Minimal dalle-mini inference and tflite conversion (produces zero-byte tflite model)

minimal-dalle-mini-inference-and-tflite-conversion-produces-zero-byte-tflite-model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/josephrocca/7dff488a71b55266c8d603709ecffc98/minimal-dalle-mini-inference-and-tflite-conversion-produces-zero-byte-tflite-model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This notebook is based on work done by @kuprel: https://github.com/kuprel/min-dalle"
      ],
      "metadata": {
        "id": "snN3kT1PWRp3"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install jax[cpu]==0.3.14 # since, as of writing, Colab currently uses JAX v0.3.8 and that has a bug in jax2tf"
      ],
      "metadata": {
        "id": "th9znbh3dBKN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b7ab180-12a5-4df4-a7c5-2f89f68edd9b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ix_xt4X1_6F4",
        "cellView": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fa298d0-baa2-4cb3-d382-107664982832"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Note: checking out '1e18ba0ffa0788a987db6a439471e27e6f8e91ac'.\n",
            "\n",
            "You are in 'detached HEAD' state. You can look around, make experimental\n",
            "changes and commit them, and you can discard any commits you make in this\n",
            "state without impacting any branches by performing another checkout.\n",
            "\n",
            "If you want to create a new branch to retain commits you create, you may\n",
            "do so (now or later) by using -b with the checkout command again. Example:\n",
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            "Successfully installed GitPython-3.1.27 chex-0.1.3 docker-pycreds-0.4.0 flax-0.4.2 gitdb-4.0.9 optax-0.1.3 pathtools-0.1.2 sentry-sdk-1.9.0 setproctitle-1.3.0 shortuuid-1.0.9 smmap-5.0.0 wandb-0.13.0\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading dataset artifact dalle-mini/dalle-mini/mini-1:v0\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact mini-1:v0, 1673.43MB. 7 files... Done. 0:0:22.7\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Artifact downloaded to /content/min-dalle/pretrained/dalle_bart_mini\n"
          ]
        }
      ],
      "source": [
        "!git clone --depth 1 --branch 0.1.1 https://github.com/kuprel/min-dalle\n",
        "!mkdir -p /content/min-dalle/pretrained/vqgan/\n",
        "!curl https://huggingface.co/dalle-mini/vqgan_imagenet_f16_16384/resolve/main/flax_model.msgpack -L --output /content/min-dalle/pretrained/vqgan/flax_model.msgpack\n",
        "!pip install torch flax==0.4.2 wandb\n",
        "!wandb login --anonymously\n",
        "!wandb artifact get --root=/content/min-dalle/pretrained/dalle_bart_mini dalle-mini/dalle-mini/mini-1:v0\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/min-dalle/min_dalle"
      ],
      "metadata": {
        "id": "EuEPj3zBIkkm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82b2ccc6-c466-4c9b-8509-92e3128549a3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/min-dalle/min_dalle\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from flax import traverse_util, serialization\n",
        "from typing import Dict, Tuple, List\n",
        "import jax\n",
        "from jax import numpy as jnp\n",
        "import numpy\n",
        "import os\n",
        "import json\n",
        "from PIL import Image\n",
        "import torch"
      ],
      "metadata": {
        "id": "ry9H5pKSQUy9"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_dalle_bart_flax_params(path: str) -> Dict[str, numpy.ndarray]:\n",
        "    with open(os.path.join(path, \"flax_model.msgpack\"), \"rb\") as f:\n",
        "        params = serialization.msgpack_restore(f.read())\n",
        "\n",
        "    for codec in ['encoder', 'decoder']:\n",
        "        k = 'FlaxBart{}Layers'.format(codec.title())\n",
        "        P: dict = params['model'][codec]['layers'][k]\n",
        "        P['pre_self_attn_layer_norm'] = P.pop('LayerNorm_0')\n",
        "        P['self_attn_layer_norm'] = P.pop('LayerNorm_1')\n",
        "        P['self_attn'] = P.pop('FlaxBartAttention_0')\n",
        "        if codec == 'decoder':\n",
        "            P['pre_encoder_attn_layer_norm'] = P.pop('LayerNorm_2')\n",
        "            P['encoder_attn_layer_norm'] = P.pop('LayerNorm_3')\n",
        "            P['encoder_attn'] = P.pop('FlaxBartAttention_1')\n",
        "        P['glu']: dict = P.pop('GLU_0')\n",
        "        P['glu']['ln0'] = P['glu'].pop('LayerNorm_0')\n",
        "        P['glu']['ln1'] = P['glu'].pop('LayerNorm_1')\n",
        "        P['glu']['fc0'] = P['glu'].pop('Dense_0')\n",
        "        P['glu']['fc1'] = P['glu'].pop('Dense_1')\n",
        "        P['glu']['fc2'] = P['glu'].pop('Dense_2')\n",
        "\n",
        "    for codec in ['encoder', 'decoder']:\n",
        "        layers_params = params['model'][codec].pop('layers')\n",
        "        params['model'][codec] = {\n",
        "            **params['model'][codec], \n",
        "            **layers_params\n",
        "        }\n",
        "    \n",
        "    model_params = params.pop('model')\n",
        "    params = {**params, **model_params}\n",
        "\n",
        "    params['decoder']['lm_head'] = params.pop('lm_head')\n",
        "\n",
        "    return params"
      ],
      "metadata": {
        "id": "0rFl-rxiNpvn"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Tuple, List\n",
        "def load_dalle_bart_metadata(path: str) -> Tuple[dict, dict, List[str]]:\n",
        "    print(\"parsing metadata from {}\".format(path))\n",
        "    for f in ['config.json', 'flax_model.msgpack', 'vocab.json', 'merges.txt']:\n",
        "        assert(os.path.exists(os.path.join(path, f)))\n",
        "    with open(path + '/config.json', 'r') as f: \n",
        "        config = json.load(f)\n",
        "    with open(path + '/vocab.json') as f:\n",
        "        vocab = json.load(f)\n",
        "    with open(path + '/merges.txt') as f:\n",
        "        merges = f.read().split(\"\\n\")[1:-1]\n",
        "    return config, vocab, merges\n",
        "\n",
        "model_name = 'mini' # or 'mega'\n",
        "model_path = '../pretrained/dalle_bart_{}'.format(model_name)\n",
        "config, vocab, merges = load_dalle_bart_metadata(model_path)\n",
        "params_dalle_bart = load_dalle_bart_flax_params(model_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSR7lFawNk-o",
        "outputId": "577fcb48-2c72-44ac-cbc7-d4945e984446"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "parsing metadata from ../pretrained/dalle_bart_mini\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tokenizers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Asn9rrNtg8IG",
        "outputId": "1d79a959-5df5-41e9-c6fb-49005a2ed61e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tokenizers\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 12.9 MB/s \n",
            "\u001b[?25hInstalling collected packages: tokenizers\n",
            "Successfully installed tokenizers-0.12.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from models.dalle_bart_encoder_flax import DalleBartEncoderFlax\n",
        "from models.dalle_bart_decoder_flax import DalleBartDecoderFlax\n",
        "\n",
        "dalle_bart_encoder_flax_model = DalleBartEncoderFlax(\n",
        "    attention_head_count = config['encoder_attention_heads'],\n",
        "    embed_count = config['d_model'],\n",
        "    glu_embed_count = config['encoder_ffn_dim'],\n",
        "    text_token_count = config['max_text_length'],\n",
        "    text_vocab_count = config['encoder_vocab_size'],\n",
        "    layer_count = config['encoder_layers']\n",
        ").bind({'params': params_dalle_bart.pop('encoder')})\n",
        "\n",
        "dalle_bart_decoder_flax_model = DalleBartDecoderFlax(\n",
        "    image_token_count = config['image_length'],\n",
        "    text_token_count = config['max_text_length'],\n",
        "    image_vocab_count = config['image_vocab_size'],\n",
        "    attention_head_count = config['decoder_attention_heads'],\n",
        "    embed_count = config['d_model'],\n",
        "    glu_embed_count = config['decoder_ffn_dim'],\n",
        "    layer_count = config['decoder_layers'],\n",
        "    start_token = config['decoder_start_token_id']\n",
        ")"
      ],
      "metadata": {
        "id": "V0LCW-ViDiSg"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_vqgan_torch_params(path: str) -> Dict[str, torch.Tensor]:\n",
        "    with open(os.path.join(path, 'flax_model.msgpack'), \"rb\") as f:\n",
        "        params: Dict[str, numpy.ndarray] = serialization.msgpack_restore(f.read())\n",
        "\n",
        "    P: Dict[str, numpy.ndarray] = traverse_util.flatten_dict(params, sep='.')\n",
        "\n",
        "    for i in list(P.keys()):\n",
        "        j = i\n",
        "        if 'up' in i or 'down' in i:\n",
        "            j = i.replace('_', '.')\n",
        "            j = j.replace('proj.out', 'proj_out')\n",
        "            j = j.replace('nin.short', 'nin_short')\n",
        "        if 'bias' in i:\n",
        "            P[j] = P.pop(i)\n",
        "        elif 'scale' in i:\n",
        "            j = j.replace('scale', 'weight')\n",
        "            P[j] = P.pop(i)\n",
        "        elif 'kernel' in i:\n",
        "            j = j.replace('kernel', 'weight')\n",
        "            P[j] = P.pop(i).transpose(3, 2, 0, 1)\n",
        "\n",
        "    for i in P:\n",
        "        P[i] = torch.tensor(P[i])\n",
        "\n",
        "    P['embedding.weight'] = P.pop('quantize.embedding.embedding')\n",
        "\n",
        "    for i in list(P):\n",
        "        if i.split('.')[0] in ['encoder', 'quant_conv']:\n",
        "            P.pop(i)\n",
        "    \n",
        "    return P"
      ],
      "metadata": {
        "id": "vMpgNCGOK-N4"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import LongTensor, FloatTensor\n",
        "from models.vqgan_detokenizer import VQGanDetokenizer\n",
        "\n",
        "def detokenize_torch(image_tokens: LongTensor, is_torch: bool) -> numpy.ndarray:\n",
        "    print(\"detokenizing image\")\n",
        "    model_path = '../pretrained/vqgan'\n",
        "    params = load_vqgan_torch_params(model_path)\n",
        "    detokenizer = VQGanDetokenizer()\n",
        "    detokenizer.load_state_dict(params)\n",
        "    if torch.cuda.is_available() and is_torch: detokenizer = detokenizer.cuda()\n",
        "    image = detokenizer.forward(image_tokens).to(torch.uint8)\n",
        "    del detokenizer, params\n",
        "    return image.to('cpu').detach().numpy()"
      ],
      "metadata": {
        "id": "pHrSOkC8KqwU"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from math import inf\n",
        "from typing import List, Tuple\n",
        "\n",
        "\n",
        "class TextTokenizer:\n",
        "    def __init__(self, vocab: dict, merges: List[str]):\n",
        "        self.token_from_subword = vocab\n",
        "        pairs = [tuple(pair.split()) for pair in merges]\n",
        "        self.rank_from_pair = dict(zip(pairs, range(len(pairs))))\n",
        "\n",
        "    def __call__(self, text: str) -> List[int]:\n",
        "        sep_token = self.token_from_subword['</s>']\n",
        "        cls_token = self.token_from_subword['<s>']\n",
        "        unk_token = self.token_from_subword['<unk>']\n",
        "        text = text.lower().encode(\"ascii\", errors=\"ignore\").decode()\n",
        "        tokens = [\n",
        "            self.token_from_subword.get(subword, unk_token)\n",
        "            for word in text.split(\" \") if len(word) > 0\n",
        "            for subword in self.get_byte_pair_encoding(word)\n",
        "        ]\n",
        "        return [cls_token] + tokens + [sep_token]\n",
        "\n",
        "    def get_byte_pair_encoding(self, word: str) -> List[str]:\n",
        "        def get_pair_rank(pair: Tuple[str, str]) -> int:\n",
        "            return self.rank_from_pair.get(pair, inf)\n",
        "\n",
        "        subwords = [chr(ord(\" \") + 256)] + list(word)\n",
        "        while len(subwords) > 1:\n",
        "            pairs = list(zip(subwords[:-1], subwords[1:]))\n",
        "            pair_to_merge = min(pairs, key=get_pair_rank)\n",
        "            if pair_to_merge not in self.rank_from_pair: break\n",
        "            i = pairs.index(pair_to_merge)\n",
        "            subwords = (\n",
        "                (subwords[:i] if i > 0 else []) + \n",
        "                [subwords[i] + subwords[i + 1]] + \n",
        "                (subwords[i + 2:] if i + 2 < len(subwords) else [])\n",
        "            )\n",
        "\n",
        "        # print(subwords)\n",
        "        return subwords"
      ],
      "metadata": {
        "id": "KAUjwzWYuKru"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_text(\n",
        "    text: str, \n",
        "    config: dict,\n",
        "    vocab: dict,\n",
        "    merges: List[str]\n",
        ") -> numpy.ndarray:\n",
        "    tokens = TextTokenizer(vocab, merges)(text)\n",
        "    text_tokens = numpy.ones((2, config['max_text_length']), dtype=numpy.int32)\n",
        "    text_tokens[0, :len(tokens)] = tokens\n",
        "    text_tokens[1, :2] = [tokens[0], tokens[-1]]\n",
        "    return text_tokens"
      ],
      "metadata": {
        "id": "28vowjQ1B0aw"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_image(text_tokens, seed):\n",
        "  encoder_state = dalle_bart_encoder_flax_model(text_tokens)\n",
        "  image_tokens = dalle_bart_decoder_flax_model.sample_image_tokens(\n",
        "      text_tokens,\n",
        "      encoder_state,\n",
        "      jax.random.PRNGKey(seed[0]),\n",
        "      params_dalle_bart['decoder'],\n",
        "  )\n",
        "  return image_tokens"
      ],
      "metadata": {
        "id": "xFGb3Gs1Th1N"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate_image_jitted = jax.jit(generate_image)"
      ],
      "metadata": {
        "id": "hxLZepOUT-qi"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# text: \"game concept art with a lush green village surrounded by a dry orange canyon with a hill in the background and a blue sky, digital art\"\n",
        "# text tokens: [[0,880,3319,241,208,58,21843,899,2595,30419,185,58,3441,2566,7308,208,58,2349,91,99,1396,128,58,789,1955,11,1189,241,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],[0,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]]\n",
        "# Image output for seed=2: https://i.imgur.com/9fBoJg8.png\n",
        "\n",
        "text = \"game concept art with a lush green village surrounded by a dry orange canyon with a hill in the background and a blue sky, digital art\"\n",
        "seed = jnp.array([2])\n",
        "\n",
        "text_tokens = tokenize_text(text, config, vocab, merges)\n",
        "print(\"text tokens shape:\", text_tokens.shape)\n",
        "\n",
        "image_tokens = generate_image_jitted(text_tokens, seed)\n",
        "\n",
        "image_tokens_numpy = numpy.array(image_tokens)\n",
        "# print(\"image tokens\", list(image_tokens))\n",
        "\n",
        "image = detokenize_torch(torch.tensor(image_tokens_numpy), is_torch=False)\n",
        "display(Image.fromarray(image))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "CEUUtqNNF7L5",
        "outputId": "df6093bd-908b-46a5-d042-dbc09f178af6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "text tokens shape: (2, 64)\n",
            "detokenizing image\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=256x256 at 0x7F67569E2510>"
            ],
            "image/png": 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pbjAEAcAJgQ/lVX5785+/Tf0S/devN7/0bY/z4yI7lNfttdlBmqs+bHz7/vUfd/zXf7y4O8TmRuy9b/fzu/tljg4LcDPf1xCEJB2xNfmWpY/4sFcEqsIpWjOYyvkE3VYtooqOBU6DEqYJxgzUYqQWrhxuTZwUoB2VpQQrS9RClN0soqyMSpmBGabgaMFmNKajHIWY0ASree1mzTqBqUnKQ5KcCDB8tlKwbjY4FJy3y/yeXTOSZc8JXEbBK1hoQmGjpfwMnGXI6CWg09QqHdlUsvNs4SgZOs+BJlQsqoJlKAFmbGElMPDs6jJXbswJDVrCuklwWWWNWtUvTY32XIcMT5YUwEJfVlOwHwg9Fr5IrtKxTucPyljrr332DgLrcbDnWvFjVlnfKEFyN1FuoAkUF85KFeSu5mZWBpFFYq1u628I01QKkMtRgpnDsZCcdJNRXobVmVggF0YCVqaEfHYCh1sVqkq0mWWNKCoEjmmYLx458tOny/5x/+m3Y3/7ePzlt+upAvzoL/LM2qs2dUkve1rc76oYly83vlyawUam5r1Xf5xATmwzGpubXzYQx2/v5p6Qm28vl1mc84MbN48wN4wFKfBJgJCw1RhhC8hbNMpC4rwCcCsWabW53cyMMGSH9UKI7iaoTF0wx+ZeQFDgMCtwEe8WrmZyW5wCSmACzI0IrVF/h9QiA9Ugs3Ikyc26FZmVQPk2pWBsrE/RJQJnEmOuha2aDHAx4UpNE90gU1O5tGV6zGCmYEqvFP02OWU9A1WLvIUgmDVrRjo22FRqUMQkepm4IH1VUzIAL5IIUpjWjGOV6aKRBVtwroC1jD0rvbiQofWPY40vePYfkPZcY20tDAtTXXAQtNC4WrwXKJWR5u4Enf7kCdarJ8DmCIObgSkCamDRgHy2JACNKCujBcxsgUlYv3A81zIAxN/XFlGiT8hK5RiSA+GVpRnBZrU3SwkqzH5LfGZd+sc5+2jv/vtZf/12/P7r/LhfvTDsp3/4nL9/9fOyYcqOxtr9Pfl9f42dLSr7e/mmPcwut7fHuXmxWbu+tFts5GVr9/eHrD79/Hrce/h1j9fz+xsqPa2k2JvbOGkmQyx+FyZbYDvIte3KF4cjcyCIZrYHN8PuZpBxwVi9xBSqPE2t0BakanCKBjdzwkwNMtZiLWmqJ7fgYhkbSxY9vHYHUeEZrKh0pLOUdKBYomUiBSFHQkVjmsFoE0JrNeaTTwINBNJJUzXQsbCmEU0QXOhlVnSqXAAOM5st81RBtVC/sEDB3auyhDIHWSrIbDqpLKdrNpWny9qdMtMMFJ6AgNkTViaQJf9Rxheyiyf+o8VDr0ej1uPHJ7K5FCNOrt3g71PQQpzXf2vBeQYDgWqxRv8Fvqb9wPtocK9FQshAib5gC9EowRxO0mBgE2g/tm+D23oRLahn8XPkUmJoEe2UPAGTbDjOl1iUZLcsatS4W5585OgfdT8duc95+fiSHx+cv3/aGfNhTcgLdn+x4+a9T/dxNp5jomFeXiJoYdpMO1omXq63fp3yuHze3d1mLdAu/Jry1y9/ypHvbwceHP2E9v3zFjhzsyYD8RR/cFUVoyh/liIFzKGFCG3QxfzifNnaYjtTmUxRzTCJhBLykqipUsKgLehWARh8k+ierGSWBBbhVERtQno73WuLscUgePUOVRg0U8A0SKxhVQNw4yNqkAJZjAjA6CJHrzBWNVuniHLIdUg1yAoyHS1RCUzwrCapZ0tEz4ZEw0kwQ0UEW3u2+nR0N0ctABB0RCnKV7s0Y5QBXqjhmJxcoABsQftGpspg65A9ZwdbYocfPNkq+KvCP+EXOvU888JTaaO/twEASx9BI83ga3R6jkVaz+pako2A0Q1rszXoB0uP5+LhWJClsegW7gHEwux/4KesEihplZKFYa0xWTbdDKKVszEAYG7Cpuk+EPfH+1e+f9ijeJa6mSbGsCPj7siOMXHZUHfkiJ3sv3+6XH2co8bbY8Y8goerRURrEdZUIy5bVek0VF1vGyLcYQWmNKrMEqkZRv30r7/85T/8Uv1RU9V0zB6xeT5OQ1sw3A/qY8H4P0AxMLh4djbDxdb+51U0mtAXSrfRzJKuEsSh6VMFV7kWJ7+mqSY2mKaqlbG4ppkn+GcMU/SGcBRmwCVOgqXMUJpOzlZnWkFq6fvMLWGeKNJSVFX5Bt8qgK1cNcFN0piaCCKG2yRTmDYnvZcq90GDWQqzLDkwdZF6mGFLKzMDsQNWc9NcuKeQQggLUEcY9ilVuTiyBuGYahhFc6BszTK1BhjCiFrymx/IG7Xg/bUHwGxR7c9JaFV3rk3bKCSMT3hqPTGLvbO/Y6oiilW0tYk6l5xkfZRW4UYkUDTA4auFAEU53ZDN3E3+RMPNlnZCIAVnKoEqFCUiIbqVS1Qmy9wZtJTe7xzjHB/n41B2HQ9/nNscMRw1IWEOzpPTWDtxXDfnmVvnRvdTrxdecb6fx/3e9aaXn5173LZGwiM+vqdbi7hi5OM84f3T9eVdpz76eT+2tsfuGjnTsubWIn5S/Cf1TBZoNb0ChvA1NGIpMp+iKqsnXWkLRpcJZDlQRlITKZaghrICLBdF2Rbch6bgBQbl+nOYC2XFkEV5EdJQoYxppIHeYbMxw7rYVUxM8hzoTjgROEw9anpOsWbqVnFFxAhpESw1kDLkmY2GKs8ibGYfQvU5EIPbyNp8c4NwsnCBFbxnEO1Ie/RUegNDu6cPj0QhCOiCTsJLLi9jyUUTNYUrETAvr7IyK2CU5AarFixAISTK6VpwPCUykLX0UE/B1cI+KdaTaBYEe4rUasFCT4HDU9azZhJyrQVGkAEYAcFYLLgv/kBBX3tJoGjgE6IwWZKGJVNiAYCqWXOkmUipKFUhjVkQaK5cZMlWSxFoQprKkYGcOrfE+Xic3z/6+dBxVGVbz8V5tEO7ygbMYH7aTQ2NR2xmUcV+zJY1p5318hovW9fp52N8+ziauV1jj83LptzCfbPecwzWmTk6OcW6hL9/fx/fBi9RPT4Oz5yytl8vYxBCYsDBqUgLwNZMl0/dCJdey0iwbL1HT26DkhdqLu2CiZgWSls0R1FKQLDmS+AYi5dXLf273IxWHFaGJGSeKFFPkZjSmc1lW6pipEpF8wleCrRxq2xWNqoNNeocc8+KOVtuYwImzawNxXTRPLarm+F4yyqk0iwaKtWvzjrPMaZfatu3PDXLrrZVbCo+6xiEwXdvQzZBr+yeF2aFBzdM3ObiBatEEFda5JbyM3Ek03VGnQUUqVqYApwGJ6SnvpOA7Km+xWJPF7mrKn9Wcpr+znCtUaOeq/GPucOevXuJ8MrE9pRji5QZw80cbnDSloJJtiwEMPEHLLBIXyw+iITgIN1SpInSmgpOJ2nXmhvViIDICczCoXnXeR7nqPwYZ9d5Wu9bqqzK3FA2aHk2mRfD5JACMq/u1TzNbT52Gt69yq+X/cXD5uM4kXO2ot+uTqnyHHOmjswSjxPfvp7VP5oh3GbNqnRzIs9xoHh0zV6+Vdsu+X4uJZRqcvNRChosKHczmGSiwWACnn1fhBtpesJrhUkjaUgKlZhVk3SzIIwasiINIGR6YmaEXPAk8wfFYmleINIFA5nBcnioYsTRanK2HGHjJeS9QyVV9TLpxTm+P/7Rr/l7fPznx/uj9zf1+3ALBadqv7bKzpqycdn2uARVjFFdeebdYv+H6z/8Vz9BH9odn7Y54v2tD/PTR3v4mNOJyvO637bTyL1fDO3+aR/JKzTl7kJlSytDeSEQbNUFc26W7D5iyeswZTD6AkgoyBg/RIJ6rrcE1j6wkBVzCE+hEn09HfiBgz7hfnJR6AUQVktNIa0BikE6xYU6BMJgRpJi1XN7KBJY8zrpJiOc4tJuomzRb1P2FIYuFSJvRhqvMjJ3z03jGMe4v43+ruMNvWLantqn6gSGz9KH5/TklKcvYs2BVrWYlVbJTJ2+FbNS9/tl+uetfbarF94+xiZhzq01uC6Xy/3tjXOuCV+lWXMkt936uGuwZve2m4VZSjZ7zu495WfOqqc3onGr6APetogtbHbY0nWSsCXNNQPXCORYSg4VDFkGmpUhk+st9aceTkWE0Ywy2QKn8WR6TMw1W7mhzCEhUPUkaJSEaJNephkwpFzl+fjk9vmclvf6qPz2ePH4FHG7s71/vu3b8Z/z/T/7/beP71+7mcsRwUDcNZHKjhr6vX/IJsNt2x4aySBn7nq8fn/5Ofj5zTarja3tyF++hGnkjqawtwc/+Mt++0z76etuF2pP3fOjalTRsJkpIYddWWW90JpbpzdzwW6IC7bD+qMIlstXATFAKFtMrEG5CF8+OciFb5aeoh9qEcY/8EY9q9NTEKQ1AhUppn4QE26+pLtGNBMdm4mG9dUgKIWJNhgVq0YJxqX9DZQsIc5JMaXMkuRJcC9EYNO256j+cdvG49vfzt/uuM94TNYQFaiLtZ3AMIpziIU2Z4pQNajxuFRrZ+4RDmt49Op1pA/Ex8hv37eX1z/8+U/2fj6GgzVml7VyXC6h12bvLhz0bcwhoYufb3v1PlMWGYwCLGILjcS9o0/NjuunFrAxJnK6SuTFtywPK2/Wylir81eVLaBYglZ7TjhUYBXZaFi2u1hKLC2xh9micgBx5A8RdKGAxRkv4tI9TWUskkugDsKsmDAtaF/wc4+t8fxyvW7fRv5z6dj6L9/2h/vE99F//bhfcLWYb7883udZR4XTaIk5OoaGCpxElUZZq4ArMPpoU0aOPD7+94/2OeqfgQ3Xy3VgtKjL5u7dldfXTxV2HBnV+8+//Hf/93/3Ba37vLS2l83RvWJVaLDEciiFr9XVKLtBlWaqzSSnY2gd2YXfY2Gcqv8/TexzZH+efpERAAo0PtGBZ/N4amyXehKlqgUhASYHiyw3c7Nw7U6CmyGcjT9aA4qiP9WOxvURL1yeeqoWORE5SlG2bTIazkOGNLuo5njcP96P98dN9Xu+4fxqHxYPYjJqFkaLseUeUslV2kR0baGEnEZVy5PHtLQ20Sy89pm9Zm/D7ZgWt5fe9l+y/BJUshW7jN5wu7rK47Klvke0Y3IcumwxdukYmf3nn27lAxZO62agpfK4j3nMfR+lpIEXQ7mOzqvcWliB0nKs1JpOUMUi6zmWOmGFSkFBkOaQVCgtXZzh79o/m6aSirQ1N1IlmdGYyxPJWvOsPQdgmRlbIVC0qoQhpC1svEa7vp+fHt4+Lt//11/9RPV66IGiWXvMoz6qksHAVUefIzvUntZLyDa6uSZMCzAFra4vl7/++t7MsUs285hu1r9lRQ3lAd2u+afrFscjGX/c9/e3M7+Nz3/920//8K9wjWMej/ejttOUHNBJeuCWsnifj7L2XUkcwyVcsqxMOcptqdlYWBpkYVnunpR72Q/sZ5UPEG1xuZCBxTUv6YdMBTKxJCV94flYcy1KcBjRgi6aaTetPhAg1qS5+AFfMHdx7WgifRnqarkX1n+ESyMbRrSes8/ziNHn8f1yz30Ox9TonDO7YRq6zMtNTbAsp0MJorw2J9poLEI5pw4jXGzmW/bEKMx5zWmPYzN397jhe75/arjZ7byP1OQelx2XF80+aPZy+zKL1WvOuTdDpmZWKcVzcss5z8oOFWtScGBq1vx4NM+w3V+Q+7w7eSDKcjCL1lF7+d/fYthTFOWSJcD0pyRlWcEEg9EFJlO2GAMrLCuhCvUUTS9rhZYYMUWt95tP+XyVrOTCUl7jUr4P8/vl8/H2+Tc7//rr+29vx/c+jncMuTrRUg+TMVrbqiZmzjGRIwKVk9GKZSCUkpRAKWA1sypnM6+qeSYysHmdZTunYJlIcjPzeInm0drIf/enPz1yPv4//1HxCy+Mfb/5HBvPQxvjGvnpHz5nx7udF3+Rwy7+za2Y+3Cwd90K1egSIbQlml7b7KowT1nE4lqeUnStqUeWKhCpJe9/Kt0Io2oB+KC0kKPlPzCSS3H4JIZziR6IlOwH71iEP4l5K5tWpBeSMHkBLktBcWvD0poLH9/78W1+vNUj58dgr9AUIC+Klft8gJ2lMmWQ0UIqk8oLYFUOhNLdJmmZSlipLsRb4gLv55vBRvF6afvOF3/14cd54AKgPH1MXMN+fr26oya3bfesOZ3zPEbfb1uOgcpk0ZtmzciRNUb2kRE3q48xlbNMpZHhzYu4+cf32fIl2uYcAMpDYhrAKrOlhJNLVrCqsiRpLgfAyaCsuAwXIEBjQcOcpZLsOe0C+ZQRkaQqJZhzCkEtaK2sWIB51dgFn4g3v53D/vn+/i+P+/w2z/P86HVOVbXGH4SOI826qc8Ux1mcENmK2xYdo+VuVtlRKsxMJh39TKjynCKzBrJhclQuzc+t8ULOnmN7qE/0Ux/bxdCofs94ryyUTfqNlPtelm+//qIL4uX15eWL/vWnX5p/oA9O0WWbKAbbGpaecFGsgyiKDi4/AmhYqBoLZRStbG1VrPXOcen5zcgiM0v8sZUCMq6dQc9PzWCQM422/CvP3XpNpU6TE3raLF2i0eAFQDNHyeasS+btfOj85f7rL+fHvd5nzOaZAZilRVKiHIe2YbWMvQUEvUXl7OsxThSlGrPEHOu8pGTuQ7Nmzrf7P3ibc1xsf9lxucBgOHGLy9X9/Tz624dqbK/RXkKPkwPaY3rLszQfYQLPmHtt2LfNw5WCSqg5MhkzM2fVmHX03rdm0+CKDg99P9rLS6wQjd1JmBtcqlY0GDOsiKQhVMb0pya0wmxqmeUmCdINAtNkpcLiKrkqWcpqJjzW+FNGTYHUxNMDm6BcVfUa1nq2R/Me+tvbt3/55X7/eplSzYubtosqnTaRqFRVcaBQ0475mJJywqHpnBa2g+d5jJ5da2kFNAJSVpFkTfNwzUxU2Ykq5AdpCpxz29KANqA8LRxQ9JOYyozmvdKNc3xM2r7HSY+Xc3vte4t9K9uJWeGb6MYYQBo4lzhHrsRTLLyWYJpQ0nMtBu1pfllZKHDSf3ADMHt6uJZ8zrhcpz8oAC0Be9hzBHK4A40KUSwjn3yWSJMtt2bVgmSZWV5AtjAG2sO8HvP7X8evX/PtHu/mxRhlZlZTiQqm080xk0IQoFmEeaVU7qmq5FQpF2xeJJvLgTS3o5x+vn3d0Qrnp0u7Ua/G6JiDYmyb1xb9t4+qA+Rm1gbPFF1ekUCOQdKqc7pdevPNNmOR5qAyOQ2Zut+POcf1db/9fK0c0XDkwdkec77zY/c9aDLIiAhsqOXptGbhIh3Mi7MBKGcuIqxIBiCly4zUcvAuoMyeiIapSBOnaDS5IGpCs0RCYNgSC1rSWAaaj7x094PbR/r34zi+2Xl42ebL2eEdQwmzpDCZLmbWzKo51q6YlY66bC+P+8ftZRs+K5c7DZkEp5ncmJhqIGeZwxhTMhtVD8PbA9R53ffXi6zNY6TIGJCj52MOjFHKa9HM9utLq3FANVkjyPNhGSVv7Wpp8BAJ+OaQG5SaZrJc4BinypYj1sWC3Bb5XoCw3G/PJsq1PtsPCKeUJOmoZaEGQK2Zx41u3MIoOUipYSnaFfSnZBoLEVqoksMVSMCSubHckprK2j9+wbdf9yNjYPQDSucWKljBa5gy+1DEIoB9LyTcTEBWn5mVggSNmm7bLaiR7rZt8zgsMXnkT/DN5qfWvrTwOXnamJoUMi3IRz/OB0qt+WV3uEg2wTf1c/bZZ++YyUp3u326oactgCAxa2TVx2OMUyHGxW6vl4YPh1rOUQOqyy3q6CGMqhkMwIwzaGZ2MbhBNS+hjdU4zVT0LEGgpow0GZ7DNuzJkvzd9tqMYFb5D0UsCrWWLYIOgwSzgqbRTDMzUYlhgxfE8TjrAUt3mRdnyhE+t1E9nDSEvJQRxjHBgKWvPdMQsJfXFwNebg0Up882s1DFUUI5HFIll/0D8oLKLffJfbL3ymR7uTDFXplh0hiVFU+/dFhZMxhmOelmY46jBn79en5pl09/7C2w2ZSctZclamkHJkqDDqawIHcAUpXMRHv+Bn+o2fS0Zy17yxLqAEDSzCjhh04N5hKYJBuxOZZ/DZJR/qR7fySdSLLVOLCsvZKms4QXa7tG6/mIg7+/zd8Ovft+Uh9HS5gL6k7AMsUGXYIjJ6yi6PKpyRZGjHnWTJQ3WrICu8MvmtNjMvDg0j6in5fgl9hfmnbFmDknjG1tbd7weD/A4oX7JfaLIxXLIOdR/TGzy2VNEbjsmy8OEUNJzTyOcfZ5nDUSDP/ptlmbRs/5oEFzfLldjvdpcQ0O7XtrrvThlUIZuRGBaj4CM4BYWTNevqgZk2BJWq0PZr2vblqrbJjL4OQYhWgylFcbVpBLiUIhwZwATfLMQEwV/JG236rloXbsrSJzUwBTlSNzjGNOnailS4sC2xIBuIzuweW7drmb08+eYCCM6REww6VspB4FnlUkyiuhlHbTOOSeShT69DHdsJezkjLM5XEJpjPDJ9pWUaoNjcpLeL+PtB69X9iiYm7Ww7aij3JBqpUmlw4hDZ54JvVo0blatgto5ektolh/FwPxh75/2e4JA4y1aMqlJpFIhXM30rXSU5aSFywAYuJpe0RRgJb2ztImRiFG9gZkP/nrb/tv7zwO3ZdAqNHeTQUiGiZkKgT3ykuzOUu0ygfjuPprr1nj4NDWNuOMthOILI7ZdjtXotx52kf/qXHn8WX3617jvQpsvq3t2VRlY/RO1UZv1x1PoJIuoZRHr1EctRn3sLZBNZSSsgzn0fuYo6tKzkb5fmnNgF5wC3huJsxwlmd83n0fYMSL18U+dtTF5D59hReVROXiz4hwmRVrJjhrK0NhuGeKyRg+SorI0iaUDdmCCYxkTbqxIIOVC1PTi4IF6VAYZ8th9gIOjsd+16Ya+qi+T2UJXlfzatFabVsaOHLSzZJbKieNqLK1cOIZ6XXknI8+kFQ+XZ5lnqkhjd6RlVZM4KRHbLFN9u0SVWdBBmc0mLIDVYMEvNFIhkSdrJaehfni6BV4vGH+jJzXZgf0U6XJeuVDU3RanFUwfzZbWxIoQUtmgIUbL/n2ClXE0/X19Ado5Zws+nGdx6d9acHTdDz/NSxpz3q6aqGotZ6f58sZHMUJSXBGIyrn7EP8dm9/uePtq4p7bA6VZUQo6xq54qGEIq1IKVT70XvaKRU3ujwUrbnIPXzsoUrUCZZj38JqfPSsF+C28UURgE3joVg2aaFKaAK86yxm820zg2mmlnNtjjpzjJxlcCJa1ER1KDOtin4ePDtHSSJC27WJqpkzB8Ngl1ka4yybCcU/NJkQ20jmxXJTbQYaCgJLxbQRZQWLxcQks3aAAbVSNAyY08aylpq80uaRKAo5yjdeFKJPU6FQSdTUlIYQRsiTDYTumNziPR+/3d/wch+45xybAlFMXdrN2sQ5W80CXRCq+vAsg+1kr8TErOpa6RvncY6eR3jIhEYWS5yqTDRHpS2MMC0L4LAzcL1e+v3sr/4489ZauAW8ot4lLQl1lT2kIlxLDyXy0SGv1njOo1k5zksFaiZrAhfjLCXKuAAvkBngSsazsqTqWeSNsjIBXLogruA1FgCzxYchVsLVysSCgjQ4CKOcoOMpb35mlf0f2l6YVh5QLWGP6EvpJas5Hb29c/9ac9z3wW3O3YDS1dA5zWxXiYU+CynRoUHiYGjMOeDEGLcWt+aaM2B/UPSuk4NXK/MczElMg+rFthdDq3nxy7ifY3S3fWg0eo2j+Xb2nlVUpEObqXIdSds933tVPWM2g+2yI3vNLGUQmt4PS22Jkk3fdlCFzNGBOsui7Dw5Hn3cK/60xZ+2tHu3rabPsPJa6RsCa9GcAMvrKRddSJ1CptBwW55JM+ZNtiFbJYmm7NJJzYjRbTObZuYHPXPxzS60omaD7RwBJefHZaLjFm+D3y/HOx7D+uNmrzRtO3fvyt6lrrN/4C3HnMNhE2nVWmtAS0mEy+gF4iW2llzgpSgWSmyGvfGcaJMShmYV5xRthHn2vG2mqTFy2DYTt/3y3r8m5jxnuicSSIo+nV4eXqyRLHZSexudI7BPnkA6boYkvKxyMbtaSvVnQScpB7T8SGtSWdFWz6jD9YW+Uuie8oinigIC9MwmI8zXiE89Q0RXPGKVAYVcO/JaJNYeN7mAUSt1GL9bb3o0xeTvPpN2ckM/jov2l/2Szo95nzbe+2PO0ztAD2GGdQyxXdvWDLvg4Q2NQyzv9xw5WmRdaI4LOQdyRovrJ1g9Puy2z531MUFz88Pm0ONuh3b207VJ0wPOQkl0BA3mZz6WKqHt4c1InwM5cyFqOW1MTzGtWosxqkXLqdpiwKwiTY/5oE3d6jjOmDhaGylmrtwCUUXmmhdkgmQwLS3KGlsjYQXMboyyrB52snp7nC8zYMelarZ+ZgnXzC99hG3yms4BK5cbEUDQLdNxbnN8p6py78fP89v18e12dlS1a0QeYZfSAWLO2eCjakubQHjY5rtdDERZ29uYmSuWDgZYZu1BR8AW+LF8exxD25xjeglVJuWYqIp0AXbovCBGTkEEe877YzzmUZybX1ioEorpM8wnVJks+WWzLB3nBXmeR2wv9K1622CntqR6gCCSiXyaB58aH//hiwHKwcLTX7ccKljicvzYYkvz6SWAVpzcyu0BQcssCPVM13v6yUQmalmFJcrA0kqw9kTRaqQTVfRHfXuJj7bVDmvDPWobuWtkJdSnevYjQI9m5Yo6Goic1oK8Ne6xAqy7svtoUWii+ryfffTzoz/27eXzp09+2293le37K8djyj12gDx10pVW0/1kEUE3BiRRWuz1zHmO7AMqm4rt0spsztSQgnWyp5dMom1VfSJia9zaBgNnlvlxVk1qqF5ajS3Sz20DgmqJrGV3eOZ+uySWLcWIDNAEpKvG1jE2vQNkb8iLfY/j/qd5vRwzZ2neK/teU1PD2/76+T3HvrE3wCatCLiZsisS1am5U//lYT8d+i+Pbft6e9nY9w+9H7tvyJOBc+YlYmL6ZjkR1VaFpIUKywfb3AyZz3gbldSKmTUHVLXofpgPZVGyVMorUrpsQfgkh4bO6/f3s7moQhnUa2CtaEjlLJUbgwW6uJCsRpdp5CczTXBrM7ZHYWz+fYUCWUb4TAkKWS0UpgSxWE9Z6FNJpQWj1ZIuYEGg/AEKCVwcMEwEEK7gSujTWppXdxDKbbnQl2sSWp1LkrjU2SSGUZpu/XNNHA/b8stnx3v385wnXre279aslh3q4y33qS1ue0ULTM9NumWQ5Z5XtouPSaRXCbgXICud54cyHf16abd9u+W5z11pyksdZadsFMouL75/UGfVNDymdRmICTqqz36OtmN73UfvM8vcCTGJCpVVL8hwuLKNaUqRDNgEbptdWrhFAGU8k+yFNRUwvG3xcjHvk9HM1FBRkEGmWskQBq00AzNLlhUnULKYBttlNumin/ly5OuvD/9eNUfOTo3H8WjNMvv2x/v2+ct2i7jUSgfLLSq5HKMIA/DlyNf6/pPmaz919D7TXndT3S7BI5N9N+aEKkYhg5BSVcQitqamZzzt4ao1MDTmRJmzoer/ECBPqzKT0RmEl+jZVZZWdml8nDNk7z2D2Twzl/MjzDnPBFysVLq5DKkJM/fQnGYep+rrDPskfsaGAea2B4OaE+UGC7gsUcpl+aWvsrzi3HIxkU80dGW/2VMCXYsQdnAB+U0wYFshibCV1vhcm5eVr0S5kDT6Ckpdhg9ppbvCC/SAtRqbHe3TRkS7v30OnP5xsFqaaljbZYNjbBrXuG3FiywnT2FovAC3FjETvdpptdWR02ZpC52Vj/HC/WUzXqYBmBnynI95Fnqhuy/mG9NbmffHOdRlbbswRg6Ym1tmSjOPGtxHpUDzsCY32Cxk0pomqmImkpFWZjE1zPh6u1w2a2hGMak5SpmWRdMW9TYjZtwccgzLHaKXiAKmVVJGDSLKUijNQjLmYKtqdn58AT49fH5/Q8G+Z349494LuaMb8WI++t1a09c5lbnvBz7XjtbUZ0kFnWDepOj15czPj7qMPr99PXpetjau/HT5nKPTG9JGJomMiTJLVtFqzqqVviFROWGWWlYSQYKBxcychSwCYhpsRUgtAimwAdLF4szJwjk7qYHTpgYfFVFzlubturHmWGpVyck54OkrVWfzZQSOOu56++X6b/78IWHfYHBXaIyZYRhKp6NShLOoWGvtyslbS/HabZcl8rkBrPRSPCHTNRote8Zi2EAAy6mXK56n9LT5eqUwVU9pOixFLLNBqLKqim0cVecL5xY19za+x8c4GvZryGWz9/ev314+73sFRE8CrZtJmP2xO1+iXWaiKtk0yzr9jusW5v7Rx/Vyk3VrFltjyFvjlsdHZn4HhtExpPDWyISXB3pSTowxHOvmDEiqnBGsHNXLc2FpApCPflT4JGec6ZlKQ4s2izl03TZHhYdZKVEzkzNtZoSGW205KtzlpQIuQIP7ek5UXjajWIhIGdduPIaAieZx18+P921+wy+/vJ4vdTDuMx5AzaYZqH1vQXVPHzZSeRmjP7Ze37edifIMuIU883X67e3+09f3l/OYb9/z+9v+sGkX8tN8ecW/fHcDuIWXmEykQWV0jARRRInORQWzDBxzLLpCVSnlkrrjybI6ljKNtsYN2SyVSqtcDlCItslaOa0EImTVx0oDWxH7UpqsZtKayWqGqKqxH/v569cv/dfNL0e/TOp6cS3BiOXFzaZSohhmKD4FEqJ8yaeeJvgf21jpCYGuEUbLlLQ6wEru13NM0srefDIHHoaydWEGV1KPQhZSK3vu3MqJ2Yqps3gUoGP4Mer3B47aNfb9dfz+xtnD1Zoo+owaVGISydwRjfnlggbrH7M0i5WYBm7bFfu2W9mdSpp7VbZCVOUwr9wuxtOD4o1MOK0OoWxMVRlPbftt9J4rPqD0zJ/Esq81FVvbBYz7vc6x7XsvoqI0l4M6YGwBaYza9wUwcGhWlYo6cnfPt6mTUWFjL/M4QjONKLCgCTFLzRJKAYaEtFm+wpJWj+/z+71//d7etenw0SyTwAL3GsnUOU6LG4j9iiwBbbRWwqsP5Ln5xkqd2HvuX++Xr8OPk2+KIT/KGDfuj/6RTJaRNgtIjSQTYU82Ok+6RUGlp29WhLmpylaq54r+FkCrlZC/FvzSFKtyybqTiXKa0rTsC40OeGu++aYcsLmZt0asYO/0UVW1fLnKbjTvmf5x3LbbX//f/1/9sfP/8m+3267sbZ1ssrJopqm27rmhkCsiWVUmJ1fk7DO/cA3/P3wwrEU3Fsv1XOjNVKqSSJKQU1IERFVxirHygijIBjCmfyCN6dldWeRUbiwv33C0Ufr91/3j+z78pW51qo5uTHMZa6PP20uWsssb6xxm+fmm7dI8rVrV4LCVannQtt3LX66Z37CCAC9eTXmg3kd9HPvrxb+kmTBndhBEs5ka3arPy9VX70Y5h0i5MTaHCVXW/LzPbQtKCqeA8PEYgs+qtGQKTDch0zefGoLlmflIOwdQMTHzq15au32Oyk5jmYYLtVj1wWKgvBW47C7Te6nmy+dXfvvNjrf43uOfvtZ44Pjw3bZ2FafNtLlxcSVTW2Tpfj1tGq+z4nG1ri/9bOfD9zPyAe/4OOx7tG99e5/+8X6ZJfVruDELb7eX63wgB0cVU6P3rHTZ7FqJJLHvc2atCWGF9xZq1krWkQorzw0/vB6LXaITyZXU6ahkFcRyMeh+DVWiOZpRTq9Pr1caZz/QUUhOlOUWVmxDNovTGD1vn24f33rv3+9l9u1/m/n1z//u357NT7tU08CEb6gKRxmZYURa0itLtpYjlsFEt7UAP4HPwkpntdW41qbzlEw8Q3JFWj7HKMKIEytwVVvKBaSmp9qA90oBqhwSbnEEio/j5eP3eGd89Nvx+Ow3zHMcaj6Rakyvh8oczXd1s5kZOXZX89CZJTX6ZJ7azvMtE5kYZ9/2l7pcchyqybOqtvv7gcfZ9j2aR3h4FuXg5m08hLHlWUTMk2G+bl3xMrdqW6OYE3NqUFU2+rITNiCpGGcfdWYKYRJyDN/pxsoxbTuOY551nOeOLIJVEUhXXC7BEM+aZqryml4Jk1sFkVjK28GKMH5un/Ghq13v1uf7e96Zyot4uWw4hnq16UoFjZNUNkKNHf04xhxH/suxv53NR3wyv84NF6jq94Euu8vvfuEf3O4IN0HYm9GUxuit6mFnP1VpNesJ+hmBVBnNDVUSkaoSSoVSqVAFspZAY+G4ZRA1meXFqnXJgZFllZorflRoLVSZJx7VjRfsNLOX20tdZldUn3PUqImCqnIp+Zsfo1+v0ed7Y+u/3DH+Nr/+5ct//3/9/o//arxC19usSalUVl6eJRo0Ck6WP01hAlD59JgumxLqqTyHmUoGl1b2zOJlBxhImJlPTELKFeOXMzC8JgdRkzUx75VlNiYZZhfpc0xYr/vpX/P6Ndr39oLXvTg+5nZonae9mce9joJembVtL/1+KjNzChuK5+OOU8n2/f1eNV9eLwFYefnpkZT1eeQcchWGv9IazSskTSBp2wWlx30m6OZjVk4Z6WC4CdxeG3Jm4RyjZx5TNbG0paW8vMT9OBJZ1DSYAZVoNiEwRo46emNUVtAGpZrmb7Q99pcaGTdrtBOUI916S9ScjrImuji6ZXqqxeUzfd6P/OXr+de/+Ntx6TM4dpztI21ummkpD6fRla1FJWoMQ+10P/O6ffA8vQbfwi/lOI3AaBiJoiNwHiyZrtl2qmZNwwSSZOXpqR+ho8+wp8RKoas1S/+4XiBXbv1SXTx1SmTOpDmgUjk9V91EFpYYAYulrQIKKrUIAaPy7f0h+evNa7IFm5PNhzFQc8AwU1vKmHOM86OnbVf6fG3W+0f9y9tt/3mGz9efRmYYUPBqcEuSwvgRcP68dmb9vM+ivlzWfEaiEMaVS77M6vKJsiLQtlKqeu82DF7qQJhmJCNGVNqpHI/UB8dHZYkj3WytmHXfa+L3j7jXyxE3mOuhM21SmYtUvr1k1pEyy2FsibQx53jEzfzCesyoevTjrCPfYrtaP+qRj8tm/LQXTqLmcae0ediX0Ku1t4mKMVmwdrkGON+P88Ds6l3plluIvN/Py76LhrjNftTR57TjGGlGt3NMwJBpZzuP7KPbdk3MGspRMFlEQfNhUNqmaHv5yO45ejG3aKN32Bk869XanWWce4ax0zOMwqyJlmU6X2ffxmHf//a3f/6n/OXj9Tu8Zhu67eauTbQw1Nwp32zfrApgzTHPc4YAv7Xq+X1wni9frvawOibxIEOzXCuikhWM6RgtBhDFylRayZJeVSzBnEEAdJFYKWvO+RTM+8RcgmHU/8Fa00CpbZueYIkvAkmV/pQSrwQph624LRHMxWHMVeCsRvN17Bz7Hmw1i/ePc8xEnXBfwY+WxDzb9Vo+Mfd7H9//5Zdo1/pk8fPPYxtqUUhWU5EwYy7nC1VBL8GAucxzqB9Xq+l5/m0B+sugLYaECl86uk7ODaOyIIbloEw55vBDdT/Q554FosegVeO8afOq1qc/jtYnu65x/by1+XZg8Kx0SIr9Voo5v1XNoqXHjj4xz83t0l5YTs4xZnXN9645sseLt9Zszod/q3EeW2vKbG7NGfvNpJJ6AYNFr86ZcR749vHej3qfx0+fXq7NZs7YvSsfj5xtq6NfvB7z1PNyMjaPkafBfv3l98u+VwNv0JHZT5q7bzSbEyLdDaYICYF7zbIFlWZY1Rk/v9j51zff/XrhrXghj3MsqVDvZz2O7XF/PB79ceBjzPd3uwMzgGnmnni9tsvt4n3DZrb77qyZ/cyafdzvSYjNet82jKO3zO0d7q3s9IuZcXqh9xUKBDOqxCx/aJzr4gUTwlzoU9wIcEUOWUIw5VNP/zSJRPGHRlIQkgwhSSMhX+nGa8n1SpkpF2Gw8r+kUi4gnSpVrcsOGwe0IZMrcxr5MWFVOWpQXkFC3YoAYt151B+wUcFml0uVjkedjxqvvGX6NmBtnzmtJXy6AFit2EmCpVr2AKsSa11SBcIlLg05VkqEcs7KUZtcwHgb59HnRySKnFLb3LaWLQmzfrQzObr7KMCj7zW8D+O+f3+7vR9tL98vl360Y7qaUtU1a9LlV+dQDRk9jM2QGlR3y1j3Pys1a/Q5j66526UJfHzMtrV+/taaZWsEG4o72YTsFH14zhp9cKbGeP/t0Y+6P2ZWYFPVqISHv70Nbte37x/IOmqW2ayirMQcM5qdH4cF4xpZfjzOGlpHnkSiymzdkgmhct6uN9Z7nuftzxe/bNHsfEPg2/v1i3g8vv7tr/PBjzw/XfSPF/71L7/k3975AXyULHM+9jH87qQ1ekDXKNd88es+ncpJbXtZ8eP83h+9H2PWw/Pluu9tcwNRYT5cxZmbl8kqS7PWJVZhp7PZntVHjns1gBV0wqqSR1lAcqe7OUuTNaWV0MUVkfO8azvy2QNqXXv1TETWj8izVUUJB8uZRdKkKkJFM0q1ZIBiIoLG8CCRAjqSY1t4KI37dDiWkKWeicIqkYUTAfd9nuzx8R7395c//fxVrbdtGbmiqrFmaSc700QDsyzWTZvkitVf/wfL0Lg87MUqkxwY1Zg+J0cfb/c5PrzL0qKVW7GPsGPsxgywLpU3+FXz2rfqhY6BcYfZbDsOP7v3685rv59WdC/6MJgFzLbHx++qRKM1xYV4H4G87BEOKKnUmOMcmSO4u1kxZRjHgG3cFnoJ2wwbwcJK3rdZR0aQk+9v8+1xPI5873l9uXmNTIchAdvi/riPY2w7XJaz3G3mrFJCAVj49nmbU93qRI5+Xm4vMC+Vs0E1q9xZyBy6H7/7Lf/xzz9lPq6fbx/fvhLXuN6+/2///n/pY3/n79tX2W/9n4/f/s3L5+NUHa6hjWFWXunl0WiacH2OZh9vry+3TZuPmnlucWHvb2/39++/WxVKznrZsG1wI3ttvtUu86k8Y2NZlQlX385yh7VynHkMvhoaEyyR5TUx7rk7swqA57qbTVbJyjQE2WEOR5a47ihjLWfyD1508cBVz3A7SJJlLnea1VN5X6CpqgRk0SzQ6ObhDMNGV7AlC6k5MdVF1t/TCukmJ56R1bWsubm99PR6/+A//TW+vHz58uePntO5CZHToMYY7GbhA4N6XuUbZtSaIwnASk9BkJFavp8q0Wmz2pj66ONIP7hu6KbPiHUlX2fK5mZWX6iXY15ROxSjcPbu86UldL/c35u75t1h2UdFnJbJUT5ev3wqdC9AQcAvArrHiBcQuaTChep5zDHWBZVq3nbv90cWLIzeAsU9uAESZuF5Y1UixOLj4/3o+710ZF0ur8aaXeFZkwfyPPr9nOaC7QUEgwKKbnTJw7kZUdjUP7JObPsNM2km5v3+HmX7hZdPP7msMTnm9bqTvL68jv4o4vrF4vf/+D/887//n+L7v4IBPePMV58baI/QCYC7OZhNrXAQDBd2t8yfPn1+ubQG69ltg3D8/ttv97d3zWjuhF62L23frXZOlSpabb5nlgfc0TZ3p2NEeNSoB4vebrQwYsvpMFX2OXN6zmZRTVke0NRMITHlE1mQjFm2IJHlIlxigBVRMavE5zMArYQKzsoUUspKrIhDcJ38lVdFrxYyyGIA+zMDBqD7XOtpmywwLHzlDa5LsmsUHCYLhY2kouXj4/jf3xx/uXR8+oc/90+NMJ9HxYaaLdrKYgBQa51Zd5KQoFhcvMwTy13LwXIBT/Met97yMXHEnAkiArycTg8Vwaxxq/KxvY7WjrGxIqfVcKRPa9mHps+Lndm2VrMbMvvddzd2GrY5sm8amFa+SImqy0/b+OVjjmxNWVkD/ZgJwbDtTpuix76POhBekO0OtHlOtApLbMhzJlSl+/FeQ+d4TDfsu6us+hzzPtNiO+b49jgFXP2am18BTIyRNJTA8H7WtrV+PqIZB43GWXN0uYj+Es12vHza24Vt3+dxZzV31OlVrL5tttEUv/yP//HLA4aPs8QR++Br009XvydES1XqznOc9X6J9IgLqtE+X+JazWTnnC7oGN8f729//bVhdximXn96ja2VeYQwCqjWzFt5CzT5bvCi0lQrGR9GWsuRlWVtVtU4Z/WRWVAFdm113eN86DxSZgh6IcSUCQw3iiOXZRXPkmmWc8qsalX5JQdbuBCed6QsRvgZ6EtI5gbApXUtXyYsMskALxduuxm9ZpntWZnJ85xZNVOZQ1SQcDMiquVDNebL1V+R+tu31iv+T4f9d9ff9MUu7iBgpSmYW4PSWJwOJmFLuqOnivV5/rVkcksJ3fsLTefRzqrHeFFtVyen9QxNzZM6t8DtnBfYHw7rmTi+X66NnKnDMt3L1m1UTkWq0/IuDVbYFc3J/iDACcrR5A5rKqG/H9sWgpCz93OOzGR4E4vmymmazTRVZHNF1uP+/f3mn7E7TNZPB885NTTGkO11vWnM2c/NkIPYfBTP+zCotdunT5cWG8YcJFpLpYPjceSjazdYeWtU3oKac3/1CPO27y8bnZf90rYtrRCXPDpU4+wx7P7L2+UPn8oqPv/T4/Oj9X/d3vKbum/aW2/4xjic1D0Jd873Bvp5bLn/4fL54rYEBaPPPgf0eLzd7+d3wBECxsvLZxJ5lF2UOfdL06RZlkyN2DDdSsOrttKK1q0JuUYOVVVmC/SqymkFTYXJG69XHQP7NaI4C+agmGWFkMeS23f0dcmvgMoVtSC3QFk91TVYBsGn3bywTLLPOySslvkOQKpYhGsoU+5EQnvWFtUsnNjaFbt+et3HGDlT2KrUR85akSHDS5lZ1EVR4y3n4/H9Xb39/H/+bz/s89yLQNlgBTvDFqlrWlcMP9sSy6AVrVQmSFQSHD1mzjN3Wem41HypuR+ilRGmR5bdxm3TfMltZ/jEnN23UJD3hDHNhsOky8tuj8mZNcbglNvl5pGGXsWkqrACioJuCM/vH+M4Xl4/GzDnHGcfyLKwgLlVzmTW7BW6t01gzK57d1psBkysmA7fVIUWehTdIG50jMpIv9pgjXMMy7bt1+3VnYQmrVeN/pQqYw4ic86Xz3tyfv5y4ZiBdrl6XJdRRXHbKFUlarX4yMyJ+2Mmr1mX+XbM+Otffn2NT8f/+tfY+9X/uF/owjEHxpxKMoKD8x4tP71wbxfNPqZ7aLCP++N+vMeGObqLbY/YLvvL5vTMfLk5WupQmHpUZ5qFNnozjYQtoY0bt5HpJo2sWkkTrXI6RqPS5Xu7MEqaScw1woSCTJlH1rp04nm1hEdrZn0MAKRRCDJTue6VMKCeV5XyaZJal+BARqetTYPKLGDdFZUq4RRgeQDXicuGzQoksxt0VIly0qXwbK6wlq0oGzl7ZcDJcfH2MYE63//6Tf63T//9/+1b/dEtRCTcnBKSMEopLlYbKFupAzUho1RlqAlTJdihjmhX06fA9Ty3s1pOV3frJbp0NefsTUd2u2I4MY6eJqAbvIktuLsLM49uvYvT2K47eM+KAJkTTKOnt/AwQPPsFhZtp7OOzJ6qMtE32k74bJhQVlyEqEG1tK3a9QpjZVVChhyJzTWF8IJPFCNFTGVgwzmZefW9ba/tunmL+f7op7LK1FFl4wQmrW5fXsLP1z98tuQmbxdza0AVhmTVz1E94LSK7SJW9ZJSKrsEyOvtc7QPJu+4NA11vPv7a1yjR5dKFoyjxvfPN+2Gy35hH6BV8FB+++17v783D5dT225+feEWzcNMapdt3Q8629QQigwgdDHTFFIUU60mk42KUvbey1wsN8KtpqczogVWoLSNYbIpQmVGm7UkxTIgK23dVlClzBYoUPYMJzLK0opQKU0ugT6zaJ7rJq3EQEly38Y4iSBXxDWEzKwwAKw+u0lVGTaX4Ebli5lbMUAN0QJumJbqMN6CGQewJUYzKh/to/kvwl++vv75j48W8G1DbbKyCt9mFYMJFeZzwV5SpjXG+XMmMphjfG7TkT+1efPzUxBvY9dwmmGDyjRay5qJHDkGDLAIndAwZJltdNRER0qz98Qy2a6gNIOoMtFmieHBBgQy81EeBpl61Mk6l/VAWxgjVCX2MqBzf7nYkeERW/NoRaunbsWyxLA2W1qPWXZ/YHPzsHCQc8x9byIu2+3owqSGHd8+vKW79qbLy+083z7/4ctkofOG9D2u14s0l9szMzlnc98um+QrjrCma2DLC3xuL3HO3F73uEZz88qsqSx9tDGMN3lhhG979k8v8+WSSFlWnvPb22iXPedxPM6dbWvNfd+32KyuV7en4Y6VJWOx5hw1hhjRLjQAfSVv4xl/LLHg3o9TsMpqbkYwghC8Eb7Q9zmreq3tf1aJgkeVVBpzAJT708FplpVJs8nSrFSq5rrqS890kefWUev6XgpldK2LQVpIte7jEqGpep4GEEWVYCbjROYK5ac5PYxmRRbRyyuH05ia7EANzcwbVe0S7SNf49X/s7r3y7/6/H2+NCtirpy3MFcFhORYHHfaSkDUU7xKBSDjNfhqFdIfrmM7rfVHXIhJy9I8RfeVTtVnrSEuR7VXWHcLSaElkNX8OJnQTMJMFhvgwiwihLQCM5sb94JNdKmEFe9tY4xj1jSVrHG3S1ONAU0hsg9wFGie3B1b8FGqkoF0eRJuW3mklVs8Ji9gYbtebxeU+x5yFcugPM/59vF5N7/l9SUul0DZn67/SOXXr2/mcMIMc04ps2pdZm43b5foAk7WyTmrptDc4mp6tH2zW/z+/hbG2HF5nyOplXvbR4kPg53ncbnU69VMDRbn+8f9/YhmNc48js3365Uvt63tF9TyawpSrFgbyxpJmaZWjlNl0C/I83m5moykmRfkrNb2Y97NWMjmTUQLtlLXgLzSKotV9F2+ugeqBONYiUISBspMlTMxNWSOROWUbNaTSSY8azmrrKCZrHW1oqNqPlPCDapnOA/mSgsGMN23KqZgilEqaGapijLvBNIJd7kjLKOtZHl6hGZSzprQ9uh1/XzJv3Qf/bO9ffwjg0q0s0kYK/KesZdmzwmX0gpATQAhc0l2EhcvXSnmvLi/GrDykFo1VrlYe5aq5BVn/0DlzLG97rVb5FZhSLOMajRD4l6hnHTWtm9mQC591KBYI411+XyBB+DKR/XZbHMtiI0YSWPbfCu6kOsu7cTZ55j3trucFgBUObUy5aiNTjO7bLd29e9989ev53yUvn7/+PJpj5dLRMyJ0e/3j7fr9fbyx7ahXl+bbTSLiFtYfvzyK2ZvW5s9Q088zqPRpqLoNpvPOTRG9V7JMpozPGhbVvBy2VBhzatQs8ydbuVBqMh+fnwJ21ks3yKqz+M+bmYGv/fjcnvdPb78dPOQy8pplb5uVmKxMM6+/JSQmLAm9ocK/mWrnKpleKpcqVpzbaBbsdNZTZpSmUpJS6FXzqpG0dJDBVZyyuaSq+YT4kemVEpBrJwQq5TrSsdaWZVTsirQVrM0gFnJRNm6DYErZXkJS59BOrb0TekRggscsFk104Bcgc+kikhJEw7Z1MpglrfyoGzbDGmt4nzocrs8jvP+H36PL4/bf5Ht0+cPbR/bTO9DciyvjOXz2uZyY5WEaeglK8HMxsDZ8ZnMj/L7wBjWwBguVGZU4Zj0zXed70nSMdl27MR9YpoSBseK/JspCY1+o0aChYY6MEcCWVr3EjKDStAtjKBnr3GeOWfA23Vv181Le2yjT3DXHDOP3Tamrci0lY0qZRZS2tncSWy3zSx1mfjAeKlQJg1nL6Vy9J9eX7abbxt9zo7DZ932S/ORx2F1vlxbBi3MLiZzkjKfpFLVM488jgfPyZwEHJdqdN+LEcn+UW7XyKvlWdVqmeQIusU8epNe9mrKecSt7b2/tw2aLuGPn/8Qm10ul72RwugHRds9LlFj1pmjpgTImTKYvNZzYfPQwxDXqrGMToI/u6KAkoMRjZPjGCpp2sgqq1lWUSyzdbmBYXM5ep97uqtWvtokXfz/8fQ3u5JlSZYmtpaI7HNU9ZqZ/0RkZWZlZxW6a9AgOOgBAb4F53xWAnwBAgQnbBBdJJPZnVWVkZER4e5m96rqOVtEFgdbLcMHEQFzuJvdq/ecvUXW+j6WdVXxZVmBBbteY53VpCVWRdhE/RuCfEXOBJCv/7+4cS96ydp5SRyxuO6UqauThXqx5ZtEq2Xgto9toSmapkAwp+90N1navJ+5W398/fr/mG+/jut/0vbpc3McJgdIq9UaW90OOtS+8MNoNDXMyTjmvqU/2tpMHZhO8yA6vQQDbgPdQaS15xxtpmvL2admowx1YpANVhplNG90SQDbpT6fj6ZlP3VcSxE/3lB3mm9mpM7nozPJ9i2uMQaj6jwraQPNLuUx7bM7N0FImXcn0Gsr/1rIHM8jsy3inNMmS41C09lxvr9X1eWNt2v4Rqu27Xq5xf4JmjZ/JXE9ZkLc3jZspuyewhoRqPqcfc5xnE3Lbhg7fHCw2oTz/pv9+MPzL89oTjN76lSoxuHdx+PpyD00v6X//tPtdpn3e6S261UT13273oaT7HLhyOnLzEBWL+B9LssCGrIxO12tbgxzg1UVDiuUiZjSSio4SBvScKhxEA/q4rMru9vaO/1IH4MbjYPFZ/UpJvPszqxZZHAtz9g0Xwh9GNH9EoCKtjjVBcnZtapXMKcKsBU9W60amdCCkYAE69lgo4xoc2uWQC6qJyip5AtCdSp3WkpdkJaVcVJjtz6DXbBuJa3m2NDP8/ynmfYx//3pn78UQA5jEV7dybIXxXyy6ajAwR5oXVRvfLzpl9v5yc7D0LYV7ADRmGp5GLQgGEkJEdoHDHa2bLONOiAls/vjkZ0kcdmFWOFwuHWmefTzudueNc0/8T37BBXhhsLxfsc8B8Ni8z1sBCj7OMUyu6Cb56Rd5MZZVmp3c3Uuk5zXEkkguh8JyzPxUZcfxhZXmJ7q+e3x6Ue/3PD2A4YDHREBen7MPu4lpmLcdr/RBltdKjOjYZr80Xlvm5qIA2asVMnOGMyUzfax54bt8jnGZs/3v9z24Vss1piFrEzz8el3t31sH//622Xoy49fLpfLkg/XgTYEfM5pGrI2d5BdpexuA4XOpsuc+eKLbBGS1SLxTLVbCTk0oEr4ZZiCpwpmPWXqUzlPIbIRgNlmRqc546gqdZXO2dVVBYZBVqsdZcZFxVnR5lZ1S9ZNmi0w1dJorHwQBZotH3ULr0IBSVh3iy+5SnUNyBhnn4Ftec5eATWjAVUgaCxCDjd1A94td+8ymI3LpunbqOezZpYyC8c5w1B9q3+/l20YbQSblL8UkWh0smgqQYPz2udA7MBWwrMB+ZaEUBNlPWnLbp6Coc4W3VweG1qqBdRlcaITWbMTpojAYPd6gzVOylDHKZGW4/azSNwn2joL162fOR8J4vrDp7Hve2w0p2WDgquKhAfDRZgqi3INwNKL52SEfNSzzjXYp3UZfKvbSN+Pbx/fHl9//3NfftL+04/2aYsEBajnzONxzq8PjLBtG5eNe6tPHa0GMNBszHPqqFd/1CGti90x5+NbCNunH/e3/escu21x3u9BMPjlZpfrkG91ZN1zu16v14Hsbbvsb4M+9LIusDGZOCmGm8NjYwMoLUFUEdnwIB2VmbkZbZEpZkmuLDQQYVUjIZMcdFNVZ2Eu8zZnVnNI9JIZRad7wKp6fswjHyd1il0ajHM2Fv9byiyt6QygBe1Zu+GlK6ZXpWACzWzFDGqtikuSYGrIxeq0JQykZjfQ1QW1U3Oe21h82XX+QveqsctkZrgMi0ELH04OOqJgw/skDvW4XJ5537LpsVU///Jx51+uv5nHD8O3jtlRaMKwDvSGNlaokM1CxHOHbQ/5ndG5j9L5YJ81T4LojWo0MVOx+OfFzTSsnkXIzdREtXJ2CepgxGV7hcLNIcM8dWZXVWvb3C6jHskx2AsvzPPxqNkuXC+ftW8FehttGLzUmdOgfbfhDlNPWBggeGgCLPrIycrM59FK7l9uMeZldb4eOJ8/o7987vhh4KYMBsI/zvPox2+PVhec7cOE6Gr17DxmM8QCZp+zp/r1ZDKy3Pees46D9Yjbj01aSO8f83mE5jGGho/wSx9GwrPGMG/1WSV++fFzgD2t0HXCBHJrg8cwopIA2aw62AfHIGDwTtmwM08Ls67wnTRZwjqRsECfRjTcyKCTVm3ZLVOqu8Ai3CDJXKjLZWw+IMxZ83yfmWcwM8wHSvu+0dgvw4fN7u5G01Z0bOHsaFkSaIjVJu3lq3nJntcG+HuDADIa168VgtbVRFtoGwaZS7DQaBSX97RVBhG4ul/ALZxhZm1mZlWA+glx9nm22q08t9gkPeY8//y1/+nL289bWcGcmuQwydDtQnUQLslqNFpp3m2n4QhzVfYkcqLUcifXpBTsPg560cy2jfP7CLf3VlUJYtaTTt8sLlZlwjI2d7XynG5VrXH7DMm3HaN5oX8QwDETu8LHuO5tpiWMgKZKpTMTrn3sY18tc8HVZRjSXcaRJ+fHMetBNW1U1X7bgLGHT2Hnw27VHj42g/rjWTPOj+OZzyaAc+xj23e/tA8SPOtUO9waQlbNkizgtFhjcVoaovpul4HLoNt5254fv/7Lv/xv8eXz58jn/ul3YPeJ5z07weS23/Ko0eZmdeZ+7bfd65x7+L5fxj5U/Hg8S4jh6OoznekX9yCsUc20IbI0LreIAQwzlVJq1uHmMnNzC3d2qVKlOpPGxtoXEi4XWL5tETFTLb9//KbOCJjHbpsPt8v+yOru9cnlaFWvUQ5oaqvW2nJE+PM8VYIFjVShVSysL9PKja7E9CrjdrtZZ5FNa7N1VZCztx0+KFlXuwiKFkGZcPFNRXY3LCfyOKo6Sz62bpWb5NhnhHWXW3h3EPc/f73Ov958dKZ5LROYMyUn5W2xOVJbn23npXXpc+8ZeeCE7k+YBEcbvHAZeByo7OcMiJc9gkiZwgocxedTVXU+JZibbaF94zvpAWtU1XH0PLqOGFcZ4NYNtmwf7MxZPYUz3/72bzDcfYiyyE4DLOtAlUGXHy9x29pdazvU5c6ejeI5j+ozujWE5nW4vwHuY1x+/cufHGdvnp8CF7Pzfn49qoZ6aEKy7dNlv0ZcvNEM1Xks1qMBeD1Dy32zRd2jrd3N8cuHN7fbbf98NfDx8e05s6Qwbv/u9z/nePNovvex6XHvvPu3X5+/3Y9I/MufftnGNjyVOWBuZhhjx6bYIhrcNlx22y99uw1zC0NcY56V77PP+9hvse6HS5WYTyFl1vSQ2WJ2JVjT8nmqiLHIf9QisZPEMNYxbbs+fvt2PE9u8ATSt+vYLxvaePKsfGYBjWqXSFhYSymxy75vU/cRZVktdZfoUNFkSkBALJ0WDMvULutcZi1zI6PMOYwOXkbE5t7sYYEVQW8zCa7SmbNT8yjTrCBhHKhKdKBhrC5gmGDHMXuEddlHjaN49LbfQjQa0UECtZiJm1SGm01WRmEvQ+52eH4cKIcJBSIYgAPdWc+yMkPEy0y2HBvtxCnyNe4ZY9tiYKLQ1gkQM/t4Vk5Wc6e2rRuZM4bx3ih/3p9twhbjemFE1aTYA/WYqq5ZNAAxLiNuO6KIWJrnenQ32lSeanEbBkdt4+2Gj3O/jV+PBwPjMuTun699Pub9Nzyz7VoFlMZ1xCVin2ZSIL3lbBg9aexEzjTfImBhPdky9/HnP/yZzstPt9vvvtj49PjzL2+frr8+3798ukY/9788cP3d5T2/Pt/1+HjqoKV9HLOyqnAQfa/rRshqFtDjMmIGha0Ot+16oeH9096/+zR+/veXbdu6T4uAPZ1jmHEfgEymGJw9cqZvHcYYDlNbVaFmnmkL5r1kogJYNDf1fE53f97fZ+b4HCXbvjgvn6rHEDUrC0H6xQtUedYE3MPN4zgSYhdy1swl4QhkN1rFNlniBWALomnGbEBiicYgKZpJazDakNHN8tm7a3M0iGrUsuV0dXapurOmFEnYNJItGeOVSnWFoc+Wp8l7liNvj/p0VncPXBwNVUDMtbcuowyyhqODuaO9jn12Hw+knE2lcyMEG0BDE9Us2ka4rQCgmYFltQRaCcEgv47eiVNNuQOBfL93lzLN4G/XuA5VhW2mzjMBVnZsseXu19EkaKLMzYDuqu6gcQQub+U0I3iqVdVxvAyAHhG3watBV8/dzTV7HkdM2W8f/NG3v7qFK799sHKt1M3IHfTUqd4IzmpMo5s7zWynbTkPjFHOrNp9M2h3f3w8v/x4s+vNI3y/SuP6w++UH6zAPmNs1+efHn/+7Y/HPAw+IsyYyk6djVm1w7eIRGfLhgn2PE9jDR8nk90fXyeU71bzQPv5+fOnbfTbxdEWYwO9nvd4uzVPqcrYMeBO2xxmymZUnX2uJhUdrhZWvN4sO2dPaW5unWhL3y+Dwzyki43wOqtLnBKYtodr47BRveDntFtUsqtjt1GYU34qkiWArEKXsmQi1LNtEXcqAV8gkoWHNKjQEMVaxjodx+TuXOVyqbNA5Cy1jllqUjL66tzTvIGmaBWkytx7HjNi7RnPS2O/Z4jSDBWJ1pRZqsFnBrLNuqiTk0qLc7PjgRR1iDIPaZpfV/WhqgpoV+jFCVpHcTh1Lp9VcZi5x6Bg1W305apB2syj4e5j3zbuF0A4DZ0LKmaCb5ttYVRXdhcgq6UnaSdimBmdBd/WH5ryOZVVsjbQh/bLZ8OFtvknBxPT53nHefT5fv3hby/bpf70R+Jp1zge1cWxuw1/5q+lXemChm+bOUzYgvLjoHojquY0t8pjTrT1uOy47BHNCHMWNu88PgInP57P+Md/+GWHwTfzcZY+HuesdLl5uHHsl2YfggqqUKMhRqA0T1xoDo4RKjvn/dej3wp24n6fpnHdvzgrH08SKLn7rELfjFV0CUKgOrKPhObiuBoXILBhtFnlwbM19qFLoGZ4DN9kBvcqYU45Z/apqjbGAo3SloIGjdQqCaiwfHhd7JVYtljMuNW7ZPnMXCPCII2eWfrucTdBcKgcntkkZkBNHWUhExEMH1BLLkrfHXgL/Lx63GorJIsLUafJbYzMbgqsPaf/69dP/+H6rBW1abMumauFYo8FiSO8m8jyBCYtiTLf2Tv5NDThRCbkTBlrvbiqV8XMQdX9WOUZmsGyfPAErcxMIT2yu9DeBIbzMl4cSQOyOxNGnbn9dMXMNkDNXhCmBropCHQXwZAZ6cGL17enkUo5GG/BoRjsaoZgjjzIzo8PCF/+7ss+OuoJd7tcW2e2bICjBiyTjNkq264ehjQ8p8qf7+fzqMw8j6PVm42x72RcPu2M6GajA8NjPx+yto9f7vnLgRvCnnq2Jp4bbVLdMFeTTe4xHrOtvQRpQ3YHWw4P72KrBZrej3OAn6/bcz7++Mfjq8/Pe/z49lZpMSR2d55zjlZnwkaMgaNmdcYhA6SZBdFiLAZyt1Y/Pgup4uYaoTLEMIsYkdl1NpinlM+aRzUDA9X97V4QGipzM3VmNbACSnSwSioUHK8PtwQS66dFahTkq3sVYTlb1SIWJlUvCUicZzI75xyD2dXFJoc8jMQK2ARQtk4+K6rTViVniFITRAotRQQK7jY/nue/frVfhv++RWkILbQZu+VOWUsryIGdUCY2eXUHgjWjqE5Mw5WwVoBqnEUb3YlKiQyiWJaNwiKmm3sisyxchZZ5dWXRHQmP3cxamiqfZjFKDYdGaxg58jjMrKr0en8QdLBRlCs5aYHhaNKilAwDknsY0U2ZiMbzmM9vsws783m/vn2JfW8Il8swexTQlrL+5Mf5tE28bNtn0ITned6/zbuqeHycJxupcLOxXa4X37ybNhzCcZxRyKbtF2lWVvJeP+TzccRj+JZq66aN9qLDc+UEqoVk14TCWID1bHNal63lKpFCcMyuU5Df3me+fxyHH7//+ROJ683nfdqoHjjUq5DCm0vykqew6WWOoOFliGgL1NFw17ONhI+meapTbz9/EaXjITQs1Z0SN6/uOhffk2pVszvVjfkykjWhxswVlwZIutRSr58DD/qT6paq6Y42AI5oa7BotjSYDVhD9MqGfGa+tBWOJ+UnSUT4BNCyE+F2WTK6lsPMeKrBNqpULIOLxgDPI69qHe8tuG16mbxIdRAvoHykN11INGM6e0WG6FakCkYZDHS0N8IuoJPNdrJDZOd89a2dtpomLYsowgncz5c9mIZoDrVjDdSScsq30TM5QoKZyZpGzTJWZ3eLRpoz2NbqpLL8BuuBSQt42z6wjZpFdSC6Ou+P8/nA8PF5++F2sTWBiz16wjd25f3DPu+pw3FsP+z2adfRNaueM6tkOKt7pDVsY4zt+ulzRPi+n7NUnWd255wyQ6nY/e2XX/ZPl+PxPt8RPfaJ1Ngyi2nJRMJM4tmFc6YJ6gIlmPtQyUi3F5I11rzaAVurvlPN3vv+OMLsboWiy/sQ0GzzwbHf9HFMPY2tdL14DTKY1CwhrHvxvIlmtI+OnM9928a+35/HVNH8yM5s0rrN1hlDqJWMtnb3LsA7RLoVlFUhFmHmNC9ls5c6uijIIqywRJgwsoVma5GoBBpXX7hXM5FUp8vhSwa1umbtYVnd6CXBljBPmdmC+rfgFt2J9pW1UNG3IZ27bf3+9DO2UnbT6dYsl5XB4LIGm5vLPb0ml3A1a5PFlNB98pXFk9Ua+xRFQWsCt0IVcr4mwGzxYgxTyWSawswmjdsxD/MNMUgXnd00oiSgzNQ4v90vP37J2ZChkY31KaC7TGrCvBN6GALcAqK7APc9SCumjNnwLjEBsQ/3y3bb+96ViotpDuw6v96RHu2defm8+zYMqOMoQUrB25IBWoTJLbbrm42dRrB3x3E/O2Swmcfb5YLM+XhUn369Zun8DeHSxXj6RTGXycpQLIPpJSkXZCuCvHS2HOAqgLvTWAYr4kgZe49BsDDvbTeabVZA+3QLIErlMdSlnWqqR5bYMJnWON6AaKN5oMTupPm2BRr72Pa3oLX3OS5IOZ61Nhwy+EpfTJhRMDCajRIAG45aIDmQ5kGB60kAdVPy9YROOp1Ad2FZK4xCLVECuOyM63XVVa0A/OwmwgG6Le/WbK227yohL+GdXh48cKWVSKjcDeakuZbd3c/f+vjX1k+wTy8zhpmZfUdjQXKSVJ/I6R7Wr8Qe5DwaJa4vHDQaB2EDooyzILOOlLHhry42qtjC7nZUTXj1rLIxZs7lWbWxYdm1l1lBEoqGnrlddzgX3VFdloAVnC8m99C47DY2LByfWlLBfKddtmrJVmVJ7lFebi/4V818SXRnx3XvLeuYcOV5fv53P9hu2yVy6vuQcOMmn5sN7mEkY+wWYzKQDbZhjiu7Gkf5xWOw7k8g4rI/7keW/vALYgyP51RlBzuizoIIa1s9LJkAGmbKIE2BXrvYNL6IzMVaz8zuPptjXM7UceL96/n73/9MHO6E0VZiDrIcBhljjboh2VJ4SeFetfa2BOkynLale7htGq7j/mTo8sP+7Z/LgmoELA2GcDfkSWcl2yLrpOA+DOygFU2FWAUyOpHVHt7oWfJmW5qChhxu9R1MaFhhie5Cc70FuhdpdBXlF8DT0Ov4K3RJFRvVAZQvmR6r5RQMbeFSc7ik8DA1Ksc+jsoz6/hD4Msvb//pb0+C0Pp9O40sdKLacbZac4btKCcqzNGNNpdrrQ0oGLwZYLcAeGMlAT3aEy2ZoFmKwpmAk9XeFqIRJ91ot4tvQWtUQ802g80SjPFpNJpZw2jKAtf0yhiBBVPn8H0nIWMvGVrLi6vK5mLB4HSXZR/oYQQM7dvuG2vyfB7WW52sVktjAEEbI926Mwl6OJk036xJkmPsqxhrSqx+ktw6Nx+5DQ/kkbbRQJzb7fb2/PZfH0BEJuDqHNOgOEOVC8JEQTBHU8SIIU2upVKZXq61dvBAgx3tbbzQT/UW+8eJt2scs8MGWA5izdgdQFnbClwCBL7b4309KU1pWF40hpwF9+FCHppA+8VL7dDRQrsb1mHBYXA/8yQGao6UVGzGHkBVp7tIX9/LiXL0rDRyp6WabYUZsIXfl/XS9Xaili1p9dFJc1aL7pBIpzoNAJeoEWrSvZfUzfVC2JKUgevVacsFYK6XAECYNS4xa378+bj+6c3+w2lPx+400ZvIgGCTglu34Dn8zqCrJ9xU4Gs260BjSnTDlElrPgt2wUnzAaYktOIyeCEg1ZTghhojnwloSpdtV2yFaIkVneWu7e06c6phYJXI13OM2apsxRqZtYzD8BZY7ywgSWGTM+8NC25h3C3Rx1dlN9CC7TtAWMjUmZl53M+6H1nHp8uPDMfNG6NT++3WY6NytCszm51eScZgn+bsjdliu4VDRaF02qewE4aoI5XAB/RA9HnC/CxLnPuwFc3vssZ6C7IEFCx8ptRJU1a6fJ1LS02P7tnmxvCN+/XG+S1PVjJzIYys2ba409hgLx+oqpHSwnEa0V1EziZVLbPoOs0iAPfonqrCZjCxzH3hEaWWVxtIR5h73B7z6EPVJ0kuaqf7QM25/NNGYniYcTNflw2Xzm4zon3sPMR59Jmptlw9SErEOhGq+dLMGIGG2wBhpBkIWrjKo53cAgJVCakXVadIY0LOhfpcxxcppMqU7u9T/99vb3/3c/+NIzAiFiKt0I6JOFAP584OnOzZZlbVPMsDJbMCrdUNTyOn40BDc5QFt3ULqbOE6Lrb2GyEEj0rczI2SufjWd1xufTRfC1CBg12M0o8ywS3QnbP6RJ9dKeq53kYmY9sd5GSGXdxYQSrg+pSB8IdFhglqHUceXaquMU13K1lLeWpOh8nvv3y69Tz8napzsDqzdP3nddGBTnOyk7LOr3Mw0kytnR4u7wZjT7znH3acEX4dhnvH3l5+3I+vn2719//JwQ25DFBo/vZQqIWlLj5AsVCpEEKM4tRvV6xcvMkXOE0EFU23HnW/mlXPr/+9vXLrXvc5vN+89mE+mSMUkKlkjSRr5WMtXEYcjHya4UrO1NmIKtbxyHrCMlQJZVgFtHZxdmxMzAEf8zMPo3ENsdc7kjs+4DP4ZQ1U1x66TCHF8H22ZjjNevNRLIjseh02fS2Rq225NI6viBZhlUtpgBiGWdQq3cAgi9UVgNumbWMe+s6YKA13KWGBX145dTAaOZTGvnxX3+LTz/fvvgqe5JUDwJUsyfStxw2rWePZfhAI5qVwgDdCNF4wXYBv3UrIoZZ6FTvnLP7eVRV3AC1aglYwwJ1VCupo8X9bZgbKhBjoZsgzHp0Hm6hK5VtA6SC0Xpa+dnTLz5n7m83Rpm5BldZz0ltzuFoa6jVmvX4eNT9UXXittHQRpvri1nalHka062JGZdbbzjPitHaN69yH50HhcwZDN6G7cNiU7U7rK3Z6rOOVkrrXFw9dQCDnr9+/aoH4veMItDd5rYgomY6Vak1LIF6DTzDQxKw1jYthiAqGo0VvLF+nAdqtO5XO61tPs7z/RiZkilbbtYGdlr2mUrMIkyCKjroIm3x0BaqquDst8ubiK45jw/ROcYaYcjBwrBhN1337ZyF1L5V7KZ0n33wOcwjzG15L3yLwLkw0k5alwi2ccAsPDxmz1OzGgd5EulRQC7yOtZnf+0oUGprx1KWvEpkKizTS0EGSS5XqKkuybEMjg13b5VRS/klUSWjH8cxtoFhx/sH/+vt8w8/3rfc//pz1aNDzICVqflwftv42Ot5WEdVDYFualUrLkJDEdwNA9TcgohCB0jx5Nk4KwG6R4y1dVGRBYxATt8AG367xNjEVVEzU3MKVTqVGisMQuUqLNjw/FA38Jz2423odMPl82eARNC0igyxb2sn2Dh75nyeOZ+zuz3CRm8+nGVm3jjQ1cp55hxbxZe3+HLt5Q7fN9ZQecNNfOY9xrXMzHdiSJt5w0U4lZVnzoW8NxuOZifrPD+9/e63v3xI+OWrYlUYlphw1VD6RclUd5OdJXNrs+iX2JZqoLsBf5lK0U1juIQ8J7aBaaca0tPyNLv1bEq2GQuc1XUW3E1cM3Z3mgttZkE/8swSinCsgUDVtEHCNM2JhpnbGN2S+5ads/v57Wketl1kNB+O9jALT1QlIBOcLpQra53LuphJlWTDGC52ZjUtFRWtF6lw7cpI606jA+2wRtuCx6FVTCVpUItYLtIVvnvpH1/ar8X2b4Op1BAYVSWQJY9xZsMhNX47H//b15//5vf9bO3d6M0LnJ2iNtems/p5t1krmY1GnWKeJm8PObk5XEllwN1cYKkP67MyQckNfht5JDrEZiQc+Tyq2uIybm+8XZp09ILR4Vk6j5limPt3aC+DBlQTyuMo9Sblc16+vKnEfStZ5USRHhbegFWtQVHn0WgPd2rbtiBpLFR355w1z+f7hznj4reffi8Mr4i3CL3CxshulNnQJnNHeq8SohxAIOo488hqsRnhI4IdOc/r5e3x+MaP59d/xO//T3uAo3GCYc5sCStLvExwqpKZA85hNOgk+8VsYi0OoKGJVQBya55ddcxzH6PyZLcGbLhnaluXVfZ5dBPdzaZ5DDoGwXIjrKBMVLXBNw94l6lw+pCGzGtl1j1EOY3b8Drt/vh6zDReeEin2lpje+aSpnZWB2O4ubEMmT0fx3kKcNHNPUIO1NE1oZzSAHprA/yQBK5JutPRFFyAuaMLsFXsX0eitT+g7MUxXP7TdZLGat3LZGKD9hIcNapFFsSb+ZmA8P5+j3/233394j/sukygWgeVLGpuVCCTR/WcPlb0v7qfDLyShGbQhNLnxOxIysViz9lTGI002xxOg5WblxkJr/IG6WNwu1gEXJRohak2HXOC7eYjBlCV4U54t9gzSzXGVnn01tvbsIjGaXaB+eTc9oGl6kFVyqasyhuCxvUasf4UKcwzMed8PtK2sJr7D1/s7da+RZD7jm0jgW9YLjAGwqKa04RKiEbHqSp1JimLEMmg3y6adMKmf/31/b/9t/fjM2y3mF/Pt8v+jBDKZbNbVigYLLWmB21G0rpFX1IVsSWT1AseRaKQphjw6jZpbBwjthG7eXX20hy2ozLvsyekoJspOPaqsrZCB6pLNbOFCJkDBs6KjT4M8HXaFqU2VTlNiTwrJ5RjptHN3Bo+Z5/Pw5VuTFiqjl6GaNWpFLKGUFVlaB/yWA5SANvL0GW2EAHCXAAhfIcrfn/Ir8nLwpd/L9QvBB1NglaqAnxxe9cvmy3Fo6LVVqpYqtPueRJ70KDUOPP447ft9739fDzwsCi1iI2SHbPvh85jfJ8ndT27e51i4QQbVqipYzLRe8hbX2cVa87wIU/fCZDug0SYfEINlRvGp53XsfzQCnbA3kuzrVsqu22L4dUfiVghkjN1QNkIN17frowhd5RrM6EYyq392Vr7q66ZR6GMwsXt6goLdn6dqKr7vSbstpU0tsv+5S1+/AlFB7UNDUdPulElN1U2KmvtH1CZW9zMbPZZJpIOttn25bYObYFR4vHL/ds3XP8Oz/eOt0+3OI8L6Bwn2sz7XCBkIFEQCYc3ECv1at5rMCxagQ3GWo1aVnnY7DZTmTR2wMa49DN7sru8iuDMk+YGjAg6XcpGV2pSYTOrmrBJ83AXIO/YCbfV8Fy0NM62MFsv1SkoyqSSSspqaXbVOUXLleW3IJlZEM9Z2d4rDmoUMU+dx5R6/UFCLhhssGezy2ii8AIGrbyGSKqxSpVr1Ic2ml4clX/bmr0ibAYsEyRWT9/WiraNXFIvI91V5d1uF8vK+788t/9u5E/pN7J72UMixNk5c1O7VpMfNad44DqwD6yc0Cpol8IGqAKxShABqvbLzuvCO66vKCkH2364WkR8etOFVC3XYKVbz7Sp4eabXzaeqUwzkxcns6upgq7blkDcnG+eLoRxtlXbW1h3HWfmI1JWezvdDeQ2bth36WApz5k5O23WiTGGe1w3f/tRFiBkISI6O0WW78wudT5PAVanC7Q9YNUSR1lNWRpjXMfYDJWJsrjsvf3pv/3LT3+Pv/yGjz9V1NnbuC6l4qW2+3y6Au6qqu4Wgg4nmo2yXjxAmq/jLJzo1yRTZl4NNaotpwkGv5D3nk7QV8z4ONhh8LEbxwXNFlTSkSIbXtkkKKcbwwjS6O6C4IvKw+pqCMVqdlaXw0anZnm7tFY2mdWj1OZmiK6SAoyZWRplqqVlLEC5AHVG5uzqnLNoO1iQkU3bVvV3gaYpLoqbVrxh/QVYuyi3ANaXU9nwFZgBl8WSWHI7r1rWa7R6wLPrEpelQdu4Z7umPn59/C5+bJtArjmT7UUcsmmz6AsTLuRJO5xmtwuscZJQV6s8uOSSZR+57m8+NrfkdTMK6cJUSmDPZ2ZpBD9d55fNn10TELQBj7PPUyju7ru10xHqVJfBcZwrajH2wc17etxufn3zxS1OgvTD7Sg9a8BkYnSIUrSxN4ssCJrqmWC1yrfwfXO3y48/dSDACsBJo1T9TJlEz5l1ppquHjCYeVBuKgmQ0pAem922ev0rwiMe90N7fI79D384np8zuPM4Z4VDXXWSFh6nOqtOpFRw7zrzYWNwqCwI0s2q1GqVgd8VPmCiCjAXw7NwHPqyRamHO9vVzBRhcsV+Y1tqTUEyVeBQIltimZOwLPMNEQ4zGM0gGAWlaj2v1aUtd5v5Al5Wl5ufObsXap9mXJHOXDVYc6lTbCq7SRmAtaddBnYiAp2zUcq1vVuuXaKbsG58t5O+5qDq7yNRrteUZZ9GWxL7CAdlvk5OBKTu9SoQCVg3jJcpjg5S2DvIsxrP+vZP3378+7cT7+tn32H2FKrAVE5pw2bShNP2oevQ7CWR18ozgnDIWN1rp++DYWG2rihJd5RaDRiHtuubxShlztnpKHe5qkSQ8OHbruwqHZqCYnlsSgXn5XKZZ43LNS5XiRybTjkMUFbZPM1bvltQompqNGGIynkez0fw0tVTLWr7dJmF609f7IdPGGaxeE7CTGWpCiy9HzlPIYlLrfvi2GVkgptXTkCgTdTFos5HSVbjkB4fuXU8vyK/Yv+7W6BZrqw0oaleNE2zqkYBxq70Laamtal1vQQUer3AScLgqTZwdi1mWBNlbODj/vyrL7uKcXOY8mj2zk5sG8eWZ7aazawF8xyd6yNiRpvdhgwFXRZjUZJJ1GIuZwlmJAfnoZorXSajzZboCLA6HIv3qMKKRaJpTl8OGaPaaU3aQoAXkhZqg4GNthXD0Qsguq6yyxX2slYsGd9L8siXvVdctwgAZrM16AuLZng1Eaf6JfwlVW5AjJHQMJ+HfCPbfNrzj9nvCh9Uw2BoHur7xNHsQlee6WbOi5yyIBLWWOl/rv26UHMFnyy8CY4ldjWI2AzzYDsQ4bGuJ3b2LGImh8uK1mUyo3nr5vX1wGLLWKlZlWgao2ENbWPIhvkFkNN9sI52iE7sYW2SrQMFVGhBoWMGQFthzInLJS5hNmwfNkZuhkbjUJVVwdJsYj5nnkSCZhTHZWw7PRrLlgWYzexu9y9v+TgMCtuqFTc/7k9O/vovhx/wXRGGPv3sIhqCuBQT7Ba4fFXonmbWleYrIaDC6rhZbKO5ssU0U2KCGpcQ68z++p4dexXpQ2bQIQLwUW7a2VootU5IQGlmkm0WJVhBXTWN25CCQcysUi1/bZtxHWE0j+kyR0/rtZqXYXYbo0wLFS9fwxHQVr8bYLS1rXSnFvunCV+wdAi9rETd1TJjdwGEvZT0WMcjwb63Xl4cIltcdUoQm1VmPPNwRavcQxLXPzpehOCVj5DS6dXFxvM8aMJU/7HqD9v1ss/zsKuFdd3POLzP3MYVR3Upgk3aGPVoYG1zCq0lrkGzpwPN2IR+8eWyfZBjVVAN1vQlEOw6c1bDL4iAO6S2tg2vF5tcKxmydh3VC7jXkE05nXtkGgtG43WvMwETJiJ8LUvWl6bVWUnr92NnF0c/C/Injusl4tPWGhihlndT1c/Z86DcrSpnZxfVCA/Y5u7BGOv7I6MZUFpsQNaE0rbuKffq83E+Hn/4058eVzw+4+06DQbfCoaCxJWAhMzkXN/LXq2Rlsxg1uwiz5Rk1ayUhYEyFQX/7rC2jd2lPJ/n6cOyn41VWbeEMpouoROt7l4WozWGGcQW4IIFASsFRRJW6j67zrIyIhqs5rx3zxblhnAY29jNWp08e11E1/eo2GunKSOd2oohsYBSZneuvoyU6uxVDhBEa6pIiF3V2VW1qgQvLBxoWEQ5F4nXj4hRYAHZkntJAltqsMg2tqxruWpsbdxq/Svr1b58HvPjl/uf/+E3/FZx+jCv+ejnMe/PsuSwmbO9elQO1PZCSMPRYGNiEAa10K8YkofTXblGm4A7xJ6nconOhEE0hxsvgX2QYsgi1voChOckWmvZ5IC6uyXBUZz7zvEW2ABDb5cZ1s7WmbZYiXMdpcQuVKr6FMqne8loIeR4u6XEy2CwQznUq1LDAhNRybOru9vIYeG2je0TRmTXopwtwCsKlVnk8ZyMaHMOn/WcZ329/3oH7js+3vD7n36Ip5oMbOqjWSvgNUCGh4SuhNDdi4kJskhlWoz5PIsGxl4BOpnrsED0UnTNPtJs9ty33SIKqW564Ozhu6AiCdYrjdhqQnIMyRibMj0GjWFOimK5z+cTgpoFSazW81zNokE7Pa0gqavVpaEoWHXXmmvSTSog2xudRUj9wqa/cOirfriAbAt5ZbRsrecdAFuuSVLd4MqJkMvF93rqo9ebRK/iwMrJrj+hrVC1AEGELeB/07lCowNC45RqNM3iPO7568ZH4JKCVzvuJyZ2jT7bzEyTcRl0vRoADoiNwkLKWJ0HQPkgSbMFF6XRjTkMX5/KilvAAzbRxpzafRQ7hq1dTa1Zk1PqaZIMsX6+Vf0Ctrf6PP3nG65Xj225nxjdPrm5qdGl3hglmCq76Yg+VANuF3QJ0+ndCvM6Ztw+4fP1tF5dhDXfVafUHbmarKC7X6AVuVtPI8JN1fOEbVd0qmb7BXArR1we71Pb+LVxDoyf4B4BHzoKYTAWKhQNkG5DpkVV/j72JpuSFAwbqBn1KHX2qC28jK0yk2IFXqIjpvDMvnmnQGMzxxjzbBveAsGsWrSCbnS13NUArSq36wYn4ViwbhWgnImiqrvRyWazXGyvEsBZi28YoqTNrAgsSKds9T5AB1otLqBh18Lwr4MQYGaqpkpLdPBv0/1VLXkd3IH1VnpFZGxtuL6P+2GCbAHY9H24StJ81Qyw7hdmC9XeABxVBooNYHlG2a64XI7fjvn13DZcQs+Pw1a2361ycmMZxm5dNBEwWIspwmOgSZPZy7/NYQtiLaetp5xVg7xduxpGd4OKoRflgckRC9VqMJJtjaOs291KRTlL7cDwrmN8uhSx7Tvi5fcLEEAP6ywClMOos2DmWP3JthKrfHPVmah+lv38Zm8bdsJ7yyqDuhFOAGXC8DB1G8hxE9imlXSVfGVau8qCDm+mXweMOFkzu/pQP6zviesN28Dqp1Cshsu4rkoeDsDkZbmG3QVS7OTYzFcSTh60pz0r53M6LQKCl7u3cYZUebNV62uo25eESwS3sRHZ57pKoaheoQEHUhLhZZBvVmiSVbX0mHmUVuhCUAkOtJf0Ut+16YS5yWSv0aRYZEElorPazGWdS+JlcgXafV0SKCMLAgGKgQW0tlUPg2z9DKzmwxpqLkkMl8cLYKsBR6tfxia8RqavrcAaGUnGVcHB2g7DXq+T9aJYRKfWGnRmw59P39MrxTRMAjuOVOV2db2C4GvJUHoZ6KtAW0lrq5XdMwKC3GhtLlrYYQy0QENj5qP7bVgEozsYhsXyaxQYZBsdkWe3B31A2cbWAcDG2KpkcXmdLikNGlpMDoZFTaGJFGRythV32LnmcEGdIT+n27bvn2/aBgJ6PqpPgZaDbsVBb0pK4xjyAIYkoxBNs5ZRqlRLGM3GoHNsHEG0NJ7P+2W/nvfaCz8Ajw9kXEPKMEuC5pOQia+xR6ux0nDV6JmXbYNZZpFW3du+jcM7KrvnkQq4u292vV0fzxl2UruHwzb08MruXBOU/XLpmTNbeErdDQLmXqlVA7B+QdasxSCS7QChewVGyUTBAUbmkgO3qY0Z4dgQoBK2Tk2WETJTdY5V+O6GNZosmMndzimDXCYozCAUm2raSiy/Av31OkybgGWWXOcfAE4sSC65agPrbUGQS/5Lk9aAaG0cJKO5EViCvn5NVI1otpkM/VoftH9r/Gne/vaH/rjrYdt0TbJlSLrsssPIjlWE7IZ1wgjEKi8gfA2eFMaVcPGQQwVUrxwg5hS6Vt8HxvD1P9REg+6NNkOjhDI4u0V3o6pWbxVGI2FURHsMX3+gU5rrAuZj6yk06LAks+C0Iat2thE6xOuObWuP2LY8u+c0J/eNIUGRDh8cDjoTLqYV6QIXlcibeTzVWNWGdoObAWqDwzlMfGY9zuNHx3jHrfDLv2ZY2yDL2H22W7eEdnquEJTW9qG7u6ZD42Uaom0RRldl6FEdyKB4Gxc7m+w0HkXbrorhY29lnwm3riqHCsfjvfoMj+6OsS/GKKwM2na3GL7RuulMFop9zK5sWq+CHawrsUJdbmuh2VJZ1dq4NtEaS+NuSrEa8dIHvMb3qDbTZVhVi2udTrHQpYZRLUjtitc7bPkaJcnU3RCwjvuy9dlFi6/odKud3q+Uw2KzYSGPwDVTcr508PhOcF+D0teAoyTzeR7nH/7zH/7qP775tuHPrQ/Wx+mc4ETBZOgBOJQstLKrWTKtDpK4ZGarDc3ujaRUpkx7iZUTg30Qqpzml7euZoRQ3cJ5AIQ70CZTq9fyOQ/J1bPzeR6nRu/bm8fuw5eZAXMWezYsNbSywyLRmVbgFLpUs1fumOhmcCRYqXgA2l00r5ZrxWgt3II23B0DlWXttV617C4aHIrMJ7hMsrThfc4+H6EAbvu+P87TVfoN2wW//AX/8f/4NwEVtY3NSj4LziJ8ROQ5aQ5LFauaQK2ZpejWcE6kBbtNKxgpnHX+knZ2uY748bpxm3n99ek/ffrZ4WYcqakUVHnOc66o3OVytfAkmd6d7oqxTo9eaKtuWvfZKYCdQlu35syu6i4Gtm2zzTsBk64xxWr6JFeVT5LZBOepPNXHpGFrk7o7KWskqWaLTY9ZuR5/6oZgHmAbmJkvfu7rMCFb/NFO9TpkvBjrJWnBna0pa5WH85WPWwpXYSXsQEmv4rWWGmN18MnJVI/TfZjd8fGPv/3V7dOv//obvxWf8ov7TW2HwRiGudRwT/asbi8aG582fUgkGyYB1YBXg6qX+OnlyuwWaxbM5S5Y7F3WXa/hQDesZFQ1FCzytUeb6iProM8CcN1x3b2d3nWs5UiyEys7Q4UP7AOPZsLaqpKzjVs+Dx9bST6AzW0MQNu+HbLi04xkWIgMs8HwaobBfHSd1iElGpQrZQza6C6Qkqm9zunj2sd9bKpDDXz88ptNHMK/+xvDu0Vet8fZ59NN4VijHNKNEWs/21lSmo+2zsnhjgVREh0uxmIrdPe4jvvzRJSNzWt44OPj48ePT/3n++0zuO3cjvNrF5hdec7tNmIfsb9oO+qmsb3W7Rtdas3zYBk2mkc3PNjSMIbZ7M4TYIW6nseg23C5XenTQSGnUMyzsuFoDKJDqE7NFcj0BVFmfx/gd3cYIRHeDrY1aGIWAl4vHQYhmi1vXS/Xk17zVmpNc8nvETgtqMT66L9u1a/wnF6DpG43B9AlkqiMCDi8xSnfkPf7b//0xx9+NvyZ568PQ/iGuGjO8ghoAj7z7D7bz3Jutq2HCEn1+jlbv0fpbELmVPiKOYs+59G2ltgGi5NCt0FQrSAT3c2IXOl1Qa0uYNYxyYb57csbtq1G1F7y4WdnG2bDvDhJMBy9oZpqeE70FNBgTbsEB+o+5bz+8OnZ1Ij65Dal3BEEJTOL8bpMWZUF0SYvgSIQr85+aiBmO9ukUw1ya9i4fLHtMv6pZs/3X3Az/Nb46e/+7td//nPMR5vjmIdlyUiz5RxymqRaY16yoEHmLE7sV4exS76P2QkMECWiTGVdOpx9yoK/ff140/n+xcZl+M1RVjP7OLPaLUbsGBvCmcT54k5YuJlXtqpAoMdZNUpxCwOqKs+cy29BxABpttE5qIphxV6Vq+5Bz8SsrTUbaZvz08/j20fPezFcpsWt7MQ5vQuFUhtVEaZZoShbjQ+6qavXTo1LM/BvN+8h0yvnsGozNFQ3ZN1yWxtiAFzKFuiVpYPQS30AK6Gzl8JJ6M4OD3CcPfHMPQzdX//L1+Mvd3vc3663yxgWbTKScOg81pg+et9cvgWG49Fo0JydWj9u1TAXiJbNwmaoQh6Gwggz82tw2Gh1dHXh9VryiLBVrSfLmpXqo+uY8yHadrvRb9vbjzUGa+1zhFkNocnNNFYOtzuhage6Gs9EiRYEqgrZSlTBv1xwNR+GcM01ZmpwtBlfHgaxbAGalsuWWAZiKSFJ8nk+mg+BSLhthBw8+/H4r4/9G74m/uP/wf/f77+Z/hy7G49JpQwvOpJsJRrXrBBdy66XEtHPZ1rsW2zu4vDzrGUAEVGzhw1xeGmcBtQ5+9vjcbw/66eNp7794d53btdhVzfVeRQxNfV8nHOmZBwxzNvm2mJKUCiWY8JQgm3cB3e4wM62HvTKR9KHv+1ZrdBCAKlnrVEmemzWF1cy1XT6bt2F73B0mu0bE7T1VNRgG66c2WNaw7s7whvdJThaKjFTmoA7rLVGpuvbg+peE9bXx/77J3/FgNZ4iuttY6ZKrJbz66Qrigs91EY3es8+H/NbNI5H3XOLc3vb3RS3wbPs5tgmz6mStRAjLkIQEnr2TKgL4WidRQ+iS7Z8jmjOZ60hI4z0wRHr+EcowAJcQZIe0IQJQziqns+cB8DM07fb+PQ5bj/j7WabYSvPlIk7UILRBnwMg8NkpwQu52HmMcbOzWU2j4/e5uWH38n3YaOvwJienW6w1cV2llpomEhvrAiTW1Qn0vI4++gAM5U5nRM4bXwZexiU8172Bjv6lz/ef8GP/yO+bZf393v/mvH8vup1MSxmpW20ZbczdsENdAPMiXnmMLbmOW2r8IBzm/ME4zslguEX06nqOotJmH3Mc/vnP//88ye9l7uPcZ2P++Pj3V3X7a202jgOWFwJLxqrTsPGwWGR2WYdo31YVa1mQcFDVmUzrcxUmA/ZYPWZapMz5Ivs7MayM+2smvOcs0BHkJye3oZE4fTGOpib09bp3SwK1cplSA9DObM0wrwMlj2IRrO58jdsmlctQeD3aNxaGHWtT/6qwKzeZ9v3agEoaAVRVsQM8FyYCVGlLcb7o51nPt/32328feZ2xXD6E2MssgaFoGEnNoMK8zgyHd3dVMPctlivHmN3E848n0ixQcC/XDj8+5ojyWrJHGVFC1ihhSqo6nh/eTcrt0+fYRG3LxpXjGtv2Pgx+0AqCO6sIdsAZgoOep00nPNZM0mOLWrsvM+A9xY+dl52bZRTxe4qMuByX8cBvbpYC0UJA9bOPB9Hz8NsZPUxHzo/fMd++3zZh+GSdWyXzYc/dbz/8c8/3nD7D7jjDf/t4/1XRCdwEu6Lqhc2VNRaPmtBmwbFNRBHp0yS05ld1uM8s19l2VrQBJMMQ9WAaRMCZ/c93f/8rJPhnPf5nAfd9t3Hp5uIfk5jOLfG6WMHbVz2GA5VoncxE/P5gPbYXexuo0AZaZ1nz2p2n2UpDOM2xIKFkdzQucwXszHzQPegGeG9qdnVwEkFYnnpYDNTBSsKcB8hpFq1IlJu6DwbgsNJ79fItAEsqbMvl4S0AE8Aq2qtx7iWHL5MU6s+yUWTXQuE1Rtra5Itjg6TF87Z+Xa5PeZvru5tx5IiohgGq25SxW2HGYcjCh/o51Q+MyOMSrgPgKppYZK7S5JO9lBrhAcsEDtB2MT3JXYL5cPgXdPPRnc9pxRkNqylsX3yy41fPhsv6bOJrM6Q7TadIMNJLXY0eEil7q5kIce+ue0oljXdt+vVt4td9jRB6nM23eiIeD0v1rhYaCwek4iEGhNdBfdqHc9nPt/Zxdvwy+faracAF251VJ/j6x/rPfD3P/1Pv/zf/zH/gHcg5lHXLdQFrgYrzV1daKPZivGb8dX1k7P0IiKAj/nwcKPUylc82JYOgcZo2n4pnx9nsYeHmVFSfv2oOC6ft89//wNs7yO3TwOx6uXXfE5ztFF9LFgK0BHDaDDHUR1Qt7pmc5atIY+H004EsBlYJpipup2YfdTzOE8pQW7LNOq2qYdsuoRAPvs4lOrZttJPWP3MmjCHuq2zshNt62prJLawCmZhdaRfF9yW0dai2M2ra60ODa8LAsG1F8aL2gOh+b1sYyuS0UZjarqM2qb0/qHN/Ytft53b9UcKaRB8u6ey81v5mPrpQrJm4/7E82QpUKDFdV8bR7oDKy9dfXRVR+x+2frKHrvB1AVzVHYBCUFha/tkSGBKZ4axQGKMcRnbW7590nY1wKW8V1diDxtWe7KpViVGktmYVXlnmZn2PcxNUGdD9O2CffS2ga7qepQ7waUc4Gt2hlqPCltfQHQpTZ3rrlg8j3POw8KcGy6BfWfQtvK2Ptvt1nPu//F6nePXP/L+p2ddgDfEPqKrOKyyoJPYkYBZdbe1QpWvXCNphZI05NnV2e5DLPhSY6nR1Qxjm8LQaDfOs+6PiNHXT/jx7ZrPMwN2G/Hlc3vM+0TQhtPN3X3j+PkNJczM7meWHWsux/16PT4eapvPZ87v5+0OEGM3bpsPytaJejlo0VW6TxdpJUKbGwxHoDGfOg4kQMEJL3dDT4a05mBKrA2XBHdTamyBTdlki21GhkM2ZFatFnKqZteaQcMZZnQ2sXwbKzm9xv0NLqIhRDli2SgpIAvWtsyUPVCWmHTQhGA4dH27ml1osPlemfLAKcKNAzAcs+bZ84FX/LpjbHDDyjYBoAlQ+8mJzRsj9mt4gIRms6uoQ5Pw4hajp8DZc2I2h2FzpTht9822sLfb2DYge5GnFnm1N98wuAIfue7SPTPPiaJK2BXurTxyYgIBt/BxA7c6qwcri7bBh4ykU2hrlKNfQRL0rKzOPNVZ7bA588wTljDENi7Xt7i429ENxO5wnHm74b//n/7D/+8/398s/7vPn77y8J8R1T2GZzdFkI3qLnS0CHW4NUprKPZKuemsxISL7U0ZUi+dESxzbthRkpElbHw+FJhR43f/bsN+QD2M+5fL9Y12s30LGws0Zgay0adbcqLzSLFX6dhiQ5O2P7/eGxbDE6kV8RH2YHOGAfuOzDPLCugmylscLMhMBY24xuB5V6k3a2uW1AnlOU9rBCLI0hDpBgvEzBzDYvdSidzW4ye9a/FwOLspM2oMd6+aqKzFEIJkNAS6amFQFjC3+9URo7EklURC4kKRrYiFEPLKDkaCs7Nbb5e9uZMbOzktvPrtwq/lVVS2rB+TzyflyzXCMQBDDDzzBce1QrO7hu9Z7dcrt8vqEIvk8cqmRJcDqGSYOtedGp0NlZqD+5cbagNjZe8QQJIWGqitw6RFb2rrMw3oc/ZMdbFs/3I5fv2zOPIxfexp3TWHR3XI2wgfb2QxzN1V6J7ksG0Ba2nV6FI967yrYImzrGV0dHmH7DLsbWNInSUhob7Zybe//tvjj38Yvl1Uf+zH9hP2K2K0WfM7DBlQm7GqoGVLrnU3rIXGsVWCtu4WNDRkQnEZoGSEdJ7n7oas2eqJNDyYVk9+/im+xLBwD3OMyxZmtgUcTV+9oso5n8/KTk6kN+hsDJ/nM9vOj+eZTfPKhhsMttOrPSbbIOksu8BLxMhuY8rysg/7FLMqEzNPPzpOdg2VEU6WMc3gO6pQrbEbNqNJswBcNps9F9W0sqjXMwlkY8rMJUivUDDR1gtaSodhpau+Q1FeJRqtt/qrPG9UW//bABVUFeDqOgmHJdJ3OAOajtFpObFdBjhAs7SaJymE+j7xfDQMLg05x/eXc8Gyj8mxy6J1mHt1eWxuV3QoEn5ioYWymfBwsF/q+GMyATMxUkVqfcsskJ2kmx3QTbYX20sDUh1oop59CCcrWYcvlP0guzsZOrMKfrPOHjHahCthBi8FaEGzKnfAthUaFKeAgs46nzVPVHExuzQyZ0kIbW/7+LzbjkKxnlXR2s2OfB7a9bjff/sv//L+hn86jq9f8OWGmJoXBn31q1Ym+bWVD49jdZGneq3rJdB7YW/MVnWrmC3JJCyOTDfaLUqnM9j+eJ7+5r8+ntdP2w+fzW/tFTkL2c4kFpIrOznn2cKpxDpJ7GYWyJon2HVWTZJKyFEg6CJi4kJ+VKs7QSHgtZkMKBpHBH3zzp7VTcEqwmNYiA+MRz2jdzSeH9VNH1YsLKYRVdlPtTlzERtbOQuzKXD9HFtXdkoSBXO6SMI8PLlK53rtAcRXaeZFWMQKyPlCH65f5etvW8Usp/AqbfppNWx8ex6/b49L4LPXGZZPNORPhwHBZ5bRYHJguKTGYKdJq7S+PE+MS0MmxvUKNVYco60xu08iEBDaKLEwpWUEh5DtvsHxqtHQ/bK1Oc0QUNLUZupOEZ6yRqFWkX3tZc1NlrRh0FkN287use0N8rLHbjVGI72rq1Qvkirb1SkWctb55JmtlCaoml2F1mGbVXL7FNdPuweJyntZRPeA7W73xq9vf/XW/6+vuPH/8w9P/g/4OIAPRG92oAMbWFwaiWBlEcYwOrILZkbrKjdfGrvqCh9rK9rFbpEuisY5ayB6iMYze4tR7vf3x6/fxs+/S277dvV+QhPneXaFzVabD5hBYrHGvjFIl/voU9ViZbsjAkc3g73CZy0rodWjmTZ1ZiIZG2OL8nPdM7GTo4SWCvN0G3bRcTxkt50xxpZnVbpfrDAzhaVVlc4sSjL2WSsCRNIsFEBxpmYnBAvjMk1w5eZNUhtBIwinGWkFh2txs9ZJkl1rm2fDWNlq2rJpwfkKcko9QZaa4zLP93bLPIXCkxyL/yoY04w2BZmbvM227/RdgxO7Idd+bu1M4XDugQjQEImc6syjvQfNE3Mtw3s5i2GQaA5zZDIbw6HmHr0NGGvj2vCtt6ToVHU1CpquphzKNnd6LsSg3N01JVc7Ob58FktvMLRPnkjKGwETK/s4+qjKQ0eanUAqW92Zs9bVQ+Zutx/e4nq1nYGexwNyYF0kXAelTfv2p2/TD/zxf8Xv/nf4yz/j044wp2V1y8zWf6ElBrHC5C6M7tX5CzipV5ylSggjseAka98jyIyzy8qDlmokwq4W8/2343zux2EOLUEOGzrPiWbD4TKntJvDjOE2XEB7MeTtFqHCOZ9GI7195RNlBp4wd1Wr1UqbMOVuXu5togi1b75dbOloxrDz7Md8PAVx3+xyImeqe6PZGO0TRz7drUrVgEwtUFU8z9UBBsxkQAsFdhtCRjOHaLTOWvclHxTxKpJ+r0o2CDCcWmrjBtzh67EKGRtCl8ParYFNgXu7xcdz/vJVX/6U11v79UPzY7tc00gITyEGd9owHt1aCLPkbmCnEiqTUhURtFHLCUlH1zwPy3P4JrRy9drYgg+0eScsfESsACFjkQQNMdYAvARTKmTZpNhCtoytXroxtPmIzsN9+IZ6zhYZYaC8eaFdQgFm6+IpYrtZyXqwD/TU8cR5Qqcpq446np1YMJHGNBs29u262765EV3ncWe4FgUA9PMQsgPoj33/q3/5L/94u+L9F1ze8Mc/ITDhgUTTsYA3oBa4uNVG9ErriWGkIHF9dAdRVT7CTAsKuPgI7oDQLQYpNpehKOrg47fMH67wznmqNPYL+6zz7KayLrerb6NpJGNdro1EJ8XN5eziDqtnEWEc8h7DzKCqbfg51TzVqM7qu8kWL5rMiNAmzsKdE/DoGDpPeVVmHuAxsxI9KSqsZebbds4T3/8jUKWu1qs8YFxaVCYIvig/nrkmPEmzVq5VQXi89ryra0mVmmLQ3I1cdUADHLV4W9adJTXayQFPnmF0MK3yPOtxcN84T2N2i0OdbeF+MWOAjmj/njOCA0dbixzNxS9Xaa4ODqYwD3RBgZCWD8EdXPN3Q5s5YAO2YTS1Ev/90lmpJdhUWW+uHoC1zaYDnXATJ/tVUYuAe67BXdVhGBHYPr1xOBaKcJ4J4ebQpmr1u+73OlA5LbswlefZz64DCAiVshEYbtsoQ8+pTI1yRLfaTW6bUvNx9n27/HjkvVpvP+OHJ+5PbIJfEMxuOZzOFrhed2IDWmHK76muhTqDwRowGYoislfEiwvdBKGLcHMoDdVw6cF5MX/Onr3NcingadZ0KySN+Uxs5BEG4DJeZOocquqschsyCN4aEePNjqPlHJcLrIaqscGhcCmrnm4b2cU+j2aNiAEWyps5kS0KEd6DOEtiNYPNRC93Zc31AbDBbXKS3l2rJYN4jTFXSawXAY5QvVig/wZEcRUN1eUY6wFQXeomjCwjfLHSEe421pRSalCv6iXb5LbW/zDth8r6Qnjh8e3beX1TjAZl54HL3t0m0akBFZeoS63X0DBTYaTDkN7Kw8fmMcR14W6upn+1b46N9sKyAu2MNTk1NfgdhkbB5NKq4alpaMlJQ2eYYDoEGgre3yf47hZGIaFKDs8z99sFF3MGoDQ3lJ4V260tGSEQXnJCrU5WCwgEgm47ArY7wpOOCnv9jhdXxxVDFuo+53F++2Zx2y9Xv1757X+uf8aXHY8D+AXxIwKqbs4uWS8aHpxIk4paBqzuBYeAaKbOxQWpdlYx2LUqhqRQxIqXKYgCF6yIpNNkebBqNGQxjEe82BrZ1vNst710bBO8uGxJmFJZZCjK6QZZF2lu1ZWhISwOFinaMLCGeaFng7Yd2TpOmu2M7lY1qgS3hsNh3d6Y6u45kYqqpqSuSl/U5EVjr7Z+QeEX/9BeZGewSb0Iao3Gy4mkxmJDDHYukkIZAbdWDbqQ5vGSqKKnoGojq/oV0Teae8+TNLrcvOltdeNm6dWDIHEV768ldxe24WFAMIhUzykOd/bZOE20agJUtUUQ1NnT0mVdJicodyIKHAxDCbTmAi2AalkL6lwwnC5zyYluOWTcjEVMqjJ5EjKVLOBl3t1mg7mwDdXzOYsYbxfzTc1k1XFnsj3ssp1/+HWMkEOz6jlLlCZKgCycMAu3MWLsdKN541Xb6FecrlCnFTu9gnokso768P3H7T3ir/1//3/+H/7x//IP/+0P0AX7QChaNblYlouVt4w2KyBbMJKuboxty+pqNtvWUPuFv2F3FkhfmKcGMKdijDX6C986j7Y4nn2/508/73nUtmvqQEycy7JJmWiYdcZhig3e3VOrmZYF25rIzO4ll9d8z8uXwbBSrgTL9ubnt6MVBZoaspx9fyau7p2Ns9F5VO9bu7eb0yUA8epb04Fqc6N185nz1WdEmfmr0r6YoPyON1+QLC526tLeOfXaahnQxhIIb5ZB7syq4SvhZS/oBnvVql4II6kFQja8ukFXcxitTINHj3PaM88rJEMpbfV5tw1uWNThswC6C9bNolLiwlSPMWjR3AUiCYhb0NuNHAvgKGSLviLPayoYIMNbSZAGMxdfSEhb976TpDqxDDmQWU2081jz6FZXs/pI0NO6u7rZ1qqEGYTn8Q3yOHaP7XGElHRT9znTaeGDJEAL3/eNY6yT+koZOiCoPTWnWAqwZ6BwWGLUuDnw6W9//O3/+b9u+3a5fXoE7lfoDfvnH2MSjq5F9TZgbS27zIwr3wZpRQMMJq9OwsQiUI0lm16Du07phcNZxCyyeZ7tG3v6fh2//va8/br93V9fdVBmxVpJrRWCAYK0NRVVR6Ln01QNtjlgbQNsXcY2Z8+JPOd5+LatpXoNC4zR26WKSiHcxalZpx5PXL+Y2jjiahdOc1xQR4PwpZKiwxulCLC6tAI8WjlBshvA2puBhFbeYSmF6b1uCQDJVYQBRVsgBXS3QdYLGaEwk4la4jyS1lOLB9ky6AUb4zJsWL4O2pWg5sDXfPiDn/94/HDbAIc50D7CY2BQXbxPNWitAyT0nFnAIMy5RZupDZXtMN8rHGYOtKVJBNqmOiVrSF3UQHijPZqM18f7FekzM6C+r7pzYmady5KGsjGsVO3GzrNKWR9shyU9xk7vMnOMhUqfWI6wfUu09WEre4b2AIBTz/DhIzwWAmxN5M9V51Bt/XoKR3uz5QOy6f208cPz0Kf9TX98P/7yl7e+/fK//Pl//s94fMb1d/wvf/w1mstdYzSsNLoEVWe1m83OrkTRxnrk0f4tm1Giocvs5Y5bw3wZ1ligu9S0qp6lQGQikT319bf361vlOYnso6r6tUskMs219bObreiqVrcR7V19GMOCHN7P9nSwyZLXJC2TjfDoyGpINQFZ0DnrwDPOUdu4RJjx0mnvH6zaqzatmyuWPEzAut9JBJyqpbcAaVVJroAB1gfYVuesvp/dG1z6GchocBi0UqBa7GhgEccoS4GOxbcB11yFrxgpjVhsFVs/e642wtRLZfLxkd+c5z23L85q3yN5QRNTLPZBtjDBgXrOOuRwJeJz2G2rRJewD2dQQSM3IhOvTn9jCVkLKCcDpFlQtjCnclFd69JaE9qchuM5p1DP12qP/R1ORRrnx0dqVpazNcgYl30vs87MKh7qLou4bFe/XMpjVMFe34Hy6FrN1dHJiL17gL5sdVKECAKF7LuKMnNtPjaRLXJHPrttTObx53/+9Z//4f/2f/32z3/G9lf46a/xD/+L/t3/iGCJayqxbnWGrpJgJKvZq7NaajpHkx7oxGIgrzchO74DQtZIb3U6GrSgw6qrC33Mvu19HM/zfts3waRq1NJymLsDplLluZqxWJwhM7BXLNtcwWCzE1j4qeyA06IwAaFs3y55HmpXh8UAog+pUEVuJm33e+K4Pj76nPgoM5f7RvVarS35QaN9zSjXSECi4PTG0oK+dGHECz5qjSbkq8VeXC1EOtSO/z9Rf9IrW5JlaWJrNyLnqOptXmNu3oRHRmZUBaqyiqgiQZATTgj+Av5XguCABIhCkQMms8DMikBEZmSEp3dm5ta85jaqeo6I7L04EH1e82cP9u5VPUdk77W+j/PBAU6mRGJCptQgedt93b4dtz85UxGqBqbRACqoKqT2sXgdEeXp5XK9FL9vcjrwkHKJpEKQRZSDonD2HD0u8BRxq1WPmmQYtBSgAhKmasocupgOZElwxDS6csoCRd2gJFLnhxsDcZt1WijyysFoPfueoeKupJSJ11DZom/7fu5ZUgQ0FbFyWLUsSFFTCjOGs9Kr3x9SdFpUMr/kQxiydxkjhXVdRRdmzRwcquEmhjR4MM9AY1GIiRdYEai21FwEImmHBT/89Mf/8f/28ofv8LP/Cg/v8Z++R/kbfAx47uE6G6sCMGN+ACTnr0ynbFlExRQqbp49BqVqoalNHEDMQ0DOiSEAmBkixQ0iiBwxrLpIad3ayByROmlzAqNTSl0Qki1HTtGQWVFRRwYB2BzQVtEcl8SgcmJ1EjCVlEXRiQTMfC19C/UC1iKjDxUKSww17EmpbffWc99jD6xHywwmVQlNMxEEnZ0h1BzUouiSg1ombXYOJQCRTKopZE7K5jNr/u8AAtfb0R80YoZ/bjKB+QHnnKZCgfmJmRvXmd6/lWYAUQkooTRwcIAltdYFr9frQjv6MeKMIpYcDj7vsQ/VQsToKcXg8LKKCRZDJrwMikZgcuELFUkyioKqvc+iidxQ+DJn3DkASZsEsSaMwMA8l8be9HJNUzWBp6wwlewmV8S+J+mrYQoVF5T7e1uWCJFQmXUxX2Mt9fiQTkswQHaaEmaZ0UTBAM0X+qLiDKFVgYtZMJApIRBohVFRhYY5VsoIa3bNqx+W0f706Z9/9/Hv8PV/h5+/1z9sea14fsJCuK6HuGxihiQoSomZfFOZBT1RMVgtLjq529TJKzfo7G0lJkRl9lxNRFLZU4/CQJHSehclZKpmrbUAnewMQMWtQITCfo3RcsxWiInAbsNsfkFICThkXObKU1x1cqRUxaXAGD1E/BDgyMiMaf5Oy9GNHVoB9+qXESQzYaIcCXOZ+gklRFWkJE08blEkMIiiELsxQ3Pi36gT9DBrqTM3qkimzYcFvngjE7fDJW6NSHwBw+F/ORnNIdvs5OitPUxR5hyhKSjIRbpGP+UaxMsn3D1UZNajZyWI2HcOnau2JHVGa9RFTY93WFbN7ogc81sHYwpBDREiB+KWggENBEzgDmgwdOa8KRkpPUhIAaKPhna9cEv1lIMUdZLsgpTIAfG0oC6macdip6PUZZZEOShygDurmy/J0DIrggkopm4toKpcal1WQIWFmKYGM+REWGWyRS8umlarSy2lgJQeVONAXEd/+/BXn/7wx//4DeIt7h/w/EP+TPD9D8AGeYBjz+LHgTH3lMnZAYaISooSCFXVaYTKZJ+TIBP90gvXeaIFMGUCpKg6LMe8B6epxkBWVa29IVEHUHWMCE+oC2nbax9Dew81h+rozIzlWCCjmkBUDRiMNkAHkRO1OTi6FoU41TRn16kooX2f6Fa6SUwGbmdZlxbCGJkmpgZRavRkpguhkyx+C+n7JFWHggxTV8nQkX1KqydWAbg5okCYCTAnRZK3yTCnRW3OEnGTx8xE3KSqzyk6FIIJjs6ZgpDbHfsGY6RgRnJLAC++j6jb1k4f8fUitnhG7OhDRg1zFLioibhCkTq0rlDByEwhkJL0eeKnhLDlhH8xQ+b0qEwtSoKZMTQFkvNhiBZMmAh7xtjH1tGTDixu1UQimkjrIiole2t0r4eCtcA0dGGprioIKQIKzcUsDSqa7cLIRCJTxTmv2CIGKiz5v9TmJMcAeQtwZBSPsR+0hGvxAqUwiYvIKMv9MYpRf/zx81LxX/9v8fsrliPOTzgHnhNygKtZctwsn7cl/c1gG8hEQEEZSGgKgRDSheQIJmkuOeUPc36HhHJWyed5IZUiyCDV9oQDLTiyoBRssbVgDxHtWw6mSlX12W8mh3RZT0gMU4MxGsUmsASgZFhIyLllqB2LxPyAhpSikyuBIMXcDJlzz+qSLRPUqtjTBMQ86uQQrksFhJKLAIlAZs8+hJpGg4qZYWQMmljcLqzIL4PRvFWFSBKJHnQiBaYaDJkTRtymnvM7cFsyCyaWEXZjx2K+PW73KiBnLyTow8yoaNKF9U/fvh7WvoqJuz1UQinCCi9L7sOGyINyOcAFyYw2enIeO2dgTmhMYcqA0FIdKoJUI6BQj0x1kMJkQpEZDAExojfm2NgTSPXi1eCIKUAqoqGZYVbUxQ+mh0MKRIEyZHZcbuHZkRm675gCrh5q7qZQxY2iqlPLEgkyKQqLSEZGToyOBiy9hlcvUlQckYkUi+v2Sh68LMudH86v+hnXhD7ip0+n3/3x/I+/x69/CT/BR+aNjxdzSx43zNO4LXxmfYmYVz8AVJWQoKTApidwzmh569JDREYgcHsYfsmUIym943Lh3szuD1HIfRt9JMeUE6rqTIMlR4ZZk4vG/cnNZIxQYAazUkTJBGMg2jiW4pSe2ftAqhZadV6aSiJkPSzw3Kk5gvuASVlGa6UW2UYTKYGEilczqplCNBUQrUT64GAwpjmDhIrRMjIVSo3IL5uxWZoWxOSkCEVATYCDaTrXLMCcft5eC7fbPvAFsp5zCj0r8zBATCNSASowqplzjKGA0lR7nv704+W04vjVKXHBGSiHwWR4pkihiWEfUElq7FP4oMqEDISoqgyZWvm0AIUi0LlyTAIm7CLs6tlTJDF6v2LLjEnHyBB10brU8rBGBzNkfjZUIUUNVNN1oQurEZPO6pxbwy//VIXmBKdOAdEXcQKFwkFyxJAGgiGKvkeIhClSC+ieSCxCOaR6ZEUfwZfCYfhq9/vD/en5x28+fv44CrqiDfxp335zxv0byD3+5b9+cIy48XFuU7AUwWy1CnWWJGfrd+IBxRSTGAUmgzMPrxMSeCMmI8XN5l8o5Az3tGhHLZ247NlzHfQsYOH10qha4Dfh25jcfxdh4+ZNd0m31Yw3TpGJo44BhgW7CcZ19FOJIdljoB3iQVSs9rxkQFK7qhhqa68Su5RVF5PWIa6iOdLUZSmKQWVHUwUYI9E3Y2AI0pFJ0RnmQYCAMiYyWW8vgtunezJQQ+YING8R/x6h5rMH8yX0fHsjSM4Lj+Ttaj2HqwKVma8QsRgtqcBgCtTZxzBIkc3GwvL9D6wvT28fpJ2Tpdei9aEv7452BJ62zmbumYjeRB1QSYGHuImCnVIVSSiIjqGqt36f3ICtIT1iNEAwGsfs/wxBgdChXovWSgpHwmYk1CmaLSilrCvcWJRFGJDEiI7ZaJdZi8uO5FARiAG9RwdzIAUSCgMEOawUSuc+YotBQw5WwVLMRQfEvBNuhVRicP+0D4Tfezm59D99/4c//jO2Deudjaf46TVOv0Q946uv8Tf/q3/lgdAIWWqmIADRjMl2FQr4hf4tkDFzcYqUW2/2NhRhulsfAzJ/myqUzBArvHVoRFWNNoakIIa8XvvrWd00XejSWxRbZ/RWqgWTCvNF5kqic38dy9GUNjgIo5pXjY4Y1jNJLBcsd+W6b2PItg0vd4dTntsZYX1vh4Nqh5sSDSHqKoU5qHCpbm6ZUEiO0TI7buh1CWOTHkYXdZ1IQN6gVknAbM4/bziJeZ9VSKreTkaU5C1NlkmI5RQEqJFz4jpfnTMtOhFCQuqkBU2iBEC4I3Ia8YhQtSlfUNEz6an15SnPm5Zji8DLdrfXt8p1lDE6aodqu+yqVX0OZFTtz7zBREYut+etEAqVyBwpnioiLRAdsiM8mQioK8VVXctipZoaFJjxP3VSyBE5dFnMq9Y6f622s+doo2sqVARhkKHMYIaQkkjPRDJSdN5PmClUpGj2feS+UR0GoYgnjj6I7A3aLQD3JuEBUYy+Si4wW6toffrP//Zv//3f4+Ff4p58+Yh395By97P/9tUdv/6r/7X3VWLAblNq3GwnN7qNEphP/dmthwBmGgnV1HCxyFG8wFUGcbvDzbqITgaCKkCYeyBE3SEtW2Q5X7fDKjAlNadLCza4I4uvPieG1dfRWqBF7+0CM4UWMqYACFVGJ1J6xHXb99xTwBy9DzmEC7T4PrbYaWZ6UDQhM2MQthzroOswQlUxegpzqhucSbEgBhPqwIjUkWqqyVuW0iYQnRBoch5gQNEklSriIzabUDaxiBDV+WWQWXXirR08d8YETTwZEM0g1GbqEDLxnHLjUgfUZeJ0iTBoAbP7x+v5q1N9ap/GdXhd2fbYWA6LOUZmeahjb340FZfprkCIGGzIyBnNThiEJqpFAcuRyMHeiZxdN53FnxCoccDKYr64L7JUFaDkOL+yp9KQCpPDQ5lzEjEngq0PTiqxQAUxoUySoRxJcWQAERSMG1EeopDArUKXmZKWEQ1dICmrZpDsQ7OEDLZSUiXSAl0hb3tmLdXv9Pt/85v/+d/Ez/4aTy/42bHkdf/lv8D/6b//33378uH5w2vd/tKLmDF6hsAoQH4JJOqNDy1zL2A6Ezuz+AFCKMHhi1KpEDWNAYhEUqdRQr/Qw0mAWgqJAQzx8znvjgUZi4gUR4wRtBGiZd930aJWKTB3Ik0YpFCCIl0E84FtlFJMpJDSE4i29xhpzL7ncz2etKhdRSGdWZRpjt5ElEYx01ZRILF1M8LQe+stNUIcERGZyMkot9l5i6SITn7bPJsww+aRYU7PbkUJqsBURW6Toi9vUJkk0NkyuVW/QMhcnwCqnM/XuduYjvnZuiYFnso5OBIhzKkU8W0fVern6/OpPMYIUkWX7crzud29PUwYixTiq4WfnRsT3dSDilT0zigEwlLUoSqDFOHesu1sWwxqhdRCqkpVl1DR02KuykrYDYhDhCipo6cUpdWhNVOELNBIttENt8JcMuYGhXNaKwbOax1BUG+3zowQpQqoVDBzIFNFeRiqJp4xOsWMCEkrVaWY12CDYlwzdDl+/dX49p9/8//4d48LvunIO5yP9df/x69+/ouvxJ8/7T8x7BQ/OHrSYtb0RG/BE7m9em+whwiqZoXPw+GXiFDOdLTMwthNnzWzcbdVsnwBe0VQEmkktY8xhqUYaCzFDxixIRFBCZKMIYaUpULUF0FK9L5HaI+pqjN1SnZM6CR8XXJcZOZaoZSIvOyXO3OtqszKEYRKFlWGTt41xmiZLYRbvK5W11WWgqS00SNNGL0J0immRWfADVSnRpCSEIookYmbNRhfUqK3qu9tnPnnNS9vn/n56J8EwflDlulZAhTJhM2Q1xSS3E4mc0CEiTAHhZIdrSTU2S/wMrLb4X7Pdjis4/J8uV7aean3lRRbVmkuIIszmKlJcr+iITQ8zKzSmjmTiZ55aSACFA9RFU2II5VhVmqpB3GZbD4SUDACojqT5EsBc/t8pcJgNA3d1IdSYJYCowTU6GkC0SRxQ3O7yLhBY4w2N+6emTH2oKQ6zFW9ghgDEFciCLO6FpMiQwCEVq/vj71V8/0//t3/+OEJ336DX/7XR9zvHz+Uf/g335V335e3408bfv3+7f/l//t/d0pmaFJVQjk3dMwkQIURMa87mTKYCp1U2RlemUNAmxuDCRzW29MxAwQmlfh2cgBAS7Gkt6YhqxU7HHpiqJY4ZwmLDE1EjhSpHCKLl0pG7yNjng5orjAjwZQxhmZqUbNKpBGJUWBOyx5MQIqZDOkZrNWzD8ZkEsZSrMtYF0tirfJwp3nOvUUT2wdbZyhba1RLgYln0iDDcpLzOV0XMyGls3ZGlRvGZgKcpntifvIziSTshkycESBgvmIJnWts+VK3mJvl+f7U1Jy7K5rNsC1IKloIRGWpjFjKqdPLspyOa88O5OvzfnA+PD4g0T+GdCmmUikFHD33EeFZoKJIqJtZixiTJMwwmAWyFMjdgSySwNCYrDgxsEcKAKdiFI3eek+v/XoBRA+FhlDrfRO0ai7mlHCAPn9+rjn3dEGn9FsAAxkEEZFC4RgjECxriUHAsohh6p5Su1A0TOrdIjoUZ/CFMAalru/qkd9/993/9PrDE17foNb33+yf/uHHjy9X/Ks133X8/Ku310N9et4dLURq5JS+y+2zP0kHYFAU3Wcemjb6cNOZGXW1eU4DAiomJpiPvRkjzNGlrjVGn8+sm2cCHnnZQ3pLvS+qxY+iC3AQMKLz+tRiJNlas9B+rAcjhDYTx1BVavRobQRdFQNhe8JyeXAJJtxUERoRGGV0jcgh4pbTViOkJqAohVZclKLVwEjFWsxVxzB1M18txSwDkmyS7mqkmw5kDMm5JoP9+fE9TwMiOpOMMq92MhlmX2ZFGRQdglJugep5Spq74LnucWjcXh365b+VmPcH0WlcIkUS4mLQIhbwPljVF2EGy/pgeRUp1ev1HEpILoaxvV6iXZkxYFLAvYseWlUFMpqKsU2ExPxnidPVqqYDBQ5U6pQzsGfPTM5GhwyJoaYlcowRphCt62HtSLFUFMIGKTA6bnbxBJBElzFdLPPdxwQSmREZqTOhpjY2UBcvwhnUg1rRWXj0UpVCGZRrjN9xe8j9zu8POOF3/+Y/9A1XwW74zeuf/sP3/R9+wv/mr/DVr/DuVKOOUR7f/fwXnkb0UJ99T6WIQcMyx4Tdc3qUVWwEzQSR9XDIpClVtbVdXVyscShsDrtnYUbotyOHW49e/RADtRjCMOTyEpc7iktxMVggM7JFywU9d0K8Hig9dysupWhOaJNIEvveYyKaDV6EnqkhQl8SMyibdHhHFmRcBzjXVyYazKQ3N4ibyVQHxSIoDnMZA9qJvaMDRbUgd+zBkmI5ILAotXpTREowxhfQic6t3/y4KiREbtHm6biaWKXUW9CTDIipTEUfEqZ/Ll9ysiRkgqJJYehsYzFBdUPe0ngp1BSBEiWQPVkGpI/qONTT5fN2vMfIsb4p49N2PV+T1NzIIAuGsS9t3yhqBz+9l9TIbGru6iOJqLZQtUJqwkiV0YYYGNqTkSJqAmYgBDFSRvQGZzkcvMA9VWscSoyNnTFSAIQEM8am8MjODERKAlSnSREF3FxXQ1HRVDhIhY9MjkgIyMDUn2Hsuygsh4y9bbtSzE6+HlZPfP7hh7/9bWv4+AHbO/zTU7/7Gf4Pf4mHR3z19XE9lvv373587o9frx4uaDFRLDrdDzPBOyf7xG0LhqQIRbOzujNgLiEomaBoha8+YmDkfIeaWo9YTDSFEFe/3XF69pRc0EYja6Y2DiRfry/X153JZKppWSrNwlSqmUpxCjTVmdYjZJHWhdRaHcpoZymxb+P4sCSgFSIukWNP8aTuEl1a6OFQq2XrSIBXkQI1EVmkLEXcaStrVjQNjUBIGrz4sGyUCE3QUhEUilKEmuImEV+ISSkyp9sqaSqTMDAFldRE6JcEKDQSyEGz24xAprh7oiRAEgbJL8laTGYos4hCMOtD85oQMYYzBQM5OZn9Zegdoh7q/SGDlz9dYk/ps7JEyU2UZkmryiUaQYztevAaISKLFqKoNPpa4YYiMJG9ZXAgw5VdlJJWBR7RNS0ZTGWaqXity8nFJJFe6WqhNS2jd1IiOsbtpamgKiGuCg3T4iJKJUoFKGbK252T2rSTumdmRCRyQLNvzE50bS/qhD/yzX+vT+dssp1fP/3b3/z07y9/usfxEdsrvn6LX/8a/+f//X/z+88/XluUu3E86Rpr5O5jZFLm85ET0jSd6Z1SkqlfRLus6dPO2PehxUSl7ZM5k2pLEew242K3h5WIGjSQaiVLjkFKt8URvJ6bi7xcNtGSvVlROmX1HLH34bNaJlrL2lAKWZRuqiIhij6SqWudbiIAeqwkUvuYq2gVE3ixvGYKoDoGVUU5CoosX4KWpIKicAlC9/Cymy+iZiahmgoraWkQIebnHaBmJiIybhCMacO6jXRkSkgn53aWJUEmwTRFZMgcExCTqxSRM3Oj8whFSE5nXg4JVY0/X6AhJnrbu4gOjtmUEGFA4CaZ++hlhMYoXhdPC/v8fYsolE0B2gCgZqBm9yLH0U9t69vrLmXo2h7Uy5r17i5d1SEiKgoBOHl3JlDvFCnhgFbNAJUp0dpoI6LThDDNEhCaIpMyZM7yq47BueiQ1MxIEzVTimhRcbUiZhSAHpIJSSVAoyHmSYRjBC0daBSyuI6a3TXTDoE33t4d3nr74Xd//J/+/p9/8+k/v8GPGw4n/PIEew8/lfX98CsOj/ubJR7fjm9e+N1P37pSMztmghEiMilwCaGmRAYAUZhZCjUxR1iSiaE5crAtVpMQVUfR22MMrj4iEkqYiNcq160VkZ5dRfZ9HO5kb+1174lRFxOtxERgKzxxSHjG0iJ47mMRTzgsdzacWB6sDWZvtS5t201dxbyILk5yIHsPjABZT6XSRjdRVY7YxlIXKymiKk7qDAJlz4Ete2ccBHRBVQiz6261Xhs7k5RZ9c25P5qzHKQm6IDe8jwC5CRlIeZEX4ykRKTY7TthpgJkpOnthpAMfDFvayo5X7t09ZGTiCm3QB1VTNFTJTSmHTEQmgbCz6/dwW3rrl6UuhziktxNKkCwuCIoJl4Fa/SI7LqEuWrCzeoR01A5ifWZREZu2xhFlaSxUFUKVKASGiQyxo4eEDFT8cXa1IZUDRDUPvotCQhAjTMqaoAwoHST9IT5ojqM7IYhcwEyIBISG7QzY1yuoTuadlXUpRyrR3K82Kl2Hg7rQWuP7Zuf/vbf/+7vXr+p+O5fQC748SfYOzxXlPfxP3z73aHhV786ffXV6Y9/XH//0/P2+dX7uVVbB0XF52J+/kpUQIwJLDOvUJlJDa/lts5HajIHUXKOUEcPpkqIuatXt8x9uC8yUA+1STAhoTlSTNHlvAXc1HOEVgGLtExbVFboIlv0UidaiynRGbrQF49KWK+6RpO97d1EewhlhOWO0IQEBEqohpFgHN6U7Jmswbz2bpwFQYDYtzZ6BpgMBU27yhKMFtECETrGMF8WxYjM8DHZVrPlNYfFegOkEDnLvCLCDAjBPqdCk/GKKVtUQJMpaooZF2EIdMZAb8uBW4kGgqxu5Jey/PwWEGoyeqeoCI00NWQwgzBxjFnQt7Ak6mIIg+nCBFUy9RSEtBJ7AulWcnS1lTvrcowtUGsOjUGBRs7hlFNi4uFmRoMMgOgRfYxooIlBzays6TAtc5QiAZmtpxiZZPCWOAqHi7kJTDFdmkXSYirOJQeyJOGt5+D1vLdtRLe8mhWty0HSVnYD+v3wU+5rLcznz3/8t//ht//u9Q+JnxRbxWh4+Hl5d1jv9SXvDuub1Z/7/vz08Ndv/vHlcmF9OJxclzWu1KUwoOqSoXMfwbhtfnXiryTJUooBpSzgANFGN1eTpfcwGhUpARMxNUEgIq1fWz2sc6lMcGSjsah0IW0dOYopo2+ZZoUFolJO0iKtGi0RUhc71moiuWZX0Uqq74ouzGXZsxkh0ZIpKjTqzKLBc+XouxDaHdTWe4ZlMmNuhEd2yaZiBoCarkLkotBc3IqJhdsuDhZFFLe9pw3pRswzHgwzvX+r4E9lw1z//ZkWg9n0BURuC2SZf3DGy2/Pxnm0Ed56YeqzXSwT1POlJYYvf5Itp99JKVCBcMw1qiUCSe+h1T2SbgRqUBzwImINYLuWDG3ZO4iMqiql+J0x+8g06xI1C2IIOmgK3AKRJmAk06R36ZiHclKoNCviKkVhpMTszUZmjj5DJPiCTRBxqQYTCGQwpsQEAg2MQASTtgS09et1357H9TqyIcIOEGNZ17KqKMKOLUrN3s574+vL//P/96ff//SPgcNf6S8C73cc/vJOrw8ffrzuuz8Mudsvb786WOff/d2Pzx+sxHLI8HEdh3L8cnK9PcnmIkznTE9EIBipk+NafDrLruc2LWvBbqgjYV5FaQY3HdHKYntrtR7noEsEOWieTjcLF2NoSt17+iGtWiRTxFbqPWyDuRKURQfHoMPrtWdWkN1Dsre+yegNsED3ihR0DbiYqohLlioaaQReOrlzDOaVmZaqsedI7cNhNamaaVHc8lhrD2da9kixNtCbEGg0qGamYgrd5wWECpuKlci8taBFMe3NmMoviEIoE3qVpNm8f0z64O3P4TYoxbzhIvPPTSPCmLP1RBMhJCP0tpGZbASNAUsLzkhF7kOWLk1REmoopSBCgNZ0pIzcLZRE5DByNMiqSEVaQopmqkgxxZhXFEwRIMcsTiMhPTRainE0JiEGJMzLuuSgqoWEJqKPHCDEVKkzUamqNuFb8x8dmsIQ0cxuqjM/BXDsl9Guve1se47mgnJcyqmW47GsRbwMks3QlvVtrd8///1vf/t3v/vh0xt8PuOv7Tj217XaUfXbvH4oewDvtQ7E86usWj9ul8vQ57EdDrtbsWSjaFBUOgBzzwxIQBVUQiPTRAdYZ3uLiMExemLmYCw5gy1jWQ0jIeLFItOrR8Si60BY8RHbXHhoKbw5dzFGYsbDXKCixcVcFk3RNqJHdo69X0yvLNrOXVxVOIIjlFxSYjFhLaWGOW+Fq9QARpUUH2w0v6I1kqaiBUPTFE5b65R3N+UU526g7pl9MBA9QKPQVQCMyDa6jLkBnZk4ufUlEmRAUwkB0jlLkjPQMDO/Y4o5zDC3nVPvpphlzPkXQjBLHzNZZy6cAIRb0hwBMlJUMmW+pgEEYW7BHaKBFLPRYy+xFN97WPFUikiAmSP23rEmxGb1NS1ijii1BdfizF1owtvfn4qki4prnUc8JBiTBDbD4KpVMAUYskSKtE7VjsAgNYWeUFfXamKh0+cskikROfOeCmWALhRGbj2uY1xbe8XowqbFyrLacbXDA++PsdqMQPpifqqvn373m//0D//0sv1mQb1/3DkuZ9gVzxF/rE9/+InXK375xn7acPmsp7L/y1+vC199ARu+v8LhGkwyJSJnUw5zbSm3vCdpRUGqGm0uaAK3DS9MpMVw1aIKpk+DDKAmGWBCzEZ2h5FhIm7CQVGNHrEnTpKql+vWEV6qFZHE3qAqmWjMjLCWilBNC7FFXaWP2XpgIuHWDXawZfEcLSJ7akZqyKBkaJfah3VbR+0xTLuplAEicWMcGSmd6g0dqTnAMBkz6KI3poNg9BhUaE5UxE2IICXzS17/NvtideuzXZFBKESB8JuoXcyEIjPxQcSNwnfrxOSfF2Nm082jt537PP7n9I5RJ32BX8oHQFIjU1JCPWHbzuKxqLYx6rJyoPfY9ok/AGR+siUDgywxyEyaSqTO0Y1y5JTVYJkOIFDooWhjFhKjj7mwLVbaCDWN644SuLPYhnqhwayIQ0rVxAwTZwiRDAqoJmkAidjFGIFte+n7OVoL2ZhbEovXUo52uK/HB7w/wU+Xjx9rUYWXtV3/9Pqb33z7u+ePPwTbEa9Pz7ud/vZl/2rHjz/iUsmEG5xetH773Z/evrW/XB8eDveHy8ui6M/wCsm0cVPlJYlZdP8zB21kAmJiCJHQ2Vwix7zWk1S4zsyDO5gSRnCMgIEqZCSnTwAzDEVGHwHNfdBDkrLnYKBhq6LDTThnyzp65kiDIpomTbQGeoOoAganVdbiMvbi0qApbR8xhksgRmag90zF0AVJUBNaUIO8jozsKcjsYqyLFQmBt9giZxnXFSpiJjZmxM9FcuI6JumWJgqhQckw/RKnEoXmKiUURgYUiYDOcn4ITU2EKXRVpI64fcYDFFXMkNGkG84XwTwrMWeRF0wqRCe6zwKpM4s0ewxgUeloRcu+D1R38x5QWr/0DgE8wNRRWCne9w0Akds5lyIQJoNhM+QxD7Q2l5uBRCI4+oa5zJMUoaZrRjUfHC2uwuHnk2tNhM9i14TzqoA9SWCmCiASMtIYmZEJdhBj5mayBFJhXsXdZPGqd0c7rFZOsQ87HPTU6lm//+0//e7//f1vL5dvZZwh/QVy5Vdf7b/b+1PFtUALPFCPQA9u+93DXSzbtz999gX1De4dtroPjKq3EQRu2X+ZHT/9EnTRFDGbPnNTAxMKhc7CzkwrBsNFVaRXciecSRo0AJnZP1NhiGC242jastcs6UjBpfV6cLghc+yobiqWrsmRKRFBmlDqzuquVDGr1VEKXdyUHEzrIwckMtEN4T0iojZEdGiKmg2KKvsYbR8EBuML623oUbNJb4doSaqpGURTklAMU42YVYTbs1dEOCdJQV8L51gvZweyqgvJWbRjqglEJBsdmFNRA0JDqOoa8/UvktnNfZ4QbqGiSaOHJJkMV8uYZWVNgAqlJnK+ckkhfBs41cM+Ut2ELBS0MGGPOd/gFMyKIXJgUV63DGhJScmWOmeBCPHb9YTSGZAQlYzR4jbaIhHAxJifFLTUnrPHIlRRWTJHAKOHsQXpGCmTthtw5ejRIzMpOYOzUqT3BqZqhZvmaTqt6sNRTtUW2V6el0WWVT/98zd/+KePv3/6/Y/t+p1wN+lnjoZ7h576m2rS4/ENjm+Bjp3reY/xcsmlPRz9Usfn520fkA1/+Zc/86zWGlRtPlluWbY5s5lkesx7jIA5HXFUZfA2kRDBrIaJBQZgUA0dpjKdfjNAnbP7Btzk5CIcqV62jbWUtNr6SOf65dqgh7ocTYU9NHsYTnu7RmRXJmjqWhTF1KWNKIwydlhhUsakpehImHhqStIwRIfQQXbEJXvMx6Y7MPNpZCwqbtIHQFjMZ6/AkLc5vTClT0zwFGyjQQK2WDFrfSIjxtwfaUhRiGWINIwYN102JXufJWICZsK4pdtGkkXL/PkzEOimrjARnc5qEUamiKakmEjol1ry/KiKumW/FLXIWErZ+wwMD9MFzEgbhDkyQdJUZSKHVbO3tTrB3qasJmpVmMhIJkcqGjkEbKPln8txmForKpBJIxMqMl/zFhBJ9miNIkGKIjgQImLRA0OSY/5UVW9mnCRCyYCyqCx1tXbZRnRlZT+FjdEujeOf/19/+4ffftwOdx/O8dOZF7PmtT1fjycs91hc/svHr/a2v/2X+1Kvn6x8+7x9+3u4Y7vi6bn/8lN/Htg77oB0OgIm0