7IvswZUudbf+nqjH3w6EEOimTqItBm2x93pz7inUXItrpUmGy1N8uPP4HVtPwgzwMz17KFoWVecck1tsA+RKyUQ5z7fHLtHuD/Ox/v343uOqmVtFOOwjHH5/Z51XQ8CnEw8HCPvNZXYoAIVyjXy5QURvzrbeDMtxuDA6syJjGzANelpiqIqgvy3O8HRm2WS+bjtcmZjn6cPr25VlSxWypWrTPtW2eJh4aG3hpdJCT+/VydUDOZGgj489vI9o0LelQQwhGj3Aj+zvZ2I+z/D0yqSQJyXvDE1yZEf3qsR23srieg7qJgKsfcF+5q15Xs+8xpXSJVJmC4+lnsSvBugPbGO4fI25+iyGYlL36o1S4xvmoaZqkjRSxI0kdbfOqdzbBNQAO8I72R3eh5Twdu4NzhfO4OGhCpVjiYfoXMPqsy8ZoycRZXg+y9dPXaI/FScZCiwr1V2ElJJcpcZLl64znskz2JpgV/vm5g/RoI+QaSxd467nMTqfW1U47EbEg34SwXerGiRwbmyGh3f20bKOoNjSbzxJzXKW2QQ12tM2P4e7radozs7zg7mXeJckCAzFoMVMEtEjRPUIMT1CVI/SYja/CJtBykYGcr78Jar2UBPCJPDrO0gGPkzU/zg+fRBNad68L6WL7c4TMHZhqO1kK9/ClWVuJjZC+IgHniOg70ZXE6hKBFWJoClhFCXIetGVm6EIHUNtJxH4ENOF/7xApkAVYQytA1PrwVQbarq6mkJXU2hKHEUJoggTIfQF4agajpuh7k5Ss69Sqp+iWH8Tr6mViIPtTjNf/gJBY++W2AHVnQky5W9Qc0bRlBiJwAeJ+B7Br29DVcJ4skbVvoqmRMlXX1lBSj1pk6/+iJLyDjV7GPDwaX1E/U8R9T2CT+9HFUFcWaFiXSRb+RbZ6vfwbhJyapD7ERw3h6Zuzg+55oySrbxApvICrlzpg6kIH6bWQ8g4RMDYi6l1Y6htqEoYIcxGoETW8WQVy52h7oxRsS5Qst6hal9ByuXXp5R1ytY51PLX8Gn9hMzNCXXETD9HU90cTHZwJjN1W2RvI3ClJF2vkJ6r8E56klPpSfrDCXpDcdoDYVK+AAkzQNTwEdAMApqBqWpoQkEVjd5cT0pc6eFKD8fzqLkOFcei7FiUbYu8VSNdqzBfKzNTLTJRzjNSyjJZLuBtUYl+ZyDK8z27eLR9gLlqmVOZyTsi/rI+BELrAxFCUTuR1n5wroI3g/RyICsg3cabXJggwgi1HdRBhHEIod+HUN8/vZId/ji/3P8YAkHMCN6xbL+haBiqhiqUddchhEAXOjo65woXiOsxtoUG7sh2/RR3FwKBoegE1TtPpPrCCT7Us5uiXecbo+fJWZtTGl0PlucyXMzw2aF3aAuEuT/VTUjfmIK3bTvMz5U4e3qUWtXGdT1i8SADg6309CYo5Ku8/OIlenqTZOZLlMo1kskQgzvaaWuPNkSCyhYXL0wyN5PHtl3CET8Dgy309TcmxY7jMjqSZvT6PPl8BSSYPp1HH99FIGiSz5W5PjzPxFgGVVN44MFBEsnQEuGp122mJnJcvTJNtWIhhKBvWwsDAy2E3idEWFUUkpFGCe/NxNZeyJAWyjVa1uhd9TxJpWY3bG2c5e/zoM+gKxVFIOjvSOC/MELuhuV1y+HK+PyyvtzVMJcrk2uSrQXoWBDBWgvzhQpXxueprCYYZWj0tMTY29e2buZ3s1gk3A/s6uHF09e4PpNp2t85MpNldDZLuWYR9K3MBobUADVRp+5ZG7YZ7fL3UHWrC320DUsfV3r8cHSMpLEdT88jhU1YCxPWt9FqtmEojXUPBnfgSoeoHiOkhTgQu4+aVyWkhVBQ6A70oCsa18vDlJ0SHpKgGsRUfGtyil0dKQ70tHN5Zp6XLl8n5DO4r6edtkiYbKVxjit1m6HZNHPFMidHJnltaAzP89jb2cqezs0R25ZwkEjAx1SuMUd2XI+rM/PUbWddEahUOEgqvJzAnhmdZiZfoicZ25hFk+sykSksqxRQFbFpD1u4XR9bITAVg6SRIGUmlshsVI8Q0oJLJ36z8GSVYu0NytbZJgROQVPipIKfIhX6BIbayloZPCFUDK2NmPokPq0fx8tRqL1607gSpIsifIR9x9DVrelRVZUwicDzC2S6umCHM0hA30PA2E1A34mqRNcnn/oAYe7HcfOUzdNo5Ti5yveaZh49WadQ/TG10AiakrylPs8b4XhpSlYagUbM/zht4V/Fpw8u8271aX3oShzLmaJsnbspK+5QsS8ukEMbXW0h6n+S9vDfxKf3L1uXqfUihEbNGaFsnVm2TGJju7PU3fFNEVvHK1KsvUam8s0FZenlUJUoQWMvMf+zxP3PYGrda/rvBtgFSGrOGPnqS2Qq36RUf2tF9taTZUrWGebLX8Bv7EAltOEgg66odAYj/NzAQSbLBWZrpbuWebQ8lwu5WS7kZlGFIGL46AhE6AxESPmCRA3/AsHV0RUVTVFQEEuE1pYelutScSzyVpWcVSNv1ZirlpgsF8jbtTvSOxzSDI629PCJ/v20+8PsibVwITfzHhHbBoSaRChPIPUHwbmEdK8j3GmkLIG0AAWhBEEkENoAaLsQSgzuUCnvnUKrL8rHu4/d8fWoisKOcAfPtB+k259ESknVXd8C6mLhIgPB/p8S258QmKrO9nAX28N3PvgjgH3xNj7at5dsvcL3J65uuUJ7zXU4m5nmT6+8TUAz2BdvI7ABVfFSsc7bbw5z8s1hgkETCSSSIWLxIF3dcdLpEp/5o5f44IcP4Toe+XyFq0AmXeYDHzqAoWu889Z1hoZmKBZquE4j2DYxniGRCBEM+RgdSfPGa0NMTmQaR0NKdEPj2EODBAIG9bpDer7I228OMz9boH+ghXg8CIpASsn4aIZTJ0eYmW4QZ+lJrg/PIZ7Zy45d7Zjm+8PKbFFNuFkWMVuskC5U1iS2luMwly+RKVSWzdgMTSUW8hEPBxAC+tsS+G86JnXHYWQ2S7VuE/aba2ZJZ3OlVYlteyJMbBURrEXMZUtcHl/pj7qIaNDH/oG1VYlvFz0tMbpTUYI+k1KT/spyzWJsLs9Mttg0axszYoyUx3gr+w6d/naUG/paU2aSiL4yCRbQAhyMHQIOLX1muS5jhRwPdPSwI35omTWXF5GULIusU6YvsB2/9u7xCGkhnmp9dvk+BfroCfRt4ihAdzzK8cFehmbSvDE8zn9/5SRvd7XSm4wxNJtBAuPZAp974wzjmTxvXZ/A9TweGOjmwcFe2qObs8Vqi4WJ35B1dT2P8XSedKlCRzyyZiAjGvCRigTx6dqSF+54Js/p0Wn6WuJ0xtfWxpGyQdJPDk8s8zNu9ApvnqbeFrFt97XxmwO/Rs2rkbcLpK0sM7U5pmuzhLUgCSNBVI/gV30rjIxXh8TxisyX/6ppVlIRfsLm/bSEfm5BJGlj2QFFmPj0bXREfp26M0LVHuJGuxpXVpgt/ilR36NoSuSWbXOWr9PAr28nHniuQZrNo/iN3atml9eDpkaJ+I7j0wdwvQKF2okmx0jiyjIl6zQ+fRvqJsqnV0cjmNAa/lV8ev8yUguNvsKw7xgh8wh1dwrbXV52KeWi6bggZBwmEfjQClILjSx3QN9F1PfYCmILYHtZ6s4EQePAhrZaIqlaF8lVX6RqX26yPh9h8zAtoV8iHnh2WWP/2hD4tF704CcxtV4m8v83pfpb3BxkcLw0ueoPSAQ+RMg8iroJdduw7uPDfXt5ZeY6L01do2BvvoH+duFKSbZeJVuvcj47s/4P3iOoQmFfvJ2nu7azN95G0aozGE3dGzZGQiCEH4xDiBtemj/F5iEQPNV2gKfaGve/7dmcL1xgSSl4FczUZ+n0d96djdwiSCmRnsSq21hVC8d28RZf+AJUVUUzVAyfgWHqiHu4r9H2HIpOFcdzqLkWhqKjCAXbcxACEkYEU9EpOVUKTgV7gTgaikZUDxLUGs/NslOj5FSxPBvHc1GFSm+w9ab1VCjZVTwkqlCI62H8C77KVbdO1bXwpIftOdQ9GyklKTNKUPOvWgWgKgr3JTv55e1HmKoUOJ2Z2vJAoyM9vjtxhY5AhICmszPasu7zq1yuM3Rlhp6+JI8+vptkKoyUEk1f9JP0yKbLxONBHntyN6qm8K2vvsPrr17l8NF+UqkwX/rimxw52s/jT+3G5zM4d2aMH3z3HAcP9bJ7TyevvHiJ+dkCxx4a5MGHd4CAYqFGNOZHURTaO2K0d8QIh/189a/eWrZ9ruNx8s1hLl2a4tnn9tPaFqVatfjPf/B92tqjtLZFaG3bOt/cO4lkJEhrvDlxzRarpIuVNX9fqlpcm0qvqE2KBExaYyE0VUFKSX9bnMBNPYmeJ8mVqszmisTDfsw1spCzuVLT/lpYVHdefQ4ipWS+UG5K3hcRC/k5sK1j1eVbAV1T2daZpD0e4uoqwkGT83nG5vJNiW1A9ZOxMlwsXqbT375kyQNwPPlAU2LbDIaq8rfve6DpMk96XMrMM1cpcbC1ne7w1l/HqqJwuK8TT0os1+XqTJoLU3OcHpteuo6uz2e5Pp9FVxXCPpN9nW387NH9PDDQtene5654hNQNveSLwmVXptP0pWIkw6uXFJu6Rls0RFciwtBM4/pxPcn3z16lMx4hvNcg5DObbpOUjXL70bkcPzx/Dct5N3AY9BlEA5uv7Lgt9tbwnLQ4n7/EN6e/w2hlHMtrlDBoQqPN18qHOp7lUOwgQTWwoQMtZSPbVay9saAkvBy6Eqc19GlUJXQLdio6Ed9x/PpuLGfmprJZl7o7Ttk6haG1o6upTY29GoTQ6I794y0Za3E8Q22nJ/Y/cXX+H1K1r9KsX7RqXcL1F4Dbn9CpIkDYdwy/tg1lDU/NiO8hSvW3VxDbRSjCJGQeWrM0V1fbCBh7mi5zvSLWKmM3g5Q22er3KNZfa7o8oO+lJfjzJALPbXjMG6EqAcLmEbqj/5Arc7+FKyusJLd5pot/xIC+Y6F3eqOBGEFQ0/m7e44zXs5xJjP9nvSLvh/Q6g/x0b69PNPZ8O/UFYXBcPLeILY/xR1Dza3xR9f/O37Vt6rJPUDOyrMv0vyZcq9BSonneji2SzlfYfzyJCMXJkhPZankq7iOi27qhGJBWrqT9O3tpGtHB2bARNNUhPLe23bdjIxV4HvTbzNTz3K1NE6HP0VI9TNTy6AIhV/qfYZ90X5O5q7wnek3maimkVLS5U/xXPtRHm+9D4CLhVFenj/NcGmKtFUgoPr4wwf+H0DjuE3XMvxw9h1OpM9TcepE9CAf6TzOseRuwlqAq8UJTuWGKDoVslaRK8VxXM/jNwc/zIPJvZjq6teQT9U4lOzkdw48zj9+7Stk63emf/+zQyeJGn4iuo/uYHTNcxmNBTh8fz9/+scvMzWR48gDA+w70ENb+/LsyP77eojGAqiqQmd3nOFrc1wfngMEkxNZxsfSvPCN00vfj8UDZNIlXE9y7eoMu/d0sntf1xJhjic21jNXLNWYnsnzxmtDXL00vWxZuVynWt2ciOR7iUQ4QFusOSHKFCukm6gQ34hSrc7Q1MqKsWjIT3vi3XE7UxFCARNVEct6Cz0pGZpM09u6Ujl5EVJK5lbJ2OqaSms0RDiwepm74zXKqmezq9urhHwmA+1bU9G4FrqSEZKR4Kq9vjPZIlPp5j3vhqKzLdTPtlD/imUBLbBkK2h73lI/pyoUdEVBVZQVy42FyjQhxIL9aCNb++pkQ0SqNxIj4QugKgJjYc4haWR8G88IiSIE2sI4m0HQNHh4Rx/b25J85eQFvn9+iPFsvlEevPAdXVVpjQT5wL7tfOzwXrrj0Vsq3+1JxehMNDKzNwqcnR6Z5Eh/J4nQ2hyuMx5hf0/7ErEFOD8+yxdOnEER8MTebZiatqziQMpGZvjy1Dx/+dpprkwvP9+pcGDdbG8z3BaxrXk1Lhau8kcjf0pEj3A8dYxWswWBYL6e5nLpKn9y/bPQD/dFDxDW1xcz8mSZqj28QDpvfnEoaGqcsP8hBLdW5gwQ9j1Axb6Aa6+8MYr1twmZR7eM2N4ZqPj17YTN+7HddFPl5ap9Bcdb2U96K1CUAGHzKEKsXX7i13eirVHG7dd349P7EWtcdorwoatJVCWK6y03Zva8Co67mnjWSpTqb1O2zuHcNA6AqoRIhT5JxP/Yhsdrvr0BAsZekqFPkS5/Gde7uce4Rr76CjVnDE2JI8TG+qegkaHaFWvhE337qdg2VwpbbzvxfodP1fi1nUd5qmv7kl2RrqgLGdv3l2DQT7E5KEKh3dfGp3t/gTazbdXvfXHirwiqm1Mnf68gpeTyW9d48fMneOu7Z5ifzOAuqCHLGya6QhEoqoKqKrT3t3L8o0d49JPH6N2z+Uj93YBEUrQr/KOdP89nrn8HRRM81XaY+Xqet7OX2RftZzDUxbbBDnyqSd21eD1zka9M/HiJ2B6MbWN3pJeaa/HS3Gm+OXViaXzLc/j65GvUXZu/M/gxOvxJJqrz/P6VvyKiBzgQ3QbAVDXNbD3Lr2/7GTp8SSpujZgewthAL15A0zmY7ORfHnmO3zv5XWaqm/dXXA+25/G5oZP4VY1f3nGYqLF6hi0YNHnw+Hb2Hujm4vkJTvz4Km+eGOLBh3fwxNPvBnI8dyUBFzQm6aoi+Du//QEOHu5bqnlQFIHp09F1FSlBChp6AJuFlCgIHnx4B7/1D55ddl0ahoZhvn+EshIRPy2xhn7AzQGNTLFKurB+xnZoYiVJiwV9tMffJbaKEHQmIoT9PnLldwmq9CRXJ9M8tKeP2CpZV0lD0ThfXklsu1IRwkHfmtoEuWKVTKG8asBGAAFTX5XgbyVS0RCR4OpJlHSxwvwqwYQdoUG2BfubLtMUjUy1wsvjI3zu0lly9UZr1P1tnXx690EOtraTXlj++ctnGSnk+EdHH+HpngES/gA11+FCeo7/eOpNTs1OAfCt4Sv4NI0newb47SMPgZRMlUr8lzNvcj49S81x2BFP8pHB3TzZu/l2GFUI2iIhfu2RI3z6wYNkSlUy5Qo120FXVWIBH62REKamYuraLWtc6KpCTzJGXyq2jGCeuDLGhw7tYrA9uWZ9bFciwoHedr7y1vllvdGnRqaYzBT47pmrHN/Zx7bWOCG/ieN6TOdKvHN9khNXxrg2u/L+6IhHGGjdfCDl9sSjrDyvzL9Gm9nGRzs/SKe/famv1vZsjtWP8MWJry7UundsiNi6Xpm6M0KzLKSqhPBpfWtmDTcCvz6IpjQvHajaV3G8rVU/3Go0XhAaQfMwxfrbTYmt7aWRTTLetwJFmAT0XesSW0NrXziuKs2sdHxaH4a6tpm3EAIhfOhKciWxlTXcTZD1fO1lavY1ml1LUd+jBI29qOL2JrxCiAZJDn6MXOX7uKxUdJbUKdRew9S6MJWNZ9CFEGhC5Wf69pK1qtSvn2W0lLut7f1JgoLg13cf45mu7aR8waXrShGCuOknYQaYrhTvuLL0T/HeQFd0jicfotVsWfPdkjDi+NV73+O1Wqrxrf/2Q1758huMXpyglC3j2OsrUtYqY2Smc1w4cZWnP/0wj37yQczArQd+7wQUodDmi5M0owQ0k1ZfjJ5AK3XXZrQyC0DOKvJG5hI5q4SHx1Q1Tc4u4UoPBYGuaAv/VPzq8v2bqWUo2hV6Ai3sCHdjKDqaUOkOtDJWmaMn0ChZDmk+AloHA8EOTEUnrPsRG1StF0IQ0g0e79jG9eIRPnftFBPllUHT20W6XuGro+fxazq/tP3wqv7iluWQnivS0hbl4KFeotEA3/raO4wMzwHvEtszp0dJJEPousrUZI5KpU5vX4pUKkRLa4Tha3MLglNJqlWLQr6Cf+H66etPMTWR48rFKaIPbkMgKJVqBIJGo0JgjeMWCvtItYSZmytw+dI0xx4aBGB2Jo+qKbfEld8r6KpKLOQnGQ0yd5PHaq5UXfJ8bdb/KqWkXK1zfWblPC0a8tO2kLFdPJbdLVEiQXMZsfWkZGhinqq1epY7UyhTqNRWiFNBo2815DPWPF/5co3cGp68AZ9BIhJE1+58wDgVDa5JbMtVa1U1ak3R0NAa2dWb5n4KgivZNOfTczzVO8CDnT04roeHpCPUOA9R08dj3X30R+P8i5e/i+fJpaZFU1XZEU/y24eP8R/eeZ2Yz89j3X10h6PETD8KkLcs/s2br/BgZzcf2rYTTSi8OTPB169doi8SpT8a31TwUQiBKgQBQyFg6IRMg45YGG9B0ElTlC0R8hJCMNAaZ2dHyzJimy5WOD8xy/b2FO1rBDXCfpPdnS0cG+zhxNWxpc8d12O2UKJ4pc758Vl8hoa2kBm3HZdSzaJYq6+wwQr7TXZ1tLCjffNJxtuz+3GrXK+M8FjqYfqDvUT15SnjoBbgcOwgP5p7hbKzdqnGIiTWqlk5RfjRtbZNqRM3g662rEqOLWd6hSLvvYqAvnNVgu56RbxN+sWuBoGBqfciWPvmUYSBKhp2Rc36o02tE01ZXx1bQUdTVgpEeVgL5b5rQ0qJ42Uo189ie82ynIKo73FMrW9LbEAEOgF9Nz69F8dLN7UBKtXfIh54BvMWSsNTviAf6d1L1bH50vWzzNU2di/9JCOkm3yyfz8f6d1LTzC2rOy4oYir0hWMMFzMUHwP+pN/ijsPTWgcit1HQF1bR+BI7DB+7d4ltlJKpJR8+49+yPf+7GWunx/DqtooqkIkEaJtoJVoMoQvYCIUget4VMs18rMFpq7PUivVSU9lKRcqWHUbKeGZX370nuq7VRD41UavqypUTMXAp5ooQuBKl4pb56sTP6Yv2M79iV1oQuFM7hqvpc9vaPy6Z+MhMVQD3wLpNVWdoOqn5lk4sjFpMhQd84bvqOu801bsh1CIGj4+1r+PdK3CC+OXmK5uTWXUIlwpGS5k+ObYReKmn4/27aORNF1+PouFKi+/eJHZmQKqquA4HqZPZ9v2RvWCQKDrKum5Il/54psUizU81+P+B7bR0hrGMDQ++qn7uXhukr/6yzca5YsCUi1hPv6zR1FVhUce38UbJ4Z47ZUrvPXGNUBgmhqf/PkHiMWDnD01yuVLU1y+OMXYaJqvfflt2tujHDu+nZ6+JIfu78f1JK+8eIk3TwwhvUYP8FPP7mPb9jbeL0U1QggiAZPOZGQFsbUch3y5RqFSa5pNLdcsZrIlipWV76Fo0EdbfDlZ6EpFV5A6z5Ncm85QqVmrKtROpYsUq83VjHsWsmRroVSzKDXZxkU0bI823k51O4gEfQ0iTnNjRk9KKnWbUrW+gtjO1uY4X7jEldI1bO/dOXBQ83M8eYyIGcGnaZyZmyFq+tifaqM9GCZsNJ4JhqqSCgQJ6AYBTV+WAVWEQtgwGYwliPv8tASCbI8l2R5vzGtt12W2UubVyVEKVo32YAhFCMaKeWqOw1ixQF80viEGIxfSnjcfb01V1/UzvlX0t8TZ19PGD84PLfkI96ZiRPy+dQNRqqLQk4rx4SO7OTM6TdWyl86d68nG9bWK2nYzHN3WzeGBTgK3IDB3W8TWkx5Vt0bciKM1EVtSUEgY8YbYg1w/8twY025qyQKNHllVbL7e+mZoIowimke0Xa+wwr7lXoWutqGsIkjkyXpDtEl6t0neGh6/upqEdcWVBKoSXJXY6mrLqkR82ShCRV2wWLrxsSals8EstKRiXcByp5ucS4GhtuHXd6ApsQ2MtT6EUFBFCJ82QNW63JTYVu1LuF7+ls6HALZFkvxM7x5qrsM3xy4y/9eU3AoaRP+Zrh380vbDDIQTq2Y0ekNxAtrE3Se2sg4osE6FA0ikdMCdAm8OkCAioLYilCib9Wv+6wZFKCTNd8uUMlYGV7q0mMttDlp8rWStDMPl64S1ECnz3mozcR2X6+fGefELJ7h+bgzP9ejZ1cHe4zvp29NNsjNBKBbA8DVEojzHo161KGZLzE9kuX52lLOvXCI9leXym0OEogF23L+Nvj33lmXUjQKSN5rneVJScWtcKIzwYHIvRxM7yVklLhVGNzx2VA+hCZW8VSJrFYnoAQp2helamp2RHvyqSY7SQvbj9tlUXyjOx/v3UXLq/GDiKllra4Phdc/hQnaGv7p+ljZ/mKMtPWg3zSxNU6OrO7FQ5SRQNYWu7gSDC8RW0pgcDwy24joepVKNVEuYnbs7MX06UkqOHtuGCszNFnE9iaoptLZFUBd69AYGG5nu4WuzFPJVhBD4Awa6oSEE+AMG8XiQPXs76d/Wgt9v4PPpmD4dRVHoG0ih6yqXL01RyFeRniQU9hEK+943pHYR4YCPrlSEU0OTyz6XEgqVGrO5UlNimyvVmJjPr/Dj9Bsa8ZCfyE19r12p6ArBHMm76st122mqSjyRzlNejdi2xAj5167iqNatJTLTDIamrRC2ulMwFspqVVVZ1eaoZtkUKnWSN/mkDpWHGS5fp+7VGauMczC6j7HqBDWvSt2r0xuO8Gh3H0FdZ65a5oXrV+mJRDnW0U3PbYpAuVJSti2Kdp2+SIzWYAgBdIejRE0fHcHwht/qrw+PM1cos70tSW8yRuAOKlEvIuL3saMjxf0DXeTKNfZ0t7C3u42DvR1E/OtXykYDPo5t7+HjD+zjGycvUqjUbsnMcU9XC88c2M6uzpZbCqTcFrFVhUpICzFVm6bu7SYg3zV/llJiS5uJ6hR+zbdMmWxNSA/XW60cQkHZArXihuJx84MlcWgeI7r3oCnhNcqDPaS0kbibUPtdCYGGooQ2XP4thG+VoIFAVaIoG1JpVhBNxpC4DSKwLjxK1tsr+l0Xxw7oe9DU+Jq2PrcCn9bX8DxuYitkubPYbgYpLcQtlNIrQrA33oayIGDwnYnLzFbLy8ysf9KhCkFHIMKTHYP80vbD7Iy2rNlP0iC2d7kkU9aR1gmkrCDUXlB7EM1U0KWHlFmk9Q7SPgnuOOCBkkRoO0C/D6Htgi143t1rqLkWRbtGxa1he+4tifD4VYOe4HKCer08wlRthoFgP6pQ6PR1EtQCjJRHuF4eoeyUiRgR9kZ202a23TO9qFbN5rWvv834lWnsusO2g7089rMP8ugnHqBnV6PCo7mapAQJQ6dHSHUneOXLbzJxdZrhc+O8/b0z9xyxXQ0C0IRKuy/JZDXNm5nLlJ0KM/Usprqx+zdhhBkMdTJVS/Py3BmiepC8XcavmQwE2wlrAaZoLkRzq7gv2UneqlG067w8NUzV3VoxpJJj8c78JH929SRJX4C+UBzjhiBeJBrg0Sd2N/2t47iAxHE8du3ppKe3eaWUoaukwia7drTR2rPyO7qusnN3Bzt3N1fCXWvZIgYGW5cI8vsZkaCPrlRz4lOs1JnJFNnZvdI7NFuqMDaXW/F5PBwgGQmuyL51JMNEF/phb3w2up5kfC7P3r56c2I7X6BUWxnEVYSguyVKoInn642o2y51e/X5laZuTcnrRrG4vtWIre14VOsrifxUdQZDNTkUO8grwCe7Psrl0lUuF6+iCR1T1diTbGFPsoXLmXl+MHqNrw1dJGb6NkFsG2JQlususy9UhSCg60QMk2Md3Tzc1UvYMLFcF8t1CegbJ6cvX77OK1dGONjTzoHudvpSMTpjEZKhAIa6dhvArUIIQX9LnE8dO0DFsji2vYdUOLDhDLGqKLRGQ/yNRw9RtWxevzrGXKG0zMJnLRiayvb2JB8/uo9jg93L7Ic2g9uaNQW1AIOhfk7lztAX6KHb37n0IrI8m9naHG9l36E32L1hiW1E85d4Aw2ydrvwFjKZTVcvDNbPTN4bUIRvnfJgjxstjW4FQuibsicSQm1KGIXQURX/CqugVUZpLjAlJXID+yORlK1zuN5KcQ+BQsDYg3Kb/r7NoGsda4zrYbuzuLKEwq31iOuKyr54G7E9D6EpKt+ZuMzMX5MeUkNR6QpGeb57F58ePERveH0v495QjIB256OcN0J6M3jlP0Y6VxC+Z1D8vwDKSkVeKfPI+it45d8H5+ryZUoSYT6BEvxNhLbzbm36HYWUEld6zNULjJTnuF6aYbqWo+zWcb2NVfPciO5Akl8fXO4VOFGd5PXMm4xVRtGEzsHofvZH9/NW9m1KTpmoHmGkPELRLvLRzg/fdkvLVsGxHN754XmqpSqBqJ9HPv4AP/MbTxNrWbs6SQgBArYf6ifeFqVWrpOdzpOfL3D+1ct88u9/8C7twdrQhUbCjCxY9Ch0+BLEjTCmohPTQ3T6k8T0EE+3Head3BBXiuN0B1roD7YT2mAJuaaoPN56kDfSl3g7e5mKWyes+flo53EGgu2Yqk5ANWkxY5hrKGhvBooQPNzWR821SdcqnEpP4G6xUnLBrvHdict0BSP84uAhuoNRtFtQe8/OFaiV63iuh2ZoRBINTYLxK9O8/u3T9O/rRlEVghE/mqGRnsrhuR5SSkKxIMGoH6tmk53JY/gNHMvB9BkIpXEdGn4Df9DEcz0mh2dp7U5irpMhfL8hEjBXJ7bVOtPZ5pWG2WKVsdncis9bYyFS0ZUaH9Ggn0QkgM/QVmRQR+ey5Cu1pp65k+k8pZsytkJAyG/SGm14jK4Fx/Vw3NWfxZqqYOh3j9jqmoquqbBKFtn1vBV9mdCY/xlCJ6g15mKaonIgupcT6TfJWlmClBgt5AkbJqaqcaitk4uZeeyFfS/bFnOVMvl6jYpjM1Uuci2XwfFc4qa/oewrBF2hCNfyGS5l0nhSEjP9tAdDtAWCPNDezRvTE2iKQlswRMW2EcDB1vYl5eT1oAjBWCbP5el5vvbORfZ1tfHwjj7u62mnIxYhFvARNI1NKy2vh/ZYeM1e2vWgqyq9qRh/+5ljpMIBTlwdYypboFSzljxul39fIWAYjfsrEeXnHjrAQzt6b8nmZxG3RWxjepTHUw9zoXCZ/z7yWbr8naTMhnJWxsoyVp1AQeHh5DFaN1j+JdBQRXMhEE/aOKuUKW8GrldE0rxko+Fh+/4wDUeIW1Mr3Mwq0Dblv9og2itvNFWEF5SsN7K9YhUfYcl6RL2RxXCp2cPN+3GFgql1o2xCnXij0ERwzYoCx8vhygq3c3UpQqE7GOWf3Pckrf4gn792mrFyHvsWyMH7BYaisj2S5Je2H+FnevcQNzd2Pfa8Bxlbab2D9GbBmwTnGjTp15fSRVrv4FX+5AZSa9K4bxzwMsja9/GQKJHfA7R7Jrt4K1jsFZqu5fnvwz/k5bkLZKxbf44rCA7E+lYQW8dziOoRjsbvx/Zsvjv7ffqDfZScMgPBfp5oeYzLpSt8bfIbfISfua192kq4rsf08Cx23WHHkW1sP9y/Lqm9GcmOOLuODnLhxFWGz4wyO3rvKKgnzAgfaD+69N9/o/8DS/9/h//dLOHzHcd4vuPYit8vXj+Lf70FUZibAxMJI8LzHQ/wfEdz78nBcBeD4a3NYhuqxqPtAzhS8r+fLDBV2XrhyZrr8F8uvk67P8zzPbtp9Yc2pHyqqgqRqB9FEbz0pTe5dnYcx3Zo6Yzz8EeOoBkqL/zpK1w9Ncro5SnmJjLsf2gH8bYon//338axXKy6xZEn93H4yT1MDc/xpT/8HoMHesnM5Ojd2YGqqyAl3dvb2f/wDgqZMv/pX/wlf/t3f56eHXfW7/RuI+gzaE+E0TUV+yZCVaw0J7ZSSjLFCuPzK0XG2uJhWpsQVEUI2uJhUtEgozcR4tHZHMXy8qyslI26ral0kcpNPYyqojDQkcA09HXfIZ7nrSiXvhGaoqBrd6+CSF8nQ+x5jYqEm2EoBpZXx/IsBILR8jghPUTds5DAZKnI5y+f5czcDLpQ8Ok6H9++m/2pRgn/cD7Ln5w9yUghR7pa4WtDF/nxxAhP9gzwM9t20hOJoSsKzw1s59++9WP+4+k3UIXg+f4d/L3DDxI2TP7pscf443Nv8wfvvE6+XiOoGxzv7GF/y+rq/TfjQHc7D27LcHZ8mmKtzsmRSd4YHice8HNsWzdP7x3kcG8niaAfXVu0EhL3hCibEILeVIzffv44T+zdxssXr/PmtXGGZzM4roeUDeqiKgotkSB7u1o5tr2Xp/ZvI2Aat6zsvIjbukoNxWBbqJ/fHvxNvjf3Iy4WrnC1dA2AiB5mX2QPH2h7km5/F6aysQmmIvwYWnOBHdcrUbfHmi7bDGrOWNNsHoCmJO4I6Xm/QggVRbk9FWpoCEvdTkn0xuFie+kFi5+VZE8gMLSOVXusbweNMuvVH8SOV2zqzXwr8Ksaf2vnAxxOdvPfLr/OK9PXKTvvj97wzUABnu/Zxa/suJ/98fZV+2mboSMQIaSbTS0a7hSkcxm8HKCC2gFaE/9UdxJpvwH2GRqBHhPh/1lQu8A+3VjmZZDWG2CfBP0g3GKW/16BJR3+3aWv8072GkXn1vsRfYpOmz/G3mj3imVhPcxgaBuHYvdRckq8kn4VWIz/CVShogv93ivfl5JauY70JKnOOJHE+u4BzRBrixJrjeK6HrXKT9azwKMhsKUIQdYqkqkXiBu3dpy2GkHN4KHWXv7H/Y/xL9/8FtYdCDI60uP3z/+YgG7wfPcuIsbazwNVVejrb+G//tnfJRAwGLsyzbZ93Tz2iaMEwr4lT9qnf+EhwokQ+x7czv7j2ylkypx/fYhIPMRHfuNJapU6L3/lLU6/fIlIPIhVtXjkI4dJdcbQdI161eK1b59i+PwEg/f1cu3cGPsf3kEgfO+Ktd0qFCEI+Qy6UhFGprPLniKlan2FqBQ0BJnm82UKTdSG2xNhWuPNr+G2eJiWaGgFsR2bzZGvLB9LAsVynVypuiKDqSkKgx1JzA1mWtekEwLuqh6dYO0NWqW6c0d4kIpTIaSF2Bke5P+6/O9RhGB3eCcpM8FgopMDLW24nsciETQUFX2hr3x3ooX/9yNP4y541goEimhkfhe/owhBdzjC7z767NLcYlHAUhGCjlCYf3z/IzjyXRKnKQq+Tcxfntg1wLFt3Uzmivz46gg/uHCNs+PT5KpVvn9hiJevjNAeDXN8ew/P7t3BfT0dd7VUfCNQFYX9Pe3s7mzhbz15P5bjUazWKNctTE0j5DPwGzqaqqCp7x7f28VtEVshBDo6vcFuPmV8lGp7DcttvFB1VSeg+onqEXSxfrRoEaoSwq9va7rMk1XqzhiWM4uuJm+5R7JincNxV0qvAwSM3VsmKrQeGpE2G8udwXKmsN05HC+D4xXwvBKurCFlfaF02sLDXhCEsvGwkJ5F1bl2h7dSQdxWjrEBgQZbINqxHqR0FkSjmk8uPFlnJPO/oiiBdVWeNwvXK2G5U2tsWx021CO8NhbvJZ+mcyDZzu8ceIIjqW6+MXqBM5npe2/ifgvwqRrbIyl+Zcf9HEl10RWM4lM3l7nUhKDNHyKsm+St1W0MthTuJMgyKK0Itaepb7G0TzdIKy6IGEroHyLMx0CEwXwcr/oFZOUz4BWQtR80ypFv0+LsvUTVtXgjc5VLxXGKTkNMoieQYl+0h3Z/DF1o/PnIS5ScGo+07GZHuBPX8yjYFcYq85zLj1H3bHaGO/lQ5xEeSGwnpK88HopQKDtlrpWHqXk1Zutz/HDuRcYrE3T7u/Fo2Dp491jpvhAK4ViAcr5RYXLLd+/CDzVdJRTd+laL9xLz9TxfGPsR49U5pJSkzBgf7Xr4vd4soPE8jhl+Hm3v5+/ve4T/fPEEhTsgWJeuV/jTK29jKCof7Nm9ZpBPCIGqCsJhP1JKHv3YES68fo0v/P9eoG93J49+7H58CyJQmq5i+HQMn4Ft5cnO5WnvTxEI+Ygkglh1h2K2TDQZxhc0ae9PLYhHCTRdJRDyU8lXyUzluPTWMMeeP/gTd/1B45j6DZ3+tgSjM7mlCgKAqmWTLlSwbBdde9c+ai5XYjZXWnFPa6pCazxEMtz8OHUkwqRiK8uUZ3MlssUKlu1gLJQWe55kdC7XtMxTVRW2dSZXKAc3g6ooKGuUtUop18zobjU8T+KtsT5l4Rq/Gf2BHjzpoQqNmBGlw9eO7Vm0mC2kzASaoq5Zzq8pCto6iTghBCqCoL7ye0I0akn8m+inbQZdU9FUhW0tGi3hIE/u3sZENs+bwxOcGBpjaC7DSDpLplThlcsj9CRiPDjYzUODvfSn4vjvgtjUehBCoKkCTVWQEoKmJOI3cT2vcf4UsSR+t5W47boCIQSGMGhZKDVeLIu41VSyInwYahem1k/dGQduvFk9bC9DtvodUsFPom6yT1JKD8fLUqq/hd1E4AcgaB5EU9fv37tVuF6JmjNKxbpIzRmi7kzgeHk8r4wrq0hZWyCyNhJngaB5jfJFPMBd6A/2kDQEIu4kGrfoVgl23QVii4PtzrN6ybKk5gzf8e1ovmZn4ZxtHQKawbZIkpBusjOa4u35CX40NcT57MyW93vdDfg1nR2RFI93bOOh1j52x1qJGj7UW+gjEQtiUxHDd/eIrZcGWQO1F5S2FcEc6RWRzvlGCbLwI/Q9CN9zoKQa94gSRegHkWovuBNI+wzifaLSvhqqrsXr85cp2lUkkuOpXTzTdpAd4Q6CmokrPb4y8Tplp8a+aA9Ptx1AFSqW51CwK1wrTfPViTfJWWVGynPcF+unzRdbsZ6+QC9Fu8iLcy8jBDySPI4iFAZDg8zX5/nSxFeoexZtvtZ7pr8WQNUVevd2k5nJk58vUincWka7kC5SSBcJhP107Wjf4q18bxHW/DzacoCSU0VBENFDdAfuHXVrTVFo8Yf4eP8+JioFvjt+mXR9fWu6zcCTksv5Ob58/RwBzeCZrh0bmmcJIdh+sI9oMsz18xNkZ/OcP3GVI0/tRVEVXMfFXRB30XQVf9BHIVPC8yS1soWqKuimjhCgaArGDfYbiqrQMdCCYzlceWeESqFKW08S/RYsOt4P8Js6A+1xXjojGnLIC/A8SaVuMV8o05F4tz9xLl9umsltjTVIrb5Khq2RsV1JbB3XYzZXIleuLZUxe57H2GwWaxVi28jYrj+H0xZ7WleB43pN13Gn4LjeipLvG6EqCkaT0uiqW6O0YC8qpSSg+vEUE1e6OJ6zVlHdXcMXR0/Q5ouxO9pJ0ly9n1UIgaFpJEMaiaCfzliYvmScR3b0MTyX5cLkLGcmZhiaTTOdLzKSzvLy5RG2tyU50N3OgZ42ehPxhbHu1t6tti8A7xLdO4ktL5hfjFZA46LypMcr6dfYFd5Bm299ZTwhVHQ1Qcz/FHOlz+HdlLBxwCEAAQAASURBVOFyvTzz5b8iaOwloO9GUTZW8tIQLykzX/4SVftKE69aFUPtIKjv25AlzWZRdyaoWOcpW+ep2UPUnFEsdwrbTdOsZPbegdiiEmLlrkwmJR6Ol9+QyNTdRqP/d+vJpqYodAYjpHwBtkdS7Iy2cCo9wfncLFfz82TqlXue5MZNP7uiLRxIdHBfspP9iXZ6QgsP5NsYtyMQIWr4uP0Gho1ByjLgIJQQNHmOSOcqOEMgi6B0IYzHG9ndRQIs9Ibdj9aDdK8j3VGWB/fef7A8m4uFSWzPJWVGeDi1i8da9xDRG4HJmmthKjoCQVDz0eqL4lcbmW4pJYOhdjSh8oWx13g9fYWYHqTdHyduLJ/4dfo7EAjGqxOoQmFHaDtVt4YrXSZrk0zXZohoIe6LHriniK1u6Bx97iBX3h5m4uoUU9dmqJXr+IIbb4kp5cqMXphgdmSeWGuUg0/svYNbfHchhCCg+TgYG3yvN2VN6IpKVzDGLw4eomDVeHXmOrktDqjVXIeT6QnCukncDHC0ZWVJ/s2oVy0uvTVMPl0kN1+kUqqjLhCYaCqMbTmcP3EV6XkkO+L07Ozg5a+8xY+/cRKrahGKBejf00mtXG9617T1JMnNFXjnxYu097Vg+gyUe8hDeSvhM3T62xMoYuWsrWY5TGUKy4jtfL7MXH6lNV9PS4xYyL9qpioa9JGKBPGbOtWbxJOmMyXShfK7xFY21JIte/kWCcCna/S0xtYkrIvwGxp+Y3VKYDsuNevuvIukhLrtNM1CL8LQVQK+lQGUa+XrXCpeXRhH4kiHilMlbkQ5ljja6Ld1bXJ2hbJTozeQuiVBttvBufw4NdemN5giucHHvBACn67Tm4zRk4ixp6OVgz3tHJvPcn0uy/B8lqsz85wen+bi1BxnxqfZfi3JjrYUe7ta2dmeImQa72u9jo3ijnaCS8DyLL4/+xIxPbohYgugihDJwIfIV39EzVk+sfNkjVL9FHOlvyQR+DABY9+C4NPq5MuTFrY7S6F2grnSXyyQyeVQhI9E4HlMvXfLemyl9PBkhbJ1lkLtdQr1V6lYF3C9lUIC9ywazvBbNNBdgPQWghb3IpG7s9tkqBrdoRidwQjHWns4lZ7iVGaSK/l5xks5ZmslcvXqPUFyVSEIaibtgTBdwQjbIymOpLo5mOygzR++bfGARXQGG8T2rkFKGudZY+XjVYL9DtK5DiigtjdKkG+6N4TwI0UM8Br9uhv0AL9X4XgeM7UcrvToDaToC7YukdoGBLrSUJq0XAfbc/EvzDOEEIR1P0+27edKcZrvTJ/iZPYah+L9PJharhjtV/30Bnpo87View1zeH2hpCysh+gP9KEKbcPvobsF3dS4/9mDvPWdM5x5+SLv/PA8HQOt7Dq2fc1+28VSyOxMnlM/PM/ZVy4hkew+tp0Djza3gfkp7iwUITiU7OQT/fupODZvzI1ScbbWBihv1TgxO0pIN0n5AvSG4ms+L6UnKReqFDJlFEWha7CV/r0NEa1oMsTg/oYYVKVQo71PpWd7OwP7uqmV6zi2y87DffTv6WJ2PMP+4ytV2gNhH5qmUspVePJTD6Bq7w9XiVuB39TpbYujqsoKC5Oa5TCVLiC3dy090efzZeabEdvWGPEmnreL0DWVRCRASxMBqZlskXT+3WoAz5NMzOexHGfFGKlokFjIh7qBQEPAZxBcwxKoZjtN7YTuBBZJ9FoZW1PXCDfxVtWEtqTpI5EoniDtpqnX6pTdxrkoOjXO58apuhYd/jjavZDG3QSEgLDfZIeZoj0apj0axpOSqVyhoUNQqZIbrXJ2fIZ40M/9/V08NNjDkb4uuhPRdRWy3++4w8RWUvPqFJ0i9iZ6CxXFJGQeIuJ/GLdcwPZuVnh0mC39BbaXIe5/joCxC1WJoArfgk2MsuB5auPJCpY7S7H+FnOlv6DujHEzwRCY+PVBWkI/t2X9tVI6OF6WYu1tZop/TMk6jSdXPuBWQiDQG2JLojE5FkJFoC6Qd2Uhg9p4eVjuzAbHvVW8v6I7EomU1h3JjL5foAiFVn+YD3SHebJzkOvFLO+kJzidmWIoP0+mXqVo16k4FlXHxvbcOx4GUBD4NZ2gZhDSDeJmY0J2KNnJg2199IVimxKG2ii6AlEOJjqpO7ceae4JxWjxbVCoRhg0ap3shX/vQnolpH2uoZgsQghtAKHvaDKI2sjcIleMASxkNk2OptbP1mwGO6ItqHegD95DUnKqeEhSZoSgtjxwKABT1RFCoebZWN7KcxXRA+yJdnEuP8p0LcfJ7PAKYlt2yszUZ5mpzWJ59WXX9GBwGz2B1Y+XbkGsqNBRMKiWa8RSEarlGq7jEU2GiG9SpRhgZ6yFuOHHrttkZ5ur5S5UZ2H4dO7/wAHmJzKceekC9UqdJ+YK9O/txhc00Q0dRVUQYqH6yPGw6jbVUo1Lbwzx3c+8xNS1GfYe38ljP3uMtt7NlelGdB87oy1Ut5CEDUSSd91u617BU53bKdkWfk1nvtpcqPJ2UbRrDBcy9ARjawaffUGTxz5+f9Nlqqby+CePrvj82V88vuKzvt2d9O1eLuxZKVbJp0tkZvLEW8L07en6ic3WQsNnsy0WIuw3sezlHtz1BWKLBESDnKULZXKlla0FixnbtZAMB+hMRlYQ2+lskXTxBmIrJZPp/ArhKJ+h0dcWRxXKhrJ0Ib9JOLB6Uqdat8mXakgp73jWr1itUa5Zq07jFCEImDqhJpZS98X2c19s/9J/SymZqE7x0vyrOJ5Lul7kfH6c1+av4Fd1OvwxfKpBhz9GVA9Q9xzydgVPelieS9Wp40qPpBmmxRdBSknOKpO2SksetmHNR8IMEdR8jUpVJPO1AgW7iuW5CAFBzaTbn1wRhLI9h7xVJWeX6fQn8Kn62oEqCTXbJl+tMVsoMTSb4fVr45wcmWS2WMLUNLa1xAn5TBzXZb5Y4bvnrvLm8DgfObSbD9+3m53tKfQNetO+H7GpmWTJKeNuInvgSpe0lcbZdMah0dfZEfkN6s44xdprePLmkh6PbOXb5Ksv49cHCRoH8Wn9aGoEgYEnq1juHFX7ChXr7Bp9lSp+fYD28N/Cr29fxWZmc5DSw/ay5Ks/YDT7ezhegdWzdQIhDBR0hNBRhB9dTWJonWgigqpG0EQERQmgCD+KMFGED0X4kHhM5f8DJevkbW/zTxTWIbWqEr1LCs03rzcIW3B9bQa6orIjmmJHNMXPDhyk6tqcy8zwTnqCS7k5rhXTzFZLVB0bV3oNK43FfzT+SiSN/2suS9UQL2wIAChiIfQilIY4gFDQFIWAZrA9kmRvvI2DyQ72xdvpDEbveNikLxznf7rvyTu8lnchlBhSmEivhPDyLLvv7TNIdxhkBbR9COMozRt+XJYIbROiqSkKO2MtfO4Dv3YH9mBhUy2Het3GsV00TcUfNFFv6ItxbJd63ca2XBRFEAyZC8Rr5RmVyCWvZZ+qozUR/QuqJqoQVJw6Vbd5T3GHP07KDHO9PMtoeW7F8iulIV6ae4mp2jRxI3bT+IE1iW1sXnDsnJ9d6XZGLk5y5KltTI/MU8yUeehD9/HBZx5B3OKEfW4szVf/4IVVlyuqgqZrGH6d1t4k85MZ3vj2Kd75wTmSnXH69naTaI/hD/kQisCxXSqFCnNjGa6fH6OQLuG5Hl07Oth7fCd9e7op5cqEmojPrIZDqS4OpbbWCmcRrudh1R3qlkM45ENRtl4s5FZRqVrYtotpaPialDXeKjRF4ZMD+/nkwP71v/w+xvjVGd74zhlqFYuHPnjfsmfETyKEEJi6Rm9rnGKlvqxUtmbbTKYLC29JQbpYIVtcqVSsKoKulijR4NrENhFpENubMZcrkSlWFhR7G72oU5liE2Krs60jseHnVizoI76KmBVApWaTLVVxPO+Ok6JssUqpunp2OOgziAb96/YOL85Y2n2tZK0cM7U0s1WPr068xVBxGlPVOZ0bRRWCXx14nMda9zBTy/GD6XPk7AoVp85weZayU+dnOg/zs70PUndtXp67xLem3qHiNLZxd6STD3Qc5IHkdiSSbL3EVybe4lR2hJxVRhGCvdFu/sHODxJYCOwKwJUe09U8P567xBvpIX5r53P0BpMrbCMXtYssx6FmuVydTfPyleu8eGmYy9PzCAE+TSMZCvBAfzfP7tvOvq5WctUa3zl7hW+evsxUvshnXzuN43r8zUeP0BXf+pbLewWbmmX//tX/zEhlc91qrvQoO7eWUTTVXjojf5dJPPLVl2kmCOTJMmXrLBXrPCv1weVCr+Xq/ZZ+fZDW0KdJBj/KVnWVu7JEvvojRrL/xzplxwJNSRDxPUDEPE7QPLjgsRpcePHfuD9i4X/f3T/XKzKnfnZLtvknBWIhULBaplkRPrYn/79oavKu99lpahxNSdzVdd4IAQRUnftbujic6lwisEW7zngpx3Axw3S1SLpWZq5WJl2rkLUqlGwLy3WwXBfLc7E9F096KAuk1VBUgppB2PARNXykfEFa/SE6AhF6QjEGwgk6AxEMRUUR75Len0ioPSBC4I4j3SEaz57GZM+rfRXcMUAg1H6E/lDzMWQNvGLjdyLwnqg+nHt7hO9/7R3OvjXC7oM9/Orff4aOnnev3SvnJvjhN09z8sdXSbVF+a1//hG6B5pnCRUEAdWk5NSoOHXsmzKyAkFE96MJlYxVIm9V6Gkyv/IpBoaiYXkORWdl7+J4ZZyQFuKf7PodQtpyUqduQEFf1VTae1M89alj/Nf/7Yv82v/8Mc6fGCI/V6RetTbV83ojMjM5vvBvv7H+F4XAcz08r/G+si2HmZF5ZsfSjffBslebRMqGcMxi7GTy6jSf+d++wFf+4AUOPr6Xf/Ynv31L27vVyGTKfPcH5/nW98/xu//843R3xe8ZYvuXX3qTE29e40PP7uejHzr0Xm/O+w7bD/YysK8bZENY6q8DVFVhW0eSKxNzy4ntQo/tIqbShabZ2vZ4mHjIj77O8UqEA3QmV5KPcs0iW6xQqVn4DJ1CuU6xUl+hINwQukps+F0bCfhIhAOYuka9SW+rJ2XDrzdTpKcltqExbxUz2WLTY7eIeDhAMrIx8ViJZKw6QdktE9RMjif30xmI86WxN+gLpvhI1/0ENBP9hndEwa7yw5nz/LN9H2dXpDFX0hUVn6KjIDic6Od4y05Cmkm2XuYvR1/lR7MXeCC5napr8ZnrL5GxyvzatsfZG+nGlg6W5yyRWgBFUZioZrhYmGCmlud39n6Edl+06bxUSkmpbvG980N86/Qlzk/Nka/WcD2Jrij0tcT5yH27eXrPIB2xMIbWmGu1hEP0PxHnE0f28q+++RKvDY3xxvAEg61JfvHBg5s4I+8vbDpjG9OjtJgpdGX9n0opqXk1LhQub3rDFold0DhAZ+TvoSutZCvfxpUrTbAbxW6bEwsSwiTqe4yW0M8SNh/ckkxtA5Ji7QSzxT9fldQKNAytg2Tw48T9z6CrKVQRRBF+hNA3bmMkVN5vpcJ3HEI0fHeFWCVJLtDUFH59YAvP+QY3DZW7oQy96voXXnAqgkWVfAkYqkpENxmMpnA8F0d6OJ6Hu/B3MWsrF7O2C9HDxfEaGVqxLEOrKQq6UDFUteERtyDOcK9MaJtBSoktXYp2lYju39Az7mYI/RDSOgHOBWT9R7joCG0X0n4HWX8JvDyofQjjPlCbm7VLr4B0Z2iUJKd4L2Qcdx7oprM3yZf+9FVK+coKH+CBne20tEdJtUY489b1NX2CVaHS6otSLdeZqeVXkFIhoMOfwFR0xirzTNey7Kd3xTgVt07NtfGkxGniFaoKlYAaJKpHNkRkb4Zh6qS64iQ6osRbIgTDfvwhH47tYtXsWya2Ukoc+9b6pKWUSHdjjQJSSuy6Q3Ymz+iF8TW/a1kO//L3vkxfb5Lnnt7HYH/L0jLHcXnrnRG++NW3eeKRnTx6fAeR2/AllVJiOy6WZeO63poFNa7rce36HJqqMHDDNt0pOI5Lve7guLcuNpjNVTh/cZJXXx9iYipHpWrhMzUSiSC7trdz5FAfOweX3+uu6zE+maVatdi9s+N2d2MZPE+SzVW4dn2Wo4f77+gzV1EVlJ/wLO3N0FSFwc7kgt3Ou1lFy3aZzZbxPIkiJFPpAvkm/rXbOlOEfOuL+ERDftoTYTRVWXF9ZotVZrIlOpMRprOFpZLYG+EztAWhq42dfyEEiXCAntYoVyeau4YUq3WuTabvOLEdn8s37U1eRGssRFu8uZrwVye/xY/TJ5Z9JoF9kd30BLoafrSKhqooaELFVHV86vJqDZ+qsyvSwWC4nYj+7rNPCIHtuYxV0rw4cwFHerjS5Upxit2RrqXy5bfS1/iFvuMMhtoIaiYSo5Fhv+FcjJbnOZm5Trs/xt/of5RWM7JU+baIQrXGxak5Xrw0zImhMdLlCvlqHct2aI2EODrQzWM7+9jX3U7UbxI2TbQbK6cEGEKlPRrm/8/ef0fXmd/pneDnze/N+SJnkARzKpKVk0ollXIHBXdyt9vjtj3uboe112P7zNnx7M56e7y79nhnxna72+5odSu3pFKpSpVVicVijgBI5AzcnN68f1wQJIhAAAxVsvTo6NQh7pvT7/d8w/N8+dg+FsoVBmcWGF7IbvRWbAie52E7Lo7rLk27VUn60KpzNjVz8/DYE9nJwdh+dPH2g7zrueStAqOV9QfZ9SCJfgLqHqRQEL+6g2zlJYrG8S1vTxT8BNV6/25YO4pP2YYsRbe8vVtRs0Yo1N6jYl1mtUyxIGgE1QOkg18hqB1Ck5uo99F+dCf8P0kQkJDF6Lqlxp5XA6S7JhL2kwwBkAQRSRbR74Jf8U8CPM9jqDxL0aoRUXzkrSqWa3Mo3oW5qJbolzSULczXBPUwgrEdzxkBexTP/TaeGK3bAC1ajAnqEQT1IQRhlevt2eDOgzMOyAhSO/dYCmFV+AMa/oBGJOqnUlw5OdP9KrpfJZYM3bb8UBUlugJpJioLjFcXyC72Jl3v5xUQ6Aqk0SWV0fIcVwqTHIx1r7BBGC7PMlXLIosS+iq9m416A5ZncaXYT2+wZ1P+6QCiJKBqMrJc9/UUJLFexiewrp/i7RBJhHjmlx/d8vpbQaJ5fcs61/MYGplHVWVqtVt6wT0olgxGxzJkcxWcDRLrtRAO+3ji0R3s2N5IOhVetwdzbr7IW+8NkogH7wuxvVMsZEq88XY/r/34CrIk0dYSQxDAMGwyuTJjExm6VzmPTLbM+yeHqdXuPrEtlQ0uXp7kzXf6ObS/Y1Wvz48iflKmQLK4SGxvURp2PY+aaZEv14iFfExni6sS257mBIFVekNvhSKJRIM+GmMhxueXJ0mypSqzuSLJSIDpTJFbea0kCoT8Go3x8IavqyBAKhqgpym5JrHNl2tcGJnhif33TqHccVyGZ7LMrmKTdB3NiTCtqdVLaXeFdxBVr5dw109eFzUa9QZSWmJDxyAJIlE1UM/Q3tQOZLsOY5V5/mL4bT7RvJ+0FsHxrlex1QP+nudRsg1Ciq+u9i8IdW+RW+5D1iwvWtpVGSnN0xFY/p149dI1Xr44yLXZDFP5InPFMqoksbM5zeGuFva0NNCRiNIYCRHxry2QWfeSldjVnCYR8HPWmN6yCFi5ZjKZLTAyn2VkLsd8sUzZMKmaFpbtcPMw+UuPHmBPWwNB/f7Pszc1YwrKQdr8LXQHOlBvY2AMdWJbsIoo4p1NmCUxgCo3IFvxVWx6QBS0xb5aA2+xN01Arfejij5kMYoqNaDKTehyF35lOz51B4qUQlxtcnkHKJvnKZtncL3VPez8yk6SwZ8n6n8aWdy8IMnN8DyL9cqsfxohIKOIKdbOjHrYbnbxObmPark/w0cGHvVBZbaa55prEZA1OgIpyrbB6exIvYRHC1GoVpgzCpRsA8/zaA8kiakBrhQmyVtVdEmh2RejPXCjBFeQUgj6J8DL1zO37lT9/wBoCOoxBO1pkDtXPzZ3Hs8ZB68AQhiUXYuCVDdg1Cymx7OcfvcqRq2eBUs2hNmxt5VILMDQwDSlfJWZyRzJxjCyLJHLlNF9CvuPdhNLhBjqn+bKuXFKi56pyYYwO/a10noPyIQmKeyJdvDewgAFq8JYZZ6MUSKl179/oiCyM9JKRPExVc1wfGGAgKxxKNZDXKtbMwyXZ3l77goz1RxBWaNJX0ncqk6NgeIgA8VBmvRGpJssHPZH9tEX3nGbI70puizcWhC2dXIXTUf43N99dsvrbwXqOuqmS/C8RQuye3ssuqbQ0Zago+32k8qRsQWuXpu9b60Kdyr6PzK2wOmzYxiGzWc/v5/W5hiyLC4S2wqyLNLSFF2x3uR0joGrM3e1r/c6srkylwemmJjK3fVt3zsIKLK0biDKdpx1K0PuFyRJpKMhhk9TEFj++thu3Wc27NeYyZYoVG6pTmGR2G7g/RSEOjltb4iuILa5UpW5fBnLdpjOFldcF7+mko6G8Kmbe74aYkH62tP88MSVVX/Pl6ucH5qmYtTLoO/Fezo2l2N8PkepurrWgq7KtKYiNMVXnz93Btpp1NNUnAqGWxegkgUZAZauk4iAhEjRri6py98KSRBXFETankvGLDNQnOYfRj5Dqz/BYHFqxXpt/gSDxRm6gmn8i17tlmujSfVSZoC0HqYjkCJrlnk/c5WYFmBPpG3pHTg7NsVLFwaoWTbJUICndnazq7lu29ObTtIQCW5K3divqiiLWdTNikTO5Itcnpjj4vgMQ7NZZgsl5gplCpUahm1j2g7OTaxWAJ7a3cOO5tvPJ8o1k2y5QukmWytREGiMBgmvonq9EWyK2B6NH6LN14q8wRJOAQFd0uj0txOQNi5kcStMe5p87R0yleepWgM3bV/Bp3TjV3ejSClcr1ZXxKWeGRUFHUnwI0txNLkFTWpGUzoQuDdeTp5nUzGvULWurfq7LMYI6w8S9T15x6QWwPUqeD/hViB3G4Igo8qNiMLqA4fneRj2OEHPANY2xr7XsByHfLnG2EKebKlC2bCwnPq9VCQRXVEI6iqxoI+GSF2FUf5vWMXuw8BkLUverHAo3kVPsIGSXaNgVbDcei/xZDXLaHm+Xt6OwJxRYGe4heHyHHmzii7JKIK0jNiCiKAeo950lgZnBM+rIQg+kFoRtKcR1EP1f68GZxrcLIgpkFII2oNwS2VBLlPi1DuD9J8bJ5YK4XkesixSKRuIosDFUyNMjWUJR/1cOj1CIh3BtuvK1z6/xtHHd1DIlcllSlTLJrblMDuVI7tQ4hd/4+4TW11UORDrIqz4KDsGU9Usc0Z+idgKQKs/wfZwC1PVLFdL05TsGtdKMyS1MDXHZLA0xVBplrJj0OJPsCe6slQ5IPtJaUlqbo2SU1pmNGm6q0+S7gd8QZ2+I70f2v7vBhYyJYZH51EUmWQiyOh4hny+bqsWDvloboouZitvBAdGxha4NjxHqXwjO/DUYzsIBpZPVgzD4vLANAuZEidODjMylsF1Pb77whmgPsHvbE/Q3ZHEv6ja6jguxVKNa8NzLGTKWJaDrss0piO0NMeIhJe/X6WywcRklompHIZhoesKLc0xLNvlTtp5MtkK+UKVxoYITz66A3kdv1DXdbncP83cfJHT58YYuDZLKKgtnSdAW0ucro4kkbAP1/WoVk3Gp7LMzRWpVE1sx0VVJOKxAG0tcVLJG2PY8Og8E5M5+q/OcObcGJlsme//8OySeFAqEaKrI0lDuv7eeZ6HadqMjGWYns1TrhiIokg07KOrM0kiFrxvYlCCAL7bEKWqad1x9cDdgCgIxEM+4iE/Y0pumX+s67rMZos0J8IsFMqUa8u/O6oi0ZGO4lM3EHiibunS0RDn7Qsjy/6eL9eYz5cxbYeZzEpiG/JrtCYjmw7aRIM+epoTpKIB5nIrS4Frps3obJYLwzPs6266rXjTVnD8yhiT84U1gxgtyQht6eiqisgAWTPHUHmEqdpMvUXRu3ER9kV30yV3EFR0WvxxLuTGeGXmPAFZZ2ekhWbf+pUuoiAQlHVa/XE+yFxjoDhNxixRsms0+aIAaJLMM017uZif4M3ZyyTUIC4eYcXHA4ke9MVEX1qLcCjeRdGq8cbsJV6ePk9E8dPki6GI9SBPSyxCVypOX2OKA+1N7GxO4dfULQUUHNcl4tfpTMRoiNze5cHzPCzH4fzYDCeuTXBicIzLk3Nky2v3Pm8F+WqND4YmOTU0sfQ3URT5+N5eDnQ2bzo4A5sktk+lH9v0DlRR5cnUozToW5sw2W6eXPV15kp/sUz9V0AmoO4mGfw5or6n0eS7a32xFdhuEdOZwnZXr1/3KdsIagdQpbvjo2i7RVzvw5uwfSQhSMhSAlmMYjlzeNwqguBQsfqJulWUD4knlmomo/NZTg1NcnxgjGuzGRYKFaqmBULdVD3i10mHg3Q1xDjS08aR3lYaousT8WLVYCZfRBJE2lNRJPHD730yLJuxhRx40JKIbOkjda+QNcsIgCrKuHj4ZY2+SAvH5wdxXIeqY6FJCttCTSiixJ8NvUmjHkUSJFJ6mLCiE1VXBuwEMYSgfQxP2Q/OEJ6bRxCioPQiCOH1lbEFCUHeBkIIQYojKvuB5cS2UjKYnsiSbo7yyMd3E0+FEEQBXVfIzBXrEWpZ5Eu/+Ti/90//klDUT+/OZsaH57h2ZZpjT/YRT4fZf7QbWZGoVS2Ov36ZD94a4Bd+vV4yezcDf4oo0RVMszvSRlILk9YjyyLGgiCgCDJPpHcxU8txKnONyWqGyWpmxbaiSoDdkTYOxbrrQSrXQBVVREFkd3gXO0IrfTZN17pthZE/pJNujRNJhlB1hY4dzfiCOqmWeF0ZWv1v2/fvdhibyPDt79XH3+3bGhkYnGE+U8KyHAIBjV07mvnsc/tpTN8I2I5PZHnrnUGGxxaYXyiSzVXYv6dtBbGtGTanz4wycG2WwaFZcrkK5YrBfKZeiiiKAk8+uoPGdBi/X8N1vToJPj3Cm2/1kytUsW0HRZboaEtw7Eg3Rw52EljsibZthwuXJ3j9x/0MXJ2pe0AGdbo7UyxkytjO1oPDgYCKz6dQLhtMTudpaoggy6urg7uux4VLE1y4PMnVoTnmF0osaDLfu4nYPny0l0Q8QCTsw/Pq5/niyxcYG89QrhhYdl1cLJkI8dCRbp59eje6Xi93HBqe5/jJIQavzTI5lcNxXL73w7NL5GbXjmb8fpWGdHipJ+7cxQneeLufkbEFqjULAYiEfRza185Tj+8kEQ+sS9bvFkRBIOjT1p2wZwqVFV6tHwauB29aUxEGJ+YxrRsTfcf1mMmVmM+XKZRry3pjJVEkFQmSCAdQNnhNQ36NzoaVZGuJ2Fo209niiqxj2K+vWaq7HlRZpjkR5oFtrfzg/dWztoVyje+/e5GOhhjJcOCu2Tu5rke+XOWdC8PM5FbT0qljf3czHem1BeiuFAf5IHsK23OIKOFlYauaUw+yxdUgh+PdjJTnOD4/iCYpJLQgzb4YulivxAoo+orMpirKtPkTPNu0jzPZESRBZEe4mQYthiKolC2DkKLz8aZ9iIhcyI9xMT+OLIj0hBo5HK+XcHcGUzT4IvgljWZfHFEQeWHyFJOVDCk9jILEntYGWmJhDne20JGI3XGpvgcc6mimKRpid/Pq+h7X4bgupZrJhfEZvvrWGT64Nk5hHZXqO8VEJs+3jl9YFszQFYnGaIiu9OYFV+/paH29tnxvdPem162/qB7F2vvMlb9xi6WNgCo30xT5O4T1h5DFDy/zdjMsZwb7VouPm+BTetHljru4v/lVbJB+ulF/4iR8Sg+GM4HjLveP9HCpWldwvdJ98WO7FZbjcGnxY/Gjs4OIooBPVZAlkcjihM91PQpVg4VihTMjU1ydzpAI+dcltp7ncWlilj978xQRn87/8PNP4VM/XGLreR6z+RL/7vm3kUSR337uYboaPjxV6FuxP9rBnFEgZ1WYqeaIayGqtonp2hiujeu5aJJCQNFwXRdBEOgKNfDW/BXw4MHU9qUo7QoIIoKUBim9qXyQoOxFUPauu0wiFWLXwXa+86dvk89V2HWwne17WvEvTuQVRSLdHEXVZfxBnXRThFRTmNmpHIVsBdtyGLoyxal3rlIpGSDAzEQORZXqwid32YdSEAQUJL7U8ShBWSelhZepQ17H0cQ25o0iVbueoTVde8nSQhIldEnh4eQOPtF0kEZfDNu1GSxdpTvQjV/2IYsyjuNguAZh5QbBmq7N4Jf86NLawdX27U2039Tr+Ev/6FMAtPauPwH4aUKlajJ4bZaRsQyf+NhuPtGeJJev8M7xq7z65mXCIZ1f/PzhJSL04JFu9u9tY2h4nu88f4ofvXZx1e0G/CrPPLWLJx7bwfMvnuPE6REO7Gnjc8/try8gQDjkIxzSl47jxOkR/vjP3yadDvO55/YTi/q5OjTHW+8OMjmdIxjQeOBgJ1DPNn/3B2cYGV3ggUOdHD7QgWnavP7WAJeuTGJtUdgLWCqxfvPtAf7yW+/z+U8dIBEP4NNVVFVGFMWlCakoijz20DaOPtDNa29e4e3jV2lpivJrX7nhGRsM6kvnKQgCrlcXpzqwr43WljiqInPxyiQ/fmeQ771wlr7tTWzraUAQYN+eVnq6Upy/PMkPX75AuWzwT//+c0vvs8+nElrctuO4zM2X+P0/egPH8XjwSDd92xsplQ3ePzHEf/6zt9F9Kk88sp3YJmyjtgpRFIgsWkGtheGZDOWq+aGM26uhPRUl4FPJlm4mti5zuRKjs1mqxvLedVWR2NGWQlWkDZOUoE+jPR1DFIRlk/5y1SBbrFKumcxmSys0AEJ+bUvEFiAVCfDUwV5ePj24LBu9tO+aycunBnnm8HYCvQp+7c4rID3Po2ZZvHHuGlfG5qjUVvfT9usKB3qbaU9H19zWZHWKuBrn51o+Q0BeXTlZlxR2Rlr455GfX/Fbgy/Kcy0H19x+XAvypY6H+dJNU/k/v3aCi7lprpUyHIi34JNUPtN6iM+0Hlp1G7/a9fiyf++KtLArstxu7emdd7eP2a8qfHp/322X8zyPYtXkxNUx/vX33mAmV8J2713LY0MkyI6mFBG/viwbfH5shkNdGTpT8U2T+o90GNrDYK70NcrGuWV/F5BpDP06Ie0gknD7lPr9gu3m1yWaipREFtcvddgMqtbAbeyEfjohIBDQ9lE0TuFQuOVXl4p5EdOZR1csBDZWEnS3MDyb5flTl3np7ACKJNHdEOe5QzvoSSeIBXx4QKZUYXg2y7mxKU4MTvDQjnZaVpH9vxmu5zGdK3J6aIr9nU23s/K9L/A8yFdqvDswxvam5D39OG4VD8R7GKss8KPp8xxN9vDKzHlGSvP1TKznEVZvDIweYDgmJauK7bmMlxdIayF6Qo339ZhDUT+PPLObw49s4+zxIV7/wVnefvkSTz63l56+prpYhHxDMV0UxUUBDA/HdliYLfCf/vUL/NY//TQHHuxBFEVe+tYHvPni+Xt63DvDreuSfAGB55oP0Rdu4e25K5zKXiNrllBEibZAioeTO9gf7SSh1Ulr1anyH6/9Af9o+9+nQ66XJn+QPcm7C8f5hzt+d2m7z0+9QG+wm2cbP34vT++/eTi2i2W7/OavPcah/e0E/Br1UmSdP/2Ldzl/cYKf/9yNiZwoCgT8KrGYn+A6itKSJNK82Icai/rRVJlIxEdH++o9udeGZnnvxDV8PoV/8jufoCEdQZJEHjjUhc+n8r0XzvCj1y4uEdu337vK+ESWh4/18td+8SjhUD0b+uCRHv6v/+PXGZ/cukJoU0OEZ5/ejWO7fP/Fs7z86iUO7G/j0Qe38cDBTtKpGwEWQYCGhvp3PBEPoGsyoZC+5nkKAnS0xfln/+hTy0q8d+5oQtcUvvvCGQauztLbnQYE4rEA8ViA+UwJv0/Btmza2+KrlhOXKyY/eOksU9N5/vbfeJJHH+olGNDxPI+d25sYHJrlBy+dY9eOpvtCbCWxXmW0XiZzYGKe2VwJ03buSQnsZtGWjhG8pVfWcT1msiWCPq1egXUTVFliR1t6w9na6+skwn7iIT8LxcpSZtajTjCnMkVmc6UVZbvhxVLkrSDo09jb1cS+ribODU2vsP7xgErN5Pe//x7/+MtPsqu94Y6ziZbjMjGX5w9fOM7cOmrID+7soKc5gU9bu/JLFRVkUcYvbV3J/acZhmVzbnSK3/ur15nOF+/5XFIUBNKRIHvbG3nj0tDS3wem5hmdz21pm/f06+B5HrZn80fDf84T6UfZFtxMBMKjWDuBYY/jsTwFLggqYf1RZPGj44cH1MuC1+l5FQU/onj3XraKeWHNsuefboiEtKPMi9/GdCZW/Op6JsXau+hyG7rSeV+P7MLYDBdGZ9Bkmb3tjfyLX3yaWNCPJktLpcOO63Kwq5nnDu2gWK0R8fuIrqN6BzBXKDM2n6uXan0UWC11qfqrMwsYlr2mQMOHAQHYFWnFcV1UUabFH+NQvAufpNLYGcVynaWMoiQIaKKCB/z1rid4ZeY8z7UcokmPcqkwTn9h6r4T22rZJLNQpKE5yv5j3cSSQb71J28zOZahp299hVXX9ahV671q4Ygfn09l8NIk/edXvid3E8IKMabVl5EQaA+kSGlhnm06gOO5CAIogoxfVhdVKm9syb3le+t6LrZnr/jbR0F05icdwiJR3bOzmYBfW8yuCQQCGuGwj1yhsqxY6frYvGTJvtZ2b7qf158TgbXL4Wfni8zNF2ltjtHQEFmytxA8gXQyRCioMz6RxbJsZFliYiqHpsk0NoQJhepZQc8DXVfoaIuTy68u9LihayIItLbE+PIvHOHhB3s5cWqYsxfG+bO/fJcfvnyBjz25k6ce7yMc1Jefzy3nvN51ubUUOBTUiEZ8CECpXFv6ti7fjlAXQBNWt9swDIuzFyZIxAM0N0UJBrTF6yLg96ls62ngvRPXKJZquK6LeI/bWmRJpC0VJRLQmc4UcVYJgpqWwxtnr9GUCLOr48OvpGhPRwncovjqLIpHKbJIZZWM7c729Ao15fUgCAKaKtPbkiTfP4Z1U49xoVJjaHph1T7eWMhPNLgxn9fV9hkJ6Pzmc8f4l3/6EtMLhRU1iB7QPz7Ln7z0AV956gD7u5u3XOljWDaXR2f537/zFtOZ0qr3XgD8uspnHtxFWyq67ry/w9/OaHWcU7mz7Ar3oUs/c7/YKDzP4/TIJH/46vvMFkqrTiUFQcCvKrQnoyRCfgKawpXJeYbntsZFBEEgGtDZ0ZxaRmzLhsV0rsBcoUR6Az3BN+PeEls8DMfganmYw9baqf211q5ag4u+tTdfXbEuCCWG+TD8HdeDKKi38aD1uBsSlJ5nU7UGqZj92O7akug/vRDqZd9KN4Y9iuPdeo08crXXCemH0eTW++pnO1coM1esEPHr7O1opCNVLzO69UPt1+ofmXQ4sIpC60pMZgoMzWY/KpwWqFsSXBqf+0iRWqh/SH2SuvT5UJG5npO41c/uZqT1MD3BBmaqOWareRRRoiN4/y1JMvNFXv3eaabGMoiShOe6RGMBtu1qXrbcas+MJIvEU0EOP7KNF77xAa89f5ZwzI8oCoQiNyZCL37rA8aG5rnwwTClYo2v/odXaWpL8NSn95FqjPLuq5cYuDTJwPkJJkcX+Ivff41EKsxTn95PS2cS7Q7UXlVRRlVltpJvqH9hP1rP20cWgrDU8nMrrntX3wxZEgmHfPh0ZdkkVhQFJEnEtu9PRUa1ajE/X2JmtsD/+H//9rLfsrkKk9M5GlJhajWLYFAiX6ggSxI+n7oUPLxOoENBH8odii2oikwiHiQU8tGQDvPgkW6uDMzw1rsDvP7jK+iazCc+tmfT2/W8OnE9f3GCKwPTzGdK9T5by2F6poDjulu2oXJcj/mFIvlClf/0R28QCt4InJqWzdh4llLZoFazsG0X9Z63tQioikxXU5yJ+fyqNjkA71wcJhkN4NMUuho/3LaWpkSYsF9bVibsOB7TmSKGZVO5hXDqisL21hTKJkUgdUVmW2uS01cnsG7q2V0olDk/NL0iaBcL+khFg8h3IPylyjJ7uhp55lAvLxy/smoW1bAcjl8exbRsnn1gBw/uaie2STI9myvx3qURnj9+mQvD01j26okhXVP4/MO72dFWF09ad5vGHCcyJ3nXe5+o8toyu55nGp6k27+N89lJXp8ZJG/W8EkKjzX2cCDeQlwLMFMtcHphgpprkalVGClnqNgWjzf28ExzXVl/qLjAd0bPUbIMGn1hJqv5pYCr47lkjAo/GL/AcCmD6Tq0+qM8mOrkQKKuBfT2zDUyZoWCWWOqWmC2ViSq+vjtnU/gl5eLQ9Usm5l8kaG5LCXD3HDlW0sszJGuzWkPjS3keW9gjAvjs8tUjgHiQR+7Wxs40tNGb2OCoK6hKRKyKPKHr53YMrEFiPp9bG9KLvub53nM5EuMZ/IfPWJbdWsYjom7BVsaZ1XVXw/XM6hZV5HEEJKwtajUvYAk+BHWUOMFcLwSrlsBcWslIlC/po5XZaH8XUxnimXSnz8DsJj5EQKE9aNUrX6qVv+KZWrWNXLVN1GlZvzq7fsO7hZcrz4ZURQRTZHXFXhaK9ruuh5lw+TU0CSz+RILpQr9k/OcH53GtG0Gphf4N997c8XAFvLpHOhq5mhv64r9Oq5LxTC5OpNhdC7HXKFMqWZgOy6SKBLQFZLhANubUnSlYysGF8/zMCyH86PTjGfyLBQrjMxlOT08hed5TGQL/OErJ4jdIhwjSSIHOps50ttG2Ld6ZNV2XYZnMgxMLzCZKVCsGrieh09VaIwG2dmapi0ZJXCbAe9OUbelaWG6msN0bSKqn6R2//v7Q2EfffvbSaTD9WddFmluT9DWlUKURPYf6wZAViSe+fxBmlrjROIBdh/qpHuHQTDs49NfOsLUWBbHcYgl6+qnluUsPW9N7Qk0n0pTWxzXcdF9KoGQjs+vIYgC6eYongdNrXEs00b3qai6TDDiQ7xPSqpQp2STtcml8WXenKfm1BgqDy8tU3HWLm37aYaqyBiGvWIy6XkepVINx/FQFGnpOyIIAqpyo8T9VtyvAJYg1PvIfbKyrNQXIJ0Ks2NbI+lUCEmuH7ckSfWAxyok0HacO/IovnFM9cxac2OU5sYoLU0xKhWDt98b5OLlqS0R27n5Iu+duMab7wyQTASJRf2kkyFESUQURS73b12dVKD+7fXpCol4kGhk+TyqtblOGhvS4SVV5XuJ68Pcsb52Lo/Orkls5wsVXjk1SLlqcmxnOzvbG0hHA2uOlZ7n4Xn1rGDVsCjVDKqGhSJLdDdtzNN0Lfg1hWQkQMCnUqzUqwpd160LR1VqyzK2miKRjgaJhfybzmxqqsy2luSK8XyhUKFmrhTTiof9NMTurEVPFAUCusqnj+5iJlPm3UsjK6yLoC5idaJ/nEyxypWxWXZ3NtLbnKQxHkJT5RViYJ7nUTUsJhcKDE7Oc354mjNXJxkYn8dcg9QGdJX9PU189qFdxDdw/doDrTzoHcHyVvbpBuUgpzPjvDlzlYQWoCeUpGJbvDE9iAAcS3VSsS0u52c4k53g6cZt7Io2Yjg2DXoIURCZqRb4i6GTqJJMX6QBVZK5WpzHXjQTrtoWfzF0Er+ksD1cF4qdqhR4afIKjf4wDXqIqWqBH89eo0EP0R1K0OwPIwt1kngzxhZyvDM4yvvD48wVytTsuu7ERmK3D/W2b4rYuq7H+dFp3h0YXVFG39eS4vGdXRztaaMrHScVrqcCrr9zt6sovB18qkJTNISuyNRuKn3PlCpMryMkthY2RWxPZE5RtDe+E9fzKNpFKs7WPsCSEFglA+rheGXmSl/DcrP4lG0oUgJJDCza+Hx4gjmSFEVcy8YDMO0ZLGceVd66Ibvjliga75Otvojjbv6G/zQhrD+0WM4+uqL32fWq5Kuvo0gpJDGMJjevsZW7i4hPJ+LXWChWuDadIVeuEvZpmyr1clyXTKnK905cYjyTZ75YJl+uUbPqH73JTIFvHb+wYqBviATRVZkjPctFCly33p/74pl+Lo7PMjqXYzZfplgzsG0HSRIJaCqpSIC+ljRP7OriUFcLyfDy3qtizeDVC1c5NzrNfKFMtlyjYpp4wEKxwktnB1YMdKosISCwp71xBbF1PY+KYXF8YJR3B8a4ODbDRCZPoWrguh4+TaEpFmJ3WwMPb+/gQFczjbdRjr4TiIJAVA2sqoR8PxGJBzjy2Er13+sIRW4MZseeuBG0icRuHPeugx3sOri2kN3ew53rHsP2Pa1s3/PhKtELgkBECdNfHGCiOgnAbG0WD48PsieXljNdC+1n5WjLIAgCiXiATLZMLl9XFZZlCc/zcByXa8P1SotgQEfTFqcJwt1Vy14Noijg4a1bOh4K6aSSISRJ5POf2o+iyCt6/BRFQltUYE/EAlwdmqVQrGFaNqoi43neorpyGcNYXajmThCPBWhIh5EVmXJlpZqoKAog1InQWpiayfHGO/3MzBT49LN76epMEQnrOK6HLIlc7p9adT1RFBBFAcdZe9uyLNLWHGNsMssjx3rYsa1x1V7cePz+Wf4AHN7WyhtnrzE5X1gxub6OkZks+VKVoekMuzoaaE6ECfrUJR9cz6tfV8txsWwH03KomiaVmkWpamI5Dm2p6B0TW0EQaIyHiAb0JWLrUbcluvXY/bpKZ2NsS1lUXZHpbU6syPTWTHtVYpsIB2iI3h3tmW2tKZ47uoOqaXJyYGJF2TPUe33PDU0xPJPh7NAU21pSNCfCRAL6kjAmgO24VE2LXKnK+HyegfE5hmeyS9duNfg0hR1tKX7ukT30tiQ35PTQG+ymO9CJh7eigq1kmbySP8tcrcyv9hylLRAlZ1b5vXM/oj8/S3eonjV0PJeSZXA01UlnMI5HXQMC6iT1xzPX+F8Of5a9sSYqtsVQcYHhUgbHc1kwynx39DyPNfbQHUwgiSIZs8JYOctoKUtar89RypZBS7yVT7bsJCBrmK6NJspL39hcpcq718b4r++d4cr0PACaLKMpEjXLxnZcFElEliRM28ZyXCRRoDESojESIrjJQH++UuPSxCyD0wvL/t6WiPCpA308s6+X1njknowBsiQS1DUSIT9TN/ky5yt1EdVNb28zCz8//RKztTk0SVsyGV4PHmB7NhVnKz0sAj6lew1xKIf5yneo2cMEtAP45C5kKYEo+G5bViogIggyAhKCoCOJfiQxhCxG7tjfVhET9b5fFDxWfpSr1gBV6yp+decWyl89HLdM2TjDbPHPqFpD/Cxbuz58Sjdh/RhVq5+KdXnF7zV7iGzlh0iCn5j/46hSE7B65Hcz8DwP16thuxkct4AsRlGkFIIg05mO05mOMzqf48zIFK+cu8q+ziaaFs3UNx7N9fDrCm3JKG3JKNO5AtdmMpSqJvGQn4OdzSsmJFG/TmdqZV+6R50Mf/2d8xSqBrGATlMsRLcaRxSEuudupcboXI6ByXnG53OossTDOzqX9wt5oKsKTbEwTbEwuXKVgal55gplIr566XXoFvIqSyLbmhLot/QdeZ5HzbR4t3+EP3zlBNdmMgR0lYZF+XdRFKgtWh18/4PLXJ1eoGJYPLOvl/AdRg9XXGnPw/E8KqZJ0TQpmyaGbWM4DpZbt99wF5U6JUFAFkVUWUaXZQKKQkjTCKrqqiXnP0lwPY+qZVEwDMqmSdW2MWwby3FwPW/pGojXr4EkockyfkUhqKqENQ1ZXN0KZSuQBYUjsQewPAt3MVqe1JIkteTSvwG2h7bRrG88mOh59SLcsmlSWrzfS+d6y/2++Vz16+e6eL+Vu3iudxuiILCrr4kfvXaJ8xcnSCWCxONBHMdleibP2QvjJBNBUskQ6hasjrzF58F1XEzTWSpTNgwL07QXs47CikBXMKgjCgKZTJmp6Rx+v4rreqiKjK4rdaGpxijdnUnOX5pkIVOmtTWOrsn1zJxhYdkugiguff96e9KcPDPC1aFZ+gdnaGqI4HoeM7MFJqdz1IytW8hMz+YpFGtoqoxPV5BkEdfxKJUMrg3P4zruiqwy1JWgZVkil68yMZUlGNBxXRdFkdA1BVmWqFYt8vkqsViA3p4GwkEdy3YYHV9gdDyz5lihqjJ+n8pI2WBsIrPo6SsgSyKaVi/z13WVYw90M/LtE2SyFWzbrV97UcSyHapVE0kSkVa5R/cSTYkwR3a0MTaXY2B8fs0AR65c4/0rY7x/ZQxVloiFfEvjZ91/06Vm2nWSWbOWbcevKTy0qwM4fMfH2xgPEQ36GJtbX8QzqGtbJtKKLC2WPesUKrUVJaK3IhHyk47dnQCvIMDDuzupmfXKjrPXplb0Dl9HsWJwenCS04OT9TJ/v0bQpy0JfRmWTaFSqytbb2DfPlVmR2uK54728cT+3g0/h6ZjkTEzZK08zk0Vn7IgY9oqZcskqvpoD9aFXGOan/ZgnKpjsVArE9P8qKJMbyhFkz+MKt34/pVtk4JVw3Bs+iIN+GQVn6zS6AsxZ9TVg3NmldlakeHSAhmjvESI+yINy86hyRehPRgjrNaTYb5bLOmuzi7w9sAIAzMLRHw63al6pjSka5wenWK2UKIzGaM7HadYNZjMFZjOF9nRlOLpnT0cat9csmZ0PsvYQm5ZxlQUBJ7e08vTe3toS0Q3tb3NQpElmmNhZvMl3MVe8lLNILcF39xNjViWa9Hqb6bD33ZbX0Col11WnApvzb+36QMDgYB2EE1pp2YP43q3npxHyTxNyTy9ya1eJ7MBVKkRXe7Er/YRUPejya1IYhhR0LaU+ZVEP5rcgiKlMJ3JFb9XrMsUjQ8IaUdQ5aYN78PzbByvTMk4zWzxq+Sqr2z62H46IRLxPUnNHsOwx1fptYWyeRbbzWO5WZKBn0eRYoj4EJA3OCmtRwU9bDzPwPVMXK+KYY1SMN6jYl4k6nuaeODTyEKIXa1pjvS0cmFsmrlCid/7zut88uB2nt7TS1c6TtivoStyPZO5xv4VWaIjFeP/9qUbKq8vnR3gj177gMGpBXa3pvmXX3kW/zrKgTdDEkUaoiE+treXbLnKAz0t7GhO0xwLoykSuUqVcyPT/Mnrpzg/Ns3JoUkODk/R15JeypAKgkAqEuC3n3t4abtnR6b4P198l4VihY5UlH/wmUfZdksfxVqwHIfRhRz/7gdvMTafpyES4pl9vXxsby+d6RiqLDGVq5PaF0/3c36sLsiVigR4tK9z6ZjuBLbrUrNtKqZJtlrlajbL5bk5hrNZpksl5splCoZBzbYxHQdJFNEliZCmkQwEaA6F6IxG2ZlKsT2ZJKrrBFQVTZZXlBzdK7ieR6ZSoWytPhkJqCqhxWNaDZ7nYToOlUVCO5TNcn5mhmvZLBOFAnPlMvlajaplYToOsiShiiIhTSPm89EQDNIZjdIbj7OnsZGk309QVfHJMvIme81uhS5p/Fzr5+9oGzfDcV2M6+daq3E1k+HK/DzXMhkmi0VmF8+1tkjmJUFAlWWCqkrC76chEKArFmNbMsn2ZJJ0IEBAVfEryn273xuFJAk88egOLvdP8+Y7/YxPZujpTGOYNucujlMs1vjE07tpa9mair9h2vVscK7CxFSOhUwJPBgYnKFWs/D51CUV35vR1hInFgswcHWG775whvbWOLbj0dEap7srRcCv0dGW4MihLvqvzvCHf/ZjHnygm3isTsoXsiVqhk1PZ4pPPlMv/z28v4OTp0c5e36MvzROsH9PK67rcfrcKJZl31GP7XvvX+P9U8NEwn4aUmECARXTtLk2PM/gtVk62xIcWVRnvhlNDRHSyRCX+6f59vdP09OVwrIcWptjdHemiIR9hMM6LU1RLl2Z5M23+0mnwpTKNfoHZxi4OrPCD/g6YhE/7a1xTp4Z4WvfPsGO3no2Nh4P0NWepLEhgt+n8ORjfXxwZoTX3rrC+GSW7s4kqipTLNWYmsmTTIT4xNO7aUjfX+/xZw5tYzZb94JdKNw+KWLaDjPZD0drpDEeJhq8vSBo0KfS3bS1nuB6C4BMe0OMuXyZirEya3pj2Xopcipy96qKFFniqYO9aGo9eHR2aGqFldGt8IBCxaCwTjZ2PfhUhb72FJ97aDefPrZzU5nukcoob82/x/n8JQp2kZAcpObWCCshHow+gSjK2J5DzbHRRAnHc6k51lKQEuqETpNkblU3EQBJEBEFgYpjEvDUeiDFcxdtqOq/a6LMb+14lP2xFlSpvg/X85BvmvOrooS0jiZP//QCAzPzBHWVo92t/O6zj9IWD6NIEv/yOy9zfGicT+zdxm88ehjbdXlncJR//+p7uK5H2KfRHFsZUFsPQ3NZpnM33iNBgIhf59l922iObm5bW4EsiaTCgWXkv2JYFFepErjttja7wqHofh5NPohvA1LaLi4ZM8uZ3IVNHxiAJARJBr6A5cxRMk7efoUNwKOG7dazaYY9RtF4H8oCgqAT1o7RGP41gtohZGFrfbB10aKuVYmt5xlkqy8hiX6aI/89srCxh8Vy5liofJ/50tepWKubZv8Mq0OVmoj5PoZhj5GpfH/VZQx7hKn8/0mm8l0aQr9GVH+qHnjYsBWQjWmPUTJOUzROUTJOLgZjKshinIj+ONebIkI+jWf3b8fz4D/+6D3ylRrfePc8f/X+JXY0p3h0ZyeP7+pkV2sD0n2MlLclIvz9Tz+6qmx/Ohzk8Z1dtMYj/Pd/8B1m8yXG5/NMZgr3rPR3oVjheycuMTafx/PgV544yKcO7SDqv/Hd6U7H+e3nHsZxXb574hKXJmb54el+Hunr3JR37FqYK5d5Z3SUHw4O8v74OHlj/UHadRwsx6FomkwWi5ydngbqg6FPUXi0o4Pntm3jWFsbDcH7Y1NmOg7/j9df568ur6xYAPj5Xbv4lf372d+0ekbTdBwuzc3x/f5+fjgwwGRhpULmzbAWr0HZspgulbg0N7f0myJJHGpu5lPbtvFEZydt0egdnNndR9EwODE5yQv9/bw5MkK2WsVZpyTW8TzMxazudKnEBYChuqpjUFXZlU7zbG8vH+/poTWydV2FewFBENje08Bv/uqjvPDyed4/OcwHp0dQFZmOtgS/8SuP8PjD2xezfZvH5f4p/uvXj/Pu+9eW/f3/9W9fAOqKxD//2UP81m88sez3PTubyX9sDz/40Xm+/8OzVKom4ZCfX/7SMVqaYwT8GooicfhgB+lUmG9+7yQvvHyBhUwJRZZIp0Ic2Nu+jJCHQjpf/MJhwmGdV9+8zHvvXyOZCPDxp3dzeH8Hb74zsKVzBGhpjnFlYJpzF8Z4LVuhVjMJBX00NUV45smdPPrQNtpbVxKabT0NPPv0bkzT5qVXL/Ct79YI+Os+wA3pMJGwj+7OFD//uUP86V+a/PnX3sMwLBobonz86V3s3NHE6TOjqx5TY0OEpx7vI5uv8Obb/bz0ygVUVeHJR3cQjfhpbKiXE4aCGv/o7z3LD146z5vv9PPaj69gWjbRsI+O9iSH9nXg891fUgsQDfr4zIO7qJoWf/n6Gex1Sqo/bDQtZmxvh4Cu0tO89dJnURDoaY5zcWR6XWIb0DXiIT8B/e7qTSiSyGN7u2iKh/gvL57gh+/fu3moADy0q4MvP3WAw9vXt4hbDZeLA6iSylfaf4ET2ZP8SvuXeXvhOLZnsTPUQ8Wc40J2ivPZSQ4n2piuFjmfneLhdBctgSgFc23LTl1SSOkBdEnm7dkhnm7aTtGsMVxcIGtWUEWJJl+Y1kCUV6f6SWh+ukNJilaNnFmj2R9B2WBCa65YFxrtTEb5tYcP0pWMLv0miyJ4LL0bsijy2PZOJnNFvvruGX54rp+2eIQdjRsXtxxfqLe1XYcqSRzqaiYVDtyRENlGIS6qLd88CbUcp+70sUlsitgmtQRRNYIqbqxkV/AEdFHfUubk+joR/XHwYE78S/K1t/C8zbP328PD82oUjXepLlwm5v8kqeAvEFB3s65PwSoIagcJmWcp1o6vWo5sOfPMlb5JxbxC3P8JgtphdKUdUdAAYbEvwMByZimblygZpyjU3sewh1f01OpyJ4qUxrAnMZ3xOzj//3YhCAIBbQ8p70tYzjxFY/XqAQ8LwxpjIve/MSX8AZrciq50okoNSEIISQzg4eB6Bq5XxXGLWM48pjOF5cziuBVcz1jM2hp4rP4yCoJAIuTnsw/0sbM1xfMnr/DahWtky1X6p+YYnc/y/Q8u0dUQ5xP7t/P03l4CmnIfShqFpe/JartSZImuhjjJkJ9MqUJllT6iu4lcucrrF4ewXZejvW3sbEkT9q3+LXmgp40LYzMcHxxjfCHP6HyWjuTWMk2u5zFZKPDtS5d4fWiI0Xy+XnrsbL3s3wOqlsWbIyOcnJykNx7nmZ4ent22jZbwvY+ErkdEp4pFMtXVS31OTk7ynUuXeGt0lPlKZalfequwHIfTU1MMzM/zV5cv82xvL1/YtYuk/8MVAJwuFnljZITn+/u5PDdHZbH0+E4sgsqmyZmpKQYWFvjG+fM81tnJL+3fT2MwuGlV1HuB6+/Rtp4Gmpti/PIXH8RxXQQEZEUi4FPR9RvfnZ3bm/gnv/scrueh36J4vXN7E/+X3/kEruMuZT93bm/iH//OJ6mtkdkRBGENb1uBwwc72LmjCdOs3wNRFAkFNXyLk3VBEFBkifbWOH/zVx/D+NKDOK63JIikafVS3Jv31dQU5Us/d4TPPrcf1/EQpbqtjSgKPPbI9qVtbxZ7drXQ05XCNB0ct561EQUBSZbw6Qr6mt9ugV07muhoS2AY9VJZUahbJ10/dk2V2d7byD/+nU9imjaeV89q+P31437oSA+KIq3SAyvQmA7za195iC9+4QFc160rwesqAf+Na1jvodb41LN7efrxPizbwfNAEus+2AG/eqO/+j5CEASak2G+9OR+GmIh/vyVU8xkP5p6IolIgHjIhyKJyxSLb4auyiQjgU0rBt8MURDobU6u698K9dLoWMh31+cLgiAgAl1NCf7e5x/hWF87X331FMPT2TVFnzYLURRoT0f50pMHONbXTlM8vK7t11pwPRdd1IipEQREQkqIJ1KP8Bdj3ySrZ3iicRuCIPDvL79F2TZQRYkHU5083tBDVPWtS2wFoDUQ4+/tfJxvjJzhm8OnaQvEMV17sT9XIKzq/Iv9n+A7o+f4n0+/QNW20GWFg/FWfmvHI7BBklg1LWqWVS9jTyeWXQdZEuuuM7a97O+PbuvgrYFhrkzN8/618U0R23LNXNazLUsSO1sb0JSNVi/eGURBQFeVZYzLdtwtPV+b+mp9ofnTRNXIuunzmyEgoEsaDyeOkNK2Fq3ycBAEFUmMIglhbG9+S9vZyJ5cr4bpTJOpfB/Xq5EK/AIh/YFNbUUWowS1w4T1h8jX3lhlCQfbXaBo1EWNZOnrSEIQSfQBEq5n4XpVPM/AdgvYbg7byd3i5SuiiAmawn8LEFko/9XPiO06EAWdoLafpsjfgoJAqfbBqkEHD3vRFziL7c5TtfoRBX2pJ7u+jIPnuXhYeItlx65nsBkbJ1kSiQR87GlvJBUO8uyB7VyemOXs8BQXxmeYyBZYKFWYWMhz4uo4X3n0AL2NiU35320Wt/tuXZ9MRvw6iiTiuO5te322CsOymS9WmMwU8DzobUwQD/rWFI5ojAaJ+H11e4yawfhCftPE1vM88rUaL1+7xvP9/QwsLDBfLt8RoV22ferktmpZlE2TqVKJU1NTfHLbNj7e23tXe083g5lymexNxPZ66fG3L17kh4ODXJidvW3mcjMwFntVK5bFQqVC/8ICv3bgADuSyftO+EzH4ccjI/xwYICTk5NMl0pU1ijZ3iw8wHAcjGqVomGQqdW4MDvL5/r6eKyz875l7G8HVZU31EOraQraGpPq1X5bb/n1IAjg09XbEk1BEFAUiVh0Y2RBkSUiYd+qGei1Sno3Al2rk9fNQhBuf42uKy1r8dWflYB/dUE0Qah730YjfqLrFApcVxIOBfVldj8fBSiyRFM8zLMPbKctFeWti8McvzTKdLa4piXMRqHKEsE1FPg3C1kUiYcCxEJ+ZnOrl0NHAj5akpE7EuESRYHeliQ+9TbENhYkHro3gUJBEFBlicZ4iCf2ddPVGOfkwDjHr4xxZWyWXGltQrgeIgGdHa0pjva1c6C3mfaGWH2escX5jiiIOJ6D7Tm4nkvOzOGX/VSdKqZr0egL8fHmPnZHmzBdG0kQafSFSeoBJEGkwRfiM227MV1nhf2fIAiEZI1HG3poDcSwXWfRnkdEk2SSegBZEOmLNhBQNApmFdtzkQSRmOpbGuMeaejmQLyVuLb2vXIXBe5kUVzh+KBKEp7Hin7ndDhIxKezUK4wmlm/7/tWlA1zWX+tJAokQ/772kazQkV7FQGwjWBTxLYj0Lb5HQgyDyWOElWiG17H8xwcr0Sh9g5F4yQ1a5CaNYrrbd1IfTOwnDly1VeRBB+yFMendG94XUGQCKi7SQQ+i+lMr2o1UyfRZWr2ENhD1EMA8qKvoMN6olACCqrcQmPoV4n6nsL1DIrGCdhaK8NPDSQxTEg7ghAWmJe+Tb765iKJXR2uV1uhpHw3IQoCPlWhIxWlLRmhOx3nQGcTQ7NZLo3PcnJogv7JeeaLZXRV4Rcf3Mv25o31p24VluMwsZBnZC7HbKFEoWJQNeu9k47jYrsuI3M5LNtd7Cu+d8Q2V64tRepOD09SqBprqvwVawZXJmcXz8Elt4ZVxFqwHIehXI4f9Pfz6rVrXJmfx7xLhHY1VG2bkVyOhUqF2XKZ8UKBz/b1kQoE7nsv5ly5TKZWW8pOFg2Dr50/v0Tu7xbRuxW1xWuQq9WVs3/90CF2pdP4lXtf+uh6HiXT5DuXLvHy1aucn5khW7t377rtusyUSswv9mRPFot8vLeXHcnkfRXmuVM4bpmadYGaeQbbmcHzHETRjyK1oCu70NXdsIo2gevVsOwJKuZxLHsc1y0jCAqSGEOVO9HVXahy5105Rs9zcNw8ljOO7cxgO7M4bhbXq+B5NTzPBkGqe86jIokRJDGJLDWgyG0oUuN99TWvH7OJ5cxiWgOLx72A65Xr1WkCi8FVHVlMIkvNqHIXitSEKG6tTPwnBYpct8gJB3Sak2Ee2N7K0NQCE/MFZrJF5vNlilUDw7IxLedGWaYkosgSPk0hoKmE/BqRoE486CcZCdAQD2253/VWCILAg7s6iIf8lKqrT8KCfu2OPXdFQaA5EeY3nztKtri2mE5r+s7Vnm8HSRSJBn1Egz7SsSC7OhsZmckyOpNlYiHPTLZEvlRdvDf1uQNCvRVFU2RCfpVY0E86GqQ5GaYtFaUtHaWjIUZDNLRpO6Rb0aw3UnNqqIJCo6+Br41/B1VUcDyXoBxAlWQafCEafKu3Uflllfbg2vdLEkVimp/YOqRUlxS2hdfOljb6wnCb11eVJRRZwnZdyoaJKt9YoW636JGrVrFdd2neoMoSiiRhWDbFTY5ppu1g3zTvERZLg+/0fmwUruetUPmWBBFJ2vz+7+oX3HAMTNfCw0MVVfRFi4Vm38YVKR23imEPk6u+Rq76JhXrAo5bWLaMgIwsxZHF6KISskK9UGIteHi44DlL4j62m8dxi6tm7gAsZ4Z87cdochu63AFsPKOiSEnC+sPYboH50jeoWv1rlqbeOD7rtkk/SQjiU/uI+z5BMvBzSGIYDxtVbkIUArjez/wa14KAgCwGieiPIQkhVDFNofYuVXvonl03AQVFSqJIyaWM74plFpV0G6JBGqJBdrU2cKCzmc50jG8fv8Dg1AKvnBtkb3sj7akounL3J12W4zCTK3Hi6jjnx2YYncuyUKwsEksPhLqEgkfdV8zZoEH4VmG73rLI4YWxWS6MzW5oXc/zNlW6YjoOl2Zn+d6VK7wwMMBk8f6VvJVMk5OTk0wVixi2zXPbt9MRjd7XzGXJNMlWKpRNE9NxeOnqVb569izjhQLWPb7PHpCr1fjR1atossyvHzzI9mRyTSGruwHbdZkrl3m+v59vXLjAcDZ717Lyt4PjeZybmSFfq1E2TT7b18euVGpTVl/3CrazgGFdpHaTerxfO4Yq9yCJfix7mrLxFsXqS9TMs9juHJ5nLxLbZnRlL0H9SYK+jyHiXxJGtJ0MVfMMpdqPqBrvY9rjuF4FQZAXiW07unqAkP40Pu0worC5zGE9uOZh21OY9iiWM4Zpj2HZo3Vi687huLmbiK0DiHViK1wntglkKY0it6PJ29GUXnRlL4KwdZcEw7pKzTyP7da/W/U5S5Kg/swSIXXdKpYzRtU8Tc26hGkNYtkTOO4CznVii7AoZqkjS4klYqsrO9CVvWhqHwLaho/TduaomRcx7OUBd1Hwo6t78Kn7t3S+a8H1TAzrChXjPW6d4NSDIXuRxLXbMYTFAPD21hTbWpIsFMrMZEvMZEssFMqUFsmTadt15W1BQBbr2XyfquDXVEJ+lUjARyzoIxEJEA34Fv2Y7w62tSTZ1nJvg86CIBD0aTz7wI57up+NYsnvPB6mKR5mX3cTC/kykwuFOrEtVylVTQzLXpovKHJdJT/k14gGdVKRIE2JMMlwAG0LyutroTvYiela+CSd/ZHdvL1wHMdz2BvZRcsmuMiHjYhPJ+LTqRgW49k8scANYhvz+9AVpV7Zli3QvqhYXDEtDNteZk+0UQiCUC/3WAxye56H7bpbyphuBY7rki1Xl7UAaYpc77vdJO74aXI9l5JdZro2y7wxT9EuAx4BOUBSjdOgpwkroQ2VLztulap1hfnyt5grfX1RCfn6SQpIQhBNblvsf+xClZuRhesqxmufioe7WEJq47plbLeA6Uxj2pMY9gSGM4Hj5lasV7NHydXeJOZ/DlVq2NR1UeUmksGfQxL8LFS+S9W6iu1k1iTS60EUdBQpTUDdS8z3DHH/c0uDroCEJjWjyk3UrMFNb/unCwKCIBPSH6gHLJRe8rUfU7X6MZ2ZxQDKnb3FgqAhi2EUMYEqtxBQ9+BXdyAIG+vh0hSZ7oY4saAP23H59y++y0y+xNhCjkKlhh65+yWMs7kSL50d4C/fOsNMvkR7KkZDJEgi5Cfir3vRabKMJIl8673zTGYLt9/oHeC6Zc51bGtMkIoENzQZaYgEadqgoJXtulyZn+ebFy/yvStXyG0iwnnd3kWTZRSxrpLoLg4Ehm1TXVRKvh0cz2O8UOCPTp3C9jw+s2MHXdHoHSsGbxSe55GpVhnKZpkplfijkycZzufx1hnNREHAt2hnpEjSUtbRdl0s16WySJI3+iZZrsv3r1yhJRwmpGl0RKP3pCz7eub0pcFB/v3x4+RuylTfDooo4lcUFElCvvV+Ow41y9pwIGA0n+d7V65guS6+ffvoSdzbDMtGYDtTFCrfJVv+s6W/JcP/kGggiudVKRuvs1D8Awzr4rL1XDeP4eYxravUrPMIgkZAfwRJCOK4JarmCbKlP6VUe4Wbv62eZ2E7VWxnkqp5BtPqJyX8AzRl54Yzka5nYtkTS+SwapyiZl3AdqZZ3wrPwfUs8Mo4bhYYXvy7hCyl8KvHCPs/i089iCwlEITNT6xq5jkWSr9PbdG1QUBFVXrxaw8ieDqul6dmXqBYfYlS9UVMZ2Sd87QXj3UBw+qnzOvIUhMB7RHC/s/i1x5AJIiwoTlWnrLxBgvF/8DN90MUI8SDfx1V7kUS756iruPMU6y+wHzh3y7bn4BGPPQ3UZVeJDamMyAIAslIkGQkyO7Ou3aIP8NdgE9VaE1FaU1FP+xDIaXdCDJElQit/hZs18In+VHE+1uNMXhpkoW5It4tbVttXUmSDRE0fe1vS2MkSEMkSL5a4+LELHtbG2/8Fg2RDAUYz+R55dJVnt29DVkSGZiZZzJXQFtsG9sMfGrdI7di1Mcx1/PIlar3PJEBNxISE9k87k3XKqAphH2bb5O4o7vsei5lu8Kp7Flen3+LOWMex3MQFv8XUcI8lnqIo/HDRJTwuuTW8xxq9hDz5W8xU/xT4OaLKSz2rh4iFfwFIvrji5naO4t0206OonGC+fK3yFVfXWEp5HkGhjVGyXifuP/TbEZISkBAEWOkgl/Cr+0mU/4ehdpxTGcK16vheQaeZ9czyUvnKgASgiAhoiAIGqKgo8sdRH1PEvU/jU/pWbEvXekipB3m1tBK3QN4Y8csCAq60oEo6otR7ToUKYEibTwaKUsxdLkDgeUvbF0ga2MTFlFQUaUGfErPsgm2ICgoUuM6a24cqtxAOvRlIr7HKRrHyVVfpWyex3HLS7Y9nmfh4VC/P9ePQ6R+d6/7Icv1igFBQRR0VKkJv9pHWDtKUDuIKm/teAOawoPb2vnDV94HoFg11lVDvBmboeaO63J2dIq/fPsM07kSbckIv/HkYR7c0UEy5F/qa/W8eq/DW5eHmcnfW2sFVZGI+DUkUcBxPT7zwE4+dajvrikwX3+mJgoF/vLcOZ7v778tqRWol1KFNA2/opAOBGgKhZbsaxRJwnJdyqbJfKXCZLHIfLm85IVau42yX7ZW489Onwbgi7t30xIO37ee2+lSideHhrgyP0//wsKqy8iiSEBRCCz60TaHwzQEAoR1HU2W8TyPsmlSMAxG8/mlstuNnDvUye23Ll6kKxqlIRjEd5dLkj3PI1ut8vrQEP/He++tKZh1M3yKsnTOcZ+P1nCYiK4TVFVUScJwnHrGu1plqlisC2xZFkXDwLoNsZ8ulXhxcBBFFPm7x44R1jaedbtfsOwRHCdDzblIrvyNFaT2ZniYGFY/84X/L6rchqBso2ZeJF/+JqXay+vux/OqlGqvoEjNxEP/HarQs6Fr4bo58pVvki9/DcuZgnWrojYCB9uZplD9LmXjx6TC/4CQ71lkqXFDpHE9eJhY9hiOW0IQfFSM9xYJ/+ts3o/ew3YmKVS+TdU6TTryTwhojyASuu2cSJFa8amHEcUg7k1ilK6bx7D6Ma1BfNrdy9oa9iA18zy3jkqy3IymbEfZZMLgbsDzPKq2zXS5SEc4uqZ2w/2G53lMlYsEFBUNiUqxhmU6ROOBJQKUz9ary3S/uqUe9rt5rJ7rMTdTIBDUCG5RPf1uwPEcsmaetWY+oiCiieqH0vbxjT/6MW+8eB7HXk4Of/23n+Hpzxwg3RRdc92OZIzOZIy3B0a4PD23NL0XBOhMROlMxvhgaJyvvnuGsmESUFXe6B/i8uQc6XCQltjmlPijAR8BTV3q23Vcj5GF/F0TB1sPtuOSLVW5OrOAfRORjvh1klvoGb8jYltzalwqXOFPRr9Ko97Aw4mjNOoNCAjMGnOcL1ziz0a+hioqHI4dJKKsHZlzvCK56qvMlb7GclILoqAR9z9HU+Rvo8ub7/NdC7IUJeb/GJrciu3mKdTeWbFvx8tTMs8Q939qS/sQBJGgupeguhfDnqBknKFknqRiXsK0p3DcIo5XATxEQUMSQihyGk1qxa/uJKQ/gE/ZjizemNRf/6jUIxsefukw7eHDrBb4dG1wbxnwRUlcEo64Dk1upq/hj7d0jjcjHfwy6eCX72gbutJBR/xfAP/ijo/ndtDkJjT58yT8n8Z28xSND6gY56naVzHsCSxnHscr4rkGCAKi4EMUfEhiCEVKosmti9nfTgLKPlS5AXGN7Oxykr4BVfGbxltVltbN4glcLz3xFrNQ3qKv2vr7KVYNRuZyjC8UiPh1fvmxgzy9t5fQKuIatuuSr9SW9WGse/yL/4N69M+DDR2TT1VIR4LEg37mC2WuzWQpVg0aIsF1171+fTdybW3P409On+ZHV69uiNT6FIVtiQSf6evj8Y4OWiMR9HVKZk3bZqxQ4O3RUV5ctAuyXXddspOt1fj6+fNoksRvHj58T0tyb8bZ6Wkuzc2xUFldw0AWRRpDIZ7p6eHjPT3sTqcJqGtPFBzXZaJQ4I2REb5/5QofTExsSHxqsljkrbExuhMJ9jU23hXLpuuwXJe3R0f5/RMnWLgNqRWoK0Iea23lYz09HGttpS0SQV3n/TNsm+FcjnfGxvjWhQtczWSoLZaErYWpYpEXBgZoi0T40t69KB+SgNhaMO1hTOcqVeMUFePdxb/WA3sslgAvn0za1KzzlGpvEhIUSrUfUqz9aPE3geVB1lvXhVzlWwR9H0OV22EDVmueZ2HZ49juHGuTWmGN/652/Nfh4rgLzOZ/D0HQCPs/hSREb3s8tz9eE8sewrCukCn9ARXjnVv2f+s1Wus6Xz8DC9O6ylTmf6A18e/xqQcRblPKLYo6qtyOXz2ymEW/AdO6SsV8H13dd1eeQ8/zMKxBatb5Fb/51cNoqwTp7wdM1+HkzCR/94d/xWu/9JvEfR+uKvt12K7LP3v9JZ7p6GGPGuetb5xlqH+av/G7z7LrQDsAz3/9fTwPDj+8jR17Wj7U4y2XDP7Xf/51nvrUfj79xSMf2nEUrRJ/MPTHq1bfCAj4ZR99oW0cjh0grsY+Ut/Y9bAtnWBnU5pLk7PEA8uf0bZElL0tDfy4f5jh+Sz/+8vvLv0mAE/2dfNg9+a4UjoSJBrwMVeoB08sx+Hc6BRVy9rQvG2r8DzIlKucG5vGsJbPLZOhAM3xzVvl3dHMKWvmeGP+HbYFe/jF1s/TqKeXsrKO5/Jw4hhfHfsGJ7JnaPe3rUtsC7XjFGpvryrYE/U9TSLwWTTp3tTHq1IDycDPL1r03EJs3QpV8+pd20/U9yQR/WFcLPCcxf3dKLcWEEEQlzKBdZGL5ZE5o2Lywp+8yY/+4l0KC5vvCfzb/8uX2fdYH8HIR+OD/tGAhCxGieiPEdYfrGdrF4W8vGUTi+sUUlyMjssIgoSAvEho1578Oq6H47qIonDbPspCxeD5D65Qs+w6sYiGSKwTuVJkCU2RMG2X2Xxpw4rFpu0sqUyKgkDItzphqZgWb10eJluqbHjbkiTgU+vZvNl8CcPaeEYlGvDx1J5uvvneBX58eYgjvS00RkOrEu6b4Xm3V3j2PI+vnT/Pm8PDzK9B5q5DFkV2pdN8Ze9enujqIqAo6LJ8W5EnRZJoj0Ro2LWLZ3p6ODs9zX86cYJLc3NU18lgzpXLvDQ4SMrv54t7965/IncJhUV/3tUmBm2RCJ/o7eXzO3fSHA7jWyw/Xu8Si4JAczjMF3bu5OG2Nt4ZG+PfvP32hkp/3xsbY19jI/sa705lxnW8NTrKdy5dYqKwfhm9Lsscam7m1w8epC+VIqrrqIvlx+tBlSS6YzFaQiE+1t3Ny9eu8e2LFzk3M7PuetOlEn98+jQPtLTQHYuh3qdgxkZg2iMUyt/FcmbqpbRy9yLxbMZ2ZilWX8awrtyi2A+l2is47hxV8zSeV0MUAmhKH0H9KSQpjmVPUa69Rs26yM3ZSs+rUDFOoMq9aBsQbJTEGNHAFynWXsJbMW+QkMQomtyLqlwXW4osET/XLWDaQ1SNDzCd0VXWB9crkqt8HVlKE/I9s+nrdys8HIrVV6iaJzHsQZaN+4KGruzBp+5BlpoQxRB4Aq6bw7BHqJonsOxRPG6t2nFx3AyZ0u+TDP99fOq+2x6HLDUQ1D++ktjaY1SNk3iBX0IQ7nxuYLuzmPY1bGfull9EfOohVHnjopw/jVBVmVymzNuvXloitj/DSmiSxgOxg6uKWXp4mK7J+9mT5Kw8DyWO0ub/cAMCG4WmyHz+0E4+tquH8Crznoe2tWPYNv/5zQ8Ymq8LoaqyxGPbu/j0gT46bvK93Qg6klHS4SADU3XnGdtxGZia58rkPPGA/7ZzrzvByFyW509eXvH35niYnobNC6/d0ShadWtMVCf4eMPTpLUkAXl5b4YkiByM7uOHMy9TsdeeQLqeRdk8R8W8yK2RSUkME9KP4Fd33yO1QgFRDBHQ9iAICp63vAfW88xF9dw776AWBBlJkIE7GzQ8z6NcqDI/mSU3t/l+x1rFXFHz/2GgaBr8H+ff4WhDGweSzcS0D6+cpR6NkpAEH7eVq9siCtUa3zp+gaGZDDuak3Sm4zRGQoT92pL6Xa5cY2gmw/uDY7x24Ro10+bYtjZ6GhPrZvASQT+N0RC24zCVLfK1d8/yzN5eGqNhREFY9ESz0WSJkO9G2WPIpxEN+JAlkapp8eLZAXoaE3Sl46iyTM20GFvI8+alYb73wSVqpr3hkp6QrtGejC6JTr1w6krdaL4hXlfus21KhokqSQR1bZkJeCLk5zOHd/H+1XEmM0X+9I1TDM9mOdLbRlsiil9TMGybYtVYzOpmyJVr9DYleO7g2gIbluMwns/zzQsXGCsU1iVaAUXhYz09/MLu3exKpYj56s/FhrLtQj14oUgSfkXh4fZ20sEg//XsWV4fGlqTUDuex8DCAi9evcrexkb6Uhv3odsq1roGh5ub+cLOnUv2NNczlrOZIm+fHqJSM9nT20R7U4yh8QUO9LUuVYLIgkBQVdGjUYKqSszn4//94x8zXigsKzW6FbPlMgPz80wUCrTeJY/f8XyeN4aGODk1tW7mOB0I8Mlt2/jS3r20hcP4FGXDZYrL7req8unt20n6/Xz70iVeGxpas2fZdl0mCwX+88mT/P2HH6YpdHfK7e8GHDdH2TiOKPoI+Z4hFvzrKHIHoqDheSY+9RDZ8p9SrL7EzZVONfMcpj2C6xaRpcb6uoFfQhJTi2Osga7uJF/+S0q1127ao4dhXcZ2Z9G4PekRBB1N6cOvHqJivIeHi6Zsx68eWlRa7kYUggiCb8m2rR549PCwcd0qbiBHxXyfQuX7VM3TLM/8etTM81TN0/jUfchS+g6vqE2x+n0ct7AUDJDEJAHtUcL+z6LILUhiGEHQ6i4JUNcF8Sq4bo6S8WPy5W9h2oMsryxzKdfeIeR7FlXuXFeMqb7PKH7tELLYtChu5SyerYHljFI1PyCgP3aH5wo18zymNcitpda60oeqdCEKHw3Lq48Sbh5ZgmGdQFBjfjrPzESWZOPmM1c/DdBElcOxA2v+7ngOjXoj7yy8x3Rt5ieG2AqCQNinE9b1VZWJY34fT+3soTsdZzyTx7AdEkE/rbEITdHQpkUotzUlaU9GeHdAWEpeWI7Lt46fpyEcYE974z0p2788OcsPz/RzbTaz7O+pcICudJxUePM9/3fEFD3Pw3JtgnIAcZUeFAGBgBzA9hwc1p7MWM4shj2O7a4kabrciS63I4n37iMoCBKyEEYS/bhOjZsHDQ8bZ6kXpa419tOEqm0xVy1TtAy2R5Mo4t0TtbFch0vZOTpDMUznTvujPvqwHZdr0xleu3CVcyPTxII+Qj4NTZGRRGFJ7jxTqjCZKZIpVdjb3sjPHd1Db2NiXdn1lkSEPe2NvHVlmEyxyrffu8i5kWlCuoYg1D9QIV3jsV1dPLS9HWmRnGmyTE9jgkNdLZy4Os7Jq5P8/37wDulIEEkUMaz68czly7TGIzTHwpwdmdrQ+caDfg52tdAUu8xMrsTL5wYZms0QD9Z7d+uKex5P7Ormoe3thG8SO9AVmW1NSf7G00f42tvnGJrJ8PzJK5wZniISqGfRbNfFsGzKhkWmWMGvKbc1sC8YBn9x/jyDmQzGOplTTZJ4treXX9i9mwNNTXfU8ykKAiFNY29DA+K+fciiyI+uXl2z9Ldq21yYmeFbFy/yjx97DOmWtoH7gX2NjXxxzx4e7+wkHQwu++q9efIqoiBQqZpMzRWIBH2cuTLBgb7WFduRRZFkIMBjHR2M5nL8+dmz62ZNbddlNJ9ncGHhrhBb1/N4dWiI98bHKRpre6I1hUI8093NV/buZftiMGGrV1wUBFKBAI+0t+N5HiXD4P2JiTWXNxyH14aG+HhvL0FVJaTdu8j45mDjenl0eTdh/+fwaw8sE8GTxAimPYxhDWLaN6qaXK+A65QQBBWf+gjRwFfQ1evVB/WrGhB0LHuEivH+MlV60x7CcVbv874VgiAiiSEigZ9DU7YhSylUeRuq3IospZHE6LJ9roAE4CHLTUhiFLHso2y8uWwRzyvXe0/tobtAbFlSSQZQ5W5CvmcJ+z6DpvQhCPoa73l9kilJaUTBT778dWrWuWVLuF6BinESXdmDpO5Z9xgEQUaW0gR9T1CofPem6+9hOdOUam/cBWLrUTPPYtrXVvzi1x5CkVruuG/5TuF6Hm9NjDJRLFAwDbqjMR5obKEzEquXYc7N8P70OLlaDQFoCUU40tTC9nhdc2S6XOSt8VFG8jkMxyagqOxKpnm6oxtRECgYBidnJrkwP0vFMvHLCocbWzjQ0IQuy2SqFU7NTnF2dhoBgeZQaFk1jyiKxFMhmtvjvPfGFZ79wqFlx1+rmowPz/PBO4OUizUURaJvXxvbdjUjSiJDV6YZuTrLc794BGVReHHw8iSToxmSDWH69rRSyFc5/sYVZqdz2JZLqjHCzv1ttLQnOH9qhGKuwvRklkBQJxjyUSxUKRdrPPbx3QTDOqIoMnptlh988wQzEzl0v8rBY91072hCUSQqJYOBixNcOT9OpWQgKxK7DrSzY28rgaDO5NgCEyMLGFWLQr7C9EQW1/F49OO76elrWjru9SAKIlF1bdLveR4+ycdrs29Sdu6PZejdgigIa3++RJFE0E/Ur7O9IYntuvhUZZmg42YQ9fvY0ZyiMxXn6syNb/CZkSm+c+IiCLCrteGuuTY4rsvFiVmeP3mZNy8NLXPCANjT1kBfc2pLVUx3RGxlUSashBkuj7Ir3Idf8i19mD3Pw3BNrpWHCUgBNHHtnhnDnsR2stza3wqgye3IYmzT0tWbQX3bIquXkXq3seq5/xAlkXRbgt797WSm81iGhWlYmLX6fy3DxjLtu5KVzRs1zsxPYjoO3eH4XSW2P23wqQp9rSkmsnnm82WuzixQNSwsx8XDQxREdEUm4tdoTYR5bGcnD21v58Ht7UQD6/dORfw6D/S0MJvfzTv9o0xmCrx+cQjHcZFEEZ9aJ7AHu5dHK0VRYFtTkp9/cA9BXaV/cp4Pro1jO27dakFRSEUC7G5r4BP7tzM6n2Nkbm3/35vh1xR2tab5yiP7+fHlYUbncrw/OI5pO4iigKbIJIJ+9nc0rShvFgQBv6bwzN5eFEnk/cFxBqbmGZnPUhiti/OIgoiuygQ1lWQ4wK7FD+FaqFoWVzMZnu/vp2KuL8R1pLWVz/T13TGpvRmyKLK3oYGqbVMyDF4cHFxTSXehWuWN4WE+tX07fanUfeu3FQSBhM/Hz+3cyeOLmdpbMTKR4bHD9f4407IxLJv53NqWWeJi9vZT27dzZnqa3KLdzVqYLBS4msnwZFfXHZ/PSC7H26OjjORyay4T0jSOtrTwhV272HEXM+Qxn49jra0UDIOrmQzZanX1jk7PY75S4Y3hYTqj0Y8Qsa2r1/rUffi1oyuU3UUxgK7sRld2LiO2dbjIUhN+7QF0ZTe3zs5kKY4qdyNLzZj2wNLfrUV1eg/3NhZ+1yER0B5FV/YiS0lEMbzB9W6coSI1EtSfxnELGPbAoqryDVj2CKY9jF87tontrg9ZbCCoP00k8EV0pe+2xwigyq2Efc9hOzNYziSOuzwAYJgXsOxx9NsQ27qFUJCw7zOUaq/hOjfeXcfJUDU/wHbmkcTYlsmn5cxiWP0rypAFQcevHUOW7n0lyu3geh7D+RyqJCECp6anKJsm6b4AsihRte16j6aiYDoO/Zl5ckZtidgenxzn4vwskiASWhIQXMx+ex5vT4xyLZfBw8OvKNRsm69dOU/c56M9HOX96QnemxwHPFL+IAXDYKFaWdqG63rEkyF27GnlB984wcNP78R1672OrusyNjTPmy9dwHVd/AENx3Y59e5VTNNmx+4WCvkKr794noee2kk8GQIBLp8dZ+TqLE98Yg/Vqsmr3z+DUbOQZRlJcpkcXaCQq/DYs7sZ6p9mfHieSCzA2NA8kigQS4ZYmCty6r1rHHt8O+CRnS+RbozWS6cXSjz/9RP8yt9+ingyxMXTo1zrn8Z1PHS/im05vPaDc/gDGl3bG8kulDh9/BqZuSLbd7fg82uYhoUo3t0Zf93JRrinPOLDgiSKBPU7HzNkSWRvWyNHeloZnc9hLWqpFKsGr1+8huk4PL6zxK7WBppj4XpT3iYJtOd5OJ7HZCbPpYlZfnx5hONXx5jKLW+pjAd9HOlpo7dxa44BdzRbCsoB+kLbuFi4TJOvgTZfKz6pPgGvuTWma7OczZ2nL7ydiLJ2RMV2MzhreIkqUhzxLvR7rAfPWyxN8mqsLDmWbvLW+2i8FLIiseNwF7pfo5gtUy3XqJUNqmWj/t+KgVExuXziGgtTuXWtO9aC5ThMV4u8PzvOm5ND+GWFsKajSzLbokkafEFs1yVjVBgqZKg5DrIg0BQI0+gPEVBUbNelYNYYzC9Qta16JsMXpDkQJqyufBErlslkpUjRNNgWSRBQtu4jeKewTZtaxcA0bARRQNUU/KGVEXXPc7DcMiXrGrZbt6dSpRg+uRFZDGDY81TsSWLaHgK6xhO7ukmEbfqnB8gUFRwrScUsUzRHcMgS0CQaoyl2NTdzrHs/6aiPonWZvKng4WI6eQREfHIDAaV9qVJCFAS60nG+8sgBOtNxrkzMkSlVsGwXWRaXyoJ7GuIrziEVDvDU7m7aEhHe7R9lNl+iWDMQEIgFffQ0Jjjc3UJnKkY86Gd0PkdQ10iG1i8REQSBeMjPVx7ZT3sqyqXxWWZyJWqWjSQI+HWVdDjIztY0+io+dqIgENQ1njvYx67WBi6OzTA4s8Bcvoxh2Usf9FhQpyMZY3tzirbk2t+ZhUqFt0ZHb9tnmfT7+VxfH/saG++6Oq8oihxoamK+XGYwk+HK/Pyqy9muy1SpxLcuXeK3F4WL7se7oEkST3R18bGeHtKB1e+v36dSrpoUSjUsxyUc1G+bKRcEgfZolAfb2hjKZhlYQ30ZYK5SYSyfx/W8O1ayfGN4mIGFhXWVmXckEnysp+eu9/UCpAIBHm5v562REV65dm1dS6C3RkZ4srPzvnsZrwdZSqDKnWsSEUVuQ1W6YRU9Lk3uRVN2rWGXIyCJcVS5bRmx9bwKrlfG8ywE4faTNUEQkaXUHRMlWUriUw/gUw9SrP5g2W+WM4NlT6+x5lYg4teOEPJ9cgOkdjkUuQW/doSqeYaK8day30xnbNFf2LktIRUEHZ92GFXuwHFzSz3GHgamPUbFOEFQfwJhg04Gt6JmnsNyxm7pCZYXn4ntiMKHX3LvAR2RKE+0deJ6Hv/l3ElOz07zeFsnbeEocZ+PvkQKSRCo2javjw7x7uQof+fgUQRgtlKfs+5MptiXaiSkaciCiEC96ual4UFEQeCh5jbCms5UqcA3r1zgibZOQqrG8clxqrbFb+47THs4ylA+w5+cP31Tq4aHP6jR0pnE82B4cBajaqH7VWpVk9nJHP3nx/mtf/Ip2rtTlAo1/vw/vkr/+Qk6ulM0tyfQNIX+CxMcONZNrWoyN51H02Q6exvILZT53teO89Sn9tPRk0aSRM6eGOLC6RF6+up6NqIg8NjHd/PaD86Sz1XYf7SL6Ykcl86McujBHlzXIxoPcvTx7aSbolw+N8a/+Z++w/jIPlRV5oN3BsnMFzn8cC+RWIBivsLrPzxH375W0s1RAKplA6Nm8cjHdpFsiGAaFrIiIct37xtouCZd/g6S6ub7NX/S4HreYgCETZcOd6RiPLS9nfNj05wfu6EPMVso8+KZAYZmszy8vYM9bQ3Eg36CuopPVaiaK8dXc7FVrC4iZ1MxLApVg0y5zPnRGd66MsLwXJaqubz9UxZFjm1r51BXC4nbzDHXwh0R24gS5pHkUQZL13h+6kUSaoKIUrepKFgF5owFApKPY/HD6z5QdWuV1SceAuo96q29AQ8Ly8nguiuJrSDIyEslTR8NSLJE+/Ym2revLabluR7/6r/7fd76/insVR6626Hm2FxYmOGF0X4uZKbxyyoT5QKyIPLX+w6T8gWZrZV5feIar01cpWpbyKLInngjH2vtZXeigaJp8MbkEC+NDVA0DSRRoDeS5MmWbo423KTYJtT3N1Up8vrENXJmjV/vO0xA2Zj3671AZjbP1XNjzI5l0HwKzV1pdj/Yi3TTx9bDw3JLZGqnmCq/guHk8HDwyU00+B8lpu0ma5xnMP9fOJz+VwTkVtoSETS/QUvrCHFtH63BZ8nUzjJdGaNsFXA8A0WqkNBiNISO4GHQn/tPaGIUWQxQskbxcAgpPeyI/S0U8YbFgySKpCNBPvfALnhgc+fr11T2dTSxr2N9gbYdLSn+ccsTG96uKAj4NZVn9m7jmb3bNndQN22jKx2nK731Qem6Uu+PBtf3epYEgUc7Ojjc3Ezcd296rXVZZn9TE89t28bVTGbNntOyafL8lSt8cc8ewpp2z8mOJAgk/X5+df9+kn7/mkS6oznOtfEFxqazWLaD47j0tm/MEuxoaytvjYysS2xLhsFcuUzNtvFvMbBw3dLj1WvXmC6uLbCnyzIPtbdztLX1nthBCIvX9HN9fbwzNoa1Tjn0cC7HYCbD3sZGUmsEFe43ZKkFSVz73kpSHFls4IaC7w0ociuq3LHmuqIYQBJvjch7uG4Jz62AdH8z14rUvCqxdd0ijnfd4/zOnxFJCBPQH8enbs1WR5O34VN3ryC2jpvBdhfwMBFuoxMhCCIiQQLao5j2GLYzufSb6xYpVp+vZ+m9tcqjV4e3qMhfMd7FuiXzLQoaQd/HkcR741O9WciiwKGGZoKKiiSKtATDzJRLTBQLpANBTs9McnZ2BsN1EIChXBZZFHE9D0kQeLilna9dOc+bY8NMlYrsTqXZEa8HWHJGjalSkelykcnijUBqVzSO43nkjCp5o0bKH6A3Vn8HuiJxGgKBJS2D6/mIcNTPw0/38f6P+6lVTXS/SqlQpZCvEIr46dpWt0yKxPy0diSZnc4xO5WnoyfNoYd6ePf1y/TubOLalWlcx6WjtwFFlckulJidynPirQHOnxzm+rOdbopgLZKNeCqEL6Ch+1QkWSTdGKWQrWDUrKWESXN7nMaWGIoqk2qMkmqMMDuVJRz1Mz9bYODCxKJNUX37iXSknnV26uNeMOwjGg8uWd/ovrs/79NFnafSj6Hf52/Kh4FMqcJcsYyuyHSlNjdn0hSZve1NfOHIbqZzJeaLNxKOVdPi7MgUF0anCft1drak6UxFSUdCjMwvr+LzgKG5LJJYD/RkyhUmMwWuzWYZmJ6nVDNudScFQBIFOlMxfv7Ini2JRl3HHTFGVVTpDHTwW92/wY9mX+NC/hIjlVE86sbI+yK7+UTj06S1FPI6xsiiIK0ZYXSprRB0urvwcNwCJeMEq5VCi4KOKjfzUcnWbhSCuHZt/kYQUjU+2bGD5mCYr/afoSeS4Cvb9i+RTcOxOTk7zjeunuOfHHyCQ+kWFmoV/vWpN3hxrJ+kL8BEOc//dvYt/vnhpzjW2E7VtvmDS8f5ztBFOkMxfHJ90ip4Apcys7w3M0pE1fntvQ8T1z9cxeYL7w7yV7//Cpfev0Y4HuTRzx6i74HuZcQWz6NkjTCY/xP6Yn+HsLoND4eruT9mpvIGQaWNqLYbw8lRtkbRpASiIFO2xjDsBdLRh3Cx6M/9B5oDz9IV+TKy4Ge2+hbDha8R0XYSUNownRwSKm2hzxPT97BQO8Wp2f+RluAniKjbkW5j8fAzQNmyGMrluLxGhvQ6VEniF3fvJr1KCe7dREs4zEPt7Xzj4kXG8vlVl3E9j0y1yvHxcVKBwJoZ1LuFkKbxYFsb25LJdUufn324j/HpHKPTWfCgrTFKW1NsQ/vYnkjQFAohL/ZYrwaPOqmfKRbpim9tcLMch/MzMwxns+uqUO9IJtnb0HBPiWRQVTna1kYqEKBiWeuKZ12cneWBlpaPELFN3tSruhKioCOKQQQ0PJYrC8tiCllcuy9VQENcRTvDw1xF/ffeQxKjqxJxDwPPq901ywtdPYAq9yKKWxvjZKkBRVrZz14PCpRx3RKitJGgnEDQ9wyl2ivLia1Xplh7laT7u4tCVJubJnpejYp5ctUy5JDvWcTbiFvdTzhu3W/a9bxFS7q6T8V0ucT/+t5b/Ksnn+XhlnY8PP7i0jleHLoRGO1LpPhnDz7BQHaBHw0P8gdnPqA9HOV/evRpwEMWRX559wF+bc9BFFFcrAysT8vmqmUEQcDz6kFXURDWFPELBnWOPraD57/xAfFEnQBeF+nzPBfHqTst4IHruvUSUVEgHPVz6KFevvvV4+S+VObC6VE0XWHHnvqzIwj16r/f+RefpXNbA+L1Y/SgUqoxNf4BknzDgkwQRERp8fm/6VBd18V1vWU2lKIoIgogSSJPfmofv/JbTyIr8tL2r79GM1M5JFlE1e5d8koQBHRJ+6kgtQA/ujjIV989S3c6zv/nr3160+vXK/h6KNVM/sOP3qNmWstClo7nkS1Xebt/hLf7R9bczp+9eWpT+5UEgXjAz+9+6lH2tDegq1uvlrvjp0lEpNGX5outn+cLLZ/Gdh3qL7WMIshokoZ4m54XSQgjrlF2dN3r9V7CcubIVF7AW8UoXRKC+JUd/KQR23uNTK3CbLVM0hfgYKoZWRBJ+QLsijcwUylyam4C03UIqzqHUq34ZRWfrNAXTXN6fpKL2VkOp+r9npeys1zMzPBkSzdf7N1HVPvwidr44DRzE+v3kjpelao9Rc64yNmF/+eSkqXtVYiqfdScDBF1Gy2BjzNfO0lAacN2y5hOjrDWiyyGqFozlKxR+nN/wLXCfwUEXM9EEn1U7Sn8SjMAMf0AEa0PERVdSqDLDZhODhd7HYOhn+E6RnM5Tk1OrruMLsvsaWigJ5HAd497WgXqKrzP9PTwX06eXFNz3QNeGxriWGvrPSe2cZ+PT/T23tba5mz/JK3pKEf31ElAqWpwfmCKfdubb7sPURBIBwIkfD5mymv35Rq2TbZWY6tdtjXH4eWrVyneppf6wbY2urdInjcDbdEXd6FSWdc3uX9hgclikUNrLnF/IYlhRPE2LQcoiKIPx71xXoLgQxKD65YTC8J1i7Tl8Dx7zQquewlB0NZQ6vXq9m+YCNz52OTXDqPIW7cuFAQfohhaDCYsrwDwPGNVy8S1oCs7UZVtGNZVXO96ZtHFdYuUaz9G8keQpY1VY9RhUzHexXFmuVlhWhD86MouVLkLYQMexfcDluvy6sgQn93Wh+N5XMtlqdkOHeEoRdMAwSOiaaiSxOmZKU7OLB8/hvJZ4rqP3licqL6HmO7nq5fO4gEpf4CWYJiBzDwfTE/wYHMblusyks/REgoTUXViuo/Zaplz8zPsjKfoz8wzVsjXRQ1vem0EUcAf1DlwtJt3X79MPBVm/5EuTNMml6lw6cwoffvayGcqXL08RSwZoqU9gaxIJFIhevoaOXP8GiODsxx9fDvN7XEkUSTVGKGhOcrbr17GH9BobI1TKlQplwx038ZJxbVFkaq2riQTo/NMji7Q2pGksSVGujHCzESW0+9d44FHt+O5HhOjGeKpID7/R+M5+G8NhuXcsRhrIuTn547uJqCr/NHrHzCZWd9B4k4hiyJd6Ti/89zDHOlpvW1r0223t5mFy3YZVVRRxBs7FQQBCQm/XI8+Xi9P2ExkU5HiSMLqg2fFvEjNHifgHUBctVfnzlCzRslUXqBsnme1jK0sRQlpD/AzYrsc9QhnPTqoSvXHSEJAWZwUW66D43kIcJNKm4As1iOA1wUSAOZrZXyKwlgpz4XMDE+0fLj+dtVyjanheXLz6/dienh4nosihtmX/OeIKEtPiSwG0KUkoqjTHHiWi5l/Q4PvIUrWCLZXo8H3cD3iio2HR2/014iqu5bWFwQFn5xeIsuKGEAWfIsCCBKiIN3igfwzrIepYvG22dqgqvLkolftvS6VEwSBuN/Po+3t/PGpU+va0Jybnma+UsF23duSzq1CWlTyPdTcvKSYvRbOD0wSCeqk4nUSMDmX5/i5kQ0RW0EQSAYCxP3+dYmt6ThL/rqbhUedGL89OkrFWrvaRxIEtieTqwpk3U0IQv27tz2R4FVZJrfOsuP5PHPl8l3pL74bEIXATRoTq6Pu4718kioJIQTBd5v3qO4DvhL2qkHmew9xsR9YZOVcwAPPvSvTAFXuQRI3VuGwGupjgIog6HjercTWxPM2lu2u3xsFv3oMw7xIzTp/068OpdrL+PUHN0VsPc+iVHsVx11ehSKJMYL6U4iCds+/rRuBKkm0BMNkjRq/9+6bZGtV4j4/n+jqpTEYJGiqPNPRwx+e/QBFOkNjIIgmycT0G5nwt8dHODlTF5wSBYGAqvLFvj2IiyJFX965l9fHhvizC6f5z2dPAhDVdH7ngYdoCAR5trOXH41c5d998C5hVaMlFKYrGiegLn+XBEFAUSQef3Y3p969im3ZyIrMzn1tlPJVvvvV43zrT99BEASa2+M88Mg2YslgPVPpU3nkY7t44VsfkEiGaO1MIi3a6kViAf7G7zzL8Tev8Af/9kUc20VWJLbvauHpT2+sTD6erPdKv/aDs8xN1+/5Z798jOaOBJpP4YlP7uWDdwZ56bun+cE3P8B1XYIhH7/464/S2rmZgMnPsFGYto1pr10VtBFIokjUr/Psvm20xsN858RF3hscI1taRUzhDhEP+nhwWztfOLKbve2N+DTljse+TRHbV2bfJKpE6AtvI6UlyZo5zuTOcSC6j5ASRBK2JnCiyi2ociOCoK34UFvOHIXaj/EpnQS1A9w9gulRMfvJVL7PQvl7yywHrkMSQvjkHnzq9k1teTiX5b3xcQYzC0iiyBMdXexJpz9SapcbhSSIyKJIzqgui9iEVZ2o5qNoGgzk5ukKxylZBiPFHLIg0h1JkK1VqdoWA7k5dsbSmK7DWClPzbZpC0aXttUTSbAtkuBKdp53pkcJqzoHU7efJN8rTF6bJTOTxzbXn1xJgoYqxVClCJaTI+U7hiwGMJ0C4CKLAUAgpHajiBEK5gAFcxBNShBR+xCQUKUYupTAdQ10OYFfbsZxa1huEUUM4SxG3wVBXOql/Rk2B9NxmC6VGF1HGRfAryg82NZ234R7fLJMZyxGczjMVLG4ZolqwTAYymbZlUqRvEdZ24iu0x2PE9ZXJzGW7bCQK3Omf4Iz/ZPYjsuV4Vlc12MuW2I2u/GqmoCq3rZ31va8dQWf1oNp20wVi4wXCkvKjquhIxqlMRhEvw+K06Ig0JtIoN/mvCuWxXy5TL5WW/JN/jBRt6C5XWZFgFu+TaLou6340w03guXwPJfVgsz3GsJi8HV11MtU7xSiEECWUncuiCmIde2RWw6pHhDYXFDArx2lXHuNmnWRG9fdpWqewrJHUaV2RPH2z6LnOThegbLxDu6yKjsRWUwS1J9kdeeJ+4v6/CTGP33wcZqCIWYr9X7+lN9PVySGJslImsiv7TnIZKmA43nEdR+iUPf3vD7pPtzYQmMgRM2piyGGVI2eWHxJLXZHIolPUZgo5qlYVt3+UlWJaBqiINC3+PtYIY8gQMLn56HmNpqDIcKCSvgJBXuRoAiiQGtnil/+208RCOk0tcbwBzQefLKPhpYYRs1CkkQaW2L1fldlMSCuSBw42o3PrxKOBmjtTC7N0RVVZs/hDgJhnXymhGU5yLJEsiFMIKTxwCPbcB2XaDzAQ0/tXBKK2rG3lVgyRCwZ5HN/7RhQ75ct5Coomkx7d4pgqG4F1NaVRNUVOnsbqFbqc3ufXyOWqBPv5rYEjzytIEo/m9vcLRi2s+64t1GIokg86OdwdysRv4+97U2cGprg4vgsk9n1kz4bQSLkZ09bI0d7WjnU3UJPQwLfHZQf34xNjejn8xdp97fSGWgHIGfleX3uLToDnQRkP9IWpeFlMYwud6NJzdTsoWW/edgUau8iiUE8HALKHkRxa+VA9YHJw7DGqJiXyNfeolB7e8U+r0NXOon4HkfeRE9IzbY5Mz3N2+Oj9Mbi6LKCLstLH8OKZZGr1XA9l9bwR99wO6rqdIfjvD09wn8dOE1Q0XiwsZ2OUJQd0RS74g381dBFYpoP03WwXYe9yQa6w3EyapVHmzt5eXyQD+YmcDyXhVqZnbE0HaHo0kS+yR/iUKqFlC/Im5ND/Gh8gLCq0R1eqeB7P3Dt/DiFhdtP1AVBJqC00eR/irnqe5StcQRBxvMcQmo3Kd8RREFFFnwkfYfJGudx3CpxfT+qFMEDFDFIS/CTVK0pxorfQxaDeLgoYpDWwCfv/cn+FCBXqzFdLK7rYyqLIjGfj554/J5lRW+FJIoENY2dqRQLixnZ1eABVzMZFiqVe0Zsr5/7WpFSYfF4r4tBmJZNtWbh4REKaBzb27nhfemStCSQshZc193y4FyxLAYWFqha1rpUpCeRIKLr9yUzKgoCLeHwbc/bA7LVKguVykeE2KoIbGSyIdzyL3UDVjFrX/c7pZCe5+J51brjgpvBdYu4XnWxVNdYzGrWM8OeVyeDnmdj2kN3Ye9roa4ELYqBu+DhujKYUMfmCbgit6IpfcjmCWznhke542aomqfR5O2o4toiYNfhetUlyyGPG5USkhhGU3agKp2sFsi435BEkaQ/wMe7etdcRhZF9qQa2JNqWHOZXck0u5Jr95D7ZIUd8SQ74qtnJoOqxu5kmt1rbCMRuVFJIggCmq5w7PEdy5ZJN0WXRJdWgyiJJNJhHn5618rfRAF/QGP3gfZV1+3svXHu11WSAfwBjabWevvGnkOda+4bQNUU2jqTtK2RnY0lgsQS97Zi5qOK0YUcs87G2wY2islcYYUn7J3ApyrsaWukORZmZ0uKK5PzDE7PM5UtMF+ssFCqUKjUHRLWgigI+FSFeNBHOhKkJR6huyHOrpYGtjUlbuuwsVlsitjanl03RV/8oJquyWRtGtM17zCaKRDU9lE292Pc8lEEMJ1JMpUXsJ0sUd/T6EoXipRAEkOIgh9RkFk5SHq4noXrVnG9Ko5bxHKz2O4CZeMchdo7VK1BHK+06hEpUpqQ9hAR/ZFNnUnRMBjO5RAQ+JuHH0CX5GVairPlMpfn51BE8SeC2Cb0AMca2pmqFBkv5dFkmT2JBiRBpDeS4LOdO/nR+CDDxSyqKHG0oY3DqRbCqo4myXxl2wFeGLnCeCmPJAjsijdwtKGNsKpTsgyONbTRHoril1UOJJvx8Dg1N1nvcfmQcO38OPn51Z+LmyEg4JPTtIU+y1T5Var2NK5nIosBgrRz8zOZ8h3DdHLIYoiotpvr+QqQaA1+itnK2xTNQWrOPCIKktoB/3/2/jtKsvy+7gQ/z7/wERnpfWZlZVWW7+7qat+NbnQDDQ8QIAmJpCBDalYSpRlpR2dXM6PVaOYMJe3O7jk6Z4ZyFEekSIIgCIAAGq6B9r68N5lV6X1mePf82z8iK6uy0mdlmW70Pae6q56LX7yI+L3f/Zp7BQHBl6kLPEJQvuE/K4sh6gKPLJQqb395/kcNs8UiM6XSmjNUQJZpjkTuelWFKorsqavj6Pj4mmWzI9ks6TV6M28XMU2jPbb6fCTLEsl4iEf2d5DKluhpr6W+JlItd1NlEtGNZ6AUSVo3K+7DmuXZa6Fs2/TPz6+b82uNRrfdzmk1CIJAXNc3ZL+QM801+3DvJgQk2AoJE6RN+snePjzfxHXTOO4Mjjez4Pc6jeNO47hpPL+A55fxvEpVDAq72s+LvSBQ6Sz8/861d0hifLG95H6BKKgE1ANUrN0UbyK2AGXjPYLaIyhy+7pBZs/LUTReX1YKLUvNBPVHNmTf9DE+xi8L3rwyxPwG2wY2g/PjMxhrrCW2AkGoZliTkSAHO5qZL5QYnE0zmc4xka4S3LJp4bgejufhuNU2RVEUUCSJgKoQC+o0xSO018bZ2VRLYzyCptyZuXBTVw3JQUpOkTlznoAUoOSU8X2fklsibxeW9N7eiqAUWHu/upeo/iRF8yyGM7hsv+3Okir/iJzxDhHtIULafjS5A1Wqr/bnCiKLRML38HFxvSK2O181C3fGKduXKVuXF8js6sseSYgQ05+iJvgCqnzD29BwHObLJcq2je9XxWaSwSAhRcH3faZLJa6lU8yVS7i+x2i2WmLSFo0hiSJZo8KZ6SlOTU1RHw4tEtu2WIyALOP6PkXLJFWuYHsuAtXFUFzXUSSJnGGQrlSIaCpZw8D1fEKqSjIQWNaXsV3QZZm+mnr6apZHFYOKyoHaJg7UriyEoUkyPbEkv3vg8RX3hxWNv7fv0SXbHmlo55GGlSOIdxq+72NbDqNXJilkV+//uxmioBBSWumJ/9aax0XUbnbX/L1l2wVBQJMStEVWUa8ToK/mHyzZFJAb2J1Yfq2PsTLmy2VS5fKax4RUlfZ4/O4M6CbIkkRPMrluJm8ynyd3B8lOWNNojqztLSmKApGQztOHe4iHdQJ6dc6xHZdSxSQS2lgljSgI62ZJfW7oNWwWFdtmOLu+f3fDXSpDhuqTKaAo6/YvA5Qti+I6old3D9JiCG4zEJC4G9m56mfs4HhpLHuIinWasnUUwzq7JPt4f0BAFALcD1nLW6Gpe9CUvZSMd5YoUlfsc1j2IEH1IYRVdFCgWobseGmKxlu3JCYkVLmdoPrwHRz9x7gOz/MwDZtK2cIyHSzTwXVcPM/HW6gIEgQBURQQJRFZllBUCUWV0XUFVVe21T92LdiWQ6VsYVQsTMPGsavWcdXftIAkCUiyhKrK6AGFYFhHUeWqAvQdRlW92adcMqmUqmO0bRdvYXyCIFTVnFUZPaASDGtouoK4LHAprDp/vn9tjPPZ1W3v7leoskRzIkpz4kYlq+t5WI5L2bSpWBYV21nU1wnrGmFdvWMkdiVs6pVaAs2czp4nY+XoCnWQdwrYvsOpzFmGSyNrliIfqXmIRn31sg5R0Ijqj2C7M0zk/k88f6WFqIfjpclUfk6m8vOFbVUhBVHUEZAXIrDVcqOtRF4FNGKBp6kL/zoRvWoGen2B1J+a51vnz3N+dgbH8+ipqeFX+vbwZHsHpuPwZ2fP8N74GJOFArbnMprNIYsi/9snXyCu67x05TIvDfQzUcgTkGV+tuCp+XuffIG+ujqylQpvjY7w/cuXmCuXEQSBT3R28aVdu2mNRnl1aJA/PXeGT3bt4LWhQbKGwd76Bn593z4ebW27L8RGVoPv+3iuh+f6C/LwHr5XlX6/ITgGICAIIIji4uR7XXL+TpYlV8fnMzOSYn4qg2XcSYup24Pv++CD47gL99Sr9j97N+wEqnZPAqJYvX+SJCKId/YebhS+5+O6XvWB61atAvBv1HwIgoAgLjx8F8YvSuKWx54ul0lX1hY9CCjKHRcRWgmyKNIai61b/jxdLJI3zW2zHLkVQUVZs8z5ZpuGhppwNaO6sFCazxQ50z/Bpx/v2/ZxbQWG4zC6AWIbVlVs1yV/F7OjVWGZtZ9MFcehfL8Q2zX7Ttc9eTtHsgzXSa3tTJAtfYtc+YfY7jDrP/eFm/7c+Hf1c6le805BEPX7UitBkRrRld0oUguWe6M1y/fLGPZ5TOcwAXXfqud7fhnLGcZyBpZsl8QYmtyDpmxOENJxPVzPW1wfXJ9rdFVGEkUsx8X1fII3qacall0VapNEPM/HclwUScReICOSKKLIEvIt/Zyu42JZDu4KgjuBkIZ4m89N368+74zy8t+0LEsomrwo6LTV61eD8i7FgsFw/zSXz40zOjjL1FiaTKqIUTYxKtWEjKJKi2Sstj5KXVOcptYaunsbad9RT6I2jCRXSe/tvveVxuq6HrblMjWW5sr5ca6cG2Pk2ixz03mK+QqWZSNJEsGQRk1tmKa2Gnr6mjnwcBdtXXUEQxrSwtjuBDzPw7ZdykWDs8eGOHdihMH+KWYmsxSyZVzHQ1FlYokQTW0Jeva0cOhIFzt2NxOO6MhKlQddJ7/CGuMUqBLF7eotharfrOncXfE9SRQJqOLC+7i3Vp2wSWL7ibonsTybU9mzXCkM4Poetm/z5ty765KqtkDrmsQWQJWaSIa+hCAojGX+9w162fn4mLietWTbViCi0xD9m9SGvoKu3DCa8Kkulv6/777Do21t/I2DhwipKu+OjvLvjx+jM56gORLh7x95hM/s7OUHVy6RNQz+h6eeAarZIFEQ+I0DB2mKRHh3bJQ9dfV8vnfX4n6A10eGOD87wz95/AnaojFKtsW/fP013gsGebarm4rjMF8uo8sy/+ELXyJvmvzv777NsYkJdiVrSQbv/RdqNdiWw+jlSQYvTDA2MMXk4CzzkxnKhQqVoonv+Si6jB7UCEUD1LfU0LyjgY7dzfQd6aamIY4k3dmFkmXanHrzEuX89iu/bSc8z6eUK3P2nX76Tw0x1j/N3ESaQrqEbTkoukIkHiRaE6ZtZxO9D3bS+0AnzV11S3147xHy6SLXzo1x7t1+hi6OMzOaopQv41gOkiwRigaI18doaE/S2dfCzkMddPa1EI5t7ftdsCyK6yjs6rJ8x/pX14IkCDSFw+sS27JtU7IsbNdFvQNZRl2Wia8iHAVg2S6ZfJmGZISp+TzWTQ/OiZkslwdn7htia7kus+uUngP8qzfeQJGku6p3n7esdcfleN6aXrcfowrfNzDsC0xn/jmWM7QgALn23a1650aRFv4IQgBR0Bf+r1VblYw37uCobydQcGehKjsI6o9ilZZqjlSsMwTtS2sSW8edomy+v2y7pvSiqxtT2L0Z54amuDg8Q6FsMJkqcHl0Bs+Hf/y1pznQ3cQP37vA+aFpfu+3P7t4zr/7wXvUxcM8sbeTsbksf/bKST5xaAevnbpGplhmb0cjn3u0j8O72pa81pXzE/zFH77JxdOjy8bxe//hby7pL90KfB/OHB3k3/w/v71s3+End/KV33icnXtbVjhzg9f3fKYnMvz0uyc4/s4As1NZbMupJhDcqj8vC4FJqAbEjYpNLlNieiKDeGYMUaySsGBIo7ktyaFHujny9C7ad9Sjqtv3vHFdjzNHh3j5r05w+ewY+QWi6Lo3vHABPNchbzsU8mXGhuc58e5VvvPH79C9q5Hnv/AADz2+k9qGO+OHPD+T551fXOAn3zlOaq6AbTnV8bk3xudWLEzTJjWb49KZMX723RN09Tbyyc8f5BOf2Y+6EHBRNRlFkbCtlYNl0YDO1w7v428/fXjbxv/vXn2fH5y+tG3X+zBiU9/YpFrDZxtf4Knax6i4Fa6VhvmriR/x1ZYv0KDXI4urX64z2LbqvusQBAlFqicZ/DyKWMts8ZuUrAurZG9vxVb7YgRkMUFUf4za4JcIan0oUv0SayHH87iaTmE6DruTtXQlEkiCQE+yhvpQmBOTk9T29BBSVcKqiibLaLJE7JaFYkBRCCgKqiQRUOQl+7OGwVAmyy8GB7mWzqDJEq7nM5LL0ptMkjMNwCeu6zzfvYNEIEBM16kPhXA8j4Jl3lfE1vd8irkyl45f48L7Vxk4PUJ6JkelaGBWbCzDqk6+CxMGcCNLJ4kMX5pAffsKekAlVhth56EOHnnxAPsf70XTt1527fs+RslkbjzN9FiK2dEUM2PzzIylmBtPk5rKkpldqvhWypd550enuHp2dM3o20qoaYzxT3//b6OHtBXKVDYHy7AZvTLJOz86xak3LpGfL1AumliV6r10HXfRJiQ9lUWSJa6dHeWDn50hVhthx742nvrSYQ482XtbGdCbMT06zxvfPca7P7phxt3YXsuzXzvCo585tOTY7HyB9358iqM/O8vowDTlfAWjbGGb9mIJkiAIZGbzTI/Mc+3sKCdeuUAgrNG6o5HP/M2nOfLCfuaMCd5N/YS24E66Q3uIKAkkYfW5p2RZlNbpOVElidg9UC0XBYFYILAhwariwvvYbmIriyK6LK85hky+zDunB/nycwf4k5eOIwoQClTvV7ZQIbXB0v07DW9BTXktobDrKNk2bHMv0nbgY2K7PlyvQNk8xlzu32DaA8v8XKGq6Kwre9DVA2jyDhS5FUlMLghiyQsCTiJVmx8R3/eoWMfuMLG9f6HKnQTUh8iVvrvkflrOIKZ9GdfNIknxFc+1VyS2ApqyC109wGbJvON6XJ2YJ182+drTB/jrn3yAkmHR1VSDLIkYlkPJWJr4qJg2hmXjeh7FisXITBbDcviHX3kSw7L5/jsXON4/zo6WWhLhG8JsjuNSKhjks8vXmdXn0vVqsq3Dtt0Vr18umjhriO6sh2y6yBs/PcdrPzrLzGSGQr6CY6+frbtegYPn497UllcpWRRyFcZH5nnjp+f49d9+moce30kscXtBX8d2mRxL8dK3jnLm6CDzs3kqJbNarbXqGMF3fTzXxbFdTMOm//wEs5NZzhwb5NnPHODBx3q2LVjvOi5njw/z8++f5OzxITKp4opZ/MXxeT6O5+M41fLvK+fGSM/luXBqhK//9jPUN8XRAyqKKkNp5edRUFWIhwIkQtsnFBgN6Hck+P1hwqbevSzKJLUaatQE3sKPISwH6Qx10B5sRV2jh3ajismioKBI9cQDz6LKTRSMoxTMU5Tty9juDNsj7CAgizUElG6C6l5C6j4Cyk4Cyg5EIbCsVMj3fSqOA0K1ZO96P5wuy+iKTN4yb9u82PU8LNelLRrjNw4cXPIYaIlGqQ9VyyRVqSpyI1BdkF73kL2fFkLjV6c5+04/l45eY/zqNPNTWbJzeWxz7RKv6iQG2C6WYVPKVTOnM2MpZkbnGb0yyeC5MT7x1SPUtyY3NSbP9ZgZS/G93/852fkCpXyFcqFCuWAs/r1SqmaOb4XreOTmC+TmN25pch0N7Ulc17vtr+3MaIoTr57ng5+dZejiBHMT6RXHCgsew5aDbTkYZZNcqsjseJrpkXnGr05z+q0dfOo3nqShrQb5NvseLMNmZmSeKyduRPnT0zn6juy4MR7XY/jSBD//5ruceesKU8OzVIorT/S+7+N41QeZUbYoZKqEKRgJYJtVEhKSo/RGHsD2TPqLZxAQiClJ6rQW4kpy2e+3bNtU1iEwiigSvkN96mtCEJAFgeBC/+VagknX38d2q+UqCyrFawU6IiGdQ7taEAWBZDxIZ3OShmS1J3dqLs+Z/oltHdNWYbkuJcvasvDU/QDf92/7efJRhu97WPZVsqU/xbAvcau9jSQmCKgPEg48jyZ3I0v1SGIcUQgv2BetHMDxfXtBFfmXE6IQRlO60dX9VKzji9t938C0BzDsi4Sk5XoZrpfFsgexnJEl2xWpGU3uRZZWVw5eC5oq06ir7O9uJKSrOK63UIa8fqm47/uossiR3W3sbKnD9+Gtc0OUDYt8yVhCbD+smBxN8fpPzvL6T84yMZKqrjNuE77vYxo2pmFTyFeIJUKL2cetolK2uHJujJ9+9wRnjg2STZW2rJ9gGjYzk1kqZYtCtkI2XeKZzxxAUbZmNXodnutx9K1+fvHDU5w7PrxiEGIjY5scTVHIV7Ash69940kkSUTVVl9jaUqVQ2wnVFlC/SW3T9rSHRUEAQmJsBymPdhGUAqgisqa4lCbu76ELMWJSo+iyW2EtEMY9iCGM4LlTmK7mar6oVfAW1Q3tG8ydRcRBRlBUBEFHVEIIYkRZDGKItagSPWochOa3IYud6HJrUhr+LSJokBDKITv3+h10ySJ+XKZ+VKJjh09G7YIuS4eUrSWLrSvlwLWBAK0RWPsra8+DDJGBU2SF4muKLCyouh9tA6aHUtz7BfnOPnqRczK7feKea5HZjZPIVMiPZPDMh2++NvPEY4FNux/5nk+mdk8P//z96iUjPvqfq2HkcuTvPPSSd790SmGL02s66+7Ejy3Ss7PpYuMX53BLFt84mtH6OxrRQ9uL6Er5cuUC9W+RcuwGe2f4qX//BpHXz5HZi6/KiFfCy3d9dQ0xgEISGF2hg+Qt9PMmhPMmRPk7BQzxhgJtZ5GvYOk2rD4oLPd9X3dJFFEuwdRzuu/a12u9o65a4zTcl2sOxDAkgVh3fkrFFDZ0VYHwNMP9dCYjBINVytOWupjRMP3h+Kp47rrBjHud/h8qKanuw7Hm6NsHaVkvsetpFaWGgnpTxMLfoWA+sACmd3MgveX984LgogstRDWn1lCbAFM+ypl6wQh/VFuFb+ynHEM+yK+v7SFR1f2oik7Edf1QV4ZQU0lFtIIL1SGKCtk5m7OpjquuyQDqCoyPc21yAvVSUFNoWza2He5//BOID1f4OT713jlpdNMjCwXIJKkallxTV2EUERH1WQkSar2j1oORsWmkCuTy5YxKxa38kxFlenpa6a5PYmmb31db5k2F0+P8PL3TnL07X4qK2QuFVUiFg9RUxdBD6jIioTruFTKFtl0iUyquKyUN58tc/7kMKZpowdUnnh+721l1S+eGeP1n5zlzNFBivnlmguCAJF4kGRdlHCkKmJl2y5GxSI9lyefKVfFpTyfXLrE+69dJhINkM+UVp1/ehqSCGGFlsT2llRrirzib+WXCbe1kosqER5LHiGmRBE3IYjg+T552+CNqQE+2byLsLL6okiTW9DkFjz9MRwvjWGPYruz1T9eBs8rV33pFiX7BQREBEElZ7tMVizmTQhISY7UHySpt6PJrchipGpwvgHIgkhTOMLBxkauZdKYrosmSYzn89SHQuypr19X1fQ6ksEgMV3n8vwcP706gCqJPNjUQkzT2Ftfz3y5zM+vXWUsn6sqTts2DzQ20XgPhG22Cj2koenqiqRWlET0kEY4FiQU0dGCKrJSVbpzXR+zYlLIlMjOFzBvEVtwbJeJa7O88ufv0dxVz2OfOUggvFFPY786WRbvDxuNjcD3fOYm0rz2naO88d2jTA3NLTsmFA0QrQkTigXRAlWjc8/1sE1n4T7mMUrWYoTU93zSMzl+/s13cRyXF/7a4/QcaN/W3lujbFIpGlRKBrNjKX76X9/i9e8ew7jpoSYrEsFIgGBUR1HkxXGbhkW5YFApmrgLCxBBEOjY3Ux9SwIAx7fJ2SnydhpJkIgrdVTcIoZXZs6coOjkSNa8sPha7gZKOwVBQLlL/rUrQZGkdXUKHM9bFFHZTggbUCm+GS31cSbncvSPzAIQDmr0dmwtK7PdcH0faxvM6T/G/QvLGaRsHsPzcku2C2gE1YeJh75OSHtk09f1ccG/c8JRHwbIYpKg9giSGMf1clwn+o47jWldwHFTyFLdTWf4WM41DPvcrVdCVw+hyl1sFaIgIK2wrpJEEUWqtmrlShViIZ1s0SBTrFATCS45X705G7Ywx33YiyF83+fa5Snee/XiMlIryyJ1TXE6dtTT0p6koSVBNB6sEkZZxHU9TNOhXDTIpkqk5/Ok54pk00UyqSLZVIlS0UAPKDzxyT1EooHbEmkauTrLmz87z9G3rlC5ZT0Xjug0tdXQ2lVHS3sN9c0JQiEdRZNxbIdSwWB+Js/48DxjQ3OMXJvFMu3Fz69Sthi4MMFPvnOc9u46mtuT1bLfTd7LQq7CGz87y7kTwyuS2tqGKO3d9XT1NtDcliSaCKLrKqZpUy6azExmmBhJMTY0x8RICqNSVVF+82fnCIZ0KuWVq9Oe2d1NNBmiIxnf1JjXQ10kRE99kqb42i4HH2XcFrGNKVEeS25ext3zPSbLOf7p8e/z8qf//prE9jpEQUGVGlCltQWobsZAfo6ZwjVene2n4tgcrD9MQK1b9OHdKARBQJNlfuvgIX7Uf4V3RkewPY/OeJyv7ztAQ+gG6dRkieZIZNWyxo5YnIMNjXz/ymW+d/kiIUWlK15DXNc53NyCKkn8/NpVvnvpIq7nUx8K0Z1I0BSJUBsMsfMWs++WaBRloUfufkH7rib2PtrD0ZfPUi4YSIpEJB4kFA0Sqw1T15KkuauOhrYksdoIekhDXmiwz80XGL82w9UzI4xemWJ+IrMkWue5HvNTGb7/H1+h98FOmnRlY6RMEAiEdXrXMBT3F7K62bn8kj4VSRYJx0PUt9Zsusc22RivKh5u8tngez5G2eStH5zg1W+9x+x4+sZbEQX0oEZdS4LOvha69rbS2FFHrCaMosnYVrWMe/zqNFfPjjLWP838VGYxiwpQyld47S+PEooEiNVGaGxf2UB9K/Bcn0qxwvjVGU6+eoGf/vFbOLaLIAqEY0HitRGSTXEaO+qoa60hGNZQNAXbssmnSsxNpJkdT5Ody1PMlfF9aN3ZSKy2GtmsuCWuFc+TtqZRRZ32YC/d4T0EpQhTxjAnMq8vGY+7gdJOATZkxXKnIIniul8R7w4S281kta4Mz3B+YIrZdLU0v74mwr6dTZvysr1T8Hwf58O+cv0Ya8DHdsYw7IvL9ihyMyH9SYLq1oRYfN/G3ZCex0cXohhY6LV9cMH6p7oo9zGx3FEq1ikigU8tHu95ZSz7GpZ9bcl1FKkBXdmNvIn12kYhSxLJaBBVlnjvwgidjQmmMwWyxcovRQm/UbHoPz/OhVvErgQBWjprefpT+3nqhb20ddVtaL2Sniswcm2Gq5emuHpxkvGReQJBlUef2Y0e2Ho1VyFX5p1XLnLy3YFlpDaWCLHvoQ6een4fB490k6hdOXHj+z6FbIVTR6/x8l+dpP/8OMW8sRior5QtLp+tljl/9RtPUFMX2bCWie/7+J7P+RNDnPlgkPTc0lYzQYDahhhPfHIPz37uIF29jasKaaVm85x8/xqv/fgMl86MUSmZ5DJlcpnV55MH2puob4pvaKybwe7GOmRRJKLfH1VU9wL3kA3d+QloZ7SOndE6WoJx/nzwxG1fry0a4/92+MiaxzSGI3x934FV90c0jWe7unm2a7n8fVhVebytncfbVvZxfbFnJy/27Fyy7W8cPLT+wO8ywrEgXXta2XNkB5dPDJFsjHP4k3s58OQueg50UNMQW3PCvS7w9MHLZ/jz/99PmLg6s4Tc2qZD/8khrp4ZJZYME4mvL2wgyxLd+1r5N9//71c9xjJsXvrD1/nRH75OeuZGNiAUDfDopw/wd/7l11A22W8iiAKarmyKOPi+j2XZjFyZ5Pv/4RXmJjM3ridUyeGeIzv42j96kZ0H2tFDq09glaLBydcu8rM/fYfTb1zCMm+UaZZyZd7+4QlitRG++DvPLcrUbweycwWO/uwsP/ovb+DYLqIkkqiLcvj5fTz95cP0PbyDYGTlbLvv+1SKJsMXxzn5+kVyqSIN7bWL4/N8F0EQeKbuK+hScEm1SERO0Bs5tOR614nben0993JJ5N1kd3S/4ydvXeTwvnZefLIPEDjXP8FLr5/ngd2t93pom+pPVTeQJb8X0CRpw60tv2zwfR/HncN2xpbt05V9aErvgijUVq5tLMsC/zJCFCNEg5+nYp3C9W5kmxx3mpLxDmH9eaq2fAKmM4jpXFsm8BnUH0ORW1f9LCzLwTadapA2sNz/U5EkApqypP/Qc71Fy7p93Y1MZwr88c+PI0siezsb6WxM0lIbQxJFVFkieouvdmCBlNxpd4U7jcmxdDUzeAtZ1HSFz/3qEZ797AEim3AQqKmLUFMX4YFHezANm7npLFPjGVo6klvqW73+nD3+zlVOvHeVuZmlYpyKKvH0i/v5/K8+TEfP2oEPQRCIJoI8+fxe9hxq5/d/7yXOHhuidFPlXblk8sNvfcCDj+8gFNEJBDdI6PxqkOAH3/yAmansLa8LwZDGl3/zcZ777AEStWtnP5P1UT75uYP07G7iD//ty5x6/9qGRLzuBLrra+iur7knr32/4P5J832MjxTaehv59f/uM2TnChx4chf6de+xDWYv9ZDGk194iFAkyB/93l9x9czS6KTvw4X3+uk91LEhYgvVSXKtflJRFKrkadn4qkbhWlC9bSGFjSI9neOP/re/WtaTGq+N8NhnD/G3/vmvEIjq6/rf6SGNR148SE1jjHAswKvf/mDJ/smhOc68fZl9j+2k94HObRv/6bcuc/HYIJmZHIIo0Lqjnr/1//oqex/tIRgNrDvuQFhj10Nd9BzswPd95JsipaIgoggauhRYZn4elmP0hJcGlmRRRBJFvDVKVKuiVfdOgK3q2bg2IZNF8b4gPIlokPbGBLXxapS9rSlB9OpGWwLuLERBQNngYuxTPT1ENe32JU+3Gc2RCB3x+L0exn0J368skKjlv1VFbkEW65aftEG4XnaZANIvI0QhRFh/njnx/8D18lzvY3bcecrWCTy/iCiEAQHTuoBlDy67Rkh7ElluXvU1Trw7wBs/OUeyLsKv/I0nSNYv7TPc39XIno76JcQqNVcgGg+i6QqttXF+8/kH+fqzh6pjFkWg6lUriQKtdTGe2N+55Jpff/YQPqB8yIV10nOFFTOBvfta6eptJBzdujCWqsk0tydpbL09YlQpW7zy0imGB2aWbJdlkSNP7eLzv3aEts6NV4mJokCyLsrXf+cZCvkK508ML9nv2C5v/fwiDc0J2rs31hZjGBbnT44wfHUGs7JUlyEY1nnsE7t5/vOHiMQ3FiQQRIG2rjp+5//+Gf7lf/unTE+k11RV/hh3DlsmtnnL4HR6gr8aPUPRNnE8j95YPZ9p3cPBmhZc3yNjlvne6FnOpacoOiaKIPJUYw9fat8PCMiCyC8mrzBazDBVztEUjPFsUy/PNu3E931Gihl+MHaO/twsru/RE63jxdY+9sarvmITpSw/m7jE6fQEpufQEozxhbb97Es0oYi/3M3T9xrhWJCdD3TiOh6BsI4gsOHo3/XjZEVm72M7OfjkbjKzeVK3RNWunR+jlN946dhGXn8tl0GB7TUrXw35dJHz7w5w6djgkky1HtI48GQvv/IPXiAUD27ongpClax37W3lk7/22GJp8s09tyOXJjn68rltJbZVpWkDSZZo6qzj7/+/f4Oeg+0EI/q6pULX35MkSyuWmZuewaw5tuTYm8+VWHqOLIooorimgNS97s00HWfdTON1gn6v4bguP3v3EscvjoIP06kC85kif/XqWQAeP9RFQ/LOeAyuh+v9dxvBV/fuZUdNzX2XtVUkiaBydwJoHzZ4voHvr9yzJgpBBGHrARbHncO0L2/5/I8OREQxQlh/inw5h+td7+N0cd0UZfMDQvrTCCgYzpVbggEiqtyBpvQiCavPAfsf7GR+OkexYKwY0JMkcTH46bkexbzBu69e5OGnemluSyKKAqooo67yMxHF5WJT6jarz94rVEomhrFcvySRDKMH1Ntao1yvbrqdx4zn+Zx4d4CZieyS9YsgCkTjQb76N56ksSWxYeHPG+OCzp4GDhzuYm4qy8xkdskxx9/u54nn+mhqq0HZwGddKVm8/8ZljFtIrSBAsjbCF77+KKGIvuEeY0GoegHX1kd48Vce4rv/9R0y88UNv8dfFvi+j72gcC4ufK7bjS3/0sfLWY7Pj9IajHOgpgXP91FEiRqtGt2YM4r8ZPwiA7k5PtHUQ1TRsTyXpkB0UaTF833mjSKP1nciIHAqNc5rU/3sTzQRlFX+r4H3SeohPtWyG0WUGCmm+Yuhk/xu39OEZZ2/GD6F5/t8onEnIUVlqpznv1z9gH+89xO0BOObIree75GzDb47epzxcpreSCOP1fXQGd6+3sPV8JOJc5xMD5O1yvRE6vlc60Fagx/uUoLVSMlmEQzr7H2khysnh5YR25nR5eU4HwXMT2Y5/ovzS8SWADr7Wnj4Uwdo6qzbtKCDFlBp29XE4599gG9f+ymuc2MxkZrOcuXkIPOTGZJN8W0h777n4+NT21zD1/7hp+l9oJNAWNuWa4uIyIJCwckRlmPrWokFZBldlimvoZbreN66Xrd3Av5CCXLFcdbNGOuKcl/00u/sqKdQMhYXjm2yREtdbPHfdyP4sxpkUUTfICmMaBp1odCGhf/Wgu1VmDcGGCufoGDP0hV+nJbgITRpeQnbtcKbiIJEY2AfASm2bP+8cY2Bwimy1iiKGKAz/Dh1+i4U8f7Iit9LCEjAyp+Xj8dWGwpcr4DpDGDYF7Y+uI8IBEEAXyaif4qy8f5NxBY8v0DReJOg9iiWO43tjOH5N3oTBRRC+jPIYu2aJeHhaIBwNIBpVOfc6+T2tZ+cJZcuYZk2Ta019B1sp1QweP/1y1w4NUwuXWLXvlY6exuRZJEzHwySz5WxbYeDD3fT3l238VLUDy1urVOqolwy71n563X4flWk891XLpFJLe1ZDYU1Hnp8J23dtWta4KwFTVfYe6idS2dGlxHbbKrIyLVZduxuorZh+bx6MzzPo5Arc+r9a1i3KC5HYkF2HWijY2c9krwFPZ6AyhPP7+G1H58hmy5tyQViJfi+T9EwmSuUKRjVZKIkioR1lfpIiLCu3XaQ1vd95vMl5otlehqTyKJ4289z1/OYzOQ5NzpN/9Q809kClu0iiSJBTaExHmZnUy19LfW01Kz9uW0UW14lyUK15GOynMdyHXqiddRoQSJK9eGbMkq8NTPIJ5t6eaaxh4QaxPZcfHzkhZ44D59dsUYeq+siJKtkrQrvzgwyXcmjiQoXstP8zq7HeKK+G0WUeG92iHOZSS5kpmkJxricneGZxh4+2dxLUFa5lJ3mtekBLmVnSKhBYurGSzJsz+VcZoyfTp5jtJRiIDJDSNbuCrG9WpjhzdkrTFdy5OxunmroveOv+WFCx+5mkgtWLzejkCnh2C6+79/TxfR2wqxYTA7OcuGDq0u2S4rEjv1t7H+sd0sBA0EQiCXDPPTJvXz/P726RB3aNh3mJzIMX5qo3udtupWhaICdhzp59MWD6KHbiyTfDNd3ydlpPkj9jKAUQVxYQDXq7XSH9y4fh6oSUlXSlcqyfddhuy4Fc+VM0J2ET9Wf1nScdZfkIUUhdC+8dm9Ba0McXZWRJJG5TJFcwaC57ob9TyR07wiYIkmEVXVdT2CAvGHguO62EFsBCU2KEFNbGSm9T43dQYO/h5WW2EG5BgERaYXHr+0ZTFbOMG8OkNR2oEohNCmCyL3P1N8PEEQdUdCpTlJLP1/Xy+L6BTaf6/Yw7HOUjHdwvfT6h/9SQCCgHkRVurHdscUeWs8rUTaP4vsVDOsijjvNzWXhgqgT1p9HFLe2QE3WRQgEVGYns8zPVhVxG1sSBIIKerBqXxNLhnAdl5mJDOMj8+zc00w2U+Li6VEUVaa7t3E7bsB9i2BYXVHUaWxwjpnJDF07G7bdwm+jcB2P+Zk8AxcmqJRuVUEOcOSZXWi3mVXu3NmwouiS5/mMDc2RmS+uS2zNis30RIbZySzeLf6/iWSYPYfa0PWt3UNRFGhsSdDaVcfMZJZS4facOEqmxdB8hjOjU4yls2RKFcqWjev5SKJAQFGIh3TaauIcaG2ku75my8JRmVKF9wZGeevyEDsakvQ21bKruY7aSAhtCxUP2VKFE4MTvD8wyqWJWSbSOTKlCq7nL4jySiRCAZprouxsrOWRnW080tNOSFdvi6Rvmdg2BCIcqe3grZlrDOTnmKrkaQ8n2BtvojkYw3Ad5ioFDtY0E5arkQRNqr6c492IKu2JNxJVdSRBJCxraJJMwTbJYyIJAvV6ZFE1OamHSGhBxkoZFFHCB2r18CKBTWhBGgIRpso5LG9zkSvH9xgqzpEyixiuzUQ5w2Qlu9Xb8zG2EYmGKKEV+kZs08GxHHzPR/iQC0JcR26+wOiVSeanMku2x2sjtO1spO42el9UXaG5u56ahijThr1opwNQKlS4dm6UB5/ds0o8ePNINsV54Jk+YusIL2wWmqRTp7VQdgsYXnlxvLa3MjGNaNqqKuXXYboumTWI752C53nMl8vrZmtlUSSsqvdFxnYmVWDvjkZMyyFXqJArGqiKxN6epnseYJJFkYCiENI08sbaC4p0pYLpumyHlrMsqtRoXdRoXQwV3loMtqyEpsC+VfeZboG8PYkqhtkX/zLSNnnDf1QgCCqSGEMUo8uEnixnENsZR1d62Xh0zse0r1KovEzFPLbt4/2wQhAEJClBQH0Q076C5VT7aH0sbGcE252iYp3CcedvnIOGKneiq3sRhc3/qhzHRddVZFnCqFjMTmbJzBfY+0AHu/a3kZorsP9wFx076pmeyDA+PE82VSQY1nEcj/HBOQrZj76qdW19jHjNchXh2aksR9+6QiQWoHdvK5HY1ntttwrLchgamCGXLeG6Nwc8BCKxALv3tyFvMgt6K+LJEInaMJquLGb8r2NyNE0mvX75b7lkMj4yv2SM1xGrCbFzT8uWx3e9nLu7t5H+8+O3RWxTxTKnR6d49dJVPrg2xky+iLtCBlgAGmMRHupq4dnd3TzY2UJDdPMWoaPzWd6+PMzPzgwQ0obpa6lnf3sjnz7YS09jEnWD649qdtnizUtD/OD4Rc6NTlO27GXHGLbDVLbAVLbAxbEZBqbnKRoWT+7upCYc3DK53fIqKSirHKxp4UBNCxczU/xiqp9j86PY7S7NwRiyKBKUFWYqBTrDSVRRWoigL1X/1CTphv3OQoOjgEBIVvDxKTompusgCQJlx6Li2MTUAGG5SnZLjonh2iiCRMWxKdomYUXftKWPiECNFiYoqeQFiYQaIq7ee/uKj1Eto5VXkFn3fR/bcvBcb1P9Gvcz5qeyjA1ML9ve3FVHfWvytjzlrvfbNnfXk5rOLSG2laLJyOXJbfX4SzbFOfDE9lcfROQER2o+ScUt4t00m2jiyg/yuK4T19fOIhq2zWzx7vfDOL7PZD6/LrGNaBohVb0vxKNOXxmnIRlhbDrD+EyWmliI984M8czhnns9NKCqdlwfClEwjDWz4LOlEqZzd3xLfd/H9AqUnBS2V0aXooTlemRRWyhH98jZE6TNYcpOBlnUSJmDSIJMXG1HEhRc36bsZjDcHJ7vIAoSQbmGgJRYk0h/lCAgIksNqFIHhnd2yT7DuoRhnSGgHkCWalmL3FbvuYntjJEt/SWFys9xvNk7PPoPH0LaY5TN97CcYa5nZn3fwrDOY1inl2S4RTFKSHsaSYggbHL95bkeqdk877x6ET2gYJQtjIpNTV01KCoI18uVF3x1bYdCvszsVHZRSKixJbGqbcxHCfXNcZrbawiGdco3VV55ns+7r1ykUrZ4+oUyPXuaSdSGCS1U0tyNoKNp2AxemcJxliaWVFUmURuhtuH2tRckSSIaCxKJBZYR25nJzIaCG+WSydTY8uqM633ALR3J2x5nW1cd4UgAyKx77EqoWDYnRyb50/dOc2xwDF2RaYpFCGpqVdFfFPB8H9txKVs2qWKZH525wng6h+V6vLBnB0Ft41ln3/cZnElzZXJukZgeuzbOmZEpOuoStNfGN0RsrzsTnBya4JvvnOHSxOyGbAortsPJoQnmC2U0Weax3nbioa0FZ7ZMbHNWhYlyjno9QmckyWelPfynK++SNatZj5gaYG+imZ+MX6ROj9AQiFBxbVRRIrEOYVREie5ILSFZ43JuhqQWIiApXM3PkTXLHKppIamFiak6Q8U0l3Mz1KghBvKzTJVy7I03EpJVyo6F6ToUbAPbc8laBhmzTETRUcSlVg+qJPNo7Q7en79GKD/DY3U9HE52bvX2fIxtxHU15ZUsWzzfX1dN9sOE7Fye6ZH5ZdtrmxPE62//oSBKIjUN8WXlzGbFYm48vW3u9ZIsEkuGaeramELhZmB5BhOVQcYrVzGc8uIDuzXQw57Ycl/t2mCQZHDtOafsOEzdC2LreYzmcusS26ZwuKrgex9AU2TSuRKFkklzXYxdnfWMTKbWP/EuQZdl2mMxBtPpNeeG0VzurvVVe7ikjEGu5H/GRPk07eEjHEx8jbjaBoDrW1zMvsSscZm8PY2AQM6aQJeiPFX/jwjKCQr2NIPFt5isnMF0iyhigNbQQ+yOvkhAim2aTHxYochtaEofhn2Om8uRXW+OgvELZKmWaODzCEIAQVAAceE4Hx8XfAfPr2A5o6SLf0DJeGuBoAkIyICAz0dPu2Er0NV9qHIPonBssZfWx6VincS0r+L5pYUjBWQxQSTw/MI9Xx2e52EaDkbFxjIdKmULVTMZuDiFqsocfnwnxaLB1YuT1SsLoGoKlulSzBtUSiZ6QKWlvZZKyeJLf/1RoGrpt9XezQ8TgiGNHbub2bmnmbPHBpc8so2KzXuvXuLi6VH2P9jJM585QN+BVgIhDU1TkOTb75lcC5bpMHhlGtde+jwLhFTqm2Lb9tpaQFmxHLuYr2BU7HXb0ypli+mJ7LLtuq4QjQe3pU87WRdB07decTM0l+a1S9c4NjRGRNfoa6nnhT097G6uozYcQpMlLNclVSwzMJPi5xeucmF8hrNj09RGQnQm4xxsb9rw69mux0Q6x3hqaSVMazJGa02M8AbLmz3fJ12s8J9eOUr/1NyGSO11+D6MzGX4s7dPEQ1qPNLTjryFpNVtiUf98dWjnJofRxJFdEnh+eZdHK6terC2BGN8o+cIf9D/Hv/85EuUHAtNkvlM6x5+a8fyxefNEAWBoKzyT/d/kj+5dowfjp7H8Vx2xRr4W72P0hpKIAB/d9cTfGf4NP/zqZ9guDZtwQR/v+8peqJ1qKLEN4dO89rUACPFNHNGkf/l9E9IaEF+p/dxHki2LvYDQzVjW6uF+RcHvoTv+4iCuOms78f4GLeLKrGdW7Y9kggT3oQ33WoQBIFAWFuW+XUd77Z7QW5GOB4i2ZTYVm/c60hbs7yb+jEHY0/Sb5wmrtbh+y6Ov/JitDYUoi60tiVUwTQZzmwtsno7sF2XK3Nz6yoyN0WjRNfJOt8thIMaf/nyaQ7tbuHxQ12UKhaGdXcynxtBUFHorqnhjeHhNQM111IpitbdITAiEk3BAzQG9/L+3H9EvGXxLws6j9b9DhlzlIu5l1DEIA8nv4EAi8deyL1EYIHoBuQEWXOMn0/9LyTUDpoDB9Ckj362CkCTdxLSHydf+SH+Lf6phnWWOXeakvke0cBn0ZQ+JDEBgOcXsd0pDOsCZfN9isZbC+dXf3uSGEeVuxAFnZL5HvfW2fp+gUxAO0jFOknFOr6wzSFf+TGeV1o8ShB0FLmNgHqI1cS9rqNYMHj75fO8++olykWTXKbM3gfa2bmnhbdePsfEyDyhiI6qKiSS4WqVUXuSctHgr/7kXfYc6uDBx3vo6Kln4OIEv/97LwHQu6+Fh5/aResmbGQ+rNj7YAfzMzn6L4wv62UFyKVLvPvqRY693U9Ta4InX9jHY8/20dpZe1tkaz3YlsPo4BzOLSW+mq6SSG7f/KSoMuoKVXymYVeVmH3W7EYwDZvUzHLP6lBYJ5bYmH3keqipi6AHtn6vz47PcG58mnhA5xO7u/kfvvAJVFlGqtphLKoMtMRj7Gtp5LMHdvFvX36Hn50f4MrUHEeHxjdFbKezeWZyxWVrkcPdrdRGNn5P8hWTP33rFEOzaSxna2JmZ0anOXZ1nI7aOK3J+KbP3zKx3RVt4J/u+yQVxwahSgwjik5koR9WFkSagzF+t+9pKq6N53sIC8eEFY3uSJKXnv9vaA7caPJ+prGHh5PtxNQAArAjUss/2vMMhmPjA7okE1MDi5nWjlCCv9P7GF/vfgh/QZU5oQZQRQlBEPhsyx6ebujB9lxc31sQvBKpUYPo8tIv3PXojip89CN+9xJmxSIzm2dqeI6Z0Xmyc4UFa5gK5aKBVbGwLRfHsrEtF9uq9tHOTaydffkowHM9SrkK+XRp2b7X/vIDjr9yHmWFyXxTr+H5ZOfzy2ySXMelUjAWy7pvN7KqB1SCkcAdiQ6LgkhQitATPsCMOUZPeD8paxrXX3kSrQ+FaIpEUCVpVQJpuy7z5TIT+TyN4fBds9WxXZdzs7PrlsR2xuPUBO5+z9RK+PJzB/jU430ENIVISMOyXf76Zw/f62EtIqQo7K6tReQ6ZVkZA6kU2UoFd0Fd8k5CEISFXnARQZCW9LEv2luhIAkyoiAhCiKyeCMjUXbSZK1Rhq1RRkofICDh+TaOb1G0Z3B0E41fDmIrCBq6eoB46Otkin94y14fx52nWHmVsnkMUdCoLnN8wMP3nQXLoPICqa0+UyQxTiTwItHgl6hYJymZH7D2t+eXA4IgEFAPoat7biK24HlFbhaNUqQmQtrjayohX0corPPkp/bx4OM78T0fVZPRAyqarvDb/+RFBKHqSysIwmIGVlVlvvEPn8fzfDRdIRDUEAT48m88jm1X505NVwiG7o+qljuNYEjloSd2UimZfOs/v0mhUFkWh/E8H9OwGR9J8cM/f5/XfnyGjh317D/cxcGHu+jc2bDYD7pdcN2q2vCtSsCpmRw//PMPePPl89vyOsW8QX6FkmPfr2aNLctZk8C7jktlBUcNPaASimxPADkU1m8rsD+TKzCTK9JdV8NXH95PcEEn5ObPS1j4j+BDUFX4wgN9DM1lODU6yWQmv6nXG53PMZdfvvbc19ZAYoOCkJbjMp7K8f3jFyneYkmlSBJttTG+emQf+9obUSWJmVyBd6+M8NblYaayN1S0fd/nvYFRdjXX3V1iG5AVAvLqymOCIKAIEg2BlYVjJEmkN7a0TDGq6ERvyqJqkkxjYPXyS1WSqZPCrGbJHteCxLWP+2TvJTzPIzdXYODMKANnRhgfmCYzm6NSNKmUDCzDxrFdHNvBsV0818PzfDzPw3f9G3/fJsn0+xmWaWMa1jKVPqgqQBcyyyed7YTruji2i6zIt62MrGjyHVNmFBBQRA1NCuD5HhOVQTLWLDVqw4rHq5JEYzhMeyzG1fTKqqc+ULQszkxNkezuvivE9jqZvpZKrZmxFYCeZJLadcqp7xYc1+PMlQkqhsXu7gZa6mJk8mU6m+8Pi7KQqrK7ro6gqpJfo882b5oMpFL01dVRH76/SaHrO3i+Q2vwITojTywhxjGlBU28v8e/nRAEEVVqIxb8Co47S7Hyc3xuFo5z8fwinrux1gJZaiIa/Dyx4FdQ5c6FO7teWOSXB7LUhCbvRpGasd3Jha1Ln1GK1EJIf4KNPDgkSSQaCxJdoQJpJbVbqF52JaXb1Xpqy47FrJEnbxn0xZs2Zf34YYAoiiTrozz1qX1EEyFeeekUV85NYFSWkzXHdsllyuQyZTLzVUucD964TFtXHfsPd7H/oU6i8eCib/BW4fs+nutRLpnLkhCO45FNl8iuELTfbjiOi+O4aGvoo7uuh2Esb0ORle0rZ1d1+ba0XwzbwXQcIgGN3obaNQMQ1/d119UQDwWwHJfKJtts5vIlsuUbVXsCENQUuhuShAMbCxjN5Yu8fuEaqUJpyXNXlkT2ttXzjWce4kB7E4lQAFEU6KxP0FlXQ2ttnG++c5qpzA1yOzyb5tpMiqJhbrgMevH1NnX0CqiKMPgMF+cZLs4zbeTJ2xVM18bDRxYkQrJGTA1Qr0VpDSVoCsTQpbUXvdf9HWeMHOOlNFOVHGmrSNE2sX0XSaiWP8eUAE2BOF3hWlqCCcQNlg+fyYxyJjPGnFFYcX+dHuGBRAf7E62bvSVA1Re3aJtczE0yUponY5WxPafq9auG6QjV0hdrIqzoVZPi7fJYuU/g2C6Z2Rzn3x3g/PsDTFybYWp4nsxsDmuFCeVjVMtjLPPelXT6fnXC9/Fv+/soydIdKUMGCMtx9kYfRkSgO7SHjD1HjdpAo96x4vGCINAUibC7rm5VYgtQsizeHB7miY6ODXuh3g7ypsnZ6WmKlrVm0WNDOExrNLqusvPdwntnhrBtl1SuxMRMlkhQ48LAFA/s3tpcud2QRZG6YJBdtbWcnZ7GWCUb7vk+p6amONTUdN8TW1UKoctxFDFAVG4iobXj+S4VN4MuRpeVNn/UIYoBdGU3NZG/hSzWUjLfwHYmNtUbKwgquvIAkcBzhPXn0JSdgIgit6FITQsk7v4psb9XEAUNTelFVw9gVyaX7xfjqEoPqtx9D0a3MnJWhTPpcVzfZ2e0/iNHbAEURaKuKcbjn9xDojbM+ZPDnD8xzMi1WYr5lduKyiVzUTjp2uUpBq9Mc/bYEH0H2ug72EZD83L9jY3C83wsy7nnXrqe56+bCHFdD9NYPldIsoiyTesWSZKQJHFB+Gzz52uyjCrLiIKAskElaVkUq8dLItomHRTSxTKFyo0AoSSKtCXjxIMB5A38fhzXYzyd57UL15atZzprE7ywfyeP93YsEbQK6xrBBhVBgNlckT9569TivrJlM5bKMp7Osbt5c1ott0VsXd8jZ1U4nhriVHqE/sIMU+UMWbuC4dp4C+XBIVmjRg3REIjSEarlgZp2PtHQt1gyfCscz2XOLHAmM8ZAfprB4hwT5Qwps0jBNrA8F3mhrzehBmkJ1rA72sThZCeHk11oorxueUV/foYfjp+mP79cgRagN9pIWNa2RGxN12a8nOG9uascSw1xrThLxixheg6qKJPUwuwI1/NQsoPH63YCLHr7fhRglExGrkxy9OWzHH/lAlfPjC5R4F0JsiKhaDKKKiMrMpIsISkSkiwiiiLZufwdz1jea1iGjW1+NEi/IAiIdzDrafs2V4qncT0b060gCQqKuDrxa4lG2d/QwE8HBlYVairbNscmJpgpFgkqCso2+JuuBt/3mS+XeWN4eN0S+weamqgNBu9aefR6GBiZ5akHdyCKAqblYFgOM+mVA4T3AoIgoCsKT3V0cC2dXpXYApydnubK/Dx9dXW35RFsuWWmjQvYXoWiM8+8cQ1VDBFX26lRu1BEjenKRQw3T9YaQ0BkrHScoj1HUu9GF9cWVlHFIK3BB0mbgwwW3yRs1OPj4fgm3eGn0aXYbQWjJLGGoPYo3OKXqyv7EIS1S+AVuZ1Y8FeW2O/IcjOK3L7meaIYIaA+QCL0W0u2B7TDSML6FmGiGCSoPowk1qAqnVSs09jOKI47h+vl8H0DH4dqPYaMKGiIYhhJjCNL9ahyJ0HtcULaI8hSHdezjZKYJBH+TWx3CnxnYUwPI2yiTUmVO4kGPkNA2X/TVgFV6dmyx+vS63cQD35toST4BgLaw0hi/Lavv+z1lG4C6oMUK68uCx6ocjsBdR+iGML3fUqOxaXcFGmzhON7xJQAneEkTcEY05U8Q4V5eqP11C9U4xVsg/78DJok0xOpx/d9hkspxktZLM9BFSU6w7V0hGsQEbiYnSLvGIQklclKVXivLZSgN1qPKsqMlzN8MD/EB3PDBGQFURBQRZn9iWZag4l7bkm2nRBFkXBE5+Ene+nsaaB7VxMXT48ydGWaidEU6bnCis8Xz/Mp5CpcODXClfPj9F8YZ/jqDAce7qJndxOJLVj0ea6HZXw4AkG+5+PYy9cBoijcdub61mutJHq6ETTEwtRHwxi2w2y+RFvN+vPGbKFIybRIhoM0xzf3GWZLFUrmDWIrSyLdDTVoisRGfjKZUoUrE7MMzi7VKgkoMgc6mni6r3tFlWZREGhKRHm6r4sfnrhEvnyjymomV2Rs/i4SW9/3yVkV3py9wh9efYuJcgbwCckaEUUnoQYRELB9F8OxGS2nuFac5URqmFkjzxN1vairRAEc32O8lOHf97/GWCmF63tokkJQUklqYSRBxPU9Kq7NVCXHaCnN6fQI5zJjaKLMoZoOlHV6PRr0KPvjrYRlDdf3sD2Xgm0wXclhr9KrtxG4nsdoKcWPJs7w3dET5OwKkiAulFlX+4MN1+ZYapBjqUHmjAJFx7zFBOnDC7Nice3cKD//8/d47S8/wFyhj0HVFcKxIOF4ED2koQc1ghGdQFgnsPBvRVNQdQVVq5ZzvPfj05x/b+AevKO7B9d2V4121jTGiCbCSLfpAbcWEg2xhdKZ+/vBb7glrhWv9+r4OJ6N5RmookpbcGXLmdpgkN11dTSGw4znV+49cTyPiXye98fGSG5ASfl2ULZtBtNpjo2Pr/nLlwSBJzs67uhYNougrmE5LqWKheN6zKWL6LfZ+73d0CSJT3R18cPLl8lUKnirLCwmCwVOTk6yr6GBAw0NW170Or7BTOUiZSdDUK7B8U1mKpdxPZuI3IgkyMybA+TtKcSFx+68eRXbqxBW6tHFKFVlWZ240ookLi29EhDoCj+JKgaZLJ8lZQ5VS8XkJJ5/+4tJRW4mJv8KsdCvbPrcgLqfgLp//QNvgSwliQY/TTT46VWPsS2HuakspmGjqDI1dRGC4RvtSoIgoSu96EoPpj2IYV/EsgewnHE8v4DnVUAA15aQpCi61rigqryLgLJ/QTl56ZwqiRFqo/9g0+/nZgS0QwS0Q7d1jTWvrx4goB64Y9e/FbJYiyp3I4oBXO/mZ7qIJu9EVw9W7fc8l+OpYU6mR0kZVWKrizK9sQY+17qfWSPPN4eO8mtdh0loIWRBZLKc5VtDxzhc20lbsIah4jzHUyOMFtOYnoOIQFKf4Oudh4mpAX46eYHB4jz74s1MlLIUHIOQrPKNHY/REkwwXs5wNj3BQGGWiFK105IEkXo9Qkswcfefbv76GcTtQF1jjGca93PwSDdXL05y6v1r9J8fJz1fIJMqYpQtvBXG4dgu/ecnGLk6y5Xz4zz72QM89PhO6ho3p2Ls+9XWs5WgKBLhWGDReuhOIhoPrhtUr/o0C9yaRvD9bTOGWHyd6j3c/EV3NdWxt6WeqzMpPrg2Sm14N5oir+jt6vs+Fcvm6OA4c4USPQ217G9r3NTrlcylIpCSKNKajG84wD82n+XE0MQyFeTWZIyDHU101CVWPTegKjQnovQ01HBmdHpRfCxTqjCb27xbxZZXI47vca04y7/vf5XpSg5FlGgLJumLNdMZriWi6AgI5G2DiXKaq4VZ5swCcSXInlgzYWX1mmlJEGkMxpAEkYgSICipNAfj7AjX0xSME5I1irbBeDnDhdw4I8UUZdfiQm6CP7j6Bv/qgV8jrq5t7vt0wy4erdtBxbEoOiYZs8TpzCj/17W3mDe3bvuRscq8MnWRb48co+iYqKJEa7CGB2s6aA8lUUWZebNAf36a89kJ/mzofVqCCQr29inS3iv4vs/41Rl+8sdv8fp3ji4jaYoqE44Hae6qZ/fD3ew+3E3rjgYa2pPoIW3Vycg0bCaH5j7yxFYQqj5qK+HICwd45NMHCN1B03VVU9B0ZUPRuXuJmJLkqdovLPzLp+BkGS33o0mr3xtJFGmORHi6s5M/O3t21eNc3+d7Fy+yv7GRmKYh34Gsref7DGUyvDk8TMZY/XcvCQK1oRCHW1rW9eG9m2htiHPx6jSj0xkc16NUMelsuX3fv+2ELEn01dezu66OmWKRnGmueuy7o6O0xWJ0JxJEtmipFJRreLj2G2secyDx1XWvE1Ea2Jf48or7dCnCjsgz7Ig8s5UhfihRyFV46U/fwzQskvUxHnthL127VlL6FNGUHjRleWDLcz1mJ7OoikLNJrMYH6MK37fx/Qq+v3TRKophNKUXTe6p6hQ4Jv+h/02+0v4An289gCbKvDHTz2vTV9ifaGFfvBlREJkoZcjESsSUADNGnsFiin+89wU0UeYHY2cIyxpf7XiQpBZitJTmX5z+IQcSzRxItFJyTCqOxd54M3+v9xlSZpG/+96fMFCYpSWY4OmGXpJqmG+PnKA7UsfXOh4kKN+7Ng7X83GcjVue3C7iiRCHn9jJoUe6yaZKHH+nn6Nv9TNydYZcpoxZsbBXCKCbhs3ZY0NMjaWZm87z1W88QSCobrjySpQEFHXl52W8JsRDT+xk34Odt/PWNoTuXY0o2trPbVES0TQFo7KU2rqut22l1L6/oA+zRaa8t6WeJ3o6uDaT4i+PnaetJk57MoauKIjigiqyX62ctRyXazMpvnfiArmKwbN93exsqCVfWXl9oUgSAXVp+4rluEvUrEVRoCEW3pDdjuN6DM1lODMytWS7IAgc6mxhX3vjuutKXVXY3VLPhfHZxXHkygbzhc1Xam6Z2KbMIucz40xVquVHfdFm/uHu53mgpmOFPlcfy3O5VpwlbZZoDsTXHpQg0hyI89X2hwjJGvsTbbQGEwvlujffHZ9rhTn+69C7/GDsFBXX5lx2gqHiLHtiLQTWmcwUQUJRAkSVALVamLxjoIq3l3l4b+4qb872L5BamZ3RBv7lga/QHkou6fMo2gbvzV3lX1/4ESOl1EciY+s6Lq/95Qcc+8X5FSeHnoPtvPhbT/HIiweJ1oSqpXP3OYm6m5BVGXmV/pZEfZQdB9qpa1k96vXLAkmQicjxxX9H5AQzxhimW1nzvOZolM/v2sW3z5/HXiWy7Pk+Z2dmeGNoiGQgQHs8vuJxt4OKbXNicpIf9/eveVxYVflKXx+1wbWDdHcbn3tmL0MTKUYnq/3KHU01dLbeX8T2Or6wezeDmQy5mZlVj5ktlXhtaIjWWIyv7d17F0f3MdaDWbG4fGaU/+n/+BvEk+EtBd2K+Qon3x6goTVBTd3HxHYrsN0RKtapRS/b69CVfWhKLyDjeC4zlTzDxRT/5+XXF6vmfHyaAjGmyjn2xJp4vL6biVKWwcI8jYEoVwtz7Ik3UqeFmTeKjJXSnM9O8qPx84uWJj4+05U8vdFqRmlHpI5HajsRBYEaLURTIIbpOBiuTYz7Qz3+Olyn6u5wtyFJIsn6CJ/+8kN86ssPMnJtjqNvXuHdVy4ycHFixewtwNx0jh/9xVESyTDPfe7ghlWCJUlcCIwvL73Vgxo7djXxyc8fut23tT42MEdcH+utcB1vRdK/FThOtQJvtfu8HmzHpachyVO7uvijt0/w3/yX77GvrYGeuiQ14SCyKGI6DulSmaszaS5PVwnhs307EAWBH5y6uMx26Tp2NtbyXN+OJdss18X2brx3AYGgpm5o7TGdzXN1ep5UYalSdURX2dfWwI6G9YUlVUmkKRFFuim5Y1g2JXPzlnxbZnEV12LeujHJHappp16vZlmXly8IaKJAT7gBL7S+tYIgCIg+fL71ECICmqQgr3LdjnCSx+t6GCrMcjY7jut59Odn6ArXr0tsl17v9uWbSo7J+dw41wqzQFWA6p/0fZrWYGJZP3FY0XikbgdfaX+I74+dYt68f3rUtopz7w7Qf2qYQnp5xvvIp/bzxd95jr6Hd6yZnV0Rvre99SH3KQIhHX0VuwKzYmGb9keqP2iryDtpLuSOLv7b9ixS1hTNga41z1MlifZ4nC/u3s1PBgYor6Ea+Bfnz5MMBvn8rl0kttFmx/U8fjYwwPcuXqS0hoeqKAjUhUL86r59hFT1nn/urueRyZf5wevnuTQ4TbFsYjsu9YkITzzQTWtjHGWLoiN3Atfv1iOtrRxoaGA8lyO7Rna8f36eb509S0hR+Exv790Z5AI836di2+iyfN/0Ua8Gz6uQLv0FmdJf4XpLhdhEMUxt+BvEgp9Gus0+0mK+wuDlSd59+TyzExm+9e9eZef+VvYf6SabKnLy7X5y6RK+7/Oprz1Ma2ct7796if5zY5iGTSis8+KvHcFxXN788VkunhgmFNUZG5ylvaeqnj4xPMfTnzmIJIt88OolAHYdaOMH//UdWrvqyKQK1Dcn2H+kG9OwOfb6ZbKpErZl86mvHqZjZ+MdU37fCgy7n1TxTyhU3iCg7qc58S+QxbXVVDd+7SsLFkhLEVAPoSm9VTJDlYBKgsg/P/A5eqMNi4EIWZCIKjqSIPJEXQ+/n36doeI8hmszWJjj+aY+xIVrCILIX+s6wpfaDi5ZVF9v5QJQRAldWpgXfWHx9e9HuI53T4jt4ucuVFe3rR1Jar92mMef62Pw8hRvvnyeD968smISopCv8K3//Ca9+1ro7m3csM2grMhEE0HymfIScus4LuWSuWpF2t2GJIkr/nZNw6Jc3J7qSaNi31am/o/eOcn3TlygZNpYjosPXBifoX96foFnLWRsPQ/LdRc9Y98dGOHE0ATAqgmzF/f3LiO2IgIiAu7COYJQJZsbmT4uT85zZXJu2fYHupppr4tviBxLokgsqC+Zr2zX25IX7paJrSJKhKQbi/CxcoaSa6waLREEAU3a+MsJgkBUWX9BqYoyTXqM1mANZ7PjePikzCLObfTJbhWDhVkmyhkMzyYs6+yONrEn1oImKcseLqIgElF0nm/cw1sz/R8JYnvh/QFmRlNLIlSCKBCvi/Kp33iC3Ye7CUU3TxJM494r7d0NaEGVYERHUeVlD8J8ukgxt9y37ZcRIhIB6YZheFCK0Bhop1Zd24xcFARqAgF+89Ahzs7MMJTJrCokNVcq8e3z5/F9n8/u2rUtVjuW6/Lj/n6+e/EiV1OpNRdiTZEIn9+9m5ZoFPk+IDtzmSKvvN9PQFP48nMH0FUZ34d0rsRsusgr71/hxSf33OthLkNQVflsby+ThQJvDA2tes8t1+Xy/Dx/cOIEs6XSYkDjTmXKfd8nYxhcmZvj5NQU56an+WdPP017PH7PgxhrQhCQhAiSGK1a6nglXC+H6+cRhciCaNPtl13qAZX2HQ1YT9uc/WCQxz+1l4bWGlzHZWokhR5QeeDzO0nN5nn7p2d58VeP0PdAO61dtWTmi0wMz3Hx5AgPP7OLnftaKRcMmtprOPT4TgRRYPjKNPlsGdf1EIQqkQYolwzGBmd5/FP72PNQJ8GIDr7P5EgKWZZ49ouHSM/mOfr6ZULRAK1dq5kN3n34voXtTGM6g0hiDf429F4DOG4K07qM5Qwu2S5LDWhKH7JU7eWTRZGkFqZejzBezrAn3kRzME7JMSna5uL6rzEQpTEQZbqSZ7qSx/JcDtW0ISAQV4PU62EKtkHRMdkbb8bxXKYreTRJxl34bgkIy3+bN/1TEgVkUSRtlvC24ft4OzAq1oqeq3cbiloV5wyGdeLJMC2dtTz8VC8//stjjFydxbzJrcJzPVKzeY691U8iGV7dhukmCIKAokg0NMcp5Ss4zo3Z1qhYpOfvnzWuqsnEa8KMDi4lY+WSSW6bPqtCrnJbAY2SaZFbKCUOajeyy77vL+M3iiSi3FQybLlrv+5K6x5FFpEk8YbQq189br2ckuW49E/OcXU6tWzfQ92ttCc39kyrij7KS1KMzt0mtlElQGe4Fl1SMFybc9kxvj92irRZYne0iaQevmsWNkFZu4kE+1Rca1WxkDuJkVKK1EJ/blTR2R1rWrO3Q0SgK1xHUgujFKXbEq26l7gemRs8N0Zufqkwj6xI7H9sJzv2t2+J1AJUisZHRi14LUiSSCQeIl4XYW5iqbJcaipLdu7+eTDca8iCQnOge2GBI6JLQVRx/ZIpVZLYVVvLl/r6+ObZs0ysIiTl+T798/P85YULZAyDT3Z3s6eubkuG9q7nMV0s8vOrV/npwACX5uaorKHUG1ZVDjQ08Nne3juqzLwZlMoWQxMpvv7ig7Q1JVAXsrPZQoXTVyY4eXHsviS2ArCvoYFnu7qYLhS4PD+/6rFl2+bCzAwly2Ikm+Xx9nb2NzRsiyK17/tUHIfJfJ7xfJ6RbJahTIahTIbRbJbJQoHfffTR23qNuwEBhaB2GFmqw/WL+L5J0XiXdOnPt/V1ZEVaWHzXoQdUOnoaiNdGuHphgvGhOdJzeQJhnVKhQjFfwTBs8pkSqZk8lZJFNlXCNGy0gEp9U5xEXYTG1hpau+oo3LJw9aGa+hCEhf8JdO1qIhILIEoiQ1emGB+cY2osRSQepFwyKeTK6yr9f1RgWGcx7LP4/tJWj6D6MJrSjShU510RgZga4OtdD3OtMMc3h46hihI+0BSI8sW2g0iCiCrJ7E+08sZ0PymzyMGaNmJqdW2gSzLPN/VxOj3GD8fO8urUFXw8BAR+reswoXVsIq8joYbojTbw2vQV/tPA24RljWcae+mNLvc6F8WVqgGrcN3rPZJbX8uWiibp+a3rtmw3JEkkEg0QCms0tiQIhnV+8Gfv0X9hYondoO/7nDk2yCPP7NoQsYUqeW7rqmPk6uySbGW5aDI1nsF1vWp/6D0O3ulBlYbm+LLtlZJFPlPCtpwNZ6lXQ2o2v6JX7kbx/N4eOmvvTOtZxwrX1RQZRZYWiaTn++RKxrpcanAmxdWZFLny0kx3S02U3qZa4qGNr/tXeqmtMLktf3IhWaMn0sDjdT28PTvAnFHg55PnGSul2R9vYUeknrZQkrZgDUFZ3bC/7K1wPI+sVWLeLJK1yhQdA9N1sD0X1/dwfY85s8C14uziOd6Ct+7dxuyCwjFU7097cO2+M0EQCMhVpWddUrA/xA9Ky7BJz+aWGYTLssTeR3cSigS2PJllZ/OUCx9+ca2NIF4XpaGjdhmxnR6dZ34yjed5d9RG58MA27fI2Sn2xo4grqN+fisEQUCVJD7b28toNsurg4PMl1eO0Nqex6W5ObKGwVQ+zyNtbfTU1NASjRJdR1jK9X1KlsVUocBgOs3pqSleGRxkIp/Hclf/nSuiyJ76ej7T20t34v7pp3Ycl2yhjON6i72112HbLunc/WvFFdE0nujoYL5cJmsYTBdXX2TansdAKsVkocDVVIpDTU301NTQFI1SGwwS13VCqrroF3gdvu/j+j6u52E6DkXLomhZFCyLgmmSNQxmCgXGbyK2M8XimlZE9yMEQUJT2tGUG1Y+vm+RLn3rro3B83xc10cUBaLxEM3tSSolk2sXJ3Fsl0BQwzJsVK26vBFEAd/zbyzQhOri3rFcfN/HKFlYpoO60B8oKxKhiL6gEH/9PXp4rocgCoQiOoce7yFWc397H28HXC9DyXwfw764ZLsg6IT0p1Ck1pu2VW11Pt28h/fnhhgrZ6g4FqooE1GWVj/sjTdTtE3ydoUHk+03kiACHE52EpRULuWnyVsGIiJxLVjVRBElHqntWqJXIgrwicZeeiI32s/iapDDyQ7ydnW9uBYUVUKUVl6bWKaD53q3ZQFTyFeYm86tf+BdhiiKhKMBHn1mN1NjaTKpEhMjSwN/o4NzlIsmvu9vaP2m6Qq9e1t4/7VL3CzXZ5k2qdk8c9M56htjCKvc77uFYEijqW1536fjuOSzZeZn8ivu3wxmJjNUSquLFq6HBztaeLCj5bbGsBlEAzpBVaG04O/reh6TmfyavMT3fY4PTjA4k15GgB/qbqE5EV0Mgq8Hz/cxbGcJdxNFAXkL5etbJraSINISTPDXOh/B8T2u5meYN4u8MzfA8dQQzcE4hxIdHEq00x5K0hyMk1CDaNL6RvK+X31rU5UsY6U0VwszDBbmmKhUvWyLjonhWthetdnZWSC49xoFu4LpViM0miST0ELrnFFFTA2gSTKFD9caZxG+T7X0xHKXhVdESaSpsxZF23r0a3Joluzcypm17Ua1B2RlOfW7gdrmOG07mzj/7lIF6NRUlsmhOYqZMtHkR39BtRZc36HoZLE8E03Ul9l1rAdBEOiIx/lSXx8F0+TtkREKq/S7er7PRD7P9y5d4p2xMR5rbWV/YyOt0ShRXUeTJBRJQhQEvFuIzVSxyMXZWY5PTHB5bm7dUJsoCHQnEny6p4dPdHXd86j2zZAkEUWSONs/sWxc2UKFYOD+6TVcCZ2JBC/09JCuVHjpyhVya/TbApQsi3dGRzk2MUFTJMKu2lq6Egmao1FqAgGCirJYIu77Po7nYXseluNQsm1S5TJzpRJz5TKzxSJThQLThQL3/in14UaiLkJrVy3BsEb37iYEASLxINNjaUzDJpEMk2yIUcxXFq1HAiEV1/WYGc8wNjiLHlCJxIMUcmVGB2awLId8tkxdU7Uv+NafXTwZpqWrDlES6d5dbXeIxIMEw1tT0P4woPq8cymbRymbH+C4N4uviehKHwH1IJK4dPEvCgIxNcinW9YWYWsMRPlS+8EV94UVjSN1XRypW1kz4bOtS62lREHk1zoPL9mmSTLdkTq6I+uXiuu6sqpoYyFXxrqN7J3v+WTmC8sI4/0EVZPZ92AHF06NLBtnPlPGMu3rBQ3rQg8o7N7fSiCoUS6Zixk436/eyzPHBnn2MwdQt8krdqsIhDSa25MrCl3lMmWuXZ68bWI7em1uscXhw4C6aIhYUGcuXw1SO57Hlak5Kra9amBjvlDm5NAEk5mlgRtNlnlyVyc14Y23cHm+T8k0l2RtJVFEXsUWdi3cVq49JGs8lOyiPVTLD8ZP8c5sP+PlDCXHZLiYYqg4z/fGTrAn2sxzTX08UruDrnAtIVlfs3fJ9T3mjDzfGz3BTyfPMVnJIgCapKCJ8oJwgEJQ0hCE6gdQdIzFbOm9guU5i7XroiCibVBhWRVlpC1mtO8L+D6WYa+o/iYIAoGQtmXRAMd2GLo4zvx09jYHuQEIVfGDW8fq+z627bC1oojNoa6lhq69rciKtKSv2DJshi9NMHBmhAef3XNfkZ67DRERWVSZqAwSV5KIQvV3pokBgvLGSf+jbW0UTJOSbXN0fHzN7JnjeUzm83zn4kW+c/EiMV2nMRymPhQiomkokoTreZQti7lymcl8noyxfhnPdQhAQzjMl/r6+ExvLyH1/iKKNbEgzx7ZueK+WFhnd9fyEr/7CQLQV1vLXz9wgKxh8MbQECXLWvcXbbkuI9ksI9nsku2KKKLKVU9BZyGYcS/aXz7qkBWJuqbYYvY0WR+lbUc9s1NZfv7d44iSyK4DbezY04wsi1w5N0Z0LI2qyUTiQQQBauqihKMBhq5MYZo2Bx/ZQWtXta3gg9cuVY8TBcKxIKomk2yILVnEJ2ojtO+oZ2Y8zc+/exyA3gNtPPjETlRt/UD9hw3VxIKN7YyQKf4Zhn3hpr0CohAkHvpVZKl500HF+xHBkIairLx4np7IUC6ZW/ZfNSoWU2NpxoeXC+vcT0gkw6u8Rx/X8zdcjq2oMh09DTS2JsjnyktKmwu5Cm///AIPP9FLPCnd08qzYFClpSNJKKJTKhhLyG02VeTS2TEee27Plsqmfd/HMh0GLk7cF73VG0VjPEJN+EbZsO24nB+bYTpboD4aRlNu8Bnfr34v3r48xLXpFJWbeokVSaStNsaBjmaigY0H/1zXI1Nc6jlffc7eZWILC56zgRh/d+cn+Erbg3wwP8jLUxc4mRqm6Bj4wMX8JBfzk/xs8jy/2vEwX2x9AF2uPhBu7cP1fZ95s8i/vvBj3pu7hunZiAi0hGp4sKaTA4lW2oM1xNUgYVlHlxRGy2m+P3aS74wev923s20Q2FiEC6q9KXerH/mOQBCqSscrlJf4vo9p2Js2KL8+0cxNZLh8fJD5W0pz7wQEQSAY0Ze9D9dxKWRKC+bdGyvJ2SqiNWE6djXRvKOe0ctLPcGunh7h+CvnOfjkLuTb7P/4MMPyTEbLA5zNvossKIuLqwOxx3im/subutYnd+xAFkUs1+X9sbENn5czDHKGwZU1ejY3g4Ci8HceeojP9fZSH77/MvI1sRCferzvXg/jtiCKIr21tfxPzzyDJAi8OjhIcQ1l6rVgex72Fs/9GBtHXVOcf/Zvf3PJtp37Wtm5r3XZsW3d9ate5/O/8diybf/gX3x5xWP/0f+63G+4a1fTKv65Hy5spPLIx8JyhpnO/HMq1il8/8biXEBFVboJ659dlq39sCKeDBMI6YiisCw4P3hlmgcf7aG2ProlgjN0dYarlyaxrfu7zcwybZwVAruqpqCq8oZLsQVBQJYlHv1EHzOT2SUl2JWyxfkTw5w/NcyDj/UQCuv3LEAvyRLxmhD7D3dy4p2BJQQ8NV/gzNFBsukSiWRoU2P0fR/Hcbl6aYKZicyS697v6K6voTEeXfy3DxQqJj85fYWacJDu+hu/d9fzSRVLfPv9c4yllmZrw7rGlw7vIRrQNnXvLMdlNJXFvUnYSldlgtrmg/zbujqu0cI811jNzM4aOU6kh3ll+hKXspOYnsNQcZ4/H/6AvF3ht3eubDKftkq8NXuF9+evYXk2uqTwK+0P8bmWg7QEEqiSjCyIiIK4QB4FNEO+L4ihKt4oT3N9D8PdWOO45dn3RSn1ViEIEIoGUDRlUYL8OjzXY/zqDDsPdRLY5Hrd93y+/x9fYXxgdQ/K7YQgCERrwki39E4aZYuJqzOYhl2N0N/hr1pjRy1Pf/lh/uRf/2DJ9nymxJm3r/CLb73Hi7/11J0dxH2MOq2FX239B3i+uyR6pIqbLw0UqGZuw5rGN8+c4fuXL2/jSNeHtGDr898/+SSPt7eT3Ab15Y+xOgQgGQzyP37iE+yqreUvzp9nNJu9b61CPsbH2BzWfjj5voHlDFI2P0CW6hCEIKKg4ePhenlsZxzDPk/FPIbjzeP7S6vgZKmZ+tj/A0mKrvIKHz5crwqIJUJkUkv77898MMgTz+2hfUf9qlnd1WBUbI6/PcDpDwbXP/geY3hglrnp5e1eidrwYq/6RiFJIs+8uJ+jb14mPV/AvUlEyjRs/uvvv0okFmTPwTY0/d5VJgVCGo89s5tzx4eXimZ5PvMzeX7wZ+/x67/9DMFVLBhXQ6Vs8Z0/fpdM+v7VnVgJLTVROuriRAIahcqN3/2PT16mZFg8u7eHnsYkjutyeWKO7xw9z9XpFPZNmiGyJNIQD/PZB/qWKDlvBJbjMjafWxJcCmkqseDm13XbSmwlQSQoawQklZgaoF6PcjjZzZn0KN8dPcHVwgxj5TTHU8N8oqGPnkj9snk4bZY4lhpeJIVHart5qn4XOyMNq/bnOr6L5d37yEhMDaAvjNF0HVLmxr7YedvAvA/GfzuQFYnG9iSjlycp3dRXYFsOx185z5FP7Sdas7Hol+/7lPIVXvnz9zj28jlyqbujKChKIu27mtBu6Rf0XI9cqsiJV85z5IX9hGJ3lnwkGmI89Oxe3vr+ccYHphcfDL7nM3F1hp/+8VuIksgzX3l4UfBkK3Adj+mROUr5CjUNMWqb7x+xorXg41Fxi1zKH6fk5tHEAO3BXlqDO9Y/+RZUJeYV9tbX87cfeojORIJvnz/PbKm0qhXQdiGmaTzS1sav7dvHgcZGYrp+3/uYftghCFWbkGQwyJf7+miLxfhxfz8fjI+TqdybfiiBqhK2dMvv2HZnyZV/TL7yC6KBTxELvIAkxjHsKxSNdzHsK7heDgQZWYyjKT2EtccIaiv3Lvq+i+1OUzLfp2ydxXam8XwDUdBR5CaC6gFC2iOo8t0TLPF9D8/LUzTfo2ydwXLGcf0iAjKKVIeu7CaiP4MqtyIIqy9XPK+CYV+hYp3HcK4tvLciICIKIVS5kYC6j6D6IJrSuc6YXBwvRcF4G8O6iO1O4XolBAFEIYIkRlDlDgLqHnRl14JX7Mq/2+2857Y7TdF4l7J5GtudwsdBEhLo6i6igReq6sHrlAb7WJj2ZVKFP0AUNBAkQAR8fN/B9yu4fgHPy3Fr640idRAPfY2A+hACW3/u3G8QBIGW9iS1DdFlxDaXKfHmy+eJ14TY+2DHht9zpWzx0+8c5/3XL1HYRps+x3Z5/43LWKbN7v1tNLYmbrukd2o8zdG3rjA+tLxcundfK+HNulkIUFMb5skX9pHLlhm5epOwq+czNZriT//9q3z2aw/z0GM7idVsTItmJZiGzdRYmvMnh2nfUU93b+OGxxsIqBx6dAdNrQmGr1pLsuqFfIU3fnqO9h31HHmql8gG13zp+QK/+MEpLpwcwax8uCp6ZElib2sDhzqaeOvy8OL2kmnzzpURLk/OEdJUfN+naFhMZQpYt2T5G2JhPn2wl2Rkc1Z5rudRMAxG5pdmbMO6Riy4eTeVO1LPKAgCqiBTp0ep06PUaxEyVhnDtRgtp5k18lwtzFSJ7S0oOybjpRuqm7uijbQFa9YUncrblfvCB7ZBjy7aDhUdg+Hi2n0Vvu9jeQ5zZoGKu70/As/zV20J9RZ7JrYH1yf7rr1tXDk5vITYuo5H/8khjv7sLE9+8SGSTWt7WlmGxeTgHO/9+DSvf+8oM+Opu2arIIoCtc0Jkk0xJodmsW6Saq8UDX70X94gkgjR9/COLVsXbQSqptDa08AX/s6z/On/54dk5wqLpdxmxWLwwjg//IPXmB6e59Azu+nsayEcDyGu08fsOi7FXIXp4TmmR+YY7Z9m/Oo0tU0Jnv7K4Q8NsS04Gc7l3iOq1BBTkni4TBujuL7D7uhDm76eAAQVhV21tcR0neZolDeHhzk5McFMqbTtvZNRTWN3bS2Pd3TwWFsb+xoa0CTpI7NQvN9x/S43RiI81dFBYzjM/sZGPhgb4+z0NNl1hKW2CzFNo7umhr319fTV19MYiSzZ73kGpn2NgvEWitRIQN2Fab5LvvwzDPsytjuH51cQEBGEAEF1H4rUtCKxdb0SZfM4ucpPFwjWFK6fx/cdBGQkMUrJPE7JPE48+AVC2mEE4c72j3qegWH3ky1/n7J5EsudwPVyeL6JgIgohlGk45TMD4gHv0RIewRZii+5xvV+0PniH1MyP8ByxnHceVy/gO9Xn6mCoCEJEYrmUULaaWKBzxDWl5cmV8dUpmJfIl38JhX7MrY7jevlF68lChqCoCOLCRSpiWT4N4kGnkMQlj8Ptuue+76DYQ+QLn2LknkC2xmvjgkPUQhQtk5Ssc4R0o7g+evojPgurl/Edkc28AndgCK1EQ1+hmjwi0hiZP0TPmTYubeZ9u56rl6aXFJx5roe544PoWoylYrFnkPtq/bbXu+tHOyf5vjb/bz/+mXGh+dX1B7ZKlzX49yJYS6dGaWlrZ+Wzlraumppbk/S1FpDIKRtqGzYcz1KRYMr5yd455WLnD0+RKm4dN4TRYEHHukmvkn1b0EQkGSJI0/vYmI0RSFbXmJ35DgeV86N49gug1em2XOonR27m0jWR5Gkla2Xrq9Xy0WTzHyRmakM0+MZJsfSTI6mmB5P8/lfP0JrR+2GxylKIjW1EZ793EG+/6fvMTOZXdznOh6zU1l+8M33yaaLHHlqF02tNcirZO3LJZORqzN88OYV3v7FBfLZ8l0THN1O7G6u59HeDi6Mz5Iu3gjI5MrGMjufWxHSVPa0NvD8vp2bDtAXDZPhuSwlw1xCW+IhnfrY5gMfd6VRr1aP0Bdr4nhqiNFyGsdzydsrR8c9/CXZ14CkoqxxkyqOxWgpzXDp3qvOXfekBchZFS7mJsnbBmFZWzF64eEzUJhhzihie+uTt1K+wtx4mny6iG07OJaLc/P/bbf6x3KwLYexgWk8d3nW6dgvzjE3kSYYDSArErIiIysSiiohLfxdViQURSZSE6Kpsx49uH7JyL7HdnL6jUtMj8wvvq7v++TTJV799vtUSiZ7H+mhoT1JOB5CUWU818My7erkN51l4toMl48PcfrN6nUAwrEggbCOWbHIp+9c9lYQBLSAyu6HdzB+dYaZ0RuG047tcunoNX7yR28yOThL+64mYskIWkBFWOjNcWwH23SwDBuzYlUtJ3SFQ0/v3jRpCUYCPP65Bxjrn+Kt758gM5tbfOBahs3VMyPMT2YYuTxB555WGtqSRGtCaEFtUcHRcarfB7NsUi4YFLNlcvMFZsbmmRlLMTk4Sz5d4uCTuzjyqQPbdh/vNCzXIG3N8GDiGUJSFMs3OZN9m5y93CB8M1AkidZolPpdu2iPxeirq+P8zAxX02km83nK9tY96SRBoCkSoSMep6+ujgeamznY2EhT5KO3SPwwIarrPNjcTHs8zu7aWs5MTXFpbo7RXI7JfJ6SbW9bYEOXZepCIRpCIRoXvgu9tbXsrq2lMx5HWsNP03anyZV/jmH3Y7tTqHI3Qe0wAjKeX8Z2Z1GkRkRh+SLA9x1K5lHSxW9RNN8BJILqARSpAUHQF8pTRzHsATL2EI6bQRKjaMqORY/S7UaVrF0mXfoLsuUf4fkVguohVLkZUQjj42A7Uxj2FbLlH+G4WQRBJaQdXpFYmfYApj2IKAYJag8iizWIQhAfF9dLUbEuYliXsZ1JfN9GV/qQxNgt99vHdmdIF79FpvQ9JClOQNmDIjdX74Pv4fplXC+H7YzheCk8vwwrZEm36577vovjzpMu/hnZ8g/xfANN6SGiPIskRvF9B8edp2Kdw/UyWM7ktn5OgqChyTsJBz5JJPAZNKV7w+c6rsuZoSkM2+GJvs5tHddG4Pk+hmXzo+OXeXpvFw3x1efa5rYkO/c0c+7EMLNT2SX7Mqkix9/uJ5cpMdQ/TVNbDfHEQomuUCVBlbJFIVdmdjrHtctTnD8xRDZVwnU9wlGdWCKEUbFIzd5+AiaXLtF/foJrl6eIRAOLpLahJUG8JkQkFiQc0dAD6sLaTsT3q2TWNG3KRZN8pszMZIaBi5NcPD2yRMEYQFVleve1sHt/G6HI1uaAxpYETz6/h2K+wgdvXFmiEGxbLpfPjjM9Xh1DV28jdY0xYvGqgJuiSCAIuI6H47hYpkOpaFDMV8imSsxN55iZzDA/k18k5JWStaiEvhFUCbjI48/t4dLZMYoFg9JNtpKe53Pl3HhVAGw0zY6+JmrrYwTD1TWWuzCu3MK9vHppkounRxf7ihO1YXRdpZAvU8x/OOwqa8IBHt7RytBsmh+fvEzZ2tiaR5Ek+lrqeWH/Tjrq4pt+3UyxwqXxmWW5uLpoiJaa2Kavt2ViW7QNMlaZoKwSUwJIwuoPZctzyNmVxfJiWZSIqiun9mVRIqLc+CFNVbLkbYPGFRJkpmtzMTfFydQw0+V77xPWHqqhI5QkIusUHIOB/AzvzV3l8boeQreQW9fzSFslfjZ5nrS1MbI2P5nh7R+e4OrZUayKjWnYWIa18PcqkbKM6t9ty1k1Y/vGd4/xpngcRZPRdBVVVxb+qKi6jKqraAvbduxv58XffBI9uL5QxI79bex+uJuxgallPqz9p0ZITee4cnKIHfvbqW2KowVUHMfFKJmkprKMDUwzdHGc6eH5xWiXFlDZ/0QvycY4QxfGufDB1Q3dq9vBw5/cx8DpETKz+SVZW9fxeOelUwycGaVjdxON7bUEowEkScR1PEzDrpLIokEpX8EsW9S2JDj09O5Nj0GSReJ1Eb7wd57FLFscf+U8qZncYubW9yEzm+edl07x/k/PUtMQpbY5QSgaRA9pVaVq08EybUq5Mtm5Avl08UNXHnMzik6OilMkZ6cRESk6OSzPxPUdXN9FE2+/RFwQBDRZ5khrK/saGriWTnN8YoLzMzNMFgrkDIOiZVG2bUzHwXZdXL/qkSkulLkqkoQmSQQUhbCqEtU0aoJB9tTV8UBTE3319dTepV5aURDYV19PZQ1SHlQUdibX9tzeTiSDQQ41NS3qEayE1mj0rpF+YaHP+ZmuLh5ta6N/fp4z09NcnJtjKp8ne9NnbjgOllNVv3f9G5UvkigiCcKCPYGIJssEZBldUQjKMkFVpSEUojORYEdNDT3JJC3RKEFlY1lR0xnCckZR5XYSwS8T1I+gSq0IgoLr5TCdIURkNGV5Ob7ljJEr/4iC8SaiGCIaeI5E8Ctoyg4kIYzrF6lYZ8iWf0Su/DPylZ+jyR3URr6BKDdyJ0QFHHeOQuV1suUf4vsOEf0T1IS+SkDdiyQm8H0Lw+4nX3mZdOlbFM23UcvNKFIdAXXv4pgEQUBAJRJ4FlXuQpZqCai7UaVWJDGG79tY7ji58o/Jln+IYV+hZB7HdK4RVA8BNzIwvu9hu1Pkyj/GxyKkPUJN6NcIqvsRxUiV2HoZbHeGinUBD4uAuq9a0nuH7rnnlyiZJ8iUvofrFwlrj5IIfZmw/hSK1IDnW1jOCLnyS2TLP8J0hte+8YKEKISRpUY8r4KPie87sGBEJSAjCCqiGEESE6hyJ2H9E4T0p1Dl9rWvfQts1+ONC4NkipV7Qmx936dkWPy7n7zHjsbkmsRW0xX6Drbz4MAMr/3kLKaxdL7Mpkscf7uf8ydHaGpN0NhSQyBYDWpbpkMxX2F+JsfUeKa69lqAqsr09DXTd6CNa5entoXYXofreGTTJbLpEhdPjwIQCuskasMkasNEooHquk6TwasGusslk2y6yPxMntRsYcWsoqLKtHbV8vlff4SGlsSme4tvxp6D7QvrEIfTH1yjkFua0MqmS2SPDnLm6CCKKpGojRAMaWgLWia2XSWPRtkkmy4tcYrYDgiCsEDA95LPlLh0ZmyZ4NPI1VlGB+dI1kVobksSqwkRCKhYlkO5ZDI7lWV2Kku5eKNaQg8oPPzULjRN5sKpEYr56W0d952CIAjsaKjhV47sJVeqcGp4kkypgrtG1UFE1+hpSvLiA7082de56SSO5/vMF8qcG1t6j3RFpjEeoSG2eTHNLRPbsXKa12cukVTD7I21EFEDaKKMLEqI1yflhezrrJHnzdkrjJRSSIJIjRZmR3hlf7GIrNMTqedkuloqczo9yt5YK3E1iC4qiIKA6/uYns1YOc23R49xIj2CJslUNijWBNWb6VNdkHq+j+HaGJ69aA7s+T6W51JxrIUFq4goCIv6xSt9eEFZ40CijQu5CU6kRkhZRf5g4HUCkkJ3uI6ArCIg4PoeObvM2cwYP5k4S8WxkARxXQGp7FyeM29d4dy7/Rt+n6vB93ysio1VWfuelfMVnvzCg9S1rk9stYDKY585RGoyw1s/OEG5sDRKlZrKkprK8v5Pzqw/QAE0XWXXQ1188e8+RzCi89M/euuuENudhzp58Jk+5sbTjF6ZXCJ+ADA7lmJ2bP3soKLK9DgdWx6HKIq07mzkq7/7adSgygc/PUN6OrfkwQnVEuO5icyyYMJm8GGogp0xxhgrD2B6FUzP5Fj6VSJyHMMrYXkW3aG1vRNvhe97mF4Z0y3hLpYtSkTlOkRBRpMFdiQ1WhONfHp3DeO5AiNpi8m8wWhunslCioxRpOKY4CvIooAgOtQF4jRHkiSDKg0RiT31rbTHdXRZQpfCizZgru9guSUMr4jve4CAKgbQpDCyoOL6Nnl7Fk0K4Xgmrm8jCQqaFEYVg9iegeHmCckJ5AXhLMczMdwioiARkKKoksRvHz7Mbx8+vNptuOvYU1/PnvrVFWzvFQSqWdUDjY0caGzE8TxmikUGMxmGMhkm83nmSiXSlQply8J0XRzPQ1wIhmiShC7LBBWF2lCIhnCYxnCYpkiE5miU5khkU31HN8NyRgmo+6gJ/Rqx4IsIwo0KGkWqRV+B0F5HwXiDsnUaH5uQ9hAN0f8WVW5e3C8SQAk8jyhEsZwxSuZRMuXvEA0+vyAwtP2FXSXrNAXzHTyvhK7upjn+P6LKzTeV4oYIS4+gyq3VcmzzbfKVXxDSHkFXdi0r2Y0HP7vwt6X3VxBkdHEnUvjrOO48hj2A55UwrEsE1QMsIba41YysXyUfIfUhAspuJLGm+rwXQBSbqr2x2qE139923XPHTS1kaktIYoya8K8SC3wGcSGIJwkyAbUPVW7Hdmdx3NnF8a8EAQ1N7iEW/JVqafRC1tnzDQQERDGIJNagyt3o6n5C2iNIa/QQf5TQs6eJpz69j/GRefovTCwjOL4PlZLJ4JVpBq+sT1QUVaZ9Rz1PPr+X/Ye7sCyXo2/d/tptLZSKBqWiwfjw1ioYVU2mrauOT3z2AE99at+67U3rQZIlDh3ZQTCsI8kip9+/Rj5bXrE827ZcZm8qB94shC1Y81zHE8/1UchVqJQshq/OLP/sFwSl5meWC2wtGYMgEAip7NrXypf+2iOkZgtMjt5eJdndhirL7G6p57/73FP8yVsnOTE4QbpYxrSrzzvf95FEAUWWCWkKe1ob+MJDfRzpaSO0BQVjw3IYS+e4PLG0dbO9Nk57bZyAuvmWmC0/sTJWiTdn+rmYm0QVJHqiDXSG6qgPRAjLGgICecdgrJTmVHqYnFXBx6cxEOdgoo2d0ZV9D+u0CE/U9fK9sZPYnstAYYY/uPo6pzMj7Iu3EpJVCpbBhdwE781dI20V6Y02ElHq+GB+Y+pztudiujZl16LsWJQck5xdYSA/g+lWv9AVx2S0mOJcdpyQrBKUNIKKRlBS0CQFGWnFRcqjdTsYL2foz8+Qtyv0F2b4Z6e+zeFkJ13hejRJJm30kea+AABDlklEQVQWuZyb5nxunKCk0RdrYbycYsZY+0fzYcDOQx288NcexyhbvPPSyS1F2AQBtKBG7wOd/L1/9XXadjZi2w5NnXUrGmpvNyRZ5IW//gS27fLDP3iNuYn0pu2KthNtvY385j/9Am07G/nxH73J6OVJPNfjdm+DIAqIooiiyqv2jtxPCMtx6vWqzUdrsAfPv+EZDZBQNkeWbM/gauFd+vNvkbbG8fEJSBE+1/zPiKkNFOxZLuffYKDwLpZXIiDFeKD1i3w+/BCT5UtMVFKkrQwlJ02j3ovru0yUr9AXe46eyH6mKlc4n/0xnvoAr8y8h+Ob9EQepy/6HLVaJxUnx5XCm5zL/gzHNxEQ6Ag9yJ7oJ2kI7CRtjfFXY/8ze+MvMFW5QtaaJKY20hd7jp3hJxgrn+VU5q94qu5v06BXPWbnzVEu5F4mItdyqOaLqCv0/n2MjUEWRVqiUVqiUZ7q2HqAarsQC3y2Wn4sbGzxcH2eLJnHsJwxNLmDsPboEoJ1M1S5hUjgGUrmB9juNKY9iCbvQJa2t/fe930M+yKGfQlZqiWiP42mrHx/JTFCPPQFStYJHC+F6QzjuPMo8q3WO2svaBWpAUVuQhRC+Di4XhYff8lZoqAgi1EkMYbrZcgbrxJQqyXLoAKrV6Xd/N5ge+551SsyT8n8AB+XkPYQmrxjkdQuvU8hguoDVKxzVJb4zi6FKOoEtIMEVhEY2yz8hcSA51UTBfiAICypxvB9H8f1qvO1X+3dFMVq+uP6ucpNXpXOQhuTKFaTCNezRT5+9Znn+4iiiCTeXAFXHQe+Xw1ACDdITvUaHrbjLiQuBCRRWNYHKEkS+x7s5Bu/+zz/+d++zLVLk9i2+/9v7z2jJEvP+77fe/O9lau6qqtzT4fJOW3OC4BYAAQIEKBIgTRFW7QoWcfSseggffGRLQfZxx/sY5HykWSCpCUGiKQAEiB2kTbv7GyamZ2ZnTzT3dM5VK662R9udc/0Tp7tXXGl+p2zZ3e7bt16662qe9/nff7P/7mv9vWSLBgcLfBzv/wwjzyznVbToX/4o6tihIjWJ7IsbahXihACRZXZvKOP575+iKee27P294+KrEhs3dVP8R98nhe+8y7f+ddvUFqp43v+R1/DiCgBIMkSpqWt9bu+VxRV4bNf2U88YfCnv/8a505N37SM705jiSV0tu0d5G//d18i351E01VSmfs3x/r3hSrLDHal+Yc/9zTnZhY5cn6SszMLLFbqOJ5P0jIYzmd4eMsQW3vzxI17dy5eZXqlwtnpBbwgQLnu89s33MvmnpsnQO/EfQe2IrKsAMAJfc5W5rhYW0BCrAV8QRjihwFOEF1QBqwsXxrYx1cHbm3wYioaO9N9/Pr4k/zexdeouE1mmmV+MH2CH8+eQiAIwhA39HEDnwe7Rvna4EFavsup0jR2cOes7T89+T2OLl2i5rbWsrYhIW7g0/CizM1sq8yfTr7N96aPr2VoJQRCwM/07uYrA/sZvYn5lSXrfKZnB0EY8DsXXqHsNql7Nq8vXODo0iVojz8IA1KqxW9sfgpNUviTibf/gwhsAbYc2EQ8E2Nway/f/9ZLLM2U7voCLCsyvSMFnv2Fh3j2rz1EKhdHVmVkRaarL0NhILuu9vXjwowb/Mw3H6V3pMD3v/US7714esNlMPdCPG3x7C88xLZDoxx94QQv/slRJs7O3PPFd5VYymTL/k0cfGYnex7byuDm4gaPeOPp0otktQJuYDPXmuTN5RdIqjn2ph/DCVt44b3JrE9XfsJs6ywj8Qd4Jv5fIISg6VdIqHmC0ON46S+xgzpPFP4zUlqRsjPDS/P/El2O4QYtvMChx9hClzHMj2d/m4fzv0xeH6bqLbLiXMUNWjT9Cmm1l68O/GOq7iJvLv0hl+pHSah5NNliyNrPcOwAmmTR8FY4Vvoel+pv0W2OE4Y+NW8JN2jxWP5XUSWT05Ufc7H6JoPWHgasXby19O12wNuDLlnUvAUW7Svsy/wsyl0GQB3+6iNQ0dVRVPnebvR+sITrzxGETRSpC00ZuOWxkjBRpGv3NMe/ih/WUNjYwNYPy7j+PH5QQVEK6OrYLY8VqKhyH6uZVc9fwA2WUbn3nrKSsJClGFGIdPPrpir3kYv/EvOV36LeOsKke4mYfpCE8SQx49Bt52/t/W3QnIe4eGEJL1gGQnRlBFm6dZsdTelFlj5ZA0DH93nr/BQvvHuWczNL+H6Apav8V195nMF8GkkIJpfK/KsfHuX1Dy7TsF0e3jrMlx/cTk8myffe/oDXTl/mf/sbX1w757944U0sTeXJXaNoisyLJy/SdFwWSjXOzSxRabR4Zs8Yv/TEPkxNpWE7fPvV4xw9N8VyrYkiSxwc6+fXno1UKrIs8fKpS3z3zVNcnFsml7B4Zs84P3t4+w3vR9MVtuzq5zf/x5/n+T97h5efP8H8TAnPu7v7rBCCWELn6S/s5ekv7GForICuq0iSoH/4/hbp16NqCj/3zYfpG8zx5ktnOP/BzEdel6iqzPj2Ph773E4OPDxGoSf9kcd5M1KZGF/4+mEOPLKZV54/wes/+YCpK4v3PX7T0hjYlGffA6McemIzw6Pd99ya53pUVebBJ7fSM5Dlxe8f54fffY9quXFXwbeiSAyPdfPUF/by5Od3kc7GkGQpqhvOxD6RhMzHxXAhQ282GW1OBQEh0caRLEnoqowif7SkSC5u8rk9m9k7vH7zb6yYo/8+6mvhIwS2W5M9/Oebn+LI4gUuVOeZa1VYses0fIdW4CMQ6JJClx6nP5ZlR7qfvZlBtiaLZPXYLfvOSkKQ1iy+3L+PHjPNq/PnOF2eZr5VoeG5aJJMQjXZlOjicG6EA9lhRhJ5zpRnGIhlOV+9c8/TFafOQqtCzbu1g6AfhlFG9yZuxWW3cUuzJ0kICkaS5/p2MxjL8eLcGU6VrzLXqmD7Hoas0m+l2Jnu57HCZnal+1loVYmrd/5Bju4a5G/9z79AvfzJtaWIpUyK93hBVnWV3pECn/3rj7Dr4XFOHbnAxfcnmTo/y/J8hWa1heu4CElCN1SS2Ti53jT9o0VGdw8yumuA4mAX2e4UCNZkYPuf3E7vSGGdfHpoWy/yx5BtlCRBPGWx59Et9Ax1MXV+jlNHzjN5bo65iUWqKzVaDQfP9VAUBT2mYZg6sZRJtjtFYSBHcaiL4e0b0zpDkiXMuMHglh7S+QT7n9rO1QvzXHx/gslzs8xPLlNerFKvNHEdFxCouoJmqMSTFqmuBLliinx/jqEtPfRsypPuSpDMJYinLNR77Dm2SqE/y1f/zmd48muH1/3dsHSyPfd3UboVslCQBZTdJT6ovs2e9GOcrb1HQEDZWSYIfQatzXd9vpnmGeJKln5rF0k1DwgsOY0sFFacqzS9MimtSNHcjCp0NGGQ0npYsaciqbIcI6kWiMlZVEknrw9R98pUvHncwAYCdMliMLabmJLDkBMk1AK2X6fmLRJTcpTdGS7X3yba8PKYbp5mwNpNGEY3kICAodh+Mno/itBIKHlWnCmaXpm4kWMkfphF5wpppxdTTlF250mqBZJqN4K7272+WDtK1V1gJH6YhHr3rpJ3ououMNl4n8u1d6l5Szxa+BXy+jDqffQbXuV89Q2W7SlGEofo0u89i7pkT3B85QdsST5KwRhFkT4dwb8kxZGEBfcoC/aC5XYv0pCm+z4zpf+V+cpv3/TYMPTwg2vlDH5wzVl4I/GDCkHQIDJrmmK+/M9Yrv3hLY4O8IN6u/0MBGGDMLz5/c/2Jmk5p7Dd8zj+LH5QIgibhKFLiIvrXcXzl1HkW5XVCBQ5Tzb2C8hSklL9u9jeZSqtH9Nw3kOpd2Oo48SNh4nrD7fNp26892zUnIehQxBUWa1/laU04ib1vKtIUgJJ+mQVGkfOTPDm2Um6Uwm+dGg7kiSoNh36cilkKTItsh2PpGXw97/8OKV6k9/7yTuM93WRMA0atstKbf3nWW3aBEGIHwT4gWCp0uDHJ87zy0/u56ndYwRBQDZhYagKlUaLP3ntfS4vrPCFg9soZhK4vk9M19BVlZbj4QchtabDk7tG+eLh7Ry7OM2Pjp3jgc2D5JPruwkIIVA1hWJfmi984xCHHhvnwgczXDo7y9UrS8zPlmnUWrSaDr4XoKgy8YRBNp+gZyDHyJYi23YP0N2bIZtPoOlKOxuqMLKlyD/9l//puveaTFvk7+E+KYRgYFOedDbG4ce3sLxQ5erEEpOXFlleqFBarlMp1WnUbBwnMhZ1XR8hCTRNRtNVYgmDbFeCQm+anr4MY9t7yRfTdBUSJFLWx6LeEkIgZIEV1xkcyfPc1w/xwBNbmbq8yKVzc0xdXmB+pkRpORq763qAQFVlNF3BNDXSuTiZfIJ8d4r+4S56B7Jk8wmSaYtUJoamKYiPIJ0WQqAbKsNj3aR+6UEOP7aF08cnOf/BNNNXllherFKv2fh+gKrKxJMm+WKSwZECm3f2M7q1h2JvhnQujmivWzVd5Uu/+CAPPbVt3WsV+zK3dZtueHNcrHyHxeYxJKEikFAkg4J5kN7YY5jK3d+n/aDFqZXfocd6iJQ+hirdWwZZlWWm6t9DERZ5cz+GcufNszAM8cMWJ5Z+i4J5gL74E7c8NmEabO7pYqR7vaLBUGXU+wya7zuwTWkWB3LD9FtZlp0aNdem6Tu4gU/Q1m4oQsKUNZKaSbeRpEtPYCl3XkgokkzBSPJ4YTMj8S4WWlVqno0fBshCQpdVMppFn5Uhq8XRZYWRRIG/veVplu06m+J5Umok1wnCACeIzD7ibVOqrw8d5onurXh34UR8MwZjOYrmrS9GqiRTMFI8lDfpszLMt6ocnZ/g+NIMI8ksz/RvRgpl3pmb5XBulIGYyq+MPMJne3aSNxL0mumbnjeetoinbzRweH9ploVmnbF0jv5YiiAM+TfnjnGo0M9oKndbk5ZVgjCk6bn8u0uneKJ3E33xjxaUaLpKV0+abCFJcbCLfU9spbxUo1m3cR2PwAtARP1vdVPDSpgks3Gy3SmS2TiycuOY0/kk6fydG8OHYUjL8/jBqXNs6e5iW/H+6vlEO7i1EgY9wwWGt/VRXqpSKzewGw6e69O0HSbLFd6YmOIbh3ZTyCQw4waxpBV9XqmNXWhE85ohV0zTP1Zk874hyks16uUGrbqNY3sEvg8IJEVCUWRUXcWIaVjtcaXzCWJJE5+QVz64zPS5Kl0JizPTi7Rclx393Tw4PkhXIka9ZXNudokj5yeptmx0VeHgpj629RU4N7vIxGKJLx3YTrIvzT//4REeHB9k92APF+aWODc7x5OZGLENbsLuhS4Nv86gNc652rHI9CWwCcJ7+z07QQNF6sVQEms1ZKtZTjdsERJEAW17sahIOrocxw1tFEJkoaJIOkJIyEJBk2LYohnJ4ViVSSsYcmLt+apkEBLg+E0cf5KT5R8yHD+AJafxQ4+KO3dDNimldqOK6HUUSUNCwQ89BBKjiQc5svSHlN056l6JmrtIv7XrngK2tBoFxdoGL4o1yaJLH8LxG7y6+P9h+6u1xPdPzV1m2Z6i37q3eupVHL/JfOsiQ7E9BLfI2v1VRAgVgXzLDeFbEQV10fv0gzJ+cDeqoNXX8OFjmKMwdNvnhiBsYnvnsb278U4QRM4d18YUSWErlBrfo946gu1dxg9WCEIbSRjteVNAyO1WOLd/P5LQ0JQB0tZXMNRtNJ2TbXnvKRr2e9juOZrOCaraS6TMz2Npe2+Qam/cnAeE