tts.ipynb

gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "713c9f6c-02da-466e-8d9f-f51437565eb2",
   "metadata": {
    "id": "713c9f6c-02da-466e-8d9f-f51437565eb2"
   },
   "source": [
    "# 00. Homework description\n",
    "\n",
    "For this assignment, your task is to develop the [FastPitch](https://arxiv.org/abs/2006.06873) synthesis model, train it, and generate several audio samples.\n",
    "\n",
    "The training and data processing code has already been provided for you. The training will be conducted on the LJspeech dataset.\n",
    "\n",
    "The total score for the homework is **10 points**, distributed as follows:\n",
    "- 1 point for visualizing the input data\n",
    "- 8 points for writing the model code and running training\n",
    "- 1 point for pitch and duration manipulations during inference\n",
    "\n",
    "The homework submission **should include**:\n",
    "- Completed notebook\n",
    "- Attached WER and loss graphs from TensorBoard\n",
    "- 1 audio file - the result of a regular model inference\n",
    "- 4 additional audio files: you are encouraged to experiment with adjusting phoneme durations and pitch slightly and listen to the results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ec30da-c017-4e76-9235-bde5f55cedc4",
   "metadata": {
    "id": "97ec30da-c017-4e76-9235-bde5f55cedc4"
   },
   "source": [
    "# 01. Preparation steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ebbf0771-c271-458f-8850-56149ae94767",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda\"\n",
    "gpu_avaiable = \"1\"    # Run nvidia-smi to find free GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "UPSEbEsA8z_5",
   "metadata": {
    "id": "UPSEbEsA8z_5"
   },
   "outputs": [],
   "source": [
    "path_to_sources = \"../\"   # Path to the the current week's source code, e.g. /home/user/speech_course/week_08_tts_am_vocoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b371ad4-695f-4da7-bc26-9d4936a6ec70",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5b371ad4-695f-4da7-bc26-9d4936a6ec70",
    "outputId": "768c54e0-6071-4fab-9ac6-e611d5415295"
   },
   "outputs": [],
   "source": [
    "# If running in colab\n",
    "\n",
    "# clone the repository:\n",
    "# git clone https://github.com/yandexdataschool/speech_course.git\n",
    "# !pip install -r speech_course/week_08_tts_am_vocoders/requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gibPkbI76I9y",
   "metadata": {
    "id": "gibPkbI76I9y"
   },
   "source": [
    "### Dataset\n",
    "\n",
    "We will work with [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) -- a single-speaker dataset with 24 hours of speech.\n",
    "\n",
    "The data we will use contains pre-computed [MFA-alignments](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/alignment.html) alongside with the original wavs and texts. If you are interested in the process of extracting such alignments, please refer to this [tutorial](https://colab.research.google.com/gist/NTT123/12264d15afad861cb897f7a20a01762e/mfa-ljspeech.ipynb).\n",
    "\n",
    "Download the dataset with precomputed alignments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "WCHQ6ev56vpI",
   "metadata": {
    "id": "WCHQ6ev56vpI"
   },
   "outputs": [],
   "source": [
    "# import requests\n",
    "# from urllib.parse import urlencode\n",
    "# from io import BytesIO\n",
    "# from zipfile import ZipFile\n",
    "\n",
    "# base_url = 'https://cloud-api.yandex.net/v1/disk/public/resources/download?'\n",
    "# public_key = 'https://disk.yandex.ru/d/PpgePfWcQTAbug'\n",
    "\n",
    "# final_url = base_url + urlencode(dict(public_key=public_key))\n",
    "# response = requests.get(final_url)\n",
    "# download_url = response.json()['href']\n",
    "# response = requests.get(download_url)\n",
    "\n",
    "path_to_dataset = 'data/ljspeech'    # Choose any appropriate local path\n",
    "\n",
    "# # If running in Colab:\n",
    "# # path_to_dataset = '/content/ljspeech_aligned'\n",
    "\n",
    "# zipfile = ZipFile(BytesIO(response.content))\n",
    "# zipfile.extractall(path=path_to_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13f0767a-cea0-4cce-b851-dec751c6a91c",
   "metadata": {
    "id": "13f0767a-cea0-4cce-b851-dec751c6a91c"
   },
   "source": [
    "### Hi-Fi GAN checkpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2811e21-c864-413e-9e9a-c53a7b651dbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-03T13:22:30.324480Z",
     "iopub.status.busy": "2024-03-03T13:22:30.323558Z",
     "iopub.status.idle": "2024-03-03T13:22:30.398663Z",
     "shell.execute_reply": "2024-03-03T13:22:30.397377Z",
     "shell.execute_reply.started": "2024-03-03T13:22:30.324430Z"
    },
    "id": "d2811e21-c864-413e-9e9a-c53a7b651dbf"
   },
   "source": [
    "\n",
    "Download a pretrained Hi-Fi GAN checkpoint (to generate audio from the predicted mel-spectrograms)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2306394e-8d09-437a-9eda-a7383749ba8f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2306394e-8d09-437a-9eda-a7383749ba8f",
    "outputId": "ac9e584b-25df-41d9-e342-0befeb568b6b",
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/dle/hifigan__pyt_ckpt_ds-ljs22khz/versions/21.08.0_amp/zip -O hifigan_ckpt.zip\n",
    "# !unzip hifigan_ckpt.zip\n",
    "# !rm hifigan_ckpt.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b6f6634-2eb0-491e-8f80-6b36122b1ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In colab:\n",
    "# !wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/dle/hifigan__pyt_ckpt_ds-ljs22khz/versions/21.08.0_amp/zip -O /content/hifigan_ckpt.zip\n",
    "# !unzip /content/hifigan_ckpt.zip\n",
    "# !rm /content/hifigan_ckpt.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "78d3a5e8-094a-409b-b1a3-b17960ea3f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_hfg_ckpt = \"hifigan_gen_checkpoint_6500.pt\" "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e6d3354-5dba-4515-8e52-89767be5d3d4",
   "metadata": {
    "id": "5e6d3354-5dba-4515-8e52-89767be5d3d4"
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4be695e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install 'numpy<2.0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b5208b7-46cd-47bc-b43d-fc0a3d7b990d",
   "metadata": {
    "id": "0b5208b7-46cd-47bc-b43d-fc0a3d7b990d",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import json\n",
    "import dataclasses\n",
    "import torch\n",
    "import subprocess as sp\n",
    "import matplotlib.pylab as plt\n",
    "import soundfile as sf\n",
    "\n",
    "from g2p_en import G2p\n",
    "import IPython.display as Ipd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3562828d-37c6-41a7-9780-5269f4c20c0a",
   "metadata": {
    "id": "3562828d-37c6-41a7-9780-5269f4c20c0a",
    "tags": []
   },
   "outputs": [],
   "source": [
    "sys.path.append(path_to_sources)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed4cee2-5589-443c-81d3-3bc51e55c3eb",
   "metadata": {
    "id": "3ed4cee2-5589-443c-81d3-3bc51e55c3eb"
   },
   "source": [
    "# 02. See a data sample (1 point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2d9b1ba7-124e-4c0b-a13f-92d31fbfa41d",
   "metadata": {
    "id": "2d9b1ba7-124e-4c0b-a13f-92d31fbfa41d",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sources.fastpitch.hparams import HParamsFastpitch\n",
    "from sources.fastpitch.data import prepare_loaders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "FqDAP7aA_E1H",
   "metadata": {
    "id": "FqDAP7aA_E1H"
   },
   "source": [
    "The mfa alignment provides phonemes and their durations, which we will need during training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "_WBHdedW_WRj",
   "metadata": {
    "id": "_WBHdedW_WRj"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'start': 0,\n",
       " 'end': 9.655062,\n",
       " 'tiers': {'words': {'type': 'interval',\n",
       "   'entries': [[0.0, 0.69, 'printing'],\n",
       "    [0.69, 0.82, '<eps>'],\n",
       "    [0.82, 1.01, 'in'],\n",
       "    [1.01, 1.15, 'the'],\n",
       "    [1.15, 1.45, 'only'],\n",
       "    [1.45, 1.97, 'sense'],\n",
       "    [1.97, 2.14, 'with'],\n",
       "    [2.14, 2.39, 'which'],\n",
       "    [2.39, 2.54, 'we'],\n",
       "    [2.54, 2.71, 'are'],\n",
       "    [2.71, 2.89, 'at'],\n",
       "    [2.89, 3.28, 'present'],\n",
       "    [3.28, 4.0, 'concerned'],\n",
       "    [4.0, 4.41, '<eps>'],\n",
       "    [4.41, 5.01, 'differs'],\n",
       "    [5.01, 5.04, '<eps>'],\n",
       "    [5.04, 5.28, 'from'],\n",
       "    [5.28, 5.65, 'most'],\n",
       "    [5.65, 5.82, 'if'],\n",
       "    [5.82, 6.1, 'not'],\n",
       "    [6.1, 6.29, 'from'],\n",
       "    [6.29, 6.63, 'all'],\n",
       "    [6.63, 6.78, 'the'],\n",
       "    [6.78, 7.09, 'arts'],\n",
       "    [7.09, 7.22, 'and'],\n",
       "    [7.22, 7.8, 'crafts'],\n",
       "    [7.8, 8.45, 'represented'],\n",
       "    [8.45, 8.66, 'in'],\n",
       "    [8.66, 8.79, 'the'],\n",
       "    [8.79, 9.6, 'exhibition'],\n",
       "    [9.6, 9.655062, '<eps>']]},\n",
       "  'phones': {'type': 'interval',\n",
       "   'entries': [[0.0, 0.04, 'P'],\n",
       "    [0.04, 0.07, 'R'],\n",
       "    [0.07, 0.18, 'IH1'],\n",
       "    [0.18, 0.21, 'N'],\n",
       "    [0.21, 0.28, 'T'],\n",
       "    [0.28, 0.46, 'IH0'],\n",
       "    [0.46, 0.69, 'NG'],\n",
       "    [0.69, 0.82, 'sil'],\n",
       "    [0.82, 0.94, 'IH1'],\n",
       "    [0.94, 1.01, 'N'],\n",
       "    [1.01, 1.03, 'DH'],\n",
       "    [1.03, 1.15, 'IY0'],\n",
       "    [1.15, 1.27, 'OW1'],\n",
       "    [1.27, 1.34, 'N'],\n",
       "    [1.34, 1.39, 'L'],\n",
       "    [1.39, 1.45, 'IY0'],\n",
       "    [1.45, 1.64, 'S'],\n",
       "    [1.64, 1.76, 'EH1'],\n",
       "    [1.76, 1.84, 'N'],\n",
       "    [1.84, 1.97, 'S'],\n",
       "    [1.97, 2.01, 'W'],\n",
       "    [2.01, 2.05, 'IH0'],\n",
       "    [2.05, 2.14, 'TH'],\n",
       "    [2.14, 2.19, 'W'],\n",
       "    [2.19, 2.26, 'IH1'],\n",
       "    [2.26, 2.39, 'CH'],\n",
       "    [2.39, 2.44, 'W'],\n",
       "    [2.44, 2.54, 'IY1'],\n",
       "    [2.54, 2.65, 'AA1'],\n",
       "    [2.65, 2.71, 'R'],\n",
       "    [2.71, 2.78, 'AE1'],\n",
       "    [2.78, 2.89, 'T'],\n",
       "    [2.89, 2.95, 'P'],\n",
       "    [2.95, 2.98, 'R'],\n",
       "    [2.98, 3.05, 'EH1'],\n",
       "    [3.05, 3.13, 'Z'],\n",
       "    [3.13, 3.17, 'AH0'],\n",
       "    [3.17, 3.2, 'N'],\n",
       "    [3.2, 3.28, 'T'],\n",
       "    [3.28, 3.34, 'K'],\n",
       "    [3.34, 3.37, 'AH0'],\n",
       "    [3.37, 3.43, 'N'],\n",
       "    [3.43, 3.57, 'S'],\n",
       "    [3.57, 3.82, 'ER1'],\n",
       "    [3.82, 3.89, 'N'],\n",
       "    [3.89, 4.0, 'D'],\n",
       "    [4.0, 4.41, 'sil'],\n",
       "    [4.41, 4.47, 'D'],\n",
       "    [4.47, 4.55, 'IH1'],\n",
       "    [4.55, 4.64, 'F'],\n",
       "    [4.64, 4.8, 'ER0'],\n",
       "    [4.8, 5.01, 'Z'],\n",
       "    [5.01, 5.04, 'sil'],\n",
       "    [5.04, 5.11, 'F'],\n",
       "    [5.11, 5.14, 'R'],\n",
       "    [5.14, 5.17, 'AH1'],\n",
       "    [5.17, 5.28, 'M'],\n",
       "    [5.28, 5.32, 'M'],\n",
       "    [5.32, 5.49, 'OW1'],\n",
       "    [5.49, 5.59, 'S'],\n",
       "    [5.59, 5.65, 'T'],\n",
       "    [5.65, 5.7, 'IH0'],\n",
       "    [5.7, 5.82, 'F'],\n",
       "    [5.82, 5.88, 'N'],\n",
       "    [5.88, 5.99, 'AA1'],\n",
       "    [5.99, 6.1, 'T'],\n",
       "    [6.1, 6.16, 'F'],\n",
       "    [6.16, 6.19, 'R'],\n",
       "    [6.19, 6.22, 'AH1'],\n",
       "    [6.22, 6.29, 'M'],\n",
       "    [6.29, 6.59, 'AO1'],\n",
       "    [6.59, 6.63, 'L'],\n",
       "    [6.63, 6.69, 'DH'],\n",
       "    [6.69, 6.78, 'IY0'],\n",
       "    [6.78, 6.87, 'AA1'],\n",
       "    [6.87, 6.94, 'R'],\n",
       "    [6.94, 7.02, 'T'],\n",
       "    [7.02, 7.09, 'S'],\n",
       "    [7.09, 7.13, 'AH0'],\n",
       "    [7.13, 7.17, 'N'],\n",
       "    [7.17, 7.22, 'D'],\n",
       "    [7.22, 7.32, 'K'],\n",
       "    [7.32, 7.36, 'R'],\n",
       "    [7.36, 7.51, 'AE1'],\n",
       "    [7.51, 7.63, 'F'],\n",
       "    [7.63, 7.68, 'T'],\n",
       "    [7.68, 7.8, 'S'],\n",
       "    [7.8, 7.88, 'R'],\n",
       "    [7.88, 7.93, 'EH2'],\n",
       "    [7.93, 8.02, 'P'],\n",
       "    [8.02, 8.05, 'R'],\n",
       "    [8.05, 8.09, 'IH0'],\n",
       "    [8.09, 8.16, 'Z'],\n",
       "    [8.16, 8.24, 'EH1'],\n",
       "    [8.24, 8.27, 'N'],\n",
       "    [8.27, 8.34, 'T'],\n",
       "    [8.34, 8.39, 'IH0'],\n",
       "    [8.39, 8.45, 'D'],\n",
       "    [8.45, 8.61, 'IH0'],\n",
       "    [8.61, 8.66, 'N'],\n",
       "    [8.66, 8.68, 'DH'],\n",
       "    [8.68, 8.79, 'IY0'],\n",
       "    [8.79, 8.85, 'EH2'],\n",
       "    [8.85, 8.94, 'K'],\n",
       "    [8.94, 9.03, 'S'],\n",
       "    [9.03, 9.07, 'AH0'],\n",
       "    [9.07, 9.15, 'B'],\n",
       "    [9.15, 9.23, 'IH1'],\n",
       "    [9.23, 9.36, 'SH'],\n",
       "    [9.36, 9.43, 'AH0'],\n",
       "    [9.43, 9.6, 'N'],\n",
       "    [9.6, 9.655062, 'sil']]}}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(os.path.join(path_to_dataset, 'mfa_aligned', 'LJ001-0001.json')) as f:\n",
    "  utterance = json.load(f)\n",
    "\n",
    "utterance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "YhRhYzEMANqN",
   "metadata": {
    "id": "YhRhYzEMANqN"
   },
   "source": [
    "Phoneme `sil` here denotes pause -- a period of silence between spoken phonemes. The phonemes are from [ARPA](https://en.wikipedia.org/wiki/ARPABET) alphabet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ae6c396-7b8b-4e4c-99cb-d63bb606c580",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9ae6c396-7b8b-4e4c-99cb-d63bb606c580",
    "outputId": "f0086f71-e688-4715-8497-b4265fbf69d0",
    "tags": []
   },
   "outputs": [],
   "source": [
    "hparams = HParamsFastpitch()\n",
    "train_loader, val_loader = prepare_loaders(path_to_dataset, hparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "u31B2Ofw9Zl4",
   "metadata": {
    "id": "u31B2Ofw9Zl4"
   },
   "outputs": [],
   "source": [
    "train_iter = iter(train_loader)\n",
    "batch = next(train_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "K5Jb_3Aw9tRZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K5Jb_3Aw9tRZ",
    "outputId": "d766ec17-2980-418f-e38b-1ee28817f18b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['texts',\n",
       " 'text_lengths',\n",
       " 'mels',\n",
       " 'mel_lengths',\n",
       " 'pitches',\n",
       " 'durations',\n",
       " 'paces']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dataclasses.asdict(batch).keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "qu_9scr2-0N4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qu_9scr2-0N4",
    "outputId": "de84900a-b7e7-4fec-e2ba-2337b1c7d320"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 80, 869])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch.mels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f4c54d1b-68e4-451d-b42b-87cf71df9a87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 107])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch.pitches.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f82c3a76-bb00-48d1-8e2b-b18514baf83d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 107])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch.durations.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71fd436-c5f2-4df4-ac89-63b7586613b7",
   "metadata": {},
   "source": [
    "## Task\n",
    "\n",
    "**(0.5 points)** Draw a combined image showing both the mel-spectrogram and pitch for a sample from the batch. Use durations to ensure proper alignment of their shapes in the image.  \n",
    "**(0.5 points)** Include phoneme labels near the time axis on the image from the previous step. (like in Figure 3 in the [paper](https://arxiv.org/pdf/2006.06873.pdf)). You may find the code from the seminar helpful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4326347e-bf71-4f1a-af60-d5d2cbba195a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# <YOUR CODE HERE>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cded88b9-abb6-4b3a-bfec-1f31d8fbec5e",
   "metadata": {
    "id": "cded88b9-abb6-4b3a-bfec-1f31d8fbec5e"
   },
   "source": [
    "# 03. Implement FastPitch model (9 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c224ecd-1529-4c14-b7e4-d9bb2d63f2e4",
   "metadata": {},
   "source": [
    "Please implement the FastPitch model in the cell provided below. Running this cell will overwrite the model file in the repository. \n",
    "- Run training (see next cells)\n",
    "- Run inference (see next cells)\n",
    "- When submitting the homework, please include\n",
    "   - the Word Error Rate (WER) and loss curves obtained from TensorBoard as attachments,\n",
    "   - the generated audio (see inference cells). If attaching an archive, use the name: `prediction.wav`\n",
    "\n",
    "By the end of training, the loss should reach approximately 0.69, and WER should be close to zero (around 0.006). The training process will take about 30 minutes (3,000 batches).\n",
    "\n",
    "**Important:** By the end of the training, the generated audio sample must be clear in terms of speech (WER close to 0) and maintain audio quality comparable to the original recordings (as logged in TensorBoard). **If these conditions are not met, the entire section will receive zero points**, regardless of how closely the model code resembles working code. Here is an example of how an audio from a well-trained model may sound:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f7764e4-b02f-4097-97e6-b5e7835985c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,UklGRiT0BgBXQVZFZm10IBAAAAABAAEAIlYAAESsAAACABAAZGF0YQD0BgAJABMAEAAOAAUACgAKAAoACQABAAAAAQD5//b//f/7//n/+//2//v/+//9//n/+f/2//3/9//9//v//f8DAP//AAABAP//BwAAAAcAAwABAAcACQAJAAcAEAAKAAwAEwAQABMAEgAQAAwAEwAQABAADAADAAUA///3//D/8P/t/+X/6f/l/+X/4//l/+X/4v/n/+n/7v/2//v//f8AAAkACQAKABMAEwASABIAEwASAAkACgAJAAUABQD/////AAABAAMAAwD9//3/AAD9/wEACgADAAkADgAKAA4ACgAKAAMA//////3//f8AAP//AAAAAPv/AQD///T/8P/y//L/8v/u/+n/6f/e/97/4P/l//L/7v/0//n/+f/9//3/+f///wEA/f8BAP//AQD///3/AwAHAAwACQAOAAkACQASAAkAAwAOAA4ACgAKAA4ABwAHAAoAAwADAPf/+f///////f/7//f/8P/5//T/8v/w/+v/8v/0//L/8P/0//v/+//9//v/+/8BAAEAAwAHAAcAEAAKAAUAAwAFAAUAAwADAP3/+////wAA/f/9//v////5//f///8AAAUAAAAAAP//AwAAAPn//f/3/+7/8P/y//T/8P/2//T/6//0//b/9P/7/wEA//8DAAEAAwAJAAAABQAFAAUABQD///n/BwABAP3/9//0/+7/7v/3/+X/7v/u/+v/8v/y//L/7v/2////AQD9//v/AQD//wMABQADAP3/AQABAP//+/8AAAMA/f/5//T/9//5//v/9//9//b/AwAJAAUAAwAFAA4ACgAHAAcADAASABUAEAATABkAGQAHAP3/AQDy//D/9P/l/+L/4P/g/9X/2v/V/9P/2v/c/+D/1//j/+P/5f/i/+L/4//g/+n/7v/u/+3/8P/2//f/9P/5//f/+/8BAAAAAwAHAAUAEgASAAkADAAOABMAGwAkAB4AJAAgACAAKwAmACYAIAAmACAAJgAyADYAQQBBAD8AMgA9ADoALwApAB4AHgAZAAcA9P/w/9f/xv/I/8j/yP+0/63/pP+M/4f/gP9x/3f/df9k/2T/Vv9U/1j/S/9S/1T/Uv9U/03/T/9W/1//av9m/37/gv+L/5//l/+b/47/mf+V/6T/qP+t/9P/6/8BABMAIgAtAEoAbgCGAJ8AswDWAPgAEwEwAVEBcgGWAbMBzgHpAQgCKQI5Ak0CYQKFApIClwKoAqgCsQKtAqECjQJ1AlgCNgIXAvQBwwGSAVYBDAHMAIQAQwDy/6D/VP/7/p/+T/7//ab9SP3x/KP8SPz2+7D7dPsv+/j6vvqN+nD6Vfo++jX6L/or+kr6U/p7+rv67Poo+2X7svsb/HT80Pw7/Zr9Dv51/uf+Uv+//zYAnAATAYkB9gF1Au0CagPtA3ME6wRvBfIFbwbwBm8H1gdBCKgI/wg/CYAJrAm8CcMJvgmoCYAJRAnvCHsI9QdlB8IGCQZWBZMEvQPiAgECLQFXAHr/m/6+/eb8E/xQ+4j63/lJ+bj4KvjB90f37va09oL2WvY+9jD2FfYn9jD2T/Z39qD23PYP90/3kvfh9yr4iPju+EH5l/n5+Vz6xPoo+5j7EPyN/P/8bv3e/VP+2f51/yIA1wCSAVMCGgPuA8QEkwVpBkEHAwjLCIkJRgr2CokL+QtaDLYM4wz7DO4MvQxxDBMMqAsTC34KywntCBIILwdBBnEFkASpA9wCDwJTAaEAAQCA//3+bv76/Yn9K/3k/Iv8Pfzs+4v7OPvu+qX6cvoq+uf5sPlp+TX59/i4+Gj4MPjr95f3ZPcG97/2b/Yq9vj10PW19YT1bPVz9Xn1mfW89eD1MPar9i73uPdC+Of4svmV+m37O/wg/SX+RP9nAJkB2QITBFAFegaeB84I/QkjCzMMFg3aDZkOUg/WD0cQjBCpELcQkRA+EMYPNQ+XDtcN7AzwC+IK1AnECJgHbQZUBU0EKgP9AQEBKwBq/87+J/6j/QX9jfwn/ML7i/tG+/r6svpy+ib68fmw+W/5L/nM+Gr4GPjP96f3Y/cL98j2avYX9sn1gvVY9Tb1D/Xe9L70wfTA9OT0AfUj9VH1rfUY9n72Avek91L4CfmZ+T76Dvvs+878wf3S/vb/SgGHAs4DBgVDBp4H1ggKCksLlAy5DbcOgw9BEPwQnREVEjgSKxIHErURSBG8ECQQUg9UDgsNpgs2CgcJrAdABhEFuAOCAlMBLQAe/1H+mP0F/XT82/t2+xn7Avvs+uP6z/qu+pb6dPpg+mL6PPoC+tT5f/kd+an4Ofje91/31/ZJ9sP1MPW89Fz06/OA8ybz3/KT8lDyNPIm8lnynvLf8jTzpvM59PL0v/WZ9pn3jviB+ar6A/yP/Rj/zgCZAhEEpwVSB/EIrwoyDKQN9w40EE4RVRI5E+YThxS4FMcUlhQ1FO0TUROhEpMRfRBOD+UNdQzpCmkJuwdQBqcE/wLFAZoAnf+t/tn9//xl/Nj7eftn+yb7EPsM+wP7Gfv6+h37KPv6+vX6oPpb+ij60/mZ+Tj5zPhL+MT3Sfei9hz2m/Uc9br0T/T683DzOvMA88fyf/JI8jbyC/JO8oHypfLs8h3zgvP485L0TfU+9oX33/iY+hP82f3i/7wBFAQCBh8IPQp1DLAOghBqEgYUgBXjFrQXWRi1GL4Y+hiaGAcYERfyFa8UIhN2EVUPkA16C4sJqgelBQsEUQIgAcj/XP5q/aj8/vtT+0H78/oH+0b7Svuw+7P77/sT/A78K/z1+xL82fuW+0P7u/pX+rb5B/kA+Ar3RvZE9Xv0kfPA8urxIfGg8LjvAu+Z7kTuIe4K7gjuMO6n7vvuqe+T8FXxffKh86/06fU399z4EvtL/Q3/EwEBAwAFiAduCSILzAzuDgMRjRLOE6oU8BX1Fn8XhRfwFhQXSxe7FtcVCxV5FIIT8RKwEJkOvw2oDNELCAoiCKMGegXyA8UBtwE+A1oDiQHb/W37i/tV/Iv7Avng9533bPet9mz1FfXr9U32hvQr8q7wXvGo8sLx/e707dzvcvKs8iXv5OsS61Hrhun25cDj4OSZ5yTo2OYq5tznbOrb61HsROyO7jn1cP6HCpcVOx77IPsdXBwaHggk9SolMHIyPjIEMgswIi3NKr8loh51FrcOowpKCXAHIwRh/1D6IvX/7mboHORR5DzlFOYA5pnm7+rK7/7zIPWC9ef4afy0/4MDxweXDTsSRhLRDvYLiQzwDjEPygz1CCwGlQWMBK8CX/8O/Cz4+vIo7jjriulZ6D7m+OPk4T/iRuGg3SrcGdqf27/dBd8N3nbest+H49nlBeqJ68DrLuy99hEWjS4APPYtexjMG4QtrD27Qis+0UJSSaVFTjq8LvIu/CxjHb8F1/Yb+kkDlwXd+mTpMOIj3QDYg9Mc0TvZIuGf4mXilORo8J39HAKsAQMApQULDTIUYxsoHrkgIR9MG9MWARLNDsMLXAkdBfj7xvei9234LvaE6SzeINrQ2+zeQNvL2NHXEtj8123WsNW82T/ZINnI1VzaHuEA5h3oZeoE8MLyf/B98eYS9jh7VWREVSMlFjgyP1BCVCBHfTyLSK5OHUOWLf0kiiIvG/b/5ei843bxcfmC9CfjGtOXzv/QrtW72IvbxOAZ6ATuCvRv+hsF0Q+KFY4VmBMwFkYd3SPuJmIi/Rv8F0AUtQ8fCLMA/PvG9iXvjueJ5KfnUOgO4kjX4s9a0J7U1Ne01vDWDdfH2H7bt96K4MLhG+Hy4wvmsesP8Hr2h/YX9tbxO/xfKFRJVFyDQxYiGCFSOt1PdVEoQkA7vj9zPxQ0aiRIGiEP1AAS6x7cvddH3srkvOl/4IrVVc46zdTXTOCQ5Grsr/Xw/0YJmg+7GL0hFiibJY4gKh7NIMYoByqNIs0X4gvGBrQDofy/9jnvZOcD4bPaTdqc3cbe5NmL0x7RIdNw2O7c8N4A4GTfXt8L4eji++bf6C7sMOyd67zr2/GR8+X3We4iAe0tQ0v5XSo8uRqeH2Q410ccSGA6yzQ/PME54yphHiwNF/587ifcV9X20vDWEN8d6DnkNNto1GzT9tz86artePcRBBENvhoDImUmYio1KVMlsCLwILchEyTWIrgbUxLxBir/PPrz8oft6eUk4ArcJdxK4MvisN6c1v7PidFs2RvfLeL/4uLjS+Qv5yboK+v07CbxQPCy8bbuP/ID9mj5VvYs+ZcfD0IZX39OOihTHJIssz2XQ4E4DC9DNK0xhyTjGOwMd/4L8HzbptDszPPOq9WU5Vzs+ujJ4ZbbOeKw74L0xPuMByYQ4hpLIJ8kPiqaKWcjyB3vGmsYRxYpFJQRgw8tB0T8m/SS7M/m9+CJ3K3dnt9K49nlN+M13h/XktSu2CPfs+M859jpbuzi6vPqMuqz7cDxpfJi8hP2mPSN8W3uueqhEG04jF6dY4I9ySLRIeEx4UJ8P50wKy0kLPkngSBzFr8Gpfhl5JnS8cgJxOrHtdln6YPvt+qh4aPipO5s9wn8vwWKDS8YfyInJdgpfStxJeEfCRpLFG4Q3AxvDDUL7wfp/wb2MO8G573gNdwB2RHbldxT38DgHNtg1uXTmNep37Pi/OJc4srltehe6mXrAPA+8Tj0xPKk9ef2KvYi8cX1PyHBSX1nMFt2MaUg/yycPKhH2zygL7gurimGIIEXaAwvAH70JuLV0uTG9sFTyIDccOvP7pTq3uUb6x/33fxCA6MKGxL6GxghIiWHKJkouCb6IYIbYxRDDm4LggkmBwMBnvjh8fHrZ+i142DecNu716fW89bj0x7SbtCz0dLY1d7Q4FXfiN8P36fh0+Mv6UTt0PIZ9Qn6mfne9lPr/u/UJD1Q0nNYZU80Tiv3OUVEN0xQOTEo2SyyJy4fGhwWEPQA+vO83L3MCsIeuZfBtdqR61DyfO7j6UjyJgDRBOsKNxThFhMdFiK1JD0tCTFpKSgitRsiEQYNbAk7AgoAOvq98dHuUeqF55nlNuGj2qXWBtVN05vTd9Fa0CbW/dzR3fDdyN1R2zPfXOG/5ZXpPu4J8av41Pv2+7Dvk/GzJKNMmHRaai02dCzLNHY9s04MPn8rQC7SHJYShBH9AQX60e1S16XOj8SXupvB3daV5xTzTPLl7XP2OgX3DVcZAyS5ItsixSKlIVAopSu8JXQgvBmQDIMEUQCM+aD4JfTA6nbocOJb3r7g7tyE2SfUss/F0lLVPNeo2QLei+SF5PLj4+C53zjgaOYO6qbtFu978+H6aP75+LfqThELQ/VvwHnOSvAuCjWYPURI9kD4LNIs4CNNFQcQOwPz8wHqjdguywG+OrNUuP3SW+53+2f+O/2UAckJeRFLHNgnQi3mKr4ltyTZJrUlvx2bFC4K+v2f9tvxpusP6Kbjxt/W4Urgv92C4B3gaN3I1kDTqdYN3eTi7eRI6kjt0ejH5Lzjr+ad7GbuF+we7ibyb/aQ9+D3gu0r+6Aq+FoMfl9rgj0wLdAzAT8sROMzTCrEJzEaPg8HCLf8M/GK3iLJlby7s0OyRsIW4Af53QaAC2QM7RAAGf0euSjNMtkufChYJ6QnoSocJU8VmgdN/aXxiui54AzbINv/2yHe1d30253a1tZ30sTNKM2x0aba5uM26U/uEPAs8XHuSOxp6WXrbe7C8Uj0KPmm/KX8TvJj62INxDlNauh4oVbbPRY48DIQPDc2JSQ2JZUY5wrQCrz/zPVx74faw8uuv4uwqrbqzIbi0vsYCzoQlxiiHgAhQSpZMCAtGSrUJCYg/iDJIToaWw/pAVnxo+q36PbiMuEJ32vdXd012z3bktxu3HTXltJB0CfTF9fH2KveyeKe5ZjqX+yn7hTyou9Q7p7y0fiN/OT+Kvnb++kem0lVcCZ4LVbnOMUtcShAMKQt/STcJikbbw2NB6P6Ee634eLPy8XDv7O4ab8o1m3vXgZbFRwc7iEuJ8onxSyZMekt6ClZImEexiCqHFEUeQlo+N/p0OIs3C/YwNht2Mvel+j86cnoneWr3e/XGtNJ0j3ZiN5a4xLoxevp7vbstucJ5xjodOlq63LxJvqS/3n+3vjYD/s61Ga1fHRmnEQgNDIt1S3RKQ4cixnMFr4LfAJH9prnz9yGzRbCO8CRvdC/K9Iw7XQIrxxkIocktimyKlUpaSu6LeAsTCqmI10doBirDIL8dvBH5n/fyttn1oHXTt6H5FzrtO1m6Arin9nw08PR69Tb227iAOo07abuiO+f7EfoH+fL54jpRu4L95f+1QPR/zkBSSAnSGtpRGyJUDk/CzvBNuUwtB4zDyILfwQX/lP6tPHr5ejZGs2ox6nEqsDLxv3bxvZmD10fBicPL5o0MTSlMR4vsijkI0sgtRx1GwEU+Qaj+vDvXefv4rrdUNjO2MzZqd9o5XLiWN1N2UDWy9bh14LY2dvz3wPi1+M55Y7mOeaR6kPzt/ne/cX/1wKBBxQNhAinF0g93F4edENffDryLR4tdjCBL+AbXQ7oB5f5zvTA8cfpN+Ny1kvMYcrkyJrIg9Me66sFoRj9HvMi9yiFL/Aw+y1IKwUl5R4LGAUTPRJ3Cmr/UfZx7SrpH+aP317fE+Nb5UbqSejS4YLb7NXO0k3SG9GTz5TRMdYi2/XfSuP/4zPm2Ofv6ujxbPxZBKQKjgsjDxAr+0wOa4hwzVbRQcY8hDtNOSopHBP8CYf/d/TC7znrwefz4vHX79DFz+HLesyW2PTuGwkAGjMglCOTKP8twS+lK+sjnRvdEjgPNQ49CoUDk/rw8BfryOhb5nPnpOuB7rLwPe0Y5WjePdkv2FraE93I3oreAdv51rTUf9SI1V/bJeR37Hn1d/zbAjcGXAeiAjAS/zXKWl12fm6gVBdF3DuWNfkwXCEKEzQKcP2X90T2Qu5F5rbcttN30LjIjsHsxePW+PI2DyYhNCtIKuYiDx0xF7QWvxeDFE0VmxVlEqEQMgr7Aiv++vMH6uDiCN0k39rmZu0f8QTte+Ib2Y3R5c1XzUrPRtHQ06zXmduY4CflDutd8Of3yfuW/vv++gBRABoVQTx7YP9/gHVAVZ1GSjsyNRA2BiBBEUcMxfun+Cr5hvCi7PXgndNz01/L9cLxxc/PvOqTBi4UDSCjJNkjFSUWIPoZDxTrCTYHiAk5DTwQLgrjBLUALvhn8/XuDOsB7bXqoOW447HcQ9UI0T/OlM+yzxHMZcf1xfTIRs+j1prgferh8pH8SwW/C8IPDRDGDe8ijEEyYIB2qmxCXWRVo0duPpYzHB0BE88HzfpP/Y/7Y/SF7Y/fRtnj1/fNBclozBfa/PGQBF4SrxzxG+MXLhTyFIYZnhXnC7gDUwKaBkMHuAMkABX97/r7+Lj1ofPc7xfsXum05XThCt3411/VHtSPzk/JCcWtxHzJbtH62mjld+sC7gnv7fMA+8MABQKlB/klI0pxa4l0mGFqVKFRO092TdQ+cSluHdgOiQQ2Aoj7qvQ57bHi3N0d1t3IbsCywh/UhOyi/+sLPxEREx0YJBppGlcXmg6CCscIKws8DuUOnA+KDlgKxgVG/ZTzf++G68nro+sP5nTfptl/1B7THtGGzeDIWcNPwurG38552JHizekJ8Dv22vmC+6r9Y/1YDLosT0qdYhVkqFjZWB9VektxP4oqAh7/G94TuBKeD3cCcfgO6rffvtu10tzJqMnd0DzgPO/w+eUGvw+3F8IZ+RSqEC0JDQSeBCoHIwwQD8ELHAqgB7sECAS6/SX4n/YX8unvi+vt5Ebhz93Z2UHWjdKczcLM8c5h1EHaLt5p4PzhBuS25WXtUfWr/scBtgRBGugx4km3V9hNGkv+TbRCDECoNSQhJR6lEBgG8w4uC6UJAQkM/Gv4au8L3TjWs9EH1rHlHO44+9wF7AY3DMYN2g/+EWcKHAS9/7P7sf7b/vb//gbvCIkLgwwqBuAAbfri84/xK+035xDiLt6m3QrdNNrY13TV1dSQ1dPUHdWq2JncJOHm6DrwZPeh/iACGAbVFHAlJjUzQ4RFKUhyTCxJjEQ4OgYoCByCEbQLcw6QC/oHgQM6+y72pO4w473dwdu13z3qufHl+KT+ugHcBqsN2xNtFqMSDAyXBPb+8v6k/9IBQgW4BVsGXAcxBf8Bfv1d+Mf0tu5452Xhad7A3zTi7+IO4kzgS96o3NLapNpW3DbfbuIk4qHhIuNx5d/p/u8Z+iwLIR7DLZ45UkBLSMtNhE14SyFD+TfPK5sdLBTYECgOugwKCUAC1vsy86/rR+aA40jiMuGn4D3hZeS56tf0DgEoDQ8UEhcEF3cUZBCHCoEEXAB0ANUB6AXZC+4O6A7rC8EEKPuM8bTlYd1q2uLZLNyE32niguTe5kvl4ON24Xrb5NdV1t/W69oS4onq1PTL/GoBLwYkD+gbKicHMjU4xjxAQrZC60D3OosuDiJFGN0Qgw4vCccAT/wY+cz4ePhC9M/wC+6G6uPpeOnf6bXsdPCO+HwDtQoADgkPnA29DHcLSAgxBbEDLgHiAJsErAdqCvoJNwYcAuT9oPjt8+fwWu3v6ffmouWb5WHmW+de6KvoBucP5Azh+OFa5EXnXOzn77nzKvcu+PX7XgH+By8SEhuJI5MqeCmHJ2UlGSGuIYceiBfXE54NggegBlAFdgUNBx4EQgGt/mf7vfiU9bLyz/EL8vjzw/Zb+Uj8pv7yAhQIHguHDIIJKAUqBTcFgwZeCMIHTAiSCYYHCwWvAUv+1PwA+7/5r/e+9MjxW/CX7xvwYu8E7bXrOOq36pHqEunU6LDqWO0L8jD2Dfnr/bMAEQQkB2MIGwm8CkANuw9REnATrBPOES0QWQ21CrcJQwgqBxsFOQPVAiMCggAgADD/fQDnAtUBgAIDAY3+Qv+H/3UCowYFB+MG0gg7CTcMsgz6Cf8IeAYTBDQCzv4I/cP9NPw9/ST9L/xN/hP9Q/uR+hj3zvSu9O3yDvMS82TwGu8L7m7t/+357Yzuf/Fd9Wj5T/34/oIA8QBMAEYBcQJ+AxsF7gUFBxsJ4Ak2CcsHfAQ6AGz+JPxH+mv8n/6dAMwDLwbSBwcJUgfnBKgDUQPwBPwGcAkYDLYMVAvrCvIKswurDG4LmQoZChQIqwXGBAYDqgGPAcUAtQGLARH/OPxU+dP4evmt+BH3hPXb8/r0P/VM8yDy3O8G7lvtLO9p8/X4y/zH/WH+2/59AG4BZP91/6wBcwQ6COQHwQV/BucFDQTDArj9Ifoa+Q/1VfQ09Z307fZ/+dj75f+BA6kGFQr9CnQJMQeMBssI1QpxDZ4NKg7lD5MPNQ9UDioO9w3eDPYI4AS6AQP/Y/6D/9f/OAHDATsBrgC2/hX8Ifqn+ef53frj+bH3J/Q/8z/yyfNY9LH0svfT9xv5j/vL/JT8Of4R/xQEaQkkBgQEqgH9AJ0EpgMKAA3/Sv2g+rn62vib9nP1//Tv83Tzs/ad94P6rP2o/v8AJQJ3AS4Bxf8vAPADhwWXBjoHYAdlCTQLJQr6CncLQQqWCXkH7gbhBlsGEgUhBCcDNwQEBdcEBgQMA08CKQGbA/oBcP6m/Hv5bfma+575dPvt/BD6Yf0Q/fz5Q/zN+V344vxG//oAvgI/ArMAfgJaAPL9T/+k/cf8/f0q+uP3Uvh59a/1QvhB+Vn6HfrW+af5CflM+i/61Pvk/kwAzgFvA4ME9AToBcsFGwXuBTwFUgUZB4IHXgf+B04HdAbBBR8FygStBDMEWwNHBKcFCQfEBuoHEgbcBGoFfAKNAjID3QFsAbUAEAAcAYIAhf5z/d/53Pnb+hn6wPxw+4D9df+Q/18BY/9Y/dv6yfnL+S/62vkU+gD7B/vA+gj4/fVG9o74p/gb/Bf9JPuL/PD5BPpw+s36A/2AAZIBSQQbBZkDtgUsA/QDbgNiBMoDTwL7AagBTQLtAsYEqQRgBXwFeAWkBe4DLgRbBC0Grge1B4IIAwcNBoMFsASQBMEDOQMNBIEDWAOxAq8AAQF+ABsAWgCY/Mf8xvqu+gD8Tvua/Ln69/lT+ur5Kvln+iH5RfjE+mr3RfpK+5767f1V/Rv9Lv7D/p3+OwB1/nQARv9v/8z+mf5N/wH/J/9jAI8A0/+d/wz+HgCG/fgAmwHcAlYE/QDtAiwE2gThBHYF0QT1BCMFIwWXBH0GewYbBsYGCwTPBJIEQgMcAlgDpAERAq8Awf5x/mn73/w0+9D9ufxh/Yj78ve5+av2p/vx+fz6KPtF+bX80vvu/0n/FQFU/2H8ef6O/nP+fv1l/mH9twEGAuACvAIh/8MA1vyW/jf/NP/u/ycB7f9Y/8j/YPyU/eL9nfz2+6P9ugDYBDUFoAb3B+4FeAakBUAGoAUACPUHrQr0Cc0IRApIBy0JugSSAqz9Cv2y+xL58frC+vP7r/yq+0T9OPuT+lf70/Y+98z2UfSz9bj2IfeU/Gb6TABo/5n+nwJ4/xoFxQM2AR4C4P5GAl0CbAMYBH7//QGt/Tv9E/yc+jD88Plk+WP2m/jC+nv8UQBXANr/0QS/BpgGGQfeBKQF4wOeBbQEDwXpCZIJYQvIDX4Nvw5fDXQJmAl7CKMGdQTOAzcCNgDp/yX9wPp4+T759/m0+fP7rfhU95L3LvUM9VX0NPN78Z301fRL9Tj0TPkR/eD9kwQKBDwGggeyBxUJggcxBy4E9AMjBfQCef5G/Y/8Of0K/tb4//T28t/xe/H99AL3Tvs9/Yn8wf15/gMAIAEaBE4HJQqCC4MMmQs8DsgOgw3IDU8LlgmACbMKRAqsCUQJ3wUWBQcGPgRJBL4Brf3t/Xz9APxj/8L7d/0M/F35WfsN+Gz27/GQ9DjzhfVA+O74X/SQ9oj5BPlrALf77f2x/u8A7gUfBK8DGQApA+3+DADp/pP3hPon9+v3Sfgw9vD1Fvfn9U74dfeV+Nb78/uYALEA3gFCBbQGmAdfC7YLog1vD10O6g6GDjkN5AvHC7oKuglcCp8LOwv9ClwJhQbfB44GcQYzBSADDQM7APv+nPxe+nz4xvmg9yL1afOi8FXw2PDz8gP1mfmI+pL/3v1OAFgEXgFZBkQCXwEABJgBkv9J/w/+K/zq+6D3I/au9JbzYvKR8BDxb/Cc8vT0gPUa9yr3r/Wn+Kf6pP5bAz4GbAs7EYUWPRyFH/Id7xzoFy4U1hBADngOiQ0xDcEMcgodCFsFpQBu/2n9F/zm+4/59fi992z3l/gU+fH65fog/KT9O/yv/dv8JPq5+1X5xvdF+fz5Af4O/n0A+/5P/aH+Y//B//T8gPUO9Ebz3/DZ9NPvIfFp9JL19PeT+GH0IvKn8v7wOPQF9KHyMfJw8hr1+feU/IYArAbvC+EQnheaGngeiCB0ISYiCSN2IRQfBhxOGOUUoxDaDXIKkQaFAtT9GfqF95b1MPQA85PyhvBd8LvwyfLR9kf5Avt5/S0ALwBwAksCugDKA78D+QIGA0YDlQTqBYYGcwIu/kj6CPiL9JXy0fB98A33yvbc9zf4IPNs82nwauzy6/Xq3+mf7K3uFPDN87Dw2vFO8cjvNPMH9OX3NAAFCpwP4RnyHxAkWSv1K98r1inXJYYhMB7DG7kYxhYpE9gPxA2SCWIEVv7c+Gf1afPU8Ljvtu6B7l3wU/In9Uf2ivfM+AP77fzF/oL/l/9LAksBTQOVB20HGQq7CMECcAI5/+r6Cv2V+jr4ufjf8zjxNvK0767wC/Hu7Sfs6ugs6KPoO+tM6vvn4ehw6Knoiunv6/bsm+5A7hbySvsQBhkTzRnQIxMu6DVRPIQ7LDhiNIEvhCrKJ9siRh76GGwSjA+kC9MFJf+49+PwK+0t6ETjd+Em4LXigeX05n/pt+sw7wX10vp3/38E9wfMCSEMewpTCXsIVwYqB+4HPQgcCnMKdwhBCDUGQgOo/+H5i/R67uTpR+WU4Sjgyd+q4XrkVOaZ5sHkvuJl4gPiJOL64onkjub16N/qauw489f/vAyAHo8sDTNoPwBEy0PJRi5BTjvgODcxYyzEK40lJh/jFmQNpwbI/s721exC5HPe0Nm913DXK9f31uTXhth43CnhbeWF7Lfz1PvIAyYJWA1eEwcYfBwHIUUh4yFvIRgeFRulFi8QDAu1BrgBP/zF9a/ttOU64P/amtdg1pPVYtXl1YPW5tcB2/Lcb98n4rTkBOhe6n7sMu6R8NLxYPKI+PcE/RIGJQw0rj14S9BSDVXfVQdO1ERtO0Iv3CauIPUY9BLDDJwHmwSF/sH0G+kd3irV2868ygnHW8VXxuvI4c7Y1yDg5edO8Mj3MAD2CEkP9BaQHggkkCh0Kt0qiiv5KXgn6iPrHDoWQA7fBUEAafk88vrrDOWO3vjYHNO3zs/MkcxpzsfRgdX92Yze4eHk5Drn6ugV6hfqY+uE7DHvpvS9+PYBhBDuHl0wYj1eRJFMFVBwT3BNOEZWPj83Qi+7KaokRx44F7kODQc0AAv4BO614jTY1M+OyW3F8MKxwhvF1cla0ZLaO+NV6ynzLfo9Ag8LPxLhGakguiV7KuItRy9+L1Eukyu/J30hARqxEdIIBQGl+Mzvbefd31jZrdSs0ZrPA8+Pz8LQUdIW1C/VYNaY2KHbYN8348HnkesW75HzlPby+CT7Dvz0AnIQLiB/MtM/jEnDUv5UX1PzTeNC4jhPL3cl7iANHTMY6xOHDb0HpAGe+Qfws+Qp2s7QbsiOw4vBIMG7xGXKc9Ke3fHnjfEt+hkByQcxDckRthaAGjkfsSSMKPMsui4XLu8tnio0JUcePRR3Cp8Bz/fY7sjmD9/22X3WytK80PjOHc1yzVnOCdC/0vPV4Nnn3YThw+QL6MfrUu9D8yf3ZPop/hX/EwOnDeYYsCjONQg+GElwT8NQDFCqR8s9MjW1KhElXCFaHLIXIBGQCv0DDPub72jji9kc0hTMSMnSx4/HkMqZzWXSi9rO4bfpQ/OE+7YEeA2EE38aZCA9JNEntCluK2MtvixGKyQpIyUYILkYfQ8PBVX6f+/K5dre1th41C7Sd9AO0N3PW85rzjDQ+NEJ1TrY+du+4b/m6+qU7tPwV/KJ9BP3mfkM/BP8GP6TB1QUMiQkNKo9E0jSUP1Q0k9lSEM8iDROK+QjYiGsHHEXtRINDd0I8gLB+N/rXd5v0wTKBMP2v7O/pMP0yrbTld526X/xBPia/E8ACwX6CGgO1xUVHUolqiy9MGIzVDJWLXYouSHEGfMRWAk5AlH8mPVQ7oHmdN8Q2n3VAdK8z+/Oa8+S0RLV0thk3B/fWeGz42bl4uWZ5l7oNOsT7lzx2fRs93j5Q/lg/HIH4hVGJlk0mD3BSAFRN1GoTqhFYjvQNJosTSg3J+IjDyAeGskSUgzvAar0iucB3OzUEs8syk3Ipshoy+bPKNWf2zzhKuXZ6RrvZvad/p4FcQ4mGA0hJClTLpUwsjH0L58swiiyIoUcgBW/DvwIQAI6+yL0Te2R56PhZduj1inSbc9SzXrLeMzuzZ/Q3tRE2ZreSuO45S/nZuiu6cTqCOxQ7lvwqvNA9qX6jQdbFwcp9zmTQwBNYlXEVHlR0UlaPk03rS+GKeMoDyh4JjsjSx1SF/0Nh//i7rbdO9AkxoK+PrvGu2G/LMWty9nSG9lo3Uriv+fy7uP34v+3COYSCByPJL4r6y/EMwM2wjX4NMgxzCyOJvIeoBa9DTkEnvqG8ZjpbOKH2wnW6NCkzNDJK8g5yHnJjsvwzWzR1dXF2cPdU+FE5Gvmf+iC6lvt9/FY9ij6QP72ASAKaRiwJwo4dENqSa9Q51L0TwtLekDkNzozGy2WK/orUClzJowg6hjNENkD4/KA4XrSRMfXvuu4K7eZuCK90cMyy2bTWtq13w/lvOqp8NH2gP2OBlkRDhxAJ5kwbzhsPj5ABEDWPS45hTKKKiIifxnlD98FK/ul8S3qO+L029bVFc/Oyf7E9MF8wXfBV8P8x6XNbdTv2SPd99/b4VbjJea16Uvt4/Lq+IL/2AY9CigPlhmvJZ80b0AnRjFO+1IMUi1QTUgTQNo6ozPaLyMujykHJMEbEBK+CO37Bu3w3j7TesunxfLB5sC4wcLDk8Xxx2jL+s3x0JzV09vB5TPxTvxKCaAVeiADK0EyNTfzOhA8HT36PWU8tDntNDIu6yZqHa8SLQit/azzeepw4T3ZrdEnyvPDcL/jvIq8xr3ewHLFrcqU0H3WlNvj39viZuVQ6FPrC+5Z8jr4+/3MBIYJCQ87Gx8pfjeVQytIykw9UDZNzEjrPxM2MzIYLmEsXS5OLMgoriOLGhwSqQaf9jHnftn+z53JacSCwQXAcMCdwhbE4cZmytTNdtSW21zjyuws9f3+zgm2E/kezyk+M2U8VkLWRaFHNEWtQNw5ZDH5KFkgPhfjDb8EBftw8SHoW95x1SvOvsfVwyDBHL+fv0nBfcWry/zQC9bf2YncXd+d4T3jlOXc6FjtMPT9/OMEowreDKYNvBPvHGInYDHHN9c/+kdGSjlJPkPIOoU23zEYL00vEiwSKFciGRrVEr4IXPs+7inijdkY02TMgMdixJPEf8d6ykjO+tC80l3VK9g53fbk+Oz39mYCeA5aHFIoQjFwNwA6dDshO8E4FzV8L3IquiYiIlIdcRUQCjX/G/Nb6P7fNNeh0bLOvcwSzrXONc6lzrnNL84T0OTRBta22tbfgeUx6u7uqPMy9tr22vcS+Zb6ffz2+1v/HAxzG/8s8zo+QHdGgEmKRWRBmzdlLn8rhyfYJw4qjigCJ0oiORy/F5MPlwQR+BXsFeT03CTW9tAEzQLNpc9o0knW1Nih2QLcoN915Bvrl/C99j7/yQc4EFIXbhxbIb4lmil3LFwtziyiKQglJCDvGOER8QqgA/3+xPn+82jv/OhL44jeStjO1E3Rq83RzRDOxdAb1rfZnt4T4w3lYucA6FLn8+db6VrssPFi+SsB3weoCzULaA64FQQetiYWKossQzOvNos3ZzU+Lykudy53LQgvei49LJEoLSH4GQUSGQfs+gTvpehb5zHmUuVh4zjiO+PN4ZneSNso12rV8db/2gXjJetc8Uf4Qv9JBqYMvw8QEjsVyBcBGq4atxmeGMMWKhUTFD8Sow+/CxcIGAXdAFX7rPSb7pbq7ObD4ureOdvN2I/Yrtmm3K7gzuNv5gLpf+ok60PrNOtj7Njuy/DU8vT1tvhQ+6H8Nvy8AOsJcxSDH00mOiwwNZo6YjtxOPMxQi+8LkotUS0/LD4qESfCIQ8d1Be3EE8II/9V+SP1FO8x6YbitN0n3PzZsdjJ2JPYZdkF29rcTuDQ40vnO+zb8Tz4Iv6OAr0GGAvUDvMR1ROUFPsUvBQpFGcToRKdEawPCg3dCTMGGALx/Vv5BfVc8VTtL+r351LmuOU75YnkieRa5PjjZ+NV4h7ikuJB48Pkhefr6gbvefSw+Qr/PgPTA4oG6g16FgcgIyZXKUUwljWdNjQ1FDAcLWwseymiJ4Um7SRcJNohah6+GnkTCApDADP42/KQ7YrojOV35PTkoOSu4krhq98J3m/det1i3+/hN+T95inqvO3A8cf1dPqM/84D+AcKDM8P2RLHFPkVpxb0FrIVChPsDxUMhgi2BQYD9gCA/h/7BPhW9Cbw5usJ5/ziyd/N3dfdNt+s4QrkUuaw6UTt8u+T8Sny3/Kk9B737vmH/UYAEQKeBr0OIBkxIlYlaiYUKSgrRitUJn8fkB0VHgYgOiIzIiQjbiO/IFAeJxq4E/AMfAXUAC7/7fyT+eb1U/MQ8tjvwuti5o3iY+Er4T/hiOC739Tg/OLD5Tzpc+yD8AH1fPiw+6394v6IACMC+wNKBjYIEwp+DKcOnhCWEQ4RmA9iDeYK/AeBBKMA+Pwv+q343vf29jf2jvU59bX0bvOD8YrvAu7m7Hnsw+yO7QjvxPAf8oDzXPRT9BH0DvOw8xj45f6YBtELPA46EWYV6hhVGiYZuxglGxQf+CJWJS4nyChrKdYo9CUkIWAbMxWzENcNOQvQCMgEwwDH/a75hPXz73vpGOWU4c3eoN093JLcSd4Q32fgPeHx4SXkS+Zg6Z3tyPFN98z9bAQRC/cOWRDEEOMPMw8ADj0MugviCxgMXQyoC18KigiwBSkD+ABw/vr74fgh9lb0WfKi8NPvFu/l7lfvu+8E8fHx2vFe8pLz1/Q99tz26/fL+bP7uv4UBB4LMhHrE+kTxxOHFEsUZxLSEMURUBUUGVYbKxwMHNkaQRjkFAMSaQ/5DGgLbgrACbsI0wVAAmb/bvyl+dH2sfOy8QLwpO1762fpJug+6Kvo7Okj7InuhvFe9L32KPnz+rP8zP6hALoCzASTBiIIwgixCDIIEAeBBXcDDgEp/4v97/vu+uH5TPmT+db5Mfo8+rD5Pvme+J333vYn9u31s/a79735bvzO/tAARAINA6kDnwOQAkoB+/8K/7b+nf7y/v3/LQGHAiUESQUZBl4GAAbaBfMF2gXzBQcGGwaiBpUGLAaBBW0E+QMCBI4EigVXBmAHtwhwCYAJaQiOBlQFEQRPA7YDpgReBu8HXggrCKcHEgZSBHUCAQFlAJ3/tv4V/uD9Bf68/dL8nvs++tj4c/ch9in1i/QQ9NTzy/Pd81j08fTv9U/3XfjV+Or4KPnd+RX7Gfz6/Dn+Wv8OABAAfv83/1r/Yf8y/9P+ef5u/lr+//3J/en9Uf7M/gP/8v6X/gj+fP0b/Wr9Uf5R/+n/AwAAAIYAUwHpARwCYQJYA9gE8gU1Bg8GHQa0Bj8HXAeeB08IOwk/Cv8KXAubC0YLrAqjCoAKPQriCSQJ5AiuCJYHMQa6BHoD/QL9AVgAs/6s/Nb6Qflx91H2Y/b29jn4jPmM+lf7KftQ+vv4zvdS95D3qfiG+lP8j/3i/Xn96/wX/Nr6v/lk+Qn6ffvr/DT+Y/8nAJYAowBRAND/yP5z/c78Cv26/WH+0v5s/3UA/QBlAND+zfwr+w36EPkz+Hz3H/eK93P49/nm+9f91/+QAagCaAP0AxwEaATRBHYFoAbvB2wJUgs3DdYO/g90EHkQdBDqD+UOhw3QC24Kewl3CEEHvQX2AzsCYwBn/rX8Kfs2+rv5cfmn+Rn6TPp/+rL66voz+zz7L/sz+1n7NvvU+qH6t/of+6H7Ffy+/Jj9RP58/k3+//2j/Z39fP1R/SD97fy5/G78/vuG+1P7Mfs/+3b72Pt1/Bn9j/2k/Wr90vzf+9v6L/pe+jr7nPzv/RH///95AKUAbgBEAPn/9P9XACsBSAJxA28EPgXuBTwGTAYEBmQF6QSmBJMEsgTsBDoFqwXWBYMF9wRzBEQEIwQEBFYE6QRZBS8FaARzA6oCzAEKAbkAnwCJAAwARv/H/pL+P/6j/fr83fya/Wf+/f41/0//fv9x/w3/l/5+/sz+r/+lAJYBYwLOAr4CEwJTAagA6//2/u39rPyq+wL7Z/oS+gv6KvqD+sf66PoV+2v78/uI/Mn8pvxV/C38hvwB/XL9uv0u/hz/KQDJANsA0ACzALMAPQBf/8j+h/6b/uv+R//0//MA6AHeApIDxQOgAw8DTwKqAfYAYABeAMcAmAFIAmwCRAI9AkYCNgLkAWoBSAGZASICzAJCA5QDvAOiA18D4gI0AjABGQBC/+f+Vv9BAI8BKgO2BNYFIQZtBTkEAwMFAjIBpgBwALkAOQF3AUQBpgDB/5T+XP1n/Pj7Wvzi/Ev9t/05/vv+jv+//2r/7f51/vr9vP2A/ZT9pP1+/Z39rP2H/Wn9Pf0u/Ur9WP18/Yn9OP2h/ND7H/vb+tr6B/tu+/77n/z8/DL9T/2s/UD+lv66/of+A/5n/er8Af3e/SD/XACJAagCNwM7A8ECFwLpARwCygLjAxsFFAauBssGmgY6BrkFLAXIBLsE6wQPBfUEvwRoBNUDCgMpAnwBKQFKAXcBmAFpAcwANADj/wwAcgD2AIkBJQLZAkcDKQOQAvgBlgGvAQMCTQJ+AmECwQGmAFL///0D/UT8wPuL+3L7U/vm+lD6jvm5+Pn3LPet9pL2vPY398/3gfhy+Y36vPvm/O39+/77/wwBvgEGAgYCJwJoAq8CwwKZAn4CegKZAp0CagLOAeIAvP+Q/m79jfxl/Nb80v35/uf/WABhABMAuv+b/8z/aQAkAb4BVALQAloDtgPIA6kDrQMCBMMEgwUJBowG4QYhBxkH4waiBlUG8AV/BQsFegQABEsDiwLQAT8BgACO/7r+D/6v/UT9kfzN+zj7xPpQ+rv5QPkL+fn4C/kt+YP59/ly+sT6EPt/+w78pfwb/Xv9zv09/rb+E/9s/8z/SADiADABDAG6ABcAfP/r/k/++v3K/bf97f1s/iD/8v+LAKoAXgDF/zv/vv5q/kj+gv4K/+v/wwBpAeIBEwIuAjsCJwLtAZYBfgHUAVwC8gJ4AwsErQQLBSEF2ASHBGoEiQT0BJUFSQbuBjEHBQeGBpEFLgSNAgcBIgCt/z3/4P6E/iP+1/3b/ej9D/5I/lH+jf6r/sf+q/5T/uv9rf2U/cr9O/6i/ir/rf/9//v/lf/V/hX+T/2l/D/8F/wt/GH8r/wK/Vj9Wv02/f/88/wI/Sn9if0T/rr+QP9k/zn/+/61/on+qv7p/nr/EgCqAE0BpAGjARwBcADG/yn/7f75/n7/QQAlAccBJwI/AvgBcgHQAHkAmgBGATkCKQMCBLAEIQUzBf0EmQT3AxwDJwIuAYAAKwAtADgAUwCjAAUBWgE/AZoAw//c/in+t/1w/XX96P13/vn+ZP+D/2P/Lv/Z/rr+qv7V/kT/+f/QAFwBmAFWAbwA+f9J/+D+zv4M/zD/I/+m/tX93fzW+yT71vrz+mL7+PvO/ND9vP5u/9X/7f8BAPL/sv9b/xr/MP+L/ykAzgBEAZIBwwEDAiMCIAIwAjACXAKdAqsCvgKLAl0CDALMAagBZQFKAUIBRgFPAS4BCAH/ACUBXwGGAY0BcAErAd0AiwCAAKYA8QBEAXsBjwFqAQcBjQAZAK3/b/89/xb/5f6b/j3++v3p/fT9NP6E/vD+T/9z/zf/0P56/pn+Kf/n/9IAnwEwAj8CvAHtABsAjv8g/xP/df/w/3UAqgB3ABcAUv+Z/uL9T/0t/S39K/1N/ZD9vP3K/YT9NP0Q/ST9j/36/Sz+Kf70/dX92f0s/pf+Gv/R/34AdwFcAvkCUQN3A5sDtgOoA4EDgAOxA8wD0AO8A50DXQPXAvQB5AC9/8j+Bv55/Ur9X/3F/RH+U/6Z/sf+5P77/v3+BP8c/4L/RAAeAe8BegLpAhMD8AKWAiwC7QG8AaQBqgGCATAB6wB9ACQA3v+C/13/S/+O/73/8v/7/9n/sv91/zL/v/5C/qj9Lf2q/FH8Q/x//BP9s/1R/u/+cf+v/3X/4P49/of9CP3i/EH9yf1E/oL+s/7//kv/w/9GANYAqAFJAocCogJsAugBRAGuAEgALQBPAGEAhACjAOgAIAEXAfEAoQBaAB4A5//u/z0AmgATAXcBiwHKAfIBAwL9Af0BLAJoAoUCdwI9AskBSgH8AL4AlACPAIYApgCuAHAA6/8K/0D+lv0c/fH85Pz4/Fz9vP0G/k3+X/5L/hz+D/4T/gH+NP5h/v3+yP9sAAEBMgEZAb4ALQCp/yD/tv6k/tn+Y/8MAMIAXwHQAfsB5AGZAUEB1AAeAG7/yP6i/hz/vf/2/0MApgAkAbwBOAEZAN7+rP39/MD8CP3t/Sr/PQBKARcCXQIIAhcB+f8s/+3+Jf/T/8sAzAHBAl8DlQNUA/sCnwIyAscBYQFEASQB1AClAF4AJABTAI0A+AB7AcwB7wGmAf8ATwCi/xb/zP5u/mP+gP6H/tX+Jf9N/2H/aP8T/5v+Mv7D/dX9DP6X/m//JgDHAFEBdQFuAUoB0ABcAPT/ev/9/pv+Y/6H/sX+9P4I/+T+Yf7k/ar9hP2Y/cX9Nv6x/iP/df+b/63/jv+A/27/bP9z/2z/fv9a/3X/RP///vL+0v7r/hz/Y/+H/8b/BQBjAMUAUQH4AY0C5QIeA0YDIQP5Ar8CqwJxAl8CbgJ1Ao4CogJsAkgCGgKhAVEB/wC+AH0AMADg/5D/RP/9/rj+m/6J/oX+if5n/lj+O/5Y/o3+0P4+/5X/9/9cAFoAOgDp/27/Kv8M/y7/eP/w/3IA0AAXARMBmgDr/zn/nf5w/rX+Sf8eAOgAZwFqAdQAGQBm/97+pv7Q/ir/l//i//f/AwDR/5D/Lv/X/pv+Wv56/o7+8v6f/zoApgDiAOoAswArAMr/Uv8h/5//JwDMAFYBlgGmAWMBtwD2/yn/pv7M/hj/gv/M/wEAQwC+AJwAKwCt/2r/4/8TAND/vP+S/27/eP85/2b/7f+hANcAXgGDAyQGxAcSB44EBQKL/7r82vlA90n25PYq+Tj8Df8kAVYCaAIeATv//fw8+0X6nPr6+yP+vgDbAngEhwWiBdcEVgOSAZ8AEAAZAMkAOwHVAfACkgOyA1EDRAJ+Aa4AuP96/yP/Sf+t/8H/TwBOAND/DADg/3f/Yf8E/wT//f62/kD/aP+U/2kARABTALUAhADFAOIADAAnAPD/Ef9R/xH/c/+GAEEA7wDBATsBkAG+AJn/uv+W/r/+LP9I/pf/HgBo/yAARv9c/nn+Wv3U/Rj+1P2X/+f/4/+uAB0AjP9z//n+O/9L/5v/vABPATQCegKqASIBpQCv/6b/If/X/q//AwDHAGMBQgHmACABTACt/x7/Af4T/0//0//9AG4AMgA7AJL/0f+X/yH/SgB3/7r/0P/X/sj/kP8bAMAAQQCxAJ0BCAF8ASsBEwBx/7/+if5P/pT/Of+L/1MA+gA5AQEBBwCH/p3+N/7U/Vr/AQDI/3kBIgHvAF8BJAETAaoAuv+f/9v+2f4p/yf/+P4DAuEE4gEBAbz/rwD5AuoASv3d/XIA3gGYAFj+I/+p/xUBZf2K+9T8gP3SALMAqABLA1cF9QSvBDADagN8BN4DAQHv/dP+ZAPkC14KpgP3BDEE9QQZASf1IvJ59V31bPMi8m/2WAGPBq4F0gA3AxQIIwWmAFb4m/jk/sD96vnL+ez6sv/K/2D8z/uS/Yf/qP1I/cH9Rv9YAr0DlgJHBI4ElQQGBnEEKARWBeMFKgdJBW4CIQTaBcMEvgEX/mH+jP+l/Lv5AvdB+WX7Zvny91L2l/fW+eP41/Yu+HT5rPv2/a39lf/SAUsDYgX6BlUGewdjCnsJlgrbCUUI5AlTCaUIPweVBhIFLgSmAlMBsf/t/UT9K/va+U74UvcD9hb3ePaF9WL5P/t0/Bf+wPs6/Gb/U/7F/Q//PQF0BhAIJAY8BzIIxQj1CHMF0QMsBXMEMwW6BIcDIQSMBOsBN/9/+2b4yvcD9gT2MPbn9335a/od+zj7Gfu5+gD7Kvrh+kP8yf3/AOkCmwQHBm8GOgYABpMEvwO0A0YCygIIAxYDagP2AmwCkgF9ACX/mP3h++/78fuT++r7kfxG/bj90PwI/T3+zv7g/q3+lP/qAJQB1QEwAkYCkgNZBDwEaASKBG0EPAUdBXYE/wOgA7gCCgLiANf/tv/O/pv+vv5P/qH+2f8K///+rf7F/U//7f5e/A78+vs5/vT/df2k/av/ZP8R/4D86PkC++b6ufk8+gn6/Ppu+6r6hPp0+j76/PoU+776XPuG+zv9cP4p/g/+if6i/lv/bP/n/2AA4gBpAQMBxwD//6L/GgSACUIMAg2kC94MiRHgEmARqRBKEEoTMhQ+EBgOHQ/qEPUR0w4VCWAGOwQXAXP9rvgI9u3zMfJo8PDuFO/78ILys/MM9bX0bPWY9cH29fiV+ub7zvyD/1gDpQWaBb8FNQbqBuwFSAImAMf+9v1R/Wv7BfpM+Xr4m/ch99z1/fT88y3ysvB+7qjs8es37UvvNvHs8UTzY/V29yH4pvfn9tf3yflI+xP8wPx6/0QDmAfDCZEJdQ25F74haCm0Kc4lzSlxMOUvZSw7I9Ac3h33GQ4RbAvCB08IzgjWADr53vRm8JDtNukY5Prgv99b30PhMOVb6Bnsz/Aq9sH49/rh+qH85AElBfoG1AgeC1wQyhYHGkIclx2oHN0bUhioE0MP4AmdBDv/ffop9u3yC+9x7C/q3udq5Tnj6OGj4eviSOSL5bfnlumv62ruBPDA8AXySvJ98+f1MPeK+IH5cflI+pj7dPue+zP60/nc+Nj3ivhU+RT7CP0D+x4BHBMZI+4xODLuKXwwcTmZOaE2Cym/Hxkk6xzZEnEOkQnBDNEO8gUR/3j5LvBx7KPpHOaH46Lem9sJ3/Dl6unb7JzxdveJ/A//df3X/fkCWwb6CHALnwvGDpATCxY7GhwcshjcFdYRdQ01C6kF4v69+iH2EPI871jrYuhz5zzmeOXn5X7l5eUf6YTsSe/s8Qnzw/Xl+Sf8OP1C/eb7Z/vx+tP5B/of+uz6EPxy/Bv7M/kn9zL14fJy8NftCuus69XtOPBi8zjzpveGCZ4fpDETOFkxsjIkPv5DE0K6NXgoSSaIInIYJBF1C/gKnQwpCPgBlfoL8M3oM+a25friCNwS177Z3eFR6d3s9/CM93/89P4I/jv9pP/UASwCsQMlBY0HwQq8DLsP3RO/FpkXZhXdEDcNTwpTBzcDOP0+9xLz8O8s7XLrHerN6ZPqdOqP6cfpCOvw7UXygPWv97/55PvO/k0CVgRWBIcCgv8V/af6Mfh19on1IPV79FzyPPBQ75fvLPA38ODvC+4a7fnuxvHt9DT1GfPE+7QPXSXkNI01NzE7Ny4/6UFePScwlCaGIWUZ9hHHCyMGhQRAAwoBY/3O9Onr6Off6OLq7OcT4vXfhOIM6aXvpvPj95b65vtf/r//LwBwAIn+qv6jAMwBCAV0CKgLmhDBE9cVaBdfFRUSeA62CjoI4wTgAFX9/vn596r1vvKg8Czuxew77Bvr4er16lPsdPDU9I74xPuL/r4CGQaVBlsFmwKZ/xL8Vvde83LxufDT8KnwtO9o7o7tIe0r7WzsYOtR6pXpwOu47hjxIPM48Ur0mgZmHhc07TwDN1s4D0PHSQtKcD6QLhcmTRu7DiYG1/7t+6r7tvlO+AH0QOxt55Pnjuxv7hfql+WC5EzpKvEc9lX6r/3F/rEAOQE7Af8B2wChAKwB7QDXAYkCvQN/CNML1w36D28PVQ92D8oNMwyxCOcDXf/s+tr3XPTW8KnuPewF7EvtUe1Y7s/u8O+f9Zb7IgBoA9cD5QTGBioHvQZoBIAA1vyX+Gn0YvCm7Grrt+sr7Krs7ese6+bqTevp7GzuWe6R61fp1evG8A/1yvVK84j8kBSFLvBC+kbyQUhHRE9YUXxMBDu9KfkeVRHJBsj+VvUE8Z3ui+0f7ibpMOI43wnhw+Z66MrlneU16Abvp/ej/X4D7AZ/B4YJ1glBCPUFzAAM/g7+Gf22/kQBUAQXCo4O+hGgFIITEBE8DsUJKAUV/4z5/fa69Qr1OvN98Bbv3u5/8KPyU/LP8QvytfML+FH7IP1q/5gBZgQ0B14IhgcABeIB4P6c++f3Y/MC7w7suenY6JXp8+nL6STp7+jh6YLqc+s97PHr2e2/78DwmvKZ76XyxAePIyY+GEt9RnNJAlSKV6VV2EUAMGkhOg6u/LHzj+mD5OfjguOG6XXssOgY6LDqiPD99LnyG/Cr7+bx2Pex/akDDgg4CNAIUQl2CC0HtgIr/vX7dvlU+Uf6p/oV/pkClgevDH4NMQ0mDkIOIw4GCzMFAwHz/KD5wfeW9OHxFPBq7+HwnvE48fHwgvKg9278QP+qARUDhwU8CLAHjAUwAmz98fls9sbxlO256YHoOOoI7HXtLO607o7w3fBv7mrrTOj351PpcOhp6WjsV+8p84jyd/V3CLsifzyvS0ZJIEkuUGJTZFPMRwAzciKCEGr/UfZW7S/nfOUs5OjnEOsL6Srpv+wH9Kf6OvqT93f1gvWX+Vr9ZwEEBQIFrgVgBowFsgXRAxkBgP/Z+4H5S/lA+U/8lgBkBTcLpA1CDrYO/g1mDV8KBgWp/+P5fvbH9O/y9fEz8BDwBfM59f32dviZ+TL9cAAGAlEDIQOVA28E9wP0Asr/u/tS+WH3gvV/8uTtZ+qh6GDoEukf6bTog+hw6Urre+zb60/qB+tq7QLvR/G88yX2p/g09gn4SgqUJD0+XUu5RsZFikycTz1PQEIBLOsaQQjl95nwoOj941jkMOUO7Bjx4O5d7tPwZPd+/QD7PfYJ88TyifeL+47/oAPgAmoCGAN+AlgDZwGb/WH9YPyj+4n8A/xo/hED2gbRC8QNzAvrCjsKNgo3ClUGkAFU/kr7yPkJ+e73l/eg9574K/qs+nL6mPoD/UgA2QB4/6b9x/wK/rj+d/2I+3H4+fXK9d70u/Fu7R/po+cf6UPq3+r067XsWO6O8B/yQ/OV8gny6vL+8uTzDPSK8m7yjO8+8UADjB1kOGxIL0fOSI5R91X+VCZGjS42HHQIfPav7DfjaN7L3mLgZOi078jwn/Lc9i7+uAQMAzb9ivjw9tP53fyt/jgAdAC3AREEHwUGBecCtP/B/Sb8pfpo+WT4Pvph/4UF4go8DWEN9gz7CxEKogYuAc36efWC8qrxqfFM8sXzBvfW+pH8d/xR+4363fvy/V3/+f+k/6v/+gApAkYCZQCf/HH4LfRb8E/sZOdy453hpeJL5sLpA+zV7czvG/Nf9gL4SfcX9ZLz8fEL8mf0P/Vn9TPyAPG+AVcfnjz+TpNM10efT4NVpVQISAEu5RdDBcjxN+cZ4NfZqtrN3rTn6vF+9CL1Uvq6A+8LxQryAhL7vPY3+Ir6DPy8/Vr8YfzO/wYCLgSfA+L/If9N/vP7vvr79133/vvgADMGtQk/CMYHYAniCqsMagonBLb+MfoE+PD3qfYI9bPzlPNU9Vj3Qfnq+vH8IgD2AUkCSwLiAIgApQCN/iL8Jvgy8/nwAvCK7uDr/udL5uXn3+rJ7JDt4O7A8FjzdfXm9Vz1NvXa9un3sffK99H2rfY09fPwW/r/E4UvrkWgSfdCd0jHTk5NxEYCMgob3wkD9W3o2eSr32vgM+Ww6hP00feW9mD6uQAtBhQHTwF7+1f4yPd5+i39xf5l/pH7y/oX/Pz89P3X/An7BftX+gX7G/1w/n4C5gfwC4wPfw+kDYwOzw4GDbEITQHR+vb2BvVc9C30A/QK9KL1FPmy+6z8DP0F/hMBqANuA+8BUQDX/47/Jf6U+7T37fOe8fvvKu5563LofedK6bbsmfAv81jzrPJD83X11/eA95D0SfE97fjq+uxW7z7xOe9j6/n3wxOOLwJE5EZkQ51K6k/vTbdGADT9H14Pe/wf8V7sb+Vl4hHk6Ojf8I30X/T59z3/OQUABo0BWvzN+ZP6hvzt/YL+hvyM+pr70P3j/6L/Ovzo+c/5Pvp5+/H7s/zkACEGYQuMD6sPGA9VDyUOiwzQCF8CKf1U+BD09fEC8CHvEvCV8Wn0W/cW+Ur76/0IARwDrQLzAJf+IP1B/D76u/fD9MDxyPBk8HPvxu7k7ZnuVfH089D1c/a69XD1mPW89L7yX++i7bHu4+438GbxLfEJ8y/xJvL5A6Qc3zQWRUJC+0AcSW1L2EvjQqYsuhrsCNX21vA37IHmVubn5ZjpW/Hh81L24fv4AA8FLAQ+/8f70fn1+XL7IPx3/BL77vjP+HL5U/ty/Yn9+P1c/s7+XAE1A7oEAAh3C5UO/g8sDo0LPwrNCHEG4AJf/an3hPOV8P3vFvD/7mHv8/B387T24/cG+dv7Zf7oAMoBlACF/7X+CP/p/0b/4Py5+cb3Gvcq9jn0l/H37yjwe/HU8971AfYX9UL0vvMp8y3xju557Yfttu/k8nTzwvKx7mjukv9aGlU0L0XnQ9NBtkjRS8RLfkI7LKcXkARD8+fsFOmi5PLkcefc7Tv10PYC9/z55P5oAjAAM/qv9Gvy8fSw+Lv7+P3Z/C/8zv2E/gwAIgAc/cL8Vf0D/ksB9AIhBQ4KdQ1tEKwRyw5QDAYLLwnEB+kDef3K+ID1rvPh853zL/NO87PzBfUh90z5F/sn/dH/NAGdAaoBvADLAAgBr/8j/lD7r/c99uv0WPPo8fnuMO468ITyAfU99nH1E/Xk9IDzQfFJ7TbqF+kJ6P7pjuzX7Znv7+z17/sFpSHlOYtG/UC6QA9K+UtYSYQ8nSRREp8BR/Ky7t/rf+dH6ZnsKfPU+V34kPYJ+er7S/16+Tvz/e887+jxdfYo+vT8Uf2z/aMAAQNkBPADxwDk/hr+9v2i/2kApgEfBX0JlQ0xD8QOMw6cDXgMOAkCBNf+tvmO9hf2GPYn9kv19vQB98n5XPwI/vT+iACPAQ8CdQJnAQUA2f5z/d38Mfug9z/1T/Qr9FX0PfIs7zXuge8X8/L2C/g+9j/zZ/EN8UnwS+577Mzr/Ot+7kryhvTZ9V7yZfIEBREfaja5QzA+1jvaQwVGj0WhO0glIxNUAhfz1+6x6kTkauMn5mjt4PTv9KXzHvdw/V0CdwGG/aX6qflG/K3/RAK6A64As/16/nr/8QA/AIL8Nvwc/Sn+qgHnAvADzQYHCPMJtAuLCrUJ9giYBhgECAGm/cX7Uvru96T1IPTt82r1PviK+tT7Bf2z/Zn/ygLjA7EDtgIpAHD+Jvxg+BH2LfNZ8M/vd+6Q7U/tKew+7jHyUfUI+LH3MPUb89LxDvJZ8trwce+H7jPuvvG69LP1n/VX8Cn0BQrvIqY4fECtOcQ8HUVyRthDrjQhIF4QNf/z85zxR+7d6orp6+o/8n73CPZC9an3K/xY/zL9bfo4+hf7Jf45AUQDGgT7ARkA2v+y/+L+4vv5+IH4hvnb/JYAMAPCBloKBQ91E+cT2RGwDfEIQAWcAET8FPmA9c7zBfM/8pzzpPRb9p75gfoU+9j7gvvB/V3/x/5v//39U/yU/Nj62PmK+KH0AfP+8STw9++v7p3uOvHN8rj00vUa9V/0NvP88jHzV/IO8pHy5vHv8e3zGvWp9kb0me5i+jkViS+MQ5lBhTgRP5NDEkMoPfQmkhKHA+bytu4W8ATtfu3i7pHyPPq9+v/2dve3+Vj84fvV9iL1w/aO+SP//wLuBPUETwHQ//3/3v4p/ob7q/lV+y79zgAsBcAHzArlDTMQqhHwD84LXAjoBbIDzACj/OP43vbF9cH1qvU29CLzu/Ku87X1gPZL99j4Lftd/u7/OAAMAMz+SP6A/TP79/d182/wQO+e7knulO317oHy9PXl+Jn5avhC9w/2D/V/87vwKu9h7wDw2/KL9Qr26fUL8RfzTAl+JTE9PUY4PEA5x0A8Q4xC4DWSHh8Nbv2z87b1QPVw8nbxjvCG9CH3rPP18VfzFves+ZL2zfMg9Cf37f2BA6sGfQdAAwkAhgChANkAwP1o+Gj3KPju+j8ARQTvByALPg06D8kPGw55Cy0JFAZ5AQP99/h291j3K/Wu89byo/Iu9Qn4lfly+vn5xvmw++b9Yf4u/fH7Qfsb/Fb9//yA/Jr6b/iv92j1IPPR8ALuY+7n7+PwRfL58aPxEvOs9Pj1LPXQ81/0KfS39DX4aPgd+Cf1I+yC9T8R9SlnP1JAijNDOv1BS0LuQpAwmhl1DHD8+fd1/VL6dfcn9ojz+/W89Mrt3eud7RTw1PFO7+3sa+9U9nH/xgbYCPIENAAa/wwAwwFIAcP8JvnG9/f4ev7lA2cHkArRC6YMeg2hC6oJdAjlBdUDFwJP//39Gf13+yb6X/d989bwOPDW8EnxmPGT8Zby5/U6+HL6gP36/Ub/VQHAAJIBugAX/VH8wvm+9bPym+0r7E/uYe8U8d/w9+6w7wXyifRL9a7yFvDw7oPuyPG49E/1S/Zk8B3y3QqpJnM/9EqjPhY8e0TLQ2lEqzk/IvQTwwSr+O372fx7+vH67vdt9w334++P66TsCu6g7v/r2+lj7fbzMvyvA04H3wb7Avf/9P+x/77+ffz5+aD5e/rp/XYEKQs/ECsT8BOJElIP/wtqCZMHTQMb/KL2cvTp9dP5ffv3+ln4uvOj8TXxp/AU8JntyuzK783ym/bC+u/8EAC4AggDnQOHAZL9d/xB+3351/Zb8Uful+5W793wjfEd8WfxA/Ip8+bz4/IM8i/xsO/R8ITx7vBy8WjsUO+lB2AjYjtuRS87UzvURfRGM0XrN4MhthQLCK3+cwGEAbb+jf3d+fD4Jfen7xXsRO1o7uTtduqB6SjumPSp+mr+IP/v/f76a/kO+gD6u/l/+SL6kvyD/+IDagmyDRYQ1BAtERQRxg6qC7kINwZ4A7T/ef39/In89vtp+jX5ivhP9t70bvQx80Hyt/De7yDyFfRl9fX3Z/oI/Vj/v//Z/1r/gv1w/ML6Uvhu9nP0ofS49o73//cs+On2r/UT9KXxS++W7K/rF+y+6wruPvBM8X/zze+58l8KUSXlOm9C+zYwNjdCKkVFQzU38yJdGG8O8ATyBRsGMAMeAQX8TfjZ80rrW+fq6HPrc+s659rmxe1H9lH9Y//d/eb7KPir9SX1bPQ99Xf2lfhz/cwBkwYwDN0PnxLvEjYSBROJEXEPFA2GCCoGdQTXAcMBMP/o+QP20fEf8LLwzO/17/fwZvHm8sPzmPQu9uT2Ifhg+TH6KPuG+6/8d/79/pn+ifxt+Nv0UfLG8X703vZj9wH3P/W68/rz+vTV9c70SPKy713t5+1Z8KfxRfII77TtNv4eGyA1HUJpO3kzpTqSQudBITgNJo0YIBH1CFkG5AeGB0oGagHA+xH3aO4G5wLl9uW/513l6OKT6KXymvvg/4f+G/0e/H35uPeL9nz2GPhd+Sb8cgDuBGoKnA5REZQT0xOkE/8T9BJnEXoOyQkSBtwCiwB8/vH6c/fv9GXyAPEJ8H/v5fBB8hDzP/TF9VD4SPse/aP9Ef3f+/r67Prf+gX7RfuV+l/4CPUb8ZTudO+X8YTyk/Jr8RbxcvPH9Qb3PvYr9BLytO+97xnycPO886vwp++S/zgbiDOoPw85zC/dNJ47ZDs2M7IiGhcGE80NagzwDdMMHAs6BfT8QvY77fnld+TX5YzoceeZ5MToZvEz+Vb95vsU+nH5m/eM9r/2svgt/Kr9zv5RAXoDtQZhCREKwwtLDcENXQ/tEH4SQRPzEMMNwQo8CLIF5gAm+7321PJS8FnvaO/L8XX02fXD9kD3+fcY+Xb5xPk/+nj55/eZ9tP17/VU9vj1bPQJ8izvn+x57JTuo/BT8gHzGfPd9DP33PhA+b33LPbo9Hf0I/Yf+P34u/dj9DH6wg5mJzw5wTrgMCsusjMaN6U0ZCqdHvkX5BJTEFgSvhNREo4MlwN/++7x6Of/4UrhUuSO5fjjw+V17DL1PftV+w36evk19yX14vSp92z91AB3ADsAewFZBDQHfwc8B1sH6Ac0ChgNKhDMEi4TEhJkEFgNBQlPAxX8W/aT8jjwh+8j7+DvK/Ik9Kb15fax9jL2N/Ur833y6vIt8/rzDPWF9gn4Ovhx9lPzjPBE76fvlfGl85v0m/Q09Az1bvZU95f3NfZK9W714vWB+Hn8i/6W/vP6lvtUC6MhXjJ3NEsoWiPtLEY2mzeSLYogoB11HV4ZKRWAERQRvQ9lB3n8ivJp69rnIeWd5M7lz+XT5gvpDO2z8/T2VPYc9WfzI/XG+Pf6of61AnMF2gepB9EGsgc8B2cGCQYoBhUJsgwLD58RChPLEh4RSQ3zCLsEN/+l+e/0KfLu8QnyJPI28jPyFPK18S/x8fCD8JDvp+6K7q3wO/SB9x/6Dvty+k75Mvfx85fwQO6Z7ZXu8O8i8R/yNPQh9zX5wvoZ+5b6UfuR/G79Hv/c/9f/2v9f/9UDSBDYH3crRixRJhEkXSiFLtMuvyeVIEQd7xsiGkcWMhTKFNkSpAsJAJT1ZvHI8DXvB+sf5u7kzef+6oLsuOxP7U3vcfAN8Ljvl/Fl9kr7n/34/d7+UwFkBOAEqAIFAQUC3gXvCYsMqQ4bEY0T2xTZE/wQrQ3MCX0FqAAn/Gj5Nfhm+BH4WPbU9BX0wPOs8v7vd+2E7C7sGuzK7P/u4fIR9sT3DfkC+hL6q/n3+Mb4cfkG+aD4pfnU+63+FwFoAvICWAJMAAX+wPw9/Nn7+PoC+ib5g/iK+cX8wwLECOALDwzXC+MMpw87El4TnRTiFY4XMRmLGbUZMRqAGvUZNRfSEkkOWgppB/UEiwET/in74fjw9zX3Gvb09CT0NPMv8s/wge+p71Px7fLh8qXxEvHx8iz2Gvip+LD5j/wKAR0FPgd5CKUJ0grvCycMiQuECjgJPQieB8AGsAXyBCgE3AKxAK39SPv++Qb5r/em9Y/zmvIp82f0NPWv9Jjzy/JX8hbyevHd8DrxYPKJ8xP1MvfA+YL8Mv5w/tv9Bf3h++r6TPqV+ob7NPzJ/MX9JwCxAy0GMQfJBqIGTwhzCsoM6g4AEesT+RZtGcwb/R3IH14gah7tGs8W6BJREKANagpkBxgEkgEHAFr+Wv1a/Q79s/t2+KT09vJO8yT05PM68vfwLfEX8jjz+PMQ9G70I/Wi9g35cvu4/T8A0gLYBJEFBgWnBKYECARfAgMAcP6b/rL/LQBf/8P9sPsi+gL5sfdd9uT0vPNi86XzF/RW9C30vvM989byB/JM8Qfxf/HE8lH0fPZB+R38Rv6g/9AACAINA/QCgAK+An4DAAREA9ICMwXUCcgNxg52Db0M3w63EeYSJRMpEwoUOxW4Fb0W1hijGssashesEgQPsQwYC3QJYAblAkMAwP0v/N36Hfn39zX3avaq9az0K/T09PL16fUt9M/xwvA/8jT1qfcj+JD32PdZ+cL7uP09/1UB0QMmBtoHCwg/BzwGLgWFBJkD7wFuAGr/tv5o/sP9s/wK/Pr6VPlt93z1+vMA8y3yNvF98EDwfPC78KnwLvCF72jv7O/G8Bfy2fPy9b34p/tq/tkAAwJ5ApADaAVQB+8HXQZHBS8HZgtQEPYSfhL1EC0QihCdEXISixLdEh4T0hLrEggUThY2GK4XohTQEIcNagt0CcAGxgO+ABz+6vtt+V33wfbX9oX27/Ra8uPwlfFu8zb1x/WS9cz1vfaT9yH4Gvg++NT5+vvr/ev+O/8gAPYBIANCA/0C3gKgA1sE3gNUApoAZv9R/0f/v/4V/uT8eftX+u74mffu9o31A/TE8r7xGPGB8FDwBfFI8qzyZPKs8U7x9/F38gzzrPQ+9/76Cv/2AdMDlQRoBVkHNArgDLYNIQ1+DPAMwQ6qEWoUuxVGFcASURByD+MPThE4EokRYhBKDwQPbhA/EgMTGRLfD/AM+AlIB68EggJBAGH9FvrR9sP08fNs86fyUvFJ8BLwaPDA8ALxd/II9YH3avjK9wv3xveD+eX6/vvo/Ej+BwCtAZcCRwPRA6sEnAXRBYgFrQSvA60CFQJ8ATIAjv4K/WX7R/oY+TL3MvVD86zxlfCV713uY+1w7Ivsju2g7m/v0+8m8HDxbPMi9Tz3OPnP+2//1QKnBWkHigikCmYP1xXrGusbexkLGMAZ2xxEHUoaoBcEF9MX2BcWFl0UHBSAE1wQrQpqBPYAXACaACQAlv7m/LX8//2Z/iv+P/xt+S73FfUb817xsvBH8XnyP/NG867z//Qc9974Wfm7+R37JP00/jn+9v1d/lH/A//D/S79Ef7y/0oBKQGcAAMByQHOAqQCcgEvAEn/LwC5AKj/mPze+L/2xvaH9vzzMfBu7JHq0umV6Jfo1+pl7fXu9e4s8Jr0JPrz/FX8GPrJ+WX9UgRNDRcUrxbwFSUVqRZHGXcaoxp3GzYbTBiXFMMTtBc0HWgeRholFOwOkgsrCLEDYAD6/dD7Cfm+9fby2fJI9EH0rPIH8O7ugfH09W35dPoY+j378P5+AiEEiQNaAgMDPAQSBeMEbwRoBRYH5gcSB2oFjgM3AkgAyv3L+3L61Pl6+Lz2g/Yj+Lv6Ffyn+k74ePfl9yr4Jfca9Zb0E/bP97/3E/WI8bHuRO1l7ObruupR6XPndeWe5QDpbe9h9Y74pPhZ+bz++gnsFdQbhhnKFD4XPyJoLq0xDiv/JK0lhSnAKVMjZR30G/gbfxgFEKAHdQOkAvD/x/rr8x3vW+3p7Hvqf+eQ5JnjT+Ra4x7ht9/J4SHnn+2g8Uz0cfeY/P8BWQboCGEMrBG4FvwYKBgPGB4a8RvmGQEUnA7BDdgOhw0kCN4B1f5z/tv8JvgA8hbukO1s7jLtP+oN6FfoEurm6hXqFOnf6QrsVO3Z7FbsNe207izwqfCR8S3yNvPv8mnzSvMv8p7ynvpICWYUIBBw/SjwJfZkDM4dth+3GJMZYyMMLIIrkCZoJWgo6SfnH30ZvhqYI5MoQSMaFmwKlQPV/9T7CPfC8vfuwOl35H3iHuRO5qLl4uH33ibeR98J4UDkuepO8YT1uvaO+Fb99QXEDRsSQhOxFOoXRB2pIe8hByBIHT4drByLGbwSygsCB8wEswHv/Ov3qvQQ9PryCfGS7V7qGOgq5wLmmeMF4o3hjeOi5ujo9ehK6HrnIed8513nz+gF6mjuVfHH8gPy3/JB9Pz0nPJj9JwFVB4hLyclJg4KAvUR+ShwNNYs0iUtKrYxCDC1JS8gfCCFIMATTAVV/a8CZQruDXYIav/69N/ruedr6ArsfuzH6bPkWuNW5NHn2Onv69fsquwM7I7t3fPg/a4HnQxODpMOFBAQErMUNRhRHG8eXR28G0UaOhknFSMOngfPBfsFTAYnBGUAgfsY9lXwzOvF6kLstO4b8J7wV+9z7hftTezj6WLmFeKp37nhkOex7bnwz+/4693orea75UXngupY7ZzvsPHL8xf2t/Td9DsCuRq2Lp4rSBR4BCsSRi9CQnk9BDD4LKMzuzRuLcElOiLuHjAUEAdN/yz/lf8X/ir5WfEG5svZaNUK2z/kkOWO343Zedv14TboRuva7cbxs/QB9sb3m/4XCBcRMhVEFg0WLBe3GW8eMiRbJ5sk0xxfF3gWhxb4EWAIOf9y++H6evmz9rP0sPOX8XntZel/6FrrE+8O8tLyUfKO8GjujuwV7O3rzuus617rauuq6ubq1+rJ69/qo+hU5+LqyPBm9235RPbj8BLqEuqU+6Ma7zPJNe0chgdADlcoBTxKPOIvlCynMm40pC/uKeImFyU3HEAOugTD/9T9bP68/Ob17+gS2WjSp9jb4WPk0d6Y2K7Y0dwq4PjiEee97Tn1PPr9/WoCCQiKDZQSXRbvGAEa1xu4H2glJSjQIz0bvxbuGCAbpBU6CCD83vjo+6/8Ifg18HLqG+hV6CLp/ukv65TtS/D88Sbwiexs6iXrb+3Q7VTsfut57cbuFO8R7WfrfepD6y/r0+yX7yjwTPHJ8j/1LPY989XudfxyGOYyUzYCHgQEzwZXH5I1Ej0gNXYxnjPwMG0nZyKFH9gfrhrxDysIFARpAPL9xPsV9S/q+9oE1QHcmedN7LfnWd6m2RnZQtqi3g/mFu6s9IX4+PutAbcHxA1qE28XXhgvF3EWEhtjI4UpEigWIVoZxhUVFPMP4ghaA3ECtgPpAbn5nvD867zsAu7b7FnpjOcg6SDsH+6t7Rfr9eaQ5PbkQOhu7LHu0+6d7a/suOsX7RHv5vE99NP16/OI8u3yHvRj97z2vvJV+WcQtCZhL7McCAKqAK4ZXjLmPMQ0pyvQLjcuQCV3HXMbNx+7Isca7xCbCfIDYACU/Uf3APAL5w7jzOab60HqHONY2UzVp9bD2U7fM+WE6dDsb+5r7yf0LPk3/1sGog3uFF8dYCG0IbYfGhyuGqYcPB+VItIjsx2lEq4H3QFdAocE5gEd/Br1UO4C6VLmFue56uftyu3X69LoxubZ5DXkaOTI5d/nDOqs7FTvM/Az74Dum+187izv6PHc9aX5gP0l/yX9k/o79XLy0ALXHLYvWC7XE2f8twZFIK4z4jeILIopkjBrLKohvhuwGoEhKyJ6F/sOzArwBkAF7wCI+YHxdum25nnqw+2U6vrhJ9sK2vjaeNwf3o/hJuf+6fnnyOa86cvxbP3wBU0KlAtzDKUPFhbiHFcgsh/iHn8gcCPSJE0ePhT5DEMJdwgOCE0E7QBR/pz4vvJa7dTp8emm66/rUesZ6X7lMOIq4FPhWOQo5/vnnOZA5czmIOk06xHswOsu7F3u2vEe9cX2b/lt+xn9T/5R+8L7Cg1tIgQveCezCiL8HQ2eKAs85TtKLGonjiklJpohAiDvIoUpQihvHswTCgsABrIDeQEt/RP1H+7K7f3vJvGH6yvg5NoK3NreF+Oq5DLk3uZg57jmWeg764Hx/vl8/oIApgH/A+cKchK3F+MZ+RjTF8kYXhouHfYb3xfoEp4NswpRCSYGjgNyAOr7+fh89bnyEvFo72jtBuxT6ZznCOZx5enlPObG5fbkr+TI5Yro2ela64Xs++1U7sPule7u8Lj2kvxj/qj+ePoD9nUBNRR/IlsmRBS8AZgKKR0QLYoyyCi+I44oqCVtH9kdzSDNKAAqgR+xEpgKBQhaCvYKIAkaA1776/Xo8k7xQu5Q6W3mb+ZU5V3lieTe45XlLueO5SfmdOe36ujwCPae+XX8c/7WANADvwVrCPsKzw8rFCoWlxRkEGENxQzMDMYMhQuACVMI/QTHAF79rPxq/nP/pfxC9zjyl+9U74HuIe2466rqM+nf58rlCOZH6C3s9e5/8BrwQu+I7z7xnPPQ9M/3yfls/eX/yv2q/MIGGROZHR4c9AqJAw8Ogx5wKxsrWSG2H+wgYx5MGmAapSBxKTgpGB+ZE0AN0Qz+Dc8NmAq/BV4BK/3j9/Hz5e8I7vfuse336J3kOOGd4Tzl++a55zXoEukt6xjuHfCC8ov1YPmm/RIA8QA9AeICAgX+B2gKtAvDDCgNgw0RDdULtQfBBIsDogSTBkkGagOAAFP9Wfpk+I727fbI9lb1/vLA8LDvue8U70LvJvBl8hf0JfQ9893x+vFu8wH14Pac+L35n/zD/mf+hvye+CH4YQJHDf0T0hEWBZf+IwaaD9gX8RkwFQkXhBnyFXkS3xIgGRcjoyR8HHITew+wEEETmRPAEfAPtA6bCpIDSP2R+T75d/tF+lr1Z/Ji7yju4O1g6lDpNuuS6/LsBO3C64XuH/G18g70rvPN8/32ivpB/Uv/Xf9rAOQBfgJmAvICqQPwBH8E6wLkAScBHgG1Ad0BTwHZ/3L9zftG+9v7MftG+6j7DPyS/Ej8EPsC+5T7IvtM+837Svwp/AH8g/o6+n/7Hfyc/Ev9Evzu+n36XvkZ+kz6cPre/WoESgiRB5gB3fuX/6cIGxBBE1wSmBB3EWUQyA5RED4UIhhpGXoWBxI6ENYQ0hFaEmMSwg8vDhYMFAhbBFQCFwEuAvYBpP1i+bP1xfNM85XyxPAz8NzvZO+V7r/tkO3V7uPwd/JD84nzyfN19O31jPat9xL59fmY+4L9qP7j/xIAzP7I/rz+cf+LAK8ALgG8ASsB4gDXAIIBzALKApABdACCAM4A2wBuADAAoP8pAKEA9ABIAbz/tf11/FX8DPxe+0D5Mfir+Cz50fkq+nv69frP+qf6kfub/cAArQJEA0QDnwKkAjUEagVwCOkKCgw3DSMNegz3DAsOsg9DEqMSuREZD2wM9grdChMLkgsgCp4H2gQVAqoAnwA0AAEASgDT/kj9Wftr+Uz5EvrI+f75vfl9+YH5k/ii9qr1E/Zk90n4JfhC96D2vfb99jP4AvnA+TP5fPhi+Yr7lv1q/qz9Lf2+/ar+gP9sADACbgOSA3wC/wAeAG4AxwCJASMClAKWAj0BD/99/Bf72Por+wL7x/o8+h36hvld+ND2N/bZ9gL4aPkL+/H7Nvy3/MD8SP0l/r/+E/+mAGUCQAT7BSwGyAVQBmkGwAfJCQwLYQxmDX4NlQ2ODRwNXQ3EDa8NHwwnCqgIJgi3CMsIKQjvB1wHeAZABakDpgIaAzID1QJRAjgBiwDQ/0n+Ff0V/FX7cvsJ+/r56fh+94v2g/Z39gH3T/dx9yr4+fiD+S36p/qE+x79Tf61/g//kv+5ABcCAQI0AagAiQApAfgBLgI5AoIB7f8Y/uj8pvzZ/Hv8Qft/+WD48Pcq+KT4G/kq+Q/58vhO+Cb4//c5+FD50vrd+3/8bPxu/Gf9Z/5s/w4Awf8rAP0A4AFbA1IEnQTrBK4FZgaFBmoFQgRzBDMFEgZJBsQFjAVdBdgELgXaBd8G8wfJBzoHCweBBhQGIwYkBgMHqgczBygGRQWrBEcE0wM3A6oCoQKQAuIBvAD//mr96/yN/R7+hf5q/hP+Iv43/m7+0v7H/oT+df5E/rH+Tf9m/x7/fP6f/Wr9+/09/iz+U/0H/FH7jfsO/Gz8lPzz+6f6R/nO9yj3oveI+F75xvmi+Sr5EPkz+an5dPpD+9D7nPxz/Qj+yv6o/3cAtwGbAo0ChwK8AnUDbQQ5BCADQgLVAdIBnQEKATsAwf+C/03/6f6N/if+GP6+/m7/EwDLACsBswFwAhEDlwPrA2EEYAVmBkwHHwiTCB4JwwnrCaUJMAl/CGsIbQhlCAEIngb1BPkD2QNOBFQEVgNAAhwBhgBOAPn/xf/0/6v/Lv8p/sD8Ffz6+zv8wvyI/Lz7Ovsk+iH5tvgz+Dr4svjw+Dz5Sfn1+Hz4Zvhv+JD4yPg8+W/67/sB/ST9rPw2/GP8E/0G/vb+LQBpAUQCfgLZAVwBYQGSAd4B/QHvAWwCpAIpAqoBHgH0AAEBbAB4/wb/w/6f/mH+xf1R/WH9xf0l/kb+Pf4G/gX+I/5P/rj+uP8iAWYC/QLQAqQCAQPlA5UErwRSBB8ElwSIBT4GdgYkBrYFeAWFBUcG7AbdBqUFLAQgA+UCFgNWA5cD1wPQAxMDzAEyAH7/QP94/xcANACF/3X+7/wO/G78xfzv/I/8z/tt+3b7hPtl+yL77/oU+1H7ivsi/NT8s/2C/nf+6P13/XP9CP6h/u3+/f7F/mr+i/7//m//w/8DAGkAoQBEADX/NP7U/Vr+MP+Q/yr/1P2G/Nn70vtl/OD8T/0O/pn+8v49/yP/MP+m/9D/7f8iAKoAJwFhAUEB+gAcAXIB2QEeAkACSAIMAsMBtQEwAtMCWANfA0IDTwNJA0ADCAPIAtwCDAMsAywDPQOMA7YDWgOvAlwCeQLtAksDkgMABAIEsQP4AhMCtQE2ArEC7wLIAgYCYwETAdAAcgD//1b/J/8n/wj/1f7B/uD+2/6v/gz+jf1w/bX98f00/lj+D/57/aX8Bfwi/LP8Lv1K/SX9y/wJ/Dz7vfrl+mL71vuq+yv7ivom+i/6Nvpr+l76RfqP+i/79fuo/E39+P3B/lv/r//w/1oAGQHeAWECmwLOAjcD+wNEBDAE7QNvAxgD3AKZAr8C4gLMAr4CXALBAUYBwgCxABMBVgFEAc4AQwD0/wEAAwAZAD8ATwBwAGUAUQBcAIAAcgBrALwAcwFIAr4CfgIjAgUCIAJ5AucCXwPeA0UEGgS9A6ADRwM3AxoDBgM9AyED5wKiAncCqAKrAiMCQgGoAE4AMAB+AE8AqP+m/on9zvxl/BP8svvj+sD54/g6+PD3Efgq+E34ZviR+E75Ivrx+lD7d/vS+2D8Qv0Y/gr/DACxAPwAFQHqAP8AiQE/AtwCFQMEA8gCiQL6AXABOAFKAZgBswFGAXsAv/9+/9X/XgDZABUB4gCuALMAAwE5AYYBrwGoAY8BMAG6AK8A/AAQARcBFwEgAV4BmQGvAcEB3gG4AXUB7QCYAMwAdQETAiIC1AFYAT8BvAFLApQCbALDARsB7wAVAZkBPQK+AvQCwwJAAtcBtQGLAV4BAQGYAEEALwAgAOD/gP/B/gb+Kf30/Ob84vz2/Pj8//zv/M78RPzU+6f7HvzO/KP9Dv40/lH+F/6v/SL9Vf1U/qv/8QBKAd8AOwCd/zv/I/9a//b/YwCWALoATwCr/8P+HP6+/br9w/0X/pv+8v5U/zT/D//2/vT+I/9Y/2P/9P+fAOsAYQFRAYQB3QGCAuUCkAJCArEBfgHBAfQB/QEIAsMBnwFYAcwAaQD7/4AA6gCoABMAKf/Q/mr/MgDxAJsBuAFqAQoBFwFqAagCWgOXAhwCCgGjAJEApgADAagB+AFeAWsA2f4T/kv+zP7I/uL+WP5P/Qn8Nvs//hwCwQL9/0r8B/yh/hsB6AAD/+T9w/3K/j3/Pf+//mr+Fv+g/wkAcgADAT8BDgFaALj/Yf9u/9r/RgBXAAwAlf/n/m7+T/5I/sz+Pf+v/hP/If8Y/5n/zP/7/1//Uv/g/zAAbAADAXkBrABjALb/MgAsAkYDgAPVApQBZQHvASIBcwH2ADYAwgDxAKgAbgA9ADX/sf+m/97+s/5P/iP/5gBEAe3/HP9d/6wASwH8APb/Vv/p/+v/RgBBAOf/uP9z/1L/uP/u/9P/ZQAwAAwABQAmAIAAWgFyAZ8ASAAVAOoAHgFGAWUBRAFLAeQARgD3/2cAOwCy/zf//f4l/37/0f+M/+/+xf0e/aT99P0i/kn+Dv6X/uv+6/4N/x7/tf4Y/nD+Pf8KAB4ADAAXAEEAJgDO/9H/nAC3AVEBrwDHAMcAHgH6ALUARgGbAe0BqgGlAN0APQGxAJwAtwDDAAgBOwCi/2j/7f4h/z3/9P+o/27/ev8i/tL9Of4c/4P/D//b/XT8HvzJ/AX+//6H/8r9y/yN/QH/owB0AB0AGQCCAMcAWAAq/z3/BwF1AjkCIgGEABIA1QHbAVMBPQESAJ0BOwIiAWAAEwG3AeIBcgEyAF3/OgA7AdX/tv/e/uf/MAH7/+7/OAADASUB6wGHAQwAKQHMAUgCdQK+Abb/rQGp/4IABwDAALUKKgSf/Mz2QfqTCMAGrQFt+Ij4UwEMAukDpgDg/O/7ifwXANAC1QG4/8P9e/yx/J/9owAQABP+pvwc/bL/n/+sAJv+2/68/27+0P/D/0f/7f20/zX/Ef54/0L/OwGl/Kb8JAAl/uAAfgAP/xD94/8/Am4CSP7o/NME/wKjAaH9+vzdAHYFAgWN/TIA//6mATICt/2uAEQA+gH9AM7+Fv+U/7UAzgFBAaD/YwHB/ykAewCd/2kBYwFzAY0AVQEcAUkCFgMKASAAmf/3/9QBoQGzAP0A9v85AT8ANACfACH/zAAMAp0AnADyAfb+qP1j/DcC8QoYA175pPWYAOEHMQTW+1X6QP63/eX/tfycANX+Tvuy+xT6Pf3K/078wPrS/dv+U/2J/Bf9Nf9PAO/+sf2d/24A6AGzAED/6AGjAckBgABvAy4FegNjAk8AiQB4AzAEBANLAsf+dwBdAi0AHAHK/1r+Pf5j/9L+qv2cAD8A6/+Q/sr+bP9j/wr//fyfADQB1f60/wcBTf8u/6QBGP9LAagAWgA5Aqj/DAEXAcr/aQBYAkgCmwMFAa39QwA5AlAEfgEzBLYC3v13/Y/7jBD/C0H9EP0T9t4E2wetAfT/2Poe/T38cf78AFH/O/0Y+XD66vpV/f39CP86/Br5Yfy+/cD97/w2/GH8pv51/xf+5f9A/6r+hgCm/ef/qgBCAn4DhwEeAbYCigS9A/QCTQGrAucCXwH9ALoBWgHgAV4BDP80ALUAUQAHAN7+hv2h/WwBUQFu/nr+IP/e/Vj/dABj/8cAlf+X/8wBpAFk/3QASQIIAzQCJgCGAEgBUgPkAL/+UQFWAWAARgBJ/8b/NAEw/2MBN/9G/Sf/9AEcA+b8Cv3m/IQB3gLO/IT9Xv1h/7b/h/9G/t37Y/7g/Rb/PQEHAGn9Ef5E/xP/Ff9l/UT8nQLTA3v9SP4y/BsALAOv/rH/pP+X/xj/D/98/mH96ABLBLr+VfsB/2X+iwCPAaoA5P4t/U/+Gv8nAPD/yQDr/4QA+/+p/84BbgC8Af//lP9yASkDVgI5/xMCIgKfAswCJQFYAC4BvAJzAhkAyv+oAZsBAwF9AEgBxwAlAX0ALwDUAUsBVP/XAOcDDAFb/8sAlgGXBC8A1P1BABz/vAFWAo8BlgEDAbb+2/7r/u0AygJDAAP+Ff6r/rL/2wAs/wr/3fyz/dz/YwCr/8D8Vf3r/wEAtf6Q/Sz/jv9n/pL8jP8FAvT+Kv/b+6P9bv8r/on+Tf0m+xv7w/wM/gb+cvlf+GD8I/6k/dT7hflQ+wH+lf9R/t39fv07/swB/QDWAAECZwEkARgC2QMuBNMDFARJBCMFkwSvBNUFUAZrBkAFXQW/BKcFmgXGBPIFUgWFBLoDbwXXBBQEXwQBA1IDlANfAmoCdQNYAnIAvf8pACABXAD3//f/fP7N/CX+1/9I/c38NPtY/c79NvxF+1L5n/sH/Or7mvnX9y35l/nC+Or4M/g1+Nb4MPZF97j4b/i7+OT2qfer9hj6E/zr9jr74/m091j8QfwJ+7r9cf/6/Mr9kP1U/94DlAGLAM7/OwDwBNMGzwTxACcBxQPeDNEPpwguBCwCywdrETwVkxA9DN8J1QxuEUERzxByEEoQ+Q+lDuEOpA0YC34KXgp9CYgH8QZ1BOkBIgCq/vL9bP3N++H4I/fp9Cz1OfQb9Iv0RPPf8sbwy/Ad8WvxPfMK9ET0zfJs8xP2jPdO+SH5cvn++gL7ifwl/Sz+8P9N/wAAVQDp//EACAFuAdcBdwAs/4f+mf6N/Rn9df3W/BD9z/tb+f/3uvab9yH4E/eU9r/1N/ZE9u32jvd891D5FvfeBLQMFwHxAHH35/+FFBYZPRSpD28NABHWGAUYYBdPGj4fvB1AHRMbaBYuFVAYUxj1Fx8V9Q69DVgLAAemAy0BY/8FAH38zvUN8bbu/+5475/ty+qf6YPoiOe/59XmD+dp6unsCu4R7l/sAe0C8Zb15fnN+sb60vrk/J0AZAMaBYUFrAYxByIIewi1B4sIhAk7CQAIlQWtA78CegIyAHz+T/1X++74tPfM9F7zAPPu8d/wGO/I7QLube9h7vXuju2i7RPsufCq8m8DIglY/Wn9tfHCAGsW3h+UGvQUWBQvGskiliMrI8UkMiuLK90rryWOH9cedSPxIS4grhvXFEQSgQ4cCS4BI/5B/S7+uvfI7hDqdOe55wDnFeSg32beTN8H33bgVN6X3ubgvuNX6Lnq2+v97OjwyfKM9x36x/2fAdACLAY/BwkJoguaDqsPCQ/KDrAOLQ9rEWkQXQ5dC/YIlgcCBvUEYQFPAAb+UPof96HzwPDy797ume2j6zHpl+cq5w/ot+dv6CbnTug26GLqtuzyAWIE8fty+bHrif6JFVchzx5SGN4TlRgXImUmzie5K4AvoDBCL9EmIiIMJPQmGykaJqgbQBasEQwSDAy0BnP+t/qT+1T2xPD+6KjjwOE54qPfBNzt3Nrclto13C/Y2dlO3q7ia+ZO6FLoaef07UTz+flU/rUACv8OAiIHrAh4DrUQcBDoEt0SKRK6FJcURhPAE8QQdg0wDRMLpwhTB44Dfv2Y+hL4KvaL9WDzve3H6ffmKeUq5gTmT+WZ5MHkKeMR5oDlPOcA5gXqk+odACMMzgHHAjXvDvv2FeAnwycgItwcEx10KHsqNCukLsU1YzaBM1ArhiO+Idkm6yfbJQEdwRMQEPIO2Ql1ARr51PNm9ljzVuvi45PeGNwu3Gra9dSQ0+bW6Nfb2jHY8NT11xTeQOWL6RftSuzL8YD3yfsFASUEwgY1DA4SqhLRFMoVnhUhGAIZehgAGi8ZqReDFcYQoA7eDTsM4QjwBSABDP2c+nz3x/Ph8cjvm+1q68PmUeS84rHituOk44rh0uKu4U/keuZt5xrlWei86Qb5gQ+zCN0IofWP9AINrCQyLJcoNSbMH0knOioJKc0rczhzOnA3JTCHJJEgryQcJk8l+R2zEhEOGgvACE/+EfeH7hjtUewa5uzggtuR2SHWl9Kl0KfObtGk1ZrXL9mn1vfXP9vA4lfpVu768tP1dPy0/yMEbQgSDpgR6RaLGtkaFR3OHJsdOh83HkIc7xvdGtwXthQbEF8MRAppCIAD+//d/JP3GfT+77XsLerl52jlVuXQ4svgp98o3ibeeeC34e3j7eTp5OLlH+fW6fHqtu4/8TMPARUXEvgJqfHgAVcfVzPqNAIxYSa/JoQs5ioNKh80RzvcOYY1GCekHHsbkh73Hg8ewBKACDUDuALv/SD1fuvR5WnokOV04LXaR9cA1rDVhdJ9zmzR+NcZ2jHdatxB2r/ftOZu7UXy//f7+Of/vQU6CAgNVxDgE6EYfh3GHkAgfyBCHtEdzBy5Ghkc5BpyGUQWBA9CCuYGugJTAZD+Kvkg9oXwJ+yQ55PnbOWA493isN+X3Ubcn9qe3KTd7N5c4f/ifOPp4/PniOlA7pTtPPDB7AUJiBpbFuMWkfrb/VsYYTDvNGw1Jy9VKQ8wEy3IKDUvRzmcOf83vy7cHnQZ2xz+GkYc1RRVB0YBTQHk/t70E+0y5ArkoePc3+LagdXj1CzV8tOu0ITPodIx10neO+Kp3xzib+am7hn0BfrH/g0D0w3tEEITTRPwFMAZkSADJQMixiAjIFcfMR+mHAAX7hV8FhkTng1tBsz/4PyT+2n5fvRO75TqC+Yq5bLge9/s3XjdUt773XHbY9q/223fneHg4pnjr+WC6Svt9fAQ8e3yOvE99WX1TxOpIRMe4hr0/j0D0hzvM2E22TgZMsIsLzQ0LoclvCynNHI0uDfbLZQdjRinF/IT8BZEDWgCWP7S/VX7W/Gt597ee+CR3tXbzdhr1VnXEthd08LQgNAj03Xa0uJC5SPkCedc6iTz7PkK/2oB+QbnDroSFhh0GP8bpB7GIEUiHyFrIF0g2iC2HrAaNxXDEn0O1Qw7CaYCDP+R+Xn0wvAc7XTpC+j55BHkj+Cn3mPcRtvc3bne1N/k4GvhvOPH5Mrmrukl7anw8/KS9UL2+vpq+Db6N/YGDAchOCQTJn0JcwIMEkcpkTEBNqMzNi1tMj8tmCKWI3Qryi+oNN0tOR5KE98PSQ9hE6sM6wFg+zj5yfmu8lfq39813fTcVtu+2K7VG9eV14PWutO10E3RVdko4Z7n0OlX6u/sp/HV92z97gOBB40RDRVkFyYZiBemGpEgTSUeI3shph61HHEeZxsEFtATQxDwDKYKsQOF/lr7xfYx81Dx8uuI573mhePr4gvhz92B3frffeD14C3hleCS4mHkE+bo5yvrQu0f8dbwGfRa9K34YPlT/Bj6xAURH1IgrydFEUYDdhFrKJUzTzeLOCsuiTXwMQojUyFpKicvAjjPM9EhBhQEDzAMMRGGD4UCrvxL+Gz3SfEo6MjcUds+3eTZDdfp0uTSp9Y62HfTY9DC0BLW1uCL6dnpROuI7031f/xnAe0BHQV2EWcYoh/hIXEdIR2FILMigCN5JKkhDiIUIk0coBVKEAsNcQwDC/AEuv2694/yB/Ae7iTqeOaZ49Tg397N3Unc59v73STfQt4J3hrewODs5fXpvOz/7Kjtbe+383P0tvhp+lr96f56/4b9NQP0HMglBSxNHVgJTg6GIHUvezSSOAE0RDg4NZslQh/JIxQq/jFzL98huxVrDvEI4ApjCYX+Pvjm8Qfx2u3s5jDcWtoT3MfZG9cn0YLR+tZf3I/Zhdbe1PPX4eFi6i7tOe4Z8nT4JQFyBkcH5AhZEGQY4B1xH+ccqx4ZI9MlgCU2Iq0eOx6pHg4caBahDz8KKwl4B7gCY/wR9Xrw3O6O7LvoYubd4dzdzNyf2rzamduV3ZXe+t8F3yzdL99Y40HoA+1m7pfvyPFj9DD2VPnX/dAAegRwAtcBHP/xEEgkQC4wLj8YNw3JE0olSi1qNfQ11DaxOMkskB9IG54f+CWxLDEoTR5uEmcHagORBREE4PyL9lnyhvEh7q3mad6g3L3cGtra107V9dZf25HeJd7B3XXbVt0e5H3rTvEM9hL5ffxlAhkGHAoUDZYSaxaaGWYd9h39HhAiSCM4IpwfCRpQFlcWRxUeFH0RHgyDBiIAnvk39Wfyf+9U7XfqfuWf4NHcqttr3Tre1d0e3ODbpNwf36XgMOOt5qPp9uw+74jyhPS4+C37ZP+U/7wCKgOrBrUIgAl7B4QP1CIjKf8umx6gD68TrR89JJgqLSzWLHQ1Py6MIZga/BdrGWAg4h5/Gs4T4grqBTQImQUr/Qb2jvC78Gfx/OtY5DDjG+Ka373d6NgJ2OPcbd+94H3ipeAy4Z3lrOnD7t/zRPbd+kwAqAPBBfMISwyEEN4VfhYcFiMX5RfuGK4ZihhXFqIUcBFHDaoK0AheBzEFVAKh/Qn5DPUC8Kbs1urJ6gfq+eiQ5kvl4OX35TzmAOY854vptu2I8OPx6PJV8/T0UvfN+uv8wf6EAboEqwawBuUEQATIBCMFRgNFBzYSJx3fI2wc4xBSDOoOdRSwGTscXR9uJNAjnR6PGR0WXxXsFs0WVhXQEb4LxgYZB3UJTQmHBHL7s/RT8ojwiu7M7ULt3O4V7hDqjOUX4/TigOWO6M/prOpV6grsD/BM9MX1jPZs94r6r/8lA6cFUwjBC0IOEg48DRMM4gxTDwwRtRHGDyMNywn4B3YHpwbnA7r/2fyq+6j7H/ow9yn0XvKV8Szwqe5Y7W/u1vB68X/xxe5Q7o7vTPJ19bP21viw+Sb7IPwI/SX9d/2A/ikA6QGbAjACewHiACQBov4QACQJzw9oFqQVuAwgC54NEg78EaYTRxXSGlIdqhmRGCEXcRY6GEkW9BIOEJkN2wn2CokKvAi9BlYCMP4k/Oz5MPW78oXwve3X7uDsA+v469frqOum6wfqDegJ6p/rhe+s8o/zSPUo+PX6IP+zAMr/EwHoAScE3gU/BwAI1ApxDL4L+AnaBp4FwgViBsIGOAdgBfkCZwCA/qT+F/1a+8j5OfjB97z1i/Ts8snzSPWN9R72gvWr9Tn1+fWi9ff2Hfk2+3L8YPyT+7D6Bf6vAL4CdwOfAnMCPASPBmoEZAMVAT0CIQf7Cm8MvwuLDNMLnA5EDT8L+Av3DLkPYBEHEUAPtw/3D4MPnBDfD/4NjQuGCKcFuQUNBooFHQZNBE0ClP4Q+7j4D/gu9vPz7vFQ8tbzrvMH8tXvOvBJ8GnwevDh8QP0l/YC9wj3zPc5+L76MP1U//YAXwHHATEE+QZnBqAG2gY2B4sJTwgvCMYH+AeGB7AFigRqA6oAbv9h/nn7nPwF+g/5JPng9wb2RvWS9Fb0GfWx9MD0tfS49X72+fdm+SH6U/rw+Wv6SvuI/Kv+gv+tAdAC4gMMA7oC3ALgA/cFhQaTBSoGogYxBvEGVAXJB2oLSw0YC7cHYgazB3sJlgl3Ce0JaAvTC/MJKwkgCfEJaQlICdIJTgiwBqIDqANOBMgEbwPUAGH/uP1l/P76nPpp+nj6nPnV97b3UPiO+Hr4N/mX+L33T/cu9kf3Lfkb+hn7pfs6/Ij7f/op+wf76/zg/lL//f+O/ub8WP00AJEAMAAr/m78df9zAWkBTgAc/xL8GfwH/X/6Uf1q/xsBQgIp/jX6ZvlR/VL/eP8T/Oj68fx3/Wr9CfyJ/Yn/BQF5APT9Tf2a/Qz+SABcApQCcQITApQBWAKbAYkA5AC1AQQDvwMVAvQBwQLHAo4FRQQ3BBgFTAUDB8kG/AZBB8cI0AmQCpILPQrbCM0HMwcHCIQJKQmUCEUInAYHBiUE5QJLAq0ClgIOAYP/+Pxs/Hv82Ps/++b6Q/uY+x37Cfos+c/4+fh8+BL48/gt+nv7HftI+7T6M/tt+3f7OPy8/K/9J/77/u/+Hv+s/Z39fv1E/Xf9c/5A/6b+mv2f++/6z/sS/Hf81PvN+m78gvx/+8T6Kvqy+Rn7B/0//sz+DP4e/p3/FwCJAMr/RAAVA0wFawUjBCEE+QITBJMFBgUNBcMEcwRhBAsFlQTDA2oDhAK/AuQCHgMFAokC6QIuApkCEQI2AUgBgAGxAOQBkAHiAZkCrwJ4A+ICLAK1AVMCUQOZAyoDWgJfArwCAwP2Am4B/wGHAksC5gFsAfEAiACS/+v97/0X/qH+fP5J/xkAuP/M/p39Qv3F/Zr9Kf3b/br9gv5G/t796f2C/eb9Wv3X/Nj7Mfsz+yT8Mv3q/Ir7ePru+gL7yfqu+jr64/p0+6/8gPxX/K/8EP1P/tD9kv6L/uD/nwBuAD0A8wDkAUYCgAGCAEYA+gBYAu0C2QMhA1sDfgLDAtsBiwCfANIA6wEEA9kBhgDO/5v+IP+x/7wA2QAwAEf/qP47//T/pv/T/84A3QEiAW4Aw/+k/wUBQAKkAoICGgMeAkIDRwPRA10EDQR6BEwFBQeFBhIHQwZqBYcFNQVdBdUFkQYkBksFZAT3A3UE2gPQA9IB0AHzAIgAN//b/en+Pf4s/wP9x/u7+Zr5L/km+cv5X/fk9qL3Fven94P30faQ9/n4uvf1+KT4yPjo+gD7qvwH++T8k/ux/mb/R/+0/wj+Bv+k/gP/cf8pAEYBugPKAscBNAA3/4v/nf8YAgMDxQMEBCsBVQGy/18BlgEHAc4BOgCCAdYAkQB+AOsAEAEyAU3/if8pAF4A5ABd/5T/HP9rAGUA+P7AAQ4BWAEcA4cBiwI0AoUCaAMTBIkDFgQSBSgFOAY8BYEFCwZxBoUG2gXsBc0FaAUNBbgDZgP4Ap8D+wKNAgMCxf8E/1z+Qf1z/d/88fv2/P77zfqt+OH4pflS+Yr69/jq+BL64fnG+R/5ZvrJ+vr79vwy/BH9Qf2h/ED/d/9EAMkAcf8cAckAvAGPAYYBmwLXATAB+ACC/0YAYQB5ANr/JAD5/z7//f3o/CD9j/1W/4/95v2k/aj+Ff/e/az92f4wADIBewEyASsB/wDHALMANwLHApcEFARuAkkCAwI7AnMCRAMgA9cCCgM2ArMCdwJdAsoCwwK4AoIC1QKLAlYCkgLMAu0BGAMcAtIBpgP5AhYDPwKbAQ4BPQFNAXUBbgEpAVYBm/81/9n/lP/I/9D+9v5n/pT+4P0e/XX+wP2U/Rf9wvuY+9/8o/wj/t39rf0k/LP8WP0D/k/+OPym/Gz9l/4+/xz/vP3O/hX+l/7C/GD8xfsT/dX++f4M/5b9Vf3W/LL7RvtI/Y/9c//Q/7//Tf9+/wX+gv8tADIA/wH5/z0CEQJCA3EDtgMwA94B1QTAAdcDqQOAApAD+AK9BBUCGANxAgoCDAOPAOL/IADQABoCKwFaAPb/l/8kADQA/QAQAaYAjP/g/5f/cf/j/+v/lAFlAagAAwD7/04ApQCJARUBwAHFAe0AggCd/0gA4v/4AV4BOQGLAcX/0/+O/mEAn/4gABABY//CAEMAn//j/wj/aP7gAEn/+f+Z/tv9RP/m/E//uP7o/F/+yfvQ+0b98/vq/Dj95PuE/NH6vPt3+xT78f3L/Mv8Xv0p/N/7E/6z/az9QP4P/0gApv+o/5T/tv9PASIAxQClAAwBcgHmAdsCWgInBHAB4ADUAIcB9gJ+A60DGATVAYICSgFGAMEBtQGOA58B5AK8ALH/FwEN/wkAwwBGAXAAJABs//D+tQCv/gwAEAATAKsCfAFYATYBtQF3AvEBZQJAAyMDlAOdA60EQwWrBAYEMgNaA5cC5AHAAUsC7QLVAk8BRADe/6v/YwAl/vr9U/3L+7H9ofzE+x78OvsD/BX7JPs/+6P6DvrN+jH7WfoQ+/H5mPxc/AP90Pv+++b83/w3/w7+pP/M/x0AYwDSAen/QgK1AkQBqAPmAIYBNwJ5AS4DRgLGA9QB8QCcAID/DgEyAeICCgAIAS7/if6CAJoAVQAiAJIASgCUAGwBUwDe//v/5gBjAboA7QDZABUCLQEeAUMAWAGH/4AB1ADF/9cCKwAXAQMBl/9eAW4Asf/vAND+XgG1APb+qgAg/74AHgCt/2MADAC4/8cAewA9AAwDTf85AX0AKf6D/27/NAG2/7oAiQAg/3H/2f1c/ZL+ev5c/tf9c/7y/YX/0v24/cr9OP3t/mf+if4K/zoAcf4vAFb+bP/V/xcAugGi//0BLv/6ALoClgDUAMkAewD/AK0BFwC6/8P/TgCi/1gAUQA9/5v+Uv/Q/Uf/eP/t/tr/Rv79AGH/Uf/G/xb/dwHvAI8AuAGp/+sBlAKCAfEBpgDKAXwCFQJnAW7/GQE2AcABYwEyAYz/Df8KAR0ArADl/isBOwBuASQBn/4M/r4ATAABApcCq//4AEr9BP8wAdcCzv9pAEgAjP91/5n+5v2+AIgAqP6C/2f8CgAj/+f/8v4K/+X+XP6J/Yb9Of7U/FEC9v8Y/oQAbv9d/4X/1f6A/aj+TwAG/+4D8QF7AAP/fv3dANf+vP/e/vYBEQJmAgb/0v13/0b+HgCJ/yQBpgATApn+Zf0Y/47/c/8gAJn+3v5RAev+Uf4KAHH/mf97AJ3/bP5aACQApv+qAgb+QQAs/xwBUQHM/hsAW/+o/3P/OgDS/gkAq/9EAAUAR/8V/8b/bABU/w4ChwFjAdkCAwCvAp0C+AFzA9kCkAN2BGwDpgRoA5ACiQKxAbwCQgMhBaQCRgFnAJL/kP9nAOIB1wDX/df9tfyz/pQA3fyL/RX+bP32+zL9bP3H/fv/4v2x/t7/ev9U/uT96//8AHIA0v5TAJv//ACCAaUAqAJ3/1YBPwHw/yQBxf4IAe0AxwCg/8z/EgBA/0//3v4y//39GP40/Sz/fv4l/fb8x/51/cL85/6u+yX+MP9a/fz8HP6k/lP+s/04/cr+Pv8Y/4gAQv8R/0//Af/LAFMBwgCaAPv/YADJAaoCZgIeAGMChAGHAqYCnACQApsB8AKiApACwAFTAggEmQK4/37/+wFGAgMDugB6/x4BpAJ9AMr+iwCUAO7/DAC4/2MANAHl/i7/bACSAEQBBQDy/0MABwCWAIv/2wBsAYYBwAEpATsA4/9PAEn/OAHKARMBwf/O/7UB+gBpAPn/zP/G/6n/fv4G/+7/uQADAXr/Yf9G/fH8yv1d/vH9cP3S/aT9N/43/sP8Pftu+9f8pP11/qv+yv2l+3f7df2j/NL81P2AAc7/Qv72/Z/8WP+L/2b/WABPAGH+sv+IAAoB2wGUAbMAowC1AUYCjAMpAnMB/wLlBEQEqgJoApYCoANYA0sEJwRwAhwCrwGzAngD+AKRABX/bgA7AZwATAB5AOD/Xf+2/hH+fv8kAI7/sf/d/ev/XgGm//j+sf/4AIYBpP/D/8cA0f/JAbUBZQC1APn/qADgAen/cgDV/y0AFwHg/3f/NAB+//T+oP8yAJn+yv1c/kL+GwBv/5T96f30/8z9kv6a/I/9Rv6A/xAAnPtj/YD9vv1R/sj+3v0R/dv8yv0R/nL8Hv/7/on9kv/Z/KH8bv87/7//CgL2/gz+qf+dAXUCQv+D/ykBxQK1ApD/TQKDBDQBtwETAZQBmwLTAoYBbgEVAs4AJwFcAUEBAQGk/24A+ADO/vD/+AC3/dD+8v+0/wwB0PxB/U8AkQAuAbL/F/5I/iYAqAG9/+AA7wJ1AfIBvAF3ALoB4gK4AiED/wFnAQUCRANlAscBHgLtAR4CpAHX/+3/r/9KAVgDkAHi/1H8lP9BAVwAbP/l/mn9xwFeAW/64v5OAPL/lgCq/Hb7gv+1/tf/tP/o/ZH8a/y//lMC8P9a/Ff7E/wI/7H/of5/+qj/PwE7/cf9nf7Q/Jr8mf4TAGEAqP4n/97/+/5YAHf+f/x1ALUCrQGlAGsAZ/6y/wT/wABqBNICYwCo/73/HgDeAzgBR/8iAvsB3v/w/1UAvf8yArMBAf+U/2UBswFlAI3+6f0uAQoDtwB5AEQBKwIwAccAnwCkAZ0C+ALGA3UCPQI5AV8C6QMwAxoCiwJ3A00DxQKvAS4BDgJhAqYCqgFlAd0AAwF+AdsAiAABAFoAS/98/pv/RP8D/kwA+v2A/Jr9nf2E/AX8vv23/Lf8wvzN+sL5OPqj+y79DPzW+8D6efoK/B/68/lf/uX+Jf7A/Pj6Yfz//sr/kP/t/sz+BAM4AIb7iQGeBLoBsQI1/5QAlwMgAdICBASiAmUA5f89AOICoQJ1ASsC7wEuAv3/N/47/xUAyQGbAk4AOgAuAXX+HP8XARkAlgDa/+gAdQIQAdD/iQBeAVQCEwE7AD8CUQOQBLwCfgCtAXcCdQK8A10DVgNHA0kCGQHAAdwC0gHFAV0C1QKCATL/Pf/9ACACPwEh/4L+2/4p/zX/VP+M//v+9P1M/Ej9bv83/yn+zfyj+8P8if79/Uv9t/vS+939Pf3t+3f9yv34/fH8hvtX/LP94//F/5b9V/vO/S0AW/++/pD+Qv+X/9L+ef5k/2kA9AB3/5n+kgBRAOoAbP/D/ocB6ABBAKD/xf8wAXECyP+h/ZIAswHMATICVQAV/37/QQGAAloCYACLAIsAMgDgACIBDQQ7ApX/BwC+AR4DhgHw/woAIwLGA0QCnAA9AbUACgE7A/cDdwNJAnIAswGVAzEEIAPBAQgD1wTRBRYEaQHkAAwCGgMqBMgC4gElAnMBewA1/+n+5f+L/1H+pP42/vT9i/xb+mn8Vv/8/OH6u/qw+pz83/zs+7T6MfpB/D39FfwM+577YvrJ+xf+wvvL+lr+yv+N/K76oviN+kEBOwEi/YL86Ppg/Gf9GP5uAHP+Gf2v/a39+gDG/1j8qf/Q/osA3wDv/P8A5gH9/r3/Lv7l/24DiQJhARsBNADa/woBgAK/Au0CjgMjAqIChwNsAhABBQJSBNcESgb1BIQC7QEDA1IFkwYqBrkFPAakBewE6AV9B7kGJga1Bq4GUAdMB3sGVQe7BnoFgwTcA8YEbQTBA18EwwHM//b/Uv9E/43+Nv3E+/H8L/xz+Pb2xPj++DH58Pj/97/3H/e49Ev1DPZb+Fr7cfmJ9//1tvdR+437YvkX+5382/zd/KP9PPqC/vQByv0k/VUBRAI2/moBXf/9/SwFegIy/Z0BXgGx/4ADrwC6/aID3v8M/wMArvvD/scCmQJ+AKb+SPuR+/b+jQGuAFH/4P5z/y4Bav+N/TX/mAHpA2wDwgChAIEDeAWrBQcGSQQuBaoHWQhNCa4IMQcTCWcKcwrVCk8JCgpeCvwJqAlgCN0HiwjmCBIHsAR4A3cDWAPTAs4BnQCU/1j+S/1M/M36iPqp+mf6bfrs+L/2ZvZx9lv2MPfT9yj3DPYK9qL1RfcS+cr2jfQf9x/5y/md/Dz7xPcE+ST5xPjr/sAAUf0A/LX7Pf0iAKj/DP6h/FMADwP3//3+NP6dAAYDdf9x/gEBc//OAXwCif5T/oX/VP8lAdcBD//r/nD+pvyL/8AAWP3MAZsCdf1j/Zr9kP0lA88FEwLxACcCPQIzA30GywcMCHIIxgcmCPQKyAwRDT4OEg6ODbgNCw6aDrkPsxDPD0AOGAzHC1oM2QudC7MJcgbGBZIEDAPOAhkBGP/x/L76JPmu+AL5tvdT9RX0BfNB807zn/I+8dTwMfHh8cvyHfJg8qXz/vP281H0bvRs9tH3q/cR+G/4j/m0+gz7JPy3+278NP6m/vn/GP6H/Xr/dQCmAOsBnwE4AfT/K/5lAHcBzAL6AZ/+Sf47AUgB+P5n/ZT9FQDBAaj/Qf39/On9BwA2AL7+Xf/IArsEkAN1ALr+NgAdCOAMnQzbCTMFPgYGDKkPBw/0De4NLRFfE64SZg8IDdoOoxDkESsSABBHDusMNwspCYQHSQZiBugHtwZwAif+1PsQ+437Nfol+KD2VvYE9zD0H/F677jup/BT8qDxnvAo8IPvV/Bo8BDwEvIF9DT1qPVC9eT0i/V29yj5Yvq1+g770vs9/Cb8Q/yf/ef+xv+Z/zT/VP9jAIz/kP6U/0YArgATAD3/vP93AJT/0P1Y/YT+m/5d/jn9mv0Y/tv9kv0X/QD82fu0+kT9NQVfA0QBHv3V91oA0AmZCgoJuwWdBOwIQgy6DEQMhg8vEvgSeRPZEbkQ1RLyFHoVthQlFBMUURNRE1kRLA5JDHUMeAwwC7wIVwU2AjIBnQCH/j/8t/n59xH4rfdw9Njxt/At8bXxk/D07jfuf++c8I7wv+/s7ijw2PEB84D0VfTL87P0OfdB+Y75zfjw+db7K/yv/Jz8jf1+/j3/nf/I/i7/WABm/0gAiAAV/8P+Af93/4v/lv5V/Lr8Z/0M/n/8Y/tn+7v7RvzN+iD7tPq9+Uz7wvi++5IEFwHw/8f7uPWz/gwJSwsOCgsGzwQACPQLWQ1aC0wRqBNzFREWCRE2ENASGBXWFh8XwRWQFPQSyRMAEdENYwyHDFANHwxeCSYFfAH5/yn/XP3q+9P5wfi795/1APNJ8EDvUvAm8T/xtO8u7x3vNe567+7uDfCg8VPzA/XU9NT0zvTw9hb5TPvN+0z8dfxY/dz+Pf9L/xIAFQEIATkBlf9N//D/TAA7APv+fv2J/V79i/xa/BX7V/pV+Yj5VPn5+FT5zfjK+Or4Dvrw9wv3S/cM9kP7RAMgArz+ufuo9JL+qAk7CukKDggJB1wJtA1QDLANShO9FeMWkBVeEhUThxXDFtMWphTZFFwUpBTrE3QRDQ3eCzALDwv7ClkHdQQpAT8AbP4F/Yb6nvnw+Wj48vSe8o/xZ/EU893yQfHR8KrxB/HA8TrxrfDW8TL0bvU09h/3Ifcw95n3+fj1+u/8bv09/3H+df6W/gj+qAD/ATMD7QHO/8j/0AAcAY0A6f8s/97+LP7z/Lv7Hfvm+o/6t/nK+Lb3Qvfr9vn29/ZA9h72q/Vs9UL1zvTA+Q4A8v9I/bb4ifTA/OQHPwspDFcIjgZTCTwN0Q7UD8ETmRasGSsZcRU/E7MUgxehGcsYVxchFkQUChQxEToNZwoOCvYL9AoNB+IBXv0H/DH7jPqg+Lj2DfbA9PHyfu/T7UvtIe5r8HLx/e7e7rDva+8v8XLwxvDJ8+L2xPhB+bj36fY5+Gn7K/7w/7cA7v8cAdIA5f+3AJsBQgNdBcYDrwGGAYsBWgElAVoAlv5w/sH+VP5l/H36wviI+Ij5efom+c72GfX/9Kv20/Zl9RH06vN+9EL/HQCE/KX7JPOn9+0C6AkDCrUKdwnIBvIK+AvnC9kR7BcbGYoYRxUeEt8SrRUAGdsZoBgoF6oTZRNMEoYOKgyZC/ILEQz2Cs8EeP/W/LP73/vA+kP4q/Zx9ujzV/HM7lbsDO0P76nwXfAd73fuX+2/7unvhvBB8RnzR/aA9674qfYg9mD5o/tw/UL/6f4c/84AbAB3/3IANAL9AsYEnQOGAVoBmADXADQCnwGRAP//xf5R/Y/81Pua+/r7V/sr+iT5GPlU+Jz4H/fn9TT0LvdsAroB7v9g+tbwJfdzAy4LmwsiC7MHMQZnCq8JcwtWEmQXCRqVGRMVHhFREVQU/BgBGtQZCRh5E68SKRFxDbgKyQr7CqoLlgnKAub9Cfx9+ib6XvnM9Yv0d/RD8hTwne3b6sTqlO3s8PfwOPCU7Y/r2u0Y74rxi/Pm9ED2LvcW92P1RPYS+YH7Cv6A/x7+Xv13/tv+0P4OAKoAAQF+An4Bhf81/8X+of75/1j/6f42/rX8xfuG+9H68Pme+tL7nfxD/Lz7kfnn+Uv4aviT9yD+FQoJB4gGzvwR9cf9WAluEUYSrhKkDZAMiA4BC4ENzhVlGgAfrR3FFAkRBA/LEIgWaRf5F0QV+hBmD0gLbQcuBDwELAYhB7YEqv2w+NP19PPQ9MvzP/EU8iTxqe+B7s3qtOjd6LfqnexX76Dv++697/nuc++B72TxwfRA+EH7Tvz6+nH5ZftP/AX+CgFPAV0CRgNsAWsAFQDbAEACIwJTAQ//4P36/WX9Zf2H/c/7Rful+nH5fvjC+Hv5o/na+lf7Yvs4+/H6evm2+KD4XwWWCkgLTwv0/XP+igWeDiEUEhhbGKYUxBY7Eh8PsRNHGLQdpyHKHT4X6BHVDbQPfBPpFMcToA5YC5oHqwPtAKP9jf2+/jL+ofrT9T3yGPAu8Njv6+2g7W/tX+6l713u3uxK64nqfu168AnxF/M79NvzXfb99V31k/db+Vj8b/++/u/9XP5E/j8AmwLpAoAC7wIMAxECowG5ANf+DgETAYL/HP/X/Jj7jfs8+mT55/jG+Dr4xPfB9+n2R/eK9xj50foZ/E75Ofej9IX+YAjxCEUPIQOm/YED/gjmDyoVcBkhGIIZeBipEHIQ/xNiGJghpSL0HL0Wyw8sDVsP7g/JEYMPbwzvCh0F7v8t+3T4kfhe+jj6RPbm8YHubuyi7QPt3+pD6rrqJ+2z7Wbtiex567Xsme9y8YL08Pa09+P5JPnP+Ib6APyq/vICHwWvA6gCcQLSASADYgQeBD4FCwXuAyADYwJVAP//0//p/Y/9EPwU+oT6iPrf+Ir3c/VL9mr1LPZP9tr2jPhi+oH7TvlL+ab2lfp9+z8H4Q9uCtoNZgPxADIJAA/9FH8ZUh21GhsZRBXNDzYRKhU0Gp0eAh7LGLwRPA3pCqgL9gykCw4KkwZuA7MAUPvP+fn4zPUR9c7z2u8R7yrvA+2v7fDs0uoM69Lq8+pa7ffuOPDo8YTyOPJ78vjz2/VQ+XD8Nv5T/mr/bgBlASoD1QKqAtMDogW5BeUF2gSUA2YD6wJhAdv+E/7b/Xf+6/0Q/A760/Yj9fL2DPZ2+B37mPW58zbza/Gt9kj+pfzb/m//4foV+6f7if/0BLYOURKTEeMPOQsBCtoNSBRgGP8d9x1aG3gYkhSFE/gSwRVOGYMX/RQDEUYKHQeyBssGjAZMBckAWvsd+Cr29PPk8lzzM/F28Gzu8+rx6T3qj+tK7ATtDuxj6xXtKO7z733yvPN+9Bf2Wff09xL65vuL/jQBcwJjA8cC0gKvA6cExAVVBkEGbQa/BlcGpgSCAsIATAAtAMX+EP3C+r34ePfX9rH1kfh9/Pf3t/Rf8JDssfP5+er7RP6d//r9Zf5b/8H9GAKLCE0N5BF3E9sQSQ/GEFQTIBmZHC4cURsMGUgZPRoUGaUXthaQFT4UjxJkDaEJjwhlBw4H9wOS/m/5avYT9BfzUfIQ8Cbvxu4W7nXst+l25xHnSegd6jTsyO1i72nxvfGl8SbxYPHU8/X3XPxs//gATgBaAIYAdwD2AXMDsATCBskH8QYvBrsEswKdAjcDGgJCAYf/x/1l/Ur8svok+Zv3dfWr9QfzF/Nm+cL43PZ19S7wj/AA+ZP7sPuRAJf/2f8wBNwD5QPqCHYNOhCgFa0WfhN8EzcU0xd8HSgg6R26GugYYBhBGGYWiRUuFKwTCBM3DqgI+QTeAq0BHAHk/cL4O/Xu8QvvM+4Y7cXri+sH677pf+ie5sblduee6VrstO737s3v9fEV85b0gva091z64v17AGMDeAVoBSwFDwbABooHZQjvCMAJiQoeCjoIMwZmBI4DtQJuAW//N/63/F77JvoP96z0x/LI8WTw2O9U7F3uw/S+9b34rfZv8RnyYfcm+PX7GgP/A60KEg8EDUINtA9iELETOxpyG3Ac1xyNGyMdBCDoIQIgrxxcGqAYkBbDExUR+w6lDrIMKwh6A+j9M/lt96H1MfMi8fLt2eml6HLo5ebP5uzleuQO5DfkIOS25Rbpn+vD7e7v/vC08Yjzn/Vm+KP8ggDOAkkFcAdyCP8JJwroCQwLkgwLDpcOdg7vCwEKaQhvBp4F1wQYBGMCaQAB/av5aPYH9Bnzz/ID8ojxEPA87tDtp+nh6CvtivCb9OP3l/Z49/390P/xABQFlQTUB1kOKBBeE1kZQhs5HRYhRSDEHr8duhu+G5QeUiD7H0sdlhpOGGwV7REzDckHRAQRAjL+7fvp+NX0MfMD8vPv1+2T6dfj+OCj4ErhwOMW5ePl/OfP6Ojo7ugF6a7qh+5M8mz1/ffY+T38yP8cA7kFywZVCKUJ+gr9C1sM/wz9DaUP9Q9bD8YNpguUCawHSwUMA3AAc/7//E/8Q/v5+FT2pvPC8Q/wW+457kTtLu2q7PXqHer77vzy4PZO+yT6bfuo/+ICFgNGCIIJcwwOE3UUQBZpGqYc1R3jIdohKiB2IGYdcBv/G0Yc5BqeGJIVPRLbEGINEQpLBTACA/7G+MLzHe9K7B7rYuqe6CjoMeet5dvk/eOi4wrkGuTQ5CTnZek96w3u5fD79Sj6Nv16/pn/fgDFAcgDMQWnCCwLaA3lDdQODw/zDuEONg/cDnMNyAuGCL8FDwQsA4QBTwBn/sf7LPkG9y71wPOL8mnwge627Vzspuu67PvtPO4+8PvuOe1D8RH0N/iH/TL/pv8FB24KagwvETIRxRFSF8QYCxi6HGccoB4tIy0juyGlIeId4hr6GhkYIRaCE/sOyAvQCdgGcwQDAXz9vfph99nyVu7U6bDnBub/5Ablh+Rl5DDlSeWd5VDot+h26lTs/+3j7zPyAPOJ9af5Nv26AVgENwYOCPYJZwrgCxMMbwybDewN1w0sDiUNYQygDE8MRguACXQGdQOmAVH/Bf3Y+hT4kvXO84/xX/B+7+7uDe/g7onu++xh65Hr4OsP7bjtSO3Q7C/yBvcm/EQBZgMGBmMMlhDZESUVWBOAFSEYEBrhGekclBweHuEgyyD3IKcfIx3ZGbkY9BSdEYUM6gd6BMcCowCP/ar6Kvj59frzLPFW7TTqcuiw5kDlN+TO4injjeRt5jro4epP7Rrw5vLo9IL2WfcC+f76I/7QAekEZwZrCMMKTwzuDQQOlQ7ND08RyxDmDyUOYwyCC2cKwAgDBwAGIwSXAgEBGP/O/HL6jPcw9bPzmvEU72/tquzK7E3tY+057Z3tZu4x747vqe8x8HPvUvEM9Kv4DP3u/0ICeAY7DFcQSBRqFLIVMRdOGZMZdBpOGnkawBuJG+Qbext3GskYkxiyFtgV9BLPDjsKJgfrA9n/EvwA+I313/PW8Xju2+sX6tbpnOlX6XjoB+j55wTohOhw6T/rOe3z78bye/XG94P6hP2tAVYFpwd9CDgIlgj4Cb4LDQ3YDuwP8xBZEdAQiA8ADggNYwzQC24KdAhxBXECuP/F/VP8vfqy+Mb2qvUH9Kfy8/CO7yHv/+6Q7l/uDe4C7rruIe/h717xTvI788LzxfPg9HX3afrz/LH/ZQDuAyIIZgu5DZAOBw78Ds8QFxBHEF4PBA94DyQRaRE4ElMTCBOEE60TDhPLEW4QaQ73DJILNAmBBQ4C4v7x/Dz7tvh+9iP1XvNc8pPx8+948DjxXPHu8UryjPEz8rLyzfIX9Xj38vic+1z9hP6fAXUDsASjBtoHbgjzCZEJOgm5CQMK3QqtC7gLdQpeCZUH/AWVBFYDtwHSAM7/x/6f/ej7P/oj+Y74zveF99f2ifam9rb2S/ZC9sn1kvX79RH2tfWC9Vj16fRU9TT1YfUz9zf5efu1/br+X/+EAfEBIwLiAqsCOQO0A00C5ABVAQUBkgHbArYDEgXqBh0HPweoCEsKIwzEDfINzA0ADqsN7AwPDGoLmAoFCncIKgfNBbAEvAOoAukBrwEwAV3/s/2n+/r6pfvj+uX5B/lZ+KT4rvnN+eP5yfoZ+9T7iPyv/OT8Vv3B/aH+fv/O/mr+rf7T/wMBswJhAyEDoAMRA1oCeQIuAlMBOAFuAC7/K/66/FX7+PvZ/LP9RP42/hX+LP6+/QP9CP3//JL9rf0I/X/8vPuG+q75jvjG9wL4u/gE+fH5p/o2+1X8E/3J/TX/XgCWAP0AVwCp/6D/6f4G/uj87Pvf+lP7Q/yq/Vj/kgAgArQDrgUiB4IIagmHCn4LWAymDKYMZgwTDKELEwtcCtYIpQdtBr0FzAQqBD0DIgJsAagAw/9A/8z+K/4//lH+hf6//pT+7/3O/en9F/6F/jb+IP5z/qr+n/6E/ub9m/3H/fb9Mv5u/gz+8v1W/mH+4P5a/5f/lf+b/zf/1f6L/rP9F/2h/E/8Pfz++3b7C/v4+vX65frq+mv75PtO/Gv8mvxT/Vr+eP8SAJwA/ADBAVQCcwJYAhUCJwKmAqQCjgInAoYBewGHAagBQgGaAPb/kP8a/+v+bP55/d/8NvwZ/Pz7Cvwk/KX8P/0X/uf+pP+jAGMBAwJYAvkCRAO/A8EDNwOfAkACfgE2AdkAiwB1AE4AVwByAMMAFQFPAiADTgT3BKUFCQaaBuEGNgd4B1wHPgesBu4F8gQCBMwCGgJpAf8AfQAVAMP/Y/8p//D+qv6k/qT+T/7//TL9afxg+2v6QPlL+GT3qPaC9nH2i/a89i73xPfz+Fn6t/v4/FH+jv8DAVQCIQOgAyEEugRdBQ8GXgZQBi8GTAYhBucFcwUSBWEEgANPAhkBQwB4/+3+hP7Z/fT8DPzG+mL5q/gu+FL4BPml+Uz68/od+wL79fru+lr74vtM/N38hv0s/sr+9v7r/iD/VP+p/wUA7v+S/0b/Fv9k/x0AEQETAikDjAQbBlcHkQi5CZQK5AviDEANUA0NDZIMEwxmC3MKcAkQCPUGuQXGBAIE+QLkARcBJgB3/9D+yf36/Dr8TPtt+i35l/dS9l/1m/QQ9Gfzx/Ks8tvydfMH9MD0nfXF9oH4V/oQ/LH9Zv++ACICYQN1BL8FzwawB1EIdAhDCNoHOAfRBqAGMQaMBeEE9gP/At4BWgBk/6T+Af5+/Z38tfsZ+4b6PvoJ+q75g/lt+X/5/vl2+tL6Y/sK/JT86Pzo/MX8+Pxp/b79GP4y/jn+cP6C/lz+fv4E//T/OQGXAt4DTAW9BjEI1wlhC/0Mig6TDxkQQxBTEC8Q0g/1Du4NmQw0C84JKQiRBs8EBgNNAcH/q/6Y/cD89vvs+ij6ePmD+J73w/bX9QP1B/QD8/7xBPFV8EHwXfDs8Obx9fLA9LP2ufis+qr8mf7HAPQCzASYBvwH4Qi3CUQKrQoBC8oKVgrJCVMJcggtB7YFNwQTA8cBYQAV/8X9zvxD/I/7zfob+oH5Ifn3+H/4Dfj99xb4m/g1+Wb5UvlJ+Sj5EPl6+cb5QfrU+lr7ufvP+8T7gvuR+w780vyk/YX+bv+IAMkBIQPcBMQGwAi6CosM5w1KD7kQ8RH2EpQTsROUE0YTWBIeEd0PQg6dDMMKrAiRBocEegLQAFr/Bf7D/Gv7L/rl+Mj34PbQ9cD0lvNp8m3xv/A38Nzvpe/G70fwGPEo8nnzUfWM9yb6qvzp/h4BLANdBXIHSgnFCvgL1wxfDYkNGA0qDOQKlglBCJwG7gRhAyAC+ACk/xP+ffw8+zH6Qfmb+E74LPgA+En3cfYy9gj2Rvba9hz3Pver94f3rfcs+LT4hvkU+oz67/r3+q76dPov+nL6Hfuz+2f8T/2C/gMAugE9A0MFswc0Cq8M5Q7UEIISDxQcFc4VahaTFnwW+xX2FI8T8RHlD8YNfgv4CGcGHATSAZv/s/27+yL6u/g+9wr2//QK9DLzdvK58RDxVPDP74zvTe8o7xrvW+8H8EHx0PKf9Kj2FPmh+0L++ACZA1AG0AjdCpIMDw4WD7UPpw88D4EOkA0+DJIKlgh2BocE0gJEAdz/tf5B/fj7Evtb+rf5Fvk5+H73M/e/9l32x/Vd9XD1ofW89bj1bPVo9aj16fU99n72rfYe98P3Q/jM+Ff5/vnJ+o37afxz/dn+aQCbAg8FewcICmoMvQ7hEAoTCBW9Fi8YDhmqGQUaEhqzGdQYVBdsFVgT3xBvDtkLvAipBXECY/+R/BD6jvd19cXzSPIf8eDv9e5G7sPtVO3e7IfsZeyO7KjsCu2r7WjunO/u8H/ylPQf9wX6/PwMAO8CBAYeCQoMpw6wEDISFxNoEzITiRJ/EQkQTA5UDD0K4Qd/BR4D4AD7/mn97PuI+kP5yveS9nz1Z/Rg81HyVfGT8A3wkO9L7xzvGO9h79HvVPAA8ebxG/Nn9On1bff9+IT6DPyE/df+JgA7AXMC9wOaBTYHzQhuCnUMwg4HET0TQBUWF/4Y6houHA8ddR1jHVEdixwFGygZhRaQE2UQ5ww7CYEFswH4/XD6Hvf+8yrxtO7i7Inri+qp6froxuja6D3pp+n46WXqDOsR7EDtbe7V71LxRPPK9W34IPv9/QgBPASgB8wKtA0+EI0SlhRAFn0XNRhvGCYYXxczFmUUChJdD00MWgmBBm8DVQA5/UH6fvcM9dnyAPFb7+Xt5uwi7HfrCuum6nLqnOro6n7rROwh7TXule/88G3yBfSN9VL3LfkH+838df7p/4ABDwM5BDkFLwY/B/oI8QqODEwO7g8IEp8UsBY4GJEZMBv2HF8e5x5zHqYdmBzrGr4Y1xWAEt8OBgvIBn4CLv4L+mP2HfNU8AvuBuw76uroHejB57nn2Ocf6Hvo+eiy6VjqHusr7K3tZO9i8YvzqvU1+O764v0BATkEXgevCvANGRHpE2oWbRjhGcAa6BqfGr4ZZxiVFmMUwhHKDp8LOAjhBI0BK/7Y+pv3ZfRc8cPupOwF69vp9ehm6C/oEuhS6ALp0OnU6unr5+wP7nbvEvHk8t704va4+Hn6Mvzx/Yv/CAFCAnMDqwStBYUG5gY0B5YHhAi+CZgKcwtsDBsOrhCSE9EVeBffGIgaShyDHd4dgB3ZHNAbKRqXF0kU8xCHDf0JQwYuAgb++fk79hXzR/Dw7brrj+ne583mMOb05RHmVOYz51nocOmw6gXsv+0G8I3yDvW090P63fyd/z8CGAX6B5gKNw2ED2cRGhONFMYV7BbAFyQYyxfTFkkVYRNREcQO6wvZCHQF+gFN/o/69Pbb8z/x5+7i7C/r3en56KXoqehQ6VfqT+uS7OXta+9D8eTyHPQ29Tv2RPdX+Af5q/lv+i37/vuP/AX9WP3m/Xr+Lv/g/3cAQQERAloDTgWpB/wJCAxCDiIR1RSXGK4b/x0qIFEiFyTZJIQkTiN5IT4f/Bu9FysTGA7sCOsDs/6i+en0TvCF7Kfpfefl5a3kouNP48PjlOSo5bvm8Od96RDrc+y67QDvq/Dd8jT1gPfn+UT8/f49AtEFdQkjDc8QDhT0Fh0ZmhpwG7UbnxsDG7wZjhe4FLARYA45C90HTQTHAGn9Ivrk9tfz6PCK7rPsOesS6gzpPuju5xnoveie6XLqbOvF7GjuOvA/8in0JfZX+Gb6a/x3/kEAlAG/ApID7QMNBMMDgQMhA7wCCAJyARUB3QCqAK4AaQEqA+EFfQiQCrIMxg++EwQY5hqZHEYeCyBmIY0h8B9WHfQa3BdPFOMP3goPBmMBufyD+LP0EPHw7Vrrvuk66QnpqehS6CzooOhr6bPpx+kF6nvqaut37HztIe+B8ZD0X/gM/O7/OwRRCNEMHRHpFB0Y5hrgHAseiR6vHS0cFxptF3cUOBFqDYkJ9wWtAh4ArP08+xr5//ZG9cvz6PEA8DvulOw/6/zpiuhS55fmbeYU5zjoyen0663uofHL9Dn4iPuq/moBiQM1BZEGLQcUB5EGpwW4BKkD0gHl//j9JPx/+jP5TfhW+ML4M/my+qH9LgKCB8gLSQ+4E4gZECDbJD4njCi2KakqKio1Jw4jqR7mGdAU4Q5TCNUBwvsu9v7xjO6f6/noJ+Zm5ArkJ+T744vjAeNK45DkieVf5n/n9ehq67LuwvEu9f34+Py4AQAH5wueELEU8RcXG4Ad6x4PH/0d7RteGSwWTxILDn4JRwV5ARr+AvtS+Cn23fRT9B708fPW89DzrPOa80PzdPJ68VDwR++K7hjuJ+6t7orv6PAO85n1nPj8+zX/egK2BT8I3wlyCswJwAhZByoFFwJh/lL67fYp9KHxr+8e7jftkO1b7uDvcvIj9cr3Cfuv/3oGUg4YFHgXtRqVH6QmyyyBL78vgC+SL/8uNCwuJ4MhvBsEFtoPrgjmAPf4j/EM7D7oFeXt4S7eYdvi2hXcbd1z3hvfcuBj40XmsOjx6k3t9fCE9ST6h/7HAvcGqAupEGEVPxnVGzkd0R3lHU0dzhsmGc8VEhKTDjsLdgemA1gAlv3N+xn6+fcg9rX07/PL85/zxvLI8Z7wv+9o7xjvwe5+7l/uAu998IHy2fR191X6j/3HAG4DNQUoBnYGYAbLBVgEGAIc/+T74/hx9j/0OvIb8OPul+4o7+fvu/Cw8Tbz4PQ99vT3j/l7+yf9nf9EBEIMSRS3Gr8ewiJyKYoxTzc6OWE4MDYxNPsvKinhICwYYA/YBjn+BPay7qLnDuIU35PeSt8x3+HdVN0C36zhD+TR5LjkyOWp5/XplOye79LzOPnK/sgE7wqKEAAWuhqAHr4hpSNpI48hZB51GpkWKxIADdsH0gIu/qH6h/f09HfzivIm8vzxwvG58QLybvLv8pLzT/QM9br1b/Z89w35+vrQ/CX+MP8OABcBVAIVA2QDRwONApQBVwDO/k39nvvL+VL4xfYl9ZrzPPJ/8bLxP/Kh8jLz6PPJ9NP1rfZz94H4p/mE+uH7c/07/x4AmgD2AmkJ/RK4G6UgpyKzJagrVTI6NSQ0TjAgLKYnBSFOGGsPSAf3//H5YfSK7xPriObO40bkgeYz6Ffn6+TS48jka+Z/53Hn9efZ6Xfs1e+L8+H3RP0DA8AIWw76EjwWSRifGfEaxxv8GuUXZxPsDlQLPQivBLUApvwf+fT1SvNb8WnwW/Cr8BbxvfHJ8ubzGfVu9hr4APqz+5H87fyd/dX+hgAIAtwCGgM7A1IDRAPbAvgBBQHQ/5/+Uf3b+4z6OvkP+Ab3HvYK9aPzIvJB8S/xk/EX8lryEPO19Iz2Lvh7+aD6LfyE/fj+cABRAmwD8gPcBWwM2haEIcYntyllLFIyXTnbPC07rjUUMC0p8h8fFccJVv8h9ontmebf4SHdsdgk1lXXLtzo4DDie+Hm4Xzkcujt6hDsee257+Hybvae+tr/5QURDD0SKhiCHc8gByL8IWYhQyAeHTMXuw+cCFwCwPyD93byl+4T7LXqP+qu6ivsPO6T8NLyuPQg9mz3ZPij+dv7Uf6+AMoCJwStBXIHyQivCWMJOAi9BtgErQJPAP/94vtp+gD5tvef9nX1rvQ09PPz2fOx8/Hy8/H+8Kvw7PBV8ZPxrvGu8ob0T/bc90n5+Pqs/ef/+gHlBK4GdAfmBywLzBR2Ih0sPS+SLsgwRDhKPtk9kjfML9UoDSGAFdgImv1K9ILsCeZs4RTeX9rK1WbUa9gU3zTjZ+Iq4CDhxeWY6jXt8u578rv3RP0DAuEGvAy8EsgXjRvYHlcgkB9sHLUY4xXrEhgOlQd7Abf8f/l39nfz/PCH72TuYe217IDsKe1A7vvv3/El9IL2H/lR/GkA/QQpCWYMGg5VD3AQMRHtEFAPhwysCb0GiQM/AO38wvlU94n17fPt8qXxEPD97r/uuO6n7qvtO+xg64DrPeyq7Y7vofEg9Nn2Nfoc/SP/8P+AAOIB1wIwA2wDrQPDAxYDwwIMCssY6SiBMyQ1nzWuOyxDaUZqQn44Uy6IIlQUegYY+lvvXeVk3OzXTtcx1hXTWNCk00/c1eMl5lbkO+Mc5vzqUO+j8+z3gPzdAawHzQ52FlYcKCAKI6Yl/SboJEsfXhijEgkOhAgeAST5hPKy7sXshOvd6jjqsOmI6bXpdOoX7Mrt7u/L8iz2k/ms/PD/xQONCIcNWRE0E8wTKxMcEuMQQg6dCjUGYwHX/NX4cPX18n3x+/Dq8BbxEvEk8FDvp+537rrup+4G7sHt3O3s7mbxB/Tk9kX57vo//bb/FwGCAeYAGQFjAgEDFgOrArECjgJxAskGQROCJAczSTcXNSQ2OjzlQk5DNTscMIckMRdICtL+D/WQ7NPjsN1k3PjbA9l21CzTudgo4KbjoeKq4AripubX7Ej0i/vvASoH5wuzEscZWR6IIMIgfyBdH+IaxxOrDLkGQAID/ir5ofTE8DPuEe3k7Artauwr687qY+t+7Kbtv+698fv20vwjAm8GWArNDjQT2hYZGSgZMxf7E5EQdg23CeUEwf88++X3bvUH8xvxz+/g7sPuVu/97yTwXe9+7qbuku8b8CHwHfDP8FHyavSo9t/42Pqs/F3/rQIoBQ8FOwNPAgoDGAStAzkCGwHWAKv/ZQBiCPMXZCmeM0U0FzM2NuU72T50OtwwNyUvF7kIjfw48xHsauSM3YXbPtz72xzZF9cc20TjxOni6wPrWutb7tnyIfkHAN8F+gmpDKMQehYlG+YcGRwXGg0YcxRfDnAH6wCE+2r3o/M68DftZerR6H3pwOs37ofvh+/3783x0PRN+I/72f6FAmAGewr1DUUQGxKSEw8VshXpE+UPLgv8BgoETQET/qP6Ifc29LnyzfKc81H0LfSd8zTzmPIv8e7uI+0/7Ebs4Oxf7Qjub++E8bz0N/lc/S0AtwF6AokDdgQUBJ8CbAGfAJX/jf4O/tn9jf2N/d4CIhIjJw84lj23OmI6+j+NRYRE3jrZLModXw0G/q7ycuqb4hfaPNTE1LfXGdcB1ALUndre413oHed15efms+ul8qf6/wKzCQYOGRJpGLYfEyTdI8ggiR0kGmUUDAwNA1P7uPVv8W/tAOod563lI+ft6jnvqfHN8XLxZfKx9Iz3UvoV/QkAAwO1BucK6g4BErgTzBQUFvkVuBMDEK8LKAgSBWwBJ/3w+CX1HfI68FDvku6o7dXsQuw97Fzs6+ud60Tsx+0S8J7yWPXy93j6m/00AbIEngbPBSUEswJhARcA0v3H+xT7Q/qG+rr8zv87AjkDfwedFX8qPDwWQus9hDvbPgBDVkK9OdksGB51DI/8t/Hd6RfiTth80eTRPtQD0ynP9c7L1lPhzeec6ffpX+xZ8V/4SQIYDBcSkhQxFtQaPiCVIYUebRkyFXIRggvZA178BPaq8aLu4uz462nqEumT6YXsj/Bg8nbxoPBB8cvz7faF+TL9GgJBB2MMvBDVE8UVYhanFqAWrRTmD2AJTwNx/+38g/oU+Kv1hvMp8tTxrvKL80zzSvJD8dTwYPDi7oPtvO2Q73Ly7/Rh9s73qfm3/NcAVgQWBksF/wJ8AYIAwf5E/LD54fhk+h38x/3c/tL+1/6FA5wR/SbaORZBXj6jPEdB5kYKRss8RC/WIH0Q//8/84jpn+Cw1//R4NI31dzSe871zlXX/OFD55fnoOew6q3wh/hCAmgLchFCFXQZ5R+fJQomlSLMHm4bGxdDD7IE1voJ847tG+qO5+7kB+LN4Jbjn+lf71HynfO89Tj57fyU/wECKgUpCDAK3Av9DeUPPRHfESASMBIJEBELcQU2AWz+6vuX+Kz0P/Fq7nHsSOzV7ZPvcfCc8J7wCfEO8bfwIfHH8tf0VvYw9134PPqj/Jv/1wIQBpEHWwZ4BPICVgLvAWcA7f57/XT79/qq+5j92f5G/48HsBl8Lig8YDsiNr83SDyuPl05dSwlH48PGv+39Efupelw48Pb+tqK3ajc29g51cnYaeHu5ULnc+f36HruXPX4/t8JCRCUEpYUdhhtHjMgrBz+FwMU3RAzDAYFaf1m98fzM/Jp8UXw4u1N7HftwPCC9Lz1zvRp9Gr1oPf1+ez7X/6EAS4FTwknDTYQsxHAEaUREhHjDsAKQwVwACX9qfq2+Av3U/Xk82Pzh/S99tj3/fbD9U31avVp9BbySfAP8EfxmvJp8zL0y/TV9f33Avs0/k3/Hv57/Q7+jP9uAD3/Af6s/Y39fP9RAoMEJQTAAUUH4RcyLE46fzogNjk4Yj3QQDQ9bTKOJvMXtQeP+zzycOon4lXaNNmP2gvYr9LoznHT59xs4ivkMuRX5tfsMPVuAK8LeRH6E+MVEhrsH+EgbB1cGb0V1BINDooH8gFf/Yb6L/lm90b0Te/16tLpRerv6TznguRH5bTom+3o8tP3n/1OBMoK1hDrFHgWtBYwFiwVsRLwDTYIHgNh/z/9mvuO+TD3wPRY84jzefTe9CL0IPNw8n3ysvKP8TjwsO928PXybPWd9qv35/gJ+4b9qf9zARUCTwJAArEBDALrAYcBMgIRAjsCRwMcA18C0ADaAysTrSc/NuI37jC7Mbc6SEChPegy7ibkG3cMK/5a9ULuiuYG3afXp9ii1r7QnM3V0sTePObN5+/p1e3882v6aQG2CnIPdg7+DeYQWxY/GBMVZRIUETUPUQuQBRMBxfxo+Kv2VPZI9fPxCu7g7iDzN/YC9zX2XfaK9/X3TPme+zT9w/0R/h4AUgPVBC8FdAauCCAK+gh/Bo4F0wWiBScEjQFq/g77X/iM9/T3bPdK9f7yEvJy8l7yA/Je8pjznfT99HD1PfZx9zz4dvgA+Sb5/fip+Vn7sf2//yUBjQLGA6AEuAQNBbUGsQhsCXsH7AVEDMkbSS7ROF82RTHwMlI5Gz1cN9oq1x1bD1MBVvcY8F7qIOL82fHYd9on2WjVLtPF2HLh1eXl5ijnxOmX75738QFLCmoNww1pD4EVRhsOHLUZ6RW3EuoPOQwTCZcFvAAQ/e/6qfm09h/xIO2N7AruI+9m7dfqwunU6uLu1vN+9335x/r//R4DggckCmYLiQxHDhYPoA2oCmAHzQQNA+8Asf16+dz1VfRR9WT3RfiD92/2g/ZU+NT5GPrc+Uv5APm2+N73PPfn9hH36/dM+er60Psg/Ob82f13/8cAWgAwAAcAlP8QAYkCYwNdA8MCwAhdF2InaDGyMEQtnS+RNFQ3FzQkK9EgyhS8CHABB/0I+BLxNOrW58/mhuKH3EbZAdx94OHhhOEr4UTjfegL8Ff6RAMUB2sIagpbD18VABicF54VgBKKD4AMCgrdB6cESAGb/lf87Pkf99n01POu8xfzb/F67//tgO277r/wsvJV9Jj1bPcO+gz9iQDnA4wGZQhpCcUJOwqfCsMKqgqNCeYG6QNPAZT/4v6z/Zr7Qfna9gr16vPQ8tvxB/Ef8HzvUO+I8LzyF/V+97b5Jvx6/+YBXQMRBegFEgYlBdcCwwHtAWUCNwOJA50C4gEPBF0NxxtJJx8qCidPJskqZC8pL4gpsCHoGIgOEAZnAbf9+fgk8sfsB+tH6IXjP+Am4PziuuTS48HjM+VT6F/uuvVY/T8CwQKkAxIHWAu2DioP5w2FDEgK8QegBg0G8wVgBUkEIwNqASf/hv1p/ar+1f6h/I74r/Sl8/L0fPYN9zX2jfUl9qv36vkk/JL9D/5R/hr/IgBeALz/ov+LAOAB4AHkAC0AEgCJAD0BkAHdANz+Vfyz+7z8+PwM/CL64fjW+Dj51vmy+u76mPp5+vP67fsd/BL8/Pxx/m7/Vv8+/+L/ywDDAbYCDANUA1wC6ADiAFMCRwbBDKwS2BV1Fi8XJxuMIMkjCiS9IWQe1BlAFOwP4AyPCRYFMv8W+qj2xfPs8TbxtvBd8LLvwe7K7m3vePBV8u3zpvRj9OTzcPRG9qT43fqj/CL+g/9sAS4EWwdlCh4MswstCrwI8QecB4oG1QTpArkAr/4T/fP7Nvsk+hr41/Xv85PytfHz8GjwkfAW8bvxmvLZ84v1I/hm+oT8wf5cAMEBQgOHBIcF4wUuBYEERATgAwMDaQEDAAT/Hv6J/QP9j/xe/FH8mvwK/WX96P1l/mr/zABhAWkBEAERASsCRwN6A+UDvwQxBmcHcAfjB6oK4Q7QEk8USxNwEp8S3hOZFDATkxAlDQUKewfhBG4CCQCH/Vf7v/nR+Hj4Jvjl9yj4wvhm+YP5+fin+HP4H/jO9wv3O/Z89Sv12/UE91n4KPrA+4f9df/tAIQCbgOvA9oDoANhAyEDpAJWAl0CDwIXAn4CsQLbAkkCuQD5/l/9kfue+br3ZvaY9b706PM/88nz8fQP9lv3ovgi+sf7Jf2q/loA1AH4AtoDXwRxBCgEDQSDBLsEIQQEAw4CkgFaAeIASgAbAO7/3v///04A1wArAaYBjQJ6A7gDiQNCA/ICGAPDAj0C1wGNASQB1gCxANIAWgHdAXUC5AIVAxgDIwNUA4MDdwP2AnACGgLxAdkBBgJTAmUCCgLSAXECWwOyA1YDpgJIAh4CowEyAboA8P8R/3X+cf6+/qT+DP6W/Zr9Of6//rz+m/6S/sr+gv8VAGMAmgC1APoAOQEyAcMAFwBJ/2f+8f3r/Z39sfy1++j6yfoU+yD73/qy+tL63fro+tv65fo0+2D7vPsJ/HD88/x7/UT+1/4h/xP/Df9P/6T/if9R/37/uP8HAAEA3v8pAN8AjQEuAtsCbgMUBKsEMwXRBRsGIwYjBuwFkwW9BJID0gLpAfwA1f9U/tn8zftr+5z7Ffxg/Nv8uv3Q/vL/vAAnAZABAQI2AiIC6wHSARMCXAKkAvICFQP7AtwCGANkA1gDdwJBAYIAGwCx/xH/XP70/Y/9Xv2E/T3+Pv/R/xUAcAD/AJ0BvgFeAdAAZwDX/yf/rf5G/kv+cP6q/j3/lf/3/2sAwgBCAVoBwwAQAGz/uv7k/fT8EvxG+6D6WfqE+t/6f/sb/JL8J/0K/gj/xf9XAF4AdABVAC8ALwASAP//zv/i/z0AxwA9AYIB1wFmArwCDAN4A8wD6QOmA2QDPQMsAwYD4gKvAncC6AE5Aa8AaQAtAKn/GP+W/jn+A/7e/en9XP6//vv+df/y/4QADAF5Aa0B3QHKAWMBGQHzAMwAawDr/3z/QP8l/xb/5f6O/iz+0P26/ZT9Y/1E/U39gP28/eD9A/53/hX/wf9aAJQApQDZAAUBFwEbAf8AyQBsACcAOgCfAN0ArgChAO8AUQGUAWMB7QBwAEMAMAAgAMz/Jf9+/jn+VP5d/j/+D/4w/nX+x/7Z/rX+Xf4X/t39wf2f/Uv9Lv0t/Wf9m/3b/UD+s/4N/5//UQAQAcoBPwKNAtcCHAOBA8MDvQO6A5kDhQN4A10DOwP0AnUCDwK3ASUBowDu/3X/R/9o/0f/6f7D/rz+Bv9A/zD/8v7Z/tD+5P7v/jL/ov/2/zoAlgAcAZYB6QHFAagBfAEtAeIAewATAKD/If/g/tD+6f4D/zL/ov/9/yYAPQBDAJQA3QDLAJ0ANAB1/7b+Jf7b/d793v3Q/ev9P/7S/mj/KQDtAHsBswGoAXcBJwHQAFcA9/+m/zD/kP7x/cH9Ff6Q/tz+5f75/jn/aP+X/6j/nf9v/z3/Hv8n/yD/Hv8a/z7/nf/2/0MATgBhAPoAxwFjAvkCPQNJA3cDhQOAA0kD1wIrAnsB/ACqAGcAVQB0AKwA5ABGAZsBwQHgAawBUQEkAd8AYwDI/w//gP49/jv+pP7g/vD+A/9Y//D/QwBRABkA3v+V/zL/tv5z/mH+N/42/k3+i/6//gP/ev8AAD0ADgDn/9P/vf+X/zL/uv5A/tv9fv1n/Zr9tf2+/cD96P0s/pf+DP91/+n/VwDJABsBmQEwArgCDwMGA9ACoQJjAiICpAEHAWcAyP9J/7r+SP79/eL9pv1n/S79Bf1B/br9K/6E/pT+jf7g/kf/0P9KAJwArwDmAEQBmAH7ATsCdQKiAucC9gK2Ak0CswFWAeoAjQA/APD/pv9v/1//df+J/6j/0/8JADgAPQBTAKEAHgFeAXUBgAFfATsBHgH0AOgAuQBeADYA//8AAO3/vP/M/7b/qf+H/2b/VP83/0v/S/8+/1H/Y/+F/8z/0f+i/3X/X/9L/yX/qP4w/h7+O/6H/r7+4P4l/27/6f91AN0ADgH0APMAAwEZAREB9ADSAK8AwAC+AJ0AbABIABcA9P/T/5v/Rv/D/nX+bP5w/nD+SP4B/ub98v0P/i7+Xf55/m7+gP6W/tX+Ef9G/5n/IADLAI0BJwKdAg0DXwOpA84DsQNhAwoDdQLMASUBawDF/yz/8v7c/tz+4P4n/53///9gAJgAtwDzAAcB8QDXAHkAIgDw//n/EAD//+L/vf+2/63/rf/D/87/q/+J/1H/Pf93/7//9P8mAFEAkQDrABUBRgFcAUYB/wCjAEgAyP9H/9f+bP4B/sH9of2N/Yf9lv3M/QP+Rv5z/pb+zP70/ir/bv/X/zIAhgCzAOAAEwFIAUoBNgEMAdAAlgB5AGwAYwBnAD8APwA7AAwAzv+S/1L/GP/e/qT+Yf4T/vj9Bf4e/kD+gv6i/tf+E/9u/9r/TgDCABUBSwF8Aa0B2QEjAl8CYQJAAjICCgLgAbcBbAFGAQoB1gCPAEYAFQAHADQATABOADYAFwD0/9P/6//p/+3/9//i/6T/pv/M/x0AXgBgAHUAeQCNALkA0ADWAKwAfQBTAEoAVQAyAO3/oP9U/yr/E//w/sj+rf6q/qv+2/4q/2j/bv9a/0f/If/i/qT+hP5N/lr+df7H/lL/5f+aAFoBFQK1AhUDMgMBA44CBQJhAbEA//9G/8P+cP43/jf+WP6z/iz/n////yYALQAiAEQALwAJAKL/Jf/i/qT+fP5w/o3+pP4I/4L/EgCzAB4BggHOAQ8CDgLbAXIB3wBTAOD/fv8I/6T+av5f/p3+v/7v/iP/aP/j/04AswDzAO0A/QATASQBTQFfATgBGQEgAUYBXAFEATIBEQH2AOAAmgBRAPn/wf94/1T/Uf9H/3P/jv/B/+3/BQAJAOn/2f+x/4v/Pf8E/+n+w/6Z/o7+if6h/sr+wf7t/iX/Xf+m/8X/CQBjAKoA4gAeAVUBUwFTAU0BTwFCAQoBwgBBAOD/WP/Z/n7+Of4V/u39wP2v/cH9Dv6A/uv+Nf9k/5D/qP/T//n/CQAAAAkAFQAdACQAMABrAJYAqACuAMMAtwCLAIAAiQCGAJYAgABjAGkAhgCaAL4A2QC3AJ8AeQBwAFMAGwDn/7T/nf+p/7//7f8XAD0AiwCoAMIAugChAKUAwwDbAM4AxQDQAOgA/wAXAR4BDAH6AOIAywCfAGcAMgDr/7T/mf+F/3H/Sf8l/xz/Kv8a/xj/DP/e/rP+lP6o/rb+vP6+/rz+0/4B/0f/m//3/1oA1wBBAY0BtwG4Aa0BjQGJAWoBAwG1ADgA/f/c/3r/D/+o/l/+J/4K/vT9+v3//Tf+jv4R/6b/NADDAEsBwwH4ASMCNAI5AhoC4gGHARcBugBOAOf/r/9s/zv/I/8W/yz/Nf8w/0T/WP9q/4v/jv91/z3/D//0/gH/If8e/xz/Jf9b/5v/4v80AIgAtQDOANIAwgCxAJwAhgBuAFUANAAmAAwADAAnAFwAkQCzAOoAFwE7ATABOwEuATYBKQHxAJ8AOgDi/47/Pf/n/pn+F/6z/YD9bv2C/cX97f1A/tv+Zv8FAG4AxwAIAU0BXgFEATsBJwEKAeoA3QD2AOgAyQDCAKoAfQBOABMA6/+g/y7/1/6L/lP+Qv4r/jb+XP6f/vn+Uf/D/xUAdADFAPYABwHxAOIA1gDJAJgAXAAeAP3/CgAFAOX/1f/i/wcATACfALcAzgC6AJQAeQBgACYAzP91/xr/2/6o/pn+sf7g/if/bP/c/0QAowDmACQBSAFEASABzACGAEYACQDi/7j/q/+//+D/FQA9AHAAiwClAJ0AhABlAEQASgApAAMAAwABAOX/5//K/73/rf+Q/3H/Wv9R/0//ev+Z/9r/PQCYAM4A5gAOARMBLQEtAQUB2wCoAGEAJADy/6b/Vv8p/xj/8v7O/sX+w/7M/tv+3v7X/sf+yP7Z/vb+Ff87/2H/h//g/w4ARACNANQAGQEtAQ4B4AC6AKUAkQBjAFMAOwAvAD8ANgA/AD8AJAD5/+n/tv93/zn/+f74/gT/CP8e/0L/d/+//xMATABnAGwAYQByAH4AiQBnAEMAJgAQABMAFQATAAoAEwA4AFwAiQC8AOAAEQE4AVEBSAE9ARAB3wC5AH0ASAD//7j/g/9S/0L/Ff8N/xP/Bv89/13/bP+H/47/zv8AAAcANABnAH4AnwDJAN0A8wDvANIAkgA9AAEAtP9H/8z+cP4u/gb+Kf5c/r/+J/9U/8P/EAByAKYAvADWAMwAwACoALoAzgCvAKgAugDJAOAA1wDFAKwAfQBMAAwA0f+d/2//Qv8P/+/++f4D/yz/cf/R/yIAHgAwAEYATgBPAFcARAAbAAwA2f+9/5L/cf8+/0b/Uf8l/zL/R/93/7j/9P8vAHAAkgCzANkA+AD0AMUAoQBpAEoAEwDl/8P/qf+y/87/FQAkADIAYwB5AHUAYAAiAOn/sf9N//j+w/6k/rH+yP7g/gT/Rv+r/wEAcAC+ANcA2wAXASUBJQETAfYAwAB5AEMADgDl/6n/fv9L/0f/NP8w/yX/Hv8Y/yH/N/8h/zX/IP8g/0//if/u/0QAaQC3APYAKQFfAXcBWgEgAd8AtQB0ADYAAwDX/9P/0//V/+D/7v/r//T/+f/9//T/9v/r/7j/4//O/7b/uv/B/+L/AwAiAB4AAQDa/8j/xf/l/wEAEwAMAEoAjQDCAPEA4gD6AOgA3wDqANkAugCaAGsAKwAKAPv/0/+f/5X/kv+Q/53/h/94/4f/nf+g/7r/gP9a/2H/VP+O/6T/zP8MACYAVQCAALMAyQDXAMIAugCaAD8AEADu/97/rf9z/13/Mv8c/xz///72/tP+w/7Z/sz+2f7k/uT+A/80/3j/zP/5/zgAWgCJANAA3wDxAN0AvAC8AKYAewBEAD8ASAA6AFcAbAB1AH4AjQCPAIAAlgC6AKoAhgCJAGMAeQCRAGcATgAiALT/h/9S//v+5/7I/tD+0/7V/g3/Tf93/6j/EABVAHAAcgBwAH0AbgBjADQAIgApACAAHgAXADYAYACEAJIAxQDfALcAuQCjAIkAYQATANn/i/9P/xH/4P7T/sr+sf6k/tP+DP9G/5v/DABXAI8A1AD2APoAEAEVAdsAowBaAAkAv/+V/2z/df+S/6T/xf/a//v/AAADANz/gv80//T+s/7B/pT+if6r/sr+Fv96/8P/8P9uAHAAqADUANcA4ADkAAEB1ADWANIAwwDAALEAugCaAJEAaQBaAFEA+//n/8P/if9s/1L/S/8w/13/lf/D////PQBgAHcAoQCGAGMAKQAMAOD/oP+i/4D/bv+F/6D/lf+m/+7/7v8MAG4AUQCfAOYA7wAgAUoBUQE9ATQB9gDCAIgAQwAHAPT/yv+v/5//Sf/Z/uL+n/9TABcAhf9b/4P/3v8BANr/wf/5/woAMAA0ACAALQAiABMAIgABAOn/9/8OADgARABrAGkAfgB1AHQAZQAyABIA8v/n/6T/pP+U/4n/n/+r/37/hf+M/3H/bP9x/03/Nf9a/zv/Mv9a/3P/bv+F/5//tv+D/5T/w/96/6T/9P/i/+D/ZwB+AHcA6AAHAQcBNAEwAScBXwEDAesA/wC+AI0AdAAVAO3/7v+J/3r/Xf9W/0v/c/9k/2j/i/98/4D/kP+C/3H/w//K//n/PwBeAHIAmgDSALoAtwDbAMMAhgA7AAUAqf9L//D+1/7K/r7+zP7F/g//Ff9C/6j/7v8eAEgASgBjAG4AVQBwAIQAbgByAIAAVQA/AGkAXABBAHQAewBnAHUAhACLAHkAdwCLAGUAOwArAOL/uv+k/3f/X/8n/+X++P4D//j+If8n/0D/RP8l/3H/lP+L/7L/4/8JADQASgByAKUAvACzAM4AIgETAfQA2wDJAJoAdQBMAN7/sf9j/0L/Wv+D/27/kv/l/9f/4v/D/8r/4v+i/2z/Sf85/yz/g//O/xAAbgCvAOYA7QAHAe0A2QDWALEAYAAKANz/wf/Q/7T/lP+Z/6b/zv/G/63/5//X/+D/HQAeADoAPwA9AF4AZwBsAGkAZQBMAEwAZwBaAGUAYwBTAGsAVwBEADoALQAmAA4A8P/n/9H/qf+Z/4v/c/9W/2j/eP+Z/73/w//r/yIAMgBhAI8AdwCaAI0AfQBnAEoAOgAeABUAyP+Q/4L/lf+O/3r/lf9k/3f/c/98/6n/m/+x/5//jP+S/5v/jP9u/2P/W/+J/5//l/+8/9X/3P/5/xAAFQAmAC8AOAAwAAwACgAAAPv/HgA9AFUAWgBVAGwAcgCLAHcAfQCIAH0AhgB1AF4ANgDw/8X/r/94/0f/Pf9J/1j/X/9j/4L/rf/D//f/IgAgAFEAZwCJAMsA+gADAdQAzACuAKMAeQA9ABMAAAABAPn/AAD7//b/HQAgAC0APQAyAB4AFQAFANn/vf+f/5D/c/9m/2j/eP+D/5f/tv+9/8r/zP/c/9f/6/8BAAAACgApADsAWABwAGcAdQCNAIYAgAB1AFcARAA0ABIA4/+//63/n/+o/6L/lP+d/63/uP/Z/+L/1//Q/+n/9v/9//T/+f/w//f/HQAQABAABwAHAAoAHQAVABkAHgAHAAUA5//c/9z/v//D/87/uP+y/6D/l/+k/7H/uv/B/8r/4P/c/+n/AwAkAD0ARABTAG4AiACRAJgAnQCfAIYAggByAEYAVwAnABUAGwADAPD/5//a/7//sf+F/2//VP9b/3P/fv+X/8X/5f/9/xsACgAgACkAPwBKAD0AMgArAC8AIgAbACcAKwA/AF4AUQBhAF4AOwA/ACAACQAHAPf/4//T/8z/xv/V/9D/sv+f/47/lf+J/5f/pv+0/7L/wf/g/97/9/8XACQAOAA4ACsANgAnAB0AEwAOAAUAAQAQAPf/7f/G/7L/sf+//9P/4//G/63/q/+t/5v/g/+V/3j/WP9m/4X/c/+Q/4z/r//y/wcAFQAQAEMAZQCNAIQAdQBjAE4AVQAyAA4ABQDi/9X/7v/l//L/KwA/AFwAdwBrAHsAZQA7AEgAMAAbAAMA+//5//v/FwAZABUACQD2/87/w/+0/5//l/93/3j/n/+p/7r/7f/y/+f/EAAKABMANABBAEMANABMAEYARgA7ADsAMAAmABcA9//a/7//tv+i/7b/1f/R/9n/7v8FAAUA9v8BABIAFwAbACYAOABOAHUAtwDgANkA+gAkARUB/ADCAJwApQBlAB4ABQDK/6T/qP+M/2j/bv9+/2b/if+x/8j/3P/j//n/9v8FAPD/7v/c/7r/zP+//8H/xv/p//D/+f8QACAAIAAVAAcA+f/5//n/6f/w/wkA///2//L/4P/c/9z/xv/R/7j/0//Z/7H/zP+0/6n/qf+2/8r/vf/O/9f/yv/i/9D/tv/I/9H/zP/V/9P/1f/j/+7/9v/7/+f/7v8VABIA+f/p/9P/4P/w//f/CgAZAB4AMgBPAFMAXABVAFwASAA/AEMARAA6ADIAOwAwADgAKQAdAAwABQADAAcADgAAAAkACgD2//D/9//R/87/3v/X/+L/4//y/+n/4v/c/9n/4P/r//D/7f/y/woADAAKAB4AKwA4ADIAMgBEAEgAPwA9ACkAGQAdAAEA/f/5/9z/2f/Q/9r/4//l//T/7f/5/+3/7f/w/97/5//g/9f/1f/V/9X/3v/p/+X/8v//////DAAdABMAFwAkADsAOAA7AEQAOgA6ADYARAA9ADYAKQAiABkAEgAHAAcADgADAAEA/f/5/+7/6//i/97/4v/t/97/2v/g/8z/1//e/9X/4v/T/9X/4P/n/+D/4P/X/9f/3v/i/+L/4P/y//T/AwAKABcAHQAnADAAOgA4ADgAMgAiACIAHQAZAAoAAQAAAPv/7f/0/+P/4P/i/9z/zv/R/9f/2f/0//3/+/8AABUAFQAbABcACgADAOv////9//v/DAAKAB0AHQAMAAcABQD2//L/5//T/9f/zv/M/8H/vf+8/8P/xv/K/9D/0P/T/9z/1f/K/8z/zv/a/+X/6f/l/+v///8MAAwAHQAgABsAIgAnACIAEgAbAB4AEgAgABAA///7/+X/3v/Q/9X/0f/G/8X/w//I/9n/2f/j//T/+/8QAB0AMgBEAD8APwBEADsAPQA/AEMASABMAEoASABOAEQATAAvACIAIAATABAA///t/9f/5//Z/8j/zP/T/9r/1f/j/+P/+////wcAFwAOABMAFwAkACYAJwApABMAEAAJAAAAAQD5/wEAAQD//wwA+//r/+f/8P8DAAkABwAFABsALwA4ADgAQQA7AC0AJAAkABUABQADAPb/4//y/+f/6f/u/+D/+f/3/woADAAQAB0AIAAVAAkACgD//+f/4v/c/8z/vf+t/8P/yP+//9H/0P/X/+f/4//p/9z/3P/n/97/4v/e/+D/4P/Z/9H/6//u//L/9P/i//L/AwD5//D/7v/2/+3/7f/t/9n/2v/R/+f/8v/0//n/+/8TABcAFwAVACQAFQAOAAkAEwAOAAoAFQAMAA4AEAAXABcAJwAgABcAGwAdABMAEwAVAAwAFQASAAMAAQABAO7/4P/g/8j/vf/F/6j/vf/I/9D/7v8MABUACQASABAAEAAAAAUA//8DAB4AHgAkAC8ARABRADsALQA4ADQALwAvAC0AJAAkABcAEgAFAPf/AAAAAOP/3P/M/7j/r/+y/7j/mf+O/5L/mf+k/6T/mf+p/7//0//g//L/AADj//3/CQADAB4AIgArAC8ALwAyACsAMgA2ADsAQQBGAD8AOgAyAC0AIAAdACQAEwASAAMAAwD9//n/7v/l//D/5//p//b/8v8HAB4AEwAyABsAGwA7ADQAMgAvAEMAMAA6AB4AEAAVAAAA7f/R/9n/0P/g/9z/7v//////CgASAB0AFwAgACYAIAApACQAGQAQAAAACQAKAPT//f/p/9n/2f/i/+v/yv+9/8X/zv/M/8r/xf/G/8X/vf+8/8P/w/+9/9D/zv/B/87/yP/Z/+D/2f/j/9P/2f/e/+n/6//3//f/6/8BAP//9P////f/8P/r/+L/+/8MAAAACgAiADAASABGAD0ALwApACcAFQAOABIAEAAQABIADgAgACQAHQArACQAJgAiABIACQD9/wAA7f/e/87/w//F/7z/xv/K/9P/4v/y////BQAFAAEABwAFAAMA9v8BAAUAAwAOABMAEAAXABsADgAOAAoABQD9/wUABwD9////BQAKAAwABwD9/wMACgAQABAACgAHABIAEgAdAC0AJgArADQANAAkACIAIAAVAAMACQAXABIAJAAiACYAJAAnADQALwAvACkAIAAdABMAFQATABMAEwASABUAEwATAAMA9//t/+D/zv/B/7z/v/+0/6L/pv+X/5D/if9+/5D/mf+d/6j/sf+2/7H/zv/X/9f/9P/u//n/FQAXAB0AHgArADAAPwA9ADIALQA0ADoAHgATAAwA/f/7//3/7f/V/8j/zP/M/87/0//R/+f/6//0//f/+f/9//f/8v/2//3/9v/7//D/AAAHAAcADgAFAAcA9v///wUACgAZACAALwAwAC8AOAA9ACYAHgAgAB0AFQAeACQAHQAVABAAEwD9/woADAAJAAwABwABAAcABQAMABIA///9/+v/8P/2/+X/4P/w//b/+//u/+3/7v/3//f/5/8BAP3/9//2/+7/7v/r/+n/7f8AAPn/9v/2/+L/9P/3//T/6//w//b/9P/u/wEAAwD7////9/8DAAAA///9//3/+f/5//L/7v/c/97/9v/y//v/9P/0//n/BwAQAAEAEAAZAAcACgATAAcADAAVABsAIgAiACkAKwAwAB0AGQADAAAA7v/K/8H/sv+i/47/l/+Z/5//pP+v/7j/uv/D/87/0P/T/+3/9/8DAAwAGwAgACYAOABDAEQAQQBPADsALwA4ADQALwA7ADoAMgA7ADgANAAvACQAEwAQAAMAAQD9/wMAAAABAB0AGQAVABUAHgAiACcAEwD//xAACQAbAA4ADAAgAB4AKQAeAB0AHQAVAAkAAAD9//b/6f/n//L/zv+8/8j/0P/Z/97/9v/y//L/+//7//T/5f/Z/9z/5f/l/9n/1//2/97/7v/2//T/+//2/wEAAQD//wAA9//t/+X/2v/e/9D/2f/c/8X/0//K/8X/zP/R/9P/1//c/+P/8P/3/w4AHQAKAA4AEAAOAB4AEwAQABAAGQAKAPv/CQAOABkAFQAbACIALQArAC0ANAAeACAAIAAXAAkAAQAAAOX/5f/i/+P/2v/F/8z/xf+8/8r/w//B/87/1//p/+n/4//n//L/+//9//3/CgAQAAoAFwAeABsAFwAbABUAAwAFAPf/7f/u/97/7f/n/+3/5//u//v//f/2/+v/9//2/+3/8v/u/+P/6//l//L////5/wAA+//7/wEABQAHAPv/AQAHAAoAGQArADAALQA2ADAAQQBIAEEATgBRAFoAYABRAEoATABKADgALQAiABUAEwAKAA4ABwAKAA4ADAD///f/9P/p/+L/1//e/9f/1//a/9f/1//n/+n/7f/3//L/9v/3//f/9P/3//b/9v/w//L/7v/r//n/9P/y//L/9P/7/wcAAwAKAAkABQAQABcAHgAdABsAJAAiABcAEgAAAPn/9v/0//D/6//c/97/4v/p//L/6//0//T/7v/u//b/9P/y//D/8P/w//b//f/7//v/AAD///n/9//3//f/7f/i/+L/2v/R/7//vP+8/7b/yv/D/7r/0f/M/9H/0//Z/97/5//5/wUAEAASABsAGwAiAB0AEwAQABcAGwAXABcAFQAXABUAGQAMAAwABwABAAkAAQABAAcAAwABAP//9/8AAP//9//7/wUADgAbAB0AFwApADYANAA7AEEAPQA2ADgAOAAvACsAJAAiAAkA9v/3/+v/4//e/9P/0P/I/7//xf/B/73/tv+2/7b/uP/D/8X/0f/X/+f/6//0//3/AwAQABIAEAAbABUAEwAmACkAOABIAEgAPwBMAEQAQQA7ADQANAAnAC0AJgArACAAFwAeACIAJAADAPT/AAD3/+3/9P/0//D/9P/t//f/7f/j/+7/5f/j/+7/8v/7//b/AQAJAAoAHQAVACYAKQA0ADQANAA2ADIANgAkAB0AEwABAPv/9v/c/9P/2f/I/73/v//B/8H/xf/T/8z/2v/Z/8r/yP/M/8z/vf+6/7z/xv/F/73/uv+y/7j/r/+6/8H/uP+8/8j/2f/a/9r/4//u//L/9/8FAAcADgAbABsAJgAdACcAOAA/AEMAPwA0ADsASAA4ACkALQA0ACQALwArACYAJAAVABUAFQAdABcAHQASAAEACQD5//D/9P/9/wAAAAAHAAcACQAQAAcA9//5//D/3v/a/9X/0f/V/9z/1//e/+3/7v/2/+f////9/+3/9P/e/9r/0P/Q/9n/2v/a/9r/3v/y/+n/2v/y//D/6f/n/9r/4P/w//b/9v/0/+X/8v8DAA4ABQADAAwAGwA0ADIAOAA2ACsANAA9ADAAJgAgACYAHQAMAAkABwD///b/9v/w//3/8P/w//D/+f8AAP3/AwAAAAoACQD3//n/AwDw/+n/9P/u//D/9P/5//T/8P/2//T/8v/3//b/8v/2/wEAAAAFAA4AEgAdABIAAAAFAAwACQAFAAkAEgAVAAkADAAOAPL/9v/0/+7/9//u//v/+f8FAAwAAwASAAwAFwASAAcABQABAAMA/////+f/6//r/+n/6f/y/+n/5//r//b/+f/0//b/8P/2/+7/AAAMABAADAAXACkAJgAkACIAKwA0ADYAOAA0ADgARgA/ADYAOAA7ADYAJgAdABMAGQAnAB4AJwAdABcAGQAFAPv/+f/y//b/8v/p/+P/3P/a/9H/zv/M/7//rf+m/5f/jv+U/5D/kP+U/6T/r/+4/9D/5//0/wkAEAAeACIALQBEAD8ASgBOAEwARgBOAEwAQQBBADIAMAAgAB4AEAADAPv/9P/u/+D/4P/F/87/zv+6/8P/vf+6/8P/yP/D/9H/0P/g//T//f8VABkAGQAgABcACgAKAAUAAwD2/+v/8v/t/+v/4v/R/8z/zv/V/8j/tv+x/5n/nf+i/5v/pP+v/7z/v//T/9r/2f/p/9z/3v/g/+D/6//y//f/9//y//b///8OABkAJAAnAC0ANAA4ADoAPQBMAEQAPwA7AEQAVwBXAFwAawBpAGwAawBaAFgAWABTAEwATAA4ACsAMAAnAB0AHgAiACAADAABAAoAEAAKAAAAAQD5//b/9v/3////9P/y//b/+f8FAAcABwASAAUACgASAAkAAAAHAP3/9v/w/+f/0f+8/7H/rf+k/5f/n/+d/6v/rf+2/7H/vf+8/7H/xf+4/8H/vf/I/87/0//T/87/1//Q/8r/w//D/9f/0//e//D/+f///wUADAAXAB4ALwA7ADAAKQA2AEEARABIAEwAXABXAFUAUwBEAEMAQQAmABsACQADAAAA6f/j/9P/0//T/8r/wf+9/9D/zP/M/+P/4v/i/+X/+//9/wwAHQAdADIAOwBPAEwAVwBnAGkAbABpAGkAXgBeAFwAUwBKAD8APQA6ACYAFwABAPb/5f/Q/8X/vf+r/5//l/9+/2z/WP9W/1//X/9b/2b/df96/6D/tv/I/9n/8v/5/wMAFQAOABkAGQAbACAAGwAgAB4AIgAeAC0AJwAVAB4AJwAiABIAIAA4ADsANgAmACcAKwA7AD0ARABaAFUAUwBaAFgAVwBsAF4AZwBaAEMAQQAdAB0ACgDw/+X/0P/K/7j/m/+Q/3f/ev9j/03/Zv98/4X/lP+y/87//f8kADAAPQA7AFgAewCSAJwAnAClALEAtwCjAIgAdQBgAEMAGwDn/8r/lP9j/0D/D//w/tD+of6J/of+bv5c/kj+NP4n/hP+IP4r/jD+P/5P/lj+cf6Q/rb+3v4M/z7/b/+m/87/DABOAI0AwgAHAU8BkgHJAQMCRgJqApICvALTAucCAwMEAwEDEQMTAwoD5wLQAq8CjgJcAjICDwLQAZQBWgEkAfQAsQBpAEwADADl/7j/kv91/1v/R/8R//j+4P7M/sH+vv6i/qL+ov6i/qb+ov6N/oD+c/5W/kv+I/4R/v394P2+/aH9if1W/SX99vzZ/LP8hvxc/DT8KfwK/PX74vvY+9L7x/vZ++j7Afwr/HL8ofzm/D39nf0F/mz+2f5P/9z/ZQDzAIABDgKfAjkDwQNZBOwEXQXYBUoGqQbxBjEHZQeKB54HsgewB6wHmgd7B1cHIgfmBqMGTAbyBZwFPAXnBIcELgS/A0sD4AJjAgoCsQFlARUBwgBgAA4AzP9x/zL/1/6H/if+0P17/RP9yfyC/ET8/PvC+3b7Q/sg+/H61PrE+qz6nPqW+oP6g/p2+mL6Xvo8+iL6Dfru+c/5rPl4+Uf5GPnq+Ln4gfhW+EL4KPgm+CX4HPg8+Hb4zfgm+Zf5Ifqy+nb7L/wD/ev96/7y//8AKwJNA4EEvQXlBgkIDgkOChELDAzwDK0NRw7EDjMPeg+VD4wPaQ8hD6cOCw5QDYMMlgt+ClMJDgjTBqIFXQQjA/YBzgC8/6j+jf2q/PH7Rvu3+lL6B/rT+bb5rPnC+dr5DfpO+pX61PoU+3T70vsr/Hv8r/zz/C39X/2L/aH9vv3B/cz9zv3H/az9jf1q/Tj9DP3Q/IL8SPwK/Lz7afsO+8T6a/oW+sL5b/kP+cL4f/g3+Av40fem95P3lfen98b3/fdU+Lb4TPnz+cb6t/u6/OT9Hv9pANIBVAPhBG0G+AeICQYLWwygDdMO8w/sELURUxK6EgUTGRP0Eq4SOxKREcAQuQ+eDn4NMAzQCkYJvgcmBpkEBANwAQcAmf5K/RX87vr++Tf5jvj093j3FPe99pv2kvaX9qT2vPba9gb3T/eg9wL4avjK+DH5hfnz+V76vfo6+6P7APxP/K/8Ef1w/cX9Cv5C/nf+n/6t/rb+pP6S/nf+Uf4P/sP9Yf3o/I/8Gfyj+y37ufpD+sb5Yvn9+LL4avgj+PD3u/ep97b37vc6+KX4Kvm5+Vz6Gfv++xH9Sf6f/x4BqwI8BNoFfwcpCbUKKQynDfwONhBuEVYSMhPXEyMUTxRGFBoUvhMTE0YSVRE0EAcPpg03DLEKOgmjB98FVAS+AlUB9v+f/mH9QfxV+2/6rvkH+Wr49/eS9zn3//b39vL26fbl9vf2MPdf95L3uPfs9zP4f/i9+P34Q/mG+dj5GfpO+oT6tPrz+jP7Ufty+4/7u/vN+9D7y/vJ+9376Pvi+9n72PvN+8f7tfuz+5P7gft9+1P7PPst+zH7Mfsv+x37JPsr+z37YPuC+7D77fsm/In8DP2G/Tv+DP/y/+IA/QEwA4EE8gVDB58I8QlLC7IM5Q38DhAQ6BClETsShxLdEuYSuBJeEsARFBEfEAsPzQ1+DDILpgkBCGkGzARYA/gBnQAs/979pvyW+6r6yPkW+Xb49/eA9x/35/bX9uf2/fYR9zf3b/fG9yX4cfjE+Cz5evna+T/6rPob+177svvs+xn8O/yL/Kj8wvzm/OD89vz//AX9//z6/Mf8iPxK/AD8z/ue+3n7RvsM+8T6hvpZ+jj6G/r8+eP52vnT+er5DvpQ+pj62Pof+2P7p/v6+4L8Hv3g/Yf+Nf8AAMUArAGUAp8D0QQbBjwHVQhyCZQKygvsDOkNsg56DwcQhBDxEBQRKxELEZ4Q9w8vD1kORQ0nDPQKiwk0CNEGYAUCBKgCUQEdAP3+8f30/B38bfvb+l766Pma+VX5KvkL+Q35H/k4+XT5j/mw+dH57vkW+jz6Vfpk+oH6oPq3+s362Prv+gz7Evsb+yT7SPty+3n7j/uT+4/7j/uW+4H7bftO+zT7IvsC+9j6rvqK+nL6Z/pO+jr6NfpF+mf6k/q3+u76Jvtw+8/7Hvx//PT8gP3m/UT+s/4j/7H/QQDAAFgB6wGhAl8DGATjBMgF1AauB6MIfQleCnALOwwEDaQNRw7aDjUPmA+9D98P2g9xD/wOQw6mDQANCAwVC/YJrAh2By8G5QSbA1QCHgH5/9D+x/3k/Pb7KPtt+sb5RfnY+Jv4b/hm+FT4V/iF+Jf4zPjs+BL5XfmI+cn5Fvp4+r769fo9+3L7qvvS++z7CvwS/BP8EvwF/O/73/u1+237Kfvl+pr6Svr1+Zn5TvkJ+cr4jvhS+Bz4APj099H39fcW+FT4uPgS+XT52PlT+rv6Q/vt+3/8Bf2H/QX+jf4Y/6//awAbAeQBmQJdAzEEBAUmBi8HUwhqCUsKWAtPDEANMw4FD9IPSBDGEB4RJhFMERQRzxB2ELUP9w4PDhwNDQzDCo0JLQjaBpMFRQQEA9kBvgCp/5b+rP3F/O37Q/uN+hL6qflm+ST55fjV+Nb49fgH+R35Pvl0+Zn5xPkA+jH6Xvpy+oT6ufq7+s364/rW+uP62vq5+qn6gfpb+hn60/ms+WD5OPkE+db4p/hv+Cj4+/f998b37PcW+Dn4Xfin+Of4Kvl7+cj5Ivp9+uz6VfvP+0T8yfwT/Vj9uP3v/VP+xf5d/0QA6gDQAZ8ClAOrBL0FMQdFCLoJEwtmDLINyw4OEAURDBLVEicTxRP9EycUPRSdE1QTmhKwEY8QIw/VDUQMzAoTCUoHjAW9AywCiQDb/qH9NPwX+yL6Dflv+MP3UPcG99D25fbc9gT3Uvdq97v3EviD+An5PPmu+fX5Ivpe+qX6+Pop+3L7qvvF+9b7wPuz+4L7afsx++j62/qG+lX6M/rz+Zz5S/kA+ZH4Nfju99731/fI99P3tveD9573cfeb9/33Jfh6+KD49fhv+bT5DvpX+tH6G/tu+wn8Qfw//WH+kP+xAFoBvAIqBPUFqgfSCK0KMwzKDUkPDRDHERATURQ8FWMV4BUnFs0WzBZ2FhEW7hQeFMISFRHED+kNhwyxCnkIkwZzBOAC8wA5/0/9n/uT+kf5Sfhs9wb3lPYh9vn1gPV89dv1I/Z49pT2yPYs95X3CPgz+Hz4v/gH+S35QPmj+e75ZPpH+ij6L/oU+nT6tfq5+pH6ffpV+ib6Bfrq+ef5z/m3+YX5KPkS+SP5APmu+IX4ovic+LD4CflB+Wn53Pkf+lv6o/q3+mX70PsT/H38XPzr/Bz97/03/xAAlAHJAfYCLAROBVMHrgj7CmMMIQ6TD/kP/xEXE5YUohWZFQgWGBbaFuAWzBaHFjkVhRSqEtgQQQ9FDT0MzAlwBx8FhwIHAd7+Of1D+335Xfjg9vn1vPRI9Gf0UfRe9D/0cvSx9In1//UG9mr2xfZZ98z3R/hk+L34XfmO+fn5PPp4+rf6pfrJ+hf7SPt0+4H7UftG+3n7Y/v1+uj6t/qD+nL6BPqK+SP5GPnz+KL4HPjI99r3sveJ95D36/eX+OH4l/hF+KD4mfi7+Fn5v/nC+V75Pvrj+lH8WP+SAJABWgJPAzMGNAglCusLwQ6hEbwSJRRcFHEW7BhTGj0aXRipGGsZ+hr/GpUYpBYLFQoU3RE6DwINmAowCRYHRgNhAA//Rv7H/P76Kvhv9m72D/b79Zn1uPXD9XP1jvaj+Qz7nvgC95v3MPg4+fL4MPiY+qH7dPoS+S/55PuR/Nr6PPg3+Gn6SPtM+/H6Lfu+/CD9DP5q/z39L/qQ9/b1v/Xv9cnzGfJB9Hv01PEd8MTvu+/W72Hv6e0+7hvxHfNK8+TyZfKY8tL0pPY19vv1U/S/9iX+IQbODEAM5gg5Ck8RXRc1GN4XoRvUI0MpmScYJvMo8ywmK9AifB37HTEi0CPQHToWPhShEwcQHQiX/4370/kW967wsOlB6Nfr+uzW57rh997a33Dj5eSd5C/nmOyN8kT2nPhG+/n+ogJWBK0E5gfhDiwVGhe8FFoTSxPUEoEQfA02CvgIkwcABN7/9fuj+nz38fGq6/7nb+fL57jlXuEm383eAt8j3MXb2tzS33DixeI24i7kj+kj7V3uAe1m78nyiPl1/u8AiAAMAG7+FgVcIQY5okALM0ga+hrtNC5D3EFSOKg3GUeCSfs52S3PKpwpGCEgCtr6sQE/CkEGJPqu6BnfaNxD1YTRY9BR0a7VG9Ys0nDSv9bj3hzk4OKn3qLjmPGx/WQFVQeKCMEOOxSwF14YIByhIzMoQCchIZ4e+CJjJtogqBPFCWoK/wsaC1MCN/mo9fPxVOwj597kHuQa5DTjAuCO3m/g9eBX4XPezNvT3G/faOTR5VvlH+d36tfrzuos6JPq0+7+83X1tfOM95z6yP43/6L+3/tP/dz4Rv7YFl03GUjJP98gsBBGJYM4vUGqPKQ5UEQoTlxA4C6UI80hKiABEff/x/uzAt0H8gSs86ffV9ZV0C3Rqdbq1rHYX9wF243a39kE3Pjj/Onk7dHu+PT7/94MdxN+EvUQ+Q8rEhIZMB6+I4MmhiJxHQIYmRYsF8MTrwyBA9X+Rv5T/iT83PWK8OrqFOZu4zTiJ+Vr6KPpSulf5Z3iX+PB5Dfn0eRV3/XdSOKf6sHttO346mfoUOh46T3qpu5J8SL1TviR+Ej69fmR+vr6APnH9DoPKjOKSmNKbyeFDgUhXjxhS3pMd0D4RQtNlEGQLnQjiR2oHGcQwPyG8zL0uPbz+PDuItiSyZnDTcgE1NjV4tI42JDbD9133Evd3OXR8c/3aPlL/XEFSBGFHSQgvx5jGigYtyD+KBMsRSqRIwodhhiGEhYP6wtaCIP/WvaP8Irutu4X7Ffmu+C73YndFt9E4wfnYukg68nopubP5xDrAu/77lXrxOeX6NnsjPCT8gvxCu2K6cvo9eoG7oHwpfLx8ZjxBfIc9Ab3lfqM9kQLhy6kSJ1RCTMzFWgdkjeyTCJWy0zWTG5SREjWNQEnJhoyFTwOkP429bTxaO167i/pKtb6xti82b4ozKDT3tQt2Yncft0j3+bgNunj95f/PgPoCHMO6BjvIk0mnyVSIVsdux42JConryVDIEAXpQ92CEQEDwOE/bz1Iuwf5RblY+Wt5W/kK+Fy36ndh94Z433pjPEW94r4MPeO9cb2hfch93n0t/IB9ij4cfg19vfxA+2n6SHlrONG44Dl+ed17GLwnPCC7XfrR+k8768TqjQQUElNFStCHgYunkLxVTFW7EwuUUBM/jyiML4laRvOExUDKfPd6q3j6uA55gnhc9IbxLu6qsBOzcDRnNXI3VHhceUW6LzrD/geA2cIgQ1GEgsWrR1RJiYsPS7uJ9AdMBzHHaEhkyHPFsUMygGG+7X6NPXO7QPkndxj2/LcZeCG4tDiB+I04N3g0+Ny6mXzu/toAvkDzAHxAL7+9vxc+t737vhX+j37jvny9R/yVO3e5XDgjNyl4Mrka+mq6m7qred26SHl6PL/HeY7G1Z5RbwidCLENJZIA1i1TstHfEw2Rls81y4pHU0N0gCK8i/peeFs3NreI+fr4eDRm8GruZ7CWNAb1sXcjuiv7z74Ovua+8MAUwfIDPsUphuxHqkmPy7wMBEvOyOrFLIQyw/0EnwXyA/LBar74/Cv7ZHn1t/M3M7ahd3w367gmeOH5WvoWur/6pDuPfNX+78EyQnUCq4IeQJs/ZH4+fYQ+wz/yP5F+UXxc+wo6vDnouQ+4G/fO93a3kLjhuoV7IPtOOL48k0f1UH1XAVGnSM9JNYz6kMGUphICkfPTUJDTzdDKY4UsgYa+T/qJ+JR2ILR89fo4VnfzNNLxEi+O8gK027YHuTn7jn29v5yAakF9gyBEOkTYBoNHmof2SKgJrkorijhILsXXBITDagKKwibAoD87/Vm8bTuPugA4XPcFdyw4EblYuij6oLrzutw7GzuxvG49Xf8lQXlC/AMxAjkAPP4RvWJ9En3V/vx+fr0t/Az7jbpkORp3mjcqtq+2+zdV+bB7FTtLOXG5vwQSjzdYNFcZzS8JKAx2EI/V+9WeUk8TJ5D8TMMLXUakwc0/DrppN4X153JF8pR2IrdtdmDyyu9T8ENzhfXwOM/8dz5lgLaBwIMOxM4F7kZEx5nIuQiVyPpJZUpVyvmJCYZSQ90B6IDoQJd/2L6ivLf6sjmxeRM4jjec92332XjcuiR6wruNfGn8hz1g/jX/CUCjgaxCsoLGwcHAJn3t/Oz9dP3GvcW8h/pAeR/4YrhZt8A3ebaZdop237b+93U36/k2eTM/58sT1qocidbVTbmKlI340onWt5UtlF1UgNH3DgSKDEP4fpn6OrYd9AyyKy/zcWt0jvZDdYByQXAZMQzzubXTeWK8mz92AiGEaIXqB1pIBsh+CMDJmckBialKLIpLSqYJJIbTRJ7B+v+xPoc9yLyZev55DTi/t/X3CnckN3H4cXl7OVi5yDrsO+F9oD9UQKTBmkJNwqECvAGjv+X+Hz1o/Tr8wXyq+5Y6zbokeHp2njUM9Sk1dvZc9yk3n/eetxB2nbfmwn9Pllvt3l1WKc5RTkiSGdZOl5lUu5SW1J0RaQ4xyPfCGr1u98Zzk/Cy7N1rq67rcrZ04vQeMRqwXfGKM1P2kjrCfplBx4RLxluI6IptCr+KqAqYieUJXYodyyJL3IshCG/FkgLtv+M+Lvy7+sI5Indx9s+3RrdENqf2BXbFt+G4kLmBeoj78f11/35BeUL+Q4FD/IO/QvBBKr9M/kx+NX4nPhe8vHodt6G15PYhtnv2KLTw9DM0+nb5uBw42ri6+0SGrtLM3HjbpFNWzuRRJVTrl7CXadWfFpJVA9Aly7TFoT+vO1W3BTPisLMsBCrK7iyxHzKOMUDwJXFns4b1+Pgve5M/IYJzhWIIJMpVi2NLXEwOjMmMdAsQyluJRIjphuoEXwN3wduAHj2mue42k3U+NC+0aLU1dPl1aTbpeCk5cTnkenu7vD2T/+pBzYPphMcFEwRFgylBWcA4fuK+sL6nPjU8tDrXOMc3SvYYNXG1v7X0daj1iLaId7p48zjJ/ajI8lVFXPhaHtG7DhBRYhOQVebUs1NDVOvSwU7kSxmFB36uee21BfKWL4srw+zHcO1zpXN58IHvsDHYdEg2eDkSvMEBYASGh4uJyctcC53LrowbTC7K4smQCc+J74luhq6DOsCvfcq7sHl5dwm1cXSONFW1LbVLtNg1SDcluMq5yLp9urP8qP9EghaEt8X7BbCEPsLEgh/BfgBJf2G+2b4qPUh8bPso+cM4f3ZGddp2WvZf9435OzoKewP51jr4BK9SqNyt3gNVLk6nz/8RARLoEpOQyNLbkj7MfIlrBBN+OzooNTWx229wah1p7m7JM+F3LvWMNGY1z3ag9705ub0eQiQFXweUSs+MEcxqTFtMNMy3SlVGqQTMBMxFy0ZZxCrBv74cecR3lXXStEazRHL+M/D2orf1uAV43vo+uxk7zTyVvSu+54EVg2SFSwXNw1vBOn/CP6sAKL+5/m694/xcOo+5Yfeudg+1avWy95L5XzmEOjt6fntmOwC8METJUkCd2h/CV5UQbxAT0enTfdJ7TzZPRE4oyv0JaAXdQL065TSgsEFtAChSaDjsz7O9eFI4VjcZN0m387iG+q799kJNRjcJaw0bTzpPq055jNtMY4mYhrmEwER9BIkD9gFzv9h9XznCtuLz63KCMq1yYLQ/thT32zjUuZL7CbxQ/N58wj16PkeApkKARR4GI8T/Alz/pf3T/Q08/b0g/ik+LDyEOii3MHV29Bs09bXd9ys4DbgfeJ953nrRv/oLeVdbH68cqBKdj2LSExXxlx6TA08BzxZMC4mCSEgEvQERu0S0Da+katlnPGizLfD1OPmGeHc3Lbbvd+h6Mrv9fuSCvQVQCZ+OElDJUkfQmQ6DjSSJLIVBAxECUUNRA12Bcz+5vIr5FzX68ujx9/ExMW9zJ/YO+L+5urnievz8p73yfuH/bH/nQOhCeMOYhF7D7MLzAqqCMUBsfaf7bzqAOov6MHlweRS5N3jEuDS2lXYDttw4d/pZOhv5PD4lSYQXvB+NHKQUwNFED/uQ55F4j2+P7A8OjJILrompBZtBBfqVdbHxlyvtJ8Wn2axmcw631DpPeyT5/bkq+RO6oX3/f4NDLodrCzsOCA8DTlHOdwy6ScqILoSfAq9BVYEIgjXBMz20ea+2AnO7MbVwKvEac501yvgQuRv6BTqXuq27+73Af73/98A8wXuDtYPWA3dCUMHVwcRBMP+C/uv9SPve+w/6q3l+d7K2izfTugN7urqmeb24lDffeteEflDlW02cAhUcUNfQnNIVEyMRJk/4j+INRssjyTyFrcH/vEL4KTUusF5rC2k3q4yyJTckuT+6IrmSON74bvl0/ULBLIOvBpYJgIwpDYxMzoxWCwzH+cVgwz0Cb0MDw1rDUEJevhA53nYfc8F0DLKOMfwytDRtt2T5oTrVfAK82r0BPfQ9dH2gfoaAh8QBBZSFPkMGASi/tL7xveY9eHzme7D62LplehZ5g/kA+P25Uzre+h15B3g998Y92MkzlCpbdtj1EMJOu450D3kQA84UDkLQSw4fDDDJo0SlAEk6WDWGc5pvGesKa3PvfnbffBu9A/1gO1k5ljj4uUi9PsCVxEuJdszFDpsN8YqESTXG6kOKgbM//3/MwS8AqMBzvwY77/kHNzt2FPZhdPPz1LTvtqB5o7vOfRk+Ur7jf6AASYAv/7k/TMDLw5hFIsTtw7lBroBAPsA8dnqRObp5Izm3Ogv6gfq4uVk4F3fvuM46UbsSutW6/L+aiQwS6tl0WbdWXVRv0jdPs42YSyZKKUqKiroK60nIRaU/z3p+tflyou5VqnepRKyGsod4ebxmvsP/qj9Ufvu+PL46PtgBUkXUirKOVM/7juLNc0pWBpeCbH3hOuV6Lzqa/F59cLxhOp74Y3Y9s+xyGvGv8wH2cbnJ/SY/QYEJgejCjMNzgzxCX0GUgbQCuwNUA3MCgwIdAXB/532ceyZ4wjeMNrs17LXNNgH2grds+HI6GLvA/OL8vfxaP/eHS9DCWGCaGJgmFg3U0NPBkl7PEky5CtfJhkimhe9Bd/yqOLf2OLRj8VItyStBa1ouYfLm93S6lPz/vqEAXwEtgNCAYoGChRGIlouhDTUNqg3xTR3LnklExYgAzPy6eYI4zrhKeG05Nboqej84K/UWcvkyJPLp9Sj4f3u7vnxAfYJmhHnFfQU2BGYEMkQbw7XCskI5AjHCqgJgwS+/DPxT+S+2evTRNIh07bV2dlS3w/meetV8Kz0S/Ue/vIU2DJcTwNbXlaxUphQtU1dS5lCfznaMX4mBB8oGTcNaP537priqdz60V/CRrapscm2xcB4zSfcsOhO8d72bPx6A/UGtwdaCx0RyRmlIVQmTCs0LnAsHylRIt4W0AiO+MXsa+cn5MziieNA5afmm+Wz4SrectoX2NnYYt4Y6ALy/vvnBTwNXhA6EDcORw7VDYsKTAjtCIcMvw9vD1AMaQevAPL3ge435/jiluAM4pnkb+fk6c/oRecj5wjrg/isDzwpxz23RUNF2kPtQFw9TTd6L4gqziZTIj4fhBsuFcMLmf4Q8uflItn+zLTDvMECxrTNt9d/4aPoAu6p8Wr2TvvH/G79vf/EBcgN/RNDGMEcxiCzI7Miexy1ErkGDv0396HzQ/GT737uC+9t7wvubuvI5yflg+Qd5knobOpo7tL1Gv+7Bm4JxAisCHYI9wfcBgIGNAd0CVoKPwqSCQMHQgPx/TD4H/Jm7bPqy+kQ6qrpsOm17Gjwf+/n7EfvP/wxERMk+y1/Mmw3JDv3O4c4CzHaKR4knB+mHi4f1xuKFVQNJgVG/iz12ujR3B7UK9Ac0W/UPNir3EPiM+jm7C/wWfHG8gT2g/ms/dsBJQXHCfgQ9xdfHaocThY+D00JAgU4Af/87Pk++bL5Xvoz+Wr2DPTf8e7whfBo75DttuxP7cvwOfcX/TYBZgM7BCcEkwTpA28DkgSeBS8HqAgVCPIF2gPLAGX+ffv19xj3Pvd19973Yff29uT2Z/Uu9YD2nfdq9zH4h/6JCasWHyAxIuwhbSHpHcUbihnaFfkUDBJFDqAOvQ3yC7oKAAcnBKwB/PqG9ETvXutU7JzvtfLi9X73svi1+1/9Rv0Q/GL6WfoZ/FH+Df98/S37Ifo2+yn+zP/c/on+w/2f/HL8f/t9+mn7Gfxe/W//n/2w+kH5l/h2+SL6Rfog/W//R/9d/vH9QQAYA4kEvwXjBuQHUwc8BTsEBgNsAMH9rvrl+Ff4Ifet9u32u/dD+MT3Kvd49sH27PgS/CsA7gNSBtgHeQdgB5gHdgdsCQwLiQorCoIJ1Ah0CQMJVQjkCNoHLAa2AxIAhPxo+Pr0JfRK9Sj4H/vQ/ID/9gFfBAIHywdKCHYIfwdABpAE6wG6AJoA5AABA8MEXwVhBOQBsv+S/rj94Py5+8f6GfvC+cP3GPf09i/5Rvzv/Nv8SP6k/4cBoQK6AHP/x/5Y/sP/oQFGAjQCBwH5/oL9Hfxi+qT4/fdd99P2AfcR+JP7wf6GAMcAEgB3ALUBHAMJBUoHyQgnChwMoA2BDgoNFQmOBWYD5gFwAFL/Tf7H/nr/W/9C/VX5QvWY8nTyFfOS9M718vY6+BL6qv1nAcoDxgXmBs8HdwjZCNkJkAr7CjILWAo3CrwIkwVQBHoCwACx/iD8Rfsv/Dr8pfvH+oz5Xvq++gP7vPuU/ND+ZQIGBncIcAggCG0HZAYZBpAEFgPQAVv/cP0r/Jf55/cq9iP2qPZJ9lH2gPYf+Nj4VfkQ+gD8t/0P/6/+xf4KAdEDkQexCAMIxgYqBrUHuwiuBxAGiQSeBO4FCgRPAdD/Hv7T/mj+QfxF+2/58vdF9yz3HvdU9hr1n/Vz92/5jfoO+mn6Af2PAUwF5gc/COoHOgfcBYYG3AbEBg0HGwYaBU4EpgLAAccArf+H//v+hf5G/xf+t/3K/tX/fAJPA7oCzgIYAscB7QIlAu0D4ASVBKsGxgQ2ArUBLv9U/on9vflJ+e75+PqC/n7/Kf3f/Ff8NP1Y/1H/J//r/zsAugAKAZ0AfgJ8AvkDSwWtA08DlgIpAJD/N/9u/osAXgApABkBxQD7//L+efy3+lX6v/kD+6H7APsA+6z6nvo2+3/7nvuu/DL+sf8ZASwCqwJcAtsBCAHJAO8AnwDi/2MAiwCjAO8AewCSALoAaQDZ/wMA5AAsAqQCbANxA7oDfwQIBF0EpwSeBM8FLQcJB48GlwSbAdr/uP8JAPD/Of+t/Rf9XP1w/eD9A/85/1b+DP2n+x/7sPuc/LH9Gv+g/3kAAwEKAWUB2wEXAscCsQKkAcwAggB5AEn+dfy1+0T8Qf0R/eL8Yf2d/SX+0PwS/PH8NP3D/kn+3/zZ++j6YPv2/F/9Vv23/L79bP/rAFgCXAFhAaoAYwC6AWMB6AG1AQcAAQHmAQgDhwMyAl0CkALpASkB2f+6/wUBrwKXBPwFQwjfB28GpAWoAzcD3AKNAWcBQQHgAHIBEQLJAZEAoP+J/g/+zP7U/Uj9ef3A/IL8nvv8/L796f4pAcj/T/9q/tf9SgFaAhwCuAIeAWkBugPZAjUDiwHbAOQA8v8s/0b7OPwN+S366vjj92f6R/ql+2H9x/3i/TL8Uvm3+cj47fxt+//9GP6v/oQAlf8bAY8A/wEeAbgBXAKBA8UCgAFGAR4CxQKyBOwE4gMpArMAxv/a/6QBugETA+cDvANZBNUBjgKrAoAD1AgEBacFfAMNBBQH4QYtBgAFwwKAAJ0B4/82ANkAowBj/gkACvzj+a759Pde+rT5PPtw/Mn8Tf2h/dL8r/4ZAIkBdQQqBYYGSQZbBVgEVgMgAcUAXADi/0j+5vz4+077WfqT+CP5mfkJ/G77i/wi+lf56vqy+qD5lPt//B37Of2R/F//Fv9nAJQAuQBfBHUDOgUNB90GbQhXBh8FgQSoAVr/Zf32/TL/3v4iAOgAQv8E//r8r/w9/VH9Ef2C/Aj+LQBaA24DMAT2A84CFQN5AtED9QT7BeoGBgYHBq0DTQFuAM79ufyA/RH9w/0+/xP+4Pzd+xn96/2+/d37O/wn/4n/kgH0At4CSwQSBcsGqQQeAsYD4AKpAw8EgAEs/5b+vv2f/c78jfri857ys/It9Bz3kPbQ9uX28PcH+7v57vfP+xT70v7M/rMAkASTCOsKgwtrDicNJQ7ZDO0KBwqBB3wCgAEN/+r8vP3x/LX9pP0u/YL72PrN+Sz2IPZx95b6yv7CACcC5AL9Ah8EFgUvBb0FUgbaBdcEmQSUAxgDqgLQAcUA1wEGAq8B5AEVAOsAUwK4AYcB2wCO/0//yP+C/nH+GP8a/sIAIgG+AYsDDwJVAWAA9wPnBB8E5wTuA8oBQAJ3/sX8zvz1+Zb6A/QD9hXzvvKH9fbyMPbm9PT1s/Rd9Zn3dPpv+bD73ftF+2wBGwGAA+UDDQOYBpgIIAilCX8ICwZsBPkCZgIBALr/9P4gARUCbAF3ArUBMgEJAGX9/Prm+6j9Pv93/zYCIQWuBnQJmAhZCC0IfwbTBPIDwwMKA6YCbwMhBOsE3gRaA6sCrQErASICXAGfAeQByQGNAl0CtQKzAWEASf8c/mv80fpB+hL8SP2AAM7/8P70AID/hAHQAO39Ufyx/Ob7t/nE+ef3Gvji9jn0BfTS89vycPPU8iX08vjI+cD7pfvf/CL91vtR/VP9bAABA7QDAgYCB0sFiAYsBdwCAAZWBc0EngWZA+cChgAi/Kv4JfWT8hX0qPW9+NL+SQLjBYkJygozDAQPVRAAEc0Qcg86DycNbAwnC1EJbAu6C2wMMAwvCC0GUgTbAA3/CP2L+zz7cvs/+5760vq++6P9E//H/hf9H/so+ur6mPwM/o3+w/8BAHcBRQQDAlwB/QFS/8wBMwOsAIQBHgBR/swASAAy/kj8gPc99IbyrfDs8aDx2vB98pbzh/UV9g32dfZS9n74Avrx+tD+HgC6AGwC0AE5AgoC3P+U/w4Av/+r/3f9E/xK+8/4tvdA9bPyR/KW8nr2jPp7/cEDPQinDYcUMxd1G3of/CDZInAiTh/SG4wXeRMeEkgQRw6ODPYJPAgEBvQCtwB5/JL3F/Mn7hHteewO7NPsZeyz7XHwOvIe9Eb2evjU+rz8kP4TAPkCTAdRCrwL+QseDGQMRAw5CwUKSgi1BswEsQDX/Tz6w/Vz9dvzH/LW8ufwxu767IfrNe3P7wPyvvKI8yH2CfiF+YX5KPii+Vz7uful+0H7UPs//Bf9mPxe/EH7wvkQ+U74ufjW+ED38vfC+Vn4ivjV+Jb7kwU8DtEV/R6pJh4uFDR7NnU2/DESKwkjeRu2F74T2g8WDW4KYQl2B/ICCv7j+Cf0wvBW7PfnNOQq4OPdut0234visOZG64rwGvXc+Af7vPwN/8kB3wU2CY4NJBLkEzEW8xe9F9oYkRdBEzEQ4guCBykDwPx69n/xWu346RTnfuMO4QDhQeJG5VLo4urT7ePw1vPS9fn2JfhU+Xb7Yf24/hcAJwBcADgAd/5R/KD55/b49E30bvRK9CL0sfTO9Zn38vef9qn4GP6pBgUSiRvbJO0uUzTiNl84fjW4MTYs1ySTIZcfvBv9FjERFg3UCugGWAE/+xP13O676D/jkd7D2+jZ7NiC2qTdmuBf41nmB+qQ7p/y5vWw+dL9YQGeBAwIsgyoEYwVYhn4HHwgNiQcJuIlciM7HV8U0gpKAQf5rvJ17XjpKOcV5aXiseGj4R7ifuOt5FvmV+nJ7OfwWPXe+Kf77f0+/0v/Mv5R/MD6IfpU+SX4z/ee+MT5Ivod+dr2vPQc9HP0WvWd9Vr0GvUA+hMB+gqmFS4ftCo7NdY6wD0APaA4TjMHLIYkaB/2GoEWQxICD+kMpgl2BAr+KPeP8HvpDOL52+jXNdX20zPUq9aQ2nbei+J85sDpm+zu7mDxNPXx+UD/TAXXC0QSaxgNHlEjLih0Kx4sZCr5JvEh0huQFFAMGARD/B718O4o6QzkYuDf3vne9d8i4c7iSeVm6PHrS+9e8tD0nfan+O769PwT/tL99Pwr/Iv7+Pqc+hn67Phz95L2N/fs+Kv5u/nq+Jn3Qvf49Djzl/bD/M0G3RNQHkwq5DVdO+k+TT4AOfwycirHIl8fxxyEGsoXghTFEl4PVQlsAR342u9O5ybf39my1SXTltII0jHUvdfg2dXcB+Bs4+Pnhuuv7vzydfcy/W8EhQuWErcYMB7kIwYpYSyULcUspSoyJxcigBtzE+cKRgIO+uPyn+xf527jXOCX3tPd992730ji3OTG5+jqY+5u8sH1u/eM+Tj7r/zU/c799vwO/Ir7efuU+0r7qfrR+Qf6IvvU+gL6R/kN+Gb3fvU68sXzyfkGAg8NyhayILcsUzSBOFY6MjcvM2UtoiUYITUdoRjiFCsR4Q9kD4sLgwXy/dX1ce6v5Znd99cy067Q1s/F0MrU/Nhq3NzfluIE5VTnvOkc7hn0lvrBAagIABD7FjAc2yGDJ2cslC8VL64s3CnTJVkgQxm7EKwIXAFb+n70Au/Q6abldeIV4eHg+ODL4STj++RA6OLrbe/z8t712vjb+zD+6f4//l793fyW/GH8Tvyu/GX9Kf6d/qv+pP47/dr63viz9sv0wPL57zXxM/cI/1MJJRPrHPcpuzMBODw5ATUVL8goQh8zGOcUixKEEp0ShBIKE1AQAwoFAt74B/BL5xbfr9m91u7UBNSy0/DUjNfW2Xfc1t9S5I3p8u2K8ZL0S/fR+pn/+wV6DhsXVB88JzYtyDC5MasvRizOJ0kh7Rn6EmoMIQYp/7H3C/EK7MvofOaU5KjiA+Ft4N/hceRD5wPq/+zR8J304/c2+uj7gv2b/qT+I/7m/FD7TPom+uj6G/xN/Tb+8P7I/tD8b/oz+Kv1DvO67hHs5e+F9ysCTg4kGH4k7jAjNzo60DeDMOcpVyEtGkMYyhZ+Fg0XJBeRGHQXdhGhCPT97fMg6ivgYNka1WjTw9M71O7We9rT3PneOOD84evku+ba6CLsDfAg9p/9rgVgDhgWSx11JNQq2i+BMhszQTIKL4gpwCIFGwYTXQu9A878D/eP8eLsiuk65u/iud/03FbcYt3N37zjB+i27BbxmPRJ+Jr67PqW+qf5Sfml+WL5yfl5+7r95/+sADsAW/91/ob8l/mi9jLzKvAg7RXqDuy19OYABA/HGoYkoC/xNlg4wDRrLM4kkB63GJEXsBnoG+Adfh2EHIIbTRUVCk39z/E46S/htdlf1djUTNdG2tzc49/m4RXixOGn4Yvi0uO25Afn7+vL8sb6WgO2DCEWNx7gJBsq1y3eL/QuGyzNKIkkqx/XGlIVoQ/pCV0Daf05+CDz2O4/66XnaOR24djfB+Cj4PbhD+Sp5k/qbu0o8FfzifWd9rT25/U99Xf0UfOG8271mfjf+wb+w/7X/pf+wvwQ+k33QvQM8hjwuO6o88f+oAxDGk4jnitNNVQ6HzqnNB4sMCbaH1cZhhcxGEwa4BtpG2EcWhusE3gGO/dE6/Xh2Nhc0i3QI9NZ2A/c2t9u45bkE+RY4vrhA+TK5ern8eta8oj6DwJqCX0RGRkCICslvSgSLIIt+Cw/KzMoQST7HlMYCBJmDC8HaAKU/XL5+PX18RHto+eb4vzedd2t3RvfQeIq5jHqWO5p8WfzP/Ts8s/w2O4w7W7tz+5T8Tn10/ic+zT9RP0g/Cv6KPgT9g71cPXy9WH2mPWz9on9nAh+FYcfQCamLrg1VDgMNpQt8iUbIS0cORsrHSogwCM6Iy0hxB4bGDUNEf6y747mud8w28nYbdhq24ze7N9F4Snh5d963pXcFt3U39fi7OYV7ObySvsgA98JCRAGFlgcxiHBJdcndSfZJWwj5yCOH/Qd1xuRGZIVuRB1C9MEIv609yLxyet65xrkJuIk4Sbh2eEI447kr+Vr5s3m7uZv5xbo6ugy6rfrKO5e8YD0yvdk+iT8SP1h/WP8A/vd+QD59fjU+mP9qP+WABb/qACnB+YQKxr/HkchJyYrKnIrvShiIs8eZh36HDkfUyHvI4AlOyPYIOIcHBWtCgP+K/Qh7wHrxuf05ODjT+Uo5jfmR+Xt4hTgv9ym2rraidv73Ejgg+bu7zz6NwNGCqwP6xP3Fs8YaRkOGQcZjRpdHZUfRyC9HuQbbRlUFmcS/Q36B58BxPvp9fDwS+xT6M3mQueP6Hnpqejy5gHl8+KU4fzgZeHH4vbkQ+iJ7JXwcvRE97j4FvqY+uH6dPuy++j80/96A2AGfwdcB44GJQUOAiv++f5iBWIOFBdqG2IfdSWTKFYomyT5H8QfniBmIQ4kESYcKBMnAiInHcoWIQ68Awv5BfO08MHuAe0p66HqU+pt56HjPt8X2+PXYNX81f/Z498n5uvrWfJr+Kj94gHrA/4FZwjpCWYM/Q2QDgUQgBHiFLUYjRoQG0UZQhZ7EuQLjgSH/Qn4xvae91352vpK+vD4BPdy8yzvgupx5rrkn+Tn5Y/ofesf7kvwJvJP833zRfJy8Izw4fI99mj5zfuX/sABJwSvBG8D8gHbAK//R//6AKYDKgYNBocEPAj8EP8axyI0JREo8S1vMMwtvybnHr4c8hyYHM4dZh5PHhkczhVKD2UIx/6J9ITr7uZf5nHluuNn4qbizuNT48vh6OCC4CjhU+Ie5MHntetL7wPz7fag+ob9d/9fAZMEKQg9C4UN2A7ID5MQMRHEEeoRfRAuDqgLlAkACPsFlwMFAksB6AAVAL79o/qn9/b0u/LN8Hzu9OyZ7JTs/ez07HDs5Ow77fTsFe037U/uVfAv8sn0kfjW+z3+8P6z/t7/twE1A7gElwZsCU8MqAyoDCAQvRYVHgAiYCF7IqslwyYFJc0fzBuiHAgdYxu7GVcX3BUnEywNEAjeA9z+Bfr49L7xBPFQ7+fsY+vW6mfrOOvC6dzoPOgt6GfoTOg26THq9epK7I7t5++s83732fsyALIDbwdCCroLygwIDXgNMw6eDhAPbQ+9D0cQcBDqD4gOtAuGCP0EDAG1/YP6g/h49yH2ifSB8jHwW+5x7GDqUOko6fzpZesw7LXsHu157Q/utO777+/xMPS/9o/5NPy+/n4AJwKHBDMGrgeaCMIIqAlGCroLrBCMF8cd9yDCH9kdMh15G0oYIRS3EeASMBWFFlsWehVYFAES6g0DCScE2f+h/O75b/jD96T2mPUb9NnyG/LN8Pnun+zG6V7ooOiI6Ujr1ezV7jHyRvXE95P5xPql/Ov+PwARAdsBmwJtBG8GTwjBCvIMxA7qDyoQqw8UD0UOKg28C9cJbwdbBA4BzP1R+1n5M/dT9Q70XvO88nrxu+9J7h7t1OvO6mnqJ+vD7G3uL/Ad8hP06fXE97T5Hfyx/rEAagLtAyMFSgZQBzIIWAnZCjMM7gwsDMMJfwbgA1gDWwW8CNcLng1kDlsPBxCKD4UNFQtLCrELoA3hDs0P6hAQEu8R9w9vDNsIEgbTAxcCsQBx/7X+Wv4O/pT9Mvwq+gL4NfbU9LrzG/MX85jze/T89DD1aPUP9cv0A/Wx9TP3Sfkx+1P9WP/CACICJwOvA0kEgwRQBCgEYQOXAjQCsQFzAfEAzP+S/vj86vos+Z73W/Za9T30x/NC9Bn1DPYu95z4efoS/M78e/2i/hAAlAFdAsgChQM8BMQEQgXTBTwGYAbhBV0FHQXPBBME6wLoASsBhgDD/+/+i/5z/lT+QP5U/nn+lv5d/vr94v0T/hz+Qv6Z/lb/kQBfAQUCegJuAp8CxwIPAwQE/QSiBUcG9wZgByoHGAbRBEkEIQRCBKAEFgXNBVkGXQZOBjwGFgbRBYUFWwVkBRIFOQRaA7MCNgJeAR4A5f76/UH9Q/xV++/60vrJ+pr6IvqR+cb4pvfK9rT27fZs9wb4u/jP+Rv7O/wu/e/9h/4j/6D/GQCNAMcAqgBRAPT/6/9RAK8A0gCuAKgA7QB5AdsBwwGGASQBXgBG/+T9kvys+177UfuU+wH8Q/xV/D38EPzb+5H7KPsH+2X7Afyq/JD9ov7j//gASgFfAbcBUQIGA3MDxgNUBMQE2ASTBEsE5QOUAzsDJQM9A1IDIAPDArMC9AJRA6QDlwOVA+MDMwRQBAsEtgPFA/0DXwTeBEUFlQW/BZMFMwWbBHMDRAJaAbUAPQBN/xr+e/1K/UT9K/2d/Ff8U/xD/FX8ZfyU/GP9Qv7v/oP/jP9h/zv/7f7e/u/+8P43/1//Mv8h/9z+nf6L/on+aP42/vr9qv1p/cn85vsi++X6SvvE+zb8hvzH/Bf9Ff3k/LH8kfzv/I39Tf4y/+7/ggD6AAgB3QDfAOsARAGhAbwB/QE/AjcCxQH/AD8AEgAkADQAMAA6AGMAhgBrAOP/HP9C/n79Ef0H/V796/3D/sb/nQDzAOsAnQA6AND/Lv+X/kL+VP7b/r3/vADHAYQC5wIgA2YD9gNZBEsEAAT/A1AEzQQfBZcFQwZKBzQImAhXCFUHBwa0BK8DRANLAwQDrwJIAsUBSgGcALz/+/6i/j/+Cv6f/QP9iPwD/LX7qvun++b7Qfyh/Bv9bP1y/Wn9e/1+/Yf9r/0M/qT+O/+r/wMAOABTAIYAlAB5AC0Aq/8p/6b+VP4c/uv90P1+/S797fy1/J38o/z2/Lf9fv7//lj/W/8D/1/+df2s/Er8WvxO/D/8SPyc/CX9pv0K/jn+cP5z/lb+P/5q/uv+Yf/T/ykArAAeAT0BCgGzAIYApQAcAe0B+AIhBB8FvQXNBW8F+wRbBMYDZANLA6QDIwRkBGwEPgTnA8MDbAMNA+cCBgMwA3MDaAPtAmoC5gGWAVMBAwGjAGsAOgAMANf/df8g/7j+m/6O/rr+LP96/63/uv+f/4D/Of/t/q3+kv6A/iP+qP1V/RP93/y8/JT8lPyL/Hf8WPwv/Bv8Rvyo/Aj9af2C/Xf9rf37/V3+h/5c/in+6f2o/Xn9Tf1p/bH99v1n/rr+9v4p/zL/Cv8B/+f+0/7p/hz/lf8pAKUADgFuAeYBggI+A9wDTgSHBG8EOwTnA50DbgNhA2MDhwOxA6QDYQPVAjkCsQE4AZ0AFwCd/zf/5f6b/mr+T/4w/vT9wf2m/Z39t/26/df9K/7V/pv/VQAMAY8BMgLSAj4DvAMUBIME3gQNBYcF6gXzBbAFGgVHBF0DYwJfAXAAsf9C//3+uP6F/j3+6/2v/YT9Vf38/Jr8IvzU+4/7NPv8+uj66vrP+oP6SvpI+or62Po4+8v7Vfzm/FX9mv3H/cn9uv2v/d79RP74/tX/ugB1AQMCcQLDAicDYQNUAycD9gLXArUCgAJYAjYC7wG8AXMBUwFCATQBKwEwATQB+AC1AEEAl//5/mX+Ef4T/lj+s/4E/0v/mf/c/ycAYAB1AJoAxwAXAWwBqgHoAVECugIpA5kDwwP5A0AEkgTuBA0F/QTMBHEEGATBA4wDQgPkAlgCwwEbAYAA5f9j/yP/Cv8I/w3/8v6+/pn+fv5c/j3+SP6J/vL+Zv+p/5D/MP+8/if+nf04/dL8efwX/MT7ffsU+5r6R/oz+kr6hvrG+hf7YPuh++37IPxh/Mn8J/2b/UL+9v6y/2sACgGZAe8BGgIuAiwCBgLyAbcBkgF1AW4BYQEkAeYAgABYAD0ACgAFAAwAJgBMAFoAZQB1AIQAdwBKAEYAbADMADABmwEBAlECqwLcAvQCCgMlA1sDiQOiA3wDJwO1AjsCzAF+ATgBKQEgAfMAugBXAO7/lP8n//T+8v4j/2b/qf/g/wwAEwAHAOL/wf+v/4n/aP9G/z3/T/+i//f/QQCEAL4A1wDMAKMASgDc/2H/Kv8T///+xf6k/nr+i/6r/q3+vv6b/nH+RP4n/jn+fP6W/sH+5f4R/0n/QP8G/9P+zP6d/lj+5P1K/er8r/yI/Fr8PfxT/HT8uvwg/ab9MP7H/nX/BwBYAHsAiwCvAMcA9AA9AZkB+AF+AhYDpgMKBBME+wPBA4MDJQO8AnUCNAIFAu8BxwGUAVYBCgGjADgAv/9j/yD/1/7B/p/+ov7M/jf/mf8JAGEAmgDXAMUAnABRAC8AEwApAJ8ASwEnAvQCqQP2AwYExQMqA24CzgF+AWkBhgHDARwCfgKrApsCRAKkAQoBWACk/+T+X/4u/hf++P3X/bj9kv2E/Uj9Lv1h/dX9VP7l/lj/if+Q/zD/yv5c/vH9nf1n/Vb9cP24/ev9HP5I/oD+r/7Q/uD+zv64/o3+Sf4T/uL9uP2Q/Zr9vP3d/Q/+Rv6E/tX+If9J/3r/kP+6/+f/BwAyAEEAeQCsAOYA8wD6AAgBCAEkASQBGwEQAQMBBQEFAREBSAF7Aa8B8QH0Ae8B0gGmAYYBdwFKASkB/wDkAPMA+AD8AAUBFQE2AVEBXgFNAREBzgCEAFcAJwAZADAAYAC6ACkBkgHBAckBkAFRARwB1gCaAGMARgBVAG4AmACsALUAjQArAKj/6/5P/uD9X/0u/Ub9wf1x/iH/0/89AIYAhABBANf/Wv9E/0L/Rv+H/7b/zv/G/4D/Kv/B/j3+yf1W/fP86Pz4/Bn9Of1E/Xf9rf3//V/+vv4w/27/4P9eANkAVQGPAdQBDAL6AcwBqAGmAZkBmQGxAcMB/QEYAhcCCALBAYkBTwEKAccArACWAH4AfgCSAKgApgCsAKgAjwBeADYAKwAbAB0AAwDr/9f/wf+m/5//lf9q/2T/fv+X/4n/d/9J/zT/Uf9Y/3X/gP+S/4n/i/98/2P/aP+D/7b/6f8kAEoAmADHAOoAHgE4AUEBGQHzAKoAbAAgAMH/fv80/+3+uv6f/pn+q/6k/oL+Xf5L/lH+Uf5x/nf+fP53/qj+sf66/sH+rf61/rP+zP7w/kn/tP9DAMUAPwGPAbgBwQGZAUsBCgEDAfYAEAEBAeoA/AAFAQUB2wCcAI0AkgCsANsAAwH0AOAAnwBuAA4AoP9C//b++/4N/yz/QP9s/4X/v//p/ycAaQCsAPEA9gAtAQEBrwBlACcAGwAZAC0AZQCfAAwBfAG+AegB2QGhATsB7wCfAEwAFwDl//n/FQAyAFoAdQCAAG4AVQAQAJ//Mv/I/kb+9P3X/Zj9uP3p/Rf+ef7k/hj/Lv8N/8j+vP5w/lz+Yf5l/qj+4P4n/27/ov/K/wEAEwAgABAAAwAvADoAWgBwAI8AswCfAJwAcAATANz/rf+L/6L/qP/g/xAAHQArAEMAYABpAJQAqAC1AKMAkQBwADIACgADACIAQQCRALUA6gAFAe0A4ACWAGMAJgDy//3/GQBEAKEAAQEcAVwBcgFnAXkBaQEuARkB4ACjAH0ASgBPAFoAZwCJALkArACUAJYAYwAtAOf/uP+f/3H/Uv9G/yn/If8c/+X+zv6v/qL+v/6+/gH/5/7T/u/+3v7i/uT+6/7l/g3/I/9H/2//lf/I////JgBPAHUAmgDMAPYAOQFcAY0BnQGGAYkBfgE/ATIBHAG8AIQAYQAgAP//4/+t/2//Sf8K/7X+kP5d/jf+Jf5N/mP+hP6m/sr+8P4h/z7/jv/X/9P/EgDy/+L/2f/p//3/QQCxAAoBYwGsAdcBygG6AY0BcAFcAVoBZwGWAboBwAG8AXsBEQGjAEEAxv9+/0//RP9H/0f/Wv8+/xP/2/6F/lr+/f3S/ab9sf0G/if+pP68/u3+Lv8g/0f/Jf87/13/WP/T//f///86AE4AkgCmAHAAfQCuALEA0gCPAEwAXgAiAAoA3P9f/yX/7f7M/gr/Of9P/8P//f+UACsBMAHbAMwA9gD2AMIA8v8KAPQAoQE7ApYCjAPWB0oK5gcGBoED7QB6//76oPaM9hH3mfhQ+6r8Y//ZAgoEqQOzAuYApv+O/jb9nfxn/Y3+ev+hAI8BhAIPA5ACtQGQAWwBqgASAL3/WP+8/6n/Sf8y/+L+BP8l/7H+2/7T/pL+hf6S/nH+Of7//Qr+ef7Z/uX+BP+b/9n/JgBnAJ0AtQAIAd0AmACjAG4APQB3AEwALQB3ACIAPQBGADoAtwByANcASAH9ADQB/wCEABsBSAH0AEQAYAD9ADYAfgAbATIArgA5AWsAmgABAJ3/iQBwAK8A2QB3AF8BwAERAa8ATgAJAIYADgA3///+of4a/6b/c/9s/+n/hf/Z/5//tf7v/rP+uv7v/pv+Af9f/5f/IAD0/8j/CQCk/6n/Zv/w/oL/Rv+g/3H/ev/T/24AiwDLAHIAw/9cACcA4P/Q/gr/6/2a/V/9Xv0w/Wz+9/+t/+IBgwOZA+oFnAZWBMYE+ACG/SD+MftM+eX5avdi+5L+Uf0V/0QACQAlAqgBCP4n/vj+uP+L/jn/QP9qA2oMLAw8CIoHygOPCLsIKvqk+Pf5wf01BbP9cPyVBZkKaAoGAxv9yP4DAdv8t/So8zz56/w2/mn7NPvOAcoCKQAO/eH7Vv1V/Vn7KPn1+w3/5ADkARUDRQQJB0EHQwXTBZIE1QTiA/QCJwKLAJYCygE/AWoCCAHBAr0GxgVkBqsGRwfACVMHyAI//tn8u/pb9uXwjO2T743xXvHl8FXy0feY/B79/fyS/fMANQNTASX/jv5hAlQFpAXzBW8H4gvKDtUNkAtoCtsJcgjpBMIABv9U/+D/A/5u/CD8IP0r/e76ufgc+A35FPgE9hX0v/a5+q77xfuo+47/qwRYA0gABwChAooGnQSv/9z/LAKpBJcCBf6h/p8ASwH7/1X9/PyH/5D/jv6a/XX9Y/6S/jb+Hv5s/kD/aP/0/t7/JAClAGMBkgGZAk4ElQSIBUcG3QZ5CHII5AcvCAkIcAhaCH0GZAWMBJkDDwPSAYsAnwBlAFMASAC6/qT9//x0/Cf8uftj+yb8Nv1V/Zb92/3O/q//0P9s/9f+uv5U/lz+Z/47/uD9RP0X/T39Zf1+/f38MvxT+6P6rPkL+WD4I/h/+NX4PvlS+Xv5yPnc+c/5wvlp+bb5/Pl0+9b7wPst+9H6Zfuq+1z7BfsB/Ln8lv1c/r79fP2N/pf+kP6X/nT8gAJEDEQTphoZGosd1SeaKkkomCO6G0scFxs0EIcKpQc8CLYLsAd+AvEByP/s+4D26e8w7bXqb+b/4xHkJOdP6tLsxvDp9Y756vpn+sf8UQBEAQ4AlP5MAMQERwdOBz0IWgoCDTANtQn+BkIFVAQDAoX+EvxQ+vH57PhC+G34Efhm94f2cPWH9JLzOPKq8TTy7vGM8ffxZfMN9iz4dvgS+cv60vvi++r5H/hS+KD4ufi0+HT4hvmY+k77PPuY+mj5UPkt+Q36n/uG+2f9o/y6/RcIzBWSI0gsjyppLFM1IzimNu8tSiPqIpEf0xWmDS8JfgrwDeoIJQHQ+0n2FvFP6hjlG+IC3z3cgNpA3Z/jyuci7JPxuvW9+Xn67PgH/Gb/tQF3AzMEkwcNDeoPjRKHFGgWKhiiFsMS3g7BC+8I/gYaBLEADP35+RT3BPZl9d3yg/DR7XLr8eoZ6rfpkevc7YjwufP09fH5Of47AXUDugPwAmMBIf8M/WD7QPnY98r2kvbg9nP2BPb79Sn1QvRk8mvwT+4M7MLqwOt77JDtre9w8f/1MPgA+qsGABriLHU4mDWhNaRBzkk3ShpAkzHcLgEs0R8nE8UJFwgKC38FY/zp9K3tlefE4cHd6dtv1+nRd89d1CXeceR26RnxxvmxApwFOQOaBTIJcAu9DNsK4go4DnIPghH0E18VgRYIFMgPgwwKCVAGAwNN/0H9svos+CX2MvRs9PTz1vFL8B7ujO2M7lLvqvF59Dn2tvhl++/9pQA0AboB2wL2AjQBCP76+hD6Dfoo+Hf0UvES8JfxrPMv9LXyevFT8UryhvOY8hnx6e/18MvyDPMk8+jzw/X1+FT4lfgWBYgZbC5NOUM0qTOzPaNGXUihPagv3SpuJCgXpgpLAYf/w/759xTxj+sj5jLhnt664VrlP+Mm3nDbxN9r6AHtkfAq9lf8RwPCBnQI9AxOEJoR/hDpDSoMDgtjCEgHywalB2wJYwhcBzEH9wYCB/0D0P7A+vT2mPRV83by6PKl8zT1bfha+yX9efyl+hn78/uB+436FPqf+yX/ggFhAk8DugO9A4MD/QEI/xT70fbQ8x3yb+8u7IbpIulw67/tTe/f8NjyzPVx+NH5jvlL+L335/Yg9v32N/cY+Lv3+vND+S0K7h4GMU40tDDeN+5BnUZmQioz1yc4IW4U2Abf+131lPQO8/zwDfGy7z/sH+kt6lLukO7q6eTkceS86obx3PWs+g//wwPNCIkKhQtUDDQK7QgzB1sEcwO+Adr/QwAZATID2gTcA+cCiwMYBvcHVQZfAq/9a/vF+4/7EPto+Vb3zPf++Db67/o6+nT6jfsn/MD8jfyS/Ob9IP8OAJ8A2f+O/gr9Y/vT+eD3ffSX8MHtr+y87SzvFPA88cnycvSm9Vv2X/d++Kn4fvfM9Xv1Uvdi+GT4I/my+e/6rvrM9h/6Bwk9HFMuhjQtMvI300H9SOBJLj9/MusnbxhpCbH9KfRQ7oPn7eHZ4cLiL+J04Wbk8etr8VPxh+5N7tDyr/eT+iz+kAFfBNMGrgigDMIQCxHfDl8MrAqqCQIHMAP2/8P9bP3U/Rz+IP5G/TT9Y/5H/+v+tfw++v74Jvgo9771pvQ59ev2LPkB/Pb9bP/QAPIBUgPZA5AC3wDj/6T/r//l/jL9ZfsE+hL59Pfr9TjznPAz74ruXe3F7Dvuu/FI9WH21/Vf9qT4zfpp+rL4J/eL9jf38Pap9tH3N/ig+Cb4mff//tYOhCJgMwo4UTaZOCY+dkQaQzs2FCdJGGEKzP/49VTuXOpv52jn7uhQ6U7pzekC7pb0BvgI9xXzf/FI9a76w/+ZA8EFEAiRCY0KvAzYDUcNEwsOB+cDFQG6/bv7GftM+wr8E/z6+7r80P3V/qb/bP93/QD71Plb+pT7rvsU+8v7DP5OAFYBcwGvATkC2QKQA+ADRwOYAd7/GP+J/s38kfmS9tn0/POx85TzSPNu84nzs/NE9Kzz8/F28UjyCPRW9e30+vQa9qn31viR+LL3ifa/9Wj24vaV92v5Svqu+vP4dvfu/5wR3iQzMkg0tzSVOwlDqkbzQh44uixNH1kPHgI798juC+j+4bXfu99H3+Xep+Dh5nru9fLZ9Fj1UPda+xj/LgSsCXEMIQ1EDCEMTg62DrgLKAehAmUAQv80/X/7ofoD+2f8Rv3v/cX+gP8QAOn/zP78/MT6GPld+EX4fvgv+Rv7aP7tASgEQwWMBgsIEgmPCI4GWQT9AisCSwHi/yn9XvmN9V7yBvCH7gLuce677xvxo/E/8mzzXvRl9QT2uvVj9ZT0ufMM9Dn1d/b79qv2vPWS9Wr2UfZf9jD4xvm9+sT4m/azACoWiCw6O5w8EDzEQ3RLLU2mRgE4KCiSFtcCF/Qd6k3iSdx32Ibak98i4vjiNebi7rj4yfzF+xj5Pvip+on9hAH3BaMHNAiECCAJNAsYCxUI2AQIAeL96vu0+aL4Evlv+lP99/9PAWUCMwPcA6ADOwHd/az6BPiQ9lH2WfdV+d/78P47AmAFuQcrCV8KCgtaCpEIvwU2Aon+1Pqy9zb1hPKO72HtIO1j7nrws/Ka9If2S/iV+Rf70PsA+6D5SfjT94z3MPZw9GnzMvQY9mr34Pdv98T3l/lH+kb7F/zb+hb5Y/QM9PICqhkWL9E6qzn3PB5I400iTetBJy9FH+UL//aq6d/e9dZ809nRxtaT3s7iDehA8AD6bAIWBfYDrwK1AbMBXwIlBK4FFAV8BDkEdQQqBSMEnwLBAfn/T/7x/L77i/yO/goBhwNkBFkEjgTVBIwEXAKJ/kz6yPZx9br1v/fL+in96/8MAyYGeQk1C3oLOQsTCWYGSwM+/7r81Prw+MH31PRt8XzvNe5L7tPuGu+X8JryDPX/98f6if3O/t79d/s1+KH1cvT086rz9vJK8i/ySvL58W/x3/EA8mDyZfPo8inyV+8h7cT3JQ4RJ386PkALQhtM51X6WYNUcUF9K6AVWv6O7pjjutmy1BXS7NRh3XPjFuj27Y71Of6rAvYBX/+L/B38bv3a/4MDqQRNBM0E4wUTCQoLUwkzB6AEiwIYAkoAFf76/FP8j/1j/97/VwCzAN0AjQB6/vH7Avoo+Uv5OPlg+Xb6s/zkADEGLgoKDOcL4ApuCk0J8AWLAXX9pfr3+CP3xfR78kzw9O4871/wa/Fm8YPw4/AF8wj2zfi7+Zz5tvnn+eX6Pfuc+Qb39PM68ivyyPEC8ePvB/A18QXxNfF98jD0E/Yi9e73Hwc+HbUykT4FP/JBpEnjTilPB0R2MFYcmAYG9Y/qAeEM2ZLTLNK82LvgnuVc6m3wb/k5AvsFMQZWBNsC9AM3BgkJwgn3BmEEnQMsBawH0wZGA6j/Wv07/h0ApQBYACIAoQGXBMkGdgcfBpQDKwHH/sv8hvpN93f09fJj8471Oviy+1gAbwVeCsQNVQ/fDwUPzwxjCcoE6f/u+vv1e/HD7d/qFumy6M3pMOw57zTyZfXM+Kz77/3O/gb+TPzf+Z33CPZh9F7yRfAT74fv7PDv8nf0bPUy9lb10PSt9ib4MPgj9eTyOf3XEisqCTthP35A6EbhTN9Okkg5NysiWgyD+PLrguJK2f/SYdG51kXfbeWw6S7vJfjUAVAHKwjEBaIDWwSaBZgG8AV5AmH/MP4n/9kBvAKQAd8AUwFkA4cE/wJLATIBwwLTBP4EjAMBAqEAPf8R/Rn6m/aJ8xbyH/J786317Pct+yYAogW1ChYOEg+VDm8NzAviCSgHOwNz/oz5ZfUk8knvSe0c7dPu3fHo9Br35fls/ZEAfALDAQz/d/tH96PzkfC87XLraemE6IHpZev/7ZPwwvP39g34RfnW+u/6Avrb9YL2mAYeHfsvQDlLOI09pkqYUe1Q7UUoMuweRgr59mnrwuDV1XDPb8581LHcUeKG6ZD0FwDCB4kJqghkB5oGxgaPBi0GJwS8/1P9Bv75/18CGAL//3f/2f/rAYcEzwSFBOwEogWPBs0F7wJeAHn+Lfwq+u73I/WW8zbzx/M09ur4ffuy/8MEPwluDCMNngzGDHgM1woxCEsEbAAM/QT5w/Qx8fvtgOwB7WHuxvAm81T1R/gd+6X8dPyn+vf3GvUo8r3vle587ofure4x7y/wtPEm8wX1FfYu9bH1s/YU98b2K/IF9HwFIBt5LjA4NDelPV1JL061Tu5EITLzH1MKZvcT7vLkzNt01njUgtkr4XXlDuvi80H9pgSzB8cHawfxBnsGxgW0BP8BCP2K+Yb5xPu2/rT/Mv8gACsCfgS0BqcHEgiKCKwIKQjEBsMExwLQAPv9Qfph9gDz8/B08GnxZfRC+Gv7if5VAXEE9Qi9DCQP1A+BDagKXgjuBbEDJwBZ+0D3gvOM8LbuI+0P7bLuy/CS80L2rfhy+7H9bv4l/Sv6ovYZ82rvcuv858rm++cg6hzsRu6c8eT0SfjW+zL96Pyu+U317fsEDdUeOS35MPIxxDwBR35Lg0lAOyoqJhoHB/74g+7o4TbZbNSO1JTbt+E55pftKvedAWcJjgv4ClcJ1gfUBioFKgPO/wz7wffc9of4vPtL/Wr++AB8BOEI0AvgDCUOkQ8JENEOGgveBU0Bh/0Q+uf22/Ji79Xu4O+N8qr1w/d2++P/iQRsCZILqAsOCxkKMgqCCW8GYwIp/gP7G/kf96b1ofOT8ZPwKvBT8cDyqvN39eP3cPo2/BD8dPpW+Iv2A/Xm8gnwheyc6cnoiulR6zfujPBp85X48/yH/8f+nvoB/yYOyB7DLLYwkC+zN2ZBoUbORnY61ikmGsQHQ/pb8CPkYdrR05fSQdl04D7mBu5U94ABUQneC7YLBwqwB44FIwPZAGX94fiM9vv2t/kD/eb9Vv77/8MCPAePCqELpAswCxMMlQ3KDOsJ1QWqAcf+6Ptg+ND0u/EG8GvwdPLQ9ET3wvrQ/8wEDAjCCDgIvghPCUgIkwXBAfT9Z/uu+dH3RPYl9BvygfGs8QPz2/Rq9lT4g/nT+fn5lflU+Zz51vhf9+L0UvEC78jtxeyh6x3qn+qW7DrvcPNv9nH4c/h79Yb6mwrKHEgsmTH+ML051kVKTUZPUkT8Ms8iow9hAFH1WeeA23bUjtOt2rHh/eXQ7Nn2tgJsCx0NkgsJCcAGngV6A5IAcPwz91z13PYA+kb9af1B/VT/yQFiBeoHdAcqBwkHMQfYCEwJdAigBxIGBAShAR7+ofqc+BT3LvZ39ejznfP99VL60P+bA54EgQXuBssI1AmwB4sD2/6I+oH3efSl8Trvgu0h7jjwvvJ89YH3FvpV/ev+J/7u+v32+PRG9IbzhvGx7XLqdukt6kTsM+717+Hxi/I49Ir3Q/nj+bT21/U5BIgY+ikTNWk0WzkZSL5PrVHlSbw2NyamFGYC1/eS63Pdr9R00BbV3N3p4tbpTfTK/5oJcQxGCw4KtwfwBTAD0/4v+xr2JPPv9Lb2OPm3+uj5MPwj/yIBCASrBOAEBwesCOYKNwztCkIKDApZCFIFjwAH+8T3RPaj9Fj0xfRl9cb4kP2xAe4E3AWrBcIGigchBmEDQQCz/WX8sPoP+Dv2i/Sa8/Tzw/PJ8/Hz//PO9bb3bfgz+I72vvU59lL2VvUp8mju3exL7bruf+/p7nHvtvDJ8sX2zPhN+JD1/vDr93EMWSFZMfo0zTJaPdVKJVFqULhBRC8vIOcNdf+19L3m0tuM1anUU9so4YPlyO2Z+F0DMAlwCBkH4wZSBsYEWgBt+xH4NPU39Tv36fie+rv6HfuN/bz/wwFmAn4BAQKkAz4GmgmkChAK9gkDCv8JXAl7BjsC3P40+/X3O/YO9aj1g/ge/Hz/8gH5AggE/AUhB04GnwPG/+D8+Pqu+JL2T/Qo8i/xt/Am8S/yZ/Mc9U325/bG9qr1cPXS9fL1pPUQ8+HvO+5b7jXwP/Fe8ZXyj/M29fP4Kvq5+dz2j/DZ9gUKaBzhK/IuZy39OYJHC040TmE/ui3yH4EPpgM++ubsieK83MrbC+El5UDnguw99D/8rgBhAID/ZQDcAocEQAM2AFH8afmD+XD6Wfvh+mD41/d9+WH8qf97AFcAkAEjBGsHrwlBChkJggh5CNMGywUnBOIB1wF+AD3+y/wC+sj4+vnS+kP8EP0V/Sz/agLcBC8GBgZtBMwCfgAy/c/6I/jZ9Wz0UPJ78Q3xIvF/8970U/WQ9GTyWfKE8530+vQ08wnxyO+Q72Dwz/Dq8KHx+fFB85L2kPiD+Br2MfGv99cL3B8UMDY0sDE+PJ1Jj0/bTsA/Mi01ICYRhwWW/CbwOeZ04JXesOE55Dvl0umY8Q353fxO/Jr7mv2CAP8BEwHT/lb9Nv1S/68B3gFwAFz9lvsy/Gf8/PtF+0X7rP3KAcsFuQjbCb4JBQr2CmwKrgc9A2r+/vuy+oz5EPm4+KX5+PtU/vf/AQBS/wMA4AGxA28DvgGdACsAiwAkAF3+L/y5+K/0KvEo7kbs6etu7B7tzO207Unu+fDv8wj23vUt84XwM+8a77Dv+e988PPxufKl87j2VvgY+ZL33/OL/HQQPyK6Lu0uIi0HOvRHjU4zTFc8Jy0bIw0Ydg/IBdP4uu5K6JDmceeb5jzmSuhq7cLyifPE8tLzYfYb+8X98/yL/N37S/1wASUDIQP6AGf9w/2r/wwB0AFDAGr/pgC1Aq0EPAU+BdEEQAUJBr8E8AOxAjQBmwHT/3X9Z/y9+g77NvwK/H38MPwe/Lz9S/9PADgAeP91/6gAPQGCAN7/P/4B/bP7UPiF9U7yC+/O7frrcOrf6ELnf+n47c3yX/Za9iD1mvTZ9IT1DPWY88LydPGY8V70FveT+b/5J/cX/gcR9CNUMXoyvC6UN5pEKEukSYQ8Py5YJDEZbRAXCLX9U/S17NDpHeoM6WjnW+cH64PvUO++7LzrfO0g8ov1h/aF9x/4Svol/+UDRQeMB5UFgQVVBqcGPAYcBI4CowFuAC0AnwCvAT0DfATaBTcGpAWDBFQCywC4/rf7g/lJ9/T14vaO+GL6u/vq+5j8Af43/+L/uP9a/yP/SP4u/ST8f/uE+4/7a/o5+Ab2MvTt8uXwse3N6nTpn+qM7YrwMvMP9dn24/hT+rf6b/lA+JH4LfkL+t/7ifw0/Qr9hPwABk4XUSZRL4Itkys4NUs/ZEJyPB4u7CJ5GzISrgljAaz5RPSD70vt8Oz26/zqseoD7aTvUO6v6/HpvOqV7pXwFPEp85b1PPmN/UoAOwN6BWYGAQiLCEYIlgdvBVQEdQR2BGQERwOxAXMBlwLuA2gEAgRzA4ADwQNsAun/X/25+jf5W/gY96T2d/br9sr49frL/Br+0P5P/0//Vv44/Tr8WfuN+t/4Uvcj99P3Lflt+ZD3RvVV8xfzRPSj9PPzGfIf8HLwn/IX9f32k/di+Mb66PxU/5kCBgVMBsEEcQLiCLYWmiO1KjUo0CWCLHszUTUTMHkkCBz0FewNlQYM/w35MvUs8fTuGu5d7ZDtCO7+7y/yjfE37+TsSe1t8LvyiPM09AH2Ifrp/nMCdAXaB6UJ7QplCywLLQoBCEkFJQMTAj0Bq//y/U/9c/6EAEkCvQPrAxMDrAH9/7T/bv/g/UP8x/qI+mf7n/sD/BH9pP1R/sf+4P7e/sn9EPzE+rv5jvjT9qH0+PLf8Q7xWfCp71bv9O7y7vfvYvHH8pzzTvNR83P0YfYs+IH50frS/Mb/bgKiBGsH0gm+C2YMZg7WFuMimiyOLnIp+SbxKqsv5y4pJTUYXQ9hCfQEaP/z+O/0ofHF7qTt9Owu7izwu/DA8ejxu/DV7zzvfPCA8zf18fTU8/PzMvfi+/f/PQLcA2IGfglaDF8OMw8+D28OXwzDCQAHOQSPAYv/gP42/gH/TgArASABmf/H/Zj80vv4+tP47fWz88LyXPNs9Z73Hfp7/Kb+zgBIAq8CGgLgABj/wPxd+a313fI48VXwl+/R7gLuv+1f7vnvCfKL827zf/Jn8j3zRPVP90P4TPlb+uT7QQBUBqgKlQ2jD6QVWSKiLo80yzL7LScubzLWM2Mu0CLLF58RLA3bB00BFPu99+b1qvLl7hzs7+sK7kLvd+7p6wfq/uru7dHwf/Gj8IPwOPJz9In1U/Wt9UP4wvt3/gMAygF0BVgKRw7aDwcQbhAeER4RjA9oDF4JKgeKBQIEZQLWAKL/Gv96/n79cPwv+9r5LPgs9lb0F/Nc8orylvNK9Y73dvmy+lH8/f1a/6n/uv0g+wD5kvc392P21/TS85/z2fTX9rT3uveg9633tvi2+Tj5Nfgo96v2TfhF+vH7uP39/Yv+GwGOBqUPJhn3HVYeyh0CILElXSmKJ4IiWh0UGr8X4BN4D7wLdghmBaMBSf4K/L76W/kU99T03/K+8cbxy/G+8cvxg/Gn8Xby2PJI8xD0D/Wm9qn3+/ck+er6mv2lACkCIQNYBMIFmAfdCPYIaQiTB6AGMwbVBQsF6wOhAhEBxf/n/j/+Iv4u/rj9wvyC+4r6Z/rv+hn7G/pq+GP3BvjA+QL7tPp6+Sj4m/cI+Dr4JfjX92T3OfcU9+L2LveS98b3VPg1+ST6mPt7/I/85Pzm/Ib8Vfzd+/76B/rY+CH5KfwiAXYGxwlRC+oNmhLLF/waJhoHGEAX0xenGKkXyhXTFOITXBLzDwYN2wvRC50KOghsBFoAtf0M/Jj7yfv++mL5tvfe9ir39va49Zj0BfRV9MX0o/TZ9Nf1FvdO+Oz4sPkX+wP9zv7w/6oAXgHSArQEKAaOBhYGpwVoBdEElQMFAggBCAEIAWMAPf9G/r/+yv/t/wj/Xv0K/Gf7cPqF+ED2E/Td8jHyM/E38M/vmfCc8u/0q/b79074X/hv+Of3/ffC+LD5nPpZ+5z7Yfyb/eX+0/+S/9f+vP0t/Kz6sPlK+ov9qAF1BEwFjAb0Ch0RUBbnF0sXiBcSGfUZUxkbGFcXnBfaFo4U0hJIEqgSOxKED0sLVQczBAMCMgDk/WP7YPkE+DP3NPZ99ObyGfI08lDyyfGR8XDyIvSJ9dz1r/WF9n/4SPte/ev9Kf7D/oIALAMuBSgGdgbPBQQFlwTnA94DvAPvAoACygFEAU0B6gBDALz/Pv8D/3r+Wv0X/Cb68vcK9gf0qPLh8Xrwwe/B733wFPPn9T74cflA+ez4N/km+rD7qvwD/O76afom+1b9ev8MAOL/Y/81/1j/kv7W/JX6+/gJ+pn+sgRXCaoKSwsCDwYWaB0GILodnRpHGQUanxqBGQ0YnBY3FFURxg51DeUN7gxTCSEEKv9I/A77jPmZ9p/yq+6J7C7sRuxG7OvrBuxE7Ubu5e5/76vw7PIe9bT2tvdO+TD8oP+OAuwEOgdjCV8LnQzeDAoN7AxQDBEL6QmECd0JwAlTCDEG1QQhBYwFUgQMAXD9YPvS+iL6xPjB9uL0vvN/8j7xOvC977DvTvCi8Lfw8PCl8WvzI/Wk9V316/Wv9wX6Nvtr+kD56vhI+on8gv3O/Er7Nvpc+tv6lvr++UP5Rfjl9xL5Of7mB5QRDxcoGPwY8B5HKGYupi3RJ/4hjh8+HjkbcReNE4gP+wq/BRoCjwE9AggBCv3K92H0ifN187vyUPDe7JTqEuo265/s5uxW7UfvmvL29Q34TvlT+1b+GQF+AuICvQOgBWMIwAoCDE0M8gvICzkMUgxlC6oJmAczBn0F4wRJBBwDZwHQ/zb+Cv31+xT6M/i49ov13vQ/9FfznvL+8d3xFPJF8rPyZ/MH9Kr08vTt9Ej1Uvb/90n58PmK+i/7Lfyq/O/7hPvx+kz6qfk8+ED3W/aA9Wf1LvY798T41PkA+gv6a/q3/UcGCBJnG/IeCB5dIK4pFTTnNygyViijIaUfzx5BG+sULw7bB5IC3v5z/Yv9zvwf+bzzve/B7unv8++f7R7qFOet5R3myOd/6pDtDfAA8tvzavZB+vv9AwH7AuMDpAR0BbsGVQgeCggLRAtPC4ALSwwNDREN6QsuCv4IfwiqB6IFqwJTADX/aP5y/dD7Z/rC+UX55fgx+Pf2VPVs89vx5/BM8NPvq+/+767wmPGL8mnzcvTM9ZP3V/nS+mf7a/tu+2P7d/tc+4H6a/li+Jv3BveD9mH2nfbc9u32Nfcs+Iz5WflD+OH6igRBEtscVh9GHvgi1y52Oi09kjZwLQkohCUrIuIcUBZVD5wHlf+l+iv6H/vJ+Yn09e7H7APtI+386gXoPueu597n5edX6MnqMO7e72TwjfGz9Gb5gv34AIUEMggyCxMNmg63EOYS2RPCEjYQyA39C6EK4QgZBrUClP8I/bz7FftF+qz5zfhZ+NX4qfm5+jz7Avuh+iL6j/nI+J33l/a69a707fPt8m7y8fIB9I71LPer+Gv6RvzM/f/+IP8//iD9yftV+tP4wfZz9JjyRfFt8OfvAPBk8BjxhPGB8qH0hfby91z6HgFzDS8abSAOIQckbC4HPShFc0HvN9Mvjyy+KgElOxxeEhQIbv8J+YL1qPQZ8nnsyOaS4x7kaObn5gbmM+VS5VTmReel6KbqYe1X8Bfzavbu+vD/ygTFCNEL9w6CEV8TmxT9FBEVkhTOEjgQDw0DCmUHWQTUAOv8TPn09tn1v/UX9kn2evb39jP4VfpY/Jv9DP6A/cn8mPw6/Fn7tvlq90b19vMb86XymvJX86j0GvZ69+H4rvqC/BH+wf5h/kH9wvs1+v34rfeq9TLz+fC574fvqfBX8ujz0vS+9Dv1RffB+Lb4ovmk/zUM9xgYHxog8SPiL1E+XkVoQeU3gTBsLVwqHiSjGq0OswLG+GXyxO//7QHqceTA4NrgS+Qz6DTqvOrf6gPr0Ouq7ervMfKa82z0gPYZ+lz+hQIYBmkJ7gzCD3cRYBL9Em4TChNMEb8Ovwu7CL8FMgI0/o/6I/dM9JjyOvIU85v0PfbE98T5TPz4/mcB+QKUA78DygObAy4DtQGy/+b9LfyE+pn4WPb99NL0IPXe9ab2N/fl94r4aPmh+jH72PrL+XH4WPf79T/0YvLN8Jvvhe517Yvtw+4W8Gfx6vJh9UX4BvmZ+CL8uQbrFIMfWiJsI20rGDnRRC1HBkBPNwAxZyykJ80f3BQZCEX7/PHX7cPrg+hK4/vdntwP37ri5eWX55zoXOk76uLrUu4Z8T3zlPTr9bD4Xv3TApwHHgu2DXsQ2RO0FqAYmhnAGKkWxxNlELINTQqBBeP/xPmm9MvwWe7w7FbsUez67DHvvPIB9277Pv9lAs0E/AW7BpMH0gcUB7oEiwHt/gr9Ufvz+Eb2ZfSP88LzbPTb9DL1v/Xw9sH4Vfrv+sL6B/pD+dz4EvjZ9g/1J/Np8eHv4+4e7vTt5e1C7YDtMO+e8WfzuPRo+V8FTRRXHxEknSadL/w9S0hzSXZCpzlPNBgwYCpMIoEWvAjY+3fyN+7Q6yrnBeG/22zaDd1V4E/jbeXj5rfoluon7anwNPQh90D5K/vZ/YkBsgWxCV8NwBAVE1wUHxXDFdEWoBaoFHsR0Q1cCvoGHAOk/o75RvTs70ntbuxl7JTskO3w73LzbfdF+wr/jQIEBXEGGwd5B2IHawZJBIYB8P6u/ML6oPjn9WDzofHP8X/zbvVk9+n4cvq5/Br/rgD0AGr/gv0K/Db6z/dq9CbxV+857jTt+uu86uLq6+tu7ZLvgfGe8mfz+PXI/lYMrhi/HxcipydtM2c/x0WaQ7k8STenMikuqyh0H24T+QXf+WfzNfDQ62HlAN6H2gbcNd//4XLjceRf5g7pT+zj7zLzvPWT91L5y/sM/+UCEge1CoUNRQ+pEJESuBSeFvcWTxWUEl4PkgwgCjgGIgEz+8z1L/J/78HtA+2f7NfsCu7z7wDzPvYS+cX7Sf6aAI4CLgRDBVkFRQR1Ar4A0P+v/rP8dPqV+I73rfcJ+JP4YvkZ+ij7kfzg/dD++P5+/q/9TPw/+tf3YfXj8pzwxe557a/sVOwI7PHrieyO7ePvcPKY82z06/cFAVAOMRnHHf0fCCbeMRU+gUIlP445rDUUNGgx4yoJIb8UvAjI/xv6bPX97v7mV+AI3hjf1uCz4Zjh/OEg5LDnUet67mTwFPKu9Nf3dvvk/sUBUgQCBxMKUg1FEBwSexPnFMYVQBVUE0MRoQ9hDXsJ9wOZ/kr6vfbJ81nxnu9J7sztsu4O8Xf0QPc8+R37Wv2k/9sBmQNdBA8EvAJcAY0AJgBJ/z39ufp++FL3H/cU9+n2evY09rz2LPjP+cv61PoH+lD5PPk4+c34tvfk9Tv01PLd8Unx0/Cl8LLwrvDG8fjzXfZm+FD4Qfl+/wcJixJ0F30Ylh3/JzozrTmUNwMzDTF1MMEvxip2IUkX7gyuBSAB0Ps29absbeUg427j1+Ql5efjauS25i3qH+608KHyhPQj9l345fqd/RcAIAIeBOUFIAgnCu8L/Q2sD6cQBxGMEB0Qjw+2DVUKFgYTAvL+MPx/+Hn0Q/Gk7+fvhvAL8c3xDPOf9SH5RPyJ/sb//AC1AvkDYgTOAwEDhALJAU4AK/4V/Cr64fjT95T2r/UX9Uv1HvaO9vb28vYU9xj43PgP+fL4+fdj9w/3Efam9Ur1sfT69D319PXO9i73Nfg6+i38vv0M/3MCzglpEX8WxBjVG90jXC3wMrQyQi6cK9grAivDJyMgAhZmDQAG/wDr/F32k++h6V/m0+a/5wnovedo50Hp6Oux7jzxUPKu8/H0RPZA+Pz5tfs9/U/+3v8gAo4EWQdpCRoLtAwfDogPghCVECQPCgwrCPkEiQJnAHf9EPlj9Ubz+vIk9MH02fQs9dv19/ee+pL8yv0K/rb+0P+UAOIAJwB4/5n+rf3C/Mf7vfrT+ST5iPhC+Nr3yvcE+DD4dPgu+Oz3Vvjn+Gv5ivmt+Mb3Uvcy96L3iffI9pD2zvaZ90L4Tfhr+H/5cPq7+hD77fxuAsUJFA9nEncV0Bv4JV8tlzBvL8UsIi0kLTwrISaDHUAVbQ4kCS4FPf9Z+Dbyq+1n7Gnrx+nC59rlYuYx6DrqDOzX7MftQO/j8HDzwfXK95P5A/sK/a3/0ALnBY8IsQq9DEsOjA/zEI0RpxE6EG0NIgsBCRIHBgVyAcr9wPpd+HP3vfax9cv0zfMO9Fj1m/YI+Af5KPqa+2X8Lf2G/Yn9lP3r/BX8YPus+ir6tPn1+IP4f/hW+B/40fei9zn48Php+az5VPnp+Oz4OPmI+Zn5Evka+Sz5ffnU+Y/5YPkL+UX4Bvg3+HP44fhH+TH79P9bBkAMkxBLFGUaniKTKZItjS23LAMtGS0rLOAnDiEZGoQTvQ4BCq0DZ/zb9OPvEe5L7QHsgekL59XmBemz60vtou3Q7ZvuVPAm8vTzofUL9wD5dPsg/koBIQTdBpYJ0AtiDUAO7g5/D5EPzQ77DOcKEwlyB6AFUgOjAP393fs4+ur4Ofd+9XL0PfTZ9Bz11PRG9Lf0NPbn9zX54fkA+nL6afsZ/Nn83fxV/BP8afsJ+8D6SPp0+pP62vo9+0b7mPvQ+/j7XvwX/LL7L/uI+lP6DvrG+eH47vf79nP2GvYK9uL1mfa796T4tvnE+pr9zgJiCKsMCxBoEyQZVSCvJW8ohSjrJ0cp4ynCKHolAh8zGfYTRw95C7kFKf8x+TL0DPJt8PTtT+sH6W/oNOk06nLqi+rz6tnrgu397kzw7PHQ8yX2YPhM+tv8fv+AAlcFHwfqCHcKUAwxDpUOWQ5ODXwMgAz7C8oKLwjsBIQCCAE0ALj+Mvxo+ZL3Qvfg9xr4pPfy9hb3RfjA+bT64fq9+qz6Jvus+8T7j/sv+wv7gfsJ/Fr8SPzh+/z7Mvx3/Ez8i/u0+h/6Ivpi+oj6GPoC+eP3WPc59yH3/faH9mH2Nfav9nb3CPjK+Jf5RfqU+yf90f8KBIwHRAtHDikSGxiAHXkhVSPZI0IlKienJ5AmHSPnHgUblRebFGkQ3grRBF//B/xp+cX1p/Gv7Vbr/+qU6ujpwugQ6Nzojep+7PLt4u4J8NjxCPSC9oX4SPr8+8X9IgBUAmgEawZ3CEgK+QvyDD4Nmw2kDYoNUAwcChkIGQawBDkDVgFG/6r9s/x9/Ej89vtp+zT7jfvS+7n71voN+kz5Lfne+Pv3sfZU9fz0IvVY9T31+vQO9fv1H/ca+Mb4NfnW+aP6OPt5+5T7YvuB+8X7nPuq+4L72ft1/NT8Rv1c/Wf9D/5I/u3+rf+6/0wATwAyAcwDNwYZCQMLHA1jEcEVGRrwHLQdah8OIRQirCLUIP0dxxpQF9AUTxF4DL8G6wDH/MD51/Z08+fvkO177JDspOw77PTr/esD7RPuge7O7tfuhe+w8L3x0PKa8x71CPcz+bL7bv3M/4IC/QS7B2AJXgqxC5cMxA08DpkNygx3C3wK8QnOCFUHPgWSA6oCDAJqAR4A2f7v/Wf9RP13/EP7+vne+Gr4nvep9tD1EfVI9Zb1lvWk9dP1tPb39+n4TPlk+c35kfqP+zT8V/xw/K78DP13/YT9Rv34/PP8CP0y/XP9jf3V/QP+mP24/dv9A/7r/T/9SP3F/mEBRwSgBmMIkAtHEBYVDhnrGv0b2h2/HxYhvSBsHtsbKBngFtMURhEEDaUIwQRUAvn/OP3h+bH2uvRc8w7ybfBL7ufs9utE69vqKerQ6erpT+r86rPrkuz57dPvBfJc9Hr20fiN+8P+OQI6BX0HPQnVCqkMHQ56Dh8OaA2ZDBYMDAt5CcsHGwYvBW8EPgPmAUwAPv+v/nH+qv1r/FD7j/pF+tb5JvkJ+ET3KPdF92H3PPcU92/3Jfjp+Ev5Pvlp+fD5qvom+1X7DPvE+vX6TvvY+2D8YPxc/Kj8JP3b/WH+i/62/rX+1f7k/if/cf9v/2j/Mv/F/4sBqwPhBTgHCglYDKkQTxUZGI8ZJRsTHTMfNSA5HwMdlBpOGFIW0hNFEOcLnAcGBHUB5P6U+yz4+vTo8qfxQPCn7sHsdev46p3qVerj6cLpEuqN6qPruOwI7urv6vGh9D73q/lc/I7+bAEPBA0GsAfmCA4KMAviC+kLOQteCtAJAwk2CLUG8ASfA5sCIwJVAVEAWv+r/tX+9P6h/h7+Z/1C/Wz9df0l/ST8d/sr+xX7+vpk+vr5vfkQ+mv6xPoL+yT7zftw/Pr8kv2t/dv9Sf5u/qb+ov6Q/rj+v/4T/zf/lf8JADoAdQCUAIIAiABTAEEA3P8q/9z+R/+mAHMCCwQzBdgGkgnsDAcQ/xGuEl8TPRQTFSUVnxOoEfkOmwzFCooI8wWUAkL/Hv2I+2b6JPn09sH1I/Uj9W718vRh9Nnz1PP+8/7z1/Oz87zzqvSo9Zv2k/cu+GL5y/r8+wH9c/03/kb/OADxAGUBwwFuAusCGgM5AwED0gL5AhUD5wKAAiACGAI0AoUCswKUAs4CCANWA68DlwNzAxUDqwJjAuABLQGoACsAvP+v/1b/E/+6/pn+av45/iL+o/1E/cf8kfyP/IL8hvxh/Ez8a/zN/D39wf39/UD+jf4E/2z/m//M/5f/lf+D/0//fv87/+3+3P4a/1gAnQFRAjMDKAQ3BmcIiwlBCgwKGgqmCq0KQgrkCAIHJgXZA/sC9AGUAA3/5P1h/X79Wv30/FH8u/uK+6z7OvuM+iv6f/mu+av5nvm9+Yj5Nfrm+mL7Gfw4/NT82f16/kT/Rv9q/9n/+f9sAAoAif8l//T+eP9+/27/lf94/0QAEwGYATkCmwJWAzUEFgXcBSEGeAaVBsQGJAfYBo8G3wUbBaAEsQNCA0sCRgGlAKv/Of+b/uj9mP3d/Jr8bvz4+/r7svvH+9/7x/sB/Or7JPxg/Ff8bPwv/B78lvy5/Cf9af1w/fH9Vv7M/mH/lf/T/73/yP8/AO8AmQHAAeYBwQLcA88EgwUzBfsELgUUBT4FegQwA4QCfgFhATYBRgDI/7/+RP6b/p3+l/72/XX9fP1e/WP9CP1g/Nn7ofvF+837o/u7+9D7TvwT/Yf9GP6X/vT+df+p/6b/m//Z/+3/2v+8/yD/zv6b/m7+pv5N/vb9uP3g/TD/fQBhAfgBjQIoBCgG3wf2CCYJTAn9CU0KnQpPCkEJRggCBy0GdAU5BLYCbAFRAIL/D/9L/ob94vy1/JL8i/xa/ML7efs6+0H7Cfuw+oH6TPp5+rX65voo+2P72Ptn/JL87/z8/Ar9df2U/bX9uP2Q/bP9Wv5P/xUAxQCYAZkC+QNiBU4GDQd4B7cHMghiCDwInAf6Bl4GyAUmBf0D+QLdASkBdwCb/9z+Ff5u/QH9V/x3+9/6TPrh+aL5UvkY+Q35JPl6+c/5B/pB+lX6kfoQ+1f7tfvq+zj8wPz4/HL9pP2s/f/9yf3v/dT9lv0O/pT9Y/2a/U/+lACZARgCjgNbBBkHHgmzCR4K5AlGCmUK8Ql5CcAH7gUdBdkDnwOCAkYBnQASAMcAIgE9AZkBmQFdAhMDGgN6A8UCkgJPAsUBdwFjANf/Zv+m/oL+zP1C/Uj9/PwH/c78Qfy++4r7hvst+9b6cPoQ+lD6p/ox+/j7o/yv/eX+cgDkAT0DWAQ1BSMG9QaNB5wHdgc0B9YGHwZZBWIEOwNEAlUBGwC8/nX9JPz3+s35v/gW+FT3kPZR9g/2XfbI9g33uPdr+PX4hvkx+in76Pts/Az9Pf1s/RX+jf7S/gT/yP53/kb+5v1W/ev8qPwA/Kz7zfsH/Q3/HQAZAVQCjATuBxoKgwtEDK0M5w3fDkkPUw8PDmYMVgu6CkYKEwmaB9MFpATeBP0EIQRWA5QC4gHpAZABhABS//L94PwA/MT6Wfnu9+n2c/Yn9pL1RPVo9TD28PbE93T42Pj8+Ur7pfzm/aT+vf8IAcUCjgRWBSoGzQaMB7wIUwlcCcsIPwisB04H8QayBV0E6QKWAccAov8X/pr89frW+dz4u/dj9sn0QvTd85/ztfNE83DzTPRq9dn2z/c8+YP6Ovx8/gwApAGrAoEDaAQRBRYFjATZA9cC3QH0AIP/vP2J/OX6v/lb+G/31fY19lv22fVN9kn4efs5/40B9wNKCG8NTRNdF1oZFRsDHOkdgB7/HXcc7hcGFVMS4w/YDUMJ+wQFAsz/8v6f/fj7Kvpm+CX4w/aZ9Sv0YPEC8P/tU+wv6wDpAOkW6dbpmuud7KTv/PKH9pH6fP19AB4DGQZNCfQKXwyXDH4MYQ2iDcwNCA3pCzsLhAphChMKxQiVBzEGQgVqBKYC2QCS/nf8ufrp+Ov2X/U/9NDyYvJg8n/y0PIH85zzwfTb9ab2ifd/+CH6z/vJ/Cv+7f87AQYCEQNxA18D2QLXAtcCXgF+ACz+Rvzf+2n5vffi9ZLzcPJw8VDxTPGu8cnyL/KN9Wn8KwFTB5kKsA73F/kfzCaNKmsreSxRLScugi0UKhwk3hw4FycTgw4SCfoBCfwm+Fb1+PL373zt0Op76Y7o3Ocx56DlJ+Xp5D7lgebs5lfouep37eXwFfSk+Mn8lgB8BT8IMAtoDaIOShAiEN8P1A7sDCoMIwvfCfgIlQeVBjoFgQQ7BGwCSwFq/4f9bPwD+7L5fPj791D3xvbR9hH3vffs+Bj5Yvm2+WD5EPpw+vf66Prf+gX7A/yq/b/++P4D/4v/pP/AAJgAJf/M/Sv8EPsb+rT4PvbD80fyQ/K18cvxC/EG8Hrxx/IX9Ab2OfiG+SH6MfvOATUMJxWqGqobEiBkKqE1ODv3OYI1rTHuL34umCpFIV0VAwqvAq3+jPrf83nrMebT46jjEOOR4erfwd7z3t/eD9823w3gYeLi5PPnpuvT79P2RP6kBL4KWQ5hEiYXuhp8HHAb1hglFiMUCBM/EJ4MPQmcBX4EfALX/5L9zfk59/T08fJX8XHvd+4N7yjwrvEk82r1M/nS/ND/aQGHAt4DUAVJBr8F+wTQA/gCbAPiA4UDcAI7Ae8AcgFhAUgAif6a/Az7Ifq7+NX2kPSD8cjvkO7n7Q3tZ+wX7Lzs/e6p767wrPE98nnzgPRG9nT5QfwM/nn+NAHFDIgaeSUHKX0ojS6KOgVFJ0azPlU0qi08KpQlDx0JENQB6ffA8wvxLO4P6GDiu+A04I/get813VTc1dzc3XbfnOFL5S3riPKO+aT+gwO5CB0PlBRtF88X1hbBFl0XvxdLFsAStA68C50J1gc5BGj+b/mi9Qzzk/Dm7JzpKOeH5mLnQOj36TLssO9e9P74E/4XArkGvAsSDxARFBG5ENYQBRHmD/YMZQmPBuUEaAQRA1UAYf3G+or5sPhZ9wX1dvJU8Pvuie6m7Zjstetn67HrQu037lTv6vDm8ebzpvVZ90D49fil+VD7Cv0p/94CmQQUBgkGPwn3FV0lui9kMo4uZjGWPBlExkNPNzInSB0zGLMUnA3xAKjzpuql58/nCeYF4WraRdcx1vHW99c/2GrbI99L5JTqUvH5+HMBYQk8DykTCBWQF/EZGhwPHMAYyBRVEeYPMw+/DfEJ9QSNAFH9+vp69yLzh+0H6fblpOOP4yvjaOSQ59LrZ/EG91r8DwK5B0QMyQ9lEQUTjRTBFWEWMxVnE+8RJBCIDsELcAc1A37+IPvj93D05fC47SPs3+se7NvrseuE65vs4u3g7knvZu+i8I3yw/Rs9kX3jvjE+sn8Iv45/mP+uP7V/mz+gP37/f3/DwJCA+0DsAflFC4kfDCwM58ufi8INtg7BzpTLPYalQ9TCQkHrwFA+HfuyOYC5VzkrOLz3hra89cX2MDZ/9xD4dHnnO8890/+MASWCiYRDxY/GHoXtBXOFGYUqxRwE4QQjA0HCtsHVwbDA9r/Kfvp9fnxBO/H7HfrU+ld6FPoB+rD7DXwbvSO+Pj8vAAsBF4HyQr5DFYO8g7hDu4PNhGUEm4SOxGBD28N7QuxCHYE3P82+6v3SPS98EnuauxL7BPtVO3s7mvvovCP8dTx9fJp85TzefNK89nz0vS29Xr3fPhB+pP7UfzF/Tn+Pf9MABECswLTAmQFjwZgCOEHPQt5Gh4tbzjgN24uDi7gNuM5+jMxINUNhQTg//39Yvns8G3pIeRf48PkluKT3g7Z9dc72ZDbJOB45mvwu/psAmkI/Q3tEioYQRqjGW8W9RGKDy8O6g2QDFgJdgUBArX++Pvu977y+uwC6N7l6+T/5YznbOq47mXyMvbY+V/9SwGyBOMGtwgBCYAKqQ2AEecUIRVjFMETexMiEoYOLwkwBLT/dPvr9wH1ifNl8prxOvAA7xru6e1X7wXxcvLJ8t3yTPRh9vX4pfoO+xX7ofsi/b3/fgFEAoQCxwETAiIB1AAVALz+6vwD/Fj8a/xY/nj/hADy/w3/twaTGjss7zP8K1khUSYNLxIyQihoFWUJZAapBbYFmgB2+azyPezf6A/lkeDS2/7YCtmF24/fZOaB7+z5IAKuB3UKtgtrDm4QoxEAELQNoAy5DcQOGg7iDF4KWwbvAGP7LvY08wjvj+u56k/qN+zQ7THwJ/Sn99j5d/sJ/JT9EgCSAuMGJwmJC68MtA2uD6UP6g0YDJEJZAepBcECmwH4ABAA+P4B/Z76z/cu9YvywvC67pDtuu1Q78bxVfNu9Cn13Pae+FP60fp5+677E/xz/f/9hf+5AFUBmQHZATkCyQGvAJL9U/xy/Gn8hvxj/ET8F/y4/aAEaRkMLjw51jPTJckiECkVLigpfRo/CzQHYAdRCj0K8gGZ+I7vSeio4trcHdf11lPZsN0743Lp9fEM/IwFLgukC4QIyAYOCbIN9w+hD6UOuw7GD9wPkA7dCmoDYvk88Ivqt+ju5/LmPudn6DjrAO+u87j3NfnK+L348/o7/lUB8AOCBxwLuA2VDvUOTg8WD6QNEwyCCcQHegZkBs0G7AVfAlb+Bftq+E32zvM/8sTxZPJ08kTzzvRP9gL3WvVr8hbwCfAr8mz1/fdO+cf6d/0uAXgD0AONAm4BAwE0ACz+2/ya/FT+fQDXAAUBXwOKBYEH0AfHCk8akiyOOD81ZieKH/wj9SmgKQgfng8FCHgFHwcNB73/lvRK6UPg8tpe14fU1tbF2m/guebQ7ET1RP65BksM7AwBCugHsQkzD/gT0xRNE6wQKg8JDoILPgdU//D16+4n6+vqmOuR6w7squ2B743xhPPm85jzTPIS8u3z8vcK/KEAEAbrCsgOnA+rDlgNOQ3GDCcMGAtPCuAKVAzuDJcMmwmZBKT+VPlw9RTzjfGn8PvwSPI59KT1/fY590v1ZPJm7k3tGO8Z8yr3iPlT+vr66/zX/mEBNABA/m36g/iD+Jn5f/vJ/OX+v/4HAS4BKQMUBFAGEBJRJgw4sjoIL04gbSJiKrMtDSf1GN8Qhg/JD9cNGQh/+m/tpeGu2dzVkNPE1SncJeOl5/rqre+v9qj9zgJABYEF1Ab2CaoQEhdeGdEYyBZxFDIRagyeBosB5Psc9330kvOI8zTyI/DZ7TLr6uf55Qnm3+j97FTw7/NZ90b73P9bBMQHiAiaB7AGjQj3DDASERWmFSEUoRHJD5MNUwqVBXj/0/kR9sn0nfQp9Rv0L/K073HtKezJ69DsX+5h76/vR/ER9bD6tv6GAIkAWv+c/IT6qvoV/l8BtwG0/0j+av8HAbEDsQLoAIn+7/5OAIADcgaQE6koejlyPPEtLh+4G1AhyyJvILYWuRAhDggNYQxXCFH9nu9U47fYzNSr06fYb+Cg5xXrbO7R8Lj0q/ie+4X+tP+8ACwEcwq8EE0WMxiDF9MT0Q2CCPQEZgKZ/0v9Qfz4+zr6vfgz98n1k/Kx7DHnpOWD6KTu2/U++mP8yf0OANAD2AXgBDQCewFxA3kHEwshDXEOuw4sD9wNCgy5CJsEsQBz/Y37K/qi+ev3h/cj9k/0NvLP8JXwBfFr8STxIvJV8/n1hfja+Rj6ovhU+AL5nvm0+hD7d/wn/d39U/7Z/9ABBgKxAY0AGgTuA6AFoQLECCAalC0YNzIvPh8sFewYchoWHsQZWxYhFZQTQRD0DP8Drvg68BrmmuDI24vbC+EF6QbsS+wF6j/qre0S8i73NfrW/On/tAVoDBsScxT0FKAUuhI0EAsNBAuSCY8IkQa9A08AiPyZ+Tz3HvSv7wrrdugd6TDtIvEF9OvzbvJB8WLzaPfk+wH/zAAlBN8ILw7WENgRJBF9EN4OSwyxCLIFaAS0AxMDUwAp/M73T/XF85ryk/F/8MTwxPGc8ovzYfTt9ET2gff399/4jvh/+VD5FPmp+Hn6Mv26/4gARP6z/In8N/62/58BIgJEAXECrQNwEeQmCjh7O74ueB7YGIId6x7CIy4gqB1MGk8TYwyTBov8hvEx6nHfodsV2eXbeeOe6ePoyOZY5LTkf+lk7qr0XvrV/t4CaQgLDUoRBhSWFA4UyxGjD9EOBRAiEJwPng1+CasEY//Y+5f5ovcb82HuUet96tLsM+/f8Gnwxu+r73LynfV0+M/6Mv5hAuMFYge5B50INgqoDG8NqwyFCsAIVQeaBpcEbgGt/jv83frq+X35N/gy90v1TPO08bjvKu9m71/wjfE082P0kPZ19w/4TPn8+fr6sPum/Mf8tf4rALEDEAb/CF4IcQbIA1YCUg3GHtUuQDE3J98YrRalGfAcFB8bG0UYYxVREqAP4gxOBCf8h/Td7E7nzuIX40HoAeuG6jrn0+Vr5p7pzO0x8w/3oPlE/Pf/LgUvCCwLRw1SDgsObw0xDkwRqhKcEa0OugmwBCIAw/yn+yb6UPcO9YLzvvJM8s3wV/CV7w/v1+6g8BX0CPcE+vj7Rv9IAcMDzAM6BTUG8QZPCJ0IAQqFCmoLlgrbCAsG3gIIASz/zv79/Jz7VPlZ9yz3oPZ+91b2rfUb9HfzOPMp9Fz1evZW9xL4H/nL+QD66vjI+JP5tfod/CkAgP/7//z8mAGKFrcs/zUFLKsWqQyUFaYdAyYhJzsjSCIaHlAXIBQpC5n/hfik72LqtuWh4/no4+8+7tbnrt/B3A3gDeV77F7yhfZF+UP8SAGHBQ4IXAqxDC4N9wzMDDwO0hHNEWAQjAx9CBYFpgPZA/ADbgEn/Wn57fbO9TT0u/L+8O7u2+xa7cXuLfG78VPydfSK97D7w/6EAsEFmAiCCRcJHwjaBXEFwwQJBpAFzwSBAyADqAP0AmoBkP2q+tz3dfdJ92b3nfeM9wD4oPfn9rb1hPVH9tX3ufnU+f75SPoB/Jr9KQDdARQE9QQqBNADAAbSCRUM+Q6nEJsVmhlwG0oafxg3FpsUrxVFFugXaBZjE98PQAvfBRsAffzl+EX3F/TI8Qfwne0/7DDr2esD7KbsvO3a7zvzhPV0+IT6MP2h/icAhAE9A/wF7gctCkYKiAkkCOwGyAXMBMUCowH/ALEAewADAHH+Tf08+/X4lfed9v32Nfd/+DP5/vkz+vX5qfrx+6j9Yf49/2z/RAD6AecCxgNWA8wCBgIiAlMBywCSAEMAfQDDAE8AvP6t/RP8ofzm/YT+VP4H/cD7t/k++gT5TvkP93X2uvbT9uH4xPdJ9sbwtu5t8cYGUhjOHc8WPQJsAA0Hig/AGCUe6R37H2YeEBjdG04WOxIEDwsHgAGx/pb+hwOgBr3/F/ao7H/nC+ff6WHs0e4l7erqV+l169nt0e+o8wz2C/lc+3X+wwPkCDALQg35DT4O4A1EDeoOwhHhEakPBgzoB9MFDQOlACD+Ufuu+dr4m/j09if0jvCe7kDuaO7776DxI/We+Pf6DPsS+mL62Puq/sUAGAJdA6AEBwbIBjwHtAUNBCsCjwGqATsC1QIEA5ADdQKUARH/kP2I+437rvuy++X6xPgj+KD3jvlc+lf78/oZ+539wADTBSYGgwblBbkGGQrODGsQbhLMFTcU4BLTDuIM+w6PD1MQ9AzoCJ4E0AJaALj+Kf3u+Lr3c/Uj9Ur1GvVY9fT1kvam9gT3Sfdo+cn6XPzK/bX+3wCbAmgDqQRvBRsG1gaFBvkFcQVmBPAC3gGmAAcAUv/X/RX8z/li+C736fb39pb2+PW49bz1//XM9U/1Z/TQ9XP2cffY96T3dPmI+pr7j/oi+j37Of2Z/g/+Yf6R+2362vk5/YEM4hR8F/kOvQPMAVwKCBJyGI4c6BfPGRoXwxSVFccS+Q5QDNoGXwIOAikCfwQqBMn8XvQo7hzsFu9S8aPyNfHM7kLtBu0H8D/yEfXF9lT3Pvjq+WH9lgEYBSoFuQXdBo0H2wlTCh8MkA1JDcoLOwoOCoYJDAoFCSgI+QX2A7YDbgMqA8j/Kfwd+RH4q/fR9q/2/fVG9gr2lvUu9f/0h/X79W/3YPjW+Qf81f1DAPYAbgBU/ov9cP5PANkBSgFBAF3/mf+X/4f/1/6z/RX94v2z/ov/Cv8r/mX9c/1V/WH9hP5x/+YAOQGWAZ0BDALMAnUDNQVrBnkH4Qf1B8QHRQczB9wGrAfbB7UH7AYkBn0FngTZA3kCXgGhAGMATf/K/kj+Uf5+/pL+8f2s/Yb9L/xG+4H6cPqn+pT7e/yE/eL+1/+dAOgAYABo/0T/xf+vAL8CWgNQBOkDbgNzA0QDPgOBA5cE4QQ5BSMDhAFMAIf/3v7H/SD82PpU+cz3mfdz95D2vvWN9PPzNvQZ9MvzwPTp9Qr3tPkg+wX7ufop+0v+yARpBxIGSwIiAJ8CwAg1DdYOig4BDB4MXwwWDWAO5Q5SDv4Nzgx1C9ELRgusCpwIDQSWAPj9zvyj/B38Svvf+Sr3uPTq87rzh/QV9A7zn/N39L/2b/lQ+/r7/Pua+x785v0a/+gA5QKQBF0FoAXwBaAFbwVABYoEGAUSBbIExgR1BHoDUwJVAMz+Hv+0/8sAKQGlAN7/Y/79/In8mvyE/jQACgEbAWAApP9E/47+1P3k/Xz91/0V/tD9Dv6t/Qj9jfz2+2L7Zfvq+1P8d/wk/Bf8Mvy1/Pb8/Pz4/fD+JgB5AaEBDgJmAloCswLpAm8DNwTXBM8EzQRUBGoEogQYBPkCBwEN/9X9O/2Y/Xn+yv5n/tL94Pxw/GX84ft3/GX9yv6i/7T/QP+v/6gATwHSAaQBlAGbAdsB7QE0AtcBMAGCABcA6/8HACkADgDO/5X/5f9eAC4BtwEjAiwCfAE0AXMBtwGZAbEADgBGAHsAJgAHAOX+WP5W/jb+MP9A/0j+uv3H/vn/wABL/939Sf7//gcAuQCRANIA2QCv/5X/NgBCAaECCANWAokCWgIVApICMAKdAf0ALQAyAEYB6AGjAaMA0v6Y/VX90v0G/8z/6f9+/+X+yv4T/3P/TADxABkBXgGbAX4CMgMeA9AC6QE2Ad8AeQD2/4L/c/6G/XL9Y/1n/az8f/ty+uj5t/m0+ST6j/pn+j/6FPpi+kX7Cvy1/Dj9pv02/lj/qgDOAVYCkAKkArUC6QLTAukChwPMAwsE4gMzA+cCVgPuAxwEJwOkATkBhwEcAmYCZgInAikCRgKkAuAC2wJ+Av0BxQEDAmYCGAOyA60DfAO1ArcB9gCSALEApQAbALr/Yf8e/xH/Y/4Z/c37Yvot+t/6qvs2/HT7C/pM+ST5tvmn+g77nvsT/Hn8S/24/d79jv4E/6v/VQC8ANABCgPFA1IERwTrAzUEYQR4BMoEMwR1AwMDsQJaAroB5AAtAND/Ef83/nL9//zq/LP8V/zZ+5b7jfu++xn8Wvy1/I39P/7p/kv/O/+y/14AnACJAD0AFQDbAHsBpgHSAcwB9gElAhMC9gFdAukCcQPIA9MDvQOoA6IDYwMRA/8CBAMIA5YCswHUAIgArwDLAKoAWAD9////HQDy/2T/Of4g/bz8P/0u/vn+RP80//b+5f58/xcAUQD//9r/LQADAboBDgIRAsUBZQH/AGAAhf8D/4X+d/4B/zX/Ef/g/jv+1f0F/tX9yv3p/fv9cf7y/lv/v//c/87/vf91/27/IADmAMUBUQLmARsBkQByAPEAVgE9Ae8AWAB6/yH/NP+H/7//qP+C/0D/6f5C/uv96/0e/kj+Rv5a/nH+d/43/uv9+P0//v3+yP/5/woAIgBMAJwAzABlANr/eP/F/5EASwG4Aa0BkAEnAUQAav/M/on+pP72/lH/2v9TALoAAwEOAQwB6gDvAEEB5AG1AmwD7QPrA5IDUgMPA9kCbgIKAgECEwLbAXUBLgG6AGcAqP+S/gj+D/40/o3+l/4c/qb9Ef2u/ID8cvxg/K78P/3M/Y7+Ff8g/9v+yP7g/oD/ZwAVAYsBxQG1AW4BFwGdAGAAswAeAUQBEwGmAE8A8v+d/0n/Kf8l/1H/lf+b/1b/2f6N/m7+zP7w/uT+7/7B/s7+xf6X/p3+v/7Q/gT/Jf9s/8r/AQA4ADYABQDc//3/MgCuAPEA0ACoAJQA1gBhAZkBpgHvATsCsQIKA+QCyALrAiMDgQNJA4kCvgFRAVUBcAFjAfgAgAAgAAoAawDJANAAdADy/2z/Gv8M/xb/bP+f/6b/7f8rAFwAWgDM/xX/n/7B/un+6f6h/jT+Uf55/o7+XP7K/aP9hv1h/Yn96f1R/mj+kv6H/pD+wf7b/if/Vv9f/3z/fP93/7H/xf/5/4QA2QAkATAB6AD9APwA4ACRAEEAPQBXAIsAjwCEAEoAeP+v/oD+qP45/6D/qf+M/27/fP/D/1oAnACoAJgAcACGAN8ANAFjASsBvgB3AF4AwwBWAYYBMAGsADIAKQCsAEEBdQEFAVwAuP+y/xIAEwDg/1H/l/77/aH9rP3p/Tf+Mv7b/Yv9cP3K/YT+GP93/9P/TwDvAHsBygHOAf0BTQKtAjIDdwOMA7ID2QO0AykDdQLAAWwBKQH0AMsAaQDT/zn/0/5j/gz+o/1c/VP9vP0a/hP+wf17/WP9vP0a/iP+Of5l/tP+Pf+M/7L/2f/l/9n/kv8l/wb/Gv+f/1MAjQBEAK3/ZP+S/+P/9P/c/7b/r/8dAHcAvgDiANcAywCxAIYAtwAIAY8BJQI9AhECygGfAaQBnQFRAS0BEQHbANAAXgDT/2j/W/80/9D+X/74/Sz+qP75/u3+pP5d/n7+v/49/97/PQCYANkACAFqAQMCegLpAvkC2QLXAsMCrQJsAhEC1QHdAeABswFyAUoBGwG3ADoAg/9A/y7/DP/v/pT+DP6+/ar9of28/cH9s/11/Rf98fz0/Gz9+/1h/qj+2f70/uv+Af8h/2P/m/+y/5D/ZP9q/9P/ewDJAIQA9v/K/xsAtQAcASsBzgB3AE8AZQB7AGsAMgDi/9D/9/8tANr/T/89/8P/KQCRAGkA8v/T/9X/IgBcAJgApQCqALUAxwDOAL4ArgCqAI8AqADOAA4BkAGfAU8B8QB7AP3/n/85/wb///7//kT/VP8s/+X+vP7F/t7+Fv9W/63/9v8pADYAUwCCAIgAmACNAI0AwwAiAboBOQI/ArcBOAHmANAA6gCdAB0Aqf+k/8z/GQB0AHQAEwBm/5n+Ef79/Tn+c/6J/p/+xf7X/u/+Fv9G/3X/aP9P/0T/X/+d/7H/mf93/4X/+f9OADYA+//i/+3/CQDZ/6D/0f8rAJ0A5gDOAJgAggBMAHsAzADAAFMA8v/i/+n/KwBBAGkAbABYAE8AWgCaALMA5gD2ABcBXAGEAWwBJQHSAJwAdwB9AIgAmACCAAAAaP8p/z3/R/89/+f+pP6k/tv+/f7v/qT+N/4I/u/9Tf6//hr/lP+i/3f/O/8c/1r/5f9jAKwAnQCSALcA7QBzAagBlAFcATABSwGfAcoBswFaAdkAjQCRALkA8QD9AOYAzACvAJEATgD3/4f/Wv9J/0//N//X/mz+8v2x/a39wP3D/aT9pv3O/Rz+Uf5I/j3+K/4p/kb+ov4+/9z/OAAkABMAVQByAKgA0gDxABcBEAEFAdkA0AD/ACcBMgHzAN0A4ADgADkBaQFeASkBzAB7ADoAEgBDAGkATgAmAM7/i/+F/3r/pP/D/9D/AAAyAIkAawBIADIAdAAcAYcBzgHoAawBbgFuAWwBXwE/AfoAowB+AHQAYQBDAMb/W/8e/xP/Vv+V/4D/HP+N/lH+kv7T/lv/vf/B/5v/kv/F/9z/MAA0AAEAyP9+/3X/0P84ADQA+f+Z/0v/E/8K/+/+5P7O/pb+ov6q/uT+Fv9C/0D/Jf8y/1v/tv8dAGcAiQCUAE8A9v/T//f/OABwAKMApgBsABsAqP9h/1L/QP9b/4L/i/+O/3H/X/8y/zT/Wv9z/9f/5//l/7z/LQCfAAwBXAE7ATIBIgFnAb4BKwI9AjAC0AFjATQBSgGSAYkBFwFgAOX/5f9cAJQAaQAKALr/w//j/7r/qf+V/4z/7f9aAGUASADn/9r/4P/K/87/uP/a//b/JwBRAGMA7v+O/3r/i//M/8X/q/9G/z7/Kv8q/5L///8nAPf/oP9q/3r/rf/5//b/HgBVAE4AGQD3/xUAVwDWANkA1ACjAH4AywDtANQAfgBPAAcACgAtAAEA0/+8/4P/D//y/pf+Yf5x/nz+d/5j/gb+/f03/j3+Z/5d/lr+gP7I/gr/Zv+V/5L/5f8AADsAKwDg/+n/EACqAF4BlgGqAEn/7/42AOIBDwKIANn+Mv+PAKEBlAE0AOT+l/5G/04AEAEIAWAAwf8JAGwAkgD6APYADgHdAMcA/QBqAe0B1AGoAUQBKwFEAXMBzAFVAcMAmAB1AJIAGwH4AA4BAwF3APb/d/8P/yn/Jf+Q/nP+6/1z/VP9S/36/LX8nfyL/CD9cP0F/of+yv4w/zn/m//3/1wA7wBBAZQBqAHZAfYBRgJRAlMCjQKJApYCNgKLAQwBoQBuAJwAoQCmAEYAwf9C/+f+3v7v/hj/CP+z/hH+1/3F/eL9Tf5w/mf+i/7M/tX+Bv8c//D+MP+Q//f/HQA9AFwAkgDXAMcAqAB9AHIAnQAHAegAAwHWAKMAoQCjAMwA/wBTASIBGwEQAWMBcgGAAZQBnQGxAWUBfAHSASwCTwLHATQByQCWANQAyQDXAM4AtwBXAJ3/+/6U/nD+U/5I/iL+9v3o/QH+Iv7x/aP9E/3A/O/8Uf0s/nH+aP6A/pL+8v5W/3f/Yf+o/63/g//j/+v/GQBYAE4AdACUABABcAFwAWwBVQH6AJYAawAXAbMBYQEmAMz+6f7R/5EAlADK/9z+v/4D/7b/TgABAAr/if6v/of/SAD//6n/Tf9x/wMARgArAOP/lP+v/+7/XgCSAMMAJQEeAS4BKwEKARMBAQEeAYQBxwHVAaMBsQGoAbwBpAGAAY8BiQFqAQEByQBpADoAbAA4ALr/Tf///hP/Kv/Z/q3+Cv7//WH+SP5R/nz+cf5U/mz+Uf68/hj/A//l/gr/9v7r/jT/cf/X/+L/l/+J/+3/QQA7ALr/r/+0//b/7v+8/9z/1f/t/+7/zv+Q/5L/ev+k//D/BwDj/7T/tP/a/xsAKQAOAA4AJgBwANcAPQFKAeAA1gDWABcBiwEcAv0BiQF1AY8BQgJ+AgMCaQEBAfwAcwHSAVwB8wArAIv/lP/F/zYAVQDc/+f+Jf7v/Uj+uP74/v/+lv5Y/iX+Bf5q/kD/6/8gABAAoP9N/z7/QP/Z/24ABQEeAZIANABOANsAQQFuAREB7wDSANAAtQB+AF4AXAA/APb/yP/I/wcAuv/V/8z/X/8T/wr/+/6H/9r/Zv8M//b++P4s/wr/vP4D/yH/WP9U/yH/zv6k/nn+lP75/jX/Hv/b/uL+3P7T/sP+uP64/v3+GP9P/4n/Pf8c/1j/df/a/9P/wf9XALwAGwH9ANAASgH4Af8BIwIRAjIC7QLvAtwCtgLVAngDJQQuBMUDPQMBA24D5QOSAyMDggIlAo4CNAKEAdIAKwBOAHcAAwA+/03+xf2S/Xf9P/0B/bX8d/w6/Nj7nPuU++z7Bfz++/77B/wm/G78nfyo/Lr8+vxP/X79Kf6F/mj+WP53/l3+Wv4K/6n/PwBIACf/yP4n/5X/gACdAAEA3P/Z/zAAwgAwACIApQAeAREB1wCUAFwB3ALkArgBPwBhAOgBlwNaA2UCSgFrAOoAowGLAroCLALdACIA2QCSAegBRALSAZQB0AHgARMCggLlAjAD7QMnBC4EZgRvBG0EZgRCBCoESwRYBGgEEQSFA2YDHAObAkACxQEFAbMAOAC2/3r/zP4R/ub8Vfz1+6z7cPsf+8/6xvrN+mn6H/rN+cj5Kvqg+rf6G/sv+zP7gvv++3f8Ff1V/YL95P0X/jn+Pf6h/hz/jv+f/5//ov9u/1j/ev/Q/gP+tf0l/lP+Ff6N/Z38nfxV/XP9B/3O/LX8mv2F/nX+hP1R/I38h/7HANcB0ACQ/7b/9gAsAnMCLgIhA2AFDgegBioFZAS0Bd0HtwgHCVoIewjUCNYIVQiTCOEI2QnZCk0KkQn6By0HOgd9Bz8H8AbGBaAEHgQDAwYCOwFBAMr/VP9a/jT97fsv+3L6pfnM+HP4RfhX+EX4zPdm9/v21/Yy99z3fvgA+fv4H/mB+UP6DvtB+6X7KfyS/LP9Vv6q/kb/sv/a//v/JgAwAMIALQHrAJYAnACoALoApgDt/0n/Lv+t/mP+//2N/Tj9Wv0c/aX8Svzh+6r7fftj+2770vsK/D/8G/wt/Cn8Ef1T/iX/0/5c/iX/8wCAA80ETQQ7A70DdAUkCCAKrwqJCsMKhwtQDJ4MzAyKDaQODRCED6QNLAy8C3oMcQ2oDBELOAl5BxQHmgULBKsCUQEwAC7/fv2K+2T6JPmD+PD3c/Yg9WP0wPMn9FP0jfM68/7yUfPq82z03vRl9Tn2HPcS+Jz4KPna+Vn7iPxV/cz9Kf6r/nX/gAB7ACABLgE/AZYBmwFcAeoAhAClAPQAxQAXALr+5v1q/cH9fv1p/RH9Rvx9+wL7NvrG+a76wPoQ+0b7H/tw+vj6k/ts/Pv9df5f/qj+ZwC8ApAFQAb5BDEEbwWUCO8L/Q3yDUMOkw50DsgOJA94EFYSIRQPFJ8SbRB8DmYOLw8zEGkPWw0eC7MJtwfjBZQDugEBARUApP4B/F35bPYl9Ub0i/ON8obxYPCK72TvoO7y7T3tCO4R76vw6PFF8g7zMvRa9Wr2Avh6+Xb7af0B/8P/bgB3AV0CxgP9BDkFSQVFBSYFCQXYBGoE7gOfAwwDvgFeAAj/Iv6Y/TT9d/zz+vn59/jP+EP4Yfed9nH2kPa99vn2FPeQ98z3L/n6+X/6kfpX+nf77wAmBVAGCwbwAhwDCQglDJoOiRGJEfgRoRInEm4RJROOFY4X2xnAGF8WphMbEvwR5BLzEkURAA/ODGUKaQfhBG4COQGIAMP+mPtd+Fj12/K18fnwfu9J7sXtZezF6yvrPer66brq3+vw7E3u/e4G8H/xL/OE9Az27vfo+YL8qv75/74AugHeAnME9wX6BvUHbghtCHkIUwigB+4GiAYxBvcF8AT5AkQBGQA0/1T+0PxI+1X6Q/km+P/2BPY/9cz06fR99Bn08fM49LH0VvUT9jf2jPZA93/4SPuQ/9UBTQNNAtYAVAPhBr8LGw9RERISKxOkEykTGhRkFQAYiBpGHN8aCxl2FvYUGhVSFZ4V8BP+EXQPqwzZCcIGIQQ0AsMAEf+C/EX5VPa+86rxAPAA7yjuau3k7Krr2+ox6ujpmOoQ7Ljtxu7N74/w/vF38yz1sfag+NH69vz9/mcADALVAsoDbQRZBTcGIQfxB20IjwgFCBYH3AUWBWoEKgRvA5kCJQHI/+/9SPwS+zj67Pn5+FT4+/bH9bj0YfQO9Az0GfTx81P0ifTM9Cv1gPV19eD1Lvb7+LX9oQAKAi0BKv+5AKYEeQi4DKsPPRHgEsATFRNLEywVtxfNGv8c7RzdGtIYXxe/FucWshZPFXATzREhD+ULZQgPBSUDugGx/5z8bfnX9qz0a/Iu8DPu6+yt7CfsO+tP6nvpSukD6j/rE+wV7XfuMfAS8ovzkvSk9Xz3Bfq5/PL+3wD6AfAC8APPBLsF0QZTCAkJfgmCCegINAhvB54G1gUGBRgE9gIOAtYAJf+h/cD7vfmD+Kn35PZH9nH1E/Q2877yA/L68fnxYPIr88nzU/QT9Yv11fW+9TD3bvy3AJ0DtgM4AW4BwwTUCGwMchAQEjUUGBbbFPsTZhRxFrsZFR1qHRkczRnqFwsXshabFloVmRMZEjYQUA1ICvwGUgRuAlEAtf3Y+hT4KvY29Pzx3u+q7S7snesw68Lqi+ro6dLpY+on6+/rNe2U7irwAvJ08+/0avYs+Cr6Vfx6/icAhwEGA00EjgWgBlUH3wdyCNgIxAjbCEgIZAd2BrYF7ATRA1gC5gB1/+395PxF+4P5Bvjw9r71cfWq9Hfz2/LP8VLx5fAz8bnx3/IT9N71W/aC9rj1i/SK94b9AwPWBYMGRgODBK4HkQlJDaEQrxNSF+8YRxamFdwVERcFGgYczBvzGuMY1haSFtMV6xTJEooQAA/KDOkJKAcYBNkBTwCN/WD6fve49Obyj/HM7wbuZ+wQ6yTqyemP6a7p4elT6i3r4Ov47CPuk+++8SD0D/Zq97b4efrg/Az/IAHiAi4EZAV6Bi0H4QeYCEYJLQp7CjQK6gh4B1kGrQUPBUsEYwP9AfYANf9E/Y370fl6+Nj3Y/fX9mz2YfVC9FXznPJ08s3yqvP99KH1bPav9q32GPeQ9lD3i/suAdgEMQbjA9IBTgS5B/8Kqw7+EN8SuBWUFcETAxTiFFcXmBpKG9sZFhgcFpQVIRbuFZ8UGRJJDw8NrwoZCLYFXQPmAaEApv3N+Vb2qvN58qfxMfCK7vvspOv+6rnqQ+pX6sLqOesw7CHt6+1m72TxhPNo9cj2w/f1+CD7zP1DADACeANbBG0FUAYLB9AHrgh3CRMKQQpKCSsIDQdvBgYGmgWKBM4CCAG4/77+if1O/O76iPmD+Oz3v/bn9TT1lvRc9Fz0J/TC8wH0d/QM9bX1fva69pX3Fvh8+Dj73v72Ac4DlwMGA18FPAglClgM0w3LDzATpBQhFFYUoBTYFb0X7hfTFhEWVhVmFdwVJxVnE90QDQ4NDC0KRQhdBo4EDwNyAcX+Ovvs94L1c/Sa82Ty6vB/7yjueu2/7NvrZetp6wbsE+0C7nXuRO+D8BfykvPt9Ar2N/dU+X/7Vv0R/0wAZQHrApIElwVABs0GEAdeB+YH9QfLB7cHWwfuBvUFmwT0AnIBdQDZ/2H/wf6t/S38zfp2+WD4xPdQ9//2/fbe9pb2Ofbp9Qb2ePbc9tr3vfgq+UP5l/gd+Uj8NAADAzEEEwO/AuUECQfYCAMLhQzuDvoRdxIiEhcSnxIyFAYWDxY3FZAULBRvFMMUXxTMEooQZA6MDLMK8Qj6BmAFCAQ5AuL/3/zh+a/3FfaY9F7zrvEA8GTv3u4C7n7trezx61rs9OyC7Yfur+/P8DjyiPN19Fb15PaR+HD6ifwI/j3/nQDeAecCEQT5BLgFeAbGBhQHXgdDB+oGYgblBYcFwwSfA3ECYQHXABUA+/6W/WX8L/te+uP5Uvmg+PT3zPeB91L36fab9uX2Y/fX9xb4VPgS+fP5Uvrz+q/8ZP8yAocDzAJ6Au0DfwZ0CU8LOwy9DXgPuxAJEbIQWRERE5cUcxWHFPESPRIHEi0SahJOEWsPxA3vC0EKeQgLBtEDjQIDARr/zvyj+WH3ZvaU9Yn07/Ld8Onu1+1G7ebsPu0a7vTuUO8h71/uUO5S783wAPNj9Vj35/hK+iv7dfyH/Yn+uv87ARwDwQQjBuEGMwcFB80GZAYEBuoF+QUZBvwFYAV8BAMD0AEFAToArf/O/rz93fxs/Bn8y/tV+6H6rvkj+cz4b/hd+Gv43/iD+d/5tvnd+T76cvpD+vX5F/sa/isBJQOfA+kCrwNbBXgG8QdaCSML4Q3mD4EQ0hD+EJMRtRIjE+QSlhJWEkwSoRJpEpYRRxA1DmQMzgoFCUUHYgV8AwgCUQBu/lP8Lfqe+Pb2HvVg86DxYvA68BDw2u+n7zzvw+6X7truKu838Jfx7/J59OL1zvaH93745fll+9/8Yf6m/wEBRAIyA/YDfATsBDcFUgVMBSMFRwU+BRIFkwSQA6ECiQFsAKj/5/7i/YD95vwX/H37ffpD+bb4Ofi/9973vff598b4JvmK+B340/fa9xr5k/pw+7X7wPsg/AP+AQGzAq8CiQK1ApsEkwdhCRkK5gpuDDMOkQ/mD7sP/hDXEggUDBSEEloRghFaEnwTShN9EbAP3g0IDOQKWgmzB78GgwWvA6YB0/5j/Nr6p/m7+Gb3h/XW84jyufFn8cjwL/Da77Lv8O8E8NPvafBv8cTyJfTU9Gr1cfZ49xD5tfqU+9v8Bv4p/8wAugElAtMCNwNaA7oDDwT5A9oD3gOpAyUDkgKtAbcAZQBcAJX/9v7K/aP8bPwn/MT7ffsX+9T67/q3+nD62PlZ+Xb5mvlg+i37gvvi+xP8Hfwp/PX7xPtV/KH9XgAcAyUEqANlAjICugNJBh8IiwnQCk0Mng0YDlQOwQ6nDzER4REoEdAQSBBQEHcRdBHJENQPzQ3nC3kK7QgXCCEH1QVxBGgCPwA5/jr8rPqF+Sr4vPZc9fTzA/Oc8kzyAPKI8fnwiPB88OPwa/E/8nDzO/QI9fL1YfaO9wD59/kx+0H8Kf05/kn/JwAcAQ4CdwJ3AmgCXwJ3AuAC8gLbApACHAJuAZgA0f8+/+T+if4X/p39Cv1R/N/7gfsV+7X6nPqp+s/6Cfsi+zz7FPvY+lf6dvpK+2n8af30/e39wP21/en9Kf5w/rT/fgFmAxEFjAW0BJ0EhQX1BvwIPQojC24MmQ1MDhYPTg+XD2sQ1BDsEKUQHxArEG4QgRAHEPUOkA3tC1EK4QiRB2AGGgV+A7MB2f/6/Y38RfvW+T74oPZn9Yn0y/ND8x3z7fLN8ovyB/LC8Sjy6PLC88z0P/XM9ZT2KvcW+D75M/od+w78zfxn/ev9Tf68/kT/9//FAC4BNgERAZoAYQBjAIAAtQDkANkAZQABAKj/QP/b/pf+Y/5x/qr+XP77/bP9m/2W/Tj93/wi/Z/9o/00/ej8jfy6/H793v0G/kL+T/6O/uf+pv5a/qH+7/7a//YAhwH6ATkCZQKQAicD3gO/BKcFUgb8BrkHlggvCcUJJwqxCk8LpAveC/8LZAzVDLEMSwytCzILwAo5CqoJJgmICJEHVQYEBeID2QLrAesA5f+q/ov9y/zW+9r6QfqG+R355/hm+P/3lfdm93r3vfcA+An4D/gw+EX4kfjM+N/4Dflv+a75t/nc+Rn6lfr6+gz7HfsZ+0H7qvsk/Jz82fz0/Dv9n/32/VT+cP6L/sX+5f4E/zv/jv+i/6T/pv+o/8z/0P+o/53/l//Z//T/sf9U/yf/T/+J/6//d/87/0L/If8V//L+3P7t/vT+N/9b/2r/df93/3f/Uv+H/9X/QQAQAcUBMgJuAo0C2wJGA8gDqQSTBVIG+QY0B2kH8QdDCLcILwlnCd0JFQoXCuYJxwmWCWoJGwlKCKcHaQcWB7IGAgbgBNkDBgNaAqoB8wA4AH7/5P4V/l79vPwQ/H379/qD+vz5iPle+XT5XflM+TH55fir+H/4g/iy+Df5lfn6+W36ffqK+rD63fo8+8/7Zfzt/FX9jf2o/cz9Af40/oT+q/7k/vD+Cv8P/+n+0v62/tz+/f7e/pL+T/4a/gj+Dv77/fj9Af4R/iP+Gv7Z/ab9qP30/VH+mf66/rX+w/74/hb/RP+V/8j/9/8/AEMAdwC3ABMBgAHkARwCMgKHAuACagPuA1AEqwQNBXMFzwUHBjMGpwYrB7IH+AcdCEEIhgjLCMIIdghFCEEIHQjmB7MHbwc8B9EGCwYGBR4EPgPBAk8CpAHxAA4AT/+S/tX9G/1n/Nv7s/uN+z/73fpg+hD61vnz+Qf6H/pT+nb6mPrR+vP6Avsv+4377Psd/E78dfyU/MX8A/1B/Wz9bv1T/VX9Zf1l/Xz9jf2L/Yn9c/1j/WX9fP2L/Yf9hv2Y/Yb9w/3M/cz99P0a/jT+Rv4p/iP+h/7F/v/+Fv8I/xX/O/9f/2T/WP9Y/6b/+f8yABUA4v/c//3/QwCcAOQALgFeAWwBZQFGAYsBDAKQAiEDbAOQA5sDqQPcA0cEtAQbBXQFsAXGBbYF4QUtBlkGgwajBqsGjAZrBgsGwQWBBTcF/gSVBD4E+QOLAxEDhALeAVwB7wBuACIA///T/4f/Ef9o/gj+1f2Q/Xn9Yf1V/V/9Jf3v/Af9F/00/Sv97fya/HT8j/y3/N/88/wK/df8wPys/JT8nfyh/Mn84PzD/Jr8Y/xr/Iv8rPzC/OT8IP0y/UT9Wv2A/bz9Bv5L/nX+jv6i/vD+S/94/4X/lP+Z/7T/5//t/+f/FwAwAD0AQQAwAAkAAQAdAB0ALQArAC8APwA4ACsADAD2/wEAJwBTAGUAZwBaACkAIgAvAEEAggC3ANkA7wDmAO8AFQE9AWcBqgHyAV8CiwK8AukCCgMyA2EDxgMaBFsEegRSBEQEZASgBJ4EfARkBFsERwTuA5sDPgMBA9ACjQI3AtQBbgETAcwAdABMAAkAjv83/9L+gP5j/hf+4v2j/X79Rv0l/Q79uvyJ/ET8DPwJ/Ar8/PsH/AP8CfwA/Pz78/vU++H7Cfw4/Ej8bvyC/Jz83/wT/S39Qv2J/ev9RP6X/rP+5P4h/2H/tv/u//T/FwBVALMAHAEcARkBHAEbASQBGQEkAS0BQgE0ARwB6gCzAKEArwC8AKgAcAAwAPf/1f/X/+7/+//u//T/1f+r/7j/4/8vAGEAkgCcALEAyQD4AC0BeQGxAc4BBgInAmgCtgL/AkcDUgNHA1QDPQMuA2EDegOmA70DhwNGAwwD4AKbAnUCYQJoAmgCFQLmAaQBZwEnAe0ArABcABsA9v/I/4P/LP/X/o7+Xf4r/sn9ef1L/T/9Rv0w/fz8/Pzz/Pj87fzS/Nn80Pzv/Bf9O/1p/Zr95P0M/hj+N/5P/n7+v/4G/w//NP9U/2H/eP98/4D/c/+X/7j/0f/B/53/g/+U/5f/h/+M/3r/i/9k/yH/7/7X/tX+6/7y/uT+tf6A/mP+VP5n/pv+0/7i/u/+0/6+/tn+Cv9U/7L/9P8kAEEAdQClAKEApQDLAAMBEAEyAU8BdQGzAckBrAF3AUQBNAFYAZ8ByQHvAfQB5AHKAccB0AH2AU8ChwKXAocCYQJYAmYCfgKrAuQCBAP5AsoCogKEAlwCOQIrAhgC7QGNASsBBwHmANAAsQBXAP//if8Y/+/+5f7t/v3++P7T/qr+ef5w/lz+Zf6O/qL+zv7M/qL+i/6C/oX+r/7n/vv+/f70/tz+0P7X/sr+uv6+/tP+3v7K/rr+pv6O/pT+gP5j/mH+Z/6h/tD+uP68/qv+jf55/mr+XP6L/tn+I/9A/zf/S/8+/0v/ev+Z/9H/FQA4AEwAYwCCAHkAiQCuAMkA4ADZANIA9gD6AP8A/ADSAMcAlgCqAMcA1ADSAMUAnQBlAE4AUwBcAGUAhgB1AGwAWABGAE8AbACIAIgAawByAIkAmADZAPwALQE/AT8BRAE0AVUBkAGqAc4ByQGtAaoBhAFzAXIBdwF8AYYBaQFKASUB9gDoAMUArgCJAHIAVQAgAPD/xf+V/4f/eP9H/w//uP6L/mf+SP4p/hf+CP4F/vH9yv2s/ZL9jf2d/bf92/3//Qr+Hv42/kb+VP5+/qT+0v79/jX/Xf9v/4n/gv+m/7//2v8FACYAQQBhAHUAeQB9AEgAJAAtAD8AYQCCAH4ATgAVAPf/0P/g////DAAQAP//+//3/xsAOwBRAEYAPwBKAEgAYwCmAN8AGwFIATABEAHkAMsA/ABVAYsBtQGhAX4BXwEtAR4BMgEyAUoBOQE0ATgBEwEeAQ4BCAHiAKUAmgCWAKYAtwCNAHAAXAA0ADgALQA2AAwADgApANn/q/93/4P/vf/p/9z/qP89/x7/N/9A/1j/Ef87/4z/MP8a/47+yP6D/2H/+f6A/vj+0//Q/0//I//i/2kAewABAKb/dAD2AP8AEAHmALoAsQD3/0wA6f9zAZcLeAz8+9TzC/u4A/UF2v9I+or74P6E/kz7EPt5/CD9A/vz+T39vv7f/Hf7TPum/VwACv/N+wj9hAEXAlwBJ/8I/4cCdwHkAXMDJQLlAgYDhAHOAp8CfgFzAzkClAIIATQCuwTj+r//igc2AB0AXf6y+6YCJQPyAbP9GPaP/RkHKgQ4/OP6V/yjAXMC9Pc9+4ACMgJjA5H7hfbX/sMASQOCAvD4fP0I/2kB2AWW/X/88v0YBUMFMfqa/XwFGgQwAkT/tv81BdQAugFxBSX/fgGgBiIB9/8+BG4D+gDuAzkFrf7D/8QEPgPO/u4DNwXL/Iv/cAE9AJsBbv4E/6wBhP7B/Yv+Ufu2/8cAJ/xK/QP7EvzZAOX+tPpq/6oC0PwF+s38FQCx/5j9YfymABEF0PwA/HQF/QAi/RX8rP08BWH/cfZE/agM1AoM/UHy7fYZCdQKDP/Z8zv86wmbArP86P3j+SIBMwfA/Ub/pQDXAIIHS/81+uf+QAMRBC7+fP4AAFP+mf98/2X8s/v0ACQByftM/GH9vv5W///9AAD//Xv9TwFf/sP/lwJl/GP+iQM3A5IBX/7j/6sDTAXzAIT9+gA+BSYGDQO5ABUB8gPOA0EBDgIWBCwDjQJJAjgBSQPeAykDDAPJAQYCvAE5As8EwQLSAa8CHANfBFIDdwDt/00EkwUBAxEB0/+HAaID6QGv/2sA3wApAN8AAf9e/ET9cP13/SL8EvqR+TD8APwf+pn5uvek+Kv5fPjX93r5KPoU+Of38Pgz+F76x/o8+CH6wvuL+x/7pfrq+3f9/fyR/FX9Cv7X/w3/L/x3/tn+zv44AV//pv9uARv9pP1kA08CKv9f/4f+IgCkBFoAjfvV/94CrAAT/6b+3wDZApQAdwAy/2b/HgCL/JT/Kwl4EBAR2wnkAMMBywlOD34SORKsEhMWghTjDicNHw8tEqYUEhHmCtQKLgzKC8wKqwbLAOv97fzd+4j75/m490n27fL77pjrEuvc7bTv5+6+7CvrqurZ66Tt0e/+8FDxdfMI9dL18veU+yv+ZP8HABMA7wE+BZEHTQmYCY8K2Qs7DE4NEw0cDUkM5ArOCv8JUQk4CY0JkweeBH4BfP6N/nr+nfxV+qL4q/Zs9JHyAvGr7w3v8u7p7RXui+3a7bTvAu987dDsOe658Pbzg/aL9qT21/YY+Z/7Ef3i/u0AvwRgBVAF7AaUCiYZrSAvGw0VoA0/EtogDCbmJIIkKyVLJxEkTRxRGk8diiCQHc0WbhJgEFwQLA0fBgj/Cfil83Dy2/He70jsweY04i/f1Nq52KPZlNx93xnfxdx43GLfSOJo5dboN+xM8A714fl+/kYD3wavCZQMkQ+SE2AX4hvOHsEehx8qINYfQB91HiMdoRtFGk4X4hKsD1YNGQr3BrgB9vuS9z/0RfIh78DrduhJ5U3jEuJT4nDhNOA+4Nbff99p4SnkeuYW6WLqietW7P/txvDf8wj4ZvpS+ib6pv1+AFoBkAJvBGkHoQcPBvoGeAduEAomvSrnH5sTlwZqEogoIy4RLS0qECtaLIskuRmPGNQg4CQPHZoSEw0pDHEMWwdTARb6OPDO6oHpIOuF7BTosOAw2wHY09SJ1OLYyN6v4gvhGN1h3e3hp+e67XDyRPUA+X38IgEyCF0PfRK+EYQS6xTQGSgf7yFMI0MiOh8FHBAaKBmnGV4aXRihEsELXgccBN4CawDY+gv3lfKD7UHra+qj6EDmwOOE4TbhaeIu42PkHeZb5ovlCef/65XxYvOa8s7zLvau+HD60Pvy/RcAQQEeAbgBYQGzAbsEbwUTAir/BwDtANADaQHF/3sTHCSyIUYVJ//C/FAWJCSvJo4nBSTTJmclIhqOFCQaXx8aHdMXORIgEKUPugtQBbP+6/VS7jft1+6j8UfwuefY3vHZKNbp1VTbnt+z42HmEeTV4rjlo+ku76T15fiI/KEBngXmCmAQpRJMEocSPhTjGG4ddh5AHawaFBlHGAQXnxSREvwQJQ4OCmEEO/+S/ZT9E/2V+p/0Se4t6x7q7elF6pXpeOgU6JHnb+hK6v3rc+2Z7YDtbe+V8gr2g/la+wv7B/om+2v85P1TALUBlwLHAfn/9P30/Az+W/91/878Y/tR+3f7mPw9+8z/ARM3IBcdDw5m+Sz+yBdvKYsvXCp5I1wijCE9HRkbdh6tHvgZlBVGEakOEwxABq0B4vsO8wbsd+rp7WvwCu0B5JfdZtyU3MTeguI35uXo+On86C3pxezW8Mr15voY/9kC7gUfCDULTg9IEaoRDBLtEooVRRhQGfkXRxWxEsAPDQ5bDKEKQQg1BW4Cv/4i+7T4Qvf99RD07u/v69fqLeum64nre+u16zvs++zy7UTvo/AA8sDzmfWZ98b5zfqU/DL+WP6h/mr/TwBDAHkAPQHUAckAjv76+3L5Fvj092f6j/18/jL87/X78Jby8vdGCIcckyDjGSQKxf3ACfsd4yrBL0Ys5SgCKZ8m4SKaIR0gdxxSGDcV1RKqEMoKPQMs/k/3m+8t6mnofevJ7GTo8eHD3C7autmq21XfXeTm6APq9erw7PDuXPIG90r8sQFABjgJygunD3sTzBT9FO0UehQNFlQX0xWJExkRGA9dDagKaQg3BpAD8QC5/Ir42/Uv8wzy9fEQ8VTwWe5R7IDr7eqm63fsLO7Y8IjyqvNh9Ar2Rfgm+W36RftM/Gz+Of+h/h7+wf6k/53/Qv6N/Db8Qfz++tz4N/Ya9TL3a/n6+aT4r/eC9tn09vX29boBWhqyJ2EmqRXV/0AFAxwgLdw36zVLL74raCWUHiwd1R0bGwcXNhM8DrMJ9wOS/Zj6MPUG7JLlzOLY5gjs9+mZ5MvfO9zD27vfMOVa6/vwl/Ha8OHxAfP29V76VQBABiYJJQo5CuQLmQ46DzwPHw94D4YRexF/D+UN/QuCCuEIPgXeAa3/cf5n/rX80/k8+Cf2IvXB9YD1/PSL8w7yTvK88tLzs/Tk9P31bPam9s72UPeV+Lf5M/sp/Hv8//xB/QP9B/yN+mD6bftR/Vb96PsZ+un4KPgG9sz0pfNH8uPyRvTV9vz5N/g79A3xSfF3AzcdwiqMKvcW8gOPChUbOCq4Npk4wTgTN/Mq3B2KFwcXvBlaGz8YlhK8ClcAePld9pPw4+iz4yniC+bm6K3l3eKn4OHdUNza3B7hSOl28Dn0Tfb39of3GPlV/QIEMgrMDbcPJBDfD+wPxA4YDqYN4A02D0MPAA6fCm8F+AHa/5f+WP5z/eH7tPoJ+mL5RfiJ9n30IPQG9Rn1m/SU8zrzWPMv9N301fVC9mj2b/di+Ej6Qfs2/I/8SP03/i7+Pf5w/vT+xf9G/0/93fr59zL2Evhg+ov7C/mW9Pzwe/FM9On0FfV89l75xPom++X2tQDxGQUstDBcIXwLRgzwHtUuxjoxPJg2pzIqKIsbLBbtE0QSjRLcDy8JhgGO97/wAvA77qvnruJZ4Kjiruej50LmKeVB4yXjWOWp6Zzwjvd/+mL7Kfu++rv6rP07A0YJmw3+DV0N8AsEDPoKjQhXCCgIOwqrDO0L6AgNBPn/I/5z/XL9uvxs/Oj8Y/uP+bb3Uvaf9pf3uPdY97j35PYN9lr2d/YI9/f3Sfm7+bv5tPkE+uP6JPxh/Uj+EACzAPD/Vv6X/qb+Pf5X/Ob6f/qa+Qb5gfgu+Cf3d/Wh8gDxSPJ/8xv0ifRb9iz4g/iT+HP2EQNNHOsssy3SHCYIyAuFHPEpKTSINYQ0QDGtJ90aYxWsEb8Ozw14DI8IBQIS+RfzEvOl7+jofuJy3/3joekp64DsaOx/6lXpFukt69/xl/cv/BH/vAA7AGb/lAExBbwJ8Av5C2MLTQxCDfgLNAnLBksFgQUqB/4G+wMBABX8hPr1+Y/5Uvkv+ZP5wvi/9nn1S/XI9xX7Gf0p/uL7afr5+Xv6sPvf+/38GP58/2r/Qv6j/aH9d/7X/Yv8i/sQ+6X70vue+/r6/vkY+WL4nvfy9lj1s/QZ9Cvz1/Mr9Jj1//XK9fL0q/Vv9zv8wPyE+7L5yfzVE44oeDMOLbsX9A11FeUgrCuRMngzmjGMKjkcEhG+C9EGHQUdBXMC9P1j9mbvAvDy7wHs0eZK4ivkhukD7Q/wnPKq8i7wfu/j777zffm6/UACiQRxBJICmQGkA9gG4gn5CxELgAkgCHgG/AWBBSoF7gOzAk0BBP8n/UP7hPpZ+5r70fqO+Tr4rfhd+c/6Y/t5+6r7/vr8+qD61Pm3+fr5tPrb+2P83fy+/E/8tfta+yn7O/x+/sj/DAEXAmEBxQD9/uD8pfu7+vX5fPjw9732RPYV9T3zvfGI8ezxcvIQ86jzofTO9Mj2svju+lP8Bfs4AAgUOCs9N5cwsxrIDTYRJxtHKKcwLTQAM14pWhpJDiYGrAAnAc4CyAO5AL/4zfEJ8Knuh+tD6D7lC+eL6pnuVfMR9rH1hPMi8ZHwdPMs+Pb8nwFABNoERwNRAfL/7wDiAlYERQVZBtQHowjQCLUGJQTBAV4ARv8V/9D+n/8FAaYBLQDQ/AT5Nfcc+OX5Y/uh+3X89P26/9YAHQDF/ST7PvpX+pb7rP3//oYA/f/V/rP8pfqj+af4l/kA/ED+Rv8j/v76Gvjp9gv3PveX9w/3KfZd9rT2pPZP9rH0LfMC8gvy7/L89Gr2EPmJ/K/+hf4S/Ej7aQkHIrw1LTvxKlQXIBHsFVUh0y5uNI80gi2tH0MSpQgHAZb+QP82/tj68fTw7qLusPAQ8A/u6OkL55nnoeoz8L/2+vkv+qb3HPZ19tX4A/23AD4DTgSbA/YCAQJVAQEC4wO/BoAIYwiBBhMECAIXADD/w/41/zYA9v6t/aP7QPkh99f1DPWS9Xr3Z/qP/a//av/9/IL7qvpG+wX9nf5DANcAfQAl/xH+dPzv+pP6C/py+oj7wPwY/hb/HP8l/wz+Jf0b/LX7o/uN+0b7CfpV+bj4S/du9hv0IvIb8X/xbfJ58iLztfVo+M35i/v4+tL7iPoh+pgI8SRrO9tABjG6GoUUrRd+HecovzCBM7AwwCKdEwEKq/94+Tj5KPiL9aDwe+ze7mPz6PFs7r7qDejo507pm+7t9lX85vwK/Db7l/mH+GL5Dv0PAqAE0AOMAyAD7QGfAZsByAIaAwUCcAC8AP3/5v00/Pr6+voi+uz3+fZA+Bn6d/s8+2/69fj3+Hn6G/06AMABzgLQA1sEjgQsBJcDxQJhATYALwDXAKUABwG8AIf/F/5G+7D5efo9+/H8Lv4a/jT9e/uN+sv6Hfuq+gT59PbZ9Gzz9PPX9Ob1c/WN9Kb06fSh8zrzaPXl+Oj8eP9c/Qr8Bfo/+i4L+CS/OE8+pi3YGJ8TbhTdGR4kjSqALLQphRxcEU8LqAGV+tH3X/Qv8yPwUe3A8lL4V/ha9DTtsufl5tvpPfLO/H4D+wOsALr89fgh9xT4HfydAJACygHLABkBQgJcAq8Bhf+t/WX8iPz0/g4A0gAbAWz/E/2j+Zv0lvM39Zf4P/yk/ZT9D/6o/lb/4P/2/0QBZgNgBa4HiwnQCfUIZAXdAZf/Iv72/eL9IP1j/Ej7g/qI+RL4Bvbb9M71UPdD+cv5q/mn+c35Q/q2+Q34HvfG93v57PqV+o/5MPhf+Ff40feX9973+PqE/Yn+nf8AAFgAEAC5/HUDYBf/LEY4ajCsHBsRbRCSE8AamiGvJFUkOx28ERkKXgE3+TD29PX/9TbztO3X7v30R/li+fj0APA77n7uKfJp+Yn/lAFyACL+NPw6+836e/37AaUFOAaVAzAAIP5W/aL++ADgArYDkAKRAJv9qfoU+ef4CfoU+/P64/nj+Jz4IfqE/Fj9Pf2m/Ar9w/5+ADsCZARbBlkIkgmWCP4F+AGJ/r78pfxj/U/+9P7g/ej7dPkf92P1PfSN9E32YPhb+sT7o/yY/c38lvsf+ij5KPhZ+Cb6JPwV/Z/8qfpJ+Y743vfn91335fav93L5afw0ANQAHQDk/Gv6OQTzGgYwDTtmMjMfsxTtEGUQ+RW8G48h8CXcH4wVzAsiAKn3efVn9A/1mPLc7czvLPZT+iD8tviw85nwFu6074z2rf2NAlsEwwKlACX9QPmt9wT6Zf3fAFwCGgJjAYkAcf8//mr9Qfxw+xX8Ff2G/SD+Lv2a+2D6fvji9oX33vhZ+5T9U/6d/kQAKgOpBgoJbgmUCLAGUAXKBD4FUgaVBrAEtQHA/aD55Pam9Zv1LPb09gb46vi0+Nj3//a89m/2s/XQ9Ob1VPj4+hv9mv2N/Ln7lvqR+cr49/fq+Lz7Gv+aADoAmP1w/MD8Ef0r/jD/w/3C/NH5lPwLD7snijkUOrwm9hHXCSAJYA+fGZ4hJSe2Jq8cEhJeB4b7fvUc9eT1LvbW8SzuRfKc+CD8qvup9tTwHO3266fvNPYt++D8SP3v/L78JvzN+/H84v7r/zQAtP8M/1r/SgEPBAAGcwW/AmkAX//Z/hX+6Px0+6X6+fl4+fX40fiu+RX8s/6cABABqgBBAG4A/ADiATID0wQxBnQGhQW0AzkBpv4d/EX6mvk++fP4V/gc+DX42Pf59iD14fKh8fnxifR0+Jz7Nv0k/VP8tfrT+Jv3avd6+Bn6mPyZ/58BUwFT/h/6g/ce9lL3nvvn/y4D9wRaAyAA0Pvw+EEBpRaLLWs7XDezJXgXPhC0DlEUhhnBHAYfKRpBEpIMKgXp/r77kPeA9FLxw+zt7az0L/sMAEf/b/lu8yzuXuyr8G/22Pqm/SL+Ff7b/TL8t/rG+oT7A/16/kn/UQA9AgsFzwfvB7YE//8m/PP5xvkV+yf9Gv9v/+D9FfxO+9L6y/qw+or6aft3/VcA6QOnBhQI0geaBvcEyALDABsAIACCADIAO/6o+z75m/YZ9EzyLPEE8VzybPQq9k/3pvcP+Lv4a/g897T2W/eT+M35wPvX/cj/QQA9/6v+pP7H/Qz99PzH/tABbgM+BEQDlAAs/7X+Dv4B/cr4IPvBDogpLTw8PKAnPRNqC2EJ9w18FjkbwR7HHVkWFxE9CpYAIPwX+yb6dve97wrsBPFi+Hf+oP8L+4v18+/y7Czvx/Lu9k76Nvuo+7D68PhT+sP8Qv9qAXUB2QB7AFMA9gELBMQEHAT/AVgARP8t/dD7gfoa+dH4+feM97b4I/li+aL5svjE+Ef6j/xeAHUDfQWlBhAGdQQTA1MC4AIgA90ACP6P+2/5dvhD+OP4p/pX+3L6uPje9tv19vXk9q34Dfr6+vH7G/yL+w76fvgq+Br5cPoO/KH9jv+zAXwDvQNmAiD/4vtw+ib5L/o7/Cn98v9TATX/T/wE9v74zQ9MKuo6AThxIJ4OewrCCUMS8BzKIHUkmSB2FncRHQdV+oz2PfXH9Zr0I+377O/0NPs7AMr/EPsE+LHze/LZ9uX5F/zi/LX7vPy5/Ej8N/85AdcBWgGQ/qT9vv3D/QwBJQSkBM4DJgAb/TD87PmZ+Lv4Qvg1+Bj4tvfO98T3MPcE98j3tPlI/GEA9QTHBykJeQgQBrIDDgJhAowETAaFBgIFEQLg/o/72Pj/98T3pveM94P3uPfg97b3I/fO9pL2cfWx9MH1UPgg/Ab/4/8wAHX/Af6a/GP7cvvF/Gr+EAAMAY0Adf9s/mH9/fwk/JX6VfvA/SP/8P5Z+6X7Wgl1HcEt6DISKVgcFhWeD4wPfBPeFBMWyBRbEIUO9gleAXT72PcD9ob0N/Bm7u7xBPf0/OQAiQH9/+T7wfhx+Mj4N/mb+O73W/kk+1P9iAAyAsoCogJYAaYAjv8T/qH+PQDbASwDkgJRAZn/WPx4+cP3VPbX9BvzDPKC8/T2WvuA/7UBqAHl/2j+c/7w/ywCsgQUB84IkQmICdkILQdOBMcAWP2V+gn55/i/+Vn6/vnC+Fj32/Vc9OrzifXW+Hf8kv7B/jn+3f24/Yb9Sv0I/V791f1p/Xn8iPso+8X7ifyN/LD7y/nB9132WvUj9vn3LPns+/j+JgDt/1r8s/voB/QaxCt/Mxkt7SJ1HEkWIxT7FSMWmRbKFPMPWw6WC4gFygGQ/qP7GPma8xbwevFw8y72hfiw+Gj4HPdf9ir44/mg+rv6e/py+4j8rf3K/2cBdwKAAz4ENwUuBd4D5wLeAYAA0/61/IL7P/sr+2f7+vqD+Tv3DPV+9I31BPeV+J753/rv/Bj/mAGrBD4HIAlCCgUK0AgtB+EEfALWAHj/Rv7A/Qj9z/vP+rD5l/gJ+CP3/fV19Wj1b/b39xL5ivlM+Rb5cvlp+X74hfdh90345fn8+vr7d/3Q/eT7yvhm9sP1bvaK90H5Wfut/TL/av5N/Ur8ofq1+in7tv4sDhwkvDXNO9Mw0SBlGQsWuBYtG3AbHRpAFkANUwiiBVj/Hfvj9+/z7PA563TniesO81P7cgFCAv//7Pve+K75ffuN/P381vw0/lUAYQLyBPIF9ASXAmb/hv2G/CD8+v2EADYCcQJKAIL9XPto+RH4evfn9iz22fVR9nj3xvj6+fj6i/yQ/l4AeQK9BBAGWQbNBf0E2gR0Be4F+wXCBY4EGALD/yX9KvqB+Hb3J/eS9zD3uvbr9yj5dvmZ+WD5ffmP+UD4D/dq9+f4C/vJ/Dj9rPsa+Yv23fQa9f32XvqU/hUBIAHt/z3+o/y3+hn62PrY+rD7of00/+YAS/8/AE4PgiR9NG04nSy5H8ca8hWNFIEV+hGVD24MdgfNCHYI6QE7/Wb4IvR98TbrCeh+7K7yJPq0/5wATgBl/t/8uP7M/z7/J/7g/Dn+QQAnAXwCTQIBAUEAof4V/nD+wf2o/v//tP8g/6H9RPxV/G77jvn/9/L1x/Ql9YX2a/lG/HD9uP2C/Yb8BfxV/VEAvwQ6Cb8LkgzvC7kIhQTCAIL93/tu+7L7zfxq/W79SP1w/O/6lfgn9mP1Mvba9nH3hfcy98j3VvhQ+AT4Bvcs9nX2yPcY+vP8Wv9BAIP/N/5c/er8WPxK+7L5u/g6+c36Bf4kAV4B/ACbAUIC/QIRAeD+Jgd0GMspMTRJMNIkfhzwFdISIRTLEs0PNQx6BvQEVwWPAXX+9vu7+L/2hvLD7s3wZ/WR+/oBPATuA94BR//T/ykBPwDF/av5YfZJ9tP3kfqm/dn+0/6J/kj+c/6f/pD+qP72/kn/T/8n//b+l/5w/g/+P/38+0H6xPga+AL4b/jW+Sv8vP+6A7kGKQiYB1IFbgIOALz+zv7Z/woB7QHdAc4AQP+Q/fz7wPqP+Q34bvaJ9bP1avb59h/3uvfN+PL4xPfJ9WX0T/Vo+Dv8+/7i/pr8Yvp0+Vz6d/u5+577GfvW+rD7Pf24/pL/Y/4X/G37tfuf/P//fgNgBekEJQE5Az8RbSLOLuAv/yUyHW8X1BLwE/sVKhV1E0ANnAdHBuQBQf34+7T6DPsU+k/2+/Y4+r78uv9R/2v8rvlU9hX2yvjx+VL61Plf+Hz4Fvli+lz9aP+6AGECTQPjA6sDMgMNBE4FtgXNBKgC4v+o/MT5H/h194f3m/ca91b2GvW88+vz5/UJ+Xf8HP/2AD0CAQO4A4oErgWPBqIGkQbCBfkDlgGW/h383/pp+t/6qvsK/Jb7afqV+VT5Nfn1+GT4ivfw9mP2MPbp9sT3GPjc93z3X/dS98/3Cfk2+lH76vvZ/Eb+Qv6h/CL7hvtV/Un+pP4c/7//6wElBK0EsQPR/6j/pgt3HYksEDJDK5QjZB+LGqMZYhpZGFAWYhDzCJUG3AII/rz8YPuq+mr4mvGm7XjuSfG29uz62/zv/a/85Puh/Cb8IPs++R73GPe99335cv2GAX0FCQgbCFkH4wRhATL/D/4l/hj/fv8eAHsAD/+s/BL6pPcy9YTyGfEH8gr1/viP/Jn//wEEA3cDRASeBX8HeQgpCEcHXgYtBjwGgwXBAxwBav6A/BL7Lfqi+Wr46fYu9UHzKfLz8cny3fSg9iH3n/ai9UL1qvX/9Wb27fay9xT5dPq5++/8h/17/Tb9S/16/mkAzgHbAYAAYf6W/Dv8P/yG/MD9c/5T/kL9OPwlA8oTliW/MYMyliqNJB0grhtuGtgXixMND8YHmwOFA7wAI/5G/H/5lfco8mzrgur77JXxNfcO+jD85vzd+z/9rf/gAHUAUf3G+sb6svu4/Xf/LQBLARwCdwK4AjIC5AH4AZIBvAD5/uD8vvtn+677hvvn+an3i/VR9A/1aPf8+kv/6QJzBc8GqQb1BacF/gUxB84IYQqFC2wLkQn8BYQBLv2c+Wr3n/Zj9ir2D/be9Y31J/U09M/yZvEq8OfvDvGG8zn2V/js+Zj68/qL+6z7p/sn/PH8//3T/v3+7f7F/vH90Pxh/CL8Nvvf+Qb5Nfqf/C7+VwB1AokCrgA5/Yf/6Q14IDAvqDTKLrsnnyItHHkZFBhcFGwRyAtHBmYGLAU0AmcAZfyH+E/zO+t26PjqUvA++Kj9nAB1AXX+3/t5+kn4ZvbJ83ny2fQo+LP8LQEyA6YEeATrAvIB7v95/ir/YAAnAjMDqgLmAWsAqP58/Tv8Rvue+jz6gvtn/Zv+VP+Q/z8AkAHcAqgDlwO/AkIBIAB0AGkBVgLlAloCcwEnAD/+mPwU+4r5w/eY9dDzefId8mDz4PTv9Xf2Efat9nb4BPql+5T8Nvwz+4b50fgF+3n+nAATADb9RfrI+Jz4L/qo/Fj+D/8g/iT9HP1//AH9HP4V/gAAzgHzAD3/H/t/+9gIFxpoJ4YsYSaZIDkfYxxUHeMedBriFBEMgQMsA00CPf/9/lX9G/zJ+t70TPGT8cvxafTn9d30pPTo83X1nvqv/gwBSgGF/5n/kQBIAUYClAGEAGMA1f8vAMkA0ADKAT8COwIyArEA1f4p/dn7y/wW/9kA5AEpAQT/Iv0H+1T57viM+d/7d//HAhIFDQYEBl0FCwR6AnsBQQGJAQUCtQFrAEn+e/tv+ZX4YPhW+PD3Hvfp9Wn0HvOw8kbzuPRb9rv3q/gf+b35sPpO+wP7SvoS+lv67/rq+x79Wv7O/j/+4v2b/tD/TwA2AHf/tv77/jX/q/51/p39V/zE+8L7zgD0DGcZviGcI88fbh1sHGUa3xlOGCUU7A/KCrAHPQg/B3wFogSLAvb/A/u880Lv5O0N75zyqPX398b55vpP/ScAkgG6AW4Adf6x/IT67vjf+P751vuh/eT+VP9J/5X/9/+qAKMBWgKBA6IErwQYBCcDTwKbAioDogPjA7YCYACt/Y37APsb/Mn9kv84ARUC6AEgAQoAJ//n/un+Lv/a//L/RP9c/vP8gfsJ+jD4lPYM9WXzR/IS8rDyiPOG9DL27vhO/IX+yP6o/Sn8x/ts/bT/+gB0AGz+A/30/Gz95v0i/jL+bv75/qv/+f8n/2P9x/us+zr8wPyo/R7+9v1K/ev8kAE9C1oU4Rn1GQcXkhZmFkcVWBXSE8ARsxAJD7kPnRGwDzAMxAeUAgH/x/pF9x/35fcP+YP6oPqe+tH5Avh291D3v/bt9c70NvXZ9uf4a/sQ/Uv+If8q/7//JwAbAJIACAFWAgQFXAfYCOwIMwdZBaYD/QFEAQoBDAGSAP/+OP3k+2X77Pud/M78mPzh+1P70PvJ/Ev+VwDKAZ8CGgPDAu8BnQC8/gP9rPs/+hT5Jfg19z72RvVN9aT2bfgE+o/6Bfof+Vb4aPi7+f76Tvus+lL5GvgL9872Avh2+c36vPvD/LP+OwBlAO3/rf/2/3sAsQCcADQA0P/Q/zsA5AEqAxUC+f9C/ZT9gwS2DJIT8RfzGE8cVyE9I6ojtiBIGawRiQlmA1ECRgHr/8j/d/40/QP7FvcX9Wr0KfRL9UT2k/er+Uz77f1rANQA7f+E/VH72vro+kT8Ef5d/xUBoQIjBM8FLQZzBYcE6wJVAcr/bP5A/oX+BP+S/+3/jwDWACYA7/70/KH67PjR97j3svg2+qP8/f+0AxAHJwk2CZEHrQSsASP/Yf2Y/Mv72vq0+Tf4IfeD9vL1+/WH9iH3UPij+T/7wvz0/B78vvql+UX5pfht9w32fvU79/r6S/72/+P/+/7i/g3/fv4//j/+P/7n/h7/gP95ACkACv9p/RL85vuC+5r7of01/woAN/+d/sgFTxJqHXckjyQvInAiVSChHIQZDBNaDGQGrwBBABMBRP5I/GP76vqh+1L5VPam9hz3Ovgz+jz70vxj/lb/6AGyA7YC3v9e+/L37vaU9jP3f/jx+c785f9jAnoEFAUIBc0EDQSJAycDugK1AosCXQLgASIA+v0g/HL7f/zU/eD+EwDgADIBIAF9ABMA9P+p/xj/VP5a/ST8o/q3+dP5YPra+lf6BPn592z3R/eA93H3yvbc9dD0kPSm9Tv3vfiu+QD6afpI+tr5TvoF+5j77fu7+7f8VP/KAS4E2AUQBrsFkATeAhkBIP6s+jP4uva/9g33u/eO+UH7qv3qAOsCGgRvA4UDIAoBFA8d2yJtIhghOCGvH5UfJh+mGp8U9AvGAwoBrf4X/D37ZvrH+0T9DvzS+z38lvwi/s79Kfwo+m72mPRL9Rr2pvei95b2uveM+Y/7df3H/Yf+8v/zAGYCXwOmA/kDXQTfBXAIDAoOCgMImwQ0Acf9dvu5+oP6qvq++hT64/n6+Rn6bvty/cH/nQKnBHMFYAUUBIACLgE0/5b8Nfln9YLyCfHP8HbxrvKf9EX30/nY+uz5zPfe9aj1Zver+Wn79fuT+277XPvF+4T9rf+kAcMC2QIuA3oDjQLgALr+L/zY+Wz32fX29X72+/ZH90n4ffqG/Hf+zgCbAiUEJQQNBCsJahLrHJkm0CuqLv0wvC/9LJcpECNNG30RZQf9AWz+nvoC+B71zfO38+Hx5fC58CjwmvBS8e3yfvUC91v4rPk8+mn6W/lJ+Ef4QvgP+b76hvx1/5ICNQULCGoJkQlECaEHDQZtBRsFAgXFAwwBkP6m/JT7gvsz+336ZPna91j3APhg+Yj7m/0+/5gA8QDiADYBjQG4AVMB1f8P/kz8j/o3+SX4xvcC+KX4e/mn+TH5fPiD9yX38vcj+TX6f/qn+fv4+fiG+QL7lvwc/sX+K/6o/RH9zftM+gf5jvjz+Hr4fvdC9zX30ffn+Pz5Wvst+3j6rvsO/ar+N//Z/xIHrxOnIKUriTBxMXAzXDNgMbouGidxHW4UnwqVBHIA4fm89E7xwe/j8azzKfS19VH2mfZH9/b2kPZW9ZHzZ/Mg9Of18vew+Tv8if47AI8BcAGEAM7/q/+kAeMD4wSuBVYFRwUWBkwGoAZABigEyQGZ/3L9Svtv+M71VvTF8y30BvWp9lL54fsF/un/BQEiAksDHgQvBegFlwVUBJsCpgDi/iv9Qfue+ZP4UviV+BL5Q/mt+Mb33vZa9RfzAvGy7/PvWvJW9cb4efy1/gcApQDa/5D+Qv3Q+y/7Mftt+yf8nPxp/OT7qvvU+6f7M/tM+pz46fjJ+tT8LP/n//ACfA0iGlokNipMKv4q6CyKK+wpryYJH3oWNw14BjwFYQJI/Nz2hvLv8V7zIvPX89X0GvXF9sz4nPrL+zz6evgz+Lb3Efek9Wr0w/X593j6RP2d/p3/vgDmAY4E9Qb8B5gIOAh2B9YGKAU5A48Bpv+O/nn96vvj+uj5nPlX+pX6G/oo+f/3/feF+cD7kP6YAS4EowaCCGAJTQkUCOUFkAMrAfD+Jf0f+xT57vbJ9KrzTPO685r0pvUI9w/4zfhx+Yz5QPnz+O74cvn6+fH5EvrN+nT7wvu++xD8zvwF/cv8TvzC+0X72vqT+qf6FPvb+qP6Avv++l77B/tD+gX9xARbD04aoSFjJTopICzFLZ0uVSxLJ5cflBUuDh4KgwZAAtn8g/gE9372HvY09qL2UveB95X3Gvhf+DH4xvdt94X3GvZr87Lxk/E689n1nvfW+Bb67/p3/NP+CAGfAg0DygLXAmoDHwSmBD4F8gV2BmYGCQVcAhX/hvu2+Lr38vc++dv69fsT/Xr+NAB+AggFOAelCEMJtQghB+4E1QFd/jr7HPhj9STzAvGg73/vdvCo8mf1xvd4+Y/6XvsZ/Gz89vsX+8f6/PpD+z/7y/ox+oX5N/lc+tb8Mv8pAHr/lv6f/r7+Wv7f/Er6IfgB9yr3l/k7/GH9bP1W/S4BEwqiEzsbXx/IIdIlxClsLO8tGyzaJg8fsRVJDz0LzwXu/1z6xvZb9lH2LPYG98X29vWW9Vj1Sfbc9sP1IvXe9Gz0T/Tt8xX0dfXe9h/4Ifml+VX6t/t+/V//mABPAQ8CWAOIBewHxQl3CqwJPQjRBlIFpANsAbb+4ftb+Yn3xvZx9x35YPss/i4ByAPYBR0H7weaCDgIdAagAx0AtfzA+V/3lPVe9ErzgvI48mvyIvNT9Mr1ifeG+Ur71Py1/Ur9Tvx3+z/7ufvZ+xn7TPrL+YH5Dfoi+wr8/fww/Tb9lv1s/X38Uft2+lX6C/p0+RL53/gU+YP55frK/0EIsREKGo4e6CDiI3wmPinxKt8pxSb+IMAZSBQDEB4LfAXQ/mj5Avc59SD09vOs88fzB/QI9H70sfQl9AH06fT99e72Xfe79374RfkC+lP6mvqY+oj6Evw7/hcBWQR6BiQIhAkcCn4KiQpKCaoHzwV+A5ABRv/6/EX78/lJ+Vf5TvkC+RD5jvkJ+1z9oP/xARwEfQX+BYgF+wOjAdP+zfuO+JD1tfKc8BDwovAJ8v/zefUe9pL2FvdN+Cb6ffs4/Jz8Jf0i/kn/bAD/ABABjwAg/8f9Kf1s/ZD+uP8QANP/1/5K/W77cfkC+Bb3xvZm98r3z/d19z73oPoyApkKSBIaF1ga4R9AJb0olSrBKD0lIiFjGxYX3RPyDgMJoQJl/dr6sPhj9iX1ifQI9cH1IPYI9wL38PVG9cv0IPVs9cf07fTi9e32xPg++mD7gPzN/Gz9jf5f/2kAXgGSAnwEZgYiCAMJswgACP4GtgWmAxUA2Puv9zj0gvJK8mvzpvX199r6Tf6WAecErAd+CV8KsQmVB1kFJQM5AaL/2/3z++r51fc19m71aPW29Q/2aPba9pv3oPgW+qj7s/wr/dn8Pfyc+z37dPtB/DL9i/1f/Qr9o/zC+176LPkz+MP3fvdh98/39ff/92j44fiO+U75fvhk+j0ARglIEx0aTR7PIg8nHyvVLWcshSieIuoaLBVXEW8NBQlfAxP+Yvt/+Wz3uPUi9FfzLfN180/0CPXM9PbzSPOP8/PzCvRs9ED1+fYE+an69fvU/D398v3//hcANgHBAYcC6wNoBYUGxgZ6BnYFMQQVA7gBZQC//mH88/ng9z72CvbI9ln44fqJ/SsA8gI6BVMHJwl5CXcIAgZ6Au/+ufsf+UL3+PX29Fr0OPSs9Gz1pPYE+P342vkx+jP6mPoO+3b76Pvi++L7rPzo/UL/CgA0ABIA5f/n/2H/Cv5X/DH6MPi09mj1vvTQ9CX10PZ4+cf7s/wQ/D/9WwPFDDoWhxzeH84jLCi3K+stnSzuJ0MgkBZVD1QLawfHAlP99fj590740/gE+qD6rPo4+gn5c/iy97b1WvRM8xnziPO18g7yQ/Kz8hH0n/Xc9gb5HftN/R0ALALOAwgFtAWuBssHEgjAB8sGSQUTBCcD4gFPAGr+iPye+5z7E/y+/CX9c/3X/a/+r/+UAHkBSQLKAhMDygLbAdsAif/x/aX8TPvw+an4WPdu9kn2jvby9nX3EfjT+Of5Evv1+4D8e/zb+z372Pqw+sb6jfoq+ir6Svqh+tb6Pvo3+UD4R/cR91v3dffj90n4f/iP+QP7Jvwr/U//0wT7Dc8W/xzKIHIjFicfKv4qjCpLJwshWhpUFHgQ2A0pCYUDA/93+3T5RfjG9oD2QvbH9VL2xfbl9nP2IPWb9MP0kvTO84TyqfHJ8Z7ybPSD9nH4zfoc/QwAZgOHBbsGTgelB3AICgnOCPUHewanBAwDfgHa/03+9Pz4+1z7mPvo+1P89vyH/XD+Sf/G/+f///8/AFoASADG/+f+2/2s/Hv7ZPox+ff3/fZv9mH2tvbr9oz3u/ii+X36XPsJ/Ob8X/3m/B78MftF+mb69fob+7D6cfkS+F/3Rfe794P48viO+B34Rfhp+ez6L/xw/UT+gv7y/voAbwYxDtsUixlNHf4gNyUjKOEoeCiiJU4geRolFU4REg4pCSMEfQCN/dn7U/rc+KL4CPgq9yf3v/ZW9mz1E/RN9Gf1XfZ699P37Pdr+FT4Vvik+Ef4QvjB+OH5xfuE/ef+0f9+AFUB7wFCAoQCXAKHAtwCnQKSAkYC+wEjAuQBXgGEAMz+jf0l/V/9S/7v/lb/BwChAGEBBgLyATgBuv/H/ez7BfpZ+PD2+PXJ9SD29vZD+Hr5k/qK+x38r/wI/dv8lvwn/Nb7rvuW+2774fpm+j/6pfo2++j67Pm0+Kb3Dffn9r32AfdQ9373bfjs+bf7XP1z/icBpwYSDjAVlRk0HDofXiJEJbQm8iXHIwIfbRlaFVMS+g9uDHAH9gPZAfT/8P4e/bn6u/jm9dfz8fJ48Xrw4+8m8OrxqPMB9R726fbj95f4tPjf+Ff43Pcq+M/4u/rW/Mz+QgEwA94EVQajBioHawe/BioGzQQuAwoC+gAIAbEBvAFNATsAA/+N/nH+lP6k/t79x/yU+9/6B/s6+6X7/PvZ+4j7wPpe+UP4PveD9of2s/Yj95n34/dg+Av5oPk6+tj6Wfvi+1f8Ef1P/oL/DADT/y7/Vv6S/bn8p/vv+nL66vl7+Rr52PgQ+TP5JPk++cj5XPr4+pr8OAFNCboRdhcUGggcux91I3ElTyX4IpcfchuQF34VkBQeEqANvAj1BDUD0AGJ/9n87PnF9hH0KfIi8WTwTu9j7onug+8O8aXySPRj9oH4ffo9/JT9q/54/6n/rf9v/7X+XP6Z/ov/IgEwAlMCCALVAWgCgAM8BE4EkgOfAkICWAK4AoQCfgEpAJ3+Of04/CL7JPpX+TX55/nP+mL7lvu1+xX8V/wS/GL7PPr++Nz3//b09q/3rvi/+Wn67/ql+0T8//zv/Xr+5f4n/+L+0/7Q/qL+cf4G/lz9pvyh+4H6ZPkC+Hj2CvXZ83fz1/Mn9RT3wfhI+jT8IADqBh8O5BNgF7wZmx0LIicl/yXrI4EgCh3ZGVcYBxiOFp8Tjw95C5oIVwVhAaT9mvlq9i30TvKR8fnwQ/DL8MDxtfJe88fyBfK+8azxdPLJ83f1h/c6+Rv7HP3X/roA7QGCAtMCXQIyAq8CYQO7BPAF9QYtCKwIqghyCMAH/gbKBR4EXwJKAEj+hPy3+kD51fe69nH2gvbZ9pD3W/hV+fn52vl9+d74Sfgl+Cz4Pvhr+Fb4UPjq+O75Rfsy/Ln83fzQ/Ar9df3M/fr92/2Q/ZL9af0c/Zr8rvsf+/z6vvqK+uf54/ig+Lj4QPkA+iH6+vnP+q/9gQPXCsIQZRRWFnsYkRsPHv0eIR5RHHQahBj7FksWvRWtFPoSsBAjDk8L8we0BOgBcf8g/ez6H/nI99z2F/Ye9Rf01PKI8cbwrfAQ8ffxOvOb9D727ve7+Yb7xfyt/Wj+5f4c/yn/Kf+D/40A7wGyAz4FAgYhBpoFXwVHBeEE1wNhAroAVP+O/vb9i/0I/WD8y/tQ+8/6TvoU+gD6Jvof+vX55/kF+o36FPsi+6r6yPkj+VL5Jvok++T7Tvx1/Eb8svsg+9/67vo9+6f7NPzf/H79F/7l/nf/qP9m/87+RP7t/Wr93/y3/NL8av1U/hr/q/8JAN8AswJxBeMHLQlNCeoI+AiaCZgKpAt+DB0Neg3BDSMOAA/zDxsQaQ8jDvAMQAy/Cz0LewoyCb0HeAZOBVQEAwMpARz/Bf0i+8T5u/gP+L/3q/cc+KT4KPnA+fX5ufmB+ST5yviw+Hz4c/iO+B/5Pvq5+xX9Ff64/gj/LP9f/6v/EABhAHsAmgDdAFEB4AGJAlQDCARNBAQEgwMPA50CCAJ5Ae0AWgDM/yr/tv6E/k3+yv0F/TT8cvv++nL68Pk6+VT4gfcB99X2zPbR9oz2bPZS9lv2uPY59973Q/ig+P34afkb+ur6ffvd+wD8G/xr/Ar94P2h/kT/yP9cALEA0gBBASIC9gMqBjYIGQriC44NGA9BEA4RmBHZEeQRuhGREagRyRHAEXAR/BChEO4PpQ4YDXEL3wk0CDoGzAOEAZX//f3F/LD7vfrf+QL5M/iJ9/f2h/ZY9g/2ofU99Sn1jfVC9hH3xPdi+Of4bfkL+qH6SPsH/Or8A/4W/wwAIAFAAloDJQR+BF8EEQS6A4wDhwNqAyUDqwIIAlYBhgBx/1z+OP38+wL7Dfpp+fX42vgd+Vn5wvnN+Yr5Tvns+F/4pvcG98b27vZL94n3jvdt92338vf3+Cv6Qfsw/D/9qP7t/w4BmwHFAcUBhAEuAcwAYQDT/37/m//XAEkD5wU2CLEJlApfCzkM4AwaDfYMqwyHDIUM7AzYDf4OGxCqEK4QKhBHDyYO4AyXCxcKXAipBgQFqwNzAm4BfgB8/2z+Xv0v/Bf7Evr1+PL3D/dz9gj2jvUs9QP16fQ/9cz1TfbI9h73Zvf09wL5JvqR+8781P3i/gAAPwFhAjIDtAPwAwgECgTaA6kDlwOUA3EDCgNuArwB+gA0AH7/pP64/a/86Ptw+w773fqy+sf6y/qq+o/6Svoh+tj5Zvnq+Iz4N/jl95v3dfei9/X3k/h2+Xj6j/u+/Mn9i/5L/5v/w//K/8X/tP+i/9D/DABGAEQADgAJAKEA7wF8A7sEpAWFBnYHgghsCSkKxwpYC7ELDQy8DLYNuw5mD7IPmg8WD1IOZg2mDNsL6wrXCYIINgdHBrgFBAXRAzcCIgAr/oj8LfsJ+uH4oveQ9sz1QPUI9Qb1HPUs9S71D/UO9YT1I/be9pP3Tfgv+ST6H/sd/C79N/49/04AWgF5AncDWQQ+BcIF/AXPBRYFUARsA5sC5gFPAboATgD2/7r/gP/7/oT+5P0l/UP8f/vb+ir60/lX+QT5nPgq+Ov3xvfy9/n39Pc1+LD4j/lw+iL78/vg/M79if7Q/qr+VP4e/jL+h/68/tf+1/75/jv/sv9cAF4BmwKfA4EEPgU+Bs0HggkDC1sMig14Dl4P3Q8rEGQQaRAqEHQPcQ5QDU0MZgvFCkEKnwnsCNQHwAauBXgEDwOGAQwAlP4b/e37Cft5+vH5f/kY+aX4aPgf+KT3//Yg9if1e/Qy9HP0IPX59RH3SfiR+RD7ffyo/aj+MP98/7j/EgCPACsBvgEcAmwCugLtAhYDFQPbAnkCBQJ8AeoAXAD5/7L/Xf/Q/iX+av2U/LP7+vpg+tr5S/nE+HH4Rfhg+KX4FvmR+Qn6ffrH+h/7bfuj+9j7Cfwy/Ej8T/xh/Jb86/xf/fT9d/7r/iX/Kf8h/3z/8P+hAGwBNgL9ArgDnQSFBZcGfwcXCIYIBwntCSULkAzBDZEO+Q4oDy8P+w6kDjoOpg37DDkMeQubCpsJjQhvBzUGuwQEA1MB2v+1/sz95PwH/GP71PpZ+r/5GvlL+ID30fZW9ir2J/ZU9q32RPf79+X44fnE+pz7XPzr/D/9lP3H/Qr+WP6d/vT+W//F/yQAlgAMAXwB4gEpAmoCdwJWAjIC1AFVAYkAUv8a/ub8FfyB+wn7lfot+ur53/n5+fn5Dvod+j76SvpI+kz6P/pe+lv6Yvpb+lf6W/pZ+oz6rPrR+hn7YPvQ+0/80vxp/RP+1f60/5YAZwElAvYC+QNHBdwGfQj6CVgLhwzIDdoOrg9FEIgQsBC7EJUQZRBFEAsQ2A+hDxYPZA41DaQLzAnNB54FagNaAWT/lv3m+5P6p/kL+av4ZvhH+A341feX90n3HPf99gH3QveA99H3D/hC+LD4V/k1+hD7nvvh+xX8Q/x7/PT8ef34/X7+Af+U/zYA9ADHAXMCvAK8AnUC6QGYASUBrgAZAF//zP5f/in+Bv7e/cX9fv0i/dD8ifxr/D/8+vvF+6r7o/uc+4v7hPuE+277Z/tr+437s/v1+1r8vPw2/ZD91P0P/kD+VP5n/n7+pv7p/iH/kP/5/4kANgE5An4DxAQoBkoHXghcCSAK1wp3C/8LVgyUDKsMsgyyDLgMcQznCzkLVgp1CZMItQfIBsoF0QQEBA0D7QHWAJX/cP5l/Xn8xPs8+8b6efox+s/5YvkS+f74BvkH+RT5/vj5+Cj5o/lp+hD7f/vS+xL8RPyd/DL9tf0g/pT+I/+y/08A1gAuAVEBNgEKAd8AwACYAGkAKwDy/73/R//B/kD+wP0O/Sf8f/te+5/75vv2++b7G/ym/CL9Qf38/Hn8APyj+zH7MfuI+xL8r/wp/az9Rv6b/tX+5f6d/nH+Qv49/nn+wf77/gH/VP/a/4gAfAFTAioDNwR0Bb0GwAe+CMsJ5AreC4AM4gwhDVgNiQ2DDWEN/wyXDCcMpAviCgUKDAkQCBAH6gWgBGEDWgJVAWMAYf9j/rf97/xM/Jz74fo1+nb5zPgl+Iz3FPfO9rj23PYK92H38PeZ+FX5FPqu+hn7bvu++w78XvzQ/FP99P11/hP/gP/D/9D/uv+Q/1b/D/+Q/kb+DP7r/fv9F/5h/rH+zP7p/tn+pP6O/jn+wP1K/eb8kvxr/G78hPyz/Oj8Af0e/Uv9m/3p/Rz+NP43/jT+MP5R/qL+1f7v/vL+7/7T/tP+Af9x////oQBTAf8B4gLiA/sEIQYFB7UHPwjhCHsJ/Al1CsEKJwudC/ALKgwqDC4MMwwjDMgLSQtsCkYJAQicBjwF5QOdAkIBBwC1/oT9mvzs+2f7z/pO+rb5Kvml+CP4m/cj9+T2qfaD9oP2uPZN9w/44fiT+R36jfrq+j/7Z/uf+9b7Hvx7/Pj8vP2F/ir/h/+U/2z/Sf8M/+f+8P4T/0v/lf/u/ysAZQBpAD8A+f+D/xj/tv5j/hz++P3b/br9xf3r/RP+Sf5z/pD+sf7g/gT/N/9m/4D/gP9h/z3/5f6A/jT+4v2j/Xv9af2G/cf9NP4D//n/BwEGAgwD/QP5BPAF4QbxB/UI4AmdCicLygt3DAoNdg2iDa8Nmw1FDd4MPQxzC6gKxwm7CJ4HSgbuBLYDjgKAAWUAE/+1/XL8Y/uB+rv5+/gq+HX36fa69sr2GPeZ9wv4c/jR+D75vflg+uj6P/tw+4T7jfuq+9n7Hvxh/KX82/wO/Vz9o/34/Uj+hP7I/s7+zv7c/uf+8P7k/tf+l/5J/gz+r/11/S39/Pzd/JL8gvx5/FX8Mvz1+777vPvU+/j7OPx5/OT8Yf3Q/U3+tf4a/5D/2v8SACsAVQCRAN0AOAF3AcUBNgK8AmYD5wNFBL0EZAUdBrcGQQeuB/cHNAhgCMcINAmNCcAJ0AnOCeQJGgo7CkIKAwqECdgIHwhpB7sGIQZoBZ0ExQPVAt0B7wABACn/O/5c/Z/87Pt7+y/75vqp+mT6KPrq+eX55/ns+fP59/no+cn5q/mD+Xb5g/mp+dH5BfpF+r76Wfvd+2H8vvzv/Bv9P/1h/Y/9yf0F/kb+bP58/o7+jf6Q/nn+gP5z/nD+Yf5d/mz+VP4y/hH+Bv7t/dT93f3d/f/9T/62/hz/d//M/w4AVQCEAJIAdABBAB0AAAAiAFgApQDdAOAAAwFVAZYBzAHpAfoBJQJwAvkCzgO2BLIFjgY0B+EHdAjfCDQJVwlPCSYJ6gihCGAIEAjJB3sHJAenBhYGWQWJBMwD9AI9Am4BiwDD//L+N/5e/cn8Vfzm+5T7FPuK+jr6H/od+hL6B/oZ+mT61vpO+8f7NPya/Ob8Gf1E/Wr9n/3D/eD96P37/Q7+LP5o/q/+6/4q/1r/X/9f/zv/Hv8a/+v+w/6U/mz+Rv4j/gz+//3k/br9fP07/Rz9Af0K/Sn9Sv1s/ar94P0D/iP+Sf5u/pf+0/4I/z3/Uv+b////ZwC+AAwBMgFeAZIBrwHoAfoBHgJTAqECHgO8A0cE6wSOBSMGtQb+BhQHBQcDB9MGpQZxBiQG8AW2BWgFFAXGBH8EFASiAywDwwJGAqYBDAFsALz/D/9q/uT9Wv3Q/Gz8Hfzq+9/72fvb+937x/u8+7f7z/vq+/j7G/wr/D/8f/zQ/D39qP37/S7+Tf5x/rP+Af8y/3j/kP+U/5L/ov+d/5v/jP9b/zD/7f6o/kD+6P2P/UL9Gf32/Or82/zZ/Nb85Pzk/M38tfy+/OL8Gf1u/cz9NP6f/vT+S/+Z//L/ZwDOADIBhwHtAWUC5AJHA58D7gMTBEQERQQ+BFQEhwSbBMME4QTpBP4E5wTYBMMEoAR4BEQEEQTTA60DeAMwA+QCswJxAi4C+AGvAWkBIAHQAIsAWgAyAP3/4v/Q/7H/if9s/z3/8v6X/iL+x/2A/U39Mv0g/Sn9T/2Q/cH9//0g/jb+Yf6A/pv+qv6q/sP+4P4B/zL/Uv+F/6L/n/+o/47/aP8+/xb/1f6v/oT+Vv47/jb+QP4//hX+1/2d/W79SP0t/QX92fy5/KH8rvzA/O38J/18/eL9VP7S/jX/m//5/zsAcgCcAJwAoQCYAJEAkgCdAK8A0AD0AAUBLQFNAXIBkAGZAaMBnwHMAekBGAJoAoACuALcAuICCgMYA0YDYwNdA4EDnQOFA2QDTQMqAwYDzAJ5AiIC2wHHAa0BeQE0AeoApQBeADYACQDT/7H/Tf8V/+T+uP6Z/nX+VP4V/sn9i/1q/Wf9Sv09/Wn9fv2o/dX9Cv5U/of+of6q/sX+5/4l/1r/gv+o/9z/CQApADoAKQD9/8H/if9m/0v/I/8c/wj/7/7p/rz+jv5T/jb+Bv7y/e/9xf21/a39o/2d/a/9x/3v/RH+NP5T/mz+pv7b/iH/Xf+U/9r/JwBwALUA+gA4AYkBvAHiARcCOwJRAnMCkAKxAtcC9gIeA0kDYQN3A3wDaANSA0YDVANAAywDAwPXAqECegIyAuQBkgE7AeYAnQBaABIA9P/3/xsAIgAnAAkA4v+6/3X/Kv/n/qv+Wv4r/hH+DP4R/gP+DP4T/vv9+/30/fr9Dv4r/lr+ev6N/q/+uP68/uX+HP9U/3j/jv+p/7z/yP/i/+L/w/+o/4X/gP+A/6L/n/+b/7H/kP+D/2P/MP8R//b+xf6X/nr+gv6m/tX+//4P/yf/Uv+A/4z/qf/K//f/MABRAG4AjQDDAPYAFQFEAVYBZQFwAV8BXwFpAVgBagFzAXcBjwGQAZgBnQGGAXIBagFfAUYBLQEgATIBOwFWAV8BZwFeAUsBMgEVAQUB8QDtAOIAzgC5ALkArACcAJEAeQBnAEoASAA/ACAA7f/e/73/xf/O/7j/jP9J/yD/BP/n/tD+zv6b/oT+gv6Z/qj+2/74/hj/X/9j/3P/ZP9s/3j/h/+F/2j/Vv85/yD/CP/2/g////7g/u3+8v7//hP/Ef8R//v+yP62/qv+n/7I/uT+Ef87/2P/kv+4/+3/AAAHAAMA9//j/+v/BwAeAEEATwBgAGsAYwB3AH0AcgB3AH4AcgBwAIQAawBgAEgAPwA2AD8ASgA6ADgAMgBGAE8AXgBpAHsAoQC6AOIA7QDkAMkAwwDCAKgApQCjALEAuQCvALoAugC+AMcAuQChAIkAfQBuAGsAVwBOAFcAPwA9ACYA9v/a/8r/lf+Q/47/jv+k/7j/1//n//n/7v/u/+v/5//p/9n/vf+Z/5n/jP+F/4f/g/+F/4X/cf9h/1//Vv9C/zv/R/87/zX/RP87/zT/Kf8c/xX/Fv8h/xH/D/8G/yD/Sf9S/3P/cf+D/7H/yv/w/wMAAQAFAP3////3/+f/AQAQABkAQQBPAGkAiQCuAMwA8wD9ABABGwEcATYBQQEyASQBHgEQAQ4BFQETAREBCgH8APEAyQCjAGEAOgAeAAkA+f/a/87/wf/O/+P/6//g/9f/w/+8/7r/q/+b/6L/lP+S/7j/zP/n//v/9v/r/9P/1/+4/7T/uP+k/7T/qP+f/6D/lf+4/8H/tv/l/w4AIAA9AFgAbgB5AHIAZwA/ABAAEgAQAP//1/+2/6j/pP+0/5v/l/+Q/1//Yf9E/0b/Qv8y/zn/Pv89/yP/If8j/wP/+f4E/wz/Of9a/27/ev+L/6L/tP+8/7j/0//i//b/BQAyAGcAjwClALMAuQClAKwAsQCoAKUArwCuALwA3QDgANQAtQChAH0AgACEAF4ATAA2ACsAIAAXAAEA9//w/+v/4//Q/7r/ov+x/63/vP/I/+L/AwAgADoASgBwAHsAiwCfAK8AswC6AK4AnwCdAIQAfQB7AHAAewB9AG4ATwBEAEYAOgAtAB4AEwABACAAGQAiADAAOwA2ACYAEgDr/8b/tv/B/7L/tP+x/7r/w//B/6//r/+p/4P/df9R/0n/Uf9S/2T/ZP9S/1T/WP9b/2b/c/9x/3P/cf94/2H/T/9N/1r/bv9o/27/ev+Q/6//0P/e/+D/0f/w/wMABQA0ADAAOABDAEoAPwA6ADAAGQAKAAcADAAJABAAGQAeACIALwA0AEEARgA7AEgAYQCAAIsAnQCvAMMAwACqAJgAnQCfAJEAmACEAHIAbABYAEYAQwBGAD8ARAAbAAUAEAD///3/7f/g/9H/w/+//7r/uv/B/+f/9P8VACcALQBGAFcAbABuAHsAgABsAFwATgA/ADIAIgAiACQAJgAyACIACgD//wkABwDn/8H/vf/B/8X/wf+x/6T/jv+D/4X/jP96/1L/Uf9b/03/av9f/1//av9j/3P/Xf9q/4f/lP+m/9D/1//V/9r/0f/M/9f/1f/a/9r/yv/t/wUAEgAgAB0AHQAkABUABQD5/+f/5//i/+f/4v/n//n/9P/7//v/6f/g/+D/2f/e/+D/yv/V/87/yv/V/+X/9v/3/xMAHgAyADsAPQA0ABMAGQAKAAcA+f/n/9f/5f/2/wMAGQAmAC8AIAA2AE4ARgBGAEwASgBIAEEAQwBKAFoAYwBVAFgAXABpAIAAdwB+AIQAcgBuAHQAXgBaAIAAgABrAFoAOgAwACAADAAMAAUABQD9/woAEgAgACQAHgASAP//CgD3/+X/yv+4/73/rf+0/7H/w//a/+X/6//l/9X/0P/F/7//vP+v/6//sv+y/7H/lf9+/4f/lP+J/3X/df9q/3j/kv+Z/6L/rf+r/8X/2f/g/97/0P/i/7z/yP/R/9D/5//u////AwD//wUA9//5/wkAEwAtAC0ANgA9ADIARgA7ACsAIgASAAMAAwADAPn/9v/9/woABwD7//T/9v/2/wcA9//0//T/6//r/+X/6//j//L/7v/5//v/AwD//wEACgD0/xIAGQAeADIAMAA7ADYAOAAyACAAEAAKAB4AJAAnADoAOwBIAEYANgBMAFgAUwBeAGcAaQB9AIYAjQByAEwAUwBDABsAAQDe/8r/uv+2/7b/uP/G/6n/tv+p/7L/yP/K/9z/5//u//L///8MABMAFwAbABcAHQAdAB0AFQAQACcAIAABAAwADAD0/+n/0f/O/9P/4P/p/+7/9P/2//D/AwD///v//f/i/+L/8v/3//n/9P/i/9D/2f/e/97/4P/c//b/+f///xAAFwAZABsAEgABAO7/3v/a/8b/1//e/+P/5//u//L/9/8QAA4ADgAFAPv/7v/t/+L/7f8DAAkA/f/j/+n/8v/y//b/4P/i//3/7f/u/+P/3P/T/+L/8v///wwAHQAiABUAEwAHAAMABQAbAB4AHQAXACAAIgATABIAAADr/97/1f/F/9H/4P/7/wMAAADw//D/6f/Z/9H/yP+6/7//0P/i//L/9/8BAAUAEAAZAAMA9P/5//f/JABBAEYATwBRAFoAUwBwAHQAZQBYAEMAKwAZABcAEwADAPv/+f/i//D/9P/u/+X/0f/T/87/v/+6/8P/xv/a/+D/3P/t/wAAFQAQABIAJAAiACsAOAAwAEQAVQBOAFUAPQA6ADsAKwAgABkAHQAdAA4A6f/Q/87/uv+6/7L/tP/D/9n/6f/w//T/3P/R/9D/wf+//6//m/+Z/5n/sf+6/7//3P/j//f/9//0//f/7f/r///////9//v/9//7/wEAHgAdABsAJwAkACkAJAAeABsAHgA2AEQARgA9ADAAJwBDAEoAOABEAEgAOAAyACsAFwAbAAcAAQD0/+D/4//5/wUACQAOAPn//f/2/+P/4P/a/9H/zP/K/8z/w//M/9D/zv/e/9z/zP/K/9P/4P/n/9n/7f8AABMAHQAeACIAHQAdABUACQAAAAUACQAQAB4AIAAdAAkAAQADAAkADgAJAP3/8v/n/9H/yv/X/87/sf+p/6//pP+b/5v/qf+v/73/xv/Z/wAAAwAFAAcA/f8VACQAJgAnABUADAAKAAwA7v/l/9P/1f/c/9z/8P///yYAJABBAE4ASAA9ADYAQwA4AEEARAA9AEgATABcAFUAUwBhAGUAYABRAEoAPQApAB4AFQAFAP3////5/wEA///7//f/8v/u/9r/4P/c/9z/6f/w/wUA7f/r//T/2v/O/8j/xf/T/+P/9P/9/xsAHQAXACQAIAASAPn/5//Z/9r/5f/0/wMA9P/w//T/BwAXAAMA8v/t/+X/7v8FAAUAAAD5//3/AwD//wEA+//n/+7/+//5//n/7f/9//n/BQADAPv/AQDw//v/9P/p/+v/6//i/9f/1//V/9H/zP/M/87/2v/B/7z/uP+8/7j/vP/B/6//tv+8/7T/wf/R/9D/v/+t/7L/xv/O/+D/5f/w//n/BQAQAB0ALwAnACQARgBMAFEAUQA/AEwAMgAyADIAPwBOAFMAWAA9AC8AJAAkABsAJgAVABIADgAJAAMA///w//T/CQD5//b/9P/5/+D/1//e/9n/0/+//63/sf+9/8H/0P/Z/+X/+/8FABcAEAASACcALQA2ADYATABaAHAAcgByAGsAYQBKACsAFQATABcABwD3////6//M/9X/1f/c/+3/6f/c/9H/zv/Z/9P/0P/e/9f/3v/r//b/AQAOABMAJAApACcAJgASAAoABwAFAAkAEAAKAAcACQAFAP//9//l/8j/xv+9/8r/3v/y//T/6f/7/wUAEwAeABUAEgAZABMAAwAOAAUADAAHAPb/7v/r/+n/5f/j/9f/3P/e/+D/5//e/9r/3v/r/9X/v/+8/8X/v//B/9D/w//R/+L/7v///+f/1f/Q/9H/4//g/+D/+//7/wAAAwADAPv/9////+7/9P/2//L/+f/2/wMAEAAdACQALwApACcALQArACQAJgAbAA4AEwAbACcAOAA9AEMAOgAnACsALQApAC8AHQAdADIAKQAmACIAJwAyACkAMAAkAC0AKwAkACAAEwABAAAAEgD2/+7/6//X/9z/1//n/+D/2f/T/8z/1//Z/+X/2v/T/+L/4//t//n/+/8MABsAEgAXACAAJAAnACkAMAArABsAGwAQAAUABwABAAEA9v/5/wEAAwAgABMAJwA2ADsAOAAnACQAFwADAPD/5//V/8P/wf/K/8b/w/+4/7z/xv/D/8z/xv+8/7j/uP+//8r/4//g/9H/1f/T/9P/v/+y/6b/vP/X/9P/5f/n/+L/8P/g/9r/3P/j/+D/4//u//T/AAAHABAABQD9//b/8v/l/+n/9v/7/wMACQAdABkAHQAiABMADAAQAAkAEAAbAAkACQAJAAUABwAJAAwAFwAkACkAHQAXABkADgAMAP//7v/0////+/8BAAEA/f///xUAIAAdABUADgAKAAwAHQAZAB4AKQAvADAAQQA4AD8ASABOAFgATwBYAFgAUQBRAFEARAA9ADIAJgAeAB0AFQADAPL/5//T/8P/vf/G/87/yv/G/9H/4P/j/+n/8P/7//f/BwABAO7/AADw/+7/9//9/wAAAwAMAAkAAADu/+D/zv/O/8b/vf+8/8j/zP/D/7//sf+t/6j/sv+y/73/xf/R/+f/8v/9/wwAFQAZACIAFwAiACsAJgArADQAOwBEAEwAOwA6ADYAJwAvAC0AHgAKAAMA+f/e/9X/0//e/87/xf/M/8P/vf+6/7T/r/+o/6b/tv+//8b/wf/I/9f/5f8AAAcACgABAAMAAwAMABcAHQAXAAwABQAKAAUA+/8MABMAHgAwADYAJAAwACsALQA4ADsAOgAnADQAQwBRAEEANAAyAC0AIgAFAPD/+//y/+X/9//7//v/DAAHABIAEwAFAA4AAAD5/wcABwAOAAcACgAJAAEAEAAMABAAEAASAAcABwAKAA4AGQAKAAEA/f/y/+3/9P/j/9r/5//g/97/5//w//L/7v////T/9//p/9z/0//e/+X/1//a/+L/8P/n/+X/3v/M/8P/tv+//8H/uP+9/87/3P/p/+n/8P8AAAEACgASABIAFQAdAAwABQD5//b/CgATAAMA+//p/+f/7f/j/9z/6/////f/EwAnADAANAAvAC8AJAAnABMACgAAAOv/9P/n/+n/8v8BAAwABwAHAAcABwAFAPv/7f/y//D/5f/u//b/9P/0//n/7f/0/wEA//8JAPn/DgAQAPb/BQD5//T/7v/n/+n/4P/j/+n/5//2/+v/0//e/+P/4P/l/97/5f///xIAFwAZAAoAEwAtADsANAAtACkAMABGAEQASABGADIAMAAwACQAJAAeAB4AEgADAAUACQABAPb/9v/y////9v/r/+f/9//9//v/DAAOABUAEgAJAAkAEAD7/+7/+f/w//D/+/8HAAAA+f/3//T/6//t//L/6f/p//b/7f/p//b/+//5/+n/1f/a/+P/3v/j/+v/9v8BAPn///8KAPn/AQADAAEAEAAVACsAKwA2AC0AGwAbAA4AFQAKAP3/7f/g/9f/zv/Q/7z/uv+0/6//rf+6/7j/tP+//9X/1//g/+3/5f/l/+f/AQAKABAACgATABkAEAAQABcAJgApACIAIgAiACIALQAmACIAIAArAB0ACQASAAwAHQAwACcAJgAXABIADAD9//D/6f/j/+f/5//j/+X/4v/r/+7/7f/u/+v/4v/u/+3/7v/0//n/AwAFABcAHgAiACsAMgA0ADgAMgA/ADYAOwBEAD8AOAA6AEQALwAtACQAEAD///T/5f/R/9X/0f/c/9P/4v/j/97/5f/V/+3/9v/r//D/9//u//b/+f/5/wEA9v/9/wUAEAAdABcAEwAbABMABQAFAAAAAwD7/+v/7f/r//L/6f/j/+3/7v/5//b/7v/t/9X/4P/i/9X/5f/r//L/7v/3/+7/2f/j/9H/1//T/9D/0P/c/+f/3v/Z/9X/3v/n//b/+f/t/+v/9v/7//b/9P8BAAcAAQD5////CQABAAEA//8AAAoABwADAAkADgAOABMAEwADAAEAAwAKABMAFQATABUAEwATABMAEgAOAPv/8P/y//n/9/8BAAMA///y/+7/AAABAAUAGwAkABMAIAAgACAALQAtACQAKQAmAC0ALwASAAcABQAJAAMA5//X/9z/xf/F/8X/w//O/8z/2f/R/97/2v/K/9r/3P/l/+D/3P/e/+L/3v/R/9X/xv/Q/9r/4//t////CQAKAAUAFQAVAA4ACgD//wwAEAASABAABwD9/wcACgAHAAkABwATABMAFQAXABUAJAArACkAJAAnACYAFQAZACAAHQAnADQAKwA6ADQAMAA2AEgASgA7ACkAJAAVAPn/9v/t/+P/4P/g/+X/4P/V/9P/yP+//7//vf+6/7//zP/V/+X/8v8BAAMAAQAFAAcACgAKABMADgAVABMAEAAdABsAEwAVAA4A8v/y//L//f/7/wAABwD//w4ADAAOABIACgAQABsADgD3/wUACQAJAAAA+f/0//L/9v/y//T/9/////T/7f/t/+3/7f/j/+n/2v/K/8H/zP/T/9H/1f/O/+n/8P/p/+v/3v/i/+f/6//y/+L/2f/r/9z/3P/T/8P/vf+9/8P/uv/B/73/tP+//7//0f/X/+L/9P8BAAkAEwAgACQAJgAwACsAJgArAC0AOABGAE8ASgBIADoAQwA/ADsANAAZABMAFwAHAP//8v/r//T/6f/y//n/CgAQAAUAEgATAAoAEgAdAAoAEgAQABIAJwAbAC0ALQAtADAAMAA2AC8ALQAmACIAJwArADgANAArAD8ASAA2ACcAJAAdABMACgAQAAMA7v/u/+X/1f/X/87/0f/O/8z/zP/O/+D/zv/j/+v/8v8AAPL/+f/7//L/6//u//L/7f/w//b/8v/3//f/BQASABAAHgATAAMACQAKAAcA//8DAP//+/8AAPv/AQD5/+v/4v/w//3/+/8BAPb/8v///+7/5f/i/+L/3v/e/97/2f/a/9z/4P/V/9H/1f/a/9P/1//e/+D/5//p//f/7v/2//n/8v/5/wUABwAQABsACgATAA4ACgAHAP//9v/t//L/7v/l/+P/6f/j/+n/5f/c/+v/9P/0/////////wEAFQAZABkAFwAZABcAEAAZAAoACgAHAAcABQAJAP3/AwAKABIAHgAVACQAJgAgACAAJgAmACIAHQATABAAFwAdABkAFwAXAAwAAwABAAMABQD7//L/9P/0/+3/4P/g/+L/3v/w/+L/0P/Z/8j/xv/K/9P/1//g/+7/9v/3//f////7/wUABwAFAAUAEAATAA4ADAAHAAUAAQAHAAMABwAHAAoAEAAFAAAAAQD///n/9//p//L/9P/r/+7/9////wcAEgAJABIAGQAZACcALQAmAB4AJAAdAAwABwAHAAwA8v/r/+7/5f/e/9X/0P/G/8H/v//M/8z/yP+//8X/yv/Q/+L/4v/r/+f/9v/9/wMAEwAgACQAIgAeACIAFQAHABUAHQAeACIAJgASABkAFQASAAwADAABAPD/9//2//v/5//0/wAAAQD///L/6//7//n/7v/5//n/9/8AAAAABQAKAAoAEgAKAAoAFQAXABcADgAVABsAGwAmACQAJwApADIAKQApACsAKwAyACsAJAAnACQAFwAZAAoA//////v/9P/t//v/9//9/w4ACgAOAAwAEAAMABMADgAFAAMABQAQAP3/+f////n/9//0//D/5f/a/9z/4//e/9z/2f/Z/9z/2v/X/+X/6//r/+v/2f/Q/9D/zP/R/9r/zv/M/9r/2v/V/87/vf/F/8z/w//K/9H/2v/Q/+D/6f/p//f/9P/5//b/+f8AAP3///8AABAADAAMABcAKQAyADIANAAvAC8AMAAnACAAKQAmABkAFwAHAAkABQABAAkAAAATAAoAAQAAAPT/9P/g/+L/3P/R/9n/3v/X/+P/5f/c/+f/8P/n/+P/6f/5//3/9v/u/+n/7f/0/wEAAQDy/+7/6f/5/wEA+f8BABAAFQAVAB0AHQAeABkAHQAbABIAEgAQAAMAAQAMAA4ADAD5//f/9P/5//3/AAAHAAcAFwAdABUAFQAbAAoABwAQABMAGwAeAB4AGQAXABcAGwATAB4AIgAkACYAJgAmACAAGQATABIAAwD3//f/9//r/+L/3P/i//D/5//r/+f/4P/p/+D/4v/r/+3/9v/t//b/9v/3//3/9v8BAPv/+//3//T/8P/t/+f/0f/c/9z/2v/e/+P/4P/g/+X/8P/u/+f/5f/r//f/7v/3//3/+f/3//b/+f/9//T/9P/7//n////5//D/8v/0/wAA//8JABIACgAMAAwABwAKABAADAAXAAoADgAZABkAHgAiAB0AJwAkABcAEwASABMADAAJAAMABQAAAP//9v/5//n/9//r/97/4v/a/9z/3v/l/+P/4//t//L/7v/2/wMA+//9/wMAAQAFABMAFwAeAB4AJAAvAC0AMAAmACkAJgAnAC0AMgAwAC8AMgAkAB4AFwAKAAcACgADAP3/+//0//v/8v/r/+7/9P/5//b/9//7/wcABwAFAAcABwABAP//AQDt/97/3P/Z/9r/1f/V/87/yv/Q/9D/xf/F/8P/vf/D/8X/v/+4/8X/v//K/9H/2f/n/+3/8v/0//3/BwAFAAoADAAKABAACQAOABIABwAOAAcAAQAHAAMA/////wAA////////AwABAPv//f///wUAEgAQAAkAAwAFAAMA9v/3//v/AAD///b/+f8BAAAABwAKAAEADAATAB4AHgAiACkAJAAnACQAIgAkAB4AKwAnACIAJwAbABMACQD9/wMAAwD2/+7/7v/0//L/6f/r/+v/4//n/+f/5//i/+X/8v/w//b/7v/w//L/4v/t/+f/7f/w//D/9//7/wEA//8HAAkABQAJAAkACQAMAAkAEgASAAkAFwAVAA4AFQAXABIABwAHAAUACQAHAP//AQD9//n/8P/3//L/8v/y/+v/8v/3//L/6//0//f/+f8BAAEA+/////3/DAAJAAAACQAKAA4AEAAXABUAHQAVABIACgASABIABQABAAAA9v/t/+v/4P/p/+f/5f/r//b/8v/u//b/+f/2//T/+f/3//f//f/2/wAADAAAAAUAAwAHAAMADAAOABcAFwAeAC0AJAArACcAJwAeAB0AFQAOAAoAEAAFAPn/9//u//D/6f/g/9f/3v/i/+L/4v/i/9f/4v/i/9n/3P/j/+n/6//t/+v/7v/u//b/9/////v/7v/u//L/6f/y//L/7f/p/+n/7f/e/+X/4v/g/+D/5//j/+L/6f/i/9z/5f/u/+v/7f/w//f/+//5/wEAAwABAAUAAwAJAAwACgAKAAoABwAHAAAAAwAHAP3/+/8BAP3/+f8BAPv/+//5//b/+//3//n//f8OABAADgAOABIAEwAQAAoADAAXABMAEwAKAAoADAAHAAUAAQAAAP3/+//3//3/AAADAPf/9P/7//n/BQAHAAoAEgAQABcAJgAnACYAKwA2ADsALQArACkAIgAmAB4AGwAOABIAEAAJAA4ABwADAPf//f/7/+v/7f/i/+D/5//e/+D/5//e/+X/5//j/+v/8P/y//b/9P/0/wUADAAQAA4AGQAbAB0AHgAbABcAIAAgABcAGQAXABkAEgASABkAGQAeAB4AHgAeACIAJgAZABcAFQAMAAMA8P/p/97/2v/Q/8z/zv/M/9H/0P/R/8z/0f/M/8X/zv/O/9D/2f/g/+L/4v/l/+f/6f/y/+v/4//p/+n/5f/r/+n/3v/Z/9P/1//a/9r/0//Z/9H/1//e/97/7f/w//n/AAAOABAAGwAbABcAEAATAB0AGQAeABMAFQAQABMAFwATABkAGQAZABcAFQATAA4ACgAKAA4AEgAMAA4ADAAMAA4ADAAOAA4ACQATABAADAATAAoACgAKAAcABQABAAMA+//9/wEA8v/7//v/9P////f/9v/5//b/7f/n/+3/8v/u/+v/5//r//L/+//t/+f/8v/2//f/+f/2/+v/7f/2//b/8v/y//D/+f/5//f/+f/2//v//f///wEABwD//wAAAQAFAAkACQAVABkAHQAVABkAFwASABUAEgATAA4ABQABAP//8v/t//L/4//V/9z/1f/X/9f/0//a/9D/0//R/9H/2f/c/9r/5//n/+7/+////wcA/f8DABIAEgATACYAJwAyADQALwA0ADgAMgAgACkAHgAXABMABwAOAAEA+f/5////9P/p/+f/4v/u//T/+f/w//b/+f/9//T/9v/9////AAD9/wwACgAHAAoAEAAOABkAGwAkACAAIgAtACcALwArACcAKwArACYAGwAeABMACgAKAP3/8P/u/+7/5//t/+v/6//t//D/7v/t/+f/7v/y/+3/9v/3//v//f8BAAUA///5//D/6//n/+L/3P/Z/9r/2v/T/9D/zv/O/9P/1//a/9z/4P/a/9z/2v/g/+L/6//2//f/9v8BAAkAAAAHAAcABQAHABAADgAMABAAEAAOAA4ADAAQABsAEAAJABAADgASAAwACQAOABIADgAKABAACgAOAAkABwAKAAMA/f/9//f/7v/r/+v/7v/l/+n/7v/n//n/+f/0//D/6//p/+P/5//n/9r/1//l/+D/4P/g/+P/6//u/+7/7v/y//L/AAABAPv/+f/9/wEABwAMAAMAEAATABcAIgAnACsAKQApAC0AMgAtAC0ALQArACQAJgAgAB0AFQAFAAUABQD///b/9P/w//b/8v/u//b/9//w/+v/+//3//T/+/8BAAAABwAKABAAGQAKABMAGQAZAB0AGwAnACQAFQAeACQAIAAiACYAHQAXABUAEgAHAP3/+f/2//D/6f/j/97/4P/g/9r/1//c/9D/0P/X/9H/1f/R/+D/5f/p/+v/7f/2//D/9v/5//n//f/3//f/+f/7//v///8AAP3/AAAMAA4ADgAOAAwABwAHAAoAAwAFAAUACgAFAP3///8AAP3/9P/n/+n/5//a/9f/zP/T/9D/0f/G/7r/yv/G/8X/zP/T/9z/4//y//b/+/8DABAAFwAkACsAKwAyAC8ALwArACYAHQAXAAwABwD5/+X/4P/Z/8r/vP+2/7H/ov+d/5//nf+g/53/qf+t/7b/v//F/87/2v/0//b/BQATABsAJwArAC0AKQAyADYAPwA7AEMARABDAEoAQQBKAEwAQQA9AEQASABPAFMAUwBYAEwAUwBhAGUAdQB1AHcAhAB7AHAAaQBaAFgAVQBDADsANAAnABIAAQD7//L/5//V/8r/vP+o/6T/m/+F/4X/gv98/3f/d/9s/1r/Y/9b/1r/Uv9R/13/bv98/3r/gP+O/5n/qP+v/7b/wf/T/97/5f/n/+n/8P/2//b/7f/l/+n/5f/c/+D/3P/e/9D/w/+//8H/yv/K/9n/2f/j//T///8SACYANgBGAFUAYwByAH4AlgChALEAwwDWAN0A8QD/AAcBEwEcAS0BMAEuATIBOAE0ATkBKwEeARUBAQHkAN8AugCWAIgAXgBEACYAFQABAOL/xf/B/6D/g/93/1v/Uv87/zf/Lv8a/xX/A/8M/wT/+f7w/uv++f4D/wP/+P4G/w//Ff8h/yz/Kf8u/zX/Pv8+/0n/Sf8+/zT/If8Y/w3/CP/5/uv+4v7O/rX+ov6U/oT+cf5o/k/+Rv5C/j3+Qv4//kv+Xf5w/oD+qP7K/gH/N/9v/6j/6f8pAGUAvgAKAVgBqgEFAlYCswIYA2gDxgMYBHwEzQQRBV0FnAXlBRkGOgZUBlcGVQZJBjMGDwbsBbYFcwUmBcgEbQQABJUDIQOZAhEChwEHAX4AAwCA///+i/4X/p/9Of3k/ID8Qfzt+6r7e/s9+x37/Pru+uP67vr4+v76Evso+zz7Uftw+4j7p/u++8v73/vq++376Pvq+/P77fvt++z75vvt+/b79fsH/Bv8K/w9/Er8Xvxn/IT8mvy6/Nf88fwl/UH9bP2j/dv9J/5w/s7+MP+o/zgA1AB1ARwCyAJ+A0AEAAW5BXgGIQfuB6UIQwnbCVEKwwoeC1gLdwtxC1oLMAvOCl4K0gktCXIIqgfdBvsFJQVABF8DhAKxAeAAEwBR/47+6f1L/cf8Xvzx+6X7afsk+wv78/ru+gD7C/sp+0z7f/uu+/H7Nvx5/Mf8Bf1N/YT9tf30/SX+Sf5q/oD+i/6F/n7+aP5I/iz+9v26/WP9EP26/GP8B/yw+1n79fqj+lX6Dvrc+af5e/lt+VL5UPld+W35l/nE+QD6NvqI+uH6Pfuz+yT8lPwX/Z/9Kf6+/lT/6/+fAE0BDgLiAqADfwRrBUoGKwcZCP8I1gmkCmwLJwzBDEcNpw3wDSgOKg4dDuENmQ0oDYwM4gsMCx4KEgkBCMgGjgVdBCcD9gHQAK3/nf6j/bH80Pv++kr6rvk4+dP4nPh8+Ff4VPht+JP4u/j9+E75oPn6+Vn6u/og+4L76vtI/Jz84vwi/Uv9af2E/ZT9lv2P/YD9Vf0k/ej8i/w6/Or7kfsz+9b6b/oh+uX5pfmB+WL5VPlJ+Wn5iPmg+dP5Gfpr+qf6F/uC+/r7gPwI/Xz9CP6d/jX/9P+xAJABcAJLAzsELgUkBi8HPAg0CSQKEwv7C94Mtg1fDuMOSg+sD/UPEhAXENEPbw/3DlkOiQ2iDKYLggpNCeYHjwY3BfQDpAJRARAAw/6H/Xv8j/vE+gn6W/nR+F/4Evje9733xvfh9wL4MPhm+ML4L/ma+Q36afrh+kX7mPvm+0P8iPzZ/Bf9Iv0//UL9Of0b/eT8lPxE/PX7ivst+7n6Wfr6+Yz5EPmX+Ev4Avi49373VvdQ90n3O/c591T3c/e99xT4aPjN+EP53/mT+jH71vuL/E/9Wv51/4gAtwH5AlIEnAXaBiQIeQnHChoMKg08DlIPMRAMEbURLxJpEpISixJNEg4SnBEMEUUQRQ8vDvsMlAsQCnsIBwegBSMEqwJVAQUAs/6A/Wv8i/vR+iL6evkC+Zn4YPhk+G34dviO+Nr4JPmR+ff5YPrW+kj7rvvx+zT8bPyv/PP8/PwX/fj8wvyL/EH8/PuW+0X7x/pO+tP5Xfnc+F/49feD9xr3v/aW9lT2MvYc9hH2A/Yh9kn2ePbF9g33Uve290D4pPhJ+QT6ofpF+9j7mvxy/Y7+tv/WAB4CUQOdBPIFZAffCFgK3gsdDSoOPA9TEH8RfhITE3sTlBOhE6ETeRMcE4sSsxGuEKEPXQ4cDcELPQqfCOEGHwWMAxECrABb/xH+5Pyj+576y/kL+X74KPje97r3kveb99f3Gvh0+K34JPl4+cv5QfqK+tT6JvuG+5T72PsH/PP76PvL+4r7QfsJ+8D6b/oF+pr5LPnV+Hb4I/i690337vaU9kn2Dfb59df1v/Wt9cP18vU+9qn2DfdN99X3UPj3+I/5Kvrs+sD7HP1z/sj/PwGCAg0EsAUtB+wIggosDLYNFg9eEH8RvhK1E3wUyhT4FAgV7hTMFIUU2xP6EgcSrhBZD/kNagzZCiYJeAeXBcUDNgKcADL/uv1y/Eb7GPoo+XP4zvdP9w333Pbg9gH3Uven9/v3OviV+Pn4ffkA+mn6G/tu+8f7QfxY/In8s/zF/MP8iPxG/A78x/uU+zH7o/o++qn5Jvmt+Ab4ovcR95L2Ifbp9aL1UfVI9Ub1IPUK9Xv1jvXm9V322vab9xz4zfia+c369ftE/b7+CgCQAV8DLgXPBjYIsQlsC+IMLg5XD1cQHhHkEcMS4BL8EiIT1RKjEhcSMRFpECMP9A2iDOkKeQm7B0cG5wQABO8CPQE9ANf++v3U/Z39MP2d/JH89Pwt/cX8OvxJ/pIBDwLn/3v9E/3g/ev+l/6A/a39D/53/Sn8Lfs++qn5zPjK96T3pfhZ+tv6tvmJ9o/0vPQu9TL1dPPP8c/wZPAu8Bjw2O987snsG+uN6mPs9+6P8N/wpfBX8EzxCPSA9gb3nvd6/gMKFRNtFVURzw4QEkUa+x+lH9Qg5CSDKKIonyVRIwwkyyPsHhkYIBQ0FPQVURRiDrEIrwTVAT/94vXa8Kvw1vAC7pznX+Ot5BjnOOi75Q7i1OFZ5rPrwe/o8yj3H/sM/2UB+wMgCKgM2A8XEfYRAxQfFsEWExXvEqwQZg/yDWEKnAcqBiMDuv/U+ov1xPIq8IfsdOh65TbiSd9W3jzedt5v3WTcS9qP2cXbTN/V4grl4eZ96N/psey38Wz0W/ZW9bT44xDDLpE8GzXgHlAXxinhPOVBKz+vPpZGzUtFQtk2ZDEcLmIoshlYCgsHewo5CloDfvU55pDb5dOc0NDPYM/LzUnLwck0yiHNjdGe1oTaGt3W4Nbo0/XKA5wOnRP3FWca9x8sJsksRzAbMvIxTS6PKvcpOShpIjMYVgzVBAMBaf1E9zrvI+db4PzZT9QX0WPQbtAbzjfLhMkpyobOX9J41b/WCdiz2TreCOQ96p3uDvOm9TP4gvzy/wwD5QNCBRUUyzJYSjhOezs9Jhsq0z/ISfBJu0WYRx5QnkwAO7Eu0yfRIdYW/QO4+CP4kfnp967wtN8y0IHEt76JwL/Eo8QcyLLMls8G0kfUgNri5ZXwbvaW/KAHgRaSJaYuFDIrM3YyKjP6NNU3AjkMNY0rBSEdGTQRTQn3/zv18+o44p3bcNfu1K7Qu8xGyGfGd8c0yLjJrctIzz7U6tin3THgY+OC5ALnJ+sv8IX1cflp/Xr/mQQHBi8G0QUYBn4SZDBjSb5NXD88KNUoVTwXRr5H2kKLQKhFxUHqMuUoKyLpFq8LU/qA7HTo2ucf6CjnAN06zW/Brbs9vqXDSsUczPnVEdzA4NrlTeyi96QDFwiyEHccYyYbM9I7/j3WPPs4SjVeNEAwtCnxIaoZZxJXCAj9U/GM5sjb/9PLz6rO5c0PzNDKIco5zNvOCtHQ0pnUFdkv363mbOtd7vzwkvP+87Xx4/DP8tb5cv21/p/8mP3c/u8A6/6zCGYpDUNNUIs/syTQIuA3gEegTYdHgEGaRRZCFTXlKKYdchD5BZr04edP4zThKeK14uPU68K4t6q0obywxY7JV9BY24ThAufI7if3QwVGEkUaCSO3LCY01T5aRyBHnkIuOUkxFy1EJ9ce1RQDCLL6KO495LLd9daKzurHIsWNxdDGPcmry/POv9PL1xHbRN1/4ZXnkfCV+TD9+Pwx+0z73flS+Zv2H/gL+OX6Qfo1+Sf1xvAi7Nn2kSKSQm9UM0HYH5IcdDIGQEhHeETVQOFLpkY0N2UtdCIhFdQHI/A/4NLbvNsB4d7mP9pYyOe74LYwv2zIpc1O1z7lsum67j31fv95EdMcRSEcKH8wOTfgQqFHp0VsQNgzvymvJKgcUxP1Bwz7/+784pvZ+9FWzC/HFsMawt7C28XOyyrTzNkG3vHfMeHp5IDqOvDu+D7/KgOpA1QDkgDm/er4yvbQ9dP4GPr8+eb1ie6G58nsSxS3Oh9UNki1JfUX0yeUN29C+UTBQLVHUEJSMoUnCR+dEfkE2u+O3ePVDtJ/12nieN8B0dXCiLtJwkzNYtSu3sPs9PXU+8MAdghFFk4iHyomMQw3TDqqQFFF/EM4POstSyBMGHgPcwSn+JntYeM92PrPfsn3xC7BAL57vofCF8jNz6jZquFN5I3kvuTL6v7z6vv0AxkIwAlyB2IFvwLe/+j98frV+Jf3IffL9P/zW+7E6v0BTiliS4BT5TexG88hwzBwPMc+Jzc6O9pBZTdXKgAf4Q81BKzxQtwB0TzLI8282LbeY9q60AfIn8my09TadeK07Qb5/wOFC08SyhxxKakypDeiOL83djk6Pbg+Hzn0LSMgehTCCbX9XfD74zDZANBsyyLHAMSdwHS96b8CxLHIqtDK2nbmtO+X8ADwuvMz+ZIDpglhC58J1gbFA+kBb/8z++n3n/O88lvwIe9n6+7kFuhHB7YvTk+mUDcv3hy8JOEylkDtPoI01TmZOAUtMiWQFvYIZf5b6F3WVMziwm7IzdbG3MHdkdXuzMzShds64eTpRvTT/8cLzBVZHxMt6TalOkw7DzokNjQ0mjMLMv8vKyWBF84Jd/tx7UjgX9WpzX/HKMJdwc7AUsGvwWrCLMlG0v3c+eg68UrzT/Rq9cT7owcuDNQOtAwMCN4EiQBi++f4l/f18sTyZvBS7ZrorOFJ5UAGdjOJU8BWjzaMIKgmhDRPQOJAUTcaOr86Di34IXITfAOz9hPkvtHYxoq94b2iyk7WINvj1onRJdUh30DnSe/8+oYIsxOCHeklmzDEPIVC70QnRmpAzziBMBgpASVPHDEOSACc8kXmytqRzvXGob/Rue6407r0wAfGQspH1KLfEunI8BLzgvQ6+OH78AUDEnkU5g9HBugA9v7A+nP0z/BO75ft4OuF5yTnquIw3SHtehfxQxZb7ksHLOMoLzV6QPdE6T54O3k8STHjIvgbzw85AW/u0ttFzq/CxrmxvkzO/Njp2dHUPNWg3IDkc+yD+fEIExQuHbomvS/vN/473T9OReNEczkwLhwntiDCGFsMXf+688/ndthvzfrGrcGUvlq+x8Eix5DLA9D12Kjhz+lL7gfxAfbj+XkAowlgEWMTeg1WBFz+f/tz9mPzafAK7t3rwOhm5UTjwt8w3S73ZyNHTLRbYES9KK0mpTFKPT9EjT/gPsw54SvSISIYNgnG91vldNYlzLq+QrcpwCfQ/dui3sre/+KN6Ynt1vFB/RwLSxXBHu8qojjPRCdKjkg+RME3/ydsHEQVEhJXDoYIhwGC9ivjRNCSw0W8RrnluDa9dMbEzgHTA9oa47rqY+4Y7srv+vM6+eUCuA0TFkkUnQoMAnX9EfhK8gruQeth7L/sEe0b63Pm2t508jMgHEpLXG9DpCSfJcQzXDzbPUoy7S6AL14hwBeAE6gIwvy16vPXiMwVv9u2/L9A0oDjSOov6ljuWfK39Dv2RP0TDGAXKCHsK5E0Vz0IQV4+nT5NOOQqQSCoFKcNQgpAAuz6F/KC4sPUWcycxuzETMVXxhjNfNKp02vYxuBp6gDygPQ79GX1r/WZ+b8C5QyxEa8MBAXu+v7z9+4I7YfuKPHm8bzslOr44c7jhAKrL01UkFpHOzsjRixWNsw+bzgpK6wsNyjPGNQP0AcI/kPzVN4m0LXHSbvrt7i/j9F843DpNuxI8u74A/0r/lECrg8QG5slTjF+OdNCh0L+PM43mS4pJKoZQA8HCU4Et/oB9EXpHN3506vMW8r2xxbGf8VFzALWtN/05o/rse5L7hbvF+0b8Aj1Iv0SCNwNhw3LBcH9wfV19O/z+PTX9HvynO/F6vfoT+Js89Ab5kg1ZO5TfC5VIwkx5Dx+QlguFSTjJ94fGRhMDyMEE/yS7ODULMprvOCxSrVGvxTV5ORT6OXt4fe8AIoF4wQXB0gRjxqJJ6My+j2+SJ9Iq0QSOuwpzRpSDkIFuAEO/DL1bfBE5srcxdDdxjnCcMDpwk/JLNNh3Czk/Of27Ffxn/T39tL18vaK+HD+IwU/CXMKjAYDAjj8Zvjq8ijuU+vX637tSe1F6rLuDBMcP9Rgk18mPM8nGS3iN8c90jXSJOQlTR8BFB8P6wEu94/rWNyU0obHVLN3rVi20MqA4mXr4/Iv/N4C3Aa6CewOBxeHHfoidCnuMa49okK3Rb1BPDLvIz4U7AUH/bTwCeft5HHfEdwM2NHOGcm2weq9JsN8zBfYoeOS7LXzKvdU9+X3VPf++Er67PvtAL4BQwBc/gf8efz//KD3I++C607pa+q76BbgfObuDKY/KGb2a71LpDmlOuM5sDnqLM8f4CPEIVUXgxf4Cif/IPPB3jnVZ8X8rTal0K54xFTeS+fV7Tz3jfoOAssGHQ82GEwZ/xxtJ2QyZDx+QE8/LkJbO+8tlR5oDIz/sfSU66nlH+CR17/Sd9A1zl3La8eNxnrKftMr2kjjOey98d72GPhD+AD6kfvQ/O3/cf8V/1P9xvhv+I/5vPt/+a31UO+N6h3puOMT6zYKMTTvVg9j7U4lQCs/AjyLPi018Cb6IhwcnRSNE2UHb/ga7hbg/NlxzCu2Y6wWsHHAcdOr3mLnG/L++VQCFwrjEM0XjxpcItktajnsQQ9C/UDdP+E5WTCRI/8SvQYX+5Hwwunu3kDVQdDczcDN5cu7xufElscbzmTXd+F36zPym/ae9wP2hvMk8frywvrVAU0Dbv+T95L09PWR+Rv8gPyU+2v4ovah8ePvxf03HEg+FFbjVKVFvD5mOyg74DV1J4IbMBT5DeQLsQhb/0r1sugx3j7U7MSzttmwr7gFyX/X8+G86fvwevnQABMJURTXG6MimCpQMlc7iT2eOcg5Jj2hPoM6UStkF9oGuPda7ZDnC+Hi2b7SUM2By6/LEctMzVvT/Nnn3ozglOFH5RrsAfPU+VP9d/yW+1H8Kv/MAXMBDv4b/HL86vvH+kD5kPd+9jf19fLA+rsOCia0OW49vDa1M/UyMTLKMGsoNyCJGtsRLA2qCMMAG/qU87Dv6evt4GPSE8hYx4vP2NfK3SfjBue67nz3sv//CWIRMxiMIT4pJy7VMAgvLC/lL1YtRStmJkkftBYrC/3+kvRY6mfiDd4723fZudW20n7Tq9Y725HfpeIs5J3kY+Wz6RLww/Uf+VP8iQHgBDsELgGa/Q77OvrT+UX6A/uo+9H5avjs9/L0z/osCwseyi1tMDEoPijAK7EsYS7BJgAf/hq6E0UQ8Q8pCT0BhfkH8irvD+f02jvTztKN2JXde+Aw4yHnQO2J87T4zv+HBdIKLBQ0HRMmSiwSLP8rYivXJ3ckYx4sF2oSZgzCBQUBS/kW8PXnEOAq3DDdhd013Vff6uDS48Doj+ux7WTwefJU9rv6Vfyo+y/6DPsg/N/6Yff889TysfTB9mP3UPjM9x72hvOI8dLx4/rpCfcXnCMIJ7kpNzBnNBw13zFmKmki6hkbEaINPQsiCYcFA/9B++D1ce3i5R3g092n3tHe7t/C4g/kUOaC6SzwK/reAX8HygtMEfEY1R/+I/Qk/CJ0IAEe0BqiF98RTwsUBw8Dcf54987uUucZ43fheeHS4l7jmOPZ5G/ooO0z8v7z7fQl9/P4B/rY+Ir3M/dk+IT6cPzy/Wz9U/w2+xf7Fvoj+TD4q/fD98r2Nfh1/pAKPBYhH/0k5SjEKyQrmyf6IfYdShnZFAoSGw9+CwMHTwNfARcC5P6p+BLyieuQ52jkOeNq5GjnBenX6kbuV/K993n8xQG/BQ4IKQksC/MOTBKDFFgU5RTwFP0S8A70CUwFawDk+2r1D/Ah7cPs0+1f7grujewF7Hvs0e6K8ALyRvP/9Gb4jPq8+5z7/PuA/Az9NP1l/eT99P1o/sr+zAAIAYn/OP3o+rL5xvgE+sj+KwjkEF8WGxgoF3QXRxlsGgEbvhk5FMYPHwx9CSwLYQvUCsoLSQuRCUUGYQDJ+wj4E/S78ULvfu8f8Fvvk/Du8bH08vg/+9T81P26/OL96f95AQQF8QeAC5kOtg0gCsgGCwTOAQMAVf02/Pz7G/so+g/55/dW9hn0VfPV9KP0jfT09H71nvhF+Q/3bvb09lv3lfmT+pH7m/6o/rP+SP6a/Ob7d/zU/bj/XgETAbj/Mv5N/mz+6/+SAYkCYAXGBxwL4w29DjwP6g6VDt0PaQ/KDiEPzAwMDCsLYQmACPUFEwP2AXwCEwOJARD9TPmv9vj1gffH+mP9Lf3h+/P5f/s9/R7+bABJAnEEzQX3A7UCXQOkAZIBwQGA//L92/rG98r3Hfny+LD5FvoH++L8G/xX+wz7MvxV/cz9sfya+wn7uflS+vX7Z/2A/jL/IgAkAccAbP8X/rf86vyR/Ar8Ufy++oX5ffnH+kv9oQATAiwD1QQRBQkFHQWSBNEE9wXTBpwIFwsEDZwNPA3dCoAI1gUYA6QBoQDR/xX+nPyv/MP9lv02/e38ifwD/pn/8gF4BKsFRwYmByEH3wYtBsQEjgXEBuUGIwVYAcH9ifwg/Pz7qfos+YP3MvYP9qv2avcP9l/1gPXb9T326/Yj97b4GfvL/KL+Ef/i/ov+3P5J/gb+I/79/nUCTgTpBBwE4AEmACH/VP6h/oz/yP84AW4BJwGJAJ3/MADgABEC5wI5A60CrAGYAHf/JQH9AXUCaAKmANz+n/13/dL+8wD4AK0BYQILBGgFaAUIBfkFPAdrBwwICwjHCHcKrAqmCQoKbgigB2QGnQPfAAz+Wfu2+UP6Jvk1+En4SPoW+i38NvoN+cT7xPcj9kD2APgK/X7/W/95Adv+//76+xT7YPti+Br5u/cd+5T85P7y/eb8WP5P/4IAOQEcAV3+pP4g/KL/kgLvAakD6gB8A/cEiQS8A+QA2wCEAkkCyAJEAe3/BP/p/fYAKQDmADYAAf0n/hkAwAARAn4AmP2N/Q3/9AKFBRkGgwUfBJsDAwPZAicCaAPuBOsEdgVOBZkD8AIRAi0BowG+AP3/r/9Y/6b+7/4D/vH8CP5L/k3+Uf+d/3MB4gGNAMX/l/4c/VP72vqp+mD7FfuI+vf6OP0r/hb/RAA0ASUC6AG4Aq0BzAG3AeP/dAAs/z3+3v58/pv+3P6v/mj+Y/82ACsBLQE/AboAMgHdALH/MgC//kb/fP7k/jT/if5z/1T/fv9q/5IA+AEpA1oDTQNdA0AEfwUPA9QAvv6m/cD81vwB/SD7bP2k/U/+W/8X/Y/9Dv5n/kL/ugCkAXcBRALJAcwB/wDt/3IAOwIzBFsECwT4ARUAWv3p/ov9QfxY/Of5/vtj+2D8Dv1j/5gBogJ8At0B5gHH/qH+Nv74/kb/2/6J/ysBjQLiAYsAxf8/AHcAuv8FACz+8v8kAMH/CgMGAvID8AQ+BXEEwwLZ/2kALQH8AKQBq/+RAOT+Xf+2/3r/6/4X/hH/8v4FAYv+kfx7/XT8LQBPAJv+Qv8G//L/YQFxA/YADACd/VP96/6x/csAP/1l/pL95f+dAEv/vACU/mEBtf02AJwAvgApARH/LP7HAEEBrADyApQAZwH0ADIB9AKQAWwD6QPTAqkEggA3/0n+1PwK/IH65fnH+0r9X/6CAZEAtfwW/wr9D/6jAP3/BARkBBsFmQWyBGgCaAXXAz0DGgO8AQoBsQHbAN7/kgBd/2cB1P2vADsB4v/gAET+dPy8/XT8dPwT/7j97f6PAMcClgFTAGf+6Pvv/Lf8Ff6o/ov+Qv0r+gv6Avkr+i/7f/vv+9n7L/xM/H38s/4MAeIB2QIMA/IDqwSMBrIGoAZ4B5gHZwhgCBYGYwPAAI0Alv4g/aP7yPj7+Fv5Pvi994X5afkS+fP6S/1u/YD89vyH/v//6f/GAzwHqAjSCsEKzgl3CAUK2wlXCPwGkwQuBIkEnwNzAukCXwE4AV8B4v6E/sX9Vf1o/77+If8k/TT7o/3Q/Uj8+PvE+yX9of7x/CP/lP+JAlgALv4//D74mvy5+RT6YPtH+RD68P6U/pYAqP4n/ED+O/25AOv+0P9aAAUCcwEBAJ0BpQDAAHUAGv8vAIAAWv9A////iABOADL9IPzi/nT8NPyT+6H8wvxV/Tn+i/8VAg4C7QPsBV4IPwgQCYcF8gMfBn4EDANfAZL+rP0e/0wA4gAnAqYDcAIcAzADwwIdBREFPAQRAyICwwK8A7sEGAL4Ad8AQQCt/97+yf36/fn/4P8tAHD9rf8BAEEBLAMgAN7+/f5j/Kj9tQBo/qr9WP6b/tP+5f8n/iD8PvqH+Nr5u/lH+U74I/hL+dT7vv3X/A/+uv3K/f/8NPzu+ez5DfrB+FL6sf1a/qH9if5z/Ub/m//u/yX/DAB+ArQGWgk0CjALUQkrCcsI2AdpCN0HzQZ7B+oFigWeBG8EoAPXAlEDugMEA2YC9gMABHEE5wOmAmMCLQDV/eD9Vf1s/Pb7+Pp8/kn//f6ZAeoA/wGNAo8BfQBaAnEDEQGrAmUAA/9h/9D78v7B/p/8tfsw+J74UvhF+B34GPc8+034J/Ze+bv3fvh9/JH8F/uT+/D3wPqw+G72gPZk9wX92f6oARf+r/09/kT+4v5T/t79Pfxl+zz6j/tV/dD/lgKiBtsKpAypDaIO5g9ZES0SgBGeDvsNLAx0CU8IRwfTBh0GGwVsBKkE8gIbAAz+z/sh+bb3qfZ79e32T/dm+X/82/2WASkDXwX8BmcGbQbWBtIHiAjSBzcGmgVWBGYF/wKPAJ//f/o2+jb62vjU+Sb4m/aL9uD2pPTv8TD1VPZF+nL8fvcY+QD8w/2o/rz9IPwD/lz9Wvzq/KP6jPpB+X354Pwl/1X9SvsW+lj3C/fZ9ZLzWPRm9zv8lAHzB/QLHw57D/UPQxKEEywVohShE7oS8RBJD8wM3grsCO4H3QZMBi4F5wOHAVb/zv1P/Mv70vrd+jT7i/zM/rr+Iv7r/a3+gP5o/oL/QP/5/73/pQDMAP/+bv8I/u38S/50+5j68/t/+wP9Sf7P+mD7XP1e+i7+BP8j+T76fvj29R387Pev9Xv5h/Vv+Sz3I/YU+634j/ru9770Zvcl92j3B/re+Nr5OvvJ+YX5J/ds9xX2RvWk+Er8ugHxBkkLZA+kFOoZVh1XH0sf0R02HH0aABi0FMURjg9WDfgLUQqzCHIHfATXAYD/zP3P+zX6YPhT9afym+8B7ljthe4f8ADyCPUH+Z39DAHEBIoHbgkhDM8Mqgq+CIYG2gRvA00DBAUPBHME0AGy/2H9xPn7+PD14/e/9ZXxafMI9JL2I/nQ8pLuPO+I70T01PBg8KPzzvSj+ZL3bPaO90/3w/Wm9DD0j/OP87LxW/B78X3zxPJY93L7Df85C2ETWBywJ+wqQi0LMCAuPyzhKeskeyHRH78dyxtQGMATHRCWCygGTgAx+kL1EPFf7GvoCOQZ4B/ed9ym3G3eTOD448vo3u519VP7tQAxBo4Lxg/9EyYXThh9GRsZpRgoGBoWNRRnEXMO9AqDBlgC7/wn91fy6e2m6oznYeV65NzkPueI53/px+uz613tpu3p7fnvBfIA81XzL/QB9J30VPUX9X70dfSv9A71QvTU83f03vT9/JcFvQy2F5AdlybNMbE2IjtmO1Y4yzQMLqsmQyGJHPUWJBJkD1YO5Q0pCx0FCv+b+A3x3eqS4wvd7tfi0l/RLNJU1ObWr9nl3dXjteok8SP4VP+iBgAO9BP8GB4dIyAnIyUlZiaSJtYjOiFWHbcXDBIFCpACRPyh9b3vV+of5SThh97F26ramNon25vdq98M4nPlcuji6zPvffII9f/1J/Yw9Sv0JPRE9dr2MPhi+Sj6svoF+3b5Sfn5/ucF9w/qGjQikC0VOOE9Y0FUP5A5pzKVKrwivB0nGhYXehXME30S3w/vCioFov6692Dwwedk377YHtPxzgTMGMuizI3QUNYY3Zbk0Ots8/P62QF/CCoO/RMXGmQfJyNIJdEmdSd4J5cmiCObH00bDxa8EEgKXwKB+crw2OmF43jdwNiV1Y7U7tVK2ETb89424pvkv+Zt6G7qY+1Q8ObyIvWH99j5+Pqc+5j7Svtw++D8X/7r/sf+1vus+3kCsQiYEREclSIsLlE3pznJPJM52zMTL8MmgyFdHtcaTBgwFYQTKREaDfUHtQDH+tDzJOtP48zaztQw0BLMwcvIzcfRUNfX3P3in+kv8Bz2k/sXAb0FLQppD+kUFxvhIMMmRCyLLu8uZSwEJwAh8RjmD8cHVwB7+c3zz+5p6pnmauId34zcHNog2fXYx9qp3svhH+XA6B7szO/b8YjzD/VP9jn4z/iZ+Br5j/k4+uP5Lfo8+g/5jPn99jn35v1bA4UNOhhNH5wsDjcNPPBALT6FOWU0UCvBJAAgMBx0GbsWwRXiFBsSvAypAzX6xPFi6G3g6Ni40RvNrcngyCzLG86b0m3YsN6V5jLu3fSS/PID+AokEUAV7BikHRIiFSYoKrUsIi4MLlwr1SY8IGsXSw6VBH/6W/GB6VLkJOEf33rdO9yC293aDNuf237dn+AX403mAepa7ffwf/NI9X73v/n4++38qv3b/VH94PwB/I/7ovnX9471rPLu9+3/UQgsFlsfyCnBN8s7Rj6APck1TDHSKgUkQSJWH3wc8RnpFYAT0g99CYsBtPiM8WXqdeO/3WDY9dRl0jTQGdCP0Z3Ty9aF27Hh4ehO8G33Mv67BMAJQw4lEzYYWB38IeUmmiviLmMvmy1AKWIiMhqTDwIFwPyh9Czuqemx5GriV+Fg3zrfCd6D3M7cntyK3Sjf2+DI5Jjpq+6I8xr3efqJ/I/93v3o/Db8KPv3+ez4fPjV9wj2rvTA8fHxIflLAesMAxqhIxIxrjvOPl1BYD1cNrgwNSe/IIUe+htWGyAbbhvkGy0ZmxKnCOL9HfO359Xda9bA0fHPd88n0L7R3tKJ1BLXXtoF34LkVeqy8fX51ACqBwYOuBMHGksfZyTuKXUuvzBmL5MrDCYnHmgVJwxWA/38FveP8gLwaO1T60XobOT34OLcwtkJ2KXX4Nl13Hrfh+Sz6SbvpfMP9sH3p/iw+H74QviM9xP3cfbg9Rj3Kvhk+JD4VPar9q/89wM6D8kacCQvMnY9uUORRwJE9T1WNystlyZBIv0dhBxgGs0Z/BrUGO0SaQkF/hvz2ucA3drT3syCycHIncmHzM3PutJb1iLaxN5x5PHpM/D59mz+5gb8DgcXAB9fJVUr7S/LMVcyGDAXLJcnTiEeGokRJAjV/xL49fFd7Qfpkuao5Ani1N8Y3D3Y59UI1DXUHdYv2ZHe3OTU6oHwWvT29of48Pj++HH40/dE95P3GvgU+W/6Dvr6+Rj4F/Yx+58Dsg5aHDAlfzD+PVtEI0h4REc7fzT6Kvgi9x5zG6QbPRxGHLodJxvKFIAKhP1e8vDmPtxU01LMCMqSyvnL6M4s0pzVt9lQ3T3h9uUD623wLPZl/F0D4gpyErcZBiC6JXYqHC51MJUwlC4oKrEjDhy2Ez8LgQNy/L/2efJU7oLqIedC45Xf+NvY1/nVjtZQ2DLcL+CZ5GXq4O6y8nD1Rvax9uL14PS49H70mPTA9D/1ZfaI+I/6yfrh+Yn2EPRo+PD/EQqKFl4grS35OypDHkeBROg81zXWK08jJR8iHOIbiRyWHZEf3B2eF9wM4P//86vnsdzw09bMOckXyMHIX8wr0WjVmNmV3RDi4ea36tXtf/Gt9W77wQKqCv8T9hwBJXAsgzH8MxU0UDFuLMMlPR2FFGwM4wWCAJP7bfci8xrvn+uM57Pj1t/V2zTZJtgD2JjZAN0v4b3mj+uH7mvxSPOE9IT1jvXD9e/1Pvak9pD3wvh6+QL7A/xI/Ez7Ofh0+UoAzghlFEkeZCcgNHY8WEAcQfE77zV5LpIlRSGcH4wfRSDhH3QgSR+5GdEPxwIh9qXqEODh15LSANAM0JTRMdRr1xXZydjQ2ODZGNyG34Li1eaL7cn0Af31BCoM5xPbGrUheihILcwwgzFqL6orNyVCHVoVhQ1bB44CT/46+/D3hvSX8PTrLucM4mLd89n619jXXtlC3FDgHuVP6tXuB/Jg867yLPHN7+zuXe8x8CLx/PLy9EX3APo8+z/7mfl89xf76QLsDPcYoyJELTg6Y0FHRChDWTzbNdAtcCUHIpEgMyCjIEcgmiBAHmIWIgs7/lzymuev3bTW+NKo0T3R89DA0f3Sm9ME1AnVDtig3IvhX+c87gP1QfvxAK4GwQ0YFdUbdyLNKDAutjBLMIQu0ionJSceCxYWD7wJhQQbALr8nvmr9jLz7O4v6rjkNd+o22zahNrX29rd+OBj5bfoo+r964Ls1+yH7FzsT+0W7wzy+vQL+BD7yfya/TD8+fmS9nDzevesADoN9huFJnMxkT24QqBDhj5MNLcs8iSRH9wfaSE/JBgm9CX0JSkiWRkRDJT86u+X5PHaeNQe0MDPO9En0r3TZNSo0+vSRtJk0zjWmNlS3rTk4uxd9sr/KQmUEk0bsiLBKGotJTACMRwwLi2sKQwm9SDkG/QWZxF6DIgHwQE2/O/13u676GziC91l2W/Xy9h32zXeHeG+4v3j4ORj5Czk9uPX49HlcOm47iv0jPid/AUAtQFyAT0A9P2e+yz4/PPp9jIBzw2kGwElRCwRN3Q8sDuHN9wuLylAJk8jxyQxKDUqoyqJJ2Uj9x2fE7sFuvd+7T7nG+In3WXZLdgg2HbXbdUh0wHRC8/azsXQ7NQj29jhH+k68Rv5IAFVCNQOrxU0HPYiGinFLEsumy6ZLa4rbyhEI2wdQRh5E54O2wkPBAH+W/hD8nHspOYg4ULdRtv62lPb29u03M/dbd/Y4CbiOeSV5nnp4Ow68CT0Gver+DX5o/kh+kP5l/g8+J745/lU+eX6JQOpDV4ZOCIKJ/YuTzWsNhc1TS8bKtAmnCJ5IXkj7SVzJ8ElNCNbIFkZ7g14/zPycOlI47DeSdzV23jdEN/E3hTdddlt1FHQnM6v0C3WQt1q5QruGvet/3sGcQwrESEVaxn2HJkf3yH1IlMjxyJKIaIf8h0/HJMZxhWCERYMcQUM/kn2uO8B61nnzOQ24+TiHuRS5RHmlOVW5HXjieJs4rjjseWs6Gjs4+9589D1UvZL9lj2SfY59sr2zvc8+jj8y/w0ABkHwg/6GDAf7yQkLakySDTIMQcsGCiLJdIjsSQ1JksnyiaJJEMiah2gFBwJU/xM8mrrzOVc4sDgHeC+4HvgpN6L29TWltLF0I3R59Qg2m/g4efl7zP3Bf1TARgEpwYwClIONhOaGM4dwiKgJh0o0SdqJakhPh3nF8US3A3tCPIEcwET/gv7Qvc/89PvHOxe6K3k/OAS37De4d4o4EPhn+JS5cbnG+oR7DDt6e5B8TjzpvSr9Vb2u/c8+sP8gv93ARUCPgW4C3sTdRw2IhUmHiwDLwgvoyxxJosiaSDkHQIfKiGeIqwjGSF5HT8ZchHdB379+PTC8DnuXOyY62PqZOk26EDlrOEu3ZPYSdai1gHZgNza337jiuio7afybfea+wAAFAU3Cl4PbhRkGKwbpB5gIB8h8SAUH74cTBr5FpITtw9GC2kH9gMZAM379/b68enty+pM6IPmMuVq5BfkIOSW5OTk4OT05CPlBuaM5xLpDOv07EvvcvK/9Q/5xPvD/av/JwGjAaQBtgNXCcARmBrfIHwl9Sn4LEItTCoIJbIhuSB6IK4hPSPHIwwjOh/1GJsSiQpGAR/5VfPG8LnvN+5C7GXqOuhN5Z/hHd6A29/Zqtk325Pe7+JF50PrKu9K8473nPuH/3cDzQe/DLMRExZTGZEb1xzrHNAbnhlfFvESaw/vCxwJvwZCBKEB3v4Z/P75z/ca9cvy1vBf71juzOwi69vpiOjB57Ln6Of56APrJ+1U75zxMfO49NH2nPgS+uP6Svul/G7+nf8DAEYBpQV4DVIVfxpRHXofCSJsI9YhzB4hHXkdAB/5H/UfhR9hHlYb7BZ0EqsNrAh3A4X+d/ul+a/2zfIT79frxulb52XkquI/4t/iyuP/5LLmz+jv6vLsf++Y8r71ufjY+yP/8gIABi0I7QkrC5sMwQ1FDssOCw+rDvQNhwxNCkYI7gVHA/oA+P7O/cv8oftK+ib5Kvga9771HvTx8jTysPHo8IjwrfCK8fHyF/To9OT1/fab91b4Uvmu+mv87/3k/j8AOAGsAJ//FQACBBoLxxFWFQ0XThgHGasXqhMLD1sMRAywDX8PuhHOE3oUZROGENEMCgmkBIQAxf2P/Eb9Yf7l/lj/Jf9z/nP9e/st+Qb32fR087vynPJr80/0QvWZ9p33ufi7+db5/vlX+qD6YPsV/BD9d/6y/9sAswEwAnUCaAITAvgBFQJ+AokCIwLOAfMAEwBS//H9qPya+4P6Z/qE+n/6Cfs0+x37UPsS+yn7d/s8+wP8h/3v/joAyQDiAE8BOQGxADYAAABrAIAABwAu/2P9NPuK+UH5mPvw/kgBKgN2BFsFAAbPBPkCbAJ+Ar0DuQWNB7oJggsYDIwMDw1UDbANeg0fDTAN/wxjDDcLeQkMCJ4GqwS4At8ADP98/cf7BPqn+Cr3hPV/83bxKPCt79Xv9/CI8o30qfbc97L4Uvmu+Wn6cvvC/OT+IAEEAwsFxAbQBwkIFweyBXMEhQPbAo4CrQJGAw8EfwSvBGYEpgOkAloBuv8K/q/8ufsL+9H6Hfu5+078Tvyo+yL7APs2+3b7tfsZ/Ob8qv3X/Vb9HvzA+mn5a/jw9yH4APmP+o38r/4yARYDrwNhA2oC4AEgApICrwO0BUwIFwtLDW8ODw+nDm0NQgw1C94K+AqzCoQKbgrtCfMIGQerBIcCSgAG/gr8L/rP+Ab4Ofd+9jT2xfWh9eT1bvZ49134Yvl5+pb7i/z6/A79Sv3H/Z/+yP/CAJ0BdwIjA+kDfgSHBKsE6QQxBW0F6wQxBKID5QLmAWsAif4r/WP8xfuo+/b7OPxe/Nn76PpI+sD57vj09x73zvby9v327fbc9gH3fvca+On4Cfo/+278if1T/tL+Lv96//f/5gA9AsgDiAVVBwcJggo0C6sL7QvwC9ELwQu2C78L9AvOC1ILewpECewHjwZCBSgEJQMeAjIBXgCS/77+wf3m/I/8WvxD/CT82fuc+2v7K/vz+gL7dPsm/CL9K/5G/4sAtwGNAh4DagO2A/8DQAR/BK0EdQTXAyMDTQJ5AXcAjP/p/o7+Qv7Z/YD9Ef2c/Az8Z/va+oj6QfrG+Un59fgU+V75o/nq+Sr6kfoD+1P7d/uy++r7F/wy/I389PyQ/SD+fP4E/6D/KQBKACkA6f+g/0b/2f6b/o7+2/5h/xsAFwEjAjID+QOBBBsFhQWnBa4FzwXnBdoFawXgBHEEHATyA8YDsQPDA8gDbgPDAhMCOQGjADAA4//g//v/JACCAAUBggEKAj0CMAL7Ae0BDgI5AoACnwKrAs4C+wLOAnUCygEeAcAATwDa/3z/S/8q/9f+df4w/j3+U/5N/hP+zv24/Yn9K/2x/Gn8Z/yN/J/8ifwm/J/7NvvG+kr66Pl7+UP5UPl/+Rn63/rW+838pP1A/lr++v11/RD96PwD/U39wf2Q/oD/YAAbAV4BbgGGAa0BCAJTApICwQLiAs4CoQI7ApkBxQDD/9D+I/7J/Zj90v13/nX/uQDVAa0CkgNfBPIEWwVrBbgFLQauBhAHSgeNB8sH+AfzB+EH4wfLB2cHnga9Bb8ECwR1A+QCjQL7AWkB1gAiAIz/0/4p/rj9d/15/Yb9T/3i/I38JvzN+7L7rPuo+5T7Z/sz+8n6Nvqg+Qn5uPju+Fn5o/kZ+oH6KfvL+1r81vxL/a39zP36/Rj+O/5q/pb++f69/2kACgGHAb4B4AHOAbEBqAGvAa8BugGjAWoB+ABcAKv/E/+N/hf+x/3J/QX+VP5o/jn+HP7x/Zv9Of3f/M78RP1c/sb/hwE5A3MEZAUNBowG+QZ0Bw4IqAgnCXIJhgllCUoJFQnUCGsI7gdrB70GuQV+BEADPQKUAfMAVwDp/3r/9P5T/on9vvz2+x/7g/o6+kz6mvrS+vH6/PoV+177vPtO/PT8nf37/UL+IP6W/cv8+vuN+6X7NvwH/ev9tv5k/5//bP+k/pb9n/zx+8T7Hvz6/O390v5f/5v/kP87/6L+2/0D/Yn8iPzi/HL98f1L/o7+of6b/mf+Ff4M/kD+uv5A/9D/dQAHAWcBmQGsAagB1wEgAo0C+wKOA9kD9wMfBDAENQQRBMEDgwN+A5IDzgMKBGYE0QT+BAYF+wT9BO4E4wSgBGEEIwTVA4cDLAPvAtwC/QIjAxMD6QKUAiICqAEZAYQAFQCL//n+Yf6+/Vj9Gf3r/Nv8zfyI/Cv8wvtp+0b7Y/u3+x38lPz9/FX9c/1y/VP9P/1p/bj9Hv5o/o7+av4P/p39Ef1g/K77Nvsf+1z7s/sb/Hn86/xp/eb9T/6A/nz+cf5d/mX+uv5C//v/wwCPAZQCgAMLBFAEVAQNBKQDOQPDAmwCRAIVAu0BmwEtAaoAEgB4//3+kv58/qr+5f5L/7z/JgCYAOoA+gABAeAAsQCYAJoA6gBPAeIBiwJbAycEqwT0BPUErwQfBHMDwwJGAgMCxwGZAYkBhgFsAUQBHAETARcB7QDOAJQANgDR/27/Nf8T/yr/Rv93/8X/5f/I/3X/Fv+d/iv+of39/Gv8AfzQ+6z7sPvf+xL8RPx5/Mv8Qf2f/e/9N/6v/lT/BQCsACsBnwHxARcCxwEVAWsAyP9U/wr/wf7M/hP/Y//Z/yQAZQCIAI0AVQAgAPT/ov9R/xb///7p/gP/Ef8V/wz/1f7I/sP+8v4+/4z/xv/g//n/9v/g/+L///9DAJ8AIAGdAfIBHAL0AaoBiQFwAVgBeQGkAQoCcwKzAvkC+QLvAsMCiwJ3AnoCrwLVAuQC5AKQAj0CxwFPARsBIgFLAXsBrAGxAawBiQH/AEQAav+4/jT+vv1W/Qr9Dv1G/Yf9r/3B/c79wP2v/bf9o/2G/U39Ef0D/QP9K/17/cP9Bv5d/oX+qv6x/qv+of6C/nn+X/5A/h7+A/72/Rz+Z/6q/tz+I/9U/1L/Pv8V/wP/DP9N/8r/nQByASACcQJjAiMCtQFYASIBFQErAWkBewFeATsB+gCuAF4APwAvAEQAbACvAPQACAEZASIBJQFIAUYBKwE/AT8BZQGGAZIBmwF8AWkBJwHdAHUADgDZ/8X/rf+6/9r/GQB5AOIALgFuAagB0gHtAbgBfgE/Af8A6AD6AE8BqgH6ASIC+wGbAQgBPwBh/5T+3v2A/UH9G/3z/Mv8s/xP/ND7SPv++sD60fob+5b7Jvy1/Bn9XP2h/d79NP6A/uT+Y//t/3UAMgHpAYICDANPA1EDCAOfAiwCvAFNAfYArgCGAHUAZwBRAE4AOwAtAAoA1/+t/3X/VP9G/1T/cf+g/+P/GwBnAJgAzgD/ACQBQQGNAeABSwLFAgQDOQNLA1sDOQPXAnAC6QGCAUQBHAH0AAUBFQERAQgB9gD8ABMBEQHXAH4ABQCD/w//pP5o/k3+aP6r/vv+GP/i/nH+2f0p/YD88fty+1H7lvsd/Mv8fv0w/sz+Jf87/xP/vP51/jL+Iv5I/p/+7/5E/6v/+f9MAHkAggCWAKUAugC6AKwAhABaADQAEAD3//b/9v8AABcAJAA4ADsAGwD9/73/eP9N/yX/LP93/8j/SgDrAGwBzgHBAWcB+AB+ADIADAAmAGcA0ABNAa0B7wEYAjkCKQL/AaMBQgEZAeAA7QAkAY8BBQKAAtkC/QIPA9ICaALUAS0B5AC5ALkA9gArAXMBoQGPAVMB2QApAGz/sf4B/nv97fyh/Hv8a/yU/Mn8DP1N/br9HP51/tn+E/9b/6b/CgBlAKgAsQCdAGsAIADM/3f/Vv9u/6j/qP+9/9P/3v/3/+f/0P+0/37/Y/9u/5f/tP+2/5//VP8K/5L+HP6m/XX9d/26/TT+lP4R/3f/sv/n/+v/8P/9/wMA+//y//3/EAA0AFgAiwC3ABABhgHpATQCdQKxAsgCrwJ+Ak8C5AF1ARABugCCAIgAnADmAFEBnQG+AbgBhwH9AHQA5/93/zD/6f7S/tP+6/4K/xz/NP85/03/i//B/x4AdwCmAMcA1gCUABUAaP/g/of+df6q/tz+Qv+9/z0AewCJAFUA//+f/yz/8v7Z/un+A/8e/0f/Yf9P/0f/Sf9G/1T/gP/D//v/EwAnAAoA5f/K/7L/wf/V//n/KwBYAFEARgAvABIAFwBDAHAAXgCUAIgAjwCfAHAAYQA/ACYAEwABAOf/1/+y/6D/ov+o/5v/qf+p/7T/0P+i/6D/lP+Q/6b/vf/K/87/3P/p/+X/4P+d/3j/h/9h/3j/gv+F/9n/CgBBAJEAowCUAFMACgDR/7j/v//l/zoAhAARAWEBfAGGAXUBRAH8AI8AFQAAACQAYQCqAPgASAFsAT8B6wCEABAAnf9A/yH/E/8q/z3/Yf+H/63/7v8SABcAEwAVAPv/vP91/2r/Uv8+/4D/uv/a/zgATwBaAF4AEwDV/3r/LP/7/uD+2f4l/2T/rf/w//v/0P+Z/zT/0/6t/l/+N/5U/nX+qP7p/hP/bP+///T/XACdAN0AHAFRAXsBggGHAW4BWgE9ATsBLgEtAU0BMgEVAcMAYAADAHj/E/+//nD+SP5w/qb+wf7//v3+5P7X/tX+2f4P/4X/IADiAKYBYwLQAiEDZgOJA3cDagNNAwwD4gKWAloCAwK4AWUBEAHWAJgAaQAmAMr/ZP8G/4T+Mv77/dD96/0R/kn+kv7O/hX/Hv8s/x7/5P7y/tf+3v7r/hX/Lv9u/9f/5f/e/87/pv9k/0D/Pv85/1j/q//t/zAAVwBBACsACQDB/5L/d/9x/5f/qP/a//f/EgApAAAA5//B/3H/MP/i/g//Jf8j/4z/vf84AGMAdAA9AND/w/9k/3z/sf///6oAPQHQARECyQE/AbcALQCL//n+tf4M/zX/vf9MAHAAugDCAGcAJACt/zX/I/8D/zv/R/9j/+f/kgD8AFwBmwHeArYFtwaMBcEDzgH7/9v9nvol+M/3c/hM+gH9N//oAU4E4QTwBBgELgJcANf+K/3N/Bv9uv0D/xcAnQG/AuQCZgITApAB+AB9AMX/Nf+L/5X/gv/X/5f/zP9IADsAoQCEAD0AewDV/4L/vP+m/rz+Pv/H/pn/xv+2/9AAiACdACkB3P/0/3AAHP94/63/Bv/c/73/c/8eALL/Qv/3/xj/6/4N/xr+6f6D/+D+xf/g/9X/WAESAO7/KQDr/rj/NP/b/tz/Uv80AGoBCgApASIBFQCYAYQA8v/bACkAbgBGAXIAmAC6APT/RgCo/27/EgASAHcACAH8AHQAsQBhAGUAYwDQ/7z/Wv+C/8X/uP8c/7r/xf/9/6UAJgD//wkAewAHAAwAuv95ACUB3QAnAJ8ASgHVAZ0CBgJPArMBHgLvAGEAUf8E/5D+//7e/un+AAClAE8B7wBjAZT/9/+x/nn+w/1I/mH+EgC//tf/wgBEAeMD9gBaAhgD4wM1A4cBF/2b/QP8Ifov+ff2QPgS+6T9Ff4h/+3+PQHYBREFcP4N/1P+AwBRAsP8nvqjAXoDkgLBAfz7cAAeAn7+PPs/+qX78v8T/9X9+/6dAF8DwwF9ALP+LP8VAIv/cP0s/+/+9gHrAmcAxgW6CiALkAptBtMDCwhzBbP97fsi/Kr+qgFf/UL9dQO9B8EFtQL5/jAAlAFP/fv4Mfid/A7+av+1/tz/tgR/BTMDGwEAABP/F/6B+mj42Pg6+Rv6b/pM/Lr+EAFfARMBXAI5AmoCFQHc/yIBmAF8ATIAzABwAlQD4AGvAOQAMgNqA+0BdwH5Ap0EDAOdAGP+RP8h/2P9Xvql+XD6e/qI+Fv31fjx+tD81vtw/GP+iQBcAVEAOgDOAI0AIP/C/If9Fv/9AEICLgPzB/cOKxCeD5QMkQjRC8IHJAEF/f34g/lQ+r/2Hvaw+PH6ifxg+zr6NPu7+y37/vkf+W368fof+/z7zP3M/9cBlgL2A/cFKgfjBs8GYgbxBg4HcQVYBGQEQAT7A9UDvgJPAx4DiQIHAcP/kv5f/fH8Jvyn+7P7Y/sM+2n7P/vi+4n8rPx8/RP+fP47/2r/XgArAWcBxwGOAmED9QSnBewEuATyA94CmwJBAYL/N/93/VX8vvt2+mT6UPrw+an5YPkG+S359fhU+Xj55/mN+pX6n/t1/I390/7M/xABegJNA+sEbwZcB6gICQmfCE0JGQnJCPMIRQhhCfIKvAwfD98PEBFuEX0PrQ28CWsFRAIr/hD7v/lv9w/33vYM9h73Kvcy9i714vPN8qXyd/KP8gr0FfZN92n5VfvJ/Gf+MP/4/tz/WADt//0A7wEwA60ELwUzBfwFpQV6BLgD6QGjAB7/YPwH+6761PrJ+9j7gvuu++j7YPyL/B38BfxX/LH8Z/0Z/eT8Vf1f/fj8bPw6/Ff7kfou+Fj2fvZx96T3kPdA+U/8bP8XAj0CnQPWBbQDCQWHCgsQYBctGWEWXx6oJGEmVCbaHnEchx6vFggNmgX5/vb+a/sy9BTyhPHf8PvvjO207cXuHO4T607pG+rO647sY+5t8W/2Kfyt/sMAsgR2B50JLQpBCL4IUQmLCEMI4QeuCNcKUQqCCYAI0QZtBowDAQCm/ZX6nPgo96j1dfYq9+f21fcC+Ff57vq++u76a/qM+fP5Svpn++r8Qf3X/dP+J/+d/8H+c/3N/Dr8QfvJ+ZX4r/f7+M/5MfpX+pn57PiD+Vv5C/km+Rj4v/hF+oj6p/pt+gr8vP5PALUAXABuAHkBrf9+AFUH7A4WGGAb5hqDIeopfi6pMKMrgydkJ8ggmhflDpgGBgN6/4z5efUO8/Xwpe/37kTvyO796zfnlONo5KnlGObs5+TqFPHT+Kz8YACrBUEJDQ2IDowMqww5DJQK3QqvCqwKyAuoCtQJWgpGCYIHWQSCADf+rvx5+jf4q/aJ9bP0//Mp80bzcvSx9N30vPVl9u73bfk1+jj8O/4w/97/0P8E/xj/IP6I/Pj6xvhP9+n2v/bt9tX2fPaT94f47vi5+Pv2jPbu9lL2gvXi81zzw/PZ8/H0T/Yj+Cv6ZPlc/QwKyRjtJpEsICszMGo46DzQPXM36S9TLPwjoRnZEa8KKgaxAWX7ZvaV8Rnqo+Ik383frOBZ3r7ZOte82mDgmeSs6YrurvMU+b77nf4RA+cFwgjTC+cNQxB5EOcOcQ8HEeYSqBP4Ee4Pzw03C3QJzwfRBf0CJf5X+TD2yfMW8sjw/u+n8JzxXPIm82z0HPZj9+n3M/g6+Mb4QfoO/Gr+YAA2AUICzAN4BWsGuwVLBNACOAFP/0r8Evn/9gb1nPNI8l3waO8x72jvHfDI77ruHu6e7njwTvFS8TjygvNR9rb3ZPhKAEwOxB7OLPIx+DR7PC9DAUh5R18/rTYmLK0eHBT2CpACJvyk9UPxSe8k637la+Ha4HniQ+KE39nbcNqB3N/fweVu7YDzQ/lJ/scCbAl6DrAQ0BKYE10UNRVsExkRqw9bDj4OFA3/CbsGcQOUAbEA6/59/Dr50PXC80Xy9fAG8DnvKu9v75nv4+/a8BTz0PWy+BD8QP+3AaID9QTJBhcJ/Ak/CZUHrgVOBOAChgDZ/af6Kvdp9OjxR/BL79DtqOz663frruuW603rDOyo7fPv8/Hs8mXzpfOi9Q/5V/wI/3D+7/5KCAQYECpfNws6ezxkQ5hI9UtASKM9wjMfJ94XSQyHATn3m+5W5sXiuuJR4Efco9lq257gq+RU5jPmm+YL6Rjtf/M4+lj+qAFZBWUKwBBIFHUVuRb0FrAWHBXUEIwM2Qh9BeADEwIu/zr8ePmV+An53/j59w/2CPTN8sjxQfEJ8b/wcvFR8771R/gt+gn8cP7QAFoCyAIRAx8EQgX8BSwGwQVoBcMEMgN+AfT/zv3S+ij3CfP771juLu1+7BfsmOv2637sh+yJ7PrsAO8r8q/0zvVz9YX1lPaV9575+vvp/f/+lP2d/9cK2RzBMAU9+j68PyVCiEVKSH9DsjjNK4kbQA21Aln5UfJA7ATodug/6UfnkONw4Cbiheb66OboHec158TqOvCm92z/pAVyCqsN7RBlFD4VlhOREOoN9wxjCnQFmAAD/Y38K/7v/u/+Z/2y+qX5afoB/P77tvgT9WDzmvPX9CL1QPWH9rT3OPkz+6j8ef4XAJIBqQOTBLQDLgInAWcBAQLHAe0AbP/r/Yb8LfsQ+gL4t/SG8enulu097R7ti+0P7oDuBu+278bwyfHA8svzh/TB9Ar0iPOY9GP2wPkF/dX9x/xo+UH5qQP5FJ4nFTUbOjI/CkfqTF9QiE0zRDk44Ca4E9gEQ/ju7VLlPN6A3MHdv92H3bneQeOC6Rbuy/Ak8j/zJfWI+L/+ZgWUCXELNQy2DnsSQhQVFDASBA/tC+QHhwMrABn96vqy+dP4z/gU+WD5FvpV+hb6LPmB9971OPQp8x3zx/OH9Z73y/lT/L/+OAHgA+wFMQd5B20GpARxAh4Aef5+/d/87fsz+mT4D/cj9nv1T/SW8j7xKvC57zzwVfAY8O7vnu848D/xl/HN8bnxCfLt8qrzhvQX9QT2gPcR+Gj5cvuj/PP8RvsF/agJPh3eMFE9q0B7RCxLt0/oUCNLzj7gLoIayQf++0/zQ+u14j7cDdxF3hvfst9y4cbllOqJ7QvwNPJG84D0b/c2/fYDPwjbCeAKAA2OD9sQVRA+Dl0LIgjyBHkCuQBo/+D+xf4h/+3/XAA/AIX/Bv61+8r4PfWh8SzvAu7a7SHvyfGL9VP6lv7XAboEjwZvB8AHRQc4BowESQJyALL/Kf8j/mX8dPrl+L/30fYX9gz1j/MJ8vDwu/D58Orw7vAo8Q7x+/AY8XbxffJu89X0jPY19yz2UfOY8YryTfT59gf5NfnR+Oz3/PtzC4ogfzOkPtxBMka9TNRPsk6YRzg7iyuiFwIGf/uL81rrteIq3Z7eO+I749DjQuav62vxcPRP9kL3D/dP93/5XP51A9oFcgYVCHULyg5VECAQtA6BDLUJkwY3BEgCvP98/d/73foL+2X7ufs2/Ib7ivnu9vTzWfEd8EnwYvGz8tTzh/Xl+K/9NAJ6BR0HWwcrB6UGkwXlA68BVP/V/ar8Pfvu+bL42vdY9zD2x/RY82nxou9N7vLth+5C7x3wlfDY8DjxjfFk8ojz4fNP9Pr0+vRq9GvyevBk8FXxi/RS+Ir60foC+eH7eQo4III11kJ5RqtJEE+XUpNTKU5JQS4w1hqyB8/7OPLm6BfgF9qZ2vLdq9804Q7k4+i47jrz1fWd9gj2Kfb1+JL+vQNpBiII/Qn7DPAPLxDEDv0M1wrvCJcGzALt/iD86vrx+7X9jv6A/rz93/xM/Lv7k/pf+A/1afHc7k3u9e/h8j32y/kb/Q4AjQLKBPUGrghRCZMITga2A6gBHgAh/8z9Rvsu+A/1qPL+8eTyCPR+9OvzhvKN8cLxsPI59Df1LfTd8ZzvoO5k76nwMfHW8B/wxu+y8Obyo/TD9H71Nff5+EH5g/YU+DkF7xlWL+Q9UkPbR7BNX1FmU05P7EJJMF4YNQO89uXuReh/4VLc2tzS4PLkR+n27K3wYfQz97v5mvrB+Ov2Kvgc/QQDcQZBB8AH1gk3DfkPqhBkDtkJbwWrAhUBw/8B/sP8G/02/hr/MP+4/m7+GP6j/FX5YfR679XsN+1L7xfyEfUc+FH8AwFSBf8ICguOC44LrwroCIMGmQNYAfb/7/08+1b4ovUF9AzzJPIA8hTy2PGT8fvwv/A28arx1PF68YPw9e9F8AXxyfHj8S/yRPPM9Df27fbu9/74Ovnh+ff5JPko9+3z0fYJBeoZ2S5cPKlBiUYNTMVPbFBsSX86OyY6D/P8ZPKb62jmUeGr3nvhc+bq6jXuRfD48uD14fcQ+fD4r/f39rb4ef0eA9gGAwjuB7MIOQreCnwKtwj7BWoD8QDF/lr9V/zv+5H8gP0//hH/0f9cAE4A0P6o+7L3x/PT8K3vfPCw8oD1gfiP++n+BAM4BxoKJwtNCjYIqQaDBUcEEwNRAQT/lvwH+on3OfUZ88bxwvEH8uHxNfG78ITxIvN+9Cv1mvSf8mjwuO7t7abtku0I7u7uf/DY8ajzCPee+QP7vPxT/Zr8Xfk29db60QwxI9s1Wj6FQahHmk0wUNRNBkMvMk8drAep+VHyAexC5vPheeLR59/rvO0Y8CTz4PZS+TX6oPqc+f334/fb+gAAjAN4BLQEQAWwBvMHAQjHBz4GNQN5AE3+JP0u/ab9Y/+UAbgCcwOiA1gDnwJgAFr90fnQ9EDwuO1a7R/va/EZ9FD4JP1RAbAEyQaPBx0HhQWdA/IBAQA3/hP9d/zF+7L6v/k1+db4HfgI9xX2CPXD84by7vE28r7yT/OE83TyxPAu73Puuu6r7onuJu9v8Ffy3/Pw9VT4Gvm2+Vn6Z/od+vn3gfqYCNscOzCpOyk+qUGsRu9I20gmQqU0UyOgDmf+bPan8PXqmeV347DnouyD7iXvXfDq8/X3Xvko+YD3VPVo9Sr4X/15ApkESQUqBs8HQgphC/0K4AkDB18EAQPtAf8BRAJlAvIDaAXqBQQGsASOAgAANPzj9xDzO+636+TrQO698Yf0gfea+xsArQQJCAoJqAgiB2QFdgQuA1MBeP+d/Vj85fqt+CH3kPaF9g33VPed9yr48vdf9/f2ZvY99dDyfu+H7Mvqx+p16znsHu1j7RHuBu8v8GTylPMO9Lr1lvak9un0TvJB+UkLTiBFMgY6OjxNQsVHzklsSD8/pzE6ISgO1QEr/Nr20fHy7LHrbe9c8ePv4O7n7xTzx/Rw84Hx5e/Y75PxLPXE+gH/0ABfAi4EQwffCWUJhAi7B30GIwYCBQQEJgXyBeEGHQiWBxAHIwaAAyIB1f0h+cv0OvBP7aDtKO8+8djyY/PZ9V76bv9YBKMGmAYHBjUFxATPBBgEnQLdAPj+J/2B+8D5OfiX9xr4k/lM+2n8vvyG/An8WvvG+WT3x/Tu8c/vOe7e7APsVeuE60jsBu2J7ajt0e4u8BnxYvPw9Tz4a/kW+F78OwvDHjox/Dr5O8k+k0LPQ1RDTDx4MLIiYhETBPT9H/mk9JPv8use7rTw8+8I7lPsDO1L70fw3fDN8CTwz/BO8zf4fv04AHwBVgLRA20GDAhnCGUIJggFCQMKbAkmCCIHCwjHCokMNQzACWAFGwHb/UH74/hT9cjw3O3e7TPwG/NR9QH3k/iK+vT8zP98AjwEgwRCBDkEagOkAXf/of26/Lz7yfnp91b3Dfjq+Wf8fv7O/3z/if2U+7n5nveh9HzwuOwe6vfoPOn36RfrCOyL7HHt1+418dfzWPU+92j5svq7+mb4pfmKBcIYVSwcNzk2FDSsNsg78j+RPMMwpyEUEVQEsv9P/0n//vvm9QDznfOP9K7zB/AK7XHsguzd7CHtqO3z72fz+fdc/d8ABgIcAWT/Tf8OAGEABwFRAngEcAdVCa8KUA1/EAwTNBOwDxkKigQbAG79o/sC+U31e/Ed78TvsvIh9mr4jPhJ+PL4mvot/Sn/fQAiAhUDMgPDApIBeQCv/5L+IP7y/aP92f3S/iIB4gMmBeIDiQC5/Oj5dPj59hv0ZO/H6v7oXOqf7UnwxvCi8NPwhPG78v7yZPKu8ePwKfLO9LP2zPZh9CP2VgNJGD0tnTc4NNUuHC7wMOgzOy+LI8EWEAn2/9P+AwLAB2AJ1wSLANL92fxK/Gj4y/N27/rq3+iT6NTq+e8v9Gz33/kQ+/j8Jf64/dD9Nv4N/1gAswDxAWgFeQmcDeYQAxODFBgURRGFDZ0J1QX0AV790fj09BvyTPGW8if1u/cv+fr5B/sV/Mf8ZfwO+/r5B/qf+zb+FQBVAF3/vv77/3UCMQQuBFgCEwCt/qP9WvyB+tz3RvWL82Dyj/FZ8Kvu9u3F7nrwHfId8kzxxvGu8pTzvvMx8jrxjPHj8c7zDfa69qb25PVk+SYGyxddJ9stTisaKLAocCtELDkodCGEGusSGg2FCyYPYRaqGYcW6A+ECK8Dxf/n+Ub0++426iTnfOTD5GXptu4F89T0X/QV9UD2DPY29ZT0D/bj+ab9fgB4A/cHGA6mE5wWpBbkFNUSjxBCDZYIJwNx/sf7rvqV+hf7vvvU/Lj9yf1j/Ur86PmU9h7z8fBQ8ZjzO/aV+Pn56PrW/AcAUgMzBX4E6QFR/zL9M/tv+Tn4pveX93j3S/et91f43vjl+FT4HPgz+PL3T/fB9TD0cvMy8yfzEvOh8mfyXPK38Wnwqe/d8fD4+wODDvATURTMEk0TzRafGXcadBp9GosbhBxPHWAg7SSwJzcm5R8zGL4RgAtxBZ//YvpC91r0l/Hf8PHwmPH18TzwT+6Y7F7qL+mn6OboE+tJ7g7zFvlc/rUCqwZ3CpwOOxH4EBsPGA1qDLEMGAxLCrUH9ARPA7oCMAN+BOUEZARHA3IBX/+o/Ib5fvd69uD10/Vf9gv4k/pp/Hz9+/2x/Yb8Z/od+Nr2T/Y59nf20Pba93L5cvsO/jYAAQEwAPv9DPxR+1P7nPvd+ij5rffk9nr37PjR+WT6Nvp0+Sb5qfg++G/4nPil+ZH7y/xu/WH9bv1+/24CNQUQCP0JWgzID5oSCxXgFsAXtRjNGAkXahS5EeoPdA9tDx8Ptw6tDRYM+gmBB8MEiQHy/S/6vfbi86DxBvDE7xnxt/PZ9iH5hvpM+1f7OPu0+qX5p/jl98b3xPhm+pj8Nf9sAVoD4wTjBZcG3wYWBn4EcQI4AM7+bP6+/pL/cABaAXkCnwOMBLsE5QNzAnAAS/40/Fz6Sfkk+d/5Mfuo/O/97/6J/8b/tv/S/t/8qfoQ+dP43Pki+9D71PtB+wz7EPxc/ej90Pxc+jP4PvdF9973RfhX+E74hfgQ+af5DvpV+qn6d/sZ/SP/RgEBAyUEQAWsBpEIKwrZCsAKAwopCYsImAjSCf0Lvw1bDhsOPA2SDMwLUQp/CFsGHwRYAssAGwCYAEoB3QEOAp0BcgGNAWEB/wCC/0H9M/ti+dH4TPlD+gD8+/1z/4AA/wDgAMwAYACv/8r+U/28+6D6vfpM/OL+XAGZA3EFrgZeBxQHPgZiBSwEvgL/AOn+5vzo+tz5PPqw+zL93f1y/U78XvuB+ur5afm/+Df4Qvj5+G/6J/wQ/WH9j/2+/Sf+J/5N/Rn82/oQ+t358flm+ub6Y/vf+1z89vz//GP8lvsO+077MPzd/Dj9Wv1l/XD+sv+YAPMAfgBjAPQANgFPAbEBygKuBdIIfArVCpkKXwv0DLQNVg2dDF0M8gxQDTcNvw2BDlQO/ww3CsIHGQfTBlAGcwR5AeT+3fxh/CL9S/4s/xX/IP7Z/M37XPsg+z76Pvg09XTyTPGK8tn13/mS/TAAswG8Al0D5QNOBHgE2ASTBKgDZgL9ACYA0f8gAOIA4gEyAqQBgAAV/0/+ef1P/KH6Fvl/+Lv4p/lF+iT6lfna+E34W/in+HP4h/dE9sP13Par+Oj5Fvq0+cj5M/oQ+u74fvfc9sH36PmU+3f8pfwk/B78Xvyz/Bv9Vv2H/Rj+qP5A/1EARgEFAo0C9wMpCHEOQhR/F8QXThelFzwYbxhJGMYXLBfVFWoT9hH8EcUS1RKuELYMXgjeA8H/7/sf+Aj1wPIW8UzwcfAq8UHykfK78fvvou2s67PqkeqR66rttPDr9PD5BP8ABIYIRAwkD2UQkxCEELkQ7xDNEMkPFA5hDJsKswjLBuAE7QInAef+Yfzd+av3KvZA9dX0VfRX8yLy/PA38FTw3PBp8dTxXvKY80/1+fYc+M/4ZPlI+ij7gfs6+0z6vfly+mX8Ef+AAbECFQPHArcBXABL//n+LP8E/xH+Ff2u/MP8MP1l/TT9Nv26/tkDbwy0FdIb4hztGisZtBgOGcgYXxc1FXASEBA8D2kQkhInE9oPggkyAhL85fcB9TTytO8L7rjtQO9Z8VzzmvRj9Afz7vC07mztb+2H7sjwNvR2+Jj9DwMMCH4MAxB5Eo8TORPfEeYP1w3TC/QJZQjdBtMEwwJnAC7+w/yn+zj7CfuM+pz5SfgT94z2jvY39xT4Xfjn98721/Wz9Wz2RPed9633xPf39zf4Xfjq+Kn5Nfrl+R/5jPir+IP5OvoM+9j7vvwQ/U/8PvpW+Pv3z/gq+nn6g/kh+Dv3Efce98j25vWX9iv7lQRaEbwcYiPbJA4jNSGBIMYfth7mHIYaxhjLFxsY4RkHGzEZahPZCi4D+P12+ij3G/On7wLu5O0G71fw2vA88BbuAeuG6JDn4+ee6frrMe+d89X4av5mA3IHmwpJDbsPtxHLEvgSTxKzEOoO4wyCCmIIVQakBDsDwwGhABsANgBgAKL/t/1t+zX5nveJ9mr1gvS+8ybz2/JT8qXxEPEu8JfvpO8o8DPxWvJ/8yv1m/dr+sL81P3e/dv9nf7///0AvAAe//j8M/tI+qf5YPhl9k/0o/L18YrxDfHY8Gbw7u948NfzSP2SDOkcsCmgL2Qw3C+oLtYsRSoTJo0hohwNF+0T9BMuFiwYJRZnEHcJYQJe/Lj2nvC46xjoCOYz5v7nTOu27kfw1e8T7kTs0Os57HrtI/C18734bv7rAxMJrQw6DxURMhJfEyMUmxP+EWkPWgyhCckGfgQnAzYCQQHF/+L9wPyq/Bz93v39/Sf9UfuX+O31DvQD81ryjfG28OPvKu8j7+HvpfH887r1ePak9nr2ifbG9i737ve/+LL5f/qw+4b9W//dAL4As/4B/Mr4HPbx83jxWe/O7Qzt+O3G7/XwFvHK8JLz2/y0C24cPioSMpI1SzaUNfEz3jBeLLglpB2HFt8S1xNkF80YHBbmDy0IJQEt+vPyluxQ56LjsOHj4JjiM+Z/6Urr+Oo96qrquuvO7L3tLu/o8cP1yfpEAToI0Q6sEw0WGhf+FiwWHxVsE50R5g/NDXkL9QhmBo4EEwPBAVMAlv4u/QD80vqr+V/4Mvc79vT0f/Pq8TXwlO4V7TnsP+wY7ePuC/Fa8+v1D/jE+QD7a/u8+9374fs2/B788fvd+wz8Y/1z/7MAQwCL/UX5NPVZ8QDvYe4w7pTuyu5C7iHuyu0a7tDyxfzpC78czylKMr42JTgpODc26DIVLzopDCI9G6sWHRY8F60WNxMTDRAGR/8m+IHxs+vn5t3j9eEy4vnkZOh+693sx+wc7aLtJe667m3vUPFa9Cb4CP2iAmMIUg10EFYShRP/E9MTIhJeD6AMGgpeCJwGfwSAAl4AOf53/G37wvvm/HP94vwf+0P5GPgE90b2zPV39Ur1IPQ88pzw4e908MLxOPNz9TP4APtG/fL9df0Z/FX66fjI9yz3g/fg93b4Uvmw+QD6tPl/+Bz3VPUm83LxEPDp7ybxqvIk9KP0MvQ29En2of1wCiAaEinfMnk3zDjnNyk2LTMiLgYoeCDhGBoUTRLpEj0TKBCdCv0DGf3t9uXwcOt/53fkkeIw4p/j5eZP6n7see3l7ePu+e+K8PHwXPEk87T2WfspAUcHmwyqEN8S9BO/FOUUdxTVEvEPtgx+CRcHuQWJBFgDwwEbADn/pv5Y/vT96vzm+wz7W/pF+gf6Lfm696j1sfNi8sDx/PG78ujzOfV89sH3dPj5+F75u/n5+Zn5jvh29+32GPei9wT4Uviu+CP5iPld+Zn4QPfe9PzxkO8q7p7uTPC38fjyRPPC8gDzF/WK+/MGSRQzIRIrxjAgNLs0ZDNvMcktBikyI50cYBiHFm8VYRT+EBoMZAcTAiL9HPh38q3tnOmy5rHlq+W45t/nTOj+6Njpauv57VfwDvPF9SP4EvvQ/ckAKATIBmkJxQt1DUAPFBA8EEoQjg8QD2IODQ15C7MIUgVNAmP/5P1f/S798f0I/sP9i/3r/OT8hvz1+gT5dfZn9EHzjfLY8mLz+vPM9Fj1BPa29uD27fZ+9iD2Xfbc9vv3Ovmy+W/5ZviS9+v3DflO+vr6XPrW+Iz2dPMH8Y7vZO928LnxwvKE81fzrPIJ81T24P51C6kY6yPCK+sw5jT7Nuk2gjRdL8oo2iErHDMZBBigFjQTuQ0VCDsDev84/Gr4XvQN8Ibr8+et5ZLkI+Rf49fikOPi5bPpjO2p8FXz+PVZ+fj8YQCkA0cGkwibClAMhQ7zENIS8BO2E6MSkRFBELQOZAz8CB0FDgE4/VL6hfgE+P34rPp3/Eb9JP1p/A77l/m290v1x/JZ8EnuFe2m7DLt4O4F8S3zw/SC9f/1fPYY90P40/lt+8P8B/2q/Fr8nfz4/HT82vp8+KD2xfWS9Z31aPXO9Fj05PM4843yZvEf8LDvZvHY97ECnA8cHLglziy5Mp826TibOMA0Ki/BJzog4RovF7EU8RFmDX0IAAQiACD9Rfn09MLwkOx46UDn3OUT5dnjmOIm4k3jnubk6truU/L29Kf3nvqG/eYACAS7BlgJTQuMDSAQWBLBFMoVMBWWE8AQuw3FCsQHDQV6Aj0AKf8e/w4ASgG4ATABvP+o/X/7OPn29tv07fJS8WTwMfC78K7xivIm82PzY/N9877zCvSs9HX19vYJ+Sb7CP07/sX+3P6O/p/9wPwM/N/7HvyS/EL9XP21/B372vi69tf0d/Nc8krxlfDf8MDygffk/owHXhC3F+QdjyMUKH8rEC07LMQpYyUaIP0b4RinFkAULRCQCx0HPgOSAPH9GfuQ+LH1UfOX8QTwyu4u7U3ryen66DjpU+rJ64ztdu+18SX0RPaI+AP7i/2xANMDEgedCm0NYA8xEO4PLw/XDScMhQrECDMH6AWkBM4DGgOHAh4COAEkABb/6f30/Lv7Dvp0+C73qPYh9+z3m/jC+CP4Nfc19mP1//TJ9KH0sfQI9cH10fZC+Kf53/oS/AH9xf0c/i7+0P0u/Y382fuE+1z7Evuc+rf5lfiJ91j2QvW49FH0cvQK9QP2RfgB/C4BsgcvDt0ThBgZHPIe+yALIjMihCEfIIAe0hz/GssYQhXxEGYMWQhtBRUD6wDk/uv8/vpF+bL3VPYr9fbzxPKY8b/w9+8L7yHuN+0B7czteO8F8i71tvhT/Nf/7wLBBf4HbAkQCvEJdAniCFMIBQgJCFMIighMCMcH4wa9BWQEoQLZACP/CP5q/QH9/fwK/Rf9Ff2u/Ln7e/rB+Kv2Z/Tq8Q/wAu/a7oXvnvD88WzzyfQs9sP3YvkD+078vvyU/Dj8FfyY/Er99v1w/mf+av5h/i7+yv3N/FH7Cfrf+C74+ffB98r3PPhd+c37ev9UAxIHdwrwDWkSGhdWG1geuB8QIJMfjB4EHc4aIhgKFeQRaQ+cDUAMrQpZCMsFQAO6AIL+hvx0+nr4NPbt8yTyffD/7oPtDuxn6wXsve1Q8ErzMvYG+Vz7av1f/wUBagI3A4cD6QOSBHgFqwbWB6cI9gijCM0HwAYNBnMFGAW7BEUEAASdAyUDuAIVAjQB2f8B/i/8jfpF+UL4UPeQ9h72uPVE9XL0Y/Ng8rvxz/E88q7yQfPv85b0XPVE9mP3zfg8+p/76vwY/jD/9P9BAL3/E/9C/rj9mP1V/dD8zfuV+kP5Ovie96v33/hc+9n+zALRBtUK7g7MEhgWVxh4GY8ZMxmuGIoY+hj8GDMYeBbwE2URQw9ZDUQL7AhKBvYDAwJaAPv+jf0S/E76a/jV9l31L/Tk8oHxVPCK74XvI/B08Sbz7fS29jf4q/lD+9b8Vv7p/yUBGgK4AicDzANiBM0E6QTYBPkEfAU4BrUGyQZ7BtEF4ATXA8ECjQETAGj+5Pzi+3T7Zfs9+8v6TPqy+QL5MPj79pn18fNt8nbxIvHE8XXzZ/VA95v4VflH+l77dPwI/fP8qvys/Hf9jf5h/63/Pv9s/rH9Mv3S/Fr88/uu+8f7Ufwk/Tn+df8uAYkDRQZnCYMMsg/gEr8VDxjkGQobdxtRG7Ea8xkWGakXehXLEs0P8AxKCm0HuwQOApX/cP2C+wv68PgC+E33dfZ59Vb0IvMH8ijxvfCu8OrwafE98lrzy/RW9v33z/mN+wP9Cv7S/nf/HQDHAMoB5AL2A8oECAXPBDwEVgM7AuAAfv+L/tD9gv2b/e39aP7w/i7/Tf9m/wT/bP5y/Rn8x/qG+V/4mfcf9/L2C/dm99f3Vvj9+Of57Prv+9/8x/3M/rb/YQDAAOgA6ACuAEMApP/2/kT+j/3Q/BL8ffsd+9L6z/rq+iL7jfsg/Lr8Tf3//Sf/HgGMA48GyQncDOEPmhIRFQQXbRjUGIMYnhdLFrgU/xIiERQP4AxeCtgHdgV3A+kBiABU/zT+S/17/PP7dPu1+pn5Gvig9nD1o/Qw9NDzifOm80L0XPWJ9rb31fjN+cf6sPt1/E/9VP5W/1cAVgElAnACMAJwAZQA0/8g/4n+LP7t/eD9Gv6E/h7/w/9jAMMA6gD8AMwAeQDp/xz/MP5R/YT80PtR++j6wPrJ+tT6APsL+xL7Rftw+5r7mvtp+077U/t/+5j7bfsU+8L6gfp7+pP6wPob+6X7RPzq/Jv9Hv6U/gT/N/+v/14AcAHlApAEuwb2CDsLig2BD1UR3xIIFOsUWBVfFQ8VTxQ7E+ERZxDGDjANjQvmCS0IVAaOBAoDiwEmAK/+Ff23+3/6g/nE+Aj4Fvcu9lP1rvSd9NX0SvW19QH2QPa09nj3V/gk+cT5bfod++b7sfx3/Tf+rf4+/8z/HQA9APv/i/8n/9D+lv5J/vr91P37/Vz+8P53/8z/BQAAAM7/qf9f/9P+Nv53/dL8OPzd++37Mvx9/NL89vzQ/K/8J/yY+wP7lvqK+rT6L/vC+2H85Pzd/H/8/vuY+2n7XPta+6r7afyC/dv+RgC6AQQDHAQlBTUGXge5CEYK+QugDUcP0BAcEjIT1xMKFNsTZxOREn0R2A/3Dc4Lrgm1B+UFRASXAhABhf98/tv9cv1l/VH9P/3r/G78n/up+t/5Gvk++EX3TfZq9Qb1O/Xv9cz2ifdL+CH52vl9+uj63/qg+k76OvqY+lP7/vuc/A79WP2W/cf9//0y/jb+LP4e/iL+P/6h/l//EgCzACsBHgGjAAkAav+v/gb+Z/38/MP82fz9/P/8w/w7/Mv7qvvY+yn8Zfxp/ID84vxV/dT9Tf5w/kb+4v2N/Yf9rP3i/fb9Bv5R/tL+b/8pAMMASgHAAWwCgQM3BVAHkQnIC88Njg8QEUgSPxPeE/IT3ROAE/0SrxL+Ef4QcQ/VDTkMZwp9CBIGhQNEAVL/qv07/Pf6y/my+MP3+faA9hj2qvUw9cf0nfSb9Mf06/QT9U31v/Vd9j73UPhe+Wn6Wfs//AX9kP3X/fH9E/4P/jL+Sf5d/nf+l/7e/i7/X/9d/xX/xf7D/uf+Xf/a/wEA7f+y/2T/Cv++/l/+7/1R/eD8j/wp/IL76vpr+kj6kfrs+lP7ofvQ+9D7/PsX/Er8wPw//QP+0v6F/0EAywA2AZYBbAEcAdYAjQBnAFMALwBIALoAmAG6AhgEfwXhBlcI6wmdC20N3w4DEAkR6BGAEgYTaBN7Ey0TeRJsESoQyA46DXELewl7B3EFdwOjAff/ef4X/cD7Svrh+JL3QPYi9Rz0SPOj8h/y/PEA8jrysvJ18yD0xfRz9SP27vbO98j4q/mN+ln7HfzQ/HX9Cv6L/v/+ZP/I/wcASAB0AJEAlACCAIIAdwBGAAwA2v+x/5T/g/9J/97+Vv7o/aH9Lv17/I/7ofoC+pn5Xvlt+b35IfqK+tv6Tvvz+4b8Ff3Q/ar+i/9cAMkABQE0AUQBMAEiAT0BSwFnAVoBRgE2AVwBRgEHAcsAkgDDAGUBkAIlBO4F5AfMCdALjg0HDxcQtxAiESgR7xCaEDwQ9Q+gDzoPvw4dDlYNhQxwCzIKzgghB2QFrQPQAQwAOf5j/Ir6wfhS9wH21PS688byA/J48TXxHfFJ8YPx6vGL8jLzAfTy9Of10/aH9zX44/iP+VX6L/se/P/88f35/uD/mgA2AZQB4gEOAvgB6wHSAZABSgHgAGMAOAASAAoA7f+F/8H++P07/Xn8wvvu+lP66PmR+Uz5KPlD+WL56Pmw+nv7a/wg/dT9hf4w/9z/VQCzACkBmAHoARwCHAIIAgUC9gHBAccByQHbATACswJzA1kEXQWpBv4HUQmvCvQLCw1ODlAPThAmEeQRkhL/Ek8TNxPVEhwS1BBTD6ANxwvtCd8H9wV6BEsDLgLZAEn/kP3N+/z5IfhG9k/0efId8Qnwhe9i73/vuO/971XwyPBZ8frxt/J982X0gPXy9qX4U/om/I39rf64/4QALgHDAU0CvwJSAwIEtgQxBXgFgQUmBZUEzAPgAtkB4AAOAED/Gv7r/Kr7bfqV+bb48Pdv9xT3Avdd9+X3avju+Gj5Bfqg+kX72/tX/Aj9vP2q/sj/xQCqAS4CcQJoAiwCzAFIAcwAiwA0APn/3v8JAKwAqAEGA3oE6gUxB0EISAlICmgLbgwzDb8NTA7YDngP9Q9OEI8QqhC3EOQQFxEmEe0QTBBJD9UN0AtsCZMGbgM/AA79EPqD93D14vPJ8vnxk/Fw8V7xnvG58cnxvfHb8RLyWvLj8lHz8fPL9NP10/a/94H4Q/n6+az6TPvi+438Sv1P/mz/rwDmASUDRQQRBbQFGwY4Bh0G0QVgBbIE6QPvAs4BoQBH/+394vzx+0P70vpp+lX6U/qI+vH6d/sT/Jz8Mv2v/Vj+3v41/1v/aP9v/1b/J//5/rr+cP47/gz+Af7//fH9m/0R/WX8x/uN++L7yfw0/iQASQKeBAAHtQkfDCwOkw9KEIwQexBQEBIQ2A+ED04PNg9pDw4QihDAEM8QbhDaDwcPgw2dC1EJqwbiA/0A6P3L+qb3vPQL8pvvou0e7Cnrn+qT6v/qvOvJ7Cjuku/W8A7yZfP69Gz2KvjY+Xb7IP2A/sr/+gAcAgwD4gPDBHwFJAbABiYHXAczB8kGEgYxBTkEMgM7AhsBLQAs/yL+Lv1K/Er7UPo4+S74h/cI99H20/b/9jX3rfd4+Hb5ePpX+1f8Jf0l/kv/YwBzAV8CDQNkA4wDlwOdA5QDPgOoAggCPwFuAG7/Ff7Q/MX7P/sx+7D7A/3t/mUBeAS3BysLeA5VEZATXRW4FkwXNxdXFgYVixPmEVMQHQ/7DdkM0QudClcJAwiTBsME0AK+AJf+lPyn+r/43vbi9Ory2PDw7jXtlutp6qXpgenW6cvqWuxm7sDwHfOC9cj3z/ma+xf9IP7i/oz/MAAHAQECAwM5BG0FngasB2IIxQjOCKgILQh4B7cGywXaBOIDDQMPAvQA1f+O/kL99fu1+oX5avhj95b2/fWd9ZT1vPXw9U/25/a297L4rPnJ+u/76/zS/Zv+QP/X/3AA5gA5AawB9AE9Al8COQLJAQgBGwA7/zf+HP1O/ND7rPsX/Fj9ev83Aq0FjQmODcQRcRXEGH0bIB37HasdtRxgG20ZPBfOFBMSow9iDSILzgh4BiEE4gG8/6r9u/uM+X73i/Wj8+bxH/Bq7qLsNuvY6eboduhO6N/o0Okg69vs4+4z8YTz5/VF+I/61/zQ/pIAWAIPBJAFzwbNB5oIHglpCZoJiQlVCQwJ4gisCHQIPwjdByYHHwbgBGYD6QFeAMr+T/3J+2v6IfnK95L2gPVj9HTz1PJM8kXyo/Je82H0r/Uj95X4G/p5+9L8I/51/4AAagEcApkC+QL0Ap8C4gHxAAkAUf+J/vb9mP30/ID8MPzU+437ivvs+5j9bgBFBB4Jhg53FIIaGCC8JOAnZik6KW0naSS2IJ8cLxiLE9MOgAoDB+MDIAGx/lH8IvoI+Nn1tfOT8Sbvrewy6r/nqeXb42HiaeHd4Pjgx+Fa46/lp+h17N/ws/W9+sP/kwSoCNALQg7wD/UQfRGAEUgR6BBZEOEPIQ9zDoUNLgySCqoIbQYGBKoBT/8k/Tr7rPlH+Av3DfYa9Uj0afNw8q7xFPGl8FnwI/AL8Djw1vDY8Qfzd/T79aD3ZPk0+yT9D//8ANUCcwS9BckGZwfEB4IHhQY6BbIDHAKvABj/X/3d+y36wfiO91j2mPXx9J/04vSo9ob6bgD6Bz4Q3xh9IUopGDDUNEg3pDdeNR8xjytCJbEejhfYD3YInwHW+wH3svJz76/sK+oC6DnmseQk4zjhFt8a3arbJdui2/3cON+G4s/mEOz88Qb43v2AA04IoAyyEOsT0RbIGKoZ4RkSGWkXLhU7EuUOaAu9B0kESAHH/q782/o++cT3ZfZA9XD0pfOJ8+HzrvTc9T73svjw+ez6x/tE/FP8TvwZ/AH87PvL+9j7NPx9/NL83fy1/Jz8kvzS/Ej9uv3e/cn9Vv3H/C38pfsC+1X6nvm/+Dz40/dt9yH3tvZH9gP2uvWv9Sn2rfam9/n4sPs9ATIJ0hJmHZ4nijF9OhhBWkWIRl1EFz/VNictLyNIGXYPVAV7+7zyGeut5Ijfg9v+2AfYFNhy2cHbV9694FriauNW5JflW+e+6Rzta/GD9jT8RgL1BxoNKRHQE9UVDRd9F6sXBxfjFSAUpREHD/ILOAgKBE//nPpb9pby++8q7vTs5uxY7cjuFvFr80T29/gi+zb9w/5BAK8BhAIuA4sDfAMgAwUCpgAa/zL9qPsJ+pv44Pd496L36/c1+Lv4LfmG+az5fflQ+QD5wvjf+Ab5ffnc+dr53/kA+m362PrU+sv6CftZ+wn8Iv16/qb/6f91/1cA1QSJDc0YaiS0LpI3yT5FQ1FFYkQPQNU4Dy6XIQQWXwudAVj3XOyx4hjbYNZW1KTU3Nao2Vbc+d4Z4pLlhOiz6mHsGu5/8BH0MPi1/AoBvQQ4CEgLmg4tEgIVzxZkF/kWMxa/FEgSDQ/HCq0FiwCq+033zvPC8ELujeyq61rr6+t37XHvFvLM9L/3Q/vi/nACbQX4BwEKfAsuDPYL7woTCX8GpgPoABz+f/sC+cr2LPX080TzWvMF9N70S/WE9av1uPX79RX2LPY+9gH2bvYT9yX4xvm3+nT7cvwl/dT96/2H/aH94vw2/IL7gfrW+bb3VPVx96b/FA9cIUIwdjwARGFInkv7ShVJrkRzOhEtVB0LDoUCBPd566zggdad0OjNIs740d7VYNkY3IrdL+FU5ubrJvIV9i/5TPwN/zMDDQd3CtcNlw8gEYISIxNlFBoU/BGED6gLQwhWBa0BuP5X+8T3CPVk8vfwWfB670Dvre4C7m/uYe/W8Tf1q/gy/eYBYgYIC7QOsRHOE+0TrBIJENwMewlrBXkBNv0Y+eT1d/NB8sbxlfFX8erwtvDw8GTx+fEb8sTxffFS8QXykfM99Yn3wPmc+7H9tf4eACMCJwP5A3gDNgLDAZ0AY/+o/mz9/Psk+Uf21PlQBYEXsCq4NxhAPERVRcRG1kauRRZBvTPaIMwMH/sh72rlbtyR1fHPGM3hzWXRQ9h/3ynlo+kw7e/xbfce/J3/9gBVAWUCKgScB3wLNw7lD1UP5w28DDQL2Qm5BzUECQCN+/f3w/Vf9Az0SPRn9Jr0RvTW86zzGfNI8qHxtPEk83715/gi/TABqQV1CRMNeBCPErwTVBOPEe4OtgvGB8MDm/9l+8T3QfSE8Z7vh+6H7uvuiu9J8BbxGfL+8s3zKfQc9Eb0AfVC9ir4lfrQ/C7/ZQGzAm8DcwMjA5YCkQB1/T/60fdJ9s714vXc9mj3T/bi9ff6lAlWHjEyOT94RAVGV0YdRuxFoENZPBcuURmQBPT0lure46TdZ9c70/3QJ9Ef1XPbx+I06XntivH99cT6Xf+6AjkFGwe3CPEKTQ3TDmAPBw7tC7EJGQcjBRYD1gBN/ln77vhP91r2EfYu9uf2//dd+P/3D/e/9fH0ZfRB9Aj1MvYA+M36cP7vAr4HOwwfEJYSrxNcE8ARUg/ZC3AHegIu/TD4EfSr8Izun+1P7fbt2O7j70rxqPJC9Lr1xvaT9+f3N/gv+RT6FPse/HD8Cv2G/Zb97/2z/S39wPxZ+5f5fvc79az05PRH9hb56PpR+7D66vuyBaIXUyw9PeNELUf/R99HFUiyRa4+EDLZHTMHAfXG6C3ijNyt1QPRu85wz8PT7dkw4iTqjO/f84r3f/vB/+cC7AXJCPoKPA2ODooPXBDJD6QObgzbCB8FywCm/Df5wfUX8wXx0+868HbxQ/Nq9Rz3Zvjy+Ij4Lvi/92j3fPen9zj5UfwdAL8EBQnIDPEP5BGlEvMR7g/3DDoJLgVzAcn9a/oy9/7z4fHK8IrwNfHj8ajy1PMj9Xr2BPh6+Yz6F/us+i/6Yvof+4T8pP1n/lT/zP/7/5X/ev5a/cL7y/mm90L10vNs81HzyfTa9lT4yPi5+BX+Yg3WIoM3+UMKRw5Hn0Y0RlxFBEEgN0Imig8E+jvrMuMb3vjX0NH8zVTNn89+1PTb4OS27LXycfcA/AoBIQWzCIkMqQ/JETsSBxHRD28Okgw3CqkGagLH/YX5h/Z+9D/zPPIq8bbwXPEg87/1xPeK+IP4z/dC9yj3fvdg+Cr59/lM+4n9EwEfBT0Jag3kEBcTmROCEngQog30CdgFqgGo/aL5s/Xh8knxy/Ad8Vvx+fHN8mXzSPQI9Yn1KvYu9g/2d/Yu98/45vqS/Dn+VP89AGcBVgEFAVcAl/70/Nr6g/jG9/n2O/Z49rz2hfdL9xf2BPqKBnca3i/tPT9EiEZYR/pIHklgRXk8nCs5Fcb/eO+55n/hU9sZ1jnSJ9HZ0+PXfd5W5mjsoPGQ9f34Gf1IAH4DVwemCpkNDw9KD44PPg9kDq8MQQkRBfn/ffpm9ivzcPHj8HrwU/Fa8w/2XvmB+wH8RvtA+Y73avaL9Ub1CPVq9QT3ivl+/X4CbQcVDEoPARHZEdsQ1g4cDG4IzQQwAYf9R/pU9/z0zfMd883yjfI68oHyAPNj8zD0CvXJ9dn2Y/cj+Ev5FvoX+8L7cvz9/cb/cwFYAtQBGQGIAKj/mf6d/Ff69/gj91j2AfeT94D30vUG+DwFiRpOMBM+pEEoQ+pF5kf/R/VCpDcYJhsOuPfU6YTiC97316bRMNDF0obXZt5E5gbvFfag+Z38VP+PASMDMwMcBPIFewaPBkoGigYbCKgIfQh5B7gEcgHd/Wv6Q/hC9t3zYvIx8u3zDffG+Z/7Ffyy+xD7OPqX+dr4tPcI9xz3nvg7/GcA4ATYCK0LBg4QD+cOCw6ADE8KaweVA5L/1PuD+Aj2QvRP8zbzUfON8/TzZfQF9en10/bP9+z4ZPmG+fn5Wfpn++j8+v1m/4QACgFBARAAkP53/Uz7cfhR9bPyU/Ji8qHyRPRs9XX1WPXl+D8IGiCWNRFCg0Q8RYBJ0UyRTBVHPDrQJgQOxveb62/ms+HO2azRmM+k0k7XaN1I5OjroPEi9P321PoX/i8ADgEpA5MGMgiPCKwITQnVCu8KlgmzB5IE9ABs/Xb6D/n798P2KfZA9sb32vlc+4/8lPyw+0z6Xfjl9tD1FfVw9e32u/mj/VoBQwV3CR8N9w/kEPMP3g2UCtoG0AJx/ln6GvaC8mDwSe9q71/wk/En84n0A/Zx93j4Ovlt+Wb5D/mg+Kv4Vflk+rz7HP30/jIBQgK6AqgChAGv/1H89ffr9APzB/Ik8kjzF/UN9nn1evcPAkIWFS1MPTZEV0WxRvJJ9UtSSlBCBjL9G5EFXPWd7V7olOFg2a3SctHt077XN93b4hboauwj757yKPcF++/+BAMQB+IKSQzbC18LFQvnCrwJcgeKBNcAJf12+kP5yPmI+tL6Qfu7+2z8B/3z/IT8/PoC+Mz0B/Ki8M/whPF98//2NPsdAIMEkwjDDO4P1hG+EckPAg1VCSMFEAG+/MT4U/UC8vXvTe9h76vwOvKo85b1Mvdk+Fv5sPnj+fz59fkh+jX6iPq9+rX6Ivus+xL8G/zQ+0H7Vfq4+H72OfSy8nrx7vC98QDzpvMn9Lb3WAQDGvswfkEjSLhJUEyPTx5R/k3CQ7kyNxw+Bp33h++p6QHidNhw0kbRD9LG1H3Y6dxj4rbmper+7zT1iPodAG8F1wqXDrUPfw/PDuwNugxBCvoGgwPZ/x79j/ti+5b82/3l/h0AAwGtAXsBDABE/v77HfkV9vXydPAu7zPvZ/GW9cT61//9A30Hjwr0DIgOrQ5CDe8KfQczBE8BNv57+6D4mfVP83vxKPC075vvJPCB8R7zy/T79eD2z/eg+EP5VPk++UH5V/kk+n37Yfzi/Kj85vsO/Hv7bfqu+Rr4b/bt9MDzuPRN9S714fhsA5cWjSzYOzpDn0W3RpJJ2Ep3R8U+ZS6nGXQGOPmR81LwEOtN5ELe9NtP3EXdXd9Z4dniQuQa5dbnaOzP8Bj2rPt5AXgH0Ao9DHwNHw7PDiMORAvbB3MDn//J/Rv96/2S/l/+Uv+cAEkC/wOxA4kCawBY/E34c/QS8anv7O6c77ny1/b1++QA9wT1CLwL0wx8DFgKtQfDBGMBZf40+//3HvVF8sTw5fBS8QDyHfJt8bLxrvJB9Iv2Fvi2+Af5Bvlb+Uf6Bfvf+wP9Ef4D/2//Rv+o/kj91vqR+Df3BPen95749fko+x36oPjr/McKwiBXNVM/3EAgQHdBdkWuRi5C9De9JnIS1AG69/rzEvEO6nDieN3727vdo9+z4WjkZOXs5Xzn3+qg8KL1iPlJ/k8DUQj2CzcN3g2SDe0LyQn6Bo4ENgJf/8D9yv0l/34BCgPVA6sEzATPBDEE/QGt/qf5AfQH8MXt1+2F7z7xHPTY9yf8dQF2BqEKkA3sDfkLnAh+BC0BfP7A+8j4LvVa8vnw9/At8nDzfvR19cP1hPU39cH0gPQp9Kbzj/Mp9Gj16/bw+B37y/zA/cr9df0k/bL7b/k59631MvU29XD1N/dB+bn56vma/YAKJh+IMt091T+CPjtAwkNMRrlEGzyoLXIbfgpXAH376fdF8kHpruEx3rLd9d8y4inj2+Oo41zkhuf860zxxfU6+UH9ewHEBQwJTQrnCpYKXAkvCBIG2QMyAmEAdQCOAiUFLQhgCSYJRAlKCGYGUgPb/U74EvNA7uDrKesc7MHu0vEP9lP7fgBtBQEJswoPC5sJ+QYIBK8A1/0A+/f3zPUw9K7zlvSi9ZL2Lvfl9v/2q/dF+M34APjr9YDzjfE/8bzyK/Xj9yL6HfsS+4H6Hfrw+UX5bPcB9XXzJPNC9NP23/i7+Rj6Uf3FCIYb5C3BOFs6mzgoOoc+HUKeQV47ZjDIIe0SNAlXBdMD5f+99qHsC+YQ4/HiuOJp4Yzgot9m3yLh2+R06gbw2/Pu9kf6av56Am8ELAXTBbUGQwg9CbEJiQo7C74LCAyUC/sK6wm3BywFgAL2/3X99fmH9R3x5O3H7CHtNe6M75XwA/JC9Ar3hPoe/gwBgAPVBB8F5wQzBFoDOQKNAGH+9fu/+S74rfdb+K75mvpv+lT5bfic+KP5XPrs+WL4gvbO9YD2+fej+Vz6P/oF+qn5UPkq+YH4yPcW9yX2avYC+Gb5nvpD/FYC+Q/EIUcxCjkxOeY25jX9NQc14TASKV8e0BLzCfMFRQY4B2oEhv039R/uT+ox6Pbl+OOT4ZzgF+Lk5CvqFvBq9Jf3z/ii+eL7MP40AFMBywBuAesDhgcyDFAPvhChEegQXQ/7DBIJDQWCAI37mfd59MnyL/Kj8e/xdvK+8ljzsfOx9LT28veZ+KX4LPgx+Y37r/4cAsUDAASkA+QCMAL9AEb/n/07/Ij7G/up+qr6zfoH+/j6K/pi+cz4EfhW93P2QvbB92T5C/qD+Tz48vd2+Gb4Zvez9ZT0rPSU9fL2D/iB+IT6GwAPC1MaESjjL+UwzC23K+or2yszKnMluh5eGGcS0Q4jDxkRjxFtDR4EPPql8kTts+lJ5gPjruAJ33rf3eI46JvucPNL9Vz1J/Rp8/jz/PTF9kv5UfxPAAgFuQlZDlUSOxX3FrAWqxQrEV8MeAftAmT/r/1C/Vz9fP2U/Cn7Dvo++S35Zvns+Kv3hfXx8nrxZPHG8gj1qPZf9+P3Ofju+B364/rC+/77vvsK/AH8Hfxh/EH8V/y3/KP8Kf2S/jsACgIgA5UD+wMABLgDzAMABA8EbAOLAZv/Rv45/Yb8s/vN+pH6qvoT/Lr/2ASZCi0PMhKWFOUV0xX/FI0TghKLEUwPzwxAC/oKFgwADc8MMww/Ct0GcwJW/Qv5vPVp82fykfI49Nr2I/lM+gf6jvhY96j2UvZd9jL2OfYI96L4U/sK//ACjgaxCHsItwYWBKgBJgAc/6H+h/43/gj+S/4K/4kAAQKqAqgC0gGjAHf/Jf7o/Az8cPta+2D7UfvQ+1H8y/x8/ej9I/6S/lb+Yf3Y+8T5Jfjk9jD2Cvb99f31Kfar9gD45fnP+zT9vv2z/Y39lv3d/UD+Tf5W/nn+s/7//rH+9v04/WH8xfsB/Dv9yv8NAw0GCgnXCw8Ohg/xD+gPQRDWEDYRWhEQEc0QzxAJEcQRdRJBEowQfg3/CbAGRQRPAnUAyP5c/Uz8UPuB+t/5jPkx+Tr4w/ZK9Zj02/Sh9df2avgh+sD79vzD/a/+EABTAQoC4gFeATQBXAGhAZgBSgH4AJgAIABa/27+yv11/Ur9Tf11/Y/9cv3C/Hn7afoO+nD6H/ua+6f7gvs9+8/6lfpw+vP52vj59rX0x/Kg8Q3x6PC78Jrw7PCV8bfyMvTn9Uv4M/tn/scBwQT1Bj8InQidCBIJrgkcCiUKeQkJCVwJlgq4DOMOQxDYEJ4QMxBiED4QfQ8aDnoMzgsYDAYNww3RDQYNkgvHCQkIUgZNBMkB5f4v/Cr6p/ie91b3w/e7+Fv5D/kw+Dv3w/a89hb38vcz+Zj6B/yb/UT/4ADoAUYCRALDAR4BswDMAFoBnQEZARUAMv+//nH+qP1e/OX6dvlz+PD39fdZ+KD4qfhD+M73uveO91T34Pb79V31U/Uw9hj4/Pl5+y38i/sq+sH4kvdZ9+z3Sfgl+Av33vUp9k34O/z8AEIF1gjOCxsOLRDiERMTthNEE4sRlQ+DDtYOAhDxECIRxhDmD4oObAwwCjYI6gWQA+0A1f4V/vT9NP55/pb+w/64/vL99vwX/Fn7zfoF+iT5f/hZ+KD4S/lB+mP7uvyx/Sf+7/13/Wz9w/1h/vL+av+6/yYA3QCLAXcCjgOZBDwFLgWZBMgD5wJCAmcBggB+/0j+Sv2d/Eb86vtB+wT6m/gs9wT2ofXT9TL2Uvb29bP15PVj9u32gffh9+P3uPeX9/T3Yvil+Kn42PiZ+Rv7CP18/s7+Y/4M/hf+5P6mAOUCyAUbCQwMAg8ZEkQVfxiGGtYaiBn9FlIUlBH5DngM/AktCFUHBQcCBwkHOAbNBHUCcf+q/G365fje9w33s/YT98z3uPjT+bX6Svus+6z7/vuS/Ob89Pw//Jz7hPsJ/EL9Cv+LAJkBHAKvARsBuQC3ABMBQgHdADsACgCjAA4CPQPlAxMEpAMPAx4CBwFIAHr/ef5C/dL7kfqF+dH4UvjP9yz3ovY59sX1UfWz9Dv08/Pk8/TzgPQi9ar1S/b/9l34TPq++zT8e/zQ/J39uP6V/5oAvgGvAoADyATWBr4JHwzBDB8MZQtPDMgO/hEnFVQXvhgfGaEYOBdoFJ4QeAyACCgFXwI/AD7/q/7x/QH9tfuB+s359/iv9/L17/Os8lzyMvMl9VD3KvmW+ib7yfvH/Hn9c/4B/yX/I/8M/0b/zv8DAXcCzgNiBGIEkwTRBA8FFgUjBLgCOQHV/zD/CP9h/ykAIgEcAuQC3gJ1AokBQQD4/oL9sfzP+3v6Avmy9+L2kvaA9gr2kPX49HX0bPRR9FX0IvTz88DzofM080ryK/JT80L2U/rA/cX/rwCjAWoDvwUJCKYJkArDCpYKwArHC/0NIhAZEewQ7RAyEusU3xfcGJ4X5BRjEVsO8AvJCaAH/gTeAYf+lPte+Tf4nffy9pL1d/Oh8Y7wcvD88KDxrvL886j1oPfI+eL7d/2Z/gH/1/+QAecDPAYDCN8I9QixCFMIRghBCBcI6geKBzYHFAeOBlQFGgNuAMz9Wvs8+Yn3mfZG9on2N/eQ+JH6XPzV/Rr/RgDWAMb/lP2K+wX7nvtY/I38TPxX/Ez8TPti+aD2jfNF8XrvMO7T7WHtI+0c7lnxc/e2/ioFAQkRCtQJJAllCPcH9Qe7CB4KNww2DxoTfRdrGskawhiiFb4SnhCnDiwMcgkDB2YFDQVdBaAFLAVNA2cA5Pwv+dz1F/Po8EXvWO5h7lvvDfHZ8of05/UW9zn48PiO+RT6cvqR+2n9+f/tAowFuQccCfwJoQo3C44L0Au6CwwLCAomCVoIrgfCBrQESAKM/w79UPtx+Sr3kPS98dPvLO9L71vwrvGY8mnz7fPJ87Xz/vOa9B72+/fn+ST82f2//iH/Cv9o/nf9+vtM+u74TfgU+Aj4BPgh+CT5SvxAAjAKEhLYF/Qa4Bu+G/8aQRlAFhcTjxCyDx4R5hMuFwAZThiSFUYRqwxFCL0D1f75+cf1vvLP8fXxpfJT8x3zFvIQ8WbwR/Bm8CbwEPCT8D/y4vQC+MT64vyO/qb/OwFsA/sFnQhuCg4LLAtJC4MLDwwYDLgLVgvJCi0KmgnCCKwH5wVRA8wAgP76/IL71PmQ+IX38PaX9nP1lvPN8RTwku6C7U/sA+uP6lrrN+3+7y3zxfVh9+P3g/fp9v32+/dk+QX70vyq/oQAswEuAcr/uv2G+9b5OvgN+N/6rgDoCKER7xh2HjMiMCSaJPYiFCBCHIYXChQvEnUS6xSTFiMWXxT4EAYNQwmTBPn/hPv59vzynu9f7crsLO3B7X7u0+4R7/vuNe7e7DvrYOq36hPsu+5a8pn2Z/vT//QDwgfKCswMpg2BDTANjA2nDg0Q7BAeEZEQjg+VDiwNpAvSCUcHfgSHAX7+2/s1+Rz2kfO98f7wp/E88i/yTvES8Drvxu627r/uI+8Y8CbxMfIH86Pzs/SU9r344/pP/PP8yf2h/bP8f/t4+SH4uved93/4QfoZ/PL9hP6U/hsAVgPtCIMPMBUeG/MglCXEKZ8roCqdJ/chfhu9FsMTTRI2EfcOwQwTC9cJdAjTBbcBB/xG9S7vzuq56HToSej85/Pn6Ohz63HupfAM8jHy8/Fe8jHzIvX39y37av5cAV8EigedCggNpw59D8kPeA98DjcNkAtYCpQJ7wiPCPwHxgYUBRoDqAAM/lP7iPg09kj02PLz8VzxEPHu8MvwsvCg8K3wxvBr8XDyhvOf9HP1X/bu9yH68fsy/Zj9Ef0w/fL9pv4l/1/+rPxD+5r5Gvgy9zD2uPWQ9bz0SPQi9IL19flhAHQIBRHCGBsgNCYZKkMsEiyBKXolWSD/GxQarhmyGX8YSRVDEQ8NwghmBLH/ZPoZ9THwF+xw6c/nq+ax5a3ks+Qf5pXojevB7Qvvyu9Q8IrxAfRZ91H7Vv/kAn8GMgrqDewQ6xKiE64SCxEkDwINQAuGCfoHaQcJB9YGTgblBEADQQHv/sv8hPpf+LP22fRn833y8fHm8RnyIvIk8hDyNPLJ8s7zWvX39nr49/kQ+8/7FfxX+6n6XPpn+nT7i/yq/T3/RgCcALb/if0U+5f4QvY99I3yR/F48E7wofLe99X/TwkyEmwaPCFHJi0q8yunK0opASQEHhAZ2hW/FAQUmhLhEG8OtAuYCFIE+f/d+lz1Q/Dd6yTpu+fw5uDmPudD6GDqjeyX7kfwDfHP8S/z/PSi99/68v39APYDsgaYCXwMlw4QENgQzxBQEN4OlwwiCr0HqwXgA4IC9AEFAk8CRgJwARIALv7A+9X4KvZe9KbzrPMw9Hv0VvRE9C30J/T283nzIPNI8wr0U/Xu9tb4FPst/Q7+6f3d/JT7APun+r76a/s9/Nv8Qf0g/aP8u/sC+qn3D/WK8jjwlO5f7trw/fZ3/58JBhRdHQYmTywwL2gviyxJJxshiRqdFWcTqBJYErwRjBBiD78NpgpDBokAQfow9A3v5uuc6kbqLepb6WfooOjP6cXrD+6K7/nw9vIj9ff3qfrm/Kj+w/8wAXEDrAbQCkUOfRD1EQcSSBHYDzENXgrHB5UFogRWBKcEoAUxBlQGsgXcA5kBvP6I+5z4r/Vl87XxePBr8N/wM/HC8dLxafFe8UXxofFM8qzyYPOQ9Gz2nPgQ+sD69/ok+9D7T/yf/MD8ifyI/GH8Hvzt+wD7p/lA+IL2bPU49AzzPfNd9er6QgM9DFoVYx0ZJDwqGi7KLxUvhir2I9wcdhZNE80RYBBdD6INPgxCC+oIhwX4AMf6mPTX7hnqN+d65W7kEeS85MPmdOkQ7BHujO8L8afyY/SD9un4n/uv/rgBMwWICBMLVA3qDgsQGRHWEWwSzBKEEmkRaQ+QDMcJPAfjBBgDWgHc/6v+df2G/HD72vkA+LX1vPMZ8hbx4/D78IPxQ/KN8qfywPKh8v7yxfPD9IX2l/iB+jD8B/2Q/bP9Iv2Y/Oz7sPvo++z7Cvy++yD7H/sJ+8L6Kvq0+Cr3BvbU9Mv0WvbT+QkAWwczD4YXyB4XJXYp+CpHKhQnPyJoHIUWtxHpDUALvgnUCGsIcAiWB9EFqwIV/vL4gPN+7pbqo+fw5W/lFuYi6P7qGO508Nrx3fKh8+T0uPan+N/6Hv13/24CuQXLCI0LiQ2lDmAPyQ8XEE4QGxB7D4wOOQ2hC/0JeQjmBh8F+QKhAFH+MPxn+uX42vc+98b2h/ZA9jD2CvZq9Xn0+PKy8R/xGfHv8WLzDPUT97b4uflZ+i36y/lU+f34OPmg+dr5B/oW+hL6OPot+ij6Gfqe+SP5aPiA99H24PXm9Nv1gfhf/XwE/wtAFGMcPSPfKEws6ixTK8MmrSDXGqQVKRKjD28NFgw5C34KkQl0B0IE+//m+rz1y/Dk7Drqhujc5xDo8eh/6lPs2u0U7+fvsvDx8aPzmfXR91D6Jf1nACEE0AcjCwcO/g9lEVwSsxLVEhwSdBB0DmEM4goBCisJnwjxB8YGawWHA2EB8v7++1L58vYl9SD0RvPf8uzyufJT8nvxVPC270TvMe/T78TwgfJy9C725/ch+bf5ovnW+On3CvdA9jL2hfZj9wb5lvrv+zL9lv1G/Sv8vfky97f0JPN58/v1/vopApQKTROCG+YiGil8LTsvUS7mKu8loiA4G3YWTBK2DvsLCArzCFEIQQcsBQ8Ctf20+HTzN+4A6sjmceRo43zjuuTT5g7p+urZ7JLuQfAx8g70W/YJ+cD77f64AgIHNAvRDmMR6xKfE8MTnxM9E6MSXBGrD9gNTwxqC8AKHAr6CEgHWQU7A/gAsf5P/Oj5ivdh9ZbzUfJO8UDwJu9A7rztzu1U7gTvJvDN8Z/zcPUE9wv4yvgP+fP4tvh8+Jz4Hfk6+tv75P0AAMkBugLSAuYBFQAY/tL7OvnO9pb0NPOJ81z1+fhf/sMEPgz6EzAbtSGFJiopkSlbJ40jOR9/GlYWvhKhD4oN4AtjCgoJFwcRBAAA9/r79dTxOe5h6xvpI+c35nbmkOcq6ajqzuv77GHuPvBr8ub0ePcU+gX9EgBSA38GIglYC+kM8g2iDvMOLA9vD38Pcg9AD80OTg5tDR4MdQpyCGYGYQRzAtQAcf9J/in9yftM+pH4tPYO9ZHzFvLu8Pvvc+9H71nv0++F8HbxafI/8yf0NPVA9kL3HPgJ+Tb6hPv8/C7+Ef+k/8P/fv8a/1r+Vf0t/Mb6oPlX+Mr2s/XF9Az1I/eq+hUAeAYuDTUUdRqyH74j5iVYJugklyGAHTQZeBW8EoYQsA4oDbQLXwrJCKkG4gMtABf8Dfg09Dzxr+7J7PTrmuuL64nraety63PrmutE7Lbt8O+a8m71oPgV/J3/CAOuBbUHJgklCmML2gx/DjgQiBEmEi8SlhFrEPAOMA1fC5YJ5AeVBnwFjgSLAy4CVwAM/nv70fgn9vzz8/FA8OzuAe6Z7YXtuO357ZLuVu9f8I/x6PJl9Aj2rfch+Yb6mvuA/Dv9s/0Y/jf+GP77/d79XP1//IT7afqK+aT4tPcI96j20fbe9+P5Hv1jAasGaAxREhkYIx3+IEYjESSaIwMioB/pHGIaMxgLFtkTsRFxD94M7Ql7BtICR/+z+2/4vPWR84zxxu/97Uvszupw6Yjo8efE5wLorugX6o7sxO9Y8/L2V/p+/UEA2wImBWkHhAmSC3MN0Q4QEAURwhEmEtgRBREmEBkP8g3nDHEL6QkfCBQGDQTOAW7/uvy0+fT2j/R08sTwWe877nrtBu3t7ETtw+1f7hjv3O/38Fzy5vNw9b329PdF+aP6Cvxp/Y3+kP+EACABcwGZAYkBSgHSAPv/Uf+2/v/9Hv0M/Bn7g/q++hD8hP4cApgGwwtDEa0WvBu2HxAiySLGIVAfKxzRGAAWmRMyEfMO4wxJC+IJOggSBrEDAQEe/lX7nvgN9rnzYPEj7yXtVevb6Xjohedb59rn5uio6gbt3O8282/2svnS/KT/WAKiBJ4GZQgICq8LGg1rDqwPsxB9EaURKRFOEBkPeA1oCz8JLQcsBSkDCgHI/lf8C/oW+IP2P/Un9D3zU/K08T/x9/DK8HLw8+947zzvk++D8ObxnPOL9Zn3o/me+xz9Y/45/5v/0f/n/zYAgAABATYBLQEVAckAIAAa/7X9Evx7+uX4tPco97H3u/k7/ekBkweQDS0TPxheHFAfwiBMIFAemxuXGHUVkhI/EM0OFA7IDb8NiQ0NDaQLPwkPBmMChP6D+pD23fK07w/t4epB6UPo0efu57Lo6Omu6+LtTvD88p31C/gr+gz8wP1+/1UBMgMaBeYGpwhPCucLOQ0mDooObw4PDpUN7gweDBUL/QkgCTwIMwfTBTUEWgIvAAj+Bfxy+iT5C/io9gr1hPPP8Ufw4+7H7e3sfOyk7HftD+8k8Zjz9vXn93r5gfpD+837E/x9/Oj8e/1j/nj/lABuAckBrQEQAfv/pP4g/X/7PvpF+R35KPpI/Kj/1QNaCEANIhKHFlMasRyiHWEdExxDGi0YKhZzFDQTWBLqEZ0RQRGMEB0PxQx9CZcFqgHt/Yb6jvfo9LfyxvAG77rtcewZ6+PpDun36KXp1uqk7OXuM/HD8z32ovgQ+yX9Kv/tAJ0CZAQPBrkHYwnUChoMKA3pDasOWw/aD9wPhg/GDqINWgyWCqcIbQYABHwBw/4D/G35AffS9NTyFvG775vuee1U7HLrK+t36wXs4uzk7T7vzfCC8q708vYq+U77T/0M/4kA1AHIAk0DbAMpA6ECBgJhAa4AGQA+/3z+hv13/Kj7/vrY+kr7s/z7/jsCTgbiCrsPXBReGFEb9hxAHWEcphpvGBgWwBO1EUMQdA9VD5EPjg/5DrsNmwucCPsEDAH4/DH5vPXQ8lDwNe6Z7Grrxepj6kjqmupj66zsce6B8J7ykPQ99on3vfjY+d36/vs0/eX+PwEuBGUHwwq0Df4PlBF7EtQSfhKdEWkQCw+MDf0LSwpjCCgGlwOoAJ/9y/pH+Bz2VvTv8tvxAvEu8HrvyO4l7sPtd+2C7TLub+9K8WnzqvUW+Ff6Pfzg/e3+c/+Q/0L/BP/t/g//VP+v/xAAVwCIAEoAvP8W/1r+fP15/Jr7Cfsv+3/82f42AkEGrApMD7oTshfdGuYczh2JHVMckxqlGMgWUBU0FGwTAxOsEjASOhF0D7wMFwnMBDsAz/uV9/rz+/Ct7vLsoevN6j3qzeli6ePooeil6O7ou+kF6/vsRO/d8bf0kPd5+lH9IADrArYFggg5C7kNJBAiEqYTeRSgFBwU8xJGEQsPvQxPCugHxAW9A/IBSgCX/gf9a/uj+bH3fPUV87bwfO6A7O3q5Oma6RfqSuvi7Nfu1PDS8sf0Zfav96T4afkO+uX69fsr/Zn+QwDJAekCCATMBGYFUgWXBFsDpgHw/yf+d/zz+sD53Pip+JP53/u9//sE6wpKES8X9BtiHzEhoCGeIHoesxv6GJsWBhV8FJIU/xRUFQoV0hNwEYoNRQj/ARn7X/Rv7sfpceaL5LrjwONN5ATl/+Wy5lXnGOjm6E7qZewd72XyD/bC+UL9pgAoBLcH9ArwDZoQGhNEFZkWEheVFhwV5BIdEDkN4gocCeYHQwf3BuwGYgYhBdkCaP9O+6T2EPIf7hLr9+gH6A3op+jC6TjrquwE7l/vj/DY8V7zFfUG9wn59/qd/Pr9Of9OABMB1AF5AscC8ALbAoAC7QEMAff/yv5R/fr7y/qM+av4OvgJ+AL4Gvgw+H35U/wFAckHWw9JF6IehCSyKJYqKyqyJ0gjOR5rGZ4VzBPBE9UUQBatFl0VOBLlDOoFzP0l9RrtdebW4affmt8r4bjjUOZr6NDplurm6jvr2euk7FTu7PBC9E74j/zHAKkE6AeCCs4MlQ4SEHARRBKhEsASjxL6EegQVQ94DYULpgkVCJ4GHwUqA2UAP/14+aT1HfJr7+TtJ+1A7QbuFO8z8CHxWfH58CPwTe/R7g/vLvB58mH1tPhB/Cr/hgHIAhEDrwLxAe0AHQCo/0D/av+F/z7/wf7o/dD8Ufui+e73VPYc9RD0OPMB82PzP/Qg9uH5EgDYCBUTlB0qJyIuPjIvM7gwEiy1JRQfARo4F3EXIhp1HaQfYh9gG74TjwmL/fHx4ec84AHcGNs73SnhuuU06cfqT+of6DflmuK14briEeZz6xfyV/kKALsFBQq9DCoODQ+TD2sQ1BFEE6gUmRV3FV8UghLuDz4NpgotCBgGTQR5AgEAGf3J+Un2SvOn8NfuyO0w7UDtre0l7pTuqe6J7oPur+6n72bxqPNd9vD4Ovtq/RP/ZQBLAawBoQGAAXUBhwHJAesB2QGNAZoA/f4Q/cb6l/iM9q/0iPO58mnyG/OA9Hj2OPm8+/39n/+mAFQD+AdKDyQZMiNYLH8zrTYiNngyxyv6Iw4cWxXJEaURBBR4F/oY3ha7EJoGYPqv7YTixdoi12fXJdsF4U7njuwu787ulOsd50HjhuEl49Pn9O5899X/AweBDPUPjRHhEX0RIhF7EYAS2xMPFVYVXxQnEhkPsQuECK0F3AKYAJb+nPy9+mr4HPaU8+jw4u6D7S7twe3B7j7w4fGI8w/1WPZt9wb47Pe298/3c/jC+Yb7hv2U/60BSwMzBEcEYQOtAU//tfxp+uf4+/cS+Oz48Pkv+/z7AfzZ+6f6q/iz9kb0mvJZ8g7zVPWI+Cv7gP2L/44CYggXEdAbAiewMNA2hzgpNiExVSpGI+kcVxj0FjMYNhupHX4dXBl9ENoD4PVd6Ird9dZE1JfVmNnW3lbkcuhG6urpb+eg5P3ifOOw5j3skfOc+1sDQgrLD8ETdhZSF/0WERZdFO0SZxGyDxEOAgzFCZEHzwQYAg//bfs8+Cf12/LU8WLxmPFB8vzy/PMM9c71cfZ19mb2ovYs97v4t/p3/Mf9SP4G/lr9bvzZ+y/83fzm/cP+Df/4/kn+Hv2s+wX6pfh69xT3sfeI+G357Pl6+UP4r/Yw9RX0jfND8/ryMvP480L1MPcN+er5ufqu/LwCvQ6JHmgvCT1TREpFOUBEN7MtpCTuHSIapRg4GicdpB77HFQVMgiV+G/on9sj1BvRd9JV1kjbEuFQ5pPqRO2r7aTs/+pO6q7rM++S9OH6bgEHCAkOIhP0FpwYGBiyFdARQw6LC7EJ+AhPCIoHowbrBJkCeP9O+wL3FPNH8MzvOPFP9Dr4Mfum/HD8nPpf+BP2K/Q09On1ZPkP/g8C4wSOBYwDYf8q+kv1cvIF8ojzWPaK+bn8yv41/8n9rvr39sLz/vHs8S/zD/Vf9wv5CfqP+gv6yfmc+aL4m/dW9mj1PvbE94P6Cv7CADID8ATxBxkQEx3qK3E5YUAsQMI6CDE8J9Ef0hqVGM8X0xfaGJ4Z3xd/ESgG//fd6ezeJtmA2M7b1OBJ5ezohuuS7Vvvu+/57vbtS+3c7sby1fd5/cMCewfbC1kPrBGEEpQRJg/0Cy0JnAf8BsYGqwVYAwcBuv7g/Jz7tvmb95j1CvS384n0F/bM99j4Uvld+Uz5t/km+sD6rPtB/dH/ugIlBSgGugQXAU78H/eG83DyofNY9pn5DvxB/Xn9NPwQ+oX3+PSq88Xzy/TQ9a/2JfeQ9zn4mfgQ+Zz5SvrP+ub6APrf+Aj4b/dS+DH6tfyb/lj+/f4qBXATVijLPEtJQEu0Qp4ziyTxGP0TPBU1GMUbmx56Hp0bzBI5Azjxpd+C03/PNNEh10ne2ePd6E3tdPFW9sr4TvgB9mDyOvHo80X57wC7COwOgBMuFcwTMRBCCjMEvf+Y/Q//EQP+BqMJUwkxBicC7/0J+u72SPQM8kPx7/Fs9OX4s/1cAWUCzgC+/cv6UPnh+c78QQEWBs4JIAuzCX8Fsf92+Wr0yPG+8fHzGPe9+R372PqV+dr30vWR81vxLvCF8GnyifXs+Az8nf7X/0n/Lf0r+mP3pvUi9ZT1Tffs+YH7cvwK/cP8Afyr+BP18vgUB60eMzkYSpNOlEcyNw8nmBu6FdgWrBieF4cWohTYEQIN1QFI82bl7dkC1PvS99Qt2m3gnOfA8KX5IAG0BFEC8/y49yv1h/cR/aADUwoED5wRGxKBDxULXQVJ/7z7FPvO/BkATwLXArYC0gEMAXAApv7S+yX4HvQS8ojyDvXz+Hn82/4DAMP/cf6q/Bn7Svo9+zv+qwJJBscHXgYDAiX9kPgG9aHzXvPb8970yfUc95n4Wfl4+Zz4lfcq9wj31/fP+R38aP6k/5L/Yf/p/tn9Y/z4+hj63/mM+U74gPft9tn16/Vs9tX2mfZD8+TyWv6FFVE0vk3qVfFPGT/WKTIaFBG5D7EUHRfGF9oYnBccFAoKqfgS6CnbWtOQ0avSdNdk4KHqRvZuAZgIlgroBd/8N/Ww8ZLz9/lcAQ4JLxBUFCMVnBEOCtUB2Pp39oD2zfmU/s4CfAQjBNsCugC+/ZX51fSZ8Aju9u2r8Er1Hfqt/WH/fv/F/qz96vyz/Iv99/+xAy8IwQtWDJoJnQS//i35h/RX8Y/wu/FB9FD3nPkJ+wX7t/lx+JX3yPfE+OX5rvv6/VoAegKFAvMAyv4F/HH5MPdR9U30B/SS8wPzrPOk9D/18PY++Nj4oPcv80b1YAYVJJZFw1nWV+JH8jBTHDIS1hDIFkAe2h70G8kYWBQEDbP+dexz3mnW/9NS1YrYmt+56cDz4vz7Ag0EcADC+CzxVO+S83/7FATSChAQdxPpEksOsAbD/kn5n/Yz92/6zv6tAzwHsAgvCA8Fyv88+UjyzO1h7afwyPbt/MIAWgKQAX7/bP21+7L6TPsr/eX/aAOGBpMIJwlBBzkD+v0c+Pryp++Z7gDw1vII9kX4m/jj93r2T/WO9Wb2Gvic+hz9uv+UAdIBwwAG/rX6BPg+9pb1+fUR9yb4Bvmc+Cr3qfYV9iX16fUh9+v3HPeJ9EX7EBKBMfBMLVWbSSw3kiRVF4kTahYCHpQjjyADG/YVBw98BKj0Y+We3ZbaStkX2pvd4uXW8Kz54v+oAokAL/v69GDxtfPY+eQAywfwDHIQbhEjDgkIWAAQ+RP1fvR+92P9+wKCB+gJ5AjYBWkBZfxF+Jv0JvIo8jL0FPhX/HH/oQEFAokAT/4Q/OX6rPvX/RMBFAVgCAEKZQmaBncCc/3g943yv+537eLuvvHF9Bz3Kvgx+P/39feX+NP5Rfvx/JT+jP8KACQAkP8p/sL7JPmy90D3xvd8+DP4WfcZ9dDyu/F98Pfws/KS89vzKfLH9UgJwih+SJRZCFS2QjkwtCDJGbUakR9zJCYiJBvWFo8SvgnA+vPo4Nxr18PTXNJZ1YfdFumA8xv7+f8eADb75vTv8S30R/lP/gYDtQd1C78M4gr5BnoCyf1e+rf5MfsG/sUA6wL7BNgFxgTAAbP9K/qg9zf25/a/+Pf68fzt/Uf/qACzAPD/D/8I/0YA7QHeA2AGkwiLCWMISQVcAd/8a/jH9Cny1PBM8BDwivD88Ub0uPZW+Pv4OvmV+Y/6Tvz7/fv+/f5W/nv9jfyw+wL7svp9+tT5ivjk9j31wPJ/8L/vJe8b8CTz0PV19yD2bvWWArgfMEGYV7lWhEV4M7wlrx7sHpUh/iMLIqUaHBXJEeIJ4vvt6mbee9mB1qbTVNSC2qblxvFK++0BmwOM/9b5mfbn9/77X//4AvgHqAyGD5wOdwpXBVL/+vlY9yz3Xfm6/D0AWwTwBigG8AI2/kf6BvhN9q31bPYW+BX7x/42AiwEfAOzAID9gvsw/MX+IAKkBe4HEAkXCfkGLANn/j75zPRD8eDuS+7j7jfwJPIO9OT1+/bi9oz2d/bw9sj32Ph0+pb8vv79/2T/df2K+/z6Z/tM+xj6FviC9nX1VvRW9I/0vvMV9AP1cfag9/H0aPdWCngoIEadUz5KvzinKbseqBtYHeseqR6+GCISghEpEUsKcvxT7PPhr9wr2H3WcNpz5MLxAf1YBFcHpgTl/lL6nPmc/GH/kgBzAmAFiwhVCn0IMASq/qL46fQr9BH2NfrK/i4DCwaIBXEC4v1d+Rb3KvYp9mj3/fhc+6L+/wDyAQgBP/6K+3b65PsbAF8F8QmiDJcMlgpZB2YD8P+U/Bv5UfZX8+PwEvBD8NLx1/N+9D30kvM782r0OfZ++H37Mv4yAOYAVQDV/z7/WP5q/fb8wvxg/LL7Lfq5+BP3N/X09Er0yfJp8yL1c/eT987z0/mAESow/0dSSnM6pytVIggdFR0nHhof8B3lFqoROhGpDsIGM/pf7gnofeLO3ODc8uPy70H7PwGZA7MCev4W+ij4Evn4+nL7Cvx1/koBpAOVBAoEnwJd/wX7Efju9gv4OPv0/jID2AXnBWIE6gCu/Or45vVT9SP3pfmf/Mj+df+k/xj/1f41/8z/4gDgAS4DYgXNB58J2QmWB44D+f5M+gz2s/Jp8DDvNe+/7wfwcvD58M3xIPOC9NL12fbg96z5XPws/4gAw/87/lb9kv1W/jv/kP/D/uD8sPnD9h70ZfKs87H1tvc4+t35wffH81bupPXTDgwu1Ea0Sj09gTFQKXcjriFUHjgbUxjAEb8P0BIbEpUMVgJv9zfwQud83v3ch+Ic7Q337/x1AbUCBQAw/VD75vpb+jP4h/gn/D8AmwPwBCwFWwSWAAf8vfgo94j4o/sTAOcFAwlMCOAEJ/9Q+vD3VPfG+PP5XPqP+xv9CP8dAA3/lP2f+4r5rPma++L+5AJvBbkG5gZSBc4Csf/U+/32oPFR7XDrDOym7j/yyvWQ+NP59fkq+n36o/oL+1z7+PvZ/Fr9P/40/1T/qv7k/IH6yPfU9GXycfAY79ztJe5Z8fjzO/X49TL0hPEG7dvq4/o3HRxC+lmhWF1IMzrXLXElLyJCHx8ewxsjF7cXEBnBE98J2/3O8+bqb96w1IXU0dxI6dTzSvuaADsBrf46/E763veG8yrwWvJO+GP+wQK7BH8F0APg/rn6v/jI+KP7bP+rA1sHZQfTBQoE+gDJ/Yj6a/jW+FD5nvm1+rf7vPw4/R79wf21/cn89Pzi/okCBgbaB9IIpwiXBuACvP2R+Aj0KvCx7nrvHfE986/1jPiE++38mPwk/MX77ft3/SQA3ALeApX/z/sS+YX3+/XW80ryM/Ge8OzwnPEr8g7xZPD+8aPzwfVF96T20vXJ8zP4HQ56LnNLbFhzUMhBNDUJKRsiah9lHWcc+ReHExUUCBJGCsj/QvXt7b/lWtvP1fjXSuBN6yD1av2YAZL/nPu4+Cr3X/Wu8Q/w5vJW95z8lAG2BOoFjAOV/xP+0P1L/vT/5AGmBGIG/gXzBYMFRgMbAC38YPlF+Ab3tvZ691b4Xvlw+qz8bv+SAJoA5/+A/z0AuQAIAtwExAa3BocElACo/M34//SV8pzxp/GI8gX0WPbl+OX6n/wr/hz/W/8y//v+gP5n/On4HPYi9QH2uPfc+B/5JvgX9jj0XvO589fzm/Q19uf2mfgD+4T8sf1Q+7v7XwsPJTs9VEmiQoQ2Jy6UJakhjCHsHvYa/RIYC9UL9wxtCFEBPPk28z3sDuL221vdeeSg7s73Df/oAVb+Nfov+Q36mPr39/D1kPeF+Yv8cgAzA7sFMQV3AvsBhAAg/sP9A/4gAD8CfgHmAecCHgJKARX/cvxS+iz25PJr8ifz5PVt+cP8YQDtAdUBnwG+AGcAugBYApwF5ge+CHQIewaFA7/+mfjJ83TwTu+c8FryffRm9rv3v/kO/Pj9E/+X/gz9s/tD+7n7Pfx//Fr8APwe/DL82Pv3+gb52vYG9qL1Afbu94j45fhv+h36oPkw9+/y8foMEecpbD34PgszYiqaImYd/R73HZgaTxVSC2MIygt5CsAGMgHW+rj26+6i5dTiH+WP65D0z/t5ADQAIPxT+sL7m/0B/bb5mfeK97346vuO/wgD6QTXAyEDegIbAAH+Ffzq+/b+yQGJBEgH6Ab0BGoCdf5w+kr1kfDc793xw/Vg+jD+EwK0BOwEPASCAhcA7/6A//oBsATLBVkF6wNEAkb/8fq29mDy1e4a7VTtevAp9Q35G/zb/e3+IAChADQB0AETAUT/A/1i+8n7Lv2Y/RX8PPjk86PxxPL79tb6Mvwr/Nr5zva89WP0qPMz8iHvSfY+DEIn4j3nQyg6HjAnJzEfjhxQGWoVNBJcC6cIZAyFDdwLTgeF//X4uO935GLfZeDh5rDwBvmO/woC6//m/df9r/7S/bD5x/Wz9CP2gfrD/zEEDQf3Bf0C6f9Y/E76k/pI/fgBfwXCBpEGfAStAdL+efu5+CX2A/QT9LP1ovfA+dn7Y/61AI8B6gAV/1P9+Pyz/isCUgU4Bs8EQAKF/5T8rvmZ90f2ofXU9MPzOPTX9nf7FQGpBdAH8AaOA87/m/21/RP/9/+2/4L9APnt80Pwpe8A8nX0SPVE9en0fvQI9R71ifNl8pXxb/Hh8t/yn/bqB1oi+jyBS41Fnzf8K38iCx8qHhkbxhgtE3EMWAz9DCQKvwQ6+77yCOyZ46LeSd9l5J/t//bp/qAECAVlAjYAzP7V/Sn7aPdm9uv3ZfvX/34CZgNnAd/8IvrT+X375P4iAbwCkARLBSwG1AagBaQDawAV/Hr4qvX/9EL3Ivqs/HH+0v4B/1v/gP/2/xkAtv/t/9YAuAHSARcBjwCfABMA6P2W+jP31PSd82fzbvRf9t/4L/xYAC4E+wXgBKQBgv7N/ND7vvoa+YP2Z/NS8YbxNPTe9+f5oPgy9Wvx8u3D7ODtde7N7/rxw/Pi9uz3Qfn1BukfzToLTA9IcTjSLGolmCPrJOogphrvEv8JKQkdDewMUQlVATP3b+5+5IXcptt44L3oAvIN+db8O/0w/Kz8Wv4K/uP5zPRn8oDz3vd3/XMCEgYdBgoD8v/H/X79uv/FAvcF/geTB4UGfwULBEICI//4+gb3qPOB8g70jvb5+M36WPzo/ev+Pv+M/woAyQCEARECtgLVAmUCOwIjAj8BHv8Z/HH5+/f09vj19PUK9wT50vvg/vQBsgSlBZUEFwI2/qX5//XF9Iv24/m5/Ar+Ef55/fP7L/mZ9aDxwe1z61zsne5I8pP3Ovuf/Yn85/df/sUTYy3IQWJDVjZBLRMnDCOJJNghWBwzFiIL9QX8Bw4HygVsA0b+TvnP75/kt98F4N7kquxi81n4Yvkw+H35Mvzx/bn8f/mc+Lv5s/v//rgBwQO0BLED/wIMAtn/6f6f//IBRQS6AwwCtwB1/8r++vzu+cr2TPO98ZzyIvSb9sT4dPqv/If9Pf7MAMMDtwboBzUG3gOAAQcAAQGkAokD9gIJAM38xPmd9sz1UPfW+TD8DP3p/QcAfAIKBNAD6wFc/nv5i/V38/bzD/b19y36ifwu/mP+Q/xi+Nn0bvLP8B3xCvNE9P31l/jN+YH6OviU9HX9SxPxKZM55zeBK0okwh8KHqsgHx43F9QOVwW7BEsK5wuoCmsGcAC3+uHwEucM487jF+pu8nH5bP4M/u370vxE/9QBHgHH/XX8bvw5/j8C/QSRBvUFyAI0Aa//EP3m+4H7OP0wAF4AVv/p/XT8P/3X/Zz84/m49OfwLfG886/33fqA/N7+JwLBBVEJXQtIC+EI1QRyAZ//vf+1AaYDuwQPBBEBGf0v+dz2xvY++KD6Vfwb/Rz+Ff8/AHkBeQGUAM7+lvx0+077Dvue+oX5lfhg+Kn3m/Yw9XvzvvKw8kzzafRd9V33rfhm+Pv4FPmM+C72BPGo9L0Gph1qMHY0OCsmI/oceBisGTgZMhX3DocFIAMBCa0L+AmQBHv9j/ly8yvrC+fD5gjrEvIW+HP9qf8u/5IAZANQBhQHGgTMAaMBbAKHBM8F8wUoBVoC0//7/ST8wPuq+wH86vwK/GP7Ufy1/dP/vABk/0j9Kvrp9yb4e/lI/If/pgKBBpgI/AekBYkBw/2G+6X6T/yd/rr/jwAlAVwClQO4AsAAr/61/K77/Por+878Yf6A/xIA4v+f/zD/9P5s//b/awDQ/xH+Q/zG+W334PV+9Dv0xfRY9b32D/gs+bn5/fie+B/3HPW+9Fb0afRC9LXze/qUCZoZsyWkJx4jfCDIHekbyRtkGCkTiQzMBOsDawY5BboBeftm9nv0rfBl7TXt1+7E8tX1O/cH+VX5k/ph/loCJAbLBiUENwLgAPoA9AI3BAkFdQQPAqUATAAtAbYCSAJf/+H6uPfC+ab/8wWdCQ4JvQeVBsME9gOWAnUA4P5j/Jz7A/3f/ND7wvpD+gf8fP0c/Xn9zP1w/uv/ggCEAR4CmAEFAhYDNwTBBAQDcABn/pH8cvyJ/a3+bgByAUACVgMsAyUCnAC8/n79dfyU+1r7z/rT+dP4g/gG+S/5b/hd9sPzGfJX8Q7ybPRY9nP3LPhb+eT9iQSmCoYP5BFfEwQVghRjE30SAxEdEKkNMAmTBXMBY/38+nb5YvoX/Bn7p/hW9YbyvvI99DD2V/js+HT5FfvS/IT+J/9w/hX+Vv7n/p3/qf/5/3cAZwGMAy4FmgUqAyX9Lfl9+4sDfQ7uFfkX4RcEFmETVRFJDhEK5wPF+yz2RPQg9Mn0VPW89sT5mvtp+9H6S/kl+DX4lfh4+lr8Kf1G/+QBZAM+A6MAs/5N/1cANwJABEUFqwZvB/oHtwkTCi0JLQgzBmEEagKuABcBOwJaAoIBuv8a/n/86Pkf94b0hPL58Xvy9vPg9UX37Pjo+pL8wP1o/jL/+ACdAkID3AITAhgC8ALjAw0E1QLdAPn+1f3D/UD+pv4u/yP/1f2q+yT5HPg8+eP65PvL+yn7U/t3+4v70vue+yT8Wv0Y/on+cP1I/Dj9Bv9hARwD4AJcAZj8jPeN+jUFXBMYH8AhDSHLIS8hOCE1IDoakRGRBpz7qfZo9Ur0ifRW9S73PPlb+Jn1BfOV8cnxx/KA88XzPfRW9or65/6QAVgBS/9C/sH+VwARAwYFLAYhB/4GLwcZCF4I3QggCYgI/AewBigFugRFBMMDWgNeAb7+3fvC+Mb3jvhb+T76yfof+1j8rP2+/rb/7v+U/xb/hf6d/cX7wvn1+Jr5Hfv6+8X7hPuR+6X7M/v8+b34KPg4+Sj7/vu8+1L6D/k8+oj7e/ty+uH3avYP93T4svqN/JD9ov5m/0oAUQCM/x7/hP2G/K78n/tZ+uD2U/TH/G0N0x7bKhQr6ScLKHom4CQJIk4aNBHCBf76PvgC+fD4cfgf95L3pPe8837v5OyA7B/uVu948XL0XfYY+TT8/f5GAU4AP/5z/Y/8Ff3I/vT/KwJWAy4DngSFBd8F4wbsBhsI3wmaCa4J5gluCUEJSAfgA6gAo/xr+lX7Iv0q/2r/A/4n/hH/AACNAUICXQKjAWj/X/2z+0X6tPnJ+YT6Ivug+uP5ZPmD+GT3zPUK9B7zEvOC9NH3afsI/kD/ZP+4/9H/Ef9+/Sv7KPlo+IP5u/s9/VP9RvzC+or5Bvjl9nj35fdS9xH3fPaz9mb3S/gpANMOWx3KJ+opuSg6K2Ut4C0kLDAkIhlJDUQCkP5P/1r+e/zx+dX45fmQ+Jb1GfPR8OHvOe867zHwR/BO8V/04/cO+2f7K/rN+rD7bPxN/R79qP1U/nH+UQDIAvsEgQcDCUQKKwsXCh4J3Qh5CDEIYgZxA9IAMP7x/I397/5jAPf/pv0T/Ln7xfxu/3kBwQIgA40BgP+t/dL7mPqn+fD4sPh4+Cz43vd69wb3UvYl9ebzmvPe9Pv3+Ptk/3wB6wFEAQwAl/70/Mn6I/hx9Y/zhvNq9a33+fgz+OT1BfRK8+3zCPVC9DjzLfO684n1h/YO+uMF+RXvJI8tGC56Lj4xgTJXMwYxfSnNH2oTiAkfB1QGNQREASn9BfsQ+c704/Ev8DrvC+8s7mzube/C7zrxYvN79YH3+fcu+A35a/mK+lX8+v2H/xIAvgChAngEXgb1B6gIjwnxCTcK+gqoCkQJHwemBMECsQAn/lH8Mfu9+lX6OPnP+Nz5O/xL/74BNwOmA24DugI/ARH/PfxL+Wz3i/ZN9oD2i/Zz9rT22fa/9tH28vbB92D5Gft//GH9jf04/Y38p/uT+ur4Dfe49SX2Zvil+mf76Pnk9urzivF48PfwR/HJ8WLz0PQB9974hPuwBG4SWR+OKCsrbiyHMEwzxTRkM9ksWiQJGrsQgQ0sDH4J1gW3ANT95Pwk+uD2MvM57wTtNuwK7bruS+8C8KHxf/Pm9A707fKU80v1TvhG++v8qP4JAIYBmwRgBm0GTgXOAkAC0AN2BXAHcAgtCEMIdgdmBQMDrf9e/EX6c/gl93f2RvbI99b6MP7MAP8BAwJYAY8Al/9P/nD8UPp++JL35/dZ+Jf4pPhD+Dn42vi5+Z76Svs2+1P72/uJ/Iv9RP6A/jf+Mv2s+2f6g/ko+V75QPnR+Br46/a29Qf0NvKD8U7xs/J39TD3D/hs99H3MADeDpIdNSgbKx8rDi41MDExAjH6KyIksBoLEVINUg2NC8UIIQUuApQADv12+Cn1ivKK8ETvFu8J8GjwYPAh8dnynfQr9ITyf/K68zf2tvgk+hX8//2V//EBCwQUBdoEFQMGAvICEgXdB7wJeQqNCgcJfwZAA4n/vPxn+iz45Pai9rL3Ovpa/XsA2wJ6AwwC2v8F/sn8u/up+sD5ivnz+Yb6/Pqq+nb5Kvit90v48vin+B/4QPgd+T/69fpV+wn8kvyC/MX7+flJ+I736fca+XH5gfgY97P1c/Vl9dv0avQg82fy9POJ9UX3Tfgv+WAAAA2uGYIjzCYaJ5wpZyuJLNks9SiaIgAaTxGRDj4P3w4PDKcGzAKoAbz/5Pwq+eb0L/J98GjwvfGV8XjwyO/979/x9vLY8nTzbPRa9iz5NPsQ/ZD+d//qAL4BqgFeAe8ACAJNBIgG6gj4CQEKfgpVCnsJsAemAwT/+PpZ95n1zvUa96D5Z/yE/j0AAwGmAJT/6/3t+wn60/i9+IP5rvq1+8L86P1I/jL9Jvs4+Tn4BPjP90D37fYP96v3+fi3+qj8K/64/oT+1P1e/Vj9df1G/ST8UvpQ+Hf2CvWm8wfzmPNI9Ab1ZfZt94P4+/iK+qoCFBDMHD8kmCTLI34mHSlHKvkoHCQqHlkX2BHAEfQSKBFGDMsFFwLAAET+FPvK94v0yPFh76fuZO9b76DuM+7X7rfwmvHI8e3yafSX9h/57/qf/ND9SP5C/1MAEwHmAa0CRQSKBnIIBQpnCqEJuwh/ByYGcwR1AeT9qfoE+Ef3W/gv+jb8d/2d/ab9hv1P/Uv9HP0B/dv8ofzi/H79fP56/0n/Y/7g/KX63PhS9yf28PXT9Zb1e/Vj9T72Kvg4+vb7wvwM/e39MP+WAE0B7QDj///9pftF+cj2RPWW9K/0e/Wr9Xf1+fV69kf3W/hp+38EFxHgG8shYCI9I8wmMymcKdwmPyEKHFAX3BROFuMWVhRdD0oJygWpA+3/Uft49nDyGPC/7p3uHO8A70/uw+1A7tXvL/Fk8rrzBvWz9uv3L/lw+5/9sf9cAScCQgN8BMgFPAjBCnUMHw32C+0JBwgPBlkEWgJJ/xP8Evnw9mz2ePZN92v4AvmK+Qn6tPq1+8X8yf1Y/jb+Wv0J/BX7g/rE+f74v/d19hX2dfa/94z5pfrS+kr6cfl0+fz5jPoL+yT7kfs9/MD8mv1R/vn+c/+C/vj8SPtX+dz3WPb09Df14PVJ9tD27vbG92D5DvsHANkJhxX0HmkizyHkIikm/iibKKYktiDeHRkcjxtzGxMboBhoE/UNvAlFBmoCw/w591HzNfDl7f/rcOqI6ifrn+tq7Cntou5F8fHzKfZm95v3ZvhQ+gj9DgCoAQ8COwLcAiMFAQglCkYL0AoTCb0GygMIAdP+9PwA/L77vvuK+8b6OvqI+l77B/yK+x36tvgP+K34Svrh+838vPzH+8f67vnz+Oz36/Zh9tP2GPiD+af6TPtt+1n7y/qc+UD41/YP9p32Cfj8+c37Lv21/t7/9v87/7H9j/vz+U34VPeB9yb4TPnL+tv7YfwK/Jj7xf2vA84MAhZKG8ocGB2eHgsiUySsIxQh2R0iHBkcAx14Hl8emht+FtQP6AlmBe0ADvxa9rvwre267PLsie1o7YXtm+7M7w7xBfJD8nTyU/J28qrzofWt+BL8N//ZAXoDagR8BXgGPgfQB6EHHQcPBmQERAOtApsCHAMnA6ECbAFv/6T9V/wi++j5f/hd9x73X/cC+PD45fnu+nn79/qw+c739PWo9Cn0n/SH9en1QPZh9z75rvte/U39WPwi+/n5mvnh+ZP62fsF/Rf+c/+dACUBwwBN/+v97/w//Kz7Ffs0+4v8ov7fAMUClQNHA0gCNAJOBdcKrBDrE8cTyRL6EkIVHRnpG3sccxuCGWYYuRhMGbkZeBiJFBkP/Aj/A1EBr//2/YL7aPjn9e/01PTv9F70RPMQ8tHw4+/77nfuqe5X72bwivFK8xH2NfmP+6P8nfyo/F79RP5x/5wAowHgAhMEcwUbB3AIAwl2CIUGwQMgAVj/uP6N/iz++v3v/Xr+Vv+y/73/Of/t/Zr8Rvsz+sj5b/lJ+W35ePlv+U75Ovka+bT4Hfie97r3QPii+Mj46vhg+Xb68/v6/JT9rf2G/Yf95v1I/ov+gv6h/j3/5f+YAAUBOwFRAfQAIgBk/8r+LP5n/Zj8wPwj/kgAqAKMBO4FKAhqCzYPGRLZEkESvhETEhMT7xP4E0ITLRJ7EcURqhJKEwESpA6QCgkHiQTIAgcBl//i/mH+6f3k/Av76fje9m71vvQ59LPzOPMp89nz1PRq9Zv16/WX9pn3pPi7+Sv7CP3H/tP/PQBnAIIAiwBRAB0A0f+X/4D/wf9wANcA3wBXAMz/9v8vAIAAnQAMAFH/bP7F/Wf9pvye+0X6xviK99X2ZvYY9jv2SfZo9nr2W/aQ9sH29/ZC90T3UPek9yz4D/lB+pz77fz4/Vj+ov4s//L/gACEABkAhf87/z3/WP+x/xIAkQCQAQYDBAWpB58Kmw0XEOIRIBNvFIEV/xUjFh8WpRauFyIYlxdbFv8UAxT0Ek8RIQ9xDKYJ6gZ4BIcCSwF7AJX/Of7x+1L56fbv9JrzufIZ8n3xBfHA8MbwEPHS8c/y8fPr9Kr1eva492v5+voS/ET86PuB+1n74vvq/Mn9RP5x/nX++P7K/4gA3QBlAJX/QP98//L/JgDD/yX/gP4M/uT9i/3k/KP7JPql+GH3Uvar9V/1LvVx9dP1WPb29pn3VPgL+aL5B/ov+kP6nvof++/77fzO/ar+QP+J/+n/TACaALwAbADV/4f/jP8tAJIBjAO/BcsHUQkYCyoNKA8eEbMSSBSiFVYWXxYxFqQWjhcUGGIX+RV5FEYTNBK7EK0OrwzyCjQJHwdfBMMBwf9j/jD9SvsN+SX30PX29Dj0e/Mv8y/zJ/PA8tTx7vCT8A3xG/JX84L0+fXB95X5+vqU+5T7Pfvq+rL68/q5++v8GP41/wUAxQB1AXMB9AAFAD7/Kf+v/1EA5gAyARABuQAeAJX/4P64/Qn8Hfqc+Kv3M/cL9yP3c/cC+Kn4Hfk4+ez4zPjq+CT5dvmV+dr5hPpX+178Sv0T/jX/RgAuAeYBTwKzAqsCLgKZAXAB1QG1AmoDGgRCBdoGJgmiC70NUg9eENQQBRFeEboRBRLSEWsRQxFeEaMR4hGTEa4QRw9/DeQLkgpKCcQH8wUGBEIChADc/mX9F/zJ+jf5hffQ9Yf01vPH89/zuvNP867ydPLC8nnzd/Sb9dH2CPgQ+Zf5H/p5+s36IvtV+5777fth/OL8T/2d/cr96/1I/sr+Of9z/3r/jP/R/xsAMgDu/zf/X/6P/S39K/0X/aj8Hvy8+237OvsA+8f6y/rj+tb6Z/r1+dP5SPpK+xn8f/yf/LH8//xY/b79J/6f/jD/xf+qAMcBwwJNA/YCXQIiAl8C8gLBA5cEhwWOBrsHPQnVCjcM+QzRDCwMqgvQC1gM5QxSDcoNgw5bDwsQUBDzDwkP1Q1qDPQKnwlgCF4HYAYfBbEDGAKLACH/i/3F+935Kvju9mX2KvYj9gP2jfUT9YT0A/S688PzRPT99Mz1cfbp9qT3cfhL+Tj6/vpV+0X7PfuE+yf80vwr/VP9fv3i/XX+7/43/yn/Df8I/yP/O/8s/wT/6f7b/tz+q/5j/hj+if3Z/ND7xPot+vP5NfqV+uP6EPst+0X7Ufs2+xT7Gfsx+1P7U/uC+yn8Sv2//iAAJwG3AQMCEwLoAZYBbAGAAcABEQK2AtADSwUJB+IIYQqACwIM0wuQC3UL0Qt+DBENsA2FDnYPtxDUEYQS7RKJEmkRwA/3DT0MtgpECZwH3gUIBAUCAQD2/RD8XPry+L/3pPbB9Ur1SvV39WP1J/Xy9Pb0MPWd9Sz2wfZo9/33dPi0+Pf4V/nz+b76oft1/BX9qv0M/j/+S/5A/kn+O/72/ZT9Tf1W/cH9XP7B/gH/6f6+/p3+ov55/lP+I/7S/Y39K/3d/IL8O/zo+337JPsM+1f7t/sJ/Bn8Ffw6/Kj8HP1s/Zr9yv02/r7+O//Z/1cAugD6ABwBcwHJAQoCPQJjAqQCCANWA6sDVgQuBTcG/AZgB4wHxgcQCKMIWAkDCqgKNwu4CzIM2QyODRgOSQ7aDdoMoQtlCjoJUQhFBx0G9ASiAzACywCL/4f+sf3g/An8NPuP+iH6z/mg+XT5FPme+Cj4u/d19033PPdA9zv3WffK91D46fiB+QT6tPpe+7z7yfuh+6H72fsi/Gz8pfwg/ev9v/5W/3z/Pv/M/k/+if23/CL87/tE/PT8pP0l/lP+QP4B/rr9X/0O/c78pfy6/Oj8P/2J/bj9x/3Q/fb9Zf4G/4z/+f9PAL4AVQEIAoICmwJlAhgCwQGNAW4BggGxAaQBcgErAT8BuAFjAu0CIANxA8oDTQQbBdwF6AYUCOIIdAl3CW4JiwmsCekJ5gluCb4IJAigBzMH0wYqBjEFAATTAv8BWgHqAH0A7v9b/+n+pP5R/g/+gv38/G78u/sf+6f6iPqs+tL66vrv+gD7Evsg+0z7SPtT+5b7DvyY/P/8Nv1V/XD9hv2Q/W79Lf30/Ov8+PwV/U39c/18/VX9CP3N/Jb8j/zA/BD9Uf2L/Zv9sf3O/cX9pv2Y/Y39fP2x/Q/+m/4e/2T/nf+//zgArAARAS4B8wCuAHIATAAXAOf/ev/y/lP+3v32/ZT+c/9KAOAA8wDoANsAAwFKAXIBXAFEAVMBkgEVAq0CTQO2AwIEJwQKBBoEDQQeBDMEfAS6BOME9ATIBHwEJQTjA6IDnQO/AxoEXwRmBD4E+QPlA9ADrwNoAw0DogJRAgMCmwE/AeAAewDu/z7/vv5o/hr+5v24/VX9B/28/Lz86Pwc/UT9Nv0k/fT8o/xD/Ar84fv++xn8Ivwd/PH75vvH++b78/vQ+7n7o/u8+x78yfyo/Yv+Sf/K/9X/sf9+/3f/r/8SAJQA9ABIAX4BuAEKAlQCswLVApYCCAJLAbkAOwDn/4f/BP+F/kb+CP4p/of+1f5H/4D/WP/r/l/+//0g/kT+jv7p/jn/3P+JAE0BCgKSAusCMwNHAx4DrQIjAusB0gHtARECFwIrAvQBugGEATABDgErAUIBXAE0AfgA5ADDAOYAOAGLAbgB3gHoAdcBwQHFAQoCHgJYAmYCYwJoAnwCoQK/AtACnwKEAj8CCAL7Ad4B0AGZASUBowAMAFv/BP+z/kn++P2E/eL8Wvzm+4L7Rfv4+tT6qvqD+pb6y/oZ+2773/sX/EP8cvzO/HP9Jf7p/pf/5//5/wMA9/8SADAALwBEAPv/0f+k/1L/bP+Q/6b/qP96/xz/zv6v/tz+If9u/4z/h/9J//n+Ff9m//T/ewAQAVUBnQEDAkICpAIIAykDFgP7AqICSwI5AlQCcQJmAkgCwQFGAeIAVwAwAAwAyv+V/0f/zv55/l/+c/6Q/uL+Of+Q/yAAYQCsAMcAugCvALUAswDfACUBjwEMAp0CFQM5A1YDUgNHA0sDMgMjAyUDIQMuAykD7QJIApIBrACp/+v+9P1T/dT8d/xn/GP8gPya/Nb8A/32/DD9Ef3f/CX9kP3Z/Vz+5/47/3r/O//V/uX+E/9L/0f/cf+O/63/5//R/7r/fP8T/+X+7/4B/6r+d/6h/g//Kf+//q/+i/6r/sH+zv4R/8H+WP4F/vb9IAMrC+sKvAOF/qb8Zv/lA+UDOgB1/Er7nPwG/6wALwCQ/Wf7Jvwa/uP/5f+Q/pL9e/3i/vb/Y/89/x0AJwHKAm8DwQNoBDwEDQSVA+sCMwPMA7wDeANLA6sDGgTOA08DFgOtAgwC0AEyAW4BtQLuA+cCPwFKADD+Mv9eABv9hP3c/i/88v1aAIf+T/07/WMAdQHe/T3+vv6v/wwC9AEqA2UClP7Z/hUA0/87/2D88v0uARsBU/7o+X/7if2N+9z/kgQcAtf8m/WW9g//TQK+AQ78IPYW+bj9rwBsAwMAQ/om+yL+Uv9aAbMArvz4/coByAPDA+7/i/0V/58CLgQiAUL+mf5R/1EBVQFP/XX9fQB1AN39DPwi/d7/TwFA//H7EPxj/nr+nf3Q/D/9Cv8a/3r/0ABaATQCGgLJAOsAAQPGBG0FtARqBMoFZwb1BbsFBAUdBTwGFgZkBpcFegR/BW0GXQa9BOUCkAGjAWIEuQYhBScEkAStAyADjAMaAgwCnwMpAS8AIgH6/ZH7p/oiAZUMighp+4bzifPo+7z/APnN8s3ygPel+Eb2Ovh4+Cz2TfXq8grz0/Vh9qr6Df9W/fj9AABgANwCggJq/4kATgQNB0kFLgHQAcoESAhBCekBi/uB+03+CAREA575cPXf+Kv+bAL++ibyavSA/JMF0wQq+VH0/PnbAV8F6f8z+UD50v3SAkYDJwA5/fX74P2+AegHJQ4fD2ULywZWBdsJUw88Dq0KVQqFDdQRrBISD4MNYBBhEgkPcgf5AnEDLAaUCOYG+wN3AisAD/6l+7b4MfjM9z/1z/Lz8A7x2fMV9QX0uvNu83TyrPLb8n30eviq+vH69/pZ+y39Yf/Z/87/lgAKATYB3gFmAlgDrQSTBZ4FpwT0A+QCcQKUA58DhAJ+AWb/2f5z/vP8af17/Hr5BPkH+X74lfie9yf3xvc192T4LPny9jH4l/l0+JH6yft9+ov7T/zL+0v+JAGd/5T8Qv0eAGUCaQFY/QX9EwGvBFYEGwAp/dT9WgGsBm8MNBE4EfcM9AqQDsoTbBWuEhkRyBS8GS8arxZGFcQXARrEFrcOXghMBk4HBQmVB4MDRABK/dj5Ofcg9K3wQu497HLrCuuz6THqbOwo78rwFO/O7BPtPPCL9Ar3v/el+df82f9aAqID5QRyBlUHGwilCBUJzgmWCtsKfgpDCQcHEgUYBKADnQLFAOf+yv3D/Wz9tfue+ZP3UvYP9fTz3fNI8yfzvPO58tTxA/IQ8vHz7fW19MXzBvWi9+/6Nvz8+6H8N/5lAJ0Auv7K/g4BWgNFBNQBdf9yAK0CiQTjA58ARv0V/FcAowpAFLYX9hJsC3oMghSaGuQZeRSLFFEcKiEGH3kaYhg2G18cYRVlC2AFuwTlBj8HBgV3Adb8PPha9Orw3u2I6tPn6uav5r/mMed76Dvr4O2i7uXt6exS7Tbxv/ZQ+hz91/9PAi8FLwdICJoJjwqmCjQLjgvZCxgM2QtfDIAM6wm3BvIEegNbAwoDUwHV/5r9AvsQ+XH3QvaW9JPy+/Cc8JXwZO+V7u7u0+9H8HzwovDE8PXxEfSL9XP2J/dd9+r4cPtz/Xz+1/5m/2kAxQAAACP/Gv+UABUCrQFjAAUAkQDp/xf8NfogAuMQZxvqGKAMDQe9DwMc6CByG0sVrBgQIUIkwCH3HukfZSLBHjwVSAtdBWoFDAh2CIwFkP9F+KPzjPHE7+TtOur/5YvjfuJ34rPjAOZT6QrsqOvY6YHpfOxr8s/40v3qAHoCgQNdBSYJWA3zDh8Ogw1FDqwPKBDWD3sQZxEtEPYLJAfIA8cC8gLwAjkBmP3J+Sf3Ufb09Rv0BfHB7rTtaO1q7brt7u037pXuuO6n7qnum+8H8kL1b/hI+tT5Avm9+XT89//6AYQB4P+Z/1oBkAKEAo0Bd/83/lz9o/s2+8X7k/s9+/z5Nv00CbYVYhoIFbgMfw75GPIfxB9WHMMcjSMoJ9cjGCAPH0wgjh7XFREMegaeBLYFCQaJA8X+8Pdp8Y7tt+pV6FLmUeQg5O7kBuVv5XHmTujk6vjr+usT7MHtJPPs+Uf/DwMABfUFnAdnCQML9Av5C8oMuQ6eEDYR1BABEYgRThApDEoHugRzBFQE7QJaAGz9Pft6+af3O/as9PHyxvB67szs/OtC7Ovsg+1L7iXvr+9/8Kfx5PK19Mr2JviT+Ev4JfiB+WX87f4rAK4AlP+A/rz+xf6b/sf9Z/z1+0H8/Pym/Rz9iPun+TH6PQL5DwEaChp9EaAM+hPsHjsjrR/AGwIg+SZCJ8Qioh/GIC8iCh24EhcK3AUsBmUH0QUDAkT8hPXo8CHuhOsU6VbmVuTV44fjj+M/5PLlMemf6+br8eqJ6kjtGfOX+eX+QAIuBK0F8Ab2CJYL8gxrDbgNsA7bEBUSxBGhEYgRkRD9Da4JUgbcBEUEcQP/AQEANv1V+iH4LvYn9JbyDvG/76Dueu0E7XXtX+6k7qfu4O5v77fwV/Il9Nz1ePdq+CT5NfpG+1P89vww/UH9zP3M/m7/Lv8i/h79T/yT+6H6q/kH+m77Yfxe+xT53/z/CZMXyhxrFkINNg8SGmchdiA+HXgf+yY2KmUmMSK3IWkjfCBrF/4NnAhyB0oIJAiMBQkAivgk8nzuXOx46enlCuRc5HzkpOM345vkq+jm67zrx+lV6UDs5vHY92f8iABGA7IEvQX1BgcJags3DYgO2A+7EGIRtxFeEskSShF1DXQI1QTnA3YERwS8AlMAo/1R+0X5C/eP9FDyHfAE7+vuuu5o7vLt3u1z7knuHO1E7bLuTPFG9E/11fXu9gL4aPma+uz52vlT+4L8yf0y/rj+AADMAD8ABv7j+t35C/tg/Df+zP4n/gr+e/3V/+8JdRUBG8QW7A3MDSYXFCBcIbod5R0TJDwoPSWXH5QdlR8aH14YVQ99CWIIbAkkCbkGpgE4+7/1R/Eu7h7sUenD5rrl7eSN5Krk8uTj5jHpXOpT6nvpO+pZ7kT0b/kT/TT/1AA3AhYEZgbJCMULVA7xD6wQJhCuDkwO8A6nD7cOogt9CH8GyAXCBW8F9wPAATL+8fni9gb12fNc8yDz7PFp8Mju3uxL7JTsHO137Zftg+468HvyGfQG9Tf1X/Xb9av2N/iK+fr6t/x1/oz/qf9v/47/g//e/rz+zv5z/if+s/0p/hMANAG9/xP/5wMQD04ZRxnzECoMwBFCHMQh2h7MHC0hGif/JzQjDx94H5UgnxyLFK8MUwkXCpgK6AhiBEj+Ifnk9FvwBO286rLo0+fV5nPlH+UG5gLoT+oe66bqxunm6WjtKfKJ9uz5DPz//VgAqwKpBLIGigc/CAoJLgrtCxMN2g30DW0NTQxuCvoH8AZQBzQHMwa8A4QBnQAw/w79f/rc91T2hfVK9LDyivG38IbwLvAE7/vtYe1H7vXvafE68mfyXPMR9Sj3q/gt+a75ufoA/HP9ef7O/sX/VwC0/yX/if7x/Sf+Of56/pT+nf5j/1v/WP2e+1b/tQiGEtsUXBA3DTsRVxrcHv0cqhrLG2IgiCMWIkMgpyDoIKIe/BioEfcM8gonCoQJpAVcAMv7MPgR9gDzKO/i60Hp8uYs5UTktOQN52Lppequ6hLqVeqj6zLu0/BK87z2lvrA/ef/0gG/A/wFfQcvCKUJaAv5DMIOSBCoEZ0RUA/cDHAL6Qo5CtIIOgYWBDMDxQHZ/wf9pfmC9in0YPK38Pfv5+9t8KXwsO8l7tLslOyg7SrvzfCR8v7zXfX79pX4sPk4+i36RfoX+zL8+/3w/xEBTwFsAPL+8f3//ar+R/8g/z3/KwAwAfIBUQFfAoQIwBBjFWUTCQ7cDVAV+BtvHWMbdRofHr0huSAEHnkcPRyJG+AWNBCHC7MJJQo3ClUHtgIP/r35tvax823wO+5P7OLqqen15zrnU+j86YfrWOtl6QLpKOqh7A3wufJL9ZH47vqE/GX+TACWArYEUgZwB4AIkQkGC/IM9A30DcYMSwspCi8JBQktCXAI+QbGBDYCggC//ub8NPtV+TD4M/fS9Sn0vvKp8WfxNfGX8Cbwju8L8JjxUfPS9BH2MvbZ9XX2b/f++PX5Kvq3+oT88P6sABMBxwADASsB1ACPANAAgAF6AjkDugPuAycDPwL7AlkHFg0zENMOYQuDC+EP2RQYFkQUARTVFloZ/hg1F2EWYheZF+0UxxAYDVYLgAsXC14JjwaoAsH/pv0J+4P41/Ux85fxxu9j7W7s9uzp7ZvuAu6+7Ersd+xJ7ZvuEvCG8l/18vfs+R/7wPy2/n0AiQFEAlIDVgWaB6UJnQtjDE8MggtRCvYIJgieB2kHiAesBjoFrwOHAjkBSf/q/Az7iPnY96n2s/VE9a/11/Xb9NLzx/L+8UrySvNP9Jr0x/Q79dX2fvhS+db5wPpu/J395P3J/Uj+6f4N/yr/GP8N/6j/NABDAB0ATf/0/mH/Yf/i/qj/AwOCB5IKCAqACPwI9gtQD0EQWRCoERoVSRiPGOEWVhaXF/MXJxZ3Eu4O4w0hDhgOCA2oCsIHBAUpAsz+sPvs+Kj2+PQ683DxUPAd8HrwmfDV72/uVu3p7F3tle4G8HnyP/VA9zf4sPi/+Tz7//xI/jn/VwDtAeID5QSiBV0GBwejB7MH9wYmBl4G3QbCBgcG5wQGBC4DjwHR/6j+O/5x/vj90vyU+5X6Lfqe+Yj4ZPe49qn2FPdN9xT32vYI90T3AfeA9mr2FvcW+P74I/np+Bv5tPmu+qP73fsr/BH9Bf6J/tP+8P4+/wkAawDHAE8BBgKMA2YFnAYDBwMHZActCUsL2QzsDfUOpRAkEqgSuBIKE+YTdRRUFG4TahIMEg4SxBGjEPwONQ1SCwMJWwYoBDkCVwDZ/lr98/t7+t/4lfc99s70e/NQ8gDyUPIg8jHylvL18svzRPRc9Lr0mPXB9uP3zfiX+YP6FPvd++v8t/2A/nz/OADOAAUB/QAXASIBNgE5AVwBggGUAWMBOAEeAfEAfQBj/3H+vv0w/c78qPyo/G78tfue+u75ZPkY+f74yPjV+Pv4Lfkj+aD5GPpX+rn62Pod+6j7U/wX/b79J/5f/nH+pP7r/kb/b/+Q/9X/CgBhAKUAAwGGATACzgIKBE4GUwh1CRUKzgpaDH8OBRCnEJYR6RJfFAYVbxSZE4ITNxQpFJQSQRB4DsgN5wz/Cs0ImAbcBD4DCgGk/nf80fpQ+d73d/Zd9cH0VfTN8+3yvfEN8TPxk/EW8tDyufPo9Lz1DPY99nX2T/eQ+KX5Z/oO+w78Z/2A/uX+9v4n/3r/1/8MACcAowBeAc4BpgEyAaYAfQBKAN7/df8h/9z+ov4//nL9E/3Q/D/8u/sF+4H6SvpZ+nL6Vfpc+mD6XPpT+nn6u/oD+3f77PtX/Gz8Pfwy/I/8Kf2h/fH9Qv6L/v/+bv+M/6T/CQDMAHACWATRBfUG8QctCYQKrQubDKANAA8kEFwRPRLtEqQT5xMBFHkTeRJcEXkQGxC9D9QOkA09DLUK4QjlBukEIAOJAev/Pf5l/AD74/nV+MH3mfZj9Vj0dPOK8hTyJPJn8oHylvKw8ubyO/OU8yv0x/SW9ZL2Qvfj9+n43/m9+pP77/te/Lr8G/34/cf+X/+b/8b/KwB1AKMAdQA6AGMAhgBsACQADgAAAA4A2f+H/x7/tv6H/mH+LP6a/Rf9xfyu/IL8TPw9/Cn8Evzz+677Zfs4+1f7gvvE+/r7MPx5/ID80vwt/aH9Cv6J/s7+Kv/a/8IAYQL9A0IF/gWKBisHDAhTCWUKlwv7DLsOKBDWECIRnxFlErwSWhKfESsROxFZEQcRHRAoDyUOlAysCpMIzQZrBQsEdwKRAID+y/yh+236FPlq97j1WvRE8z3yoPFV8TrxVfEm8fPwyvDT8EnxH/IA827zB/Tt9M71wfaS90P4Cfnh+ZH6H/vJ+5H8fP1h/gb/Xf+r/+L/MAB+AOQARgF5AaQBmAGYAV4B9gCfAFcACgCf/1H/DP/g/p3+MP76/df9qP1n/SX9EP1P/aH9t/24/fb9SP53/mf+Mv4w/lT+vP7t/un+Af8+/3r/oP98/6T/YQA/ATQC0wIRA6sD3ASiBmsIxwnvCg8MRA1/DsYP1BDSEdcShxP9ExUU+xPiE84TdxN9Eh4Rkw8HDp0MzAoeCT4HBgW/AlcANP5R/KX6yPjO9jb1wPOu8urxPPHf8HbwN/Dc737vku/G70Hw1vCs8XTy+PLO85L0GfWU9Q/21/bT96T4Rfnn+bn62PvD/Bf9Z/3Z/Wz+Lv+Z/xcApgAVASQBBQH0ANQA2wDOAL4AxwCPADIA9P+9/6T/Y/8n//D+0v7k/uD+Df///r/+rf6z/rj+w/7S/uf+D/8c/wb/1/6//r7+zP7t/gr/Qv+F/+D/MABuAJ0A2QAwAQUCVgO2BBYGUgc/CCAJKwpNC6kMMw52D3sQLxHvEYISJROvE7YTghPrEjkSSBEkEOMOrQ1GDIAKnwiVBpsE0AL8APD+xfy7+hj51feU9kr1MPRG82nydPGD8MHvju+27xDwWfBd8GLwt/BQ8cLxNPLP8mXz/vPB9KH1lPaK91f4Gvnc+ZH6Pfvo++D8A/4G/7z/LQChABsBZQGkAe0BDwIrAjICNwJIAnECcQI7AsoBVQEFAa8AjwChAMAAwwC5AE8A4P+4/5L/gv9L/0b/Wv9G/wz/+f7r/gj/Kv8W/wP/5P7p/vv+Vv/O/wkAPQB1APMAxwGzAoEDeASaBb8GkwcgCNQI0gkyC5cMtg18DgUPig8CEDgQLxANENwPoA/aDtgNwwziCyMLLgq1CBcHZAWUAwgCcAAN/979qvxu+zj6+/jK98z20/UG9UL05vO+84nzZ/Mx8/jyxvKy8q7ytfLk8jbzo/Ml9LP0S/XT9Tf2gvbQ9lL3BPjf+LD5ffpI+wn8tfxG/Zv9Bf6h/jT/vf82AKwA/wBEAZIB5AG3AZgBjQGGAccB4AHvAc4BxwGbAXABLgHoAMsAywDgAOoAxQClAKgAxQDvANcAnwCUAJwApQCzANIAAQEwAU8BaQFaASsBGwFWAboBEQJNApcCDwOiA1AE8gSFBTUGyAZ5BzEI+gjbCbMKVgvgC00MjgwCDUQNXQ1ADcoMRwyoCxcLTQppCXcIZQc8BuwEmwMuAuIAq/9U/uv8qvu0+tz5Kvlt+Lv3GveM9hP2nfVK9UL1SvVG9TL1HvVa9br1HvZv9rP26fYh91/3kPca+MH4Qfm3+Q36RfqK+sL6CftV+5j7zfsm/I384Pw0/YT93v0j/lj+Zf55/tL+NP+S/8H/6/8TAGEAmgDHABABQgF1AYABjwG4AdUBBQIrAlECbAJqAkACFQL4AdkBqAFjATYBCgHfAKoAbgAdAOv/4//5/yIATACAANYAaQHFASUChQInA+4DzwS7BV0GFAfsB6wIYAnCCREKggrvClILdwuNC7ELuguxC1ELqArtCS0JgAjWBxAHVAZzBW0EXwM2AicBOgBR/2X+Zf1Y/HT7sPr++XL50/hA+LL3LPed9iX24PXZ9dz1rfWA9W71rfUG9lb2ifaM9tz2Mvdv97v3H/jK+Gb56PlZ+s/6Y/vz+3382/wg/Xz98f2Q/if/ev/Q/x0ATwCIAKgAswDOAOIAGQEbAd0A1wDbANcA2wDbAPoA8QDfAK8ApgDHAOAADgE2AWoBrQHFAbMBmwF3AWEBVQFeAVwBZwF7AXwBggGCAYIBeQFqAYsBtwHKARMCdQLtAlIDwQMoBJ0EBAVqBdEFVwbGBh0HoAfzBz8IdwhZCEwIPwgbCAAI4Qe1Bz4HsAYbBo4F/QRbBL0DCANaArUBEAFcAJ//7f5a/q39Ef1h/ND7gvsZ+5r6Uvof+tz5nvk8+fv4wfiu+L/4xPjK+KX4jviT+KX45/gm+Uz5a/mX+eH5C/p2+u/6Wvu8+xD8XvzL/Cf9Wv2W/df9Y/7n/k//kv/O/xMARgBhAHQAowDdACsBXgF5AXkBewF5AXcBTQE/ATgBCgHoANcA0gDWALwAkgBGAAAA5//g//L/5//M/73/tP+g/6D/ov+o/87/EgBEADAAdACjAN0ANAFyAZgBmAGvAekBRgK2AggDGANPA5sD5wMNBEQEkgTNBBQFEgUaBSYFPAVXBWIFWwVFBS4FCwXaBLAEdgQuBOUDXwO4AjACqgFRAQMBnAAkAL//Lv+F/gz+bv0K/bX8VfwX/Lz7dvtF+x378frb+r76vvrY+u/6LftQ+337sPv++2D8kfzF/BP9RP1z/bj9I/6h/vj+N/9b/2z/lf/I/+f/BQApAHUAqACuALoArgCmALoAjQBuAEgABwDp//T/+//u/+L/kv85/wj/7/7Z/rH+qv7S/uL+2/4D//b+Df81/yX/D/8B//n+Qv+d/7T/vP+v/6v/ov+U/47/tv/c/+7/JAAnACkAMABGAHkAlAC3ALoAwADmAP8AGwEwAVwBggGvAZsBtwHbAe0BJwIyAjACBgLmAeIB9AEaAiwCPQI/Ak0CKQL9Ae0B7wEYAkYCagJmAloCXwJWAiwCDgIBAuIBtQF1AT0BEAEHAeIAuQB+AE4AEgDn/+L/sf+g/5v/nf+v/5n/g/+V/5T/r/+//87/zP/M/97/6f///9z/0P+o/4f/Wv9C/yn/9v7p/sP+ov5Y/hX+4v2W/Xz9WP0u/R79CP0H/fH80vyx/JH8hPx0/Gf8Zfx9/Lz8//xC/Xf9bv1K/Ub9Wv2J/cH99v09/n7+qP7I/vb+Cv8R/yX/Jf9W/6v/4P8/AJIA6gAgATgBagFlAXcBjQGhAdQB8QHyAQgC+gHeAb4BjQF1AWwBbAFBATAB/wDmAMMAtQDAANYA8QAXARwBDAE2AUgBcwGQAe0BJQJGAmwCdwKmAtMC2wL5AtICxwLZAp8CkAJhAkkCOQI7Ah4C3QGdAXkBTwE4ARUB6wDAAHIAOAAiAP//2f/X/7H/jP9W/zT/Ff8G//j+9v7k/sf+uP6i/rH+lP6Q/nz+Vv4//iP+Af74/eb9vv2q/Z39hv1q/XP9d/2W/ZL9lv2o/br91f3y/Q7+Mv5U/nH+wf7b/gT/Ff8a/zn/T/9E/2H/kv+y/9P/3v/n/9P/xf+y/5D/c/9u/27/i/+d/6D/jv9m/1j/Rv83/y7/Lv8p/1b/Zv93/5T/sf/i/xAAEAAXAEQAWAChAOoAMAF8Aa8B7QEpAjsCdQJuAosCsQKiAgYD+QInAwoD4gLyAtICvgKrAo4CSwI5AhMC+AHVAa0BnwF7AVwBOAHtAPEA3wDOAM4ApQDLAKwAnACmAJEAmACqAK8A0gCvAKgAkQCEAJ8AfQB3AH0AVQBcAEgAEwAMANn/0/+t/27/Nf/l/rz+nf6Q/mj+Pf4i/uv9mv2U/Xf9X/1P/UH9RP1E/VH9G/0p/WX9mP26/ar9qv2Y/dT9CP4Y/kL+XP56/qj+r/7X/tL+5/4u/9v+0P6Z/u/+W/8G/0//1f4E/6j/g/+i/1T/sv9z/yr/Nf8e/7H/KwCoAOgAxwDHAPoA3wA9ARkBcwGtAeIBZQKvAgoDvgJYA9ICjgJwAUIDvgtcEDEGTvnR+CQATge/BgkAUPvh+5/9eP9hAQP/qvxV+gT5/vs7/yr/rf39/GX8Tf7AAKD/5/5h/Tf/2QKUAgECggD0AAIFIQPtAG8FhwIlAn4E3gQYAyX/ygXzBSIBSwQk/c/7AgfVBBQE1f7B9mb/8Aa5Bnf83vXz+mgEBwiY+lL3Af30//4F1f7O9J77mv2oAs4DQPgi+nP4SgBVCJ77nfMd+uYHIwI8+tj3n/1zBVgAKPhD+7YEB/wP//r9QfsTApcFIfhr+ZUHqP2//uYAIADO/HMC4wNn/Iv7MgiYB8/5vvyNAuEGzgOJ/Kj8egRABdQBWP5m+uQC3Abe//r91/ys+/oG7gVt97/+DgJ5AUoBG/ziAK0CIP5w/q8DMgPV/xwBTwHm/f4EqwZc/r3/JwK0BDkCUwDTAygEwADM/wAEOgVAApT+rgDDBEsFLQDM/pX/gQQmBen/8P5xA6cE+f9eAJwA3gG6A7UBP/1lAdwC0AKM/3f+1f+W/qQE8AT7+J77lgH/A+n+OfVjAZUF+vra+EL/jfx6BBn6qPQpAt4CAfcw+CkCxf2N/k/2x/uU/3D70AAd+0n5lwKL/4z5QPmqAikDvPz6+4L8pP+mAoQAif0T/wb+rgZK/Rn6hQLlBBv98/lJA4kCUgXH/YL2KAQSCCD+xf+L/loAQgFVAQgBCP6vA14Aq/4eAeoApQB0AO/9GAJABKn/sf4n/wYCWgG/ApsB+//IA4UEaQDHAUAD/QKRBVQEyQDHAQkFXwVXBlQESAKXA6cEuANOBOUE8gKTBDUDAQLSAbUBsgWdAYv8O/8KBNIAn//9/2r9tP89ANL+u/ti++r7Jf4HAfz6yPdx9sD7bAPSARD09u2dAHMDY/x39FX9UQDz+H37dfyk/5D+APsm+O0DTQTe9Z76n/8J/HoDnwF5/MH3xvoIApsEkfuT+UsEzP7998f86vyIAGQGf/rh+mUAP/z2/qYDj/pT/kkDPAY8+8j2EAjaBbgBcgCGAUMA0P2UAvwGOQLy/en/FQizB1j/AfwtAQMJXQuZBMH/yAWICMYHvwYoB/EITQpuCXMKXgiNBw8L4gv4B18F4QU3Bp4HsgW/BCEEJAYxBnkCQv6E+9L7Z/zt+x35yfkm+qf64vsS+Aj2yfUT9d725fbe9Q/3LPnz+FX7j/vE+cT5Ifql/FH8B/29/9QBWgAwAtQBQAK2AsMBRAMRAu0CJwCaALoD1wO//87//QJlAtb6Xvk5/jD/8P/n96T3qv2t/ur5Cvdx9oz4ggEo97vumfc0/yD9xvd0+a74dvuV/77+yfrQ9dL6CwZcCMn9UvfC+ccJew9v/3v9TACJBFwIAAXDA3gEZAXLBg8GhgfBCtkK+At1CckJ0gm1CKYJMgvcDcQOzA3wDJ4Mdwy/DFILHAr6B20GtAYAB9YF1QV6BnUEwAFP/k75iPh0+ij6ivi29qL28vZx9QfzafMK9Cbz8PB88O3yjPYC94P2ovj8+Sv6YvjT+AP81/5b/zn/ZQKtBH8EJgVsBJkEDgfyBWYEyQYhB1IGoAaOBTEGMQUGAlgA5AFR/mX99P/B/d38svp4+U75OfjP91v3rfdl9mf1yPfD93r5bPSf8on8Fvrl9tH5dvj1+/b+1vwS+yD7uQAy/kr9LgKm/zIAmf5GAAkHbAIi/cf9PQB8BQgFTAU9C8gLqgpXBw0HzAvnDaAM4Q0tEc4SeREmERoV1xVjFDkSpw9JDswMdQv/DDwOcw3QCqkG7QIZAIb9CfwO+k33K/Wa8hbynPE+8L/uhe3D67rqWOot6mDqY+wz7yHxivGO8DbyvPS/9ir5/vr//G7/kv9pAW8EGwfrCZQJdAlPCgwLNQsEDHgNCw5+DSoMFwvrCbEJOgebBKQFkgPCAOL+D/66/E76YPgz+cX23fDN8ub0WPRt8Vnug+7n9hP3/e4N8Cfzn/a2+IX10POc+LX8lPyF+Kj97f9T/XUBkAGvAj4D4ABPA+4HOgfuBMMBCATHCoEOdRLeDksLUg5SDuYQ6hFTD7oShhcmF6kVMhQ1FF4YNhi3EvANWAwIDaIM5AqjCeAJ4gl7BkQAePoU+fn4aPfX8zXxq/Bo75Xui+wB7Grsuepb6OfmKuYE5+TqlO2/7S7uV/EV9CD0NPS89UP6CP6b//0AJQNFB6wJqwtMDiwO4gzwDOoNyw/EEKAPRRA/EGIPXg/dCioHXgeGBn4DWADQ/Qn8Vfza+iX41fUk87fwce988GnxJvBD647s+vFT9CfuJuot82L5r/fx8G3yC/oc/Y398v2I+Y/5SQN0BioDrf/a/xgFFQpIB0kE4ARZBUUGhQb9CSYQ0hKoER0OZA3xD1oS1BHTDuYQrRckGb0VixQPFQ4Z0RhpEeMMAQxLDMoLjwn4CE8J9weyBOT+mvlu9pb0GfMS8OvsFewc7Abs4urv6IznfOcn5qHj8uM55irpBezH7RvwKfIF8/HzlvbY+fr7+/2oALEDiAasCD0Lkw4ZEEcPmQ63D8cQRREkEmMSQxLvEDEPsg2rC/8ILAZ2BWYEAQF6/k/9n/vd+s73IvMb8pbyWfCL7J7woPEY71zxMO9X8Sj3zO9x7vL1PPcT97Pyjvf0/8n8gftF+wj9wQPSAJT8rgC9BOcEEQOQAoMDIQewBoQC5QPhCCgPwRVYEvYMkg1IEUgUJxShEisUchrMHEgZVxfPGHMbFx1zFjwPKA4HDhoO2QvmCIYJ7wg1BO/9fPdT9K7zf/EI7krqH+go6MrnfuZ55OniGePi4hvhvuDo4UvlDuq17NntVO+Y8Sz1EvjW+Vf8gv9zA9oGLwmACrwMdhB9EtsRQxAJEAMRlhLJEuEQBxGCEaIOGAy5CYMGMwbTBNAALv+v/NL7XvzB+Lz1WvUR9NHwku9k8lLxRuwq73D0/vIA8eXvgfHY+Jn4M/AY8YT8ugFv+s73fP3pAo4Ds/17+28DpQVcAugBmABHAxgGgQZXBvYCT/8yA+4HkgpXDvwRBRFbDZUNgQ9nEp0SaREIFLsZ5ht5GaAWaRiJHD8bCxWVDvQLqw3uDvIL4gg2CH0HqwMO/Xj2dPM28o7vo+t45/TlmeYL573l9OPm4pHiP+IX4YzgseI+56/rzO7u7z/x8vSQ+Or6F/wn/2YC8gSnCJAKvAwXENkRhhJ0EnIQaw8bEekS/BFtD1cPsg+SDfQJmAcbBRMDXAE3/gz9KPss+UP6PPib9T315vGY8YHxaPBT8WbvsfSJ97nxqPRb9kD3WvzB9vb24v03+UX74AFP/pD9Uv8u/+IBtwDC+9L+fAOfAXAByQC2/swAgQMiAtn9fP30AMYErAq3D1cOjwqPCmkOaxHSEC0QqBJOF8UaghpSGCEYlhuxHEoYTxODD9EO0Q4uDWYMVAsJCQIGcAFI/BH4vvSI8l/wluwZ6VPo8eh96JnmreRj5FjkpuJ04QziX+VF6cDrCu7N71Py+/W4+O/61vxP/8UCTgX+BjgJWAzUD4sRbRDLDyYQCxDaD4wPeg/oDs4MGgsuC7AIKgb9BOQAVP+C/1v6jPcO+i36Ovif89TwI/ax9rbvju9X80j0VvT+8dny5/li+kz0Y/Xk/Lj9Hfm7+ev9jgI6AH/5ov4ACLIDrvyq/MUChgeoAlH+4//MAeUFwgXk/r7+0gF9AKMBVQadCe4MwwzDCbUI8AuDD6cO+Q36D9kTTRZPFRoUNxYqGc8YgBRtD1ANkg27DfILOQpyCUEIwQX2AGf8S/mk9if08PA+7XDrLete6xXrp+mD6ATo/uao5WPkEeYv6qzsce4d8XXzWvY8+Vn7pv0K/3kAugKrBdAHzAmZDGsNhQ6/D3QODQ3sDF0N9wy6C0QLPQrJCGcImgZfBOkCrwEY/0b9LfyB+tr6lvp4+c/39/F490IBeO946dv8vv6B8kXwxvdK/er6uPgx8rT3KgMS+f/2qP5a/pYAm/6P+7oADP4n/bMBSQJrAFX8D/81BScEWP8N/6gCaATkAOL+JQEtBn4LpA2xDBMLWgrgC3IPqQ8ND54QoRNGFcgUsRRUFYwWrRcIFPANuAvbCnMK+AmwBwAHsAYLBLj/e/tA+PL1cvQQ8a/tRux+62Przutj65zqT+ov6vfpTunH6aHru+4f8hnzrfV2+CT6/fzH/m//mgBAA9YFvwaaBpIJogxWDYENlwytCwwL6wtuC4IJWggfCIwHIQZxBsoEhgFuAYb8Xvu4AYL7uvQz+vn56Plf99LyP/sI9Lvv1/4Y+u/qe/Go/aYBhvtG7cLyIAOZ/z32jfoF/a//HP89/TAA1AGJAY37Mv3NB7YCjfo1/4UG5AqNAYL8NQS1AkoBWQTHAEQD2woGDJgJfwivCWYLtg3hDZ8LpgqJDfUQkRLdEQcSMBS4E9AQiQ3VCy4L1QpnCiAKIAhJBbgF3gVaApr9Avtb+V33pPS08YHwZvB/8QDxFvDl7mPsDOxl7Dbr/+qS7Wjv7vCD8W7zO/fG+N35cPuA/ef+vACqAiMETAXUB2ELrwuMDLMK6wlFDeoNxQybCu0Iwgi6C0gJKgaSAX4BXgnjBB35nvkbBU78jfVT/Pb2uvXt/A/3a/Cn+rzzI/bl+qDv8PhR9rLx5ALW+cjnlP9cB1H14ekc/mUSNvVY9gAFMv7fBp//NvQbBtIKg/8TAI0ArgWwBvkDAgTrAgYGbgPKA/wFmQO3B9cMPwmyBmUKVg3BC6AG6AfEDVQOPQvMCiMO2xC3DxMNnA15C1wH9QcQCOgFWwXYBFIDCgNYAkL/P/s/+kP55/Xq81HyLfNN9L7y1PLY8hLy6vEQ8STxYPEq8TjzJfVu9kv33/jY+8f8WP2q/Vr9TgAiABr+bwWWCCkD9AQBCh0GFgMrCLcHbgJ+BF4HwQVC/y4EewpS/3D9Bwjk/pLzdQD//jn+tv+w79f8CAq08GX0gv+u9Or8z/ui7qX43wWd9JT0I/+L/075f/ic+E3/6AWC9jf2hAJfCsoBQvQSAGoMXwFu+7MBvAqQAvf2IQSvDKsCYf7QAUIF1gk8BiX/w/8BCNsIUAUwBOIBIAi6DCQHTAWvCboL0gnfB04GYAhhClgJnQgZCCsJUQrxB+UFsAZyBoEEcQIVAi0BpP9EANX/i/4H/bD6hvrY+Uf48vet9nD1W/eo9iv17vZY9QX1RPa/9aT2nfeY9Uv4hP5u+0v20vvTBkT+aPVqBG0Hwvy8AMcHVANbBJUFuwYLBk0DrgVcAXEFFgbS+xoFbgkX+3D7MAEdALIHlfHd9IwMbfh2+Vb9BPAW/+gI9emd9WIE+voX9SX31QHs+xX1KvmHArf9XvqX97MB7gYc98/4VgT9Ayz++fkEBQ4AOPvYBMkB2QARAUD/FQELBdACE//u/9sH/gYI/w//QQZTCWcAPf87CXkHkQAGBsgGRgPkCWUHfgNaCAkIywXLBscIyQjjBoAIiwoxCFAGHQZPCawIiQJ4A4EFJQPtAaoBkAEDASr/O/3L/LX73/nP+jH7D/jY99H6hfhb96n6FPqp+LL4Rfgx+qz7Nfj1+lT+r/0Q/Y/8SQLn/y/8HvyDA+kCt/mxAKQB2QD9/94Bxgep9qb3VRAeAvzrmQWiAzr6QQCp9j8I5vw39qn67gWO/hz1Tf56+LwKuP2e7yv8Og3OA+DthPs4B7UGPvpA+E4AUwhBAPz7pAKm9IUL+AjW74MDGgSzAXAAHfvqAN4FLQB/+S79QQnFApL3mQSH/1gAYgQd+3UAtAPG/8z/hAKOAukB1wAeA4YGLgPu/yoFoQguBHoDCQb6B5oJjAeOBUEGWAk3CqcGDQftCXQHtQbFCIcF5QU6B+cD/wJ+A/0A5AA7AgwAr/7S/t/8T/61/UH5EvrN+jb74/ru+Zv2gPwi/AT3D/iJ/Jf+1PLB96z7nfzA++r45faN/IQIWfdT80sCNAH8+7L6XvvTA6QCaO41Bo4Nce85/fcFaAQd+i39dPqdCgAEeu0ABYsDbv9x/ywCjPGwBa4JrfBB+3kKewCf7PYJDggo75IBTwiZ9YkBnQCz+7sGK/qz/uACSABYA5L8FPr0/+wMF/yA9zQCD/6EChAAn/KLANwDUgOy/2P0CANcB+j7lAKDAxH9KwLJBqwA5QKKBLgE0ggPBWYDQQqPCi4E5gaWC0YJMwbWB1YLFwpXB90GAwqzCgoE9gKuBrsFUgTwAqwBvAPdAcH++f/F/tv8Kv+L/An5Svt7/CL7R/iW+9/6wvmL+8D0iPgT/Fb49fjg9bH2/wG2+LPylwOl+PL2jgQr+37u+AL/AgH3Of7x+3T5zwVMBoPufAKAAa/8/wDz+iT64gICBzj0yv49C8f8mfVXCHQIAPMXACwDgQXLBmvvDANwEFsFrumVBToZ5uuD+lAOuAEn9yYGVPhrALUSXPXi7v0DWhQI9vDvlgJxBnABrfcN/5b6pAFMBd35H/nl/+EHDv3y9RMEUQiI+wP9FQOjB+v/wvxhDAgCFf+MB44FZAXyBPUEqgnEBwgEBQrACMcHCwdgBdkKrAnyA/MFYggdCIwGqQPaBBQEugPFA0gBfv9WAVoBSgD9/or6bvzy/8D9Evje9j/81f6I+CP3R/p7+z76+PXb/fr6hvIb+3L9u/kr+3n67fRsAxsBvvIZ+koBzgOu9Jn3WAPg/V792/7E+Kv/5Agf+u73zfvnC34CyPAa/gMLpQXw9TD4ZQm4DAbwbvvbCkYBePrBASX+V/rpC3H+8/GiBd4NFe6Y/MURtfPz/K8E+fdGChAAj+kEDtcL9epNA1UHJP2f/vz59wbHCkLt6fjWF1gDsep3AFwSVv1i+tIB4wZYA0b8IwPIAkkEDwV1/+IC/gczBEADFAZ8AwgE9QbfCB4DKwE/CZQJHAQTBMsHGQapBMgFcwOmAmsFJwRYAa8BlAGlAPAC8gFu+8P+OwBe/HX8XPud/OL8O/zs+gX6RP2f+9X3Kvrz/Ir4kvyH9gT5MQWN9ar7K/0g+/0Be/uz80sENgdt8DD8fwQJBwr3fvXbC0z84P2HASv1dQn0A5/t/wjhB7nwEwQGArz8QQYi8zIAiQrU+lH/V/lHA08DbvtxAkj6Rv58C1325/mkC3L8RfhpAZUEvvyu+UgCWP4M/iMDU/V2Bqb+w/S7DZr7r+1EDVgKk/B/+rUKJQVA918DzgLc/04HzP+m/z0CTAbPB3z9CP/bCD4HFwFLAmMBtAZwCZD/0v5TB5wH7wD6AFIFpwW6ABoEawckAawAWwZrBQMAxQF+BHgE9/9q/8EEmQNyAHP/Pv+VA2wE/PwQ/DABAQKzAFj82vlxBK0BpvYP/gYCiPqE/Jv+rP0c/hj+qPVpAeUE+O1SBKj9tvANEJL2C+g4EusAwe7e/qwGtPq58IcNfAPP51EKuAUb8EMOHfi9+Z4HJABh/w7yuRD8AAHrOA5h/4P3/woz+Gv4UwmFAgD5tvYaBbQE5fnf+W79EwSRBZHz5PtbB6r8uANG+6b2LgWjCjsAqut/CJkTK/NJ/4IKH/tz/5QL/wHH+2P+lgggCt/6VfqmCj0K1vpI/rIDfwVZBqz7WPwZCY0HXvzW/MkJ4AJ6+ZEIUAf1+bz+5gdUBb78lAIRBVT/ugTeBOD8oQCmBNcCswL6/LP9XAftA2v69P4CBWT/IvzO/zgAl//C+6r9OQMF/Ej7jv8sAoD2HP8fBcz1rPzyA9v8w/bPBmf9MPbvAcMChgDf8VgEXgfk9v38GwE2Am78rwS78FoCaBOR60cDO/5U+ccTJPG38xgMGP8A+/L9G/0RA7n6T/2yBGn5bv9o9w4HcwQd6LANVgUk584RtQEh7lQGJf5IB8z3Lfq1Cv/zdAigBz3tOAgiB3f+of5w+qQNHP6c+OgHZwj/9xf+kgsE/5b+s/5MBzkD+fa0AxEM0P1E9l0FvAh1Aa759P4/CuQCSvrrBO0Itfo9/lQLawg/9Yb8UxELBUT3MP2bDFcIDfpz/V0G1gfr/yT7DALyBGwB0gI4/CD+Ighs/wn5UgMhBGn98fwi/N0GGQA3+cr9cPyNCov8yu43BcQFzfug+m/2mAmkA87qjwnfBzD0s/U+BsQNlO7E+lYKYvgGBpQCCO37DuICJvE8B/YC+vlj9qAWu+/j8RMj0uzY8lAHmgdfBZjsl/HUGMUKSuDF/egOewCb98v8E//t/4cDCfxt+hv9YgRsCavt0APTCxXschNfAufmbhAxCIX3I/4TAKELHv5h9fAMlwVR9G8HagTj+bYDUgbN+0H9mwqh+5D9IAq4/+H5NAAZCnf/lvQtCZsJ1PQO/koKUgXG+Hb6gAiNCUj6k/iiBGcI6QIY+Qr/pwdAAyT96PwiB5IBBfosBOUDqP2L/gMBTQEVAwv7IgDHABgEr/5M9DgOKf2H9hIFuv0dBRT69fqVBYL9Bf5gBXn0Wv7lC6H0LP/5+VMKHgHC6OUOugMz8LELavXT98AP0PZK9agJBv4q7zQSo/3I5iwVY/zy74oHdwPD9c/7xgcs+TUF9PWE/gsGWP3cAyjwogO/C1X7zfq8/IoEdAcF/ZT7/ADb/tENKPtJ+P0C8QYSCHn0Af+KCDMHy/lt+GMKPQsg9Ff5ghSN+oX24gy6ACT7owFjAx8GNf+p+G0IbwZN/sD9YABOB9MCVv3s+mcHfQa4/2H8o/tfDGEDcvul/KP8oQoHCG3yHPh/D5ACqvXDAdMCZf16BUX4QP8xDXDy1/ymCtX+NvuU/sEBBfzzBl8BXOweCvQJUvBeBq8A0foGAqX8awUW/4z4KwFYA73/1P1a/osALgNjAaL1fAFnCh/5/PpyAQD81g4e9erzEBDR+oD+dvj7AsAHQu8PBKEC4PRaCS38q/DJCDYHfu8z+6AOJPJS+kUOnPD2AZcC/PNjFIP3GuwRFoUFa/HT/ikJPARV/Tn+7QFeCEoBuvZTEWb/P/FpEpwG4vOxAccIpwTb+wX+iwnn/yT8WAGRBYn/bP2S/r8DfwX4+3z4AAfSB3/4mvtLA9wCRv7JAHQAWvyq/iIHogOA9+L9fAWdA0f/Wvsh/3QFVwWn+Ib7GwfCCBH16vmBDDkCV/qD+CIIpgMd+j8A+gFd9ooIzQeA7pcGjfuPBp8JvuS8CHIIXABK/AbvoxBnAHT73QFY9T0IWgHW+NsIGfMK/AUbS+bR+f4XR/Ds+I8IYwKE9Wr41gdECwfq/f94/2sGEAkI5gb+wwtBCQny4+7JD+8L2+tPA3ABegSh/lz0fhV69pv1/BFP/PH7ZAfq+bUHHQfl92v8+gr9CcPzif2mDboB0flqAqEIDf8y9/gLOQso7gQE4RBB/K/3lwN2DkD/wfZTAPgIlwVD/Gb5CANcCHsAs/2P+34Aqgl5/Iz3jQfpAhj3twA5AQ0Ggv8T70YIWw3Y9yf0fQUFBxv9cfknBB/67wB2Dt3sd/8YDKr7Uu5vDt0K0+zi/FYDJgXOA3z4JPHrEtT9AfWGAHENtPq369sKsQplAUDvXvuRCQ0DpQBsAhTp9/9JHeb9qOTp+FQXxQgr5A8C/wgU83kTK/IW8jcNsAZL9kL4P/6uCK0EU/W5+EYCvgn4/Db90vrB/wMI7QGH9CcDDQY5/sH9XQNz/3UCqQca8HAHUw88+Mj5cQVDBbED/vmZ/3kLRfo5BHwEDfrHAnEDsQim/Pf3+Ah2BaICZ/zE+YUNBgV89zAAiwLaBr/+gfn8BowD7/7z+okAyAu3+vf3OgYHAdn/nQKQ98oExwLHAHn6/PtJC8H4fP30A3T5pgJoA5n1aAwI97X77QtX8Z8L9fce/SIKzO5UDtH4yfrtCr32xf839x4Rnf3+6TYKwwHvALH94PYXAhoEpP4Q/HP4ywiyAwvwfQUN/+cEQ/lT+k4IHvefCNv+he3nDcIFuvOkAvn4jwddBVnxmQIvBmMAEPul+YgI5AHN+aP86wAUB44Cpe8TA3cT4fc/9JYKLAJV+qoHsQN0+M4BnQldAhYDwPk1BQIM0Py6An8HS/0MA80FeAYFAbn84wdoBcj+RACQAyQG+/7s+iIC7gam//H6qvyfAHYHR/pt+4D/lAAV/RX+P/7H/EkCw/dHBLv7ZvdDB1z9z/fiCPb+rvJ/CEIBL/qOA+sAYfeIB5MEE/X3A7ID4v3K/8j+K/xlCYP5Q/mxCF/9kvzx+SEM7/0y9WoE0vtKCRgFq+jB/T0Z0vxX8Kn6zwS/DM78QPWn+XkHhwJEAT/+pu7KBKkHcv2q+lT4owCXBuz37fsOAvoAbgDo8gcBgggmAMv0Cvy4BQoENP7m/Gn7JAFLC6f5ffvIBZIBhgDSAUoA6QFBBksDv/7QAUoHnwFTAAMIQQGEAuADhAFXBs0FMANj/00DWwXuA3z/EwF1ApIBpAT9ADL+N/7jA1MCWv4V/b/+qgHU/VX9sf4jA9sBG/qH/fn+1ABbA677OPpY/9wEwf+1+sj+lQPc/lf8HAIl/mAAiPzS/IMFggF2+0z6CAPKA73/D/n2/MoCnf7DBKX7h/aXBAEB9P32Acn7Hfzo/EL/fwYi+9n2twFYAU//RP1g+sEB2/7++0b/bvt8BDj8yPcnAuX/HgIu/Sj66gDRAz/7lvzyA1/+vP2O/34AQwDm/K//wwP9/9/82f+xAtkB3/yJ/kUFBgS8/VL/HAMvBaICPwEWA/oBAwNsBKICTQGgBWwDLANTAk0EOAb6AeoA9gIvBgYD3QGCAcUC4AN8AV//IgIEAzoA+/8q/z8BagLt/X38ugAnAHD+Wv64/Qb+i/8Z/Vr95f45/YL/r/5//Gj//f66/Zv/fP4Y/j8AEwSD/6777f5PA+0Afv4DA68Bufm+/XIGSwGAAXf7dvkJBvgB1/3J+Wv4xARdBBv7UfN4+qQDkALS+8LzAPtpAJn/Cfoi/Mf7ivs0/lb9ZQCs+XD8OwLS/bz71wKZ/+H78P+H/pQAZgRVAVX9cf8VAIcCSwJhAVL/WgDMARgD4QX3AzcGeQfWB/AGZgWnBrsHAQjFClwJ/getCo0J1AjkCRsJ7AiPCG0FlwOtAgYDBgNfA9cByP5s/sj+NPt4+VD7dfzj+ev1yvYl+JX4PPiT+KD43Pj5+VD55fhw+jv8rvwu/Tn/tv4l/rj/9AERBEsCNAHbAZIDMQQsA+ICZAOKBaADVQFYAk0D2QKmAbT/R/9UAp//9v1P/Z/9RP9Q+p/7hf4++bL6gP46+ED4sPmU/PT90/i297f5hPyN/UL+I/hv+Mf9IP2X/uT8h/gk+Rr/kP/C/Mn7Qf3P+z/8YACZ/on96/y6/wP/t/v2/ioDygH2/ar9fAKNB3UKMw7yC7AISAjWB40L3xFwEjUPJw22DS0SmBPQEDoRoRAcDYQKsAfwBSQGdwN3ArIFBgQFAEr8q/kz+M72uPWs9LXyAPCb7jHw/PH+8I/xFfOM8V3vy/CL8nDzHvTZ9oH56vw0/ab8HAGOAkcEVgUzBhkIfQmRCPQJJQ1kDdQO5w01C1UJPwpcCbkIYgj7BGgE3gMiAdf/Yf6C/Vj8yfks+Mn15vUw9gP2jveL9Hvyh/Qe9HDzKfUY97z1m/UA/Av6APjA+yL8s/wT/Ez8OP1w/Hv90gFlAVP9qv5hAgoC3P+3/Gj+agLgAcr+Xf7G/2wD8AxuESQSjg5nCBUInwq5DSkT8hSHEhIQPRGoE1sVMRYRFbwQ2wrkB+EEsAXXBLoB1gADAQkA7/vY98f0P/FL7q/u2e3H63nrL+sQ7KTtLO7E78TwQ/DT7/3v/PGk9xv6Dvss/msAFASgBRYGMQcVCZYLMguoDDMO4Q9eEJUPWRC3D98OzAuhCQ4IdAfhBoUEogMVA3kCP/5u+zb6dPjm9VHzE/R989by6PH+81H1xvKN82n0DvU59HP1p/e99u73Pvqj+439nf1q/v/8Pfx+/xz/if/fAJf/5f8MAab/T/6d/Qz/0P3//L//+P0R/mr/Uf05/VcAHQj5D/wPQgyzCZMIQgqyDbUQgBTjFTAV2xQNFrYWthXvE4YRVQ9JDMUI8QcxBvsC2QCtAVEC8fxt+Uv23PAR7fHpA+m36qbrdetB64nqS+yC7c7tu+5S70fyTfRW9GT3Ovpc/hoDZAQ2B9IJzgtzDA8LugsdDtwNfg1DDtQOchBHDq4PBQ/yCoQJAgWAAoIBewFeAI/8EvsX+9r4PPgq+VD3I/b/8zjxI/Dh8U7yK/Lc9Sr39Pcw+A34hPo/+hj6x/vx+7f9J/8I/lr/ZQCuADYB6ADd/WX9wP2C/Q/+Ef1j/fT8Tf1B/Er8efyq+9D98gRFDbARARQWD+YKags6DUoSZBZZGK4ZHRprGPIWyxdmF7YUtg5qC5IL4giVBmEDTf9L/Rf9Evoe9qzyse6z7AXpxuZi5tzmfemI6aPnq+jU6mPsyexL7cHulPPw93T6D/6zAOsDngRoBVMIFQudDAcOzw9OD5EQQxL6EWcQThGTEF8MnQvpCUUHFAaJBKIELgOPACr/5vz2+xD5bvSN8grz/PJR8oTxf/Jl9Uj0y/RK9ZL08fRu9Ov2u/iw+jv+Ef8QAOIBrADe/nwB+wHp/x7+QP+H//T+lv3N/GP/Vfzh+lD6BviT+I/5KPmY+8b6gfk9/T0BwggoEG0WQBdTEhIPbw8VEfYUNhkwHssgYCGpIOsejh20F1ESYA8wC5gHPgdABPv/jfzI+Cr3e/NY7nvqTOeH5Kngud6B38PiouXl5qfnEung7MftKe2C7aDw+PSn+KX7nQIDCv0KTwwuDpwPjxGHEn8RlBFBERsOHRBjEn0RexByD2QNkQgoBuUEmAB//B36+fja+FD5Effa9nX3sfWH9T3zhPFv8TPxrPHb80v1Tvht+r35Xvt9/Ov9yfxl+9b83/zo/RP/+P2H/vEAnwHu/5L+hPwr/In8KPsQ/Gf8dvom+nb5c/ht+cD6sPtQ+9v7X/5NAYoHshDaF88Z6RZNFDcUDBPtFMYYJBslHm8hpyJ2IeUfZxvkFKQM+QUABMcCqgDb/dn77vkq9ybz/e7Z7NroVOQ94sbgj98g4Hvii+RC5+7oZ+vr7vXviPHm8gP1N/h0+87/AgWYCaELwQ3cDp8QDhJpEn0SzRFKEv8SXxNpEoIQIw81DbwK+Ad9BQgE+gH2/u38TvsH+Vv3qvVp8vnwrfDp7hrvVfBm8ZXybPMy9Dz3kfiQ9zr4yfkc/Zn+qABIAiADfwXBBVIFpQV1BBYDTwI6AID/I/8e/nf+QP4k/Qn6Jvh4+KD4hfat9Wz3W/e49yP4BPms+YT9ZgIdCAkQzxULGcAXdRZ8FJ8TqhTXFUcYQRtPHpUfwR+DHVEZFxN5C38Elf9z/Q78q/lQ9wz2rfUm8yHvyetU5/vjKOEh31zhweTe5dHmhunm7M7uJe+M8WH06/Vv+BP8swHjBPoHAQxHDlsQ+hFqE8cTkhNoE0gSdBBXD/INlwwNDE0LLAsTCcgFCgKJ/Rf7uPiC9VPzcvID85v0LvWE9ev1//Vo9QHzxPDv8U30kvVX+DD8lf9TAZIBEwIXAgoB9v+X/3z/7v+dAFgAOwIjA3sBagE2AXsAI/8F/UX7+vnC+Qv5h/f29nz4Avho99b4dPnL+UH6t/2vAeoI9RBoFA8WyBYNF7QViRUuFQsWRRjzGPIbjB9RIoQhvBznFe4NowemAR38GPgN97/2g/ax9Zr0hvQb8AzqxeOn3l3dW9w+3QXgX+SP6TPuqvEr9Bz20PZ293r4J/wmAPIDKAhjDPYRTxQwFWEWihXiE48StxCiDk4N1Qz0DDcM5AqsCKUFYQLi/Qv7+fjD9VH0EPT287Py3/Gl8l7xGPFD8mvys/K58gz05Pan+Kz6tfzd/f0A1QGxAYEEVAX8BbcHgAjfBwwInwkTCWsHfQWOA6ICTQGO/nn8Xvuc+gn6gPcR9n30V/Of8vPxuvQn9nf1A/Wz9Wz3DfnX/QEIHw9BEhoWThhrGmManhgfGCoXihZJFwsZTxxxHsgdARq9FA0QiQkGA+b8uPbA83DxLO5W7qfv7u4j7Z3qdOjB5JThquCC4O/il+bY6YDusPOH+HX8Mv4e/wwAIgF1At4DAgdTCmgN+hD8EnEVBxf5FAwToRBFDXoLfQkACPUGxgRSBIMDRAFE/678C/px9v7z5vN78jzyYvLb8aP0s/ZE95D4iPgb+RD6SPrv+zT+BwBNATYB0AHMAmwDqwRABBwE4wQ8BNMCswFyASQA2f3N+xj6H/mj+aD5o/mn+Ov28vi79zv01PSQ9O/y7/ED82r1Fvhn+6L/sAXCDuIVJBhPGtka7BnlF2QVdxWZFeUW1BlCHFsg9iLAIFocBBUEDI4Dx/u49uvzffKP8kHzKfTU8+7x8O1O6IDjON8h3eHdNt8p49LoLu8r9WD4P/sF/ab84vz//ZYApAQdCAQMUBDeE6kV0RQKFEMS9w5dDdEMuAtNC+0JewhHB0cF3gOfARz/2fwH+v/3RvZl9Bv0afRI9GP0ffQK9UT1DPXX9Sr2+PVJ9vn2W/jj+t79uP/rAHABNgJNBEIECALvAvYDTQMaBBYFdgX5BP0DVAN1AhABfP4e/Bf7H/oC+db4YvhS+EP4qfZc9fL06fXB9g/3h/ie+qX8MgBxBsUM2BE3FakXpxmeGOIVMBQKExISwBG3EkcWMhoKGyAawRZlERwLrAGx92nw4uvU6bfpbuz+8GH08PXU9DjxfOy55vPhXt/W3g7jDuoo8dj4O/+7BEwI3QffBU8DnQC2/4P/9gFGCJkObhLkFEsXlxf4Ex0PswqrBgoExwHHAKQCNQTQA7MCrwE7/+b7EPlb9tn01/RL9br0hPQl9RP2X/YP9s727vc3+SL6Hfuh/D3+HP/g/74APwGSAq8DkATKBFgEcwSOBHcDVgFKALH/m/7Z/YD9lPw7/FX89ftQ+9360vrn+Jf3Ifcn9mj2cfaD9yv7eP+VBBcKxg5oFPkWMRbTFo4WohU/E2IQxhAHEnkTWBVtF80Yrhc5FIQPDglqAY/6QfTy76DtI+7u7+jwJPP287DybfBU7djpg+bX5PLkz+aY6R/u4fPe+Or8zv8gAS0Amf/i/6UAVAIfBagIbwxgEPoS5BRvFfgTzRHqDuALaQkgCB0HhwWyBKIDnwEKAOT+Rv02+mr3b/Yt9HbyofNK84byTvMg9I31xfX79af3/vi0+kj7T/yx/pf+i/2x/tH/DgDrADkCbwNzBNEEaASQBAIE4AIOAkQAif9f/zv+f/yu+ln64foo+SH3ovYG94H3qfY891D43PhX+939LAIRCsQP2RJ1FfkWfRekFU0SRxD5DZ0LvAwyER8WxxkSGzYZHhWuDtEG6f6F+FjzUO8Y72Xycfbw+Qn70/mm9x3z4O3u6AHl5uOg5Enou+6A9BT5cv2y/zAAAwDX/9z/1/+CAYAD5wWGCQgMBA5HD8QOIw4IDccKxwn8CEEIEAh2B/oGxAX9A+kBN/9l/Fv6mfiB9xz3QvYG9gH2WvaH9k32VvZ49sX2/faF9674jfpD/Hv9vP5wAAwChQITA18D8gJ1AlMBPQCV/6n/FwBm/zT+gP3d/Er8afpN+Mz3b/aS9MXzSvQR9uz3u/hn+jT+zALlBu0LBxDtEvUW5ReKFoUWrRVqE+wQlw/HEdAUpBaeF3gXVRfOFDUOQQfAADP6WvP47UvtyO6I8WP0fvUe9vH01vFt7iLqgeYy5aTktOYb63LwVvaE+qP9nf80ADgAyP8ZAJQBlANkBoYJJw28EBMTDhTOE5gSZRDsDbYLownxB2AGsATKA/YCrQErADf+Dv2P+xr59ffF9sX0hvNu8j/ynvLx8jL02/VH9/74JPq9+gf7t/o6+5772fvg/N39O/9lAE8B7QK2BIEFgwX3BBQEiwO3AZf/kP5L/Tv8qPvu+gv6mvne+ED4HfjT+Dr6x/u8/tUB8ASECFELZg4JEWIRARGJEdIRDBKuEmcTVBTnFPgUkBSvExwS3A4rCgQGiQLX/nf7oPjV9i72jfQH82vyb/HB7xft6OoH6nnpsOhO6ajrqe768fz0Ofj6+q78+/09/2cAygG8A0kF/gYSCesKWAwIDWENOQ2BDPgLqgsuCzQKiQl7CKsG8AQGAwUBEf8c/XL7cvrw+FT3R/br9HD0TfRW9N30RPUV9if3IfgY+ez5cPuE/cX+TgA2AlEDvwOfAxwDoQIYAlwBJgC+/kL+5P2f/cr9Rv6J/gP+WvxM+v340/dz9on1bvWC9uf4qPuC/1QEdwh3C4EN6g7lD/wP6g6ODQgNFg2rDUwPLRHpElIUfhR1EzISdg8uC/AFNAA0/Kf5vfdz92L4R/lX+mT6e/l493vzYu+j64zoo+eD6NLq5e4i87/36vsg/pv/nf+f/jT+qv2N/v0AqQMLB4QKgQ25D/cQrhAhDzUNbAviCXYIzwdkB2kGPAVOBC4D6QF9AFP+afzb+m354Pcu9sz0TvML8lDxPvG+8ezyE/Qp9Z32kfhO+lX7cvyj/eT+2f++ABMCVgNfBOcEEgVFBZMEiwOhAsABLgEkAL/+zv0O/R/7WfkG+Nr2HvZn9Wz2ePmW/EYAOgV3CYwMOA6gDtMOgw3QChsIqwaMB7cJyAynEAgUQBZ1Fu0TTBDeC5kFc//n+Vj2Bvby9sz4svsK/oT+NP2K+ir3MfP57t/rsOoV7PnuZPIP9zz7CP6Z/ysAvgC5AAMAAADrAJkCbwUVCJsK0QwNDrAOVg4wDSMM8QoFCQ4HZgX0A+0CewFk/2X9wvtv+r34Y/eD9gz1Y/M28mvx1PDl8Hbxk/IT9Nn1TvgZ+iv7vvzo/Zv+LP+9/8kAFwIjA4UE4wUUBrIFCAXyA5ACJQFyANn/oP/j/6n/gv87/0T+1PxV+2T6zfr2+3P+8QGDBZEJzAuoDCoNCAx9CZwGSQSpA2EE/gW3CcQNgRBTEgESxA8nDB0HpgGU/AL5c/dJ9zr4z/l0+678Ef2n+w/5avaA88Dwiu4c7jnviPE59E33KfvQ/osBEQMxBKsEfwTMA/sD8gT5BYIHEwmWChEMwwzTDJ4MsQswCi8I5wWoA8wBAwB3/t38WvtM+lX55fhC+Ef30/Z+9k/10vSP9BH0RPQQ9F70ovXc9k74+fkp+0r86/2L/xcB8QH5AnYEcQVMBq4GjAYsBvAEhQMDAjsABv/m/fb7mvrn+TP5wfh/+D/6cv1lAN4D0AcMC7sN2A75DRMNoQthCZ4H6gZrCLELsg6wEbYTfhShE0MPlAliBMH+R/mF9Vr0J/a5+Mf64vwa/g7+i/tN9wnz+e4G7G7q6upZ7kzzUvh9/L//NgIhA7UC4gHzAOX/if9VAAoCRAQNBzYJoQrRC+ILcQusCn0JUwj5BmYFHgSHAwgDwQFTAFT/hP7t/Bv7zfnP+Ar36/Ss86jy8/Ft8W/xTPK682H14PYs+GD5Fvo4+q76L/tZ+4j7wvxL/vL/SAKMA9wDDQSOA4cCwQHJAJX/b/9u/1L/J/+4/h7+wf0R/rb/+wKXBsAJ4Az6D5YRIBEAD8ULKAg6BccCvwJ6BhcL8A8pFAcXkxipFr4QBwkwARL6e/Rg8Qfyh/XE+TL+ZwH9AtUBt/0N+GnxwOtp6NjnV+px77H1vPt3AOsDVwVbBF0CfP8Q/Sv85Pyx/4ADBQe4CvQM1Q1JDvQMbAobBzcEnwGk/yX/pP5w/v3+1f7Q/g//Dv7Q/Mn6v/hd97P1jfSR8yvz8/Om9B72CPin+XL7pvxV/TT9ffxM/O/7SPuL+yL8e/0w/y8A8gFAA6QDKATgA84CSAH7/nT8wvpD+VT38vYA+TD9xQEJB5IM1BCWE4kTQxGGDjsLEgf5A6YDWwbMCvwP3hRkGD8ZJBcvEgwLxQOv/NX2s/Ne9Gj3Rfv2/isBWAFd/0P7qvXP8LjsL+oM6j/s4fA59aT4CvwY/k3+cP0n/B784vxa/Vr/ZgJHBYgHJwlVCmUKMgqACXkI9gh0CQEJ2AjjB38GzAQIAov/of36+9L62vna+UH6Dfqi+b/4g/cX9qz0//PF89Tz+vRv9t73j/nd+s37XPzF/Cn9Mv3v/Rb/4//kAN4BqgIaAxgDQgJyAU4A8v43/nn91/wV/eT9/f4bAcEDPgZaCPgJ/QosC14KPQlaCAsH9wUvBlIHHgkGC6IMVA4xDxIO8AvkCBoFIgH4/Gb6+fmD+vr7IP5OABoCJwJhAGH9W/lP9bDxUu9272nx5PSV+Sz+zAFmBDEFlwMTAWz+T/wF+wD7nfw9/5cC4QXWB4gILwgvBowD2wBW/gr94vyN/en+WgBwATsCqAEFAJ3+5vyP+5X6H/pg+pz62/qI+sD5B/kd+Bb3zvYh98r30/j8+U77afyf/Bn8ofsS+8L6k/p7+o37A/4RAXEEawgwDIMPbBHdEawRFhB+DWgKfwfCBd8FeAeuCR8MPg6ID0oP8AwXCRQES/4k+ZT1VfRu9e73EvsD/k8ASwGYAHX+wvrB9nXzePF68T3zRvYC+qP9rwAVA2QEUAT/AgMBY/+v/pT+E/9sAGwCfwQoBmIHEgjJB6sGFgU+A7wBbgBN/4D+A/77/Sv+NP4w/gP+Xv1c/AD7qfml+Kn3qPZf9rj2X/dg+IX58foO/Kj8/fwk/Tn9NP1B/V79Bf7y/s7/0gCZASMCMgKmAfYAQQCL/9n+if4B/zIApAHyAhQEbQW9BnsH0Af3B+wHtQdQB+oG6Aa3Bo8GkwZnBqMG2AbYBm0GdgUzBLECHAGQ/zL+Ff17/FP8sfxj/dv9Y/5C/nP9nPyu+9L6EvqF+W35APoQ+5T85v3w/rj/CgAwADsALwAXABMAdAA5AQoCCAMUBK0E6QSOBMwDEQMTAggBOgDO/7z/m/9+/2T/CP96/qb9n/zS+yn73/rb+tj6APso+0b7IPvh+oH6FvrW+Rn6u/q7+9D85P3F/gj/J/+Z/tD98fwH/Jb72fsg/Sz/wwGJBFAHiAnKCh4LdQpBCYgHxAXnBA0FBwaEBysJ9gpYDIcMygv0CYEHzASvARb/Sv1P/Cv8WvzL/IL9Hv4F/mn9Evwt+gD4E/a69D/0IPWS9qX4Mfvg/ScAhAH7AdkBBwEAADv/8P6D/7UAXQIlBA8GeQdRCGUIZAfLBdkDCgKRAL//oP+4/ysAwABBAZIBJAEpANL+Uf0J/Ob68PmZ+b35OPoD++T77fyS/d79wf2G/U/9CP28/KH8Bf2J/T/+Ef/I/z0AZwA/ACYACgDR/4P/bv+X/9D/4//R/6j/jv+t/6D/xv8rAAcBwwFUAt4CRAOMA10D8AK1Ap8CswIVA6kDmwS5BUoGdAZdBq4FzASrA1MCEAErALz/rf///3cAzADQAK8ABQDB/lz95Puy+tH5a/mX+Tb6Ovts/KH9cf70/iH/Hv+d/jD+Gv4n/oX+Of99AMAB1QKoAzsEsAR2BJkD0wLxARABHgBu/0f/Yf9m/2r/rf/T/w4A6f+f/3j//f5z/sr9Vv3m/JT8d/xa/LH8P/2U/bP90P3K/Y/95Pxu/Ob7cvt9+6f7HvzH/Jj9CP+AAPIBwQNrBasGMQdrBysHiAZvBTEEXwM3AyUEYAXdBoIIiwnFCRUJKwfeBIQCLwCJ/rj9yf22/u7/zABIAeoAjP+o/Ur7EPmF9+L2O/cs+NH5s/s2/V/+i/4T/lX9efzP+5z7K/z//D/+qP/zAAUC1QJHAykD3AKOAkgCTwIuAgwCBQLBAdIBowGEAY0BSwH8ALoAmAA4AJD/0v5A/tL9j/2o/Rj+lv4E/0b/bP9z/xr/ev6Q/RX9Gf00/YD9HP7I/lb/w/+t/1r/5f4s/pv9Wv2G/TT+vP6H/0QAfQDDAMsAiwA/ABUAMgB3ANQAmwFUAtkCeAO2A6ADZAMcA9kCdQIOAtIBvAHiAckBmQGvAV4BBQF3AA4AyP9k/yH/9P7p/r/+rf5T/uT9xf2m/XD9if2v/dv9Kf5W/oX+pv62/sr+Af9G/+D/bgAKAbEBXAIDA34DwQPjAzAEUgR4BH4EvQTTBM0EuAQxBKADGAOAAgYCrQFIAfoAlgBBANX/S/98/sf9J/2j/Jb8x/z2/D/9wf3y/f394P1q/e/8bPwX/Dj8qPw//ZT9Bf5U/j3+2/1h/cP8Svzk+337e/uw+3L80vw4/c79Wv6+/vT+J/8N/9P+nf6v/if/2v/vANcCwQR0BgkI1gijCOwHegaVBA0DBgLXASICdwN8BS0HIggyCFAHPAVEAiP/0PyI+w775Ptn/Sn/yQC6AdsBmABY/u/72Pl6+Cb4B/me+rP8+/6RAGoBNAEQAKr+rP3L/KP8cP2v/i0AaQHcAmgDVgO1Am4BVwDZ/7T/8v/fAJgBUQLTAuQCbgITAZX/Pf5C/ZT8lPwk/dX9mf4n/yr/XP5W/U78P/te+hT6UPpV++v8Z/6m/6gADgH/AHkAr/8y/6v+4P5v/2sA9gE+A0cE3gQABZUEogOkAqwBIgH6ABwB/QEGA+0DZgROBNoD+QLpAc4Avf8w/yX/Y//9/58ATQF7AWoBEAE2AKv/O//X/rj+5/49/+3/iQCxAEQBiQGkAawBQgHzAIQATwAFAJ//O/8j/2b/ov+8/4z/if8M/0j+sf0M/Zb8MPwM/Ez8wPxc/QP+ev6h/sH+pv6E/q/+lv6q/uX+D/+M/wMAUwCaAMkA4AChACcA9P/G/3P/d/+f/8P/PwDDAEoBpAEKAg8CyQF8AeQAewDr/2P/Vv+J//v/iADLAAUBRAE/AdAAGwBm//j+vP6L/pf+Hv9d/3H/mf+d/3X/CP+z/kL+J/4g/lT+3P5f/87/BQBGAD8ANAASAL3/6f8OAGsAFQG3AUsCgAKSAmoCLgIMAu8BuAHFAR4CnwIKA0cDdwMqA5cC4AE2AWkA5f+8/4n/jv+i/3X/Wv9E/8j+T/7Z/WP9Cv3z/Pz8Jf1w/Zv91P0K/in+T/55/o3+of7c/k3/sf8MAFoAdACPAKoAiAB5AFoAUQBsAIAA2wAOAU0BjQGUAWUBLgHWAIAATgAJAP3/CgBRAIYAgACJAKYAbgAiAPL/v/+D/0n/VP9x/2//df9+/6v///9KAKwA3wD/AAwBywBMANr/l/9s/37/yP9MALkAUQFqAUoB/QAVAE//mf4I/tT96/00/qr+Mv+i/87/nf8n/43+9v2o/ZD9qv1j/lT/IgAcAb4BDgIPAscBZwHxAPEAAQFlAfYBdQL7Aj4DPgMNA3oCoQERAWwAKwD5/97/FwArAEwAEgDV/4P/Hv+f/kL+//37/Sf+aP6z/u/+Mv80/xz/0/56/iz+MP5A/nn++P54//L/JACJAKYAXADQ/1b/0P6W/q/+4v5H/7T/WADdAP0ABQEOAaYAVwD9/+X/8v8tAHUA5gCdAegBMgJCAjsCDwLHAVoB8QDOAKwAiQB9AHsArACcAIgArACxAIAALQDI/3f/BP+E/nf+aP5N/pD+7f5E/37/lf+H/0f/9v6N/mH+Yf6N/vT+bv8QAI8A2QD9AN0AkgAQAOX/vf98/73/JgBgAKgADAH0ALEAUwAAAJD/Rv8c/xX/Tf98/7H/uv+9/5T/VP8D/8H+s/6z/r7+x/4M/zL/Pf9W/2//d/9o/1j/Zv91/1b/Tf9R/1b/Uf9x/7r/8P8bAHsA1gDMANAAtQBRABIA7v+X/6v/FwCIAOIASAHBAf8B1AFNAREBtQBgABUAHgBDAF4A1gDzABMBKQEpAQ4B9ADfAK8AuQClAHQAWgBBAEwALQBGAH4AggCAAJYArACJAFMABwDi/4X/qf/F/6L/5//i/97/6f/Q/7T/eP9E/zD/1f7B/sj+4P4g/1v/sv/r/x0AHQAdAB0A0P+0/6L/l/+8/9z/DABaAHUAlgCqAKYAiwBjAE4AIgAtADgAQQBRAEgATABPADAAGQDp/7L/l/91/z3/I/9A/yD/If8T/yD/RP83/zf/Qv85//T+4P7t/un+xf7S/v3+1/7//hP/Jf9A/xr/Qv9z/5f/2v8HAFEAkgDHAP8AKwEnAQoBOwFEASkBSAF5AZQBowGsAaQBVgH6AKwAOwC6/6n/lP+F/6//qP/u//b/7v/D/63/mf9W/0D/Jf9S/4P/lf/i/zYAUwBPAGcAiACWAIIAaQCGAIgAnAC3ANkA3wDtAAEBAwERARABEAHLAIIAOwATAPf/vf+4/8r/wf+t/7j/nf9R/zf/v/6C/on+kv7D/gH/LP83/1L/Y/+J/1b/J/8w/0T/Vv9z/8b/EgBTAGUAYwBEACsA+/+0/8b/xv+8/+X/AwD//yQAQQBGAD8AFQD2/87/vf+0/7j/0f/u/ycAgACaALEAvgC8AK4AkgBsADAANgA4AD0AbACCAHsAfQCEAJIAiACYAJEAeQCRAJoAfQCEAJoAiQB0AHUAkgCoAHUAZQByADsAGQAAAA4ABwDp/+P/2f/Q/9X/0f/D/87/v/+9/5D/RP9L/zf/Jf9P/3r/rf8DAAkABQDp/8H/bP9G/1H/I/9o/5T/gv+g/7b/v/+g/4D/d/9v/3z/oP+p/8j/HQBEACIALQAQAPf/KwAeADYATgBXAH4AuQD4ANsAYQDQ/7H/yP/R/5//rf/O/9r/9/+2/7H/ov8g/7/+jf6z/uT+Af87/5D/v//a/9D/w/+4/47/hf93/7H/9P9hAMwAIAFEAVYBUwEcAQwB5gDfAM4A7QAQARwBIAE5ARwBCgHZAIsATAD0/9r/wf/I/+D/6f/K/7j/kv9h/zv/Kv8n/zD/W/9m/5v/rf+M/5//rf+v/5X/fP+o/87/5f/3/0QAYABeAFUAOgAtAAAA4P/n/8H/qP/M/+L//f8/AGkAfQCWAIIAZwA0ABIA8v/e//3/GQA7AEoAcACGAGsAZQA9AOv/uP+x/5D/if/V/+D/5//0/+v/w/+Q/1L/Df8B///+Of+V/8P/4P/t/97/sv+Q/4z/pP+p/9z/RACRALEAywDoAOAAnQBDACAA/f/g//v/JAAvAEEAYAA/ABMA7v/n//f/EABKAC8AQwB1ABUArf98/1H/MP9u/8j/MADMAPgAFwHbAGcAAwCf/zv/BP9d/8X/OwCcAOQA7QDHAHkA4/+i/1v/Bv8s/2j/qf/j/0QAhgBwAGwARAAtANn/df9H/0v/Y/+Q/9n/JgBwAJgAiABEABUApP9L/zf/GP8+/2r/ev/D/xMATgCSAGwAZQAwANf/5f/M/8b/4/8kADsATwBwAKYAmgAiABkAQwAtABkAHQAgAD0AXAA7ABkAGwD5/9D/1f/K/9P/9/8KADsAHQAJADIA9/+8/7b/g/9j/7H/5f8DAC0AMgABAAMAGQDX//f/DADO/+7/PQBKAEoARAAQAAEA5//a/x0AKQD5//f/8v+RAHAAWP83//f/AQB+/7T/2f/l/5//r////6j/lP9U/5T+Zf4B/13/vP+v/7//1f+F/13/df9+/yH/jP/n/yQAiwC+AC0AJABnABUAvP9IANYAkgBjAJgA5gB+AbgBuQDJAFoACQCIAM4AXgCp/zIA+gHZASUB0ACQ/3P/BQDy/zIAGwGPAC7/aP8XAO7/lf+v/1L/2f4B/0L/pP93/yf/Yf8s/8j+yv8JACAAsQAmAO7/VQCGAKoAVwAmAJwAvgDOAA4B8QA9AI7/3P++ALkAlAC8AHUAdAChAE4APwArAFT/7/6i/5T/Nf/5/lH/jv+H/+3//f94/1v/T/+L/pv+MP8Y/97/aQAW/8z+m//2/4YAjwAmAGUADgBJ/5n/GQAiANr/sf9yAPQASACM/2T/O/8a/3z/kQAVAR4Ai/8c/zL+mf42AD0B4AEZAcb/CQAG/+/9AQDdAOIAeQLvAdn/yP9KAEYA7wD4AAoAxf9gAEoAFwCRAGcAev68/WEAJQFh//b+eP90AMcAMv/5/v3/7f+A/13/W/+aABEDmAH4/kYAq/+Z/wMC1ADB/zsAeP+fABEBjP+O/xkBwAGp/y7+Qv9yACUB5//k/vb+nf53AFYBQv5G/jYApgDu/0j+DAABATn/BP9J/sH+xQF1Asj/Af6A/2kA7v+PACMCPQK2/4/9mP1uAboEtAP7/6H7ef1IAZwAPQF7AYgA2v/r/VMB1QGs+zL9ywC+AZ8CKQBc/KP9CgLtAa8AXgGm/+39lv3D/pwANwITA5T/1f+sAQX9lv1RAVEAxQGJAA3/1gCS/bz+RgIDAWwAtv8s/kf/ev/a/y8AYQAlAYf+Yf+zAUL/9v6hAIL/NgHiAAb+UwAyAB0AWgKzAW7+x/wB/gcAEQHoANUB9AA9/8f9GP5cANACDQNXAGH/8/yU/XcBCATcA47/7voJ/HAAagLIBOUC2f1z/on/0v3T/soBVAI9AU//Uf4K/b/+wgBNAWwAqP6i/8sABwG+/Az+MAJjAGb/bAH7/lv/owHJ/br+nQD8AOsCDAGz+/D+bwQPAmf7EPy0A60E6AAk/ez7HP+xAUEAYwMVA6/+Vv93/qEA8P8X/X0APQKv/bz96QHn/+f+6QFrAK/8d/3VAvEBy/y//g3/QP7xAFQCJQKH/Qf8PgNqA6r+rf+//uj9wgDZAUIBXP5eAOsAIgCsACQApgG+AOL9SPuFAiQHhvyu+HEFcQMj/roB//0b+5YC2QOp/7b+Af68/ugAswH6AHf+n/6kAUj+iABvBA8Cdvp9/KoBEwD9AGwEewCu/P/9F/vIAnsHTf3q/FEDEP3m/Br/cwJEA24Asv+t/sz+lv0pAXwCOwEGAqb/i/2sACUBh/5RA60CawAG/i/7+/+L/ZMExQlr/Hb3SgZYBMD5oPoy/4MEcQJjAUr9BwDSAPT8LQCHAXUA0gIO/qD6GAKHAz/+nQH2Asv8cPzxAcMCO/8MAVoAJ/1W/WQDDAJV/bIFKwHM+Hf9/QFsAcP9SQIWBVP9y/oF/DoAOwT4AE0BfQA4/Gv4+P33Bj0JhgDp9iz4lwULBe/9wwNcAZH8G/tL/ikCWQVq/yD9LAI4AHX8KQHKAz7/MP6J/tMD4ABT+uADTQGs/dv8uP0+BtABIgEi/Ov2VAO7CEYBj/tA+CUCmQWD/4YAvvzg/OADiwJ8/kf/j/xPAkUFA/7K/sz/Bv7DAaoCnwCzAfn+Uf80AV3/HP3rARQHsf+e+wMBIf9+AeIDlv3oAMH/uQDC/MX8lwRR/Yn9XQRUBHj/IvwQ+lX7AAgeBCz5bAJDAH/5CANkA/j7if5nAZf+uAM7Awv5FwB1ArT6VgNLBQz8If91/rUBIgJq/uIB4gAT/3H/Wv0f+rQFKgWi/pL+8PmGAAwLP/2P9EQDugFqA2gC5vrj/2wDGv9R/kf/uv9zAj0DOf/r/e3+kP4CBQP/afwuAQMAeP8MAyP/l/jtArgD4P79AHf9SP7JAPT/HAEnArr8Xf5sBCX+pP3vAbYDrwGR/K78CP41A3MF6/+6/U/+1f7xAJb+2f05CpMEvPY++a3/UQmeBKn6Mv2q+w4CfQZW/aYBMAO29zT+8AQ3AokCy/pW+G4DsQvi/732HP8RBUn/Zf74AVgB+/4c/wX+TQEjBOj90P9qAXf+WABJ/nX99ARG/w79bwYe/XD61wCsAHAAvAD9/mP9EgBC/8j/XgDCAHf/O/+CAVv/9v0B/iz/gwQcA6j9LAL///X6HP6QAhYEVv/B/mEBXP2j/TMDE//t/xsFi/7C+U39BwaSAuT+IADy//r5Cft4B9UEc/7h+uL+HAKZAQUClv40/BQEowHZ/CMCS/2lALoC8gFu+yn8+QSJAtIBOQHV+Kv+GwjH+4z/+QJj/d0BcwTr/V79//3gANgH1f0U+fQBvwTA+xX8egQJBnL9Lfow/xoCbgIn/0b+gv6Q/0MAkwSvALf7zADv/jAA1AGh/egAbgFC/loBDP8W+gMBnQnpASX3tf0KAkf/MgPUALH8av/H/qwBoANa/sP+D//9AMkAiQGy/xT7nwIKAhj/XwIZ/c/6Mwb5BA35Fv/iCFX8ofWcBb8EAf/tARH9PPuLAtQBGP9lASL+3P42AjYB0f9D+9f80AIbBaT/RACoACz5+/+8AkgBbAHBAtv9pv33/5b8TgR6BC38Yf8XAnX8ZwFlAGX+rAEOAT/+CAOy/9P5aQF3AZIBbAHe/c78SgBzAVsDNQMo+yz+T/89A5oAg/jFAuEH2/7u+Q4CJQF1At7/b/mN/ngD1QVRAHD8IP2GAcEC8P5o/rgBO/9I/cEEHAGN/b4A9v1o/iwEugLj+h7+WwMPA7n7Ef3mAUgATQKqAmz9s/xJ/l/+pwR3AKv+0QNH/7f6cPsDAR0FFAaQ///3uP3dAJEA2wGTBBwD2fy793z9pQXtCY0BrfgQ/HX/mwPOAtYArvxs/PICYQPb+64ATABP/j0Av//bAhb/vv0KAhL8wf9dBLj9EP0W/1YEiwKj+z/+fP+hAlYDNP11/0n+cAHZApr86/3VAvQCkgSh+6v3ugKcBV8Clf+h/cP9kgFaAOsATwIrAjQAP/5u/mP7/wK5BUMAoQLe/0b0kP3sCIUDOgY4+2z1+PwKBM0ETf0u/kMAIgGRAD39Rfsn/s8F+ABu/D8Cs/77+PkCUAQu/a3/Lv43/2QE0gFV+xMBDAIe/QkAhAKqANr/9v2h/mUBcQOiA+H7T/z/AlMB4Pzw/1wCCQDM/gz/Ff8IBQH/FviX//gA6wKOAyD9VP4cBG78vfoVAEkDLwXIAxz93PdYABAG4gJl/hj/U/4yAwgBbP15AFv/LgPrAaX7Nv6HBcoBsPvF/UcEQgGc+7wANwMQAWEAZf3y/SkA8QAeAgwAMgGx/9T8NgDxALr/J//t/+IADwVIAID31vzyBCAIRAK9+h/6F/7y/1AH8AOJ/VMByftO++UCEQSr/nf+iQAFAXH/HP3j/9YAJwJA/w/+cALS/nv7wQHbAnUALP9g/HwC3AI8+34ChwLP+E8CzgO+/vYCQv6I/Lz+1wDIA9MCMP7Z++D+Zv/X/zADpgFA/hr+Kf9s/xAAiwJ6/6H+IAHy/eAA+wKS/nX/d/+L/i4BYQD4AJL/d/z2A60CF/36/S39lAAjA0kCDgE9/e38wAA4/ScCjAVU/wX7uvy1AicB2/6H/7cBDwKq/gr+c//I/nECjgP2/Kr+DP8q/+AAWAMyAAn7mQETAgb+Wv+HAT0Bi//F/dkBMv/Z/5sD8v0n/jMDlgEn/jb8ZP9VBtYAD/7r/YgAwwJyAFP+rwAy/uv8iQNnAUYAbAGU//v99/+8/9f+hQLQAv//K/xz/Y7/cQNdBRj+zfp7/YIAwgBQBsoDPftt+Mf8lwJXBcABDP2XAvj+A/3X/l/+DAKMA/oArgDn/jz70v5sArwBSwEPAvD+SP3w/qn/mQGxAF3/CgHdAcr/3/xh/moBAQImABr/HgHAAab8d/1fBJsB9P80/+39xv97AQcAfP/yAgkA7/4MAMH9rwBNAxz/OP3p/0MAeQGxAHX+LP43/y8Awf+d/hEB9P+u/FwAQgGN/vT+9gDc/9X9Yf9dAjIB5f+b/z/9I//0AWkBQALD/xD9of72AGMCsv80ANQB6/7T/r7+7/3QAgMD6f8iAEj+D/5j/5n/1QObA+v9sfw5/a3/+QIDAuYAjP9l/Wb/uP9Y/1gDDAPD/X79wADpAWUBKf3g/jMDTwKL/2X9nf4RAfMAtP+uAMsAzAGS/3f8cf+8AG4BSwLF/tX+kAGy/+/+kP/e/zgAxQFGAtf+F/xl/g0DUQL///3/Of51/sIAfP8/AEQCaP9m/6v+d/84AR7+6f+tASsBgACx/Sv+CgGxAeABjwDt/Hf+awCNAN4BDAHu/5f/0/6f/2X+zv8EBD8CE//d/Zj9F/44AQoEhwFs/mj+zv0I/zICnQH4AIz/T/1T/r4AJwA7Ak0BUf4//qz9pgDVAY7/zv5uAff/r/5VAKMAbADn/jD/ZQCCAJgAQP+m/7YC+/8H/VcAggDSALwDxQBN/m7/XP7i/vQClgEiAOkBRADJ/Wj+Rv89AXACTAAa/z3+N/5zAVv/7fz3/xkBsQGC/yX91Pxx/+QCGwHy/Xv9jf4tAZ0B7v8KAGz96P1aATICTQHWAE//MP42AAUCCgFz/x4AugCNAmkBnf5A/0wAhgHgAXH/Bf5wAAEBEACCAET/4v4gANz/UwDzAAH/pfy2/r4BjQGi/4v+gv4vAE0BRv/S/kQA8wDzALcA5/7g/oAAvADCANP/hf+D/6oA8gEeAND9CP9b/2AAXQIIAcH/IP6d/ef+OAGfArUA7/7H/vr98v5sAI0ADAKjAHz9d/4wAeYAmf+d/+n/mgCGAJT/sf+RAGcBLwDV/n7/qgBTAUoAKv/w/1MBqgFYAJ/+5f+oAL//UQC5AJEA3P+8/xIAqf+F/4n/uv7n//oAQgG6AP/9nf0h/z0AFwEtARMBLwDT/gz+LP73/zICVAKuAJT/wP2G/Qz/EwEaAksCPwCv/Kr+4P/n/oL/NgLFAZf+gv0s/v3/3gGJAUoArf/H/q3+VP8FAP0APwLkAG7/r/4a//f/rACoAdkA1f/w/8j+Af/Q/2EAJwKuAE//N/+U/wgBcAHZ/of/wwE2ABABLgHw/7wBwgAiAHAAaP+J/6YAOQHJAA4Bw/+H/uX+FwDkAFwAmf93/1r/3P7e/hX/yv8SADL/mf4G/iX/lgCX/xz/XP5a/vb/+/+d/oT+Df9+AIAAav5T/uP/6gAbAK3/zv6r/xMBv/8MAEwAPv+X/8UADgH8AK3//f5IAKEBAwJBAY7/d/9sAE0B3gGzASQBpQA9Ac4ApQCCAbEB6QG8AXsAEACdANsAYAAyAaMBtP/V//wAVQDB/w4AfP/G/8wAN//c/mUANgCX/z3/bP+X/pD+VwCH/3r+5/+d/5D+jf4r/tX/sv/y/aL+zP7w/jX/m/0Z/Vj+K/5o/m7+df2P/VT+rvwA/Fz+VP4I/WH+Cv6L/P77z/vD/TYA2v+Q/ST8RP0I/mj+lP9m/8z/4P+Z/vn/hgFIAfgApP/Z/2wBVAMWBEUENwOhAqsDOwR8BWsGbQZFB+YGEgZiB+QHFQkHCkMJkQiNCJQIsAheCSsKDgkkCJEHeAZkBp4GBwYIBSwE0AL6AEEAgv8n/8X+cPz4+ib6xvhm+CH4AfcP9un0zvRP9Cf0BfRE887z1vOU88fz/PO69R72qvQ39jP3wfYu+A76/Pot+7T6dPvv/WH/lv4W//T/zAGEAoAA3v/xAdcD9wSwBCsCRgHqAEYB9AI7BCADDgI7Ae0BgAI9Afv/9P4dAJcFewmcCCgEHv4w/2gDrAgaDTUNeQuFCqcIzgh8CvQKJg6CEWsQSQ+DDZwI3whYC2ML2QyxC6kHpwbRBEkDyQFL/zT/A/8g/sP8LfnG9tv1RPTF9PT0ifND84jyXPGX8dbwH/B/8Krxd/NW9Eb0vvPC8631k/ds94P49/ml+rH8qP1+/U3+Wv6N/tYAhALQApkClgLtApsC+wHtAYkCkAOkA4QCswHxAEEAHQAvAAAAyP7r/d798v15/Sf9F/0Z/Tn9rPz1++/72fxC/poAqALjA4IBs/5A/3wCAAiHCy0K5AiNCXcJwAnOClkN5Q8BEloR5w6HDUsNLA2pDt0PZw+kDmoLPQlICZQITAZQBDUDOwPTAosAJP1X+mD5f/hx9632ePYe9tn0YvIN8ZzwdvAE8SvyCvNr8z/za/Lm8hf0fvVo9ij3iPgv+qP7Afw6+0b7YfwF/nP/SADJAAUBSAE/ATAAD/9GAP0BrQJ6AmcBBQAh/6T+6f7I/3j/ev7r/YD9kfx7/F/9EP0t/fv9gPw9+0b7ffqj/SUE/QTOAj0A5PvO/XwEAAj0CkQMxQkSCRAJsAjACukMig6YEYkSrBC/DgoMzArTDOwOYg8oDoALbgmUCOoGXQRGAk8BVALTAqgALv2y+SX3VPa29sb2g/Zx9Wfz4fJ/89TyYPHc8DzxOPPe9XP11/Np83f0RPZv+JX5ePoM/HX84PyN/cn9Y/77/9ABiwPGA6oCvAHvAcwCEQN+AhECqgIWA4IC/AAa/wb+NP6b/q/+i/1R++r5uflH+nL6cvn1+OH43/jN+ZP5Avgz9+D2gfmPAHwE6wLc/8/65vp6A9kI+ApkDm8NWwzTDXcMjAzAEBoUXxZTGYQYdxWzEnAQixHpFGgWhRSqEXgP1w1SC8AINQYeBMoDlAMQAdX97PqQ94f1bvTY8rfxLfEY8JzvfO/u7SLr+ukp62PtV/Ce8Y7wB/CV8FnxqvL880T2YPkm/DD9ofwX/Pr7s/2lAEsDWQSyA+8C3AI9AzADRAIOAtMC9AMsBIsCw//K/YL9S/0I/aH8TPyL+5X6iPlQ+FD3R/dF+Af5SvpX+qT4k/ce91f4Yf2QAgsFogRRAVj/XQItB6EKrQ7NEDgRChJZEVkQShHZEs4U3xfzGYEZxhajEq4Q8xE9E68SshDfDi4NGAtVCM8EVgHa/0b/6P3f/G36PfZi88jxaPD173/vFu6o7Zvt3ewj7Nvr3+vk7MjuivAS8WTxCvP99IP2RPfK9yb5xfs5/ikAnQG+AWwBGgJfA0kELAWKBegFEAdkB/IFqANlAqYCYwPtA0sD7QF0AEv/LP7q/NL7TPq9+cL6Yvsx+lb4ovaN9T72Wfft9sH1mPWA9Ib0tvnH/qwAQwDm+gb3VfvLADwEYwm2DK0N1g+2DRoK2wo6DRIRVxejG78cihmEE4oQjRFwEw8VOxUhFHwUwhL1DT0JXQUzA34DUQOLAiQBe/2e+R/30vSf8gfxiu/17zPxEvGF76vtsezK7BHuD++K74XwBfJ08yP1kvbF9oH3e/k2+4/9if84AN8A7QEsA6YE4wWZBYgFngagB5YHjgaIBWYF8wX7BfUEpAMFAlEAgv+J//v+hP0B/Ef6VPn9+G/3X/Xv9Gj1vvWv9en0BfQn8/byP/Ii8rX0Dfl3/awASACe+2v5i/sn/zEFcwr7DMYPCxGlDmEMSQwTDcQQDRbfGfwbAxpxFcIRJBFgEr4SChK5EScSNhGBDgEKVwUzAxgCOQHJAIv/A/3L+g359/ZL9fbyafCk7+fwrvHC8L/vN+9f74zvc+8N70nvG/EQ8zD1Effe9733CPhZ+Zj63/sX/YX+6gBzA0IEjgNWAyADkgO2BHEFBwZUBpwGuQaBBioFFQPBAXMBugH2AWcBmgBj/6H9Cvy7+pX5avjM9/n3Wfgx+Gz3+/Vc9eD1ovUR9S30uvPK9rn7Wv8kAcf+BfvR+gH92f+4BL4IrwunDzgQUA7jDJALfgtyD90T6RaEGDcWhBMgE64SiREfENYOBQ9nD3QO4AviCAYGlQTMAs4APv+A/Gv6ffkG+Rb48PZV9OjxOPHA8MHvGO/07lLvmfCp8aXxIfHK8K7w3fEF9OT1YfdZ+RX7T/xC/ev9NP/zAI0C1wMIBdUF3AbEBy0ImAhlCC0HMQYqBkcGQwY4Bm0FlwSUA58BgP87/l79UfxB/Bv88fqr+Qj4uPYB99P27fXX9aT1vvUa9nD11/Sv9HfzQvX6+rT/VgJzAXX8hvpp/bH/WAMiCSkMpw4gEQAPvwzQC+sK3AxBEvAWUhhxFw8UaxGIEMQPog5YDRQNPg3TDHoL2AguBccBNACf/hX9i/v1+OD3yvee92/2rPTI8Vfvbe/u78rwgfHR8RvyhPOU9IbzYvJn8gP0GPfL+e76K/ux/LP9hP6r/0n/Bv9jAFMCuAOKBa0FZAXCBmsHzQb8BdcE0APKBPcFAgY3BqQFkATiA4sCiADB/ub9pP3K/lT/uP76/KP6Hfkq+Jf35PbR9jf3vff59573oPYM9frzlvJB85z43v0ZAW4AOvw1+jb8RP6AAPQENgj2C7UPQQ+/DUcNrQuzC9oPHBSgFb8VYxMVElETNBPqEDMOFA2HDHMMfgspCcIGWQU+BHUCEwAb/af51/ex94H33PZU9cfzj/Ko8uHxZvCy7h7uC+/T8Gfy8/Je81P0VPU19mb2E/aM9gD4mvp3/d7/8wDSAT0CvAJoA5kD3AMjBYEHzAkMC4QKEAltBzMGRwX0BNcE0wTYBEIE0gLFAHn+mvzo+yL8o/t9+g/5HPdA9n72jvZq9jX2wfV59Rn14vQM9Vz1pvby95D4+/gs+JL35PuhAaQFAAdqA4L/+AFmBP4FOQpFDZgQaBQyEwUQiA/IDZ4MCw/qEVoUKRUpEWYOkw+lEMsOfgv8B54G7gZfBVEDPwLmAQ4BpP+A/Zz6YfcD9Q70/fSA9ir25vOo8qzytfKP8hTyvfGl8j307fR89Rz2pvbe9+z5mPtE/GD8YPwM/ZL+dwC6Ae8ChwMIBGYEQASrA00DlwNfBGIFygUABcUDIAP7AmECtwFVAcsAoQC+AHQApv8B/yD+6f2J/oT+nf2I/C/8sfwc/dn8Cfzd+p76Ffs0+y/70vrS+qz7T/wZ/KH7H/sz+oP5FPnJ+90AwwQWBk8Dd/+t/sP//wAlAxIGAQmkDCwO/QxxC58JqAg6CdcKlAwCDTIMjQtEDBENagxlChkHhQXlBJ0DLgL9AJoAvgC+APT+7/w8+4H5svie+LT4Tvim9yf3Tfd6+MH4FPh191T32PdZ+Mb43vgU+Tb6k/vD/ID9hP05/XP9pP6b/+7/GwD7/7EA8gEYA2gD8ANhBNoELgUwBAYDUQLVAmoEngWcBQAFIQSzAgUClgCC/lr93/wp/TD+ef5h/X38/Pp0+ZD4uve29un2avjj+QD7/vrA+Z74sPhZ+dP5JPt//OL8fP1c/q391vzi+0r6QP6wBPUIewq1B74CVgJ4A+8CLgUJB/EJdA5/EEAP+Q3cCz0J5AnoCcUJ1AilBrIGdAmpDAgN+woAB0cEoQJ3/zL92PtN/VMAuAI/AtX///yi+Rb4+/a99gH3evep+Ez6BfxM/Hv72/pn+mf69fnY+Hj4tPmR+2P9Qv/M/6D/Hv///XX99P1N/tn+0P9GAf0BEwJNAbcArgDtAOX/t/3i/Lr8Iv1s/l//bv89/2H+rPyE+7T6pfn6+fP6Zfyo/a/9Ff2m/HP96P0r/jT+n/2+/dn9x/1T/vj+fP+8/4IAQQDr/+n+Yf1f/fv9cf7v/Wf+Sf7m/dD9Bv5sAJ0ErAb3BfcEHgSUA60EYgUXB6YK6wzcDZ4Naw2MDHEMmwyODUIOmQxPCrMI0AiACgELhwqECX8IzQVaApX/O/04/Yb9kv11/Xf9jfsW+Xj33PWb9Wz1RPQ99E31bvU/9fb1lPay94z4bfem9lj3S/jq+DH6a/v4/In+Lv8s/+v/xQBpAVYCrwNhBG8EiQSkBEUFHQbhBTUFYAVtBYwEzAOoAuIBmAGCAFj/2f6N/mn9kfyB+6z6afrA+ST5j/nh+ZH5cfnn+L/4e/lZ+aX42PhB+bv5hPq++qf6gvst/HT85Pu8+277GfzV/Rr/R/9P/tb70fp6/1AFiwmhC2EJCQkCDB4LlgjjB5wI2gy3Eq0T/xMnFRUTThHNEP4OJw18CoMGvwZBCoIL1AmlBw8FgQOYANv6X/Yi9Sn1FfZN90L3ovYV9VPyJPGD8ZrwjO+7717xt/OC9V31E/Zf+L36EvxE/Ij8Sv1+/gcAcQIIBQUHnge1B8AHLwjLB2cHDAhqCUgKhgmBB+wFjAUvBi8GtAXYBHUDrAHB//39o/xT/K78t/2U/kb+Zfxc+hT5VPgE+LL33Pfu+Ez6XvuB+yL7Rfrj+XT6IvuP+6X7ofsQ/HD96/6S/5v/cf9C//3+n/49/n7+Uf9lAHUBTQG+AL//7/7n/oL/NP8Y/1L/RP8n/5/+6/0I/84BAgR4BfsFQgW9BGQEWwRZBZwH2QnOCpkLgwvbCnUKAwq6CbUKzArDCaUIcAdmBjwGXQUjBPAD+AJRATD/2/wX+436k/oS+pX5oPg+92P2DfYP9g/2W/Yh9nH2tPaU9mz2CveK+FD6Pfw//ar9+v0I/qv+nf/SACcCjAMLBe4FBwbhBfMF0QXGBZkF/gT0BB8F3AQYBbsENwO+Ab//+/1L/c38Lfyu/IT8i/vf+nT5NfgP+If3c/fs+P75kfoC+336hvok+z37t/qT+in70vyJ/mz/4P9z/5X/+f5o/p3+Ff6d/RH+GP6t/qH++v0VASgHswt0Dv0LRghcCMYHDgciCNkJcwx5EX0SjRIrE1EQRQ2gDFYLDgv9CbkFqAMuBQYGIQZdBZACvADe/r36W/YH9AzyHfKd8670kPWb9dLzkfLY8jTzSPPm8vjyJ/RU9h/4Rfn6+vz8//51ACIBEwGoAJYA2QBxAngE5wVnBg0HzQf4BwMH2gT0AoUC6wJGAzkDTwNkA1QDCgOsAYL/yf11/M/7O/ys/I38j/xO/Nv73fu++wX7EPuw+yb8bvxy/C/8f/xW/Sf+If+r/7b/gP+U/9P/1/9h/wj/Gv8tAOoAJAGPABAApP9k/0b/zP5x/sP9Wv3W/M78rPym/MP8+vzo/BX9ffz+++z7vPxx/qQBMATCBRYGOQWRBZgG8QfZCBEKKwt+DE4NCQ75DvAPJhDlD1kOFg0XC2sI8QYZB/cHMQg+B5IEXwLK/2f8G/nM9oL14vSS9B700PPX8zLznPJp8i3yt/GT8afxRfLA80/1Pvew+dT7EP3v/Zv+lP/0AIkCNwTuBWsH9wcOCGIIDAmzCV8KkgoeCqwJyQghByQGcwXsBIkEzAMPAssAI//x/DH7tvmt+G/4MfiM94X3UPfQ9oP2b/ZC9r324vYe9x/4H/ld+fP5b/pZ+xv94v0p/nH+0v7r/uL+8v51/3z/q/98/6r+Bv76+335wvtlAbkG2Qp+ChUJyQrHCoAINAjNB9sJDw4qD1wQ2xKGEh4RRhFQEBsQgQ50CT4GXQZ/BXEE7wJWAbEBjwGZ/jT7YPhd9YLzxvHu8NTw3PAf8EnwQfFc8sTy6PIp8zn0T/XF9Sn2YfeO+Sn8IP9cAUQDjASXBRgGEgdgB4gHzwdlCBAJDApTCloKYwphClEKPQmGB1AFegOrAhUCbAG8AL3/3v7F/a/8gft0+nH5Dfk4+Rv5qfgP+IX3//fG+Dj5BPpp+qz6Z/us+/r7rvwn/f39Nf/Q/6T/S/+m/sz+Yf+L/5D/d/9U/xj/xf6Z/kv+x/1h/fT8wvzd/Kz8Pfxp/Ej8JPxM/AP8J/zt/L/+hgGBBBIGAwfuBwoJQQpnCgoKmAr0C6INMQ+sD+gPdhB7EAAQjw8GDjkMewrECI8HhgZhBCkCOgCi/kL9UPvC+Jv2efXv9GH0L/PI8cvwcfBx8HjwZvAk8UPyw/NY9YD2Y/eB+K75jfu4/TX/owApAr0D1QVrB0EI/whXCckJRgp+CkQKPQpeCr4KxwrJCW4IHQfVBaIE/QIyAdP/kP5f/Vz8XPtg+jf59fcL9yn2avW89Jr06fSJ9f/1Y/az9hP3nvca+HP47viD+Q76wvpX+z38Qf03/o7+0v74/jD/S/8y/9D+LP8nAFwBfAPnBcIHagl1CkYLsQxCDcYMWgx6DHYN1A6cDpAOMQ+GD4YPfA5xDBULBwpeCKoHBQe/BUIE+gF8/2H+6PzY+l75Jvhd98z2fvXb8wHzgfJF8mDyvvIF84/zhPRf9cH2EfjT+Jz5t/rL+8v8o/02/gj/bACYAa8C7gPaBLgFlQbdBsYGwgZ4BlQGWwZVBjoGHwaVBdgEcQT/A4kD8AIDAhwBLQAI/+b95PwB/Kf7tftw+177B/uE+mD6L/rh+fz5DfoJ+jX6Zvqn+vj6Qftu+8D7Gfxp/Fj8Evy8+6r7rPus+5r7kfu++wD8GfzL+5z7XPuc+xL8hvwQ/a39U/5s/0EBJwO0BBkGbQeECdwLIw1mDVsNzA3uDg0QaxCeEA4RiRFpEeYQ3w9CDqYMwQomCfwH+QVLAykBg/96/n79XPvw+Gb3+/Xb9D/0rvNp80PzlfIi8iLyDvJO8gfzSPRL9ib4SfmG+vX7i/1f/6MATwEGArUCcwN8BJAFkwalB5gIEwl7CXQJ2QgiCJ4HKgfJBkwGMQX7A8gCcgEVAAH/Ff5q/VP9wvyz+5j6QPlA+AD4sfdd90X3CvcG9yP3Nfcq92P3sfch+Of4dPnG+ej5BPo8+or6k/pS+vP50/nn+fH5SPro+kr7Ufzt/d8AyARBCIIKHAygDRAPchCYEBQQZxAHETsRwhF7EToRtxFFEVAQrA/0DZALIAlABjkE+ALqAKv+Lv1p+zb6pfho9sH0w/PH8hTyWfHG8ALxM/GT8Y3yjfPJ9DX2R/fe+Jr6HvzD/Tn/cADSAQwDzAOkBE4F8wXGBlsHggfLB9gH/gdZCE8IDAiVB6IGzwVFBVgEqQPbAsAB1gDF/3z+Y/1T/EP7t/or+rb5hfny+FD4Qvgl+GD4Jvms+XD6cvvb+0H8uvzA/O38Jf3z/Pr8Af2j/In8bPxV/Lr8rvw9/Cv8EvzY+wP8n/tI+3f7Wvsr+4H7Hvxn/WP/+gDKAgsF/AaRCOsJ4gqUC0QMdQx8DOUMGA0zDY4NgQ16DVYN1QwIDHwLzAr8CUgJBwhdBm8ERAL0/8z95Ps1+uf4yveS9mP1ffTN80zzDvMn89Dz0PS+9Wr2Kvdk+Lb5LfuN/Ar+Vv9yABMBrAF6Aj0DCwTGBFsF9QV7BlsG4QWZBaQF1gXNBUIFjgTrAz4DhwKJAY8A6/9W/7r+Rv7B/Tj9pfwJ/Ib7SPv4+pP6Vfo1+ir6OPom+i/6dPrl+nT70PsT/In8s/zW/Bv9X/3H/Rf+Sf5+/uf+DP8I/+3+A/8s/xz/Ef+r/pL+A/9q/5v/EABnABcBIAKkAjIDGgQUBWcGrAeLCHQJPQp5CqMKfAogCqoJRAn6CG0IxAfWBsgF9wQjBAYD7wHxAKj/c/4e/cD7Evte+qv5LPlz+Nj3h/cu9yP3h/fh91n4B/mT+T/6/PqI+3T8Xv1U/mT/ZQCUAQEDSwSaBZ4GNAfYBygIMQhrCG4IDAigB8QGmgW2BJcDhQK8AfEAMgCA/2P+Lf07/B379/kG+TX4uPeA9y73s/Zs9iH2DfY39kb2lPYL92j33Pdf+AL5xvlw+uX6hPsQ/Fz80PwQ/Vz9zP0n/p/+fv/ZAHUCmwSjBkMI2wlmC+MMkQ7GD64QdhHfEQgSixExEMsOqQ1oDIULWgrbCIQH0wU3BNsClgE7ACf/0P19/IT7BfpZ+BH3//Wo9Zb1HPXH9LP03vQr9Wr1qvUn9uv2pvd8+HL5cvrS+6j9c/8ZAVQCIAO0AzwEgQS9BCMFhwU1BugGNgc2BxAH2AaXBpgGSQbVBUcFkwTTA+sC1AGSAGb/Y/6Q/dD8Cvw/+5P6APqB+SP5v/hi+En4V/hk+KD47vhM+d35ePr1+nv7BfxY/ML8O/1y/eD9P/5u/tD+Df8u/3j/nf+V/5n/g/9k/1r/Tf9k/3r/if9o/0T/Wv+6/xUAcADOAFwBTQJoA1IESwVHBmUHwAjMCYsK8Qr4Ch4LMAvtCm4KsQnfCCAIHQfeBWYE5AKzAc4A0//v/jn+i/0I/XD8nvsD+4T6BfrL+Z75nPnh+fn5y/m9+cj55fmN+lD7Hfz0/LX9bv6X/9cAFwJoA0UEFgW0BeMFAAbsBd8FKgZnBkcG3gUfBTMESwNAAu0AzP/X/vL9MP1X/Ez7b/q3+fL4Yvjy93j3UPdE9yf3O/dW9zz3ZPed99f3R/jB+BL5gfn6+W36OvvP+y38r/z8/G791f0i/rX+lP+oAEgCMwQmBi0I9AmfC2IN5w4XENAQGxFREUMR3xAZEAsPBA7VDGELvAkmCHYG2AQNA00Byv9w/kH9Tvxi+3D6pfnM+O73TffQ9hj2ovVT9TD1ZfWk9fL1ifZF9/336vjP+c/6/vsb/TD+av97AJsBsQKgA4wENQV/BbAFywXEBa0FjgVZBSwF2gRbBAYE0AOBA2MDKQPFAmwC1QErAXAApP/i/j3+pP0B/Xf8/Puf+3T7M/vd+rL6k/p9+qP6y/rm+jH7efvJ+yD8gvyl/Pb8X/3U/VH+pP4I/03/h/+g/6n/r/+9/+L/3v/r//L/AQAVABkAEgAkADQAKQAMAA4AJABcAJQAtwDZAP8AJAETAQUB9AD9ADQBWgGNAe0BcwLVAk8DrwMYBK8ESwWrBQIGMwYfBhQG6AW4BZUFRwXaBGQE0QMaA2MClgHCAAMAfv/4/mz+6f1c/df8cvwm/Pj71vvh+w78Vfyc/ND8DP1w/c79Kf5x/rj+O/+0/z8AxwA4AYABwAETAhoCLgI9AhcCFwLZAW4BEQF7AAoAmf8u/9X+Z/4B/oD96Px5/PH7Y/v1+q76a/pM+i/6Hfo++kP6RfpX+kr6Tvpm+l76cPqV+o36tfr1+jb71vty/BD9Hv5m//8AEQMJBd0G1gioCnUMAA4QD7cPGxAtEB0Qmg+3DqcNfgw9C9sJUQijBgQFXQOtATAAyP5l/RX88/pH+sj5DfmD+BH4q/do90T3GvcN9zf3UPeM98/3MPjl+M35yfrY+7P8fv15/mz/VQAwAckBbAIuA44D4gMLBBoERwRhBFkEDwTFA44DVAM7A+QCiQJAAusBfgH/AHkAAQCo/zn/yv5+/vv9bP3m/HT8O/wi/BP8Cvwr/E78lPzf/Az9Nv17/eT9Rv6x/g//ZP/B/yIAfgDXAAUBOQFuAWwBeQF+AVoBSwE9ASQB/QD0AKEAYAApAP//9P/p/97/v/+0/5f/hf9o/1H/S/9S/1L/Rv9E/y7/GP8V//3+5P7p/tn+3P7y/hj/R/+C/9D/TgDAAB4BdQHoAVMCtQIzA5AD2gMsBGgEkAS2BNUEwQSrBHwEMwTlA2oDxwI5AqgBAwFaAJv/7/5h/vL9lP0k/eb8wvy1/NL8DP1B/Zj98f1l/uv+Qv+b/9f/EwA6AC0AOwBhAF4AUQBDAPL/0f+p/4D/fv9U/zD/J//r/sj+lv40/vL9tf13/QH9rPwi/Hn7Ivu0+ob6R/pB+mn6xPo0+4L7Afyl/Gf9Kf7b/pf/hACEAYQCWgMTBMYEYAXjBWcG3AYtB5UHuwfuBzIIYAh2CH0IYAhKCCQIuwdXB9YGKAaBBboE0QPBAosBVwAh/+n9wPyL+336svnz+HH4Mfgh+Cr4f/jq+Gj5APqW+lD7Bfy6/JT9fv47/wwAzgBwAesBVAKiAuUCEQPyAsMCfAIeAsMBWgHSAFoA+f+L/y7/1f6E/lr+Ff74/Qr+8f3v/fj97f0R/if+P/5h/o7+vv7c/hz/Pv9s/5X/qP+8/+3/9////xMAHQAnABsA+f/c/7r/lP+X/4v/if+X/6D/tP/F/9r/3P/V/8r/sv+L/1j/Ef+2/mP++v2o/Uj9/PzF/I38mvy1/OL8JP2a/QH+cf4K/6j/HgCzACsBqAE9Aq8CLgOMA/sDWQS2BP0EVAW4BQIGZwauBvkGJAc0Bz4HMQcJB80GcgblBTwFeASkA8cC1QHrAPT/8v4B/hP9P/yI+/H6a/oE+s/5o/m7+ez5Nvqy+i/7p/tO/Pr8fP0V/p3+MP+4/yYAeQDCABABPwFNAT0BMgH9AOQAnwBKAA4A3P+m/2T/N//7/s7+ov56/lz+Jf4P/gX++P3t/eL9zP28/ab9mv2H/Vr9Vv05/Rz9Iv0R/Qr9Kf1K/Zr97f1Y/sr+QP/c/2MAEQGxAWUCCgO0A34EEgWXBfsFNwZyBnQGYgZKBgcGvwVtBQ8FtgRHBNEDSQO/AjQCmwEKAXQA3P9W/5/+8v1q/dv8cvwd/L77jfuR+4r7vvsF/Fj89Px3/Qb+lv4w/7L/FwB3ALcA6wAFASABJwElAQcB2QDDAHsATgAmAAEA8P/c/+P/+f8AAAUADAAgAEMAaQB3AHcAhABwAFwANgDn/7H/df81//n+zP6r/oL+Wv5R/mX+Yf6S/rr+yv77/if/Vv+A/8H/2f8MAD8AXgCGAJIAowDDAMwA2QDiALoAkQB+AFoAFQDT/5f/bv9G/+3+kv5Y/gH+xf2a/XP9bv1e/Wf9bP2U/a39yf3t/f/9LP5T/of+qv7l/jv/cf/w/04A0ACCATQC9AKyA4MEUAUbBt0GlgcOCIII0gjoCNsIjwg4CLcHCwdABlQFOwQsAx4CEAEVACn/MP5n/br89ftX+8L6Rfrh+Y/5Uvkd+RL5GvlA+Yz56PlT+s/6U/vh+3n8F/21/Vz++f6M/yAAowABAWMBqAHkASICSwJTAjYCFQL7Ac4BmQFPAdcAcAAbAMj/d/8n//3+0P6f/mz+U/5A/in+K/45/kT+Tf5c/l3+cf58/oX+sf7Q/uX+Af8c/yP/S/9q/4v/0f8TAGEAzgATAXwB6QE5ApIC8AJPA4EDyAPuA/IDFgQTBOMDtAOLA1oDIwPZAnkCDgK8AXcBKQHLAIIASAAKAOn/pP9j/zf/A//p/rz+kv5c/kD+NP4g/iL+Gv4I/gH+CP4G/hr+J/45/lj+X/6N/qv+yv7S/tD+zP7H/sj+vv6r/qv+pP6U/o7+jv6i/pv+tv6+/rX+s/6x/r/+tv7Q/uX+E/8q/1b/hf+y/+3/GQA6AHIAewB0AHkAWgBOAD8AKQAdAPD/tP+v/4z/h/+8/9P/KwCWABsBwQFoAg0DtAN/BDUF2AVeBsgGQQeRB6oHkwdtBxAHhQbnBU4FlwTQAwMDSAKGAb4A+f81/4L+4P1G/c38bvwA/Lv7ivtX+yn7Cfvx+t361vrl+vH6F/tB+3/7zfsg/JH8Bf2A/f39gv7i/lT/tP/y/04AlgCxAMAA1gDWANcAzgC+AKwAjwCRAHsAZQA6ABIA6/+//5n/c/9J/wT/3v66/oL+bP5Y/kT+N/42/kb+T/5n/o3+vP7b/gz/R/+D/9P/CgBVAKEA7wAgAVEBiwHKAfEB9gH/Af8B+gHJAa0BkgFqAWMBKQHgAK8AnABsAE8APQAtAD0AQQA/AEEAUQBYAE8AZQByAHsAfgCEAJIAnQCmAKwAtwDCAMIAwADDALUArgCcAIgAiABTAD0AQQA6ACQACgD///L/5f/D/6T/hf+A/3r/Zv9Y/1v/T/9A/0v/RP9N/0D/Pf9N/13/Zv+O/6j/zP8TADsAbACRAKoA0ADUANIAywCjAIAATAAmAOn/pP9f/xP/8P7H/qH+gP5z/l3+Xf5z/nz+jv6O/o3+n/6X/pn+rf6z/r7+2f72/g3/Mv9j/27/ov/i/xUAQwBaAIkAnACdAKUApgCvAJ8AoQCUAH4AhgB9AH0AiwCUAMIA8wAcAU0BiwHJAScCbgKvAvsCLgNxA6YDxQPaA84DzAOgA3UDQAP2AsECXAL9AaYBPQHZAHUAFwDB/2T/BP+8/oL+Mv7x/en9w/2m/Yn9bv1W/Ub9K/0b/Rv9IP07/Ur9Yf1+/Z/9x/32/SX+df62/uf+HP9U/5n/qf/F/9f/2v/2//b/8v/V/8b/pv98/2z/Kv/5/tL+r/6N/nn+aP5n/m7+ev6H/pD+qv61/tf+8v4D/yP/Rv9Y/2r/jP+g/7j/yv/V//n/DgAmAEYAYwCGALMA6wARAVgBmAHyAT0ChwLSAhgDbgOxA8oD9wMRBBMEEQQCBPADzAOfA2YDEwOvAlgC6AGEATQBwgBeAP//qP9A/+v+pP5s/kT+Ef76/eb9x/3J/cP92f3k/e/9Af4G/hf+MP45/kL+X/6C/qT+vP7T/gH/MP9a/5T/qf+6/+X/BwAvAEEAYwBgAGkAgABwAGsAXgA4ABsA///T/7T/m/+L/3j/Zv9R/zf/J/8V/wr/+f4R/yr/Qv9k/4L/sf/a/wEAGQAwAFwAgACjALkAxQDkAAEB+AD6APQA6gDmANcAuQCoAJQAewBuAFcANgAdAB4AEgD9//n/5//i/7//sf+g/5f/qP+i/7L/yv/e/+7/GQApAEMAWgBpAH4AfgCYAKYApgCSAJYAkQCaAKUAqgCaAJgAnQCfAI8AewB9AGsAYwBjAGUAOAAtABMACgATAAcADgD//wwAAQADAP//AQAgACIALQA2ADsASAAkAAcACQD7/97/yP+m/5L/gP9U/z3/J/8B//b+6f7k/tz+5f4B/xj/Mv9H/13/fv+f/6j/vP/Q/8z/0P/D/7T/pP+Q/4P/eP9b/03/Vv9J/1T/Zv9k/2P/aP+D/3P/gP+M/5T/pv+9/9f/1//p//D/AAASAB0AJwAeACcAFQATABMABQAMABcAHQAOAAwAGwA0AEEAVQByAI8AwADqAAUBOAFKAV8BiQGQAa0BxwHJAdUB6wHZAeAB4gHOAa8BpAGUAXIBSwEOAfEA0gCsAHQAOgADAMj/qP9x/0L/GP/5/tX+vP6i/pL+kv6J/oL+i/6k/qv+uv7g/vj+Cv8c/zf/Vv94/5T/l/+4/8b/3P/X/7r/uv+U/4z/kv+M/47/jP+F/4P/fv+C/3z/c/+V/6T/uv/X/9f/2v/V/9X/2f/i/9D/0P/B/7L/r/+i/53/rf/G/9r/8P8DABIAHgAwAEoAYwBlAGkAaQB7AH0AdwBjAEYAQQAmABkAAwDn/9z/3P/Z/+P/6//e/97/2v/O/9P/1/+//7j/v/+0/7j/xf/j/+v//f8VACYARABGAGAAbABsAI8AiwCLAKoAvADDAMMAwADMANIAzgDgAOYA6gD8AOsA3QDDAKwArAChAKgAjQCIAIsAbgBrADoALwAVAPT/7f/g/9D/pP+f/5T/ev9z/2//df+D/3j/c/94/37/kv+o/7L/uP/c//D//f/9/wwAKQA4ADQAQQBDADAAMAAVAAEA9//n/9X/yv/G/6b/mf+J/3f/Xf9b/1r/Qv9N/03/W/9f/1L/Rv9L/0//T/9a/1H/S/9E/1L/WP9k/2z/fv+b/6v/uv/R//3/HgA7AFwAeQCNAK4AswDCAN0A4gDqAOYA4ADZAMwAvACsAKYAnQCJAHAAaQBjAEMASgArABsABwAFAPL/3v/Z/9f/3v/O/9X/xv/O/9X/4P/e/+3/7v8DAA4A9//9//D/+/8KAAwACQAMAAkAEAAXAAoA/f/9/////f///wUA+/8AAAAA+//5/+7/6//n/+X/6f/e/9H/2v/Q/+X/5f/Z/9z/3P/j/9f/8P/9/wMAEgAdABsAHgAkABAAAAAFABIABQAMAAEA+f/r/9D/0f/F/63/m/+X/5X/m/+f/6L/tP+4/8j/vf+//9r/zv/R/+D/3v/p//v/7v/2//T/9v8KAP//8P/2/wAA9//l/8z/1f/a/9D/zP/G/8P/sf+v/5n/kv+O/4z/nf+k/6//wf/I/9H/6//9/yQALwA2AFUAZQCNAJ8ArgC3AKUAqgC5AKwAnQCSAJQAkgB9AHsAcgBlAGsAZQBeAEwAPQAnABIAEwAAAPT/4//l/+P/2v/j/9X/5f/5/+v/9P8KAAcAIAAnADoATwBMAFMAVwBOAEYAWABTAEYAQQA2ACYAHgAeABsAGwAiAB4AIgAnAB0AJwAnACYANgAyAC0AGwD9//T/4P/X/8H/tP+m/4X/c/9W/1H/Pv9C/0n/Qv9H/0D/QP8y/zn/QP9J/0//av+F/5T/tv/G/97///8KAAUACgAVAAwA//8FAAkA9v/9/wAAAQAVABAADgAkACsALwAgAAkACQAQAAwAFQAmABcAHQAVABkAFwAeACcAJwArACIANAAtADQAKQAvAEEAOABOAFEAVQBRAEQASgA6AEEASgBPAE4ASgBXAE8ARgA2AC0AFwAKAP3/4//Z/8j/v/+r/7H/sf+k/6j/lP+k/53/kP+m/47/if+U/5D/ov+4/8z/0f/l/wcABwAkAC0ANgBVAGcAaQBaAFgAXABnAF4AXABXAEQATABEAEMAQwA0AC0AMgApABkADAAAAO7/4v/g/+D/0f/B/7z/sf+4/7r/tP+x/7H/xf+6/87/0P/G/8z/w//T/9n/4v/g/97/w/+r/7L/r/+r/73/zP/Q/9f/3P/l/+f/+//3/wkAFQAdACkAOgBKAE4AWgBaAEYAPwA9ACcAHgAZACIAOgAyADoANAAXACIABwDr//v/7v/n/+L/5f/t/9f/2v/M/9n/0f/D/8r/zv/V/9X/2f/G/+D/6//2//v/AAABAAAA+f/5/xAAEwAVAA4AHgAQABAAFQAQAAUAEAAgABsAEwAJAA4ACgAQABUADAASAA4ADAAHAAMA9//t//T/4//X/+D/9P/w/wEA+f/9/wkACQABAAAAAQAFAAoA8P/y/+3/4v/l/+3/8v/y//b/+f/r/+X/7f/3/wAACQAeAB4AKQA7AE8AWABjAG4AawBnAGEAcABYAFgAVwBKAEwAOwAnAB0AEwD3//T/7f/t/+L/6f/n/9r/4P/X/8j/xv/T/9D/1f/Q/7//2v/c/9z/4//y//T/AAAHAA4AIAAmACsAIgAeAB4AEgAFAAAA9v/X/8r/uv+2/6v/ov+v/6D/tP+6/6v/qf+O/47/lP+b/6L/kv+f/7//xf/e/+n/6f8DACAALQAyAEEARgBKAFMATwBKAEEARgBVAFEAOwAvACsALwAvACAAIAAmACAAJAAnACkANAAnAB0AGwAeACAAJgAXAAAA/f/y//D/4v/V/8j/0//e/+L/9P/7/wkAEgAMABAAHQAQAA4AHgAmACYAKwA6ACYAIAAkACcAIgATACAAFwAKABUAAQDw//L/7f/l/9P/w//K/8z/tv+v/6j/r//D/7H/r/+x/6n/rf+k/7j/tv+t/7j/vP/X/8r/0//g/97/7v/5/wMA/f/9//f/9P/3//L/AwATABMAIgAwACsALQA7AEYAOgAyACcAEwASAA4AHQAkABsAEgAOAAcAAwDr/97/7v/c/9n/zv+//7j/uv+9/7r/yv/Z/87/yv/V/87/2v/l/+f/7f/t//v/BQAVAAoADgAFAAEA+f/t/+f/2v/V/73/v//B/8j/yv/O/9z/7f/w//b/+//9/wkAEwAiACcAJgA0AEQASABPAFUAUQBYAF4ARABPAFMASgBBAEMAOwAyAEgAQwBBADIAMgAyACQAMAA2ADIAMgA4ADQAKQAbACAAIgAQABUAHgAgAC0AMAApABAAAAD2//n/6f/c/+n/6//n//f/7v/i/9z/0f/R/8b/xv/B/7r/vf/B/8z/4P/l/+7/7v/5//v/4P/0/wAAAwAbACYALQAmAB0AIgAdAC0AKwAmACsAMAArACYAHgAbAAwA+f/2/+n/4P/O/73/tP+t/6//qP+d/5T/g/+S/5D/jv+t/5v/r/+0/7//0//l/97/4/8BAPn////3//3/AAADAPv/+/8OABUAIAAkAC8AIgAbABkAFwAgAB0AFwAXABIAIAAiAB0AFQAFAAUA+//u/+7/6f/t/+X/9P/0/+L/8P8HABMADAAZACAAIAAbACAAJwAtADYAJwAiABMAEwAOAAMACQD9/wAAAwAJABIAEgAvADAALQAyABsAEwAeAA4AGwAdAAEA///3////AQDu/+n/9v/9//n/9P/2/+f/4v/Z/9f/0f/T/9f/wf/I/8H/zv/M/8b/0f/T/+P/6f/r//b/9//3/9r/0//a/87/yv/F/87/0//e/+n/9//7//n/AAAAAP3/9P8DAAwAEAAeACIAFwApADYALQAtAC8AHgATAAoAAwAFAPv/BQABAA4AEwAFAP//+//5//3/+////xAAAQABAAAA//8FABIAIAAmADAAOwBGAD8ANgApACcAMAAvACYAFwAMAAMABQAKAAEA9v/5//n/8P/r//b/5//j/+L/5f/t/+3/+//0//L/7v/p/+D/1//O/9D/0f/G/9D/xv+9/7b/uP+8/7L/vP+2/7b/0P+9/9f/7v/u//n/AAATACQAOAA4ADYARgBaAF4AWgBeAGkAbgByAF4ASABOAEwASAA/ADIAFwAFABUACgD7//b/6//3//L/9P/3//T/8P/0/+f/5//w/+L/5f/p//b/+/8JABcAFQAbAA4AHQAgABkAGwAeACYAJgApACYAHQAMAPf/8v/j/9H/4v/Z/9D/v/+0/73/q/+0/7j/vP+2/73/vP/T/+D/7v/2/+n/7v/g/9z/5f/w//D/CgAVABAABwAFAPv/AQADAPT/DgADAP3/+f8BAAUA8v/p/+f/9v/c/8P/wf+0/8P/yP/R/9D/1f/Z/+L/2f/j//T/8P/y//D/+/8DAPv/9v/7//f/AAD0//n/7v/l//L/6//7//3/+f8BABUAHQAZACcAHgAOAAkABQD//w4AJAAgADYAOgAyACkAMgAtAB4ADAAFAAoACQAOAAwAEwASABIAFwATAA4AAwABAAEAAwABAPn/+//5//T/9//2//b/+f/9/wMABQAQAAoADgAKABkAFwAZAC8AJAAeACcAJgATABkAEwAZABkAHQAZAB4AHQAXACAAFwAVACAAKQAQAPn/9P/t//T/6f/c/+L/8v/5//3/AQAFAP//+/8FAAcACgASAAoA+f/j/9n/w/+2/7j/tv+p/53/sv+0/7z/yv+y/6v/pP+m/6n/pP+m/7j/vf/a/+X/4//5/wEAEwAZABAAEAASABIAEwAiACAAKwAtACsAMgA7ACsAFwAOAAUAAQD9//n/+f8DAAwAFQAbACIAGQAdACIAKwAnAB0AFQAKAA4AFQAFAPb/9//n/+n//f////3/AAAFAAEAAQAQABcAEgAXABIAHQAmAAwAIgAiABsAIAAHAAMA9v/g/97/3P/a/+3/+//u/+L/5//r/+D/2f/c/97/3P/j//b/+f/y/wAABQADAA4A//8OABAACgAXABMALwAiACsAKwAyAEYAMAA0ADAAHQASAAwACQAFAAkACgD///f/+f/9//f/7v/2/+n/1//T/9X/2f/g/9r/4P/p/9r/2v/g/9z/0f/M/9H/4P/5/woA///2/wMA9v8FAAoA//8HABMAIgAnAC8AMgA/ADgAMgAvACYAEgABAAAA+/////L/9P/g/9n/zv+8/7r/tv+8/8r/1f/M/97/3v/n//n/8v/r/+f/9/8BAAcACgAOABMAHQAdABcAJwAyAC0AOAAtACYAJwA9AD0APQA9ADsALQAVABcABQD///b/7v/i/+P/2f/g/+L/1f/e/8z/0//Z/9X/0P/Q/9H/2v/i/97/2f/i/+P/5//w//n/8P/u//L/+//9//D/5f/j/97/2f/O/8b/zP+8/9f/4P/T/97/zv/M/8P/3v/r//n/CQAKAAwAEAAeABkAEgAXACAAHQAtADQANAAyACYAJwAeABcAEAAKAAAABQAMAA4AGQAkACsAFQAXACAAFwAdACAAGwAtADQAKwA7ADAANgA/ADYAMgAvADgANgAyABcADgATAAwACgD7//3////3/+v/6f/j/8z/xv/G/8b/xv/G/7b/tP+4/8z/1//O/9X/0f/Z/9z/7f////f/8P/7//v/9//n/97/3P/c/+X/3P/j//D/7f/7////7v/u/+3/9v/5////AwD3/wAAGQAZABAAJAAkABcADgAFAAUAFQAKABAADgAAAAEAAQD9//n/+f/0//T/7f/c/9P/0P/R/9P/1//T/8X/zv/K/9P/7f/7/wAACQAOABMADgD7/wEA/f////v/AAAFAPv/CgAVACIAKQAyAC8AJgAnAB4AFwAJAAkAEAAeADQANgA0ADoANgAkABcAEgD//+n/7f/u/+v/8v/9/wkAAwADAPv/AQAdABUAEgAFAPn/CgAVABUAJAAdABcAIgAeAAoAAQDw/+3/6//w/wMADAAkABUAJAAnACIAIAAZABsABwAJAAEA9v////n/AwD7////CQAVAB0ADgAKAAEA9//0//b/7f/0//b/6f/u/+n/8P/t/+v/9P/j//L/7f/t//D/7v/9/+f/4//i/8r/0P/X/87/1f/a/9r/2f/t/+f/1f/Z/+X/6//i/9X/zP/T/97/6f/t/9z/1//V/+f/9//y//f/BQAFAAcAFQAZABcAEAASABkAFwAdACQAGwAkADQAOAA0ACAAGQAHAAcAAAADABIADAAdAB0AFwATAA4A8v/X/9P/zP/M/9D/0f/K/9D/xf/G/8H/yv/I/8z/0//O/9z/6f/t//n/EAAOAAAA+/8BAAkABwAKABAAHgAgACYAJAASABcABwADAB4AHQApACQAJAAwACQAJgAgACsAKQAiACIAEgAOAAwACQD3/wEA+//9/wEAAADy/+P/0//e//L/7f/y//f/CQAAAAoAHQAiAB4AGwAbACAAJAAgACAAHgAvADQAJwAnABkAGwAdABcAEAD7//T/7f/w/+7/9P/2/wMA+f/0/+7/6//r/9z/xv/M/7//n/+p/6v/sv+6/7j/uv+6/7H/sf+i/5//q/+y/7j/vP/O/9P/4//u////+//5/wEAAQADAAwAGQAQABcAGwASABMAGQAOAAcA///w//n/9v/9//b/9v/w/+7/+f/5//T/9/8DAAAADAAXABcAKQA0ADoAOAA2ADAAQQA2ACcALwAtADYAOAA2AC8AMAArACAAEwD9/+n/3v/p/9f/zP/X/9n/4v/l//f/9//n/+v/7v/l/+7/9v8DABAAJAAkABkAJwAMAAcAAwAAAAoACgAKAAUABwAKAAwACQABAPv/9P/r/+3/+//w//b/9v/l//D/7v/3//////8MABUAGwAnADsAKQAnAC0AJgAtACAAEwAMABcACQD9/wwAFQAeABsAGwATABcAEwAQABkACQD///f/9P/r/+v/7v/V/9H/yP/G/8X/vf/O/9D/0f/t//D/7v/2//n/+//y/+7/7f/w//T/9v/5/wEAAwD2//L/7v/i/9n/2v/R/8j/3P/e/+X/9//5/w4AEgAdABAAEAASABUAEgAFAAkAAwDu//T/+f/l/9z/yP/O/9r/3P/p/+7/6//n/+X/7v/r//b/9//2/wAACQAQAAAADAAKAB4AMAApADYAPQA9AEMANAAwAC8AMAAbAAoABwD///3/9//9//f/8P/p/+X/1f/T/9r/3v/j/+L/8v/y//f////0//f/+//w//L//f/3//n/////////CQATACAAKwA6ADsAPwBIAEEARgBBADgAOgA/ADYAOAA2ADAAPQBDAD8ANAAeAAMA9P/u/+X/0P/V/9n/0f/V/9z/zv+8/7j/v//G/8P/0f/O/8r/0//e/+7/+f8AAAoAFQAZAB0ADgD9//f/5//c/9X/0P/a/97/1//l/+L/3v/Z/9H/1//e/+n/4v/c/97/wf/X/+f/4//5//f/9/8FABAADAAAAP//DgATABcAEgAJABIAGwAZABAAFwAXABkAJAAnABsAEgAeAB4AGwAZAA4AEwAFAP//BwADAAEAAQD9/wEADgAFAAcACQAAAAcADgAdACYAKwAeACIAIgAVABAADAAHAPv/7f/t//T/8v/9/wUAAADt//D/7v/p/+P/4v/0/+7/AQAMABMAFwAZABUAHgAQAAcAAwDu/+7/6//w////9v/p////5f/a/9f/vP+6/7L/vf/D/9r/3P/R/9r/4v/y/9r/0f/X/97/2v/M/9n/3v/u//b/AQAAAAAAAAAJAAcAFQAmACYAIAATABUAFwAZABkAJAAdACAAFwAVABsADgAOAAoABwAJAP3/AQAHAPf/7v/2/+f/2v/a/9X/1f/V/+D/2v/p/+X/2v/i/+n/7v/y//D/5f/l/+3/7v/y/+7/8P/r//b/9P/t/+7/9P8BAAMAAQD//wEA///3////+f/9////AwAQAAwAFQATAB4AIAAiABsAHQAnABsADAAOABUAFQAiACAAJgArACYAIgAgACQAJgAtAB0ADgAXABMACgADAAEAAwAOAAoAAQAKAA4AAwD//wMACQAHAAcAEAAJAAkAAwD3//b/8v/3//f/9P8HAAcADAAZABcAFwATAB0AFQAbABUAFwAgABkADgD5//D/7f/Z/8P/uv+x/6//sv+i/6j/qP+p/7j/v/+8/8r/yP/V/+3/5f/p/+P/4v/2/wMADAAKABMAKwAtADoAPQBDADYAJAAkACYAJAAVAAwABQAOAA4ADgAVABMAGQAbABUAEAAQAAkA/f8AAAcA+f8AAAcA9v/7//3/BQD//+7/8P/0//b/7f/y//T/+f8KAAoADgATABAAEwAJAPv/BQAKAAoACgAKAAoAEAABAAMACQD3//f/8v/7//v/5f/n/97/4//j/+P/+f/3/wcABwADAAMA+//7//3/AwD7////DAAKABAAIAAXABUAIAAnAB0AFwAOAAcAAwD9/w4AEgAOAAEA/f/7//b/7v/0////+f/y/+7/6//p//D/4//a/97/5f/n/9f/1//D/7z/zv+//8X/vf+2/8P/xv/O/9P/0P/Q/8X/zv/T/9H/4P/r//b/AwAQABIAFQAXABsAFwAQAAoADAAXACkALwArACQAJwAmABMAGQAJABMAGwAnACQAIAApACIAJgAkAA4A8P/3//D/6//n/+L/4P/B/8j/0P+//9H/0//g/+3/5//2/wwAEwAgAC8AJAArAC8AJwAkACIAJAAyADIAPQA9ADIALwAwAB4AAwD0/+v/7v/l/9n/0f/G/8b/1f/M/8z/yv+//7j/tP/D/7j/0P/n/+n/BwAVABUADgAHAAwADgAZACkAKQAiABsAEgATAAoACQADAPn//f8BAAMADgAHAAMABwD0//b/9v8KAB0AGwAQABMAHQASACkAIgAmAC8AKwAdABkADgABABIAAQD0/+v/8v/0/+X/8P/5/wcA+//2//f/6//l/+X/8v/y/+n/4//i/+L/5f/j/+P/3P/c/+f/6f/0/+3/7f/w////FQAbAAoA+/8AAAcA+//y//3/AQD2//L////n/+P/6f/p/+L/6f/i/+X/9//3/wwAAQAOABMADgATABcADgAQAAoA/////wMAAAADAAEA//8SABkAEwAOAAoAEgATAAwACgAFAAoADAAKAAwACgAJAAcA/f8FABUAHQAdACcAJgAtADYAKwAwABsAFQAMABIAFQAFABUAGwAnACsALQAgABAAGQAVABMACQAKAAcAAQADAAAA+//9/wMA+//9//n/6f/c/+v/4//T/9P/1f/c/9n/4v/a/+L/9P/r/+n/3v/V/+X/5f/e/+f/3P/Z//L/8v/l/+X/4P/a/9H/1//c/9X/4//g/+3/8v/3//n//f/3//D//f/2//b/9P/w//f/5//u//L//f8FAAAAAQAJAAwACgASAAwAEgAOAAwAEgAOAAwA9v/y//f/8v/5//L/+//5/+3/9v/j/9P/2f/T/9n/5f/u/+f/5f/0/+X/9//w/+n/9P8JAA4AAwAFAAoAFwAdAC8AJgAXABUAGwAdABMAEgAQACIAIAAgACIAJwAnABMADgAQABsAEwAVABAADgAZABkADAAAAPv/7f/w/+f/5//l/+L/6//7/wAA/f8BAPT/7v/9/wUABQAKABMADAAHAAUAEAADAAMAEAATABAAHQAXABAACQAAAAEA+//2/wEACQD7//b/6f/t//f/6f/n/+n/4v/l/9z/3v/c/9r/2v/R/+f/5f/p/+7/5//0//b/8v/r/+n/5f/l/+L/0//i/+f/5//t//v///8BAA4AJgAmACAAHQAXACcAHgApACsAJgAiABsAGQAbAA4ACQAFAPf////t/9X/0P/Q/9r/2f/e/+L/1f/X/9r/2v/g/+X/4//y/+7/9/8AAAcADAAQAAMACgAMAAMABwAFAAMA9v/t/+X/6f/n/+X/6f/u//L/9//w/+f/7v/w//b/9P/5//n///8KABcAFQAZACQAGQAZACQAIAAkADAANAA4ADYAQQBIAEgAQwA2ADgAMAA4ADoAOgA0ADYANAAkABsAEwAHAPv//f/y/+7/8v/t//f/4//a/97/5f/j/+L/7f/r//b/AQADAAMABQAHAAkADgABAPf/9//y/+n/5f/t/+n/3v/e/+n/4v/a/+D/3v/c/+X/4//Z/+L/2f/g/+X/9v/9//b/+f/2//f////9/wMAAwABAA4ACQAJABIABwAJAAMA+//9//f/7v/j/+X/5f/n/+v/3P/p/9r/4P/y//T/+//7/wAA/f8JAP3/9v/2//L/7v/p//D/7v/3//3/+//2//v/AAAAAA4AEAAbAB0AEAAeACAAJgAnAB0AHQAbABMAGwAgABUADgAVABIAAAABAAcAAwD9/wAA///7//b/8v/0//T//f/2//n/9//2//v/+//7/+v/5f/n/+P/5f/l//3/AQAHABAADAAXAB4AFwAkACkAFQAbABUAFQAXAA4ACQAFABIADAAHAAUA9//0/+7/9v/2//v////r//L/7f/0/+n/3P/c/9z/4v/p//T/+/8JABIACQAMABcAEAAKAAUACgABAAoAEwAdABsAGwAkABsAEwAFAAwADAADAAMACQD//wAAAwD3//b/9//y/+n/5f/g/9n/0f/Z/9f/2v/g/+L/3P/c/9D/0P/a/+D/7f/i/97/2f/Z/9P/2f/a/97/5//2/wkACQAKAA4AEgASABUADgAFAP//AwD///f/9v/u/+7/6f/n/+f/7f/r/+L/3P/n/+n/7f/3/+3/9P8BAAcADgAVABsAIAAeACQALwAvAC0AIAAkADIAJAArACkAIAAtAC0AJwATABIABwAJABAAFQAXAAwAGQAbACAAGwAdABkAIAAdABMAEgASABIACQADAO3/5f/a/9P/2f/R/87/1f/K/8z/0f/O/9n/1//a/9r/4//l/+P/4P/Z/97/6//t//L/+f/5/wEABwAVABMAEwAQABsAEgAQACIAHgAbABkAEAAKAAoABwAJAP//9v/r//D/6//u//T/6//w/+X/6//n/+v/7v/y//3/AwAAAAcA///0//3/+f/5/wMABQD9/wcACQADAPv/AAD///f/9//w//f/3P/a/+P/6f/2/+f/3P/w/+7/5f/t/+3/9P8AAAEADgAKAAcAFQATABcAIAAmACYAIAAmABsAGwAkABsAIAAXACAAGQAZABUADAASAAMADAAbABMAGQAeAAwA//8FAPb/6//j/+L/3v/g/+v/5//5//n/9P/r//T/9//w/+3/9/8HAAMACgAMAAkABwABAAkA///u/+f/5f/j/+D/2f/V/9f/zv/M/87/xf/O/9D/0P/T/8b/zv/a/+D/4//t//D/+/8QAAUA+f8HAAwAAwASABAAEgAFAAUAAQABAA4ADAAZAAcAAQAMAAUA+//u//L/8P/0//f/+/8AAAcABwABAAkADgAKABIAFwAMAA4ADgAAAP//+f/7//L/4v8AAAAAAQAMAAoAEAASABcADAABAAEABQD9/wMA/f/0/wAACQATAAwAAwADAAcAAwD9////9v8AAAoA+//u//D/4//e/+L/4P/Z/9P/1f/e/+7/9P/5/wAADgAKABMAHgAnACAAGQAbAB4AJwAmABsAEAAiABkADgASABAADgASAB0AHQAbABkAEwATABkAEwAZABcAAQAKAAcACQAJAPb/9v/r/9r/3P/X/9H/3P/r/+X/3v/g/+f/5f/Z/97/4v/n//T/+////wAAAwAHAAUACgD7/wEAAAD5/wMA8P8FAAcACQAJAA4AGwASACIAKQAkACYAJwAmACcAMAAvACsAHQAXABkAHQAVABAAEAAKAP3/+f8BAP3/9P/2//3/7v/p/97/3P/X/9D/0P/T/+L/5f/j/+X/6f/p//3/8P/n/+7/6//w//T/8v/y//v/AwAFAAEA///7//n/9v/0//n/8v/y/+7/9P/y/+v/8P/l/+7/9v/2//D/7f/j/+L/4v/c/9z/1f/V/97/4//j/+f/7v/9//L/+//7//v/DAAbACAAJwApACsAMAAvADQAJgAkACAAEgASABAAAwADAAMA9//u/+f/3P/i/+P/3v/l/9r/2f/Z/87/zv/X/9f/4P/l/+3/9v///wUADAAHAAMACgASAB0AEwAFAAcAAwADAAkAAQAHAAkAFQAOAAEACgDu/+7/8P/y//3/AQAJAAAA/f///////f8DAAAAAQABAAEAAwAAAAUABwADAP3/+//2//T/7f/y//f/9v///wEACgAJAAkAAwAMABMABwASABkAHgAmACkAHQAnACcAHgAmACYAJgAiAC8AJAAZACAAIgAmABAACQAOAAkABQABAAMA+//2//D/9v/2//L/6f/j/+D/5f/w/+7/9//0//v/AQAFAA4AEgAOAAoABQAJAP3/7f/w//v/+f/y//T/4v/r//D/6//j/+X/3v/R/9X/1f/a/8j/2f/l/+L/4P/a/9f/5f/l/+L/8P/2//D/9//3////AQABAAcAAAD//wUABQADAPn/+/////n/AAABAAMABwAMAAUACQAOABUAFQAQAA4AEwAXAAwAEgAKAAkABQAFAAAA9v8FAAMACgAbABsAHQAZACIAIAApACcAJAAkACQAMAAgABUAGQAOAAkABQD5/+3/5f/t/+3/4//i/97/3v/Z/9X/0f/e/+P/4//t/+D/1//g/+X/6f/y/+P/6f/7//f/9P/2/+v/7v/y/+3/8v/2////8P8BAAcAAwAOAAwAEAAHAAoADgAKAAUAAQAMAAMA+/8AABAAFQAJAA4ADAAHAAoACQD//wcACQAFAAkA/f8BAAEAAQAMAAUAGwAXABkAFwAQABcAAAAAAAUA9v/5//f/6f/u/+f/1//Q/9r/zv+9/7z/yv/Q/8b/vf+6/8X/yv/X/9n/zP/O/9D/5f/u/+n/9v8KABUAFQAbABsAIgAiACsAMAApACcAJwAbABcAHgAeABcAAQAAAPv/+f/2//v////0/wEAAwD///n/+f/t/+X/6f/p/+n/6f/n/+L/4//j/+X/4//0//L//f8JAAkAEgAVABAAEgAbABUAEgAXACAAIAAbABkAHgAkABkAHgAVAAcAEgAFAAAACgAFAAwAAAAAAAMA/f8AAPn/BQAAAAcACQADAAcACQADAPb/AAD//wAACQAMAAwAEwAJABMAFwAJAAUACgAXAA4AGQAZABkAFwAVABcAGwAXABIAEAAMABIACQD///3/9v8AAAAAAQD9//b/9P/p/+X/3v/g/9z/5f/e/+L/5f/p//v/9//y/wEA+f/r/+3/5//p/+v/4//n/+n/5f/n/9z/3v/j/+P/1//O/9H/zv/V/9r/5f/j/+X/6f/r/+7/9v////v/BQAHAAkAEgAbACAAKQAiACIAJwAiACQAFQAXABUAFQAdABkAEwAXAA4ABQAHAPn/8v/3//f/+f/2/+7/6//2/+n/5//r/+3/+f/7//////8OABMAEwAXABIACQAFAAoA+//0//T/8v/3//b/9//u/+X/5//n/97/4P/g/9r/4P/j/+P/3P/n/9z/6f/0//f/BwAJAA4AEwAXACIAIAAdABsAGwAgABsAIgAmABkAIAAVAAMACgABAAEAAQAAAAcACQAHAAwADAADAAcABQAJABcADAADAPv/+f/u/9z/4v/a/+D/3v/V/9P/3P/g/+X/6//i/+X/6//u/+7/9P/5/+7/9//y//b/9v/r//f/9P/w/wAA+//0//n///8FAAcAAQD/////AQD///v////9//f/+f//////+/8AAAEA+f////f/9v/0/+X/7v/u//L/7v/5/wAAAAAAAPb////9//b////9//f//f/2/wEADgAHAA4AEgATABMAGQAgABMAIAAkACcAMAA2ADIAHQAeABIAGwAbAAwADgAQABUAFQAQAAwAEAATAAwAEAAOAAcACgAHAA4ABwAFAAwAEAADAAEADAAKAAMA/f8KAAAA+/////n/9P////f/9v8BAPv/AAAFAAMAAAAAAAEAAQAAAP//+f/9////9P/y/+7/5//p/+7/5f/g/9n/1//Z/+L/5f/p/+v//f//////BQD7//v//f/9//b/9//u//D/9P/5//v/8v////n/8P/3/wMAAQD///3//f/0//n/AwABAAMACgAQAA4AEgAZAB0AGwAXAB4AIAATAAEAAQAFAP//DAD9/+v/+f/t/+v/4v/e/9X/2v/l/+f/6//p/+7/9P8BAP//+f8BAA4AEAAOAA4ADAAMAA4ACgAAAAAA/f///wcAAAD9/wUAAAAFAAAA/f8JAAAA+/////3/+//7//f/8v/0/wAA//8FAAcAAAD///n////9//b/9P///+7/7v/3//L/8v/n/+L/4//g/+P/8P/r/+3/4//l/+f/4//u/+7/8v/l/+v/7v/y//n/AQADAAAAAwAKAAAA9P///wAAAQAKAAoA//8KAAEA/f/2//b/9//y//T/7f/3/97/4P/l/+X/6//c/9f/5f/j/+D/4//e/+f/7v/t//f//f8HABAAEAATABsAHQAbAB0AIgAiACsANgAvADAALQA4ADYALQArACQAJgAbAB4AJwAbACIAKwAbABAAEwAFAP3/9//3//b/+/8BAPv/CQAHAAMA/f8HABAABwAHAA4AFQAOAA4ABwAFAAUABQAHAAAA+f/3//D/6//p/9z/2f/X/9X/0//Q/87/1//X/9H/2f/O/9H/1f/R/9X/4P/l//D////7//3/BwAOAAoAEwASABMAEAAVABUAEgAZABUAHQATABUAIAAiABMACQAJAAcACgADAAEAAQAFAAcAAAABAAcACgAKAAoABwAKAAkAAQAAAO7/6f/g/9P/5//l/+X/5f/j/+n/4//e/9r/1f/c/+P/2f/a/9f/1//X/+D/6//p//L/+f8FAAMA//8QAAoADAATAA4ADAAOAA4ACQAJAA4A///0//f/9P/3//f/+f/3////AQAMABAAFQASAAoAEgAZABMAEgAOAAEABwD7//T/8P/y/+7/6//5//T/8v///////f8JAAcAEAATAAkAGQAVABUAHgAeACIAIAAgAB0AHgAeABsAHgAZABMAIAAdAAwAAwABAAcAAwD//wMA+f/w//v/+f/0//v/8P/9//3/9//2//L//f/p/+7/4P/n//L/6//5//3////7/wAABQAFAAcABQAAAPv/AAAKAAkABQATABAABQAOABIADgAKAAMAAQAJAAoABwAFAAEA8v/j/+n/4P/a/97/0f/Q/9n/0P/R/9D/zv/T/97/3P/T/9f/3P/0//L/8P8AAAkAAQAFAAkADAAOAA4AGwAMABAADAABAAUABQD//wMABQD3/wMAAwAFAAkAAwAAAP//AQD3//L/9P/2/+7/8P/t/+L/6//w//L/9P/r/+v/7v/3//b/9v/7/wMAAAD9/wUA/f//////AQABAAAA9//9//3/+/8AAPb//f/7//T/9//7//v/+//3//L/6//3/////f8BAAkACQAKABUAGwAeAB0AGwAkACYAGQAFAAMABwD9/w4ABQD2/wEA9//2/+7/8P/t/+7/9//9//3/9/////3/AQD//wEABQAMAAwADAAOAAwADgAJAAkAAQD9//v/9//5//L/7f/3//L/6//r/+f/9P/2//T/9v///wEA/f/7//L//f8BAP3///8DAAAAAAAJAAkABQADAAEACgD7////DgAMAAkAAwADAP3///8AAAUAAAD9//b/9//2//f/AQD5//n/8v/0//b/8v/5//3///8DAAUACgABAPb///8FAAEA//8JAAMACQAMAAwAAwASABAADgAOAAkADgDw//n//f/0//D/6//l/+7/6//g/+P/4//j/+v/6f/y//v///8DAAkAEgAZAB0AGwAbACQAJAAnACsAJgAnACcANgAtACYAJAAbABsADAAJABAACQADAAwABwD//wMAAAD2/+3/8v/t/+7/+//7/wEAAQAKAAEACgAOAAoACQASABsACQAFAAMABQAAAAAAAQD7//b/9P/y/+v/5//i/9z/3P/X/9H/0P/O/9X/0//I/9D/zP/F/8j/yv/B/8j/0f/T/9r/1f/T/+L/6f/l/+v/7v/2/+v/9v/3//f/AwABAAkABwATAB0AHQATABAAGQAXABcAEgAXABsAFwAVABAAEAAVABAACgAJAAUACQAFAAAAAwDt/+X/4v/X/+P/4v/l/+n/9v/5//L/8P/y//L/9v/7/+7/8P/t/+7/6f/w//f/9v8BAAcAEwAQAAoAGwAXABcAHgAdABcAFwAZABMADgASAAMA/f8BAAAAAwADAAkABQAHAAoAEgASABMADgAJAAwAEgAJAAcABwADAAkA/f/2//T/+f/y/+3/+f/5//f//f/9//v/BwAFAAkADgAFABUAEgAQABUAEgASAAwACQADAP//9//w//b/7v/n/+7/8P/i/9r/3v/j/+P/4P/n/+P/4v/y//L/8v/5//L///8AAP3//f/7/wMA9v/5/+3/8v/9//T//////wAAAQAFAAUAAQADAAEA/f/7/wEADAAJAAcAFQASAAcADAAMAAwACgADAAUAEAASABMAEAATAA4ABwAMAAUABQAMAAUACQATAAwAEAAOAAkAEAAXABUADgAJAAMAEgAKAAUACgAJAAUAAAD9//b/9//y//b/6f/t/+f/3P/c/9r/2f/c/+D/2v/l/+L/5//r/+3/7f/n/+7/7v/n/+f/8P/y//n/+f/0/wAACgAOABUAEwAZABsAJAAmACYAKwAwAC8AJwAtACAAIgAeABsAFwAQAAcABQD///b/9v/n/+n/6f/g/97/3P/Z/9n/1//R/9H/3P/i/+P/5f/r/+f/8v///wEABQADAAMACQAOAAUA8v/t/+3/4v/w/+3/3P/j/9r/2v/V/9P/0P/Q/9f/3v/c/9z/5f/l/+X/6f/w//L/+/8AAAMACgAKABMAEwATABIAEAAMAA4ADgAJAAwAFwAbABAAEgAQABIAFwASABAAHgAgAB0AHgAVAB0AHQAbABsAGQAZABcAHgAXABIAHQAdABkABwAFAA4ADAAHAAcABwD//wAA/f/9//n/9//y//T/9P/5//3/+f8AAP3/AQABAAMAEAAQABAAGwASABMAEAAKAA4ADgAOAAcADgAMAAwAEwAQAAUABwADAP//9P/r/+3/2f/X/+P/2v/R/9P/0//g/97/0f/a/+P/4v/p/+v/9v/2//T/9P/y//T/8v/3//b/8P/5//f/8v/3//n/+//2//3/9v/0//n/+f/7//b/8v/3//b/6//r/+v/6//l/+P/2v/T/97/3P/a/+f/7v/0////BQAAAAUAAwADAAkADgAeABcAFQAbABkAFwASABIADgAFAAAA/f/5//n/9v/0//L/7f/n/+3/8P/t/+3/6f/i/+X/6f/p/+3/5//0/////////wEA/f8HAAoABQAKABIAGQAMABcAGwAbACkAJwArACkAKwArACYAIgAgACcAIgAeACIAKQAtACcAMAAwAC0ALwAmAB4AHQAXABIAEAAFAAkABQAFAAMA+f8KAAcAAAD2//v////p/+n/7f/j/97/2v/V/9P/1f/T/9X/2v/Q/8r/0P/T/9P/0f/O/87/1f/R/9P/0f/B/8r/0P/X/9n/1//j/+7/8P/0//f/9P/0//f/AQABAAAAAwABAAAAAQAHAAoADAAFAAkABQAKAAcABQAKAAcACgAFAP///f8FAPv/7v/2//n/9P/0//f/8v/0//b/9P/w//f/+f/5//f//f/9/wMACgAOABMAFwAOABAAFwAQABAAEwAbACQAHgAgACQAFwAeABsAEAAVABUAGwAVABMAEwAOABIADAAOAAkAAAD//wAA+//y//D/4v/p/+X/4v/a/9z/0//Q/9H/2v/e/9z/4//j/+3/6//2/wMACQAKACAALwAyADQANgBEAEQAQQBEAEEAQwBEADsANAAtACkAGQASAAMA9//9/wEA+f/3//T/9v/3//T/8v/3//f///////n///////3///8BAAAA9v/w//D/6f/l/+X/4//n/+X/4v/X/9H/0f/V/9H/0f/Q/9H/zP/K/8z/xv/Q/9H/1//a/+D/4P/l//T/8v/3//b//f///wAABwAFAAoAEAAKAAkAFwAVABcAHQAXAB0AHgAeAB4AJgAkACsANgA7AEYAOwA4ADAAJgAkABsAEAAMAAMA9v/y/+n/6f/g/9X/3v/V/9n/2f/Q/87/xf/Q/9D/yP/Z/9X/2f/n/+X/4v/l//L/8v/7//f/9v///wMACQAHAAMA/////wUAAAAAAPn//f////3/9v/p/+n/6f/g/9z/3P/e/97/2f/Z/9X/3P/V/8z/0f/T/9z/5f/0//L/+/8HAA4AGwAmADAAPQA7ADoAPwA9AEEAOgAwADQAMAAkABsAGwAVAA4ACQAQAAoAAQADAP//9P/9//n/+f8AAP3/+f8BAP///f8DAPf/9v/7/wEAAAD9//3/CgD9//f/9//y//L/6//w/+f/5f/i/9P/4v/c/9r/2v/Z/9r/3v/g/9r/6//r//D/AAAOABMAHgAnADQAOgBOAFgAVwBcAFoAZQBhAGEAXgBYAFUAUQBGADAAJAAbAAoAAQDp/97/1//G/7j/mf+X/5v/jP+A/4n/kP+U/5//sv+v/7r/xv/I/9X/5f/0//f/AwAHABMAEgAVABsAGwAgAB4AGwAVABcAGQATAAkAAQABAPv/8v/n/97/3P/V/9H/0//R/8r/zv/M/8b/0//Q/9X/2f/c/+P/5//t//b/CgAJABMAFwAdACYAJAA0ADAAOgA0ACYAJwAmACIAJgAnABsAJAAkABcAIAAtAC0ALQBEAE8AUQBMAEQAQwA6AEEANAAnAC0AJwAZAB0AGwAXACAADgASAAoAAQADAPT/9P/u/+3/9//l/+n/3P/M/87/qf+Z/4L/b/9j/1//Sf8s/yr/Gv8P/wH/5P7H/rH+rf6d/pL+gv5+/nz+c/5z/nX+hf6f/q/+v/7e/vv+Jf8+/1v/jv/F//T/KwByAKYA+ABGAY8B0AEaAmYCswIIA10DrQP5Az4EdgS0BO4EKAVMBW0FjAWQBZAFigV/BWIFMwUABc0EfwQnBNADbAPyAnwC7QFhAdcALQCQ//b+T/63/S79i/z2+3/7A/uT+ir6y/mF+TH57vjB+IX4ZvhW+E34W/hZ+HH4h/i/+P74Jvlk+Zz56PlB+pP62vo4+5P77PtT/LH8/fxe/bX9Bf5J/pL+5P4n/2//q//5/0MAjwDfACcBhAHvAWgC1wJYA+cDeAQWBaQFPgbcBmkH7wdlCN8ITwmmCeQJJQpICk0KSAo5Cv8JugluCf4IhAj4B04HigbPBQQFPgR8A7EC8gEeAV4ApP/t/kD+qP0t/br8Rvzt+6f7gftp+1f7UPtM+0r7SvtZ+2f7gvun+8L71vvk+9v7yfu++5/7gftR+yv79frE+n36OvoQ+tP5p/lt+UP5D/np+NX4uPjB+Mb41fjq+Af5I/li+bb5Avpc+sL6K/uf+yf8ufxW/fv9pv5m/y0AEQEDAg0DJwQ6BU4GXAd2CIsJmQqOC3gMUg35DYoO+Q4+D4EPhA9eDw0PkQ4ADkkNegx3C14KPQkJCNEGoAVsBE0DOwIpARkALv9J/oD91/w//Ln7P/vP+nj6Qfoi+hT6B/oQ+ib6Qfpm+pb6y/r4+h37NvtQ+3D7j/un+7z7sPuY+4T7Wfsb++76svp7+jb68Pmn+WL5JPnl+LL4a/g3+BH49/fu9+f38vcC+Cj4PPhU+J747vhU+b/5Hfqe+hT7pftG/Oj8of16/lj/UQBsAYQCqAPTBBYGYAefCOIJIAs+DE4NRw41DwcQtxBBEZMRzhG+EZYRaREHEY0Qzw/UDrsNgAxEC/EJqAg2B7AFVgTwApkBWgA0/yn+HP0g/EH7jPr++ZX5QPnw+M34xPjG+NX48Pgk+WT5pfnf+Sb6Yvqh+ub6Gfs9+2L7dPt2+3/7efta+zb7B/us+mb6Evqr+UD5yvhi+PX3m/dC9wb30Pav9oL2YfZP9jn2S/Zu9rH2+fZE96f3GPiB+PL4f/kW+s36mvt1/GX9ev6M/8sAKQJsA8YEGwZ2B9gIJQp5C60M4A0JDw0Q6BCqET8SvhIIEwYT4BKNEggSVRGBEH0PaQ4uDc4LUQrUCGsH7gVtBOsCcgEQANz+w/3S/O37C/tH+qv5I/nP+Jn4hfiH+JP4tPje+BL5TPmM+bf57Pkm+lf6g/qw+t367vrs+uX62vq1+rL6j/pc+ib68Pm2+X35V/kH+cj4k/hU+Br44Pex94H3WfdE90L3S/dd92z3nvfV9xL4Vvil+Bb5oPlH+gP74fv6/DT+jv/dAD8CxQNkBSgH0AhNCuULTQ2tDvEP6hAMEvoSqhP9E+8TwRNuEw4TYxJ2EVkQKg/VDV8MxQorCZEH6AU5BH4C/ACr/3n+Vv1O/Fz7efrN+Uv54/il+JH4pPjW+Af5Tvme+e75Vfqy+v76WfvA+zj8jfzH/Oj8/fz6/Or8rvxw/D382ft9+wX7g/r5+Xv5/fhi+NP3PPfF9l/2EfbQ9aL1d/VT9TL1N/Vd9Yn1x/Uh9pn2LvfE92b4FPnE+V76G/sT/Dn9l/4KAIIB1wJJBMgFNgfHCEIKsQsTDUwOgw+zEMURxRJyE+kTERQrFBUUuBNnE8USEBIeEdIPQA6bDPgKPQmCB7AF/QNUAtYAXf/x/bf8mPuq+tj5PvnR+Hz4cfiB+JX4zfgm+X/51vk6+or6wvop+2L7mvvL+9v77fvi+9v7u/t0+yj72vp7+jb6xvlg+er4V/jc92T3//af9lv25vWt9XD1VPU39SL1OfU79VP1efW+9R72Y/bl9mj33vdQ+NH4ovmP+gr8lP0T/68ADwLaA4gFUgc/CdkKugwWDnoP+BBBEsoTiRQzFWgVWxV1FUYVUBXVFHoUqBMyEugQIw9fDaYLugnAB9MF8AM0AqMAMv+1/Wf8Jvv1+ff4QPjc97L3vffu90v4wfg++b35Q/qs+iL7o/sH/Hn83fxP/Xv9h/1z/Sf9wvxj/Oz7Q/ug+gX6VPmn+B/4ZPep9gj2efXr9I/0MPTi87nze/N981PzV/OC867z7/NP9JT09PRq9fn1i/YL9xH48viG+gX8XP0w/4IAegIeBN8F8weSCboLkg0kD8IQ+BGUE5kUbRU1FjoWmRZiFo4WGBZUFfQUXBMVEkwQRw6LDIIKrAiFBpIEqAKaAEL/hv0v/AL7yfka+Tz48PeZ94r38Pfr91L4ufg8+cD5Ifqw+gD7Rfun+wX8Pfxr/Ln8ufy3/Iv8Ovwv/N37ffsO+7v6Xvrx+aP5D/mn+Cj4l/cf9532N/YP9sn1e/Uc9fr0tfTO9BH1AfVh9YT14vUX9qD2gfeg+Cv6cPvJ/CD+i//DAS4DzAToBn0I7wp+DFIOYA+YEBsSmhKZE6wT6xNvFHEUURReE3US2BGuEJoPtg2mC/oJGwiRBt4ETwOEAQUAGP/m/b78E/z3+pj6efpm+gL7OPtc/ID9aP6z/nX+0f87An4ESQQBAtsATQEsAnsBg/9W/hX+Qv3m+jr4qfd4+dn7G/xO+cn1wvLl8VnxX++C7artBO4a7Hbpaeg/6Wfq+Omy5yHnUOmq7ALvG/Ef8hX0avdZ+00B8gV1CU8KJQqrC/AOfhNZF8cZARt1Gzsb8hubHewebx5xHBIYLBQnE+ISQRNTExcR4AwVCOcCm/4m/Ff7/Pnl9+30CvOH9MX2Q/iA9870i/Nl9Eb21fiG+6T+OwLlAyADnQPEBc0HDAiVBWQD6wR0CCULdwotBvkC4AE2AOj9Evq89if2xvak9Mvw+O2k7C/roOgn5dLi9uGV4D7eh91Z4UbkMuNN4griE+P95nLp6Or27HruSfAx+c8O4ydcNjQugBsWGE4oeDxbQyo7QDdLP+BHMUWpORgxXy4WKK4XtQasAbMH0ApaArnx1eWW4Xrff9gw0B/N/s2eznjOg87506neAObT56DnPev085wAgwumE3YZuh0ZItUm4SqELQctbShKInsciBovGs0YOxNlCTf+LPbS8UnvmOtS5ZnejdqC2RTYh9Ss0DHNw8tFzOzNHtJz1b7X39YU1ezV6NhW3Ynksuhu7THyKPeG+gD5j/u5Eeo5YFX8VVY66ycIOFBVdF3NVeBHRU1SW9RVj0QQNGcsiiL1DZ/zjuyw76jz4Osn3OrMHscYwQ+6tLlpu0TBE8gSzQPTf92D58byR/iH/fYDuw98HwwtBzbTOZU63DkBNxk1lzEgLBQnQh8pFbwKvAHN+0/1Duqn3ULUi9FP0NDPGs3Xy47KVcgBx4/IrswB0wXYaNvR3njfaORS5+fsAe3K7XrvWPSr+cIAiwIhA7EB+AI4IoNDT1iVTtcvECOrN5BISU0pSZFFNk5eTEc6NitNJEAd4w+x9cDj+ODo4Zvk5+Sm3O/RssVkvOy88sHGxW3M9df746TuYfWd/REKUxL3FaMYNyB+LHw650PaRNVAwTg9LQMl2xz9E3wMLgSa+jrxrOiV4DvaGtK8ykTHFcfgycrNU9ER1I7UXdTN1XDb2OFH6Nvs1vCI82z19Pbc9mP07/ID9Kn3j/2aAFwAsf3o/L779xYTNytN7kktLOscwy32QZRGuj4bM3Y6Nj7mM3UkthzwE5gIrvIm3zrZFdoN3T/gG9821/HO/sfiyFHPGtRG2QjmK/MM/2cIZg65F7MiBCf/J20pbiy3Nbo9KT2JNR0pJx7WF14PWwS9+Fvu8OVT35bacNdU1DjOc8grxyHLJNFn1xjdt+BE49vis+Pq59HuBvbv+oj8wPoz+vf4fPem9FPz9+8e8xj2Bfp7+SH57/KIBa8u702CWjM7MRnCGsI0K0XmR+k3HTTuO2g3uCjbG10MGv7n7C/Wts2Izj7Umd2d5bvf0dVYy6LKD9XY3zflz+6c/EoIIBMAGd8gCSvnLkItry1uL8QzSTgrNVYuVSQlFpsLvQMN+prwWuVE21jU9tBc0AzR5M9FzI7Kzc1f0/ja+uDB5ePp9+ia6Yvt+PWm/Dj9NfgT9qT16fYO9Sf0/PH88GnxafKq81fyve6Q7f0S9T2jYfRccDOMFUEgVzr9S7hKrzllN/o2gC+hI38Yzwbv9e7deckqwqTDTcpQ2DjhR90B1LjKJsxO2PHjAesB9+0CMQ+UGwMkDC/BOME6yTW8L2QqYS5BNX00Py07HgcPDwXo/MLzv+dc2oLPXcnhyzLQ49N402HQqs+f0kzY7N/r5RXrMe/P8gv3Q/zR/8AAMP2M99jyGfKK8rLyL/PG8Dfwnu6Y6yXl1+La3Vf7ly4wXLptc0k2Gu8SHC3NRr5Rwz+fNeE5YzixLwwlmhGL/aTkSczpwRvEVMnj0w3gm+L03VfVqtEB2gXofu/v9R4AOQoWF6MjNi2dNwE+HTwDNsAsrSVuJnEm2CKlGe4M9AQg/tDz++YB2OLPociWx+7Lws+M1ZjWrNZm20ziEum87VTwS/Dc7zrxW/a8/q0EpAKI+3D1X/CQ7fXptOeX5Grm5+bo6QbnO+R13ATwvCNZVY1z+FhfKLoUjSWWPqpPXkbWOxg7+jXzK/MhyRK4/tzlvcsHv5680L8tyMnXFeKs42Tc7tW62UDmtvC5+50IlhGRG78mhTAMPVtEGkKIPKcz7CihJNYhzB7uF0kMhgHE9wHukeJp11XQU8lZxM3Dvshu0vbYWN344dPlHemh6qzrX+/o8xj4ewD6BoQI0AGV+mH2+/Ub8CTp1+Kq4b7j0+a16Hbn4uJ83ZYCHDZfZlZtu0F7HMgfdTUoShhNmj4OPZs4zytZH1USTwGb773XncdFvtC49re9w4zWquKL5ILhmuGL6on07vgwAXELzBMEIbEtQjr9SHBOR0rnP4ss5xz0FfITihWGEu8Juv+l823mF9muz+jHj8B5v1jCdszz2MLgTOhR7Rju7Om45PLl0e3C+Y8Gag1MELUIWPz486XxDfA377zpLeEc3Hrb4uGo6vbtgeWm/A0o41O4ZOxEeiZILeBAu03OSOk1EzU9N10umiSSFaL/zerg0WbEE79nt4qyNbrwyqHbV+Ju44/pFfMd+on8GQEVClET7h4VKxM5TUjZTvpN20dqONMm+Rj8Dh4M0grCBef+TfQu5svYNNB9yCHBAb+DwevKYtUe2fXd4uSU6sLvVfFe8kL0NPTK9+T9nAUoBugBi/zC+q32nPBb6C/iZeJq5XfrS+2T6Wfgo/p5KwlfD3QsU7QwDi5mObxFw0INMeoytzJ6JjMfLRGfAZ/zO9nmyVPAl7AGqrCxBcV83hbnZuW86cbu+fZ3/W8EsREMGoYgpSk9Nb5FmE+aTR5ICzo1KBUbhA93CfcFT/5h9o7uK+KY1inPEMhFwwi/eLxxwz3PT9mD5JHroO1q7UHpE+uP8qf59AAWBjYKaQkIBQ3/bP2K+075M/Av533in+M86b/sZ+j+5g4IrTbDYzlrIEiFLgAw8Dn5RNk+NzB1MFQoKxxnG6URWAQ68yTXGMrpv+Wv2KzXtoXKOeLA6FPrffKH9ob9agPoCNcSFRrJIcMt5zlSRBVILUXDPucwByNJF2YNeQdpAIn3+O3Q4iTWDc4Ey27I28XawrrB78bPz0HZMebp7iTyu/H47DXtH/G498cCAgzaD3UM0wUmAA798PaN86Duzuxp65zprOnR6MLhauMgCcE5wWTeZIA/5iuANA9AJkW4N2omGikeJREdLh7kFCgI/ff84JvUgsfGsr6sgLmCz4Xlhun66qXx4/dR/qICIAiIDk0VHB+WKyc2bjxuPXA8kjlwLGweERPQCXcCe/q19B3vAuhj2bbNTcqFyXLInMbZxg3Orti339ToFe1v7nzwevFG9Kz0U/R7+rMHtxEFEP4FhP34+074VfPS6kXmJOjb6qDtR+4M6gnpHwW8LllWyV/zQ/QsRC+5OhxCcTsJKooonyZBID0dYxIaBCn0seHY1WnNrr3Bshq30Miw3+PpD+338Ur8PgZwCTcKIw1lExodlShIM389m0EDPvs4tC/WI3QXpQg5/Z74i/Wg8TvrXd7Q04TPGMz5ynzJdci3zrLXUN8S55TrJe638WvzfPV297H2Vflm/3oGPwkUBcf+Sfgk9KfwKO5y6t/qm+7f8pL3k/Ja7c38VSLvRmBYlkeDMUUzkz3cQtU40iV4Hj0jECG4G64Q3QEw9urp39770y/Gh7lLuKHF7dmK5/vtFvHk9U/+1QK7BKwJew/oGUQnZjCmNwg5eTNOMHUtrSj8I8AYDgo6ABj3M/HX7F7js9sO1znSrM8AzszMBNPX2pXgm+Zn6crs1e8S82718PYp9oL1c/iv/agDAwOjAMf6e/Uf8dXtSuxY60fvKO/a8X/vu+6r//AgbD9JTMhApSxdMbs6rz6iNyooDCOvJdohxhabCzf/2vgC8fvm4ttLzKbBd7+EycLYcuK253fr8/Bi+IL9Qv9mBqcOBRk3JsAr2jFGNEoyJjH0LgsrrSaBHs4R6AiIAP75B/JA6MTe/9g31SjP+c01zlbTRtwF4g/nHer56GbogOsP7V7yWvV++Cr/CwRHBUEBgPzQ9mX0RfLx877zLfGV737tUvCe8svyavg7EzQu10GDQlsvECxDNTw8Wj2GMx4jtCE+HdgVqBHgA+z6n/T27ADmrtn0x26/x8GpzJTa1uD85y7wzfiqALMBTgQZCdgPOhqlIpEq4TIaNo82dTYRMWMtoCaLGugR8wcR/x73Luxk4MnZc9Vh1PrW3dZO2Kraytxm343jqebQ6fPv7/Mj+ML5Rflc+iX/cQLvAJr7L/Nh7yPujO+K8C7wz+9E75PvR+/F7SzvzALiHcE2LEK4ODozIzn9Pt0/2TjTKJog3xkxEWQOjQi1AuT8yfQG7J/gIM8zwoC/XsX60N/YVd+g5lDvrfiZ/5EFAQn8CTgO6haBIQcs9y8wMD8zCjUiNdEw+CVTG8IQAgcZAB73Oes24NTX/tXo2W/cb94X4FfgtN+r3nHfwOKt53vsL/L/9xX8svtt+gX7mPzD/W77kvbG8XbvAO/h8I3x7/Kl8j3ycPMQ8Qb4mAp2ISY1nzxYOFs7CEIcQvtApjU8KogizxcPD0gKRAO++8r3bvPa8Zjp0toYztbGXMeXyrLPKtdV4T3r2/P1+q4ANQaSCusReBidHfUgyh/EIaAmICztL2wuJye2HqQWpQ5yB5T8DPI45xbdTtcU1YzXFdya4dLj4uQq5Uvlweaj6BDrBO2Y8bPzIfY6+Nv6AQD/Am8DyP99+2T4NflD+yn9Y/25/LP90vwb+5j0uvXvAcsREiIEKCUmcyh7KzoqOCuUJescuBbtCwQF3gPVArEB0AAh/5X/Tf7/94bxmOse6obr5uzG7kzxHfPS9TH6F/1pAe0DLQYXCukLTQy+CtAItwctCRwLyg2nDuUMaAo8BgYDRv6r+SD12PC87WHs2ey/7m7zZ/TB9GP1DPR+9ID1+PSE9fL3LPjf+dT6t/ui/r4AIAFj/t36YffR9/P5Mfmt+D748vht+9D8qvus+078t/3I/gP+pP5EAMIFJwtDDz8TWxYWGC4XFBYIFJMRUg5PCm8GnAVJBTcEsAWRBm0IxAg8B30FBAMp/5z7+/gh+DX6+Pzy/xkBOwF3AXkCIQNAA9kCNAFjAFcAnwD0AjADFwGC/2f+pvzl+a32w/Te9t35rvqg+W/4Wfj1+JH53vhC+C747PnR+hT7ofq5+PD4nPmE+0j8pvyc/JT8n/wH/EX7f/oL+zb8nf7WAPIBwAE/AWMAx/7K/nX/MgDDACkBiQL+BAwIXgkwCyUMSQwwDPQKeQj3BZAEpAOpBMYF0QYxB0cHdgZOBN4BJ/8r/aP8fv4kAaID5wSKBfoGtQeeB+4FFgQWA6QDRgNRAgMCPwG8AVoAc/3A+av3+fdb+er5a/iz9mj1g/YI+MT4HPgN92P3I/h6+Rr5Evi99y35Ovx6/p///f5T/bL75fpa+yD8A/3F/sz/7QD8ANH/2/7v/hr/JACoAHX/bP8M/mn9av3v/vYBIQUfB4EHnAZsBBgDfAEbAXcBfAG6AicEfQWGBjoHvgeYCNIHEgYGBgsGrgesCMQIIgmJCuILTwuNCpoIAwcvBaEC4AD9AOgBTwLOAZwA5P51/eT9Yf2I/HT7gfeO9vv37fZL9X/z2/J+9fT3LPhJ9hH2vfYB98/3C/fO9yr53vdC9/D4ZPp5+xD63vhy+3v90P7S/Xn8DP+8AVQDNQP0AMX/qgG8A2oFOQVmBdMF8AVvBtoDjwG0/938SPv1+jb6dPtu/Gj++gC3AZ8DkgSgBJUEIwR6BRkI+AmjCnsKVgv0DEsOFg8mD2IO4wxlCtYHtARRAuv/Y/2k/fv9zP3D/r/+uv9lATsBcQJAAkEAyv4J/Kf5KPrs+pr7k/sH+l/41fdL9/b24PeK+Dj5PPlz9xz2Dfbr9k75o/kk+Q/5KPhA+Sv6Cfqm/Ej++P4bAHQArQJ4BfkFpAVzBRsFwQQeBEgCjQATADf/i/5a/TT8iPob+4L7Nvy+/UT+v/51//8AugErAuYBqAJvBEgHJwmdCucLmQuDCwoLRgqfC18L/QpVCmsIVQepBUkEwwIcAtACYQNoAiQBWgFUAqQDwwLy/yf8vvqa+zj7j/tY9w/2Nfgx+fz5wvl0+G34C/nC+Db6M/oq+V754fdq+MT56Ply+wD8E/2A/MT7G/x7/SX+GP9R/8b/OADa/8cA/f++/Z39SP6O/q3/1P3C+yj7JPp8+LT4P/pI/Fj/EwEnAoADJQODA6kE5wUbBxQHbQUABhIHrghjCe8IhgmSCeYJEglTCEkGOgXGBGkGCQjCCGMI0gdjCE8JgAj1BWgEHAPpA6kE7QPFAdAA9v13/a772Pmi90v3Tvhb98n5Jvia+db4M/eF9/34zfnF+6j8gPwc/tj6/fyL/Fr/EAHZATkE0f8bAMMAeP84AHX+z/lb+nT7efsU+lr2fvcA+ff3/fcr9WX0r/a09kv2PfYs+AL75/7tAPQCSwQaBa4GzQcbCdYHcAcNB3sGqQZxBXgFegXaBdoFigXRBTcGFwdBCNsImAhgCUwJ/giRCfUI9QiACeQInwjjBw0HLwZqBecDnwIHAWP/7/xy+nT5+fb79jL2tfR183307/Vq+Az73/lV+z76svpy/XL9Jf8u/xP+NP6U/a39Kv/d/Q//Jf2R+Xj6v/de+n38lvrh+Xr4lfme+SP2F/N/8fDv4fCN8vr0O/z0AWkGOAkyClILKQqfCcUIoQn9CloM5wrFCeoHdAbqB2sGegVxBCEDcQJ5ACAA8wBmAl8FAgfEBxkHrAaeBkUHaQgVCTgIvQfTBr0E9ANrAFT+o/3J/cX+Zv+o/w3/mf8c/uD9XP36+4j7y/pw+6X87/1+/qv+wf5C/pT9Qf3C+7L6UPpb+TP5WfmV9533uPf7+A36nPlg+V34YPjF9hH2N/XF9LP01vNp9LH2t/kd/Jf+4P79/nr/2/6C/ykAuP/4AHwCHAR2B9oH3wcOCnAKeAzgDR8N+wwBDGMLxwr0CmULugopCoIJLQndCSUKoQnSCL8GFAYbBfQCPwEW/9n9lvz8/Kb+A/81/5L+Uf2o/qH+4v/tAPgAiwGC/wr/Of9N/xsBKwKCAH4CEwDy/f/+y/oM+2n6fPjE+/j6m/h4+Wb26vPi9nP0kfPh8ofu/vFY9ArzV/Lz8P7wb/Zp+Vb2vPat9nP4U/2h/dn86vzi/L79df33+uP6SvsJAIUGGwkjDdYPuhNlGfIccx6FHx8fWh0JGgYWDBEYDQYMGA1tD2cP5wwMCFEDrf/0/OX5avcT9RDzXPK08SHx+fDj8dnzC/dD+iL+wgD5AtACBQHZAEQB6QO5BuQHOghkBygHHQerBG4C1f2B+ef4sfdZ93f2XfWJ9lb2Z/Ps8AvvBu/l7oLroueP6F7smezT7cjvBvCl8zv1OfQp9Sz1HvWV+DL97wBTAQP8Rffy9t/51P1aAcAGvQ+KGWAipyfsKVMszStUKXMm/CIhIZAeOxvuF00U+hBUDRoK1gjuBEMAxfxs9nLxj+uC45zf0d6w3yXj8uZR6/vwFfb3+u3/XQSoCHoLfg0fEGUSfBP2EyITsBG+EHoOvwvWBwYC1P1w+mj3LvYb9BnyKvCh7BvqxuhB6EzoqegC6GrmjuYL6PnoSuvt7MfsLO7572vxkfMh9hL4+vnJ+/r6OPkP+Ab3Nfcl94z4ZwD2CuwWjSH5JuAuLjagOHM5qDSmLRQqDyW/IHQfFRx4GCcV6hCiDIYI5wPm/UD5zPRW7bvlkd/z2anWGNZn17HcXuM66bTuT/Mf+JL8kP/BA9kJqQ8dFt0aOR05Hx8f0R1LHPMYTRQYD3cJZgQc/zz5cvMu7j3q7OZL5BPigeA64HHfI9/e3ijef95M4ErjAudI6szsN++I8YDzA/Ti8y/0gvWd96v5u/uG/E78Rvup+RP9VAY2EZkegSlAMbI64j8eQTtAKjufNUcvhSeaIMcaPBaHErkPvw0QCo4EK/3v9EvuFOib4o7dx9ja1ZnT3NJx1KfXXdxX4obphPHA+VMCLQoZEX8XiRzuINkj4iTrJPgj1CLNIPIcOhf4EMEKkwRz/v32+e4J6Lri49+k3v3cn9vB2kTaCNvX2pnahdz339Xkkel37M/u/vCe8o3zYvMJ8wXzwvM59dP3Wfob+2X8o/u+/TwHpQ90Gk8mEy1NOBFDE0dESoZGIj+ZOOAukifAIUQbfBYSEF0LigjwAgH8efK06Gric90c2sDWhdI/0AfPnM8s0kfUY9gr4LXpE/Vf/04HeA/yFjIejyQlKFornS02LiIucCr4IwYdvRTqDZUHsv8C+dLx7+ry5eXf3dqq13/VLNZ/1x7Z6due3SvgHuPD5Kfm9+fW6Mnqsey07rnwnvJo9an3FPlZ+pr77/vN+9b50fZi+7sF1hHsIBks4DXYQZZHWkm+R9lAsDpOM4orjiZgIdscaxdnES4NXgd9APP4tO9X6Irho9oz1sPR0c1Sy4LJpMoFzqbSTdm94FzpJPNR/PwFLw8sF+of4CfyLsQ0iTbtNWkzjS6pKfYimBqLEmMJLgGP+bfw5ehj4efaNNcC1E/SD9Jo0gvVJthC2inc0dwU3vHgPeRg6ITsv/Cu9NH2Cfgf+dz5g/pT+7z7SPsi+6z58Pn0AZUMUBn3JyExGTvbRSNJVEqYRww/HzhbMIwniyKtHYwXcBLRDccJUAYpAcH4ku/Y5gLeeNbdz2PKucfSx8XJ8MxP0J3U5tqh4rrrX/Wd/kwI2REXGvUhLCniLg4z/jR1NW00CTH2K7gkrBy8FBcLRgFb+DHvkee74AjaetUK0V7NoMxHzRvQ8NMH1u3Yb9yM3yXkPujF64rvNvLO9CH32vhB+vP6WvuW/Fz+5/7e/lb94/pN/50INhMSItkssTWTQnVIAkoVSthB9DnYMo4oZyMSIPgamhcXE2kOzgpmBFP7oPEU6HLfx9e30CXL88dDx4TIbctyz5/TlNgf31fmeO5s92wAhAqkFDkdPSWIK+MveTP4NJ81SjXTMWssSiW1HFEUpgrO/xz1UerC4bHbo9aV01XRxc8t0M3QrNGq0sbTntYK2vTdEuL05GLo1ev07n/yavXG9wf62fsR/f3+pgFUA6cEOwNlAHgFJA8XGksozTBtONhEmUmfSTtHPDxgMiIrJiLjHvIdEBoCF5gTYA8BC9UDivkY73PmVN5y1lXQ3srCx03IkMkLzV3S9dZm3RPl2+tl8436OAFaCoATExyUJU0tTDP2N/Q4hTftNLwvQymsIosaeRFlCCP+xfOs6rnhRNoW1bHQR86gzlDPcNBP0d/QE9Ll1GDYvdye4O/jMec26nXtafG19ej5s/09AIQBVgPaBEwF9wXaA0cD/wmqEt4dKiqtMQI7eEXHSOlI80MEOUswPijwIEce9htmGG0VPRHPDIYHrf9J9k/shOP/2zzVwM8Ty7XHDse+yJPMD9Lz14PeKeW+6wXzIfqhArwMpRa0IHQpvC/zNBw4tDitN4Iz5i3MJ3ogjxkgEW0Gofua8BvnXODL2U/UO9AbzXbM6cwOzljQQtJ41ErXftk73DPfK+JC5p3qJe/o85P4xfzn/3cB7wGXA20FJAYLBvkC+AIrC2oVcCLTLqU0FD3CRNJF6kUnQNI1Li4PJlQgHR8pHbAa9RYrEmYNcgas/Zzz4em14kvcnNaS0hnOD8v7yQPKZs3y0SjX99705qTvtPi//80H9w8NF/cfxiftLTQzzjShNcI1hjLbLdMmph52FxsOdQPC+DXtIOSb3GnWWtMc0W7QMtE20cHRodHQ0GjSGNWh2P3c1ODM5WrrnvBu9cz4Lfv6/P39fv54/08BnwKtA70D/AAaAo0JgBOcIcksqTJpO61BjkIVQSE5zC/WKWkjtB+7HgwdTBrcFUYRzgz3Biz/yfSm61vl7N4T2rjVzdAtz43Ons6f0TXUONhg34rm++6m94f+9QZJDx0W+R3vJIorWTLoNcE3NTizNA8v4CaFHMsShggP/hH1IO3R5iDi2t1j2n3XqdSL0jbRctAy0GnQ+9F21Xfaa99q41nn0uv38BX2zfmL+9n7e/uG+j/67/uf/ZT/fgDy/tUBmAqvFZEjcy3oMg07I0BoQO89SjXoLH0o8SN/IRYgBh1cGSkUFg/mCtwFzv8R+K3wfesc5krg+tlG06XOV8xXzCTPNdPJ2FzgQej58Mv5YwGlCUgRFBdRHUMjpCjMLZMw+TEGMtAuNymlIYgZsxFnCMz+OfZf7m3oSuNo3pvbKdnN1rjVndR61ATVgdXA1wPbdt5B4rrltena7cjw1vK68+3zX/Tk9fL4Rvy9/0sCRwP3A6wB0f94BCgNJBq0J44v7zccQDNCrUHcOxgyoysMJSofTx3iG04awBc7EyYPtgqDBBX8RfIJ6jLjMN042HXTo9AK0FfQetKM1SnZ1t475bHrVfMk+/kCzgpnEXoXdR0HI5UnUCoXLEgtSCyKKfskCx7GFoUOzwQ4/Er0vuw453Titt6D3MvZ2Ndg1xLXDtj+2O/Zjtzw3mfhx+M85XbnDOqL7B3wnfNL9vv4u/sB/o8A9gJYA/IC4gDN/MH9lQU/EbQfhCreMZg7LELqQnE/TTbpLb8odyJWHg8dmxtaGiYYCBVeEswNIQXq+cjvPOjo4S7cFtdH013SD9ME1JjWUdrR3lLkCelN7UHyNfcr/QYEtQrZEjIbPSLYKG4tqy92MDItcya4HncVBAx4Axv7e/Xd8tzwz+8h7jjrpec74kfcj9fO037Sw9O71qjbC+Aw4yXm5ugc7C7vk/Hq8771xvbG9yP5JPpt+4L9Y/+JARoCI/8h/yoGmBCXHVknTiydNFU8wz46PRI1vCtSJsQg7h2vHkMfvyDPIGEerxt+FT0Kx/wb8NbmPuD62dPUgNLi04bX/9oE3jTgIOFg4djhn+OB55vsWPNK/KMG1hFpG6EiTSiWK3IsfyveJ3IihBwuFnkQugs4B/ACE//u+r/1ze9D6UHiINwk1/vTw9N01THY9Nuj333iS+QW5T7miuik6ybvofLB9R/4D/m2+Rv7Vfxh/eX+SgBTAg0DSAArAZ0IExM+H3Um+Sn5MRY4ZjjqNOgr4iR0Ih0fpx4+IacikSMMInMeZxqEEuwFZPhd7i3o2eII3ifa9dj02kDcOdwn3YfdZN2k3fXd4eAc5sLrV/PL/CEHjRHcGAodPiDRIcsi2yL5IHUeIxxpGqkYshXmENQJ+wE1+vHx9eoE5TPgeN6i3hTgNuIv4sfgeN/53fTdCd/44Pjkz+lq7jPyTfRl9ab1UfWb9d722vf++Eb7Zf2t/+v/x/yW/o0HbhNbIFgn1innLy80tTOTMNgovCMbJH0kwSfMLJIvxDD4LXMnSx+kE0MFdvfn7WXpUObX4+ThmOBy4azgVt2P2m/XSdXR1QLX2dqP4aPo7vEd/NwDHAofDnIRdhc3HfAgJyOlIvwhwiHPH/0c4RgZE0sNzQZN/xr43PDf6m/nheU05GPiot943fvctt3E3izfG+DV4tbm5up37eDtCO6d7nPv6vBw8kz0pPan+Eb7m/7MAOYBd/8B/Cf/xAbEEKwaGh5nIvcqai+FMVIv+Si/KHkqbyrJLHcsbSpCKKAhiBr9E/oKtgL2+1b3XPVB8mHt7uik5fTiON9L28fY4dfJ2BHaxdu932HkIukh74f1WPxGA5gIPA3bEUsV2BdGGrUcbx8EIb0fgBzeFwcS7Qt4BfL/ffzc+XH34PQt8SDtH+l+5bzjZeOF43DjBuNa40Dk8OQ35XflHect6yTwcPRE9yj4h/ho+c352vmr+Vf5cPrv+2z8Ff/8BZMQwRxNJCcmgycxKc8ppifaIccdEx+LIrElvybtJd4k4SFIHOUVAA/qBzgBWvvM92z2vPTY8X7uzOuK6fnmm+OV36vcN9sV2+XcFuCX5Ivqt/De9Ur6A/4DArkG+wqTDp0RDhRzFb0VDRWFEwwSrhAdD2oN5wpSB44DGwBP/Sn7SfnR9xz2ufMC8Szu6ese6gToBOaS5eDmZek27Izul/Cl8rXzcvOs8jPyy/I29Hv1O/e0+Wz80P5E/xcAuwSUDOkUOBnlF60WrhgcHJ0eSR0eG8kcRyDAIpwi4x/7HSAd9BqQF/oSHw5NCp4G2QN3AgUBvv4/+6D3n/RF8aHsLufm4rDh6uH14azieuQZ6K/s3O9Z8uD0D/cG+Vn6TPu5/GP+PwAjA2cGqgn4C+IMuw1ODigOBA1lCnYH5QR3AoAAKf/r/Yn9/Pwt/Jb7M/qi+BH3pPUB9Xv0IvOw8Rbwnu4L7jntseyW7SPv/PG19Vn4tvna+jP7/vqr+Tf3kfiPAFYM0xXPGJUXmhfGGSsalxZeEnsSYBcIHY4fkR+/H9gfTx5jGpsUSQ/6CjMH0wRRA/YBiQAX/lr7+fjH9TjyKu536tHoUui+6OTpf+o57MHuJPAm8RLxkfCs8dbyvPNP9fL2ovkR/dD/ggINBbsGLQiYCCYIwAcFB5MGVQZABqkGhgc0CKMIOAgQB9YFIQS+AYv+vfpd99n0V/Nl8qfxevHm8W7y0vLZ8h/yhPEk8aDxgvPM9en3ufk0+2n8OPyh+jX6pfyvAc0HAgx4DgUR6RK4EysTURGKEMQRxxN8FvUYNBo0Gx4byxktGIMVlBLaD/cMfAqICJEG4QQNAyUBlP+H/S37V/gI9e7xsu7D677pZOi/583nIuis6dTr0O198N3y1fQW94r4z/nh+3z9Yf/bAXwEcgfZCc4KDgsyCyILmwonCRQHJQWbA2MCIgGt/3f+MP0g/B378fkP+f/3uPa/9Sf19PQw9Xn1n/Uj9tr2qfe2+Ib5P/rP+hX7p/sl/Sr/fAGHA+cEPgY6BzYHngYmBqcG1ge5CHkJJQojC2EMCg1fDecNGg4LDrANDQ3ODDIMFQvvCQUJiwgZCCQH1gV1BPYCZwGU/7X9OvxB+8/6qfql+m/6ivlD+PD20PVT9fz0F/VN9vn3zfmI+338av0e/gP+zP1f/f/8Wv2S/bH9WP6X/ov+fP6s/ev8r/wk/AP8Evz4+wX8xfuT+677APyC/Pr8cv0p/rH+wf45/kL9ifw9/BL8SPym/OT8Rv2m/Tb+3P68/k/+Y/4g/0MAlgC//5D/2QD7AgQFGAa5Bt0HGwmbCTsJMghXByYHZwcDCHQIkQh3CA4IuQeRB3kHlQe5B2sH9wZ2BsQFHQVWBGQDegKJAWsAc/+m/vr9kv0R/Z38Cv3t/dD+Pf8D/6r+kv5q/hj+1P1u/Tb99vyP/E/89vuc+3/7ffvH+3/8+Pw2/Xv9sf3r/Rj++v1u/bn80Pv++lv68Pnj+fP5Q/qa+vP6f/uU+x37Qfos+cH49fhd+Qf6LftR/Gf97f3o/f392/2s/bH9I/4q/34AjwFJAhEDzgNdBKYE5QR/BQQGLwYLBvAFFAZ2BtYGAAeIBwUISghPCBkInAcSB10GoAU6BfAEpAQABB4DSQLQAYsBagG3AQwCJwKNAVMAHv8R/mH93/ys/Cv9I/70/nf/0f/g/7b/Hv9z/kb+T/49/iv+Zf4e/+3/4P8j/0D+pP3Q/RH+Of5f/mX+Lv7Q/RP9Dvw9+7D6xPo6++37t/xz/QX+D/6z/Q79QfyB+yL7AvtD+9/7cvzX/FP9vv0p/nz+gP6N/tD+LP91/1r/If83/5L/GwBuAFoASABnAMcAcwEKAo0CMAOMAwoERwQ7BFkEegTjBI4FMQa9Bj8HowfhB7sHFweMBiYG3AVbBUcEHANoAtkBaQHOAP//fv9P//3+aP6N/cP8t/xK/SL+Ef/n/+IA0AE5Ag8CbAHWAGkALQD5/7T/fv8K/7H+s/74/j7/Kv+d/vr9kP1B/bz8B/xQ+/j64/rE+pr6UvoJ+q75Rfnf+Nr4gflD+ij77Ptj/N/8JP2H/RH+h/5P/ysA0AAZAeYAMgBm/+/+xf4G/4n/SgBNASUCiQJhAukBdQE7ARsBOwGkATkC4AJ6A/kDmwQ8BZoFhwUhBdEElQRbBNwDOwP7Av0CQAOZA6IDlANPA9cCXQK8AQ4BlACIAJoA6wBwARoC6QKOA7EDMANIAj0BWAC4/37/2v+SAFEBwQHOAaEBOAGSAAEAqf9z/0D/v/7g/eL8JPzQ+8X7EPx7/Oj8JP0t/e38qvxn/B78+Psn/Kj8Nv2S/Y/9c/1N/RH9sfxn/Gn8zvx7/Qr+Wv5z/lr+O/7//cD9hv1p/Y/93f1a/gH/yP+IABsBQgFRAR4B1wCUAGUAbACxAOQAzgDOAOYAAQEDAQMBNgG4AVYCjgI3AoIB6wCYAFcAQwBgANAAjwElAnwCdwIaArwBjQGUAf0BjQIaA5AD1wO8A4MDEwOvAloC2wF5AWEBdQGZAZABNgHZAHsAOwAOAOf/9P/9/9r/lf9j/zD/HP8E//3++P7e/qv+QP4B/vj95P2t/WX9MP04/V79fP2v/eb9Jf5E/jT+DP7k/cz9wP3b/Sf+r/5J/+D/RgCfAOoA/wDmAIsATwBcAIAAfgB9AB4AvP80/0n+XP11/M/7lvvA+yn88/zJ/VP+c/4//jv+Zf7D/hX/i/9XAFwBUQLiAhUDEwMYAyoDOwNLA0QDkgM3BBQF9QWDBs0G3AZ6Bt4F2gTIAywD6wL0AhUDGAP2Ao0C4gFGAa8AGwCD/7z+Hv6J/er8K/xy++z6nvrq+jH7f/vs+zT8tfwl/bf9Y/7v/kv/vP9BAPQAygFfAs4CLANUAyEDqgL4AUQBhgC4/yz/8P7l/iP/df+//8b/Rv9l/iX9IvxK+7n6j/rh+rv71/zZ/YD+Df9j/5T/tv+A/0D/+P7T/tL+0P7V/qT+n/7Z/kv/1f9lAAMBfgHFAaYBUQEFAd8A2QDbAOoALQGoAUICrwLBAo4CoQLvAksDxQMqBHUEagTrAx4DPQKHAbcAGwDT/8P/0/+t/0b/5f6q/p/+s/75/jX/lP/w/xcAXAChANsAEQFVAWoBTwEyAREBDAHxAOgAywCjAGsAHQDa/8H/4/8OAPf/q/9h/xP/sf40/rX9cv1N/T39Rv1c/ZT95v0M/h7+P/6L/vv+R/9q/2P/QP8g/xr/+/7l/gj/Kf+A/4v/VP8G/8r+tf6Q/qb+mf51/nX+aP6L/sH+6f4N/1H/wf89AJIA2QDzAPEA0gCaAGMAJwAMABIATgCuAAoBTQFlAYYBfAF7AXMBWgFEASAB7QAKAScBSwGfAb4B2QHAAWEB3wCGAGsAfgCfAKoA0ADkAAoB6wCzAGMANgAtAAEA9v/p/7T/Uv+q/vj9bP0X/QP9Hv1W/Zv9yv3r/fv9Ff79/cr9rP3g/ZL+Xf8BALkAewEGAmoCdwJlAj0CDAIeAkICWAK/AhEDXwPBA/cDEwTGAzUDSAJ8AZEAzP9G/wz/Lv98/5L/jv91/w3/lv70/U/9zvxD/NT7nvuC+7L7DvyP/B79yf2h/pv/swCUATsCjQKQApYClgK/AggDeAMUBHEEhQQIBDkDNgIuARcAPf+S/gH+7/3e/dT9qP1n/Tj9Nv2U/d39LP5f/oD+m/6t/s7+0P7F/qv+n/6L/qr+BP+b/zYAuQArAUEBHgGxAAwAhf8n/+v++f40/5v/+/8nAEEAOwA2ACIA9////yYAnQA4AbcBIgJuAo0CVgIFAncBnQD3/3j/Tf9f/7//LwCaAPoAJQEHAY8A4P8q/87+uv72/mj/zP8yAJgA1gDfAMIAjQBOAOn/rf+C/4D/kv+9////AwAOAPT/3v/O/6b/lP9o/5T/FQChACsBXAE0AQEBjQADAIn/MP8+/2r/xv8iACkAQwA6ADQAWgA7ABkA8P+d/2z/Pv89/03/Xf+L/5v/r//R/8P/sf+d/3r/eP9x/3P/oP/t/3UA5AAKAdsAnQAkAO3/8P/n/3sA2QD/APQAowAbAIP/Kf/l/iH/b//j/zoAFwDX/2H/+P6i/pv+yP4E/9f/ZwDXABEB0gB9ACsAHQBOAKwAJAHkAUkCQgJCAugBQgHvAEgAsv/Q/wMAfgCmAJYAzAAkATIBGwGuACUB+gEcAkQC3gFuAagAGv98/SL86vtV/F788fzc/zsEKAcxBwQFvwKSAMX9lfq492/2Off++Fn70v2O/wEBYwFlAAb/Yf0F/K77Dvxe/eP/BgKSA38E9AQIBfsDIgKzAL//5/4P/+n+5P5R/7L/+//p/37/3P58/kT+LP7B/Xn9Vf17/f/9tf6C/yIArwDQAPYAkQBaADYA/f+oAGEB/QGbAjMD4AK6AqsCDAKZATYB/QATATIBLgFjAXkBWgEiAY8A0f8M/03+vP1u/Wr9j/1c/ZD9HP5N/iz+jv7p/h7/EwA/AIAArwHlAnUDuAM5BIcEwQSxA90BnQHgAXwBagEnAfcEBgsnDBAK0wblA5sCLP7s9+D09PMg9R/3Ovig+hH+oQDOAPn+Uf3W+xv6xPgc+AD52/sR/lj/WgEYAkIC5gFVACf/av6N/lr/uP9YAFwBIgKWAiACqgEtAeAAKQH6ADsBUQGzAX4CCAL6AQ8CmAHbAW4BbACUAJQAhADa/6D/+//w/4sAIgAs/zv/rf+t/7j/GP8n/wEAMgAcAaUAVwC3AIsB6wFTATQBoQDXALwBjwGSAOYA0gBTAY0ADv5E/9z/OgAgAMn9nwOHCk8IzwUiAhsAoAUiAt362/qs+hz+Ef/G+s37t/2t/qH++flD+GL6jPoQ+wX6M/rW/D3+uv4M/hz+gv4R/4n+2/5I/k3/AQHQAXUCRAJHA7MCfAOmAuUCiwKLAl0CwwGQAjYCwQKZAnUD8AKfA0QCdwGxAVYBGQFGAEMAUwCxAI8A6/9z/nz+Rv/r/Wf9Qv2c/Jb9RP5N/iL+d/6O/+0AKwA/ARwBCwQUBiwEMAL5/9sAuv+r/n79Xv28/tQBQgP0CjERYwyLCVgCx/2m/yf3NO0j7FTvWPSI+XP3AvuGAfACSwNX/ML6R/qs+0gCOQPmAJUD+ALZCXARkQjbCDIJzAkADi0HZP+JA6YDqAAH/Gr0lPYG+cb5vPXx9KL3OPmH+Bj3jfUW9y35/fav96P5xft//If9m/2L/7MBtwHMAW4CUQNrBQAF3gQkBpoHmwk7CycKXAknCrwJwghiBVgDbgHgAY8A+P3U+x37f/uM+rn6evnL+kr9WP7V//YAeQEWA/IDagLeAkoBTgA/AokBxQAe/879TQPMDXoOjwl7BsEDxAjwBer5K/Us9Tr4bfhT9Pn3YQHWCAoKtgX0BCsHxQO8/tb4Lval+Xz3afQ19mD55v3g/zT+Of5sAFT+oPlF+O73Dvqp+rv4+fhW/Q4BJQLHAagAyAIzA0IBfv65/AH+tv/c//D+wf+fAZAChQJ+ASwCbAJEBDkFKAj2CicKHgr4CZsLJQ3bCzoJaQmxCWoKIAkdBm0FsASvAzQCNgC2/43+0Pzx+l35qff59gv3UPjn+Wb6Ffsr/JL+4P7v/LP7HftM+4/8H/pX+Sn8Cv2k/0YBLQCdARcC9v9j/z39oft5+/75G/rz+Tj5H/kj+d/4EPlS+JD2O/bS9df1xfZ497/4+vns+lL63PlH+bv4uPj59+H35/dZ+Dr4Ifik+Bb6iPqJ/zIK4hNfHiohYCCnJ9ktPS8dLBUjLh5JH2MajhRcELsNKhDIDtIHagJa/Wj4gPPM7PfmmeNT4Ifezd4e4T/kk+ZM6vLtoPFf9Iv1fvcx+4L+SQI6BZQIqQ0kEUkUmxb7FoMYOhkJGHoWORPPDywNwgiUAzT9//ZP82Tw/+3661vp5+eY6Bvpx+k66Z7omOqX7YPvWfBZ8D/yQvW793/4kfhZ+Yj6svqn+W/56fgz+s/6V/qw+Uv5Lfl6+D74KPq5+x78RvxQ+e0BFRRAILApwSezIj0ubjYVNHYxsSZgI3EnxRz2EsIPxA11EikRYAc2ATP7vvQ68HDrJug24z7dEds72wLgoeOv5Azr3fHM9cb4h/hV+34DPAbHB+4HoQfXDUYT+RV7GWcaghshHhkcnBeWEjcN9Qg3BBr+Bvcr87TxDe8j7VPpH+VJ5A7kaOPy5BPmYufU6m7tFvEV9eP3Qfme+Tf5iPim96b2mfbi9m33APir99r3xvfs9wL3aPXi86ryZvB+7SntlO108G3yPO849FkHqx5DNHs7YzVgO6ZH2EzPS/o94zHjMH8q+R6OFigQ6hDLEekKyAIA+fLsQuS03sXcF9oq09jM3szI06vb+t+q5PLrF/Mj+b/5Q/n6/SUDAwhkDEINHRCNFBYY8B3bIVAixSJmHwMcpxm1Ew8O7Ad0AE77rfWZ71Hshukd6Fnn/eSx47bjXeSQ5gPprOsY7/rxVvUo+Qn89P7kADsCdQNYAdv9cPsv+Xr4Efg99nr29PZz9g/2bvQ68WbtSOl96I/oiOZH5vfn9uxM9MnyG/NHBVYdHDcARGQ8wj2FS/1S5FbqTFM7bjZYLjMg3BXWCS4C8QA9+1z1Ye4b4gnZ3NZM2Yfa+dXuzmvN/dMI3S3jJumX8FL4zP9mA4UF/AiACwYNog5JDi8Ofw4HD3USOhb1F+wXSxakFGMT1hC0DH0GTwBp+xT33/N+74vqhee55rDnM+na6DHomOim6jzve/Pr9lX6s/0uAiYHQwmUCcUIKgeeBZgBZftU9Szxze/h77/uvO0K7ZfteO9o8HPvh+3X6+3qF+ne5sPm5ecc7EDu7u63/PcWIjOUSFBKR0VpTPJUmldpUIw8/itMIrsVgguBA8v78/ja9yP2HfNM6/nf5Njf2eDcPdpb1JTP7dKa3wHrtfMy/GkB6AaVDO4Neg/UDuIKNAoVC5kLRw3sDNoNChP9FgkZBRnXFbwSoA+kCp4F8QDN+2r3PfNN7gDqQ+e45anmmOil6Urqueo/7IXw5/X4+sz++gAABLMH2wrnC+sKagkACOcFIgHf+UHz/e6X7QjuOe2E69Tp5egi6v/rRuzF6k7onOeI56LnMek46ubs9+7i7a33lw8MKxlEN0yZSAxPsVezWcZUq0FcLdwfMw64/hr18utT6J7o/ujA65zqouPA37rixuhD6xTo1ePD5LXrNvNK+scCBwkIDbUPoQ8rEOMPtAvACBkHMQTlAiQBaQAlBekJ9wxnD54NfAslC/oI9QVEAR36uPT38PLtvuzO65vr5Ozr7hLyw/QG9mb30fmL/QgBoQKiAwIFLwfrCQYLdQqUCOEEQgGH/Yz4dfNt7mfqtOhZ6ITo8eg26M/mh+ZA54zoy+j15yzojOhy6qvui/Le9or3U/U6AH8YOjNWSMZKtUUaTB9TW1QxTw4+Ty1FINcNbP689Jbrq+a45HPkm+a65TrhRd/g4zDrce7X7Ynsau1p8hT42f1SBagJIgpfCoQKTwyvDQgLNgnOCboJswk6CFIGhgcpCVUKwQtYCgsGWgF3/sH+2/7J+632pfKg8GrupOsp6jLqvuuv7TnvAPI29dr4ov4oBaYJRApuCOwHHAm1CSQILAUnA3sBlP7Y+pv22/Kb71bsV+mt5Qziq9+23oPfZODN4K7iz+Vn6ZTr9Ozq8Rz4J/2Q/b/3kfwpE0svXUiUUE1L8U8uWoxegVxmTOQ2iSVMD0762+xK4QfamtYv1V7ZKN0w3N7cGuPD7fj10Pbq88vy+PWV+mP+GgPABgMInAh9CBMKoAytC+AJOwn6BzEHNQWrAqgDEAYdCGcJggcGBIgASP5N/gr9W/mN9O7vse4R7wvvFvH18jL07/Xm9f/2Vfkx+lf84v5TAGoCvQOKBe0IKQqICfgHHwVmAoT+Avp+9l7yeO7/6iboyude6AzpG+rQ6SLpH+nh6S3rN+xd7V/uffFA9zz7r/2U/Lb5AgW7Hq05xE2cT6JIG02TU/1Sb0u8N2siCxHW+9TqpeEe2ZnUN9Vu2XnhZOae5b3n+/Bg+0YAcf/h+/r50Pvg/f8AHwYMCK4HRgidCWoMdQ0lCrQG8AO1AGf+gfuR+Rv7mP3vAFIE/QT1BEMFVwVDBTsDm/82/P34xvbH9Kfy+vGN8QfyffTK9sH4rPor/Pv+XQKtBCgGPgbEBVIFAAR1Ap3/SPsG+BH2pvQD86vvYezz6p/qG+ub68Pr1eth6zvrNuxH7trws/Pw9T72Bvde+Tb72ftF9zHygvxqFDQu/UAoQwRB60h7TzRPsUcXNNceUgx++Mfr6eSJ3KrXHtmi39roWO1q7MXuE/e2/50DrwJj/539HP9RAdcEjwiYCLIHMghBCbEKcgmBBLcA3P7Q/YL9DPu09yf3svk5/2QEYAU8BCkD5QI5BIkEUQL4/gT6JfUd8+rydfP/89LzavQN9sP3JvpV/YsBxAVFCDIJ9QgrCLAHuwbnBKMBifz39x71dfMg8gnw3u117PrrCOxo7KvtDe+D76XvR+9f7p/tKO7K8GTyzfLA8yD00fad91P0DP1uFEAvzUbLTCdH00swUu9RbUwKOQkiShA0+6rqvOI22uzVKtdj2nnirOjc6NfrDvRV/fcDIQQQAFP+Nf/OAPsDgwYCBh8EjQHK/6oArwAg/vP7x/rh+q787f3w/n4BdgTxB8AKiQrNCHgGBgSZAjYAV/zV+IX1mvPh8wH1r/a69+n3q/jn+dj79P13/9QBUgTPBSgHlQfmBh8GWwRzAogA/fwf+SH2hPQ49Lvywu/m7CfraevH7NHtFe7H7a/u+/DJ8pbyKPFc8bDyafTe9Sz1Z/QX8t7tpPTeCrMmgD/4SBJH+0vRU9ZVS1EGQWArUBdIAGjsW+Do1vrR4NJy2B7ieekB7CbwrfiFAo0I9QexAysArf6i/qUAsQOeBGQDEwEl/6L/5/+b/QP78vgo+Ev5R/oS/BUArwTDCewNzQ4hDhwMzggUBlECvP0m+vv2aPV39RH25/fh+RT7Kfz8/OD9lP5j/pf+pP//AIkC0ALtAZYBMAGzABX/FPuC9gPzrPGe8UHw0O1l62nqHOxv73LyqPTv9H708fT29K7zu/CS7r/tpuw77eTtTe5b797scfBkBkMj8TxOS9JIzkmDU0NVs1B6RPEtJBqgBsvwVuWZ3tTXiNiF3HTiQepK7LPtN/UH/RUCwQJR/sv6Yvmc+EH6efxq/aT9av3r/tABegPpAw8D5AGzAQEB4//v/mj+PQAfBDoH5Ai7CBsIYwkpChUJvQY9AlH9MfoE+JX3oPgu+JP3/ffK+Mv6F/0E/2oBUgMlBDcEqAPgAjYCvAE7AaD/YPxQ+DL1L/Tx8z3yg+9n7ADqXOrZ7FLv6vBA8Hjuju6I77vvAu/07lDw7PA28sH07vZk+aD3bPTT/wAYrzAvQ1lFI0EBRxhL0kg4Q1sy+x5LDkX5qupm5KjcddlA3Cbhpenw7jzvdfPW+5UD8wcFB9cDZwEOAGMAowHBAjACqf+J/p3/AwHiAYYALP51/fT88/xY/Z/8t/0BAf8DcQZZBqYEOQWGBlIGUAQ/AEb8OPpO+d74MPi99pb1UfZF+eT7Gf0a/of/ygKgBjQIiAgACCwGkwR5AqL+b/r29erx3u+g7WPqAOgq5gjm9+fZ6d/r++2V7v3uivAM8mzzMPTB9HP1HvWZ9jH5TvuJ/Dn4KPfYBYccmjLVP9Q9GT2IROJHgkesP1Mtwxu4Coj5PvEF7GblseKP4sPmw+2t76DvB/PW+AT/WgFv/4T9zvyv/cb/6QHBAzIDwgBJ/8P+1f5J/mf72PhU+MH4XPqR+9/8ewBvBSsK4AzlC0YJMwdxBTwEzAF3/VX5h/bp9Tz3v/gU+pH7Bf2m/rb//f9YABcBpAI8BLgEJQQVA6QC1wKLAqEAIP3M+A/1ZfLg7+bse+l+5sbl4efk6jXt+O117Xfug/F+9Nv1FfVs9NX0nfUR+O75rPrs+oP2HPdOB+4dTzRaQEM8STtAQnlF+kRLOtIlChVMBdD2fPA96yHmxuUW5/3rtPFr8ZPwd/NL+Gn89fsd+IP2FviB+27/DgKbA1gDtwEKARABfgDF/oH7G/lS+aP6NPxp/Rr/5QIJCCUMrw1LDKoJ7wesBuUEugE0/bn4PfYn9if3UPjI+Dz5EPuL/en+s/68/dn91f/4Ae8CoQL9AWECFgPeAt8AO/0d+ev1Z/O38HrtBeqa5+fnker27XTwcfAP74fuxu+08RfyJvH58FPx/vEr9JD1Sfak9YTxQvaYCqYjuDjdP7Q7Lj8TSaxNTUtRPQ8pWRggCML6BfRb7V3neOVA5wHt0/C97wTvofLN+NL7ePm29TT1evhL/UQBCwQ6BX8EiwNPA4ED1QLT/yD8yPk4+Yb6Ivz6/fQA2ARjCYAMngwgC58JyQiBB9kDY/5r+dz1s/Te9OL0X/U59pD4Ovz7/l//zv07/IT84v1j/lT++v07/rr/DAGLAY8ADv4/+0v5jPc29PvvaOxw6ifrSOyZ7AbtZ+wZ7JftuO8m8u3y6PIZ9NTzafP29BP1cfU48mPqtfJqDPQmRzzpPkc5AEQQUPZSEk8xPMgnDBp7CSX+ovh275Po0+Z451Xr1Osz56Xn8O3b83z2n/SE8l31/PovAEIE+QSoAkwAYf///5QABP89/Or6YPtY/S0AHgITBAMHiwkhDHEN7Qu2CqEJ9Qb5AxMAOvxw+iP4i/Wh9CT0aPUP99r3nvnz+Sr5+/jN+Hv6SPzg/Mr+zAC/AtwEtAQ+BKAD0ABG/h38svha9dbxl+4p7ejrsOpr6v7qGuyo7FjtkO4U7yrvePDW8YbxrPGE8ojzYfWq8Qjv+f6AGq81/0YWRA1BakrJUNtR+kinM4MgBRAcAaP6m/aD8Kzr9+nQ6w/u9OyA6uvrvvG69Tb1o/Rw9VD4cv3Z/74AnwF3/vX7A/xK+5H8hP3C/Gj+n/82AeEEwAaTCB4KhAoeC6wJawd/BRMDcgCP/Nr5Mfkj+N72hPS38ifzXPK18Xfy8fJL9VT3dPhY/IAARwOMBZUF+wQqBXgE2QLrADb+a/uG+tP5nveJ9fbyG/F/8SHxz+9G7h7spepp6sDqy+rd6f7pJ+wT7hTxIPUu96X4R/an8IH4tA6fJoE6dz20N9Q+fkhGTp9OMj9TLNkd1Q31BAUBwvm584nuxeuD7Uvt6+qq6nft4fEp8zjxZPDf8Sn2/Po9/fb+A/+z/BD8u/vz+7j98/x2+0H7d/vp/jUDnAYYC8oOjxFIEkUPJwzCCfAGUgNI/UL3dfOD8RnyufNy9J30OfTO9J73u/lp+Z74GPmq+7b/IgLZAvkD3ARzBXQFgAMgAdf99fnj9+T1+vNe8qLvve5t72bvyu9Q73Puse6S7jvuq+2o7IPtTe/Y77nxgPNc9P31sPKe8gwDixoLMiFAcjtHObZBoUeKTFZHdzXGJjEYWAqOBT0ARfmm9PXuoO2D7m7rxOly6lHsl+6F7OPpDuvj7j31OvqL/Dv+DP4u/sz/pQCUAQwBov6P/Yv9av9GA64Gggr1Db8PsBAHD2QM+wp2CCoFyQC5+iD2ofNM8kzynvJn8iTyivL68471AvfO96/3gfma/GT/NwIlA7oCrwLoAf0AGwD7/Zj73/ng9z31gfIW8FbvhfAb8f3v4O0r633q6+ya8EjzPfPl8erxAPI089z1/fV79UHyc+9Y/RIXlC2ROw05IDQkPiJI00r8RqQ2HyemHMgP3QjIBPP8+/da9HLyK/Nq7wfr/+rB7L/uO+wf6OjoYew98iH3C/nW+039IP7rAKEB0gEZAUT9mvyo/UL+8gFLBDoGzgq0DOUNsg4fDCsLQgrhBlsELwCC/PP7APom+Br35vS89Cf1MPQ59FfzxPKz9G330PsMABUCbgOAA6oCKQLFAPn+t/xS+ZL2CPXX8wXz2/Hz7xPvre6o7Tvtcex3637sI+7q79rx9fKk9PT1ifVl9vv2qfZ79fPwdfZCDJskzDYhOTkwIDQ3QTVJSEliO9YpDSG5GLwRdQ0PBpX/y/nQ8y3xDO3C55flCObJ6Uzr3OfW5iLqafCF9wv6OPq7+nn6wPtl/dv+rgDl/4X+NP/fAOsEJAnBCv8LbwyBDNcMkgsrCeUGCAXRA7gCdQFDAJ/+Xvzw+Vv4jPds9tn0yfId8a7wt/AX8oX18Pnt/WcAPQGjAcEBSAH9/+L9APw1+sH40fei9tn1dfXU9BP0FvIz75DsF+rz6LDot+jH6Ujrau7W8qr1X/d2+Nr4ivlD+K/3YwGkFFcpMDb8NAAyDznjQ01K3URBNc8nWR/IFxcRYQqOBb4Cef7j+OTyCO7D6wPq5uhS6CXmMuQw5IbnZu479Aj2e/VG9dj3K/vS/PH9+/5PAKQBoQJfBdYJFg39DbgMwApaCVcHgQXsBMEERAQsAtD/3v8XAe8Ajf61+qn3m/Vr8zXxm+/E71Lx6vJU9Wv4t/v//j0BTQIMA00DqgKvAI39jPrn9/v1EfS38cbvS+9d8EPxL/EL8IfuIe5Q7sjtKe3S7MrtNfB78WLymPTX9rL5Yvor+jMDERVNJ+oyIzEHLbUzKT44RIA/OjEqJ4Ij+x6GGIEQMAuCCmAIzgHw+Krxm+6b7Rns8egT5abjtuSF55Tr+O0+7o7tSOwN7b/vGfLB9ED3/vkr/vsB1QVyCvQNhg/sDpcMtgrrCXUJ0gm2CmMLSQsQCgcJCQnYCNgGjgIR/aL4KvbV9Bf0//MH9PHzJfQF9YL2Uvja+G/3AfYp9uH3Pvp/+yL7ePo++jj61PnG+GP3WPYa9sn1WvUO9ST0HvOw8uzy+vMR9Vj1vPUE9zr4KPmu+QT6YPpX+kr7twGcDjkdoibiJu8jWiVgK4owmS5WJkYe+hltGKUWgBM9EZ4QMQ8pC2IEVv0N+bz2xfPu7nTpWebI5pzpBuy47LXs1+wa7vXvnvGI86/1+/fC+hn9kv4ZAPgB9gOeBb0FsASeBFIGVwh1CUEJhgjHCPYIkQgLCF0GPgPQ/1H9J/0i/gP9ufq/+QD6Yvqj+bT3c/ey+Kf43PdU91T4YPvy/RH/tP/I/zv/Vv5w/e/8sfzx+4r6m/jp9gz2qPVh9vf2Xfcz+OX4iPkQ+rb5afm5+Qf6A/tP/FH9D/5o/m7+bv/UAXMFYwlAC5YLWwzwDewQcBNGE+gRmhCrD7UPMRAQED4Qvw8YDqYMUQsDCpMIBwZoA5AB2v8Y/pL8Bfxs/Mv8QfwQ+4b6Gfs0/Ov8U/yP+tr4PPj1+GT6IPsd+5j7V/wI/Yv9wf0M/hH+df25/J38MP3K/TT+of5m/0YAywAOAScBMAEgAXkA0f+4/8j/dABzAUAC4AIyAxwD6wKUAtIBVwA3/jb8ePoC+Xj3nfWm8w7yy/A88N7vZO+c72jw1PGL85D0RPVl9gb4APrd+z/9cf56/zsAbAAJAD8ADwLjBLIHNgntCOwIOQpEDIUOuw+/DxQQchDoENARJBLFEicTWBLLEFIO7Qu1Cp0JPQiOBs8ECASmA7MCmAGLAPb/HgA4AEEAJgBA/6j9u/sH+jP5L/kh+fv40/hb+J73Pvdj9+P3DfgC9xP2wfay+Mb6vPvx+0j9vP8GAvYCSQJhAdYAiwBRAJ3/R/8AAFoBKQPwA2YDogLvAbUBYwHp/4L9iPpS97z0f/LA8PDv9O437lDuKu+T8U30A/ZA91f4+vno+9n83/yP/ID8bv3t/rT/3v87///9d/4RAeUE1AcDCKAG+QZaCTAMzQ2pDRQOtQ++EKwQchDNEesUxBagFXsS4w+uD2sQABAADjULAQnjBxQHNQbeBF8DcQLZAZIAqP5E/EH6JPlq9xP1RPOn8gX0v/XZ9df0OPTe9Lz2+fd691b25/VS9+P5BfxB/SD+pv93AqQFiAeaB88FlwN3AnECTQMIBNkDjAMsA7gCWgJTAf3/v/5s/Rv8hPr59030k/DT7ens+OyA7M7rFexS7tLxJfU390n4Gvna+W36M/po+a74rfjT+b77lv0e/wcAGwCx/8z/IAGmA+MFVQYkBsgGAwknDGsO3w92ESsTphSmFW8WQRiNGkgbxhnlFmwUwBOWE4sSmhACDqoL7wkLCCEGnQQ9AysCkgC4/Vn6N/fv9KrzFPKr79XtHO3R7fXuV+8U7xHvC/DJ8arz1PTB9ff28PjE+6H+RAGxA30F1gbWB3AI7QgyCU8JiwnCCToJvgeTBYwDLgK5AP3+6vyW+rj4+fYI9dDyRfDa7S7sSOsD61jrE+xj7ZPvFPJW9Hr2/fdQ+cT6A/zD/JH8rPsQ+7z7Z/0l/9n/W//0/cD8G/0BABYF6AmbDNMMbAypDZoQlhNLFYMViRU6FnQXKxk0G3Mdux7KHWMaVxZsE4ISPxJiEIUMzweZBKYDBgMRAd39xvmQ9jD0LPHi7QHrHekF6bXpDupM6h7q7+oI7ZzvG/LA8+L0lPaV+Zr9DwLsBb4IDgv7DJ4Org+7D0oPGw9kD5gPlQ4wDDAJjwaeBO0C/QCL/rv7tPho9gP12/On8orwve0B64jou+bK5XXljOX25Wbm/OcV67/ugvJA9dn2xPeX+Ab5C/nN+K74uflZ+6H8EP2G/BP8O/yG/ZsBJQoGFSMd4h5IG/4YKxzhIUokWyE2HLMZFxtUHeAedh9zH1keSBpzE34M0AdQBlAFjQHJ+8H2mfVS+Hj69/hN9P/unet26nLpFOgN55fmxufQ6SvsW+9n86b38frb/P/92v+mAgQGJgmdC8wNoQ/3ELUROxKEEnISfRFeD1AN0wuSCt0I6AWSApv/Iv3Y+k34/fXQ85jxKO+Y7HLrU+u369DrYuq36CToHekt64ftD+8Q8LDx5vMP96L5svoq+jD40PaH9tf2vfbJ9c70t/Rx9VL2J/eX9gj0FPK19e0C0RXPIssjOx19G+QkSzEpNt4w6ihCJtYo1CqcKpEpxCeJI1oaig6/BQcBFf7A+Xfzh+7K7BftE+1p62/oN+WC4dXcONkV2UTdX+Pw56PqO+2p8UD37fu+/jAAoQE1AxgFOAirDDsS8BZMGc8ZxRqFHBodsRolFeYP/Qw7CyIJ/QSPAED+Rv2E/FX6evbz8e3tk+pn6IrnUOfY5/noIOps60Dsoezi7Cft+O3s7gbw+fBp8QvyhPN59XX3bfj195v3GPgb+TX68/mr+Kb3cfbi9en1BvUr9AP2lv6RDwUiwisUKtQjiSMOLDQ0yTPJKzQjYCBQIn4koiVGJToi/hqaECgHLQGs/En31vBT7MLrh+xo7JTrmOtf7b3t8eke5PzfteBf5ZXp9utQ7ozxR/Yi+wb/nwJrBSMG8gWsBksKFxC0FKIW7BaTF04ZgBomGTwVAhA1C9YHawVaA4QBRv84/Qv7z/hS9l7ziu+f68vomuf751Log+jY6NjppusV7e7t4+7g73bxXPPt9If2LPgC+bf5VPlL+Lb3I/fu9jD30fb39jD3MPZG9UH0XPQX9QDziO/L8Kf7vBD6JNAsjikBJCAlSi1eMoEw+CreJVUkeSQlJbonPilnJaYb4w4zBdn/gfrk8+TtlOts7Rrvge6S7bPth+7S7LTm39+F2+bafN0x4UXmwewx8iX2e/le/WUCIQbEBmsGkQeLC6EQhRTuFhYZjRsnHTscrBikE5oOuAqeByUFeQKX/+T8ofqD+JL1l/H07brrhOsa7IXsRO2m7sbwB/Nz9O/03vTk87PyvfHx8Uz0N/dB+e75hvkN+h/7M/uB+T32B/Ni8VvwTPAU8dvxHvP681P09vSs8+Dvau709AMH+hw1KhkqNiSfI9krEzWtNyEzuyvpJdQiAyMeJzIrwCn3IMET7Qi6AbL6VfIy6n7lH+Vo5f/kVOW95vPomOny5kTjXuAx3+jgBOUD6wvy3PdR/C8AaAOuBXEGYgb6BskIEwuHDfEQ9BVeG1kfnCDMHs0aDxV/DsQIkgSUAQ3/Z/xT+iz5Ifj39k/1dfOP8X/vLu316s/p8+ms67Hue/GN89T0ZfVC9pP3Wfjc+Mj4EfjM93z3WPed96n3qfee9133r/c6+PX3dfdP9hX1dfRK80fyWfKy8UfwC/D09IUDwxbgIywm3CDTHRsiTihVK4oq1SeZJboj8SKAJYMoxia/HkETAQozBCz+KPdV8cjuI++57/XuBu4P7UPrxOj35d7jIuMy4yzkEuek6+Xw3PWw+XT8fv5s/wAAtwEaBXcJzwyTDoYQ0BPYF7oaphoCGLMT5Q4pC9IIrAc3BnoDpQCz/sH9HP2f+575q/c79Wfyc+/e7NTrT+wC7ozwvvLU89DzTPMv89Lz2fSi9YX1sfQw9Ob0Mve5+e/6U/rq+Bz4TfjK+I74Mvev9UD1YfZ2+Fv6XPt3+6z61PkX+wwB8As8GLkgnyKBILYezx9YIwYmsSX4InEfLh1AHW8eyB6qHCgXQxA5CkMF4ABV++30BvBf7ZLsT+xR63vqKepw6hvrVevm6kHpN+e45n3o/+tx74Px5PMW+Kj99gIZBt0GPgdiCDsKgAzaDacOhA/bEN0SkhRCFYkUixLjDywNwQqsCKsG6wR+A0gCQQG6/1X9KvqJ9jrzX/A37tLsGuzo69vr1+u+63Dspu0N77nvR+9N7r/tKu4Y71/w1vFg8970Afbn9j748/m5+2H9zv4gAEEB0AEBAlwCnwJqAj0CVAMhB8YMkRHtE1IU5RRAF0EaexsJGtwWNxTdEx8VkBa7FlAVxxMQE5QT0xMgEj4OdwnaBTUEmQNCAsP///wV+5r6svrP+Vn3wvMA8H7tmezk7IztKO4q7wXxqvNA9sP3gfhL+Y/6bPw0/pL/6wBlAn4E5Qa7CNYJ6AmaCSAJPAhkB6AGwgW0BN4CtQBN/yv+rPyP+uX3/fU79Yv0e/PP8Vfw8u+B8Jfxe/K+8o/yWfJK8hbyQ/Gn7/3tGu137cPuGvAJ8ffxgvMN9iP5o/vi/Bv9X/3c/ncBKARgBiIJmQ2UE9IYwhoxGtAZcBt8HhogtB6dGx0ZtxgmGsAbDhyRGgcYnRVWFI8T9RFoDqUJnAVaA2gC8wAI/oz6Zver9UD1BfWb9Ijz8/Hf8IjwmfB68G/vRu5W7qDv8/Fe9PT1R/cJ+F34C/nl+a76NvuT+9T8GP9RAb8CEQO+ArUC6wLMAkYCLQHc/6b++P2m/WP90Pyn+5H6tPnz+Ff4l/ef9nD1XPT881z0U/XS9aj1T/XD9Fj08fOI83DzqvMR9Ov0Kfb091f6O/0iAKYCVgT7BFAF8AUzBxAJzAqMDEwP9BKHFuMYaRnAGSsbAR3MHWMctxmiF8gW5Rb9FggWSxRNEscQQRCjD7IN6AlCBUQBtv6o/KP6fPhh9vL0MvQe9Fz0XPTS8wHzU/Ku8efwJvCD72HvC/CI8ZHzgPWo9jz3pvcz+K742Pja+Nr4UvkZ+kr7ifyx/YX+vv6E/in+zP15/TD90vyJ/E78VfxB/NT7A/tB+h/6XvqW+j/6cvn1+Bb5ePmn+Wj5+fiw+E34p/c79yz3fve290n3xvYa90L4/vmI+/b8NP8eApEF0AgeC6IMjA1CDooPuhHnE7oVgRbEFpAX+hidGmcbuhr+GBQXzxWAFTUV3hNuEdQOFA1mDJALdwkjBqICNADD/rz95Pvy+J/1FPPz8bXxQfEz8EDvRO8u8J7xtfLm8rLyTPI/8ivzd/TJ9e72tPfI+Cb6efuq/Hn98v1E/nn+kP6z/q/+8P53/yYA3QAeAdkAGwA+/27+rf3S/Mn7svro+Y75iPna+Sb6BPqw+XH5ovkm+kX6z/kQ+ZH43/jG+Y/6Hftp+8D7s/vd+sL5GPmr+Zz71f30/9QBfgPRBZwIIwtLDJAL1gn4CG4Kig1HEFcRZxFVEtEUnBf3GKkX7hSsEuYRPRIpEpUQxA0eC6YJkQmbCVwIuQWzArkA7f+p/+/+Jf23+kP4bvZY9Uz07PKR8dzwB/Hu8bXyAfP48vPyQ/MB9N70YfWm9fD1Avfa+IP6qvs7/Lz8av0Y/o3+hf5s/kL++/26/ar9+v2C/g//Kf/T/mX+Dv7X/Wz92fwi/GX7FPva+tL6wvqj+oH6Nvr6+cD5mfk++a74CPik98H3KvjV+G35Cfom+2P8hP1z/vv+1f82ASEDYAXAB9cJtAtkDQ0PQxHSE9MVahabFYIUExSbFCEV4hSzEyYSLxGYEPEPkw5HDJgJ6AbrBMYDEwNPAkYBEgAY/2H+av0X/K76Xfkz+FT3tPZ89tP2Vvfn9+f3NfeC9uf10/U09mH2X/Y79pT2fvei+Gj5b/lL+Xb5Hfrz+kP72/o8+h36afra+iL7Ufvb+5/8jf02/lj+Af5q/e387fwr/Vj9Vf1B/X799v1h/nr+WP7p/Vb9x/xI/Oz7n/v3+jH65/kt+gX72PtE/GX8dPwg/Wr+h/8JAAkAHgBaAasDNQYBCAMJrAkOC0IN2A8FEjITahNqE58TCBRqFPoT5hJ0ERsQ8w77DcwMGgtGCT8HhwUfBNcCbAHG/wX+ofyG+4/6y/kH+Wv4//fI94H3QPfk9oz2pva89qb2kvat9l33aPhB+cD5GPrH+lP7s/uh+0z7Svuj+zj81/xl/ZD9Vf2I/LP7PPsA++b61Pr8+ln70vs2/CD8ofvU+v75TPkb+Wb5K/r8+nf72/sT/Jr8Af1E/Vb9O/3x/K/8Z/zv+3v7M/tQ+5773fvx+zD8x/y+/eT+JACZATIDgwRDBeMFtQYBCN8JGAwdDrIPvhD4EZ8TXxWgFs0WHBY8FXoU9hNhEz8SyRAQD4ENZAwEC0EJ/AamBJ8C3QDl/qz8g/qr+Jf39vaC9tf1K/V79OLzrvOU84DzP/Mm80Pz8/M99cj2GPgH+X/5yPlp+hv72PuN/C39IP5C/2UASgHHAfgBDAITArcBBQEAAOv+/f1K/Rn9Jf1I/Sf9efy5+y374fpm+pz5c/hm99z2uvYu98P3ZPj7+Jf5ffp5+xL8E/yW++76ffpX+k76UvpH+kX6ePrA+in7xfud/NL9If8gALEAWgG8AksFIAhCCksL2QsLDVIPAxIPFCwVshUuFjMXUhjhGLAYxBekFv0VVhVWFJgSEhBbDecK5AjUBlIEQQE//vr7M/qb+M724PQ980fyKfJi8rLy1PKl8n/ybfKV8ujyPfPF85v0r/Xk9hH4/fi2+Uj6qvpg+zb85vx+/dn9bv4Y/5X/+f8KANn/qf9d/+f+qP43/tL9qP3H/Tv+i/5d/sP9Kf2m/AP8RvuT+hD62vlv+dj4aPgw+Dz4R/gs+On3vffy9574evkY+nL62/ot+4H7n/t9+0P7RfvC+8L8cP5XAFwCRASTBc0G5gdTCekKXQxODbINkA7+DwUSBBREFeIVYhZCF2YYIBnSGIgXphXpE6MSixHzD78NdwuECeMHWQZdBAUC5f8F/nL8Cfss+c72o/Qe80zy6vFr8e7wsPDf8NLxFfMp9Gz0HvQK9Fj0UfVN9if30fey+Mn54/rh+7z8X/3x/Yf+5f5a/3X/Uf8y/zT/fv/c/x4APQBYAGMAfgCLAIYAawABAE//gP7o/U/9d/yf++b6ivpm+hj6mvkb+aT4evh++Jv48vgq+V75kfm9+fP56Pmy+bL5B/pT+vz6PPsz+yj7L/uo+7P8Xf7//5QB/QLEBPoGPQn7CvQLjgx/DSYP/hDFEg4UdxUYF9EY7xknGjMZdBfpFY0UQROGER0PmwytCoYJpwhrB3wFAwN+AHr+1vyN++H5gfeU9Tn0t/Oj80bzAPMH82nz2/M59EH0SvSH9Db1KvbV9v/2uvaZ9vL22vfR+FX5ivnC+TH62Pqj+2P89PxI/XL9qv0M/lP+of7t/nj/VwA0AawBrQErAXUAuv+4/q39lvzN+3L7gfsQ/LH8zvy8/Ej8jfv4+vD5yvjI9zL3Qvfp96n4mvlp+uP6Avvf+lL6oPkE+af41fhk+e76Z/1BAMECuAR0BnsIBAtLDeUOxA9tENQRIBStFnoYCxkiGVMZFRrXGoQaxhhdFt4TBRJ7EGQOEQw/Cc8GSQXrA2YCdwA7/kP8Dvvq+Zz45/b69Jbz7PIy88Xz//PQ843zqPNl9Fr1I/aA9tr2sfeX+I75Bfr3+aL5UPlv+eP5j/oL+2f7zfs9/Mv8Dv3r/J/8ffyY/Nv8Jf0u/Ur90v3F/oP/tv81/xf+IP17/Dv8GfzY+3v7d/vm+1j8hvw//G77jfrs+UD5sPhN+Cz4fPgb+db5K/pV+kj6V/p4+l76Avqa+Wn5lfn8+bf6F/ws/qUA8AJNBGgFqwaCCBwLAA2DDmcPhBBWEiMUhxXlFRgWdRaRF5oYpxi3FxYWgxRLE2oSAREED78MoQoQCc8H4wZUBSADzgBU/pL8xPq7+Ab3SvVP9MPzqvPF85bzmvN/85jzB/R19PL0XfUX9sX2WffY91T4Nfk/+nf7hvxB/eb9SP6X/gP/DP8N//j+i/53/kn+d/7F/uT+RP+X/+D/0f+H/zn/mf4Y/tL9gP1L/Sv9Lf1Y/V79Bf1n/J/7ufom+pz5PvnM+ED4v/dY98r2avZU9jT2O/Yw9lH2WvYq9ov22fYB92334Pc3+Sv7ef2V/24BjgO4Bc0IOQvjDC4Oyg4fELwRORM+FEYUnRQqFToWfBffF44XhRaJFbYUjRMDEmUQ7g0GDLEKVQmTCCEHqwVtBA0DtwEQAFz+jfza+nT5ovdC9sv0nPM/8wDz7fIe8/byj/Jy8m3ye/LY8p3z+vPb9Kr1pPYE+OX4R/qG+3/8av3M/kn/oP+aAKgA0gC6AQMB6AAbAcsAnAAgAGj/Of5j/rb+tv7X/qb9Z/rG+Dr42vc5+M/48frO/BH/BwA7/v38OvmY9G7ylfCF8FLwre838HjxNvPh8wf0y/AB6ivp7O8+99IAeAa9B2oN1BIGFrkYABqPGvQc3CArJVwqmCz1KuEq5iqVKQon4x+9GO0Tuw9DDmoNPwp/BL//C/u+9R3xSOss5UXhYODk4Ojg+t+Y39vgluO55qXojelD65Xu0vNO+Vj9dQBPA30G7wk+DUMPihB7El8UQBZ/FuUUYBI4EWMRnBCtDl0LEwnHB+EFJwSHAWr+LfuT9yL1tfJi79LsbOt5643riepi6M3m4Obh5/jpdeu87NzuYvHZ82z1ZfZb9wD59vvT/j8AJwDn/98ADAJEA3gDJwFE/u396f3K/V/9kP00/er6Ovh19n8E9BTgHTkfOBGfCNwMXBR6HuUmninHKpUqGCdiIpUfChwUGIgYDxZXD5MIaAPwA3YHKgNV+obx8eiS5bTl6eUa50XnbOSJ4l7hst9r3xnhAeXr6oru3O7L8Bz1QfpKAFkEYAbHB0EJnQxyEesVEBggGTwZKBnUF78UHhO3EmcT/xN7EVAMsAUtAWb/Bv/r/aL59PXt85zyZvHQ7ZHpl+b55aLmR+c6563mweeI6mfsUe1m7V3tiPCb9Dv3jvgI+Gj5afzQ/+QAOf/i/Sn9lv2W/lb+Iv1w/Cf88/yv/Hf7IfoC+v/3Jvi49In/mxUjHgQmGheEBxoM5xU0Is4s+y9xL1IvYCz3JqchIx/2GsEcDx3eFBgOnQj5BNYG9ADi9P/snOcq5zHqterl50jkVd983AbejOAc4q/mK+ve7UPx/PGN8zX5if6FAtMF6gevCaYNphIdFj4W4hKID0cPUxLpE7cSOBAwDSUMFQv8CAcGrwF5/qz8Y/s4+Qb22fM289/zIvFu7Anpeuim6nDshe2v6/Pquuwz7hLyPfRq9Ob1pPa29zf5pfjl+Qf99v/SAIL+U/3o+yf8o/y8+076qfhh9xD6qfm09zT21/PC8UPzLfJG9dQRhhpUHnwW1Ps0Aq8V9CO7M342Ty3LLJUqQie8JWwmoSGKH80ggRcoEW0O5ghECesDVvRI7U3r3uyT8HHuNeUW3/TbedkR3jvjm+O/5V7o++b+6CnrH+5N9QH8d/45AcQEIgf7C/MP1g/qDoQPlRBsFTEajxrGFvMQLA2CC08MqwxuCiIH0AJW/6/9Ufsd+ij49vSC82TvRuyq61/szO5O7xjuPewR7FTuV/Do8mz0m/Sm9u74tfvA/ab9Ufzq+uX6p/vo/PT/nACV/1P9Xfke93f2aPag9yj4yfV19aryj/JL8DPx+e9sBA4bgRhXGaEBFvkzD0Yiei/XNfErUiZzJ1Em0CVoJbEkCSGpIbAaTRKiDCAK3AYCBHT7SvJS8S/yW/HG7gTmRNw73ffdE+I15unl9OV25w3nueYF6gnvd/XJ+xr+Mv0MAKsDqAjBDhQRIw/3DwgSZRT5GBoXuBOPEU4PDRCNEAsQaw3iCKcEyv+C/fz8Yfzz/Pz57/WM8XntNe1j7k3vYe/77lvuce7n72bv4O7L8FfyfvV8+NH4o/mq+j/6lfoH+yv7o/yQ/zAAI/+W/cD6g/r4+sv6j/rs+Vj22vaL9YD2/fVk99P15wLuGOgStxIu/+jyIAsoH/4rdjIlJRoccx18HbsgviF5Ig0frR+5Gq4S3gyECgMH5QS0/4X2q/ak9vb0vfFr6s3hx+Iy5GbmF+ru5x/mFOjx6LDp5+zV79nzAPja+dP5d/xBAL0FTQrpDKgKFwv0DOANJxMOEi8RmhAkD3sPcw4yDJQJsgaFAxgDAQJcAAP/gft29zT13fJi8Z7x0vFm8QTw3u4u7e7tAPFi8TPys/Jw8iv1s/YP94z4OvmO+a76rPsk/Qb+7f87/2X+av7B/fv99v6F//L/Kf9p+2v7mfma+2L7t/2q+toMyRkNED4OM/eR+QMToSRyK5Mn3BdyE54Zfhx7IV4gfB5jHJgb6RQaDvEIBQkVCLgFDgD7+N/42Pif9MHvH+iW4vvlRenJ7HHteOhI5JLkoOUM6YXuU/Pi9iz5S/e19Qv4h/3BBHkKVA0HCu0J9AmpDFsQ8Q+eDl8NJg/8EDISpQ+SCoUGAQP5AkIEZAN+AiD/x/rz+AP2hvRI9Frz1PJ28XTvi+3T7Bju2e1U7uXvTvB08frx/PFe8t3zPfWz9sb4u/mK+mH8U/wZ/Af9Ef3e/tsAWgLbAY8BlP4B/+D+zAGYATkB6//iCiUdqhOsEeAAhvnYERsilyeAJ6UYIhBzFb8VcBr6G30ayBj5FzIRhAp0BXsGGweCB7oD9/rw+X743PUZ8/fuoeoV7tPvS/Br8M3qJucv6ILp9usv8O/yY/VY91/2U/Tn9RD6Hv8NA4QHwAZvBzQJ3QeqCmUKewo3DYQPoxEUESEOAQpbBxsGGAa9B/oHdAYEA4L+hPtL+MH2gPbQ9e30hPKc7z3tBu277p7ule6g7knt0+3a7m/v1PBO8UXy+PMa9vn37Pdz+J74sPpl/Hz98P6vACUB0AK8AkYC9gJxBPsDIwNdA3sIxR2KF4IUIgrq+CUNfh5CJV0mTBsWDzsRQBRXGVoaQRtiGIcVdBG5B5ACWgPYBdQHOgiU/an4kvYR9VfzHfLT7uTtq/Ax8Grvyew06rvp5uyO7yHwePFs89vzX/Yg9lL3Q/sh/7wCmwR0BW4DzwVeB7UKGA4sDpAMmQvGDLINrg+kDYsKtQhZBiwFsAXlA/MAvf90/G/68/hl9vr0SvTC8hTx6e8R7kvv3fDa70Tv9O0g7bLumvCc8hP0QvS39FX0pva/+Cn7E/0D/Sv+vP4G/+X/KQF5ApsE1wJ6Aw8DDQMcAgsF/QIOEHIboA73DyD/6//ZE3AceB/6G/oPNwtAD7URPhbnFhAYnxLCECEMeAX/A+MHEwrVC84Ihf/C+5r5zPgY+sv5qPa690v1SvGg7zDtYe1T8Vfz/PJS8RrvPu8Z8aXz9vV6+Nr6Yvtp+4v87/we/8oDrAbSCGEJGQhIB7cHqggyCiUNLA1SDP8JwgXwAzUD4ANjA7wCOwHp/eH7TvmW9s71//Ry9Db0JvMO8rfw0++47zrwTvGu8R3ycvNq9A71r/Uq9t73QfkZ+yD8P/0g/nz/BwCjAKgAbgFaAqQD1wSBBBoE/wJ6BPkEaREiEIcKDAny/8IJiRUFGcYXmBJ3DPIOXhHKE3oUXRTvE2cSMBLYDYcKOgkEC0gL4Ap2BwgCpv/U/aH8SPzv+437U/rI9ubyPPBf8O/yVvVq9vrzrvEN8FnwnvKb9Cf3kfi7+Bj59fe/93/5TPu6/mcAzgETArEB/QFlAvgCcQOVBNMEuAWrBToFBAS/AmcB9//T/v3+hf8I/4T9x/rq+B/3cfaf9ij3Rfce9v/zTvLh8pbzSPSO9f31O/aU9iP34Pcz+Gr44fj3+e/8JAByAZgAHv6o/jgAeQL3BNgFMQVWBZMFbAQjDWkOHgykC+UDjwiWEQEUPBWcETwNAg6gDeUO/A/1EfIT7RJ4ELoKmgdIBysJXwtlCkgIPAR+Abj/qP4R/of94P21/c377PiX9lj0h/Wv9jf3fPeX9nP2HvUt9Gvz+vNa9uX3yPhA+Gj33vdo+Xv6afxy/a/+Lv/e//YAhgAYAiUCfgL9AisCAQJUAloCbAG6AGUAyv/r/yz/IP7X/XL96vuI+wP72vp9+rn5Jvoo+mT6OvmD+L34W/lx+bv6F/sp/KX8+PtB/D/7ifzO/UMAEQP5AnMCsQEXAY8ByAIGBYUGDgfbB4cFEgjzCeUEPAbeBOUG4Q2qCgUITgatBHYHFgd0BskH7gY/BxkGIwSmA9IChwRDBaAFrQS1AugAaQFqAoIC/QIyAQcBBwEdAAb/xf7X/nr/Pv8X/gH9RPxj/A78bvxu+8T6g/om+RD6rvro+QL6G/rG+Tz6SPqP+s37Ufwm/AH8/fz9/Fr96/4a/+7/DAAh//D+5/+cAHAA4gDT/7T/2f9P/1b/Vv/4/sr+ef1O/JH8OP2x/XP9Nvwt+4T7Rvy8/Az8/Pue++/7TPyI/E/9MP4r/uv+h/8wAJ8BvgFjAZQB/wKJAzwE5wQSBd8FJga2BQAFXwUoBSYF7gVzBcoFTAbqBbQEHgSmAscCSQSbBNMEZAOqATkBKQEtAZgBlgGAAb4A0P9o/w//bP+6/y7/lP5L/rf9w/3V/aT9v/5P/qP9DP2S/Iv9DP7H/Wz9cP2W/Sf+uv7D/mj+pv6D/7j/pv+F/zn/OAAcAboAOADO/wcA9ABqAXsAKQAkAC8A/AA6AHf/CP9H/zAALQCd/8j+Uf7w/gP/pP7F/hj+5v02/gP+rf2L/ev8Bf2U/Vj9Hv0D/e/8tf1L/hX+5P0e/sf+OACfAIYAnABpAMAAMAFGAhUDkgNvA9wC7QIwA2wDTwP0AgoDvwOgBKcE9gNHA40CSwJJAmEC1wLFAgoCmQFnAAAAKwDu/x0ABQCV/y7/I//X/tf+LP4V/nz+VP5h/lj+0v3e/XH+Dv5c/kL+m/7p/7b/Ff9L/2b///+jAFgAgABVAbMBiwFVASkBOQG3AekBjwETAbUAawBcAFEA6/+L/wT/av5J/jD+QP5c/hH+Hv4c/tD98v0P/t79XP6O/vL90v3t/Tn+0/4E/9X+uv58/gb+9v2Z/vT+A//t/p3+of6z/lr+P/6F/jL/CgChACQApv+f/xcAyQD6AFUBzAG4AoADjAPnAnUCFQI3AhMDcwOUA5IDOQMzAwQDTQLBAdIBCAIGAh4CrQHiAH4AHgCm/9D/4/+9/2j/MP+Z/pb+d/5T/qH+6f4c/xr/5P6J/hH/qf8JAB4A9P8ZAMwA6gB+AAoA6f/SAIYBmwETARMA7f8bAEYAyQDJADgAsf8e/8P+Fv9u/9X/uP/v/hf+CP5a/u3+RP/B/pv+cP5n/q/+1f7b/k3/Nf/F/tX+m/6Q/o3+1f41/2H/Ff+t/lb+Iv4//jD+RP6k/u/+qv6E/mH+J/5L/m7+rf49/6j/lf+p/5f/1f++ACsBTwF5AY8BqAHkAQECCgJqAu8CTQN3A8wDrwNuA0IDKgNoA60DdQNEAwMDaAI/Av8BtwFwAfgArgCRAA4AS//t/rr+9v7l/l/+9P3J/Q/+hf7Q/rr+bP5h/oD+ef6q/u/+g/9EAFMADACp/3j/zP91AN8A5gC6AIsARgDR/+f/EgApAEoANAAVAL3/df8P/+/+GP9C/4D/Xf8e/+L+vP64/mP+Gv5n/pD+hf6b/k/+Tf5n/kv+Lv4w/jf+kv62/on+3v7v/qj+jf6f/qT+9v41/2b/6f8VADQAOABhAKUA0gAQAVwBxwH0ARMCFQIgAlQCPwJoAk0CNgI5AkYCnwK4AmEC2QF1AVMBXgE0AQcB9gD2ALwAKQDD/6v/9P9BAO7/X/8a/9n++/5h/5f/jP+M/1r/Pf+4/xIAZQDfABsBCAFyAND/zv+hAIsB1QFwAcMARABDAC8ADABVAEwAUwBEABUA2v+o/37/W/9j/6b/JgAmAOf/d/9P/2P/Y/9h/3r/v//g/7b/Uv/K/qT+sf64/tf+6f43/zL/x/5G/uT95P0r/qb+Af8I/8z+d/4T/v/9Nv6E/vb+Ff8a/yX/4P7B/u3+Yf8BAJYApQBeAIgAiwCzAE8BbgGAAYsBlgGtAdQB+AHmAbEBeQFzAaQBwQGoAZ0BmwG1AW4BrAADAND/IACGAJoARgBBADAAMABDACYA+//l/wAAZwDZANcAjQA6AEEAkQCvAIQAEgDO/6j/q//t/+L/4P+9/0L/5f6X/pL+Bv9z/9D/9v+4/xz/nf6d/vj+mf/c/9P/qP+0/7//kv9s/0n/fv/7/wcAqP9L/wH/GP8u/yH/E//T/vv+Mv/k/lT+Pf5w/t7+T/83//n+A/8Y/wH/Sf9L/5v/IAA4AEYAFwDt/xAAcgDAAOoA4gAiAUoBjwGNAUoBOwFlAZgBugGmAXkByQH7AegBsQFWARsBIgFGAUQBLQEHAcIAgABRADYADgD0/7b/jP9+/2P/Mv/2/jL/E//5/lv/1f/u/4X/qv4w/pb+Pf8yAMsAiADZ/xX/m/7O/pf/RgCmAHkA2f9J/0L/yP8dAC8ANABEAGAAQQAQAOv/NgBpAGMAowCzAJYAbAA4AEQAlgB7AAMAev8T/47//f/V/3H/6/6F/qL+0/7X/vD+1/7c/pn+P/7m/d79Yf6S/r/+zP6A/kv+q/75/lj/yP+9/5//0//7/yQAiADHAP8ATwE7AUEBPQE4AYsBwQHgAdABtwGLAZ0BzgHbAb4BggFjAc4AjwCoAOoAJwHvAJ0AcgBjAPD/i/+b/+f/LQAXAL3/n//g/ykABwDI/9z/6f8vAG4ARgAvACQAGwByAFoACgBPAK8AHgETAScA0P84AM4AWAHoAAkAn/+8/08A1AClABkA1f+M/2H/b/9v/4n/h/9b/2j/g/80/8j+4v75/gz/Mv8R/zT/pv/Q/5L/Ff/p/sP+1/40/8z/MAD0/+3/pv8E/9z+v/4G/6T/w/+4/4L/Hv/v/gb/DP9C/5X/mf+g/4X/Of8T/+v+5/6A/y0AiACEACQAhf91/wEABQHVAZQBFwHSAP0AVgGAAYcBcAF8AW4BLQEBAbEAYQDrABEBswBjADIAPQBaAEwA+/8FAE8AkgCzAG4AGwDu/+n/JgBlAGkAUQD//8b/9//9/5D/Sf9u/5v/4v/g/2z/Kf8Y/xH/Pv+U/6T/sf+f/3z/X/9z/3f/c/94/2H/cf/7/2wAXgAkANX/5f8tAGMAQQDu//b/OgBrAIQAUQAeANn/rf/O/+X/7f/p/+3/8P8XAAcAov8W/zX/VP9s/6T/pv+i/6b/Rv+t/rb+QP/t/4QAiADa/yP/QP8eANsAPwFEAU8BNAH8AL4ApQDdACUBfAF+ASIBAQHdAIsAhABjADQAkQD8ABwB/AC0/+D+pv72/pn/xv+o/5//3v+x/z7/yv79/tf/TAAiANn/Vv9v/73/xv8QAFUAVQAnACIA5/+2/6v/JgC3ANkAUQBb/xj/mf+UACkB7QBgAIf/9v79/pn/wwCPASAB6//X/hP+uP44AMwAFwFlAbz/lv0V/fb9HgDkAagB7v+Z/uj9XP5DAFEBSgHJAMP+P/1A/uv/pAHgAXf/1f1h/kYADAF+//L9xf3D/zkBdQCg/+f+Bv+M/7j/MgABAAcA+/+k/34AggEBAdz/if9MAD8BBwG6AIsA5ACEAWcBQwBh/1wAUQE5AtcBr/8W/3H/vP/SAJgBBwDQ/ir/8P/6AGr/9v6IAAUAiwByAAH/BwAMACAASgF8/5QAlgGcABEB0/5+/vwA+AJWArz/kv9IAckA6/8W/6v+6wG6AeT+Lv11/TQAjwFzAaj+e/2Y/SP+DAGPAVMA1/7r/XX+LP/i/9AAwwBP/0b/Zv+m/tH/1AA0AWEA5/70/vD+TgDSAToAcP6r/1MBYwEJADf+rf8MAmYCDf8B/u3/Of9uAGkAFwGxAlr/4Pzk/cb/dwLHArz/lv2Q/TQADgIVAU4AGP83/7z/LwAbAdYAkv8K/6n/awDgAIgABQEiALH+3v5rALgCugHB/0v/vP82ATABaP98/94BYQEVALb/mf9eAM7/qf89AAgB6ADF/+L+5v2d/7wASgDt/qz9vP+6APn/Vv/v/Qj+lACSARkAq//7/nz+QP+v/24AOAFwAAEAZv/l/5gBgv+Q/kgBuAFu/3z/lf8JALMC2f+YAPf/x/03/+oARgHzANACNv3D/kP8+/6+CtwCt/xm9tb84giXBmAAu/kE+rkANAGfADEElf+J/WX89v1oAnACoP9J/rf98f04AP0ARAHe/5v+HgAs/zYA/QDt/xMAnf95AVoAzv5GAJgA8P/tAIQBhAA0APv+6/8eAlMC1AFP/7z/sf+UAMUBhAGYACX+DP9XAFEC1wG8/pD+b/9x/gH/iwEnAfb+xf4w/wMB5P7o/XAAb/8lAQwAn/6fAM7+SvyLAQoC6QMHAEL41gCkAlYBqf+g/1oBjv40AML8QwC6AsD9NP53/rcAav9C/cr9g//AAAEARv5a/5v/1f6O/2UA4gD2APv/+/5pACIAdQBlAWkAvP+jAZEAVv/JANIAjwE6ALz+qABCAVwAov7t/h4BNAFR/zgAMv9x//oAE/+Q/08AUQDe/1v/GP9jAQMCd/9c/kv+///eAbwCQQCs/aP9zv9aAokCKQBI/n4BGALM/nP+7f9RAoUCDgBd/xz/NgC4AeIAvP+M/7oAOAHc/6b/sf81/xkBYwHl/27+S/4eAGwCnAAt/B7/9P80ANcDJAGd/zT+jfxH/40ClgKF/7/+pv0g/df/cwLDAqT/LfxY/eL/IQOWAZz8nf/D/1/+owBL/6oBoQLS/qv+LP6f/1ECnwBcAGcAn/8I/nX+dQFNAu0Cyf3i+8sAIwMiATb+1/+vAdQAXf8h/wwA/f+fAAUBI//p/8MAnf87/mn9iQArAhAA0P7i/rT/6f9z/37/HAGy/4D/pv5G/7YCxv8j/k3/LgFTAncAw/1I/hwBuAGhAPv/TwBcARIAP/1z/mECygMKAoj8Ff05AdQApQDvAIQB3wCL/NT9nQDZAM4CMABx/8z9qv30AHIBlADe/fH8EgARArkApvwl/pQBCgAVAFoA3P7n/isATgAnAfYAGwCq/gMAnwDDAWEAvv4iAFwBRgLr/sf9/wApAgoBYQCz/fH9tQG1AoP/0v7I/53/6wDg/uj9VQGtApABfP2z/Jn/4QRhAyb8lP5nAb4BAQGV/5v/9ADZ/5n+dwCQAhAAUf8g/63+0gGjAOL/I/+d/JX/HAMgAFL/7f8g/pv/ZwCNAF//Vv9DAIf/O/9OAKwBm//y/f/+fAFuAbUAx/7r/XIAFwCIAGMBDgEq/yD/Sf+6/8IAbgA9AUD/cP50AMsA3QBlAAAAQv7r/jsBDgFRALj/Zv+A/ab/5AJKAJL9+f4lAaEBN/9a++D+IwVgALH9+vyv/DoGUgSU/bv6Mv3MAjIC///u/xz/JP13/vMASQNKAET9ef0wAxYDx/2b/Tf/HgLc/2r/aQFhACH/6//HAPn/pv9jAREB2f61AOf/EgDSASL9hgFYAnf/9P/9/KT+/QEFAbT/7QA0AQ7+GfxKATYA7wBwAsX+ZP8bAO/9vADwAvwASf9N/cf+KQFrBcIADvx8/yYASALr/4P/OgB1AvEB1vwR/2UALgKWAOD+JgBjAq39rP3j/woBaAL2/xX96/34ACf/6gCC/nf+PwAXAeD/Yf2Q/kgAmAEw/2AAZf7O/TABUwGx/zf+0gI7Abr+Z/61/qsDPQI5/pf+OgAGBNYATvyO/xoCqgIw//v/zgC9/y79ov6vAoUC/f4R//b8EAC/BNv6pP9aAvb+hgF1///+Kf8iAdQA3P8nAGEAof4i/lMAbAKzAQb+CP47/lgAJwIuA7j///wq/4L+EwCmArMCCgB1/v/+Jf7I/xYE6wEFAJb9SP22Ai4Cw//K/4YA3v4c/4n/dQKRAKj/xwBM/IQAIgDgAKQDJf9a/Oj9OgCSA74AHP42AOYAYQAM/RX/qAGGAYgA9P6o/+IA8v/i/XIAnQPWAFj8IP0HAUUFOAAV/df+NP6NAdIBUwE0/pb+FwCZ/hABowCO/t0AkgAj/tMCKQDS/G//yQGNAXz+m/9lAMEBdf8y/mkAoQGvAqz8d/swAU8CugGUAJ/92/5z//H7qP/BAw4CEf+S/Tb9qP8IARP/sf8XABAB5f+s/Q7+0AGZARAA5/83/rL/SAKxAKwACP5P/T8CfAHkAZwA2/xf/isCJgDkAFwCXf9C/sAA6gA5/y0AIgGaAHQAIf/H/mkBhgEDASYAbP+9/9D/CQC+AC0BaAI2AG//3v8JAEIBFQHMAHsAcAATAFgA+f/V/zsAzACdAXf+//15AOD+8P/AAfv+7/2x/5v/yP/e/yr/rf60/0QA4v7O/3QAav7F/mcBwACt/yP/uP4wACMCrAAM/osAcgFs/8wAdf+m/2kBbgC6/uv/VgFrACIA2/6M/8j/XAFGAfD+gv7v/mwB3gGN/X79RgDy/+YA3v91/nj/BwBu/zn/WP+aAGEAn/7D/9IApv/G/4P//AD0AQEACv4K/j8A4AGAAff/OgD3/+7/3P+r/13/kQC+AWEAkP/S/pT+vP+LAHIAvgCZ/z/+If8KAL//nf9L/5L/yQDR/z3/Af9T/t7/lgG+AeT+RP5L/zn/Rv/mAYYBnf9KAAz/DP8e/x0AZQI9ApIAqf98/if+5AB+AqYBEwG0/xz/4/9hABUBtwETALz+if/a/ycAJADmAHAArgCh/jb9PwBpAP//dAAyAOL/WgA5/9X+wgB1AJgAnf+1/p0AUwCy/8j/aQAwAW4AHv+f/6UA7wAXAO3+bAAOAT8Aqf+f/0//DAE0ALr/sQDn/if/bgClAMUASf+W/vb////9/xcAwgBuAFT/LP6i/84AEwHMAIv+5/6p/0n/eQAeAVT+Bv/3/9X+IABnAF//6f+g/3QAHgBx/uT+CgDkASUBGQDw/ov/yQDn/7j/KQEeAQkAov/r/u/+6wA/ApT/owC+ANX9A/+YAJIASwHtAEgApgBOAKn/qf9jALoB/QFhAe8Ah/+oAC4BrwApAboA5AEBAtH/UQArAED/1AEFAiIAkgCJAB7/Qv9+AHsAGQA0/4T+NP+1AET/cf4w/1H+kv1N/uL+4v5h/7r9V/yE/Z/9WP05/pb9pv0T/Cn8cvwb/Yf+Ef1e+xL7Gfwg/pL+bvzE+9T8B/0G/q/9t/3v/mP+Xf9Y/rP9UwCCAK8AJQJwAHcAcgEyATYCCwQjBIwDVgMnBH8EiAXhBfUFJgjaBkAGGwfPB9kICgn2CBUJbggmCcAIgghYCYYI3wfoB0oHNwYNBesEGwVWBPAC0gDG/w//J/7B/fT8y/uy+gT59/dE9732Pfbi9f31rvR589nyXvO588LzhPNO87707/Xn9Rz1h/VP9QL3JPl4+aX6K/tZ+wz8APwH/bH+sf/V/98AJQLSAGkAqP+cAF8DUATrAwAECAQwA00CggEh/ywCjgvVDNUMWgm0/90BmAetClMPFg/IDR8QSg9IC4gJowovDmkRRBLsEIoNkgroB38HlglBCkoIBga0BF0DoQBI/Rf7oPq++5j7W/h39dv0d/OP82XzhPEd8bvxEvIz8q7xhPFr8r7yKfTo9Dn1VPbF9ib4Mfqa+jb67Pl5+0L+0P7F/hz/X//V/zIAxv+r////oP9gAB4BzAA3/9v9aP6b/g/+yv1B/WP9nf21/dv8J/2H/bP9mv3D/Qb++PuE/u0BnAV9CaUH3gLSAv4FyweAC8oNbw+dEfoRhhDwDmsOvQ5QEAUTixSHE1kRQw6eDH4MHgtTCe8IvAhkB+AE1wEl/+/8zfuR+sb59/gs9zT1zvPt8p/ykfHf8AfxYPEz8v7xg/EC8qPy5vKR8+j0wfU79oP3xPgA+kj6iPpO+xn8av11/tf+DP+i/4IADAE0AZ0ASACxAAwBmQGdAWEAR/+X/4X+6f2A/iX+I/4O/jb9Pfxn/ej8bfua/J77APu2/hEC0QOZBRgDQQHnBDUFfwYMCh4K5w2dEdoQBxAkEF4P2A/kEagSzBKAEjER/A9nDw0OtAuqCj8JgQfhBnEENwI9AGX+9PwD+6f4kPY/9XL0e/RO87LxYPEk8aPw6e9E7+nuFPCC8gnz9vMT9BLzNPTT9b/2PPja+f765vzy/U/+c/7y/sr/VQAXAUQBFQHSAdsCQAJ7AUIBZQBYAHQA3v6W/X79Sv0H/Wf9Afxc+0P8gfvu+hn7SPov+gP7pPgW+Vv/mAHpAx8GOQG1AKQEfgSeBocLYQuuDhATHhFOEWMRoA9GEa0TdRSkFBMUNhLWEacRTBBTD60NXAtKCToI8AWbAysCGwAa/j38pfmi9yH2lPR081DyQ/G38Evv9O3Q7TDtre0z72vvPvB08WTxxPEp8kjyXPQI94r4gfov/Iv8qvw//aP9mf6aABECJwIhA5sDkgL5ArwCrwEiAuABNgCAAMwAHv8T///+n/2l/Jj7+Ppr+4375vrU+rD6gfqB+Yz44vvM/0ACMwUsBLgCCwUjBvUGSgmLC+MONhNvFCAUPRTVEj0TOxS6FLsVkhUuFBoT/xIHEjYQCw5lC1oJ/Ad/BT0C5/9A/jj9Nvzh+Qr3j/RB8ojwJe9q7r/ua+/V73rvQu5A7eLs5Owq7lXwDvIr8wH0BfVs9of3+fd0+I36U/2m/hj/qf/CADYCqALXAvICcQOKBCwFGgUIBPACRAEnAND/yP/I/9v+s/2N/H/7IfpB+QT5b/hU+JX3fPZz9+f2i/W4+LP8c/83A1ECaP9WA0AGUAY7CmULDQ32Ev8UQhWVF44XwRbGGIwY6hdMGM0WbRYkF/AWgRVKEmQO7wswCgkHCAQgAfj+Y/6P/Ff5fPbt84/x4+837tfsouzb69Tq/upe66zr0uvo663sZO7w7zXwzfAz8mn09va4+BD6FfvZ/Nn+uP/2/6MAVQH7AXwDgwTMBAAFlwQnBLgDyALQAZ8A2f96/1H+qPxI+3T6Gfq3+eP4avjl94D3X/cg9oX1uPTF8wr3hvvS/sMC/wKkATkF7gapBlUJGQqoC+IRrRT9FJcYuxm5GYkb8xo9GcAYOBcLFjwXxBerFicUXBFSD1ANzgo6B9wCOf8r/ej6QPgu9pr0OvNF8uzwpO7m7GrrDOqL6bPpSun36ZTrcOzI7tTwV/FQ8vbzdfUa90L4dPhI+gr9Xf9hAXcC+QLFA2EEugQLBTwFMwXEBFYETwMlAlwB6wD4AJQAGv/z/DH7a/lr+Gb4I/ik98H2ePZs9pn1DvUr9c7zpPUb+gf8E/+SAEoAWQSLCOoIugrRC+ILUBCFE1QTyhVQGEoZvhz9HQgcCBsoGWIWwxVzFcMTTxLtEKEPZA6hC/kGRAKF/3n9P/sI+CL0mPGG8Lbvsu697abs9uvD60rr5uqA6kzqDOuY7JXuJPCh8cfzFfbh92j5VfqK+sD7Y/2b/qEAqwLRAwgF1gWBBQAFkgTIA9wDxgR/BC4DqgHn/8r+Z/4H/bf7Y/t9+vn4YffJ9cn07fPf86/0RvRE9C/0xPEA8r32a/ms/Y8BbgCkAz8IoQeLCXgMqAtnDzYTDBMaFiYZEhmLG70dhBx3HBwb4xcYF68WCBWoEkEQBA5UDNcKKwh8BAgBpv6T+3T4VPXq8aTvne4q7sXtZu3D7O3rrutj7MXsJexh7G7t2u7I8GTys/P59VT4Uvpa/Gz9n/7t/wMBRAKQAzcETAUoBi8GVQZKBg8GHQYJBrgELgNzAZX/Ef74/Ej7ufkC+Rr4CvcV9vL0T/Sj87fyOvIf8crwqfDg7xvx1PQs+HD8zv8ZAA0DjwbNBwgK0As1DHYPxRKbE88V0xeeGf0cZB/cHnEeXx2AG8kaghmQF3MVBRMrEA0O0ws/CZcGqAPdAEv+ffqt9QXyRfDu7rTtjuwK6wXqEuod6kXqseoT64frfOy/7aLuRfAz8l/06fYz+Yr6tfuL/Zn/6wHRAw8FzQVpBgUH5gbABjMGQgVUBW8FwwRLAycCXgDZ/g/+/Pu/+T74tvak9b/1tfTC80PzkfIJ8qPx/vDI757uVu/m8oP2B/vA/cz+wwIXB1oJZQuZDAANkRBuEw4U0xXEFi0YWBtIHY4dCx6LHWUcfBz/GqwYxRVVEmkPCw0cCo4GdwPZACr/NP19+qT2KfO78JfuzuzA6zjqR+nC6d3pHerd6kjrgOy97n/w0fEm8/Tz8vT/9uz4x/qE/JD+wgDTAhQETgSmBG0FeAbUBm8GoAUoBb0E7QN+AhABLQA0/zn+Bf2I++j5ivhU90n2RvUV9Gfzo/JT8vby6vIM85jyJPJy8wj25fhX/Pj+7QBmBfgIyAuuDr0P7BDBE5QVCRaVF30YDhp5HCEdJx3bHAUcURvQGtgYRxbAE38QmQ3kCpgHBAQ5ARz+KPs1+YP2Y/MQ8XjvFu4u7aHr2+mf6QHqx+n46Ujqzuqx7Jnuk+/C8IHyGfQw9k74tPmN+5/9w/6JADsCNQPtA4wE7ARMBSoGVAYbBt4FZgWBBJ0D/QGJANf/1f6z/aH8Kftm+X/41/dv9xr3X/a89CT03/N58zL0xfN98w/1pPd0+TL87f26/4kEDAn7C54OOhDvEFYT6RQfFYUV9xXRFpUYThpPGi0arBnUGGQYmhfcFV8TIBESDjsLMQhkBLUA4v2T+/D5HfhT9QzzOvE68PXuve1W7OnrBexu7K/shewR7SPudO988LfxqvLm8471NfdD+aP68ftN/Z/+GwBTAScCTQLbArgC4ALQAnkCPQJqAoIClAEVAED+IP2N/Hn8tfsF+yv6Uvm4+Mr3bPdh90/3evep95738PaM9o72PPg6+3n+6gBuAvQEtwfMCgINBw7YDhAR2BH/EY8SZxG8EfoSMBPKE9wUdxSxFCwVahQBFAoTjBDcDVELXAgGBngDRgDi/d/78fns+JX3w/W39JrzIvJr8a7wq+/s79Xv3u/n8FXxePGE8vXyjfMF9YT18vV198L4Q/pB/P/8vP3X/pX/wf8DACYAawBEAQUC2wGbAT0BowCCAHAA/f+p/3r/zP7O/nP+6/3B/Xv9Qv0k/db88fte+8T6Vfpc+iH6/vkf+gL7dfzO/rwAzAI6BUgHlAmWC2gM7gz+DakOeA+1D6sOUg4FDyYPUA9xD9YO/g5XD88OLg5WDSEMmQonCdEGfARjAvb/nf2G+6P5HPgU9/T1MPWj9FP0B/Tk81zze/Oo84LzxfMK9HL0P/WZ9eT1pPYz9+z3z/h9+WL6p/uR/Ev9DP55/tn+Xf+o/2AAqgBKAND/r/9v/wj/6f6F/mj+zP7k/rH+Rv+m/87/UwDu/zT/Lv8a/8j+6f6i/gj+F/7o/eT9bP4R/8P/SwFfAjcDRQTRBLgF6gbQB5oIFQknCXUJ8wkcClMKZQpeCpsKiwpKCicKpQkOCRMJsAgpCEcHAga0BMwD0AIKAaD/Mv5G/Zj8j/tt+rT5L/nT+Ir49fd+90f3VveH98T3pPev99j3Jvjw+EH5iPk++sT6UPsw/JL8mPwH/Sv9MP3m/SP+nf4w/z3/QP9f/1b/Kv9S/1r/q//e/2//Af/B/rb++f4p/yP/4P61/mX+rf2q/WP9Z/36/fr9P/6//rr+Af/V/5QAYwE3AhUC7wF3AgED7gOpBPkEYAUABoUGtwYHBzoHhgfoB/UH4wfGB5wHfwdQB98GTAY4Bt8FWwWvBKIDGAMGA4ICKwKQAeQAkgAKAI7/wf7e/Wr9Ff2E/Db8u/s9+zP7/voV+yD7y/rd+vz6JvuR+4v7YPs/+yT77/o8+3L7nPsw/Hn8rPwH/S793fzx/Pj8Ef1l/Vb9mP0n/o7+Mv/X////TwC5AAMBWgH/AUAC9gK2A94DGAQ3BBgEQgRFBP0DEQTDA0kDtgIrArwBeQEkAY0A9v9m/+D+Gv7Z/Zr9cP1w/T/9CP0l/Qj91/wO/f38Ff2A/c79NP7S/n7/EACvADABcwHbAQgCHgInAiACHgIaAjQCSwJfAosC8gIcAwQDxQKOAhUCqAEeAbkAdwDj/1L/9v6H/i7+Rv4p/vj9Lv42/iP+Of7r/Y/9hP18/ZT9r/3D/S7+l/72/qD/LQDkALEBPQLVAmEDiwOpA7oDjAOUA4UDPQMVA7oCbgIyAtABdwFVAfYAtwBjAOv/kv8q/6b+S/4a/sP9lv1W/Tb9/fzQ/Mn8bvzF/H791P09/oX+cf7B/g3/Ff9a/4X/kP/Q/8r/lP+y/wAALQCsANcA3QAXAUYBYwF+Aa0BtwHpAfsBtQFwATkBIAEKAQUBsQBlAEQAGQAgABMA5//0/+P/uP/a//L/BQAkAAkA0/+t/1v/9P4E/wj/Pf9j/0//jv/R/xsAlADdACsBhgG4AeQB/wHoAfEBDgIaAiMCDALXAWwBLgHSAIQAWgD3/8z/sf+L/1T/DP/K/qv+mf5N/ub9s/15/Wr9Xv0w/Sv9Lv0u/R79Hv0//WH9mP2Y/az9x/0G/lb+jf70/jn/pP/y/wMAMgAwAHkAyQDfADsBkgGsAboB1QH4AS4CVgJ5AokCWAJEAiIC1wGkAXcBXAEtAQEB1ACdAIQANgA9AC0ADAD9/7//lP91/0n/Ef8D//L+zv7n/jT/Pv9Y/2//ov/7/y8AfQDMAAMBPQF8AZYBvAHeAcoBsQGmAWwBRAEeAf8ACgFIAUIBFwEXAd8AqgCNAB4Ayv+Q/1T/DP/I/of+Of4X/vH90v2f/Xz9WP09/T/9Qv1e/XL9df2A/br9yv30/Tb+bP6r/vD+If9Y/53/3P8tAKYAGQFaAZkBvAG8AckBugGQAZsBnQGGAYIBWAEbAQEB+ADtAN8A5AD6APQAJAHzALoAhABpAEYABQDI/6D/pv+2/wMADgA0AF4AiQChAK8ApgC8APwA/QArATIBXwGjAaoBqgGbAVgBMgEQAcsAkgBRABcA/f/i/+D/sv+X/4n/ev9Y//T+ov5Y/iz+/f3v/dX9r/2G/Yn9j/2H/dn97f0X/jv+Uf5L/j/+P/5A/mr+kv68/rb+0v7//i7/W/+J/7b/8P87AFgAqADrABUBOQFRAYQBqgHKAeIB8gEaAhEC+wEGAvYB2wHkAaYBYQEkAdIAjwBGAOP/nf+b/1L/If8I/+X+5/7X/sz+zv7D/sf+//4e/1H/i//D/wEARgCCAK8A7QAZATABTQFpAWwBYwEwAREB/wDSAMMAlABlAFoATwAbAMX/mf+d/5T/c/85/+X+n/5R/h7+Cv76/e/91f3x/Rz+Jf5s/oX+sf7n/gH/N/81/13/kP+6/9n/FwA2AFwAqgDdABMBWAF7AYcBggFcAYQBmQGUAcMBzAGvAbUBdwFKATIBBwEBAe0A4AChAHQASAAgABkACgDr/73/vP+Z/47/l/96/6v/xf/T/+L/3v/3/+n/EgA4AFcAZQBhAGAAOwBPAEEALwAZAAUA7v/a/9H/0f/K/7//w/+o/6//tv98/1b/Qv8I/+T+zP7F/r/+uP6t/nP+av5z/on+vv7T/vn+Qv9f/2z/fP9x/2b/l/+U/3z/oP+x/8P/0//2/zAAYwCGAJgAwgDqADYBSwFIAVMBPwFYATsBNAFIAVoBewFjAVMBJwEKAQcBCAH6ANQAuQClAHcAZQBDAB0AFwAHAOf/q/9+/3r/pP/i/wAAEgBhAHkAhgDDAMAArwCoAKMArwCjAKwAlACGAK8AhAB+AF4AHgD7/9z/w/+f/2P/Sf8y//j+6f7K/sH+kv5x/kT+A/7r/bz9mP1y/XD9d/2j/dv9Ef5W/qr+0v4B//v+DP9q/3j/pv+x/7T/vP+9/6n/sv/u////QQBwAKUAywD9ACsBQQFuAVoBcAGEAXIBfAF+AYsBdwF8AXwBYwFGARUB6wC3AHQAYQA0AAEACQAHABIA+//V/9r/5f/2//f/3v/Q/6n/kP+x/8b/xv/p/wUA///r////9////ycAIgBOAD0AOwA4AB0AGwAQAPn/+f8DANP/q/+f/4n/d/9h/0//bP9v/0//N/8n/wj/Pv9P/0L/Xf9k/4X/l/+8/9D/9P8AAPv/AwAVABUA9v8KAAAA8v8KAOL/9v8QACYANABDAFgAYACCAKEA1gDqAAUBJwFKAUoBSAFWAV8BagFVAUYBFQHzANcAqgCoAKgAhgCJAH4ARgAZAO7/zv+o/6L/jP9v/2j/Tf83/yz/RP93/6j/w//g//L/CQD0//v//f/e//D/5f/Z/9H/tv+o/6v/sv/G/73/xf/V/9n/+////+3/6f/i/9D/sf+C/1v/Nf8R/wr/Kf8l/zL/Of8u/0v/Xf91/2//aP9Y/0f/Qv9E/0//Y/9h/03/g/+Q/5n/3P/r////QQBnAJoAqADHAK8AvgDkANcA4ADSANQA6gDoAMAAoQBjAEoARAAwAB4ADgDw//v/BwDc/+f/0P/O/+f/0P/B/7b/tP+2/7j/zv/V/+3/AQAeAFMAdQB+AKUAnwCUAKYAgAB+AIgAfQCGAKMAtwC5AMMAugDbANsAxwCxAH4ANgABANf/q/+Z/3X/Tf8g/+n+qv66/rb+ov6m/o3+lv6O/qb+yP70/hH/Rv9S/2P/cf9o/3H/d/+U/6T/uP/X/wcAJwBrAIsAmAC3AMAArwDAAO8A7QAQAQ4BDgE9AU8BbAE5AQ4BMgEiAQUB6gDiANAA0gCxAIIAUQABANf/0f+t/47/jv96/1b/Uf8u/yr/N/8n/zL/Kf9H/1T/Y/+b/73/4/8BABUAMABMAGcAZQBlAFcAcABhAEEAYwAvACIAFwATAAAABQD2/7T/sf+A/03/Lv8Y/+3+3P7S/rH+qv6X/rz+vv7b/gT/HP9G/27/vP/7/ykAWACCAJoAnwC6ANQA4AAKAREBCgHgAMwAxQCaAIAAWABDAEoAaQBEACkAPQBEAGAAlgCoAK8AuQB1AFgARgAiAAcA6//I/5v/qP+J/3r/h/+V/6//1//2//3/JwA4ABUAHgAXAAMAAQD9/wcABQAVAC0AUQB9AJEAiQCCAEgANgAkAPf/7v+i/3X/S/8n/wr/Bv8j/yX/Sf9C/yr/Qv9U/2r/eP93/3H/aP96/4n/fP+U/5n/h/+Q/4v/av+b/7//8P9IAEQAUwB0AE4AcgCEAFwAbgBGABMABwAFAAAACQAVABkANgBeAGkAaQBjAE8AYQBsAGUAbABXAD0AUwBBAC0ATgA/AD0AYQBhAEYASgBKAB0AKQApACIAFwDp/xAAHQD7/ycALQBEAGsAYABgAE8ARgA7ADsAKQASABMAGQAdABsAEwAkAB0ADgApADQAIgBPAHAASgCEAHUAjQDAAHAAXAAnANn/vP+6/6D/hf+C/1b/VP9W/zD/D/8T//n+0/7O/u3++/4M/xb/D/8p/zn/O/9Y/4D/kP+//8P/pv+r/9D/3P/5////EAAyACsATwB3AHIAjwDMALwAwADqAOIA5gAHAegA1gDmAMsAqgCmAIQAYQBsAHkATgBPAEoAGQBOAEMADAD///T/vf+2/7z/if+L/5f/bv9z/4f/Y/96/5f/jv+8/8r/pv/y//b/4//2/wMA4v/T/wkAzP+6/8b/tv+k/6v/r/+A/3z/i/+i/4v/sv+p/4n/pv+t/47/lP+i/2T/gP+Q/3H/i/+0/4D/rf/j/7H/+/8FAPf/KwAvAB4AcAB0AHcAgABOADYACQASAOn/2f8/ACkATADMAPwAGALMA7oESwVoBesEYgSZA0sC+gCx/0T+JP0X/Cv7/vrC+hf7xPtB/DT9Iv7y/vb/1AARAZABxwFVARwB3QD7/53/b/+2/pv+9v7T/kT/LQBKAPYAwAHJATkCpAIaAjIC/QETAdcARgBG/w//vP4F/iX+J/7e/Wf+0/7B/lL/xf/M/zoAawA7ADoADgDD/37/Nf/0/vD+yv7p/g3/VP+2/ykApQD/AGMBhwGLAbUBsQEtASQB1wBIADYABQCV/5X/v/+Q/+7/IAAbAHQAvAB1AOIA6gBBAMMAdQAOAHAAMADT/40AYQAZAOoAcgBjANQAHQAFABMAjv9m/z3/4v7t/hb/NgD6AE0BSwI3AgMCegK3AeYAGQHM/77+uv58/br8Lf2U/Ij8SP1T/eL98v5E/ykA/wA2Ab4BvgFwAV4BEwFpAPn/lP8a/+/+5P7B/qr+Fv83/2r/5f8QAD0AdQClAIQAowBRADoA5f/F/4n/bv9z/3P/yv8SAFMAcgAIAT0BjQHQAegBewGZAUYBrwB3AMj/Lv/y/tv+Af5T/j/+Qv6x/hz/0P6d/y0A0P95AJYAhAB7AKUAEgBTAIsApQCmAAEBlgHJAa0DDwVqBdMFVAV1AyICLwAy/aj7nPkz99f2D/aJ9U33avi9+Z38rf5XAD4DqwThBXYH3QfkB8cHfQbRBEsDCgF+//b9efzx+8L7xfuW/Ib99P0g/xkAUwDrABsB7QD9AB4BgABjALT/GP8+/z3/ev9b/8r/UwAMAYIBDgIeAggCYQKSASUBDgFEANH/vP+q/pn+/f6C/pn/ggCLACsC8AKoASUCDwJuAAgBhgBq/rX+yv7D/e3+X/8p/6UA6wBcANkAZwD0/0YAbv+z/sH+LP58/Z/92/yv/Aj99vxN/Sf+hP7Z/tf/9v9XAOsAFwHUADQB/QCdADYB4gD2AOIB5gGhAdkCVgLDAdACEQItAeIBdwFEAKQBOAFVACABRgD7/qb/2f4a/vL+MP5J/g3/N/7X/VP+6Pyz/PT8UfzU/Iv9Ef0T/iD/N/9eAPoAXgEuAu8CZAPTA94DEQSDA+QC+QJTArgB5AGjACkApgDg/9H/awBH/wH/lP9U/mf+vv4j/iL+Y/6E/YL9nf1E/dL9U/1C/az95P0c/gT/WP9W/40AowAkAYQByQG8AUsCJQLVAY0CvgHZAQwCAQK6AWoCWgEMAZ0B3wDSAOoA2QAQAGUAyv+k/in+Af5c/YT9df6q/dL9ev5s/hH/8P9b/2//tP9a/kj+ef7p/dz+8P+v/7MA1wFjAYcCYwPbAuIDzASdA24D6wONAnwCPwL4AK4AowDn/qT+Df9s/kv+ev6C/uD9pv6v/lr9Hv3e/cL8sf0+/yf/jP82ALz/Qv+m/63/0P8mAO0B4gFBAcwB7wC1AF8BQQDy/tD/oP/2/2EByv9H/2EAeP+W/qj/zP3k/WP/tf3i/dX+Zf5f/tn/7f5v/0sBFQEbAZkBuAFGAV8CZgJYAUwAv/6q/rP9SP5E/jv/KQBEAeQBnwJSBGYEtAWQBLgDCgP7AWT/of2l/Gf6z/rP+n36pfr4+8f8Kf1l/rH+4P82AcoC4wOrBp4HdgX1BukEJwN1A4ICbP/X/6/+3fyz/nn+IP8r/j/+Qv1R/ev8xPuE+u/6IPyA/FH+rf8ZATgBagIPAgUCZgJwAp8AMgCp//D+lP7Z/r3/w/+fAMsALQH0ACICqwKrAhUD4AFyAGkAKf+J/en9OP2G+4v8E/w/+7H9Kf40/osAmAEtAUYDXQMBA+kDFgPFAbgB1gDp/tv+LP7K/fj+N/6G/Yf+aP+L/1wBbgLoAYsCwwGJAScB8wBrAFoArf/r/mj/o/1f/Y39DP6k/q//YwDkAJcCYwLDAVwBPQGxAC0BZwGS/kb+if5+/oL98f18/TL+DAHXAMoBGATTBHEEWwTmAQMBBQGS/v/8vPwr/In86/05/pf+VP8bANr/T//LABEBmQHKAgYCKwFDAJD/Xf6L/on/MgBf//L//QEQAdIB2wKiAp0B7QGQAfT/SADg/0wAvf+f/+T95f7X/ykA0f+o/lT/lf+AAG//BwBz/4P/pP83/1z+4v1U/n/8gPyl/Lf7s/1nAHoC2QN6BFMCqADKAssAwf8rADD/fv2L/qL+bP4VADX/wADJARkBFQFoApoAiQDZAbUBXwJMABH/Dv7J/c38Mv7i/YL9Gv/j/7wAKQCvATgBqf87/+38i/vk/WP+DP+uAFEASQI5BP8C5gHFAvoAbgFWAe3+Tf6k/Uj+yf09//EAlAGkAuACTQIKAgMDYwKJA08BaP/j/6T9F/4n/hn9tf6t/sT7F/0i/TP71vyN/KP86f6f/9D/IwIIBMEDHgOtA68DTwPcA9cDhwKUAX4Bpv+Z/jT+lP53/in9WP1a/O/87f3Z/6YAVgEKAR0AJwFyAJIAIgFaAd8AnwAFAaL/5P6F/iP/w/6X/1T+nf2m/1j/Mv/v/tf/n//xAEoB1wCoADoAEwHDAVoBDAHMAaEAhgCJAMz/fgABAE/+j/00/rj+3v2E/u8ANwJzAuIC3AKfA3oCxwBwAEj+vP/0AesAAQAD/+L+Af6X/1/+4v2i/pv9gv6//rf9Of8DAXX/WP5E/Sb8VP6S/2wAJQHtAPQCzgPpA1UBT/8c/lUA5gA7AfQAef5IAOQAawC4/z3+lvys/RH/EwDAAekBCAFhAiICNgIVAbX+d/6H/Rn79vv//An8df/y/zgA5wLrBFQEqwJRA8cCggIhA2IElwMbASkAj/1R+/r8Gv5r/OD85/7S/MP9d/4+/74A/wD4AXUDZgTFAmoDvAAu/of+c/17+w79+/8P/9D+6/4l/qD/NgIDAcH/fAEXAhEEIQTpAawBqALJAUT+jv4i/ob9K/1G/Yv9yf1L/8r/EAAeABAA8P6L/53/3f34/lT/P/6F/pn/TQE7Aq0CXgFjAF4AEgDr/7/+m/4//nr+6f/V/84AdQJwAo0BSACGAGwAGwDkAeMDSALxAcIAvP5z/i39Zf32/FH9cP0n/F78zv0P/sb/5gAeALwB7wHiAZIDugPKA4sDdQL/AIn/Zf59AMUAIP77/8MAav+A/qb+uP6A/6v/0/87AcUAHQAMAE8ANADc/xcAc/5s/p/+9v2W/cf9rf7y/xEDlgKXAnoDMAIKAcwAPQCz/mX+WP1P/fr9Gv4K/0wA1AGzAs4DIwR3AwoCnQHqAKD/r/+N/q/9Uf1P/QH+7/1R/f/+JgDbASICDwJJA0QDpAJnAa4AQP/A/WX8Dv07/sz+MgBuAAoAEwBYAAMAbv8KAMH/zP/HAKD/aP/g/v/9Vv6m/tn/AQG3AVEChQLXAkICTwHX/03+DP6J/SX+Cv/w/kEAyQDp/6j/HgHSAA4Ahf+E/vj+5f7i/oD/JgDy/2wA7wCoALoABQLXAd7/iwBT/vb9WAB1/l/+6f+//7H+6/4SAAMBJwFeAVQCdAAQAL4Auv8u/yP/GwC4AeICFwLLANQAQP98/u/+9vxp/dz+d/69/1gBEwJDAEv/Zv8/AMsATgDc/27/IAAkADAAOgAtAOX/Uf9U/2P/yP+hAFMASgAnAfQA/f8l/9T9af2C/tD+zv89AXQACAFeAZn/vgCtAhgDOwKWAXwBywB0AHAAWACQ/6T/Mv9c/YT9fP6r/vP86P3k/XD86f7t/+YACgN5AugBDwP0ArgCAQFa/rr9MP3F/Rr/OwB7AdABegJUA9sBIAGNAHsAaQFlAP/+yv47/n/8s/wO/eL7vPx3/dT9yP6ZARUD0wL7Ak8COQP7AogAlP0S/Ob7y/y8/R79Ff18/YAAggJTAlsDKQPOAwIENwOUA2oEHAKf/yn/5vtn+vX7x/y6/UQAdADQ/xAABwBz//b+kQCqAKYAggFTArYC7wG6AVgBhQJwAdP+T/66/Ur8Iv2Q/0gARAElAX0A/wD8AOIAE/8D/pv+Ff/F/1//BwBRAagCmwI9AZIBdAAM/in9YPyW/Mn94P+o/wr+9P9yAZYBzAL0Al4BLgJCAnAA8QDLADsA/ACk/pv9Of5P/fv9RP83/uD94v/r/84AhQKiAtcBkAGiAjYBkv8s/rj9I/7p/RD9XPwr/SH/5gAAAIv/4P8SACUBFwI5AV4BIgHxAJsBswCb/1H/lf/B/y0B9gB3AOoAFQCuAI8Bc/8B/0b/D//B//L/lf89AEwA2f/MACIBOAGfAKj/Y//K/rj9JP1q/UT9cf4kAIsAMgHBAYICcQJ8ARkB6f9Y/+3+XP4M//L/OgBRAEEAQQAtAbUBaQHXAPT/X/+D/9P/Kv/Z/jD/tv9E/1//awDgAE0B1wAQAO0ACAIZAQEBSwHF/6j/uP8g/tL9Qf1E/Jj8SP03/3cBbAJdAnoCygGsAAkAi/9f/y0AKwFuAdkC8gITARUBBQHn/13/s/5W/kL/qv7p/bj+4P72/vD+5P62/6L/LP+m/8r/DgClANsAYACg/6gA8QAMAJEAWADu/7EALQAV/+L+gP4y/tX+3P7t/l3/tv+GAA4BLgGPAC0A1ABGALL/ZwAmAPn/YQBTAIAAuQATAJv/ZP83/8b/3P/c/+f/EgBBAA4AAwDbABUCbgJmAiIC+gEXAl8BggDk/ub84vzA/Sf+t/0l/aH9Of6y/0MANgBPAGEAYQA4AKUAjQCNAOgAZQGAAXABQgEDAfQAugBDAP3/gP+r/on+gP6q/r7+r/6r/4gAgADFAMMAZwCF/0j+lv2t/W7+Cv+F/6n/1f+CAI8AJQHBAckBBQJzAdcAQgGzAFwA1wAVAKT/yP+H//v/NABrAMwAlgDJAP8ACgG3ADQAXf/i/iD/gP7O/Zv+WP9v/9r/gv/0/4IAfQCfAKYAZwClAPQApv8u/13/3v4N/7r/6f+aAHIBRAEnARkBFQEOASkByQAOAMj/xf89/8X+d/56/mH+Of4O/vr9c/4e/7r/TwBhAEoAbgA2ACIAxf+m/9D/l/+Z/0v/9v5f/6b/NgAbAQ4BjwBeAIgArwAOAdYAnwD0AM4A1AAuAdsATgCo/0D/Jf8+/xMAAwCJ/2AADAEtATkBQwCr/5D///61/nH+lv7B/ir/BQCjAO8A3gGXAmoCegIPAvEANgAl/xH+pv3r/Gf9VP5l/iD/vP8OAIsAYwEVAiMCxwHkASACkAHiAAoAdf9N/6L+8v3O/Rz+of5S/xAA/QCWAYYBCAIsArgBUwFwAKb/9v6//qv+kv7F/v/+W/+0/5v/jP/G/37/eP/T/9r/AADc/wkAVwA/AD8AcgBgAG4ApgBaAOf/tP+L/3j/BP+k/gj/Ef8e/zX/+P5q/kn++f45/7z/jwAFAXMB8QEOAgEC0AFPAckAfQASAL3/tv+p/xAAdACLADQAsv9+/wT/wf5f/iL+c/56/lT+r/4G/zf/zP9EAEEAhAC6ADQBxQHmAfEBjwFeAS0BowAZAKT/Pf80/yr/Fv8g/x7/pP+9/9z/UQBnAHUAeQBwAEEAEAASAAAA9v/u/xkAfQCdAFUAIgBOAJQAmABKAMX/sv/l/6n/QP/e/l3+WP7F/i7/tP/u/+X/ewDCAKYA0AAbAGr/bv9k/6//LQCIANQAMgFBAfoArgBaANX/Yf8e/0v/w/8kANYAcwGSAaMBXwGuAC8AqP+1/iv++/3e/QX+nf45////zAArATkBVgFsAT8BIAFEATsB4gDOAJQAUwBGAC8AdwBBAMj/EwA9APv/PwD7/47/pP+C/2r/kP+M/47/vP/B/+P/AwDc/3z/c/9R/5T+E/4G/kT+m/6O/uv9t/2d/Wr9Z/2s/fr9jf7b/gz/Zv9s/0n/+f6A/vv9//0n/iD+D/5E/oX+pv7e/vD+E/9h/7//OACLAPMAbAGqAeIBSALTAmQD2gPMA+4DVASOBJUE1wT7BOEExARtBF8ETgTtA+sDeATMBCgFHQbdBpUHbQi7CL4Inwj4BwcHyAVQBMoCMgGt//L94vs1+t74wffa9vn1bPVj9av1evbP9y35qfq++1783fzb/Kj85Pvo+s35k/hk9yX2IvWd9K/0QvV39qT3EPmN+gf8jf2J/pT/nQAiAXIBvgGLAVgBGwFyAPn/bv+J/tL9Lv3A/Ij8a/yL/MP80vwb/cD9fv6m/18BmwM+B84LnxHLF00doyK8Jv4osik8KNskaSBjGqgT/Qx6BpYAfftb983zFvH77u3t6+1+7pPvv/Dx8VHzlvTQ9b/2X/f99xb4GPgJ+PX3Hfhv+Oz4l/l9+sv7Z/2t/sz/iQDMAKUAKwAB/4L9Q/zu+sn5+fgu+Lb3pve09973PPhv+Ir4aPjY94P3Lvfc9uv2D/e/99b4APqG+yD9kv7j/9AAbAHSAaMBZQFfAUEB3gFLA2sFywjNDY8TBRp/IPglmCpuLfEtlCwWKcUjUR3gFWAOaQfbAFD7BPd0893wwe6x7HLr5urf6nLraOyx7VbvMfFI81/1RPe4+Jf5Qfp4+sb6rPu6/CP+5//HAZ0DQgU3BmQGyAW6BCwDFwHv/r78rPqu+OD2e/VT9HXz0vJi8lfymvLs8mPzFfTS9Kr1b/Yj9wL45/gm+rL7O/2//gkAmgDZAO0AmgAnAJn/0v7p/ST9kfyv/AH+uQAjBQoL8xGBGT8hiChJLxU0nzZ7Ni0zYS1fJfwbFxI9CFL/0/eG8a3sYOlz57bmh+Z/5m3mdub95uXnIukT63ntTPB98/L2gfoO/mMBZgRKB+0JYQw6DmsPyw9HDyYOLAyUCZEG+QIW/4v7aPio9abzP/JZ8SbxNvGl8VryG/O68yn0JfTx89nzx/P481j08vTk9Rj3g/j3+Y37Rv2x/tP/tQAMAQEBeQBa/+/9Tvyj+kn5VPi69zz4Svrd/doDYwv6E3EdrSb4Lpo1yDkFO/k4IjQBLS4kiRquEFMHIf9v+Pryve5h67DouOYq5SDkbONa4zDkv+Uh6C3reO7b8TD19/dB+jL86/0FAEsCFgWaCFsMFhBPEzsVzBXrFDQSLg4HCXcDOf7I+YX2fvSq80/zHvO18uHxq/BL7+3t4Oyd7CHtie6u8EHzBvZX+An6K/vS+wf8y/uC+0z7ffv6+4D8K/1h/Tv93/z8+/76Avqe+ID3wfbB9nr4HfzmAU8KQhT5HvopsDOMOydBlUNfQkE9wjTPKYwdPRFoBTz7gPOM7ZXpDec85Y3kHORP4+bie+JI4vjiJ+Q65jPpsey98Lj0YvgO/GT/qwIQBmcJBg2NENcTtBaDGGAZ/hgPF0YUKhABC28FeP/++Xz17vHP77HuDe7c7Z3tUu0a7dPspuzn7Jntxe6I8LfyVPVD+GD7Wv7fAIcCMgPpAtIBRAAY/rL7L/n39of1U/R585HzMvTX9MP1RPbI9l/38ve3+bH9fAQJDm4ZdSZKMxc+bkY3Si5JKERUOhwtqR4iEH4DmflO8oXtNOrB523lkuLL3yjd9tr92RDaEdwH4FDlE+wy82b5fv7ZAYsDrwRSBUMGmAjcCwcQPRTeF8sa/xtsGwcZdRT3Dm4J0AMw/3n7gfiL9vT0CvMi8R/v9uyH667qsOrF6/vty/Ai9IP3Xvrt/Kb+n/9uAGkA3P9a/yz+3fyl+x/6yPjB96321fVU9QX1BfUs9bb1A/a+9Vz1UfSP8lvxb/AJ8tj31ADRDfIcDCyFOlpFHEt4TIlIEUB9NPklNRc0Chj/5fYJ8bbsAOpJ5xPkKeHa3n7deN2n3u/gLuQP6CDsPPBu9LL3wvm3+5r9rf+/AlsG4ArzD2EU6hfjGS0aCRmiFn4TShDpDM4J8waxA40AQv2I+bP1/PFv7s7rK+pt6VfqMuzT7qnxCvT59XX3rfi7+SD78/xN/w8CUgS/BfwFXwRBAQX9YPhC9Fnx6u+Z76vw1PIn9c/3+fl7+vH5EvhP9THzZ/FH8Qz1k/upBckSFCBpLfc4lEBgRINDwD4jN5osBCGUFfgKZgI/++L0DfDU60PoZOXi4iDiqOJG5LLm7Og/65Lt8u6075HwFPE/8nL0h/cT/NABwgeHDYISHBaBGGkZKxkiGGgW6RPCEDwNYwm/BRUCr/4D/Lb54Pdu9t70NvOE8avv/+2W7JbrOeuh67bsyO7h8cr1UPrc/usCJAYOCDgI0wYnBNYAx/0f+x/58vf59h72OfUO9PXy1PEW8S3xqfF38rPzIPWA9qb3KPmq/JkCbgsPFysjQC+gOXc/LkHxPkc4Ui9fJJwXswswAYj4hPJS7lXrQemD5oTjDuE+35ffhuGU5C/p8O0r8r/1Pvjf+Sn7Pfys/en/MwOwB60MpxHYFYQYwBlZGc0X4hU5EzgQIQ2CCZMFwAHH/RD6O/fx9GXzd/IM8uXxZPGc8GLv8u3T7D3sqOyS7h/y6fYF/PwA4AQhB80H6gYzBTID9gCz/qz8y/pF+TH4ZvdL92P3c/eM9yX3lPYw9nH1nfSA84vyDPLA8RDzzvav/f8I0xbxJJcyRzzBQKtAUzsxM0Eq/h/nFZkMkAPo+1T1f+/r6h3n6+OY4UHgguCa4lvmJeuO79/y1PS69SX2r/YR+Fz6WP1eAakFfQn+DVUSNRZ7Gc4a3RpZGQIW/hFYDW0INwQ/AMX8nPoC+bj33vZw9XTzxPC27RzrXOlB6X/q0Oz57xDz1/Vd+HL6e/ym/pwAkAJhBJUFLAaOBQgE8gEh/2X8P/pv+IP3HPf99kf3bPdP97H26/Uj9az0wPTm9On04vR59Ab17vgBAWsOjB8UMEM9tUQQRYQ/CDbjKuMfHxUnCgT/F/TS6qLj596e3dbeo+Go5arpfO168ZL0YfY+96f3ivhn+rH9GgJ0BssJTQsPCycKSgk7CUoKCgzuDX0PFxDGD7AOqQwFCp4GYQLd/Zf5v/XS8vfwBvC97/3vR/BQ8GjwffC08G3xz/Lt9Pv3rPtx/7gCJQW0BpMHwgdMB6UGywVYBDYCUv8Q/PD4QvZW9Ezz7/Ib82nzZ/PW8vfxZ/G78ejyJ/Xg91f5cvnc+F75GP/tCk0bmS0rPBtEkUTHPaczeykAILsX5Q6FAy73U+tr4QjcSNuO3f/hR+aE6crs/e9Y8+f27vlr/Jv+cABWAk0EgQX5BS8GTAbxBgoJ5QuDDhsQ0g/uDa0LiQmlB4MGPgVfA/gA+v0H+3P4q/YD9uf1Pvba9gT3pPbF9SD0dvJ78RvxIPJz9En3YvqG/RsAcwLVBKwGAQgpCLcGHAQtACf8Mfku91j2/fUX9YvzgfHq7wbv9e7c7+HwOPJq9Lz2q/k2/b3/tQCaABsBeAYSEgAhpDCWPIlBVED3OXgwMCfMHsgV6wqz/RDwfuSS3ErZr9ny3LzhReZB6mruFfNb+IL9wwGOBZYIWAszDUANDQzbCQcHwQTeA/YDHwVgBmIGqwXKBOsDnQNqA6gChAFo/8D8Vfpg+Bj3S/a+9TT14PSW9I/09vR39Rf29PY1+Fv5gfpl+9v7DPwA/BL8kfxz/e3+xQBjAp0DLgTRA+ACSAGf/nT72vcb9CbxI++27hTwmvFp8lXyrPEt8TrxrPLU9Hr3gflS+sn8hQQtEo8jDDUVQUxGP0X2PTw0VysVI38aYA/4AG7y9+UL3aHY3dcc2treyuN66EDu4vM4+eT9lgCtAnMFDAjKCtwMRw3cDJ0LlApuCvsKXQspCkUHxgOGAKr+cf61/qv+x/27+1n5mfed9oP2uPa49uT2l/fT+KX6dPxf/Ub9gPyL+zT7dvvb+0j8L/yC/MX9EABGA6UG8QhgCfMH/QQiAer8Xfix89zvI+0R7L/s2u2y76nxUfIF82PzJ/P080b1mfa7+N/62PsJ/NT7cf+GCU4ZoyvPOflAq0CxOKEtpiNeG0QV9w0RAwL3F+xC5MTgB+AD4iXm6OnM7YrxDPUm+WP8pP6AAecE0gjiDNYOoA5YDbMKHQjWBiYGrQU7BBUBJf0Y+ir57vkb/M7+sQDQAckBggC2/tT80vpQ+av4PPmj+zv+8P97APT+jfyB+sL4gPdL97L3kfj6+o3+yALYBkQJYAlkBwsEewBK/Q76w/Yb84rvhe0s7X7uGPFa8wP1+fUM9o72Mvfu94P4I/hQ+Ab5Xvm0+VT4H/jl/soLKh7PMcc900FaPlIz2ij1ICQaORUuDO/9HfCP47PbRtoV3E3iSuqp8Mj2YvpT/Gz+fP5o/+sCHQd8DCYQGRCrDq0LxAjvByIHaQZdBKb/NPuJ9771//YN+Wv7Ff7p/8wBxQMEBaIF9wN7AI/8nPiQ9in2vPVC9ij3jveH+IP5L/oz+4j7e/tr/HP+hwHjBHIHawgvCKwGsgN0AKr8v/g09W3xr+6F7U3tkO6K8Ejyj/SU9vT3pPgE+HX3m/Zx9rb4EvtG/Wr+NP1DAJsL0ByeMYJAykPmPooyhiT+GmYUXBCsCpb+bfBq5AbdntwD4PTkrOuy8YL2aflO+rf6Z/p2+tT8XwGYBzwNJA/+DfkLvAo5C2YM6QxRC6sGPQD5+Zn1K/Tt9Fj2yvfa+RX8cf5MAFEAQv8P/qz8lPy6/ZD+2/7g/b776vlF+d35tfob+0X7s/sO/Yz/FwJdBJwFyASxAgwAVf0f+974LPb084Ly2/Ev8lHzavQs9Ub1/PQP9TL2MfhK+mn8nf7X/24AfgGZAYIApP0Z+s786ghYGycuyjiiOT80YipBIX4bIxYSEHMFkvTT5A/bJNjk2tPeneOU6gnynvnw/x8EAQjUClsMeA57ELIQXQ6KCPEBN/7m/Yz/GQGZASsBYwA0AJQAcgGxAk0C0P/8/F768/i4+HP4svjN+RD7Bfzf/P/9Y/9RAPgAHgGlAFMAN/9p/V78/vs7/Cn9HP4n/3UAoQGrAkQDOwMjAhP/5voh9prxVO5P7ELsT+7A8dn13/ic+qj7Dvu7+Vb4z/d0+Gj4GPnE+u37Wv13/J77tARLFhcqVDqxPb83zi8rJdIcnhiPE7QMv/8I7QfgSNsE3JPfV+Gt5MXrmvJO+JT8bgC/BeYJDQ3mEAYTyxLsDosIZgVxBTEGdAYYBBUBn/6P+7D5Tvlx+WD6g/ok+ib7Svwg/WX9yfxB/T/+bP7M/uT+bP9GAa8C0APcBHwEgAKd/zD8wPnl+C359fqb/f3/vAAdAGr/2f5o/jn9IPue+Ar2dPPd8FLvBPD58QH0HPYP9/33+vkm+9v7ZfxY/In86PsQ+x38IP6dAHwBMgGjB/kVgCZBMwM1oS7cJy4gYhnVFB0PaQjk/WvvjOX04iPkp+aM52vpve8D9n36yf0eAFYD3wU+B9IJWQ3GD2cPxQy8CjQK3QmlCAkG+wJsADD9MfoS+CP2IPWN9Fb1X/g//Lr/cwFeAYYAlf8Y/yn/pv66/Vj8gfpc+q77e/39/vD+mP0p/Iv7svtX/PP8if09/kv/2wBhAucCSQLLACv+IPvn93L0evF07+ntTe0s7unvZPKA9Lr1lPan94b5dPuC/fEAkwQJB8kGbgP5AzsMKBm0JlwtSCwkKfYj9x15GbgTcw3lBXn6UPFS7Ursse0C7izusvHX9Uz5Jvxp/f3+iQDzAP0CLQbYCMoKagpjCSsJ8wdkBi4FvANcAs7/DPwS+W/3EffD9+74pfov/Cv9Ff6b/oP/2QCNAWYC1QKkARj/hPt696P0kfMB9Nf13Pei+RL7J/zA/W7/CAGrAtUDAAQEAyABjf5j/JP7DvuM+lX6dPmw+FD4m/dd9+f31fgJ+vX6fft2+4P6UPkn94f1VPax99/5+PpQ+wwCZA/TH+QuazRvMpkuBCg6ImccoROmCt7+sPFc6lnoP+qq7SzuG/BT9Tz6SP4c/8r9//xn+5b61PsB/koBpAPwBGQHvgmxCkgJWwWzAfv+pfzL+r/4p/dm+Ln5vvvF/oABvAMNBaIElQM/Aq4A4P6q/IT6XfhL9lH1SPXy9TX3pvdW93r3xPdr+Qr9agHnBZoIAwk9CK4GtATbATT+9fpU+Nn1efN28WLxkvMh97T6efw4/Vj94Pyq/Cv8mvt2+rj4IfhC+Pn4ZvlA9335jgZgGY8tFDm6NnoxUyz0JvMidxp/DbP+rewv3+XbmN/+53zumvLz+RECYgibCjEIjAUaAwUAfP3s+vz5a/qI+gj9VQGaBToJSAm9B4YH3wY+BawBx/tF92r10PWi+F78aQDlAzUFNwUoBCsCQwCh/fr6rPli+CH3X/ZK9Zb0T/RP8wXyDfGr8DryCvb3+t7/vQPYBo8JggsVDDcLJAkCBtABHP0Y+CL0+fGF8IHwUPHP8UHzIPWt9mD4Yvly+jr7V/tu/Z3/6wBXAIb72fxVCs4dADGmOLIzdy6MKZIkRSEOGcoNgP+x65vdJNqK3dnkYuoa8M36yASoCrIMswsuDOkLRgj3A1r/gfpd9u3z2fVi+x4AHgLUAVUBwQGYAVgA3v40/X/7dvqD+nX8bv8TAkQE4wUSBwcHOgXvAssAX/6n+9b4Jfaj9ObzJvPf8wT2dPhy+mv7Ffz9/MX9QP6m/pv/VQGUAhMDIwPHAvEBLwCk/T/6fPaR83vxKvGz8p/0IfYG9zP3d/Zb9if3H/hM+y8AsQMmBfQClAAfCHsYGypyNZozaSyDJyIj7iB8He4V0wul+8TqJOKD39DgKeIZ4proIvMf+5EAAQLQA6kHdAgfCIwH1QTdAXr+xfyA/1QCtQLpAXkAEwCA/3P9qPw5/U3+Jf8y/64ApgP+BS0HgQbtA24Bw/44/Ij7tPrE+K32lPTo8zL1wfab98T3aPeM96T40frm/aUAuAIWBNwExAVQBm8FCgOi/xf8FPm49rX08fLm8djxBfMc9b331vkt+lX5Evhh93H4S/mV+bX7Ef7y/ysAWP6UArcPxh/ZLAIwbSsWJ6EieB53G1IWKBCCB/z79PR08kfxD/AD7Wrsf+8f8W3xrPE68yz4Bf1KAQUHZgtdDbQM1wlyCNYHAgaBBGoCNAA7/tT6lfja+NP5hvvL/Kr85PxI/eb9OADkAhQF3AVbBEsCSAAM/kj8SPoh+ED3jPYn9p32D/fl9wf5a/p//Mj+wwCfAksEIwZyB8IG0QRPAk3/d/yg+f/2MPXD8xvzw/OQ9aD35/gJ+mX7RPyN+/v4Pfbk8/HxWvJ19d35qv2F/oACNBDdIrcyOzjfMgMs+SV2HqsXHxBrCO8Alvae74HvhvFI8+rywvKZ9jH5GPfx8nbvG/BR8872E/zbAT4GQQkwC04OeRGcEFgMfwe2A9cAuvwA+EL1uPWT+HT7sf1E/xMAJQHHAjwEuARsA4YAWv2l+nH4r/b49Zv2Nfhr+gr8ofzJ/Cn8Rvy6/Xr/rwHgAh4DjgPgAzwEGgSEAuX/XPzR927zsO/i7ZLujPAD8yz19vaD+CP5BPnW+C/55/kY+e73xvcE9yz2bvSS9b8CYhf1KZ4zDTHLK1ErJCtZKVUjsBaRCIz5d+106lHrhOzI7ZTuB/Ni+GD5xPgx+Kv5o/yW/Kf79fo4+v77qv4nAg0HOAnDCagK8QpdDOAMdwpyB1EDh/+o/Tr8V/yC/ZL+4/93/1P+d/4s/90A2QEDAND8LPl19sP2zPjH+sX7PPtI+rn5wPln+k77tfz9/TL/VwDUACkBdwHXAT0CnQGm/1P9/voA+W/3yvU79Mvy7/HU8aXyfvSm9eD1gvW89Lz0afRT9H72c/jJ+Wn6DPyoCGweRzEEOwY3LS15Kp4pjCbaIPESRwPb9PLmhOI35LvlJukj7DbxI/nq+0T8VP5sAZgGZwgABqsDlgA7/6wA/wKOBhkIBwfABrQGpwZoBZ8B0v5B/VD7+fk5+Mz3Ffv7/0MFhgj3B98FEwTjA1IEMgLv/Wr4WvNe8WTxlfJB9MX0WPVs90f6c/1k/zoAggFjAqoCzgFS/7H8rvpU+d/4Wfi49nf09fJD87r0UvZP95X3Qvg++Wb5MPgy9t3zSPKn8qHz9PUJ+2kAfgT8BWIFrwt3GzIs7zZlNRss3CbqIyEgURsrEegFTPu979vqBesv62rs7+x28Ev4gPxe/Yv9h/25AJQDLgQGBmcGBwagB64JRQ1nDxENYwnEBaECev/l+un2QPWU9an3GPl9+aX6NvyA/5IDQAXuBB4DywBPAKMAHQBj/qX6jPYO9CDz7/OW9SP35fis+jj8Mv7QANkDaQZeB4oGOwReAZD+svt6+MP0dPB37FHqhupn7HjvufK19a34RfqV+mf61vk++gP7Wfv4+gf5q/hc/KoBVAZvB80IChPAIuAvljQ3LignMiQfICAcgxVcC9ABIPaJ7C3rRuyA7SjuzO3Y8eX2ePgA+hv8UwBfBWsGBga9BfkE6gVvBzQJKQvQCUkG2QOUAlwCBwFy/RT6nfdH9lj2S/e/+UL99gAbBVUI2AgJBw0Drf4p/Kr6OPoq+i/5tvdN9sz1jvZY95X3QPjs+N35APvC+5/9fQCxAuIDxgObAuQAi/6o+4H4h/XA8s3wj/D+8aj0v/de+rD7e/tn+ob5H/pX+/j7L/xG+/76rP18AQsFNwYvBY0KWxg3KGQz7DKxK8MmsyL/Hb0XVA1lAlD3N+zP58LogOq8627rJe4F9U76Z/1J/7wB2gU0CJEIOAieBlcFdgQ3BJUFbwVbA68BgABgAD8AvP27+tj4uPcW+Jf4rPkn/E3+r//T/2b/QgERBG8F5QSGATL9ePqT+Kv43fqs/CL+N/4Z/XL81PtX+9v6OPrY+bn5v/kN+nn6WfvQ/DL+8v7p/UX7Wfg+9sz1Yfav9sr20PbB9hT3DfhS+jn9S//R/7b+Jf5R/mr+DADiAWgCTAAO/FH+DwuWHNoqBS41KSwmTSTfIZ8dsRPWBx/7LO0f5STjK+Tj58XrxPGG+nQAsQLOAvkCuwUpCLsIUQiwBhgFkAP0AbwBuAHFAK//Cv+X/0gAY/+S/bn7JPuf+zD8LP5PAQsEjAVUBcwDsQGZ/m36Avc59Vj0wfTe9jH65P11AJgB4gJxBEIFzwQ5A2MBLv8k/T/8gvwj/mj/Lv/U/b76uvZr863wju8H8HrwoPFj83z1xPim/N7/0AH7AVEAIP6G/Cv8Dv0V/Xb7V/qa+mD7svr59zr6lQfOGjssoDMwMFcrWCdPI/4fpxhdDGz+4u7y5HPjpONk5Tjo2eyS9VX8hf6D/2AA2wLnBbkGFAcsBvkDfgM1BSIJeAxGDEQKEgghBicE1wAw/c362PhC9zT2s/UG9wL6nf1IABsBTwCm/uT9Ff4l/rP9s/zQ+2H8Bf7j/6QBRAL6AegAGv+E/Tb8FPuc+g772PsB/f/9+P3Q/Fn61/aJ8zzxh+9S7kvvOPLB9S/5MfuW/Jf+WgBcAVMBE/9a/M/6pfxsAOQBGAIiAZL/pv46+yb83wg2GxssijP4Lo8p3iUzIBcbLxJ0Bc34s+qd4iHkX+fz6jXuffEW+bj+Mv89/9v+KwDFAlgDEgWjBjEGLQf/CNULgw4hDFsHGgRVATgAPv9//C/6tvdA9Ub19/aT+Ur8kP1N/nD+D/6r/n7/EACb/5D98fuP+8n74vze/QP+Bv7S/HD7Qfvx+kb7a/wc/rkAxwJYAxUDQAIuAVj/MPyu+J/04/B47vDss+2y75XxZ/Sg93b7HP+xAFMABv/g/cr9zP5VAFcArf8vAEQABwB8/Qz7igRZGLErajaDMIskpx+mHNsZChW+CPT8svJe6WvqYe8F8uT0N/X599L9Yf0k+sb4Fvln/QEBUwFhAg8DpwQgCqcQKBYCF9kR/Qs4B2ECdf1P9x/yVPDY7wDxIvPv9MP3t/rm/SQB/wGHAWkBggEhAzEE+wJwAZD/VP7g/sr+4P2f/Bn7SPsd/I/8+vxc/cz++gBNAp0CWAFo/j37tvfV9PryoPCt7vbtju4O8Ur0ZPeg+lb97/6D/3P/4v7t/Rn9JP2X/i0Amf/k/pf+3v2A/Gn5L/xnClgcZCq1LQonbSJdIE0dxBrgE50J0/9n9Hzuou9d8D/xJPGR8VL2ePk/+lf8Mv5NAbQDKgPOA2oE7QO9BHEGDgl5CmMI3gUNBcgFiga2BP8A5PxQ+D/1yfPC8971ePgi/BUAXwLuA/sEGwYqB9YFNwKP/fv3gPNV8fHxofV6+fH7Xv0X/vv++//AAHUBswEIAcz/Zf5j/cn8tfy6/OL7EvrR9kbzy/Ha8R7z6fS49Qb2gvYC98j3HfnN+kv9awDGA8sF0QbSB3gHVgUdAD76tfwJCbcZlSaVJ0whqBwJGjQZNxdZEBUIc/7O80DuGu0I7qvwG/Jw9AT5/PrJ+ub68fta/wUC5AH0AIz/d/5W/4cCfwdaCz0L/wjhBkMFcwO8/077N/gM9tn0RvU+9/X6W/9aA+oGXgmFCq8KMAoOCQkGtQHb/YT7ffuz+5P6cfh19ebyXPL+8qP0Dffy+H/7xf7rAUwFDghwCToJYAaWAeL8Mfkj95D1G/MW8fXvju/T7+zvJPFu9Hz4kvyp/8cAOAEMAZgB4wMUBa0FegVYAy0A4foz+IAAARGuIWcr/iiVIiMewBiUFJoP6AbJ/UTzwuq56jXuPfIG96r6PwAjBQ0FhQMDAmcB5gEwARMAJf+S/Ur9av/pA0EJIguCCa4GMgOWAE3+5voh+Dv2S/UY9076IP62AkMGjwioCXsIQAYUBEYB+P44/Qf7evkw+GH21fRT8zPyVfJK8/b0X/fm+in/DwNiBu8IIgrkCfUHqQTXAH/8aPi19Rr1n/XV9dD1mfVd9aT1MvXD9E/1+fXX99r6Lf3i/tX/RgBRAawAfv5W/az8A/yI+nD68AItE+YiZyvlKWEkSSC3G5UWPBCyB8z/4/dr8gPzifTv9GP1uPUq+V79d/2h/J/8XP1h/1gA0ABUAkIDEQRCBUMGUgelBuUESwTMA8MCYQDC/Mb6Dvs7/F/+AACSAKwBagJvA+MEeATZAssAyv79/fH9w/2H/cD8WfvE+e73b/bv9LrzsPOH9K32cvkS/Hj/CgMjBk8IEAgCBukCFf+x/Er7B/oL+Vn3WvaD9u31/fRY9Ob0bPcS+mn7NPsA+o74avhv+s78sf5YALT/4P0K/MD7qQazGp4rQTMlLY0hKx36GVwU5Q3wArn5IvQ+7qvvI/U+9xL67fsP/jkC8P90+dX2d/a7+Dj7jfoD/KT+rwAzBc4J3gw8DfgI4wSoA4kBVv5T+hr3v/cH+sX7F/4XACUCEQVlB/wIMQglBIL/ofx7/Oj9Xf6E/R38M/pi+Ab2efNk8nvy5vNz9tP4gftE/0QDKAfZCfQJXghoBYcB3v0V+9H5Wfk1+fL4kPhm+B/4M/dW9iH2HPZS9ir2m/XK9Sz3e/lB/DT+hf5U/kL/WgAiAAb+e/wDAnEOOxuxI+AjEiDjHuQcPxldFKQLcwNe/XP3q/bs+PL4G/ne+Db6kv6t/8f91PyW/Dv+d/+Q/sP+q/+PAIUCJgV4BxcIZgZSBM4DVgS4AzsBMv6g+q33ifYh96H6Df8VAqIExgV0BXoEMAJVAFH/h/25+wT6APl2+UX6OPsb/EP84vv++iT6MfpK+p76p/tn/HX9Pf4w/lH+Pf4V/iL+Nv5d/hf+lP0F/c37V/kp9l7z/PGn8drxAfOL9f75aP4wAuAE7gRtBFYDCgAB/Aj3IffKA1sWrSUJK14k7R2NHJ8aAxmhE3IKkgKt+DzxhPEJ81/1TvjL+YL+EwFz/fH55fe9+Jb7oftp+3D81vyo/l8BYgRrB4EHewbdBisHgQbXA1EApv4r/vL95v0//ZH88ftg+zj8ef0B/i7+9P37/ef+yQBjA9EFiAaJBJIAmPyG+bH3YfdS9zP3QPdA9/33Kvms+Zz5wvnv+jv9i//8ALgBIAI7ArcBzgAZAAr/fP31+pv39vSu8t3wW/E79DD4nvsm/MT7rvzJ/JL8JvxX+lf46/Qi9B4AHBboKzM4mjRtKxclQB4QGN0QLwf0/rX1+O1d7l7ytPbx+hX99ABPAw3/Pvk09Rz1b/hc+k776PzV/V3/5gGHBcIJnQpuCNwFgQMYAmsARv77/br+3P66/vj9n/0B/gz+7f4gACIAFQCb//T+tf6z/fr8rf3X/db8U/vc+Rb6IvsB/GP9Of9+ADIAqv6G/XL9Af6E/jD+RP7M/o3++v2v/LX6ffku+Fv2lPSy8jjypvPw9Xr4gfoe/HX9of0t/eb8s/0w/6n/d/9o/nL8VfpD+A78PwnYGf8mPyrXJAQgix3oGvcWzQ4CBfH7lvOt8Kzyh/Ud+Kn4lfmo/Fb9mPu0+Rr5hvsr/iP/PwAeAdIBOQO0BBQGDwaAA5YA4v89AdkChQM3A+0CqwM8BB4EwQOxAscBugEgAaT/m/2K+wf7ofvW+2L7hvr8+fX52Pmn+Q/5KPgf+An5UPqU+3n86f1aAK8CnwNWA5kCugF0ADT+XPti+JT1O/MU8kfyOPNN9GX1b/eM+hz+VQG8AoQBI/7n+SX4TPkh+rn6GfvL+mn7xPqq/CkKzh5hMIs2xy1yImYdIBqVF+gSowrrAuH5p/It8+f1uPYZ9afxtfGj8iPwQO7974z2Uf/YBDEIrwogCyMLFwsiC/0KKwf9AKj8Evsw/Az+bP8DAQwCcAH5/03+1/zt+8v7Vf0AAF0CpgPiA1QEagXYBewEgAJN/rv5jfUM8kHwGPBr8eLz5fZg+nf+RgL9BEcFUgOaADn90fmf9rXzC/L18f7yuPSd9gL5APxz/g4A2v85/uD8A/s1+eX3DPZf9FjznfMV9iz5B/1CAhgGYAfXBEsDuAvoGtwnLSsvIwobyxjcGD0aLxlWFO4NKAQi+5f3SvWE8uzvWO5M8Xn0RPSN9ND22Ps/AW4DXQREBIUCBQGCAFQCYgUvBrgF3AUGBoEGlQWUAgoAlv3z+3/8of2A/sr+sf7T/4QCkwTaBC4D3wCi/0n/sf9m/zL94/lR9tDzpfPe9LT2pPic+XD60Pv//WEBWQToBWkGfQWDA/8A6/3s+qv30PP+70/tce2277fyDPZ2+Mb5K/o8+XT4nPhO+UH6Nvsp/N/7bfvL/PT/UAQSBgQFbgoZGOUnojHrLIog0xeREpgQZRCHDYQKsgXy/c369foh+nr4ffTl8Snyiu/m62Drge7i9fr8lgGnBTEHywaCBz0JxQsfDD8IaASrAj0CmQIMAsMAjv/m/e38t/39//kCkQWuB8AITgcqBDIAOvw/+pX5iPmN+pX6WfnO90j1NPNZ8lXyJfS89lv46vk2+3T8yv4pAREDuATTBG4DdwHZ/kz8Hfoz+PL2R/ZP9rz24Pa/9gz2MvW+9H30EfW99t/42/qP/Pr9S/+mAbsEOgcFCEsFMgP/CV4Z2CqGNN4vliQXGykUdhHmD/IMPQqHBLf9x/qX+H71qvFs7U/tBu/u7Z/sQO2R8U759P9+BNgGpwUlA6QBXAJdBNUEZATuBEkGRwfGBXUCtwAVALb/h//4/u3+h//t/y0BXwM3BYUFugPrAB7+OPuK+Gj2mPVa9kL3Cfin+D75Uvqe+3/8n/zH+1z66vm1+hL8n/0h/xUBdwOvBKgDpQB7/Lj44PUI9J/zIPQO9Tf2+/bX9yj5GfqV+qz6TvoE+nb6mvu8/FT+nwBPAh4DjwGQ//4EkRI/IuErNykzIAkaYBcEF8UVxhDOCgsEZf0V+/r6e/pt+XP3C/f99nDzaO4p63nsP/KI+OL9SwHiAYABWAHFAnMFQwaFBe4ENQTKA+cCFQGEANYAzgGJAzkE9ANmA4sDcwXqBvIF3AJY/lz6Yvjn9z75JPuw++/6Ovkh9+31RvaF+AX8X/64/gH+uP3Z/oQA+gHpApkCRgH5/nX8PPvf+q76hPo8+ZL3BPcC9wj4u/nm+oH7NvsA+tj4LvgI+Kv32vcL+Uz6Jvxz/VX9Af07/Fb/rAotGZ0knSYjH5EY3hbCFx0aMRmDFTIRMAqgBIcCSgCO/sv7lfi295v0g+7Q6SboluuE8WP1IfiI+dT5dvuk/qQDUQiqCbsIJAf7BTcF7gPwAvACvAIiArUA1f/CAF8CRATqBQQG0wToAdT9oPoG+bb4hflB+tT6NvvY+jH60/lt+uz7d/3o/eD9Uf5q/wgBfAIKA8ECTQHi/qP8bfsx+2D7IPyz/Lz80Pt9+ur5BPq/+Zz48vYj9nr27vZ++EH6F/tg+xj6qfiZ+Z76Q/uo+wv7i/46CPsT9h1BIPYathUpE9sSfhNEEikRrhAYDwsNsQq5B6AEawCj/Of5fvb18Wzth+ur7YHxPfX/92j5yfpu+7n7B/1s/oP/PQGJAswDMQQWA68BwwHBAgoEWQSrA+kCswIqA8gDMwT2AykDLgJuAYkA4/+//mn9A/xQ+lD4zPax9V32ivgC+zb8xfuY+pj63fuk/eL+2f/4ACUBUQAe/2H+q/5U/5n+Sf4R/sX9O/1r/K77FPuu+f74APlZ+n37gfr5+Vz6oPrC++/7Cfue+z37nf3IBacPIRixGnoV4xCnDpEOBxHdEhEUIRYnFZkTjxGDDScJuANR/tD7q/jr9A7yyu/K8DbzRPRl9RP2FfYf9873V/nb+t/67PpY/F3/XQTwBh8HuAXTAq8AjP+m/jIAhQJFBPAFTgV6A7gBGwBRAP8CLwUABuUDrf+n+xD5Qvf/9nX3l/iF+Zz5UvkC+b/4wvi2+HT59fqE+yn8J/2X/p8AGgJ3AS0BxwBRAD8AAQDv/sP+2f10/FX8Y/zr/Hn9EP3d/PH9lP6C/6j/Cv8X/tT85vxcAFQFVQkMChMJjQldDKsPrBBKD34NXwsDCpsJWAmUCQoJ9QUWAysAS/0C+2r4MPeb+Mv5t/qw+vX4wfdx9q31sveq+rX8wf39/Fr9Uf8tAUkC6wIVAmEBDgC4/on+Sf9rAEICRARLBWAFOQRJA28DyAPtAqoBsf9R/oT9HP1h/Y7+w/6i/p/9vPwr/H/7LftD+8/7hvzF/JT8mvxD/E780vx8/cX9//11/aj9Wv6F/jT+s/09/cH9sf7c/ykBmQH9AJv/nf6x/oP/6AAIAx8FwgYQBmIEHwThBXYIUwoQCjAJVwjTBjUFrwQmBcQGUwdtBeYBJ/4J/IL8N/7e/t79UfsN+bT4Dfpy/Pn+MAC6AK8AMABS/wr+/fwa/lUAZQL2AusBrwCf/0b+Q/yI+sL5oPpe/Hz+DAD0ABAB6wCvAD8AIf+6/ZL83/yA/nIA4AEVApsBHgGhAOD/Uf9Y/ub91/2z/Uj+v/7V/kv/+P6z/pf+cP0y/GD7K/o1+t/6WvuJ/N39MP6z/lr+j/0i/g3/hgCCAloDMAN6AnIAwwDXA3AIVgzlDDcKhAdkBT4E1QN/BCYG1gdFCDYHTAVbAxwBMv9o/tn9df1a/DP74fqK+7P7wPtp+0P7t/vQ/D3+BQAnAQUC5AKHA0AEZAQfBL8DVgJwADv/of5m/7H/GP9o/nz9r/xR/N/7XPy8/bz+kP/u/3X/Gv+d/jD+zv5+/97/6/8K/1b+ev7M/tP/RgDp/wH/8v2H/Y/9rf0c/iL+P/5N/pL9+vwK/Vb9av6M/7j/vf8D/779af3//Vv/fAFYAfb/xf7i/lMCZwb+B/AGfAOjAEwAOQFYAl0DLAR0BVIG3gTXAdz+m/1W/tf/gADFAHIAHgDu/3H/Rv/B/kv+gv7S/vT/cAA/AIkACgHMAXACVALyAYUCbwPDBCwGzwZxBjEFDAMFAdH/A/+o/sz9GfyK+tX4+feS90333veI+LD53/rL+3f8uvxe/C38efwu/Ub+0v5S/z0AAwHOASwCAQLrAV8BOAAR//b9bv3X/b7+OgBIAf8BWgLHAQEBuP+Z/qv+W/+0/yAAbgAOAdUBUQE9AOL/9ABEA0IFuAU1BcwDvwI/AhMC8gILBJME9QQYBFMCrgAu/1L/nACkAekBFQGm/9f+l/7//uD/nQA5AZkB2QHdARcBGwBk///+r/9RAOsAPwJRAxoELAS/Aj8BSgC2/2MADgFTAVwBTgC+/hf9U/sY+qz5k/kS+mL6yfkJ+Vb4LPg1+Xb6wPs5/e/9GP4P/qP92/3y/kYAtwG6AuICnQIaAjAByQC6AH4AYQAkAF4AbAFCAhECPQFRAOX/+f8HAND/nf+F/0//fP8kAAwB1wGYAVUA+P4//j3+Fv80AFUBVALZAY0ASf9a/uD+GwAXAQYCQgKZAS0BJwFfAd4B1wHXAWECcwITAlgBHQCV/7L///9RAdMCpgO6A7gCggEpAeIArwDMAP0AWgF8Ad8A2v89/5v++v3e/fT9Of5u/vL9SP3b/Ij8efys/Pr8Lf1B/Qf9A/1W/en9rf5E/27/T/8E/7b+0v4W/0f/W/+t/2MAgAHVAncDFgPkAZ8A4/9GAE0BIwL0AgQDQAJWAT0A2v9GAAwB1AEPAnIBSAAy//D+n/9sAPgAzAAZAIv/4v5h/oL+9v7K/5oAqgCJAIIATAAeAIX/wf51/nD+lP7k/jn/l/8XAH4ADgFcAVoB/wCIAHcA5gBTAbMB+AE3ArYCLgMsA5IChgHAAFcAJwA7AO3/if9C/6r+/f1N/VX83fv4+z38wPyo/Ar87fvN+9b7CvzE+xv8wvxa/U/+zv4G/4P/ov/V/wcA6f9OADIBNwJPA4AD0AI3ApsBUQGhAaoBsQGtAXkBFQHFAMcAKwEBAlgC/QFBAaUApQAZAXcBlAGdAWcBLQEMAcAAjwCfAIYArADAAH0AYwBRACsANAArAPL/7f+r/0n/Uv9+/73/AwD5/+3/SAByAFMAEADp/y8A6ACtASACRAInAsABPwGzABcAl/9q/2b/pv/i/9r/r/9d//n+zv6z/pL+zP7y/g//+/5h/rf9af1V/fv95/4l//D+Pf7X/TL+yP4s/5f/oP+Q/2j/BP/H/pL+uv4N/4L/EwA7AID/4v7H/qD/OQE9Am4C0gHXABAAv/+t/x0AwgBNATACmQJWAg4COQGuAPQAFwFGAYYBRAE/AXMBPwEHAY0Aov8y/wj/6f5A/87/LQCAAHAAAQDD/5T/qf/R/9r/qP8c/9z+7f5L/+7/TwCIAN0A+ADxAEsBggF5ATABbgDn/7L/0f/g/8X/r/9k/wj/m/4a/qr9af0r/S39fv3p/Z/+Fv9j/7r/4P/3/x0AKwBXAGkAFwCQ/xX/v/7K/vv+Fv8g/yf/Tf8j/+n+c/4R/l/+vv4K/2b/g/+v/04AqAD0AGMBlgHeATYCIgKsAUQBGQGdAXECJQOBA24DPgPMAl0C4AFnATIBXAFnAUsB2wDl/w//if5n/tn+df/0/0wAOgDa/zL/0v4K/5//NgBaABMAwf93/27/mf+x/xIATwBVAEQABwDX/8b/6//Z/9D/uP+J/5X/uv/9/z8AVwAOAG7/wf4s/r79qP21/fH9Uf55/mX+Xf5U/qj+VP+p/87/uP9W/1r/bv9Y/1T/Bv/e/v/+J/9f/5n/tv/G/9D/uv/a/ycAgADSAPYAEQEbAf0ApgBDACsARACRAO8AIAFYAY0BggFIARkBAQHUANcA6gAwAaMB1AEIAvEBkgF5ATYBFwEuARwBLQEZAboARAC6/0f/Qv9v/4X/q/+A/1v/ZP9m/6D/AQBXAJ8AywCRADIA0/9q/yH/6f7O/j3/4v8/AAUAO/9T/pj9XP1h/bf9Wv6d/nn+Of7H/br9Kf7M/m//8P8SAMj/if+H//T/nAA/Aa0BzAGGAQwBugCfANcABQEeAfoAdwDB/zf/Hv8u/5T/+/8eACsAFQD///v/EwATADIATABsAH0AmgClAJ8AiwB5AIkAxQABARUBNAEgAeIAYwDc/6L/5f+SAEYBtwGoATIBmAD//5X/X/9H/1j/ev+p/+X/JwBVAH0AbgByADQA7v/p/w4AVQCmAKYAcABpAFwAWgBKACAA8v+d/z7/yv5n/kj+if79/kb/Yf8w///+8v7T/t7+5/7y/kL/df98/5v/jv+L/5v/tv8SAI8AugCuAEEAnf8n/xz/aP/3/3AAewBMAPb/w//O//3/RgCPAIIARgD5/6b/n/+8/wkARAB5AIQANgAOAAoAJAB9AMMA/QAVAfoA4ADFAJYAgABjAGUAiACLAF4AEwDF/4v/eP9u/2//jP+0/+D/KQBrAJQAqAB3AA4AlP9U/9v+kv5q/lj+l/7D/qr+pv7Z/gj/i//O/9P/zv+o/7L/zP/a/+n/FQAvACYA7f+X/3r/kP/F/xMAVQCNAH4AWAAvABsAYACWAMwA9gDJAJYAWgAyAFoAwwAiAU8BRgEVAdQAqADFAN8A1AChACYAqf8s/wT/CP9R/6T/yv///wEAIAAwADAAVwB3AJoAyQDfANAABQEtAUQBTQElAd0AugB5ABAA2v/B/+3/HQDX/3z/Lv/D/rP+vv7V/vn+Kv94/9D/KwBPAD8A4v+J/zD/7/7p/tz+5P7Q/nr+Sf4y/nP+5f49/1v/RP8N/8P+of6o/u/+X//a/z8AgACSAIkAqADDAMkAtwCPAFoAYACYANsAQQGjAaQBlgFYAfMArACJAJEArwDSAKgAUQD3/5f/Zv9H/yH/HP8q/0T/df+S/5X/eP9+/5//5f9XAIkAowCRAGEATABcAJQA2QAVATsBTQEyASkBEAHdAMAAvADbAPoA6ACoAFUAHQAVADQAWgBuAGsASAD5/4X/I//n/gb/S/+V/+v/KQBlAJQArwDFAN0AvgBpAAcAmf9a/1b/Kv8w/zT/If8s/yH/Gv8K/wb/3v6q/qL+gP58/o7+mf66/uT+Gv9A/03/ZP+g/97/IABXAGUAdwCLAFgADADj/+n/WgDfADIBEQGlAA4AtP+r/7r/3v/2/wcAAQAFAO7///8bABUAFQDc/8X/wf/Q/+7/DgAwAEwAdwCfAMcA1gDDAKYAggBaAGkAcgBjADYA9v/Q/6v/v//w/zQAVwBrAFUATgBVAGsAkQDAAN0A1gC3AGEAKwABAAMAJAAQAAMA+f+x/5D/nf+m//n/GQDr/7T/bP81/yn/WP+d/97/3v+O/0b/8P7B/sX+7f4I/yH/Mv9N/3z/wf/w/x4AQQBYAHAAYABnAHAAjQCxANYA+AAOAQUB+gDoAMIArAB1AE8APwBKAH4AiwCUAGEAAQDF/5T/fv98/47/fP+A/2j/R/83/0n/df+v////LwA6AAUArf9m/0v/VP+m/wcARABpAFoATAA7ADoAPQA7AC0AIgAXAAcACgD//+D/xf+J/13/QP8Y/xr//f4D/yD/Pv9x/5f/r//R/wcAGwA0AEQASAA9AFUAVwBaAF4ANgAHAOL/6/8MADYASABBADgAJADy/7T/vf/X/xcAZwB5AGwAUQAyAB4AGwATACYARABjAI8AlACYAKYAwgDDAKoAiQBVACIA+f/g/6n/l/+k/6//zP+2/5v/pv/K//D/+f/g/9D/q/+o/8r/4P/5/xMANgBVAH4AmACPAHcAUQBeAIAAggByAFMAIAD//+P/3P/j//f/AAAMACAAGwAOAAAA6//T/6L/WP8u/xb/GP9C/2z/h/+8/8r/4/8ZAD0AfQCYAJIASgABAMH/nf+k/8b/8v8BAAcAzv+y/7T/nf/M//n/MABjAGkAbABlAGUAewCcAKwArwC5ALUAoQB7ADgAFQAQABAAOgBYAFMAQwAXAOD/r/98/3H/c/9v/6n/xf+//6T/gv93/2T/df9v/4X/qP/B/+v/AADy/87/lf9v/2T/S/9G/2T/n/+9/9H/2v/r/w4AFQAdAAwA6f/3/zAAnAAVAWMBXAH9AK4AYQA6ABsA7f/K/6D/fv9J/0L/Zv94/7T/uP+p/6D/jv94/2j/T/83/0L/av+m/8b/6/8OAB0ABQDy/+7/8v/7/xMAMgArAC8AMgBDAGsAnQC+ANcA1ACsAHkAYwBKACYAFwAAAPb/5f/O/7//zP/w/y8AcACLAIsAcgBgADsARABOAFEAbACGAIIAYwAkAOv/yP/c/x4AOABKADoAIgAeABcA/f/j/9X/sv+b/5L/pP+0/7r/3v/9//b/4/+//6b/l/+J/47/kP+S/7z/4/8OAEMAYQBpAGsAawB+AIgAbABVADIADgD7/9n/xv+i/5T/Uf8p/z7/Pv9b/2H/Vv9Y/2P/Xf9b/2P/if+9//v/JgAnACYAMgBEAD0ATwBaAHcAmACUAGsAOwAvADIAOgBOAEQALwATAPn/7f/c/7b/tP+0/63/0//u/x0ATwBeAGUAZQBlAFcASgA6AD8ARABMAFcAYwBnAFcATABGAFUAZQCRAJ8AhgBTADIAHQAZACYAMgA/AC0AOABBAEQALQAmAAwAEAAHAN7/sf9j/0T/Pv9P/3r/m/+A/2//R/8w/1//h/+k/7j/2v/Q/8r/rf+D/4X/lf+p/4v/bv9v/1//WP9j/4L/jP+U/63/2v8OAD0AXACAAH0AWAAvAAEA4v/n//f/FwBGAF4AdABrAFwAPwAmACsAJAAdABcAFwA4AGAAeQBuAD0AAQD0//T//f8rADAALwASAPf/5f/t//3/7v/u/9f/tv+S/3j/Y/9h/37/jP+k/7H/tv+k/7r/2v/0/woACgAOAAoAAwAAAAUAFwAtAFMAZQBlAGEARAA2ABMADgAXACYAPQA9ACYAFQAiABUAFwAdABcAAQDw/8X/pP+Z/4z/qP/F/+n/EAAgAA4A+/8XAC0ARgBYAFgATgA9ABsA7f/a/9X/zv/i/woAMgBaAIIAnwCPAHsAYwBTAE8ATAA7ACQA/f/l/+X/6f8VACQAMgAvACAAAADe/8z/yv/p//L/8P/a/87/3v/y/wEAEgAFAPf/9v/V/8z/yP/B/8j/5f/r/+3/1f+9/9f/6//9/wcA+f/0/+7/8P/y/+n/3v/B/73/uv/B/8r/2f/t////FQArADgAMAAvADgAOAAwACcAAADe/7b/m/+Q/5T/h/9f/0L/Ff8B/xb/Jf9j/63/9P8kAEgAbgBwAH4AkgCdALEAuQDkAPMAzgDFALEAiwBuAEgALwAtAC8ALQAOANn/zv/V/+3/CgAMAA4AAQDy/+3/8v8OAAwAHQA0AEwAcABwAHIAYAAZAND/oP+k/7T/0P/w/+n/5//R/8H/3v/r/+v/yv+i/3H/fv+v/+v/LwBpAJoAiABlADoABQDF/5D/fv+S/6//xv/F/7L/v/+x/8H/uP+X/37/Zv9W/y7/Fv///vD+If9R/4P/uv/e/wAAHQArAC0AIgAmAC8ANABBAEMAXgCIAKoA3QAIAQMB6gDSAKgAgABeACcAEgAFAAAA9//X/73/v//Z/9H/2f+8/7T/uP/X//D/7f/7/woACgAbABcA6f/V/8P/uv+9/73/vf/B/+D/EAAXACIANAArADoAQwBOAFMARABBAEoAMgAHAOP/wf+d/4v/cf+A/5v/xf/0/wwAHQA7AEQASABsAIgAqAC6AK4AlABwAFcAawBuAFoAOgDn/73/tv/g/+3/7v/7/87/yP+8/5T/if+D/4P/h/98/4v/pP+6/97/5//2/wkAIAApAD0ATgBrAHIAWAA6ACQAOgBGAFgAXgBXAEYAEADZ/6n/lP9x/4X/fP9u/4X/mf+6//D/AQAFAB4AGQAMAP//9v8DAP//EgAVAAwA+f/j/+L/zP/K/9X/+f8HAPf/5f/w/woAKQBGAEEAHgDc/7j/vf/T/+n/CgAOACIAMgBEAEEAKQAdABkABwAAABMA9P/V/8X/0//V//L/5//j/wMA9v////f/BQD9//D/5//n//D/4v/G/6L/if+S/4X/g/+t/9f/AwBDAFgAVQBBABkA/f/p/9X/+/8QACYALwAnACcAHQAkACkAJAASAP3/zv/M/+n/CgA0ADgAOAAtACIALwBYAHQAbAB0AGsAYQBrAGcAXABBAEoAPwBOAFgATgA9ACQA9/+x/4f/gP+f/9D/BQA4ADAANABEAEwASgA2ACsAHgADAPD/3v/r/wMADAAKAAAA4//T/9n/uv+p/5n/gv+f/7L/pv+g/5D/gv9+/2j/Wv9W/2H/Uf9k/47/pv+6/8X/2f/w/xMAKQA9AFMAVQBjAFgAVwBTAFcASgAwABsAAQDy/+7/8v/w//b/8v/u/wAAGwAZAAMA9P/R/7b/tP+b/6j/sf+v/73/pP+6/7z/xv/5/xAAJwAmACcALwAkAC8AMgArAEwAcgBrAGUASAAmAB4AKwAyAC0AEAAKACYANAA4ACcAGQAyAEYAWABuAHcAWgAtABcAAQAFAPT/7f/e/8P/w//O/87/v//X/+P/AAAVAB0AJwAdABUAGQAVAP//7f/c/9H/3v/7//b///8VAP3/9P/a/8z/vP+o/7b/0//r////9P/V/8X/zv/V/9r/5//3/xMABwD2//D/5f/g/9f/0//Z/+3/5/8BAB4AEwAQAAkAEwA2ADYANAAdAA4AFwAiADoAKwAXABAABwAXAA4AAQADAAAA8P/u/+P/0//g//L/0//O/9X/1f/O/7//w//F/8j/zv/e//D/6f/O/73/tv+t/6n/qP+0/6v/yv/0//v/EAATAAwABwADAP3/8P/y//T/6f/V/9n/1f/w//L/8v/y/+f/2f/i/+f/6////xMAOABDAEEAMAA6AFgAfgChAKUAnwCPAHkAcgBcADIADgDw/+f/5f/2/wUAEgAQABAA9v/T/8j/yv/M//v/DgAZAEMAWgBpAGkAiACSAJYAkQBpAE8ANAA9ADoALQAmACcALwApABUABQDr/87/zv+9/5v/mf+k/7z/vP+y/7L/zP/i/+7/7v/M/63/vP/F/9z/+//7//v/9/8BAAwAAQD0/9D/vP/D/7z/tP+2/87/4v/i//v/AwDn/+f/+f8DAB4AHgAmABsAKQAnABIACQABAAAACQAXACAAKQAnACsAMAAvACkAGwAFAOn/3v/l//b//f/t/+3/3P/V//f/5f/t/9r/yv/O/8z/pv+H/4P/Zv+F/5L/m/+t/8H/0//g////CQAdACYAOAA9AFEAYABMAE8ARgBIAE4AKwAbAAMAAwAVABMAHQAgAB0AKQAtABAA+f/9//n/6//p//L/4v/K/9X/4v/y//3/AQAHAAMAGwAQABAABQABABIAEwAVAP//9//3//L/+f/r//L/4v/j/wUAEwApAEMANABDAEgAIgAdAAUA/f8BAOv/4//u/wAA9v/r/97/qP+F/3P/fv+b/8b/7f/i//D/7f8FAAoABQAMAAMABQAFAAUACgAQAAwA9//9/xMADgAOAAEAAQAFABkAMgA9ADoAJwApACIAGQAJAA4AAwDy//D/9v/j//L////p//D/AAD0//b/8v/j/+7/6f8BAAkAEwAbABIAGwATAAEA/f/0//D////j/9f/vP+t/6j/xf/i//T/AwAMABcAAwDj/8z/xf/I/+f/9//3//v/DAAKAAkAEAAQAAoA+f/u/+v/DAASABUABQD5/+7/AAAiAB4AJwAgABMACgAJAAoACQD//wUAHQAnACAACgAHABMACgAtAD0AJwAyACYAMgA4AE4ATwBRAFcATwBDAC0ALwAbAAcA9//t/9z/6f/j/9P/0f+2/63/ov+i/5f/qP/B/9H/4v/j/+7/+//2/wEAHQAmACsAMgAtACIAJwAmACAAFwD2/+X/9//2////AAD9/wEA9//u/+L/3P/G/8j/tv+6/8z/1f/i//T/+//t//b/AQADAAoACQD2/+3/3v/p//L/5//w/+L/2f/K/8r/xv+k/4n/lP+f/6j/sv+x/8X/3P/2/wwAJwAnAA4AFQAVAAcAEgAKABsAMgA6ADoAIgAmAD8ATABGAFcASAA9AEoARgA9ADoAJgAeABIA//8KABUAFQAdABUAAQAFAPv/7f/g/8b/v/+8/8P/0P/T/+v/6f/t/woACgD9/wEAAQAJABMAAAAAAPb/9//7/wEAAQD7/wEA+//2//b/AAABAP3/CQAMAAAA6//a/9H/0//V/+D/4//w/wAA+f/9/wkABwAHABIAHgAbAAkABQADAPn/AQD7//v/CgAKAAkABQAOAB0AJwAnACcAKQAmAEMAVQA6ACcADgD//+P/3P/c/9H/2f/R/+X/7f////v///8FAAMAIAAgAB4AGwASABUAAwAJABcAHgAkACAAIAAQAPv/4v/O/8H/v//F/9H/3v/n//n/8v/u/+v/6//l/87/0f/O/9D/9/8AAAwAGwAXACIAKwA0ABMA/f8OAAcAIgAvACkALQA6AEEAMgAZAAUAAQD9/woADgD9//3/CgAZABUADgAAAPL/9v8AAPv/+//y//L/+/8KAB4AHgASAAUABwD7/w4AGQAVABcAAAADAPb/6//j/+7/5f/a/+7/+f////b/7f/n/+D/zv/F/87/zP/O/9f/2f/V/9H/yP/G/7z/zv/O/8P/xv/V/9D/yv/F/8X/0P/D/8X/vf/D/9z/5f/0/xIACQADAPv/9P/3//v/FwATACsAHgAMABIAGwA6AEMASAA7AC8AHgAXABcACQAFAPf/9P/w/wAAAwD///f/AwAKAAMADAD7/+X/4v/w/+3/8P/u/wcADgAOAAoACQAMAP3/+/8FACQAJwAvADAAJAAgAC8AIgAMABUADgAbADQANgAtACQAJAAKAPL/1//V/9f/2v/e/9X/1//c/+7/9//9//3/+f///xUAIAAmACYAMAA4ADgASgBDAEYARABIAE4ATABIAD0ANAAvACcAHQAMABUAKQAQAAEA6//B/5f/gP9o/2P/av9h/4D/m//I/9n/7f/w/97/9v/3//n/AQABAP3/+f/7/wEAFwAMAAUA+//u/+3/2f/X/9X/1//j//f/AAAbACYAFQAZAAoADAAHAPv/BQD2//3/CgAFAAkA9v/3//b/7v8HAPf///8bAA4ABwD5/+X/0//p/+v/0f/D/7j/v//R/97/3v/X/9f/7v/7/+n/2f/O/9P/3v/e/+X/9v/n/+D/6f/t//v/9//u/+3/8P/5/woADAAFABIAEgAiACQAJwAnAC8AOgBBAEwASgBKAEEAMgAwADgAIgATABkAGwAdAB4AJAAMAPf/7v///wUAFQAiAB0AHQAZAAwACQAMABAAJgAmABcAHgAyAC8AJgAeABUAEgD3/+7/9P/R/87/zP/I/+f/5//t/+v/9P8AAPf/CgAMAB0AJAAZABUAAwABAP3/AQDt/+7/6//e/9z/4//O/8H/tv+4/8P/wf/I/8z/1//K/9n/6f/l/9X/1//t/wAAEAASABUADgATABUACQAXACYALwAtAC0AJwAdABsAFwASABIAHQAQACIAEwAJAAoACQAJAAEA6f/e/9n/xf/T/97/3v/g/+f/9/8BAAMAAwD3/+7/8v/y/+7/8P8BAA4AIAApACkAFwAJAAcACQD2/+3/8P/c/+L/8P/u//D/BwADAPv/7f/X/9r/0f/a/9P/1//O/9P/8v8DAAoAAwAQAAAA8P/0/+n/9P8BAAcA+f/7/+3/9v/w/9r/4P/g/+7/+f///wEACgAXACQAMgAvAC0ALwBIAEwAQwBTAFEAUQBMAE4AOgAKAPf/8P/l/+7/8P/t/+X/5f/e/8j/1f+0/6v/sv+0/8P/w/+8/7//yv/e/+3/8v/7////CQAMABAAIAAVABAAEAD7/woAFQAgADQANgA0ACYAHgAZACcADAD9/wMA/f/9//D/6//l//b/5//g//D/+f8FAAcAFQAHAPn/8P/j//T/6//e/+D/3P/c/+n/+f/n/+n/6//w//f/7v/2//L/5f/7//v/7v/0//v////7//b/+f8AAAAACgAOAB4AJgATABMAEAAJAP//AwD7/+v/AAD//wMADAAJAB0AIAAwAC8AOAA9AEQAQQA7AEEANAAbACAALwAdAA4A//8BABUAGwArAC8AHQASAAoAHQAkADsAQwA/AEQAPQA6ABMACQD2/+v/6//G/7//vf+4/8P/sv+0/7b/wf+8/7b/xf/G/8j/xf/I/8X/w//D/8r/xv/B/9D/1f/c/97/6f/p/+3/9v/p/+v/+f/2////DAAQAAwADAADAP//BQAHAAkABwASAAoACQAOAAwAFwAXABkAGwAdABAADAAFAP3/AQABAPn/7f/c/8j/yv/Z/97/0f/c/+v/5f/5/wkABQAAAAMAEAATABAAGQAQAAwADAAJAAkAAwABAAMADgAQAAkA9P/e/9X/zv/O/9D/1f/e/+v/6////wcACQAQAAoAGQAnACkAKwApADIAIAAtAEMAQQBeAEoAKQAnABsABwDp/9r/2v/i/+3/5f/c/+n/+//5//T//f/2//n/EgAbABIABQAVAC0AQwBVAE8AWgBMAD0APwA4ADAAJwAiACQALwAiACcAIgAOAA4A//8BAAcAAwD2//D/8v/j/9n/zv/B/6//lf+M/47/if+b/6j/qf+f/6b/uP+8/8z/zP/g/97/7f/7/wkAGQAdAB0AKwAeAAwACgD3//T/9P8DABcACgAHABsA/f8AAAUA7v/2//D/8v/0/wEAAQDt//L/9/8QAA4ABQAKABMAFQABAAcAAAD7//n/AQADAAoACQAQAAkACgAQAAoA/f/j/+f/6//w//T/+//5//D/6//t//b/8P/t//D/8P/y/+7/AQAHAPb/8P/u/9r/1f/j/+P/8v/y/+7/6f/w/+3/6//0//n/BwASABAAAQD0/+7/8P///wcABQD3/wMABwAHAA4ADAAXABcADgATABkAEwAVAB4AFQAVABUAFQAXAAkAFQAHAP//AQD9//L/8P/0/+n/2v/O/9H/zP/X/9H/1//g/9r/5f/n/+P/6//7/+7/5//3//b/AQAJAAcADgAgAB4AFQAbABcADAAFAAoADAAMABIAGwAXABUAFQAQABAACgAOAAwA/f8HAP//+/8BAPT/9//9/wwAEwAiACkANAA/ADoAOAAmACIAKQAeAAkA///n/+D/4P/Q/87/zP/V/+P/7f/w/wUAAQAHABsAFQAZABcADgAXABsAHgATAA4ADgAFAAwABwABAPD/5f/r//D/9v/y/+v/2v/Z/9r/2f/g/+L/2v/g/+L/4//t/wAADAAZACYAGwAgACsAGQASABMAHgAOAAoAEwAQAAkA8P/7//f/5//7//////8TABkAGQAQAAAABwAMAAUABQD///f////3//f/7f/j/+f/6f/5/+v/3v/g/87/1f/D/9P/7v/t/wAACQAHAPT/6//p/9r/2v/V/8z/1f/V/9X/3P/e/97/5//3//D/+f/2//L/5f/n/wEAAwAAAAAAAQD5//v/AAAKABAAEgADAAkAGQAdACYAGQAeAB0ANAA7ACcAMAAdABMADgDy/+P/3v/a/97/3v/r/+n/6f/3//D/9P/n/+L/5f/r//f/+/8MAAwAFQAkACQAIAAiABcAHgAtAC0AMgAwAC0APQA/ADoAMgArADIAJgAtACsAJAAwAEEANAAwACcAHgAZAAkABQDw/+X/4v/R/8j/0f+//73/0P/R/9D/1f/Z/+D/7v/r//v//////wwABwAFAAcA/f/5//f/+//9/wEABQAKAPb/3P/R/9H/1//M/73/uP+4/7z/yv/M/87/zP/X/9D/0P/i/9P/4P/p/+f/8P/5//3//f8KABMAEgATABkAFQAQABMAEgASAAcAAQADAAMADAAbAB4AIAAZABcAGQAKAAMAEAAiAC8ANgAeACQAJgAVAB4AEAAOAA4ADAD5//f/9P/p//T/7f/a/8z/zv/Q/87/0//a/+f/0//c/+L/4v/w//L/AAAHAAEAAwAOAAUABQD//wEABQABABMAFwAbAAkAAwAFABIAIgApAB0ADAASACQAGQAHAAoABwD5//L////r//D/5//l/+P/7v/j/+D/5//n////5f/0////+f8FABAAEwAeABIAAQADAP3/9/////v/AQAVABcAFQAQAAwAFwAZABMAFwAZABkAIgAiABkAEgAKAA4AAwAJAAoACQAOAAUA//8BAPv/8P/3/+3/4//t/+7/6//g/+v/5//r//b/9P/5/+3/9v/2/wkAEAAKAAAAAwADAPL/+f/5//v/9/8AAAcAAAD9/wEA/f/0//b/6//p/+7/6//p/+7/7f/7//b/4//2//D/6//3/wEA9//3/wMAAwAOAAwADgAdAB0ADgAOAA4AEgAFAAkACQAJABIADAAMAAUAEgASABAA/f/t//T/6//r/97/4//l/+f/8P/w//T//f8BAP//AAAAAAwADgAOABkABQD5/wAA+/8MAAwAAwD//wMABQAAAAAAAAD2//v/CgADAP//+f/9/+7/9v/2/+v/9P/r//L/7v/c/+P/1f/X/+f/5f/y//n/AwABAP//EwAAAP//BwADAAAAAwAJAAAAAAABABUADgAMAAEA+f8HABIABwD///L/4v/e/9H/0P/R/9r/0f/R/9X/1f/T/+L/5//e//v/AAAJABUAEAAiABkAGQAeABkAGQAVACIAHQAbABkADgAMAAAA7f/5//3/6//p/+7/9//5//n/CQD9/+3/AAABAPv/9//u//v/+f/9//f/9P/3/9n/4P/Q/9f/5f/g/+v/7v/y//L/+f/5//n///8BAAAAAwAKABkAFwAVADAALwAiADIAOwA0AC8AKwArADIAPQA9AD0AOAAwACYALQApACYAJwAVABMAGQAQABAADgADAAUADgAKAP3/+//3/wMAAAD3/wEABQAAAAcABwAHAAcA//8DAPD/9P/0/+f/4P/e/9X/0f/X/9H/4v/l/+n/6//p/+X/5f/y//D/5f/c/+D/2v/R/9H/yP/Q/97/1//T/8j/yP/K/9P/2v/l/+f/9v8AAP3/CgAKAAkAAwAHAAAAAwADABAADgAHAA4ABwAHAAkABwAAAAcACgAJAAAA/f/0//v/+//0//b/+//3//v/AwADAAAA+f///wEA/f/3/+P/1f/e/9H/4v/p/+X/6//t/+3/4v/y//T//f8FABIAEwAXACIAHQAZABkAJAAeACQAIgAmACYAGwAeABMABwD9//v/9//3//L/6//j/+L/3P/Q/9H/4P/g/+P/8v/2/wUACgAHABAABwAOABcAEwATABsAJAApAC8AJAAkACsAIgAgABUAHQAiACIAFQASABcACgASABkAEwAKAAUA9//5//f/AAD7/+3/9v/0//3/+f8AAAwAAwADABMAEgAQAAUAAAAAAP//AQDw//b//f/2//3/+//y//f/9P8AAAEA+f/3/9z/3P/n/9r/yv/Z/97/3v/g/9X/0//e/9H/4v/e/9X/4v/j/+X/8P///wMAEAAQABAAEgAQABAADgAQAAoADAAdABIAFQAdABIAFQAVABkAIgAeABkAIgAmACkAJwApACcAFQAZABIADgAJAAoADAD//wMA//8AAPT/9v/3//b/+f/u/+X/7v/5/+3/8P/2/+n/2v/i/+n/3v/Z/9n/1//X/9n/0f/a/+v/7f/w/+v/5//2//D/5//w/+X/6f8DAAcA//////v/BwAHAAEAAAABAAoA9v8BAP///////wAACQD//xAAEgAOAAUA+//9/+v/6f/r//T/+//2/wAAAQAAAAMAAQD5////AAAAAAAAAQAOAP////8FAPf/AQD7/wUAAAAHABMAAAAFAAMA9//9//3/7v/p/+f/4P/T/+f/4v/c/+f/9/////T/6//2//////8KAP//9v///wEAAQD2/+n/4v/t//D/8v/5//n/AQABAA4ADAAQABUAFQAOAA4AFQAXABcAFwAXABAAHgAOAP//9//w//v////5//f/BQD3/+7/+/////n///8DAP3/BQAKABIACQAJABAADAAJABMAGQAVABMAEAAQAAUA+f///wUA/f/9//D/9/8FAP3/AQAAAAMADAAFAAwACQAOAA4ADgAeABAAFQAQAAMABwAHAAUABQAQAAoABwD7/+3/9P/0/+7/7v/3//D/7f/3/wUA/f/0//f/+f////b/AwAJAAwAEAASABAAFQADAAUADgAFAA4AAAD7//b/9//9//f//f8AAPv/AQADAPv/+//7//f//f/5/wMADgAQAAwAEwAOABAAEAAFAPf/9v/u/97/1//K/8z/wf+9/7z/vP+2/7j/uP+0/7z/vf/D/7r/v//G/87/1f/e/+v/7f/5/wAA+f8FAAwADgATABUAFwAdADQAOAA9ADgANgAyACkAMgAnACYAKQAtACsAIgAbABcAEgABAAEA9//w//f/9P/y/+n/5f/l/+7/6//n//T/9v/3/wEAAQD//wcAEAAVAA4ABwABAAAA///3//D/9//7//3///8MAAcA8P/2//T/7v/2//L/6//l/9r/3P/R/9r/3v/Z/+D/5f/n/+3/6//y//f/9/8BAAAAAQAFAAMABwAHAAcACQAFAAEA/f8JAAoAEgAgABIAIgAXABUAHgAkACAAJgA0AC8AOAArACsAKwAtAB0ACgASAAoADAAKAAkA/f/7//b/7f/3//L/8v/w/+f/9P/0//D/7v/n//D/8v/l/+f/6f/p/+f/9v/y/+L/4//u//b/9P/5//n/+//3//n/+f/7/wEA9//7//n/+f/3//b/+//w/+n/5f/e/9n/0P/i/+D/4P/p/+D/8P/3/+3//f////f/AAD9/wAAAQD3//f/AAAKAAkABQAKAP3/AAABAAMAAAADAAMA9/8AAPb/AAD3//L/9v/3/wMAAQAFAAkAFwAiABMAFQAdABMADAAAAAEA+f/y//b//f/3//n/BwAFAAUA+/8HAAMA//8JAAUA+f8JAAUA/f8FAAcABQADAAUA/f/9//b////5/wEAAAD2//v/9v/2//v/9/8AAA4AAQAFAAUAAwADAAcAEgAXAB0AJwAyAC8AKwAkACkAKQApABsADAADAAAAAAABAPf/7f/3//n/7f/p//b/6//j/+D/5f/n/+3/9//0//T/+f////v/+//9/wcABwABAA4AEAABAPT/9//0/+7////u/+f////t/+3/4//j/9z/2f/n//T/+f/w//v///8OAAoABQAJABcAHgAXABMAEgAVAB0AIgASABAADAAJABAACQAJAAwACQASAA4ABQAOAAoAAQAFAAUAAAAJAAMA9/8AAAcA//8DAAUAAQD//wEACgABAAAA+f///+n/3P/p/+D/3v/c/9P/0P/M/8X/yv/K/8j/xf/I/8X/xv/M/8b/0P/O/9X/1f/a/+X/8P///wEA//8KAAcAAQAOABAAEgAeACIAHQArAC0AKwAkAB4AGwAQAAwAAQAAAPD/7f/y//L/8v/e/9H/4P/e/9f/3P/c/+D/7f/t//f/9P/y//v/9//7/wAABQAFAAMADgAKAAkAFwAZACIAJAAyADAAMAA6AD0ARgA/ADgAPQA4AC0ALwAiABkAFQAMAP//8v/y/+v/7f/y/+v/8v/2//T/8v/2//L/7v/y//f/BQD///v//f/5//3/9P/y//T/8P/w//L/7v/y//n/+f/5//T/8P/2//n/+f/5//T/7v/y//f/+/8BAP//BQAQABAAEAAOAAMACQAJAAMABwAFAAkA/f8DAAEABQAOAAkADAAKAAoABwABAAAAAwAJAAcABwAOABMAFwAXABsAFwAVABUACQADAAUA+//2//b/6f/p/+f/5//p/97/7f/t/+X/4v/g/+f/3P/g/+L/2v/c/97/0//Z/9z/1//l/+X/3v/g/9z/4P/l/+P/4P/e/+D/4P/j/+P/0//Z/97/6//0/+3/9/8BAAUACgASAAkACQAMABUAGQAVABMAEAAKAAwAEgASABcADgAQAA4AEAATABIAFwAVABsAFQAJAAcADgAHAAMABwAFAAUACQAFAAEADAAHAAUABQAJAAMABQAHAAEABQAMABIAEwAZABkAEAAOABIAEwATABsAHQAkACAAGwAVAAkADAADAPn/AAD7//3/9//w//b/7f/0/+7/9//0/+3/8P/w//T/8v/5/+3/9//3//n/9//5//T/9v/y//T/AQD7//n/9v/9//L/+/8FAAUAAAAFABAADgAJAAAABQAJAAMABQABAAcACQABAAMA+//u/+n/6//c/9f/4P/r/+v/8P/p/+3/9P/2////AAADABAADgABABAAEwAJAAoACgAHAPv/8P/w/+n/6//w/+n/6//t/+n/4P/c/9z/5//r/+7/7v/u//D/8v/7//3/CgAMABUAIgAkACYAMAA7ADIAOgA2ADAAMgAvAC0AIAAkAB0ACgAHAAUAAAADAP//6//0/+3/5f/n/+n/7v/7/wMACgAdABkAHQAbABMAFwASAAoACQAHAP3////7////9v/r//L/4//n/+n/1f/M/7//w//B/7z/xv+6/7T/xv+6/7T/tv+9/7//xf/D/73/yv/Q/9f/2v/T/9H/0//c/+P/8P/p/+3/9v/2//n/9P/7/wUAAAADAA4AEwAeACIAIAAdACQAIAAZABsAEwAXAB0AGwAQAAwAEAATABcAEwAbACIAFwAJAA4ADAAMAAUA//8FAAMAAAAAAAoACgAHAAwAGwAdABMAIgAgABIAJAAnACcAMAAyACcAKwApACYAIgATAA4ADgASAA4ABwABAAoAAAD5//T/7v/r/+L/7v/g/+D/3P/T/97/2v/V/87/zv/O/8r/yv/F/9H/zP/M/9n/4P/n//T/AQAMABMAKwA4ADoAPQA/AFEAVQBcAF4AYABeAF4AWgBKAEQASAA4ACkAEgAHAP//6f/c/8P/v/+6/6n/nf+X/6D/pP+p/8P/v//I/9P/3v/r/wAAEgAbAC0AOAA/AEEASABKAEYARAA/ADoALwApAC0AIAAKAAUA+//r/9n/yv+//7L/qP+k/5f/lP+D/4X/gv94/4X/gP+F/4n/if+H/4z/lf+b/7L/uv/I/9D/4P/r//b/CgAMABsAIgAkACIANAA7ADgAQQA/AEgATwBKAEYATwBIAE4AXgBnAHIAZwBnAGkAVwBVAE8APwBBADoAJAAmAB0AGQATAAEABwAFAP3/+//y//D/7f/0/wAA9v8MABAAEAAeABMAEgAMABIACgATABAACQAXACAAIAAbABMADAAKAA4ABQAAAPb/6f/Z/8P/sf+f/4z/gv9v/1r/S/87/zD/Gv8I///++f7l/tf+2/7c/un+9v4D/wz/IP8y/0f/aP+M/6n/yv/l/wEAJABGAHAAhACmAMkA6gAHASUBSAFhAXsBiwGsAbcBxwHeAe8B9AEBAgECCAIIAvEB4gHdAcABqAGZAWcBQgErAREB7wDCAJ8AjQBYADIAEwDl/8j/oP+D/2z/RP8p/wP/+f7k/r7+pv5+/mr+XP5A/h7+F/7//e397f3p/eD94P3e/dn90v3S/dD9vP24/ar9qP2j/Zb9gv1u/V79Vf1L/T39P/1B/UL9U/1N/V/9d/2U/bX9yf39/T/+gP6+/hj/bv/I/y8ApgADAXAB4AFEArUCIAODA9UDMQR8BM8EFAVMBZAFsgXjBQIGFAYbBiEGKgYmBgsG8AXTBbQFiAVSBRYFygRvBBMErwM+A9cCcQIFAosBFwGUAAcAi////oD+CP6b/Sn9x/xR/PH7p/tM+w77yfqW+lv6JPr5+cD5sPmi+ZX5j/mD+W/5ePmO+Zf5tvnd+QL6Jvpg+pj61voX+0z7iPu++wP8Q/yG/Nb8HP1c/ab9+P1L/q3+CP9u/+f/VwDoAHcBGALVApcDYgQoBfAFxAaTB2sIHAnFCWoK+AqNCwYMXwyZDL8MxQydDF8M4gtJC6oK+gkeCSYIFgf1BeMEzAOxApsBkgCg/6j+vv34/Er8u/sp+6X6R/oO+vH57PkC+g36K/pe+or6vfoM+1r7ufsK/Ff8nfzf/B79Uf1+/Zb9rP2q/Zr9if1a/SX95PyW/CT8pfs8+836Wfro+Wj5+fh6+Br4yPd490/3LPcL9wj3I/dJ94n3xPcW+IX48viD+SH6xPp3+zT8B/3y/en+/f8XAU0CdwOtBAkGWwfHCBoKRgtuDG8Naw5XD0MQ8RBiEcIR6BHoEb4RcBEHEVkQbw9fDi4N8guqClMJ3QdmBuwEZAPpAX4AQv8j/iT9Mvxc+6H6C/qa+Uv5HfkP+QD5Cfkz+Wb5q/n6+UP6cPqu+ur6C/sz+z/7U/tp+337dPtj+077IPvq+q76dPob+s/5hvlB+fX4l/hH+PT3kPc79/b2qfZ+9lL2LvYX9hP2CvYc9k32fPbK9iH3ivca+KX4WfkZ+hD7HfxP/aj+EACWASoD0QRVBvoHpglAC+AMWw7JDyIRaRKAE0kU+RR+FbgVvRVzFf8UYxSEE4QSXhH5D1QOhwyzCrwI3AbwBB4DZwG9/0L+0PyG+4H6nPnM+DH4uPd491T3ZPeg98/3Qvi4+Cr5k/ko+qD6IPuu+/z7SvyU/MP8zfzA/KX8Z/wX/K77JPuc+vX5R/mk+OX3J/du9sP1KfWS9BX0sfNn81zzVfNn84vzyfMr9KP0NPXb9Yv2VPcu+Ab50/nN+qr7x/wT/lb/+AB6Av0DdgX6BogIFQrHC0sNyA4ZEGIRhxKZE34UORWiFbgVqBVJFfgUgxSxE6wSXBHdDxoOTwyJCpwIvQbEBPACJwFY/8P9UfwC+/75GPlJ+Nj3h/eD9373ivfG9xT4fPgG+XT5/vmq+v76ffvt+yb8Y/yU/Lf8s/yc/Gn8Rvz4+6P7K/uY+hD6bfnN+Ef41fdP98r2UfbV9Xz1EfWo9FX0EfTH87nzw/PU8wj0e/QO9Xz19PV19vT2lfdC+PP4H/qP+2r9b//9AMcCbARvBm0IVQosDK8Nuw9pEQYTlhSkFbYWUBd4F5AXYhdLFxgXZhZhFdsTRBJnEKIO5wz2Ci0JBwe6BJACrgDv/oD9U/z4+q75rvjr91b3O/cf9wv3QPds99j3ZvgJ+c35Q/rx+m370Pth/Kz8G/0//RX95vy1/Gv88fuP++76Qfqa+en4JfiM9/T2YfbD9U317/S19Hv0U/Rs9E30Z/Sa9MX06/Q/9cr1Tfbc9h73nfcS+F34C/mX+cL67/tG/cr+7f+vARUDGAXzBooIewo5DC8OkQ8pEcASwxMTFcEV+xV2FpcWwxatFksWbRU1FMISxhDhDqkMpgqoCIMGbQRcAoQAwf4r/bf7SvoL+Qn4Nfei9nj20fYu95n3QPiR+BL5Lfo2+9T74Py3/Y7+m/8bAHsAOQEeAsUBqAA4AAgBIAInATT+5Ps6+zT7Evq69tnzWvLu8WLxr++87d7sE+2W7FPsw+z/7dXuve7M7pzv7/H682f1E/br9hr4iviN+iD/rQQdCIIHIwa5BqYKJBC3ERcRyREhFMEWnBgoGfoZ8RtyGlcW1xMyFAcXOBjwFF4Qrw06DdMMwwlZBVoCnwDr/Tb6Yffe9+H5YPlY9ifz/vKt9VT4q/fH9aL1z/cJ+y79uv2W/U/+WP8a/3z+0v5lABYDMgPmAb4ANAB1AUgCfv/W+x37B/tw+nr4G/RV8ZfwCO4k6T3k/eJd5lPpxufk4h/fA+DG5Ynq3euo67rrFu4h8K7wj/T/ArAYmyjMKP4ayRKWGz0sUzZbMxMtGjBGNx84ijFxKcclJiOYGowOzQeqB9kIaAX++zHzc+6C6sXlwN8p27bb+d3a3ZTcgdyV4Crn8+ph69nrgfBv+QgD/QlMDrARCBYVHNggqiKKIpwgbx6UG5EYwRZqFV4S0Av9ApL88vh+9TjxJ+y0587k1OJn4ODcXNo42erYJ9mP2KzY0tq/3V3f8t4z3h3fNuK95X/pm+0F8Sv1KPmj+s/6Mfm6AaIf2z/7UFlFBCnKHmEwF0XPTcFJ80M4RiBFhzlLLSomYR5zFEEBHfIj7ebrr+vv6nvjiNlXznjEVMK1xWHLs9E02HHd2+LB5y7v3/kVAysIUA1WE8geySwHNXM2SjSpMDYt8SpSJ4YjQB75FGUJ2/5O+X30Qu3C4kjYwdI20STQtM5TzrnOktCJ0TDSUtMZ11HaOd2c4eTkA+t+7nrwQO8m70fvtfOg90z6ffze/n0Aef3a+eYAySI7RmlXcketJ/cdGTLbReVNnUfTQAJEqUIlNsAprR9jFFkHf/Oi5l7joePt49Dk9tyL0rLGOb/Gw3rNUNSX21PiRelH8FL2of6fCaYUUBl6HQojiSx3OH4/TT4QNjgsLiUrIkQdMBXtCbb+n/Rl64Di+Nrn1CTO/8hOxlzH/8qu0G3VNNkT27jag9sS3q3krOqn8pf4gfvW+v75dPjk9hX0V/Oq9IP3BfqN+lz6oPcU8tTztRN/OohUzEnFJO8TBijsQY9Pw0hMO7w/+0FSNyMptxrcDNADafLr4/DeSdyp30fmPOGp1RTH1LyFxE3SxduH5RzvvPaV/+cEKQqzFK0dFCL3JqYsyzHnOKw80znaMf4j5xX+DRUIXwLE+RrvpuRs2qvTXM72ymPINsadxzDMotII267iGOi56JPnz+ZT64n0Vv3rA78FKQNn/mn7VviC9un0pvVe9Mn0mvEC8WruGO466Br5KibySY5bgD4YF9cVBS7HQPtKikHmPDVDxDo2LcAjnRSLCCb6Eeat21TT+NGJ3drnXeQX2PPGuMM2zvXV2dzI6EL1MgIsC5EOJxauIeoobis8K6ApOixyNM05ljfiK0gbVw7VBCv8wvHp5n/evdZT0S/N/8l0x9vFgsjNzU3SM9fp3a3nse6c7+fswe7F9lj/6QRDBcoCBwDJ/fb7Mfr49cLyJ+4q7nTwRvQv8lfvI+Ql9YAkyEppX19Dthx+HQkzjz06RPg3ZTc4Q8M5ACt8H7QOjQIG9cXi5tkr0CXLFNVc4jzmnuD71IbRq9Zv3APjWfBIAYkNExUZG48gUCm7MXI0dTanMs0rbSu+K3QpESTzGDUOLgNz9Abn8t0f2I3RIc4SzELKX8rhx6LJ/9FT2bDe4uS552Lqzu3z7yX2LfwD/kH9+/3k/RP/8v+G/Ar00O0C58PmI+yP8HjwGPCD6DPu3hexQF5flVMFKu8acSmNNrZBIj1AOK5D/D0tLdEgPg4m/HXuYdyJ1BLOoMRLydnaOubw5lDdjtXf2eHgz+UH8t4DYBHbG9wg4CRPLUIxkTHxNSU2rTHXLVknWiLnHrsVBgxsAvH0V+eg3PnVidDuzMXKzsj7yT7KicuH0pnaAOA85b3nsOlW7cDwc/fg/nsBc/9G/Mb5Dflf+CH2bPN68dvrK+nS6gruou6S7OXnyvYdHxRF6Vr0SugqyiCNLaM9OEdmQXg73TvsM3omBxpRC+T8VO+B4MHV+8qbwFbBgs833r7imt662uLcfOQG7Ab2SQXWEEwZ5iEZKkY1lj0nPsI8ETcGLt4omiN2H5gc1RUmDtcCx/LZ4/jX8c/tysHI7clFy1fMbc3k0WnZR97E4J/kHerE70rz2vbS/VIFAgciAt367fXF9K/2BvfL9HTvbOrv6Xft2evg5SbgUeS3AYUpPkwoVqtBMSgSIrwtwj4zQ3Y68jYWOII1jytuGvwHIflH6VjaBc/Nxl3D/cgC1lfih+VH4ODaCN7W5pfvVfmTBS8SKR2hJKos5ze+P0ZA5zkhL18nEiLbHNAZhxX+DSgFZPj86sDf99QazZTI68fkybHLIc5I0YPXg9+v5TjqDe397rDw0PJN997+AAa+CLAE1f31+Gr3E/Yp8+7tCeqP6THqUelH5VXi7eLj91EaY0BiVeRJ9i+GIHwm3jcHQxU+TzaGMyQzry8ZJdUSbgA376fgatULy9zDCcZ+0ZPeieTd36/ZP9sn5Lnva/vLBfgQchvvIm4rsTV3PktCTz6dNbou9yq4JiQhwhdJDLoB2fV36r7gKNiN0XXKpMOov/a/0cS7y+DSXto84KLkTudD6SztrPMZ/IMEbAl5CRgGDAOZAUgA4/ps8xztSOl76erq++zX6uHnzOMR9U8aWj9qUjVCtClhJIoxBT+wQm05zzM7NQgxXydwG3MMNf+B8b7jjNfkyhnAN8AGzBrb+OJX4iLhfuSC7Nnyq/joAesLNReIIuIryjfqQkRH90T5OAsqniBnG8cZaxecEEoITPww7dHeFdNJysPCrr2svbjCysqQ0o3Ze+EI5ovl6eOr5FPrr/a+AsMLTA6YCC8A6Ps2/U3/Ivoy9MPu3+pb6G/oS+088eDu6eMv87EaLEKCUs4/RisbMrE9UzuGMwUquzErPKo2xyybHrcJuPR45WveSNmvyZK67b3UzffeX+Nx5ILpBO4Y8dzuJvLG+b8DZxN1J142TUDWRKxEP0YROrkp7Ry8FDIT9w9EDJMF0vtb7Z7fX9NqyCS9n7dCui3ADcvQ0Jnaw+Wm6gfwge/961fq6+oA7xn7bAIAB/8K6wlVCTQARvTt6g7pTOnP7qT1tPfn+dHvn+qJAfwqOUrEU+U6CCZCMPo0KTePMogpyi/MNToqmCS5GaMG1Pt/6sLggNj5w6S5n8BI0NTgVOP534flL+s68zH5s/utA9wG5Q/KHz8ulTxZRThHh0gjQKgthSApFMIO2gx7BmcAYPld7snhdtefyX2+ZriZuCrCGM4O10nfL+g962/tVOyu6SXsoO/K9dL9jgTYBQAHOAgzB2gDmPpX83TvLO5h7pHwZ/Tt9YTza/q5FmY3aEwfRfgqiiF1J94vXjW4Mdsrui0GKU4inR3xEB4Efveq7CHkcdXRxD3A8cjL2QjmjuYW5sjmneuE9Iv7dwNTCHcMgBMgG1gkdTB6OPM9nD0vNCAs7iF6Fn4NbAPf+kz0/ups5Fzgtdic0C/IfMOPxqfLUdB01vLdsuUr69Ls/es96v3rV/Eb+zEFdQlRCBgFmAHe/gX8GPnc+Uj6iPp4+P7za/Dc7Xz1Sw2xK/lAu0GpMPQkDCZnKsotLSkeJKwjOiIQIB8fQRpXEZAE+PRF6EvasMwJxwvM0Nkq5Xvopubb5BjnUe0F9LX6Sf9JAx8NZRnDJREu0y9DMSIz5TJxLwgodR5rFyIQLQig/0j1Y+3K52DiodvF0qjIRcPjxLjMk9el3ibhkeGs4qTk4uW/5afo2/Ja//UIGgoUBuUCUAQxBvQDRv9o+Mn1CPgg/Ov+/f+4/lsEjBVNKAM3izXyKCcj3iWYLJAwaSqGIQkfoxuRGt8XKA/8BY/65+/q5zzd19A9yUXLbdeZ47/n+efn5fPnMu1F8m/6xQJ3Cu8S7RrJIowq/y5OMl40NjNgMaMr+iIDGbkNIQXV/3j6K/OW6vrgB9n/0enLzsfQxwjMq9KU2nnglONf5PjkqOUH54vpFO/K+G8D8QlPCZcEBwDe/koANAK8AS7+nvvU+tv8mv3R+j8AUxNVK8Q7CjmEKo0lUCnmLMksiyRsHScafBYyFd4VORMaDD8BbPbt7FnewM7IxbPIMdQc3OzeseFz53Pu7/KP9Af5SP6fA+sKthPNH8IqZjGhN3s9tT43OukvAyXiHPYTjQmGAHH51/Ry8APqteGG1z7N5sZpxWfGusi3zUrWRd9L5F/kIeQa5qzqD/CW9jD9CAGXAhoDJgW3Bs0HAAjmBjwEJf9r+Xj2Dfar9RIAMhSsKa02ITJdKMwn/CrcKVQlMBwqGOgXwxUoGKEbchpsE8cIzv2185DkVtS7zKPP0NgL39Diiuaz63jxLfRa9Ub10PNr8/z5TAWAEsEetCfVMB43CDgZM7krRCQ0HWQVgQ2CB2MATvpT9M7uOunt4Vba19ND0K7N9c1R0STXqNyc4IDkyOev63PuqPLO9zL8Wv40/s7/SwLPBc8HIAoEC8kHhwIX/oT7uflH917yfflTCkwa2iYuJyQjviS8I04frxz0FHkQABBxDVwSyxfRFq0UUxCmCd4DTPlC7s/pyOg/6+/spO1i7+PwH/Kl8oTy3fIc9DT1gfil/EYC0Al6D0sUbxYZGBsbjRwpG7kXgBJLDZ0InQEJ/L33EfXF87Dw6+zh54Xj+uHb4k/kN+bl57DpaOxf7mDwAfPX9xv8T/8iAKz9mv2J/oIAWgPGAzsC+gE0Ae3/h//0/eb9Hv3++jL36/Vc+tsBsQhUDDgQlhJAFAoVHBNPEZwQaQ46DRgOxQy/DHYNsA08D5MOpgzDCvoH0QNs/0P7ZPgB91H2fPdI+lf8t/ze/ar+vv5x/oj8Qv1L/wEAagLVA/UEPgfTBpkE+QJY/+L80vzb/Dn9mPuT+D32ZfUc9Ur1U/WN9L71oPZY9+z4l/nj+aP6kfrf+tn7xPsy/cr+EQHKAYP/1/2s/Mv7vvwp/VX9l/56/if9/Psr+8v6B/sL+676Cfv8+6j8Af82AtoFxQn9C3oMBg1fDMcLBAuhCoQK7QlBChUKSAkLBw0E9ACi/2f+if2a/Un++f8FAegBFQIKAroCiwPGBCoF2gRqBHEEgQUtB/oHlQZbBCIBnf9j/jL9J/wd+s/4V/hb96312/NB8n3ysfNy9DL1TfaQ9wD5BPpG+8/7ifxP/S7+r/9S/97+c/5R/xUCCAQnBBYD4AFaAOP/+/5U/p/96Pyd/B78dvtw+tL6dfzG/+UC+QRrBnoGPgZnBoUFyAVSBrkGcgfEB14H5QZ2BXUDeQEa/7z+GP/R/4YAEwGbAaQCugMGBPkDQgWuBcAGkQdyB8II1wlzCmUKMgpPCCoHwQWSBPQDCgKt/z/+GP/D/fr6YPhL9WXyIfHz773w8/I68vXyK/Xc9Z/0LfOW9Pz5o/xK/ej6o/rH/Sn/ywAHAB0AKwHQAp8C0gH4AJ//O/+U/6v+cPz4+iH6OvyA/3MBlwJ8AmwBnwAZALcAAQG1AAMB4AD/AusEAATgAgYCdwLbAZgAS/8F/qb/ZwF8AyoHsQmJCjAJvwYjBucFUAXzBYEFjAafCIgJUgvRDJAMjQsgCloI9QXTA+gB6ABLASYAd/8n/qP7+Po++ib49Peg9/f3xvit9kv22fb99lv4WfhZ+BD5L/nY+j/7a/tg+4z60fqP+4r6cPrf+5L9Kf8R/7H+Bf45/tT9zvwe/CD7PPsw/DD9ev7O/lj/OABBAWwC8AOZBGoFpgStAdz/D/51/qL+9v3r/Nf8E/09/gwAswEEBF0FtwblBnIHiwjACPUIPQlDCdQIZQi3CHYIOAkBC+8LQAs2CrUHOAZdBdwCbgHc/mD80vxK/Z/9O/3Q+5j8lPu7+mf6LPkx+Rr5Bvj4+sT77/vr/H/8ev6s/bP7P/rq+AL5xvl9+qz7hvx7+4T6IvsY+lz6APqj+pb8Wvy7+4/6vvqz+2z9LP5b/ykA7f8lAosCcALMAicCXAIaA7oBtv/9/qT9Pf5l/An8Iv1x/n4BggI3AgoBDf+t/pQALgLuBA0G4QYpCSkLWwyMDL0MjA3TDrsPlQ54DVYLQwnPB+gFWQSFAhEBuP5N/X/7a/vf+ZP4JfgP9x35dvd2+TL8TPw3/9n+yP7i/6j/0P8w/dL+zP8j/6YCif0y/Cf9Uf90ABz+0v2c+er7cPov+uX5SPs6+zH75Pt7+SX9FPhX+7D7yPdF+1D7xf6sAUoACP7e/WX7d/tS+uL2dvi/+NH6c/0g/9UBXAFTAEv/av6H/mUA/QDnAxQH3QZ9CAcIqAryDRsQrBE/EUwRMRALDxgOGAzSCbUIXgdUBqYEswLoAMz/RP5//O/6ZvlM+Zv4Evpp+rX7G/0K/lT/Rv9pAMUAjQFCAWwAr/8BAYQAwf98/hf75vq0+vX5jvdg+b78lPtM/Gv7YPmh+m36oPqj+Rj5GPkR93j2OvjG+c/6Bf7CAOsEWATLAKj+Vf0j/gb+O/0Z/F78Wfvk+6X6+/fV9/33ovh4+X36U/wK/pIAZgQzB08KuAr7C5cN2g7hD1kQnBF/EV4RJBAhDq0LCApXB7YEJQNCAcr+bP1V/XX+TgDi/2//h/6+/TD/AAAtAOABmwI7AxYDAwIBA1wCXwGRAKH+nf0R/dH6Lfqf+8n5OPk6+Fb3lfiZ9k/1uvU399j4dfcw93P4bfrO/MD7p/sn/SL+bP40/7b+IP68/qL/ZQDM/zT/of6Z/lP9dPx7+nT4Avkq96v3gfrd+9T9Ff/4/tL+NgHpAhME9wVgB8UK6Q3EEOIRZRJpEaMPwg7jDUwOig6rDUgLTwkvB04FlANlAvQBOQJfA8gC4AGmAiUCuAKzAnz/fv4R/g78G/yS/YD9qv3S/ND7TPqg+DP5MfqM+vf5CPiv9QT2QPjy9yr4BPh496r6oftM+yD7DfdE9eL2Ifh2+qn5wvrq+4H7WP6P+ir6L/tM+ez68fnr9jD3Hfil+f38Y/xC/ej9hP5b/wP+VP6C/Lv6XPsc/a//ZQJCBEoGMgvsEBgWIBnrGjsbjxl2GOUVkhNeEsIRGREFEMYNUgtBCUwG7gPSAcIApv9d/6T+DPzn+TX4ZvcB9xb3w/b/9Qb27vaT+O/6vP3D/00BKgOkAzgBvv5L/Rn7NfrW+Rv7c/2h/e/67vhk+JD42vda9Z/1tfSE9KD3mfgd+qP7VPay8R3zbvQj+H74ifXn9pP52fuP+/P5afrx+6b8t/wD+4X4+/e/91v4H/ka+NP30/mY/dwCOwoXEcYX5R5wIeEi1CQ2JfslmyZzJRUjMyEIHqEasBaUEdMMDggqAw7+mfm+9Zzy2u+z7KXoFeWt42zjxeRF51vp3uz18ND0R/ma/BAAwQR5CIkMiA/UEBcRTg9LDX4M+ApPCHEFXwEE/4v93fqj+e73//Se8r3v3evL6M/mpuXf5xzrO+wa7zHxAPKQ9Oj0jfSo9tr3nfd29zT2xfWd97n4u/jE97j3FPko+m/56vgD/Q8FhhBFGg8fNiQqKpIu3jHfMpAwFy5BK00mgSIsH2QZqhP3DrYKDgncBaL/ZvrB9IjvL+sC5kjieN9z3J3bCtyJ3SLgd+Io5iDsz/Lc+dIAywaXDPYRIxWZF2cZHRqCG9cc4ByAHFUahxV5EIUKzgOj/XP2au9/6jPn5+Rh4z3h+97T3Szdztzw3VngeePW5kXpOeuX7UPwFfNY9kz5lvp/+nH55fjj+PX5HvxC/bH9mPzm+o7+GwdkEDgbfyORKkg0fDlkOro4nDOQLgkqkiV0Irgf7xuTF1wT2g+bCyoFzvzg9DXu9eeo4hPdZdhz1QTUMdVT2LTbz97H4brlc+v58cL4Kv+wBeAM+BORGjEfeyIpJXYngSnuKbgnTyOvHP0Uyg1fBaX8cPRe7Njm3+IW36vcmdta2+XbT9t92ePXYtcH2S7bR94y44roWe+o9Wn5zfxA/wkA2f+x/sX9U/0c/kD+8f2Y/Tj8mgBaCvYU2iCFKP8uqDjEPA49ezuxNHov1Cn2IpMfrByLGRMW0BKqEJsMigYs/+T2++9Q6LDfe9iW0uPOWc3EzdLQuNWo2pHf2eSz6jrxEfjt/vkF9gzSE4IaiCADJvEpAywXLZwsRyolJvAgdxpwEi4KPwEL+cLy7+uZ5erfm9lD1fbRNM/dzwPRUtNb1zbanN3t4K/jQ+it7WfzbfjH+/3+VQFWAocCggHZ/7z+Uf4V/bH8L/y5+xMCJwtWFOIeCCVKLMw1QjmyObw3FjJ3LYEnsiAXHbkaABglFTUURhTtEWQMQgOB+T/xk+eg3S/W69BFzo/NXs6f0SbWctqp3pbjleko8PL2of1/BKELOxOxGkohgyblKZ8svy4NMNAv9CwfKf8jjhyYEw4IF/wo8snoruGb3P7Xv9Wt1NXTwdS/1ELUV9Vd1rfY69vc3sHkP+y38xn6E/16/gj/av5P/oT+6f0l/eb8Evyl+wz7G/mo/akHQRHBHO0jGyokNe45VzriOK0xOyz/JpUglB5HHqAdARxFGTgXaBO8DLIDkfk48SrpiuAO2WzT/M82zprNrM4X0RHUXthL3lDl/+wp9Gb6/wH4CrgTSxwJIzInuSrDLSovDzATL1wrRSdbId8ZQRLFCCr/s/af7V3mXOBn2mvX+9S60mHTwdN81C3XCtkT3A3gseKm5j3rue/Z9Jf4dvtL/bn8Gfv5+A/3h/bO94H53/qo+x36Q/wWBNoMUBjSIRopZzQHPCA+pD4bOp01kTEvKnwl8SNBIlAgdR3CGUYVzw4GBYT6OvKq6YTg1NiO0qfOXcx3yvbKPM5d0vfWE9w74r7pVfFH+DYAdwg6EFMYNR9oJWMrXy9iMbkxvS++LPAoAyRSHukWiA7sBQP9i/QR7ETjvtsd1pTSt9AT0M3QWNLX1PjXGNsJ393iV+bC6pXuhPFK9GP2jPgY+sT5KPlp+ZH5TvoV+6z67PmO95j17/rCBdsSfSDwKSY0Zz/WQ6BDdz/INzwyOywPJockLiSwIqIgNx3mGaIVKA0aAtD24usH4prYudAhzH7JcsjtyJvKAs430irXu93R5NDr2/GX90T/xAdyENgY4B+CJlEs5S86MuoyrjHkLuUplCMGHHITDguWAmX7WPUT7rLmM9/f10vTbNDRzp7PONEW1DHYJdyE4JbkR+ha7KDvLfKd9HX2Ofhp+Sr5avgI+Nz25PWO9gv3yPeQ9g70Fvl0BQwUOiNpLWo16T8HRMVBPT2nNBguzylUJQ0moCnoKq4qGChVI44cZRCzAG3yqeYC3RbV+s4dzBjMiszazLTNSs970M7Rw9TA2cTgG+l48Tb73wXhDigXbB6qJBUrFDBMM3k1xDSkMWEtPieOIEcZnhClCEsBo/lg8vHqpOP13cnZ0daH1bvUh9Sa1aDXhNqc3bLgm+O45s3paOyM7gfwEPFM8qzzyfSC9k34y/ms+7r8BfzE+h34Zve6/kgKehd+JOosIDbQP3NCUkFKPUE2VjFDLFsnBiebJ4kmRiV/Ixsh9hoFD5EAHvNK6E7ek9SszT3Kyskcy7jMOs+G0UnS99PA1rPajOCv5uvtg/fkAF4JiBGpGJ4foCalLOgxLTYEOD83ADQ5LgYmFxyIEegHOgDz+Xn0f+/16rnmEONy35nbhNed0/3QndB+0i3Wvto434vj3uYJ6W7qlOtA7XHvy/FO8xP06/Sq9WT3K/rU++L8Qf1U/usEQQ87Gp8kkStyMlk62D0UPWE5uzMzMaQwoC8aMU4yqzBLLTsnGB9sFaEI/voh8Dro/OHV2xjWBtJBz2jNFszTywnNns4p0UPVYdp24KnmSuxt8pn4Pf8kCJYSLh0oJ3cuojJ0NPAyny/+K1kn0SEyHJUW2BF1Dd0HowFr+//0tu6+6PriPN452jTXXdY214rYvtmS2m7bTd1/32vhluPB5TjozOun757y/vMl9KT0SvUM9lv3pPgA+3L9Af+rBNgOzxkVJMQqgTBAOSA/pj/4PPQ34jQmM5AvVi0bLCopuCU+IXccQxddDtkClfgU8YvqvuLg2qvU2dBpzyrOwM3fzijP0M9T0pXViNn/3Jzg1ucA8uT8FAeBDzEXKh4rI68muCjEKXkqZip2KR4nAyP5HR0Z9BR7EOAKxgR1/lb4ivIr7B/mVeGZ3cfbdduq2//bRNx83Abd3N3j3nvgfeI/5Knlf+el6YvrzOwT7k7wQfNh93D7gP40AZYB1wJTCsgUrx+ZKEgtWzPUOnk9NDxfOKozpTGQL9AsOCyEK2gptiYOIxYf+RhvDuYBH/dO70XohOAg2VjTYNCUz67Okc40zzjPpdBo03LWHNpf3VfhVejI8Kn5agJuCusSTBsMIvYmmSlKKugp+yiTJ1glhCJhH3MckRkLFZkOdgduAEr6hPRP7pXoDOTt4LffZt9b357fmt/P34rgteBv4HTgx+DN4ZTjr+VO6FXrku1i79bx1PMT9pn4M/p5/Hf+CQBSBlcQ7BkvIpYlpCeLLSoxOTFZMKotYS1aL+Iu3C/1McwwDy5QKeoiSBzfEvUGx/zV9RDwz+nb46XfyN3j3eDci9rd16LSPs2fynzK+s1Y0xvZCuKd7MP11/3cA6UI1Q0QEdsSRBWuF5warR5QIhMlHCYkJKIgpBx2FxQReQoLBVoBv/4T/Pn4BPZB8zjwRu2d6tHnX+UQ40XhL+Da3kve9d4H4QHlf+k37SbwcvHE8A/wBPDy72bx9PNS9tP5cvw3/nEElQ1vFvAeaSNPJ8UtKjABL/8syynaKWcreSrWK/stdS10Kx4m9x5wGF0PzAT2/Oz3nfTx8AXsSehU5oDjwd7z2b3XiNfo157XDNhd25HfXuOe5xDsIvFH9r/5/f0gA3EG/AiNCywPSxRtFw8YexhVGfEZzxhxFRMSHQ8jDGAJuwbGBHwC6/4t+/v3MPVB8s7tU+nG5pflFuY45/fneOkw62zsX+1R7BvqV+ic5+zoWOtv7SjwofPa9vn5qfq/+QD7HgBqCUQUvBtkHx8iASVFKFkp6yYMJE4jDCXXJ2spbSn0J4sldiJGHoMYvhAACBkB1P0X/Fn6Gvdj8x/x9+5M6/bltN/V2mfYhNeq2Ebbzd6142/o7+xn8cXzgvTA9ID18vel+/n+EwOjCPMOIxUMGdIacxsDG2QZNRaUEmIPyAyZCugIngeOBpcEfgGH/tj7Tvky9pHyv+/T7QjsJuqw6PPnHehM6AXoXug26e/qbu2M8OTzUvZ491f4Ovn5+db5nPja+S0BlQzfF3wd+BwRHM4cHh19GmoVARMTFgMb/R5gIH0gSyC6HfwY+hOkDiQJlQNq/6/+h//7/t/8wPr3+VD5jPZI8iruYOue6QToEudp55HoFOqL68PsN+6e76fwgvJ19d74tfwyALgCQAW3B0oJewoGCy4LCgz/DPsMygwuDHcLJwv0CTwIjgZUBCMCBQDp/TL8u/ql+Jv2VvR/8SbvD+0I7Evsquxf7bruD/A48Zjx+/Bw8UTzw/Ub+WD82f+oA6AFnQTyAs4C9wWNC/sOmA+ED6APQRCRD4wMXAqzCp4MOg/SELURvhLkErURkw8zDUsL6wm7CEoIWgiuCMQI7Ad4BokEIgJU/wH8LPmM9qb0t/MX807zKfQK9QT2yPa99u72AfcB92r3lfeH+Jz6t/xL/8wBYwOiBEkFPgVLBfsEjASBBKQEmQTQA1MCUQA2/hn8Ifrq+Mb41viV+N73X/bM9B3za/Gt8KPwIvEX8nXzN/XT9hz4W/nG+oj8yf2d/j7/uv/3/0QAGQCjAIsBlALDBOEGdAgVCtcKzgoBC0QK0An/CW4KPQtfDFkNZg7wDsoOww0TDAcKDAh/BqUFSwVUBecFvQUEBXgDMgEW/1j9+Pvk+7f8uP1z/oT+1/3o/GP7xvnq+Cr53fmV+mP7Wvw0/uP/7wCfAQEC1AFNARIA1f4X/sf95v1h/gP/m/+9/yf/Xf6C/df8Nvw2++z5PPlM+QD68/q5+1f8ofxs/AD8j/uc++T7T/zd/K/9SP47/qb9Qv1a/a/9pv1a/Zb95v1f/oX+VP6J/kv/8P+dALEAawCoAOYAEQGCAa8BZgKHA3oDzALvAcMAggC6AJQA3wD8AEQBBQKbAp0CaAJwAi4DdQQWBTcFWwXfBbUGTgePB9AH9wcBCNgHdAfzBvwFJQXcBL8EjgSgA2gC8gHQAXkBugB+/2j+1f0i/Ur8yftV+2D76vt9/AH9F/3D/F78zfsL+xn6W/km+WL5sPk++h/7Ivz2/Fz9hv3b/S7+9P1w/d/8vPy8/FH8iPsH+yD7o/sv/Lr8uv2m/tn+Sf70/RP+i/7X/tv+If+f/+7/+f+o//T+VP5n/dL8Xv1d/jT/5/8gALMA6QGFAuUCDANfA0IEQgXqBToGVQarBn0HWQj/CGMJmAn8CW4KlgqNClgK6AlVCXYImAeYBkwF5wPTAiAChgHbADQAvf8Y/zL++vyw+/P6lvpg+kz6NfoL+tr5gflD+aX5WfpZ+2P87/x3/Qj+QP42/u/9WP0k/Wr9CP7b/nX/xv/G/5X/Gv9N/k39cPwt/E/8kfyU/JT8yfwR/U/9Z/2Y/ej9LP4P/rr9vv3y/S7+IP7Z/b79i/0O/XD8Nvxs/M78vPxg/Eb8Vfy+/Az9T/3M/cr+9v9sAecCDwQ+BVIGXAdRCCYJwAlcCr4KwQrvCh4LOwsYC6YK7QkiCSYIzQYvBc4DzAITAmoBnwC6//L+N/5s/a/8J/zz+wP8Ufya/Mn84PwH/WH94P05/on+Cv+C/yIAUwAMALT/IP+U/hj+zv3x/ZL+av9lADABkAGPAf0AIAAG/7P9o/we/PX7NPxX/Cv8+vvm++b73/tu+6z6M/oU+jb6ePqK+tH6UPu1++z7zfvH+/X7SPx1/H38j/yf/AH9xf3F/rb/NABEAFgA4gDFAaYCagMABLgEQwVfBRoFhwT7A70DwwPpAz4EsASyBHUE8ANGA+sClgJwAmYCYQLHAkcDogPMA9kDrwNAA+QCfgJCAj0CYwLbAloDoAOoA24DDQObAtUB7wBXABsATACqAPoAbAHDAeABowEwAcsAPQCQ/6r+5v1y/fP8n/yv/PH8Tf1z/Ub91vwp/Cn7FPpk+Qv5YvlF+k77QfzX/MP8Lfxu+7f6Svoh+n/6i/vk/Db+NP/R/xsANgAtAGMA7wCmAXcCNwOmA5cDQAPIAmoCQgIsAhwC/QH0AbwBYQHgAE4A5/+8/6b/b/8T/6b+Uf4w/jf+T/6J/rj+1f4a/2r/2v9uAAUBnQEPAlYCaAJoAm4CnQIWA7gDeAQdBaAFywXPBYoFHwW4BIEEVgQaBLEDNQPVAqYCcwIMArUBSwHzAKEAIACL/wH/h/4V/lP9i/wJ/Ln70PsS/H/86/wO/bf8Cfxa++H6xPqu+pj6tPrb+kz7y/s9/I/8zfwT/aT9Sf66/gr/Fv8u/1T/pv8yANkAfAEGAlgCiwLDAqQCSwLFAXIBEwGcAB0Anf9U/zT/J/83/1b/Vv8R/3P+5P2L/T/9Gf0//cz9pP6S/0MAyQAXAT8BQgEtAUEBhgEaAtICgAMnBKkECwVmBa0FtgW7BV0FsgQjBLQDiwOxA+MDMQSgBNUE1QSBBNMDAQMsAmoBowDG/+L+SP7V/Zb9WP0M/cn8hvxn/ET8APyc+yj75vra+s/67Pov+7L7Zfzx/GP9qv3e/R7+av7O/l//IgD2AJYBAwI9Av8BUQFhAJv/MP/b/pv+Pf4T/g/+2f2o/TD9sfxK/Pb77Pse/LH8gv1U/uv+df/Q/xMAXgCPALUAIgGtATsC4AJ3Ax8ErQTrBO4EuwQxBKIDQgMWAywDRANWA0ADDAOZAvIBWgEFAe0AAQEIAd8AzgC1AIYAXgAJAOD/3v8kAJoADAFeAWMBSwEQAbwAYAAtACkALwBOAD0AGQDy/7T/W//b/l/+GP4X/kv+m/7v/jf/fv+V/3X/RP/0/qb+ev5f/k3+Of5P/mf+jf7I/vv+N/9z/3z/R//n/lr+s/34/EH84fvq+wn8TPx3/Jj85vwr/XD9cP2N/eL9jf5Y/wMAmAAHAVEBcgGQAboB8gE5AlYCVAIeAtsBmAE5AfQA1wDfAPQAAQErAYYB5gEsAlQCRAIsAu0BaQHHAFUAQQB7AOQAOAFaAV4BIAHWAGwADAD0/+f/CQBRAHsAoQCcAJYAgACPALMAtQDMAPoAUQGxAeYB4AG6AaQBdQEgAaEAMAD5/+X/4v/X/7r/uP+X/1T/7/5Y/q/9IP3X/N38JP1n/aT9w/3O/cr9pv17/TT9Dv3t/Pb8J/1u/dL9I/5q/rX+DP93/9D/AwBPAJYArACRAGUAJwABABMAZQAbAe0B0gKFA+4DAATcA2EDwQIeApYBZQFwAZsB3gElAgwCtwFLAdQAmABXACAA+f+Z/0f/Bv/I/tD+2f7g/gP/HP80/zD/QP9N/1v/if+O/5T/i/+f/8j/9P/e/7T/m/+t/+f/MgBpAJ8A5gAVARcBzACYAHsAiwCcAK8A2QDiANcAxQCuAIsAdAApAKn/BP9U/ub9c/0w/Rf9Jf1Y/Vr9Mv3Z/J/8V/w//Hf8DP2z/Y3+Uf/3/5YA6gAIAeQAtwCAAIAAkgD0AJgBQAIIA6QD5wPKA3EDyALyARABhABEACcAMgBTADYA8P9s//D+n/5c/iz+DP4l/pL+LP+C/7H/3v/7/x0AAQDV/8j/1f/G/+f/GwBeAKoAyQDoAA4BXwGHAXABRAEIAbwAjwCJAGwAqADWAP0AGwEuATIBJwEgAfEA4gC+AKEAvADXABwBOAE9AUsBRAEbAdcAggA7APv/m/8s/4v+D/77/QH+I/51/s7+Nf96/4f/S//S/nD+IP5P/r/+X//l/3QAEwFWAXwBSgHmAJgAOgAAAOn/4/8QAGAAqgDSAMIAjwAvAND/Yf8K/9f+q/6i/pv+fP5z/lb+A/7e/fT9Of61/iD/Lv9A/0D/J/8l//D+A/+A/xUAbgCNAHkAbgBsACAAyv+Q/5D/gv9q/3z/m/8pALEAIgGAAagB5AHtAcoBjQE5Af0A7wAlAYQB+wFYAoUC2QL2ArwCYwKqAc4ABwBW//D+nf5U/l3+ef6N/u3+Ef/9/vv+xf6m/m7+Rv47/nD+tv4N/5n/GwCcAO8A8QDxAOQAlABcAD8APQBPAEMAQQBrAJoAugDWAOQAvAC3AM4A8wA4AUEBHgG1ACsAq/9S/y7/BP8N/wz/5P7K/mH+6f1p/UT9JP0w/VX9O/1T/Yn94P0j/nH+yv5J/87/OgCoAN0A+AAIAR4BMgFBAVEBXwFTATQBAQHZAPQAHAFPAa8B6QHpAcoBcwHtAHkAAQCy/5n/qf/X//b/AQDn/7//jP9N/z3/Uv+0/0MA+ACPAeAB+wHAAS0BggDO/zX/GP9z/0MACgG6ARECCgLMATABbgDI/2r/Sf+f/1cA9ABlAVUB4gAyADD/Y/7B/Y/9vP0r/pb+8P4V/w3/3v5d/uT9cv1h/YT9w/0V/lP+tv5J/7r/9/8iABcA/f+o/4f/Zv9k/8r/DgB+APMAEAEFAckAdwBIAAkA1f+v/6v/1/8ZAHcArgC+AKgAcAAvAP3/5//l//v/SgB+AMMADAE4AVoBUwFhARsBnAB3ACkAEgAmABsATABXADIA9P+t/1//Kv83/1//0P9DALUA8QDbAJIAJwC4/zn/+/7Z/iD/9v/rAAYC0AIDA68CNAKsATsBwABRAEwAOAAQACcAKQDI/63/bP9A/1H/N/8+/yP/vP5d/g7+mP15/XL94P2k/kn/8P89AFgAGwCX/xP/fv4//nD+Af/G/7oAeQHiAQgCowEFAVUAv/+A/2z/if8DAEQAYACaAH0ARADt/4D/O/8R/9n+Lv+g////oQDOAMMAeQDy/7b/r/+r//3/XgCIANcAzAChAI0AJADr/63/bP9j/5f/BwBaAGsAUQAHAJv/hf9d/1b/hf+U/5T/hf91/37/oP+f/ysAWgA4AGMAIADg/wkAeQCGAO0ABwE5AboBggFpAScBowB1AEMAn/9+/97/AAB9AL4AswDvAI8A4P8s/6b+vP7I/l/+Gv7T/iQBqAPTAmcBbAH0Ad4ChgBI/Bv8Af6V/8r/K/4vAHUDAgTDAsb/d/6z/mX92/s6+5L8IAAsAgoD6wIjBM0EiwNnAHL9Lf0//ab9j/2E/QwBqwMeBBQFagO8CTUOXwUZAHn7I/gF+0T2JvKv9jr6zv7CAJ3+xwDXAoICgP7b+t/5cvqo/BH90P2YAXoFZwdKBv8D+wI3AgEB7/2m/GH91/57AHAAtwCdAcMBOAAc/zb+if3b/en+nf9RAAwCSAINA9wCeQLxAScBgAEiAf0A6ACzAVoC9ALZAs4CvAITApQBEwDj/xAA0P+t/pT+I/8M/+3+yv2q/SX+6P3Z/FP8nPxC/dX9Xv3f/ET9gv5u/uT9vv5E/+X/0f9PAIX/8P8rACsAlP9E/2H/Pv/5/8r/KwAmAA4B7wDkAGkA+f9RAF4AUQDw/87/9v9T/gb/rQLTA6QCOwJyAWwDlANU/pb8Wv3Q/oL9x/xE/O/+vgF9AMj+Af+YAIYADgB+/i7/OgDBAaQCcgEPAq0EJwR8AqYCUQMrCvANmAmCCB8H+wU8BgAAdPoR/fr8o/us+6L5gvws/4b9APvU+bv4u/mg+Pn2xviw+tn8MP4R/+3/NAEFAY0Arf87ANsAMgH9AdMCtAMsBNcE6wRbBKAF9AQNA7UCnwEhBpYKBQd+BMwBQwAMAnL8Pvhm+M73zfjw9yL14/hR/Cv8MPw6+hn60vxy/GT6+Pv//LX+EQH4AKECpwTcBFcFDQSJAsUCCAJGARgDagMGBBQFzQUQBlAFhwPgAfYAqf82/hf8Wvzk/D/8Af3W/Bn9Yf5E/e38m/1M/Pz8wP2x/U//zv+lAEICGgOSAggCfgEpAscCOQKdAOQAHAITAt4BwgB7ABMBaQFG/0L+n//g/9H/7f9A/hr/hACi/qb+tv5d/j8Acf8l/j/+X/6m/3UAHgCF/wkA6gBgAK8AAwG6AO0BvAFRAMMAugDkALwBbgG8AVwB/QA2AIL/cf+i/73/O/+x/jD/fv8K/9n+T/5h/oX+if4R/6MAKQDe/4QA+gCjAcUBggB3AbwC2wIKAzQCIwK/AnMCXAB+ANQAsQHXAL//Bv9z/9UBXQPZAcUAxf6z/agAif17+bD4UPgD+2n8ZPiZ91f5Tvmn+I31Y/RF90n5Vfoq+hL7r/0mAH4BCgFCAYYB1AAwAL//lv4q/+P/aP64/pT9xPuN+2n6DPt6/u8BDQSuBv4HGAyhEDASNhMGE6ETdRQMFKwTmRT/FR0XJBezFGUSfw/OCwEIiQP4/rD6xPdK9Qf0DvM98qrxG/Ex8Inue+zE6snq/OsX7eLuevE09bn52/wl/z8BAQM8BKQERwTgA8QErQVpBlcGsgXIBbIFpwRmAof/zfys+jz4Nfbe9D30XvRy9KH1M/eD9o31kfM88iTyz/GD8TbyzfM99cT33/iI+Rj64/lB+rn5V/kA+Tj5vfrO/b//mQFo//L9QAsvGjAn0C2SJfsl7DBhMEgtRya2HFMiwiKTFgET5BBREfQUfAwPAlz8wvJW69rmYOKY4IHekN3k4ArlTOgb6dbqL/DA8j/zgvK78g344P4nAkcGfglSDRwTzxVtFl0WYxSREqgRVg6YCqUH8ARdBB4EZQHW/Nb5Dff29HDzwe5Y7Azsn+sS6/PqTOsY72nz8fSJ9l32Sfa29nP2aPf3+Of5cPqP+7n7bPzU/JL86P3F/F77XPro+cn5Yvn197r2sfaS9ib4evhm+fz67PtN/nT8t/saBK0TKSSmLSYqXyc5LYkwbTItLbcjtCAjHfQVpRJVECgODQ4JCbYCJvz+8Abn/OAk4Nbggd+k3DfcZ9+O5S/rXe8b9Iz2jvig+jD8Lv+zAt8FFQpJDQ0QBRIrE9gVnhhZGVIYfhScD3cMLwl7Bs4DugAY/ub6bPeA89rwG/Da8HjwEe4J6lDnMei+6lbul/Bi8vT0z/gX/SUBXQNABGIFsAXpBCEDeQHDAZkCEQIkAF79Rfpk+Gj36/YE9j3zTPAb8CLybfKy8E3vCO+g8bz0F/S39Fb2BPpFBxYYsCLTJoQhGh42JYwnLyNPHeoXhhujIEocdBriG7oc2R1OFiYJNf/m9PLt5OzM693prucK5W/msOph7bbuZPDb8qP0kvWA9aj2y/r//6IEXggyC70N8A+BEOYPGA/IDR8MKAg1A1L/4PxF++P4gvXx8hTybvJr83DzmvPd9AL35fn1+rD5bfk8+yP/DwP/AkYBBQG1Ag4HJApICDwEl/9q/eP/GAK4AcIAhf8mAJIB6/+1/AL65/ef9q70iPEP76vtGO6F78rwGfI/88D0RvZl9vT12vYW+Yj76Pvj+l77bP9ZBAIHtQfEBwkIgQaWABj+oAdVGbkryzIOLdMpoS25MeMyWyreHLETwQoFAc36jPYw9XP2Z/WK8oDuYOe74L3fBuMI5p7lzuJe44LqrvPf+hkBVwYVCucLGAulCfwIKQjuBrAGNQYzBLoB0ACOAn0FQAbTA3QAVP5L/Z38lvw//fD+9gC1ARkBiQDWADQC+QK6Abb+y/tI++38X/4T/gf8ffma+fz7bv6GAKYBxQIzBf4GpQeGBywGSwTUAaj9KPjz8TDsYunb6WDrqOwD7ZbspOz47G/t3O7p7yjw7O8z8NDypPUh+BL8bP9jAh4DVQBgBgIYUyvqOVI68DGTMqE2hTalMk8mShpjFGoKqgA++hDyee3v6wDq5umZ5Uncldd+2R/ffOPJ43HlO+z/9Wb/dgYWDc4SMxa+GOgY9BXJEXgMhAlBCoQJwAa/A7wAJgB3AOP/W/9J/i79Dv3o/DL88frJ+nn9rwGDBO0DJwH7/k/+7/4e/279SvvT+cv6Cv6aAAoCIQNtBKwGeQfTBXMELAMpAncBcf+Y/SL8xPl19xH1K/Kb7wPtMusi6svoiuih6d3qNuxR7a/vL/Rk9/D2WvU59Wb4Tf74ASUDagTBBdYImQvpCV8KXBIbINwvUzVWLnQodyZdJmwl0hxZEZQIav7m9U7y4e8j77Huq+0w7x3wD+0d6hDrI+918wz1P/VC+MH9GgPZCMsOARMRFZ0U1xJBEfUN4QjgBFYCiwAT/939O/1//Gn6iPjl+A36VPmZ9qb0nfX7+Jb8uv4D/9n+pP+FApcG+ge4BVEDFwJ1AWsAd/+NAYUFZgYeA0L/4P0D/7//Qv+z/ob9Evtr+Of2qfYR9qbzGPHG70Duwez/6z3sjO077o7t1+yi7Grtwe8i8wb2fvd2+NL6BP/4AiMF8wZ0CHkKOQ2yDWgMYAimAcEC3A4aHwotSy+MKWspsCv8KwcpHx4rEkMI4fvY8hjuTup26nDszu4x8hjxDe2F7Mrv5PQ3+AD49ffa+VP9IAN7CoYRmxXiFe4UdRP4EFsNdgj0AyIANPya+Xz4QPep9n73j/k8+z763Pc399/4afrA+uz7+v2r/z8BIwIgAxwEzgJ8Ar0F0gfdBkQDi/99ALEC+wEZAFr90vrI+QL5G/mM+bL4Gvi9+GT5VPjH9VXztPFF8CHuAewK643q/urO7J7vy/Is9Wz3cPr//IX+c/8KABz/T/xB+gn60vwHAXcBoQAI/4/8gAM5ExckjjLkNMEwHTPENDYzZC8nJFoZqQ/iACH2u/C463vqmOqW6szsI+yX6KDoi+za8X72OPmj+4D/lQS1CQUPchPkFIcUixNiESwORgl1A2H/U/xU+fL2afSa8inz1/QL99z4M/gf94f3Evi7+Nj5P/uG/QwAwQHQAhQE3gR9BRcIPQrtCL0FSwJ8Af0DCwTrAMX9p/oz+UH5UPm++qP7TvuL+1n7TPv1+r/5ffmi+F32ZfSy8pjx6PBL8PDwwPHw8FLvFO8i8Uz0+PUB9tH2q/i9+jn9CP7V/X7/BQGoAen/Tvqc+9cJmx2SL901uTGZMXs0fjWsM38omhlODYL/bvWX7ybpfuaw5ibo8OwL78Httu3P7+f1tfyD/wwAxf+tATwHnQzqENUSmBHqD2sNDgrYBgUC+PxX+Wb2A/Uv8w/w1e7573fz+/dS+h78Cv/eAecDTQPJAIX/FwCfAr8FbweKByEGBgUkBoIHWweKBA3/Yvrh99r2M/iW+t38l/+mAbUCKQPBARb/DP1G+7L5Sfi89qj2zvcP+Ab3YfRM8cjvHO8W71fvwe477k/uwe7n77nxafSv9yL63fkP+L33JvmS/C4BWAPVA40Bif0VA4UTwyZBNuA4jzQ0Nlg46TbCMa8lFxpVEHMCYfZx7izoQOa95mToHuyX7e3rQ+sW7pHzKPhX+k77hP0GAzAJxA27EPcQWxBQEN8O+QttB1MB0PwW+un3lPZ39KryWvMI9Vn3qfnH+qH8aP9+AQ8DygMlBKcEjARiBB8EpAPZAhEBRgCSAfkCbAO4AW7+s/wO/Hn7RftK+sL5vfqw+6z8ofyc+jX5+/hX+Ur6mvp7+4390v45/vf6+/Vi8bjt0usH64rpL+jR5yvpEe0b8Vj0svfq+b76Z/q9+QL6jPqR/CX/rgD/AJL9i/4uDuQkpzkNQ6o9xjrCPok/zTypMWYfDRBs/6TvBOhG4tXe1N8v4oHoeO6i7pvuVfG89nz92QCCAXwCXQS3CDEOvBJqFZ8UORJ2D9kKCQXp/RH2LPEG72ruVu+e78rwavUB/N4CzQf/CHsIUAcIBY4Chf/q+674GPb99En2KPlr/P//BgObBLIESQNqAZIA4/9q/p/8p/qj+bv66/xC/5QA8v9x/rf9hP1N/eb8u/vN+iH6Fvjx9FPx0+0j7MXrx+tE7GPs9Oxt7iTxRPWK+H/5zPef9GnyGfLC8271l/eu/HsBpwQNBZYBFQjvG