slJejeT+T6dd83ZdQIRTDMJ3rLSwj/HepUB+CzR6UTci6qaJVOm3ZsZuHFEhTDx1qIsOsY4YtnamMOP7dpa7jWWdfU4b3Usy8EVBVISsZ7eRjYg5is7XcOmvFS2nRq6q8ZOyxVICQWyuvXRtZKJzu5ShyUz9CFGB5KuFoOMgKJzX6YIbOQYMW//ahaS5O07oEVSOkWYJilGSYbsjIXFvI0+53lNB5uM0afL21WEToW5DILRKZO9hpiYweB8w6YOmTE+UkTbGAqKzTxWzLgFOCdzN4KJ3nTUgoCrRqkM9MjkOJiOIXvexsAAEhJMNyMyGCFgSqpYcVDH2GGeYxPVHAH16ABpEogcMhgxFb3gZLB7klCjaGRmpLn4eieHEpnZR+QsyqUgR0BvoWlOcQBVkm4lxMSm6ii7IgIKZfVSvA4Z9auv5d3PtrOted2e+9//w3/87d//6XLQ8fLj9QwcUccDmozDNQF3qPuID794fIyS5WQL9U31530MID8hD/hExA4VDMeHp6ujJRYanF9Yt2JzpiPglxGwigyYGVIZwkAGhGZ6Y6hMKPQ8FjNpxeWWNpi4f+EIKbMfME8cnCjWaEMXkgiXLcMBREpkdHIfeEF1t7JW07oOr66ZOi3smr5KEtylD5WRbDkuIm1ULtXEDf11o4dlu7tb6/r42vajUSR6MFcZKdChJm3T3MdaoXVY9UttuY1smqmBUFFVxiysiLJkH4FOFVLSTRg5drobtcT8MoEpnYepfNGE+FwaJLOnViUoaklKaGfARSQSapj0RyC/dMbmSRsippPaSiYDZgaBmBLiMCJNdGRaes9QF1EMpIGh2QLBVE/h3DRRVb8wG9OtLKv17KfDMs7dDg5lqopSuijIwdGHWCCTDNDM55MerpqG5BAVf1OrGSlIismes78AQJMAY3JjRk/kEDeF2GG+BpHT1ERCRatXKVoMq5/efEW3rknpTfp/+nf/6cd/+oD7h9bw1D9FQDv0WrdsT4Gvj8CO7/e+vMPl/KEX7GccPd5+jb/5L/F6wfU9RoMF8tFeW0Ti5fPmOaG+TrOZtxOIkjZR9gCnypl2K/jd2KdIM4mQmc0y0UhMVDDUBNOdd6MfJGY7fkAcwCDFZHRiLdvWtXJd9FiO1ZeOXt0i2TonriIsXRnJfaCOURwisbgIcRKgMAWX585o6DpGLHKMgddoI4fG0Dr8KEPbrarCroWrS8L3FvNUvCxyeLiTtJZq6H1BqqYk0hAkEF+QbWI6VGSpaeQurLc95uresptK5AimudpBcl4UlMXJBoSii7rGzbXYAUoV3LQMlcjiRaCxI3ZgCEmZBBqQYE78uhKWAdEQztOgGqk56Gqtb2pK0QCyww3m3gZdBG2y3GR+JGdjUbSYiRVn91G1dT3cFSZyp1qwx4iRs/gcjCS7QwWi7iszKCEiVoNrVVHOdFaPzKKqI0WZiZx3yMzITEiki6mqZTBErE/uX9t771bManVf4O7HYyklywqN9R7/n//r//B3//O3P/ubv9Clf/p0Pf+p9Jf+xoByef38cnnF04al4u0vkXd4egIDd2/w7msk0S5w4qtHUIUXe31dtu0cL/LSd5cUSR0UN8zw+m2mN2tLU0t8m3BwZPOwkH1y2zRzEIODVMjkwJukCnOye+Z5UWkRQ1BjtqbmKcalTD8lU3V4rZEF9B4UhTgiR7bRmWYNMkrXRc1ViiML3LTXQI+epIVZ7WCs2XY0nHu/ZM9S5b7e92KX8z4+Xs2iLKskCnxeSzLhKGUpukTvQO9SsagCEb5E72jeI3IfTEFRTcii131zeIuooqkqMtK53tchW4zsjM6pUHKoefWJDtAIaSKZOjf9SAZ77zTVIgMQk1FZDNaY58iuaJoBgXAmzW6wFZ2PpTGmO11LCsE2+nxrUMJUMkNkqNck7VZ1RERUk9kyyAwxLEthDwSXN6XHea0lIvTg/bKXyNABBgB0UCKUIV3FgTqyVS1piSpYDZOKw6GpwwBLUMwkRQwiJTEA0ZiQbaSq99hiaEhPbhgw74f7Qz0svh7MiqhRdmDnddhX9+ff9c9/9Es9COp9lrel9vxcCn6x6pnX94J/+XNEmXkpnByy4Dwmrle9mHiXjmh+fhr0eL70uNpLj/sFnhrT56Cp6pYzwy6SamrASEBVIKKMEPOBSKpzhvipAkmLMQMICkFmmCqDAk0ZEOkcLjrYxMShYpKQoBSKsHD0th2vGrASaKQqBa6jZQvNCLMhFqskLSw1QqXWpL4MH4HL62XsuaodTY8P9x8/Ly/PcTlLdHvzeLe042pyPJ1+evokFgFzs5sqYg8wh44r/ePrNvaMPopL9qKk+Pwk8bp1cCBNUgDLCKJvfQctTa/tWo+sTqccT2VgVxWamDsk3BaCIgtHJrLSe7+6JCUtlF3Qcd5GBieKY6M4Qyz8BMndmkRq23MMsaOCChr3jBYiJgVKCkZIxph0/DQTBUYLQ0oxqRBKJE1FZmFVxKDmunghIlq3IpMnuK7H6F2qYScyW7tkh5CGMF/UCjp1SUFRsyS2PpSqrjJAy8yU1MlX0SWR0rMTliP63nOkJiaPuqgBElmoIWLuYBUXXcxEXIoKrtv2PLZOHpkPfh3Xfb/7b9fD38uPH7/Rd79ef/7rN6/b5fLh3V88+MvneBmnI375r/yc+uw5ciz3zn0IsH/IelgEsrfx4fN4+gQ9scXQLavgq/d3DhPe1ovqMBFVurpXFWTkvG4RgRAVN5JgDjGdd2ctNtgzNQEVdS0qoiYTiVvEewxTywg1m5tF5iy42hjMHLowGqSaGAEZkeKeg0G0HnMCbUVGUfaQBE5mwKX3y1OM3UZKZbHTMRif075/vezjqiyHdT3I3eKH9WilXMvpeN0vhAgs2GLs0bvQzdYcfT9zRBQtSi3LmskYHK0A0npnUKscTivI0MFCrqmSLft6J8sxR14f3r8h28lL15FAVXetKpFUwWDSUnpexnW/7GOEWLoAabzqJkNHpgxz9ZB2W7SlqSWpo1AKFK5CG66OMILdxDXECUlRaG+ixUSUCKlKZij7AI1mAhWqgdlyiKtAA+LG4UGVZr0Ug6O6jMz+dLUMMbFFaGq6aHOGMwMqZnWwM5OQVKgKW8QUJVNFhExeQVrPEBuZSQ0TkwU2s6ypgZJEDKQHERVi3ohhGOzXJp9H9q5ey3FsnpmXpw//6Z/+9nW71sB33/zuUB/vT4/18vLpud+vp1fd7xZ8/fb+2zHivJ+fMWwsBfXOl3sg8fTUfv8H/PAjeocc8fWx/uK0pOYP3704EslMSqDP065qqCrGTUNAyUyqO5MGZSYEVpw5B34iY64IkMgcUb0IRMREMjgIyZw/HzFPoY4cAuigVhNSWfbWtAlVUCRT5IYnmtR+BsWyxjVC1dS21HEhRaUQPQXVSrn2asnLuUevpfpSD2upB1tOy6kmax7eHf3Z/RrRW7TRWjQdVuzAXE2tlF79zk7+9v4+2Efn09O+t0bymlkONhTAdjge1RBKcfEqS121aL9ezCwYS3U1B+lFVDPHluOW7EqmU2stYZ0alg6LpI4+XD2K24rlWOb1ESk6lJ0EYlC2Tq3zaL1rM3G7V3d1E2uSPTnIFlKEneYqiiCTQmaTMZslQ6kqrjXHto8cZFIXU68WY/QMO92PfbOh6Jsyc9qxkEYGSveCAWQXeCoyZu1DmL120EgGI8ULMxK8gcKnREZDBMkhUJnSBAMZQBfmLHm79VLDdd73DFIhi8hwUXXr4+Xj9Ru5s+0Jyz34fN6+/f2a5ZBLXgKm93eoq287hqFFE4PTYN42nhMp8d0P2F7gClG8ETxKfrg8Pfzq3bu2uig4iLn3m/z7QCYkORh9dDUWtcwoxQSEysybYVpSJr9fVDGpLzltD+qKoM0RHkJ1XobnIwJkqDkCuhQwXbSPxu5qKupCzj1iFgnS4YKAzmlMcuw9DFI0ZWbQQWTr2bM32L0JyhAvth4ox8jXly21buJX2DZSFnu5Mqku4mOsGpIDweVYM/z5vIt4H5G1lkXUFG3EiL6fl3Vp8uQHrHeVBRxDFTS++8WbN28fcjSTpqmfn1+EUMNosY+RHZGVmQmqlvVwt9yHSI4eW4s8TzqvCX05yfXSti0ym6kVN0bsMWR1svfedZkuNIWOJNIKF8nG0XZUR5gObzHKCoOqldHG2Ac094gCN0ofDQSz65CW/Rq+rBj79b76xw8fH+5WqbWfw9jEXIWmYmaiqRiswZKixpQdOU2pohhk9oYkLZgcNwZeiKoEJjtDPHwRMRoG2VSLmmDhjU4tefS0muYC3SUWhQjCbfiyKaV/ev7pw3cfPr5yoPr6/lB+wvUfn7/1z/z1V7qJrBX3cvrm+35ejFrrSRJ43XZJQOv5peGMN/f4WiDEaOje7g+4f3j9Lx7fenLGGBTzBD/nvjBKZnSyM9GYxcuERRR3FaF9mXPO8nKqmFJpYjf4JAmQkTl1SXmTCieYSQNisB7EZ0yaYdBt7+upQglm7wmLWrUUZyikQyOCOTIopiilmEg0DIzQ0RhbRw6ttZipSLw8ne+Ww4ufn19f9hhwRzXPftm2ZDNdUoiiaaGSqtp5OZ5wencanTrEiASS+xHSWrtf1sH97u5uOtDUdb2vKZmEUvcLvT5g3c7n82uTslTry/l6ziwGWCmqQwC/w7LI9UqGN1IUZZHFlc2ECkEvulj0kP1y7i2YQamMXWuhTNnMjDlRVAdSBF26lFDxuqxVFvcyMBQpEuyDnruYwZEYA5qQ2cKZWo9CFlNZdl7RL1rriC0ZoqpIO+hSJSSQAFMKhVBhzwGZp5+Ecow+7yKkZYDTGAnXFDOzomKcrBylQodgCDaqi6i7QkORKWlmFO1MpIhpkW62l9gv1/6HH377w9PzqJAjzqPI+nCn8d2TdonzllLw0wvKv+jvvvr5999/+xTtDU66ytuH4+noqNvRx2NNVzwsLrDvP+z6iHqCmYxPzz6zJKJq6ky1mWgfFCBHIgI0V7XpRDKFTBDatAfLaCMYqhAOlwq92YBuL8HJEJ2rhBt1Tmblz7RMNgipMTR20dWzpTg7RgA5ad1EMWk9aYgBwgARd8gUM47sE1IIS5QO7ntVluo2xjhkk7Lb5/Sz29HrEinaRk2F7cvhoErXvLs/tO2lmom+kOP0ePr8dOmtTa1k0Tzcac/t/riWA7OlnZblYVHj2PrINDUu9funZ/lxD477x7fbte9D4PcZmxSvhmoVzMveL5ctB5lDRbc2qpVTLctqqWHlcBz1w/m5Rn18LNt+vvy0j6YsTLCeNDI4IBqR2UaKuISYiruxIrQNjXKqq4tYjoF2jb2OaF0CGu5CHebFV1uwS2/IYF7aYfGWrYhE263cJ6Ksa1mKrTskBDI4MKN+ksYcc8yngFhGVzUoM77kAUU4LVomJnCj2kRYgNmZQdmEJLqI0IqYQCrVB3uOnQGhSYiOEVvP5Kenbz989/q6l8OxXLbLq0hdXauuXV+v8ePAcsLDW3U8/uGP38PL/XpQkbHF1+/f/+v/5td/dT/+8NsfXvfPa90/fH/d9+Q93r5fqi2v+zrOT17UJ10mJIpOsME8RlJCZjVyxhnElEK9Fb8hQGRkDp30LDCyO4q4ZM7EWApnn3YOliZbOsFpJ+Et25UpQhVJYY5QqJgobnugSVWe+WfJFLFEMCWpVRiyL1VPVRfPfuXsdNZVwbEccTrsx0fc/cpbSLaRmYMiHZnmXiP3h/v7pZalqHNR5U8fP12fnvZx6Dt77IsvsqgmXW1dVxVgDAGKx51nKPbYBlSho23u2T3b6/bh+/60d5USiIfH43ootSqiX68xIk2sST8cV3f9an2Urlu/vr62fcv1WNTM825r171dFWu5K7lrj+7VwqWUqrDtfO0qoW5aTF00NIdTJHIgL9fdYLX68W4pbmI5CFHVNMTQHcwWQs9je9mxsxCUfjp5WI6jikZ9OLqjmEvuIdz3LUneKKUajKBmUlTYx01aMEgmXFVE1K0aVaFqSRCRORVyg0Hp1AxXBQ0iQgmHOlNSGGPjuKwgIa/bdv5pRB8/9Jd/fH0qx4d6uJ6vCDW16K/98tLPr1jeYjksb08P29NY8UYqudRPr98/nh4//Pjp99/o610R9PsjqsaTU/uo7+Xh0F9fY/vcFxNXF6Unh89gh83aTgISDFHhNPmYZIMtfjv66RQtTFWlYF6EBLSkTElsYqbhJz1aCJnl5szZLpdooUUNImnCUPYQ1ZbDF2EofXZE0sQC6ooW4GBmZCeEXOWocjJ5qLxfZQs/X7cgvOnptNaDrVX3a6ur36/v/YFWsoe03tsm3HI51K9+cVc9Xl/PvY3Xl9e2XwVS9VBP+tXdu1otcZOOUjC39Iqo4jnG9Xrdzj2oW4zR+9aug60uvq6lkh8+fij1kK2Oc2vb7dt9t9bUuLM7DD0tq6hfY+zXlurHh6IjTA56YDktmo852raP5k023dgPvqzH0vY2EOcLxA/L4mKhDOoIwAhn3q3mBlUWySt32GCWAJey+BH6QEvhaBhCdNbcRrPjHe6XGOPazqI4nSpiZGm9n4lgjNkLgUKZqSqCQoUiJKH2BaJfUGym58xMIOKSiX3fe78aF2pPbCFq7g6FGBKaqyy0ci9CaeP84bJWCWlt708fzx8/XkcpP7Tr8d3xQ7u+PvergaVnfIjUzSEnHB9Rl8oqPfetXa0ezepf/6u/XEt898dvf/fPT79xfX/3psZF0E/+eLzzP33/+ukV23YZsUHhdUgW7IPMUcxFDYm0VLqJZdLcSRoVJpkpU000aeC3gueE3qdrQU5XzDTm3kr2vNHwRSipsPmCQTJT1ROcBJzJZmdgWJr5YCAR+yDVRAdTlAhyTMUnXMv94iVZUiTMsh1KD+JwOJ7udD0UX3CHY0pTVRVx4qC2ldwidYFW7Fc8X2PQVKwc6s/vPBcBTFtClMPVMpFAF6LPNpNYD2lXtpi3xKplUaNqQmxdDnfHw7pcj/UkVauayHQ+wkVa68txWf2ky5ItDPV4LCn68fPzNq4hnftlu257jrUuZrr3sV/6eXttF8qbGlsTwqzWkj37vnOpx9FerNC93J8Kcruv3tvWs1/2uMaIjn2YqLTtpZRSvChsKdYx9BFsWcTC2Dxr8cwYHNceJowzxy3nYgnVmJpALeqmNstMBiVTILfvgKkAgxExSHgaRSOGqjAyKemp0Oi5LkfYIrpSzQ6lXSX7danux4elRjS+tKfPlw/7ES9971o+fn/9eMElMDqGvj5+/e71eoaADlkhdO2eEYdDvXD/i794LMu+9R8ovV3i7S9OsW2fLq+rLlL85ZmXp3g4uL1kCt7/i9Vz7FYciBgxoD7pfkmCKDInankj0+q054wRpc7GANUkc1alZcRwLZkp0MyhMz+NBJlAZhTIZOIzoYYYEcGZHCbojohgjMhBHSFBY131ilANrzNJRlN1iVPhwZppUfY+lv56KUN/9uau3BdRtep2KIelQDVQhNrRYK6SHMxA6xhnuTuUFFOXiUTtXbY9QmGK1rrLCJsvcqhD1SDeI4xAl2L17rS27ntIZy8monpYfDnUTDs+OJxidA+JPjJdptZX0LtRXvfWxrnHGASE1/N5jIgerW295SufxSBw6OJSj+/v3E0y7g5r5Ov5dZxfL6GuSLfYM44Rh9IXyUtunXreNgV7ZOpQLx2EuFihhYNiqC7Ivh71zrCaLQztYYHgtg2uVuCZlj3T6FaXGz8DUBETghBBSs4cM4FMib6rWoIQ2M12RlONMRU0EChFyn3JflFf+lHL8e789GE8jzfvD+th3XB0bE8/fP704/M2tqvkS5fvf7rysD5/t9FA4vFx6dGlMCvyFfd3b4II6Yc7y9zv347yKL/9zTdF9up297aqLJdrG1cQmlX3S1pEvbue3i77FTgf/LRI4yhOpoxMUCTVFgNUhlFV3ACmQE0CCGA2mAcpOu25ABAZXkowRCQlZVZaTSAw0zHS9WZTlVvzI9M48ag5ybhDRvYWETq/AEmnWDUrQkPCyNUSEFddqiNH24eQznY6LW+Py6mUbGw5SPYY7NcEhg6GXnrbt+D0nhFEHI5Lo52vbYtBSmdGZB/di8kiHKJAVpVgcT+Vda0iRE8yWjJy99QSiZ4BUtQXVA2LrTmkLIxIMyteSz31aFAZoxX1aySRUqjSDksJZTRfT18xWt9Hx75d+t6az4gwllpX6vrhw0dJXLerGJKicmh9nC/x8Hga6D+17eV559i/Oi4nXcPAirH3oX1d8vH+TrTun5FImIqIGK5PrzH87m5dDsMZEHSpJioVfXEo4SyqYycgx3WFiCCkR89khHqIaY9u7DmXzO60IWkwIlVEQTGXyJ65kAN0eJW6rA/Yrnp+2eSpP51/fHt3eP708fKM5X4Zdn3pP2w4Xxk/XvDJuJsMyuErq6vd1TdM+fB8QZR1bRvRczx89TP2S98vUnk63X/zx+/EtCgWq1Vr4s7kqojKh7IfTrLsa9mfvvu+76ef/fy/+vkv/FUb/YAu0A4yeioFVdVMLG90IuRcEcfoVINqxHzoq1BvHhOVzGFaxghTA2eLb54AADAYmi5TfjYyRZgcrZeDz340R0zujohR4F5QTFUo1sn9ldNfBEJKzWEcjGQQdFFD3Wpdqixo59fXD+c0KMlkZmRaSLZBLUqlm65vl6K4Xi/XnvvoveXoo++hHn6qxasWqIquPKy1ljS9Hu6KoLSdbdi45lDeUPI6YK2oeIX4GKlqxbyMxGghqmqqVk1lPda6Mj5dR9uzdSvFiqpE8aQgutXiI8pXD0sQ55dnxeJSR+eF5S9+8evWr+jgiHHM63r9+PRCRcgC1d4uaXLNmk1/9dCKDeqAIV+zGz1Qi+q9th6mIhE99vV4KEMg1reEOVMCOZgsJpUjmBF1UBRjnFuYm0rGkBjsNCrokkmGTOZ3MrOWuaxJgypAC4LmPsYI9sw8+Ikvo+s4X/YPny5y7adHz9cff/j+yWJ/eFvj0Z9/er3s7SfiXHBp8McDaD9/OB7v3cbdy4e9MzPb8VTxvq31OGJIxhP65dLeXw5+qjpCDrre+ePp8WUsuAyvy5LrwRbf/fx5e91GFjzcHc/PT67Hw8vLXtwKjRANo1ofuhhMhOYcCVpCOeY8i20MU1NLEx+jQYmkwOZteH7Wb9Igg5gwSZ09qpiV6JszlBpJGSkOjBBXoTicijSBqtKMIg0pqaLR01SEEjd9k6GDmSKInZv1y7MIgADh842rUHeIIQw1wUR4oDBa7Emry6mgaHaJoYFjXxddanjZFaIVdS1S8rAuKUXVJUTEvQBa+9CWUamGRdRW0+oookkB3HTZdM+MgtkBAwRjjBK2roesIcoxRKaZ3aJRVWBHP+EuY/eRh7cPLYQJ6fSNbB2hoMBcRNQF+tIaluRaHuRUrbfWXj6GXEUPXo66r2U73ksx2Vu/tqZRzESorqhWsvdCI7JjZA5JH5pB6YmeS45IktvFOHL0T/XqXUSqCFQTImZcXRFDlSq5eBW1bYQb3ZSjqytnrKpuIZmjlXJ0ofmB+75/2P7zNy8Sr//FYV0ejgP986enfe/Xz8v+Ys9hYwkvWBUfPoz7d4/0aven18/7Zvr2Z3eX62bX3R708fHwcPQ/fPjww8vWFXJ5Pcn5L3791sb27s1adbmy3q9HiVzStSPbq79uj4mHh0N52rOlW7aC8InXCzNFGDD5XzY0PJiqJLuomUgwy2y4a5WJxRk5i4XQJKZh8AbJURUIkFTMxRm+iO4CMyfIuCUTXVoMAimaSc69BJkjBvu8RdSyrKUCkGwgHYVQEUVP7VUhXdO9HpYjZYzLZspiOq4RmgGRxVWppY5xKdWFWNROxb0usKFka68S/XhcyoEwDEJNmrAWSdos9ypDVANYmqh5pqygqpnCTER9RPahbcTzuV/281JqXftal7qKjmHixZdQHu/L6COyZ7IPKdSs4ShAqPgeYzYfUNTFrO2XvQkOh7K2EuzNJE1PvnSn3C+Hc6Mvekj0OF83C8+tx2Ljrsb9aS2GYKK3Vb0CiGGr3xXErm0fr/miUiHXffQWevR1lVotHMRRt70RIT0NppwtZkDGgcaQQ3VQaD2Rwl4WNbIWXY4rJNh776N40dpleNM99heGyOt4/uOn54+v9XD+/ONmiX7N8xWvr3JvjnPhowevl9FfNg4vl36tPv70zXPrHSqX1vZ9v3tcHpfD2L6zx/dv3j3uGz+9XNl3yVV21mqaLXMbe3L0VfVElcTezusdjsfDHb39GC/b5nG+Hr0EsmBJNzLTDV0zsZg37VrZt7RiRoVIEY8g3YB5L0QaQU4/QE56DKb/Kql6AxvDMCU4Nrkl07FHFxs5IGOMZuJZxIXBAcj2+rrUIpIenOkjMVX3Q6myXyREiqOIZ00PvqLvA2phaK+dAvdDldDCdTm07BBtPbJ23cfxfRX26RO/XDZvWEr1k9ydHjJ7RMMmddVaFGBGn42IjBFd5+3PTVlZQPrEVYUKVC0SouoFYiwm1RdZvedYXLXU6zVrCsTH6ApVWcxt5GYFSAxSWgyMbAASRhmACxReoKWzacsrh/Xn7TXPHW1chd5fnl6uW4j1a+ym6r70y56CQMkGGlYptIIYTfTuKNZGa23PKOOIlIFMeR0cjHjJvsRBTHi8H+KOgxoj7Pn5tYaopQhcxRfvIw9mWqXSGJEpmaNvTcWLcImiRmH6ISVSFxexIv366cX3bkP26/MbdOnl/Aw7qq3L4euHdg6OR4TGeKbw6Zo7xt621e3x7eE/f/OHzxfe31kp9e5n9yerl/OHvGK/++7TWc8bd8Gy56r+9OFyVv/8qS1Fzz1938vztUcsXL5aD7YeFs97Lx9/d/Hlna8Fd8f7101CdoxZfk22ECxZKKkgrChUOoalJ8N9HmuEnEbNjIkFT0IlxxAvYAro0C5QaIKkSMbocDMS0SGSLdMP3vvoNjyLGvoWUoU5qmIFnIY6/ZFpRGlpV7FcHdABsxKjtW2McyyyWlEDM5Xg+bVvHO4z24f00CLjShSaxOGuWHFtvtb1cCrR8/y0RTIEKIV1SBupVEIPZRUnzbQyRTWBqmXCAqhADqjQXTKAzD5GjKGKh8fDKZfL637ez0OiUQ+HlaqtsV8jmAaDE1LNCTIbJIXDiLkzhy0qKIvWt/fvN2ufP127XD/FdauNHHs8LQe7+JltfRnJK0cwjnnnOJxWYqForcey+mXk87V5Gnd92tu7hXcifu8HSjVBHhwXtyPYR6699+v1p+v1dXl498ouW1Pz5euHvO6vfb92GFOHnQytZAeKdGgqMi2bDYlBylFYKItayVRrB9Xr03NnW+vjerLt25fE56+Pdy9jPIf3c6unWA9+9He4ymXfIuilju01DetJ3//s9NMPH68v/Muv3h3N376/+/lB7bLf/8Xj41fb73/zw56jHvSv65uv3vrxVK5jPD2Nw35su4br0tJ3c4mHoz2uoiqLLOOFj8efnxb39+/t7vjm2OzH/ftsWwzsezO3bTQNgQrSVEVMNVyEGEIXKCk5BsYYvE12Ju2GmWmTliwzOmGqcqPkTduThomTEWQ1JYalqlfpyC2MNDAjqpVV7f3dsQUzwyOxq7aRvUek5aJVXFFh2/OrNdhazCzJ0YOYzBK0PSgU0NT9YOLVMMbraD2wWRqabr2mLdTF3Ey1UnlpY4wzQa0om2QfVtzcApBOVVnUVMghvXd1KaUIRUKDPZCER4KRwaGqixUtXqSMFiKOHK0PDm8IekDyuJiIxMDE4rKLiMhIDqmHUo+LDLz0nofL8/MPV0bP+vGVl/B9VBYZV2xXXcwb0qJdaFrHOFstB8FRRc7b589X6B52sG5Wup6BHy/2APn1Y/58zYOt59ckZAH2go+ffoIva2DbM9kWq6WPYnZ3LLzG6FFWVy0hvAK7TG4YQY2UjjRy49AcR8OKisjr3rbIZfEC99BP333SrJ9Hftx9e1ivTVTt4bQeudzXYugHLwosYYe66GEht5X9v/vX7y9Xl6Gn2v/6L8pf/ex9G1e269PqD0suWg9HeaOHFtqRJou8Vopoafbcjzse7v1UKuOCoa3v3BbdYq2Ll7/o67vicuKHH64f+6fr7kcZ57Gdt3UcGBWVajVTgkPTUP/sOwIxMiJJMRMYRJgqppHDRAA10UUdzj3CYa2P2bIUk85BjEEXLKVWlRzJAhUdyHE61GJyMMcmd7QxVJLFTJJlLVDLWbhpum3XGoULRRUhE/aUKgZjslaJ1szgbmQaHCPkIuyyb6GuhL0gKEPA5eDr26Xe0+sqd6ceIzigWaqKH5OY1kDL6apSMz0e7yx1bx3ExrBhRgydhjSteihHedTjCB5Px9F6IEfAUYZGzTIgKMMNGFaAYBasx+VA7FZ8XWrrw+hi9fHnlZ9yGF9fPu1t1INfN1/dWu79EMuqyYP2OPiyup33qyghyhE/PfXvX/br9XpXlkV1wWkbG0P3C7alRPY/yR6BR+PP3uRj0ZdPO+oK8Ze+X0YokmqqKRBsWULMF4MD0pMNUYpWn/kBM1VkGfsgumA/S9viKozn2McWXz/8rOL47T/9cO72cZOfRj0/t0I1W173XWUnpUpmkTf3y5OMr97c79JVNfaoa+0dpeil5x9/OJeFQ+T61ApBe3x4e04vqHdFD69tbGftFy3FT3I4f/7jSXB/Otwvy+gtRdpAjMgYcuTBL748cvSf7Ff6cJR3j6We5cPnz4NVziWwuYqpijE6SIhkNYsYWHRk2CSdEDFoRhOxSa4sPhBF1MzFAI0MJnKqfnxqUAZnT8oFdTUz99Ygo6yyrofRG9Ms8uReiGImVQViqroI05oQI/fIE0pZ1j5igpZ18giZ7iYiRUR8FYwcBEySttZFiw4ilU3UQGpnUpRd95b9OfEw7ODl4F6OIpmM8cJ9Z9/o1JDBA5bVr3HxVZVQ1RTtkEiI2vKwLidrO1pngZ1W62MznXbVpRTVLBzJISP3EWB3oy0COegyJ1aU69CrdmcRXdu4vDxvZank4eN2ucRdQ71KdCIxol2VWWpkk5P68iqLySVe9Jhl3femow10DAxe6n7Vujwwm4JXxvddi9e6HPbI83OedGxsn64NEnePZQsWmJR1h0dMTSMAHcnIQAi1BGrr6apVEpYhMYgdYbCl6OraZTdJW07U5enT/vqZT5/wSo9APZQEA9ZyPO29s8eIN3eHX/3N+uGfXxbs4nz/y8eQ/OGbD0+fX63qcN+7ffOEe31+txy+/tXXHz+ft8943SI6nuR6fW3b82tu1bC2P/10ute7u8O6e+/NWJAT+qrpLG/qfn12uXv66U/fye+Xu/tjff/m11+Xf2EP//Bvto8fU1igrtUlFyjRTWpOatRc6ErBjZefAWjrw225v1/37Comqrc6UuoErrQMMVIsY3TsqXCZ2MgA0iphen9YRLodjypSra0SDs7+AaygKCqQppKdww+pC9lMJPeuGUx2EXd3E7FS3I29SxqSiE6I0Iye2XP0MRE/TIEDAx0qJu55ZRhUAtl0ZOzYW7O+LnrywELUNAt1euxbAjSRYlZoXtS9rutoMibtFXm97uqmKT5FqcFEuy1VVUSVHQCEYV1CsKoGq0S/dMDO2K5P+6dcVg/74fzy1F+3saht7RJUUqKc9rd3h8T5VB4WpDwtL089DiWhO7HFgOoN103S5BopEErPfRQOU133y+a5ub64DcJPD6oCVi0V8O5HjlCbboiBGJoQrYkRYL/0mekX6TQ2tC0IlIe1vLHoetExVvcDzJlPT/u3P3xCHPZeGClupR6fPu9n6Uvsx4cj3vH79tP1J//p8nwZfHy3OhJ9vH2o/dxfLnF8G+/u7z1jFlDAcTjh2C3Un88VnhsvL/31LX7G1/1+tUO9u0c/HhedkBq7mhnqQ314eL20A4p/3i67oaz78X395Wl5Peu1Z11lud/kgt4BdUq6T0sXRWnVwFCbuE9rMZKS2d1tjE5ZF1szu80172Q/RUaMHDd10KRq6039nECd+/njwe+O5rJmpkUHC4JtEJlrXZTaIjPENfoAUk3cnfveBK6ZI5iKaM1pp0OpXkwkjZQxGJ1dZ/Zv9M4csRNFYFAVwOvBiy5gDsaVe0T38CXqo4xtN3XLru3qYmLeR2wjhuyQTHU1RoYw2Qfrwqo5tG/bTAFWk0MpIFxsZPa4MlxEXBw5JGMp1VQchxwxHxadmREfPn50ZgasSrVskLXV8lyfzxZil+f7esLDXXz9iF+9sTdv35y29+Pl009IWS379mF7Sj/s3auXWFVqLstdDGaPzDFGGznWMjzL8XASaWnYBhpWhZ2OtbNkpeI4du0R6loc0UZDe7z3/Tq2YGQKtc3Sk8HSUrVTEskhpjq01DgwQoayb6/fnSWPz5u/jGRJVz2ZlhCOfjyWHz//WNa3WfSn1j8HT/f22rf86fuJnH3zdv3Fr/XxzlZVq1667Bd8++F62ffs2kfuwbhc9k+bX3EQHk84EEVLMcPoR2m0VqzJuoQ1OV53Q3998U+f8e4B54scD/fAuKacR6xv8bCt5+8WtJLN082KqWC0SMbIXou26Av9C7Is1IrdWFkiAoUn0fqYmKFAZo40EmIBZEKg5gICuvfdRVYtj26LL0JtrUWOloEBDhQeLn3p7lfdDWNRVqqrIXRiBlruqpbaQdaDmeJYHaKugHgMEFllYXIG8rIn4EQSKSw2gZVhfToquJh2QgXsn8et2DAE1CGCKtnH5/FhMJdllTWVEIN1tUJnYN97pwgVoWZ18WomWoGM2N10BgFjdDSW2aKIzMFgBoYaiRi5LyfzIdeW/bBfrYnwpX/MoI51uxf2y/FN+eoX8e4OX93Lm2P1uNOv8uXTs2yfDmt9Z+vLeRfVsQ23o3SkHRWj7c/ZMgZThXlQL71hrYee3U00e1aIaZc4LCdked3OQl2ove3mYiGidlx18SY8nLfR9sJMVw6hq2vGCCp3puw7uTegVC6XD/n0g7Wmlw4R5Rh1XUNGz+jRr5f97deP9e3h06fnTz9eikKLGqIcpSjuj/fMlJSqq5i4xjC9yP76su/XYNfjugZl7xiASFkUGYE7rFaWKFWb5Su6pRQry8lOeeHHT08CeG/IDe/eP5Qlzq2dSVZ5/Pl65/1F4vtv8HoJIbSIW0LUbNJewkQzx+prdgy0SbhWtchURWAwxKoJMJhgTtSlMpmqkGJL5nB3M1NRDWbBdUddzF0e7quYXLpt561vlimhbZAL00VP5mYyWnAqmSxAUUotrqpmkiNUc6nL/G3sSc8h89BBBOHqwZhaS6bC1FAylZbaxcjiMkQpzBZippriIdIlXRN7NAvooRwORQ7qVaSklaJWy+JSRVNFU1SdqaIKXaxEdK01EZoKm2VSzT5iEImwURKmCvSRvWreoXDwdBibuCB//PBc9tPP3vPnf3EnPrb2ula+e2tr9XISVNEyttef1PvpPooW5oJhW8suPksUbex5ucTYRqKKOWTVdakHp43RNLjr/5+n/2izZEnS9MBPiKqZHeIkyKWZeYt1oTeYwWL+/58YPHh6AHQBXZVZlwRx90OMqArBwiInluFnd46Ziop88r4bgSi4bTieBzf/fP3kgEp4lt6aFM7eqB6ej+duPfqqVB6P1ZJDhWr+3Q3L2chgY0opIo2K8XKltnLPmMZBI9aa5UQZqsN6Pun40HWMbWs98/gwDizrej8edRpHFqJC62216Be/uSWhPtX3sfHS1vsWUx7G4Vxvtv2xnDEej9+dejwNjxPTFFTM7X5LXjY9T9PHCWpmfVtxzTU2PXzAeMPW3npW91vKKEYU2/FpCrwM/X77jw+2FUGpRah24Z2RHAGTMihT6ZrWdmr+vvSVRADvPuHkDLcgNwpmBguBhCWaj/WwQ4736A1Zts3vt+XhYaLKbaNlzuxFuSBB6ZJZkAMPh0HB7GBr5D3AmR75TXuEJGcOZWImVqlaS9EIJiYEe6aDNjMxWOIbii5BAWYOi84e4eg7IZpCSUocHyfVkhGRCKZhsyQWxjjR8FiUM0VYlGpRlVQZ9+EWFWUhcK7ORDxUTQecUfY4sahg4IzY+haRHu7ReqyaidyqUGyaFNU8J/6Xx6m+3C1tPOV6L3MJ8z4NzOGyHRrK8vZG20rh04NNLtzpsRw+f7ltget1mT2GM7vb4/mo4JpFmAtnhnDGurYFJsRSpFAG0bZ1qdV7vnx91SqHh8NYJMNA6Oav8/3tthalD89lGnBdOyF0z4tXtsAGtgUBk5TRdfuK29cNxJKslVtrczQmIrclL9uwcEJCb5erO7MkGFpLmcR2Uj88qkZLJhVWp7KmQAAuLe/nx9N//vp2ubuu8fPh6efx4/uHgjWWbWPVefnVtlvKXM/fT4dHa+zbfLvcXl43k1W/3GACu1D+X/n942lUm068XvP69ebDeXrfni/89jW0rO7JHEyRof//1Cuwc2IpnVTYKSnB+S0gB8rwoIjMlPimQqFga41APZqGIDKNWibtd9vM5hGr7y46EiEXF0p3YhLpUwGjIwsEqQ4welg4gSiFmTgRwU4pcIYMldkkbCAiJxAzWVDRuRsswSikLTwcVbIn4NkbzDIWI7YoXs6pFXrK4VC1SiK3XiYFMY9l0FEhpqUkVYj+3SdARJrYizSp4xHY90sjd4vFDrlFZI9AcBZCchIoqg4NUWA1wjMhTMS9CiX9/OdTt6Z6zIf4Cr68zaSb5Lh9au128td1GLMeuYhQ5+l4vt97nMraut/NAR3LcDxPhT6oYLG2+Gab95axdl4SMT0fh1oCDinDcKi1/nX+clvsu4fTYTrZti19E0Leoxw3rgrCGu3p0bWg9RbupYBghCE0UbxEiEM3375uuDMJw3MS/ez47fP1A1s5jG8xM2EUJSlSnZOmYSyawvU4STmQkmhhN85xQPBy75ptWb8uy6hmh8Ppfrn//p//+U/lHx/zfL7Qd88HvtykmzJflhu/3WRcyilPZymUYQjD9W1eVtzqop9+BU1cz0PL9T7HdKKi0Icperm+zs+PR/nObFtoO0OktaaqSeYWUjU8UyM5MpDIFi4qvYeyRuaAAZTWPSgjIymFGaAIE2EPI1CSW1hkChW2MEdO4t4k1R2FRx92ZCooQoVYExxEZNa/RVR5FzXtoJoE2CR4p9EbBcrKlLnzWffUYhKpEISyp4XB0kBslN0sKT06M9iRHqFBsNywzH3r+cjDKFUqTYdRtRFlKZUQqNXTC+cW4QRhZkQmIQIZIIpokbvYJTOYYhflOiVtvqUT8y6Yp8JDEFeyoCHFLYlMQEJoZtnXmbyTgm2YMBU5DT5mjI1L03589xT1zX1NErgS5HSoP34sb5frQ2lfv9wPel97HOv0fNCrL916LcQDMxQk0zAs20Y7VhFOkMu8Mtfzw1GHcbW+bUsSD4fau9PGpRTz7b7R5xdJzzAw60jE7NGj99XmqjYM9IDPS7+4IObkpV+zOmkvMd9XnJ4noZHGVirSzGKrOgqnEjFrEJDaw22j5Z5rL1vGPHeL1m8lWjseaSzy6et/viM+XG/fnz58/+G9csNm3vq99N/ba3ta3vGo48fSufB9tkPPqx5i/rz+cYdSwS1K/7xYQj8+4a439kIkxUjTuw3DeHqXednW5TgO2raAcFEGUcApRFiS9xY4evSkMdzBokrNAv5N/MaZ35RuTEzkzIRkkkwmBBwW1gbuibSw7KqUQLJIkQxjkoCzlM0zd2l6GrOCEuRcaFutUNn5yWCwwMMsUZJEuVP4muFOtTITgkQY/G1rOeGI9ACYE+Iulk7C0Y26O2dwDg+HgJNsh+MwVbaa0TJ8C+7VVVl6zAhWFUmJfU3cPTjZrTkomGCsIry7GuHuMPJwR4rzoKMohRkIEYBCKkK835caj0fWxto8bd3oocnZj8hU1za4nKXVGsNAmSORHQzJTOb+NFQteqj0/YfTbbnZ6u3eNTFIO36k1njr6ZuZN9rY1tezjvdtdqM5l6kcWutD6nF8f6xjJp0ep/RmYSkem/WIzZpsmjHoTo0HrHshUu8Sa9louJXaGdeKbbNCL/dX6AqTd+fp/nbsiPcyfqGjt2W7L1E2GagU1MpMkUjqklyak3UJnqyVe895y8VZUSsrOv3fnz6fGR/H0w/1/N3zcywbKd62LyDMt+ukWy1lmo5RTnd9iGj3HlufbvP18yV+SyiP7+nLKHq5vlxfqJXvcbR0N2/JCq7OOQ+JILr3jYJZWVQoiP6+90giTBwcmR5gz9BkIg4wIrp33+EwcKQARMKZIaREPZIso4J2DG1m2NY4i8kupawgQCgLW7oSg8g7uhiSmQuwEy2Usys4LXisQJSCTCgzuTFURIi1bzOId8cpkpl51KG1HknJO8kUEclc3JNQmJ15goCVp6rCcruuBlmslaKHQ5FRNl/f3i7jWESoqFaZAsmKdM99WOKU2SkSpJneF98ZOhHEhCoFhOgRbDlvgtRa0pwSSdJvtLSrUSO54V62ToxJPPhOWMchUsXZKqkk5Epbk6s3sU4sDHIEQryHF3bP7flU5Yl9DbOuokTaD+ietiK7FCrT8fu2Li+XdZnzS8uvn7+CmZ3I++fPF5nqNBYVowyz1iJ8bcFRCm3NgsQyFblmPk2sbrHycxsOfujXfJvnFX67btu1PzyebAur9uO701bXCfgMNLdqMk461SoKhtuG+b42PQrTstTWanK5XqjVthGtwDk8N7/dFlvbz+8fvuf60+O7M48vvS0+r7mBInV9L5p8JD4WOvUYPejrfZmj//Ul/rqh/gn63X/5s6/Xr5dbD355pYaeZM+1tC2jQUcph6JaZ1K/ZbYkSCmaPTKSK2ckkyaYhNOJgrYtuaIW7GFmAHBiCCSJIGAPYybRvfFCHNS7CxRB3mAVQU4WNcSzDVNlCpX9xOAIJMMSO26cRa0HkLtRyC3CTZh3qRkRJRELMYI4Q3YyeIiyRYpQQiwDHZ7Y+UVJ3HrwDmyhwhFFD3CWRsOxglMkqlYpapnhbMSFy7oxJirhPb2yeicDAJZkAu+xEfjOp+YAR7h3D0+TDBIkEOlhnF2jlYIMB7Au82XernmtehurjjZyJZw4cAM3SnWBlu5oSWUwgUszdoc337D01d9KR2ax0JFFSdwZicqJrCWYyoFDnrVAc4GgYBxUby9sdgvhfjocer+//HG94Y7tcKMcSxJzKWjmFkCRBMXdCxLk5lwHuoxchvHkQS92CPq6LNt9mee22EZqBOIayVuT6/SYVm/b+lq8P5wOp/MIeGy9O18XSx8tNX2wVtdW5262gUZq6FKwcq8mubT/QvX7q/3yT7+8G5+2TzNnI9qOpwwg+Uwsrqfp8EFAtMZyx7out7D/4wtmxc9/PqrMr+9/qZ9/Fb/Flm2yaZvLpr17a07SZDrIoCTvbLq434/bzRO8B/13Vcge8yckE3e2CDMndXHKSPdIEAoV7D5RDmUmgMBgpKV7DEV51w5lZCbFvm9s7INbH0s1Ch7AHaBd4JP7Jv3OSiZK+ebMjcxgUvp23LgoQD11IA4WS+hu01Ldt/m5ajqnb8FMEZFEArU0zqJJDGFQuHRLtdBQbOosg0SdSh68ROF64MVoHyILI4mUyUCdUlgkA7GHkzIpdrw5q0yCvfvk2T0QQdkTW9/QmjM06dsxS3MxxsKZI0068jolT8a3nmyIxTOjJIkAJIJYq5QsEBYb3Uk327q7UUgEO1caJcFMvmO7pZj17NmaUd7IyM1r4dMQT8fD5XLj3n74+NwvfO8hya3ZUBBBOhQidicEs00pLQc/MGDpr2FZDv6YF/O0+WVZe1vNDLJ562mOObzTqS9hX9Z7q/FuGPlIZr2vS5psS4/xsG0SvZoVstFiuN8vwl1WezzJpCxDbonrFU/fl/f1/PT+FL9jvQcKHNpQpSS4bFc/vj+fHo7tNnv42/wyTvn7y41PtH3OX/7lL3ouV9yvwxh9ht36bNLGJzwmdKteInLpPpSxBx0fyMB9iQzLkF0duEuwkjnSSUFGHmZJHgXm1iMRbklEkbnDzSIhwpTJKeaOHlEriBlCTID47grtBM6RZKftmQcDu3QdwN5ld8uI3d/cwZyU7tYMQ6kIiggzSnA6bEvhms75TXQHheROfC6KtYXvMtOkdDCHRzTmscKQSdmsryyDQzgqbRthosGqEIMnOdg2v+EOq6Hilu4pmYhmHQqOQQZzcAKUKkIR0XdmRrr0QKQzwGRgylrFurubyBBhx1EXa73SanHZ3kRZXaiwWjIzuHsWTu8UR0DKStzX+WbgaRoxDWMOS2/ZYgd2uy3EJYLcmQLz3Je2srOZ3W7GSYXlQZ6PT8Mk8lT4ctnm5A/T4ySbkMfGLV2IpYzX2zJvC2e6LMPEx5onaH9rpR3KKhls1/jVL2/LQmJ7srYQQGQ5Px4fxqeHrW/erwl94OGkKg6fcm6Q+vBiYWXcQtdWKYO3iEgpqUf76U/yz+PHhvi3vy1//vH809P0/cOJtvblywV2qGMxt5NMwv3eSwyHMj7OyzKdT+vr14lwTfnbPf+3e37/Hf7y8190vS4T88Ha6lhnXG/rK38dh1prOHXmcl+irYsbkXSerI4p84NFWGZaJFMpxLwrAzOI0jMjIjoFzDtYaPd5JhKeAEiRu3E29sVITuYUglEq9joH4KDWnEoXHcJgQT1DEJoQxZ52FpB75K402Lv0nON+GwvL5ORsK0uJUoSdmTg5I3aXGXYJuIWRMFOGBZKS8tv5guzBQszC6Ox32ii5din2ogAAnKJJREFU2eiUA3hMrYYiTDoC8nxum9nsq3mubq6AgxVstdakht2ykLQnaODMxJ7fluVYSSUrT5muHCG0em9hp3eTI7aZr+tLC2ubH0wEhbOciDVDUYda0rvN7R6tXeZxGqfD8H44eLRoYckDjVkporU2m/WteSceZIjcwjosl+jMcjgMPUm1avZh0r88+u1Gv6F+Nm7SEGyRPB629WW95TJvqYLWMtxkOWv98PnM4GkZn7LWHFpbrut6X27tZjjUbBTCTLlcLsOBnx6ffvz4oa1v37WPG/oI8CwtFi/1Fv0meQ2x5Htr3X2qJwo7nkoJ/PiQ/5/z++bjb5eLtPjl+eGX6Xho48tvn7Zcn06PE8tq5mR9daqHw/tfLtfLutwO40uhYfPTv3/97d9///x4xD//RauN+vBB2ieaKnBAn9FvuH5pTu3pGcPEMVHYyn1t9/5wOt7X++H8tM2e+2+ZHSAL2vme35xtvP8hsGu/3Un3XWFEhuzdlx2xSRxhcO8eVTKZTN2Cmb+hU5LhQeZORJHGRBnpxAjSSunUM5LJiY16p0hKQSKCcu8gZWo6mQfcPBmAEAVJpgVAQenkSQBTeDjIIveHL4kqVQr0CEaWQppAR3PPwOFcX/64904yqqpwAQyKwqJra5EChFFQLiRIqY4S+02AhJAJY+JdTBj7D4PLDpFhkGWHMIuyByU8MQ0nEwyZCzUJC3ajdXMMg0502MVb53cfTbbTLz/fvl5sdVuFZMzew++EVOaGlcM9wlqNRBcirb0lMsswiPK2slmZZ7/0Bn9d3g2TkJbDwyBFc9OYGy2NH/U039eIfu+zMuDtpm3o2yWHd/ruCfro26PEr22NfqVqeqK5+ZLk91sc8Pzd+O7duwzE7Bzys5wsfd4Wn+Xa+mvkX5ftk90/3Rbm6bauY6Fffvr5w/nx3q4fPj7/03D8vz5/+bXdX37/8uN4rrGeVL78+9d+3959/zSViXuG9bWtc9fhMKYvXHO6dr7Fov3/fP36v74sr44//YThmK2Lvv/u8bf19cf3380vN7V5M4Tj9Xf4hnd/KSXAYeu6YMu150jnIF3hAUowQL358TRYs72CjwgWznSGRlpyfKMMJUUG7VvCYAILiJKDGEgmjsxxGNLbrkXfcaKRTkYZu1Caw4Plm+GQY0cuJjOUsjtR7g5dJvkGaydieKQSYJ76raQuhYU8E0AaAu4pIUSmQP9G+wqQaMAlhAnuzkRsyb2z0Hqdu9mEsRXvt4VrspAUllIp0Xb8byLVhSg63Mw5HPlNeYpMmKYwF0kSUVWO8LBc0NdtMzRlFdYQbxGrRW+BqlPyqEWpU+/iLElTP3S3jkbkTCWNW2yQIYd+vzuss5skp0VQtbZuSY6aMmybMxgruJ4yc2u9bhJEHRSSzD60tt2VUOtAh3E4n3l4GrZZ/2aY58tJhvs2N82VOkzZ8zGOD3Q4b/JYy0c6/sf119ecoV1dLLaJ6ufltRwLDoOf6u/ryrn97fOnKeyfxnfvHk5lyyXwx82+9vyjx+fNWkeNoKUvyF/f/m1+BANFrv/9+/d/9fX3L3/8y2F4Nzw/S728XF775fnx41gO4zi9va2Xq892LafvyEKud9ZtJmvut8X+x9X++sflw5EfHyMORc9FX//61/9yeh7OdBGcy+nt2C6fsb213Gq7j34gzZkjw5ljzDxbVOcdEigRjJ28wCV9PwEyLYLT0wVlD9sYInc1KCMSChDII5VzR4u5G3HJdBaOCIKTYCe2B3Hf18PRRWUXWBNFJFGAOJVlQ8vMNGOQEisTBxKZErsNV6TIfl/e503gLZt5WpDvyAowExLIoMyAEGdi/yvnvpPhgFoJjSwlgatfpg4dKh49ETwUEALcPAQKISHJzDB4Zc/YTxcCBYLCQQF3IvK11EAm4BnUN/eEhydLKgIOZJCyIrgkEsGNQUlgqn2lxX0OGHx7uxLAbG2FkkhSRK/FH4dJili3W+eNZMtCclyzo6tUYhKLFhg2lvQglCqMXFSQsMXTLcV4dFmleQ4jbS2Nhl4Y6YWKlGG8t63O48/b6RGP9+31/3z5ulArpNHGSrlgW+yNBoPb+9NjqXIh/zLfdYjanbfl0+e18nGjYTaexe5OVPwwlCFKoYlyPk94P8rp6M8PtM5v9OX+rzn+Kx9/OdKk+vL7/P7xacQ4HE63pX399frl9vL+wwMHDgWosbWMxeH5dVn+86uZ5SBhG3741z8XZj2e8bf/fJlW/PD+6eN52B7PX57v//4pevQSjG0aj2M4lnVwmtI0nDOSRPbKm9X3ynZfi2GAaQep5t6dzAQ57cowSlJmThCId86ugwsYkEwhDe8kMHOKzAQpw93MlCGUSSG8+9MpPBHOJJTExvBAkoAEtD91hKTMQJIwJIOCWUF7q8h7b9aoBSIjHUyZIrzuj0FSEjOlgwmZlDvIwizBno6wMBIKX0g4pTHV3fObrW8sRJLJmjDPAJGbhnwbVGcisOMzSARCWgjEgUjXjBTSQJIQ77SNtbc1IlJkqJHq3rwJtVBohFDY7OvNPAh7DpKJh+ORXTOsr7YtBtvOZRrKUSbSIs1zddogqiKVPSKNPDw8jbZRuR7yu1Ifu4eN101mMkL77WYMJnrpW2O3Ufh5kKM8FMdyubc7nXN4itO/XX5tDbM5D7SZ01BuuLRcv//h3b+sxY1Hx//2dv//Ygm2s0Tb+lcsj1pK843zJv2NbaFWj1q6VirPj+NED0Pe3h/5zHSkcd7yYz19l/GR5GOv8zy/f/yxHuZJPsyz/vbr758vv02Tg+l8PhwfTkZ13V6MZF79j0/zr5+uD89PMrydnn18PBur/rf/A4fAI+FP37NnyrE90+XM+N1x9/UwHOrxEThQElxzoQyWSWxBOoi1VBEl2kMF4UGBJKQhdGdqIynhkbtNI4kYzAJ2J49Awi0wInanagCcaQEOAwhOym7JChHsrmkAu9WGQPvNOmL3jQcItapyku+Xbt8DU5GJ5HCwwslBEp6WkSZIcrjszXggAsy73RhMZOmcTMkRAZG9U9RbpwoFJ4eM4ksoMer+DkhCHI+6mics9ne1ZCKIxBFMzJLMIgEW4SwBiixBngkHdd9rRpaEU7a9zRgRntGsB1pLNh9UhCItV0uDLtmDJDKHYADsVFLEuTvPcM6WhVnVLZfNzaDMCkZ38vTmyLZREHT1pXpceEPph6SMx7nrkn2J9LsfmSj7YF2x/XBUXdG/toH7odfX6/wqv7J7UGjlOWbp9cqGWn/4y2Eok3/C523RJ5+Fj1YsZeuzDZgPYO5KC7RGxvt6esc0HgovNFY6ANt9PZSHZpeXTT+/vo1S/zIdH9EOg93mV7vI8PE8/vCn+Ves/3m/fL6y+3jg6TwcJhaFJbnHdltfvrZ/+/SV9CATnT/I//L//ulLb8HQzcAD2PE/5utRn24vy+UVsaAWTAPG85qmQyl2tNgW614OClfr6YaImIYatvPPM/bWPIESHrFLwPdXKDMRaMflMhGlAC093a2qYq9ngiIjHOmRIIpAUji7RoF4hMrf6ULpiaTkDEL83RlNJEkCAngP4BCEhHOfHMBINMm/0X/d4chAJoTqXvmDIaC9Wt8h7wzZr+OAIPfqpJMgkc5BZfRMConOYknCun+gRaXSvRAnAeksWoJJkUzJFLJ3fvaqLMj3tCDBw5IMIBCDaY96w5O4RiUK3aL72siiBjqUOiMOW9rGCaVaKLlbv7etDVxa3i7zZ2QrOP5o35PXKIUz3to2UlHygRAUrNk7VHAaxkNJ9ltkvZrNjm7NMXCj2qlmGd1HHGh9PWVsd9eXa+0KPXy5XK7dqQKTjkJ331bxe519kMPz47+jfyoeH8AdWhytU9siNougCfWMp+NJCLVUHY6TPtagV9u2fH04tGLlsvZOwxZarY1YjorD+9PT8cNhvS+0vns+0ya6frh9/f3r29f7vJwfyvndNJYkz77Iddm+flluEb+/3rcFyv3j8fw//+vjkeVtqYMMujW0GesZp6WXsTWTy4w+41BABi1LrZP7jo9hIRGlfSfSCd4NVBAMiwQjv/0SwbsBHYi0FplBLN+2pmnvPQZA3Xtm8o5QZuyYQ/NM2usdIidKVClAMhGESIgAT6IM7E5QpyTKIOwtFiIC0kEiSNprLwb2tX0iCcIOpHCn8H2yhh3ozqQ7C2//R0wZvoOx99tKekKUAqxUyCR1h8OyCiiIk4hTsH3LwSENCeIKhhWWwKrQYJNUAORIdhgTEzwd8O5mXVRZkqAeubmhlLDwTeAem/W2tfu6OT1ULjkYtIiQoDDcVvgyDlVqv6zXeqA/vX9fYi5tYu+x0dd+D9GPTyfb1kMtyt9KPcJQFGHd1t7IGNAYGFMLdjSYcR16kVO3737v5c2ORHzkcUwqbe49s3v09mU9/fn91VzEk+6Pj8fn94d/u768tPb1bstmt217KsMhN3eUCfWJjofD+5Mca313lh5xm6+r33vy9bYh8/U1Xq7XZeUy4VFwjP7zePz5afjLc52CWcfTccrMuef89uX69XXZ7qTx4y8/ut17dxK/tfvbbfYt5s2XNdBoeubvfxmHoy1b5eE0t1m3O06NacC6hAzrFlEIHZhXlERdcJhkse4ZiVJqIBoKJSGCQG6bCNfmHUB+Y4Fy7Dl7DzBFemRShlJJosiMSJZiEUGeHE4Zkd2ThXaVTOY+7IWAaH+ng4hJ97z/XhrtsNZEUEaCdlUYAgQPJDISKpRgBKVSAKK0T67B4dmCNTkylNxj108LEoSgvXEi2CX3nLFr2yMIhNxLOC5oa0KzDJrJTKpEUkJSYieuMwt5MFMiu0UaYCGAE4TT90FKemQIou1LFKhlEPI0mFu42CwotGSs3q73pW1bb/czjY8Px1FFEWSWkRTJ4UQ+DDXJFfjL++fKNDA9lme2ELLl4rqWxXC53Q7I84hulpt7blz0pW3b0ld31nFUqkRCHi0bWKUa94ekybjM/Unk9K6eDkNxyqDPc/JYZGmL2lBRiOe4TtyfxUe7/3j0ttyiewt8HCC0/fjzkcfTWw+qdj5Op5Gvt8ucGmIdft9uzbNxKa73ebtfctJ8HnWoceD44afTf/34UdZ+/d2YxhET+WK3bblerpdroH/3y0MtmJdY/VanswXuts3ervP6ta025Pksx8pv9/n84499kSTRTZ76NvPv42U1+3Go8HFCu/XeMQvOO/090jOQPTl8sySxXZLG1cmREcFEosRBFJS6+zAyOfdJbQBCABNnkEjyzpFLzXBmASPh5ilMouo9sPPZnQWSse9Y7m0aSkckwBS2pyqIAE7GDjP9tr+4U3mZmcCpBC2K/T3uyNytfpn7uz328wacUOak2OEO3TqzsoZAYl8c41RmuMFIukCZHdkMXdMilQtEVDaLcHM4iDLNJEGNnSJcPetQic09dnFmhBM4yEE8CCMtaSd1JyKF83pbX1uGcukSqQMdpkqU3peOKoVkPKUoPTxMkMliNuuMoZTkRiM0+2JGs/ll2S5Lf7m2efE6Tfe7w6i5c1XmMKJ1cbCYdZchpDyMMTDCNJmeIKdleT/7d5Hfn07Nr2UGuEajIcjTHo66gj+HveK2EPWBaFyPp0ERH97XY1tvc64FQvjugyQffW3JgPSXdY7AChqk8JGUDj24LX67LeB8+l4fC34YCnv+9OHx/fPUW7RlksJo0gzLK+4L5pc3N3989/DwdLxdrgE5HobK4E36XS5vb6+9fYn+Bvx8HF9vr//0Dw/t8pbyKMT63/731x/HcSxZon0ifTpNZzmKfw1s2UFSwGy9uyWDkYgu3sgjHCyckUB2QnrsJFvmpG82PLdgYtqDrfim8Saib00Qzh5SaL/DMsgyMAhHEhf3LT2Z0SzJQLpH+cmTIi2R6V2peFpGoMCbcSIpQft/USUJUECQjqJJtGN9Ipz2R2a3WgV5WITknqggpFMmeTZRTjiiQLDXUATs5wty71+V7Ft2smbt1thjGAYdZOtm5i22QFo2riwlqYgQSxUTUOynSeEMUgmyQYp5inA6PDsy0rpDi8KjjSm3eQsNKDPTw3R4eirzS+vWLMpBBy2pWZZL70ShqkwZ0Iluy7b0LlmW1tYMV6jy+8ejiJbDuTUbOqeje3YmlozUDLm35FJXLO9Ghk/LuokvHygf1vu7Pz1t85Zbh1S0iuBsVhTXM///7p+ubO9Pw3F6uJVsQ2pNzPE0nMdBTiftzMm5rH7H6uGbrb5FUksTi1ROTRQ6N2e6fn1e6Kgycf3lp+c//sflp8fxH797fxTElWKJ0qcw5to/30ko7m/X8cP53cdHu6+rGQulHkOkzX3ZtkvXl/Y6D+gFL7oeTo8+jM3WHl1A+tf/wHJcx7L+4y+yrpvxcUuiforYqKAU9Nast0wwCcgizDv7jsClvf/+DZNoAS3fEnLfJAq5X1uFAGJypCBBfx+KqexMlYhMSt2lUrmzsxhwOAScnkYZSTthF0wZkbuiCRThKkS6/77TCSKlL02FdbeWl0K0sxn3BAa5JynRt4TSt95S7N1ZfFOjCe3NJk7xPesHzrBkTgg8LFwEeymYZCkTe5CBPI0r6YAwbpuTUkIdySRINS9Fi6XtV2ticg+hksGDViJf05FIgowlGw2V3+XRenlAby0CiN6K1Ers43BvSw3LGMLKdQ3rWD3GoQTwGr0vaJE1R6HIGAJ2OpTnw4ikNO0QLWlC62alkBGDo/cxg1mZUouUDSBSuy8CPoQ/jEM5d7ua8BE5AWKxBXkf8Nn6lwHrUc5jDR/MRhL6SuGRdaBhON3nexGUUfTxeHvN+3216JahE69t22KoXYodpnrC29v3XieJ48Dfn56+fLrFHM/fTQOXvqxmVrgUHbZXN2SIXXK9neL/9cv389traiQbRdE6wdPM77b+4fbF8AI8f+TE/I/v/qHdw8r7bajrtuoA+IrLBb+OXjEencZpDDhjDKy3OY3WQHIdqmgu2iyaO5IjcpqGbTVOEOJbTzJIGMTMJGFOnJSZoESGhSgDCA9WJVCttbeNA6QQ1fBkERBTBJOEJVUOR0nhSDhl2dV8jMy9+8MZyQgCK9BBzAS4R63Veh8Oo6hIIWFEIJEUFJmZISiGxiyBffBLBISnqDBHkO+llDDv/Vbm/aoRhGQWpIcHyFjT3djIetdSOJoSkbKDIVKGurbs6LUMOqj3iNya7/IoUFAmN0NGJ5aBqZgMNBgrSU9kqaRaqQTLZIJFe0NTJvRcugXYie8J6o0st7fG5OfT2A2X1e692ybNUrMRUtVL8iDD8XAIl1uPt2W53efNWFU2Ty7iPvagIO7WI5b7ladJS1tN4qdB1svb47sDCvRUoIxV0YbwW5a45LJsMQrWOv7+ctFjWftXbqxPE0p5Afl6J+uUc9mGwz1Wc4+ZCzM83JOwtHXd+iFk8Ppey0/POua7OsRY+T//+6fn01TGwZ1y4djscOC4NnOsL/QAzT68/+Wp89Yz+norLKQTyU/Xr1/hl6v517utG74bMb7En/7l/ODLugYfHsBRRtXhNBw7z7bcZtwv2wWNnR5O1VO7YTMuRrUe6nigfpuv1q2YeURqKSRJxOa+302Z0t2JpaqkExF8FybzrkZ1IUpKYmIhUlbm3nsymcXeIc2MhHoiiUjEg9g9U5PIEZxEQUwREWCPICSJMrqXXSkQvFdLnlEHAWWET4NGegLI8MhIRVIidKC+hIAS0jOZQcxI26cXIPA+eFVOSjCYKG3fH0suBAITqTJYQhJBHqQkECXyoiUimBQyVC7RSVKT9+pHnMD7SeiWFj0ZPdy303gYVbP534XKtPQls1jcuAyHMUaIMELdjeaMALcAK0d2MCVkWdNI2laMopE3k1sDEwpiqnycdG6GTHP3bi3ibWmRrjLkwop9qSeIMnyRnNYFzduH05k+//GuSqkFrWM4ggLGjtuK+9dt/c3ImX8ax9++Xg9D3vJ2oz5Nh4fKnfRt7eqnQU3MuuW/v745cxHxzYK8nnUzW1ZM4h2N2Q6H/l9/+OHl0+xB27b8l3/6U3U5Hc6UtPRXrPX4/WOD5adbvtmh1uHhzAWU+bbNrfn5u/OoA6zPS3ttC9eDipUqQ/Vtw+OxPk4HHLfQFx1P3Gd1ItIqj4sRbs1OaB/PA8alXTY9gc9Mh8Sm97dcF4k790YeINkvgA5BkFNSxrftE2EAzMzWY7+j7nqIfSSWmaJClEWYaW93J0lEGLP+PaoMJASMbEgOE5CYRHbiZOIAp4czE+2gOUVm8ECSkUhyygZRMvNa2ck4GQSnYNI0OIggQmScIdy3JNYAAs7MSGfljABDiJyDed/n533XdwcBiO77mKLCjgZIy8ydkJKg2B0grixJxYp7+s7DAILBGZnmCdSx5mIbLdGTxmM3b26rbQYiQpNIap3I5+Wow6HKXrLpkCJKFwprfWvuHRxVi1v64r1xY1kSWyY8p8JMYMk1+8vXi3gdx6OMB6RjyWhkCVEEUIjTgRbeIvKWJIfTc9SBk0YtwAQeAMaW0eal3nNcpvcP33HZkWxlPF5v/reX5f3To8ckWw0aR2sZQaAfn85rX4dn+etvX7dm44EUHCmbOWVS8kEPI4fd/Pdr25BfP920lPc/HB4Ox+kw5DLLILmGN7n937O9iWSkuJoyeVpy0vvvfzieRt7mbXu7Zbv0vN+3vuRhOE397d0Tnh/K8/HQxlrqW7nfhuNJ2Vc+tGUBOxbC537hZXn4cDw9vZ+OPTe9b3DDdqvtqrkUTh2k0rcKXxTolN+06pFwcC27EphVAYtvQVACcTixEBggEmUEWCg9KAiKRHBykO+tHAsvu0nbnGRHCkUkITLJWZn2QPM39zZYhMB7zpLgZCTTbiImrZwWktTNc3/pZzAJISioCFsifLfa5L4rnEEUnAKAYAkFU6pwZpKQiAtJOgGRDnMnITaSJDaA0p1SB6GMdM4UIiLK/LYQvSdWd3mys4gQr5sMjARpjdZ2TVrIvkTKIw9bttu8hlHY7Wl8RAntNBCbEnsICKHoYc4SkiEZiGDDxuQm4bk+7Vg46JZpK7m6N5GhMMLcoQbPffZn1mJbFXk8HxfK/vk+uhzGKT3JHWsHaMubs3HV88NjdfXAQTT75RPd87sTnyqIxLH0+0q9OQ4qN9pOhwJuzx9PMKuS6719vW/XG8jgGdO58eHx/vr1f/8buB7Pz49Lb5+uGxetUfgapTzWd0fq8vW3V+r6+PPDu0Nebx1Gl9v1VKfTKApzWVvHGuoxtrb0Fqvf5Ihaq5YoerUUCh/9st6Omnf4Gm93UGBu+Ok9OKw+TW3VV4txwqAiTbZL5ZbaiaX4vgbpnKAIMEk4IR25SyMdTEHYG4b0TaNKwpRhpIUJGSa6G/Ug36JDSSBzFxMiQgYjWAjsEJi7eiEmy0CacFAScyhRftsG6OQJFECab8zS0YcyaqU9OBqcEUYkSRneDCAwSY0Iz+wOEuxI9yQIse+tdUPyN68ccYKSyrervUdkQ2qyBph7ayySSzjY2TzBqqSsolAAoeCEUEhhhARJRk0PpJnWPOoEzmYLey+Fg5Qyv+UGod6Fwh+qbOsyKsMCrhoYWVAHZ0+j7mZb+kYJUCSLs1jNTjBpMZQ6arm3pbmlKNRbRAc3T2bqmYutWJycp/EYCeKsWh3DWfSh+Y+PxxqeDlJCQbfelJN8kCfkQM0/p80P+b/eln8bbhc5HENPg7a2fJq3nq21nPu09fj5PPDQT0yN8935/Da2T19WYOsrxoK29rfXt5iXNi9eb5fb+O74fFlzffkfr7bVNX/+0y//8PyX+6XLNKya89qdDYNdfv3aVvvxH75Lu80LI2Nxuxja3eQedl+mR61HGh6mQHmxedu6ZEtsw/isT0foDYcrlkANmMF6WluXl3Uag8rEIkZw876SLerZhlqVlV1A1fpmnpSRGcqMRHoEBfZ+KLN77rrIPV0TEW4otez3Y8r02EdgaWYQSnLPTDfZN8MhwmQUnp5BJRRkINpXEXInjAZtsVIMcBPOUjSQRWQfX2RoqPvm29bJ4T2CLEiIIskBTnzzmSQIlCogJEOsmwrtZRrvyCEBsyR5Zu7Ta2Z094RR4Z1kQJGRCdWEsdb9NABzCLQgLN2RJinBQYiQSIjvG5XgSA8RIlLJ7OEB3sJ6bkIeboVJmTI5QEw4jGWCRphv6L3e+rYxu5MoSqUxUQSFK8MYOpvNC7UUAnXyiNxi9ewsNdzt3rbrGqQe8t3pZKUsGA39p7b+gx5+fBJavrIMkAyP6FtyQxmpCk96vVyN9VPzl/A6lIJwir9t19z6EhJdnfqW7SC6os9f3oS5lNKX8XJxtfHpUBc0tG79apCaKIphkBS7tiUxLa+v13/HO+DPj+2e1+y1TCfr2+02X5u1tPXWfnp/4FzC4Fa2nqvRfLe3mW7bUkbi4pB8fpwS1gzTaaI8Jw5jeacnnV7mZXjGSXEaIInTUXKbx7St+bA1ejxyGbrPmxm1MTIlBgykJNbdg8Iz9x3zADjBSE2iDIvwICIIhAW0d5pdRMINzNadiUiVEMmc8G8K4X2IRcTCXBIULEh1Fs3oWoQ4SpU97xxAthjKxKkCjVRmcQpRkgrSsFjJ4C1kV4CLcmj/pmolYa68ryUgPJmVCAS2cBGJAIhAIKEgCDi+zfRAqilu3vUoXAfTZCYGZQQzIyBVKeDkFpQBb9k1hJXA27ZxfmsDBIFTcz9WKPYwd2FwgIQ2i0wbi0GpoigrZSvOe982PSPMABHJ9FKH3prtdyVHreM4iTBDXYKsbWBrHekZ3NOQHmYBNvQYWaLyHNllm6VsRK7tbHrK258+PB0qtTZartqjpzmljBMdVI8VWwvZlphvL9to6B6HjvVEJUtxvcEt7DA8fP/8+P2T3rcXogHsP/78y3JrxGVZ59hAQb4FBNfmk2EFaovz8eTMZcG84PiE89PgUrb1brG1OW+3eV5fH4+09V7HrBNvLTICKpnU7rS9tpWWjcAZNtv7f67/9JcTxfL+HM/nO9vHP14fvvvprPdPyw8i2+Q0YjhCO09jDkJ9dSLc5lz67VhnZa2jULTKJ2SSAh1J2X1LZmLO7llYAC0qLAQKTiKKALNm5P67T0G4SS2WToTY4eS7HjWI9vQNEum1FnCSqGcDkSWDcBjU+r1CSk2lEp3MXFRlYKKRnPybmNIpw2tjCS+Z1r0kUdHgsCRIhmYkFbUMFjXPJBQdMoKYQYCjm6XAPTgowTrAEQB2gp2n15JlUiiYWZQDTBS+c/OT0sgkgxDhkB1+ZH1biQhJLR0pScqqIt+MMkwgDtrDgEjJViLLTl1yHpgpU7NQJoLWsKVR754ZkwyniVqbdexbeIAcoT4yk/e8vtnb/WbucBhUhJC+Qx7ZOdwE2CoJxhppg64Z62U5WX4n+l+fPr4fHD3YCcYpwQPHmac6Misk7huWtr3Y7XIcOpski+RlvT2+fybMdrdxrAlw+nfP0+evp/Iom4fdcZvn+/X69rb6BmYUQweaQyp0GA7DqKpnrfbpWhJ/+dPT+/FdYb77ti7z7dJfv7w9nIu/rYczPT4/RLobS0oWts6X1zbf2pfLnY+1zssSeD6VH98fL5f7kZtguTf//Nq/5hc9P6pslOLnUZCUbtsrDpRP55MPMxU5Hk61KhPBOTvbDXZdu5G7WSdgv1J6CA+q/C2SH+GgTHcQUyKIOGD7XkvSrlAqKWDivesmqhmmIpmmqjACoKKJDIo0zywF1Nx4B9GRBjvIRTBUJlGPSGHPhgRE3VeuG5R5kEZdVCICZOQaLZHR05mShxIWFCkiGUECkkTQHl4NBDOFB1OCZRd9BzI5FBHMIUwUzB4M1bELAj13STLSkzw9iWiPfjffk06ZEBJhUk4gzLfWIiNZvaiqIAOI5IRSEECRwiwEFlSCdd/6lsEhBvIMmmORIIsW3BasLfN+m9+/+35Z++W2bC6jFi0jBQoAbMIYqJRRmawkGPx4GPK+iceF+tbGqd3f9e3H8fbLh78czu/88kfnOzm7hoznqR6QG0xdt8Xv93VZhNfWJx1D5NbvNNROsfYuUmtG2/xy274sp3mLZbXuOo8Ss3Cj4jKNOZY68noSPCpqrVweK1eVspD7ZMf301jPj49P23pft/XtNn/943IgjRb5QNPjiavaBs4Es7fhfs3r/f56u4WogK9bf/yAh1Mvsh21zMt2z8N/+2y/4qv8x0U5DaCn8/E8RZGRDl1G+/h4RraQB1KWtVQaz6cBiUVs62H3ZsFKXscScO9AJqVIUQIDYbZbuoQkIwIAhwoz4JEIcyJmdiWFkGdAau64BEb0SHZRJg4pwsLWgoA6sMPSXIo70GODqtTIJGOjjE7co7cILSqlFnGq0agNj2MN7nODe1hkp9hzp5kh6dTTDZXBIlKJNRGZuu8kUCYhk5HIji4igv2yDKqRkh4g960vI00s2LdomDgTtn8MsutUMpGpcCcWptjrpX1UYmngThKUO+BIgH1lHioKhDIKmPesPyXciDpYKDPCUwoLbeGdwqWXU758XWmo0enrS29mMpUN6yl0qDyRVXFXOsowb/k0ia9u2/ZxUKN4QF5U//rFi+YD7B9/PJwOtS+r31bzudQTxgOoIChnp8xla5++vr54W0hHQXK7rZFy0OC4+9aQwcNRNDKtf/k8Vxl73NmJvAXu6LPfAEV5wj+///5R4fM1y8FKZUZmz+aDCA+jPh8dESz3Nn/962ffSKditn787h+cKLMnVQtnl9Xx6dN8v9p9jjZSbFupwtWf3w3HY5vUb7f1iu2e/IVqed2UFhzKIAnOKJS1HJz729aeHycRZOYwKqosbRmLjqOAvHdCa0qAJ9B14GjGMaCIrUadiFiYHNDCYZaGYO9BhbkQMQeDIpKFmISk2J78yozIUhkZGaFamTTMhSqJE3aOD+dAXZdh1BwREmHek9qGdbOE0TDJxFxCJrU+W7S7JYNoUl8s2p5QK52BAZDIYhgsMknHSKdkgqS5aIY7MlhEIBlGYN1vJ6QUzgoJohbpLofintnSeu8FMnARIdqDqpFId3zrI7EIEUh3jvWukoB1IWOiMiYLI52RLHsDgMGuLExgp0RmBEdif34sJDlTqFCYrxyr03g8H20KrnNnOk3THXSxrsulb4relIZBKo99wsNwQnqXbsV6f7suC8ZyRvkwMdf6j8P47mkM6klz4Y6HieoB6Wozlm2bLxD6vLXPNq9I16o1H4Z6InnrVBVtvSwez8/nk2Bul+t1/rfXl6fnD6eHEb7GYj89DH96/uF/+kexNjwch+cTz5f11fqtbVpMUwcUXm+Pqn/6OI0xG1Ob6e0/56/39tP79y8vb7/8w496rLFF33prjakG6HLx22qvNudAHLz2fniajk+byeHthg9P46fPPjuvt25blxdonY7cpa1pG601S7XxeXg+yOU+W3dEYenMNhQ6SiMt4V2samWpPdGhTG5uErPBt+GRCYwIa57OmSoCKEASRPU4UnSBR6ZkBiDKO6sn0MxcidxCRxVlEoKSUm0W4DQLOYKnkuJ0lDyTnFgL3DaYy9bLDlH0zpw6DBAXLUzJFTJwrI7IyO5b5JSCMTPoQDmFVjdPbDeRA6GkBYzQQz28BxBMEOLw2IsaJqFKRBSdSEEVGcEsCd8Ph/QIJYEHh7tF4JsskwPEezJw38lv+7KoBBCsIswUhPg2CNwJvhxEFh3IaARiBCQF8PQie+x1dCSFWihB2sojj1brur6JWRVwDbVYYq5DnQYeVCVtEoivhWUsoEIS/TQlU5boDx3vjsNfjk+HTN/i+uWv1K/j9//Ue2pWzJfcurktsX7N1sYQkuOEUrANQg260LJeb7fbNJQHIFb/cr8t23bSyqUeqLj3Z6J/eDqVPGn58G+friO7aixot2Zbu408PNPwrvN0G5/fjz8ehvm2Xea8vczX315/OD9eX+/f//mH9z/+OK9bX31ZfV1ymEq07csXmy1Wy3I8rfd1aVlP9vGHQ1+zb2IOn3VOe1vQChaCbus2Wl2sCQnxVurYl75drDwmg61tbolwIqtB01BYotRNp/o4Hcogg47jURGjr9iu3rZNkhJZhVqzWI1l9AiC1aOWMSKCGpXUFBIQgQIW0XKH7QIQ0QFMqUxC5GECTjBPLk9ZDj6clWsczki25CCJzkY1ihWpgxzOQxHbXEHh2uFgx8DTY43Y2gX3+9rfNsCySDnV4WhayRNoaetGmZKVmQiFGu9CIXemZCrpPdJ38FEaQcSlBIi0KBEinTJ2SwHnvs+eRYQGSeo7/SITHiCi8EQSiHeJPYsoF/4G0EiCcpY9G7K5RfQMIockheQg5J6aTIUG1eYeRhC0jG50my/Ko4TX3A5UBylZWsvt8fR4GmgspLCH46iVyaCcCE2n6ICnNvGWSvleJrkJ2DM27+vD+Vwt0Je43ZbsHrEu18/WL2XwgnHMQ6nL1i5bu63xeluu88tbww+Vt+0aWwj4IMV7l+h/+vA+pf1wuP08aKz56fYy37ffPr3JeVg204WOwT8W+XkdPp6+PxzLxHX7mlWA+Xr77U4Qu/bTcfrzzz9W0c+frvN9tYZG4+KMjS/r/e6b6BQbrluQFbuulDRvufS2riJyxvo6KAbDdYOa6y0tUwUskOxmyHSKBcLMWgXBocFMFJ0AASJBPuc6jMUK4FIqqY7Tu1H0oEoI6ubz7b7NvfVOBiEibPdtHg9TkUpB7vA92EyU4pKACqWrAIyi4tkDFQQX46HLI5d3PpxZa0iJ4VmlRDcK5zEVozIK0+DOmcyHBEiZxCoYydjCmZhqKoglvLcAdBAu6egRBBBqpnvAgFaz6DHJ0Rsti/tmFAJlZwfRLjsO5lKo1gJtQUxUklhARKZCkGAVFk5jEEDZ05AkJYkcPcEgUAZVUiiqMDHId9RkValE5s0zgxKBJIok3jOqlVWISYq17mhpLiEPg3y9z4Q+r537/aeHw5G4piw3+3Aex8M2IMJsHPQ8IhA8Blump4OX1TOirybWDwPH66cux9Npass6PR1Yaf7t8y2umWIlvmzrNeYm48oo0s863Etbs7/e7pb6eOSh1h/kJAQw50QHJyOtw+HdsX43RVx1DMU9tyV+f7n89Y/7JeaD08RjVZ1sOA3lw/PhEKD09W3lNx2O2m5tuV58a/RQP/7p+fw0fv1631r6nW2oJoO1bJdlXZpDG+Ml+3xdjkc9jOcyDt5su/v9iByE+2m5rTyiFKj1IAfXkCArwaCMTq69IZWViYo6CMlbGhhF2IV0lrVmGUyHfLtQGbZRh3Eo43E6TuUwVRWZzufe/LJc2g1b35BWCpMWd8TGESxQEBdFODNCnDgLvvUBiVU81jopF6Jz1ofQZ0yPEe4yhEw5HbQ61jn3hA2gSUBSMJCphbRymGxt8/AI43BRqpU7udGsRRbu3EGVkohZpCZDMpbotpqoM6BOMMA0ZHeuFk1JTiGCZFABOEmEOIAgRvBuUwRTKigMCbfWiZkLCYOZQYzKyaAgBAu7KDOJw1i+fYApHe5IohJBe5obiczY0geqAFRlubbWLBwZfiDcqoiB8vZDfTrZ8v75o/W301kERsbIbax8ZN79Ze7u+/wxKGCIDI1pKnG9d7tN538wey0Vhzr0udu2XfMyj2Wz+M22zkmUjwcUodu6vC2wwkREfR14EBrHUk5y+MNeFdqEUOTIw+DXhym/jOXFxjEef7/43+blFn1txBaPj/LI5WGez4Xk6XR5cxs1N+FcvOs125u3998d3h3ff/f+h3nxz58vsVqwgjTc78t1bpcGuZsvwktvMloqyfFMJC3j1vpDZj11tvXpe1nv/vwBqlPhZTMnhCmJ6kBgixTWzP1dv0+mCFoY8k3WCxEQmSRR38JWtNK2at3Smq7bOtSixLQvIUornFxlPLEUNrNYNRpxd3QWqiPt5DaqquENZJlZB6kH5YdeDhJV6OB1hKgPk0gBM0e4lFIGsRaWSN8scw1X6KDqzmKcaSQhYNpHUARwL5UVpW9mc+MyUJEEIcSRrW8UrLmlsAcTDQ7zvfKQSGYmkgqGIJmjgxFkaRmS4ICE7lguz+Rw/H04iL2ziRBw0rcNt+RdJ8Ms7J3FgYSCdjALXCihbB5ECjeC7mgVkewRy3zjZS36XPhmW0r4fdsk6azl6fx01jzDRr4eHnnZXqN3kBfZBubCnES+MhG698WNTVJYRU/TqNnm15ePP5xgryxFZHANHMK8L8hf1fuW15IzEZVAiWPlAN7mS6qgyoBkshY9MzeKk07r1hWlVh0yDlP97evSu70tubX1dc6X3i23Qx1P0+k0HH8avdrbx6OSz9PhQAe+2ra+XTuGt/usHY/Hh+ePD+P58PLpLee0SCh1xNzbZb4jY7HcgrlI96WMEJYj8SgPzZf7fbnc6lTSsyptHyRO7x90HCZu0nL18OxkvlVVVUUioZLw5ESCd55yBLH1NPa2EmuycJFk5c6+aS7zWgaepkEGVK1VqPumhctB6qHWCRlxeipJlC18I9887o0Odbn2WHYvY4QgqaMSTza9qySGQVGdyFiFS4J3iJVGdylIUHSz8EROJVWVyCl6hhKMI6G7LJOVXaR2z7bG1tes5NzDIz0pWYZRh0MmFRGhUGt9XbgFD6yDKHuIMJkbZxhnBQTUk5ESSXDvMBCJCGEfb8Dym7smERFJss8+ksK/meeVlTyThUU4I1NA4NxHEJwCAZk7WGJ/yohZPCmIs/VuuEkph9K50+Y46AAqw1gG8rSvy+2trwtbq0xCJFKZsft8wrsnmXdpwUyH0zTRKEnc7fHjc1m7LMvwYRBBb1sZZbTim2ZS2EoZ7EaJt7SX+0aWQcV7dGEppQ40DByWRqYknBrmlMqMbfM/vmyW633rX9tlnuNOg+lwPg/fvz9+Nw6jW62i0AHl6fjOtyRX85v3oD9uD9CznD58/H692f1lmW+hXKG6Oe7rutvYOycl39sMQZ30KFmnmEYf6+HusWQ9PYPWVYMOZXg6HjXm2+N4MmRbb2iaTp0sQkWpUEQtzLR3cZg5EH/HamL/FpklFWJZBgEHOYfHsjY26uKdhlQKc1Du0LbDGVw7Q4zTsoMLsaLlpPXib0FUikRkcK7YpmGa+yyZRkvhfWrGZVQtnOS+We8rS4pKqeAilMRC8BbhQDgiyXJHskRn0CiUgDsrYTqdL1vr7p4SkZEx9Hw4lrEO3Dc0AkPEhiOROHnj5CAKFBUHy9a6aklIooOSOAhkGZouKkSOzG/YLWIgkggeEVvhwsoBIrgkhIBgh0jX5GRwhjnvlF7apUpCRNQolTmUuTBbtiIl3GA2DKK1+nb5/jR2s+gu1AWZhPk+x7A9nw6Pp1JLIGjr3lqb18iQSCoyjR8mNxbLJLWIQNFBZ2tlhK8W2SM4mkZiTl6s+RT9tpLkKOT10PngRpt5Epo7yIYyHo7cGjb3gFXlCFvcI2QL9O4JjGWwmEmmTY4DT9+f+E8/SLnNI9n5eDoexml4kCTtcTzKpfLtt4v27fDj6ePPz0Xx6eU2b42CLcKlb67LsjV3IiYtLaKtvtyW8+n88HQ8PB56sz7e//i9/8fvv7/7uFQWl2w96Fm0HOodCweqjE7+7YYbFpbuJQN1UKoipHu+HX/HOif5t68ZENRcIDGaORt2S7wrkkMHpoOG87oGDwmJ4UBAHzSHZ1ru83xluydx0Q/UrzflQzYbjnV8gjykTuh217HSyKwcKWahAtXRdYObJdJNpAgbQ5OcNRFIT1DPdEDJomdusM1ljdy6A0MLebvL0qIhkttRhznw6+1FSz6PeF9kkFCFik/jmXpa88jYPFvbWEbWsfeNWZL3igbJIFikm/EwSqYTkSSFA0GJIKVUEGOHw8dOSUQSJQV1cRiIPNMod5cYkKBMZYAVSE4SSc4g9zogDO73GrouywDUgS9tfbtcujasfQx6/njSSoPUgx4n2q6B27xsHR7EUZlT1Nelb0tfwzJAmdNU++NDbA0NkhHb5gkqkqq3I3+5xTGljPWY+Tgefrfxcp8PcmTKLTd3U+0CEpiRMUlJC1FMQYt7j6rjeBhu9zYWfuSxlyltqBGPcXtaY5Tz/ev8859/GI+MdJ+xfZ3bVY/9sGL7+aefh6fH43i6vl6/fn1zByqyY2vxMt9ad+YanoRx67N1H0cdhX44jkSyrVit+eLrZl96VrHFMByP/329K4fXUlu0pORaolkmpROnJWtYmIYEMzMFIsyT+RuhTRIUycrcAdLiSKXaPTUoCWTRiWoIoSoraJvX4AtQ2FbnSjLm4azlgXqLvpj0itthe11K1jL1j//4HnTLGpMOJAlmHgSEJAILYESpmh7MLJnYB0SRlHCHkyUxiRBliPDWwkJWwz2o92KGMp46dPFlS245d5Pj4dBK/Hr9/Ba0HOkjx4HUEZL9YRzKkFJzdfASblt0LibeGzzNNyNKhVAmm4ggkpjcPCyRU3JAmDlVnACGDwzjAAwoQckoFHuQEAnOMOTu0whJivRAEvEgydTTQekRmAYOKtEuBD+dTm2Zj+J9tGWb6ZADcdHb4fTQbu3TrSmjdbxtQa5EtTXx9NfVBFpEeNBtna/b5RA256Q6nASx9dk7aLxJ9JJchnHobU1kLkDvEkZrUr/1zrn2m5Adz7ne1o1zbogwgtUyVOHjKI2Cw5UPolhaS/Xn02Nfiyzr2/2Pv/6P3/wP/PPH4Qc9xd1LlOXrvL61WHIcp3fPcrft3Yfv+/12/bL0IGKOyBZx9+buCN56JGiDsSRz14Nu4jhb63T/ate23pa2ZeOGxyOIsL20NrGKFpUyJDY3y4CUCFCgJ9gap4YHO2OAMEOYmCh1n/JnQoDY11GItRQgRTQyhAhI6+EePWyMrMldrIXNjsqJAUeCTHh44umMZT36vNnS+DVo7WUkG74oswwoVGTIVHaQ0n4mt9QEMmXHGHp4umX0yMjYaVfChRhJUouIivXu1ruYpwUnD29L++NlWcLMVafjw/F0TXtblq8XD8pBqjCax4hiilDUzOMoD0wyyHyjTS0j1AXdeHdxIZKchSktjBhEWSmIwYQ9YLQRe3gEpUAm4mR2JIgDzJzhSYAHgowpA6GEJEhEukuiN0pySerNwRpGybbcb8uttboIj0FdM461esxQo2Fc+32NaH2loNbZnTM0k7yje6ZyWpu4SkmmaLdl7X042g/DKV2lxrkcVtEqkUnmKMO5r8ua8mVrQh7rcncfnAwLk9eJJ3IRCbJhQq0lqWQILAtUuERSBFUdbsva057k/sPhh5Yo28EutxPjf/rzv7bX9CU7vM/98fR4sY3TrNswDR7zp98/fb15xBm7z9Zzi7z3pUdTVkh5nW+dY+G5loEP9W/Lhbus9zU5K5XWdB2MAxUYR35+elQaCgczUSlUZVjF2MTQ4EgovtHywwxOpLEb8fYF3qAdltY5G9buh6xSJQBFDQ5kMJXA1tZkGEocTq3P3tdFB9Zp3KJ3HplBoHEgfaxX7XwoEVnBQ6VaSPdoRHBERM9QQ6FtcdVIlaIAURjS4Atl4x2qyyUogsBDnYR5GIbltnYvi6F7bSjz7Hdr68Zv93Y6H4bxeHeE1rXPo0zLdn3R7Xwc21A8cg1eYxDOw9LfDylsWgH23iNckh0hWpkz8A08F5zCAMPNyLNnCSZwdoRlkrBSZsALVVZm9gh47rHBFIayILoICQFJjiYeoOyObpLpQcJu5n29L75tJhI9os/W1yJcT2U8fAj1xXpm3PvqIZy8RnUjeFVRD/JCKOpsM7o4Ipak5Ejp94RIOelR5t5DUg7Mm/elM/g4INagzBfbrMeRKgsqqdacDnJ+4ooy91V5v7GPVBJjRaBbixCLq0upQpIVYuOU05IZ9sz4pz//OIKX17vKEG09H49xR4a/vby9rXHQ4+9/W/7z682zpvYw2ix70rx278lDgfBbW6TSHXc+oR45qOdGroVrzUhAu44pxuPKiub1+eMHXbo74motIpWZBwmmcM0IhVmSZG+9k+9dIFEtOuwJh4zY1yB7UrCJmw8HLVOxaCT7zN8oHZlGgRsgfTqMybm2Vpok5TWMfR3LUKnpyKexOHnSoCVHATNxpLlH8xU9AyUhSCmwaN6kURRU6xldpNVYU1hZiNxUq6YVVyHta3qTbGiLblDr4S6RdYt7ElGqJGfUtkV6HaZz2mZrfPm6XOV2kiqCmUyktAOP5AN7Ca6V6FDBoFA2bkZh0Sy3DHZJGFmCCysoepfMki4I4FjAkuwkUBahoOQ91LklSEgTQZECUsLeGCrJGGTbIiM6kXH0dWlLS7TsFFS8IUiEJAhyGKfTwaVXzbld7373ZE+18JbmCJYazFxrcvY2e3Pm+8CFi5w/vr+/vXTGOgoX68kdhs7zEi/z5iuCMIjWqo+dqHWDRmzE43QohwmHQz2fhr62giFA3c0369lbW7unCvnioJyG0TfqvhSRMmzL/Y8H0Xfnx6cfvrt93ZgLhI6nU4qut9e32/J6uyTXl1e7LZvnIKKe3aENsXg068EZaeYyiHC2Pz89hZTt1eE2Ph8EvPRcgbm1rSSNg8saTKfzQ3LXeqz3l8u9behJ2QgqIshvysR0JMlOP2dkkrlzbqAqVQVBYT3ByHBDo5Z3by2kICWVCv1dYm3pTDhp4aBhEkJeWhfYdNIsebSc2ElWMSqVVECCsXBwbm3pnvNqklCtJEmxR5kzPdHFmb2HYCg8QtktCJSpnaQ8nIO8LQhChGb2pcfVLCIWi1vrpchYtPU3bwiYbc17N2xUsHW/XFVrMY5hzElNPJNkwnAuMjiP0SeNgatwgtMk14qtc9xXZ9JMOZRIq8HCWCg6UVDUoJqkkfueeFp4hNkWDGYjDoudcj4QKVHxCCZmzrBQxkpb63xvy2qbRXDH7tsY6qnUY6x+mPh4qsIUZrd2XzoRj8maFmbUjd1FmFW4u3froOQRB51KiXHU6Il35wBm0kY0BolzZL+n9E4e6G7NgcJ9W4/DARMHjcI5VTk/TMcDsWQXrpTz6vMabe1GtG0KDQ0x72OlrW20LQ9TPRHh7Y9f6mmo4w8P45PWrSYFShAJ379ur6/X69viTtvaeiCpVKkRIUEAW2Lr3Ty33rhowltvKvpYT5ZVPyDJBhEIa0dcr0u/zayxzmOFWx4/cGooM9p9qywM93ACLLdChSYpwZJE+MZpyGRChm8J6Wu4KhOk8P6FcRLvi94RMBZQSBLzbt+CRXayjVIQIBr1OObi3DcgTmFic2y0DVQqQRCCQ6aneZiH+zBQEQ3b6VURgUB0DyRVYri4OdaAkUdCMiJY9eW6ZuRQBIKX67JafF3WL0u7NzOik5SCcUCubd3u0t0vy7L1NhSpAkgjodYdVSEs4ZV5wdpG2RpGpsPpqGnStkiHcBBxMDMVYdtMZdRUlQp4JqbMio4kLbRzhZPNYciIdILzNzh2Fq5ERCC4UWHsTiPv0W2Z/W1ZLpuv3h0AsgqnsmppHmu/11L0ULfim4fl1sJIJqPmoRt5t9Y9AE1yCIaTjoU8IZ00/TjVSXmxxqUacXdOz5bB7B7aG7aeEhwGMEWPyvX0dBJi6/38XCLBkjrCeiPJTFlt2dawXoiLikDTfRtKEQInD0MZWOK2sufp+fRhePj+/UMFUeFMKtB+2V7/uFxfl7XHfXETHoYpAevmTQzeUprl2luLzqN2NCBu3YYp/72/juNhGibJtnaeynTmcrN8WfKL9Z/fY634+Aw/t//49KL3L5cPj49LNlsaQA02SOEsWnWU4lvCezrHzmfeRUQWzqbfoOeqCgDM2JO+CHgYnKRAmCiDCFk5qffOq7Gjc8lC/TDUyNbu5FtZyzYOUQcexJQhuoWQRwR3GahCe5pk3bJnZjJbz3U1HbQwcYc19ZZh2XpjIZoQ23pbt1LqlSgCWwNXHlSezqczt9/nu/eeCYH41u952zLuL5dNHTlwKVXgrRPR3Ny6YirlOLbt7XK7Pn3/oWRlczEigBLwFNoze6RTGYgsUnY+qlCslsE61GEYM9YMC0Nm+4atBjggoEgqlEQ9U8E9AnNf0tLTvTtgW2LjNeq3RGmRAxMKJkj2TZxjtr4t8FtP5DAwGCBPcHhGzx5JymCrSrXGVBPckd4RY9GxKEccBl23XsQzyNk9SsvjZnHfnGhwRDnUoWgPH7hYNxKuhGOhwDD36/26hnsIurtHyCip7F3EOVoMEYJ5nA4kI0fS2o5aHpSflT4cRl7WdVUBs0pf+pc/3q6X7bL0eUmnmKYHJunAtmWPiKDN821Z7r0FZ6roON1ub8czFgWKNVr+4/UPShTRD3n4x4fv7q/r1zve/wvqGR8+4M8/Hq08UlR9fH/qSzAVgbhsNSSJBgaLshKFexIl9j3XjEAQ3FLIo4PFQQRm2fkdQKQUIYP17hElUqciSlCJDHC03kRLtIaCYdRUL1WL6JA4VHz4+HCcEkj3SGrmNJ2Ux2G7G1/RycnJkda79VVMZZUeVFERJQ2WyaVwSaP/h6c/2ZGlW7I0sSXN3qpm5u6n+Zsb90ZENqwsZBKVTLBPcESCw5rxJWpUz8A3IMA5AT4CwQdgTYoAOWSTTIKoBLMyMrp7/+407maqureILA70BOc+cjNT3Vtkre+rT/f7ttW6pKtFKUPJdtFbF3y4fP/PXtrb8ThqzDiuq3767cGQd5frW+zP1q7t4o4ZRa1GyVEH9QuOHz+8W7pALmb96Ul87nKgyDSKJt2BroXd89hywkJkxLRUSDjblexPt1nBDFJ3CclJMYuSam6tiQSzqrYdR+7BAQiTqQyOETiKA6xq2hfJk1tcBneRIgMpxX28LZf++csOSzMXmEKbL+KiQmg1SWFw7ioqVqv1bk2cDi8Z45Fx1H1MqrEg0bbSk+TVWu+XZqhxP3bPx+M+metlOe6DdZ/7pq7Liqf1mtiXCyrqcd95x9rVc9Lj0uaL91wXbHUtWTm/u/246mU8bDsORK62WJtvv43Hr3HszFp00b64qGRh3/PIOgKZ+XXOe4wDoarN27TwKwoIZledr/v3F1yXtuiLNP0tPi3f96ev8Y+/w4cP+PDu6Z9++Iv7/fm7v3hxprysF79iH3Hcl5rHtzi6sxnKxG+tqGNMDhCCBLQp5Vy7NhFRniYIo5/qC4O27gmKZI0smCu6SRZO6o5ds63oKoW+2sIjo6vo4mJNdW08IvY9msiyShJ9MS9+/RJaKVlas2tRyBEmPqMGo7UmqtbUOl7fXvcjWrtebwuQnpIzYFARXRuw28IPl/WY64wxl3tO/e3za2S64eXFbssi0OGRzOYUWOtiHk2tYeGeQ/m18qboaRUpEKzgiPGAZa3Zr9Mic05Gl9R1m1tkjMmiuHQsDWEXyOQrg14Kdp2tssaU4HFojZSpTkKMmRj0XSxaQbKjEadnocQArYwc2OcRIRoIz1kyl75WEKy26hzTapYUMw8GXOSYJXy69qfFQY6jtpFzzreRBN+2nBXmKyWDVEk1mBnAXz99bs3mUaOyXGxpR3KOzT3bYn5po+px2P2Yk49ICqcRL6t6q0vzhr6F1b2wz+frk20o1jHuuadF7iKD3F73LZnS5Ay9aiNzH3OM2rfcjkzUPkBCxKxpu/iUXQ2ffoU+4+3rfL6iXVEybdmW/jQST13/xX8PywV/8edPT9ePM78+Hlvc/8Yv5ldFwd517FEPLhSU1NlSN2sqamrrdUVijpBARikEgor6JoBJaUsToYm4mxhh2sTIpFIBZqWmK5pCFJqioXNLdy+Jgn667/z58WVfP75bv3tpfSGD6vRyaZ44XPLphRtoG2NLmU1MbLkE5Y2HaZaPZX2Gj3EMsf3DezErb2nNKxjDYpbo3MaQ60IJA3tfHa2xP/fxKWMptrU9P61u3lWXO+dRUnkcm3g9f1hXEmPMeHvTJzEZ1lYp1gBTjtxrq1deeW20bRv5mKHqT1eodltjf91lw7YufqzXp/VllWDD7T7ub2PUt+SSs3JIZmUCU1kQlB85NomJUaFm6xRxMxNmpArHUW9vxz73mdWVUmBfr+s6IhXa2pIZ237MOOipSgDXpW3HWHrr62WS84htj5oGlZHr43j79HqIdGvsKv1CNTMhK367v53ggfv9yIjLchNZxvamps/X64gYA1B9nRy7aqmMtIQTXXCR1egTLTZq1ct668LWc10ls0znDA48xlFBTs9HckoRTWtE1D7H17m/jUlpGQygLHWB34CWNXE/4Df89oqPv0e/wR0vH56frh/WtnbPT5/++slxWfHyfH1py9/89vf/7m/n/RNclQS2feA4/9+sClFJ4dL89LAIVEg1b1cHkbM4iwK/nMwrcVUTJVQkxZBJg6pKb2uyiIA7JJraAuaRc2o8mnZvq76EXqR9/fq6RYKD8Leh6vtL1vXWuqRni3S26sBSNZFysXZbx0PzVbc9ca3RuVxa1JagovrFq0qYsEMss1lbOQ7BwYvwGOPt7U1VFH7RNmIi56XBbvSS/khpXJzMY7u/5dy9t3dP60vzpUpnTKGa3lVD4wBYgQj0qGNecCmT+32/f3nFTtdWgWGtJJfewyVe8bD79tj1N13WnhL7GIMIWllIQZFpknAxZRVbzQS9W6O2au41PaGkjTEoScUecZ+ZqcgCIBbdvUIh6tLlsNAUMVGJg3RdFm3i/vSsos3Wba/HmDPcXEsxRr7dK8vUbDV992RjH8eI44hEjCMvt8v2OCpZaoI2x7xcuyHNICH7PibeUFc7ka4GvWAVXP1lo8tWNQ4v/n69XWs8LfbU+z5H3oNTWTYeE4Yj5W3Y1y3mjGZqQFa8jv3tOErarAmYmtXI6bE011aqzIntgbagr3j//dPvfvzg3V2erCYqLs82A93b3I7Pj6+f91kdbHBIarWKGceYUVFZlQnQmA7rSyBVVUZ1QE3ETjQDXFwNzXszRcl5Wi2gZgLIs1JLWnNfXI3euiMv0iMmzQ9KZe0Vn37Sd++Pl6eb8bXheFZcynJ7OnCvOJ5xoKf3Ju6KpFAnUd57SQa4hkfMw0S8uVEyvFoxmarEoaqJXLoX1H32BbFHi/HkMqv6elWp5/d9uSwff/wYMfPYv23yVv/u/cf94xLjcNW1qafE2C2keQdMaZVC0Vm7wmZOWNYmM7V+nuNeSK4X9Dfi4uq2v6UunZwIz0elStwjGYdKRY0W6KYLRXyAkx4zY854S2+9XdXYioo5FSXdxnbMEVIxKrftMctYo6lA6/lpbSYz0P1qdGlxbPt2HKKZdgZ61XzpvcMlJR6PPRLSNCPnqGOfSXEzFFtzl5m2RzAyiqLds+oYKYRpB2pp+nRb3r58/vxl7numYpLKWNutIXrGuKM1zFDkWjms7k+mN9P36/PlSux5vI7YrA6VSOpy1P71mK8P7GO49Igcwhhj2wbym8kqTBI8ZMwZvo13Ly+LN5EHCs/PeP+CH75/eff+SVvtb4/7fl8a4hjHhHDK/X7w6z7x/jv8cIWHqELMbTqYcVZKUODEDEqmNMmaothwioChYuaiAlIz0E6FUXHGqFMMKgREKjkrKxSOxhnUq7/G0dg4mKLZRHY+PuGP/wG327g91YXM7X67ss/bU3/66veNb7fL53bTm1xUNCtiVEU/JLeRFZ8PlbHI4xGG57Z0LRxRKrLFRtw9/d3HW+ndpHdgHiXu1AbWnLbFEKgbL09O6YILcz04TZuINNP36/P2uDJOo94wNjMVb0qTWkEdqOA1io+jImnztuy6pG1jjre3j5TlDx/e7qhJXW+9Oo+7YW2de806JCQHd15U186rjJQMJlYCATxGqnjznoWsPPaH5GFy+UreX7/UPFZDV9dqMlKad+Otr7d1YTVtuqfVwJyzZjaxWXSzBjXB4q2pCu3tbYsI1TbHzKiZwawuGhkAL2bzmLNk7U31JOcpxdzWOev5crtc9bpKbffH16+PgotVs0VuYq3TVr3I5bgKV3RDthEqebN2RX1oeZWlHrW91uMoHpGHm1uk3Mu+POZ2hIpNJESjOINHBtJoWibFto1HZpQBT+jvO2Af9Wm9PG7v6w//6ENw++3r7q2/fT2YHFl75Dbx2PDbDDgKsIF/+vsPHsaHGRA6VDcJ1PkrFi0hMyKHnWswxVQ9NbZJmFjqqX0REwj0mwYmQX7jF0CiVCTG0VaBcCTWSw9hc3XpWRhjDJH+qTUNVXk7Ui71+OnvPl6f/+LDn7XM15Rtj/Y4Xpe6XTpN8yhmiOrX45jgFlQ2W69vQ+QYzftAYea258HjebHX/eHCl0tPzGZaBhEHLJS9yzHzbR+Ncb12FbAg0oPFku3YgFRAFEg1NW3WtRksxPYq+pESR80j4sFJS6vH0/UqqNfH5/t8iy8X+/4Zz09U2cGvX+5r7GYDFGsLLpZ2TMRD57HtWg4zoZqKpr0d90pdrk6GkCP2qmE6A4a0pSuau+wu7ZhDl2rNr9Zu9tLYAzUqmBnJ5Dktgnlra7+0UyJoMD3249jqONyAMasQKBFR9zK4wqRy3+cg4ZGgNV8XN2tztpp11d0eFcfIipfbcmvtFBxXuUqXyUZ97h9Ps2UGb4s+23rNtytbCx87RHS+MdNzC23LoeOIsR25R7IxAFFWZVET2WSp5NBqbXnL+Wn7eul4/h5PH+T6zmvkDy9P3q4N+Z/8k9//3U+/Lcq19YssQ6OXt6hf/vRWwHqBNFThesPXx+G6oLa9pLONutncjywWVFJZKl6acoqiS52ogkoFgkLviiAjplsDoaIThUIKJU8zBgiqalWJahw51UkJV3M087LrnMfjc9XMy9OQJUO3n3T75aXt4+sP37+8+Pdj/joaMxk4PJVip6dF2lPNcVkXpCLbHJppW0H9sq4vsX0ezGxPf/ry260v8GWfwipXM4X6ZJqw2kmxFkZMEKYgtLxlTCJj4tK7WZhrZWlbS1BVMzLLwiIqZ02eSI0O9nnf7m1d8eHpu8vNjvb5QT4e1x/e7ezyPWQLyhe4ll7UV197VeWktoJVzSliRJZqv0DN3JrKHGMUU8+6p5Qj1r40wp0zFeJuBcFyu16yYyKDMXeUSFGwWyYV3ttl9ebCWdtjGw85kDNnbfW6jRJah5u7GqzBhVHH/hgxSjADAq6rXRZeLvb5y4P3/ZgPJ56frpL25K1xLfPp2KqaSMywrstqftREmfDJ7V1bbzmf10XG+vefvy7LdUywGt0n+zZlizyipLkQmQdF9hxzhF5UEl0tVrzy64PHux+7ySzn8rS8e/muPV7X3l5eblFf9s+vPvOPn/+kF8iAOl7WJ/24/4vfIQTTgAkx/PiH948/vnNIA++pJQvcRFWnBEGdkKpzuUsSNKiri6gWvxmHJtEJgRWgoqkCemkhwFO9SwDMwKmlE6tjy64tE0qb9O4iMMfQDSJXbm9t8Ruuv70d6w9vFMry/cW+397+tC9pe7j3TvXeTBWWF79kJETQXGF1UKJt2xxHmj9doPthff1de5EtalZW+kBplROMmqWlIsVEKlQbHDqZdWQVBdHNk9vYZmtNmxOaxSOPgczSjIoMMikHq7zk4lM9B6Q/P737eMn7fe760+v2tq3Li737flnehnx5WxZvi+Cqw1oNN6t97hHTu6OQmQHe2iqwfRzQ6E1NTAiEpmhbVBBe4go1C6MwV2gf6FjG2Of+NmJM5WKm1vyqXVQsX5ptwS9vb8csM0BNRX3BpfVEqUG1LJRVkcyqkkBDsMTdBW2Ja++L40mPB8aRUAU8V3XXjmGMXsvek6i7EUi0tmp/WNYq9pRd3yziOvMiEapi6o7oy/LYuYlk+swokVQNZDAqcnscpL17urSP9eVtf6t9uV6+b9dxz+2xWZ8fr9/3rX7/8uPNn36ev+bl8m/+6r8ZrL/6GW8HIrEpOt4+PuPPvsf7D/j+49PyTr7/Ds98ef7nf+5z7JIZtV9aK1lzCVGfjCqyWlSSJSoKGIuiogTIKopInKXVdBilXCQgZ1+AGQLVhJxlKCmSYKvEQHVvWWGsoDhF1ZEqd3C5KFrg9tv8beGr4a5rvv9wbdbGvB8zb1dju46AVJRWh7ury8IhTdz16ZFvvS8nZQtYGLTS3OUxtgAaU0vgGulMKSr4jbZGRBXSQrKA3WHqkCiUEFmTruWr7XNMyCCmVAFTcGIVJ2muaUcaYrBpW9CaXGM9Pv9W9eXzh9o81FauqwteHZvo89veZ0oRWOW23MyISOUibFntmEeHimuedK04oLzY0rJFRgHqbmpNxaWuOXV+nfeIUZL7ooVmLkFbShZmfWj6hH3LqiwZSe9wb+au0lQmDhVZm2bNIwqJGYCom3lvU9Awnpp31hqq7fnlu+tPv/zSyhrQmc76emxvc3utL02wgk3b0vp2/3Qb/Yf1xw/9+/xyzz1iIoOlaNKYaJdeWF91jshIzsEjic4cc+4j83G9vacvampIu/RL90iy347xOrTevSxHO96iDnz9s8uHEP78+nN7f9v2NzEuz/j5F9wVtaI5euH69DzbHyqftmzb1/IfyuN1k9A3ZDQs7aJws1FLYwVKAEMVKDynnGoqpwkyScKkkGPmTJiodojBoGqK1IgCimICVOEU5pJWqTO1qTK0CShMsquGsbW1OBBjOfpn7NePcT0+/3DtH3+QRZdPXwPEari+PDX9Vvtyad08g0ql2/Lu9uXLeLztc46SQ9V9yihOKdOWQlUxO+8qhSoVExVnCMnKxWBLz0imQliUMaeqmhjkJPYILE8PcYJTkhi9JKcE5I319mXzTJszXkbs+17Lf/z65a/ffvuLm/zP/vHv8H7h0n890IpVW0geYiGpuoBqwedrN/cx5LGHJsSYEosa9NKuV6BsGkMkO2A+YZ1mIaVGdMX1OsT86+GDswNiUMk9otei9xmH9CnP2nJdh6qoXLguapFyJyJ3HTXBGmc1WVJMrUE9xnbR9D2y4zji+/b80V0uj8/7OqbUPi7seR8X5rv2odlwKa649Pbx9t3tTfpDeQ+kdlEBHnObFVF1WdRL74LNaxyp0+awyeGCuYUWF3+ZbJf+1Brvn3+SQIF355ff/vbrL/n+Parb343Pm83fJn56/eM//sOf/f7Hpz8sy08//fav/tV3X1/f/vq3X3799Xi7o1/wh/fX9/pMakn76bN/3WLz8CjazlpyZ9U4TtdIVlW5gpyVTELMRUWVsH8QQLqrKr9VPVgJxqD6aeOBQpo7K0kpUkQJAFJlRZZkbyI87aqs0sykWgxt5rXvF2Pv9X6BKmt727/e13d8bnocEvt95FdaF22LNZZtwTKt6Pd8rXoCdX3Xr9CB2RPWOfdKOwUZbNJM5PTKw0tRi5EIlTzGmBnF5gZQydJmRc2ZokspgxVSsyp6HXuiwIrjOKbIGPE4gEk50KG8z7buc3vUtnq0+OO93filz+eXP//uh3dvf7dtu3QYW7mKeM3jHiHry/PrvhePCAiEIP1yUTdNEzNYpbaJ++OwGbbA1E3CBGDd3G+8rP3bsuV18lPpCI6drly8epBHLsFL9/K6z6rGWw0tjIxdp4zNykHhkIFmc88sa6Kd8xiZr59e89bVFv887+/92Rf/y5frf/y7zy+3i/ykN2lPeh1zv63rrbklFmvrLggRRyVNzNS2yvsW+4G8yB5asAGwQRbRxKk6HzFLxbvPgq7+Ou6deM37z4+8T6Dj5694foflBXIxW/3dBR9+5x9fnl+PX//TP/tn8xH6++/HQ2+Xj79/993LOq7LGqK556Wt2wq7Zr5+qvb0yy9/8oAsZXUwJktGISsUAFNnVCL9lPpUpaZRk+JnVB0KECCIkkBBTVH4JrRWLZSIQ1iR3yxhJSp1Ek3G1L5qZWaVZmkTjHCxAmdJWC5PHb2PN3u9REaOR7qbgSFzHK+ARMIorV0UNoYu67vsa1Nr3hdbRGTxd0t3QR1LPFhVmQhFSmmWqKFTTNgkk/W0LnPt1Fx92WNmQFQ48CrTpAetdKqiVhq/UX5jpC1Ld6kjr1ybm/ZVW79/lhj6x7+Z+taeRjsO59a+Qt/wu7/++XmneV5vTbq0fYDtGBiRKa77eIX0Y8ZjS9BE1EUjonW7wDRGG4tFXbXJ1bmEKrz7cvPWFx+B38aFi3SvuUjNg1rkzcUzlnMFLDFLYrO0bGJHxu77Y78zM+mrNiU6VtOYB2x6bHvIMFPJpZuuGk+oZnx0/YTsd1vEX+za3ei7Tjxf7fb8vvlRwp7WdgEfomuIwyd02cf4Orb7HCGs4aGi4jMjKzF1StkFckRzatcxIYugHe25f318GbdcrrhP/PoJHz/g43d48uen5/ftun/48LKu+sP7p7/7BX/7H/+4j9g3iw1TLrU8r72n6Diy9vGV+fg6Lsuxv33atnW5PJxib3XmPaQqJpIJlioaM05aDkRUJDLkML2hSkWFOFGyKJYSIkrCCChYFQRFtbJgNSMBVT3tQpAU0xxzgN1QFpnJQW9iZBxDWBcs2BmLLX6d8hUSy6KwhIhEciBFNBFITmEx5ZrceFRoZfXYv5qxX5bHjvy2zTZw0lzMKWlQRS2tJ3PbU0RfwRkBx9d5oGi6KFLZbr4cCG41j9r3wWsvJsS1ysqd4u0mRszSJLLX1o/H/Ou//1xbvtTlVe/HyNVXXvvP/lJ4x1/G1VSfcw6m9Q2xoVwAiRRRFljzgMIGZ+MwxFINxQzpj3i5+F/++P3b4+uxvcmqOvOatkbTnF0haV8+H8cbE61PrjysyZOJtGxwSho7JyKami/PHz8/fqGsblxFHGWqOXJbr7vUUtpdDhWNzK4fl+v3sC73xewhy9juguyHpGrbpy7t+aV3R6/dHskmmj6y2tpPOVxf1rdj/vz59V7cFVHUK5aueUQxdUpKHWMwGLm//+HlNe6ruaFNhN90rpfx+XGd9I/944+07li49g9msi4q8nXf+Pc//yJt2d+SS+/rNTl52Hh9bDmtdyjGfauvu8+Wy/E4XlPHUbvvRUGKGxAxSRTz23HXxRM8HV7f7r6cNdVdwdJUUZAlRQH0xB0rFUJQQZKAkFkolELq1GbTREiQUkkDUgjEFIBtlRR196ijw02usdkRwstCgVFpVOXysYtQyYqzUmsROuccoyZGiS9mbVl2jX3WfUyVtfnS3UF/3PdKIjfTNjUhelueZx0FmMWMeXLmR1ZkSc2kxpE7K9PabVE30erKnL0wL8s1Sz7/9Es+pgkbUvH4usew+fzUOeceKWu/XV5+97uPzVvFfUBfvzzePuP5cvFVjhvr0nlJ1UyoNtdSkSQ5Z/QbUJlk9WVduotcV+VeH/zavuv3/egXLF21EhCafPn0+PrbmLCBGNtwx7XZj+/7MXPfIwomtMXkqLVutre/sHcpR3dpvh8WKFzf+V995vNyvXlln1ttLWPH+Nj53eVjzbQhDReXbi/Y75NK0754rDAZsW3hUsuqaLy+uyYkRnZrcx+ff7vvgd15n4nL7baoSA3OCVHDPKiqX/KzNVj3H//wu/vjp7HthH/aHvc51vcvP3z/cvTlID8fdzHs+3jEnjWlzZpj/wRfxtKev/zy1aBHYm5Blpgu1DmGy25Gx8jluDrK9g+uzj3X1gdqBiSl6oSeGFGqliZEUU7LodTZ4GDSTSAC+wb3JCigiKtSVapOsKuK4iypkyAgIiIolDBx4npYLD15oxCpbC4zsJh3q/y6q950vY45OU26VFZT9VJmmaSKoqgFJXvjbbEQdaoiGW9zcA6YIMDI3LdYYJfLRcS2yP0eajC0Oh6u7e01QsNMqFUV+GZ8XUIRPpFoCxQWiUqNzFar+UtDS2BZ52N7rYiRuRqeXmp9v7JUy3pq7i1e5Yfv3oNx6RDkZXn3V/+fn14+tsWXJ7m6Hte2L6uo5bt3L3/782+/3n/ty8ol2QYHZozrZUXNlT0fU5Vr78fXcVkXjSO+PO7bEa9ze9R4i5glAiI+XldULLbocBxlUxQmqQLxXO6f/rjqJZ/G9da0uQre26Jdj7fxkWExdezNTLre7CrNOydqlgscez5Q4BtWoPUuXB0Vj70NvCwXlNzH46j6Gsfiq196iP/29cvbgfvMQF0v6774OUzQpjCOweqVmIut0mZf/Xg8cgg0J22uKFe5NfvhOhPFRB5rW14//zb2cZRS9B//o7/867/7WefzLyOg30nrt+crbnPUa0IUD0muayvZlP0qLvvoHR9X+PVyy+1Q1XIvZs4SgxNaetIm1KSgIGFqKsUSc6OpqyoYSCGh/6D3FRNSFaDBSKiIAESRUPhJuDx7JKR8k3CVgAKBluaea79My9q3Yxu62utvD5V5XbQF13Wx7mQeMYCImas3Kc6KhrWv2aXLtweiVJOnJhtgO444TgnZ4zHGGDWcaZHD2Nv1Yth2ZtTkubo4R1ZC1CNQhUSzGkzVsRVd1NTEXGeEHGN8/bqNoQprkJrzx8uz2YKGzkyosI83D9/Wompe28K1/9N/+Y++fI62anZfFiVLZpg0xY6adm1wQerjbVhv12U9GsfIGW/fXz7e5jK/zJJlH7F9etvHtj1IwjYKrXsrxcuiFrleL9J8P5LTukhlKkUEg/H0wWUZ7bokZNsmRV98Fef+6dEO/viUfvXKRHdzZ0JUq5miCOmRlanlWmpYOVBTgGVwt9nvOVP6YwzECBvtsh4Zj7exK3b46zbf9baut8xdNWWZHc5ATYlXqurz+/cP3d/2cYytq4UIFWXy6+tx/PQput2uVlLbce/PWauJ8sMPf96vz4aj7OnpXbs8XQXj7bdfIwdke76+XN9fWyOYUhKbj81v9ogDvcrxiJfLbTCTmcyyOGsFANX1GFUpkFJTwXlWcgSkCygJikLOKwFVVUzAEoBFiuSp/1VInm+ESoMWqfqNkOk0Qiin3gYZNBoCpX1E8IEr9mg5h/z2eXv/3bWFbnPEcQSHcFrnPbKiml6IybSme2sak+YwMW16U7ebivWM2vfYzySjVUYUtDmkRJfWCm12tC60yCrJYo3iPkodEEubZAXH/jbN+3Z8CXKMhNptuV193Y63LYcLn9roq8eIpYt1G3m4y9unr1kZFXcsrWu7+jMuIceSzaLJeDdnftnftuH3Vyi7TK0R7tdW1hR64DFHj6fPn+6f3wbLKmrK5I1B26A9sRieVndI92Wfs3Vzwdj31mrtMqtomDFY1TqfPqywtYKsevr9d3CVX4+Y5Zcbx2t9fvP36+X9zeB7EN7OGJi7S3PzpueydHB/GxGR+8xC660ohJNxwfK5vqzXy5faj6/bHhHtWZf1w8tLF1+kP3IPjQWJNtZ1uX/a0rhcn2Y3tcv2uKeZX5acvO+vW+wfvnv/cr3sNe5ffr22q1+O//S/8xf/7X/4+Yen98Dx8x8/rS82xluTy/jyKhFN8rKI+Vq1x+e7r957LVZbMHUL4HaVGpT/1f/i9509TKsmkiwAlcWx59wjSsCzxnLqwMSU3ux27apmQhBFAUPVqOou6l41hQDU0UidY2QWpCB2UkZFxdWXi7kQZ9FSW7/CZTaFLECL13rgQ4qObqNfxuXFbi96ezLNR3ACaAoAc2K9Nq3u1hfvN2smqVBr4m2hAHXy1NjZ7OLebd/nkSKtN7M58/W+HfeZ1uPEW/ZeI0akqlvXgiSzTF73t6PKupj7l6+vIuq9H2Morsv10rmS9bb/FsfxdGv9cqkjckbX21O/WtO/+g//4fXTpx/ff3z37lnamFRLV9rz7UW3uPblclk/vz3aTR8V/bs1a0AFBc3wHLUfV6meV85je+TYQx3SrKwW4KLP33u7bPdLX7xdm6t5COXxuqlwvaSyCtiTOao/2Uu/Hsd2DNacVM+N2+PetaMvzVkWglDou/fvtQtizhG2NF2hLBBWxnIRIzP2HF9zFjBRqIjY7jH9KDTesG+7WB+vX3Tp2VbXPukBDMHnx2c2pBwD4bKEmtlFmt/vc/J4zI1IWz0Tu+7tqYloVqyrvHx3W5rcrn5hbRv6Ev/NX/39X/09/uyG56enP//hQ3A7jruE0QiOE10IRpd2uahXhdYj6uKXd/3m12uzw8/JeGrZScXCbL3tcmw7WXKiCYhMElWSEkUXkjSYgGKOlASbWAWSQqSJnnMigUK+HfLNrFCSLJxQHeG5jF0SaSJWLAtVlYb17betX691ab/+sulxvETvXxo0LytuF1TiSIggsjzhDi6kz2tXcF69oypEkGGLd/dFVBJ5RI20EjCTUwpP7pcPXtSUVkUS3a4TEUkIim1GDma/3b5lEpWX75/34zjmvvRu5smwOrLqam20EPjYyXLxp0PapPIOffrh/fIsJq8w8KriTazmrE+jqRZ9jyxZXW/dUCkVcwjG/XGMu1f64mWRue3JbUa6N23W3VwXxdddt6/Hd02fpb+9ZcxNsD2h3ZbFdcrgurAyK+RyXWD+d3/7KXJWRLNMk9xDfDkYJotqM1qKUet1JrbAnEt36cg9K49jL6SyzAhXPSuXvS2BOTdkijVPTSVFuj6bq7aPvz+OnLQkGts05n5cny1Ud6NOIWztt+11rxrbGOu6SKAt/fLxak2nXejorr2v67nHmXtPZqEon1+j2e3jx/utt9s7j/n45edfm2Jpna4KeKeC16Vpx/tbf3nyHSXob2H/9OM7P8ZYoIWCgZQNkxCqjNOBUhWTCLL+4VENZFZWqdpiXqSaKZg1Y9Yh1ftaBcE3YCaEJ15aTiM86vxBfNMECQBR4Rw0KXEIpZCXVCqF1JlH7KNiXZD3eS8JkdlteHqDK5qD3UvtmBX78YDu5t31uIe1moQ0xOsUkS64Wi8pptCad5iqiaII4vQgtd5ONZfZeoILE5jF1/vbNqCqKr04u+r7556eDOaUOQs8ElDXqndVE9pHzd60EM3XsfHWX7BfVnUKp4i6NkFzn9sWr/n19WuNCvWPtVxfLnx9jGN7Ox7b417ktTcM/Qya+pFx7CqFm/SLPetdDjxk1ttB0PKKr/vDHpu05tfm4sNiK7M7Tawt/XXDeGz7ZIoCIJdjH11761ftDURUJmBl7RJW2/E6BHiwJO4SyDkEq8G+Dbq9iyqgMSOOkaRa9cUul5td9bENOz/t4tLUxNSEojtj5zRUlXRXt87sINoiI+t28f5sWhfYvD7DLzph2u156TdrUvF06eptPx73bSet+aX75XffXa5uq3N7/fLDe316uh7bHMdu6qa6XH313he8e1qfrrhSA62zPV3EU/TLvoUkS1w8pVJoZeKWVWLuIqkgCwkKRMS7Q4REVDX3kwwx54wY4Dpj9+7MKone27kPYCUAoZ43ZUJIsJAKQAo0AyNTxQxVsiO0W24yESFIggN1uIqy8IiUK8pQBTUxLK+vqaMLtJsPQXOua6sLBJmPINZsJsybhVqehdpW7E0XEyo0oQYt7FNk4uyaimni/H3i+RrPzyIuUlDpx7Zl7DN3CqpbtgwoEzMK6rastGBZ5XHEmG87Z3Pr601ef/t5ztmXJZVQBOxqvl76kbXFWDp627f78eX105cvb1NrFbVbj8A42PoKmmIBdmkVOi7Wjz2xWc5paXtrf7MdTeV2uVBw1wweisSRTRdCbD+DLN1uTzWnt9on5RLotgOaVlkglXJtNqeNfaRYkR66+K10wFY0q4yCsAgIUhS6z4pZpbU4Lh+NKnohjr1yBiUjC6WwMqSa+rpcgpWSqHZo9vJdSmFYACzaukJ7Gv1C9dHcRPLZ6LarZK+oMWobuW3FLhSbtRhaPjKz6bSlar9r8rqoNVlat6VXzGZrpvzyZb6O+cioqNrFD0EYYw6Zxk4zwwFKVZnBgiWU7g5mSWZy7dYvixgQJCWBS1941oMVVYPiGOnuQqB4TodEtTIFKMBU5WTwsQgVAfJkKwgnI2sxhWLu2VVRXjwKlpERc13l+Z0dkSNhKf5sH75rP//p9e++0OL+7vocMbL3ObHlgY22mF0nxeagCyNm5iBVLe1NmsllNSFESIq5eBOAFDHvzYRUE22LSGjVUKoIoLK6f56POopqzrq6lajdCL/MQkyBehxVJi+XS4lWqpeNI8dC2DRNBoT6tLys65IHhle/mHV7bJ8i3Tpfnpcv98fy3L1J5O5mpWJcCPV1WWQ2QVVQxBqkNYNA5pzH69i+du/qS/ASeNf60/vGx8hqAhaY0XZqpErI2nvzyhpRkyyAoK3qg5qhKClRmCVrDCLNSsaRVBTSzrufiDt3BFup1nJZa1mqjohdn+zi52OOSRRrJjlFJIz+7smv7rv1Ki8qcs7Img9RX6+Ea8FnbaqtLzCxksdjRuQ43malxsz72Lf8HMQEOrC63NTMa2lShWVd1ZUG7W3MMPUH5eevx6/7p0FuCRB//zO8uGhVViXIvVRm9yWCYqlhRhGaQiBmzZYrm6qICkGlippbkWrSl1YbhcUztZsh3oslagLihOQUXbVIE/3G6ACQAhEmhRQXUc0kRHhqyVRRTayLFKxn1fNiLz+804guXuSnX4+f3vA6cDV9PY7n9++vvXkaKHTOxP6QFGvtFjVQBTqEEpotXJYtRMVEqyxX1aiUMne2OsG/FVJenlolrtYEkiybYF0O0g6rLh8+3iT1sW3714C1ZbFESiszUQNZIsYs9Pr9H56hz5FHsVDI7Ygc0tr1o0lKlqq40B575q0/v6y+aGmhloOzzvR1aGve4J6csuuitlBhDkC8oss+I1k5C4yl3cdBtqW1nLknAdFmr/ftiOyGqFqS3QsOMmGiVomoVK9GKRMHGTWP+54Bb6a6ZCacqqVCNY6Wcj3vAlI6p0B13N53vSyVB5LJYGUyR8h2tDnUVL3pIlyDQ5tI0W1DlWC94MN7J2pkJe3ImchKjCwmZ44z6LvL/pC5A1siEn3FomweICz5dLs9tjyG0ISwqh7pc+jng1/DKTMK2pCEHzV1bRlNqmyiVILsulQQJQQgRaGecisypCRmIgS6WFcVM0WhWYseGGRBtCBSGeCKQpGnUD6lUuDawMKJUiEgJaLEpCBThanNUIkoOlH0hjNowb2GzHpblnX1fupE90gLWayPPVX7Ndu4Pc+rtO2QYzSvZQxJUcHhblEDFSIUER4Rx+i2ppoCsII3NWNBhWEUrYC5YcQsYZQwS8QcvCy2H4Z6j76W4OsmiGHtKpeKbz0IplimwVTFrFPJVZxZNbHIBQJV9g+dmPujSK00oKF4uz49jXUEZsTY9tZdm1x8heaJuIqQbmaVrKEpS3dXqqiI3dhS3zNYlTXH/vmOEj1iP/aOpQYAmmhUJJOj5Qo0m9hqzJd3T2Zobgxt6tZEykfsOomRMSZFhNWWfrl07TQvIc/BiHkT5Xq5uNdZnRMRiUZqFifnnDHnZMmRFoI9g4+C2Mia3AEVHVLalhtD72+RHCUBVgorgwKt1OKiKVJinDbvwFvBFqyKi+Ny0aYtm/W27Ie+ve1jtr5ec6wD3MPTlGw3yWqj+tgefH7+4N48a/iyMCdOb8/g4DR3g0Ql5DyxA0l45UFjSRMpBHJEqKmLmtnZkjyZ0QJjIWb6YqpgyTlQjaRItmYAIfkPDYMqyAy6lfI0ypzINKVKgz1GJrauQsWvn7+a93bVJvi6vT2C+5b7UZ9mvTT+gOXn17fnnovW+3cvb/M4jrBiWTKE06oKEEq5NxF5HNO0VnuSMIlF1NjSREvs7L4I0R4CtSqqui1SXkiHm8weu04cx52A+gI1hYsZSckslDOQkeYiLCMFpVSt4ARR9zhE1GBj1sjBHG1ZH8cXYwOKyCx5PB4l6ibeRFjeDGbbCGF9O2TOvCytWFVSCRE3gmFN/PbdjZOxV0z1ZVneXY7Y9seoTqlMAt3S4vq8NlvHY19g9PQV1+vVWCZqtmjGtq3Hix9zCrMwsiqnZopqqomWCsW112McRjWBijpjPvb9gfNsJCzqTM45gxhKiIhL+dUUZu4tnm4XaJqwXcA4yJQWIkgdq2uMEFhrZaQUi7kf05zeR3M0W8pc5vLlbvd77F+PsVH4EnqrbIdm9VVtyTHFv7+//pyy1WLPf/id+xEX9TcEux91YFAZVpLHcUgDoQYCCihZg9AicKp9UKUJp+O8HGsv3SUBKIWQmlWWFqg64aJCgTC0Wuk5UQILpSWuGjUr9VREn251y1VFrVCwOdu4z/7Ucj9e7zt3Vo37sR2Fnfj8iuVJvOrLOD4D8rY/Q17085Qysupo/alfe78o2Axwb2aOapSMotJMe6H2cRwRpq7WAYGKG1RXlpNHbHxso0QajU066uJm3gZHUTIJh2sNycJ5KypRRaIUoiiFkmCyqtIYFOnnu20MHkEwLyLBlBqt9eXWhDt3Ay0pOdHgp0U1MotmLgQieWR1V/GSppLMgmmLCriLVYrMhj25xFQ3vF+a9ouhmeAY2z3qIc/vLtfvn+p+f9w3oeVMJFTZTW7vnm7ft7VmRYCjiMljHFVZDGSWSqnyOB6ibY8hdQeMzL5cJnC6EnKvIGfJEFW2ZX3//v2Tt+tW27wfYw4ch6peVm0uNYKtGZsQLHl75CytSaEXSvXoi5nqE0uKjntlG7E+grnVY8fcLcd8d1sn+qf9KFlDYxwV90emhqyv+/rH335797unf/d//3fermKgbUiJdTUKpQSDUgxMiBjhKiQL8s12qGIoiBGZFYVSqKqY0aKVUqWKlpwgItPsxPafw9CzyQLVM0190kalANJmpQAS0hpM1ES0TFKayiC3bcuesurjmNpmxDYSY+AotEWmiTQejsduk/Jz7s9WF5t/eZWXi90uvbVWpZlVwaoCY+YBqFADYx5vB9NEys5rz1A5zUCSnBQ1wFZdFFHpNJotbiIzYqhLL2fV2PNRFLSAIgMQkVIovZp5NprqKc9FqvbmbCUSM2bMpK1rHzKlhIl7jH1oCY4JJClpolNEDESAokKFSbFS30ZEVpSAQ+kqkDqQ1XsXwePrxoKYeFtFBQWDrV2X1dd+aYsm+Pa5HHOxvjTXKoZBBGwCub+5PVIqMQM5SrJQYKnL5XJRWmyPKviyVIUKzSWdUpItpcqjeWv+fFHvopdUWmpElwlOv6wvl+eqjDq+1va2vz6+jHFUKVLMGlb6UuOSYZwiBtZhsqDBOHtHW2r2zMHXx3h7o+YSqvt9jAMp44H70XPE28AbS/Z5vG1fanq7LS3u4/Hllzt8XTxLG6eX5IESUqhGyKwZqt1UQIopzlR/MVlQFYQYK2dlwN3gaO4oLeQMEQmhUVElahrA6ZErUJiZBqXWKS6VM00houYpmRQkjQYqSwQoAUol2tdfx8H7d++lfJYDhYzma59VLaeFzEP2r1Nzr4m3SrtAL/zdu+ti62JaaGtreTLUgRFF0YAcQ5MF+pgFoYnA4eYkKkv0bIBqTVKzEZl7oW3HFjVnBqw5F1Vv7qtrEvOonFJJhZioqR6knDKnNDiZRYidFqVgUaCGgjUvpOm5PxRTLE9XJE9hEiR56s9UhZJFQGdNhX57S4sTZjBIta4kY2Zb1iEpYkkUqaKlYOHYx9dtGtKdzeCgDLYj390kWrqqKKWprigpCXopc6mo4xgZyeKUWhYTf2KWCN7/+L7dNDlEv2nDr09drAENomBDOXRgsEa+ftp/+fmvH3/codH71Vsh5zyiKrs2gRUc1Vy6ypq2qCkqKDbmfX9s4Nyk1BId5wDLYJXgDg3rvR05Pe1+39RwsV6jnrP/2N+xVanEQ3Xgl8/wp6vKWLugxPOM7gdYeU+lkCU4eyhZEDkRD2TmTAGUxLLMKs/ShsYWFpBy9CNnFy8KKEEWSgQCARMKQIOhBQpVlEUqcfLnYICoGA+dEtqkEFTEGGN7JA/TePuaskC75SFut1Eqnqn7fn8VXsfY20ADVocHMPDYSCW9Lj3JaRYqJhJ9LUnCuVsLCtjlZqsrKHPoYIaINDcXpAUzTeP8elGiigSoM9oQBbOAyhpTI1XVfLkggRCl9cVZPGIwSuGcEEkAWSnQ7qAKNJsJGtRcBXK6907JPCClSskUBrIXYVWiYkxZ+lUNbgYcbglUTREwKfMQUTyOI1Pt0ry5nHDqKZWFKlWdsJyybSUVomwib4MqFEwC8KmAMZq6q1pLs4LTTFtr7OuW0dnWd+vTU5+V909JVRrJ6Bf59Pmeab2famW42uqjSaBG6/L7P+f4S+u60i7X202K25e3fRvbHhkzhqs42QE7vhxzS4VWbVk7K9Tgy0UUjzoqMaLGAcwsxpzc5y46lqv9xfsrPS9Nr08fNYd/OnjMbb7Vuuy6/8vbR7+axjieLk6tqTWLx147E6zKEmQUpFxMK+p85fL895ZUFcZQtxxhcG+lKoSB2dCHVjc7P2MRiGiSVIowC0yGwNLUUtSyKAR5rsJgYlbCksEJmVWcY8gsRNElAYeOXZquMSyidtkr+OG9rFdp0q4fbC3LjQL9tOHr35Lx6emyvjzd1MmKjEfvaqqX5irSl+ZOltLwdpQ376ZrX71RVRiVTE0NtpkxqiqrQaTOHB1YOgNZk00JK2hkuJi2laQGtIlae8FCJhVoDToFWZWZqeCJHSgZcd5si6xze4JzZ2sQhTbRwNQsCgUdkt9ezSwZQ1iRUVIUJRPiVMwKUVkbwMmZJLyrNQXIZMTIFBZKpShS7KKeKlUlUVU8FAyhQBIERQGtohuawWUD4Rbdpwitq0gJKcqlmfs89qPkajrPvXNnc5nvPL57Rn+e7nPpl762idzun5A9O3tfLh9f2KjQqlTheLB92XIWisqIMeqYCjFvy3XF6qkjE8aOYswYj/3tdRwHkqFyF9GX7jI/qV/bxyvHYUS/al1eLkf4coNnovsOKi1IEM1s93ISUygVkapwoLLkDPerUqglYhJ7BHMMrjc3g9EMzZwml8zMqoqkiYAiVac1vbIoYiLFyWgOt/PVb1Vg6SIG1GRFJnUwlckZmRSUgVKjNXGWpDJiD6/1HawrcppNM5B9bR3lb6+beKceg3KMvFwAeM1aVgqwe3XI2vPyZJh5zBE8KQyidW8q60UrKEZEutjtoh9ul+XCrtJSQnnMEgWkp2hUcsdbPEZa867ckbaNLUIJXdcnV06tYooeYimZ1Q4EZ41tCyXCpFLFqFQ1NzMxF6pA5wl2yYNAVwPmrd84TprVhBurqmmdH5H05KTY09IKWWQy6gSWiQoPVcAUFy8qztA6URmSYEFKz+96kixnEVCefyNKSqKKfgAQUYgUkiXjfF2pwI1eoSxGqZ1eRYmXa/vwvS6/8y/2+g6/+kq/qLRYZMml51QpQ2Tw0HEkC71SJ5d4+lG1smWhQs9+VZTWVC1tLOxVMuOoUCyonj88yRwyDqkQEVVJ0jVnE/XLOhO1l73W04fVDz1q5T3DV3pbnoCnm3x5y1S/S5PB2kJAJXluxMAzui+q+s2/UGBVO1gmZuqnA1qYImGsQdB1IaQgRBRZRRModCbdrYohBSA13YTBTC0izqG6LzkqSwMoK1Epmk6FNgI5mEZzu5rwGPvInAidXvPCtdv1enlH6azpkV3lBq1Uyqoxr7e+Xri4G2W9uNy6PO5zFIeUqPelLfbyvLrMbcy16aKWc77e+euXTMlT5TQ5UAI1NzUXaRKSU/ZR2axn4uCxc7y9PvgKgbobS1ARtGQIZFFfbu12acvqqn5yKUXURAGpTIFCaVgmo0pYxQqlPTUdqH1sQg3SVKTo7iIsyKLXqFBVlJVUUrVJEpUF6hlm0dJSUVgVTRSmNFaBTFZLQCAFCJT8h3gXkDzn4igyo44RNQCYm6pKiqMIJcUppOJI1EYHWtxfWrV3z08/3J6WVNlLRBVMQAyWqsoGKy1bFExEokTVoQpAIRQByEQNhAKaSVENZqbEZG5gNZ3ZpHmr9KoZFQQJjcwjw9nMO/Kar5b+y6+vmu2tCA59vFFmVXQu7nJ7+rO6h1vNCHLUEIIqqqoUSpnKme0RoCwlEY0i8ETpFFZFnQPTKlLPAFGyRlGEkJwUlfyHXgwUGSYKUc2oEhIipZlCEYluzAKoU0UJj2SKHjFT2JtL8vEZWhCiKXqTi/iFS94rqUEPc7pvn6FhMHq/fH1lW3jr+nxrI8oQhDRb/GoqtKG116cvr6T21appLCp90YvILIyMGTkj4WQJ55ZSOUfORwTMuh0NvYuC+bSY3/w48wCsFJRRQtRMxaht3zm30ToMKQaDu0lzzcpMgLNKFdO7BCNzKkwkcttADwor1c5jpMaYUgpRb2XWjmO3U8otGmAChBEp5aIsZVVUTlFJFhMQrfPKzaBYUiBy1j5AZQFwkqxTyQxQvRlcQSVQRAVAVKomm/Yjg0Rpvh2Rdaz3Y/+3n3/9sPz4O/vx97e1iURKqQAlxRinhD1APUXPdhEIz+ELhCWisNXUI+YEAHeAhK69YQWfIDNxJO85x97mEXZkVZs55+ju7XIpaTkzJKnw69WODWTFAdWMzKLuMzia+qteOiswgTR11IGsQkFFKUWBilAJMQhQNeIYI6Kmloq6CLISAhMkz8yNnKJ0EjzRO4SpJYop0NC0kpwnrEpQ51MKEKWwIZhhcHhrIjLmoIi6umrupQu8eRNbV+tLk5IZsW9xxD5DMlTDb225iojxLlMM2vDa5Fev3mCeYmlWrdva+O7ar0ton3/6FfsnNdVlsaY0x9JtvepTt6WZio8Z5+9b12oLE5mRl3Y9M0YzM0ZtMzIxK3Ai6BPCNirmVscelVXis4po3puoijVThwCeOAupLKgZwXKlVkkIYxyEinoVTOjNtIlSFCYqZiXqee7+KKAKaYKkQRiZKK1KEUWkGFAiZ2iLLBHUP/RYFYRUFVHfin8UJc9VB8/gLhOlZ6KFLAWoOTinxsww2Ns2fnq7f/mNf/l+/e0eP3+a96/4w++uNyt1ds1KJuTUM0KlSkqthFJCE2MSStHeTaiGFNWIPR8wqvsCCnSCYlkgZQGhgJpDWey8mYsTzYANXPYHl+fmrdm1L2tkKibyGMc+LJSzZGYxZ1PiVFqAVGFWVY3KMxJkZs3NTRVSDBaJEBEqE2GiCZ6ec1WDwMRGkSylCJWSgAhERYNVtDKlIE4yE1XMihSVhhYMxDTojMGEd6eESJkSTNPF262La19K9XHgrar2Md7uyEy4sBsD4DlcMTc3IFBD0rx6o0fKLEkCcPmF2bq2M8qUUqrb1OmlIo8ErLrWeoU716d2WZspa8fbax6phfZFU9RcjWKVYzti7AHn0hrUjKrwi/r1ifUEs28EWyaOPY/jeGxzkmPf9rMn4aaokiI4KzpaSTq7ubl4twVCKR51ZwkrADVoCBUGkTGjKkREAHEhadKo5dKoKWIKUdUzuXuuelBUoUKAMlGAVCmUSFaWQAt0ozTPzEx8y5SLJEuBpJjKTJOIilBZG6mcb/P4afMPutzv+JufImN/XrpD24WqaK7makCZVinTUhSlVKLS2tLcTDUDezzmHpINIMhjf80BVqjKeRatMUZsVrs4tPK5q5nhSB6afGQdwPr1/qs/P+m7OLZr3klK228+Hv56yDayDi0pqSwRUs6LEL99oVkUuNg/JIbgxQSzKIIiKwFJKQCUmpwOaBoAVa1JFSFJURcvFhKmCqKiSoHSlFLImSSLooqKNZFRlaqWxTEfaKW94NDF/OnCpgZXau4yj3ydM77sOEZTdzNzLfeR9ZBx0YakAAaHkMQeR2mpsLl29SFzSuajHWw3r7VVYkoUwyNLVVV0GsZkcdQvO6y8oXezi4hbMVycgGhhgFKEISujcqcYFSmiZI0ITiyXJgkA6rKsfbndqFqMo5acYio0N6YqIIUiRDIJSiWRooE5s/XefYkKky7wc+QQkwmqCyCmdi4LFKAqCqCYdMhpOVQx7d6C08VSzjxLsahgzjpBH2Ln1z2UqCKk8K05hHN70apAzZASkVletJPp63x6enrqdluW3q++1NO7GlL3lG4Ww0Sz3rKSoISkSHZj0BTVmpt33WWP2BW1gWVKF5/EHmPMeeJcCRNnVNR+bCmbY6gA4l87NO8utfY+Y5oP4mFPNx9Tftml2iqtQmlh63PGFXLIcQBsoo46792YR4qllWSJAqhvd6So7CkKKXEixeT8iZyxsCLUJIkSaIlAISxIIUydAoVWFQBVy5pIMXclR5XSeM7VCHfX1nJSjYkQUUo186X5O3vuuOTEjD2q5jHHW+2fH3OEN0E3pZmqHK28HVA41pXNvahZGtQz6leqMyEBlCpmMs01yxZwNfSLm62VmZkxaoziuZJtmpX7QT0071Uc67osi7WuakIpQzPxkqUqzRcTK4A8J/ZcL9q7WJ4O2CY7jioB4eraFxOofrsEiqhRqCDE/TyGM2WOuTQ5D+bLKVpVqFihxGVWoLTKSNT5kUAy4GKqnRMsVTk3lVkzRVBaPCc7rJwlonNyzJwZIDNLXFl1Xi0BEXEKze2Y6ULCTq3drDJv6mosXxpdluXaexNpqH1+ZUg7pJuLeYOYIBUgkRJFnsN3AURgKa2hqa0GocqEiPCQ0hZpQaSQOTWgYlGcZipNW4LVgc18H8dFD3kb7lyP7OAYD3+7x6W11xEqZEp928RyuYg1HbMQ4m11DeE8HjJVM9DgyVIIslCp0IKaqBRBTRK0QoHfoFmT0UUUdl7k3SUiFWrndo0pYjwf/SJFjBluKmaVRVUyR1I74SJlrKxZ6WWLL9fr6tZ01U1ZeRw45sidx9t8vM4uWmXibrCElgJZASRlD8aVLlRGg4GiejLtYBClk02MEePssmVmzlwvw2wYrK3ieinkmDFm0CjKwbkfIw37Qe0hrlYK04t3a+quSb6+BbUEcAKkKncJmbAqVXGf6tASqrg1M1SdpYoUmBhCgEyku6urVCk1UDYjzd07KiOjqljIyiLZ1FuDCk0lz8cPkAXOMADWIJlJpo0xIRiRip5FAVgFSDNTkZXNGUlAkQkhE2fZA1UKlTy4sBGpaEmuaxeZ3m0pKwDigC7i7t4E17WLWoVumVLAmKJQpEDFBC3VtDIII4amI8WKVniImrqrsjIRs2LKt3nUuVkt6pTacTE3PMb15faC+vzz1+3n7b03P+p6s+V66O3qePLdEDG1t95FU5HG4pyZSRXVBSGGCdBV3VuoCqtIKwXPE8sMZhEIFJQZeaKwBOeIqwowtRn0TqmTDCRtWUxZSVUR8UqKakqqSTJVzjlAUf4Bs66MIRCLygxhGos2Gx6mz4sEImPf5uMxM6iHxgOyyxA+rWuTTm1WLSozKDPKIE3Hvrv2p6tTo4mlkg49DxkUUCzMrTVSSkfXSox7uoYjlq6q2ZouN4VeJytipt7m3GZVTWYiR4lDUmPEl5D7MUTV3N3d6M1KXUVSeX7Lv42CRSey3LTbKp1MSqm4KEw8VWFeXRcVNRigUQc2qDmOGow6C6kq54adwIwaE0Eiz3/oebInCjGiKlxVzcyrmFU4/Q4qODl+lIoKEyvivClARAUCcWGdSaU6sWcsIQpRBbAiulrXxgbXijnFs2s5dq1WyUSboBoS6aalyVJCWVa7ADSVEQdkCukiJoBZEwjpZSp2CGbFPisLDc1UEyB6SITmXvndyw8BDCwffv/jDx9G++loH8pe8gmZb49PW3lKc0hMIeHa1A6WFnTm2VFRaR2WjIoqVVCLCZBOlW61MA9wpJZQWAk7U0MFDRRxhqVpNGiBrYFBP4kOpWbnGVPVNVkQTVDsTOYkvs0t6oxLVBVAhUATZmLo9HzEPqRMBMhptQFoc0+Waqq2zmrmlyzmETEKlNPnx0xvEKk5h7jF+W4KoVJVzc6EazbigEoN7gGHSbZW3Wwp6Y2epYeoHCIippLspjfvdhVIs+JxREEL80l4z1RoMecWIi2t1UizmgwSSgJKTQSEjWKqQGpzJUBh1sjJKlaKcEsRoy6iACQBZVcphlRCVAUFFktoajR3fMsTnagz9xPPpCByQjxlFEg5h5zKFPl2WDr3v1Ui0G95XgWUJ9qmqTaXDESlyDkhFYYVU4oVI2qosayatQyEIStdgymZU6AhNGnz2x0ChVAWrbQ0MsFEiBqaOwySVUx3lMyUcFdfV2ONA2OrLBUVBRvLT3rb6xfXy/34GrK+X/rb/tuH9en908dWx5/+9POP/9m/9P3rzGhhUJPMoU5TglUpFaLQZCImc5ryfOScUIAkmaWAqciyoEpUBMKTBZ2oRAYykizWeT3OKHEV5tlxwSyDpMxz6KNCQrUQJSIgpSiqApLFFBEpuhjatSRc+zyOJlWwrVNVKqile0yBqohfLlUCw3YcOTnGNNcuqlaZMHcQmTmOygFR8kwAKwGq0Axr7wSseqakQIdayywNl4NiB1RpShMxy8XYFIujM+1IB124ulJyHzkiblcwI4wxK7nPUlEVL4pl7zkVYRRlppUikAUcMqMCUiev5vynQFVsajowVUBdmrt6FM9vHuhgZTIyRdWkZVWd3hLQSDLdUKgcpCaglHSYCLQVCm5kBXhiDc5EoAFUg9D4jfRRwPnuFzWrLKh+o2MClDP6WsKyBAhW9gbi/J0JUYSbQsVEs3UXU8P5eqqqMy5FUCKmq4iEaKeU2SI0MwCSGbEhAAjW3geLcRwZ2qlmq9q21drbTz/9+7e6frL56d//9J/8+X/3S/3t97f+qf3yT/7yybFe59dS0TlmFUMSxfMA4OUQBgWxSVFIE7o2MEhqgqpCRXMVce8plKTwbNVCnN9WisnIyMopqeXNmmpDgkyIREZVIqFUMzFRUZgKwIJ0E+sq1CrNGUk1wFVMljE3OWxZltZ76VRZYs9Z89qXIF2aGO9fj/E2hWkdjWbs0qUC3v3MBUsUVFRFEjA7QY+nOpaF7THTOzgKpU2pIYfIUap0LypdIaCoNivTvDT11+qG3vXauXS0xNLl9vwi72rsUxxUCquEkNyDY2RKDo9QllXlORiWDD3T9lJoZt47lcWECNRULFrMDIBgvkb2KIYxTaAMN+J8BSiE0mElYDsnP1pGgKUQbfN8Z4soGQL6GbQW+mKkKKW1diZfqs6Wn6pYCQwCUAxLa5Po3SBkCossZmXEqBBIoaANTkFATOys6oqyVGaJmQKoyhPBdD7lv3mqtTftvsYsFNrqZ2ctqRCpLLi2Vji4HzUijlnBDaAhGy9fai5++eOnP/5v/zf/7T/7gP/yv/gf/bT9+//8f/1//c//M/wP/vl6/Lr/j//74scOC82YSHIizoo4KNA8sZ5nFSYn6CWIEj9n82lJGOAhcFGwiZZUMEhGBUKzUMmsCpCgiZYyisYQ0XNOSpESAYuaCWDAXBYz664KwJAisCaWllrKqkIx0703VxSOiKeXTkrTpV9aBKoqZ23HziOsKwPqTpGqwWi+uIgkShMCLSIpMMtKgMS58pcSullMFgD1ONLh5+NXRdLKGmh0FaVEibVlJOla0GPGfcJG3Vzea425m00kx5GqtZhJASJN2cWGxJU5vZoboXMWyAhJMiMiSWolY0LAEhGsUSJWl6bnA8fEzs7yRJso6urlnEmWw9ydFYJgqqAg6qq9sZvuoibqvYgEupJKLQYEJEkxUYi4amum5yMd9f+PwjCYLKHIKBkizQQKK3WKOcWTOKn6lDLYZEbkZBIQQrSJSlGaG1Z1MUeUnM/9NDMpySIKql5aJ7x5TjGnsGaNea8oVkrNmjGjpqCI6HLdjk3Khz2q1//tT/h//wn/w//L//ef//N/8a/x//qbv8X/6d/uA/j3/+X//mwuqKiISkJEKoMlOOu7ZqpkpQq6UJlWrCmi0pCswmT5nhtEoQqKJzUTIkoHNTTOYSi/ITdVRSUr+Q+hdg2Jk7rLpItagwjHzGSqwSAqLgOmfQpDjnYRs4KKlmj2+/GgypfjAPW2LoZszXPTMpl7JFBM0ZSkuJUwuKusCtG0b8BqShSBgkowhSIFClQ0svBtJWo0hAcgmtmbgzlnYXBaF52ucgQVtiyCqohpwmb6q86/01x7d8sVaY61yXc/rC/Py4Is8arjmJORmbWuuK4tYWqMkH1ym5LFslIalTEQs44xo0RLFl9EDRQqghrMKJnFbd9xxhdJISwVykqtrOCs4E4KvsVj4OFdS+lSYmZucoY++e1Dg4AFFwOqMgk9q1GCbzkWt0aCJM5UCMxMxSDKyAlhv/azho2mIIh2yoLmpKmygCpWUKS8xKwqWaDpuW0isgIzQuEz81vfsAo4D5HKgrm4AelZqYLWNRJacF9/ffz0O8GfiP/df/35v/gn//p//r/8t/0987/Cv/2C/8P/E9576jrgroQ6mWWtRVaiTASzqkgoSxRSlIJkFjGUdtqz5ixqSCqkJM4el6pUmjfRFGRlEtLE/NzylugZ6ygUExSliIrwhCgwAopR5720mpXBZ03RUm9jhKtQNRJrkcO3epsbKPnpFTBt1FVWOWG8raUmxErIs31vTFYjiVSqnGcYAYUCulkw1IwkhFlaPCmmJKWiVFRMjyiHQmCNVeWQYHGqakVI75alMzMZ0pWtcagKB7CuUpr7r9tPvx1PK9yleRWzmZjpY45SCmFNzPSylC0VPIcscIjdGrPGI7ZBMYvImgSRU01EqZ16an3ljM2CkNbTodTUFAyUjBaopBC1jwxRTpmMEiFoEgQUDSips5OTESkC09ObQyhMROR8I51ZdrBAnloIURUIGUWBNt02Us4DprkqIMZznSGDwazAnpWlYZ0KVWNmmVmSZ5LepZmVSZ0HVaiIGE9X3bndzzStjuZNRB2Il9tTYexvb5enD3/5z/Cnf4e//Qn/j//jv/nX/+R/ssj2P/1X/+bf/p8BwHsLe5aA5aRAem+kIkRFZZqa5p6SDSIJoSPJYBUIpJ+XM8lKKoee7wE1VdAEdY7hRFxM/Ow+ghSKmNT5bDFUEaB+mwFIaaEkkapSNVXayFBCwZRUhLvMqImxlB3/v5rOZ9e6LDnqEZG59j733q/+qKrbtOW2BQjLkgf2hAEjvwo8gyVLjHgEHgMGzBkxRVhiaCxZaokJyAJ1V7ervu+ec/ZeKzM82NePsI/WWbkyMyJ+yi/zPMpBODCG2Y3UrJO9IwP01rwSnNhSWkB0SIJSlyWNlyK+LqF3RPrsiI8IoIsAEiSAKwS4ShtlIpp1toSphqjow601nydRK4Ta3bNwtjzMGhm4Y4u+pWOsbSClPXuMyhAJyYMr02huI5PLAY6P7W0HuRZQKny7aWx6fcU8dTxZSyfQxdme0+d6VufhOpbLeHLWupItBUp7AXERbrAhNAAP6COwTIRpgxhooFlMagabgJSSrgxtUgGRUF5GNYjRZFq69NXJwgcbwrqS+tFQ4AoZvEBCgwMRASxfjSDh8CAv2355BcVrUBwgudxwsK/zJELF2hRIkGmv2DLUc2HfX+Lbt/t9vf4C+BUO4G/Pv/+zP3h94e///CpuQL5u2N+2x4xergPd9HKFJ0qb5lHxCk96tRbbDJqJx1GnsWfIIipERaLZQJfDhhDpans1CA6lisxGAaxuBmkgELgk/pfDrykiiNZyQ6PchFUIuNvn6RreUkocrsdzFhyJGFBIbWUEREVscuYmUVVLVeVmC4gqxCmPoHXNTDCvbGuJl3lnbAslhy82r6MuLYKv2XCfdjAKDYsGu2hW81IsNmHF6YW7GRpboqHYanpsQnoaR9f90UEpENQWEeER/bpF1iL63ivSbPe5EpmJly1u25hVdZBzbXN8PsIXm4eG1dGX1mEPCtzK+9R9YYJ1Xjd+reXlrot7uCCSnECYix/0BpfhaxMdAXfPawDFCLOjPvCqAbgNN5aBoBJoEwGpPhgRYMZQulme0Qbqssg2qwle9lCQTWqjyrzgg2wzI0J94bnQpbFJWqtCQ1JZEMg0EHFFXFZ1yzoP5HC5vryfv/5h/fC7n/7kj77/5V/88Ae/wC/+4p/f/t/X3+ef/s3f/Y8//sP1q/8D/uW//6PEpx/b65zz8HPN85hoFOVmqLp9PLc6ls5sdxVateTZyWIUA00voAXVsQx0GWJIY2yAJWpQYaUaqOUg6tIVUh2Baiyji/+kEbVBaXG5uzCzRnDDhOwVLiyjB7Hh1lwOYzO7kxfGEkIQATE4eMnvS75QWNFhZI6MwQI411qUFIRMUnEdKRAB9pXzniFEyMKqLtsN4WMjREC+FLu4QB8i7HXOFE0KzlCzMxQBAEOhREQlAoCdGQkhzdsw/URYm0dQqn3gUp7J2MXQ3LccL5EkKSFGKDNouQ2kqW1wT5zPdTx9LC1eWcCYC7NR1jH7fvTzrLXsPsvRaGNcqJOqBSe1DWW23NVdERfvHNdnX+1TCB+tXwRmg0CIIqjmSnMLAQ6FB23bFtML7JjdYkChsHFaDkW7GLV8VQ4023QKscceSZid7FgrnLLUZKT0cZtCmOt5h0wdfTAQR8Pib99/+tu/+V//5T/hAAD8m6/wb//qj38z6r//1/+dX307xjuC+dxKn3Iin8c+z2cd7OXAA+XjpVx4fO7jPNV8Xq548XYbPcEDuH4WM7ZtVUW4MEEfftAQYkPQnO09lLuqKqEmIhgZiezV3cM1q112L65YVZfmpy0HiT18TFRl4ZQf7HO+c+SAujpkCCNMqUw2AE7Onmj2jQrEljEniuKKcql8nCdFasa6ol0gB1hJkUWgy6XoWS8ZRANGB9V9xbxcVaVR3WBdnml1EMptq27blA5QiDU7psU8gzoRDDFDUOa51MKGca4IvYArFiI6pAcNWHtl4L4WO/UlJI/BzA4404oeYXIFLVNgjLAnqkP1CWM2TYyRTdSs1+CreCbabsQqrPKqWbAXathd7qcYRLSZA2S1G4zldQn5cO2Ua6VVq8TqpquoAlLsiJSBrlbz6EiK22X/72bS5Mi4tdHZUrEhXWDdbJwIpwgbKD2tbAWz3Vw7t3K6tSJrQoGCOX2mFK9YX1Cteq11UpXR+zzPv/84/QD++jP++j/86vUN93dk9+Sr/OhvbtxxsH0mz1t9vtdxZ/cGFDxD66tv9ez+cmAs9oonkbVCt9wCds/04pJHYp3LIumrVRLRvhZ8rEnKMVDhQW4B1znUCJ6WOCqqwLrePTLksDZzR89CDchRXc0yjt6hYHMLDQy3xIho+VrooE97BKrjnMsuWZGZEc/17Nl0X5vpNq8qj+WPm8wmGEGRZ2oTVCuKiwUsNBl9bUWrLfsS0NFBCw2Tc1naRbt8Rf6KFJERtoxqwBbgOg301Z243dWR7IViyo2PIEoYlREReYttTzyfix9UwiYNFcAR9iUciKucIZR7XHgeN5YACigO9y1GDtAX3S2MXoVn4X7irIIUdqAVO4yii3outxRU9UIBdkQE2Y024PK1JtYKZZrHOq+eMQy008XMVRXBxZOO6NeX7c3hCTM9qwCiF32BtZa6FyYU66xuD0Bx29nNWKjlgYiJZcwNu7mdWK+8f1yBMVXyo247bj/Dz7/Hr38AgJ/9Er/5v/juD3H/O+S2zXiO1/1l9f2+aFB90LzFyBdj5XJvxFo4VnP5JRw9nezEp4y3FevAebqppbbs0v62QyOsEuZqYYqjYfXQ8rlVGZh8CfVsE4ecjrhCqpDEurLTpQX1zVs6EhRxEmjlyHsCVnu150gEoyFzDNxewYh1oA3ECpdfOso91W3H1DFXd7fqrMp1UXZGX/EtpBAN1PUtjb7+DGs5AmA0FpcrUaiuRk8WfDI6HKmNI26XtDIGnKTSIULsWQcYDb70ZnlpBkcYKxvVso5zljgGh7cdFmR1FwV3R4jt5SX79pinmINs0F4Xw1kSmh4HoxViFUXTx7payA4tKbKV0S8veK2opC2QKDa6glHYd4TURJjPY9aspgwdfX4aWt2oM0fGPix4lq6LimIOG55LOCVn5rdjc8xryg22GDF2q0NeLoiq4XJLbxEKl6Nt6UI5X69ZnMXqnutxiYOuQoPubG2NQgET+wl3duuWfuSlQmNm9Rq5K/LP//W/+P6f/fq//c8vf/onkV0Sfv7d1z/7d7/H//if/2X8uP/ux7m6XJw+PVctqMLnkNnQ/AgzaM755f0hUx1zcMs9j62r51zvz1WYS3sw2u0qgzO0B3Cc8+G1a10ZW0BV7NKrgYWZvWg5Q8zChElPYI7eEtFKLOIDpHFf1V22Ch3BNeJmvzxP1Hb/FAyldwVuqj31POY8QKKWKrpQXm520zgmxEcvujd6733kZnil3FC1BSLUkSwEBmZqk65tNaI9w0S3tARyRXkLdUJ1IQeEiOuhQBpiBjfPecyJj20Rdr0gkzHXSagkhKfrZei1gollggpQkoLuPjijMbYNmquOqDEQGchi7trKw7rvJftbx+sLzq3MGYEwShoqUuo8zF71lqNAuBYEts1EXKLRDACMDCP24V54nGa0tq1HjGHOXh1FbMma5zwmY4AcSmSaxxDuXzzP54jIvPHGEdTy4/HsRgyObWwj4Iysc/n5XJGYUrub0LWPZIqRcsK9efh2YK25fNZacoM9aBn1ji9z+ejAntvIXueb3OeJyqbY9f7bn1Tr///w6318Avrs+7Zv683Z5/34zdxf33iemI54aFSP0TQ63+9PHTFiVPJ4+mXU11/jeWAUMFReGpv3Gmdv1JPRdSmoq+w0JqL7+G4fGHr2ssbp2lZol13H44nUM9yhdgdwu/LmkvDwpsUaSLVdByIZI0bWefZFllw6nMD89DIaOq7ckLYVP6J5+NuR5HnM6X0/BZe4pb3m/RHEUp4ZPh8LFSphdeQJGsghX0bUpMlch7UOtbV1M8J7o9lkY7HH1dOiveY80wXlsWrNBbCFEA51Ad9vinDFbOc5/Oze7DecLzqJMbe3leFdt099+939UbVyb0aXG7WaA+C+pXwamXuOeHZ8Lqt8a7zdNOZa9vq0Z0ZMvdeX4zgkI0zEYgrY4K3KO3MXc2bjyn6a0fCmYKq3eW4JBpmD2Md2+H5oGxO9znk86/QzbWo4x2R/+wlfVj+Pg9ZTlVzb7rjh977RfY3jxDx7PuTufdN+y1pHQl1rcYK37od7KXHSj0N9JU4R6iwaXK8scuGIz3RwBE2vhBbY7TbQZ94eHlFDd7e2+bJv46ffGD312rM+H//w8m338fltf/j9PesMoogbvsrb/q+++eVP//C7k7ftqWPN9GzBnmbwtt+OeZ7H8/EFisEY1SRrDh/3JzSOuu9j7Lcts3n3PpjBNZ3VZUQ/dsRZEemvoAONUKuxpmP1S6+al2ck1GkwfTmyQfnxgPw4eh8MRZfZHYw9srLXnLEp1xGNYyPQr1LNObYx5/2yhlQF2QwuF8qDOZ8zr0pmcvaLgvEiTJtPlyQCMdQNVzMA9/IK8egGQJUHRkRp2TZLqa6nj/p81LZT7JOd0MstjnkNAoHCvtYAw1tEr+OMzaP7FiFwfIiDrOdz5/jxt8Uxv//+Fqg+HpUd2xCQq5JjvZ+kXsc43s8S33b1BqncvZ7Pp5yZx+fj0e7X2/ff3bZjPY/nRSYpT1aJQOg4Ss+ufXu74fk4A7NTbZzGxFzh96NXFWKM3W8zXrYx1eeaeKGqMBEKUGcXGZ+fbHG8lpluLvvz+/HyHPj6ZS7bNT4J3WvyOPrLOlyn5Xmeq9bj0V+/3W6JExj7yJft0v96dbOLlVKpa87zWDE+ba95HgvVHFq9GOCWXq41+uSj1qDcyy/z7bvfP+djfn7gub31N16Pea94cB3r/fi8fbPPE/Uj/xGhi8/cXWGQDwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Below is JAX-to-tflite conversion process"
      ],
      "metadata": {
        "id": "h5GG1Qw1SqDT"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from jax.experimental import jax2tf\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "kVF1_1Gqnwwi"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_model = tf.Module()\n",
        "generate_image_jitted_tf = jax2tf.convert(generate_image_jitted, enable_xla=False)\n",
        "my_model.f = tf.function(generate_image_jitted_tf, autograph=False, input_signature=[\n",
        "  tf.TensorSpec(shape=[2, 64], dtype=tf.int32, name=\"text_tokens\"),\n",
        "  tf.TensorSpec(shape=[1], dtype=tf.int32, name=\"seed\"),\n",
        "])\n",
        "tf.saved_model.save(my_model, '/content/dalle-mini-tfsavedmodel', options=tf.saved_model.SaveOptions(experimental_custom_gradients=False))\n",
        "# restored_model = tf.saved_model.load('/content/dalle-mini-tfsavedmodel')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-74vChXbStPY",
        "outputId": "5ad31b85-7c4d-4bde-b1fd-17e90f4941b4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:@custom_gradient grad_fn has 'variables' in signature, but no ResourceVariables were used on the forward pass.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The notebook may crash when running the next cell due to memory usage. If so, just run the cell again after the runtime reloads.\n",
        "# The above-generated SaveModel will persist between crashes, so no need to run any of the above cells again."
      ],
      "metadata": {
        "id": "zccB03McIPH5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# This doesn't work due to a bug - resulting model is zero bytes: https://github.com/tensorflow/tensorflow/issues/56629\n",
        "import tensorflow as tf\n",
        "\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(\"/content/dalle-mini-tfsavedmodel\")\n",
        "converter.target_spec.supported_ops = [\n",
        "  tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.\n",
        "  tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.\n",
        "]\n",
        "tflite_model = converter.convert()\n",
        "with open('/content/dalle-mini.tflite', 'wb') as f:\n",
        "  f.write(tflite_model)"
      ],
      "metadata": {
        "id": "cD4UtFEXe_4h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f4b34e8-5068-4a6c-c089-4133407278cc"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Importing a function (__inference_converted_fun_6851) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
            "WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This approach doesn't work due to a weird bug \"Jax transforms and Flax models cannot be mixed\": https://github.com/tensorflow/tensorflow/issues/56660\n",
        "\n",
        "# converter = tf.lite.TFLiteConverter.experimental_from_jax([generate_image_jitted], [[\n",
        "#   ('text_tokens', jnp.zeros((2, 64))),\n",
        "#   ('seed', jnp.zeros((1,))),\n",
        "# ]])\n",
        "# tflite_model = converter.convert()\n",
        "# with open('/content/dalle-mini.tflite', 'wb') as f:\n",
        "#   f.write(tflite_model)"
      ],
      "metadata": {
        "id": "yfH6UYUTtcQh"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Minimal dalle-mini inference and tflite conversion (produces zero-byte tflite model)",
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}