113TorriW9/LBQrX1wx/EpycmMPxfJ7cNcDekV4kIWg6Hroq47Slv7mkxcHRPkZ7ctiuxx+9cpxSvUnDvrtNE0kI4obG9sFuhguZVdswZEmi3nL4yYkLfOHgVg6M9ZFPxfH9gKBdBwjRd2Rrf569m3pJWjrVps1bF6ZYqNTIJaw15eEqq+1qCj1pcoUkvYM5Nu/tYnZpmnq1RYwePM8nDAIkSYoCL0snkTbJ5hLkCkkkeX29pyQJYnGD3Yc2feQ51w0V3UiRKyQZGi0wtq2X8kqdZsOm2bhmHur7AYEf4vsBQghqpQaGFQWIZkwjljBJJE2y+QSqpty2njbwA0pLNWrlBpl8gkT6/uS1QkTBar6YJleIAtQtu/uprDSoVaMNA9fx2ko0gSRFzsWqqmBYGqalEUsYJNMW8aSJpm1s/f9qcNvdm6GrO0XPYI69D45QKTWvrV3DELn9uccSkeN1rpAkmbZuaLUkSYLegSy9A3f2qLkeP7Cpu9MokkF/7GlCAtygylT9pxhylry5H02+O3NFIRQK5n5MpYD0IW+CMAxoegs4QRlLKaLJN19fp7XxdqnG3Ttkh/iUnLMktNubzimyhCJv7Brxvr8VkhDEFJ2RRJ4RPrrE4sMIIUhpFqlbuCd/mJwe53BuhNlWmT4zg9xeqPphQN1zsH13LbB9oOvuLevvl9X52ZbqZVsKHEdiumzTqxfYmx5isVXnt+be4pe37CetmzyUv7Uc626YqpdZtht0Dyc4W1rkUmWZnbnuu14GhYQ4vs/rs1fYnSt+5MAWrrtBDOQoDHxyrqsQSeVjuob2EWUSENVwmHGdwS098CEJXLVl8+7UNEdes9n3uV30pW8deDcb9tqO8OoFMGzXGDmOFwWhd9gtdWyXVsslmbKIJU1iSZP++/zqOLbLu5dnODk1y6NbhkmaOiEhPzh+lq5EDEvT+GB6gZdOX0IIQdoycDyfl05fwg9CplcqnL46zwPjgyzXGrx1aQpdVejNJLkwt8zZ2QWe2r7xvzVZKOiSwVTjPFWvxETjDG7gktbuLdsYU7K0/AoVZxar3R/TC1soQsOUk0hCoelXqHsrmHKSll+l4s7RrY8CAt931wUaN6tHcoMWS/Yk3cYoLb9Owy+tZXiXnSnmWxd4ovtvklByzLcuId+kj6UklFuat2S0XmJKhqq7QNOvYAcN+q2d9zQPWX1jVAUfRpdjFOQRVMngyNK37zkouymCj2bS+1Gf/+8JgbivcQthINrBjt5uEaTKd7fRF9MPoHwM0tbIRThaeihSnrjxCJrSf1fPNdStaPK1a3CIR6nxFyzX/gDbvYCqFLH0fWuuyEKYSEJDCIVq6yUqjefv+BpCKGhKL5rSi6ntwHYP0nLP0nLP0XLP0HRO0HLP4fmLEIe4/hDSdQu+jZtzeV09dZQFvvXmXYh328c/Dsr1Fpoq05dNrtWsWmvqn2gsactgqJBBCIGhqcQMLWqPt9oSENbknkJwrR63jSxL9GaTZGLmDRkczw+YLVUZKmSIm1HnCaldr+tf1/plKJ8hFTOQJQlLUzFUhabt3lEeKrd7nQbxBaQ+F11OMRz/q9Eab3Utkc0nyObvHOS889IHpHIJRnfc3/W+WbeZubJIdaXOjsO3Nqu7WyRJEE+axJMmDN/4eGmphu/5ZPPJj5SJvVsuVhcICOg1M1iKhqLIdPem6e5N33CsF/i8tzJJv5Uhq8dQpcg758zyLPOtCo8VtpDSzLU45H6QhEJaH2c4+RxB6GP7JabqL1Jzp0jrm+86sJWEQrd1+KaPBaHHkn0KL6ihSLFbBrYZ4947e/z75BPpY7uKHwSU3AZLdg1Fkug2UtQ9m5bv4gQuhqyR1SKZ8tXmMkEYktXjxBWdhuew4tQBSKgmSdVkoVWh7tvEFYOUarJs1/jp/GkezI2R0xMkVIOS02C2VUKTFHrCNG7oM9MoYQcuSdXEkDUank0rcHF8l6yeIK1ZaNLGT01c1ak6NhfKy6T0a5KiK9UVynYLNwiIqxoZw2S6VmFnrsiJpVnSuoGlqHhhiCEr5Iz1wf5YKsdso8o7i9NcrZd5fvIs+7p66YslmW/WmW/W8MMARUiMpHJYisr58iKO76/VQyc1g9h12fSFZo2yYxNTVHpid86SrnK1VKblevhhyEojsgkvJGIMZFLoikLTdZmv1pgp1wjCEEWWGOvKkjQNFEnCCwLKzRaXl1Zouh5hGJKLW/SlkqRMA9f3WWk0ubS0gh9Eu7J96SSFeBxNkXF9Pzp/pRa1XLnOUc3xfOaqNUrNJiO5LDFdww8CZis1pssV9g9EO84122GyVKbcjFydE4bOSC6LqUayolKzxdRK9HgQhsxX6zedC8f2qFWbZHKR5GR2uoSiyOTyiXW1IJ7nUyk1iMWNOwa2pVKDySuLHNiAGwuA7XpIQmLnQJGDI/3UWjZ/+1/9GVNLZYrpBCen5rm8WOLvff4RhvMZVupN/o+/eJlTV+dIGDopy+D87BKzpSr7h/tYqTeZXqng+j6mqmLdh0venTAki15zEzOtCWRkSs4iOb1Il35vdXcD1m6uNk9ypf4uDb8ECPzQZTC2F0tOk9c3UXJmuFB9A0tJ0fDKKEIjpw9R91Zo+XfKxAjcoMVM8xQNf4WGt4IXOKTMbmJKlpq3REzJMNM8zZJkUXJmaAU1UuLu3kfU6kRh0NrDROMYS/YEeX2ElHp3z294ZcruHA2vhC7H6NKHMeRoJz4IfeZbFwjCAEnI7fmBhNJFRutDkTSCMKDplyk7szT9KiEhkpApGCNYcnrN1OtmBGFA1V1k2ZliU3z/2t8XW1cI8KMstWTQ9Kss2pfxAxdNtqh5y2vHhmGIG0YbBy2/Rhj6a/LwVUl1y69FsnK/iixUau4Sd4oQwzCqway4M+0a50gqqUkxiuZWJBQa/jIVdw7brxICitBJa73ElBx1bwnbr2G33a51OY4fejT9ElltiJiSQyCo+8uUnZk147BEW0KubmDmPOrnGgNkNGWIbOyrxI1HNuz894MsJdu9aBUUOUfKeo609fl7Pk8YBgRBmeXaH9B0TqEp/aSsL5K2voSuDH/IzTnE9ReoiB/d02uocgFVLhDTD+EFS9Ttd6g0n6fS+AHV1ovoyhiGOoYmXauj3ag5l4SGLBIINEIcXH+B4BYybIiyw5HE+5MjbmpUmzbz5Trd6ThCCBzPX5MhQ7QxfL051OqvT2qbTIVhSLnRImXplBs2pXoT9TrFliByar3ZxqEsS2QTFtMrFcZ6u7A0tW0kFawz91QV+UNmUdcec/w6JXeaplcixEMSCjE5R0YfIgh9yu5VJutvsWhfJK7k1ko8eqydqJKJ7VepunM0vOW2IZkgpfURV/Ioko7tV2l4K/h4bdf6CiEBWW2QhFpEuomcfSMIghCn5TJ9aQFZlTl3fJKhLT109aRYnqsgSYJcMUWt0iSZjmHENOrVFs1qi2QuzuyVRYIgJN2VIJ4y271bA2Ymlth+aATP9VmYXqHVcIglTayYTrNuR20obZdsIYnnBTRrLQI/QFEVwjCkeyDL4kwJu+lgJUwMU6PVdLCbDo7tks4l0AyVc8cnqCzX2bpviGQ2RjxlbYiZ1a2ouE38MKBg3Hm9GxCy0KrSpccIwmg93vI9LtYWeHH2A7an+ogr+g0Z3PthtVOLJCQUYbazrgLbL2P7KwShhx82sf0KEJDURrCUImHo0fQXqLlX8YIGaX0zppJHFhpB6OMEFUr2GabrLyGERIBP1b2CKRdI6aMQBrT8FWruFE5QwVK6SagDKNeZ1zl+lZo7ge1XCPGRhIqpFEiqw+0jBC1/haXWSWy/hEBE59GGkD4Gt/1VPtHAtuQ2OF2e5kJtFkPWGE8U27WuLRq+TUq12BQroMkKP5k7iUAwEi8wnigy01zhRGmKjBZjOJ5nJF7geGmCy/VFikaKLcleKm6DI4sXMGWNzckehqQuZlolzlVm6DEzBITMNEocXb5Iw7Mpmmliis6SXaXqtmj5Dr1WloPZTeTv4st9r4yncyw2G5xcnuOBYnQjbHoeP716kZl6Fdv3yBkWffEkL169zP/wwGf4vTPvsjndRW8sgR+GDCcyNwS2hqIyEE8zWSvz46kLnCkt8vOjuzAVjR9Pnee9xRnkdruiJ/tG2NvVw59dPEXL98gbUY3J1nSe3bkeQFB1ba4uVLhaK7M5nb+nwPbNK1NcXiohhGCuWsX1Aw4M9pGLWWiyzMRyidcuTfDB7AJuEBWjP7djCweH+slaJqVGk6MTV/nRmQu4XmQvvqe/h6c3j5A0dFYaTV65cIU3r0zh+T5eGHJosI/HRocZzKZxPJ8z84u8eO4Sb09M87cePUzvrmj8Lc/jxPQcL3xwjr/z+IOMdmWpOw5vXJrgp+cusaeviJBlTkzP8sblSWbKVfwgIBe3eG77Fnb3FRHAe5MzvHThMgvVGklDR5XlyJHxOnzPZ2G+zMljkxx4YBTT0rhyeTG68TsemVwcy9LQdJXlxRrlUh1VlQlietQfrdYiCEHXFQxDpdl0cB2fycuLnDwxtWGBLUBvJsHuoSKSJEhaBrlEDMf3mSvXqLZsMjGTTYVISpONW2wqZKjbLqaqkomZnJ9dZGq5zLO7xvneux9wcW4ZVZHouU3fwPuh5TcICUmoaXalHuJK4wx5vZeYkiSnF4kr96Yy2BQ/hBASl2tvMb1yCgBDTtJjbkVXYownHuFy/S0u19/GDhoYUpw96efI65GczAka6FIMRehktQFkoaJJBnElhy7FsP3G2sX7ROkv8UOX4dhBBqw9WEqanD7EpvghTpZfQBYaveY2uo0xMlofAoEq6RSM0XVZXENOtIOfaxmiHnMbZ6uvEoYhvebW2waU11N1F7hQPcKF2psk1QKP5L+JIUfvzQsc3ln+Ll5ok1ALLNtTeKFNSi3yUNdfI6HmafoVJuvHuVh7i7q3QkiAJBQe7PoGppGA2wa2HlONE7y59G02xX9r7e9nKq/ghk12pJ4hrua4VDvKmcorhGFAQs3T8Eto7RtrgMdM8ywXakcoO/P4oYspJ+m3drA99RSK0JhunuZs5VWq7iKmnEKWFPzQvaPRacWd41z1JRZaF3CCOiEhSaVIXh9BkmSW7Qku1l6n5EwRhAGqZNBr7mBX5otMN08w1zxH3VvED712eyaTmeYptqU+w0j8YfzQZbL+LlebJ7D9qI95Vh9iS/Jpcvow8gbc9IUQKHIGTRlAdt/H9Wdpueew9ENItzEWC6+bnQ3Jsn8IWUqiKX0ocg4/qNB0TpA0n0ag3Xbhujqu1TGFuJGTsHeZEJuYfpCk+QzGTcyo/KDelig3kMSt5HSr7/vGMQgho8oFUuZniOkHaTrHabnncbzLON7VNYOojZxzIVQUOY2qFHG8SWzvLJ6/TKj4N9T2hmGA403gBffX5uV+2dpX4Oj5Kd48O4GuykiSRK1ps6k7uy6YvRmKLJOJmxiqyhtnrjDWk2NmucpipU42fndKPUtXeWz7MO9dnCYTM+nPpXB9H01R6O+6u/vBon2R89WfUnKmgBBFmPRYO0hp/XhBk6uN41yuH6HqzmHICVacqGdsRh9AlUyq7jyXa28w2zzV9kYI6Da3sS31OVJaHxV3lku112l4y8hCpeRcxQ9tdqS/SEzp+tgCW9fxmL6ywBsvnCCRiVFerlGvNjl/YpLTb11G1RVGd/azslBleEsP3QNZpi8tMHlhjrGdA7zyvWMIAYPj3WzdvwndULHiBo1atOG/cHWFE0cuUC3VyXWnSGRilBaqNOotWnWbgbFuKqUGpYWo/ZSiyji2x6PP7eHdV85QWa6T702TzMaplhrUK03shk1hIEfvcBcXT11l+tIiiioztKWHWNL6yG0JK20z19W+82nVwpBVql6LLj2OJiuYsoYb+CzZNSQh8AJ/7bpkSCoJ1aDiNtmSLJLTEyhS9Pn1Wmkezo9xrjK3dnxISNVtUXNbayWaXXoCrZ3h9cKg/XsPMRWNhGJc99yAlrdM2T6HHzrU3En8sEVcG0CX05SdC8w0XsP2l5FQqLqT+KHNeOobmHIXbtBgxT7LZPUF5ppvsafr79AbexxZzhKGHg13lsnaD1lovoskVOruLKpkUrAOkdJHCPCpuVNM1l5gpvEGvbFHGE99nfh1gW3JPsNE7Xnq7iyIEFWKkzf2k0wNAyCQKNvncYIqNWeCIHRJqAPsyP5NdDnzsbUR+0QD2zOVaWpeky/07gUh+K2zP6TPyrA5UWQsUeRCbZ7nZ47Ta2XIaXG6jTTvrFxCkWQc3yWhGnx96IF2YXLAoewIOT3BRH2RuVaZbclehmNdfL53D/HrviAt32HJruH4Hj+ce5+HcmMMxbt4Y+E8by6eZyCWY3uqj63JXv7fiy8yHMt/LIFtTFGpKwpO4FF2omzfmdICS60GT/ePMpTI8PrsBN+fOENC1ag6NjnDZMVuUHcd+mJJ+m8hER5JZlls1fmf3v4Jf2PbQdK6wdnSAst2k0eKQzzSM8zZ8iL/57FX+CcPfo5lu8nmdBdfGdlOVrfww4CKbSMJwcvTl0loOnu6eni4eO8GLRMrJTYX8vz3zz2LJkv4YYgsBK7v89qlCRZrdX7zM48jC8FrF6/wl6fO0RWPkTENTkzP8cMPzvPE+Ahf3LkFSQhsz0cWAj8IOL+wxA9On+Mff/FZ4prGe1Mz/OTcxciePJsmpms8NT7C3r4e/tF3X1g3rrim8cBQP7/98hGW6g2GsmkuL5VYqDXY29+DKsu0PI9vv3eSpzeP8GsPHWSpVuflC5f5/aPv8U+KnyUk5PunzjCYTfN3n3iQSsvmd4+8e8NSqFazOXNqmh/94ATlUoPxrT00Gw4LcxXOnJwikTTZvW+I8a29HHn1HJquEk+YxBIeVy7Oc+TVcwRByMhYNyObi7z/3hXmZkqEIcjyxi42FVnCUD9UewFIbfVjEEZGHpIQhEStFQSQS1j4QcCR85NMrVQYL+bQVYWrKxUGcikGcxtrHnWpfgoncNiTfhg/9JlrTrAv8ziWcn8BtCrpjCceZjzx8E0fj6s5dqY/x8705254rN/auU7y+4W+/zZ6jpKj2xzHDVqcKH0fU0myL/tl9mW/fMM5kmqBB7t+8Zbjy+lD/Mqm/3vd30bihxmJr8qKrlvohyEZrZeiefeSoW5zjG5zDFNJsdC6sVValIX1GY7t5fHCf8JC6wp/PPGP2Jx8GFNJMlF/j0u1t8lq/Xym5++iCJW6t4Ihx5Gl+zMjWyUkpOLO8/riH/JQ1y8ymjiM7dc4svhHlN05QsD2G7w8/y32Z7/EwezPoQiNs5VXOVt5hW5jlLwxzNGlP6XP2s7h3NfR5RhnKy9zqfYW3CG0fb/05zT8FbanfoYBax8hPnZQR2tntNNaH/syX0WVTEICpprHeWnut9iaehYAL7TZlHgIgHeW/piH8n+DvDHOQuscNW+RudYZluxL7M/+PGm1Dyeo8/zMPyWu5LDkNPENNPGKGw/RdE/Tck9Tbb1I3HgMTRloy2VXryUha27BoU8oAiQM+JgW3aa6E0vbRbX5IpXmD0hbX0JTBpHQuVZHujqmEEKvrQjQrhlohWG7brZNuw45DMPrXEhDQjwa9js0ndO3NcOKghIPwgCEfN38rJ8jWcTbcmOJEL8tAV7PRs25JCWJ6Q/iejPU7XdpuWcw1M3tFkSr5wnwwwoN+20cb/JePoaPzKPbh5Ekwffe/oC/ePs0QQgJU+e/+dqTbCpk0RQZ/UMBrq4qKLKMJGDbQIGppRK//f03UGTBzqEiPZkE/bnUWsseVZHRFPmmWyxpy+BXnjzA7/70bb7147co1VuoisyDWwb59c8+gBACq+1KvIosCXRViTK4Aibrb6FJMQ7mvknB2Bz1Fw/qyEJBUVLsznwZS04z03yformd8eRT68YQV7rYnHyGHekvIguNqjvN8zP/C73WLpJq5PTa8JaZa37Asz3/NWltAD902jWLH595XbVU5/03LnDgia10D+Z44Y/e5PLpabp60uT7M8QSJj/9d2/z2HN7uXpxnuX5SIHUrNsce+0s6a44+d4MJ49eJJ62GN1+rVwgDEJef+F9tu0fZnC8yKm3LvL2T0/TN5JnfNcAQ1t6+O7vvMTs5DLjuwYpDua4cnaGRrXJmz8+hapFwersxBIXT00zMNbN2M5+xnYN8O3f/hH9m/L0jxRI5xI8+tzem3qu3CshIa/On+PNxYvYgUdM0fnKwH42J7s5uniRP796jOFYF98YPoxA8C/Pv0RatbjaXMGQVSBkPFnksz07+fOpY/zF1DF+Y8tTHMqNkFBvXXt6ZPEir8yfpelH0vdfG3uckXieb0+8xZJdI6boBITsSvfzbPGaU7cXNJmpv8rV+svta0TIePoXSKpDyO1Nsqa3QNk5z6H8PyKhDeEFLWShIUsasqQzEH+aovUAL03/l+vqa2VJJ2tsI6H9fY4t/l+YSp6B+LMktWvrfVlo5M295M29vLPwv9/UdGq68QqG3MWm5JdJa6P4oYMXtK6bc58Qnz7rMbqye1i2T/Hq7G8yGP8simGhfEztCD/RwNZtt/3RZRU38HHD6P8VSUaTFARR6yDbd4nrCXRZ4YnCNrJ6jCu1RRJq23CCkNcXzzHfqhBTdBQhRaYkd6ifihZDLqqsoAg5WqQToAiZtGqhywrh2pEbT0gkG/aCkJevXkIAV+tlTi3Pca60RFLTUSSZoXgaRZJ4be4Km9N5LlWWmWlUKVqJG7K1qyiSRFo3GUpk2J/vw1RUFpp1VEkiZ1joskxON5lpVPHCEE2S6DIs0pq5bnwT1RInl+f465v3Mp66v8XVYCbNtmIXWluGIbc3GOardSaWy7x0/hLHr0bu1a7vr8mFQ2ChVqfacnh2y8jaR7l6npVGiyvLZY5Pz/IP/uT7CMANAjRZZnPhzmMVItrh3dPfw8XFZfrTKS4trbBUb/Czu7cRhCETy2UmV8r8P68c5Q/fPtFuBQXFZCQnnilXUWSZ7kSclBldzPb293B+YX0LokTSZGhTnpHxbp77yn40TaFcbtI7kGFwuAvH9nj3rUts2zXAvkObOHViCrvlEgQh9ZqN5/r84q8+hqxIvPnqOVJpi607+liYq/D+sYn7+lzulXwyTjZucWJyjvcn59g9WGS+XOPk1Dy7BrrZNVjkykKJ6ZUKhhI52A3k0hy7Mk3K0um7T6v2W1H3qthtWagXOlysn2JH6gEsNjYz/GnjYu0IIQF5YxPWLWpk7pduY4xecxsSCjElQ1LtxvbreIHDijODJBTGEg+itG+alpLekCyfF9g0/Spu0GQ4thdDiqFLFll9ACdoEoRuJGW2p3h5/nfXMth+4BJT0pTdWQw5TtMvU9A3kdaKCARd+jAJpYvby5FDZpqn2Jp6hrwx2s6oyFjytYVB3VviQvUVlp1JQkJsv4YbNCIDszCaB0tO4wY2lpIlqw1RcieZCz0a3gplZ4bzlVeYaZxEiCgYc4I6DW8FJ6gDGxfYJs2naTqncL1Jaq3XmFj6e2RjX8XU9qHKXYQE+EEFz5/Hdi/QcN5BljLkk7+Bfhftbe4HS99PwrtC0zmJ7V7i8uKvk419DUs/jKb0IZDxgyqev4jjTVG330AIg2z868T0qI2LECqaMoAkLHzK1FuvYyijqHJP2/k4xAsWKDeeZ6Xxb2m5Z2/bA9j1Z6k2f0LVfpm4/gimtgNNGUSR0kDk5txyz7NS/zNs9xIhLpoyhKbcuPm7UXOuSl1kY1+j0vwBflBhsfqv8IMVUtaX0JUBwtCh5Z5nsfYtavbRj8XF+nboqsKj24Y5ND6A76+2AgHL0JAliV979tANaqbf/LknUORr8uRffGwfX31wV2Qm2ZYLy7KE1n78G4/uJgjCdWVFqwghiFs6v/bsIX75yQMEYYgQkZOrqasQwu/9/V8kbl773PeN9LFtoBtDVVAkif7Yfk6W/px3l/+QPmsfA9Z+MvoQd1vU3vIrXK69wXTzeHvlGFB1Z/EDl9W1pC4n6Da3ktEHEcgfqwxzlTAIcR0P3dQxzKhm1PMiQynD0EhmLJ77pYcZ2tLDj/7tUc6/P8nmPUNs2TvEe6+eJZe30HSVB5/dSc/Qjdcjt+WiKBKq1q5pbptUqboaqc+CqN+woskomoyqRdc5u2FjxaIer9v2D1MtNSgvVUlmY2j6tbUg8JEztB9mqrFC3kjw1cGDxBUDQ1aQhcSjhS0s2XX862q7JSF4sDDK89Pvsys9QNltYPseGc3iZ/v3caW2iHkXpkePF7bwUNcoJafBuytXeHPxAj1mihWnTo+Z5md6d5HSTJQPbSIqksVo6qtszfwKYehj+yucWPpnyEKhLxZtrmhykqy+g4Q2hEBGlSw+SROJHuthzpX/mLJznoJ5iIJ5cF1wDJAzdpE1tiMJFUPOYilF7KBCEN64IbhRfKKB7Ui8wMnyVf7txJuYssbB7AhVt8H7pSlOlaexZI1D2U0kVYuXFz6g5DYYtHKkNatdT3ZdQ+4wYL5VQRYSqiST0ix0SaFoZPju1LtsT/XRZSS4Ul/kneVL2IFHv5XlgdwYRxcvcITzmLLGcCy/1qf2k/hC5M04Ld/lQmUZRZIomHH64ikOFfrZnumOMpS+xzuL07w+M8GXR7azYjdZajVQpGs9gj/M6vwo7fkAyBoms40aJaeFE/iUHZucYSG3j5XEjefrMmJ8ZWQHFafFa7NX+PzgZjT53r4mmiJjqOoNsrIgDDFUhYdHhvilg9caxCuyRE8yca3ZOyG6cu01V88ThNFu60guyz/83JNrj8uSIGvdeedHCIEqSTw+NswblyboSSVYrNWRJMFYV7adjQxIGjpP797G/oHIZEESUY2Ppshr2UpZktbmz1RvnJ9VNz9FkdENFVmWoh53MZ1MJka51MBxvOgm3K6rXb2YK20redOKLpqtlksqbZFMWtSqrY9FHngzNEXm8NgAjufzuy+9jeP7yEJirDvHg+ODdKcSTC9XcD2f7f3dqLLMpnyGN85N4HgBxcyt7ezvh/BDjqiRpPTT2Rvuo+IETa423uf98vPYfp2R+GEGrb0bLu1RJQNNMqPrixBIyGtzvupCrUjXJKT39N1cO/SaBNQPXfz2DW/1s5aFihCRd+mqkVbkOx29/qOFb5LVrqtxFBqWksINWhBGdb+rcr/r//t2+KGLJFQkIV93HRPtALTB8ZXvktSKHMh+A0XSWWid58W5f7b2fFmo0VhxkJBQJLWd4VvtoRrSa+3kUO6X1s2ZqaQx5Y3dEJJEgmz86wghU6r/GS33NPOVf44sxRGsLiQ9wtAlCJsEYZO4/iB8aOHhB1Ua9tvU7aP4YZ0gaBCETRzvEhAQhE2W639E3X4LWUoiSSZCGOjKKJnYV9ZJjSVhkTSfIsRlqfotbO8KS7XfZ6X+nbZkV0S7/aFLGNr4YQVT27M+Q4uELBJkYl9juf4HON4kC9V/QanxXSQpBqGLHzbw/GUMdTNp8/O0vIu43tQtZsrHC5aoNl+mYR9HkqzIWbm9TArxCMImnr9IENZIGE+RMB5Hlbs/tjkXwsBQt5FP/AZL1d/B8SZZqv0+pcb32yZc0bz7QZWU9Rwt9wy11sv3+hW5b4QQaKqCdpN7IHBTj4W4ub5lkalLURB6C0ztNk7QIvr1WLoGN+uEJCATX782UBV5nUw6b4xzMPdNSs4US/ZF3lv5Nmmtnz2Zr92xF7gbtLhQfZmKO8vuzFcx5RRO0GCxdXHdnUkSMpoU+0QC2lViCZPRHf389DvvRHW182UGx7sxYzoXT10lX07Tt6mApEjE0xYL0ysgoHe4i1bD5o3n36e8UqN3OI+VMFier3D89XO06jb9owV2HB7hg/eucOLIBQxLY3hLD/Vqkzeef593Xz5DKhsn8IP2+joakyzL7HlkM8dfO0d5uUZxMBfVoQqBkKR1gWwqG2ducpmf/OlbbN43RP9I4bbOzXfDA10jvLt8hX9z+Q16zTRf7N+LKWvosoImKdjBtVaSAkFKtdBllaRq4AQert9EEhKGrKJIN1cRXI/jexxdusQH5ZkocdOqkNLMthRaIqvHKBjX5MzXIxAokoEhZwjDEE1KkNLHaXjz2P4KQshIKEiSct336pN1RswaO9glZ6m6E5Tsc5wt/WtS+ijjqV9YO0YRUWY2WkNIkaqmfQ/8uPhEA9uCkUQSgl4zjSrJZLU4ryx8QFZPkNPiZPQYGS2GIuToiyYrpBSThGpiKfpa71iBYGuyj7yejGpahERCNYkrBo8XtlD1WuT1BJaiMxzLk1AMQkJ6zAymrBJTo3PFFR1VigraM1oMATxT3EGPkf7Y5kCTZWKqRl8syVKrzlgqx0S1xGStzFKrQVIz6I+n2JTI8AfnjpHSDLK6ybymYyj3Ju8bTeaYqlV4d2Gai5UlbN/n50d3EVe1m37/BaArMjuyBequw/nyEi9NX+aZgbF7+rms3mw+TMo0sDSVuuOQMg16UlGmreE4aLKMJARJQ8dUFI5dnWVff2SC4/pRjYOlqaTN6LPUFYWBTBJFlrFd76539iRJYl9/D3956ixHr1zFUlVGchlMTSUIQ/KJGLIUtRIoJuOkLRM/CGi5HgLIxky8IGCl0aRuOzRdl0tLpZu6K+q6Sjxu8NMfnmR0vBu75aDr1lqQ6/sBK8s1Tp2Y5NL5OVRVxjDVyFTjuhtv/2COi+fnWJyv4Lo+iroxwYuqyHzpwLYbdtS/+ehecokY6ZiJLARP7RhlOJ+h5boossxgLk1PJoGmyIx25/j1Zx+gkIxjaAo7B7r51ScOkLYMLG1jZVYtv8H52gnqbhU3dFhxF3l54bvobbOdwdg425IHN/Q17xdZqAzFDpDRPp6MlywUMlo/W5NPISGR0wc3VL66ikCsC5av/5nF1SwVd57p5hkSah4JGSdoIAsV+TZOzhAt9JR2T85l+ypprYequ0jZnUORoucbcgJZqMy1ztNrbcMLHKruYrt2WcaSU8SUDLbfIK7kSKg5nKCJG7Qw5ASK0FBli5IzS81dRpUMKu4CDa98x/ed0QZZtq/QpQ2jG/FI0Bq0UCQDN2xRcWfJG2N0GSPYfm3NXOtWs3j9zKmShalkqHtLBKFHb1vSXveiMd5pMX2vCCGhKcNkYl9HVzZRt9+k6ZzE8WcIghoIkISJJCVR5V5MbQtx4wnkD/V7DYIGDecYK43vtPvJeu1/VoNND9u9gONNrbXZEUIhph8ibX1xXbZUCAlVLpIyfwZVLlBvvUHDeR/Hm8IJyxD6CKEjS0kUKUtMPUTceBRd2XTdOQSgk4l9DUVKUW29TMs9T8s9C/gIYaDK3STMJ0kaTyGEQtj4i1sGtrKUxtL2kDSfwHYv4gYLuEGVIHQAgSR0ZCmNoY5HGWfjcUx1501rZzdqzoWQkKUkmdiXkaV4+z2ewvYuQugjS0l0dZR07CskjSdZqf8Jdfvo/XxN/qPFDRrElByWksFSMkzW3+ZS7TV2Zb4MRJ+tJJRImRHU1j3XC23q3lJbMTOGLFQu1964IRv14evoJ4Fuaoxs70PRZAxTY3zXAKlcHEkSFAdyaKZKMhND1RR2HBphcKybRCaGlTAZ2d6HqqmoukIibWHFdKyEgRXXCYKQfG8G3VCJpUycposZ12lUW5w9NkH/SHSebD6BY3tohoIZM8h0JbCbLsXBHIal4XsBVsJAUWV8L4j6v0qCRz6/h1wx2tyLJU2CICCdjX/k7K1AMJooEFd0LtTmmaqv8ObiRR7Kj7WlxjciCwkJsfbve2Wyscx0o0RcNei3MjiB194YDpEAVcg3DWpvJMQLWzTcOQwlGxlA4UcbAuFHaxMgSzpuUGtf5+4NN6hjKT0YSg5dzjLTeI2ZxmvrAtvVgPZDb+dj5RMNbHVZpdfK0GteayGQUmP0WhkGrBxxRV/b0U2pg6w6zAkhSKjXdt0kISiaKQpGYu3xVYZiXQSESO2/J9Wol+z1bFGNtYn9cFZxa7J3o982ENXA2r5HlxFJnj8zMM6mZJaileCB7gGm61XswCOuaKQ0g4xu8nMjO+iNJTBkhWIsQcG8fQYsb1p8bnAzSS2yvc8YFjuz3cQUFTvw0SSZQ4V+TFnlid4R+q4zhRIIDEXh84NbGGxLoeOqvlbwvhHEdY2dPd28PXGV75w4HQWpIQxl02wr5slYJqNdWXb1dfPjMxc4M78AIRRTCbZ15ykk4gznMuzp6+EvTp4hZ0W7QIVEjM2FLgYyKWq2zWsXJ1isN5ipVHl7chpDVRjMpNnc3YUQglw8Rk8ywem5BXb2dLOzt9ieA8hYJg9vGmSmXOVPj52KWgMoCpu6suzsKZA0dLYXC0yXK/zxu++jKwqVVuum7zeZMtm9f4hyuYmmqQyNFLAsDcPSyAjB3oObUGSZfCGFoih05RMYhka+KCNd56bXP5jDdX0adRtFkRkevb++vB9GkSV2DRZv+Puh0fXB2EAuxcAt6mW7kjEeS15bbOaTcfLJjc3UrtJnjiIQyCIqGTiQeQJFaGsZL0O6O8ORTwJJyOT0QXL67Xu43S+yUElrPaS1e3OCvp7J+gnK7jxXG6eougucrbzCon2ZfmsH2l3UvvSYm7H9Olcbp6h5S1FGMvQZSRwio/VRtmeYbZ2j7MzgBA0u1N5kxZmmaG4mpw2QVLvIG5t4b+V7JNU8Qsi4oY0lRa2WEnKSscSDnKu+zlzrAppk0vBW0CULCQlTSbEz/RlWnKucKD2/1iM4pmTYkngUVTIZjR+m5ExzrPR9TDlJzVvCVO4k1xaMJx9nqnGMC7VXudo8AUSuyJuTT6IKnaw+RMmZ4v3S9xAIGt4yuhznbnbMFaHRa27HDZpcqL3KXOtMlMkNfYbih8lqAwiigCZhPoksZZAkC02+/++SJDQMdQRVLmCo27C9C+3MY+SiK4SOJOIocha9La+VxPrfkySZmNpuMu2a0LtFUwY+5FBM+zUVNKUHRf4MhjJOzLuA58/hBzUgQAgVScSQpRS6MoimDLVrS68/h4ShjiCLn0VXN+N4k/hBhRAXSRgoch5T3YamDBGG0XXaUDdjaXsQH+r5KokYprabLmHh+FN4QYkgqBO2gxQhVGQpGc2hthVV7mlnTT++OY+Ok9GUftLWlzDUMWz3El6wAgRIIoamDGDpe1GkLEnzM8hSBkXuQpY+nuvwf2jMNc9QdWcJ8Ns90ZvktE2I63oGJ9QChpxkpnESP3CRJZWxRHT/SWk9zLfOcbr0l8hCwwlqKJKO/DHVp98tsiKRyFjsODiydllaXe/mezNrtehCCLr7s3T3X9tUSaRjbD+4iSAI1o5JZll3DESBJyEgYP7qCpl8kpHtfeSKKWRFWre+zlzXkmh0Rz9B0Hb7/VAWdmT7tXZEyXSMMAzXrYfuF8f3OF+dY7ZZpuba1L3Ijd72Pc5UZjlbncUJPFKqecs2o34YsGTXOLY8wXSjxPHSFCEwHO/CDwLeWbrCbLPE20uXqbkthICm71D3HJKqgSQE/h1aTF17LYeF5jFOr3wLCPFDBz+0yerbMZUCdW/mts9vePMstU7Q9BZpeUvMN97C9itk9C2ktTE0OYmETE7fwUzjVSZqz7PQeo+UNkLBPIAf2Mw238T2Vyg7F5CFjoRGShsha+zEkLMsto7T9OYjvwM8gtAlqW267bg+CT7RwBZu7Mc3HM+TVI2oxva6H8GHpcc342bun0JEbezvZQyfBKOp9X1cDxT6OFCIfsA7c0V25m4MMH516wEgki9vuYtewXkzzheG15vHjKe7GE/fmMl5un+9q64kBKairnt+9hb1vLejP50iruvkYjc+V5Yk9g/0oskyb09Oc7VUIQhDisn4Wvg8nIs2IV48f5mrpQphCKam4vmRedFAJsXP7trGj85cYLZSJQhDzLaNPIAXhMzX6lSaNrt6i1iayny1TtaKxrP6sT86OowiS4zmcwzn0tFjQqAIwee3b+G1SxNcWS6xXG+SNHWGstExqizz+Pgwb16eYnKlTMYy2dnbjaWqN0iSY3GDrTv68P0Q+UMN2y1LJ5uL3Dv3HBi+Ya66i+m1/44nDHbuWQ02xYbXnXxaGIlvZyS+/c4HdrgrGn6ZqjuPKSfRJBM3tKm6C7iBja7EGLB2retxqwiN4fh+UmoBWah06cOAYKJ+nLIzS0iAKulrEmU3bFF1F7CDOpvihwBBzVvG9msE+KTVXrYmn+RS/W3K7hxprYeh2B4SShe6HMeSU+xO/wxnKi9T85ZJKDmK5jiGHMeUk6hCY1f6M1yoHWHJnqLuLaNIGpacjDY8EWxLPsHF2lFWnGkAkmqe7amnSag5JG69WBq09iMQzLXOUnZnEUgklTwg0KQYY4lHmWmeourOE1OyFI1tyEJHlXQSajeKZGLJGTTJos/a3ZZHZ+k2tqyZQ8lCY7L+HmV3miAMMOV0u6Fn9ANX5DRJ8xmS5jMb9IkLZCmBpe/G0u+9F6csJUmaT5E0n7rzwfcwJknoGNoWDG3LfZ9FVYqoyo330PXESJpPkzSfvvlIhIwiZ1Hkw8S4ed/He+ejzfn1KHKWuPzIbVsHxY2HiBsPfaTX+Y8NgaDpl7D9GkLIWHKW0cTj69zoU2ovPeYO3KBJ2Z1GkXT80MOQk/RZewmBqjuHJll0G1sZTzxNXCkAAl1OkNM3rV0XP9H3Jm6+1l0NVu+EJN0+oLz+/LGEwcBYN7GkgSRLdzz/3ciKhXTnOOBuCQipOE3mWhUkouTY9nQvshBUnAZpLTJRtYOoXeX2dC9xRWdLskiXkWhLkk0c36PsNhlN5FElmYbn0PJdvLZv0HiyGy/0qXs2Q7EcQ7EuLtUWaPouvWYaQ1bRJIVtqV56rfRNx6pIFll9OyucpupMtMuAFHpiD5E396IrGbywQVob41bBjB84NL0FWt4iXeZeZMnC9ldw/DJBGEmuJaGSN/fT9BdouHPU3asYbdVI5Mq8RNObJ64OIhC4QY2mv7SW3RVItLxlvKCOJBRMpZtu61B0bhS6zcPE1WsbsoqwKFoPYqnd68ysNhoR3qlDdYcOHTp06NChQ4cOHTp06PBXmE9W9N+hQ4cOHTp06NChQ4cOHTpsMJ3AtkOHDh06dOjQoUOHDh06fKrpBLYdOnTo0KFDhw4dOnTo0OFTTSew7dChQ4cOHTp06NChQ4cOn2o6gW2HDh06dOjQoUOHDh06dPhU0wlsO3To0KFDhw4dOnTo0KHDp5pOYNuhQ4cOHTp06NChQ4cOHT7VdALbDh06dOjQoUOHDh06dOjwqaYT2Hbo0KFDhw4dOnTo0KFDh081ncC2Q4cOHTp06NChQ4cOHTp8qukEth06dOjQoUOHDh06dOjQ4VPN/w+RXvTPp8VSuQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from wordcloud import WordCloud\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Load the training dataset\n",
+ "with open(\"data/wiki.train.tokens\", \"r\") as f:\n",
+ " train_text = f.read().split()\n",
+ "\n",
+ "# Create a string from the entire training dataset\n",
+ "text = \" \".join(train_text)\n",
+ "\n",
+ "# Generate the word cloud\n",
+ "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)\n",
+ "\n",
+ "# Plot the word cloud\n",
+ "plt.figure(figsize=(12, 8))\n",
+ "plt.imshow(wordcloud, interpolation='bilinear')\n",
+ "plt.axis('off')\n",
+ "plt.title('Word Cloud for Wikitext-2 Training Dataset')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 🟡 Clustering Words by Semantic Similarity and Visualizing Word Clouds:\n",
+ "\n",
+ "- This code clusters words from the Wikitext-2 dataset based on their semantic similarity using a BERT-based sentence transformer model, and then generates word clouds to visualize the most representative words in each semantic cluster."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sentence_transformers import SentenceTransformer\n",
+ "from sklearn.cluster import KMeans\n",
+ "from collections import defaultdict\n",
+ "from wordcloud import WordCloud\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Load the BERT-based sentence transformer model\n",
+ "model = SentenceTransformer('bert-base-nli-mean-tokens')\n",
+ "\n",
+ "# Load the training dataset\n",
+ "with open(\"data/wiki.valid.tokens\", \"r\") as f:\n",
+ " train_text = f.read().split()\n",
+ "\n",
+ "# Compute the BERT embeddings for each unique word in the dataset\n",
+ "unique_words = set(train_text)\n",
+ "word_embeddings = model.encode(list(unique_words))\n",
+ "\n",
+ "# Cluster the words using K-Means\n",
+ "num_clusters = 5\n",
+ "kmeans = KMeans(n_clusters=num_clusters, random_state=42)\n",
+ "clusters = kmeans.fit_predict(word_embeddings)\n",
+ "\n",
+ "# Group the words by cluster\n",
+ "word_clusters = defaultdict(list)\n",
+ "for i, word in enumerate(unique_words):\n",
+ " word_clusters[clusters[i]].append(word)\n",
+ "\n",
+ "# Create a word cloud for each cluster\n",
+ "fig, axes = plt.subplots(1, 5, figsize=(14, 12))\n",
+ "axes = axes.flatten()\n",
+ "\n",
+ "for cluster_id, cluster_words in word_clusters.items():\n",
+ " word_cloud = WordCloud(width=400, height=200, background_color='white').generate(' '.join(cluster_words))\n",
+ " axes[cluster_id].imshow(word_cloud, interpolation='bilinear')\n",
+ " axes[cluster_id].set_title(f\"Cluster {cluster_id}\")\n",
+ " axes[cluster_id].axis('off')\n",
+ "\n",
+ "plt.subplots_adjust(wspace=0.4, hspace=0.6)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "idRexFij4wgN"
+ },
+ "source": [
+ "## 🟠 Transform the data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2VjvBOtvHu2v"
+ },
+ "source": [
+ "🛑 Make sure to perform the transformations on train, validation and test datasets."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ApisIcGeGSsJ"
+ },
+ "source": [
+ "🔰 Reshape the dataset into an `N x B x L` or `M x L` format, where `N` represents the number of batches, `B` is the batch size, `L` is the length of a sample within each batch, and `M` is equal to `N x B`.\n",
+ "\n",
+ "The two data formats mentioned, `N x B x L` and `M x L`, are commonly used in language modeling tasks, particularly in the context of neural network-based models.\n",
+ "\n",
+ "1. `N x B x L` format:\n",
+ " - This format is often used when working with batched data for training neural network-based language models.\n",
+ " - `N` represents the number of batches. In this case, the dataset is divided into `N` smaller batches, which is a common practice to improve the efficiency and stability of the training process.\n",
+ " - `B` is the batch size, which represents the number of samples (e.g., sentences, paragraphs, or documents) within each batch.\n",
+ " - `L` is the length of a sample within each batch, which typically corresponds to the number of tokens (words) in a sample.\n",
+ " - This format allows the model to process multiple samples (batch) at once, which can significantly speed up the training process compared to processing one sample at a time.\n",
+ " - The advantage of this format is that it enables efficient batch-based training, where the model can learn from multiple samples simultaneously, leveraging the computational power of modern hardware (e.g., GPUs) to accelerate the training process.\n",
+ "\n",
+ "2. `M x L` format:\n",
+ " - This format is simpler and more straightforward compared to the `N x B x L` format.\n",
+ " - `M` is equal to `N x B`, which represents the total number of samples (e.g., sentences, paragraphs, or documents) in the dataset.\n",
+ " - `L` is the length of each sample, which corresponds to the number of tokens (words) in the sample.\n",
+ " - This format is less efficient for training neural network-based language models, as the samples are not organized into batches. However, it can be more suitable for certain tasks or when the dataset size is relatively small.\n",
+ " - The advantage of this format is that it is easier to work with and can be more intuitive for certain data processing tasks, such as simple text analysis or feature extraction.\n",
+ "\n",
+ "The choice between these two formats depends on the specific requirements of your language modeling task and the capabilities of the neural network architecture you're working with. If you're training a neural network-based language model, the `N x B x L` format is typically preferred, as it allows for efficient batch-based training and can lead to faster convergence and better performance. However, if your task doesn't involve neural networks or if the dataset is relatively small, the `M x L` format may be more suitable."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def prepare_language_model_data(raw_text_iterator, sequence_length):\n",
+ " \"\"\"\n",
+ " Prepare PyTorch tensors for a language model.\n",
+ "\n",
+ " Args:\n",
+ " raw_text_iterator (iterable): An iterator of raw text data.\n",
+ " sequence_length (int): The length of the input and target sequences.\n",
+ "\n",
+ " Returns:\n",
+ " tuple: A tuple containing two PyTorch tensors:\n",
+ " - inputs (torch.Tensor): A tensor of input sequences.\n",
+ " - targets (torch.Tensor): A tensor of target sequences.\n",
+ " \"\"\"\n",
+ " # Convert the raw text iterator into a single PyTorch tensor\n",
+ " data = torch.cat([torch.LongTensor([vocab['']] + vocab(tokenizer(line)) + [vocab['']]) for line in raw_text_iterator])\n",
+ "\n",
+ " # Calculate the number of complete sequences that can be formed\n",
+ " num_sequences = len(data) // sequence_length\n",
+ "\n",
+ " # Calculate the remainder of the data length divided by the sequence length\n",
+ " remainder = len(data) % sequence_length\n",
+ "\n",
+ " # If the remainder is 0, add a single token to the end of the data tensor\n",
+ " if remainder == 0:\n",
+ " unk_tokens = torch.LongTensor([vocab['']])\n",
+ " data = torch.cat([data, unk_tokens])\n",
+ "\n",
+ " # Extract the input and target sequences from the data tensor\n",
+ " inputs = data[:num_sequences*sequence_length].reshape(-1, sequence_length)\n",
+ " targets = data[1:num_sequences*sequence_length+1].reshape(-1, sequence_length)\n",
+ "\n",
+ " return inputs, targets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([68333, 30]), torch.Size([68333, 30]))"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "inputs, targets = prepare_language_model_data(train_iter, 30)\n",
+ "inputs.shape, targets.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([68333, 30]),\n",
+ " torch.Size([68333, 30]),\n",
+ " torch.Size([7147, 30]),\n",
+ " torch.Size([7147, 30]),\n",
+ " torch.Size([8061, 30]),\n",
+ " torch.Size([8061, 30]))"
+ ]
+ },
+ "execution_count": 69,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sequence_length = 30\n",
+ "X_train, y_train = prepare_language_model_data(train_iter, sequence_length)\n",
+ "X_valid, y_valid = prepare_language_model_data(valid_iter, sequence_length)\n",
+ "X_test, y_test = prepare_language_model_data(test_iter, sequence_length)\n",
+ "\n",
+ "X_train.shape, y_train.shape, X_valid.shape, y_valid.shape, X_test.shape, y_test.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PgLgP04P4-aX"
+ },
+ "source": [
+ "## 🟠 Custom dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XkxH_IR2PBNq"
+ },
+ "source": [
+ "🔰 Write a custom dataset class for LanguageModelDataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {
+ "id": "1cjpSkrtexap"
+ },
+ "outputs": [],
+ "source": [
+ "class LanguageModelDataset(Dataset):\n",
+ "\n",
+ " def __init__(self, inputs, targets):\n",
+ " self.inputs = inputs\n",
+ " self.targets = targets\n",
+ "\n",
+ " def __len__(self):\n",
+ " return self.inputs.shape[0]\n",
+ "\n",
+ " def __getitem__(self, idx):\n",
+ " return self.inputs[idx], self.targets[idx]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {
+ "id": "o0qUkL0CfQmr"
+ },
+ "outputs": [],
+ "source": [
+ "train_set = LanguageModelDataset(X_train, y_train)\n",
+ "valid_set = LanguageModelDataset(X_valid, y_valid)\n",
+ "test_set = LanguageModelDataset(X_test, y_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor([ 12, 3852, 3872, 884, 12, 20003, 86, 3852, 91, 3,\n",
+ " 3872, 24, 783, 3, 5, 6185, 6, 3852, 7, 4,\n",
+ " 5026, 91, 23, 5, 1840, 1021, 10, 17, 3852, 3872]),\n",
+ " tensor([ 3852, 3872, 884, 12, 20003, 86, 3852, 91, 3, 3872,\n",
+ " 24, 783, 3, 5, 6185, 6, 3852, 7, 4, 5026,\n",
+ " 91, 23, 5, 1840, 1021, 10, 17, 3852, 3872, 884]))"
+ ]
+ },
+ "execution_count": 72,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_set[0]\n",
+ "# len(train_set)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NCQjacybOfqV"
+ },
+ "source": [
+ "## 🟠 Define a dataloader if needed"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HqKMEyFNS-1a"
+ },
+ "source": [
+ "🔰 Write dataloaders for the training, validation, and test sets."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 354,
+ "metadata": {
+ "id": "KMCJ3UMD0U_f"
+ },
+ "outputs": [],
+ "source": [
+ "train_loader = DataLoader(train_set, batch_size=32, shuffle=True)\n",