10wGED2QQo54jeZODg1ezIPJzcXZQmZ90jtzuz86QLoQOcN5xHtZ/GP8cz08/hY/XMBDALZAQoBn/+8ATgGFwrgCvMHyQahCOsJpQlFBqEB2/4p/Db6zfgh9ub00vXe95r6M/uM+sL79P24/5//cP0k+9b5yfmG+gv7F/vs+lH8+f9RA0kF4QWgBewFCQZtBVsFuATHAoIAP/5z/fb9Nv3h+5j6xvgI+I73Yfbn9SL1WvSb9N3z+vG275vtRu227ent5+227dzuUvFE9J/27vZv9vb1Xfd5+tb85f+EAiACdQDS++/6SgjrG24uvzkNOPA3wj13P3c/TziKJ+kW/wPb8prrp+aJ4i3iIOMm5zDrnetm7QfyQPfU+oT77PvJ/BP+EwE6BXIKYg5zDmoNhQxxC+cKuQidBG4AnvsU+Nf2vPVG9UD2m/is/P3/fAFhArwC/QJzAqUAaP6n+9j4H/dA9lb2vfcD+8cAdAZ9CcAJnAgMCZYKaAqICJME0P8I/Sv7IfpF+gv56ffX9yP3CPc198P2UveT9yz2dfRc8r3wv/Ak8bLxAPI28UPwD/AS8XLzO/Z4+Iz5nvkC+bb4+fmR+h/6svpI+ur5JPka9yn/rhIBJ8M3gjxhOSA8/D3xO783/imgGO4HY/Za7Y/rYumg6Orowupf7x/yYPJe9K/3dPpK/C/83fsb/dr/qwR7CsYOng/gDSkM5AriCQEISwMB/qP5gvaW9rr3Uvhb+Yb59/lj+y38Hv3J/Wz9DP0Z/N/6OPrd+cT6x/yg/wgDLwWOBoEHjAeEBzEGagNNAQ3/if1u/er8E/1I/e/80v30/cv8qvvU+cH4+/c99k31y/Ql9GLz5fG38JXv/+1m7dDtce/d8ZzzFfWM9u73MfrS/LH9mPvn9o/zwPPH9QD66vwK/SX9KPuS/iwPtSKlM6E78jZuNYM4rzfgNRAt0B25EP0BJ/a78lfwVO4K7o7tPO9Z8DzuzO3U8AP1fvi0+RD6iPu4/WoBIQYrCuILcgriCPgIBQlPCH8FzADk/Of5Jvhb+KL47vg4+hv8pv2x/rb/HgE5A6gDJAE2/ur7Wfvo/Mr9NP7e/ZL8of2V/7kAggEpAED/iwAuAYACzgO2A2QEjgRSBJMEDAPbAKj+EPwW+on3/fTt8x7zKPJx8OftLexB6wHr8et37e7utu9L8PnxjfQB91T4UPiH9//1FfQb9O32xPkR/XX//f/LAPL+vf+tDPIeby8wNxYy9i7TMnc1zDYjMfUimxTfBdz5PfYO9DHw3O097ArtJu9m7h/uZ/Fl9Zz4ovmX+DX5E/xYAccH+QshDVILxAgDCLAHnAYWBF//MfuT+an5IvvS+8n7av2//50BmwLyARsBqgAD/+r8mvu++ij7x/yY/dv9Xv2j+0773fwD/1oBugLOAicDNQSXBS0HbweOBaECO/87/AL6a/hU94D27/XQ9ef1evYU90/3jPex9rj0t/IU8VLx2PLF853z+vFt8BrwW/D88I/xy/Jn9Lb1lvYz+EP6K/ww/xABwADM/r/5y/vXC4UfjDATN2AyGzPOOD48dz3bNNAkXxUfBoT7T/at8K/rRuqm7BDxXvM28orxX/Sl+GD70fpQ+Jb26/fx/EcDXgheCi0J8QcFCMAHMwfhBOQAs/6f/a39WP6J/U/93P4KAQ0DCAPXAF/+j/yU+5r69fjK9rr1RPY+97v3hfdA+Af7Kv91Ar8D4gMUBPwFvgh7CcIHkgPD/o/81Pu3+ln52fZL9bT2yPjR+jD8yftp+wf7nPlW96H0qPIX8l7yG/Kr8NHuxe2S7gTxzvOH9fL1hPWU9fj1yvZk+Pv31/ZZ9972Qvbi83TxLv2xFTIt9zzSPS46dUC8RrxGmD91LeUYAwgo+ovz/PBG7Avppur57t/yofJv75zvcPLr82Xzl/D57lfx+/Y+/4YGHgqAC3cMJQ5SD8ENRgq5BccBmf9L/gX+sf09/dv+wAGoA9cDDAL9/3X/Wv5r/HL6R/gY93z2I/ba9ib4Zvph/iICpAQlBeADyAPMBGsFbQSzAbb+2fyW/Ln8kfu0+fX3bPch+QP76Psg/LL7y/tV/LP8E/yg+gD5ivcW9472sfQW8tfubuxf7Jbsn+wn7M7qHOs77ZnwefQI9X702vez+w//D/4d+SICKhmAL4Q/aECjO61B5EaRRc4+LS33GTYKjfvX81Dw4eom53HnO+qQ7YntSutf7JDvv/A48PLuwu9K9PH5pgAQB/YKDw1XDg0QCxEPD4sK2gR7ANf+0v7K/14AtP+m/00BYwMzBD0DOwG0/8X9C/td+Xr4ZPgY+LT2pvbM91n5ufyYAJYCaAJYAAP/uv/g/wP+dvvR+Vv5Nvqn+7780P21/d/8DP1E/TL9Y/2C/TL+If/Z/p/9IPxi+r34rfYZ87LuIOuP6VrqIOxC7a/tQO5r7+PwdPHu8fjymvOx9R34ePd+9n30aPY+B7oesDJ1P3U/kD/ERuBIPUaWPZosVBwqDB79ivdl9hf0IvIA8LDvevCS7hHsm+vV6+3qGel96Vvt2fKc+a4AeQfwDBgOvw2tDrYOaA23CRQEswDl/pb9D/47/of+QQAFAk0EigVtBEkDGAKL/8v7yveU9RX2DfcP9wv3oPcx+Wv8VQAMA74CD/9Q+2T6jftK/Mv7GfuL+3n9gP8bAWYCDAJ1APn+c/2l/NT86/wt/e38kfpb97P0XvK/8Fvvhe0w7NLrZewq7v7vyvAN8fzwoPD+763uNe7N72/xCPRU+AL77/yN/ND85gncHmAxzT23Pas7X0B3Qdc/ejsqL/Mh+hPfBV4AIP9g/HL5S/U/8crtJOmr5fDkqOS34lfgi+GT58DwBfuVAzAKqw6yD8YP9w8HDiwLyQcABHACPQGm/yYAdwERA8EEvQMwAcH+Uf0Y/5QB7QBY/ZH4R/YW+Mn6AfwD+6D4PPfw+Mr9fAIaBMMCSgBq/ysAQQBDANYABwFLAUQB6ABpAcEBvgGvAaEAuP5V/Mn53vfy9RXz5e+47FrqxOkk6uHqEOz47MjtCO+T72/viu/T7yzwLvDw7oXuI/Ba8rP2gftu/TT+afxE/9QQ8CdvOjdCyzynOWc+YUCvPh42qCZOGUkNWARsAvMAuP3w+fr0/vBn67bjTt+t3gvgG+EO4ZLksezX9sAB1AnaDRkPNQ0GDH4MLAsDCYMGLgQjBHYEcQQ1BSEFcwQuBGQDhwIQAUb/J/9v/xj+YPvK+ED4jPmr+eD3efUc9I71t/nS/p0CjgN1AvYBAQMoBDUDhAAP/iD95v3Z/jv/Uv/p/in+2/y3+i74GPbm9IL03fNZ8rDw8u/770PwovCK8JfwsvAv8L/vRe9U74HwrvEm8qHxVPCP8LXxa/JT9eX4IPvv+wn6hgDIFWssuzrIOi8yKTQvPKg93jgtLIwg2xvRFRcQmQy8COEG5wNV/aj0fenS4Rvhw+Mz5qnlIeVg6rXzAPweAbgB6gC6AK//Tf89/2r/jQKIBpIJkgvSCmcKiwu4C7gK1gdzA5gAb/9U/1oAGQD0/lP+Hv0C+5n3Z/OK8M3vW/Cw8XDzBvaT+bz9ggKYBrMIowicBooEogNxA2oDaAK6AHr/kP5h/sf95vvh+QL4yPZU9mr1OPSU827z5PPC82vzJPN38hDyLPFd8GTwsPCM8bnygPNl8yjyHfHs8ADxrPKx9XH3zvcy9sz4zQYAGqAppC/gLIktZDMXN4U27S+eKOQkNSA9GiMVdhDBDcAKpASW/Rf1gu3k6VLoTOij53jl4+X16N3srvBr8bvwjfHW8uT1Z/o2/uUCMQdKCqkNxA8eEc0R4RCYD9gNZgstCWUHcQZFBgIFzAJnAJj9kfsP+dL1cvPR8f7wyvD88DHyzvTT93D6mPyE/icBuAMABcQEEwNVAaEAvgDfAGAAPv9l/X37q/nc9x739/fw+c/7Cvw6+tX3tPYB9033Afbj8tbvyO507/HwYPJ084/07fRa9Gnz9vLz84T0T/Qg9JjzLfQB9gv6YQTpEn4fvyYNKC0p6y5eM2czgC8vKTslXCFNGzwXrRU8FbYTuw1QBjQAR/rQ9FvvNuoR5+Xk4OOb5M/lq+ci6oXsJe8A8QPyHPQ3+Jb9lgJZBlUJaAxDD/MQ9xCpEKoQrg/eDWoL/AjWB1kG7QPBATv/0vyh+gD4b/aE9f/zFPN/8kfylPPe9CH2Tffe9xv5EvuW/Ov8O/zi+3z9PQCrAkcDUQG1/uv8Cv1j/oX+Cv5E/vT+1/+D/+b9bPzd+fv1UPIJ8M3v+e/I70XwL/EJ8hvy1PHt8u3zrvO98c7uGu6i8Mf0YPmm/H4AkQkaFmshxiYGJZEjZyWXJ8wnDiTwH54edx3tG/QahBowG+MZSRVQDy0IRgGW+yX2cvLM76HsNusB66brlO0z7ontZe0u7bPtf++p8XP1mvrZ/icCbARDBrcITwqPCgoKBQmTCGAIQQiGCA4JGgpfC58LjwplCIcFlwPMARP/xft0+Iz2R/Y79rH1UfUp9Xz1rfUu9fH0NvWi9Tv2pvbK9p73FPme+kP8r/2x/lb/HgDxAOAB0gHp/9T8lfkG92f1UfQr8zjyUvGy8OzwnvEQ8jTyg/Gp71bu4O0172TyovXT98L4H/km/OEEjxDfGfwbJBjyFQcZKB5HIPkddRtlHM4enB8KHmMcUR0aH6IdohcUD/UH+QREBOsCuP+N+xv56fhV+Sz4VPWw8tzwSe8j7ejq3erV7ePxfvQX9bz0MPWv9hH4N/nq+bn6ofxS/xwC2gRKB50J+AvXDP8LMAo9CDEHwAYHBoMFjgStA9cDXwMaAkoAm/1t+9P5gPfn9WX1VvXv9b71/fUY+BD7Cv6X/1H/2/7I/vL+rf84AEQA4/9E/tv7f/mZ9473tvj9+H/4oPbA9CL08/OG9H71cfVT9Qj2nvdp+nf8Mv02/iQARgJ+A+IC+ADD/woA3gHVBBkHHQgBCaoKiQ3sD0gQPA8WDr0Ntg18DYENDQ7uDhYPJg7iDBoM0wuOC1UKNgj5BTAE2gOtBNMEIwTkAmUBbgDQ/jj8EPqi+B/46/cR96v2FPf792n5JPqr+QL5nPik+F759flt+jr7MvxL/V3+IP8VAC4BFwKhAp8CJQJYAXkA7f+v/1T/1/76/Xv9tf1R/nj/2wApAg0DMwPgAo4CIwI/AeL/cP7b/Fr7wPka+ED32faM9kT2tfVN9V/1+vTm8xLzcvLZ8hn0hfUN90v4Pvno+VX6o/rP+s36+vpQ+7P7Wvxl/dL+bgDeAUQDMwWBB+QJjQsMDD0MiwxFDZUOABAxERcSSBKqEV4R7REKE5YTnRLHEKsPUg8LD0wOiQzOCgkJ2gZQBFgBNf8e/oD9lPzz+v34zved99X39/dz93r20/Xe9Tv2qPa09sz2bPdD+Cb50fmV+t37d/2i/hH/Jf8T/wb/Bv+x/kv++P3d/Sv+of4a/yYApgHwAtcD0ANfAzADDQONAo0B3v93/rj9nfyP+wf6a/hz98X28PVR9eT0o/Sq9CL0a/PC8sfy1/NK9Z32zvfs+BD62/rS+pP6afp/+qP68fqN+6H8Af5o/xABuAL7BLUHGgr7C1oM7QtPDLgN/A/2EaYSIBO6E1QUuBSfFD0UqhMpExsShhCiDroMzAseC30JRQYlAir/6/1n/dD8hPt2+hn6svlD+cT4yPhB+Sr5PvgG92j2KPeO+KD5K/pI+ob6B/uz+y/8cvy6/JL9lv6i/2kAnQBpAKT/qv6H/SL8wvoN+s35nPps/FP+FQAgAU8BOwEKAUgAlP/B/vT9Yf2Y/Cb8Bfz2++H7SvtZ+nj5k/i991n3DffX9tf2FPfn9974nvkr+nD6oPrA+tr6xvpZ+sL5j/nw+YH6Xvts/Lf9aP+RAO0AcwHtArgFrgiSCosL5QzlDkwRNhPVE2EUiRRYFNsTZRMuEwgTjRJGEegPnA5WDWgLkwh0BZ8ChgAh/1r+tf0M/Uj8Svtn+pr54fhZ+Mb3Rffl9u72lffl+Gf6V/u++4H7/PpK+sn54fk4+tr6nPtK/C39Pf47/0oA1AB9AF3/DP4n/Tb9F/4Y/zYAfAHbAtUDGASrA+ACTQKfAZ0AVP8F/mH9Mv2h/IH7f/oE+uz5oPmI+A33lPXD9GP06POs887zmPQB9mr3MfjE+FT55flM+i/63Plk+cr4ZviT+G35Yvs0/nUArAH9AWECYQRTBzkKpAxiDsgPuRA2ERMSdxPFFLEU9hJgETsRCBKAEp0R6g+/DuENcwwTCpwGiQPAARsBAQFBAAj/7f11/Yf9Xv0v/Ez6m/gN99n14PQy9G70gvXl9qb34fcL+I74gfkU+nT6g/rW+gX8e/0g/6oA1AFxAl8C6QFcAUoBjQGUAXIBlAFlAoUDsQNCA50C6AGEAfwA/f+Q/kT9IPyB++P6t/lO+Pb2RPYy9sP1mvRj85zy2fKx82H0/fSQ9dP1c/bT97v5jfta/OT7Gfva+hv7Ivus+pz6tfuo/rwBPgPMA0kEaQZ3CRMMig2uDoEQjRJaFOUU+xSSFWEWAhdLFjcUShKEEfgRkhL/ES8QnA0cC6wI1gW8AuP/1/3o+x36v/je9xH4evg6+M72vvTS8pPxEPEv8cDx7fKP9Kr1IfaA9s73M/qd/L793v0D/g3/QQFdA4MEzwStBMMENwVbBdoEAgT0AvoBLQHgAD8BLgLyAscCqgGb/+D9P/1K/VH8ovmJ9gj1pPWZ9sr1vPPG8oTz4PRq9dn0e/Q29Qr3S/mj+vH69/m/+DP49PcE+WL6IPvW+lz6ZPof++b78/uH/X4Bvwa1CewIowfOCV4PVBTwFQEVnhUgGVEcEx2+G6EaIBvjGYkVcg8IC1oLeA03DX0J+QQlAu0Agv77+Pjy/+6L7dDsQesg6vjrZPCA9Fj1EvNH8RnyifRL9n72+fZO+hH/ugLFA8oDgwUBCO8IFAeHBGYEMQc3CoMLWgsgCw0M1wz/C9kJeQewBVgE7QIkAcX/h//w/lP9wvpb+LT3b/fS9bfy0+8U70Xwf/HE8BTvuO2t7avuPO/Y7+Hw2PLt9K32Sfip+RX7rvuj+xf7e/od+v34LvfF9YL2z/ie+j/6wvle/T4GFA8QEYcL5wULCFsQIhgDGdUVkha8GzcgkyC8HYkclx1sG8gUdg0wCwIPzhKjEVoMhAdrBdEEcwI4/WT3gvKc7/Ltw+zi7GzurfDE8aDwaO6U7V3vUfIy9Hv0NvWm95b77v8NA1QFFgfzBygIQQg7CboLQg5BDwcPoA6IDp4ObQ1LC/EI8AZzBHABO/9n/oD+uv0b+733NPX882nzYPLo8B/v8O2J7Qruu+7X7uDu6+4L73rvsu+4747wvvEm8pPy/vKN9NX25feH90b2I/Z497L4hfgU+NX3c/dG9ir2kv2QDTYcpR+bFkIOhBOjIRkqPia2HcMcjyRbKrgoViSsIkYiNhwvDosBgP39AK8EzgJT/Yr5iPjl98z1bfEE7d3opOSx4tvjCeho7QTxxvLm84f0FfU99tH35fre/jYCSwWNCL4LtA9CE5YU6xLsD8gNZA4tELIQGQ8oDQ8MYQtuCbIFtQE//sf7H/lo9iv0dPMH88fy1vE48KvuyO3B7VTuFO+47+PvYu8W7/Xu6u8b8UfxnvBv8IzxyfSt9+n4dPi79/D3gfk8+nr5PPhP9wL4z/iu+RL6t/l+9372P/xjC2EcjyKCGrAPjRBvHTwo0SZLHtcbNiOlKhIqqiTdIWchmxz7Dg4AKvkt+03/1/4m+gP2gPT+833yb++R673nq+SP4tTiduah62DwWvOs80HzG/Tw9Uf5SP3FADAEDAhoDOgR/hbbGfMZ0xfbFI0T7RMIFRwVCBNFECUOSQxXCaIEN/9e+2j4i/XW8SPvT+7D7qDu6+wk6nDoR+jx6IbqzOtq7BHt9O3V7wHzO/W49vD2l/Zx9q32n/aX99P4ZPmZ+eP4Avg8+En5H/tp/Fr7Wfp9+yT9nfyr+DX49wRiGHUkGiADFBET1iCtLcUsfSHqGhQflCO0IAMauRccGxcasA9GAm365frB/R78c/bn8IztPu1b7nrvFPAT737suenv6ITqou168Wz0Uvbr9gr3Ifgx+9v+dQH0AU0C2gPTBiULLw8gEpQTJBJREIIQIBJEE14R0wzWCM0GHwUzA5EAPf4m/AT5zvRn8RvwbfBJ8LTu4Os96Qfogegt6lPsQu7F7jfvV/DG8WnzOfS+85rzMvSb9Qn4mPqE/Ab+vP6k/if+Uf2U/A79RP2N/O/8A/7t/uv9E/yvAZgRfyG4JR4cJhIYFtIhESdTIVoZ+Br1Ik8l/CAIHZIdEx4oFt0HzfxU+Tr7lvxg+4z5X/df9c3zAPOW8rfw5OxX6T7oZ+pj7nbyKvZx+Kv5a/lQ+JP4rvoP/u0A7QG8AksFqAn3DZ4Pcw3QCUAGWQRtBKkEJwQVAzsClgLnAr4Bmf/4/G36/ffV9EjyxvFy8lzzPfM68rbwVu8c7rztle5q71nv++7a7zjyc/Vk91v3hfd4+Nj5Q/tX+2v7a/wc/hz/uP4c/dD7V/yx/Zn+s/6Z//8AlAF5ATUDUg2QHIMmRiUXHPkYGCGKKiIscSWyHusewCC6HmMbIBkSGH4UxwpPAJX5Lvd29x/3VvWR8mzuMOwI7XTvffHg7+TrKOnA6LXrmvDZ9F/4tPlM+Uf5efqH/coBUATBBMUDmwPVBIYGdgc0B78FtgMTAQz/J/8gAOYAiQC2/tD8Yvuu+VD44PZj9TL03/Nc9FP10vWt9RH1SPSj8+3yafKC8rzyOPOW9K/29/gO+iT5mfdH9+z36fjV+Iz4EPl7+9P+BQHMArgD+wNmBFgCEgDmAJkCWgPZAcb/6geDF3kiGyTrGrAWlR6AJWgm8SA7GwsfqiIqIGEdvBriGgUaMxALBiv+evnq+af4i/aj9DjxLvAj8Fbvr+6W69TojOcx5gnoL+vy7u30tvcS+Q76Q/o9/bMAhgHOAdcBjAORBhcI7AdgB38G9AQpAlv/CP5n/Zb9JP1E/Nv7bft/+7L7u/qR+ED2cPXT9Un2QvV59MP00PV39pL1uPSa9LP0DPT48vzxNPJX84/06/XZ9kv4a/pK/MX+7/6//l3/w/+sAAMBeQA0Af0BHgIpA8MDEQXGBhcKphPtHFcgbBzHFKAVWhyjIBggQhyjGzMfSR8rHWcbsBlOFwcQ8wWQ/xP9wP2a/Xn7oPj59U/1w/TW8o7wou7e7jfwle7Q7NLsM/BL9V331fXq8+3zkPYU+UX6wvoS/Jv+vADOAD8AQQCCAVYDnQKaAHr/0f+mAHkAdf7h+xn6B/kS+GT3ePZu9hr3oPdL9yP2gvVd9m34sPnq+af5wvmI+p/79fua++76Nvqi+av5p/ko+kP7uvw4/cX8hvx5/Dv+7QAwAtICDAMcBIEHiwhyBhIFhQZNDbETwRPZEpATtBiFH04g0R5fHZgbjRu3GCwVKRUYFRwVohMqDqgKNgiBBaECifzw9r70/PO39H306PON9H30QfT48izxL/Dh7x3wGfE+8TjyMvRW9gT4R/i695X3z/c5+Df48Pij+gz8kP1s/vv+AABsAFH/nf0K/LD69flb+fX3Fvf09gb3uvds9xH2jfVR9Zv23Pdb+Dz6QfwP/lH/lP/e/3QAiQCaALwAKQFVAVoBjwHMAY4CAQP9AXABzgBcANQA+ADZAEICGAVZB74IwAhsCQ0NkxAgEo0S0hJqFVcYZBlwGZwYxhe5Fo4U0hJ/EA0OEws+B4UEOQLG/7H+1/zf+937t/lU+Hf2gPQr9KXyUPGn8eXxifM99en0DPTC80j0uvVq9gT2YfVz9uv3C/i9+Mv5V/tY/KP7cvqg+vb7OvwH/Ez7tfo8+0X77Pom+0H7oPqj+Zv4g/hD+a75KvpZ+qP67vo8+/H7G/xP/EH8sfxh/lgAqgGWAn8EBwb1BgAIoQjxCZoJrgdnB90HSAnUCvwJUQs8DWEMgQ2ADJAKSQymCyML2wtBCh4KJwtmCzwN2A1cC8cIigVqA6QC+AE5AZv/LP+Z/xcAiADp/wT/df78/Dr6Rfho91L3qfdH93X3fvcK9xb3tPYM9n72HveV96T4Kvkr+jT7L/sD+0H64/kA+pP6Vftp+yn7u/pr+v76IvuR+sT6iPpv+qX6zfrN+938XP2C/Rz9s/xf/aL+fP8pAIsA9P+U/z7/Df8FACQAav9h/9D+m/5N/9n/1wAiAZoAQgFmA1YF4Qa3BvwGAQnACSkKRgoPC6sNHQ4CDbQMoAynDo4Pdg0CDQIMJQsKCzoIyQYjBuMFNwZtBKgCdQGLADgAZf4o+5H5KPjg9pL2DPXB9ab2FfbI9nH21/Zm+Mr48vch9wb4Hfon/K77Pfsk/Cn+MgA7/87+Iv4P/swAdABU/u39gv3D/qT+Mvxj+yT7OPs4+wP7lvva+Vz6OvxT+nD7Gvkd+vn+UPvl+ov80PspADABHP5aACD/JwAeAtP/PgPbAdQA/QT5AlQDSwTnBG0GQASqAd0BuANzBdoEUgNDBaQEAgcSCEoG4gnJB1IGOginBz8ICgmRCJQJeQomCIYHWweOBjEGfAOzAUYBWADqAPQA0v5E/g7+gvvH+4r6cvrv+3/6sPr1+j/83fy++2D75PvJ+nn7sf3q+8/6xPvx/Cn9IPw7/Fj9s/xR/Jb9zvzE+4D9Pf7N/LX6V/xY/Qn8Yf+m/6H9nPyS/mMAwf6h/j39GP+3AVT+qf89Aj8BzAD5/8P+KwCi/4/7vPyW/Jr8lv47/dL88gHbAY3+wAHZAU0CpwTt/8MBrAfSAuID3wVzAyEDGwBdA1ECCP/0A8EDHALFA9kCbgOOBLgBQgFsA20F6wHB/pIEBARPAdEGvwTUACUEjQKxAO4DKwBA//kFqgGH/00D3v9aAMMDhP3D/K8Bd/zD/aT/7/th/ZH8nvvi/MT7Nfp3/Hn7A/u1+r34EPvm/cL8Xfmk/jv9xvplAfX7bvt8/1H+OP1a/Zf/lAB9AFv/UwLgAtP/j/0RBJkDpv3dAJD+bwP2/KX59QX3+sv6dAYt/N/7ywZj/QX8MQSs+xb/eQI0/HIBvANH/woD6gdu/BgC7gVF+ggEcQO2+WEBRANq/swAJf5HBp//PfurBe38uv7NBTABBfw4AEgC5gC9/24DJwCSA2UC6PvuBdUCMADuBCICXf9RA/UGb//xAY4GN/+JA0cEv/7e/j8CRgO3+47/RAFk+Nf/ov+l+oL/7PmD+X4CWv6L85IEFwH39icEm/2o/XsB1vvK/ooGP/u7+lEL6AEk+ZEGWAB8/RAI9fsH/AIH7v+P/JkCFwC8/9z/kP5yAST95PxLBYb8S/+QAuL+nQBr+5sCUf/q/E0BlP4L+pIAIQOe+j0DWv1k+ekD3fx1/A4AB/y8AJsC+f6//mED/wCR/EQCcf8gATD9nwLnBUj7IwTkAaMB5QVy+2T/bguG+2H8LQj7/qT9+AIsAlr1ywjnA3DxuAM4ASb6VgSGAZn5ngaQArH8+wKkAkv+XQI5AqwAegZB+jIBoQ95/BT7lAzXA539VQaf/SUFHv76/MsRePff+O0LbP4Q/U4FP/p5/fQEI/dABcb6HvxFBhz3BwF+/9b5u/u+/QD6JgB19xn7OQNw/Gj4mwQNBmHteAxkBcTmQQ+BBSzxbAGZApME6PnIAv0E/vpv/5YCcv1A/7gCMfrFAzUFsfdLBFMHLfos/1YKBfux/WsI2QBg8gcOLgJV650RkP8T9G0NsPoP9icbsevs+nIY++SvBMIRa+kvB8oKtOalEukBot0wHdn7NOstEn72j/3iC2r1qgFADaT2kwVmA878/QqO/g//PA8a+Fv/PxJR9kn+TQyX+NAIzgAJ+eMQ/vJaAIIQL/QXAcP9U/oqDMz1Xv3jBgT4KgZH+gb/rgil+Zb8rgfMAUb7YQRA/zMGn/yZ/rYLxPAIBWESIO02/kkV6+urAhAKD+Y3FX74sOi/Hnfu5+9yG4T6uvSmBAMAiQTx/bT3SAcHAFL63AMt/J8Ixfv09BsSuPjC82cK7wCf9VsDkQCp+pUEIfa1Bj/75/hSB+P4g/oUB7P0g/hsE+fu+fFTGmruc+1iIKXzKeU6ISAC9uFRGzn9J/PIDzL/hfjODOH6IwToB5b0Ohnj74X/ixS66hAS8v1D6s4kiu5/850e9efyAz4Pre4/CvwA0PtgBsH+pwS0+fYLJvmRAAUPiuZVEfoHJ+2sBkAEX//GBAn5sQLqB0/3SQU1+VkQoPEj940cvOnN+T0aI+/x+8YWK+v6CGwDMvZNC8DzkAOfAJ74QASk/gn6K/wy//H8dAV290D+TQyx82v54xVn7Cn70RR/6MoLfP6P+ogG6/TPDjT9vfGnDhH93O8RFUr6zO0oEVoB1PR1BOUGu++wD5r9c+4fF5LtJ/60DqbulwaSC7PriBEOAf7x8hOS7VgMhwO+7BgNRgHf+8wAQv2pBeAAvvGGEX71zPYyGz3iXArHE1LfTRbjBxTmahRtBELunBDo/R/x4xVv/x/uLBci/EPymQ51/e3+4flbBU4G6PKIBk0D6/X1BxT5SwP0/iv18Asb+rT6CAoT/lfxPxBs/aX6JQLd+y4N1vFN/wYM8vjJ+wcGL/y8AZX/3fyvDavtKQovDqblrQ20BQ70ZQni/V34JgmJ/8P8agFC/hkJx/O4/S8R4O7dAd4M3fMMA1gEi/5ABWX2qQw1+g768BdS6EsKMQZV+bcOr+80CrYOGekTCdcTGei+AhUTR+gEDP8DGud3G4H6XOF/Iyny2Oi7Kobgn/NWJ27kwfRyG/7oxwfM/s36Lg3Q4/AdzPV46f8nJOD8ABsb2N3hDZwQCd4uFFgLPuDBDWsFpfiH/Zr0dxKY/QLlyB3q/JDkWyEt8yD2gRaT52gNyAUf8fwJCwab+KYAJQzc+RMAVAIk/TcL4/lS8MMcE+yo9rspjtIrCZMh3toYDhkKFvHg/gAOVP945VsYPAf44IcV3P+k9CkJXfjiCCoDgffrA/oQGfHz+p8dxex9+4cWOe9g/OEW4O0x+90aDeYl/5cUZ+lFBQEDy/m8AHIA9/+M+e8Jhfa4A2IG1vC0DNH/NfHYEWrvhwREDBvg5x+J/JfcZCoN+cHaRC/z6kjr4iUP6OHy8Svv4+3hFkuFzXfrQkPSxSEMth1VzWcirgfJ1jkl9AAy4r4jofTh7/MSqwOX9xH32Rsq59QA3iRc0qANNRhS3qkQKAZM8Xz/MhEy7vkFmwke5VQVDQW030wijvbO4nszG9ePB80XmNbfKu/qRO7uL6XWZAfkHFzg6AibEwHi3hUCB/LlpBbAADz5EPtjDEUF9ealDlESsdw2Eh0HZu2VFi/0ewDBA0X5xhCR6n8HPgaX8foP3evFEZ76ZvjSENrmSxTr9hf1lRdS5uUFqwxo5GIfcezf634488xIAo4lZ860Hxv6L+kJIKvtvvXzGAXxq/YZGd3jxRpd7336ACKJ0y8aVQm/5HkJhhjA2FQVww0k0TA22+PQ9h4ct+lQBDkE2fxa/uf+qgJ0+j4DnwLC8yMPFfRmBiEMIOK5IzTyBfo6GL/m/hr97fIC9Q588H8IhPuw+8kQV/IG+Xcb898kEfgB9fcdB2DxgB6H3HIPoxA11fApjfp52NwxEvPa38ot5OzF7owY2vpb8WgNkwRd9kb9ewhz/8f1fQ9O8UkFKwjW6roVbP/D4ngedwNa2lknffnu5BEdif3K7EkVxPlW5KYnf/DZ6QEcNuz3AxIJOPRH/50DpAFa/Ub/OvioEkrpuwiBEIDiehbDAvbq+RVsAQvoKxz9+ODuERzE8GLykCbi5J/zHSnp5tf0rhjE+ezv0BA7ABztZxoj9QbtNSBJ+H3naB8u+OjwhxXZ81/+Nwp4+Rn7TxPu7vL96g/29cr4nA91/JLuzRpS95Trkhts/VbecyY59w/bvync8KvuFxzk7NIB5gadBCTxbwPBHFfPDBpvFCbQtB3JB8PlExKS/8v8//PVEqgBLepgEAj4GA5C9Vv5hRe47OIBWBO75pAOVwB89+cFvgHE+gj4tBgj7nvyQCA4+ojevCu18+LltSXi88DrJiEY8F/wFy3k2gYGihUu5hwTKfUs/gsHjO46F6PowgXxEMbg2xK8A+f3RPwJABUSuOw393Ik6uDK+IQjPOmL8qAX8v7v6R0XxPpA+RQIMPcoEfjz5vRUIObg/AdgF7rZ+xbABhvndBnH8mb2tBhB4ZkUK/0I7Y0Z0eUUDi0Kndw+H176+e/cD0L2HAOVBJHxUhZu6SsBrCOIz0gRmR2q194CnDPtwTUPPSt0x0IbmQsN3HYivP2A2ngmTwNZ1k8m1QOp1Hwvp+5M8mMS3PcCBab8DgHmAScCGPmfEYXuU/vfI8jW1wxMGJTcNAp1ElfpYgQhDkDmsBpo7LsFbBN5z0cvRAMrx9A2qgK0xIM7bvSs2mQxst68As4dvNPOHQgE6+ImI3ToKAQYFuDR9SiF/1/SOzju5gTxNiPQ5H0F+w6160IOxf5G9pgJZQcU6YgWIfil8jAlidBiFz0LeOZNCpD+UQBnAMX9afynBy72PgZ/BBTwEg9p9DIC1ghO8wAHTvqOCxjxDAjDCubgrBgCD63RSiTBBA/TkTVz5cDzSB0h8MDz9hL7A17jShryAVbk1Rvu+Cbxlhyv43YPTQnD5RwV+QIE7cEOrAAR9YwMMvYWBT3/dADiAEP5cgot+0P8OAc3AxjxyxBZBMXjaCXk9GTmEiMl96Dv8xBFBTzmEBpC/63m+CE37ub1ohwj7kryvx7h6kH77wv8+Tb6AARX++kCUAQ151QgMfik3n8ziOEZ8vUwpMxbFZoOFOHGGJr5UfSCE5r0lwPr//775gZe+8ECivfjDAH3bvuzFKnlqwxQBSjyUg008w4JHP9898MLJPxo+f4Gg//SAPX6oAP//sP+dQED/poAYQDWAAgEAfZKEVzz0vrvHODZLxlYAJ7peRSaB+fcOCLQ9tztSx9y8Fr2FwhzFNXaLBg5ARHuExSj/NzvSxeA98z0ChWi9pT90f8vEVHjKRK2DprXzR9wAWfgpSFp8970GREh9j8BjfqLDPzwLQCyEOTgKhVvBLHhKCD18Mn1Fxwb4RMU0P6273UbfuabEjn1Jf9UFsPbRxYCD+HYARy6Cs/VES7G5lf5+R8t130YWwcU37EddvoY9vQL8vWTCDn0ohPh6hIAFBhN4xUJdAdz9jQAGAP4AiX3ZQgPBKfpwh9J8Fvx2inL1Z8K7CCUx2gwBvex2v45rtV3Cr4QGNv4IgD4qeZbJ1folPYJGtP1FPti++IVzekSACEWzuMTC5oHxfNRA24DyvaYCg/5Y/wlC/7vOQslAwX0bQaDBpn2IwJ6Blz76PwFChr5DvuMF7jlcQLSISzURhN0FyLJ3zNT8ljkcS4G3X4KIw4s7agIlACoCYnt/AUiEw3gvxdx/3D0hQLUAUIO7e1N9+4hG+v88oke0Ou7BQ77xQPiArP0HwUgA0v1PQu9+pbzkhxo5IAKwQoI3DI1PNhH9r40gMgND6IgV8fvI+ANC8uPM0nuoO+kFsP1cwVC9HUNKQK55t4ftO8n/YIJx+2LHWLnPPkbJGPc5wQsHZva+RU9AQrq9R7p63P9Bwl2+ZgI2/GQBPUNUuS/D2YNWOXACAUKtf5O6r4ihuhk+eoirOCcB78LWfKDBMMM2+zuBWQMB/LV/7YLvfbNBt/yJw1uACDtARsC8Lf5gBRo8A4C7xEA3XMfGgPb21kenAUx50ALkAyU69ATz+8PBGMJ7/KWCEv2hQSPB27zLv9rDwrqDBRh9on9+wrv62AaG/Gs9OYcKOqw+sUic9WbHBj+iermKpfWwwpAFqvo0//nDjv8J+zVHYPvTfY2HK/jAAQ9FDDvA+y1KwPilfd0H3Dj+gqpBXv53PYUGbrrz/mTIS3if/x3JNTg2/W8LiDblvylIEPq1PPqITDsr/QCIVLmLgVWAYMGpfz48o0U0PIl/+oOC+YOE8j/++8nEvjzHgSt/RQEdfzm/SULtO/FCD4DnvIdF47lCwiiFnnXPRxrACznghoK9VD4iwx8+EcD1/yLAtMCFfU1DOsBoersEFEJ6ODaGJsDzd+4JJHwU/PvG6bjeA29BTftsg7O/a397Pk5Ehfyy/n6GCvpkAHRFUvlHAJxHPXejgXeFZHoA/81Ft7t+vq7Fl7rkwTQE4PfThj1+v30Shp03lIYFf/E8L0MGQYl7wMJ2A921+Ius+wi6KAvet1HBYMXKN+hE3QFkOxJF53uVwgmAI37twn38P0NhPyG80cV0/Ca+lkRJ/Y/+/UFTf/8ANn8DgJ4/4b9eQsV5XckwOt26tI299Np+lkw1tBBCZglRMOFKUsF59VuJfH6+upgER0ABfV0BusDxfMfBscJr+XcF2kAPOXlH8Pr9AObCX/wwgcnBAX6LfvmEKjsiAlfA8LxrA8k/G3yIgtmBdHx3AUkBsX1YQCzEGrvlfnbHO7muP0WHrjcpQ55CXPnER147j75DCJ/1F4crgny1VUpZ/MK938IWfsXCd71vgCBDoHpZg0WDMzjHQ+1EjLbhhKlGbDGkzFQ+6fYdjEN5gb4+Bsb6DAD3wdh9kwJEfbJBoT7Vv78Bjr8O/YQETz5avSuF3nqdQlEA57yvBLD9MT46hr22S0bYAWF3o0lSun0AR8NBu9CCysAsvKpFyjoLAQEF1zYMhtcBzXWaSvl+QfZ8DKg55n2+xcP6LMRyfTTA3UDsO/AGZLr0PsfGAnozAKzEObpEAmADHTnnxSv/TzyShO585EFKwDn/hb5hRZj6vsBVBZB4qoTKQHx+Qf8aQ8A+OD2Ehkt66f7Qh8d3dkBBywkv/8apSgLsYgyuQUk2Ywfi/bk/BD9iQlL/VPxEBFEABzsjxj8/EnmeRxvBUjhng7lGATWdRRcEdrcIhv09Bf+wg8F6G0P6wQ97cIOcgBG9g8DAAhE9yAAPgzK7XAI4wy057wKYg9D4RoVNQTr7SMLeQcY7h4JJxSN2ZIT/RUJ1QEU3h4JvFsx5QX6190gm+8qBqj+tgKC9UwGYAgB7pcFDhRU2ykU4w+J2vwZHgDE8HYFSQut9mX0YBhd7936fRgb3/wYs/sW6D4nfONnAXASV+lTEHX12gN3A07xNht83FIURw8pz9YyMOs+7kUiGu6T+vYJBQJL+FwAhgaf/gb1vhNO8HEC9w5h5Mcbk/fG7pYb2/Kf9s0R+/VzBFr/YPmgDRr2jgRR/X0FS/7w+VILNvox+wYGdwAl+KgJJvlY/6EBSAgS84L+fRsJ2Y4VugrR3SAld+6Q9FshLt6DDpoQgNjiHsgEd9nvLDjpA/SeHgfroAPvAYwDFfZYDSX06gbCANX0PxBy+sH0Fg4XARHsIxwF7ED4zB6b5s7/IQ+A9hX+BgRWAkz6jv97B3j5B/yEENvqEgkYDwnfhBtwANviuSBF997uYRQT/qHsVBTWAKzoJxYdAHXrSRV7+b3wLSFL5FL/8xho5iwM6QQ56xwdvuoBAaERiewADdz2qgjg/oD3Mgq5+W8DHv+J9WUTUPd+7R4jW+nI8egrd9thACcaFueeBc0NJ+U3FEAEAeQoH7PyEPRmFwPyLfxJDZ74X/giECP/GOa/JQLvHer/LWjle+roNFXYOvzYKs/MvB1LATLu/hCE/Kb21gniAfL1GQZzAqv3GwHLDzniqRkt9M/51RTk5NcUy/ToAcoDfv76+kADmQTA+hMD0flWC9P4f/tdDPH8RfCfHMfpSABiFrzh1hZU+Pz0tRPe95j0DQ82/df3dQl8+DQCLAVY9zgBygqI7/sKEQEq8bAX3uwI/3gV6+SBEO/+B/EmGCXr/wGuERDrgPzCIOvc5ghbDtbvvQZ5/TwGku+GF//uGwDYEcvp1wyTBOjyeA3k9OYJq/6E9TQSpPQt+zsR+fBGAuMMa+moEsb61vl3/w4RVemMBuEOg+akE4753v4d/JAOtO4+BrYL1um9DqAGl+5xBJwROOqMBXMLz/jc+ZIJFwI28UsTVvjy9+8Lnvsl/+MEIfHOEjD4r/X8EDjxUgtD8qcNNvvJ8wgbquHOCgUT19tGHlr1l/BJIRPlEwEpEuDus/2bFCDs2PoiGtTq3v9VCJQAffs++rMRNPKG+ooWuuxl+zUdzd/5C2sOxOeIESj7df2OA27++AHv/NsCAABj/XMF8P6a+iQJpfshBM37fv6rDc7zEPpsGtTqVvd1HjbrPfu8EMT3Tvq2DUL0Dgn68pMN5wNe4t0jQ/Bg8nIYuvYs7dIjc+WU9owpBNblDmIHa/H0CjoAu/FRErLySgCMEEvmPBDx+qoCXP3w/ucCMv8S/DYHn/tB/XcCygIg++j8kQfc+D4FB/rBBCH50Ap59NEDfwVd97b/iQl/+HT5mhBr8G0QBekNF9/5BvWZDg32TQmi91gBTgT0A1/uxRPY94r6CweaALL7aQD3BY/0tRFe6gMRvv5L7o0ahOy9BDoHi/J4DXb4X/3VDPXv5ghsAlLwwBdE83Hu8iaD5nH1vCSH5Db6Bh/c5G0FBxHt4HIb9/f/9ZMRhvAVC0D+zvNMGeXuAvGBKznbLP+BIZPY1Ben+wn5jwjI9kcNOvJ8BEgIJvNAA9gQmOOOD1kO/t8hFV0G8uQNFrkFQ97iHOYBTeNyIxTnQv4BGoLjUQkpCWDxvQcoBeXtEhfz8PgAdgWmASH4HQcJAFX6HgqX+NAJ/vFPDIH7PAbb8wcJvQaZ7K0N7gNw9GX9tBQi6oP/1hjO5OIDuhQ86L0G4AyG6FQOaQZi8ar+9Q8Q+XDxNxUU+Wz3GwnM/h/7JQTDAhXzmg/4/Hn0Zgt8AWr3HAI3DnvjwhrD9zH4cAuv9ZYI2/wKAQP7HAqW9bIDPgfr9XX/Twsu9dv+uQ6h6J8L9RCP2DkbJwN/6GwRb//g9dgE1wMd+R4D3/z+BKX53Qh19UADsAg29G8H2PkGDWHtUg1/B1TmHhwn9H7+agVJ/nQAEP0oBfD/8vb9CR4Az/EAGhPmAQkQEU/j2BHGBSHtNgcYDZHzDfbvGsXuR/byH5LitAYKDeTzr/6pD4PuNf8tE1LxTPvPD3TxFwLwEz7cpR848zjzyB6r5AIHFweJ868N4fBSCwL7fvSgF8fp1AfmBnjvVxAh+WD56BIj5+cUwPkY8f8dcuE/E4f/ee17HD7obAT3DRPtEQuT+UcGJADD7Vke4OwV9CompNqvCfYVTt/JE4v+cfa6C3X3ewHUBhX16gjH+kYApAvf6DQSegVk5x0PLA5K6Q8CERO97lcGKQNg8u0TpPTf/MwJLvdaAmYFZvny/aIMnfSO/vUIdPwH+jIKefyH9/AMNgBq7z0St/u58q8VzfLg/V4INv6m9wYNpv567Tsas/MF8nUjKt+BBV0WJeOWEID8wPuMB9H2wAaPANz4UgSAA/38cvxSB/X6D/8PBjT77v+iAmkBaPkrBy7+j/mxCO/+0fbFC0n40PvoEX3x5P0sDu3yEgVjAyn2uw1J7+AL9gLC8AgKeATR9wkA1QSq+m0FAvkQBwb55AIsA3P35gmt9k4Exf9XACn+jfzoB3z41QLmAV78av8ZCOzywQqO/lr0KRUr7HQH3wmJ7CoMegOz8v8LEvogANUCP/uMBo/5DwXy/gb+ZgRc/Yn+gwUA/C8AaQby91YFcADJ+koHRP5F+AQMGfyv9lwQX/f0/QEJE/bPB/r7xf9E/s4CdQEF8a0TYffB+IgJDgJT87UJSAAy/rkAI/kMEUjtOAiZA6r6FQD3BQb5bgJxBSf12wg4+6//EQEX/dAC+P3A+14KivAXCeIAq/WUCab+PvmpB1/9hvrrC+by7Aiy/176hwMACOPySwSdCt/xZwhW/Zv9NAB7CKPyxgR2Byn0mAqG/LX94gFcAjn+yAJB/Yz/KAa6/QkA4gMwAln5cAgIAsT7MgCtBE//XP41Awf9AQPZ/dD+bQTx++383QYI+HsA8wg08ooEZwYO9b0EJQFM+gMCjQHL++YAugH++wEBwwOc/IL+5QVh/en/Nv6JAlMCPfyLAYQHb/mf/3YIPvpfAqP9WQVX+y8Aof4vAJ8C9ftv/1YCD/9N+M8FcPwXAqf3BgOyA530TgiI/Lf5cAqx9nH+VwZo9gYFgALG+qr+FgRd/sUABgRq+MEEvANr+RIJefok/aEK1fdPApcFk/eHA0AFoPojAwz/wAAIASL+rAHZ/p8Bzv/j/+D/3gH3/2kBcf9hAaT/6vysBin9BftZBxX/XvwvBf/89P8RAsP+PwBIAcP/YfxZBF8BLPltBjL+9/kbBgf8s/5VAaT+BQDi/iD+yALS/gH88AMa//T8EwHMADb9DwQ2/aEA3gL8+R0FHgIS+hYDmwIR/ukCrvyhAHAByP/7/roAtv8//h4CTAAl/ikBzgCv/AUCUf+j/e0Bvv4nAE0BtftCA+IAEf6UASkA0vu2ApQCyvjKAf3/df1GAqD/x/7u/48As/5m/10DPfx6AmEChP7JAI7+DwQ7/VECKQHk+1QDbAFW/lUAYQCZARz/Rv8OAWP9+wJjARH9wwF3ALv7ngQwACT7nQH5/xf+EwNJ/gT/LAJ3/lYBXABCAcP95AIDAkD+8AN1/9n9tQab/lUABgUb/H0FTQGo/fsDX/6dAPoAOwC6/Tn/+AB+/m4Asf2o//n+Vv9A/wb+w/6qAFH/SP72/zv+SABu/eYA4v45/pQAEf4tAF3/EgBE/7//I/4rABP9rf9k/1//IAKN++8BWv9l/XsA6/5E/g3/ov6b/mEBa/zbAa0C6PyxAQT/rf78AAEAkgF+AaMBcAHg/7kALAP2AJABOwK8/a0EYQFY//cEWgD2AQgC8v/6AKwBmf/MA18Bq/4NBVr+SAJyAcD9NwPw/zv/IQPZ/icAHgPZ/ZsBewA7/+X+nwEJABr/eQB5ASn/yP+jAWf9hgFW/0YA/f+F/yAA1f+1AMn9bP95AHL9DP5a/wn78fwR/vf5mPyp+oT66PqQ+Jr6HPhy+ez5cfkF+u73OPrk+1D7XPre/cL5EP1DAML6zP9+/lH+ggC9/4X+VgIHALwBbAOg//IC1wLaBfgJSw15CzUP7A5dDTYRjg0iETgRARGWEroStxK+EqwSrhHfDikKNAgLBCoEfP5o/uP/Ivqt+M362fL+8QPzRO9m7oft4O8067nzUPIR9D73zPZZ+mb4TPsD/Fb9nf9SA/QAIwXOA68DMwSdAscBgP8B/4L91f3i++b7ofuV+SH2qfah9cLzx/Oa9D72n/Lh91v2UPIi+/HzK/U/+0TzDfgK/JH4VflK/RD8hPzOAND+rACpA5IAQgFuAcIIlhvRJsAraijzIDskpCSXIHYfUB+lIeklLB3oGD8ZNRXJEwoLpfp872zrHOYh6PznnOZr6QjmP+Qb3yLaWN1O4cThN+Rf59boBfM2+hcBQASqB5UHLwmQDuAJ6BCrGIEY4hzvHBYWKxqwFu8R9Q6rBeEE5gAV/Qn6D/jp9q7zSfBM6R7lHuJE5D/ixeS550LnZu9W7A3uYPL37g/1BvYV9Wj5IPsh/zoFnwI6Bx8IeATcBY0C7QLX/74B1QEkALf9Uv8G/oD89P08BI0TgBy9H0MZ/xPyE3AYoRsPHGYdlCPkIuggoB6FFw4ZQhaSCt4BMflK8STzJ/X89M3yfu817pTqI+Tc3onk5OKZ5cvr2Oax7oz2JPvr/s4BiwC/ApMIrgepDZ8QyROQFVsXghPjEI0QcQ41DqAD4gKm/Zb9+/73+f75qPIL8Q/uQeiM5zPpV+pN7NXt4+757jzxZ/Ed8W3yPPBB9ID3lPUX/if94vs0AuH7n/ss/oL8qvv/AFz7hvx4/2H+1wDM/t4BzANnGGkkkCitJ/0ctRyGIhQiIx/gIyck9ydSKHkcABmXGAMSHAzQ//Xx/+pA6Ffp9+mb69jpGOY24xHd3tuq2rbdh+V76MXqxu8D9q77ywXRBcIHyQkZBs0O4BItGE8cBh7aHo4dTBtfFE0UrhAPDYQK5gEZAKX7S/hq9ILtV+o55OTiKN/k4HfikeIq5g/n4+a160vwJu8K87XyyfTe9iH5jf1p/YsBkAEnAEn/+P3g/D7/bv0d+ZT7wvjz+gv7yPiK99X3Dfl9+rEI1hoiKjUv+ShnG34bMiW8JgEsXy5NLuUyGS2sIokdGRlOEbkI0/dH6H/miOd06uzuYezQ4/Xffdf4z1jUHtQM3A3l++XE6AfwBPgt/doGcgZ2B2UKMw4zFmccbiV8KGkpeSRuG6UXDxQYFkgRkg34ClYBkQAk+971he1q60vnNuIO4pjfFeXI6GPtRu6J7Hztbe758O30Ifjc+ar90vwg/TL8y/s2/V/92/3p9rr3K/VY9Zn4RvVV86HySfCd7eXwA+1e8RzvTPJf8BABDyi2NtFCgTGtIAQnEy5ZMtQy1TFWNjM8ITJLJtAcJBmiF4EFNvLu3XzTeduO3gLlZ+HW14nU+9Ntz6rQuNR92Gnppehq7kn35vpSDKIUThEjE+0S3xApHOwgDCOnK0colSbfICMWahK/DOwM7gdoBVH+jPib9jzulu1X6s7ibd9r3tPcf+Az5VHkKey08JDvp/FB85T1ofr9ACQBUgNFB1sE3AWoA337h//r/Gv7EQG5+yH5OffE8q3v7u+R6ZPodOkV6r/tJe8o8mvwQvTd8u8IFilfP9JG0TB3IlElWzPsO/k7kzs2PqE7dDLUIsUVdRTPDjn+uu1e1uHMbNKW2ITfh94A1Z7Nb87szK7POtZp3wLuEfSw+Nn9TgbqEM8YDx4NHcUb0hyVIA4jjCaNJWAh7h8iGMMNFAgXAZ39H/vZ823wFexp6l/nVuXG5Sfj5OKx40PiFOcj7dXv4PdT/GP+7QPlBnIGVgpPC2ELeBDJD9oOyg6uCS4DRAD5+Yn1n/bi9PXxOe3G6OnkT+W95jnltOTz4uPgj+MC6b7rwvLQ9E74w/w8DbErbUKQSlU9vy4KLTkxGDdHOL41jzz0ObEsrR5QDy8FbgBV8WvhHNVNyDDLUdLA2DXdjdvZ00/SyNQE1KTdQ+tK9FQCLgXpBK0NrxNuG0EhRiK6HkocXx2/HTwiXCMxIFEbQRF/Bjb+1PoW+FT4MPXI77jvnOmj6KDnlOLF4Tbh7+I/6yvy0vXs+tT9nf+3AVoDEwT1BzALwQrUCiMM+QzTDGYOcAhLAYj6wvFx7Rfr5Ota7aDuw+zX5XLiQ+Hn3gniY+Lv4pPngOpU7w71aftP/eIBAwAqDyEqI0IeUZZGtTRYLxA2qjbZOI81jzPbNCgqJBcYDU4EDvu49cPlQNYEzeLIB8792ancPdov11bT6dVb3KXgG+vX947/+gbjDFUS5hiCHWEfyiDyHoQaGRkbGpYdhx85HgkXoA1SB3z9hflM9N7she1i6hzrXOpq5szlceZQ5mDnYui+6jHvx/Pd+cT78QFgBzgHCgoTDFIMEQ1HDgYNWwzXC7YKAwiiBSQBbfpZ95PyN+417WjsDOvE6CXm7eL25Kfmmefn5+/qzOsh7hfyLfMq+lj8aQBl/RMKmSbPQdRQYkIzL00l/ClJMKAypi9qMNMu9CTlFikItv/v+mLyOOdv14/IyMTuyz/XTuH84a3ccd5k3gniPeoQ8XP4WAKPB24Iwg+WFH8arCNPJewgphv1FlsVwxaVFVET4xATDEAF6/65+ETzbfF37ZftjO3a6HbpcOiI6s3vW+8o8rj0+/Vt+f39d//tAUwI1wm4DGkOzwyrDEsNFA0eCjAKBQmwBaYCJPzS9bzyPe0/6Rvn9uTT5MXkyOSB5pXm6Oco6ejpVerd7EHy9PU2+5L+xQLyArIFh/6CCVgnLz4DUC5DVC+pKRUsTiwuL+MppSkiLKkgIBPWBxP+9ffo8vvklddqzPjHtM5Y2T7gbOFQ4E7g9uKQ5/DskvU//pcG8AwED94TnBlNH+IjLyIcHQ0XpBMXE8kRaRLEEFAOmgn/AKD5A/X38Ift4Ouj6ZPpAOhJ5zLqOesp7QbwafLS9HT4rvxzAfcG9gk2CscLhQ3MDVsOHw79C2wKWghZBo4ElwJY/h34rPQ/8eDrBOgP5SzkqeWM5jjnC+c85pLm3ejP6b7rRu3I8ZL1K/p5+wX+DgL5A+QB8wB9GsYzokyiTDc2oylqKBwtpTFUMqQvijGVKaIcsA/3BCf8Xfg87+bhiNURysbKFtXB3mDigeAM3A/deeE55jvuHPcR/m8FCQmmC9oP4hT0Gr0dcBtNFrETvhOeFaUW2xRpEvINKwem/6L5//Mu8Gjviu5P7gHtbuoi6bfqO+057knwSvP99uj6wf3O/+sBdQQgCAoLcQ5wEAcQEhCXDcgLwQplB6UFfgMp//n59vSg8C7tY+q06BHnfOX04n3gSOAX457lMejj6O3qoezn70L1z/jU++3+ZASwBFQG+f4KDKAqYkL/UapAtysCKTcvezMRNhQwmiwAK3oe7A/pAwf8Pvmk9ifryN0O0eTJnM9i2M7b6dzK29nc9uPb6nbwHfmdAHwExAfUB1YKUxEBGgIglSBmHSYYVxagFWMU8xJ2Dh4L0QbgAQb+A/sl+Fz0YPE57anp4+by5fDn1Ouy76PxAfTr9or5C/sy/F3/NQQ7CXUNCRAZEmESXBDrDPoIrQULBMcCTQIwAE78N/ie8oXsledh5YHlu+Zd5x7lj+KY4w3nuelY7JvshOxL7zTzl/mW/CYAJwLLBhIHtQZu/sEDOCJCOcVOy0MwLKUiCCURKKosQSxpKtgrjCGmFLMH+ADA+sr4Ju//40TbsdJH1I7c8eHb4u3h3t7d4DvlIukS8Bb59v0qA0sFnAhBD50U/hejGMoWnRLoEYQSZRO9FJYT0BB+CjYCMvx/+E/37/SK8irwpe+Z8HrvQPB/8VXyJPPU8wr2hfnt/ZAB8gNKBsQG3wdfCqYLXQy4Cl4IRwd4BUcFbwRmAoQBuP0m+ZD0iO+i7HPr9Ot87InqvOnE6OPnrudX57Lnueq9777zYfYw+Lb5PPiT+0j9agNxA88EWv6WCcEmfzn0SRg7pyfRILwkvSj2L6QuJC3HKiofwxIxCJQCwPyv/Iv0O+oq3jXUWdQK3BvhteOR4s3fx+Ly5k/rjPHO9wv7Qv8MACsCIAjLD6sVPhiDFz4UixJcELQPQw9HDrQLpQetAwgBZwBG/9j7k/el8+fwfu+F7mjv6e/18CTzwPTc9xL7e/wb/Sn/5f8TAq4Fdgj2CrMLNwqqBxkG8AMFAokBJAHtAO/+Bfyy97DyiO/97TXuLu8W7prr8OcR5bHlTuih67/uI+/i7aDuEvBc9Bf22vkX/g8FEAYtBhD9BQKlIE822k3CQxQrCyJwJHUnTy//L7wsIS8ZI70VUQriAvz7oPly8Ffmot3004rUjNxG4friseI639bhtuY96qrx2Prl/lQD3ATGBm8NFRQLGIoYNRY0E1gT9BMIFK0TyRGeD+8LQQcVA3D+d/tk+Wr2QvXQ80zy6vCe7yXvBu9U78jwtfM8+LP81f80AMz/2wDxAPkC9AQGBf0EmwQRBYMEPQP6Aab/Jf6q+3T4Cva49N71bPYZ9djxh+z56PHoEOoy7N7soO0C7gXx9fEX8wXzsfR89Xz3tPhL/b4ANAKPAQ/+gRdpK7U+xD3AIgQWCBspJGgvuTRcLV8t3iUrGZINXgY7ALb/Uf1Y9EPrnuAO2i3g0+Xc5ULlMeEt4vDmF+vT7sH2qPuZ/j0AYABEA/UHwwwtD2IQeA6mDakOfxAZEjgR5Q4VDDgIDQVAAi8ARP8d/Kz5pvfd9OHyrPEC8oryGfOL8ibzyvbf+G/5g/py+yL+cAFPAzMF3AXKBDADWAFTAQwCgAIcA+cCCAMZAQ//PfzI9/L1MvU59XX2tvbc9YT0qfD47ArsIe6p77Dxv+9S75fw6PNw9Ev3rfco+9/8pfwC+8/7Pxq4Le5ABDpLHbgTsBh7IyUvqTMNL/AvKSWtFpQK/wPUAGwAOP2d9Mfs1eSh4PvlfOeY4jjgYd3P4IPmNOqX7en09vaA9Uz0TPQK/OoF4A3LEGURZA4zDgMQZRG8EugQjA9nD2kOfA0sCyIIjAUOAPX7tvcg9Uj1Y/YL+fX40/aA9NjxqPJN9ND0g/jm+iT9r/+UAbgDjAQcAzYAuP/qAA8CBgS6BOcEFgUFAkL/KfyM+Mz1dfT29Jj1WPXb8oPuF+1T7EjtOe6D7cjtA+3m7Bjv2fJW9KP5L/oK/d350/nj8qAEfiUrM7o9BSNbDpYTtyL7Lco2CzEcLgMs8hxMER4LVgwxDecMPQGk9RvrH+Yu5kjq8OZV4gXfmd0/4T/kg+ZQ6QvwLO/U8Gvx7fTg/EwF2QmAC6ILBgstDwoTvxXVFgQXVBXeFFESKhCtDYILUgc5BZQDGQGS/u753vfV9Vjz4/Gs8cDyMvYw9yX34/ed9034BPoQ/FP9Zf5u/9sAYwLKAjkBfgBb/+n9svs6+RP3Uva49pb1vPXF87Dx/u9f7nPsfu0z7jzw1PEi9Db0LvUv9AT24PV698f6Uf1PAEj+3/vb83UWeypZOsc2Qg14BmoSZSJrMmM2Hyo9LSQjWhNZDm4K9wwlE68M6AC89Ojo0+Ut64zva+mc5ijf2uBP5DfnFOmv7c3vWO0L8HjvxvfJAREKpAubDHIHxwfDDbMTEBjxFzMXphP/EW0Pfg2CCkkM9gh9CPMFhP7l+vn2m/av95v1X/Tf8430d/X09AP1HPTS9WH2jPdV+h37Dvte+1z8efyY/AP8Yvqj+k77e/qh+xv8sPtR+/74+ffM9xT3N/c3+Ov3Ifn1+O73cfce9yn1efWE9Kb3c/jY+tD7OvzG+E/2We+f9UsVuSOPNkMjGQZUBCgODSC0MLIwdSxILWkhtRihEAkQpRI6FpgRgghg/F7y++xm74zvd+tK673mbedA6LvlPeP75tHluuV46Hvp0fBO+Z3+YQByAcz+HgIXB78LaQ/dEVIUtBWlFvYTcBAzDogOPQwNDvILpwe9BbEBnQC3AOT9G/tS+Un3zva19T/0TPQp9Uf2Ufb49dn0t/JY8zn0KfRU9h/3Pves+cb53/gh+Uv4k/n8+xr+cgAw/zP7ZPg39uX3efrs+zj72/rC+X/4//a89nX3Jvj++d35aPnp9e30xPDwBGcYUyRFKLYM7wBtCBEVuif7MBspiCvXJ/Id1hf/EZERkRf+F0wQQwfS/PL1WvSb9cjwhfAY7Vjqs+r95pbj++Qm53/opuvz6l3uiPMY99z3VPnb+tX/PAZVCbYLXArHCroK6wtpDmIQsBElEwkR3A2ODLUJjwlGCbAHJAbwBM4D7wHX//z7Zvdq9e30dfad96n2fPWa81nxou4T7Zntp+/H8971cPXg9X/zC/I09N71Tvo//bn8zvwp+xD6vfpM+mf83f0p/uj9Z/7J/eL7UvnP9+z3GPnh+lz69/nn9qb22/PSCEgZbx/gHVwB5vtVCeEYLSqBL6YlwSWiHucUghLSE28YWR6sHNUSowkeAL36lvqJ/Fb33vct9Ojx9+8L6Z3jluQR533nGeqX6O/rme/h8VXxtfFt8bj1HvydAKYDPARkBRcI+wv3DdwPXg+CEmUQZRB2ETMP2BGBEA8OtAx1CY4GnQR+AcH/3fwm+zH6KvfT9Qf04/EM8sjwie7G7gvv5+6x7tHt+e048G7yafS19aL2p/cY+Mr3Uvh2+sn8iQA2AlQCKQHl/wH+I//O/2kB8QHkAkEASf5R/CH6y/qT+W/6Yf0YFZUYThrcC93xTPzmD5UfoCqYJTgXrBt4FZERDRVhFUUahR2zFA4LLAK+/eb9wQJ+AEn5n/Y68Lnw0O366SflMupE6/zrI+066evrE++g713w7fNW9eb8SAGOAwoD4ALnAxsH+As+DwsR4hEpFLcQVRJDD94NsA7VDMgNbAsiCK8EhwIkAGP/WvxB+p75Tfj59vb0iPOR8sDxFPEP8Kvu1e2d7Z3uxPC683n0uvQ79YT1x/Xi9oH4L/rz+1f8Qv2E/Aj9m/0Z/Wz94P8VAIn/F/4F/Cj5/fg/+0b8uP2Y+6f4jvX9DBMW3hfzEVz0avhxDhge7ipSKnQadRxfF4YQFRSGF+ke6CM7HQsRlwXk/on97wCbBCX+rvuS9gf0UvGu6/DkxOaz6Ujqluy86Ybrie5d71Ht+e4m8Df1dvqx/dT9K/6GAE4ESgrlDdgOPA3IDp0MYg7LDwcQaBMtEzsS/A6ZC1wI+QURBagDDgHM/+T+G/0T/PH5tPa19ErzNPIg8s/xbfG58AbwL/BH8ZXyMPTc9VT1jfOo8y/00PV6+Uf6ufpi+3T7vvo++hb6cvsT/XD+kP5W/u37FfsD/H/7KfuO+f33rfdoDoUWuhVtEAb3k/oYDq8byiXQJl0dAh6YG3cTARLTF+oftiWRIp4VCgmbA5ICxASeBx4BFv9Q+nf2JvL/6sPkk+c97BDsF+0t6Hjm1OdK6SbnCeoj7bDym/cL+pH41fg6/FT/uwQ8CMMK+wvzD+4OoxByD5AN3w7RDgwRtRE/EcAPagxXCPcEGALkAsYDwQOCASn9oPhE9eD0DvUV9V70IvJL8CTwH/CD8DPweO4K7u7u0e/f8d3z9PNI9O/yXPHh8cz0wfcY+pP6Cfsi/DL8A/uY+g36m/iU+5v9eP8Y/x0Ai/wmD3QZkxcGFa/8uP7/EhsivivgLd4fMh4pHCcU0xT8G/Ik2ilwJE8S+wNL/gEBSgYVDJwFwf9r+KvwkesP6HHl8ejI7q/sgusy5WniE+U16IHnper77M3wv/WZ9vz0SPUw+Ib8fgK0BgELtguSDR4MyguXDEMPmRNxFSoWlhOTEPMO8gwcDDcMtgtkDHcLUweZAg/+BPo++R/5S/fv9U/0dvGk74ft5OqY6lzrEexU7WHt0Oym7RPueO5+79zuB/CV8uv1V/jl+VX69fo/+5/7cvuL/On+tQA3BCcEIwTfAPv+JP1KEUoaqhniE6P57vpUDf0cfCVHJnoYFhcYFaINYg1cFPcd0CSTIFkO4gB5/QEC0An+DrsHHgP8/Dn20vGc71nv6/Sj+a31JPGw6pfoPetW7pTtfu+E8erzwfaU9fPwR/FK9F75ov73/ykC5wIfBdwFbwVUBnQJMw29D9IPwQ2zC+cL0QtfDEIM+Aq4CqcI0QS6ARcA7/6V/+X+pfvn+ND2EPT48uHxk/A+8W3yP/IY8fXuzOyH7aTuMe+F8A7xTPPm9Ov12fXy9WP2APlo+Sr6Wvsw/lwAPQG+ATb+o/2E/In+FQBKE3oV9BPyDOX4+/4+D90Z2B/hIWEWeBhoFdMNhg88F78enSSDHgIPKAdDBksKZxDOEmYNkgkjA5j7OfXo8hD0u/hc+pT02u2g5wLmoej46VnpROu17Lbs6ewc67Lpx+3s8mH3Qfvb/Hn+6wDZAnUDOgUxCMEMZRCGEZgPRw73DdwOxg41DhQOOQ1QDKoJYgZ1AwwDPQJRAbT/Iv0L+0v5KPd+9XP1ZfWt9Sn1SPJm76/uJu8Y8ZbyB/P482f0x/TZ9M3zL/Qn9gn5WvvC/N38xfy6/fn+3P6i/iYA3wBsAo0C8wA7BDsMsgwjDOUDZfsjA3MMVhN2FmAS3Aw3DWgLcAklDGcR+ReJGgoVFQsSBgIGyQq7DxkQ9A2UCnEGpgBj/Jr6a/wgAN0Acvyt9sbxW++K8JPx0vFK8yv0gPMQ8lvvHO+g8b/1MfhQ+d74ePrf/P3+AwGdAeQCqwTPBdYFQwYSBjwHUwhpCNYHswcSB7QGfQWMA/sB+ACdAE4Asf96/j39V/vW+Sr4sfey9+738vdQ9wj2JfY79yr57vl8+O72v/Vj9kX4dPlg+qX7Svww/Eb7KPq3+gH9GwAsAmUCWAKbAYkC1QKUAosB9gFAAuMGSArUCNIHzAAT/csAKgYyC3QPSwwrCoIIJgUABLAEAQkUD98SXhAGC5gG3AW5B+IKXQyLC2cKvge/AzgBVP9aACEDSwSNAqL/eftd+Hb4sfec+Hj5Wfnc97b2S/W/9cj39fgC+ij5Q/i09+f3pPgU+i/7cPxe/QX9t/wT/dL9ov/ZABMBvAAgANr/Pf/K/lH/ggBoAhYEqQMgAp8Adf8w/5EARAEeAsEDlAOxApIBUv/g/Rf+jf3q/C79kv34/U/+Of17+tb5AvrY+p/8jf2U/Sn+qP5h/S39L/zY+5/9Qv+r/6b/if4D/eL8P/xY/BH+VQB5AUEA4P2z+8P9TwGmAzwF2AT3BCUFNwQIA+AExAceDCMOwwwEC24JSAhuCAwKDAsWDTMNSwtRCDMGbQVZBSgFTgSxAp8BbADS/p/9e/yn+wn8YvsC+mD5zvcy9+n3gfiQ+HH5Q/iD+Ir5afnq+Qf72Ptf/Vr+mv0M/i7+Gv/SAO8BBQJmAlMBcgDFAAcBrwGmAswCaAKNAaoAEwC8/+L/pv+J/8H/O/8n/jn+gP3V/Yn+3v1R/ej9Uf4e/7//kv7X/Xv9Mv32/bH+Af+S/xz/Af5B/V79qv0e/gr+hv32/Gn8p/vS+ib7V/tg+4r7mPsS/PT8/fzN/Lr95f70AIIChwJAAhgDfANJBCYGCwcxCOoIrghyB0wHPAfqB48JGQoMCnQJUQi0BvIFcwXzBYIH4weVBmoEIgIyASkBBwEFARkB+ABBAGr+KfwL+2L7o/x1/S79lvz1+3n7dPso+9b7nPxf/dX95P2m/cD9K/4n/qH+pv4B/4f/OACjAGsA9/9b/2X+Dv4u/iX+cP7D/p3+pP7K/hj+r/30/IT85vxw/SP+Zf5G/nD+xf4h//D+jf6f/gH/cf9h/9D+mf7c/gT/sf7m/RD9Ff3r/en+i//7/nX9d/xy/PH84P07/of+RP+i/z7/df7t/Ub+Y/97APEAEQHxAKgAfgBTALUAhAFwAv8CRgN4A9cDMwSyBCMF8wWXBuYG0wZmBjoGGwaPBiIHJAcDB44GWwUWBP8CcQKxAqYD/QNNA4cBlf9Y/m79A/0Z/UL9t/3r/cf8Rvv1+W/5Zvq8+2D83/yf/DL8IvwH/In85P0B/3X/i/9m/3H/8v9PADAAJAA2AHIACAE/ARUBywBnAPL/gP94/+3/0gBlAXUBvAC0/0//T/+D/+3/EAC2/yz/X/7J/aj9zv3Q/e/94P1C/ab8SPzt++L7CfwB/BL86vt2+077cPvh+zb8WPwA/MX78fuR/In9Vv4h/+P/6gCWAYkB/QDmAGcBlwL5A8EEMwWMBbYFGQZBBtgFkAWwBeoFLQZDBucFywW2BTEFQgSOA9ICqAJ3AggCeQFjAVYBHAEFAaoAgABRACAAGQB3AOYAYwFYAdsAfQA0AOD/c/8y/4z/ZQAeARUBDACv/t39d/2m/TD+c/6v/sf+tf6Q/oD++P3m/dn9jf2H/az9SP4l/6T/Tf+X/tT98f2E/vj+c/+r/4P/HP93/nn9MP2Y/Yv+hf+p/yX/N/5p/ST9EP0u/Zb9NP7Z/vb+hP6j/eD88/x+/U3+9P4D/9D+h/4g/g7+aP6x/gz/T/9U/37/0f///+7/FQA4AHUArAC8AKMAFwHHATYClAJLAgwCVgKhAscCHgNUA10DRgO2ArECQgPGA/IDuANzA3cDiQNRAyoDVgPGA0kEHgQqAwwCSgElAWUBnwGfAZQBbAGcAHH/9v23/LH8S/3H/a39Dv15/Az8rvvJ+x38gvwH/bX8EvyC+1f7Gfwp/T/+Sf8yAIkAdwDt/2j/i/+2/2AAeQE0ArMC5QJRAu0BkgERASABMgErAVMBBQGaAJgADABJ/6b+8v1h/VP9ef2j/ej9pP2S/ZT9r/0D/g/+E/4Y/kv+kv7k/iD/T/+m/+L/YACPAMAASAHVAZICwwIRAjQB2wAkAd4BSQJoAq8C4gJjAnMB1ACvADAByQGkAfwAjQAvAGUACgFIASIBHAEXASsBhwF7AawBSwJ8AjACTwFsAFoABwGvAcwBQgFYAHH/4P5A/sz9/f1x/g3/T/9A/+/+aP7i/WH9i/2q/gUAmACNABMAuv+Z/4D/lf8HANAAiwGzATQBZQD9/+X/5f9OALEACgERAZYA2f8K/0n+Ff53/sr+8P7p/pL++/1+/fz8s/z0/BH9Jf1q/Qr9wPzW/Aj9i/0l/iD+1f0T/hj+N/6t/vD+qP8gAD0AWgB5ANsAMAFGAXAB1QH6AdQBnQGNAZgBwQGSATABFwEpAWoBewElAcIACgFeAesBSwLVARwB2QAyARwC3ALbAtMCxQJ6AvEBKwGlALUARgGdAZkBUwHHAKgAcgC2/xX/c/45/o7+uP7T/vL+1/6N/lz+U/5d/s7+Mv+f/+7/TABwAGsASAAAABsAVQB7ALwASAGqAccBSgFyANX/W/+F/w4AIAADACcA6f8a/4f+7f3X/XD+rf53/lb+qP1a/bj91P2H/uT+v/6L/kD+5P3o/Tn+7f6d/8H/Qv/I/o7+sf4D/xr/d//c/zAAJADe/6D/NADQAO0ArgAHAOn/kQAlAVoBYwEXAdYA/QAtAXsB2QG3AVEBywDtAP0AagFWAqsChQLXAfMAggDFACUB1QFAAvYBkgHJAOL/Jf8R/1b/zP///8j/Sf+N/tn9Qv0M/Rv9o/0G/h7+Ef7B/Vj9Cv39/EL9O/7t/lT/lf+O/4n/bP+Q/+L/lgBPAQgCZgKqAvYCYQLbAcMByQE9AtwCTwNUA/0CbgLKAYkBOAHiALcAuQDbAOQA3wBuAOX/4P+4/63/ev8G/7j+bv6k/tD+IP9d/2z/cf/7/pn+Mv4X/hX+2f5S/2P/g/+Q/+X/9/9rAAcA8P8JAC0AUQBRAHsApQD2AXcCdwLZAa8AhgBEACcA7v86APQAPwEwAVcA4v+f/0n/Ef/5/kb/+f/c/73/tv91/+L/tP9u//j+4v5o/6j/AwCH/+7/JwC5AMsAVP+h/gj/vf8l/z/+0P0kAR8FpgQpAqL/c/7H/nf90/na+JX5ZPpe/KH8lv07ANkBeQEdAGX+kP3p/Q/+7/2H/xABIgJhA8MC+wJSAzICcQJuAvkC7gPTA4UDiQJLApIBwgCcAEgA0P8VALb/Pf/2/oD+if5L/rj9X/1y/dv8Uf35/o7+Hv+C/0v/VQCUAJgARAG3AdIBHgLFACQAXAA2AYsC0QNsA5ICcwLgAJYAHgAB/0MAugCvAJgADgB9AAcBGwHqAFr/BP8j/77+zP+H//f/oQBMAB0Aq/+U/zgA0gC8ABIAjv+A/8H/AQDT/9X/vf9b/wT/Sf5W/oT+hP7D/sf9HP1z/SX9fP1l/nn+hP6O/gj+zv0F/pL+Ef9u/7T/6f9MAJn/IP8T/1T/+/9KAJQAhgF1AW4BaQEgABUArADZAPYBugE7AHf/NAAaAroDzAPZAVUBfgL5Ao8BJgAP/3QAPQF3/7b+pP7j/ysBxQA7/03/fv8B/1/+i/2t/bj+Kf9j/9z/TABlAKwA0gAJALL/1f+0/5T/TgD3/6gAUQGbAQoC+gGxASAB0ABlALoAkQDHAFgBIgJIApACygH4AJAB6wCoALwACQAgAK8A4//D/9f/MP8h/zD/ef6o/kb/tf7K/v3+2/07/q3+t/02/pb+0/7T/9H/gP/R/9r/WACPADsAQv+v/m7+Cv6EAMEB/ABsAbwAWAJtBUYCzP4g/tT8gv8I/yD7e/xW/o7/vgDK/gH/DAK+AuoA0v58/Wf95v1j/Sf8zv0XALH/0gD6AAEARAGWAF//GQB8/1r/+/8SAJYACgFfAaMBQgFLAUQBiQBOAMb/hf/K/x4AkQCqAHAA9gDFAZgBdwE/AY0BVAJ1AX4BNgLXAQYCDgAtADUDtAQ8BO0BQv+1AAUCYwC4/4z/GQCoAMz+ofuY+iL7bfsU+/H6hPrJ+ov74vtc+3X88fzJ/Jv9JP1h/EL9fP73//EBqAJbAwgEBATDA6ADQgO2A4AD/wKQApICzgK9AwIE0QPrA7YCSwE+/6j97f2v/hr/NP+//hf+Vf3r/Ln85vw5/aH9T/1n/aH9kv2m/g//8v9TAGEAvABKAZ0BewHgAdcBbgKxAlECfAJ8A0YD6QNxA4ACxQK3AVUAjv+V/9n/yQCkAZkB7QFPA+sC/wKqAkYBFQGfAHf/3P/B/3H/BwGFA7IFDAggCCUFVgUoBJYAx/5I+1T53fsU+0P6y/qP+pj7Ovvh+Jb2IPYI9qH0tfMH8wDzhPRy9G70m/V+9lf4evlO+Sj64/pX+wf9A/47/lH/iABpAR4DcwIVAesDnAhoC+kNMA07DCYRChMrEkgTchKYEtAUlhCiCzALDAkZCAEIvQSiAm4CT/9y/IT6Efii9qv11vM68lnxuO9U71XwhPEU86/0rvTl9i35ePp1/EL9MP+1As8EWQauB3kIRgumDQsOSQ6TDi4Otw5ODmQMGgvdCW0HvwbrBOIADP9R/U77cPuF+XH2cfdz98P26/ZT9KHyTvNc8gLxj/Gw8lb0Pvb/9v/3dvo2/fj+GQCPAG4BSQKXA4wEwgXmB68JEwu6C5kLZguFC/YKZwp9CTIIcAeKBesDrQIwAfT/QP+E/hP+t/0v/G/6zfiJ94z2tvXv9MD0A/W39Er1DPaJ9kX3Nfei9oD25PUi9YT1Ifbs9wX6Xvsk/Lf8Ef39/Jz8cv17/SL+1/5+/Tv/vQbDC4sRqBTEEX4VFRqRGQUbXhqKF1McNBzxFw8XHhVNFC8X+hOeDnoLIQfKA7cBGP7h+YD2EPNo763tE+yu6W3puekf6ePoOOcp5U7n2ek56wrtyO0w7zbzevYo+kD/CAQyCDkM3g0HD24QnxHDEiwURBWFFTcWXRX9FDQU3xKfEbcPvAyPCYcFEAEn/d35vfY29OjxDfCg7l/tBexO6ujo5+e759znR+mW6cnq9urg6x7u3u+w8Vrz7fTu9mj5L/pu+4n8XP6Z/4YAEwHMANkA/QGiAkIDRwO8/13/3we3Dv8UNRcQElYWOR/7IJkgGB99GjcfDiHWGRYXaxf+FoYaOhpEEmYO2QiLAV4Av/4f+rj2VfKe7rTuZu2y6NXmz+Ym56TmJ+TP4JbhmeWu5+3p7euz7a7w1fSB98/64v8lAwMHbgucDWAQfhOoFdgYOxxIHLMbSBrKFyoXahb/ExwSTBD/DBwKdgYRAiP/wPsc+KT0ePAB7ejpmeep5izna+ZL5qvmsucx6qrr5OtA7QnvDvF/83L0Ifag+Iz6ufum/OL8rP0W/0EAqAC+AFv/yv2d/Tv9Ivx5+xD62vr/AGAIvw3+EbkR+hFTGGob4hy6HcMbJRwCIBofTR2MHm0eKiAhISUd7hZwEY8KxgXwA8P/0fr49djwGO7D7Xvr2Og86B/m5uOs4cbdM9xh3cLesOBa5JzmaeqZ7sTyaPgI/vsBCQWlCI0LbQ+UEnEUGxdRGjQdXR8UIEcfuh5GHncc8xloFQkRlQy8CHgE0/90+5X3cPQt8QruJupr52LmOeaw5mvmveUA5p7mNedM6FDpJetS7hvw6PEk8wj0qPV/+Hb7vP6dAWwC5wJ8AhkBBQDQ/3j/vP8XAL7+Zf2z/Mn6Z/ydA5kKXBHEFh4VGBh4Hn8fsCG3Ik4gUCJPJCgfoB6vH1geOiBWH9IYfBR0DhoFpgBe/aT31/N87nbofubb5EPhguCI4PDe8t4w3Xfb/dwC38LfTeP55mDqeu9D83z3df7EBD0J5Q1gEXwUFBkgHDse3CBXIgAjEiPfIWsfnR1eG/kY4hUXEeAL8wYaAiT9rfcm8uDto+p/51rkcOHE3wXgu+Ca4aHhmuH64p3leujr6rbsou418dvzRvbE92j5svvO/QEA3wCYAP3/hgDXATADMwM7AhkAzP5z/5b+nf0y/H74hvo7BDUNuhXqF+AT1hhwIsYm6SgIJZcgmyQ9Jsog2h32GqQbNyCxHsAX5BAqBzQAIv7A+hX2a++05xzk0ORE5PbhEuAU4Nnhm+I44Lvd9N0g4h/nBexq71nysfXb+mEALAbQC98O2xHgFE4XaRlBG5QbLB1bHw0f9x2kGxQZ8Be4FYARZgwWB4sBRP1X+H30JPGW7abq3ueM5a3j/uED4VXhDOLk4hrkpOWj6FPseO/b8nD1yPfA+TP7nPsS/GX8O/3H/okAiwEDAgECBQLDAVwBc/8P/kr9rPtw+4H6u/j191H2O/QQ/LUGeBAiGI4WwRbNIOAnCypKKkQlViZ7K6QnDCPxIHwewCADIjQcBBUwDGcBJvx/+UH0he3O5SLgjN+s4LDe2dzw3cbfaeEm4ZneeN2z4Brka+h37dHvcPMG+ZT+PAWJC94OmBKwFmkYZRr6GrUaXBwnHmoexB64HXUbrBqMGNcUJhEGDGkGNAIr/UP4kvRV8BXsKukw5g7kquIJ4Z/go+EZ42XkVuUd5x3qFe6n8RX00vU59+r41vo9/Bn8V/zk/KL+8wA4ARcAjv8MAMIAOABI/ln6S/iu+EP5rfix9g7xIPPn/xEMKhchGLoSRxdkIjAnQyoNJ+QjcCoyLL8mNiNdIEUhUSZEJLgcaBSuCBkA4vyy+Qb12u0w5bfgSOGc4GDezNyg3GLe8N5620zYgNiJ24HgAub66dHtG/LB9mr9tAT6Ct8ONhJ+FaEYURuzHLMdkR93InQjYiMrIQYe1Bs4GXYWYBJ+DPsFWAD8+un2MvOn7sfrL+kE53zlF+O94CjgsuCK4WHjuuPD5aPp+O2s8sz1sveu+LT5efp9+7f7lPyA/Z3+YQAwACQAvgChAYUC2wFaAHv9/Poz+Q34mfcI97XzIPWZ/xMLLxd0GvAWoxrZIsMm6ijFJQMidSYLJzgjtCHPH+EfQSK7HwcZmhFyBmr90fmZ9yf0b+5k5wzjd+OY4kPg597N3qXfNuDV3Rfbetyc33zkSuq27sfyW/cm/BoCJwlmDlgS9BSIF4IauhzHHdwdvR7CH48gmR95HVwaoBe8FGwRAA47CV8DP/3D9zHzpO/D6/DnIeX94mvhjt9H3q3ev98H4srjbeXo5z/rl+5t8sz0PfbI99b4Bfv6/O39D/9eABUCDwSXBB8EdQRxBEsDZwE2/uz6e/mP+TH40fe89STzPvneBT0R8xl/GJ8TZxm2IOIjJSV0IWsgyCW6JB0g7B7RHhAhWiOpHlYV9Au3ABr5N/cB9vPx3uzR5xjmA+lB6crmXeV55H7jpeI63xHdLeAB5PXoge5c8uT17Pk9/egBUQhHDK4OEBATEpQVOBnQGvIbFh69H0cghx7XGogXPhUeEjUOLwnTApr9p/l39obzjO/f6rLnI+a25ILj6+F24XLipuRE5ujnYOoB7ivymfVj92T3jvcq+AD6NPxG/Tf+K/7b/jIAUQF5AZABrgBR/yP+JPxy+qT4VfkA+eX4LPe88lX0lv6ECj4UgRZDEdATIxzEIHkjXCF8HrkixSX2ImYhnCAjIFcjXiFuGu0QGQbm/Pn5DfnF9VTwFOpZ51DobelQ52XkJ+Ol4tXiZ+Hy3i/gX+Q76hrwdfTy9jP7h//eBEsKfw1MDxIQRhHwE8AX3RrnHYMgeyL8Iq0guhwAGRQW+hLsDXkIwQE0/J73h/Q08t3wC+4X6uzmK+Ry4vrgZODx4F7jVub56APsUu9Z8tD1v/dL+Cr4rfen9xn62/sw/Zb9gP3U/Rj/qP+m/xz/cP0n/C37LPmK9/X3rfdb+eH4QPVU+NMDRQ7EF4MXdhF1FeId/iBwI2AhDR9WJA8mdCLxIYgh2iA8IpsepRaQDqYEFfyn+Q34L/QY70Hq1OdR6QnpfuUe4wHiKOF/4C/extw44KDlmupv777yMPbf+g//aALuBfEISQsLDokRfhU0GVYc5R3eHvIfqR9dHnMcXBlUFT0RxQzQCLsFxQF8/Rj5xfQL8OLs/ukv6Djnm+W24xnjlOOv5Ljmf+hg6unsfu/h8dbzHPUB9lb3FvnL+pL8Vf1f/rT/5gEMA+AD6wIyAYv/ev6L/fH7svsT/B78nPvC+MX1EvzoB1USpRipFWARHxcjHdUf9yAwHsUd6iLvIWgf9R5UHv8eQx++GR0RwAdE/uf4ovcw97Xzku+q63jpIOl/5yzk0OKh4Q7gAt/p3creAeQm6SrumPLi9DX3afoa/hMCwAaLCf0Law57EQ0VMxl5HHwevx8JH0IdKRssGMEVbhOREAQNsQi9A1H/hvs8+E/1xPGF7vrqyOdx5aHjRuNL487jKeRx5B/mPOk57SHxJ/Tw9Zf3KPi7+DX6yfy0/zkBQQGvAB4BSAKMAyoEvwPnAmkBQv9p/ZT8qfqy+P/3Ffbf+ScDKQsKEnoUcBAJEX4V3BU6GOMYShh5HA8fTR3pHbkfZB+lH6YbzhPODJUGhgDb/hb/6f3W/Ob6h/b2893wfOx/6Zfn1eNR4avf1t4g4nXmXukH66/s5O0x8Rv0J/a7+Gf9SAJeB+ILTA8FEzMX7xnmG08doRyAG08bphuzG8ka7BbfEuoOfAo6BYYACvxF+UD2FfP976rtt+pT6Gvm2eQR5AzjneLy4+Xmiuk97LztKO+j8JPypvTk9mz3gffa95r5P/yQ/sX/iQCfACsAvP+X/5L+lv2G/UT8MPya+Sn1YfdEBPgRgByWHLYT1hFUF8UavB3cHTAbzB5FIuMgbSHdIZkg4SDcHEsTZQqAAk3+3P++AScApvwz+Df1A/R68evsZuhJ5Y/jBeI24Dzg6eI457nqm+uY6pjqaOzy7xn0dPjA/MoB4wYCDIEQHhOKFqMZURxdHSMcnhnCGdIb1RweHC8YERPsDosK5QWPAYf9q/kP95/zBPDt7LLpVOfT5VHkBeLJ4Pzgr+Lg5QLoG+lr6fPqs+xH79vxHfMQ9Hv1UPfj+P773f1VAHUCXQNaApIBEAEtAbUAnwHXASABqf+N+838zgoXGloiRSCEEoENMBX6GegaRBtMF7EdayJhHpYb0xwYHcoebBoWDiMGiwBW/9ACWQRjAVP8i/aP88/xfu4m6rDnl+W+5GfiaeC64U3mrOt+7Rft8+oH6//t0PKO9hL6uP2QAVkG3wlmDH0PFxMYFs0X4xfWFoEXZRluGwUcrhlbFYQRzw5QDDgJsgXZAt0Aev6g+kf2iPId8MXt1+oj5+3jxOGW4iHkVuVG5czk4+UJ6LzqXOuU627sou8Z87z2Tvnt+7z+7QHrA1IECgRABN8FEAgQCSYIowfABzYIvwbTAiwCDAthFfAcbRkEDwcKuQ2REusTxxGaDr4TThiKGAYWIBRLFM0WahOQCxoFzAH9A2cHrAemBE0BS/2n+h341PT68UnwMO+U7kvtbuxo7E/u9/B98drvYe1l7E3tKvD18dDz3vbz+nX+QgFxArIDYgaJCdwLqwyMDUMOeBBhEksTNhPZEXYQxA43DTAL1Al0BxIFmwI2APH9xPsh+Tv26PMq8dPu8uzp63XrketY61rrU+t+6xDrWOvv61Ht5e7d8GnySPQn9rv3jvnC+5r9yv7vADACdQNtBEwFtAa5CCIJLwkvCSgIBAaZAQ//gwPOC18TqhJyCnEE+wXkChkPBA4rCygOIBOmFcgUjRKzEhoWRhUbEGoK8AZ0CHgMyA6RDyQP0wxcCXgFYwHt/hf9t/sY+nP3CPWY81jzQ/OK8sHvDe2P62Xq9elI6tDq8uyr7yrxYvKG8zL1qfcF+g77Q/wM/lUAcwPVBeoHrAoADbsNdg2tDKQMBg5kDqsNmQyFC18KKwlQB3oF4AOxAVH/6/zA+kf5avgd+DP4p/fO9lb28vWm9rb37Pe2+Mj5jfq3+678/fwi/rb+Wv4a/pb9RP3i/S7+6f0P/nL9qvw//O/73/s4/ND78ftw/Iv8qPxr/Kz8Vv4XAH0ANgBS//v++f/6ALUBvAI9A58DMwRJBJkDfgKdAdQB0AIBA8MC4gLOAzoF2AU1BfUEsASeBP4EngU3BvAG4QZ6Bj4HAAj2CKoJHAl7CIoI7AdMCKcI/Ad2B+EG9wXEBaYEyAIGAi4BdQAeAKb+Iv3S/AP8ivsO++r52viH+Gv4q/jE+B35yPm1+l77H/uI+oP6lfqM+sT6U/qW+qz74vwT/iz/S/9P/zL/nf5W/iv+J/6X/sr+mf58/oD+qv4V/3f/Sf/2/nr+Rv4l/lb+ef4M/mX96Pw2/Ob7z/uf+xn8x/w//XX9Tf2u/K/8Kf2+/QH+o/0b/Tn9uv2+/Zj9RP0w/Ub9K/1j/Z/9rf1p/Wn9d/53AEIC4gIaA9ADMQVQBtgGZwewCIsKMAwjDUsNaw1NDewMeAx1C08Kfgn8CMUIpwjLB54GLwUGBGMDwwL0ARAB/f8u/9z+h/5c/iv+3v2U/XL9+PzZ/Pz8Kf28/Uj++/26/ZL9jf3J/fL9vP3b/Rz+9v21/eb8lvzJ/M78nPxn/EH8/Pzv/Ub+MP7t/c796f0M/tT98f1s/uL+N/9P/wP/3P7p/rb+X/6S/YL8Gfxh/MP8EP0w/S79cP2C/Rv9lPxP/D38Pfw9/MX7IPus+oH6H/sA/Jz8mPxO/Fr82/xn/bP9HP66/of/6f+D//3+HP8SAC0BuAHJAQEC0wKMA6YDfAMqAzUDbwMgA34CFQL9AY4CagMRBIcE5wTNBIkEoASyBOwELgUABeAESQXVBV0G5gboBpEG6gX9BEsE5wOXA4kDnQPDAycEHwTMA7QDWAPOAksCZQGPABIAnf9q/0f/Gv/p/lT+cv3k/MX89PwT/VP9w/0T/hP+o/1V/UL9Vf0t/Qj9G/0p/Wn9if2+/Vb+4P5C/3H/GP9x/hH+9v0K/kD+6f17/W79i/1T/Wf8QfuV+p76tfpy+uP5gfl6+Yj5dvk8+Vf53/nP+n/7zfsm/Jj8P/3B/Qj+aP4I/wUA/wDMAXcC8gIwAyAD5QKHAloCZQKhAg0DAwO6AqECqwLZAvkCVAJTAagAcgDAAGwB2wEDAlQCnwIpA6kDyAMKBIEEDQVfBWAFIwUuBacFPAZ6Bm0GTgYsBtoFWwW0BEAEEQTXA6IDeANsAycDuAI2ArUBOwF1AGz/Zf6L/SL9P/1c/a/94P3v/fH9of00/er8Af0w/YT9vP3K/fL97/2+/Zv9uP30/d79bP3d/Fr8FfzN+4r7dPtO+wf77vr3+iT7j/vN+wn8RvxD/I38Ef2a/S7+Vv6F/gH/Zv/Q/wEAGwB0AO8AFQHWAGUAOgB1AL4A3QCWAPT/Nf+q/m7+Uf42/t39RP3t/ND8vPzH/Ln8zvzi/O38+Pwg/XP96/1u/r7+Bv94//n/rgB7AfYBhAL9AlQDrQPpAxMEOwSbBAkFngUbBrcGWQfUBzEIawiUCNYIGQn6CJEIHwjfB8kHswcqB3IG1gVDBbsE5QPgAvsBbgHoAEgAjP+k/sf9Nv2R/AD8mPuT+/z7cvyN/ET8DPzN+677jftR+0j7kfv6+078lPzN/OD8x/yd/JH8hvyL/LH8HP3F/Vz+vP7c/hP/If8a//D+1/7H/uX+Cv8R/xz/MP9R/13/GP98/vb9lP1L/QH9wvym/PP8Qf0g/c38cPw4/Dj8Yfya/NT8Ff2H/fj9WP6m/un+NP+g//L/JwBpANIAjQE3AqEC5wIqA5sDLAR/BG8ERQQPBA0EIQQ3BFIEfgSdBJcElQRYBOkDbgMeAwoD+wL9ArYCbgKLAjAD8gOKBGgEvQNGAwYDHAM+AywD+QKxAkgCwwFjAS4BXwGLAUQBiwCo/wT/rf58/jb+wP1Y/RD9y/yf/Kz8wvzD/Kz8Z/wt/PX74vsg/Hf8B/11/dX9bv4Y/5L/uP+k/4z/qf+8/4D/N//9/vj+Gv8j/xr/9P6q/kT+8v3B/br9x/3K/eT97f3M/Wn9Af2z/Kb8yfzU/P38Uf2U/cX94P3m/Uv+2/4p/zL/3P6f/q/+6/72/uX++/4l/0v/Ef+6/qb+3P4n/yH/GP/7/jn/1f90ACcBswEjAnMCrQLgAgoDRAOJAwsElwQlBbkFGQZxBo8GZwZMBioG9QXYBdUF/gU3BkAGLAbwBYwFPgXTBBgEbgO+AisCrwE/Ab4AMACX/w//yP6Q/iL+j/0F/db8B/0k/Uj9lP3v/Ub+cf51/oX+uP7//mH/vf/V/wEAVQC1ABsBEAG+ADsA3P+J/xz/rf4u/vT96f3O/Zv9Vf0K/bH8j/xh/Db8Hvzq+8L7s/us+6H7xPsb/Ij8xfzX/M78vPzO/Pr8NP1l/ZL91/0n/kb+U/5o/qj+Kv+g/87/7f//////JwBBADYABwDV/9H/9v8dAAkA8v8QAFEAkQCaAGUAVwB5AMMA/ABVAbUBHAKNAtkC9AIEAxYDTQPGAzAEigTVBC4FawVqBVsFLwUEBeEEvwSOBIEEjASXBDAEYwO4AmECLALpAXsBFQHJAIQAQwDt/6v/qf+6/9H/9v8VADYAQwBIADoA//+X/xz/w/5u/mr+WP5N/iv+2f1a/cf8bPz++9j7wPt9+177V/tp+577xfvo+xf8SPxw/HD8gPzA/C39qv1C/rr+A/9h/73/AwAQAAUAIABnANkACAEFAf0A+gD9AO8AnABYAB0A2v+6/1T/0v5j/g/+Bf4a/jb+Rv5w/qT+2f7r/hP/W/+y/wEAEAADAPn/JwCNAOoAGQEZASABOQE/ATYBPQE/ASsB+ADrAA4BTwGYAf8BagL5AnUDkANsA0YDbAPGAxEEOwRvBIwEzwRCBaQF8gUYBuUFbQUaBYkECASQAxgDwQJqAvIBXwHgAIAAIAC6/1L/8v58/iD+Af7r/cn9s/2G/Ur9B/3D/Kj8ufzW/Dv9qP3k/QX+8v37/fT94P2j/Uj9HP0Q/Sf9Vf2W/c798f3Q/Xv9J/3W/Nb8Bf0n/UH9Qv05/TT9X/1K/Rf96vzU/Mf8t/zL/NT8F/1W/Yn9w/0r/sf+T//I/xcAZwCoANcA9gARAUoBcAGPAb4B5AEnAlgCdQJ3AkgCEQKqATYB2wCsAJ8AnQCLAH0ApQDMAOAAzACWAHAAqADiAEYBvgEaAnUC2wJLA4kDugPZAwYESwR6BIoEZARCBCUERARLBCUE+wOoAyUDjgLmAVoB9ADJAK4AiwBsACQA0f+V/0f/zv5E/sf9av0t/RH9Bf0u/Xn9r/3b/bH9lv2N/Xz9pP26/e39NP5l/o7+nf6+/t7+7/4K/w////4g/yz/Hv8j/zn/QP9C/x7/D/87/2b/hf+H/2//ZP9o/3X/bv9J/w//5P7O/qT+hP5Y/kb+XP5x/on+if5s/lT+Z/5x/oX+mf6i/rP+xf7g/vv+DP8T/wb/BP8h/1H/kP+v/+n/OwBEAFcAYQB5AKEAywAOAVwBqAEDAjQCRgJRAmYCjgLMAikDXQODA4wDegN+A3oDYwNJAzcDCgPyAsoCtQKmAoACbgJcAikCCgLZAY0BSwHxALcAeQBcADIA6f/K/5T/b/9G/yz/I/8e//3+4v7F/q3+of7F/uv+D/8q/yX/NP83/03/T/9b/3X/kv+0/6//n/+d/6L/g/9k/zv/+f66/ov+i/53/lj+P/4l/hz+D/4B/uv97f3r/e398v0c/iv+Wv5+/pf+uv7l/hP/NP9f/4L/xv/5/x0AGwAVACYASABrAGwAewCGAHkAWABIADIAFwD///T/1f+y/5v/l/+r/9D/7v8HABUAKwBTAGwAjQC8AN0A7QAFAQcB8QDxAO8ABwEZATgBSAFPAVYBTwFpAV8BSgEtASABHgEbAVEBgAGtAeIBCgL/AfsB4AHbAfoB+AH0AdUBtQGtAaoBtwG+Ab4BwAHHAbMBjQFVARABvgBYAAEAuv+O/5L/g/9h/zL/BP/l/qv+hP5E/vj9wf2Q/Yn9fP2Q/YD9ef2Q/Y/9j/2N/ZD9r/3O/dD91P3k/Q/+NP5a/on+qP7F/sf+xf7O/tz++P4R/xb/IP8V/wT/Bv/4/vD+8P7p/t7+xf6r/sr+Cv8+/2T/g/+k/7r/0f/I/8j/zv/u/wAAAwASACYAYQB7AJgArACcAK4AnQCPAKUAuQDtABkBOAFCATQBJAEIAfwA7QD9AP0ABwE2AU8BPwEyAUIBSAFVAWEBaQFnAVoBbAGQAZABqAGZAYkBmwG1Ae8BAwIpAiMCPwJTAkgCTQJEAk0CSwIIAsEBjwFWATAB+gDMAJYAYQAyAAkA7f+//5X/VP/9/rz+if5n/lT+QP5E/jn+J/4T/gP+8f3o/fH9Dv4a/iX+LP4r/kn+Wv5G/l/+cP58/qH+uP7S/s7+3P7Z/uD+CP8B/wP/6/75/gH/IP9A/0//VP9Y/2j/W/9J/zL/O/9A/1L/Y/9x/4v/lP9+/2b/Y/9k/2z/b/+D/4v/lf+d/7H/9P8XACAAPwBaAI8AvAAIATkBoQHxAfYBIgI7AkkCLAIeAhECIAI3AkYCNwIlAi4CCALBAWwBJAHrALcAiABrAEQALwAwABMA2v+y/6b/qf+d/4X/ev96/27/if+C/3H/i//D/wMAGQAiAAkA6//y//3/4//p//L/AwAmACcAKQAFABMAGQDw/+v/1f+p/5T/kv+x/7//0f/Z/9H/3P/I/7T/uv/B/7j/n/+g/73/0f/g/9n/1f/V/8j/tv+k/5X/lf+D/4D/bv9G/1T/Kf8l/yP/Ef8R/xP/A/8B/zX/Kf8q/xj/Cv8N/wz/DP8I/xr/J/9N/2j/gv+F/5//xv/X//L/AAAVAEEAWgByAHUAYwBjAF4AdACRAJwAsQCdAIIAYwBgAFoAPwBIAGMAdABlAGsAaQBlAGEAXgBOADYAPwArAC8AIgATABIACgAbABsAJgA2AFMAbAB+AJwAjwCIAIgAbABnAHAAUwBYAFEAJgAdACQAQwBhAFcASgBnAHIAfQCIAIsAewBsAGMAUwBEAFEAbgBgAHQAYABKADgAGwAVAAAA8v/e/8X/wf+//8H/qf+r/7r/qf+i/5f/mf+k/7z/zv/j//f/9P/9/wAA9v/l/+X/2v/K/8b/uP+k/7L/w/+2/7L/sv+U/4z/lf+Q/5n/lP+b/6L/r/+6/6L/mf+i/6j/vP/M/9f/7v/i/+v/9P/w////AAAOABUAGwAnADAANgA6AD8AQwBTAFwAaQBrAGEAWABTAFMASAA0ACsAIAAXABAAAQDg/9P/0P/M/8b/r/+r/5v/n/+o/6b/qf+y/63/sv+2/7b/0//T/8z/1f/a/97/6/8AAAoAFQA6ACQALwA6AC0APwA6ADYANAA4ADoAPQBBAEEAOAAyACcAHQAKAAUA///a/+L/1//V/+D/3v/Q/7r/vf+2/6T/nf+M/5L/mf+b/6j/pP+f/63/rf+r/6n/kP+S/6T/qP+m/6D/sf+9/9n/3P/p/+3/6f/9/wUAEAAbACcAOwBGAEMATABXAGcAdACPAJEAfgB5AGUAWgBKAEEAKwAkABsAGQAMABsAFwD/////4v/R/63/l/+d/5f/n/+9/7H/n/+m/5f/w//X/9P/4P/i//L/2f/c/9X/8P///+3/+f/7/xMAFwAnAC0ALwAtADAAIgAOAAUACgAOABMAIAAXABMABwAHAAwADAAKABkAGQAZABcAHgBBAEQAYwB0AHkAkQCYALwAvACvAKYAjQB1AGAAXAAmACAAGwAJAP3/6f/Z/8H/zv+2/5n/jP+A/3z/lf+k/6b/rf+r/7T/l/+X/6b/sv/B/97/EAA6AFEAawB7AGkAXgBuAIAAfgCIAHUAcABhAFMAOwApAEEAPwAmAB0AJwAgABkAGwAXABkAJwAmACIAKwATAAoAAwDl/87/zv+t/5T/pv+Z/5X/kP+F/2//Y/9S/zX/Lv8n/xr/Jf8u/yn/Kf8u/zT/Jf8P//b+8v7y/uX+8P4P/yn/QP9R/2z/hf+V/63/qf+9/7j/yP/l/+7/JwBjAIQAsQDfAPMAEAEbATYBOAE7AUoBOAEnASUBFwEBAQcB6ADSAMMArACuAKEAjQCLAHAAVwBKAD0AMAAvACsAIgAwACkAJgA9AEEAUQB3AHIAkQCLAHkAewByAG4AcACIAIkAlgCjAJoAjQCLAGwATABTADIAAQAFAOD/tP+S/4z/bv80/yz/Af/5/uv+yv7Z/sj+Ef8g//b+N/9H/zf/N/9H/4X/lf+F/6L/tv+v/8z/6/8BABMA//8TABAAwf/a/1wA2wDFAIQARAAbABcAwf+S/3f/Lv8N/wz/E/8T/wP/Df8w/yf/HP8a/wz/IP8q/0b/Y/9k/2r/ev+V/7//1//j/xUAGwAZAD0ANAAyADgANgA6ADgAOABMAGcAbACsALMAswDZAOIA6gAKAS4BMgElATIBGQH9AOYAwwCcAIsAawBMADsA+f/y/8z/sv+f/4f/bP9m/4X/bP98/5T/jP+i/73/yv+//9n/3v/i////BwAiACcANABOAGAAawB0AKUAkQCPAKwApQCmAKgAvACmAKgApQCLAHUAYABPAD0ARAAtAD0AOAAiADQAKQA0ABUAAQAkAPT/3P/Z/7j/mf+J/3j/av+V/5f/lP+Z/7L/1//B/8b/xv/I/97/4v/Z/7L/pv+D/4L/jv+O/6b/lP+d/5D/c/9s/2r/Zv9u/4X/q/+v/6j/uv+U/4f/kv+S/7j/tP+0/97/5f/5//v/8P8BAPL/AAD3/+f/IAAVABkAPwAQAC8AKwBEAHcAJwBjAHAARgBsAGcATgBeAG4ARgBlAEMAPQCEAGEAfQB5AGEAawCCAKgAvADbALUA/QC8AI0ArgBXAGkAQQBhACcASgCCADIAHQDp/+f/3v+2/7//eP8w/0T/c/9k/0n/Mv/g/sH+Mv/K/g3/6/4G/0b/4P5S/0v///7w/j3/O//X/0//Pv/M/mj+ZgM0B7QEkgKtAuAAdwAcAWz/nf4P/xP+Zf6k/1r/QwDXALwAHgHDAD0AVQEQAXkACgGhAB0A5//7/t7+Uv+q/o7+K/4T/vr9E/5I/oL+WP5E/o3+gv6S/sH+qP7t/sX/8v8DABcAAAAQANn/+f95ABMANP9N/w//7/7t/kD+WP6v/iz+Bv4X/vH98f0//WH9Jf5h/jL/DACmAKQBCgKkAlsDkgPOAw0EVgR6BLYDgAMEBB4EIwSdAyED2QIYAn4BmQFRAe8AewD2/+P/sv9h/0f/QP8a/1T/qP/T/ycAaQCuACkByQFqAscC/QIGA/8CJQPwAo0CXQJAAhcC/wGUAV4BpgC6/z7/7f5j/nP9Y/wr+2v6PPq/+Qb5fPgN+O73z/e995P3UPfT9h727fVY9sH2H/ek92b4y/nW+mP7Rvxh/U/+8P7w/uX+lP6Q/j/+mv0F/fj7zfrj+cT5cfkU+RT5nvin+IT74gMgEWwddySeJ0orPDHANRc3eTb3MvgqyB/pFGEN+Qaz/eTyn+pG5ePgPdzs2JzYqNm12u7cdOAO5NrmIOkI7f7yivgr/ZkBfwYcDHYRFBZeGhcd9h0EHgQdZxsHGbYUJg9RCWoDr/1i+IDzYu8Q7CTpV+e05vDmAujb6YXsLvA29Nj3afve/lECogXSCHwLfw2GDo4ONQ5xDbwLGQmpBaoBDv46+k324/Iz8GTuDO217BXtuu3l7vfvqfCV8YLyzvNK9eT2vfh7+iD8QP5IAJkBDQPgBGcGYAd0B5EHGQh9CNAIowhIB8gEDwPWBUwPARtwIkIk/CIJI1QlYifnJuwidRo6DtUBtPg980buAOhn4qDfQ98N4J3hkuQ06VnujfPY+Lf9xwB+AqQE0geNCz4Ocg+uD1AP4Q7cDkAP7g4YDdsJbwY1A14Adf0O+pn23fLn78HuWe6A7oPvZvEv9Lj3IvtN/mkBCgRbBmsITwpdC+0K8wlECT0JRAkoCIcFNgLl/rL73vh19uHzYPFU73zuEe+38LfybvTF9dr2GPgx+fD5xvro+pj6a/qG+gL7dvsB/OD8lP3B/XD90vzD/N/8s/y6/SP/uP8QALMAmwG4AlEDEghlFBMjQS0eLycs7yqnKxMsKirfI+MYCglq+GjtdOio5TrhAtwn2k3cPuBq5YTrPfK7+MD9aAJJBp4HLQenBgAIuAo1DNAL/wm1B4gG/AbECBAKqgjlBGEAkfxI+h/4v/Ui86nvT+1G7Q/vo/I+9qX5xf0eAq4GcgpJDEsNBg5zDucOAA5PC8sHGgQrAq8BQgETAID9rvq4+EL3h/Yn9mP1efT080j0HvWU9dX1Vvbt9g/48/jf+KT4hfeH9nX2bvby9hr4/fhZ+j38Iv4pAMMBlwIuA+kC4AEgAJ/9TPsa+VT3wfYG9rH1T/Z29675YPx5AqwQ0CP4NNA9lD35O/U8DD47PSw3kSraGKYDdvHN5vrfddlq0l7ND810zyHTXNgs3wnnce6r9a/8fAEoBAcH+wvoEqUYTRtwG9QZGxh2F/4WkhVPEf0JwQFp+qP0I/B77LDp1ufc55/pluxi8Af0pPfi++P/LANXBdwFCwYxByQJegvXDG8M+wpICdsHAwctBrAE7wFT/gL7oPgl92j2A/bb9ZL1FfWx9Kb0//S/9bz25/c++Ir3g/b49R/3f/mI+wf9wf3B/QH+Xf5J/xUBBQKEAhYDMwOxAuIAMP5R/Ff7Qfom+UX43veg+DX61P23AcwDzQbWD4Ef4C5gNV4ykC0/LJws6CpqJfoaegtV+aHqT+Q94hreKNiF1DPWaNs+4CHmwe0u9aH8WQTvCyQRkREUEB0R7xNCFVMTqw7kCcQF0ALxAUoBqv5S+SvzjO8P7jLtfOzm617sQO7U8MH0XfmE/fgBYgbZCi8PWhGJEdAQ1g90D6QO/ws/CL0DVP8v/D/6ivnM+Lr26PRI9K/0qvVE9hj3g/hM+Yz5dPl7+Sj6/voe/LX9df5G/mr9efxs/E78wPvL+tH4Sffr9hr3gfiR+af5Pvqu+pz7bv2X/rT/7QARAc4BegLVArIDAARtBDcFbwXaBUcF9QRnCbgUDyWaMos2XDPnLrMsHixCKNMfrhJuAHjuj+HM2gDYk9Re0UbSttY73X7kCuwK9Wz++QbfDi4U5RUhFTIUHhXPFdUTrQ5pB+sAffx2+jj6Q/lq9obyc+/p7szv3/AQ8lzzDvX29r34Mftz/jQC9wV2CH0J4QgFB9wFDwZHBzYI/Ab3A8sAjf4T/on+lP7x/dL8JPzo/Gj+jv9d/zn+J/1a/IT73fl291b18/Oz8zj0wPTb9KH05vR39tr4vvpg+1D7+Pvb/dz/agEgAk8CHANhBK0FtQYfBqYDIACS/GL6Vfm/91r2e/W49eP3APvc/vACdgV5CF0PmxsoK+s23zrNOU83HjbFNA0vKyRCFP8Axu504A7XB9HlywzJzMpw0GDYaeDR6MnyLf0xB3oPRBSeFT4UJRPiE3cUXxOpD6YKawY+AzgBYf9a/DD4yfNf8OPuSe7w7eLtb+6Z8BP0yPd9+7b+6AF9BZgImwrKCjYJDgdxBbIEVASoA1QCIgHFAFwBnwIfBEIF0wVLBUQDJwDi/KD6p/k1+ZX4LPdf9azzpfIV85r0MPYL9z321/Q/9JT0zvap+fj7Z/5gAIAC2gSTBnsIJQpfCuQJJgi0BWoDIgBI/az70fkf+Ef2xfRY9Vj2Yfc8+cL6mv3ZABUDkwQNBeYI1xNKIg4uRzFwLV4qKiq1KhYp7yFxFrsHuvc/7HPlNOCC25PX/NcW3cfiKugs7nD1Cv6nBQQLrw3aDJAKVQnSCbwKXAksBtUCRgCC/2P/0P5e/ZH69/d19p31CvWd9Pj0b/Zm+EX6xPvq/Dv+KQBoAkIEvQR4BOsDnQPOAz4DFQKxABr/P/4a/hj+O/7H/tP/3gEEBP4EfARUAmj/RP1M/AH8APuK+GX1DPMo8nbyB/Mt80jzvPPH9Lr2Fvnb+wH/2QGiBBkHWQg8CMgGUgWyBDsErQIbAGX9bvsQ+7f7Cfz++wL73/kJ+sn6ffws/l3+uv/4AYMD2gOUAz8Koxq+K4Y1/DSBMCMwFDDbLMEmXhvFC2b4ZOYj3WnYyNLYzDTKIs8W12Ld/+SJ7q75CwVfDsEWDBteGaQWoBVvFn4VjA9ZCGwC3f2B+5P6TvoL+fT1iPPG8i3ykfCb7ozuq/Bi85n2y/kT/WAAzANjCOMMzQ7NDV8LAQlOB20FUgPtAbwABQAOAMIA1QFGAv8BAwL9AQcBGv+1/OX6v/mI+IX3Ufbk9NnzV/P680L1r/XO9bb1Bvax9+X5iPyx/qT/qgA7AboBvAJdA1IEwwQ1BBgELAM7AZL/0P1Y/az9dfz2+8X7o/pc+oj6L/zM/0QCkATxBqAHeAcwCYsRYiGBMOA2UDOBK1smaSPNH4gZmQ7X/0vvZ+Fj2hfY/tba1s3YZt/T52Pux/Se+1oDMgtpEAETdxKBDtcKrglaCpYKigeNAoT+t/sz+tr4Ife+9FfxE++n7pXvIfGI8hn1hvlE/gYD9QafCW4L3gsNDNMLwwlbBqQCEwCv/sn8g/pZ+Zz5OPvx/Pr9l/5+/q/+DgDHAQEDzgJWAfv/Ef+L/oL+9P1c/Kz5tPZy9EHzivKw8UXxufEQ8271ZvgJ/CsASQTjB2UKiwv9CkgJSAc+BeUCFQBG/dj6e/lV+Qf65vpi+yD7IPsT/BH9Sv3v/In8X/0T/84AEQIMAtACMgjHEzYj/S7zMfIuUysrK8MsTio0InoUqwLN8Qbl4Nwt2CXTCc/dzw/VtNx+5BHsIPZjAagLHBQgGYkamBh8FdkTShJrDr4HdABp+5f46fYq9l31JPT28qXy4fNC9a/1T/YP+D/7qv4iAf8CYQQsBS8G6AeWCbUJVwd+Ax4AmP2n+5r6hPpM+1r8MP2f/rEAtgK4BLUG9QfdB4wF4gGh/rX73Pjw9fbyePB67p/taO6D8O3yP/WX9y/6Vv2sALEDpAVHBqcGNgfuB48HhwWLA7oBMAA5//L97/yo+/z5e/mO+S35D/kL+ff5RPyP/Y7+PQARASsCHANABFcG5QYNBhsJaBNgIu0tpC+IKoklFSNZIZsdSxbBCi37VusX4bTdLt0n3EfcwuCp6GLwmfaI/B4DUwklDo0RfhJnD2wJYQSUArYCwQHB/uH78flx+Bj4vfiR+Yb5Gvii9tP1A/Uv9Lr0fvfv+///0AIJBcIGjQgeCiULgAvXCfMGHgQKAvwA0f8j/iv9Cv3q/Pj8//zx/DD9JP0r/Y39df1e/J763/id9/T2mfbi9Vb1XfUn9rT38/ho+Zn57vkX+/b8q/7e/30ADAF6AmwESQbjBn0FRgMtAZ//Cv6Y++z4rfam9cz1r/bT9zr4zPgi+9n+ZANQBigHoQdKB98HiwlNCUMHmQPgBLwQxh82Kn8rwSYKJlsnkCb4IzYbBw4X/kvt7uS14c/dc9rU137bA+SY6RTvffRo+WEAqwXHCqAObgwKCWsHAwjbCloKOgdmBMUAjf55/Vr8p/vo+QT47vdQ+Fv49Pfc98j5nPxb/9ABdwMEBVUGkwd0CZAKzgmEBzEEBwFP/ob7I/mn9xP3bfeT+JP6Tf07AOACPgUNB6EHewZFBO8B1/9u/Rb6QPYg81DxwPA+8U7yi/MR9br26vj2+/3++ACjAVgBQgGkAT8BTAAV/5D9o/wO/K/8Tf6O/zQAMAA4AIgA6f8G/7H+LP7V/SX9wPtp+z380P2cAFoDMwWnBRgDDgLQB9UUDCQRLUstQClWJVkiKB9MGk0SkQbr99DqheOp4NrekN0b32bkNOu08Hv0ufjk/VQDRAkbDssPGw6tCvMIDgo9C5AKXAgYBRUC3P+P/W37a/iQ9Gnxtu9U7xzvkO/R8YD2x/zHAgIHXgmUCoULtAwlDR8M3wg+A3v9UPmH9x/4f/m7+h78Zf3e/tAA1wJ+BCYFfASUArz/XPxk+bT3HPfO9lL2DPYK9rH20/cG+Qf6o/or+5r7Ovzb/If95f6LAFgCMQS5BZUGlQZABe8CZwDm/Tr7rfjK9rr1E/Zb98T4SPuE/t4BWwUrB98GlwVbA1MCOwI7AU8AAf+d/YX+CgJ+C7ca7ie0LvEtrilgKPAm3yH6GZkNKv+38EbjM9wK2lDY9dcg2i3gmujX7mX0bvsYA2gKpw/AEusTARLzDl8N+QxbDCAJyANd/wf80/l++Iz2Z/S78dfuqO327WHvH/Kb9Vv6vf/2A0gHrwm+Cz4OFxDLEMQPCAzhBpYBK/2a+p74H/fB9iX3h/go+gD8zv5sARYDFAS4A2gCVwCa/bf7ffo4+Qj4zPYV9tL1v/Uj9vf2gPfr96D4wvlQ+9v8Iv6t/3AB9AKyAyUDMAJyAdkAJgBv/z7/IP8K/w3/Uv9PAAcBhgDO/9D+ef1u/Bn74/k4+vX6Z/yd/v8ATQT3BjQHdgYbCUoSpB6yJ+Mo0iOUHm4a+xbiE+MNMwVe+lnvNOpu6u/rCu7T77Hz1PnD/ScAtwE2AiMDdQNxAzMDhgCh/Gv63/op/QX+0PyU+1X68/lF+mD6qvrh+YH4k/jw+Rv8gP53AE0DogZPCTAL2QtlC8UKUQr0CT0JpQZPAmP9UPke9yP2lvUi9cX0x/T99UL4hPtJ//sBegPnAz0DQAI/AY0AhgCjACQAHP/H/aH84fs2+zr6/fiF9zT2N/U09Yz2h/gr+in71vtO/Aj9pv0g/vn+Gv+X/jv+CP4y/nD+U/7b/v3/4ACbASICiwKJAicBUf/p/Vz+9v/WAMoBLgKdAdIADP+ZAXMMiRqbJkoqPiY4I0UhZB98HbAWZg3SAk32++9S7unsBu3V6/bszfGs80j0o/Q49OX2Uvpe/UsBYwJLAgsE3Aa+CmwMCAp6BoQCTf8t/YT6FPhd9cfyH/K78v/zqvWe90H7rACpBX0JZguUCzULTwoXCewG9AKJ/b33NPPl8F/wP/EK8yP1GvjU+xIAlQQ4CP0KtA3+D1oRrBC4DaEJSQVKAYf9tvkN9sfya/AI7/fuafB58kb09vVh99/4KPpO+nv6MfsO/Pz84v0j/2wAUQEKAhYDUAQPBR0FsgT/A7UCBwBV/ez61vgL+Gj3sfer+AT5mvqL/Hn+nQFsA0ADAwLAADoHIRZ3JXov8i5kKRwnYSVcJLchVxl9DhIAgvLy7EHqeui35+zmcOup8WP0m/eX+Zb7mf/QAQ0EAgXFAdD+w/1+/scAVgGcAP//l/6J/bn8f/uK+hj5h/fe9nz2//bs+LX7kv/nAs0EsAUbBTUE7gOZA68C/QDD/cv6S/np+Dr6IvzQ/R7/kv98/2P/qf/WAMUClwQsBWoDRABf/SD8Af1N/r7+RP4X/Sb8mPtO+1777vpp+Tn3E/Uw9Eb0w/Tk9VT3hvlg/Ab/jQEuA5cDkgPbAk0BR//b/GD7MfvE+7H8of3X/joAbAFAAmgCBQKUAIv+jfzY+iv6Xfmc+LL6sf1gAOIBBgNqDe4g0zISPf46MTOLLr8o3yJAHRwSXwR18wblquKz46Tj++Of5Fbri/JI9Bf2DffM+D393wD1BQMJsAZ6BUEHRAuhD5wOmQpUBrcAYPyw+PL0rvEP7l/sCO508fT09PdR+3r/DQNZBbIFTgRcAkQATf9j/xz/qv6J/kL/CgGqAmoDxQOiA1QD8AIgAh4BUwDg/8j/w/+i/yn/gP4G/oL9MP2m/D37q/np92z2n/XS9Dn0EfTv8/zzZ/T09J/1tvYj+KP6E/5GAR4DqAMuBHwEdgQzBD0DpAEw/3D7k/hd97H26fa29+r4bfoC+8T6K/tV+4H7dPzb/jQCxgSlBsAHrgcLBq0E4AorGjIrBzbJNI8sUSXVHoIajhesEX0JFf6a8nXuNe5U7tzvo/HK9V35S/df9EjzX/Rd+OT8RgGOA6wB0v5o/sUANwONAhMAvv17+9L6iPtT/U3/O//i/nj/rf9R/1j+3f3p/gAAhADtAFwBIAInA2YECQX5A6QBDf9K/Wf8IPso+ob6bfvQ/CL+D//MAFQC9gITA6gC2wHFAFT/7f0g/XT8fftn+pn5XfkQ+cH4Ovh494D2/fSf8wDzNPMv9A32Sfkl/W4A5QJkBNgEugS9A5ICnQEI/8n6mfYZ89LxPPJD86b1Sfhp+mn8+v3y/u3+0/7b/tH/zgGAA/kF6Aj0CT8JiAf8CZMWnSdMNHI2Uy2lI9MdFxkUFzcUpA4JCBz+Bve49UzzsvAN7+LuV/E87+PoMuVY5WzqrvK3+vEBigSoAhECXwRjCKQKXgn3B/AGJQVRA8cBXAFcAWsAmf+A/rz71/eq9Jb0Uvf8+Yb7ffx3/fj+OwERBNYGFwgoB1YFnwPXAU8An/+x/8r/kv65/Dj85PwD/vv+ov82AEMApP8y/zv/Ff8a/qr8IPvY+c34CPja98z3z/c3+Ab5hPqE/GX+l/86AFUA5f8l/6b9e/u0+WL4hfer9+v3nffy9wT4rvhr+mn7YPxn/Z/9e/2h/Cb7IfrA+Sv6Xvsl/dX+qf95ASoEcQasB/UHNw1RGzYsyDcfOFwuJCPzGawTpRF/D8AKJwNI+gT2BPYp9UTzRfGT8G3wGO366GnnaenK77D4JQKLCR4L8QjmByIJtgvkC7EIsASRALz9J/15/dT9O/3x+4T76voA+X720PQD9jH5RvxA/hr+Ff1j/Wr/pALsBOID7wCf/bn6cvlD+RL63fvD/aj/xQGbAx8FRwbTBugGRQatBHkCxv/Z/Uv9af1B/T/8nPrj+JL3DfdS9573qfbm9Pbzh/Tn9d73gfpW/ZL/BQCt/14AKwFpAUYBwgBPAIX/zP2R/Mn7ZvpL+fn4pfl0+tT5QPmD+QX60vqf+z39AwD9AYMEYgefCPwHKgcnDd4cySzdM+UuKCIbGOQRLw7aDhkPrwxDCBwCZQB1AZ3+2Pq699f1FfVf8Mnq3env63TxTvlh/94CPwHD/Bn8BP/nAlAFWwVfBXoFMwVqBSEFDwTDAVz+yfvh+ND0G/Lq8U/10fqS/rEAiwE4AUkCMATRBb0GXQV3A5cCZQEQAJT+Of2a/XP+2f7T/nL9qvuE+n36kvwe/5gA6ABm/2H9vPsC+mT5ufkF+sT6G/sH+8f6MfrR+Qf6YPoZ+5T7DPsA+sT4bfia+cn6Y/vh+4/7afue+8T75Pw2/f775fr++a75U/qK+nb7d/2v/o7/jQB5AdUDBgZBB3kIMQhSB1kGkgRhAoP/zv/1CF4YwSYrLFYm7h3NF8MTFxPAEusRFxA3CqAESwKy/+b8M/qV98P2TvOb7N/nl+bz6anwxvau+4v9JvwO/BX+TwEaBG0EfgQCBRIFNwUNBY4EyATyA3MC8QDD/Qz78flI+kz8sf1y/a/89vs6/Aj+zv9YAAz/Ufw1+s35U/v4/VEAkgGWAaEAtv+f/vv9CP7Q/gwA3QBjAcEBNwLIAucCnwIMAi0AJ/2g+Un2VfS18zTzhvNw9Fz1ovY19x/3YffY9474RfqG+6P8XP2t/aj+AACqANYAawB8/9f+4v2W/eD9IP6E/vv/ZQHwAlsD+wFlAIX/Jf/i/84AcAEKAmwBbAFZBeEQtCBlLIQsPyM4GOsRABDCEH4TnRU1FRARtAvNB+sDRP0897HzDPKF7x7qxuUH553rk/FJ9+j50fm09/D1fPi6/ZYBcQQ+BgsIAwn4Bw0G6wTFA9MC2QHbAPT/l/4B/uD+MgCfALH/uP1R/G77K/uh+2n8P/2z/uv/vAADASAATf+X/pv9bPwF+2L5BPmc+YT7Y/7DALUB4gF7AVEBeQE/Ac4AKwBN/yn+Lf2J/Mv7KvpZ+Jn2T/Vz9MXzWvNI9Er1bPbK90f4hfj5+Cb5tfqh/Ab+6/+CAeQCZgT5BPAENQWyBKkE0wThBCMFDwWBA94BzP8j/wcAXgHkAngDHAJVAXMDWAs1F/0eOh92GUMSQg5JDYUN5BCkE00UfRJ2DuIJkQWk/x78i/vm+nr40PNF8LbwJ/OE9bv3sffM9VHyve+V8Gfz6/Vr+P76av5hAHsAwgC6AQQDygMnA5kCeQKHAckB0gI5BEUFegTVAoABnf/p/eL8J/zJ/Cv9A/19/AD7v/nG+er5t/pX+5b7ifyP/Zf+hgCjARgC8gEeAckATAC4/7z/hgCsAaoCTwKCAZD/Of1a+7n5dPgn9970VfOo8sLyxfP69Cn25/cj+XT6Hvwu/X7+Of/Z/48A8wCdANQAGQBjALkAYwAmALH/N/6+/mH/HgBYApsDcQR4BTMGhwvlFo0hbCcZI/kYURGiDjMP4BKdFGQVKRO5DaEI5QNY/qn5W/YK9lv3pvUb857xgvLm9ez4APr5+b/20PPs8kT0avf3+fr6tfyP/bb+FQC5AFYC1QM3BUcHowdyBkwFWwMlA4kDJwPMAiACMgEeAt4CfAJfAfv+Qv2d/JT7XPqc+e74M/my+Rn6Nfov+o/6vvs4/U3+q/4w/1v/fv/a/+f/BQBs/zn+cv1C/RP8e/sC+x/7ffu0+h/53PcE90/2uvaH9xD5ZPrd+mn6FvrL+cD5Fvqq+vj76P3M/+gALQGfAasClQRdBvUGWwaDBaIE3AStBVcG5QbxBrsFWwOxAIsAYgVhDYcTxxNzDr4H4gPVAy0HxwuaD+YQ7g//DFoKXgf5BP8DpgQ8BYUEiwEw/m78L/zb/ND85vpC94vz/vCw8L3xLfMX9OT0K/Xy9eT2TvjC+eL7mf7SARgE8AQIBRIFBAZvBzoJAQppCTwHtgVzBRIGrgWMAycAMv3++pH5Yvkk+QT5rfj99yj3Ifc397j4Evtz/b7+CP8l/mf9m/13/mAACAIqA1sD4gIRApgBwgCjABcA5f4i/cT6c/hU96v2HPfM+Cj6KPsv+6D6e/ro+u76V/sJ/FX8G/wF/Ib8Nv7y/9IAkgDr/87+d/5J//0AOQIuA5ICXwHtASoGAhAIG1QfzRqREZYJYAglCw4Q4BR2F4EV+BAkCkUE4P8//Dz71/wp/lT+T/w8+S35JPqB+0P8Uvq09l70afJ38wj2ZPfV+J75Uvlw+kr75Ptz/Q//KwFHBAcGzQZ9BzMHTgdkB0wHAgc+BmIEdwOoA0AERAR1AoX/tfzN+oX5j/n++WT6sPqw+iH6qfkQ+R34IfjY+CH6AfzO/KP8RPzA+7P7jfxW/U/+QP8B/zD/sf/I/8j/qP8R/yn/+f4D/oD9Af1c/KX7hvoW+cb4kPjP+JX5K/qw+iL7p/uf/A7+b//8AIkCsgNYBCUEfAQ1BRYG6AapBykIuQjWB2IGVgVXBaAGyQgIChEK2whVBqYEWAOLAl0DgQZNCiMOPg4PC1cHzAQ+BL0F8AaEB70GUAS4Afv/T/94/yD/0P34+0z5W/cp9mX24feI+e75B/qp+Jn38Pax9qL3EvpV/Ev+cf+d/7UAIgIqA78DiQPwAvgC7wLlAtcC0wIPAwIEkgRLBIUC9P/K/d38zvwi/bn8t/u0+t35sPlX+Zv4C/hL+HL5Bfsm/Oj8Gf2x/Pj73/tO/Kb91/4gACUBuAHmAG7/Gv4i/hb/GwA2AHz/Sf7k/Jb8+Pzt/b/+Zv9J/7r/df9v/8P/FQChAM4BAwKEAgEDagIOAh4C9wO+CDcOVRDPDjQK2AZtBpoItgt9DrcP3g43DIYIBgWmAlEBEwG4Aa0BxwCZ/nX8wvpS+hD61Pnd+fP5C/oC+pP5rviD+Nj32Peb+L/5L/uj/Az9mv0I/o3+6/40/yz/Mv85/6n/ZwBNAWwC/QKdA+MDsgNkA6sDnQNJBFgEJwQjBIwDmQLFAZQAxf9x/4L+yv3J/IL78fq5+n36wvq5+lX6Bfpv+Uz5PvoL+837IPwV/O/7QfzU/Pj8P/2m/fL9sf8BApQDHQUuBY4DogJLAT0APQDO/4X/rf/B/pb9U/y5+rn6YfwK/l3/mf6s/CD9IgFFB8gMOQ1XCVYFkgMvBskKog7oEH0SdBLsEDMNlAiiBVYFSgYvB/sFtQKq/u/7V/uY++r7wvpy+Vn5Pvme+Oz3bPbt9cH29ffU+dj7wvxs/Qz9NPx/+9v6hvod+xP8i/3S/if//f5u/r795P1h/l//mgCvAdsCIwO4AjIC+AGOAtADZATpBHgEQAPJAR0Awf5d/pv+ov+lAAgBhAAK//r9Qf3d/P/8F/1p/Dj82Pt9+837xfsM+2L7VfuB+yb84fvs+6X8af36/cj+NP/w/6YAqAF+AlgDSwQCBOsC5AEnAAb/P/6C/YD//wLzBQsHEQUXAeT+SP5A/xsBRgJcAp8ChAKkAe8ARgC1AL8C6QSFBUsFHwRhA1AE9wULB7UHKgcZBvsFXQWkBBgEDwPZAvsCSwJzAb3/yv2P/Iv7Kft9+5H7J/x5/Mv7Wfs4+7v7Vf1u/j/+6P23/Mn79fsH/LH8Qv4T/+3/TwB3/wT/pv66/tD/DAFfATgBFwBH/xz/Qv+D/7//vf/B/+P/4v8BAKj/Ff9u/g7+7f3x/dn9rP2t/a/+3v/AAPoAJwAW/wb/tf6o/jf/5f4D/03+zvxM/H/78fr6+8781/2v/sf9o/2W/vD+iwDdAT0CMgPBAnUBqAEKAvACbQR+BBEE4wMcA9wCQANYA3cD5QIuAjACzgL7A8wE7ATXBP8DYwNEA8EDkgQ+BVAF8gREBD0DqAJLAgMCfAGSAEf/fP4F/rX95v0r/lT+jf6F/vT9jf3z/H381Pxz/eT9GP7V/Z399v37/hsAVgEKAgECvAEcAY0AJwBEANIAkAHdAZQBhgBv/2z+cP32/Pz8/PxC/RP9YPzo+1f7NPuC+6P7u/vW+6X7xPsm/Ob8r/1C/mj+if5q/m7+Mv4R/rj+4/85ATICvAF3AFH/fP6X/u/+T/8wADkBCgKAAqwBhACZ/4D/dACSAb4BiQEVAXsBfgL2AiMDDwMEA3MDuAOrAwgE0QPQA8UDKgPyAgYD4AKgA6QDJwP0AgYCbgFLAUgAn//4/sz9Z/0T/ZH8dfxV/Cf8dfxn/MP8mv2C/jX/J//F/nr+0/6M/1UAAwFsAVMBIAHrALkAJAHBAT0C3ALDAiACtQH2AIsAkgClAPEAoQGWAT8B8wAQAGz/Bv+N/o7+N/+o/x4AAwAW/9X9f/ya+6P7bPx8/TL+2f0I/eb7K/uy+7f8fv47AAoBRgG3AN7/+f99AFMBggKmAokCGAJRAUIBhwHxAaQCyALXAaMAQv8R//D/vAD2AFEAE//v/Yb9N/4q/4f/df+N/oX+6/9jAdUCvwPQAxYELASOAykD5AIcAx8EqwQnBB4DfAFaAJEAtQB+AFUAN/98/m7+s/2N/aj9Z/3B/a/9zfwT/BX7bfoC+7n72fwV/uD+Y/9E/9v+hf5W/nH+3P4P/6T/3P+x/zoAlAA2ASkCegKWAqoCHAIRAtcCYQOQA+cC+gGtAdcBRAIwArMB/wADAP/+8v0F/ar8qPzb/Bn9zvxa/BX8vPv1+678af1A/sj+0/4N/0b/kP8MAGsAwAAMATIBCAG1ALcABQEiAUQBGwHkAKwBWgLbAj0DlAKHAeoA9/9o/53/3v+NACABugDr/zn/If8eAE0BAwL7AUoBgADF/7H/TgAiATACGgMuA+ACFwL6AGkAPwBXAOgAHAH4ALEAEgC2/1L/Ff9N/47/tP+d/xP/if77/Yv9gP02/T39Tf1e/df9Ff4K/vH9yv2v/RP+i/7X/hP/4P6O/lb+MP47/pL+4P4R/9P+RP63/Zv9Af5f/pL+pv6S/q/+I/9v/woArAApAf8BKwKHAZ0AbP8c/9r/pgCJAfEBwwGbAZYBpAERAoUC3gJHA0cDNwMhA8gCYQIiAvgB1QHJAaMBhgGEAXUBJwHmAG4ABQB4/8r+i/6//jL/tv8vACsA6f+4/1v/bv/Q/wEAewB5APn/aP98/uv9Cv5P/tL+X/9m/7T/CgAeAJ8AKwGWAR4CRgIrAiwCHAIeAiUC5AF7AWcBrwEMAhMCmAHoAEMA5/+J/3H/uv+t/1b/9P4i/qH9c/05/S79MP0M/eL8wPyP/JT8ofzL/DT9pv34/f39zP2S/Yn9tf09/v3+if/Q/+X/BwAMAFMAqgDJAOQAoQBKAC0AYQDCAFoBxwEYAi4CGAK4AUIB9gDzAE0BtwH4Ae0B6wHSAccBtQGLAWEBWAFsAYQBXgHQABIAUv/F/oT+gv6m/tD+8P4Y/zL/Y/+b/8z/+f8yAAcA6/8VAHIAFQGZAZsBWAEcAd0A1wDkAOQA7wC5AGUA7f9o/1L/n/8gAF4AIgCD/+v+ov6x/in/jv/F/+n/pP9m/4z/5f+PAAwBDgHoAKMAJAD2/6j/WP80/w3/DP8Y/xH/zv6A/iP+Bf4a/jf+cf6v/sH+3v4E//n++f7D/n7+WP5Y/oX+tf4p/8j/NgCRAJgAggB5AHQArgDzAA4BFQHXAJYAhAB1AIkAtwDkAPQA4ACaAFwAWACRAOIATwGNAW4BNgGuADAAEgBlAJQAsQBhANf/sf+p/87/MgCoAPoAcAGJAXsBbgE5AVEBcwF3AV4BJQHdAJ0AYABEAEgASAAiAPT/uP94/xj/yP6x/tf+Nf9Y/1r/O//7/vn+7f7r/iX/bP+t/+X/AwD9/8z/h/+C/4z/kv+r/5v/tP/D/87/0//X/9D/wf///y8AUQA/AO7/w/+t/9f/OACEAJoAsQCUAF4ADgCS/zD/Kf8+/zv/RP8l/yr/J//e/sX+r/53/oL+l/7B/uT+Ef9k/7T/GwA0ACYA9v/g/9f/+/86AEYAcgCCAJEAzgD4AD0BeQGSAZsBkAGHAVwBSgFeAYsBowGdAYYBPQHWAFUA/f+//5T/ev9a/yH/Cv/g/rz+yv7X/rr+tv62/q3+2/4D/0D/if/n/woAEgD9/7b/sv/O/9z/7f/T/7H/vP/R//b/KwCaAN0AEQEeAeYAzACJACYAyP+L/2b/Wv9h/5f/4v8DACsAIAD0/+P//f9OAI0AmAB9ADAACgAQAEEAkgDFANkAuQBVANn/ev9U/1r/c/9o/03/Of9C/1j/Xf96/7z/CQAvADgA5f+g/3f/Wv+f/8P/zP+6/8j/8P/l/+3/2v+8/+n//f80AHsAiwCUAIYAbAA4AO7/7v8bAE4AhACfAL4A0AD/ADABSgFqAWEBPQEgASkBJwFRAU8BFQG6AFEA9P+//5T/g//F/7z/vf+J/0v/NP9P/4L/m/+o/2H/MP/7/vn+E/81/1b/Yf9o/0n/Xf+d/9P/BwD//97/rf9x/13/Wv91/6v/xv/V/73/l/9d/yr/E/9C/7T/AwAZAAAA3v+y/6n/yv8JABMA8P/t/9P/vf/a/9P//f9VAI0AxwDMAM4ArgCxAJYAiQCNAI0AqACYADsA3v+g/2r/ev+A/4X/jP9x/1//Xf9m/4X/lP+C/3X/jv/B//3/NABOAIAAwgDzAD8BagFqATIB6gDCAKUAowCjAJ8AjwB3ABcA4v+2/53/yv/l/wAA/f/r/9r/4////wcA/f/n/+L/xv+o/4f/c/9s/3r/jv+f/6T/m/+d/3z/Yf9P/0T/VP96/6D/0f8JAEEAeQCGAI0AWgAZAOP/vP/V/wAAJABIAEMANgAnAEYAggDUAAcB6wDDAH0AIADT/73/1/8FACAAGwD2/9z/+f8JABkAEADp/8b/lP9s/1L/LP8u/1b/qP8AADsAOwADAKn/Yf9S/2T/lP/Z/wMAEAAkADYARgBBAB0ADAABABcAWABlAGsAYwA9AEMALwAmAOf/yP/R/7j/zP+6/6T/qP+o/63/q/+m/8H/6/8VACcAGwAbADoAaQCGAKoAswCsAKwAqACfAJ8ArAChAJgAggByAHcAewCEAHUAaQBBACQA9v/D/8P/sv+Z/5X/ev9E/yf/9P7l/gj/IP9J/1r/gP+m/8j/9P8VACQACQD7/wMAEwATABMAGQAbACAAGQAOAAwAGQA/AGEAWgBMADQAOwBeAIIApQCxAKMAnQCIAHIAYABOAFgAUQA6ACsACQDQ/7r/qf+y/+v/DAAdAAwAAAD0/9f/sv+J/1//O/8l/wr/9P4I/x7/QP+A/6j/r//T/+f/1f+//6T/rf+y/6T/xv/Z/+7///8OACkAIAAMANX/ov93/13/O/80/1T/fP+m/7T/zv/r/wkAFwAtAEEAPwBTAGcAeQClAL4AvgCuAKMAbAApAAAA2v/l//n/GQAnACkAMgApAB4ACQD//9z/w/+9/8X/4P/0/wUAFwASABMAJgBaAJYAswCvAJoAiACGAKgAtQCvAKgAiABgADgA7f/T/9H/2f8DABcANABKAGkAUwBEADQABQDg/8b/v/+r/6T/n/+S/5T/jP+S/4z/i/+i/8r/6f/7/wkAGwAmADoAWABhAGwAdQBsAGEAXAA/ACQAHgD2/8P/r/+Q/5X/pP+k/7z/ov+y/7H/lP+F/3P/i/+V/5f/if9d/1L/Sf9P/3f/ov/F////KwA4AFoAggCSAJQAbABaAFwAVwBMACkAMgBYAGMAZQBVADYAEADi/87/v/+k/5D/mf/I//L/GwA2ADAAJgArACIALQAnAC0AOwAgACsAHQAHAPn/0f+t/5X/fv+A/5T/ov+t/6b/qP+f/6//yv/2//v/CgAyAEEATgBeAHkAdAB9AHQAWgBgAFgAPwAiAAkA/f/7/+P/5f/a/87/xv/M/9H/5//0//T/6//e/8j/pv+v/6v/2f/p/+3//f/y/+P/0//O/8j/1//w/wwAEgAZAP//7v/r//3/DAAnAC8AMgBVAEMASABIADAAKwAeACIAMgBIAEYAPQA9ACYADgAAAAEA6//i/+D/1//G/7L/v/+2/7j/tP/D/7//yv/F/8X/zP/Q/+n//f///wcA9//O/8b/1//e//T/FwAwAEwAVwBGACQAAwD0/+3/6//y//L/5f/7/wcAHgA6ADIAGQAQAPb/6f/y/+7/AwASABkAKwAvADYARgBGAE4AYABaAGMAWABTAEYASABnAGcAaQBTADIAIgAvACAA9//p/8j/vf/K/97/6f8HAP//2v/B/6n/sf+9/7r/r/+d/4v/h/+r/8P/4v///xkAHgAJAPn/1//Q/87/tP+X/3f/Y/9u/5X/q/+//8b/1f/i/9r/yv/X/9D/0//n/9n/3P/X/9H/4/8AAB0AKwAwACkAOgBGAFEAVwBXAFUAXgBaAEEAMgAeAA4AEAAVACIAJAAbAP//2f+//6j/pv+x/7b/xf+4/7z/4P/p//v/AwDw/wEABQAKAAoACgAMAAwADAABAP3/BQAVAAAA+//3/8j/0//M/+P/CgAZADIAOABBAEMATwA7ACsAKwAyAE4AYQBaAGAAbABeAGkAcABnAGkAXgBjAGUATgBXAEMAKwAnABUABQDa/7H/nf9+/37/av+D/7L/1f8DABcAEwAFAPL/4//t/+v/4//2/xMAIAArAB4AGwAnABIAEgABAO3/8P/0/wAAAwATAAoA6//w/+3/4//D/6//nf+r/7r/uv/G/9H/0//l/+v/9P8FABAADAABAPf/5//n/+n/8v/r//D/9P/r/9H/w//Q/9f/4////y0AQQBXAGUAYwBMAE8ATABEAEgAOAArACYAEADt/9f/xf+y/5v/hf9x/3P/gP+F/4z/l/+X/53/vP/F/8z/2f/r//v/FwATAAoA//8FABIAEgAkACAAGwAnAD0APQBDAD8ARgA7ACIAHgATABUAFQASAAoADAAHAP///f8AAPn/AAAAAPT/9P/u/9f/xf+8/7r/uv+4/87/9v8QACQAJwArAD0ALQA9AEMALQBEAEYASAA9ADgAIgAgACYAJAAbAAwAEAAFAAEA/f////b///8BAAAACQADAAwAGQAnACIAKwA2ADsAOAAiABAAGwAZACcAPwBDADsAIAAFAOX/0f/F/8P/w/+r/5X/ov+k/6j/rf+d/5X/g/+D/4D/g/+U/8H/1f/2/xMAGwAvAEEATABPAFgAWgBTADYAJAAQAA4ADgAdADQALwAtABIACgAHAAwAEwABAO3/9P8BAA4ADAD//wMA///5/wcAGQAKAP3/+f/2/+f/+//n/9r/6f/j/+3/xv/K/9n/3P/c/+f/3P/c/8r/qf+m/6b/ov+v/7//0f/w//3/BwAOAAUAAwAHAP//+f/7//D/AQAOABAAFwAQABUAAwD5//L/3v/g/9r/4v/w//D/6f/2/+3/4v/5//v/AwAMACYAMABDAFoAYABsAFcATgA6AEQARAAwAB0AHQAbABUAKQA0ADoAKwArADAAKwApAB4AGwAMAPv/7f/g/+n/5//X/8j/uP+9/7z/qf+v/5v/hf+O/5X/lP+o/8X/1//3//f/7v/3//D/4//2//f//f/r/+3/9v8HADQARABPAEYASgBGADYAFQD5////9P/5//T/+f8BAAAA///u/+L/3P/R/8r/1f/T/9f/1//X/+P/xv+2/7//vf/g/+P/1f/O/9X/3v/y/w4AHQAiADIAQwBBAEgAPQA4AB0AGwAeABIAHQATABMADgD7/wkA+//2/woAHgA/AEwATwBBAC8ALQAMAPf/8P/l/+L/7v8AAAUADAAQABkAGQAVAAoAAQD//wkA+////wAA8P/0//D/AQAMACAAJgAwADgAOwA9AD0AOgAgADsAPwA4AEEALQApABAAAAD3/+3/4v/O/8z/uP+y/8H/v//I/8b/uP/R/97/2v/t//v/CQAkADoATAA/ACQAMAAiAAkAAwD2/+7/2v/V/9H/0//n/87/3v/j/+3/AAD//wwAEgAOAP//+//y/97/1f/O/8X/yP/G/9D/0f/M/9X/xv+r/7b/vf+x/6n/q/+x/73/1f/t//n/9v/r/9n/8P///wEAEAAFAAMAEgATABsAJAAkAB4AIgAXAAEA/f///xMAEAAQAB4AJgASAAcAAADy/97/0P/X/8j/3P/t//f/AAASABsAJwA4ACkALQAwAC8AIgAeABsALwBDADoAKwAQAAEA9P/p/+7/4//y/woAAQADAPb/6//t/wcAEAAgACsAOwA6ADAAOwApAB0AHgAwADIAOgA0AEQATwBEAE4AQQA0AC0AHgAXABcACgAFAPv/5//T/9H/0P/F/8j/zv/F/8P/zP/T/8z/sf+g/6j/tP+9/7j/uv/I/8b/+f8SAAcAHQAVAB0AIgAwACcAKQA2ADsANAAbABcABQADAP//8v/X/9P/xv/B/8b/uP+9/7r/vP+x/7L/uv/M/9f/0//R/+L/4v/e/+f/8v8DAAoABQD9/wcAAwD9/wMA7v/u//f/9v8BAAUABQAJAAkAAwDw/+f/6//9//f/CgApAC8ANAA9AEMAOwBDAE4AVwBDADAAHgAbABcAJAA0AC8AMgAkACkAMAAyAD0AMgAbABcABwD//+3/0f/a/9z/2f/i/+X/0f/O/9D/0//D/9P/0P/M/9r/1//i/8P/xv/g/+v/9v/5/+v//f/u/8z/zv/Q/8j/yv/M/9X/3v/X/+D/4v/j/+f/5f/a/87/1//R/97/9P8BABIAFQAnAB0AGQATAAcACQAAABAAIgApACsANgArACIAOgAwADAAMAA0ADYAPQBYAE8AVQBKADgAHgAbABMA+//w//D/+//u/+7/6//n/+f/1//T/8j/vP+y/7j/v//D/8H/v//Q/9z/7v8BAAEADgATAAkADAAFABUAJwArACkAMAAvADIAPQAnABsAJAAiABkALwAvAC8AIAAiACQAIAA4ADAAOgApAB4AIAADAO3/1f/j//D///8FAAkADAAKAP//7f/t/+v/5f/i/+f/4P/e/+P/2v/l/+D/2v/g/9f/BQATABUAJAAiACYAHgAXABAABQAFAAcA9v/y/+v/4//e/9z/3P/B/7b/qf+m/6j/nf+v/6//qf+4/7//wf/O/8r/v/+9/8H/tP+0/8P/1f/r////DgASABkAEwAbABcAGwAQAAAA//8MABUAGwAdABIAGQAJAAkAEwAeABsAHgA2ADsAPwBDAD0ALwA4ACkAFQASAO3/8v/n/9P/3v/V/87/wf+x/7b/vf/I/9r/8v/p/+P//f8HAAoABwAQABUAHQArAD0ANAAmACsAHQAMABMA9//9/wAA8v8AAPv/GwASABkAHgAgACsAEgAbACAAEgAJAAAA9v/5////AAD3//D/9v///wEACgAXABcABwADAAkAEAAZABMAIAAwACIAJgArADIAJAAdACIAJwA9AEYAOAAnAC8ADAASAAoA7v/y//L/9v/0//b//f8TABUAEwAKAAMA9v/l/+P/4v/w/+P/6//j/+f/6//j//f/8v/9/wkADAD0//D/3v/a/+7/6f/r/+P/6f/u/+n/3P/V/9f/4v/R/8r/yv/K/8b/2f/M/73/v//F/8X/xv/I/7z/tP+x/7z/uP/M/9H/2v/y//3/+/8FAAwAGQAwAC0AOgAwACIALQA2ADoASgBIAD8ANgAvACcAIgATAAwA+//j/+P/4v/p/+D/xv/B/7j/tv/O/9X/2v/i/wMADAAAABkA9v/2/w4AFQAgACIAKQAvACsAIAAeAAcADAASABAABQADAP3/AAAHAPT/9//w//b/8P/n/+f/8P////v/AQAKABkAHQAdAA4AFwApACQANAA4ADIAOgBIADYAOgAtABcAJgAbAA4AAQAHAAEA4//a/9f/1f/B/7z/wf+8/7r/wf/D/7j/r/+f/6v/sv+2/7b/vf/R/+f/CQAOABIADgAXABcAEwAOAA4ACQADAPf/9//0/+7/BwATACIALQApABUAHQASAA4AKwAmACkAGQAgACkAJwAvACAAHgAtAC0ABwDu/woADAAHAB4ALQAbABcADAAHAAMA7f/2//b/3v/t/+X/8P/t/+f/9v/l/+7/4v/e/+P/4//X/9H/yv/K/9f/0f/R/9P/zv/Z/9z/2f/i//f////5/w4AGQAtAD8ATgBKAEQAMgAQAB0AJgAvACsAJwAbAAUACQD0/+X/3P/u/+3/9P8ZACAALwA9AE8AWgBRAFoAbgBuAG4AbgBrAFoAWABOAEMAKwAbACYAHgABAOv/zP/D/9n/0f/g//D/+//y/+v/5//V/9z/w//D/8P/r/+r/5v/n/+i/7//yP/F/97/8v8AAP3/9P/t/9r/1//I/7b/uP+y/7L/w//F/8b/3P/i/9z/5//9/+3/7f/p/+L/5f+//9H/0f+//7b/pP+6/8b/wf+4/6v/qf+J/3H/i/+Q/4f/qP+t/7j/0P/V/+f/+//t//3/EwAvAE8ASgBRAGcAewCJAIkAfgB5AHAAfQByAGAAUQBIAEwAOwBDAEYAMgAdACYAHgAdAAMA7f/5/+v/AwADAPT/9P/j/8r/uP+2/8j/0//Z/+3/7v/u/+v/6//5/w4A/f///xUAFQAZACkAOwA4AEQAQQAtAC8AMAApACIALwA9AFMATABaAFoAQwA2AC8ANABBAEYAQwA9AEwAQwAkACQABQAZABUAAQD//+n/1f+8/6n/jv+H/4v/lf+L/4z/fP+H/5L/m/+X/6L/rf+4/9r/0f/w/xIAGQAXAB4ALQAyACsAEwAHAAwADAABAPf/+//0//3/AAD9//n/7v/5//D/6f/w/wMAAAAZACIAJgArACQAJAAkACYAJAAyACcAHgAmABIA/f8SABIA///3/9z/xf+2/6n/pv+d/6D/sf+k/6v/sv+x/7b/wf+//87/0//R/+L/4v/3/wMAEgAdAB0AEAADABIADgAbACsANAAvADgANgAtAEYASABGAC8AKwAZAAkAEgD9//b/9P/3/+v/9v/u//T/EgATABMAEAAJAAcAEAABAAMACgD//wkABwAOACQAMgA6ADgANAAwADQALwAvABcACgAdAAUA6//T/8H/0//X/97/wf+X/5n/gv98/3z/df9x/3j/jP+S/63/xv/a/wAAAwAOABkAJgBBAFgAVwBaAGAAXABhAFoATABRAEEAMgAyACIAHQASAPn/1f/Q/9n/4P/u/+D/1//r/+X/0//e/+D/+f/9//3//f/7/wUA/f8QABIADAAVACIAHgAZACQAJgA7AC8AGQAdAA4AFwAVABUAKQASACIAOABDAFgAbgBhAFMAVwBKADYAJgAbABMACgD9//L/zv+9/6//n/+D/3f/Yf9H/2T/Sf9H/0n/Sf9Y/1j/VP9H/1L/Y/9z/5v/rf/I/+f/+/8bADAAWACRAKgAswC5AMMAwwCxAKgAmACmAKwAowCLAHQAXgA9ACsAEwAAAP//4v/a/8r/vP/M/6v/sv+x/7H/vP+8/8z/yP/t//n/+/8dACAAJgA0AGAAcABuAIAAggCIAHkAcgBaAE8ARAA0AC0AIAAVAAwABQAVAAAA5//t//v/8P/j/+3/8P/t/+L/8v/w/+7/2f/a/9f/rf+V/3r/ZP9A/yf/8v7B/rH+jf6E/nD+U/45/iv+Ef76/fv9+v3y/fH9Bf4P/if+Uf5s/pb+zv70/hX/LP9G/27/gv+p//n/PQBlAIYAsQDvAAcBNAGJAcMB/QFaApsC9AJUA6ID+QNLBKcEBAVHBZoF7gUtBmcGjwa7BrsGzwbJBpEGfQYsBsYFRwXeBHYEAgSQA+0CWAK+AQoBSgCH/8j+Ff6C/eL8MvyR+wf7mvom+q75R/n1+NH4sPiR+Iz4nvi7+Nj4Avk1+Xr5svkC+mn60fot+3/72fsV/Fr8kvzQ/Bz9U/15/Zv9vP3Q/eL97/0G/v39/f0D/vr9A/4T/in+NP5R/mr+nf7Q/hP/Zv/G/ycAnAAIAZQBMgLFAmwDLgTlBJAFWwYQB80HiwgyCdQJSgq2CiILXQuHC7MLoQt+Cz8L2QpzCtAJLQl0CKMHxga7BbQEqAOrArgBrACi/7j+1/0F/VH8vvsi+6H6LfrR+Yr5W/k++Sj5LPk6+Vv5hfm9+fH5Lfpv+qX61voM+0r7gfuu++L7+PsX/DD8RPxe/GH8cvx5/Ij8hPyd/Jj8mPyd/Ib8dfxe/Er8J/wb/Pb7y/ul+3f7U/sz+yT7CfsC+/r6C/sp+0j7gvvA+xL8bPzH/Dn9r/1G/u3+m/9OAAwB1AGdAnwDWwRZBUUGPAcxCBMJ+AnXCsMLmwxoDQkOkw4HD1kPhg+XD44PQw/fDjgObQ2UDIULdQpDCQsIsgZMBeADVgLqAJL/Qv4V/fz7+voZ+lD5pPgc+Lr3b/c89yr3Ofdh96T39PdQ+Lv4M/mi+SL6o/oi+6X7E/yE/Nv8MP2A/bf95v36/RX+Ef4T/hX+Cv4F/uv91f2s/YT9Zf0//RP94vyj/Gn8Lfz1+7f7dvsk+/P6xPqK+l76LfoO+gX6AvoE+gf6H/pT+oH6wPoM+2n72Pta/Pj8kv1W/iH/+f/gANkB2wLgA/kEAgYQByAIJwkyChML5AuyDEkN6Q1oDsIOCw8SDwkPyA5zDgIOdQ3VDPkLEwsRCvYI1gegBnEFPgQIA9IBpgCM/3z+i/2l/ND7Jvt/+vP5dPkU+c/4oPiI+IP4l/in+M34B/lJ+Zr56Pk1+pz66vpB+5H75Ps7/Hf8wvz0/C79Yf2G/a39vv23/bX9rP2b/YD9Vv0e/fH8t/xy/DT84fuR+0j7+Pqj+mL6L/rz+cL5oPmK+Xb5bflo+Xr5kfmw+ez5Jvp2+s/6LfuU+wn8f/z6/Jr9Jf7S/pD/cABPAT0CSQNOBGsFgwaKB40IlgmJCosLgAxiDSUOtA4sD48P0g/jD90Puw9eD+EOPg58DakMvAu+Cp8JawgrB+gFpARmAzkCHgEAAPj+E/4r/WH8tfsH+3b6Dfq3+W/5Tvkz+S/5Q/lm+Zz53Pk8+pX6Evt/+9v7Qfyj/AX9Pf2J/bj91f3t/e39xf2Y/VX99vyf/Dr81PtX+8L6OPqr+RT5jvgP+Ir3Cver9l/2HPbt9cP1r/Wd9cH1//VL9q/2FveD9xH4tPhM+fr5tfp2+zb89vze/cz+4P/xAP8BFQMLBBIFRwaTB9gI/QkRCyMMHQ0bDv4OyA+WEC0RmBHLEd8R7RHhEagRSBGVEMYP2A7DDaIMdQscCtsIjQcCBp4EKQPJAX0AKf/S/Zj8j/uc+uP5JPmZ+Df44/ey9473oPfK9wv4Xfi/+Dr5qfkW+oz6APtg+8v7Nvx//MD86Pz8/Az94vy6/Ij8MPz8+7z7Y/sf+8n6e/od+qL5M/nB+En48veB9zX3+fav9oz2ZvZJ9iD2I/Y39k32qfYI95f3Jvi2+Ev53flp+gz7wvuu/Lj9rf6t/8IA6AERA04EkwX3BlUIqgneChUMXQ2QDuEP7xDAEXQSMhPOE0YUhRSNFIkUSxTTEw4TKRI2ES8QDw+QDRwMcgqoCPkGIQVYA4kB7f9T/tL8bfsZ+uH43vcT91/20vVz9WX1WPV79cn1Kvad9jv3z/do+B/5u/lm+gz7tfsb/Iv89vwQ/Sf9Af3b/KX8Z/wi/L77afvj+kr6vflF+cz4VPju93j3CPe09nX2WPZA9gT23PWS9WP1QPVN9XX1x/UX9mr21/ZH9873Vvjh+KD5Yvog+xD8MP2//mEAOQLaAzwFsgZGCC0K+AvhDbAPWhGuEo0TehRNFUUWBxdMFzEXpRZWFsUVLBWmFKITYRIUEVUPdg3MCwoKYwiwBr0ExQLkADn/0v1l/Af72Pmr+Kf30/Yy9tv1q/Wq9bz13vUG9m/2Gven90f42vhv+fr5o/op+6j7Jvx0/Mv8Bf0g/T/9Qf0k/dL8ffwt/KH7TPu7+vn5ZPm2+Dr4zPdb9/v2gvYE9qv1VvUg9RH1BfXF9LH0wfS89Oj0RPWL9QP2aPbc9lD3yPdq+An51vme+s/7Z/0l/wUBtQJsBFcGJAjzCd4L4A0NEPYRjRObFHwVuRaaF/sXPhgSGKIXSxfGFrYVpBRsE9sRaRAxDvkLBwrkByYG9wPJAeD/Bv6S/Cj7ovmV+Hz3kPbg9Xz1FfXt9Hz1uPUX9pT2Sffj95v4QPmj+ZP6JPun+1r86/xh/fj9bv66/sP+3v4E//D+n/4F/u39gv0e/f/8JPyN+wv7gfrC+fn4WfiB9yX3gPa49Sv1mPR79F/0JfQD9Nvz5PMr9K/06/QG9c71J/Ya9yz4Wfk9+6j8Ef5W/4IBuAN2BcIHTAkGCycNtg7oD9IQrhI5FOQUqBXDFPsU+RUuFg8WsxS1E+YSQxJDEYoPXw6iDPQKOgmiBr0EIAP2AQMBw/6z+7/5GftV/fH8iPlf9ZT01fY8+I73J/dQ+db6Hfr9+IX4YvlS+nj6UPpm+tT7Kf3J/Gv8HP3H/V79Jf3r/GX96/7w/yn+wPsr++z7DPzc9zbyNvG19DX3ffP66qbmf+iE6y3r/Odb5YXkRuWe5Sjm0+eS6x/uau2J6y3sQPC8+ykMexi0HlgbqRUEGOoiGC5DMu4wdDHMNsg7ETqoNP0v8SsTJ18dLhSUETATyxJHDEoBjviW9BbxG+v/5H/hM+Cy38XcNNoc3HDgn+IA4GTc8NzK42Pt+PRA+PX5o/14BOAL0BBlE+AVBRn6G9AdzB6gIWcjqyBMG/sUtRBHD28NEglJA4n+dPq09jjy5+yV6UPnFeXA4w7iJuHd4c/gxN6h2t/X89dL2zjfT+Ew4g7jlOPM5Nbm++Yt6h7tQfNz9/X74v5wAbYDJQFT+8sGOyUURLJNsTYeHGkZvzGYRBJFgTuiOopD0EVsN6kq2SQmI5EZJQSW9TL1df1zAaf5T+qZ2wXS/M8v0OfSetJ302jVYNfm2oHdxeFZ52Pr6+w37371+wO1EagafxrLGMEbYCE3KDgpGykCKJkllSFfH9UdMhxwErQDTfYb8YTzpvND8OboTeOw35DcDtsn257cH9+u4BrjtOYS6ADoyOQM4jThyeEu5+TqaO7F7jvtRuyJ62HrtO/H84r4g/pw+9P+1f2e+0H0fgmqK7VGhUkgLNcVQyEIN61Bzj60Nw49FkR3P6gvIiq8JF0d6g4p+9LyFfPS9Av6E/ea6tfc287Ky6fPPdEn06zW99w24tDjauXf6dTy5flB/YABbQiOFMAgEyfAKd4m0yWAJionaSpvKPokiRyNEZQLgwW9/w33xeqN5MPiK+IL4UTcP9kt2NrXFdgi2wLgzuWW64rule4L7nXtSuzV7iHv5e+28HLxNPIt8tHxX/BE7sPtp+/e7xvyRPP39033E/aC7Lr/tiV4RQlP8i7SE6cZ1TGHQBtFsjs7PltE8z54M60tsyLIFiYJKvg98nfuE+158lv2J+xA3BTM/8ckz9HT9dRc2yvhmeZB6tXra/Kc+cr+9wNRC+QUthy8JPEpcisuLWsp+yY5JrciIiIKHvgUFwtCAWL6FfPG6JDdkdjM207dztvk157UCdd12vDcaeF15vjsVfOy95n4Efcp86fxQfJI9Pn1Y/dA+BH3T/T57ojpueZb6Wrr6e6j8Jbz0fG18erpK/tuIx9CJ1AvMaMSpRjfMuJHFE40P2k6Rj5DO4s1ZDDWIqgTigbn94vyFexn45LllOza51vdcc3vxi3P+9Xb2ELdpeA85q/sF/MQ+7cBvQdzDN4UqBy2IIAn4ixqMFsxYipsJuklhyQhIaIWmgmf/d73Ffbd8bLpndxs0yHU7Ngw3WLeFt2Q3PDcnt8X5CXtg/ZZ+vX4/fTI8ObxYfSz9tz3m/aq9e30QvRm8bzsUekF60DsQOxj6rDqRuow7Ufo8fmcINhBFlR1Oeoa+hg2KxU/mEemP8Q8sz7aOvY1IzJHJgsV5QK18rXrnOYf38Lgo+n46afhbNIRyOfJfc5e0ejYjeHL6LvwEfifAaYJJwxfDEMRZBgRHvAmSy9qNUA4/jF6KTIkNR7kGs4VeguL/uPxW+5+7mrrEOLV1Q/SHNSV1wHbqtvT3DfeV+F46GvxVvjU+hn6GPY68c/wlPQq+kz7FPeP8LruP+v554vl6Om67oruN+d549rlO+rd6hHnNwVwLuxX014kPRMfjh7jMKhHTk8rRjpDX0CnPF05ujA7HYcKi/bW6C/hO9od1trdMuVj5LzadMwZxTrHm8y/0+PceeQV7O31IQPYDzoYLRmBFioXwBtLJugyBDknOKMydS1jLDAmIhr1DYACKfz29AbuhOkY5fHfM9fU0KnOW84a0j3Y9N2A48XlzecP70L4g/9CARX+7vaq8qXzqflKAGUCof3m9GXsXOQH33bdLeFB4/nnb+jJ6qLnVOWm3OvtNBv8RqRmmVFsLKMhZi9pRMVSykiGQ9lFvECgOTYywiIBEkb/AesL4ZXXC84Az/3ZgOFs4XzUr8hBx9XKac/J11vfgebn8Jv9KA7mGzAf9hwPHg4j7ijbLdIuHi/LMUoz/DO/L8Igtg0y/OrvbelR4/3dJdoi2EHXIdVP0krP5c5o0qPa6OJn6XTwf/ooBdwLMgmjABf71vsDAa4G6AZPAdP4evBj7EjrC+mX5bjkCeKa4OLcJ9yv2/rgDd5M8TofIUyma/5VXS7eHdEoGT3gT+9OY09KUWxIyz2/MTAfNgpQ9wvoPuAQ1ynKzseo0vvcuN3r1IrLJMhNyozOLddX5kHxjPneBMAP8htbIbIfCyDoJDMpZDCENN0zXTLkLDEp5CM+FyQGzPRu6c3hbtun1jvTvtLi0NLOkc4zz+vRANek3bjlpOw787X9vAkkD+QLvwKN/SsCNglADWwKrwG69pzvp+gW5c7h2eGP46/lUeAX27nWpdbt2HXdFQM1L2Za7l6xPoEpeC+bQQZTglE7RqdF3kGRPgxAKTZbINgGEutd3HPRncFVuwLFoNNg3u3bbNPQz5jQcdMY24PkT+rK71z7/Q1KJM8xLTKbLsApOSSYJVspriy2MMUsFihnJKAX5QZz9Sjmp96x2CzTMNAzzg/Ncs33zrfRbNTv2OTgj+n78Mr1F/0JCDEP0Q5pCPYC6gXACgYM4QfH/lP1rPGw73ruGOfr3P7UqdO716Xed+Qn5TThPdjJ8RMftlEJaVpQOjQ1MZ47z0pcTWZB/UHmPu82qjVTLOgZ8Abl7drgZ9kCxj+3jrnox8bcs+Sw3sna+tcm1z3cFuUo70P51wOkE+skpC7yLeoqAy3BMFsxdS3iJgUkNiK5H/8bTRKBBHD0b+YW3rvYetRc0pbPz8+G0QrTENcn2jreb+VT7BvyxPf7/dAHiBBsEVkNjgYRA10DpP9w+7z0Ee/v6yvq/OiX5mzicds2183Wr9wk4UTjRuOi56AFQTLlW4RrTlb2OIwwuTnKSGlO+kTMP3Y7bTOcLO4gRQ/q/BDosdkQ0YHCp7T5s63CbNnY5k3l+t/93A/fvOSB7s/5tgNNDQkZdSfxMjY1CzIuMUcwnS1fJiMcKhf3FREVAxNoCgP8luyT32vYZNPuzNLG0cNeyCfSQNzw5Fzpuuva7uXxsfUQ+s4A1womEosSfAxfBbYF6wq/C0sFpPVo5UTbNtg92BLZL9ln2nHeWuI35NrgLN8S3obrSg88OtNcpmJ5TuY/SURJTbpT/UkFO7Y2ezOVMRwviyJ2D6j7hueb2cPIabLjqLCxbsjh3W/lF+Sa4fPg6eJS6Izxy/wvCYgY3CbRMCI0iDLSM3w3xTbyMFwk7BWgDnENFRErErsIRflu6Yfb89BcyOXCk8OSyLHQYdmV36Tjg+bJ6+zxgvQQ9ID2FQEXEYAbiRqaEUYINwUxBagDlP088u7nOOKW4X/he9/S2krXt9Z+2VLc3NuP2YLYWOt5EzVBFWCnYAxQt0aaRwJK8kmyQWU86jxFPI89MzpZKmARRfdg4mHUhcJarpam/68kxfPV1dwd3v7edOD84bLlqOty8u37/wqWHVsv6jk5PhZDXEURQao1biT/FJsMowrfDusS1A5fBET2Wefo2BDIlrhZsky2r8F/zivYGeBH6YTzA/s8+/L1YvKi9lIDmBAfGSscTRucFwUSIAmJ/hz1vO1i6K3jKN/53ETdDd6H3uHeUN7D2izTotPx65UWC0KKVolRhUkBSrZMoEwZRSo8pzvqObY2IjSCK6gdJw0r/dLxauNozMW2V62ztH3E19A/2j7fpuJJ5snoZu1K8vr0vP12DQQeLSxdMAYwVTPdNb42EzV8LdIj6hlcENcJZgPR+iLyL+mw4ATWVsqSwrO/pMI0yK7Mt9DC1Vvdt+ry+FgDcglPC10NxA70DU0MrwueDU8RQRMJEdIJXP7j8o/oW9+12MLVl9Vl2QXfSOLJ40fgI9vL6U0MQDGFSU5GAjnvPNBF8kmJSghBjzwiPGAyKCv9JGYWpQic+5HxN+wT3BfHaLojuXjCfMxL1B/e2eTm6eXuOvHB9QD4oPoSB1sXKSVzL2kzCDjJPFc6hjRpK2QhDBrpEvYM8Qfi/u30xesp4jDaptFhykjHkccPycPK/czi0czamefe9Kr9jgORB7oLuw8QECoNbAphCVEJ+ggNB9MDtv+T+8P22PCK6ajhi9sr2PjZ/t1T4irmI+bL6/IBsiFUQLpPZUmAQGU+ED3mPSY7cDTPMSctJyZuIk4aeA3xAP/0vOyh4ujR3sKVu1G+Y8ci0dPbIeVs6sHuFfPY+CP+Zv9JAnsJthNxHvYlbCzONbw9wUDwOlUq7hbEBz0A+f42/uH6g/a38arsQuWs2gXQHsdXw9bEe8kQ0WXbsudw9ND9mQKBBGQEeAWnB8sJKwvLCSgHygXhBX8GywVcAhn9i/X97FDl499h3Tvc4NyG3yLjV+bG5yLsV/t3FOsvUkOfR65E6UFuQB5BHkAsO9k0ySxxJZwhIxxlE+oIzv5H+IHwi+In0h/FGcBJw7rKVNMe3Azkuup68L/2svv4/iADIgiQDqIUHxfWFzgbjCGgKbEu8yu5I4gZoxAtCqAE6/3c9W3uB+mg5uXlWOSY4Bbd0trM2arZodoS33jnIvKn+mT/YwJDBZMHLQltCPIFKgTOAgQDgwRtBLEBd/1A+UT2svLi7VXpVuag5E/jA+QN5ibomulh68f0RwcgGnEo+y+TMtk3YDyfPE48wTcsMH0p9SHnHdAcnBYjD40IfgGR/Ar0Pui74Enc8dmf2gLcUt8u5MjnGuyY8b32UvkL+nL8X/+LAaICWAIoBJYICAw+EGoUsRWyFV8UJBJ9EMEM7AZwAbX8FPmA9t70c/QT9GTyfPAI7+3t+Oxf7K/tIfC18YvyGvU4+rwA+QTTBasGKAdDBjUESwET/0n+/fzC/Ob94v0A/CH4SPSE8qrxqvGS85L1HPeH9zf35/a+9XH1g/m1AnwOVRcBG+IcOx7GHvYdoRvxGBQWvhJHD7EMxwqUCFAGtAVdBrAGtgWJAjv/lPy0+dr3BPeD9u72ave5+I37Ff0X/YL9hv1j/ej8z/tI/Oj9tv6U/3kAnwGFAzwECwQeA0QBY/8//ej6+/h199j3gfkZ+m/6svl6+IH4N/j99+f5TvyO/sUARgHDAcEBiQAV/xj+Yf0b/RP9HP2s/cv8G/sZ+hL5g/fK9Tj04fO49Br2KviI+5b+AABDAPb/d/8q/9v+K/76/Qr/TQEPBWUJegtCDF8MEwumCvQLpQ7vEUQTyxEkDywNSws9CRQHeAVCBS4FsgTTAxgDcwNoBRQHyAZABfgBdf6a+8j5Bfo0+6z8Ff75/h7/X/5K/Hf7Ivwi/ar9MPzb+tL7Dv4VAFYB0ADp/6T+P/2S/X7+QP87/2z+F/5L/rf9T/yw+0P7KPvx+mj5evhH+E335PYK96n3ZvoX/Bn8B/wX+xD7U/wD/XX+2QAPAxYFIQW4BMQEegP0AZwAT/9BAPMAKwCJALL/JP0p+1D60Pv//i7/d/5v/58BVgWWBwAIXgepBkEGzwZ/Bx0HIQeBB9AIywnYCGcHogb5BvwGHwYjBSgErQKtAVwAlv5u/nv9w/zV/cj+kv/X/13+MP04/Wf8ffuc+vn5Qfqe+8f9SgCmATQBHQC8/nD+lv5I/uX+2v8VAP//0v5q/eb8rPuN+ln6svmB+RT6f/lN+En4oPjE+Zr73fzc/roBUARZBkcHVQarBEcD6wL5At0B6f80/rH8j/tG/D/8Dvyc/A78HvyC/Ej8uvxp/Uj9Kf1h/Mf7Gfzv/Ev+qP/MAS4DVARdBYEF9wXYBskH8wgQChwKywnWCKwHAAfUBkAGMQU+BDAD2wLcAgEDFgMKAzsDmwKLAY8ABwBj/5/+fP4l/9cAaAJNAroBaQEdAPb+of2a/Gf8qPwl/VP+Gv+h/qz9J/y1+uX5BPo8+vz6cvwe/UL90v0c/jL+Of97ANQBeQIVArMBiACO/6j/OAAgAEL/Y/4e/rf9SPwg+zb6yfkA+cz3jvcq+B34rvhm+fP5Mvz6/HD95/7j/z8BBgJaAcUAFwEpAi4DmwTwBUoGWwd7CMAITAi9Bi4FcwT+BPAE6QPpAlwBYwC//zX/sv+CAOAATwFyAdABUQIDA4sDwwNABAoEXwTlBH4EegQKBNoDyAPHAggCaQCy/zT/Uf0D/Jz6k/nT+cT5j/qI+/z7gv1z/on+bv5Y/Qf93f2S/TD+n/2G/dD+cACWAmQDsQFs/wz/IP9nAAAAZv8//RP8efvh+xj/3v6N/nf9xfzA/MX7Vfob+lr75vyf/kv/fv+p/3IAXgAOAHkAY/9gANIAvgCCAsgCIwMBAv8A2wD8AJYBrADF//T+gP5l/qP9uP1q/kD/9ACoAggE2ASQBW0F2AXoBigIiwjqCOII0gijCKoHQwjoBkcFPQNuAPL+cv1I/Mn7F/tr+3b7C/v6+lP7Y/xB/Hv8E/61/nAAugDj/8cAMABOAK3/5gB+Al8DqQQaBZsEdQKi/wj+XP1p+/r7P/un+/L9Qv2t/Yb8vfpy+aP6x/tr/AH/RgAlAXAATwBC/3IAgAEpAB0AqP9s/6z9w/ys+277XvuI+/X7IPxP/Ij7wPuf+yL8EP02/vL9Zf4V/uT83/xu/TL+ov+YAPYAtAMhBaAHAwpmDMsO4w6XDjANNQvkCa4H6gWDBRwEagPTAo8BlADT//3/cgAI/0T92/qn+I748PgZ+rv6nPsu/az9HP5s/nX/WgFxAjAD+wL9AlsDDwNGAc4AmAAkARwBiAABAIT+3P5+/UT+/fyY+q75H/vr/Lf9Z/2u+wP9X/3//7H/w/9k/6b9Tf4K/uT+7f4g/+3+hP7l/kv+E/23/EP8EvyU/TD9U/xi+5r7/fwX+3f7UPvU+678Qf2A/YD9J/42/aj+iwCSAhYFAgc9CKgLtg1rDkAPgw2JCwMKngdXBRYDewGGAHP/vf+0/7b/Uf9G/kv9K/wp/In8bvx5/EH9m/4OAK0BMAL7AhoE4wTIBSYGpwT0A2oD1wG4AZIAAf8R//n+i/63AG7+GP5R/Ib7p/td+TH6gPco+7X6Q/x7/cf8nf2C/Df+Tf1f/Sf9BwD2/nUBEQIiAJABE/4V/q/+n/+//tT6x/ti+Qf7HfxT/NT99foT/eP6Cfp2+2n9Kf5YAO7/xv+SAgMChQQABLgCbgLgAUYBOwI7BF8FPweLCCAIBwmjB94FzwZvBlkGdga1Bo0HqgchBhgFeAR2BNwDfgKPAUEAZv/g/+n/sQBNAb4AOAB3AGEAKf8j/gz90PxE/Oj7MPzZ+/H7G/0c/c79WP+D/4D/3v6X/vT+6/6CAO7/uP4/AHX/GP8c/dv60PvT+aD6zfja9+r4V/nj+af3avh6+Ln79PwZ/AH9qvyN/IL9mvxy+1j9OPy3/bj+xf3c/l3+Y/5U/kv+SP5U/gUA+ACx/3P++vzf+3L8Ff24/tsCsAbyCvIOmhDvEhEVGhWCEx0R/QyfCe4FJQPKA/YD4ASkBQYFIQSqArH/ef11/Hv8qPxl/RH++PsW+nz49/bY9+j5n/uz/aL++P4j//j+3P7K/nj/sf9IAWkBLQAIAWwCBAUSBk4EdwKdAl0COQJnAJb8mPo8+UD47Pen+DD4Fvjc+Cz4tPjn+Gj26/Wk+FX6Bf46AKUAogJUAt4BrwBm/xj/pv2I/JD9ov7i/u3+U/2W+7n5yvg6+FL3Mfgh+PX3G/kz+aH6B/2Z/qsCGQgNDTIS4hUMGQ8c1RwcHF4ZahWREZEOGg3eCwoKlggDB2oFiQT0AXn+9vtr+aL3YfY79InzgvMn85L0vvXi9tj4Vfql/Cz/ewBEAQ4B5ABuAZIBvwJ1A7gDUQPQAGP+vv2Q/RP9Zfye+gv6p/pF+t/5gfht+Dj6K/oF+iL6hfli+mX7lPsd/HP9IP/b/pj91Ptw+sL80v5+/vT9mvyg+mD55/h8+NP46vin+Ij5u/kH+Xz4MPc59lT2kPW39LPzrPPt9cv7DwR6DHwV7RuBIdwnZSuSLe8tMyneJJwhbhyIGMcUMxDXDPwIHARgAJr8qfio9QHz1vBv7oTrFOmr50nniuf15yvpvOsW72fy4PRN9z76+v03ApcGsQrGDRkQmhAsDwAOVgwcCmIIfQaRBdEF9QTIA3MB+PxX+U32PfOq8dHvLO0B7V3uBO9o8HLxIPIw9YP4kfrO/CL9av28/l//6/+C//v9pvz4+/z6K/oi+mf6Yvpy+RT4lPYG9pD1lPSL9Cv1MPXL9DT0v/YW/woK+BS9HZIkkyvNMGkzMjQfMvguoCpjJLAfQRvMFQARogvqBukD8v/8+gH2svDV63Doc+UV48nhx+A/4XvjJ+ZQ6Bnq+usJ75Tz2PeT+5v+hwGlBRUKXw1DEO0S+BRoF/MY7hfBFegSeg+2DCIKyQZHA+P/zftU91jzOu+36xLp/ubG5W/lcebq6Fjsle8O8mP0ifY1+Ez5qfqE/GP+Of/r/lr9L/s4+TX3QvbX9Q71ZfSW85bzifQV9Zv1evZN9iz1zfLc71XyY/ulBw0WRiITLGc26j1EQUtBnz2CNywwViegHuwXNhJZDcAJfwdLBf8B7/wI9rjvMury447dedhb1aTUa9aI2ZfdEuJE5j/qY+6B8nz2Z/qk/r0DzQhqDa4RExYdGmgd2h+OIFkgkx8EHqMbPxgBE9wMAAemAEz6TfR07z/s6OmX5+vkuuKl4UriA+RQ5uHorOsE7/7yn/b++DP6vfpc+0T8w/zQ/O/8av03/sj+0/7v/Sf8Xvoj+S74LvZM8/nwpe9h7wDvGO7h8XL7cgfXFFIfESfYMFY4Wzx7P909gTm7M9YpdCC+GWgTTg79CR8G9gOoAY391/ch8S3qO+Nd3YjZH9fB1VDVGNbF2B/dTOLh51jt2PKZ9xT7fP7dAYgFrQoDECkUyxcrGj0cxh62IOUh2yGBIP8dbhq0FZgPGwgwAIr4QfEl62LmY+Oa4vHipONl5OTkz+WO50jpc+v57aXwyfPa9jr5Wfsr/RH+Y/7k/T39Dv25/HX8YfwA/EH7Tvpi+WL4fPa19PryePGl8D7u3uyN8bX64wfeFpoiLC4qOUI/jEKDQYY7oTTYK6UihRwjFzgSUg7/CcAGOQSo/075SPI0677kFN9C2ozWVtTZ0srRs9Ke1fbZC+Cb5oPtsfSK+rz//gQDChsPqhOgF8sb1x7KIJEi8yOiJa8mlCWaIice7xhwE8wMcQTP+nvx2OlC5JPgTN583QneAuC34lblk+el6XDryexS7nbwQfPE98v82QCLAzwE7QM9A+YBQQDZ/uj9qP3r/C/7+/jR9lH10vNB8iTwkO2G6xDpXegn7RH2rQLqEF4cwSd2MwU7BEDrQHA8+zabL9AmtyApG18V+BDlC5gHagTZ/2L6ffQj7sLnnOFL3G3YONY+1pbXNtoo3ifiZuZr6gjuKfIy9gT61/1NAaUFPQvYEIUWuBt4H58iqiSkJSAmziSEIeYcUBfxEb0MoAYHACb5ZPIE7QXpxeW444vi4uH+4WfiWON35W/oXuyP8FP0uPc/+jj89v3H/nH/bAA5Aa8BEQF8/1r+w/3Z/H37nvmb91/2RvXf8lfwlO5e7LXqF+k86LPtyPiyBnMW6CJcLQY4QT7pQHNAoDpVM7sqvSAyGkQWzhImEOkMhgmOBuQBWfvS83PsDeaM4BjdXdt12rbaT9vr3MbfIOJP5Mrm3em/7iD0Lfm1/oMDxAfDC3EOGxETFNoWMho0HXYfFCHuIG0f0xwoGFMSdQxVBqMAYPtJ9vrxm+6u68bowebO5ePlUufo6PrpeeuF7UfwffN89r34qvqs/Gf+w/+YAI8AIACV/6v+gv1R/Ej7wPqY+iv6oPgM9pjzB/FE7x/uT+wS6zzpjugc7lT5ZwjjGI0l4i9zOWU+YT+YPMM1ei4oJzwgLRx9GUQWTxJMDsULeQl9Bdf+WPYP78DoP+IP3f7YD9Z81R3WYtik3LvgZeQb6APssPBA9ff4lPwQAIcDngfyC8QQtBUUGiEeXCHZI+kkHiQ6IvUe3xm+E/8MIQZXABf7vPbL83TxR+/M7BTqqeco5lTlPOUT5pnncuky7Jvv4/In9pn4ffrf/Ov+JgCEAAAAbP9+//n/aQCIAKv/1f1D++732fRr8gnwq+587knukO5u7ZDsOvF2+tEGChQNHkIoiDPNOro+7z2JN+0vfCYTHTEYMBWxEgkQAgxGCYEGFQEW+ujxtepb5VvgWtyC2aHXitfN2PjbBeGA5afpUu0h8ILz6/bn+cP94AGcBbcJkA2CEZAVOBl+HFYfiiFuIr4hYR/8G8YX4hJJDRsHJQHA+1n3hPMU8DLt0OpR6azoaegi6NPnAOhH6YLrh+7E8Sv13Ph3/Fb/8QCUATsBVwBC/979j/yL+7D6P/py+pP6f/rI+Uv4oPYv9G3x4u536y3oneSh4u7nhPPIAqoTKyFRLYk5h0CtQpJAxjnoMdooDx8QGecVdRNVEZ4OEw2dC48HfgB1903uZOUf3TPX49Mn05DUq9bm2qngzuUx6mztAvAH8wb2wfhD/BcAbASGCecOvRTJGXodXiCVItck3CVlJGIhNx1MGNkSgwywBWj/kfly9GDwVO2Y63nqVenY59flCOTD4uTi9OQE6IvsnvGi9mX7nf4XALEAhAA7/1P9/vpk+V75nvpK/PT9S//Q/y7/5vxt+W71p/B77BvqV+iV55zm4OX46p31/QIiEgsf9yqON3M/LkKMQdo7RjRZK+MgeRrcF8YV3ROsERIQVw/7C8EEi/vE8X/oBeCE2R/WYtWO1mPYHNsE33nikOXW6D/sg/C89Oz3u/vt/9oDTwhEDCQQqxTGGLUcgSAbIw4krCIqHxsbwRbEEV8MmgYbAYD8TvhN9Nbwb+3G6fXmFuWh4+vioeIe45Xl2ugV7J7vDvNs9or5f/tr/Dr8qvq/+B73kvaO92v51vub/jQBBgMKAyABo/3J+dX10fHg7oXsXuv16jTq9Oxs9VEBlw/VG/EkyC5jNok5nDlaNXUvvylwIk8dIBxYHBodpBxlG8UaqRdTEB0GtPpi8AnnjN7m2IrW7Nbt2KrbrN/Z47Dmb+gL6c/pHOvQ6wTtnu9p84P4Lv5UBGoLWBJBGKEcOR98IAQgGh5GHMca/hhCFnUSdg6+Cu4GcAJR/Z73b/FY66/m3uOA4oLihePT5TTpJezr7XrvFPHA8jD0/fSr9X72m/cC+bn6lPzJ/SD+K/5I/kj+qP1E/JP6KPkI+Hz2kvRE83Ty5fEf8YrvUPHj+JMEhxK6Hg0o1zDdNr04ljeyMr4srSY1IJkdbB7MH98gsh49G0cY9RGPCCf93fBD54rfptll1+zWFNhf2rTcf+AP5JvlN+ba5UvmKugg6h7tl/Bu9H/5wf6XBIMLuhF8FyccqR5bIGYg6R79HGUawBfIFFMR4w1uCq4GJQJu/NX25fFu7dDqTOmu6EXpo+kv6pHrreym7aTuiu/38GDyTvOd9DT2AvjG+Qv7gvz//Qj/GP9a/hv9nvsN+pX4vfcR+H74sPiZ+cD5p/np95T0v/XN/GQHhBOmHOAjcixxMhk1nzQhMHkrTyYFIX8fux/7H00f2xvWGBEW0g/JBh/7/e5L5QjdW9eg1TzWAdnw3KngjeSZ5tflIeT24nDjbeUq6MDro/DO9rz9sASSC5ERABYXGaEa9BodGi0YBBaOFIATJhKIEEMO3Au3CToHUAQtAZv9Bfoe94L0BfIb8GPufO247UDuNe/W7xrwX/Ad8VzypvNh9LP0YfUq9un2AfeF9hH2DPaz9rv38vhK+kb77/vL/O/8o/zU/Gv8sPvY+s34Vfkq/30IIxSSHuslbi0DM4g0gzK3K9cjUh1AFxMVfhZXGHQapxnTFh4U5w1CBLj4quxx5MLf/dxo3RTfVeE05Lrl9eYx5yflTOLq38vfcuLV5pDsIPNS+voBhgiZDUwR9BIEFKgUQBT6E5gTUxNUFIkVSRYsFmYUrhEdD6AMsQnRBR4BHfxH9ynzsO9U7W7sh+xW7b3u+++i8Hzwr+/37njuHO5Y7kLv3PAi8zv18PZ8+Df5LPnW+Gr4h/gt+XT6EPzQ/Z//rgDdAEQBLQEpAPn+IP1w/gAFww31FoId+x+sIsAkrCMhIcEbXxbHFEYUzxV2GSQbrxv+GYwVfxG6C5QDiPv886/vnu7O7eXtNe4f7p7uFu7S7JLriunC597mw+YZ6DbqEe388G71UPqm/u8BpASrBtgINQvRDOANrg5vD8QQKRItE24TNxPfEtsRcBC/DUEJRQRx/yv7H/hK9bvyQ/Fp8FLwOPBh70buQu1u7PHrt+t165LraOzg7UHwAPM/9Rz38Pin+k785v3Q/lH/Yf/T/lr+LP4G/gP+x/7V/6oA8wCy/9X/5wM/CmcRqRb1GB4bDR2kHNQaRRc0FMoTNRR8FgAamRxLHeYaaxYbEhgNpwap/5756/Zq9jX2Qva19V/1GvWI8+Xxk+8y7APpIeao5G/l6uY96Xzs2u849E74U/us/SX/nQAOAjsDbQSaBS8H6QkqDd0QRhTPFcYVYRSIETwO/wk8BSIBIv6E/A78Kfwy/Dj8f/si+uX36PT58RHv5+yW6yXrluut7B7uze/q8QX0+PUj96L39/ca+Iz4/vhZ+bT6rPyb/g4BJQN+BNEF3AViBWgE+wHrAE8DCQluEKgVGxeIF0MYPxjRFsMTUxGJEawTExYAGO4YyBeLFLkP0ArYBk0D9//V/cX9Lv+RAD0AAf5Z+yj4j/St8Ejs7OiV51TnR+hD6kDsE++h8SfzyfTt9dX25fcQ+ff6yv2aADADFgY6CcEMCw+hDxsPuA1fDCMLnwmcCDQI4QcJCN8H9wbKBaADpQCb/Rb64Pbv8zzxXe9H7hruce7F7mbvfPBM8drx4fHd8ajyzvMe9WP2xPdV+SD7pfxd/oIAjgJHBAYFHwW9BLIDZgLAAF3/X/7z/IL91wFNCckRcRejGCgYjhaiE0wQjAzJCvsLdg5eESwUGhaQFqAUZxCkCygHSwMTAIb9J/yn+1H7x/rN+W/44vbO9HnyOvAq7jvtQO3l7ZnvCfGe8o307/VQ93r4V/mI+t37Tf1Y/4YBxgMNBrcHVQnJCokLtAtUC2wKhgmsCNsHSgeXBp4FRQS+AggBfv/4/ab84fuL+237M/vs+ij6OvmX+Nj3jvfD9/f3z/h/+bf5OPqj+jj76Ptp/BX96P1N/qb+Df83/2T/Pv/b/uT+I/9C/0b/D/8G/wr/1/6J/jn+F/7x/Yn9Tf1C/Y39kP7M/z0B5QIhBAsF5wXjBb0FuQXlBawGZAewBwUIiggnCcMJJQo7Cv8JbgmLCIEHhQauBaYEOwOmAWAAT/+F/i7+O/7V/tH/rAD6ANcAFQAE/9n9sfzf+6X7xfsn/Nb8wP2Z/vn+mf6v/c78Gfx3+wn7NPvH+9b86/2q/lr/rf+b/13/Jf8W/xP/A/8R/0L/WP9S/zf/Gv8I/7b+Mv5L/Wv8wvtM+xX7OvuI++z7d/yS/Cv8aft5+hv6cvpT+6H8Ff58/68AcAFuAdYAw//r/l3+D/51/hH/4//zAAMCugI5A/YCfgInAnUB5gBeAEoAagEyAyUFLQcKCaMK2wv7CyIL9gnWCBsIjAflBpgGdAZABvUFTAVmBKQDzALxASQBCQAa/zf+qv2S/Uj9EP3L/Lr86PzZ/EH8Q/sd+hr5h/hx+Mr4sPnY+iL8gP2Q/nP/AQAdABMAFwA4AKgA8wDiAL4ARgC4/yn/sf55/tf+W/+o/7//df8D/4f+6/0n/bf8lvyR/Kb8o/zU/BP9Dv21/EH8Mvxh/Jb8ufzL/Af9Tf2A/ab9uv2d/Zv9vv3O/c79kP1q/Zb90v0r/mP+hf7Z/mz/KQA7AWECPQPQA+IDogN3AxUDoQJsArUCqQO4BHEFAgZiBrcGYAfsBzwIigiGCGUIEAiyB1MHDQelBkEG4QVbBbYE7gMcA24C5gFKAcwAdAAiALT/9v70/fH8+vtc+z37ivv6+2v87/x1/dL9yf1q/db8V/wO/Or7Evxa/Mv8MP1P/Vr9X/2s/QP+jv74/jf/W/8c/5n+0P3x/Ej8IPxr/NL8F/1h/aP95v0T/tX9hv1E/R79F/0r/Vr9pv3v/VH+5f5f/6b/vf+X/2T/bP+L/7H/4v/w/wUAKQBhAH4AmgC1ANQA+gApASkBEwH4AAUBdwHMAQYCXALbAosDRwThBDMFhQXsBWsG1AY0B3gHggdTB/wGsAZkBvMFSQVZBFYDXAKCAaoA0f8T/4D+F/6m/Ub9F/30/Or85Pzg/BD9E/3S/Jr8WPxh/K/8CP2E/R7+pP4R/0L/RP8e/+v+wf7Q/hP/Qv+o/9H/vf+f/z7/8P6X/n7+ef6Z/uT+/f4a/z3/Uf9N/wr/rf5Y/jL+Mv4w/jT+Rv6H/rz+yv6o/jf+x/1E/df8efwk/Ob7A/yd/IT9pP6p/3sA+AAQAeQApgCoANYAMgGkAScCrwIcA18DhwOoA60DogOVA34DMwPKAkgCtwFKAQ4B3wCqAHUAKwDj/4X/Rv8I/8r+hP5f/mj+s/4e/3r/4P9gAN0ARgGjAaYBhgFhAUQBgAHpAZQCZgPrA2gExATIBK0ESwSdA+8CGAIpATQAZv/F/jf+2f2J/Xv9d/15/ZT91P3//Rj+IP4D/sn9nf1p/Uj9S/1s/bj97f0r/k3+Z/5s/oD+lv6X/nX+Nv7e/WX9+vyE/Db8DvwA/C38dPz2/I39Jf68/iD/Uv9f/2j/R/8y/1L/w/9MABUBwwFUAuACHgMeA9ACcQIiAvQB7wEuAqQCBgM+AxgDnQLQAfMANgC9/3z/g//D/xIAbACoAK8AlgBpAFoAbAB9AI0AjwChAN0AKwFuAagBzAHdAQYCGAJIApkC3gIGA1YDmwOkA3cD9AJsAv8BvAF7AS4B7wCfAD8A1/9z/xP/CP/5/vn+Hv8y/z3/J//2/pb+MP7Q/YT9Zf1P/Tn9A/3C/In8U/wS/Nb7qvuI+2P7Qfs9+z/7gfv1+3T8G/3D/Vj+5f5L/47/lP9N//n+mf6L/un+pP+EAGUBOwLOAkcDeANYAyUDxwJ1AisC1QF3AS0B5AC3ALwA6wA7AZkB6QEgAjACBQKbAQ4BXADI/3H/Xf+x/zAApQAOAVEBWAFBAeoAdAAZAMz/v//e/yYAbgC3APQA+AC6AEQAv/8+//j+yv7g/j7/ov9hADsBCAK+AgoDEQPrAqECXQIYAvEB9AErAnACjQJlAusBWAGWAJ3/vv7v/Tj9xfx7/ET8Cvzm+/r7P/yS/CD9mv32/WX+of7D/r/+x/6//qb+fv49/g/+Bf4B/if+if4p//D/jwAkAcABSAKEAlQCzAETAU8AjP/5/pv+av5u/p/+3v4T/yP/DP/B/m7+Pf4n/kT+bv68/g//Y/+t/9r/AQD0/yYASgCCAOIAMgGYAfIBKwI3AlQClgLwAh4DPgNHAzIDFQO+AmMCHgLmAb4BqgGWAZYBmwG8AQoCPQJfAocCkAKFAmwCQAIBArEBWgH6AKMAZQAwAAwA8P/K/47/LP+W/vv9cP0b/f38Ff1I/YT9x/3x/QP+A/7U/bP93v1U/vT+gP/7/xcA2v+M/x7/qP5c/lH+h/7c/iz/Y/9W/xH/vv5s/iD++v32/Tf+fv6x/uv+Bv8a/y7/Kf8q/1H/if/R/xkASgCJAKoApgCSAHsAWgA4APD/wf+L/2T/S/8w/zX/MP9b/3X/c/+d/8H/9P8/AJ0AEQGqAU0CwQIEAzsDWgNqA1oDRwM+A1EDhwOdA5ADdQNLAyEDzAJWAq0BDgF5AAkA3v/e/+v/EAAZAOD/df/D/gX+Uf2u/Hv8nPwi/cf9av79/mH/h/9q/yz/6/68/pL+n/7i/lj/0f9pAMkA6gDfALEAcgAZANP/mf98/1b/W/9Y/z7/9P6O/h7+7f3X/cD93f3Q/eD9//0a/lT+lP7b/kv/sf8mAMIADgE4AT8BNAERAc4AbgD5/9n/7f8ZAEoAYwCEAHAAQQAFAPn/+/8DAB4AOAA9AD8ARABeAHQAlADDAO8AIAFCAVYBVgE0AeQAiAAZALz/bP9C/2P/lf/u/1UAlgDLANAAswBRAMz/Tf/w/rH+kv7S/hH/cf/c/1oAwgAcAT0BIgEQAdAAnQBcADIAGQAyAJEAHgHSAWMCuAKZAkgCpAHUAAAAQv+//oD+ef6h/uD+Fv87/0f/LP/y/r7+c/40/tD9kP2Y/bz9+v0c/iv+T/6N/pL+r/74/nH/CgC+AEoByQEuAiAC/QG3AWwBLgH9ALEAbgBjAEMAJwAJAAMABQA0AHsAswABAS4BJwEZAfgAuQBlADQACgDt/9D/v//B/8j/zv/K/9P/0P/l//3/CgAVABIADgAAAAoAxv+A/1j/Nf9S/2r/c/+V/8r/HQB+ALEA6wAKARkBKQHrAJoATwAVAPn/JwBXAKUA/wBKAZQBlgE5AbEAHgCF/xH/w/6N/o7+w/77/lH/lf+//8z/r/91/0n/IP/p/tX+uP68/v/+QP9u/5//tv/R/+7/3v+g/4n/av9d/3j/bP9v/3j/eP+D/47/6/9eAN0ASAFuAaYBowFwARUBiABeAFMASgCdAO0ADAEnARkB4ACRABMAjv8a/63+l/6x/s7+Kv+D/9D/CQBBAFMAUwBDAO3/5/+//7j/6/8QAGcAqgC8AMAAowBjAP3/pP9j/x7/HP8p/2H/sf/g/woAGwAVAA4AIAApACsAPwBXAG4AiACPAIYATwD5/7L/h/9h/z3/Uf89/xj/Kv///uX+wf61/rH+3v4c/x7/R/9m/4D/m/+y/73/2v/a/+v/FwA2AF4AjwDbAAgBOAFsAZkB0gH7ARcCKwJEAkYCLAIVAtABdwEpAckAcAArANr/rf+O/2H/R/8g//3+0P6h/oD+U/49/in+Pf5d/qj+8P4n/1j/m/+g/5X/pP+f/+P/WAD9AHIB9gFAAkACFQK1AVEB/wDUAKwAqgDSAMcAoQBTANn/ZP/Q/mf+N/5A/nn+1/4Y/zv/MP8j/+v+qP6S/pL+5/5P/8z/PwCmAO0AKQE4AR4B/wC+AIIABQDi/6v/Yf+H/4f/lf+r/7H/sv+f/3P/b/9R/0b/Uf9R/2//fv+2/+3/WAC5AA4BUwFjAYABbAE2AQgBAwHgAOAA+gDdAK8AowB0AAwABQDp//L/HQAnACkAAQDu/9z/qf+b/5n/uv+4/+f/BwBpAJ8AXABMADIAEwDp/3X/+P7w/h7/Of9m/6j/OAAcAYsBwwHFAocEmQX0BLYC1//A/dT75/nI+Kv4EPqN/PL+/QD/ATcCxwFMALH+IP3W+2n73/vX/Kj+bACNAfgBBQLxAR4BGwA+/9D+v/43/6T/FQCuADQBhAFzAfwAeQAQAKn/fP9o/2z/lf+//wcAFQBjAIkApgB3ARoCLAIuAswBTQF1AZ8Avf+8/3H/5/8kALH/qgBRASkBgAH2AAEA9/9x/+v+r/6v/sP/lgBRAUgCZQJhAocCwAErAYIAev9J//b+HP/Q/97/bgAgAcsA9ACJAIL/g//S/on+6f56/gb/xf/R/4IAiwArAFEAr/+C/zD/qP4e/wT/Df9h/yH/wf/MAF4AdwBjAHf/ZP8M/1b+NP6i/sH+Uf+O/97/XABaAGUAyP87/yn/5f4n/3P///66/5gArgDSASABXAC3AJv/pP8p/xj+Nf8kAMX/zAATAXcAJQGcAAUAO//r/gb/0P5j/9P/SAB7AbwBnwBcAPb+jv5h/qr9I/55/qL/5gCYAYcBowHvAYYBvgC//+v+kP7p/h7/+/9lANX/JwFBAdkBxwEHAfQAFQCNACr///ym/Xr/qv4R/078WP9OB6AHIwZfBNUD7wdwB5D+JPx1/H384Pxw+sb4Qf1PAYkAY/+m/8j/OwHoAJ39qv1k/97+GP8p/wH+AQHQAbMBHgITArUB5QNvA6EC1wIeATIBAQHw/yL+VP6z/cX+ef5y/SX+Xf5o/lH+8v21/S7+df7p/jb+c/58/6T/GwDkAEMAAwEIAj0CIANjA0EBNQMjBEgC1wHT/q0DzAnvB/AG4QSoA3kHkgIb+hD58vaO9l/2f/Lh81n6r/0w/Xn98/yE/on+PfsG+Sb6e/yj/Xn+0f8qA88FPwdKBpEFhQWSBDsDcgHLAK0BCgI/AnECVAMTBO4EygMqAysCHAJsAS0APf+m/hz/+v2C/c38E/07/Vj9dfyu/N39Ef43/r79Bv7k/iz/1/7r/gMAOwA4AAkAQwB3AcUC3AJ1AmoCHAKsAaoApP+2/9kAiQBH//b/7QL3A3EC3P4M/h4AVwCu/Kz5efpq/tQA7f5R/Qz/qALrAlcAfv3U/Tv/BwCk/Rv90P77/yIB7wC6ABcC9gIwARMBgAABAMH/2/7V/tr/fv+8/9H/PwDXABwBeQAVAa0ByQB1AA//A//B/tL+1f4T/6n/GQElAXkBjQJuAhgDggIQAYkBnwLXAmMDxQKbAoMDAATMAoAC/QLKAqICcgFU/2H/R/+x/o3+Nv6W/mT/fP+Q/3X/WP7g/dL8x/xE/BL7cPq3+uz7U/1n/gb/xwAlAlYCMgJTAf8ANAGxALj/yP7H/fj8PfwU+1r7TPti+9T6nPm7+Fb45/db93P3CfhX+Zb6DPy6/Pj9BP9d/9v+i/2q/FX81vvq+wr80Pyo/ZD9+vyb/W7+QwA/ACn/if9rAEYCvAK1AOQCrQzwFKQbbBsrGTUe9iNuIucdMxfdEsUT4Q6WB2YF2ARvBXoFfgCY/KX6mfW08KDtNOp66NjmWOVM5yvsr+5Q8Wf0Qves+h38M/vo+3H+swD4Am0EIwZBCUsMQw5yD3IPkQ7RDDAKgQcjBYUCAQA2/rP8p/tZ+iX4SfYP9aT0vvPW8trxNvFp8eXxSvKG8pHysvLb85b1N/dX+Cb5XPrz+6H9c/4a/9r//ABJAq8CXAIcApACfgP7A1EDKgM+A+AD9gNaA8oBdwCt/wj/Kf/e/4IADgHBATABegRCDP8TLRlpGcEWIBljHbUcpxnDFPoRoRMxERMMnQm9BwAHmgWmAFP8uPgr8xjvee3k7GjsP+tP6l7sj/Cu8pjyefOL9WL48/pn+wH9AQH5A+oFMQc2CJIKlQy6DOUMgwwcC9AJ3Qf5BXEE5AHQ/ub8Z/sv+kn5svd89vT1DvUO9M7zU/Mg8y/zKfP689T0BvWJ9S73dvmw+uP5FPk8+gH9QP9L/+T+ov+8AC4BdAC0/1cARgFeAbEAGP+W/Uv9yfy8+0z53PWq80/zWPRE9Zb2rvgU+7X98QDjBW0PbhsFJRcrLyzdK+Itzi4BLbgoMyF3GsEVtQ9hClsGOwKX/6r73fTK7d7mueAE3Y7bJ9uq2xHcW91G4WLnhe3E8iX3/Poc/34C1QRFB0EK5Q17ERcUIxaZF7AYeBlaGd4XKRXoEAIMKwfZAeL8VPhC9B/x0e5G7XPs+Ou36/rr6+w87kTvo/Be81D3Afzn/z0C6wNmBY4GzQYfBpAE0gJyAXsA/f8KAEQAYwCjAPf/aP6A/EH6PviU9hX1//PA8qLwDe9v72/xkfOm9EL0vPOz82zzafSr9qf32vjh+s782v/xABYDOA8gInkzED2rOro2WDimOQY56DMjKJ0ctRHNB8oDUwDh+sP2i/LG7xPtieUd3X/YcNje22LeG94I3iLhoOhD8m37OQK/BVEImQrsDA4QThHsEN8RmBPFFcYWehR7EoASExIOEB4LHAQa/hD5SPX+8vHwku7k6yLqV+qU6yDtBu/P8aT1Rfgk+bb5wPpu/QEB4wNtBsQHgQdlB8IHgAgHCeYHlwUNA+f/w/zq+Wb3QvXm8vzwoO+07uTtLO2J7W/uzu6k7jfule5/8DTzbPaM+cn7av2C/ir/HP8P/1L//f/bAa0DUgQWBOUC5gZ8FRsp8DncQII9WTsSPbs84jjiLhIhpBRgB+/7Lvbs8TzuIuti6EPorebE4AXbAdmW2yDgj+Il5DPnT+xj9Cv+6gd7D50SMBOCEwQUYxRUE+MQMQ+eDlAOdg0MCysI4QaOBmAFUQI4/cj35PNF8Y7vkO4y7cfrbutw7MHu0vGm9Fb3Ivrm/Jv/YQLnBNMGUQhBCSAJyQdqBT4D7wGRADD+xvqQ98X1LvVI9WX16PRY9OHzzvMQ9A70Y/SS9cz23PdF+En4Dfms+az5B/pI+nL6f/pO+t362/tV/B785vtw/Lr8Tf13/zsCbAR8BPcERQ4sICoyZzxHO3I2DjYiNuUy2CueIKYVQAuF/872R/Fh7K7piOg+6PHn++Nk3n7cDd/k4+Hn7Okp7B3wIfbg/RQG5Qz3EHkSBhMeEzkSKBCODZALZQotCcYGIAP5/9z+5f9nAZwAOf06+fT1B/QF83/xue8s7iPt+e2a8OvzePfL+tD9sQDvApUELAbYB4QJ0gplC1QLlgpKCQMImAabBI0BOf2Q+PH0AfOR8vzyRPOu85j0//Wy91v4ePf79aH0O/SA9IT0zvQB9tP3Hfrh++L8mv0P/nH+ev6o/dD7uflQ+RL63/vO/ssA2wGEAckAAwgtGMspsTaiObw3qzldOyo5fzOlKFgczQ8uASr2Te9g6HzjTuG64XnkHuQr4Tbh9OR96m/vMfJz9E33nPp+/9YFcQtgDqUOBg4jDg0ONQwnCcYF5AL2ADT/Yf36++P6qvpw++37f/tg+jr5JPl0+WL56vj39zP35fYB91v4dvp9/NL+OAFYAwsFywVnBtgHTAlnCeYHOQU3AkYATf9T/iv96voG+NP1WvSf8+TzZfTH9Cz1SvWf9bj2ovca+H74iPj++AL6j/rE+s/67Pq5+6H85vyq/Fz8SPzY+/P6y/nY+C/5Lfop+3v90//XACsB2QJfC8Uc0C9nPD8/cjurNys1LzGNKl0goRJEAz31T+xO6UPo6uYH5+jpfO3D7kjtVuym7l7yvPT79cP2q/ci+v3+NQZ8DZYQZw+eDasMjQsQCMUBj/v/9vTzk/Jy8hXzUfQn9rn5Sf42AYIBPQBk/3H/of4//B35DfYF9Cv07faL+5f/0AGQAy0GXgnRC5AMEwyfCkoIQQZLBSYF4ASkAwoCdABI/lP7UPji9R707PJu8ovyAPOW85j0c/ZL+Ab57Pjq+GD5Bfp4+qP62vru+jP7/vvt/Pr8eftr+R/4S/eJ9k31ffT89GP1PvYC+Tj8Rv+NAHECPg1dIDw0qUHNQ8VASj9ePTU6KDSQKEEZPAfS9QPr+OQD4JTci9ut3pjjKOb358XroPFL+O38bP+RAOn/wf8PAjEGAQqACisIdgblBc0FLAWzArj/rvzN+TH4FPev9Yv0YfQj9o759vyV/1EBgAINAzICCQBP/EX3qPJz73XuBPCs8iD2cvpz/isCrgUrCf8Msg+sEBQQ9Q3iCngHbQRxAnUAbv2V+fn1cPOw8kjzVvRE9Q71TPQ99M700vUa9yH4Ifkk+sn6wvvo/Cf9OP1+/Zb9t/2a/G36Cfme+L34Vfm/+Sb6B/tB+y/7a/wV/k3/xf5b/x8IKxq/Ll48/D7HPNE71DpsN2MvHSP5FBwE5POh6XHk5OFV4Ezgr+Sc6mHtde6B8O/0ZPph/UD+5/4w/8b/AwKuBZ0JPwslChUI2AVUA7L/bvsh+OL1J/Tv8nvxrfAb8lH1xvrWAO4E6gZ6BnMEogLWAIn+y/sh+MD0u/Lx8ejyaPUd+cr9iwLTBnMK9wyZDlAP3w7GDb4Luwh6BSMCCv8t/BD52/Vc88nxBfHN8AXxj/Gw8n70ovYS+TP7OPya/P38MP1l/QX91Pvx+p76G/tw/Iv9Cv61/WX9Rv4B/2z+x/tq+Gb3ovhi+sP85v3d/fT85Ps0AkQSDCXAM/A4WDeFN/03xzV8MU4oPxvpC0f6N+3q5nfi3N9V3wriyOip7kPyzPYp/JkBgQU+BjwF0wKL/8n9CP5x/2wA7v+m/08APwGkAUMAuP0o+5f4uvZL9Rz0QvR19fn3vvuy/4kDRwYDB2sGrQQaAjX/hvt+9wj0y/Ga8S/z0PWB+Ub+HwTACWgNEg9FD54OjA2QC20IhQS0/8D6jPZK86fwju647aDub/CL8rj0yPYE+SL7CP05/7wAtQD3/7/+j/2A/FP7dPoo+j/6ufrC+5H8lPwF/MD7hvul+pn46/VM9CvzC/Iv8971wfhV+nD6lAFJFBsriz1tRB1C4kB+QKM9VjjtLHMcMAq69mXpUuSW4EXeW95i4c/oMO9Z8r/1V/nQ/HP/yv///k39Jvsx+3z9rwD0AjsDKgP2AxYFRQV+AwcA4ftO+BP2lPSs88Dz9vRr+Jr9xQLlBrUITwgQBygFGgJq/R/36PBz7OLqOewN78/ycffb/CwDiAmkDp8STxXrFZQUZxFYDC8G7v8Y+kr1yPEL7/Tt8u4W8eHzxvZU+Ub76Pw//mj/iAC5AA4Aev+t/jT+1f4G/xH+Gfwz+Wr3WfcT9zv2CPXk88vz//Tt9hb5svuG/eT9Gv9OAHIApP9Y/VoAwQ3RHnctmjTLMyA08zWhNWk0jy0oIJMQ9/9u9EPwQuxi6IjmeObJ6nHvofGC9Df33fnJ/Br+Nv7v/IH6YPkL+qj7Zfwf+yL6mvo6/Dn+gP5u/SL8lvoi+nb67Poy/FH97f5IAasCRwMMA+kBVQGCAKv+T/wd+Vj2GvX09ND10fa494/57/y6Ae4GEQugDQ8PuQ8sDyUNrwmBBSUBIv2G+SP2OvPf8MHvhfCK8gb1nvdS+iT9KwDkAkAE5QMKApn/fv10+wn5ePZw9Arzk/J/83P1b/do+Mb4rPkg+yv8JPuI+S/5cfmK+rH8bP6//2j/GP+eBlYWMyepM6I3SzcIOZs5gDctM38pTRzPDVH/HPaa8JzqUOb/5Cjni+tG7sHvbfLK9Xj58ftj/I37G/k+9zD4kfo//U/+pv1s/lEALAJ3A50CnwCo/qj80Ptp+5r6W/qT+rv7Yf1N/gz/ov/u/1UApP8O/rn79/fg9BfzXvIe8zj09vWu+Xf+vwMXCUINWRD8EdkRXBBiDfYIgAO1/c34+PTq8dzv1+4T78Dw9fJ+9Q34Hfrv+9X9l/8tAD7/Vv19+8D67PoX+677KfzL+9/7hvyL/U/+Dv2g+pf4OfcE91j3sPhm+tv64vv0/RAA7QF5AaIDHQ6hHC0q9zFrMvMyzjTFNAM0WC7qIrQVBwfq+8727vFE7Tvqkeg061vuTu/n8FzyK/SO9hj3QPfZ9jL1dfWV95r61f2z/u/+hADdAREDHgMkASf/EP2l+8D7sPtp+1H74vsF/v//rADDALb/Z/7//Fv6lfd99PnwM++w7xfybPYU+y0ABgZLC/oPURPXFPsUMhMtEHMMuQfKAjL+Nvq099P1JPTY8mDxjPD18CjyE/TZ9ff2cfjs+c/6j/vA+5T7K/te+vH5kfq1+2f8x/yP/Pr7qvua+4T8Rv0I/Rf8lvq5+ef5Hfq8+639Cv6G/aH8n/9GCx4baCmBMvczKzTMNeQ0ozKNLBQh7hQgCFj9Tfgr9EHwne4L7h3w0PK18qHyLfMv9Cn2yPZN9oL1cPRd9fD3wPpR/e39//1o/7cAtwGjAWb/pv3N/Pj7Hvzx+6X7Q/xy/SP/OAGWAhMD8ALyAVUAqv3J+YX13/Gl7yHvLPCI8in2Nvv9AMQG6wupDwgS8xJPEtIPrQs4BiAAtfqi9oTzNvF070DuWe5/73DxcPRk95X5Ivs6/IT95f6V/5T/BP/i/WD89foS+sj5+fmp+tb7w/x9/Cj7l/lQ+Kv3r/aF9TL1NPS69JL3b/oI/fH9gP87ClobGytYNow4sTdgOqc6BDkmNV4p/hpuDEL+ovdG9Crvjewn6yvsjPBZ8hnz7fSh9TD3Wfj59mr1qvLd8J7yd/WI+K76o/vX/bUAIAOvBJADZQGf/yn+Gv6o/XL8A/zm+xz9tP+8ARYD+wJqAQUA+/23+vL2HfOy8PnvCfBc8e/zyPdE/YUDbgn5DVMQKBE0EfAPOQ2wCA8Dn/1/+P/zNfBW7fbrOez57dbwbvQm+Nv7Xf9mAsQE8wXnBYoEgAJEABX+IvwC+lb4bPdU91L4R/mj+Xj5evi09yr3l/ZU9jT1RPRs9cb2xPis+QX6RgIbEiQjbTH6Nj83NTr+O887WTqMMRkkTxUJBkL9L/nA8zPvNOyE62Pu1vBF8jv0hPWi9i73c/aY9Ffx3u7V7vnwEfTF9Yv2jviC+2T/1wL2A+cDYQJ7AOv/Qv+4/pn+//2v/k8AGwFTAiADmQK3AUb/kfvB96XzsPCi79Pv+fCC8g/1kflU/5MFFQuVDnAQpxCrD80NbAoEBmEB9vw8+Wz1j/Ec7yju0+7z8HvzvPYC+mf8gv6AADIC6QIpAoIAfv7C/IL7Vfr8+Qv6o/lb+Yz4kPfD9rr1rPRc82nyYPK18r71JPpg/Lf8Evr3+skHkRkQKrw0WDbiOEE+jz89P9E5dSwPHhQOrgBg+wr24O/d7KPrYe5r8VLwyO8S8EfwPvHd8FDwGPAE707wE/SO+JL8Cv6b/1oCVgSeBVgE/wBx/vP72vql+6f7cPwc/tX/CgPWBWkHggiMBzwFOwKQ/Wv4iPOe71Tuve4H8Ivyr/Xo+RX/rQNyByQKugu9DPYMaguLCBYFfgFx/sv7yvix9RvzQfHP8KnxV/O+9R/4APqo+zD95/5IALEAQQDv/u381vqp+B/3w/br9rL3Dfgj+H/4lfiH+Jf3WvZK9X3zd/MG9XX21/fZ9jH57gbjGVwsCDm9O08+D0PEQ/JCzzyxLmofAg9uAQX8b/ec8kvw/+5c8Rv06PKp8f7wivAH8bTvse287Ovr8O2h8kL39fqs+1P7bPxR/Rf+7/11/NT7nPsp/Mf+ggGtBLMIqwvDDUMOmQzrCtAIiAWGARv8YfZt8bHtWOyE7Hftu+878/v3SP23AVkFKAivCTYKqgk4CNgFDwIG/oP6mfdq9bDzefJl8grzFfTr9Tz4sPoH/eL+KQDMAFEARP8u/t38nvsQ+kv4O/cf95X3gfjh+K74NfhC9xX2ofT/8wj0pvP/9Fb3+/jc+dP4YfyWC0Af+S/MOEc4qTgSPKE8mDuINZUomhowDDsBif0Y+s71SPOG8Try2/Kr8IXv1e9v8DXxLPCV7h/ulO6I8dL1OPlR+5P7t/sg/X7+m/+o/xX+E/3i/NL9SAApAj4Eewd1Cs8M3g0jDScMhApTBxUDrP3l9+PyLu/t7Yfuju+38cv0svhE/cUAoAMxBngHgge5BukEaAI1/7v73/hG9m7zB/EC8KXwZPLL9KT3vfpl/UT/wgDiATYCJAHn/s78dvv8+tT6b/ov+jX6efp4+v75Kvl492j1+vJc8aXxIPIM8871UPjx+hP8gPxMBRYW5SbfMhc1pTIZNTk4HTmtN9UuvCL9FnALmQV+Azf/UfsA+Mf1QvbM9PHx7vAq8Jzv0+6x7EHraev07NrwPfVx+Of5Svrd+8n9//7v/iv9DPy8/Df+fQBmApQDpAWCB/MI6wlDCTIIBQfGBHkCRP9c+734X/a19H3z1PFK8UPyhvQl+Ar8Pf+dAqAF8weuCToJPAeHBJoAqvx0+LHztu8g7Vzs/e3z8BP0xPdn+5b+WgGEAmUCRgF8/8X9SPxp+9b6KPqe+Wj5yPm9+gv7gfrh+YP4X/fQ9X70HvTh8orySPT99Zv3/fcA+6gIexx1LXA2LTZKNjw7fT76Pb43bivKHloSHwiOA7z/Rvto+MX2ifcs9/zybe9A7nzuN+7W6orn1ubj5y3rjO/482/48fpe/Nn+hgAkAVcAFf7O/Zn++/5GAGkBrQPAB18KxQvcC6YJvgcPBicDiACj/Pn3MPVO87vyQ/Np84n04vZe+Tb8av44ADMDEgYtCLUITAYVAyQANv0J+0L4wfQX8srwVfF98xj2zPhG+yD9d/4G/6v+Gv4i/WP8CvyR+4/7O/xK/UL+d/7J/Y/8G/tp+QT3F/RZ8Xzv4e8q8Vfy2fSV9x/6L/yG/cIFcRbrJy00lDYDNN81sjnhOrM3xy2YIYwWVgwhB6IFXQNrAHn8qfkW+b32TvKe7lzsRute6cjlMOS/5Z7p0+6A8yX3rPme+qz70P2Z/ycAuv7H/AP9/f77AY4FPAhTCokLKQteCrEIqwUeAr79l/nR9pL0ifN78yvzbvNh9E/1BPcH+f766/xA/pv/ZwFGA54EbQRmA44CPQGF/0b9EPpt9/n1VvUV9mb3mfg++tL7Af3F/bX9xfwr+zH5bfc59pL1TfXZ9cz31Pqa/XH+Pf0m+974Zve29dvzV/K78BnxCvS092L7Tf3OAIwMwx4xMFA5gDfkM1w18DjwOdoyLCYXG3ASwQzfCXsHaQZxBVgCKv8H+6H13fA77ProweZ549rgSuCz4proC+/H85L2WPdA+DP6x/ty/C38AfyE/VgAlAPfBgEK4wxrDxQRThF2D7wLggeSAxkAa/wl+Pj05PMy9HD14vaX99f3nfeH93H4sPkf+gf6Zvql+1X9lv5E/3f/O/+C/jv91PsU+vL3DfYO9RH1vvXI9jn4k/lk+jr6b/k1+ZH5GPo2+or5D/k3+Yz5efqw+5/8RP2c/B37l/lv+Fv4TvjY9yb43vi7+sD8Pf3w/7kIJBdCJ8YwrjHyL5Av1jHUMlouRiY1HRwVzQ93DCwL3gs1C5MI9wOY/Rz4ufPg7xftrun/5SnkQuQY57rrwe8d81j1LvbO9h/3nvcP+cD6j/xE/of/LgHQA4wHDwxMDwkQhQ7yC1oKggkgCNMFQgLk/mv8efqT+Vf5Sfl4+UD5rfgN+FD3kPYD9l31HPV+9Un2tvcv+TP65fof+zz7H/us+sD5Tvid93b3hffO9+v3k/iK+ST6p/rY+rX60fp9+uH5+vkJ+jz61vr4+qX7e/yz/Nf80Pyj/C398f3O/pn/8P82ADoAQQCSAcsF7A3aF+MetCEDIogiHCWJJn0kJCBjGn4VAxLBDtcMKgzlC5ILXgkfBbcAxfxI+of4S/UJ8YPt8uv27P3uSfDR8AnxjPE88qPyt/LZ8oTzMvVP91T55PsI/9MCewa7CJQJuQmYCXkJ9QhTBzcFUQO+Ad8A4/98/pv9Qf05/Xz9Ef0J/Lf6Hfkm+BL4Zvjw+ED5lfk6+vf6Wvwp/mP/9v9G/wr+Lf1M/Hf7Cfv4+r771/yQ/RX+Rv6J/vb+zP4K/pz8dvqt+JP36fb79i73nffz+H/6F/xL/ab9wf0e/k/+Sf6k/Sb8G/ul+vf6ffw9/y4DtQf4CqkMaw67EG4TnxTSE+kSZRIXEs4RVRFiEVYSYBJ5ESIQgQ79DHcLyQnuB3gFUwISAA//A////qz9B/ys+kz55/cs9pL02fOY88fzCvRT9Pr0NPYo+FL6EPz2/I/9O/77/sH/9/+r/1v/Sf+L/0YA2QAuAYsBygEOAk0C+AH8ANP/lP4V/nD+Uv87AIIAYwDO/yH/kP76/Tv9RvzH+kP5Lviv91D4g/nH+nv7B/sb+kP5Sfgs9871QvRj8wzzK/MO9BH1ZvYU+Jr5Lfuq/LX9Vv5A/q/9Y/34/Kz8e/zb/N7+wwEnBPsFhAdqCX4M+Q7wD/4PmA/CD0MQpxBaEVoSQRMsFH4UfhSSFFIUmRNVEjMQxg13C2AJwAcSBp4EeANxAjABLP/H/NL6TvnB98z1P/M88Xrw4fD+8d3yHfNg81H0yfWA9/P45/kS+/b7Vfxg/JH8gv3t/uX/9/+t/5//KwARAZ8BtQFjAScBPwE5ATgBJAH/ANsAfQAKAID/5f4i/iT9Kfyj+5H7lvuC+wn7g/qW+gz7Y/sd+1f6cvm5+Cr4avek9kf2kvY897b3v/fa96T43/lR+7n8kP0u/o3+kv7H/hb/T/+S/ysAXgFWA1AF3QY0CI0JxwpoC2wLXAvpCxQN2g0bDi8O9w61EEES0BJqEp0RIBGyENoPmg4uDe8LIwtYCjoJAAirBlcF7gPQAQ//OPyT+WT3SPX+8k7xhfCe8ITxQ/KL8tjyf/Os9On1C/fM95H4b/lB+i37F/wt/SX+2f5d/7L/EAArAMj/ZP94/yYAAQFzAXMBYQGhAfQBAQKfAQEBRgCt/1b/A/+F/sX9A/2C/BL8Q/sx+i/5Zvja9yX3SfaW9Vb1i/XX9Q/2KfZS9uL22PcP+Rj62/qa+1j8Z/26/q//TABpAAMAif9R/8b/VgFxA8EFoAePCBcJ3wngCuILqwzTDNoMvwxsDF0MzgwEDrQPuxB/EJwPsg6FDpoO9Q2tDBoLjwmPCCQIAwj+B4YHWwaOBCMCav9n/Lv5rffZ9V/0L/Ol8mPzzPRu9r33xvi0+XL6qfpm+kf6ufqs+2H8f/wt/B78ffwn/cr9Bv4F/gr+Dv79/Q7+c/47/0QA+gA7AdAANAD0/6v/X//F/jn++v05/qL+yv6f/j3+pP0T/Uz87/pD+br3Efft9uv2WPaJ9Ub1vvUI9234Yvmy+Wv5Kvma+Yz6Z/v6+078lPwr/fr94P6Q/8z/CQDrALYCQAVeBz0I6Ad/B98HuwiSCT8KLgu4DJoOLRAiEUQSrxPwFPsUTxOsEB8OjAziC4cLBgsFCv8ISAiKB4EGWQWUA0sBbP7N+oH3aPUG9Zj1/fXm9b/1Jfbk9o73nvdE9z73WPek9//3C/hz+A/5FPpI+0H87/xR/Yv96P2X/v/+If8N/9P+8v6U/1wA1wCWAJX/VP/AAE8DmwTeAq3/9P0M/48BdwLtAND+fP3X/OH7hvqw+YX56fhE9zv1JfTo9Mj2fPgJ+QT5KPkk+mv7RPyI/A78XPva+ln6Hfqe+ob7rvzJ/XP+rf7S/pn/NwKsBg8LbQ0PDVgLGgsNDQUQHhJnEjgShxKAE0YUCBX3FVkW+xQdEUIM3QgQCPUIVQnWB4oF0wMnA/kC0gFS/0b8XvmU9hX0G/Jc8UjyyfMK9Tf1mPS+9HP11/UK9sX13vXu9kD40fl0+8r9owDnAtcDvwO/A1YEKgVgBdEEHgS/A7wDiQPQAjkCAQLxAXIBDAA//hX9yfyo/K775fmb+Nr3WPdz9jb1OfQK9Gn0mvSo9H30m/Qn9RX2evdb+DX47fa/9Qr2zvcU+uP6Lfpg+Zf55foT/Hn8Ivwd/In8E/2d/fT9Of6r/ycEWgziFesb6xtvGMwW5Bk+H3ghcx+4G1oZ6BmAG5YclBy6GkkWOg+VBz8CpQDZACsA9PyZ+Mf1r/SY9LzzsPGK75DtzOuw6g7rgu2G8Wf1Xfee94H3ZviE+u38Wv7b/lL/nADQAigFPwfHCCsJPwj5Bi0GSQYmBlIF2QMBA34DOQTQA4kBjf5E/Fr74frU+Tf4Pvem97T4BPlA+NP2yfUa9Uj0kfM088Pz/PTF9Vj2//Zv99z3O/fn9Vr13PUW9xb4xvgH+vj7IP7p/vv9rvz2+8f7Y/vv+pj60foC+yb7VfvC+rn5+fla/34LKBlpIFserBigGDcgIShyKXUkeB+OHygi3yL+IJsdHRpmFE8LagFQ+m33kvYP9bfyt/Az77Pt+utO6n/pyOgs55XlpOXo6Enun/Nm99T5o/st/Qz+SP4T/2UBcQSKBzYKjAyKDugP2g+wDpsMAQqWB5AFBgRkA1oD9wO6BB4E9AHv/if8M/rn+Cf3e/XH9JL1M/er+Cr59fga+L/2DvWh8x7zbvNY9F31DPYW9+v3Hfi79yX3sfbl9i73WPcG+G/5efuP/Oz7/vkl+H72uvUV9b70+PSL9Wz2QPiZ+an5M/jd+V8DwRO1IUwkdx2XF7wbbCbFLTossyb4IlgjVSTNI0oiaSAXGzsRzATj+jf2S/W+9Ez0cPMp8srvnew26+jrLuyI6czkiOFc40fpIfCF9Wb5YfzX/r3/rf/F/4sBpwShB24J5gr2DBIQrhIwE5MRNw4KCjwGcwMgAu8BxwFRAdAADgDc/jL9Rvt/+VD3WvUw9Jb0T/YP+KD4l/jl97r2U/Ul9GLzofM/9E31EfdF+VP7+PtR+wL6FPkz+JP3E/eX99z4bfoU+z37ofpb+tj5xPjR9jb0pfEh8WDyDvRd9gn4rfj39yH4S/7eDeUfQyqkJ1YfQh2mJMMspC6nKWglxyS1JPYiPiBjHdQZnxFSBX/58fA77eDsyu2Q7yzwOe6P63bpnOmc6hTqBOiT5kLnOeuT8Bj26vpn/kgAqgArAJoAZQLKBZoJsgzYDt8P6g/lD3sPiA6bDLEIRQQZAen/nADOAUYC2wErAMr9i/vL+a749/cC9xH3yvdM+dv6svuw+2/6GPjb9ejzEvMx843zpPSL9tz4APt3+5b6jvmD+Gb4g/je90f3yvbI9l34rPlF+sD5Mfi/9uL1sfTt88LzwfQ99kT3fvjd+Z75Qfl3+yEFhRUgIjskCB57GaAeFiioLHgqSiSeILgfxB7QHdUc2Rl+FCkLSgG3+aH0MfIC8i/ySPIz8KLt0+zg7U7wU/H37yHu+OzV7UfxZfXf+V/93v4R/7j+X/9hAugFOAnFCr4KnQoeCw0MIQ0eDEgJJgVNARAArgD4ARUDOQIFAGf9qfp/+X/5gfk6+Wv4AvhA+UH7Lf2A/R78Gfpg+CX3pvYT9lT2Pvdd+G35iPmw+NX3H/dS9wL4VvhL+OP39/dx+T37Ovzq+7D5tvc+9nD1nfWr9cP1Ave/9zD4Uvf/9Tf3e/kH+kP5xvoEBaAV6CHUI8EejRw2IqUoQClxJewgjCCXIQQh7h+iHn4buhWODC4Ds/sP9uby8/HY8ZzxzO+W7RXtwe1v7pvtput96ifrjO0x8T31v/ly/WT/ov94/5gAvQPdBtIIvAmvCrYMtw5DDw0Ofgt3CMYFvAN+AoIBrwA/AL//BP/F/dT7SvoH+W/3CvZz9Sz2uPdQ+Av4gfce98z2zPW69Dn0X/S+9Mf0r/QG9av17fYx+Kf4Dfl2+VL6mvvE+w77Pvrs+Zz6Dvti+mD5JfiS9/X3QPja+Bv5nvgu+AH3r/ZL+KL5TPpv+h79xgfcFuEhXCPVHRkbuR+ZJgcpQCZRIqchiyKwIqchIR+uG+kVlQ0qBUv9UvdK9KrzpPSG9NvxbO5W7Ivs++3Q7cnr3+l56dfrAvAv9OH3nvqs/O39jv5s/xkB/QMhB4QJMgsIDJIMUA35DQIOngxMCWoFpgIDAcUAQQGjAXcB4/8//Zr6QPk1+VT5YPh19lH0APMr8/bzkPTq82vy9fAN8DXwz/CT8bXyyfN79Db1QPYq+L761/zy/Qb+Mv2U/Nn8A/6A/2EA6f8P/gP8x/rj+hf82fzv+7L5pPeF92/56PxP/zf/Bv5C/3sGWBLmGxweyRtTGqQc+R98IMEf1iDWIvEiPCCLHI0a/Bg5FfcOcAdeABT76/fB9qb2x/Xh84zxa+/M7Yfs6ev/6+br6etE7JTt4fAD9RT42vn8+d35n/s5/k4AhgGtAn4E3wbqCFwKagu6C8kKPwmwB2AG8wVbBZAEQgNwASAACv+Y/W771fjG9sr1y/Rr8xLyFPF68VHyiPLP8UnwMO9d773wK/NP9T72l/fh+A77Dv6b/3r/Tf68/ZT/XQIKBAsE6wFuAJwAtwBpAHn+Vfxw+y37rPuf+/f6RvuK+4L75PsV+6f6k/s0//4HGRF+FjUWchK8EmsYoB1fHzceph1HIGQhPCA8HlYbKBnsFZUQqAvCBmgCDAEkAAb/pvzu96jz9/CH79fu7e2v7Ovr3+ss7cPuAvBg8V7y4fNT9eT1/fYQ+cL7Ef9PAa0CiQTcBbQGUgePB+YHTAgMCH8HyAVoBP0DdwN8AiAAfv1X+z/6f/nf+FD42Pfr9pL11fTv8yLzkvNC9Cv0CPRu8yTzefRH9sr3NfhD+CP5p/qB+2D8nf16/sz+Z/72/ksBFAR6BEkCLwD2/lP+Cv7J/Zf+dAAHAdQAEAHrABkBmQHpAboCZgPcAvgCwgVPDLMThRbVFAMSORLBFUkYMxdaFcoUahVJFoAV4hOHEv4QKA6bCi0HBgQnASX+cPyG/Nn8mPsf+Eb0JPIL8sLyufLU8Wnxl/Gz8kH0m/Vf9mX2I/ZE9gH3yPcQ+eH5cPr1+j38S/7D/5YAOADw/g7+X/4y/37/D/91/jb+pP7F/lr9ufu7+mL6R/oj+dr2EfWA9R34yfmb+A32KfV499L6k/vY+Nn2p/lf/+sCPwEg/uf+TwMXB54FHAIiAl8FXgi3BhoCYQGmBAIHNQbBAjYBNwM1BfQE5QOgAzUEfgS6AvYAOQHVArQDpgKbAUQDZgZBCXIJyweuB/wH3wjACZQJuArHC2wMQA2JDF0MsgyDC8kJ1gZABO4DNQTwA5kCBQEtAO7/s/5w/JX6BPqR+mn6ZPl6+GL4Zvn5+Wb5k/hJ+DH4Jvgq+Fn48Pi2+an5GfrY+mT66Pqh+tT60Pvw+Vn7fv0b/Fr7wvqq+1j+D/6N/CD9/f3r/Y/8OvvH+2j/qP+N/V/9+vt9/E/8i/ws/yf+xPuT+9D+qAIc/xH+uAHtAF8C7wBy/b4AEwIRAvIBzAABAsECpgEeAtcDEQT3A2UCN/+///8CmQPHAvEAuv9rABcChQPgAUkC8AOQAzAE7wKGABUDmgbIBkUGWQUIBecF/gfvCGMIywdBBs0GoAfTBVkF6gW0BBQEAgRxAxoDgAI9ALH+Uv+i/4v+/fy8/EL9kv2f/r/+P/wr+6b81vy5+zr6iPtT/hv9pfp5+uH64fsD++P5ffsJ+yn7Z/z1+rv6A/zD/NX95Pxt+wP8rvx8/Zj9n/wt/Bn9pP1q/R7+vv1n/SD7zPg4/eb9Qfw3/yn9nPxTAOX+x/59AKb/2f4//Xf/lwIMAQkA3v8RAiwFPARlAiICqwS7BpoFSwSgA9MEFAeIBsMEbANbAzkEUgSVBDsDKwIeAloCoAOoA+gBGwHBAfkCxgMWA5kCrwL2AhUDgAJEATQAiQDAACAAY//l/j3///8QAD3/yv4B/+f+qv4p/hH+Jf/G/xAATwCb/wwAXAA2AFgAIgCqAPQApgCqABAApP+//wj/Bv/0/p/+qv4F/t39qP0V/cP8Ffw4+4T7/vsx++b6Pfya/Cb8ifwk/FX8e/wO/Gz8ofwe/ar8ufs2/C794v09/mf+R/9L/1b/tv9a/6T/9/8SANn/nf+p/9X/ggDHAAUBtQG1AT0B4gDHAGoBQgJ3Am4CegLVAlEDkANsAxYD9ALXAqgCKwLXAngDBgPnAtICpAJmAv8BKwJUAusBZwETATkBTwEpAeQAMgAvAEEAr/8M//3+Rv+O/6b/Rv/r/gr/rf8tAEEABwCg/zIA+gDZAOIA/AC1AMkAFwH/ANsAcgBKAFUAXACvAH0AAwCt/5//4/95APMANAA5/z7/w/9DAJn/ov56/k3+wP1N/V/9bP0t/TL9Cv0n/az9s/34/FH8RPwd/HT8wvzq/Jv92/0D/gz+Af45/ov+av5P/of+U/6q/kn/c/8p/xr/jP+x/7r/nf94/8b/4gCWAbgB5AETAucCLAOkAjAC6wGbAtEDBgTVA44DxgOKBAIEeAMWA6gCagL/AaYBjQEiAUoAAwAMAJn/Uv8P/6r+3P7w/tv+If8l/yn/kv+o/3j/hf/9/7wAeQERAicCDAJcAdYAYwFlATsB1ADu/9z/QQClAGMA9v/O/+P/NgDr/z3/m/8BAKv/Bv+v/in/X/9H/87+gP6m/lj+Af5s/Yb9+/2S/f39qP6x/vn+7/7H/qj+K/6A/k3+zv0n/lj+Wv5P/mf+N/4j/lT+yv4c/9n+SP68/oX/GwBrANP/4P8vAB0A+/86AKEAtQARAWcBcAGvAcoBDwJIAtIBdQF5ASMCmwJwApsCDAM3AxwDqgJcAjcCCAKPAaEAWgCxALoAlABnAO3/nf9d/13+A/7y/dL94P0M/kn+av6Z/o3+wf56/9P/v/+y/9D/GQB5AK4AqgDOAPMAOwGvAagBPwHzANsArACjAJYAUwB7AOoAQgEOAYAAJwBIACcAZv8D/wb/Nf8W/+f+vv66/pT+T/5z/q/+XP47/pv+x/74/mr/aP8B/yH/av/X/0YAiQCLACYAMACAAHsANgDu/6T/d/9d/4D/yv+d/4v/ov/O/wEA8v+S/4f/GwBeAI0AiwCoAP8A9ACsAK8AtQAKAawBwwHZAbUBsQHHAXcBWgGCATIBpgCfAGUAkQDfAAoBBwHWAGUAif9d/6j/w/+o/1j/Mv9a/47/4v/i/6b/3P/y/xIAQwBTAHIAtQDUAMkAtwBVAFgAjQBTAAAAAAAmADIABQCV/5v/DAApAKT/mf+v/87/EACt/0f/Kf8T//j+3v5d/un9wP2z/dL9gv3B/TD+NP4V/sH90P0a/nD+cf5G/lP+if7S/rb+xf5m/xIAZQBDAAAA/f8rAFoAQQAJAMz/0/8kAJwAswB9AGsAOABDAJgAEAH9AIsAmAAHAUgBrQGzAXIBjQHiAUQCKQIYAg8CCgLDAXcBEwGqALkAzgDDAMsAjQBeAHAAOAD7//D/if8s//j+q/75/hX/4v49/3f/VP9U/4n/3P8pABAAIgB3ALwACgEuAWoBugHAAY8BHAHkAAUBGwH2ANAAYwDD/97/GQBRAFMA0/+C/1//lP+A/yf/8v7e/tL+sf6F/nD+gP58/l3+8v2G/dn9WP6k/tz+3P7c/g3/WP8w/0f/l/+F/4z/d/+U/8X/3v8pAIQAiQBnAIYAUQC6/8H/PwBYAFEAKQDy/wcAQQAkABcAOgAmAG4AYAByAKwArgDHAI0ATAAQAAwAJAB0AKMAjwDDAKwAlABKAAAAKQA2AIQAwACaAF4AEgA2AFoAVwAgALb/pv+o/6//4/8dAPn/r/+S/+X/PQAwANf/zP8gABcAQQCdAGcAfQDCAIAApgCSAGkAUwBuAAUB7QDJALcAtQABAcIAQQASAPD/GQDe/1b/Uv8q/zT/NP8G/+X+gv5l/mr+XP55/qv+v/7w/gr/Kv+S/+v/4P+i/7j/if93/73/tv+8/9P/IABIAFEAfgAHACQAPwAJADYA8P/2/58AFwFLAQoB5gDLAFwATAAQAE4A4gCsAMsAOwAvADgAw/80ADAADAAVABIASgBXADQAiAAkAMP/8v/l/+n/oP8tAA4AIABIAPD/SgDe/+X/CgD2/zQATwBKAF4ARgBcAGkAYACUAB0AVwAFAPf/RgDt/2AAdQBXAJEATgBwAIsAEgBRAAAA4P8kAMr/FwDy/30AaQApAE8AEAA2AC8AAQCV/2j/Of9G/9n++P5A//b+If9w/qb+5/7e/in/Bv8a/x7/DP96/7j/vf8FAMj/tP9v/5D/JADa/yYAvf94/7z/DP9W/3j/Uv+b/2P/xf/K/x0AYwC2/yQAIAAAAGEAJgDSANIAmgC+AIYAyQDCAHUAZwBuAN8AyQCvANAATACRAGkA9P/n/4z/9P/n/xUAnwApAFgAYADy/yIAr/+p/9D/7v9rAEMAOAA2AJ8A+AD0/wMAEACJ/yYA0/9L/9P/rf+9/zsABQDu/wMA3v+2/0MAFwCX/ycA+//OANAAYACfAFoAiwCSAAwATgA2AO3/AQDZ/zIA4/8OAEQAtP8/AL3/n/+r/zD/Af+h/sH++P7e/kL/sf4l/7j/m/+8/5X/FQB7AGkArwCLAHsAHgDy/of+y/sc/eoHgQef+936kQDlA9MFXgFy/I8AlwSuAND+rwBNAdkB4gAB/jb+TQI/Ad79If8W/90A0gC6/C79AABGATAA5P2d/UgBZwEQABkA9P68AFUAA//r/lL/aP+YANIB8P4P/8MAAwH0AXQABf6vAEoAr/+qAkL9vACdAiv+6f/y//YA1/+x/ev+EwJ+/zL/pv5d/iYGoP/C+ksBewEiAXACIP4H/EADHAErAkYB/fxbBP8C8QES+NL7kAJuAnEEuvdk9+8BlQZM/Kf5CQDD/WwDe/2Z+OsEJQLY+k39agO8AaX7sQAYBQP8r/5kA/j+lP8c/gwIewAV9goJMgNc/a0DJ/2CAREEF/yzAlsFbvaLCSL8RvyZC5jxpgOpBvz7pv3nArQDhf+c+Gr/BgX2A48A8fQZB3z/hPx6BtH5xAQ/AvX5Gv8LBxsAyfz9/3f9YAmw+gwCOgCj+bQGY/08BF4BUfQDCCQGdfeqCVP6w/+OBhH1ugsDAtD0Sf7jA+IJCPcW+SoGQAPy/wn6Of1NAY0CX/4j96QBAwNG/YwHe/te8h0IsQJE9EMOugPG5ngV4gIY5jEXCwbB7KsDMwzyA3r3A/uWEW343Pf1DjH68fMOCi39fAImCcHltQa3Cfn3ggei97/3LwiJAyP+iwFy+YIAHgKwBgEALfOGD2/6GP/TFhfpT/22FAH3NwPL+/T9QQ/Y+Qj9yP7H/j4HGALA/Tf5afvFC8T6bfvQCQnvRwe/CwfzBwGX+BgFyAx17hn7jQvsBBb4EPxfBZP7Ogj/A/Hz0AKlBkX7MAR0+QoE8AtQ774A5QZOBUQBrPLOC9IILu+xA28DEPxSB0/9+/aeBo0Cgfi0+kYJ4P+p9p0EL/ymAeD93v9bBiL+i/6k95QBWA2B98z+GP8t+2YLkvyQ9h4K9//1+Wj+LALBBbb2q/+pA9H/GgPO/nD7HwQiALz7WQYf+T0Bzwfr8yoErggO/JT/s/4V/1UIb//G9hQEAwqs+wD8nwMp+28FdgbU8rr9JQyH9gED1QON9A4CsQLDAcj3q/70A68DgP34/XoGlPsTAhYF6vvL+94DdAApARX88f0LBgkAdwMd8+7/+Rba9zfwGQhrB3wCcv3E8v4GlQ12+gX9tfwjA4gAzv3eAz39X/2ZAv//PAVq/qb0dwibBLT5lAFU/pD+ZgTv/av/Ff8r/o0HXf4S+j8Cvf/MAIv/DPyRBRz9k/peBnf/Rftj/yoFyfxf//YB7vr2ADoIm/+28O0I5QUt+wgBbv9VASb8FgWz/oD9RANy+eEEYAVh9SEDEQH8+1YLlfm09uEOJPmX/rIMRfJpAO4Hzft8/qH+5/4FCEb74PfAB1cFufgc99ULTf94+J8CAQIIAYL78fzqBvsBtvZGChn8BvlNDLr9oP+t/2z/NAJ1/P0BlwPdAPz6n/7GB9v+Of3g/ID9IQVzBE39qPJFBOIJ6PkF/NsC+QJa+7EAM/vUAEoGofzu+YwEngR89QYEvAPmAQX9S/h2CAr/O/13AHkBKgU1/zP7IP3hBcP/cvkJBhIJA/X1+TIKfgOn+yD/7QH1+L8FdwjG9+38NQTXA0MAwPtN/uf/2QEfBjL8OvgYAwkFQv4uAyL9W/hQBeQBfgMV/wP1aAQPC+T8S/h+BH4C5P49AuD+Rv9RAT8A///tAbH/YPxL/z0I6vqT954HSwKo/rn5pP74B6756/3iCS/57PnxBmIF2/yU9GcHKgUx+y4CD/l1BLkI6PkV/IEGffve/pAFe/wEBM38WvvuBfQAB/xjAa//CgBWAU396//4/XkAXwSQ/sr9CP+4AbwBiwDW+FoApgkV/Cb8UQBAA1j+TvzuBpMFRPOv/DgOVv7C+gUB9fs7BBoF8Pmj+s7+VQf6B+T78/OWAAUIswBd/vH9Xv0eAE8CfAIHAaD4TPxRCfMFoPoD/DT8FwHIDen9ofJoAugGU/4T/68Cq/4t+xEEVgSK+ysALgKZ/yD9LAInA/n41ACbASz+1AA7/EoBcwQl/mX+CgAT/pUDGgKr/n38XwXABpf5wwBhA7X+EQIp/sv8tAW6/0//+/4l/jkCxf4B/y79DAMHAaD6gwbKAuT2x/7aBycAMfiC/AIEjQL2/oX+Ef2N/oMD4v8/+0oABQC3AA3/EwHl/+0APgST+04ArQP7/UD/rQHZ/g0DMP+k/WgD3/ySAnoCfP1rAEf/NP+J/tcB6wK+/dQA0ABu+6T9HASAAlX8QQABAjIAPv+7+8EB7wea++/7vwPiAIIASPx+AGYGWgB3/K//HAHB/wsE1gDe/bj/aP5wAW4Cd/0DAAQDqv3A/QMBvgH7/XsApAJz/fn+FwJpAAP/fAFaADn+pAFCAQj+fQDZ/30AlAG1/mT/kgApAuv+cv1u/j0CKQL0/XL9iwB2BIT8Uf49/7P+ZQFn/okAE/5H/3AC4gAP/xP/uP+NAIABm/1cAPT/zgFuAXz9zv/r/2kAjP+8/2//HAGo/qv+qgKH/lr9fP/FAl0CqPsK/HABSQOhANb8eP9dAu7/3QDa/yn+eQJLAbr+Rv4F/tIC4gMg/rD7iwCHA34AGv8E/0f/7/7vAMcB+/70//8Awf2sAMAAofy9/9sA6gDkAMf+Ef7xAb4AU/2S/5wAnQBnAAoBzgAHAI3+JgBAAmH+vP+CAdD/WAEY/sH9jQGr/zL/XgAyANf/mf/gAMoBDgAe/1EBzgBU/1EBPQEy/xj/qAJ8AjL+CP9s/z0A+AC0/3f/w/8K/5D/uQC4AQoCfP9R//YAKQE9/gX+hwHlAhMBLv6h/a//rABu/r//rwBm/2P+c/69/+f/yP/ZAO7/4v1C/0YBIgG9/zgB1wBe/SD/GwFd/yP/+f8cAnwBnf0k/RkBmwJJ/wj9nf+PAGT/PwKSAFb9gP4bARABGwCr/lz+5gCNAQcAxf3kAFMCtQCA/9f9xQC8Ahj/Qv5gAPL/6wD2Ae3/rf1h/2EAJAB+ACsA5/+//w8CfgIVAKr+Sf5rALgBugAY/yIAjwAvACIBAQDn/0L/n/9aApEAP/02AIED0AGk/8b/MP9q/n7+YQDtAS8Ah/41/y0BVP4I/S0AJAG8/9n9i/5z/xcAxQHvArwB+P4O/hX+cABv/zYAVgIBAIn/bgGdAJf+sQE9Aj8ACgD4/g/+tP/tAj0C6/5J/oz/IP8i/lEA1ACO/nv92f8VANv++f58/tYA5/6J/QEABQDQ/hsAiADQ/8sAcADHAO0BQQFVACH/Gv9IAFv/awAOAdH/i/+CAHf/mv0c/3r/oP9uAN8AAwHM/wr/CgCf/6//9P+v/4sAdwGuAOf+If8gALcAqP6h/r4AvAFGAUb/fQCqAG7/ewCjAIIB1wHV/4z/BQFVADD/ZwAOAdD/xf5P/8AA1gCQ/oIAPwEa/1T/XAD0/wz/If9lAL8CHAGg/0//2wBPAXf/pv8q/1//0f/t/wkAwf8BAB0AQP/2/7z/zP4s/9P/nACO/2//QQDK/yAASAA2AMH/swCAAfYAewBu/53/lAAwALH+WACNAIX/S/+z/qv/8v+SAFj/HP6J/m//ev9P/xMB8gGWANn+w/++AOsAgAAa/0YAiADV/oX/OwCIAPEApP+i/qn/1ABTAL//bgCRAJ3/i//G/9f/HgApACX/ef7O/ykAJgDa//j+7/7Z/jYA3P+D/ysBswGqAUgA0/+JAOIAdwGQAf0A0/9gAD0AyP9cARUCzv+U/pT/Pf/i/3P/JAGiAxECWAAKABkBpgDbANYAOAAOAOv+MP7Q/wgCswBP/5X/vf/K/on9S/4HANz/yP/a/0QB9ABl/vb9//6cAE4Azv/k/mz9B/1e/U//rf4g/Wf8Lfwt/Bf8RPxI/IL8B/w2/Kb8T/3d/Wf94vzM/bf9i/6F/wj/LwDu/+T+EADJAFr/jP+3AEQAHgCi/1H/eP87ALEAmgCEACkCyANGAW//IACPAekBuv/a/w8DGAT9BDUFsAR+BHME2gYVCtsJ1gZQB3EMTg9gDnMNTwtlChwLBgusCb8GLwWZBdMEYQIO/gj9Qv2q/VP9L/od+Mz4PvpI+kz62Pg8+DP5H/vv+836/vro+pr6U/q5+oz6XfkL+uP4Uvj3+K33W/Z89hT3T/fE+KP5lfr6+gX8e/2x/aH93fxw/KP96/wk/Fr8Vf2m/7b+//zF/Hf8APwJ/Mv84PxK/BH+BQE7AYsCugN/BJMFOwSAAwIFHwVsBAAGFwelB2kGDgcTCacI+AjkC/IMvQzWEAgVkBaUFKwTbxS2FcUU9Q8KDeULaAxLC2sF2f9a/SP5DvUQ9GXy5fAl7yPwdvFB8YbyzfJj9Cz3Vvi9+N/5APsK/VH+kP7//mX+Y/6k/Rv8j/wS/KH6qfkq+Tz5gPft9Q/2FPfe9HX0Y/a29qD3bfc897/3ufia+Xv6wPuR+1X76vvt/LH8SP1C/9H/Zv8a/xUBkANjA9UDmQXBBOMDzwTYBE0E4ANQBYAIqgcBCPIKvQ5ZF1geTh8cG7cXUBm1HOAcHxloFbgSKhC/DPwIXwVWAUb96fi88wLvDOyL6qbsMO9+7/fvlfHF9Yz5JPwc/wP/VP4p/37/RAD0AaEBXAG1AUoA0//Q/sD9cP3A/Hf7Avp6+Bz3nvdo9iP17fOg8YPvR+4J7/LuoO8J8QDyy/Ks84L1J/fP+Jz6HP6SAZYCogPMBG8EHQUdB6kFYQPOAqoB0//o/Hv6YPkx+or6rvp7/HH+4P/0//gCVwe0DbUauCjiL2wu8CjBJ3IriilaInsa8BVKE/kM1AbKAsX++fjK8NToP+Gv2XTYkNzd4WDnU+pA7kb1Lfpe/YkBzgJUAk8CTgQMCLMKWAx8DsQQ6g8KDCoHpAP/AOn+bPwA+fD2c/W89QP27fQH86DwI+/I7qLu7O5D8OfwUPLQ9MH2p/fs90n4APoT/Jz8zv7oAOkBnwNYBCEFgwYZBswD8wDV/oD+hv3Z+8b5tPjN+N73b/hm+Hb3oviG+mv7ufwI/b/+EwlJHW0xZDpHOK8vIC3gME8vLighHV8TlQ4kCcoDFwGz/Wv6fvSW6avf+tZp0ZfSB9jP3bvgGuVz7c72tP+OBpgJugrpCVgJyAtFDosRjBX3F14YnhZPE4wPrQqdBLP+q/iC8mru+uzF7Fvuyu+I8AfyR/KN8cvw0e8f8DbxP/Om9Q34afxm/7b/ygFAAtABvwKSAFT+ef5L/yAAaQCCAI7/fv3Z/fj9KfvB+LP2ZvZb9o/0sfNp83XzPfXu9oD3D/id96D6aP/5A1MRNCbHNlc7AzNELAQyYDVfMcwokhtYE78L1gBh/AD69PS97t7mn+Ko3CHTN9Jy1/fd7+Km4+7nSfG9+ZsCQgrYDiARDhCTDvAOtw/AELwSWhTKEw4S8Q9bDQEMnQnMBLz/H/mR8WPt9uvv7MrvrfBm8UHzJfTy9Aj2Y/YV9qT1nfbs+Fz7E/7mAPYDFwfACFUIHwdtBRQE9AJPAMH9rPuP+fD4dPnj+V764/hx9in1IvPj8VfxQO8L74fvwPAt9OX2JvoF/Y7+SgAqA98QTCkxPBpCDTlRLtMxmzcgNC4o7BcNDeUEffuF97f0rfBs7djnuuKd3H7Uc9Ki1s7cm+L55D7ov/Dh+9QHnhCDFJIV+RQIFGoTWhKAEYEQtQ+0DhEM2wlbB/IEKgSSAm7/MfoS8w/u3eyV7iPwQ/Af8c3xtfIy9cH3evkZ+vr5yfqz+2z8ev7iAMgDJgdqCZsJmAgFCOoH/AdKBwsEUv+E+1L4RPYP9Yvz4/HI8DPwOe9L7gHu6+2n7knwk/IO9eT2zPih+27++gHIA4oERA3/Hqsxezv7N3ExEjKANYo0tyuZHSARRwbL+zb0pu5Z6QLlx+FV4GbeENl81dHX9Nxp4QjkTOiO8Kr6zwRNDZQTFBjNGfMZARrzGGIWNxMdEMYNfAsbCacGMwTeArcAofzr92Lz3PAF8fPx2PKw8s/xYvKE9I73Bfov++j7ofwe/nsAwwHKAnYENQZyCPgJ1gnhCOQHJge5BfsEGgUUBIkBXP1e+fL2bPSj8OLst+o26qPpPek96bno2OgV69HuEvLq84n0qfa8/PIChwV9Ce0T2SUGOJY/Dj0WOKM2yjgRN8cs5BwzDRAB2vdZ8RXsS+bG4LHcT9x+3VrcYdux3A7hZubq6NDsHvRs/MYE8QpkD/sTtBY8GFcaOxvdGYwVlhDMDHkJHQcuBTkDZQGE/ov7p/lL97P0/PH776LvVO+Q7mbvIvIM9Un3UPn6+hf8wP3n/yMCkgOQA34D8gSDBiEGegW/BSMGLwYRBbYCmgBY/rP7rvl196707fIF8rbweO4B7eDsNO3i7UTule4+7yHxifNn9Zn4zfzT/+ABnQONCicc2i+ROgg4dTBbMj460Tu9M8Uj0BOzBzb+fvhK85LsHebH4PDfbeDp3fXdgd/Z4QjmIeco6Qbv0/XI/jQH/wtxD7wRARVKGpkdlh2WGfsTFBDgDBcKcAfcAwkA+vvy+DD4v/f09qb1ofSJ9CLzPvHq8Hjx6vLz89LzXvTg9Qb4Wftc/i8A4gApAjUFOgjbCeIJTQlYCcUIFgb2ArkArf5f/WH8Qfpo92n0YPHc7vrsJOte6dbnxucM6arq/exQ78TyoPbK+O/6Of1C/x4CYgRADK0c5isyNCs0nDFCN7Y/AEIOPI8s/RsjDlwBzfkn8wHq9uGz2rvXCdkk2S3aCNzj3THh7eNt5+vt9vSs/MwDPQnsDdQQghSVGSEd8h1sG2kXdxSsETMPqAzLCDsEVv+1+wv6GPji9RP0iPJH8i3yTvEN8VPx2vHs8s3zTfWX9z76yf2AARQEWQUtBsYH5gmkCh4J5gZMBb0ETgQRAysB7/5D/Ir5KPfU9Efyiu8X7Zbry+rm6Urptenk6VHqwOv27Y/xBfUw94r53fyRAGwCHAQhDFYb7Su3NYI1DjVkO0BDxEahP9kvzx/xEPsFev6L9EHpid2F1EvTEdQN1SjXj9jn3LPiuOZ+7G3ysPioAPcFhAm/C9cMyxD0FZUZURsZGooY+Rc4F/0VqhJbDfcHrwLI/jv8z/j09Avxle6p7lvvou+T72Tvl++R8DPyFfQY9tj3W/ll+2j+lgEwBEoGxgdtCKcIYgiTB54GMQUGAy0ACv1k+j74PvZc9JXyp/BF70fu1e2o7Sztdeyz6zfss+wY7czuZvDG8j738/o7/lwA+gHHC3sbKypnM9QyTjLTOBVAvkR3PwsxkiOXFvkMOgbC+pDuL+Jw2CvXPNcL10zYE9lm3YviBOY46yjwFfY4/coBqwXLCMoLUxHjFRkY/heSFUQUAhUWFpwWTxSOD+QK3QZ4BPgBif2k+JLzu++D7rHtd+3y7Ersme3R76jy6/Sd9Y72C/il+pL+jwGmA7gFpQeWCYkK9AkMCTQIcgfoBcMCgv/0/Az7jvlC9wr0JPHR7rrtY+3S7MnrcOuz6wjsEe137urwyfMR9bj2wvj1+s79tf5xAm0NpRrnJjYsDixSMhY8uUSxR2U+6jGAJjYbLBTfCQX7eeyr3afWKNXg0gPSpdDt0WLYud6B5RnsEvFY96j8swCMBKMGZQkWDYgPYxHzEUQSfhQuFu4W4xYIFCkRMw+dDBELfQfFAc384fem9JXyPvBb7nvryOih6O3p4Ozq7y3yMPUq+C37wf63AdUDLAW2BcAGyQeWCG4JSgqtCpIJVQenBe4ElwN0ABP8zvej9IHyW/An7gPs6Onl6Efpwum86aHp3eq07VLwA/M39b32APnv+hsA5AvoGK8knCmhKnIzKz8RSXNLIUGSNVMsZyPkHK4QYQAN8YTiRtyA2avV4tILz7DPrdQk2efequPE6Cjw1/Xx+lj/XQJHB+8LBA+GEUMSTRPlFRQYZxm5GfcX5xV5FKUSjxDaDDQHJwGa++L2MvOy7yDsA+nK5/noVutS7l3wU/Hx8gj1w/f3+q/9gP8cAREDMQWNB00JcwphC4kL/QqRCegHnAbGBAMCCP4L+Sf1LfK074Lt2eoC6XvorugQ6TzpNunY6VPsle4W8EPy6PM09rj4pfs6Bf8R2R3nJe4nLC90O/lE9kmFQ+A4ijF/Kf4jGxu6C4L9z+7c5RziX9zU1xrS787S0eHUEdpM3+3iW+kP76T00fpq/qQCrAa1CGwK+Aq9DFkQ0hIeFPYT6RIIE84T4hSHFZgTjA9WCm0FGgLH/kj67fQ373nrB+pc6iPsuu207rvvxPE99VD5nPxl/nH/wwBhAhoE+QVSBzYIDggUBw0HEgfRBiQGcQNTADD9mPoq+br2WPOy77rs7+u16/bqIOpV6fjppuwL78zvl/D68Wn0bPcS+br+TwnZE4QcmR9YIyUu3DiyQXVCnDoyNcovTix4KfkdPA9k/4HxS+yc5wPhINrt0cvPZdLG1ZbaVN2w4BHmvOuL8vn3NvxMAHUDMQYpCNYJ8gsmDsgP5hDQEegSMBUoGLIZThnTFjsTjxDcDU8KPgXV/kH5//QD8uPvqO0Q7BDr3+r668XtV/Ag81P1mfd/+Rv78/yf/v//kQCYAIAAswDtAcUCbwPwA2YD7QI0AvgAsv/4/Kv5yPYg9FnyhvAl7lHsTOtW69DslO0u7nbwGfKf9Fv2vfeD/zYJtxF0GNIZuB/xKt0zaTswOToy6y+vLLwsYCmhHIYQYQNV+l33ce8C53HeudZi1yTZ1dpm3VndqeDX5cTqAvG89L33dvvk/dcAAwN6BEEHPwojDRYPFxBeESMTlRU8Fy8XhRVYEwoSsBBADp8JmQOH/ur6D/gM9T7xOe4Z7GHrZ+y97bvvFvEZ8tnzkvVq96L5hPtl/Yf+3v58/woBcQOiBc0GtwZxBpUGwgaVBvkE6wFw/vr63/gE9yf0C/He7WXs8Oxv7ZTtie6S70nx/PIi9FD4kv9kBg0NoxAlFfAejihiMSY1dDIAMfcvSy8KLpIlURrjDgsFXgCc+mLyG+mG3znb4Non2//bCttf29rezuPs6efu8/Hd9CP4f/tN/uP/OQHQAp4EhQaxCHcLEA9KEoUUFhZ2Fh0XoxdXFxMWuBJFDsAKsgfIBE0Bffw8+G70hPEj8NHuzO2q7PHr0uye7q3wCfLq8kr0/fVH+Ej6wvuS/bH/4AHiAx8F/AWsBjEHBQe/BUcDcAAO/sv7Pvk+9tnyM/A17x/v6+5Z7uft7O628Mvy1PRS+L/+HwV+ClkOIhPZG8sk2yrJLA4rHys5LagtyStlJJwaYxNhDckIaALj99Pt8uXd4S3hst+23TXcc9xb4GzlwOkp7YrvofKp9hb6WP0VADcCpwSnBhIIYwnvCcoKQgzcDcQP2hDLEYUTqBR4FSMV4BLkEBYOpgoDBwUCif23+UT20vNS8ffun+287FTtge4a75nv4e/c8MDyavTy9W/38PgX+4T9sf+QAaYCQAO0A8UDDANuAWT/qv2n+0759vbH9MXzAfS581jzOvOA85v1R/fG9w35dPvJACoH0graDcsRLxjTIH4lnybMJdAkJydZKAgmcSBCFxQQMAxICJkDOvtw8Svrjuf55ojmIeSR4kbiguS96APsg+5Z8G3ysfWX+LT6WPyj/Zf/nQH7AlsEpAVQB8kJ1Qt/DQcPOBAtEg4UkBTmE/gRbRCBD18NSgoEBsEB2/4g/O75Uvc59ADyz/Az8RvytPHn8HrwAvFa8mPzVvRK9Rf2UPfP+Kn6t/xP/mH/KQCRAIkAGwCm/9L+7/1l/Jb6xvlm+XL5LPnn9zn3D/fg97L40/ev9wf59vyzAg8GGwj4CssPuRe8HRsgNSFFIZ8jQCaCJVwi8RukFR4S9Q02CbgBUPj18SPuteyq6xvoSeUX5BHl2uha663s6+3g7t3xO/V293H5PPpI+xf9Kf4TAOYB7QMHB3UJ5AsmDvAPmBKvFF0VORWsE0wSOBEfD5QMLwlzBUQCZv/Q/OX5dfZG8wvxDfA57/jtdeyq6yXsX+2e7nPvN/DU8f/zyvb5+GL62PuA/Xr/RgHbAcoBvAGsAeYBZQH2//D+8f1I/SX9ufvC+ln61vmI+tr5nvgf+Zr68P5NA+EEIgf8Cd8Pmhd1GzsdUR0sHu4hDiSzI0kgEhq0Ff0SZBCUDNMEcPzZ9RvymfD47fPpsuYN5X7ma+k062zs7eyJ7pfxRvQh9hj3Zvec+Gb6HfxI/tX/5AFoBKcGYAneC3MOXBEyEzUUMBSAE40TGhOPEcEOuApcB70EWAKQ/7v7//cZ9WvzSvJ88FTuWuzQ67PsCO7B7h3vCfAA8sH0Pveg+J75xPp1/Hr+OAA4AdQBfgIqA+kDqwPQAuABgACo/3P+cPwC+3v56vgU+c/33vZ49rj3tfvg/iUBiQNUBtkMxxP8GHkc8hwCH3QikCQsJawh+ht0F3wTghAYDIoE0PwD9nbyIvFf7tDqJudG5RHnYOlY643shewn7rbwJPM59VH1TfUy9oz36vni+3z9xf+SAtMFTAksDMgOPRHzEkIUyhTbFPQUcRRzE8IRpQ6fC+oISgbBAzAAAfx++Kr1y/Mv8tHv4O3J7J3sl+0w7q/uFu9f8LXy8fTR9rL3iPgS+kP8Xf6Z/x0AdQA/AXwCgAPRA1oDjQJWArwB5gBW/+b8RvsA+nb4Tfei9Zj1VPhu+xb/hAFfBP8JpxBHFysbXxwoHnwgByMQJDUhyRwEGOYTtRAGDGsFWP4J+OD0JPMU8V/u4erG6c7qXuy97U/td+z77ETuYvAA8vfxmPJw8131V/hK+o38mf7FANwDfQYBCU8LaA3oD48RoxIBExkTahO6Eg4RKg7UCmMI8gWkA64ArPxO+fn2DfYe9bfzA/J98KfwYvEX8n3yKfK58gP0zPWi98L4qfms+ib83v0T/5//pv/D/+7/OwAwALr/Zv/K/o3+h/6x/TD9L/yU++H7x/pQ+jb67/ry/vIB5QMJBu4Hng1lFD4YuhrUGScaZh2QHjUf2RukFegRVg4aDE0JQALb+472KfMZ86fwT+3b6pjoPepL7Obs+O397CfuXPFB88P1ovYf93v5TPtT/Uf/NAB1At4E6gYmCW4KVgz7DvoQNBIQEikR8xChEM0PGw7kCgMIiAVoAxUCcf9Y/JP5Ifc19nD1y/MW8pnwcvAq8dbxM/Jg8uHybvQc9rL3H/kA+nb76vzx/aL+of7B/k//Uf8u/4X+gv07/S39P/3Z/Mf70vpF+j/6hvo6+h35TfiD+Wf8TgDyAlsECweQC1USyxe8GZMauhogHa4gwCCyHrMZhRSjEhcQCA2uB4X/Z/o79631vvSy8GPtZev/6hztZu077TntEe2c76HxHfO89DL1Xffs+dT7HP53/24BEQRvBvEImQr7C34Ntw6BD3sP7A5dDh0Ocw0aDBkKvQcZBtMEDAOhAIf9mvqu+EL37fXi87XxyvCe8Hbx7/Hx8Rvy9fLm9AL3lfg1+dT5DPvH/I7+Gv/2/gr/MP8ZAGMAv//S/j/9qvxn/K77xPoW+df3nff/92T4LPjs90n4FfuJ/wgD5QWKB/IKQRHuFoAbkhx3HIkeriDjIvEh5xxbGO8TtxGBD9QJOwP6+1j3s/V5883wquwC6Z7openr6wzt4Oy/7UfvrPKO9en22Pc5+Pz5afz6/Vv/BQBsAXMDUAVBB3AIEAolDPINKA8zDwkPZw+XD2APlQ2dCgkIxgUzBPYBcf7C+sj3avbt9cn0SvPd8cLx4/Jy9HH1WvWx9df20/i1+mX7Ufs0+8f7Af2o/Vz9bvzN+x38Wvx9/AX7jPkJ+an4afnM+Pv2Afbt9Ab2tvcS+GD4jvdZ+dL9cAIOB24IJwpgD3wVcxxqH8QeTR8WIHUjIyX3IbocXxbbEkYR6g29B+/+Y/eW80zyFvGJ7RLpbeYA52LqK+0P7ovtju2p77XyVPXD9Wr1Rvbn9wP7av3l/gEBeAMhB6gKuAzaDm4QLxIwFOQTDhMtEUMPOg6DC4IIjATDAAj/Yf1w+9j4Bvbv9EL1CPbV9Wz0bPN18xH12fYG97z2Yfa49yb6LfwZ/eT8Uf2E/lEAsQEuAd7/s/77/f39o/xX+s/3qPVc9a70O/QO8xny3/KN9JP32vgS+bb5t/sRAqAHTwp8DDENqhM9HOogkCSIIbkg7SP9JNAmUiFdGKgS5QzKC0oI9P7n9m/umOuU7FzqsufD4qngSuOc5o3qB+u+6rjsDfA99aX44/le+wz9TwC/Aw8G4QeNCScMpA73EOIRQRKvEw4UHBSGEiEPJQ0GCxkJiAaoAe/98/p4+UP59Pbo9AfzrPK49Br2l/Z39o72M/hr+gn8Rvzb+wD8zfw0/rb+7/0D/Zj8Ff21/UT97fuw+iT6+vmK+UX4MvYw9c70bvXt9Ur15vTe9Gr2gfg6+vf6Qfoo+879XwKKB6UIiQluDHAREBpWHqsfrx8jH9cjmyarJscjphvaFtITZBCmDEsDovmf8hXuVu1D6srl5OGg35Ti++XL6FrqSuqJ7e7x3vb6+jr83f39/xUDgQYkCCYJkQkeC/8MoA1DDqANGw4sD6QOqQ3rCrwIZQeBBYAD0f8O/Hj59PfK98H2A/Xt8x707/Xs9+f43/gU+Yb6EvxC/QX9+vu5++T7wPyu/IT7nPok+pr62PpB+mv5LPhZ+K34M/gY+NX2s/a793b4b/lv+Y75qvrE+9L9x/72/nX/0v5RAPgCYAV5CH8I7wmtDa8SRRndG6QcWB2ZHlkiByR/IicenBdoExkQbgyPBnL9efVZ7zTsTupb5qbi6t/W33Pjyuaj6fLrO+6T8nb3pft+/iAAqgHOAy8G7wfoCA4Jown7Cg0MzgyLDFIM0wytDG4M0AqoCCgHlwWkBFECN/+P/Pr5u/kx+a/3hfY29Qb2xvcs+Qn6v/lV+uH72/1k/w//av79/W7+g/8G/+j9PfwZ+6H7V/uR+g/5O/dN90v33Pd890n2kvYW98r4H/rl+Sr69/mq+sL7/vsi/AX74/q1+9n92QH2A1sGbgkwDesUuRqtHhQhwCB0I6QlICatJHMeLBjtEj4OnQrDA177i/Nu7Y/r0Olm54Xk4eHZ4pvlZOkI7Bftzu428TD1Gvk0+1j87fzH/sABKATLBYoGhAeECfgLCw6wDrkO3A7CDsoOJQ21CnkIVwaDBNQB0/7S+xT6YPmc+Mr3WPaQ9XP23vf9+Or4Vvib+Mj5SPvx+237vvqN+o/73/xV/Qf99vs6/OL8WP3S/bn8Q/z1+/z7f/zS+7f7Yvsk+/X7iPuy+yj7+vlT+oX5pfnz+bH3kveO98v6ZQB6Al0FBweUDIoWwxx9IYYh2CBAJQIo0SnrJtAdUBhqE58Q0Q1xBGL7AfMj7trtwusd6GrjoN9D4XXkf+cv6ejn2OlJ7e/xAvdz+B/6BfzO/iADsAVvB7sIMgpNDToPRxCYEC0QZxGdEcAQjA5sC/0J0AjJB60F6QFG//r91/3d/d/7APnl9kb2FPcY9871yfPY8qjzE/UM9uD1uvV39kX4Nvr3+vr6yfqo+1b96/3O/Qf9n/xq/bP9s/2S/IL7iPtu+xf8hvvU+R35N/ip+JD4N/jp9132pvZU95b6jP9+ARoEjgakC20VtRsmIN8hbSFWJlkpQysZKi0ivBw3F4cTxxC5Bwz+n/R67f3rG+l65Wvh+9xL3oLhc+XR6APpYevG7o3z/vgS+/H8K/73/7YDKgauB6wIAQnvCiENOA7wDvMOiA9BEE4Qew/wDc8MzguJChkIigRfAe/+cP3q+x35yvUx81Xy9vJs84vyM/ES8bDyN/UK95P3r/cm+Pn5SPyY/Yn94PxT/cX+FQCUAHr/Bv9C/63/eQCA/xX+MP1s/Or8kvy7+236Gvn++Jv4ovhq+Ov2lvZ49lT4Wvyv/pkBnQN9BzMPWxVcG9MdCB+zInEl8ijqKEIkUh+sGc0WYxTNDcsFx/uS9NjxH++m7EXnHuKy4ILhd+WT533nPOhe6drt2PLt9Uf47vjq+mr+ZQGgBMYF2gbzCMAKkA3fDqEP3xAQEbMRShELEDoPrQ0WDLkJeAaUA9AA9P4e/Tz6Wfev9HDzkfM084byo/F68b7yvPQj9tX2DfeS90P5EPs7/Kr8a/wB/e39tf45/53+X/4y/tn91/3C/EP82fst+4r70vpy+or6rvkh+or5nvjw+DX4g/iy+CH4hvrH/MP/jQLuA7wIOA58FGIZIhu/HYogOyRZJ6smOCTBHxMcTxpEFhARtQiU//X5NvUr8tHtnudw4/7gMOKt5JTl++Uh5rLoQu2N8Uj1vPZU+Av7Gv4aAhoEAAVbBrIHewpuDHYNPg6tDvUPrBCMEM8PZA58DTUMEQpnBwoEpgGO/5L9F/vj99X1gPRW9DT0XPOB8kryLfOj9L713vW29Uf2dfcQ+R36Nvqa+lX7rPx5/hb/d//M/0gASgFPASIBZQB1/0D/NP7X/df8gvsz+w76tPlX+Tf45fg8+Nf3XfiD+Er8J/8/AWoE1QXTC8wSXxe1G/IbDx5uInUlyCd+JbIgYx0BGmIYrRTlDCUF0vxd+Nz1W/F17E7mY+KU4mjjneSC5FPj5eTR5/brm+/+8EjyMPSX93b7av40ALMB1QMCByAKZAxQDsYPdhEKE5kTrBM2E1YSoxGpD84M1AmeBokERAK4/3/8PPmm9632MPYp9VPzK/Lq8Vfy2/KE8snx8/Gj8iv0cPXO9aT26ff6+Qn8Vf0P/o3+bP9cAI8AQQAM/879of2l/KX89fsU+2X7YvtV/OD81vyA/fr9m/5L/43+8P7oALYCrgVHBmQH+wq5D/gUmRfoGA4aLB3PIHsiVSFUHqgavRgmF08TzQ1UBWX+t/kc9rLyA+1m52zjU+Jo46/kxeSW5J3lg+g37aXwDPOv9Lj2Q/rX/cUAbAKoA2gF5gebCl8M8AyyDRYPTBBBEckQqw/3DiUOaA2bCwEJnAZiBOcCwgD9/Xn7FPk6+E33i/X48z3y7vFg8jby7vFJ8UnxhPLv8wP1cfW89fL2k/gh+h/7e/sD/AX9Af7Q/m7/ZP8kAKMA/wCNAcUBEwKmAnMCDAKYAa8AfQDV/+T+0P0b/JH7sPuN/Sz/0P/kAHgDQQjYDRASIBREFlIYMhwxHwcg1R47G3gYWxY1FH0Q7woEBEv+gfq69zL1/vAV7YbqNOpG6yfsquvW6mnrE+3p767xU/Kj8sXzHPaw+NT62/vr/OD+YQHcA/sFdAcMCSALwQw3DhsPxA9OEK4QeRB7DzoOygyvCxwKtwfGBAMC1//7/Tj8xvlN9yz1E/TC8yLzM/I18dHwevFe8tTy4/L68vTzXPXF9sr3+/d6+L35PPuJ/Dn9G/1y/Tf+WP8OAP//qf9N/2P/sv9N/2f+RP2C/HL8WvzP+9T7hPyC/vwArwL1BGMI3gyJEUkVUhfLGXEcgR+MIeggGh/2GxUaLxgwFVERdwsbBg4C7f5B/HP4t/Md8PLtVO1E7czrhOpi6bPpi+vw7Mrtxe0j7qfv1vEg9Lj1yPZk+Kn6j/2xAAwDRQV0B90JkAypDlsQtRGREvoSxxIeEnIRIhB6DhUMHgmXBroDfgHw/rz75/g+9ub0JPTk8rfxBPAq78HvFPCi8JzwPPDU8OXxiPPo9Kr1i/Zm9734R/qs+538O/34/Zn+sf+IACQB4gEFAnAC5QLtAmMDxQLVAqsC7wG+ARwBTwHgAtAD6wTYBaAGQgqJDfMQ+BJwE7sVkxcvGrgbThrvGLQW0xQnFEMRVg2UCAMDVwCd/af6nffL8kPwEe987vXus+2Q7Ins0Oz37gbwBPC38JrwNvI99Cz1D/e090X5DPwI/v8AzAKXBDoH0AgPCzMMMA1tDmgOuQ5DDlINygyXC1UKlgg6BokEhAIlAbb/Iv1i+135APhf9/j12fSW84vygfJ78mLyKPKp8d3xhPJM8+3zGfR39Az1X/ab93T4g/kh+mL78fws/vT/zgDbAfYCnwPYBM8EqQTuBAIEKASHA1YChAL/AfICVARfBPAFGQeACTMNOA9sEUQSwxMhFk4XVRhpF5UVURSlEmIRdg8sDOEIVgXkAmkBOf++/PP5aPc19r71A/XH8xDy0/Bp8JnwjPDs71/vAO/u7xbxVfJ785/01fbz+Iv7+P3G/xEC7gOQBT4HFAgHCc4JLQrACksKPQoHCsUJHgowCWIIfwdnBgAGMwURBLMC9ADB/43+S/3Y+7/5Hfip9mz1YfTk8rXxyvCB8KvwmfDE8P7w7PEv82X0tvXp9qD4f/pR/B7+VP+8APgBUgNhBOsEuwTwBJsERARdBBUDFQJpAdAAZwFGAhgC0wJjAxYFMgibCaoLZgx/DfkPHhGzEq8SpxFVEbwQgRA/EPkNBgzrCR8I8QcoBiMEiQHI/t39qPxc+2L5kvYM9Qj0gPMH86DxovBH8C/wAPF78aHxP/LP8gf0ofXp9j74tPk6+wj9MP/8ANUCpASRBnYIFwqdC7oMyA0lDjMO3A03DbEMcwvrCRQI1QUEBEgCfgB3/u/7zfkL+LH2x/U99MfyePHC8NbwsvDR8NrwtPB/8bXyA/SY9ZD2q/dQ+fX6y/xc/nz/vACjAQMDzANQBIkEFgQTBBoEbAMPA1YC2QGWAosCEwNNA2QDQwVKBpwHcghcCNcJhQqzC9wMWgySDFYMmwwSDq8NYg1PDOYKcQs9C/IKmwkiB+gFTQSkA4sCzv+k/dv6Fvkh+FL2zvSl8vDwbfDe7/LvuO9h78/vS/Cl8THzd/Rh9uf3+fll/K3+bAHMA/cF8QemCY4L/QzcDXMOCQ6/DRQN+QseCzoJdgfEBRwE+wJzAR4ApP47/Tv8M/t7+nH5Qvgy9zv2v/VT9eb0e/QQ9P7zOfTA9FT1rfVm9gT3M/il+Yr6E/zS/On9GP/O/+8ATQG8AV0CdQK2AmoCdwI3A1sDygNYA0IDbASQBPsFfwUGBZAF8gTNBvoGyAZDB/cF9QY6CHsIrgm8CMIIqAkMCroLCgskCq8Jfwj4COgHTAaQBMMBsQAs/6j9TPzW+ZH4cfem9pn2v/VK9fT0BfXV9Tv28Pak90L4svnb+kH8pP2O/tf/+gAuAkkDBAS2BPUEVAWOBWIFrQWHBSMF7gQuBPYDrQMgA6gCpgG5AP3/Pf/D/gz+pvyR+436M/rL+ez4EfgG92r2ifaD9nj2YfYK9qj2h/eR+Lb5P/pF+zr8ef3Q/kf/7f/n/9cAagGmAeABkAH2AagCXQMCBK8D0wOpBBEFOgYUBkkFPAWgBO4EygT7A/gC2QFTAUQB6AE9Ad8ANgAyAKwBLgK2AqoCIAIaAwIExARtBbsEzwTaBCwF3gVWBSMFWAS/A8EDFQNoAkQBTgBo/3f+3f3o/Fj8Ivy8+/P7wvsS/Lz8Hv39/Xf+7f6H/zgA5gDSAAEB4gDmAC4BxwByAP//qf/u/+L/3v/K/zX/Vv+A/47/gP/k/nD+0v13/T39QfyB+1L6cfk++bv4rfhS+Pf3c/j3+D/6FPu7+6H8OP1U/in/oP/5/+D//f8bACQAWgD2/2UAmAAiAf8BGgL7ArIDdQQzBS8FxAWXBcgF8AXuBI4EOwMrAsUBhgDZ/2P+9vzA/CT8yfyc/Dv8sfwD/a/+xv9uABUBbgHSAv0DvQRCBfcEAAUoBVIFTgVtBKgD3gJdAv8BXAEBAcj/WP8g/8z+sv/F/5X/EgADACsBJwJGAvkCfgK2AicD3gJkA3MCkAEeAWEArwA/AK3/Pv+Q/uv+Ff/2/uT+GP4e/gP+2/3J/bz8NPyo+1D7Xvuj+iv68/me+SH6b/qR+u760vqI+/j7WPzX/Lz8OP04/aT9Kf4P/q3+Lv+0/7wAZQE5AjADoAOTBO4EVgUABqcF7gWcBRsFzQTyA4kDnwKYAdQArf/T/o7+zP1+/c38OvxI/Bf8gvy6/Ij8tfwy/Z39nf7w/kb/6/9hADgBkgHpAUICPwL9Au8CjQKNArEBygEDAjsBEQEpAGUAugHpAfYCtgLyAtwEqQUtBz4H0QZVB9QGRQdiB+4F6wQEAyMCQAIRATIAP/66/ID8TPxr/H37SvrN+Sb5y/k++kX56fgI+Av42vjV+AT5m/i0+Kn5Hfrl+mf7kft9/A79//2v/vn+7f9lAEoBDwKmApADWARZBcgFYAYmB3IHBwhKCBUIywcmB5cGuwWwBH4D6wHrALj/n/7F/Xf8t/u++mf6ZvoQ+vz5ivmG+e75dPoi+3/7u/sv/ND86f21/j7/xf/l/8sAeQHBATACmAGWAf8AtQAwAOv+S/5a/Qf9r/wL+/76MftV/Pj+h/4pANUB4AMDCbUJHgzXDKsMvhC1EDkSQRKRDoYO5wxGDCMMmge9BfsBEADMAHv9pvzC+Sj3+/c39uD2v/Ug8wr0n/Ka8yn0UPKf8+/y5PO19Vb19PZm9w/5sPuc/Lb+zP93AY4EJAbEB1kIzQhCCkALHgzOC/8KmwoiCisKiQmnByMG2ATnA1QDbAEp/4b9z/s0+/r5+feO9t70gPSb9Aj08/NI86Hz7fTr9dj3S/ga+V77bvyf/sb/GwDeAbUB8AK/AwQDkAOLAmYCVgKGAFoASP4//fr8Zftc+z/6PPhF+Nj4wvvM/U3+3wAaAo8IKg5DEWgV5RRDGIYbcR3uH/IcuxkSF7gU5BThEcML7AahAGP/wP0U+m/3afEo8E7vpu627wjtfeuG6/br0e1h7sftJ+627sjw3fLf88H1H/ec+ar8Af9yAfsDzQbZCQEM4AwYDsYOyA+IEGkP0Q0PDKwKewmPCJEGmwMbARH/Yf45/Tj7TvnI9pT1QvWG9LXzSPJw8WLxe/LA8/7zofSZ9QT3qfkp+2H8lP0n/ncAPwGFAmYD+AJqBPYDVASbBPYCvgJGATAAWP/Z/Eb7ivkl95v3dfVT9a/0xPJx9236MP9NA1QEQQoSErUaByLzI2Ul9yZgKXwtKyypJ38gaRhqFCYQhwroAY73D/AB7AHqJOj/4/Hf+d6q4FTkaOcQ6frpTeyg7/Pzq/Zz91L5vfls/H7/JgC3AdcCGgUxCBoK7QtADb8OCxGuERwSvhCFDjwODwwuCpUGjwF8/mL78/ng94/0xvEa8NHwbvJ/8svxivEt8lX0Lvba9mb2dfax92D5Wfsv/Eb8cvyq/usAQAIGA4kCcwNQBPsEEgVSA08CjwGt/9n+iPxV+uH4KfbD9Uzy5fAf8I7u7vAY8aPx3fJ/80v5EQGKB4MPQxHRFk4hkSmANOQz8zKKMpkxHjWfLxgomxz5Dw4JyQGP+mfx2+N63NbYf9eS2oHX1NdE2ZDc6eWf6lXwnPP99Vr8DgBvBHQH2AbmCYULhwz3DtgNig/hELoRVhOxEaoQIBBpD8IObAuVBpsCCP+Q/ZP61fUO8pDuKu6B7knvGu8a7ofvk/IR97D6UfzU/Kj+kAGnBOcFcQRHA+kBKwK8Aan/c/1/+yr6q/k8+Sr41/ex9/L40fnl+ez5p/lK+nL6L/rs+MH3BPeJ99H2MPei9pn1R/dj95P5dPlI+rr8Lf1z/tL+hwL0CxATJhezGXIanSYuMKU1TTYFLUgsgypkKQglzhWNB0r6u/EW7wDoxN1v1TrPttMi2sDfTeTp5GfsAfXI/qsGfQhcCtkLtw4gEcYQbw5vDPQKtgphCoYICQfsBWQGqwYfBR4DHAESAAAATf4A+5P3pvRK9Lz0MvSu8t/wtfGL9L33wvkv+rT6efy4/9cBIANNAsAB6wIuBBgFQATkApkCbgLSAeIAXf6j/NH6Rfra+Xz3Tfaq9Bf2nvcR94L2IvWL9ZD3Vveg9g/13/PH9YL2UPip9yD2+fbI9+j6vvv9/J/+Cv85/8H/UgaaEokaGh6EHOweBi5UOD8+4Dc/K3QoMCYnJZYdxwoW+srtk+cb5wfgU9fH0CHOQdb33W7kCelg67PzEf22BOgJggp8C9UNvQ4xD9MM/QmbCfYI/giYB+4EqQRLBT4GUAbGAzABn/8a/xz/3/wH+Tb1T/O+8wr0SPNl8r3x/PO69xv7I/4Y//gA/QNSB8wJNAq5CcUJwwqFCvEI2AVGAycBMv82/ID3ufOK8H/vq+5C7cnr3epe7DPvafK686H05/US+ej74v2E/on9cf64/l3/Pf8y/Yj7H/po+dj5Uvk++Ff65vow/Sz+HQBwCj4U5B2yIVchkCiFMqA6FT2aMyYq6CSBH2McLxCM/+XxUuZe48bgYdvh1qbSCNXj3DLk2+ty8AX1P/1LBFILwQ5KD4QQ8xAOEZMPXwyoCeoHrgblBa0DeQE/AP//nAAmAFT+4vt7+iL6nvoi+iz4/fVE9Xf2WfjC+dj5mPpc/GP/NgLGA4MEsgTTBfMGjAewBvAEKgOdAvEBRgAR/jz7/vmp+FL3qvUd8wPysvEz8mvz6PNy9Lr1p/eD+v77Pfwn/P77Tvz++2L6m/jp9pf29/ZZ9w33UfZz99z4dPt//LP8NP+IAAMD2gPyBeMO8Rf3HvghvyBmJ6cwCDZxN+ssICTrHjwZUhWnCG34desU4a7eX92E2JPVs9LW1sLgPOkS8qL2nPvRA/0KuhFdFHMTGRM7EjoRVQ91C0MIfwVPA6QB//63/Fn71Po/+5P6+fiM9/b2+/dD+Tz5Ofg79+f3j/ps/ab+Zf7S/tcAdQOMBfsFHwV6BGEE7ARmBX8ERAOCAoICWAKGAT0AuP7D/T/8G/rR92r19vN184TzQfMo8nvxKPI99JL2Fvex9jD2C/f1+L35nPnu+KD4Mfrb+zn9pv0c/U/90v0Y/0b/wf8yAX4BhgHQADcFchGvHJ4i9yHdINMp7zTcOo02oChfHlcY+hMLDl//ou6j4UTaMtuH2//YPtdt13HfuurO84j6bv5oA5sKNBC6Er4RaQ+DDp4NvAtlB44Cg/8D/vj93/w1+vf3hfdO+Uj8Hv36+5X6mvol/Yn/+/8s/gX8vvuS/YD/EwDS/jn9wP1q/2EB+AFpAdsAhwEjA20E9QS4BJIEbwRbBIwDDgInAPH9vvv9+LP1xPK/8O7v9e/P74HvKO9f8N/y8PUA+HP4pfgz+lz8t/0F/i79HP2z/aT+J/85/iL9lPxc/a/+HP7S/dL+E/8E/+r8ZwB8DJEYgSAxIEMgoyogNxc/fTyBL+8kmByvFTwPAQE48DHhF9eG1mDXOtaH1XvWc96C6nP1Iv4sA1EIgw7QE78WZBU4EvIOBAySCeoFgAGq/R/7cPpn+gf6N/lX+S37Rv3O/oT+Jf0w/Sv+ov/t/8f9iPuK+ij7o/ys/Ir7lfrj+ur8WP/MAA4BHAFqAn4EygU6BuUFBwalBh0GmwSzARH/O/0L+2/4lvTU8A3v7O6v7wTwyO/z71vxRvR+9wL6bfvh+4v8NP0c/cD8UfyU+7D7Zfte+/77d/wD/ZT9O/7S/pQACgIRAtQA0P4yA+UPRhz2IuUhbyBdKbM0jjpzNngo0xxcFOAMngVD+Hjoptuk04nU6tfW2Hnb3N/U6Qv3DgGICKINNBIoF38ZtBcXE28N7AhfBQMBdPs39m7zYPOY9UT3p/cm+PP5t/1TAeAC6QHn/xb/W/+9/yP/+PzR+hL6sPoQ/Ln8nfzC/In9s/6y/2cA+gDAAaQCaAPlA4MEVgUZBqUGfwWSA1YBfP/7/dn7zPgT9VnyFPHd8I/w4O/Y76vwefLM9Dv3mflp+4/8zP3M/v/+0/6C/qH+T/8B/179jfur+VL5ePq9+a747vca9+z3HPjW+ssG2RQWIHclliVWLrk7wkOIRLw4UiohIOsVVA4IA/vw8+BQ1G7QfNRe1tLXn9kL3kHpm/VGAOgHWAv8DvoSMxUPFSsRqQw4Ca4FogIy//77vfqT+o37E/02/T/99v0I/64ApgBz/1b+cv2S/TL9FfwV+xn6UPoA+wP7H/si+/j7x/2E/tL+Pf8FAOgBlwN/BDcFfQXLBfkFZAWtA/wAlP5y/J76PPjo9AzyBPBQ75vv8u9Q8A3xu/Jh9UD4kfp3+8f7Vfwc/bX9wP2v/G37sPpV+v75VPlS+Pv2zPbM9vD2afkg++j7+vvH/EwIqhrnKG8v1ixsL888vkfjSu0/iyx4Hh4TewpTAIDtY9rBy6PF1cl7zcfPUdNE2W/mMPU0AUALJBGTFtQb0R1IHVoZBhQoEFYM/gfBApT9Bft7+uH6u/pJ+Sb49/e9+FD67vqP+TP41fdS+Yv7NPzA+237+vvZ/Yz/6//u/+D/wgBLAqgCFQKjAVYC7gPhBNkDDgI2Aa8BQgJ8AeL+Y/sG+b/32fZ+9bnyNfCF77vwBfM79RH3hfgZ+kT8Mv6d/1r/yv28/JT7PPqK+Pn2Mvam9VH1yfRw9Ez03fRh9uX3Bfo4/XX/awAl/1QClhFyJN8xuzOrLjE0jkG2SmZJ3DdkIqUS5QWm/cjx3N5JzbHArr8gyLzPgdZU3ATl4fPcAngPzxcyG9EdHR8cHssaSRSiDRAIDwM0/or4x/Ol8Qzy2/Pi9Lf0GvWx9hL6zP22/7H/nf5h/h0A+gFhAjIBI/8r/oL+7/4j/3X+2/21/j0AsQFAAikC/QKgBAsGQwYGBb0DVgNEA8cCYABn/NH4tPaF9ZT0CvOw8Rby0POd9rn5tftc/Pj8Kf7X/5wA/f63/Kz6Sfmb+Kf3s/aW9VX0+PMv9P30n/Vd9Zj1tvXI9tP5XPtQ+wX6CP4GDiQikjCPMzcw3TWXQ3lNRUxVO50lSBQxBvH8dvHj30POAcFGv93HFdE92Wffeudc9SMECRGNGUAcuh0qHlYc2hg0EtQKAAXc/4/7iffZ8z3ytfJl9OL1Y/b39hz4y/rA/T7/T/+h/tf+xwBfAjICywAW/6j+yv+RAOv/Pf4R/dv96/9zAWoBwwBaAf8CvQRgBS4EqgLAAUoBgADH/Xj5mPWd8wDzpfIf8YXv7u9I8gz2YPkm+2D7k/sM/Rb/QwD2/o/8FftT+3T82fwV/H/6ZPlb+fX4AvhJ9iv1/fWX9oD34fhH+Wn5zfj4/RIOOCHMLowxXy/tNs1EOE0WSrw33SHHEDQCQ/ji6+vZksnNvtC/LMrr0+3bauNm7SL8oQrMFaQchx60HsodsxrwFRsP8wceAgf9p/je9FHyrPHZ8hP18PYA+JP46vmJ/Pj+YQAeADv/S//tAMECQgMgAuD/0v7S/tD+4P2f+9r5N/m/+Ur72/xC/lwAzAK5BSIIlghTCCQIqQdrBhED6/0b+Z31gvPh8afvn+1f7Y7vK/PM9nL5Svsb/YL/+gEsA94CmAFOAGj/Qv6J/OH6JPlF97r1RPSw80PzCvOJ86H0jvec+jb77vmV+Ov//hEZJZMxAzPlMQk6rkVOTKxH5jQGHxgMnfxg8wfoYtcyyEa+scBkzBLXdOBy6NHxKf/lC7oV0hveHJsbeRnKFYAR4gtmBToA0vtF+O/1FfQ29Nf1Zveb+Of4TvmK+iD9EwBIAhoDrwKzAv0DSQU5BXgDOgAu/f76xPjp9pj17/T09eP3GPq+/K//agNKB1YKzguUC4IKKQlHB38E//9y+rP1EPKy73HuLu3e7Cfuu/Co9IX4Rft1/Yn/wAGOAxgDnQCa/Ub7P/qB+U34aPax9Az0m/T59Qr35/ag9m/2N/cU+u/72/u++s/7bQiQHC0tzjX8NME2vUG/SYNJbD1iJwoUpgMK9uTsIuBW0S/Hz8MfyxnX498J6MjwOvvABssPMBbzGckaBRljFeEQ7QtQB4ADQQDk/Fv5oPYB9nX3V/nc+eX4JvhO+Yv8TAAKAscBSAGoAcYDmgUYBWwC9v3++bj3QPbt9Azz1vFn8/f2Uftq/y4CAgVlCBwLOg0nDcAKxwfgBGYCDf+u+frz4+8I7trtie3p7ELtOe9O81D4afz0/m4AnQG4Au8CoQFv/z/9dPuB+lf5ave49RP12fUB99D22/Xp9XH2IfdD+Tj7V/wS/AD8agXmGKMrZzbVNk01QTzIRJpGzD4bK2MUnwFp80Hr/OJ91VHJZMQZyS7VpeDl6BLx8fmpA6sNtBQSGHAYSxZhFCsS3A2RCJUDJgAn/n37H/jV9YD1IfeO+Y/7SPyj+8n7+/02ASMDVAKNACsA5AAQAdH/df14+rT3wfXB9ND0mfX/9tj5Jf26/3oC0QV1CSoMPgx+CgkIsgVfA5IA7fyt+Nf0ffJt8QnxV/E/8u3zWvZM+bf8v//gAIQALwBuADAAI/5u+7/4//Y79mH1PfV39ZD1WPai91/47PcY9/32CPhT+pz8ufzA+8b6ugEeFQcqHjf2NyozxTdxQn5HSUKHL3YXIANM8wXrtOQ72WrMGcWqyHbVdeII7CX0GfxiBF8MrxKyFowXbxXoEh8Q+Au9B8wDZwEKAI39e/rO92b2D/f9+Jb6lvp9+cb5dfwkAB4DwQM9A4AC9gGfAZ8Ad/7f+pD3SPW+81PzqvPi9SH66f1yAFEClAO9BZYINgpCChcIVARTATf/XP2u+g33rvM+8SjwffA/8WLyzvMi9Vb3P/qh/GX+zv6o/vv+Sf7J/HT68Pcs9/L2w/ZP9lj1jvWi91z6mvwZ/fb78fqj/BsA8gFYAHL8CQB5EXwmyzT2Ng0y9jSTPTxCez+JLyoYugI48ezow+Qj22DQKspdzULasOen8UX6vgFMCYoQRhUfF00WDBRjEn0QlQwFB8cBev9f/+L+a/wR+If01vPD9aX4pflr+K/3EPn9/DIBxgOkBEIEcQPmAb//rf0U+x34kPUb86PxAPIy9MT4pv3XAMoCngRrB6gKNwxxC6wImQQtAY7+ivtH+CL0TPCZ7vjth+7M7yjwL/GC8yr3vvsc/+AAXAKHA8wDtQJuAOD9rPue+Q33pvQS85PypvMV9cX1uPb++Ez7Zf2v/34A5f9n/qb9EgiqHCUu6TaCNAAySjunRNlF1juJJQQPzvzn7jroWeCk1JfL/ci50KndzedZ8Av59AFyCiAQOxOpFTAWTRWxEwAQXQv8BiADFQFo/4/7T/em8wfy1PNW9t73+ffI97n5D/5xAhYFTgWvA/gBnQCv/z/+wPph9kPzmvEd8jjzd/QN+IT79v5hAr0E7gciC/8MxA22DLEJDQYlAjT+iPp19o3yXe9U7bHsMu0N7x/yUfVi+Fn7wf3MAHEDFATwAqL/Cvyr+ef3uvad9M/xW/Bm8LzybvVl9gv39fhi+9D9yQCCAUQAEf5w/BIIoh7BMRI8ITleNWc+DEYARvE7hiPRC2/4sOhq4wDeI9RVzZXLZNOR4fTrG/Q9/IUDfgroD9QS0RTXFN8SrhHAD5AMiwhQBNkBzP8r/J73Q/Pd8IPxWvOF9Wb3ePma/b4CDgdMCTsJ6AfnBQEDUv9y+6/2dPIN8FfvBfE08zv1Mfip+u/8tP/wAhcHVgqACw8LvAkVCPIFRgPQ/2L7yvXL8BXucezm6zfsKe2B8DT0qff4+1L/OwGEAcj/sf02++f4gfeq9S/0FPNj8xz2Tfiu+cL67PpI+yb8Q/zg/CH/mADQAJ3/1/2gBj0b0S9VPeM8ZTbcOCk9MD7ZN08jiQth9vfmkONK4iXdvtfO1MvZO+TO7HfzXfhl/KMALAU/CqQOxhDLERcTDxR1Ew4QqAsZCE0Eg/+2+arzR+/B7VfvVfPk9iz5JPsY/rECtAZuCHYHSwRTAJ/8l/mx9gH0LfKV8Wvyy/NR9Xz46vyCASUFpQZOBykIZQmzCpgKmggdBbUB0/9J/pz7+fce9Dzxyu8I70LvxvAp8rnzVPar+Qr9gP58/Zb7hfnV90v2//RR9Lnz2fO69ZH4sPve/Qj+pv2s/RX9fv0+/1j/X/61/J39NgpdHiowSTpCOd02fDrNPIg6vS/VG7AH5vXk6b/mJeQd34naTdno3wzqxvDg9Gj3wPnb/UYCKAbUCBIJiQkMDGsOlw43DHIJTwhcB6YEq//I+fn1GvWb9jn4cfdJ9m/3nPumAE8D0wKEAFT+2/z8+1D7Mfkw9kH0//NC9uX4/Prx/agAbAPGBfwGHQjYCNAIuQiyB3gFuAIpANP+2f3o++f4fvWj8qvw7O868EHxOvI287702faD+bP79PwX/Zb7nPnn95T2GvYj9l32vPb99vf35/nt+1H9ev5U/2AAggKvA78DmQJlApsMYiCVMow8hjpwNbo3qTkGN1otVxmJA5HwkuMb4bDgM90a2v/ZnODt6lnyvfdB+8f9XgELBZEIvAq8CoALBg7zD28PBgxRCKkGwQWfA8j/vvqm9tX1ZPdD+dP58vjz+OP6Ff1L/nD+uv2u/BP8vvsL+8j5Zvjr9x354/rC+678vv6EAV0EBAb3BaUF0QU8BuoG6AWvAiH/sfzb+3f7Sfmo9XvyQPBx763v/u9S8KfwwPFR9A34e/t+/Sn+5P0B/d37dPrh+ED3NPUQ86ryfvR69zj6GfuR+yL9XP6xAHECVQHT/vz7swF1FewrEDvrPTA4wzh0PVU9STcRJlIPrPsa7AbmH+Yg4tfclNoo3Wbl3+t87vPxr/W/+Vz+TwE1BHYHnwp9DysTdxLeDpAKEgjvB4oGogK8/ff4VPdX+e37sfzN+v34M/pY/YL/r/8//gz92/yP/Af80frB+FT3UPcz+Ir5Bfud/a8BdAWhB/MHlgfhB3kITggWBzMEXAHQ/4v+E/0o+qT2B/QD8i/wre4j7onuFPBy8lr1l/hc+yv9uP4dAJIARgCL/qX7jvjc9XD0QfRy9CL0NPTx9JT2PPkV+9b7gP0//lP9Qftx+eQBoBZfLRA9kj+IOgA64zxMPbQ3PiePEJH7jezo5y/o+OTh353cKt5G5C/pXuuW7Zfv9fI1+P38IgG6BO8I2g4rFIkVFxMmD5sLCglmBmgCrP19+V33m/hD+/b87fxP/LX8P/62/9D/uv7Q/Kf6tPn++OP3vPbt9fv2RfkU+6X85P4uAhQGHgn6CeYIywYmBeAE3ARoA9AAbv4V/ST8FPuD+Xr3i/Wm81PyAvLf8Tzy+PM+9uX3bfiZ+D/6i/zr/XL9Ffsm+Ar2Z/XB9Tn2avXX8z/zAfTm9W/4CfrL+c/6OPyz/C/8LfoZAaQVVSxQPIQ+qjeCNq048DgtNYAm8xEG/4TxE+9r8cPucOmQ5Ynlk+ki7BDs+Oxf7hTxfvb++/gA8gXvCloRABZmFZMRhw0OC5EJwgYcAsn8g/iH9zX6o/0w/mX7FPm5+VP8lP66/rX9Wv1+/ZD93fwo+oX2JPTx8/D1Jfir+Wf7yv2fAUEGRAlYCn0JTgeeBeADHALzAG7/Mv2E+qv4aPjI+Jn4X/cD9nn1VvX49cX2wfaL9gb2zPX99Tv1J/SC87HzwPPq8vrxC/IB9MX2sPjN+XD6tPqy+xf9bv2r/mUAxQApAVj/4AA+D8Ui2jP6O2E2uDDDL3MtQyyYJUsW9Qbz+Bby2/QX9sfz2PBI7bzslu3/7AbuW+9/8NX0Nvrr/mMDnAY7CzYQchEAEGoM9QfrBHUCiQGsAEb95vq7+uH7j/0X/Lf55/jB91v4e/pr/Hf+3P6//uL/Zv/k/ML5m/bk9Vb2Xfa4+Fr8YQCXBUwIIgnxCeYIvgcZBiIC1f6R/Bn7F/v4+rn6Bftp+/X75Psi+176qfn9+K/3UfYg9WP01vNy8k7xp/A68GbwjPDl8GnyvPQI+HD7if3r/q//2v93AE4Acf9rANkAb/9x/rj+xwjTHGotMjU3Ma8mKSSOJdUkbSLYFm0Iuv+D+qb8WgCJ/Aj3TPKD7tXufO6x7fnva/LZ9R/7i/8jBNkIQAyDD5wPzgvUB3MEOwNaA/gBmf+a/Av6tfrO/D/9tPqv9sH1u/jQ/OX/iQDa/1//gP8FAYYBpP5r+kv3O/ea+bX6cvvM/d0AzwTqB5QI2AerBSED7QGmAJ/+6vwi/AH9N/7V/QX95PtS+oP5ZPnT+Yj6jvke97j0MfKR8FXwjPA68c3xefJB9P319PZb98T3PvjM9+v2TfbG9sL4tvmE+gr9CP7r/u0ATQNwC58TmRNsEUUNygsKE40YzhvgHggcQRroG0IbGxkFEv4HkgPGA18EOQKG/bn5h/gp+8f+5AA7AaH9G/rv+o/88P6oAC7/pv/XAI8ARgFaACP+MP4B/ob93/wF+g/5uftj//YCSQMXAA7+Af6D/3wBzv+N+0f4tPY++KH7Qf3F/fT9s/1S/3ABUwI5AxoDeQIXAmAAsf6q/kD/yQD0AXIBxQDt/7j+Rv4w/bD61fiA9zP3OfeJ9Tb02fRa9vX3z/gA+QT6M/vz+3v8s/un+bL3IPbZ9JjzNPIr8kr0ovZg+Ij66vwq/wMB5gEPBDYHbAniCdYI9giPCqsNnxHHEtkRQRABDI0H+gH6/HcDJBLTHswk+x7jFtUWThdNFtkSdAdy/NP2GvXx+vD/zv3d+1f6ovhD+MP0p/Ck7yzuju7v8Tv0jPf++yQApAWyB/sECALT/iX9e/0D/cD86Pv++jT91gBRA5UDnwEOAe8BbgI7A0AC5gD7/+/+MgBWAeX/vP4B/bv7a/zL+0j8hP7k/sX/nAD0/+n/df4Q/L361viH91f4K/pC/eD/AQH6AfYBewBo/kH7AviJ9YLz+PLt8yD1aPZ490P4tvnx+ob78fuB+xv7cPst/KL+owF+A44D7QFeAAAA5gFUBAoEUQOrAhwBDAHI/uX/rQ3sHmsroi7DJA8dIR07HJAdEhtwEN0ILASAAzIKcgrlA37+H/eu8Tfuv+cT5ZnlYebL6/HxpvX5+AX65vt6/yQAdf8R/k78zfzI/g8CtAV0BkUG/AVQBfkEJQNfAd4BVQGNAOD/sf0D/WP9h/1m/xz/E/xH+uf4u/lD/Mv8mv36/Sf8nvst+yH6jfoY+rT5TPtK/Fb+3QEaBBsFQgRzAYT+cvuI+ID3oPdf+GT5JPoQ+8T7o/sJ+9r5JfhS9on0zvOs9Mz2Xflc+wn8Kfwe/GL71vkE93TzvPJM9Gb3uvyb/xMATwAVAJMIPRyPLVI37DNEJmgg/CDIIZYlYiJnGR4UaQ4CDpwRmwwqBJ38AfQq78/pfuOx42bnY+wM88r1GPb/9a/17Pij/In80vrK+An6VP/XA0UHPwhkBmIFjASUAz4D8wCQ/zsAvADOAcoBOAAZAGH/v/6L/kr8lfqa+hL7zv3v/iv9NvyG+or5Ifp/+Fb3OfgC+fH8LgHVAlQE2gOUAlMCHP/W+lD3XvTL9Ab3VPmq/F//FQHVAtUCFQEi/QT4d/R78zn0x/UP9sX1WvVL9dn2u/gL+Qr31vMb8mLyPfQ79474M/vy/mAA1QE2Ae0ANw3wH18wMTo4M6gmDiLaHaceAiJKHDUXMhMnDeMP+BAXCSsCTPkx8Unuk+gG5XHnJup471z1iffr94f1wPP29Sz4D/lA+Dv3+fkg/mgCSgZVBioFagQpA2oDyAIDAU8CfwTfBjQJQQgoBvcDlgC4/8H+U/yg+rb4Pvi3+vz7S/2q/hX9JPtH+TX37vcJ+iT8Zv+SAWwC2QKCAu0BFwGX/nv70fiV92T41vrt/eoAFwKsAPr9ofsZ+p74avZB8/fwSfDa8LPyBvVY9tn2LPc+93j3X/dC9jv1aPZ+9/L3cvlt+eP49fj/9xb/ghIaJuozHzSUJoAdkRsDHHIiKSSQHvgadRUyEwQXbhOWCsECM/l79OPxBu2W7IHvAPIe9hT37/QF8o7u/+2O8NjxZfIU8mfzr/fi+2H/jwG+AYUCuAOyBEMGEgaaBkoJ5ws6DqIOlwt7CBsF7wEgANT97/oE+uP5MfvF/Lr8t/vY+TX3bPWN9F70sfUS+Bv8gABEA6IEZAVgBcwE4gIpAMz9TPzt+3798P97AWkBhgDp/lX9sPra9qjzM/HR7x3w2vB08bLy7fPS9TP3J/cp9pv1e/UT9b/1Eff/9of3X/gx+Ff5k/fI9y8GxBonLaY1+iolHbYXgBVhHFwlyyTYIv0c4BU1F2IW6A7fB5L+/fcg9UXv8+qo6mnrM+888qrx7O6C6sDoP+wY8Rz2Evht+FP6Yvtf/ToAwwAsAqQD9AMZBwUJUwp2DmARnxK6EksOGgotB5kDQgJhASD/lv27+8n6y/uw+1771vos+Zn3YfW684f0NfcH+xz/1AFhAy4E9AT7BfMFAAXTAs4Axv98/4f/IgArALT/Kf6f+8347fXh8yjyzfDG7zXvoO9b8WLzVfQb9C/zofLH8jbzP/JT8cjx6PHq8wT4TPrz+7f6v/f2AaIVNynHNiYyrySfHSYZkRscJKsm1yeZJv0fqBzxGMsOgQbl/8b6Evia8UHpHeZQ5vXpMu4d78rsf+im5ePn/+wm80T3PvnZ++L8EP3O/b79gP/7AooFugk9DL8Mzw4LEIgQdBHcDe0JigaUAYn/yP4X/Rv9Wvx9+mn6z/hF94r3rfcf+Cj4xfaA9sj3H/p1/YkAqwIqBAoE+QLrAG7///6f/2MBGAIgAuQAUv91/nD+7f1R/Ln4P/Ws8rLxbvKG8+j0E/VT9F7yWfBq713vhvBD8p7yR/Lm8oTzfvUh+uj8Gv/i/uP6eQD6Eb4kyzQANMMmoh/OGgwbWSIBJhsp0iurJZAfvhlfDkcFWADA/NL7RfdR7RnoEedi6O/rHu2661fpKOai5uvqW/Bz9Mj2zfgt+rb5mvlb+qr98gJ0BvYJtgsRCywLfgtPC28MdQoFCNQHvwZHBoYGlwRkAxoCfv4v/PH5m/c3+IP5UPoM+3j5jPgO+nX8DP8RAVwBkgGCAWsAaP+Q/nf92f2//mb/1/8Y/uL7f/oz+br3tfVs89/yPfN99A71xfSo8wPyJPGe8Rnzs/Tm9aj2s/bR9u721fV89bb1Pvbu+ef/jgJ8BF8Bd/2TBW8VlCZ/M6QuciIZG6sVOhkQIWolVymVKVkiDBz6E3IJ1QFp/dj6/Pmf9Vvu/+ov6yPuqvHs8s/wfuy56EXoserK76HyCvRa9tz2zveq+n/8kQD7BDUGfwgrCcYH3QifCfwJWgxYC3AKTQoUCNwGmQWqAXX+Svq09oL2i/aK9zX6yfs0/bj9F/xM/Pz8w/32/oP/m/+dAAUBXwEiAVv/Qv2E+7n65vrf+i/6TPk++Hr3Avc39sr1TfY192D4PPgG9/n1tfUc9jP3OfhL+Sv6b/oC+rb4fPfS9cz2pfkg/Of+cAF7AMkAN/4u/R4L7xteK5Ux/yMdF8wSVhIcHfQmwCkOLIcmuBxSFkkNBARW/3L8TPyj+3X1Zu/B7ZzvIPNa9Cby5e0C6QLnmugO7EDwPvGy8Wn0Ifbf+Db87f2GATUEbQSgBsQHLQhzCtQKKwsWDNAJqggyCMYFSQQDAkT+/Pzh+nT5hvqn+iL7Kfxa+736I/m49of3z/nv/J3/AQArAK4AGQCt/2H9e/o5+Fv3N/gh+qr6a/rs+Zr5sPkQ+RL48PfN+Lf6TPyW+7T5k/e/9iX4APo4+3b7YvtH+tP5zPh+9w/2ovYa+P76vP7CAP//9v4T/Bb/BxGMIK8uUy3cHMwTxxFsFYQijCkOK14rCSIMGcETxwuwBTAEVgIVAzoADffT8JzvxvAR9Ebzbe8S613mzOXx6NPs3u9i7/vu/PDW8lv22vmu/FgBDwTYBDcGTAUYBYEHNgkPDAYOUQuhCbMHQgXEBdUEhwLHAZ3+4Pzz/IT78fuK+1X5jvd39C3z+PWK+UD+BwGIAKL/WP7O/Mn8KfyR+xP8Cvzo++b7Mft5+8D7mPuC+7769/lX+QL5t/nh+o36Jvnu9oT1h/UW97b4l/nI+UD4/fZb9mz1vvQN9pD3bfqf/ar+U/7K/V77dwDaD6YeWSpbJ8cZSxM/E5MZMCYeLVIwfC9NJqQcgxXKDEMHNQVtBGsFTwIQ+SbyR+/Y7trw++/p7BDqVuZo5Sjoo+tm7wnxGfL48wX1yPZ2+ZH8SwENBPIE8gUdBqAH0gq6DI4O7A6MDCML7QhXBnwFxQMnAukByP/b/QX8lfj59q320vVN9qb18fSD9jX4vvop/Sn9wPzZ+z/6VfqO+Wb5R/p9+zL91f4w/7H+cv3z+yL7K/tD+2D7qvv1+9L8Kf0r/dL8Xvyw+1X75fra+Un45fZs9Ub1T/Xd9Jj1v/fq+Ln6wvwH/Ob7Zvom+O0BixJeIQMtbCfDHKEY+RbnHLsnJCy6LpQrayGqGfoSfgvTBr0ENwMRAtf8EPQl7tLshe2775vvh+xi6GblHuXu6DDu9/BQ8tTzRvTw9Q34APkn/Kb/rQGHBd0H6gc2CWEJjwrrDLwMrQvpCj8JXAh2B7QFvAPoAAX9Tvpr+O72uPew+I368/sJ+yL6iPlJ+Yj5MflO+a75cvnC+WD5KPo9+5z7U/yx/FH8Ufxn+5P6F/u5+zD9vv4e/wj/l/7x/cH9x/xZ+7n5M/hF92T3RPep97b3Jfd699f3aPeW9oX2fvYw92v5pfrN+qj79fhs/UkPeh+PLT0sphuUE7oS9BZlJB4sWC3kLccjZxkMFGQMOAd5B2YG5QaoA/34sPG57yHwQ/PS863wgOwG57PkN+c56xPvGvAC8U7zBfTm9Qn42vlK/Sr/b/9aAnMDGAVPCUkMhA8MEfcN/QuPChQIrgcbBu0D7QKCAFL/CgCU/53/6/7m/DP76fcG9Vb1yvXO9yr5m/hp+aX5mfgQ+X74oPfY+En5NPvm/Zb+qP5A/jn9CP1N/Wz9uP1N/XL8jfsf+/760foJ+yT7Dvu0+sn5yPjn9/T3g/jw+Wv6OvkT92b2O/co+LL6Evx7+4T7QPi9+SkKUBnwJg8pXBkVESQPWRC0HQ0n4SlNLcck4RpzFn0OcAnSCKUG5QVLAnj4tPHI7wvw4fK38w7xxeyn5t3jLOY06/nwcPON9DX20/Xp9qn43/lz/YL/cgDgA/wFSgirDPkN6g5xDkYKsQiNCDQIrAl0CS8HxgVdAjYAyv/g/m7+Y/0/+u73efXf82/2Tfj3+iL8yfpI+ij6Cfqs+3/8Vv36/Tb9Cv3D/Dn9r/2k/Uj9DP3W/E/9Vv4n/5D/P/7A+5n5Wfgc+Br4GPjY9zH4C/jP96b3svca+En4uvdC9lT21fY5+PX6hPw9/JH7GvjC+TYH1RZ1JtEp6x4KFR4RNhPVHh8oDiwFLQEl1xuoFfMORgocCS0HEAc5Ay/6p/Im7yXvP/IM8/7wQO1H6Bzm9edp6+PuSe/y7pfv5e+38Rn0JfcK/JQAegOpBhYH3wYgCP4I5gq8C/QJPQkFCRwJhwqCChwJ5QY/At7+yfxX+1X7Ffy3/BP9C/va97j2KveD+UH7vPtu+9T6FPoW+ib61vqG+9j7Ovzk+1n7wPuo/Eb+v/8KAJv/Pv8D/1j/kP/9/bX7tPi89nH2z/fc+eb7wvs1+i74VPdC96b2avVj84nzi/Uq9+X4g/oW+gz78/rq+vQEIhNcIUgqRCMZGBUSRREmGiUmSCwGMKgrFyI2GpoSGgyfCHYGpwVbBFH+pvZH8fnvH/E68tHwDe0b6OTkAeXx6KvumvGK8jLzp/JG9BP3aPn2/VwBugLYBDUF9ASwBtsHMgpSDIMLOQowCUoIRgnJCVEIGQbJAfT96Py1/D39VP4T/Zr7Sfk39nX1VPb79y/5RfnP+Dj5K/r3+j/7EPue+mv69flr+rv7av1S/+7/CQBBAJ3/Uv+X/hz9svt7+Wv4Tvns+o38Pf2m/N/7U/p6+MH23PUw9eL03fQa9aT1pve/+Lb5XvvE+wn7e/o398n76wqYGqQoUiiHHJIVFxQiGP8jBSouLVwtryWxHbQXCQ87CdMGjAS9BLoBovmo86vwre9v8XTx/e5E6/DmGuUh52Xrq+4G8HDx6PHm8un0NfZX+dn9mgBhBGsGpwXEBkMH9ggjDJkMlAsBC08J2QiICeQHyAZsBOIB6AA9ACH/Wv5p/MD6ePkW93P2FfZ290H5XPqK+or6j/od+9/7ffzZ/BD8bvtB+xP9c/9aAZgBvgC9//L9SPwk+9L6d/uo+1D71PpQ+qD50fj79y73//XH9A70HvQe9Xz1qvUy9VX0LfTZ9YX3B/qG/Mf7CfzP+hL7BQcEGAonji/uJggbLxfFFcMcISYEKR4sJClvH0EZSBI3CmkHMAQwArUBEPvQ9CLyv/DU8cfyiPB67kXqm+bG5nbp0e238KPxJ/MR9Gf12vfY+Xf9hgBnAS4DzAMRBP4FngdwCnMNEw30Cn0IuwULBr8GTgYbBdkBS/5c/K76Dfov+ob5YPnK+Dz3yPa09pL30fl5+/X7NPxu+5779Py1/Xr+2f4B/6v/YQA7AEQAqP8e/5v+pv1w/Q/+Df/V/1H/Rv1p++f5yvh0+N732vbQ9U31WPXO9eT1lvR785PywPHR8cXzfPX/9736z/v2/ID8dPssA34ShCHCK5UnphzVFo4UAxn6IvUodSzEKrUhTxp+FAQOqAoZCC8FkgKI/Jv0BPAq7nfunO/T7ubrL+gP5RblA+lZ7tjxo/ON9LX0nfXD9vn3RvvK/moBZATPBWYG0AccCX4L0Q32DAELsAjRBuUGXgZoBZUEhAL5/7f9lvu0+sv6svrq+mT6EPkR+An4Sfli+qr6rPo0+6z8bv4K/2b/Lv9n/sH9efwX/HT8fP0a/+n/sf/w/sX9+v0a/4P//f7k/B364Pe49mH2Vve0+Pn4pfir9/b23PY896T2lvWh9Nvz1/St99H5Dvwc/tX9+P1q/Un+vgccFh0jGSr2JDQbHRY5FZMamiPMKLErmSgmH5wWOg/JCOMFzgKGAFz+G/lO84rvN+7c7g/vme0l6x/oUuZ65jzpgu1/8BLy4fKj8rfzifWV9/X6uP30/zUD8AT8BVwH6gcXCg0MugsRC+YJAQndCaYJXAhmBswCOAC//qb9r/1s/az8+vvY+Xz3O/Z49kH5E/yA/bX9F/0F/fH9Rv5R/pr9DvwA+436UPtz/V3/uQDdAPL/2/6Y/br8cPz1+0b7Yvpg+V75mflT+uj6pfrG+DX2CPTW81/1wfZm9xr37fYR96D4hvoD+9j65fo4+5j9pP+HAnMNsBowJUUoKiBCF8wUnRXAGzojMCb5JnkiChtUFhwS7Aw9CTkFTQLO/gf5K/RD8kHyNPO18ufwzu3W6aDnR+jz6kvupO+M8PPxpfI49FT2Fvmz/OX+l/91AUIDBwYgCRMLuAxSDWMMIwzpCysLwAmXBnEDfAGJ/43+vv3J/EH8Ovvs+R/5H/i490n4Lfn3+nb7Q/ug+nL65vrN+7z7qPuG+wr8uv1x/90AxQFGAS8Avv49/fj8Y/2f/SD+1f2v/I37JvoN+nD6K/rg96b13fMI9O/0IPYC9/D3Jfip90T3JfcK92r3Uvh6+ZL8jv9sASkCSAGQBPwPjh3OJycn0x3wFocWfxoiIkYmhScIJvIfdhmOFdsQ8AxPCeEE/QH0/HP1YPDT7uXv7PHL8LbtwOnZ5Wbk3OXf6JLrN+zb7M7u7PEX9Rj3zfhg+1P+rQFvBDwGRgg2CpUM3g5MDxEO+AuACQwIKAdKBt8F9ATiA8oCIAGJ/8z96vuy+nv5S/gA+DP4VPmy+nL7o/vY+8f7s/v4+6P8pP2Z/mj+ef23/K/8c/1w/vL+0P4i/ov9E/26/ML8lvxG/NT7hvoH+f33Rfcu96L3uPfp93X3g/bp9Qz2avbV9tP2vPbM9xj6tfyv/i8Axf8g/939w/1YA0oQhR7WKAYoNx+uFxoVAhjlHTQiSCSWJCYhwR0OGvAVfxEwDK4FrACj+qb1SPNl83D10/bt8wvv8en25dXlXedn6eDr0uxq7bTv2PEs9Wv4hPrQ/Bz/EAAcAbUBBANkBiIKTQyyDAMLaQkFCRAJOAm5CCYHEgXeAlEB0gBuAO7/wf4i/YH7tvmy9632O/am9qn3S/iw+FX5kfkO+rT6H/vW+1z8hvwF/dT9LP+oADQBEwFyAB4A2v+p/9D+rP3x+2/6TPnz+Ib5vfkP+bb3rfXi85Tzj/O69Jn1rfWH9ev1lvZH+FT5IvrN+nD7Mv1J/xsB5AHOAm0HLxLHHXMkkSF/GdsUihevHQgkFCeZJzMnuiMuHrUYxRPUD9cMUQgcBJX/2/qX94n2+fXo9Orweeoc5XviMuNZ5qPp7+ve7XXu7O5r7wfwevHt8732R/q8/YgANwOgBbMH0AkpCwwLlAqaCT0J4Am2CiIL7wpKCcIGMQTmAY8AWv8a/t387/sd+5j62vlA+bD46/cq99H27fYC+JP5KPtV/KX8gPy3/BH96P2x/pf+SP64/Uv9XP0F/eH71Prw+dj5zfq1+338B/2f/Nb7A/sQ+tr5PPrz+v77kfya/D38xPsi+436DvoJ+mb6Avu++5r8Nv1w/fv9EAE8CPMRfxonHRUaXxX2ExIXHBwWH1cfXR5vHUgcKRoEF4sTPBBQDF4HrAEk/Fv4Wfei9xr48Pao84zvDOy86WXpTOrz6j3rHOuL65ntTvHo9Ab40/mV+nb7Z/zD/RAA8AIhBrsI5gngCegIzQfsBq4GxgYLB/MGhQarBXUEUQPDAgYDBAOtARj/mvxt+/j7Ff30/VP+NP7V/Y39DP3i/IL8BfyC+/j6P/p4+Qf56vjA+Ur6P/qj+Q354fiG+Wn6Lfvi+078iPxw/Pz7mvuq+6r7Rvuu+mn6mvq7+hT6f/nC+bT6UPs9+wX71vtn/Yv+Lv93/87/AAADAI0AfgPdCL0ORhIbEugPNg80EYsUuRZ/Fm0VqhR6FGoUBBSxE3wTHhI+D+YK3gWPAbb+Tf0t/Vr9r/xF+xr5ZfbL82fxnu+x7pDuJu8f8GTxxPIH9Db1UvYw97T3Avho+IP5OPsT/bb+FwAnAfQBaAKNAv8CqAMKBPYDlwP9AqIChwKQAhoD9wOgBAIF6wSTBIUEigSSBGQE9ANqAxgDxQJRApQBXAAh/5L9t/sH+tX4Zvjw+Lb5Rfpe+pf5l/jB9zv3Rfei91D4ovk4+4n8Qf1G/Q79Af3g/Jz8XPxa/MX8of3r/lMAiQErAv8BrQF+AS0B4gCUAN0ArAFGAnECNwLyAZsBLQGcAB0AGwBYANsAuAG/AhwEbwViBvAG6gauBoYGigbCBmkHPAg2CWUK/Qq+CvEJ+AiWCKoIqAhRCEcH3gW0BNcDWANNAzADggJRAXj/5P3i/Eb8wPv++vn5wfj39973Uvju+Of4FPjy9uv1tfWM9jn4DvqL+4/8Qf0I/pT+9v6O/4gA0gEBA4wDrQPXAzsExAQEBdoEKAQIA7oBhgDr/+X/bgDrAMsA3P9s/gf9JvzL+4b7ZftM+xL7wvpb+tT5S/n9+NP4yvig+Hr4jvgo+R360vpB+337tfvv+xX8MPyx/GP95P03/kn+lv5f/xkAZQAnALz/0P99AEsB1AEYAmECEwMNBP0EgQWiBZ4F/gXRBhQIRAlKCjsLDwzODDANzwzyC00LFwtYC8UL/wvgC3ALpAp7CRAIlwZWBWQEmQPKAsABPwCL/hv9Jvx7++j6H/pF+WD4h/ez9vj1WvUl9VP1Z/W69Vj2RPdm+EX5tPkW+r765vtc/YT+b/8rAOoAzAHgApUD8AP0A60DSwN5ApYB/AAnAf0BhQJUAn4BbgCm/yX/sf5P/uj9gv0n/dv8mvx9/H38j/x0/OT7M/u0+o36o/qw+qX6kfqI+mD6SPpm+tj6k/tI/Kj82fz0/Ob8//ww/bf9s/6o/0oAbgAtANX/sv+y/9r///9EAKwAHAGNAawBfgFsAdkB0gLZA4wE0QTaBAQFqQXYBkgIRgl0CScJ6ggrCf0JHAt+DNgN2g5HD+4O7g3DDIcLRgrsCF4H8AWTBGYDhwKEAe7/Kf5B/Ib6YvmB+Aj4vfeQ91L39PaO9m/2xvaO92/4Bvk8+Vv5y/mn+ub7Cv3U/WX+yP7p/v/+E/9A/7j/TAADAXkBiwGJAX4BfAE5AbwARADr/6j/Yf8n/wH/GP8R/+L+c/7B/f38WvwV/B38G/zF+1X79frG+sb6nPpX+gf6xPlr+f743Pj3+GL58fmT+kX7Evzd/Fr9pv37/Yn+Lv+8/x4AhAAOAdQBlgI5A58D9AMfBA8E5QO0A70DAgSBBFIFRwYCB2UHhgd0B4EHqge1B7IHuQfsB7wIugmCCtIKpgo9CuQJjQkFCYoIIAi+BzQHYAZCBQYE4AK8AcIA2f8p/77+Mv5T/Tr8JvuD+j76Uvp9+n/6afpO+lD6cPqM+lv6BPrG+Zr5gfmX+ez5k/pV+9D7NPxs/M78kv1f/hz/g/+x/+X/FQBOAFgATgBKADQAJAAJAMb/g/8+//v+x/6L/v/9Zf0D/RD9gP3e/eL9n/0r/c38jfx9/Kj88fwO/dD8Yfzk+7P7xPsM/IL84PwI/R79K/1E/Zj9A/6C/gr/bP94/0T/DP8Y/2P/sv/F/7H/xf8DAGUAmgC+AOYA0gD8AEsBzgF5AgQDdQPTAx8EpgRLBQIGuQZrBxUIvgiaCU8K1wr4Co0KEAqaCUEJIgkSCd0IlggSCG0HqwapBasE8ANfAw8DqAL0AScBKwBb/9D+bP7e/cX8nPuB+tP5lfmT+bf55fnR+YP5Gvnp+C/5y/lX+pb6wPoD+7L7nfyC/Qb+Kf4X/iL+bP7i/m//4P8SAOf/ov83/7H+N/7O/ar9Y/3v/JH8SPxn/LX86vz0/Oj8pfxP/PP72PsK/Lf8bv3p/QX+4v3X/eL9E/4p/mH+ov7X/uT+wf6x/sf+BP80/0T/Bv+b/mr+qv5H//D/fQDzAC4BPwFNAWkBsQErAokCzgLOAvACbgMTBLQEPAVdBRIFwQRQBPQD1wO8A+0DZgQlBfAFTAZABtgFfQUxBdMEcwRiBNMEsAXqBqEH3we+B0MHpQYmBr0FVwX0BHMEAASOA+kCbAKsAaEAi/9I/mn9zvw//GL7JvpF+dz4B/kv+Rj5sPhF+Cz4JfhJ+Hj43vhO+dr5PvpT+mn62voB/DT9NP6W/nD+hf61/vD+GP/v/rz+ov6f/tD+If+Z/+7/0P+A/yn/GP8h/wj/CP8y/47/4P+D/7b+t/39/L78zvzD/NT8zvyA/Fz8K/w2/Ib8zfzk/B79hv3t/U/+kP4y/zAACAFTAf8AqgCuABsB1wFlAsEC3ALnAtUChQL9Aa0B2wFaAtcCEwMPA+UCnwKtAu0CSQMqBPQEjAX3BTcGoAYDB1UHlgfPB+EHEAjJB3AHPgf+Bi0HYAcxB5wG7AUxBZ4ERwTGA0IDhwLFATQBewC8/zL/ev7J/Rf9T/y5+6j77PsJ/O37Z/vl+pP6nPrP+hX7K/t9+078K/0R/pD++P4D/6b+F/5z/SL9Bf0g/Ub9Z/1P/f/8uvwV/KX7Kfvx+pH7Rvyd/GX83fuT+8v7L/yN/Gz8RPwX/PH7E/xy/Ej97/1L/lH+Cv6H/aH9bP6J/4gAAwHJAH4AtQABAZYBRALMAg8D+wJhAmwBjQAiAC8AtQAVAUIBcAFPAVYBPwEiARkBDAFBAZIBHgK4Au0COQNuA5AD9gMlBPQDgAPrAosCaAKzAoADSQS7BMQEMAStA4MDfANsAyADWAPtA4EE1wTXBMEEdQQLBKQDSQP/AgoDDAPnAl8CdwGsAOf/+P6O/kb+2f1V/bn8NPwS/Kj7L/vv+h/6vfmc+bv5Z/ox+wH8Y/zQ/Db9Lf13/bH9+P2Z/if/0P8XAE8AuQDJAO8ACgHJABAAr//R//T/2f9h/yf/GP8P/8r+m/4Y/tD9F/6U/tP+zP0p/Ij73f0bAfgAJf2b+G/4qPzCAG4Bm/6a+2b6NPvZ/PL9O/44/Zz7NPuc/G7/iQE9ArcB0gD0AF4B4gElAq0CdQM1BIwEogRSBOUDjAOZAgUCvAGjAYcBSwETAS4BhwHMARoCagGdAGcA7v+8/6MAJQE0ARECJQLxAe0BFQINA1YDqwIgAmwCAARbBXYF4AQhBOcDwwMBAxgCDgIsApkCogIeAhkBnQAVAZ8B/QGdASQBbAC9/wH/mf41/9H/AwC6//n+fv5s/sf+Pv8P/yL+Mv0R/WX9SP7l/r/+1/77/v3+lv47/d37lvuq+w78F/1j/hz/of0Q+0f5ePnk++/9d/0v+4H5nPoe/WH+HP5f/f/8B/0c/Tj9Xv1Y/rb///8DAAUA0/+p/2b/xf9GAT4DNQRvAzABEACRAGwBbAGjAE8AaQC5AMwAvgApAN7/jv+i/vj9Cv53/o3+/f6i/zsAwwCAAXwBWAGdAaYAn//e/4AAswGvAlEDGASyBCYF2gTpA+UC/wGGAf8BagMPBU4FKgS/ArgBfgFjARcBLwC6/8b/qAA9Av8C/wJ8ArwBZQFnAVMBXAEgAQEBlgGQAjEE2gSSA5kB2v/I/3wBlwIrApEARP94/6oAMgH2ADsAN/+k/mf+Iv55/m7+uv26/XL9pv3r/U39xfwn/Dr8DP1s/e/8Nvwm/M38hP3d/cH9Dv2j/MP8hP16/ov+HP6a/TL9qv3y/pL/Qv98/k3+h/4y/+L/rf9H//3+IP9h/7r/2QAnAXsAEACm/wwArAAkAPj+GP4c/in/YwCjAAcA1f4X/iL+jf4P/8X/HgAKADYAhgCGAJ8AHgFNAZQBygGhAe0AFQDX/1UAYwHKAesBuAGEAc4BYwHdAGcB6QFJAnECmwLDAr8CEQOHA/8DKATTA4MDlwNRA6EC8QG1ARgCuALcAokCAwKWAWcB7QCzAIsAdQBuAJoA0AC+AHcAn/+6/pD+aP4r/tv9nf3Z/Tv+IP7O/YD9df1q/Xv9U/25/Oj8kP0e/mf+Wv7K/YL9of3B/e39J/40/nz+T/8nAIYAOgCt/+T+of7S/jD/6/6L/tD+l/8XAAcAGQDI/5L/7f+4/xb/E/+o/z8AYQD0/+v/9v/9/9z/aP8w/6j+D/4X/oX+5f7X/hf+4P2W/kv/Nf/c/on+lP4n/1L/vf8mAMcAkAF5AdYApQCqAEQBFQJNAo4CwQKvAqICoQLHAv8CvgIlAhcCAQLxAfQBzAGqAUIBuQBaAA4BBQKzAaoAv//e/5oAcgHKAaQBRAHSAMUABwGAAU8CuAKNAhEChgFwAWcB9ABcAAUA7v+qADkBEAHiAEQA//+v/9X+O/68/p//GQAbAOv/8P8TAKj/6f7K/v/+I//0/nf+Tf5C/pT+0v4D/4X/2f+A/5/+8v3i/fD+cgAwAbcAjv9n/lH+RP9yANAAr/9o/sX95v1A/u3+T/9S/1H/W//i/u/9Jf3Z/Lj9wf5m/7P+Af1j/HX97f7//+n/0P4X/vv9uP4SACcB1wAgAO7/ZwBzAQ4CrwHLAEEAwwCtAUACQALFATQBeQE2AmoCBgJCAWEAFwAdAJwAkAHgAaMBOQF9APT/wf+X/6L/fP+g/1MAPQHBAVoBwwBwAFUASgBEAC0AVwBnAF4A8QCGAboBjwHSALz/Gv9W/4X/vP/V//n/EgAgAEoA/f91/9n+QP4u/o3+GP9+/5X/4v82AA4Alf8h/wT/T//l/2MAiQCAAKoAtQBOANz/8P+PAJIA4v/v/in+hf6C/zgAwgABAWsAw/8+/wH/hf/i/0T/3v7k/jX/zP8iAE8AJwAHAMP/nf9m//3+x/7F/hP/df8tAHAAQwBcAGkAhgCmAJoAOwCA/wH/1/58/1oAtwB+ABIAJgB3ALcAkQBRAB4AUwDOAAcB/wABASQBSgF8ATsBiQBMAGEAYABrAAkAoP98/8r/nwDmALcAQwCp/5//pP/B/9D/yv9s/yP/NP93//b/6/+H/2H/+P7i/ir/W/+Q/6v/wf/i/9r/sv/G/+L/FwAKANH/q/+r/8X/kP9x/4z/9/+3ACcBOwFRAUEB+ABYAKn/Rv9u/9P/5/8bAFcAjwDWAHIA1f8G/53+bv5w/v/+fP8MACQA3v/X/8X/vf+Q/yn/Pv/K/04AoQCmAE8A///0/zIAWAAQAJf/Kf/I/sj+QP9s/3f/7f9RAJwAtwBTADYAvP9P/2P/sv8wAG4AwgAgAYQBgAG8ABcA4v/K/9z/0P/p/wcA8v8iAHIAsQDXAKYAAABx/1j/Zv+d/zYArADqAMAAVQD2//D/qf9A/yn/GP9J/7b/IgBDACQA7v+X/6//1f+Z/z7/3v7M/hP/rf97AC0BRgHiAEMAuv9x/5L/AQAnAGMA1ABsAccB4AGEAfoArABYADsAhADJAKUAXAAmAE4AywAeARkB4ACRAC8A7v+i/zT///4B/x7/Tf+A/6D/lf8BAE4AYwAyAMz/VP8V/yf/fv8HAIQA/QABAeoAxwC5AGwA0P8Y/6r+wf4V/7b/EwBEAEMA8v+r/2b/+f7l/gH/IP9+/7r/0P/O/6T/lP9J/wH/0P7Z/jT/pP8SAEoAYQA4ACYACQDX/8b/2f/0/+7/bAClAKEAkQBIABMA5f+b/3f/av91/4P/Yf+C/7b/0//i/wUAHgBDADsAw/+y/wUAiADdAOIAtQCEAEoAMABuALEAowA2ALj/LP8G/1//r/80AIgAoQCvAJoAOwDB/53/sv9DALoAugCWAGcAkgCvAKwAfgBKAAAAxv+f/4v/0P86AHkAkgB+AE8A9P9z/yz/I/9b/5n/0f8DAEwAowCjADsAuv+U/5L/l/+V/6D/tv+0/9r/CgBBAD0A0f+Q/4P/g/+Z/5v/h/9z/7//+/8tAFUAJAADALz/yv8tAH0ApgC1AF4AMAAmAAAA8v/G/63/vf/I/+P/QQCGALEAqgBIANX/l/89/+/+1/6x/v3+if8yAOoASwEpAZEAAQCo/2//ev+U/6//2v8HAP3/IgAdANz/qP9a/0//Tf9k/4L/zv9RAMMABQHgAL4AYwABAOD/EgCCAPQAMgFNAUsBMgEFAX4AEwDV/5f/mf/i/0EApQDSALcAiQA/APT/bP8E/97+7f5S/3r/d/93/1H/VP9b/2P/i//r/w4A4v/7/xcALQAZAPT/EABVAKwA2QDxAOAAywByAPv/vP+V/6//l/96/4L/g//G/xMAYwCNAFEA4P80/2H+x/2v/T/+Ff8QAJ0AnwCJAGwAVwAVAKL/E/+4/uX+aP8VANcAJwETAQwBxQB0AB4Axf96/yf/NP9h/4n/7v8HAFUArgDSAMwAYQDB/wr/ev5u/sP+Pv+//y0AmgDrABkB/QCIAAkAlf85/zL/jv8rAOoAbAGtAZkBWgG3AAkAVP/0/kv/m/8bALEANgF1AYYBDgFhANz/Xf9d/4X/3P8eAHAAzgAKAREBtwBDAL//Tf8N/xr/aP+U/7T/wf/e/yQAQQAvAOP/av81//v+qv6N/qj+8v5W/8z/OABrAFgALwD2/+n/DgAdADAAKwAVAPn/AADy/9z/5/+2/4f/aP9o/63/5f8rAGcAewBlAEoAFQD2/+3/tP94/5f/5/8iADYALQBhAI0AbgA9ABAApP9f/yn/Bv85/5n/8v8eAEoAcAClAKoAqgB7ADQA4/+D/3P/fP9+/5L/fv+g//f/PQBsAG4ANADy/73/pv/G/wUAUQCaAPEAAwHZAK8AhABEABAA7f/I/+f/DgASAC0ASAA6ADIAMgAnACQAKQAgADgAiQDXAO0A2wCWAD0A8P+p/6L/zv/i//3/GQA4AD8AOgAiAOf/4//K/7L/w/+//9H/IgCGANIAAQHqAJIACQC6/5X/eP94/4D/vP8OAEgAcABXACYA///B/4n/Yf9P/0//Tf9E/27/n//O//D/AAAMAPL/yv+H/z3/Kf9G/2P/kv/I/+f/IAB3AHQAhABOANn/Zv8R/+X+1/4q/2z/tv8yAHAAkgCAAB4Aqf9S/yr/Df/7/i7/rf86AKgA4gC6AHUAQwDl/4n/Sf8l/0L/if8JAIkA6wD4AOQAwwCLAE4AIAD2/9r/1/+4/9f/IgBgAK4AqACaAIgAWgA7ADIAOgA6AD8AKQAKAP//BQAeADIAJAA/AFEARAAiAPv/9v/R/9H/yv/n/xcATwBeAGAATwAnAAUA6f/Q/5//kv9x/2b/eP+x//L/AAAyAGAAOwA2AGUAWABwAGcAQwCEAMIA1wCmAGkAOAAbAAEAw/+o/4P/Yf9W/53/2v/u/9D/l/9R/zn/Pv9a/2//aP9v/3j/sv///1gAeQB1AG4ATwAQAND/r/+b/7H/0f8dAH4AowCaAFUACgCp/0D/E//X/sX+Af+C/+7/ZwCLAEoA5f96/z3/Gv8w/0L/WP+C/63/8v89AFwAVQA4AP///f8DACcAWgCLALwAyQCxAGcA9v+f/1//Uv+M/9z/EgA7AFcATABXAEgAOgAwACQAIgD9/8r/uP+v/6n/4P84AMIAOAGHAZkBhAFWAegAdAAHALr/mf+6/+n/JwCYALMApQCNAFoA8v+L/yP/tv6X/rb+9P5k/9f/MAB7AJwAhgA4AMX/bP89/yf/N/9f/5v/tv/c/wkAHQAXAPb/2f+9/9H/zP/O////JgBYAGAATABOADYAJAAnABIABwD2/9D/yP+4/6D/ov+g/5f/1/8BAB0AJwAmAC0AMAA2AC0AFQDy/8b/rf+Z/6L/uv/B//3/QQCGAMsA8QDoALoAggBKADYAOABPAIQA1gAkAXsBsQGmAVgBwgAMAH7/Lv/i/rr+x/7w/lT/zP9EAHsAjwBTAP//wf9a/x7/Df8h/3H/yP9hAOoAKwFpAWcBBQGsAGEAIAAKAAMAHQA9AFEAbABlADIACgDa/7r/uP+//9P/4v8KAPn/9P/Z/6L/cf83/yz/LP80/1H/ev+d/6T/tv/0/woA8v+y/37/jv+b/6T/sv/G/8P/uv/F/9f/9/8FAOv/rf+D/3f/bv9d/0v/b/+//+P//f/9/+7/5/+b/4X/gP+Q/7j/1f/7/wcAJwAZAOn/zP+S/1H/Nf9f/5L/1f8gAF4AfgB1AEMA4/+M/zn/Gv8g/0L/kv/l/xkAOAA6AAAAw/9k/wb/6/7//jn/g//e/1oAzgA5AWoBhgFuAUEBDgHFAJIAYwBcAEwAXgCmAMsA7QAOASABDAHiAI8AGwCZ/zn/Ff8D/zL/fP/a/1oAoQDWAN8AiQAiALr/S/8I//T+IP+F//f/awDMAPwAHgH0AJwAawBGADsATABlAJYAzADgANsA0ACsAHcARgAVAPb/5f/R/9X/4v/K/8P/q/+V/4P/bP9G/wH///70/vn+Fv8K//n++P4B/wb/Jf9q/7z/+/8ZAB0ADgDr/7r/bP83/z7/d//g/1gA2QBCAaMBwwGdAUQBtQAeALL/bP9f/4z/v/8QAGAAggCUAGwABQBv/9f+Xf4M/uL98f0s/o7+HP+Z/wEASABOAB0A6f+S/0f/E/8B/yz/av/g/1oAzAD0ABcB/wDLAI8AXgBaAKUAKQG4AaQCgQNiBEIF5QVyBsAG0QbhBssGkwZvBvMFSwWrBPIDEwP9Ae0A2f/k/hj+SP28/Dj8svst+6f6Pvr3+c/5yfkN+p76Wfse/Pj8n/3e/f/92/2d/Uv9IP0y/Xn9E/6//oz/SgDMAPQA5ACoAB0AWv+m/u39bP0i/fb8//wn/V79h/2k/Zv9m/1f/Qj9s/xs/FP8YfzH/Fb9Lv4G/73/bgD4AEoBewGCAVEBJQHWALkAnAChAN8AJAFqAZgBrwGzAdAB/wEyAnoCrQK/AsUCkgJAAsEBSgFBAccBGAP0BEgHwwkBDH4NOg4sDiwNcQsnCcIGwwRaAz8CowE0Aa4A4v+J/uD86Pra+ND2K/Va9E30BfWg9qD41PoK/dP+GQDdABkB9ACNACkAAAAiAK8AZwEnAsoCHgMKA4cCwQHQAMr/yv7i/Wr9RP1K/Yn9mv2Q/Z/9cP1n/U/9IP0b/fb8EP05/V79Z/0u/Qz95PzS/Ov8F/1V/ZD9xf3K/cn9o/00/br8K/zY+/r7d/w//UL+Tf9GAAgBeQGYAU0BwgAdAIL/E//2/gz/Nf+k//n/DAAQAKb/GP9f/sn9lv3m/Qr/xQBuAxQHlgteEHoUXReaGPAXxhUpEpkNJgn1BPIBaQA7AGMBqAIMA3wCqgC1/fH5uPXY8SbvFu647i3xuPSg+Ez8HP/DAEgB2wDM/7j+2/3e/dz+jwDnAkMFSAedCB4JyQjCByoGRwSAAu8AtP/k/lr+s/39/Fz8d/uN+rT58PiV+Hb4lfjV+Fv5AvqN+hX7mvsk/LP8U/0M/vv+9P++AIAB3gHtAdsBWAG8AAMAbP8E/+/+DP9j/8z/9P/c/0n/fP5R/fz71voN+qL5xvmB+oL7nPy1/YL+5P4B/8P+Wv7i/aP9tf1c/lT/gADbAeAC8gO4BPAEAAWgBC4EFgRWBJUFuwdnCoENNhA9EgoTMhJKECoNTAl2BRMC9P/9/vT+6f8pATICSAIuAQj/Cfy5+GX1y/K78RnyxfOg9s/5+vxv//EATwGEAID//f3L/Gz8nfzB/Xr/TwEyA54EjgX5BZUFBAXgA2YCQgHu//3+K/5W/Sv9Bf0R/Zj93v1l/uT+BP8g/6L+Cv5j/Yn8Hvzk++j7tfxp/TL+GP9A/zL/ev5e/UP8Gfte+iT6bfom+x78HP3t/Wj+mf5o/hX+3f3B/fH9Of6X/kD/lf/X/+X/gP8q/4T+Af7p/cP9Rv5A/5IAaAPRBpQKpw6zEU8UExZFFncVXhNrEFYNSgo4CGIHXgfGB9YHkwesBpMEYwEZ/W34NPSD8ETuxe2y7r3wPfPc9Tf4sPk6+vn5CfkY+Kv3CPiG+Rv8ZP/0AnoGYQlhC1sMLAwXC5EJ7wd0BkIFYgTQAzUDggKfAWwA1f4V/Ur7dPku+Cr3v/YN94n3VPhQ+S36F/vS+zr8mPzg/DD9h/3Q/Rr+X/6b/tz+5P7b/r7+lP51/lr+WP51/oL+kv7I/sr+0v64/mH+1f0g/Wz8x/tZ+936lfpr+j76iPrz+mv77PtK/Ij8ufzo/ID9RP51/1MBrwPYBwQNYBKcF3IbYx6/H2MeTBtmFmkQ1wqKBSMCJwF7Ae0CPgQSBQAFygLS/jz5zfIe7Tzog+WJ5W3nWuve71z0f/gL+1P8SPyE+wD7zfrx+4L+JQKsBg8Lyw6CEZ0SYxLGED4OYQtKCMgFvQMeAhEB6f+8/pL9CvzA+nj5MPh69+72BveJ9/33m/jp+Cr5ePm9+aP60PtY/Un/HAHtAgQELgSgAy4CYwCi/hz9dfyC/Ev91f5YALUBdQJTAjgBbP8K/X36a/j/9m726/Yh+Nb5vvs//Wf+q/5J/on9VfxT+5P6Q/rA+o/7A/3I/oIApALEBLkHVgvIDkwSSxWaF4oZ8RkUGUIXSBQFEcYN8QonCTIIeAe7Bq0FHwSxAT/+u/nL9BDwCuyG6ZfoVemN64fuy/EB9U33svgd+bD4QPj991T4p/nz+zv/7QKDBskJFQxxDcgNGA3tC1UKtwiEB3oG4wWKBRYFzQTyA8UCRgFR/179UfuD+U34J/do9j32FfaD9jf3BPgU+Tb6cPuU/Jr9U/6+/gj/LP8l/1//bv+8/2sA6AC6AWYCmwK4AikCSwFsABb/CP4e/UT85vue+6H77fv1+9T7jfsM+6f6B/qR+Vv58vgt+a75Q/qP++/8tf4nAekD6geoDBURKRUiGNQZjRppGdgWwxNTEGgNGAsFCloKRgsTDBwMDwvzCD4F+f8L+vjz0e4O6xvpfelz623u1vHe9EX3oPh++Iz3CPZ19OTzT/QP9gn5kvydAIME6AeSCs4LEQyFC0QK7whgB9UFxgTMAxEDkgL2AZkB/wBcAK3/5f49/jL9EPwH++f5Kvne+NH4Uvn8+cL6t/t7/ND8mPwD/DP7dPoU+lz6Rvvq/AP/NgFSA94EhwU5BfIDQAJPAI3+Zf28/NL8Zf0n/uf+N//4/lr+Dv2y+3j6Qfne+Dz5B/p3+xX9Vv6Q/xAAEgD5/37/eP8FAC4BsQMOB+AKAA9wEmEVZBewFyEXUhWlEvAP9wzgCqEJoQg6CMQHLwdiBpsE4gFA/hn66fUD8jnvce3m7Iftwe6w8OPyxfRG9hz3SfdP94r3FPh0+YL78f3tAPYDqwYkCaMKLgtAC4cKjwmICGcHrgb7BXQFMwWOBL0DpAIuAbT/CP6E/D37Lfqu+YP5p/kd+nD6vfrq+rD6SvrA+UP5EPkC+XT5Vfox+2f8n/2f/mz/5f8OAOP/0/98/2P/lf+k//f/WgBnAHQAKwBU/4f+af1h/Iv7jfr5+Yz5S/lV+Rj50/iV+B/4Efhb+ED5DPsb/TQAzAQBCsIP1RS5GNcbMB3HHCUbXRhfFZQS+g+yDsEOcQ8UEMYP0Q6HDIYIJwOz/Df2WfB+62/okOd26KzqbO0q8KryK/SP9BX09fKq8RDxVfED8wj25/ld/tICEAdRCnMMig1SDYsMhQtBCmAJoQj1B7AHBwdHBj4FtgMXAiQAfv44/Sb8nPs/+xv7DvvN+mf60fkL+Yr4FvhA+Pn4/PmW+yn9x/40APYALQH/AF4Avf8p/8H+4P4g/6j/OgCPAK4ALwA+//r9jfx5+5X6Lfo1+m/6EPtB+wX7YvoJ+dj3ovat9cf1MvaM96v5dPvA/bH/BQEVAgYDpgQqBx4KrQ2CEcYV2BlWHIsd9hwMG+gX1xNIEJcNrwuWCrwJYwkTCakHOQVaAar8nfe88v/ubOxR62XrP+z27cTvHfH68ejxe/FH8S/x9fGw8w32L/ma/Pv/GAOXBWUHmAhyCRUKwwqCCzkMzgwaDeIMJQy8CtII0wawBNsCcAFGAIL/yv4n/lX9MPzz+lv52PfD9g32FfbG9t73lflI+6P8tf3i/dD9OP00/Hn7+PoU+8v78fy4/qYAUQK8A04EOQSxA0sCqAAn/439dPzF+0X7RftD+xL76Ppm+sv5KvlZ+NH3dvc892P3rfdL+Ez5a/or/On+rwKRB4sMARFWFawYDBvAG90aTBm5FtsTmBEkEKMPABA+EHgQURDIDvgLlQfoAcf7avXy7/3rrOkr6RXqJeyt7g3xofIX82ny/PAx78ztee0j7kvwuvMj+C792QHEBZ0IIgqbCiAKBwkMCBQHpwbxBp4HrgihCS4KNAqSCXQI8wY8BXEDxwGRAKT/DP9+/ub9Sv2L/AX8VfuR+gX6e/k6+Wv5oPkz+uz6cvsM/Fr8o/zU/NL8xfyh/Kb80PwT/YT93f0F/hj+1f1C/bH8Afxj+x/7EPtc++r7ifwt/bz9+v3x/ZD98fx9/AP8y/sD/ID8ef3i/pEACgP8BQEJIQyTDrMQtRLKEz0U8hPdEvgR8RDND0oPxA5iDuENugxoC2EJWQbOAon+j/o39zv0nPKh8XvxEvJi8qzyi/K38Xrw9+7K7XrtBO7C74/yDfYA+sz9BwF6A9MEXQUdBbgEoATVBL0FGQfsCMoKYQxiDXENiwzDCmMIxAU1AxwBfP9T/rf9cv1W/VH92/wK/Bn78Pnf+CH4p/fM91b4JPl2+pz7pvyG/Qb+c/6z/gj/gP/y/5gAGQGsAQgC+gHmAXIB2wBKAJ//Kv/i/nr+QP7p/U39vPzi+8/6Dfpx+Tj5afnJ+XT6G/uB+6z70Psb/Gv86/xL/usAfwSsCKAMFxArE/kUfhUPFXITuhEiEHwOGA6GDn8P5BBGEdoQVQ8jDNYHZQKo/Lb3YPOO8GvvaO+98E7yrvOu9JL0vvMQ8sHv/+3H7HzsuO3Y7x3zBvfq+sX+CgJ6BEoGKAd7B4wHKwcbB/wGDQd5B9QHVwjhCB4JYAlaCcQIHwjqBlQFxQOzAbb/2f0T/Nr65/lx+Vn5l/n8+W/64fo9+0r7M/sC+7L6f/pc+jb6V/rR+oH7d/yt/en+FwAXAboBAwLZAVYBYQBU/zv+P/2c/Eb8KfwM/CT87Ptl+7L6k/lC+O32wfVL9XP1QPb09yT6t/yi/3kCqQVBCYwMYg+oEa8TmxWrFswWfha2Fe4UQBRKE64SMhIMEVsP0wzLCSwG1QFT/S/57/XF87XylfIp88LzFfSa84LyzfCQ7onsEOvf6k/sJe8x8w345vxCAV8ELAaTBt4FsARYA6EC1wIKBCgG3QiLC9ENPA+RD5EOvQw2CkoHwwRaAnsAT/+X/nn+l/6f/pT+Vv7K/SL9Hvzx+qP5R/hE92b2//Uj9p/2qffc+BT6UPsD/HD8ifwb/LX7U/s2+5/7YPxl/dL+OwA5AQoCQgLtAS4BGQAD///9Iv2J/Cf88fsn/C/8QfxX/OT7gvv++lX6+vnd+Qf6z/pe/A//xwKwBnsKrQ0SEKwRDhJuETgQpQ52DekMZA0JD+0QyRLZE7wTjRLmD7gLzQaWAdv8jPlz9632E/fX91/4UPhz97r1TPNO8JftvOsQ6+Dr+O3u8Db0g/c1+jv8jf0X/jD+Qv7O/u3/twHuA3gG7Qj0CmgMLg0wDZUMgAsHCsQInge1BucFLAViBGgDiwJqAQMAv/5P/fj7DPsO+l751vhN+B34+/cd+G34wfhL+ef5nvpZ+9j7WPzi/Fj9E/6z/mH/AwB9AMUAwgCuACsAh//e/kD+8f3v/RX+Zf6U/qT+xf6t/lz++v1C/Xv83fs/++r68fo4+937/fz9/l0CNQbMCdUM7g5TELUQnA/jDeALCgorCVgJ2wpSDb0PCREtEYMPQAzPB/gBNvxx9yv0/PKs8/b15/hD+7H8kfzE+vD36vME8FbtBezr7G/vQ/Pj9+H7Kf9PAeIBhgE6ANX+Bf79/Rr/DgGtA4YGFQkXCx8MGgxwC+0JWgjhBnYFuwQ5BCEEKATwA6kD+QIFArcAIf+a/RL8mPqP+cr4Yvhr+Hz47PhX+Z75HfpK+oT60vrL+jH7svsk/AP9tf13/kD/wf9lAMAAwADtAMkAowCCACIA5f+J/xP/qP5R/tD9av0c/eL8Ef0w/V79yv37/Sf+S/4M/r79gP0V/fb8h/3M/skAFQNfBYoHjQnMChwL1AolCicJSAjsBwwIMgm2CvsLHA2vDXYNGAysCcIGzAP4ANz+mP0F/TL9cP2b/Wz9jfwM+wf57fYi9eLzj/MI9HX1JffW+Fn6Mfue+2778/qe+uH6j/vL/Jf+mACUAk4EcQXyBfcFWwXKBFIEGARSBMYEgQU3BsAG4wZ9BnMFDwSOAtsAfP9R/oT9Ef3H/Jz8cPxT/Pz7dPvS+ir6zfmZ+Xj5jvnR+Wn6Nvvk+5L8G/2A/R7+Zf6S/tn+6f4N/xX/+f4G/yX//f7O/qT+jv6q/tv+5P6r/m7+F/4w/UP8dPuI+mD6xPr4+3D+2QHjBUQJUguZDIkMaguICRsHhwUABYcFUAcTCjcN5Q+7EMIPhQ3XCT4FswDo/LD6Q/og+xn9Tf/iADIB8v80/bD5Cvbk8vfwX/Cj8TL0X/d7+pz8pP2m/er8p/t7+hD6efr4+yX+uQB6A5oF7gZbB/oGUgZ2BcwEfwSmBCMFywVrBrQGoAYhBgAFvwN+AhcB7v8K/2P+9v2b/TT96vyS/EH8z/tD++r62vro+uz6KPuK+/j7Tvx7/KH8uvzQ/Pb8J/23/Sv+ov43/6//OgBrALEA2QCjAL4A1gC5AK4AlAB7AGsALwAAANP/n/+U/5D/df9Y/wb/pP5n/jL+Kf4e/nD+Of+8AK0CgQRpBu8HywjYCAUIywawBdUEbQTDBBQGswc7CUoKcwrWCRUIfwXBAuv/j/0y/Kj74ftw/AX9Zf05/Xn8Evso+Uf3AfaZ9eL1+faD+Bv6ufsM/cz9Ff4g/rX9av2G/S7+Pf+WAOQBDAMYBKIEuARdBAYEaAPVAnECSAKFAqsCswKQAjcCoQEZAT0AWv+d/tv9J/2P/D/8Dvz1+8n7nvvC+7n7i/t2+zj75vrh+iT7k/vz+5r8hP0//j7/4/86ABUAkv/n/pD+Z/5I/qH+Kv/u/8sArQFJAokCDgJeAZEAFQA0AH4AhAEYAy8Fewc9CU8KfArSCZQIOgfwBQsFHwXlBXAH6AgVCq8KMApuCL8FtgKb/379K/zs+1j8NP1l/un+xf7Z/Q784/kG+Fj2XPVU9VH2svcH+Y/6tfts/Ij8TPwH/Nn7NvwQ/TT+yP85ASsC4AI7AxwDygJqAiwCSQK6AjAD0wNdBH8EPASUA4ICJAEtAFr/1/7F/tv+Ff+g/6L/ev9H/2r+uv3Z/An8nvt3+4r74fsk/Ib8Kf1y/e/9XP55/q3+yv6m/qr+Ff+k/xUAeQDoAKoBXAJYAtQBQQGUALb/LP8s/5X/OACAAMsAOQEbAYkAov9z/lb9/fxf/QP+1/7c/8kAOQGSAW4BJAG8AC8A4/8wAP0AugHBAocDJwR4BEkE/QMTA1ECHAK+AV4BhAGYAbEB0gGsAYsBdwEpAaoAEwB+/2P/Vv8p//L+5P6v/mH+Rv4//lr+cP6f/uX+NP9j/4D/1f8kAGUAnwDLAOoAJAEwAeYA7wDbAAgBXAGHAccB9AEIAu0BoQEgAXsA1/+H/1r/N/89/2j/tv/n/97/qf87/53+J/6d/f38wPy+/L78MP2j/eT9HP47/hz+6/3O/ZT9T/1q/ab9Kf7X/nX/OgDDAPQAGQEiAfgA1gCIAF4ATACcAOYAKQF+AYsBvgG3AaQBjwFnASQB6gCaAHkAlgCzAPgAGQFNAbwB6QH2AcEBdwEnAb4AjwBEAAkA9/80AEMAIgBXAJgAmABcACsAw/89/7P+K/7o/cf9o/3S/R7+lP4h/3H/Zv9q/xH/d/5A/tv9pv3V/T/+zP5s/y8AmgDbAO8ArABcAPL/qf+H/4X/0/9TANYASgFwAWoBXgEgAawAYAAvAAUAPwB5ALUABQE2AT8BRgFIAScB+ADWAOgA9AAOASkBJAFpAXkBfgFWASIBCgGvAG4AGwDR/5D/Uf8e/+D+i/5N/hj+9v3D/cr91P21/dX9uP3D/df93f3b/fb9Pf5R/l/+kv6z/uf+Uf+v/x0AhAD2AE0BfAGoAZABcwEuAQEB8wATAWEBowEGAnkCjgJjAl8C2QFfAeAAUQAwAEwAiADDABkBQQEtAdAAPQB3/8P+Ef6d/ZL92/0l/on++P4j/zn/+P6k/kj+8v3Z/fr9Tf7K/oP/GQBjAJgAswCYAGwAYAAnABMAIAA6AGcAiQDAALoAyQC8AKMApQC6AJ0AlgDDALkAvgC1AJgAdAA9ABMAIgAiABMAOABTAH4ApgDDAMIAmgBwAD0A7f+i/3H/S/8+/2H/fv+f/7r/vf/G/4n/Sf8h/wb/7/77/iX/bP+9/+X//f/7/wEA9v/i/+3/LQB7ALEA/AATAQgB2QChAD8AyP+Q/03/Hv8h/0v/aP/I/wkADAAbAPD/ov87/9z+lP5o/mX+c/62/v/+Sf+S/7r/uv+V/3j/MP///un+4P77/iH/Uv+y/ykAPwBlAIAAdABcABMA2v/B/7b/vP/t/yIAiwAKAWcBqAHBAaMBeQFRAQUB3QDDAN0AEAFPAZ0B5AHvAdsBwQGGAUYB9gCzALoAqACjAMMArACjAIsAewBgAE8ASAAyADsAPQBPAGwAiACSAKEAvgDOANsA1ADWANAAjwBgAEQAEgDZ/5//T//5/rj+kv5E/jD+O/42/k3+VP6L/pb+fv6A/nf+ef5w/on+mf6x/vL+Kv98/8X/7v8/AI0AtQDAAKoAkQCWAHsAaQB1AGkAiQC1AMUA1ACuAJgAZwAgANz/gP9P/xH/7/7r/tD+zv7H/s7+4v7H/p3+hP58/j/+Dv45/lH+df6k/vL+Wv/R/y0AdQC+AOgAGwE7AUoBVQFVAWMBcgGdAfQBNAJPAlECNwIVAsoBiwFIAegAtQCNAGwARgAwABIA7f/5/9f/vf+m/53/mf+f/5v/oP+//8X/vf+6/7r/pv/D/9H/wf+9/7j/sf+i/5//qf/I/97/2v/0/wkAEAD0/7b/if9u/2//Xf+H/7T/BQBTAI0ArACjAJoAcgA0AA4A7v/e/wwAQwB1AK4A1ADiANAAtQCqAHAAWgBXADsANgAkAB0AEgD0/9H/vP+b/4X/gP9z/3H/hf+D/4P/Y/9A/xP/7f7g/gT/E/8Y/1r/lf/I/8P/qP+H/03/HP8Y/xr/N/9+/7H/9/8bAEEAJwADAPD/rf+b/47/fP+b/9f/IABuALUA6wDzAM4AiwAwAPL/zv/Q/9z/EwBnAMMABwEgASQB/wDMAHkAJAD3//L/DgA0AE8AhADCALkAugCdAGUALwDy/63/hf9m/0v/Yf9s/3f/kP+2/9H/tv+y/6D/jP9v/1b/QP8p/0D/b/+k/8X/3v/g/9P/tP+i/4v/ev9+/5T/yP/9/y8AOAA4ACkAJAATAAoA7v/t/wkAIABRAHIAmACaAIAAXgAvAPf/7f8DAC0AXACLAKUAkgCCAFMAEACx/3P/Wv9N/13/qf/7/1UAiQCPAJ8AhgBMACQAAQDw/wwAJgBgAKUAyQDrAP8A/ADbALoAiABTACQADAD9//D/AwATACYARABXAFcALwAFAOv/xv+d/5n/nf+b/7b/w//c/+7/7f/g/9n/8v/X/9r/8v8AAP3/+/8BANX/nf9h/z7/E/8P/zX/VP+D/73/+/8ZACYADgDw/7r/fv9C/z3/c/+v/wMAWACoAOYAAwHoAKYAbgAmAOn/zv+b/53/ov+r/9z//f8ZAC0ARgAiAAMA6/+9/5f/eP94/2P/h/+4/+X/GwApADIAHgAHAOf/5f/j/+f/BQAyAG4AiwCYAKoAjwCEAFgANAArAP//7f/0//n/+f/u/9D/3P/M/8r/2f/I/87/uv+o/5n/kv+U/4X/lf+8/8P/9v8pAFMAeQCRAJEAdQBhADYAEwAiABcAJwBcAHQAkgCmAJIAhABrAD0AGwDu/9f/1f/T/+f/+/8XACcALwAiABUAAwDg/73/h/94/2P/Yf9v/3f/pv+y/8X/4P/M/9r/yv+d/4X/bP9h/2r/i//B//v/LQBPAFUAVwAVAOn/0/+V/4P/l/+p/9X/AABEAHsAbAB+AHsATwAmABMADAAXACAAQQB0AIQAnwCdAIgAawBEADsALwAiACQAHgAtACAAFwAMAN7/0f+9/7j/sv+i/6T/kP+o/6v/kv+b/6j/nf+X/6n/pv+g/6//vP/O/+X///8rAD0AVQBsAH4AfgB0AGUATgBIAEYAOABcAGEAWABjAE4AOAAHAOv/yv/c/9n/8v8VAAkAEAD0/8z/ov+V/2r/ZP+O/5T/tP/F/8H/vP+H/2b/Mv8P//b+CP81/27/vP/Z/xUAJAApAAEA1f+8/3H/X/9z/4L/rf/0/y8AZQBlAGEAQwAZAPT/9v/T/8z/4v/w/ysAHQBXAFwAWgCAAHsAfQB7AH4AhgCIAHIAZQBTAFMAdwCRAKoA0ADSAOAAzACUAFMADgDe/9P/AAAnAGMAjQDUANIAvACYACAA5/+t/3P/pv+9/+X/EgBpAJQAgABeAOX/rf9U/w//A/8M/zf/b//F/+f/SABOAPD/tP+Z/4X/Tf8n/z7/c//Z/wEAJABcAHIAcgA2ADAAawBMACIAOgBKAFEAzP/O/+7/i/+H//T/5P5sAJUFbQbvAikAXP5p/fj9Hv5l/lH/JwAkAOX/CQA4ALr/q/4l/rX93f2r/jn/0P4I/4sA6wD2/8r/TADy/63/sv8ZAPwAbgHxAOoAEQGdAPwAVQFjAE4AmAAKAIkA8wAmADgA0P9L/zD/Ff9H//n+jf6J/+3/Mv8j/0//GP8FAAEAKf1L/fwA6AFEAU8BVv8Q/Sv+hAFdAhsBnwBOAKwAiAAq/4X+if8/AVgBTgAtAa8B6f/n/in/rf/j/xkAWAHyAYIABv9W/87/7f/u/4gAbgHu/43+NADi/0n/UwFpAD7/CgFq/9X9nf9jAJ0ARADH/pX/XgBJ/xsAZQGUAZEA0v3N/NP+QgFwAmoCowDV/nz+Ff7i/ef+WAFRA+YBE/8T/97+hv1J/wYCZgLFAN7/VwDg/yP+aP6z/mz/0AGLAhgCeQCW/pL/eP87/oQAkgHSAAMBT/7p/SkCPQKS/sf+KwHpAeIBAABG/Ib98gHgAdv+cP5EAGkAyQCqAJT/yQHeAbj/ogItAWn6PfxlADIChgC//iMDnQKs/OD86/8M/8X+TwGCAUb+x/0TAVH/x/28AAkAcf6b/1wChwEb+7X7WAOMA0YAIP6a/fEAyv/C/MoC2gQ7AFH/xf/e/+D8U/4zBFED5f6EAR4B+PuJ/YkBCgIeAgoAR/rF/HoEfgOf/2cBGv73+BP/fgOJAVUAcAAmAAr+W//0Ae3+Z/3iAVwBeP/Z/3H/X/+fAWIEBf0//eIBoQDc/g793P4oBw0H//yG+9n+xf6k/VMBxgQeA6D/GP6U/ckBWAHp/3wF2f3e+PH9fwYqBEP8wPs6+vkG2QuN/Sf2/QAtAQb/Pf7L+ZwFWgrUAU76RP1q/gUAcABEAOEFKwG99+/7PAhyBmn5fgC2BBP9pvzO/EYBVAL7AXACEvzb+6UF4v7J+sQFGv7X/EkEwPy1+wwAmf+OAqsGVv3L+jkEI/9L/UEB/f4B/kEAoQH0ALYChwKa/HT5dwM/CP8Ce/qH+CABqgAiArECSf/8+pr7GAUXCFX78vfiA2QEIfoo98gGagxT/aH1dfwMCoYAzvXXBGIEuv0E/777AQP1BRz+LP4Z/Yv9OwGlBo0Bevk9/1cFv/+h+xMBtv5b/2EDoP/e/Z39fANuCqT45fbnCg8DH/kOAab+a/sRBfEHwAGa/DD/9PzS/jEGnwJN/bP92wFh/xX/I/4Y/msHsQFy/NYA6//y/+r7XP0eBID/hAFqArv3Tf7oByT9JPu1B0EAove1AoEEj/zk/qb/Rv9JAiwDPf7q+9MEZQEH/CUBtv/u/xEDdQLT+ez52wukBITyvAL+B8f92/rF+8EET/9f/aAF7wLI/wb+Z/q6/y8AVgPyAvj6YAB5AlMBHAT2/6/0I/4mCFsDOvob+pEHtAWy+MT66AVcAjL+gv5s/ScACALZ/6T+n/+yBUQD5voO/VMB9P+aAP8Cuv4MAGwAJAHF/zH7vwJz/9kDJQUf+xT6H/swCpMI2vh6+X4Asv8KAqD/ufxFBQUASvz0AZX/1/zOAg/+yfrKBbIFjvk8+dIHkAWC9f38JgeUAfoAVfqo++oGzv3S/KcIE/6a+9MCSvxwAfMAWfshBt0G8vgk++UCnf+rA2EA4v9yABT4RgG0BDD+cAK8/zr6LgJ4BC7+JvuPAGgFlv15/uf/tPrUCOAE3PWEAKQDLv+x/1v/EwBTAawB9P5P/zAB4P2s/RwEyAJE/UQAD/+m/ZoAygWU/KP7w/47AdAIEPro+fYDVwUDAij7JPmlBqkHW/kH/VQDdQLK/e0BvgAO+9QB8QGi/okEoP+k930FFgbr/cf8d/uzAGsGowAB/zsAYvl3AucFOvyV/wwBO/7MA1P9FfyTBA0D7/6W/iv+GPrqAM0HIP+N/pICdf09/jcC2/00/ykCkP2A/vYC2QHm/UL9If8u/zYCPgP6+73/kAJ5+wH90AIi/pQCvgLO/NwEQP+A/f/+/PtLA0QB1/5UBXkAmfdWAfgCFf8UBcf+yvcVA6UJmfkM/HsBFf98BfsB0P2h/nL9r/1iBzQCVPj5/1oBcAFlAqYB5vwk+2P/RAN8A5n+owG//sD6JAD7BcgCkfqx/gT/JwAKAx/7zwXkBxL58fqm/LwBOggMAMD6PQLbAB38xv+t//EAGAbv/Uv4OwCpA+IB3v7s+wYCPgR7/fH68fwcBHgE+gDK/Vb/XACz+2r/9wRj/x78sQNLBMr+yfqD/9UETwBU/uYB3wCS/Jj91wFxAvEBnQK0/936Ef+tAs363/qEB18KYADt8pL25wU6CfH90fmGALAE3/xQ+CgEvQNs/mUCE/47/PQB+/9bBa3/tPd1/nAJTAbq+2D7x/1iB/AGl/jv9ZUGxwdR/F3/WAJyAIv9vvw+/yADiwIbAE3+s/xcAfMAvP/KA+L+DfiVA2oEAPj9AvEGC/s0/a8E4v02/NkD0APj/734pv38CLUBX/gwASADuv03/hcCYQHg/KgDXf+499cD2QhM/PH9kAFz/rr/zv49AsYFBfwF+tcEggB4/zcD/fww/psCiQHkAv76uPYzBNcDnQDp/nL8kQBSB94BivF5AKoI0/50AFP7wvksBB4ELQHp/+L9w/4z+08B7wf7/973Pf1VCVwCPPdpAIcDeP/pAfEBwvwZ/X4C9gBnAdD/n/1zAeL9NP+tAfwAFgcB/bP0AQK0BfD/jf1b/xYEc/8Z/Z3/UQKx/+P6egInAGH9mgVPAo/9XQIt+TP5NgkoBPT+IAFj/KD6WQWqAmL72gbt/tn2rAgNBA/24v+VBmcA0v1lAskA1vyx/6UAGgQq/3D7eP+tAqkE7f/q/PD+qAGI/JYCWwWa/Ej9qAO/Azn+QP+3/Yf/FQNsAHH+j/zF/qEITgCd9wQDqgH6/J//fP1MAIcEggHk/Cj7agPnBWH8dvlk//cDMAI9AqP9+P3b/k77VALtCT8B0ffq+7MAngTB/hj+WwMpACIASf/O/hr+Zf68AdQAgALFAxf+K/p0+tAAEgiXBpL+kPip+bz/pQZLBSn+yv4eAmr3Xf+ACHr+xwFmA0H8jPcIAbQFWwMP/nf7W/93Ao8AK/2f/moByAZd/uX37v/j/wcGWAQ09i/7+gemAyr4FQIMAgX+hAHb/V3/hQL//TL+WgLF//QCcf/z+eoAygMiAcAAofv0/AIGoAQG/tT8XvoRAkMHzgCNAbP8JPp5AjEEagN1/yT5yv2aBkADXPsiAKgAsvtbA0YDD/6S/Y/7tv93AhgFHALj+LL/QAS8+1/+7wKdAaQB0f8L+nABUwc6+6/+EwOP/e0DmAH3+HoFXQQN95T9/wNtBLoBF/yo+0sBGAPoAU/+DfhNBCQGOvxB/WX9Hv0QCDwEfPcn/w7+pQDkB3f/9fejAFUBEwOzB8b5FfYcAooGsgNNA1z6t/luAN4EhQKlAHcA4/ou/U8CEwH7/W4DtQLs+jT7jgIuBXf/Qv6O/1j85v3eAcoFJAE2+wb/aAMq/7X7cAJhAzgA0P2J/Df+sgUcBGr9DPzF/c4BQgJGAQwA/QFXAOX4/fywBQgDcgBj/eT9fwZWAYL1P/vdCVcHT/zU/SsArPvFAPf/MgBlCBEC4frv+vj+MgP0AnABqf8e/ZgA8ALVAeX/nfeZ/vUHxQGH/4f/Tvyz/OQBcwMOAloD1PpL+dYALQFYBCMCK/7//Hv9EQTRA8X7cvwPA1QEYf3l+RAG2gb2+2f8dwDBAff/RvvFAO0D4v5cAG//Zf5MAOv+7f13AIsAVv+1AX4ClP/R9zr8NwYKBMf+j/qP/JAB2wK6//b/TQK5/O766gXzAFb4AAZqA8D8ef43/iD9EwCICEUHDPsW9yAB1QEy/y0AxgS2A+T+2f58/pH70f92CMMCn/yS/s4At/vA/TsC2gTOAxr+yPiv/LkF7v8R/hgDGv86/D4DowA7/jX/Wv2sBpcCMvcB/xgFTgAN/wT/t/zkAFsF0/8F+9D+dQNOAAEBewAF/J3/7wFpAI4CxwHS/O/8iQIzBOP/r/x+/wQEuP/0/goAkfz9/kkF0wXb+0P5LAWZ/6X4jAZ6BXX8K/0k/Yf+VQfZAJr5VQF8Acf9ZQEeAvT9TvwDAsEDWv/S/ef/YwK4/sj+AwG1ARcARgH9/IL96gUgAJ/8OwKq/uf5jgKVBdkAwPwX/A7+9gNuAHf+XwID/yL+Af+YAOf/bv/mAF0Dpv/d/fT9+/73BZ0Bw/7g/kP8Gv8eBDgBF/tkA20G6/34/ML8/f7FA/MAIvz5/78E2wLQ/Tb7vvzKAkICK/6XA/8B6Pkw/iEDzv5TAfoBf/zMAGEDWv6S/Br+JwEWBdsBtfw5/loA+v1U/m8DdAWdAjz74/hB/cEDtwnpAtv9f/yM+Yn8EQMsBioFcwHP+nv5OPxdA4gHKgSv/pf3XPohBTUGov6W/vb8O/1fA2kAXP0SACwCGQHS/ab+sf+H/+AA3gIuBBr+rfhc/agBCgSeB1b+/Plm/6b+qAJRA3n+av26ArEC7/1w/ov8mgB/BD4DowA6+ST8yASfA0b/AQAg/Yn9gAPXAdT9df9VANP/JwSLAEr7Lv9m/1YBLwVn/vr88QAT/jQBGAT7/Q78XQKIAFr/uv7P+00CigVAAwX8uPgrAVIEeP9H/5wAtv89A08Bg/qj/ccAEACNB+8Cf/nk/lb+Jf4ABnsBw/wuAkv9Lf1uAmwA3v75/jYB2QIvACr6uP70/q/+GwVgAIf9DwK+/Yr6qABPAV8CBgQe/Xb6JwKtAW7/SQMk/Vj8AAftAVL6hf42AiwEzP6v/VEATgBRAVoAr/3c/j0BDAHtAj3+Evxz/8kAoQJzAsr/4P2c/Lz+1wAfBO0BO/04AYn9efx1AdcDLAPI//H7Uf3qAJICpALe/2MAyP6Y+7j/JQWrBRgE1vrQ9CX/UwfaB27/PPtI/hX+2f8xBCcCDv72/iT71/3JBrMBxfvg/iUB0gGh/Qj+vAKvAbz+Jf4FABgD4v5U/8EEAf8w/Yv/0f8/Av8Amf7tA3kAvfnMAJAEvf87AbX9/PyJARcCjQJ1/ob8b/9aAXwBawDmAKgAHvxW/6YBZ/6rAoQCMv09/60BSf6z/Qb/vgDy/+gA5AJn/Zj8NgA0/8n8TwNHBRb/o/yp+hX+3gJZBpUE+vxN9or6dQQ3A6kDsgN1/EH6ZP8JAAr/JwJzBeABXvxc/tT5fv2ZBZoHkgBS+ib8AwDyBLMA1/xVAaICPPtN/0IBEwK/A5D+2fzv/v8B0gCh/ir/sgMcA2P9/fjQ/vcGVgR7/fb+2f9a/JL/gAGkApkCHv7k/nIBv/4B/dMDUQKdAOD/Hfji/kEIpAMv/KH6Y/18ArEDegK8/vr9Qf2h/SwE6wKS/HT8n/8aBFoDWv0O/qr+FQApAeQB5ACW+3H/egQmAAP9mf8wAvb/MgCxAFj+xwF3ANj60AKrBHD+fv4K//j+JwC6AHkAdwCo/ysAkQA5/6/9sf9EAMj/6wL4AEL/FQAQ+0v+LAKQA0kDBQEO/cv5jQCAAscAAwMpAE/9IgDF/If+pAP5AlL/Af0c/bMAIAJXAHkBKQB8/fH9HQAbAV8B4gCCAKn/gv/AACn+vP0nAokBZgJDAKf7of4TAyAC5/6v/mP/dQBEAKb+U/68AmwCx/2W/l//W/+xAXj/3v8cAwUAKf0a/iIAhQKqAZIAiQB3/vb+BP9z//QCsATa/9b89P9CAfEAi/91/9kB1ABJ/6wAUv+Q/tL+g/89ALcBWgP7/nj6NvymAIUDYwPe/6z7J/xGASUBWgCEAfL+IP8G/qr9uAFuAj8Avv6h/QwA6ABJ/9cArgBVAIQApP7m/Zv+BgO0AxAAVP7O/Cn/rwJEAsUAfv95/oX/SAHkAKr9+f9LAlgBb/8O/sP+jP/JAV0Csv+S/KD/FQD9AE0Cr/9T/R7/kQD0AGoCbP9L/nUAVQFyAJYAN/+f/lMCMgLvADQB5Px7/b0EhQKJ/qv/Yf4kABUA0ACkAmEASf8M/a/9RAHJAfn+cgBYAYv/Hv4M/U3/9AOoAZL8zP9yASkBZwBA/rj+oP+PAGMCWAGH/lgAMgCZ/gEAFQCLABMDAwER/kL91/0kAVIDwf/K/aUAhP6X/1EBjf01/+IBpP2k/4ACpP47/qb/Yf8s/74AKQAc/2P/Ef/V/8wANABjAD3/qP1A/6gAwgCvABkBZwEj/hH9VP85ARUDtwGJ/yz+D/6m/vMAWgOSAMX9Bv/DAAUALP5A/yIAjwCLAsz/qP4ZAAz+R/+HAkgB0/85AZT/kv5YAFoAbP84AMwCfAEY/6/+af1J/5QD9ACO/68Byv4g/Cz/pgDIAuUCev5D/J39cwFIAgoCJACm/cr9dQH9Ao8Bd/97/dcAiwM3/uL+/wI6AN0An/+x/aEA8gHF/3r/BQGdADb+XP2AAGMDSwFx/kv9Uv8eAsUAA/6G/Lr/UQJjABAAyv/m/WP/hf/y/mj/RgDxAMkAVwCf/pb9pv5cAF4BwQMMAdD83fzT/gMCGgP4AQoAzv7S/aj/vgH6ADQBAwFG/8f9Jf8KAbb/rf5qARUCAwAl/vr8RP9EAOP/vAF8AYIAdf9G/aH9GQAOAY8BMAKtAa3/bv1u/Yf/cQJdAwYCpv9y/bX9AQG8A9wCAwG1/Gz8UwFYAQYCugFj/3UAqP91/qv/vP9GACcB2f42/t7/0P6m/8r/LwAIAY7+df2+/icAfgDG/xj+Nf/V/1b/Hv8e/7T/2wCZ//D+RP/l/uL/4P9+/7T/Uf/UAK4AJ/3m/R4AiQK/Ahb/A/sy/WoBRAI0AqT+V/wp/s7/fAFWAS796/0eAOL/XAFPAbz/Yf5P/oAAcAKQAZEAuQCXAjkC/wFYAskBGAMsBD4EsAXTBZQDagN0BawGAwdeCJwG3wXaBoUFAAWFBtYG1gZBBvcEygPTAtICSAL/AIIBYwGf/hX9VfxN/ST97Pna94j4gfj5+OX3QvVW9Zb0+PK+9Zv4tfV19YvyJu9o9XL60feT9zn4m/Uh9uX4lPy0/2n9Ofih+oYBjwFu/8kAkgDmAagDAQLVAWYFEgUuAQECcAKbA0UFiQNCAikD9ALBAnUCYf+L/loCFAd1CzsJbAJy/b77fAMuDWQPTA5PC7gF9wRiCJYJGA0FERcR2hCODtsJ7wcyCSULJg7wD8MMkQiSBPEBTQIeAw8CvgCEAFT/7PvR98f1N/Wb9Tf2aPWS9C/z6vCI71bvDfEt8fnvLPGa8j3yC/Fn8Tbzy/Np9JD1dfaT9+74Yvkm+qH6pfgW+qT9BQC6AM7/m/7X/cz/ZgLgAasCxQKoAPIBvgFfAokEAQNVASICKQGcAHcD0gKZAUcEJQOC/+YA6vk0/YsMnQllCIoGEfjD/QcIwAgCD6QNdwkcCgoJsAhmC/sLng+CEyQSjRBHDToJDAxSD2QQ7BCeDTULRgpKCVkHuwXuBEAFXwT/Aun/ffyo+wf65feZ91v2P/XH9cPzIfFr8P3uCO4a7zXwUPAo7/fueu8Y7wbv+++p8TrzIPSq9Eb1LvcW9wr2c/jC+h78qvxw/Ej95/62//v+hf+lADABewGQATgBMAB+AHMCGgNEAkYBO/+4/7MBEwI2AQMBkP+D/w/+lvz0AbsH6giXBnsAxPlW/x8GDAuyEAIOdQmdCK4Irwm4DIUOHhIDFKsUexN6D8MMjA3+D0YS+hOCEjEQTg00C5EJhgjJBrQF4wXsBWwD5P4O+1/4Dflg+dH36PTJ8ojxAPHz70vuyuwu7Vbu5e7R78zubO2/7c7uoO8F8Z/y4vR89lv34vY79uX2b/km/Gj+9v+C/xr/6/1s/gwAewEaAgoDuAMgAxwBev8M/xP/iQCxAZgAxf0n/cP9NP7K/fT8efqc+jz7Xfmo/aIE+wRqAsr//fdF+30F0AhfDr8PjQtlC4MLNwolDfwQlhSaGTsbWRfFExIRkw/rEkQWyxe5FiIT3w6ZDBoKFwdCBecEwgYJB9cCd/ye93D0AfR+9Mvz2/M68wvws+0X7ITqwOkV6jTri+y27ybv5+zV7JDsYe5O8WTyQvRb94H4ovi0+NP3rPls/WwAzAKjAWAAhgBNAeQCvgL9AbMCIwQ5BVkEfAH0/n7+Gv+2/5YAO/8e/rH82fsZ/B377vh8+Fb42vfxAKQFDQMgAlb4tfSX/jkFiQorEFYOmQtUDYkLbgnnDccRjBYGHE0cPRkpFLMQmhG6FcYYBRpDF9QStxCeDu8LfQlOB+YGcAdqBDYADPzC+IP2kPZe9D3ynvBz7k/u+OyJ693ojOh56WXqQuxd7TDsLeuC7PvsXe6p8Dby7fS5+MD5p/nE+EP4a/qb/s4BRgMRBOUD5wNRA10DdQJ1AgIEHQauB/AF7QLl/yn+yv1Y/vn+uv6L/r7+fv0k++z3KvZd9mX2tvYy/swD2wJfA9H6gPRb+lwAUAf1DpgQAA4HDh4MnwlJDN0PGhVjGzAeKx3jF+ERsg/oEjoYwBorGVAVFRLeDtULhAkQBiMFfwY8BQwBJ/1x+MX0rvRu9Krya/CQ7q3sdexo7WrrVek46dbplurT7Ajuh+207rruW+9n8YryEfQn9/f5iPu5+7T6x/pK/YYAwQI7A0cDrQP9AjMEIwSFAxYD3gOiBbAFPgS6AD/+J/75/rb/n/9s/ob9E/x7+mT4bvao9dn2EfWG+cMCyQEKA4v9kfKk9cf9AQN3CyQQ+Q0FDy4NhAhGCZAMLxEzGbgejh5wGu0TNg93EeAVCxkAGvAVWBPJEZwN8Ql/B7kFgwaKBvkCjP8F+yf30PZz9XX0+vLK75LuOe7n7VvtcuuP6tbqAevp7NXtjuwf7sTv/u+Y8RLyTPLd8/f2lfkp+zr84ftD/M79Rv9hADkCyAJGA8QEaAS4A08DZQKdAngDTQMPAmkAn/+Q/5X//f4b/YH7vvp5+lL6FPlL9z32YPMg9hr+MAAIApX/tvak9hP87f/CBSALjAw8D6MQcQybCRwL3A2fE08bCB/0HVcZLhTfEoUURRd6GO4W4xaVFqETWQ+fCjEHQQbsBj4EYwHX/jT77vjM9yn1pfGt72btwex87XDsSutB603rP+vS6x7rA+ps603tX+/v8QzzTPOv9A/35fjx+cL6dvtV/YX/SAEuAjsC9gITBBIG9wU8BJkCvAHXAhEEwQMFAuL/Af+t/vj96vxw+1D7OvsS+6v5fvdx9SX0uvN39kn+iAD/ATn/r/fT+BD9cgCrA2oJLgzIDy0TcQ/RDPkMcQ7/EhQZRBwyHBUa8RdHF+gXXxemFVMTARQzFWUTDhDkCqMH7AYLBvACbP+n+0L4qfca92P1IPOZ75nsmOuL6vzoYOg26XfqNOy17CnrKOpG6p3rCu7L8AzyffNA9pv4CfqT+pH6F/sg/Z3/oQEyAx8EvwSIBZcF6wQsA2MC5AJFBKIF5wQVA7EAJ/8j/u38Q/wd/I38hPy1+4H6V/jb9bHzgfEI9Er8vADvAuABB/rP+NT7cv0MAqoHLAsqDwoTuxDnDgAPbw4eEhEXpxknGkMZbxfKF04ZiBnjF1gUphKxEbIP4Q3bChIIEggHCBEEKv8d+9f2gvWZ9b7zj/JM8h/wbO1x7DrqGOic6LfpgOu47WruTe1f7Tvu1e6T8DTynPMD9u74hPu6/PP8NP2h/ZT/ggHKAlQEoAWuBhAHOAbKA9MCsgM5BF8FxgVLBLEBGv+E/Wv8jfwg/H37hvu++jr6evcn9XnyVPAS87n5HP8uAcUBxPpv+Tv91/xOAEAEvQeHDSUTSBJ5EH0Rpw9TETkVLBayF1kY0RdiGM0ZPRlAFp8TARIQEZcPHQ1GCSgGngYCBpcDrwBc/G34HPZu9B/yR/FD8KXvle9A7sPs/uox6gPqMusT7eXtKO6i7s/wn/Lb8zv0O/Tb9en38Pn1+xP+3v+1AZ8CNwJTAhoC6wLhBHoG4wfGB5oGdAUhBIkCSAFcAcAAiwAFAf3/cf7m+4T6Cfmp94H37vbk9un1K/Nt8SH4cP1x/isB7Ppz+Pb8efw//esBwQXSCSgRdBEzDx4RNQ+tDggSHhQWFWoWgRa9FtIZzhrxF40UaRHlD94NKgyNCqcIFQleCEIFJwFj/Un5zvaJ9hD07PKu8pnw6+4E7jft/+sR7D/rRuoK7LHs3uzK7a3uYvCY8p/zqvPr9Zz4yPmq+wr9xf0SAKgBvAL2AzEFiAaaBzgIFgeVBssF+wSRBbsFBwYUBfkCJwHM/9n+w/3t/D/8P/vW+eX4tPd79fH0R/Jx8Kv29fvt/YX/y/sj+Gf8w/6b/VYC7gN6BREMgQ18DfMPtxEXEZ8T9hV+FPYU/BKzEr8VIhhwGYoXZBUjE9AQ9Q21CsQHVwV0BecDpQB8/lH87PoN+Z/2Y/NM8IXtKerW6Xnq4Ov97f/tne3V7Pbrrupg6uvrAe5M8YvzCPVH99z4Uvog/F79Iv5v//EAEwK4A/sEGAYFCEEJ3wnkCdgHfAUoBLYDgwQuBYUELAPMAev/GP74/PX79/rG+QL5Bvif9in17PJ68d70Z/r4+zD+ef0m+gb+//8a/74ASQK0BNAJmg7aDxkSjRTZFEcWnBaZFQ0WahVQFSMX3BjvGUoZsheJFYkTsxBNDDII+wRLAyICrACf/qz8e/r797/1p/Ki763sDOq36O7oWeme6Ybq6+qG6zLs+uvr6yfsKe3B7wzyqPPv9av43/r6/LH+S/8FAHsAbgE9AqIDWQVDBq4IPwpsCloJjAbVA/oB9gEcAjcCgALgAYYBOgCA/dj6N/l++E/3H/dS9hn12fSw8bDxS/bM+AP8h/63+0b9KQHt/3oCmQMyA4YIPgxgDnsRAhUAFgsYDBtYGt0aEhnIFdwUuxUmF/UWVhZQFb8UNhMFEM4LlwZoA3cAtfzP+e32IPXe9P30TfQD87/wHu0/6h/o9ObK5lLnj+go6ufsse6v7yzxM/Jy86b0QvXr9aD3BPpp/Nz+TwFsA/AEEgY+BmsGKgcCB98GogYSBhYGDwYCBuoF5QSQA3kBNP8a/t38Rfv1+X35h/gj9xP2qvW29Ur1VfRy8fnvx/KO9SP5+PyC/BP/OQQ8BSYGWQbcBGAGrgmZDNEPmROnFhAZJRw7Hckc0htlGUMXhRYaFiMVBhSLE0sTRhMyEuwO+gpwBzkEmgB5/F34j/SG8kHxH/Ct73zvkO4+7XHswOpK6JzmAub95eHmWejm6WPs3O/H8vL0Hvc1+Cj45/jI+f76ufyz/iUB7gOsB2UKcQuoC0gKIgm9B8gFWATRA1sEUgR4BIADpgG8AFH/j/3S+6v5kvc+9u/0w/Na9F700vMm89HwVfGk9Hr3qvqa/HX8/f4RAyEEowbjB9gHbgunDkoQ7RInFnAYghvyHV0drBwTG7kYRxg/GI8YxhchFuITsBHqD/8MkgksBmED9gAI/qn65fal80zxUu/O7ZDsTezC6wjrkeqw6QXprOgJ6I7oduoG7NDt7u8O8nL0nfZq+IH6CvzZ/Xj/dwBWAcEBqgJJBBgGtQfbCCAJ/gioCG8HvwV8BGoDhwKQATAABv/r/dn8+Puw+9v6R/nT9/T1rPNt8o3ymvKC8pPywvCY8Rf2OvlN/cP/W/8RArIF8QbzCPQJ9gliDeQQdxKxFT4Ychk0HGoe/x1dHU0csBk2GB0XHxUZEx4Rkw5UDDULgAlKBh4DIAAM/aX55vWI8gLwou4K7SDr8enb6fzpo+r26k7qMupn6vPpMuqo6xjtVu/C8f7zn/Yf+bv6nfzn/uIADgJzAsUCgAMhBd8Gswc6CDsJvAlECS0IfwcHB0UGxgTnAu0AUf8K/sX8bPwp/LL7hPrK+Mj27/Ul9fPzifNP82XzWvOB8rLyFfaR+br92f/JABgEwgc5CucL4gyODAAPHhF+EqQUpxYJGG4alByWHY4d9BtKGnIYFBd1FWcS5Q5xDF8KAwliB0IERgEI/4L8BPp49tLy/u/X7Tbsvuqu6Wfpo+nd6d3qYevQ6+Tr9OuN7N7t6e4Y8DzylvRb99r57fvV/Vb/tQBoApUDSwRABUEG3AY2BwkHHQczBzEHQQfNBmYFxgPXAQkA7/4b/bL79/pe+tj5hfmr+IX3q/Z59ar0kfOR85zzDvRN9dn0w/O49d/4yftjANAB7gONCAYMgQ1kDw8Pig9REl8TbhTwFToXDhmJGzkcXx15HUocIBvEGewWrRPRDxUMdwnzB0kGlQOYASH/Qfy3+Xf2FPMY8bTue+y66qDoLudQ51Tn4eeI6UHqEus27Pjs/+3970nxz/KU9GH2f/g2+gf8vv0MABoCsgPPBPcF8wbWB4oIeQg2CMQHWQf1BssGBwbyBJIDLAKlAA3/0v21/K/8bPye+yT6kfj59rb1K/Ws9Fb0y/RE9Wz18PVh9Tn1wfcU+w/+dwBaAvsE+gjeDCQP1BAHEpITIRUGFiMW2hawF2AZxxqRG/8bKxvtGWYYMxclFY0SCQ+xCzQIpQWrAkv/5vyp+tX4E/eC9J7x/e8T7jLst+pI6UXotegA6YLpuupy64TsBu4I7x/wYvEp8h70wfZ/+Xv7Xv3Q/mcAwAGqATQCXwNFBLoESgapBmIG3AaXBigG7gWKBSEEDAOHAdz/0v4T/pb9m/05/W78TPvI+SP5Qvhj9/f2D/at9VT2qPbk9mz3//YW9z75DPuE/SAAwQHnAyYI+woWDaAOBQ9OEBUSCBO+EpYT9BP5FB8WfxYsFh8WSxUMFOIScBGYDy4NpAq5ByYFLgKM/3f97fuq+h35Mvc99ZLzCfJ98DzvS+7V7Y7tee2Q7evtlO7N79rwU/Lb8xX1+/WU9mP3B/nJ+jD8P/5o/8UACgLFAqgDJgXeBUkGvQZKBoMFHQXGBEcE/wNWA10CRAF7AP/+Qv4V/tX99P3m/ZL8APz2++P6yfqK+kP6TPpv+tj58/lX+mn69fog+677efwO/oP/IAG+AmwESgbvB98IsQnBCh4LtgsaDJUMYQ3VDdoNlw1qDYENZA2bDOALZgubCggKlgjfBocF/QMnArMAgP8Y/gf9Zfuu+XT48Pdd98H2UvbQ9XH1NPXL9Mf08fRU9d710fZj9x/4zPhA+VX6XPt5/DD95P1n/kf/IADAACsBvAGAAhwDtAOkAwoDpgJmAvQBoQH4ADgANgDi/5n/Df8R/iD9Af1W/WP9RP2G/AX87Pvz+1X7/vog+8/7KfxO/M/7gfs0/Af90v1f/un+pv8BAf0B7wLjAxEFPgZTBzYIywgMCSIJmAnQCR4KVQq+CfYIxAilCIIIPwieB+MGyAZgBnYFdQREAykCdQF1ACf///3D/J77xPod+p75hflF+a34Q/g++I74DflS+af5PPq3+jH7o/vx+2P8G/3K/aj+df/i/zoAzgBCAeYBkgLVAtsCygLOArgCEQJqAfwAugC1AD8ATf95/sH9Uf3v/E/87Pvq+9v7Hvwr/M37qPte+077bvtD+xf7OPtt++37qPwQ/Z39Lv7I/qv/XADgAIQBPwIpAw0ElwQPBfAF1AahBx8IQwhwCG4IUwgdCIQHVQdDB6wGMwbeBTkF8gSSBMoDIQNfArUBJQGmAOD/A//o/aX89vtX++76vfqN+pr6z/rf+u/6F/tn+7f7JPyS/MX8Gf1h/Zv9Jf7n/oX/QQCNAHQArgDDANAAIAFpAZQBIAJAAt0BhAEXAaMAWgDg//b+aP7m/Sf9mPwO/N/7HvyP/ND88fz6/Oj82/yI/DL8EPww/D/8L/yY/AP9i/1R/vT+jv8bAKEACgFfAa0B3gFUAg0D0wNxBBIFhwX1BWIGpwayBoUGWwYoBugF1gXIBUsF2ARtBAAE3APMA3gDJwMBA6ECRAJnAZIAIACx/2T/Df9R/tv9if0c/er8vvy1/Nf87fzU/O/8Bf0V/SD9Ff1E/ZD9sf2x/QP+QP6Z/gH/If8s/yn/MP83/1H/aP9j/1//av9s/2j/Wv/y/rr+lP4i/vj96P2W/Wf9Wv05/U39h/1q/WH9Z/1y/Yv9lv23/fb9VP6f/vn+Gv87/5n/sf+4/9n/CgBXAK8A+ABNAccBHgJRApkCvgIBAzUDJQMPAx4DHgPwAq8CXQIrAj8COwIaAkgCmwINA6ID5wPtA/cDHwQUBB8EDQTDA4ADPgPnAnMC/wGGATAB2wB9ACkAxf9h/+3+pP5U/vb9rP1C/cn8Z/wT/PP73fvq+/r7IPxE/Dr8Pfxu/LX8CP1G/Wr9o/3M/eD99v0j/lP+lP7c/vj+NP9j/3r/4v8JACsAcAB0AHkAdQCEAHAAewC+AOgA9gAMAeQA0ADfAK4AewBMAOX/vP+t/4L/fP93/4D/oP+6/73/vP+b/4v/lf+d/9H/6f/0/wkAIABMAI0AtwDrADABfAHXAWUC6wJdA+4DZATwBHoFxAXBBYwFMwW0BDcEgwPBAjcC2wGNAT0B1ACAABcAuP9S/+X+Wv7y/XL9E/3x/Mv8tfzD/Nn87fwi/fr86/wQ/Rv9Rv2A/X79tf3Z/RP+XP6Z/u3+\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "audio, sr = sf.read('prediction_example.wav')\n",
    "Ipd.display(Ipd.Audio(audio, rate=sr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "94899563-e8d7-44f4-8669-b67a612b21d1",
   "metadata": {
    "id": "94899563-e8d7-44f4-8669-b67a612b21d1"
   },
   "outputs": [],
   "source": [
    "# %%writefile ../sources/fastpitch/model.py  # commented to enable syntax highlighting\n",
    "\n",
    "import torch\n",
    "from torch import nn as nn\n",
    "\n",
    "from sources.fastpitch.common.layers import TemporalPredictor\n",
    "from sources.fastpitch.common.utils import DeviceGetterMixin\n",
    "from sources.fastpitch.common.utils import regulate_len\n",
    "from sources.fastpitch.data import FastPitchBatch, SymbolsSet\n",
    "from sources.fastpitch.hparams import HParamsFastpitch\n",
    "from sources.fastpitch.common.transformer import FFTransformer\n",
    "\n",
    "\n",
    "class FastPitch(nn.Module, DeviceGetterMixin):\n",
    "    def __init__(self, hparams: HParamsFastpitch):\n",
    "        super().__init__()\n",
    "        self.hparams = hparams\n",
    "\n",
    "        n_symbols = len(SymbolsSet().symbols_to_id)\n",
    "\n",
    "        self.symbol_emb = nn.Embedding(n_symbols, hparams.symbols_embedding_dim)\n",
    "        self.encoder = FFTransformer(\n",
    "            n_layer=hparams.in_fft_n_layers,\n",
    "            n_head=hparams.in_fft_n_heads,\n",
    "            d_model=hparams.symbols_embedding_dim,\n",
    "            d_head=hparams.in_fft_d_head,\n",
    "            d_inner=4 * hparams.symbols_embedding_dim,\n",
    "            kernel_size=hparams.in_fft_conv1d_kernel_size,\n",
    "            dropout=hparams.p_in_fft_dropout,\n",
    "            dropatt=hparams.p_in_fft_dropatt,\n",
    "            dropemb=hparams.p_in_fft_dropemb\n",
    "        )\n",
    "\n",
    "        self.duration_predictor = TemporalPredictor(\n",
    "            input_size=hparams.symbols_embedding_dim,\n",
    "            filter_size=hparams.dur_predictor_filter_size,\n",
    "            kernel_size=hparams.dur_predictor_kernel_size,\n",
    "            dropout=hparams.p_dur_predictor_dropout,\n",
    "            n_layers=hparams.dur_predictor_n_layers\n",
    "        )\n",
    "\n",
    "        self.pitch_predictor = TemporalPredictor(\n",
    "            input_size=hparams.symbols_embedding_dim,\n",
    "            filter_size=hparams.pitch_predictor_filter_size,\n",
    "            kernel_size=hparams.pitch_predictor_kernel_size,\n",
    "            dropout=hparams.p_pitch_predictor_dropout,\n",
    "            n_layers=hparams.pitch_predictor_n_layers\n",
    "        )\n",
    "\n",
    "        self.pitch_emb = nn.Conv1d(1, hparams.symbols_embedding_dim, kernel_size=3, padding=1)\n",
    "\n",
    "        self.decoder = FFTransformer(\n",
    "            n_layer=hparams.out_fft_n_layers,\n",
    "            n_head=hparams.out_fft_n_heads,\n",
    "            d_model=hparams.symbols_embedding_dim,\n",
    "            d_head=hparams.out_fft_d_head,\n",
    "            d_inner=4 * hparams.symbols_embedding_dim,\n",
    "            kernel_size=hparams.out_fft_conv1d_kernel_size,\n",
    "            dropout=hparams.p_out_fft_dropout,\n",
    "            dropatt=hparams.p_out_fft_dropatt,\n",
    "            dropemb=hparams.p_out_fft_dropemb\n",
    "        )\n",
    "\n",
    "        self.proj = nn.Linear(hparams.symbols_embedding_dim, hparams.n_mel_channels, bias=True)\n",
    "\n",
    "    def get_encoder_out(self, batch: FastPitchBatch):\n",
    "        '''\n",
    "        Return: \n",
    "        enc_out: \n",
    "            Output of the first series of FFT blocks (before adding pitch embedding)\n",
    "            shape: (batch, len(text), symbols_embedding_dim)\n",
    "        enc_mask:\n",
    "            Boolean padding mask for the input text sequences\n",
    "            shape: (batch, len(text), 1)\n",
    "        '''\n",
    "        embedding = self.symbol_emb(batch.texts)\n",
    "\n",
    "        enc_out, enc_mask = self.encoder(embedding, batch.text_lengths)\n",
    "        return enc_out, enc_mask\n",
    "\n",
    "    def forward(self, batch: FastPitchBatch, use_gt_durations=True, use_gt_pitch=True, max_duration=75):\n",
    "        '''\n",
    "        Flags `use_gt_durations` and `use_gt_pitch` should be both True during training and either True or False during inference.\n",
    "\n",
    "        Use the function `regulate_len` to duplicate phonemes according to durations before passing them to the decoder.\n",
    "        \n",
    "        Return:\n",
    "        mel_out:\n",
    "            Predicted mel-spectrograms\n",
    "            shape: (batch, time, mel_bins)\n",
    "        mel_lens:\n",
    "            Number of time frames in each of the predicted spectrograms\n",
    "            shape: (batch,)\n",
    "        log_dur_pred:\n",
    "            The predicted log-durations for each phoneme (the output of the duration predictor).\n",
    "            shape: (batch, len(text))\n",
    "        dur_pred:\n",
    "            The exponent of the predicted log-durations for each phoneme. Clamped to the range (0, max_duration) for numeric stability\n",
    "            shape: (batch, len(text))\n",
    "        pitch_pred:\n",
    "            The predicted pitch for each phoneme\n",
    "            shape: (batch, len(text))\n",
    "        '''\n",
    "        encoder_out, encoder_mask = self.get_encoder_out(batch)     \n",
    "\n",
    "        pitch_prediction = self.pitch_predictor(encoder_out, encoder_mask)\n",
    "\n",
    "        if use_gt_pitch:\n",
    "            pitches = batch.pitches.unsqueeze(1)\n",
    "        else:\n",
    "            pitches = pitch_prediction.unsqueeze(1)\n",
    "\n",
    "\n",
    "\n",
    "        duration_prediction = torch.clamp(\n",
    "            torch.exp(log_duarition_prediction:=self.duration_predictor(encoder_out, encoder_mask)),\n",
    "            0,\n",
    "            max_duration\n",
    "        )\n",
    "\n",
    "        if use_gt_durations:\n",
    "            durations = batch.durations\n",
    "        else:\n",
    "            durations = duration_prediction\n",
    "\n",
    "        pitch_emb_out = self.pitch_emb(pitches).transpose(1, 2) + encoder_out    \n",
    "        enc_rep, dec_lens, reps = regulate_len(durations, pitch_emb_out)\n",
    "        dec_out, _ = self.decoder(enc_rep, dec_lens)\n",
    "        mel_out = self.proj(dec_out)\n",
    "        return mel_out, dec_lens, duration_prediction, log_duarition_prediction, pitch_prediction\n",
    "\n",
    "    @torch.no_grad()\n",
    "    def infer(self, batch: FastPitchBatch, max_duration=75):\n",
    "        encoder_out, duration_prediction, pitch_predicton = self.infer_encoder(batch, max_duration=max_duration)\n",
    "        mel_out, mel_lens = self.infer_decoder(encoder_out, duration_prediction)\n",
    "        return mel_out, mel_lens, duration_prediction, pitch_predicton\n",
    "\n",
    "    def infer_encoder(self, batch: FastPitchBatch, max_duration=75):\n",
    "        encoder_out, encoder_mask = self.get_encoder_out(batch)     \n",
    "        pitch_predicton = self.pitch_predictor(encoder_out, encoder_mask).unsqueeze(1)\n",
    "        log_duarition_prediction = self.duration_predictor(encoder_out, encoder_mask)\n",
    "        duration_prediction = torch.clamp(torch.exp(log_duarition_prediction), 0, max_duration)\n",
    "\n",
    "        return encoder_out, duration_prediction, pitch_predicton\n",
    "\n",
    "    def infer_decoder(self, enc_out, dur_pred):\n",
    "        enc_rep, dec_lens, reps = regulate_len(dur_pred, enc_out)\n",
    "        decoder_out, _ = self.decoder(enc_rep, dec_lens)\n",
    "        mel_out = self.proj(decoder_out)\n",
    "        return mel_out, dec_lens\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7e56cee5-2819-4e64-8e02-f7b58fe54084",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    " # Allows reloading code import without kernel restart\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "44733b3d-8031-4c55-8a02-b1f7ba00eae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sources.fastpitch.model import FastPitch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d500860d-72f1-49ba-9da7-d74f836a514c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = FastPitch(hparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fdde33be-187f-41d9-92e0-91b105d0c5e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc_out, enc_mask = fp.get_encoder_out(batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "00a2d810-490d-45b3-9592-e90e12d91882",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert enc_out.shape == torch.Size([hparams.batch_size, batch.texts.shape[1], hparams.symbols_embedding_dim])\n",
    "assert enc_mask.shape == torch.Size([hparams.batch_size, batch.texts.shape[1], 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bf709b60-8765-471f-9f48-ad04fcf34bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "mel_out, mel_lens, dur_pred, log_dur_pred, pitch_pred = fp.forward(batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dcb1f96e-2dc2-4702-9711-dd196aadf3a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert mel_out.shape == batch.mels.transpose(2, 1).shape\n",
    "assert mel_lens.shape == batch.mel_lengths.shape\n",
    "assert dur_pred.shape == batch.texts.shape\n",
    "assert dur_pred.shape == log_dur_pred.shape\n",
    "assert pitch_pred.shape == batch.texts.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ba68b7-d3e8-4ea2-88dd-de4db7385ec9",
   "metadata": {
    "id": "97ba68b7-d3e8-4ea2-88dd-de4db7385ec9"
   },
   "source": [
    "### Run training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2e164200-31c5-4c38-8f23-93de7ecaeaa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "logs_dir = \"logs\"     # Choose any paths\n",
    "ckpt_dir = \"checkpoints\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "406ae58f-2cd6-4569-babc-7abb1c6b9630",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(logs_dir, exist_ok=True)\n",
    "os.makedirs(ckpt_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "459e61e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install matplotlib==3.7.0 --force-reinstall  # Reinstalling because I caught `AttributeError: 'FigureCanvasAgg' object has no attribute 'tostring_rgb'. Did you mean: 'tostring_argb'?`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "783a90a0-1356-4216-a47b-fe1d80fcefb8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "783a90a0-1356-4216-a47b-fe1d80fcefb8",
    "outputId": "30504c3c-1ee6-46a5-d5d8-759bd31f4593"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fvelikon/miniconda3/envs/ysda_speech_torch_26/lib/python3.11/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\n",
      "  WeightNorm.apply(module, name, dim)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HiFi-GAN: Removing weight norm.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "22207 14 08:16:27 INFO Epoch num: 0\n",
      "22207 14 08:16:38 INFO Logging GT data\n",
      "22207 14 08:16:49 INFO Logging 20 images\n",
      "22207 14 08:16:50 INFO Logging 20 audios\n",
      "22207 14 08:16:53 INFO Logging 20 texts\n",
      "22207 14 08:16:53 INFO train : 1  loss 36.0241  elapsed 6.56  load 4.22\n",
      "22207 14 08:16:53 INFO Running validation\n",
      "/home/fvelikon/miniconda3/envs/ysda_speech_torch_26/lib/python3.11/site-packages/transformers/models/whisper/generation_whisper.py:573: FutureWarning: The input name `inputs` is deprecated. Please make sure to use `input_features` instead.\n",
      "  warnings.warn(\n",
      "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "22207 14 08:17:22 INFO Logging 20 images\n",
      "22207 14 08:17:23 INFO Logging 20 audios\n",
      "22207 14 08:17:23 INFO Logging 20 texts\n",
      "22207 14 08:17:34 INFO val : 1  loss 35.74470138549805\n",
      "22207 14 08:17:34 INFO Saving model state at step 1 to checkpoints/fp_step_00000001.pt\n",
      "22207 14 08:17:35 INFO train : 2  loss 36.2633  elapsed 0.52  load 0.00\n",
      "22207 14 08:17:36 INFO train : 3  loss 36.1810  elapsed 0.56  load 0.00\n",
      "22207 14 08:17:37 INFO train : 4  loss 36.1064  elapsed 0.56  load 0.01\n",
      "22207 14 08:17:37 INFO train : 5  loss 34.7805  elapsed 0.58  load 0.00\n",
      "22207 14 08:17:38 INFO train : 6  loss 35.3339  elapsed 0.54  load 0.04\n",
      "22207 14 08:17:38 INFO train : 7  loss 34.6046  elapsed 0.68  load 0.00\n",
      "22207 14 08:17:39 INFO train : 8  loss 35.1483  elapsed 0.59  load 0.02\n",
      "22207 14 08:17:40 INFO train : 9  loss 34.5151  elapsed 0.62  load 0.05\n",
      "22207 14 08:17:40 INFO train : 10  loss 33.7697  elapsed 0.65  load 0.03\n",
      "22207 14 08:17:41 INFO train : 11  loss 33.3248  elapsed 0.69  load 0.05\n",
      "22207 14 08:17:42 INFO train : 12  loss 33.5310  elapsed 0.56  load 0.01\n",
      "22207 14 08:17:43 INFO train : 13  loss 32.3785  elapsed 0.61  load 0.08\n",
      "22207 14 08:17:43 INFO train : 14  loss 32.1704  elapsed 0.56  load 0.07\n",
      "22207 14 08:17:44 INFO train : 15  loss 30.4854  elapsed 0.57  load 0.00\n",
      "22207 14 08:17:45 INFO train : 16  loss 30.9662  elapsed 0.53  load 0.22\n",
      "22207 14 08:17:45 INFO train : 17  loss 30.3923  elapsed 0.59  load 0.00\n",
      "22207 14 08:17:46 INFO train : 18  loss 29.6566  elapsed 0.68  load 0.00\n",
      "22207 14 08:17:47 INFO train : 19  loss 28.6302  elapsed 0.69  load 0.04\n",
      "22207 14 08:17:48 INFO train : 20  loss 28.3878  elapsed 0.58  load 1.27\n",
      "22207 14 08:17:49 INFO train : 21  loss 27.6924  elapsed 0.61  load 0.00\n",
      "22207 14 08:17:50 INFO train : 22  loss 27.5262  elapsed 0.66  load 0.00\n",
      "22207 14 08:17:50 INFO train : 23  loss 27.4874  elapsed 0.58  load 0.03\n",
      "22207 14 08:17:52 INFO train : 24  loss 26.7642  elapsed 0.68  load 0.80\n",
      "22207 14 08:17:53 INFO train : 25  loss 26.7050  elapsed 0.61  load 0.00\n",
      "22207 14 08:17:53 INFO train : 26  loss 25.6365  elapsed 0.64  load 0.00\n",
      "22207 14 08:17:54 INFO train : 27  loss 25.4432  elapsed 0.63  load 0.04\n",
      "22207 14 08:17:55 INFO train : 28  loss 25.4728  elapsed 0.53  load 0.61\n",
      "22207 14 08:17:56 INFO train : 29  loss 25.6827  elapsed 0.55  load 0.00\n",
      "22207 14 08:17:56 INFO train : 30  loss 25.0224  elapsed 0.66  load 0.00\n",
      "22207 14 08:17:57 INFO train : 31  loss 25.4071  elapsed 0.63  load 0.02\n",
      "22207 14 08:17:59 INFO train : 32  loss 24.7555  elapsed 0.49  load 1.33\n",
      "22207 14 08:18:00 INFO train : 33  loss 25.0480  elapsed 0.62  load 0.00\n",
      "22207 14 08:18:00 INFO train : 34  loss 24.3856  elapsed 0.64  load 0.00\n",
      "22207 14 08:18:01 INFO train : 35  loss 24.1730  elapsed 0.62  load 0.00\n",
      "22207 14 08:18:03 INFO train : 36  loss 24.0276  elapsed 0.56  load 1.26\n",
      "22207 14 08:18:03 INFO train : 37  loss 24.0225  elapsed 0.61  load 0.01\n",
      "22207 14 08:18:04 INFO train : 38  loss 24.0606  elapsed 0.66  load 0.00\n",
      "22207 14 08:18:05 INFO train : 39  loss 23.1130  elapsed 0.57  load 0.00\n",
      "22207 14 08:18:07 INFO train : 40  loss 23.0313  elapsed 0.69  load 1.63\n",
      "22207 14 08:18:08 INFO train : 41  loss 23.0459  elapsed 0.68  load 0.00\n",
      "22207 14 08:18:08 INFO train : 42  loss 23.2232  elapsed 0.75  load 0.00\n",
      "22207 14 08:18:09 INFO train : 43  loss 23.5154  elapsed 0.66  load 0.00\n",
      "22207 14 08:18:10 INFO train : 44  loss 23.1601  elapsed 0.60  load 0.68\n",
      "22207 14 08:18:11 INFO train : 45  loss 22.8810  elapsed 0.82  load 0.00\n",
      "22207 14 08:18:12 INFO train : 46  loss 22.9930  elapsed 0.64  load 0.00\n",
      "22207 14 08:18:13 INFO train : 47  loss 22.5086  elapsed 0.61  load 0.10\n",
      "22207 14 08:18:14 INFO train : 48  loss 23.0560  elapsed 0.59  load 0.76\n",
      "22207 14 08:18:15 INFO train : 49  loss 23.2530  elapsed 0.66  load 0.00\n",
      "22207 14 08:18:15 INFO train : 50  loss 22.9687  elapsed 0.56  load 0.00\n",
      "22207 14 08:18:16 INFO train : 51  loss 22.9546  elapsed 0.63  load 0.00\n",
      "22207 14 08:18:18 INFO train : 52  loss 22.1351  elapsed 0.57  load 1.11\n",
      "22207 14 08:18:18 INFO train : 53  loss 22.2968  elapsed 0.74  load 0.00\n",
      "22207 14 08:18:19 INFO train : 54  loss 22.6244  elapsed 0.62  load 0.02\n",
      "22207 14 08:18:20 INFO train : 55  loss 22.7245  elapsed 0.62  load 0.02\n",
      "22207 14 08:18:21 INFO train : 56  loss 22.9756  elapsed 0.66  load 0.88\n",
      "22207 14 08:18:22 INFO train : 57  loss 22.0094  elapsed 0.58  load 0.00\n",
      "22207 14 08:18:22 INFO train : 58  loss 22.3222  elapsed 0.60  load 0.00\n",
      "22207 14 08:18:23 INFO train : 59  loss 22.1019  elapsed 0.64  load 0.08\n",
      "22207 14 08:18:25 INFO train : 60  loss 22.4231  elapsed 0.51  load 0.81\n",
      "22207 14 08:18:25 INFO train : 61  loss 22.2954  elapsed 0.77  load 0.00\n",
      "22207 14 08:18:26 INFO train : 62  loss 21.9066  elapsed 0.56  load 0.00\n",
      "22207 14 08:18:27 INFO train : 63  loss 22.4433  elapsed 0.55  load 0.08\n",
      "22207 14 08:18:28 INFO train : 64  loss 21.1412  elapsed 0.56  load 0.85\n",
      "22207 14 08:18:29 INFO train : 65  loss 21.9028  elapsed 0.70  load 0.00\n",
      "22207 14 08:18:29 INFO train : 66  loss 22.2544  elapsed 0.67  load 0.01\n",
      "22207 14 08:18:30 INFO train : 67  loss 21.9992  elapsed 0.62  load 0.03\n",
      "22207 14 08:18:31 INFO train : 68  loss 21.7414  elapsed 0.58  load 0.61\n",
      "22207 14 08:18:32 INFO train : 69  loss 21.4878  elapsed 0.52  load 0.00\n",
      "22207 14 08:18:33 INFO train : 70  loss 21.3512  elapsed 0.59  load 0.03\n",
      "22207 14 08:18:33 INFO train : 71  loss 21.3372  elapsed 0.65  load 0.01\n",
      "22207 14 08:18:35 INFO train : 72  loss 21.8348  elapsed 0.65  load 1.13\n",
      "22207 14 08:18:36 INFO train : 73  loss 21.6747  elapsed 0.58  load 0.00\n",
      "22207 14 08:18:36 INFO train : 74  loss 21.1853  elapsed 0.58  load 0.05\n",
      "22207 14 08:18:37 INFO train : 75  loss 20.9362  elapsed 0.64  load 0.00\n",
      "22207 14 08:18:38 INFO train : 76  loss 21.4265  elapsed 0.59  load 0.94\n",
      "22207 14 08:18:39 INFO train : 77  loss 21.2650  elapsed 0.63  load 0.00\n",
      "22207 14 08:18:40 INFO train : 78  loss 20.7817  elapsed 0.59  load 0.05\n",
      "22207 14 08:18:41 INFO train : 79  loss 21.0191  elapsed 0.65  load 0.10\n",
      "22207 14 08:18:42 INFO train : 80  loss 21.1474  elapsed 0.68  load 0.94\n",
      "22207 14 08:18:43 INFO train : 81  loss 21.3123  elapsed 0.71  load 0.00\n",
      "22207 14 08:18:44 INFO train : 82  loss 21.1903  elapsed 0.68  load 0.14\n",
      "22207 14 08:18:44 INFO train : 83  loss 20.5951  elapsed 0.69  load 0.00\n",
      "22207 14 08:18:46 INFO train : 84  loss 21.0148  elapsed 0.69  load 0.71\n",
      "22207 14 08:18:47 INFO train : 85  loss 20.7752  elapsed 0.56  load 0.00\n",
      "22207 14 08:18:47 INFO train : 86  loss 20.4521  elapsed 0.63  load 0.01\n",
      "22207 14 08:18:48 INFO train : 87  loss 20.7022  elapsed 0.56  load 0.08\n",
      "22207 14 08:18:49 INFO train : 88  loss 20.2241  elapsed 0.67  load 0.86\n",
      "22207 14 08:18:50 INFO train : 89  loss 20.7298  elapsed 0.59  load 0.01\n",
      "22207 14 08:18:51 INFO train : 90  loss 19.8008  elapsed 0.65  load 0.00\n",
      "22207 14 08:18:51 INFO train : 91  loss 20.6139  elapsed 0.59  load 0.05\n",
      "22207 14 08:18:53 INFO train : 92  loss 20.1077  elapsed 0.60  load 0.70\n",
      "22207 14 08:18:53 INFO train : 93  loss 19.9990  elapsed 0.67  load 0.00\n",
      "22207 14 08:18:54 INFO train : 94  loss 20.1665  elapsed 0.57  load 0.08\n",
      "22207 14 08:18:55 INFO train : 95  loss 19.9834  elapsed 0.65  load 0.07\n",
      "22207 14 08:18:56 INFO train : 96  loss 20.2983  elapsed 0.62  load 0.69\n",
      "22207 14 08:18:57 INFO train : 97  loss 20.6095  elapsed 0.57  load 0.01\n",
      "22207 14 08:18:57 INFO train : 98  loss 19.4115  elapsed 0.64  load 0.07\n",
      "22207 14 08:18:58 INFO train : 99  loss 19.6714  elapsed 0.62  load 0.16\n",
      "22207 14 08:18:59 INFO train : 100  loss 19.5679  elapsed 0.58  load 0.56\n",
      "22207 14 08:19:00 INFO train : 101  loss 19.5044  elapsed 0.56  load 0.00\n",
      "22207 14 08:19:01 INFO train : 102  loss 19.8637  elapsed 0.61  load 0.02\n",
      "22207 14 08:19:01 INFO train : 103  loss 19.4409  elapsed 0.61  load 0.08\n",
      "22207 14 08:19:03 INFO train : 104  loss 18.7309  elapsed 0.58  load 0.87\n",
      "22207 14 08:19:04 INFO train : 105  loss 18.8129  elapsed 0.71  load 0.00\n",
      "22207 14 08:19:04 INFO train : 106  loss 18.4664  elapsed 0.61  load 0.02\n",
      "22207 14 08:19:05 INFO train : 107  loss 19.1890  elapsed 0.63  load 0.00\n",
      "22207 14 08:19:07 INFO train : 108  loss 18.5611  elapsed 0.58  load 1.11\n",
      "22207 14 08:19:07 INFO train : 109  loss 18.7299  elapsed 0.66  load 0.00\n",
      "22207 14 08:19:08 INFO train : 110  loss 18.3969  elapsed 0.57  load 0.00\n",
      "22207 14 08:19:09 INFO train : 111  loss 18.7440  elapsed 0.54  load 0.00\n",
      "22207 14 08:19:10 INFO train : 112  loss 18.6599  elapsed 0.58  load 1.13\n",
      "22207 14 08:19:11 INFO train : 113  loss 18.5145  elapsed 0.59  load 0.00\n",
      "22207 14 08:19:11 INFO train : 114  loss 18.7467  elapsed 0.61  load 0.01\n",
      "22207 14 08:19:12 INFO train : 115  loss 18.4347  elapsed 0.67  load 0.05\n",
      "22207 14 08:19:14 INFO train : 116  loss 18.1365  elapsed 0.63  load 0.88\n",
      "22207 14 08:19:14 INFO train : 117  loss 17.9152  elapsed 0.59  load 0.00\n",
      "22207 14 08:19:15 INFO train : 118  loss 18.5020  elapsed 0.50  load 0.00\n",
      "22207 14 08:19:16 INFO train : 119  loss 18.0918  elapsed 0.54  load 0.01\n",
      "22207 14 08:19:17 INFO train : 120  loss 17.8202  elapsed 0.61  load 1.03\n",
      "22207 14 08:19:18 INFO train : 121  loss 17.6451  elapsed 0.65  load 0.00\n",
      "22207 14 08:19:19 INFO train : 122  loss 17.5315  elapsed 0.60  load 0.00\n",
      "22207 14 08:19:19 INFO train : 123  loss 17.0624  elapsed 0.59  load 0.01\n",
      "22207 14 08:19:21 INFO train : 124  loss 17.4648  elapsed 0.68  load 1.22\n",
      "22207 14 08:19:22 INFO train : 125  loss 17.0470  elapsed 0.59  load 0.00\n",
      "22207 14 08:19:22 INFO train : 126  loss 17.0399  elapsed 0.57  load 0.00\n",
      "22207 14 08:19:23 INFO train : 127  loss 16.6768  elapsed 0.58  load 0.00\n",
      "22207 14 08:19:24 INFO train : 128  loss 17.3173  elapsed 0.52  load 0.95\n",
      "22207 14 08:19:25 INFO train : 129  loss 16.8984  elapsed 0.59  load 0.00\n",
      "22207 14 08:19:26 INFO train : 130  loss 16.8815  elapsed 0.57  load 0.00\n",
      "22207 14 08:19:26 INFO train : 131  loss 16.3856  elapsed 0.62  load 0.06\n",
      "22207 14 08:19:28 INFO train : 132  loss 16.6376  elapsed 0.53  load 1.23\n",
      "22207 14 08:19:29 INFO train : 133  loss 16.3637  elapsed 0.57  load 0.00\n",
      "22207 14 08:19:29 INFO train : 134  loss 16.0925  elapsed 0.64  load 0.00\n",
      "22207 14 08:19:30 INFO train : 135  loss 16.6959  elapsed 0.67  load 0.00\n",
      "22207 14 08:19:31 INFO train : 136  loss 16.2599  elapsed 0.56  load 0.82\n",
      "22207 14 08:19:32 INFO train : 137  loss 16.4281  elapsed 0.62  load 0.00\n",
      "22207 14 08:19:33 INFO train : 138  loss 15.9807  elapsed 0.57  load 0.00\n",
      "22207 14 08:19:33 INFO train : 139  loss 16.2213  elapsed 0.62  load 0.06\n",
      "22207 14 08:19:35 INFO train : 140  loss 15.9185  elapsed 0.60  load 1.30\n",
      "22207 14 08:19:36 INFO train : 141  loss 15.7430  elapsed 0.67  load 0.00\n",
      "22207 14 08:19:36 INFO train : 142  loss 15.4415  elapsed 0.60  load 0.00\n",
      "22207 14 08:19:37 INFO train : 143  loss 15.3660  elapsed 0.59  load 0.04\n",
      "22207 14 08:19:39 INFO train : 144  loss 15.3749  elapsed 0.60  load 1.01\n",
      "22207 14 08:19:39 INFO train : 145  loss 15.0429  elapsed 0.58  load 0.00\n",
      "22207 14 08:19:40 INFO train : 146  loss 15.0908  elapsed 0.60  load 0.00\n",
      "22207 14 08:19:41 INFO train : 147  loss 15.1621  elapsed 0.51  load 0.00\n",
      "22207 14 08:19:42 INFO train : 148  loss 14.8761  elapsed 0.64  load 0.97\n",
      "22207 14 08:19:43 INFO train : 149  loss 14.8379  elapsed 0.56  load 0.00\n",
      "22207 14 08:19:43 INFO train : 150  loss 14.3978  elapsed 0.57  load 0.08\n",
      "22207 14 08:19:44 INFO train : 151  loss 14.2945  elapsed 0.58  load 0.00\n",
      "22207 14 08:19:46 INFO train : 152  loss 14.5390  elapsed 0.56  load 0.85\n",
      "22207 14 08:19:46 INFO train : 153  loss 14.5399  elapsed 0.61  load 0.00\n",
      "22207 14 08:19:47 INFO train : 154  loss 13.9890  elapsed 0.70  load 0.02\n",
      "22207 14 08:19:48 INFO train : 155  loss 14.1086  elapsed 0.69  load 0.00\n",
      "22207 14 08:19:49 INFO train : 156  loss 13.6583  elapsed 0.67  load 0.94\n",
      "22207 14 08:19:50 INFO train : 157  loss 14.2121  elapsed 0.66  load 0.07\n",
      "22207 14 08:19:51 INFO train : 158  loss 13.5189  elapsed 0.63  load 0.11\n",
      "22207 14 08:19:51 INFO train : 159  loss 13.2650  elapsed 0.60  load 0.00\n",
      "22207 14 08:19:53 INFO train : 160  loss 13.3148  elapsed 0.53  load 0.84\n",
      "22207 14 08:19:53 INFO train : 161  loss 13.6663  elapsed 0.64  load 0.09\n",
      "22207 14 08:19:54 INFO train : 162  loss 13.1872  elapsed 0.65  load 0.01\n",
      "22207 14 08:19:55 INFO train : 163  loss 12.9850  elapsed 0.58  load 0.00\n",
      "22207 14 08:19:56 INFO train : 164  loss 13.0719  elapsed 0.62  load 0.77\n",
      "22207 14 08:19:57 INFO train : 165  loss 12.7011  elapsed 0.66  load 0.35\n",
      "22207 14 08:19:58 INFO train : 166  loss 12.3523  elapsed 0.64  load 0.00\n",
      "22207 14 08:19:59 INFO train : 167  loss 12.9462  elapsed 0.55  load 0.00\n",
      "22207 14 08:20:00 INFO train : 168  loss 12.3861  elapsed 0.61  load 0.43\n",
      "22207 14 08:20:01 INFO train : 169  loss 12.2773  elapsed 0.62  load 1.07\n",
      "22207 14 08:20:02 INFO train : 170  loss 12.4663  elapsed 0.64  load 0.00\n",
      "22207 14 08:20:03 INFO train : 171  loss 12.0153  elapsed 0.67  load 0.03\n",
      "22207 14 08:20:04 INFO train : 172  loss 12.4697  elapsed 0.61  load 0.26\n",
      "22207 14 08:20:05 INFO train : 173  loss 12.3983  elapsed 0.56  load 0.82\n",
      "22207 14 08:20:06 INFO train : 174  loss 11.6984  elapsed 0.76  load 0.00\n",
      "22207 14 08:20:06 INFO train : 175  loss 11.7261  elapsed 0.65  load 0.00\n",
      "22207 14 08:20:07 INFO train : 176  loss 11.7527  elapsed 0.64  load 0.04\n",
      "22207 14 08:20:09 INFO train : 177  loss 11.6695  elapsed 0.59  load 0.95\n",
      "22207 14 08:20:09 INFO train : 178  loss 11.2559  elapsed 0.63  load 0.00\n",
      "22207 14 08:20:10 INFO train : 179  loss 11.3170  elapsed 0.66  load 0.00\n",
      "22207 14 08:20:11 INFO train : 180  loss 11.4511  elapsed 0.58  load 0.32\n",
      "22207 14 08:20:12 INFO train : 181  loss 10.9842  elapsed 0.59  load 0.56\n",
      "22207 14 08:20:13 INFO train : 182  loss 10.9972  elapsed 0.62  load 0.00\n",
      "22207 14 08:20:13 INFO train : 183  loss 10.5021  elapsed 0.68  load 0.00\n",
      "22207 14 08:20:15 INFO train : 184  loss 10.7435  elapsed 0.66  load 0.94\n",
      "22207 14 08:20:16 INFO train : 185  loss 10.8574  elapsed 0.76  load 0.07\n",
      "22207 14 08:20:16 INFO train : 186  loss 10.2718  elapsed 0.58  load 0.00\n",
      "22207 14 08:20:17 INFO train : 187  loss 10.3473  elapsed 0.56  load 0.00\n",
      "22207 14 08:20:19 INFO train : 188  loss 10.2983  elapsed 0.65  load 1.13\n",
      "22207 14 08:20:20 INFO train : 189  loss 10.2555  elapsed 0.57  load 0.05\n",
      "22207 14 08:20:20 INFO train : 190  loss 10.0303  elapsed 0.67  load 0.00\n",
      "22207 14 08:20:21 INFO train : 191  loss 9.6676  elapsed 0.64  load 0.00\n",
      "22207 14 08:20:22 INFO train : 192  loss 9.9005  elapsed 0.61  load 0.78\n",
      "22207 14 08:20:23 INFO train : 193  loss 9.6382  elapsed 0.65  load 0.27\n",
      "22207 14 08:20:24 INFO train : 194  loss 9.4035  elapsed 0.51  load 0.00\n",
      "22207 14 08:20:25 INFO train : 195  loss 9.5109  elapsed 0.70  load 0.00\n",
      "22207 14 08:20:26 INFO train : 196  loss 9.5816  elapsed 0.65  load 0.63\n",
      "22207 14 08:20:27 INFO train : 197  loss 9.7412  elapsed 0.61  load 0.17\n",
      "22207 14 08:20:27 INFO train : 198  loss 9.4341  elapsed 0.51  load 0.00\n",
      "22207 14 08:20:28 INFO train : 199  loss 8.8596  elapsed 0.57  load 0.00\n",
      "22207 14 08:20:30 INFO train : 200  loss 9.0540  elapsed 0.71  load 1.25\n",
      "22207 14 08:20:30 INFO train : 201  loss 8.8396  elapsed 0.61  load 0.01\n",
      "22207 14 08:20:31 INFO train : 202  loss 8.9425  elapsed 0.59  load 0.00\n",
      "22207 14 08:20:32 INFO train : 203  loss 8.6131  elapsed 0.67  load 0.00\n",
      "22207 14 08:20:33 INFO train : 204  loss 8.5502  elapsed 0.60  load 0.75\n",
      "22207 14 08:20:34 INFO train : 205  loss 8.3027  elapsed 0.58  load 0.09\n",
      "22207 14 08:20:35 INFO train : 206  loss 8.6384  elapsed 0.61  load 0.00\n",
      "22207 14 08:20:35 INFO train : 207  loss 8.4333  elapsed 0.66  load 0.00\n",
      "22207 14 08:20:37 INFO train : 208  loss 8.3081  elapsed 0.62  load 0.98\n",
      "22207 14 08:20:37 INFO train : 209  loss 8.0380  elapsed 0.62  load 0.00\n",
      "22207 14 08:20:38 INFO train : 210  loss 8.0269  elapsed 0.69  load 0.00\n",
      "22207 14 08:20:39 INFO train : 211  loss 8.1108  elapsed 0.61  load 0.00\n",
      "22207 14 08:20:40 INFO train : 212  loss 7.8924  elapsed 0.58  load 1.09\n",
      "22207 14 08:20:41 INFO train : 213  loss 8.0007  elapsed 0.59  load 0.00\n",
      "22207 14 08:20:42 INFO train : 214  loss 7.5347  elapsed 0.65  load 0.00\n",
      "22207 14 08:20:42 INFO train : 215  loss 7.4523  elapsed 0.61  load 0.00\n",
      "22207 14 08:20:44 INFO train : 216  loss 7.3190  elapsed 0.62  load 1.01\n",
      "22207 14 08:20:45 INFO train : 217  loss 7.7040  elapsed 0.64  load 0.02\n",
      "22207 14 08:20:45 INFO train : 218  loss 7.2666  elapsed 0.68  load 0.00\n",
      "22207 14 08:20:46 INFO train : 219  loss 7.2526  elapsed 0.64  load 0.00\n",
      "22207 14 08:20:47 INFO train : 220  loss 6.9675  elapsed 0.59  load 0.43\n",
      "22207 14 08:20:48 INFO train : 221  loss 6.9837  elapsed 0.52  load 0.35\n",
      "22207 14 08:20:49 INFO train : 222  loss 6.9064  elapsed 0.67  load 0.00\n",
      "22207 14 08:20:49 INFO train : 223  loss 6.9298  elapsed 0.64  load 0.00\n",
      "22207 14 08:20:51 INFO train : 224  loss 7.0518  elapsed 0.62  load 0.75\n",
      "22207 14 08:20:51 INFO train : 225  loss 6.8705  elapsed 0.62  load 0.01\n",
      "22207 14 08:20:52 INFO train : 226  loss 6.7047  elapsed 0.56  load 0.00\n",
      "22207 14 08:20:53 INFO train : 227  loss 6.3839  elapsed 0.59  load 0.00\n",
      "22207 14 08:20:54 INFO train : 228  loss 6.0777  elapsed 0.54  load 0.91\n",
      "22207 14 08:20:55 INFO train : 229  loss 6.2038  elapsed 0.67  load 0.03\n",
      "22207 14 08:20:55 INFO train : 230  loss 6.1956  elapsed 0.62  load 0.00\n",
      "22207 14 08:20:56 INFO train : 231  loss 6.1045  elapsed 0.61  load 0.00\n",
      "22207 14 08:20:58 INFO train : 232  loss 6.3264  elapsed 0.60  load 1.12\n",
      "22207 14 08:20:58 INFO train : 233  loss 6.0840  elapsed 0.59  load 0.05\n",
      "22207 14 08:20:59 INFO train : 234  loss 5.8884  elapsed 0.57  load 0.01\n",
      "22207 14 08:21:00 INFO train : 235  loss 5.7511  elapsed 0.64  load 0.00\n",
      "22207 14 08:21:02 INFO train : 236  loss 5.7499  elapsed 0.67  load 1.29\n",
      "22207 14 08:21:02 INFO train : 237  loss 5.6895  elapsed 0.56  load 0.00\n",
      "22207 14 08:21:03 INFO train : 238  loss 5.3323  elapsed 0.59  load 0.00\n",
      "22207 14 08:21:04 INFO train : 239  loss 5.3580  elapsed 0.56  load 0.00\n",
      "22207 14 08:21:05 INFO train : 240  loss 5.4388  elapsed 0.60  load 1.23\n",
      "22207 14 08:21:06 INFO train : 241  loss 5.3221  elapsed 0.56  load 0.00\n",
      "22207 14 08:21:07 INFO train : 242  loss 5.1095  elapsed 0.49  load 0.00\n",
      "22207 14 08:21:07 INFO train : 243  loss 5.0861  elapsed 0.58  load 0.00\n",
      "22207 14 08:21:09 INFO train : 244  loss 5.1794  elapsed 0.58  load 1.41\n",
      "22207 14 08:21:10 INFO train : 245  loss 4.9728  elapsed 0.59  load 0.00\n",
      "22207 14 08:21:10 INFO train : 246  loss 4.9037  elapsed 0.57  load 0.00\n",
      "22207 14 08:21:11 INFO train : 247  loss 4.8437  elapsed 0.60  load 0.00\n",
      "22207 14 08:21:13 INFO train : 248  loss 4.6788  elapsed 0.56  load 1.25\n",
      "22207 14 08:21:13 INFO train : 249  loss 4.5304  elapsed 0.59  load 0.00\n",
      "22207 14 08:21:14 INFO train : 250  loss 4.8988  elapsed 0.64  load 0.01\n",
      "22207 14 08:21:15 INFO train : 251  loss 4.5948  elapsed 0.66  load 0.00\n",
      "22207 14 08:21:16 INFO train : 252  loss 4.6349  elapsed 0.51  load 1.06\n",
      "22207 14 08:21:17 INFO train : 253  loss 4.4576  elapsed 0.66  load 0.00\n",
      "22207 14 08:21:18 INFO train : 254  loss 4.6730  elapsed 0.52  load 0.00\n",
      "22207 14 08:21:18 INFO train : 255  loss 4.6282  elapsed 0.64  load 0.00\n",
      "22207 14 08:21:20 INFO train : 256  loss 4.2077  elapsed 0.61  load 1.34\n",
      "22207 14 08:21:21 INFO train : 257  loss 4.2210  elapsed 0.60  load 0.00\n",
      "22207 14 08:21:22 INFO train : 258  loss 4.2456  elapsed 0.66  load 0.00\n",
      "22207 14 08:21:22 INFO train : 259  loss 4.4098  elapsed 0.61  load 0.00\n",
      "22207 14 08:21:24 INFO train : 260  loss 4.0325  elapsed 0.61  load 0.70\n",
      "22207 14 08:21:24 INFO train : 261  loss 3.9153  elapsed 0.60  load 0.00\n",
      "22207 14 08:21:25 INFO train : 262  loss 3.8814  elapsed 0.62  load 0.00\n",
      "22207 14 08:21:25 INFO train : 263  loss 4.0367  elapsed 0.63  load 0.00\n",
      "22207 14 08:21:27 INFO train : 264  loss 3.8292  elapsed 0.59  load 1.21\n",
      "22207 14 08:21:28 INFO train : 265  loss 3.8438  elapsed 0.58  load 0.00\n",
      "22207 14 08:21:29 INFO train : 266  loss 3.6915  elapsed 0.59  load 0.00\n",
      "22207 14 08:21:29 INFO train : 267  loss 3.5617  elapsed 0.56  load 0.00\n",
      "22207 14 08:21:31 INFO train : 268  loss 3.7210  elapsed 0.66  load 0.94\n",
      "22207 14 08:21:31 INFO train : 269  loss 3.5817  elapsed 0.54  load 0.00\n",
      "22207 14 08:21:32 INFO train : 270  loss 3.5748  elapsed 0.65  load 0.00\n",
      "22207 14 08:21:33 INFO train : 271  loss 3.4805  elapsed 0.68  load 0.00\n",
      "22207 14 08:21:34 INFO train : 272  loss 3.3358  elapsed 0.56  load 0.93\n",
      "22207 14 08:21:35 INFO train : 273  loss 3.5552  elapsed 0.54  load 0.00\n",
      "22207 14 08:21:35 INFO train : 274  loss 3.3001  elapsed 0.62  load 0.00\n",
      "22207 14 08:21:36 INFO train : 275  loss 3.3005  elapsed 0.70  load 0.00\n",
      "22207 14 08:21:38 INFO train : 276  loss 3.1504  elapsed 0.63  load 1.04\n",
      "22207 14 08:21:39 INFO train : 277  loss 3.2588  elapsed 0.65  load 0.00\n",
      "22207 14 08:21:39 INFO train : 278  loss 3.2186  elapsed 0.61  load 0.00\n",
      "22207 14 08:21:40 INFO train : 279  loss 3.2044  elapsed 0.69  load 0.00\n",
      "22207 14 08:21:41 INFO train : 280  loss 3.1206  elapsed 0.55  load 0.67\n",
      "22207 14 08:21:42 INFO train : 281  loss 3.1963  elapsed 0.64  load 0.00\n",
      "22207 14 08:21:42 INFO train : 282  loss 3.0917  elapsed 0.60  load 0.00\n",
      "22207 14 08:21:43 INFO train : 283  loss 2.9651  elapsed 0.64  load 0.01\n",
      "22207 14 08:21:45 INFO train : 284  loss 2.8525  elapsed 0.64  load 1.02\n",
      "22207 14 08:21:45 INFO train : 285  loss 2.8388  elapsed 0.62  load 0.00\n",
      "22207 14 08:21:46 INFO train : 286  loss 2.7903  elapsed 0.59  load 0.00\n",
      "22207 14 08:21:47 INFO train : 287  loss 2.9383  elapsed 0.60  load 0.00\n",
      "22207 14 08:21:49 INFO train : 288  loss 2.7986  elapsed 0.59  load 1.26\n",
      "22207 14 08:21:49 INFO train : 289  loss 2.7869  elapsed 0.62  load 0.00\n",
      "22207 14 08:21:50 INFO train : 290  loss 2.6676  elapsed 0.54  load 0.00\n",
      "22207 14 08:21:51 INFO train : 291  loss 2.8586  elapsed 0.64  load 0.00\n",
      "22207 14 08:21:52 INFO train : 292  loss 2.5959  elapsed 0.55  load 1.28\n",
      "22207 14 08:21:53 INFO train : 293  loss 2.6131  elapsed 0.58  load 0.00\n",
      "22207 14 08:21:54 INFO train : 294  loss 2.5901  elapsed 0.54  load 0.00\n",
      "22207 14 08:21:54 INFO train : 295  loss 2.6099  elapsed 0.64  load 0.00\n",
      "22207 14 08:21:56 INFO train : 296  loss 2.4711  elapsed 0.58  load 1.02\n",
      "22207 14 08:21:56 INFO train : 297  loss 2.4630  elapsed 0.63  load 0.00\n",
      "22207 14 08:21:57 INFO train : 298  loss 2.4641  elapsed 0.59  load 0.01\n",
      "22207 14 08:21:58 INFO train : 299  loss 2.5106  elapsed 0.56  load 0.00\n",
      "22207 14 08:22:00 INFO train : 300  loss 2.3678  elapsed 0.57  load 1.36\n",
      "22207 14 08:22:00 INFO train : 301  loss 2.4410  elapsed 0.64  load 0.00\n",
      "22207 14 08:22:01 INFO train : 302  loss 2.3316  elapsed 0.59  load 0.02\n",
      "22207 14 08:22:02 INFO train : 303  loss 2.3807  elapsed 0.66  load 0.05\n",
      "22207 14 08:22:03 INFO train : 304  loss 2.2976  elapsed 0.58  load 0.47\n",
      "22207 14 08:22:03 INFO train : 305  loss 2.3159  elapsed 0.63  load 0.00\n",
      "22207 14 08:22:04 INFO train : 306  loss 2.2835  elapsed 0.59  load 0.00\n",
      "22207 14 08:22:05 INFO train : 307  loss 2.2794  elapsed 0.64  load 0.00\n",
      "22207 14 08:22:06 INFO train : 308  loss 2.2200  elapsed 0.58  load 0.77\n",
      "22207 14 08:22:07 INFO train : 309  loss 2.1265  elapsed 0.71  load 0.00\n",
      "22207 14 08:22:07 INFO train : 310  loss 2.1235  elapsed 0.59  load 0.01\n",
      "22207 14 08:22:08 INFO train : 311  loss 2.1436  elapsed 0.63  load 0.00\n",
      "22207 14 08:22:10 INFO train : 312  loss 2.1904  elapsed 0.64  load 1.27\n",
      "22207 14 08:22:11 INFO train : 313  loss 2.0849  elapsed 0.71  load 0.00\n",
      "22207 14 08:22:11 INFO train : 314  loss 2.1248  elapsed 0.61  load 0.00\n",
      "22207 14 08:22:12 INFO train : 315  loss 2.0216  elapsed 0.63  load 0.00\n",
      "22207 14 08:22:13 INFO train : 316  loss 2.0545  elapsed 0.59  load 0.80\n",
      "22207 14 08:22:14 INFO train : 317  loss 2.0627  elapsed 0.58  load 0.00\n",
      "22207 14 08:22:15 INFO train : 318  loss 2.0212  elapsed 0.64  load 0.00\n",
      "22207 14 08:22:15 INFO train : 319  loss 2.0764  elapsed 0.52  load 0.00\n",
      "22207 14 08:22:17 INFO train : 320  loss 1.8999  elapsed 0.62  load 0.99\n",
      "22207 14 08:22:18 INFO train : 321  loss 1.9690  elapsed 0.69  load 0.00\n",
      "22207 14 08:22:18 INFO train : 322  loss 1.9359  elapsed 0.55  load 0.00\n",
      "22207 14 08:22:19 INFO train : 323  loss 1.9332  elapsed 0.64  load 0.00\n",
      "22207 14 08:22:20 INFO train : 324  loss 1.9043  elapsed 0.61  load 0.89\n",
      "22207 14 08:22:21 INFO train : 325  loss 1.8347  elapsed 0.57  load 0.00\n",
      "22207 14 08:22:21 INFO train : 326  loss 1.8550  elapsed 0.58  load 0.00\n",
      "22207 14 08:22:22 INFO train : 327  loss 1.9003  elapsed 0.63  load 0.00\n",
      "22207 14 08:22:24 INFO train : 328  loss 1.8247  elapsed 0.60  load 0.97\n",
      "22207 14 08:22:24 INFO train : 329  loss 1.8966  elapsed 0.57  load 0.00\n",
      "22207 14 08:22:25 INFO train : 330  loss 1.7998  elapsed 0.59  load 0.00\n",
      "22207 14 08:22:26 INFO train : 331  loss 1.9074  elapsed 0.64  load 0.00\n",
      "22207 14 08:22:27 INFO train : 332  loss 1.7783  elapsed 0.58  load 0.77\n",
      "22207 14 08:22:28 INFO train : 333  loss 1.7628  elapsed 0.66  load 0.00\n",
      "22207 14 08:22:28 INFO train : 334  loss 1.7604  elapsed 0.65  load 0.00\n",
      "22207 14 08:22:29 INFO train : 335  loss 1.7355  elapsed 0.64  load 0.00\n",
      "22207 14 08:22:30 INFO train : 336  loss 1.7889  elapsed 0.60  load 0.56\n",
      "22207 14 08:22:31 INFO train : 337  loss 1.6782  elapsed 0.61  load 0.00\n",
      "22207 14 08:22:32 INFO train : 338  loss 1.6932  elapsed 0.66  load 0.00\n",
      "22207 14 08:22:32 INFO train : 339  loss 1.6595  elapsed 0.65  load 0.00\n",
      "22207 14 08:22:34 INFO train : 340  loss 1.6971  elapsed 0.62  load 0.67\n",
      "22207 14 08:22:34 INFO train : 341  loss 1.6927  elapsed 0.64  load 0.00\n",
      "22207 14 08:22:35 INFO train : 342  loss 1.6375  elapsed 0.53  load 0.00\n",
      "22207 14 08:22:35 INFO train : 343  loss 1.6120  elapsed 0.61  load 0.00\n",
      "22207 14 08:22:37 INFO train : 344  loss 1.6238  elapsed 0.64  load 0.69\n",
      "22207 14 08:22:37 INFO train : 345  loss 1.5398  elapsed 0.70  load 0.00\n",
      "22207 14 08:22:38 INFO train : 346  loss 1.5999  elapsed 0.60  load 0.00\n",
      "22207 14 08:22:39 INFO train : 347  loss 1.6024  elapsed 0.59  load 0.00\n",
      "22207 14 08:22:40 INFO train : 348  loss 1.6063  elapsed 0.64  load 1.10\n",
      "22207 14 08:22:41 INFO train : 349  loss 1.5539  elapsed 0.63  load 0.00\n",
      "22207 14 08:22:42 INFO train : 350  loss 1.5072  elapsed 0.53  load 0.00\n",
      "22207 14 08:22:42 INFO train : 351  loss 1.5638  elapsed 0.61  load 0.00\n",
      "22207 14 08:22:44 INFO train : 352  loss 1.5058  elapsed 0.66  load 0.88\n",
      "22207 14 08:22:44 INFO train : 353  loss 1.4804  elapsed 0.62  load 0.00\n",
      "22207 14 08:22:45 INFO train : 354  loss 1.5128  elapsed 0.63  load 0.00\n",
      "22207 14 08:22:46 INFO train : 355  loss 1.4761  elapsed 0.51  load 0.00\n",
      "22207 14 08:22:47 INFO train : 356  loss 1.4732  elapsed 0.66  load 1.12\n",
      "22207 14 08:22:48 INFO train : 357  loss 1.5119  elapsed 0.59  load 0.00\n",
      "22207 14 08:22:49 INFO train : 358  loss 1.4250  elapsed 0.58  load 0.00\n",
      "22207 14 08:22:49 INFO train : 359  loss 1.4918  elapsed 0.61  load 0.00\n",
      "22207 14 08:22:51 INFO train : 360  loss 1.4684  elapsed 0.61  load 0.90\n",
      "22207 14 08:22:52 INFO train : 361  loss 1.3871  elapsed 0.60  load 0.00\n",
      "22207 14 08:22:52 INFO train : 362  loss 1.4545  elapsed 0.63  load 0.00\n",
      "22207 14 08:22:53 INFO train : 363  loss 1.4360  elapsed 0.59  load 0.00\n",
      "22207 14 08:22:54 INFO train : 364  loss 1.4029  elapsed 0.57  load 0.98\n",
      "22207 14 08:22:55 INFO train : 365  loss 1.3538  elapsed 0.57  load 0.00\n",
      "22207 14 08:22:56 INFO train : 366  loss 1.4473  elapsed 0.76  load 0.00\n",
      "22207 14 08:22:56 INFO train : 367  loss 1.3410  elapsed 0.53  load 0.00\n",
      "22207 14 08:22:58 INFO train : 368  loss 1.4554  elapsed 0.59  load 1.17\n",
      "22207 14 08:22:59 INFO train : 369  loss 1.3949  elapsed 0.55  load 0.00\n",
      "22207 14 08:22:59 INFO train : 370  loss 1.3948  elapsed 0.66  load 0.00\n",
      "22207 14 08:23:00 INFO train : 371  loss 1.3659  elapsed 0.59  load 0.00\n",
      "22207 14 08:23:02 INFO train : 372  loss 1.3274  elapsed 0.59  load 1.25\n",
      "22207 14 08:23:02 INFO train : 373  loss 1.3809  elapsed 0.64  load 0.00\n",
      "22207 14 08:23:03 INFO train : 374  loss 1.3018  elapsed 0.58  load 0.00\n",
      "22207 14 08:23:04 INFO train : 375  loss 1.3584  elapsed 0.67  load 0.00\n",
      "22207 14 08:23:05 INFO train : 376  loss 1.3252  elapsed 0.61  load 0.65\n",
      "22207 14 08:23:06 INFO train : 377  loss 1.3636  elapsed 0.60  load 0.00\n",
      "22207 14 08:23:06 INFO train : 378  loss 1.3110  elapsed 0.61  load 0.00\n",
      "22207 14 08:23:07 INFO train : 379  loss 1.4175  elapsed 0.57  load 0.00\n",
      "22207 14 08:23:08 INFO train : 380  loss 1.4160  elapsed 0.65  load 0.95\n",
      "22207 14 08:23:09 INFO train : 381  loss 1.3344  elapsed 0.68  load 0.00\n",
      "22207 14 08:23:10 INFO train : 382  loss 1.3532  elapsed 0.61  load 0.03\n",
      "22207 14 08:23:10 INFO train : 383  loss 1.2975  elapsed 0.59  load 0.00\n",
      "22207 14 08:23:12 INFO train : 384  loss 1.2769  elapsed 0.67  load 0.93\n",
      "22207 14 08:23:13 INFO train : 385  loss 1.3690  elapsed 0.53  load 0.00\n",
      "22207 14 08:23:13 INFO train : 386  loss 1.2630  elapsed 0.56  load 0.00\n",
      "22207 14 08:23:14 INFO train : 387  loss 1.2670  elapsed 0.57  load 0.00\n",
      "22207 14 08:23:15 INFO train : 388  loss 1.2550  elapsed 0.59  load 0.80\n",
      "22207 14 08:23:16 INFO train : 389  loss 1.2398  elapsed 0.63  load 0.00\n",
      "22207 14 08:23:17 INFO train : 390  loss 1.2436  elapsed 0.71  load 0.00\n",
      "22207 14 08:23:17 INFO train : 391  loss 1.2499  elapsed 0.65  load 0.00\n",
      "22207 14 08:23:19 INFO train : 392  loss 1.2803  elapsed 0.67  load 0.68\n",
      "22207 14 08:23:19 INFO train : 393  loss 1.3408  elapsed 0.60  load 0.04\n",
      "22207 14 08:23:20 INFO train : 394  loss 1.2825  elapsed 0.62  load 0.00\n",
      "22207 14 08:23:21 INFO train : 395  loss 1.2824  elapsed 0.62  load 0.00\n",
      "22207 14 08:23:21 INFO train : 396  loss 1.2174  elapsed 0.52  load 0.24\n",
      "22207 14 08:23:22 INFO train : 397  loss 1.2786  elapsed 0.51  load 0.00\n",
      "22207 14 08:23:23 INFO train : 398  loss 1.2492  elapsed 0.51  load 0.00\n",
      "22207 14 08:23:23 INFO train : 399  loss 1.2092  elapsed 0.46  load 0.00\n",
      "22207 14 08:23:23 INFO train : 400  loss 1.2320  elapsed 0.49  load 0.00\n",
      "22207 14 08:23:24 INFO train : 401  loss 1.2345  elapsed 0.14  load 0.00\n",
      "22207 14 08:23:24 INFO Epoch num: 1\n",
      "22207 14 08:23:28 INFO train : 402  loss 1.2245  elapsed 0.61  load 3.87\n",
      "22207 14 08:23:29 INFO train : 403  loss 1.1733  elapsed 0.54  load 0.00\n",
      "22207 14 08:23:29 INFO train : 404  loss 1.2239  elapsed 0.65  load 0.00\n",
      "22207 14 08:23:30 INFO train : 405  loss 1.2062  elapsed 0.64  load 0.04\n",
      "22207 14 08:23:32 INFO train : 406  loss 1.2420  elapsed 0.62  load 0.84\n",
      "22207 14 08:23:32 INFO train : 407  loss 1.1638  elapsed 0.63  load 0.00\n",
      "22207 14 08:23:33 INFO train : 408  loss 1.2203  elapsed 0.58  load 0.00\n",
      "22207 14 08:23:34 INFO train : 409  loss 1.2694  elapsed 0.66  load 0.00\n",
      "22207 14 08:23:35 INFO train : 410  loss 1.2219  elapsed 0.62  load 0.59\n",
      "22207 14 08:23:35 INFO train : 411  loss 1.1950  elapsed 0.60  load 0.06\n",
      "22207 14 08:23:36 INFO train : 412  loss 1.2024  elapsed 0.57  load 0.01\n",
      "22207 14 08:23:37 INFO train : 413  loss 1.1771  elapsed 0.53  load 0.00\n",
      "22207 14 08:23:38 INFO train : 414  loss 1.1902  elapsed 0.58  load 1.03\n",
      "22207 14 08:23:39 INFO train : 415  loss 1.1348  elapsed 0.62  load 0.00\n",
      "22207 14 08:23:40 INFO train : 416  loss 1.1549  elapsed 0.57  load 0.04\n",
      "22207 14 08:23:40 INFO train : 417  loss 1.1566  elapsed 0.60  load 0.00\n",
      "22207 14 08:23:42 INFO train : 418  loss 1.1514  elapsed 0.51  load 1.09\n",
      "22207 14 08:23:42 INFO train : 419  loss 1.1518  elapsed 0.56  load 0.00\n",
      "22207 14 08:23:43 INFO train : 420  loss 1.1778  elapsed 0.65  load 0.01\n",
      "22207 14 08:23:44 INFO train : 421  loss 1.1873  elapsed 0.66  load 0.04\n",
      "22207 14 08:23:46 INFO train : 422  loss 1.1877  elapsed 0.72  load 1.12\n",
      "22207 14 08:23:46 INFO train : 423  loss 1.0982  elapsed 0.58  load 0.00\n",
      "22207 14 08:23:47 INFO train : 424  loss 1.2347  elapsed 0.56  load 0.01\n",
      "22207 14 08:23:48 INFO train : 425  loss 1.1745  elapsed 0.70  load 0.01\n",
      "22207 14 08:23:49 INFO train : 426  loss 1.1471  elapsed 0.56  load 0.83\n",
      "22207 14 08:23:50 INFO train : 427  loss 1.1560  elapsed 0.56  load 0.00\n",