+ "valid_loader = DataLoader(valid_set, batch_size=32)\n",
+ "test_loader = DataLoader(test_set, batch_size=32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([32, 30]), torch.Size([32, 30]))"
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "x_batch, y_batch = next(iter(train_loader))\n",
+ "x_batch.shape, y_batch.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 355,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Input batch shape: torch.Size([32, 30])\n",
+ "Target batch shape: torch.Size([32, 30])\n"
+ ]
+ }
+ ],
+ "source": [
+ "x_batch, y_batch = next(iter(train_loader))\n",
+ "print(f\"Input batch shape: {x_batch.shape}\")\n",
+ "print(f\"Target batch shape: {y_batch.shape}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3ttl0AK3Hvyh"
+ },
+ "source": [
+ "# 🔴 **Model**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "06p-oBowTf-R"
+ },
+ "source": [
+ "🔰 Use the following template to create a custom model.\n",
+ "\n",
+ "Your model should consist of three parts:\n",
+ "\n",
+ "* an embedding layer\n",
+ "* an LSTM layer\n",
+ "* a fully connected layer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "id": "ISnnHE0BMVqp"
+ },
+ "outputs": [],
+ "source": [
+ "class LanguageModel(nn.Module):\n",
+ "\n",
+ " def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers, dropout_rate):\n",
+ " pass\n",
+ " \n",
+ " def forward(self, src):\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torchtext.vocab.vocab.Vocab"
+ ]
+ },
+ "execution_count": 90,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(vocab)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 356,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch.nn as nn\n",
+ "from torchtext.vocab import GloVe, FastText\n",
+ "\n",
+ "\n",
+ "class LanguageModel(nn.Module):\n",
+ " def __init__(self, vocab_size, embedding_dim, \n",
+ " hidden_dim, num_layers, dropout_embd=0.5, \n",
+ " dropout_rnn=0.5, embedding_type='random'):\n",
+ " \n",
+ " super().__init__()\n",
+ " self.num_layers = num_layers\n",
+ " self.hidden_dim = hidden_dim\n",
+ " self.embedding_dim = embedding_dim\n",
+ " self.embedding_type = embedding_type\n",
+ "\n",
+ " if embedding_type == 'random':\n",
+ " self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
+ " self.embedding.weight.data.uniform_(-0.1, 0.1)\n",
+ " \n",
+ " elif embedding_type == 'glove':\n",
+ " self.glove = GloVe(name='6B', dim=embedding_dim)\n",
+ " \n",
+ " # Create an embedding matrix with the size of your vocabulary\n",
+ " self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
+ " \n",
+ " # Initialize the embedding weights with the GloVe vectors for the tokens in your vocabulary\n",
+ " for i, token in enumerate(vocab.vocab.itos_):\n",
+ " if token in self.glove.stoi:\n",
+ " self.embedding.weight.data[i] = self.glove.vectors[self.glove.stoi[token]]\n",
+ "\n",
+ " # Freeze the embedding weights to prevent them from being updated during training\n",
+ " self.embedding.weight.requires_grad = False\n",
+ " \n",
+ " elif embedding_type == 'fasttext':\n",
+ " self.fasttext = FastText(language=\"en\")\n",
+ " self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
+ " self.embedding.weight.data.copy_(self.fasttext.vectors)\n",
+ " self.embedding.weight.requires_grad = False\n",
+ " else:\n",
+ " raise ValueError(\"Invalid embedding_type. Choose from 'random', 'glove', 'fasttext'.\")\n",
+ "\n",
+ " self.dropout = nn.Dropout(p=dropout_embd)\n",
+ " self.lstm = nn.LSTM(embedding_dim, \n",
+ " hidden_dim, \n",
+ " num_layers=num_layers, \n",
+ " dropout=dropout_rnn, \n",
+ " batch_first=True)\n",
+ " self.fc = nn.Linear(hidden_dim, vocab_size)\n",
+ "\n",
+ " def forward(self, src):\n",
+ " embedding = self.dropout(self.embedding(src))\n",
+ " output, hidden = self.lstm(embedding)\n",
+ " prediction = self.fc(output)\n",
+ " return prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 357,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "LanguageModel(\n",
+ " (embedding): Embedding(23654, 300)\n",
+ " (dropout): Dropout(p=0.65, inplace=False)\n",
+ " (lstm): LSTM(300, 512, num_layers=2, batch_first=True, dropout=0.5)\n",
+ " (fc): Linear(in_features=512, out_features=23654, bias=True)\n",
+ ")"
+ ]
+ },
+ "execution_count": 357,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model = LanguageModel(vocab_size=len(vocab),\n",
+ " embedding_dim=300,\n",
+ " hidden_dim=512,\n",
+ " num_layers=2,\n",
+ " dropout_embd=0.65,\n",
+ " dropout_rnn=0.5,\n",
+ " embedding_type='glove')\n",
+ "model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 358,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 3.76832, 12.134502)"
+ ]
+ },
+ "execution_count": 358,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "num_trainable_params(model.embedding), num_trainable_params(model.lstm), num_trainable_params(model.fc)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "24qT-sgUO2-d"
+ },
+ "source": [
+ "# 🔴 **Config**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 359,
+ "metadata": {
+ "id": "Ma28M5Z36gsq"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'cpu'"
+ ]
+ },
+ "execution_count": 359,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+ "device"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bwYDJKjuduUT"
+ },
+ "source": [
+ "🔰 Define the optimizer, loss function, metrics and other necessary parameters in this section, and ensure the model is sent to the appropriate device."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 360,
+ "metadata": {
+ "id": "9ubk3xKaIG6i"
+ },
+ "outputs": [],
+ "source": [
+ "optimizer = optim.SGD(model.parameters(), lr=0.5, weight_decay=0, momentum=0.9, nesterov=True)\n",
+ "loss_fn = nn.CrossEntropyLoss()\n",
+ "metric = tm.text.Perplexity().to(device)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 323,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "clip = 0.25"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "W0QNbC0YPCKZ"
+ },
+ "source": [
+ "# 🔴 **Train ➰**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yS6EF4HUhi5e"
+ },
+ "source": [
+ "🔰 This is the template for train function, change it if needed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 361,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def train_one_epoch(model, train_loader, loss_fn, optimizer, metric, epoch=None):\n",
+ " model.train()\n",
+ " loss_train = AverageMeter()\n",
+ " metric.reset()\n",
+ "\n",
+ " with tqdm.tqdm(train_loader, unit='batch') as tepoch:\n",
+ " if epoch:\n",
+ " tepoch.set_description(f'Epoch {epoch}')\n",
+ "\n",
+ " for inputs, targets in tepoch:\n",
+ " inputs = inputs.to(device)\n",
+ " targets = targets.to(device)\n",
+ "\n",
+ " outputs = model(inputs)\n",
+ "\n",
+ " loss = loss_fn(outputs.reshape(-1, outputs.shape[-1]), targets.flatten())\n",
+ "\n",
+ " loss.backward()\n",
+ "\n",
+ " nn.utils.clip_grad_norm_(model.parameters(), max_norm=clip)\n",
+ "\n",
+ " optimizer.step()\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " loss_train.update(loss.item(), n=len(targets))\n",
+ " metric.update(outputs, targets)\n",
+ "\n",
+ " tepoch.set_postfix(loss=loss_train.avg, metric=metric.compute().item())\n",
+ "\n",
+ " return model, loss_train.avg, metric.compute().item()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "G9HgVWslPGsH"
+ },
+ "source": [
+ "# 🔴 **Evaluation**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TsszJ7GVj2l3"
+ },
+ "source": [
+ "🔰 This is the template for evaluation function, change it if needed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 376,
+ "metadata": {
+ "id": "uV0_67_ZQ0xf"
+ },
+ "outputs": [],
+ "source": [
+ "def evaluate(model, test_loader, loss_fn, metric):\n",
+ " model.eval()\n",
+ " loss_eval = AverageMeter()\n",
+ " metric.reset()\n",
+ "\n",
+ " with torch.inference_mode():\n",
+ " for inputs, targets in test_loader:\n",
+ " inputs = inputs.to(device)\n",
+ " targets = targets.to(device)\n",
+ "\n",
+ " outputs = model(inputs)\n",
+ "\n",
+ " loss = loss_fn(outputs.reshape(-1, outputs.shape[-1]), targets.flatten())\n",
+ " loss_eval.update(loss.item(), n=len(targets))\n",
+ "\n",
+ " metric.update(outputs, targets)\n",
+ "\n",
+ " return loss_eval.avg, metric.compute().item()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "o_5f69nwPtY2"
+ },
+ "source": [
+ "# 🔴 **Training Process**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "De7VreNxQdct"
+ },
+ "source": [
+ "## 🟠 Finding Hyper-parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lpJ3wtyctQJH"
+ },
+ "source": [
+ "### 🟡 **Step 1:** Calculate the loss for an untrained model using a few batches.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 377,
+ "metadata": {
+ "id": "QnE4F4GkzzaR"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor(10.0660)\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = LanguageModel(vocab_size=len(vocab),\n",
+ " embedding_dim=300,\n",
+ " hidden_dim=512,\n",
+ " num_layers=2,\n",
+ " dropout_embd=0.65,\n",
+ " dropout_rnn=0.5,\n",
+ " embedding_type='glove')\n",
+ "\n",
+ "inputs, targets = next(iter(train_set))\n",
+ "inputs, targets = inputs.to(device), targets.to(device)\n",
+ " \n",
+ "\n",
+ "with torch.no_grad():\n",
+ " outputs = model(inputs)\n",
+ " loss = loss_fn(outputs, targets)\n",
+ "\n",
+ "print(loss)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 365,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(torch.Size([30]), torch.Size([30]), torch.Size([30, 23654]))"
+ ]
+ },
+ "execution_count": 365,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "inputs.shape, targets.shape, outputs.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 366,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "23654"
+ ]
+ },
+ "execution_count": 366,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(vocab)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BrHQCv7q7LF_"
+ },
+ "source": [
+ "### 🟡 **Step 2:** Try to train and overfit the model on a small subset of the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 367,
+ "metadata": {
+ "id": "G0ji0MXsWaPt"
+ },
+ "outputs": [],
+ "source": [
+ "model = LanguageModel(vocab_size=len(vocab),\n",
+ " embedding_dim=300,\n",
+ " hidden_dim=512,\n",
+ " num_layers=2,\n",
+ " dropout_embd=0.65,\n",
+ " dropout_rnn=0.5,\n",
+ " embedding_type='glove').to(device)\n",
+ "\n",
+ "optimizer = torch.optim.SGD(model.parameters(), lr=2., momentum=0.9)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 368,
+ "metadata": {
+ "id": "kPRZQpPWJ2qv"
+ },
+ "outputs": [],
+ "source": [
+ "mini_train_size = 1000\n",
+ "_, mini_train_dataset = random_split(train_set, (len(train_set)-mini_train_size, mini_train_size))\n",
+ "mini_train_loader = DataLoader(mini_train_dataset, 20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "bNrg4d9hWaPt"
+ },
+ "outputs": [],
+ "source": [
+ "num_epochs = 100\n",
+ "for epoch in range(num_epochs):\n",
+ " model, _, _ = train_one_epoch(model, mini_train_loader, loss_fn, optimizer, metric, epoch)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BLT4w0ZfAhlJ"
+ },
+ "source": [
+ "### 🟡 **Step 3:** Train the model for a limited number of epochs, experimenting with various learning rates."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Jxz5DXoj61mg"
+ },
+ "outputs": [],
+ "source": [
+ "num_epochs = 1\n",
+ "\n",
+ "for lr in [2, 0.9, 0.5, 0.3, 0.09, 0.05]:\n",
+ " print(f'LR={lr}')\n",
+ "\n",
+ " model = LanguageModel(len(vocab), \n",
+ " embedding_dim=300,\n",
+ " hidden_dim=512, \n",
+ " num_layers=2,\n",
+ " dropout_embd=0.5, \n",
+ " dropout_rnn=0.2, \n",
+ " embedding_type='glove').to(device)\n",
+ " \n",
+ "# model = torch.load('/content/model-ppl_147.pt')\n",
+ "\n",
+ " # optimizer = optim.SGD(model.parameters(), lr=lr, weight_decay=0, momentum=0.9)\n",
+ " optimizer = optim.SGD(model.parameters(), lr=lr, weight_decay=1e-6, momentum=0.9)\n",
+ "\n",
+ " for epoch in range(num_epochs):\n",
+ " model, _, _ = train_one_epoch(model, train_loader, loss_fn, optimizer, metric, epoch)\n",
+ "\n",
+ " print()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uC2GhaXfA8vC"
+ },
+ "source": [
+ "### 🟡 Step 4: Create a small grid using the weight decay and the best learning rate.\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "a7UeNW3WWaPu"
+ },
+ "outputs": [],
+ "source": [
+ "num_epochs = 1\n",
+ "\n",
+ "for lr in [3.]:\n",
+ " for wd in [1e-6, 1e-4, 1e-5]:\n",
+ " print(f'LR={lr}, WD={wd}')\n",
+ "\n",
+ " model = LanguageModel(len(vocab), \n",
+ " embedding_dim=300,\n",
+ " hidden_dim=512, \n",
+ " num_layers=2,\n",
+ " dropout_embd=0.5, \n",
+ " dropout_rnn=0.2,\n",
+ " embedding_type='glove').to(device)\n",
+ "\n",
+ " optimizer = optim.SGD(model.parameters(), lr=lr, weight_decay=wd, momentum=0.9)\n",
+ "\n",
+ " for epoch in range(num_epochs):\n",
+ " model, _, _ = train_one_epoch(model, train_loader, loss_fn, optimizer, metric, epoch)\n",
+ "\n",
+ " print()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Mjd9Z3N1ef3I"
+ },
+ "source": [
+ "### 🟡 Step 5: Train model for longer epochs using the best model from step 4.\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "IWgkMgC6JWpU"
+ },
+ "outputs": [],
+ "source": [
+ "model ="
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "YVwLp-02JWpV"
+ },
+ "outputs": [],
+ "source": [
+ "lr =\n",
+ "wd =\n",
+ "optimizer = optim.SGD(model.parameters(), lr=lr, weight_decay=wd, momentum=0.9, nesterov=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "zqxSVVB7JWpW"
+ },
+ "outputs": [],
+ "source": [
+ "loss_train_hist = []\n",
+ "loss_valid_hist = []\n",
+ "\n",
+ "metric_train_hist = []\n",
+ "metric_valid_hist = []\n",
+ "\n",
+ "best_loss_valid = torch.inf\n",
+ "epoch_counter = 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "eVqS9SEPJWpW"
+ },
+ "outputs": [],
+ "source": [
+ "num_epochs =\n",
+ "\n",
+ "for epoch in range(num_epochs):\n",
+ " # Train\n",
+ " model, loss_train, metric_train = train_one_epoch(model,\n",
+ " train_set,\n",
+ " loss_fn,\n",
+ " optimizer,\n",
+ " metric,\n",
+ " epoch)\n",
+ " # Validation\n",
+ " loss_valid, metric_valid = evaluate(model,\n",
+ " valid_set,\n",
+ " loss_fn,\n",
+ " metric)\n",
+ "\n",
+ " loss_train_hist.append(loss_train)\n",
+ " loss_valid_hist.append(loss_valid)\n",
+ "\n",
+ " metric_train_hist.append(metric_train)\n",
+ " metric_valid_hist.append(metric_valid)\n",
+ "\n",
+ " if loss_valid < best_loss_valid:\n",
+ " torch.save(model, f'model.pt')\n",
+ " best_loss_valid = loss_valid\n",
+ " print('Model Saved!')\n",
+ "\n",
+ " print(f'Valid: Loss = {loss_valid:.4}, Metric = {metric_valid:.4}')\n",
+ " print()\n",
+ "\n",
+ " epoch_counter += 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rjGQ-M02cusP"
+ },
+ "source": [
+ "## 🟠 Main Loop"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4AdYaMU4x34g"
+ },
+ "source": [
+ "🔰 Define model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 378,
+ "metadata": {
+ "id": "JCtZXDybxexf"
+ },
+ "outputs": [],
+ "source": [
+ "model = LanguageModel(vocab_size=len(vocab),\n",
+ " embedding_dim=300,\n",
+ " hidden_dim=512,\n",
+ " num_layers=2,\n",
+ " dropout_embd=0.65,\n",
+ " dropout_rnn=0.5,\n",
+ " embedding_type='glove').to(device)\n",
+ "\n",
+ "optimizer = torch.optim.SGD(model.parameters(), lr=2., momentum=0.9)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AUKZRiQPxqrB"
+ },
+ "source": [
+ "🔰 Define optimizer and Set learning rate and weight decay."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 379,
+ "metadata": {
+ "id": "bowjVB5yIXUP"
+ },
+ "outputs": [],
+ "source": [
+ "wd = 1e-4\n",
+ "lr = 2.\n",
+ "optimizer = optim.SGD(model.parameters(), lr=lr, weight_decay=wd, momentum=0.9, nesterov=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AUyFFIzlyaiB"
+ },
+ "source": [
+ "🔰 Write code to train the model for `num_epochs` epoches."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 380,
+ "metadata": {
+ "id": "CAXagB4yvtZd"
+ },
+ "outputs": [],
+ "source": [
+ "loss_train_hist = []\n",
+ "loss_valid_hist = []\n",
+ "\n",
+ "metric_train_hist = []\n",
+ "metric_valid_hist = []\n",
+ "\n",
+ "best_loss_valid = torch.inf\n",
+ "epoch_counter = 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "PovABWnU3ld0"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 0%| | 0/68333 [00:00, ?batch/s]\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "Input tensor `preds` is expected to have 3 dimensions, [batch_size, seq_len, vocab_size], but got 2.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[381], line 11\u001b[0m\n\u001b[1;32m 1\u001b[0m num_epochs \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m30\u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_epochs):\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Train\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# model, loss_train, metric_train = train_one_epoch(model,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# metric,\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# epoch)\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m model, loss_train, metric_train \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_one_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_set\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloss_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetric\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# Validation\u001b[39;00m\n\u001b[1;32m 14\u001b[0m loss_valid, metric_valid \u001b[38;5;241m=\u001b[39m evaluate(model,\n\u001b[1;32m 15\u001b[0m valid_set,\n\u001b[1;32m 16\u001b[0m loss_fn,\n\u001b[1;32m 17\u001b[0m metric)\n",
+ "Cell \u001b[0;32mIn[361], line 26\u001b[0m, in \u001b[0;36mtrain_one_epoch\u001b[0;34m(model, train_loader, loss_fn, optimizer, metric, epoch)\u001b[0m\n\u001b[1;32m 23\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 25\u001b[0m loss_train\u001b[38;5;241m.\u001b[39mupdate(loss\u001b[38;5;241m.\u001b[39mitem(), n\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(targets))\n\u001b[0;32m---> 26\u001b[0m \u001b[43mmetric\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtargets\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m tepoch\u001b[38;5;241m.\u001b[39mset_postfix(loss\u001b[38;5;241m=\u001b[39mloss_train\u001b[38;5;241m.\u001b[39mavg, metric\u001b[38;5;241m=\u001b[39mmetric\u001b[38;5;241m.\u001b[39mcompute()\u001b[38;5;241m.\u001b[39mitem())\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model, loss_train\u001b[38;5;241m.\u001b[39mavg, metric\u001b[38;5;241m.\u001b[39mcompute()\u001b[38;5;241m.\u001b[39mitem()\n",
+ "File \u001b[0;32m~/miniconda3/envs/pytorch2/lib/python3.10/site-packages/torchmetrics/metric.py:466\u001b[0m, in \u001b[0;36mMetric._wrap_update..wrapped_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 464\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enable_grad):\n\u001b[1;32m 465\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 466\u001b[0m \u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 467\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 468\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected all tensors to be on\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(err):\n",
+ "File \u001b[0;32m~/miniconda3/envs/pytorch2/lib/python3.10/site-packages/torchmetrics/text/perplexity.py:83\u001b[0m, in \u001b[0;36mPerplexity.update\u001b[0;34m(self, preds, target)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdate\u001b[39m(\u001b[38;5;28mself\u001b[39m, preds: Tensor, target: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 82\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Update state with predictions and targets.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 83\u001b[0m total_log_probs, count \u001b[38;5;241m=\u001b[39m \u001b[43m_perplexity_update\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtotal_log_probs \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m total_log_probs\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcount \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m count\n",
+ "File \u001b[0;32m~/miniconda3/envs/pytorch2/lib/python3.10/site-packages/torchmetrics/functional/text/perplexity.py:83\u001b[0m, in \u001b[0;36m_perplexity_update\u001b[0;34m(preds, target, ignore_index)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_perplexity_update\u001b[39m(preds: Tensor, target: Tensor, ignore_index: Optional[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[Tensor, Tensor]:\n\u001b[1;32m 66\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute intermediate statistics for Perplexity.\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \n\u001b[1;32m 68\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 81\u001b[0m \n\u001b[1;32m 82\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 83\u001b[0m \u001b[43m_check_shape_and_type_consistency\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 85\u001b[0m probs \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mfunctional\u001b[38;5;241m.\u001b[39msoftmax(preds\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, preds\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]), dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 86\u001b[0m target \u001b[38;5;241m=\u001b[39m target\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+ "File \u001b[0;32m~/miniconda3/envs/pytorch2/lib/python3.10/site-packages/torchmetrics/functional/text/perplexity.py:45\u001b[0m, in \u001b[0;36m_check_shape_and_type_consistency\u001b[0;34m(preds, target)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Check shape and type consistency of input vectors.\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \n\u001b[1;32m 24\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 42\u001b[0m \n\u001b[1;32m 43\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(preds\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[0;32m---> 45\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 46\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput tensor `preds` is expected to have 3 dimensions, [batch_size, seq_len, vocab_size],\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(preds\u001b[38;5;241m.\u001b[39mshape)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 48\u001b[0m )\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(target\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput tensor `target` is expected to have 2 dimensions, [batch_size, seq_len],\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(target\u001b[38;5;241m.\u001b[39mshape)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 53\u001b[0m )\n",
+ "\u001b[0;31mValueError\u001b[0m: Input tensor `preds` is expected to have 3 dimensions, [batch_size, seq_len, vocab_size], but got 2."
+ ]
+ }
+ ],
+ "source": [
+ "num_epochs = 30\n",
+ "\n",
+ "for epoch in range(num_epochs):\n",
+ " # Train\n",
+ " model, loss_train, metric_train = train_one_epoch(model, train_loader, loss_fn, optimizer, metric, epoch)\n",
+ "\n",
+ " # Validation\n",
+ " loss_valid, metric_valid = evaluate(model,\n",
+ " valid_loader,\n",
+ " loss_fn,\n",
+ " metric)\n",
+ "\n",
+ " loss_train_hist.append(loss_train)\n",
+ " loss_valid_hist.append(loss_valid)\n",
+ "\n",
+ " metric_train_hist.append(metric_train)\n",
+ " metric_valid_hist.append(metric_valid)\n",
+ "\n",
+ " if loss_valid < best_loss_valid:\n",
+ " torch.save(model, f'model.pt')\n",
+ " best_loss_valid = loss_valid\n",
+ " print('Model Saved!')\n",
+ "\n",
+ " print(f'Valid: Loss = {loss_valid:.4}, Metric = {metric_valid:.4}')\n",
+ " print()\n",
+ "\n",
+ " epoch_counter += 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oK20iNRI3Xxb"
+ },
+ "source": [
+ "## 🟠 Plot"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IKlLvCwuzEAA"
+ },
+ "source": [
+ "🔰 Plot learning curves"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "KYFzTsdIOkVp"
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(8, 6))\n",
+ "\n",
+ "plt.plot(range(epoch_counter), loss_train_hist, 'r-', label='Train')\n",
+ "plt.plot(range(epoch_counter), loss_valid_hist, 'b-', label='Validation')\n",
+ "\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('loss')\n",
+ "plt.grid(True)\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KZ9UIdmkfxlA"
+ },
+ "source": [
+ "# 🔴 **Test**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SO8iPWH1zVYn"
+ },
+ "source": [
+ "🔰 Test your model using data from the test set"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "35sn67IhKcm_"
+ },
+ "outputs": [],
+ "source": [
+ "loss_test, metric_test = evaluate(model,\n",
+ " test_loader,\n",
+ " loss_fn,\n",
+ " metric)\n",
+ "loss_test, metric_test"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FzcQQwFuar_7"
+ },
+ "source": [
+ "# 🔴 **Generate**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jh2_9jUp0GF4"
+ },
+ "source": [
+ "🔰 Your mission is to write a `generate` function and use a desired sentence to evaluate the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "pskvb--R-wJ0"
+ },
+ "outputs": [],
+ "source": [
+ "model_path = 'model.pt'\n",
+ "model = torch.load(model_path)\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "f5SvSDLal8YB"
+ },
+ "outputs": [],
+ "source": [
+ "def generate(prompt, max_seq_len, temperature, model, tokenizer, vocab, seed=None):\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "pVedneOVD6ul"
+ },
+ "outputs": [],
+ "source": [
+ "def generate(input_text):\n",
+ " my_sample = torch.cat(torch.LongTensor([vocab['']] + vocab(tokenizer(input_text)) + [vocab['']])).to(device)\n",
+ " prediction = model(my_sample.unsqueeze(0))\n",
+ " prediction = prediction.argmax(dim=2)\n",
+ "\n",
+ " generated_tokens = []\n",
+ " generated_tokens.append(tokenizer(input_text[0])[0]) # Get the first token of the first input line\n",
+ " for token, index in list(vocab.get_stoi().items()):\n",
+ " if index in prediction:\n",
+ " generated_tokens.append(token)\n",
+ "\n",
+ " generated_sentense = ' '.join(generated_tokens)\n",
+ " print(generated_sentense)\n",
+ " \n",
+ "sample = \"What is your\"\n",
+ "generate(sample)"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [
+ "w_a3OXnSeV0z",
+ "RwaY_YcgRayy",
+ "RTql4Ftiunfr",
+ "ujIVtjsYvxOI",
+ "wCi-ofSLCzop",
+ "B29jrEvwRqXA",
+ "A3rnR739GbYb",
+ "a4HyLPqcsF43",
+ "cfBasjQCE_aI",
+ "idRexFij4wgN",
+ "PgLgP04P4-aX",
+ "NCQjacybOfqV",
+ "3ttl0AK3Hvyh",
+ "24qT-sgUO2-d",
+ "W0QNbC0YPCKZ",
+ "G9HgVWslPGsH",
+ "o_5f69nwPtY2",
+ "De7VreNxQdct",
+ "lpJ3wtyctQJH",
+ "BrHQCv7q7LF_",
+ "BLT4w0ZfAhlJ",
+ "uC2GhaXfA8vC",
+ "Mjd9Z3N1ef3I",
+ "rjGQ-M02cusP",
+ "oK20iNRI3Xxb",
+ "KZ9UIdmkfxlA",
+ "FzcQQwFuar_7"
+ ],
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "pytorch23",
+ "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": "3.9.19"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Readme.md b/Readme.md
index 91844e5..ce2977f 100644
--- a/Readme.md
+++ b/Readme.md
@@ -4,43 +4,91 @@
This project is a step-by-step guide on building a language model using PyTorch. It aims to provide a comprehensive understanding of the process involved in developing a language model and its applications.
# Step 1: Accurate and concise definition of the problem
-Language modelling involves developing models that can effectively understand and generate human-like text based on input data, enabling tasks such as machine translation, text generation, and sentiment analysis.
-There are several common types of language modeling techniques, including:
+Language modeling, or LM, is the use of various statistical and probabilistic techniques to determine the probability of a given sequence of words occurring in a sentence.
+Language models analyze bodies of text data to provide a basis for their word predictions.
-- **N-gram Language Models**: These models predict the next word based on the context of the previous N-1 words.
-They are relatively simple but suffer from data sparsity issues.
+Language modeling is used in artificial intelligence (AI), natural language processing (NLP), natural language understanding (NLU), and natural language generation(NLG) systems, particularly ones that perform text generation, machine translation and question answering.
-- **Neural Language Models**: These models leverage neural networks, such as recurrent neural networks (RNNs) or transformer models, to capture complex dependencies and contextual information in the text, resulting in improved performance.
+
+
+Large language models (LLMs) also use language modeling. These are advanced language models, such as OpenAI's GPT-3 and Google's Palm 2, that handle billions of training data parameters and generate text output.
The effectiveness of a language model is typically evaluated using metrics like **cross-entropy** and **perplexity**, which measure the model's ability to predict the next word accurately (I will cover them in **Step 2**). Several datasets, such as WikiText-2, WikiText-103, One Billion Word, Text8, and C4, among others, are commonly used for evaluating language models.
-**Note**: In this project, I use WikiText-2
+**Note**: In this project, I use WikiText-2.
+
+# Step 2: Advancements and types of Language Models:
+
+## Different types of language models:
+The research of LM has received extensive attention in the literature, which can be divided into four major development stages:
+
+
+ 1. Statistical language models (SLM)
+
+SLMs are developed based on statistical learning methods that rose in the 1990s. The basic idea is to build the word prediction model based on the **Markov assumption**, e.g., predicting the next word based on the most recent context. The SLMs with a fixed context length **n** are also called **n-gram language models**, e.g., bigram and trigram language models. SLMs have been widely applied to enhance task performance in information retrieval (IR) and natural language processing (NLP). However, they often suffer from the curse of dimensionality:
+it is difficult to accurately estimate high-order language models since an exponential number of transition probabilities need to be estimated.
+Thus, specially designed smoothing strategies such as back-off estimation and Good–Turing estimation have been introduced to alleviate the data sparsity problem.
+
+
+
+ 2. Neural language models (NLM)
+
+NLMs characterize the probability of word sequences by neural networks, e.g., multi-layer perceptron (MLP) and recurrent neural networks (RNNs).
+As a remarkable contribution, is the concept of **distributed representation**. Distributed representations, also known as **embeddings**, the idea is that the "meaning" or "semantic content" of a data point is distributed across multiple dimensions. For example, in NLP, words with similar meanings are mapped to points in the vector space that are close to each other. This closeness is not arbitrary but is learned from the context in which words appear. This context-dependent learning is often achieved through neural network models, such as **Word2Vec** or **GloVe**, which process large corpora of text to learn these representations.
+
+One of the key advantages of distributed representations is their ability to capture fine-grained semantic relationships. For instance, in a well-trained word embedding space, synonyms would be represented by vectors that are close together, and it's even possible to perform arithmetic operations with these vectors that correspond to meaningful semantic operations (e.g., "king" - "man" + "woman" might result in a vector close to "queen").
-## The goal of solving a problem or challenge
-The goal of solving a problem or challenge in language modelling with AI is to develop models that can effectively understand, generate, and manipulate human language, enabling various applications such as natural language processing, machine translation, text summarization, sentiment analysis, and more. The aim is to enhance communication and interaction between humans and machines, enabling more efficient and intelligent language-based tasks.
+**Applications of Distributed Representations:**
+Distributed representations have a wide range of applications, particularly in tasks that involve natural language understanding. They are used for:
-# Step 2: Advancements in Language Modelling and different approaches:
+**Word Similarity**: Measuring the semantic similarity between words.
+**Text Classification**: Categorizing documents into predefined classes.
+**Machine Translation**: Translating text from one language to another.
+**Information Retrieval**: Finding relevant documents in response to a query.
+**Sentiment Analysis**: Determining the sentiment expressed in a piece of text.
-## Different Language model training approaches:
-Language model training approaches can be broadly categorized into three main types: **causal language models**, **masked language models**, and **sequence-to-sequence models**. Each approach has its own characteristics and training methodologies. Let's explore each of them in more detail:
+Moreover, distributed representations are not limited to text data. They can also be applied to other types of data, such as images, where deep learning models learn to represent images as high-dimensional vectors that capture visual features and semantics.
+
-1. **Causal Language Models (e.g., GPT-3)**:
- Causal language models, also known as **autoregressive models**, generate text by predicting the next word in a sequence given the previous words. These models are trained to maximize the likelihood of the next word using techniques like the transformer architecture. During training, the input to the model is the entire sequence up to a given token, and the model's goal is to predict the next token. This type of model is useful for tasks such as **text generation**, **completion**, and **summarization**.
+## Different training approaches of Language model:
-2. **Masked Language Models (e.g., BERT)**:
+
+ 1. Causal Language Models (e.g., GPT-3)
+
+Causal language models, also known as **autoregressive models**, generate text by predicting the next word in a sequence given the previous words. These models are trained to maximize the likelihood of the next word using techniques like the transformer architecture. During training, the input to the model is the entire sequence up to a given token, and the model's goal is to predict the next token. This type of model is useful for tasks such as **text generation**, **completion**, and **summarization**.
+
+
+
+ 2. Masked Language Models (e.g., BERT)
+
Masked language models (MLMs) are designed to learn contextual representations of words by predicting **masked or missing words** in a sentence. During training, a portion of the input sequence is randomly masked, and the model is trained to predict the original words given the context. MLMs use bidirectional architectures like transformers to capture the dependencies between the masked words and the rest of the sentence. These models excel in tasks such as **text classification**, **named entity recognition**, and **question answering**.
+
+
+
+ 3. Sequence-to-Sequence Models (e.g., T5)
+
+Sequence-to-sequence (Seq2Seq) models are trained to map an input sequence to an output sequence. They consist of **an encoder** that processes the input sequence and **a decoder** that generates the output sequence. Seq2Seq models are widely used in tasks such as **machine translation**, **text summarization**, and **dialogue systems**. They can be trained using techniques like recurrent neural networks (RNNs) or transformers. The training objective is to maximize the likelihood of generating the correct output sequence given the input.
+
-3. **Sequence-to-Sequence Models (e.g., T5)**:
- Sequence-to-sequence (Seq2Seq) models are trained to map an input sequence to an output sequence. They consist of **an encoder** that processes the input sequence and **a decoder** that generates the output sequence. Seq2Seq models are widely used in tasks such as **machine translation**, **text summarization**, and **dialogue systems**. They can be trained using techniques like recurrent neural networks (RNNs) or transformers. The training objective is to maximize the likelihood of generating the correct output sequence given the input.
+
+ What's the difference between Causal Language Modeling and Masked Language Modeling?
+
+- Given a sequence of tokens, Causal Language Modeling is the task of generating the next token. It differs from Masked Language Modeling, where certain words in a sentence are masked, and the model is trained to predict them.
+- In Causal Language Modeling, the model only considers words to the left, while Masked Language Modeling considers words to the left and right.
+- Therefore, Causal Language Modeling is unidirectional, while Masked Language Modeling is bidirectional.
+- GPT is an example of a pre-trained Causal Language Model, while BERT is an example of a Masked Language Model.
+
+
It's important to note that these training approaches are **not mutually exclusive**, and researchers often combine them or employ variations to achieve specific goals. For example, models like T5 combine the autoregressive and masked language model training objectives to learn a diverse range of tasks.
-Each training approach has its own strengths and weaknesses, and the choice of the model depends on the specific task requirements and available training data. Researchers and practitioners often experiment with different architectures and training methodologies to improve the performance of language models and adapt them to various natural language processing tasks.
+Each training approach has its own strengths and weaknesses, and the choice of the model depends on the specific task requirements and available training data.
For more information, please refer to the [A Guide to Language Model Training Approaches](https://medium.com/@tom_21755/understanding-causal-llms-masked-llm-s-and-seq2seq-a-guide-to-language-model-training-d4457bbd07fa#:~:text=CLM%20models%20focus%20on%20predicting,good%20for%20tasks%20requiring%20the) chapter in the "medium.com" website.
## Different Types of Models for Language Modeling
-Language modeling involves building models that can generate or predict sequences of words or characters. Here are some different types of models commonly used for language modeling:
+Language modeling involves building models that can generate or predict sequences of words or characters.
+Here are some different types of models commonly used for language modeling:
1. N-gram Language Models
@@ -49,10 +97,10 @@ N-gram language models are a traditional approach to language modeling that rely
In an N-gram model, the probability of a word is estimated based on its occurrence in the training data relative to its preceding N-1 words. For example, in a trigram model (N=3), the probability of a word is determined by the two words that immediately precede it. This approach assumes that the probability of a word depends only on a fixed number of preceding words and does not consider long-range dependencies.
Here are some examples of n-grams:
-- Unigram: "This", "article", "is", "on", "NLP"
-- Bigram: "This article", "article is", "is on", "on NLP"
-- Trigram: "Please turn your", "turn your homework"
-- 4-gram: "What is N-gram method"
+- **Unigram**: "This", "article", "is", "on", "NLP"
+- **Bigram**: "This article", "article is", "is on", "on NLP"
+- **Trigram**: "Please turn your", "turn your homework"
+- **4-gram**: "What is N-gram method"

@@ -60,20 +108,20 @@ Here are the advantages and disadvantages of N-gram language models:
**Advantages**:
- 1. Simplicity: N-gram models are relatively simple to implement and understand. They have a straightforward probabilistic framework that can be easily computed and interpreted.
- 2. Efficiency: N-gram models are computationally efficient compared to more complex models. They require minimal memory and processing power, making them suitable for resource-constrained environments.
- 3. Robustness: N-gram models can handle out-of-vocabulary words and noisy data reasonably well. They can still provide reasonable predictions based on the available n-gram statistics, even if they encounter unseen words.
+ 1. **Simplicity**: They have a straightforward probabilistic framework that can be easily computed and interpreted.
+ 2. **Efficiency**: N-gram models are computationally efficient compared to more complex models. They require minimal memory and processing power, making them suitable for resource-constrained environments.
+ 3. **Robustness**: N-gram models can handle out-of-vocabulary words and noisy data reasonably well. They can still provide reasonable predictions based on the available n-gram statistics, even if they encounter unseen words.
**Disadvantages**:
- 1. Lack of Contextual Understanding: N-gram models have limited contextual understanding since they consider only a fixed number of preceding words. They cannot capture long-range dependencies or understand the broader context of a sentence.
- 2. Data Sparsity: N-gram models suffer from data sparsity issues, especially when the vocabulary size is large. As the n-gram order increases, the number of unique n-grams decreases exponentially, leading to sparse data and difficulties in accurately estimating probabilities.
- 3. Limited Generalization: N-gram models often struggle with generalization to unseen or rare word combinations. They may assign low probabilities to valid but infrequent word sequences, leading to suboptimal predictions in such cases.
- 4. Lack of Linguistic Understanding: N-gram models do not incorporate linguistic knowledge explicitly. They cannot capture syntactic or semantic relationships between words, limiting their ability to generate coherent and contextually appropriate language.
+ 1. **Lack of Contextual Understanding**: N-gram models have limited contextual understanding since they consider only a fixed number of preceding words. They cannot capture long-range dependencies or understand the broader context of a sentence.
+ 2. **Data Sparsity**: N-gram models suffer from data sparsity issues, especially when the vocabulary size is large. As the n-gram order increases, the number of unique n-grams decreases exponentially, leading to sparse data and difficulties in accurately estimating probabilities.
+ 3. **Limited Generalization**: N-gram models often struggle with generalization to unseen or rare word combinations. They may assign low probabilities to valid but infrequent word sequences, leading to suboptimal predictions in such cases.
+ 4. **Lack of Linguistic Understanding**: N-gram models do not incorporate linguistic knowledge explicitly. They cannot capture syntactic or semantic relationships between words, limiting their ability to generate coherent and contextually appropriate language.
- Here's an example of using n-grams in Torchtext:
+Here's an example of using n-grams in Torchtext:
-```
+```python
import torchtext
from torchtext.data import get_tokenizer
from torchtext.data.utils import ngrams_iterator
@@ -90,6 +138,9 @@ print(list(ngrams_iterator(tokens, 3)))
['i', 'love', 'to', 'code', 'in', 'python', 'i love', 'love to', 'to code', 'code in', 'in python', 'i love to', 'love to code', 'to code in', 'code in python']
```
+**Note**:
+- The n-gram model, typically using trigram, 4-gram, or 5-gram
+- N-gram model inadequate for language modeling due to the presence of long-range dependencies in language.
@@ -113,7 +164,8 @@ RNNs are the fundamental type of neural network for sequential data processing.

PyTorch code snippet for defining a basic RNN in PyTorch:
-```
+
+```python
import torch
import torch.nn as nn
@@ -141,7 +193,7 @@ print(hn.shape) # torch.Size([2, 3, 20])
- 3. Long Short-Term Memory (LSTM):
+ 3. Long Short-Term Memory (LSTM)
LSTM is an extension of the RNN architecture that addresses the vanishing gradient problem. It introduces memory cells and gating mechanisms to selectively retain or forget information over time. LSTMs have proven effective in capturing long-term dependencies and maintaining contextual information.
**Advantages of LSTMs**:
@@ -165,7 +217,8 @@ LSTM is an extension of the RNN architecture that addresses the vanishing gradie
- **Output Gate**: Controls output flow, determines output selection.
PyTorch code snippet for defining a basic LSTM in PyTorch:
-```
+
+```python
import torch
import torch.nn as nn
@@ -192,7 +245,7 @@ For more information, please refer to the [Long Short-Term Memory (LSTM)](https:
- 4. Gated Recurrent Unit (GRU):
+ 4. Gated Recurrent Unit (GRU)
GRU is another variation of the RNN architecture that aims to simplify the LSTM model. It combines the forget and input gates of the LSTM into a single update gate and merges the cell state and hidden state. GRUs have similar capabilities to LSTMs but with fewer parameters, making them computationally more efficient.
**Advantages of GRUs**:
@@ -209,7 +262,7 @@ GRU is another variation of the RNN architecture that aims to simplify the LSTM
Overall, LSTM and GRU models overcome some of the limitations of traditional RNNs, particularly in capturing long-term dependencies. LSTMs excel in preserving contextual information, while GRUs offer a more computationally efficient alternative. The choice between LSTM and GRU depends on the specific requirements of the task and the available computational resources.
-```
+```python
import torch
import torch.nn as nn
@@ -274,81 +327,21 @@ Despite these limitations, transformer models have revolutionized the field of n
## Evaluating language model
+Perplexity, in the context of language modeling, is a measure that quantifies how well a language model predicts a given test set, with lower perplexity indicating better predictive performance. In simpler terms, perplexity is calculated by taking the inverse probability of the test set and then normalizing it by the number of words.
-Two commonly used metrics for evaluating language models are **cross-entropy** and **perplexity**.
-
-Cross-entropy is a measure of how well the language model predicts the next word in a sequence. It quantifies the difference between the predicted probability distribution and the true distribution of the next word. Lower cross-entropy values indicate better predictions. The formula for cross-entropy is:
-
-- **Cross-entropy = -Σ log(p(x))**
-
-where p(x) is the predicted probability of the true next word x.
-
-Perplexity is derived from cross-entropy and provides an alternative way to measure the performance of a language model. It represents the average uncertainty or surprise of the model when predicting the next word. Perplexity is calculated as the exponentiation of the cross-entropy value. Lower perplexity values indicate better language models. The formula for perplexity is:
-
-- **Perplexity = exp(Cross-entropy)**
-
-A lower perplexity value indicates that the model is more confident and accurate in predicting the next word.
-
-Here's a simple example of how to use the cross-entropy loss function in `torch.nn`:
-
-```
-import torch
-import torch.nn as nn
-
-# Define some dummy data
-# logits are raw outputs from a model and typically haven't had a softmax applied
-logits = torch.tensor([[0.2, 0.5, 0.3], [0.1, 0.2, 0.7]])
-targets = torch.tensor([1, 2]) # ground truth labels
-
-# Initialize the CrossEntropyLoss
-loss_function = nn.CrossEntropyLoss()
-
-# Calculate the loss
-loss = loss_function(logits, targets)
-print(f"Cross Entropy Loss: {loss.item()}")
-```
+The lower the perplexity value, the better the language model is at predicting the test set.
+**Minimizing perplexity is the same as maximizing probability**
-Here's a simple example of how to use the Perplexity metric from `torchmetrics`:
-```
-import torch
-import torchmetrics
+The formula for perplexity as the inverse probability of the test set, normalized by the number of words, is as follows:
-# Define some dummy data
-logits = torch.tensor([[0.2, 0.5, 0.3], [0.1, 0.2, 0.7]])
-targets = torch.tensor([1, 2])
+
+
-# Calculate the loss using CrossEntropyLoss since it's often used with perplexity
-loss_function = torch.nn.CrossEntropyLoss(reduction='none')
-losses = loss_function(logits, targets)
+### Interpreting perplexity as a branching factor
+Perplexity can be interpreted as a measure of the branching factor in a language model.
+The branching factor represents the average number of possible next words or tokens given a particular context or sequence of words.
-# Initialize the Perplexity metric
-perplexity = torchmetrics.Perplexity()
-
-# Update the state with losses and compute the perplexity
-perplexity.update(losses)
-final_perplexity = perplexity.compute()
-print(f"Perplexity: {final_perplexity.item()}")
-```
-
-Let's illustrate these metrics with an example. Suppose we have a language model trained on a large corpus of text, and we want to evaluate its performance using cross-entropy and perplexity. We provide a test set of sentences, and for each sentence, we calculate the cross-entropy and perplexity as follows:
-
-1. **Sentence: "The cat is sitting on the ..."**
-
- The language model predicts the next word as "mat" with a probability of 0.8, "chair" with a probability of 0.1, and "table" with a probability of 0.1.
-
- Cross-entropy = -log(0.8) = 0.223
- Perplexity = exp(0.223) = 1.250
-
-2. **Sentence: "I want to go to the ..."**
-
- The language model predicts the next word as "park" with a probability of 0.6, "store" with a probability of 0.3, and "school" with a probability of 0.1.
-
- Cross-entropy = -log(0.6) = 0.511
- Perplexity = exp(0.511) = 1.668
-
-By calculating the cross-entropy and perplexity for multiple sentences in the test set and taking their average, we can obtain a quantitative measure of the language model's performance.
-
-In summary, cross-entropy and perplexity are widely used metrics to evaluate the effectiveness of language models. These metrics provide insights into the model's ability to predict the next word accurately, with lower values indicating better performance.
+The Branching factor of a language is the number of possible next words that can follow any word. We can think of perplexity as the weighted average branching factor of a language.
# Step 3: Choose the appropriate method: Language Modeling with Embedding Layer and LSTM
@@ -383,7 +376,7 @@ The **WikiText-2** dataset is a small version of the **WikiText-103** dataset as

-### 1. Prepare and preprocess data
+### Prepare and preprocess data
This repository contains code for performing exploratory data analysis on the UTK dataset, which consists of images categorized by age, gender, and ethnicity.
@@ -391,338 +384,718 @@ This repository contains code for performing exploratory data analysis on the UT
1. [Download WikiText-2 dataset](#Download-WikiText-2-dataset)
2. [Tokenize data and build a vocabulary](#Tokenize-data-and-build-a-vocabulary)
-3. [Plot Histograms for Age, Gender, and Ethnicity](#plot-histograms-for-age-gender-and-ethnicity)
-4. [Calculate Cross-Tabulation of Gender and Ethnicity](#calculate-cross-tabulation-of-gender-and-ethnicity)
-5. [Create Violin Plots and Box Plots for Age (Separated by Gender)](#create-violin-plots-and-box-plots-for-age-separated-by-gender)
-6. [Create Violin Plots and Box Plots for Age (Separated by Ethnicity)](#create-violin-plots-and-box-plots-for-age-separated-by-ethnicity)
- 1. Download WikiText-2 dataset 1. Download WikiText-2 dataset
-To download a dataset using Torchtext, you can use the `torchtext.datasets` module in Python.
+To download a dataset using Torchtext, you can use the `torchtext.datasets` module.
Here's an example of how to download the Wikitext-2 dataset using Torchtext:
-```
+```python
import torchtext
from torchtext.datasets import WikiText2
-data_path = "path/to/save/dataset"
-train_dataset, valid_dataset, test_dataset = WikiText2(root=data_path)
+data_path = "data"
+train_iter, valid_iter, test_iter = WikiText2(root=data_path)
```
-
-
- 2. Tokenize data and build a vocabulary
+Initially, I tried to use the provided code to load the WikiText-2 dataset, but encountered an issue with the URL (https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip) not working for me. To overcome this, I decided to leverage the `torchtext` library and create a custom implementation of the dataset loader.
-To build a vocabulary and save it in PyTorch using `build_vocab_from_iterator` from `torchtext.vocab` for the Wikitext-2 dataset while using a tokenizer from `torchtext.data.utils.get_tokenizer`, you can follow these steps:
+Since the original URL was not working, I downloaded the train, validation, and test datasets from a GitHub repository and placed them in the `'data/datasets/WikiText2'` directory.
-Import the necessary modules:
+#### Code Explanation
+Here's a breakdown of the code:
-```
-import torch
-import torchtext
-from torchtext.datasets import Wikitext2
-from torchtext.vocab import build_vocab_from_iterator
-from torchtext.data.utils import get_tokenizer
-```
+```python
+import os
+from typing import Union, Tuple
-Load the Wikitext-2 dataset:
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext.data.datasets_utils import _wrap_split_argument, _create_dataset_directory
-```
-train_dataset, valid_dataset, test_dataset = Wikitext2()
-```
+DATA_DIR = "data"
-Define a tokenizer using get_tokenizer:
+NUM_LINES = {
+ "train": 36718,
+ "valid": 3760,
+ "test": 4358,
+}
-```
-tokenizer = get_tokenizer('basic_english')
-```
+DATASET_NAME = "WikiText2"
-Define a function to yield tokenized sentences from the dataset:
+_EXTRACTED_FILES = {
+ "train": "wiki.train.tokens",
+ "test": "wiki.test.tokens",
+ "valid": "wiki.valid.tokens",
+}
-```
-def yield_tokens(dataset):
- for example in dataset:
- yield tokenizer(example)
-```
-Build the vocabulary using build_vocab_from_iterator:
+def _filepath_fn(root, split):
+ return os.path.join(root, _EXTRACTED_FILES[split])
-```
-vocab = build_vocab_from_iterator(yield_tokens(train_dataset))
-```
-Save the vocabulary to a file:
+@_create_dataset_directory(dataset_name=DATASET_NAME)
+@_wrap_split_argument(("train", "valid", "test"))
+def WikiText2(root: str, split: Union[Tuple[str], str]):
+ url_dp = IterableWrapper([_filepath_fn(DATA_DIR, split)])
+ data_dp = FileOpener(url_dp, encoding="utf-8").readlines(strip_newline=False, return_path=False).shuffle().set_shuffle(False).sharding_filter()
+ return data_dp
```
-torch.save(vocab, 'wikitext2_vocab.pt')
+
+#### Usage
+To use the WikiText-2 dataset loader, simply import the WikiText2 function and call it with the desired data split:
+
+```python
+train_data = WikiText2(root="data/datasets/WikiText2", split="train")
+valid_data = WikiText2(root="data/datasets/WikiText2", split="valid")
+test_data = WikiText2(root="data/datasets/WikiText2", split="test")
```
+
+#### Acknowledgements
+This implementation is inspired by the official torchtext dataset loaders, and leverages the torchdata and torchtext libraries to provide a seamless and efficient data loading experience.