      "22207 14 08:23:50 INFO train : 428  loss 1.2071  elapsed 0.67  load 0.03\n",
      "22207 14 08:23:51 INFO train : 429  loss 1.1904  elapsed 0.57  load 0.00\n",
      "22207 14 08:23:52 INFO train : 430  loss 1.1803  elapsed 0.58  load 0.87\n",
      "22207 14 08:23:53 INFO train : 431  loss 1.1621  elapsed 0.56  load 0.00\n",
      "22207 14 08:23:54 INFO train : 432  loss 1.2150  elapsed 0.54  load 0.20\n",
      "22207 14 08:23:54 INFO train : 433  loss 1.1450  elapsed 0.67  load 0.00\n",
      "22207 14 08:23:56 INFO train : 434  loss 1.1586  elapsed 0.60  load 0.74\n",
      "22207 14 08:23:56 INFO train : 435  loss 1.1628  elapsed 0.58  load 0.03\n",
      "22207 14 08:23:57 INFO train : 436  loss 1.1077  elapsed 0.63  load 0.15\n",
      "22207 14 08:23:58 INFO train : 437  loss 1.1413  elapsed 0.56  load 0.00\n",
      "22207 14 08:23:59 INFO train : 438  loss 1.0895  elapsed 0.64  load 0.67\n",
      "22207 14 08:24:00 INFO train : 439  loss 1.1652  elapsed 0.64  load 0.00\n",
      "22207 14 08:24:01 INFO train : 440  loss 1.1567  elapsed 0.53  load 0.30\n",
      "22207 14 08:24:01 INFO train : 441  loss 1.1542  elapsed 0.68  load 0.00\n",
      "22207 14 08:24:03 INFO train : 442  loss 1.0980  elapsed 0.58  load 0.66\n",
      "22207 14 08:24:03 INFO train : 443  loss 1.1494  elapsed 0.66  load 0.09\n",
      "22207 14 08:24:05 INFO train : 444  loss 1.1298  elapsed 0.67  load 0.95\n",
      "22207 14 08:24:06 INFO train : 445  loss 1.2080  elapsed 0.60  load 0.00\n",
      "22207 14 08:24:06 INFO train : 446  loss 1.0579  elapsed 0.60  load 0.01\n",
      "22207 14 08:24:07 INFO train : 447  loss 1.0997  elapsed 0.60  load 0.03\n",
      "22207 14 08:24:09 INFO train : 448  loss 1.1046  elapsed 0.63  load 0.99\n",
      "22207 14 08:24:09 INFO train : 449  loss 1.0658  elapsed 0.64  load 0.00\n",
      "22207 14 08:24:10 INFO train : 450  loss 1.1513  elapsed 0.62  load 0.05\n",
      "22207 14 08:24:11 INFO train : 451  loss 1.1315  elapsed 0.60  load 0.29\n",
      "22207 14 08:24:13 INFO train : 452  loss 1.0923  elapsed 0.68  load 1.24\n",
      "22207 14 08:24:13 INFO train : 453  loss 1.1237  elapsed 0.62  load 0.00\n",
      "22207 14 08:24:14 INFO train : 454  loss 1.1008  elapsed 0.63  load 0.03\n",
      "22207 14 08:24:15 INFO train : 455  loss 1.1064  elapsed 0.62  load 0.05\n",
      "22207 14 08:24:16 INFO train : 456  loss 1.0808  elapsed 0.63  load 0.94\n",
      "22207 14 08:24:17 INFO train : 457  loss 1.1213  elapsed 0.63  load 0.00\n",
      "22207 14 08:24:18 INFO train : 458  loss 1.0642  elapsed 0.61  load 0.08\n",
      "22207 14 08:24:18 INFO train : 459  loss 1.0861  elapsed 0.67  load 0.00\n",
      "22207 14 08:24:20 INFO train : 460  loss 1.0630  elapsed 0.66  load 0.83\n",
      "22207 14 08:24:21 INFO train : 461  loss 1.1024  elapsed 0.60  load 0.00\n",
      "22207 14 08:24:21 INFO train : 462  loss 1.1569  elapsed 0.63  load 0.06\n",
      "22207 14 08:24:22 INFO train : 463  loss 1.1344  elapsed 0.69  load 0.00\n",
      "22207 14 08:24:23 INFO train : 464  loss 1.1185  elapsed 0.62  load 0.76\n",
      "22207 14 08:24:24 INFO train : 465  loss 1.1201  elapsed 0.64  load 0.00\n",
      "22207 14 08:24:25 INFO train : 466  loss 1.0886  elapsed 0.58  load 0.01\n",
      "22207 14 08:24:25 INFO train : 467  loss 1.0784  elapsed 0.60  load 0.01\n",
      "22207 14 08:24:27 INFO train : 468  loss 1.0831  elapsed 0.57  load 0.81\n",
      "22207 14 08:24:27 INFO train : 469  loss 1.1135  elapsed 0.62  load 0.07\n",
      "22207 14 08:24:28 INFO train : 470  loss 1.1823  elapsed 0.56  load 0.04\n",
      "22207 14 08:24:29 INFO train : 471  loss 1.1054  elapsed 0.61  load 0.00\n",
      "22207 14 08:24:30 INFO train : 472  loss 1.0986  elapsed 0.59  load 0.81\n",
      "22207 14 08:24:31 INFO train : 473  loss 1.0926  elapsed 0.58  load 0.03\n",
      "22207 14 08:24:31 INFO train : 474  loss 1.0820  elapsed 0.60  load 0.07\n",
      "22207 14 08:24:32 INFO train : 475  loss 1.0860  elapsed 0.55  load 0.00\n",
      "22207 14 08:24:33 INFO train : 476  loss 1.0128  elapsed 0.67  load 0.65\n",
      "22207 14 08:24:34 INFO train : 477  loss 1.2051  elapsed 0.62  load 0.03\n",
      "22207 14 08:24:35 INFO train : 478  loss 1.1204  elapsed 0.57  load 0.32\n",
      "22207 14 08:24:36 INFO train : 479  loss 1.0639  elapsed 0.63  load 0.00\n",
      "22207 14 08:24:37 INFO train : 480  loss 1.0493  elapsed 0.63  load 0.67\n",
      "22207 14 08:24:38 INFO train : 481  loss 1.1090  elapsed 0.57  load 0.03\n",
      "22207 14 08:24:38 INFO train : 482  loss 1.0762  elapsed 0.63  load 0.27\n",
      "22207 14 08:24:39 INFO train : 483  loss 1.1077  elapsed 0.65  load 0.02\n",
      "22207 14 08:24:41 INFO train : 484  loss 1.1087  elapsed 0.57  load 0.82\n",
      "22207 14 08:24:41 INFO train : 485  loss 1.0880  elapsed 0.64  load 0.00\n",
      "22207 14 08:24:42 INFO train : 486  loss 1.0707  elapsed 0.57  load 0.20\n",
      "22207 14 08:24:43 INFO train : 487  loss 1.0773  elapsed 0.59  load 0.04\n",
      "22207 14 08:24:44 INFO train : 488  loss 1.0921  elapsed 0.65  load 1.05\n",
      "22207 14 08:24:45 INFO train : 489  loss 1.0507  elapsed 0.62  load 0.00\n",
      "22207 14 08:24:46 INFO train : 490  loss 1.0870  elapsed 0.58  load 0.01\n",
      "22207 14 08:24:46 INFO train : 491  loss 1.0841  elapsed 0.66  load 0.10\n",
      "22207 14 08:24:48 INFO train : 492  loss 1.0916  elapsed 0.53  load 0.86\n",
      "22207 14 08:24:48 INFO train : 493  loss 1.0581  elapsed 0.57  load 0.00\n",
      "22207 14 08:24:49 INFO train : 494  loss 1.0616  elapsed 0.58  load 0.12\n",
      "22207 14 08:24:50 INFO train : 495  loss 1.0682  elapsed 0.63  load 0.00\n",
      "22207 14 08:24:52 INFO train : 496  loss 1.1180  elapsed 0.58  load 1.35\n",
      "22207 14 08:24:52 INFO train : 497  loss 1.0778  elapsed 0.60  load 0.00\n",
      "22207 14 08:24:53 INFO train : 498  loss 1.0741  elapsed 0.68  load 0.06\n",
      "22207 14 08:24:54 INFO train : 499  loss 1.0739  elapsed 0.69  load 0.01\n",
      "22207 14 08:24:55 INFO train : 500  loss 1.1120  elapsed 0.61  load 0.61\n",
      "22207 14 08:24:55 INFO Running validation\n",
      "22207 14 08:25:17 INFO Logging 20 images\n",
      "22207 14 08:25:18 INFO Logging 20 audios\n",
      "22207 14 08:25:21 INFO Logging 20 texts\n",
      "22207 14 08:25:32 INFO val : 500  loss 0.9557861089706421\n",
      "22207 14 08:25:32 INFO Saving model state at step 500 to checkpoints/fp_step_00000500.pt\n",
      "22207 14 08:25:33 INFO train : 501  loss 1.0754  elapsed 0.51  load 0.00\n",
      "22207 14 08:25:33 INFO train : 502  loss 1.0737  elapsed 0.56  load 0.00\n",
      "22207 14 08:25:34 INFO train : 503  loss 1.0822  elapsed 0.62  load 0.06\n",
      "22207 14 08:25:35 INFO train : 504  loss 1.0800  elapsed 0.60  load 0.07\n",
      "22207 14 08:25:35 INFO train : 505  loss 1.0883  elapsed 0.60  load 0.02\n",
      "22207 14 08:25:36 INFO train : 506  loss 1.1431  elapsed 0.60  load 0.00\n",
      "22207 14 08:25:37 INFO train : 507  loss 1.0719  elapsed 0.62  load 0.00\n",
      "22207 14 08:25:37 INFO train : 508  loss 1.1037  elapsed 0.57  load 0.01\n",
      "22207 14 08:25:38 INFO train : 509  loss 1.0487  elapsed 0.62  load 0.00\n",
      "22207 14 08:25:39 INFO train : 510  loss 1.0130  elapsed 0.62  load 0.08\n",
      "22207 14 08:25:39 INFO train : 511  loss 1.1029  elapsed 0.62  load 0.04\n",
      "22207 14 08:25:40 INFO train : 512  loss 1.0281  elapsed 0.62  load 0.03\n",
      "22207 14 08:25:41 INFO train : 513  loss 1.0293  elapsed 0.63  load 0.01\n",
      "22207 14 08:25:41 INFO train : 514  loss 1.0515  elapsed 0.63  load 0.01\n",
      "22207 14 08:25:42 INFO train : 515  loss 1.0754  elapsed 0.58  load 0.06\n",
      "22207 14 08:25:43 INFO train : 516  loss 1.0610  elapsed 0.66  load 0.07\n",
      "22207 14 08:25:43 INFO train : 517  loss 1.0460  elapsed 0.63  load 0.01\n",
      "22207 14 08:25:44 INFO train : 518  loss 1.0509  elapsed 0.65  load 0.01\n",
      "22207 14 08:25:45 INFO train : 519  loss 1.0775  elapsed 0.64  load 0.01\n",
      "22207 14 08:25:45 INFO train : 520  loss 1.0181  elapsed 0.70  load 0.01\n",
      "22207 14 08:25:46 INFO train : 521  loss 1.0605  elapsed 0.68  load 0.01\n",
      "22207 14 08:25:47 INFO train : 522  loss 1.0158  elapsed 0.57  load 0.38\n",
      "22207 14 08:25:48 INFO train : 523  loss 1.0244  elapsed 0.58  load 0.02\n",
      "22207 14 08:25:49 INFO train : 524  loss 1.0139  elapsed 0.63  load 0.33\n",
      "22207 14 08:25:49 INFO train : 525  loss 1.0431  elapsed 0.63  load 0.05\n",
      "22207 14 08:25:51 INFO train : 526  loss 0.9861  elapsed 0.54  load 0.58\n",
      "22207 14 08:25:51 INFO train : 527  loss 1.0170  elapsed 0.58  load 0.07\n",
      "22207 14 08:25:52 INFO train : 528  loss 0.9969  elapsed 0.61  load 0.37\n",
      "22207 14 08:25:53 INFO train : 529  loss 1.0333  elapsed 0.58  load 0.02\n",
      "22207 14 08:25:54 INFO train : 530  loss 1.0435  elapsed 0.61  load 0.91\n",
      "22207 14 08:25:55 INFO train : 531  loss 1.0746  elapsed 0.61  load 0.08\n",
      "22207 14 08:25:56 INFO train : 532  loss 1.0412  elapsed 0.57  load 0.08\n",
      "22207 14 08:25:56 INFO train : 533  loss 1.1266  elapsed 0.62  load 0.00\n",
      "22207 14 08:25:58 INFO train : 534  loss 0.9916  elapsed 0.66  load 1.31\n",
      "22207 14 08:25:59 INFO train : 535  loss 1.0270  elapsed 0.64  load 0.01\n",
      "22207 14 08:26:00 INFO train : 536  loss 1.0627  elapsed 0.63  load 0.03\n",
      "22207 14 08:26:00 INFO train : 537  loss 1.0171  elapsed 0.70  load 0.02\n",
      "22207 14 08:26:02 INFO train : 538  loss 1.0029  elapsed 0.51  load 0.94\n",
      "22207 14 08:26:03 INFO train : 539  loss 1.0482  elapsed 0.65  load 0.03\n",
      "22207 14 08:26:03 INFO train : 540  loss 1.0401  elapsed 0.66  load 0.04\n",
      "22207 14 08:26:04 INFO train : 541  loss 1.0547  elapsed 0.63  load 0.01\n",
      "22207 14 08:26:05 INFO train : 542  loss 1.0274  elapsed 0.58  load 0.90\n",
      "22207 14 08:26:06 INFO train : 543  loss 0.9886  elapsed 0.63  load 0.01\n",
      "22207 14 08:26:07 INFO train : 544  loss 1.0430  elapsed 0.54  load 0.04\n",
      "22207 14 08:26:07 INFO train : 545  loss 1.0169  elapsed 0.56  load 0.00\n",
      "22207 14 08:26:09 INFO train : 546  loss 1.0513  elapsed 0.55  load 0.87\n",
      "22207 14 08:26:09 INFO train : 547  loss 1.0696  elapsed 0.60  load 0.01\n",
      "22207 14 08:26:10 INFO train : 548  loss 1.0005  elapsed 0.60  load 0.01\n",
      "22207 14 08:26:11 INFO train : 549  loss 1.0446  elapsed 0.63  load 0.01\n",
      "22207 14 08:26:13 INFO train : 550  loss 1.0809  elapsed 0.56  load 1.42\n",
      "22207 14 08:26:13 INFO train : 551  loss 1.0239  elapsed 0.66  load 0.00\n",
      "22207 14 08:26:14 INFO train : 552  loss 1.0373  elapsed 0.65  load 0.01\n",
      "22207 14 08:26:15 INFO train : 553  loss 1.0924  elapsed 0.68  load 0.07\n",
      "22207 14 08:26:16 INFO train : 554  loss 1.0589  elapsed 0.57  load 1.03\n",
      "22207 14 08:26:17 INFO train : 555  loss 1.0720  elapsed 0.54  load 0.00\n",
      "22207 14 08:26:18 INFO train : 556  loss 1.0304  elapsed 0.59  load 0.00\n",
      "22207 14 08:26:18 INFO train : 557  loss 1.0154  elapsed 0.66  load 0.01\n",
      "22207 14 08:26:20 INFO train : 558  loss 0.9905  elapsed 0.64  load 0.97\n",
      "22207 14 08:26:21 INFO train : 559  loss 1.0409  elapsed 0.60  load 0.00\n",
      "22207 14 08:26:21 INFO train : 560  loss 0.9973  elapsed 0.62  load 0.00\n",
      "22207 14 08:26:22 INFO train : 561  loss 1.0387  elapsed 0.61  load 0.01\n",
      "22207 14 08:26:23 INFO train : 562  loss 1.0289  elapsed 0.61  load 0.72\n",
      "22207 14 08:26:24 INFO train : 563  loss 1.0258  elapsed 0.69  load 0.03\n",
      "22207 14 08:26:24 INFO train : 564  loss 1.0603  elapsed 0.50  load 0.01\n",
      "22207 14 08:26:25 INFO train : 565  loss 1.0323  elapsed 0.53  load 0.25\n",
      "22207 14 08:26:27 INFO train : 566  loss 1.0728  elapsed 0.58  load 0.84\n",
      "22207 14 08:26:27 INFO train : 567  loss 0.9948  elapsed 0.57  load 0.00\n",
      "22207 14 08:26:28 INFO train : 568  loss 1.0898  elapsed 0.65  load 0.01\n",
      "22207 14 08:26:29 INFO train : 569  loss 0.9820  elapsed 0.61  load 0.60\n",
      "22207 14 08:26:30 INFO train : 570  loss 1.0542  elapsed 0.62  load 0.44\n",
      "22207 14 08:26:31 INFO train : 571  loss 1.0047  elapsed 0.64  load 0.00\n",
      "22207 14 08:26:32 INFO train : 572  loss 1.0422  elapsed 0.60  load 0.02\n",
      "22207 14 08:26:33 INFO train : 573  loss 1.0139  elapsed 0.57  load 0.38\n",
      "22207 14 08:26:34 INFO train : 574  loss 1.0114  elapsed 0.60  load 0.55\n",
      "22207 14 08:26:34 INFO train : 575  loss 1.0329  elapsed 0.55  load 0.00\n",
      "22207 14 08:26:35 INFO train : 576  loss 1.0152  elapsed 0.66  load 0.03\n",
      "22207 14 08:26:36 INFO train : 577  loss 1.0116  elapsed 0.58  load 0.69\n",
      "22207 14 08:26:38 INFO train : 578  loss 1.0331  elapsed 0.59  load 0.53\n",
      "22207 14 08:26:38 INFO train : 579  loss 1.0241  elapsed 0.64  load 0.00\n",
      "22207 14 08:26:39 INFO train : 580  loss 0.9959  elapsed 0.58  load 0.02\n",
      "22207 14 08:26:40 INFO train : 581  loss 1.0071  elapsed 0.62  load 0.15\n",
      "22207 14 08:26:41 INFO train : 582  loss 1.0118  elapsed 0.60  load 0.95\n",
      "22207 14 08:26:42 INFO train : 583  loss 0.9679  elapsed 0.65  load 0.00\n",
      "22207 14 08:26:43 INFO train : 584  loss 1.1101  elapsed 0.64  load 0.03\n",
      "22207 14 08:26:43 INFO train : 585  loss 1.0072  elapsed 0.62  load 0.01\n",
      "22207 14 08:26:45 INFO train : 586  loss 0.9597  elapsed 0.60  load 1.23\n",
      "22207 14 08:26:46 INFO train : 587  loss 1.0291  elapsed 0.67  load 0.00\n",
      "22207 14 08:26:46 INFO train : 588  loss 1.0232  elapsed 0.61  load 0.02\n",
      "22207 14 08:26:47 INFO train : 589  loss 0.9713  elapsed 0.62  load 0.01\n",
      "22207 14 08:26:48 INFO train : 590  loss 1.0309  elapsed 0.66  load 0.66\n",
      "22207 14 08:26:49 INFO train : 591  loss 0.9760  elapsed 0.65  load 0.00\n",
      "22207 14 08:26:50 INFO train : 592  loss 1.0123  elapsed 0.66  load 0.00\n",
      "22207 14 08:26:50 INFO train : 593  loss 1.0295  elapsed 0.58  load 0.00\n",
      "22207 14 08:26:52 INFO train : 594  loss 1.0210  elapsed 0.70  load 0.50\n",
      "22207 14 08:26:52 INFO train : 595  loss 1.1962  elapsed 0.68  load 0.00\n",
      "22207 14 08:26:53 INFO train : 596  loss 1.0256  elapsed 0.60  load 0.03\n",
      "22207 14 08:26:54 INFO train : 597  loss 1.0276  elapsed 0.58  load 0.02\n",
      "22207 14 08:26:55 INFO train : 598  loss 0.9757  elapsed 0.64  load 0.50\n",
      "22207 14 08:26:55 INFO train : 599  loss 1.0234  elapsed 0.65  load 0.01\n",
      "22207 14 08:26:56 INFO train : 600  loss 0.9989  elapsed 0.55  load 0.54\n",
      "22207 14 08:26:57 INFO train : 601  loss 1.0134  elapsed 0.55  load 0.06\n",
      "22207 14 08:26:58 INFO train : 602  loss 1.0590  elapsed 0.60  load 0.33\n",
      "22207 14 08:26:59 INFO train : 603  loss 1.0486  elapsed 0.62  load 0.01\n",
      "22207 14 08:27:00 INFO train : 604  loss 1.0107  elapsed 0.56  load 0.88\n",
      "22207 14 08:27:01 INFO train : 605  loss 1.0066  elapsed 0.57  load 0.00\n",
      "22207 14 08:27:02 INFO train : 606  loss 0.9579  elapsed 0.63  load 0.22\n",
      "22207 14 08:27:02 INFO train : 607  loss 0.9984  elapsed 0.62  load 0.04\n",
      "22207 14 08:27:04 INFO train : 608  loss 0.9851  elapsed 0.56  load 1.40\n",
      "22207 14 08:27:05 INFO train : 609  loss 0.9579  elapsed 0.59  load 0.02\n",
      "22207 14 08:27:06 INFO train : 610  loss 1.0232  elapsed 0.65  load 0.02\n",
      "22207 14 08:27:06 INFO train : 611  loss 1.0262  elapsed 0.67  load 0.03\n",
      "22207 14 08:27:08 INFO train : 612  loss 1.0604  elapsed 0.66  load 0.83\n",
      "22207 14 08:27:09 INFO train : 613  loss 1.0825  elapsed 0.56  load 0.07\n",
      "22207 14 08:27:09 INFO train : 614  loss 0.9523  elapsed 0.60  load 0.06\n",
      "22207 14 08:27:10 INFO train : 615  loss 1.0540  elapsed 0.70  load 0.02\n",
      "22207 14 08:27:11 INFO train : 616  loss 1.0266  elapsed 0.69  load 0.85\n",
      "22207 14 08:27:12 INFO train : 617  loss 1.0022  elapsed 0.60  load 0.00\n",
      "22207 14 08:27:13 INFO train : 618  loss 1.0201  elapsed 0.59  load 0.08\n",
      "22207 14 08:27:14 INFO train : 619  loss 1.0051  elapsed 0.70  load 0.00\n",
      "22207 14 08:27:15 INFO train : 620  loss 1.0077  elapsed 0.69  load 0.66\n",
      "22207 14 08:27:16 INFO train : 621  loss 1.0416  elapsed 0.67  load 0.00\n",
      "22207 14 08:27:16 INFO train : 622  loss 1.0198  elapsed 0.56  load 0.06\n",
      "22207 14 08:27:17 INFO train : 623  loss 1.0111  elapsed 0.76  load 0.03\n",
      "22207 14 08:27:19 INFO train : 624  loss 1.0259  elapsed 0.65  load 0.83\n",
      "22207 14 08:27:19 INFO train : 625  loss 1.0230  elapsed 0.61  load 0.00\n",
      "22207 14 08:27:20 INFO train : 626  loss 0.9642  elapsed 0.62  load 0.06\n",
      "22207 14 08:27:21 INFO train : 627  loss 1.0082  elapsed 0.67  load 0.02\n",
      "22207 14 08:27:22 INFO train : 628  loss 0.9852  elapsed 0.60  load 1.16\n",
      "22207 14 08:27:23 INFO train : 629  loss 0.9402  elapsed 0.65  load 0.01\n",
      "22207 14 08:27:24 INFO train : 630  loss 1.0343  elapsed 0.75  load 0.03\n",
      "22207 14 08:27:25 INFO train : 631  loss 1.0374  elapsed 0.71  load 0.04\n",
      "22207 14 08:27:26 INFO train : 632  loss 1.0090  elapsed 0.59  load 0.61\n",
      "22207 14 08:27:27 INFO train : 633  loss 0.9952  elapsed 0.73  load 0.00\n",
      "22207 14 08:27:27 INFO train : 634  loss 1.0029  elapsed 0.61  load 0.03\n",
      "22207 14 08:27:28 INFO train : 635  loss 1.0081  elapsed 0.57  load 0.01\n",
      "22207 14 08:27:29 INFO train : 636  loss 0.9969  elapsed 0.57  load 0.75\n",
      "22207 14 08:27:30 INFO train : 637  loss 0.9662  elapsed 0.60  load 0.03\n",
      "22207 14 08:27:31 INFO train : 638  loss 0.9714  elapsed 0.75  load 0.01\n",
      "22207 14 08:27:31 INFO train : 639  loss 0.9869  elapsed 0.66  load 0.01\n",
      "22207 14 08:27:32 INFO train : 640  loss 0.9262  elapsed 0.61  load 0.45\n",
      "22207 14 08:27:33 INFO train : 641  loss 1.0337  elapsed 0.70  load 0.09\n",
      "22207 14 08:27:34 INFO train : 642  loss 0.9530  elapsed 0.69  load 0.00\n",
      "22207 14 08:27:35 INFO train : 643  loss 0.9988  elapsed 0.70  load 0.01\n",
      "22207 14 08:27:36 INFO train : 644  loss 0.9762  elapsed 0.71  load 0.73\n",
      "22207 14 08:27:37 INFO train : 645  loss 0.9859  elapsed 0.72  load 0.13\n",
      "22207 14 08:27:38 INFO train : 646  loss 1.0068  elapsed 0.71  load 0.00\n",
      "22207 14 08:27:39 INFO train : 647  loss 0.9708  elapsed 0.68  load 0.00\n",
      "22207 14 08:27:39 INFO train : 648  loss 0.9598  elapsed 0.71  load 0.03\n",
      "22207 14 08:27:40 INFO train : 649  loss 0.9460  elapsed 0.68  load 0.16\n",
      "22207 14 08:27:41 INFO train : 650  loss 1.0375  elapsed 0.62  load 0.00\n",
      "22207 14 08:27:41 INFO train : 651  loss 0.9553  elapsed 0.56  load 0.02\n",
      "22207 14 08:27:43 INFO train : 652  loss 0.9930  elapsed 0.61  load 0.53\n",
      "22207 14 08:27:44 INFO train : 653  loss 0.9420  elapsed 0.65  load 0.38\n",
      "22207 14 08:27:44 INFO train : 654  loss 0.9587  elapsed 0.63  load 0.13\n",
      "22207 14 08:27:45 INFO train : 655  loss 0.9945  elapsed 0.76  load 0.00\n",
      "22207 14 08:27:46 INFO train : 656  loss 0.9888  elapsed 0.54  load 0.18\n",
      "22207 14 08:27:47 INFO train : 657  loss 0.9713  elapsed 0.68  load 0.66\n",
      "22207 14 08:27:48 INFO train : 658  loss 0.9728  elapsed 0.61  load 0.01\n",
      "22207 14 08:27:49 INFO train : 659  loss 0.9742  elapsed 0.75  load 0.00\n",
      "22207 14 08:27:50 INFO train : 660  loss 0.9574  elapsed 0.65  load 0.57\n",
      "22207 14 08:27:51 INFO train : 661  loss 0.9706  elapsed 0.59  load 0.30\n",
      "22207 14 08:27:51 INFO train : 662  loss 1.0250  elapsed 0.65  load 0.00\n",
      "22207 14 08:27:52 INFO train : 663  loss 0.9880  elapsed 0.76  load 0.00\n",
      "22207 14 08:27:54 INFO train : 664  loss 1.0474  elapsed 0.64  load 0.76\n",
      "22207 14 08:27:54 INFO train : 665  loss 1.0215  elapsed 0.59  load 0.01\n",
      "22207 14 08:27:55 INFO train : 666  loss 0.9858  elapsed 0.65  load 0.04\n",
      "22207 14 08:27:56 INFO train : 667  loss 0.9953  elapsed 0.62  load 0.00\n",
      "22207 14 08:27:57 INFO train : 668  loss 0.9595  elapsed 0.58  load 0.93\n",
      "22207 14 08:27:58 INFO train : 669  loss 0.9745  elapsed 0.71  load 0.38\n",
      "22207 14 08:27:59 INFO train : 670  loss 0.9495  elapsed 0.66  load 0.01\n",
      "22207 14 08:28:00 INFO train : 671  loss 1.0030  elapsed 0.61  load 0.00\n",
      "22207 14 08:28:01 INFO train : 672  loss 0.9597  elapsed 0.61  load 0.75\n",
      "22207 14 08:28:02 INFO train : 673  loss 0.9818  elapsed 0.70  load 0.23\n",
      "22207 14 08:28:03 INFO train : 674  loss 1.0227  elapsed 0.72  load 0.07\n",
      "22207 14 08:28:03 INFO train : 675  loss 0.9565  elapsed 0.62  load 0.00\n",
      "22207 14 08:28:05 INFO train : 676  loss 1.0002  elapsed 0.61  load 1.12\n",
      "22207 14 08:28:06 INFO train : 677  loss 0.9881  elapsed 0.74  load 0.04\n",
      "22207 14 08:28:07 INFO train : 678  loss 0.9611  elapsed 0.64  load 0.06\n",
      "22207 14 08:28:08 INFO train : 679  loss 0.9839  elapsed 0.80  load 0.00\n",
      "22207 14 08:28:09 INFO train : 680  loss 0.9453  elapsed 0.63  load 0.64\n",
      "22207 14 08:28:10 INFO train : 681  loss 1.0579  elapsed 0.58  load 0.08\n",
      "22207 14 08:28:10 INFO train : 682  loss 0.9439  elapsed 0.61  load 0.18\n",
      "22207 14 08:28:11 INFO train : 683  loss 0.9425  elapsed 0.73  load 0.00\n",
      "22207 14 08:28:12 INFO train : 684  loss 0.9427  elapsed 0.63  load 0.19\n",
      "22207 14 08:28:13 INFO train : 685  loss 0.9181  elapsed 0.58  load 0.40\n",
      "22207 14 08:28:14 INFO train : 686  loss 0.9265  elapsed 0.72  load 0.06\n",
      "22207 14 08:28:14 INFO train : 687  loss 0.9714  elapsed 0.58  load 0.00\n",
      "22207 14 08:28:15 INFO train : 688  loss 0.9577  elapsed 0.53  load 0.29\n",
      "22207 14 08:28:16 INFO train : 689  loss 0.9406  elapsed 0.54  load 0.63\n",
      "22207 14 08:28:17 INFO train : 690  loss 1.0285  elapsed 0.68  load 0.00\n",
      "22207 14 08:28:18 INFO train : 691  loss 0.9607  elapsed 0.56  load 0.00\n",
      "22207 14 08:28:19 INFO train : 692  loss 0.9862  elapsed 0.66  load 0.33\n",
      "22207 14 08:28:20 INFO train : 693  loss 1.0555  elapsed 0.65  load 0.48\n",
      "22207 14 08:28:21 INFO train : 694  loss 0.9396  elapsed 0.71  load 0.08\n",
      "22207 14 08:28:21 INFO train : 695  loss 0.9541  elapsed 0.72  load 0.00\n",
      "22207 14 08:28:22 INFO train : 696  loss 0.9754  elapsed 0.68  load 0.01\n",
      "22207 14 08:28:23 INFO train : 697  loss 1.0463  elapsed 0.60  load 0.31\n",
      "22207 14 08:28:24 INFO train : 698  loss 0.9871  elapsed 0.69  load 0.43\n",
      "22207 14 08:28:25 INFO train : 699  loss 0.9774  elapsed 0.57  load 0.00\n",
      "22207 14 08:28:26 INFO train : 700  loss 0.9383  elapsed 0.64  load 0.11\n",
      "22207 14 08:28:26 INFO train : 701  loss 0.9248  elapsed 0.53  load 0.28\n",
      "22207 14 08:28:28 INFO train : 702  loss 0.9552  elapsed 0.61  load 0.65\n",
      "22207 14 08:28:28 INFO train : 703  loss 0.9894  elapsed 0.63  load 0.00\n",
      "22207 14 08:28:29 INFO train : 704  loss 0.9606  elapsed 0.63  load 0.28\n",
      "22207 14 08:28:30 INFO train : 705  loss 0.9380  elapsed 0.55  load 0.00\n",
      "22207 14 08:28:31 INFO train : 706  loss 0.9708  elapsed 0.61  load 0.52\n",
      "22207 14 08:28:32 INFO train : 707  loss 0.9162  elapsed 0.61  load 0.00\n",
      "22207 14 08:28:33 INFO train : 708  loss 0.9872  elapsed 0.62  load 0.50\n",
      "22207 14 08:28:34 INFO train : 709  loss 0.9261  elapsed 0.80  load 0.01\n",
      "22207 14 08:28:34 INFO train : 710  loss 0.9641  elapsed 0.61  load 0.09\n",
      "22207 14 08:28:35 INFO train : 711  loss 1.0066  elapsed 0.71  load 0.00\n",
      "22207 14 08:28:36 INFO train : 712  loss 1.0697  elapsed 0.59  load 0.54\n",
      "22207 14 08:28:37 INFO train : 713  loss 0.9304  elapsed 0.65  load 0.01\n",
      "22207 14 08:28:38 INFO train : 714  loss 0.9885  elapsed 0.69  load 0.04\n",
      "22207 14 08:28:38 INFO train : 715  loss 0.9586  elapsed 0.73  load 0.00\n",
      "22207 14 08:28:40 INFO train : 716  loss 0.9381  elapsed 0.73  load 0.86\n",
      "22207 14 08:28:41 INFO train : 717  loss 0.9355  elapsed 0.75  load 0.02\n",
      "22207 14 08:28:42 INFO train : 718  loss 0.9406  elapsed 0.64  load 0.08\n",
      "22207 14 08:28:42 INFO train : 719  loss 0.9837  elapsed 0.68  load 0.00\n",
      "22207 14 08:28:44 INFO train : 720  loss 0.9471  elapsed 0.70  load 0.49\n",
      "22207 14 08:28:44 INFO train : 721  loss 0.9321  elapsed 0.67  load 0.03\n",
      "22207 14 08:28:45 INFO train : 722  loss 0.9295  elapsed 0.74  load 0.00\n",
      "22207 14 08:28:46 INFO train : 723  loss 1.0212  elapsed 0.76  load 0.05\n",
      "22207 14 08:28:47 INFO train : 724  loss 1.0932  elapsed 0.65  load 0.37\n",
      "22207 14 08:28:48 INFO train : 725  loss 0.9724  elapsed 0.69  load 0.01\n",
      "22207 14 08:28:48 INFO train : 726  loss 1.0042  elapsed 0.67  load 0.01\n",
      "22207 14 08:28:49 INFO train : 727  loss 1.0056  elapsed 0.60  load 0.01\n",
      "22207 14 08:28:51 INFO train : 728  loss 0.9316  elapsed 0.76  load 1.28\n",
      "22207 14 08:28:52 INFO train : 729  loss 0.9453  elapsed 0.72  load 0.01\n",
      "22207 14 08:28:52 INFO train : 730  loss 0.9746  elapsed 0.58  load 0.03\n",
      "22207 14 08:28:53 INFO train : 731  loss 0.9266  elapsed 0.55  load 0.01\n",
      "22207 14 08:28:54 INFO train : 732  loss 0.9504  elapsed 0.60  load 0.39\n",
      "22207 14 08:28:55 INFO train : 733  loss 0.9476  elapsed 0.64  load 0.01\n",
      "22207 14 08:28:55 INFO train : 734  loss 0.9562  elapsed 0.56  load 0.06\n",
      "22207 14 08:28:56 INFO train : 735  loss 0.9567  elapsed 0.66  load 0.01\n",
      "22207 14 08:28:58 INFO train : 736  loss 0.9882  elapsed 0.59  load 0.96\n",
      "22207 14 08:28:58 INFO train : 737  loss 0.9044  elapsed 0.63  load 0.08\n",
      "22207 14 08:28:59 INFO train : 738  loss 0.9469  elapsed 0.56  load 0.01\n",
      "22207 14 08:29:00 INFO train : 739  loss 0.9739  elapsed 0.57  load 0.00\n",
      "22207 14 08:29:02 INFO train : 740  loss 0.9561  elapsed 0.68  load 1.33\n",
      "22207 14 08:29:02 INFO train : 741  loss 0.9219  elapsed 0.64  load 0.01\n",
      "22207 14 08:29:03 INFO train : 742  loss 0.9715  elapsed 0.54  load 0.03\n",
      "22207 14 08:29:04 INFO train : 743  loss 0.9687  elapsed 0.64  load 0.01\n",
      "22207 14 08:29:05 INFO train : 744  loss 0.9554  elapsed 0.54  load 0.75\n",
      "22207 14 08:29:06 INFO train : 745  loss 0.9644  elapsed 0.61  load 0.00\n",
      "22207 14 08:29:06 INFO train : 746  loss 0.9596  elapsed 0.66  load 0.02\n",
      "22207 14 08:29:07 INFO train : 747  loss 0.9450  elapsed 0.48  load 0.02\n",
      "22207 14 08:29:08 INFO train : 748  loss 0.9543  elapsed 0.62  load 0.96\n",
      "22207 14 08:29:09 INFO train : 749  loss 0.9273  elapsed 0.61  load 0.04\n",
      "22207 14 08:29:10 INFO train : 750  loss 0.9586  elapsed 0.66  load 0.03\n",
      "22207 14 08:29:10 INFO train : 751  loss 0.9038  elapsed 0.58  load 0.06\n",
      "22207 14 08:29:12 INFO train : 752  loss 0.9704  elapsed 0.56  load 1.14\n",
      "22207 14 08:29:13 INFO train : 753  loss 1.0093  elapsed 0.60  load 0.00\n",
      "22207 14 08:29:13 INFO train : 754  loss 0.9080  elapsed 0.57  load 0.04\n",
      "22207 14 08:29:14 INFO train : 755  loss 0.9355  elapsed 0.66  load 0.00\n",
      "22207 14 08:29:16 INFO train : 756  loss 0.9389  elapsed 0.61  load 0.98\n",
      "22207 14 08:29:16 INFO train : 757  loss 0.8786  elapsed 0.60  load 0.07\n",
      "22207 14 08:29:17 INFO train : 758  loss 0.9678  elapsed 0.60  load 0.00\n",
      "22207 14 08:29:18 INFO train : 759  loss 0.9619  elapsed 0.69  load 0.00\n",
      "22207 14 08:29:19 INFO train : 760  loss 0.9367  elapsed 0.63  load 0.60\n",
      "22207 14 08:29:20 INFO train : 761  loss 0.9639  elapsed 0.66  load 0.03\n",
      "22207 14 08:29:20 INFO train : 762  loss 0.9667  elapsed 0.64  load 0.01\n",
      "22207 14 08:29:21 INFO train : 763  loss 0.9405  elapsed 0.62  load 0.00\n",
      "22207 14 08:29:23 INFO train : 764  loss 0.9365  elapsed 0.65  load 0.98\n",
      "22207 14 08:29:23 INFO train : 765  loss 0.9543  elapsed 0.62  load 0.05\n",
      "22207 14 08:29:24 INFO train : 766  loss 0.9684  elapsed 0.62  load 0.00\n",
      "22207 14 08:29:25 INFO train : 767  loss 0.9556  elapsed 0.69  load 0.01\n",
      "22207 14 08:29:26 INFO train : 768  loss 0.9860  elapsed 0.64  load 1.05\n",
      "22207 14 08:29:27 INFO train : 769  loss 0.9329  elapsed 0.67  load 0.01\n",
      "22207 14 08:29:28 INFO train : 770  loss 1.0749  elapsed 0.68  load 0.00\n",
      "22207 14 08:29:28 INFO train : 771  loss 0.9285  elapsed 0.68  load 0.00\n",
      "22207 14 08:29:29 INFO train : 772  loss 0.9359  elapsed 0.62  load 0.42\n",
      "22207 14 08:29:30 INFO train : 773  loss 0.9703  elapsed 0.57  load 0.05\n",
      "22207 14 08:29:31 INFO train : 774  loss 0.9063  elapsed 0.60  load 0.04\n",
      "22207 14 08:29:31 INFO train : 775  loss 0.9255  elapsed 0.60  load 0.00\n",
      "22207 14 08:29:33 INFO train : 776  loss 0.9478  elapsed 0.52  load 0.99\n",
      "22207 14 08:29:34 INFO train : 777  loss 0.9289  elapsed 0.63  load 0.13\n",
      "22207 14 08:29:34 INFO train : 778  loss 0.9175  elapsed 0.65  load 0.00\n",
      "22207 14 08:29:35 INFO train : 779  loss 0.9374  elapsed 0.60  load 0.00\n",
      "22207 14 08:29:37 INFO train : 780  loss 0.9351  elapsed 0.58  load 1.27\n",
      "22207 14 08:29:38 INFO train : 781  loss 0.9287  elapsed 0.63  load 0.04\n",
      "22207 14 08:29:38 INFO train : 782  loss 0.9438  elapsed 0.61  load 0.00\n",
      "22207 14 08:29:39 INFO train : 783  loss 0.8803  elapsed 0.62  load 0.00\n",
      "22207 14 08:29:40 INFO train : 784  loss 0.9468  elapsed 0.56  load 0.71\n",
      "22207 14 08:29:41 INFO train : 785  loss 0.8757  elapsed 0.60  load 0.15\n",
      "22207 14 08:29:42 INFO train : 786  loss 0.9247  elapsed 0.58  load 0.00\n",
      "22207 14 08:29:42 INFO train : 787  loss 0.8870  elapsed 0.57  load 0.00\n",
      "22207 14 08:29:44 INFO train : 788  loss 0.9125  elapsed 0.56  load 0.84\n",
      "22207 14 08:29:44 INFO train : 789  loss 0.9321  elapsed 0.67  load 0.12\n",
      "22207 14 08:29:45 INFO train : 790  loss 0.9101  elapsed 0.60  load 0.00\n",
      "22207 14 08:29:46 INFO train : 791  loss 0.9314  elapsed 0.62  load 0.00\n",
      "22207 14 08:29:47 INFO train : 792  loss 0.9506  elapsed 0.66  load 1.02\n",
      "22207 14 08:29:48 INFO train : 793  loss 0.9955  elapsed 0.58  load 0.02\n",
      "22207 14 08:29:49 INFO train : 794  loss 0.9106  elapsed 0.70  load 0.05\n",
      "22207 14 08:29:49 INFO train : 795  loss 0.8842  elapsed 0.67  load 0.00\n",
      "22207 14 08:29:51 INFO train : 796  loss 0.9344  elapsed 0.57  load 0.75\n",
      "22207 14 08:29:51 INFO train : 797  loss 0.9414  elapsed 0.53  load 0.04\n",
      "22207 14 08:29:52 INFO train : 798  loss 0.9600  elapsed 0.56  load 0.00\n",
      "22207 14 08:29:52 INFO train : 799  loss 0.8851  elapsed 0.52  load 0.00\n",
      "22207 14 08:29:53 INFO train : 800  loss 0.9628  elapsed 0.49  load 0.01\n",
      "22207 14 08:29:53 INFO train : 801  loss 0.9026  elapsed 0.49  load 0.00\n",
      "22207 14 08:29:54 INFO train : 802  loss 0.9059  elapsed 0.14  load 0.00\n",
      "22207 14 08:29:54 INFO Epoch num: 2\n",
      "22207 14 08:29:58 INFO train : 803  loss 0.9066  elapsed 0.65  load 3.78\n",
      "22207 14 08:29:59 INFO train : 804  loss 0.9662  elapsed 0.60  load 0.01\n",
      "22207 14 08:29:59 INFO train : 805  loss 0.8884  elapsed 0.56  load 0.02\n",
      "22207 14 08:30:00 INFO train : 806  loss 0.9176  elapsed 0.65  load 0.00\n",
      "22207 14 08:30:01 INFO train : 807  loss 1.0116  elapsed 0.47  load 0.85\n",
      "22207 14 08:30:02 INFO train : 808  loss 0.9108  elapsed 0.57  load 0.31\n",
      "22207 14 08:30:03 INFO train : 809  loss 0.9777  elapsed 0.63  load 0.00\n",
      "22207 14 08:30:03 INFO train : 810  loss 0.9272  elapsed 0.54  load 0.00\n",
      "22207 14 08:30:05 INFO train : 811  loss 0.9721  elapsed 0.58  load 0.81\n",
      "22207 14 08:30:06 INFO train : 812  loss 0.8850  elapsed 0.55  load 0.75\n",
      "22207 14 08:30:07 INFO train : 813  loss 0.9142  elapsed 0.61  load 0.00\n",
      "22207 14 08:30:07 INFO train : 814  loss 0.8975  elapsed 0.61  load 0.00\n",
      "22207 14 08:30:09 INFO train : 815  loss 0.9142  elapsed 0.69  load 0.34\n",
      "22207 14 08:30:10 INFO train : 816  loss 0.9481  elapsed 0.64  load 0.76\n",
      "22207 14 08:30:11 INFO train : 817  loss 0.9405  elapsed 0.53  load 0.02\n",
      "22207 14 08:30:11 INFO train : 818  loss 0.8891  elapsed 0.68  load 0.00\n",
      "22207 14 08:30:12 INFO train : 819  loss 0.9279  elapsed 0.67  load 0.01\n",
      "22207 14 08:30:14 INFO train : 820  loss 0.9112  elapsed 0.64  load 1.22\n",
      "22207 14 08:30:15 INFO train : 821  loss 0.8973  elapsed 0.69  load 0.00\n",
      "22207 14 08:30:15 INFO train : 822  loss 0.8464  elapsed 0.60  load 0.00\n",
      "22207 14 08:30:16 INFO train : 823  loss 0.8894  elapsed 0.63  load 0.06\n",
      "22207 14 08:30:18 INFO train : 824  loss 0.9468  elapsed 0.66  load 1.08\n",
      "22207 14 08:30:18 INFO train : 825  loss 0.9513  elapsed 0.59  load 0.00\n",
      "22207 14 08:30:19 INFO train : 826  loss 0.8615  elapsed 0.58  load 0.00\n",
      "22207 14 08:30:19 INFO train : 827  loss 0.9216  elapsed 0.56  load 0.01\n",
      "22207 14 08:30:21 INFO train : 828  loss 0.9198  elapsed 0.63  load 1.34\n",
      "22207 14 08:30:22 INFO train : 829  loss 0.8860  elapsed 0.63  load 0.00\n",
      "22207 14 08:30:23 INFO train : 830  loss 0.8749  elapsed 0.72  load 0.00\n",
      "22207 14 08:30:24 INFO train : 831  loss 0.9457  elapsed 0.66  load 0.03\n",
      "22207 14 08:30:25 INFO train : 832  loss 0.9104  elapsed 0.61  load 0.56\n",
      "22207 14 08:30:25 INFO train : 833  loss 0.8554  elapsed 0.63  load 0.00\n",
      "22207 14 08:30:26 INFO train : 834  loss 0.8760  elapsed 0.59  load 0.00\n",
      "22207 14 08:30:27 INFO train : 835  loss 0.8503  elapsed 0.55  load 0.45\n",
      "22207 14 08:30:29 INFO train : 836  loss 0.9291  elapsed 0.60  load 0.86\n",
      "22207 14 08:30:29 INFO train : 837  loss 0.9001  elapsed 0.62  load 0.00\n",
      "22207 14 08:30:30 INFO train : 838  loss 0.8832  elapsed 0.59  load 0.00\n",
      "22207 14 08:30:31 INFO train : 839  loss 0.8919  elapsed 0.64  load 0.23\n",
      "22207 14 08:30:31 INFO train : 840  loss 0.9690  elapsed 0.52  load 0.21\n",
      "22207 14 08:30:32 INFO train : 841  loss 0.8881  elapsed 0.59  load 0.00\n",
      "22207 14 08:30:33 INFO train : 842  loss 0.9140  elapsed 0.53  load 0.00\n",
      "22207 14 08:30:34 INFO train : 843  loss 0.8639  elapsed 0.60  load 0.54\n",
      "22207 14 08:30:35 INFO train : 844  loss 0.9301  elapsed 0.57  load 0.57\n",
      "22207 14 08:30:36 INFO train : 845  loss 0.9129  elapsed 0.62  load 0.00\n",
      "22207 14 08:30:36 INFO train : 846  loss 0.8991  elapsed 0.65  load 0.00\n",
      "22207 14 08:30:37 INFO train : 847  loss 0.8653  elapsed 0.61  load 0.48\n",
      "22207 14 08:30:39 INFO train : 848  loss 0.8585  elapsed 0.59  load 0.63\n",
      "22207 14 08:30:39 INFO train : 849  loss 0.9068  elapsed 0.61  load 0.00\n",
      "22207 14 08:30:40 INFO train : 850  loss 0.9182  elapsed 0.61  load 0.00\n",
      "22207 14 08:30:41 INFO train : 851  loss 0.8667  elapsed 0.65  load 0.01\n",
      "22207 14 08:30:42 INFO train : 852  loss 0.8985  elapsed 0.59  load 0.87\n",
      "22207 14 08:30:43 INFO train : 853  loss 0.9000  elapsed 0.58  load 0.00\n",
      "22207 14 08:30:43 INFO train : 854  loss 0.9023  elapsed 0.49  load 0.00\n",
      "22207 14 08:30:44 INFO train : 855  loss 0.9146  elapsed 0.56  load 0.32\n",
      "22207 14 08:30:46 INFO train : 856  loss 0.8843  elapsed 0.61  load 1.25\n",
      "22207 14 08:30:47 INFO train : 857  loss 0.9289  elapsed 0.65  load 0.00\n",
      "22207 14 08:30:47 INFO train : 858  loss 0.9180  elapsed 0.68  load 0.00\n",
      "22207 14 08:30:48 INFO train : 859  loss 0.9027  elapsed 0.62  load 0.01\n",
      "22207 14 08:30:50 INFO train : 860  loss 0.8689  elapsed 0.54  load 1.04\n",
      "22207 14 08:30:50 INFO train : 861  loss 0.9084  elapsed 0.55  load 0.00\n",
      "22207 14 08:30:51 INFO train : 862  loss 0.8760  elapsed 0.55  load 0.00\n",
      "22207 14 08:30:51 INFO train : 863  loss 0.9176  elapsed 0.67  load 0.02\n",
      "22207 14 08:30:53 INFO train : 864  loss 0.9640  elapsed 0.64  load 1.05\n",
      "22207 14 08:30:54 INFO train : 865  loss 0.9527  elapsed 0.65  load 0.00\n",
      "22207 14 08:30:54 INFO train : 866  loss 0.9011  elapsed 0.61  load 0.00\n",
      "22207 14 08:30:55 INFO train : 867  loss 0.9166  elapsed 0.56  load 0.04\n",
      "22207 14 08:30:56 INFO train : 868  loss 0.9029  elapsed 0.64  load 0.70\n",
      "22207 14 08:30:57 INFO train : 869  loss 0.9314  elapsed 0.57  load 0.00\n",
      "22207 14 08:30:58 INFO train : 870  loss 0.8833  elapsed 0.58  load 0.00\n",
      "22207 14 08:30:59 INFO train : 871  loss 0.8612  elapsed 0.58  load 0.25\n",
      "22207 14 08:31:00 INFO train : 872  loss 0.8864  elapsed 0.62  load 0.77\n",
      "22207 14 08:31:01 INFO train : 873  loss 0.8947  elapsed 0.63  load 0.00\n",
      "22207 14 08:31:01 INFO train : 874  loss 0.9252  elapsed 0.62  load 0.00\n",
      "22207 14 08:31:02 INFO train : 875  loss 0.9142  elapsed 0.59  load 0.49\n",
      "22207 14 08:31:03 INFO train : 876  loss 0.8828  elapsed 0.57  load 0.46\n",
      "22207 14 08:31:04 INFO train : 877  loss 0.8811  elapsed 0.62  load 0.00\n",
      "22207 14 08:31:05 INFO train : 878  loss 0.8970  elapsed 0.69  load 0.00\n",
      "22207 14 08:31:06 INFO train : 879  loss 0.8772  elapsed 0.63  load 0.28\n",
      "22207 14 08:31:07 INFO train : 880  loss 0.9066  elapsed 0.55  load 0.92\n",
      "22207 14 08:31:08 INFO train : 881  loss 0.9182  elapsed 0.62  load 0.00\n",
      "22207 14 08:31:08 INFO train : 882  loss 0.9025  elapsed 0.62  load 0.00\n",
      "22207 14 08:31:09 INFO train : 883  loss 0.9324  elapsed 0.57  load 0.42\n",
      "22207 14 08:31:11 INFO train : 884  loss 0.8848  elapsed 0.56  load 0.70\n",
      "22207 14 08:31:11 INFO train : 885  loss 0.8898  elapsed 0.55  load 0.00\n",
      "22207 14 08:31:12 INFO train : 886  loss 0.9003  elapsed 0.54  load 0.00\n",
      "22207 14 08:31:13 INFO train : 887  loss 0.8940  elapsed 0.56  load 0.34\n",
      "22207 14 08:31:14 INFO train : 888  loss 0.8714  elapsed 0.57  load 0.82\n",
      "22207 14 08:31:15 INFO train : 889  loss 0.8596  elapsed 0.67  load 0.00\n",
      "22207 14 08:31:15 INFO train : 890  loss 0.8772  elapsed 0.59  load 0.00\n",
      "22207 14 08:31:16 INFO train : 891  loss 0.8885  elapsed 0.57  load 0.39\n",
      "22207 14 08:31:18 INFO train : 892  loss 0.9713  elapsed 0.58  load 0.93\n",
      "22207 14 08:31:19 INFO train : 893  loss 0.9534  elapsed 0.54  load 0.00\n",
      "22207 14 08:31:19 INFO train : 894  loss 0.9170  elapsed 0.52  load 0.00\n",
      "22207 14 08:31:20 INFO train : 895  loss 0.9185  elapsed 0.64  load 0.59\n",
      "22207 14 08:31:22 INFO train : 896  loss 0.8948  elapsed 0.63  load 0.67\n",
      "22207 14 08:31:22 INFO train : 897  loss 0.8871  elapsed 0.60  load 0.00\n",
      "22207 14 08:31:23 INFO train : 898  loss 0.9309  elapsed 0.59  load 0.00\n",
      "22207 14 08:31:24 INFO train : 899  loss 0.8892  elapsed 0.58  load 0.35\n",
      "22207 14 08:31:25 INFO train : 900  loss 0.8717  elapsed 0.55  load 0.73\n",
      "22207 14 08:31:26 INFO train : 901  loss 0.8963  elapsed 0.60  load 0.00\n",
      "22207 14 08:31:26 INFO train : 902  loss 0.8767  elapsed 0.63  load 0.00\n",
      "22207 14 08:31:27 INFO train : 903  loss 0.9057  elapsed 0.55  load 0.44\n",
      "22207 14 08:31:28 INFO train : 904  loss 0.9000  elapsed 0.52  load 0.19\n",
      "22207 14 08:31:29 INFO train : 905  loss 0.9612  elapsed 0.58  load 0.00\n",
      "22207 14 08:31:29 INFO train : 906  loss 0.9153  elapsed 0.66  load 0.00\n",
      "22207 14 08:31:31 INFO train : 907  loss 0.9490  elapsed 0.60  load 0.47\n",
      "22207 14 08:31:31 INFO train : 908  loss 0.9451  elapsed 0.65  load 0.20\n",
      "22207 14 08:31:32 INFO train : 909  loss 0.8912  elapsed 0.63  load 0.03\n",
      "22207 14 08:31:33 INFO train : 910  loss 0.8262  elapsed 0.58  load 0.03\n",
      "22207 14 08:31:34 INFO train : 911  loss 0.8663  elapsed 0.64  load 0.49\n",
      "22207 14 08:31:35 INFO train : 912  loss 0.8942  elapsed 0.58  load 0.41\n",
      "22207 14 08:31:36 INFO train : 913  loss 0.9414  elapsed 0.63  load 0.00\n",
      "22207 14 08:31:36 INFO train : 914  loss 0.8884  elapsed 0.69  load 0.02\n",
      "22207 14 08:31:38 INFO train : 915  loss 0.8606  elapsed 0.71  load 0.47\n",
      "22207 14 08:31:38 INFO train : 916  loss 0.8488  elapsed 0.51  load 0.08\n",
      "22207 14 08:31:39 INFO train : 917  loss 0.8882  elapsed 0.59  load 0.00\n",
      "22207 14 08:31:39 INFO train : 918  loss 0.9616  elapsed 0.52  load 0.14\n",
      "22207 14 08:31:41 INFO train : 919  loss 0.8905  elapsed 0.56  load 1.22\n",
      "22207 14 08:31:42 INFO train : 920  loss 0.8665  elapsed 0.60  load 0.04\n",
      "22207 14 08:31:43 INFO train : 921  loss 0.8449  elapsed 0.57  load 0.04\n",
      "22207 14 08:31:43 INFO train : 922  loss 0.8743  elapsed 0.64  load 0.01\n",
      "22207 14 08:31:45 INFO train : 923  loss 0.9189  elapsed 0.63  load 0.97\n",
      "22207 14 08:31:46 INFO train : 924  loss 0.9310  elapsed 0.69  load 0.15\n",
      "22207 14 08:31:46 INFO train : 925  loss 0.9340  elapsed 0.65  load 0.00\n",
      "22207 14 08:31:47 INFO train : 926  loss 0.8839  elapsed 0.63  load 0.00\n",
      "22207 14 08:31:49 INFO train : 927  loss 0.8578  elapsed 0.64  load 0.98\n",
      "22207 14 08:31:49 INFO train : 928  loss 0.8905  elapsed 0.57  load 0.00\n",
      "22207 14 08:31:50 INFO train : 929  loss 0.8402  elapsed 0.66  load 0.00\n",
      "22207 14 08:31:51 INFO train : 930  loss 0.9449  elapsed 0.64  load 0.02\n",
      "22207 14 08:31:52 INFO train : 931  loss 0.8940  elapsed 0.58  load 0.92\n",
      "22207 14 08:31:53 INFO train : 932  loss 0.9497  elapsed 0.62  load 0.01\n",
      "22207 14 08:31:53 INFO train : 933  loss 0.8766  elapsed 0.67  load 0.00\n",
      "22207 14 08:31:54 INFO train : 934  loss 0.9367  elapsed 0.56  load 0.00\n",
      "22207 14 08:31:56 INFO train : 935  loss 0.8683  elapsed 0.63  load 1.02\n",
      "22207 14 08:31:56 INFO train : 936  loss 0.8989  elapsed 0.62  load 0.01\n",
      "22207 14 08:31:57 INFO train : 937  loss 0.8913  elapsed 0.66  load 0.00\n",
      "22207 14 08:31:58 INFO train : 938  loss 0.9048  elapsed 0.65  load 0.00\n",
      "22207 14 08:31:59 INFO train : 939  loss 0.8613  elapsed 0.60  load 1.14\n",
      "22207 14 08:32:00 INFO train : 940  loss 0.8189  elapsed 0.60  load 0.01\n",
      "22207 14 08:32:01 INFO train : 941  loss 0.8996  elapsed 0.64  load 0.00\n",
      "22207 14 08:32:01 INFO train : 942  loss 0.8941  elapsed 0.63  load 0.00\n",
      "22207 14 08:32:03 INFO train : 943  loss 0.8406  elapsed 0.58  load 1.40\n",
      "22207 14 08:32:04 INFO train : 944  loss 0.8863  elapsed 0.54  load 0.00\n",
      "22207 14 08:32:05 INFO train : 945  loss 0.9075  elapsed 0.62  load 0.00\n",
      "22207 14 08:32:05 INFO train : 946  loss 0.8827  elapsed 0.54  load 0.00\n",
      "22207 14 08:32:07 INFO train : 947  loss 0.9300  elapsed 0.65  load 1.47\n",
      "22207 14 08:32:08 INFO train : 948  loss 0.9189  elapsed 0.62  load 0.00\n",
      "22207 14 08:32:09 INFO train : 949  loss 0.8501  elapsed 0.58  load 0.00\n",
      "22207 14 08:32:09 INFO train : 950  loss 0.8634  elapsed 0.65  load 0.00\n",
      "22207 14 08:32:11 INFO train : 951  loss 0.9390  elapsed 0.67  load 1.06\n",
      "22207 14 08:32:12 INFO train : 952  loss 0.9046  elapsed 0.64  load 0.00\n",
      "22207 14 08:32:12 INFO train : 953  loss 0.8997  elapsed 0.56  load 0.01\n",
      "22207 14 08:32:13 INFO train : 954  loss 0.8601  elapsed 0.63  load 0.00\n",
      "22207 14 08:32:14 INFO train : 955  loss 0.9478  elapsed 0.58  load 0.62\n",
      "22207 14 08:32:15 INFO train : 956  loss 0.9496  elapsed 0.64  load 0.00\n",
      "22207 14 08:32:15 INFO train : 957  loss 0.8890  elapsed 0.60  load 0.00\n",
      "22207 14 08:32:16 INFO train : 958  loss 0.8889  elapsed 0.65  load 0.00\n",
      "22207 14 08:32:18 INFO train : 959  loss 0.8975  elapsed 0.57  load 0.87\n",
      "22207 14 08:32:18 INFO train : 960  loss 0.8652  elapsed 0.63  load 0.00\n",
      "22207 14 08:32:19 INFO train : 961  loss 0.8644  elapsed 0.59  load 0.00\n",
      "22207 14 08:32:19 INFO train : 962  loss 0.8843  elapsed 0.59  load 0.00\n",
      "22207 14 08:32:21 INFO train : 963  loss 0.8895  elapsed 0.59  load 1.45\n",
      "22207 14 08:32:22 INFO train : 964  loss 0.8723  elapsed 0.57  load 0.00\n",
      "22207 14 08:32:23 INFO train : 965  loss 0.8373  elapsed 0.59  load 0.00\n",
      "22207 14 08:32:23 INFO train : 966  loss 0.8683  elapsed 0.66  load 0.00\n",
      "22207 14 08:32:25 INFO train : 967  loss 0.8890  elapsed 0.62  load 0.70\n",
      "22207 14 08:32:25 INFO train : 968  loss 0.8535  elapsed 0.65  load 0.00\n",
      "22207 14 08:32:26 INFO train : 969  loss 0.8478  elapsed 0.64  load 0.00\n",
      "22207 14 08:32:27 INFO train : 970  loss 0.8951  elapsed 0.63  load 0.01\n",
      "22207 14 08:32:28 INFO train : 971  loss 0.8950  elapsed 0.64  load 0.61\n",
      "22207 14 08:32:29 INFO train : 972  loss 0.8649  elapsed 0.65  load 0.00\n",
      "22207 14 08:32:29 INFO train : 973  loss 0.8608  elapsed 0.65  load 0.00\n",
      "22207 14 08:32:30 INFO train : 974  loss 0.8944  elapsed 0.58  load 0.06\n",
      "22207 14 08:32:31 INFO train : 975  loss 0.8625  elapsed 0.62  load 0.74\n",
      "22207 14 08:32:32 INFO train : 976  loss 0.8916  elapsed 0.63  load 0.00\n",
      "22207 14 08:32:33 INFO train : 977  loss 0.8706  elapsed 0.57  load 0.00\n",
      "22207 14 08:32:33 INFO train : 978  loss 0.8460  elapsed 0.59  load 0.10\n",
      "22207 14 08:32:35 INFO train : 979  loss 0.8896  elapsed 0.58  load 0.94\n",
      "22207 14 08:32:35 INFO train : 980  loss 0.8669  elapsed 0.62  load 0.00\n",
      "22207 14 08:32:36 INFO train : 981  loss 0.9057  elapsed 0.65  load 0.00\n",
      "22207 14 08:32:37 INFO train : 982  loss 0.8857  elapsed 0.65  load 0.01\n",
      "22207 14 08:32:38 INFO train : 983  loss 0.9634  elapsed 0.61  load 0.68\n",
      "22207 14 08:32:39 INFO train : 984  loss 0.9021  elapsed 0.66  load 0.00\n",
      "22207 14 08:32:39 INFO train : 985  loss 0.8990  elapsed 0.70  load 0.00\n",
      "22207 14 08:32:40 INFO train : 986  loss 0.9219  elapsed 0.62  load 0.00\n",
      "22207 14 08:32:42 INFO train : 987  loss 0.8513  elapsed 0.61  load 1.36\n",
      "22207 14 08:32:43 INFO train : 988  loss 0.8568  elapsed 0.66  load 0.00\n",
      "22207 14 08:32:43 INFO train : 989  loss 0.8766  elapsed 0.61  load 0.00\n",
      "22207 14 08:32:44 INFO train : 990  loss 0.8480  elapsed 0.69  load 0.00\n",
      "22207 14 08:32:46 INFO train : 991  loss 0.8493  elapsed 0.64  load 1.02\n",
      "22207 14 08:32:46 INFO train : 992  loss 0.9672  elapsed 0.62  load 0.00\n",
      "22207 14 08:32:47 INFO train : 993  loss 0.8787  elapsed 0.67  load 0.00\n",
      "22207 14 08:32:48 INFO train : 994  loss 0.9595  elapsed 0.61  load 0.03\n",
      "22207 14 08:32:49 INFO train : 995  loss 0.8669  elapsed 0.66  load 0.83\n",
      "22207 14 08:32:50 INFO train : 996  loss 0.8786  elapsed 0.57  load 0.00\n",
      "22207 14 08:32:51 INFO train : 997  loss 0.8384  elapsed 0.69  load 0.00\n",
      "22207 14 08:32:51 INFO train : 998  loss 0.8816  elapsed 0.60  load 0.00\n",
      "22207 14 08:32:53 INFO train : 999  loss 0.8557  elapsed 0.57  load 1.12\n",
      "22207 14 08:32:54 INFO train : 1000  loss 0.9106  elapsed 0.65  load 0.00\n",
      "22207 14 08:32:54 INFO Running validation\n",
      "22207 14 08:33:15 INFO Logging 20 images\n",
      "22207 14 08:33:16 INFO Logging 20 audios\n",
      "22207 14 08:33:19 INFO Logging 20 texts\n",
      "22207 14 08:33:30 INFO val : 1000  loss 0.7898404002189636\n",
      "22207 14 08:33:30 INFO Saving model state at step 1000 to checkpoints/fp_step_00001000.pt\n",
      "22207 14 08:33:31 INFO train : 1001  loss 0.8748  elapsed 0.53  load 0.00\n",
      "22207 14 08:33:32 INFO train : 1002  loss 0.8904  elapsed 0.52  load 0.00\n",
      "22207 14 08:33:33 INFO train : 1003  loss 0.8657  elapsed 0.67  load 0.01\n",
      "22207 14 08:33:33 INFO train : 1004  loss 0.9250  elapsed 0.68  load 0.07\n",
      "22207 14 08:33:34 INFO train : 1005  loss 0.8456  elapsed 0.57  load 0.00\n",
      "22207 14 08:33:35 INFO train : 1006  loss 1.0120  elapsed 0.63  load 0.01\n",
      "22207 14 08:33:35 INFO train : 1007  loss 0.8356  elapsed 0.55  load 0.07\n",
      "22207 14 08:33:36 INFO train : 1008  loss 0.8775  elapsed 0.63  load 0.03\n",
      "22207 14 08:33:37 INFO train : 1009  loss 0.8664  elapsed 0.50  load 0.03\n",
      "22207 14 08:33:37 INFO train : 1010  loss 0.9066  elapsed 0.64  load 0.00\n",
      "22207 14 08:33:38 INFO train : 1011  loss 0.8753  elapsed 0.58  load 0.05\n",
      "22207 14 08:33:39 INFO train : 1012  loss 0.9474  elapsed 0.68  load 0.03\n",
      "22207 14 08:33:39 INFO train : 1013  loss 0.8742  elapsed 0.62  load 0.01\n",
      "22207 14 08:33:40 INFO train : 1014  loss 0.8682  elapsed 0.57  load 0.01\n",
      "22207 14 08:33:40 INFO train : 1015  loss 0.8888  elapsed 0.63  load 0.02\n",
      "22207 14 08:33:41 INFO train : 1016  loss 0.9108  elapsed 0.56  load 0.03\n",
      "22207 14 08:33:42 INFO train : 1017  loss 0.8959  elapsed 0.61  load 0.04\n",
      "22207 14 08:33:42 INFO train : 1018  loss 0.9006  elapsed 0.57  load 0.05\n",
      "22207 14 08:33:43 INFO train : 1019  loss 0.9303  elapsed 0.69  load 0.01\n",
      "22207 14 08:33:44 INFO train : 1020  loss 0.8287  elapsed 0.67  load 0.02\n",
      "22207 14 08:33:45 INFO train : 1021  loss 0.8667  elapsed 0.58  load 0.37\n",
      "22207 14 08:33:45 INFO train : 1022  loss 0.8629  elapsed 0.60  load 0.00\n",
      "22207 14 08:33:46 INFO train : 1023  loss 0.9202  elapsed 0.65  load 0.08\n",
      "22207 14 08:33:47 INFO train : 1024  loss 0.8397  elapsed 0.65  load 0.18\n",
      "22207 14 08:33:48 INFO train : 1025  loss 0.8386  elapsed 0.62  load 0.83\n",
      "22207 14 08:33:49 INFO train : 1026  loss 0.8397  elapsed 0.60  load 0.00\n",
      "22207 14 08:33:50 INFO train : 1027  loss 0.8738  elapsed 0.54  load 0.00\n",
      "22207 14 08:33:50 INFO train : 1028  loss 0.8147  elapsed 0.66  load 0.10\n",
      "22207 14 08:33:52 INFO train : 1029  loss 0.8411  elapsed 0.56  load 0.95\n",
      "22207 14 08:33:53 INFO train : 1030  loss 0.8841  elapsed 0.58  load 0.00\n",
      "22207 14 08:33:53 INFO train : 1031  loss 0.8404  elapsed 0.57  load 0.01\n",
      "22207 14 08:33:54 INFO train : 1032  loss 0.8791  elapsed 0.61  load 0.04\n",
      "22207 14 08:33:56 INFO train : 1033  loss 0.8673  elapsed 0.63  load 1.12\n",
      "22207 14 08:33:56 INFO train : 1034  loss 0.8733  elapsed 0.65  load 0.00\n",
      "22207 14 08:33:57 INFO train : 1035  loss 0.8697  elapsed 0.62  load 0.02\n",
      "22207 14 08:33:58 INFO train : 1036  loss 0.8551  elapsed 0.57  load 0.00\n",
      "22207 14 08:33:59 INFO train : 1037  loss 0.8829  elapsed 0.59  load 1.19\n",
      "22207 14 08:34:00 INFO train : 1038  loss 0.8519  elapsed 0.64  load 0.01\n",
      "22207 14 08:34:01 INFO train : 1039  loss 0.8309  elapsed 0.58  load 0.08\n",
      "22207 14 08:34:01 INFO train : 1040  loss 0.8742  elapsed 0.59  load 0.07\n",
      "22207 14 08:34:03 INFO train : 1041  loss 0.8185  elapsed 0.59  load 0.72\n",
      "22207 14 08:34:03 INFO train : 1042  loss 0.8662  elapsed 0.64  load 0.00\n",
      "22207 14 08:34:04 INFO train : 1043  loss 0.9177  elapsed 0.59  load 0.02\n",
      "22207 14 08:34:05 INFO train : 1044  loss 0.9005  elapsed 0.62  load 0.15\n",
      "22207 14 08:34:06 INFO train : 1045  loss 0.8777  elapsed 0.62  load 0.72\n",
      "22207 14 08:34:07 INFO train : 1046  loss 0.8413  elapsed 0.62  load 0.33\n",
      "22207 14 08:34:08 INFO train : 1047  loss 0.8521  elapsed 0.65  load 0.03\n",
      "22207 14 08:34:09 INFO train : 1048  loss 0.8825  elapsed 0.63  load 0.01\n",
      "22207 14 08:34:10 INFO train : 1049  loss 0.8512  elapsed 0.55  load 0.47\n",
      "22207 14 08:34:11 INFO train : 1050  loss 0.8730  elapsed 0.68  load 0.79\n",
      "22207 14 08:34:12 INFO train : 1051  loss 0.8518  elapsed 0.60  load 0.00\n",
      "22207 14 08:34:12 INFO train : 1052  loss 0.8850  elapsed 0.57  load 0.01\n",
      "22207 14 08:34:13 INFO train : 1053  loss 0.9121  elapsed 0.59  load 0.23\n",
      "22207 14 08:34:14 INFO train : 1054  loss 0.9186  elapsed 0.59  load 0.57\n",
      "22207 14 08:34:15 INFO train : 1055  loss 0.8987  elapsed 0.59  load 0.00\n",
      "22207 14 08:34:16 INFO train : 1056  loss 0.8492  elapsed 0.61  load 0.17\n",
      "22207 14 08:34:17 INFO train : 1057  loss 0.9002  elapsed 0.56  load 0.17\n",
      "22207 14 08:34:18 INFO train : 1058  loss 0.8770  elapsed 0.59  load 0.53\n",
      "22207 14 08:34:18 INFO train : 1059  loss 0.8803  elapsed 0.67  load 0.00\n",
      "22207 14 08:34:19 INFO train : 1060  loss 0.8976  elapsed 0.61  load 0.39\n",
      "22207 14 08:34:20 INFO train : 1061  loss 0.8466  elapsed 0.61  load 0.00\n",
      "22207 14 08:34:21 INFO train : 1062  loss 0.9334  elapsed 0.60  load 0.61\n",
      "22207 14 08:34:22 INFO train : 1063  loss 0.8579  elapsed 0.53  load 0.00\n",
      "22207 14 08:34:23 INFO train : 1064  loss 0.8410  elapsed 0.63  load 0.58\n",
      "22207 14 08:34:24 INFO train : 1065  loss 0.8360  elapsed 0.58  load 0.02\n",
      "22207 14 08:34:25 INFO train : 1066  loss 0.8924  elapsed 0.55  load 0.87\n",
      "22207 14 08:34:26 INFO train : 1067  loss 0.8770  elapsed 0.74  load 0.00\n",
      "22207 14 08:34:27 INFO train : 1068  loss 0.8377  elapsed 0.61  load 0.40\n",
      "22207 14 08:34:28 INFO train : 1069  loss 0.9045  elapsed 0.56  load 0.01\n",
      "22207 14 08:34:28 INFO train : 1070  loss 0.8727  elapsed 0.63  load 0.20\n",
      "22207 14 08:34:29 INFO train : 1071  loss 0.9241  elapsed 0.60  load 0.00\n",
      "22207 14 08:34:31 INFO train : 1072  loss 0.8653  elapsed 0.63  load 0.96\n",
      "22207 14 08:34:31 INFO train : 1073  loss 0.8652  elapsed 0.68  load 0.00\n",
      "22207 14 08:34:32 INFO train : 1074  loss 0.8732  elapsed 0.60  load 0.03\n",
      "22207 14 08:34:33 INFO train : 1075  loss 0.8922  elapsed 0.56  load 0.00\n",
      "22207 14 08:34:34 INFO train : 1076  loss 0.8328  elapsed 0.64  load 1.17\n",
      "22207 14 08:34:35 INFO train : 1077  loss 0.8319  elapsed 0.67  load 0.00\n",
      "22207 14 08:34:36 INFO train : 1078  loss 0.8612  elapsed 0.63  load 0.01\n",
      "22207 14 08:34:36 INFO train : 1079  loss 0.8949  elapsed 0.60  load 0.00\n",
      "22207 14 08:34:38 INFO train : 1080  loss 0.8805  elapsed 0.58  load 1.09\n",
      "22207 14 08:34:39 INFO train : 1081  loss 0.8545  elapsed 0.53  load 0.06\n",
      "22207 14 08:34:39 INFO train : 1082  loss 0.8835  elapsed 0.64  load 0.00\n",
      "22207 14 08:34:40 INFO train : 1083  loss 0.8784  elapsed 0.60  load 0.01\n",
      "22207 14 08:34:42 INFO train : 1084  loss 0.9228  elapsed 0.59  load 1.15\n",
      "22207 14 08:34:42 INFO train : 1085  loss 0.8340  elapsed 0.65  load 0.03\n",
      "22207 14 08:34:43 INFO train : 1086  loss 1.0386  elapsed 0.59  load 0.09\n",
      "22207 14 08:34:44 INFO train : 1087  loss 0.8664  elapsed 0.58  load 0.07\n",
      "22207 14 08:34:45 INFO train : 1088  loss 0.9315  elapsed 0.60  load 1.01\n",
      "22207 14 08:34:46 INFO train : 1089  loss 0.8857  elapsed 0.60  load 0.08\n",
      "22207 14 08:34:47 INFO train : 1090  loss 0.8479  elapsed 0.65  load 0.01\n",
      "22207 14 08:34:47 INFO train : 1091  loss 0.8704  elapsed 0.64  load 0.03\n",
      "22207 14 08:34:49 INFO train : 1092  loss 0.8246  elapsed 0.60  load 0.75\n",
      "22207 14 08:34:50 INFO train : 1093  loss 0.8921  elapsed 0.57  load 0.09\n",
      "22207 14 08:34:50 INFO train : 1094  loss 0.8648  elapsed 0.63  load 0.01\n",
      "22207 14 08:34:51 INFO train : 1095  loss 0.8929  elapsed 0.53  load 0.02\n",
      "22207 14 08:34:52 INFO train : 1096  loss 0.8776  elapsed 0.66  load 0.84\n",
      "22207 14 08:34:53 INFO train : 1097  loss 0.8704  elapsed 0.70  load 0.30\n",
      "22207 14 08:34:54 INFO train : 1098  loss 0.8227  elapsed 0.67  load 0.00\n",
      "22207 14 08:34:55 INFO train : 1099  loss 0.8608  elapsed 0.69  load 0.01\n",
      "22207 14 08:34:56 INFO train : 1100  loss 0.8206  elapsed 0.58  load 0.40\n",
      "22207 14 08:34:57 INFO train : 1101  loss 0.7905  elapsed 0.61  load 0.69\n",
      "22207 14 08:34:58 INFO train : 1102  loss 0.8427  elapsed 0.58  load 0.00\n",
      "22207 14 08:34:58 INFO train : 1103  loss 0.8369  elapsed 0.59  load 0.09\n",
      "22207 14 08:34:59 INFO train : 1104  loss 0.8672  elapsed 0.62  load 0.21\n",
      "22207 14 08:35:01 INFO train : 1105  loss 0.8502  elapsed 0.65  load 0.67\n",
      "22207 14 08:35:01 INFO train : 1106  loss 0.8946  elapsed 0.64  load 0.00\n",
      "22207 14 08:35:02 INFO train : 1107  loss 0.8695  elapsed 0.63  load 0.01\n",
      "22207 14 08:35:03 INFO train : 1108  loss 0.8524  elapsed 0.61  load 0.04\n",
      "22207 14 08:35:04 INFO train : 1109  loss 0.8802  elapsed 0.60  load 0.91\n",
      "22207 14 08:35:05 INFO train : 1110  loss 0.8658  elapsed 0.63  load 0.00\n",
      "22207 14 08:35:05 INFO train : 1111  loss 0.8529  elapsed 0.58  load 0.01\n",
      "22207 14 08:35:06 INFO train : 1112  loss 0.8464  elapsed 0.60  load 0.27\n",
      "22207 14 08:35:08 INFO train : 1113  loss 0.8758  elapsed 0.65  load 0.72\n",
      "22207 14 08:35:08 INFO train : 1114  loss 0.8960  elapsed 0.62  load 0.00\n",
      "22207 14 08:35:09 INFO train : 1115  loss 0.8982  elapsed 0.58  load 0.02\n",
      "22207 14 08:35:10 INFO train : 1116  loss 0.8700  elapsed 0.66  load 0.56\n",
      "22207 14 08:35:11 INFO train : 1117  loss 0.8610  elapsed 0.56  load 0.06\n",
      "22207 14 08:35:11 INFO train : 1118  loss 0.8683  elapsed 0.60  load 0.02\n",
      "22207 14 08:35:12 INFO train : 1119  loss 0.8440  elapsed 0.70  load 0.00\n",
      "22207 14 08:35:14 INFO train : 1120  loss 0.8781  elapsed 0.58  load 0.97\n",
      "22207 14 08:35:15 INFO train : 1121  loss 0.8697  elapsed 0.62  load 0.08\n",
      "22207 14 08:35:15 INFO train : 1122  loss 0.9313  elapsed 0.63  load 0.01\n",
      "22207 14 08:35:16 INFO train : 1123  loss 0.8428  elapsed 0.56  load 0.07\n",
      "22207 14 08:35:18 INFO train : 1124  loss 0.8246  elapsed 0.63  load 1.32\n",
      "22207 14 08:35:19 INFO train : 1125  loss 0.8161  elapsed 0.64  load 0.11\n",
      "22207 14 08:35:19 INFO train : 1126  loss 0.8452  elapsed 0.61  load 0.00\n",
      "22207 14 08:35:20 INFO train : 1127  loss 0.8700  elapsed 0.57  load 0.02\n",
      "22207 14 08:35:21 INFO train : 1128  loss 0.9064  elapsed 0.60  load 0.67\n",
      "22207 14 08:35:22 INFO train : 1129  loss 0.8768  elapsed 0.58  load 0.45\n",
      "22207 14 08:35:23 INFO train : 1130  loss 0.8584  elapsed 0.64  load 0.01\n",
      "22207 14 08:35:24 INFO train : 1131  loss 0.8411  elapsed 0.61  load 0.02\n",
      "22207 14 08:35:24 INFO train : 1132  loss 0.8498  elapsed 0.57  load 0.21\n",
      "22207 14 08:35:26 INFO train : 1133  loss 0.8565  elapsed 0.58  load 0.70\n",
      "22207 14 08:35:26 INFO train : 1134  loss 0.8511  elapsed 0.61  load 0.00\n",
      "22207 14 08:35:27 INFO train : 1135  loss 0.8508  elapsed 0.63  load 0.01\n",
      "22207 14 08:35:28 INFO train : 1136  loss 0.8653  elapsed 0.57  load 0.03\n",
      "22207 14 08:35:30 INFO train : 1137  loss 0.8541  elapsed 0.60  load 1.31\n",
      "22207 14 08:35:30 INFO train : 1138  loss 0.8619  elapsed 0.64  load 0.01\n",
      "22207 14 08:35:31 INFO train : 1139  loss 0.8580  elapsed 0.66  load 0.00\n",
      "22207 14 08:35:32 INFO train : 1140  loss 0.8115  elapsed 0.65  load 0.08\n",
      "22207 14 08:35:33 INFO train : 1141  loss 0.8845  elapsed 0.62  load 0.83\n",
      "22207 14 08:35:34 INFO train : 1142  loss 0.9107  elapsed 0.63  load 0.00\n",
      "22207 14 08:35:34 INFO train : 1143  loss 0.8816  elapsed 0.65  load 0.00\n",
      "22207 14 08:35:35 INFO train : 1144  loss 0.8472  elapsed 0.61  load 0.01\n",
      "22207 14 08:35:36 INFO train : 1145  loss 0.8260  elapsed 0.55  load 0.70\n",
      "22207 14 08:35:37 INFO train : 1146  loss 0.8247  elapsed 0.66  load 0.01\n",
      "22207 14 08:35:38 INFO train : 1147  loss 0.8335  elapsed 0.57  load 0.00\n",
      "22207 14 08:35:38 INFO train : 1148  loss 0.9160  elapsed 0.66  load 0.01\n",
      "22207 14 08:35:40 INFO train : 1149  loss 0.8616  elapsed 0.51  load 1.17\n",
      "22207 14 08:35:41 INFO train : 1150  loss 0.8243  elapsed 0.63  load 0.04\n",
      "22207 14 08:35:41 INFO train : 1151  loss 0.8847  elapsed 0.65  load 0.00\n",
      "22207 14 08:35:42 INFO train : 1152  loss 0.8856  elapsed 0.62  load 0.01\n",
      "22207 14 08:35:44 INFO train : 1153  loss 0.8260  elapsed 0.66  load 0.85\n",
      "22207 14 08:35:44 INFO train : 1154  loss 0.7907  elapsed 0.63  load 0.21\n",
      "22207 14 08:35:45 INFO train : 1155  loss 0.8766  elapsed 0.66  load 0.00\n",
      "22207 14 08:35:46 INFO train : 1156  loss 0.8386  elapsed 0.62  load 0.03\n",
      "22207 14 08:35:47 INFO train : 1157  loss 0.8455  elapsed 0.59  load 0.79\n",
      "22207 14 08:35:48 INFO train : 1158  loss 0.8343  elapsed 0.60  load 0.29\n",
      "22207 14 08:35:49 INFO train : 1159  loss 0.8110  elapsed 0.69  load 0.00\n",
      "22207 14 08:35:49 INFO train : 1160  loss 0.8430  elapsed 0.65  load 0.03\n",
      "22207 14 08:35:51 INFO train : 1161  loss 0.8859  elapsed 0.61  load 0.65\n",
      "22207 14 08:35:52 INFO train : 1162  loss 0.8156  elapsed 0.63  load 0.20\n",
      "22207 14 08:35:52 INFO train : 1163  loss 0.8550  elapsed 0.66  load 0.00\n",
      "22207 14 08:35:53 INFO train : 1164  loss 0.8569  elapsed 0.65  load 0.01\n",
      "22207 14 08:35:55 INFO train : 1165  loss 0.8270  elapsed 0.54  load 1.11\n",
      "22207 14 08:35:55 INFO train : 1166  loss 0.8574  elapsed 0.53  load 0.00\n",
      "22207 14 08:35:56 INFO train : 1167  loss 0.8183  elapsed 0.71  load 0.00\n",
      "22207 14 08:35:57 INFO train : 1168  loss 0.8654  elapsed 0.68  load 0.02\n",
      "22207 14 08:35:58 INFO train : 1169  loss 0.8387  elapsed 0.58  load 1.03\n",
      "22207 14 08:35:59 INFO train : 1170  loss 0.8709  elapsed 0.63  load 0.01\n",
      "22207 14 08:36:00 INFO train : 1171  loss 0.8917  elapsed 0.65  load 0.00\n",
      "22207 14 08:36:00 INFO train : 1172  loss 0.8481  elapsed 0.63  load 0.04\n",
      "22207 14 08:36:02 INFO train : 1173  loss 0.8201  elapsed 0.65  load 0.78\n",
      "22207 14 08:36:02 INFO train : 1174  loss 0.9127  elapsed 0.67  load 0.00\n",
      "22207 14 08:36:03 INFO train : 1175  loss 0.8293  elapsed 0.68  load 0.01\n",
      "22207 14 08:36:04 INFO train : 1176  loss 0.8301  elapsed 0.60  load 0.07\n",
      "22207 14 08:36:06 INFO train : 1177  loss 0.8584  elapsed 0.54  load 1.17\n",
      "22207 14 08:36:06 INFO train : 1178  loss 0.8086  elapsed 0.67  load 0.00\n",
      "22207 14 08:36:07 INFO train : 1179  loss 0.8233  elapsed 0.65  load 0.00\n",
      "22207 14 08:36:08 INFO train : 1180  loss 0.8935  elapsed 0.67  load 0.01\n",
      "22207 14 08:36:09 INFO train : 1181  loss 0.8682  elapsed 0.60  load 0.78\n",
      "22207 14 08:36:10 INFO train : 1182  loss 0.8757  elapsed 0.66  load 0.00\n",
      "22207 14 08:36:10 INFO train : 1183  loss 0.8079  elapsed 0.67  load 0.00\n",
      "22207 14 08:36:11 INFO train : 1184  loss 0.8311  elapsed 0.51  load 0.03\n",
      "22207 14 08:36:12 INFO train : 1185  loss 0.8474  elapsed 0.62  load 0.85\n",
      "22207 14 08:36:13 INFO train : 1186  loss 0.8429  elapsed 0.68  load 0.00\n",
      "22207 14 08:36:14 INFO train : 1187  loss 0.8951  elapsed 0.63  load 0.00\n",
      "22207 14 08:36:14 INFO train : 1188  loss 0.8379  elapsed 0.60  load 0.02\n",
      "22207 14 08:36:16 INFO train : 1189  loss 0.7991  elapsed 0.57  load 1.05\n",
      "22207 14 08:36:17 INFO train : 1190  loss 0.8814  elapsed 0.63  load 0.00\n",
      "22207 14 08:36:17 INFO train : 1191  loss 0.8685  elapsed 0.63  load 0.00\n",
      "22207 14 08:36:18 INFO train : 1192  loss 0.8819  elapsed 0.63  load 0.02\n",
      "22207 14 08:36:20 INFO train : 1193  loss 0.8475  elapsed 0.62  load 0.93\n",
      "22207 14 08:36:20 INFO train : 1194  loss 0.8035  elapsed 0.59  load 0.00\n",
      "22207 14 08:36:21 INFO train : 1195  loss 0.8468  elapsed 0.59  load 0.09\n",
      "22207 14 08:36:22 INFO train : 1196  loss 0.8942  elapsed 0.63  load 0.01\n",
      "22207 14 08:36:23 INFO train : 1197  loss 0.8194  elapsed 0.63  load 0.93\n",
      "22207 14 08:36:24 INFO train : 1198  loss 0.8393  elapsed 0.55  load 0.00\n",
      "22207 14 08:36:24 INFO train : 1199  loss 0.8447  elapsed 0.51  load 0.00\n",
      "22207 14 08:36:25 INFO train : 1200  loss 0.8727  elapsed 0.50  load 0.00\n",
      "22207 14 08:36:25 INFO train : 1201  loss 0.8401  elapsed 0.48  load 0.00\n",
      "22207 14 08:36:26 INFO train : 1202  loss 0.9194  elapsed 0.48  load 0.00\n",
      "22207 14 08:36:26 INFO train : 1203  loss 0.8308  elapsed 0.12  load 0.00\n",
      "22207 14 08:36:26 INFO Epoch num: 3\n",
      "22207 14 08:36:30 INFO train : 1204  loss 0.8529  elapsed 0.55  load 3.84\n",
      "22207 14 08:36:31 INFO train : 1205  loss 0.7920  elapsed 0.71  load 0.03\n",
      "22207 14 08:36:32 INFO train : 1206  loss 0.7980  elapsed 0.57  load 0.01\n",
      "22207 14 08:36:32 INFO train : 1207  loss 0.8280  elapsed 0.62  load 0.02\n",
      "22207 14 08:36:34 INFO train : 1208  loss 0.8347  elapsed 0.60  load 1.02\n",
      "22207 14 08:36:35 INFO train : 1209  loss 0.8565  elapsed 0.60  load 0.08\n",
      "22207 14 08:36:35 INFO train : 1210  loss 0.8324  elapsed 0.57  load 0.00\n",
      "22207 14 08:36:36 INFO train : 1211  loss 0.8080  elapsed 0.68  load 0.03\n",
      "22207 14 08:36:38 INFO train : 1212  loss 0.8334  elapsed 0.63  load 0.88\n",
      "22207 14 08:36:38 INFO train : 1213  loss 0.8566  elapsed 0.65  load 0.00\n",
      "22207 14 08:36:39 INFO train : 1214  loss 0.7976  elapsed 0.67  load 0.05\n",
      "22207 14 08:36:40 INFO train : 1215  loss 0.8283  elapsed 0.64  load 0.00\n",
      "22207 14 08:36:41 INFO train : 1216  loss 0.7980  elapsed 0.62  load 0.95\n",
      "22207 14 08:36:42 INFO train : 1217  loss 0.8284  elapsed 0.64  load 0.00\n",
      "22207 14 08:36:43 INFO train : 1218  loss 0.8285  elapsed 0.61  load 0.11\n",
      "22207 14 08:36:43 INFO train : 1219  loss 0.7927  elapsed 0.57  load 0.00\n",
      "22207 14 08:36:45 INFO train : 1220  loss 0.8004  elapsed 0.57  load 0.98\n",
      "22207 14 08:36:46 INFO train : 1221  loss 0.8813  elapsed 0.59  load 0.01\n",
      "22207 14 08:36:46 INFO train : 1222  loss 0.7925  elapsed 0.55  load 0.05\n",
      "22207 14 08:36:47 INFO train : 1223  loss 0.8351  elapsed 0.51  load 0.00\n",
      "22207 14 08:36:48 INFO train : 1224  loss 0.8510  elapsed 0.56  load 0.95\n",
      "22207 14 08:36:49 INFO train : 1225  loss 0.7962  elapsed 0.63  load 0.07\n",
      "22207 14 08:36:50 INFO train : 1226  loss 0.7961  elapsed 0.59  load 0.00\n",
      "22207 14 08:36:50 INFO train : 1227  loss 0.7819  elapsed 0.60  load 0.00\n",
      "22207 14 08:36:52 INFO train : 1228  loss 0.8369  elapsed 0.62  load 0.97\n",
      "22207 14 08:36:52 INFO train : 1229  loss 0.8146  elapsed 0.57  load 0.07\n",
      "22207 14 08:36:53 INFO train : 1230  loss 0.8193  elapsed 0.61  load 0.04\n",
      "22207 14 08:36:54 INFO train : 1231  loss 0.7932  elapsed 0.58  load 0.00\n",
      "22207 14 08:36:55 INFO train : 1232  loss 0.8438  elapsed 0.54  load 1.05\n",
      "22207 14 08:36:56 INFO train : 1233  loss 0.8077  elapsed 0.62  load 0.03\n",
      "22207 14 08:36:57 INFO train : 1234  loss 0.8356  elapsed 0.66  load 0.06\n",
      "22207 14 08:36:57 INFO train : 1235  loss 0.8614  elapsed 0.65  load 0.00\n",
      "22207 14 08:36:59 INFO train : 1236  loss 0.8852  elapsed 0.67  load 1.34\n",
      "22207 14 08:37:00 INFO train : 1237  loss 0.8145  elapsed 0.68  load 0.00\n",
      "22207 14 08:37:01 INFO train : 1238  loss 0.8358  elapsed 0.61  load 0.04\n",
      "22207 14 08:37:01 INFO train : 1239  loss 0.7878  elapsed 0.60  load 0.00\n",
      "22207 14 08:37:03 INFO train : 1240  loss 0.7977  elapsed 0.66  load 1.12\n",
      "22207 14 08:37:04 INFO train : 1241  loss 0.8241  elapsed 0.61  load 0.00\n",
      "22207 14 08:37:04 INFO train : 1242  loss 0.8812  elapsed 0.64  load 0.02\n",
      "22207 14 08:37:05 INFO train : 1243  loss 0.8291  elapsed 0.62  load 0.01\n",
      "22207 14 08:37:07 INFO train : 1244  loss 0.8305  elapsed 0.57  load 0.94\n",
      "22207 14 08:37:07 INFO train : 1245  loss 0.7754  elapsed 0.62  load 0.07\n",
      "22207 14 08:37:08 INFO train : 1246  loss 0.8003  elapsed 0.60  load 0.00\n",
      "22207 14 08:37:09 INFO train : 1247  loss 0.7787  elapsed 0.71  load 0.03\n",
      "22207 14 08:37:10 INFO train : 1248  loss 0.8292  elapsed 0.63  load 0.96\n",
      "22207 14 08:37:11 INFO train : 1249  loss 0.8155  elapsed 0.63  load 0.05\n",
      "22207 14 08:37:12 INFO train : 1250  loss 0.7982  elapsed 0.66  load 0.00\n",
      "22207 14 08:37:12 INFO train : 1251  loss 0.7692  elapsed 0.63  load 0.01\n",
      "22207 14 08:37:14 INFO train : 1252  loss 0.8356  elapsed 0.56  load 0.58\n",
      "22207 14 08:37:14 INFO train : 1253  loss 0.8641  elapsed 0.63  load 0.03\n",
      "22207 14 08:37:15 INFO train : 1254  loss 0.8051  elapsed 0.57  load 0.00\n",
      "22207 14 08:37:15 INFO train : 1255  loss 0.8089  elapsed 0.58  load 0.05\n",
      "22207 14 08:37:17 INFO train : 1256  loss 0.8293  elapsed 0.58  load 1.17\n",
      "22207 14 08:37:18 INFO train : 1257  loss 0.8373  elapsed 0.59  load 0.55\n",
      "22207 14 08:37:19 INFO train : 1258  loss 0.8378  elapsed 0.63  load 0.00\n",
      "22207 14 08:37:20 INFO train : 1259  loss 0.8300  elapsed 0.62  load 0.00\n",
      "22207 14 08:37:21 INFO train : 1260  loss 0.8299  elapsed 0.62  load 0.44\n",
      "22207 14 08:37:22 INFO train : 1261  loss 0.8216  elapsed 0.62  load 0.50\n",
      "22207 14 08:37:23 INFO train : 1262  loss 0.8178  elapsed 0.56  load 0.00\n",
      "22207 14 08:37:23 INFO train : 1263  loss 0.8845  elapsed 0.52  load 0.00\n",
      "22207 14 08:37:25 INFO train : 1264  loss 0.8071  elapsed 0.61  load 0.89\n",
      "22207 14 08:37:25 INFO train : 1265  loss 0.8077  elapsed 0.63  load 0.04\n",
      "22207 14 08:37:26 INFO train : 1266  loss 0.8164  elapsed 0.61  load 0.00\n",
      "22207 14 08:37:27 INFO train : 1267  loss 0.8190  elapsed 0.70  load 0.00\n",
      "22207 14 08:37:28 INFO train : 1268  loss 0.8352  elapsed 0.57  load 0.70\n",
      "22207 14 08:37:29 INFO train : 1269  loss 0.8145  elapsed 0.50  load 0.23\n",
      "22207 14 08:37:29 INFO train : 1270  loss 0.7538  elapsed 0.57  load 0.00\n",
      "22207 14 08:37:30 INFO train : 1271  loss 0.8318  elapsed 0.55  load 0.00\n",
      "22207 14 08:37:32 INFO train : 1272  loss 0.8302  elapsed 0.60  load 1.46\n",
      "22207 14 08:37:32 INFO train : 1273  loss 0.7776  elapsed 0.58  load 0.01\n",
      "22207 14 08:37:33 INFO train : 1274  loss 0.8180  elapsed 0.62  load 0.00\n",
      "22207 14 08:37:34 INFO train : 1275  loss 0.7845  elapsed 0.68  load 0.01\n",
      "22207 14 08:37:35 INFO train : 1276  loss 0.7987  elapsed 0.52  load 0.75\n",
      "22207 14 08:37:36 INFO train : 1277  loss 0.8139  elapsed 0.57  load 0.13\n",
      "22207 14 08:37:36 INFO train : 1278  loss 0.8455  elapsed 0.62  load 0.00\n",
      "22207 14 08:37:37 INFO train : 1279  loss 0.7974  elapsed 0.65  load 0.08\n",
      "22207 14 08:37:39 INFO train : 1280  loss 0.8471  elapsed 0.63  load 1.09\n",
      "22207 14 08:37:40 INFO train : 1281  loss 0.8917  elapsed 0.56  load 0.13\n",
      "22207 14 08:37:40 INFO train : 1282  loss 0.8146  elapsed 0.64  load 0.00\n",
      "22207 14 08:37:41 INFO train : 1283  loss 0.7740  elapsed 0.57  load 0.01\n",
      "22207 14 08:37:42 INFO train : 1284  loss 0.8233  elapsed 0.67  load 0.81\n",
      "22207 14 08:37:43 INFO train : 1285  loss 0.8009  elapsed 0.56  load 0.16\n",
      "22207 14 08:37:44 INFO train : 1286  loss 0.8224  elapsed 0.64  load 0.00\n",
      "22207 14 08:37:44 INFO train : 1287  loss 0.8012  elapsed 0.57  load 0.04\n",
      "22207 14 08:37:46 INFO train : 1288  loss 0.8452  elapsed 0.57  load 0.84\n",
      "22207 14 08:37:47 INFO train : 1289  loss 0.8075  elapsed 0.59  load 0.24\n",
      "22207 14 08:37:47 INFO train : 1290  loss 0.8271  elapsed 0.55  load 0.00\n",
      "22207 14 08:37:48 INFO train : 1291  loss 0.8264  elapsed 0.64  load 0.04\n",
      "22207 14 08:37:49 INFO train : 1292  loss 0.7902  elapsed 0.65  load 0.76\n",
      "22207 14 08:37:50 INFO train : 1293  loss 0.8163  elapsed 0.52  load 0.53\n",
      "22207 14 08:37:51 INFO train : 1294  loss 0.7862  elapsed 0.56  load 0.00\n",
      "22207 14 08:37:52 INFO train : 1295  loss 0.8176  elapsed 0.68  load 0.02\n",
      "22207 14 08:37:53 INFO train : 1296  loss 0.8136  elapsed 0.58  load 0.24\n",
      "22207 14 08:37:54 INFO train : 1297  loss 0.7980  elapsed 0.53  load 1.24\n",
      "22207 14 08:37:55 INFO train : 1298  loss 0.8058  elapsed 0.58  load 0.00\n",
      "22207 14 08:37:56 INFO train : 1299  loss 0.8293  elapsed 0.59  load 0.04\n",
      "22207 14 08:37:56 INFO train : 1300  loss 0.8309  elapsed 0.63  load 0.00\n",
      "22207 14 08:37:58 INFO train : 1301  loss 0.7990  elapsed 0.59  load 0.81\n",
      "22207 14 08:37:58 INFO train : 1302  loss 0.8063  elapsed 0.68  load 0.00\n",
      "22207 14 08:37:59 INFO train : 1303  loss 0.7917  elapsed 0.59  load 0.01\n",
      "22207 14 08:38:00 INFO train : 1304  loss 0.8319  elapsed 0.60  load 0.53\n",
      "22207 14 08:38:02 INFO train : 1305  loss 0.8018  elapsed 0.69  load 0.67\n",
      "22207 14 08:38:02 INFO train : 1306  loss 0.8362  elapsed 0.62  load 0.00\n",
      "22207 14 08:38:03 INFO train : 1307  loss 0.8896  elapsed 0.61  load 0.04\n",
      "22207 14 08:38:04 INFO train : 1308  loss 0.8506  elapsed 0.55  load 0.80\n",
      "22207 14 08:38:05 INFO train : 1309  loss 0.8561  elapsed 0.58  load 0.26\n",
      "22207 14 08:38:06 INFO train : 1310  loss 0.8156  elapsed 0.66  load 0.00\n",
      "22207 14 08:38:07 INFO train : 1311  loss 0.8953  elapsed 0.55  load 0.08\n",
      "22207 14 08:38:08 INFO train : 1312  loss 0.7773  elapsed 0.57  load 0.71\n",
      "22207 14 08:38:08 INFO train : 1313  loss 0.8292  elapsed 0.57  load 0.05\n",
      "22207 14 08:38:09 INFO train : 1314  loss 0.8315  elapsed 0.60  load 0.00\n",
      "22207 14 08:38:10 INFO train : 1315  loss 0.8115  elapsed 0.59  load 0.29\n",
      "22207 14 08:38:11 INFO train : 1316  loss 0.7619  elapsed 0.62  load 0.67\n",
      "22207 14 08:38:12 INFO train : 1317  loss 0.7948  elapsed 0.59  load 0.08\n",
      "22207 14 08:38:13 INFO train : 1318  loss 0.8278  elapsed 0.56  load 0.00\n",
      "22207 14 08:38:13 INFO train : 1319  loss 0.7690  elapsed 0.53  load 0.32\n",
      "22207 14 08:38:15 INFO train : 1320  loss 0.8326  elapsed 0.63  load 0.71\n",
      "22207 14 08:38:15 INFO train : 1321  loss 0.8710  elapsed 0.55  load 0.01\n",
      "22207 14 08:38:16 INFO train : 1322  loss 0.7860  elapsed 0.62  load 0.00\n",
      "22207 14 08:38:17 INFO train : 1323  loss 0.7724  elapsed 0.55  load 0.21\n",
      "22207 14 08:38:18 INFO train : 1324  loss 0.7949  elapsed 0.59  load 0.59\n",
      "22207 14 08:38:19 INFO train : 1325  loss 0.7923  elapsed 0.61  load 0.39\n",
      "22207 14 08:38:20 INFO train : 1326  loss 0.8436  elapsed 0.50  load 0.00\n",
      "22207 14 08:38:20 INFO train : 1327  loss 0.7716  elapsed 0.55  load 0.27\n",
      "22207 14 08:38:22 INFO train : 1328  loss 0.8319  elapsed 0.65  load 0.45\n",
      "22207 14 08:38:23 INFO train : 1329  loss 0.7754  elapsed 0.60  load 0.65\n",
      "22207 14 08:38:23 INFO train : 1330  loss 0.8474  elapsed 0.54  load 0.00\n",
      "22207 14 08:38:24 INFO train : 1331  loss 0.7831  elapsed 0.64  load 0.05\n",
      "22207 14 08:38:25 INFO train : 1332  loss 0.8210  elapsed 0.54  load 0.21\n",
      "22207 14 08:38:26 INFO train : 1333  loss 0.8128  elapsed 0.59  load 0.62\n",
      "22207 14 08:38:27 INFO train : 1334  loss 0.8053  elapsed 0.64  load 0.00\n",
      "22207 14 08:38:27 INFO train : 1335  loss 0.8144  elapsed 0.57  load 0.02\n",
      "22207 14 08:38:28 INFO train : 1336  loss 0.7803  elapsed 0.62  load 0.32\n",
      "22207 14 08:38:29 INFO train : 1337  loss 0.7934  elapsed 0.64  load 0.47\n",
      "22207 14 08:38:30 INFO train : 1338  loss 0.7890  elapsed 0.67  load 0.00\n",
      "22207 14 08:38:31 INFO train : 1339  loss 0.7985  elapsed 0.59  load 0.07\n",
      "22207 14 08:38:32 INFO train : 1340  loss 0.8003  elapsed 0.62  load 0.96\n",
      "22207 14 08:38:33 INFO train : 1341  loss 0.8254  elapsed 0.53  load 0.07\n",
      "22207 14 08:38:34 INFO train : 1342  loss 0.8017  elapsed 0.62  load 0.00\n",
      "22207 14 08:38:34 INFO train : 1343  loss 0.8271  elapsed 0.66  load 0.01\n",
      "22207 14 08:38:36 INFO train : 1344  loss 0.7886  elapsed 0.62  load 0.75\n",
      "22207 14 08:38:36 INFO train : 1345  loss 0.7988  elapsed 0.61  load 0.01\n",
      "22207 14 08:38:37 INFO train : 1346  loss 0.8142  elapsed 0.53  load 0.00\n",
      "22207 14 08:38:38 INFO train : 1347  loss 0.7724  elapsed 0.60  load 0.16\n",
      "22207 14 08:38:39 INFO train : 1348  loss 0.7962  elapsed 0.62  load 0.91\n",
      "22207 14 08:38:40 INFO train : 1349  loss 0.7616  elapsed 0.58  load 0.17\n",
      "22207 14 08:38:41 INFO train : 1350  loss 0.8055  elapsed 0.57  load 0.00\n",
      "22207 14 08:38:41 INFO train : 1351  loss 0.8310  elapsed 0.56  load 0.04\n",
      "22207 14 08:38:43 INFO train : 1352  loss 0.7874  elapsed 0.54  load 0.96\n",
      "22207 14 08:38:44 INFO train : 1353  loss 0.7717  elapsed 0.54  load 0.44\n",
      "22207 14 08:38:44 INFO train : 1354  loss 0.8105  elapsed 0.64  load 0.00\n",
      "22207 14 08:38:45 INFO train : 1355  loss 0.8069  elapsed 0.66  load 0.01\n",
      "22207 14 08:38:46 INFO train : 1356  loss 0.7590  elapsed 0.61  load 0.32\n",
      "22207 14 08:38:48 INFO train : 1357  loss 0.8224  elapsed 0.63  load 0.70\n",
      "22207 14 08:38:48 INFO train : 1358  loss 0.7868  elapsed 0.61  load 0.00\n",
      "22207 14 08:38:49 INFO train : 1359  loss 0.7803  elapsed 0.62  load 0.03\n",
      "22207 14 08:38:50 INFO train : 1360  loss 0.7600  elapsed 0.57  load 0.17\n",
      "22207 14 08:38:51 INFO train : 1361  loss 0.7985  elapsed 0.62  load 1.08\n",
      "22207 14 08:38:52 INFO train : 1362  loss 0.7691  elapsed 0.57  load 0.01\n",
      "22207 14 08:38:53 INFO train : 1363  loss 0.8384  elapsed 0.57  load 0.01\n",
      "22207 14 08:38:53 INFO train : 1364  loss 0.8434  elapsed 0.62  load 0.04\n",
      "22207 14 08:38:55 INFO train : 1365  loss 0.8216  elapsed 0.59  load 1.37\n",
      "22207 14 08:38:56 INFO train : 1366  loss 0.8134  elapsed 0.57  load 0.00\n",
      "22207 14 08:38:56 INFO train : 1367  loss 0.7690  elapsed 0.57  load 0.00\n",
      "22207 14 08:38:57 INFO train : 1368  loss 0.8297  elapsed 0.62  load 0.08\n",
      "22207 14 08:38:58 INFO train : 1369  loss 0.8124  elapsed 0.55  load 0.56\n",
      "22207 14 08:38:59 INFO train : 1370  loss 0.7998  elapsed 0.59  load 0.00\n",
      "22207 14 08:39:00 INFO train : 1371  loss 0.8862  elapsed 0.65  load 0.00\n",
      "22207 14 08:39:00 INFO train : 1372  loss 0.8143  elapsed 0.63  load 0.02\n",
      "22207 14 08:39:02 INFO train : 1373  loss 0.8017  elapsed 0.57  load 1.30\n",
      "22207 14 08:39:03 INFO train : 1374  loss 0.7726  elapsed 0.65  load 0.04\n",
      "22207 14 08:39:03 INFO train : 1375  loss 0.8208  elapsed 0.63  load 0.00\n",
      "22207 14 08:39:04 INFO train : 1376  loss 0.7905  elapsed 0.58  load 0.10\n",
      "22207 14 08:39:05 INFO train : 1377  loss 0.7829  elapsed 0.62  load 0.52\n",
      "22207 14 08:39:06 INFO train : 1378  loss 0.8090  elapsed 0.61  load 0.00\n",
      "22207 14 08:39:07 INFO train : 1379  loss 0.8323  elapsed 0.64  load 0.00\n",
      "22207 14 08:39:07 INFO train : 1380  loss 0.8146  elapsed 0.62  load 0.01\n",
      "22207 14 08:39:09 INFO train : 1381  loss 0.8147  elapsed 0.61  load 1.15\n",
      "22207 14 08:39:10 INFO train : 1382  loss 0.8052  elapsed 0.70  load 0.00\n",
      "22207 14 08:39:10 INFO train : 1383  loss 0.7783  elapsed 0.54  load 0.00\n",
      "22207 14 08:39:11 INFO train : 1384  loss 0.8375  elapsed 0.64  load 0.12\n",
      "22207 14 08:39:13 INFO train : 1385  loss 0.7845  elapsed 0.62  load 0.78\n",
      "22207 14 08:39:13 INFO train : 1386  loss 0.8573  elapsed 0.60  load 0.00\n",
      "22207 14 08:39:14 INFO train : 1387  loss 0.8250  elapsed 0.55  load 0.00\n",
      "22207 14 08:39:14 INFO train : 1388  loss 0.7687  elapsed 0.61  load 0.03\n",
      "22207 14 08:39:16 INFO train : 1389  loss 0.7915  elapsed 0.57  load 0.86\n",
      "22207 14 08:39:16 INFO train : 1390  loss 0.8171  elapsed 0.58  load 0.00\n",
      "22207 14 08:39:17 INFO train : 1391  loss 0.7908  elapsed 0.60  load 0.00\n",
      "22207 14 08:39:18 INFO train : 1392  loss 0.7754  elapsed 0.61  load 0.14\n",
      "22207 14 08:39:20 INFO train : 1393  loss 0.8327  elapsed 0.56  load 1.24\n",
      "22207 14 08:39:20 INFO train : 1394  loss 0.9403  elapsed 0.60  load 0.00\n",
      "22207 14 08:39:21 INFO train : 1395  loss 0.8269  elapsed 0.63  load 0.00\n",
      "22207 14 08:39:22 INFO train : 1396  loss 0.8289  elapsed 0.64  load 0.04\n",
      "22207 14 08:39:23 INFO train : 1397  loss 0.7812  elapsed 0.71  load 1.00\n",
      "22207 14 08:39:24 INFO train : 1398  loss 0.8382  elapsed 0.70  load 0.00\n",
      "22207 14 08:39:25 INFO train : 1399  loss 0.7973  elapsed 0.61  load 0.01\n",
      "22207 14 08:39:25 INFO train : 1400  loss 0.7868  elapsed 0.60  load 0.06\n",
      "22207 14 08:39:27 INFO train : 1401  loss 0.7870  elapsed 0.57  load 0.82\n",
      "22207 14 08:39:27 INFO train : 1402  loss 0.7695  elapsed 0.49  load 0.00\n",
      "22207 14 08:39:28 INFO train : 1403  loss 0.8076  elapsed 0.64  load 0.00\n",
      "22207 14 08:39:29 INFO train : 1404  loss 0.8416  elapsed 0.51  load 0.27\n",
      "22207 14 08:39:30 INFO train : 1405  loss 0.8304  elapsed 0.63  load 1.01\n",
      "22207 14 08:39:31 INFO train : 1406  loss 0.8138  elapsed 0.65  load 0.00\n",
      "22207 14 08:39:32 INFO train : 1407  loss 0.7612  elapsed 0.59  load 0.00\n",
      "22207 14 08:39:32 INFO train : 1408  loss 0.7459  elapsed 0.65  load 0.04\n",
      "22207 14 08:39:34 INFO train : 1409  loss 0.8042  elapsed 0.56  load 0.80\n",
      "22207 14 08:39:34 INFO train : 1410  loss 0.7699  elapsed 0.58  load 0.00\n",
      "22207 14 08:39:35 INFO train : 1411  loss 0.8031  elapsed 0.64  load 0.00\n",
      "22207 14 08:39:36 INFO train : 1412  loss 0.8544  elapsed 0.59  load 0.13\n",
      "22207 14 08:39:37 INFO train : 1413  loss 0.7589  elapsed 0.62  load 1.04\n",
      "22207 14 08:39:38 INFO train : 1414  loss 0.7833  elapsed 0.64  load 0.00\n",
      "22207 14 08:39:39 INFO train : 1415  loss 0.8021  elapsed 0.61  load 0.00\n",
      "22207 14 08:39:39 INFO train : 1416  loss 0.8223  elapsed 0.58  load 0.08\n",
      "22207 14 08:39:41 INFO train : 1417  loss 0.7786  elapsed 0.55  load 0.64\n",
      "22207 14 08:39:41 INFO train : 1418  loss 0.7911  elapsed 0.66  load 0.00\n",
      "22207 14 08:39:42 INFO train : 1419  loss 0.8289  elapsed 0.58  load 0.00\n",
      "22207 14 08:39:43 INFO train : 1420  loss 0.8002  elapsed 0.64  load 0.03\n",
      "22207 14 08:39:44 INFO train : 1421  loss 0.8035  elapsed 0.58  load 1.08\n",
      "22207 14 08:39:45 INFO train : 1422  loss 0.8005  elapsed 0.65  load 0.00\n",
      "22207 14 08:39:46 INFO train : 1423  loss 0.8094  elapsed 0.68  load 0.00\n",
      "22207 14 08:39:46 INFO train : 1424  loss 0.7630  elapsed 0.59  load 0.13\n",
      "22207 14 08:39:48 INFO train : 1425  loss 0.7785  elapsed 0.64  load 0.64\n",
      "22207 14 08:39:48 INFO train : 1426  loss 0.7634  elapsed 0.67  load 0.00\n",
      "22207 14 08:39:49 INFO train : 1427  loss 0.7961  elapsed 0.65  load 0.02\n",
      "22207 14 08:39:50 INFO train : 1428  loss 0.7705  elapsed 0.56  load 0.23\n",
      "22207 14 08:39:51 INFO train : 1429  loss 0.7620  elapsed 0.56  load 0.81\n",
      "22207 14 08:39:52 INFO train : 1430  loss 0.8815  elapsed 0.56  load 0.00\n",
      "22207 14 08:39:53 INFO train : 1431  loss 0.8040  elapsed 0.71  load 0.01\n",
      "22207 14 08:39:53 INFO train : 1432  loss 0.7860  elapsed 0.59  load 0.05\n",
      "22207 14 08:39:55 INFO train : 1433  loss 0.8385  elapsed 0.60  load 0.97\n",
      "22207 14 08:39:56 INFO train : 1434  loss 0.7488  elapsed 0.60  load 0.00\n",
      "22207 14 08:39:56 INFO train : 1435  loss 0.7685  elapsed 0.56  load 0.00\n",
      "22207 14 08:39:57 INFO train : 1436  loss 0.8312  elapsed 0.68  load 0.07\n",
      "22207 14 08:39:59 INFO train : 1437  loss 0.9154  elapsed 0.61  load 1.42\n",
      "22207 14 08:40:00 INFO train : 1438  loss 0.8317  elapsed 0.71  load 0.01\n",
      "22207 14 08:40:00 INFO train : 1439  loss 0.8020  elapsed 0.64  load 0.00\n",
      "22207 14 08:40:01 INFO train : 1440  loss 0.7960  elapsed 0.62  load 0.01\n",
      "22207 14 08:40:03 INFO train : 1441  loss 0.7577  elapsed 0.59  load 0.97\n",
      "22207 14 08:40:03 INFO train : 1442  loss 0.7713  elapsed 0.50  load 0.00\n",
      "22207 14 08:40:04 INFO train : 1443  loss 0.8001  elapsed 0.63  load 0.02\n",
      "22207 14 08:40:04 INFO train : 1444  loss 0.8418  elapsed 0.64  load 0.10\n",
      "22207 14 08:40:06 INFO train : 1445  loss 0.7737  elapsed 0.64  load 1.17\n",
      "22207 14 08:40:07 INFO train : 1446  loss 0.7834  elapsed 0.66  load 0.02\n",
      "22207 14 08:40:08 INFO train : 1447  loss 0.8455  elapsed 0.60  load 0.02\n",
      "22207 14 08:40:08 INFO train : 1448  loss 0.7939  elapsed 0.57  load 0.04\n",
      "22207 14 08:40:10 INFO train : 1449  loss 0.8046  elapsed 0.59  load 1.03\n",
      "22207 14 08:40:11 INFO train : 1450  loss 0.7972  elapsed 0.65  load 0.03\n",
      "22207 14 08:40:11 INFO train : 1451  loss 0.8247  elapsed 0.59  load 0.03\n",
      "22207 14 08:40:12 INFO train : 1452  loss 0.7570  elapsed 0.65  load 0.07\n",
      "22207 14 08:40:14 INFO train : 1453  loss 0.8311  elapsed 0.58  load 0.93\n",
      "22207 14 08:40:14 INFO train : 1454  loss 0.8333  elapsed 0.53  load 0.00\n",
      "22207 14 08:40:15 INFO train : 1455  loss 0.7903  elapsed 0.70  load 0.01\n",
      "22207 14 08:40:16 INFO train : 1456  loss 0.8017  elapsed 0.69  load 0.01\n",
      "22207 14 08:40:17 INFO train : 1457  loss 0.8124  elapsed 0.51  load 0.77\n",
      "22207 14 08:40:18 INFO train : 1458  loss 0.8110  elapsed 0.63  load 0.06\n",
      "22207 14 08:40:18 INFO train : 1459  loss 0.8274  elapsed 0.62  load 0.03\n",
      "22207 14 08:40:19 INFO train : 1460  loss 0.7486  elapsed 0.66  load 0.00\n",
      "22207 14 08:40:20 INFO train : 1461  loss 0.7867  elapsed 0.49  load 0.68\n",
      "22207 14 08:40:21 INFO train : 1462  loss 0.7723  elapsed 0.56  load 0.00\n",
      "22207 14 08:40:21 INFO train : 1463  loss 0.7479  elapsed 0.61  load 0.02\n",
      "22207 14 08:40:22 INFO train : 1464  loss 0.8096  elapsed 0.55  load 0.04\n",
      "22207 14 08:40:24 INFO train : 1465  loss 0.7899  elapsed 0.56  load 1.27\n",
      "22207 14 08:40:24 INFO train : 1466  loss 0.7847  elapsed 0.63  load 0.00\n",
      "22207 14 08:40:25 INFO train : 1467  loss 0.7873  elapsed 0.57  load 0.07\n",
      "22207 14 08:40:26 INFO train : 1468  loss 0.7736  elapsed 0.67  load 0.00\n",
      "22207 14 08:40:27 INFO train : 1469  loss 0.8595  elapsed 0.57  load 0.51\n",
      "22207 14 08:40:27 INFO train : 1470  loss 0.8650  elapsed 0.55  load 0.00\n",
      "22207 14 08:40:28 INFO train : 1471  loss 0.7800  elapsed 0.58  load 0.04\n",
      "22207 14 08:40:29 INFO train : 1472  loss 0.7598  elapsed 0.58  load 0.00\n",
      "22207 14 08:40:30 INFO train : 1473  loss 0.8151  elapsed 0.54  load 0.99\n",
      "22207 14 08:40:31 INFO train : 1474  loss 0.8025  elapsed 0.52  load 0.00\n",
      "22207 14 08:40:32 INFO train : 1475  loss 0.7996  elapsed 0.68  load 0.00\n",
      "22207 14 08:40:32 INFO train : 1476  loss 0.8342  elapsed 0.60  load 0.05\n",
      "22207 14 08:40:34 INFO train : 1477  loss 0.8509  elapsed 0.55  load 1.57\n",
      "22207 14 08:40:35 INFO train : 1478  loss 0.8035  elapsed 0.66  load 0.00\n",
      "22207 14 08:40:36 INFO train : 1479  loss 0.8958  elapsed 0.66  load 0.01\n",
      "22207 14 08:40:36 INFO train : 1480  loss 0.8076  elapsed 0.62  load 0.03\n",
      "22207 14 08:40:38 INFO train : 1481  loss 0.7528  elapsed 0.63  load 0.94\n",
      "22207 14 08:40:39 INFO train : 1482  loss 0.7895  elapsed 0.64  load 0.00\n",
      "22207 14 08:40:39 INFO train : 1483  loss 0.7980  elapsed 0.61  load 0.04\n",
      "22207 14 08:40:40 INFO train : 1484  loss 0.7679  elapsed 0.59  load 0.14\n",
      "22207 14 08:40:41 INFO train : 1485  loss 0.8368  elapsed 0.56  load 0.79\n",
      "22207 14 08:40:42 INFO train : 1486  loss 0.7792  elapsed 0.65  load 0.00\n",
      "22207 14 08:40:43 INFO train : 1487  loss 0.8224  elapsed 0.63  load 0.00\n",
      "22207 14 08:40:44 INFO train : 1488  loss 0.8089  elapsed 0.58  load 0.12\n",
      "22207 14 08:40:45 INFO train : 1489  loss 0.7643  elapsed 0.60  load 1.13\n",
      "22207 14 08:40:46 INFO train : 1490  loss 0.8226  elapsed 0.61  load 0.00\n",
      "22207 14 08:40:47 INFO train : 1491  loss 0.7563  elapsed 0.64  load 0.00\n",
      "22207 14 08:40:47 INFO train : 1492  loss 0.7771  elapsed 0.59  load 0.05\n",
      "22207 14 08:40:48 INFO train : 1493  loss 0.8628  elapsed 0.57  load 0.30\n",
      "22207 14 08:40:49 INFO train : 1494  loss 0.7950  elapsed 0.63  load 0.00\n",
      "22207 14 08:40:50 INFO train : 1495  loss 0.8039  elapsed 0.64  load 0.05\n",
      "22207 14 08:40:50 INFO train : 1496  loss 0.7925  elapsed 0.57  load 0.07\n",
      "22207 14 08:40:52 INFO train : 1497  loss 0.8219  elapsed 0.63  load 0.76\n",
      "22207 14 08:40:52 INFO train : 1498  loss 0.8185  elapsed 0.54  load 0.00\n",
      "22207 14 08:40:53 INFO train : 1499  loss 0.7564  elapsed 0.62  load 0.02\n",
      "22207 14 08:40:53 INFO train : 1500  loss 0.7815  elapsed 0.61  load 0.01\n",
      "22207 14 08:40:53 INFO Running validation\n",
      "22207 14 08:41:15 INFO Logging 20 images\n",
      "22207 14 08:41:16 INFO Logging 20 audios\n",
      "22207 14 08:41:19 INFO Logging 20 texts\n",
      "22207 14 08:41:30 INFO val : 1500  loss 0.7064799666404724\n",
      "22207 14 08:41:30 INFO Saving model state at step 1500 to checkpoints/fp_step_00001500.pt\n",
      "22207 14 08:41:31 INFO train : 1501  loss 0.7708  elapsed 0.52  load 0.00\n",
      "22207 14 08:41:31 INFO train : 1502  loss 0.7702  elapsed 0.52  load 0.00\n",
      "22207 14 08:41:32 INFO train : 1503  loss 0.7794  elapsed 0.53  load 0.01\n",
      "22207 14 08:41:33 INFO train : 1504  loss 0.8370  elapsed 0.65  load 0.07\n",
      "22207 14 08:41:33 INFO train : 1505  loss 0.7519  elapsed 0.57  load 0.01\n",
      "22207 14 08:41:34 INFO train : 1506  loss 0.7691  elapsed 0.65  load 0.00\n",
      "22207 14 08:41:35 INFO train : 1507  loss 0.7806  elapsed 0.65  load 0.01\n",
      "22207 14 08:41:35 INFO train : 1508  loss 0.8467  elapsed 0.55  load 0.00\n",
      "22207 14 08:41:36 INFO train : 1509  loss 0.8339  elapsed 0.54  load 0.00\n",
      "22207 14 08:41:36 INFO train : 1510  loss 0.7534  elapsed 0.58  load 0.04\n",
      "22207 14 08:41:37 INFO train : 1511  loss 0.7876  elapsed 0.51  load 0.10\n",
      "22207 14 08:41:38 INFO train : 1512  loss 0.7712  elapsed 0.59  load 0.02\n",
      "22207 14 08:41:38 INFO train : 1513  loss 0.8103  elapsed 0.56  load 0.05\n",
      "22207 14 08:41:39 INFO train : 1514  loss 0.8125  elapsed 0.60  load 0.00\n",
      "22207 14 08:41:40 INFO train : 1515  loss 0.7742  elapsed 0.59  load 0.01\n",
      "22207 14 08:41:40 INFO train : 1516  loss 0.7451  elapsed 0.64  load 0.00\n",
      "22207 14 08:41:41 INFO train : 1517  loss 0.7633  elapsed 0.61  load 0.06\n",
      "22207 14 08:41:42 INFO train : 1518  loss 0.8231  elapsed 0.56  load 0.13\n",
      "22207 14 08:41:42 INFO train : 1519  loss 0.7992  elapsed 0.62  load 0.32\n",
      "22207 14 08:41:43 INFO train : 1520  loss 0.8047  elapsed 0.63  load 0.03\n",
      "22207 14 08:41:44 INFO train : 1521  loss 0.8075  elapsed 0.60  load 0.16\n",
      "22207 14 08:41:45 INFO train : 1522  loss 0.7743  elapsed 0.65  load 0.54\n",
      "22207 14 08:41:46 INFO train : 1523  loss 0.7684  elapsed 0.61  load 0.07\n",
      "22207 14 08:41:46 INFO train : 1524  loss 0.7837  elapsed 0.59  load 0.00\n",
      "22207 14 08:41:48 INFO train : 1525  loss 0.8119  elapsed 0.59  load 0.76\n",
      "22207 14 08:41:48 INFO train : 1526  loss 0.8185  elapsed 0.59  load 0.01\n",
      "22207 14 08:41:49 INFO train : 1527  loss 0.7886  elapsed 0.70  load 0.00\n",
      "22207 14 08:41:50 INFO train : 1528  loss 0.9084  elapsed 0.58  load 0.08\n",
      "22207 14 08:41:51 INFO train : 1529  loss 0.7866  elapsed 0.62  load 0.94\n",
      "22207 14 08:41:52 INFO train : 1530  loss 0.7920  elapsed 0.58  load 0.15\n",
      "22207 14 08:41:53 INFO train : 1531  loss 0.7733  elapsed 0.69  load 0.02\n",
      "22207 14 08:41:54 INFO train : 1532  loss 0.8456  elapsed 0.62  load 0.00\n",
      "22207 14 08:41:55 INFO train : 1533  loss 0.7394  elapsed 0.61  load 1.16\n",
      "22207 14 08:41:56 INFO train : 1534  loss 0.8229  elapsed 0.56  load 0.08\n",
      "22207 14 08:41:57 INFO train : 1535  loss 0.8247  elapsed 0.65  load 0.00\n",
      "22207 14 08:41:57 INFO train : 1536  loss 0.7602  elapsed 0.61  load 0.02\n",
      "22207 14 08:41:59 INFO train : 1537  loss 0.7802  elapsed 0.57  load 1.11\n",
      "22207 14 08:42:00 INFO train : 1538  loss 0.7524  elapsed 0.58  load 0.00\n",
      "22207 14 08:42:00 INFO train : 1539  loss 0.7826  elapsed 0.63  load 0.01\n",
      "22207 14 08:42:01 INFO train : 1540  loss 0.7775  elapsed 0.60  load 0.03\n",
      "22207 14 08:42:03 INFO train : 1541  loss 0.7856  elapsed 0.60  load 1.28\n",
      "22207 14 08:42:04 INFO train : 1542  loss 0.7972  elapsed 0.65  load 0.04\n",
      "22207 14 08:42:04 INFO train : 1543  loss 0.7978  elapsed 0.61  load 0.06\n",
      "22207 14 08:42:05 INFO train : 1544  loss 0.8085  elapsed 0.66  load 0.00\n",
      "22207 14 08:42:06 INFO train : 1545  loss 0.7945  elapsed 0.50  load 0.84\n",
      "22207 14 08:42:07 INFO train : 1546  loss 0.7945  elapsed 0.63  load 0.01\n",
      "22207 14 08:42:08 INFO train : 1547  loss 0.8011  elapsed 0.65  load 0.04\n",
      "22207 14 08:42:08 INFO train : 1548  loss 0.8443  elapsed 0.71  load 0.00\n",
      "22207 14 08:42:10 INFO train : 1549  loss 0.7262  elapsed 0.62  load 0.54\n",
      "22207 14 08:42:10 INFO train : 1550  loss 0.7619  elapsed 0.63  load 0.04\n",
      "22207 14 08:42:11 INFO train : 1551  loss 0.8003  elapsed 0.66  load 0.00\n",
      "22207 14 08:42:12 INFO train : 1552  loss 0.7925  elapsed 0.55  load 0.01\n",
      "22207 14 08:42:13 INFO train : 1553  loss 0.7538  elapsed 0.65  load 0.92\n",
      "22207 14 08:42:14 INFO train : 1554  loss 0.7889  elapsed 0.60  load 0.02\n",
      "22207 14 08:42:15 INFO train : 1555  loss 0.7313  elapsed 0.61  load 0.12\n",
      "22207 14 08:42:15 INFO train : 1556  loss 0.8062  elapsed 0.64  load 0.00\n",
      "22207 14 08:42:17 INFO train : 1557  loss 0.8223  elapsed 0.52  load 0.83\n",
      "22207 14 08:42:17 INFO train : 1558  loss 0.8615  elapsed 0.59  load 0.10\n",
      "22207 14 08:42:18 INFO train : 1559  loss 0.7354  elapsed 0.68  load 0.01\n",
      "22207 14 08:42:19 INFO train : 1560  loss 0.7770  elapsed 0.61  load 0.06\n",
      "22207 14 08:42:20 INFO train : 1561  loss 0.7104  elapsed 0.53  load 0.76\n",
      "22207 14 08:42:21 INFO train : 1562  loss 0.7831  elapsed 0.64  load 0.43\n",
      "22207 14 08:42:22 INFO train : 1563  loss 0.7610  elapsed 0.65  load 0.00\n",
      "22207 14 08:42:22 INFO train : 1564  loss 0.7793  elapsed 0.60  load 0.04\n",
      "22207 14 08:42:24 INFO train : 1565  loss 0.7946  elapsed 0.66  load 0.88\n",
      "22207 14 08:42:25 INFO train : 1566  loss 0.7912  elapsed 0.58  load 0.07\n",
      "22207 14 08:42:25 INFO train : 1567  loss 0.7516  elapsed 0.56  load 0.00\n",
      "22207 14 08:42:26 INFO train : 1568  loss 0.7579  elapsed 0.67  load 0.00\n",
      "22207 14 08:42:28 INFO train : 1569  loss 0.7588  elapsed 0.63  load 1.04\n",
      "22207 14 08:42:29 INFO train : 1570  loss 0.8209  elapsed 0.61  load 0.32\n",
      "22207 14 08:42:29 INFO train : 1571  loss 0.7800  elapsed 0.61  load 0.00\n",
      "22207 14 08:42:30 INFO train : 1572  loss 0.7363  elapsed 0.66  load 0.00\n",
      "22207 14 08:42:31 INFO train : 1573  loss 0.8116  elapsed 0.65  load 0.30\n",
      "22207 14 08:42:32 INFO train : 1574  loss 0.7455  elapsed 0.63  load 0.46\n",
      "22207 14 08:42:33 INFO train : 1575  loss 0.8512  elapsed 0.59  load 0.01\n",
      "22207 14 08:42:33 INFO train : 1576  loss 0.7618  elapsed 0.58  load 0.00\n",
      "22207 14 08:42:34 INFO train : 1577  loss 0.7660  elapsed 0.57  load 0.61\n",
      "22207 14 08:42:35 INFO train : 1578  loss 0.7519  elapsed 0.64  load 0.34\n",
      "22207 14 08:42:36 INFO train : 1579  loss 0.7828  elapsed 0.59  load 0.00\n",
      "22207 14 08:42:37 INFO train : 1580  loss 0.7802  elapsed 0.65  load 0.00\n",
      "22207 14 08:42:38 INFO train : 1581  loss 0.7355  elapsed 0.56  load 0.83\n",
      "22207 14 08:42:39 INFO train : 1582  loss 0.7823  elapsed 0.57  load 0.45\n",
      "22207 14 08:42:40 INFO train : 1583  loss 0.7341  elapsed 0.70  load 0.00\n",
      "22207 14 08:42:40 INFO train : 1584  loss 0.7713  elapsed 0.64  load 0.00\n",
      "22207 14 08:42:42 INFO train : 1585  loss 0.7970  elapsed 0.57  load 0.80\n",
      "22207 14 08:42:43 INFO train : 1586  loss 0.7542  elapsed 0.55  load 0.08\n",
      "22207 14 08:42:43 INFO train : 1587  loss 0.7950  elapsed 0.56  load 0.00\n",
      "22207 14 08:42:44 INFO train : 1588  loss 0.8286  elapsed 0.60  load 0.00\n",
      "22207 14 08:42:45 INFO train : 1589  loss 0.8493  elapsed 0.56  load 0.95\n",
      "22207 14 08:42:46 INFO train : 1590  loss 0.8311  elapsed 0.62  load 0.24\n",
      "22207 14 08:42:47 INFO train : 1591  loss 0.7939  elapsed 0.61  load 0.02\n",
      "22207 14 08:42:47 INFO train : 1592  loss 0.7597  elapsed 0.60  load 0.00\n",
      "22207 14 08:42:49 INFO train : 1593  loss 0.7652  elapsed 0.56  load 0.76\n",
      "22207 14 08:42:50 INFO train : 1594  loss 0.8051  elapsed 0.56  load 0.35\n",
      "22207 14 08:42:50 INFO train : 1595  loss 0.7531  elapsed 0.67  load 0.00\n",
      "22207 14 08:42:51 INFO train : 1596  loss 0.8280  elapsed 0.57  load 0.09\n",
      "22207 14 08:42:53 INFO train : 1597  loss 0.8038  elapsed 0.68  load 0.83\n",
      "22207 14 08:42:53 INFO train : 1598  loss 0.7821  elapsed 0.59  load 0.15\n",
      "22207 14 08:42:54 INFO train : 1599  loss 0.7615  elapsed 0.61  load 0.00\n",
      "22207 14 08:42:55 INFO train : 1600  loss 0.8184  elapsed 0.57  load 0.00\n",
      "22207 14 08:42:55 INFO train : 1601  loss 0.7846  elapsed 0.50  load 0.04\n",
      "22207 14 08:42:56 INFO train : 1602  loss 0.7679  elapsed 0.50  load 0.00\n",
      "22207 14 08:42:56 INFO train : 1603  loss 0.7794  elapsed 0.48  load 0.00\n",
      "22207 14 08:42:56 INFO train : 1604  loss 0.7884  elapsed 0.13  load 0.00\n",
      "22207 14 08:42:56 INFO Epoch num: 4\n",
      "22207 14 08:43:01 INFO train : 1605  loss 0.7671  elapsed 0.59  load 3.99\n",
      "22207 14 08:43:02 INFO train : 1606  loss 0.7503  elapsed 0.63  load 0.00\n",
      "22207 14 08:43:02 INFO train : 1607  loss 0.7586  elapsed 0.58  load 0.00\n",
      "22207 14 08:43:03 INFO train : 1608  loss 0.7552  elapsed 0.53  load 0.01\n",
      "22207 14 08:43:05 INFO train : 1609  loss 0.7270  elapsed 0.68  load 1.53\n",
      "22207 14 08:43:06 INFO train : 1610  loss 0.7805  elapsed 0.63  load 0.04\n",
      "22207 14 08:43:06 INFO train : 1611  loss 0.7861  elapsed 0.57  load 0.00\n",
      "22207 14 08:43:07 INFO train : 1612  loss 0.7527  elapsed 0.65  load 0.00\n",
      "22207 14 08:43:09 INFO train : 1613  loss 0.7444  elapsed 0.60  load 1.55\n",
      "22207 14 08:43:10 INFO train : 1614  loss 0.7524  elapsed 0.57  load 0.00\n",
      "22207 14 08:43:10 INFO train : 1615  loss 0.7530  elapsed 0.66  load 0.00\n",
      "22207 14 08:43:11 INFO train : 1616  loss 0.7079  elapsed 0.59  load 0.00\n",
      "22207 14 08:43:12 INFO train : 1617  loss 0.7431  elapsed 0.57  load 0.95\n",
      "22207 14 08:43:13 INFO train : 1618  loss 0.7270  elapsed 0.58  load 0.00\n",
      "22207 14 08:43:14 INFO train : 1619  loss 0.7399  elapsed 0.59  load 0.00\n",
      "22207 14 08:43:14 INFO train : 1620  loss 0.8062  elapsed 0.52  load 0.00\n",
      "22207 14 08:43:16 INFO train : 1621  loss 0.7249  elapsed 0.58  load 1.30\n",
      "22207 14 08:43:17 INFO train : 1622  loss 0.7928  elapsed 0.60  load 0.00\n",
      "22207 14 08:43:17 INFO train : 1623  loss 0.7364  elapsed 0.69  load 0.00\n",
      "22207 14 08:43:18 INFO train : 1624  loss 0.7461  elapsed 0.57  load 0.00\n",
      "22207 14 08:43:19 INFO train : 1625  loss 0.7316  elapsed 0.68  load 0.77\n",
      "22207 14 08:43:20 INFO train : 1626  loss 0.7622  elapsed 0.64  load 0.00\n",
      "22207 14 08:43:21 INFO train : 1627  loss 0.7747  elapsed 0.66  load 0.00\n",
      "22207 14 08:43:21 INFO train : 1628  loss 0.7703  elapsed 0.58  load 0.00\n",
      "22207 14 08:43:23 INFO train : 1629  loss 0.7660  elapsed 0.68  load 1.00\n",
      "22207 14 08:43:24 INFO train : 1630  loss 0.7344  elapsed 0.60  load 0.01\n",
      "22207 14 08:43:24 INFO train : 1631  loss 0.7600  elapsed 0.60  load 0.00\n",
      "22207 14 08:43:25 INFO train : 1632  loss 0.7596  elapsed 0.61  load 0.00\n",
      "22207 14 08:43:27 INFO train : 1633  loss 0.7386  elapsed 0.54  load 0.96\n",
      "22207 14 08:43:27 INFO train : 1634  loss 0.7443  elapsed 0.55  load 0.00\n",
      "22207 14 08:43:28 INFO train : 1635  loss 0.6882  elapsed 0.61  load 0.00\n",
      "22207 14 08:43:28 INFO train : 1636  loss 0.7794  elapsed 0.58  load 0.00\n",
      "22207 14 08:43:31 INFO train : 1637  loss 0.7397  elapsed 0.59  load 1.53\n",
      "22207 14 08:43:31 INFO train : 1638  loss 0.7657  elapsed 0.60  load 0.00\n",
      "22207 14 08:43:32 INFO train : 1639  loss 0.7251  elapsed 0.58  load 0.00\n",
      "22207 14 08:43:32 INFO train : 1640  loss 0.7379  elapsed 0.60  load 0.00\n",
      "22207 14 08:43:34 INFO train : 1641  loss 0.8060  elapsed 0.55  load 1.23\n",
      "22207 14 08:43:35 INFO train : 1642  loss 0.7366  elapsed 0.53  load 0.00\n",
      "22207 14 08:43:35 INFO train : 1643  loss 0.7271  elapsed 0.56  load 0.00\n",
      "22207 14 08:43:36 INFO train : 1644  loss 0.7937  elapsed 0.66  load 0.00\n",
      "22207 14 08:43:37 INFO train : 1645  loss 0.6853  elapsed 0.57  load 0.82\n",
      "22207 14 08:43:38 INFO train : 1646  loss 0.7998  elapsed 0.59  load 0.00\n",
      "22207 14 08:43:39 INFO train : 1647  loss 0.7560  elapsed 0.62  load 0.01\n",
      "22207 14 08:43:39 INFO train : 1648  loss 0.7942  elapsed 0.59  load 0.00\n",
      "22207 14 08:43:41 INFO train : 1649  loss 0.7814  elapsed 0.58  load 0.87\n",
      "22207 14 08:43:41 INFO train : 1650  loss 0.7423  elapsed 0.56  load 0.00\n",
      "22207 14 08:43:42 INFO train : 1651  loss 0.7550  elapsed 0.55  load 0.00\n",
      "22207 14 08:43:43 INFO train : 1652  loss 0.7454  elapsed 0.58  load 0.00\n",
      "22207 14 08:43:44 INFO train : 1653  loss 0.7572  elapsed 0.63  load 1.29\n",
      "22207 14 08:43:45 INFO train : 1654  loss 0.7375  elapsed 0.60  load 0.07\n",
      "22207 14 08:43:46 INFO train : 1655  loss 0.7383  elapsed 0.63  load 0.00\n",
      "22207 14 08:43:47 INFO train : 1656  loss 0.7358  elapsed 0.65  load 0.00\n",
      "22207 14 08:43:48 INFO train : 1657  loss 0.8232  elapsed 0.65  load 0.71\n",
      "22207 14 08:43:49 INFO train : 1658  loss 0.7368  elapsed 0.66  load 0.05\n",
      "22207 14 08:43:49 INFO train : 1659  loss 0.7288  elapsed 0.60  load 0.03\n",
      "22207 14 08:43:50 INFO train : 1660  loss 0.7284  elapsed 0.56  load 0.01\n",
      "22207 14 08:43:52 INFO train : 1661  loss 0.7336  elapsed 0.58  load 1.23\n",
      "22207 14 08:43:52 INFO train : 1662  loss 0.7586  elapsed 0.66  load 0.00\n",
      "22207 14 08:43:53 INFO train : 1663  loss 0.7384  elapsed 0.67  load 0.01\n",
      "22207 14 08:43:54 INFO train : 1664  loss 0.7314  elapsed 0.71  load 0.00\n",
      "22207 14 08:43:55 INFO train : 1665  loss 0.7178  elapsed 0.65  load 1.07\n",
      "22207 14 08:43:56 INFO train : 1666  loss 0.7735  elapsed 0.63  load 0.01\n",
      "22207 14 08:43:57 INFO train : 1667  loss 0.7872  elapsed 0.56  load 0.00\n",
      "22207 14 08:43:57 INFO train : 1668  loss 0.7488  elapsed 0.59  load 0.00\n",
      "22207 14 08:43:59 INFO train : 1669  loss 0.7484  elapsed 0.52  load 0.92\n",
      "22207 14 08:44:00 INFO train : 1670  loss 0.8241  elapsed 0.63  load 0.00\n",
      "22207 14 08:44:00 INFO train : 1671  loss 0.7968  elapsed 0.54  load 0.00\n",
      "22207 14 08:44:01 INFO train : 1672  loss 0.7964  elapsed 0.58  load 0.00\n",
      "22207 14 08:44:03 INFO train : 1673  loss 0.7519  elapsed 0.61  load 1.39\n",
      "22207 14 08:44:03 INFO train : 1674  loss 0.7937  elapsed 0.63  load 0.00\n",
      "22207 14 08:44:04 INFO train : 1675  loss 0.7827  elapsed 0.62  load 0.00\n",
      "22207 14 08:44:05 INFO train : 1676  loss 0.7250  elapsed 0.60  load 0.10\n",
      "22207 14 08:44:07 INFO train : 1677  loss 0.7530  elapsed 0.53  load 1.41\n",
      "22207 14 08:44:07 INFO train : 1678  loss 0.7487  elapsed 0.63  load 0.00\n",
      "22207 14 08:44:08 INFO train : 1679  loss 0.7878  elapsed 0.61  load 0.00\n",
      "22207 14 08:44:09 INFO train : 1680  loss 0.7596  elapsed 0.66  load 0.00\n",
      "22207 14 08:44:10 INFO train : 1681  loss 0.7500  elapsed 0.62  load 1.12\n",
      "22207 14 08:44:11 INFO train : 1682  loss 0.7523  elapsed 0.62  load 0.00\n",
      "22207 14 08:44:12 INFO train : 1683  loss 0.7615  elapsed 0.62  load 0.00\n",
      "22207 14 08:44:12 INFO train : 1684  loss 0.7772  elapsed 0.62  load 0.00\n",
      "22207 14 08:44:14 INFO train : 1685  loss 0.7427  elapsed 0.54  load 0.91\n",
      "22207 14 08:44:14 INFO train : 1686  loss 0.7547  elapsed 0.52  load 0.00\n",
      "22207 14 08:44:15 INFO train : 1687  loss 0.7684  elapsed 0.58  load 0.04\n",
      "22207 14 08:44:16 INFO train : 1688  loss 0.7213  elapsed 0.54  load 0.00\n",
      "22207 14 08:44:18 INFO train : 1689  loss 0.7539  elapsed 0.57  load 1.24\n",
      "22207 14 08:44:18 INFO train : 1690  loss 0.7284  elapsed 0.55  load 0.00\n",
      "22207 14 08:44:19 INFO train : 1691  loss 0.7258  elapsed 0.56  load 0.00\n",
      "22207 14 08:44:19 INFO train : 1692  loss 0.7403  elapsed 0.73  load 0.00\n",
      "22207 14 08:44:21 INFO train : 1693  loss 0.7450  elapsed 0.65  load 1.11\n",
      "22207 14 08:44:22 INFO train : 1694  loss 0.7338  elapsed 0.64  load 0.00\n",
      "22207 14 08:44:22 INFO train : 1695  loss 0.7388  elapsed 0.58  load 0.00\n",
      "22207 14 08:44:23 INFO train : 1696  loss 0.7695  elapsed 0.58  load 0.00\n",
      "22207 14 08:44:25 INFO train : 1697  loss 0.7386  elapsed 0.58  load 0.97\n",
      "22207 14 08:44:25 INFO train : 1698  loss 0.7562  elapsed 0.58  load 0.00\n",
      "22207 14 08:44:26 INFO train : 1699  loss 0.7570  elapsed 0.67  load 0.00\n",
      "22207 14 08:44:27 INFO train : 1700  loss 0.7214  elapsed 0.62  load 0.00\n",
      "22207 14 08:44:28 INFO train : 1701  loss 0.7499  elapsed 0.56  load 0.74\n",
      "22207 14 08:44:29 INFO train : 1702  loss 0.7577  elapsed 0.61  load 0.00\n",
      "22207 14 08:44:29 INFO train : 1703  loss 0.8067  elapsed 0.59  load 0.00\n",
      "22207 14 08:44:30 INFO train : 1704  loss 0.7607  elapsed 0.60  load 0.00\n",
      "22207 14 08:44:32 INFO train : 1705  loss 0.7603  elapsed 0.59  load 1.40\n",
      "22207 14 08:44:33 INFO train : 1706  loss 0.7137  elapsed 0.64  load 0.00\n",
      "22207 14 08:44:33 INFO train : 1707  loss 0.7209  elapsed 0.61  load 0.00\n",
      "22207 14 08:44:34 INFO train : 1708  loss 0.7500  elapsed 0.68  load 0.00\n",
      "22207 14 08:44:36 INFO train : 1709  loss 0.7155  elapsed 0.55  load 1.11\n",
      "22207 14 08:44:36 INFO train : 1710  loss 0.7227  elapsed 0.65  load 0.00\n",
      "22207 14 08:44:37 INFO train : 1711  loss 0.7551  elapsed 0.61  load 0.00\n",
      "22207 14 08:44:38 INFO train : 1712  loss 0.7028  elapsed 0.63  load 0.00\n",
      "22207 14 08:44:39 INFO train : 1713  loss 0.7438  elapsed 0.60  load 0.77\n",
      "22207 14 08:44:40 INFO train : 1714  loss 0.7464  elapsed 0.66  load 0.00\n",
      "22207 14 08:44:40 INFO train : 1715  loss 0.7568  elapsed 0.60  load 0.00\n",
      "22207 14 08:44:41 INFO train : 1716  loss 0.7458  elapsed 0.53  load 0.00\n",
      "22207 14 08:44:42 INFO train : 1717  loss 0.7541  elapsed 0.59  load 0.80\n",
      "22207 14 08:44:43 INFO train : 1718  loss 0.7334  elapsed 0.65  load 0.00\n",
      "22207 14 08:44:43 INFO train : 1719  loss 0.7253  elapsed 0.63  load 0.00\n",
      "22207 14 08:44:44 INFO train : 1720  loss 0.7796  elapsed 0.66  load 0.00\n",
      "22207 14 08:44:46 INFO train : 1721  loss 0.7670  elapsed 0.59  load 0.84\n",
      "22207 14 08:44:46 INFO train : 1722  loss 0.7509  elapsed 0.66  load 0.00\n",
      "22207 14 08:44:47 INFO train : 1723  loss 0.8255  elapsed 0.62  load 0.00\n",
      "22207 14 08:44:48 INFO train : 1724  loss 0.7827  elapsed 0.68  load 0.00\n",
      "22207 14 08:44:50 INFO train : 1725  loss 0.7878  elapsed 0.63  load 1.37\n",
      "22207 14 08:44:50 INFO train : 1726  loss 0.7213  elapsed 0.63  load 0.00\n",
      "22207 14 08:44:51 INFO train : 1727  loss 0.7507  elapsed 0.63  load 0.00\n",
      "22207 14 08:44:52 INFO train : 1728  loss 0.7645  elapsed 0.65  load 0.00\n",
      "22207 14 08:44:53 INFO train : 1729  loss 0.7586  elapsed 0.55  load 1.12\n",
      "22207 14 08:44:54 INFO train : 1730  loss 0.7660  elapsed 0.57  load 0.00\n",
      "22207 14 08:44:55 INFO train : 1731  loss 0.7627  elapsed 0.63  load 0.00\n",
      "22207 14 08:44:55 INFO train : 1732  loss 0.7439  elapsed 0.55  load 0.00\n",
      "22207 14 08:44:56 INFO train : 1733  loss 0.7319  elapsed 0.60  load 0.54\n",
      "22207 14 08:44:57 INFO train : 1734  loss 0.7550  elapsed 0.62  load 0.00\n",
      "22207 14 08:44:58 INFO train : 1735  loss 0.7453  elapsed 0.53  load 0.00\n",
      "22207 14 08:44:58 INFO train : 1736  loss 0.7528  elapsed 0.64  load 0.00\n",
      "22207 14 08:44:59 INFO train : 1737  loss 0.7447  elapsed 0.61  load 0.66\n",
      "22207 14 08:45:00 INFO train : 1738  loss 0.7804  elapsed 0.55  load 0.00\n",
      "22207 14 08:45:01 INFO train : 1739  loss 0.7325  elapsed 0.57  load 0.00\n",