+
-EDA(Exploratory data analysis)
-These histograms can provide insights into the dataset's composition and help identify any imbalances or patterns.
+ Tokenize data, building and saving vocabulary
- - Histogram for Age:
- 
+Building a vocabulary is a crucial step in many natural language processing tasks, as it allows you to represent words as unique identifiers that can be used in machine learning models. This Markdown document demonstrates how to build a vocabulary from a set of training data and save it for future use.
+### Function Explanation
- - Histogram for Gender:
- 
+Here's a function that encapsulates the process of building and saving a vocabulary:
+```python
+import torch
+from torchtext.data.utils import get_tokenizer
+from torchtext.vocab import build_vocab_from_iterator
- - Histogram for Ethnicity:
- 
+def build_and_save_vocabulary(train_iter, vocab_path='vocab.pt', min_freq=4):
+ """
+ Build a vocabulary from the training data iterator and save it to a file.
+
+ Args:
+ train_iter (iterator): An iterator over the training data.
+ vocab_path (str, optional): The path to save the vocabulary file. Defaults to 'vocab.pt'.
+ min_freq (int, optional): The minimum frequency of a word to be included in the vocabulary. Defaults to 4.
+
+ Returns:
+ torchtext.vocab.Vocab: The built vocabulary.
+ """
+ # Get the tokenizer
+ tokenizer = get_tokenizer("basic_english")
+
+ # Build the vocabulary
+ vocab = build_vocab_from_iterator(map(tokenizer, train_iter), specials=[''], min_freq=min_freq)
+
+ # Set the default index to the unknown token
+ vocab.set_default_index(vocab[''])
+
+ # Save the vocabulary
+ torch.save(vocab, vocab_path)
+
+ return vocab
+```
-
+Here's how you can use this function:
-
-Calculate Cross-Tabulation of Gender and Ethnicity
-Calculating the cross-tabulation of gender and ethnicity using the `pandas.crosstab()` function. This analysis can reveal the relationship between gender and ethnicity within the dataset and provide useful insights.
+```python
+# Assuming you have a training data iterator named `train_iter`
+vocab = build_and_save_vocabulary(train_iter, vocab_path='my_vocab.pt')
+
+# You can now use the vocabulary
+print(len(vocab)) # 23652
+print(vocab(['ebi', 'AI'.lower(), 'qwerty'])) # [0, 1973, 0]
+```
+
+#### Explanation of the Function
+
+1. **Function Definition**: The `build_and_save_vocabulary` function takes three arguments: `train_iter` (an iterator over the training data), `vocab_path` (the path to save the vocabulary file, with a default of 'vocab.pt'), and `min_freq` (the minimum frequency of a word to be included in the vocabulary, with a default of 4).
+2. **Tokenization**: The function first gets the `basic_english` tokenizer, which performs basic tokenization on English text.
+3. **Vocabulary Building**: The function then builds the vocabulary using the `build_vocab_from_iterator` function, passing the training data iterator (after tokenization) and specifying the `''` special token and the minimum frequency threshold.
+4. **Default Index Setting**: The function sets the default index of the vocabulary to the ID of the `''` token, which means that any word not found in the vocabulary will be mapped to the unknown token.
+5. **Return Value**: The function returns the built vocabulary.
- 
+#### Usage
+To use this function, you need to have a training data iterator named `train_iter`. Then, you can call the `build_and_save_vocabulary` function, passing the `train_iter` and specifying the desired vocabulary file path and minimum frequency threshold.
+
+The function will build the vocabulary, save it to the specified file, and return the `Vocab` object, which you can then use in your downstream tasks.
+### Exploratory Data Analysis (EDA)
+
-Create Violin Plots and Box Plots for Age (Separated by Gender)
-These plots can help identify any differences or patterns in the age distribution between men and women in the UTK dataset.
+ 1. Analyzing Mean Sentence Length in Wikitext-2
+
+This code provides a way to analyze the mean sentence length in the Wikitext-2 dataset. Here's a breakdown of the code:
+
+```python
+import matplotlib.pyplot as plt
+
+def compute_mean_sentence_length(data_iter):
+ """
+ Computes the mean sentence length for the given data iterator.
+
+ Args:
+ data_iter (iterable): An iterable of text data, where each element is a string representing a line of text.
+
+ Returns:
+ float: The mean sentence length.
+ """
+ total_sentence_count = 0
+ total_sentence_length = 0
+
+ for line in data_iter:
+ sentences = line.split('.') # Split the line into individual sentences
+
+ for sentence in sentences:
+ tokens = sentence.strip().split() # Tokenize the sentence
+ sentence_length = len(tokens)
+
+ if sentence_length > 0:
+ total_sentence_count += 1
+ total_sentence_length += sentence_length
+
+ mean_sentence_length = total_sentence_length / total_sentence_count
+ return mean_sentence_length
+
+# Compute mean sentence length for each dataset
+train_mean = compute_mean_sentence_length(train_iter)
+valid_mean = compute_mean_sentence_length(valid_iter)
+test_mean = compute_mean_sentence_length(test_iter)
+
+# Plot the results
+datasets = ['Train', 'Valid', 'Test']
+means = [train_mean, valid_mean, test_mean]
+
+plt.figure(figsize=(6, 4))
+plt.bar(datasets, means)
+plt.xlabel('Dataset')
+plt.ylabel('Mean Sentence Length')
+plt.title('Mean Sentence Length in Wikitext-2')
+plt.grid(True)
+plt.show()
+```
-
+
+
+
+ 2. Analyze the most common and least common words in the dataset
+
+```python
+from collections import Counter
+
+# Compute word frequencies in the training dataset
+freqs = Counter()
+for tokens in map(tokenizer, train_iter):
+ freqs.update(tokens)
+
+# Find the 10 least common words
+least_common_words = freqs.most_common()[:-11:-1]
+print("Least Common Words:")
+for word, count in least_common_words:
+ print(f"{word}: {count}")
+
+# Find the 10 most common words
+most_common_words = freqs.most_common(10)
+print("\nMost Common Words:")
+for word, count in most_common_words:
+ print(f"{word}: {count}")
+```
-Create Violin Plots and Box Plots for Age (Separated by Ethnicity)
-These plots can help identify any differences or patterns in the age distribution among different ethnicities in the UTK dataset.
+3. Count the number of words that repeat 3, 4, and 5 times in the training dataset
+
+```python
+from collections import Counter
+
+# Compute word frequencies in the training dataset
+freqs = Counter()
+for tokens in map(tokenizer, train_iter):
+ freqs.update(tokens)
+
+# Count the number of words that repeat 3, 4, and 5 times
+count_3 = count_4 = count_5 = 0
+for word, freq in freqs.items():
+ if freq == 3:
+ count_3 += 1
+ elif freq == 4:
+ count_4 += 1
+ elif freq == 5:
+ count_5 += 1
+
+print(f"Number of words that appear 3 times: {count_3}") # 5130
+print(f"Number of words that appear 4 times: {count_4}") # 3243
+print(f"Number of words that appear 5 times: {count_5}") # 2261
+```
+
-
+
+4. Word Length Distribution
+
+- Compute the distribution of word lengths (i.e., the number of characters per word) in the dataset.
+- This can reveal insights about the writing style or genre of the corpus.
+
+```python
+from collections import Counter
+import matplotlib.pyplot as plt
+
+# Compute the word lengths in the training dataset
+word_lengths = []
+for tokens in map(tokenizer, train_iter):
+ word_lengths.extend(len(word) for word in tokens)
+
+# Create a frequency distribution of word lengths
+word_length_counts = Counter(word_lengths)
+
+# Plot the word length distribution
+plt.figure(figsize=(10, 6))
+plt.bar(word_length_counts.keys(), word_length_counts.values())
+plt.xlabel("Word Length")
+plt.ylabel("Frequency")
+plt.title("Word Length Distribution in Wikitext-2 Dataset")
+plt.show()
+```
+
-### 2. Dataset Splitting
+
+5. Explore Part-of-Speech (POS) Tagging
+- Perform part-of-speech tagging on the dataset to categorize words into grammatical classes (e.g., nouns, verbs, adjectives).
+- Analyze the distribution of different POS tags and identify any interesting patterns or deviations from standard language models.
-This repository contains code for splitting datasets and analyzing the distributions of age in the training, validation, and test sets. Additionally, it provides instructions for saving these sets in separate CSV files.
+#### Example
+```python
+import spacy
+import en_core_web_sm
-#### Contents
+# Load the small English language model from SpaCy
+nlp = spacy.load("en_core_web_sm")
-1. [Plot Histograms for Age in the Training, Validation, and Test Sets](#plot-histograms-for-age-in-the-training-validation-and-test-sets)
-2. [Save the Training, Validation, and Test Sets in Separate CSV Files](#save-the-training-validation-and-test-sets-in-separate-csv-files)
+# Alternatively, you can use the en_core_web_sm module to load the model
+nlp = en_core_web_sm.load()
-
- Plot Histograms for Age in the Training, Validation, and Test Sets
-This histograms will help ensure that the distributions of age in these sets are similar, indicating a balanced and representative dataset split.
+# Process the given sentence using the loaded language model
+doc = nlp("This is a sentence.")
-
+# Print the text and part-of-speech tag for each token in the sentence
+print([(w.text, w.pos_) for w in doc])
-
+# [('This', 'PRON'), ('is', 'AUX'), ('a', 'DET'), ('sentence', 'NOUN'), ('.', 'PUNCT')]
+```
-
- Save the Training, Validation, and Test Sets in Separate CSV Files
-This step is crucial for further analysis or modeling tasks, as it allows you to access and manipulate each set individually.
-
+For Wikitext-2 dataset:
-### 3. Transformations
+```python
+import spacy
-The defined transformations include resizing images, applying random flips and rotations, adjusting image color, converting images to tensors, and normalizing pixel values.
+# Load the English language model
+nlp = spacy.load("en_core_web_sm")
-#### Contents
+# Perform POS tagging on the training dataset
+pos_tags = []
+for tokens in map(tokenizer, train_iter):
+ doc = nlp(" ".join(tokens))
+ pos_tags.extend([(token.text, token.pos_) for token in doc])
-1. [Resizing Images](#resizing-images)
-2. [Applying Random Horizontal Flips](#applying-random-horizontal-flips)
-3. [Introducing Random Rotations](#introducing-random-rotations)
-4. [Adjusting Image Color using ColorJitter](#adjusting-image-color-using-colorjitter)
-5. [Converting Images to Tensors](#converting-images-to-tensors)
-6. [Normalizing Pixel Values](#normalizing-pixel-values)
+# Count the frequency of each POS tag
+pos_tag_counts = Counter(tag for _, tag in pos_tags)
+# Print the most common POS tags
+print("Most Common Part-of-Speech Tags:")
+for tag, count in pos_tag_counts.most_common(10):
+ print(f"{tag}: {count}")
-
- Resizing Images
-Resizing images to a resolution of 128x128 pixels. Resizing the images ensures consistent dimensions and prepares them for further processing or analysis.
-
+# Visualize the POS tag distribution
+plt.figure(figsize=(12, 6))
+plt.bar(pos_tag_counts.keys(), pos_tag_counts.values())
+plt.xticks(rotation=90)
+plt.xlabel("Part-of-Speech Tag")
+plt.ylabel("Frequency")
+plt.title("Part-of-Speech Tag Distribution in Wikitext-2 Dataset")
+plt.show()
+```
+
-
- Applying Random Horizontal Flips
-Random flips can introduce diversity and prevent model bias towards specific orientations.
-
+Here's a brief explanation of the most common POS tags in the provided output:
-
- Random Rotations
-Random rotations can simulate variation and improve model robustness to different orientations.
-
+1. **NOUN**: Nouns represent people, places, things, or ideas.
-
- Adjusting Image Color using ColorJitter
-ColorJitter allows you to modify the brightness, contrast, saturation, and hue of the images, enhancing their visual appearance and potentially improving model performance.
-
+2. **ADP**: Adpositions, such as prepositions and postpositions, are used to express relationships between words or phrases.
-
- Converting Images to Tensors
-Converting images to tensors is a required step for many deep learning frameworks and enables efficient computation on GPUs.
-
+3. **PUNCT**: Punctuation marks, which are essential for separating and structuring sentences and text.
-
- Normalizing Pixel Values
-Normalizing the pixel values ensures that they have a standard range and distribution, making the training process more stable. The provided mean and standard deviation values (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) can be used for this normalization.
-
+4. **VERB**: Verbs describe actions, states, or occurrences in the text.
-### 4. Custom Dataset and DataLoader
+5. **DET**: Determiners, such as articles (e.g., "the," "a," "an"), provide additional information about nouns.
-The custom dataset allows you to load and preprocess your own data, while the dataloader provides an efficient way to iterate over the dataset during training or evaluation.
+6. **X**: This tag is often used for foreign words, abbreviations, or other language-specific tokens that don't fit into the standard POS categories.
-#### Contents
+7. **PROPN**: Proper nouns, which represent specific names of people, places, organizations, or other entities.
-1. [Custom Dataset](#custom-dataset)
-2. [Define DataLoader](#define-dataloader)
+8. **ADJ**: Adjectives modify or describe nouns and pronouns.
-
- Custom Dataset
-The custom dataset is designed to handle your specific data format and apply any necessary preprocessing steps. You can modify the dataset class according to your data structure, file paths, and preprocessing requirements.
+9. **PRON**: Pronouns substitute for nouns, making the text more concise and less repetitive.
+
+10. **NUM**: Numerals, which represent quantities, dates, or other numerical information.
+
+This distribution of POS tags can provide insights into the linguistic characteristics of the text, such as the predominance of nouns, the prevalence of adpositions, or the usage of proper nouns, which can be helpful in tasks like text classification, information extraction, or stylometric analysis.
- DataLoader
-The dataloader is responsible for efficiently loading and batching the data from the custom dataset. It provides an iterator interface that allows you to easily access the data during model training or evaluation. You can customize the dataloader settings such as batch size, shuffling, and parallel data loading based on your specific needs.
-
+6. Investigate Named Entity Recognition (NER)
+
+- Apply NER to the dataset to identify and classify named entities (e.g., people, organizations, locations).
+- Analyze the types and frequencies of named entities present in the corpus, which can provide insights into the content and focus of the Wikitext-2 dataset.
+
+```python
+import spacy
+import matplotlib.pyplot as plt
+
+# Load the English language model
+nlp = spacy.load("en_core_web_sm")
+
+# Perform NER on the training dataset
+named_entities = []
+for tokens in map(tokenizer, train_iter):
+ doc = nlp(" ".join(tokens))
+ named_entities.extend([(ent.text, ent.label_) for ent in doc.ents])
+
+# Count the frequency of each named entity type
+ner_counts = Counter(label for _, label in named_entities)
+
+# Print the most common named entity types
+print("Most Common Named Entity Types:")
+for label, count in ner_counts.most_common(10):
+ print(f"{label}: {count}")
+
+# Visualize the named entity distribution
+plt.figure(figsize=(12, 6))
+plt.bar(ner_counts.keys(), ner_counts.values())
+plt.xticks(rotation=90)
+plt.xlabel("Named Entity Type")
+plt.ylabel("Frequency")
+plt.title("Named Entity Distribution in Wikitext-2 Dataset")
+plt.show()
+```
-## 5. Model with Custom Dataset
+
-The models used in this project are ResNet50 and EfficientNet B0, and they are trained on the custom dataset you provide.
+Here's a brief explanation of the most common named entity types in the output:
-### Contents
+1. **DATE**: Represents specific dates, time periods, or temporal expressions, such as "June 15, 2024" or "last year".
-1. [ResNet50 Model](#resnet50-model)
-2. [EfficientNet B0 Model](#efficientnet-b0-model)
+2. **CARDINAL**: Includes numerical values, such as quantities, ages, or measurements.
-
- ResNet50 Model
-The ResNet50 architecture is a widely-used convolutional neural network that has shown impressive performance on various computer vision tasks. You will learn how to load the pre-trained ResNet50 model, fine-tune it on your custom dataset, and use it for inference.
+3. **PERSON**: Identifies the names of individual people.
- 
-
+4. **GPE** (Geopolitical Entity): This entity type represents named geographical locations, such as countries, cities, or states.
-
- EfficientNet B0 Model
-EfficientNet is a family of convolutional neural networks that have achieved state-of-the-art performance on image classification tasks while being computationally efficient. You will learn how to load the pre-trained EfficientNet B0 model, adapt it to your custom dataset, and leverage its capabilities for classification or feature extraction.
+5. **NORP** (Nationalities, Religious, or Political Groups): This entity type includes named groups or affiliations based on nationality, religion, or political ideology.
- 
-
+6. **ORDINAL**: Represents ordinal numbers, such as "first," "second," or "3rd".
-## 6. Training Process
+7. **ORG** (Organization): The names of companies, institutions, or other organized groups.
-This repository contains code for the training process of a model, including finding hyperparameters, the training and evaluation loop, and plotting learning curves.
+8. **QUANTITY**: Includes non-numeric quantities, such as "a few" or "several".
-### Contents
+9. **LOC** (Location): Represents named geographical locations, such as continents, regions, or landforms.
-1. [Finding Hyperparameters](#finding-hyperparameters)
- 1. [Step 1: Calculate the Loss for an Untrained Model](#step-1-calculate-the-loss-for-an-untrained-model-using-a-few-batches)
- 2. [Step 2: Train and Overfit the Model on a Small Subset of the Dataset](#step-2-try-to-train-and-overfit-the-model-on-a-small-subset-of-the-dataset)
- 3. [Step 3: Train the Model for a Limited Number of Epochs](#step-3-train-the-model-for-a-limited-number-of-epochs-experimenting-with-various-learning-rates)
- 4. [Step 4: Create a Small Grid Using Weight Decay and the Best Learning Rate and save it to a CSV file](#step-4-create-a-small-grid-using-the-weight-decay-and-the-best-learning-rate-and-save-it-to-a-CSV-file)
- 5. [Step 5: Train the Model for Longer Epochs Using the Best Model from Step 4](#step-5-train-model-for-longer-epochs-using-the-best-model-from-step-4)
-2. [Training and Evaluation Loop](#train-and-evaluation-loop)
-3. [Plotting Learning Curves with Matplotlib and TensorBoard](#plot-learning-curves)
-4. [Save the best model from .pt to .jit](#Save-the-best-model-from-.pt-to-.jit)
+10. **MONEY**: Identifies monetary values, such as dollar amounts or currency names.
-#### Finding Hyperparameters
+This distribution of named entity types can provide valuable insights into the content and focus of the text. For example, the prominence of DATE and CARDINAL entities may suggest a text that deals with numerical or temporal information, while the prevalence of PERSON, ORG, and GPE entities could indicate a text that discusses people, organizations, and geographical locations.
-The process involves several steps, including calculating the loss for an untrained model, overfitting the model on a small subset of the dataset, training the model for a limited number of epochs with various learning rates, creating a small grid using weight decay and the best learning rate, and finally training the model for longer epochs using the best model from the previous step.
+Understanding the named entity distribution can be useful in a variety of applications, such as information extraction, question answering, and text summarization, where identifying and categorizing key named entities is crucial for understanding the context and content of the text.
-
- Step 1: Calculate the Loss for an Untrained Model Using one Batch
-This step helps us to understand that the forward pass of the model is working. The forward pass of a neural network model refers to the process of propagating input data through the model's layers to obtain predictions or output values.
- Step 2: Train and Overfit the Model on a Small Subset of the Dataset
-The goal of Step 2 is to train the model on a small subset of the dataset to assess its ability to learn and memorize the training data.
+7. Perform Topic Modeling (To-do)
+
+- Apply topic modeling techniques, such as Latent Dirichlet Allocation (LDA), to uncover the underlying thematic structure of the corpus.
+- Analyze the identified topics and their distributions, which can reveal the main themes and subject areas covered in the Wikitext-2 dataset.
-
- Step 3: Train the Model for a Limited Number of Epochs, Experimenting with Various Learning Rates
-This step helps us to identify the learning rate that leads to optimal training progress and convergence.
-
- Step 4: Create a Small Grid Using Weight Decay and the Best Learning Rate and save it to a CSV file
-The goal of Step 4 is to create a small grid using weight decay and the best learning rate, and save it to a CSV file. This grid allows us to examine how weight decay regularization impacts the performance of the model.
-
+8. Generating a Word Cloud for the Wikitext-2 Training Dataset
+This code generates a single word cloud visualization that highlights the most frequent words in the entire Wikitext-2 training dataset, providing a high-level overview of the prominent themes and topics present in the corpus.
+
+```python
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+
+# Load the training dataset
+with open("data/wiki.train.tokens", "r") as f:
+ train_text = f.read().split()
+
+# Create a string from the entire training dataset
+text = " ".join(train_text)
+
+# Generate the word cloud
+wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
+
+# Plot the word cloud
+plt.figure(figsize=(12, 8))
+plt.imshow(wordcloud, interpolation='bilinear')
+plt.axis('off')
+plt.title('Word Cloud for Wikitext-2 Training Dataset')
+plt.show()
+```
+
+
-
- Step 5: Train the Model for Longer Epochs Using the Best Model from Step 4
-The goal of Step 5 is to train the model for longer epochs using the best model obtained from Step 4. This step aims to maximize the model's learning potential and achieve improved performance by allowing it to learn from the data for an extended period.
- Step 6: Save the best model from .pt to .jit
-The goal of this step is to convert the best model from .pt to .jit format. This conversion is primarily done to optimize and enhance the model's performance during deployment.
+9. Clustering Words by Semantic Similarity and Visualizing Word Clouds
+This code clusters words from the Wikitext-2 dataset based on their semantic similarity using a BERT-based sentence transformer model, and then generates word clouds to visualize the most representative words in each semantic cluster.
+
+```python
+from sentence_transformers import SentenceTransformer
+from sklearn.cluster import KMeans
+from collections import defaultdict
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+
+# Load the BERT-based sentence transformer model
+model = SentenceTransformer('bert-base-nli-mean-tokens')
+
+# Load the training dataset
+with open("data/wiki.valid.tokens", "r") as f:
+ train_text = f.read().split()
+
+# Compute the BERT embeddings for each unique word in the dataset
+unique_words = set(train_text)
+word_embeddings = model.encode(list(unique_words))
+
+# Cluster the words using K-Means
+num_clusters = 5
+kmeans = KMeans(n_clusters=num_clusters, random_state=42)
+clusters = kmeans.fit_predict(word_embeddings)
+
+# Group the words by cluster
+word_clusters = defaultdict(list)
+for i, word in enumerate(unique_words):
+ word_clusters[clusters[i]].append(word)
+
+# Create a word cloud for each cluster
+fig, axes = plt.subplots(1, 5, figsize=(14, 12))
+axes = axes.flatten()
+
+for cluster_id, cluster_words in word_clusters.items():
+ word_cloud = WordCloud(width=400, height=200, background_color='white').generate(' '.join(cluster_words))
+ axes[cluster_id].imshow(word_cloud, interpolation='bilinear')
+ axes[cluster_id].set_title(f"Cluster {cluster_id}")
+ axes[cluster_id].axis('off')
+
+plt.subplots_adjust(wspace=0.4, hspace=0.6)
+
+plt.tight_layout()
+plt.show()
+```
+
-#### Train and Evaluation Loop
+### Transform and prepare dataset
+
+The two data formats, `N x B x L` and `M x L`, are commonly used in language modeling tasks, particularly in the context of neural network-based models.
-The train loop handles the training process, including forward and backward passes, updating model parameters, and monitoring training metrics. The evaluation loop performs model evaluation on a separate validation or test dataset and computes relevant evaluation metrics.
+1. `N x B x L` format:
+ - This format is often used when working with batched data for training neural network-based language models.
+ - `N` represents the number of batches. In this case, the dataset is divided into `N` smaller batches, which is a common practice to improve the efficiency and stability of the training process.
+ - `B` is the batch size, which represents the number of samples (e.g., sentences, paragraphs, or documents) within each batch.
+ - `L` is the length of a sample within each batch, which typically corresponds to the number of tokens (words) in a sample.
+ - This format allows the model to process multiple samples (batch) at once, which can significantly speed up the training process compared to processing one sample at a time.
+ - The advantage of this format is that it enables efficient batch-based training, where the model can learn from multiple samples simultaneously, leveraging the computational power of modern hardware (e.g., GPUs) to accelerate the training process.
+
+2. `M x L` format:
+ - This format is simpler and more straightforward compared to the `N x B x L` format.
+ - `M` is equal to `N x B`, which represents the total number of samples (e.g., sentences, paragraphs, or documents) in the dataset.
+ - `L` is the length of each sample, which corresponds to the number of tokens (words) in the sample.
+ - This format is less efficient for training neural network-based language models, as the samples are not organized into batches. However, it can be more suitable for certain tasks or when the dataset size is relatively small.
+ - The advantage of this format is that it is easier to work with and can be more intuitive for certain data processing tasks, such as simple text analysis or feature extraction.
+
+The choice between these two formats depends on the specific requirements of your language modeling task and the capabilities of the neural network architecture you're working with. If you're training a neural network-based language model, the `N x B x L` format is typically preferred, as it allows for efficient batch-based training and can lead to faster convergence and better performance. However, if your task doesn't involve neural networks or if the dataset is relatively small, the `M x L` format may be more suitable.
- Plotting Learning Curves with Matplotlib and TensorBoard
-Learning curves visualize the model's training and validation performance over epochs, providing insights into the model's learning progress, convergence, and potential issues such as overfitting or underfitting.\
-TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more.
+1. Function for prepare language model data
+
+```python
+def prepare_language_model_data(raw_text_iterator, sequence_length):
+ """
+ Prepare PyTorch tensors for a language model.
+
+ Args:
+ raw_text_iterator (iterable): An iterator of raw text data.
+ sequence_length (int): The length of the input and target sequences.
+
+ Returns:
+ tuple: A tuple containing two PyTorch tensors:
+ - inputs (torch.Tensor): A tensor of input sequences.
+ - targets (torch.Tensor): A tensor of target sequences.
+ """
+ # Convert the raw text iterator into a single PyTorch tensor
+ data = torch.cat([torch.LongTensor(vocab(tokenizer(line))) for line in raw_text_iterator])
+
+ # Calculate the number of complete sequences that can be formed
+ num_sequences = len(data) // sequence_length
+
+ # Calculate the remainder of the data length divided by the sequence length
+ remainder = len(data) % sequence_length
+
+ # If the remainder is 0, add a single token to the end of the data tensor
+ if remainder == 0:
+ unk_tokens = torch.LongTensor([vocab['']])
+ data = torch.cat([data, unk_tokens])
+
+ # Extract the input and target sequences from the data tensor
+ inputs = data[:num_sequences*sequence_length].reshape(-1, sequence_length)
+ targets = data[1:num_sequences*sequence_length+1].reshape(-1, sequence_length)
+
+ print(len(inputs), len(targets))
+ return inputs, targets
+```
+#### Usage
-
-
+```python
+sequence_length = 30
+X_train, y_train = prepare_language_model_data(train_iter, sequence_length)
+X_valid, y_valid = prepare_language_model_data(valid_iter, sequence_length)
+X_test, y_test = prepare_language_model_data(test_iter, sequence_length)
+
+X_train.shape, y_train.shape, X_valid.shape, y_valid.shape, X_test.shape, y_test.shape
-## Todo
+(torch.Size([68333, 30]),
+ torch.Size([68333, 30]),
-...
+ torch.Size([7147, 30]),
+ torch.Size([7147, 30]),
-### Contents
+ torch.Size([8061, 30]),
+ torch.Size([8061, 30]))
-1. [Inference](#inference)
-2. [Experiments](#experiments)
- 1. [Train and Evaluate the Model Using Various Datasets](#train-and-evaluate-the-model-using-various-datasets)
- 2. [Train the Model Using One Dataset and Test it on a Different One](#train-the-model-using-one-dataset-and-then-test-it-on-a-different-one)
- 3. [Analyze the Loss Value with Respect to Age, Gender, and Race](#analyze-the-loss-value-with-respect-to-age-gender-and-race)
- 4. [Analyze the Model's Sensitivity](#analyze-the-models-sensitivity)
- 5. [Create a Heatmap for the Face Images](#create-a-heatmap-for-the-face-images)
-3. [Use the Model to Perform Age Estimation on a Webcam Image](#use-the-model-to-perform-age-estimation-on-a-webcam-image)
+```
+
+
+
+
+2. Custom dataset
-#### Inference
+This code defines a PyTorch `Dataset` class for working with language model data. The `LanguageModelDataset` class takes in input and target tensors and provides the necessary methods for accessing the data.
-- [ ] Implement code for performing inference using the trained model.
-- [ ] Provide instructions on how to use the inference code with sample input data.
+```python
+class LanguageModelDataset(Dataset):
+ def __init__(self, inputs, targets):
+ self.inputs = inputs
+ self.targets = targets
-#### Experiments
+ def __len__(self):
+ return self.inputs.shape[0]
-##### Train and Evaluate the Model Using Various Datasets
+ def __getitem__(self, idx):
+ return self.inputs[idx], self.targets[idx]
+```
-- [ ] Conduct experiments to train and evaluate the model using different datasets.
-- [ ] Document the datasets used, training process, and evaluation results.
-- [ ] Provide guidelines on how to adapt the code for using custom datasets.
+#### Usage
-##### Train the Model Using One Dataset and Test it on a Different One
+The `LanguageModelDataset` class can be used as follows:
-- [ ] Perform experiments to train the model on one dataset and evaluate its performance on a different dataset.
-- [ ] Describe the process of training and testing on different datasets.
-- [ ] Report the evaluation metrics and discuss the results.
+```python
+# Create the datasets
+train_set = LanguageModelDataset(X_train, y_train)
+valid_set = LanguageModelDataset(X_valid, y_valid)
+test_set = LanguageModelDataset(X_test, y_test)
-##### Analyze the Loss Value with Respect to Age, Gender, and Race
+# Create data loaders (optional)
+train_loader = DataLoader(train_set, batch_size=32, shuffle=True)
+valid_loader = DataLoader(valid_set, batch_size=32)
+test_loader = DataLoader(test_set, batch_size=32)
-- [ ] Analyze the loss value of the model with respect to age, gender, and race.
-- [ ] Provide code or scripts to calculate and visualize the loss values for different demographic groups.
-- [ ] Discuss the insights and implications of the analysis.
+# Access the data
+x_batch, y_batch = next(iter(train_loader))
+print(f"Input batch shape: {x_batch.shape}") # Input batch shape: torch.Size([32, 30])
+print(f"Target batch shape: {y_batch.shape}") # Target batch shape: torch.Size([32, 30])
+```
+
-##### Analyze the Model's Sensitivity
+## Model
-- [ ] Conduct sensitivity analysis to understand the model's response to variations in input data.
-- [ ] Outline the methodology and metrics used for sensitivity analysis.
-- [ ] Present the findings and interpretations of the sensitivity analysis.
+
+Custom PyTorch Language Model with Flexible Embedding Options
-##### Create a Heatmap for the Face Images
+The code defines a custom PyTorch language model that allows you to use different types of word embeddings, including `randomly` initialized embeddings, pre-trained `GloVe` embeddings, pre-trained `FastText` embeddings, by simply specifying the `embedding_type` argument when creating the model instance.
-- [ ] Develop code to generate heatmaps for face images based on the model's predictions or activations.
-- [ ] Explain the process of creating heatmaps and their significance in understanding the model's behavior.
-- [ ] Provide examples and visualizations of the generated heatmaps.
+```python
+import torch.nn as nn
+from torchtext.vocab import GloVe, FastText
+
+
+class LanguageModel(nn.Module):
+ def __init__(self, vocab_size, embedding_dim,
+ hidden_dim, num_layers, dropout_embd=0.5,
+ dropout_rnn=0.5, embedding_type='random'):
+
+ super().__init__()
+ self.num_layers = num_layers
+ self.hidden_dim = hidden_dim
+ self.embedding_dim = embedding_dim
+ self.embedding_type = embedding_type
+
+ if embedding_type == 'random':
+ self.embedding = nn.Embedding(vocab_size, embedding_dim)
+ self.embedding.weight.data.uniform_(-0.1, 0.1)
+
+ elif embedding_type == 'glove':
+ self.glove = GloVe(name='6B', dim=embedding_dim)
+ self.embedding = nn.Embedding(vocab_size, embedding_dim)
+ self.embedding.weight.data.copy_(self.glove.vectors)
+ self.embedding.weight.requires_grad = False
+
+ elif embedding_type == 'fasttext':
+ self.glove = FastText(language='en')
+ self.embedding = nn.Embedding(vocab_size, embedding_dim)
+ self.embedding.weight.data.copy_(self.fasttext.vectors)
+ self.embedding.weight.requires_grad = False
+
+ else:
+ raise ValueError("Invalid embedding_type. Choose from 'random', 'glove', 'fasttext'.")
+
+ self.dropout = nn.Dropout(p=dropout_embd)
+ self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=num_layers,
+ dropout=dropout_rnn, batch_first=True)
+ self.fc = nn.Linear(hidden_dim, vocab_size)
+
+ def forward(self, src):
+ embedding = self.dropout(self.embedding(src))
+ output, hidden = self.lstm(embedding)
+ prediction = self.fc(output)
+ return prediction
+```
+#### usage
+```python
+model = LanguageModel(vocab_size=len(vocab),
+ embedding_dim=300,
+ hidden_dim=512,
+ num_layers=2,
+ dropout_embd=0.65,
+ dropout_rnn=0.5,
+ embedding_type='glove')
+```
+#### Calculating Trainable Parameters in a PyTorch Model
-##### Use the Model to Perform Age Estimation on a Webcam Image
+```python
+def num_trainable_params(model):
+ nums = sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6
+ return nums
-- [ ] Integrate the model with webcam functionality to perform age estimation on real-time images.
-- [ ] Detail the steps and code required to use the model for age estimation on webcam images.
-- [ ] Include any necessary dependencies or setup instructions.
\ No newline at end of file
+# Calculate the number of trainable parameters in the embedding, LSTM, and fully connected layers of the LanguageModel instance 'model'
+num_trainable_params(model.embedding) # (7.0956)
+num_trainable_params(model.lstm) # (3.76832)
+num_trainable_params(model.fc) # (12.133476)
+```
+
diff --git a/data/wiki.test.tokens b/data/wiki.test.tokens
new file mode 100644
index 0000000..6f3fc88
--- /dev/null
+++ b/data/wiki.test.tokens
@@ -0,0 +1,4358 @@
+
+ = Robert =
+
+ Robert is an English film , television and theatre actor . He had a guest @-@ starring role on the television series The Bill in 2000 . This was followed by a starring role in the play Herons written by Simon Stephens , which was performed in 2001 at the Royal Court Theatre . He had a guest role in the television series Judge John in 2002 . In 2004 landed a role as " Craig " in the episode " Teddy 's Story " of the television series The Long Firm ; he starred alongside actors Mark Strong and Derek Jacobi . He was cast in the 2005 theatre productions of the Philip Ridley play Mercury Fur , which was performed at the Drum Theatre in Plymouth and the Factory in London . He was directed by John and starred alongside Ben , Shane , Harry Kent , Fraser , Sophie Stanton and Dominic Hall .
+ In 2006 , starred alongside in the play written by Mark . He appeared on a 2006 episode of the television series , Doctors , followed by a role in the 2007 theatre production of How to Curse directed by . How to Curse was performed at Bush Theatre in the London Borough of and Fulham . starred in two films in 2008 , by filmmaker Paris , and Punch directed by Blackburn . In May 2008 , made a guest appearance on a two @-@ part episode arc of the television series Waking the Dead , followed by an appearance on the television series in November 2008 . He had a recurring role in ten episodes of the television series in 2010 , as " Fletcher " . starred in the 2011 film directed by Paris .
+
+ = = Career = =
+
+
+ = = = 2000 – 2005 = = =
+
+ In 2000 had a guest @-@ starring role on the television series The Bill ; he portrayed " Scott Parry " in the episode , " In Safe Hands " . starred as " Scott " in the play Herons written by Simon Stephens , which was performed in 2001 at the Royal Court Theatre . A review of 's performance in The Independent on Sunday described him as " horribly menacing " in the role , and he received critical reviews in The Herald , and Evening Standard . He appeared in the television series Judge John in 2002 as " " in the episode " Political " , and had a role as a different character " Toby Steele " on The Bill .
+ He had a recurring role in 2003 on two episodes of The Bill , as character " Connor Price " . In 2004 landed a role as " Craig " in the episode " Teddy 's Story " of the television series The Long Firm ; he starred alongside actors Mark Strong and Derek Jacobi . starred as " Darren " , in the 2005 theatre productions of the Philip Ridley play Mercury Fur . It was performed at the Drum Theatre in Plymouth , and the Factory in London . He was directed by John and starred alongside Ben , Shane , Harry Kent , Fraser , Sophie Stanton and Dominic Hall . received a favorable review in The Daily Telegraph : " The acting is intense , with performances from Ben ( now from his performance as Trevor 's Hamlet ) , Robert , Shane and Fraser . " The Guardian noted , " Ben and Robert offer amid the . "
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+ In 2006 starred in the play written by Mark . The play was part of a series which featured different , titled Burn / / . In a 2006 interview , fellow actor Ben identified as one of his favorite co @-@ stars : " I loved working with a guy called Robert , who was in the triple bill of Burn , and at the National . He played my brother in Mercury Fur . " He portrayed " Jason Tyler " on the 2006 episode of the television series , Doctors , titled " Something I " . starred as " William " in the 2007 production of How to Curse directed by . How to Curse was performed at Bush Theatre in the London Borough of and Fulham . In a review of the production for The Daily Telegraph , theatre critic Charles Spencer noted , " Robert brings a touching vulnerability to the stage as William . "
+ starred in two films in 2008 , by filmmaker Paris , and Punch directed by Blackburn . portrayed a character named " Sean " in Punch , who along with character " Josh " as the " quiet brother ... who hits it off with " . guest starred on a two @-@ part episode arc " " in May 2008 of the television series Waking the Dead as character " Jimmy " . He appeared on the television series as " Neil " in November 2008 . He had a recurring role in ten episodes of the television series in 2010 , as " Fletcher " . He portrayed an emergency physician applying for a medical . He commented on the inherent difficulties in portraying a physician on television : " Playing a doctor is a strange experience . you know what you 're talking about when you don 't is very bizarre but there are advisers on set who are fantastic at taking you through procedures and giving you the confidence to stand there and look like you know what you 're doing . " starred in the 2011 film directed by Paris .
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+ = Du Fu =
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+ Du Fu ( Wade – Giles : Tu Fu ; Chinese : ; – 770 ) was a prominent Chinese poet of the Tang dynasty . Along with Li ( Li Po ) , he is frequently called the greatest of the Chinese poets . His greatest ambition was to serve his country as a successful civil servant , but he proved unable to make the necessary accommodations . His life , like the whole country , was devastated by the An Rebellion of , and his last 15 years were a time of almost constant unrest .
+ Although initially he was little @-@ known to other writers , his works came to be hugely influential in both Chinese and Japanese literary culture . Of his poetic writing , nearly fifteen hundred poems have been preserved over the ages . He has been called the " Poet @-@ Historian " and the " Poet @-@ Sage " by Chinese critics , while the range of his work has allowed him to be introduced to Western readers as " the Chinese Virgil , Horace , , Shakespeare , Milton , Burns , , , Hugo or " .
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+ = = Life = =
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+ Traditional Chinese literary criticism emphasized the life of the author when interpreting a work , a practice which Burton Watson attributes to " the close links that traditional Chinese thought posits between art and morality " . Since many of Du Fu 's poems feature morality and history , this practice is particularly important . Another reason , identified by the Chinese historian William , is that Chinese poems are typically , context that might be relevant , but which an informed contemporary could be assumed to know . For modern Western readers , " The less accurately we know the time , the place and the circumstances in the background , the more liable we are to imagine it incorrectly , and the result will be that we either the poem or fail to understand it altogether " . Stephen Owen suggests a third factor particular to Du Fu , arguing that the variety of the poet 's work required consideration of his whole life , rather than the " " used for more limited poets .
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+ Most of what is known of Du Fu 's life comes from his poems . His paternal grandfather was Du , a noted politician and poet during the reign of Empress Wu . Du Fu was born in ; the exact birthplace is unknown , except that it was near Luoyang , Henan province ( Gong county is a favourite candidate ) . In later life , he considered himself to belong to the capital city of Chang 'an , ancestral hometown of the Du family .
+ Du Fu 's mother died shortly after he was born , and he was partially raised by his aunt . He had an elder brother , who died young . He also had three half brothers and one half sister , to whom he frequently refers in his poems , although he never mentions his stepmother .
+ The son of a minor scholar @-@ official , his youth was spent on the standard education of a future civil servant : study and of the Confucian classics of philosophy , history and poetry . He later claimed to have produced poems by his early teens , but these have been lost .
+ In the early , he travelled in the / Zhejiang area ; his earliest surviving poem , describing a poetry contest , is thought to date from the end of this period , around 735 . In that year , he took the civil service exam , likely in Chang 'an . He failed , to his surprise and that of centuries of later critics . concludes that he probably failed because his prose style at the time was too dense and obscure , while Chou suggests his failure to connections in the capital may have been to blame . After this failure , he went back to traveling , this time around Shandong and Hebei .
+ His father died around 740 . Du Fu would have been allowed to enter the civil service because of his father 's rank , but he is thought to have given up the privilege in favour of one of his half brothers . He spent the next four years living in the Luoyang area , fulfilling his duties in domestic affairs .
+ In the autumn of , he met Li ( Li Po ) for the first time , and the two poets formed a friendship . David Young describes this as " the most significant formative element in Du Fu 's artistic development " because it gave him a living example of the reclusive poet @-@ scholar life to which he was attracted after his failure in the civil service exam . The relationship was somewhat one @-@ sided , however . Du Fu was by some years the younger , while Li was already a poetic star . We have twelve poems to or about Li from the younger poet , but only one in the other direction . They met again only once , in .
+ In 746 , he moved to the capital in an attempt to resurrect his official career . He took the civil service exam a second time during the following year , but all the candidates were failed by the prime minister ( apparently in order to prevent the emergence of possible rivals ) . He never again attempted the examinations , instead the emperor directly in , 754 and probably again in . He married around , and by the couple had had five children — three sons and two daughters — but one of the sons died in infancy in . From 754 he began to have lung problems ( probably asthma ) , the first of a series of which him for the rest of his life . It was in that year that Du Fu was forced to move his family due to the turmoil of a famine brought about by massive floods in the region .
+ In , he received an appointment as of the Right Commandant 's office of the Crown Prince 's Palace . Although this was a minor post , in normal times it would have been at least the start of an official career . Even before he had begun work , however , the position was swept away by events .
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+ The An Rebellion began in December , and was not completely suppressed for almost eight years . It caused enormous disruption to Chinese society : the census of 754 recorded 52 @.@ 9 million people , but ten years later , the census counted just 16 @.@ 9 million , the remainder having been displaced or killed . During this time , Du Fu led a largely itinerant life by wars , associated and imperial . This period of was the making of Du Fu as a poet : Even Shan Chou has written that , " What he saw around him — the lives of his family , neighbors , and strangers – what he heard , and what he hoped for or feared from the progress of various campaigns — these became the enduring themes of his poetry " . Even when he learned of the death of his youngest child , he turned to the suffering of others in his poetry instead of dwelling upon his own . Du Fu wrote :
+ on what I have lived through , if even I know such suffering , the common man must surely be by the winds .
+ In , Emperor was forced to flee the capital and . Du Fu , who had been away from the city , took his family to a place of safety and attempted to join the court of the new emperor ( ) , but he was captured by the rebels and taken to Chang 'an . In the autumn , his youngest son , Du ( Baby Bear ) , was born . Around this time Du Fu is thought to have contracted malaria .
+ He escaped from Chang 'an the following year , and was appointed when he rejoined the court in May . This post gave access to the emperor but was largely ceremonial . Du Fu 's compelled him to try to make use of it : he caused trouble for himself by protesting the removal of his friend and patron Fang Guan on a petty charge . He was arrested but was in June . He was granted leave to visit his family in September , but he soon rejoined the court and on December 8 , , he returned to Chang 'an with the emperor following its recapture by government forces . However , his advice continued to be , and in the summer of he was demoted to a post as Commissioner of Education in . The position was not to his taste : in one poem , he wrote :
+ I am about to scream in the office / Especially when they bring more papers to pile higher on my desk .
+ He moved on in the summer of ; this has traditionally been ascribed to famine , but believes that frustration is a more likely reason . He next spent around six weeks in ( now , Gansu province ) , where he wrote more than sixty poems .
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+ In December , he briefly stayed in ( modern Gansu ) . He departed on December 24 for ( Sichuan province ) , where he was hosted by local and fellow poet Di . Du subsequently based himself in Sichuan for most of the next five years . By the autumn of that year he was in financial trouble , and sent poems help to various acquaintances . He was relieved by Yan Wu , a friend and former colleague who was appointed governor general at . Despite his financial problems , this was one of the and most peaceful periods of his life . Many of Du 's poems from this period are peaceful depictions of his life at " " . In 762 , he left the city to escape a rebellion , but he returned in summer when he was appointed an advisor to Yan , who was involved in campaigns against the Empire .
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+ Luoyang , the region of his birthplace , was recovered by government forces in the winter of 762 , and in the spring of Du Fu and his family sailed down the Yangtze , apparently with the intention of making their way there . They traveled slowly , held up by his ill @-@ health ( by this time he was suffering from poor eyesight , and general old age in addition to his previous ) . They stayed in ( in what is now , ) at the entrance to the Three for almost two years from late spring . This period was Du Fu 's last great poetic flowering , and here he wrote 400 poems in his dense , late style . In autumn , Bo became governor of the region : he supported Du Fu financially and employed him as his unofficial secretary .
+ In March , he began his journey again and got as far as province , where he died in ( now ) in November or December 770 , in his year . He was survived by his wife and two sons , who remained in the area for some years at least . His last known descendant is a grandson who requested a grave inscription for the poet from Yuan in .
+ his life by concluding that , " He appeared to be a son , an affectionate father , a generous brother , a faithful husband , a loyal friend , a official , and a patriotic subject . "
+ Below is an example of one of Du Fu 's later works , To My Friend ( Chinese : ) . Like many other poems in the Tang it featured the theme of a long parting between friends , which was often due to officials being frequently transferred to the provinces :
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+ = = Works = =
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+ Criticism of Du Fu 's works has focused on his strong sense of history , his moral engagement , and his technical excellence .
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+ = = = History = = =
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+ Since the Song dynasty , critics have called Du Fu the " poet historian " ( ) . The most directly historical of his poems are those commenting on military tactics or the successes and failures of the government , or the poems of advice which he wrote to the emperor . , he wrote about the effect of the times in which he lived on himself , and on the ordinary people of China . As Watson notes , this is information " of a kind seldom found in the officially compiled histories of the era " .
+ Du Fu 's political comments are based on emotion rather than calculation : his have been as , " Let us all be less selfish , let us all do what we are supposed to do " . Since his views were impossible to disagree with , his expressed enabled his installation as the central figure of Chinese poetic history .
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+ A second favourite epithet of Chinese critics is that of " poet sage " ( ) , a counterpart to the philosophical sage , . One of the earliest surviving works , The Song of the ( from around 750 ) , gives voice to the of a soldier in the imperial army and a clear @-@ sighted consciousness of suffering . These concerns are continuously articulated in poems on the lives of both soldiers and civilians produced by Du Fu throughout his life .
+ Although Du Fu 's frequent references to his own difficulties can give the impression of an all @-@ consuming , Hawkes argues that his " famous compassion in fact includes himself , viewed quite and almost as an " . He therefore " lends grandeur " to the wider picture by comparing it to " his own slightly comical triviality " .
+ Du Fu 's compassion , for himself and for others , was part of his general broadening of the scope of poetry : he devoted many works to topics which had previously been considered unsuitable for poetic treatment . Zhang Jie wrote that for Du Fu , " everything in this world is poetry " , Du wrote extensively on subjects such as domestic life , calligraphy , paintings , animals , and other poems .
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+ Du Fu 's work is notable above all for its range . Chinese critics traditionally used the term ( " complete " ) , a reference to ' description of . Yuan was the first to note the breadth of Du Fu 's achievement , writing in that his predecessor , " united in his work traits which previous men had displayed only singly " . He mastered all the forms of Chinese poetry : Chou says that in every form he " either made outstanding advances or contributed outstanding examples " . Furthermore , his poems use a wide range of registers , from the direct and to the and self @-@ consciously literary . This variety is even within individual works : Owen identifies the , " rapid stylistic and thematic shifts " in poems which enable the poet to represent different of a situation , while Chou uses the term " juxtaposition " as the major analytical tool in her work . Du Fu is noted for having written more on and painting than any other writer of his time . He wrote eighteen poems on painting alone , more than any other Tang poet . Du Fu 's seemingly negative commentary on the prized horse paintings of Han a controversy that has persisted to the present day .
+ The tenor of his work changed as he developed his style and adapted to his surroundings ( " @-@ like " according to Watson ) : his earliest works are in a relatively derivative , courtly style , but he came into his own in the years of the rebellion . Owen comments on the " simplicity " of the