      "22207 14 08:45:01 INFO train : 1740  loss 0.7590  elapsed 0.58  load 0.00\n",
      "22207 14 08:45:03 INFO train : 1741  loss 0.7569  elapsed 0.54  load 1.46\n",
      "22207 14 08:45:04 INFO train : 1742  loss 0.7681  elapsed 0.71  load 0.00\n",
      "22207 14 08:45:05 INFO train : 1743  loss 0.7704  elapsed 0.68  load 0.00\n",
      "22207 14 08:45:05 INFO train : 1744  loss 0.7638  elapsed 0.66  load 0.00\n",
      "22207 14 08:45:07 INFO train : 1745  loss 0.8097  elapsed 0.61  load 1.21\n",
      "22207 14 08:45:08 INFO train : 1746  loss 0.7808  elapsed 0.64  load 0.00\n",
      "22207 14 08:45:08 INFO train : 1747  loss 0.7839  elapsed 0.56  load 0.00\n",
      "22207 14 08:45:09 INFO train : 1748  loss 0.7624  elapsed 0.60  load 0.00\n",
      "22207 14 08:45:11 INFO train : 1749  loss 0.7609  elapsed 0.59  load 1.08\n",
      "22207 14 08:45:12 INFO train : 1750  loss 0.7437  elapsed 0.73  load 0.04\n",
      "22207 14 08:45:12 INFO train : 1751  loss 0.7805  elapsed 0.59  load 0.00\n",
      "22207 14 08:45:13 INFO train : 1752  loss 0.7504  elapsed 0.59  load 0.01\n",
      "22207 14 08:45:14 INFO train : 1753  loss 0.7511  elapsed 0.63  load 1.05\n",
      "22207 14 08:45:15 INFO train : 1754  loss 0.7362  elapsed 0.58  load 0.00\n",
      "22207 14 08:45:16 INFO train : 1755  loss 0.7560  elapsed 0.62  load 0.00\n",
      "22207 14 08:45:16 INFO train : 1756  loss 0.8145  elapsed 0.55  load 0.00\n",
      "22207 14 08:45:18 INFO train : 1757  loss 0.7434  elapsed 0.65  load 0.98\n",
      "22207 14 08:45:19 INFO train : 1758  loss 0.7302  elapsed 0.61  load 0.00\n",
      "22207 14 08:45:19 INFO train : 1759  loss 0.7643  elapsed 0.51  load 0.00\n",
      "22207 14 08:45:20 INFO train : 1760  loss 0.7390  elapsed 0.65  load 0.00\n",
      "22207 14 08:45:21 INFO train : 1761  loss 0.7858  elapsed 0.54  load 0.89\n",
      "22207 14 08:45:22 INFO train : 1762  loss 0.7093  elapsed 0.63  load 0.00\n",
      "22207 14 08:45:23 INFO train : 1763  loss 0.7777  elapsed 0.60  load 0.02\n",
      "22207 14 08:45:23 INFO train : 1764  loss 0.7447  elapsed 0.65  load 0.05\n",
      "22207 14 08:45:25 INFO train : 1765  loss 0.7258  elapsed 0.65  load 1.09\n",
      "22207 14 08:45:26 INFO train : 1766  loss 0.7783  elapsed 0.62  load 0.00\n",
      "22207 14 08:45:26 INFO train : 1767  loss 0.8381  elapsed 0.58  load 0.00\n",
      "22207 14 08:45:27 INFO train : 1768  loss 0.7542  elapsed 0.57  load 0.00\n",
      "22207 14 08:45:28 INFO train : 1769  loss 0.7647  elapsed 0.66  load 0.74\n",
      "22207 14 08:45:29 INFO train : 1770  loss 0.7872  elapsed 0.69  load 0.01\n",
      "22207 14 08:45:30 INFO train : 1771  loss 0.7995  elapsed 0.71  load 0.00\n",
      "22207 14 08:45:30 INFO train : 1772  loss 0.7938  elapsed 0.60  load 0.00\n",
      "22207 14 08:45:32 INFO train : 1773  loss 0.7366  elapsed 0.63  load 0.46\n",
      "22207 14 08:45:32 INFO train : 1774  loss 0.7626  elapsed 0.66  load 0.00\n",
      "22207 14 08:45:33 INFO train : 1775  loss 0.7775  elapsed 0.60  load 0.00\n",
      "22207 14 08:45:33 INFO train : 1776  loss 0.7387  elapsed 0.59  load 0.00\n",
      "22207 14 08:45:35 INFO train : 1777  loss 0.7313  elapsed 0.57  load 1.27\n",
      "22207 14 08:45:36 INFO train : 1778  loss 0.7576  elapsed 0.56  load 0.02\n",
      "22207 14 08:45:36 INFO train : 1779  loss 0.7683  elapsed 0.57  load 0.00\n",
      "22207 14 08:45:37 INFO train : 1780  loss 0.7215  elapsed 0.60  load 0.00\n",
      "22207 14 08:45:39 INFO train : 1781  loss 0.7262  elapsed 0.57  load 1.48\n",
      "22207 14 08:45:40 INFO train : 1782  loss 0.7601  elapsed 0.63  load 0.05\n",
      "22207 14 08:45:40 INFO train : 1783  loss 0.8282  elapsed 0.60  load 0.00\n",
      "22207 14 08:45:41 INFO train : 1784  loss 0.7651  elapsed 0.60  load 0.00\n",
      "22207 14 08:45:43 INFO train : 1785  loss 0.7619  elapsed 0.56  load 1.00\n",
      "22207 14 08:45:43 INFO train : 1786  loss 0.7156  elapsed 0.60  load 0.01\n",
      "22207 14 08:45:44 INFO train : 1787  loss 0.7780  elapsed 0.56  load 0.00\n",
      "22207 14 08:45:44 INFO train : 1788  loss 0.7311  elapsed 0.58  load 0.01\n",
      "22207 14 08:45:46 INFO train : 1789  loss 0.8198  elapsed 0.58  load 0.86\n",
      "22207 14 08:45:46 INFO train : 1790  loss 0.7435  elapsed 0.56  load 0.00\n",
      "22207 14 08:45:47 INFO train : 1791  loss 0.7516  elapsed 0.57  load 0.00\n",
      "22207 14 08:45:48 INFO train : 1792  loss 0.8467  elapsed 0.53  load 0.00\n",
      "22207 14 08:45:49 INFO train : 1793  loss 0.7286  elapsed 0.67  load 1.07\n",
      "22207 14 08:45:50 INFO train : 1794  loss 0.7593  elapsed 0.65  load 0.00\n",
      "22207 14 08:45:51 INFO train : 1795  loss 0.7270  elapsed 0.65  load 0.00\n",
      "22207 14 08:45:51 INFO train : 1796  loss 0.7905  elapsed 0.64  load 0.00\n",
      "22207 14 08:45:53 INFO train : 1797  loss 0.7443  elapsed 0.57  load 0.57\n",
      "22207 14 08:45:53 INFO train : 1798  loss 0.7456  elapsed 0.56  load 0.00\n",
      "22207 14 08:45:54 INFO train : 1799  loss 0.7500  elapsed 0.53  load 0.00\n",
      "22207 14 08:45:54 INFO train : 1800  loss 0.7543  elapsed 0.55  load 0.00\n",
      "22207 14 08:45:56 INFO train : 1801  loss 0.7777  elapsed 0.65  load 1.29\n",
      "22207 14 08:45:57 INFO train : 1802  loss 0.7635  elapsed 0.62  load 0.00\n",
      "22207 14 08:45:58 INFO train : 1803  loss 0.7832  elapsed 0.60  load 0.00\n",
      "22207 14 08:45:58 INFO train : 1804  loss 0.8018  elapsed 0.66  load 0.00\n",
      "22207 14 08:46:00 INFO train : 1805  loss 0.7838  elapsed 0.60  load 0.99\n",
      "22207 14 08:46:00 INFO train : 1806  loss 0.7866  elapsed 0.52  load 0.00\n",
      "22207 14 08:46:01 INFO train : 1807  loss 0.7776  elapsed 0.64  load 0.00\n",
      "22207 14 08:46:02 INFO train : 1808  loss 0.7395  elapsed 0.60  load 0.00\n",
      "22207 14 08:46:04 INFO train : 1809  loss 0.7586  elapsed 0.59  load 1.29\n",
      "22207 14 08:46:04 INFO train : 1810  loss 0.8116  elapsed 0.64  load 0.00\n",
      "22207 14 08:46:05 INFO train : 1811  loss 0.7524  elapsed 0.75  load 0.00\n",
      "22207 14 08:46:06 INFO train : 1812  loss 0.7719  elapsed 0.64  load 0.00\n",
      "22207 14 08:46:07 INFO train : 1813  loss 0.7574  elapsed 0.57  load 1.02\n",
      "22207 14 08:46:08 INFO train : 1814  loss 0.7747  elapsed 0.61  load 0.00\n",
      "22207 14 08:46:08 INFO train : 1815  loss 0.7476  elapsed 0.58  load 0.00\n",
      "22207 14 08:46:09 INFO train : 1816  loss 0.7300  elapsed 0.63  load 0.00\n",
      "22207 14 08:46:11 INFO train : 1817  loss 0.7714  elapsed 0.60  load 1.13\n",
      "22207 14 08:46:11 INFO train : 1818  loss 0.7871  elapsed 0.63  load 0.00\n",
      "22207 14 08:46:12 INFO train : 1819  loss 0.7231  elapsed 0.66  load 0.00\n",
      "22207 14 08:46:13 INFO train : 1820  loss 0.7557  elapsed 0.64  load 0.00\n",
      "22207 14 08:46:14 INFO train : 1821  loss 0.7564  elapsed 0.54  load 1.14\n",
      "22207 14 08:46:15 INFO train : 1822  loss 0.7564  elapsed 0.65  load 0.00\n",
      "22207 14 08:46:16 INFO train : 1823  loss 0.7674  elapsed 0.57  load 0.00\n",
      "22207 14 08:46:16 INFO train : 1824  loss 0.7310  elapsed 0.62  load 0.00\n",
      "22207 14 08:46:17 INFO train : 1825  loss 0.7332  elapsed 0.54  load 0.52\n",
      "22207 14 08:46:18 INFO train : 1826  loss 0.7667  elapsed 0.54  load 0.00\n",
      "22207 14 08:46:19 INFO train : 1827  loss 0.7359  elapsed 0.67  load 0.00\n",
      "22207 14 08:46:19 INFO train : 1828  loss 0.7994  elapsed 0.66  load 0.01\n",
      "22207 14 08:46:21 INFO train : 1829  loss 0.7555  elapsed 0.59  load 1.09\n",
      "22207 14 08:46:22 INFO train : 1830  loss 0.7531  elapsed 0.74  load 0.00\n",
      "22207 14 08:46:23 INFO train : 1831  loss 0.7753  elapsed 0.59  load 0.00\n",
      "22207 14 08:46:23 INFO train : 1832  loss 0.7267  elapsed 0.57  load 0.00\n",
      "22207 14 08:46:25 INFO train : 1833  loss 0.7347  elapsed 0.62  load 1.20\n",
      "22207 14 08:46:26 INFO train : 1834  loss 0.7850  elapsed 0.63  load 0.00\n",
      "22207 14 08:46:26 INFO train : 1835  loss 0.7677  elapsed 0.60  load 0.00\n",
      "22207 14 08:46:27 INFO train : 1836  loss 0.7152  elapsed 0.51  load 0.00\n",
      "22207 14 08:46:29 INFO train : 1837  loss 0.7253  elapsed 0.58  load 1.22\n",
      "22207 14 08:46:29 INFO train : 1838  loss 0.7365  elapsed 0.70  load 0.00\n",
      "22207 14 08:46:30 INFO train : 1839  loss 0.7374  elapsed 0.64  load 0.00\n",
      "22207 14 08:46:31 INFO train : 1840  loss 0.7895  elapsed 0.67  load 0.00\n",
      "22207 14 08:46:32 INFO train : 1841  loss 0.7634  elapsed 0.55  load 1.06\n",
      "22207 14 08:46:33 INFO train : 1842  loss 0.7047  elapsed 0.63  load 0.00\n",
      "22207 14 08:46:34 INFO train : 1843  loss 0.7347  elapsed 0.60  load 0.00\n",
      "22207 14 08:46:34 INFO train : 1844  loss 0.7285  elapsed 0.61  load 0.00\n",
      "22207 14 08:46:36 INFO train : 1845  loss 0.7482  elapsed 0.56  load 0.94\n",
      "22207 14 08:46:36 INFO train : 1846  loss 0.7358  elapsed 0.60  load 0.00\n",
      "22207 14 08:46:37 INFO train : 1847  loss 0.7443  elapsed 0.59  load 0.00\n",
      "22207 14 08:46:38 INFO train : 1848  loss 0.7986  elapsed 0.60  load 0.00\n",
      "22207 14 08:46:39 INFO train : 1849  loss 0.7259  elapsed 0.64  load 1.09\n",
      "22207 14 08:46:40 INFO train : 1850  loss 0.7649  elapsed 0.64  load 0.00\n",
      "22207 14 08:46:41 INFO train : 1851  loss 0.7420  elapsed 0.62  load 0.00\n",
      "22207 14 08:46:41 INFO train : 1852  loss 0.7605  elapsed 0.69  load 0.00\n",
      "22207 14 08:46:43 INFO train : 1853  loss 0.7277  elapsed 0.55  load 0.74\n",
      "22207 14 08:46:43 INFO train : 1854  loss 0.7422  elapsed 0.60  load 0.00\n",
      "22207 14 08:46:44 INFO train : 1855  loss 0.7608  elapsed 0.67  load 0.00\n",
      "22207 14 08:46:45 INFO train : 1856  loss 0.7909  elapsed 0.60  load 0.00\n",
      "22207 14 08:46:46 INFO train : 1857  loss 0.7501  elapsed 0.67  load 0.89\n",
      "22207 14 08:46:47 INFO train : 1858  loss 0.7037  elapsed 0.62  load 0.00\n",
      "22207 14 08:46:47 INFO train : 1859  loss 0.7634  elapsed 0.60  load 0.00\n",
      "22207 14 08:46:48 INFO train : 1860  loss 0.7254  elapsed 0.62  load 0.00\n",
      "22207 14 08:46:50 INFO train : 1861  loss 0.7680  elapsed 0.61  load 0.86\n",
      "22207 14 08:46:50 INFO train : 1862  loss 0.7517  elapsed 0.62  load 0.00\n",
      "22207 14 08:46:51 INFO train : 1863  loss 0.7266  elapsed 0.65  load 0.00\n",
      "22207 14 08:46:52 INFO train : 1864  loss 0.7495  elapsed 0.57  load 0.00\n",
      "22207 14 08:46:54 INFO train : 1865  loss 0.7259  elapsed 0.66  load 1.32\n",
      "22207 14 08:46:54 INFO train : 1866  loss 0.8043  elapsed 0.63  load 0.00\n",
      "22207 14 08:46:55 INFO train : 1867  loss 0.7134  elapsed 0.64  load 0.00\n",
      "22207 14 08:46:56 INFO train : 1868  loss 0.7801  elapsed 0.60  load 0.00\n",
      "22207 14 08:46:57 INFO train : 1869  loss 0.7263  elapsed 0.59  load 0.64\n",
      "22207 14 08:46:57 INFO train : 1870  loss 0.7512  elapsed 0.67  load 0.00\n",
      "22207 14 08:46:58 INFO train : 1871  loss 0.7399  elapsed 0.61  load 0.00\n",
      "22207 14 08:46:59 INFO train : 1872  loss 0.7887  elapsed 0.66  load 0.00\n",
      "22207 14 08:47:00 INFO train : 1873  loss 0.7601  elapsed 0.61  load 0.75\n",
      "22207 14 08:47:01 INFO train : 1874  loss 0.7317  elapsed 0.62  load 0.00\n",
      "22207 14 08:47:02 INFO train : 1875  loss 0.7733  elapsed 0.71  load 0.00\n",
      "22207 14 08:47:02 INFO train : 1876  loss 0.7881  elapsed 0.64  load 0.00\n",
      "22207 14 08:47:04 INFO train : 1877  loss 0.7645  elapsed 0.56  load 1.45\n",
      "22207 14 08:47:05 INFO train : 1878  loss 0.6867  elapsed 0.61  load 0.00\n",
      "22207 14 08:47:05 INFO train : 1879  loss 0.7081  elapsed 0.58  load 0.00\n",
      "22207 14 08:47:06 INFO train : 1880  loss 0.7604  elapsed 0.63  load 0.00\n",
      "22207 14 08:47:08 INFO train : 1881  loss 0.7454  elapsed 0.59  load 1.45\n",
      "22207 14 08:47:09 INFO train : 1882  loss 0.7424  elapsed 0.57  load 0.00\n",
      "22207 14 08:47:10 INFO train : 1883  loss 0.7382  elapsed 0.64  load 0.00\n",
      "22207 14 08:47:10 INFO train : 1884  loss 0.7518  elapsed 0.54  load 0.00\n",
      "22207 14 08:47:12 INFO train : 1885  loss 0.7756  elapsed 0.65  load 1.05\n",
      "22207 14 08:47:12 INFO train : 1886  loss 0.7233  elapsed 0.62  load 0.00\n",
      "22207 14 08:47:13 INFO train : 1887  loss 0.7540  elapsed 0.54  load 0.00\n",
      "22207 14 08:47:14 INFO train : 1888  loss 0.7141  elapsed 0.58  load 0.06\n",
      "22207 14 08:47:15 INFO train : 1889  loss 0.7875  elapsed 0.52  load 0.71\n",
      "22207 14 08:47:16 INFO train : 1890  loss 0.7101  elapsed 0.65  load 0.00\n",
      "22207 14 08:47:16 INFO train : 1891  loss 0.7544  elapsed 0.56  load 0.00\n",
      "22207 14 08:47:17 INFO train : 1892  loss 0.7280  elapsed 0.61  load 0.00\n",
      "22207 14 08:47:18 INFO train : 1893  loss 0.7064  elapsed 0.55  load 0.88\n",
      "22207 14 08:47:19 INFO train : 1894  loss 0.7366  elapsed 0.59  load 0.00\n",
      "22207 14 08:47:19 INFO train : 1895  loss 0.7682  elapsed 0.60  load 0.00\n",
      "22207 14 08:47:20 INFO train : 1896  loss 0.7209  elapsed 0.63  load 0.00\n",
      "22207 14 08:47:22 INFO train : 1897  loss 0.7137  elapsed 0.65  load 1.43\n",
      "22207 14 08:47:23 INFO train : 1898  loss 0.7238  elapsed 0.59  load 0.00\n",
      "22207 14 08:47:23 INFO train : 1899  loss 0.7279  elapsed 0.60  load 0.00\n",
      "22207 14 08:47:24 INFO train : 1900  loss 0.8103  elapsed 0.65  load 0.00\n",
      "22207 14 08:47:25 INFO train : 1901  loss 0.7838  elapsed 0.60  load 0.66\n",
      "22207 14 08:47:26 INFO train : 1902  loss 0.7612  elapsed 0.63  load 0.00\n",
      "22207 14 08:47:27 INFO train : 1903  loss 0.7604  elapsed 0.60  load 0.00\n",
      "22207 14 08:47:27 INFO train : 1904  loss 0.7339  elapsed 0.64  load 0.00\n",
      "22207 14 08:47:29 INFO train : 1905  loss 0.8142  elapsed 0.51  load 0.79\n",
      "22207 14 08:47:29 INFO train : 1906  loss 0.7648  elapsed 0.63  load 0.00\n",
      "22207 14 08:47:30 INFO train : 1907  loss 0.7312  elapsed 0.64  load 0.00\n",
      "22207 14 08:47:31 INFO train : 1908  loss 0.7136  elapsed 0.64  load 0.00\n",
      "22207 14 08:47:33 INFO train : 1909  loss 0.7512  elapsed 0.58  load 1.21\n",
      "22207 14 08:47:33 INFO train : 1910  loss 0.7404  elapsed 0.58  load 0.00\n",
      "22207 14 08:47:34 INFO train : 1911  loss 0.7053  elapsed 0.62  load 0.00\n",
      "22207 14 08:47:34 INFO train : 1912  loss 0.7586  elapsed 0.60  load 0.03\n",
      "22207 14 08:47:36 INFO train : 1913  loss 0.7319  elapsed 0.55  load 1.43\n",
      "22207 14 08:47:37 INFO train : 1914  loss 0.7308  elapsed 0.66  load 0.00\n",
      "22207 14 08:47:38 INFO train : 1915  loss 0.7583  elapsed 0.58  load 0.00\n",
      "22207 14 08:47:38 INFO train : 1916  loss 0.7909  elapsed 0.69  load 0.00\n",
      "22207 14 08:47:40 INFO train : 1917  loss 0.8102  elapsed 0.59  load 0.89\n",
      "22207 14 08:47:41 INFO train : 1918  loss 0.7580  elapsed 0.66  load 0.00\n",
      "22207 14 08:47:41 INFO train : 1919  loss 0.7704  elapsed 0.63  load 0.00\n",
      "22207 14 08:47:42 INFO train : 1920  loss 0.7526  elapsed 0.65  load 0.00\n",
      "22207 14 08:47:44 INFO train : 1921  loss 0.7841  elapsed 0.57  load 1.40\n",
      "22207 14 08:47:45 INFO train : 1922  loss 0.7169  elapsed 0.63  load 0.00\n",
      "22207 14 08:47:45 INFO train : 1923  loss 0.7503  elapsed 0.65  load 0.00\n",
      "22207 14 08:47:46 INFO train : 1924  loss 0.7646  elapsed 0.60  load 0.00\n",
      "22207 14 08:47:48 INFO train : 1925  loss 0.7631  elapsed 0.56  load 1.41\n",
      "22207 14 08:47:48 INFO train : 1926  loss 0.7180  elapsed 0.57  load 0.01\n",
      "22207 14 08:47:49 INFO train : 1927  loss 0.7334  elapsed 0.57  load 0.00\n",
      "22207 14 08:47:50 INFO train : 1928  loss 0.7198  elapsed 0.63  load 0.00\n",
      "22207 14 08:47:51 INFO train : 1929  loss 0.7510  elapsed 0.60  load 0.78\n",
      "22207 14 08:47:52 INFO train : 1930  loss 0.7865  elapsed 0.63  load 0.00\n",
      "22207 14 08:47:52 INFO train : 1931  loss 0.7599  elapsed 0.57  load 0.00\n",
      "22207 14 08:47:53 INFO train : 1932  loss 0.7354  elapsed 0.55  load 0.00\n",
      "22207 14 08:47:55 INFO train : 1933  loss 0.7434  elapsed 0.60  load 1.06\n",
      "22207 14 08:47:55 INFO train : 1934  loss 0.7457  elapsed 0.64  load 0.01\n",
      "22207 14 08:47:56 INFO train : 1935  loss 0.7423  elapsed 0.69  load 0.00\n",
      "22207 14 08:47:57 INFO train : 1936  loss 0.7360  elapsed 0.58  load 0.07\n",
      "22207 14 08:47:58 INFO train : 1937  loss 0.7011  elapsed 0.63  load 0.82\n",
      "22207 14 08:47:59 INFO train : 1938  loss 0.7406  elapsed 0.55  load 0.00\n",
      "22207 14 08:47:59 INFO train : 1939  loss 0.7175  elapsed 0.59  load 0.00\n",
      "22207 14 08:48:00 INFO train : 1940  loss 0.7068  elapsed 0.60  load 0.00\n",
      "22207 14 08:48:02 INFO train : 1941  loss 0.7746  elapsed 0.60  load 1.07\n",
      "22207 14 08:48:02 INFO train : 1942  loss 0.7305  elapsed 0.54  load 0.00\n",
      "22207 14 08:48:03 INFO train : 1943  loss 0.7160  elapsed 0.61  load 0.00\n",
      "22207 14 08:48:04 INFO train : 1944  loss 0.7650  elapsed 0.60  load 0.00\n",
      "22207 14 08:48:05 INFO train : 1945  loss 0.7411  elapsed 0.59  load 1.33\n",
      "22207 14 08:48:06 INFO train : 1946  loss 0.7074  elapsed 0.61  load 0.00\n",
      "22207 14 08:48:07 INFO train : 1947  loss 0.7586  elapsed 0.60  load 0.01\n",
      "22207 14 08:48:07 INFO train : 1948  loss 0.8245  elapsed 0.56  load 0.00\n",
      "22207 14 08:48:09 INFO train : 1949  loss 0.7414  elapsed 0.64  load 1.18\n",
      "22207 14 08:48:10 INFO train : 1950  loss 0.7542  elapsed 0.68  load 0.00\n",
      "22207 14 08:48:10 INFO train : 1951  loss 0.7902  elapsed 0.62  load 0.00\n",
      "22207 14 08:48:11 INFO train : 1952  loss 0.7132  elapsed 0.64  load 0.01\n",
      "22207 14 08:48:13 INFO train : 1953  loss 0.7469  elapsed 0.62  load 0.83\n",
      "22207 14 08:48:13 INFO train : 1954  loss 0.7696  elapsed 0.58  load 0.00\n",
      "22207 14 08:48:14 INFO train : 1955  loss 0.8243  elapsed 0.63  load 0.00\n",
      "22207 14 08:48:15 INFO train : 1956  loss 0.7800  elapsed 0.71  load 0.00\n",
      "22207 14 08:48:16 INFO train : 1957  loss 0.7581  elapsed 0.61  load 1.11\n",
      "22207 14 08:48:17 INFO train : 1958  loss 0.8449  elapsed 0.57  load 0.00\n",
      "22207 14 08:48:18 INFO train : 1959  loss 0.7637  elapsed 0.62  load 0.00\n",
      "22207 14 08:48:18 INFO train : 1960  loss 0.7413  elapsed 0.73  load 0.00\n",
      "22207 14 08:48:20 INFO train : 1961  loss 0.7586  elapsed 0.61  load 1.24\n",
      "22207 14 08:48:21 INFO train : 1962  loss 0.7562  elapsed 0.65  load 0.00\n",
      "22207 14 08:48:22 INFO train : 1963  loss 0.7268  elapsed 0.62  load 0.00\n",
      "22207 14 08:48:22 INFO train : 1964  loss 0.7946  elapsed 0.64  load 0.00\n",
      "22207 14 08:48:24 INFO train : 1965  loss 0.7049  elapsed 0.67  load 1.24\n",
      "22207 14 08:48:25 INFO train : 1966  loss 0.7296  elapsed 0.58  load 0.00\n",
      "22207 14 08:48:25 INFO train : 1967  loss 0.7463  elapsed 0.53  load 0.03\n",
      "22207 14 08:48:26 INFO train : 1968  loss 0.7332  elapsed 0.65  load 0.04\n",
      "22207 14 08:48:28 INFO train : 1969  loss 0.7788  elapsed 0.59  load 0.91\n",
      "22207 14 08:48:28 INFO train : 1970  loss 0.7621  elapsed 0.61  load 0.00\n",
      "22207 14 08:48:29 INFO train : 1971  loss 0.7214  elapsed 0.67  load 0.00\n",
      "22207 14 08:48:30 INFO train : 1972  loss 0.8513  elapsed 0.72  load 0.00\n",
      "22207 14 08:48:31 INFO train : 1973  loss 0.8005  elapsed 0.60  load 0.82\n",
      "22207 14 08:48:32 INFO train : 1974  loss 0.7122  elapsed 0.56  load 0.00\n",
      "22207 14 08:48:32 INFO train : 1975  loss 0.7350  elapsed 0.69  load 0.00\n",
      "22207 14 08:48:33 INFO train : 1976  loss 0.7364  elapsed 0.64  load 0.00\n",
      "22207 14 08:48:35 INFO train : 1977  loss 0.7617  elapsed 0.62  load 1.34\n",
      "22207 14 08:48:36 INFO train : 1978  loss 0.7368  elapsed 0.64  load 0.00\n",
      "22207 14 08:48:36 INFO train : 1979  loss 0.7036  elapsed 0.62  load 0.00\n",
      "22207 14 08:48:37 INFO train : 1980  loss 0.7724  elapsed 0.57  load 0.00\n",
      "22207 14 08:48:38 INFO train : 1981  loss 0.7810  elapsed 0.57  load 0.70\n",
      "22207 14 08:48:39 INFO train : 1982  loss 0.7731  elapsed 0.67  load 0.00\n",
      "22207 14 08:48:40 INFO train : 1983  loss 0.8049  elapsed 0.64  load 0.00\n",
      "22207 14 08:48:40 INFO train : 1984  loss 0.6999  elapsed 0.68  load 0.00\n",
      "22207 14 08:48:42 INFO train : 1985  loss 0.7295  elapsed 0.60  load 0.69\n",
      "22207 14 08:48:42 INFO train : 1986  loss 0.7704  elapsed 0.74  load 0.01\n",
      "22207 14 08:48:43 INFO train : 1987  loss 0.7441  elapsed 0.65  load 0.00\n",
      "22207 14 08:48:44 INFO train : 1988  loss 0.7340  elapsed 0.72  load 0.00\n",
      "22207 14 08:48:45 INFO train : 1989  loss 0.7690  elapsed 0.65  load 0.80\n",
      "22207 14 08:48:46 INFO train : 1990  loss 0.7653  elapsed 0.63  load 0.00\n",
      "22207 14 08:48:47 INFO train : 1991  loss 0.7237  elapsed 0.66  load 0.00\n",
      "22207 14 08:48:47 INFO train : 1992  loss 0.7651  elapsed 0.62  load 0.00\n",
      "22207 14 08:48:48 INFO train : 1993  loss 0.7465  elapsed 0.55  load 0.53\n",
      "22207 14 08:48:49 INFO train : 1994  loss 0.7219  elapsed 0.63  load 0.00\n",
      "22207 14 08:48:50 INFO train : 1995  loss 0.7805  elapsed 0.67  load 0.00\n",
      "22207 14 08:48:50 INFO train : 1996  loss 0.7064  elapsed 0.64  load 0.00\n",
      "22207 14 08:48:52 INFO train : 1997  loss 0.7388  elapsed 0.65  load 0.61\n",
      "22207 14 08:48:52 INFO train : 1998  loss 0.7144  elapsed 0.68  load 0.00\n",
      "22207 14 08:48:53 INFO train : 1999  loss 0.7383  elapsed 0.58  load 0.03\n",
      "22207 14 08:48:54 INFO train : 2000  loss 0.7626  elapsed 0.58  load 0.00\n",
      "22207 14 08:48:54 INFO Running validation\n",
      "22207 14 08:49:22 INFO Logging 20 images\n",
      "22207 14 08:49:23 INFO Logging 20 audios\n",
      "22207 14 08:49:25 INFO Logging 20 texts\n",
      "22207 14 08:49:37 INFO val : 2000  loss 0.67400062084198\n",
      "22207 14 08:49:37 INFO Saving model state at step 2000 to checkpoints/fp_step_00002000.pt\n",
      "22207 14 08:49:38 INFO train : 2001  loss 0.7977  elapsed 0.51  load 0.01\n",
      "22207 14 08:49:38 INFO train : 2002  loss 0.8565  elapsed 0.47  load 0.00\n",
      "22207 14 08:49:39 INFO train : 2003  loss 0.7704  elapsed 0.49  load 0.00\n",
      "22207 14 08:49:39 INFO train : 2004  loss 0.7148  elapsed 0.48  load 0.00\n",
      "22207 14 08:49:39 INFO train : 2005  loss 0.7675  elapsed 0.12  load 0.00\n",
      "22207 14 08:49:39 INFO Epoch num: 5\n",
      "22207 14 08:49:44 INFO train : 2006  loss 0.7003  elapsed 0.63  load 4.18\n",
      "22207 14 08:49:45 INFO train : 2007  loss 0.7314  elapsed 0.56  load 0.01\n",
      "22207 14 08:49:46 INFO train : 2008  loss 0.7353  elapsed 0.61  load 0.00\n",
      "22207 14 08:49:46 INFO train : 2009  loss 0.7218  elapsed 0.61  load 0.00\n",
      "22207 14 08:49:48 INFO train : 2010  loss 0.6772  elapsed 0.56  load 0.94\n",
      "22207 14 08:49:48 INFO train : 2011  loss 0.7303  elapsed 0.54  load 0.00\n",
      "22207 14 08:49:49 INFO train : 2012  loss 0.6949  elapsed 0.63  load 0.00\n",
      "22207 14 08:49:50 INFO train : 2013  loss 0.7295  elapsed 0.61  load 0.00\n",
      "22207 14 08:49:52 INFO train : 2014  loss 0.7360  elapsed 0.60  load 1.48\n",
      "22207 14 08:49:52 INFO train : 2015  loss 0.7852  elapsed 0.59  load 0.00\n",
      "22207 14 08:49:53 INFO train : 2016  loss 0.6761  elapsed 0.61  load 0.00\n",
      "22207 14 08:49:54 INFO train : 2017  loss 0.7056  elapsed 0.65  load 0.00\n",
      "22207 14 08:49:55 INFO train : 2018  loss 0.7420  elapsed 0.59  load 0.95\n",
      "22207 14 08:49:56 INFO train : 2019  loss 0.7420  elapsed 0.56  load 0.00\n",
      "22207 14 08:49:56 INFO train : 2020  loss 0.7324  elapsed 0.63  load 0.00\n",
      "22207 14 08:49:57 INFO train : 2021  loss 0.7127  elapsed 0.63  load 0.00\n",
      "22207 14 08:49:59 INFO train : 2022  loss 0.7512  elapsed 0.65  load 1.03\n",
      "22207 14 08:49:59 INFO train : 2023  loss 0.7115  elapsed 0.58  load 0.00\n",
      "22207 14 08:50:00 INFO train : 2024  loss 0.7497  elapsed 0.65  load 0.00\n",
      "22207 14 08:50:01 INFO train : 2025  loss 0.7676  elapsed 0.63  load 0.01\n",
      "22207 14 08:50:03 INFO train : 2026  loss 0.6893  elapsed 0.59  load 1.36\n",
      "22207 14 08:50:03 INFO train : 2027  loss 0.8131  elapsed 0.65  load 0.00\n",
      "22207 14 08:50:04 INFO train : 2028  loss 0.7707  elapsed 0.59  load 0.00\n",
      "22207 14 08:50:04 INFO train : 2029  loss 0.7012  elapsed 0.57  load 0.03\n",
      "22207 14 08:50:06 INFO train : 2030  loss 0.7193  elapsed 0.71  load 1.12\n",
      "22207 14 08:50:07 INFO train : 2031  loss 0.6954  elapsed 0.60  load 0.00\n",
      "22207 14 08:50:08 INFO train : 2032  loss 0.7595  elapsed 0.66  load 0.00\n",
      "22207 14 08:50:08 INFO train : 2033  loss 0.6645  elapsed 0.58  load 0.00\n",
      "22207 14 08:50:10 INFO train : 2034  loss 0.6911  elapsed 0.60  load 0.82\n",
      "22207 14 08:50:10 INFO train : 2035  loss 0.6868  elapsed 0.69  load 0.01\n",
      "22207 14 08:50:11 INFO train : 2036  loss 0.6951  elapsed 0.72  load 0.00\n",
      "22207 14 08:50:12 INFO train : 2037  loss 0.6952  elapsed 0.69  load 0.01\n",
      "22207 14 08:50:14 INFO train : 2038  loss 0.7211  elapsed 0.58  load 1.25\n",
      "22207 14 08:50:14 INFO train : 2039  loss 0.7441  elapsed 0.63  load 0.00\n",
      "22207 14 08:50:15 INFO train : 2040  loss 0.7368  elapsed 0.73  load 0.00\n",
      "22207 14 08:50:16 INFO train : 2041  loss 0.7219  elapsed 0.57  load 0.00\n",
      "22207 14 08:50:18 INFO train : 2042  loss 0.7116  elapsed 0.65  load 1.17\n",
      "22207 14 08:50:18 INFO train : 2043  loss 0.6853  elapsed 0.59  load 0.00\n",
      "22207 14 08:50:19 INFO train : 2044  loss 0.7218  elapsed 0.52  load 0.00\n",
      "22207 14 08:50:19 INFO train : 2045  loss 0.6974  elapsed 0.52  load 0.00\n",
      "22207 14 08:50:21 INFO train : 2046  loss 0.6765  elapsed 0.63  load 1.07\n",
      "22207 14 08:50:22 INFO train : 2047  loss 0.7270  elapsed 0.63  load 0.00\n",
      "22207 14 08:50:22 INFO train : 2048  loss 0.7041  elapsed 0.59  load 0.00\n",
      "22207 14 08:50:23 INFO train : 2049  loss 0.7186  elapsed 0.63  load 0.01\n",
      "22207 14 08:50:25 INFO train : 2050  loss 0.7018  elapsed 0.59  load 1.01\n",
      "22207 14 08:50:25 INFO train : 2051  loss 0.7056  elapsed 0.61  load 0.00\n",
      "22207 14 08:50:26 INFO train : 2052  loss 0.6873  elapsed 0.59  load 0.00\n",
      "22207 14 08:50:26 INFO train : 2053  loss 0.6877  elapsed 0.65  load 0.01\n",
      "22207 14 08:50:28 INFO train : 2054  loss 0.7139  elapsed 0.57  load 1.26\n",
      "22207 14 08:50:29 INFO train : 2055  loss 0.7343  elapsed 0.67  load 0.00\n",
      "22207 14 08:50:30 INFO train : 2056  loss 0.7263  elapsed 0.65  load 0.00\n",
      "22207 14 08:50:30 INFO train : 2057  loss 0.6973  elapsed 0.53  load 0.03\n",
      "22207 14 08:50:32 INFO train : 2058  loss 0.7495  elapsed 0.54  load 0.90\n",
      "22207 14 08:50:32 INFO train : 2059  loss 0.7188  elapsed 0.53  load 0.02\n",
      "22207 14 08:50:33 INFO train : 2060  loss 0.7314  elapsed 0.60  load 0.00\n",
      "22207 14 08:50:33 INFO train : 2061  loss 0.6797  elapsed 0.60  load 0.00\n",
      "22207 14 08:50:35 INFO train : 2062  loss 0.7105  elapsed 0.59  load 1.26\n",
      "22207 14 08:50:36 INFO train : 2063  loss 0.7340  elapsed 0.60  load 0.00\n",
      "22207 14 08:50:37 INFO train : 2064  loss 0.7106  elapsed 0.60  load 0.00\n",
      "22207 14 08:50:37 INFO train : 2065  loss 0.7505  elapsed 0.66  load 0.01\n",
      "22207 14 08:50:39 INFO train : 2066  loss 0.7088  elapsed 0.57  load 1.01\n",
      "22207 14 08:50:40 INFO train : 2067  loss 0.7117  elapsed 0.71  load 0.00\n",
      "22207 14 08:50:40 INFO train : 2068  loss 0.6863  elapsed 0.70  load 0.00\n",
      "22207 14 08:50:41 INFO train : 2069  loss 0.7451  elapsed 0.65  load 0.01\n",
      "22207 14 08:50:42 INFO train : 2070  loss 0.6817  elapsed 0.60  load 0.47\n",
      "22207 14 08:50:43 INFO train : 2071  loss 0.7241  elapsed 0.67  load 0.00\n",
      "22207 14 08:50:44 INFO train : 2072  loss 0.7055  elapsed 0.62  load 0.00\n",
      "22207 14 08:50:44 INFO train : 2073  loss 0.7323  elapsed 0.54  load 0.00\n",
      "22207 14 08:50:46 INFO train : 2074  loss 0.7180  elapsed 0.61  load 1.12\n",
      "22207 14 08:50:47 INFO train : 2075  loss 0.6843  elapsed 0.61  load 0.00\n",
      "22207 14 08:50:47 INFO train : 2076  loss 0.7043  elapsed 0.56  load 0.03\n",
      "22207 14 08:50:48 INFO train : 2077  loss 0.7149  elapsed 0.60  load 0.02\n",
      "22207 14 08:50:50 INFO train : 2078  loss 0.7531  elapsed 0.55  load 1.25\n",
      "22207 14 08:50:50 INFO train : 2079  loss 0.7135  elapsed 0.61  load 0.00\n",
      "22207 14 08:50:51 INFO train : 2080  loss 0.7150  elapsed 0.65  load 0.03\n",
      "22207 14 08:50:52 INFO train : 2081  loss 0.8129  elapsed 0.64  load 0.02\n",
      "22207 14 08:50:53 INFO train : 2082  loss 0.7124  elapsed 0.63  load 0.86\n",
      "22207 14 08:50:54 INFO train : 2083  loss 0.7474  elapsed 0.51  load 0.00\n",
      "22207 14 08:50:54 INFO train : 2084  loss 0.7178  elapsed 0.66  load 0.01\n",
      "22207 14 08:50:55 INFO train : 2085  loss 0.7353  elapsed 0.68  load 0.03\n",
      "22207 14 08:50:57 INFO train : 2086  loss 0.7025  elapsed 0.67  load 0.98\n",
      "22207 14 08:50:57 INFO train : 2087  loss 0.7015  elapsed 0.61  load 0.00\n",
      "22207 14 08:50:58 INFO train : 2088  loss 0.7403  elapsed 0.54  load 0.00\n",
      "22207 14 08:50:59 INFO train : 2089  loss 0.7279  elapsed 0.67  load 0.01\n",
      "22207 14 08:51:00 INFO train : 2090  loss 0.7257  elapsed 0.56  load 1.11\n",
      "22207 14 08:51:01 INFO train : 2091  loss 0.7823  elapsed 0.68  load 0.00\n",
      "22207 14 08:51:02 INFO train : 2092  loss 0.7244  elapsed 0.61  load 0.02\n",
      "22207 14 08:51:02 INFO train : 2093  loss 0.7075  elapsed 0.63  load 0.04\n",
      "22207 14 08:51:04 INFO train : 2094  loss 0.6895  elapsed 0.53  load 1.00\n",
      "22207 14 08:51:05 INFO train : 2095  loss 0.6995  elapsed 0.62  load 0.00\n",
      "22207 14 08:51:05 INFO train : 2096  loss 0.6881  elapsed 0.56  load 0.09\n",
      "22207 14 08:51:06 INFO train : 2097  loss 0.7069  elapsed 0.59  load 0.07\n",
      "22207 14 08:51:08 INFO train : 2098  loss 0.6994  elapsed 0.59  load 1.16\n",
      "22207 14 08:51:08 INFO train : 2099  loss 0.7369  elapsed 0.60  load 0.00\n",
      "22207 14 08:51:09 INFO train : 2100  loss 0.7224  elapsed 0.58  load 0.00\n",
      "22207 14 08:51:10 INFO train : 2101  loss 0.7117  elapsed 0.61  load 0.40\n",
      "22207 14 08:51:12 INFO train : 2102  loss 0.7069  elapsed 0.62  load 1.05\n",
      "22207 14 08:51:12 INFO train : 2103  loss 0.7237  elapsed 0.59  load 0.00\n",
      "22207 14 08:51:13 INFO train : 2104  loss 0.6871  elapsed 0.55  load 0.00\n",
      "22207 14 08:51:14 INFO train : 2105  loss 0.7544  elapsed 0.59  load 0.07\n",
      "22207 14 08:51:16 INFO train : 2106  loss 0.7353  elapsed 0.58  load 1.52\n",
      "22207 14 08:51:16 INFO train : 2107  loss 0.7127  elapsed 0.67  load 0.00\n",
      "22207 14 08:51:17 INFO train : 2108  loss 0.7236  elapsed 0.57  load 0.00\n",
      "22207 14 08:51:18 INFO train : 2109  loss 0.7114  elapsed 0.56  load 0.05\n",
      "22207 14 08:51:19 INFO train : 2110  loss 0.6838  elapsed 0.61  load 1.13\n",
      "22207 14 08:51:20 INFO train : 2111  loss 0.6755  elapsed 0.62  load 0.00\n",
      "22207 14 08:51:21 INFO train : 2112  loss 0.7361  elapsed 0.59  load 0.00\n",
      "22207 14 08:51:21 INFO train : 2113  loss 0.7131  elapsed 0.59  load 0.01\n",
      "22207 14 08:51:23 INFO train : 2114  loss 0.7030  elapsed 0.65  load 1.09\n",
      "22207 14 08:51:24 INFO train : 2115  loss 0.7449  elapsed 0.64  load 0.00\n",
      "22207 14 08:51:24 INFO train : 2116  loss 0.7468  elapsed 0.58  load 0.00\n",
      "22207 14 08:51:25 INFO train : 2117  loss 0.6965  elapsed 0.55  load 0.07\n",
      "22207 14 08:51:27 INFO train : 2118  loss 0.7057  elapsed 0.58  load 1.14\n",
      "22207 14 08:51:27 INFO train : 2119  loss 0.7518  elapsed 0.67  load 0.00\n",
      "22207 14 08:51:28 INFO train : 2120  loss 0.6963  elapsed 0.60  load 0.00\n",
      "22207 14 08:51:29 INFO train : 2121  loss 0.7275  elapsed 0.61  load 0.06\n",
      "22207 14 08:51:30 INFO train : 2122  loss 0.7190  elapsed 0.69  load 0.99\n",
      "22207 14 08:51:31 INFO train : 2123  loss 0.7203  elapsed 0.57  load 0.00\n",
      "22207 14 08:51:31 INFO train : 2124  loss 0.7539  elapsed 0.64  load 0.00\n",
      "22207 14 08:51:32 INFO train : 2125  loss 0.6947  elapsed 0.60  load 0.05\n",
      "22207 14 08:51:34 INFO train : 2126  loss 0.7246  elapsed 0.54  load 1.03\n",
      "22207 14 08:51:34 INFO train : 2127  loss 0.7329  elapsed 0.60  load 0.01\n",
      "22207 14 08:51:35 INFO train : 2128  loss 0.7293  elapsed 0.66  load 0.00\n",
      "22207 14 08:51:36 INFO train : 2129  loss 0.7459  elapsed 0.68  load 0.04\n",
      "22207 14 08:51:37 INFO train : 2130  loss 0.7134  elapsed 0.61  load 0.96\n",
      "22207 14 08:51:38 INFO train : 2131  loss 0.6979  elapsed 0.66  load 0.00\n",
      "22207 14 08:51:39 INFO train : 2132  loss 0.7148  elapsed 0.62  load 0.00\n",
      "22207 14 08:51:39 INFO train : 2133  loss 0.7221  elapsed 0.63  load 0.03\n",
      "22207 14 08:51:41 INFO train : 2134  loss 0.7252  elapsed 0.60  load 1.39\n",
      "22207 14 08:51:42 INFO train : 2135  loss 0.7091  elapsed 0.61  load 0.00\n",
      "22207 14 08:51:43 INFO train : 2136  loss 0.7584  elapsed 0.59  load 0.00\n",
      "22207 14 08:51:43 INFO train : 2137  loss 0.7185  elapsed 0.63  load 0.05\n",
      "22207 14 08:51:45 INFO train : 2138  loss 0.6856  elapsed 0.55  load 0.82\n",
      "22207 14 08:51:45 INFO train : 2139  loss 0.7431  elapsed 0.55  load 0.05\n",
      "22207 14 08:51:46 INFO train : 2140  loss 0.6823  elapsed 0.62  load 0.00\n",
      "22207 14 08:51:47 INFO train : 2141  loss 0.7094  elapsed 0.62  load 0.01\n",
      "22207 14 08:51:49 INFO train : 2142  loss 0.7517  elapsed 0.67  load 1.38\n",
      "22207 14 08:51:49 INFO train : 2143  loss 0.7592  elapsed 0.62  load 0.00\n",
      "22207 14 08:51:50 INFO train : 2144  loss 0.6994  elapsed 0.60  load 0.05\n",
      "22207 14 08:51:51 INFO train : 2145  loss 0.6682  elapsed 0.52  load 0.05\n",
      "22207 14 08:51:52 INFO train : 2146  loss 0.7555  elapsed 0.61  load 0.74\n",
      "22207 14 08:51:53 INFO train : 2147  loss 0.7013  elapsed 0.63  load 0.00\n",
      "22207 14 08:51:53 INFO train : 2148  loss 0.7449  elapsed 0.57  load 0.00\n",
      "22207 14 08:51:54 INFO train : 2149  loss 0.7298  elapsed 0.59  load 0.06\n",
      "22207 14 08:51:55 INFO train : 2150  loss 0.7368  elapsed 0.60  load 0.82\n",
      "22207 14 08:51:56 INFO train : 2151  loss 0.7273  elapsed 0.65  load 0.00\n",
      "22207 14 08:51:57 INFO train : 2152  loss 0.7141  elapsed 0.65  load 0.00\n",
      "22207 14 08:51:58 INFO train : 2153  loss 0.7025  elapsed 0.58  load 0.09\n",
      "22207 14 08:51:59 INFO train : 2154  loss 0.6930  elapsed 0.53  load 1.00\n",
      "22207 14 08:52:00 INFO train : 2155  loss 0.7292  elapsed 0.64  load 0.00\n",
      "22207 14 08:52:00 INFO train : 2156  loss 0.7025  elapsed 0.60  load 0.00\n",
      "22207 14 08:52:01 INFO train : 2157  loss 0.7356  elapsed 0.64  load 0.03\n",
      "22207 14 08:52:02 INFO train : 2158  loss 0.6984  elapsed 0.54  load 0.69\n",
      "22207 14 08:52:03 INFO train : 2159  loss 0.6803  elapsed 0.61  load 0.00\n",
      "22207 14 08:52:04 INFO train : 2160  loss 0.7300  elapsed 0.71  load 0.00\n",
      "22207 14 08:52:04 INFO train : 2161  loss 0.7309  elapsed 0.57  load 0.01\n",
      "22207 14 08:52:06 INFO train : 2162  loss 0.7093  elapsed 0.60  load 1.35\n",
      "22207 14 08:52:07 INFO train : 2163  loss 0.7128  elapsed 0.59  load 0.00\n",
      "22207 14 08:52:08 INFO train : 2164  loss 0.7145  elapsed 0.68  load 0.00\n",
      "22207 14 08:52:08 INFO train : 2165  loss 0.7078  elapsed 0.75  load 0.03\n",
      "22207 14 08:52:10 INFO train : 2166  loss 0.7264  elapsed 0.56  load 0.70\n",
      "22207 14 08:52:10 INFO train : 2167  loss 0.7174  elapsed 0.60  load 0.00\n",
      "22207 14 08:52:11 INFO train : 2168  loss 0.7169  elapsed 0.58  load 0.00\n",
      "22207 14 08:52:12 INFO train : 2169  loss 0.7099  elapsed 0.56  load 0.14\n",
      "22207 14 08:52:13 INFO train : 2170  loss 0.6947  elapsed 0.54  load 1.13\n",
      "22207 14 08:52:14 INFO train : 2171  loss 0.7073  elapsed 0.63  load 0.00\n",
      "22207 14 08:52:15 INFO train : 2172  loss 0.6730  elapsed 0.63  load 0.00\n",
      "22207 14 08:52:15 INFO train : 2173  loss 0.6826  elapsed 0.50  load 0.09\n",
      "22207 14 08:52:17 INFO train : 2174  loss 0.6688  elapsed 0.57  load 1.39\n",
      "22207 14 08:52:18 INFO train : 2175  loss 0.7170  elapsed 0.63  load 0.00\n",
      "22207 14 08:52:19 INFO train : 2176  loss 0.7264  elapsed 0.66  load 0.00\n",
      "22207 14 08:52:19 INFO train : 2177  loss 0.7668  elapsed 0.53  load 0.02\n",
      "22207 14 08:52:21 INFO train : 2178  loss 0.7450  elapsed 0.59  load 1.33\n",
      "22207 14 08:52:22 INFO train : 2179  loss 0.6904  elapsed 0.61  load 0.00\n",
      "22207 14 08:52:22 INFO train : 2180  loss 0.7403  elapsed 0.72  load 0.00\n",
      "22207 14 08:52:23 INFO train : 2181  loss 0.7086  elapsed 0.68  load 0.09\n",
      "22207 14 08:52:24 INFO train : 2182  loss 0.7182  elapsed 0.60  load 0.53\n",
      "22207 14 08:52:25 INFO train : 2183  loss 0.7316  elapsed 0.61  load 0.00\n",
      "22207 14 08:52:26 INFO train : 2184  loss 0.6888  elapsed 0.65  load 0.00\n",
      "22207 14 08:52:26 INFO train : 2185  loss 0.6998  elapsed 0.59  load 0.01\n",
      "22207 14 08:52:28 INFO train : 2186  loss 0.7176  elapsed 0.62  load 1.03\n",
      "22207 14 08:52:29 INFO train : 2187  loss 0.7560  elapsed 0.56  load 0.00\n",
      "22207 14 08:52:29 INFO train : 2188  loss 0.7155  elapsed 0.66  load 0.00\n",
      "22207 14 08:52:30 INFO train : 2189  loss 0.7134  elapsed 0.69  load 0.10\n",
      "22207 14 08:52:32 INFO train : 2190  loss 0.7164  elapsed 0.62  load 0.91\n",
      "22207 14 08:52:32 INFO train : 2191  loss 0.7462  elapsed 0.65  load 0.00\n",
      "22207 14 08:52:33 INFO train : 2192  loss 0.7366  elapsed 0.54  load 0.00\n",
      "22207 14 08:52:33 INFO train : 2193  loss 0.7292  elapsed 0.64  load 0.01\n",
      "22207 14 08:52:35 INFO train : 2194  loss 0.7227  elapsed 0.57  load 0.96\n",
      "22207 14 08:52:36 INFO train : 2195  loss 0.7752  elapsed 0.62  load 0.01\n",
      "22207 14 08:52:36 INFO train : 2196  loss 0.7251  elapsed 0.68  load 0.00\n",
      "22207 14 08:52:37 INFO train : 2197  loss 0.7505  elapsed 0.56  load 0.07\n",
      "22207 14 08:52:39 INFO train : 2198  loss 0.6982  elapsed 0.71  load 0.97\n",
      "22207 14 08:52:39 INFO train : 2199  loss 0.7234  elapsed 0.65  load 0.00\n",
      "22207 14 08:52:40 INFO train : 2200  loss 0.7262  elapsed 0.55  load 0.00\n",
      "22207 14 08:52:41 INFO train : 2201  loss 0.7039  elapsed 0.56  load 0.04\n",
      "22207 14 08:52:42 INFO train : 2202  loss 0.6916  elapsed 0.57  load 0.71\n",
      "22207 14 08:52:43 INFO train : 2203  loss 0.7187  elapsed 0.64  load 0.00\n",
      "22207 14 08:52:43 INFO train : 2204  loss 0.6842  elapsed 0.62  load 0.00\n",
      "22207 14 08:52:44 INFO train : 2205  loss 0.7002  elapsed 0.59  load 0.07\n",
      "22207 14 08:52:46 INFO train : 2206  loss 0.7004  elapsed 0.65  load 1.01\n",
      "22207 14 08:52:46 INFO train : 2207  loss 0.7127  elapsed 0.59  load 0.00\n",
      "22207 14 08:52:47 INFO train : 2208  loss 0.7259  elapsed 0.64  load 0.00\n",
      "22207 14 08:52:48 INFO train : 2209  loss 0.7910  elapsed 0.66  load 0.02\n",
      "22207 14 08:52:49 INFO train : 2210  loss 0.7204  elapsed 0.60  load 1.05\n",
      "22207 14 08:52:50 INFO train : 2211  loss 0.7326  elapsed 0.58  load 0.00\n",
      "22207 14 08:52:50 INFO train : 2212  loss 0.7364  elapsed 0.59  load 0.00\n",
      "22207 14 08:52:51 INFO train : 2213  loss 0.7192  elapsed 0.64  load 0.00\n",
      "22207 14 08:52:53 INFO train : 2214  loss 0.7063  elapsed 0.61  load 1.32\n",
      "22207 14 08:52:54 INFO train : 2215  loss 0.7046  elapsed 0.63  load 0.00\n",
      "22207 14 08:52:54 INFO train : 2216  loss 0.6612  elapsed 0.58  load 0.00\n",
      "22207 14 08:52:55 INFO train : 2217  loss 0.7085  elapsed 0.58  load 0.01\n",
      "22207 14 08:52:57 INFO train : 2218  loss 0.7412  elapsed 0.63  load 1.30\n",
      "22207 14 08:52:57 INFO train : 2219  loss 0.6689  elapsed 0.56  load 0.02\n",
      "22207 14 08:52:58 INFO train : 2220  loss 0.7127  elapsed 0.59  load 0.00\n",
      "22207 14 08:52:59 INFO train : 2221  loss 0.7418  elapsed 0.61  load 0.02\n",
      "22207 14 08:53:01 INFO train : 2222  loss 0.6886  elapsed 0.67  load 1.78\n",
      "22207 14 08:53:02 INFO train : 2223  loss 0.7046  elapsed 0.63  load 0.00\n",
      "22207 14 08:53:03 INFO train : 2224  loss 0.6885  elapsed 0.67  load 0.00\n",
      "22207 14 08:53:03 INFO train : 2225  loss 0.6946  elapsed 0.59  load 0.03\n",
      "22207 14 08:53:05 INFO train : 2226  loss 0.7485  elapsed 0.56  load 1.10\n",
      "22207 14 08:53:06 INFO train : 2227  loss 0.7131  elapsed 0.63  load 0.04\n",
      "22207 14 08:53:06 INFO train : 2228  loss 0.7171  elapsed 0.60  load 0.00\n",
      "22207 14 08:53:07 INFO train : 2229  loss 0.7370  elapsed 0.76  load 0.00\n",
      "22207 14 08:53:08 INFO train : 2230  loss 0.7193  elapsed 0.59  load 0.71\n",
      "22207 14 08:53:09 INFO train : 2231  loss 0.7075  elapsed 0.59  load 0.00\n",
      "22207 14 08:53:09 INFO train : 2232  loss 0.7570  elapsed 0.60  load 0.03\n",
      "22207 14 08:53:10 INFO train : 2233  loss 0.7093  elapsed 0.63  load 0.00\n",
      "22207 14 08:53:12 INFO train : 2234  loss 0.6890  elapsed 0.59  load 1.16\n",
      "22207 14 08:53:13 INFO train : 2235  loss 0.7733  elapsed 0.59  load 0.00\n",
      "22207 14 08:53:13 INFO train : 2236  loss 0.7229  elapsed 0.62  load 0.00\n",
      "22207 14 08:53:14 INFO train : 2237  loss 0.7792  elapsed 0.62  load 0.00\n",
      "22207 14 08:53:15 INFO train : 2238  loss 0.6760  elapsed 0.60  load 0.77\n",
      "22207 14 08:53:16 INFO train : 2239  loss 0.7280  elapsed 0.64  load 0.00\n",
      "22207 14 08:53:17 INFO train : 2240  loss 0.6940  elapsed 0.59  load 0.00\n",
      "22207 14 08:53:17 INFO train : 2241  loss 0.7515  elapsed 0.57  load 0.00\n",
      "22207 14 08:53:19 INFO train : 2242  loss 0.7297  elapsed 0.59  load 1.05\n",
      "22207 14 08:53:20 INFO train : 2243  loss 0.7102  elapsed 0.65  load 0.00\n",
      "22207 14 08:53:20 INFO train : 2244  loss 0.7171  elapsed 0.65  load 0.00\n",
      "22207 14 08:53:21 INFO train : 2245  loss 0.7580  elapsed 0.59  load 0.04\n",
      "22207 14 08:53:22 INFO train : 2246  loss 0.7334  elapsed 0.60  load 0.77\n",
      "22207 14 08:53:23 INFO train : 2247  loss 0.7261  elapsed 0.56  load 0.02\n",
      "22207 14 08:53:24 INFO train : 2248  loss 0.7420  elapsed 0.64  load 0.00\n",
      "22207 14 08:53:24 INFO train : 2249  loss 0.6937  elapsed 0.57  load 0.00\n",
      "22207 14 08:53:25 INFO train : 2250  loss 0.7202  elapsed 0.56  load 0.65\n",
      "22207 14 08:53:26 INFO train : 2251  loss 0.6814  elapsed 0.61  load 0.00\n",
      "22207 14 08:53:27 INFO train : 2252  loss 0.6892  elapsed 0.62  load 0.00\n",
      "22207 14 08:53:27 INFO train : 2253  loss 0.6957  elapsed 0.62  load 0.01\n",
      "22207 14 08:53:29 INFO train : 2254  loss 0.6939  elapsed 0.54  load 1.18\n",
      "22207 14 08:53:30 INFO train : 2255  loss 0.7966  elapsed 0.57  load 0.00\n",
      "22207 14 08:53:30 INFO train : 2256  loss 0.7498  elapsed 0.60  load 0.00\n",
      "22207 14 08:53:31 INFO train : 2257  loss 0.7230  elapsed 0.55  load 0.01\n",
      "22207 14 08:53:32 INFO train : 2258  loss 0.7225  elapsed 0.59  load 0.79\n",
      "22207 14 08:53:33 INFO train : 2259  loss 0.7650  elapsed 0.56  load 0.00\n",
      "22207 14 08:53:33 INFO train : 2260  loss 0.7228  elapsed 0.59  load 0.00\n",
      "22207 14 08:53:34 INFO train : 2261  loss 0.7195  elapsed 0.72  load 0.01\n",
      "22207 14 08:53:36 INFO train : 2262  loss 0.6983  elapsed 0.65  load 0.98\n",
      "22207 14 08:53:37 INFO train : 2263  loss 0.7169  elapsed 0.61  load 0.00\n",
      "22207 14 08:53:37 INFO train : 2264  loss 0.6941  elapsed 0.61  load 0.00\n",
      "22207 14 08:53:38 INFO train : 2265  loss 0.6944  elapsed 0.63  load 0.06\n",
      "22207 14 08:53:40 INFO train : 2266  loss 0.7032  elapsed 0.64  load 1.04\n",
      "22207 14 08:53:40 INFO train : 2267  loss 0.7454  elapsed 0.56  load 0.00\n",
      "22207 14 08:53:41 INFO train : 2268  loss 0.6937  elapsed 0.59  load 0.00\n",
      "22207 14 08:53:41 INFO train : 2269  loss 0.6937  elapsed 0.52  load 0.07\n",
      "22207 14 08:53:43 INFO train : 2270  loss 0.7022  elapsed 0.56  load 0.90\n",
      "22207 14 08:53:43 INFO train : 2271  loss 0.7340  elapsed 0.59  load 0.00\n",
      "22207 14 08:53:44 INFO train : 2272  loss 0.7228  elapsed 0.65  load 0.00\n",
      "22207 14 08:53:45 INFO train : 2273  loss 0.7448  elapsed 0.50  load 0.01\n",
      "22207 14 08:53:47 INFO train : 2274  loss 0.7496  elapsed 0.60  load 1.29\n",
      "22207 14 08:53:47 INFO train : 2275  loss 0.7695  elapsed 0.59  load 0.01\n",
      "22207 14 08:53:48 INFO train : 2276  loss 0.6894  elapsed 0.56  load 0.00\n",
      "22207 14 08:53:48 INFO train : 2277  loss 0.7407  elapsed 0.56  load 0.00\n",
      "22207 14 08:53:50 INFO train : 2278  loss 0.7090  elapsed 0.56  load 1.35\n",
      "22207 14 08:53:51 INFO train : 2279  loss 0.7206  elapsed 0.61  load 0.00\n",
      "22207 14 08:53:51 INFO train : 2280  loss 0.6804  elapsed 0.59  load 0.00\n",
      "22207 14 08:53:52 INFO train : 2281  loss 0.7152  elapsed 0.61  load 0.00\n",
      "22207 14 08:53:54 INFO train : 2282  loss 0.7059  elapsed 0.67  load 1.50\n",
      "22207 14 08:53:55 INFO train : 2283  loss 0.6715  elapsed 0.58  load 0.00\n",
      "22207 14 08:53:56 INFO train : 2284  loss 0.7159  elapsed 0.59  load 0.00\n",
      "22207 14 08:53:56 INFO train : 2285  loss 0.6738  elapsed 0.63  load 0.01\n",
      "22207 14 08:53:58 INFO train : 2286  loss 0.7321  elapsed 0.55  load 1.18\n",
      "22207 14 08:53:59 INFO train : 2287  loss 0.7123  elapsed 0.68  load 0.00\n",
      "22207 14 08:53:59 INFO train : 2288  loss 0.6973  elapsed 0.60  load 0.00\n",
      "22207 14 08:54:00 INFO train : 2289  loss 0.7053  elapsed 0.65  load 0.02\n",
      "22207 14 08:54:01 INFO train : 2290  loss 0.7038  elapsed 0.56  load 0.85\n",
      "22207 14 08:54:02 INFO train : 2291  loss 0.7269  elapsed 0.63  load 0.00\n",
      "22207 14 08:54:03 INFO train : 2292  loss 0.7377  elapsed 0.68  load 0.00\n",
      "22207 14 08:54:03 INFO train : 2293  loss 0.7098  elapsed 0.59  load 0.06\n",
      "22207 14 08:54:05 INFO train : 2294  loss 0.7294  elapsed 0.64  load 0.83\n",
      "22207 14 08:54:06 INFO train : 2295  loss 0.7065  elapsed 0.67  load 0.00\n",
      "22207 14 08:54:06 INFO train : 2296  loss 0.7171  elapsed 0.63  load 0.00\n",
      "22207 14 08:54:07 INFO train : 2297  loss 0.7203  elapsed 0.62  load 0.03\n",
      "22207 14 08:54:09 INFO train : 2298  loss 0.6963  elapsed 0.61  load 1.25\n",
      "22207 14 08:54:09 INFO train : 2299  loss 0.7289  elapsed 0.60  load 0.00\n",
      "22207 14 08:54:10 INFO train : 2300  loss 0.6887  elapsed 0.69  load 0.00\n",
      "22207 14 08:54:11 INFO train : 2301  loss 0.6938  elapsed 0.65  load 0.03\n",
      "22207 14 08:54:12 INFO train : 2302  loss 0.7219  elapsed 0.65  load 1.14\n",
      "22207 14 08:54:13 INFO train : 2303  loss 0.7382  elapsed 0.66  load 0.00\n",
      "22207 14 08:54:14 INFO train : 2304  loss 0.7050  elapsed 0.73  load 0.00\n",
      "22207 14 08:54:15 INFO train : 2305  loss 0.7363  elapsed 0.54  load 0.07\n",
      "22207 14 08:54:16 INFO train : 2306  loss 0.6991  elapsed 0.60  load 0.70\n",
      "22207 14 08:54:16 INFO train : 2307  loss 0.7576  elapsed 0.54  load 0.00\n",
      "22207 14 08:54:17 INFO train : 2308  loss 0.7501  elapsed 0.58  load 0.00\n",
      "22207 14 08:54:18 INFO train : 2309  loss 0.7382  elapsed 0.55  load 0.03\n",
      "22207 14 08:54:20 INFO train : 2310  loss 0.7039  elapsed 0.62  load 1.16\n",
      "22207 14 08:54:20 INFO train : 2311  loss 0.7091  elapsed 0.63  load 0.00\n",
      "22207 14 08:54:21 INFO train : 2312  loss 0.6881  elapsed 0.74  load 0.00\n",
      "22207 14 08:54:22 INFO train : 2313  loss 0.7014  elapsed 0.56  load 0.02\n",
      "22207 14 08:54:23 INFO train : 2314  loss 0.6627  elapsed 0.55  load 1.29\n",
      "22207 14 08:54:24 INFO train : 2315  loss 0.7192  elapsed 0.59  load 0.00\n",
      "22207 14 08:54:25 INFO train : 2316  loss 0.7343  elapsed 0.53  load 0.00\n",
      "22207 14 08:54:25 INFO train : 2317  loss 0.6951  elapsed 0.50  load 0.00\n",
      "22207 14 08:54:27 INFO train : 2318  loss 0.6923  elapsed 0.56  load 1.55\n",
      "22207 14 08:54:28 INFO train : 2319  loss 0.6996  elapsed 0.60  load 0.00\n",
      "22207 14 08:54:28 INFO train : 2320  loss 0.6868  elapsed 0.67  load 0.00\n",
      "22207 14 08:54:29 INFO train : 2321  loss 0.7168  elapsed 0.62  load 0.01\n",
      "22207 14 08:54:31 INFO train : 2322  loss 0.6914  elapsed 0.61  load 1.23\n",
      "22207 14 08:54:32 INFO train : 2323  loss 0.6989  elapsed 0.60  load 0.00\n",
      "22207 14 08:54:32 INFO train : 2324  loss 0.6988  elapsed 0.69  load 0.00\n",
      "22207 14 08:54:33 INFO train : 2325  loss 0.7313  elapsed 0.67  load 0.00\n",
      "22207 14 08:54:34 INFO train : 2326  loss 0.6821  elapsed 0.65  load 0.68\n",
      "22207 14 08:54:35 INFO train : 2327  loss 0.7373  elapsed 0.61  load 0.01\n",
      "22207 14 08:54:36 INFO train : 2328  loss 0.6969  elapsed 0.72  load 0.00\n",
      "22207 14 08:54:36 INFO train : 2329  loss 0.7199  elapsed 0.55  load 0.00\n",
      "22207 14 08:54:37 INFO train : 2330  loss 0.6932  elapsed 0.54  load 0.45\n",
      "22207 14 08:54:38 INFO train : 2331  loss 0.6797  elapsed 0.66  load 0.00\n",
      "22207 14 08:54:39 INFO train : 2332  loss 0.7154  elapsed 0.61  load 0.00\n",
      "22207 14 08:54:39 INFO train : 2333  loss 0.6769  elapsed 0.59  load 0.05\n",
      "22207 14 08:54:41 INFO train : 2334  loss 0.7133  elapsed 0.56  load 0.78\n",
      "22207 14 08:54:41 INFO train : 2335  loss 0.7172  elapsed 0.60  load 0.02\n",
      "22207 14 08:54:42 INFO train : 2336  loss 0.7292  elapsed 0.63  load 0.00\n",
      "22207 14 08:54:43 INFO train : 2337  loss 0.7293  elapsed 0.64  load 0.01\n",
      "22207 14 08:54:45 INFO train : 2338  loss 0.6741  elapsed 0.62  load 1.54\n",
      "22207 14 08:54:45 INFO train : 2339  loss 0.7610  elapsed 0.58  load 0.00\n",
      "22207 14 08:54:46 INFO train : 2340  loss 0.6976  elapsed 0.50  load 0.00\n",
      "22207 14 08:54:47 INFO train : 2341  loss 0.7078  elapsed 0.60  load 0.01\n",
      "22207 14 08:54:48 INFO train : 2342  loss 0.7935  elapsed 0.64  load 1.16\n",
      "22207 14 08:54:49 INFO train : 2343  loss 0.7709  elapsed 0.62  load 0.00\n",
      "22207 14 08:54:50 INFO train : 2344  loss 0.6820  elapsed 0.64  load 0.00\n",
      "22207 14 08:54:50 INFO train : 2345  loss 0.6844  elapsed 0.61  load 0.00\n",
      "22207 14 08:54:52 INFO train : 2346  loss 0.7342  elapsed 0.50  load 1.24\n",
      "22207 14 08:54:53 INFO train : 2347  loss 0.7195  elapsed 0.60  load 0.00\n",
      "22207 14 08:54:53 INFO train : 2348  loss 0.7393  elapsed 0.57  load 0.00\n",
      "22207 14 08:54:54 INFO train : 2349  loss 0.6912  elapsed 0.60  load 0.00\n",
      "22207 14 08:54:56 INFO train : 2350  loss 0.7620  elapsed 0.63  load 1.00\n",
      "22207 14 08:54:56 INFO train : 2351  loss 0.6895  elapsed 0.76  load 0.00\n",
      "22207 14 08:54:57 INFO train : 2352  loss 0.6844  elapsed 0.67  load 0.01\n",
      "22207 14 08:54:58 INFO train : 2353  loss 0.6822  elapsed 0.70  load 0.01\n",
      "22207 14 08:54:59 INFO train : 2354  loss 0.7114  elapsed 0.55  load 0.57\n",
      "22207 14 08:55:00 INFO train : 2355  loss 0.7161  elapsed 0.64  load 0.00\n",
      "22207 14 08:55:00 INFO train : 2356  loss 0.7090  elapsed 0.68  load 0.00\n",
      "22207 14 08:55:01 INFO train : 2357  loss 0.7314  elapsed 0.56  load 0.02\n",
      "22207 14 08:55:02 INFO train : 2358  loss 0.6872  elapsed 0.58  load 0.67\n",
      "22207 14 08:55:03 INFO train : 2359  loss 0.7004  elapsed 0.59  load 0.00\n",
      "22207 14 08:55:03 INFO train : 2360  loss 0.7277  elapsed 0.60  load 0.00\n",
      "22207 14 08:55:04 INFO train : 2361  loss 0.6885  elapsed 0.65  load 0.02\n",
      "22207 14 08:55:05 INFO train : 2362  loss 0.7105  elapsed 0.55  load 0.71\n",
      "22207 14 08:55:06 INFO train : 2363  loss 0.7630  elapsed 0.56  load 0.00\n",
      "22207 14 08:55:07 INFO train : 2364  loss 0.7419  elapsed 0.63  load 0.00\n",
      "22207 14 08:55:07 INFO train : 2365  loss 0.7584  elapsed 0.61  load 0.04\n",
      "22207 14 08:55:09 INFO train : 2366  loss 0.7060  elapsed 0.54  load 1.03\n",
      "22207 14 08:55:10 INFO train : 2367  loss 0.7635  elapsed 0.65  load 0.00\n",
      "22207 14 08:55:10 INFO train : 2368  loss 0.7351  elapsed 0.64  load 0.00\n",
      "22207 14 08:55:11 INFO train : 2369  loss 0.7433  elapsed 0.60  load 0.00\n",
      "22207 14 08:55:12 INFO train : 2370  loss 0.7358  elapsed 0.62  load 0.94\n",
      "22207 14 08:55:13 INFO train : 2371  loss 0.7071  elapsed 0.56  load 0.00\n",
      "22207 14 08:55:14 INFO train : 2372  loss 0.7117  elapsed 0.53  load 0.08\n",
      "22207 14 08:55:14 INFO train : 2373  loss 0.7612  elapsed 0.63  load 0.04\n",
      "22207 14 08:55:16 INFO train : 2374  loss 0.6931  elapsed 0.59  load 1.19\n",
      "22207 14 08:55:17 INFO train : 2375  loss 0.7456  elapsed 0.54  load 0.00\n",
      "22207 14 08:55:17 INFO train : 2376  loss 0.7197  elapsed 0.65  load 0.04\n",
      "22207 14 08:55:18 INFO train : 2377  loss 0.7345  elapsed 0.64  load 0.00\n",
      "22207 14 08:55:20 INFO train : 2378  loss 0.7154  elapsed 0.61  load 0.97\n",
      "22207 14 08:55:20 INFO train : 2379  loss 0.7566  elapsed 0.64  load 0.00\n",
      "22207 14 08:55:21 INFO train : 2380  loss 0.7710  elapsed 0.66  load 0.02\n",
      "22207 14 08:55:22 INFO train : 2381  loss 0.6630  elapsed 0.63  load 0.00\n",
      "22207 14 08:55:23 INFO train : 2382  loss 0.6930  elapsed 0.57  load 0.86\n",
      "22207 14 08:55:24 INFO train : 2383  loss 0.6914  elapsed 0.62  load 0.00\n",
      "22207 14 08:55:24 INFO train : 2384  loss 0.6739  elapsed 0.66  load 0.03\n",
      "22207 14 08:55:25 INFO train : 2385  loss 0.7101  elapsed 0.52  load 0.00\n",
      "22207 14 08:55:27 INFO train : 2386  loss 0.7168  elapsed 0.62  load 1.62\n",
      "22207 14 08:55:28 INFO train : 2387  loss 0.7007  elapsed 0.67  load 0.00\n",
      "22207 14 08:55:29 INFO train : 2388  loss 0.6741  elapsed 0.60  load 0.00\n",
      "22207 14 08:55:29 INFO train : 2389  loss 0.6855  elapsed 0.67  load 0.00\n",
      "22207 14 08:55:31 INFO train : 2390  loss 0.7398  elapsed 0.62  load 0.71\n",
      "22207 14 08:55:31 INFO train : 2391  loss 0.7567  elapsed 0.67  load 0.00\n",
      "22207 14 08:55:32 INFO train : 2392  loss 0.7214  elapsed 0.58  load 0.04\n",
      "22207 14 08:55:33 INFO train : 2393  loss 0.7294  elapsed 0.59  load 0.00\n",
      "22207 14 08:55:34 INFO train : 2394  loss 0.7227  elapsed 0.60  load 1.03\n",
      "22207 14 08:55:35 INFO train : 2395  loss 0.7213  elapsed 0.55  load 0.00\n",
      "22207 14 08:55:35 INFO train : 2396  loss 0.7052  elapsed 0.58  load 0.00\n",
      "22207 14 08:55:36 INFO train : 2397  loss 0.7292  elapsed 0.57  load 0.00\n",
      "22207 14 08:55:38 INFO train : 2398  loss 0.7016  elapsed 0.62  load 1.20\n",
      "22207 14 08:55:39 INFO train : 2399  loss 0.7036  elapsed 0.64  load 0.00\n",
      "22207 14 08:55:39 INFO train : 2400  loss 0.7118  elapsed 0.55  load 0.00\n",
      "22207 14 08:55:40 INFO train : 2401  loss 0.6911  elapsed 0.53  load 0.00\n",
      "22207 14 08:55:40 INFO train : 2402  loss 0.6974  elapsed 0.50  load 0.10\n",
      "22207 14 08:55:41 INFO train : 2403  loss 0.7129  elapsed 0.49  load 0.00\n",
      "22207 14 08:55:41 INFO train : 2404  loss 0.7378  elapsed 0.49  load 0.00\n",
      "22207 14 08:55:42 INFO train : 2405  loss 0.6580  elapsed 0.50  load 0.00\n",
      "22207 14 08:55:42 INFO train : 2406  loss 0.7455  elapsed 0.13  load 0.00\n",
      "22207 14 08:55:42 INFO Epoch num: 6\n",
      "22207 14 08:55:47 INFO train : 2407  loss 0.6960  elapsed 0.60  load 4.11\n",
      "22207 14 08:55:47 INFO train : 2408  loss 0.6720  elapsed 0.55  load 0.00\n",
      "22207 14 08:55:48 INFO train : 2409  loss 0.6613  elapsed 0.57  load 0.00\n",
      "22207 14 08:55:48 INFO train : 2410  loss 0.6794  elapsed 0.51  load 0.00\n",
      "22207 14 08:55:50 INFO train : 2411  loss 0.6895  elapsed 0.57  load 0.89\n",
      "22207 14 08:55:51 INFO train : 2412  loss 0.6918  elapsed 0.61  load 0.06\n",
      "22207 14 08:55:51 INFO train : 2413  loss 0.7058  elapsed 0.63  load 0.00\n",
      "22207 14 08:55:52 INFO train : 2414  loss 0.6909  elapsed 0.70  load 0.00\n",
      "22207 14 08:55:54 INFO train : 2415  loss 0.6555  elapsed 0.56  load 1.12\n",
      "22207 14 08:55:54 INFO train : 2416  loss 0.6689  elapsed 0.57  load 0.10\n",
      "22207 14 08:55:55 INFO train : 2417  loss 0.7187  elapsed 0.71  load 0.00\n",
      "22207 14 08:55:56 INFO train : 2418  loss 0.7140  elapsed 0.64  load 0.00\n",
      "22207 14 08:55:57 INFO train : 2419  loss 0.6849  elapsed 0.65  load 1.02\n",
      "22207 14 08:55:58 INFO train : 2420  loss 0.6643  elapsed 0.64  load 0.01\n",
      "22207 14 08:55:59 INFO train : 2421  loss 0.6743  elapsed 0.61  load 0.00\n",
      "22207 14 08:55:59 INFO train : 2422  loss 0.6833  elapsed 0.69  load 0.00\n",
      "22207 14 08:56:01 INFO train : 2423  loss 0.6854  elapsed 0.58  load 0.80\n",
      "22207 14 08:56:02 INFO train : 2424  loss 0.6801  elapsed 0.59  load 0.04\n",
      "22207 14 08:56:02 INFO train : 2425  loss 0.6848  elapsed 0.63  load 0.00\n",
      "22207 14 08:56:03 INFO train : 2426  loss 0.6808  elapsed 0.62  load 0.00\n",
      "22207 14 08:56:04 INFO train : 2427  loss 0.7090  elapsed 0.58  load 0.74\n",
      "22207 14 08:56:05 INFO train : 2428  loss 0.6722  elapsed 0.64  load 0.10\n",
      "22207 14 08:56:05 INFO train : 2429  loss 0.6522  elapsed 0.57  load 0.00\n",
      "22207 14 08:56:06 INFO train : 2430  loss 0.6517  elapsed 0.60  load 0.00\n",
      "22207 14 08:56:08 INFO train : 2431  loss 0.7485  elapsed 0.57  load 1.16\n",
      "22207 14 08:56:09 INFO train : 2432  loss 0.7295  elapsed 0.64  load 0.16\n",
      "22207 14 08:56:09 INFO train : 2433  loss 0.7282  elapsed 0.57  load 0.00\n",
      "22207 14 08:56:10 INFO train : 2434  loss 0.7248  elapsed 0.64  load 0.00\n",
      "22207 14 08:56:11 INFO train : 2435  loss 0.7054  elapsed 0.68  load 0.89\n",
      "22207 14 08:56:12 INFO train : 2436  loss 0.6610  elapsed 0.68  load 0.01\n",
      "22207 14 08:56:13 INFO train : 2437  loss 0.6932  elapsed 0.68  load 0.01\n",
      "22207 14 08:56:14 INFO train : 2438  loss 0.7037  elapsed 0.58  load 0.00\n",
      "22207 14 08:56:15 INFO train : 2439  loss 0.6427  elapsed 0.61  load 0.80\n",
      "22207 14 08:56:16 INFO train : 2440  loss 0.6471  elapsed 0.53  load 0.01\n",
      "22207 14 08:56:16 INFO train : 2441  loss 0.6673  elapsed 0.61  load 0.00\n",
      "22207 14 08:56:17 INFO train : 2442  loss 0.6641  elapsed 0.63  load 0.00\n",
      "22207 14 08:56:18 INFO train : 2443  loss 0.6430  elapsed 0.51  load 0.91\n",
      "22207 14 08:56:19 INFO train : 2444  loss 0.6768  elapsed 0.60  load 0.02\n",
      "22207 14 08:56:20 INFO train : 2445  loss 0.6915  elapsed 0.65  load 0.05\n",
      "22207 14 08:56:20 INFO train : 2446  loss 0.6606  elapsed 0.60  load 0.00\n",
      "22207 14 08:56:22 INFO train : 2447  loss 0.6930  elapsed 0.56  load 0.67\n",
      "22207 14 08:56:22 INFO train : 2448  loss 0.6598  elapsed 0.60  load 0.05\n",
      "22207 14 08:56:23 INFO train : 2449  loss 0.6613  elapsed 0.63  load 0.01\n",
      "22207 14 08:56:24 INFO train : 2450  loss 0.6748  elapsed 0.67  load 0.00\n",
      "22207 14 08:56:25 INFO train : 2451  loss 0.6255  elapsed 0.57  load 1.10\n",
      "22207 14 08:56:26 INFO train : 2452  loss 0.7853  elapsed 0.55  load 0.01\n",
      "22207 14 08:56:26 INFO train : 2453  loss 0.6653  elapsed 0.62  load 0.00\n",
      "22207 14 08:56:27 INFO train : 2454  loss 0.6724  elapsed 0.61  load 0.03\n",
      "22207 14 08:56:29 INFO train : 2455  loss 0.6667  elapsed 0.59  load 0.93\n",
      "22207 14 08:56:29 INFO train : 2456  loss 0.6716  elapsed 0.55  load 0.05\n",
      "22207 14 08:56:30 INFO train : 2457  loss 0.6849  elapsed 0.60  load 0.01\n",
      "22207 14 08:56:31 INFO train : 2458  loss 0.6837  elapsed 0.63  load 0.00\n",
      "22207 14 08:56:32 INFO train : 2459  loss 0.6708  elapsed 0.59  load 0.92\n",
      "22207 14 08:56:33 INFO train : 2460  loss 0.6576  elapsed 0.62  load 0.15\n",
      "22207 14 08:56:34 INFO train : 2461  loss 0.6679  elapsed 0.69  load 0.00\n",
      "22207 14 08:56:34 INFO train : 2462  loss 0.6718  elapsed 0.57  load 0.00\n",
      "22207 14 08:56:36 INFO train : 2463  loss 0.6806  elapsed 0.57  load 1.19\n",
      "22207 14 08:56:37 INFO train : 2464  loss 0.6656  elapsed 0.55  load 0.09\n",
      "22207 14 08:56:37 INFO train : 2465  loss 0.6476  elapsed 0.62  load 0.05\n",
      "22207 14 08:56:38 INFO train : 2466  loss 0.6821  elapsed 0.56  load 0.00\n",
      "22207 14 08:56:40 INFO train : 2467  loss 0.6725  elapsed 0.60  load 1.13\n",
      "22207 14 08:56:40 INFO train : 2468  loss 0.7139  elapsed 0.65  load 0.03\n",
      "22207 14 08:56:41 INFO train : 2469  loss 0.6864  elapsed 0.64  load 0.01\n",
      "22207 14 08:56:42 INFO train : 2470  loss 0.7273  elapsed 0.56  load 0.00\n",
      "22207 14 08:56:43 INFO train : 2471  loss 0.7066  elapsed 0.52  load 0.81\n",
      "22207 14 08:56:44 INFO train : 2472  loss 0.6465  elapsed 0.58  load 0.08\n",
      "22207 14 08:56:44 INFO train : 2473  loss 0.6936  elapsed 0.65  load 0.01\n",
      "22207 14 08:56:45 INFO train : 2474  loss 0.6870  elapsed 0.65  load 0.00\n",
      "22207 14 08:56:47 INFO train : 2475  loss 0.7311  elapsed 0.61  load 1.20\n",
      "22207 14 08:56:48 INFO train : 2476  loss 0.6753  elapsed 0.65  load 0.01\n",
      "22207 14 08:56:48 INFO train : 2477  loss 0.7033  elapsed 0.56  load 0.02\n",
      "22207 14 08:56:49 INFO train : 2478  loss 0.6645  elapsed 0.68  load 0.00\n",
      "22207 14 08:56:50 INFO train : 2479  loss 0.6998  elapsed 0.71  load 0.67\n",
      "22207 14 08:56:51 INFO train : 2480  loss 0.7145  elapsed 0.70  load 0.04\n",
      "22207 14 08:56:52 INFO train : 2481  loss 0.6741  elapsed 0.64  load 0.00\n",
      "22207 14 08:56:52 INFO train : 2482  loss 0.6742  elapsed 0.63  load 0.00\n",
      "22207 14 08:56:54 INFO train : 2483  loss 0.6806  elapsed 0.53  load 0.81\n",
      "22207 14 08:56:54 INFO train : 2484  loss 0.6570  elapsed 0.59  load 0.02\n",
      "22207 14 08:56:55 INFO train : 2485  loss 0.6742  elapsed 0.68  load 0.00\n",
      "22207 14 08:56:56 INFO train : 2486  loss 0.6795  elapsed 0.66  load 0.00\n",
      "22207 14 08:56:57 INFO train : 2487  loss 0.6820  elapsed 0.63  load 0.82\n",
      "22207 14 08:56:58 INFO train : 2488  loss 0.6899  elapsed 0.60  load 0.10\n",
      "22207 14 08:56:59 INFO train : 2489  loss 0.6903  elapsed 0.68  load 0.00\n",
      "22207 14 08:56:59 INFO train : 2490  loss 0.7025  elapsed 0.62  load 0.00\n",
      "22207 14 08:57:01 INFO train : 2491  loss 0.6453  elapsed 0.56  load 0.84\n",
      "22207 14 08:57:01 INFO train : 2492  loss 0.6311  elapsed 0.63  load 0.01\n",
      "22207 14 08:57:02 INFO train : 2493  loss 0.7128  elapsed 0.70  load 0.00\n",
      "22207 14 08:57:03 INFO train : 2494  loss 0.6870  elapsed 0.63  load 0.00\n",
      "22207 14 08:57:05 INFO train : 2495  loss 0.6944  elapsed 0.60  load 1.18\n",
      "22207 14 08:57:05 INFO train : 2496  loss 0.7015  elapsed 0.59  load 0.00\n",
      "22207 14 08:57:06 INFO train : 2497  loss 0.6938  elapsed 0.66  load 0.00\n",
      "22207 14 08:57:06 INFO train : 2498  loss 0.6950  elapsed 0.52  load 0.00\n",
      "22207 14 08:57:08 INFO train : 2499  loss 0.7151  elapsed 0.55  load 1.15\n",
      "22207 14 08:57:09 INFO train : 2500  loss 0.7078  elapsed 0.59  load 0.00\n",
      "22207 14 08:57:09 INFO Running validation\n",
      "22207 14 08:57:30 INFO Logging 20 images\n",
      "22207 14 08:57:31 INFO Logging 20 audios\n",
      "22207 14 08:57:34 INFO Logging 20 texts\n",
      "22207 14 08:57:45 INFO val : 2500  loss 0.6631200909614563\n",
      "22207 14 08:57:45 INFO Saving model state at step 2500 to checkpoints/fp_step_00002500.pt\n",
      "22207 14 08:57:46 INFO train : 2501  loss 0.6741  elapsed 0.49  load 0.00\n",
      "22207 14 08:57:46 INFO train : 2502  loss 0.6793  elapsed 0.51  load 0.00\n",
      "22207 14 08:57:47 INFO train : 2503  loss 0.6562  elapsed 0.59  load 0.01\n",
      "22207 14 08:57:48 INFO train : 2504  loss 0.6689  elapsed 0.60  load 0.03\n",
      "22207 14 08:57:48 INFO train : 2505  loss 0.6891  elapsed 0.58  load 0.00\n",
      "22207 14 08:57:49 INFO train : 2506  loss 0.6558  elapsed 0.59  load 0.08\n",
      "22207 14 08:57:50 INFO train : 2507  loss 0.6780  elapsed 0.64  load 0.01\n",
      "22207 14 08:57:50 INFO train : 2508  loss 0.7043  elapsed 0.60  load 0.01\n",
      "22207 14 08:57:51 INFO train : 2509  loss 0.6591  elapsed 0.60  load 0.00\n",
      "22207 14 08:57:51 INFO train : 2510  loss 0.6919  elapsed 0.53  load 0.01\n",
      "22207 14 08:57:52 INFO train : 2511  loss 0.6652  elapsed 0.54  load 0.01\n",
      "22207 14 08:57:53 INFO train : 2512  loss 0.6709  elapsed 0.57  load 0.05\n",
      "22207 14 08:57:53 INFO train : 2513  loss 0.7019  elapsed 0.62  load 0.01\n",
      "22207 14 08:57:54 INFO train : 2514  loss 0.6677  elapsed 0.61  load 0.01\n",
      "22207 14 08:57:55 INFO train : 2515  loss 0.6807  elapsed 0.55  load 0.04\n",
      "22207 14 08:57:55 INFO train : 2516  loss 0.7081  elapsed 0.66  load 0.05\n",
      "22207 14 08:57:56 INFO train : 2517  loss 0.6981  elapsed 0.55  load 0.09\n",
      "22207 14 08:57:57 INFO train : 2518  loss 0.7012  elapsed 0.62  load 0.01\n",
      "22207 14 08:57:57 INFO train : 2519  loss 0.6785  elapsed 0.53  load 0.05\n",
      "22207 14 08:57:58 INFO train : 2520  loss 0.6636  elapsed 0.58  load 0.03\n",
      "22207 14 08:57:59 INFO train : 2521  loss 0.6822  elapsed 0.67  load 0.01\n",
      "22207 14 08:58:00 INFO train : 2522  loss 0.6790  elapsed 0.59  load 0.72\n",
      "22207 14 08:58:01 INFO train : 2523  loss 0.6875  elapsed 0.51  load 0.04\n",
      "22207 14 08:58:01 INFO train : 2524  loss 0.6753  elapsed 0.58  load 0.00\n",
      "22207 14 08:58:02 INFO train : 2525  loss 0.6769  elapsed 0.69  load 0.35\n",
      "22207 14 08:58:03 INFO train : 2526  loss 0.6692  elapsed 0.60  load 0.54\n",
      "22207 14 08:58:04 INFO train : 2527  loss 0.6896  elapsed 0.58  load 0.29\n",
      "22207 14 08:58:05 INFO train : 2528  loss 0.6997  elapsed 0.61  load 0.01\n",
      "22207 14 08:58:06 INFO train : 2529  loss 0.6795  elapsed 0.60  load 0.07\n",
      "22207 14 08:58:07 INFO train : 2530  loss 0.6977  elapsed 0.61  load 0.74\n",
      "22207 14 08:58:08 INFO train : 2531  loss 0.6829  elapsed 0.59  load 0.49\n",
      "22207 14 08:58:09 INFO train : 2532  loss 0.6816  elapsed 0.55  load 0.09\n",
      "22207 14 08:58:09 INFO train : 2533  loss 0.6948  elapsed 0.56  load 0.03\n",
      "22207 14 08:58:11 INFO train : 2534  loss 0.7260  elapsed 0.63  load 0.68\n",
      "22207 14 08:58:12 INFO train : 2535  loss 0.6649  elapsed 0.61  load 0.36\n",
      "22207 14 08:58:12 INFO train : 2536  loss 0.7135  elapsed 0.60  load 0.00\n",
      "22207 14 08:58:13 INFO train : 2537  loss 0.6607  elapsed 0.63  load 0.48\n",
      "22207 14 08:58:14 INFO train : 2538  loss 0.6709  elapsed 0.67  load 0.17\n",
      "22207 14 08:58:16 INFO train : 2539  loss 0.6915  elapsed 0.53  load 0.83\n",
      "22207 14 08:58:16 INFO train : 2540  loss 0.6783  elapsed 0.58  load 0.00\n",
      "22207 14 08:58:17 INFO train : 2541  loss 0.7112  elapsed 0.56  load 0.06\n",
      "22207 14 08:58:18 INFO train : 2542  loss 0.6519  elapsed 0.61  load 0.01\n",
      "22207 14 08:58:19 INFO train : 2543  loss 0.6463  elapsed 0.59  load 1.00\n",
      "22207 14 08:58:20 INFO train : 2544  loss 0.6876  elapsed 0.66  load 0.00\n",
      "22207 14 08:58:21 INFO train : 2545  loss 0.6574  elapsed 0.62  load 0.03\n",
      "22207 14 08:58:21 INFO train : 2546  loss 0.6295  elapsed 0.62  load 0.01\n",
      "22207 14 08:58:23 INFO train : 2547  loss 0.6909  elapsed 0.52  load 1.16\n",
      "22207 14 08:58:24 INFO train : 2548  loss 0.6720  elapsed 0.67  load 0.00\n",
      "22207 14 08:58:24 INFO train : 2549  loss 0.6829  elapsed 0.71  load 0.06\n",
      "22207 14 08:58:25 INFO train : 2550  loss 0.6668  elapsed 0.54  load 0.09\n",
      "22207 14 08:58:27 INFO train : 2551  loss 0.7626  elapsed 0.61  load 1.43\n",
      "22207 14 08:58:28 INFO train : 2552  loss 0.6819  elapsed 0.67  load 0.00\n",
      "22207 14 08:58:29 INFO train : 2553  loss 0.6940  elapsed 0.69  load 0.00\n",
      "22207 14 08:58:29 INFO train : 2554  loss 0.6544  elapsed 0.64  load 0.02\n",
      "22207 14 08:58:31 INFO train : 2555  loss 0.6549  elapsed 0.60  load 1.09\n",
      "22207 14 08:58:32 INFO train : 2556  loss 0.6693  elapsed 0.61  load 0.00\n",
      "22207 14 08:58:32 INFO train : 2557  loss 0.6737  elapsed 0.67  load 0.00\n",
      "22207 14 08:58:33 INFO train : 2558  loss 0.7139  elapsed 0.54  load 0.01\n",
      "22207 14 08:58:34 INFO train : 2559  loss 0.6554  elapsed 0.59  load 0.67\n",
      "22207 14 08:58:35 INFO train : 2560  loss 0.6459  elapsed 0.51  load 0.00\n",
      "22207 14 08:58:35 INFO train : 2561  loss 0.6900  elapsed 0.66  load 0.01\n",
      "22207 14 08:58:36 INFO train : 2562  loss 0.7222  elapsed 0.60  load 0.00\n",
      "22207 14 08:58:38 INFO train : 2563  loss 0.7147  elapsed 0.56  load 1.37\n",
      "22207 14 08:58:39 INFO train : 2564  loss 0.6795  elapsed 0.68  load 0.00\n",
      "22207 14 08:58:39 INFO train : 2565  loss 0.6808  elapsed 0.66  load 0.00\n",
      "22207 14 08:58:40 INFO train : 2566  loss 0.6494  elapsed 0.66  load 0.01\n",
      "22207 14 08:58:41 INFO train : 2567  loss 0.6858  elapsed 0.70  load 0.73\n",
      "22207 14 08:58:42 INFO train : 2568  loss 0.7184  elapsed 0.54  load 0.04\n",
      "22207 14 08:58:43 INFO train : 2569  loss 0.7605  elapsed 0.56  load 0.05\n",
      "22207 14 08:58:43 INFO train : 2570  loss 0.7128  elapsed 0.55  load 0.06\n",
      "22207 14 08:58:45 INFO train : 2571  loss 0.6787  elapsed 0.61  load 1.04\n",
      "22207 14 08:58:46 INFO train : 2572  loss 0.6705  elapsed 0.68  load 0.01\n",
      "22207 14 08:58:46 INFO train : 2573  loss 0.7274  elapsed 0.64  load 0.02\n",
      "22207 14 08:58:47 INFO train : 2574  loss 0.7530  elapsed 0.59  load 0.05\n",
      "22207 14 08:58:49 INFO train : 2575  loss 0.6771  elapsed 0.60  load 1.12\n",
      "22207 14 08:58:49 INFO train : 2576  loss 0.6511  elapsed 0.60  load 0.01\n",
      "22207 14 08:58:50 INFO train : 2577  loss 0.6489  elapsed 0.65  load 0.00\n",
      "22207 14 08:58:51 INFO train : 2578  loss 0.6584  elapsed 0.61  load 0.00\n",
      "22207 14 08:58:52 INFO train : 2579  loss 0.6854  elapsed 0.55  load 1.07\n",
      "22207 14 08:58:53 INFO train : 2580  loss 0.6823  elapsed 0.50  load 0.03\n",
      "22207 14 08:58:54 INFO train : 2581  loss 0.6877  elapsed 0.69  load 0.00\n",
      "22207 14 08:58:54 INFO train : 2582  loss 0.6506  elapsed 0.67  load 0.00\n",
      "22207 14 08:58:56 INFO train : 2583  loss 0.6426  elapsed 0.57  load 1.06\n",
      "22207 14 08:58:57 INFO train : 2584  loss 0.6799  elapsed 0.54  load 0.09\n",
      "22207 14 08:58:57 INFO train : 2585  loss 0.6742  elapsed 0.63  load 0.00\n",
      "22207 14 08:58:58 INFO train : 2586  loss 0.6601  elapsed 0.60  load 0.00\n",
      "22207 14 08:59:00 INFO train : 2587  loss 0.7174  elapsed 0.53  load 1.46\n",
      "22207 14 08:59:01 INFO train : 2588  loss 0.6896  elapsed 0.56  load 0.00\n",
      "22207 14 08:59:01 INFO train : 2589  loss 0.7131  elapsed 0.65  load 0.00\n",
      "22207 14 08:59:02 INFO train : 2590  loss 0.6951  elapsed 0.60  load 0.00\n",
      "22207 14 08:59:04 INFO train : 2591  loss 0.7069  elapsed 0.60  load 1.58\n",
      "22207 14 08:59:05 INFO train : 2592  loss 0.6996  elapsed 0.54  load 0.01\n",
      "22207 14 08:59:05 INFO train : 2593  loss 0.6772  elapsed 0.55  load 0.00\n",
      "22207 14 08:59:06 INFO train : 2594  loss 0.6800  elapsed 0.66  load 0.03\n",
      "22207 14 08:59:08 INFO train : 2595  loss 0.6633  elapsed 0.58  load 1.04\n",
      "22207 14 08:59:08 INFO train : 2596  loss 0.6742  elapsed 0.72  load 0.01\n",
      "22207 14 08:59:09 INFO train : 2597  loss 0.6371  elapsed 0.66  load 0.00\n",
      "22207 14 08:59:10 INFO train : 2598  loss 0.7075  elapsed 0.58  load 0.00\n",
      "22207 14 08:59:11 INFO train : 2599  loss 0.6391  elapsed 0.62  load 0.93\n",
      "22207 14 08:59:12 INFO train : 2600  loss 0.6735  elapsed 0.63  load 0.06\n",
      "22207 14 08:59:12 INFO train : 2601  loss 0.7174  elapsed 0.60  load 0.00\n",
      "22207 14 08:59:13 INFO train : 2602  loss 0.6589  elapsed 0.63  load 0.00\n",
      "22207 14 08:59:15 INFO train : 2603  loss 0.6536  elapsed 0.62  load 1.02\n",
      "22207 14 08:59:16 INFO train : 2604  loss 0.6595  elapsed 0.59  load 0.08\n",
      "22207 14 08:59:16 INFO train : 2605  loss 0.7509  elapsed 0.57  load 0.00\n",
      "22207 14 08:59:17 INFO train : 2606  loss 0.6873  elapsed 0.50  load 0.00\n",
      "22207 14 08:59:19 INFO train : 2607  loss 0.6917  elapsed 0.61  load 1.39\n",
      "22207 14 08:59:19 INFO train : 2608  loss 0.6826  elapsed 0.63  load 0.01\n",
      "22207 14 08:59:20 INFO train : 2609  loss 0.7003  elapsed 0.62  load 0.00\n",
      "22207 14 08:59:21 INFO train : 2610  loss 0.7032  elapsed 0.61  load 0.00\n",
      "22207 14 08:59:22 INFO train : 2611  loss 0.7291  elapsed 0.59  load 1.03\n",
      "22207 14 08:59:23 INFO train : 2612  loss 0.6721  elapsed 0.61  load 0.04\n",
      "22207 14 08:59:24 INFO train : 2613  loss 0.6681  elapsed 0.61  load 0.00\n",
      "22207 14 08:59:24 INFO train : 2614  loss 0.6561  elapsed 0.67  load 0.00\n",
      "22207 14 08:59:26 INFO train : 2615  loss 0.6781  elapsed 0.59  load 0.67\n",
      "22207 14 08:59:26 INFO train : 2616  loss 0.7156  elapsed 0.71  load 0.01\n",
      "22207 14 08:59:27 INFO train : 2617  loss 0.7005  elapsed 0.62  load 0.00\n",
      "22207 14 08:59:28 INFO train : 2618  loss 0.6989  elapsed 0.60  load 0.00\n",
      "22207 14 08:59:29 INFO train : 2619  loss 0.6924  elapsed 0.51  load 0.92\n",
      "22207 14 08:59:30 INFO train : 2620  loss 0.6679  elapsed 0.61  load 0.01\n",
      "22207 14 08:59:30 INFO train : 2621  loss 0.6615  elapsed 0.56  load 0.01\n",
      "22207 14 08:59:31 INFO train : 2622  loss 0.6660  elapsed 0.65  load 0.00\n",
      "22207 14 08:59:33 INFO train : 2623  loss 0.6683  elapsed 0.63  load 0.95\n",
      "22207 14 08:59:33 INFO train : 2624  loss 0.6568  elapsed 0.64  load 0.02\n",
      "22207 14 08:59:34 INFO train : 2625  loss 0.7334  elapsed 0.61  load 0.00\n",
      "22207 14 08:59:35 INFO train : 2626  loss 0.6788  elapsed 0.63  load 0.02\n",
      "22207 14 08:59:36 INFO train : 2627  loss 0.6603  elapsed 0.59  load 0.72\n",
      "22207 14 08:59:37 INFO train : 2628  loss 0.6661  elapsed 0.57  load 0.58\n",
      "22207 14 08:59:38 INFO train : 2629  loss 0.6582  elapsed 0.56  load 0.00\n",
      "22207 14 08:59:38 INFO train : 2630  loss 0.6978  elapsed 0.64  load 0.00\n",
      "22207 14 08:59:39 INFO train : 2631  loss 0.6615  elapsed 0.61  load 0.20\n",
      "22207 14 08:59:41 INFO train : 2632  loss 0.7156  elapsed 0.60  load 0.79\n",
      "22207 14 08:59:41 INFO train : 2633  loss 0.7243  elapsed 0.63  load 0.00\n",
      "22207 14 08:59:42 INFO train : 2634  loss 0.6682  elapsed 0.55  load 0.00\n",
      "22207 14 08:59:43 INFO train : 2635  loss 0.6874  elapsed 0.57  load 0.23\n",
      "22207 14 08:59:44 INFO train : 2636  loss 0.6424  elapsed 0.57  load 0.88\n",
      "22207 14 08:59:45 INFO train : 2637  loss 0.6700  elapsed 0.54  load 0.00\n",
      "22207 14 08:59:45 INFO train : 2638  loss 0.7023  elapsed 0.58  load 0.01\n",
      "22207 14 08:59:46 INFO train : 2639  loss 0.6798  elapsed 0.56  load 0.56\n",
      "22207 14 08:59:48 INFO train : 2640  loss 0.7864  elapsed 0.62  load 0.62\n",
      "22207 14 08:59:48 INFO train : 2641  loss 0.6752  elapsed 0.59  load 0.00\n",
      "22207 14 08:59:49 INFO train : 2642  loss 0.6961  elapsed 0.52  load 0.01\n",
      "22207 14 08:59:50 INFO train : 2643  loss 0.6962  elapsed 0.56  load 0.51\n",
      "22207 14 08:59:51 INFO train : 2644  loss 0.6668  elapsed 0.61  load 0.38\n",
      "22207 14 08:59:52 INFO train : 2645  loss 0.7070  elapsed 0.60  load 0.00\n",
      "22207 14 08:59:52 INFO train : 2646  loss 0.6968  elapsed 0.56  load 0.00\n",
      "22207 14 08:59:54 INFO train : 2647  loss 0.7030  elapsed 0.64  load 0.97\n",
      "22207 14 08:59:55 INFO train : 2648  loss 0.6931  elapsed 0.58  load 0.30\n",
      "22207 14 08:59:55 INFO train : 2649  loss 0.6536  elapsed 0.57  load 0.00\n",
      "22207 14 08:59:56 INFO train : 2650  loss 0.6856  elapsed 0.61  load 0.03\n",
      "22207 14 08:59:57 INFO train : 2651  loss 0.7170  elapsed 0.62  load 0.68\n",
      "22207 14 08:59:58 INFO train : 2652  loss 0.6804  elapsed 0.57  load 0.06\n",
      "22207 14 08:59:58 INFO train : 2653  loss 0.6785  elapsed 0.60  load 0.00\n",
      "22207 14 08:59:59 INFO train : 2654  loss 0.7083  elapsed 0.66  load 0.00\n",
      "22207 14 09:00:01 INFO train : 2655  loss 0.6914  elapsed 0.59  load 0.89\n",
      "22207 14 09:00:01 INFO train : 2656  loss 0.6910  elapsed 0.62  load 0.00\n",
      "22207 14 09:00:02 INFO train : 2657  loss 0.6731  elapsed 0.62  load 0.04\n",
      "22207 14 09:00:03 INFO train : 2658  loss 0.6875  elapsed 0.58  load 0.00\n",
      "22207 14 09:00:04 INFO train : 2659  loss 0.6779  elapsed 0.55  load 0.87\n",
      "22207 14 09:00:05 INFO train : 2660  loss 0.6751  elapsed 0.58  load 0.60\n",
      "22207 14 09:00:06 INFO train : 2661  loss 0.6313  elapsed 0.55  load 0.00\n",
      "22207 14 09:00:07 INFO train : 2662  loss 0.7506  elapsed 0.64  load 0.00\n",
      "22207 14 09:00:08 INFO train : 2663  loss 0.7043  elapsed 0.58  load 0.68\n",
      "22207 14 09:00:09 INFO train : 2664  loss 0.6753  elapsed 0.58  load 0.13\n",
      "22207 14 09:00:09 INFO train : 2665  loss 0.6856  elapsed 0.58  load 0.01\n",
      "22207 14 09:00:10 INFO train : 2666  loss 0.6615  elapsed 0.62  load 0.00\n",
      "22207 14 09:00:11 INFO train : 2667  loss 0.6805  elapsed 0.64  load 0.90\n",
      "22207 14 09:00:12 INFO train : 2668  loss 0.7228  elapsed 0.61  load 0.01\n",
      "22207 14 09:00:13 INFO train : 2669  loss 0.7170  elapsed 0.65  load 0.17\n",
      "22207 14 09:00:13 INFO train : 2670  loss 0.6933  elapsed 0.62  load 0.00\n",
      "22207 14 09:00:15 INFO train : 2671  loss 0.7159  elapsed 0.64  load 1.02\n",
      "22207 14 09:00:16 INFO train : 2672  loss 0.6673  elapsed 0.56  load 0.01\n",
      "22207 14 09:00:16 INFO train : 2673  loss 0.6868  elapsed 0.55  load 0.08\n",
      "22207 14 09:00:17 INFO train : 2674  loss 0.6793  elapsed 0.64  load 0.00\n",
      "22207 14 09:00:19 INFO train : 2675  loss 0.7047  elapsed 0.51  load 1.05\n",
      "22207 14 09:00:19 INFO train : 2676  loss 0.6893  elapsed 0.56  load 0.02\n",
      "22207 14 09:00:20 INFO train : 2677  loss 0.7068  elapsed 0.56  load 0.05\n",
      "22207 14 09:00:21 INFO train : 2678  loss 0.6518  elapsed 0.55  load 0.00\n",
      "22207 14 09:00:22 INFO train : 2679  loss 0.6767  elapsed 0.55  load 1.18\n",
      "22207 14 09:00:23 INFO train : 2680  loss 0.6871  elapsed 0.51  load 0.01\n",
      "22207 14 09:00:24 INFO train : 2681  loss 0.6899  elapsed 0.60  load 0.06\n",
      "22207 14 09:00:24 INFO train : 2682  loss 0.6420  elapsed 0.62  load 0.00\n",
      "22207 14 09:00:26 INFO train : 2683  loss 0.6651  elapsed 0.56  load 1.51\n",
      "22207 14 09:00:27 INFO train : 2684  loss 0.7174  elapsed 0.60  load 0.04\n",
      "22207 14 09:00:28 INFO train : 2685  loss 0.6430  elapsed 0.66  load 0.00\n",
      "22207 14 09:00:28 INFO train : 2686  loss 0.6947  elapsed 0.62  load 0.00\n",
      "22207 14 09:00:30 INFO train : 2687  loss 0.7031  elapsed 0.57  load 1.32\n",
      "22207 14 09:00:31 INFO train : 2688  loss 0.7389  elapsed 0.67  load 0.00\n",
      "22207 14 09:00:31 INFO train : 2689  loss 0.6730  elapsed 0.61  load 0.04\n",
      "22207 14 09:00:32 INFO train : 2690  loss 0.6927  elapsed 0.56  load 0.00\n",
      "22207 14 09:00:35 INFO train : 2691  loss 0.6467  elapsed 0.69  load 1.76\n",
      "22207 14 09:00:35 INFO train : 2692  loss 0.7043  elapsed 0.63  load 0.00\n",
      "22207 14 09:00:36 INFO train : 2693  loss 0.7145  elapsed 0.56  load 0.01\n",
      "22207 14 09:00:36 INFO train : 2694  loss 0.6383  elapsed 0.61  load 0.05\n",
      "22207 14 09:00:38 INFO train : 2695  loss 0.6900  elapsed 0.59  load 1.15\n",
      "22207 14 09:00:39 INFO train : 2696  loss 0.7200  elapsed 0.58  load 0.00\n",
      "22207 14 09:00:40 INFO train : 2697  loss 0.6865  elapsed 0.64  load 0.00\n",
      "22207 14 09:00:40 INFO train : 2698  loss 0.6835  elapsed 0.56  load 0.00\n",
      "22207 14 09:00:42 INFO train : 2699  loss 0.6585  elapsed 0.57  load 1.71\n",
      "22207 14 09:00:43 INFO train : 2700  loss 0.7214  elapsed 0.55  load 0.00\n",
      "22207 14 09:00:44 INFO train : 2701  loss 0.6687  elapsed 0.60  load 0.00\n",
      "22207 14 09:00:44 INFO train : 2702  loss 0.6962  elapsed 0.57  load 0.00\n",
      "22207 14 09:00:46 INFO train : 2703  loss 0.6933  elapsed 0.60  load 1.03\n",
      "22207 14 09:00:46 INFO train : 2704  loss 0.6744  elapsed 0.64  load 0.00\n",
      "22207 14 09:00:47 INFO train : 2705  loss 0.6985  elapsed 0.57  load 0.00\n",
      "22207 14 09:00:48 INFO train : 2706  loss 0.6872  elapsed 0.57  load 0.00\n",
      "22207 14 09:00:49 INFO train : 2707  loss 0.6566  elapsed 0.60  load 1.21\n",
      "22207 14 09:00:50 INFO train : 2708  loss 0.6620  elapsed 0.61  load 0.00\n",
      "22207 14 09:00:51 INFO train : 2709  loss 0.6739  elapsed 0.55  load 0.00\n",
      "22207 14 09:00:51 INFO train : 2710  loss 0.6605  elapsed 0.65  load 0.00\n",
      "22207 14 09:00:53 INFO train : 2711  loss 0.6864  elapsed 0.58  load 1.26\n",
      "22207 14 09:00:54 INFO train : 2712  loss 0.6990  elapsed 0.61  load 0.00\n",
      "22207 14 09:00:54 INFO train : 2713  loss 0.7192  elapsed 0.60  load 0.00\n",
      "22207 14 09:00:55 INFO train : 2714  loss 0.6855  elapsed 0.69  load 0.00\n",
      "22207 14 09:00:57 INFO train : 2715  loss 0.6888  elapsed 0.59  load 1.13\n",
      "22207 14 09:00:58 INFO train : 2716  loss 0.6995  elapsed 0.66  load 0.00\n",
      "22207 14 09:00:58 INFO train : 2717  loss 0.7117  elapsed 0.58  load 0.01\n",
      "22207 14 09:00:59 INFO train : 2718  loss 0.7000  elapsed 0.64  load 0.04\n",
      "22207 14 09:01:00 INFO train : 2719  loss 0.6384  elapsed 0.56  load 1.00\n",
      "22207 14 09:01:01 INFO train : 2720  loss 0.6606  elapsed 0.66  load 0.00\n",
      "22207 14 09:01:02 INFO train : 2721  loss 0.7082  elapsed 0.65  load 0.02\n",
      "22207 14 09:01:03 INFO train : 2722  loss 0.6829  elapsed 0.69  load 0.00\n",
      "22207 14 09:01:04 INFO train : 2723  loss 0.6997  elapsed 0.58  load 0.93\n",
      "22207 14 09:01:05 INFO train : 2724  loss 0.7014  elapsed 0.67  load 0.00\n",
      "22207 14 09:01:05 INFO train : 2725  loss 0.7167  elapsed 0.61  load 0.00\n",
      "22207 14 09:01:06 INFO train : 2726  loss 0.7223  elapsed 0.55  load 0.02\n",
      "22207 14 09:01:07 INFO train : 2727  loss 0.6872  elapsed 0.54  load 0.77\n",
      "22207 14 09:01:08 INFO train : 2728  loss 0.6835  elapsed 0.63  load 0.00\n",
      "22207 14 09:01:09 INFO train : 2729  loss 0.6601  elapsed 0.54  load 0.00\n",
      "22207 14 09:01:09 INFO train : 2730  loss 0.6882  elapsed 0.62  load 0.01\n",
      "22207 14 09:01:11 INFO train : 2731  loss 0.6745  elapsed 0.63  load 1.46\n",
      "22207 14 09:01:12 INFO train : 2732  loss 0.6596  elapsed 0.59  load 0.00\n",
      "22207 14 09:01:12 INFO train : 2733  loss 0.7222  elapsed 0.54  load 0.00\n",
      "22207 14 09:01:13 INFO train : 2734  loss 0.6800  elapsed 0.67  load 0.00\n",
      "22207 14 09:01:15 INFO train : 2735  loss 0.7058  elapsed 0.57  load 0.97\n",
      "22207 14 09:01:15 INFO train : 2736  loss 0.7052  elapsed 0.67  load 0.00\n",
      "22207 14 09:01:16 INFO train : 2737  loss 0.6930  elapsed 0.66  load 0.01\n",
      "22207 14 09:01:17 INFO train : 2738  loss 0.6536  elapsed 0.54  load 0.00\n",
      "22207 14 09:01:18 INFO train : 2739  loss 0.6754  elapsed 0.60  load 0.91\n",
      "22207 14 09:01:19 INFO train : 2740  loss 0.7207  elapsed 0.64  load 0.00\n",
      "22207 14 09:01:20 INFO train : 2741  loss 0.7096  elapsed 0.63  load 0.00\n",
      "22207 14 09:01:20 INFO train : 2742  loss 0.6940  elapsed 0.69  load 0.00\n",
      "22207 14 09:01:21 INFO train : 2743  loss 0.7488  elapsed 0.55  load 0.41\n",
      "22207 14 09:01:22 INFO train : 2744  loss 0.6792  elapsed 0.56  load 0.00\n",
      "22207 14 09:01:22 INFO train : 2745  loss 0.6847  elapsed 0.67  load 0.00\n",
      "22207 14 09:01:23 INFO train : 2746  loss 0.6964  elapsed 0.66  load 0.00\n",
      "22207 14 09:01:25 INFO train : 2747  loss 0.6671  elapsed 0.64  load 1.53\n",
      "22207 14 09:01:26 INFO train : 2748  loss 0.6871  elapsed 0.60  load 0.00\n",
      "22207 14 09:01:27 INFO train : 2749  loss 0.7195  elapsed 0.58  load 0.00\n",
      "22207 14 09:01:27 INFO train : 2750  loss 0.6931  elapsed 0.69  load 0.00\n",
      "22207 14 09:01:29 INFO train : 2751  loss 0.6644  elapsed 0.59  load 1.26\n",
      "22207 14 09:01:30 INFO train : 2752  loss 0.7188  elapsed 0.63  load 0.00\n",
      "22207 14 09:01:30 INFO train : 2753  loss 0.6960  elapsed 0.65  load 0.00\n",
      "22207 14 09:01:31 INFO train : 2754  loss 0.6660  elapsed 0.52  load 0.00\n",
      "22207 14 09:01:33 INFO train : 2755  loss 0.7090  elapsed 0.58  load 0.97\n",
      "22207 14 09:01:33 INFO train : 2756  loss 0.6544  elapsed 0.60  load 0.00\n",
      "22207 14 09:01:34 INFO train : 2757  loss 0.6828  elapsed 0.65  load 0.01\n",
      "22207 14 09:01:35 INFO train : 2758  loss 0.7103  elapsed 0.70  load 0.00\n",
      "22207 14 09:01:37 INFO train : 2759  loss 0.6814  elapsed 0.60  load 1.29\n",
      "22207 14 09:01:37 INFO train : 2760  loss 0.6695  elapsed 0.56  load 0.00\n",
      "22207 14 09:01:38 INFO train : 2761  loss 0.6620  elapsed 0.62  load 0.00\n",
      "22207 14 09:01:39 INFO train : 2762  loss 0.6597  elapsed 0.62  load 0.00\n",
      "22207 14 09:01:40 INFO train : 2763  loss 0.6994  elapsed 0.63  load 1.13\n",
      "22207 14 09:01:41 INFO train : 2764  loss 0.6696  elapsed 0.63  load 0.00\n",
      "22207 14 09:01:42 INFO train : 2765  loss 0.6643  elapsed 0.55  load 0.00\n",
      "22207 14 09:01:42 INFO train : 2766  loss 0.6594  elapsed 0.58  load 0.00\n",
      "22207 14 09:01:44 INFO train : 2767  loss 0.7133  elapsed 0.61  load 0.88\n",
      "22207 14 09:01:44 INFO train : 2768  loss 0.6590  elapsed 0.65  load 0.00\n",
      "22207 14 09:01:45 INFO train : 2769  loss 0.6423  elapsed 0.67  load 0.00\n",
      "22207 14 09:01:46 INFO train : 2770  loss 0.7269  elapsed 0.55  load 0.01\n",
      "22207 14 09:01:47 INFO train : 2771  loss 0.7308  elapsed 0.58  load 1.07\n",
      "22207 14 09:01:48 INFO train : 2772  loss 0.7046  elapsed 0.55  load 0.00\n",
      "22207 14 09:01:48 INFO train : 2773  loss 0.7419  elapsed 0.56  load 0.00\n",
      "22207 14 09:01:49 INFO train : 2774  loss 0.7122  elapsed 0.58  load 0.00\n",
      "22207 14 09:01:51 INFO train : 2775  loss 0.7233  elapsed 0.63  load 1.14\n",
      "22207 14 09:01:52 INFO train : 2776  loss 0.7280  elapsed 0.64  load 0.00\n",
      "22207 14 09:01:52 INFO train : 2777  loss 0.6642  elapsed 0.55  load 0.00\n",
      "22207 14 09:01:53 INFO train : 2778  loss 0.6590  elapsed 0.67  load 0.00\n",
      "22207 14 09:01:55 INFO train : 2779  loss 0.6641  elapsed 0.58  load 1.43\n",
      "22207 14 09:01:56 INFO train : 2780  loss 0.6803  elapsed 0.63  load 0.00\n",
      "22207 14 09:01:56 INFO train : 2781  loss 0.6857  elapsed 0.58  load 0.00\n",
      "22207 14 09:01:57 INFO train : 2782  loss 0.6576  elapsed 0.71  load 0.00\n",
      "22207 14 09:01:59 INFO train : 2783  loss 0.6810  elapsed 0.56  load 1.41\n",
      "22207 14 09:01:59 INFO train : 2784  loss 0.6644  elapsed 0.53  load 0.00\n",
      "22207 14 09:02:00 INFO train : 2785  loss 0.6875  elapsed 0.66  load 0.00\n",
      "22207 14 09:02:01 INFO train : 2786  loss 0.6804  elapsed 0.57  load 0.00\n",
      "22207 14 09:02:03 INFO train : 2787  loss 0.6812  elapsed 0.73  load 1.15\n",
      "22207 14 09:02:03 INFO train : 2788  loss 0.6516  elapsed 0.70  load 0.00\n",
      "22207 14 09:02:04 INFO train : 2789  loss 0.6839  elapsed 0.69  load 0.00\n",
      "22207 14 09:02:05 INFO train : 2790  loss 0.6697  elapsed 0.63  load 0.00\n",
      "22207 14 09:02:06 INFO train : 2791  loss 0.7531  elapsed 0.61  load 1.21\n",
      "22207 14 09:02:07 INFO train : 2792  loss 0.6937  elapsed 0.68  load 0.00\n",
      "22207 14 09:02:08 INFO train : 2793  loss 0.7410  elapsed 0.63  load 0.00\n",
      "22207 14 09:02:08 INFO train : 2794  loss 0.7338  elapsed 0.63  load 0.00\n",
      "22207 14 09:02:11 INFO train : 2795  loss 0.6964  elapsed 0.68  load 1.34\n",
      "22207 14 09:02:11 INFO train : 2796  loss 0.6729  elapsed 0.58  load 0.00\n",
      "22207 14 09:02:12 INFO train : 2797  loss 0.7057  elapsed 0.60  load 0.00\n",
      "22207 14 09:02:12 INFO train : 2798  loss 0.6912  elapsed 0.62  load 0.00\n",
      "22207 14 09:02:14 INFO train : 2799  loss 0.6708  elapsed 0.61  load 1.33\n",
      "22207 14 09:02:15 INFO train : 2800  loss 0.7194  elapsed 0.65  load 0.00\n",
      "22207 14 09:02:16 INFO train : 2801  loss 0.6570  elapsed 0.57  load 0.00\n",
      "22207 14 09:02:16 INFO train : 2802  loss 0.6629  elapsed 0.62  load 0.00\n",
      "22207 14 09:02:17 INFO train : 2803  loss 0.7138  elapsed 0.50  load 0.51\n",
      "22207 14 09:02:18 INFO train : 2804  loss 0.6580  elapsed 0.49  load 0.00\n",
      "22207 14 09:02:18 INFO train : 2805  loss 0.7086  elapsed 0.49  load 0.00\n",
      "22207 14 09:02:19 INFO train : 2806  loss 0.7100  elapsed 0.50  load 0.00\n",
      "22207 14 09:02:19 INFO train : 2807  loss 0.7058  elapsed 0.11  load 0.00\n",
      "22207 14 09:02:19 INFO Epoch num: 7\n",
      "22207 14 09:02:24 INFO train : 2808  loss 0.6566  elapsed 0.73  load 4.00\n",
      "22207 14 09:02:24 INFO train : 2809  loss 0.6540  elapsed 0.62  load 0.10\n",
      "22207 14 09:02:25 INFO train : 2810  loss 0.6613  elapsed 0.59  load 0.00\n",
      "22207 14 09:02:26 INFO train : 2811  loss 0.6648  elapsed 0.64  load 0.01\n",
      "22207 14 09:02:27 INFO train : 2812  loss 0.6667  elapsed 0.62  load 0.63\n",
      "22207 14 09:02:28 INFO train : 2813  loss 0.6594  elapsed 0.55  load 0.04\n",
      "22207 14 09:02:28 INFO train : 2814  loss 0.6471  elapsed 0.64  load 0.00\n",
      "22207 14 09:02:29 INFO train : 2815  loss 0.6504  elapsed 0.59  load 0.01\n",
      "22207 14 09:02:31 INFO train : 2816  loss 0.6787  elapsed 0.68  load 1.25\n",
      "22207 14 09:02:31 INFO train : 2817  loss 0.6374  elapsed 0.63  load 0.00\n",
      "22207 14 09:02:32 INFO train : 2818  loss 0.6589  elapsed 0.61  load 0.00\n",
      "22207 14 09:02:33 INFO train : 2819  loss 0.6607  elapsed 0.63  load 0.01\n",
      "22207 14 09:02:34 INFO train : 2820  loss 0.6333  elapsed 0.59  load 1.02\n",
      "22207 14 09:02:35 INFO train : 2821  loss 0.6231  elapsed 0.63  load 0.01\n",
      "22207 14 09:02:36 INFO train : 2822  loss 0.7397  elapsed 0.60  load 0.00\n",
      "22207 14 09:02:36 INFO train : 2823  loss 0.6674  elapsed 0.62  load 0.04\n",
      "22207 14 09:02:38 INFO train : 2824  loss 0.6565  elapsed 0.61  load 0.77\n",
      "22207 14 09:02:38 INFO train : 2825  loss 0.6733  elapsed 0.72  load 0.00\n",
      "22207 14 09:02:39 INFO train : 2826  loss 0.6651  elapsed 0.52  load 0.00\n",
      "22207 14 09:02:40 INFO train : 2827  loss 0.7010  elapsed 0.59  load 0.01\n",
      "22207 14 09:02:41 INFO train : 2828  loss 0.6373  elapsed 0.65  load 1.11\n",
      "22207 14 09:02:42 INFO train : 2829  loss 0.6575  elapsed 0.62  load 0.00\n",
      "22207 14 09:02:43 INFO train : 2830  loss 0.6492  elapsed 0.63  load 0.00\n",
      "22207 14 09:02:43 INFO train : 2831  loss 0.6682  elapsed 0.64  load 0.01\n",
      "22207 14 09:02:45 INFO train : 2832  loss 0.6601  elapsed 0.66  load 0.75\n",
      "22207 14 09:02:46 INFO train : 2833  loss 0.6313  elapsed 0.71  load 0.00\n",
      "22207 14 09:02:46 INFO train : 2834  loss 0.6436  elapsed 0.63  load 0.01\n",
      "22207 14 09:02:47 INFO train : 2835  loss 0.6639  elapsed 0.54  load 0.03\n",
      "22207 14 09:02:49 INFO train : 2836  loss 0.6907  elapsed 0.62  load 1.05\n",
      "22207 14 09:02:49 INFO train : 2837  loss 0.6567  elapsed 0.60  load 0.00\n",
      "22207 14 09:02:50 INFO train : 2838  loss 0.6815  elapsed 0.66  load 0.02\n",
      "22207 14 09:02:51 INFO train : 2839  loss 0.6440  elapsed 0.57  load 0.04\n",
      "22207 14 09:02:52 INFO train : 2840  loss 0.6845  elapsed 0.52  load 0.88\n",
      "22207 14 09:02:53 INFO train : 2841  loss 0.6627  elapsed 0.69  load 0.00\n",
      "22207 14 09:02:53 INFO train : 2842  loss 0.6506  elapsed 0.61  load 0.05\n",
      "22207 14 09:02:54 INFO train : 2843  loss 0.6734  elapsed 0.67  load 0.00\n",
      "22207 14 09:02:55 INFO train : 2844  loss 0.6622  elapsed 0.61  load 0.67\n",
      "22207 14 09:02:56 INFO train : 2845  loss 0.6718  elapsed 0.60  load 0.00\n",
      "22207 14 09:02:57 INFO train : 2846  loss 0.6504  elapsed 0.62  load 0.00\n",
      "22207 14 09:02:57 INFO train : 2847  loss 0.6500  elapsed 0.52  load 0.02\n",
      "22207 14 09:02:59 INFO train : 2848  loss 0.6664  elapsed 0.70  load 0.97\n",
      "22207 14 09:03:00 INFO train : 2849  loss 0.6736  elapsed 0.66  load 0.00\n",
      "22207 14 09:03:00 INFO train : 2850  loss 0.6400  elapsed 0.63  load 0.01\n",
      "22207 14 09:03:01 INFO train : 2851  loss 0.6385  elapsed 0.65  load 0.01\n",
      "22207 14 09:03:03 INFO train : 2852  loss 0.6492  elapsed 0.60  load 1.07\n",
      "22207 14 09:03:03 INFO train : 2853  loss 0.6393  elapsed 0.50  load 0.00\n",
      "22207 14 09:03:04 INFO train : 2854  loss 0.6710  elapsed 0.65  load 0.00\n",
      "22207 14 09:03:04 INFO train : 2855  loss 0.6424  elapsed 0.64  load 0.02\n",
      "22207 14 09:03:06 INFO train : 2856  loss 0.6667  elapsed 0.66  load 1.11\n",
      "22207 14 09:03:07 INFO train : 2857  loss 0.6371  elapsed 0.64  load 0.00\n",
      "22207 14 09:03:08 INFO train : 2858  loss 0.6504  elapsed 0.64  load 0.00\n",
      "22207 14 09:03:08 INFO train : 2859  loss 0.6200  elapsed 0.58  load 0.04\n",
      "22207 14 09:03:10 INFO train : 2860  loss 0.6548  elapsed 0.65  load 0.82\n",
      "22207 14 09:03:10 INFO train : 2861  loss 0.6712  elapsed 0.56  load 0.00\n",
      "22207 14 09:03:11 INFO train : 2862  loss 0.6113  elapsed 0.57  load 0.00\n",
      "22207 14 09:03:12 INFO train : 2863  loss 0.6371  elapsed 0.56  load 0.01\n",
      "22207 14 09:03:13 INFO train : 2864  loss 0.6626  elapsed 0.64  load 1.29\n",
      "22207 14 09:03:14 INFO train : 2865  loss 0.7234  elapsed 0.61  load 0.00\n",
      "22207 14 09:03:15 INFO train : 2866  loss 0.6108  elapsed 0.56  load 0.00\n",
      "22207 14 09:03:15 INFO train : 2867  loss 0.6633  elapsed 0.66  load 0.03\n",
      "22207 14 09:03:17 INFO train : 2868  loss 0.6569  elapsed 0.60  load 0.62\n",
      "22207 14 09:03:17 INFO train : 2869  loss 0.6507  elapsed 0.62  load 0.00\n",
      "22207 14 09:03:18 INFO train : 2870  loss 0.6670  elapsed 0.62  load 0.01\n",
      "22207 14 09:03:19 INFO train : 2871  loss 0.6514  elapsed 0.63  load 0.00\n",
      "22207 14 09:03:20 INFO train : 2872  loss 0.6578  elapsed 0.58  load 1.36\n",
      "22207 14 09:03:21 INFO train : 2873  loss 0.6511  elapsed 0.60  load 0.00\n",
      "22207 14 09:03:22 INFO train : 2874  loss 0.6338  elapsed 0.60  load 0.04\n",
      "22207 14 09:03:23 INFO train : 2875  loss 0.6522  elapsed 0.76  load 0.01\n",
      "22207 14 09:03:24 INFO train : 2876  loss 0.6752  elapsed 0.62  load 0.85\n",
      "22207 14 09:03:25 INFO train : 2877  loss 0.6251  elapsed 0.55  load 0.00\n",
      "22207 14 09:03:25 INFO train : 2878  loss 0.6935  elapsed 0.55  load 0.00\n",
      "22207 14 09:03:26 INFO train : 2879  loss 0.6471  elapsed 0.64  load 0.02\n",
      "22207 14 09:03:28 INFO train : 2880  loss 0.6794  elapsed 0.55  load 1.19\n",
      "22207 14 09:03:28 INFO train : 2881  loss 0.6519  elapsed 0.62  load 0.00\n",
      "22207 14 09:03:29 INFO train : 2882  loss 0.6454  elapsed 0.63  load 0.04\n",
      "22207 14 09:03:30 INFO train : 2883  loss 0.6266  elapsed 0.66  load 0.01\n",
      "22207 14 09:03:31 INFO train : 2884  loss 0.6568  elapsed 0.59  load 0.95\n",
      "22207 14 09:03:32 INFO train : 2885  loss 0.6636  elapsed 0.62  load 0.00\n",
      "22207 14 09:03:32 INFO train : 2886  loss 0.7119  elapsed 0.57  load 0.00\n",
      "22207 14 09:03:33 INFO train : 2887  loss 0.6546  elapsed 0.63  load 0.01\n",
      "22207 14 09:03:35 INFO train : 2888  loss 0.6135  elapsed 0.59  load 0.81\n",
      "22207 14 09:03:35 INFO train : 2889  loss 0.6299  elapsed 0.67  load 0.00\n",
      "22207 14 09:03:36 INFO train : 2890  loss 0.6315  elapsed 0.71  load 0.09\n",
      "22207 14 09:03:37 INFO train : 2891  loss 0.6477  elapsed 0.66  load 0.00\n",
      "22207 14 09:03:38 INFO train : 2892  loss 0.6409  elapsed 0.59  load 1.05\n",
      "22207 14 09:03:39 INFO train : 2893  loss 0.6466  elapsed 0.55  load 0.00\n",
      "22207 14 09:03:40 INFO train : 2894  loss 0.6683  elapsed 0.64  load 0.54\n",
      "22207 14 09:03:41 INFO train : 2895  loss 0.6421  elapsed 0.78  load 0.00\n",
      "22207 14 09:03:42 INFO train : 2896  loss 0.6561  elapsed 0.62  load 0.79\n",
      "22207 14 09:03:43 INFO train : 2897  loss 0.6131  elapsed 0.67  load 0.00\n",
      "22207 14 09:03:44 INFO train : 2898  loss 0.6638  elapsed 0.71  load 0.01\n",
      "22207 14 09:03:45 INFO train : 2899  loss 0.6721  elapsed 0.63  load 0.00\n",
      "22207 14 09:03:46 INFO train : 2900  loss 0.6192  elapsed 0.60  load 0.91\n",
      "22207 14 09:03:47 INFO train : 2901  loss 0.6646  elapsed 0.63  load 0.00\n",
      "22207 14 09:03:47 INFO train : 2902  loss 0.6782  elapsed 0.57  load 0.02\n",
      "22207 14 09:03:48 INFO train : 2903  loss 0.6596  elapsed 0.60  load 0.00\n",
      "22207 14 09:03:50 INFO train : 2904  loss 0.6331  elapsed 0.61  load 1.23\n",
      "22207 14 09:03:50 INFO train : 2905  loss 0.6438  elapsed 0.69  load 0.00\n",
      "22207 14 09:03:51 INFO train : 2906  loss 0.6404  elapsed 0.61  load 0.06\n",
      "22207 14 09:03:52 INFO train : 2907  loss 0.6480  elapsed 0.56  load 0.00\n",
      "22207 14 09:03:53 INFO train : 2908  loss 0.7097  elapsed 0.56  load 1.06\n",
      "22207 14 09:03:54 INFO train : 2909  loss 0.6582  elapsed 0.58  load 0.00\n",
      "22207 14 09:03:55 INFO train : 2910  loss 0.6608  elapsed 0.65  load 0.02\n",
      "22207 14 09:03:55 INFO train : 2911  loss 0.6497  elapsed 0.59  load 0.00\n",
      "22207 14 09:03:57 INFO train : 2912  loss 0.7057  elapsed 0.64  load 1.23\n",
      "22207 14 09:03:58 INFO train : 2913  loss 0.6352  elapsed 0.66  load 0.00\n",
      "22207 14 09:03:58 INFO train : 2914  loss 0.6434  elapsed 0.58  load 0.00\n",
      "22207 14 09:03:59 INFO train : 2915  loss 0.6955  elapsed 0.60  load 0.00\n",
      "22207 14 09:04:01 INFO train : 2916  loss 0.6415  elapsed 0.55  load 1.15\n",
      "22207 14 09:04:01 INFO train : 2917  loss 0.6442  elapsed 0.55  load 0.00\n",
      "22207 14 09:04:02 INFO train : 2918  loss 0.6642  elapsed 0.61  load 0.05\n",
      "22207 14 09:04:03 INFO train : 2919  loss 0.6584  elapsed 0.56  load 0.00\n",
      "22207 14 09:04:04 INFO train : 2920  loss 0.6217  elapsed 0.60  load 1.00\n",
      "22207 14 09:04:05 INFO train : 2921  loss 0.7176  elapsed 0.60  load 0.00\n",
      "22207 14 09:04:06 INFO train : 2922  loss 0.6765  elapsed 0.60  load 0.06\n",
      "22207 14 09:04:06 INFO train : 2923  loss 0.6504  elapsed 0.62  load 0.00\n",
      "22207 14 09:04:08 INFO train : 2924  loss 0.6440  elapsed 0.65  load 1.21\n",
      "22207 14 09:04:09 INFO train : 2925  loss 0.6849  elapsed 0.73  load 0.01\n",
      "22207 14 09:04:10 INFO train : 2926  loss 0.6569  elapsed 0.61  load 0.01\n",
      "22207 14 09:04:10 INFO train : 2927  loss 0.6637  elapsed 0.61  load 0.00\n",
      "22207 14 09:04:12 INFO train : 2928  loss 0.6549  elapsed 0.60  load 0.94\n",
      "22207 14 09:04:12 INFO train : 2929  loss 0.6652  elapsed 0.68  load 0.00\n",
      "22207 14 09:04:13 INFO train : 2930  loss 0.6952  elapsed 0.66  load 0.00\n",
      "22207 14 09:04:14 INFO train : 2931  loss 0.6434  elapsed 0.63  load 0.00\n",
      "22207 14 09:04:16 INFO train : 2932  loss 0.6571  elapsed 0.55  load 1.17\n",
      "22207 14 09:04:16 INFO train : 2933  loss 0.6849  elapsed 0.57  load 0.00\n",
      "22207 14 09:04:17 INFO train : 2934  loss 0.6404  elapsed 0.54  load 0.00\n",
      "22207 14 09:04:17 INFO train : 2935  loss 0.6828  elapsed 0.55  load 0.00\n",
      "22207 14 09:04:19 INFO train : 2936  loss 0.6383  elapsed 0.56  load 1.24\n",
      "22207 14 09:04:20 INFO train : 2937  loss 0.6411  elapsed 0.61  load 0.00\n",
      "22207 14 09:04:20 INFO train : 2938  loss 0.6471  elapsed 0.57  load 0.01\n",
      "22207 14 09:04:21 INFO train : 2939  loss 0.6643  elapsed 0.61  load 0.01\n",
      "22207 14 09:04:23 INFO train : 2940  loss 0.6508  elapsed 0.68  load 0.95\n",
      "22207 14 09:04:23 INFO train : 2941  loss 0.6478  elapsed 0.65  load 0.00\n",
      "22207 14 09:04:24 INFO train : 2942  loss 0.6430  elapsed 0.56  load 0.00\n",
      "22207 14 09:04:24 INFO train : 2943  loss 0.6525  elapsed 0.62  load 0.02\n",
      "22207 14 09:04:26 INFO train : 2944  loss 0.6731  elapsed 0.60  load 1.22\n",
      "22207 14 09:04:27 INFO train : 2945  loss 0.5990  elapsed 0.69  load 0.00\n",
      "22207 14 09:04:28 INFO train : 2946  loss 0.6062  elapsed 0.70  load 0.00\n",
      "22207 14 09:04:28 INFO train : 2947  loss 0.6382  elapsed 0.67  load 0.01\n",
      "22207 14 09:04:29 INFO train : 2948  loss 0.6601  elapsed 0.59  load 0.29\n",
      "22207 14 09:04:30 INFO train : 2949  loss 0.6355  elapsed 0.64  load 0.01\n",
      "22207 14 09:04:31 INFO train : 2950  loss 0.6394  elapsed 0.65  load 0.00\n",
      "22207 14 09:04:31 INFO train : 2951  loss 0.6749  elapsed 0.66  load 0.03\n",
      "22207 14 09:04:33 INFO train : 2952  loss 0.6540  elapsed 0.57  load 1.14\n",
      "22207 14 09:04:34 INFO train : 2953  loss 0.6730  elapsed 0.76  load 0.00\n",
      "22207 14 09:04:35 INFO train : 2954  loss 0.6547  elapsed 0.66  load 0.00\n",
      "22207 14 09:04:35 INFO train : 2955  loss 0.6717  elapsed 0.63  load 0.07\n",
      "22207 14 09:04:37 INFO train : 2956  loss 0.6633  elapsed 0.53  load 0.99\n",
      "22207 14 09:04:37 INFO train : 2957  loss 0.6657  elapsed 0.64  load 0.00\n",
      "22207 14 09:04:38 INFO train : 2958  loss 0.6831  elapsed 0.57  load 0.00\n",
      "22207 14 09:04:39 INFO train : 2959  loss 0.6549  elapsed 0.56  load 0.00\n",
      "22207 14 09:04:40 INFO train : 2960  loss 0.6362  elapsed 0.53  load 1.22\n",
      "22207 14 09:04:41 INFO train : 2961  loss 0.6760  elapsed 0.63  load 0.00\n",
      "22207 14 09:04:42 INFO train : 2962  loss 0.6874  elapsed 0.66  load 0.00\n",
      "22207 14 09:04:42 INFO train : 2963  loss 0.6584  elapsed 0.62  load 0.05\n",
      "22207 14 09:04:44 INFO train : 2964  loss 0.6582  elapsed 0.55  load 1.15\n",
      "22207 14 09:04:45 INFO train : 2965  loss 0.6981  elapsed 0.62  load 0.00\n",
      "22207 14 09:04:45 INFO train : 2966  loss 0.6543  elapsed 0.58  load 0.02\n",
      "22207 14 09:04:46 INFO train : 2967  loss 0.6536  elapsed 0.62  load 0.00\n",
      "22207 14 09:04:48 INFO train : 2968  loss 0.6480  elapsed 0.62  load 1.22\n",
      "22207 14 09:04:49 INFO train : 2969  loss 0.6690  elapsed 0.57  load 0.00\n",
      "22207 14 09:04:49 INFO train : 2970  loss 0.6696  elapsed 0.58  load 0.00\n",
      "22207 14 09:04:50 INFO train : 2971  loss 0.6550  elapsed 0.52  load 0.07\n",
      "22207 14 09:04:51 INFO train : 2972  loss 0.7097  elapsed 0.57  load 1.07\n",
      "22207 14 09:04:52 INFO train : 2973  loss 0.6316  elapsed 0.64  load 0.00\n",
      "22207 14 09:04:53 INFO train : 2974  loss 0.6934  elapsed 0.59  load 0.00\n",
      "22207 14 09:04:53 INFO train : 2975  loss 0.6433  elapsed 0.71  load 0.12\n",
      "22207 14 09:04:55 INFO train : 2976  loss 0.6918  elapsed 0.66  load 0.96\n",
      "22207 14 09:04:56 INFO train : 2977  loss 0.6770  elapsed 0.67  load 0.00\n",
      "22207 14 09:04:56 INFO train : 2978  loss 0.6232  elapsed 0.62  load 0.00\n",
      "22207 14 09:04:57 INFO train : 2979  loss 0.6469  elapsed 0.61  load 0.05\n",
      "22207 14 09:04:59 INFO train : 2980  loss 0.6849  elapsed 0.65  load 1.16\n",
      "22207 14 09:05:00 INFO train : 2981  loss 0.6271  elapsed 0.66  load 0.07\n",
      "22207 14 09:05:00 INFO train : 2982  loss 0.6433  elapsed 0.59  load 0.00\n",
      "22207 14 09:05:01 INFO train : 2983  loss 0.6663  elapsed 0.60  load 0.05\n",
      "22207 14 09:05:02 INFO train : 2984  loss 0.6909  elapsed 0.59  load 0.77\n",
      "22207 14 09:05:03 INFO train : 2985  loss 0.6423  elapsed 0.57  load 0.00\n",
      "22207 14 09:05:04 INFO train : 2986  loss 0.7401  elapsed 0.66  load 0.00\n",
      "22207 14 09:05:04 INFO train : 2987  loss 0.6511  elapsed 0.75  load 0.01\n",
      "22207 14 09:05:06 INFO train : 2988  loss 0.6258  elapsed 0.68  load 1.03\n",
      "22207 14 09:05:07 INFO train : 2989  loss 0.7005  elapsed 0.64  load 0.00\n",
      "22207 14 09:05:07 INFO train : 2990  loss 0.6529  elapsed 0.62  load 0.00\n",
      "22207 14 09:05:08 INFO train : 2991  loss 0.6471  elapsed 0.57  load 0.00\n",
      "22207 14 09:05:10 INFO train : 2992  loss 0.6639  elapsed 0.70  load 1.19\n",
      "22207 14 09:05:11 INFO train : 2993  loss 0.6737  elapsed 0.55  load 0.00\n",
      "22207 14 09:05:11 INFO train : 2994  loss 0.6713  elapsed 0.51  load 0.00\n",
      "22207 14 09:05:12 INFO train : 2995  loss 0.6674  elapsed 0.62  load 0.00\n",
      "22207 14 09:05:13 INFO train : 2996  loss 0.6241  elapsed 0.58  load 1.12\n",
      "22207 14 09:05:14 INFO train : 2997  loss 0.6481  elapsed 0.63  load 0.00\n",
      "22207 14 09:05:15 INFO train : 2998  loss 0.6456  elapsed 0.67  load 0.00\n",
      "22207 14 09:05:15 INFO train : 2999  loss 0.6596  elapsed 0.58  load 0.00\n",
      "22207 14 09:05:17 INFO train : 3000  loss 0.6450  elapsed 0.63  load 0.90\n",
      "22207 14 09:05:17 INFO Running validation\n",
      "22207 14 09:05:38 INFO Logging 20 images\n",
      "22207 14 09:05:39 INFO Logging 20 audios\n",
      "22207 14 09:05:42 INFO Logging 20 texts\n",
      "22207 14 09:05:54 INFO val : 3000  loss 0.6555802226066589\n",
      "22207 14 09:05:54 INFO Saving model state at step 3000 to checkpoints/fp_step_00003000.pt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.check_call(\n",
    "    ' '.join([\n",
    "        f'PYTHONPATH={path_to_sources} CUDA_VISIBLE_DEVICES={gpu_avaiable}',\n",
    "        f'python3 -m sources.fastpitch.train_fastpitch',\n",
    "        f'--logs {logs_dir}',\n",
    "        f'--ckptdir {ckpt_dir}',\n",
    "        f'--dataset {path_to_dataset}',\n",
    "        f'--hfg {path_to_hfg_ckpt}'\n",
    "    ]), shell=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6e02abc-0240-4eae-9bb1-4419a5b244c3",
   "metadata": {},
   "source": [
    "### Infer model on an example\n",
    "\n",
    "Execute the code provided below. Then, append the generated audio to the homework results.\n",
    "- if attaching an archive, use name: `prediction.wav`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9ad28736-5626-4a50-a6ba-4dcdad9bc304",
   "metadata": {
    "id": "9ad28736-5626-4a50-a6ba-4dcdad9bc304"
   },
   "outputs": [],
   "source": [
    "from sources.fastpitch.common.checkpointer import Checkpointer\n",
    "from sources.fastpitch.model import FastPitch\n",
    "from sources.fastpitch.data import FastPitchBatch, SymbolsSet\n",
    "from sources.hifigan.model import load_model as load_hfg_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5e2a451e-06de-4e9c-b45c-2461acd561cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def get_symbol_ids(text):\n",
    "    g2p = G2p()\n",
    "    phonemes = g2p(text)\n",
    "\n",
    "    symbols_set = SymbolsSet()\n",
    "    \n",
    "    symbols = []\n",
    "    for ph in phonemes:\n",
    "        if ph in symbols_set.symbols_to_id:\n",
    "            symbols.append(ph)\n",
    "        elif ph == ' ':\n",
    "            continue\n",
    "        else:\n",
    "            symbols.append(\"sil\")\n",
    "    \n",
    "    symbols_ids = torch.LongTensor(symbols_set.encode(symbols))\n",
    "    text_length = torch.LongTensor([symbols_ids.shape[0]])\n",
    "\n",
    "    return symbols_ids, text_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f1541feb-f8e4-4e08-bc8a-cb66979480d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpointer = Checkpointer(ckpt_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b0cb51ef-c337-4796-88df-50fdf928a7f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fvelikon/miniconda3/envs/ysda_speech_torch_26/lib/python3.11/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\n",
      "  WeightNorm.apply(module, name, dim)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HiFi-GAN: Removing weight norm.\n"
     ]
    }
   ],
   "source": [
    "hfg = load_hfg_model(path_to_hfg_ckpt)\n",
    "hfg = hfg.to(device).eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2ced55d2-5b54-4957-bed1-ea8a5834ceaf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ckpt_dict = checkpointer.load_last_checkpoint()\n",
    "hparams = HParamsFastpitch.create(ckpt_dict['hparams'])\n",
    "fp = FastPitch(hparams)\n",
    "fp.load_state_dict(ckpt_dict['state_dict'])\n",
    "fp = fp.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c9418e2e-8693-4497-b827-adcdcdec0970",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"Freestyler, rock the microphone, straight from the top of my dome. Freestyler, rock the microphone, carry on with the freestyler.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "91a7612a-d3e7-4347-8e5c-e4c80fc277dc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "symbols_ids, lengths = get_symbol_ids(text)\n",
    "\n",
    "batch = FastPitchBatch(\n",
    "    texts=symbols_ids.unsqueeze(0),\n",
    "    text_lengths=lengths\n",
    ").to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c844557b-ee3b-488f-acc5-dd43d7d17dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,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